diff --git a/doc/tutorials/estimate_cmi_with_unsuptree.ipynb b/doc/tutorials/estimate_cmi_with_unsuptree.ipynb deleted file mode 100644 index e69de29bb..000000000 diff --git a/doc/tutorials/test_gabormorf.ipynb b/doc/tutorials/test_gabormorf.ipynb deleted file mode 100644 index 37474bf0e..000000000 --- a/doc/tutorials/test_gabormorf.ipynb +++ /dev/null @@ -1,1401 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "20a5bee8", - "metadata": {}, - "source": [ - "# Gabor Forest" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "10cfcf5b", - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 1;\n", - " var nbb_unformatted_code = \"%load_ext nb_black\\n%load_ext lab_black\\n%load_ext autoreload\\n%autoreload 2\";\n", - " var nbb_formatted_code = \"%load_ext nb_black\\n%load_ext lab_black\\n%load_ext autoreload\\n%autoreload 2\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%load_ext nb_black\n", - "%load_ext lab_black\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "bbcf4202", - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 2;\n", - " var nbb_unformatted_code = \"import sys\\n\\nsys.path.append(\\\"../../\\\")\";\n", - " var nbb_formatted_code = \"import sys\\n\\nsys.path.append(\\\"../../\\\")\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import sys\n", - "\n", - "sys.path.append(\"../../\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "72569afc", - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", -<<<<<<< Updated upstream - " var nbb_cell_id = 17;\n", - " var nbb_unformatted_code = \"import numpy as np\\nfrom oblique_forests.tree.morf_split import Conv2DSplitter, GaborSplitter\\nfrom oblique_forests.morf import ManifoldForestClassifier\\nfrom oblique_forests.tree.morf_tree import GaborTreeClassifier\\n\\nfrom sklearn.datasets import load_digits\\n\\nimport matplotlib as mpl\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\";\n", - " var nbb_formatted_code = \"import numpy as np\\nfrom oblique_forests.tree.morf_split import Conv2DSplitter, GaborSplitter\\nfrom oblique_forests.morf import ManifoldForestClassifier\\nfrom oblique_forests.tree.morf_tree import GaborTreeClassifier\\n\\nfrom sklearn.datasets import load_digits\\n\\nimport matplotlib as mpl\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\";\n", -======= - " var nbb_cell_id = 3;\n", - " var nbb_unformatted_code = \"import numpy as np\\nfrom oblique_forests.tree.morf_split import Conv2DSplitter, GaborSplitter\\nfrom oblique_forests.morf import ManifoldForestClassifier\\nfrom oblique_forests.tree.morf_tree import GaborTreeClassifier\\n\\nfrom sklearn.datasets import load_digits\\nfrom sklearn.metrics import (\\n balanced_accuracy_score,\\n roc_auc_score,\\n roc_curve,\\n plot_roc_curve,\\n)\\n\\nfrom sklearn.model_selection import cross_validate, KFold\\n\\nimport matplotlib as mpl\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\";\n", - " var nbb_formatted_code = \"import numpy as np\\nfrom oblique_forests.tree.morf_split import Conv2DSplitter, GaborSplitter\\nfrom oblique_forests.morf import ManifoldForestClassifier\\nfrom oblique_forests.tree.morf_tree import GaborTreeClassifier\\n\\nfrom sklearn.datasets import load_digits\\nfrom sklearn.metrics import (\\n balanced_accuracy_score,\\n roc_auc_score,\\n roc_curve,\\n plot_roc_curve,\\n)\\n\\nfrom sklearn.model_selection import cross_validate, KFold\\n\\nimport matplotlib as mpl\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\";\n", ->>>>>>> Stashed changes - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "from oblique_forests.tree.morf_split import Conv2DSplitter, GaborSplitter\n", - "from oblique_forests.morf import ManifoldForestClassifier\n", - "from oblique_forests.tree.morf_tree import GaborTreeClassifier\n", - "\n", - "from sklearn.datasets import load_digits\n", - "from sklearn.metrics import (\n", - " balanced_accuracy_score,\n", - " roc_auc_score,\n", - " roc_curve,\n", - " plot_roc_curve,\n", - ")\n", - "\n", - "from sklearn.model_selection import cross_validate, KFold\n", - "\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f2b234d0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1797, 64) (1797,)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 4;\n", - " var nbb_unformatted_code = \"X, y = load_digits(return_X_y=True)\\nprint(X.shape, y.shape)\";\n", - " var nbb_formatted_code = \"X, y = load_digits(return_X_y=True)\\nprint(X.shape, y.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "X, y = load_digits(return_X_y=True)\n", - "print(X.shape, y.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, -<<<<<<< Updated upstream - "id": "22db9c2d", -======= ->>>>>>> Stashed changes - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 5;\n", -<<<<<<< Updated upstream - " var nbb_unformatted_code = \"height = 8\\nwidth = 8\";\n", - " var nbb_formatted_code = \"height = 8\\nwidth = 8\";\n", -======= - " var nbb_unformatted_code = \"X, y = load_digits(return_X_y=True)\\nprint(X.shape, y.shape)\";\n", - " var nbb_formatted_code = \"X, y = load_digits(return_X_y=True)\\nprint(X.shape, y.shape)\";\n", ->>>>>>> Stashed changes - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "height = 8\n", - "width = 8" - ] - }, - { - "cell_type": "markdown", - "id": "f6b10ac7", - "metadata": {}, - "source": [ - "# Classification Tree - Convolutional Tree" - ] - }, - { - "cell_type": "code", -<<<<<<< Updated upstream - "execution_count": 7, - "id": "763bdcae", -======= - "execution_count": 6, ->>>>>>> Stashed changes - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", -<<<<<<< Updated upstream - " var nbb_cell_id = 7;\n", - " var nbb_unformatted_code = \"from oblique_forests.tree.morf_tree import GaborTreeClassifier\";\n", - " var nbb_formatted_code = \"from oblique_forests.tree.morf_tree import GaborTreeClassifier\";\n", -======= - " var nbb_cell_id = 6;\n", - " var nbb_unformatted_code = \"height = 8\\nwidth = 8\";\n", - " var nbb_formatted_code = \"height = 8\\nwidth = 8\";\n", ->>>>>>> Stashed changes - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from oblique_forests.tree.morf_tree import GaborTreeClassifier" - ] - }, - { - "cell_type": "code", -<<<<<<< Updated upstream - "execution_count": 8, - "id": "99d3f9b2", -======= - "execution_count": 7, ->>>>>>> Stashed changes - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", -<<<<<<< Updated upstream - " var nbb_cell_id = 8;\n", -======= - " var nbb_cell_id = 7;\n", ->>>>>>> Stashed changes - " var nbb_unformatted_code = \"random_state = 123456\";\n", - " var nbb_formatted_code = \"random_state = 123456\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "random_state = 123456" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a80a744a", - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 9;\n", - " var nbb_unformatted_code = \"clf = GaborTreeClassifier(\\n random_state=random_state,\\n image_height=height,\\n image_width=width,\\n patch_height_max=8,\\n patch_height_min=3,\\n patch_width_min=3,\\n patch_width_max=8,\\n discontiguous_height=True,\\n discontiguous_width=False,\\n)\";\n", - " var nbb_formatted_code = \"clf = GaborTreeClassifier(\\n random_state=random_state,\\n image_height=height,\\n image_width=width,\\n patch_height_max=8,\\n patch_height_min=3,\\n patch_width_min=3,\\n patch_width_max=8,\\n discontiguous_height=True,\\n discontiguous_width=False,\\n)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "clf = GaborTreeClassifier(\n", - " random_state=random_state,\n", - " image_height=height,\n", - " image_width=width,\n", - " patch_height_max=8,\n", - " patch_height_min=3,\n", - " patch_width_min=3,\n", - " patch_width_max=8,\n", - " discontiguous_height=True,\n", - " discontiguous_width=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "8d2ac52d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GaborTreeClassifier(discontiguous_height=True, image_height=8, image_width=8,\n", - " patch_height_max=8, patch_height_min=3, patch_width_max=8,\n", - " patch_width_min=3, random_state=123456)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 10;\n", - " var nbb_unformatted_code = \"clf.fit(X, y)\";\n", - " var nbb_formatted_code = \"clf.fit(X, y)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "clf.fit(X, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a5d839bb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "463\n", - "18\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 11;\n", - " var nbb_unformatted_code = \"print(clf.tree.node_count)\\nprint(clf.tree.depth)\";\n", - " var nbb_formatted_code = \"print(clf.tree.node_count)\\nprint(clf.tree.depth)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(clf.tree.node_count)\n", - "print(clf.tree.depth)" - ] - }, - { - "cell_type": "markdown", - "id": "a1e37733", - "metadata": {}, - "source": [ - "# MNIST Analysis With GaborMorf" - ] - }, - { - "cell_type": "code", -<<<<<<< Updated upstream - "execution_count": 12, - "id": "b7124cda", -======= - "execution_count": 8, ->>>>>>> Stashed changes - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", -<<<<<<< Updated upstream - " var nbb_cell_id = 12;\n", -======= - " var nbb_cell_id = 8;\n", ->>>>>>> Stashed changes - " var nbb_unformatted_code = \"import torchvision.datasets as datasets\";\n", - " var nbb_formatted_code = \"import torchvision.datasets as datasets\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import torchvision.datasets as datasets" - ] - }, - { - "cell_type": "code", -<<<<<<< Updated upstream - "execution_count": 14, - "id": "403a9ef2", -======= - "execution_count": 9, ->>>>>>> Stashed changes - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", -<<<<<<< Updated upstream - " var nbb_cell_id = 14;\n", -======= - " var nbb_cell_id = 9;\n", ->>>>>>> Stashed changes - " var nbb_unformatted_code = \"mnist_trainset = datasets.MNIST(\\n root=\\\"~/Downloads/mnist\\\", train=True, download=True, transform=None\\n)\";\n", - " var nbb_formatted_code = \"mnist_trainset = datasets.MNIST(\\n root=\\\"~/Downloads/mnist\\\", train=True, download=True, transform=None\\n)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mnist_trainset = datasets.MNIST(\n", - " root=\"~/Downloads/mnist\", train=True, download=True, transform=None\n", - ")" - ] - }, - { - "cell_type": "code", -<<<<<<< Updated upstream - "execution_count": 15, - "id": "de412697", -======= - "execution_count": 10, ->>>>>>> Stashed changes - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", -<<<<<<< Updated upstream - " var nbb_cell_id = 15;\n", -======= - " var nbb_cell_id = 10;\n", ->>>>>>> Stashed changes - " var nbb_unformatted_code = \"mnist_testset = datasets.MNIST(\\n root=\\\"~/Downloads/mnist\\\", train=False, download=True, transform=None\\n)\";\n", - " var nbb_formatted_code = \"mnist_testset = datasets.MNIST(\\n root=\\\"~/Downloads/mnist\\\", train=False, download=True, transform=None\\n)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mnist_testset = datasets.MNIST(\n", - " root=\"~/Downloads/mnist\", train=False, download=True, transform=None\n", - ")" - ] - }, - { - "cell_type": "code", -<<<<<<< Updated upstream - "execution_count": 16, - "id": "9b111178", -======= - "execution_count": 11, ->>>>>>> Stashed changes - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([60000, 28, 28])\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/adam2392/opt/miniconda3/envs/sklearn-dev/lib/python3.9/site-packages/torchvision/datasets/mnist.py:64: UserWarning: train_data has been renamed data\n", - " warnings.warn(\"train_data has been renamed data\")\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", -<<<<<<< Updated upstream - " var nbb_cell_id = 16;\n", -======= - " var nbb_cell_id = 11;\n", ->>>>>>> Stashed changes - " var nbb_unformatted_code = \"print(mnist_trainset.train_data.shape)\";\n", - " var nbb_formatted_code = \"print(mnist_trainset.train_data.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(mnist_trainset.train_data.shape)" - ] - }, - { - "cell_type": "code", -<<<<<<< Updated upstream - "execution_count": 20, - "id": "18b9a7db", -======= - "execution_count": 12, ->>>>>>> Stashed changes - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(60000, 784)\n", - "(60000,)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", -<<<<<<< Updated upstream - " var nbb_cell_id = 20;\n", - " var nbb_unformatted_code = \"X_train, y_train = mnist_trainset.train_data, mnist_trainset.train_labels\\nX_train = X_train.numpy().reshape(len(y_train), -1)\\ny_train = y_train.numpy()\\n\\nprint(X_train.shape)\\nprint(y_train.shape)\";\n", - " var nbb_formatted_code = \"X_train, y_train = mnist_trainset.train_data, mnist_trainset.train_labels\\nX_train = X_train.numpy().reshape(len(y_train), -1)\\ny_train = y_train.numpy()\\n\\nprint(X_train.shape)\\nprint(y_train.shape)\";\n", -======= - " var nbb_cell_id = 12;\n", - " var nbb_unformatted_code = \"X_train, y_train = mnist_trainset.train_data, mnist_trainset.train_labels\\nX_train = X_train.numpy().reshape(len(y_train), -1)\\ny_train = y_train.numpy()\\n\\nX_test, y_test = mnist_testset.test_data, mnist_testset.test_labels\\nX_test = X_test.numpy().reshape(len(y_test), -1)\\ny_test = y_test.numpy()\\n\\nprint(X_train.shape, X_test.shape)\\nprint(y_train.shape, y_test.shape)\";\n", - " var nbb_formatted_code = \"X_train, y_train = mnist_trainset.train_data, mnist_trainset.train_labels\\nX_train = X_train.numpy().reshape(len(y_train), -1)\\ny_train = y_train.numpy()\\n\\nX_test, y_test = mnist_testset.test_data, mnist_testset.test_labels\\nX_test = X_test.numpy().reshape(len(y_test), -1)\\ny_test = y_test.numpy()\\n\\nprint(X_train.shape, X_test.shape)\\nprint(y_train.shape, y_test.shape)\";\n", ->>>>>>> Stashed changes - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "X_train, y_train = mnist_trainset.train_data, mnist_trainset.train_labels\n", - "X_train = X_train.numpy().reshape(len(y_train), -1)\n", - "y_train = y_train.numpy()\n", - "\n", -<<<<<<< Updated upstream - "print(X_train.shape)\n", - "print(y_train.shape)" -======= - "X_test, y_test = mnist_testset.test_data, mnist_testset.test_labels\n", - "X_test = X_test.numpy().reshape(len(y_test), -1)\n", - "y_test = y_test.numpy()\n", - "\n", - "print(X_train.shape, X_test.shape)\n", - "print(y_train.shape, y_test.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(70000, 784) (70000,)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 13;\n", - " var nbb_unformatted_code = \"X = np.concatenate((X_train, X_test), axis=0)\\ny = np.concatenate((y_train, y_test), axis=0)\\n\\nprint(X.shape, y.shape)\";\n", - " var nbb_formatted_code = \"X = np.concatenate((X_train, X_test), axis=0)\\ny = np.concatenate((y_train, y_test), axis=0)\\n\\nprint(X.shape, y.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "X = np.concatenate((X_train, X_test), axis=0)\n", - "y = np.concatenate((y_train, y_test), axis=0)\n", - "\n", - "print(X.shape, y.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 14;\n", - " var nbb_unformatted_code = \"X_train = X_train[:100, :]\\ny_train = y_train[:100]\";\n", - " var nbb_formatted_code = \"X_train = X_train[:100, :]\\ny_train = y_train[:100]\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "X_train = X_train[:100, :]\n", - "y_train = y_train[:100]" ->>>>>>> Stashed changes - ] - }, - { - "cell_type": "code", -<<<<<<< Updated upstream - "execution_count": 21, - "id": "b15bb73a", -======= - "execution_count": 15, ->>>>>>> Stashed changes - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", -<<<<<<< Updated upstream - " var nbb_cell_id = 21;\n", -======= - " var nbb_cell_id = 15;\n", ->>>>>>> Stashed changes - " var nbb_unformatted_code = \"image_height = 28\\nimage_width = 28\";\n", - " var nbb_formatted_code = \"image_height = 28\\nimage_width = 28\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "image_height = 28\n", - "image_width = 28" - ] - }, - { - "cell_type": "code", -<<<<<<< Updated upstream - "execution_count": 22, - "id": "42391118", -======= - "execution_count": 16, ->>>>>>> Stashed changes - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", -<<<<<<< Updated upstream - " var nbb_cell_id = 22;\n", - " var nbb_unformatted_code = \"estimator_params = {\\n 'image_height': image_height,\\n 'image_width': image_width,\\n 'patch_width_min': 3,\\n 'patch_height_min': 3,\\n 'patch_width_max': 28,\\n 'patch_height_max': 28,\\n}\\nn_est = 100\\nclf = ManifoldForestClassifier(\\n base_estimator=GaborTreeClassifier(),\\n estimator_params=estimator_params,\\n n_estimators=n_est,\\n **estimator_params\\n)\";\n", - " var nbb_formatted_code = \"estimator_params = {\\n \\\"image_height\\\": image_height,\\n \\\"image_width\\\": image_width,\\n \\\"patch_width_min\\\": 3,\\n \\\"patch_height_min\\\": 3,\\n \\\"patch_width_max\\\": 28,\\n \\\"patch_height_max\\\": 28,\\n}\\nn_est = 100\\nclf = ManifoldForestClassifier(\\n base_estimator=GaborTreeClassifier(),\\n estimator_params=estimator_params,\\n n_estimators=n_est,\\n **estimator_params\\n)\";\n", -======= - " var nbb_cell_id = 16;\n", - " var nbb_unformatted_code = \"estimator_params = {\\n \\\"image_height\\\": image_height,\\n \\\"image_width\\\": image_width,\\n \\\"patch_width_min\\\": 3,\\n \\\"patch_height_min\\\": 3,\\n \\\"patch_width_max\\\": 10,\\n \\\"patch_height_max\\\": 10,\\n}\\nn_est = 100\\nverbose = True\\nn_jobs = 8\";\n", - " var nbb_formatted_code = \"estimator_params = {\\n \\\"image_height\\\": image_height,\\n \\\"image_width\\\": image_width,\\n \\\"patch_width_min\\\": 3,\\n \\\"patch_height_min\\\": 3,\\n \\\"patch_width_max\\\": 10,\\n \\\"patch_height_max\\\": 10,\\n}\\nn_est = 100\\nverbose = True\\nn_jobs = 8\";\n", ->>>>>>> Stashed changes - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "estimator_params = {\n", - " \"image_height\": image_height,\n", - " \"image_width\": image_width,\n", - " \"patch_width_min\": 3,\n", - " \"patch_height_min\": 3,\n", - " \"patch_width_max\": 28,\n", - " \"patch_height_max\": 28,\n", - "}\n", - "n_est = 100\n", -<<<<<<< Updated upstream - "verbose=True\n", - "n_jobs = -1\n", - "\n", -======= - "verbose = True\n", - "n_jobs = 8" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 15;\n", - " var nbb_unformatted_code = \"clf = GaborTreeClassifier(random_state=random_state, **estimator_params)\";\n", - " var nbb_formatted_code = \"clf = GaborTreeClassifier(random_state=random_state, **estimator_params)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "clf = GaborTreeClassifier(random_state=random_state, **estimator_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GaborTreeClassifier(image_height=28, image_width=28, patch_height_max=10,\n", - " patch_height_min=3, patch_width_max=10, patch_width_min=3,\n", - " random_state=123456)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 16;\n", - " var nbb_unformatted_code = \"clf.fit(X_train, y_train)\";\n", - " var nbb_formatted_code = \"clf.fit(X_train, y_train)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "clf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "49\n", - "14\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 17;\n", - " var nbb_unformatted_code = \"print(clf.tree.node_count)\\nprint(clf.tree.depth)\";\n", - " var nbb_formatted_code = \"print(clf.tree.node_count)\\nprint(clf.tree.depth)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(clf.tree.node_count)\n", - "print(clf.tree.depth)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 19;\n", - " var nbb_unformatted_code = \"clf = ManifoldForestClassifier(\\n base_estimator=GaborTreeClassifier(image_height=image_height,\\n image_width=image_width),\\n estimator_params=estimator_params,\\n n_estimators=n_est,\\n verbose=verbose,\\n n_jobs=n_jobs,\\n **estimator_params,\\n)\";\n", - " var nbb_formatted_code = \"clf = ManifoldForestClassifier(\\n base_estimator=GaborTreeClassifier(\\n image_height=image_height, image_width=image_width\\n ),\\n estimator_params=estimator_params,\\n n_estimators=n_est,\\n verbose=verbose,\\n n_jobs=n_jobs,\\n **estimator_params,\\n)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ ->>>>>>> Stashed changes - "clf = ManifoldForestClassifier(\n", - " base_estimator=GaborTreeClassifier(\n", - " image_height=image_height, image_width=image_width\n", - " ),\n", - " estimator_params=estimator_params,\n", - " n_estimators=n_est,\n", - " verbose=verbose,\n", - " n_jobs=n_jobs,\n", - " **estimator_params,\n", - ")" - ] - }, - { - "cell_type": "code", -<<<<<<< Updated upstream - "execution_count": null, - "id": "d48465c0", - "metadata": {}, - "outputs": [], -======= - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 4.7min\n", - "[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 12.1min finished\n" - ] - }, - { - "data": { - "text/plain": [ - "ManifoldForestClassifier(base_estimator=GaborTreeClassifier(), n_jobs=8,\n", - " verbose=True)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 19;\n", - " var nbb_unformatted_code = \"clf.fit(X_train, y_train)\";\n", - " var nbb_formatted_code = \"clf.fit(X_train, y_train)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], ->>>>>>> Stashed changes - "source": [ - "clf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", -<<<<<<< Updated upstream -======= - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ManifoldForestClassifier(base_estimator=GaborTreeClassifier(), n_jobs=8,\n", - " verbose=True)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 22;\n", - " var nbb_unformatted_code = \"print(clf)\\n# print(clf.estimators_)\";\n", - " var nbb_formatted_code = \"print(clf)\\n# print(clf.estimators_)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(clf)\n", - "# print(clf.estimators_)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 32;\n", - " var nbb_unformatted_code = \"# y_pred_proba = clf.predict_proba(X_test)\";\n", - " var nbb_formatted_code = \"# y_pred_proba = clf.predict_proba(X_test)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# y_pred_proba = clf.predict_proba(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Setup cross validation experiment" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "ename": "TerminatedWorkerError", - "evalue": "A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker.\n\nThe exit codes of the workers are {SIGKILL(-9)}", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTerminatedWorkerError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m scores = cross_validate(\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"roc_auc_ovr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"accuracy\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_estimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m )\n", - "\u001b[0;32m~/Documents/manifold_random_forests/.venv/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/manifold_random_forests/.venv/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m 244\u001b[0m parallel = Parallel(n_jobs=n_jobs, verbose=verbose,\n\u001b[1;32m 245\u001b[0m pre_dispatch=pre_dispatch)\n\u001b[0;32m--> 246\u001b[0;31m results = parallel(\n\u001b[0m\u001b[1;32m 247\u001b[0m delayed(_fit_and_score)(\n\u001b[1;32m 248\u001b[0m \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscorers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/manifold_random_forests/.venv/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1052\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1053\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1054\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1055\u001b[0m \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1056\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/manifold_random_forests/.venv/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 931\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 932\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 933\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 934\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 935\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/manifold_random_forests/.venv/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m 540\u001b[0m AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m 541\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 543\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 437\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 440\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/lib/python3.8/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 389\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTerminatedWorkerError\u001b[0m: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker.\n\nThe exit codes of the workers are {SIGKILL(-9)}" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 20;\n", - " var nbb_unformatted_code = \"scores = cross_validate(\\n clf, X=X, y=y, scoring=[\\\"roc_auc_ovr\\\", \\\"accuracy\\\"], n_jobs=12, return_estimator=True\\n)\";\n", - " var nbb_formatted_code = \"scores = cross_validate(\\n clf, X=X, y=y, scoring=[\\\"roc_auc_ovr\\\", \\\"accuracy\\\"], n_jobs=12, return_estimator=True\\n)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "scores = cross_validate(\n", - " clf, X=X, y=y, scoring=[\"roc_auc_ovr\", \"accuracy\"], n_jobs=12, return_estimator=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(clf.n_classes_)" - ] - }, - { - "cell_type": "code", ->>>>>>> Stashed changes - "execution_count": null, - "id": "bf7595e6", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "sklearn", - "language": "python", - "name": "sklearn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/doc/tutorials/test_morf_split.ipynb b/doc/tutorials/test_morf_split.ipynb deleted file mode 100644 index 22f15ff18..000000000 --- a/doc/tutorials/test_morf_split.ipynb +++ /dev/null @@ -1,4063 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MORF API Working Demo\n", - "\n", - "Here, we want to validate visually that MORF API works as intended. First, we show the 2D contiguous convolutional splitter." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 1;\n", - " var nbb_unformatted_code = \"%load_ext nb_black\\n%load_ext lab_black\\n%load_ext autoreload\\n%autoreload 2\";\n", - " var nbb_formatted_code = \"%load_ext nb_black\\n%load_ext lab_black\\n%load_ext autoreload\\n%autoreload 2\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%load_ext nb_black\n", - "%load_ext lab_black\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 2;\n", - " var nbb_unformatted_code = \"import sys\\n\\nsys.path.append(\\\"../../\\\")\";\n", - " var nbb_formatted_code = \"import sys\\n\\nsys.path.append(\\\"../../\\\")\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import sys\n", - "\n", - "sys.path.append(\"../../\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 3;\n", - " var nbb_unformatted_code = \"import numpy as np\\nfrom oblique_forests.tree.morf_split import Conv2DSplitter\\n\\nimport matplotlib as mpl\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\";\n", - " var nbb_formatted_code = \"import numpy as np\\nfrom oblique_forests.tree.morf_split import Conv2DSplitter\\n\\nimport matplotlib as mpl\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "from oblique_forests.tree.morf_split import Conv2DSplitter\n", - "\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Splitters: Convolutional 2D Patches (Contiguous and Discontiguous)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Contiguous 2D Convolutional Patch" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 4;\n", - " var nbb_unformatted_code = \"random_state = 123456\\n\\nn = 50\\nheight = 5\\nd = 4\\nX = np.ones((n, height * d))\\ny = np.ones((n,))\\ny[:25] = 0\";\n", - " var nbb_formatted_code = \"random_state = 123456\\n\\nn = 50\\nheight = 5\\nd = 4\\nX = np.ones((n, height * d))\\ny = np.ones((n,))\\ny[:25] = 0\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "random_state = 123456\n", - "\n", - "n = 50\n", - "height = 5\n", - "d = 4\n", - "X = np.ones((n, height * d))\n", - "y = np.ones((n,))\n", - "y[:25] = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 6;\n", - " var nbb_unformatted_code = \"splitter = Conv2DSplitter(\\n X,\\n y,\\n max_features=1,\\n feature_combinations=1.5,\\n random_state=random_state,\\n image_height=height,\\n image_width=d,\\n patch_height_max=5,\\n patch_height_min=1,\\n patch_width_min=1,\\n patch_width_max=2,\\n)\";\n", - " var nbb_formatted_code = \"splitter = Conv2DSplitter(\\n X,\\n y,\\n max_features=1,\\n feature_combinations=1.5,\\n random_state=random_state,\\n image_height=height,\\n image_width=d,\\n patch_height_max=5,\\n patch_height_min=1,\\n patch_width_min=1,\\n patch_width_max=2,\\n)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "splitter = Conv2DSplitter(\n", - " X,\n", - " y,\n", - " max_features=1,\n", - " feature_combinations=1.5,\n", - " random_state=random_state,\n", - " image_height=height,\n", - " image_width=d,\n", - " patch_height_max=5,\n", - " patch_height_min=1,\n", - " patch_width_min=1,\n", - " patch_width_max=2,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "779 µs ± 41 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 8;\n", - " var nbb_unformatted_code = \"%%timeit\\nproj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))\";\n", - " var nbb_formatted_code = \"%%timeit\\nproj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%timeit\n", - "proj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 9;\n", - " var nbb_unformatted_code = \"proj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))\";\n", - " var nbb_formatted_code = \"proj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "proj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(50, 20) (20, 20) (50, 20)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 10;\n", - " var nbb_unformatted_code = \"print(proj_X.shape, proj_mat.shape, X.shape)\";\n", - " var nbb_formatted_code = \"print(proj_X.shape, proj_mat.shape, X.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(proj_X.shape, proj_mat.shape, X.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Text(0.5, 1.0, 'Sampled Projection Matrix - 2D Convolutional MORF'),\n", - " Text(0.5, 28.5, 'Sampled Patches'),\n", - " Text(28.5, 0.5, 'Vectorized Projections')]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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(nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\n", - " \"talk\",\n", - " # font_scale=1.5\n", - ")\n", - "fig, ax = plt.subplots(figsize=(7, 7))\n", - "sns.heatmap(proj_mat, annot=True, ax=ax)\n", - "ax.set(\n", - " title=\"Sampled Projection Matrix - 2D Convolutional MORF\",\n", - " xlabel=\"Sampled Patches\",\n", - " ylabel=\"Vectorized Projections\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":32: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - " fig.tight_layout(rect=[0, 0, .9, 1])\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\\nfig.suptitle('MORF 2D Convolutional Patches Sampled')\\nfig.tight_layout(rect=[0, 0, .9, 1])\";\n", - " var nbb_formatted_code = \"empty_mat = np.zeros((height, d))\\n\\nfig, axs = plt.subplots(\\n 3,\\n np.ceil(proj_mat.shape[1] / 3).astype(int),\\n sharex=True,\\n sharey=True,\\n figsize=(7, 7),\\n)\\naxs = axs.flat\\ncbar_ax = fig.add_axes([0.91, 0.3, 0.03, 0.4])\\n\\nfor idx in range(proj_mat.shape[1]):\\n proj_vec = proj_mat[:, idx]\\n\\n vec_idx = np.argwhere(proj_vec == 1)\\n patch_idx = np.unravel_index(vec_idx, shape=(height, d))\\n mat = empty_mat.copy()\\n mat[patch_idx] = 1.0\\n\\n sns.heatmap(\\n mat,\\n ax=axs[idx],\\n xticklabels=np.arange(d),\\n yticklabels=np.arange(height),\\n cbar=idx == 0,\\n square=True,\\n vmin=0,\\n vmax=1,\\n cbar_ax=None if idx else cbar_ax,\\n )\\n\\n# remove unused axes\\nidx += 1\\nwhile idx < len(axs):\\n fig.delaxes(axs[idx])\\n idx += 1\\n\\nfig.suptitle(\\\"MORF 2D Convolutional Patches Sampled\\\")\\nfig.tight_layout(rect=[0, 0, 0.9, 1])\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "empty_mat = np.zeros((height, d))\n", - "\n", - "fig, axs = plt.subplots(3, np.ceil(proj_mat.shape[1] / 3).astype(int), \n", - " sharex=True, sharey=True,\n", - " figsize=(7, 7))\n", - "axs = axs.flat\n", - "cbar_ax = fig.add_axes([.91, .3, .03, .4])\n", - "\n", - "for idx in range(proj_mat.shape[1]):\n", - " proj_vec = proj_mat[:, idx]\n", - " \n", - " vec_idx = np.argwhere(proj_vec == 1)\n", - " patch_idx = np.unravel_index(vec_idx, shape=(height, d))\n", - " mat = empty_mat.copy()\n", - " mat[patch_idx] = 1.0\n", - " \n", - " sns.heatmap(mat, ax=axs[idx], \n", - " xticklabels=np.arange(d),\n", - " yticklabels=np.arange(height),\n", - " cbar=idx == 0,\n", - " square=True,\n", - " vmin=0, vmax=1,\n", - " cbar_ax=None if idx else cbar_ax)\n", - "\n", - "# remove unused axes\n", - "idx += 1\n", - "while idx < len(axs):\n", - " fig.delaxes(axs[idx])\n", - " idx += 1\n", - " \n", - "fig.suptitle('MORF 2D Convolutional Patches Sampled')\n", - "fig.tight_layout(rect=[0, 0, .9, 1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Discontiguous Sample" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 60;\n", - " var nbb_unformatted_code = \"random_state = 123456\\n\\nn = 50\\nheight = 5\\nd = 4\\nX = np.ones((n, height * d))\\ny = np.ones((n,))\\ny[:25] = 0\";\n", - " var nbb_formatted_code = \"random_state = 123456\\n\\nn = 50\\nheight = 5\\nd = 4\\nX = np.ones((n, height * d))\\ny = np.ones((n,))\\ny[:25] = 0\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "random_state = 123456\n", - "\n", - "n = 50\n", - "height = 5\n", - "d = 4\n", - "X = np.ones((n, height * d))\n", - "y = np.ones((n,))\n", - "y[:25] = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 71;\n", - " var nbb_unformatted_code = \"splitter = Conv2DSplitter(\\n X,\\n y,\\n max_features=1,\\n feature_combinations=1.5,\\n random_state=random_state,\\n image_height=height,\\n image_width=d,\\n patch_height_max=5,\\n patch_height_min=1,\\n patch_width_min=1,\\n patch_width_max=2,\\n discontiguous_height=True,\\n discontiguous_width=False,\\n)\";\n", - " var nbb_formatted_code = \"splitter = Conv2DSplitter(\\n X,\\n y,\\n max_features=1,\\n feature_combinations=1.5,\\n random_state=random_state,\\n image_height=height,\\n image_width=d,\\n patch_height_max=5,\\n patch_height_min=1,\\n patch_width_min=1,\\n patch_width_max=2,\\n discontiguous_height=True,\\n discontiguous_width=False,\\n)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "splitter = Conv2DSplitter(\n", - " X,\n", - " y,\n", - " max_features=1,\n", - " feature_combinations=1.5,\n", - " random_state=random_state,\n", - " image_height=height,\n", - " image_width=d,\n", - " patch_height_max=5,\n", - " patch_height_min=1,\n", - " patch_width_min=1,\n", - " patch_width_max=2,\n", - " discontiguous_height=True,\n", - " discontiguous_width=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 72;\n", - " var nbb_unformatted_code = \"proj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))\";\n", - " var nbb_formatted_code = \"proj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "proj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(50, 20) (20, 20) (50, 20)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 73;\n", - " var nbb_unformatted_code = \"print(proj_X.shape, proj_mat.shape, X.shape)\";\n", - " var nbb_formatted_code = \"print(proj_X.shape, proj_mat.shape, X.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(proj_X.shape, proj_mat.shape, X.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Text(0.5, 1.0, 'Sampled Projection Matrix - 2D Convolutional MORF'),\n", - " Text(0.5, 28.5, 'Sampled Patches'),\n", - " Text(28.5, 0.5, 'Vectorized Projections')]" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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(nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\n", - " \"talk\",\n", - " # font_scale=1.5\n", - ")\n", - "fig, ax = plt.subplots(figsize=(7, 7))\n", - "sns.heatmap(proj_mat, annot=True, ax=ax)\n", - "ax.set(\n", - " title=\"Sampled Projection Matrix - 2D Convolutional MORF\",\n", - " xlabel=\"Sampled Patches\",\n", - " ylabel=\"Vectorized Projections\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":33: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - " fig.tight_layout(rect=[0, 0, .9, 1])\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 75;\n", - " var nbb_unformatted_code = \"empty_mat = np.zeros((height, d))\\n\\nsns.set_context('paper')\\nfig, axs = plt.subplots(3, np.ceil(proj_mat.shape[1] / 3).astype(int), \\n sharex=True, sharey=True,\\n figsize=(7, 7))\\naxs = axs.flat\\ncbar_ax = fig.add_axes([.91, .3, .03, .4])\\n\\nfor idx in range(proj_mat.shape[1]):\\n proj_vec = proj_mat[:, idx]\\n \\n vec_idx = np.argwhere(proj_vec == 1)\\n patch_idx = np.unravel_index(vec_idx, shape=(height, d))\\n mat = empty_mat.copy()\\n mat[patch_idx] = 1.0\\n \\n sns.heatmap(mat, ax=axs[idx], \\n xticklabels=np.arange(d),\\n yticklabels=np.arange(height),\\n cbar=idx == 0,\\n square=True,\\n vmin=0, vmax=1,\\n cbar_ax=None if idx else cbar_ax)\\n\\n# remove unused axes\\nidx += 1\\nwhile idx < len(axs):\\n fig.delaxes(axs[idx])\\n idx += 1\\n \\nfig.suptitle('MORF 2D Discontiguous Convolutional Patches Sampled')\\nfig.tight_layout(rect=[0, 0, .9, 1])\";\n", - " var nbb_formatted_code = \"empty_mat = np.zeros((height, d))\\n\\nsns.set_context(\\\"paper\\\")\\nfig, axs = plt.subplots(\\n 3,\\n np.ceil(proj_mat.shape[1] / 3).astype(int),\\n sharex=True,\\n sharey=True,\\n figsize=(7, 7),\\n)\\naxs = axs.flat\\ncbar_ax = fig.add_axes([0.91, 0.3, 0.03, 0.4])\\n\\nfor idx in range(proj_mat.shape[1]):\\n proj_vec = proj_mat[:, idx]\\n\\n vec_idx = np.argwhere(proj_vec == 1)\\n patch_idx = np.unravel_index(vec_idx, shape=(height, d))\\n mat = empty_mat.copy()\\n mat[patch_idx] = 1.0\\n\\n sns.heatmap(\\n mat,\\n ax=axs[idx],\\n xticklabels=np.arange(d),\\n yticklabels=np.arange(height),\\n cbar=idx == 0,\\n square=True,\\n vmin=0,\\n vmax=1,\\n cbar_ax=None if idx else cbar_ax,\\n )\\n\\n# remove unused axes\\nidx += 1\\nwhile idx < len(axs):\\n fig.delaxes(axs[idx])\\n idx += 1\\n\\nfig.suptitle(\\\"MORF 2D Discontiguous Convolutional Patches Sampled\\\")\\nfig.tight_layout(rect=[0, 0, 0.9, 1])\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "empty_mat = np.zeros((height, d))\n", - "\n", - "sns.set_context(\"paper\")\n", - "fig, axs = plt.subplots(\n", - " 3,\n", - " np.ceil(proj_mat.shape[1] / 3).astype(int),\n", - " sharex=True,\n", - " sharey=True,\n", - " figsize=(7, 7),\n", - ")\n", - "axs = axs.flat\n", - "cbar_ax = fig.add_axes([0.91, 0.3, 0.03, 0.4])\n", - "\n", - "for idx in range(proj_mat.shape[1]):\n", - " proj_vec = proj_mat[:, idx]\n", - "\n", - " vec_idx = np.argwhere(proj_vec == 1)\n", - " patch_idx = np.unravel_index(vec_idx, shape=(height, d))\n", - " mat = empty_mat.copy()\n", - " mat[patch_idx] = 1.0\n", - "\n", - " sns.heatmap(\n", - " mat,\n", - " ax=axs[idx],\n", - " xticklabels=np.arange(d),\n", - " yticklabels=np.arange(height),\n", - " cbar=idx == 0,\n", - " square=True,\n", - " vmin=0,\n", - " vmax=1,\n", - " cbar_ax=None if idx else cbar_ax,\n", - " )\n", - "\n", - "# remove unused axes\n", - "idx += 1\n", - "while idx < len(axs):\n", - " fig.delaxes(axs[idx])\n", - " idx += 1\n", - "\n", - "fig.suptitle(\"MORF 2D Discontiguous Convolutional Patches Sampled\")\n", - "fig.tight_layout(rect=[0, 0, 0.9, 1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Profiling the Projection Matrix Sampling\n", - "\n", - "Let's increase the sample size and the height and the width of samples.\n", - "\n", - "Note that if ``height`` or ``d`` is increased too much... then it's pretty slow currently." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 44;\n", - " var nbb_unformatted_code = \"n = 1000\\nheight = 100\\nd = 80\\nX = np.ones((n, height * d))\\ny = np.ones((n,))\\ny[:25] = 0\";\n", - " var nbb_formatted_code = \"n = 1000\\nheight = 100\\nd = 80\\nX = np.ones((n, height * d))\\ny = np.ones((n,))\\ny[:25] = 0\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n = 1000\n", - "height = 100\n", - "d = 80\n", - "X = np.ones((n, height * d))\n", - "y = np.ones((n,))\n", - "y[:25] = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 45;\n", - " var nbb_unformatted_code = \"splitter = Conv2DSplitter(X, y, max_features=1, feature_combinations=1.5,\\n random_state=random_state, image_height=height, image_width=d, \\n patch_height_max=5, patch_height_min=1, patch_width_min=1, patch_width_max=2)\";\n", - " var nbb_formatted_code = \"splitter = Conv2DSplitter(\\n X,\\n y,\\n max_features=1,\\n feature_combinations=1.5,\\n random_state=random_state,\\n image_height=height,\\n image_width=d,\\n patch_height_max=5,\\n patch_height_min=1,\\n patch_width_min=1,\\n patch_width_max=2,\\n)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "splitter = Conv2DSplitter(X, y, max_features=1, feature_combinations=1.5,\n", - " random_state=random_state, image_height=height, image_width=d, \n", - " patch_height_max=5, patch_height_min=1, patch_width_min=1, patch_width_max=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.53 s ± 101 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 47;\n", - " var nbb_unformatted_code = \"%%timeit\\nproj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))\";\n", - " var nbb_formatted_code = \"%%timeit\\nproj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - 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] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " " - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 35;\n", - " var nbb_unformatted_code = \"%%prun\\nproj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))\";\n", - " var nbb_formatted_code = \"%%prun\\nproj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - " 72026 function calls in 0.171 seconds\n", - "\n", - " Ordered by: internal time\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.084 0.084 0.167 0.167 morf.py:192(sample_proj_mat)\n", - " 2000 0.017 0.000 0.027 0.000 morf.py:131(_get_rand_patch_idx)\n", - " 6002 0.014 0.000 0.035 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}\n", - " 2002 0.010 0.000 0.010 0.000 {method 'randint' of 'numpy.random.mtrand.RandomState' objects}\n", - " 2000 0.008 0.000 0.021 0.000 index_tricks.py:35(ix_)\n", - " 4001 0.006 0.000 0.006 0.000 {built-in method numpy.arange}\n", - " 2000 0.004 0.000 0.043 0.000 morf.py:160(_get_patch_idx)\n", - " 1 0.004 0.004 0.171 0.171 :1()\n", - " 8000 0.003 0.000 0.005 0.000 numerictypes.py:285(issubclass_)\n", - " 4000 0.003 0.000 0.009 0.000 numerictypes.py:359(issubdtype)\n", - " 1 0.003 0.003 0.003 0.003 {built-in method numpy.zeros}\n", - " 4000 0.002 0.000 0.002 0.000 {method 'reshape' of 'numpy.ndarray' objects}\n", - 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" 2 0.000 0.000 0.000 0.000 fromnumeric.py:70(_wrapreduction)\n", - " 2 0.000 0.000 0.000 0.000 <__array_function__ internals>:2(prod)\n", - " 2 0.000 0.000 0.000 0.000 fromnumeric.py:2912(prod)\n", - " 2 0.000 0.000 0.000 0.000 fromnumeric.py:71()\n", - " 2 0.000 0.000 0.000 0.000 {built-in method builtins.getattr}\n", - " 2 0.000 0.000 0.000 0.000 fromnumeric.py:2907(_prod_dispatcher)\n", - " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", - " 2 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%prun\n", - "proj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " " - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 23;\n", - " var nbb_unformatted_code = \"%%prun\\nproj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))\";\n", - " var nbb_formatted_code = \"%%prun\\nproj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - " 76006 function calls in 0.170 seconds\n", - "\n", - " Ordered by: internal time\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.083 0.083 0.170 0.170 morf.py:201(sample_proj_mat)\n", - " 6000 0.021 0.000 0.021 0.000 {method 'randint' of 'numpy.random.mtrand.RandomState' objects}\n", - " 6000 0.015 0.000 0.036 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}\n", - " 2000 0.009 0.000 0.030 0.000 morf.py:131(_get_rand_patch_idx)\n", - " 2000 0.008 0.000 0.021 0.000 index_tricks.py:35(ix_)\n", - " 4001 0.006 0.000 0.006 0.000 {built-in method numpy.arange}\n", - " 2000 0.005 0.000 0.044 0.000 morf.py:169(_get_patch_idx)\n", - " 4000 0.004 0.000 0.009 0.000 numerictypes.py:359(issubdtype)\n", - " 8000 0.003 0.000 0.005 0.000 numerictypes.py:285(issubclass_)\n", - " 1 0.003 0.003 0.003 0.003 {built-in method numpy.zeros}\n", - " 4000 0.002 0.000 0.002 0.000 {method 'reshape' of 'numpy.ndarray' objects}\n", - " 12000 0.002 0.000 0.002 0.000 {built-in method builtins.issubclass}\n", - " 2000 0.002 0.000 0.011 0.000 morf.py:193(_compute_index_in_vectorized_data)\n", - " 2000 0.002 0.000 0.009 0.000 morf.py:198(_compute_vectorized_index_in_data)\n", - " 2000 0.001 0.000 0.008 0.000 <__array_function__ internals>:2(unravel_index)\n", - " 2000 0.001 0.000 0.009 0.000 <__array_function__ internals>:2(ravel_multi_index)\n", - " 2000 0.001 0.000 0.024 0.000 <__array_function__ internals>:2(ix_)\n", - " 4000 0.001 0.000 0.001 0.000 {built-in method builtins.isinstance}\n", - " 4000 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}\n", - " 2000 0.000 0.000 0.000 0.000 multiarray.py:1001(unravel_index)\n", - " 2000 0.000 0.000 0.000 0.000 {built-in method builtins.len}\n", - " 2000 0.000 0.000 0.000 0.000 multiarray.py:940(ravel_multi_index)\n", - " 2000 0.000 0.000 0.000 0.000 index_tricks.py:31(_ix__dispatcher)\n", - " 1 0.000 0.000 0.170 0.170 {built-in method builtins.exec}\n", - " 1 0.000 0.000 0.170 0.170 :1()\n", - " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%prun\n", - "proj_X, proj_mat = splitter.sample_proj_mat(sample_inds=np.arange(n))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Classification Tree - Convolutional Tree" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 52;\n", - " var nbb_unformatted_code = \"from oblique_forests.tree.morf_tree import Conv2DObliqueTreeClassifier\";\n", - " var nbb_formatted_code = \"from oblique_forests.tree.morf_tree import Conv2DObliqueTreeClassifier\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from oblique_forests.tree.morf_tree import Conv2DObliqueTreeClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "super(type, obj): obj must be an instance or subtype of type", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m clf = Conv2DObliqueTreeClassifier(\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mimage_height\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mimage_width\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpatch_height_max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/manifold_random_forests/oblique_forests/tree/morf_tree.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, max_depth, min_samples_split, min_samples_leaf, min_impurity_decrease, min_impurity_split, feature_combinations, max_features, image_height, image_width, patch_height_max, patch_height_min, patch_width_max, patch_width_min, discontiguous_height, discontiguous_width, bootstrap, random_state, warm_start, verbose)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m ):\n\u001b[0;32m---> 96\u001b[0;31m super(Conv2DObliqueTreeClassifier, self).__init__(\n\u001b[0m\u001b[1;32m 97\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_depth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0mmin_samples_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_samples_split\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: super(type, obj): obj must be an instance or subtype of type" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 53;\n", - " var nbb_unformatted_code = \"clf = Conv2DObliqueTreeClassifier(\\n random_state=random_state,\\n image_height=height,\\n image_width=d,\\n patch_height_max=5,\\n patch_height_min=1,\\n patch_width_min=1,\\n patch_width_max=2,\\n discontiguous_height=True,\\n discontiguous_width=False,\\n)\";\n", - " var nbb_formatted_code = \"clf = Conv2DObliqueTreeClassifier(\\n random_state=random_state,\\n image_height=height,\\n image_width=d,\\n patch_height_max=5,\\n patch_height_min=1,\\n patch_width_min=1,\\n patch_width_max=2,\\n discontiguous_height=True,\\n discontiguous_width=False,\\n)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "clf = Conv2DObliqueTreeClassifier(\n", - " random_state=random_state,\n", - " image_height=height,\n", - " image_width=d,\n", - " patch_height_max=5,\n", - " patch_height_min=1,\n", - " patch_width_min=1,\n", - " patch_width_max=2,\n", - " discontiguous_height=True,\n", - " discontiguous_width=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Conv2DObliqueTreeClassifier(discontiguous_height=True, image_height=5,\n", - " image_width=4, patch_height_max=5,\n", - " patch_width_max=2, random_state=123456)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 6;\n", - " var nbb_unformatted_code = \"clf.fit(X, y)\";\n", - " var nbb_formatted_code = \"clf.fit(X, y)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "clf.fit(X, y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Classification Forest - Convolutional Forest" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 7;\n", - " var nbb_unformatted_code = \"from proglearn.tree.morf import Conv2DObliqueForestClassifier\";\n", - " var nbb_formatted_code = \"from proglearn.tree.morf import Conv2DObliqueForestClassifier\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from proglearn.tree.morf import Conv2DObliqueForestClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'Conv2DObliqueForestClassifier' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m clf = Conv2DObliqueForestClassifier(\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mimage_height\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mimage_width\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'Conv2DObliqueForestClassifier' is not defined" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 11;\n", - " var nbb_unformatted_code = \"clf = Conv2DObliqueForestClassifier(\\n n_estimators=100,\\n random_state=random_state,\\n image_height=height,\\n image_width=d,\\n patch_height_max=5,\\n patch_height_min=1,\\n patch_width_min=1,\\n patch_width_max=2,\\n discontiguous_height=True,\\n discontiguous_width=False,\\n n_jobs=-1,\\n)\";\n", - " var nbb_formatted_code = \"clf = Conv2DObliqueForestClassifier(\\n n_estimators=100,\\n random_state=random_state,\\n image_height=height,\\n image_width=d,\\n patch_height_max=5,\\n patch_height_min=1,\\n patch_width_min=1,\\n patch_width_max=2,\\n discontiguous_height=True,\\n discontiguous_width=False,\\n n_jobs=-1,\\n)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "clf = Conv2DObliqueForestClassifier(\n", - " n_estimators=100,\n", - " random_state=random_state,\n", - " image_height=height,\n", - " image_width=d,\n", - " patch_height_max=5,\n", - " patch_height_min=1,\n", - " patch_width_min=1,\n", - " patch_width_max=2,\n", - " discontiguous_height=True,\n", - " discontiguous_width=False,\n", - " n_jobs=-1,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Conv2DObliqueForestClassifier(discontiguous_height=True, image_height=5,\n", - " image_width=4, n_jobs=-1, patch_height_max=5,\n", - " patch_width_max=2, random_state=123456)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 16;\n", - 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" 1 0.000 0.000 0.000 0.000 _parallel_backends.py:80(start_call)\n", - " 1 0.000 0.000 0.000 0.000 multiarray.py:143(concatenate)\n", - " 1 0.000 0.000 0.000 0.000 contextlib.py:59(_recreate_cm)\n", - " 1 0.000 0.000 0.000 0.000 {built-in method builtins.iter}" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%prun\n", - "clf.fit(X, y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plan\n", - "\n", - "- cythonize splitter in adherence to sklearn's `_splitter.pyx` pattern\n", - "- incorporate sklearn's `_criterion.pyx` code\n", - "- develop feature importances in Python\n", - "- develop graph sampling\n", - "- develop generalized convolutional filter bank: gabor, FT, wavelets\n", - " - to patch or not to patch?\n", - " - allowing Python lambda functions to be passed arbitrarily into the Cython tree builder?\n", - " \n", - " \n", - "Gabor filter:\n", - "1. When we sample a filter, it is KxK, and if we convolve this filter on an image, HxW, then we might sum up the response to represent as one number?\n", - "2. imaginary vs real filter response: (symmetric vs anti-symmetric), or taking the l2 norm of the signal response (square root of sum of squared real and imaginary components)\n", - "3. how to do patch-selection?" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 5;\n", - " var nbb_unformatted_code = \"from skimage.filters import gabor_kernel\\nfrom scipy import ndimage as ndi\\nfrom scipy.signal import convolve2d, convolve, fftconvolve\\nimport scipy\";\n", - " var nbb_formatted_code = \"from skimage.filters import gabor_kernel\\nfrom scipy import ndimage as ndi\\nfrom scipy.signal import convolve2d, convolve, fftconvolve\\nimport scipy\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from skimage.filters import gabor_kernel\n", - "from scipy import ndimage as ndi\n", - "from scipy.signal import convolve2d, convolve, fftconvolve\n", - "import scipy" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6.0\n", - "(13, 13) (13, 13)\n" - ] - }, - { - "data": { - "text/plain": [ - "(13, 13)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 6;\n", - " var nbb_unformatted_code = \"def output_shape(n_stds, sigma_x, sigma_y, theta):\\n y0 = np.ceil(\\n max(\\n np.abs(n_stds * sigma_y * np.cos(theta)),\\n np.abs(n_stds * sigma_x * np.sin(theta)),\\n 1,\\n )\\n )\\n\\n x0 = np.ceil(\\n max(\\n np.abs(n_stds * sigma_x * np.cos(theta)),\\n np.abs(n_stds * sigma_y * np.sin(theta)),\\n 1,\\n )\\n )\\n\\n print(y0)\\n y, x = np.mgrid[-y0 : y0 + 1, -x0 : x0 + 1]\\n print(y.shape, x.shape)\\n # print(y)\\n # print(x)\\n return y.shape\\n\\n\\nbandwidth = 1\\nfrequency = 0.5\\ntheta = 0\\nsigma_x = bandwidth / frequency\\nsigma_y = bandwidth / frequency\\n\\noutput_shape(3, sigma_x, sigma_y, theta)\";\n", - " var nbb_formatted_code = \"def output_shape(n_stds, sigma_x, sigma_y, theta):\\n y0 = np.ceil(\\n max(\\n np.abs(n_stds * sigma_y * np.cos(theta)),\\n np.abs(n_stds * sigma_x * np.sin(theta)),\\n 1,\\n )\\n )\\n\\n x0 = np.ceil(\\n max(\\n np.abs(n_stds * sigma_x * np.cos(theta)),\\n np.abs(n_stds * sigma_y * np.sin(theta)),\\n 1,\\n )\\n )\\n\\n print(y0)\\n y, x = np.mgrid[-y0 : y0 + 1, -x0 : x0 + 1]\\n print(y.shape, x.shape)\\n # print(y)\\n # print(x)\\n return y.shape\\n\\n\\nbandwidth = 1\\nfrequency = 0.5\\ntheta = 0\\nsigma_x = bandwidth / frequency\\nsigma_y = bandwidth / frequency\\n\\noutput_shape(3, sigma_x, sigma_y, theta)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def output_shape(n_stds, sigma_x, sigma_y, theta):\n", - " y0 = np.ceil(\n", - " max(\n", - " np.abs(n_stds * sigma_y * np.cos(theta)),\n", - " np.abs(n_stds * sigma_x * np.sin(theta)),\n", - " 1,\n", - " )\n", - " )\n", - "\n", - " x0 = np.ceil(\n", - " max(\n", - " np.abs(n_stds * sigma_x * np.cos(theta)),\n", - " np.abs(n_stds * sigma_y * np.sin(theta)),\n", - " 1,\n", - " )\n", - " )\n", - "\n", - " print(y0)\n", - " y, x = np.mgrid[-y0 : y0 + 1, -x0 : x0 + 1]\n", - " print(y.shape, x.shape)\n", - " # print(y)\n", - " # print(x)\n", - " return y.shape\n", - "\n", - "\n", - "bandwidth = 1\n", - "frequency = 0.5\n", - "theta = 0\n", - "sigma_x = bandwidth / frequency\n", - "sigma_y = bandwidth / frequency\n", - "\n", - "output_shape(3, sigma_x, sigma_y, theta)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(50, 20)\n", - "(50, 5, 4)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 22;\n", - " var nbb_unformatted_code = \"X = np.random.normal(size=(n, height * d))\\ny = np.ones((n,))\\nprint(X.shape)\\n\\nsample_X = X.reshape(50, height, d)\\nprint(sample_X.shape)\";\n", - " var nbb_formatted_code = \"X = np.random.normal(size=(n, height * d))\\ny = np.ones((n,))\\nprint(X.shape)\\n\\nsample_X = X.reshape(50, height, d)\\nprint(sample_X.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "X = np.random.normal(size=(n, height * d))\n", - "y = np.ones((n,))\n", - "print(X.shape)\n", - "\n", - "sample_X = X.reshape(50, height, d)\n", - "print(sample_X.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(19, 19)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 25;\n", - " var nbb_unformatted_code = \"frequency = 0.2\\nkernel = gabor_kernel(frequency=frequency, n_stds=3, bandwidth=1)\\nprint(kernel.shape)\";\n", - " var nbb_formatted_code = \"frequency = 0.2\\nkernel = gabor_kernel(frequency=frequency, n_stds=3, bandwidth=1)\\nprint(kernel.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "frequency = 0.2\n", - "kernel = gabor_kernel(frequency=frequency, n_stds=3, bandwidth=1)\n", - "print(kernel.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", - " sample_X = torch.tensor(sample_X.reshape(50, 1, height, d))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2, 1, 19, 19)\n", - "torch.Size([50, 2, 5, 4])\n", - "torch.Size([2, 1, 19, 19])\n", - "torch.Size([50, 1, 5, 4])\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 32;\n", - " var nbb_unformatted_code = \"pad_size = list(map(int, ((kernel.shape[0] - 1) / 2, (kernel.shape[1] - 1) / 2)))\\n\\nsample_X = torch.tensor(sample_X.reshape(50, 1, height, d))\\ntensor_kernel = kernel.reshape(1, 1, kernel.shape[0], kernel.shape[1])\\nreal_kernel = tensor_kernel.real\\nimag_kernel = tensor_kernel.imag\\n\\ntensor_kernel = np.concatenate((real_kernel, imag_kernel), axis=0)\\nprint(tensor_kernel.shape)\\ntensor_kernel = torch.tensor(tensor_kernel)\\n\\n\\noutput = torch.conv2d(sample_X, tensor_kernel, padding=pad_size)\\n\\nprint(output.shape)\\nprint(tensor_kernel.shape)\\nprint(sample_X.shape)\";\n", - " var nbb_formatted_code = \"pad_size = list(map(int, ((kernel.shape[0] - 1) / 2, (kernel.shape[1] - 1) / 2)))\\n\\nsample_X = torch.tensor(sample_X.reshape(50, 1, height, d))\\ntensor_kernel = kernel.reshape(1, 1, kernel.shape[0], kernel.shape[1])\\nreal_kernel = tensor_kernel.real\\nimag_kernel = tensor_kernel.imag\\n\\ntensor_kernel = np.concatenate((real_kernel, imag_kernel), axis=0)\\nprint(tensor_kernel.shape)\\ntensor_kernel = torch.tensor(tensor_kernel)\\n\\n\\noutput = torch.conv2d(sample_X, tensor_kernel, padding=pad_size)\\n\\nprint(output.shape)\\nprint(tensor_kernel.shape)\\nprint(sample_X.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pad_size = list(map(int, ((kernel.shape[0] - 1) / 2, (kernel.shape[1] - 1) / 2)))\n", - "\n", - "sample_X = torch.tensor(sample_X.reshape(50, 1, height, d))\n", - "tensor_kernel = kernel.reshape(1, 1, kernel.shape[0], kernel.shape[1])\n", - "real_kernel = tensor_kernel.real\n", - "imag_kernel = tensor_kernel.imag\n", - "\n", - "tensor_kernel = np.concatenate((real_kernel, imag_kernel), axis=0)\n", - "print(tensor_kernel.shape)\n", - "tensor_kernel = torch.tensor(tensor_kernel)\n", - "\n", - "\n", - "output = torch.conv2d(sample_X, tensor_kernel, padding=pad_size)\n", - "\n", - "print(output.shape)\n", - "print(tensor_kernel.shape)\n", - "print(sample_X.