API Changes

Added matrix:linspace(a,b) and matrix:linspace(a,b,base).
matrix:transpose() returns a matrix which references the original. Any
modification to the tranposed matrix, will be reflected at the original.
matrix:cmul method is now in-place, so, if you don't want to modify the
original matrix, you need to do m:clone():cmul(...) instead of
m:cmul(...).
update property is added to ann.optimizers.sgd object in order to compute
the momentum.
Optimizers execute method receives a functions which computes the loss, the
gradient (mandatory). Any other data is optional.
Removed dropout code from activation functions.
Deleted options from ANN components.
Added methods unroll and get to ann.components.stack class.
Added inf and sup limits to Hard-Tanh activation function.
Added random:to_lua_string() method.
Moved ann.loss.__base__ to ann.loss.
Moved ann.components.actf.__base__ to ann.components.actf.

New features

Added matrix.dict, a hash set dictionary in C++ binded to Lua, which allows
to execute basic math operations and reductions over the whole set of
contained matrices. It major purpose is to represent a set of connection
weights or gradients in ANNs.
Added dataset.token.filter, which allows ANN components as filters.
Added trainable.train_holdout_validation class, which replace
trainable.supervised_trainer:train_holdout_validation method.
Added trainable.train_wo_validation class, which replace
trainable.supervised_trainer:train_wo_validation method.
Added trainable.dataset_pair_iterator and
trainable.dataset_multiple_iterator, useful to iterate over datasets
following different traversal schemes: sequential, shuffled, shuffled with
replacement, shuffled with distribution.
Added method precompute_output_size in ANN components.
Added ann.optimizer.cg, Conjugate Gradient algorithm.
Added ann.optimizer.rprop, Resilient Prop algorithm.
Added batch_fmeasure_micro_avg and batch_fmeasure_macro_avg for
multi-class FMeasure computation.
Renamed loss function local_fmeasure as batch_fmeasure, and improved to
work with multi-class models.
Added ann.loss.zero_one loss function.
Added DEPENDENCIES-INSTALLER.sh.
Added syntactic sugar for matrix:slice(...) method: m("1:2","3:4") or
m({1,2},{3,4}), like in Matlab or Octave.
Added matrix.svd and matrix.diagonalize.
Added stats.pca, stats.mean_centered, stats.pca_whitening.

Memory leak due to the GPUMirroredMemoryBlock pool was solved.
Solved bug at stats.correlation.pearson.
Solved bug at trainable when using use_dataset, train_dataset,
validate_dataset, grad_check_dataset methods without a bunch_size
parameter, and with a trainer which has not a bunch_size defined at
construction.
Stabilization of log-logistic activation function.
Stabilization of training with cross-entropy and multi-class-entropy.
Solved bug when reading using matrix.fromTabFilename. The loader failed
when the file had empty lines.
Solved bug at Matrix<T>::select(...) C++ method. The matrix offset wasn't be
added to the resulting matrix offset.
Solved bug at SlidingWindow::setAtWindow(...) C++ method. The matrix offset
wasn't be added to the computed window position.
Solved bug at buffered_memory.h. Such bug introduces an early stop when
reading matrices, ignoring the last lines of files.
Solved problem with rlcompleter, which didn't work properly when loaded as a
Lua module.
Modified configure.sh to inform about any error during Lua building.
Loadable modules are working on MacOs X.

Added Matrix<T>::random_acces_iterator, which reduces the access overhead
for random access of a Matrix object. It retains the memory pointer forcing
an update between host and device (GPU) memory.
Generalized GPUMirroredBlockBase to allow the reinterpretation of the
underlying memory pointer using different types (reinterpret_cast).
Added MatrixSet class template, which stores a dictionary of STRING->MATRIX,
useful for ANNs and gradient descent purposes.
Added StochasticANNComponent which is base class for stochastic components.
Simplified coupling between ANN components, ANN loss functions, introducing
automatic binding between MatrixFloat and TokenMatrixFloat.
ANN Components has a pointer to a MatrixFloat instead of ANN::Connections.
ANN::Connections is a static class with helper functions, and it is binded
as ann.connections.
Old-weights property is removed from ANN connections.
Added automatic conversion between DataSetFloat and DataSetToken in
dataset.token.filter and dataset.token.union.
Added FunctionInterface class, in Lua as functions class, superclass of
ANN components.