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(5, 4)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 31;\n", - " var nbb_unformatted_code = \"test = convolve(sample_X[0, ...], kernel, mode='same')\\nprint(test.shape)\\nsns.heatmap(test.real)\";\n", - " var nbb_formatted_code = \"test = convolve(sample_X[0, ...], kernel, mode=\\\"same\\\")\\nprint(test.shape)\\nsns.heatmap(test.real)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "test = convolve(sample_X[0, ...], kernel, mode=\"same\")\n", - "print(test.shape)\n", - "sns.heatmap(test.real)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 34;\n", - " var nbb_unformatted_code = \"frequency = 0.2\\n\\nfor frequency in np.linspace(0.01, 1.0, 10):\\n\\n kernel = gabor_kernel(\\n frequency=frequency,\\n n_stds=3,\\n bandwidth=1.5\\n # sigma_x=1, sigma_y=1\\n )\\n test = convolve(sample_X[0, ...], kernel, mode=\\\"same\\\")\\n plt.figure()\\n sns.heatmap(test.real)\\n plt.title(test.real.sum())\\n\\n # plt.figure()\\n # sns.heatmap(kernel.imag)\";\n", - " var nbb_formatted_code = \"frequency = 0.2\\n\\nfor frequency in np.linspace(0.01, 1.0, 10):\\n\\n kernel = gabor_kernel(\\n frequency=frequency,\\n n_stds=3,\\n bandwidth=1.5\\n # sigma_x=1, sigma_y=1\\n )\\n test = convolve(sample_X[0, ...], kernel, mode=\\\"same\\\")\\n plt.figure()\\n sns.heatmap(test.real)\\n plt.title(test.real.sum())\\n\\n # plt.figure()\\n # sns.heatmap(kernel.imag)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "frequency = 0.2\n", - "\n", - "for frequency in np.linspace(0.01, 1.0, 10):\n", - "\n", - " kernel = gabor_kernel(\n", - " frequency=frequency,\n", - " n_stds=3,\n", - " bandwidth=1.5\n", - " # sigma_x=1, sigma_y=1\n", - " )\n", - " test = convolve(sample_X[0, ...], kernel, mode=\"same\")\n", - " plt.figure()\n", - " sns.heatmap(test.real)\n", - " plt.title(test.real.sum())\n", - "\n", - " # plt.figure()\n", - " # sns.heatmap(kernel.imag)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(19, 19)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 25;\n", - " var nbb_unformatted_code = \"frequency = 0.2\\nkernel = gabor_kernel(frequency=frequency, n_stds=3, bandwidth=1)\\nprint(kernel.shape)\";\n", - " var nbb_formatted_code = \"frequency = 0.2\\nkernel = gabor_kernel(frequency=frequency, n_stds=3, bandwidth=1)\\nprint(kernel.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "frequency = 0.2\n", - "kernel = gabor_kernel(frequency=frequency, n_stds=3, bandwidth=1)\n", - "print(kernel.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Image shape: 3 4\n", - "Initial kernel shape: (19, 19)\n", - "The final output size (21, 22) of the vectorized convolution kernel\n", - "(21, 22)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 86;\n", - " var nbb_unformatted_code = \"image_height = height\\nimage_width = d\\nimage_height = 2 + 1\\nimage_width = 3 + 1\\n\\n# reference: https://stackoverflow.com/questions/16798888/2-d-convolution-as-a-matrix-matrix-multiplication\\noutput_size = (image_height + kernel.shape[0] - 1, image_width + kernel.shape[1] - 1)\\n\\n# zero-pad filter matrix\\npad_width = [\\n (output_size[0] - kernel.shape[0], 0),\\n (0, output_size[1] - kernel.shape[1]),\\n]\\nkernel_padded = np.pad(kernel, pad_width=pad_width)\\n\\nprint(f\\\"Image shape: \\\", image_height, image_width)\\nprint(f\\\"Initial kernel shape: \\\", kernel.shape)\\nprint(f\\\"The final output size {output_size} of the vectorized convolution kernel\\\")\\nprint(kernel_padded.shape)\";\n", - " var nbb_formatted_code = \"image_height = height\\nimage_width = d\\nimage_height = 2 + 1\\nimage_width = 3 + 1\\n\\n# reference: https://stackoverflow.com/questions/16798888/2-d-convolution-as-a-matrix-matrix-multiplication\\noutput_size = (image_height + kernel.shape[0] - 1, image_width + kernel.shape[1] - 1)\\n\\n# zero-pad filter matrix\\npad_width = [\\n (output_size[0] - kernel.shape[0], 0),\\n (0, output_size[1] - kernel.shape[1]),\\n]\\nkernel_padded = np.pad(kernel, pad_width=pad_width)\\n\\nprint(f\\\"Image shape: \\\", image_height, image_width)\\nprint(f\\\"Initial kernel shape: \\\", kernel.shape)\\nprint(f\\\"The final output size {output_size} of the vectorized convolution kernel\\\")\\nprint(kernel_padded.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "image_height = height\n", - "image_width = d\n", - "image_height = 2 + 1\n", - "image_width = 3 + 1\n", - "\n", - "# reference: https://stackoverflow.com/questions/16798888/2-d-convolution-as-a-matrix-matrix-multiplication\n", - "output_size = (image_height + kernel.shape[0] - 1, image_width + kernel.shape[1] - 1)\n", - "\n", - "# zero-pad filter matrix\n", - "pad_width = [\n", - " (output_size[0] - kernel.shape[0], 0),\n", - " (0, output_size[1] - kernel.shape[1]),\n", - "]\n", - "kernel_padded = np.pad(kernel, pad_width=pad_width)\n", - "\n", - "print(f\"Image shape: \", image_height, image_width)\n", - "print(f\"Initial kernel shape: \", kernel.shape)\n", - "print(f\"The final output size {output_size} of the vectorized convolution kernel\")\n", - "print(kernel_padded.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(22, 6)\n", - "21\n", - "12 (462, 12)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 87;\n", - " var nbb_unformatted_code = \"# create the toeplitz matrix for each row of the filter\\ntoeplitz_list = []\\nfor i in range(kernel_padded.shape[0]):\\n c = kernel_padded[\\n i, :\\n ] # i th row of the F to define first column of toeplitz matrix\\n\\n # first row for the toeplitz fuction should be defined otherwise\\n # the result is wrong\\n r = np.hstack([c[0], np.zeros(int(image_width * image_height / 2) - 1)])\\n\\n # create the toeplitz matrix\\n toeplitz_m = scipy.linalg.toeplitz(c, r)\\n\\n assert toeplitz_m.shape == (kernel_padded.shape[1], len(r))\\n\\n # print(toeplitz_m.shape)\\n toeplitz_list.append(toeplitz_m)\\n\\n# create block matrix\\nzero_block = np.zeros(toeplitz_m.shape)\\nblock_seq = []\\nfor idx, block in enumerate(toeplitz_list):\\n if idx == 0:\\n block_seq.append([block, zero_block])\\n else:\\n block_seq.append([block, toeplitz_list[idx - 1]])\\ndoubly_block_mat = np.block(block_seq)\\n\\nprint(toeplitz_m.shape)\\nprint(len(toeplitz_list))\\nprint(image_height * image_width, doubly_block_mat.shape)\\n# print(doubly_indices.shape)\\n\\nassert image_height * image_width == doubly_block_mat.shape[1]\";\n", - " var nbb_formatted_code = \"# create the toeplitz matrix for each row of the filter\\ntoeplitz_list = []\\nfor i in range(kernel_padded.shape[0]):\\n c = kernel_padded[\\n i, :\\n ] # i th row of the F to define first column of toeplitz matrix\\n\\n # first row for the toeplitz fuction should be defined otherwise\\n # the result is wrong\\n r = np.hstack([c[0], np.zeros(int(image_width * image_height / 2) - 1)])\\n\\n # create the toeplitz matrix\\n toeplitz_m = scipy.linalg.toeplitz(c, r)\\n\\n assert toeplitz_m.shape == (kernel_padded.shape[1], len(r))\\n\\n # print(toeplitz_m.shape)\\n toeplitz_list.append(toeplitz_m)\\n\\n# create block matrix\\nzero_block = np.zeros(toeplitz_m.shape)\\nblock_seq = []\\nfor idx, block in enumerate(toeplitz_list):\\n if idx == 0:\\n block_seq.append([block, zero_block])\\n else:\\n block_seq.append([block, toeplitz_list[idx - 1]])\\ndoubly_block_mat = np.block(block_seq)\\n\\nprint(toeplitz_m.shape)\\nprint(len(toeplitz_list))\\nprint(image_height * image_width, doubly_block_mat.shape)\\n# print(doubly_indices.shape)\\n\\nassert image_height * image_width == doubly_block_mat.shape[1]\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# create the toeplitz matrix for each row of the filter\n", - "toeplitz_list = []\n", - "for i in range(kernel_padded.shape[0]):\n", - " c = kernel_padded[\n", - " i, :\n", - " ] # i th row of the F to define first column of toeplitz matrix\n", - "\n", - " # first row for the toeplitz fuction should be defined otherwise\n", - " # the result is wrong\n", - " r = np.hstack([c[0], np.zeros(int(image_width * image_height / 2) - 1)])\n", - "\n", - " # create the toeplitz matrix\n", - " toeplitz_m = scipy.linalg.toeplitz(c, r)\n", - "\n", - " assert toeplitz_m.shape == (kernel_padded.shape[1], len(r))\n", - "\n", - " # print(toeplitz_m.shape)\n", - " toeplitz_list.append(toeplitz_m)\n", - "\n", - "# create block matrix\n", - "zero_block = np.zeros(toeplitz_m.shape)\n", - "block_seq = []\n", - "for idx, block in enumerate(toeplitz_list):\n", - " if idx == 0:\n", - " block_seq.append([block, zero_block])\n", - " else:\n", - " block_seq.append([block, toeplitz_list[idx - 1]])\n", - "doubly_block_mat = np.block(block_seq)\n", - "\n", - "print(toeplitz_m.shape)\n", - "print(len(toeplitz_list))\n", - "print(image_height * image_width, doubly_block_mat.shape)\n", - "# print(doubly_indices.shape)\n", - "\n", - "assert image_height * image_width == doubly_block_mat.shape[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 25;\n", - " var nbb_unformatted_code = \"test = convolve(sample_X[0, ...], kernel, mode=\\\"same\\\")\";\n", - " var nbb_formatted_code = \"test = convolve(sample_X[0, ...], kernel, mode=\\\"same\\\")\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "test = convolve(sample_X[0, ...], kernel, mode=\"same\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 15;\n", - " var nbb_unformatted_code = \"from scipy import linalg\\n\\n\\ndef toeplitz_1_ch(kernel, input_size):\\n # shapes\\n k_h, k_w = kernel.shape\\n i_h, i_w = input_size\\n # o_h, o_w = i_h-k_h+1, i_w-k_w+1\\n o_h, o_w = i_h, i_w\\n # construct 1d conv toeplitz matrices for the kernel, with \\\"same\\\" padding\\n n = i_h\\n\\n K1 = np.zeros((n,))\\n K1[:2] = (kernel[1, 1], kernel[1, 2])\\n K2 = np.zeros((n,))\\n K2[:2] = (kernel[1, 1], kernel[1, 0])\\n\\n K = linalg.toeplitz(c=K2, r=K1)\\n KK = np.identity(n)\\n\\n L1 = np.zeros((n,))\\n L1[:2] = (kernel[2, 1], kernel[2, 2])\\n L2 = np.zeros((n,))\\n L2[:2] = (kernel[2, 1], kernel[2, 0])\\n\\n t = np.zeros(n)\\n s = np.zeros(n)\\n s[1] = 1\\n L = linalg.toeplitz(c=L2, r=L1)\\n LL = linalg.toeplitz(r=s, c=t)\\n\\n A = np.kron(LL, L) + np.kron(KK, K)\\n\\n L1 = np.zeros((n,))\\n L1[:2] = (kernel[0, 1], kernel[0, 2])\\n L2 = np.zeros((n,))\\n L2[:2] = (kernel[0, 1], kernel[0, 0])\\n\\n L = linalg.toeplitz(c=L2, r=L1)\\n LL = linalg.toeplitz(c=s, r=t)\\n A = A + np.kron(LL, L)\\n return A\\n\\n\\ndef toeplitz_mult_ch(kernel, output_size):\\n \\\"\\\"\\\"Compute toeplitz matrix for 2d conv with multiple in and out channels.\\n Args:\\n kernel: shape=(n_out, n_in, H_k, W_k)\\n input_size: (n_in, H_i, W_i)\\\"\\\"\\\"\\n # reference: https://stackoverflow.com/questions/60643786/2d-convolution-with-padding-same-via-toeplitz-matrix-multiplication\\n kernel_size = kernel.shape\\n input_size = output_size\\n # same padding should result in the shape as\\n # input image\\n # output_size = (image_height, image_width)\\n # T = np.zeros(\\n # (\\n # output_size[0],\\n # int(np.prod(output_size[1:])),\\n # input_size[0],\\n # int(np.prod(input_size[1:])),\\n # )\\n # )\\n\\n for i, ks in enumerate(kernel): # loop over output channel\\n for j, k in enumerate(ks): # loop over input channel\\n T_k = toeplitz_1_ch(k, input_size[1:])\\n T[i, :, j, :] = T_k\\n T.shape = (np.prod(output_size), np.prod(input_size))\\n\\n return T\";\n", - " var nbb_formatted_code = \"from scipy import linalg\\n\\n\\ndef toeplitz_1_ch(kernel, input_size):\\n # shapes\\n k_h, k_w = kernel.shape\\n i_h, i_w = input_size\\n # o_h, o_w = i_h-k_h+1, i_w-k_w+1\\n o_h, o_w = i_h, i_w\\n # construct 1d conv toeplitz matrices for the kernel, with \\\"same\\\" padding\\n n = i_h\\n\\n K1 = np.zeros((n,))\\n K1[:2] = (kernel[1, 1], kernel[1, 2])\\n K2 = np.zeros((n,))\\n K2[:2] = (kernel[1, 1], kernel[1, 0])\\n\\n K = linalg.toeplitz(c=K2, r=K1)\\n KK = np.identity(n)\\n\\n L1 = np.zeros((n,))\\n L1[:2] = (kernel[2, 1], kernel[2, 2])\\n L2 = np.zeros((n,))\\n L2[:2] = (kernel[2, 1], kernel[2, 0])\\n\\n t = np.zeros(n)\\n s = np.zeros(n)\\n s[1] = 1\\n L = linalg.toeplitz(c=L2, r=L1)\\n LL = linalg.toeplitz(r=s, c=t)\\n\\n A = np.kron(LL, L) + np.kron(KK, K)\\n\\n L1 = np.zeros((n,))\\n L1[:2] = (kernel[0, 1], kernel[0, 2])\\n L2 = np.zeros((n,))\\n L2[:2] = (kernel[0, 1], kernel[0, 0])\\n\\n L = linalg.toeplitz(c=L2, r=L1)\\n LL = linalg.toeplitz(c=s, r=t)\\n A = A + np.kron(LL, L)\\n return A\\n\\n\\ndef toeplitz_mult_ch(kernel, output_size):\\n \\\"\\\"\\\"Compute toeplitz matrix for 2d conv with multiple in and out channels.\\n Args:\\n kernel: shape=(n_out, n_in, H_k, W_k)\\n input_size: (n_in, H_i, W_i)\\\"\\\"\\\"\\n # reference: https://stackoverflow.com/questions/60643786/2d-convolution-with-padding-same-via-toeplitz-matrix-multiplication\\n kernel_size = kernel.shape\\n input_size = output_size\\n # same padding should result in the shape as\\n # input image\\n # output_size = (image_height, image_width)\\n # T = np.zeros(\\n # (\\n # output_size[0],\\n # int(np.prod(output_size[1:])),\\n # input_size[0],\\n # int(np.prod(input_size[1:])),\\n # )\\n # )\\n\\n for i, ks in enumerate(kernel): # loop over output channel\\n for j, k in enumerate(ks): # loop over input channel\\n T_k = toeplitz_1_ch(k, input_size[1:])\\n T[i, :, j, :] = T_k\\n T.shape = (np.prod(output_size), np.prod(input_size))\\n\\n return T\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from scipy import linalg\n", - "\n", - "\n", - "def toeplitz_1_ch(kernel, input_size):\n", - " # shapes\n", - " k_h, k_w = kernel.shape\n", - " i_h, i_w = input_size\n", - " # o_h, o_w = i_h-k_h+1, i_w-k_w+1\n", - " o_h, o_w = i_h, i_w\n", - " # construct 1d conv toeplitz matrices for the kernel, with \"same\" padding\n", - " n = i_h\n", - "\n", - " K1 = np.zeros((n,))\n", - " K1[:2] = (kernel[1, 1], kernel[1, 2])\n", - " K2 = np.zeros((n,))\n", - " K2[:2] = (kernel[1, 1], kernel[1, 0])\n", - "\n", - " K = linalg.toeplitz(c=K2, r=K1)\n", - " KK = np.identity(n)\n", - "\n", - " L1 = np.zeros((n,))\n", - " L1[:2] = (kernel[2, 1], kernel[2, 2])\n", - " L2 = np.zeros((n,))\n", - " L2[:2] = (kernel[2, 1], kernel[2, 0])\n", - "\n", - " t = np.zeros(n)\n", - " s = np.zeros(n)\n", - " s[1] = 1\n", - " L = linalg.toeplitz(c=L2, r=L1)\n", - " LL = linalg.toeplitz(r=s, c=t)\n", - "\n", - " A = np.kron(LL, L) + np.kron(KK, K)\n", - "\n", - " L1 = np.zeros((n,))\n", - " L1[:2] = (kernel[0, 1], kernel[0, 2])\n", - " L2 = np.zeros((n,))\n", - " L2[:2] = (kernel[0, 1], kernel[0, 0])\n", - "\n", - " L = linalg.toeplitz(c=L2, r=L1)\n", - " LL = linalg.toeplitz(c=s, r=t)\n", - " A = A + np.kron(LL, L)\n", - " return A\n", - "\n", - "\n", - "def toeplitz_mult_ch(kernel, output_size):\n", - " \"\"\"Compute toeplitz matrix for 2d conv with multiple in and out channels.\n", - " Args:\n", - " kernel: shape=(n_out, n_in, H_k, W_k)\n", - " input_size: (n_in, H_i, W_i)\"\"\"\n", - " # reference: https://stackoverflow.com/questions/60643786/2d-convolution-with-padding-same-via-toeplitz-matrix-multiplication\n", - " kernel_size = kernel.shape\n", - " input_size = output_size\n", - " # same padding should result in the shape as\n", - " # input image\n", - " # output_size = (image_height, image_width)\n", - " # T = np.zeros(\n", - " # (\n", - " # output_size[0],\n", - " # int(np.prod(output_size[1:])),\n", - " # input_size[0],\n", - " # int(np.prod(input_size[1:])),\n", - " # )\n", - " # )\n", - "\n", - "# for i, ks in enumerate(kernel): # loop over output channel\n", - "# for j, k in enumerate(ks): # loop over input channel\n", - " T_k = toeplitz_1_ch(k, input_size[1:])\n", - " T[i, :, j, :] = T_k\n", - " T.shape = (np.prod(output_size), np.prod(input_size))\n", - "\n", - " return T" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "ename": "IndexError", - "evalue": "index 2 is out of bounds for axis 1 with size 2", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtoeplitz_1_ch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0minput_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# shapes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mtoeplitz_1_ch\u001b[0;34m(kernel, input_size)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mK1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mK1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0mK2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mK2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mIndexError\u001b[0m: index 2 is out of bounds for axis 1 with size 2" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 18;\n", - " var nbb_unformatted_code = \"T = toeplitz_1_ch(k, i.shape)\\nkernel = k\\ninput_size = i.shape\\n\\n# shapes\\nk_h, k_w = kernel.shape\\ni_h, i_w = input_size\\n# o_h, o_w = i_h-k_h+1, i_w-k_w+1\\no_h, o_w = i_h, i_w\\n# construct 1d conv toeplitz matrices for the kernel, with \\\"same\\\" padding\\nn = i_h\\n\\nK1 = np.zeros((n,))\\nK1[:2] = (kernel[1, 1], kernel[1, 2])\\n\\nprint(K1)\\nK2 = np.zeros((n,))\\nK2[:2] = (kernel[1, 1], kernel[1, 0])\\n\\nK = linalg.toeplitz(c=K2, r=K1)\\nKK = np.identity(n)\\n\\nL1 = np.zeros((n,))\\nL1[:2] = (kernel[2, 1], kernel[2, 2])\\nL2 = np.zeros((n,))\\nL2[:2] = (kernel[2, 1], kernel[2, 0])\\n\\nt = np.zeros(n)\\ns = np.zeros(n)\\ns[1] = 1\\nL = linalg.toeplitz(c=L2, r=L1)\\nLL = linalg.toeplitz(r=s, c=t)\\n\\nA = np.kron(LL, L) + np.kron(KK, K)\\n\\nL1 = np.zeros((n,))\\nL1[:2] = (kernel[0, 1], kernel[0, 2])\\nL2 = np.zeros((n,))\\nL2[:2] = (kernel[0, 1], kernel[0, 0])\\n\\nL = linalg.toeplitz(c=L2, r=L1)\\nLL = linalg.toeplitz(c=s, r=t)\\nA = A + np.kron(LL, L)\\n\\nprint(T)\";\n", - " var nbb_formatted_code = \"T = toeplitz_1_ch(k, i.shape)\\nkernel = k\\ninput_size = i.shape\\n\\n# shapes\\nk_h, k_w = kernel.shape\\ni_h, i_w = input_size\\n# o_h, o_w = i_h-k_h+1, i_w-k_w+1\\no_h, o_w = i_h, i_w\\n# construct 1d conv toeplitz matrices for the kernel, with \\\"same\\\" padding\\nn = i_h\\n\\nK1 = np.zeros((n,))\\nK1[:2] = (kernel[1, 1], kernel[1, 2])\\n\\nprint(K1)\\nK2 = np.zeros((n,))\\nK2[:2] = (kernel[1, 1], kernel[1, 0])\\n\\nK = linalg.toeplitz(c=K2, r=K1)\\nKK = np.identity(n)\\n\\nL1 = np.zeros((n,))\\nL1[:2] = (kernel[2, 1], kernel[2, 2])\\nL2 = np.zeros((n,))\\nL2[:2] = (kernel[2, 1], kernel[2, 0])\\n\\nt = np.zeros(n)\\ns = np.zeros(n)\\ns[1] = 1\\nL = linalg.toeplitz(c=L2, r=L1)\\nLL = linalg.toeplitz(r=s, c=t)\\n\\nA = np.kron(LL, L) + np.kron(KK, K)\\n\\nL1 = np.zeros((n,))\\nL1[:2] = (kernel[0, 1], kernel[0, 2])\\nL2 = np.zeros((n,))\\nL2[:2] = (kernel[0, 1], kernel[0, 0])\\n\\nL = linalg.toeplitz(c=L2, r=L1)\\nLL = linalg.toeplitz(c=s, r=t)\\nA = A + np.kron(LL, L)\\n\\nprint(T)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "T = toeplitz_1_ch(k, i.shape)\n", - "kernel = k\n", - "input_size = i.shape\n", - "\n", - "# shapes\n", - "k_h, k_w = kernel.shape\n", - "i_h, i_w = input_size\n", - "# o_h, o_w = i_h-k_h+1, i_w-k_w+1\n", - "o_h, o_w = i_h, i_w\n", - "# construct 1d conv toeplitz matrices for the kernel, with \"same\" padding\n", - "n = i_h\n", - "\n", - "K1 = np.zeros((n,))\n", - "K1[:2] = (kernel[1, 1], kernel[1, 2])\n", - "K2 = np.zeros((n,))\n", - "K2[:2] = (kernel[1, 1], kernel[1, 0])\n", - "\n", - "K = linalg.toeplitz(c=K2, r=K1)\n", - "KK = np.identity(n)\n", - "\n", - "L1 = np.zeros((n,))\n", - "L1[:2] = (kernel[2, 1], kernel[2, 2])\n", - "L2 = np.zeros((n,))\n", - "L2[:2] = (kernel[2, 1], kernel[2, 0])\n", - "\n", - "t = np.zeros(n)\n", - "s = np.zeros(n)\n", - "s[1] = 1\n", - "L = linalg.toeplitz(c=L2, r=L1)\n", - "LL = linalg.toeplitz(r=s, c=t)\n", - "\n", - "A = np.kron(LL, L) + np.kron(KK, K)\n", - "\n", - "L1 = np.zeros((n,))\n", - "L1[:2] = (kernel[0, 1], kernel[0, 2])\n", - "L2 = np.zeros((n,))\n", - "L2[:2] = (kernel[0, 1], kernel[0, 0])\n", - "\n", - "L = linalg.toeplitz(c=L2, r=L1)\n", - "LL = linalg.toeplitz(c=s, r=t)\n", - "A = A + np.kron(LL, L)\n", - "\n", - "print(T)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(5, 4) (2, 2)\n" - ] - }, - { - "ename": "ValueError", - "evalue": "not enough values to unpack (expected 2, got 0)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtoeplitz_mult_ch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mtoeplitz_mult_ch\u001b[0;34m(kernel, output_size)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mks\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# loop over output channel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# loop over input channel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m \u001b[0mT_k\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtoeplitz_1_ch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_size\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 68\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mT_k\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mtoeplitz_1_ch\u001b[0;34m(kernel, input_size)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtoeplitz_1_ch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# shapes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mk_h\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk_w\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mi_h\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi_w\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# o_h, o_w = i_h-k_h+1, i_w-k_w+1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 2, got 0)" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 16;\n", - " var nbb_unformatted_code = \"k = np.random.normal(size=(2, 2))\\ni = np.random.randn(5, 4)\\n\\nprint(i.shape, k.shape)\\nT = toeplitz_mult_ch(k, i.shape)\\nout = T.dot(i.flatten()).reshape((1, 4, 9, 9))\";\n", - " var nbb_formatted_code = \"k = np.random.normal(size=(2, 2))\\ni = np.random.randn(5, 4)\\n\\nprint(i.shape, k.shape)\\nT = toeplitz_mult_ch(k, i.shape)\\nout = T.dot(i.flatten()).reshape((1, 4, 9, 9))\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "k = np.random.normal(size=(2, 2))\n", - "i = np.random.randn(5, 4)\n", - "\n", - "print(i.shape, k.shape)\n", - "T = toeplitz_mult_ch(k, i.shape)\n", - "out = T.dot(i.flatten()).reshape((1, 4, 9, 9))" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(4, 3, 3, 3) (3, 9, 9)\n", - "(324, 243)\n", - "(1, 4, 9, 9)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 94;\n", - " var nbb_unformatted_code = \"print(k.shape, i.shape)\\nprint(T.shape)\\nprint(out.shape)\";\n", - " var nbb_formatted_code = \"print(k.shape, i.shape)\\nprint(T.shape)\\nprint(out.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(k.shape, i.shape)\n", - "print(T.shape)\n", - "print(out.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 115;\n", - " var nbb_unformatted_code = \"def _convolutional_kernel_matrix(kernel, image_height, image_width, mode=\\\"same\\\"):\\n # reference: https://stackoverflow.com/questions/16798888/2-d-convolution-as-a-matrix-matrix-multiplication\\n if mode == \\\"same\\\":\\n pad_size = ((kernel.shape[0] - 1) / 2, (kernel.shape[1] - 1) / 2)\\n image_height = int(image_height + pad_size[0])\\n image_width = int(image_width + pad_size[1])\\n\\n # get output size of the data\\n output_size = (\\n image_height + kernel.shape[0] - 1,\\n image_width + kernel.shape[1] - 1,\\n )\\n\\n # zero-pad filter matrix\\n pad_width = [\\n (output_size[0] - kernel.shape[0], 0),\\n (0, output_size[1] - kernel.shape[1]),\\n ]\\n kernel_padded = np.pad(kernel, pad_width=pad_width)\\n\\n # create the toeplitz matrix for each row of the filter\\n toeplitz_list = []\\n for i in range(kernel_padded.shape[0]):\\n c = kernel_padded[\\n i, :\\n ] # i th row of the F to define first column of toeplitz matrix\\n\\n # first row for the toeplitz fuction should be defined otherwise\\n # the result is wrong\\n r = np.hstack([c[0], np.zeros(int(image_width * image_height / 2) - 1)])\\n\\n # create the toeplitz matrix\\n toeplitz_m = scipy.linalg.toeplitz(c, r)\\n\\n assert toeplitz_m.shape == (kernel_padded.shape[1], len(r))\\n\\n # print(toeplitz_m.shape)\\n toeplitz_list.append(toeplitz_m)\\n\\n # create block matrix\\n zero_block = np.zeros(toeplitz_m.shape)\\n block_seq = []\\n for idx, block in enumerate(toeplitz_list):\\n if idx == 0:\\n block_seq.append([block, zero_block])\\n else:\\n block_seq.append([block, toeplitz_list[idx - 1]])\\n doubly_block_mat = np.block(block_seq)\\n return doubly_block_mat\";\n", - " var nbb_formatted_code = \"def _convolutional_kernel_matrix(kernel, image_height, image_width, mode=\\\"same\\\"):\\n # reference: https://stackoverflow.com/questions/16798888/2-d-convolution-as-a-matrix-matrix-multiplication\\n if mode == \\\"same\\\":\\n pad_size = ((kernel.shape[0] - 1) / 2, (kernel.shape[1] - 1) / 2)\\n image_height = int(image_height + pad_size[0])\\n image_width = int(image_width + pad_size[1])\\n\\n # get output size of the data\\n output_size = (\\n image_height + kernel.shape[0] - 1,\\n image_width + kernel.shape[1] - 1,\\n )\\n\\n # zero-pad filter matrix\\n pad_width = [\\n (output_size[0] - kernel.shape[0], 0),\\n (0, output_size[1] - kernel.shape[1]),\\n ]\\n kernel_padded = np.pad(kernel, pad_width=pad_width)\\n\\n # create the toeplitz matrix for each row of the filter\\n toeplitz_list = []\\n for i in range(kernel_padded.shape[0]):\\n c = kernel_padded[\\n i, :\\n ] # i th row of the F to define first column of toeplitz matrix\\n\\n # first row for the toeplitz fuction should be defined otherwise\\n # the result is wrong\\n r = np.hstack([c[0], np.zeros(int(image_width * image_height / 2) - 1)])\\n\\n # create the toeplitz matrix\\n toeplitz_m = scipy.linalg.toeplitz(c, r)\\n\\n assert toeplitz_m.shape == (kernel_padded.shape[1], len(r))\\n\\n # print(toeplitz_m.shape)\\n toeplitz_list.append(toeplitz_m)\\n\\n # create block matrix\\n zero_block = np.zeros(toeplitz_m.shape)\\n block_seq = []\\n for idx, block in enumerate(toeplitz_list):\\n if idx == 0:\\n block_seq.append([block, zero_block])\\n else:\\n block_seq.append([block, toeplitz_list[idx - 1]])\\n doubly_block_mat = np.block(block_seq)\\n return doubly_block_mat\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def _convolutional_kernel_matrix(kernel, image_height, image_width, mode=\"same\"):\n", - " # reference: https://stackoverflow.com/questions/16798888/2-d-convolution-as-a-matrix-matrix-multiplication\n", - " if mode == \"same\":\n", - " pad_size = ((kernel.shape[0] - 1) / 2, (kernel.shape[1] - 1) / 2)\n", - " image_height = int(image_height + pad_size[0])\n", - " image_width = int(image_width + pad_size[1])\n", - "\n", - " # get output size of the data\n", - " output_size = (\n", - " image_height + kernel.shape[0] - 1,\n", - " image_width + kernel.shape[1] - 1,\n", - " )\n", - "\n", - " # zero-pad filter matrix\n", - " pad_width = [\n", - " (output_size[0] - kernel.shape[0], 0),\n", - " (0, output_size[1] - kernel.shape[1]),\n", - " ]\n", - " kernel_padded = np.pad(kernel, pad_width=pad_width)\n", - "\n", - " # create the toeplitz matrix for each row of the filter\n", - " toeplitz_list = []\n", - " for i in range(kernel_padded.shape[0]):\n", - " c = kernel_padded[\n", - " i, :\n", - " ] # i th row of the F to define first column of toeplitz matrix\n", - "\n", - " # first row for the toeplitz fuction should be defined otherwise\n", - " # the result is wrong\n", - " r = np.hstack([c[0], np.zeros(int(image_width * image_height / 2) - 1)])\n", - "\n", - " # create the toeplitz matrix\n", - " toeplitz_m = scipy.linalg.toeplitz(c, r)\n", - "\n", - " assert toeplitz_m.shape == (kernel_padded.shape[1], len(r))\n", - "\n", - " # print(toeplitz_m.shape)\n", - " toeplitz_list.append(toeplitz_m)\n", - "\n", - " # create block matrix\n", - " zero_block = np.zeros(toeplitz_m.shape)\n", - " block_seq = []\n", - " for idx, block in enumerate(toeplitz_list):\n", - " if idx == 0:\n", - " block_seq.append([block, zero_block])\n", - " else:\n", - " block_seq.append([block, toeplitz_list[idx - 1]])\n", - " doubly_block_mat = np.block(block_seq)\n", - " return doubly_block_mat" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 136;\n", - " var nbb_unformatted_code = \"\\ndef toeplitz_1d(k, x_size):\\n k_size = k.size\\n r = *k[(k_size // 2):], *np.zeros(x_size - k_size), *k[:(k_size // 2)]\\n c = *np.flip(k)[(k_size // 2):], *np.zeros(x_size - k_size), *np.flip(k)[:(k_size // 2)]\\n t = linalg.toeplitz(c=c, r=r)\\n return t\\n\\ndef toeplitz_2d(k, x_size):\\n k_h, k_w = k.shape\\n i_h, i_w = x_size\\n\\n ks = np.zeros((i_w, i_h * i_w))\\n for i in range(k_h):\\n ks[:, i * i_w : (i + 1) * i_w] = toeplitz_1d(k[i], i_w)\\n ks = np.roll(ks, -i_w, 1)\\n\\n t = np.zeros((i_h * i_w, i_h * i_w))\\n for i in range(i_h):\\n t[i * i_h : (i + 1) * i_h, :] = ks\\n ks = np.roll(ks, i_w, 1)\\n return t\";\n", - " var nbb_formatted_code = \"def toeplitz_1d(k, x_size):\\n k_size = k.size\\n r = *k[(k_size // 2) :], *np.zeros(x_size - k_size), *k[: (k_size // 2)]\\n c = (\\n *np.flip(k)[(k_size // 2) :],\\n *np.zeros(x_size - k_size),\\n *np.flip(k)[: (k_size // 2)],\\n )\\n t = linalg.toeplitz(c=c, r=r)\\n return t\\n\\n\\ndef toeplitz_2d(k, x_size):\\n k_h, k_w = k.shape\\n i_h, i_w = x_size\\n\\n ks = np.zeros((i_w, i_h * i_w))\\n for i in range(k_h):\\n ks[:, i * i_w : (i + 1) * i_w] = toeplitz_1d(k[i], i_w)\\n ks = np.roll(ks, -i_w, 1)\\n\\n t = np.zeros((i_h * i_w, i_h * i_w))\\n for i in range(i_h):\\n t[i * i_h : (i + 1) * i_h, :] = ks\\n ks = np.roll(ks, i_w, 1)\\n return t\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def toeplitz_1d(k, x_size):\n", - " k_size = k.size\n", - " r = *k[(k_size // 2) :], *np.zeros(x_size - k_size), *k[: (k_size // 2)]\n", - " c = (\n", - " *np.flip(k)[(k_size // 2) :],\n", - " *np.zeros(x_size - k_size),\n", - " *np.flip(k)[: (k_size // 2)],\n", - " )\n", - " t = linalg.toeplitz(c=c, r=r)\n", - " return t\n", - "\n", - "\n", - "def toeplitz_2d(k, x_size):\n", - " k_h, k_w = k.shape\n", - " i_h, i_w = x_size\n", - "\n", - " ks = np.zeros((i_w, i_h * i_w))\n", - " for i in range(k_h):\n", - " ks[:, i * i_w : (i + 1) * i_w] = toeplitz_1d(k[i], i_w)\n", - " ks = np.roll(ks, -i_w, 1)\n", - "\n", - " t = np.zeros((i_h * i_w, i_h * i_w))\n", - " for i in range(i_h):\n", - " t[i * i_h : (i + 1) * i_h, :] = ks\n", - " ks = np.roll(ks, i_w, 1)\n", - " return t" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 142;\n", - " var nbb_unformatted_code = \"def toeplitz_1_ch(kernel, input_size):\\n # shapes\\n k_h, k_w = kernel.shape\\n i_h, i_w = input_size\\n o_h, o_w = i_h - k_h + 1, i_w - k_w + 1\\n\\n # construct 1d conv toeplitz matrices for each row of the kernel\\n toeplitz = []\\n for r in range(k_h):\\n toeplitz.append(\\n linalg.toeplitz(\\n c=(kernel[r, 0], *np.zeros(i_w - k_w)),\\n r=(*kernel[r], *np.zeros(i_w - k_w)),\\n )\\n )\\n\\n # construct toeplitz matrix of toeplitz matrices (just for padding=0)\\n h_blocks, w_blocks = o_h, i_h\\n h_block, w_block = toeplitz[0].shape\\n\\n W_conv = np.zeros((h_blocks, h_block, w_blocks, w_block))\\n\\n for i, B in enumerate(toeplitz):\\n for j in range(o_h):\\n W_conv[j, :, i + j, :] = B\\n\\n W_conv.shape = (h_blocks * h_block, w_blocks * w_block)\\n\\n return W_conv\";\n", - " var nbb_formatted_code = \"def toeplitz_1_ch(kernel, input_size):\\n # shapes\\n k_h, k_w = kernel.shape\\n i_h, i_w = input_size\\n o_h, o_w = i_h - k_h + 1, i_w - k_w + 1\\n\\n # construct 1d conv toeplitz matrices for each row of the kernel\\n toeplitz = []\\n for r in range(k_h):\\n toeplitz.append(\\n linalg.toeplitz(\\n c=(kernel[r, 0], *np.zeros(i_w - k_w)),\\n r=(*kernel[r], *np.zeros(i_w - k_w)),\\n )\\n )\\n\\n # construct toeplitz matrix of toeplitz matrices (just for padding=0)\\n h_blocks, w_blocks = o_h, i_h\\n h_block, w_block = toeplitz[0].shape\\n\\n W_conv = np.zeros((h_blocks, h_block, w_blocks, w_block))\\n\\n for i, B in enumerate(toeplitz):\\n for j in range(o_h):\\n W_conv[j, :, i + j, :] = B\\n\\n W_conv.shape = (h_blocks * h_block, w_blocks * w_block)\\n\\n return W_conv\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def toeplitz_1_ch(kernel, input_size):\n", - " # shapes\n", - " k_h, k_w = kernel.shape\n", - " i_h, i_w = input_size\n", - " o_h, o_w = i_h - k_h + 1, i_w - k_w + 1\n", - "\n", - " # construct 1d conv toeplitz matrices for each row of the kernel\n", - " toeplitz = []\n", - " for r in range(k_h):\n", - " toeplitz.append(\n", - " linalg.toeplitz(\n", - " c=(kernel[r, 0], *np.zeros(i_w - k_w)),\n", - " r=(*kernel[r], *np.zeros(i_w - k_w)),\n", - " )\n", - " )\n", - "\n", - " # construct toeplitz matrix of toeplitz matrices (just for padding=0)\n", - " h_blocks, w_blocks = o_h, i_h\n", - " h_block, w_block = toeplitz[0].shape\n", - "\n", - " W_conv = np.zeros((h_blocks, h_block, w_blocks, w_block))\n", - "\n", - " for i, B in enumerate(toeplitz):\n", - " for j in range(o_h):\n", - " W_conv[j, :, i + j, :] = B\n", - "\n", - " W_conv.shape = (h_blocks * h_block, w_blocks * w_block)\n", - "\n", - " return W_conv" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(24, 80)\n", - "(5, 5)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":25: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " W_conv[j, :, i + j, :] = B\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 147;\n", - " var nbb_unformatted_code = \"T = toeplitz_1_ch(kernel, (10, 8))\\nprint(T.shape)\\nprint(kernel.shape)\";\n", - " var nbb_formatted_code = \"T = toeplitz_1_ch(kernel, (10, 8))\\nprint(T.shape)\\nprint(kernel.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "T = toeplitz_1_ch(kernel, (10, 8))\n", - "print(T.shape)\n", - "print(kernel.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":14: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " ks[:, i * i_w : (i + 1) * i_w] = toeplitz_1d(k[i], i_w)\n" - ] - }, - { - "ename": "ValueError", - "evalue": "could not broadcast input array from shape (7,70) into shape (10,70)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtoeplitz_2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mtoeplitz_2d\u001b[0;34m(k, x_size)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi_h\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mi_w\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi_h\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mi_w\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi_h\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mi_h\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mi_h\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0mks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi_w\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (7,70) into shape (10,70)" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 140;\n", - " var nbb_unformatted_code = \"T = toeplitz_2d(kernel, (10, 7))\\nprint(T.shape)\";\n", - " var nbb_formatted_code = \"T = toeplitz_2d(kernel, (10, 7))\\nprint(T.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "T = toeplitz_2d(kernel, (10, 7))\n", - "print(T.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(50, 20)\n", - "(50, 5, 4)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 112;\n", - " var nbb_unformatted_code = \"X = np.random.normal(size=(n, height * d))\\ny = np.ones((n,))\\nprint(X.shape)\\n\\nsample_X = X.reshape(50, height, d)\\nprint(sample_X.shape)\";\n", - " var nbb_formatted_code = \"X = np.random.normal(size=(n, height * d))\\ny = np.ones((n,))\\nprint(X.shape)\\n\\nsample_X = X.reshape(50, height, d)\\nprint(sample_X.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "X = np.random.normal(size=(n, height * d))\n", - "y = np.ones((n,))\n", - "print(X.shape)\n", - "\n", - "sample_X = X.reshape(50, height, d)\n", - "print(sample_X.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(5, 5)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 138;\n", - " var nbb_unformatted_code = \"frequency = 1\\nkernel = gabor_kernel(frequency=frequency, n_stds=3, bandwidth=1)\\nprint(kernel.shape)\";\n", - " var nbb_formatted_code = \"frequency = 1\\nkernel = gabor_kernel(frequency=frequency, n_stds=3, bandwidth=1)\\nprint(kernel.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "frequency = 1\n", - "kernel = gabor_kernel(frequency=frequency, n_stds=3, bandwidth=1)\n", - "print(kernel.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5 4\n", - "(992, 182)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 117;\n", - " var nbb_unformatted_code = \"image_height = height\\nimage_width = d\\n\\nprint(image_height, image_width)\\n\\nconv_kern_mat = _convolutional_kernel_matrix(\\n kernel, image_height, image_width, mode=\\\"same\\\"\\n)\\nprint(conv_kern_mat.shape)\";\n", - " var nbb_formatted_code = \"image_height = height\\nimage_width = d\\n\\nprint(image_height, image_width)\\n\\nconv_kern_mat = _convolutional_kernel_matrix(\\n kernel, image_height, image_width, mode=\\\"same\\\"\\n)\\nprint(conv_kern_mat.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "image_height = height\n", - "image_width = d\n", - "\n", - "print(image_height, image_width)\n", - "\n", - "conv_kern_mat = _convolutional_kernel_matrix(\n", - " kernel, image_height, image_width, mode=\"same\"\n", - ")\n", - "print(conv_kern_mat.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[9, 9]\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 123;\n", - " var nbb_unformatted_code = \"pad_size = list(map(int, ((kernel.shape[0] - 1) / 2, (kernel.shape[1] - 1) / 2)))\\nimage_height = image_height + pad_size[0]\\nimage_width = image_width + pad_size[1]\\n\\nprint(pad_size)\";\n", - " var nbb_formatted_code = \"pad_size = list(map(int, ((kernel.shape[0] - 1) / 2, (kernel.shape[1] - 1) / 2)))\\nimage_height = image_height + pad_size[0]\\nimage_width = image_width + pad_size[1]\\n\\nprint(pad_size)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pad_size = list(map(int, ((kernel.shape[0] - 1) / 2, (kernel.shape[1] - 1) / 2)))\n", - "image_height = image_height + pad_size[0]\n", - "image_width = image_width + pad_size[1]\n", - "\n", - "print(pad_size)" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(50, 5, 4)\n", - "(50, 14, 13)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 132;\n", - " var nbb_unformatted_code = \"pad_width = [\\n (0, 0),\\n (pad_size[0], 0),\\n (0, pad_size[1]),\\n]\\nX = X.reshape(-1, height, d)\\nprint(X.shape)\\nX_padded = np.pad(X, pad_width=pad_width)\\nprint(X_padded.shape)\";\n", - " var nbb_formatted_code = \"pad_width = [\\n (0, 0),\\n (pad_size[0], 0),\\n (0, pad_size[1]),\\n]\\nX = X.reshape(-1, height, d)\\nprint(X.shape)\\nX_padded = np.pad(X, pad_width=pad_width)\\nprint(X_padded.shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pad_width = [\n", - " (0, 0),\n", - " (pad_size[0], 0),\n", - " (0, pad_size[1]),\n", - "]\n", - "X = X.reshape(-1, height, d)\n", - "print(X.shape)\n", - "X_padded = np.pad(X, pad_width=pad_width)\n", - "print(X_padded.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "182\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 133;\n", - " var nbb_unformatted_code = \"print(14 * 13)\";\n", - " var nbb_formatted_code = \"print(14 * 13)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(14 * 13)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Matrices shape: (3, 2, 3, 3) (2, 5, 5) (75, 50)\n", - "0.0\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 21;\n", - " var nbb_unformatted_code = \"import numpy as np\\nimport scipy.linalg as linalg\\n\\n\\ndef toeplitz_1d(k, x_size):\\n k_size = k.size\\n r = *k[(k_size // 2) :], *np.zeros(x_size - k_size), *k[: (k_size // 2)]\\n c = (\\n *np.flip(k)[(k_size // 2) :],\\n *np.zeros(x_size - k_size),\\n *np.flip(k)[: (k_size // 2)],\\n )\\n t = linalg.toeplitz(c=c, r=r)\\n return t\\n\\n\\ndef toeplitz_2d(k, x_size):\\n k_h, k_w = k.shape\\n i_h, i_w = x_size\\n\\n ks = np.zeros((i_w, i_h * i_w))\\n for i in range(k_h):\\n ks[:, i * i_w : (i + 1) * i_w] = toeplitz_1d(k[i], i_w)\\n ks = np.roll(ks, -i_w, 1)\\n\\n t = np.zeros((i_h * i_w, i_h * i_w))\\n for i in range(i_h):\\n t[i * i_h : (i + 1) * i_h, :] = ks\\n ks = np.roll(ks, i_w, 1)\\n return t\\n\\n\\ndef toeplitz_3d(k, x_size):\\n k_oc, k_ic, k_h, k_w = k.shape\\n i_c, i_h, i_w = x_size\\n\\n t = np.zeros((k_oc * i_h * i_w, i_c * i_h * i_w))\\n\\n for o in range(k_oc):\\n for i in range(k_ic):\\n t[\\n (o * (i_h * i_w)) : ((o + 1) * (i_h * i_w)),\\n (i * (i_h * i_w)) : ((i + 1) * (i_h * i_w)),\\n ] = toeplitz_2d(k[o, i], (i_h, i_w))\\n\\n return t\\n\\n\\nif __name__ == \\\"__main__\\\":\\n import torch\\n\\n # generate random kernel, and input x data\\n k = np.random.randint(50, size=(3, 2, 3, 3))\\n x = np.random.randint(50, size=(2, 5, 5))\\n\\n # generate the 3D toeplitz convolution\\n t = toeplitz_3d(k, x.shape)\\n\\n print('Matrices shape: ', k.shape, x.shape, t.shape)\\n \\n y = t.dot(x.flatten()).reshape(3, 5, 5)\\n xx = torch.nn.functional.pad(\\n torch.from_numpy(x.reshape(1, 2, 5, 5)), pad=(1, 1, 1, 1), mode=\\\"circular\\\"\\n )\\n yy = torch.conv2d(xx, torch.from_numpy(k))\\n err = ((y - yy.numpy()) ** 2).sum()\\n print(err)\";\n", - " var nbb_formatted_code = \"import numpy as np\\nimport scipy.linalg as linalg\\n\\n\\ndef toeplitz_1d(k, x_size):\\n k_size = k.size\\n r = *k[(k_size // 2) :], *np.zeros(x_size - k_size), *k[: (k_size // 2)]\\n c = (\\n *np.flip(k)[(k_size // 2) :],\\n *np.zeros(x_size - k_size),\\n *np.flip(k)[: (k_size // 2)],\\n )\\n t = linalg.toeplitz(c=c, r=r)\\n return t\\n\\n\\ndef toeplitz_2d(k, x_size):\\n k_h, k_w = k.shape\\n i_h, i_w = x_size\\n\\n ks = np.zeros((i_w, i_h * i_w))\\n for i in range(k_h):\\n ks[:, i * i_w : (i + 1) * i_w] = toeplitz_1d(k[i], i_w)\\n ks = np.roll(ks, -i_w, 1)\\n\\n t = np.zeros((i_h * i_w, i_h * i_w))\\n for i in range(i_h):\\n t[i * i_h : (i + 1) * i_h, :] = ks\\n ks = np.roll(ks, i_w, 1)\\n return t\\n\\n\\ndef toeplitz_3d(k, x_size):\\n k_oc, k_ic, k_h, k_w = k.shape\\n i_c, i_h, i_w = x_size\\n\\n t = np.zeros((k_oc * i_h * i_w, i_c * i_h * i_w))\\n\\n for o in range(k_oc):\\n for i in range(k_ic):\\n t[\\n (o * (i_h * i_w)) : ((o + 1) * (i_h * i_w)),\\n (i * (i_h * i_w)) : ((i + 1) * (i_h * i_w)),\\n ] = toeplitz_2d(k[o, i], (i_h, i_w))\\n\\n return t\\n\\n\\nif __name__ == \\\"__main__\\\":\\n import torch\\n\\n # generate random kernel, and input x data\\n k = np.random.randint(50, size=(3, 2, 3, 3))\\n x = np.random.randint(50, size=(2, 5, 5))\\n\\n # generate the 3D toeplitz convolution\\n t = toeplitz_3d(k, x.shape)\\n\\n print(\\\"Matrices shape: \\\", k.shape, x.shape, t.shape)\\n\\n y = t.dot(x.flatten()).reshape(3, 5, 5)\\n xx = torch.nn.functional.pad(\\n torch.from_numpy(x.reshape(1, 2, 5, 5)), pad=(1, 1, 1, 1), mode=\\\"circular\\\"\\n )\\n yy = torch.conv2d(xx, torch.from_numpy(k))\\n err = ((y - yy.numpy()) ** 2).sum()\\n print(err)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import scipy.linalg as linalg\n", - "\n", - "\n", - "def toeplitz_1d(k, x_size):\n", - " k_size = k.size\n", - " r = *k[(k_size // 2) :], *np.zeros(x_size - k_size), *k[: (k_size // 2)]\n", - " c = (\n", - " *np.flip(k)[(k_size // 2) :],\n", - " *np.zeros(x_size - k_size),\n", - " *np.flip(k)[: (k_size // 2)],\n", - " )\n", - " t = linalg.toeplitz(c=c, r=r)\n", - " return t\n", - "\n", - "\n", - "def toeplitz_2d(k, x_size):\n", - " k_h, k_w = k.shape\n", - " i_h, i_w = x_size\n", - "\n", - " ks = np.zeros((i_w, i_h * i_w))\n", - " for i in range(k_h):\n", - " ks[:, i * i_w : (i + 1) * i_w] = toeplitz_1d(k[i], i_w)\n", - " ks = np.roll(ks, -i_w, 1)\n", - "\n", - " t = np.zeros((i_h * i_w, i_h * i_w))\n", - " for i in range(i_h):\n", - " t[i * i_h : (i + 1) * i_h, :] = ks\n", - " ks = np.roll(ks, i_w, 1)\n", - " return t\n", - "\n", - "\n", - "def toeplitz_3d(k, x_size):\n", - " k_oc, k_ic, k_h, k_w = k.shape\n", - " i_c, i_h, i_w = x_size\n", - "\n", - " t = np.zeros((k_oc * i_h * i_w, i_c * i_h * i_w))\n", - "\n", - " for o in range(k_oc):\n", - " for i in range(k_ic):\n", - " t[\n", - " (o * (i_h * i_w)) : ((o + 1) * (i_h * i_w)),\n", - " (i * (i_h * i_w)) : ((i + 1) * (i_h * i_w)),\n", - " ] = toeplitz_2d(k[o, i], (i_h, i_w))\n", - "\n", - " return t\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " import torch\n", - "\n", - " # generate random kernel, and input x data\n", - " k = np.random.randint(50, size=(3, 2, 3, 3))\n", - " x = np.random.randint(50, size=(2, 5, 5))\n", - "\n", - " # generate the 3D toeplitz convolution\n", - " t = toeplitz_3d(k, x.shape)\n", - "\n", - " print(\"Matrices shape: \", k.shape, x.shape, t.shape)\n", - "\n", - " y = t.dot(x.flatten()).reshape(3, 5, 5)\n", - " xx = torch.nn.functional.pad(\n", - " torch.from_numpy(x.reshape(1, 2, 5, 5)), pad=(1, 1, 1, 1), mode=\"circular\"\n", - " )\n", - " yy = torch.conv2d(xx, torch.from_numpy(k))\n", - " err = ((y - yy.numpy()) ** 2).sum()\n", - " print(err)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "sklearn", - "language": "python", - "name": "sklearn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/doc/tutorials/test_waveletmorf.ipynb b/doc/tutorials/test_waveletmorf.ipynb deleted file mode 100644 index 411feb923..000000000 --- a/doc/tutorials/test_waveletmorf.ipynb +++ /dev/null @@ -1,211 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "coated-python", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "liquid-prophet", - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 16;\n", - " var nbb_unformatted_code = \"import pywt\\nimport mne\";\n", - " var nbb_formatted_code = \"import pywt\\nimport mne\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pywt\n", - "import mne" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "sporting-exhibit", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(111,)\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 58;\n", - " var nbb_unformatted_code = \"sfreq = 100\\nwavelets = mne.time_frequency.morlet(sfreq=sfreq, freqs=[10])\\n\\nprint(wavelets[0].shape)\";\n", - " var nbb_formatted_code = \"sfreq = 100\\nwavelets = mne.time_frequency.morlet(sfreq=sfreq, freqs=[10])\\n\\nprint(wavelets[0].shape)\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sfreq = 100\n", - "wavelets = mne.time_frequency.morlet(sfreq=sfreq, freqs=[10])\n", - "\n", - "print(wavelets[0].shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "responsible-drilling", - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 41;\n", - " var nbb_unformatted_code = \"wavelet = (pywt.ContinuousWavelet(\\\"morl\\\"))\";\n", - " var nbb_formatted_code = \"wavelet = pywt.ContinuousWavelet(\\\"morl\\\")\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "wavelet = pywt.ContinuousWavelet(\"morl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "considerable-shelf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ContinuousWavelet morl\n", - " Family name: Morlet wavelet\n", - " Short name: morl\n", - " Symmetry: symmetric\n", - " DWT: False\n", - " CWT: True\n", - " Complex CWT: False\n", - "[array([-8.44621402e-15, -8.44621402e-15]), array([-8., 8.])]\n" - ] - }, - { - "data": { - "application/javascript": [ - "\n", - " setTimeout(function() {\n", - " var nbb_cell_id = 47;\n", - " var nbb_unformatted_code = \"print(wavelet)\\nprint(wavelet.wavefun(level=1))\";\n", - " var nbb_formatted_code = \"print(wavelet)\\nprint(wavelet.wavefun(level=1))\";\n", - " var nbb_cells = Jupyter.notebook.get_cells();\n", - " for (var i = 0; i < nbb_cells.length; ++i) {\n", - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", - " nbb_cells[i].set_text(nbb_formatted_code);\n", - " }\n", - " break;\n", - " }\n", - " }\n", - " }, 500);\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(wavelet)\n", - "print(wavelet.wavefun(level=1))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "sklearn", - "language": "python", - "name": "sklearn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/experiments/plotting_cmi_analysis.ipynb b/experiments/plotting_cmi_analysis.ipynb deleted file mode 100644 index 24d9bfc14..000000000 --- a/experiments/plotting_cmi_analysis.ipynb +++ /dev/null @@ -1,530 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b0125b7c-d6d3-46a7-9e86-c3f7e2f6cda3", - "metadata": {}, - "source": [ - "# Analysis of (Conditional) Mutual Information Estimators Using Forests - Sample Efficiency\n", - "\n", - "In this simulation notebook, we will evaluate the sample efficiency of using forests to estimate\n", - "(conditional) mutual information. We will replicate the findings of https://arxiv.org/pdf/2110.13883.pdf. \n", - "\n", - "The data we will simulate comes from the following distributions for mutual information:\n", - "\n", - "- Helix: X is dependent on Y on a helix\n", - "- Sphere: X is dependent on Y\n", - "- Uniform: X is dependent on Y\n", - "- Gaussian: X is dependent on Y\n", - "- independent: X is completely independent of Y\n", - "\n", - "The data we will simulate comes from the following distributions for conditional mutual information:\n", - "\n", - "- Uniform: X is conditionally dependent on Y\n", - "- Gaussian: X is conditionally dependent on Y\n", - "\n", - "For each distribution, we will add a varying number of independent dimensions to the data (i.e. sampled from Gaussian distribution)." - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "id": "2e7b6950-44a0-40a9-bf2a-b6eca06725c1", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import scipy\n", - "import scipy.spatial\n", - "\n", - "from sklearn.neighbors import NearestNeighbors\n", - "import treeple\n", - "from treeple.experimental.simulate import (simulate_helix, simulate_multivariate_gaussian, simulate_sphere)\n", - "from treeple.experimental.mutual_info import (\n", - " entropy_gaussian, entropy_weibull,\n", - " cmi_from_entropy,\n", - " mi_from_entropy,\n", - " mutual_info_ksg,\n", - " mi_gaussian, cmi_gaussian,\n", - " mi_gamma, \n", - ")\n", - "from treeple.tree import compute_forest_similarity_matrix\n", - "from treeple import UnsupervisedRandomForest, UnsupervisedObliqueRandomForest, ObliqueRandomForestClassifier\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "bf387bc2-f1d1-4b7a-9dab-e9c8fb992b2a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "id": "8ca87428-e8c8-46df-829f-116261c54f86", - "metadata": {}, - "source": [ - "## Define Hyperparameters of the Simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e5089afb-c359-423d-92b1-641fca6315b2", - "metadata": {}, - "outputs": [], - "source": [ - "seed =12345\n", - "rng = np.random.default_rng(seed)" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "id": "4fcde0a4-7413-4f09-bbd8-fe62fcd62c2d", - "metadata": {}, - "outputs": [], - "source": [ - "n_jobs = -1\n", - "\n", - "# hyperparameters of the simulation\n", - "n_samples = 500\n", - "n_noise_dims = 10\n", - "alpha = 0.01\n", - "\n", - "# dimensionality of mvg\n", - "d = 3\n", - "\n", - "# for sphere\n", - "radius = 1.0\n", - "\n", - "# for helix\n", - "radius_a = 0.0\n", - "radius_b = 2.0\n", - "\n", - "# manifold parameters\n", - "radii_func = lambda: rng.uniform(0, 1)" - ] - }, - { - "cell_type": "markdown", - "id": "dc222a29-5a31-486e-b2e3-6e276a61f81f", - "metadata": {}, - "source": [ - "## Demonstrate a single simulation\n", - "\n", - "Now, to demonstrate what the data would look like fromm a single parameterized simulation, we want to show the entire workflow from data generation to analysis and output value." - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "id": "3cc73c4e-78b6-48b3-83b3-091d365d8ada", - "metadata": {}, - "outputs": [], - "source": [ - "# generate helix data\n", - "helix_data = simulate_helix(radius_a=radius_a, radius_b=radius_b, alpha=alpha/2, n_samples=n_samples, return_mi_lb=True, random_seed=seed)\n", - "P, X, Y, Z, helix_lb = helix_data\n", - "\n", - "# generate sphere data\n", - "sphere_data = simulate_sphere(radius=radius, alpha=alpha, n_samples=n_samples, return_mi_lb=True, random_seed=seed)\n", - "lat, lon, Y1, Y2, Y3, lb = sphere_data\n", - "\n", - "# simulate multivariate Gaussian\n", - "mvg_data = simulate_multivariate_gaussian(d=d, n_samples=n_samples, seed=seed)\n", - "data, mean, cov = mvg_data" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "id": "aa1a67bb-55fa-4983-b2d1-a23102945b7c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# helix data\n", - "fig = plt.figure()\n", - "ax1 = fig.add_subplot(111, projection='3d')\n", - "\n", - "ax1.plot( X, Y, Z, '*', c='b', label='Helix')\n", - "ax1.legend(loc='upper left');\n", - "ax1.axis('equal')\n", - "# ax1.set(\n", - "# xlim=[-1, 1],\n", - "# ylim=[-1, 1],\n", - "# zlim=[0.25, 1],\n", - "# )\n", - "fig.tight_layout()\n", - "elev = 20\n", - "azim = 50\n", - "roll = 0\n", - "ax1.view_init(elev, azim, roll)\n", - "\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "a841ffc8-e821-4c0a-8df0-9b55b1be3aba", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# sphere data\n", - "fig = plt.figure()\n", - "ax1 = fig.add_subplot(111, projection='3d')\n", - "\n", - "ax1.plot( Y1, Y2, Y3, '*', c='b', label='Sphere')\n", - "ax1.legend(loc='upper left');\n", - "ax1.axis('equal')\n", - "\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "id": "383a17a7-6c4f-49fc-aa4d-322f820ac18e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 0.31087872 0.01905156 -0.19413813]\n", - " [ 0.01905156 1.21114085 2.27200637]\n", - " [-0.19413813 2.27200637 5.13099624]]\n", - "[6.17646933 0.35752001 0.11902648]\n" - ] - } - ], - "source": [ - "print(cov)\n", - "print(np.linalg.eigvals(cov))" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "id": "570cf521-f62f-4e62-98e4-0aea8e4f72db", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# mvg data\n", - "fig = plt.figure()\n", - "ax1 = fig.add_subplot(111, projection='3d')\n", - "\n", - "ax1.plot(data[:, 0], data[:, 1], data[:, 2], '*', c='b', label='MVG')\n", - "ax1.legend(loc='upper left');\n", - "ax1.axis('equal')\n", - "elev = 20\n", - "azim = 50\n", - "roll = 0\n", - "ax1.view_init(elev, azim, roll)\n", - "\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e642837c-d6b0-40c0-94b8-136127796ee1", - "metadata": {}, - "outputs": [], - "source": [ - "# fit an unsupervised forest\n", - "unsup_rf = Un" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "155e06b8-be96-421c-9bbe-ea8333670c8f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(30, 2)\n", - "(30, 2) (30, 2)\n" - ] - } - ], - "source": [ - "print(X.shape)\n", - "print(indices.shape, dists.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ed563ca6-fb7c-4ed0-a6c8-e12cebfad1e1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]\n", - " [0. 1.]]\n" - ] - } - ], - "source": [ - "print(dists[:5, :])" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "8d454e2c-3cee-4dcf-b386-cd613e6f8b32", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0. 0.25 0.5 ]\n", - " [0.25 0. 0.75]\n", - " [0.5 0.75 0. ]]\n", - "[[0. 0.25 0.5 ]\n", - " [0. 0. 0.75]\n", - " [0. 0. 0. ]]\n" - ] - } - ], - "source": [ - "X = np.arange(9).reshape((3, -1))\n", - "X = 0.5 * (X + X.T)\n", - "X = X / np.max(X)\n", - "X[np.diag_indices_from(X)] = 0.0\n", - "print(X)\n", - "\n", - "print(np.triu(X, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "e35dcdc7-f0d7-478e-a7f4-dc40f33af292", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(3, 3) (3, 3)\n", - "[[0 1 2]\n", - " [1 0 2]\n", - " [2 0 1]]\n", - "[[0. 0.25 0.5 ]\n", - " [0. 0.25 0.75]\n", - " [0. 0.5 0.75]]\n" - ] - } - ], - "source": [ - "\n", - "nbrs = NearestNeighbors(\n", - " n_neighbors=3, metric=\"precomputed\", n_jobs=n_jobs\n", - " ).fit(X)\n", - "\n", - "# then get the K nearest nbrs in the Z space\n", - "dists, indices = nbrs.kneighbors(X)\n", - "\n", - "print(dists.shape, indices.shape)\n", - "print(indices)\n", - "print(dists)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "b6769533-095a-46d3-ab25-5adf10503bcc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(array([1, 2, 2]), array([0, 0, 1]))\n", - "[3 6 7]\n" - ] - } - ], - "source": [ - "# get the triu indices\n", - "triu_idx = np.triu_indices_from(X, 1)\n", - "\n", - "print(triu_idx)\n", - "print(X[triu_idx])\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "6806acd9-bae0-4d08-9f34-a1026bcc9f31", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0 1]\n", - " [0 2]\n", - " [1 2]]\n", - "[1 2 5]\n" - ] - } - ], - "source": [ - "# get the triu indices\n", - "triu_idx = np.triu_indices_from(X, 1)\n", - "\n", - "triu_ravel_argsort_idx = np.argsort(X[triu_idx], axis=None)\n", - "\n", - "triu_argsort_idx = np.vstack(triu_idx).T[triu_ravel_argsort_idx].squeeze()\n", - "\n", - "print(triu_argsort_idx)\n", - "print(X[triu_argsort_idx[:, 0], triu_argsort_idx[:, 1]])\n", - "# print(ravel_argsort_idx)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "1862eae0-ef26-4cfc-8448-c835053c9edb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(array([0, 0, 0]), array([0, 1, 2]))\n" - ] - } - ], - "source": [ - "print(np.unravel_index(ravel_argsort_idx, (3, 3)))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "f321a9a1-1006-41d5-9eb0-5739929fa331", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0 1 2]\n", - "[[0 0]\n", - " [0 1]\n", - " [0 2]]\n" - ] - } - ], - "source": [ - "argsort_idx = np.unravel_index(ravel_argsort_idx, (3, 3))\n", - "\n", - "print(ravel_argsort_idx)\n", - "print(np.vstack(argsort_idx).T)" - ] - }, - { - "cell_type": "markdown", - "id": "67330326-469a-48de-9107-7d5cfdcfd4b7", - "metadata": {}, - "source": [ - "# Final Analysis Across All Possible Parametrizations\n", - "\n", - "Now, we want to analyze Unsup-Forest-KSG, Sup-Forest-KSG, Uncertainty-Forest, and KSG-estimator for MI. Moreover, we can implement all traditional RF and oblique RF." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0a875b34-9a97-4bdf-ab45-14a1be6e60a5", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "treeple", - "language": "python", - "name": "treeple" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/experiments/urerf_sickkids.ipynb b/experiments/urerf_sickkids.ipynb deleted file mode 100644 index 4dd4cca47..000000000 --- a/experiments/urerf_sickkids.ipynb +++ /dev/null @@ -1,105 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7e14c65b-82fd-4c28-9208-8c8485394d11", - "metadata": {}, - "source": [ - "# Analysis of UReRf on SickKids Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "73b38d35-fedd-43b0-9de3-e8d04b307e17", - "metadata": {}, - "outputs": [], - "source": [ - "# auto-format every cell to black\n", - "%load_ext lab_black" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9a7b7768-5ddf-4d57-9cf3-ca3b28b7b519", - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'rerf'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcluster\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AgglomerativeClustering\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m adjusted_rand_score\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrerf\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01murerf\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m UnsupervisedRandomForest\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'rerf'" - ] - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "from sklearn import datasets\n", - "from sklearn.cluster import AgglomerativeClustering\n", - "from sklearn.metrics import adjusted_rand_score\n", - "\n", - "from rerf.urerf import UnsupervisedRandomForest" - ] - }, - { - "cell_type": "markdown", - "id": "b3b350ad-8407-4b52-b08b-3d45f2143b19", - "metadata": {}, - "source": [ - "# Load Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec6f8de2-4542-4a72-86a6-324834ee5299", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "dfce531a-5a42-44c6-bc5a-92401ab9169a", - "metadata": {}, - "source": [ - "# Run Unsupervised ReRF on Data\n", - "\n", - "Since the data shows fragility heatmaps of different datasets, we will compute an affinity matrix between all channels for all maps and then compare them conditioned on surgical outcome." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "44d6747f-8ef1-4bc1-982d-195881cd4d3c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "sklearn-devm1", - "language": "python", - "name": "sklearn-devm1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/validation/cc18_benchmark/cc_18_sporf_vs_rf_benchmarks.csv b/validation/cc18_benchmark/cc_18_sporf_vs_rf_benchmarks.csv deleted file mode 100644 index 4563ea1b1..000000000 --- a/validation/cc18_benchmark/cc_18_sporf_vs_rf_benchmarks.csv +++ /dev/null @@ -1,68 +0,0 @@ -,task,n_samples,n_classes,task_id,n_features -0,segment,2310,7,146822,17 -1,jungle_chess_2pcs_raw_endgame_complete,44819,3,167119,7 -2,tic-tac-toe,958,2,49,10 -3,pc1,1109,2,3918,22 -4,analcatdata_dmft,797,6,3560,5 -5,qsar-biodeg,1055,2,9957,42 -6,kc2,522,2,3913,22 -7,cmc,1473,3,23,10 -8,mfeat-fourier,2000,10,14,77 -9,vehicle,846,4,53,19 -10,letter,20000,26,6,17 -11,credit-approval,690,2,29,16 -12,sick,3772,2,3021,30 -13,mfeat-factors,2000,10,12,217 -14,mfeat-pixel,2000,10,146824,241 -15,MiceProtein,1080,8,146800,78 -16,eucalyptus,736,5,2079,20 -17,mfeat-zernike,2000,10,22,48 -18,steel-plates-fault,1941,7,146817,28 -19,breast-w,699,2,15,10 -20,diabetes,768,2,37,9 -21,phoneme,5404,2,9952,6 -22,splice,3190,3,45,61 -23,electricity,45312,2,219,9 -24,isolet,7797,26,3481,618 -25,bank-marketing,45211,2,14965,17 -26,Bioresponse,3751,2,9910,1777 -27,pc4,1458,2,3902,38 -28,banknote-authentication,1372,2,10093,5 -29,analcatdata_authorship,841,4,3549,71 -30,connect-4,67557,3,146195,43 -31,mfeat-karhunen,2000,10,16,65 -32,pc3,1563,2,3903,38 -33,wilt,4839,2,146820,6 -34,texture,5500,11,125922,41 -35,credit-g,1000,2,31,21 -36,optdigits,5620,10,28,65 -37,cylinder-bands,540,2,14954,38 -38,vowel,990,11,3022,13 -39,wall-robot-navigation,5456,4,9960,25 -40,madelon,2600,2,9976,501 -41,spambase,4601,2,43,58 -42,nomao,34465,2,9977,119 -43,jm1,10885,2,3904,22 -44,pendigits,10992,10,32,17 -45,satimage,6430,6,2074,37 -46,car,1728,4,146821,7 -47,GesturePhaseSegmentationProcessed,9873,5,14969,33 -48,har,10299,6,14970,562 -49,mnist_784,70000,10,3573,785 -50,first-order-theorem-proving,6118,6,9985,52 -51,kr-vs-kp,3196,2,3,37 -52,numerai28.6,96320,2,167120,22 -53,ozone-level-8hr,2534,2,9978,73 -54,Fashion-MNIST,70000,10,146825,785 -55,adult,48842,2,7592,15 -56,blood-transfusion-service-center,748,2,10101,5 -57,ilpd,583,2,9971,11 -58,mfeat-morphological,2000,10,18,7 -59,semeion,1593,10,9964,257 -60,cnae-9,1080,9,9981,857 -61,dresses-sales,500,2,125920,13 -62,PhishingWebsites,11055,2,14952,31 -63,balance-scale,625,3,11,5 -64,climate-model-simulation-crashes,540,2,146819,19 -65,kc1,2109,2,3917,22 -66,wdbc,569,2,9946,31 diff --git a/validation/cc18_benchmark/run_all.log b/validation/cc18_benchmark/run_all.log deleted file mode 100644 index f1ffa471c..000000000 --- a/validation/cc18_benchmark/run_all.log +++ /dev/null @@ -1,464 +0,0 @@ -2021-05-13 09:53:09,397:INFO:mode=CREATE -2021-05-13 09:53:09,398:INFO:cv=5 -2021-05-13 09:53:09,398:INFO:n_estimators=500 -2021-05-13 09:53:09,398:INFO:n_jobs=30 -2021-05-13 09:53:09,398:INFO:max_features=0.33 -2021-05-13 09:53:09,398:INFO:start_id=None -2021-05-13 09:53:09,398:INFO:stop_id=None -2021-05-13 09:53:09,398:INFO:parallel_tasks=1 -2021-05-13 09:53:09,398:INFO:vary_samples=False -2021-05-13 09:53:09,398:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/study/OpenML-CC18 -2021-05-13 09:53:10,550:INFO:1.1520352s taken for [get] request for the URL https://www.openml.org/api/v1/xml/study/OpenML-CC18 -2021-05-13 09:53:10,555:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/3 -2021-05-13 09:53:11,027:INFO:0.4716766s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/3 -2021-05-13 09:53:11,029:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/3 -2021-05-13 09:53:11,476:INFO:0.4473505s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/3 -2021-05-13 09:53:11,478:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/3 -2021-05-13 09:53:16,077:INFO:4.5987723s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/3 -2021-05-13 09:53:16,077:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/3 -2021-05-13 09:53:16,528:INFO:0.4501581s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/3 -2021-05-13 09:53:16,529:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/3/kr-vs-kp.arff -2021-05-13 09:53:17,057:INFO:0.5282001s taken for [get] request for the URL https://www.openml.org/data/v1/download/3/kr-vs-kp.arff -2021-05-13 09:53:17,395:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/3/Task_3_splits.arff -2021-05-13 09:53:18,025:INFO:0.6304121s taken for [get] request for the URL https://www.openml.org/api_splits/get/3/Task_3_splits.arff -2021-05-13 09:53:18,465:INFO:Running kr-vs-kp (3) -2021-05-13 09:53:19,119:INFO:pickle write kr-vs-kp -2021-05-13 09:53:19,401:ERROR:Test kr-vs-kp (3) Failed | X.shape=(3196, 36) | 36 nominal indices -2021-05-13 09:53:19,401:ERROR:'Namespace' object has no attribute 'uf_construction_prop' -2021-05-13 09:54:07,434:INFO:mode=CREATE -2021-05-13 09:54:07,434:INFO:cv=5 -2021-05-13 09:54:07,434:INFO:n_estimators=500 -2021-05-13 09:54:07,434:INFO:n_jobs=30 -2021-05-13 09:54:07,434:INFO:max_features=0.33 -2021-05-13 09:54:07,434:INFO:start_id=None -2021-05-13 09:54:07,434:INFO:stop_id=None -2021-05-13 09:54:07,434:INFO:parallel_tasks=1 -2021-05-13 09:54:07,434:INFO:vary_samples=False -2021-05-13 09:54:07,434:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/study/OpenML-CC18 -2021-05-13 09:54:07,875:INFO:0.4408467s taken for [get] request for the URL https://www.openml.org/api/v1/xml/study/OpenML-CC18 -2021-05-13 09:54:08,403:INFO:Running kr-vs-kp (3) -2021-05-13 09:54:08,714:INFO:pickle load data kr-vs-kp -2021-05-13 09:54:44,540:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/6 -2021-05-13 09:54:44,990:INFO:0.4501064s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/6 -2021-05-13 09:54:44,992:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/6 -2021-05-13 09:54:45,425:INFO:0.4322701s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/6 -2021-05-13 09:54:45,425:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/6 -2021-05-13 09:54:49,855:INFO:4.4294364s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/6 -2021-05-13 09:54:49,856:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/6 -2021-05-13 09:54:50,220:INFO:0.3635781s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/6 -2021-05-13 09:54:50,220:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/6/letter.arff -2021-05-13 09:54:51,180:INFO:0.9595573s taken for [get] request for the URL https://www.openml.org/data/v1/download/6/letter.arff -2021-05-13 09:54:51,488:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/6/Task_6_splits.arff -2021-05-13 09:54:52,531:INFO:1.0429733s taken for [get] request for the URL https://www.openml.org/api_splits/get/6/Task_6_splits.arff -2021-05-13 09:54:53,636:INFO:Running letter (6) -2021-05-13 09:54:54,112:INFO:pickle write letter -2021-05-13 09:58:52,425:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/11 -2021-05-13 09:58:52,877:INFO:0.4522202s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/11 -2021-05-13 09:58:52,879:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/11 -2021-05-13 09:58:53,326:INFO:0.4464366s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/11 -2021-05-13 09:58:53,327:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/11 -2021-05-13 09:58:58,060:INFO:4.7323797s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/11 -2021-05-13 09:58:58,060:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/11 -2021-05-13 09:58:58,511:INFO:0.4510880s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/11 -2021-05-13 09:58:58,512:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/11/balance-scale.arff -2021-05-13 09:58:58,864:INFO:0.3522484s taken for [get] request for the URL https://www.openml.org/data/v1/download/11/balance-scale.arff -2021-05-13 09:58:59,086:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/11/Task_11_splits.arff -2021-05-13 09:58:59,536:INFO:0.4504781s taken for [get] request for the URL https://www.openml.org/api_splits/get/11/Task_11_splits.arff -2021-05-13 09:58:59,787:INFO:Running balance-scale (11) -2021-05-13 09:59:00,110:INFO:pickle write balance-scale -2021-05-13 09:59:30,404:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/12 -2021-05-13 09:59:30,774:INFO:0.3692670s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/12 -2021-05-13 09:59:30,776:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/12 -2021-05-13 09:59:31,142:INFO:0.3661816s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/12 -2021-05-13 09:59:31,143:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/12 -2021-05-13 09:59:35,293:INFO:4.1492281s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/12 -2021-05-13 09:59:35,294:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/12 -2021-05-13 09:59:35,752:INFO:0.4584692s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/12 -2021-05-13 09:59:35,753:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/12/mfeat-factors.arff -2021-05-13 09:59:36,703:INFO:0.9496820s taken for [get] request for the URL https://www.openml.org/data/v1/download/12/mfeat-factors.arff -2021-05-13 09:59:37,023:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/12/Task_12_splits.arff -2021-05-13 09:59:37,653:INFO:0.6301816s taken for [get] request for the URL https://www.openml.org/api_splits/get/12/Task_12_splits.arff -2021-05-13 09:59:37,960:INFO:Running mfeat-factors (12) -2021-05-13 09:59:38,655:INFO:pickle write mfeat-factors -2021-05-13 10:00:17,492:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/14 -2021-05-13 10:00:17,862:INFO:0.3688240s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/14 -2021-05-13 10:00:17,864:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/14 -2021-05-13 10:00:18,555:INFO:0.6911097s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/14 -2021-05-13 10:00:18,557:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/14 -2021-05-13 10:00:22,677:INFO:4.1200047s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/14 -2021-05-13 10:00:22,678:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/14 -2021-05-13 10:00:23,133:INFO:0.4551358s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/14 -2021-05-13 10:00:23,135:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/14/mfeat-fourier.arff -2021-05-13 10:00:24,205:INFO:1.0702822s taken for [get] request for the URL https://www.openml.org/data/v1/download/14/mfeat-fourier.arff -2021-05-13 10:00:24,433:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/14/Task_14_splits.arff -2021-05-13 10:00:25,137:INFO:0.7029622s taken for [get] request for the URL https://www.openml.org/api_splits/get/14/Task_14_splits.arff -2021-05-13 10:00:25,520:INFO:Running mfeat-fourier (14) -2021-05-13 10:00:25,887:INFO:pickle write mfeat-fourier -2021-05-13 10:00:58,686:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/15 -2021-05-13 10:00:59,136:INFO:0.4497049s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/15 -2021-05-13 10:00:59,138:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/15 -2021-05-13 10:00:59,599:INFO:0.4604900s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/15 -2021-05-13 10:00:59,600:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/15 -2021-05-13 10:01:04,161:INFO:4.5607958s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/15 -2021-05-13 10:01:04,163:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/15 -2021-05-13 10:01:04,629:INFO:0.4658747s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/15 -2021-05-13 10:01:04,630:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/52350/breast-w.arff -2021-05-13 10:01:04,986:INFO:0.3554018s taken for [get] request for the URL https://www.openml.org/data/v1/download/52350/breast-w.arff -2021-05-13 10:01:05,211:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/15/Task_15_splits.arff -2021-05-13 10:01:05,661:INFO:0.4493670s taken for [get] request for the URL https://www.openml.org/api_splits/get/15/Task_15_splits.arff -2021-05-13 10:01:05,909:INFO:Running breast-w (15) -2021-05-13 10:01:06,148:INFO:pickle write breast-w -2021-05-13 10:01:35,390:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/16 -2021-05-13 10:01:35,835:INFO:0.4446673s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/16 -2021-05-13 10:01:35,838:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/16 -2021-05-13 10:01:36,196:INFO:0.3579471s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/16 -2021-05-13 10:01:36,198:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/16 -2021-05-13 10:01:40,436:INFO:4.2383342s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/16 -2021-05-13 10:01:40,437:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/16 -2021-05-13 10:01:40,805:INFO:0.3677113s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/16 -2021-05-13 10:01:40,806:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/16/mfeat-karhunen.arff -2021-05-13 10:01:41,723:INFO:0.9167106s taken for [get] request for the URL https://www.openml.org/data/v1/download/16/mfeat-karhunen.arff -2021-05-13 10:01:41,951:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/16/Task_16_splits.arff -2021-05-13 10:01:42,568:INFO:0.6171129s taken for [get] request for the URL https://www.openml.org/api_splits/get/16/Task_16_splits.arff -2021-05-13 10:01:42,869:INFO:Running mfeat-karhunen (16) -2021-05-13 10:01:43,187:INFO:pickle write mfeat-karhunen -2021-05-13 10:02:16,129:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/18 -2021-05-13 10:02:16,584:INFO:0.4550672s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/18 -2021-05-13 10:02:16,586:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/18 -2021-05-13 10:02:17,028:INFO:0.4413283s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/18 -2021-05-13 10:02:17,029:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/18 -2021-05-13 10:02:21,442:INFO:4.4129562s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/18 -2021-05-13 10:02:21,443:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/18 -2021-05-13 10:02:21,799:INFO:0.3562033s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/18 -2021-05-13 10:02:21,801:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/18/mfeat-morphological.arff -2021-05-13 10:02:22,346:INFO:0.5454006s taken for [get] request for the URL https://www.openml.org/data/v1/download/18/mfeat-morphological.arff -2021-05-13 10:02:22,572:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/18/Task_18_splits.arff -2021-05-13 10:02:23,273:INFO:0.7001789s taken for [get] request for the URL https://www.openml.org/api_splits/get/18/Task_18_splits.arff -2021-05-13 10:02:23,575:INFO:Running mfeat-morphological (18) -2021-05-13 10:02:23,819:INFO:pickle write mfeat-morphological -2021-05-13 10:02:58,949:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/22 -2021-05-13 10:02:59,396:INFO:0.4469705s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/22 -2021-05-13 10:02:59,398:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/22 -2021-05-13 10:02:59,767:INFO:0.3694890s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/22 -2021-05-13 10:02:59,768:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/22 -2021-05-13 10:03:04,335:INFO:4.5666711s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/22 -2021-05-13 10:03:04,336:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/22 -2021-05-13 10:03:04,789:INFO:0.4528351s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/22 -2021-05-13 10:03:04,790:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/22/mfeat-zernike.arff -2021-05-13 10:03:05,700:INFO:0.9105480s taken for [get] request for the URL https://www.openml.org/data/v1/download/22/mfeat-zernike.arff -2021-05-13 10:03:05,923:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/22/Task_22_splits.arff -2021-05-13 10:03:06,633:INFO:0.7101758s taken for [get] request for the URL https://www.openml.org/api_splits/get/22/Task_22_splits.arff -2021-05-13 10:03:06,943:INFO:Running mfeat-zernike (22) -2021-05-13 10:03:07,252:INFO:pickle write mfeat-zernike -2021-05-13 10:03:46,094:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/23 -2021-05-13 10:03:46,465:INFO:0.3707998s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/23 -2021-05-13 10:03:46,467:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/23 -2021-05-13 10:03:46,913:INFO:0.4461534s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/23 -2021-05-13 10:03:46,914:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/23 -2021-05-13 10:03:51,473:INFO:4.5580873s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/23 -2021-05-13 10:03:51,474:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/23 -2021-05-13 10:03:51,929:INFO:0.4546845s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/23 -2021-05-13 10:03:51,930:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/23/cmc.arff -2021-05-13 10:03:52,397:INFO:0.4674020s taken for [get] request for the URL https://www.openml.org/data/v1/download/23/cmc.arff -2021-05-13 10:03:52,619:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/23/Task_23_splits.arff -2021-05-13 10:03:53,116:INFO:0.4968023s taken for [get] request for the URL https://www.openml.org/api_splits/get/23/Task_23_splits.arff -2021-05-13 10:03:53,487:INFO:Running cmc (23) -2021-05-13 10:03:53,741:INFO:pickle write cmc -2021-05-13 10:04:49,971:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/28 -2021-05-13 10:04:50,414:INFO:0.4434648s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/28 -2021-05-13 10:04:50,417:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/28 -2021-05-13 10:04:50,866:INFO:0.4482558s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/28 -2021-05-13 10:04:50,867:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/28 -2021-05-13 10:04:56,265:INFO:5.3977451s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/28 -2021-05-13 10:04:56,266:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/28 -2021-05-13 10:04:56,720:INFO:0.4535494s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/28 -2021-05-13 10:04:56,720:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/28/optdigits.arff -2021-05-13 10:04:57,540:INFO:0.8193641s taken for [get] request for the URL https://www.openml.org/data/v1/download/28/optdigits.arff -2021-05-13 10:04:57,770:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/28/Task_28_splits.arff -2021-05-13 10:04:58,501:INFO:0.7310624s taken for [get] request for the URL https://www.openml.org/api_splits/get/28/Task_28_splits.arff -2021-05-13 10:04:58,948:INFO:Running optdigits (28) -2021-05-13 10:04:59,372:INFO:pickle write optdigits -2021-05-13 10:05:42,195:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/29 -2021-05-13 10:05:42,566:INFO:0.3708682s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/29 -2021-05-13 10:05:42,568:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/29 -2021-05-13 10:05:43,013:INFO:0.4451423s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/29 -2021-05-13 10:05:43,015:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/29 -2021-05-13 10:05:47,195:INFO:4.1799934s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/29 -2021-05-13 10:05:47,196:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/29 -2021-05-13 10:05:47,562:INFO:0.3658781s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/29 -2021-05-13 10:05:47,562:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/29/credit-approval.arff -2021-05-13 10:05:47,998:INFO:0.4353523s taken for [get] request for the URL https://www.openml.org/data/v1/download/29/credit-approval.arff -2021-05-13 10:05:48,223:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/29/Task_29_splits.arff -2021-05-13 10:05:48,664:INFO:0.4409649s taken for [get] request for the URL https://www.openml.org/api_splits/get/29/Task_29_splits.arff -2021-05-13 10:05:48,912:INFO:Running credit-approval (29) -2021-05-13 10:05:49,166:INFO:pickle write credit-approval -2021-05-13 10:06:22,751:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/31 -2021-05-13 10:06:23,213:INFO:0.4614756s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/31 -2021-05-13 10:06:23,214:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/31 -2021-05-13 10:06:23,585:INFO:0.3702185s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/31 -2021-05-13 10:06:23,586:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/31 -2021-05-13 10:06:27,919:INFO:4.3330593s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/31 -2021-05-13 10:06:27,920:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/31 -2021-05-13 10:06:28,363:INFO:0.4429226s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/31 -2021-05-13 10:06:28,364:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/31/credit-g.arff -2021-05-13 10:06:28,911:INFO:0.5473173s taken for [get] request for the URL https://www.openml.org/data/v1/download/31/credit-g.arff -2021-05-13 10:06:29,137:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/31/Task_31_splits.arff -2021-05-13 10:06:29,636:INFO:0.4989998s taken for [get] request for the URL https://www.openml.org/api_splits/get/31/Task_31_splits.arff -2021-05-13 10:06:29,900:INFO:Running credit-g (31) -2021-05-13 10:06:30,180:INFO:pickle write credit-g -2021-05-13 10:07:10,692:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/32 -2021-05-13 10:07:11,054:INFO:0.3619826s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/32 -2021-05-13 10:07:11,056:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/32 -2021-05-13 10:07:11,502:INFO:0.4455862s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/32 -2021-05-13 10:07:11,503:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/32 -2021-05-13 10:07:15,900:INFO:4.3971393s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/32 -2021-05-13 10:07:15,902:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/32 -2021-05-13 10:07:16,265:INFO:0.3634148s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/32 -2021-05-13 10:07:16,266:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/32/pendigits.arff -2021-05-13 10:07:17,015:INFO:0.7482307s taken for [get] request for the URL https://www.openml.org/data/v1/download/32/pendigits.arff -2021-05-13 10:07:17,242:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/32/Task_32_splits.arff -2021-05-13 10:07:18,177:INFO:0.9352739s taken for [get] request for the URL https://www.openml.org/api_splits/get/32/Task_32_splits.arff -2021-05-13 10:07:18,830:INFO:Running pendigits (32) -2021-05-13 10:07:19,321:INFO:pickle write pendigits -2021-05-13 10:08:16,775:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/37 -2021-05-13 10:08:17,220:INFO:0.4445806s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/37 -2021-05-13 10:08:17,222:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/37 -2021-05-13 10:08:17,675:INFO:0.4526825s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/37 -2021-05-13 10:08:17,676:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/37 -2021-05-13 10:08:22,072:INFO:4.3955314s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/37 -2021-05-13 10:08:22,073:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/37 -2021-05-13 10:08:22,429:INFO:0.3562157s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/37 -2021-05-13 10:08:22,430:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/37/diabetes.arff -2021-05-13 10:08:22,968:INFO:0.5375242s taken for [get] request for the URL https://www.openml.org/data/v1/download/37/diabetes.arff -2021-05-13 10:08:23,194:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/37/Task_37_splits.arff -2021-05-13 10:08:23,699:INFO:0.5041053s taken for [get] request for the URL https://www.openml.org/api_splits/get/37/Task_37_splits.arff -2021-05-13 10:08:23,948:INFO:Running diabetes (37) -2021-05-13 10:08:24,189:INFO:pickle write diabetes -2021-05-13 10:09:00,966:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/43 -2021-05-13 10:09:02,409:INFO:1.4422555s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/43 -2021-05-13 10:09:02,411:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/44 -2021-05-13 10:09:02,849:INFO:0.4382670s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/44 -2021-05-13 10:09:02,851:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/44 -2021-05-13 10:09:07,404:INFO:4.5529544s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/44 -2021-05-13 10:09:07,405:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/44 -2021-05-13 10:09:07,769:INFO:0.3638318s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/44 -2021-05-13 10:09:07,774:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/44/spambase.arff -2021-05-13 10:09:08,510:INFO:0.7358899s taken for [get] request for the URL https://www.openml.org/data/v1/download/44/spambase.arff -2021-05-13 10:09:08,827:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/43/Task_43_splits.arff -2021-05-13 10:09:09,587:INFO:0.7600472s taken for [get] request for the URL https://www.openml.org/api_splits/get/43/Task_43_splits.arff -2021-05-13 10:09:09,990:INFO:Running spambase (43) -2021-05-13 10:09:10,375:INFO:pickle write spambase -2021-05-13 10:09:45,780:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/45 -2021-05-13 10:09:46,150:INFO:0.3693216s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/45 -2021-05-13 10:09:46,152:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/46 -2021-05-13 10:09:46,603:INFO:0.4505649s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/46 -2021-05-13 10:09:46,604:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/46 -2021-05-13 10:09:50,842:INFO:4.2373111s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/46 -2021-05-13 10:09:50,843:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/46 -2021-05-13 10:09:51,310:INFO:0.4667301s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/46 -2021-05-13 10:09:51,311:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/46/splice.arff -2021-05-13 10:09:51,996:INFO:0.6848094s taken for [get] request for the URL https://www.openml.org/data/v1/download/46/splice.arff -2021-05-13 10:09:52,241:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/45/Task_45_splits.arff -2021-05-13 10:09:52,859:INFO:0.6175959s taken for [get] request for the URL https://www.openml.org/api_splits/get/45/Task_45_splits.arff -2021-05-13 10:09:53,208:INFO:Running splice (45) -2021-05-13 10:09:53,660:INFO:pickle write splice -2021-05-13 10:09:53,662:INFO:Going to remove the following attributes: ['Instance_name'] -2021-05-13 10:10:49,023:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/49 -2021-05-13 10:10:49,477:INFO:0.4538691s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/49 -2021-05-13 10:10:49,479:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/50 -2021-05-13 10:10:49,927:INFO:0.4482975s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/50 -2021-05-13 10:10:49,929:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/50 -2021-05-13 10:10:54,347:INFO:4.4184308s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/50 -2021-05-13 10:10:54,349:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/50 -2021-05-13 10:10:54,803:INFO:0.4544327s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/50 -2021-05-13 10:10:54,805:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/50/tic-tac-toe.arff -2021-05-13 10:10:55,254:INFO:0.4486127s taken for [get] request for the URL https://www.openml.org/data/v1/download/50/tic-tac-toe.arff -2021-05-13 10:10:55,475:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/49/Task_49_splits.arff -2021-05-13 10:10:56,046:INFO:0.5706375s taken for [get] request for the URL https://www.openml.org/api_splits/get/49/Task_49_splits.arff -2021-05-13 10:10:56,304:INFO:Running tic-tac-toe (49) -2021-05-13 10:10:56,548:INFO:pickle write tic-tac-toe -2021-05-13 10:11:36,698:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/53 -2021-05-13 10:11:37,880:INFO:1.1825390s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/53 -2021-05-13 10:11:37,882:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/54 -2021-05-13 10:11:38,257:INFO:0.3748195s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/54 -2021-05-13 10:11:38,259:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/54 -2021-05-13 10:11:42,857:INFO:4.5978544s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/54 -2021-05-13 10:11:42,857:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/54 -2021-05-13 10:11:43,219:INFO:0.3609853s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/54 -2021-05-13 10:11:43,220:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/54/vehicle.arff -2021-05-13 10:11:43,693:INFO:0.4729135s taken for [get] request for the URL https://www.openml.org/data/v1/download/54/vehicle.arff -2021-05-13 10:11:43,915:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/53/Task_53_splits.arff -2021-05-13 10:11:44,401:INFO:0.4858766s taken for [get] request for the URL https://www.openml.org/api_splits/get/53/Task_53_splits.arff -2021-05-13 10:11:44,661:INFO:Running vehicle (53) -2021-05-13 10:11:44,914:INFO:pickle write vehicle -2021-05-13 10:12:24,035:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/219 -2021-05-13 10:12:24,498:INFO:0.4626324s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/219 -2021-05-13 10:12:24,500:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/151 -2021-05-13 10:12:24,941:INFO:0.4406059s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/151 -2021-05-13 10:12:24,943:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/151 -2021-05-13 10:12:29,334:INFO:4.3910992s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/151 -2021-05-13 10:12:29,335:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/151 -2021-05-13 10:12:29,783:INFO:0.4474154s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/151 -2021-05-13 10:12:29,784:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/2419/electricity.arff -2021-05-13 10:12:30,700:INFO:0.9160416s taken for [get] request for the URL https://www.openml.org/data/v1/download/2419/electricity.arff -2021-05-13 10:12:30,920:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/219/Task_219_splits.arff -2021-05-13 10:12:32,119:INFO:1.1991093s taken for [get] request for the URL https://www.openml.org/api_splits/get/219/Task_219_splits.arff -2021-05-13 10:12:34,112:INFO:Running electricity (219) -2021-05-13 10:12:34,722:INFO:pickle write electricity -2021-05-13 10:17:50,524:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/2074 -2021-05-13 10:17:50,976:INFO:0.4514184s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/2074 -2021-05-13 10:17:50,978:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/182 -2021-05-13 10:17:51,423:INFO:0.4442778s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/182 -2021-05-13 10:17:51,424:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/182 -2021-05-13 10:17:56,326:INFO:4.9017065s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/182 -2021-05-13 10:17:56,327:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/182 -2021-05-13 10:17:56,688:INFO:0.3608427s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/182 -2021-05-13 10:17:56,689:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/3619/satimage.arff -2021-05-13 10:17:57,577:INFO:0.8878965s taken for [get] request for the URL https://www.openml.org/data/v1/download/3619/satimage.arff -2021-05-13 10:17:57,803:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/2074/Task_2074_splits.arff -2021-05-13 10:17:58,603:INFO:0.7996097s taken for [get] request for the URL https://www.openml.org/api_splits/get/2074/Task_2074_splits.arff -2021-05-13 10:17:59,183:INFO:Running satimage (2074) -2021-05-13 10:17:59,562:INFO:pickle write satimage -2021-05-13 10:18:50,392:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/2079 -2021-05-13 10:18:50,840:INFO:0.4476697s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/2079 -2021-05-13 10:18:50,842:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/188 -2021-05-13 10:18:51,287:INFO:0.4453433s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/188 -2021-05-13 10:18:51,289:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/188 -2021-05-13 10:18:56,096:INFO:4.8070300s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/188 -2021-05-13 10:18:56,097:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/188 -2021-05-13 10:18:56,543:INFO:0.4460742s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/188 -2021-05-13 10:18:56,545:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/3625/eucalyptus.arff -2021-05-13 10:18:57,078:INFO:0.5332501s taken for [get] request for the URL https://www.openml.org/data/v1/download/3625/eucalyptus.arff -2021-05-13 10:18:57,302:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/2079/Task_2079_splits.arff -2021-05-13 10:18:57,940:INFO:0.6381929s taken for [get] request for the URL https://www.openml.org/api_splits/get/2079/Task_2079_splits.arff -2021-05-13 10:18:58,192:INFO:Running eucalyptus (2079) -2021-05-13 10:18:58,449:INFO:pickle write eucalyptus -2021-05-13 10:19:37,496:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/3021 -2021-05-13 10:19:37,872:INFO:0.3761213s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/3021 -2021-05-13 10:19:37,874:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/38 -2021-05-13 10:19:38,318:INFO:0.4435060s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/38 -2021-05-13 10:19:38,319:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/38 -2021-05-13 10:19:42,968:INFO:4.6483936s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/38 -2021-05-13 10:19:42,969:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/38 -2021-05-13 10:19:43,331:INFO:0.3620095s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/38 -2021-05-13 10:19:43,332:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/38/sick.arff -2021-05-13 10:19:43,885:INFO:0.5530968s taken for [get] request for the URL https://www.openml.org/data/v1/download/38/sick.arff -2021-05-13 10:19:44,111:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/3021/Task_3021_splits.arff -2021-05-13 10:19:44,766:INFO:0.6544523s taken for [get] request for the URL https://www.openml.org/api_splits/get/3021/Task_3021_splits.arff -2021-05-13 10:19:45,137:INFO:Running sick (3021) -2021-05-13 10:19:45,480:INFO:pickle write sick -2021-05-13 10:20:18,400:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/3022 -2021-05-13 10:20:18,855:INFO:0.4548483s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/3022 -2021-05-13 10:20:18,856:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/307 -2021-05-13 10:20:19,312:INFO:0.4554965s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/307 -2021-05-13 10:20:19,314:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/307 -2021-05-13 10:20:23,568:INFO:4.2537725s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/307 -2021-05-13 10:20:23,569:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/307 -2021-05-13 10:20:23,932:INFO:0.3628361s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/307 -2021-05-13 10:20:23,933:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/52210/vowel.arff -2021-05-13 10:20:24,473:INFO:0.5394626s taken for [get] request for the URL https://www.openml.org/data/v1/download/52210/vowel.arff -2021-05-13 10:20:24,779:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/3022/Task_3022_splits.arff -2021-05-13 10:20:25,232:INFO:0.4528861s taken for [get] request for the URL https://www.openml.org/api_splits/get/3022/Task_3022_splits.arff -2021-05-13 10:20:25,493:INFO:Running vowel (3022) -2021-05-13 10:20:25,733:INFO:pickle write vowel -2021-05-13 10:21:06,887:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/3481 -2021-05-13 10:21:07,338:INFO:0.4507515s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/3481 -2021-05-13 10:21:07,340:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/300 -2021-05-13 10:21:07,787:INFO:0.4465656s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/300 -2021-05-13 10:21:07,789:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/300 -2021-05-13 10:21:11,976:INFO:4.1873291s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/300 -2021-05-13 10:21:11,977:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/300 -2021-05-13 10:21:12,420:INFO:0.4426627s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/300 -2021-05-13 10:21:12,421:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/52405/isolet.arff -2021-05-13 10:21:16,741:INFO:4.3190730s taken for [get] request for the URL https://www.openml.org/data/v1/download/52405/isolet.arff -2021-05-13 10:21:17,093:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/3481/Task_3481_splits.arff -2021-05-13 10:21:17,838:INFO:0.7449660s taken for [get] request for the URL https://www.openml.org/api_splits/get/3481/Task_3481_splits.arff -2021-05-13 10:21:18,368:INFO:Running isolet (3481) -2021-05-13 10:21:23,224:INFO:pickle write isolet -2021-05-13 10:55:17,141:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/3549 -2021-05-13 10:55:17,598:INFO:0.4566059s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/3549 -2021-05-13 10:55:17,600:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/458 -2021-05-13 10:55:18,046:INFO:0.4458609s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/458 -2021-05-13 10:55:18,048:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/458 -2021-05-13 10:55:22,421:INFO:4.3732092s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/458 -2021-05-13 10:55:22,422:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/458 -2021-05-13 10:55:22,796:INFO:0.3732600s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/458 -2021-05-13 10:55:22,797:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/52570/analcatdata_authorship.arff -2021-05-13 10:55:23,435:INFO:0.6376035s taken for [get] request for the URL https://www.openml.org/data/v1/download/52570/analcatdata_authorship.arff -2021-05-13 10:55:23,758:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/3549/Task_3549_splits.arff -2021-05-13 10:55:24,344:INFO:0.5858352s taken for [get] request for the URL https://www.openml.org/api_splits/get/3549/Task_3549_splits.arff -2021-05-13 10:55:24,693:INFO:Running analcatdata_authorship (3549) -2021-05-13 10:55:25,086:INFO:pickle write analcatdata_authorship -2021-05-13 10:55:56,518:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/3560 -2021-05-13 10:55:56,969:INFO:0.4506559s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/3560 -2021-05-13 10:55:56,971:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/469 -2021-05-13 10:55:57,418:INFO:0.4461935s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/469 -2021-05-13 10:55:57,419:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/469 -2021-05-13 10:56:02,039:INFO:4.6196396s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/469 -2021-05-13 10:56:02,040:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/469 -2021-05-13 10:56:02,488:INFO:0.4485698s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/469 -2021-05-13 10:56:02,489:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/52581/analcatdata_dmft.arff -2021-05-13 10:56:02,843:INFO:0.3530090s taken for [get] request for the URL https://www.openml.org/data/v1/download/52581/analcatdata_dmft.arff -2021-05-13 10:56:03,155:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/3560/Task_3560_splits.arff -2021-05-13 10:56:03,626:INFO:0.4710159s taken for [get] request for the URL https://www.openml.org/api_splits/get/3560/Task_3560_splits.arff -2021-05-13 10:56:03,881:INFO:Running analcatdata_dmft (3560) -2021-05-13 10:56:04,117:INFO:pickle write analcatdata_dmft -2021-05-13 10:56:45,943:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/task/3573 -2021-05-13 10:56:46,394:INFO:0.4509931s taken for [get] request for the URL https://www.openml.org/api/v1/xml/task/3573 -2021-05-13 10:56:46,396:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/554 -2021-05-13 10:56:46,852:INFO:0.4558682s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/554 -2021-05-13 10:56:46,854:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/features/554 -2021-05-13 10:56:51,145:INFO:4.2908299s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/features/554 -2021-05-13 10:56:51,146:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/554 -2021-05-13 10:56:51,588:INFO:0.4420280s taken for [get] request for the URL https://www.openml.org/api/v1/xml/data/qualities/554 -2021-05-13 10:56:51,589:INFO:Starting [get] request for the URL https://www.openml.org/data/v1/download/52667/mnist_784.arff -2021-05-13 10:56:56,888:INFO:5.2990303s taken for [get] request for the URL https://www.openml.org/data/v1/download/52667/mnist_784.arff -2021-05-13 10:56:57,266:INFO:Starting [get] request for the URL https://www.openml.org/api_splits/get/3573/Task_3573_splits.arff -2021-05-13 10:56:58,584:INFO:1.3178506s taken for [get] request for the URL https://www.openml.org/api_splits/get/3573/Task_3573_splits.arff -2021-05-13 10:57:01,607:INFO:Running mnist_784 (3573) -2021-05-13 10:57:28,443:INFO:pickle write mnist_784 -2021-06-08 13:29:31,296:INFO:mode=CREATE -2021-06-08 13:29:31,296:INFO:cv=10 -2021-06-08 13:29:31,296:INFO:n_estimators=500 -2021-06-08 13:29:31,296:INFO:n_jobs=1 -2021-06-08 13:29:31,296:INFO:max_features=None -2021-06-08 13:29:31,296:INFO:start_id=None -2021-06-08 13:29:31,296:INFO:stop_id=None -2021-06-08 13:29:31,296:INFO:parallel_tasks=1 -2021-06-08 13:29:31,296:INFO:vary_samples=False -2021-06-08 13:29:31,296:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/study/OpenML-CC18 -2021-06-08 13:29:31,743:INFO:0.4468753s taken for [get] request for the URL https://www.openml.org/api/v1/xml/study/OpenML-CC18 -2021-06-08 13:29:32,357:INFO:Running kr-vs-kp (3) -2021-06-08 13:29:32,660:INFO:pickle load data kr-vs-kp -2021-06-08 13:35:23,530:INFO:Running letter (6) -2021-06-08 13:35:23,751:INFO:pickle load data letter -2021-06-08 13:44:26,136:INFO:mode=CREATE -2021-06-08 13:44:26,136:INFO:cv=10 -2021-06-08 13:44:26,136:INFO:n_estimators=500 -2021-06-08 13:44:26,136:INFO:n_jobs=30 -2021-06-08 13:44:26,136:INFO:max_features=None -2021-06-08 13:44:26,136:INFO:start_id=None -2021-06-08 13:44:26,136:INFO:stop_id=None -2021-06-08 13:44:26,136:INFO:parallel_tasks=1 -2021-06-08 13:44:26,136:INFO:vary_samples=False -2021-06-08 13:44:26,136:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/study/OpenML-CC18 -2021-06-08 13:44:26,583:INFO:0.4473372s taken for [get] request for the URL https://www.openml.org/api/v1/xml/study/OpenML-CC18 -2021-06-08 13:44:27,111:INFO:Running kr-vs-kp (3) -2021-06-08 13:44:27,329:INFO:pickle load data kr-vs-kp -2021-06-08 13:45:24,237:INFO:Running letter (6) -2021-06-08 13:45:24,451:INFO:pickle load data letter -2021-06-08 13:46:35,453:INFO:Running balance-scale (11) -2021-06-08 13:46:35,671:INFO:pickle load data balance-scale -2021-06-08 13:46:46,486:INFO:mode=CREATE -2021-06-08 13:46:46,486:INFO:cv=10 -2021-06-08 13:46:46,486:INFO:n_estimators=500 -2021-06-08 13:46:46,486:INFO:n_jobs=1 -2021-06-08 13:46:46,486:INFO:max_features=None -2021-06-08 13:46:46,486:INFO:start_id=None -2021-06-08 13:46:46,486:INFO:stop_id=None -2021-06-08 13:46:46,486:INFO:parallel_tasks=1 -2021-06-08 13:46:46,486:INFO:vary_samples=False -2021-06-08 13:46:46,486:INFO:Starting [get] request for the URL https://www.openml.org/api/v1/xml/study/OpenML-CC18 -2021-06-08 13:46:46,934:INFO:0.4482744s taken for [get] request for the URL https://www.openml.org/api/v1/xml/study/OpenML-CC18 -2021-06-08 13:46:47,377:INFO:Running kr-vs-kp (3) -2021-06-08 13:46:47,597:INFO:pickle load data kr-vs-kp diff --git a/validation/cc18_benchmark/run_all.py b/validation/cc18_benchmark/run_all.py deleted file mode 100644 index 408690d56..000000000 --- a/validation/cc18_benchmark/run_all.py +++ /dev/null @@ -1,413 +0,0 @@ -import argparse -import collections -import logging -import os -import pickle -import time -from pathlib import Path - -import numpy as np -import openml -from joblib import Parallel, delayed -from oblique_forests.ensemble import RandomForestClassifier as ObliqueRF -from oblique_forests.sporf import ObliqueForestClassifier as ObliqueSPORF -from rerf.rerfClassifier import rerfClassifier -from sklearn.calibration import CalibratedClassifierCV -from sklearn.compose import ColumnTransformer -from sklearn.ensemble import RandomForestClassifier -from sklearn.impute import SimpleImputer -from sklearn.metrics import cohen_kappa_score -from sklearn.model_selection import StratifiedKFold -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import OneHotEncoder, StandardScaler -from tqdm import tqdm - - -def _check_nested_equality(lst1, lst2): - if isinstance(lst1, list) and isinstance(lst2, list): - for l1, l2 in zip(lst1, lst2): - if not _check_nested_equality(l1, l2): - return False - elif isinstance(lst1, np.ndarray) and isinstance(lst2, np.ndarray): - return np.all(lst1 == lst2) - else: - return lst1 == lst2 - - return True - - -def stratify_samplesizes(y, block_lengths): - """ - Sort data and labels into blocks that preserve class balance - - Parameters - ---------- - X: data matrix - y : 1D class labels - block_lengths : Block sizes to sort X,y into that preserve class balance - """ - clss, counts = np.unique(y, return_counts=True) - ratios = counts / sum(counts) - class_idxs = [np.where(y == i)[0] for i in clss] - - sort_idxs = [] - prior_idxs = np.zeros(len(clss)).astype(int) - for n in block_lengths: - get_idxs = np.rint((n - len(clss)) * ratios).astype(int) + 1 - for idxs, prior_idx, next_idx in zip(class_idxs, prior_idxs, get_idxs): - sort_idxs.append(idxs[prior_idx:next_idx]) - prior_idxs = get_idxs - - sort_idxs = np.hstack(sort_idxs) - - return sort_idxs - - -def train_test(X, y, task_name, task_id, nominal_indices, args, clfs, save_path, random_state): - # Set up Cross validation - - n_samples, n_features = X.shape - n_classes = len(np.unique(y)) - - if args.vary_samples: - sample_sizes = np.logspace( - np.log10(n_classes * 2), - np.log10(np.floor(len(y) * (args.cv - 1.1) / args.cv)), - num=10, - endpoint=True, - dtype=int, - ) - else: - sample_sizes = [len(y)] - - # Check if existing experiments - results_dict = { - "task": task_name, - "task_id": task_id, - "n_samples": n_samples, - "n_features": n_features, - "n_classes": n_classes, - "y": y, - "test_indices": [], - "n_estimators": args.n_estimators, - "cv": args.cv, - "nominal_features": len(nominal_indices), - "sample_sizes": sample_sizes, - } - - if len(nominal_indices) == n_features: - print(f"Skipping task: {task_name}, {task_id} because all features are nominal") - return - - # get the original numerical indices - # numeric_indices = np.delete(np.arange(X.shape[1]), nominal_indices) - - # drop nominal indices - X = np.delete(X, nominal_indices, axis=1) - - # Numeric Preprocessing - numeric_transformer = Pipeline( - steps=[ - ("imputer", SimpleImputer(strategy="median")), - ("standardizer", StandardScaler()), - ] - ) - - # Nominal preprocessing - # nominal_transformer = Pipeline( - # steps=[ - # ("imputer", SimpleImputer(strategy="most_frequent")), - # ("onehot", OneHotEncoder(handle_unknown="ignore", sparse=False)), - # ] - # ) - - transformers = [] - numeric_indices = np.arange(X.shape[1]) - # Get numeric indices first - if len(numeric_indices) > 0: - transformers += [("numeric", numeric_transformer, numeric_indices)] - # if len(nominal_indices) > 0: - # transformers += [("nominal", nominal_transformer, nominal_indices)] - preprocessor = ColumnTransformer(transformers=transformers) - - _, n_features_fitted = preprocessor.fit_transform(X, y).shape - results_dict["n_features_fitted"] = n_features_fitted - print( - f"Features={n_features}, nominal={len(nominal_indices)} (After transforming={n_features_fitted})" - ) - - for clf_name, clf in clfs: - pipeline = Pipeline(steps=[("Preprocessor", preprocessor), ("Estimator", clf)]) - - fold_probas = [] - oob_fold_probas = [] - if not f"{clf_name}_metadata" in results_dict.keys(): - results_dict[f"{clf_name}_metadata"] = {} - results_dict[f"{clf_name}_metadata"] = collections.defaultdict(list) - # results_dict[f"{clf_name}_metadata"]["train_times"] = [] - # results_dict[f"{clf_name}_metadata"]["test_times"] = [] - - # initialize the cross-validator - skf = StratifiedKFold(n_splits=args.cv, shuffle=True, random_state=random_state) - for train_index, test_index in skf.split(X, y): - # Store training indices (random state insures consistent across clfs) - results_dict[f"{clf_name}_metadata"]["test_indices"].append(test_index) - - X_train, X_test = X[train_index], X[test_index] - y_train, y_test = y[train_index], y[test_index] - - if args.vary_samples: - stratified_sort = stratify_samplesizes(y_train, sample_sizes) - X_train = X_train[stratified_sort] - y_train = y_train[stratified_sort] - - probas_vs_sample_sizes = [] - - for n_samples in sample_sizes: - start_time = time.time() - # Fix too few samples for internal CV of these methods - if ( - clf_name in ["IRF", "SigRF"] - and np.min(np.unique(y_train[:n_samples], return_counts=True)[1]) < 5 - ): - print( - f"{clf_name} requires more samples of minimum class. Skipping n={n_samples}" - ) - y_proba = np.repeat( - np.bincount(y_train[:n_samples]).reshape(1, -1) / len(y_train[:n_samples]), - X_test.shape[0], - axis=0, - ) - # y_proba_oob = y_proba - train_time = time.time() - start_time - else: - pipeline = pipeline.fit(X_train[:n_samples], y_train[:n_samples]) - train_time = time.time() - start_time - y_proba = pipeline.predict_proba(X_test) - # y_proba_oob = predict_proba_oob(pipeline['Estimator'], pipeline['Preprocessor'].transform(X_train[:n_samples])) - - test_time = time.time() - (train_time + start_time) - - probas_vs_sample_sizes.append(y_proba) - # oob_probas_vs_sample_sizes.append(y_proba_oob) - results_dict[f"{clf_name}_metadata"]["train_times"].append(train_time) - results_dict[f"{clf_name}_metadata"]["test_times"].append(test_time) - - fold_probas.append(probas_vs_sample_sizes) - print( - f"{clf_name} Time: train_time={train_time:.3f}, test_time={test_time:.3f}, Cohen Kappa={cohen_kappa_score(y_test, y_proba.argmax(1)):.3f}" - ) - - results_dict[clf_name] = fold_probas - # If existing data, load and append to. Else save - if os.path.isfile(save_path) and args.mode == "OVERWRITE": - logging.info(f"OVERWRITING {task_name} ({task_id})") - with open(save_path, "rb") as f: - prior_results = pickle.load(f) - - # Check these keys have the same values - verify_keys = [ - "task", - "task_id", - "n_samples", - "n_features", - "n_classes", - "y", - "test_indices", - "n_estimators", - "cv", - "nominal_features", - "n_features_fitted", - "sample_sizes", - ] - for key in verify_keys: - assert _check_nested_equality( - prior_results[key], results_dict[key] - ), f"OVERWRITE {key} does not match saved value" - - # Replace/add data - replace_keys = [name for name, _ in clfs] - replace_keys += [f"{name}_metadata" for name in replace_keys] - for key in replace_keys: - prior_results[key] = results_dict[key] - - results_dict = prior_results - - with open(save_path, "wb") as f: - pickle.dump(results_dict, f) - - -def run_cc18(args, clfs, data_dir, random_state): - logging.basicConfig( - filename="run_all.log", - format="%(asctime)s:%(levelname)s:%(message)s", - level=logging.INFO, - ) - - for key, val in vars(args).items(): - logging.info(f"{key}={val}") - - benchmark_suite = openml.study.get_suite("OpenML-CC18") # obtain the benchmark suite - - folder = data_dir / f"sporf_benchmarks/rerf/results_cv{args.cv}_features={args.max_features}" - - if not os.path.exists(folder): - os.makedirs(folder) - - def _run_task_helper(task_id): - attempts = 0 - while attempts < 50: - try: - task = openml.tasks.get_task(task_id) # download the OpenML task - break - except Exception as e: - attempts += 1 - time.sleep(2) # Number of seconds - print("Trying again...") - - task_name = task.get_dataset().name - - # if task_name in ['Fashion-MNIST', 'segment', 'Devnagari-Script', - # 'mnist_784'] - - save_path = f"{folder}/{task_name}_results_dict.pkl" - if args.mode == "OVERWRITE": - if not os.path.isfile(save_path): - logging.info(f"OVERWRITE MODE: Skipping {task_name} (doesn't exist)") - return - elif args.mode == "APPEND" and os.path.isfile(save_path): - logging.info(f"APPEND MODE: Skipping {task_name} (already exists)") - return - - print(f"{args.mode} {task_name} ({task_id})") - logging.info(f"Running {task_name} ({task_id})") - - X, y = task.get_X_and_y() # get the data - - nominal_indices = task.get_dataset().get_features_by_type("nominal", [task.target_name]) - try: - train_test( - X, - y, - task_name, - task_id, - nominal_indices, - args, - clfs, - save_path, - random_state=random_state, - ) - except Exception as e: - print( - f"Test {task_name} ({task_id}) Failed | X.shape={X.shape} | {len(nominal_indices)} nominal indices" - ) - print(e) - logging.error( - f"Test {task_name} ({task_id}) Failed | X.shape={X.shape} | {len(nominal_indices)} nominal indices" - ) - import traceback - - logging.error(e) - traceback.sprint_exc() - - task_ids_to_run = [] - for task_id in benchmark_suite.tasks: - if args.start_id is not None and task_id < args.start_id: - print(f"Skipping task_id={task_id}") - logging.info(f"Skipping task_id={task_id}") - continue - if args.stop_id is not None and task_id >= args.stop_id: - print(f"Stopping at task_id={task_id}") - logging.info(f"Stopping at task_id={task_id}") - break - task_ids_to_run.append(task_id) - - if args.parallel_tasks is not None and args.parallel_tasks > 1: - Parallel(n_jobs=args.parallel_tasks, verbose=1)( - delayed(_run_task_helper)(task_id) for task_id in tqdm(task_ids_to_run) - ) - else: - for task_id in tqdm(task_ids_to_run): # iterate over all tasks - _run_task_helper(task_id) - - -parser = argparse.ArgumentParser(description="Run CC18 dataset.") -parser.add_argument( - "--mode", action="store", default="CREATE", choices=["OVERWRITE", "CREATE", "APPEND"] -) -parser.add_argument("--cv", action="store", type=int, default=10) -parser.add_argument("--n_estimators", action="store", type=int, default=500) -parser.add_argument("--n_jobs", action="store", type=int, default=12) -parser.add_argument( - "--max_features", - action="store", - default="sqrt", - help="Either an integer, float, or string in {'sqrt', 'log2'}. Default uses all features.", -) -parser.add_argument("--start_id", action="store", type=int, default=None) -parser.add_argument("--stop_id", action="store", type=int, default=None) -# parser.add_argument("--honest_prior", action="store", default="ignore", choices=["ignore", "uniform", "empirical"]) -parser.add_argument("--parallel_tasks", action="store", default=1, type=int) -parser.add_argument("--vary_samples", action="store_true", default=False) - - -args = parser.parse_args() - -max_features = args.max_features -try: - max_features = int(max_features) -except: - try: - max_features = float(max_features) - except: - pass - -print(f"Max features is: {max_features}") -random_state = 12345 - -clfs = [ - ( - "rf", - RandomForestClassifier( - n_estimators=args.n_estimators, - max_features=max_features, - n_jobs=args.n_jobs, - random_state=random_state, - ), - ), - # ( - # "Oblique-RF", - # ObliqueRF( - # n_estimators=args.n_estimators, - # max_features=max_features, - # n_jobs=args.n_jobs) - # ), - # ( - # "rerfsporf", - # rerfClassifier( - # n_estimators=args.n_estimators, - # max_features=max_features, - # feature_combinations=1.5, - # n_jobs=args.n_jobs, - # random_state=random_state - # ) - # ), - ( - "cysporf", - ObliqueSPORF( - n_estimators=args.n_estimators, - max_features=max_features, - feature_combinations=1.5, - n_jobs=args.n_jobs, - random_state=random_state, - ), - ), -] - - -data_dir = Path("/mnt/ssd3/ronan/") -data_dir = Path("/home/adam2392/Downloads/cysporf_vs_rf_without_nominal") -run_cc18(args, clfs, data_dir, random_state=random_state) -# python run_all.py --mode CREATE --n_jobs 45 --max_features sqrt --stop_id 29 --honest_prior ignore --uf_kappa 1.5 -# python run_all.py --mode CREATE --n_jobs 45 --max_features 0.33 --honest_prior ignore -# python run_all.py --mode CREATE --n_jobs 25 --max_features 0.33 --stop_id 220 --honest_prior ignore --vary_samples --parallel_tasks 20 --cv 5 diff --git a/validation/cc18_benchmark/run_benchmarks_and_save.py b/validation/cc18_benchmark/run_benchmarks_and_save.py deleted file mode 100644 index f1f83f1d7..000000000 --- a/validation/cc18_benchmark/run_benchmarks_and_save.py +++ /dev/null @@ -1,365 +0,0 @@ -import argparse -import logging -import os -import pickle -import time -from pathlib import Path - -import numpy as np -import openml -from joblib import Parallel, delayed -from oblique_forests.ensemble import RandomForestClassifier as ObliqueRF -from oblique_forests.sporf import ObliqueForestClassifier as ObliqueSPORF -from sklearn.calibration import CalibratedClassifierCV -from sklearn.compose import ColumnTransformer -from sklearn.ensemble import RandomForestClassifier -from sklearn.impute import SimpleImputer -from sklearn.metrics import cohen_kappa_score -from sklearn.model_selection import StratifiedKFold -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import OneHotEncoder -from tqdm import tqdm - - -def _check_nested_equality(lst1, lst2): - if isinstance(lst1, list) and isinstance(lst2, list): - for l1, l2 in zip(lst1, lst2): - if not _check_nested_equality(l1, l2): - return False - elif isinstance(lst1, np.ndarray) and isinstance(lst2, np.ndarray): - return np.all(lst1 == lst2) - else: - return lst1 == lst2 - - return True - - -def stratify_samplesizes(y, block_lengths): - """ - Sort data and labels into blocks that preserve class balance - - Parameters - ---------- - X: data matrix - y : 1D class labels - block_lengths : Block sizes to sort X,y into that preserve class balance - """ - clss, counts = np.unique(y, return_counts=True) - ratios = counts / sum(counts) - class_idxs = [np.where(y == i)[0] for i in clss] - - sort_idxs = [] - - prior_idxs = np.zeros(len(clss)).astype(int) - for n in block_lengths: - get_idxs = np.rint((n - len(clss)) * ratios).astype(int) + 1 - for idxs, prior_idx, next_idx in zip(class_idxs, prior_idxs, get_idxs): - sort_idxs.append(idxs[prior_idx:next_idx]) - prior_idxs = get_idxs - - sort_idxs = np.hstack(sort_idxs) - - return sort_idxs - - -def train_test(X, y, task_name, task_id, nominal_indices, args, clfs, save_path): - # Set up Cross validation - - skf = StratifiedKFold(n_splits=args.cv, shuffle=True, random_state=0) - n_samples, n_features = X.shape - n_classes = len(np.unique(y)) - - if args.vary_samples: - sample_sizes = np.logspace( - np.log10(n_classes * 2), - np.log10(np.floor(len(y) * (args.cv - 1.1) / args.cv)), - num=10, - endpoint=True, - dtype=int, - ) - else: - sample_sizes = [len(y)] - - # Check if existing experiments - results_dict = { - "task": task_name, - "task_id": task_id, - "n_samples": n_samples, - "n_features": n_features, - "n_classes": n_classes, - "y": y, - "test_indices": [], - "n_estimators": args.n_estimators, - "cv": args.cv, - "nominal_features": len(nominal_indices), - "sample_sizes": sample_sizes, - } - - # Get numeric indices first - numeric_indices = np.delete(np.arange(X.shape[1]), nominal_indices) - - # Numeric Preprocessing - numeric_transformer = SimpleImputer(strategy="median") - - # Nominal preprocessing - nominal_transformer = Pipeline( - steps=[ - ("imputer", SimpleImputer(strategy="most_frequent")), - ("onehot", OneHotEncoder(handle_unknown="ignore", sparse=False)), - ] - ) - - transformers = [] - if len(numeric_indices) > 0: - transformers += [("numeric", numeric_transformer, numeric_indices)] - if len(nominal_indices) > 0: - transformers += [("nominal", nominal_transformer, nominal_indices)] - preprocessor = ColumnTransformer(transformers=transformers) - - _, n_features_fitted = preprocessor.fit_transform(X, y).shape - results_dict["n_features_fitted"] = n_features_fitted - print( - f"Features={n_features}, nominal={len(nominal_indices)} (After transforming={n_features_fitted})" - ) - - # Store training indices (random state insures consistent across clfs) - for train_index, test_index in skf.split(X, y): - results_dict["test_indices"].append(test_index) - - for clf_name, clf in clfs: - pipeline = Pipeline(steps=[("Preprocessor", preprocessor), ("Estimator", clf)]) - - fold_probas = [] - oob_fold_probas = [] - if not f"{clf_name}_metadata" in results_dict.keys(): - results_dict[f"{clf_name}_metadata"] = {} - results_dict[f"{clf_name}_metadata"]["train_times"] = [] - results_dict[f"{clf_name}_metadata"]["test_times"] = [] - for train_index, test_index in skf.split(X, y): - X_train, X_test = X[train_index], X[test_index] - y_train, y_test = y[train_index], y[test_index] - - if args.vary_samples: - stratified_sort = stratify_samplesizes(y_train, sample_sizes) - X_train = X_train[stratified_sort] - y_train = y_train[stratified_sort] - - probas_vs_sample_sizes = [] - oob_probas_vs_sample_sizes = [] - - for n_samples in sample_sizes: - start_time = time.time() - # Fix too few samples for internal CV of these methods - if ( - clf_name in ["IRF", "SigRF"] - and np.min(np.unique(y_train[:n_samples], return_counts=True)[1]) < 5 - ): - print( - f"{clf_name} requires more samples of minimum class. Skipping n={n_samples}" - ) - y_proba = np.repeat( - np.bincount(y_train[:n_samples]).reshape(1, -1) / len(y_train[:n_samples]), - X_test.shape[0], - axis=0, - ) - # y_proba_oob = y_proba - train_time = time.time() - start_time - else: - pipeline = pipeline.fit(X_train[:n_samples], y_train[:n_samples]) - train_time = time.time() - start_time - y_proba = pipeline.predict_proba(X_test) - # y_proba_oob = predict_proba_oob(pipeline['Estimator'], pipeline['Preprocessor'].transform(X_train[:n_samples])) - - test_time = time.time() - (train_time + start_time) - - probas_vs_sample_sizes.append(y_proba) - # oob_probas_vs_sample_sizes.append(y_proba_oob) - results_dict[f"{clf_name}_metadata"]["train_times"].append(train_time) - results_dict[f"{clf_name}_metadata"]["test_times"].append(test_time) - - fold_probas.append(probas_vs_sample_sizes) - # oob_fold_probas.append(oob_probas_vs_sample_sizes) - - results_dict[clf_name] = fold_probas - # results_dict[clf_name + '_oob'] = oob_fold_probas - print( - f"{clf_name} Time: train_time={train_time:.3f}, test_time={test_time:.3f}, Cohen Kappa={cohen_kappa_score(y_test, y_proba.argmax(1)):.3f}" - ) - - # If existing data, load and append to. Else save - if os.path.isfile(save_path) and args.mode == "OVERWRITE": - logging.info(f"OVERWRITING {task_name} ({task_id})") - with open(save_path, "rb") as f: - prior_results = pickle.load(f) - - # Check these keys have the same values - verify_keys = [ - "task", - "task_id", - "n_samples", - "n_features", - "n_classes", - "y", - "test_indices", - "n_estimators", - "cv", - "nominal_features", - "n_features_fitted", - "sample_sizes", - ] - for key in verify_keys: - assert _check_nested_equality( - prior_results[key], results_dict[key] - ), f"OVERWRITE {key} does not match saved value" - - # Replace/add data - replace_keys = [name for name, _ in clfs] - replace_keys += [f"{name}_metadata" for name in replace_keys] - for key in replace_keys: - prior_results[key] = results_dict[key] - - results_dict = prior_results - - with open(save_path, "wb") as f: - pickle.dump(results_dict, f) - - -def run_cc18(args, clfs, data_dir): - logging.basicConfig( - filename="run_all.log", - format="%(asctime)s:%(levelname)s:%(message)s", - level=logging.INFO, - ) - - for key, val in vars(args).items(): - logging.info(f"{key}={val}") - - benchmark_suite = openml.study.get_suite("OpenML-CC18") # obtain the benchmark suite - - folder = data_dir / f"sporf_benchmarks/results_cv{args.cv}_features={args.max_features}_{name}" - - if not os.path.exists(folder): - os.makedirs(folder) - - def _run_task_helper(task_id): - task = openml.tasks.get_task(task_id) # download the OpenML task - task_name = task.get_dataset().name - - save_path = f"{folder}/{task_name}_results_dict.pkl" - if args.mode == "OVERWRITE": - if not os.path.isfile(save_path): - logging.info(f"OVERWRITE MODE: Skipping {task_name} (doesn't exist)") - return - elif args.mode == "APPEND" and os.path.isfile(save_path): - logging.info(f"APPEND MODE: Skipping {task_name} (already exists)") - return - - print(f"{args.mode} {task_name} ({task_id})") - logging.info(f"Running {task_name} ({task_id})") - - X, y = task.get_X_and_y() # get the data - - nominal_indices = task.get_dataset().get_features_by_type("nominal", [task.target_name]) - try: - train_test(X, y, task_name, task_id, nominal_indices, args, clfs, save_path) - except Exception as e: - print( - f"Test {task_name} ({task_id}) Failed | X.shape={X.shape} | {len(nominal_indices)} nominal indices" - ) - print(e) - logging.error( - f"Test {task_name} ({task_id}) Failed | X.shape={X.shape} | {len(nominal_indices)} nominal indices" - ) - import traceback - - logging.error(e) - traceback.sprint_exc() - - task_ids_to_run = [] - for task_id in benchmark_suite.tasks: - if args.start_id is not None and task_id < args.start_id: - print(f"Skipping task_id={task_id}") - logging.info(f"Skipping task_id={task_id}") - continue - if args.stop_id is not None and task_id >= args.stop_id: - print(f"Stopping at task_id={task_id}") - logging.info(f"Stopping at task_id={task_id}") - break - task_ids_to_run.append(task_id) - - if args.parallel_tasks is not None and args.parallel_tasks > 1: - Parallel(n_jobs=args.parallel_tasks, verbose=1)( - delayed(_run_task_helper)(task_id) for task_id in tqdm(task_ids_to_run) - ) - else: - for task_id in tqdm(task_ids_to_run): # iterate over all tasks - _run_task_helper(task_id) - - -parser = argparse.ArgumentParser(description="Run CC18 dataset.") -parser.add_argument( - "--mode", action="store", default="CREATE", choices=["OVERWRITE", "CREATE", "APPEND"] -) -parser.add_argument("--cv", action="store", type=int, default=10) -parser.add_argument("--n_estimators", action="store", type=int, default=500) -parser.add_argument("--n_jobs", action="store", type=int, default=6) -# parser.add_argument("--uf_kappa", action="store", type=float, default=None) -# parser.add_argument("--uf_construction_prop", action="store", type=float, default=0.63) -# parser.add_argument("--uf_max_samples", action="store", type=float, default=1.0) -parser.add_argument( - "--max_features", - action="store", - default=None, - help="Either an integer, float, or string in {'sqrt', 'log2'}. Default uses all features.", -) -# parser.add_argument("--uf_poisson", action="store_true", default=False) -parser.add_argument("--start_id", action="store", type=int, default=None) -parser.add_argument("--stop_id", action="store", type=int, default=None) -# parser.add_argument("--honest_prior", action="store", default="ignore", choices=["ignore", "uniform", "empirical"]) -parser.add_argument("--parallel_tasks", action="store", default=1, type=int) -parser.add_argument("--vary_samples", action="store_true", default=False) - - -args = parser.parse_args() - -max_features = args.max_features -try: - max_features = int(max_features) -except: - try: - max_features = float(max_features) - except: - pass - -clfs = [ - ( - "RF", - RandomForestClassifier( - n_estimators=args.n_estimators, max_features=max_features, n_jobs=args.n_jobs - ), - ), - # ( - # "Oblique-RF", - # ObliqueRF( - # n_estimators=args.n_estimators, - # max_features=max_features, - # n_jobs=args.n_jobs) - # ), - ( - "SPORF", - ObliqueSPORF( - n_estimators=args.n_estimators, - max_features=max_features, - feature_combinations=1.5, - n_jobs=args.n_jobs, - ), - ), -] - - -data_dir = Path("/mnt/ssd3/ronan/") -data_dir = Path("/home/adam2392/Downloads/") -run_cc18(args, clfs, data_dir) -# python run_all.py --mode CREATE --n_jobs 45 --max_features sqrt --stop_id 29 --honest_prior ignore --uf_kappa 1.5 -# python run_all.py --mode CREATE --n_jobs 45 --max_features 0.33 --honest_prior ignore -# python run_all.py --mode CREATE --n_jobs 25 --max_features 0.33 --stop_id 220 --honest_prior ignore --vary_samples --parallel_tasks 20 --cv 5 diff --git a/validation/cc18_rerf_vs_cysporf.ipynb b/validation/cc18_rerf_vs_cysporf.ipynb deleted file mode 100644 index 1207204c2..000000000 --- a/validation/cc18_rerf_vs_cysporf.ipynb +++ /dev/null @@ -1,762 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b11edb55-f95c-4972-a901-640d2820a874", - "metadata": {}, - "source": [ - "# Run RERF C++ vs CySPORF Side By Side on CC-18 Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d27de209-f445-4ca1-acb8-31ae16ba437f", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext lab_black" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "96b0d04d-cd41-4f83-ac09-bd16224e9279", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "from pathlib import Path\n", - "import numpy as np\n", - "import collections\n", - "from tqdm import tqdm\n", - "from pathlib import Path\n", - "import time\n", - "import logging\n", - "import json\n", - "from collections import defaultdict\n", - "\n", - "import pandas as pd\n", - "\n", - "import seaborn as sns\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "\n", - "sys.path.append(\"../\")\n", - "\n", - "from oblique_forests.sporf import ObliqueForestClassifier\n", - "\n", - "# from oblique_forests.ensemble import RandomForestClassifier\n", - "from rerf.rerfClassifier import rerfClassifier\n", - "\n", - "import sklearn\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.model_selection import cross_val_score\n", - "\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.calibration import CalibratedClassifierCV\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.metrics import cohen_kappa_score\n", - "\n", - "import openml\n", - "\n", - "from joblib import Parallel, delayed\n", - "\n", - "from sklearn.calibration import CalibratedClassifierCV\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.metrics import cohen_kappa_score\n", - "\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7526c348-061f-4de5-9ec0-999b19346533", - "metadata": {}, - "outputs": [], - "source": [ - "benchmark_suite = openml.study.get_suite(\"OpenML-CC18\") # obtain the benchmark suite" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f89906d9-8790-489d-b950-18921789d94d", - "metadata": {}, - "outputs": [], - "source": [ - "data_dir = Path(\"/home/adam2392/Downloads/\")\n", - "data_dir = Path(\"/Users/adam2392/Dropbox/sporf_benchmarks/\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "239e9b24-a92c-446b-b981-c8e1c7fc7d55", - "metadata": {}, - "outputs": [], - "source": [ - "folder = data_dir / f\"cysporf_vs_c++/\"\n", - "if not os.path.exists(folder):\n", - " os.makedirs(folder)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d5041a89-c4ed-4167-87c1-caf5a6ba6e91", - "metadata": {}, - "outputs": [], - "source": [ - "n_splits = 10\n", - "random_state = 12345\n", - "n_estimators = 500\n", - "# max_features = \"log2\"\n", - "max_features = None\n", - "max_depth = \"sqrt\"\n", - "feature_combinations = 1.5\n", - "n_jobs = -1" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "79e5b80c-a317-4cfb-8855-52a5d385a0c6", - "metadata": {}, - "outputs": [], - "source": [ - "clfs = [\n", - " # (\n", - " # \"rerfsporf\",\n", - " # rerfClassifier(\n", - " # n_estimators=n_estimators,\n", - " # max_features=max_features,\n", - " # feature_combinations=feature_combinations,\n", - " # n_jobs=n_jobs,\n", - " # random_state=random_state,\n", - " # ),\n", - " # ),\n", - " (\n", - " \"cysporf\",\n", - " ObliqueForestClassifier(\n", - " n_estimators=n_estimators,\n", - " max_features=max_features,\n", - " feature_combinations=1.5,\n", - " n_jobs=n_jobs,\n", - " random_state=random_state,\n", - " ),\n", - " ),\n", - " (\n", - " \"rf\",\n", - " RandomForestClassifier(\n", - " n_estimators=n_estimators,\n", - " max_features=max_features,\n", - " n_jobs=n_jobs,\n", - " random_state=random_state,\n", - " ),\n", - " ),\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "578329a0-19a1-44b2-962a-9145acfdedda", - "metadata": {}, - "source": [ - "# Setup Specific Task and Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "34fc6302-718e-4fb4-a8e4-3c06eb65516e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "task_id = None\n", - "for t_id in benchmark_suite.tasks:\n", - " task = openml.tasks.get_task(t_id) # download the OpenML task\n", - " task_name = task.get_dataset().name\n", - " task_id = t_id\n", - " break\n", - "# if task_name == \"climate-model-simulation-crashes\":\n", - "# task_id = t_id\n", - "# break" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "b9c2522f-ea85-4e26-a16a-17b4c217fe62", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3 is for climate-model-simulation-crashes\n" - ] - } - ], - "source": [ - "print(f\"{task_id} is for climate-model-simulation-crashes\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "748e93c3-7847-4e8e-9e7d-dd73ca4b7a35", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "36\n", - "36\n", - "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]\n" - ] - } - ], - "source": [ - "# get the task\n", - "task = openml.tasks.get_task(task_id) # download the OpenML task\n", - "\n", - "# get the data\n", - "X, y = task.get_X_and_y()\n", - "n_samples, n_features = X.shape\n", - "n_classes = len(np.unique(y))\n", - "sample_sizes = len(y)\n", - "\n", - "# nominal indices\n", - "nominal_indices = task.get_dataset().get_features_by_type(\"nominal\", [task.target_name])\n", - "\n", - "print(len(nominal_indices))\n", - "print(n_features)\n", - "print(nominal_indices)" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "495dd234-6f5b-402a-8833-154ed15a55fb", - "metadata": {}, - "outputs": [], - "source": [ - "cv = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=random_state)" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "3cbcec5f-bd42-43ee-a560-d033533bc270", - "metadata": {}, - "outputs": [], - "source": [ - "results_dict = defaultdict(list)\n", - "\n", - "# Check if existing experiments\n", - "results_dict.update(\n", - " {\n", - " \"task\": task_name,\n", - " \"task_id\": task_id,\n", - " \"n_samples\": n_samples,\n", - " \"n_features\": n_features,\n", - " \"n_classes\": n_classes,\n", - " \"y\": y,\n", - " \"test_indices\": [],\n", - " \"n_estimators\": n_estimators,\n", - " \"n_splits\": n_splits,\n", - " \"nominal_features\": len(nominal_indices),\n", - " \"sample_sizes\": sample_sizes,\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "062a8a90-d8db-461b-8cd5-147951b1346f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Features=[[[18]]], nominal=0 (After transforming=18)\n" - ] - } - ], - "source": [ - "# Get numeric indices first\n", - "numeric_indices = np.delete(np.arange(X.shape[1]), nominal_indices)\n", - "\n", - "# Numeric Preprocessing\n", - "numeric_transformer = SimpleImputer(strategy=\"median\")\n", - "\n", - "# Nominal preprocessing\n", - "nominal_transformer = Pipeline(\n", - " steps=[\n", - " (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n", - " (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\", sparse=False)),\n", - " ]\n", - ")\n", - "transformers = []\n", - "if len(numeric_indices) > 0:\n", - " transformers += [(\"numeric\", numeric_transformer, numeric_indices)]\n", - "if len(nominal_indices) > 0:\n", - " transformers += [(\"nominal\", nominal_transformer, nominal_indices)]\n", - "preprocessor = ColumnTransformer(transformers=transformers)\n", - "\n", - "_, n_features_fitted = preprocessor.fit_transform(X, y).shape\n", - "results_dict[\"n_features_fitted\"] = n_features_fitted\n", - "print(\n", - " f\"Features={n_features}, nominal={len(nominal_indices)} (After transforming={n_features_fitted})\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "51cbc703-47bd-4f3c-98c1-cdd1202d327b", - "metadata": {}, - "source": [ - "# Run CV experiment with both classification models" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "2235a805-e129-4033-bb63-3fe252146ae1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "k-fold: 100%|██████████| 10/10 [00:10<00:00, 1.01s/it]\n" - ] - } - ], - "source": [ - "fitted_clfs = defaultdict(list)\n", - "idx = 0\n", - "\n", - "for train_index, test_index in tqdm(\n", - " cv.split(X, y), total=cv.get_n_splits(), desc=\"k-fold\"\n", - "):\n", - " X_train, X_test = X[train_index], X[test_index]\n", - " y_train, y_test = y[train_index], y[test_index]\n", - " results_dict[\"test_indices\"].append(test_index)\n", - "\n", - " for clf_name, clf in clfs:\n", - " pipeline = Pipeline(steps=[(\"Preprocessor\", preprocessor), (\"Estimator\", clf)])\n", - "\n", - " start_time = time.time()\n", - " pipeline = pipeline.fit(X_train, y_train)\n", - " train_time = time.time() - start_time\n", - "\n", - " # predict the probs of X test\n", - " y_proba = pipeline.predict_proba(X_test)\n", - " test_time = time.time() - (train_time + start_time)\n", - "\n", - " results_dict[f\"{clf_name}_test_y_proba\"].append(y_proba)\n", - " results_dict[f\"{clf_name}_train_times\"].append(train_time)\n", - " results_dict[f\"{clf_name}_test_times\"].append(test_time)\n", - "\n", - " fitted_clfs[clf_name].append(clf)\n", - " idx += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "454efc6e-ec68-435b-b7d0-b8bf8eac3f38", - "metadata": {}, - "outputs": [], - "source": [ - "tasks = []\n", - "n_samples = []\n", - "n_classes = []\n", - "n_features = []\n", - "task_ids = []\n", - "\n", - "rerf_cohens = []\n", - "sporf_cohens = []\n", - "rf_cohens = []" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "8857d7fe-00d2-4f29-8da4-f3bfeb440c97", - "metadata": {}, - "outputs": [], - "source": [ - "result_dict = results_dict\n", - "# number of stratified cross-validations\n", - "n_splits = result_dict[\"n_splits\"]\n", - "fold_test_inds = result_dict[\"test_indices\"]\n", - "y = result_dict[\"y\"]\n", - "task_name = result_dict[\"task\"]\n", - "n_feature = result_dict[\"n_features\"]\n", - "\n", - "# extract metadata of benchmark experiment\n", - "tasks.append(task_name)\n", - "n_samples.append(result_dict[\"n_samples\"])\n", - "n_classes.append(result_dict[\"n_classes\"])\n", - "task_ids.append(result_dict[\"task_id\"])\n", - "n_features.append(n_feature)\n", - "\n", - "# compute cohen kappa for both classifiers\n", - "for clf in [\"rerfsporf\", \"cysporf\", \"rf\"]:\n", - " clf_cohens = []\n", - " fold_probas = result_dict[f\"{clf}_test_y_proba\"]\n", - "\n", - " # compute statistic on each fold\n", - " for ifold in range(n_splits):\n", - " y_proba = fold_probas[ifold]\n", - " y_test = y[fold_test_inds[ifold]]\n", - " kappa_score = cohen_kappa_score(y_test, y_proba.argmax(1))\n", - " clf_cohens.append(kappa_score)\n", - "\n", - " if clf == \"rerfsporf\":\n", - " rerf_cohens.append(clf_cohens)\n", - " elif clf == \"cysporf\":\n", - " sporf_cohens.append(clf_cohens)\n", - " elif clf == \"rf\":\n", - " rf_cohens.append(clf_cohens)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "aff01207-44de-4776-b4c0-d90a0a775cf6", - "metadata": {}, - "outputs": [], - "source": [ - "rerf_clfs = fitted_clfs[\"rerfsporf\"]\n", - "cy_clfs = fitted_clfs[\"cysporf\"]\n", - "rf_clfs = fitted_clfs[\"rf\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "43c21597-80c2-4dbb-a0d6-e1ef3bfc8d36", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'feature_combinations': 1.5, 'image_height': None, 'image_width': None, 'max_depth': None, 'max_features': 'log2', 'min_samples_split': 1, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'patch_height_max': None, 'patch_height_min': 1, 'patch_width_max': None, 'patch_width_min': 1, 'projection_matrix': 'RerF', 'random_state': 12345}\n", - "{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'feature_combinations': 1.5, 'max_depth': None, 'max_features': 'log2', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'oob_score': False, 'random_state': 12345, 'verbose': 0, 'warm_start': False}\n" - ] - } - ], - "source": [ - "for idx in range(n_splits):\n", - " cy_clf = cy_clfs[idx]\n", - " rerf_clf = rerf_clfs[idx]\n", - " rf_clf = rf_clfs[idx]\n", - "\n", - " print(rerf_clf.get_params())\n", - " print(cy_clf.get_params())\n", - " break" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "1cf2da0e-6eb9-465e-8e57-395764e235fc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "leaves = []\n", - "capacity = []\n", - "max_depths = []\n", - "n_nodes = []\n", - "\n", - "for est in cy_clf.estimators_:\n", - " tree = est.tree_\n", - " leaves.append(tree.n_leaves)\n", - " capacity.append(tree.capacity)\n", - " max_depths.append(tree.max_depth)\n", - " n_nodes.append(tree.node_count)\n", - "\n", - "# sns.histplot(leaves)\n", - "sns.histplot(n_nodes)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "b62f8dfc-68ab-441e-ac57-1b6329de5e21", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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bx1LgiFXVu6vqrKpaz+DP4c9V1RVMaL8ASU5K8vQDtxnMgX6VCe65qr4D7Exybje0icHhzSe250Vey+PTPDDZPT8KnJ/kxCRh8DrvYAk9N/HN3SS/AWxj8Gn4GuCWqvqrJL8K3ALMMvjlXlZV/zO+SkcvyUuBP6uqSya53yTnMNjKh8EUyD9X1XsnuWeAJOcBNwDHA98AXk/3b5zJ7flEBh92nlNVP+jGJv11vhbYDOwH7gPeAJzMMfbcRPBLkh7XxFSPJOlxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqzP8Bl5D+bbb7WLsAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "leaves = []\n", - "capacity = []\n", - "max_depths = []\n", - "n_nodes = []\n", - "\n", - "for est in rf_clf.estimators_:\n", - " tree = est.tree_\n", - " leaves.append(tree.n_leaves)\n", - " capacity.append(tree.capacity)\n", - " max_depths.append(tree.max_depth)\n", - " n_nodes.append(tree.node_count)\n", - "\n", - "# sns.histplot(leaves)\n", - "sns.histplot(n_nodes)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "499baf7d-6ddf-4a20-946f-d062822a3437", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "None\n" - ] - } - ], - "source": [ - "print(cy_clf.estimators_[0].max)" - ] - }, - { - "cell_type": "markdown", - "id": "fe8010a2-a59d-4850-8c30-373b5d6eb15f", - "metadata": {}, - "source": [ - "## Create DataFrames and Plots" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "da2feb5c-4c18-43e5-bf65-2818826e9eca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.0, 0.0, 0.0, 0.0, 0.547486033519553, 0.0, 0.0, 0.3816793893129772, 0.6493506493506493, 0.3816793893129772]]\n", - "[[0.0, 0.3121019108280255, 0.0, 0.3121019108280255, 0.547486033519553, 0.0, 0.0, 0.6493506493506493, 0.6493506493506493, 0.3816793893129772]]\n", - "[[0.19402985074626866, 0.3121019108280255, -0.031847133757961776, 0.3121019108280255, 0.4626865671641791, 0.547486033519553, 0.3816793893129772, 0.5423728813559322, 0.6493506493506493, 0.5423728813559322]]\n" - ] - } - ], - "source": [ - "print(rerf_cohens)\n", - "print(sporf_cohens)\n", - "print(rf_cohens)" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "id": "b4f88d19-e1d5-4045-a0b3-d3cd1670ba5d", - "metadata": {}, - "outputs": [], - "source": [ - "result_df = pd.DataFrame((tasks, n_samples, n_classes, task_ids, n_features)).T\n", - "result_df.columns = [\"task\", \"n_samples\", \"n_classes\", \"task_id\", \"n_features\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "7ff7dea4-12e2-43e3-bbab-9d2acdbbc68c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2, 16)\n", - "(1, 15)\n" - ] - } - ], - "source": [ - "rerf_df = pd.DataFrame(rerf_cohens)\n", - "rerf_df[\"clf\"] = \"rerf\"\n", - "rerf_df = pd.concat((rerf_df, result_df), axis=1)\n", - "\n", - "rf_df = pd.DataFrame(rf_cohens)\n", - "rf_df[\"clf\"] = \"rf\"\n", - "rf_df = pd.concat((rf_df, result_df), axis=1)\n", - "\n", - "sporf_df = pd.DataFrame(sporf_cohens)\n", - "sporf_df[\"clf\"] = \"sporf\"\n", - "sporf_df = pd.concat((sporf_df, result_df), axis=1)\n", - "\n", - "diff_arr = np.array(sporf_cohens) - np.array(rerf_cohens)\n", - "diff_arr = np.array(sporf_cohens) - np.array(rf_cohens)\n", - "diff_df = pd.DataFrame(diff_arr)\n", - "diff_df = pd.concat((diff_df, result_df), axis=1)\n", - "\n", - "# now form the final dataframe\n", - "data_df = pd.concat((rerf_df, sporf_df), axis=0)\n", - "\n", - "print(data_df.shape)\n", - "print(diff_df.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "0c0dde98-9c31-4c81-82ff-0c2159ef316a", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/adam2392/miniforge3/envs/sklearn-dev/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", - " warnings.warn(msg, FutureWarning)\n", - "/Users/adam2392/miniforge3/envs/sklearn-dev/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", - " warnings.warn(msg, FutureWarning)\n", - "/Users/adam2392/miniforge3/envs/sklearn-dev/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", - " warnings.warn(msg, FutureWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "x = np.arange(n_splits)\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.distplot(rerf_cohens, ax=ax, label=\"rerf\")\n", - "sns.distplot(sporf_cohens, ax=ax, label=\"cysporf\")\n", - "sns.distplot(rf_cohens, ax=ax, label=\"rf\")\n", - "\n", - "ax.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83f427ca-ac47-4ae6-b761-f4e4ad6b5133", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "039bf160-742f-42df-a4e3-b4332db19712", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "sklearn", - "language": "python", - "name": "sklearn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/validation/data/orthant_test.npy b/validation/data/orthant_test.npy deleted file mode 100644 index 81c51a6ae..000000000 Binary files a/validation/data/orthant_test.npy and /dev/null differ diff --git a/validation/data/orthant_train_2000.npy b/validation/data/orthant_train_2000.npy deleted file mode 100644 index 2913ec016..000000000 Binary files a/validation/data/orthant_train_2000.npy and /dev/null differ diff --git a/validation/data/orthant_train_400.npy b/validation/data/orthant_train_400.npy deleted file mode 100644 index 35127cec2..000000000 Binary files a/validation/data/orthant_train_400.npy and /dev/null differ diff --git a/validation/data/orthant_train_4000.npy b/validation/data/orthant_train_4000.npy deleted file mode 100644 index e1fb15b7a..000000000 Binary files a/validation/data/orthant_train_4000.npy and /dev/null differ diff 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b/validation/figures/orthant_diff.png deleted file mode 100644 index c8ce20131..000000000 Binary files a/validation/figures/orthant_diff.png and /dev/null differ diff --git a/validation/figures/orthant_experiment.png b/validation/figures/orthant_experiment.png deleted file mode 100644 index 512c93b1e..000000000 Binary files a/validation/figures/orthant_experiment.png and /dev/null differ diff --git a/validation/figures/sparse_parity_diff.png b/validation/figures/sparse_parity_diff.png deleted file mode 100644 index bfd52fdaf..000000000 Binary files a/validation/figures/sparse_parity_diff.png and /dev/null differ diff --git a/validation/figures/sparse_parity_experiment.png b/validation/figures/sparse_parity_experiment.png deleted file mode 100644 index 7ade24007..000000000 Binary files a/validation/figures/sparse_parity_experiment.png and /dev/null differ diff --git a/validation/generate_datasets.py b/validation/generate_datasets.py deleted file mode 100644 index 20c64c186..000000000 --- a/validation/generate_datasets.py +++ /dev/null @@ -1,34 +0,0 @@ -import numpy as np -from sporfdata import orthant, sparse_parity - -# Sparse parity -for n in [1000, 5000, 10000]: - X, y = sparse_parity(n) - df = np.zeros((n, 21)) - df[:, :-1] = X - df[:, -1] = y - - np.save("data/sparse_parity_train_" + str(n), df) - -X, y = sparse_parity(10000) -df = np.zeros((10000, 21)) -df[:, :-1] = X -df[:, -1] = y - -np.save("data/sparse_parity_test", df) - -# Orthant -for n in [400, 2000, 4000]: - X, y = orthant(n) - df = np.zeros((n, 7)) - df[:, :-1] = X - df[:, -1] = y - - np.save("data/orthant_train_" + str(n), df) - -X, y = orthant(10000) -df = np.zeros((10000, 7)) -df[:, :-1] = X -df[:, -1] = y - -np.save("data/orthant_test", df) diff --git a/validation/orthant.py b/validation/orthant.py deleted file mode 100644 index a346af134..000000000 --- a/validation/orthant.py +++ /dev/null @@ -1,125 +0,0 @@ -import sys - -import numpy as np -from oblique_forests.ensemble import RandomForestClassifier as ObliqueRF -from oblique_forests.sporf import ObliqueForestClassifier as ofc - -sys.path.append( - "/Users/ChesterHuynh/OneDrive - Johns Hopkins/research/seeg localization/SPORF/Python" -) -from rerf.rerfClassifier import rerfClassifier as rfc - - -def load_data(n): - ftrain = "data/orthant_train_" + str(n) + ".npy" - ftest = "data/orthant_test.npy" - - dftrain = np.load(ftrain) - dftest = np.load(ftest) - - X_train = dftrain[:, :-1] - y_train = dftrain[:, -1] - - X_test = dftest[:, :-1] - y_test = dftest[:, -1] - - return X_train, y_train, X_test, y_test - - -def test_rf(n, reps, n_estimators, max_features): - from sklearn.ensemble import RandomForestClassifier - - preds = np.zeros((reps, 10000)) - acc = np.zeros(reps) - for i in range(reps): - X_train, y_train, X_test, y_test = load_data(n) - - # clf = rfc(n_estimators=n_estimators, - # projection_matrix="Base") - # clf = RandomForestClassifier(n_estimators=n_estimators, n_jobs=8) - clf = ObliqueRF(n_estimators=n_estimators, max_features=max_features, n_jobs=8) - - import yep - - yep.start(f"profiling/rf_fit_orthant{n}.prof") - clf.fit(X_train, y_train) - # print(list(map(lambda x: x.tree_.node_count, clf.estimators_))) - yep.stop() - - preds[i] = clf.predict(X_test) - acc[i] = np.sum(preds[i] == y_test) / len(y_test) - - np.save("output/rf_orthant_preds_" + str(n) + ".npy", preds) - return acc - - -def test_rerf(n, reps, n_estimators, feature_combinations, max_features): - preds = np.zeros((reps, 10000)) - acc = np.zeros(reps) - for i in range(reps): - X_train, y_train, X_test, y_test = load_data(n) - - clf = rfc( - n_estimators=n_estimators, - projection_matrix="RerF", - feature_combinations=feature_combinations, - max_features=max_features, - n_jobs=8, - ) - - import yep - - yep.start(f"profiling/rerf_fit_orthant{n}.prof") - clf.fit(X_train, y_train) - yep.stop() - - preds[i] = clf.predict(X_test) - acc[i] = np.sum(preds[i] == y_test) / len(y_test) - - np.save("output/rerf_orthant_preds_" + str(n) + ".npy", preds) - return acc - - -def test_of(n, reps, n_estimators, feature_combinations, max_features): - preds = np.zeros((reps, 10000)) - acc = np.zeros(reps) - for i in range(reps): - X_train, y_train, X_test, y_test = load_data(n) - - clf = ofc( - n_estimators=n_estimators, - feature_combinations=feature_combinations, - max_features=max_features, - n_jobs=8, - ) - - # Profile fitting - import yep - - yep.start(f"profiling/cysporf_fit_orthant{n}.prof") - clf.fit(X_train, y_train) - # print(list(map(lambda x: x.tree_.node_count, clf.estimators_))) - yep.stop() - - preds[i] = clf.predict(X_test) - acc[i] = np.sum(preds[i] == y_test) / len(y_test) - - np.save("output/of_orthant_preds_" + str(n) + ".npy", preds) - return acc - - -def main(): - n = 4000 - reps = 3 - n_estimators = 100 - feature_combinations = 2 - max_features = 1.0 - - acc = test_rf(n, reps, n_estimators, max_features) - acc = test_rerf(n, reps, n_estimators, feature_combinations, max_features) - acc = test_of(n, reps, n_estimators, feature_combinations, max_features) - print(acc) - - -if __name__ == "__main__": - main() diff --git a/validation/output/cythonof_sparse_parity_preds_1000.npy b/validation/output/cythonof_sparse_parity_preds_1000.npy deleted file mode 100644 index 6a3af3bfb..000000000 Binary files a/validation/output/cythonof_sparse_parity_preds_1000.npy and /dev/null differ diff --git a/validation/output/of_orthant_preds_2000.npy b/validation/output/of_orthant_preds_2000.npy deleted file mode 100644 index 54e74cd6c..000000000 Binary files a/validation/output/of_orthant_preds_2000.npy and /dev/null differ diff --git a/validation/output/of_orthant_preds_400.npy b/validation/output/of_orthant_preds_400.npy deleted file mode 100644 index 0e8ef459e..000000000 Binary files a/validation/output/of_orthant_preds_400.npy and /dev/null differ diff --git a/validation/output/of_orthant_preds_4000.npy 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- "cells": [ - { - "cell_type": "markdown", - "id": "e4978edf-a3a7-4136-8133-351c15dddcf4", - "metadata": {}, - "source": [ - "# Plot CC_18 Benchmarks" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d20ac63c-48bd-4940-8f3c-e8026feefcc1", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext lab_black" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a8ada798-91d5-4731-a8ea-81e6539dec17", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "import numpy as np\n", - "import collections\n", - "import pickle\n", - "from pathlib import Path\n", - "\n", - "import pandas as pd\n", - "import openml\n", - "\n", - "from sklearn.metrics import cohen_kappa_score\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "\n", - "sys.path.append(\"../\")\n", - "\n", - "from oblique_forests.sporf import ObliqueForestClassifier\n", - "\n", - "# from rerf.rerfClassifier import rerfClassifier\n", - "\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "24a67130-c5d6-483a-bf83-2b0bf3eb1deb", - "metadata": {}, - "outputs": [], - "source": [ - "def continuous_pairplot(df, vars, hue, cmap, diag_kind=\"auto\", scale=\"log\"):\n", - " import matplotlib as mpl\n", - " from mpl_toolkits.axes_grid1 import make_axes_locatable\n", - "\n", - " vmin = min(np.nanmin([df[hue]]), -1e-6)\n", - " vmax = max(np.nanmax([df[hue]]), 1e-6)\n", - "\n", - " g = sns.pairplot(\n", - " df,\n", - " vars=vars,\n", - " diag_kind=diag_kind,\n", - " plot_kws=dict(\n", - " # The effort I put into figuring this out.....\n", - " c=df[hue],\n", - " cmap=cmap,\n", - " norm=mpl.colors.TwoSlopeNorm(vcenter=0, vmin=vmin, vmax=vmax),\n", - " ),\n", - " )\n", - " if scale:\n", - " for r in range(len(g.axes)):\n", - " for c in range(len(g.axes)):\n", - " g.axes[r, c].set_xscale(scale)\n", - " if r != c:\n", - " g.axes[c, r].set_yscale(scale)\n", - "\n", - " sm = mpl.cm.ScalarMappable(\n", - " mpl.colors.TwoSlopeNorm(vcenter=0, vmin=vmin, vmax=vmax), cmap=cmap\n", - " )\n", - " plt.colorbar(sm, ax=g.axes, label=hue, aspect=40)\n", - " return g" - ] - }, - { - "cell_type": "markdown", - "id": "8bfa642c-183a-4a3c-8c49-6369a7b8a644", - "metadata": {}, - "source": [ - "# Get the data" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "dc476db8-8103-47ba-93a8-e1ddf801b87c", - "metadata": {}, - "outputs": [], - "source": [ - "intermediate_fpath = \"./cc18_benchmark/rerf_cc_18_sporf_vs_rf_benchmarks.csv\"" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "421efc6d-8cf6-446b-9f68-653e74d82baa", - "metadata": {}, - "outputs": [], - "source": [ - "# if both start/stop are None, then run on all tasks\n", - "start_id = None\n", - "stop_id = None\n", - "\n", - "name = \"hackerman_master\"\n", - "name = \"optimizebranch\"\n", - "overwrite = True\n", - "\n", - "# cross validation\n", - "cv = 10\n", - "vary_samples = False\n", - "\n", - "# hyperparameters of forest\n", - "max_features = None\n", - "max_features = \"sqrt\"\n", - "\n", - "# directory to save the output\n", - "data_dir = Path(\"/home/adam2392/Downloads\")\n", - "data_dir = Path(\"/Users/adam2392/Dropbox/sporf_benchmarks\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "f88464f6-ca20-4d96-8b0b-0f84cbe3ed44", - "metadata": {}, - "outputs": [], - "source": [ - "exp_name = \"cysporf_vs_rerf_vs_rf\"\n", - "params_name = \"results_cv10_features=None\"" - ] - }, - { - "cell_type": "markdown", - "id": "20fb8a8a-80e8-4864-992c-4a4fa5c25398", - "metadata": {}, - "source": [ - "## Get Data Separately for Rerf and RF" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ea34a97-b6d7-4508-8347-82b7cd4a3e03", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "# folder to save results\n", - "folder = data_dir / f\"rerf/results_cv{cv}_features={max_features}\" # _{name}\"\n", - "\n", - "result_files = [\n", - " f for f in folder.glob(\"*.pkl\") if any([f.name in x.name for x in rf_result_files])\n", - "]\n", - "print(len(result_files))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "70ede4d6-4cef-4805-bbe8-91db03954ce8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "# folder to save results\n", - "folder = data_dir / f\"results_cv{cv}_features={max_features}_{name}\"\n", - "\n", - "rf_result_files = [\n", - " f for f in folder.glob(\"*.pkl\") if any([f.name in x.name for x in result_files])\n", - "]\n", - "\n", - "print(len(rf_result_files))" - ] - }, - { - "cell_type": "markdown", - "id": "12af54e1-a5b2-40e6-9e30-dd96927c2d14", - "metadata": {}, - "source": [ - "## Get Data That Are Grouped in 1 PCKL file" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "1b15e162-8db1-4037-9682-d334ed421e7c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "71\n", - "/Users/adam2392/Dropbox/sporf_benchmarks/cysporf_vs_rerf_vs_rf/results_cv10_features=None\n" - ] - } - ], - "source": [ - "# folder to save results\n", - "folder = data_dir / exp_name / params_name\n", - "\n", - "result_files = [f for f in folder.glob(\"*.pkl\")]\n", - "print(len(result_files))\n", - "\n", - "print(folder)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "35c0f561-f4f4-4a90-8251-98539915eb32", - "metadata": {}, - "outputs": [], - "source": [ - "def _get_task_id(desired_task_name):\n", - " benchmark_suite = openml.study.get_suite(\n", - " \"OpenML-CC18\"\n", - " ) # obtain the benchmark suite\n", - " for task_id in benchmark_suite.tasks:\n", - " task = openml.tasks.get_task(task_id) # download the OpenML task\n", - " task_name = task.get_dataset().name\n", - " if task_name == desired_task_name:\n", - " n_features = task.get_dataset().get_data()[0].shape[1]\n", - " return n_features\n", - " raise Exception(f\"Didnt find {desired_task_name}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "15a31fac-badc-46cc-8ee9-0fb74d48eb8f", - "metadata": {}, - "outputs": [], - "source": [ - "tasks = []\n", - "n_samples = []\n", - "n_classes = []\n", - "n_features = []\n", - "sporf_cohens = []\n", - "rf_cohens = []\n", - "rerf_sporf_cohens = []\n", - "task_ids = []" - ] - }, - { - "cell_type": "markdown", - "id": "6f8adee7-7bbb-403d-86fb-4598c0f33c66", - "metadata": {}, - "source": [ - "### Get Results for Rerf Files" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "36f034ff-fa36-4382-b6b9-0ab349d51efd", - "metadata": {}, - "outputs": [], - "source": [ - "for fpath in result_files:\n", - " with open(fpath, \"rb\") as fin:\n", - " result_dict = pickle.load(fin)\n", - "\n", - " # number of stratified cross-validations\n", - " cv = result_dict[\"cv\"]\n", - " fold_test_inds = result_dict[\"test_indices\"]\n", - " y = result_dict[\"y\"]\n", - " task_name = result_dict[\"task\"]\n", - " n_feature = result_dict[\"n_features\"]\n", - " # n_feature = _get_task_id(desired_task_name=task_name)\n", - "\n", - " # extract metadata of benchmark experiment\n", - " tasks.append(task_name)\n", - " n_samples.append(result_dict[\"n_samples\"])\n", - " n_classes.append(result_dict[\"n_classes\"])\n", - " task_ids.append(result_dict[\"task_id\"])\n", - " n_features.append(n_feature)\n", - "\n", - " # compute cohen kappa for both classifiers\n", - " for clf in [\"rerfsporf\", \"rf\", \"cysporf\"]:\n", - " clf_cohens = []\n", - " fold_probas = result_dict[clf]\n", - "\n", - " # compute statistic on each fold\n", - " for ifold in range(cv):\n", - " y_proba = fold_probas[ifold][0]\n", - " y_test = y[fold_test_inds[ifold]]\n", - " kappa_score = cohen_kappa_score(y_test, y_proba.argmax(1))\n", - " clf_cohens.append(kappa_score)\n", - "\n", - " rerf_sporf_cohens.append(clf_cohens)" - ] - }, - { - "cell_type": "markdown", - "id": "1cb40785-0cbd-464c-8b95-ef2962b9ad83", - "metadata": {}, - "source": [ - "### Get Results for RF + CySpoorf Files" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "7f895090-7500-43b2-b6bb-8dce610be1b7", - "metadata": {}, - "outputs": [], - "source": [ - "for fpath in result_files:\n", - " with open(fpath, \"rb\") as fin:\n", - " result_dict = pickle.load(fin)\n", - "\n", - " # number of stratified cross-validations\n", - " cv = result_dict[\"cv\"]\n", - " fold_test_inds = result_dict[\"test_indices\"]\n", - " y = result_dict[\"y\"]\n", - " task_name = result_dict[\"task\"]\n", - " n_feature = result_dict[\"n_features\"]\n", - " # n_feature = _get_task_id(desired_task_name=task_name)\n", - "\n", - " # extract metadata of benchmark experiment\n", - " tasks.append(task_name)\n", - " n_samples.append(result_dict[\"n_samples\"])\n", - " n_classes.append(result_dict[\"n_classes\"])\n", - " task_ids.append(result_dict[\"task_id\"])\n", - " n_features.append(n_feature)\n", - "\n", - " # compute cohen kappa for both classifiers\n", - " for clf in [\"rf\", \"rerfsporf\", \"cysporf\"]:\n", - " clf_cohens = []\n", - " fold_probas = result_dict[clf]\n", - "\n", - " # compute statistic on each fold\n", - " for ifold in range(cv):\n", - " y_proba = fold_probas[ifold][0]\n", - " y_test = y[fold_test_inds[ifold]]\n", - " kappa_score = cohen_kappa_score(y_test, y_proba.argmax(1))\n", - " clf_cohens.append(kappa_score)\n", - "\n", - " if clf == \"rf\":\n", - " rf_cohens.append(clf_cohens)\n", - " elif clf == \"rerfsporf\":\n", - " rerf_sporf_cohens.append(clf_cohens)\n", - " else:\n", - " sporf_cohens.append(clf_cohens)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "724d12b2-ca16-441f-bce1-79946b360bcc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "167141\n" - ] - } - ], - "source": [ - "print(max(task_ids))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "c0815dd8-2b36-4f19-af22-c7b75b30cef4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(71, 10)\n" - ] - } - ], - "source": [ - "print(np.array(rf_cohens).shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "4d003fc9-290f-46d9-ae54-b59af0b2ef72", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(71, 5)\n" - ] - }, - { - "data": { - "text/html": [ - "
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taskn_samplesn_classestask_idn_features
0Devnagari-Script92000461671211024
1segment2310714682216
2jungle_chess_2pcs_raw_endgame_complete4481931671196
3tic-tac-toe9582499
4pc111092391821
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" - ], - "text/plain": [ - " task n_samples n_classes task_id \\\n", - "0 Devnagari-Script 92000 46 167121 \n", - "1 segment 2310 7 146822 \n", - "2 jungle_chess_2pcs_raw_endgame_complete 44819 3 167119 \n", - "3 tic-tac-toe 958 2 49 \n", - "4 pc1 1109 2 3918 \n", - "\n", - " n_features \n", - "0 1024 \n", - "1 16 \n", - "2 6 \n", - "3 9 \n", - "4 21 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "result_df = pd.DataFrame((tasks, n_samples, n_classes, task_ids, n_features)).T\n", - "result_df.columns = [\"task\", \"n_samples\", \"n_classes\", \"task_id\", \"n_features\"]\n", - "\n", - "print(result_df.shape)\n", - "display(result_df.head())" - ] - }, - { - "cell_type": "markdown", - "id": "2b6f74e3-afa6-49a4-9332-b3a3b8ad4fc7", - "metadata": {}, - "source": [ - "## Saving Intermediate Results" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "id": "15d5998a-2477-43a7-b479-9a7b39926a49", - "metadata": {}, - "outputs": [], - "source": [ - "result_df.to_csv(intermediate_fpath)" - ] - }, - { - "cell_type": "markdown", - "id": "0678c653-33e8-4ff5-a139-f28d61ab01fa", - "metadata": {}, - "source": [ - "## Process CySPORF vs RF" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "3cdbd06e-3ae4-4660-a4e5-377263e1453e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(142, 16)\n", - "(71, 15)\n" - ] - }, - { - "data": { - "text/html": [ - "
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0123456789taskn_samplesn_classestask_idn_features
00.0584440.0534440.0471110.0540000.0510000.0450000.0456670.0508890.0468890.050333Devnagari-Script92000461671211024
10.0050510.0151520.0050510.0000000.0353540.0101010.020202-0.0202020.0101010.005051segment2310714682216
20.0239520.0213330.0275070.0255420.0296030.0199490.0249960.0311790.0316580.017435jungle_chess_2pcs_raw_endgame_complete4481931671196
30.0000000.0000000.0000000.0000000.0000000.0232560.0000000.0000000.0000000.000000tic-tac-toe9582499
40.051339-0.0189770.000000-0.1140770.0000000.013408-0.1180470.0000000.0000000.000000pc111092391821
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0123456789clftaskn_samplesn_classestask_idn_features
00.9021110.9080000.9136670.9092220.9104440.9066670.9068890.9111110.9063330.909889rfDevnagari-Script92000461671211024
10.9343430.9141410.9343430.9242420.9696970.9343430.9292930.8989900.9545450.934343rfsegment2310714682216
20.6785860.6840990.6874750.6639700.6785380.6529760.6780730.6937380.6658410.671090rfjungle_chess_2pcs_raw_endgame_complete4481931671196
30.9531480.9292041.0000000.9767440.9767441.0000000.9770770.9770770.9766181.000000rftic-tac-toe9582499
40.5236050.523605-0.0296850.1246060.267058-0.0162770.4747630.5268540.4747630.237875rfpc111092391821
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0123456789taskn_samplesn_classestask_idn_features
00.0584440.0534440.0471110.0540000.0510000.0450000.0456670.0508890.0468890.050333Devnagari-Script92000461671211024
10.0050510.0151520.0050510.0000000.0353540.0101010.020202-0.0202020.0101010.005051segment2310714682216
20.0239520.0213330.0275070.0255420.0296030.0199490.0249960.0311790.0316580.017435jungle_chess_2pcs_raw_endgame_complete4481931671196
30.0000000.0000000.0000000.0000000.0000000.0232560.0000000.0000000.0000000.000000tic-tac-toe9582499
40.051339-0.0189770.000000-0.1140770.0000000.013408-0.1180470.0000000.0000000.000000pc111092391821
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0123456789clftaskn_samplesn_classestask_idn_features
00.8436670.8545560.8665560.8552220.8594440.8616670.8612220.8602220.8594440.859556rfDevnagari-Script92000461671211024
10.9292930.8989900.9292930.9242420.9343430.9242420.9090910.9191920.9444440.929293rfsegment2310714682216
20.6546340.6627670.6599680.6384280.6489350.6330260.6530770.6625580.6341830.653655rfjungle_chess_2pcs_raw_endgame_complete4481931671196
30.9531480.9292041.0000000.9767440.9767440.9767440.9770770.9770770.9766181.000000rftic-tac-toe9582499
40.4722660.542582-0.0296850.2386830.267058-0.0296850.5928100.5268540.4747630.237875rfpc111092391821
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taskn_samplesn_classestask_idn_featurescv_folddelta_cohen_kappa
0Devnagari-Script9200046167121102400.058444
1segment231071468221600.005051
2jungle_chess_2pcs_raw_endgame_complete448193167119600.023952
3tic-tac-toe958249900.000000
4pc11109239182100.051339
........................
705PhishingWebsites11055214952309-0.011019
706balance-scale625311490.173092
707climate-model-simulation-crashes5402146819189-0.160693
708kc1210923917219-0.073518
709wdbc569299463090.039173
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710 rows × 7 columns

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" - ], - "text/plain": [ - " task n_samples n_classes task_id \\\n", - "0 Devnagari-Script 92000 46 167121 \n", - "1 segment 2310 7 146822 \n", - "2 jungle_chess_2pcs_raw_endgame_complete 44819 3 167119 \n", - "3 tic-tac-toe 958 2 49 \n", - "4 pc1 1109 2 3918 \n", - ".. ... ... ... ... \n", - "705 PhishingWebsites 11055 2 14952 \n", - "706 balance-scale 625 3 11 \n", - "707 climate-model-simulation-crashes 540 2 146819 \n", - "708 kc1 2109 2 3917 \n", - "709 wdbc 569 2 9946 \n", - "\n", - " n_features cv_fold delta_cohen_kappa \n", - "0 1024 0 0.058444 \n", - "1 16 0 0.005051 \n", - "2 6 0 0.023952 \n", - "3 9 0 0.000000 \n", - "4 21 0 0.051339 \n", - ".. ... ... ... \n", - "705 30 9 -0.011019 \n", - "706 4 9 0.173092 \n", - "707 18 9 -0.160693 \n", - "708 21 9 -0.073518 \n", - "709 30 9 0.039173 \n", - "\n", - "[710 rows x 7 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# melt the dataframe\n", - "diff_df_melt = pd.melt(\n", - " diff_df,\n", - " id_vars=[\"task\", \"n_samples\", \"n_classes\", \"task_id\", \"n_features\"],\n", - " value_name=\"delta_cohen_kappa\",\n", - " var_name=\"cv_fold\",\n", - ")\n", - "\n", - "print(diff_df.shape)\n", - "print(diff_df_melt.shape)\n", - "display(diff_df_melt)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "ed9dad20-2775-4fc5-924d-72a31cf089fd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[46 7 3 2 6 10 4 26 8 5 11 9]\n" - ] - } - ], - "source": [ - "print(diff_df_melt[\"n_classes\"].unique())" - ] - }, - { - "cell_type": "markdown", - "id": "55eafefb-79da-44bb-99db-4ce2c854903b", - "metadata": {}, - "source": [ - "# Create Plots" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "e17a4fcb-633e-4160-b432-1c9e8814dbf4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taskdelta_cohen_kappa
0Bioresponse0.015824
1CIFAR_100.012963
2Devnagari-Script0.050278
3Fashion-MNIST0.010190
4GesturePhaseSegmentationProcessed0.021767
.........
66vehicle-0.004796
67vowel0.041111
68wall-robot-navigation-0.000282
69wdbc0.018957
70wilt-0.001578
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71 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " task delta_cohen_kappa\n", - "0 Bioresponse 0.015824\n", - "1 CIFAR_10 0.012963\n", - "2 Devnagari-Script 0.050278\n", - "3 Fashion-MNIST 0.010190\n", - "4 GesturePhaseSegmentationProcessed 0.021767\n", - ".. ... ...\n", - "66 vehicle -0.004796\n", - "67 vowel 0.041111\n", - "68 wall-robot-navigation -0.000282\n", - "69 wdbc 0.018957\n", - "70 wilt -0.001578\n", - "\n", - "[71 rows x 2 columns]" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# df.groupby('StationID', as_index=False)['BiasTemp'].mean()\n", - "diff_df_melt.groupby(\"task\", as_index=False)[\"delta_cohen_kappa\"].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "c8af462c-de63-4c9f-861e-98d8cef93406", - "metadata": {}, - "outputs": [], - "source": [ - "# compute descending order by median\n", - "order = (\n", - " diff_df_melt.groupby(\"task\")\n", - " .median()\n", - " .sort_values(by=\"delta_cohen_kappa\", ascending=False)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "3e89d25e-49b8-4f26-b71a-c18fe6f35da8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\n", - " \"talk\",\n", - " # font_scale=1.25\n", - ")\n", - "fig, ax = plt.subplots(figsize=(25, 5))\n", - "\n", - "sns.stripplot(\n", - " x=\"task\",\n", - " y=\"delta_cohen_kappa\",\n", - " data=diff_df_melt,\n", - " color=\"gray\",\n", - " order=order.index,\n", - " ax=ax,\n", - ")\n", - "\n", - "# distance across the \"X\" or \"Y\" stipplot column to span, in this case 40%\n", - "median_width = 0.4\n", - "df = diff_df_melt\n", - "for tick, text in zip(ax.get_xticks(), ax.get_xticklabels()):\n", - " sample_name = text.get_text() # \"X\" or \"Y\"\n", - "\n", - " # calculate the median value for all replicates of either X or Y\n", - " median_val = df[df[\"task\"] == sample_name][\"delta_cohen_kappa\"].median()\n", - "\n", - " # plot horizontal lines across the column, centered on the tick\n", - " ax.plot(\n", - " [tick - median_width / 2, tick + median_width / 2],\n", - " [median_val, median_val],\n", - " lw=4,\n", - " color=\"k\",\n", - " )\n", - "ax.axhline([0], color=\"red\", ls=\"--\", label=\"No Change\")\n", - "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "\n", - "ax.set(ylabel=\"Delta Cohen Kappa (cysporf - rf)\", xlabel=\"Task Name\")\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "ac110f38-974b-42a5-b5c3-fd1bc4cd5c07", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\n", - " \"talk\",\n", - " # font_scale=1.25\n", - ")\n", - "fig, ax = plt.subplots(figsize=(25, 5))\n", - "\n", - "sns.stripplot(\n", - " x=\"task\",\n", - " y=\"delta_cohen_kappa\",\n", - " data=diff_df_melt,\n", - " color=\"gray\",\n", - " order=order.index,\n", - " ax=ax,\n", - ")\n", - "\n", - "# distance across the \"X\" or \"Y\" stipplot column to span, in this case 40%\n", - "median_width = 0.4\n", - "df = diff_df_melt\n", - "for tick, text in zip(ax.get_xticks(), ax.get_xticklabels()):\n", - " sample_name = text.get_text() # \"X\" or \"Y\"\n", - "\n", - " # calculate the median value for all replicates of either X or Y\n", - " median_val = df[df[\"task\"] == sample_name][\"delta_cohen_kappa\"].median()\n", - "\n", - " # plot horizontal lines across the column, centered on the tick\n", - " ax.plot(\n", - " [tick - median_width / 2, tick + median_width / 2],\n", - " [median_val, median_val],\n", - " lw=4,\n", - " color=\"k\",\n", - " )\n", - "ax.axhline([0], color=\"red\", ls=\"--\", label=\"No Change\")\n", - "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "\n", - "ax.set(ylabel=\"Delta Cohen Kappa (rerf-SPORF - cysporf)\", xlabel=\"Task Name\")\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "f8d5bc57-a36b-440d-91b8-be83b4cb00b5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\n", - " \"talk\",\n", - " # font_scale=1.25\n", - ")\n", - "fig, ax = plt.subplots(figsize=(25, 5))\n", - "\n", - "sns.stripplot(\n", - " x=\"task\",\n", - " y=\"delta_cohen_kappa\",\n", - " data=diff_df_melt,\n", - " color=\"gray\",\n", - " order=order.index,\n", - " ax=ax,\n", - ")\n", - "\n", - "# distance across the \"X\" or \"Y\" stipplot column to span, in this case 40%\n", - "median_width = 0.4\n", - "df = diff_df_melt\n", - "for tick, text in zip(ax.get_xticks(), ax.get_xticklabels()):\n", - " sample_name = text.get_text() # \"X\" or \"Y\"\n", - "\n", - " # calculate the median value for all replicates of either X or Y\n", - " median_val = df[df[\"task\"] == sample_name][\"delta_cohen_kappa\"].median()\n", - "\n", - " # plot horizontal lines across the column, centered on the tick\n", - " ax.plot(\n", - " [tick - median_width / 2, tick + median_width / 2],\n", - " [median_val, median_val],\n", - " lw=4,\n", - " color=\"k\",\n", - " )\n", - "ax.axhline([0], color=\"red\", ls=\"--\", label=\"No Change\")\n", - "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "\n", - "ax.set(ylabel=\"Delta Cohen Kappa (rerf-SPORF - RF)\", xlabel=\"Task Name\")\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "dee47aa6-c13b-45b0-993d-9db1fb1df0bb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\n", - " \"talk\",\n", - " # font_scale=1.25\n", - ")\n", - "fig, ax = plt.subplots(figsize=(16, 5))\n", - "\n", - "sns.stripplot(\n", - " x=\"task\",\n", - " y=\"delta_cohen_kappa\",\n", - " data=diff_df_melt,\n", - " color=\"gray\",\n", - " order=order.index,\n", - " ax=ax,\n", - ")\n", - "\n", - "# distance across the \"X\" or \"Y\" stipplot column to span, in this case 40%\n", - "median_width = 0.4\n", - "df = diff_df_melt\n", - "for tick, text in zip(ax.get_xticks(), ax.get_xticklabels()):\n", - " sample_name = text.get_text() # \"X\" or \"Y\"\n", - "\n", - " # calculate the median value for all replicates of either X or Y\n", - " median_val = df[df[\"task\"] == sample_name][\"delta_cohen_kappa\"].median()\n", - "\n", - " # plot horizontal lines across the column, centered on the tick\n", - " ax.plot(\n", - " [tick - median_width / 2, tick + median_width / 2],\n", - " [median_val, median_val],\n", - " lw=4,\n", - " color=\"k\",\n", - " )\n", - "ax.axhline([0], color=\"red\", ls=\"--\", label=\"No Change\")\n", - "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "\n", - "ax.set(ylabel=\"Delta Cohen Kappa (rerf-SPORF - RF)\", xlabel=\"Task Name\")\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "id": "7b5c104e-bdfd-4a90-97bd-a66e9e2de8bd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\n", - " \"talk\",\n", - " # font_scale=1.25\n", - ")\n", - "fig, ax = plt.subplots(figsize=(16, 5))\n", - "\n", - "sns.stripplot(\n", - " x=\"task\",\n", - " y=\"delta_cohen_kappa\",\n", - " data=diff_df_melt,\n", - " color=\"gray\",\n", - " order=order.index,\n", - " ax=ax,\n", - ")\n", - "\n", - "# distance across the \"X\" or \"Y\" stipplot column to span, in this case 40%\n", - "median_width = 0.4\n", - "df = diff_df_melt\n", - "for tick, text in zip(ax.get_xticks(), ax.get_xticklabels()):\n", - " sample_name = text.get_text() # \"X\" or \"Y\"\n", - "\n", - " # calculate the median value for all replicates of either X or Y\n", - " median_val = df[df[\"task\"] == sample_name][\"delta_cohen_kappa\"].median()\n", - "\n", - " # plot horizontal lines across the column, centered on the tick\n", - " ax.plot(\n", - " [tick - median_width / 2, tick + median_width / 2],\n", - " [median_val, median_val],\n", - " lw=4,\n", - " color=\"k\",\n", - " )\n", - "ax.axhline([0], color=\"red\", ls=\"--\", label=\"No Change\")\n", - "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "\n", - "ax.set(ylabel=\"Delta Cohen Kappa (SPORF - RF)\", xlabel=\"Task Name\")\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "markdown", - "id": "7ce286c9-70ca-402f-96b8-39a1706fdf40", - "metadata": {}, - "source": [ - "# Pairplot" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "40e466f7-6bce-45aa-8227-29afa06d2aa7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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4tNujDf2arQ7pRIJup43b7YwMkt5qDzc0832fUClQaqQWoNlsUioWh5Yfh1CpkQN+u90eKsCcNcft6zAIaTUHC3IQDfK+5xGLxbly9WpkUzBCI9JstSj2XXfTtlEizK9cIfB9At8nlkyxVR7dzFarRTKdodmoE08ksfqWZJSKbBOCIKDZGm2U2Gp3iMUOLzuMMoaESCN03n2qbQjOlkk1BJ8P/Gnf95cDn0XkfbBjZPgd41Q0IO3kMCbOW63RTBvbtonFYiQSKWw7hlKCZVkDVcOaO4OT2EkYAs3yJrX1m7Qrm3jdztEH9Rg5JI81Xo83qCeTKebn51mYX9wNuKS5XExqMXKNyIBwh5cDn1BKfQeAiDyTSLU/TcbOWy0iNlHSJQOwe7ER3IPHHpUIozfT0FqCO4Bp97Xv+wQhbFXqNJodEMikEqwsL7K5tU23e3g5IJWM02nWAXBi8aGrniJCIpGgPURLYFkWhgiIYJrmUC1BKplkWvKJIZHAM0xLkEgkZkYYOm5fG6ZBMpXuLQ0M3H9fICjDMDANY6iWILru+6+FYVoksnk69Sph4NOpVUjnS7Rah5cDdkgmk6zf3KI4N79POxC1IfJsMC2TVDIxUkuQTCSG/q5YLEYQBMzPL1BvdljfahAqRSrhsLi4xNRuoAmRKRrFao5mUhHQAfrfQi8mWjLY4VFgedJGDaHE4MxTg/JW/zzQJlrCeEvv/8+acns0lxg/UDx28zbVeosgDAmCkEqtyZNrW8yVCpjm/kfLsiwSMWfX57w4Nz/cy8CyKBWLQwfYUqkUuasZxtAlARGhWCxiWdOx/Ldtm1JpeGr4UrGIdUEt0k3TIpPLYQwZfLL5wr7vhmFQHHItRIRCoTDwWtixBFbPhbTbrBFzHJwhGodUMkkYBhimSXzAgG6aJtl8Ac91cRxraD3pdHKo2t22bUrFIvPzC9y6XWG72sQPQsJQUW92efzWFp43XcNUzWwzqUDwJPAC2NUG3Av0J3ZYIDISnDZj5a1WSr1+QOSod51CezSXENfzWd+qEg4w1gpDxWalQT6X3d2WTiVZWiiydXsdJxZj6cq1I90BDcNgZXmFeJ9Hg2PbLC0tEY/Fdl3b4vE4S4uL+1TZ8XicKysrU3U5hMgdbWlpad/gE4/FuLKycgeol4XlK9dIJPcispuWRXF+gUw2u+/6mqZJIh5ncWFh3/bEEdfdsh2KV+4mmYuEvcrNx1laWiSTTu8Kf4ZhkM/nKRaLNKpVFleuDA1TLYaxe38sLZbIpPc0QoZhUCzkmCsWhgoLEAkF9VYXzx888K9uVHC98zMWFUPG+mimw6Qi/a8B/4+ILADPBGrsN+r7JGDaiR1OnLd6FL3kFQdJn6ROzWxy0r5WClojDAibrS5LpRypVHJfYKLFlSvA0YGJIHpR2zYszM+PDBgT7WfjOM6pB5bZPZdtz2QQm0GM29c7v6E4Pw9qnv7ARIMG+J1rEYvFjnUtLNshXVokXYz6VRQUCwUKhcKhwET50ui8GJZlgWVFgYkUlIp5isX8sQIThSFU68OXLTw/GCj4nhVy4QXNi8WkAsEPEtkRvILI0O+1SqkKgIjkiDwF/tMU2tfPh4EvERHjgC3AWHmrNZrpcfQLUgEx5+QD5biD7VkOyrMsAJyUYUGAhu9//Gtx0B7gpNgnWRYSjhzwz9vTQHN2TBq6uAt8de9zkDqR/cBwsXMyHuyd7+X0eRowRt7qcVBKvejgNm1UeGdy0r4WEWzLHKpmtW1TB0qZES7Scx0EAWEYQKhAoiBY0xYeBpGMOzSHaLxE5JA9zJkhx3A71I/bVDgNKyBbKTU8E8gARORLe/9+au/vZ4vIHNBUSv1Bb9tYeas1mtPGNIT5YpZbtwdbpc8XsjjOxTSw05wPvufSaTWobW/uJraKJVLk5xcxTOvUDDYd22K+mKV5c2NgeSGX0mPtJWLSwET/GPg0pdT39m37BuCHgKSI/DrwOqXUuGGufuPA9516HwfuhhPnrdZopoZpmiQck4Vihs1KY1flahjCfD5N3NGuUprx8X2fVr1GdWt/GuVuu8ntG4+xeO3eUz2/aRpcXSqytlHB74XcFoFCNkUhmxqZC+G0MSz9LJ0lk/b0vwV2714ReQZR5sNHiGburwLeA7xxnMoO5pkesV8N+KbeR6M5FzzPZXvtBpYT49pCAdWbQwmKZnWbSt2juHz12OvRmkuKCqltD56hqzCkXt4iW5o/VS2BIXDXlXmCMEQpsHrLBM45CgOas2fS3n4G+70KXkXk6/98pVRNRP47kTr/jSdr3tmhvQwuDyfua6Xw3C6e26XdqA3dR3P+XITn2vf9kYZ7nVadTGF4DIhpMKsxJLRL4dkyqbVIAdjs+/4S4B29GTzAu4B7TtAujUajuRSMN+RpAVNz+kwqFm4CdwGISIbIGPC7+sp3QgdfGC6SNbLmZJy8rwU7Fh8aj96Jz04Y38vORXiuDctCDAM1JBRyIp1FjAv1Op0eOg7BmTKpQPCXwNeLyIeBf9yrp38J4T5g9YRt02hmEttxyM8vsXHjsYHl+blFLNshCAJUGNBpNfG7HSwnhpNI0up4OI6NbVnnarClORme5+F5UbZBESGTTmIYEgUYAjrNGoHnYseTOPEkYhgDVfMiBrniPJXN9UNlhmmSyV/csNAnRQvWZ8ukd9n3ELkA/nrv+5t34gD0UiG/sleu0dyRWLbFwtW7qWyu4/byEzjxRM9NzCYIAny3y9bNT+yf+YmQX7xGpVrH9wOWF+e0UHAB8Tyfm2ubuO6eI9V2pcbifAGbgPLak317b2EYJqWr9+BzeL3esizi6QxFy6K2tYHvRYmxEqkMubmFXaNVjea0mTQw0UM9z4LPAKpKqXf3FeeJohS+68St02hmFMtysCyH4tKVveXdvtC1vuceFgYAlKKy9iSla/fyxK1NNrcrzJdyU0tCpDl9PNfj9lZ5nzAAveyBlsnWAM1RGAZs3XyMuetPGVinbTvYtoMTT/QMUqWXZfEy3xdyDLdDLTRNg4mnJkqpbeCQ/79SqkzkgqjR3PEMcy3stppD14RB0WnWSSbj1BstSsWZWc7WjIECmq3D9iPpZJx2fXCwKoAw8PG7nZHuqNpVVXOeaF1lj4vgnqSZDmfR15472OBwh8DtYFvRKdU5Jo+50zmNvg7VYEHPtkyCzvCkVwBet0tCv1XGQxjfqFArCKbCxCacIvIZIvL7IrIhIr6IBAc+55czU6M5Zyw7NrLctJ2+qHD6bXaRMGTwa9MPQgxr9AzfGiPTpUZzXkwauvizgLcTZTr8K+ClwDuIJO/nAx8E/nZKbTwTLoJ7kmY6nEVfx1NpqiJDAxTFUzk2VjdJJRNoeeD0OI2+FiARj9E+oA1oNNtcWSrSrg1eNhDDwI4nJj3tpUQHJjpbJl0y+C4it8JPIVpSuw28QSn1DhH5R8BvAt8wnSZqNLND2w12Mxnaphqab14hFFfuYvvm4xwMKpNdWKHaaGHZFvNz+SNT6AZBQBgE7MoXfcaLELm/9XMnpyeeNl033O0d0wixx3Dvsx2bxfkiN1Zv4/dlvAyVwvVCcvPLVDf2vK7FMEjmiiRzJUDwfA9DjL0+pZfZ8AxdC/vvGcMwht7HmsvFpHfg84EfU0ptiEixt80AUEq9TUR+CfgB4HOm0EaN5tzpuiHbzYAnN33ariJmC1eLFvM5IWYfViHvDMoLdz+Vdq2K73YwHYd4Oke745FMWhQLuSMHb9/zaFW3aVa3CIMAOxYnXVwAFQcxCMOQre0yrZ4vfCqVpFgoYBhyaX3Xx6XWCvjEbZdKU2EasJAzuTZnkHCOXkl1HJtrK4u0O93dOAS5TBrLMlAqxkIyTbNWRhDi2QLlSoXtmzcBWF5cJHDbNMobBJ6HaTukC3PEUxmsUzYq9DyPTrdLuVzBdT1M0ySbzZDNpGdSkJQhyzOa02HSN0YMuNn7f0dvlukrfz/wmgnr1mhmio4b8PiGz1plbzbY9RSPrHuUmwFPXXaIDxhEdl6w9twCQehjGhZhGGI7MYwxjKV8z2X71uP7IiJ63Q7l1SfIlBaJpXM8eePWbplSikajSavV5uqV5ZP85DseBbzvE3sq/yCE1XLAVj3kk+6JDezPg9h2FFgqnYqWAQ72aX5+Gdd1uXHz5m6ugrlSkWZlg25fDozAc6nevoWbzpGdXz41V0PX96jX6pQre9npgyCgXK7QbrVZXJyfOaFAdLbDM2VS8WsVuAqglGoCFeBZfeVXAW1UqLkjCEL2CQP9bDdCut7RXgKmEcnehmGMJQxA5Lo4LDxyffv2ULfGHa2B7+tHcBjDPEJdX3Fjy8M7xrUb1qeu57G1tbUrDBiGgWOZ+4SBftqNKmFwin0Wsk8Y6KfT7dLtuqd3bs2FYFINwV8TBSXa4W3At4jI40RCxjcRGRteGLTb4eXhuH29URssDOywWvHJJGTsgX4cPNelNcQ4DQCl8LttbMsaOHg1my3mSsUBB14uhvX1KBFuvRpwpWRx0rmyAK12e/d7IpGgMyw7Zo9WvRIFJzoF2n1tGUStXicej83UUpM2KjxbJn2DvQnYFJGdO/ffEaU//kXgvxEtI3zbiVun0cwAwRFxAsIQwmlnoxNQQ/zdd1BKoV0Upk+o2DUcPQkHHUyEMfp0aDCrkzMqxfJO+VH7aO5sJg1d/EfAH/V9f1RE7gc+FwiAP1NKDdZNzSja7fDycNy+LmUsbmwN1xLMZU2sKWejMwyTeCqD1xk+q7PjCfxyfWBZPDY6DsJlYVhfy4jnupAyEDn5wCgCjuPgupEqvuu6pHMZ2rXK0GMS6eyJzzu07kR8ZHkymZyqlmsqaKPCM2VqV1sp1VRK/a5S6n9dNGFAoxlF3BbS8cEzxrgtZBPTf2mZpkkyWxia9jaezgIydEZXKhVmzkBslhimWBGBu+dtYvbJBTzbtikW95ZtPM8DwxrqSWA5MSzn9AQ5wzCGCgWmaZJOJbX74SVHi18azRHEHYNnXotRyux/XAop4Tl3j2eRPhFiMH/t3v3BbERIZAtkSkuYlsXcXGnfrM6yLJaWFmZqHXgWMQTuXrAw+7ou4QjPuR7Dtqantrcdm8WFhd2BdmNri9zSNWLJ/SYr8VSG0pW7T9Xt0LZtFubnyKQPnDse48rK0swJkEJkQzDW57wbe4cw1ltDRN4xQd1KKfW5Exyn0cwcthFy/7IJy1ZvjTkaSWKnJQzQS5NrWRSWr4NSqDBEDAPE2A0mk04JqWSCsLf2bBhGlCVPCwRHcqVgsJCLEwQKETAN6Ql3x58le66L2rU8UNi9EMWmGMRjDldXlqM1+l4Ww/ziVZQK+/pUTj0GAURCQalUoFDMR+eWqD2zJgxozodx3xr3cjDcmkZzSfA8n3KtQaXWJghDbNtkLpcilYxzFkq2YRnwfM+lUd6kVSujwhDLiZEpLuAkkqfepjsBy7JOnN3N8zyCUHF7q0azHcU1SCdiLJSymAa0GzWa5U0C38MwLVL5Esls4VzTGl8cYVGQsZcwtI5gGox1Zyil7j7ldmg0M4nnedxYK9Nxvb5tAaubNYpZj2IuvTsbPEt8z2Xr5mP47l5wHd/tUl57kkxpkURW2xCcBX4Q8vjNzX2zpUa7S7rdQbUrtOt75lRh4FPfWqfbblJYvHImGoELjc52eOZoGwKNZgSdrrdPGOhnu9ZGndObqNNs7BMG+qlv3Ua0+9ip43ku65u1Q6pTQ4S4Y+4TBvpxWw0Cb/A9pdGcJ5NmO7wHeJZS6veGlL8c+KBS6rETtO1M0YGJLg/j9rXve1Tqo4O5NFttHOdsZ+Ke5w7NqBehcLttnWqX032ulYL2gOh+iYRDt3lEAKJaGTuemD03vxlDByY6WyZdTPoPwDVgoEAA/N/Ak8BXTli/RnPuKHV0MJcjYhadDmMEkNEBZs4PYXja6x10/2hmkUkFghcCPzei/G3AP5+w7nNBBya6PIzb17Ztk0nFdo3FBhEZFp4thmkRS2fxusO1F6cV/vaicZrPtQCObeF6+0NHd7ouxbkMVLaGHpvI5LR2YBx0YKIzZdKrvQCsjSi/DSxOWLdGMzOkk3GsIZbOqYSDeQ4aTdM0SWXzGEPalcjkER3S+NSxHYeFYubQdj8ICZRgxwYLZZYdw46dvSCp0RzFpAJBBXjKiPL7gMExVTWaC4Rt29y1UiIV31uPF4F8JsHyfP5cPAwAFMLc1Xv3uRiKCKl8iezckrZgPyPiMYsrC3msvjS9tmViiEFx5TqJzH5FRDyVoXT1dAMQ3UmIaY710UyHSZcM/hT4WhH5caXUPk2BiCwBXwO8+6SN02hmAcexWV7I79oURMFcOFe3vujcNvnFa4DqtctADMGytLvhWWHbDoZhclfM3jUbMER6/WCRnVsmU1rcvW/OKgCRRjMJJzEqfDnwPhH5UeD9RIGLPonIoDANvGEaDdRoZoFZ9ek/Lw2FZg/TNIfmADjPAER3BNrL4EyZNNvh+0XkS4FfAH6YvSiGAmwCX6aUeu90mqiZJq/+6q/j1kZl9/vKfJ5fedPPnl+DLhi+7/csxKNb3jCGDwZd19+NUyDCsRPm+L5P2Jt2CuiQxGeA5wWEau+FFneO7jPP90d4FQi2fXSfBUFAGOxl1DRMA9PUfS3a8PJMmfiOU0r9vohcBz4PeCrRO+th4G1KqdHO25pz49ZGhcWXffPe999/47m15aLheR6VSoVGvY5SCsuyyBcKJBKJfRqErhvQ9UJubHZodgIEyKdtrs7HsQw11qDu+T7tVotypYLv+4gImUyGfC4/1gCjOT5dN+DmVoftuodSUVKrK3NxUnFzqDDneR7lSoVGo0E6nSaVSlEul+l2I8+UZDJJqVjEMIyh/e57Ho1mg2qlShAEGIZBNpslm81qDYPmTDnRm6U38P/2UfuJSBZ4I/DDSqmPnOScGs154Hkea6urUQrbHr7vs7mxQS6fJ5vJYDsOQRDQ7AQ8stra3U8B5YZHreXzwF3pIx86z/Oo1mpUq3uR7pRS1Go1Ou02S0uzl5nuotP1Aj7yZAPX35vpd9yQR261uDYfp5gFx9ovFHiex+raGp7nEYvFSCYSrK3td75qtVq0222uXrky8Lye67K9vU2z2dzdFoYhlUqFdqfNwsLi5e5r7S1zppyVPiYBvA5YOaPzaTRTpd1u7xMG+qlWKrv/ewE8cXuwgiwIFatbXbw+1fAglGKfMNCP63l0Op3xGq0ZiyAI2Ky6+4SBfm5udlADMiK3Wq3deyKXzbK1vT3weKUU2+VytLRwgFCpfcJAP91Od+g9p9GcBmepe5xpUU+HLr48HLevPc+jUR/tRdvudLAdhzBUeMHwKHTlhstyKcYoc4JOZ/SKW73RIB6PX+6Z45iM09deANv14QNvqKDrhcT67Alcz6PeaOx+N00Tf8CAv0Oz2aRULB7a3hoiDOxQr9eJxx0M4zIuEwnobIdnirbY0GiOQBgj93fPqOyo/ZQ6+tWlwxKfPUdd0kMhqqfUBUf2pVKEA7QTGs1pcBnFzoHo0MWXh+P2tRgGqWSS7ghVfTwRRaWzDME0hGBIkoNcyj5yWTSRGB12OJVM6rC3YzJOX1uGkEtZ3K4cTlQEkQAXd/Zfb9M0SCaTu8aDoVKYpkkwZDkokUgMXA9PplJU+pacDpJKpy+vZ4kcw8tAKwimgn6raDRHYJomqXR6qHthKp3eDRVsmLBSig3cTwRW5uI4R7gfigipVGp4W1KpoW3RHB/LMlgsxDCH+LwvFJxD7vCmaZLpuydqtRqFfH7oOYqFIvaAgd00DOLxwWGMbdsmFht8L2k0p4EWCDSaMbBtm+WVlX2zdxEhl8tRLBZ31/Nt06SQsblrMY7dl+ggGTN5+rU09hhPnG3blEolctncvpwEiUSCleVlbTtwCliG4unXU6QTe4KWaQhX5mIsFWJDhbiV5WUSiQTtdpsgDJmbm9snrDmOw8ry8r7Qxv3YjsPCwgKZTGZfXydTKZZ0X0fJjcb5aKbCJdVFaTTHx3Ec5ubno3VdpTAMY2CwoJhtUspANmkTKoUgGMI+o7SjsC2LfD5HNpdFhWEUlvicwyXfyViWhWXBvUtJFNESgCGCaUZC3iB2+mJ+bg7VS0ltGAbJRIIwDHdDFTtH9Jll2+QLBfL5fF9obNExCEBHKjxjtECg0RyDcQfkKJztyc51adeOz5HjCG07TENI04KeZhY4qzeOC/wJUD6j82k0Go3mgqMzGZ4tEwsEIvLpwDcRhS0ucdjOUymlntL7pwy8eNJzaTQajUajOV0mssYQkdcCfwZ8CRAHngAeP/B5Ykpt1Gg0Gs1l5AyMCkUkLSI/ISKrItIWkfeKyBeOcdwLReS/icj7RcQTkQsfIGRSDcF3ESUyeolS6tYU26PRzBRhtw3dXuTAWAIjNjpGAEDQbYMKQQQzlhy5b39GQ9MwZsqdMOj08jGIYPb97o4bJWxiggyOs0bQaiC+G2WlTKTBMMDrxZswDEwnQeB1IYiiECrTxrInTznt+yF+L9KQZRhYloHn+ahepKOjDBA1p8KDwCcD3wZ8Ang98KCIvFwp9dYRx30u8CLgbwEP+JTTbebpM6lAcBfwb7UwoLlT8f0ORqeD/9G/IVx9BABj+SlY9z+PMB7Hsg77jgfdNmG7hrf6KGG3hTgJnKV7MVO5Q4KE7/sEoWJju06708UwDHKZBLl08tyzGQZum7Bexl1/DOV1MeIpnOWnECZy1NsBG+U2rh+ScEwWiwlizvBsgLNMWN0k+MhfEd6+gVgWxt3Pwrx6P51bHyPsNLFKV2FuGXf1EwTVDTAEq7iCMX8N4whB7yBRfwu3K22qjShMcj5tM5ePs12u02x3sCyTYi5FMhEbGLPgUnLKyY1E5KXAS4AvVko92Nv2TuBe4EeBUQLBDyilvq93zBu5xALBDUBHzNDcsRjNJu67fg3cveiE4SPvx33yIzgv/nLI7hcI/G4Lf/0xvPXHdrcpr0vnkb/FmruGs3Lf7izb9326rs8Tq1t7FQQhG9t1qvU215dL5yYUBN027o2P4JfX97Z5XbziNVYbLSqNvZj/9bZP/Wad5VKCYiY2kYX+uaEU7tt/GcIosqACgg/9GeFjHyb2gi+gfeOjWJkCrQ//+e4+AN7qI/ibN0k+/QUY8fGFAj+Ah5+s7otgebvSZavm8pSVNI12h07X49btCplUnIVSVmsLzoZXAlXgd3Y2KKWUiLwZ+DkReUAp9dCgA5UalPLqYjPp4svPAK8WkQv0BtBoxiNoVPH+7k/2CQO7uB28v3s3QbO2b7OEwT5hoB9/80nw98LihgpubVQG7ut6PpV6a2gI3NNGue19wgCA2DH8WGafMNDP6lZ7WqH9z47A2zfQ76AaZcInHyZ29X66Nx8evI/Xobv6cXy3O9apup7Pjc3WwHDWQahYK7fJZ/YiU9abHXz/jhtrJsMwxvtMzrOAhwYM7h/oK780TDoN+Rsig8L3iMhPEa27HHpylFLvPkHbzhSd7fDycFRfiwpRQwZ3ALX2CeTAQOFvjV498zZvYKai8PlhGOL7wwf8ar1FLp04c3uC0PfwNp48tN3ILbDeGD1A1Zoucedo+4qzZmhfj0gqFDz+EPa9zyZsDk5BDeBv38JZvm+8Riih1hyeTbHW9FkspKGyl1GzUmsSj9mXPGfFsbMd5np5KoailMof2FQCPjpg1+2+8kvDpALBH/f9/185nPtrJ0Gc1iBoLh5hyOh0doqDKeiUPzpvvQo8wjDEMAzCIYmP9k4/RkrEU0CFISoYkMLXMBmR0RkA/4jfdKHwukdf/jAcu4vCcbJTHtglCJXOanl2HPGwXx4mFQj+2VRbMQPobIeXhyP72jCQZBbVqh3cLdo3mT2kpjRz83gbwz1trez87mxvWFz7HRJx53ySt5kWZrYUGdD1odo1sqUr1JrDD80mZnO9e5LnWkpXIgWCae16FxzESGZRY3aSIYJjGbhDlgEcyyA4IGCmkrGZ8jg5N47nUlgdoAE4ii0GawGKvb/bA8ruWCYSCJRSb552QzSaWUEls5jP+DT8v/mjgeXmM16ASmb3bTOSWcRJoNz2of3FjmFminv7CuQySar11sD654qZcwlla5omkl/AW31kn8YjrG+TvcLQtM4Jx8QeJ2vTLGEMH2ytZzwfb+smzuLduLc+PnAf58r9R7qU7tZnwlIpwRPrgyWqxWKcSq2x+900DNLJwRkQNVPnw8CXiIhxwI7g2b2/HzqHNp0bF+wp1mhOH9M0MRbuwnzWC8HsG5hNG/NZL8RYuH5o9mbGEiSe9nyM1P6Jp5HMknjap+1zO7Qsi/lihlxm/4BimgZXl4pY5vk9lqHpkLj/+Rjx/emX1ebj3H8tQ/yAJ0E6YXHvSubiuR2aFsa9z9k/A3USWJ/2UrxmBW/zBkYsib107/59TIvY3c/GSGTGP5VpkknarMwl9+XqMQRWSgmEgHYnMjp1bIvrK3PI5dJUD0aI3A7H+kx8lgeBPPDyA9tfCzw8zMPgTkU7u2o0AzDSObj7WZjXnoZqRcZeksyAaWMkUgOPMeMp4k/5JAgDlNtFnBgY5sCZpN0TCuYKGTzf72XWM849OJFlWWBliD/1eRAEKN9F7FgUpCdm85SVDIFS+H6IY0fZHi+cMNDDfPoLsJ72qahmFUwbiadQlo0deFilK4gTx8jOYc9fjzQ/hoFhxQhNC/OYwYlitkkxG6OQdvB6Swe2ZWBIlDkz4ZQwTQPDMHDOOQ7FJeOtwDuBN4lIichA/nXAC4Ev2tmpZ5z62UrtLRSJyDzw2b2v9/W2fWnv+2NKqfeeeuunjL7zNJoh7A786fzYx+wO/mPMIHeCz8ziADBMHb4ba+AOiEJipnp9NE7/9sUcmFR/41jRkYPiNcSc2bTBOHdO2cuiF3PgFcAbep888BBRoKLfO+LwZwK/cWDbzvc3E0U8vFDM3ptIcySv/uqvG+jHvjKf51fe9LNn3yCNRqO5oCilakSJ+r5pxD4vGrDtXZyLP9DpoQWCC8itjQqLL/vmw9t//41n3haNRqM5NUYYf2qmjxYINBqNRjObXOrATGePvtoajUaj0Wi0hkCj0Wg0M8opZzvU7EdrCDQajUaj0WgNgUaj0WhmlOOFLtacEC0QnCEf+fuHePEXvurI/bT7oEaj0WjOGi0QnCGeMga6Cx5Euw9qNBrNsdMfa06IFgg0mgO4rk/XCzFViGkoTFNADCT0kV7+k9B0CJRgEkTfxYz2UUFkCKV6KYyVYDs2ruf1EqkKZugioY9CCO29JDYCUcpb6Ytkr0AkyiY+KuGR6+1l5RNR2NbxI9+5nrebt9wQIQwVBlEqaIEol8OQF7TnRcmQlIpyMlyUTH1+p4X0+io0bBQGImD3oke6Xa9n2BZiqgAQfEwQQYiuj1JCtRFiWUIsZmAa0SGGYfTSSStAUGKgVHQ1DQHDkCg1slIoBYYKMAIPBSjLwXKicJBBEBD00i0rwDlB4ivP95FeBkclJvYEERI919393zDNC9PXmqPRAoFG00el7kYv8U6TsH6DTqeFYTs4xRViuSLuR98LrTrm1fuxcvM0P/FBBIVVWMaev4avwG01adbKhL5HuriAk8ywWa7R7rgYhpBLxsg5oD74boy7n0k3VWKz1iCdSpHL56k3GtSqVUKliMfjFPIFxDDo1OvE44ndwQoiQUCFiq1KlWarA0A6laCUz2EYvdwER+B5Ht1ul0q5jOd5UfKluRJG4NJafwK/3cCwbGJzKzi5eUwnduBYl3K5gut5WJZJLpsllUqeS8bG4xDUy/iPfRBVWQfDxFi8G5buY6PpU8ilCUNY3Xa5lhfU9hOE+WW2OzZrNQ/PV2SSJvNJkz9/zwaf/Jw8iItbbhOGioVihqQF7fXHCVp1DMvCKS7j5Bco1xpks1mUgu1yGRUGzCVsgo+8B3/9sShnwtX7Ce//FAInSaPRoF6rEQQBTixGoVDAsu1jCwZ+t4O7eQOvfBulAqx0EVm6G2U5Y/WV57oEvkd1awOv28YwTVLZAqlsHuuU+lppL4MzRVtsaDQ9FFCvdQkq66jbHyFoN0CFhG6HztqjNG58DPv+T0WFPv7H/xbvkfeRuHIfyvfwNp6g/bH3YqqA2vZtAs/FSabAivPErQ1a7S5KKYIgZLve5mbNQ571QoL3vBXniQ+STyao1WrcunWLRDxOEIYopWi329xavRUN1LZDuby9Oxv3PI8gCHn85jr1RjQQhaGiVm/x+M21gamKD+J5HtVqldvr67iui1KKeCxG2CjT+MSH8Fu16Bp4Xdqrn6DxxN8TuJ29Y2t11tZv0+0d63k+m1vbbGxsnWZXnRylcP/2D1FbNyHwwesS3ngY9cF3MpeyePzGJp7nc70oBB/9C8jM8/Eti8e3ArqeIlSQtgy+6wcf4lnPyBKEddqdJkEQkk7GcPwWjUc/QNCs9q6fS2f9cZqPP0Qhm+bWzRv4vk8QBMzZELzzV1E3Pwq+C26H8NEP4L7jv0O7TrVSwfd9lFJ0Ox3WVldpN5t4vn/07+zhdzs0Pv4+3M2bqMCDMMSvbVL/6N+guoPTcO873nXptBps3Hwct9OK7mXfp7a9webqk/iee2QdEyHGeB/NVNBXUqPpoRQ4RohRuzGwPGhW8TttzGsPRPtXbiNBgPRSGyuvi795g2Q6C0AqP8ft7drAulzPp+EbyOJdhI+8n5RjIiIEvk+z2SSd2p9RcXt7izBUCEIQRMsUCmFjqxwtMxwgDBUbWxU8b/SgoZSiVq3u25bNpOisPzb4GrTq+L3sj0opKpXqwP1a7fbI8547YRB1+AFUtwVrj5BNx2h3OqiNxxHToqXi1Dvh7n6puMFDD9fIZmwcx9+3ZFPIJOisPTrwtEGnidcok85kKJfLzGVShH/3zqg9B3E7qI/8FZlU4lDR9vb2wPYPwvc8uhtPovxBg7ai/eTD+N3uyDqUQHVzfWCZ1+3QaR0tVGhmH71k0KOX3vIg6bNuh+b0GdXXqlsfeaxbWSc5d5Wd13ew/hh2fh53IxIivPIayXueS7tRJ1RCGIZD66q2PVJXnwHrj6NWHyGWu0an3abZaFAslWg0m7v7+r6PoEimUjTqdeLxeKRB6AyfmTVbHRSjB41W3zkALNsm7DRHDjbd7TXMVHZ3iWKWGdrXI35fuP4JcnP3gAjBk08gC/ew3tivuo6bwu/+71W++KVLBMHeYGqaBnhdGNHv7vYa6ZWnslqrYWWTeJXbw9ty46Ok7n8+B8UupRTdbne8ZRkV4pYHD+YAodseLJD04XU7AwXPHZq1Mulc/ui2HBc9+z9TtEAwgxzlnvjwxz7O4hm2Z1Je/dVfB3ChXCh3jAaHocIQ1T82hP7+l1YYGRWKyMgXKBAZlJnRI6h8r2c8CGEY7v6/79xRC6Pj4Mj6o31Gl4cHlhVEBDViMANQYRAZuB2x34Ul8KPrLxL9VsMkPKhoUdDtBjiOQaj6DTqjY0bS379H3G+ocKj9/Chhc38d6sgBX3H0fT/6FONpKzSzjRYIegxKbykiFSB31m05yj3xgz/89WfXmBMwKEXzLDCsrwVyOKOVQnY6D529WbWRX8Rt7WkVzHQBt9shDAMsa7T1dSpmI6s3Ihv0heu47WimGU8k6B5Q4YoIhgidbodkMlpOMESwLBPfH/yydxx7oGDRTyKZoFIp7373PQ+zOPqWtzMFMCwSyQSUKyP3PW+GPtcy/LmW/CItNxoA09k5VGOTQmGJet8qSAB82vOKvO9DNe6+nsf3o0LfDzBj2ZFtsjIFuq6HaVkow4ZYEoas40thCXdI/8bj8YHbD6LEwEznCRqVwTsYJmKMHgqc+OFli35iydTI8okQUONmO9S2h1NB62M0mj5anokRH/xCF9PGyRbxH/tgtMGOYRQWCRp7A6q9dC/NWgWATqNKNp0cXJcIhaRN+MSHkewcvpPctQ3I5XLUG419+2czWRCh3WoSizk7tVDKDx985os5HHv0i96yLBzH2f2ulKLj+liZwuB2Gyax/AKmaWKZZl9b9jPzrmgypH0iGHc9i+1GFx8D6+rTCSsblFJg9b0tq62Az//cRd715xtYZmJfyP1Gx8POzQ+u3zCIlZYpl8vk83mqXRfj6S8Y2kzjmZ9BZcDSTDKZPFLY28F2HBJL9w4tjy1c39VUDUdIpDKDSwyDdHbw/aK5WGgNwR3EoKWGxx/9GHfd+9Td7xdlueE8EIF4KkaYeQpm7UmC+uZumZlIk7zyVIKNm6j6NpIpYt/3PNo3PhYda8eJXXs6WDEM046MA8tbFFeuYxhCtd7cVd/btsVSLgEfejcydwV59ovY2K5iWRZz8/M0m81d4UBEyGazpNMZ2q0W8wsLu+vGjmOBxJkv5dkq13ZVyKZpMF/KR+VHYNs2i0tLbG1u0uoZhm2XK6ws3YNh2riVDXYWK8x4itS1p6FMe/fYpcUFNje3afYZlcViMRYX5k7QE2eAYWDe9UyCJz+yq06XeArjvk9hs63IphOkUwluVjos3f9phDc/zAMrz+LjG4pWN0QBlU7I//f/PIvf+N1bfNkXLtNx63iez1a1ydXFq4hp4JZv767bGLEkqWv3U603yReK2I7D9vY2dmGF+LM/i/AjfxXZHwDEU1jP/RxUbg454LGRTqcpFIvHc+t0YqTufjatmx9F7ZzDMInNX8MpLB3pnmo7Dvn5JcQwaNWrfdtjFBdXkNNKU6zdDs8ULRDcQQxaavjgD3/9vm0XZbnhvJjLWbiBRejcQ3zxbgh9DNPEMAwgRPJzWJ/6UpRpgVjE735WLwqNRSgWEFJcuhqpO8MQMUyKsTiFfJYgCKOANISYbgue81koy8HDYGk5GQUDUop8Pk82m0MphWEYvThHilQ6dWgQcGyLTCoavHaC1xiGgWnI2LN027Ypzc1RVIowDHvnFGJL9xBfvN6zi4i2mbHEoWPn5oqUKBIGwe6xsx6DAMBYuR9r6SmR9b1hgGnji8WcYSBEwYKWSkmUSuDclUFUwNOv2ISYhGEURM8k5DVfeg3PV8wVith2FD7INA2MhbuJz19HBX40YIpJKAaZbBwl0TmuXr1KEIaQfibO1ftRbieyQbFjqFgcy7RZXLJRvb4xzSgokj1GfIl+bNvBT2VJ3/dJEEQujGJaKNPEGjOIlWXb5EoLZEvzhEGA9O4J2xmsJdJcPLRAMJpstVoln8/vbujaWYq5T5mosnqtxp/+6Z+deL9h5YO2H9x28Pv2X/7lvt83Tbp2pM7ur79arT6ulLrrVE54MrLVapX5WZ/ZXiBmva/nFpfOux13FKfS36eledAMRLR16HBExCeys9hxJt+xOGsMPuIQR+0/qnxY2cHtx/m+Y0g12Hl8Mo57TaqzOEiIyM6DsHNtDloXNg5sM4lsy3am4Udd00n6c9D/w5iFdhxsw6z2tc9e/41zbWG86zvo2o5zXc/7vDvbdq7JpOedWn+LSCWXTuZu/uEvjbX/lc/7SqqNVlUplZ/G+S8rWkMwAqXUvuuz49M8yHJ5EEftP6p8WNnB7cf53vOaYJoPzXGvyQzzJ3D4uu3Qu379254LvL/398hrOkl/Dvp/BLPQjhcCjVl/KSulrGNeWxjjtw26tuNc1/M+b9+25xLd0xOfV3Ox0QKBRqPRaGYQQR3hDtm/r+bk6AUajUaj0Wg0WkOg0Wg0mhlFux2eKVpDoNFoNBqNRnsZXCZOw6jwsjMr13QW2jELbTgtzuu3Xbbz9p8/l07lnvzj/zHW/tc+91VUG03tZXBCtIZAo9FoNBqNtiHQaDQazYyibQjOFC0QXCK0Om36zMo1nYV2zEIbTovz+m2X7byayRCRReBTgAIDNP9KqbeMU48WCDQajUYzk4yd/viSIiIG8FPA1zDaBGAsgUDbEGg0Go1mNhFjvM/l5VuBrwN+FXgdUYSm7wC+EfgY8F7gH45b2aW+khqNRqPRXGBeB/yhUuq1wB/0tv2NUupngOcBc72/Y6EFAo1Go9HMHApQyJifS8u97AkCYe+vDaCUagK/QLScMBZaINBoNBqN5mLSBrze/w0iOWqhr3wNuDZuZVog0Gg0Gs1MosQY63OJeRx4CoBSygM+Dnx+X/lLgPVxK7vUV1Kj0Wg0mgvMO4BX9n3/JeArROSdvfTUXwb8+riVabdDjUaj0cwk2u3wSH4EeJuIxJRSXeAHiZYMXgMEwM8B3zNuZVog0Gg0Go3mAqKUWgVW+74HwL/qfY6NFgg0Go1GM3uIjG8foEMcTwUtEGg0Go1mNtED/ViIyD8hsiW4t7fpUeBBpdTY9gOgBQKNRqPRaC4kIpIEfgf4HKIohZXe308F/omIfB3whb2YBEdyR3oZiMi7RKQjIo3e5w+OPmpgPY+LyOPTbp9m9tB9fXnQfX1x0G6HR/IG4HOBnwRWlFJFpVQBWOltezHwH8at7E7WELxeKfVrJ6wjl8vlcnCZA2FNnVnVAeq+nj66ry8Xs9rfdzKvAn5DKfXN/RuVUmvAN4vIld4+33z40MPcyQLBVPF9H6UUIoJl6cum0WgO4/keKDAMA9PULnMnJRR9DY8gC7xzRPk7gJeOW9m561pE5KqI/LiI/FlPva9E5EVD9k2LyE+IyKqItEXkvSLyhUOq/kkR2RCRPxKR55ykja7rsr1dYX19g43NLdrtNp7nHX2gRqO5FHieR7PZZGNji/X1DcqVKp7nEQTBeTdNc2fzAeCpI8qfCnxw3MrOXSAA7gO+gigO8x8fse+DwKuB7wa+AHgIeFBEDkpA3wbcA1wH/gh4q4ikJ23gE0/epFav0+l2aTZb3Ly1Rrlc1UKBRqPB83w2NrZYXbtNq9Wm0+1SqVR54smbeJ6n3xMnQWS8z+Xlu4GvFZGXHywQkS8iSmz078atbBZ03+9WSi0AiMgrgIEz/t6g/xLgi5VSD/a2vZPIzeJHgbfu7KuUek/foT8sIl8FPJ9IfdJfZ+WItuXCMBxYUKvXyWRS2LZ9RBWaWWCcvj6LdmhOn7Pu63a7TavdPrRdKcXa+gZXVpameTqNpp9XA58AfltEHgb+nsg25gHgaUTagdeIyGv6jlFKqa8eVNm5CwRKqcEj7mFeCVSJXCx2jlUi8mbg50TkAaXUQ0OOHfccx6JareM4NqZ57pdRo9GcA57nUa3Whpb7vk8QhOh5w2SomVBizzSv7/v/6b1PP8/pffpRwGwKBMfgWcBDAwSID/SXi0ieyAfz3UQ//BuBAvDXBytUSuVHnbA30xg6m/CDgDBUaNuh2eekfa25OJxlXyvFkXYCQajtCCZFXe7lgCNRSk1VYrpIAkEJ+OiA7dt95QA2UYKHpwMu8D7gHyul6qMq72WGOshIu4N4zNGWxBeQSfpaczE57b4WASfm4LcOLxnsYGuvJM0F4aLdqaP8hhWAUmoD+JSpnXGEhJrNZjAMrdLSaC4rtm1TKORpDREIEokEome5E3PJgw4dCxF5Gn2hi5VSDx+3joskEGyxpwXop9j7uz2gbGyUUi86uE1EKoZIzrbtfZbChiEsLS5cqAfd8zyUiuQpEbnUxpDD+hq9ZHDHcRZ9bZkm8/MlNje3d58xgHgsxsJ86UI8a/r9cHERkc8hikr49APbPwL8K6XUUd57u1wkgeDDwJeIiHHAjuDZvb8fOknlo1SLK8uL+H5A13WxLRPHcUYGHvG8gFCBHyoMAUOEmHM+Swue59Htdtne3sbzfQAS8TilUgnLsi7lkodeMrg8nEVf27ZNSoTktQSdbpcgCInHYpimsW9gDYIAP4AgjAZe0xAsk3N9Bn3fx/M8Nre2cF0XAMdxKJUiQeZ8lzvkGIGJLs7kbJr0hIH/DXSBnydyxRciL4OvAP5ARD5fKfWO4bXscZEEggeJLCNfTp+nAfBa4OERHgYnxrZtbNsmkYgfua/rBayXO2xWO+xMFmK2wV1LaWKWgW2f3cMfBAHtdpuNzc1929udDjdv3eLqlSuXUiDQaKbNTvTSYTPrrutTb/nc2mrhB9GLwTKFlVKSTFIRc87nVex5HrdWV/dtc12X1dVVlpeXtf3D7PMGYB14gVLqZn+BiPwA8H+Ichl8+jiVzURvi8iX9v791N7fzxaROaCplNpJTPRWohCNbxKREpHv5euAFwJfdNI2TEO16PoBa9ttNqvdfdu7XsjHbtR4+vUcZ6mIC8OQre3BKylKKcrl8q6m4DKhlwwuD7PS161uwBO39yec8wPFE7eb3LOcPheBwPM8tra2hpZvbW1hLy6e6/KB9jI4kucAbzgoDAAopW6IyM8C3zluZbMyEvzGge/f2/v7OHA37MYceAWRRPQGIE+kHvlipdTvnbQB01AtqpBDwsBumYL17TYrc0mcM9IShGHIsMBKAM1Wi2KxOLT8TkUvGVweZqGvu27Arc3W0PJbmy2SMevMlxWVUnR7ywSDcF13n02EZiapAqM86GpEKZHHYiYEAqXUWGKgUqoGfFPvM3O4/uj4R/W2T6gfMI3mUqGUousNfzd0vVAPvENQl9Q24Bj8BvAVIvJTSim/v0BEbCI7goMT7qHMhEAwC0xDtWgcce9G5Wd3gxuGgWkYBEO0BKlk8kJ5SkyLWVEja06fmejrcZ6xc3gORYRYLEa3O1irGXOcc38/aLfDI/kZ4P8C3i0i/wn4CHuhi78FMIGfEZHr/QcppZ4YVJkWCKaIZRqYhuxaER+kmI1xhjaFGIZBsVRiY2PjUJmIUCgULp39gEZz1ohAJmlTbw1OcpRJ2ueSn8e2bUrF4iGjwh12PA00M82HiAQAAX7tQJn07XOQgSORHg16TGOt0TLh+mKKT6w2DpXFbINiNnamVv2maZKIx1lcWGC7XN6NpZBIJCgVi5c2qNIsrCtrzoZZ6OuYbXJ1PsnDT1Q5OFcwBK7OJ4md5UyhD9u2WVleZmt7e1dTEIvFKBWL5y4MKBjb7fASL7h8P1P8+VogGIPA7RC6HfxWA8N2sJJZlGFiHXhgTNMkFYenXctya6tNo+1hGkIpG2MuHz+Xh37HZTIWi/XWKaUXeER3vUYzCb7XRcIQr1lFBT5WMothO5jOcLdk24Sn35VjdatNtRkJ5rmUzXIpgXWOcrllWViWxeLCwqUNTCQiaSJD9S8jMlb/MPD9SqnfHePYpxBl230xYAB/CnzrabrB96OU+t5p1qdHhR6j1hrrj36Q0O30FRikrz8dP5HBcpx9xzi2iWObXF80duU2wwDbOl9//8v0gB/FTKwra86Ek/Z1EERBxgyJBH7f7eDXtmjd+gT9EzMzkSZ91zOGCgWWZWEBV+aSrJR22sGZeRwdxay+H87IqPBB4JOBbyNyZ3898KCIvFwp9dZhB4nIApEAcJvIBd4Hvhv4ExH5JKXUjdNu+LTRAsERqDDYLwxEG2k8/vfk7v9kwDl0TNBtYwU+QauKmDZGMkMQmJix2Nk0WqPRHJvA6yJhQNBuoMQijGfZqrm0uiEx22AhH8P0urRuPXr42HaD5o2Pkbx6P5Yz/DmfFQFAEyEiLwVeQuS+/mBv2zuJcgL8KFH8m2F8K1Em3U9RSt3qHfuXRELFdwH/4hSbvg8RMYlCFxfgcM5opdS7x6lHCwQ9jr/WqOhsr5FYuI7RZ5gXdtt0H/sgQb0vIJBhEr/+AFDEjCWm1WTNhMzCurLmbBi3rwO3Q9go03nsQ5i5BTql+3j0sf3u3bYRkqoPNM4GwG9UCH0fT0y9JDclzsDL4JVEvvy70W97MW/eDPyciDwwQv3/SuCPdoSB3rFbIvJ7wBdzRgKBiHw78B1AdsRuY0mil9OqbEoE7QZhuOf6GXTbdJ54aL8wABAGdB77IMob7N6j0WjOGa9L59G/gzDAWHwKn1g/HLAnbgtBZ3iAIYi0DK3W6H00M8WzgIcO5McB+EBf+SFEJAE8hcEW/B8AFnpLCqeKiHwN8IPA+4mWKwR4I/AfiRL+vRf4qnHr02Jsj0nWGsVyoN8KVoUE1cMufju4q48g1x/QWoJzRtsQXB7G6eug28FdjZYBzHSeakcNNNv2A4Vj2wT+8Oh+hmlR2a6RSCRwnNlcl79IHNOGINfr2+H1KZU/sKkEfHTArtt95YMoEA2+g2LD9x97e1R7psDXA/9HKfXiXkj//wD8L6XUO0Tkx4kEhbHXqbSG4AQ4xWX6xUrlDs6JvkPYqkXxjTUazeygAoJWDYiE/K4/eBDaaIRYxStDqzGcOF6oIvfeyxfv61RQImN9TnqaCctOeuw0eAZ7kQh3zmcBKKVWgZ8D/vW4lWkNwRGIGJE58IHQok7pCj4Gsb6bUczRMwKxbPSbQqOZMUQQy0a5bcJum2Rh8Hu81QnwCxnsbBGvtn9iKIZJ4urTWC/XZtZi/xJQHaABOIotBmsBdpK8DM4OB2WiAXiSY6dJAOxkzdr525+g5jHgqeNWdmyBoKeWWFBK/X3ftnuAf9NryFuUUn943HrPm2HGRwpIP+WT8Kq3CVp1DDuGU1yi7YXEndj+SH+WjTiJoZoCe/46Zjx5Gs3XHANtVHh5GKevzVgSe+Euuo99kLBdJ+2AaUAwQJn3yHqX59x1H3axibe9hgp8zHQeK1NkY7uK63rMz8/hTFEo8HwflEIphYhEIckvSdryMdPcnIQPA18iIsYBO4Jn9/4OshFAKdUWkUcZbGPwbGBDKXXaywUATwD39NrUFZEngc9kL2rhp3IMwWSSJYMfB96886UX1OFPgW8kSqTwv0TksyaodyYRERrtDk0jiZ9bph3PU2l1SaRSyIHkBaFhE7/3OVHggQOYmSJWbv6smq3RaI6BlSliZqPJXnDzIZ66FMM88HwbAvetpFFiYSYyeJklvNwK1cDmydUNOl2XdDpFMjE9GyHP89ja3OTJJ57gxpNPcvPmTWq1WiQkaKbBg0TBiF5+YPtrgYePCDD0IPAPRWRpZ4OIFHt1/c8pt3MY7wa+oO/7bwBfJyL/TUR+EfgaRrtO7mOSJYNPB3657/urgBXgpUQGDH9EFOBhLL/HWWGY8ZFSKlepVA7t32m3WV5Z2bfNtm0ClSL5wGfg3X6CoFFGTAt7/hpmKo+hjQlnAm1UeHkYt6+NWILYXc8ibFbxNp5Ath7hgSv30fINmt2AuGOSSVjIbpAxk0w2g+t5BKpFPhYjk04hhjE1l0PP87h16xZB3+AfBgHl7W18zydfyN/xyxPq9M3c3gq8E3hTT/v9CaIgQy8Evmhnp56m6bMPZOb9EeArgbeKyPexF5jIJ4p8eBb8OPB3IpJQSrWB7wHuJ/oNAG8DvnPcyia5cxeJ1BQ7/GPgvUqp/w3Qk0r+zQT1ziTD0pL6vk+73T70QJqOAzjIylNQvg8i2qtAo7kAmLEEZiyBkcqBUohlUkg6FIbsvxMWPJWc/jJgEAQ0m819wkA/9XqNfCE/9fNeNnoxB15BNIC/gUhb8BBRoKLfO+LYdRH5TCLB4JfYC138WcOyCU4bpdTDwMN935vAF4pIDgiUUocT64xgEoHAA/pHuM8GfrHve4Xhrhp3FM1Gg0QiMVBKNywHrMNRDDUazWwzCwJ8GIY0G6Pf5Z1O5w7XEMgx3A4ntzVQStWAb+p9hu3zoiHbP0afJuGsEZEvV0odzHKIUqrat89PKaW+cZz6JtHHfJTICENE5AuJDAn/uK/8GmdjXTkbnHO+cI1Go9FcWn5RRF48rLAXi+Drx61sEoHgp4i0AmXgN4FH2S8QfBbwwQnqvXCk0mlsS3tuajSa6WIYBqn0aMeXeHx4dsU7BdXTEhz1ucT8IVEipuccLBCRHwX+JfD/jlvZsUczpdRbRCRkLwb0G5RSXq8BJSJjnf9y3HrPm2HuSTJEA2DbNokpWhNrzg7tdnh5uKh9bZomqVSKWrWKP8COIJvT9q8aIDLqfwfwByLy6Tu2CyLyg8C3AD+klPqecSubaHqrlPpl9nsa7GzfAp43SZ2ziohQKBapVasEQYCIkEqnKRQKd/j6nUajOU9s22Z5ZYXt7e1dewLTNMnn8yRTKf3+0aCU6ojIy4C/AP63iLwQ+Gbg24EfU0r9u+PUdyJ9t4jcR+R18KF+I4aLyCj3pGw2Szqd3g0MgmFgX5LAIHci2u3w8nDR+9q2bYrFIsViMfJ4EsG8TIGJLvdywFgopbZF5POJhIIPAMvAf1ZKfetx65rIyVNEXiYijxC5O7ybnlZARBZE5OMi8qWT1DurmKaJbds4jhO5Gl2Sh1Gj0Zw/O+6NjuPg2PalEQY046OUeowoBEAG+Fml1L+apJ5jCwQi8iKiCE3bwPfR5+/RC9X4CPDlkzRGo9FoNJodtFHhfkQkFJFg0Af4WyKB4OsOlI0d1nKSJYN/D/wd8GlEKSC/90D5XxKFfdRoNBqNRjM93sIpZlGcRCD4FOB7lFLhEAv8G8DSoALN5cH3fdQlTciiuTx4nkdvaR9AG/pNmTNIbnShUEq9/jTrn0QgMIHuiPI5wJ2sOZo7Ac/zqNVq1Op1wjDEMAyy2SzZTObCvDCVUpTL5anUVSgUGOa+qrmYeJ6H63lsb2/jutHrLplMUioWMQxjfxZUjWZGEJE54D3Aq5VSf3mwfJK79u+J0isOizXwMqIlBc0lxPM8bm9s0Ol0dreFYUilUqHb7bKyvHyOrRufcrnMa3/6j7GT2RPV47VqvOVffC7FYvHonTUXgiAI6HQ63N7Y2Le91WrRabe5cuXKObXszuMy2QecESZwN/vTD+wyiUDwJuAnROTtwO/2tikRSQI/RJQNUdsQXFJ8398nDPTTbrfPuDUnw05miaVOJhBo7jzCMGRre3B09lApypUKxWJRRzGdAlogOFsmiVT40yLyGcDPAz9KZODwq0QJjUzgF5RSvzLVVp4BFzWi2axRPyIhyyyg+/rycBp9HYYhQRAMLW82mxQLw3IkajSzy6SRCl8jIr8FvAZ4OpHr4V8Bb1FK/dYU26fRaDQajeYMmFinpZR6kCgewR3BRY9oNiukUynq9fp5N2Mkuq8vD6fR14ZhYBgGYRgOLE8mk9qIVHMhmShSoUYzDNu2iTnOwLJYLHbGrdFopo9hGEOXBESEYqGgvQymhA5MdLYcedeKyEQGgkqpt0xynOZiY9s2i4uLlMtlGs3mbhyCdDpNIZ8/7+ZpNCfGNE2SySTzhkG5XN7NRhiLxZgrzel4G5oLyzhi7C8SGQ4eRwxTRBGVNJcQ27YpFIsUCgVCpTBEEBE9a9LcMdi2jWEYJOJxQqUQBBEdmGja6MBEZ8s4b+gXn3orNHcc2uVKc6djmqbWBmguGg2iHESPDio88q2tlPqTabdIo9FoNJpRKCAcUzF9asH9LxC9WEAlBmjzlVJP9P42iQSCgehpnEaj0WhmEm0wOBoRMYBvA/4lo3MIjaXKmkggEJE48K+AVwL39jY/SuSG+JNKqYsVkk6j0Wg0movHDwHfCnwY+C1g6ySVHVsgEJF54B3AM4EakSAgwDOIUiK/VkRerJTaGF7LxcPzfFTPttKxL55iZccSWgRM8+K1X6OZRY56rnzfJwijea7jaIPD46KNCo/kNcD/Vkq9dBqVTTIy/EfgAeDfAP9FKeUCiIgDfCPwI719Xj+NBk6KiHw68OfAv1NK/dBJ6mp3umyXa3RdD9M0KeYzxGMO9gURDDzPo9lq0Wq1EBGy2SyObWuLaI3mBBx8rnLZLIZpUWv6ZFI2gmK7XKPTdTFNk1w2RSoRvzDvDc2FoAD8zrQqm+TOfDnwJqXUG/s39gSD/yQizyRaSjg3eusqbyRK83giFPDEzdu73z0/4Nb6FulUgvliHsc5m4c7CALCMCQMFWIIxphufK7rcmt1dV/s9VarRTweZ2F+XgsFGs0EDH2uYnHyxRLdrsv6xl4CJM8P6Gy4JOIxlhaK56pl9H0fpdRujBCYXXdJbUNwJB8EppZCdpK70gH+dkT5e4FXTdacqfF1RNqBE+ecVeFg+9VGs00+mz4VgcBzXUCBUiAGGAbl7TL1RhToByCdTlEqFkY+yDupiAclYul0OjSaTbKZjHad0miOwcjnqtvBFLi1VR54bLvTpd3pnptA4Pk+1UqVaq2++y5JJpPMzxVnVijQjOT7gDeJyJuUUk+etLJJ7sq/Bj55RPnzGHNmLiJXgX/bO+a5QAp4sVLqXQP2TQNvAL4MyBMZUXy/Uup3D+xXAv41kT3Dj4/Tjkmp1BrEHGtqAXc8zyP0Paq3b+F1I7vM/PJ1yrXWoZTCjUYT13VZXloc+iArpeh2u0PPV6vVSKeSWiDQaI7BqOfKMAw83yccMpEAqNYaJOKxMxcKPM9jc2ubZrO1b3ur1eLmLZcrK0taKLh4PA94HHhIRB4EPgEclFSVUuoHxqlskjvy/wb+WEQ+CPyMUsoDEBGLyIbgi4HPHbOu+4CvINI4/DHwhSP2fZBIEPk2oh/9euBBEXm5Uuqtffv9IPBjSqnqaScYCYKQIflNJkIFPptPPsqOV61hmohhHhIGdnBdj27XHSkQjCIyiNIqOY3mOIx6rgzDIAhGvxSCIETOwXM+VOqQMLCD7/u0Wm1yOS0QXDC+t+//1wzZRwGnJhD8KJFrwxuB7xeRnRHsKUAWeAT4sQODsVJKDRIS3q2UWgAQkVcwRCAQkZcCLwG+uJdlERF5J5HL448Cb+1tex6R0PD1E/yuY5OIxxBjOgOq77lUN9foD7FhxxK02sNn+ACNZpNkMoFhHM5TNWhbP47joEN6aDTHY9Rz5fs+sSO8CeIxhzDw4Yxn4+3WaG/wnXfJLGkJtJfBkdwzzcomEQjupWdr1/u+s05f6X1sxmykUmrc+fUrgSp91pRKKSUibwZ+TkQeUEo9BHwmkQfE7Z5AkgYCEblfKfVVByvtpUEdRW7YBFpEyGWS2Nb01O1uqzG1unZIpVI0m82BZYV8HtsenJnwTmOsvtbcEZx2XwujnysxhGQiTqs9WLOXzyZpVrawF1aOFNqnyhFja5SPYbYGYG1UOBql1OPTrO/YAoFS6u5pNmBMngU8NECA+EB/OfBfgd/sK/8x4ONErpATYYgQjzl0uu7uNssyWVoYGCFyqridFvnCApVqbeg+2Ux66EvFtm1KxSJBEBxadsjncsR1OmKN5thYI56rbDZH11MslLJsbCmafRo+0zRYnMvTKt8+W0GgRzKRGFmeyaR1ArILjIjcBywCH1JKVSep46L0fgn46IDt233lKKUaRMkbABCRFlBTSm0POBalVL5v33cN2OWFgLm0UCIMQ1zPxzINrJ5WYJoeBgqIpTJ0m/W9bWFI4HZIJRM0B6j74vHYkeo927ZZmJ8nCALanQ6GCIlEIso+eEqqwR23JrfbxfM8bMfBcZxzzXg4bl+fVXs0p8dZ9PXOcxWGIa12G8MwiMfj1NsBm1WXK0WLbFwo5Ut4foBpGogKaZTX8NotSlfvOXOhQETIZjLU6vVDZY5jE4/P3gRBLxkcjYi8jMiA/u7epn8IvENEFoC/AL5DKfWbQw7fx0URCGD0YvfAMqXU66dx4phjEYbRQ20IpzKo2bZDbm6JjVZjn9FSbXOVwvJ1YvE41WqVIAgxDINsNkMumxlrvc/uBSGKx+NTb/dBgiDA9zzW1tYI+ywuDcNgaXlZz0A0dww7z16sT9NmGD7ZpI0Q4rab1DfXMEwLFYbsKDjteBLrHJbqbNumUMgRizmUKxV8P0BEyGRSZNJp6vU6mcx47xTNbCAiLyIyuH8/8Gb6jAyVUrdF5BHgy9mvOR/KpLkM/imRR8FT6c3OD6CUUtN8828NOc+O/cJADcBxUEq96OC23lpkDsAwTJxTFugNy2Tu+n1UN1Z37QlsJ45hGGTSCTLpVC94cpR69TzUjkcRhiGrq6uHLLHDMGRtdZW775mqDcxEHNXXmjuHs+7r/rTf+YUrNKvbtCpbKBUihkEyVySdnzs17dxRiEAY+JSKBUzTRClFs9lkrffMzloE0yk6cd2p/Hvg74jc7Avs9zoA+EvgteNWNkkug+8mCoawTqSOGByBY7p8GPgSETEO2BE8u/f3Qyc9wRDVYvqk9R4H07QxTZvC4tXegBoN/7ZzcQz/Ws3WULescJo+midgFvpaczacZ19btk26UCKZLRCZxwkYxrlqyVQI5fLwV3alUiEWj8+UUKAZyacA36OUCocYhN5gdBbEfUxyZ34D8C7g83diEJwBDwJfTRQ2uT9u82uBh3seBncMw2YPrueBiqT8WXxgwzCk2x1sWa3RXEZM08I0o6W0IAhAKXzfPzehIDzCsct13ZHlZ4scw4bg0toamMAo3/Q5YOxOneSuzAK/Pi1hQES+tPfvp/b+fraIzAFNpdQf9La9FXgnUYjGElFgotcRGQd90TTaMctqZM/zeoFD9oKKxONxHMeZKcHAOOfZz7jMcl9rpsss9LXnedTq9chNUSmSqRS5bBaRPTe/s4oWepRboY5aeuH4eyJ3+/8ypPxlREsKYzHJ2/t9wLUJjhvGbxz4/r29v4/Ts5rsxRx4BVHo4jcQhS5+iChQ0e9NoxGzqkb2PI9Ot4Ntmlihh+91MS0bU+J02m0U4MyQUJDOZKhUKufdjJHMal9rps9597Xnedy8dWtf3oMwCAh8j26zhu92sewYyVwBZRg41uk+yyJCLB6nOyT6aTabnUnbJM1Q3gT8hIi8HdgJ469EJAn8EPDpnKYNAfDdwG+JyP9USo1KcjQWakydkFKqBnxT73N5UAojDNi69di+zc3KFtn5ZQjHdxUKw/DUH3YRKJVKbG1tHSqbm58/1XNrNLOE7/tsbW/vEwYy6TRJx2LryUf27dsob1BYvo6RSGKdolBg2zbz8/OsHhBSAGLxOOkZS3amAxONRin10yLyGcDPE0XtVcCvEhnhm8AvKKV+Zdz6JglM9Cci8tXA/xGRvwQeY3Ayha8+bt3nyVGqxSAIUEphGMbUBlXX21t1GTbLVyqksn5jYFltY5W566kjB3rP8+h2uzR7eduzmQxmb/9pxyOwbYdkSognEtSqVTzfx7YssrkcxpTCPJ+UWVAja86G4/R1lNsjeiZOMij6vk+ooqHsYDTDbCZ9SBjYobz6BPN3PRVPRctvpzUwO47DypUrNJtN2r13QiaTwYkdHddEM3sopV4jIr9FlMvg6UQGFX8FvEUp9VvHqWsSL4NPA36xd+xn9j6H2khkBHhH4LoejXod1+1imibZXA4RA+eImOXD8DyfVrvViysQ4Ng2+Xx+oE2A247WHYfRqVewSosjzuVxa3V192UHUK/XSSWTZFJJWvUq+bnFqXoy7PyGQrG4m3P9ItgWaC4nOzY69VqNMAyJJxKkUqlj37eeH6DCgHK5TKvdZnFhYV95IpHYF3hsEO1GjaYbUOi9D05LKLBtm0wmM9HvPEt0YKLx6OX4efCk9UxyF/w44BEZ8/2pUqpy0kbMAqPWGm/eeHKfK129Xiefz5PJZo8tUXuex+bmJq32XuTBTrfL2vo6hZ06+x7O4AirX99zUWEIAzQEnuexsbGxTxjYodlqEY/HCYOA2zceZ/HaXVMPljKrL5nzXlfWnB1H9bXneWxvbe2bybdaLSrlMssrK8e6hwM/Er533hVBEGBbFl7v+TNNk8Abnaws9F0Mw2Z1bY1rV6+eqvreNM2ZWh7QnD+TvLGfA3zvtIz5Zp0wDHFiMTLZ3F4gj0adarVKMpWaQCDw9wkD/ZQrFTKZzL5tVnx0/HEnlhj60lJK0R5iPARQq9cpZPOUb6/SrNdI5wr6BaG5VHQ6XVqtFplslmQyBSL4nke9VuX2+jpLy8tjPeOu57Gxubk/ymitRi6fZ3NzE4iWEhJH5A8x7Th+u4tSik6nc+lV+NqG4GhEJAX8U/YCBR68aGMv4U8iENzmGH6NF4Vha42GYeQSqRxdN8Q0IwFB7ASLyznq9TqWZY09iwiCgFp9eKIiiGYnudze8mYskUQMI9ICHG4g8czwZe9wxFIDRLMjs9f2VqNGMp29FAKBtiG4PIzqa9f1aDUbLC6vUK622FyroFQUqryULxL67vjBtJQ65MPf6XZJpVIUCgUqlQqdTodSYQm2bw9cBhQRnESSbiV6R3jeWYV50VxUROT5wP9icCTfHcZewp/EOu6/Aa8RkdnUB08ZBcQcE99r06iX6XYaxC3BD0JS6fSRg+6+upQaPLD3ERwsF4PSlXuQg0sCIhRX7gIZ3oXGiDKI1hEDf+eloyVxzeUjm8tzY61CtdHZHaO7rs+t21WUWEOjbh5k2H5b29v4nsfy0hLLS0uESlG6cveheAAiBsWVu0H2Ynk4FyhC6WkRqvE+l5gfA2zgnwBzSiljwGfsWd4kg/qfEQU7+D8i8l+IggQd9DJAKfXuCeo+N4auNSpYW1vd3eB5Hp1Oh2QyRT6fxzhG/nDpZRoctmQAkeFRPzsvh4XrT6XbaeF1O9iOQyyZRiEjVYpRXvbh58tlMzSrURjTZDqDcQm0A6BtCC4To/paBGrNLn4wWEjf2G6QuT6eq+yO99EgjUK90cD1PLKpJLWtDeLJFPN3PZVOq0HgdjGdGJYTZ7sSeeUszM+zfvv2vqRJGs0Qnge8YdxshkcxiUDw9r7//yuHMw1Kb9sdMbqoIaE+W60mmUwG6xiDqGmapFIpypXKwBdHzHGwzMNdsiMUWMecMdiWxdzcHKtra4fUj+lUClMEt9vBtCxSmcuxXKDR7BAqqDWH29iESuH7AY599GvSME3yuRzbQ/IEFPI5qhvrhGGAE09we2OTUKnI0LDRxnX30tc3Gg2Wl5aOjCp4pxNlcxnvGlxiJUGNKPnfVJhEIPhn0zr5LDHJunKjXiNWGrV0M5grKyus3769b80xmUgwNzeHPcbL5zjYts3y0hKe59Hq+RwnEwncTpvt9Vsk0hnypflzScd6XmgbgsvDqL4WOHIkGXdJ0DQM0pkMSikq1eruEoJpGJTm5og5DrF4At/3sGNxOuXK0LrqjQb5fH5mvXQ0M8X/BD6P4aGLj8UkgYnePI0T3wkcWu8fgx0V/9LiIkopgjDENE0Embow0H9O27ZJJpMEQUAYBFipNMl0BjHkVCOjaTSziiFCOhmjXGsNLBdhLO3ADrZlkc1myWQyBEGAiGAYBkpFaXzzpXmyxbkjhYxx7RbOC891d/XAhnZdPG++HfhDEflJ4I3Ao+oEN5AWQU9AIh6fOGrhebkTad9jjSbCsi1K+TTVRptwgGVaMZdCjqmM7p/Vl7e3aTQau9vzhQKJRGI3qdGw97bjODOpAvdcl8D3qG1v4HY6GKZJKlcglclNNdppPzow0X5EJGTwMv3ziTIRD1pqUkqpscb6iQUCEVkkysVcYIC3glLqLZPWPUsMW8fbCfepB1eN5uJiiOLuK3OsblRpd6IlPNMwKBVS5NKJibR2nuexeuvWvoBgvu+zubFBPl8gnc2Qy+WGJgEr5AszlbAMogBo3XaT8u09A+vAD6lt3abdqDG3fO3UhALNPt7CKZpMTBK62AB+CvgaRrstXiiBYJg1sogQi8XodvcijJmmycL8HIHnXvrAIRcR7WVweTiqry3bxgKuLOQiIzYFhiGYonZjdByHIAhoNBoDo4MCVCplMtkM2UyGIAip98UlEREKhQKx2OzZ8yigsrE2sMzrdui0m6Tt/PTPO4uqknNEKfX606x/Eg3BtwJfB/wy8Daigf/bgTrwzUAV+M4pte/cUWFIMZfFchyUitYVwyCkurmOYRpYdgxrwCzC9Xyix0gwZHbD+Go0GibOS3KQIAho9pYJdrBsm2w2uxtXIAxCYvEYhXyefD6H57qI9FyIRfaFLp8VvG5npG1Ds1omnc2fXYM0p8Ikd97rgD9USr1WRHZM7P9GKfUOEfkl4ANEvpHvmFYjz4Jh1shiGLlO16W2uUXYMxRKpVLk5hZoVLZQB7Q3nu/T7nhsbNdwPR8BMukE88XMzKkBLyvay+DycN59nUqlyGSzlLe32e5pGR3HoTQ3h2VZ1Bod6o0WXhCilCKfSVLMp2dOKDgqoNppGUKGOmDaWIjIi4FXAvf2Nj0KPKiUeudx6pnkrrsX+Nne/zt3iQ2glGqKyC8QLSf8xwnqnjmUUvvW+pRSNBoNut0ui4sL+4wKPc+j3uqyvrnnU6yAWqNNq93lrivzx7Ja1mg0FwvTNEmmUriui2VZZLJZ1lZX9+3jui6rt26xuLRErdGm6+4tL2xXm7Q6LlcWizP1rrBjo3OqxBLJM2qJpp/eEv6biXIZCHtjsgF8o4j8CvC6cT0PJjGRbxNlOwRoEI15/Xk+14BrE9Q7kwy7jp7n0XW9fUaFCmFja3CuAj8IKdea+BO4Kmo0mouBaZq7xsbZbJbKkEBFANvb2xRzhwfSTjd6t8wSIkI8NdjMRsQgnSueynmVkrE+l5j/G3g18JvAJwGJ3ue5wK/3yv7NuJVNIhA8DjwFQCnlAR8HPr+v/CXA+gT1Xjga9fpualMA3w9G+hjXG21C/1CUZ41Gc4exvLJCPB6nMyLbqOe62NZgL6VqvT3UMPE8sB2HwvwyyUx233bLcZi/etfphD1XkVHhOJ+Z9NM8G14PvE0p9Sql1N8ppbze5wNKqa8A/gj4qnErm0Qn9Q6itYpv7X3/JeD7RWSFSGXxmcCPTFDvTGNZVpQMKAxx+zwO+s1gD9oTHERbzGo0dz47nkfuMWf5hiHEYzYoEFGnErrY73ZB9VzZxUQsa2zXacu2yZUWyRYXCMMgSp5mCPYlinI6g9zL6CiFv8cxxuNJBIIfAd4mIjGlVBf4QaIlg9cQJTn6OeB7Jqj3XBnldrgyV8BolKF8ExJpmLtKpdkhnk7vczu0LQuR4QN/OhnDMC61emsm0G6Hl4fz7GsRiMXjdIdoCWzHwfMDDENYzsWJh13U7UfAMLFW7gW3C1Nam/c8D7wO7ZsfI2jVATDsGLGlewnTeewx86ToWAMzRxNYHFG+1NtnLCYJXbwKrPZ9D4B/1fvccQjAn/0mYafvmopB/lM/HyNW2L+vQDGXZquy3+0IojCppUJGux9qNJcE27YplUrcunlzYHmhWGSz3OSuUhJ539sJN5/cLfM+/GcYdz0TnvkZmInUidsigUf94+/bN1sJvS7tJ/+exNWnYeTndZC1Y9ILzvfDwBcQrdv/LfDtSqm/GOPYlwOvIvLIux94Uil19wTN+FPgm0TkfyilPnzgHA8A3wi8a9zK9OjUY6h7UuDl6BwQsFRI+J4/wHzJV0Kf9a1tWRRyKUzTYKvSIOilVU3EHZbmckxTORAEAWEYEgQhoVJYlokhogWOMThvVzTN2XGafb2TQdTzAwwRTDNKgdw/sFqWxcrKFba2NneDmzmOQ7FUwrZtihkf+fhfo/qEgR3Cxz+MFBbh7medaLD2XI/O6ieGqi47a49iZQowgwLBuNkOzxoRiQN/TKRt+pdEGQe/GfhjEfm/lFLvO6KKVxBF+v0bIlu+SXNd/3vg/wDvE5HfAR7qbX8m8HLA5Rga+0kiFT4f+AdKqZ/v2/ZFwP8LFIE3K6X+3XHrnVmGLvwrgkf/DvXMz8By9vrStizymSSZVJwwJBIChKnGIPA8j27XY31ze1foAMhl0xTz2VNLkqTRaCI8z6dcqVOtNXYth0zTYGGuQDzm7C4lWpaFZVnMLy7uvkt2gxABpiW4jz806BQABA//NcbyvZDMTNxWIcCvD8+Qq3wPFfhMPiZdSr6KaNB9nlLqbwFE5E+AvwfeAPzjI47/WqVU2Dvut4m8Ao6NUuqDIvLZwI8DX9L77PAXwL9WSn1w3PomGTm+h8jX8ecBROQ68KtE6xQbwLeLyMeUUr8wQd0ziXH1aXDvc1GWg6BQNz+KevTvULVNJDzsNXDaCYT8IOTW+uah7dVatFRRzGd0SGWN5pTwfY9KrUGltrc0aJomuWwawzAJQ4Xr+RiGYPXeA8MmBCrwYMA7ZJd2HTmpNbLqzUouoCn+gJxTs8IrgQ/uCAMASqmuiPwq0RiYUUrVhx28IwxMA6XUe4HPEJF54B6izn5UKbVx3LomEQj+AfCf+75/ea8Bz1VK3RSRPwD+OXBnCASWQ+2e51NudAjDNgDp0lOZv/YMeOyDKONs1Wye57FVrg4tr9UbFPKHZxO+5/ViKigMc3zLYo3msuF5bjSbF2OgYB0qqFT3hIFYzGG+lGerXGOrHMUhMU2DUiFLOhkfWIfvuigUpmkxcrB24pFx0gii5cOgFyk92rf/nMowsHNzeJXbA48X20EMrVU8Js8CBkUB/ABgAs8A3nOWDeoJAMcWAvqZ5C4oEQUf2uHzgHcrpXYsZ34X+IGTNGqWUMBWvb1vW6PVpev6XHv6C/YtF5xVe9rtLpZlUsimiMcclFKEoaJca9LudKNlhN77wHVdBKhWtum02oghpDNZUumMthjWaPrwPY92q0m9Vu3lG4iTKxQxTMGy9qzwwzDcDVgmAgulAjfWNgn7go4FQcjtzQphIUs2k0R69j2+5+J12zS3Nwl8l+LiFWTlXtStRwa2yXzKcwnt2NCAMZ7n4XseoQqpVWt4nodlWeTyeRwnWrqwbRtZvBuvtjVQG5FYuQ+ZUdujYwYdyvXsQ0bUp/InaU8fJWB7wPbtvvJTR0ReBXyBUuq1Q8rfDPyeUuo3x6lvkrugQs/NQURiwAuI1kx2UEQWl3cEw7R1nh/Q6nrkYme87qYglYhTyCVpbK2xvdECwLRs8oV5UskYRt+MIgwD1m/e2Bdxsby1SaNWY3HlihYKNBoiYWBjfXWfi6Df8Gg26iwsryCytwwofYZu6VSSWqO5TxjoZ6tSJxm3cZs1Uvki9c112rUoemF24QrVepPcAy8krG6imvs1f1JagesPDFUe+J5Hs16LNBZ94dV936eztkYmm4kEA9sByybz1E+mvfoofi2yJzATaeLL9yKx1MxqDM8idouIvIjBs/1BzCuldtZrR7XurBY7vgkYLE1GBERGj6cmELwf+BoReTvROkoc+MO+8nu4JJEKa40WyUTsTNfrTVOYK2TYfPIR+pehAt+jtnGL7NzybqwDz3PZWl8fGH7Z81zq1QrpXF7bG2guPe1Wc2i8gM3b6yxfvbYnEIhg2xae55NIxNkuDw5XDlHo8yAMaVa38L0uTjyKK2CYFobt0Njaot1ps/CCL8KobcDNj4FhwPUHMHNz3FxbZ35pGetAnIAgCGjUa8QSSVZv3Rp47nqtTiaTBbuXbdWyiF+5H1mJAhMpDOyzntCcLtUJNQAfAf7ZmPvu2AVsMVgLsBPDeZD24DR4BqMH+/cReRuMxSQCwQ8QpT1+D9Hi1x/1jBp2eBnwVxPUe66cZQATz/MIlEEQKjw/JGabGAIx52gpPQwVjcoGw2xS6tvrJNJReFEVqmg9dAiNeo109vJ52unARJeHcfrac13q1eF2OWEQEPgBO3Kz49gszRe5cev2mFPYSEDvNGqkcyVEhFg6S7MRjS2B77O6uYXtxInf+ykooNPukOl6WLZNvVrFPpBmPQwD3G73SLe8eq2G4zi7SdjGDUA0K5xFtkOl1Brwi8c87MNEdgQHeTbRrPwjJ2zWuKR65xuGAsZ2UZkkMNFfiMgnE9kOVIFf2ynrpUN+G/Dgceu9iGTSqWPPrn3fxw2ER2416Xp7g3o6bnLvcvJooUApOo0RM5IwjAyMOJyYybLsaMYSRDHSgyBkRt18NZozRO0+M8M4WG5ZJtevLdFsdUinEpSrh4ORQaRNMI299MHddhMrlkAMg9A/kDrddfHcPQE+DENEDMIwQHFgAtA7NAj2t8swolgIQRD0bItCwjDcl5VVMxUeBP6ziDxXKfV+ABFxgK8A3q6UGv6Sni6fAF7IfkP/fl4IPDFuZRNZkiilPgp8dMD2LeBb+reJSBZ4I/DDSqmzkpqOzbAAJiKDA5g4tkUycXx1mx8KDz/ZIDjgT9PoBHzsZpOnXk0Rs0cLBYKMtUC183LI5AvEE0lc14vUnZZJvVYl8P2L6Il0Ys4yMJFSivKIjHcAhULhVOLWa8brazFMYvEEfmOol9iheP27cQQMg1BFy4f9MUF2KOXTtKvbOIkUyWwe24lj2jZuq0kslqDdGh5V1onFaNSqZLI5jIPeTCKIIcTicajVcGIxCoUCSoEfBNi2FT3foIOVnQ5vIooC+D9F5DuJlgj+NbAC/JP+HUXkMYD+SIQichfwqb2vK0BSRL609/0hpdTw4BT7eRD4DhH5I6XUmw6c96uALwP+47g/6izulATwOuCXOTs1ytQQEeaLObardYIgRETIppOUCscPABQEAeW6d0gY2KHthrheOFogMCJ1445h0qFiw0R2Xh4iLKxcpVyusVneczkSEUqFHLlC/MKpEC8aXqvBN/7SX5PIFYaU13jLv/hcisXTSR+rOZpQCelcYVeFf5BEMoWSwTNsy7LwPI9rKwtsbldpNCOPJNsyKebTmKGHlcnhdTvUtjcJfQ87FieVK+LEE9SqlYEGibbtgIq0BKls9pDRn23bZHJ5Aj8glUqTzmRY39jaJ5Q4js3y0vykl0UzAqVUR0Q+h2iw/WkiW7q/Bf6hUupvxqjixRx2zf+N3t/vA753zKb8EPBFwM+JyLcQ2fgpokBHDwAPs9/ofyRnJTpe2OmPUoqgtc1yqdgbaBWdRpXyepXCwlVs5/CSQdBtobptgtoWWDZWfgHEIDAd6u3RKU3rLZ9McvgyhG3ZZIrzdBrVXTVkP9n55d1UpCJCtd6k0drvNqmUYnO7wpXYwhhXQHNSnGSGWCp79I6acyFUcLsWUFpcoby5Ttinhk8kUyRzc7TbPmr7Bla2BIaF2R+yvKctWCjlmC/lCDyPwPdoVzZw8iUa5S267T1NgNtp43ZukinOs7hyhY21NXx/LzNiLBanMDdHeXODpStXD2sHephmpAUoFIs8cWP10BKh63rcWr3NyvLCVCOlniWznCG2Z3vwlWPsd/eAbb/I8e0WBtVdF5HPIEoy+CoiIQCgTCSofPdxli+0LukolKLbbNBtHl4j9NzOIYEg7LbofOy9hO29/d0n/x5n5X7MheuYRyQ0sMyj1/rEMJm/dh/VzVW6zWhWYzkxsnNLWE58dzYRKkW9NnhtE2CrXMWyrQv7stBoTkrg+wjQaIe0u7A8fwWDEBUGmJZDrR3w0Ztt7l+ycW88jBsGWHNXcVaeuk8ogD3BwEfRqZd37Q76hYF+6tsbJLN5Fleu9PKS+JGdDwqUYm5xGUSGugTato1hGFSr9YGeRBCFWPY9Xz/jdzBKqSrwDSLyjcAc0QR8Qw27KUagBYIT0Kpu48Tiu778QbdF99G/2ycM7ODe+iiJTIGFfJbt+vA86dnU0V2y40KUW1jp2QAoQA6p/8MwHGki0O10jzyXRnOn4rkutWoVPwyZz2Z5fKNL/WaAYUTZSf0g0qxZpmApF783wPubNzCTOZi7OnCwtmyHTGkRwpDKxuqh8n667Sbp3OTLRaFStI94jjvdLsnkxQwNc8zARJeangAwMlJhL0PjLaKljXccLNempydAheF+lVYYEjSGG5C5tz5OzBIKmcHS+rX5OCLjC3W27WA7DrYTG2gLcJShmjGjwUg0mrPgxo0bVGs1mo0GyRik4tHrMAzBD/aew3vmLcLVh/cd6649Ct7wgdiyLJTIUPfgHQYt+x0HIQqTPIpZDTp0FIpoOWeczwyvLMwiQwcGrSE4AfFUhv5bUbmDA5vsEHYamGGXa/Nximmb9UoX11MkYgbLxTiODbEpZioUMXAcG9cdrJHIZdOY2h1JcwlRSu1Ts2/eXuP6whLNLmzWfPxQkYkJSzkTtf5xwvr+ODPKbR9pGCVALJmmO8qToBeoaFIsyyKXzVBvtIa2IZGIn+gcmsuDFgiOYshTbxgmTjK9bxYu9miLfeklKonZJjHbJBU3UYAhCucUUhY7tsXiQombN9cJDywnOY5NNpO+sLMHjeYkHFxeDcOQ22u3iMViXMlnSSQTBKsfJ/jokxAcNgQWyz56VipCLJnGMDf3GSruYMeTRyYuGgfTsshmUtTqhwWP+fnSVM5xXsyyUeGdiBYIjkDEIJ7O7gsGZMfi5OaXqVarFAp97mSGhRFPE3YGG/I5i3djxvZmBONEJjwplmly7doy1WqDVruNIUI2kyaZTBzbbVKjudPpdru47iap1FWCbmOgMABgz98F5uGlv64bEiqFbfZcEl2X4vJ1qhureN09DWI8nSWVnxvqQXAcHNuiWMiRTiWpVOv4QYDj2BRyWQzLxNFxCDRjchZ3igv8CZEbxJkgIj9HFL85BTwOfKdS6vcnqSsMQ4xYirn8HEpFcQhc1+P2xibpdBqjb/3OjCeJP+W5tB/+K5S/X01vFZYx0qfra+55Xk+kViCCwkAphYiQz2XJZdMgw3OzazQaiMVioCB+/Zm0PvpXqO5+t10zXcBeuIbR9xy13QDXC9msdAiVIhW3KWYdLNumvL1NIjdHzrF7z6NBq92m0+2SOjBY76ZeRqIMiWM+qztZDR3H7pkYy7EFfteNhB+FQhAc5/wFiaNCM2umy0Q9LiKfTpRl6alECR4O9ppSSj2l90+ZKAjDWfJjwL9USnVF5FOBPxKRu5VSleNWJCJUKpV92cR2tmey2V4+8z2UkyT5wGfgVW4TVDcRy8Kev4448UNuStPC931UGFDZXKfTbJBfWMFTJlvVJr4fIEAmnWC+mD2VpQmN5qIxyuC2UCj0jHQdkk97AUF9G7+8CmJgz11FEhkMZ+9Z7rgBtzZaVJt7YYfrLY+NSpv7rmYpFos0Wy02NrfwfR/HccgXCth2jB0bJM9zcTttalsb+L38I4lUhtxcFMNk3BDpkyQq832fIFSsb1VptiJDSce2WChlidn2TAgGmrPh2D0tIq8lirDkEYUvHjtO8llxIESyD8SAK0Spm4+FiFAqFog7NoQBYhh0PR8rniRQwsHHb8clMLZ4N0FxGTAwT3lGrsKA9Sc/gQpDUvkiLVexXdtb4lBArdGm3XG5vjIHSu0uK+7kaddoLhMiQiqVotls7ttWKpX2PQ9GLIERu4KRmwcEDCEMFaHngQLbsWm2fRK2YmUlDipAxKDuKtYqPo+t1rnvSpZ4IkUgMQyBrhfyidseXdflrsUEOYROs05lc3+S2HazTrfdYvH6Pad6LYIQHru5QdgXQdX1fG6sbbM8X8A0h8dC0NxZTDISfBdROMSXKKUG5908BiJyFfi3wPOIwi2mgBcrpd41YN80URjGLwPyRBmnvl8p9bsD9v0vRCkt48D/AsaNDX0Iu3qL7vYaO9K8mcphXH0AX5l03ZCYM9hS37RPP7VoEHjUy1u77kuJdI71W4Mzb3p+QL3ZJui2aTTqUZ6DTIZsLqdTIGsuHaVSiUKxiNvtYhgGtuMgorCsPeNgzwtoewZ+aJNyAprVGrVqlTAMsR2HQqFAOm4T334C78beOyKZyvHUKw/w6KaPAj5+s0nHPexi+Nh6m39wT4rq9mD38TAMqFe2yRTmTkVwdz2fzXJtnzDQz+3tKsnEPFoeuBxM4nN2F/DT0xAGetxHlCGqAfzxEfs+CLwa+G7gC4gG+QdF5KUHd1RKfQNRmtN/CLxtkqhNAIQB/vYq/e6FQbOK+8jfYOMjB7OQnTFhoGj3YrCblk3XHR0audZoY/WiK4ZhSLVaZW11NbI/0GguEZZt4zgO6UyGZCrKXNovDDTaio+tGTz4V0KnG3B7fZVKubybe8BzXW6vr9Oq1xHL5uA7wnv0b7hn3sEPwoHCAEQOAJ7njYxH0G7UUEdkYzwJjdZwd+kgCAfmWjgrxo1DoBmbKtFE+cODCicRCG4QqeCnxbuVUgtKqc/ncLKHXXqD/kuAr1FKvakXZel1wF8CPzroGKVUoJR6O/CPROTzJmrdEDlC+S5BdWMmsjSo44TlUJHBUT+e59FqtvD90cKERnNZ8PyAJ7fg3Q8JyRg40tmXmrifcrWKVVw5tF35LjQ2CYLhz6cwhmud2tnzFFAwy1F9lBrvo4k06CJyVUSuH/zs7KOU6iil3qyUWh9UxyQ6qJ8BXi0i/0kpdWKxVR0VymuPVxJJN7/Td6wSkTcTZXp6YETKSBN4ysGNvTSooxiZDjes3cYuLjLJZdyZkaud9XwxsCdQCYoIiVSGVr1K4HvEjjAASqdidDqHg5g0GnUSyZMFSZllTtrXmovDNPq67Rr89ceiQfiehQCvMzo/TNd1MeIpws7+WABhbYNYbnHocaECMS0S6SzJTA4RA4WiXa/SatRAKeKpDHJKAcTEENKpOLVGe2C52UuhrpldROTLibTmzxix21iLPpMIBH8DfAnwHhH5KeATwCHBQCn17gnqHsWziPJEHxQgPtBf3rMzeAXw20CHSJB4MfDtU24PYpgEyjj2RfQ8j0q5TKPRQCmFYZrkslnSmczItXzXCwhDMAxweimSLdsmW5yL1IpK0WnWyGeSVOqHB33LNEglYqyVNw+VTbqiotFcZLzAxw8EA9lnC+QF0OopBAw5+vkIlcIc4LkghoFpCJYp+8Ih75YL2JaBEUtye2OTMIxcmzPpNPMr19lev0mmUDo1w1/bMpkrZKg3OwN/41wxw5DMz2eCfi2NRkReAfx3IgP/nwW+vvfdIhoHPwiM7XI/yV3Wv87/XzmscJLetmmboZSIfvRBtvvK6Z37q4D/3GvLx4GvUEp94OCBSqn8zv8i8q4Bdb+QEb/DKF5FyfF+pud5rK+t4fapH8MgoFwu43oexWLxkFDQ9QJcH57c9Gi7ipgtXCtBzIa4Y2KYFgvX7qGysUajvEVh8Sq2lWa72iLorf+lk3FK+RRbG7cHtiuRTGKZM7D+cUqctK81F4dx+7rthqxth2w3A0xDWCqY5JMmccfAEMin4DlXusxnFaak6HSGr7XHYzG6AzRvdukqYhjcdyXFR59sHFrvfupKkq3NTVqtvWOVUtTqdbquy8LVu5FTHpENU7j7yhxrm1Xanei9ZFsm88UsibiDbWovpBnmW4G/JzLKTxMJBP9NKfUOEXkW8OfA+8etbJKe/mcTHDMtRsmLUd4/pZrA50ztjIaBs3gXdqaI8n3ENAm6bfxGmTCWJTnEw2AYUSS0wWuRzUaDfD5PEPgEQU8RIgZbNcXH1vaM/hodxVa9y7WSxZVSFArZNE0Kiyu9RTWIJQyymRQqjAITIYpquTzQeNAwDLLZLIZ+8DWXiJsbHZYzIUsxFwyDbmjz+G2TuxccUpbHy++vYKw/TLjZwH7gM6mZ5sAQxMlkgrBZgQPKSzOVx0ikMS2LhPj/f3vnHR5Jehb431upc1Ce0eQN3l3HdTbGYW2vMRiDA3BgHDFwYGMw8TDhDsMdPpI5m4zBGAdyWAdYg+NiTHDEXm/0ptndmZFGsdXqXOG7P6pa0y11t1qaltSSvt/z9CN11VdVX9VXXfV+b+RRp7MsrdYpVQMsEybzMSwjYKHSuQ5BvV7HbbgkU6mdOP01bNPCNmF6YiR6wIbPDNPY+3DDQFc73IzHAv9HKVUTkabN1wRQSt0WJen7GVpM7b3Y8htAKfWerW4zIBa5pAVopZn+r3OsXZ8opW5Yv0xECoiRk8IcjXu+RFMekfQIsWufRmBuTZ/lui6l1dWebarVKkosFgsllIJ0Kk42lSKfEgrl9uM9vOgxmTOJRQoFe5NaCvmREUSE1dVL9dNjsRjjExObVkY8SHQda+1HcODoNtZKkTteuxd1310QefDHY0lOXfUUfDePuXA/XLhnLYbIu/cLHL3yiSyurK5pCkSEdDpNfmSEYHUJI5EmqJYQO4Y9cQJr5MhaMjLTtDAjIWA8RxjeaFosLy727P9qqUQ8EcMwdl5Y1wmI9iUm4bsRoOkI0vocuxt4fb872093wO3At4mIsc6P4DHR39suZ+ddVItpfI9g/uG2haq0TOPLnyD2hG8gTHPQ9zE2bRMEimK1iuuFD6lCsczKaoUzU+Pc5fpUG+1Cwcyyx5Ux6cvxx7Zt8vk8uVyeIErDLAi2c7hyEHQd6yFFKcXy8qXM3yORYKfZnG5jLSpAPdweeaXqFbjjX4g94Ruoz7fnW1OVFfy7/4Ox6asxThwPnQFDb2AeOD+PZVqMHr0Ox7Lw/YDFiss45oYHrGmabXapTacUShGow1unXvsQbMo5wlQAKKWqIjIHPAn422j9NUD3cpvr2E8CwU3A9xLWKGhVf7wauLtHhMHl0e2O9Br48w+hjl6N6fSemTexLItUOt1mL1xPPB5nrtBe9kEpxeJygeOjOe6ZbVdZun5Ub6FPT+B+c6Nrhofl5WVe/fufwE5mcStF3vv65zE6urN1MQ483QKklMJ74FasyVO459tdllSjinf2VsxqEfPEoyitllitefi+wvddZhbbzXEBcGQ829NROJVKbUiL3ko6ncbSpjxNd/6dMBz/f0XfPwS8SUQqhHLkDwEf7ndnQ3Gnici3R/8+Ofr7bBEZB8pKqY9Ey24GPgW8S0TGCKMbXkPoIPTiy+3DdtTIwfIsxuQpYKNAEJQKzR2DHcOI6p7H43Fs2+5oy08kk9QaXkdv31rdZWzEYH1ARzZp6NTDW2Q/mgzsZJZYKrvX3dh3dB1rRY5YAmvqDEZ6BIIAf3kGf/5hgsIc9rGru+/U96jXasSTyY4RO01KlRpK9R4zwzRJJBJUqxvD/hzHwYntfLZTzb7m94CXikhCKVUlzCT8FOAt0frbCR0P+2JY3iR/s+77W6K/DwKnYS3nwEsIUxe/lTB18R3Ay5RSfUtA3ehLjWyYa/ZGAEwr/LTgl1aguop327+hlmbAcjBOPRLrqsdjpMMUwUeOHmVpaYlyKSyTbBgGmWyWWDzJuYvdXSHWJyAyDRjPaMf4rbLfTAaa7dN1rA0D58on4N31Ofz5c2BaGCevxXn0s3DP3gods/MJiCC5SYqrq4yPj/fRg946b9u2mZiYYKVQoBj59jTrLIx0iDg6bGiTQW+UUp8HPt/yfR64XkQeSzh7vHMLuX6GQyBQqj9XUqVUkbDK4ht3tkctCFhnHosxfizy2DdQpWW8h27DnL6aAGPNJujXKqjFC3ifu/nS9o0qwT1fxL1wH/azvx0jFQoFY2OjjI6OoCJbpIjw8MxS1x+AYUibx61jCY8+6WAZAYfXwrj/We8f0ET7CewwIrj/8resvbADn+C+rxBcuA/nmd+Guzyz1tTIjmMfvQIMC1SAJDI4xdDp1zJNvA6RBxCG7okInttADME0O7/cLdsmNzJCNp8LnzFG6Jtw2IUBzeaIyLMIX/ptxTCaYfYiMh4l7esrL9BQCATDQFfVomnnZPw49ZUFvFoFw7KJ5SewH/1sfNqzeInXwP3ypzrvv1zAf+B21DVPwLTjWNbGH/vEaIaHZztrCMbyaWKWwTXTNnHHIG4Lpvgd96PpzTCZDNxKiR963+dJ5EZalmk/gUHRdax9L9dx9l4t4T9wG/a1TyYoLSPxNGZugvoDX0W59WgHBqmp0xjZDGP5FBcXi9iWiWkYuJ6PHwQk4w7TY2ncpRm88gpi2cTHphE7huVsNANos19ndJ2CTfkU8CrCZESdeF60bscyFR4ulKJ4/1fbdFeNwjyx8WniY9OYLT9kVS1Do3MKUIDg4TsxTl4HdufIhJhtcnQ8y8Wl1bXqYyIwmk2RSyewbZNkvHVctWbgIOAkM9o/YLfpoYv2H7wD4+rHw+nrsfCp3fav67YN8GfvDyN0xs6QT4/wtftLLC27XH1FiolRi5FUwOq9X2ozPbiFeeyRI9gTpwjEIhnTv1/NZbOZGtGE/ivwaYEgoputUamg48OjvnABJzsOhoXZVO0FvYsDKc+jl03RdhxSIpxJxEM1pFLYdjhEWn04OIbdh2C9GaGTSaHXNoMyNzT3uZ/NF9saa99FAoUdi9G477/W7dDAzI0jpk1D2dx6R5m3/Ppd1OqXnrl/8rbH4Czc09EPwV2ehdQ4X5rN8YQzikxif17X3aJPa/Jhp5ce5elAd8/XdWiB4DKoLc2SPHJ67bskcyDGhoxlTYzJE2D1DlFsvvgdtABwWFlvRigvXCCem+y5TTM0ERiYuWF5eZnv+o0P8Jc/+ZIDaL7o/qKRiZMoQ5DAJ1i9ZMIzjlwN4yeZL4EbCIan+Nm3fqHtvZ9JWWQTAWq5czZSAFU4x1Q2zWfuNnn2dZCM6Zeepn9E5E3Am1oWvV1EfrlD0xEgC/xJv/vWAkHEduzKyq0TqEvGGWWYGGceQ3D/VzY2Nkysa5+KkRyaieihZZh8CLrRakZolHtnt2xiJwdvdrCTmYHvczfp7htkdh5rMbAf/XSMVA6/WkZMCxX4GNPXspI4xtmHQwfCkZTJBz54boMSIJ22UP7GkOJWArdOIhlQrpuU6koLBN3YSmnjw+VrUCCMwIMwCm8RWF/OWBEm6/tP4O397lgLBJdBYCWpe4Id+QiZqQxy7VPwDJPggVvBD00IkhnFeuLzUV18BzQazS5jmJjXPQ3/ni+CF77AJZXDesKNYIfphpVpY42fwJ17kCB/jLPnL0UTGAruvre0YbfLhQayye/ciKUo1sNpRKEMk9p9RLMFovIB7wEQkQeANyulPjSIfWuBIGI7tkaVmsRYJ9wbqSzWtU+Bq64HtwGmGcY4p/OD6qrmMhl2H4Kt0LTz9+NncBjpNdbmVY/HPP0oaNTDmuKmjZHJrzWyHAdj6jSBGJwrrt8xjI043LNuca0e8LWzNR43mcKvds4Ya4ye4IF7QofChLYMdkXRf5TB4VIQXEIpdWaQ+9MCwWaIgWHHCJohR9Eyf/QqanWDVHajus9IpICdrVCm0cAl34FGpUQ8N4lp785PemkptK3vZ9+CMHtoctM21pErqF1oNwOUGgHf8eJj/OcXN4YJv/2PH+T3fvk64sE9BPWWqCMR7CNX88ByjIYXJhYbzWhzgWZ40AJBRDdbY6DInQuuYHwkwPQqBIZDTSW57Y4Kj3usuW+9rw8z+8GHYCvYyaz2xu7CQMZaDBK2UGp5t9ddxdREnBc+b4qbP9Fuvl0uNFhYrHPN0WmUk6BRLRMYDsSz3D1ncXbBQICnXAkWPvoxrOkXEbl/G5sppdSV/TTUd+ImiAjJTIJ//Nd5CisNHMfjmU9Lcc21eUZz9lpYoEazG6wPL9zOtt3CCPsNM2y2y+fzW952P2LbFkdHYb7YHlY8t+rxnd9+khc8b4q/+9B5CkWPx1yT4FufO0Z+5nME/7WA/fQXo0anqTaEOy8E1BrClZNw1RHBMQNiuuRwT3Tq4g08xA5aSPTduAmGwGLN5+ufMREGKgmsVgMyaRNL9N2q2V2aIYmWbfLe1z9vS9tuFkbYbX1TAGkuW15e5vt+5x/54zd+M2NjY33te79jm3Bm0uGBuUvhhApYKns86uoMP/+mq/AaHk55FqNwJ+bp65DsKEYqRwyI2fDE04IfKExDcGyhz+RxGs0anTReg0QLBH3w2NMpVsoeqzWfuGVwcjKOKQGOoz2CNLuPk8xs21dgszDCfsMMrcTGdvs9RLEXjmMxmlHk0ikWiw3qniKbMMkmLUxDYcWjyILRq+DEVZ33YevMhFtFawh2Fy0QRPTyRo45JpOOSXtqGC3d71cOUpSBpjeDHOvmBOD4ROIyeqTRDB4ROUNYt2AK+DOl1FkRcYAjwKxSqnumrBa0QNAbc2Vlpc1Wqrk8VlZWHlRKndrrfnSgbayDICDx3DdgOJf38PdKSxhOHMPp7M3eaf36Za3fm/+DcOp/fTsAyef9EEGj2ra8m49A/Jnfw6lffV1f65vfH/cr37NWkbPZznnqy3ncb/3wWnGvZtvTv/a9a8v2y1hrBsNOjLcubrQ5IvKrwI8TzlIV8B/AWSAO3AH8PH0mJxKldTJdERGPsIJQMwq5ObPYmJGkM5u177W+27r1y7fyvelZvdKlP9thq9dkZRhfEiJrDiHNa7N+Fllat8wkrDfeVBVtdk23M56d/u/GMPRjfR+Gdaw9Lo1fP9cW+ru+na5tP9d1r4/bXNa8Jts97sDGW0QKiVQu91sfLPTV/kdenKdaXllRSuUHcfz9goj8APD7wG8B/wB8FLhRKfXJaP2fA1NKqb4cjrSGoAdKqbbr01Q/9uvYsVn7Xuu7rVu/fCvfo3ArBvmj2eo1GWL+BTZetybR9Wtddj3w5ejvptd0O+PZ6f8eDEM/ngGUhv2hrJSytnhtoY9z63Rt+7mue33clmXXE97T2z7uoNHz1U15A3CTUupHRWSsw/pbgTf2uzPt5aLRaDQazf7kEcDHeqyfB8b73ZnWEGg0Go1mKOlQQVrTTo3eaXFPERZD6gutIdBoNBqNZn/yOeClnVaISBx4FfBv/e5MCwQajUaj0exPfh34OhF5H/DYaNkREXkBcAtwHPiNfnemowwOETvhVHjYGZZrOgz9GIY+7BR7dW6H7bitx0+kcrnf/LtCX+1//NsOZ5QBgIj8d+AdgAMIl1IbN4DXK6X+tN99aR8CjUaj0Wj2KUqpd4rIh4DvAK4lFAruAf5aKXV+K/vSAoFGo9FohhKtwO4PpdQs8NuXux8tEBwiDqM6bacZlms6DP0Yhj7sFHt1boftuJq9RQsEGo1GoxlKdOridkTkk9vYTOlMhRqNRqPRHCyu4JLTYJMUl5IPFQh9CJpp6hfoP628DjvUaDQazXCilOrrc1hQSp1WSp1pfggrHFYJowymlVKjSqkRYJqwvkElatMXWiDQaDQazVCiVH+fQ8z/A/5dKfVjkWMhEDoZKqV+FPjPqE1faIFAo9FoNJr9yQ1Ehdm6cAvwnH53pgUCjUaj0Wj2Jwq4rsf6R7HR56ArWiDQaDQajWZ/8lHg9SLyahGR5kIJeQ3wA1GbvtBRBhqNRqMZOhT9Vzs8xG4EPw48GXg38Csicg/h5XgEMAU8HLXpC60h0Gg0Go1mH6KUOgdcD/wqsAw8BXhq9P+vAtdHbfpCawg0Go1GM5Qc8giCvlBKrQA/G30uC60h0Gg0Go1GozUEvRCRBwGUUqf2ui+anUWP9eFBj/U+QW0hdbHWJAwELRD0JpfL5XLo222QyOZN9gQ91oNHj/XhYuDjrU0Gu4s2GWg0Go1Go9ECQT94jQb1WoXSyjLVcgnPbeD73l53S3NA8BoNPLdBebVIaWWZRr2O5zb2ulsazZ6jAtXXZy8QkSkReY+ILIhIWUT+VUSe3sd2poj8hIh8VETOi0hFRO4UkV8Skcxu9L0b2mQQISK3dFicBliYeRi3UV9baBgGY0dPENgK27Z3qYeaQdFrrHcbt9GgVilRWLjYph+NJ1OMTE5j6fvrshimsdYcHEQkDnyC8F76YWAR+FHgEyLydKXUf/XYPAH8AvAXwDsJKxI+GfifwDeJyNcppfZkxqkFgk1QQdAmDAAEQcD8hYc4cvKKPeqV5qDgey6F+dkNy2uVMoWFi+TGp7TQqdEMH68jTAv8RKXUlwBE5F+AO4G3At/UY9sqcEYptdiy7BYRmQP+NNr2wzvR6c3QAkGEUuqG9ctEpMClutLrN6C0skx2dAzT1A/s/cSWx3qHcBsNikvzXddXS0VyY5O72KODx7CMtebA8VLgq01hAEApVReRvwB+WkQySqnVThsqpXxCjcJ6Ph/9PT7w3vaJFgguA7deI/AVprnXPdHsSwQa9VrPJkHgAc7u9EejGTK26B6Qi4S9riil8pfRnVYeDXyqw/JbAZOw4NDntrjP50Z/b7uMfl0W2qnwMjBME0OGNbJKM/QoMDeRJkX0T1SjGULGgKUOy5da1veNiFwB/BLwL0qpf73Mvm0brSG4DNK5UUxt39VsE8M0SeVGWVm42HG95TiIoQUCzeFli3kIVrajARCRG+g82+/EhFJqIfq/V+/67rmITAD/CJSBV/a73U6gBYJN6PZATmXzmJYWBjTbxzRNkuks1VKRRq3atk4Mg7GpY9i2NhdoDi/B7oQU3gV8T59tm34Bi3TWAoxGfztpDzYgImPAx4E88OytFCLaCbRA0AdTJ6+gVFjCrdcwLIt0bhTLdrAd/bDWXB6WbTN25Bj1WpXyyjIqCIgl06RzedDmAo1mx1FKzRJ692+F2wn9CNbzGMAnFDJ6IiKjhKGLR4HnKKW+tsU+DBwtEPSBE4uTHZtABQoR0bHhmoFi2Q6W7eDEEgAYpujIFY2GoU5dfBPwOyJyvVLqywAi4gAvBz6ulCr22lhERgg1AycIhYHbd7i/faEFgj6xtHlAs8NojZNGs294F/BDwN+LyM8QmgjeBEwD/621oYicBVBKnY6+J4B/Bh5HmNQoKSJPa9nk3F6ZDg6kTlJE3ikiMyJSFJGvisiL9rpPGo1Go9kaSvX32f1+qRphmOC/Ab8PfJDQD+D5SqkvbrL5FGFmQgP4XeA/1n2+b2d6vTkHVUPwm8APR4kingx8TEROK6UKe9wvjUaj0RwAIt+DV/XR7vS672cZ0kqgB1JDoJS6SynVzDfsATHg2B52SaPRaDSaoWbPBQIROS4i7xCRz4hISURUFBfaqW1aRH4rMgdUReQLIvKtXdr+nohUgS8RenLesWMnodFoNBrNPmfPBQLgKkLPzBLhi7sXNwGvAH4e+GbCl/xNIvLC9Q2VUm8grET1fOCjSg2xv+oO4XkejYaH6/p73ZUDi+d51Bsu9YYuh63RDJpAqb4+msEwDD4En1ZKTQKIyEuAbjP+FwI3Ai9TSt0ULfsUcAXwNuDm9dtERSQ+LiI/KiJ3K6X+ed0+C5v0bV8WQGm4LkrB8kqZar2BZZqM5FI4toVjD8OQ7z47MdYN16NUrlEsh0mFsukE6WT80F7jYeGg/q4PIyrY6x4cLvb8yaVU30P+UmCF0Juzua0SkfcA7xSRRyqlupkFTODKy+vp/sDzPRquz7mZxZbcmS6lSo1sOsHEaBbTkE1z6Gt603A9HrqwgOtd0r5Uaw2WrBInp8cPtFDgeR5BEIAIpmHoe0mjOSDsp6fWo4E7OggQt7auF5E08BLgA0CNUJB4DvDT63fYmvdaRG7pcMxnEAoT+4YggAsXlzsm0i6WqmSSMVTgkUymsA/wS2s9gxzrhusxt7jSJgw0cT2f+aUiE6PZAycUhIKAorBSoFoNtSLpdJpsJoM9RMm6DuLv+jCigH4tvdpoMBiGwYegX/qtLqWA1wHnCPNNvxl4uVLq1g7bHjgarocfdFe6FFareJ7PzMwFXNfdxZ4dIBSslruXLV4tVbuu28/4vs+58+dYXV3F8zw8z6NQKHD+gr6XNJqDwH6bwmxaXUopVeZSXen+d6zUDeuXRbbIfWVv9DrMWtvW+z6GaeF6HsVikZGREYxDVlHvcsdabTIf2crMZr/gui7zCwsdz8v3fZaXlxkbGxs688FB+V0fVnrMbTQ7wH4SCAZSXaobXVSL6cvZ514Qi/VW3cYde202t7q6SjabPXQCweWOtUS2826aGMs0EBnKvCPbRilFvV7vur5ULjMyMjJ0AsFB+V1rNLvBfnoT3A5cJ7KhBNxjor+37XJ/hhJTDOI9hIJcNkmpVALoaVoYBJ7n4TYaeN7BCskzDBjJpbquH82nMY2dEwh838d1XTxv99T0m2k8DppGRDMEqPC+6uejnQgGw37SENwEfC/wLbREGgCvBu7uEWHQFwdFteg4FsemRjk3u0S9cemFYYhwZCJHabW49vBOxOM70gfXdQmCgJWVFTzXxbJtcrkchmEMhfPZ5Y61ZVrkMklcz2dltdK2Lp9Jkk0ldmSm3NTslEslKpUKYhhkMhlisdiOX1fDMDAMI4wu6IAzpIWZDsrvWqPZDQYqEIhIrCVl8Fa2+/bo3ydHf58tIuNAWSn1kWjZzcCngHeJyBjwAPAaQo/hF19ez/eHatH3fZRSaw/nbji2xfEjo/h+QLXWwDAE2zJYWVmhWr30AhsZHR34i6ThulTKZZYWFy8trNUora4yNj5OLrf3z+FBjLVjW4znM4zl01RqDQCScQdBdix6QynFhQsXCPyWUMdKhXQ6zejYWGjK2CGVvWEY5HM5lpaXO64fHRkZCmFvPfvhd63RDAtbfnKJyDcBT1VKvaVl2RuAXyEs4/jXwGuUUlvRZ/7Nuu/NfT8InIa1nAMvAd4affKEmQpfppT68FbPYz/R8FwIFMXiKo2Gi2VZ5HKZnjNux7bABisSBBYXVtbWGYbB+Ng41k68PJRqFwZaWFxYGAqBYFA4TvjziTk7/yJ0XZf5ubk2YcCyLMYnJmi4LnPzCwhCJpMmFnMG/nI2TZN0Oh1qfoqXtEyGCKOjo0OrIdDsbwJtCthVtjOV+SlgrvlFRK4D3gHcRzhr/07gc8Db+92hUqovg6tSqgi8MfoMlGFVLXqeR71W5+LF+bblxdVVxsdGSaaTOFb3h79tWeRzOXLZLK7rIoaBZZoYO5RQprS6OvB9DpphHeterHfqMwyDiclJLs4ttPlolCsVYjGHI1NTA9dU2LZNNpsjm83ieh5CKJQ01w0j+3GsNZq9YjtPjOtoTxP8nUAVeIpSqigif06oyn/75Xdv9+ilWvRcFwIfFXiImCjDwHZiu9IvpdQGYaDJwuISJ5KJTfexmw/t/eBAuJdqZNfzEN8L7yUMMEys2Ob30nqnvUw2y3JhpeP1rtcbrBSLjI7kBx5B0hQyhlUAWI82GexvlFYR7CrbEQhGgIWW7zcCn4xm7wC3ABuKDe1nahfuwy3M0XRlNZNZ5MQ1KNPe8QdjuVLpuX61uIo1kh+acK9YLL4WxaBpx2s0cJcuUJ8/B0Go+jdiCRLHr4FYEqfHvbT+xZ5MJlkuzHZtXyyukstmDl1IqUaj2T7bEQgWgFMAIpIhdAT8uZb1NvswLWhX1WLg59zCxbblfqVI6b4vk7n6CYSnu3N4bu8Zt+t5QxXylUwlWVqSjn0alpfTXqiRPdelsXie+txDbcuDepXy/V8h84gnQQ+BQCRME9wUtjYb827RAIcNbTLY3wzRo+1QsJ0n9H8APxhFBrydUKhoNSFcBcxcfteGg24PXuW51AsL+P7OlhaObaJOjsWcNZPAMCAiHJ2e3vDyN02To9PTe9SrISDwQ81AJ5SiOnu2Z/pfy7IZGR0lkbhkIuqV/GiY7gmNRrM/2M5T4xcIw//+Ovr+nmYOAAmfUC+N1u8rtmNr9IuLBPnJHVXXx+OxrvHfIkI6PVzm0OaL6Njx4zQaDVzXxbFtbMcZmux9e2FXVr7bs5arX1pGAp9eGifbthmfmCAIAjzPJ5NJUyx2duLM5w5fBspOaB+C/U2gfQh2lS0LBEqpO6LIgq8HVpRSn25ZnQf+H6EfwcHH2PkUtSLC9NEjXJiZbRMKRISjR6aG5iXbyrB7nu8Jm42TGMDmY9m8prEYODEHz/WoVNuLKWUyaVLp5ND4lWg0mv3BtvSKSqklYEPsv1JqmTAEcd+xHVujMzaNtcMvvebL9cTxaer1Bo1GA9u21zQH+qG/dfbCriymjVgOymt0XG+PTMEWx9K2LCYmxgiCgHKlgiCkUklEBLtHKOphQvsQ7G+GyT/qMLBtQ6OIPAv4BmAKeJtS6i4RSQNPAG5VShUG08W9RbqoXc1kFjOxO5rH9hl39xz6muFFTIvE8UdQObux5IbYMeLj05jbsPtf0hjsThisRqM5uGzZyCgipoj8FaGfwM8CrwOa3mIe8AHgDYPq4J4jBslTj8KMhy9isRxiR86QOvVIrF3KRaDZ/5imiZHIkL7qCZjpPABimDhjx0hfeT2mszN1JTQajaZftqMh+Gng24AfB/4JuLO5QilVE5GbCPMQvHUgPdwlejkfxfLjGIkMImFVLbFsrarfx+yVo5ntOOA4cOI6pOlgaJpYWr2/Y2inwv1NDz9czQ6wHYHg1cB7lVLviIoMredODlhiIgBbq2Q1A8LWef81Gs0Qsh2B4DTwth7rC4TZDPcV2vno8KDH+vCgx3p/E2inwl1lO4HKq8Boj/VXAZ2T72s0Go1GoxlKtiMQfAZ4pXQIgBeREUInw32XmEij0Wg0w4VSqq+PZjBsRyD4ZeBq4JPAi6JljxORHwC+RBgX9yuD6Z5mUHieR6Ph0mi4O55uWQMN11273hqNRrMf2E6mwi+IyMuAdwHvjhb/BmGatTngpc1UxvuJg+qN7LougVIsLa9QrdYRETLpFLlseq2U7WFjJ8fadT08z2NpeYV6o4FpmuSyGVKpBLauL7DrHNTf9WFA0X/qYq0jGAzbzVR4s4icBp4PXEcoDNwD/LNSqne9Xs2u4nk+5y9cbPvBLBdWKJXKHJueOrRCwU7geh6VSpW5haW1Zb4fML+wRKkcZ3w0h4hgGIZO66zR9IG2BuwuW3obRJkIPwT8mVLqXcA/RJ99z0H0Rm64LhfnFztKz67nsbKySj6fxbIOV06FnRprpRTzi0sd11WrNRqNFCuFJWzHYWxsTAsFu8BB/F1rNDvFlnwIlFIl4Mk71BfNgFFK4bpe1/XF1RKBzvwxMOr1Rs8ZzWqpTDKVolIus7q6qn05NBrNULEdp8IvE5oJNEPOZt63nUoqa7ZP4Pe+nkEQrFWnLK6s6Ouv0WiGiu0IBL8AfL+IPGfQndEMFmOT8szxRKyPgruafonHe2ezjMdjNBphtcMgCHS4lEbTCwUqUH19tFfhYNiOR9krgYeAj4vIV4CvAesdCZVS6nsvt3Oay0OAXDZNYWW14/qRfA79SxocYhgk4jGqtfrGdSKkU0lmLiyvfe8hq2k0Gs2usx2B4LUt/18ffdajAC0Q7DFKKZLJOEopisXS2qvfNA3GR/OowOcyKmBr1uHYFlOT48wvLFGuVNeW25bFxMQIS0uLa8vSqRRo/YxG0xOdunh32U4egu2YGTR7gVIYIpgGHJuexPcDxBBQilqlQiKZ3OseHjhs22JifJRxpfB8DxT4vsfy4uKaucBxHNLZjI6p0mg0Q4WeHkYcxAQmYhisLMyTGxmlXq9Tr9UASKVSOLEY1UoF5xBWcdzpsbZtC9d1Ka8sk8pkUUphWRaWbZNKJjEMg9JKgZHxyUEdUtOFg/i7PjxE/gF9ttVcPlogOMCICLmRES5eOEcsHseJheaD5cV5UHDk+AksnT1vR7Btm9zIGDPnHsKybWKJBADFwhKe63FUX3uNZlP6Fwg0g2DLTyQR+WQfzZRS6nnb6M+ecRATmDRfOEdPnKSwuEipuIIYBql0hmx+5NAmxtmtsTZMk+kTpygsLVJZDR07E6kUk0emEUNb3naDg/i71mh2iu1MUa5go37GAo4ShjEuAOXL7JdmQFiWhWVZjE5M0hw2EUPPTneB5jXOj42THxuj6UR4WAUxjWaraAXB7rIdp8LTnZaLSAz4ceB7gGdfXrc0g0a/hPYOfe01Gs1+YGB6S6VUXSn1f4HPAr85qP1qNBqNRqPZeXbCkPkZ4AU7sF+NRqPRaDQ7xE4Yks8Azg7sd89ouC4oheu6mKaJaYbVAbUqWHNQcF2XIAjwPE/f45qhQUcZ7C7biTI42WXVKHAj8CPALZfRp6Fjfm5uLYYfwLQspqamMAxj7cGp0exXXNfl4sWLNOqXUi5blsXUkSN4nqcdUDWaQ8J2fuln6Z4FQoC7CIWCPSFybvwD4PlABvgS8Eal1O3b2Z9Sqk0YAPA9j5kLFzh+4oQWCDT7Gtd1mZ2ZwXXdtuVedI8fO358j3qm0WxesVUzWLYjEPwSGwUCBSwRFjr6uFJqL+u6WsD9wNOAGeBNwAeAq7ezs243pFKK1WKRXD6vhQLNvsV13Q3CQJMgCCiXy+Tz+d3tlEYTEWiTwa6ynbDDt+xAPwaGUqoM/O/mdxH5HeA3RGRMKbXYfcutU6vVyASBFgg0+5Zatbrpej+T0fe4RnMI2HKUgYj8iYg8tcf6p4jIn2xhf8dF5B0i8hkRKYmIEpEburRNi8hviciMiFRF5Asi8q2bHOLpwNyghQEIM9GJrmGr2ccYm7zo9T2u2UuUUn19NINhO2GHrwWu7LH+DPCaLezvKuDlQAn4xCZtbwJeAfw88M3AHcBNIvLCTo1FJA/8IfCzW+hP32SzWe1wpdnXJDepeJnNZjF0mmWN5lCwE2+zFNDZKNmZTyulJgFE5CVAxxl/9NK/EXiZUuqmaNmnCFMpvw24eV37OKHvwD8opTpqLKKc5r3IdXsYJlMpHZK1j+hnrHejH8OGiDA6NsbS4kYFWjqd3pemAj3WBwcddri79CUQRKGGp1sWXSsiz+rQdBR4PXBvvx3YggPiS4EV4IMt2yoReQ/wThF5pFLqjqi/JvCXwHngJ/vtSzemjx2jsLxMo9HAtCxyuRyxWEwLBJp9j23bpFIpYrEYhUIBt9HAsixy+TyO4+h7XKM5RPSrIfge4BcIowkU8HPRZz0CBFH7QfNo4I4OAsStreuj//8ISADfoXoYmJRS+V4HbFZFi8fjjE1MrNmqnCF8SHqeh1IBKgg/hmVhIJhRX91Go1lbB5BD96Dvd6y3sk/fdQlQBL4PgGmaiCGYZvu1dRuNlgOBbQ9X3i7btrFtG8uyUIS3yX6+P3ZirDWaw0C/AsEHCPMPCPAnwDuB/1jXRhH6AXxeKfXwgPrXyhhhWON6llrWIyKnCAWSGrDc4hD1TUqpf+22cxG5pcPi9Np638XwPZQIDNnD0ms0cN06y3Mz+J4XLhQhnRshkx/D81yWFxep16qICMlUmvzoGMjhEwxg87HuB9dtUCutsrI0jwpCGVUMg9zYJIlUBsu2cV0XFfgsLy5QrVTCg2Rz5DNpRAUoMbESvW34u8lBvBcGMdYazWGhL4FAKfUV4Cuw9sL9O6XUbTvZsW5d2WydUupBWubCg8BfmsW/87Oo4jwSS6Ee8USMsWOYidS29+k2GqG6JQhCp63LmDkGgc/ChXUymFKUCksI4Cuo16rRYkW5tEq1UuHo8RPb7v9hpTluQeBjxxKkciOUC0uht3MQUJifxTBNLDtH4HvMnj+3plkaH8nj1Ir4n/0XVLWIpEeQa58KmVHM+PAIBhrNUKC24EOgXQ0GwnbyEPziTnSkDxaJtADrGI3+LnVYd/kEAe6n/mLtq6qs4n/2HwmOXQ2PuwEzsfXJRqPRCNMht6SKTSaTjI+PY21xlua6LiuL813Xl1aWGZ8+yWqxiOPEonz1LkHgs1JYIj86hmUdvJnhoPE8D9/3mZ+bo9FiAkilUoxNn2TxwsM0rVnFxXmcWIKlhfk2YcA++xWCs5fkaFVZxZ17CONRX486/RiseGJ3T0oDhEJybV020q0Sj8d1eOaAUUDQZ0ihlgcGw7ajDERkCngSMEKH8EWl1Hsvo1+duB34NhEx1vkRPCb6uzMaC9/ruFidvwd1xeNgiwKB67rMXLiAH9mdm1QqFS5enGVy6sgWVbeKeq3SY61gWDbTJ0/TcBsYYmBZFpXSKsXCMrn8aNdtNZdQSjFz4QJB0O7CUi6XCYKA7NgkKwuzAHhug3BcamRyOVLpDHZtFfds51s0uP3fsI4/gtDt5RKhX4haK65lOw4igq1DXQdKrVbju//g05h2bFvb+26dP//BZ5FIaIFOs7/ZTnEjA/hd4Pvoncdg0ALBTcD3At9CS6QB8Grg7maEwXZRSt2wflnofKS6Oh/59/0Xkp/EdDY+SBruJUHCkLBYjOd5VMrlDcJAk3q9ge/7WxMIFIgYKDoHa0ydvILlQoFSqbS2TEQYHxtjbHJqwMaV/UH3se7saOb7PqVicYMw0KRarZLP5xGRliQpwtjEFK7bYHWlQO5C79szmLmf4IrHYUVhfq7rUi6XWVpebku8ks1kyOfzB9LevxP0O9amHcPq8DvW7C3DHHYYTYp/jTAnToKwbs5PK6X+vY9tfxZ4GWHenjRwAfgY8L93yAevL7Yz1fhJ4AeA9wMfJXzx/zSwCvwoYWjgz2xlhyLy7dG/T47+PltExoGyUuoj0bKbgU8B7xKRMeABwgRIzwBevI3zWN+HWzos7jn9l0YdFbS/3F3Xpe76zC8VqdVdRIRcOsHYSAZDoLJJqthqpUI8Hu+736ZlkczmKC1vjCMfmZpmZWWlTRiAcLY7v7DA0aNHOIwSwVbHOgiCTcetXq9j2Q5uo048mQYRgsCnWFgmnclAo7dK2q+VKa9WSKcSmIYQBEEY4prNUlxdXRNGiqurGKZJTifF6gvtVKjZCaI8N58gvJd+mNCk/aPAJ0Tk6Uqp/9pkF3ngbwk136vAdYQJ914kIo9SSi3vUNd7sp0nymuAf1ZKvTp6MQN8USn1SRF5H2EY4BOBT25hn3+z7vtbor8PEuU/iHIOvAR4a/TJE4YZvkwp9eGtn8YAmDyJtISYub5PudpgZr6wtkwpRWG1Qqla5/T0OMYmdsatZoUzDINMboTqahHfa88H5cSTzC2e77rt8nKByfHxLR3vcKKQTcbFMAyUCtvlx6cAKBbC37TrujBxEs7f03V7GT9BsVQjEbdZXa2wurqKUopEPM6RqSlWikXK5XK435UVspnMgM5Noxlehjgt8euARwFPVEp9CUBE/gW4k/D99E29NlZK/Y91i24RkQeAjxBqwQetYe+L7QgEVxCmAwbW9NQ2hIWFROTdhOaEX+93h0qpvqapSqki8MboM1C6qhbF6GwysGysk9dhtKhuVaC4uLjSsbnn+RRLVTLZ7NqDvRObpZLt3BWHyeOnKK0UqKwWUEFALJHaNM93rVaDQ+gItVWTgW07ZDOZnoWAHMfGS6TIjIwihkng+2umoXqtBhPHwUlAY+M+JJXDS+SYjCWYm5vD8y6Zm8qVCuVKhemjR7Esi2qlQsN1u5ovNO1sdaw1mj55KfDVpjAAoJSqi8hfAD8tIhml1OoW97kQ/d1Kpt+Bsh2BoMqlDpcIHTwnW9bPAvsunq2ratGykPFjqIVLM21JZjGe8kLqYrW5gXme37Nc53KxTHZ6jEQiQbXDyyWfz3d8Qfu+jwp8auUSbr2K5cRIpDIoMdZsyZbtkBkZI5XNN89nUw/d/ZiWdhBsR40ci8eJxWJtkSFNxsbGMC2b7NjEmhrfXffCXlgpMvGMlxF8/mbU6iVtoIxMIU/8RooumF6pTRiA0Gcgk05RLxcxA4+RTArTiWmP9j7RJoP9zRCXP340oQl7PbcCJqEJ4HOb7URELMIJ9TXA24G7gA8NrJdbZDsCwYNExY2UUq6I3At8I/C+aP2NwMXBdG/vCQJF/VE3ELcEKiWIxfEMm7lSFcstYjvO2ktAbRL8olTYYmJignK5TLFYxPM8HMchn88Ti8U2hB36vo9Xr7F4/iytwRXF+VlGp0+AJLGjsEHTNNte8m6jgR0lyOlEJpPBMPSLpR9s22ZqaopSqUSxWMT3fZxYjJGRPI7tbBg3EUgkU1QroTao0WgwV1SMPOmF2ARQryGJNGVfmFtuMDWWYWlpoW0f+VwWWxSLD9/XttwwLcaOn9nZE9Zo9h+5zepYbJbFcguM0TnUvS1RXi9EJE3oP9Dks8BzlVLdVcg7zHYEgk8SqkuaNQLeB/ySiEwTeqg9E/iNwXRv9+ilWpwvhk55pmEQ1GsoFc7uDdNsm4XbloXQPSY2lQxjlS3LIp3JXDIP9EhKpAJ/gzAQrWHpwkNMnn5E13OyHYfJiQkuzMxsMB04tk02k8E0D59j2nbVyJZtk8lmSaUuJaSync7jZtkOo+MTzJyvraU2dl2XuaUCpmUxPjXNw7PL+M0shyJtZgDDMEjG4yydf2DDvgPfY+n8WcZPXIE1ZGmQhw1tMtBshojcQOfZficmlFJNyX3TRHmbUCF0pHeAawmd828RkRuUUjN99megbOdt8BvAR0UkppSqA/+X0GTwSsAnTGv8C4Pr4vDgr1MDJxIJzBZnM0ExkkuxtFLGNAwcx0IFilojjDYYy6fXYsjXz+a7Ua+UOwgDl6gUC+QiJ7ZOGALHjx1jZWWFaq2GiJDJZEglEohO57Fl+h03AFTA1JGjVMplKpGmIJlMkcpkKNW8tvup1nCJx+NrpqR0KkWl2D3Xlu+5+K6rBQLNgWaLYYcr29QA3EX/9XeaM/rLTpQX5dP5QvT130Xknwij594MvKnP/gyU7WQqnAFmWr77wI9En31LN1tjN1utYRhk1pWHtW2b0XyadCqJ5ysqNQ/DECbHLUxDsMyt15V3673D3bxGDc/zOoageY06hQsP4nku2fGjZMbGAEWttEKhMEcikyeRzWMeskyFg7Qr1xseCIgC02RN4+I1GhTnZ6iXV4mls2TT4e4b1RILizOMHDvDxGiGxUKJIFAUihWOTeXXBALLMnErja7HBXDdOjG2nz57q7iuTwCUqx511ycZt4jbJoYEQ5sXQfsQaDZDKTUL/OkWN7ud0I9gPY8hnBjftY1+XBCRc0B3te8Oc/j0xVtEREgmk1Qql7IBOrbN5ORUR8euQAkPzVWo1tvzExwbTzKScdiqH5/l9M5JYNmxrvHoSim8RugEV5w7j2HZZCeOEkumw3BJwwzbeK5OX7xFGq5Hqeozs1ij2giwTGEi5zCRFxzbBBT1cjiZqJeK1EvFtu2Xzz/A5BXXkk0nCYIAEcEQODI1xfzCAr4fYNp2T4FwN7UDdden1vB54MIqrZM22zK46lgGXHdohQLN/mWIww5vAn5HRK5XSn0ZQEQc4OXAx6OIuC0hIlcApwhz7uwJ2xIIRCQD/BjwDcAU8Gql1H9EyYTeAPy1UmrLEtJe0s3WqBQ5M5bjxOgYQeAjhkHDVZxbqHNisj1Vad31eXC2tEEYADi/UCEZN6OXRf/EU2lWRKDLDyOZG+lxTi02acti5OhJluYv4jZaPOUX5hibPIpKJIauLO9O0Y9dudHw8QIBgZil2jRBjYbH/EqDmaVL19HzFTNLdaquz6mJBCCMHj9Do1KisrK05kfQ1o8gwIm1X3MR4dj0dOhPECSolTo/VwzLwrR2b7yUgvvPr24wMrlewD3nilxzYjhN8tqHQLNDvAv4IeDvReRnCE0EbwKmgf/W2lBEzgIopU5H308RJvb7K+BewCPUNvw4sAy8bTdOoBPbSV08AXyGMB/BvdHfBIBSakFEXkOYNOjHB9fNvUME0gmbhqcIAsEwQMRkejxJuebh2JcuoVKKUrVz7QOA2aUaMXtrQoFCGJ0+xdKFBzcIBfkjx0G6myEMwwxPQCkyY0dYWphrFwbCTrN48QJHTpyOskkcbhqeT8MzuHdWmF0B04DTE8KxEUUyFmqEFNImDEB4mU9PJUjHoFEtU15ZJPA8LCdGfvoU9VKR8vKlKALTshHDxHVdfN8PNQSGEWmdFIX5WeKJJOnRCUpL7cWrxDAYmz69azkkfN9ncaXW1ePE8xXVhk/MOZxhrJqdQw1pvg2lVE1EnkuYb+f3gThh6uLnK6W+uMnmBeA+QjP7NOGT9zxhUqK3RhV794TtaAj+D3AEeCrwEDC3bv0HgeddZr+GipXlhbXwMQDHcRgZmyJum7iuhx0JBQ23981brXtsNay2qYYdP3El9fIqXqOGaTnEMzkMw+ypphURkrlRqitLGLaDW++ePre4vMTI+CTmIVf7lmsGt9yp8FuG8isPwv1zimdeA8mYsFrZGMZ5ajJO3PRYXV6mtnopOZXnNqiVV8lPHCWRHaFaDHMQ5I+dYaVYpLiysqYWtSyL8fEJbNuiXq1Qr1YYO3KMiZNXUSkuE/g+ViyBk0hScz0Su2Tm8RVUG53rbzSp1F3y6cOhYVqPUqpjXpEmuhLiwSTyPXhVH+1Or/u+Arx2Z3p1eWxHIHgR8HtKqS+1pC5u5X6G9GR70c35SCnVJgxAVL744nmOTh9vmzVt5jRoW/05Ffq+T+B7hN5qwuzsLA3XJRGPY1lx6p7PwoUZTNPk2PR0V6HAtB3SoxOIGFEFvu64jTqBCjgMc7xejmafv79dGGiyWg2FgmuOBBvsmrYlxCwQFbQJA60U5meZOH6K6mqB/NFTlMplVgqFtjae5zE7O8OxY8cwTBPTtFAI52Zmw4gW06JSrVMvhGaE8fFx0qlUZI5QGGLsiEBnGIK9yb0ds4bzztkNp8LAbfC6d38WK7ax2qGuhHg5qC0kJhpaX4N9xXYEgnFCU0E3AkL1yYGg+fC3bZtYLIbnedRqNYIgoFQuk2nJKW8aQsw2qHfRFEzm48Q3Uat6boNqqUh1tQBKEU9lmRgfY2Fxieq6mu2+71Or1XpqCSzbIZUfw/N6Z8M0TPNQpjFeT7FlohezIBmDhgflOjwwB2cmhHTCBi6NxUjaJvAa1CudhYEQhee6TJ65FgVcnF/o2nJ5eZlUNo9l2yxHGoRWp1YItT+mITSqZUrLCwSBTyKVJZHNIwhWl/wI28EyDCbycRaLG7M0hn0huiaHF10tUXMQ2I5AMEuUqbALjyc0JewrujkfiUjuxLFplNfAq5YxUjGs8TGKpRK1aplMOgXRvDrmmJw5muGec0X8dZJtLmWTTvZ+aHpug8VzD7TN5t16DaOwyPj0KS7OL+Ctc04rVyqkUqmeRZEsx0FJlEipS+nlbH700DsVqsjRLB2HJ5xwSRh1VK0MtkNgp7l9xgIxMA3IpSxWyqG/SDh5VhsqX67H83xStk2tVuvpPV2tVslmJ0GERqNz0bOJ8TFqK4vUKyUyY1PY8TTVeoNSuUYymUAN2OvfNODoaIKZpY2q8TNH0wxrwkvtVLi/GeIogwPJdgSCm4HvFZHfBtr00CLyVODVhDmZDwQiQvnsbQSt5WsNk9TJa3HszAbboGPBtSdzLJcarFZcTEMYz8eIWSaxHs6EnudRWl7oqNoPAp/i4iy57CiLy+0viM2qJ146D4Pxo8eZP//Qhh9ZMp3FjunZjQDZBDz9TB3v/G2468b88cevw5QMjm1xairJhYUqi6su1UZA2hGceJJ6pXvW0VgizEy5mT3ZMAws29lQvbKJbdsYKqBeXmVk+jSFUp3VFodFlorksinG8tk1/5bLJeZYjGYhl3aYK9RwvYBEzGQ8FwtNCkNqMtBoNP2znafFLwLfCvwXYREGBbxGRL4feBlwAfjVgfVwj1GB3y4MAAQ+5QfvIHv1E7HWPXAty8ICJnIOIxkHwwC7n+QDKqCy0r0EtlutkB07smF5NpsNtQimBSJdZ4WWZSGiOHLyCsrFAvVaFcM0yeRGMG1bZ7yLeNoZF+/8bR3HvPbw7WQf8UTAwrFNpsfjHB2L4wdgmwrfsyktL3bMLGk5Yb4It9EIX/iWtaGQUZNUOk1pdTVMk5xMUl5nLkglk1SLy8QzOUo1j9Xyxln7SrGMY9vkMsmBFbGKOeG9fnw8ia8UlnF4C2RpNAeRLafOizwrn0ZYiOF1hBOrVxHGXn4UeKZSatO0jfsepagvzeB3eaibZqgR6EsYAFB0fJG0H3LdzD6ZwKtVWHj4PuYe/Brl5QW8LoWMwj7Z2E5YFXF06iij41PEk6lDYyroh7jUNgoDTZSivji7NuaObRFzLJLx0PlPDJPR6ZMbMj868QQjU9MsnLuf5dmHQAWMj090PIRlWaRSKQqFArMzM4yOjm4wBxmGQeB7JLKjrKx210gsF1bxO3lIXiaWZRCzt5DCWaPZDipMXdzPR/sUDoZNNQQichKYV82KPoBS6mHgxSKSJSzbKMC9+1kQ2I43sl8tXVacrOu6qPDYCOEs0lufJ2CtfwZEceqWZZHLZLBNYXn24bCBUpQLCyCQGhlfq4DYiS3l4z+A9Bprr1rqua1fXe045o5t4Xnhy3r8+GmCKFLEtCzceo2lCw8R+B6+57Jw7n4mTlzJsePHWV5aolqtYhgGqXSaVCrF/FwYyev7Pkopjh07xkqhQKlcBqUwDAMnngKkpxe25/uH/jmpUxdrNP3Tj8ngAUINwJ8DiMgngV9WSn0iSs/4+R3s31Ajlr0tz3zXdak3GqysrKyVPx4dGSEzNsXyTGd/zFR+DNuyOXH8OG69SnlpnlKtsqFdubBIOrdp5U1NFwy7ty+F2E5XH4DWFNK1apnC/AUCz9uQUCrwPOqVMk4yTSweJ5PJoJSiXKkwc+ECwFpVxeXlZRquSzqVYvro0bXkRSoe2+BguqGvIjpwRLOv2WJxI81l0o9A4NKew+4G4I93pDd7yHa8kePjx9hqcYKG67K8tBTO9iI8z6NSqXDs6BGy40coLl5se4kkMiOk8mNYto3baFCYfbi7ZkIpfN/DQpsButFrrO1EOsz+2MV8Ex871lesf620StDDfFMvrxJLZWjU6ywvtSvWkqkUjuMwMzu7tmy5UGC5UCCZTDI+NgaGiakU6WScUqWziSObTh76hDg6ykCj6Z9+NQTfKiIfiDIswSGy2IhhEFpE2k/ZGTuG2DGsHuF+nfA9r00YaOX8zCwnT5wgkcnRqNVAKex4HBCslpeQiIGiu6lCttgnzSUCwyR16jrKZ+9g/ZjHxo8h6/wtag0fFZkwhTA8z7HNNru/k0iRzI0hpoUI1CslCHwMwyCXH6G87n7I5XJtwkArlUqFRuRs6BvCxHie+oV5XK9dW+DYFmMj2bVy2zuJ73sEgQp9XCRM722ZhzsvgWYwBJv4VWkGSz9Pi98Gfgd4STTbUMD7ReT9PbZRSqkDUklRyFz9eOpLswS1MmLZOKPTmLEEaouzL8/zKBZ7F8GqVCrkcrmuXv9iGCSyI5SX5zuuNy079DfQbAuzUYHF82RPXUu9uITfqGJYTiQMtCef8eo1xAvTUa/WFEsln1zaYTQbJ5HOsbp4kezENJ7YzC6X8KKXdioRZ2J8BN/3MUyDiclJFubnUUph2zaNRqNn/HWxWCTmOFiWhR/4TB+doFKpUa6Ebj7pVJJUMj6wkMNeuK5LsbjKSnF1rWpjOp1idCSvqx9qNPuMTZ8YSqnfE5E7gOcDR4HXEBY3un+H+zYUKKV4YGaJdDKHkxvBDxQXF0sYRoUTRzt7ivfC38QJMQgCPM9bcxYTFHZL1jnLskjnR6mVCvgdVNL5qeNaQ3AZuP/xYVS5AIaJffRK7HQeKsVQ+DtyBY2Gi6F8VLVI/fw9BPXQjyOVGSV35GoeWm5Qa/gcH4+TmzpOzRMWC+0ZDMvVGrXzDU4en1orjnX8xAnq9TpKqbDSYQ8C3ydQCtf1WFypM7tcJRW3SCeSYRbEFRd3sc41J/Jbrq65FVzXZWFxiXL5ki+LUorV1RL1ep2TJ47v2LE1Bx9F/z4Eh0ZlvcP0NYVQSt0C3AIgIq8F/lAp9ec71qshQqnwsz7W2/cDKtUaObt/h2URIRGPb1oIZXZxhdVSaBeOx2ymxrLYprEmGFi2w/jxKygXlqisLodldBNpsmOTiGW2ObdptkAQhMJALEn96KOpp8O8D5mjeYq1BoVzCyTiNpOOS+Oh29s3XV2iUf48J69+KnfPNPBVEieZ5sLDFzseyg8CCsUSo7k0TjSTbs6oa7XuRaggvEdMw8AL4GIhvJfKNY9yrT0EdnGlxuRIfMciSoIgaBMGWmk0eqfK1mj6QTsV7i7beXOcATrrqzsQhSa+Hfg1pdRd2zje0FIsVUgmYn2rRk3TJJ1OUygUCDqohG3bxvXVmjAAUKu7PHhhkRNHRmm1Ili2Q2p0jGRuNFzQIymRpk+Ujzv1COZGHs87/2qWL9z6IDHH4Buf0+BlLzqKZfmMZeK4D9zRefsgwJ97gPHsaYqVBum49FT9l0oVcpnUhh+haZrEHId6Y2PWShEhm81imiblWn19AEMbhXKD0Wxsq36vfdM0UWg0moPBdhITPaiU6jwt6EyC0MwwvdVjHVSOHDmyYRYfi8WYnJjk4mJnH4PZxRXcdbMuK0o0ZDuOFgYGgnCf81he99N38m+fX6JeDyiuevz1h87zxjffSsxOY0mA8rpXjvQLc2QTl+fZb9s2U1NTxOPtNcJM0+RoFHq4Hss0yCZtMkl7F+sK6NmbRnOQ2C3d8tDHPm0ngUkykei7lkAT13VZXFpibHQUMQx838eyLEzD5KGZRbwumeVc12/TKriuSxAoVssVgiAglUzg2HZHRzLXdXE9j0q5jBgG6agY0k4JEW7k21CpVmk0GjiOE14rwxiKhEhdS10bJm/9/Qfx/Y0vuoWlBn/34Qt8/3cd7esY2aSDaagwZ0CXaXwmncTs8va2bZvJiQkCFVZJNEwTyzTDKodms5iWhW0Jx8eTmKKo1SqIGBwdSVOu+XgBWKbgeR5KKWq1GrV6HduySKZSiNAxgZXv+wRBaBJr1OvYjkMqmVjrV5NUMsnSUqGv67FX6MREGk3/aGPzZnR539u2RaUixGKKdJ9X0fN9iqurNBoNLs7NISJhGtogYGrqSFdhYD2u67FUWGWleCmrXmGlhGPbHDs63vbQdl2X2YsXabSonwuFAplMhpH84D3BXdddO2bri1BEOHLkCMkhrguvEC7Mdrfff+STF3n5S4+RsJyuWgIzP0k9MLAIHUJH8xkWlzdqfUzDIJ1K9FT5N8cm1qWUsYHiqukM83NzNFqKYq2sFEil00yMjBIEAUEQcGFmps1ZcXFpiYmJCYi3v+R936fRaHBhpn38FkU4MjXZ1i/DMDrWWgBwHK2x0lw+utrh7qIFgoiuJXEDcslEmnq9gh8ECJCIJ1Aqzh/92UP8xA/2qgS94RhtCYWUUvhRtrl6vUYy4VCpdn7RWKa5po2o1eptwkCThutycX6ZqYkRbNvG81wWFhbahIEmq6urJOLxHdESrBcGIDzX2dlZrjhzZuDH2ypdk9Wo3slqavUAz1fY01fReKiDH4Fh4ExdgWXFiTkGYJLNpLBMk8VC8VLYYTLGaD7LxfklYo7NxFgOq0eq6R5nQqGw3CYMNCmXSiTicVLJJDOzsx0jF+bn5zl+vD0SIBQewvEzTZNsOoljWwRBwNLyMlOTk2ttbdtmfHwMp2h3DDscBnRiIo2mf7RAsAnVms8fvuc83/niY6RSFn6g+NBH5/ngR+7i+15xikS8fxW4bVkkk0kqHaIMVotFxicmeajauRzE5GgGwzRoNFyWCt1zGVSqtbVZZxCojsdqUigUiA9YKCiVy12l+mGX9kUgmTCpVDunBH7iY/N4vkvJSpI+/Vi8mXvXwg7N9Aix49cQWE4kDESocAwmxnKYUZKrSrXGzMUFfD+g0XAZG9n+u2l9UqNWVgoF4rHYmtDZieLKCiMjI2s+LeH9o8hn02TiDvWFc3i1EoZlMzZyFIL2xKW2bTEykiObDdMvNzMjap8WzSDYLARXM1i0QLAJpinc8u+L3PLvi23L81mbZ3/dOMjWXnLJZBKzUNjwkPZ8n2q1wqnpcWYXVqhHDoS2ZTI5miERszFNM3yJuJ0rLDZp5jrY7AXc6JFadzsEQdBRG7FfEIFXfccJ/vC9ZzesMw34vleeou5WqNYVMpohd9UT8d1GaPqxbKxYvOM+S+UqpQ4liptsV1Da7GHpdqnE2UrDdduO36jXSacSpAyf8gO3ri333QZ+9V6sdB7z+CMwWxI0GYaJYey9b4hGo7k8tECwCfGYwY3PmuBTn5mnaeK/9uoMP/8jV5BP1JEtXkLbtpk+epS5+Xnq9UuVDVPJJLlsFtu2OTE1QkCYD9cQQQxZm8EJoaah18vcjLzQN8tjb1vWQGftO+mouFu86PlHUAre97cPU400BVMTMd78w4/g2JSDaYX2/HJplYqhsCwL27bbUku3ohSYptGzDPF26w10ijZoxerDgdO2rLbj245DxopTuf8rHdt7pQJ+tdQmEGi6o5TakHckHo8f+hoT/aLzEOwuWiDYBEHxpu8/w/e+4jTlqk/cMUkmhLRZobE4S/LY1Vvep+M4TE1OrmWla3qNN1+mdg+HLNuxyefTzM0vd1wfjztrFe5EhHg83jXRTTaXw/M8ZIA5DNKpNMvLnfu2H4gnTG58ziTPeeYklYqHYxskEwZeY4mF6JqnUimyuRyl1VWK9TqGaZLNZonFNuakME2DfDbD4vJKp8ORSia2XZFQgEQi0TXRVS6XwzCMNcfVbm1aQ2BTyQReqdC1uBNAbfECRiLdlsZZ05nAbfC6d38WKxY60/punT//wWeRGGLn2mFC6VoGu8puCAQN4F+AffmWUBjcNdMyG294UIKJbJzp/NFtz7Av5wWcSiRIp2ob1NCWZXJkYuySYBGFrl2YmcFbpz5OJhJYpsnszAyJZJLx8fGBCAWGIUxOTDA3vzF31dTU1GXvfydRwFceqLR7/jd8csDxkRHqtRni8TipdHqtTHGTWrXa8TqaZuhYWK3VqVTbBTPbtpgY336kh2XbTIyPdx3fdDoFCEemppiZnd1wr46OjnbUMojqXVZZ+T46B0H/mOtqYGg0w8q2BAIR+TrgjcDVwBgbg/OUUurK6J9l4DmX08m9pJvGar7oM5lLETN3X8li2xbjo3lG8llWiiWUUqSSCRLx2IY8BE0TRb1ep1wuIyKkUik8z2N+bg6AaqVCvV4fiEBgWRbxRJwTx4+zWirhui6ObZNOZzB2L2POtggC6CTfrZR9xjMOjuOQy+fXrtt6ul1H27aYmhjB9XyKq6HTZTqVJB5zLrsAUafxzWQyWJa1FrkgIpw4fpxSuUy9XseKNBqdNEO2bSOJ3mH6ViqHYWjlombn0SaD3WXLv2oReTXwbsAFvgY8NOhOXS4i8ovAdwDXAt+tlPrLnTjOTMHnTMzcsdSwvWjGececSzHh3QhDEL21JDnz8/ME65waiysrHVXe26GZ7GZsNIyD38zWvR+4uOJzcnQENik+1O062raNbdvEY6EPwiCvSXPfyWSy475N08Q0TUby+b7GQ0wLK5nFq3SIZhGD+NgRDF0vQ6M5cGznV/1zwN3AjUqpC5s13iPuAd4E/O+dPIjnq85Tyl2k3xeL53mUVle7rveDYEdO5SAIAwCep4hZBrKJqtz3/Z7XcSevRz/77qeN6cRJnbyGyrl7cUuXLH2GHSN18lr2RALWaDQ7znYEglPATw2xMIBS6v0AIvJzO3mcTMLs6BDmu24Yr+37YAiIiRnbWycip0u2uyaxWAzzYLy7d4R0XAjmHsDJT+A4TtfwylgshomPX6mBYYBhYjobwxGHHdOJkzh+FckgwHfrGKaFmDaBYWJuK4mSRqMZdrYjEJwDBuYhIyLHgZ8CnghcD6SA50Qll9e3TQNvJTQH5IHbgV9SSn1oUP3pF0NgMmtsmHEF9Sre0gzu7P1r6W3N9AixU49C2TEse2+ci0SMnhEH+Xz+0D/oe3k4TOeE4K6HaBTnGT19PbPzix3b5bJpKrd/BrwGiGCNHCF2/FqMPRYIt4MVCTJmPLm2TOsGNLuG2oIPgXY1GAjbmRP+AfAKERnUs+Eq4OVACfjEJm1vAl4B/DzwzcAdwE0i8sIB9WUDpgHJWPtlskzhuqMWzN2Halx6wXqNOu78wzTO3dWW694vLVO58z8Rf/NEMTuF49hMTE4SW1dBzzAMpo4c2cUKecOLYUA2uc7+bsAjjpjI3L3guwSVIjHb6nwdJ8bxZ+8LhQEApfCWZqje84W1jIb7iXrDp9bwqTf27r7VaDS7x3Y0BF8Evg34nIj8LvAAsCFOSSn16T7392ml1CSAiLwE+NZOjaKX/o3Ay5RSN0XLPgVcAbwNuHlrp9EnvscjMqv441mqrsIxBYc66tytKNNEWqIMDOVTnb2/834CD3f2AdSxq/dMS2DbNpOTk2FGwXod07SwnbBc7mHXDjS5+oiD23CpNBS2ATHTR124naAwe6mRW2MiHUeNj1OuuoBBKm7iPXgrfnFjuGVQXSWoljBiyQ3rhpG661OrB8ws1ag1fGzLYGokRiZpEbO1jkCzWyiCvvMQaBXBINiOQNA6i/9jNo6ERMv6enKo/jNPvBRYAT7Ysq0SkfcA7xSRRyqlOlSc6U5U5KQXOQD/vs+DYZJ04uC5+NEMMPaIp+CLuXaiQb3aM6GLtzKHfeQM7JFAAC0V9GKHKy6637E2Ahd56Msk3Rr4Hr5bb2tkZsfxi4u4D98Jdoz0tc/k8w8nuT5xJ0EHYaCJuzSDkR0feifLuuuxUGgws3TpvD3f5+xshXza5sRkYuiFgn7HWqPRtLMdgeB7Bt6L/ng0cEcHAeLW1vUAImITCiQGYItIHGhsQfi4hEQP8MBH1S4VkpFUHhK5do/yTVPOab38sGMlUsipR1G9/TMbhTvDxD52NY17vxR+d+v4D93KdP7JqEbvsW2Wuh52VCBtwkArhZLL1Ehs6AUCzcFB5yHYXbYsECil3rMTHemDMcK8B+tZalnf5I+A10T/PxN4L2FypFtaN1RK5Zv/i0jbuohnKDFM84onoma+RlBdRSwHY/IMwehxHlr2ODl+SdVuOHEwLejiK2CNHkWZWjW/F/Qz1kRarcCKkXjMs2g8fBf+8kUQMPNTOEevoPHwXajGpQyRwcocI0ddgvg0Mt/FXARY48e7rhsmlku9C17NrzRIxE2sIRZutjLWmuFG6WqHu8p+yy7SS1xcW6eUei3w2kEcMFCKB6oZjpx4EjFb8AOYWQ0onG+QSVioli4FYhI7fg31B2/fsB+xYziTJzHs3uF/mr3HjieABPbpx+Kc8lHVVfziArW7P3fJYbAFpRQXy3GO5abwVy5eWmHamIk0RjKLxFO7dwKXgb/JAzgI1NCXsdZoNNtjPwkEi7RrAZqMRn+XOqzrG6XUDeuXRbbIXKXmc3+HaL1s0sBscc+3nBhebpLE1XHq50KNAmJgjR7Fmb5qzanMdX0CBatVj4YbkIybJBwTQ4IdqRboui6+71GrVBAxSKZSIGAfUuGk11i3LrNicfx6lcYDt6K6RAlILEndN7FsG2fyUfiLOdyFc5jT1+LHMviGhWFCqVQByiSSqTBl8CZ5IfaKbNJmtovJIFxvYe+jxET9jvVuoasfbg1tMthd9pNAcDvwbSJirPMFeEz097bL2XkX1WJautj9TUMYzcYw16VwNVDUF2ewJ0+FsecKvJV5vMIcjBzBNx0qdZ/7LlTa6iTYpvCI4ynAHahQ4LkuCxdnqLfkH1henCebHyGby2EdQqGg21h3aqtMG/vEI2nc+4WO+7JOXEc2Y3MkLkAcmTyFjJ/mvpkq0zGT2soi1Upprf3y4gLJVJrR8YmuJZP3kpgtxB2DWmOjpsAyhVxq+Prci62M9W6gqx9qhpnhNQRu5CbCZETfsm75q4G7txph0C9hbHr7Sz/uGFxzIoUp7dKrX69SO3sb3vIs9YfuoHrPF6ne+0Xc+YdonLsbVa8QBHDv+cqGokmur/jauTKBGtyQuG6DpYX5NmGgSbGwTL3WfSaoCbEsCyM7hn36MaF/SBPTwj71aMzsGMn4pRmzr4SH5us4tuDVS23CQJNKucRqcQXf711VcC+wggZXTZpkEu1agETM5JqjDqbqnKFR0z/N6oeWE8Pcw4gjjWY9Q6EhEJFvj/59cvT32SIyDpSVUh+Jlt0MfAp4l4iMEeY/eA2hg9CLL7cPvVSLZ44kCFToT2CIIAJOpyp1gY+/2t1y4a0usVBPdnWEcH1FteETcwakklXhy6cbK8tLOLHY0Kqvd4qtqpHNWAI1doLEyFGUWwcU4sQJxMJcd+0CJSyXXK45FqcwV+jah9WVAulMFrOD+t33XHwlBIFCCINXQo2yXHZ1xF74vo+7cA5v4Rwnj1wFo2N4vsI0BakV8e+/B+v4NbCPsi4Om8mgG0qptSyi2oSg2SuGQiAA/mbd97dEfx8ETsNazoGXEKYufiuhtuAOwkRFH77cDvRSLS4slyisVtZCDFMJhyMTubVKg02U2zkt8Np6DMq13rPCSs0nPyCFZq+qfACNxuHUEGxHjWw5DuBASxrfTrocP1L9GNL7+ofrNoqGjYZLudpgbmkV3w+3d2yLqbE0nlsnnU7viJ8JAL5HUF1FeQ28c5HCzTDxg0v3bFApwsjUzhx/Bxg2k0E3arUa3/0HYS43bUK4xHYixTXbZyhMBkop6fI5va5dUSn1RqXUEaVUXCn1BKXUB3a4bywXK235BsrVBg+eX6ThtocXirXZTDvAsXtf8s3Wb4XN4t4tXcJ24BiRk2k0t+/aLpwBtq/3PZdSpc7M/MqaMADQcD0eni1gx+LMz8/jur1DA7eLmCaGva4QU9AuwMo+0g7sN0xbmxA0e4t+I0R0Uy0q1Vm16PkBpXKNbDp+6cVqWBjxFMFaAqNm0sYQK5FmMumwsNLZDmsIZBKDGxIRiMXjHX0IADK5PMY+8hgfFDupRjZEkYqbLJc8kuk0lVLnktOpdAZZV0DCV8L8cncTz+JymUwiQaMxWMfTJoZpYU+ewF14uHMDMbAyo53XDSn7xWSg2YgiDHPtt63m8tECQUQv1eJkLk4mboLnIqZF1VfMFxsUyzUyqUsSvRlPEr/icbgL57Ezo+DWwbAIAg+/VsFI5bEMmB6LcWGxXV0vwJXTya5FhmqNAKVCPwPLFAxRxDfxNbBsh7HJKS6eP7fBgS2eSJBKZzrasA86/aqR/VoV8erQqIXSlROPHjxCYNgEptPm7+G6DUTBmSMJ7jlX5sqjo7j1Oq7bLgDajkN+dAxrXf2IIFBtmoH1lKsNxvMjVMqrpFI7VBfBtHGmr6Zx4Z725SLEr7weZeyv+2W/mAw0ndGJiXYXLRBsgmEImYu3EyycpymHxlJ5Tl7zNJZqG9W+osBcnMH9/D+vqVslmcV+yjeC5xFLJRjPQT5tM1dohHkIYiYTeQfDANva+MCtNQK+dqHOcvmSHJxNGFxzTDZUYlyP48Q4cvwk1UqZarmMiJDJ5rBteyjD3oaFoLRCMHM//u3/Fgp2AHYc85FPA9vGXzyPPOLp1FSaeMzEbTSYmw8jOmzH4cqjE1TqAfmJo/henWppFQTS6SyxeHxb175pjNg8Rfb2MWMJGJvGyk/gzj1E0KhhJDLYE8cRw8TQKm2N5sCiBYKIrqpF38sFC+fa25YL+Ld+koknvgCzxevbLxfx7/hPgrNfbW9fKeJ++u9wbnwlkAnL59pwYsJAKRVWG+wyU681Ar76UJ1KvV0pVqwGfOVsncefiRF3egsFtm1j5/IkU2lEwDQP97D3o0YOFs7jf/mT7Y3cGv5XbsF68gsQ0yL46sfhsS/ANWLMzMzgeaFPidtocHHmPLF4HFMlyWQyJJPhjL6X34YhgmObNNzOjqeZdJxqpUImk9niGW8NMxZmapTj16ICD0wTc5+m3NYmg/2NTky0uwyFU+FQ0y1Nq9cgWDiH37jk4CVBQNAhbTEAgY9352fxW+LSLdPAtsyeavtqQ20QBpo0PMVKpf9YdsuyDr0w0A/B6jL+Hf/edb1/x2cxp64At4Esn8d13TVhoJV6rUZheQm3UceyrE2dOB3H4shE5/eUYQijuSSe7+2amcewLEwnvm+Fgf1AM3NhM3th63edIlqz2+i3Q8R2bI3B4nlk4iQQPjBVeaVn+eNg/hxmh1z4vVgqdS6U1GRxNWAs62PtM9vuXrLpWAdBOJZdUOUCEmV4lOXzBPneYXjlSiVMF90HjmVw+tgYc0urVKrhvZJNx0NhoFFnbGx8R3MRHDSG3Yegmbkw8FzsRJrAc3nduz+LGMauhx82hZKhCXlUWwg71LLTQNBPlsvBtNoTiGwyAxTLZqslkK1uXobNLhhg6LLKg6WfSn7NstiGxWZjamwyhq04joMDTE8Ya74CTd+BmJM+lE6gBx3TjiFitH8f4mqSmoOLFggitmNrNKevjuytUft4KsziVq92bG+cvA4SW5ucjGdNzs531xIcHbE2zTegaWfTsRYDmTyFmnuw4/YydYqguABAcORq7Fi8Y7sm6fTWbf6Oo9X0g0D7EGg0/aPfJJvRZUYm+Skk1f5MUXYM+/E30mnGKJlRzNOPxNyid7lpwLHRzn2YyBg4WqQbOEY6h/24Z0OnRFOWg/XIr8ObfQDyR/BiOcQwSaU7C3qZTAZDp6HVXAab+RU01wdBQKVSoVKpaP8DzbbQr5PNEAP7sTfgn72NoFxAnATmsWswx49hxNtjwc1YAm/kCPZzX453+7+jFi+AHcM89UjMKx6Lkcpu+fBxx+TEOOSSJg8teFQbATFbOD5mMZoyiW0SYaDZHkEihXPjK/Du/BzBzH0AmNNXYVz5OLwL9yInH40xegzDiuHYBmOjoyQTCVZWVnDdMHFQLp8nsc0QQ42myWZpjWu1Gt/5Wx/jT7//Gbzq9z6BYdn89Zte0NUXoLX8crVaJZFIrLXt149gt/wNdJTB7qIFgohezkfW6FEkmY0cVxQ4ia4Z/qx0FtJZeMKNSOQQo2JJDGf78dsx22QiB5mEoBCEzZMSabrTj6OZFUtCLIn1mGfCdU8FBCwbBZhXPgHfdHBa1DOWbZOxbeLx+FqCysNWNGoYGXanwn7ZLKWxGT1fTDuGYR0UAVRtITGRFhwGgWjVUkiXB8ezAXI5bW4cFCsrKw8ppU7tZR/0WO8O+2WslVIkX/DjyDbDK4NGBTEtxNwoAK5f1+278r22vyCU/+ltiAhKKVLf+BMAa8taUUqRfP6PUP7oO0g+7w2IaVH52G93rZjY+sxXSiEia8dp/d6LTu0GOd5NPw/T6i86x/fKACtKqfwgjn9Y0QJBD0TEI/SzKEaLmjOL7gnn29msfa/13datX76V7823XfeYuq2z1WuystcviU6ISPOH0Lw262eRpXXLTMCP/rZu143tjGen/7sxDP1Y34dhHWuPS+PXz7WF/q5vp2vbz3Xd6+M2lzWvyXaPO7DxFpEH2brj51Deb/sJLRBsgeZso5Pn8nba91rfbd365Vv5HkndDFKK3uo1GVa6Xbcm0fVrXXY98OXo76bXdDvj2en/HgxDP54BlPbDLG2L1xb6OLdO17af67rXx21Zdj3hPb3t42r2N9ojTaPRaDQajRYINBqNRqPRaIFAo9FoNBoNWiDQaDQajUaDdio8VOyEU+FhZ1iu6TD0Yxj6sFPs1bkdtuNq9hatIdBoNBqNRqMFAo1Go9FoNNpkoNFoNBqNBq0h0Gg0Go1GgxYINBqNRqPRoAUCjUaj0Wg0aIFAo9FoNBoNWiDQaDQajUaDFgg06xCRXxSRO0QkEJHv2uv+HARE5J0iMiMiRRH5qoi8aI/783XR+L55D459i4jURKQUfT6y233YKfbityMiMRF5t4icE5EVEfmUiDxql449VPe15vLRAoFmPfcAbwI+t9cdOUD8JnBaKZUFXge8X0Tye9ERETGAt7O34/tapVQ6+nzTHvZj0OzFb8cC7geeBowCHwY+sEvHHpr7WjMYtECgaUMp9X6l1MeA2l735aCglLpLKVWPvnpADDi2R935AeDfgLv26PgHlr347Silykqp/62UOqeU8oHfAa4UkbFdOPYw3deaAaAFgn2MiBwXkXeIyGci9asSkRu6tE2LyG9FKr6qiHxBRL51d3s8/OzUNRWR3xORKvAl4BPAHbvdj+gl8SbgF3sdeyf7EPHbIjIvIh8Tkcf205dBs1e/nV047tOBOaXU4m4cd6v3tWa40QLB/uYq4OVAifDH2IubgFcAPw98M+EP9yYReeGO9nD/sSPXVCn1BiANPB/4qNo8RehO9OP/Ar+plFrZZH872Yf/AZwBTgIfA24WkXSf/Rkke/Xb2bHjRur6PwR+dreOu437WjPMKKX0Z59+AKPl/5cACrihQ7sXRute2rJMgM8Ad3bZ9y3Ad+31OR6ka9rS7h+AF+xmP4AnAl9o7hf4U+DNQ3At7gKee5DGuddvZ6eOC8Sj475tmO9r/Rnuj9YQ7GOUUkGfTV8KrAAfbNlWAe8BrhWRR+5A9/Ylu3RNTeDKXe7HM4FHAnMisgB8F/A/ReRPdrEPnej3GANlr347O3FcETGBvwTOAz+5W8ftwKb3tWa40QLB4eDRwB0dHgq3tqwHQERsEYkT3hu2iMQjz3RNO31d08ge+8roryUi3wE8h3DGtWv9AP4YeARwffT5EPAOurxAdqIPIpIXkedLGCrniMiPASPA5wfQh51ir347fR8X+CMgQRi9cbkq+2G5rzV7gH7QHw7GgKUOy5da1jf5I6BKOKN8b/T/s3a0d/uTfq+pIgzJOgcsAm8GXq6UurXDtjvWD6VUSYWe6OeUUueAClBUSnXadkf6ANiEfgyLwCzwIuCblFKrA+jDTrFXv52+jisip4DviY6zLJfyOzxzJ4/Lzt/Xmj3A2usOaHaNXjOHtXVKqdcCr93pzhwQNr2mSqky8Ny97seGheE472oflFLzwJMGfNzdYK9+O/1c0wcJbfyDZFjua80uozUEh4NF2mcyTUajv4OYJR42huWaDkM/hqEPO8VendthO65mCNACweHgduC6DvbMx0R/b9vl/hwEhuWaDkM/hqEPO8VendthO65mCNACweHgJiAPfMu65a8G7lZK6WQiW2dYrukw9GMY+rBT7NW5HbbjaoYA7UOwzxGRb4/+fXL099kiMg6UlVLNwjE3A58C3hVlq3sAeA3wDODFu9nf/cCwXNNh6Mcw9GGn2KtzO2zH1ewj9joRgv5c3ofQyafT5+y6dlnCPOezhLnWvwS8ZK/7P4yfYbmmw9CPYejDQRvnw3Zc/dk/H4luAI1Go9FoNIcY7UOg0Wg0Go1GCwQajUaj0Wi0QKDRaDQajQYtEGg0Go1Go0ELBBqNRqPRaNACgUaj0Wg0GrRAMNSIyFkRuWWv+6E5PIjIDSKiROS1e92XYUdE3iAid4lIPbpmp/e6TxrN5aAFAo1Go9kiIvIc4HeBu4AfBF4FzO/g8a4XkbdooUOzk+jUxRqNRrN1nh/9fZ1SajcqAF4P/AJwC3B2F46nOYRoDYFGo9FsnSMAuyQM7DgiktnrPmj2Hi0Q9IGIvDayET5XRH5SRO6L7IZfE5HXbHOfjxeRvxGRi9G+HhaRvxCRKzfZ7htE5K9E5H4RqYpIQUQ+KiLP7tD2UdExzkfHmBWRT4nIN7e0iUeqyLtFpBLt76si8usd9ndjdKyCiNRE5FYR+cEO7Z4uIh+JjleLjn+ziDxtO9dqpxj0uPZz3iIyLSJvE5Evi8hy1O4OEflpETG79O95IvK/ROTBaMw/29yniDxbRD4jImURmRGR/9mhX2dF5BYReYKIfFJESiKyJCLvEZHJPs9NROT1IvLF6D5Zje6l53Ro+2oR+Vx0n5Sje/XPRGRiq9d0UAxqrEXktIgo4Hui7yr63NLS5qiI/L6IPCQiDRG5ICLvXH+t+70XROQtwLujr59qOeafNtdLFx8G6eCH1Nw2uq8+IyIl4MMt658kIjeJyEJ0je4WkZ8TEWvdfjZ9vmj2F9pksDXeCiSAPwTqwOuBPxWRe5VS/9bvTkTkRcDfAWXgj4F7CWccLwAeDdzXY/PXAqPAe4FzwDHg+4BPiMhzlFL/Gh1jDPhktM0fAA8C48CTgKcC/xit+13gddH+/h9gAlcDz13X5/8e7ec/gV+O+v584PdF5Eql1E9F7a4BPkZYGOUdwMXo3L4eeFy0/bBx2eO6hfN+LPAywjKz9wE28E3ArwBXAD/QYfe/Qjgu7wAc4CeAf45eZO8C3gn8GfDfgF8SkQeUUu9ft4/jwCcI77u/BZ5AOO5PEpEnK6Uqm5zi+4CXR9u+G4gBrwA+JiIvU0p9KLoOrwTeA/wr8L+AKnAyOsdJdtDO3ieXO9bzhP4C/x14ZvQ/hOONiJwE/oNwnN5FOMZXRcd5jog8SSm1Em3T773w98DR6JhvBe6Mlvd6TmzGk4BvA/6IcLyI+v/CqD/3Am8DloCvA36J0GzxHVG7fp8vmv3EXldX2g8fwpewAv4LcFqWHyN8qPzFFvaVJHyozAHHOqw3Wv4/C9yybn2qwzZTwAJwc8uyb436/N826c9S63Zd2hwlrHr25x3WvQPwgSuj7z8SHfcpez1uuzyufZ034ctIOix/X3Qdj3bo35fW9a85th7w5JblDjAD/Me6fZ+N2v/ouuU/Fi1/c8uyG6Jlr21Z9tJo2X9ft70FfIGwRG6zUNrfA0XA2uvx3amxjrb70/DxuWH5B6Pf9vF1y58UjddbLvNeuKFD+7dE6053WHeWjc+QZpXDG9ctjxMKtJ9eP34t98oN6+7Bns8X/dlfH20y2Bq/p5RqNL8opc4DXyOcUffLCwgl6bdF27ehlAp6bayUKjf/F5F0JKn7wGcJJfMmzVnIN4lItscuV4BHicije7T5dsIZ4btEZLz1Q6hqNIDnrTvui0Uk3utchohBjGtf562UqqroiSoijoiMRtfxnwmv45M6bPb7rf0jnH0D/KdS6vMt+24An+vS7yLw++uW/V60/KXdTwuAVwKrwAfWjX2ecPxPtxxzhVDo/WYRkU32uxcMYqw7IiI54EXAh4Daumt1lnDW/Q0tx97OvTAovqKU+vi6Zc8nnFy8G8iv6//NUZtm//t9vmj2EVog2Br3d1i2CIxtYR/NB89/bacDInKliPyliCwTPqQXCDUOLwRGmu2UUv9CaAZ4LbAgIv8mIr8oIo9ct8sfjbb7amRX/WMRebGItN4b10V/Px4dq/XzsWjdVPT3L6N2PwssSWiz/mkRObWd890lBjGufZ23iFgi8vMi8jVCrcsi4XV8X9RkhI209U8ptRz9+0CHtstd+n2/Uqq+bj/1aN9X9DyzcPwzhGrx9eP/lqhNc/zfSqg+/gAwLyJ/JyLfJ8PjtDaIse7GNYTP1O9l43Waj9Y3r9N274VB8bUOy5q/8z9hY9/vitZNwZaeL5p9hPYh2Bp+l+VbmQk126qtHlxE0oTqvBTwduCrhEJBAPwM6+z+SqnXSOgc+ELgGYS2558TkR9VSv1O1OaDkTPSC4FnAzcSPtD+VURujGZTzT6/mlAl3Yn7o/3VgeeLyFMItSHPIrQ/vkVEvlspddNWz3sXuOxx3cJ5/ybww8BfEfpizAEuoU3/V+kspHfrX7flHbvYZXk/5yiEL4Xv7tHmNgCl1D3RS+F50efZhHbqXxSRZymlLsfuPQgG8RvuRnMf76fFLr+Oasv/27kXOtHrWdLtGd/JZ6TZ/58CvtxluwtrB+3j+aLZX2iBYPe5O/r7eC7NrvvlecA0Yezzu1tXiMj/6bSBUuo2wof1r4lIntC08Csi8rtNdaUKQ6feD7w/UvP+CvA/gBcDfwPcE+1uoYOasSNKqc8Rqq8RkROEGpH/Q+iwdGDp47xfBXxaKfVdrduJyFU73LUrRcRpVZeLSAw4w6XZXzfuAR5BaKIobXagSDi6Ofo0HdX+Efhx4Ie21/19wb2EL2enz9/JVu6FXi/9ZujjKC05CiLT1dGoX/3Q/J2Xt/A73/T5otk/aJP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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "continuous_pairplot(\n", - " diff_df_melt,\n", - " vars=[\"n_classes\", \"n_samples\", \"n_features\"],\n", - " hue=\"delta_cohen_kappa\",\n", - " cmap=\"coolwarm\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6d7d3082-374a-4dea-b3b3-47a701780593", - "metadata": {}, - "source": [ - "# Analysis based on count" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "184a8391-6a07-4771-9138-682fffcf995e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taskn_samplesn_classestask_idn_featurescv_folddelta_cohen_kapparelative_rank
0segment23107146822160-0.0101010.0
1jungle_chess_2pcs_raw_endgame_complete448193167119600.0397951.0
2tic-tac-toe958249900.0000001.0
3pc11109239182100.1245721.0
4analcatdata_dmft7976356040-0.0266560.0
\n", - "
" - ], - "text/plain": [ - " task n_samples n_classes task_id \\\n", - "0 segment 2310 7 146822 \n", - "1 jungle_chess_2pcs_raw_endgame_complete 44819 3 167119 \n", - "2 tic-tac-toe 958 2 49 \n", - "3 pc1 1109 2 3918 \n", - "4 analcatdata_dmft 797 6 3560 \n", - "\n", - " n_features cv_fold delta_cohen_kappa relative_rank \n", - "0 16 0 -0.010101 0.0 \n", - "1 6 0 0.039795 1.0 \n", - "2 9 0 0.000000 1.0 \n", - "3 21 0 0.124572 1.0 \n", - "4 4 0 -0.026656 0.0 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "diff_df_melt[\"relative_rank\"] = diff_df_melt[\"delta_cohen_kappa\"]\n", - "diff_df_melt[\"relative_rank\"] = diff_df_melt[\"relative_rank\"].where(\n", - " diff_df_melt[\"relative_rank\"] < 0, other=1\n", - ")\n", - "diff_df_melt[\"relative_rank\"] = diff_df_melt[\"relative_rank\"].where(\n", - " diff_df_melt[\"relative_rank\"] == 1, other=0\n", - ")\n", - "\n", - "display(diff_df_melt.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "8cba3f8e-4595-4ff7-91db-983af1762909", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taskn_samplesn_classestask_idn_featurescv_folddelta_cohen_kappa
relative_rank
0.0201201201201201201201
1.0111911191119111911191119459
\n", - "
" - ], - "text/plain": [ - " task n_samples n_classes task_id n_features cv_fold \\\n", - "relative_rank \n", - "0.0 201 201 201 201 201 201 \n", - "1.0 1119 1119 1119 1119 1119 1119 \n", - "\n", - " delta_cohen_kappa \n", - "relative_rank \n", - "0.0 201 \n", - "1.0 459 " - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "diff_df_melt.groupby(\"relative_rank\").count()" - ] - }, - { - "cell_type": "markdown", - "id": "6f8afebe-0e7f-4a46-b933-6023d2c010b8", - "metadata": {}, - "source": [ - "# Analysis based on number of classes" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "aa97c16c-b289-41eb-8dbe-a156ce9d5583", - "metadata": {}, - "outputs": [], - "source": [ - "# compute descending order by median\n", - "order = (\n", - " diff_df_melt.groupby(\"n_classes\")\n", - " .median()\n", - " .sort_values(by=\"delta_cohen_kappa\", ascending=False)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "78e679cc-0829-4a2b-8433-8c8e6c3d0966", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\n", - " \"talk\",\n", - " # font_scale=1.25\n", - ")\n", - "fig, ax = plt.subplots(figsize=(16, 5))\n", - "\n", - "sns.stripplot(\n", - " x=\"n_classes\",\n", - " y=\"delta_cohen_kappa\",\n", - " data=diff_df_melt,\n", - " color=\"gray\",\n", - " # order=order.inde,x,\n", - " ax=ax,\n", - ")\n", - "\n", - "# distance across the \"X\" or \"Y\" stipplot column to span, in this case 40%\n", - "median_width = 0.4\n", - "df = diff_df_melt\n", - "for tick, text in zip(ax.get_xticks(), ax.get_xticklabels()):\n", - " sample_name = text.get_text() # \"X\" or \"Y\"\n", - "\n", - " # calculate the median value for all replicates of either X or Y\n", - " median_val = df[df[\"n_classes\"] == sample_name][\"delta_cohen_kappa\"].median()\n", - "\n", - " # plot horizontal lines across the column, centered on the tick\n", - " ax.plot(\n", - " [tick - median_width / 2, tick + median_width / 2],\n", - " [median_val, median_val],\n", - " lw=10,\n", - " color=\"k\",\n", - " )\n", - "ax.axhline([0], color=\"red\", ls=\"--\", label=\"No Change\")\n", - "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "\n", - "ax.set(ylabel=\"Delta Cohen Kappa (cy-SPORF - RF)\", xlabel=\"Number of Classes\")\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "5744e0b9-765f-4a45-b850-22cebf890d27", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\n", - " \"talk\",\n", - " # font_scale=1.25\n", - ")\n", - "fig, ax = plt.subplots(figsize=(16, 5))\n", - "\n", - "sns.stripplot(\n", - " x=\"n_features\",\n", - " y=\"delta_cohen_kappa\",\n", - " data=diff_df_melt,\n", - " color=\"gray\",\n", - " # order=order.inde,x,\n", - " ax=ax,\n", - ")\n", - "\n", - "# distance across the \"X\" or \"Y\" stipplot column to span, in this case 40%\n", - "median_width = 0.4\n", - "df = diff_df_melt\n", - "for tick, text in zip(ax.get_xticks(), ax.get_xticklabels()):\n", - " sample_name = text.get_text() # \"X\" or \"Y\"\n", - "\n", - " # calculate the median value for all replicates of either X or Y\n", - " median_val = df[df[\"n_features\"] == sample_name][\"delta_cohen_kappa\"].median()\n", - "\n", - " # plot horizontal lines across the column, centered on the tick\n", - " ax.plot(\n", - " [tick - median_width / 2, tick + median_width / 2],\n", - " [median_val, median_val],\n", - " lw=10,\n", - " color=\"k\",\n", - " )\n", - "ax.axhline([0], color=\"red\", ls=\"--\", label=\"No Change\")\n", - "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "\n", - "ax.set(ylabel=\"Delta Cohen Kappa (cy-SPORF - RF)\", xlabel=\"Number of Features\")\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "380881eb-c14a-4c7a-b785-4a244d7acdab", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\n", - " \"talk\",\n", - " # font_scale=1.25\n", - ")\n", - "fig, ax = plt.subplots(figsize=(16, 5))\n", - "\n", - "sns.stripplot(\n", - " x=\"n_classes\",\n", - " y=\"delta_cohen_kappa\",\n", - " data=diff_df_melt,\n", - " color=\"gray\",\n", - " # order=order.inde,x,\n", - " ax=ax,\n", - ")\n", - "\n", - "# distance across the \"X\" or \"Y\" stipplot column to span, in this case 40%\n", - "median_width = 0.4\n", - "df = diff_df_melt\n", - "for tick, text in zip(ax.get_xticks(), ax.get_xticklabels()):\n", - " sample_name = text.get_text() # \"X\" or \"Y\"\n", - "\n", - " # calculate the median value for all replicates of either X or Y\n", - " median_val = df[df[\"n_classes\"] == sample_name][\"delta_cohen_kappa\"].median()\n", - "\n", - " # plot horizontal lines across the column, centered on the tick\n", - " ax.plot(\n", - " [tick - median_width / 2, tick + median_width / 2],\n", - " [median_val, median_val],\n", - " lw=10,\n", - " color=\"k\",\n", - " )\n", - "ax.axhline([0], color=\"red\", ls=\"--\", label=\"No Change\")\n", - "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "\n", - "ax.set(ylabel=\"Delta Cohen Kappa (rerf-SPORF - RF)\", xlabel=\"Number of Classes\")\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "9ab2a6df-489f-44f8-84d2-de7241d17ad6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\n", - " \"talk\",\n", - " # font_scale=1.25\n", - ")\n", - "fig, ax = plt.subplots(figsize=(16, 5))\n", - "\n", - "sns.stripplot(\n", - " x=\"n_features\",\n", - " y=\"delta_cohen_kappa\",\n", - " data=diff_df_melt,\n", - " color=\"gray\",\n", - " # order=order.inde,x,\n", - " ax=ax,\n", - ")\n", - "\n", - "# distance across the \"X\" or \"Y\" stipplot column to span, in this case 40%\n", - "median_width = 0.4\n", - "df = diff_df_melt\n", - "for tick, text in zip(ax.get_xticks(), ax.get_xticklabels()):\n", - " sample_name = text.get_text() # \"X\" or \"Y\"\n", - "\n", - " # calculate the median value for all replicates of either X or Y\n", - " median_val = df[df[\"n_features\"] == sample_name][\"delta_cohen_kappa\"].median()\n", - "\n", - " # plot horizontal lines across the column, centered on the tick\n", - " ax.plot(\n", - " [tick - median_width / 2, tick + median_width / 2],\n", - " [median_val, median_val],\n", - " lw=10,\n", - " color=\"k\",\n", - " )\n", - "ax.axhline([0], color=\"red\", ls=\"--\", label=\"No Change\")\n", - "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "\n", - "ax.set(ylabel=\"Delta Cohen Kappa (rerf-SPORF - RF)\", xlabel=\"Number of Features\")\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37165522-3eb4-4f21-9eef-1f7cdf29ed26", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "sklearn", - "language": "python", - "name": "sklearn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/validation/plot_cc18_short.ipynb b/validation/plot_cc18_short.ipynb deleted file mode 100644 index ee0b9a294..000000000 --- a/validation/plot_cc18_short.ipynb +++ /dev/null @@ -1,2105 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4c30e9a6-085e-4580-af32-02a8b9fcd2a9", - "metadata": {}, - "source": [ - "# Plotting CC_18 Benchmarks" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "95468c85-3f46-4220-8661-d9d79048df8f", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext lab_black" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ef1d7891-46dd-407f-b7e6-1d76848358c6", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "import numpy as np\n", - "import collections\n", - "import pickle\n", - "from pathlib import Path\n", - "\n", - "import pandas as pd\n", - "import openml\n", - "\n", - "from scipy.stats import wilcoxon\n", - "import dabest\n", - "\n", - "from sklearn.metrics import cohen_kappa_score\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "\n", - "sys.path.append(\"../\")\n", - "\n", - "# from oblique_forests.sporf import ObliqueForestClassifier\n", - "# from rerf.rerfClassifier import rerfClassifier\n", - "\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e55f9318-ddf6-40b4-a31d-a2798e082a60", - "metadata": {}, - "outputs": [], - "source": [ - "def continuous_pairplot(df, vars, hue, cmap, diag_kind=\"auto\", scale=\"log\"):\n", - " import matplotlib as mpl\n", - " from mpl_toolkits.axes_grid1 import make_axes_locatable\n", - "\n", - " vmin = min(np.nanmin([df[hue]]), -1e-6)\n", - " vmax = max(np.nanmax([df[hue]]), 1e-6)\n", - "\n", - " g = sns.pairplot(\n", - " df,\n", - " vars=vars,\n", - " diag_kind=diag_kind,\n", - " plot_kws=dict(\n", - " # The effort I put into figuring this out.....\n", - " c=df[hue],\n", - " cmap=cmap,\n", - " norm=mpl.colors.TwoSlopeNorm(vcenter=0, vmin=vmin, vmax=vmax),\n", - " ),\n", - " )\n", - " if scale:\n", - " for r in range(len(g.axes)):\n", - " for c in range(len(g.axes)):\n", - " g.axes[r, c].set_xscale(scale)\n", - " if r != c:\n", - " g.axes[c, r].set_yscale(scale)\n", - "\n", - " sm = mpl.cm.ScalarMappable(\n", - " mpl.colors.TwoSlopeNorm(vcenter=0, vmin=vmin, vmax=vmax), cmap=cmap\n", - " )\n", - " plt.colorbar(sm, ax=g.axes, label=hue, aspect=40)\n", - " return g" - ] - }, - { - "cell_type": "markdown", - "id": "99c91a17-7277-457e-bec6-f152ace8c2bd", - "metadata": {}, - "source": [ - "# Define Data Directories" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "3204cb28-0987-4961-b1e7-3d74400e5488", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/adam2392/Dropbox/sporf_benchmarks/cysporf_vs_rerf_vs_rf/results_cv10_features=None\n" - ] - } - ], - "source": [ - "# directory to save the output\n", - "data_dir = Path(\"/home/adam2392/Downloads\")\n", - "data_dir = Path(\"/Users/adam2392/Dropbox/sporf_benchmarks\")\n", - "\n", - "exp_name = \"cysporf_vs_rerf_vs_rf\"\n", - "# exp_name = \"cysporf_vs_rf_without_nominal\"\n", - "# cross validation\n", - "n_splits = 10\n", - "# hyperparameters of forest\n", - "max_features = \"None\"\n", - "\n", - "data_path = data_dir / exp_name / f\"results_cv{n_splits}_features={max_features}\"\n", - "print(data_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "9c2fce8a-0fb5-43bc-b9b1-b4e6160ff4f2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "71\n" - ] - } - ], - "source": [ - "# folder with saved results\n", - "result_files = [f for f in data_path.glob(\"*.pkl\")]\n", - "print(len(result_files))" - ] - }, - { - "cell_type": "markdown", - "id": "9900382b-1ac8-4313-ab32-9fd0fb2889ad", - "metadata": {}, - "source": [ - "# Load Data" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "69be4199-6464-4430-8b32-9b9adf8b55ed", - "metadata": {}, - "outputs": [], - "source": [ - "tasks = []\n", - "n_samples = []\n", - "n_classes = []\n", - "n_features = []\n", - "sporf_cohens = []\n", - "rf_cohens = []\n", - "rerf_sporf_cohens = []\n", - "task_ids = []\n", - "\n", - "rf_times = []\n", - "rerf_times = []\n", - "cysporf_times = []" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "1b070e30-d299-4f81-a3f6-391d9d99fe0a", - "metadata": {}, - "outputs": [], - "source": [ - "names = [\n", - " \"rf\",\n", - " # \"rerfsporf\",\n", - " \"cysporf\",\n", - "]\n", - "for fpath in result_files:\n", - " with open(fpath, \"rb\") as fin:\n", - " result_dict = pickle.load(fin)\n", - "\n", - " for name in names:\n", - " metadata = result_dict[f\"{name}_metadata\"]\n", - "\n", - " if name == \"rf\":\n", - " rf_times.append(metadata[\"train_times\"])\n", - " elif name == \"rerfsporf\":\n", - " rerf_times.append(metadata[\"train_times\"])\n", - " elif name == \"cysporf\":\n", - " cysporf_times.append(metadata[\"train_times\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "111cf49b-b929-463d-88a3-c9012c716569", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_keys(['train_times', 'test_times'])\n" - ] - } - ], - "source": [ - "print(metadata.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "f4489ecd-042d-4d05-b362-cf36c67052b1", - "metadata": {}, - "outputs": [], - "source": [ - "for fpath in result_files:\n", - " with open(fpath, \"rb\") as fin:\n", - " result_dict = pickle.load(fin)\n", - "\n", - " # number of stratified cross-validations\n", - " cv = result_dict[\"cv\"]\n", - " y = result_dict[\"y\"]\n", - " task_name = result_dict[\"task\"]\n", - " n_feature = result_dict[\"n_features\"]\n", - " # n_feature = _get_task_id(desired_task_name=task_name)\n", - "\n", - " # extract metadata of benchmark experiment\n", - " tasks.append(task_name)\n", - " n_samples.append(result_dict[\"n_samples\"])\n", - " n_classes.append(result_dict[\"n_classes\"])\n", - " task_ids.append(result_dict[\"task_id\"])\n", - " n_features.append(n_feature)\n", - "\n", - " # compute cohen kappa for both classifiers\n", - " for clf in names:\n", - " try:\n", - " fold_test_inds = result_dict[f\"{clf}_metadata\"][\"test_indices\"]\n", - " except Exception as e:\n", - " fold_test_inds = result_dict[\"test_indices\"]\n", - "\n", - " clf_cohens = []\n", - " fold_probas = result_dict[clf]\n", - "\n", - " # compute statistic on each fold\n", - " for ifold in range(cv):\n", - " y_proba = fold_probas[ifold][0]\n", - " y_test = y[fold_test_inds[ifold]]\n", - " kappa_score = cohen_kappa_score(y_test, y_proba.argmax(1))\n", - " clf_cohens.append(kappa_score)\n", - "\n", - " if clf == \"rf\":\n", - " rf_cohens.append(clf_cohens)\n", - " elif clf == \"rerfsporf\":\n", - " rerf_sporf_cohens.append(clf_cohens)\n", - " else:\n", - " sporf_cohens.append(clf_cohens)" - ] - }, - { - "cell_type": "markdown", - "id": "7045b5c5-eb9e-496c-88a0-15659168e7f7", - "metadata": {}, - "source": [ - "## Format Data Into DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "75c2bc68-76b0-4e4c-b7fe-b497a7cf8fb7", - "metadata": {}, - "outputs": [ - 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taskn_samplesn_classestask_idn_features
0Devnagari-Script92000461671211024
1segment2310714682216
2jungle_chess_2pcs_raw_endgame_complete4481931671196
3tic-tac-toe9582499
4pc111092391821
\n", - "
" - ], - "text/plain": [ - " task n_samples n_classes task_id \\\n", - "0 Devnagari-Script 92000 46 167121 \n", - "1 segment 2310 7 146822 \n", - "2 jungle_chess_2pcs_raw_endgame_complete 44819 3 167119 \n", - "3 tic-tac-toe 958 2 49 \n", - "4 pc1 1109 2 3918 \n", - "\n", - " n_features \n", - "0 1024 \n", - "1 16 \n", - "2 6 \n", - "3 9 \n", - "4 21 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "result_df = pd.DataFrame((tasks, n_samples, n_classes, task_ids, n_features)).T\n", - "result_df.columns = [\"task\", \"n_samples\", \"n_classes\", \"task_id\", \"n_features\"]\n", - "\n", - "print(result_df.shape)\n", - "display(result_df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "01746b41-8ea1-4409-a59f-c1aa813bedb3", - "metadata": {}, - "outputs": [], - "source": [ - "rf_df = pd.DataFrame(rf_cohens)\n", - "rf_df[\"clf\"] = \"rf\"\n", - "rf_df = pd.concat((rf_df, result_df), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "e8b74b94-514a-4384-babb-f44fdad3bb8c", - "metadata": {}, - "outputs": [], - "source": [ - "sporf_df = pd.DataFrame(sporf_cohens)\n", - "sporf_df[\"clf\"] = \"cysporf\"\n", - "sporf_df = pd.concat((sporf_df, result_df), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "928dda26-770f-4421-bb58-894c704d1f74", - "metadata": {}, - "outputs": [], - "source": [ - "rerf_df = pd.DataFrame(rerf_sporf_cohens)\n", - "rerf_df[\"clf\"] = \"rerfsporf\"\n", - "rerf_df = pd.concat((rerf_df, result_df), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "0fea08f8-042c-4d67-8b9e-b41b231329a0", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "operands could not be broadcast together with shapes (0,) (64,10) ", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# rerf - rf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdiff_rerf_rf_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrerf_sporf_cohens\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrf_cohens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdiff_rerf_rf_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiff_rerf_rf_arr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdiff_rerf_rf_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiff_rerf_rf_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (0,) (64,10) " - ] - } - ], - "source": [ - "# rerf - rf\n", - "diff_rerf_rf_arr = np.array(rerf_sporf_cohens) - np.array(rf_cohens)\n", - "diff_rerf_rf_df = pd.DataFrame(diff_rerf_rf_arr)\n", - "diff_rerf_rf_df = pd.concat((diff_rerf_rf_df, result_df), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "326510d4-0db8-44ef-b473-cd60ddda3c9c", - "metadata": {}, - "outputs": [], - "source": [ - "# sporf - rf\n", - "diff_sporf_rf_arr = np.array(sporf_cohens) - np.array(rf_cohens)\n", - "diff_sporf_rf_df = pd.DataFrame(diff_sporf_rf_arr)\n", - "diff_sporf_rf_df = pd.concat((diff_sporf_rf_df, result_df), axis=1)" - ] - }, - { - "cell_type": "markdown", - "id": "361b5afb-340f-415a-b8eb-b5238ec29f7e", - "metadata": { - "tags": [] - }, - "source": [ - "## Melt DataFrames for Seaborn Plotting" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "781b50ed-4801-4672-9077-552821137aa6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taskn_samplesn_classestask_idn_featurescv_folddelta_cohen_kappa
0Devnagari-Script9200046167121102400.058444
1Devnagari-Script9200046167121102400.005051
2segment231071468221600.023952
3jungle_chess_2pcs_raw_endgame_complete448193167119600.000000
4tic-tac-toe958249900.051339
\n", - "
" - ], - "text/plain": [ - " task n_samples n_classes task_id \\\n", - "0 Devnagari-Script 92000 46 167121 \n", - "1 Devnagari-Script 92000 46 167121 \n", - "2 segment 2310 7 146822 \n", - "3 jungle_chess_2pcs_raw_endgame_complete 44819 3 167119 \n", - "4 tic-tac-toe 958 2 49 \n", - "\n", - " n_features cv_fold delta_cohen_kappa \n", - "0 1024 0 0.058444 \n", - "1 1024 0 0.005051 \n", - "2 16 0 0.023952 \n", - "3 6 0 0.000000 \n", - "4 9 0 0.051339 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# melt the dataframe\n", - "diff_sporf_df_melt = pd.melt(\n", - " diff_sporf_rf_df,\n", - " id_vars=[\"task\", \"n_samples\", \"n_classes\", \"task_id\", \"n_features\"],\n", - " value_name=\"delta_cohen_kappa\",\n", - " var_name=\"cv_fold\",\n", - ")\n", - "\n", - "display(diff_sporf_df_melt.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "759d83b3-db69-4d08-83ab-57ab211f16db", - "metadata": {}, - "outputs": [], - "source": [ - "# compute descending order by median\n", - "order = (\n", - " diff_sporf_df_melt.groupby(\"task\")\n", - " .median()\n", - " .sort_values(by=\"delta_cohen_kappa\", ascending=False)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "29abe53a-fa41-41f6-ba40-d83a0959c11d", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/adam2392/miniforge3/envs/sklearn-dev/lib/python3.9/site-packages/numpy/lib/nanfunctions.py:1111: RuntimeWarning: Mean of empty slice\n", - " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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pvkqpd5VSLRxWfQWkYqTfmamUqu3wmHsx8sj7OrGpGX+13k5XSv3CGsi3jVb4L0bv9AzgXTOF+vq6KKW+UkpNUEqFltt+GEb6HYBtDquOWW87KKXqmqmjSf/GaFBqDdxlW6i1noHRmz4AmK+UeqV8PZRS8Uqpp4C9mPzeaK2LgTnWu69jzHdxBvih/LZKqXuVUi8rpVqWWx6FMdkzOL921eHP1tv7lVJNq7lsIYQQQojLToL6QgghhBCiRlkD8qMxgqe1MQLfF5RS+5VSm5VSRzGC03+0PmS7h6JsKXiCMCY+/aaC3X5lvX1BKXVIKbVWKbXGOrmpz7TWGqMX8nFgAJCilNqulDqAEeyvD3wHvFKV/XjhMettGTBHKbXe3V+5xzyM0aO6E7BZKXVSKbVJKbUTI0XSJuBRIMT2AK11PsbzzccIEp9WSm1VSqUCszCC6KeoYVrrpRiNOYEYn4OTSqktGIHjRzF6wk/WWlc0gbEnpl8X4CZgIZCtlNpr/RynYgSxawGfa63tEzhrrdMd1iVby1+jlPrSh/p6pLW+CLxnvfs7W+OH1QMYAfcA4GXgrFLqsLXuhzFGwvwbY+6KjT7s3tYrf4z19kvrKIHy6mJ8P5KUUqeVUluUUrsx3st7gCzgGR/275HWegXwM0Zj3AuVbC6EEEIIccWToL4QQgghhKhxWusCrfV9GOli3sXIfd4Q6A5EYvSAfw5oobX+1kMZh7g06efcinrCa61/xAhGbwEaAYMxeh83qIbnchTogdF7/ChGvvnGGEH9x4CbrT2XL4dmuJ9g1fbnWO98YDxGiqAlGAHOHhivySGMgO4QyuWlt6Yv6Q3MA4ox8tdfBB7VWldr8LUiWuuXMALGy4BQoBvGxLifAN211mZT79jK9eV1mQL8DziI0ZDTA2MkyiqMfPaOOe1tJmPMAZEN9LSW2c+XOlfidaAIo5Fiom2h1rrMOjltJ+BfwC6MSY57YEz2uwkj2N9Ka/0e5q3FaJyzme1hu7kYee6/s9azM0bP/hPAO0AXrXV199SHS731f6GUSqiB8oUQQgghLhtldDYSQgghhBDiymbtdZwEtACGaa3X+LdGQgghhBBCCHH5SU99IYQQQghxtRiKEdBPxugVLIQQQgghhBDXHQnqCyGEEEKIK551QlJbzv3/ahluKoQQQgghhLhOSfodIYQQQghxxVJKTQUeBNpi5C5PATpa86ALIYQQQgghxHVHeuoLIYQQQogrWSLGJLfhwHJgtAT0hRBCCCGEENcz6al/nVBKlWI04mT7uy5CCCGEEEIIIYQQQgghPIoCLFrrIHcrJah/nVBKWQAVHR3t76oIIYQQQgghhBBCCCGE8CArKwtAa63dZtpxG+kX16Ts6Ojo6MzMTH/XQwghhBBCCCGEEEIIIYQHMTExZGVlecy4Ijn1hRBCCCGEEEIIIYQQQoirhAT1hRBCCCGEEEIIIYQQQoirhAT1hRBCCCGEEEIIIYQQQoirhAT1hRBCCCGEEEIIIYQQQoirhAT1hRBCCCGEEEIIIYQQQoirhAT1hRBCCCGEEEIIIYQQQoirhAT1hRBCCCGEEEIIIYQQQoirRJC/KyDE5VRcXMzhw4cJDg6mVatWBAYG+rtKQgghhBBCCCHEZVdQUEB2djZFRUVorf1dHSGEuGYppQgNDSUqKorw8PBqKVOC+uK6kZeXx4cffkhmZiYAjRo14sEHH5TAvhBCCCGEEEKI68rFixc5e/asPdAUECCJHIQQoqaUlZWRkZHBxYsXqV+/PnFxcVUuU4L64rqxfft2e0Af4NSpUxw+fJj27dv7r1JCCCGEEEIIIcRlVFBQwNmzZ6lduzYJCQnS0U0IIS6DsrIyTp8+zdmzZwkPD69yj31pihXXjZKSEpdlxcXFfqiJEEIIIYQQQgjhH9nZ2SilJKAvhBCXUWBgIAkJCSilyMnJqXJ5EtQX142uXbsSHBxsv1+7dm3atWvnxxoJIYQQQgghhBCXV1FREaGhoRLQF0KIyywwMJDQ0FAKCwurXJak3xHXjfj4eB5++GF27txJUFAQPXv2JDQ01N/VEkIIIYQQQgghLhutteTQF0IIPwkICKiWycmv+KC+Uqoh0AyoC4QD54F04IDW2jWfihAVqFOnDiNHjvR3NYQQQgghhBBCCCGEEMInV2RQXyl1E3APMBgjoO9OoVJqC7Ac+FRrffpy1U8IIYQQQgghhBBCCCGE8IcrJqivlAoFngYeB5oCyrqqADgHXLT+H2f9qwcMsf69qpRaDPxJa739MlddCCGEEEIIIYQQQgghhLgsroigvlJqCvAq0BgoBhZZ/7YA+7TWFjePiQP6AIOAu4CJwASl1FfAb7TWJy5P7YUQQgghhBBCCCGEEEKIy+NKmRllBqCBp4AGWuuJWuuPtNZ73AX0AbTWF7XWy7XWL2qtWwEDgDkYAf4HL1vNhRBCCCGEEEIIIcRVSymFUoq6deuSm5vrdpsnn3wSpRQzZ86ssXq88sor9rrY/kJDQ2nRogXTp08nKSmpxvbt6OzZs9x3333Ur1+fwMBAlFIsWLDgsuxbVC4xMdHpMxIUFER8fDwdO3bkgQce4KuvvqKkxPM0pEopEhMTXZbn5ubyxBNP0LhxY4KCglBK8eabb9rXv/HGG7Rv357Q0FCUUkycOLH6n5zw2hXRUx8j5c5HVZn4Vmu9CbhbKdUeaFhtNRNCCCGEEEIIIYQQ17zz58/zzjvv8Jvf/Mav9ejatSvdunUDIDMzk82bN/Phhx/y5Zdfsnr1anr16lWj+582bRpLliyha9eu3HjjjQQGBtK0adMa3acw77bbbqNWrVporcnKyiI5OZlZs2bx2WefkZiYyOzZs+nfv7/X5f3mN7/hv//9L61ateKuu+4iODiYDh06ADBnzhx+9atfERcXxy233EJERAQ9evSoqacmvHBFBPW11v+rxrIOAAeqqzwhhBBCCCGEEEIIcW2z9Ux+/fXXefLJJ6lVq5bf6jJx4kReeeUV+/28vDzuueceFi1axKOPPsrPP/9cY/suLi5m+fLlJCYmsn37dgICrpQkH6K8f/7zny497lNTU/n973/PZ599xogRI1i/fr1L8P3AgQMEBwe7lLdw4ULCw8PZuXMnkZGRLusAvvnmG4YPH169T0T4RL6ZQgghhBBCCCGEEOK6FhoaytSpU+299a8kkZGR/Oc//wFg27ZtnDp1qsb2debMGcrKymjWrJkE9K9CTZs25dNPP+Wxxx6joKCAhx56yGWbdu3a0bJlS5flp06dol69ei4Bfds6gBYtWlR/pYVP5NsphBBCCCGEEEIIIa57L774IsHBwbz++usec+u7k56ezi9/+UtatmxJaGgodevWZeLEiWzZsqXa6takSRPi4uIAoze2TW5uLn/84x/p3LkzERERREVFMWTIELc58FNSUlBKMXToULKzs3n22WdJTEwkODiYV155hcTERJo1awbA2rVr7Tnby/cGX7hwIcOHDycmJobw8HA6duzIn/70JwoKClz2OXXqVJRSrFmzhmXLljFkyBCio6NRSgEwc+ZMlFK88sorHDx4kFtuuYW4uDhiYmKYNGkSx48fByAnJ4df/vKXNGnShLCwMHr27Mn333/vsr/CwkI+/PBDxo8fT/PmzQkLCyMuLo5Ro0axbNkyt6/t0KFDUUqRkpLCggUL6NevH5GRkcTFxXHPPfdw8uRJt4+zWCx8+umnDBs2jLi4OMLCwmjZsiVTpkxxO5piz5493HvvvTRq1IjQ0FAaNmzIgw8+SEpKitvyq+Jvf/sbtWvXZseOHaxfv95pXfn31Pb8tdYcP37cKV+/bZ6H1atXA9C8eXP7ujVr1tjLqM7PoU16ejrPP/88bdu2JSwsjNjYWMaMGcO6detcylyzZg1KKaZOncrFixd57LHHSEhIIDQ0lE6dOvHxxx97fK1SUlJ4/PHHadmyJWFhYdStW5cBAwbwj3/8g9LSUqdti4uLeeutt+jduze1a9cmMjKSPn368NFHH6G1ruAdqX5XRPodAKXU4KqWobV2fVeFEEIIIYQQQgghhKhEs2bNmDp1Kh988IHXufVPnDjBoEGDOH78OC1atGDSpEmcPHmShQsXsmTJEmbPns0dd9xR5bpZLBby8vIAY1QBGBPaDh8+nP3799O4cWNGjRpFfn4+Gzdu5NZbb+Wvf/0rL7zwgktZBQUFDB48mBMnTjBkyBC6detG7dq1uf3220lJSWHu3LnUr1+f0aNHA1CnTh37Y1999VX+8Ic/EBwczLBhw4iOjmbNmjW89NJLLFmyhB9++IHw8HCXfc6ePZsPP/yQPn36MHbsWA4ePOi0Pjk5mf79+9ufx65du5g/fz67d+/m559/ZsSIEZw4cYJ+/fpx4cIFfvrpJ8aNG8eWLVvo2rWrvZyUlBSmT59Oo0aNaNu2LX379uXkyZP88MMPfP/997z//vtMnz7d7Wv83//+l9dff51evXoxevRotm7dypdffsm2bdvYtWuX0/MqLS3lzjvvZP78+YSFhTFw4EDi4+M5fvw4X375JbGxsU5zH8ydO5fJkydTXFxMr169GDBgAElJScycOZNFixaxdu1aOnbsWOnnwFu1a9dm9OjRfPPNN6xevZqBAwd63Hb06NEkJibyySefEBkZye23325f161bN6ZMmcLy5cs5e/asPY8/QIMGDYDq/xwCHDx4kJEjR3Lq1ClatWrF2LFjuXDhAqtWrWLFihV89tlnTJ482aXMzMxM+vfvT1ZWFn369CE3N5d169Yxbdo0LBaLy8iFNWvWMGHCBHJycmjVqhW33HILubm57N27l1//+tdMnz6dmJgYwEiDNWbMGH788Ufq1q3LwIEDCQgIYOPGjTz00EP8/PPPvPvuu+beqKrQWl8Rf4AFKKvCX6m/n8OV/AdkRkdHayGEEEIIIYQQQly/UlJSdEpKSsUbHTtW8V9u7qVtS0sr376g4NL2xcWVb19UdGn7ggL321QjQEdGRmqtjdcnODhY16lTR+fk5Ni3eeKJJzSgZ8yY4fTYMWPGaEBPnz5dl5aW2pfPnTtXBwQE6Nq1a+szZ854VY+XX35ZA/rll192Wbd8+XIN6ODgYJ1rff1Hjx6tAf3b3/5WFxcX27dNTk7WLVu21IGBgXrPnj325ceOHdOABnT//v11Zmamy35s2wwZMsRl3aZNm7RSSsfGxuqdO3fal2dnZ+tBgwZpQP/61792esyUKVPs+/zqq69cypwxY4Z9/e9//3v78pKSEn3TTTdpQLdv316PGDHCqb6vvPKKBvR9993nVN758+f1999/ry0Wi9PyXbt26djYWF27dm2dnZ3ttG7IkCH2z8CqVavsy/Py8vSAAQM0oD/66COnx9j237VrV33ixAmndWfPntWbNm2y309OTtYRERE6NjZW//jjj07bzpw5UwO6T58+Lq+NJ82aNdOAPlbJ9+BPf/qTBvQ999zjtBzQzZo1c9ne03KtL71G7vZZ3Z/D0tJS3alTJw3od955x+m93LZtm46Pj9e1atXS586dsy9fvXq1vczbbrvN/h3RWusFCxZoQDdt2tSprPPnz+s6depoQL/99ttO6ywWi165cqUuLCy0L3v00Uc1oKdOnepU/tmzZ3Xfvn01oJcuXer29XPk1W+w1jo6OloDmdpTrNfTisv9Zw3qV+nP38/hSv6ToL4QQgghhBBCCCG8CihBxX9z5lza9vz5yrdfvfrS9ocOVb797t2Xtt+wwf021cgxqK+11tOnT9eA/vOf/2xf5i6on5SUpAEdExPj1ABgM2nSJA3ov/3tb17Vw11QPyMjQ8+bN08nJCRoQE+bNk1rrfX27ds1oAcNGuS2rPnz52tAP/PMM/ZljsHUrVu3un1cRUH9+++/XwP6T3/6k8u63bt3a6WUjo6Odgrs2oL648aNc7s/W1C/ZcuWuqSkxGndokWLNKADAwN1UlKS07qsrCytlPIYhHbnxRdf1IBeuHCh03JbwNqxUcFm7ty5GtAPPPCAfVlhYaGOjo7WSim9ffv2Svf79NNPu20YsLnllls0oHfs2OHV8/A2qP+///1PA3r06NFOy6szqF8Tn8N58+ZpQN9///1uy3zjjTc0oN944w37MltQPyoqSl+4cMHlMZ07d9aAPnr0qH3ZX//6Vw3oiRMnut2PozNnzujg4GDdsmVLXeTY6Gi1Y8cODehbbrml0rKqK6h/xeTU11oHVPXP389BCCGEEEIIIYQQQlzdvM2tv2HDBgDGjx9vT0ni6P777wdwyWlemf/7v/+z5y2PjY1l0qRJnD59mtGjR/PWW28BsHLlSgAmTpzotgxbuhV3ef0TEhKcUsN4y/Y87rnnHpd1nTt3pkuXLmRlZbFnzx6X9RMmTKiw7KFDhxIU5JwlvHnz5gAkJia6TOwaFRVFfHw8p0+fdilLa83atWv54x//yKOPPsqDDz7I1KlT7Xnhk5KS3NZh1KhRLsvatGkD4LSfbdu22dO7dO/evcLnBcZ7pZTilltucbu+oveqKrTRydc+f0FNqInPYVXK7Nmzp33uCUfu3sdVq1YB8Mgjj7jdj6O1a9dSUlLC2LFjCQkJcVlvSx1U3e9hRa6YnPpCCCGEEEIIIYQQ4gpw7FjF6+vWvfR/TEzl21tzbwPQvHnl2zdseOn/Hj0q376aNWvWjAcffJD333+ft99+m9/+9rdut0tLS7Nv745tMlJ3geeKdO3alW7dugHYJ1QdMWKEU1502+Sqzz33HM8995zHstLT012WNW3a1FR9bNLS0ggICKBJkyZu1ycmJrJr1y63z7eyfTZq1MhlWWRkpMd1tvXnz593WpaVlcUtt9zC2rVrPe4rJyfH7fLGjRu7LLM11hQXF9uXnThxAsClocGTlJQUtNZOcxO44+69qooLFy4AEBsbW63lOqqJz6GtzNtuu63Cfbsr0917CFV/H211evvtt3n77bc9budusuiaIkF9IYQQQgghhBBCCHGJNRjtlcBAc9sHB5vbPizM3PbV5MUXX2TGjBm8/vrrPPXUUxVuW909oSdOnMgrr7xS4TYWiwWAIUOG2BsP3HEXSA4LC6tK9XxS2T4reg3NvL4vvPACa9euZdiwYfzf//0fnTp1IioqisDAQN5//30eeeQRew/28gICzCUB8bZeFouFwMBA7rvvvgq3q86JcgF27twJQPv27au1XEc18Tm0lTlu3LgKG0LatWvnsszsewjevY+2OvXs2ZNOnTqZ3kdNuCaC+kqp24Hfaa17+LsuQgghhBBCCCGEEOLq1rRpU6fe+u40tI4osPXiLe/48eOAkWakutl6JN955508/vjj1V6+Ow0bNuTYsWOcOHGCFi1auKyvyefrrfnz5xMYGMiCBQuIiopyWnf06NFq2Yeth7mnND7lNW7cmOTkZP7zn//YRx/UtJycHJYvXw7A8OHDa2w/NfE5tJX5+OOPM3bs2Gop052mTZty8OBBkpKSaNWqlVd1Gj58OH//+99rrE5mXLV56JXhXqXUXuAroKu/6ySEEEIIIYQQQgghrg0vvvgiISEhHnPr33DDDQAsWrTI7fpZs2YBOKXNqS433ngjAAsWLKj2sj2xPY8vvvjCZd3evXvZtWsX0dHRdO7c+bLVqbyMjAyioqJcAvqlpaXV9lr17NmTmJgYtmzZwq5duyrd3vZeLVy4sFr2740XXniB3NxcevTowYABA2psPzXxObxcn+0RI0YA8P7771e67fDhwwkMDGTRokX2Xvv+dkUF9a2B+qlKqcVKqX1KqS1KqQ+UUh3LbXc3cAj4FOgAFAKeExoJIYQQQgghhBBCCGFC06ZNmTp1KhcuXODrr792Wd+yZUvGjBlDZmYmzz33HGVlZfZ1CxcuZN68edSuXZspU6ZUe9369OnDjTfeyMqVK3n++efJy8tzWl9WVsZ3331nepLeijz++OMopXj99dfZvXu3fXlubi5PPvkkWmseeeQRgoODq22fZrVp04aMjAzmzJljX2axWPjd737HoUOHqmUfISEh/OpXv0JrzS9+8QtOnTrltD49PZ3Nmzfb7//qV78iIiKCZ555hiVLlriUd/HiRf773/9WSz721NRUHnjgAd59910iIiL46KOPqlxmRWriczhp0iQ6dOjARx99xD//+U9KSkqc1hcXFzNv3jy3EzKb8dBDD1GnTh3mz5/Pu+++67J+1apVFBUVAcYolWnTpnHw4EGmTJlin6/A0YYNG1i6dGmV6mTGlZZ+Zy5gmwraltCoJzBZKTUa2AF8DdxkXZ8F/Bd4Q2t9HiGEEEIIIYQQQgghqsmLL77IzJkzPQZc33vvPQYNGsT777/PqlWr6N27N6dOnWL9+vUEBgby4YcfUr9+/Rqp26xZs7jxxht5/fXXmTlzJl27dqVu3bqcPHmSw4cPk56ezhtvvFFtIwX69evHK6+8wssvv0zv3r0ZPnw4UVFRrFmzhnPnztGvXz9efvnlatmXr1544QUeeOAB7rzzTgYPHkxCQgJbt27l1KlTPP744/z3v/+tlv389re/Zdu2bSxcuJBWrVoxaNAg4uPjOX78ONu3b+fRRx+lb9++ALRq1YrZs2dzzz33cPPNN9O2bVvat2+P1prjx4+zf/9+iouLmTx5MuHh4V7X4fnnn6dWrVporcnJySEpKYl9+/ZhsVho0aIFs2fPtk+4XJOq+3MYFBTEggULuPHGG/l//+//8a9//YsuXboQFRXFiRMnOHDgAFlZWcyfP79Ko0Li4uL4+uuvmTBhAo8//jhvvPEGPXr0IDc3l71793L8+HEyMjIIDQ0F4M033+To0aPMmjWLhQsX0r17dxISEjhz5gxHjhwhLS2NZ555pkZTBjnyOaivlKqPkfImHvDYBKe1/tTL8h4AJlrvbgC2AhHACKAlRk/8k8BojGD+P4B3tNbZvj2D6qOUigB+D9wFNALSgW+BP2itXZtu3JdRH5gAjAM6Ak2AfGA78K7Wem4NVF0IIYQQQgghhBBCeGDLrf/ee++5Xd+kSRO2bt3Ka6+9xsKFC5k7dy5RUVGMHz+e3/72t/Tr16/G6la3bl02btzI//73P7766iu2bt1KcXExCQkJdO/enVtuuYU777yzWvf5hz/8gS5duvDWW2/x008/UVRURIsWLXjqqad47rnnTAWla8L9999PdHQ0r732Gtu2bSMkJIQbbriBOXPm2CeOrQ5BQUHMmzePmTNnMmPGDDZv3kxJSQkNGzbknnvu4YEHHnDa/pZbbmH37t28/vrrrFixgmXLlhEWFkbDhg259957ue2224iOjjZVh7lzjVBhYGAgUVFRJCQkcO+99zJ+/HhuvfVWgoIuT1/umvgctm7dmh07dvDvf/+b+fPns379erTWJCQkMGTIEG699VZGjhxZ5boPGzaMXbt28Ze//IUVK1Ywf/58oqOjad26NU899RS1atWybxseHs7y5cv59NNP+fTTT9m1axebNm2iXr16tGrVimeffZZ77rmnynXylvI047PHByjVEqN3fGWvnAK01jrQy3JXYATwX9Ja/9lheQgwDxgLaGAjcJvW+qypitcQpVQ4sBboDRwFfsYIyncEjgF9tdbpXpQzC7gXKMFo0EgFGgMDMNIk/Vdr/UQV6pkZHR0dnZmZ6WsRQghxVcvJySE3N5cGDRp4Nbu9EEIIIYQQ1yLbZKbNmjXzc02EEOL64+1vcExMDFlZWVla6xh360012SilGmL0oq9rvW1j/X8WEAf0AuoDBRipdMrcl+RWV4we+H91XKi1LlZKvYQR1C8F7rpSAvpWv8cI6M/DqFspgFLq38BTwBvAfV6UcwF4AfhQa33RtlAp1Rv4HnhcKbVYa72smusvhBDXvDVr1rBu3Tq01tStW5f777+f2rVr+7taQgghhBBCCCGEEKaZnSj3N0A9jLQyg4EjAFrrKVrr8RipZx7ACOYnAA+ZKDsOSNZau5tC+ID19ojW+pSb9X5hHUXwBEbv+sdtAX2r/4eRhucepVSDysrSWj+jtf67Y0Dfunwrlxo6qnfMlBBCXAcyMjJYu3YttpFp6enpbNiwwc+1EkIIIYQQQgghhPCN2aD+aCAP+Je7lVpri9balkZmBPC8ibIDMXr4uyu30PrvRXfr/WggEA2sKz96QGtdBCzCeI3HVHE/u6y3DatYznUtKSmJ+fPns2LFCnJycvxdHSHEZZKVleWyTFKRCSGEEEIIIYQQ4mplNqjfGEjRWtuC72Vg77Fup7VeBJzACO5fy7pab7d7WG9b3qWK+2lhvb2S0g5dVY4cOcLnn3/O7t272bhxIzNmzKCszEx2KCHE1apJkyZERUU5LevYsaOfaiOEEEIIIYQQQghRNWanQS4EihzuZ1tvE4Dj5bZNB9qZLL+pUuoPvq7XWv/R5P6qqon19qSH9bblTX3dgVIqGHjcendRBdtlVlKUuSm0rzG7du1yup+RkUFqairNmzf3U41qTnZ2Ntu3b6esrIxu3boRHx/v7yoJ4VeBgYFMmTKFdevWkZOTQ+fOnencubO/qyWEEEIIIYQQQgjhE7NB/VMYAXybQ8A4YBAOQX2lVG2MSXRLMacJ8HIF65tWsv5yB/VrWW/zPazPK7edL14F2gNbMCYfFj6IiIhwWRYZGVnlctPT0zl58iSNGzembt26VS6vqgoKCvjggw/Izc0FYMuWLTz66KPExsb6uWZC+FdcXBwTJ070dzWEEEIIIYQQQgghqsxsUH8L8IBSKs46oetC4Dng70qpc8AGoAFGzv1IYJmJstcB2mR9/E1Zbz3VW3lY7l3hSt0D/BrIAu7zMImwUQGtYyopK5PruLf+gAEDOHToENnZxuCS7t27U69evSqVuWPHDr799lv7/ZtvvpmePXtWqcyqOnjwoD2gD1BcXMyuXbsYOnSo/yolhBBCCCGEEOKKoZTCYvEYXhBCCFGDtNYEBJjNiO/KbFD/W+BB4GbgU631eqXUN8AdOAfwFcakty95W7DWeqjJulwJbLOteurybesenuthvUdKqZHATKAYmKi1PmK6dsIuJiaGp556ipSUFGrVqkWDBg2qXObq1aud7q9Zs8bvQf3g4GCXZSEhIW62FEIIIYQQQghxPQoKCiI3NxetNUpVqS+iEEIIE7TWFBcXU6tWVZK6GMw2CyzCSJGzwGHZvRjB+8NACUae/cXADVrrHVWu4ZXthPW2sYf1jay3qWYKVUr1BeYDgcDdWus1PtVOOAkKCqJVq1bVEtAHKCkpcbpfXFxcLeVWRbt27UhIuJQhKyYmhm7duvmvQkIIIUQVWSwWSkvNZnQUQgghhCeRkZGUlZVRWFjo76oIIcR1pbCwkLKysmpJCW6qp741/cupcstKgdesf9cb2+yr3Tys72G93eNtgUqpjsBSjDz807TWC3ytnKhZvXr1Yv369U73/S0oKIhp06Zx+PBhysrKaNOmjfTUF0IIcdXasmULq1evpri4mC5dunDzzTcTGBjo72oJIYQQV7XIyEgCAwNJS0ujSZMmcs0ohBCXQXFxMWlpaQQGBlZLUF9pfXWlsVdKfQ301Fq3vALqEgKkA2FAY611erl1J4A61nWnvSivObAeaAj8Wmv9j2qsa2Z0dHR0ZmZmdRV51SosLOTQoUOEhobSunVrn4MDWmv27dtHamoqTZo0oVOnTjJ0UQghhKgm58+f5z//+Y/TsrFjx9K7d28/1UgIIYS4duTl5ZGaaiQVCA0NlUZzIYSoQWVlZRQVFQHQtGlTr4L6MTExZGVlZXmaR9VsTn0nSqnWQFugNkZ++cNa68NVKdMLCUBiDe/DK1rrYqXUf4HfAP9VSt1jHbkA8DegHvCFY0BfKfUX4FbgHa31Ow7L6wMrMAL6f63OgL4wFBQUsGXLFjZu3Gj/IjVp0oSpU6f6NEGFUopOnTrRqVOn6q6qEEIIcd07fdq1P0RaWpofaiKEEEJceyIjI2nVqhWZmZkUFBRwtXX4FEKIq0lQUBC1a9cmJibG7XyYPpXpy4OUUg8Dvwaau1mXAvxda/1e1ap21XgVGAXcDnRXSv0MdAA6A8eBZ8ttn4DREFKn3PL/Aa2AfCBBKTXTzb4Oaq3/Wm01v44UFxfzwQcfkJGR4bT8xIkTJCcn07p1az/VTAghhBDuNG3alICAACwWi31ZixYt/FgjIYQQ4toSHBxM3bp1/V0NIYQQPjAV1FdKBQCzgTsAhTEx7kngLFAfY8LY5hi91kcAd+lrvLlXa52vlBoC/B64C6MX/nmMIP1LWuvzXhYVa72NAKZ42GYtIEF9Hxw4cMAloG9TfsJbIYQQQvhfdHQ0d9xxB6tXr6awsJCePXvSuXNnf1dLXOcKCgoIDQ31aZSnEEIIIYQQ1cVsT/0ngTuBXOD/gPe11jm2lUqpWsAjwB+A24CngH9XT1WvXFrrPOC31r/Ktp0KTHWzfGh110tc4inXfUxMjPTSF0IIIa5Q7dq1o127dv6uhhDk5eUxZ84cUlJSiIyMZNy4cbRv397f1RJCCCGE8Ltz586xatUqsrKy6NSpEzfccIPMOXkZmA3qTwc0cKvWelX5lVrrXOB1pdROYKV1++oO6ivrnxBea9++PT/++CPnzxsDJ4KDg+nbty/9+vWrtlxWQgghhBDi2rR69WpSUlIAI8C/YMECWrZsSUhIiH8rJoQQQgjhR2VlZcyaNYucHKPP99mzZ+0xN1GzzI4bbQkcdRfQd2Rdn2TdvlpprQdqrWW8qzAlODiYhx56iDZt2hAWFkZ8fDzNmzf3arZpIYQQQghxfTt79qzT/eLiYo+pHYUQQgghrhdpaWn2gL7N4cOH/VSb64vZ4HgWkOnlttnW7X2ilHpAKXWTl9veqJR6wNd9ievD3r17OXz4MIWFhZw5c4Yvv/ySgoICn8rKzs5mxYoVLFy4kOPHj1dzTYUQQgghxJWk/CTNUVFRMrmkEEIIIa57cXFxLnMN1alTx0+1ub6YDer/AHRSSsVXtJFSqg7QEaiwR38lZgK/83Lb3wIzqrAvcR04evSo0/2SkhJOnDhhupySkhI++ugjNm7cyM6dO/nkk08ksC/EVaK4uJjTp09TWlrq76oIIYS4igwePJh+/foRHR1N8+bNueeee2SyXCGEEEJc9yIjIxkzZow9tXXjxo0ZPHiwn2t1fTCbU/8lYDTwlVLqHq11evkNlFJ1gS+APOv2VeFt7nzJsS8q1aBBA/bv32+/r5Sifv36pss5duwY2dnZ9vtaa3bt2kWzZs2qpZ5VtWPHDg4ePEhcXBwDBw6UFENCWB06dIj58+dTVFREZGQkd999N40bN/Z3tYQQQlwFAgMDuemmm7jpJq8GEgshhBBCXDd69epFly5dyM/PJyYmxt/VuW6YDeoPBP4L/Bo4rpSaBxwAzgF1gfbAbRgjAP4ODFJKDSpfiNb606pU2o0GGI0IQriwzbgdFBTExIkT6dChA4WFhXz//fe8/PLLTttqrSstLzw83GVZbm4uOTk51K5du3oq7aMtW7awbNky+/3jx4/z8MMP+7FG1c9isUjPOGGa1pqlS5dSVFQEGJMcfvfdd0ybNs3PNRNCCCGEEEIIIa5uISEhhISE+Lsa1xWzQf2ZgMboGR8MTLbet3HsMf9iBeW4BPWVUk2BxHKLo5VSFY3ZCAeGAG2AzRVsJwSlpaXMmTOHkJAQSktLsVgsPpXTpEkTOnTo4NTr/8iRI/z73//mvvvu82uP/b179zrdP336NOfPn78m8pnl5+ezYMECjhw5QlxcHOPHjycxMdHf1RJXibKyMqcRNoBMcCiEEEIIIYQQQoirktmg/qc4B/Gr04PAH8ot6wSsruRxtoaEN6u7QuLaVFxcXOUy7rjjDpKSkpg9e7a9d39paSnr1q3j/vvvr3L5vio/UiAwMJCIiAg/1aZ6ff/99xw5cgSAixcvMmfOHH75y18SGBjo55qJq0FQUBCtW7e2f4YA2rdv78caCSGEEEIIIYQQQvjGVFBfaz21huoBkAKsc7g/BMgGdnqqDlAAHAW+0lqvr8G6iWtEcHAwbdq0wWKxcPjwYcrKynwuq169ei7pemypPfxl6NChpKamkpubi1KKYcOGXTNB/bS0NKf7eXl5ZGZmEh9f4bzdQthNmjSJ1atXc/r0aRITE2XyHiGEEEIIIYQQQlyVzPbUrzFa60+AT2z3lVIWYI/Wepj/aiWuBbbAe0ZGBu+++y4lJSUAxMXF8eijj9pn6DYrKiqKNm3acPjwYfuyXr16Vb3CVVC3bl2eeeYZTp48SWxsLNHR0X6tT3Vq1qwZZ8+etd+PiooiNjbWjzUSV5uwsDDGjBnj72oIIYQQQgghhBBCVEmVg/pKqVpAbSBHa51b9SrZDQOyqrE8cZ2bN2+ePaAPRgqXffv20a1bN5/LvP3229m+fTvnz5+nbdu2tGrVqhpqWjVBQUHXZK754cOHU1hYyKFDh6hTpw5jx46VCXOFEEIIIYQQQgghxHXHp6C+Uqoj8BxwE9DAYfkZYDnwhtZ6r4eHe0VrvbYqjxeiPHeTYpafONOs4OBg+vbtW6UyhHdCQ0O59dZb/V0NIYS4YhQWFrJ8+XKSk5Np0KABY8aMIS4uzt/VEkIIIYQQQghRw0x3c1VKPQFsB6YACRgT1dr+EjAmvN2mlHqqKhVTSjVWSk1SSnUpt1wppX6tlDqilMpTSq1VSnWryr7E9aFFixZO95VS9OjRw0+1EUIIIapm+fLl7Nq1i9zcXJKSkvjmm2/8XaVqdeLECZKSkqo0/40QQghxrbJYLOTn5/u7GkIIIfzEVE99pdQY4G3r3SXAO8B+4CxQH2gPPAWMA95USh3RWi/3sW5PY4wGuAvY7bD8/wF/wWhEABgErFJKddVan/RxX+I6MHbsWAoLC0lKSiIiIoLx48dTq1Ytn8o6fvw4Z8+epUWLFtSpU6eaayqEEEJU7ujRo073z5w5w7fffsuYMWN8ni/mSqC15ssvv7TPWRMfH88vfvGLa2bidyGEEKKqjh07xoIFC8jOziYhIYE777yTmJgYf1dLCCHEZWQ2/c5vAA28oLX+Z7l1J6x/K5RSzwH/sG7va1B/GFAMfGtboJQKBJ631uFJYIN1H3dblz/r477EdSAsLIzJkydXuZzvv/+eDRs2AEZv/zvvvJN27dpVuVwhhBDCjISEBHJycpyW7dixg/DwcEaNGuWnWlXdsWPHnCahv3DhAj///DODBw/2Y62EEEIIKCoqoqSkxOfOYdXBYrEwf/58+znA6dOn+e6777jrrrv8VichhBCXn9n0O92Bc24C+uX9C6P3flVymzQGTmqtix2W9QfqAEu11u9qrXcDjwAFGPn9hfDKxYsXSU1NxWKxmHpcUVERmzZtst/XWvPjjz9Wd/WEMK24uJg9e/awf/9+SktL/V0dIcRlMGbMGOrVq+eyPDk52Q+1qT7uUgnk5eX5oSZCCCHEJT/++CP//Oc/ef3115k9ezbFxcWVP6gG5OfnuzTqnzlzxi91EUII4T9me+qXAqmVbaS11kqpVKC1T7UyxAHHyy0bhNFLf4nDvnKUUklV3Je4jqxcuZKffvoJgNjYWKZMmUJ0dLRXj9VauzQESK5f4W/5+fl88MEHZGZmAtCgQQOmTZtGUJBPc6ELIa4SMTEx/OIXv+DNN9+ksLDQvrxBgwZ+rFXVtW7dmsjISHsgPyAggK5du/q5VkJcUlBQgNZaUkIJcR1JT0/nhx9+sN8/cuQIW7du5YYbbrjsdalVqxb16tXj3Llz9mUtW7a87PUQQgjhX2YjPtuB7kqpIK21x66gSqlgoBWwrQp1K8DI0+9oiPW2fNfoQoxgvxAVunjxoj2gD5CRkcGGDRsYO3asV48PCwuja9eu7Ny5076sT58+1V1NIUzZuXOnPaAPRk+dgwcP0qlTJ/9V6gp04cIFtmzZQklJCT179qRRo0b+rpIQVbJnzx6WLVtGYWEhgYGBlJWV0bRpU0aOHOnvqlVJaGgoDz30EJs3b6aoqIju3bvTsGFDf1dLCLTWLF26lG3bjEucrl27MmHCBJRSlTxSCHG1S09P92rZ5XLXXXexfPlyzp49S6tWrbjxxhv9VhchhBD+YTao/xfgO+CvGDnsPfkzEG299dUBoI9Sqp/WepNSqglGnv1zWuv95bZtgpHuR4gKlR+m6GlZRcaPH0/z5s3tJ1DNmzevruoJ4ZOSkhKXZStXriQkJIQ2bdr4oUZXnvz8fD766CMKCgoA2L17Nw8//LDb1CVCXA0KCwtZtGiR/ftfVlZG9+7dmTBhgp9rVj1iYmK46aabOHPmDGlpaURERBAfH+/vaonrXFJSEj///LP9/s6dO2ndujUdOnTwY62EEJdDYmIiwcHBTufdrVv7L1lAbGwsderUISUlhQMHDlC/fn3pbCaEENcZszn1j2BMTPuUUmqbUuohpdQNSqnWSqkBSqlpSqltwFPAC0CyUqpp+T8v9/U5oIClSqk5wE8YjRCfOW6klGoBNAAOmnwu4jrUuHFjYmJinJaZ7c0cEBBAly5dGDVqlAT0xRWhS5cuhIaGOi3Lzs7m66+/Nt1oda06dOiQPaAPRgB09+7dfqyREFVz8eJFlwa9ixcv+qk2NWPTpk289957LFq0iP/85z/s3bvX31US1znHVBcVLRNCXHsiIiK47777aN68OfXr12fMmDF07NjRb/XZu3cvGzdupKSkhIKCApYtWyZ59YUQ4jpjtqd+CkaaGwV0A97zsJ0C/m79K097ud93gcHA7cAk67KfgFfLbXef9XaVF2WK61xgYCBTpkxhw4YN5OTk0LlzZ7+ejAlRHWJjY3n44Yf5+uuvOXv20qClsrIyjh8/Lml4gMjISJdljimLhLja1K9fn1q1apGbm2tfdi3l07VYLKxZs8Z+X2vNmjVr5PdM+FWrVq1YtWoVWl/K+unPnrpCiMuradOmPPDAA/6uBgAnTpxwWXbq1Kmrfl4dIYQQ3jMb1E/lMuWu11qXAXcqpboDbYATwEbteBZtSAJ+Ccy5HPUSV7+YmBjGjRvn72pUuwsXLrB+/Xry8vLo2rWrNFZcZ+Li4ujRowfLli1zWp6QkOCnGl1ZWrVqRVRUFNnZ2fZlBw8epKCggPDwcD/WTAjfBAYGMnnyZL7//nsyMjLo0KGDXybrqylaa0pLnadvcpdqTIjLqX79+tx5551s2LABrTX9+/eX+VmEuI7k5uayadMme+ewVq1a+aUeJSUlHDp0yGV506beJkUQQghxLTAV1NdaJ9ZQPSra5w5gRwXrZ1/G6ohrRGlpKQcPHqS4uJj27dtf9UG9kpISZs6cae+xeeTIEQIDA2nXrp2fayYup549e5KWlsbu3bsJDQ1l2LBhkoPaKiAggNjYWKegfllZGRcvXpSAjLhqJSQkcP/99/u7GjUiMDCQHj16sHXrVvuy3r17+7FGQhjatWtHixYtSEpKIjIyEq31FTFRbl5eHmFhYQQGBvq7KkJckywWC5988gnnz58HjPmZ7r77btq2bXvZ63LgwAGnc1qAjh07Urdu3cteFyGEEP5jtqe+3yilAoB4IFxrnerv+oirV1lZGTNmzCAtLQ2A1atXM336dKKiovxcM9+lpKQ4pWAA2LdvnwT1ryLHjh1j3759REdH07t3b8LCwkyXERgYyMSJExk3bhyBgYEEBJidNuXa1qpVK44fP26/X6tWLerXr+/HGgkhKjJmzBgaNmxIWloazZs3p3379v6ukhAcO3aMzz//nLKyMsBIgTd9+nS/dRDJzc3lm2++ITU1lYiICMaNGycT9wpRA06ePGkP6Nvs3LnTL0H98iPZAOrVq3fZ6yGEEMK/rviIj1JqlFJqOZADnAGOllv/olJqtlJKmqWFV44cOWIP6INxMbRt2zafysrJyWHv3r0uJ3iXm7sGiejoaD/URPji0KFDfPrpp2zbto0ffviBzz77rPIHVSA4OFgC+m4MGDCAQYMGERcXR4sWLZg8eTJBQVdN27YQ1x2lFN26dWPs2LES0BdXjMWLF9sD+gAZGRls2bLFb/X5/vvvSU01+jvl5+ezcOFCioqK/FYfIa5VERERLsvczdl0OXTo0IHatWvb74eFhdGlSxe/1EUIIYT/VCmaoZQKBeKAYE/bVKVXvVLqVeB3GBPvllj/yu/rDPBHYDXwga/7EtcPxwuxipZV5siRI3z11Vf2x44ePZq+fftWuX6+cJwc1UYCIFeP8o1KaWlpnD59WvLhV7OAgACGDx/O8OHD/V0VIYQQV6mcnByXZf6aeD0jI4M9e/Y4LSsuLiYzM1NGoglRzerUqUPPnj3t5+21atViwIABfqlLWFgYDz/8MDt27KCsrIxu3boRExPjl7oIIYTwH9NBfaVUMPA8cD/GBLYVJZHUvuzDup/xwIvAaeARYBmwBih/5JwPvA9MQIL6wgtt2rQhNjaWjIwMAEJCQujevXuljyufL/WRRx5xCrouWLCAG264gbKyMlznc65ZjilFbC5cuHBF5ArPy8sjOTmZ2NhYmjRp4u/qXJHcpdrxJf1OQUEB6enpNGjQgJCQkOqomhDXjIsXL7Jw4UJOnjxJs2bNuOWWW2REkxDCtA4dOrBr1y6nZZ06dfJLXTZv3ozFYnFaFhkZKXm1haghN998M7169SInJ4fExESCgz32baxxtWrVYtCgQX7b/9Vi/fr17Ny5k4iICIYPH05iYqK/qwQYDbC2uVmaNWvm7+oIIa5SpgLu1p75PwD9uNRzPhQ4AcQCtaybFmH0oK+KpzEaBe7SWq+37t9lI631RaXUcaB1FfcnrhPBwcE89NBD7Ny5k+LiYrp27UpsbKzXj+/Rowf9+vUjLi7OaXlISAgBAQE+9fo/c+YMJSUlNG7c2KfJ1ho1asT27dudljVs2NB0OdXt1KlTfPrppxQXFwPQp08fxowZ4/Xjc3Nz+eGHHzh37hwtW7Zk8ODBVZ4ATmvNoUOHSE9Pp1WrVldEb/gbbriBI0eOUFhYCBifMTOfSTDmUFiwYAGlpaWEhYVx9913ywmiEA7mz5/PyZMnASMn9rfffnvNTjIrhKg5N998MyEhIRw4cIDg4GCGDRtGy5Yt/VIX2/mVoyFDhkgKPiFqUIMGDWjQoIG/qyG8sGvXLlatWgUYHd5mz57Ns88+6zaV0uWUmZnJxx9/bB/51a5dO+666y6/1kkIcXUy24v+GaA/MBejp/5KYIDWuhmAUqoD8CtgKjBDa/3HKtStJ3DKFtCvxDmgYxX2Ja4zERERPg2XTExMZMKECW7X7dmzh5KSElPlaa35+uuvOXjwIAAJCQlMmTKF0NBQU+V069aNM2fOsH37dkJDQxk2bBh16tQxVUZN+PHHH50uOLdu3crAgQOdckBW5KuvvrIH4U6dOkVpaSmjRo2qUp0WL15sbwBZvXo1d9xxh99TFdWvX5+nn36a5ORkYmJiaNy4sanHa61Zvny5fdKswsJCVqxYwfTp002VU1xcTEpKCpGRkYSGhhIfH+9TI5MQVxqttf23xObEiRN+qo0Q4moWFBTE2LFjGTt2rL+rQo8ePdi1a5e9t358fDw9evTwc62EEOLKkJyc7HS/pKSE1NRU2rVr56caGTZv3uyUyu3gwYOcPHnS9DWgEEKYDerfidE7/ymtdWH5YI/Wej/wkFIqBfg/pdR+rfUcH+sWDrjmFHEvDKNXvxA1yl1PrJMnT7J7926fJtvds2ePPaAPcPr0abZv307//v1NlRMQEMDYsWMZPXo0SqlqCcTu2bOHVatWUVhYSM+ePRk5cqSpcktKSlzyzmqt3fYqcyc3N9clCHfw4MEqBfXz8/PZsWOHU31++ukn00H9srIycnJyiI6Orragd3h4uM/D94uLi8nNzXVadubMGSwWi9e99dLT05k5cyb5+fn2ZbGxsdx7773Ex8f7VC8hrhRKKRo3buz0myIXTkKIq13jxo2ZNm0au3fvJiIigl69elV5RKMQQlwr6tWr57LMNjLan9zV4UqolxDi6mM2qN8GSNFa21LraAClVKDW2jHnyF+BXwJPAb4G9dOAlkqpAK21xdNGSqlIoC1wxMf9COEVrTV79+5l7ty5TstffPFFn3LzrVu3jtWrV7ssdzcBm7eqOtz64sWLbNy4kezsbA4fPmxf/tNPP1GnTh2v5h4A2LlzJ4sXL3ZJRVS7dm2vA8Th4eGEh4dTUFBgX1Y+5VF1MDv/QVJSEvPnzyc/P5/4+Hjuuusuv+eu3blzp8syi8VCcnIyrVt7l5ls/fr1TgF9MCbgW7VqFXfeeWd1VFMIv5o4caI9p36DBg244YYb/F0lIcRlVlBQQHBwMEFBPk35dUVq2LDhFZFyUVx+2dnZFBUV+f08VIgrVd++fTl58iSHDh2yL1u4cCFhYWF+7a3fvXt3du3aZb8OjYuLo3nz5n6rjxDi6mX2jDYQyHC4n2e9jcdIgQOA1rpUKZUMdK5C3VYB0zAmyX23gu2ew8jrv6wK+xLCKx07diQlJYUdO3aglKJ///4+BfRzcnJYs2aNy3KlFG3atKl6RX1QWFjIhx9+6BREd5SamupVUL+wsNBtQB+M53306FFatGhRaTmBgYGMGzeOb7/9luLiYqKjo6uceiciIoKuXbs6BcGzsrIoKCggPDy80sdbLBa+/fZbe/D7woULfPPNN3Ts2JEOHTr47aKq/IgGGzMNFp4aky5cuOBTnYS40sTHxzN58mQ+++wz0tLSmDVrFp07d2bSpEn+rpoQoobk5OSwdOlSUlNTCQgIIDc3l5CQEEaOHEnv3r39XT1xldq+fTv79u0jKiqKIUOGEBMTc1n3r7VmxowZ9jRytWvX5rHHHvPqXLYipaWlrFy5koMHDxIbG8tNN910Rcw9da0pKyvj+PHjhIWFSYNcDQsODmbw4MFOQX2AHTt2+DWo37RpU37xi1+wa9cuIiIi6N27t4yyEkL4xGxQ/xTgOIYpxXrbHfjOtlAZ+SiaYqTF8dXfMfL2v6GUCgc+clyplKoDPAv8BsgG3qrCvoTwilKKm2++mVGjRqGUIiQkxKdysrOz3QZctdZ8/vnnjBs3jm7dulWxtuYkJSV5DOgDNGnSxKtyMjMzK5wsOD093augPhiNKK1btyYzM5M6depUy8RviYmJTkH93Nxcdu3aRb9+/Sp9bFFRkUvwOz09nTVr1vDjjz8yZcoUr1+n6tS0aVP27t3rtKxOnTqmJu7r1q0bx44dc1netm3bKtdPCH87d+4cycnJpKenk5aWZl++Z88eevTo4VPjrBDiyrdgwQKOHj3qtKy4uJhly5bRpk0boqOj/VQzcbXavn07ixYtst8/fvw4Tz755GWdnHj79u1O88Lk5OSwaNGiKo+s/PHHH9myZQtgXKvYJhU1G2wsLCzk8OHDhIeH07JlS5m42UFeXh4zZsywd5rp2LEjt99+u59rVb0OHz7Mhg0bsFgs9OvXj44d/Tv1YViYa0jK3bLLrXHjxpIKUghRZWaD+nuBMUqpEK11MfADRk/6V5RSm7XWmdbtXsII/v/sa8W01klKqfuAz4B/AH8DygCUUmnW8hVQCNyjtU7zVJYQ1c3sRLblJSQkEBcXx8WLF13WlZaWsnz5cjp27EhwcLDpsrds2cK6desoKyujb9++DB061KvHVfacYmNjvSqnXr161K5d223P74CAAFq1auVVOTYhISFu8yH6yt1kxt5OcBweHu6Sl9umrKyMrVu3+hTU//bbb9m9ezcAXbp08TgZsyc9e/bkwoUL7Nixg8DAQNq3b8+NN95o6iKsS5cunD59ms2bN6O1RilFjx49GDJkiKm6OLL1+Dp06BBxcXHcdNNN1K9f3+fyrjUWi4WNGzdy5MgR6tSpw9ChQ6lVq5a/q3XNOXDgAN98843HkSvZ2dmXuUbXj8LCQoKDg6X3mfAbd43VYHSiSE9Pl6C+MG3//v1O9zMyMjh9+jSNGjW6bHVwN9F7enp6lcst3wCWm5tLeno6DRo08LqMzMxMPvroI/tcTy1atOC+++6rtjmozDp9+jRr1qwhNzeXLl260LdvX7/Uw2bLli1Oo2D37dtHnz59aNq0qR9rVX3S09P58ssv7edcc+bMITo62m/Ba601Z86cISEhgdOnTwPG9ZykYBSiZiQnJ5Oamkrjxo29TgPsqLS0lHPnzhEfH1/lmNv1wmxQfzEwERgJLAXmA/uAvsBJpdQBoD7QCCPf/j+qUjmt9Vyl1EHg/4CxXOr53wAoBpYDL2mt91RlP0JcTraT2ujoaG6++Wa3P3a2/JhZWVkVplBxPEGuXbs2nTt35sYbb7QvW7t2LY8++iiHDh2qNBVLZb26N27c6FWuv4CAAO6//34WLlzI6dOnsVguTYkRGhrqdeNATenQoQNr1qwhLy/PXqcuXbp4/fg77riDZcuWcfToUa8n/a3Izz//7DR5744dO2jUqBE9e/b0uoyAgABGjx7N6NGjq1SXm266iSFDhnDx4kXq169f5UDc2rVr7T2+srKymD17Ns8888w11WPLdoHgy/D0devWsXbtWsDo6Xf27FmmTZtWrfWripKSEp8aFh2lpaVhsVho1KiRzxf0OTk5pKSkUK9ePeLj403nwt6wYYPH37+wsDDTDY3VyWKxcObMGWJiYoiIiPBbPapbUVERc+fO5ciRI4SHh3PjjTde9tFnQgA0aNDA/jvtKDQ0VHpICp+UbwhSShEVFXVZ69CtWzd27drltKx9+/ZVLrdBgwZOHVdCQkJMz2e1ZcsWe0AfjIaC48eP+2VEXHFxMZ999pl9JHJaWhqhoaF+PR7Zrj8qW3a1SkpKcjnnOnLkiN9+b5csWcK2bdvs9/v06cPw4cOviGDhiRMnWL9+PSUlJfTq1YsOHTr4u0pCVMmGDRv4/vvv7feHDBnidQdTMH6jZ8+eTV5eHsHBwUycOFG+F14wG9SfCxQBqWDPnX8T8AkwArBFoS4CL2qtv6lqBbXW+4DblVLBQGsgBsgFjmitPecKEeIKl5WVxcmTJz0G9bOysrwuq127dtxxxx1ug7BNmjRxySNYni3Yds8993hMt7Jo0SLuvfderxsZAB566CGnk7iCggISEhI4d86YgsPsJLXVISIigunTp7N9+3YsFgvdu3c31VMvOTmZw4cPOzVW2PjSGu14ommzd+9eU0H96lSd+T2TkpKc7mdnZ5Oenn5N9NYvLS3liy++sPdqa9GiBZMnT/a6IURr7TLB8cmTJ8nOzr7swYHysrKymDdvHqmpqcTHxzNx4kTTF2NlZWV88cUXJCcnA0aKqPvuu89UI0FpaSkzZsxwSpcD0KZNG2699Vavh067SwfWunVrIiIiGDBggN+C6efPn2fWrFlkZWURGBjImDFj/Pa9r27r16/nyJEjgPG7v2jRIlq1aiUjUcRld8sttzB37lzS09OJjIwkKCiI6OhoRowYcUWkXxBXn8GDB5OUlER2djZKKYYNG0bt2rUvax0SExMZPnw4GzZsoKysjHbt2jFs2LAqlzts2DAuXrzI0aNHqVWrFuPGjTOdarSoqMhlWXV0gvHFiRMnXFKLHjp0yK9B/S5durB9+3b7NVCtWrVMpcu80rmbX8xfc44VFhayfft2p2WpqalXREA/JyeHzz77zD5a/NixY0ydOpVmzZr5uWZC+G7jxo1O9zdt2mQqqL9ixQp7I2dJSQlLly6lXbt211SHwJpgKqivtc4CPi+3LA0YpZRqACQCBcA+a8BfaR+jdkqpDCATaKe1LtJalwD7K36UEFcXT73WS0tLTZUzcuRIj8FEd0N0PZk/fz5PPPGEy8WJ1tqp1dUbUVFRLhcCpaWl9klmvVVaWsqOHTvIzMykXbt27Nu3j9jYWHr06GEqQGixWDhy5AihoaGEhITQqVMngoKCTI0cKCsrY8WKFW4D+sHBwT5Ncuzu9bicQ7hrSlZWltsUTNnZ2aaD+haLBaUUhYWFFBQUmO41VhP279/vNEz96NGj7N+/n86dvZsffvHixS4Nd6GhoT5PcldWVkZGRgaxsbFVGmFx4sQJFi9ebG94u3DhAnPnzuXpp5821dP+4MGD9oA+GBdRtvz13nrvvfc4f/68y/LDhw+zevVqxowZ41U5/fv3Z/78+fb73bt3N53iqib88MMP9s9AWVkZ3333HZ06dfL5YvPUqVNs3boVgL59+5oePZKXl8fmzZvJysqiU6dOPjVS2pw9e9bpvsViIT093eegvsVi4dy5c8TGxl4RF+Pi6lG/fn0ef/xxioqKWL9+PZs2bSI3N5eDBw/SpEkTv6UEqU5lZWUcOnSI/Px82rdvT2RkpL+rdE1bu3atPW2b1tpUJ5zqNGjQIAYNGgQY58q21IlVERERwf33309RURHBwcE+BVLcNZRXddSfr+Lj41FKOXUiqlOnjk9llZWVcebMGWJjY6vUGaBp06Y88MAD7Nixg7CwMPr16+fzHG1XooSEBHr06GEfhdy5c2e/9bRVSrm8/1fKb35SUpJL+tcDBw5IUF9c1cp/v8x+3zIyMpzu5+XlUVxcLJ0wKlFhUF8p1V5rfcCbgrTWZ4AzDo8NAmYDvs7YEwyc01q7NvcLcY3w1Fvk8OHDpsopf3KptaawsJBNmzZV2kvfUWFhIT///LNLb5/58+e7Da55EhQUxLRp01x6wAcFBXH77bczc+bMCh9vOwAEBgby9NNP28vZsGGDfd1XX33Fv//9b8rKyiodPVCnTh0efvhhtyfNe/fuZe7cuWitKx05UFJSQmFhocvysLAwxo8f71Owyd3BrirBtOqQn5/PvHnzOHnyJI0bN2b8+PGmRjOUlJTw8ccfux1OfPjwYa+fX3FxMV9++SXHjx+3n5TbUrlMnjzZr+lK3F3Ee5ubPScnx6XnEEDDhg3Jy8sjJibGVF1OnjzJ119/TU5ODpGRkdxxxx0+XRSsX7+eVatWuSzPzMykoKDA1OvtrkHH3TJP8vPzK/zNKd97vyJdunQhJiaGnTt3UlBQQJMmTSgrK/N7nvfMzEyn+yUlJeTl5fn0O3LhwgVmzpxpbxDev38/jz/+uNefJa01n332mT0Yv3v3bu644w6fL8RbtGhh76kPxm+kr42V6enpzJw5k/z8fJRS9OnTp8qpxsTVw2KxUFRU5HODp83JkydZv369/f7GjRtp1qzZVT8ZvNaaWbNmkZKSAsCqVauYNm2az4FLUbH8/HynlIlgTFp70003+SVwXVZWxuLFi9m9ezehoaEMHz6cXr16VbncqjSeupunKiMjw6sUnu5s27aNHTt2EB4ezpAhQ0yNHIyJiWHEiBGsWbOG0tJSmjZtyoABA0zX4ezZs8yaNYvc3FwCAwMZN24c3bt3N12OTWJiol/SEdUk27n/mTNGOKhx48bcddddfh2hFxoaSu/evdm8eTNgXHMNHDjQb/Vx5K6D0pXQaUmIqhg0aBDLli2z3zf7fevQoQObNm2y32/RooUE9L1QWfP7KqWU6WSzSqkQYAFwmy+VskoC4qvweCGuSLbgsdaa/fv3M378eKdeVZGRkcyePdurILNtmxEjRjgt79mzJ3/9619Zs2aNV+U4cpf6w+zIgRYtWngMAicmJnodaOrQoYNTOY4B8OjoaNq1a+dVORMnTvTYC6ZTp05elaOUIjw83H7h7Cg7O5uuXbvae4RUVo5SipiYGKZNm+Y2OPzcc8+ZKsubPzPl/P73vyc5OZmioiKSk5N56aWXTJXTuXNnjwHuOXPmeN1q//HHH3Ps2DEsFgtlZWX2ERKnTp1iw4YNXpVRE86fP8/PPzvPAx8UFOR1Plt36WDAGHo7Y8YM09+3pUuX2gPmeXl5LF682NTjwQic2fL7l1e7dm3TDSjlv/8BAQF07NjR68dXljff7MVwYWEhO3fu5ODBg3z77be88847br/Ll1N8vPMpToMGDXy+oNu/f7/T56akpIQDB7zqkwEYjSTle9eXTw9lRp8+fRg0aBAxMTE0bdqUyZMn+9wTcenSpfYRTVprNm/eTGpqqs91u1KcOXOG3bt3m2rsut5s3bqVP//5z/z973/n9ddfd2kIM8MWaHLkLtf+5XLy5EkWLVrE7Nmz3abh81ZqaqrTb1lhYaF9Lhuzzpw5w4YNG1xS54lLPDU2+6v377Zt29i5cycWi4WCggKWLFniNAmrP5QftRoYGOhzepkDBw6wePFiTp06RVJSklN+fG/dcMMNPPfcczzzzDM8+OCDPjUQrlq1yj5PgG1knbvGC7Ns6WF27tzptxRF1WXNmjVOv7MnT550mffBH2666SbuvfdeRo0axSOPPHLF5Odu1qyZU8rFFi1aVKmh6Fq0Y8cO/v3vf/PWW2/ZR6KKK1ufPn2YNm0aI0eOZOrUqaYnpB45ciRDhw6ladOm9OnTh9tvv72GanptqSz9TgPgB6XUEK31MW8KVEqFAwsxJtOtSs77WcDflFIDtNY/VaEcIewKCwtJSkqiVq1aV0wPiY4dO7JixQr7/by8PNauXet1agmAUaNGER8fz/Hjx2nUqJHpXjqOQf9jx47x6aef2u+Hh4ezbds2r1pJbeWcOHGCjz/+2O02AQEBnDp1yqsgYWU9hbztSVRZjnIzPaOXLl3K448/7rQsJCSESZMm8fXXX3tdztixY2natKnbdW3btiUoKMh0cLc61KtXzyUgGx8fT3BwsNcXMBWlWHKcPM0TpRTx8fE89dRTHrf5+OOP7ZNCe9toVVRUxIoVK0hNTSUgIIBmzZrRv39/UymYtmzZwnfffeeSgmnUqFFeB2RjYmJo27at21E02dnZHDt2zNRojfJBhvPnz1NcXGwqiLpz506Pn7ecnBzOnDlDgwYNvCpLa813333ntCw6OtrU5zkkJIR27dpx8OBB+zKlFKGhoXTo0IHBgwd7XRbA5s2bnT4nmZmZfPLJJ9x1111eNw6C8dr++OOP5Obm0r59e06fPk1eXh79+/c3NTpi8+bN7N2712lZVXqPufstNNM7zt3vcWFhoc+TJefm5lJWVkZ0dLSpUT7upKenuyzbv3+/x9/PmmKxWNi4cSPJycnUr1+fwYMH+9x7fO3ataxZswYwPtfR0dE0btyYkSNHVvn1ulYUFRWxdOlS+/3c3Fw+//xznnjiCZ/Kc9dLuEWLFj7Xz1dFRUV88cUXHD9+3L7syJEjFBcX079/f9PluWsk9tRwXJF9+/bZRy0C9OvXj5tuusl0Ode6hIQEl/OhZs2amZ7Avbq4a5g6c+aMS6Px5dSiRQt69uzJ/v37CQ0NZcyYMT7/rpU/TyouLubYsWOmA7NKKY4cOUJBQQGdO3c2dd4HriPrioqKKCwsrNLojPz8fN5//317557169d7HFVckzIzM1m/fj25ubl07tzZVAcMR44TLNscPXrUdFCvuimlaNWqFa1ame6nWu1yc3NZuHAhR48epX79+kyYMIHBgwdTUlLi1+/slejUqVN8++239vtLly6lXr16kp7oKtC4cWOfJ8YODAxkyJAhDBkypJprdW2r7AzkB2A4RmB/sNa6wuTcSqlawBJgEJAPVCVp7RvAEGC+UupRYIGv+fmFACM9wccff2wPOHbq1InbbqvKYJKqycvLY+HChU4pCmzMpJYA44SlZ8+eVZ5k8ciRI2zYsIE6deoQEhJC3bp1ueGGG0wPe7p48aLHdYMGDfK61++ePXs8DmkuLi5mz549XpWzf/9++vXr53ZdaWmpqXRHFy9edBvoMju5bEWpKEJCQoiLi7PnNb+cPF14BQYGeh3U9zTEuqioiHXr1nlVRmX5YR2DvRWxPV4pxTPPPOPUgHPu3DlWr17Nv//9b4qLi71K4/Tkk0+6Xb9gwQIGDRrkVTlgNJY988wzbr9bQ4YMsV+oeyrL8XV55plnXC5QX331VT788EN7r6nK6jRlypQKh8bff//9fP/99141oOTl5bmMQMnIyOC9995j7Nix9O7du9IyAO666y527NjBrl27aNq0KYMGDfL5AtpTqp3t27d7HdQvKSlh5syZ9rRSjnMqHDp0iLvvvtvrVB62gK6jb7/9ljZt2ph6jpmZmXzzzTekpaURGBhoD+Y1b97cVNAjNjaWnj17OvUYPnHiBO+++y4PP/ywqWNAYWEhH3zwgVMD3u7du5k8ebJPqcUSExPZt2+f0zKzv7eZmZns3LmTwMBAoqOjKSgooHXr1qZGRvzwww/2EULHjh3j3Llz3H///abqAcaxyzENjNaazMxMMjMzOX/+PI888ojpMq9FP/3k2p+nonOLytSvX59bbrmFDRs2oLWmf//+l71hCIzRB44BfZu9e/f6FNQv32s5MDDQp/Qr69evd/p937p1K0OHDr1m5rCwWCysX7+egwcPEhsby4gRI3waGRUcHMz999/PsmXLyMjIoFWrVtx66601UGPvJCYmOo2qCggIoEmTJn6rDxgTI9qOJQUFBWzZssWnOac8zcNlNrWUxWJhxowZ9tFoa9euZerUqaZepw4dOjiNZmzSpEmVJ0fetWuX07nShQsX2Ldv32XtrV1WVsYnn3xib7SwNaL4Ethv3769yyg6M50mrgfLly+3j4Q6ffo0n376Kb169TI131RNKisr48SJE9SuXdvvjQzuRtMeO3bMb0H90tJSUlNTiYqKkvR24opTWVB/PLAcI0hv67HvNtqolIoGvgP6ADnAWK11VfIjrAQUEAfMAbKVUocB1yTNBq21HuFhXY1SSkUAvwfuAhoB6cC3wB+01qbGQCqlmgGvAjcC0cAxYCbwutbafNcbYffTTz85nRzu3buXgQMHmp60s7qsXLnSbUAfzKeWqA7nz5/nyy+/dOqBPGbMGOrWrWu6rPJpPIKCgrjllluoX7++V+U5XlheuHCBxYsXk52dTVRUFLm5uURFRTF+/Hhee+01r8rSWrNo0SJ27tzpVHZISAhTpkzh1Vdf9fq5tW/f3m3QzWwKhZSUFDp16uR2XVFRkak5DKpTSkqKSy/v9PR0t3MJuBMUFOTS4zg7O5vvv/+egwcPej28OCsri+LiYrcBBYvF4jYoUpG2bdu6HZFRq1YtWrdu7RI0dKeiQGJoaChNmjRxmhy2IrfccovbQGlRUZGplBBxcXFue5wFBQUxduxYjyNmyqtsBIU3w/ltDQ1dunRh4sSJbifY+/rrr+nTp49XDR/e8qasxMRE7rvvPpfelPPmzePee+/1qpyWLVtWGMR94403eO+99yotJzg4mBdffNFlna2nri0Y4k0DyvLly+2NwGVlZURERHD33Xf7FNRp3LixSxqQjIwMVq1axbhx47wuZ8+ePW4/T9u2bfMpqH/rrbdy8eJF+/eibdu2Hn873cnKyuKdd95x6b28cuVKJk+e7HVv7f379zvdP3r0KF988QUTJkwwNTFpaWmpx1ErZ86cIScnx1SwqKioiPz8fNM9Tyuyfft2Dh8+THx8PAMHDvRpREJpaSnJyckEBgbSokUL0xNuHjvmOkDYYrGwYsUK+ygtb+uxaNEi9u7dS1hYGDExMZw9e5atW7dSr149nwOgaWlprFu3joKCArp37+52fiR3PDVMVDai0JPvv//e6X5ERITpSbLB9femqv2oVqxYwY4dOwgICGDgwIE+NVhUl9LSUr788kv78fn06dOcPn2ap556ytTxJi0tjW+++YbMzExq1arFnXfe6XOe+OrSpUsXMjIy2L59O+Hh4QwfPtznz1J1KT/nQHJysunftbNnz/LRRx85dSYJCAhg0KBB1KtXz1R9jh496pRezmKxMGfOHH75y196XcbgwYMJCQnhyJEj1K1bt1p6kZYf8elpmScXLlzgxx9/JC8vj65du5o6LtqcOHHCZRTC3r17fQrq9+vXj1OnTrF//3601rRs2ZKysjJSU1P90oB6JTpxwrl/bEFBAT/++CM///wzjz32WJUbiqoiMzOTmTNn2hua+vfvb+pYW93cXXOZ7dBRXcq/Nn379pW5ncQVpcKgvta6QCk1FlgB9OdSj32n7qNKqToYQfiuQAYwWmtd1cRXQ8vdjwYq6t7nl1781nRDazDqdhQj9VBH4HFgjFKqr9badey4+7LaABsxGjK2ACnAYOBvwA1KqVu11t4f7YUTd8FEbwOV5R04cMB+wdunTx+fhkq6G6YYGhpKx44dTaeWqA5JSUkuJ5N79uzxafhU+R7dWms6dOhg+qIejNQvU6ZMMf04R0opJkyYwJ49e1zyTnvb4m+7wN2+fTuLFi1yWT916lQ+/PBDr8vJz89nyZIlHDp0yCXY1KtXL6+Gz1fX4KXy5aSnp7NgwQIyMjJo0qQJL7zwAu+8845X5RQXF/O3v/3N6bOUmJjoU17NAwcOuA2UBAQEMHHiRGbMmOF1WRX1yHM3oa87qampHkcPaK29DnwppTz2WjP7nlaUYsVML5t169bRvHlzt+UVFhZ6PSqmdevWTJo0yeN6f012lJKSwn/+8x9uvfVW+8VlYWGhqbkZKvtOehscKikp4cKFC27fn44dO3qVX9u2r2effdapsSo/P5/nn3+e48ePs3XrVvv30JuGj759+7pN+7ZlyxYefvhh0tLSvCpn4sSJbr+3c+bM4Z577vG6PuXVqlULpRQ5OTn2cioqy1bO5MmT3X7fysrKePnll5k1a5ZXdXI3muXw4cP89a9/5S9/+Yv981HZdzgiIoL27du7nfMgKCjI1PwV27ZtY/ny5ZSWlhIaGkp8fDytWrVi4MCBpke1pKamsm3bNtLT050aFk+ePMmDDz5oqqyMjAzeffdd+7lAXFwcjz32mKkUJZ7OqzZt2kTfvn29TuexadMmdu/eDRjfD1vnjrNnz/LNN9/w7LPPmj43KSws5NNPP6WoqAgwXruwsDCveqW2b9/eJegZERHB0KFDTdXBpvzxy9fz2n79+rFw4UL7/eDgYNatW8eQIUNMn+Nu3ryZjRs32u+vWLGC+vXrm053VFBQwIULF2jQoEGV0tssWbLEpcE9IyODc+fOed2xR2vNV199ZZ8vKDc3l1mzZvHcc8+ZnnOmOimlGDp0qM+fH0elpaWsXr2aw4cPU7duXUaNGmW6sTA3N9elE0BgYKDpz9DKlStdriUsFgtHjhyha9eupurl7vudnZ1Nbm6u12nqAgICGDBggE+T7HrSpUsXpw5nUVFRXo+ws40ctDWgJyUlERgY6PXcTjbuGoCqEli+7bbbuO2229i7dy/z5s2z90ofOnSoX9JplJSUsGPHDjIyMmjfvr3fGxeaNGnithNRQUEBe/bsMf35Sk5O5uzZs7Ro0cLrFJmerF+/3mnkyMaNG+nVq5fPcz2VlpZW6Xe7efPmDBkyhJ9++gmLxULv3r19GvFTHcq/Nps3b6Z3795+H81Qnc6cOcP69espLCykZ8+epn9LhH9Vehartc4DRgNbgTYYgX37mBOlVANgLUZA/xwwrBoC+gDDTP4Nr4Z9+uL3GAH9eUBbrfVdWutOwNtAc4w0Qt76GCOg/yutdV+t9V1Aa4xA/wTgF9Va8+tMjx49nIIF9erV8+ngvm3bNr7++mt27tzJqlWrTOVRd1R+3zExMbzwwguMHz++Sjkaq9P27duZNWuW6TQwffv2dbpfVlbG119/7Zcc8Y7K9xINCgri1KlTpspo2bKlSyqP0NBQunbtaqqciIgI7rjjDl544QWni5Pg4GAGDRpkqqzqVrduXaZPn86vf/1r7rnnHlOB2JCQEJeAXk5OjsdRKe7YRlfMnj3b44Vbq1atTE0CffjwYbdB2YMHD3o9YWpmZqbHHu1KKa+HGWutPaYy2r59u1dl2Jw8edJjEMfMpJLp6em89dZbfPbZZy6pc8LCwrz+TarsNfDnRHAZGRls2LCB7OxsysrKOHz4sKkRMRWdvGutWb16tddleXpvzMzvAc4pgGw6dOjAmDFjTPWuB8+TEwcEBHidVghcG3VtbAFQX+Xm5vo0qWxFvZbNXHD+8MMPbn9vgoKC+PWvf+1Vo55tMvF7773X7fc2LS2NoKAgryYlj4yMZOHChfbjalFRkb33+GuvvUbz5s29nuC8YcOGfPjhh+zevdtlpFBqaioxMTGmJlz/17/+5fQ5uHjxItOnT/eqPjaDBg1ymzZLa13hvC3lVZTOMCcnx6fJd48dO+byefY2JVzr1q0ZP3480dHRREZG0qZNGxITE1m2bJlPk1N36dLF6X7nzp1NlwGuv81FRUX89NNPLFmyxHRZ7hrxy08uX5m9e/fy+uuv89FHH/HGG2+YPlez0Vq7rU9QUJCpPO/5+fn2gL6NLch8rVi9ejU//fQT58+f58CBA3zxxRemy9ixY4fLuVaHDh1Mp3Hy9L1MS0tj2bJlpspKTEx02X9wcLDfU0vVrl2bRx55hBEjRjBq1CgefvhhrzuHHD9+3OV81JsRp+XFxcU5BZJjY2OrJQf+unXrnI6XGzZsMH0NWFBQwL59+6o0ofmXX37JsmXL2LRpEzNmzHA7l9XlNHr0aFq3bu32OGj22n/lypXMmjWLlStX8t5773nd+cYTd9c33syDVt6JEyd45513eO211/jkk098Om+zGTp0KH379kUpxdatW1m2bFmVOrRdvHjRp+fkrvOXL+VcqQoKCpg5cyb79u0jOTmZr7/+2u31hbhyedU1RWudg5EOZgfQAfheKRWjlGoKrAPaA2nAUK317uqomNZ6rdm/6tivGUqpEOAJoAR4XGvteLT6fxhpeO6xNnxUVlZf4AZgl9ba3hCgtc617gPA+3GCwkXz5s158MEH6d27N8OGDWPq1Kmm0zyA+2Gl5YNg3hg1ahQdOnQgODiYhg0bcuedd/pUn+rirreGbfj87NmzTQ0JHThwoEuPjEOHDtl7y/nL+PHjnYJmJSUlzJs3z9QJwp49e1wuWG677Tafe7YEBwfz0EMPMWLECAYNGsTDDz/sU8qjK8m4ceOcAqAlJSUsXrzY9IlYeHg4Tz/9NH369HFZZ2aEhdaa9PR0pkyZQmJiIgkJCQwaNIhp06bxxRdfeNU4YNumol4i9913n9fluAsQd+7cmeXLl9u3qags2/rS0lKPn70mTZpw4cIFr+tUXFxMUlKSS0+x8PBwcnJyvHr/Kst57W6E0uUSFhbG7bffTlRUFIGBgXTp0sVUr6iMjAyP67TWXl8sRkREeOyxGhUVZerC7rvvvmP79u32hgpHXbt2NXVMqahXjplc5nv37nX7WfHHpKRQceOW2YCcp9czNDTU1Jw2oaGhbhtL69at63XvqPj4eI9zRSiluPvuu72uT+fOnT2WpbU21ft7yJAhbhtLmjVr5lWvNlvgf9iwYW4biM6ePUvDhg29bmR4/fXXPW6TnZ1N3bp1TTVY2HpGl/fGG294/X07cOAAWVlZ5OXlcfjwYfbv38/x48dZuHChyzlmZTp16uS03/KBZ29t2bLF7fLyaae84S5YbqYXaVlZGd9++639Ny0/P5/ly5ebrgcY57Hu3pemTZt6/My7ExkZ6TbNlq/nfrNnz+aPf/wjr776Km+99RYLFy70qYGpOpX/PUxPTzddJ3eB25YtW5quS0WpZGxzBXkrICCAyZMn24+tSilGjRp1RXSgioqKYuDAgQwYMMBUGjd33zFfJyMeNWoUTz/9NA8++CBPPvlktUzWXv58xGKxmLoGSEtL46233mLOnDm8//77rFixwnQdzp8/7xKY3Lq1av1OT5w4wfz58/nyyy9ZuXKl152CbGrVqsXkyZP5zW9+43StFx8fb6pBtqSkhM2bNzstMzPy1J3yDcRxcXGmR+tnZmbyxRdf2EfrpKSk8N133/lcp5SUFNavX09paSllZWVs2bLF7SjHymzZsoU///nPvP3227z++uumjyflX5vo6Gi/z11SnZKTk106Kvhy7K9u6enprFy5krVr115TjSg1wevxplrrLGAUsBvojDGJ7jqgFZAKDNFae9dN5doxECMt0Dqt9VnHFVrrImARxmvsOp7dla1L3ZzyK7TWOzBS+3RQSvk3eeNVrkmTJowdO5bBgwf7lCMWcHmcL8NKbeXccccd/O53v2P69Ok+5UCtThVd3GRlZZnO8e7u5NSbvNw1KTw83OWkMiMjw1SvP3fBrar2Po6IiGDgwIEMHz78mph8JyAgwCUIlJ2d7fNIjTFjxvDEE0/QsGFDgoODadWqFePHjzddTsuWLZkyZQoPP/www4cPN32yqrV2ysvqSCllKkDsbjjrsWPHTDfsHT161GPPaIvFYh/67K38/HzS050zxg0YMMCr4IfWmh9++MHjnCBKKd58802vGxlsfxaLhR07dvDNN9+wbt06+2TEZhpjtNbs37/f5SL+kUce8bqc5ORkjyNHWrZs6XU5Z8+e9fg+BwUFsWvXLq8vfouKivj222/517/+5dLoUFBQYOoi2tOFvJn0S2D0IHQXBPYmpVhNWLJkidvXQWttKoBa2THazIgmT3MLKKU8TupeXmXnMGZ6oVbWaG9mlIWnHONKKZf5VirSt29ft2kzYmNjTeUx37p1Kxs2bCA3N5dz586RmppKWVmZPf2OmQ4LNufOnWPdunX2z3RKSopLgMUdpRQREREV/i6///77phoZXnrpJafPd1JSEvXr13faxpuyPDW4pqWlmSonJCSEvXv3Oq3TWjNmzBivR2pcvHjR5bhmNpBr8/PPP7t9j48ePWo62HTXXXc5HUPatm3rU079+fPnc+TIEfvxzTaR96xZs6otraIvyncoCQ8P9zo9jU23bt2cfnuio6N9mix18ODBjBo1inr16rkct31pIG7atCnPP/88DzzwAM8++yy9e1eU0ffKV7duXadzznr16lUpNVBsbCxNmzb1KU2qO+VHbPfo0cNUI8ratWudjjsbN250O3l6RdztryoNOTt27ODjjz9m9+7dHDp0iJ9++olPPvmEdevWmS4rJCSEhx9+mNtvv53bb7+dRx55xNQ5hO23w1FVrkUzMzPJzs6mf//+tGvXjj59+jBlyhRTn4eUlBTefvttl8nbq9KZx11g2eyx4KeffmLZsmVOx5TNmzebqlf79u258cYb7a9HVlYWb731FosXLzYVQ7hSuRslbHbkMBjXC1u3bmXTpk1ep7b1JD09nffff5+ffvqJNWvW8OGHH/p1tPeVzlSiK631RaXUCIwc8t2si5OAEVrrE54eV1VKqUiMXuxtgNoYE/EeBjZY0wP5iy3fhqd8CdsxUuZ08bDebFktrGW5zh4mLpshQ4Zw/Phx+8HBYrGwatUqxowZY6rXj83p06dZtmwZ58+fp23btowZM8Z0I0F2djYzZ84kIyMDpRRBQUG0a9eOsWPHen2S0KBBA5RSHi8okpOTTU1O1bp1awIDA50COWZP7G2vTXp6Olpr+wlevXr1mD59utepE4qKili2bBmHDx92uaC0DYH3Vrt27ZwCQSEhIT5dYOTl5bFkyRKOHTtGw4YNufnmm32e6LA6PkNgNLrMmzeP06dP2ycGvfXWW31qAGvXrp1TbvDw8HDy8vJ8OkkAqFOnDtOnT/fpsWVlZaxcuZI9e/YQHR1Ny5Yt2b9/P6WlpfTp08froca2gEz53gJhYWFMmjTJ1AiLQYMG8fnnnzsty83NJSMjw9TnoLJRQmbzPf78888uJ01meunaJp8+dOgQX375pdO6itIOuVNYWMhbb73ltP99+/Zx6tQpU72QbRISEggODnaqg9meNg8//DCbN2/m6NGjXLx4kaKiIpo2bWqqkalWrVrExMR47AHpTX5md7/TBw8eZM6cOZSVlaGU4v777+ef//yn12UtWbLEbYqM0aNH85e//MVUnf70pz+5BPEnTJjg9dwcYPSI++6770hPT0cpZe9x27JlS26++eZKg02O9fnHP/7hcuEVEhLCzp07vZq8V2vNmjVrXCaBd/T55597dYwMDQ1lxIgRHtd72/hR2ZD2ikaWlFfRMVApRXx8vNeN8klJSR4b9sz01vZUp5CQEG666Sb+97//eVWO1poWLVrYe1qXlZWxYcMGVq9e7VMAtX379owcOZLY2FiUUhQVFbFlyxavGz4q69lttpHB3Wvqy6jPc+fOuYyAKykpYenSpabKad++vcu5mVKKXr16eRX8UkrRpEkTpk2b5lKXxMREjh8/bl/mzdwcTz/9tMec0KtXr7b/dpuZ56NRo0YUFxeTnp7uNL9HZeWA8f32lCblwoULpvL8O1qxYgWbNm1Ca01ISAgPPvig19+3U6dO8dVXX7n8ptSuXZv8/HxTE+/GxsZy3333MX/+fDIyMsjJyeGzzz5jwoQJpq4hsrKyOHr0KBkZGWitCQwMJCgoiLZt25qaoNJisfDZZ5/Ze1TbPou2RiazUlJSWLBggf3cKzIykgkTJpjK9f3999+zceNG+3c9MDCQUaNGuQTCKxMdHU1QUBClpaWcP3+e//znP3Tr1o2RI0eaCsauXbuWNWvWuCyPiYkxFWzOyspiyZIlnDhxgvr161O3bl0yMzNp1KhRhcc8d9yl21q5ciUBAQFeN3xHR0fTvXt3+zWbUopjx46xcOFC09dJaWlpHlORbdiwgYEDB5p6zQsLC/nuu+9ISUmhYcOGNGnSxFSDQ0hICEFBQU7ns5mZmR7n/arIqVOnmDlzpr3jVePGjWnbtq3pctatW+f2+FVcXExJSYnpBpWysjK3HUrMXHNnZmaycuVKt+suXrxoqnPX9u3bnZ5fdnY227ZtIzMzk/vuu8/rcq5EcXFxREVF2Uf61a1b13TDZ3FxMR988IG98+P69et55JFHfB7NtmPHDqfOgFlZWRw4cMB0uuPrRYURMaXUAx5WfQb8CWNy2o+BYZ6++FrrT32tnFIqCHgFeApwd/WWq5T6N/B/5VLfXC62aICnpj7bcm8St1dnWe5ZLFDRMLG6dcF2EVVWBicqaadp0ABsB/qSEqgs32XDhmA7gBYWQmUtrY0bg+3CID8fKsvr3qwZ2D6HublQUc9ypYztbbKyoKIL4KAgoz4YB7vaERFctJ7Qaa3Ztm0bscANtuGiISHG87U5f96oUzkWi4Wv5s0jy9qauXPnTkJDQxndo4fxnD2JjDTeL6tZ1oC+rT4lJSXs2bOH4MJCxg8YALVrg2Nw79Qp4z1zoHNzK7wYKU1P9/z5iYsDx5P+lBRigPtGjmTD3r2UlpXRp107mpSVGWXUqQO2gIzFAqmpLkVaLBa+nDePbDctvefOnWP+rFnc4WlisIQEsPUUKipi1fz57PKQGiM3J4f87GwibPUvKAAPvbEB2oSEMHHCBLbv3ElYWBiDe/cmvILtAXAMcGRnw8WLLFmzhgPWi9OjR48yd/ZsHho3DgIDwTHQmJlp/HlgCQzkq7lz7RcYO3fuJKSoiDFuUtYAxnfW8UIvPR2sr/HXCxdyzrovrTVHjhzhjX/9i8n33nspSHPmjPH99aRWLahTh5tuusl+QQZGr+H5X3zBg+Un4oyOBscg9smTUFGP/thY4zE2x49DRRfRdeqwcedOey/K/Px8p/yc33//PXUtFtrYXvN69cAWVC0tNerjYGS3bizcsAGtNQEBAdw4fDi9+/c3TuaLi6GC/M0ANGoE1tEGsTExZDi8twFKEZ6ebvwe2TRpYnwmwHifyvWibx0WRkhQEMXlXjOlFD27dKFFQIDn721AADjO7ZGZyUE3KbLCCgqMMhx+BwG4eNH4PLvRNjSUxIQEUsrlQg0+d854ndwJDweHYMZH773ntkHh0KFDFBw6RHh8vPFbYnP6NFQQWAuLiuKOO+7gu+++Izs7m84tWtC/YUPPr09MjPFnk5pKmMXCkGbNGFI+/VNGhvF6Op68VnC8nXbjjXy1fj2nz5516nHVvmlTmmnt/rGVHHPbhYXxzKRJnExPp2F8PNGOQQYvjrk3jRhBaGgoSUlJlBQXE6IU7Zs1Y2CTJu7rU8ExN7hcgy7gfCFeyTG3VCm+/Pprtz2gDh06hM7L4x7HIEElx9y6tWtzvFxZJSUlfPnllzz99NNEFxVVeszt3Lmzx7zA9wwfTr38/Euvk4djrj52jPV79rDKw9wZAQEBvPTSS3zypz9V+PnRWVkQFcW8efPcXvRGhoXx7PTpvPVLa9bG3FyPx1x97Bhz161j7zHP/UUOfP89dWNiPNcpIcF+/lCSm8v7H3zAeTe/DTcPGcL/fvMb47fVdn5X7phr+2ZlnTjhsdEtNjra2M5TfRyOubffcovTKIugoCAGDx5MQGYmR7Zvp4xyJ95ujrm2OtWKieG2O+5wCt6EhoZy+2238enBg2h359nljrktKwhsaq3ZXj645uaY6/jr4y6MFJWVhVMYzrFO5Y65jTAuBo9v2+YS1A8ICKCw/Lm0h2Ou7ZHKw+97v969OW4L6jvWp9wxtxkQbP1ddLy2VEoxdepUtm3ezGJb6oQKjrnNABUQUOEkjznp6ZdeS1ud3BxzXZL9nTpFkHUfxx0W1yr/3Mpp2rw5zQYOZOTIkW7Xl5aWMrh5c4oKCkg5dszjMTex3OiAOg0bMv7hh+33i4uLefPNN/nwb3/jfAXf6/rNm3MhMJDnn3/ebeeNc+fO8fwTT7DCOpl4Snq6x2OuY51G3HMPTa3zsGitOXXqFK+++irz33mHIDCemzsxMSjrZ/PRRx91aZQoKyvjT08+yS+t73lKdrbHY66tPt2HDqWbw7WC1pqtW7fyyssvc86hEdttnazHXKWU8X6HhHDXc88R4jASIS8vj1mzZvH1v/7F0bw8im3fDTfH3MTmzWnSti0jyzUElZWVsWzZMh4aN44cW9oSd/WxHnOVUjRv0IAHHnnE/h2xWCwUFBSwceNG3vjDH9i/eTOpODQyuTnmJjZvTkTt2tz5q1+5DeBmZmYyffJkfpw/37VO5Y65dZTitgceoKE14OrY+JaSksK9kyYx19pAqLU2rundHHMTmzcnKDiY+373O7d1WrpkCQ+OGkVBbq5zfcodcxsphS2E3LZnT/qNG0dAQABFRUXs3LmTj999l2+tI3W01kbMw02DemLz5gQEBnLbs89Sy0NwMi83l5bBwWiL5VKd3FznOn5Hbrz/fhpZ01JlZmayfOlSPp45E4BAoLSC7y1AYWysSycZrTVLv/qKce4aPRxjKuWOuRvXrnU6rzl58iQnT54kICCABx54gGbx8RXHVAASEz32oi4oKGDV/PmMtl2XenGdm5mby/fbtrlcA0RGRpJYu3bF8SzrMVdrzbx589xuEhgYSMvgYKOciAjjOGTj5ph74tw5jxkLkpOTKT5yhBBPjRaVXOeWlJYSGBBw6bzCh+tc+2dNa2P7iri5zv3+p5+cUvfl5+QQdPKk8V45XntUcMw9kJzslM0gLy+PDWvXMrqCVGqAx+vcDDcjKfZu3eoc1M/JgYo6nLi5zq0opmLmOhcwYk2Oo3g9xPvsyl3ncvas8X30xBpT8UZl3VxnYgTuK/JaBes04FNQXykVACzEmKRXAWeAg8BZoD7QFkgAfgf0UEqN11qbH0NbNbaGBk9XgnnltquxspRSmZWUH01ODlQ0THTOHLjtNuP/zMyKtwVYvRpsJ0rHjkFlE+nt3g22fHHbt0NlvWNPnbp0wrBqFUyYUPH2RUWXGg3mzIEHH/S8ba1axg+BzYcfwvPPe96+WTP7ASQ3N9ce0HeU8uWX3GDredujBzj0UuZ3v4MPPnB5TEZ8PFlPPeVcTkqKsa2HAxEA99wDs2fb7168cMH44Spfp02b4N574ckn4e23L60YPRrKDY9O7dIFJk1yu7vA0lL6TZrk9oQHgH//GxyfR5s2UFJCIpDobvvPP4fJk43/8/LcftYy4uPJLvfaONV3zx7P7/HPP4Mtv/HevaT8+KPzAdtBmcXCiV27aGubnPbHH+GmmzzuF6BrdjZdu3c37syebbzGngQHOwcxP/kEnn6alF//+tJBFTh1/jwlbdoQXKeO8wHzzTfh//7PY/GZffuSVS5QnrJmDdx1l/sHDBkCjkGDZ56BL76gMCyMc7/5jcvmJaWlLF68mCeffNJY8MAD4KHXAwDTp8P77xMcHOwy9C713DksLVsS4NiT47e/hT//+dL9gQMrPiH55z/huecu3e/UqeID6IwZHK+kd8ixP/2JNrZ8nd9+C7Ze1+fOuXw2uwLNoqNJa9iQxidPEjVixKXv3qFDUC7vootDh4zvB3BHixZ8un49hRERKIuF4atWEfbyy87bnz9/6UJl+XK4/Xan1bWBqQkJ/DRgAKVBQfR67TXq1q1LYGAgkZ9/DhX1aImLcz4Z+u9/uZCbe6lBzKr91KnGiU3r1nD48KUVr70G//qXx+JvvPFGZg4daj/R75CXR13b98adm2+GRYvsdy9evOj2dy2kqIjgTp2M7531Iggwjl8bN3ou/1e/ovXrr9O6dWsjYNS2LVSUU/2114zfbpvu3Y3XwZP33gOHwEpFx9BawLQ5c2D6dMrKyji2axfhEybQqKLGcS+OubUxJjkCTB9zg06dYuTIkUbAadGiKh1ze4wcyU8OqVYanzrl3NuzkmNueo8e5Few/2NHj8JDD11aUMkx98aEBL6YPJncchflFouFY8eO0e3VVys95sbPns0vfvELli5d6jRsu/nRo7R55RXn7Ss45mbefDP06uW0eTutaWadQC8+Pt4496lockDrMXfSpEn079+f/KFDSUxOJtDxGO34e17JMbdHYiL7HngA7eb71qpRo4q/t+B0zA0+dIgHf/97/vPEE+Q79LYPKi5m4GOPGcGlY8cuBd7LHXNTrLfZmzbxTs+elLhJIzRg717+DO6/Y9Zjri2Y9fof/0iumwvixxITmbR9u3FBVskx11anXU2bssDNaxQQGMiaRo1oduKEa53KHXMfPX6cYxaL62+b1oxZsoTOhw/j9Cvv5pib4vD/P/btI9+hh29cSgqHyzeGOtap3DF3PdAkMJDPu3Z1GQYcGBjIlHbt+JvjCB4Px1xbnaYmJVFWVuYycjWidm12R0URlZ3tXJ9yx9wUID06mv+6CegppejVrx8nz5wxJhWu4JibAmCx8NSpU9Rp1MhlfczFizy2fDnv2xbY6uTmmJvi8miHOjn8f49jOW7sAJ71MIoFrbl5xQpetV3gN2/u8Zhbvj6r2rdnfbll4eHh/D0kpML6fAhMr1OnwtGYTZo3v7S/55/3eMx1rNNrzZtTvumzTp06LK5dm24VXYu+ZoQUgoKCPI4yeDsxkQG27+sXX3g85trq816bNrhrzm7TvDlbHD/X7urkcMxtDqxs0ICZbn6PAgMDWdCkCXc4Tpbt5pibAixv3hx3ibqUUrwwejT32a4j3dXH4Zh7c2Kix57UUxMTGbt5M07jK9wcc1OATe3b810FPbI7tWrFZ7Y7jnUqd8z9M3AmMdFj0Kh3ixbMdVzw2GNuj7kpwI99+/KDhzoFBgXx96FDGb94sXN9yh1zl2PkirYoxRtDh5Jb7ve2e4cO9qA+AH37uj3mpgCn69Xj/Qp6G7dMSzN+0+FSndwcc1OAsoAAPr/vPo6VOzdvmphoH3XR2LEcD25QivF/+IPLZ2Dfjh2MK9doBFR4zM158EHnjo5WFouFt956izNvvMFsl7UOgoNRJSX069fP4yia5LVrL12XVnLMLQsMZOZTT5HlZmT36dOnebtOHTxHB7Afc5OTkznhroOq1kz86isiX3rJuD9pEsx1+HS6Oeam9ekDY8e63V1UQQHBbdt6Drx7uM4tCQ5mwcSJHGjfnvCCAm787ju67t7t03UuU6ca/5eUVB6/c3Ode+qxx5yCzXmFhWT16UPcxYuwYQPYUntVdJ3btSvceqvTom07djDkjjsIr6gzoIfr3NLJk+3XyzYXync8++ILeOQRz2W7uc7lxRc9b2/yOpf+/cExLdjzzxtxHk/KXefy0EOweLHn7adMcT7mVqCyoH4qlQf1a8pDGLno04FngK8dg/bWoP+dwBsYgf9pgGvUtGbZfkk9vUZmxi1VZ1miBkVGRhKlNdnlDqQJlfXQdSMmM5OIvDynC9+Gjr0NvVSrqIgsNyflDU3UyV39IyMjadiwIWNeeYXgy5wPOdrNa+OofmW94x00TEsj3UNQX1ksJFTQk6umNExLI7lVK/v9uufOEWwiNYlNdH4+kZGRTgH0hpWNmnEjtLCQ2AsXyHCTruXChQtYLBZTQ0tTU1MJDg526rlR/8wZ54D+ZZKQkFBhDmMz3xOAmKwsYnyYHLu8hNq1+eUbb3CiaVPiLlwg1sdJ8hJOn+Y22wlpuZQ3ZpzXmuJyF6xh+fnEm5gk1aleOTk8+eSTHD58mKioKFq9VlEfAFe1S0rIKn8BbW38CPJxfobS0lKWLFnC7t27ibz1Vm5avJiOfp4MKjAwkFbNmlU+2s2D3MhIVg8bRnq9erRKSmLg+vV++Z45GvHDD4QVFZHcsiX1z5xhyM8/w/vvV/5Aq/isLEJDQz2mNKmw8cONhqdP8+ybb7JmyBDWDx7stM5MSpiEhASmTZvGrrvu4rDW1ElPp39FDUludNi3j20OQf3QwkImhIYS7mVKAXd14tgxz43uXmieksKDH3/Mni5dqJWTQ+LTT3Osbl1iYmLoZDKnNkBEfj6P/O9/bO7Xj7SGDal39iz9Nm0y9bu5q0sXyjyk2DPzmjcIDCTJze+F2d/9ih6jLBYSKmqEsdJac37KFP7j7liqFAQGctukSei5c13Xe/DLxo1Zc8MNHD16lPr16zPCZJ74xGbN2BEXxzEPk5k+cPvt/NXE5JLPv/kmcz30aMyIiTGC+pWIv3CBoKIiSj3MC9GsWTMjqO+F00lJboP6BRER5HuR6qy6paWl0crh3M+m7unT9PUwWXFl6ro5Hy4qKiLAi/zDFy9epLi42GMqErO/tRdjY91+b7OysgjzcqLDikZXmP3eNk1N5Yyb66qzPhxv66WnE1ha6vr8vPz+Q8Wvp8VEupOiCsoxcz3aOimJ77S+NOqunEYm8o43TEvjlId0JpFevj4A5ytJZenpes6djNhYl8Z8s+IuXMBSVESAh9+jsybOIQ61besS0AcoOH/e3LxjWlNQUOCSrtHMZ8imtILUwVFRUZwLC6t4hLZVRSkRlYlz0tSmTd0G9AGvr0OVUvTt25cx5UeHGys5b3L+usSUFCNoX+71DQsL4+ZNm1AmUvlp4Lsbb2Rbr16UWn938yMj+faWW2hx9ChV+7T6ps7585xzCOpHZ2YSYyKFI/D/2TvvOKmq8/+/z+6ydHbpHUVAkSKIoGIBsRcQC/YSNZqosUTTk+/PFE0vmsTEqInGRGM3arD3ggULiiJSBEGqtF3qArtzfn+cc/feuTOzOzO7MAt83q/XvHbnljPPvffcUz7nOc9h75kzeeq449gcCddVnUgwt39/hsYcSbOh/9y5zI2J+r0asD7Dzo4p5II8dWGMmQIcCIy21mZs5Rhj9gfeAt601mYXGLmRMMb8AbgGuNpa+6c0+ycCjwKPWGtPrSet94F9gWHW2pT4B8aYG4FvAldZa/8c35+FrRVlbduWVaQJrVCLwu9kPj42HeeLTz7hgcmTWe89agb07MmksWPDqVdZht8BmLd0Kf97800q1q2jX79+nHLKKbRavz7r8DsLFizg3//6FzWxCnP3bt045dBDaduqVVbhdwDe+fRTXpw2ja01NQwfPpzjjz/eVaAZpiXWkib8Tp1kEX4H/L154w0q1q+nqKioNkRF25YtuXziRFpkWggwFn5nw7x5PPLaa8xbupT2bdrQvm1bPl+2jJbNm3Pkfvu5qbkZpiWmpU+f0MuuvrwGacPvrFm3jkdee41FK1bQubyckw85hO4dO+YcfodmzZi/ZQv/+9//WLNmDf169+bkgw6idaYYmHWE31m8ciUPvPxySsijAQMGcHbgcZJF+J2lW7fy97//PSn2YKeyMiaNGUPXeGdtO4Tf2VJaymOPPcbMmTNp3aIFe3TvzqwvvqAmkWC/PffkmFGjQm+XesLvpJDltMRafPgdILtysJ7wOylE81qO0xKXzZnDrf9J9sdp07Il3zr9dPdlO09LrJw7lz/fe29t2da8WTMuOeEEOgbPPz4tsZ7wO7Rrx+uffMILL7xQu6m4qIhvTppEm3SeimnC71BXx6Rjx6zD7wCNVuf+48knWRTJF4cMHcoRI0bkXedaa6las4aW9Ylv27jOnb1xI0888QRr166lyBgS/j3v3qEDp44dS8donZNlnVuTSPDYlCl8PH8+JSUlHHrooRx66KEZQwHUEgt5V285WE+d++nChbw3ezYtmjXj4KFD6bbHHsl5rUB1bi1du7r3EbIrB2N1bp2zDKDOUAAAazds4KaHH04bErCoqIhvnXYareqK8xwpB7euWsVNt9/OxkjZsM8ee3BisAB4jnXum59+ysvTptUOWBcZw1EjR3LgoEHpT4jVuXPfe497MnhmnXPkkfQfMKDeUABJ+HKwqqqKxYsX09Va2tS13lCaOve5t9/mjTSd7r7du3POpEkUR+vteurctxcu5OmXXkq774xx4xgYnQoPGevc5997jykZhIATTjiBkSNHZlXnrmzRgr/cemvafT07dXJhD6NkWecuWrGCTZs30/fAA8M1BOqpc/v07cuyZs246qqr0sYZnjNtGq8/9hhATuF3xl9yCZ0jAxdBHPm18+fXG37nS6B///6cfvrpNGvWLMnzd/Xy5bz84INU+rI9m/A7Qw4+mFFHHZX0O5s2beLee++lZuHCrMLvlJaW8t3vfjdpbYZETQ0fvPIKH0bWZcgm/E5xSQkTLrmE8i5datcN+/TTT3nkoYfoESljswm/0xPYbdAgDjnxRJr58q6mupo3n3iCuR98wBKoN/wOxnDoSSfRb599UrysF8ycyYsPPADW1ht+pzVw8imn0G/o0NrrstYy/+OPmfL441RXV2cVfgfgoBNPZM99961NJ7CrasMGHr3lFjb5urS+8Ds9O3dm7Cmn0LF7dypXraK0eXNatmnDsgULeOahh5jvZ8jXF36n75AhHBablRrl3eef56PXX88q/E5RcTFnXHstLWIOYtPfe49HvMdsfeF3ADrsvTcTYqHXAjZv2sR/fv3r5HuUIfzOfkceyT6xBeMTNTU8dO+9fOKdj+oLv7N7374sBc776ldTQtRZa/nXDTeQCBZxD9JJU+cG13b8RRfRNV4uR7jlD3+gxdq1md9bwPTtyyWXXELPNAOo4O7Rvb/5DdZaPl+4MGOdu3vfvrTr2JFTM8zUr6ys5K6bbqJtpncE3DvbvTuDBw/mtNNOS3vIG5MnM8vP1Pl8+fKs6twPP/uMp6dOpWrLFoqKijjggAMYN24czVasyBxWFFLq3P/ccQdzMrT3zz3ySPoNGZJX+J3NmzezaePG+p0nYnXuQ/fcw4xgzRGgW8eOjB89mh7B+5RDP/flDz7glQ8/TNp24bHH0qeudWIy1LmJRIJ7X3iBuf73upSXc+Fxx9EiKvTvQuF3ysvLqaysrLTWlqc7tCmL+hXAUmvt3lkc+wnQ01pbVt+xjYkx5hrgD8BvrbXfTbP/cuAvwE3W2mvqSesx4ETgBGttyqpUxpgHgNOAk6y1j+Vha0VZWVlZpoX5RH5s3bq1dmHahmCtpaamJq907rrrrtoFoMB1eK+99tqcFn+NkkgkkjrS+Sz+25hE780WP6W+eSYxvx6qq6tr73FNTQ1FRUV5LZTVmERtaggNyUNx1q9fz8svv8wXX3xBz549OfLII7NavDPg+eefZ8qUKUnbJkyYwIgRIxpsW0Oorq6muLgYY0xtPi90/m5qXH/99UmDMcYYfvSjHxX0Pq1fv56SkpKsF2uri/vuu49ZsfU1zjnnnLSekzsC69at4w+xqaEdO3YMw2XlyLx583jsscdYu3YtvXr1YtKkSZSVbdemVRJBuVZUVFQrWjRGGVdVVUVxcXHOC7eJ7cMXX3zBHXfckXbfmDFjGDduXE7pbdmyhSeeeKLWm/3YY4+lU46eemvXruWRRx5hwYIFtG/fnhNPPJFu3brRvHnznNoRW7du5Y9//GNKeLp9992XCRMm5NUmmTdvHvfddx9bt26lqKiIiRMnsk99oeAiLFy4kDvvvLP2e1FREaeeeiqDMg1U1MG6dev405/+lOJ1WlJSwne/+92s37mNGzdy6623JsX5LSoqYsSIERx33HFZe2xaa7nxxhvTLizdtm1brr322qzSiXLrrbeyzAu25eXlXHTRRTktBvjAAw8wc+bMtPsuv/xyOtfjqRylqqqKX3tBMaBNmzZ8Kxq+IQtWr17N7Nmz6dKlC328CJJPWfvhhx/y6KOPJm3L9Z2dO3cu9wRhaDzHH398zgs3Rtm6dSvV1dWUlpY2uD0TrF0GpAyEZEtlZSX33nsvy2MDmrUDVvWwfv16/vjHPya9Z6eddlpe7yy4vt+6deto27Yt69ato2XLljktJBtl69atPProo3ziZ0F27tyZCy64IKd+xFtvvcVzzz2X1B5t1aoVI0eOZOzYsTnNHJ4/fz4PPfRQ7Ro9Xbp04bzzzqvTszwdP80QCnXkyJGcEB8czMDNN9+cdtH5QYMGZRSg4wT5bcKECewXhJqN8MgjjzDdO3FmswD48OHDOemkk9Ies3nzZn75y19mldbll19e52LYv/3tb9mwYUNWNh1zzDGMHj067TGPPfYY06ZNqzedoUOHcmoQVjrCpk2buPHGG2sH5rNdJH3UqFEpz/m+++7j00jYrfrS6tGjB1+LhgyLsHnzZm688UaqqqrqXXA9ypdffslrr73GjBkzsNbSqVMnLrzwwqzet3jdD/m1sQKqqqr497//zRIvxA8fPpyJEyfmlVZAdXU11tpdvr1en6jf8J7RtqOUzPHl42wCCvGkg6Go4Rn2BwpW6ipm6dM60aeVIurnmJbYTjRWAdOQgYH4IjJxUT5XioqKePnll3njjTew1nLAAQdkXNRrexC9N/k2MAOi97ipiLmNIVBBw/JQnDZt2jB+/PgGnR9n3bp1KQvfbW+i9yeXDsGuRMeOHVkR8Uxs1apVwe9Vrh2vuthtt92SRP2SkpK8Qp41FVq2bEmLFi2S6oG6QhfURU1NDf/9739Z771MFi1axLPPPpt1R3Nb0JjlWpTGGCAS244ePXpQXl5O1BFl5MiRjBw5kq51eXxl4Pnnn68VOdavX8/999/P5ZdfnlN99Oyzz9Yu/rhmzRr++9//cvXVV+dcpzVr1oyvfOUrvPrqq2zYsIH+/fszdOjQnEThOM8//3ytyJhIJHj22WcZ6j14s6FPnz5MmjSJqVOnUlxczCGHHMIeda3HUgdt27Zl4sSJPBwLIbTvvvvm1GZu1aoVl156KR97b/0hQ4bQwntN58L8+fPTCvoAQ4M1R3Lgr3/9a1IdWVFRwdtvv51TO7l9dKZEjPXr1+ck6m/ZsoVmzZolLZqZi3gKMHv2bO6///5aAXXs2LEcFqzfkiPpRL0V9c0wjDFv3ryUbS+88AL77bdfXu2R1atX856P/77ffvvlXUcGGGMa3B8pKytj3Lhx3BcLmfj5559nJerPnz8/ZeDss88+y1vULyoqqh3Ab+hA/uLFi2sFfXDP/91332VMLPRdXRx44IEMHTqUt956i3Xr1jFkyJC8nS/69u3LeeedR0VFBR07dszp/YrSpk2b2vZRlNU5hKjclMErd349C+NGsdayfPlybr311rT9/QcffJA9Y2FLMqUT8Otf/zpFTwA32JCNpmCt5ZlnnuGtt95Ku79bt25p710mm6qrq7nlllvS3tsf//jHWQnFc+fOpaqqKqW917JlS3bbbTfm1LWmVhrGjh2bsu3ggw9OEvXr48AMYRYTiQQPPvhg2meQia1bt3Lvvfem5J2VK1fy3HPPZXWP4s4FmbZlS4sWLbj44otZunQpzZs3d2tENZBt0QfYGWnKqsZ8YIgxps5AZcaY7sAQqHMdo23F68BaYKwxJqmGMMaUAhOABPBUFmkFQn7KfDNjzDCgH/CptTa1pSN2aeLez3vuuWeDRLD58+fzyiuv1Hq1TJkyhdnRRUOEqId99903RSh9+eWXefrppwtkkciWo48+urYBVVRUxFFHHVXw2SyNRSKRYMuWLbRu3Zri4mLat2/P6aefnrMA0pQoKSnh+OOPrxXL2rVrl/cg7IYNG1I6XcvqC9kjxDaguLiY888/n+HDh9O3b1/Gjx/PCSeckJegD6ki4cqVK5M8wLNhSWzK+dq1a/Pu/Hbu3JlTTz2V888/n4MOOqhBgj6QIlpv3LiRmhzXWBg8eDAXXnghEydOZNasWTz44IMps5qyZciQIQwfPrz2e8eOHdMKIvXRsmVLRo0axahRo2jZsmVeddHrr8eXkHXl5NFHH80RRxyRU1pLly5NK1BnI1ZFyTTg0rp161ov+Wz573//myTog/PcTCeMZ+KVV15J8oieMmVK0npIudAY7YXu0XAGns2bN7OyvlCXaVi7di233347b7zxBm+88Qa33XZbzu9+Y7Ny5UqefvrptGJgtk4G6YTpRCLBa6+9xpf1havdxqR7H3J9R8C9D0cccQQnnXRSg2ZT3n777dx6663cf//93HnnnXmX22eeeWbawZx43VAXAwcOTLs9XZ6vi7lz56YV2zt06MCAAQNySgtcmR2ndevWHBULpVUX48aNY++9905bBhx99NE52fPYY49lHCzplWHdhijWWjZu3MgxkQWBo9x///21sz+zoXXr1mm1lXjZWx+ZBgOLiorYd999c0rrww8/zDgYtCjL2PP9+vVLui5jTE6z/NIRzEhoDEFfZE9THvp4DPg+8KAx5gxrbUqJaYzpCdyPu47/bmf7sNZuMcb8FWfnX40xZ1lrg2HzXwNdgHuttbXBRY0xvwROBm621t4cSestY8xbwIHGmKuttX/0x7fGhfABtyiwEEmMGjWKNm3a1E6bzcbDoy7SNU6WLFmS1ai/2LlYunQpNTU19OzZM6eOWvPmzbn44ou55ZZbkjrA7777LuPGjZOXbBOmf//+XHPNNSxevJhu3bo1WGxqSrz66qu88sortd9btGiRV+enqTF06FAGDBhARUUFXbp0yXtmRdu2benUqVOScJKvt64QDaV9+/YNnrYd0K1bt6SQB5k66HWx++67syYSk7pTp05NonycMWNGigfpoEGD8vJuq6mp4Z///GftDIlPPvmEM844I6MQVRcTJ05k9OjRbNq0id69exdkxpe1Nik8ZcBZZ52V0wLZAZnE4OgARjZ069aNCy64gDfeeKO2ndWjRw9OOOGEnGaRVldXp70+cKJftuV3XJiqqalJEvlzoVu3bvTt27dWbCoqKmL//ffPKY0hQ4bw2muvJbUfW7RoUecMh0xMnz49yft18+bNTJs2La9BpsagsrKSv//977WLwJeUlNCsWTM2b97M0KFDOeCAA7JKp1u3bhx22GG89tpr1NTU0K5du9rFo1966SXOPvvsgoUV7N+/P61ataoNd9MYQmG+TJ8+Palfu2nTJiZPnswZZ5yRc1o9e/bkBz/4QUoYniBcSjb9pAkTJgCu3N66dSuJRKL23c+FdIM6+++/P0cffXReA2vHHHMMpaWlzJ8/n44dOzJkyBD69euXUz1SWlrK6aefzuTJk2tnxgTkMpC2detWZsyYkbK9qKiIkSNH5iR+p/PGb9GiBXvttVfWaYB7xhs3bkxyArLW8uabb+aUzuzZszPWp3vuuWfS+oGZCJ7vxIkTM96Lp556iiuuuKLeQYvS0lIuuugi3nrrLaqqqth3331zHlgWTYOmLOr/BjgbOBiYZ4x5BPgEWA50BQbjxPFSnJf+bwtjJtcDR+E87Pc1xrwLDAKGAgtwi9tG6Q7sBaQL5nkh8AZwkzHmLH/+of6cJ4C/bwP7xU7A3nvvzd5717v8RFbsHl1os45toukwf/58Pv/8c3r06JFzQyUdiUSC+++/v3aGRq9evTjvvPNymm6cLmxGLl4RonC0atVqpxC748RjGC9dupSKigrKowuU7oBUV1dTWVlJp06dGiScGWM444wzeOaZZ/jyyy/p379/Tl5ajc1HH33ECy+8wKZNm+jTpw/t27dnt912Y/DgwQWzSeyYHH300VRUVLB48eLa8DC5huA7+uijqa6uZu7cuXTt2pXjjz9+G1mbPa+88govv/xy7ff27dszbNgwDjrooLzS++KLL4ivvfXRRx/lJepD+lAs2xNjDOXl5SmDMfkI+uDCeDRv3rxWkAUYMGBAXm3kPn36NFg8KSkpoUOHDmk9WnO596NGjeLJJ8PIr/vss0+DnC/OPvtspk+fTmVlJYMGDcp5ho0xhgsuuICHH36YefPmUVZWxgknnJBXyNN08cvjcey3JzNmzEjKP9XV1YwbN44DDjgg5zJp7NixHHjggaxevZrbbrutdnsgNhZK1F+3bh09evRg6dKllJWVcdRRR2XlXb0tSOexnM+Mjyj9+vXjs88+q/2+22675SSkT5gwoVbc37JlS16hnAYMGMB+++3H+++/D8CwYcM49thj854pU1JS0mjtvZ49e6aI+pkW0E1HcXExLVq0SApV1LVrVy699NKcbUl3b88555ysZ+hG+6wzZszgkUceqRXde/bsydSpU7PuQ1hrSSQS3HbbbWnLoJYtW7Jly5asyoHOnTtnDB9XU1OTVJ7XR/v27TnuuOOyPl40TZqsqG+trTDGjAPuA0YBZwJRNSgotd4GzrbWVmxfCx3W2o3GmLHA/wFn4AYaVgJ/A/6ftTbrmsNa+6kxZgRwA26gYDguDNEfgd9ba/NzmxAiB3r27MmECROYMmUKiUSCgw46SKJ+E+btt99OCmtz8MEHN3gNhNmzZyeFXFq0aBEffvhhzouUjR49mkceeaT2+7Bhw2jZsmWDbBMiXzp06JA0Lb158+Z5LyjeVFi4cCEPPPAAGzZsoFWrVkyaNIm+ffvmnV6nTp0455xzGtHC/Ahilgcdqrlz5wLwzjvvsHLlyoJ5WYodk3bt2nHxxRezadMmmjdvntfgV4sWLTjllFO2gXX588477yR9X7duHSNHjsx7zaB0sxd29DLy+OOP56GHHmLz5s20bNkyZ4/YKKWlpVxyySW88MILrF69mmHDhmVczHF7EaxfEHjCBgtE5uIZPWrUKMrLy/nss8/o2rVrg72qS0pKUkKD5kqrVq0477zz2Lp1KyUlJXmLld27d6/1YA8o5GBTusGSli1b5v3ONm/evEmFEKyuruZf//pX7cyhDRs2pAwUbk9GjBiRUk7mu+5AwIknnsjkyZNZsGABvXr1qhXo8yHftRmMMYwfP57DDz8cyH0djW3J8OHDWbZsGe+//z6lpaUcfvjhOS1MX1RUxJFHHsnkyZOx1jZowGHMmDF88cUXteHoOnfuzPTp02nVqlXOa2ts3rw5yYt+yZIl3H333XzjG9/IunwqKipi8ODBaUX9XAb2hgwZknEWRbCwbK7U1NRQVVVFy5YtWbVqFeXl5bv84rQ7Ek1W1Aew1s4HDvDi/rHAnkAbYD0wG3jaWvtSAU0EwFq7AfiB/9R37AXABXXs/xw4t5FMEyIvRowY0eAGudg+xKf+TZ06lcMPP7xBHrvpFpXLtNBcXQwdOpTy8nLmzp1L586dG9yQFqIhHHHEESxfvpw1a9bQrFkzjjvuuB2+wfrkk0/WxofduHEjTzzxBFdccUWBrWo4ixYtytgpeffddyXqi7zY2QaVS0tLk+JDJxIJfve739Uudp/rzL2ysjKKi4uT4vE3b9680ewtBP379+faa69l5cqVdO7cuUFlvrWWVatW0atXL8aNG5f3gpuNSZ8+fbj66qtZv349paWlWGvzyucDBgxo1Bl61lqmTp3K3Llz6dKlC4ccckhedjW0jt5nn314/fXXa9uwLVq0KGj/ZujQobz77rssXeoi83bv3j1tPPNcKCsrY+jQoXz00UeAE3wzLci5rVm0aFFK/PyZM2fmHKKqsejWrRtHH300r7zyCtXV1QwaNCjvRaAD2rVrx9lnn91g2zZv3szcuXNp06YNu+22W15pNCUxP8AYw3HHHdegmQMjRoygX79+LFu2jN69e+d9nX379uWqq67i3Xff5fXXX2fFihWsWLGCGTNmcNVVV+VUvwWLtkdZtWoVy5cvz2n218iRI/noo49qw4t17NiRo446Kqf6uq51IbKdiRR9NoMGDeKEE06gdevWtaGktmzZwsMPP8ysWbM0y34HoEmL+gFeuC+4eC+EEE2NuHjfGIuU7bXXXjz//PO1C6UFngX50Lt3b3r37t1gm4RoKJ06deKKK65g5cqVlJWV7fBiFZASdiHTwmI7Gr169cIYk7Yjka9nmxA7G+PGjUua0RJ4Ea5fv55HH32Ua6+9NidRNIjvHiWXRSCbKqWlpVkvQFoXkydPrg138fzzz3PmmWc2ifWmpk2bxvTp02nTpg1jx45tEoNXr776am1oqLlz57J06VLOP//87W5HixYt+PrXv86HH35ITU0Nw4YNo127dtvdjoBmzZpx8cUX1y5kvMceezTKehMnnXQSAwcOZNWqVey1114Fm43Qvn37lLo7V4/oxmb06NEFn1ETZ9WqVdxxxx216w4MHjyYSZMmFdiqxqWh/dGysjLKysoabEe7du3YvHlzUp7cuHEjc+bMyWlALV25UVxcnHN50rJlSy699FI+//xzSktLcw5NZa1l8+bN/O53v6O6ujppX1FREX/4wx/4xz/+kXV6paWlTJw4sbZPFDy3YPvvfve7nOwThaHJiPrGmKuAxdbahwttixBC7Ch07949KV5sQ6YpB7Rr144LL7yQt99+m+rqakaNGpVzTFQhmiJFRUUFj/PcmAwcOLDWOw9otLVVCk2wSOoLL7zAhg0basVKY0yDveyE2FkYOnQoPXr04L333mPq1KlJgnxVVVXtWhvZ0qlTpxRPfdX9jg0bNjBt2rTa79Za3njjjYKL+h999BGTJ0+u/b5gwQKuvvrqgs1CSyQSfPrpp0ydOjVp+/z589mwYUNBwjm1bt0673UmtgVFRUWNHu++qKioScyGbdu2LaNGjeKdd97BWkv37t055JBDCm1Wk+PNN9+sFfTBxWs/5JBD8l7vQ9RNunIn17JozJgxfPbZZ0kzUYYOHZrXLIKioqKsFzJPR/PmzencuXPtjJ+Anj175jwQUl5entHJqVWrVtvdkSZYE+Cjjz6iTZs2HHPMMY2yXuDOTpMR9YGbgNeBFFHfGPMiMN1a+83tbJMQQjRpgjiqARs3bmTFihUNFi67devGxIkTG5SGEGLbMn78eFq3bs3ChQvp1atXbXzVnYFhw4YxbNgwwE0nXrJkCX369KFjx44Ftkzs6ixfvpzi4uKcBPNtRcuWLXnnnXdSPOzLy8tz9pBt1aoVEyZM4JlnnmHTpk3069ePMWPGNKa5OyzW2pSZQ9H4yoUivgD8hg0bWLhwIf369SuIPQ8//DCffPJJyvbmzZtrltVOzpdffsl//vMfKisradasGWPHjuXggw8utFm1zJkzh+eff54NGzYwfPhwjjjiiEaZ3ZwP0cWS69omGocg5E2wSPLAgQNzXi+wQ4cOfPWrX+Xmm2+urW8/+OADhg0bVpC1B4888kj+/e9/J23Lx7Fn5cqVVFRUpF3wd8WKFVRVVeWc5tatW3nmmWeYPXs2nTp14thjj81al3jzzTdrZ8StWbOGhx56iGuvvbZJzEBryuQs6htjSnAx4Y8H+uFi3GcqEa21tjFaFYfRtAYghBCiSdC+fXsWLVpU+72kpIS2bdsW0CIhxPaitLSUY445ptBmbHO6du0qj2FRcKqrq/nPf/7D/PnzAdeBnjRpUqOEz8iXRYsWpUzBb926NWeeeWZedg0bNowhQ4awZcsWdaIjtGnThn322Yfp06fXbquqqmLlypUFHdxp3759Vtu2B2vWrEkr6BcVFXHUUUft8GvYiLp5/vnnqaysBJyo9/rrr3PAAQdkXNBze7JhwwYeeOCB2rJyypQplJWVMWrUqILYM2LECGbMmFE7UNi5c2eFKt2GtGrVissuu4wFCxbQvHnzvMOxzZ8/P2UA/dNPPy2IqL/HHntw4YUX8uyzz7JlyxaGDRuW01oa0UHq5cuX89xzzzFv3ryk7WeeeSY333xzzra9+OKLvPfee4Bbk+++++7jyiuvzGoQ7Ysvvkj6Xl1dzdKlSxs0s2FXIKfWnjGmA/AOcCtwEjAU6AvsXsdHCCHENmLcuHG1Hbji4mKOOuoodcSFEEKIRmb69Om1gj44L+k5c+YU0CI34BXvKB944IENGgQrLi5WOyINEydOTIp/vGLFCh566KECWgQHH3xwrUBVVFTEYYcdVrAY5unWQOnZsyfXXHMN++23XwEsClm9enVSqErR+MTvb1VVFZs2bSqQNcksXrw4ZfDz888/L4wxuAVcL7zwQkaOHMm4ceO44IILCjo4vCtQVFRE3759G7S+SrqytZBrRvTp04eLL76Yyy+/nIMPPjjvmSddu3bllFNOSSnD0y0OHMcYk/J57LHHko5Zs2YNHTt2zMq++BoDJSUlCkuVBbkOnf4CGAYsAX4DvA18CRR+/qEQQuyCtG/fniuuuIIXX3yRWbNm8dFHH9GhQ4dGj9cpxI7M1q1b+fLLL+ncubNCAAgh8qKioiJlW+CZWijKysoYP348zz//PFVVVQwaNIgDDjigoDbtrBQVFaXkgeXLl1NVVUWLFi0KYlOrVq245JJLWLFiBa1atSpIzPqADh06sPfee9eGBCoqKmLcuHG0adOmYDbV1NTw4IMPMmvWLACGDBnCySefLAF1G7D33nvz2muv1X7v1atXk5k53K1bN4qKipJCZjXG4tkNoXfv3vLO385s3bqVDRs2pA01kw277bYbI0eO5L333sNaS//+/dl3330b18gCUVxcnLKmTi79pWbNmnH44YfTt2/flNk5GzZsSAkXnInRo0ezZs0aPvroI9q2bcsxxxyT17oFuxq5ivoTgGrgSGvtp9vAHiGEEDkyZ84cpkyZUvv9vvvu46qrrqJdu3YFtEqIpsHnn3/OAw88wKZNm2jevDmTJk3SoJcQImcGDRrElClTaoWhkpKSJrGA24gRIxg+fDg1NTUKcbKN6dmzZ61ADNCxY8eCCfpROnfuXGgTAJg0aRKffPIJa9asYa+99mqUhelnz57N7Nmz6dy5M/vtt19O4Vw+/vjjpOf18ccfM3jwYAYOHNhgu0Qyhx12GM2aNWPOnDl07tyZcePGFdqkWtq1a8eECRN47rnnqKqqYsiQIRr83MX44IMPeOqpp9iyZQvdu3fnrLPOymvQ6YQTTmDMmDFs3bq1oF76jU3z5s05+OCDefXVVwEn8ueyps5xxx3HiBEjar8nEgmKioqw1jJ79uyUsEXpyGWmQbqZYbsyuYr6HYBZEvSFEKLpEJ/+X1NTw/z582sXmRRiV+bpp5+unQK+efNmnnzySa666qoCW9X4zJ49mxkzZtC2bVtGjx5dUI9NIXZGunXrxrnnnsvUqVMpKipi9OjRlJWVFdoswHlFy/t423PcccdRVVXFggUL6NKlCyeddFKhTWpSFBUVMWTIkEZL7/333+d///tf7ff58+dz5plnZn1+upA7CsOzbSgqKuLQQw/l0EMPLbQpaRk+fDjDhg0jkUhQXFxcaHPEdqSqqoonnniiNgTT0qVLefnll5kwYUJe6TWVGSiNzbhx49hrr7348ssv6du3b07tm7iDQ9AeMcYwfPhwXnnllbSzHUXjkKuov4Ac4/DnSJkxJtOQUF37ALDWvroNbBJCiCZNuti5jeEdJcTOQLwDX1FRgbU279iTTZGZM2fywAMP1H6fPXs2l1122U51jUI0Bfr27Uvfvn0LbYYoEGVlZVxwwQW1Xohi2xIsthgwa9Ys1q1bl7WoNnDgQF599dVar87i4uImMbtGFAZjjAT9XZCKioqUNRVWrFhRIGuaNj169MgpNFVQtt55550sXLgw7THGGN5991369evXKDaKVHIV9e8GfmKM2ctaO6veo3NnCPBSmu22jn3RYwq/vLoQQmxnRowYwcKFC5kxYwbFxcUccsghdO/evdBmCdEkGDRoEB988EHt965du1JVVbVTLQQZvT5wnZXFixenLDglhBCi4UjQ3z7EYzoXFRXlFGKqW7dunH322bz99tsYYzjooIN2qpAZQoj66dKlC2VlZUlr4Oy5554FtKjpsmTJkrw89Y877jjuu+8+KisrKS0tZcuWLbX7WrVqRZ8+fbaFucJjcolHZIxpBjwDdAPOs9a+V88puaTd4MV2rbVqYWXAGFNRVlZWpmkvQuy8bNq0ieLiYi0EKkSErVu38sILL/DOO+/UxsIuLy/n61//epOIhdwYPPbYYynC/pVXXinxQgghxA7L559/zj333FPrZXvIIYdwxBFHFNgqIcSOxsqVK3nhhRdYvXo1e++9N2PGjNHgbIyXX36ZV155BXCzms4666ycvOsTiQRr1qyhrKyMadOmMX36dNq0acNhhx2WNqpAfVRXV7N48WI6duxY0AXXmwLl5eVUVlZWWmvL0+3PVdS/A2gGnA4UAx8Cc4ENGU6x1tqv5mSx2CZI1BdCCLGrMmXKFJ5//vmkbRMmTEha1GlHZtWqVfzzn/9k/fr1AOy///4cd9xxBbZKCCGEaBjr1q1j3rx5dO7cOaewELsKa9eu5dlnn2XJkiXsvvvuHH300QVzWJg7dy4vv/wyVVVVjBgxgoMOOqggdgghcmPz5s389re/TVrQtnfv3lx00UUFsWfZsmXcfffdbNiwgaKiIo499lhGjRpVEFuaAvWJ+rmGq7kAF+YmCNK6r/9kwgIS9cVOTWVlJdZaysvLC22KEEKINKSLLb8zxZvv0KEDo0aNYvr06ZSXl7P//vsX2iQhhBCiwbRt25Zhw4YV2owmy8MPP1wby3rNmjXU1NRw8sknb3c71q1bx3333VcrCj733HOUlZUxePDg7W6LECJ7rLWsX78+SdAHkkLobG9efPFFNmxwfuOJRIJnn32WYcOGKRpBBnIV9S/cJlYIsQOSSCR49NFH+eijjwAYPHgwp5xyiqZyie3O8uXLmTNnDp07d2bPPffcqcRKIRqDYcOG8dZbb7Fu3ToA2rdvz6BBgwpsVePx9ttv89JLbtmhVatWcffdd3PllVeqPhJCCCF2UrZu3ZqyOOXcuXMLYsvnn3+eIgp+9tlnEvWFaMIsW7aMhx9+mJUrV9K8eXM2b95cu2/kyJEFsyseXaS6upr58+drofMM5CTqW2vv2laGCLGjMWvWrFpBH2DGjBkMHDiQIUOGFNAqsasxa9Ys7r///trV50eMGMGECRMKbJUQTYvWrVtz6aWX8vHHH1NcXMzgwYNp3rx5oc1qNGbNmpX0vaKigmXLlilUgRBCCLGTUlJSQvv27VmzZk3ttnxiVzcG3bp1S9lWKFuEENnx+OOPs3LlSsCF4CkrK2PAgAEMGDCgoIsJd+zYkRUrViRtW7hwoUT9DMiFS4g8Wb16dVbbhNiWTJkyhejaKNOmTaudriaECGnVqhX7778/++23306zQG5AfEHc4uJihYQTQgghdmKMMUycOJF27doB0KlTp4Ktp9O5c2eOOuoomjVrhjGGIUOGsN9++xXEFiFEdixbtizp+/r16znhhBMKKugDacsO9Wsyk2v4nVqMMaXAfkAvoKW19l+NZpUQOwB77rknL774IolEAnANK40eiu1NLoudCyF2TsaOHcvixYtZvnw5JSUlHH300bRq1arQZgkhhBBiG7Lbbrtx9dVXs2HDBtq2bVtQWw466CD2339/qqurdzrnCSF2Rvr165cUsqt79+5MmzaNPfbYg7KysoLatc8++zB9+nQA+vbty/DhwwtmT1PH5CoIGWOaAT8BvgHU1hzW2uLIMf8AjgPGWWtnxdMQ2x9jTEVZWVlZPD6VaBifffYZb775JtZaRo8eTf/+/QttktjF+OSTT3jooYdqxf3hw4czceLEAlslhCgEK1eupE2bNupMCyGEEEIIITKyfv16nnrqKb744guaN29eG4qnuLiYs88+mz322COn9Ky1vPvuu8yaNYuOHTsyZswYWrdunbd9q1evprq6mi5duuSdxs5AeXk5lZWVldba8nT7cxL1jTElwFPA4cAG4B1gCNApJuqfAPwP+D9r7S/yN180FhL1hdh5Wbp0KbNnz6Zz584MHDhQi2MKIYQQQgghhBCiTqqqqvjtb39bG4EC3CygCy64IKd03njjDZ577rna7z169OCSSy5pLDN3WeoT9XMNv/MN4AhgCnC6tXapMeY1oFPsuOeArcDRgER9IYTYhnTv3p3u3bsX2gwhhBBCCCGEEELsIFRXVycJ+gBbtmzJOZ0ZM2YkfV+yZAmrV69OWftLNC65unOeB1QDZ1trl2Y6yFq7BZgL7NYA24QQQgghhBBCCCGEEEI0Mm3atGHQoEFJ20aNGpVzOsGi3QElJSUNCr8jsiNXT/2BwFxr7RdZHFsJ5BaEKQuMMRcAfay1P2vstIUQQgghhBBCiF2NRCLB1KlT+eKLL+jZsycHHHAAxcXF9Z8ohBBih+bkk0+mb9++rFixggEDBuS1VuThhx/O4sWLWbduHUVFRRx11FE0b958G1grouQaU38dsMhau3dk22vAQdGY+n77p0Bna23HxjK2rt8TdaOY+kIIIYQQQggh0vHkk0/yzjvv1H4fMWIEEyZMKKBFQgghdiRqampYsmQJ7du3p02bNnmnU1VVxbx582jfvv0uH2a4sWPqfwYMNMaUW2srMh1kjOkFDADeyDF9IYQQYpuwZcsW1qxZQ+fOnbWYsBBCCCFEhPfffz/p+7Rp0xg/fjzGmAJZJIQQYkeiuLiY3r17533+u+++y7Rp01i+fDk1NTUAHHjggRxzzDGNZeJOR66i/mPAPrjFby+v47gb/d+H8jFKCCGEaEw++eQTHnvsMbZs2UJZWRlnnXUWXbt2LbRZQgghhBAFZ8uWLbUCSoAxRoK+EEKInJg/fz7Lli1jjz32yKm//e677/LEE0+kbH/77bcZPXp0Ssx+4chV1P8DcCHwde+NfzvQEsAYMwAYClwNHArMBm5tPFOFEEKI3KmpqeGJJ55gy5YtAFRWVvLcc89x7rnnFtgyIYQQQojCY4yhuLg4SdhvSOgEIYQQux4vvPACr7/+eu33U089lSFDhmR17owZM9Jut9ayefPmRrFvZySn+APW2krgGFwYnvHAo8C+fvenwIM4QX8OcIK1tqrRLA2Ru4AQQoisqaqqYuPGjUnbVq9eXSBrhBBCCCGaFs2aNePAAw9M2nbkkUcWyBohhBA7Glu3buWtt95K2hYV+OujvLw87fbevXvTuXPnhpi2U5Orpz7W2pnGmGHAV4GTcd755cB6YAbwMHCrtXZTI9oZ5QZAMROEEEJkRevWrenduzdffPFF7baBAwcW0CIhhBBCiKbFkUceyR577FEbNqFbt26FNkkIIcQOgrWWRCKRtC0e1q0uxo4dy8KFC1m9ejXGGLp3785ee+3F/vvv39im7lQYa22hbRDbAWNMRVlZWVlFRUWhTRFCiO3O+vXrefHFF/nyyy/p168fY8aMobi4uNBmCSGEEEIIIYQQOzxPPvkk77zzTu33CRMmMGLEiKzPTyQSLFu2jHbt2ikEnKe8vJzKyspKa215uv0S9XcRJOoLIYQQQgghhBBCCCEaG2stM2fOZNmyZfTr14/ddtut0Cbt8NQn6uccfkcIIYQQQgghhBBCCCGEALfo+qBBgxg0aFChTdllyFnUN8aUABcAxwP9gDZkXrzWWmv75W2dEEIIIYQQQgghhBBCCCFqyUnUN8Z0AF4A9iGzkB9FsX2EEEIIIYQQQgghhBBCiEYiV0/9XwDDgCXAb4C3gS+BRF0nCSGEEEIIIYQQQgghhBCi4eQq6k8AqoEjrbWfbgN7hBBCCCGEEEIIIZoM1lo2bdpEq1atCm2KEELslGzcuJHJkyczd+5cunTpwvjx4+nWrVuhzWrS5CrqdwBmSdAXQgghhBBCCCHEzs6iRYt45JFHWLNmDV26dOG0006jU6dOhTZLCCF2Kp599llmzpwJwOLFi3nwwQe54oorMCab6O+7JkU5Hr8gj3OyxhjTzhjTxxjTNcvju/rj220rm4QQQgghhBBCCLFr8uijj7JmzRoAvvzyS5544okCWySEEDsfCxYsSPq+evVqNmzYUCBrdgxyFejvBvYyxuzV2IYYY4qA14H5wJFZnnakP/7FxrZHCCGEEEIIIYQQuy7V1dWsWrUqadvy5csLZI0QQuy89OrVK+l7eXk5rVu3LpA1Owa5ivq/Bl4F/muM2a+RbZkADAH+Z629J5sT/HH/A/Y1xhzfyPYIIYQQQgghhBBiF6WkpIQ+ffokbdtjjz0KZI0QQuy8HHPMMfTr1w+ALl26MGnSJIXeqQdjrU2/w5g7MpzTDDgdKAY+BOYCmeZDWGvtV7MyxJh7gDOBQ6y1b2Zzjj/vQOAN4B5r7XnZnrerYYypKCsrK6uoqCi0KUIIIYQQQgghxA5BZWUlTz/9NEuWLGH33Xfn2GOPpWXLloU2SwghdkqstRLzPeXl5VRWVlZaa8vT7a9L1E80wu9ba21xNgcaY+YA7a21Oa84Y4xZCay21u6Z67m7ChL1hRBCCCGEEEIIIYQQoulTn6hfUse5F24bkzLSHZiV57kLgEaP858txpivAt8ABgIbgdeAH1trp+eQRivgGOBEYCSwO2Bw9+Re4E/W2i2Na7kQQgghhBBCCCGEEEKIHYmMor619q7taQguvn9D5lcUZG6GMebPwBVABfAk0AmYCBxrjDnSWjsly6TOBm73/88AngLaAQcBvwUm+fTWN6L5QgghhBBCCCGEEEIIIXYgcl0od1uyAtgt15OMC7S0O7CysQ3K4rePwgn6c4CB1tpJ1trDgNOAFsDdxphmWSa3BbgF2NNaO8Rae7q19ljcDIRpwAHA/2vsaxBCCCGEEEIIIYQQQgix49AgUd8Ys6cxZrwx5iz/tyEx7d8Fyo0xY3M8byzQHnivAb+dL9f6v9+11i4PNlprHwYexw02nJRNQtbaf1lrL7fWzoltX4oL7QNugWIhhBBCCCGEEEJsB2pqapg1axZz5swhkWiMpQeFEEKIhlNXTP2MGGMuBn6AE63j++YBv7LW/iPHZB8DTgZ+Y4wZY63dnIUdLXChaSzwaI6/1yCMMS2BccAm4Ik0hzyEi48/HniwgT/3of/bo4HpCCGEEEIIIYQQIgs2b97MP/7xD1asWAFAjx49uPDCCykpyUtKEUIIIRqNnD31jTG3A7cCfYFq3CK1U/3faqAfcJsx5rYck74HmItbJPYJY0ydArYxphdOTN8P+Myfvz0ZCDQHPrbWbk2z/33/d59G+K09/N/ldR4lhBBCCCGEEEKIRuHDDz+sFfQBlixZwieffFJAi4QQQghHTsPLxpjTgK8CG4EbgL9aa9dG9rcDLgd+BHzVGPOstfahbNK21tYYYyYBr+M84OcZY/4HvArMBzYArXGDCWNxHvClwHpgkrW2JpdraQR6+7+LMuwPtvdphN+62v/9XyOkJYQQQgghhBBCiHrYvDk1gEBVVVUBLBFCCCGSyXXO2NdxoW5Ot9Y+Gd/pBf5fGWOmA5OBS3FhaLLCWjvdGHMo8AAwADjFf9JhcJ79p1trp+d0FY1DG/93Y4b9G2LH5YUx5njcQEol8Is6jquoJ6myhtghhBBCCCGEEELsSgwZMoTXX3+dLVu2ANCyZUsGDRpUYKuEEKLpMX/+fFavXk3//v0pK5MEuT3IVdTfF/g8naAfxVr7pDFmvj8+J6y1HxpjBgPnAGcCBwFtI4esA94E7gX+kyH0zfbA+L92m/2AMXsDd/vf+rq1dvG2+i0hhBBCCCGEEEKEtG/fnksuuYT33nuPoqIiRo4cSZs2DfLbE0KInY7Jkyfz3nvvAVBSUsK5557LbrvtVmCrdn5yFfVb4eLXZ8Mq8lzY1VpbDdzlPxhj2uKE/XXW2nX5pJkOY8xbwAE5ntbXWvs5bnABXEigdATb1+dhWrBmwNNAe+AH1tr76zreWlteT3oVyFtfCCGEEEIIIYTImk6dOnHMMccU2gwhhGiSrFu3rlbQB6iurmbKlCkS9bcDuYr6i4HBxpgya21lpoOMMWXAYH98g/FCfkYx38fi/6G1dkSOSX8OlOd4TjAz4Av/t1eG43r6vwtzTB9jTCfgWVw8/j9Ya3+VaxpCCCGEEEIIIYQQQgixraiurk7ZtnVroYKq7FrkKuo/jVsI9w5jzLnW2k3xA4wxLYE7gRZAnWF6GoIxxgBnAz8A9s4nDWvtmQ0w4VNgC26Qo1maMEDBAMNHuSTqZyU8hbumu4BvN8BGIYQQQgghhBBCCCGEaHTat2/PgAEDmDNnTu22/fffv4AW7TrkKur/Ahfn/iRggTHm78AnwHKgK847/6tAR2ANkJOHuRfqvwJMAvriFpv9ELjJWjsjctyZwM+Afrh485uA23O8lgZhrd1kjHkJOAY4Hngsdsip/u8T2aZpjGkBPA6M9H8vttZus5j9QgghhBBCCCGEEEIIkS+nn346H3zwAatXr2bgwIH06dOn0CbtEphcNWNjzEjgQWA30i8Sa4AFwGnW2ndzTPsRYGIkHfxvVAHHAtOAB3BCugEqgb8CN1prV+Z0IY2AMeYY3OyF2cCh1tov/faTgYdxoXf2tNZuiZxzMvBLYKq19vzI9mLgIdyAycvAcdbaqka0taKsrKysoqKisZIUQgghhBBCCCGEEEII0ciUl5dTWVlZmWkd1Vw99bHWvmuMGYTz2D8W2BNog1sQdjZO5L43V0HaGHM+TtAGmAK8g1uY9wicR/6fgUX+NyuB3wI3W2vX5noNjYW19hljzC3AZcCnxpgXcLMUDsOF5jk/Kuh7yoC9gGWx7VcQXv9q4G9u4kLKb17QSOYLIYQQQgghhBBCCCGE2MHIWdQHF3oGFzf/zka05VycV/7/s9b+IthojCkFHsGFuBkKvAGcaq1d3oi/nTfW2suNMdNwaw2Mx4UC+h9wnbX2wxySah/5/5Q6jrsgZyOFEEIIIYQQQgghhBBC7BTkHH5nW2GMWQ40AzpZaxOxffsC7wFbgT2stYsLYOIOjcLvCCGEEEIIIYQQQgghRNOnvvA7RfUlYIz5ujHmDmPMBdn8oDHmAn/8xbmZSgfgs7ig75np/86RoC+EEEIIIYQQQgghhBBiV6VOUd8Y0wf4Iy70zf+yTHOyP/5PxpgeOdhSjAtdk0IkPv/qHNITQgghhBBCCCGEEEIIIXYq6vPUvxAoBX5prV2VTYLW2pXAz4EW/nwhhBBCCCGEEEIIIYQQQjQC9S2UewRQA9ydY7r3AH8AjsEJ/NnSxxhzXb77rbU/y+G3hBBCCCGEEEIIIYQQQogdijoXyjXGrABWW2v3yjlhY2YB7a21XbI8PgHUtWqv8X8zHmOtLc7ewl0LLZQrhBBCCCGEEEIIIYQQTZ/6Fsqtz1O/DPgsz99eA+yew/GvUreoL4QQQgghhBBCCCGEEELs0tQn6lcC7fNMuz2wLtuDrbWH5fk7QgghhBBCCCGEEEIIIcQuQX0L5S4A+hljOuaSqDGmE9Dfny+EEEIIIYQQQgghhBBCiEagPlH/JVws+2/kmO43/Hkv5mOUEEIIIYQQQgghhBBCCCFSqU/Uvw1IAD80xhybTYLGmOOAHwLVwO0NMy9t+g8YY/KN8y+EEEIIIYQQQgghhBBC7LDUKepba+cAfwBKgf8ZY242xgxOd6wxZogx5q/A47hY/TdZa2c3tsFAd3JbgFcIIYQQQgghhBBCCCGE2Cmob6FcgO8D3YDzgMuAy4wxa4DPgQ1Aa5zIHiyoa4B7gO81sq1CCCGEEEIIIYQQQgghxC5NvaK+tdYCXzHGvAj8H9AP6OA/ceYBN1hr/9mYRgohhBBCCCGEEEIIIYQQIjtPfQCstXcZY/4F7A8cDPQE2gLrgMXAG8BUa21iWxgqhBBCCCGEEEIIIYQQQuzqZC3qQ63X/tv+UyiM/wghhBBCCCGEEEIIIYQQuxQ5ifpNAWvtIYW2QQghhBBCCCGEEEIIIYQoBEWFNiATxpjzjTHHZHns0caY87e1TUIIIYQQQgghhBBCCCFEIWmyoj7wT+CHWR77A+DObWeKEEIIIYQQQgghhBBCCFF4mrKoD9nHzleMfSGEEEIIIYQQQgghhBA7PU1d1M+WbsCGQhshhBBCCCGEEEIIIYQQQmxLmsxCucaYPsDusc1lxpgxdZzWEhgL7Am8vY1ME0IIIYQQQgghhBBCCCGaBE1G1AcuBK6LbRsCvFTPeUHonZsa2yAhhBBCCCGEEEIIIYQQoinRlET9z4FXI9/HAmuBDzIcb4FNwDzgfmvt69vSOCGEEEIIIYQQQgghhBCi0DQZUd9aexdwV/DdGJMAPrLWjiucVUIIIYQQQgghhBBCCCFE0yEvUd8YcxZwHjAc6FhHOtZam+/AwTigMs9zhRBCCCGEEEIIIYQQQoidjpwEd2OMAR4ATiGMZV/nKfkYBWCtfSXfc4UQQgghhBBCCCGEEEKInZGiHI//GnAq8DrQH5iC88YvAjoD44EXcbHuL/Lb88IY08sYc4oxZp/YdmOM+a4xZo4xZoMx5hVjzPB8f0cIIYQQQgghhBBCCCGE2FHIVXQ/D0gAF1pr50V3WGtXWWuftNYeiYuN/3djzOENsO0q4EFgz9j27wC/BPoBLYFDgReMMb0a8FtCCCGEEEIIIYQQQgghRJMnV1F/MPB5RNC3AMaYeDrfAjbiBPh8GQdsAR4PNhhjioFv+9/9Bi6m/31Ae79dCCGEEEIIIYQQQgghhNhpyVXUbwGsjHzf6P+WRw+y1m4CPgVG5m0Z9AIWWWu3RLaNBjoBT1prb7HWTge+jgv3c0wDfksIIYQQQgghhBBCCCGEaPLkKuovATpGvi/2f4ekObYb0CYfozwdgFWxbYfivPSfCDZYa9cBc4HdGvBbQgghhBBCCCGEEEIIIUSTJ1dRfxbQIxJu5zXAAN8zxpQGBxljLsR52s9tgG2bgK6xbWMjvxulCh8KSAghhBBCCCGEEEIIIYTYWclV1H8StzjtGP/9AWARcCwwyxjzoDHmNeDvOJH9rw2wbSbQxxhzIIAxpjcuzv6X1tpPYsf2BpY34LeEEEIIIYQQQgghhBBCiCZPSY7HPwiU4RawxVq70Rhzgt++J2EInGrg99baWxpg2z3AAcCTxpgX/f8lwL+jBxlj9sCF+nm6Ab8lhBBCCCGEEEIIIYQQQjR5chL1rbXLgZ/Htn1kjBkEjAL64sLmvGmt/bKBtt2CmxEwCTjFb3sDuD523Ln+7wsN/D0hhBBCCCGEEEIIIYQQokmTq6d+Wqy1CeBt/2kUrLU1wOnGmH1xswC+wA0WxGPnzwWuAR5qrN8WQgghhBBCCCGEEEIIIZoiOYn6PgzOdGvtN7M49kZgH2vtEXnaBoC1dhowrY79/2lI+kIIIYQQQgghhBBCCCHEjkKunvqH5XDOcMIFdRuMMaYI6Ai0tNYubKx0hRBCCCGEEEIIIYQQQogdhaJtmHYpUNPQRIwxRxljngbWAcuAebH9PzLG/McY07mhvyWEEEIIIYQQQgghhBBCNGW2iahvjGmJi4O/qoHpXA88DRyNmyGwFTCxw5YBZwAnNeS3hBBCCCGEEEIIIYQQQoimTp2hdIwxE4GJsc0DjDF31HFaS2AU0IEGLF5rjJkA/AhYCnwdeAp4GTgoduh/gduAE4Hb8/09IYQQQgghhBBCCCGEEKKpU198/OHABZHvFuga25aJOcAP8zHKc5X/vTOsta8DGBN30gdr7WpjzAJgQAN+SwghhBBCCCGEECItmzdvZurUqaxcuZI999yTwYMHF9okIYQQuzD1ifqPAp/7/w1wBzAb+GWG4y2wCRf3/n1rrW2AbfsBiwNBvx6+BApWoxpjvgp8AxgIbAReA35srZ3ewHTLgU9xAykzrLVDGmiqEEIIIYQQQgghcuSBBx5g3jy3xN/06dPZuHEjo0aNKrBVQgghdlXqFPWttR8CHwbfjTE/AT601t61je0CF8ZnQZbHtsANKGx3jDF/Bq4AKoAngU64kEXHGmOOtNZOaUDyvwG6NNhIIYQQQgghhBBC5MXatWtrBf2ADz74QKK+EEKIgpHTQrnW2t2ttWdsK2NiLAH6GWPqtNEY0xrYi3BGwXbDGHMUTtCfAwy01k6y1h4GnIYbaLjbGNMsz7QPAS4G/t5I5gohhBBCCCGEECJHSktLKS4uTtrWsmXLAlkjhBBC5Cjqb2deAFrjFsmti28BzXEL6W5vrvV/v2utXR5stNY+DDwO7A6clGuixphS3OK/s4DfNdhKIYQQQgghhBBC5EWLFi0YM2ZM7ffS0lIOO+ywwhkkhBBilydj+B1jzPmN8QPW2n/leepvgPOAG40xLYF/RHcaYzoB3wS+D6wF/tgAM3PG2zQOt4bAE2kOeQg4ERgPPJhj8t8H9gYOB7Y0wEwhhBBCCCGEEEI0kDFjxrD33nuzcuVKdt99d3nqCyGEKCgm01q2xpgEjRCn3lpbXP9R6THGnAr8G+eJnwBqgGbAclyseQNUAZOstdvVU98Ysy/wPvCOtXb/NPsHAx8DH1hr980h3T2B6cAD1trzjTG7A/Np4EK5xpiKsrKysoqKinyTEEIIIYQQQgghhBBCCLGNKS8vp7KystJaW55uf10L5f6LAi0+G2CtfdgY8ynwU+B4XJx6gG44D/angf9nrf2oAOb19n8XZdgfbO+TY7q34rz/v53LScaYinoOKcvRDiGEEEIIIYQQQgghhBBNjIyivrX2gu1oR0astTOASX7B2QFAObAemGOt3VRA09r4vxsz7N8QO65ejDEXAYcBl1lrv8zfNCGEEEIIIYQQQgghhBA7I3V56hcUY8waoAIYaK3dbK3dCnxSWKuSMP5vo8xmMMZ0Bn4LTMUtkpsTmaZiRNKvQN76QgghhBBCCCGEEEIIsUNTVGgD6qAZ8KW1dvO2+gFjzFvGGJvjZ3d/+jr/t3WG5IPt67M050ac6H6ptTaR3xUJIYQQQgghhBBCCCGE2JnJy1PfGDMIuAoYB/QCmltrSyL7vw7sBvzKWrs2T9vmAh3zPDdbPseF88mFrf7vF/5vrwzH9fR/F2aZ7nhcKJ8bjTHR7cE6An2NMS8Hx1prsx0sEEIIIYQQQgghhBBCCLGTkLOob4y5BPgzUBrZHA9B0xz4HjADuCdP2+4Gfm2MOcha+0aeadSJtfbMBpz+KW6x3sHGmGY+PFCUEf5vLov4tgXGZtjXKrKvyYZNEkIIIYQQQgghhBBCCLHtyCn8jjHmYOBvwGbgapw3fjrB/UFczPmTGmDbjcCTwH+NMSebmPt6ofGL9L6EE9uPT3PIqf7vE1mmV26tNfEP0NcfMiOyvaKh9gshhBBCCCGEEEIIIYTY8cjV4/u7/u/51trHAIwxKQvFWmuXGmMWA4MaYNtzuIGBDsBDwFpjzGxgQ4bjrbX2iAb8Xj7cCBwD/MYY86a19ksAY8zJwERgAfDf6Al+3y+Bqdba87ezvUIIIYQQQgghhBBCCCF2YHIV9Q/CLV77WBbHLgH2yt2kWg6LfS8DRtVxfMrgwrbGWvuMMeYW4DLgU2PMC7h1AA7DheY531q7JXZaGe6+LNuetgohhBBCCCGEEEIIIYTY8clV1G+Li5OfbdoNif0+rgHnbjestZcbY6YBl+MWu90E/A+4zlr7YUGNE0IIIYQQQgghhBBCCLFTYazN3sHdGLMQaGOt7RDZ9hpwkLW2OLKtGbAaWGytHdiI9oo8McZUlJWVlVVUVBTaFCGEEEIIIYQQQgghhBAZKC8vp7KystJaW55uf04L5QKvAWXGmDPqOe5ioDXwQo7pCyGEEEIIIYQQQgghhBAiA7mGx/k9cAZwizFmq7X2kehOY0wRcJE/bgtwU2MYaYxpDRwM7IkLAbQOmA1MsdZmWjhXCCGEEEIIIYQQQgghhNipyEnUt9a+b4y5BifWP2iMWQ00AzDGvAEMxC0Ea4GvWWvnNMQ4Y0wJ8BPgSqBNmkPWG2P+BPzUWlvdkN8SQgghhBBCCCGEEEIIIZo6OS9ka639szHmU+CXwIjIrgP93+nAd621zzbEMO/1/xhwLGCAZcCnwHKgK7AX0B34ITDCGDPBWptoyG8KIYQQQgghhBBCCCGEEE2ZnEV9AGvtc8BzxphewD5AObAemGGt/ayRbLsYOA5YAVwNPBAV7b3ofzpwI074/ypweyP9thBCCCGEEEIIIYQQQgjR5MhL1A+w1i4CFjWSLXG+ggvjM8FaOzXNbyeA+4wx84C3gAuQqC+EEEIIIYQQQgghhBBiJ6ao0AbUwWBgdjpBP4rf/ykwZLtYJYQQQgghhBBCCCGEEEIUiKw99Y0xBjgVOAE4AOgCtAXWAl8CbwOTrbWPNJJtpcDGLI/dhF+wVwghhBBCCCGEEEIIIYTYWclK1DfGDAYexC1OC27h2oCO/jMQ+IpfRPc0a+0nDbRtPjDEGNPNWrusDtu647z0GyuWvxBCCCGEEEIIIYQQQgjRJKlX1DfGDASmAO38preB14EvgA1AG6APcAgwCtgbeMMYs7+1dnYDbHsM+D7woDHmDGvtkjS29QTu99fx3wb8lhBCCCGEEEIIIYQQQgjR5DHW2roPMOYdYD/gI+Ar1toP6jh2BHAXLh7+VGvtgXkbZkw58AFuwGAL8AjwCbAc6Op/42RcmJ7PgRHW2op8f29nxxhTUVZWVlZRUVFoU4QQQgghhBBCCCGEEEJkoLy8nMrKykprbXm6/XV66htjDsUJ+vOBsfWJ5tba940xY4H3gFHGmEOtta/lY7i1tsIYMw64DzcD4EwgOgIRhAB6Gzhbgr4QQgghhBBCCCGEEEKInZ36wu+cihPSf5KtaG6tXW2M+THwT+AUIC9R36c1HzjAi/vHAnviwv2sB2YDT1trX8o3fSGEEEIIIYQQQgghhBBiR6I+UX8kkCD3ePWPAHf48xuMF+4l3gshhBBCCCGEEEIIIYTYpSmqZ39fYL61dn0uifrj5wF7ZHuOMeYqY8ypufyOEEIIIYQQQgghhBBCCLErUZ+oXwaszjPt1f78bLkJuDrdDmPMi8aYm/K0QwghhBBCCCGEEEIIIYTYKagv/E4rYEueaW8FWuZ5bpzDqN9WIYQQQgghhBBCCCGEEGKnpj5PfSGEEEIIIYQQQgghhBBCNBGy8X7vY4y5Lo+0++RxjhBCCCGEEEIIIYQQQgghMpCNqN8b+HEeaRvA5nGeEEIIIYQQQgghhBBCCCHSUJ+o/yoS5oUQQgghhBBCCCGEEEKIJkGdor619rDtZEdAmTFmTB77ALDWvroNbBJCCCGEEEIIIYQQQgghmgTZhN/ZngwBXkqz3daxL3pMU7seIYQQQgghhBBCCCGEEKLRaGoiuCnQuUIIIYQQQgghhBBCCCFEk6fJiPrW2qJC2yCEEEIIIYQQQgghhBBCNGUkpAshhBBCCCGEEEIIIYQQOwgS9YUQQgghhBBCCCGEEEKIHQSJ+kIIIYQQQgghhBBCCCHEDkKTEPWNMfcYY/ZqhHRKjDFfM8Zc1Bh2CSGEEEIIIYQQQgghhBBNiSYh6gOnAR8bY+4zxhxrjCnO5WRjTC9jzPeAucAtQJdtYaQQQgghhBBCCCGEEEIIUUhKCm2AZxDwK+B0nMC/0hjzNDAVeBdYAqwGqoD2QAd/zv7AIcBBuAGKZcClwD+2s/1CCCGEEEIIIYTYCdmyZQsLFiygQ4cOdOzYsdDmCCGEEBhrbaFtqMUYcwBwJXAK0AKozzjj/84EbgNut9Zu3HYW7rgYYyrKysrKKioqCm2KEEIIIYQQQgixQ7B8+XL+9a9/sXGjkxrGjBnDuHHjCmyVEEKInZ3y8nIqKysrrbXl6fY3FU99AKy1bwNvG2PaAycBY3Ge+H1ItrUSmAa8BjxjrX1jO5sqhBBCCCGEEEKInZxXX321VtAHeP311xk1ahRt2rQpoFVCCCF2dZqUqB9grV0D3Ok/ABhjynHe+6ustVsLZJoQQgghhBBCCCF2ETZs2JD0PZFIsGnTJon6QgghCkpTWSi3Xqy1FdbaZRL0hRBCCCGEEEIIsT0YNmxY0veePXvSuXPnAlkjhBBCOJqkp74QQgghhBBCCCFEodl3330pLS1l5syZtG/fntGjRxfaJCGEEKJpLZQrth1aKFcIIYQQQgghhBBCCCGaPvUtlLvDhN8RQgghhBBCCCGEEEIIIXZ1JOoLIYQQQgghhBBCCCGEEDsIEvWFEEIIIYQQQgghhBBCiB0EifpCCCGEEEIIIYQQQgghxA6CRH0hhBBCCCGEEEIIIYQQYgdBor4QQgghhBBCCCGEEEIIsYMgUV8IIYQQQgghhBBCCCGE2EGQqC+EEEIIIYQQQgghhBBC7CAYa22hbRDbAWNMAjBlZWWFNkUIIYQQQgghhBBCCCFEBiorKwGstTatU75E/V0EY0w1bmbG2kLb0gQIRjYqm1BaTS2dxkxrZ02nMdPaWdNpzLSaWjqNmdbOmk5jptXU0mnMtHbWdBozrZ01ncZMq6ml05hp7azpNGZaO2s6jZlWU0unMdPaWdNpzLR21nQaM62mlk5jprWzptOYae2s6TRmWk0tncZMa2dNp7HTKgTtgIS1tiTdTon6YpfDGFMBYK0tbyppNbV0mqJNTS2dpmhTU0unKdqka9t+6TRFm3Rt2y+dpmhTU0unKdqka9t+6TRFm5paOk3RJl3b9kunKdrU1NJpijbp2rZfOk3RpqaWTlO0Sde2/dJp7LSaIoqpL4QQQgghhBBCCCGEEELsIEjUF0IIIYQQQgghhBBCCCF2ECTqCyGEEEIIIYQQQgghhBA7CBL1hRBCCCGEEEIIIYQQQogdBIn6QgghhBBCCCGEEEIIIcQOgkR9IYQQQgghhBBCCCGEEGIHQaK+EEIIIYQQQgghhBBCCLGDYKy1hbZBCCGEEEIIIYQQQgghhBBZIE99IYQQQgghhBBCCCGEEGIHQaK+EEIIIYQQQgghhBBCCLGDIFFfCCGEEEIIIYQQQgghhNhBkKgvhBBCCCFEAzHGNC+0DUIIIYQQQohdA4n6QgghhBBil8QYYxopnR8B5xljWjZGekIIIVIxxhQX2gaRPcaY44wxuxfaDiGE2FmRqC9EI2GM6WCMKSm0HbsCxpjDjTFXFdqOHQVjjMp6IUSTIFoe5SqoN6IAf6gx5msA1lrb0HSNMX8ArgeGAs0awcRGoaFlf1OrOxrr+fu02jRWWjsrxpgjjDHfLrQdYscl+s425P01xhxrjLnfGNPcWlvT1MomkR5jzGXAE8DlxpjehbZHCCF2RlQhil2WWEOzoR3fQ4EPgPFNTdjf2Rq+xpguwN3ATcaYKxqQTiv/t9FEgsaksTyRjDEl1tqEMaa0KXg3NUTQS5NWU3vXmsW+71Tvnqib+PMudNnSFPNfpDxqbowpzkVQN8aMxNWxDfKEN8Z0BV4G/maMuRgaJuwbY24EvgncD/zNWru2Ifb5NEv8X9MAu6L3eu8cztvfGHMugD+/yeQja62Nfs/XNmPMGcCvjDETG8WwRqYJlB3GGNMBeA74jTHm2kLa4236ljFmRKHtiNMU2lVNHANgjGkbf3+zOtlRAtwEnAbcaYwpbWplU1MiKD+MMUWNOBCe773+AngDuBK40hjTpyE2ROrGnAfP4/ci3odQftq25PPMhIiidzQzujFihyXdi51j46Vd8I+1NtEAOwxwKNAL+ANwTEPExoYUWPHOhfdoSfj/D2hAuvGGUME6nNbaL4Hv4xqKfzLGXJ1rGsaY7wO/NMZ0agwvTZ9mceT/0gamZbwnUktjzEENSKfIWlttjGkNPA/8s5AdUH9dQX5slaOgV+T/tg2uwV9bG2PMhEawrbUx5hpjzKAc7enq/5ZYa7d6++4yxnTJp1xJ8w43WnnQgHQaWtY2KrF3rVHbMQ0QUKN5++BAsG5M23K0JxB0W5om4h0XK4/eAZ712+q9T8YNnE8FbgSOMA2IXW+tXQ58HdgC/M4Y83W/Pee6wAv6VwMPAz+x1s7M165ImsVB2Qb8G3e9ObUpfH4M7vXLwK3GmIFZnDcGJ8D8whhzFmwbYT+P+3ywMeZcY8zVxs3U656vbcaYG3D39QKgq/ED/A2hoWWkF6u6GWM6QergRZ425V1GW8dq4CTA4t6TgnnsG2P+CPwW1+YbUig70mGtrQEwxpxujNm30PZEn3tE3G2MNm7Oafhndb0x5lngNWPMdcaY/XNJw+fFamAM8AlwJvBv00SEfZPsqFJoW+JtvpJoWdKA9k3QnmhhjNknl3OttZOBn+Dq/GuAK0yWwn6knT3QGFNmrU34eq0rcLNvF2R7DbVtjaD94PMVxpiL/Pe8tYCmRKHzYdQG46IYtAHwfaR2vq+13fuixpjdm0p7eFciVkbm3XY3yU6K440xhzSFvN5ksNbqo88O9wGKI/8PBUbncO4lwCPAauA94AHgQKBjA+xpiROaK4GFwAm4xlSu6ZjI/3s0wJ6/AAMj328C5gCH55FWUeT/rulszePa0v6foy1nAkuABHB1Dmlc789ZBPwM6JDP9cTSLPF/mwNf83lht3zT82k1Az4EXgc6NzCdx4BNOEGseZ7plAJHA0cArXI892Kge+T73/170ibHdEYArwDjI/d7PrAZGNKAe9QSN9Nmvbc1q7wA7OPz0k/89zbAdL/t3AY+/0lAuf+/qIFp/RC4Ks9ziyPP/0TgxBzP/wpwcEPerwz2NAfOBc7L5/0gUoc0xj2OpPP/onk73/KtkWxp5W35egPSaGybSnB17lrgNqBFFueUAn/179UmYDYwId+yLJLueUCNt+XSyPZs3/9fepv+A/Rq5PvUHDcQm/B5KufnABQD//L37BdAy3qO3wuY639zsy9bz2yMvBB/33JJE1dvbfR2BZ9pwLfzsOMn/vyngTGN9Kyi7dFRwLE5nn8Rrk26EVgGPAkcks27UZ9NOOetnNPBeVcb///xkfv+nTzSamj91cfnxQSwFTe4N7QRnlte7dAM55/t7bsbGNZQexrhuRcDbYHBuPZNq1yfRaZjcygfxwFL/X1ZE8lDz+b6/Ajb2J2BT3069wOl+eaxhubL6P32/48H/g/o14D0GlLGBveoHPgRrp6dCvwOOKUB6QblQGvgLeDNbJ9f7B05CnjVv8O/Afpkmcbe/nk/4L+3wPXf1gMT8rie/wHfjrwrv8u3bMvmuhvj2eeSXuS6mgEHALsX8Jr2wbUpLovYFLy/4xrrfmdpyxHAl8CvaeT2WkPvU11p5PjsGyUPNeb5sTLyOOAH+Tz7SL5ujWsvJXC6Vr/GuOc7w6fgBuijT66fWAFxla8wPiQLwdo3JBJABU7EW+C/L8cJjUP8cTkXor6h8UMaKOz7tP6I69wdmce5P/XX9BZuNkIgYj+Ua+Ueu9fnA88At+b7zHAdjTJgX1xHo3n0HuZyv/3/OQv7uNkUCX9/1/n7k7ewH6toJvu0P8QJmQ1poDfHiUWbgEm53CegWeT/nsBK4Pf4zn2u1+mv7WGcAPYaMDzbNIA/+3tyg//+e8KOXd8s0zC4xmCQl1/CiXof4xr2P6VhAsg1uEG+n1OP8BU77yT/+wlcZ+4jnBj2g1zSiT8T4Aqf5l1AWS7PPn4s4cDDAuBrOdoUdBJb4zqI1cCLZClaEIqejwMj83m/6rDnUZ/2e748yeX+BOm0BL7rr+0F4D6cmJbXAC9ORL/T2/XVPK+tFNcRuwg4Gdg/Uz6JbKsV8GLbT/O25CVeklz+9wKGAUPIUbQgUg8C3XD1458Iy6NshN3gnbgXV8d+Sp7Cfuz9OBVXrq0ArqjrPsfSCOqRBPC/yPZmudqT4X4fiCuTfkYOg5/E2hzAPOAO6hH1cPVycE0P4gYSEjSCsB/J2y1wThXXAdcCA7I4N2izPQOcAlyKGwwK7v0dRJwN6knrEH9PXwUGZcifudaN0Wf2TX+/1gHHZ3n+r/11rMd5IS/y3z/BD6DmYVP0fv8Y144YmG86kXsX3PMf5Hl/xgBf9XnrCpxAG7QB63vf/unf0ce9DVkLi/XcoyLcQGO7Bjz35riy+hn/HG8nR2E/Zk8b/z7m1H+IpNEK59jzPlCFayfdk4tNseubgJvZ9P+A/mQYoIudfwhukOpzXPuqOXAMrk2TIA8nAxpR2I+k1QzXfpiIa891Jst6ieR65Ie4siUBXEh+9VJgUwmu3T4Q3/7z2zO+I4RtgM64/kcCWIUTMYP39g+5lgHR9HFtm43k3k7OS9gH/uZt3gP4jLD+/xzXh/9ONnkxluYx/p1YC5xF2D+5jzzLk1xtqC8NXDl5JnCZz5dZ56VYGXCXv9bbiDhV5ZBWvD2ZsyMfrr7f6J/f13H12gZcfymnPlKatFv496RLFsfv49+HhH8nrgd6NsYzw5XZzXD9iXzfr/i9bhn7Xm+6sTw0FDgW5xx2ZK5pxdPL5/nHrws30Fjh3/1LgLa5puPz9Tu4fvY/gUHxe7crfwpugD765PKJFRC/wHXGZwHn1Ffx+QoygVuwZ19fKXT0DYv5ft/7wEH++O0m7Kcp0G/x9rwbL5CztOdBQuE6EAcHN+BeX+8r5tXA98hB9CK5kXErMINQ+P4r3ss2XoHkYFtOwj4urmPCVzAf0QBhP1bRvI0T4G8ji0ZGlukfjWuALCFLATxybluch+NY/450zfU+++ODUBlVOC+0PXM8/2RcOIcEzmso6IjllI5Pq59/bptwnectuMZh8BxyqtxxjbAeOGHlbbLoHBLr/OO80QIhZitulkbQKczqXpPcGGuGE/QW4hpA/yQHYT+W1u64ht3fcIL8h2TptR25p639vdmIE7+z9a46h2TP2qdpgLCfxp5NOEGvd47pRAfh3vS2rSb0KKwAbibPmR/AYT6dF3Ed7Kwb44QDgxti9+6PwH4Zzv0pcANpBkhx4kIi07nZ3G///7cIy+1AAP8Z0C2H9Nrgyv+TcJ3yfjm+I6P9716K86zbCswkIuzncq/9/3v5d+0OwoGvyyP706aHK1eDPB10FO+P7M9rMN+f2wpXp12PK/ebxZ9HFmm09fnvW7hOdP/67rW/D5U4Z4dhOIeAn9FAYZ/k9zZ434JPhb/WtAOyuA7bStxstb6xfUcBi306j5HdAMGl/vjjY9tLcINWw4GReb4jgTj/Ca7Oa53F+d/x5zyJ8/Av9Xb8nVC4Ls0x/0TLkmf9e7Lcvz+5tLGi78l+OI/vOwnrue/leH9+Smq5NhvnMZtRcIrkn+tx7ZjhhO3bvIT9yD1qhRtcfA6Yghts6kj9AwzR6/oebkB4o7+ehP//n9naFrPn37jZfgt8Gofk8Z696+34CDcI8n7Ernpn2pEswv489sxW4JwWdq/j/L1xAwmLgTNi+4726Txc333OkHaDhf3YO/IoTuANrm+ezxM96kovdo8C54XXyNP7OGbT44Rl22tE+jV13TNcmf2Wf86/wNW5XYH9CUXxX5Jl/RS514Gn/mzczK98Zn3kJOzj2goJ3ABZO6A9YRtkC/DDdO9jXfc28v0cnDPglkheHFDf/a3nHrXElQX342YT34sLy1vvrObYvfkZrm0bfefewtUpHXLIQ2/4dJ4iByesdPcMN5j+c1w5dyORtkAW6ZTjZjEtwQmxW3Gz5Uz8d3JIsxUu+sA7uDbEl7jQbCdkOL4LblAz4a/hQ1z/MW9hP3KvO+LKi1dw5e6TuBnNWfWT0tzrc4F/4DzQH8O17bNpS0TrpP8j1H6Cz2TgSLIcSInl62twfZBfAIfmcF3p6pGXgGPyvOfNCAeFf0JYDslLP7hHhTZAH33y+RB2hh6lDu8TwoZuKa6Dszo4PlJotcV5WAXC8Fy8sJ+jTXkJ+2kqz1/gRP3NuEZHVrMQ/PlRD+0FOCF2NXBUcM25FoC4jk7Q8Uwb5ojMDd9oIyPoaHyMa0QFYUo+ImxAZyvwmNh9q1fYJ2xEDMMJBVfhPFnn4xr1N5BG2K/vfvmK5j84seundVWamdIi2UurWWxfMDX0ZnLwJsN1CBM+D64kC9Ejw7X9y6dT6w2fRx4aiRO+a3BC3OjIvro6KT8g1oDEeS996dNaQJYekWnSbo7r6MzECTE31pcHfR75E7GGIG5gaLO/Tz+s651Ik2a0MfZNXDn1MU6ICQSUO8hC2CdVRAlCaQSDKQl/zZdkaVspbqry5mjeTmdD7LeH4Bq5QeMraFA/RcOE/VKcoFOd77vm97XANe634jop3f3ncFzIk624Mjhr0TqW/t24DlW9A8SE5VKryHN6GFcXfBtX7tbgvEDHxc7dG1dHrMXNOAjE7aA8CUTMUflch08jECw+x3WkbiYcBH+M2EyCOtIJRM8v/PsyPEc7ynCC0nU4r8E/4Oq3mbhOVGt/XGsyDPDH8ujPcAJO9P2owZWVGUPxEM40egDoAOxG6KF5X+S4fMLvFeOcDrb6v8/77Tl5fRLOalhCRMirJx+2wgktvyAUyDoQhquZT/7CfjNcp7Lavxun4wYat/j35ArSdFwJBcDL/ffS6HXgwrEFAwX/ww/wZbKN0MO/d2TbKFyHdTWhyHM/rm2QlaCOm3VQA/w323yNGwxehptptE/sve2GG8hIkMPsKlLLkk04T9Rcw9xF35MbfD4KZulVEdYlWYWrwM0WSOCE80k4L+7v4sTmjbhypVM9aYz2aRyOE3QfIRT2h0WO61nXc4vco2ibNDrY8DROjMvGGz0Y9HoJF2buEJ+XPvV5/V9kL+xHy//FhIPMG8gyxAjuPQsGPH5O8kyL4BnMI8sBa9xaIQmfRy/HCa1zIvmqf4bruNGf963I9qDt2BxX/v8FVw/3ISJWkt3AbN7Cfuwdecef+zhuJvINhO/d22ThMICbUbfFp7FPtu9Vhv0tcQJu0C8K8mcC+F2mexS5psv9sX8mVmf457YEVw9nPVCI6x9PwfVpZwB7++11vh9ZXOvRZBD2cSEwE7h+VSC2lxE6zyRw/ZtsnvcY/1zLYttfIyIyZ3s/YmnERfQErv3wBeEg2m/J/n37PqHD2/dxZeR7fttSXH+ozlCT/p161l/bz6mjz5bpXSO5/P8FrizbQnIIrTuop66L5Muu3v5qnAifdYjTNGlGy+3PcO9rlU97MXBt3AacPrAF5+iwO679MZM8hf3IdXXC9R2Dwc5ASK/EteHrbXfH7vWvCMv8WYQOI2/iQuBlE6ryhkgeuhLnEX+/f3azcTPl6nOAjTtQJWKfb5GbFhE4Fz1e37tQV74ABuDKgClkmJ28q38KboA++uT6wXkMBWLcsNi+Ipyn01CShdmevlCf6r8H3m9B4VyGE02CQutDGuYBlJWwH7Pxl7jG8mJco+w5wgbrx2TpAeIrsWP9ecHUy6mEnpFZx8n0Fckmb0uKp399lUPkXjyJa+D8JnLvdyP0HplPHcJ+NgU3WXrs4zwHlgO/iZw3FyeM/ZxQ2B9Gdl4WQ/zvPhO33VdCX8U10Ooc4cY1nu/ADVh1jmwvwTX0l+K9ttLdozTp9SHsoFQCJ2V7biSNffzvvkYOoTLizw0nTiYIheqfUX8n/jBCEXBiZPv3cQ24V3zefAPn+ZPrDITmhFP5twB3Z3oP/Pb+Pr9W4RpOQX7dE/d+PkrY6ajXizFN+kFj7BWcGHs6zmMjCBGWlbDv9/+IcNBznL/W8bhO+Qaf5sV1nB+Uiyf4e3xv/F3w79GR/ph4B/J8//vBPR1KKMLkLezjvIlX+euKhxnphZtOfS6wbz3PMrDv70QGBnDv2ge4suBn8WtOk68zpR90Sp8gi8Yvrt66idCTrjSyb7C/5mU4ASE+q+NE/zwrcO9G88j+YPB7vzzvdzDb4kki5T9udsvHft8PyM6TqCduivtanED4zfgzrOf+NMd71Ppt3QiF/U99Pt/d38frqKMD5G1O4ETYIwjDZ9zobVtDGmGfMDzNvSSvWTOERhD2cXXlhbgOXVBu96orr9WRVjAYUyuuEYmTHjs2uk5FfCZSe/IQ9kkWFDvjyv6bY3n7EsIy9cp4PsKVLQngl2nSD57JPoSCyt112Ua4LkMwc2EkrqOfwAnMjxN2pBfi3p86BTDc7LHZONFseGxfK1y9cRSujVlbbvh8uhW/Pkxke3DMqd6OG3J87sW4AZOtuDKszneznmf4XW/Dg/gQXrh6OXh/6hX2/fEVOGFiSGzfHT6Nf1HPGkS4enYt8H/+e2/COuUNXHvyVP8Mz8nw/E3kcwuuTfp7XN0xBid+bPW2HkHdA/zj/LHvEmsb+3ODwdi7yCD2ktr+X48TXbv46wsGDRLAyVk8+wP9vZ4cf+44IWQVrowrz5R3Yt9fJrJ+Aa4cPhb3zmzFCfP9Y+e083n7v5FtUWejIO/8D/fOVPp7mBL6rJ68GQi6+Qj7xbiyKKhvW0f2tff5bAmuTKpLZCrB1fEriM3wAfri2kfX49pyXdPZRbKod7nPAz/HOTCU+jSCMulP6fJOZNs/ve3RQZK2uAGCalyfNPBurbfO9sddRNh23wSclun3o/clcn+64AbkepMqrKcI+/7ZBF76wWB0N59HpvvnFYQXupM62sS4NmqQN36Gr9v87yQIw51twLW5s7onsd+IOojchA8Hg+s7v4grA35LmtlIJLfl2uDa/s+T3NZqh+s/LfD54ArqHrS8xNvyN9I4veDa4n3I7h0L3tXncYOpvXF1WTBw+B9gryzu0Um4cimYgfIFrp2T62BzKa7NVuOfZ3u/fQyujbLF7/t27LyTcW26qyPv/zm4QaqchP3IfQvWhlrtf7u7f/bfJFwL6U2ydKjBCeVBW3t/XNuhD6GH+xu496iu9+5EXFvqBVLr2qd9On8iizXI/PU94u/p33Hv6v/hBq0SuMGeeiM2+Gf2qH/+w2L7euPqyu+QxRp0wBn+t/+f/96gNbV2xk/BDdBHn1w/vnCpBr4f234sbqpe4HXzJj5uKm6K1Je4gYDAoy+pUsN52mwk9Jb5W1BppLGhzgaj/xsV9r/AiWppO/m4xVWDhm60AT2S0LssF2F/T5xYvYeveBK4Ef894vb7imJ09J5E9gVxdcfGtu+H83R/2Rf8J5G5o3CZfyZ/Ilw8cghOsEwQNrrm4hs+JDd2ov8fgOu4fR8XVqZP7LfOog5hP/JsJuO8dJrhGr1RYf9HOG+yGbhGSFw8jHd8zvS/93+RbSW4RmrgKR180nrd+WcdjIgvwTVgDyDsoJyO87x4KnJONo3qXoSejFMI8362YtpX/Lk/THftObyznXGd50sJBZhfU0+jgLBRuYBIpxbXoe5POFX1LWKdcLIbCGpJGOogJZ/H7ymuQfsm4fTmnsH1+b/HE3rpfCd2fl2duQn+nLdIbtC3wXkNBtOO6xX2cQNJi3Gi1N6xfV1x3ncbcJ7Xl8T2x/N2MDhwbNRuvz3q/T8tsBsnmOzvbT0lcl6DhX3CEGqXxOz5Ga7jGthTQx3ejThxbxOR2PD++QZ583rCRYpLY88qKsR0iaUbFTJfw3UYA0+zuuqMFrjBhOnE4kx6m6pwXjwpZay3+0T/WxUkh3+63t+LPaO2k0HcTZP2P/19Oji2/YeE9dWBWaQTlEfdCTuEH8bzZxbp/Bjn5bm7/94JJ1JV4QSiYBDzjjrS6O/z/lJSxbhWuDKqBtdZuyyyr5PPN/fhBX1/H4NrG0zjCPtB+J0PfFo3U09ouFiejObBaOiMo9IdnyGNeDugLo/9+LFxoeJnuDJnSeQ6ogNP5+LqyRRhHxceqRrnCd09k524cIpBXfvjOu7tpdFjCD0jv0HYNtgTNyi0CleWx2eKxa93tM8v18W2j8eFUqnwv/EabvZNCe59/xTnNZ1pAeEghv1LueQjnJD1Ga58jg5YGly9eT2uk/1jwvcoxfMXJyjM9PchxcEF10YK8tZ367AnKLNPjm0P2paPk73w8Yq/j2399z44j8iEtzXwlByf5tzgPQ2e86u4mUatIsf0xbXbNuHq+cPreD4X+N+6KnLP4msQvIQTRf5B6oBPYEcJTrh7AifoxAfIr47c5zqFfZyglyC53i0ibANeTyhslpGh/YUTl/fzeejs2P0zuDz/HJmF/QGkCV+Da08uwrVlb8cNrETbXz+M3x//f29vz94kz7LJS9jHtfmn49ot0QGHZoR1yM8J2wCZyt0uuProxehxOMHwNcIwKltxdWW32PlB37M5Li/fhe+XBPZEypjAueNPkecQ/F6pt/1N3DvQKXadm3F1dovIuWcDp2f53gX1fbyfk64eia579C/C2XCrcXXakUQGjkkv7AeDWefj3skFuDbruf6cXoTCflKbmOT8Voxrs3/sn0X0mQ7AtQVOx7UHNuLqh2h5UG9MeVz9lSDVQcT4e78e57BT16zS03GDn+uBCzPcy8v9s52DD0WXwZ5/+OcdF3WvwJUvQR/zP5DZOQUX+m4xrl29T2zfXwgF6JTZOhmucTBOh7nIp7vIP982kWPqm/0xGifOTya1X15KOGvmS5L7i838ueXRe4t7B+oU9klub0SdH8pw/fTbifU7cbPJ7yd0HNmtnuvqhau/5pJaT/zWp/NM/JmmSSdwDDsitj3oxz0OHFDH9UXrrxa49+42kttrEwlnOP2SWEio+DMkXNPvrdj2c3CaRFQf+Qd195NO8sf9oY5jTOR3G7zWxY72KbgB+uiT6wcn3CZ8xdILJ1RF49C9Qljhv+0LYEPYsE0KIxEpqH/oK4RTcY3ZZYQCe0rB7v8fjWvcfwsnrMQ7R83JQtj3FUAVPpRBrHBtS+hVMj1NgZ2p4RptnDxKqrDfDNeoSeAapgcG1+rvSxFuqnyCZKHxCsIGZvBZQtjgijaqWuFEkDmEo+p7EMZm/bPfFsyS+IyIx34sretwHjHBb27FeVocHbvubELx/MBfcx//vQ1uFHg2rvEYeMZcFtyr6LXhGlnBfdwX1yh4AdfRPYMwhukcXGPsBlzHv4YMFTNhg2QRLv9txlXoQ/0zCTqv8cGs+ry2exJWns9Gtmez1kMwYPGT+HsQOSaoRPf0z7vOhjBuoapgAONXpOlYkrywciC8LMB7CUWO282nkVbY98ekrEVAcocoWOshgWvgZYw/TthJmEpM2I8ccyoxYT+Wj48mNksB13GvAc6PlEvRMmckYaf1TsKOSTrvpAP9cf/M8B61xzXytvjrvTR2z1vixAxDKMj8HJfPRxMK31/gBupeJBStomGkukXvt/9bp7CfLn/Fru0w3HtxJ26wYzzh4MICXNn+N8D660vb4fA2VBKJNU6yoN82ch2TCGfIRG29nbCzPDZN/rrGp3dTpvwd+T7AH3t79L0mWYwJbCoj1eMl8NgPhP1gEC54zjnHDfX5ZA6pjfEfe5smE+mARO5lfeVRd8KB5il4T/R6zgnyTzCN+rDIvhLc+1vt03yfOuLQ48ShGrzwTsQ7OpIXvuWPWYKr7wIxpA1pQjIRvjs5CfuR6yoiuUxq7a91Fq4T+21CMS5tWySWbrQTFggkK4g4BVDPu5Ymzbiwf3Zk3yn+mSYNfuHexQQuHMBMnOCc8mxII+z780sjeSVtSKTIPRvjj1uGn6mT5hpG+PwzH1cPfQJMTmNPF3+tW8nscRwMUBxP+D7shhOlfuHPDcq5KYR1VDd/3m7UPaOwp3/2b2XzXkXOG+x/97ZI3mqJ65wH7Zrg8wIZ3j+cqLOe5EWg43X7+ZG00i6eG8kDe0e2XRe5Z8Niz2fPTNeKE4EXkiwEtSF5rYYf13FvWvtn8AdcfTo2/n7i+hS/ph5hH+fUksA7ahCWAdG8GczYWo8TLOKD7C193gi8j4P6vxnJ/Yzgt+oU9gnXi5rkv5eSpl7z+8biBp3iQvMFhG2h5fgBfVLbcXFhv87F03Hl7nJ//KGxfacQCr+HkPwuXoMrO4I2/6fAeZH9mULxnI2bxZOy2CMu5FZt/yPynqRrA7T2tqfMuPPP7zlcn2E8zjkjyO+bfT77DmH+TFnPCNdHfB/XR3sWPzhIZJ0Y/wnWWYr2m9rE7lUwQBKIvh8RE/Qj583CDSLEw31GB/6j5V3QDk/gZ/2mye/R/lEQNmYKzqFnMu6dWufT6hE5Lxpj/5e40Gcf4drYK/w515D8TgwgHPiubRNH9h8NHOyf6zh/vZtwZXP72LEXkUHY9/v7pbte//1mXNtj92ieJL2DSLM09ywoQ1/B1YEHpytPcAN/gSPcrXW8Z3/zz3wMzhFhT1weDd6vlwnXa/hrHekEjkYXxLYHZfdjpFl/hvqF+TJcnzgQ9r9CzGMf19dI13b7uv/tYOZhPO8283lrK3BLht+PtrMyCvskv1fR/zv6vPorXB/iuPgx/vte/r6vxZfZ8bwTOfYgIv3FyPYfE4rxI+q61/7an8c5q7RJk0a8rh2WwZY2uPL8W7i2VDBTNDrD8hiShf2UdWhIDrE7BSfsn4/rpwRlZDCQ903CsvsbdeSd4D59TJpwwoTlT3vc+3dEprR21k/BDdBHn2w/kYK4HDeVqApX8QcNnbcIQ4x0IvS4CBqlJ+IaCJ/hKu7oqHo7XKP6LVxDOAgX8reYDXGReR3JnaQHicW/J9VjvzYUD66CaUM4ta4HMUHPH9fDF8oJnHfJEX57tFDvj2vEHooTKuMj2f8lgDKl9QABAABJREFUFPajjZSHcCLYamIe+4SdhBdxlVh0pP8SnMd9MEX75QzP7TJ83HPcAEUgMtwROWYEYUNjLrFYlpFz3sd1lM4nHOhIEBEY/PFRYf/ayPbguoKO+PjYeb/ANYhq8AI4rgNwK6GI38rbuhzX+W9OKHYEn7nEGo+E3g3HxfJRYFMfnNi4Ctd4CWJQz8OJHnvh8tt7eM+2SD5qhvOOPhuXv+IeqFFh/5nI9jqFfcKG3aekX8wqqETbetsnRvZ1x+XJ0vjv4BrcgbD/ayIiN67zcntsW1TYj3v89SJZ2D8ysu8i3Dt3Robriy7ifIf/jTcIw7fcQEyUJb2w3yN2TIqw77ef4/PNUz6d4Nnf4o89N2pX7DcvwTU8K3CdmBSRL/Iu1YqWGa57MKFn68fAVyL3YRaujDwU5yEXdAiCsm65tzeYndDSp7E+nu8y2Fevxz7JnlzRkBU9CQcEg88CXOe5Y+Q92oAr02q99mLpB4stjSJ5wDdJ0Pd/F+DK3mh90QrXMA3KmE0+/xweOb8vrlG8CD9jLFpm+/s2DNex2M0/s2ci73MmMaYvri6ZELum5oTC/npcHfYPXKfib7ips9/x26/C1YGX4Oqx8WmeQSdc2fMR4XuSqYPQDDeoEwhcpbgBmMtxef6wWNpRYf91shD2/XmBZ+QvItta4zqYCVzneqZ/DmmnduMWjasCHk+XP/22AYQDs8tw78APM6UZe671CvukdthTwjz5PHYG7l38EidqtMOXG5Fn0gInxF3h73e6Kfe/IHx3GyLsx0PxjPfPOQijFx90HYYTqoJ3dVRsfzphfx2uTRGEiTgtcv6x6WyP3Mcgf56fqRzCDVDW4MqRRcC/47b47/0JwyD9iGQP4b/iQjCV+e9PRO5JUCa8hn9HcXVUkEfPIVVoTCdgtMUJTe9muF+ZBnQ64erWLT7P/IRwpte7OE+743ECQDUZZjThvA3X4crG5qRvl3aIXPsmkj2tg/v9R7//hHrKkGA9ieeJ5WHCsvhkf+7xkX1HELYdEzjB5W/ARWmuKQhLGQx+Tkx3L0kV9selOSYITTaF1EH6aN78CFceVONmFUVDoxyJK1+CuvWbdbwfQVs8QaSdFbvXJ/r9f8OVwUGbL6kO8ce+gGvX9UhznwInm62k8dSPHBcI+5twbeSMoThwfZhf44VAIo4b/m+wVklUsA9CiC3x1/QXwsGy70eOSyfsW9xaPimzwXB16Abgoci2lDaA3z4EN+PiaP/9AJJnE32d5MGyDThHpsMixwRhxP6Sxpa9/bMI1mT6V7qyCydofYewXXkrrp7/VeS4b/h9T/r7sAVXdsU9yP+Cy49Rr/BsQnoGfb11RBYljeWJEkJnsOtJnakR9CeS3ilcm/pFv/+nuLJrE66sfpCwvVtbDpEs7N9BOEPjMpyIeCeu7ComWdj/Oalh5i4gWdgPZjXviytP04VXLfG2bcDnfTIPDhXhQs7EPaX3x9UNa4kNNKV5v/fEDfQ+H9tuCN+jU3HthdWEYvV6XH0VCNb9/f15k1RBPUjzKm/PqZF915G+7B6Da6tE2yRn4wTb3+EE4vLI/rakCvvBINZX/L34aZr7cLr//Z/XkUdH+muroo7FaiPXmU7Y74Or43+Ea99EZyYGM9QW4d6DU4PnG0u/iPB9fC6+P2ZDMKj57ci+TPd6P9KsAYFrgz9LZAYyGepav+9VfHlE8jsazO58Bdee6UXM6cQfdyyhQ9V7RBZgx4WPrm2v4docgU4XlJH3EnGGwr3/CdI7QUUHCx/C1QHfJbmcTufI8pX6yrSd7VNwA/TRJ90nXQEY3ecL7nt8wfoBrrIMwhwEldvffAEXhODp7AustbhG2MO4SuJaXAd+M3ClP7YTrlJ8MvK70QItmOb0Pq4zeizhKPpLRCpCf3zgsb8S17E+JZbef72tIzNdP2GDqgY3ShodCf0hToAJZitMxVWm8fAQ//X7P8A1bEfjGivWb/+C2CLBOG/coDD+EiegjojsL8FVzp+S3NhNmobv//bCNdCfIzLFFCfWLSbsRE8jFC+O9df1GqnTAIMO5aO40eJo5XQGYWN7eOy8NrgK+feRbYfhPFMTuMGfSlynIohF92ecsP4R4ZTKoCFSjhM4bgK+hxPp4tOoHyOyYK2/7vjUtfNw+fM2XD4/lbCzcTehwPpjwnze2j/XNZHntIxYB4IshH3CRkb02T1F2EANpvXGp5oHotEZ/vv3cOLaZtzskuuIeXKRLOz/1ts3GjdNOQF8NXZ8NsL+Rpxw8TVcg2wh7n3ZF9cJ/52/jnPi+RQnst7pf+NNwgGNBLEQI7i8eaS3P5PH/im4TkICNwB1G2FjO56PA4++f2QqA3Gdv/WEndrfkF7A28sfl6COBbZxjdbFuAbWRzjPvRu9fb8nzNuD/PYXcZ3BUaTG633Pp5HV4km4jnIw8+Tp6P3FzaK5g9BDJP6O7InrSN+HK88HEoufjisftxAROKL3FDewVoUrN4KG6Q3EBkr8Pa7BzaQIzr2OMHzHAFzdM9P/XrXPOyfhyqPL/PnxvNwKJ2DOJpwN9gzuvR2OK+syiTH/8L8zDldGtI/YW4oTdoLOUiAw1pA8EBL/ROPDR+ule3H5e3fCmQfpOghf8fuuwJVHkwnzYPC5j8iUWFyc3BRhnwx1v7/WVv6eBYJ8NJzPS7j3uwrXQTspTb4wuJliwX3JuM6Jv8/B4JDFDUSl9QBPU5ZkFPYJO7YtcSFIHsZ5jP8KOCZNPjkDV7dX4ESddv45G3+vA+eF4PM+ri6Kv6NJwn6m+xzPA2n2dSIU9hcR1rFX1/GuB4L2C6SG6oiKNGf7+7aM5AHxIFRLBRHvq7idhOLZnUQGTWPH7I97R4N34vF4mRt5RrcQto1+5NMMhMb/EebZ1rj6+WNc/XM23gkgkvZNuDbHyEz2x/Jpe1yner7PN1FPuZNx9UvaGNC4dXw+j1zj+7g6uWPkmMDr8ZoMv9+Z0Nv2qHS/E7muqKgZjy1+JO6d/COuPEsnVFyCE8IsztPwW6SPQb2nPz9YOPkkQrHgW4RiYvDM0nnznU1YN/wlsj1e3wbC/lpc+R6sKRANr/maf6YXk9z2rQ0Dgmsj3IYr3zcSDm4EHtiTcAJLAlfHprwfkf+jwv4JpIqMZbh6ZSNh2JMfk+qZfB2uHVFbz/vtUc/sx/35SwnDNKWE68A5Ybzkj60zPChhmz/avgze/e8QEexwouIWb8fwyPG3R+7B/4tsD97ZTrj6zxIuVjwoZkdH3Hu1ENfuf4uwDRCvb+/Gz6LGvUMJXL0e9YY91d/nf+K8w+ODPMGMxwsy3JeRuFkTm/1zGxfb3xtXLm7AvbefE85G/L/Y/Qzaz1uA36b5rWtwefY5wtnTwb1rjitbvotrWx1FLHQIYUjMStII+7h+5XJcWR+vg97CvU+/JxZf3+8/AdenG0rYF1uJy88XkiziRYX9aYTtiHv8fVpJsvd8NsL+hT5fbPTXeYm/jgRwvT+mNckCZuB1vFfkGjO13RbiPOVLiawDhmvzPeTteh8/Yz96rf7TAZe3F8TvbeTYUp9HnsbVo/fi2rvRe1fm900hc3srENC/6r//lMztvz/4/NbX35+gPIh+PsD1bwPnmza49yJop96A6wcGfZJhaWwKhN/l1LFIL2FkgoGZjonloWKShf0g5HHC21OBn2Hv790FhPnzeTKE6cJFkViE6xu1iu+Pvf8JvB5B3WJ80Baq1YoIy+Vv+X1nEpan6dKoHRwheSCmA67eC5yeEqT2taNl/zGEZdEyfx03EmoygyNpD/fP90bcwGjH2DO4wJ93Aa690xPX9+we+82z/W9V+evdPWbf13D97NpZkbvSp+AG6KNP/ENyA+V4nGfhPThRb0zs2D6k93Ib41/8N/ELFPntvQgXnYlWOHNxlXggPpf6Qvt9X4hHC5XzcI2ieMfkJ4SC23tpCsPmkQI5LvL8ym9/gtADNklgxS3I9iFhhXUvruMVjEouxnVm3yYULd4nVWx8yO/bSugd8h3CTv8XpE6PHYsT13uQ2pE42p93c9Ru/3+8oxSsHXBNcJ8j+6bjGlF/it3XoIKLz4AI4sQ9Qsz7IXLMV0iNGx6IQ+8Bz/ltx+HEogSuQXuMzxOrcMJTMEtitb9nPyAUCtOGDyE5H3/d55lHcV4KLXGVzpPE4tXiGvcbCDuRg3AN8SBebPAZ568jmAY3Gef98CPCwZHnSPZ470Eo7D8R2V6Ka5ClhJ7BNbQ/wzWSf0zMq9Y/0+X+eroSdtwrcY2ZoMP/FKkzCI7GNcSr/f0OwitFZ1ekmwKcTtjviXsHv4zco4W4hsZzJN+7QJQpj71rcWH/UmIhmKLPl/qF/RNJjhs4k1gH0x8XCPY1RLwLSI0R+qy/xpn+Oo8K7Cc5vwXvzP0kh84qiVzrP3HlzfdwDfJbcB3ox0izyCixDkpk+1X++d2OD3WW6f2PnRcV9p/AzTAIyrL5OBEoELofJ9KBT/N+RT2WLse9P88RelvXCvv+uN6E3ojVuMZt3GPpcr/PEnp0Bg3WW0mOz9kT5x0+mXBNl4/99424Ac++ERv/gitLbib0SA4Egw0+H/yA1AGNb/p88hBh2LlHcSJe1PPoRMIBpYdwjejhuNAG++HqxyNwszF29+cNI1WIDESkNYRl7X6xYw7D1ZUf4961t739D+FC5X2NULSagptNFnQkosL+yyR3wkfgBJKS2DO+GVdWH4CrAxOEs6qaEXp8LiMSeovkfBmUUUlxSomEqiL0AFyEy9vZLiyYUdj36RfjOr5BubCGsMxaSeoib61w7R+Ly0vX4AaRW+E67oEn4wWE+W0B6ae1B3X8IkLP03G4Ac+jiHjaUse76/f/i7Bc+2am83D5fQjh4PidsedyJe7dCe776aQJ5UTo8b2K1JB7wQBkV39Mirdj7PgzCIWgBJEBiUhaxT5/rMO1uyqJLMiL9wCO2diG9DODDsWJo+/iBvzrLCNx5X4b3Du1jOSZS1/BlQFTcZ3w3XCD4XuRXCYd5q/za/64+OyvO4iEXcxwn4L69hP8e08kPKP//htcXXIirpy9nORQO30J3/8Ern7ZN5LWXwmF+GAQYi2uDovWV8W4+vkTXBvpLEKv5Ysj5XBQnnzkz08XOud0wrb61emehf+9nrjyZikxL3RcWXMxLj/O9fbE2xOH+P1nEXp4TsXVk9HZuqfjys0qnEjbOfZbRRGbvourIwYT6dtEjp1I2I6aTGodchmuvPmA9LMvo+2t/0Zs3i1WvkXz8FjCEBNJ5XY972E0jcBD9GjcQH0wQBaIVy1xIs5G/9wX+eN/EEkjuKddcAMXQfsinbB/Ja5+r/R54QZSw65cixNa78MNpl6GK1vX4tqGaZ0YYtc1Gte3WUiqWBt1jNkfV5/V+N+LtttKcaLqclx5sMof98vI8wj+HoyrSxO4emEPXFnSGZeXN+DaVz1j96w14QKf0c97pIZ8zSjsE86C/mpkezyUYOA80Z7UxXNb4ZwIfu6f0cWEQntSaBzC96xH5Jo34PJrn+h9ibw/9Qn75xK2G5PqN5w4OYPksE3B7KFnI9f4C1IF/d/g8tmVuHbkO7H9wwnft0dxdWbwTIP3/wifxgOEM/R+jatTv4rvNxO2M9J6quPKkGr8QEWGY/bBtU0WEta9T5DqIHcCrh65jzBCQjUuFNRwXBn4f4SOJrUDYv5ZX0QYLjmBG9SKv6vpQkz9jUh7leRy6zX/W52ppzwiuX45xz/fdbg6K2j3xttkZd7uuT4ffZdwZke0bgwGYaaSnG97kBx+qifhbLqgTTuZWMgdnAazyj+HPxJGQAh+bxzhjI+g7R9P41D/W/Pxugnu3V+Iy7+dcG2KICTth0TKLVI99s/298kStmMfJGzDZRrIKCIU9g/AlTNLfH6529/b9X7brSSvkXCt/60tuDbZdf553IOrQ5eRxQLOO+On4Aboo0/0Eyu8r/cvaDVhAzyBa9xkXJzFF1rBdMaz0uxvhutoXI+raK/DVaBxr77lwGOxbV1xjYf5hKFqmuMamxtw4udvCEOBJE3NwlXEKYuC4Rpd7/hr/S1hvNZoRfVVX8iNwol1G3BT05fixJZ9/HGdcJXpS/5+zSLm9eSv/Vn/OQfXCO5M2DBaiI8jncbWaEP0QH/OOlyn7iKcyHcZPi6uPy74G3iH/YZkke1b/nntG9kWNExewDUeo5XgdaQZhcZVDnvWl7f89xtwFcOFhCJ4MNjQEjfavdrfi5NxFVc1rqOR1eJAPq1v4yqmRYQV3b6EotF6nIdrMKrdDVehvUfy+9AT1+hb5e3Ym3Ca269Ijpk5wD+TKvw0dMIGYk/c1LoErtPTGje4EAwEPY8buAq8eNrhOurz/DN6z3+/ADeivwFXiQ7ECYNLSF7w+QDCgaiXSV3A6WBcA2UprmN7EW7gYzJhwzya5+oS9tvjOlF/xomkA3GVfg1+uh9OeAy8aV6N/EY6YX+mv6bWuIb+72O/l42wvxeu4XsO6ae6R9+NLbjObMoCZv53anBeYSd5+/6DK8viHZNBOEGqGifexIXYA3FC7M+8fRsJG2XnpXtfMuTtK/xzm+vtaIETnm8lwyBA7PzhhCLMTELR7iPCcugDwobq0zgxI1PoiWtw+W8BrjN7IO59DToBUa/E/QlDED3k70MvXDn4G/+bgUC/lNAj/EHC2V/pPIEPwZUtKwlnTi3FNchLcO/TC7gyPGqPIfSOXEGqd823/fOZh/Nkm0IYbmYsyZ6ipThv7cW4cutbpNZv0c7ulf6erfH3Ldpovz/yXCbG0hiL60BtxYVE+60/9texa9ud0OPu6yR3fLrh8nGCMCxVNMzAOJLf/6sIhaYEkVlHfn8X/6yvwInfe/htnWLHBLO80nV8/op7H+aSXL+khCDJkA+DsnYQkUHNSPnyCq5svgXnNdqc0JtzC6mLrh7in731x3wDN4W+AucpHawR1M7nk8X+8xVShf1gQGM2YYi34DOH5PAXmbz4jiX0kA7qzGJc22M/0nTKcW2sQDT6J668uDl4hrjwEnVNUS8hrO8SuDoqLsL9xO+7PHpuPC3//3mEM8UWE87SDKaufwP3fj2DK3tX4Mrfj4msseE/dQ1ejvHXvQUnGGVVRvrrfRtYHtl2gbd1Na5evQvXFq3B1fdPE3NiyPC+fw3XpnreP7OuuLqyG6lexsG7+QapoSOCeuQunNAXDGKdQbKIcQKuPZ3AiTLN/X0LyovgvKv9PfoENyhZnuY67sGVq0H5cGEkjxXjBmxvx5U5rXHlRLpwTGfiyq3KIL9E0om2u76HK5fexHlwDiYciO2Ia3utx70PN/r9LXEC3Eu4d3Q/nIA1E1fOlsXzJa4t/6FP63ukzrINvNxb4jxjn8aVqc/470GYjza4enC1//zO55UjcO2aKlxZEgxKtceVkYNxeS7eTg48v6PCfjQkXjRfZSy3M73X/v9xuPdrOs556Vhc3XtW5F24EpfH5/jvQSilBMlhn6J9ppFkEPZxZfMjuDbtDFLXxQnq27kkD0KeT7LIHK134wNnRxH2w74Wf55pjt+fsJ1xt38m0bx4Ay7vV+PK0Nq14WLlxhhCp4UEru8RtGXeJiZ4+zz1hk/3Lm/HiSTXD/GB1EDYX4nrHwV5YqLf/n3/vTmZQwkehmufpRucitbXwcyjdMJ+dMDreJ9mIBb+jtgi3mQn7B/mn//PSQ71Nc5fR9TpqDPhoGUVrk1dRHJ/7HLcu/iGf6argAf9vmg/eBhhX+lpXFkYPONDCfPSVwj76tWRZ7SCSKjR6L2JbLsU11/7hDrC0/hj/xr5jRdJdeg6GKdbrMY5xgWOKTfFrr8c9x6t9cdEr7kU55z5TVxfvDd+PQd/XrwOPxRXFm3COcfEZz1fiivXH8bl0XrLI5KF/RMJwwkmcE5y83FlW/Q9KyNc9HcpTtiPl+vB/bid8F37Ou4drNVs/PaLI7/5Fqn9yEMIZ7p8J3JsfMDtu5F918T2HUk4szPQBYpwZUkFPlyV374bYT94MuGg/khcXRUtv4/w+WmzzwtnpLm38b9/8s/xAsJ8/Q1CJ8VP/PalhHnwmki65xPWTcFnHa5tvUsK+tZaifr6NM0PbmQ34V/Q8bhR44sIwxLcRGrc9Q6+wJybpgDINXZs4AV+bfR8nBgwm3AhwhJcJVLhf7cdrrAPGlQv4Ret8scfghP3/g/nvdMlsi/wiN6I6/BFRezRuEp8Cq4RfrlP/y6cEHNw/DoJY6wlfOGXFKfa72+LE8hn+8I48FYPGoK16cbvIU5YDISVayPPJvi8ivNAisZz3N1f42xcB66fv9dLcKJplzTP4g5/T4IZDD8hjaDv992J8xhImb2RJt1ggCHwavpmbH9rnIDanzDeeiD+30bd8fpa4RoRLxMKb+k8tH/g881Wf78CQfVqf803pLHpLH8fO+I6QdNIbkA182ltxjVK26f53d64RuMoQoF/Gk6w2Ojzwq/xjW1cvj6ZVE+eICzSQFy+P85vj4dwau+fS4L0wn4x7t0Kfu8GQuEpWDi5XmGf1Niet+E6yb8k7Ii3JRSSE7h3qnfsPW+JE/tq/L0e7Y/dTGwhPrIQ9uvIJ1F7O+O8jKv9dUVnDh2K8zxfihvM6Y/rQFTh3v/ZuI581NP2eJ8PqnEi+TdwnfszCGMun4wTHDbjBMMNhIJYJkGvJc775UFCr6/BuLz5LC4vrycS/zpDOkEjd29CEX9VxOZf+98qxeX1oHN+P8llYxt/XU/5a5pNcue9LmH/QEKP6fW4DkBQHnzobQs6r0He7ZTu/qT5vi+uUzbdn/scrj74A04sOC2Sf2o9skhet+QfPk8Ejd4vcAMhr/hn9ksyews2w4WQWoQTrn5AxAs5ctyv/P2egROW28WeTzvCwYYFuLrnQlznNRDqvumf/9u4xnjrmB2v+WeTqTzqgStjhxDOWpvq7198Cn8fQjH2udj7Hg2NcZU/biuuPPsEV/e2iTz7F30683H1wTjC0BSWZC/ejOIt6Qd3ovk7yD+9cGXbBlz+DoS4vQg9o2o7k7H0DvD32/p8cT5O4ArSCGaOtca1SRaRWdj/fySvU/NrwjouAVxVR74+jDCGeSDot8K1RaKd4VdxM++ChWHjwn50BlPwbo3OdI8jv3995Jx7cflxGG4gbjWurk1pR6S7Hlyb60FCD/GX/P1diMuvy3EhX37o91f4Z/cD6pnajas3LvN5q5rwHcmqjMSJYW95G9riOr9LvQ2jCMu0KbgO8sOEAs+vM+VN/+yX+s+eOBHgE0KBbo7PW0Food0IPUgDUeYU3KDIe/7eBftfxS8cmOZ6JhHOkH0XJ5IH9m7G5znC9UoSJNdnwft0CuGsxbg3cCBiBX/P8cdtILbeg99/trdhLTFhP1I2BnktyCMzce9XuT+mM649H4RjqMDVQcEs2G/749r7Z7kc13+5lmThsggnlkwnFPY7x+xpQ9jmXuafYRVheXkErpxq5689ELSCTxWuzAtChVyJE3nW+/vwCq59HffuTyfsF5Pcfqmz3K4jPx5C2AYN2r974USu4Jkfg8s784m0rQjXIkoQaSvH7IoL+1EP+HG4wd0af99vwTmNBWXUYsIB/Gi5cQGhsH8ZqYOLo3Dl1Dp/b6/221v69J/A1bfP+mfeIXZuIOz/G9e2CgZ0gvp2Ha59cCzJbf94/+8a3ODAFFz5fBGh4F0744JwZudN8eeGE9NX4wbgSiK2FBOKll8QLm4+APcOPOKPC0TEJEHf7/sv7t3bC1fetSONwO+PvZDMwn58cMTgHB+CvHF5bH9GYR9XF/7HX0d0BmgRoaj/9ejv4trjgVPU07h+45O4wZHf4NpfS/11tvX3544M78MwnLBfgytDZuPqpbU+nW8ROlLchhucGokrT4Ly9NxYmi1wYUz+7n/7C8J8fQmu7XE3brA3GtmgiHCW+lx/nS19PphEOKMhmM38OK5cipZrJYQOKDeQOpspfv0tcW2T53Hv5GTc4EF55NmdRlj/zcfVYefinATW4N6ToD1VZ3kUeb6BPeOJhBQkefAwno/aEQr7G3G6z3ics8tNuHZ+bZmFKyNr/PFXkDqYFLRtNvtr7OI/5+H65gncu9qf5EG7eLiun0b2/RP37t5OuHbLtZHnW+6f499JdfzpgyuDErgy6zhcvtuEc8Iw/vkeEckjW3HC/IFkdr5q6Z+f9fliPa69fh0uj19HOPNhEMmDGNEBtU64gdNLcYNwBxFzStjVPgU3QB994h+cyLTSv/Tx0CRBx/MxIrH+cOLwjYRCYIq3GcmNspQp4pH/L8VVCrOIDRz4/QPwnu+4wYYZuAbn7pFjzva2bPWF8Rm+MF4TKZwSuM7jabjKoQQnggfTsBb7AvUWwk5M4E12ly8Q5+A672XR6yBssLXHibVr8CF1SBZHg3BAj+EEoN1xjemgEbwIL9BG0h5KsvD0DVwFE4Rc+BquAqggFEsCQbUNrvERNIA2+r+fEzYyric5xnbQ4PwFodj7BKmCfjC9+fr4883wzJsRijrfzHRcsM0/pzGRa7+D1NH0IK/1xnmJLPHH7R47LiqqjfbHBnnij7hwBDNwXsr7x59b5LwEcGvMzug016j31sFxG3AV4ip/bw2uwXkKTmTdimuYBEJ70LA9GyekXIerzDvhhMqV/jcfjx4f+b02pBH248dFbLvLHzuP3IT9IO8H5cijhPlvAGEj5V8kx+PuHbvOFjgPtmCB5CN9egliCxWRXtjvQd2xqaNlTlefRmdcfg8a55/hyo9gptK3/PG/IWwQfYhrZAXeKaMi6R5G6sKySY0j/7sfRH5jGl4Uiz8Xv204zsukxv/eHrhGWnDtN1NPqJLYtQcdw1W4sqMaJ9bFhcj3/DF/JDme7UBch2QlzotzD2IdQ1KF/WhHeA+cADIFJ/5MwdUBQUM8EPQCG4Mpq2m9tuPbcGXwa/5+bcINGqwnFDBqPZEj+e96wtkkQcflDm/rNX7bzYReXJkazyWEwv4Kn250UOPbPq1HiczmyHBdfyPM/8Hnvch1DPbX9WCW5VHcw6gY10EK6qNhGa6pGc5z7w+x60wnMC31eeNRQk/Fmwnf6WEki9nBxwL31/FMB+MGzc7DddRTQq7EyqK9/KcP7n1+I3Iv+hOGsvkDTuQK7Lgu+h7i8vE8n5fe9Gm1IRRagrzUijBebSZh/0H//KIDYGcTxhtPqRNxefMbuPcgKItaE8Zen+qvZSah9/gdRDx8/b17i3DWwS8jz+AlMgj7sef7FUIhMPr5gFiYkAzPJVov9cO1eZbg8q/19+BVfExenFf3Tbjy6T1/XT8lswBVThiyYDqufMm6jCT0On8W18H/CWFs32GR/PILksuyYFB9E8kepq1xwsaUiE17E7anluPEqECAC+rHIPRJe5JnSQSfYAZcyjtL+jJkLE5g+IJwIPk1XBkVOJoU4QS85ThRbCRO2IjOHPx/ZBjMT/ObV/rnupnchP2T/bN6CvdODsSJf4tx7fFvRmxqixNj/47Lg4txAtcFkfLuM8L1OYL7NweXl6MDX5mE/VJvS+Cs0RLXXtiXMLTVp0Q8qnGD4Rfi6rAf4ZwDglnAgRf2l/7ZzSTsn7xMah0aCPtvktqerbfczpDHzyacsfo/woFuQ/LA8J24d26s/97C//0Jrl771Kexf4bfigv70ZBrI3CDMtHwlvP8ccNj5WomYT/usX8l4UyYUyLvYFCnL8KV3UE7759EZkbj2q5RYT/oG03CDSgGs2KW4Dzj46Eh4+3p0sg1pGtvP4NrR7eMnROUFz8jFn8/eA64NsHjhG2RckInhEX+73WkDhR9HzfI9DfcYPcDOHHxY5wjQ/c0dmYU9jM896BPmAC+EduXTtgfTNhe+Qup61sEov750XT83wGE9WDgFBDUpdMJZ8a0wr3bt9Rh9zBcfq3EtVt+j2vzHEAYN/0vpNbpc3B1RHwm/PiIbc8QrusWhOOLft4meWZKOeG7U004MzeBKzMDTaItrgz5gORQNCmzNHBl10TC9zjIm60j938F7j0MBtd/SPKA4uGEfbngU+XzUOCUmXV5FLlP8/y5V2fIR5mE/eCerPfPbQZuQCLoV16bySaSy5WoE9FawlnKK3C6xwLcuz+HZD0pLuxfSLgeRJAnnyEsY1vj3rlv+/SS1oyJpNM7cp+fxrXVHiB1QGISTh/5tc8n7+Pya0p4Yv/blxOuWfU+rg/8Z59f0oV/PTdyLSn1uD7+PhXaAH30iX8IvWtOi20PBOhHiYWwwXUGxuME5GgIl3SC/tk4MTLaOCnCNSIewjUYoh4au+GmiqfrpPzE23S2/x5UUvv4Qj6YXh5Uiu/gGoPjcYMQX+Aaq1fjKvtmuIbFo4QhMarx4nnkd1+M7FtFGKO0JM21BxXcT2O2B6LnPFI9p0twjYagYRb12O+GGwW/B9fIGYyrxG4kFE+74kaig7AztY1e3IyKE/01Po3r/O7u953pf/PQiC0dSfYAfJLYAjiEceK+IByEKI7fi0j+Cqb17kFyTMgi6hAD/DGjCUfJ7yA2JdEf0xzX4B1G5ljk6dYmCDxQXyFcoPaXpI/T2w9Xqf87YnuKgOb39ccJLkkdYdwI+CqShYES3Ij3+7iGwI3U7fnYjHCBuq24dydlMMwfGxX2XyA2aBfNw7iGW9CYmEfdwv5cIlP+IvfzLcLGdC/C6aS3RfJW4D34KsnCfilu0O19wo71EYReHD9J884Ewn4l7t0PBv/iHkrxeLT3EHrIF/ttz+IayatwnaQgzMBIQvHpH4QDCg8Rvh/7x+ya5J/jM7jOyymR/YEXxBTCDvbNhJ4x6QZcDvH3or2/V7/x5/2a0BO6zvfIH/N7QmFhEOEMrX1ivxfN12VBXoocMxInAHXxNswELo39Vl3CfjFhB+ePhB21Nrg89lfCxbO+pI7FOmPvV5BO0Cm4P3KPpxCKOfHBWIMrQ/bBlSFtCL3sH8A19ANxpj7P5mY4j+SNuA5x4Km3N65snkb68r8Hru7rErFvkE/rPJyI1T1yTk+cMPJ0FuVRd9w7fXHsep/C5e19091fYgMg8f/996/635xMsngzyl/vJpzgHU3jdNxg2pORPBLEiA7qrWJc3fVnwtAzQcfrBjKHfYvWQ139bwThW7oQOiP8PXJcMCixFfi/WHr74sSkBK6D3SX6O6QX9j/HtY2i1zKPSJkTSf9UwsH2b8avwz+nUZG8FYRn+Qnh4EJ7XDvnfULRKii/Da6Dbv3nL7h8FYjDL5FZ2I/a2R33rs/BtaHW4oT3YWRX9sQHanbHOSxchitPOhAJNxW5d8fj3uF1/pqDQe9oG6MUV+d+xduTVxmJEwBq/LVV4GY6DMTVsS+RWm+/jHt/fkWqiPZtnGD2G1xHfTCuffZfkgd2zvNpB04aQyP7jsUN6vwHJy4dRziDbHjs93rh6olvEJmtGilXg/I0CFHZEldm9Ca5HRS0QR8kjBkdDbESve9JnsqR/6/CCQj1CfvRwaqrcWJHdLCzHU5cmIerB75JLEQQrt1XTjhzpiXufbX+2VyIK2+exJVFq3DvQ9BWMLj6Yrr//Z8SeiTW4NpPgYAatJU6E7Zt3iWyIHKGfBWUkY8QCtc9cXV6IDxPJTW2/6N+3yyciJdVuZ3m94eRPPssCMN0M6ntja64fsoHhKJg0Lf5KS7Pn0FsnbLo/fH/ZxT2/f4euD7RUYSOKg8SConphP0LfT5YjxsYjNZxBxK2J5vj2l5bcQ48nXHl8wGE4v2t+NBf/pxA2N+MKwfifZ4f4Nr/i4kI+5F7E30XrvX2xevKIv/cK4BXYtvTibHNcbMmgjCirXF1y/9n76zjPauqv/+eTpihpLu7u7tDkAYJ6W5FpUtESlRCJRSw6EZplO5uhs4BhmGYvjx/fNaatc4+53vnzsxV/PnMH+d17/fEPvvsvfaKz4r9GtVgkkWQDtyGHIWl0+EABFK+YLTgmZrvEI6OexBwW/bZgf1hyMlac44W374VHQP2v8Q2q0cOprEooG6edP8WVO39Um+bAUW+305k1T2Bleq1e2a0tn1T1FZBZEsS9HoFEWRylY1dzrwvHTADMw8kMhe3JzJNN7Y5+gdyYG5I2LXvkWjR7j+DkNmvIZ1nHar6xpNIprhztpX+1wutmTPTud5E2eRzCP12fyRrP0S28GxFv7ZAMvtkhKvMxgTyI7venxTkR33vmPaA/QEI2H8V8YMTSLwT6dovIb1xifb6Yfevh2yh65F8PojIPLgG0exAtD5+l/pVAvv9UDDJ0tgmxunanvbMW0YHa7YaJ6rA/o2E/no4Vsan4Nfn0gDsE3LLdamVif3ubrMx+nk7fOxgu/dPRi/jLRH7/9vxrXdg8vH/99GCgXhaeAZ2jyUM9cXT+TWJCPRxIEpum6oStg8BqFTSFpHAexwprq607I8iYe+i2CzE/t6IPMmL53chY+5rY1p7IwFzP/XMg2usP5dTr3vv43AXVcNif2QMvEEY3xkQKDfX8ci/C4u+r219/0V6thJ9SgiM9whPrgOeMyNhuhHKjhho193QmNLG4T0C2K9EFpQ0QHhjPWXZDaNtiKiPc9I39kXKiCs0+5JKBzTMv9c2fJh6ymzX1PdeyOu8A4rAmKq4dzmqwP6s6dpeyHhuBHnKPhXn50aRuIOoRiBsZuP5q3TvbERq7JpElN3J1EHkK5HSuiwy/qdFgPZOwO/zWKc5XoEqsO/AdtPGc32RAjzE+vO9dr69P1JS26z9qRvuGbf5F+MH9h2UfgYZmq4M9rFvcMV2e7RurizedQJSQMYi5T6nd9+D1u6O6dxaFMA+1ZqMR6L18D4y3k5CAMbAfG9amz5vWxdt9UV0OC0BAvUiovTfp5odNCeR3XALdcdnN+oG8xFESYAFkTPheaK0y4CClzTx664ICHg19bNDyhYBaPp+Ep5O7xGi3Wldj3VrJBcyAO9pyY8jB2GvYrwbS/HYN0yF1vtARP8ezT0FYdSeQgD7a+WxLdZOd2vT+dfiKIroZgRAPIPVTi/pAjlhT6JuzHomxyBk1H2HdkBBqtF5vRAgN2+67mnF+xbvXw8ZEJ8iY/9GZOjORHVtDEDyYQb75gFECbbNiDqzTfzoErQWV0/nZrcx8U1vx7vRYotvvt76vXzxXe4wGuf0KmjDadz5ycXp3BSoJIkbfaMQAOGllb5C63JApoMWfZw6zflK1ter0/XdkHz6hojq/WHRxyWI6NSrKMoqUeUhe9tYv4Cc36siI+1mrP469UyHVsB+z+JbZrNxuC/1zfvSE/GTx62tA1P/lkIy6QEieGJ+wkFwN+OJ2CdA8pFoTQ9HPPz3ZT87QDe1UgBEwMavint7Ip3jKQLYz86t5RCIlnnzBPNIe8bLxH1E8Mht7NzBxb0ZQPFSElMQTu0uNl99kANjSxsv3xsqy9RVETAyhvY3U5zV3nlzcX47m0OPVG0D/lDQ8AF2/gHC2J+B0DkfQWWFLiGiTJ+nORCgI06cCrBPnb96QMlQtKYuBs5OY+f97mv3vmHzckga79JRND0Cur6xdpdK1/ogkOJNpDPtSaJDm593kN47TRqvDeyeEsSdDvG+NuCCkq+ldrsTQUNLNND9VIROciVpbyy7fg9pzw8mkm+jNXI2spEWQGtjDEmupu/6FMncZdL5lVCwzyUkm8fGdcZyLux6CeznUjxl5qSXuxhXajPNT7n3WxvSr/ekoRwesZnqZdTtjiepl4j071kORfsPQQ7H/N5eKJPjcyJiP2fhHYJk78pE4EvNJkEA7FPAK+lcKzDW6fnw1MddkG5zddHuekSU8eVIt9oGOaU8+npBpIN+joL2+qBAht8hfv4E0lNKp8CuRLmr2n4ZDfM5PmB/E8JRvpKN2S0EsD+f3but3bN5yXeo85PFiUCba5Bt1xU5O9uAExva6ILkyXFonS5BONL+Zr9fR0ErTiutHDA97LuXSW1n+jnU6Kbcb+v3RMBACezvlcZxh/Lbiaz6HxH63ynU9b9fIj19+/QNx6N1cHb6ttmIgLHPjSZ+RvulbyeWH3VDjpRdMu+k6hxuD9ifHQH7HyHwfs/0HY61tJSl7fSpG1r771m7TfLvpNSvNVrMdy4x1AWt5aMJuv8jLUp52jOzElnffyFK7rxMPQB3OoTTOLC/gs35v5Aj6H4s6ArJm5sIXe5PDTzW+z07ov0vSNkkk4809t92ByYf//8c1AGlDCJmD+LBVJVXB7crgL5d+x1S9ppqsY9jYvZ7HwTGvdPEEIx5zk8AWR4t9zppx3Zv2/56NPuhqY3lkQH3AFKIPe1qo+J9HkF/HcUGZOmedVJ/lkaC72Ok5C6E0pa/snZ+mp7LBtoO+XrquxuHlzY844bCgghA+gYZF6v7dULxfxQpwFM0zLHXgs3Aft/iHXmOFkSK1HlFO1MhheI9e+f9SMkZigTBWKRIPGu/V2toez8btw9JpZvy/DstIu+xgxofIWFWgqQZ2L8MKYIHEUJywSbaL/7fgfpmr10RGDISKctfI0X3aRL4a/d66ZKhSEk6lroCdYS18RdkNF9u8/WCjedrNNSAR+tneSSUx6Co6unL7yCMSwf23YCoKR9pjUyB1vNRSJFfkxYbeVIF9l8jItoyvR6CgKLT7Nu2bPiWh7A6wpn/IJp8GxlPYxHg4d+0POIXj1BNTa4B+3bejaETEcjukei/o77x3aoECLpnOj+QOsjWFRmh3xC19BfO42T/z0qUZriFKjjeFRmCW6Jo47cQjb1FOCUdiHvR3pGB/ZpBa7/nIgE7dECJLvqc9xXZz9raohjPinGZrj1m49UbgUgjKTZpbXh3Cez3bPimk21+16QO1DQC+3ZtEbSm+yI+8or1x7/rIru2DTJKP0Xywcd4YQIkbiyHg3jTMCKFupZJkXjpLmX/032b2rtOs98zIf7hNZqfJHjOQ8hAP9fGuhcBaG+f2vTon9HIiPkJ9TTdw+z7riVFuSJj92vgrg7Qz1RNc2zfMBi4rjh/HKFDLJHOT48MhWzsLESAEVcgJ/qdxHp/G4EAc9ozP0agyxcYaEc126gPDY5Lu34xKYCB4BfDiU2Wv6EhVR+BBq/YPefSAOzb0Q8Zm+cS0ZMOtp5d8JhWwP7ZLfq/jl3/pf0ueXgPBIQMQ+s1j/O0BA92WdIusJ+e9xT16wnnzXpELewVmvrb0E7FCVLcs0Qar3MavisD+ychmlyc0Ak2TPdPMI+0+1ZBazBnnGyI5NRRicaaSlzNhOTXX6nykhPR+jvH54SISszzvz7SlcZQ1326pnF4COk8q9kz51lfxiL++lOiFviuqY2BBB9+Dckrz4AZV87D7p2bqNt/GdUo1RyIsRVajycjx2JZQuYg+/ZRSO8uZfIuBDjyKYoI7FLOF3Vg/2DqmyT2QQ61b2wMfY3n6M+eRKmWNwgHaTf7uxnhXHZ74ZgGOsn6+ldID24sUUCA5C+W6z61MzvSAd4ngP+mbNEuTDjfzrZerovuTouzcl/sfy8TdQMC1rZFOtloLEPTxvs0lN3xBsp03KxhjktgfyGqYLjvJbEoUb7id7QA9lEwwdsErzi0gV5Os2t5j4gyYGGAf4f9nQbp6p5J+i5yxC5E2FAlsP89o6G9iVJ58yE9e//EL8ZlsNvh43EYkSVTccZbf12/3zSdn5HgeQcU370UERjk6+ortNZdf/kb4kNZJ5zZxuQrWgP7+yLe49kQa1EPvmoF7B+Qzp+J+NDOVAHdEtifgQB1N83vaaBz50kLUwX257DxaiM5xtJzixAA9uE25osTvO9KBJZ/mp5p5YCZEvGBs8qxs+u/AK5PvzMe41HhHxDOCP8m1/EqwL5dWx3pQQ7Q/pC6/neA3fMPIlBsBqQPP0pUO5ibCFK6CNlLnrl8Ki2AfSacH/Um7KOBaO0cg2xKz47IdFQD9q1v/0JBcrvbuDmw3wfR6rg5px35T0NNeORoGgH8uuC9ec2cmfq1VsGjxumjRbvfQXLlPTt2oH3baQ4k0xdCQTxHWb9eBLYp7nVgfzSyFV4k8JQ2G2PnpVMRNP4VDc7m1K6vp1Va9fP/5+Nb78Dk4/+Po2CKm1I15s9FwMcA+70aUoL/Sfs11Dc35n0xUqxmRpHISxT3dUWgymhjtLPb+e5N/bPfBxGA+2LtfNdaiZH+BkWPeW21PYzxXmbfk+vUHUuDo8K+fTrqm1CdTkTtPUkVMN6MML5P92+2vysSQn/dou8LI8D+VVqUckAKyKdE1ODXds4B38+t7VeJNPRSoc3A/pvYhnENY9kVRcUPtW/sVsxRXwTI3WP9HkukNB+LjN8RyKgrwYWd7b4PaKfmLhLwDrTejZSoJ+33Y5izIN2/LAIWPGXUFaGFEU3uXX5j+n8/pKD8y76tSxqHnYhN+U5EEWrDkeMiG0LTEpEMrzbM8WEIfHwVGXyeWv2mzevo1G5ThJHPsyv6pxK01VT3ri+RBv4y1dT9NfM4IECgBzLCBlNEGuU5svu9HNHrRMS+g+/9kbI7yt67TUE7UyOj6AXq6fJ/QfxlSopSSoh2z6YZ2GmK2D+EADW9v38hRYTZfTMQUb97pPOrGJ19l/pafIngAW+RNost7svA/o0E6OXOKqdVX9NPGZ14SmR7wL4rh30II3cOtBbfKMcv9cmfW4aIVB1XQz7dtzphwDpofDL1aPaT7Dt+gviEg+YXMp4683YtA/uXU43G6YWAqVHIQbRGw1w4sP8RVoqHcPJ9jJTwZQieMIQqEN8H0ejriJcdhpTknnZ+3EZsVJ1bXdGGVw5u+bc2lZm7lYbayOn6PMTG348S4MGjhFNlLpQWO9b6+hVKw36JBn5kz5xLOD22KK4dad/7OjLaMsjrYNMIUgR/5kXp//Mpyiul/g7BorTsXCtZOxXKenia+n4jGxGguh+vomgj32Qy8xcHon+c5sudnPcjYOIcBLRmQO8ae25torzgLYhnDEZ84BM7v2zD9y5BlKb7Ja0j9o8ieOflRhf+XbsVfDmP85kEn9gmz1daz21UNywudakpiUjrlRu+obx/fMD+okjGvkCx+TyRiblmB96ziM3XjNT1rQzKuI5zbvG8A/uPE5uRv2P3/pAJ55Hu4FiZ6j4J05D0CAQg+NjMRWQpjgPh7J73Ccf9XPaN3VGwyhfExq8lYJ/H4Sy755Cm8bQ2D0OR5k5PwxFImDOZbjMaOrP41qnRGngVrfsnkTyaLs9DovV3Ee8pS065bPNv8uNZ6pGoRxG643kN3zUfsefTA2keSkDEgf3PCAdmCTx+B8mQz8p5J9ZQX6IEy8n+3XZ+C0L2rm7vuZagz7wuehjdvGLf9mU5t6ntZ5AOOEVDOx7Nf4716bCGPndJxyTx7dTm9Egmf0o4Mnx+FyCyO/0Yg9El1Xr1zhu+QGv3HIogLqrA/mWYnoqAzqsIh+1iBK/8PQlIxHgl0hdeQ4Engykiwe1eB0mXSc82RVd3RaWRdiAysR5AMv8RQvfblahx3xPJ1Q9tzt9E9P0mUda0dxqnq1E2WJ7zJW3c2hCNnUY9c/dAJP9fJ8kMu748kSG5WDF306KAtO8ju2Z+BJ72QTT7RyJbML9zBsIB2QrYd531UAIoHNAO/e1C0M8haI154MIW6T6X0RnYPxfpfcORTL8SOUz/YPNzsf39EwoicLougf2NbIxvMbo4D9lVP0fy9k82j27Ldkd06G04OHo2YZc1RcOfa/3eLp3bzto80a7/tZirDBJ7ebQPkPzoKLCfrx1p53yujyUCK3PWaDeEcXjJw+mIfeZ+n+77ZerTKdQdbR3iRzYOsyB+tF96p9OCH++jAI6S7jKwfyBhD5yCaHt3JKveQrTpDqVbWtBl/v8QiqAE64Prhn2LeeqWxvheu28sAez72PRDOkOZYTAdkl2fI765KfWMzEpGSmpzCsR7RtMa2D+D0KFcl3yewJy8rakJOfg4kWHYtXj/bUieVsqRTT5sfL7tDkw+/v86iLqpnvbtEQxXE7XeehKA2BhbxHMU7axiC/9jY0JXElHcbShia1y9PaQkvU8oa+1FaM2JDIsPSTXZGu7rYgzcmZW/+wuqG245yOmbrp1AA8hg165DIHyf4vyhCFAfp8gW1zcnNgK8B9WwPQcJllEtnumOwEwHtsbVeaWaqvVXpFR9gxTFxQhDZ0kiqvEPqe3SAHJgfwRi6APt/KLUU/xusTF0pbUS0Y+UgzmRA8RrcY607zyKOtDVDYFPjxJKbk0o2v8bIIGRwevZiAiVZ6gD+wsghWQoEkpz2/c+hxTAlXP/7f/xZg3YnA6yOR+NFEUf92zsLkuk6b+NlMNTiKjF95Bn/QcIIDoRCfjp7Ts/MDrZiRblkWys70GOgR2QMvoCSqU9nqoyXgP2iVIJP0n3dUWAz8loHb+IQKwS2HdFcmYiC+I1IkKnN1JmRth7mqJSehEbzu6Szu9p33++jcnlWKmLdM9cxCaFGxfXtkBK6giiFqArfGOQ0l0BINKz+5PqESK69jTKn9MQzUGs16Hel7JdO5dTJe9HNPwAWiOXoXqae6FotsEEWNsv9WV1m5OhSJn2EgN9jV72Su+7ydrYoqEv2Xh8EQGWHiHWNFcuE9qASxqu72t9foyIpLnC3j9X+c6SltP/yxkdjcFq26drAxBdDrc+r1G2SSjyY5Ax51HTPyJ41eqEE6Us49EK2M9lHu7BUrGLuX0VyYNjiKi9fM8B1vffUsiSog8roWjEN9D62IsiRd/mazSKjnwYGQyjEc/r2vDumYn170733xDg4ztEyZUSaPW5P49qBkd2uqxr9/yW+kZrPe09g+z3EbSWtT8i5PZ11IH9JZEcvRgZ0PMS4Mu4GqHW57utHY+e7ks4jdygabO+rU+A757FdwXiL48junsHRaX2QwDxKApdJPVhCSKQ4JeIH3ajGrH/D5vnhdIc+dh8QDWVPkfuLoWCK8YgUGGJog/uGBmDDNcS9Hfw1vl/xencDl2WwH7eRPKvNm+/cP5HFRD/CgFIayKHQC6N4/Q6rY3JUMQPPSAh14X28VuE1sB+dwRoXUc4AfckeGQG48fLI5EsH2lz1YUGPSKNwWgkl8YgHjEgzffOhF56P9VAgD6IbzjNnEodCHM9Ywu758J25qo34g1nIzpenWo94YtSXy7MY2d/eyE9YADiP09bm6W87I90xzZ7R16DD9h4/AHZBhsQOttwrCRBauu3Nl9f2ViV2aPzEMD+5SVNpN/zonX7DWnj7rxGidICu7Yz73vbPXembxqEZI8DznMQzrFSR8n89wXkbL6aOtDXHfGUv9OgjxV/HUg6ueG7J4Zvr0/i2xR6vf1/qtMbdT1wRuS0ux7JXs/k7o103RHICeV2zLZITxhM2qw7tXcLATKdT2Rm/4kUMEEV2P8dYUM6/W2KbIZFMf2jYZ7d5vNSV+6AaMpAfBnZVKPy/Ng1z9b4J+LNOWNmB/umNxB47BkH2b75PmHHbEvwuAFIHn6E6PnA4r3HIB3FI+4/RqBwzrb6KeJFnhE+LiOl4KfTGJ3cYbTwJgL2mzJBZrCxc2B/fZp1xu0Qnxxl/Sj5mTugehABL8OQXT0dASb3pgiWQTLlVqT3fILo7EsiS9xtT89++5K6nMzAvgdSuF3X1uIYimj3CLRul0L2yepE6aFRiG6bbIsvkRxxB6nrq9nx+SWWBUaRvVbw7sHI2Znnsz1gf29C730SBTC5M+JVCmd84nnOh1ewd15T3LMYkdXUhuRexSE/Pn5EZLd7qbLfGg24DXkx0ss9O/wLhF2UgYIZ2D8FK91p15YkNg8/F/Gud6z9XTLPpcr/drJndi7eNYuN31vEPlzdinZ6IhrzDJ1PqO4psKWd/zD3wa5Ni3jLF2gdbUbzejyCVM7J/o4P2J8Klbv7G9JpnyQcFNMUbU1NROw/Qr1c9R6Ifu+nCM6bfNgYfdsdmHz8/3EY0+mNhM0gBD56auPlFKUkjMk+SygwfQgjYHMiauEAQtG9FzH5q435jUAM28EoB6y6GxNrBfysaM/+soPftgEhnP9A2szLvntHu3YOIVCaMg++hwTh2elbtyYMrD0I43KXhn7kUjwfIaP1DCSg+pOiBNMzcxJg1PnUnQkr2zt/RtQ6vQ8BCd6vxYnU01yf3+dySZu/KW0sPFrVSxeNQYz+Kpu/F5DgWaPsT0PbU9k3j0QCqdUO7n0ooo3t/xwttbDR00fUUwZ7EBHbTcC+RxW8YGP2DK2zBr5vfX6fhqwBqtHQw6ydUdT3CsjKwGwIrB9CRJgOItKLZ0Ag3PNUDfwp7JvfQ0rHzhTAPhGR1JswfEagNeb0+DhSxHql8czAvqcftgE/LtqfGq2Lz1B0UhOw383m+gUigulJO38IQb9NAKfP8VZIUXSD91q0bt9HINI/Uh+vse9x3rGNzcEf0zeuhIwbB6wGI75ycGpnLGl/AQolrqGPs6La/wPt97yI9jJ9OFjxKRE13ATsz2bfeSgRBfwLqqnm30GK5NsIgP0hqSQGcuZ8aMd0aRzbsGgSow2fgxeo1qnNzp4f2Rj+AvGS+9A6OY5qBPU0RF3PV5Ah93v77vOQsfNR+va+CGz+lBYbNJd8I/1eljDSZ6RqtAxAIEN7wP5RiO8MNVrYp3yXPeeG1DHF8xnYf59UroaoWfkRlortY4r46PuIj1yElP5+qU8fImPZv20Lo4FjqJf7mhpLQ24Yr5WRcXCPveM9tGYc1J8q3VuJPELrwJ1CY5ExdxFykm1LbER4Nop4nhI5SN34O50CJEFr7p+I76yPDM1ciqMrwYs9gv1aUi1mu28txB9fJspu3UAd2G8v26Mr4hsOWA4nQJvzEO8+A/HeJRANf41oflPEuxYhyth9Q0R6v0+AeVsj3rIVkrXLNPRlXQKkPZtwduyO1vdnFMaWXfeSFx/SGthflDBwm4D9Q5D8vY1URpDqnkXX2Zx1uA4qVWD/DkSLPrffAKfafa6H5JrijxI8+C7qRvIUyKEyDNG0g0dtNK/hlsB+Gq+5CIN7QnhkllX32rd9isCbRj0C8WV3Gt2NnO15/R2KdIGxiH/sW/S3L4qa/AQ5dbahCIaw/x1sPgiVNzocRZMeQfv1d512nI8Ps+8aRDVCswRj/mD3VMqDpP9ftPGbLo3rsQTd9yvaexvxyONJjq6C9ochO2Ga/P0oOMOjl3ON+lKGrIlFsyPeXNa63xHxhluob17c1fp1gY3Px0Y7T9szJ1KV15sTdH1kw7h7xtrlSH453W1b3LcasS9IXvcZhN/L2sqZPJ3Bt3+FbIIe5XtRkNSLiC87KN1eqYouaG14OSnX/+YkeJaXe7iYarb0qsjx9g3hLPojxQa6du9iBLB/PRGNvjpy1L5FsYdG8XdtxCOfIXjTqdTtjFOJ0nV/Lcaoi/XzU7uvZhshXXEGYi33TOcHIH3jUKSzPI/AcOdxs6O19DHBQ68h7PDXEY93YHaItbEv4qczomC4NtJ+eEX/pkljPYIAfh+ndblO582f2ziv0eK+zYnszhqwn+47k1jXbWkue9t3PUhzEOG1af7XQfb0TIhPzGPH/DSUT7E2FiZKxr6A7P0l7VgOOYdXQXJuRQTCfkgV2O9lf7dDfHIslp2f+MkJ9sy7hK39fUR/9yEb7xgiU/heLLCOZmD/Yrtvz4Zr7QH76yM9aBDSZx5CTqxa2RzqcuASa9M3UXf+ML/RwZEoK/ZQJpAf2dg+R9DgNoj2P0f2bRk1/x6i9V2pyuD+BFD+kf3ugtbmy4iPr5bu3xvJmsdJARBFvx6xeVuumI8eKMDEdaGMZeUAzHPtPg92vJyqfrwHYTvuWry/Cdh3W7cL4eR9DXMW0HFgP2Ma3yGCdq+nDuxPRewjMQQFqhyOdEG3RSdH6bc4vvUOTD7+tw+KWt1I6d2CMPQfJ+rQlorQ3ARg/z5SCl0h+toY7mWEpzTXalzN2n+RKqjWBQnN54zBNQFs21ibXtuxPaXSa2CuQwFapu+Ykyht4ODBAkU7qyJF/l2i9qaXHspCey/aB/Y3s7H5gIgaPAIJ1OGoHMehicl2RcbaG9bmPQggmA9F896HBNEGxowvQYz7dqrA/hJExH7e0X4xO/cBEkw5ovIXSAj9AwFQHungx3P2/vOQM2gzG++ZU9+3tns98uVtYKViTGo1eYvrPQhl9G8UG7ql+7oRKckVYJ+IOHdFYRQScmXWQA+kcD6BIq/K1LIeaXxmQAL2FqQYfY2Mtr6tvgcpM0uiFM8ZkCL/Ehb1AJyQvsX71M/G931aAPt23+H2fbciMHRKowkvt/GYzXcuP+HA/puEQjFujaS2m4D93nlM7P9HrZ1fEs6zv9pzczTNW/GOvYm6vUNQhJ9n0JxOGCzv25idg6JauxBZRjva/YcQxuKaSOHshTIifk6ADWNJm3m16l9Df09CfGMD6iWt3CH2Sep/E7DvUWs3IEWuZigjRWsHZMw9iRwJp2EReohfzpPu34VkDPl7iGyR55FCfzoyJnoiAMXLQXiK66fEmn+e2K+jCzIQz0708g0RVf0IovNxBgjhIN6k1dpNcmG3cryQcfIIou2eqe0BjB/YdyMsR/mWsmwN2gf2v4f4+qtUN3rOhl0G9qe2sXUgdzBaN87H3yKisnPmgx+/pnkfjcwrVyHKNW1nY/4CAj4eQkbiqaSyEtT50QzI2F0BATY9CcdgPoaj6NFpkCz0NXovMlI3RnLLgfr9EZjwKuLdA9M7+xHRQG9TT2VeG0X6jER0vwoRTVsB9mlwDjbQzoUEbfrmd7dhqdLpvhkRTxiCZNtGiNZXQnrONzaPt2DrzebZgZSHiPVyGZFevbqdu4hweJ5ORFn/HTnHli35qf0+kBbAfvo/A/tXU92XYAEiCvFWinrDaG2NQCDCwHytAzxwISKy0ufeQacRwDp230wIGHX+cAICRG9F9NsqOm1/Avg4ggBKMpDZCOwTPLJXw3h1lEfmKMIDkJ7k5Zaa9AjvSy9UQuMZpBdch6KYd7Z3+Df/COkQ71MH9vsgXeQLpHf+gOpG2CuitTXG5n4E1TX7LDL0pyzHyv53QN8j3h9FuuR3y3vTM+6wPol6+Q+PUr4MKvX2b0frPK+1noSz7CRS1mcxT8cQuvmp1IH9eWwc26hmGTRFh/ZB/OjPRd/nJsoiXEYD2Eg4q9qImv+Z32c+tFO690rkcFkC8dUPSRtcEo7h54DNUhu97NtHI56xZ9Gf5ZE+9xnBNzqDb3sJnWGIb25RvLcHEYBwanGtUsve/p8O8bYniDUyN2EXXoj0mUeJrLG5UltzEftRjSsXSXOW5CIEL/4C6Y4eRHVAotOeNAcVOY8cAfyx5LNEOU6X4euke/qh9TYKOTFy3f1xGcnFO3PN/zuQHTXAnjmMAOW3J/jKtEie/IOwOR5GQPjsaD08aWO1DuK1Y5BjcQ7ET9tsnLw0igcFdUF6x8fIyTY/0osciL8dW38NYz+99eEVzObONJP+34IWwH4aj7OQXr8tcES6PpDAF26kDuyvhoLxxiLHVGNWxnjk2RJEiZEry3c0rIWtqAL7uSTb94nM5TcRv/PfrxD6XzekdzxHROV3R7iEZ/7eQmS5Zr16nFOq6FuHgH27PqX119vthXSejdFardV7t/EdjYHb6fxJCGSfmQCuJ4YfvUPobL9CMnYQ4QTLjtS9KYB9hJE8gOzgjbF9mexbz0Nr4j4im6irjfd5SO98GWFWM2OZIgSGsTfN2SgDCWfEtdSDBVdCvN9t/McQLyn3lXFn7fiA/aeQXdLT+jgNUcr5VZqB/SMImbJNojXPEDsW8YCXCX3iapoj9n2dtNl8XYd03HnLsZl8pDn8tjsw+fjfPQhj84TivIOfXyMP524E+FSmb/dFyvadiEG/YoxxQxSJ+g4CosuIhn8RqWkD0rXuBDh5TvGcKzauCD9KRH60qn/2I2A9+3+5xISObRgLj6D+fXFtA6K+4h6prQ+RJ7MEJcYH7G9pYzuKKMEyBAkmV0IvJRSEXsSGqA5CZoD9KBvHK5EB5N9xC4ocdKG8JAWwjwTRM0ioTlH21e7pibyzcyEl5ixrYyhhEPrhkej5vYsigeLphe9QbDxMHYgrf59NbPDzDK3rlXcjwMbHjVayQuxZA4OJqM2mNPKBRO3ShZFAdnBgCmLTq75EZMabNq/7UDVg23M69UVGvs/ZPTRE11IH9nekuunSnEhQv05dmP8MKTF3EEZRLiHiKZy7IPC71RrJwP7LCHDLgMFRNrY5pbA3MnbepkWETMOYzGrftwJSVnwddEHGyPuoBuhf7LscWN8N0fdbSDnvjkCNAfa8R/z0IqKt3ZFRAvvdy/8THXUx+jjH5u0RBAB1CNinavR6WmZljKgDEtMiMKKN2HBpLCnNP42Tp5UuXfR7AFGndiixqe8Ldu4DFDkyDK3xWZHi7WDch9Q3nt0YGfl327EFVeDJ+3QoBW+lANbt/2uQUTl7Oncq4ndvIx4ybfH8AMYP7PezPnynYWybgP2S9vvbty2A1ojTVE/EEz+ibtj1RkbRlUYjnyO5dxbh4PLIzX8hWtyPAFj+SoNhigyT3YgMroOJtT4tMjbXQAr+SBuzDOyXdJrl5Q9srG9FzuQlrX03Fl6zdyyHDOsMJI5BMmzvNGevIb7g681pcV4iIvAetE43QsCBZ5UdnPrVHrBfcwSna57Z9Rmi+TYE0L0IbNSw1qdD9DwErQu/Z1N79gmirNZMyOB0oPoCtDZfQ3T0LyR/NiaAiBWtjYXtuMDmqI2GUhvptwP77wA7tfjWRQmA/WqqfHg5tLZG2TjcgMCnK5HM+pi0SWQH+bTzxQWIaNF3kN6XAcZ/IVC6DfGIbJDPhjKBRiNj3zdZz7x2XwQ2PkqkrrcxfmD/G5uHHxLrY0J45BfIwXkxAgCcR65EO3oEVXBkQ4Juv7G/g40W5kU84vuMH9j/HMm2xxF9/hrR51giqu5BpJPMh5xGDoTuQr2EyFXWn5GI3x+IdOU2BDa00gWXJEqA/MreNS9ykg9FIIXvp9PN5vcTBGjliH7Xp08iSmb1Qjy2LK+R9e+f0XFgv+RzixIOgvOp0uGyBN+9HgWjfMf6dJC1/zqx18E7wOaZB1Hlo9+1+fFIZz+eJ5W2QPR2HFqXTxdtzoEcU84frrI5+inB+31z1c7i21Mi0OjedP0ymxcHqhciHE1LdYBPnILxLAQ8eXBG1gWOJXSl3xJOD89E+TrNvwN7TU6nAYgu37U+PktEMPdFsvcfNsfHU+X9AxFQ9o0dP0HA9qzIphiKgXLWlx3TO5+xeSozdrZB678xowzJ6musvVMJG6MlsJ/WytTWv67WThfET35qtHCwnf85WrOfIFn8IuL3uxD7AHlk8fWIzvK4zE2Ay7fROtL9O4Re1I2GzEK7tgUB7J9AVTdZBfHBs4tnclk253c3UQf2cymeC2gHYKS6Xr+T3rEI4eC5jmrATKk79qAesZ/1v3mQ7HrEvusuROvuUDkJOaAfxTLMinmemhbAPgHG9kT4wE5Ih1ywnCNaAPvUbd9+SNa5HvwWChQsM0vcgfsg4vH9kQx5175xXyadH52CgPSvkd7wBKL7cZnKqd8lsO960H2EPez6yiwI4xqFdJMNUzvz27Uv7PnPCX10uI3FeTaf59jcZzpaiAjceArp6UsjefAva+N7du+Ndl+t5CHjB/aPtusPoOCIFxENvprm7lWKDW2pAvvPAduneX/UvvVfSO5cQeBFN9Acse+60sO02Ito8lHM7bfdgcnH/+5BGKo3UI1+PAIpVj9EQMp7CFB0we/3ZcHfDXnoM8jnxmzeyb5U6N3Y709ERk9P2qyXFF1sv3Ok38GkSKyCwW5r9+yHlPbFiBI142oLFuPhyuN9COC6Hgmc0cSmTzMhZv0F1Uiv3MdGYJ9QHLawdr9BStti9t1rEcbFn6k6Nfogh8fvkHD8NVIanRn/Ayl0ZxMbhN5Ja2D/DDvXN83tAYSjpCY87fd2SIE9DynCayOA9yKkaB2LaOcqqgpbL8J7Pw7YL8Zt8eJdWXCfYmM6lBbARqJFj6zy0lBdrY8OwLhiUZZ9KDckm9nm+T2k7A0gaO+UYn52IYD9faG2QWXemK4rkaI+pc3p10jxKzfTLYH9t5Ahvm26tpr16cji2eMJQGmpsk37fwoEVPp870askSZg/yhkILyDFOfVkUHyKVLWp0/39yKcUZvlttI9TpurYGUv7HePYry6IHr/Giu9hRS4R9Ba+g0y4oZRzwxyo3Fb6pGwP6AA9ol1upjNZa7h6utjFmSUeMpmE7DvmRCfEJkC3ag6FnoSoNGeTWNk921m9/weAaCfIcX06OK+g+y+xRJ9+Jj2R8CAR85/Y9/9AAIZH0KKWlnmy6PJP6K6oXJp5PRDMuPk4vwSROmSU1p830E2ln8g+NERhHFVAxBoDeyvThVEciX/dBqM0nTv1oRS7BE1Tp99iQjD20mGDnVgP8vGrogHzYUZYenan9CayRtWL02UKfkb1dTkmYi07BdpkC3p94Y2rzVg366vSz1K6JfIGFikON8X6QltiP9NZXO9JjKCzkFraxkCHLyBxF8baGVG5GjMwNc39vvP1Pds6RCwj4D52W1sv0JA48JozQwlAFI3aErDdjokRx1Q2di+13WHL6wfbxIyNjtxlyGi5i9Jc+rfs3a6d0HkCBiDRVe2M58eZTmCFgA8dWA/8/yFbW58HbbZN97PBKZLE3ylKzIqXaaP20AT6QBPoXVxG9Jr3LGaefs8RMbn6g18ZyxaIwchANQzstqobmKegf2cLTCpPLIN8bS/Wl+3Z/x6REnrP7R7H0I62IB0rSPA/jFEVOMgpP/9Bq27z218lyjeN8zufxfJSQfPPRp7BDLGvYRCrzQPu7b4jj6IDr08xieEPvkCERHpY9nd2nwxteHAzjhA385Pi+T3TxBAlOXMesSmk+MD9itBOUX/lyeifS+gCuwvR9T+93F+I43h2UgPdFn9MGkPH+rA/gJInznb5mkHwmm1K1GebgAR/f40qfwasqm+T9WZNRKttbwfRGfw7bymv4Po/EmiDMt9yD7pTWx4vX/69sZ9sOy3y/Ml7VuuK64vhOhokLV7ntHOVogXb53m4myKzZGp0+lcKNDFMx8dtPLxcxDxn6SSgDYXzxHA/tfp3scRv17Vfp9mz7QC9LvYmL1LVX/MusHUiFedQ92+npYqsL8ddTv4TsQjM93Ngtbiy8im7oLsg8ut384XXkG8bhokJ2+3b/Rsyu7E+pqDkL01YL/o1/YI/HwU6S/fI2U82T1bINkwFgXn7IlA6ccRv67ZClSBfc/IaAXs32LzdjnNgRF5vFZHGSM7p7FfmAD2b6S+j1G5AXYG9o+kbrv0ImWw2995kS031GjklBZrZxoKYJ/kjLH+eVBYG7ILbqaeEZ8zsXcsx4LqXom3I2e26zgPULWnehLlrj5I972H+F6n8COkNznfH+7zkPpcAvvvIv5yEFGW6AHq+x3NQgSe/AsL3rBrU9h7ryTWxOlUNwL/0p4djpyIXYu270v35uOItHZfRvyuVu7I7nFHzMfUN77+DsKCfknow5shmtrT6KSN1sD+YWnuFiD01WOp8rDFiDKCrYB9/9bG75h8FPP6bXdg8vG/eaRFuQxhbFbKldjfXYxRvofAcVc8ajXGG9peg6T82LlWCv1qyJCft2jrBGQUblcwzq2Neb+Koq5KEGole9cHKOrdjZB7qQrAsszIEkgReQspBZ8hpWMLu34qYvIXA3fZuYoAT/9nYH+cwmDXpkCK6zck5diuzUxE4lSA/fweJNhdcJ1A1UBZhKh7dhcC9vPmuQ76n5Se8ei1RwnAqqn2+ZxIMbwxXXPl2hXN0fbtm1M18rpRBfZXSG3sbOeOyWNZ9OEka/tTWtRu9GdsrkqlYgnkiPA+vEFd4Oc5HIiM568QiPYGUuCPoki5T+vlTaIUj5eo2dH6/Rdi88bnCIN6CiJi/kVsZ/mGse+HhPmLSIB3Se9tI22gRQDZN1Gtib6I0cu40kLW7h8Q8HI/VUWk3AxsKmTUvVDc9w7NGywdjozCc6jzijzWtyPFeHrrz0soimlGwuEw0M4NxZwfKJLqeESTDsh+ihQSdwb8FK3lDxDv6Fn0w4H9MRhQioz856zdte3c75Ai5plLMyCabA/Y9zTFL5GxMACtsUsIGtre+n4LiWat7w4qL2XtePmpjQjl8kfpmeNJShaxjnoUf5dE6+ATpDjugUAFz2zqUcyPZ9p8TNVBNa7UAWHwtlHfLHD9dO0Cu3dam+sfIz7/JgEMLoDW2xvlmLRYGwPQmh+K6HjddM8mRLmHM2gdbdaPSOEeg5VWs/MPo7V/HZIvmS95KrYD+7k0Q1nqpwtSqvdANL5v4un+LYsQEXwlsL8uAr5WRHzkKmS8fp/6BnQlsO9RtDshOXi9zd/pKKLvX0SJGk/Jz1HTfyecI00bdS2L6OkhUl3ydH1qbP8W+93bxnIvBESejfjIx8g4Lmtctwfsd0UgxV0EaHlDMXYbE5FXf2riQ/bbgf1Pkf6zPopKPt6+7zOkX7xBZOGUm6N7mZbs2HC5c2Q6tyDSBTzqepV2+nUkcHgT7aZ72gP2uyCDcAMEMi5Ki5IK4zsQkHqtved9Yv+ZbBT2tTle3eZ0qqINp3fv787p2pZ27o6SllDWjQdg7J55URqDNqT7TQyP9LW7BOK7A6k6bhZl/HpECc4sTJTuKkGi3kj/+YLWwP5xCNh/Hxnw0yJA50Nib4AeSDcZhpwNR9u4v4GA5K2sv3/BahX796L1cwAGtLXDbwcgEOZ+a/dhBLRPjwIUHiFK7/Um9NAjCf3/FOrZA+cjHcmdRIcX18cH7M+N1mUbAp8zSJ11tBUIoOICqvQ6H9Lb/NseRcDJXPZ96yDH6nH2/GNUIz3Hyet07mYUJOWbPZ/t30fQ2QBaAPt2fS4kw36E5P4Sdn6i+DZ1vtIbAY/lXM+BaM0DsYahqNlzkY30qY1Z3lR5ZwSqzd6wzh2E2qFYb7Mi3WxPJPdyNoPLimUJ0P8sCn3G/q9lmNj3/976fjZa0ysS2QiPY5kBiWe5bfaN0cL2hENmdmIj1LcRzf6IVBKTqN09iiL4x673R2DgwUjeuQ1e8owmYL/cKNl55KZEFuO69u6zU1sX2tw9lp6bDdmb7sgajsmmNKc+d3PQAOxT1RFPTW1/TDhF/kh9H4ecxZSPQ9rhPd3TuIwP2P8X0sXKzJ9sh69C0PbmxX0LEcEVmyN5tx5ai6Ve0hSxX+p/ZZWDHkgO30MAsPPkdlP7Gdj/B7L9+iL+NArx2F1sjr1MzGDqZXkysL97cW1xpL+emfo4ldGMr5MM7PdCcvtFpMf+FdmwE6VHUvCkxLuXIRzs1xHOkSZgf0+k675kY3YpEwHs57WIZKPz5nuQbBmAMCuXN38q+tEX2fxnIZ3uDIy+kL79GuItT9D+3jcekf82KYDBrq2E+MLL1PWj3oRO1QrY/yni0zPZPS8S+F5Xgm7nJ2jqGurA/kAmR+l3+PjWOzD5+N88qAtLj2w+ojg/BQILMrCfwfil7fp3Gt6xAGFYz0Eo9CdTV+ivQ8bX/OlcXwRMfoWiIbZNjGYGpPx/af26CBlNcyHQzqMyPD3ob0Qt/FWJCNA26sB+bwQ+LGjvceE8FVGzfrQxwp7leFIH9t2438vOnYEE5RnISPmAqKXnguQ7BLD/J1Itd38HElrPIQEx0J9PzHZuAhi6lWqN/WWRoMgRolMhYLXN+lUD9u33QHv2WVJZJmR4PogUuXOp15RrBezvZHPpKbOL2RwsQEq3S+240fcJKZqruKc0MvanCrr0JWrNv0HsG5GNvzw25yBlYSyitX7FN5X1el9HRsSZKKPiA6RkrYQ826OQMN6GAP77I6WsDQHmCxXflEteleDICvac7zPhSkgF0Ldrx9sceVpiH2SID0Hg4HI2Xl5aognY70mUWfilzcnsLeZiNiL75FCalbeD0Tq/DAGoCxOOpxfQep3d7p0HOYyyU6k7AgW+SX3OabPd7B0OYLcH7Lvy6CVq7rDrXvbmH1SBwhlpAewj42Nk6tfeaM2Otm/y9Tw7AZBdWM59Qfd5o8gM7P/EznmkdMtyR4Szo2saFy/9VW5amddEBvbXaNF2Bk5/VFzbgIio/NrowksivEl1g8oNSPyZ9susdEW8em5iY+S9EG16hOo6hAyqAfvE+roo3TcWGb23Ill2Mq03B++FotI+pg7sZ/mwNsFj3wG2a8FnF6UK7M+d+jiAALi8FNtIJO+mK9rZEBktIxGYdykBTC9BbCb7qR2/tfGsZOLZ36Vs/h6hyESy6x7FXG7KtgbSMd5HfO+3VLPcsrGxN5IvDuyXvK4lsJ/o72akL9QAa7Q+P7fnj03nS6BrWuQ0eYvqep8BgZffQzRbBhS4juLreY10zUHViq6FZN1vjN5uowWwn+ipBwIdvotkeQkSL061xv54y2RMyIH4+PPI8fUNEfzgWZMlbz3I+rJ0OtcjzfsdNifZAXGyPeMlkEqa3DeNZS7FM25fC/s7UTyyiSYarndEj6jQOVGXfoOirY4A+8cguTQIybBrqO7Ds6eN4+uEjuIRui8iXf17FBsip3lYiMhyqNVfLp5ZAMmxG5Hu0p/IyjuCWAfLEPJgJEU9/kQfQxGf3TCNwfzFfeMD9ufH9Ozx9L0lsJ/Gclqbk3E6dbo+HSELK8B+umcuBC6+hnSD4xDfc35eAjEDqAL7W4znGyaWb2fweTvEd/6JbJKDKSJ8fT0jkOov1EFY3zukHxEh+ixy/JQgtZfl2rA4fxySSXMhO2Q6GkpsGi0NsjbOpuqQWQrJixUIHuARzf+0vue9HqYhgK8nkHzNvHbX9I27Fv3YLF27tqGfRyCaf4Rm29h5xmPINpi3HKt0bwb2n0b2hdP7fGh9f27HFcR+L+cRgPQChCPiEsT/5qMaqPWuXb+7gX5yxL4Dsg+RwG2iJMs/sABBZEu8gnS9ceVj0jOzIxr8K1pPWyS+eoKdP5+UwZr60gjspz4vj/SRPUk8OLWzKhHAtFc6P64SAbJFt0Sy1DPcPkUydZ2ivfaA/VaBKN2RnusOpjMx51FDf6dOY78FUQb3RMKG7Iacj267fUFdP/LgvVFIj/G+fBfJcbfpvc0pCX3rMarAfld730DCkT7BemQ63xfpxzMX71gG6bBt9m0zFuOY1+1OBPDfhwkD9jdI13zuNka86XYCHO9FbKzr5dH+SIs1XLzzUKSPeADaPu3cuxripWORrrlvurYpkr/HZ7qiamv8mQD2K7oJgQUtYt9xd8P7c+bKF9bW9RTA/uSj48e33oHJx/8fB7GhaRvJU27X+lMF9g+084shAd5GMkLtWlc7Lrbr7yED8CSKTT5RhN5IEwJlyZJpExOspCCi6I7DCVBoeGKwQ5GC/AJSdhZt+ObN0zfnerZlhEJmkvMgcMKB+v3L+/370/9ufB6MlFUHzkehFLQSXPL2MrB/OfVoj9mQ8Hy44duykfYeAewvlph5r4Z3DiRS8GrAPqFQ/d3G22u0dyGM8F8QikEJVGVg/+Q0jm1IWV8aGRmPE2n3V1GPxGgJ7Kd39CNAsaeo177tRYNBbtd2RsDSj+y3Z3qMRDS1TMN85DnfnlBCXKgugZS3EQh4bNovoiWwTyGwi3fPTkQNeWR4peSO3bcBUtxuwjZHIozTM6kaPV2JaMlyjYxXeSneuwRRZuN0Io27DzIwPkBARAme/ZSoUfiYzzWR/n1wut+ja4fZeJfpyt0QL2kP2N8OKU+foLXlzggHEv5CkQJq/7cH7B9GKHBvo3XzY+q8LgMW1yLltKeN00HW55dJ6eL2nINWbcioPcPedy5aY8eg6M0jEA/aGxlMWxPA/qEI9GlDfLgEhpuA/bGEo7QLVfrfJPWpBPaXRo6ll20sHkPgabnGParIN7lrCbAhpfNT69vUhDLcBmyd7iuB/eka2nrIxmlJ5NjdhdbgTw8EIsxAOFa3trn6ENFjyQOnsvH36Lifl3w7/c7A/nXI+O+KUoOHI4N3ZSSfvRzI3dTT3dcm0skd4Mu8ZRMi6ujeJp5mv6clwLDlGsbOa4Hu8k3Ikx8RoN4gwji+EgFZ5Ts6A9ivZbc18MEhiJ8f3c73TkPrrI4zkKGzQDqXDdgHrf/lBncbpXnIwP78tAPsU5VrfyPA1zFIxzmIapmHxWjYPJcGkHJCDqIm9kcoAva6NKe3UtC7PeNr6A9E2ZH1bIwOsWu3o7XhOsaVdr6U760iQ712tvO0HKk9MTzyRASA90Ig3o/sni2p8sOO6BFHpXPrErrL+sW3daQUz0+JvWN2IyLAvYzR2yTQHvHGkQhYG0yLjf7S/fsRIEUtejuN8RJE6v1fED/3LMa+aey72xwPQXr5oUVbP7GxeIsAJH08vVxB1pVaAvuEzO2NSvhcgNbS0dT3gymB/dqGyg3fnWlvGqrAfi7hsDvS/76PeNU9hPP1JiJDpARhBlAF9jdP15pqyE8036a6Qbu3MRbZeXl/oVJH2pYAylw/9qjhEUh/nbF4xteiy4fHENA8HdI9vKzRD5GO/J7149ck+W1tZGDf65AvRQCeG6c+3WDj+QmRbZmjUKciSuo9gQH76fquaYx2Lfqxd7p2MuIRGyHePAbR86ztzMcN6fn126M9u/8Qu/dBqoF10yDg0dfiYER3u6A1ebt919pEtuIvqO9NNAdR4uuyku5sXNZHNuvzaD17CdH1kA5/J5Gl4/r2MCI7/mZabGCbaKQfocvk41KKPfSoA/vZfjjaxmIs0uszGD0bAeg3OQy6pr54gOAjyFHiQOn7WEBGej4D+x8i2hufLtIT6SOP2lidRGtgf1qEW3Sz/r9JcvQX93pWzF3IPsl85vsIG+iJHDoLo+CApxCIX9m3ws55psA4YJ+6025i+JHb5dMQQZhXUOwth+wGjxj/Ne0A+0Xf+yBHlq+d0iaeBfHxkQj4Xz9d64107K+w0oA2x4fbXL2E7IQ3CJ3W35sDMLsh3f0qxBsGIX3CdbiKnLfnViR0iofT/wPsuttHvyrnn6DfRQn7+RVCB+yL6QgoaOl9OxbMY1qM47mEHfsgRbbK5KNjx7fegcnH/95Ba2Awp/MdUlzrjwwUrwF3DwF4HW339KIOiG+AFICxSLiUnvqDkXH4IuFpn6e4Z1pjok3Afh8klH6JDIt/ouj+NZDS+BVwS2qrZPw7pG/OUXvZOJ+BakTCPMhLPgwJ+jVpdgBkAyCXj1gICa0vkPK6UUO/MrDvZU5uKtqfmij1snqreSYiKEYgkHyRsq/FOwfSDrBv/59k11dK5+5Bwmpg01in+7KSuCECOH+MQA0X9M8ij/Az9vtdpDTnFPgTCON49fxOQhkbjhwiJUiZI/mzQb4NAhw9a8DH6nAEep+FhP/zKNKjUZGw/+e29nZAKW6H2HvOJ5TTSu3ttNYysL9g0W4XkkMmnd+FoOWHqa+jlZBSNISqsXgVWlsODozbmNb+bkHzGqlsft0BvrMkMhrGEhvDvUaAfU3R6b2RMuQAzxhkyO+KlMxXMKUw8YrDbO5epL7BWEeA/QXRGl0URRq9Z31+gWq5qKYa4a2A/YMRiDsW+HMeQ6pregOqKcnPpjF6r2mM7LkNbQ4/JyKuvk7tNB05K6qrjdvbNnY7N4xLHkdfM2WNy0wT7QH7PQgF/uQm+rE+tCEjuSkq3N/ZH63xt+z7j7e5/5pm50kG9s+jCsruZ33OETFuHM1WtLM/wSffRuCAb/C3pc336zQovwhM+KE9+xWwbTt0tUiiiY2RgfUCcphnfjglEfF1D3Vgvy+iyQ2AGRr6lCPYz8q8kiqN/s1oZN6yv4gnOoB/E7EZ7rOIF3q90ieR3HeeUxrTEwLsj0UASS3CdDz8KEdwtwT223n+QPu2n5RzTESm30hkiowD8Ij9hsYBl3Y+R+zfREQ85iwtl5G3ozV7DmFUXkQq3YZ4mIMQN1PUfp/QAwUojEFOx6XSd81F6IOHNNDFtIRD+wa0Ph4kNuv+iBQ5as/81NrzTJ1azW4ERnxIbIL8vXb6PjE8cgkEFo4qzj9AESBBB/SI1JfVCDB1fMD+3sV1B/YHUy1Dd6S16UE3PWxuZkGA26/t+/doNUb23JJI5oyiwXFX3DsrsTHlGBSs4BGeWbeZCYH9Lg/+hZxB7nh9w+YyRyC/hXSEJl3Jgf2xCKCcjqrTy8HLr4ka4mOpy6ES2K+VFGuPL1CN2H8e6SVHEIE0Lh//ar9HI0fYwHKNpDYHEMD+ExSleBrun2C+jfTZNqRjr4F0/q2I6O424Af524u2+hKZk93Sc+My2VqMV3+iHNPHiL5dv3eddyiyq4YaTQ2hvnHqMkQd7+cJcCyX2NrOzr1KKtdJXdaUwP5cxbfumsakrG29FdU9Slye30TU8y/1KOflUxOO9tcJZ2cp/52up0e66wYUmTbp3pORzPSsvKcRj9kt0YrrPmcRoLyPxewEb8wbGXdD8nY0WpfbpWf7It4yjGbg81W0th18v4Vw3pWZAN1QZvpwa3N1pKP7XF9NsXaoAvv3I8DeHVb3AZu2GKufUQX0y3HvhdbH11Q3MZ6BAPqHUsgc+/YtKPQ/tM72RHLhEJvPcfvX2dw8ZmN2Mq2B/a7IthyF7OOeVPdp8rEcgAI8vgSWbeCh/ZBO4AFr7yKZMmcxN03A/kMUwZmTyI+mQ7Lga2TrNZVudGDf6fc3FMB+0zP2t6PA/jCb283SWP+WyIbrivjBYJtbf/82hN57BQVd2/+bWNuXE3bCzgTv2qf41jXs/BAkU7ZBdqnblMvY3D6V6KjEI6a0d3oW9gNIx3wb8TrXvy+y6z9sGDv/lkONRnz8Z20a88lH+8e33oHJx//WUTCZqamnjm9Ka2C/LzKEn7Drb6IanCchw+dZY9RNG7S4wfdHYw47EwrNBxhYhQzUpgimlsB+O983D5EC2Zt6VEwXpNDdaP0YidJAyw1vHzRGnesuzkWUZLkbKTwtgX1/d/q9MGFs30axl4Dd48x7esT4f9pwz/HIWPl5A0N3Zrw7MnyvQUJ0djs/I1Liapvu0RrYd8XmYLu2RWrra+C+ch46SJcZ5DulmEd3IDyOAK48PycQ2Rmr2Lme1tZwm0/veyvB38vG7zOC9t+yOSqjbHugKL+RqIzKOGCfBnosnv2rvWOOVmOU5iwD+x8QCv8hCFB5GoFJqxZ9c0fHpyhduheKgNiRMKD38/fb9aeRAroEdePNadqjM0fTQIcTMM9z2Jy8gQy6R5CzZI4OPLs7Wmtj7VueQMrjqUhBzaVJDkdr/1H79gkF9n1OjyIM0Dbre8sa1ASw/4X1cVO07t3o8OO76ZmSL82DSgE9a3T4JHIENUY4pedy2ZubiOibtZFzYk2kcK+LQIPTqWdmHGLf+gmKyOtZXO9G8KVZEHh5gn1zbQ5pAewjWvVMKzd6vHZul9T+CzYGpRKe+YNnmZ2GjJwxRqenprYq0ckI2PdIrYcRL78U8bBBVCMpvTTdhtbOokRU4BfWznP2+xx7rrfN/XwNY+J9mgY5HdrQGtysvCf9XgIBCJcg2fc+UT6rGwmoIGq03oMB+3QcqF6fSLM9qeH6KmjNPWfztw6i1XnTPTsg2h2CDPpDqZcSe8vm6XCkVzRlu00IsD8CgcbLTyA/6jCwX55DkVSDjGZOwTYeRgb5p0jmlnsEdQTYn58Ayf5FGI7dCJlwMlWetpaNwYekSHIb3yeJ+tAfIWNwguSztbUo0r0GkdLY07u2sj5c2jR2KOrdDcJ8vIKM1TLCbXWilMysxbuc3rsjANp1yaG0U2qI8fPI1dAaWAMZvw8iOr0cAcmbEQ7m1+1+729LPaJFX1anfWB/Z8SHB6M11I0A0vog/vY24VzzSLp1i7H6ro3LCrRwCjf0zUvNjQPR2rn3S0IH+zPS1St1pO3/vvbN99mcOXj7c+rlgPoQZYN+ms6XPNxtkVXSuN2N+P+vUBTiPDZvH9m9R1NdOxnYr2XElmsf2QR5g+apCAdUqT86APcSCmp6EumlZ9DOxoJI//5Jamt8sn9C+PbKSK6+TeFssnvdWTmaos54i3fPiPjCs7QP6Gdw8Fyb++fRetrJ6OhqAoBcxvriutcFRXuzI+fNIPue0gkxDXLSO+j+e8LhVInupQrsv4kCxTIAumua2xLY9yj4A5GtuyjhyM36/LlEdHwG9q+xdv9OgN3jyrOktfNbIsBrtD23LUVNbsSTLrD73Bn5AaFnrUzsO3Mqdcf6HBTAPlpHj6M1u10eZ/t7MhEx3BXxLs90dR1kK8TPhiGbe+7iG7sgfv60zUUurbQwUVf9GiJaOQP7zqs9WOJ6mkv8NTnSmuh1JwSGXkREpi9AlGzyTOzhwDbFsz0Rz3Gn3omEc9GP16yvLtt6IBnTEWC/F+J9nxD7JJX6SRfCbiv3t+pNBIA8bGP3FkGHUxXzm9fu9Xbf3bTIHmfC+NGC1pevEM+ryY7MQ6gC++chO2G8+gxaQ5fQGtifOY3XNoleehD2wADEc94jOeGMLr4gggSuI+i6JyqHuBEKBCgDDDOwfwjSe30MTsGC8Eh7hKXvcRvmQtLm4VT1zEsQTnSF3XsMsg2+wjZMtvY9k36XFuN3BQrImInJgP5EH996ByYf/zsHVUX2+wi0/SEh7J0JtAT27XoPY04LJeb6AVGzbwxSfHP61HeRwjY6tf0JEhAZEPCoyCeo1xwtgf1tE/MbVx7GGZrd7x71DdoZl18gBcT7tYydP82+5T0E7pX7AMyJwKBRxiRXpVAWG96VjYMFiRqVlXFI9/h3ZfAtGzZzEML44BbvvAgJzVkJgOAIZGB8Ysz96nKuaQHs27UVqUY5TI8Uvo9Jka8tvmUFCkUEGdZfImWvX/HcQ8jwPYcC+Evz9xmhQK1ibV1LPUp3FgQu7IsiOtwg74Ho/jgk9OYwWpq1oT9TI0M0A/s5QnkPirquSIl6FhlS46vj68J5CiJqZW7CufE1Mt7HIoMmK9nTEkptGwIEhqT/9ytpkYgw3LWdPu1IOL7agMMmkRf1RwrSOEdXO/fmNTMXAjhGEErIcCKt8Ps2Nw8SESgPMf6I/e/REKWHonLONnrx1M9TqUfmlqV4vCzE9nZuMwR+usE87po/Tx3I7WdHN8bjMErPbIjW4SiqG47lKMYbjXbeIOrhZlD4YKrA/m6EUtvD+tOPOkD3EYpEKsuJ1YB9Uoq7/T4H8fRcS7MPIQ8eoxkgXxlFqgxC6/59JGNGooytstRbl+LZ31GN1n2JOgC9gLU3Gq3hr+y4EhkCPREg+D7KEOhV0Np0yNBfhHpZgmmIDa3LcgslPRxNGINDSPXJ7XqWgfdQB/YnhIacZ1yJQIIZkQx3w3oPlD482H5vSbVW8yxorTZtXHglATK3Ib3hj/besrTUVAjYf5fWwP7KCCgcXD7fgW893b5pKJb63gEelCPhDyfAwjYiIuq1ko6a1jqtgf1FELCRNz6f1tqtRcmhCKxhiPe5QX4+4Xh6h6Dz57DAi47ShN3r5SZ2K8ck9flLChmXvtUN8tORYXyj0dCsyCh9lWpJwJ6EIfoUDXKTqL8+P+GAPaWpf+PjkQ1z5A7ysizdQKQTfo30qHH8mdAjjkV6xOzjGdPVScA+9U2Gd7XrV6DMjM+RLnYFAgpmSfd7+cMrCXB1RaT7vUTK3GlnbFwOzIT48ZtEtmKrZ25GtOr11K8gAdYUYBqS/VMh+2EnDFRM9/tYLmDfez8JxKPKwzdGMsrb9rJ8F1FfI88jPf8cLMgnXVsera0xFFlMVNf+vkZvg6jbA5vZe0+k7qRYGOlwy6G16iUYZ216j/2eEtkgB3dwfbbi21tS5dvLIdq/qJ13+54T15Xj2PDetezey8fHUwh9uzvSb/sgGbol4t9LtFgjH5LAyTTffe0bZ088piyRtB+SzZ+gwJAyE9Xl5nSIJ42kOWt0V1oA+614SPr/V4SdV9rbUxGleG4jgP2cdfw4oSNcS2Q4fIL401QN798yPdeGMnAOQ3Lh2XT+FOoR+3MQwP7vkK30NnBnav/SgoZmSP29G/HILCunt3MuI/9J2HP9EA85CtHwEolOfJzmR3uvtAL2p0n3fEhRchfp8ivaPG6azjeVi+uJnChvEPJ0TqKc8AV2znW3L0n6fNHWcel7t7I+HE5kkp9HOFa6E8D+ECQrZ6aqM3Szw4MCf0sRqU3Q+Hp2z5FFn1ZBMufkNP6rIkygzb5zYNGm8+UBCLdYfCL5UalH7oXskbOprt0uSJdezcZ+6jRGSxOA9mmpj5sh+/cXyEEwt9NJ4heXUAD76fnZqFYfKIP6trFnj7HfWV+5lyj5dGwa11sIPfkl5NCdu2h3ZyK76y9Izz0P0dWrxB6EJxXPLUw4Lc+k4L02z0OQTTc9WtOP25i+g2iwDNRsQ3wiy/A90Jr67fj43uRjPHLh2+7A5ON/4yiY5QlEnbtDqG522i6wTyhlfYwxjzTm42Dxj5DCONIYw8D07BQo+mJrVCpkAUIwfw+lw11oz45CikfpnSyB/a1pEV1s/x9h3/AyKZWYanTjzwhQ4RA7l9NTF29nXEtgfxWqgM5SSIjti4zeMiJ4AQLYv4FmYD/PS1NEwZJErbOTsUgXu7YXAjevSG14uvDnyFB8m4gi+EvR9kAC2H+AMBbzN/o4/sHmbtfx0N/TRjs5CvgH9o6N8zMEcHgSoQAOoF7bOoMIu9szOxXzfTJRDqINGWZbUFfu+yGafhQBec8gupyBoP+pkcLlpXjWtOd2JECeaVLbHlnRRorILb7B378KpnQiA3hGpHB9jJT+lVEpnROtvdEU3nUUafhzRL83orW4UjG23jcH7D+iKC+T+jQTApN+iJTBWoTXJPCmjpTuKQHOlZFS62DxPEi5G40Up4tReubHRGR/qxr7HyDFZ6diXMq/GxFOwhqw73No7c5hc7YedfDVgYc20maEVB1Dlfq+HRmj9Gwl+ji10Z+oe3sx9QivDAofYmM3lOCDvvlUb/s9Gq35DWysP7D7j6YO1GRg/0TCQOiC5MI/CBA6A/vTEFkOzyBgcRH7ls0J/vADBJy/iRxhTxORP2UZmiwfBiBA51Akg2ZuMaZLIKX9XWt/Q6ogdh/ERx+iyusOs774fH+BeNmKxTe2AvZzW7MgcGAcwEGdDzYB+0/SsHfAeGhoAyKF+kvEGwYjI3dfYsPD+4FN2lvLVNfcStbON0Y/L1Lde2QQMlK/S9Bbd+QsetuebQL2V2DCAf2ZidJW19nfjykycYo52B8ZX1m2fA/R/1tInv6MdqJwE903AftXEgb6tMX9y1MYdtRlpEeIepTzNQg8nRIBHn7voyXtjKe/3WzeH0UBHZlProVklvflE4p1ZP1fy8ZpJpJzHq0d56v/pGoo9yVKqTxvbXhk42ooqvAdxP/nQ0D7PRPKI0taNZq7y+gxr/Oe1p8xSJ+ogWlNdDOevqxOrLN/Ug1y6U1kZ7iB/4n9fhPxLQfTpkV6rvPRCwjwb++O9KUY99/bsxeXc+ljUZyfnyjPcDlVUKAJOHPg+E0USbhMcb230UsbKQq7HT7TFcm1oSR+hxwtDxCyZ6C3n/uGeMj+CBhqitbfDzkF3iNA5BIMH1+AQjej4UZg3+6Zmwh6mdASh+3x7f3tHgf6/ljOTZrb2YisgpozvXjn4jbm17bqK7GuliZl9CGw6hkEfD2SeEI5rpsgHe0BAtwrAaz+CLAqA2qmRhnlg1FW0M6kqNa0di5DPOZDpC8e0UCTuxK8etf26LthbXgJvZvoOLDfgyjddAKhF86OQDh3RJ1Noc/ZfYsiXW45+ybnMZcSTrg2o8P2gP1biWz7FYmglX9QD1T4rl0728cmfecdSD+8iyrfPdSeedzeuWDRl44C+3MjveKmok/bpOf8OGM8c7YGFjCFeJEHVVyS7lmE4LmfkfaisOurI53vIeol2Jy//pHqmuiOsv5cb1wNyd5xJYDsvjlt/r9CmUKuN+R7PCgm7zOxHZJdb1Cvwz8fAexfSmtgv0P2COPXI7vYe8ZS3Q+hJ3Lw3GPPvo+cWV7SqivSh24gAo6cJvPxJsJn5kltl8D+0una4kTp1az3+ffvb8/9pPjOa5BOu1U614fQbR9Ea+UNYj0tULRxnLXhwWptdv9CyOH/GaLtE3K/kB3kcv5GpIvNamN/D+LN6yG6cvtvRcQz2oDzUnsHpXe/gDIgb7fftczTyceEH996ByYf/1sHEX10Ow31Vakam5ulBX5QuqcrUo6Horp87hSYjdhAabgxoMNojiLIwMppdv87KGr4NiLN8AXqu8w7sD8YGTk7I4VuNqTAlUa5p8s9TL000PJIKP8xnVvY2n2LlL7Xzpg6sD/MmPiadv4nVDeCHY3qBC5bPJ+B/espaqF3cF6XIiKTP0JC4770e770vYNRmSQv3zA3Uprd43tD0fZAQiF9mXopC6eX3RHI/QHVsjBZOB6JhMo5VJUPV+q2tN89CUNsHFhh19ZHgG6rmn7uTb8KpWhvQGy89DoqefEHpEg8Z9+Xo2I8Fft5JCRdED+JnACeijkVAnCGIkX0MQQq1KJE7P7DkcA+h3oEQB6j262NDG7uiJSjMmXQoyfHUiiU6Z6cjbCIjccyVMEKBw4exEpYUAXjTkVGVRfGs/HTf+pAzo5NUUTwLoRBlDcAXSTN0XM0R+wfYfOyl52bEhk0U1OPyC+B/WnStRUQIDmN0dGfEa2/TxGxiUBK5ws7FNe2R8p4S8CoA2PjoNUwtH56IMBwBAJQajXqi+e7IWPwUyI74zob8wEI/DyfagTrTsgQGYYMoBJM3yh98zRGS268zUhEHt1f0P60iD+643EIAtvGWt+OBHa2e2dFBthGKLrX+zJ9/raJHNOetN401aOETyUyxRz4fguBXF4mZiySRZun56cmgP3HKeooE/xpZhv3YchIX5E6gJKBfTfQZp+I7/UU6jHIaJkPOTb3osHhXfajob2dCKeqRxB+hNbN95DD4rNEIx8iUG9L5NDY357/yMa75lSbwO/rgvjgAzYn12Mb7kLVoWf/74tk54c0gPZERkS7AE/x/hLY/8bm1QHHbumeZe0ejxLsRYOMJHj5MKzeb6KJqYh9ajbuSD9Tf2dGAEOWVbvZePwZ8bw7EN+Zt/jOKYmSD7+iDgRNRQQPPEgd2HfHngPbtxL84BC7bzY7d1MHv6fCI4trcxqd3U2sva4txrsPCQhuop0O9GU9InvlHqI0xZoItLkOBW/0QTzuPKRzfI70TI/0XIiqE/FdEqA/gX1aCPHajxGfGS/fRKCt64qXI/Ax08v2WDkixE8uJHT9kUg2rVKMi/OLxjriaZ30QTrY+8Tmud1azJmXg9rQfvcjQNUP7Ntzv7dFNso4WU5dh8t6xfxI116HuoO7q81rBvZnS+N3L9I7B07I+kztN/Ht3yAwtw+yb4YiXlYp91K0c6mNx0be7wb+1RXZL5/Z+1ZraCeXIL3d6LU7stn8HZ8ged2zxbumSXS1SYvvdjB5BMnpmvjLAdbPEtifNtHga4j/eibsCxRBOFSB/V06MB/+/XMTPG58wP7NRkPzIf56R2rH+90X6b+v2XftkuirLHvlwFwZsb0zsZFmK2DfsxQPsfs84+tSGrKyiSChU4vz6xiN5PJwfRHf7oZwAI/iz3t3lcE1Gdi/A8kWDwDoifT8l5DzbC0C2G5D/ObE9A1loE/pqHQnyrQoqOBeqpuAu0xwmfohClLxvrp9tmXR7jF2/gaqQXg+7r2QTrCr3TMWAdSOLXRBa2hnxK++RNhCDgTYm1hX09sY34wc2TcA19l9ZWDIPDQD+xOrNzfxo/kJx/459q61kG2xLBF88CFaMz6+F1B1dPpa8Azou5GMWRc5TZy2b6G6h1gfAth/DDm9jrXxGmL0cRpFSVEUlPK19cnxk9Wsn98g3uLybRv77lMSPSxMrPE7qWay7EfQ6Q02f7MlmliDwJNOTM/1RvLkFUKOfpXa+jGSxT9E+vMLyN6YCzmov6Ba+nNLFLj0uT0/GGFJjRUYJh8TuB6+7Q5MPv53DqQADEHGVRPo2FR+Ikfs+4a4MyDF53EicntuAjz/DVKwvjAGcxApBapo38GtG0kbuCKQ2qN0XqQesf8dAki5xL7pa2TEPIeEtRvZsyIQu42I3vRsgSeQwNwmtb0+EkAndGRc7Zk5iRTL7xFe4xcQGLA3MszGIMG6ZvF8BvZvmRgGanNwljH+0cgYu4Oo/TY7sZHhsg3Pz4XAsjbg3OLaNMhhcTwSmPshkHoZIlKsC1HL8QObgzlTGwcjo+gl6iDnuvbcL+y3e7grgL5dc4/3TCSFj6oR5lEQfrxtY5OVHlcMvQRJT5ujsTZ/bmB0IwC6v5O8+MghcKDR0WdIqWhVfmg2ItrlUKhHddkYfYWihvql89sCD9v/3Ytv9SyHsRi4aed7UM0+uILIIhiNSi5tbdenJvY0eNfeN6eN6yE2fnciBbrDwMB/6kBrbzSwnv3OkaQzoSiIr22edqCqyPcgFLQDkZI+Cq2jp9F6zk6lDQlg/3RrfwVib459EG8cgcCuBWioNUsV2N/Jzu2BFK0PSKUVJnJMNrC2P0bRG58i464sSbUwAmp/jviD133tZuMxljCCrkaGxmvpPjd8uiC+/xQNYLrdsz7Bj86xNjew3zMQUWklsD+lzdsfkOx5AUWabGb/v1LMeTeknLbXl9VpIZfaGdNx7adz+9icvUSAM7sQci07mdZF6extyFBZP10bSNRRHkSLOsoIXP2NzcuDCAhqD9iffRJoaCMEAI1ADvp+CHR8n8LhjRwzKyDn7kbp/EJEqvTrdr07MhzabO7cuTM30jmuJHhlG1pvtxFy/GW0pifa8ZVodhli081fkeRZuu8gxN/fJZV6KNqamKyaDOxvRwC7T1BsUI949IuI3mcgnPYZrPQ07Y9tzpYmABQ3gDexe8oN/kog7RfIqG/KyuuKaNzL+ixm529EvHOpcozQevO9KM4vxxmBWg4+lcC+R0reinTKjxCPzSUAz7Rnj+voPFDlkdlBO4WN8xPpXC0jIs3Lm6TI04mkxV3T/D+I9NaDkHHt45vl+Z5ID/oI29vIrvVF63Ylis3QJ6AvPs+n2TcfkebhYMTD7kU61SbFsxnYv4LYQ2gHpNu8h0ArB4XnRoCKlx8YbHTncuJnSLZvMr7vQPJhDKH7t5qz3ki2X4qAJI9g/B31jVK72dzclb6lce3b/4cjx9MIAjQqN+jNwP5IpO8dRPDJYyaRljLfvhbxtkcJG8vLWv2RtNkkqSweUZZxGRIPQY6WxYv3efbv30kldKhmAfi+A4emd8xI8Kw2YPd0fynTDrZ7WmadELrwGDoI7KOSSZ8h/tIfAX4bEZtHfkH7wH62HUu6KAHpjgD719j1q5Du2UaUFOtVtN8H6W6jgZtbjIk75+4mZEAGRjdGcqXN5rGssT8bAZr/y941iurGnpnPL4d40tPIju+B9M+7EPjpunZfQsbPgPjd8UievEJDBmD6Ox+i57EIzP6DvaeX0cnnaX58DayV2tvWrvkm7L2QnX4EDUF1aR7ObljvjjfsQF0ncgxj4XTu2DT/i6fzSyOwu2san5xZO6ChXwORHH7D2nwP6WcP2e+PiP0KeyMnvGcWfEjwyXKtZWD/d0x6AEXmR8cgHv8BWm9bofXnJUFH2fWL7Pu62ri8h2R+b+rr7A9IZ8rj3BM5o73Uz5+p2hS9Ef9rI/bJeRetP88qvoG0TxOi08vs2pvIUeoAuvPu55HtfDyym8sycAOJPYBKYD9ncX8vnXeaWJMGYN+uTW3v/Atae5cgB8c/UpvfIPzJ1/g2Nt5/pBqg1du+YXVkvw6clPmffKR5+rY7MPn43zlQlP5oLDolnV8WKZSPIUO73AhoM2MIh9vv6ZEyv5r9/g5hUP0+PXeWnRuFFLmm9MArkVLr0cFZCexPpAi9gAEghGCfjvC2foEUeBdEbShqdlG7d2DqTz7GUtSsJFKsDiv71ND/gen/eYwJ7oaiem4gFJjuRJaEl+pZrWhrAQJY3bTVOzswzzOiCOXZCGP/TCR8bgKeTfdWQCqUWv8xEliuDDhoNxAZ3Tk9bCQCW3xztq4oGsKBwDftmx62+9+nuV7lrESmgJdEOJ56mYVjbPzOpMEJle6bEhlXf0RG4UIU+xIgJfc1AlxY3vp8DfU6+o8ajZ1N3cnQzd43L+MBCVEZDx+/01F0Qm+knP8IKTqvI8NyNxSRdx2i8zuL/rcC9h0gbso+eNDG41LEC94g0rKno+oM+SzNyUck5eO/6bDxvxfRt28aWCp9cxJAw7+wiH2qxvvphLL7Z6T8eJT6maQ0dAQGPWBz+Sqx0dERBFh5HJHVkd+T522P9I5X7e+nNDhdJ3Js1kUK8Ybev3Sth33Xm8WcH0WkNXdFiuZKRKTtIwh0nonmDU5LML1mOCCn6hCkFOdIpZbAfrqnN8lJgmTD8xSGX4u+uCNiJ8Sj/8gEgF2p3SmsrxfZd7xLFby/zOhxOfud5drshAP4b1ipBbs2DQLSDkPgyWFGI5sW73dg39d0S2C/E2golyr5ub3zmoKOPb19VKKlX9u1X6K18gvEY51muhPg0lvUgaIZbf4uIZzN+XicdjatnoDvaxfYR07chxFQMHsTf+mE9/uYbE8Au3ckOu9GyNY2ArT4MRGVPJX13SPY70vX8pr4rtHmZgR/KNPw5yQ2unM9L8/3LkiWDyHxKgQ+fE01pb0LCgK4EelHzkcuIAGK6RtaAfsenbgYWkO5Hvl+RqOvMJ7SRw3jvy4F+I3ksYNr+xAG+ynU5f+FiO43nJD3tujLbsReE39DgNVNeT2nseqD+IPz5JbllOiAg6PFc9sheTg70se9FJID1n78nGr5hKWJiMS7URbM50iPWKrFu5a0efSSHx8gWXoyAnmephnYyrTtJX3+ROg8J1MvNfcLu7YHshPakH7et2m8kMzxjOQyQr/MOnY995dIvrpuUJYx6oqc6A7ytiGd7KCmtidgzlyXd77dhvS8BdN757fxGWU0VpYAWhHx5OcIR0ZfJC+HGr3NmO6flQhquAXLeErX97X5dLDpsHRtRiJo5lWqju5cuuVIChA9j2X6fw8C2N+5uG8qFKjwGQKy9yecxaXO2ItwVrxIPcN6H8T/3JGf12XOVG4C9r30zXWkrBL7O4OdXwSBeG0UZVGLfsxEgONLNFxfmao87towZjslGjyVImLf/j/Erj9H2NxNwHtfYj+X94lSiG3YHjGIl5+H1sZFhJ48K1qvI5GevkE74zgPciC8jnRmL8s2DZJtv0P0ugaFDkpEdm+J7CMvG/YS0gXKNe5BZ5dRzXDe375ty3Lc7frZ9pw7JI+jGdDvguzku41+ehD7qR1PO5m1du+CaP257fwS0qPnKe718jtPEXx7YAueNw+R4f5rJkJPLtpzfjQW8RDPOPQAgeuQLXKhzVmW/dMi/nFv0eZhNpfvkkriUC3vuTiSW0OJjF6/3sdo4GEEfi9u55dH0e1j7PxKiXYWQg7P9xH/fMb635/Qz16w+Tvb+VjR7ympAvsLpbE4gFiL25Y8jvaB/VppPMKuHWNj/zkKqPK9Gtxe3bGJn04+Ovf41jsw+fjfOGzx3oCEZU6NPpAAdbJBXtYlLDd+molQgFdEQvW64p5ljYG4Z/jAxHS6IGH6pt0zK0XaoN3nCk4bUg6ykPfUtqupGpZ7EMLocqoG214EwHw3qcxBYprft+s301BDrviG/ah6WmdBysgrWH1ypBwejoztN4iU07uANYrvXZhOMA4b5v9Ee+eXCCCZ3c5XovDs+LXduytVRfUhEwxXosiL3YjUxk8IxaWLzY1HVTpQ/wcEfE9LQ/1jqjuw30gdaNiXiJ6ezcZ1Z2SM/QoJuzKtv1KTlChDtB+i9ysJhdLpafX0TDea6/pPme+ZwLlYEnnwx1ofniaUsUFIaWiqD9hGSvelNbDfBuxh53oicOJrpCz7mp2fSEv/mGrUzR5IGX6DqL8594R843/6IBST/dq5Z0tCuXm1GEs3BMdlDNn5lZEyOczGNzuGVjW6+wjxmx+g9TsYKYIeFdWUkZHnbWtkWD6DeE67NWwncnxWJXjodnY8SRisexp9vGNHLUoc8eJ7CVmxZP4WmsH0L2zd5E0aeyLj5lWqvDMbtTVgn7rR7UDn9XbvotAS2H/S5vASpHh/hOTOEhM5nrMTpWJupipTp7AxfNP+rziP7J6FbG2NANYuvn8gAnpK4OxqBJa5k3UmxgPsdyL95FIlHyKjcTnklDwn9fH3RkdD7Pft6dqviTXhAGU3qsB+ba8OxOcGILl8PlqPH9BJjq/0jlbA/pQou8DrXHcaoF+833nzyQSwex1VnuMlRtxwn61o5zgC1H/T+t0r95sAx/y+D40WD6DqYFsLK+mGZK2v811oAPTt2m8Rf109nduFcA7PhRzbHj12HrG+2wX2aa5z3tPWwCdoTS/c3jiX/GM8190R2ob05mOoZzkdgsCCW5jEaMbU5pqEwT8G6ZJ9int8rPoRpazW74z3N/RnCgRq30ts7DgXyso5lACtLyPpCcj5cr4941GYF6Co2T8gude0h9TMCKxx/cSd3qNIe/606GtPIoNlNAaqFPfsi+TSfdbH19Ca8vVRKzPTwXHa3/p4Pam0KdLlXK+9pGHdT4miiw+hCmhPKoi2j7XpZepOSnTTA2VEPWv9esh+L4XkpWfKuh7Zj4gavhFFSpelSlYiwNGvEah6hp0bg/jM5oRcz1mzHijWhiJutyjaXh457oeRImeLeyYE2D8c8djBiG/4d5Y6Rk8iSMkz1bODvm9+zsb1YQQ052y1JkD6KUKuO7DfG+mSnhkzN6LVoRTZ6v4+++sZ0rX1YXPaZnM9eyvaJuzsMTREZyOH7CkIND/U7h1CdR80l+sDEWD8FFoTj1AtDzTA6OnPFHoysqFPtefaBfbtfw80uJB6KZmm+1dAuvYgpMM8gnjUuOyxFjzpfgTgn4L0oVMQ73uJFJhRvHdNa/tXxEbm4wB9IgjpuzbuFxkNLIZo8zbqMmdBpK+fhuyTqXzsUWDWUja+/cv+2P/9kf7v+7McTNrjpnjX/Ijvd1iuNoxd1vW2Q2tpNPWSd11pCNKw88fb+ByXzrtN86zN5UF5TeQ1QtjGdzd84xxojZWZi/MhnXU00ldWTvPaF+EYcyLe5eenRDrQWILPDWwxLhnYv52q3XkIkR09voj9XGO/ZxqzXghvucL6MwYF3/zF/n8LBafthnT7t5hIe2jyMQHr4dvuwOTj//6RGM4PjEHdhLx3Xqf0PaQALkIoQ3cY8y83qmna8MpTmBzUdSPMFZIzkQCvRfoaUxtJAOBNAJhHI41F4MzKSPDdjICZZey+DFCvg6LhxwA/tXOrI0N3LHJwbEPUrs0K4fRI0f8Yefyz1zcLR49u2C6dm8/OefRzdwQgD7E2p7XjPpuL2ymA/dRWp3pLic1/26hG7JZz7DURT07f7SDzGdSj2N+z41jqStXcSDHoi5Tpm5AAHo0A9Zy62gtljIwm6qsuh4T3xUg5+hhlNPSjmlbWhgyOv1I1qPJ8efTIBQgUepOqEepgh9f170Hruv7bYpEGEzkXcyAA5g0EkDyC1skcqDzC1yiVeR3kXLnE+lGW7CiBfa937QrO9khgX0w4L+Yj1uzdaI18QREhRih6LTMivq2jXBtEeuxdFJkgxPqdEwEk7rTKgNVNiJd4ZLWvBa99eUt5zdtG/MIBQOcJu49v7Ip2pkGGUC2baSLGplVJKv8WP17BjLR0j6eWbt+izRkJufEoER3VBOxvgRTF9wij9WQUJXkPltVF4Vi0vzVgv/iW/MyP7b41mvps/2+c+t2GHM21jKEJHOclEdBc7uHSHYFEwzDHR4vnj7O+/NLmqgsCbR1Y/jOSPzsTpdseBtYmDOEZCWD/fgR+TDKwX9CQ/7+RvecrBDI4UDXC+rd2et7T2x3Mfd/+/or2gf1BRImIbvlvantBWuxtMKnfTB3Yn7m9vnTiu30s+hLZUkNpBvbnIjZdfxJloByO9KGxCKT6rT3/CtL7XM/JEZl3Iofeh1T5wu8QX8i1m1+xNnelOULfx8dLS3nm3u5I1n6OHJ7rGG3chkCLNgTmucOkw8A+0qN+g/SFB5iAkoVpvHsh8P4QJCvL/Zs88nsY5vhOfTwaBbMMYiL2QRpP/xZB/O8LpPfs2LAOfH68rMkP6KTsHGu33Duhzca7BJnWJQIfzs7PI4fAQkRZnFJfexyroU81Y6Ur4gs/JKKaj0r0eAbSH3+PgIkB6bl1CMfXJUiudkO89VTkjP0AAR7uuDnTnu/w5tHFGMyL1uKzWJYKAoR/iNbhIKK80KUlHbY39hPZH+e/g4isno+pAum9UESsl6HMx9dYBrPdd4utgRNpyD5MYz8n0mNHEs6Yj5EjwAHMP9mYrFA8/x0isnm0/b8HWptP23mP9O6H1muZNdME7I+mvoHpGgRYPpwA7Jvsz3URrb5IZD437puBQNWzkYx8iCrg3SW/AwW9+Zq4AfGzNQiHrfPEEwl9IAN/OZL5NgTyzVT2367fYN/alOXgfOQ3KML4SbQ+Bjbcm4Fi17tKYH9ciR+bp5kJW6IvAomPRk6a9fMzqY0OA/uo7Mgr1veZiz6U87M2kSm2J7Gh/AkUjtOGb9+F2BPIj1dpoUcifjYnsdlrG3L4LWHXPSDucCTP3ydq5ruudWgeV6Q7v5Ha+9Sen7JhfDxIbxbqGe8O7L+B1uchtAb2J3kPNUTb/0CBna5vfIzWfC2Lmap+f6h955NUg4NmQDa7t3cn1IMwE295FekcM6RzMyEnyLNERlJ22s1DC2Df+0mULfPslimRXBpmc7peO+MyJdX9FPdBtvpNVOvib9kwRhnYPy59e9aRfmBj546rX9r5Xe3cCLTun7T+nkKB70w+Ovf41jsw+fi/ddCOMoiMwQsIJe8jlPaYQdCuSBF8CQnknih9aSMEPNXSUxEQNIZ63fufoYjkPrQQmAgsbUMA5vS5bWRgz42AzqcIr+bfkNHzKXCfPwM10H0zBGiOQQJ8OGLs+7UaJ8KYOBoppfchRaBn0beVkUHyNinV3K4tT0TCL40ApLdIUXUEoPM1UlrWburPv4E+PMJiLGmDTqpprg4M5+jt+2zsehfPOOh9CpHG1xRR15tqzTkvNfI11VTcfkjZcIPfj9FIOXIHgWcN/MnGeE0iYvcB6hsi90UKwDd2vEdsaONC0hWpQ+x3Yz1Wu3YnEtiNkR0TMB/9kdHpisGUqITL61TrA85OpNE9QqHkUjVm/Lu6IwXhY0Jhm4MAjS60c4cYPbzn357nkU4ACTuBbts1cpHhcDWxEfFc5bOoduMItJ4XS9dnQTzxqqJNX6M3Ua0T6yVcupRjQxiSJ7fTVy8jMUM+1wlj1JGNDLdHxtLRRlNljdbrUSprS5AKKdN/t++8Bqv9TzOwv4nPBQJj30SG2qfARe3NNVVg/yGUzdULRSFlg3Y7u8fro/Ys27L/BxgNbE4L43cixrxx3lA0a5v9rYH+9nd5EshjdOHlG04vvmMOZGgPRZGmmV/PlN7393JOJ5WOCCCjOwI4lkbOjAtsftamvl/BEdafcxAvf5TIRvolHQf2G8tW/bvWiPUjA/u/pigR09kHYYz1QwCbR7JlYOwaqsD+lEj2fUDIyI9tPuZBPO1nBLC/BZGB+AThuJ7C7t0HyYXPU3vO55YhorGHIjCsMUsCRU2ORGDp1ta/L5C+dirSxz5FsvWx9K5fleNMFdh/nAbAFUVdblHSX8N9qxEyMJelu4s62Pw7lInYFekN2Rl6q43xk4lWJ9o5WNB3KccXs3eNJZUGSGvRv8NLyWxsv6enqCfeCTR6rr1jvvL99nsdwoG3VfGsO0ZuQXuqzIN0cNfXhmAOWUKPz21PQ5TR7Etk2vra+AQBEw7W9EO6nNeVHop0TqfhF4hyB2vYuT+Ob46QvGx00tv7viZtTI0ArKHAa3Zu1tSHy1O7kwyaNfRnOuT08Iypv9FOZhPSAc+19XAM4unevx2IdVEG9PRHttAyVG2DpdGa2xEF4QxI7e1p7d1IPQNleqo19j9E/OtGrCws4tFe2unAhj5l2vFAneGksrJoba+YaORVQo8pdbpeiP98QgdKeyF5fALSNR+mGdjvjvjby0TE/p+tX3+23x4YtiwRwHQl9eCFvZBOdSuFnZLu8ZKy7wMrthirvyNbflka9pVJ92XA9Wiagf1GOYtkwlgCsNy3nXHMwP692L4GFDqBHR4Ff1KLtuaxez63dx9E1Gn/e8OcL4j0rKNI/AzxuWMREPtTws7fCK2hg0lZaumay+mLER7SldjPZZD93Ss9swYhq7dGTjqXOS+irPLTrP+DqGdg9EXr+SEbv7dRFsByREm/DgP7ncCPvNyR2/U/IVVQaLh/SqTz/gWBzePK6qGgNHcsfsf6/QHSmQ4k9KmuJFmCAti+Qrbi0ShA7jPE84ZSxUMyjWVg/1astBihu/axuXjOaQXpVRel+WpZuhbZ/5ehoInR9q0/RvzWS5pV5CpVYN9p6wwk+15C+uKMCL9bGMmua0gZP8QeFl8ScuJTin0hJh+de3zrHZh8/PcfNESRoeiY5VBd3hkIUNo9kzujEiZlra+NkbL8a2MQtyRG/JIxjrntXldQfmLXbya80AcgQfIPio1Civf1IcDTswhv58KJyRyC1WlFhv9wBMx8agxt/qLNzJA9ivFju3+7ctzKZ+z3fATI8BgS4DOgqIDNCDB7r1Zt2DkHaTby77W/KyNDw0Gr77Uao38DvXjE/mDSxlR2bQWkPIxGEfJdjU5GAten+7rSEMVu87kq9bIA2yOF6jQkaGYgNpNsA35Y3D8XUjiOQYrVSjb23RGYNAwpaf7eedJ8+ZytULR3EVJwvkFAw/rFOxckvP4vpG8bWNx3rLVzGp1okCEQ5k/IeXWJncvA3tSJnloC+8S67Gp07HUEe6HIsXFAop1fEjmd2pBhfvh/ihY7OC5lBs4BCIDajVS7FaWPP41Amt9SjRxeATmmnsN4TBqjeZGyd3M6fywB6C+eznvESWMZACIl9Fqa683nDZafxaIUO2GM/F29kWJ7AVII96Ad0ImqUbc34q3XGa0thuTBxvY7A+kzEBFP1yIDrGumwaa1gfi2b1z1PoVDtGGcZrD22xBA8CYyrh9EgPEhSJa1Aee3aKsLnRxhTQuAOa29pZEy/77RqfOpTMu7Wr89+rAn4qmvUI286o7Ko4xCztOpGt47CwL3Jmnfi6J/30cgwivIIDqDANdKeVmmtz+HHJPrIeDibgQ+OPB3LhMB7HfiGtnV+nABku2zEdHs3enA5rmdTE+uB32FnNfrIYPtUKJe8tVUgf2uyPBdCzlbZiRFURPA/lcEmPg41XrPpQNnYQRCzJq/lwC2v0Hl2FrR/3F231+QTPkCAfoHpW9YIo3zjsQmjTUHCgK+nM/k+rK1cojtjK07mI4k1mFPBBSNRcbuwUjWu/x/BJUhcT60JQpKeA8Zxk+hiNw5OmOt+Xw23LMIUcbwFuqRgnsj3WyY0exU9g0jmUjjnGq0fA/EP11P3aC4N/dlX7vnsvT8okYHL1OUcLC23Rn5LkWt/XJerR/nIhDiVwiUPZDIWvkrEd3cw8biVBu35xEQfChpA3qkH32NbJUlGsYiO4CeRDppk57f1/ridffXRDxsUOpTX8IecYC20/kJYQ9Oa+8Yi3j4Mq2+r50x70qU7ixLsB5KrM0xSCervaPhnT2Qk/cjos5/ljszEDX27wI2b+jTHohvjEBrtwT2u6QxcDtlCNWNtbsiu8KvX0raJDaNY08kz16hmtVYG6v0/8w2bk3Aftbpn0e65l+JkixL29jcle5bF+lNnol1MQLqL0b86GMayjamcehClOh5H2W25j1JDrTxOabpe5q+N51vBey3un8/ouzTtTTsm5TunYUA7G8r5zndtxjiC7V1jMDrH9q4PUA43ra2do9N93ZHjmmXua3s08z3mkqlHlvQypZEmeMnkF1xG9LpRiIZtVIxZycWbb5q78rteqZBlo1eKsufeZgotfciVfC+P3IYOLB/EP+GTVHtPccRWQYX0KDHpvs3RHJjFNIZnIfui2TJw4Qs/w7iRZ+jwJHdKQLSkF32pc2/29Cf2FyMTrQ4depDCey7rHoo9ac/sitHWb9y9Yeyxn57dphnVo0rw0PwH19fbTRH7K+F+O/BRPlXf+cRhONpHhujG4t3r4sy6bzcZ6306uSjE9fCt92Bycd/70HyzlJVFH6MjOfhtkifRWmoTWBAVqZWRIbL5wh88bqKDyIgyxnyraRUZ+SVvIaIivEa+h/6fQg43NCY8hq5L8hj/Yoxld8hD+JUSOj90/r0IAKWvO75L5AiNIIitdLadGPE04a/oVqPrQbyUBcEiyIl/DNr4yM72pAg9hIn3cr2kHLQDwmAEaR6z3b9UBurpUhG9r+JTpqMEI9gaUNe4pMQuOFzd3C6d0qjp6fSucYodpu7p6g7C063tsdtkmh/N0r9OKoD37Kc0dX1hFDP5WR+R5RXeZSkKCGhtnx652fUgf19Un/+3PD+fZFz6Cka9gWYhDmalchkGAZcna6VUWsZ2G+Z2lfSOjJw3kTruk9x7VYiaukd/kt2uy++/SRCOfXja6pK+VYE3xqDHCRXEqDWvnbfj33sEKD2CPBqsTYqgL5d8yyWHRNdZcNpDiKiZs/i2ZwW6VkX207s2DTMbz8i1XcEEYH6BA3py0UbRyIH6duIV11LbJTZhhy6v6Ce/uoA/bVEpNsaVEHpX5MyF1Bk7d3IQD2HBLI0zT1yRG+EojzvtW8s6837fN+EHDo7oGhDrzc6SdHrTeNt/y+H5NfuyCk4tZ13Q3IIWk8HkYxXBHw/gmTAGnZuDiR7S+dpjdcimVuWCpkkxwVV+eSgynBkCL5rvz+nWiu4lHs5vd1LULnTZXMUbODAfquIfTdU36KTgH1ijbgRVtLOi8jZ7WWiuvIfAPYJ3uElBi+kXt5kHgL4vgbxq03oQKkVBIqcRugtZ6RrOc28CVD21PIuNq+vEdkWv6DKm318j0tj+gXiJVMaTQyjuYTZ2gQIcS71UjxTk/Y+mogx3gXpl8MRn+uP1u3HCDDK47AEKtM3AgUGrJGuDUAAwiI2BxNd6qYYu/0QIHq39bUEvxcmslSfR0De+sjYfxfptm8STs/PUCDEBPMDgue6Tr+Zzb9H23s2VFPJtPkRP3sCyyRCgMFoqvy/azH/XrLpAgSgtucw/Bdycjhw0g3pf/+0Nv5Gsa+S3Vcra2fPdieAl19QlKdJ/x9r9+yf6LIS1Vr080KjN99k2vncwYj/fWHt1eqkTywdtbg+DcFPH6cA5sdDl11sjH5lzx+IIukXJ0r2fIiCABykPbOgjco+BQSfdwdQ5kd5/KYnypOMy2Smqm/tgEDLkTauteAx6/+NhE42mGI/LGT3PmHXL6HQR4js5r9R358kOx/LzZhnpQrsb1WM7U+Rw7UMgJoa6TFtwAHp/LJIb/SybG1EmZ/2IoFz5LJHD39p9HAWcnqNRUB2be00zM3i5fsI4HEwsGmLNjKfPdDe95XRQsvSN8jhfgyyU3+P+ExTkMRP7Dv2bujzjAjYnTOdmwdzniDdZGdif5JXrY8eBNWGya6ibwcTtthBSP59aud+TVXnW8nmb5DN4af2TZsROlFpj+6MdLGfIP2wzKy9kbQHFuKf1yNd+CSCXhdCTuiP7ZsPI6ou9EWg8ss2fvvSuTqOr/n+aD18jGyNLWlHhiLsZkuCz56G1vobSB4NTPdOi/S3L5Dj/Vxs30cUIOF2ypFIRl5DBBesQ+Aev6eacZRpaF7k+HPcx/eZGYEcQdm+y/rmeIF9IttuncSXMi/2QMw2miP2s6N6BsRbXrL7HyPKOjm+cXDx/hkQPtepZQQnHw1z/W13YPLx33kQ6VnlZiPHE8DcBcbM3rZzzxGREWUNu+8Sm8seiLzYX6DIQFdcFibSHu9MbXVBxuOvkVB/GgHuXkPxx8Zo3SM6xhjcOkip7oGY9zMEeH6xHW6MXoWE+Sr2+zQUrTEKGWy5vmtmhgclZjinnSvrvq+BlJunUSreJUSNzumR0XktUi5ftHvXR5GMv0aA6J9RpPBcRduXkJi1nVvBGO0tVAXIRJUXoB0BTFUolZv5uLLqyti/EAC6U9knlIUxCikZnv48LlI+3e8CbH0CFOhuc/XXPP6JrjZI/TgytdWj/A6jmcFEGvgsRPTTRXZuZqTojrW+rtQwLqvTGtj3CImxKFV4RQTwXIRA5I+ZgPq9LealCURZyehoDFJ61mpB0w7sj0HOhUYlumHstrHv8rI7WSF+BQEBmzKJ0b7/joOIFL3f+rga1Q0Yz0r3LoOU62z8vIZl1aBIlbZEQ12J8gKu3F1HEXWGIiLeQgBUe+Vpvp/e+4OG63uhKJF/kkDyiRwXn9u+aP2OQornvCjl0veR+AhLX07P9kERW3cTkSZLEQDbTWjd/jydu59q2aAM7F9F1N79rl1358UNVDMkNkNrcwSSWa2MyTJazPnRrEge/QCtVy9X4vLCj88Q8HABE1kvuVV/jMY+JzbF+tDGwmsqD0Rp+B8SabUnIePKM2MOLMbyI+Cf9rs7rZ2n0yLDfP9/w1pzQ/Vmowc3UDzi+QtSCr9dm8/G4zOb/7w/x1zIwBiEwMLVCJ1kfMD+UCZx74PUDy8BN9roYV7EK36AAIbBiM+MqxlNAPsjSJGc/4Yx/zXi50sUY+H0PicBqL9pfx9CPHDuoq1yzcyG5MUQm4ddCKfueMsbIf2sBzJQ10B6ZAXYJ/jQVoSz1UucTGfz/Xh+V/HujYkglLOJiP3yWybKcYX02ReJFP39EaDkmaFZ15iLCAz4Y+7zv2HePQp1lP0diZyWpU6yMAIVc8TlJwhc9Rrlw23ca/slTcQ68bJHf0W60jpGP58REcXOF5xWp7Drt6a2dieBt8U4O+14NP+bVKNQa5kMRvM75vfa/zMTQRF/Ixxw5TrqQz14Zz3Em9uQvCr35Nkb8eXHEl2eQXJkF/fPjHjd29QdVCcjvjoPqRzMRNJO1t8WRKBvU5bstETQy6OMB9hveM8CiOePRmCnl2v4o33rQsjp/jkCKmdCvPMgZGPUStZYm5/ZGDVuwokcaOdYv/+JlTWhKoN3JID9g4hMiXHZeQjQvxUBtn+jHnDkpXgc2H8J6Yg7IJ7/sdGmBy3kshs/R07iDxHYeDTVDOHZkA4wHMmYk1BU+Qlo/T5GVS/K5V1HG61UooeRHbwtAl9XyM+Pj1bSvPg+Fc6r7yfKm3Sjypvz/6siIPNxxNfzes4A+FyMBxhGPPg9o62daUc/szHztt9AdurCVNfA6oinv894oo1tHKa0+fg6tf0KwjzymHuAw7rUedLNNhZ5r4N1bIzakP49c/FMX2TD5cDG9Qg7fIN2+t2UWXstAXwvhWTB9UR1Bv87LZJ9H6E1s1pqqw+yW56kIeNjAnjF+JyM/ZG+P9Tmcb2GMa04Fu2vO41upCgXnf6fHoHf7lQZjOTKl8iePgvx3Q+JfVxcLixHBLW1B+wPSP8fkubYHSS1QAnqwP6C+R603m616yu3MxZnJzrdur3xRzJ8XrROHHP7GcpQvRnR+YpNbUw+/r3Ht96Bycd/54HSO32B+0awsyMl6VoiTX4KpHh5ZMVLpM3fEJDokTIfIUE7IzLQ36aIfkBAhUcN3UldAZ4XCUs3jE8hjJBjkTLsUTWDUZRSb+vLAkjp+pIwdN4jaoA/RdQb3AEJoj+m9lemCnwsjyKbvPbm1kVf56eqiLQRjodHEXC0KvJ6fseecc/3aekZVwqGI2UpRzJ6ZO8IBBgcT3hQJ1ipp27kZiFSCscsjFZDTpPDinsc2H+BVFPO30UosOvZfI2yMTqVunPkQKRU3Yho7lKU7vYWAgLuKNrOUTcZ2G+3/AuKFPFIja1t3P+UxwRF6/p8vkZz6vHqtAb2D6Gq7DnIfz/t1ALs4BzmeSnrba+M1sBYBGzNn65lAT81MijaSCWlxvPelZFwv7w4vz8yyLaakO/4Tx3W78+QAbR4ce1im+M7qEfhLI6M3UWoRjKchYzSOdK5gcQmWINKekF84H4sMwhFfFyBlPefUmyISih8bYhHHYEcXb9FwMzHk0pH6V3dkPE7DK1LXxtzE5FZbdTTowcgo+Yr69echIPsVFKqM1KYfeOnfamu3RkIEOgZpDS/RESbXUEAfJmGN0V8djgFsE9rgLHV5qm3IrmxGuJVe6AIw7sR7+kUYDi9z0vOvYRk5WWEs/Nrov7rAFR67GaqvORV66MDTF7qwkttbEFkXTQ5Ty9Ccm39Tv6uWZFh9w71vWJ+TGzung2r3ghYHIJku6e3j4s2IjbVc0BufcYP7HvN7Xk76ds8Gv48qmVsvA7p5zavFYOO2BdnMJO4h0pDn3wNeXmTzUv6TmP4A1sr39jh8u0NFNU2X1PbaV5zjf3dCF2mSxr3nggY2BpFbjVld65BA7Bv1+ZC62G+dG56m+txkfot+ugOQDesJ3nfi6Jv29g8jkaZpw/RAnhCINIb1pctOnPO0zvWM5r7Bwoc2ZrQxV+lcNYj8PsvaJ3dh4DIPki+fYZk+3sUztsO9iWX3FkCASPXG709YmvEI7Y/pKjPbjTkTr8Hke7XhQjCeZoAXEsdti/hGF6h6E8ftG5PQLbEg5ieWs4dVWD/r4Sd0xfJsztt/q8jBUzYPTsQTrMXkFPncGKfno+IrOOBRMmWC6mD6P2QHPyUaum+FdG6+WsrGp1Iuv4xWl/uFHsNOS1zRPpEAftUQa9HbGwuR8Blf8Qr2oxW3iT2OPsusZbfR/rSakXbx9v1XVvMZ3cE7P8S6cODUER1WWonA/s/pKrrlfadH+dT7L2DbGF3oo9EOtq9SH8pAf1+RBDcO8RGvm0I5P1BantmRMNfFn0YhWz1OUmR52jd9CZK0W1QzsdE8ptuxfcuh3CEbE/lUr3lfKxCrK89WtDiiQQe0Rs59c5B6+SHFPIT2f/vI2D/+7QP7M+LIrR9L5YvbR43S/ecbdcOKb+5RZvTIYfymSioaTaKfTMQvxhCdZ+u/VAprtexSgHF+K5MZCyeT+sNjLP8W50Ge7ScBzt3BJFZO08671mRe9rv8lumIcoF/aG41ocWezJMCH0hvrChzfe+1MtnObD/FS2A/eL+5REvfYAGPR7Zwj2MpudGDoAPkB5+IQrGcif9HYiHOQifaXcZOgDs+28kYz4jNthtCtTLWQoO7H+AsKe86bTjZHvm54px3QDpbx4AsGMH52V3IjP6WeS8/NzooHcTfU0+/n3Ht96Bycd/30EYgmsQCsJhSOh9TrWWdC7NcJ3de29iagsikP4iY64vGQO9Ejjb7inr7k9JFdiveB7T/5sgA+o2qpt+dkPC/zMkzHcq2l8MCdoV7f9pUM0v/9YXiYiZ/kjxduXxL8hgPZxICXVHwPVEetM+hHe0DSmqxyNg+lUiOsTb+DFSALqgSLdRCMBfD6XFHUKAWK9STZE6kWrZkK+B/cr57Oi8p7HNER4XIkFUS2dDytg99u4DGq67F/w9BHjsY984juEj5fpsBGx8BGxftPETFHnyFoqgcOH4vn27R7PmyNQmYN/vO6ihnyUddrPvyoLV6XoHJMius/YajRiqilRZL3YZZDCcioDb1Ujp2Z2wjn+JDIMSjFmRALR+R2tgf1oashDaed9UhEPtDBRVeTpa7y/TSZuHdvZBlNzaojh/nJ2/Dlg2nV/Szl9QrBEvCXCJ0fHsma6Mbn3t346Ax3URL3EA8kjCsPmSSKN/H9ik6N9uhAPPjzG2NiYp06N4z4LIoL6DAE7mJYz4c4iSQp9TNYL623dPQTg2HqMagdXDvnks1Q2xK1lARrNLo+wW53cPEtHHXsojP5eB/eOQQ7l7eu/8SBasSQEwUgUj3QAuI126kaJrJmGMc6Tb1LZebiY2pe5h7/K6m8OJsjr+7FoIrF/J5uxABNgvl96za6KV0ciIHVD05RAiGqtlXdKJ/M5lkOPqtOL88danm0gOLKQLeJbL10hGLG/X8h4LUyNA64k0Z+vQPrDflU7kSUhn+Yhqpkk3ZCyOtblw2u5OyCUHOicowrWDffJ3HOzjkK6V4OdyCGwagUD9t4g6ya4XnYxk9bj9AdLzXmPfgf1d0D4rWUe8lqoz+y0kJwYWfVmDKrCfAY0mHeRcxPsObbjm870LAozvtnbX7KQxzjJzWyIjtI12stwIJ9CBndGPhvb3N3rMm7b3TWvtDeoyZRFCp9kg9XMo0meHIqfcpjTsaTIeGuyPZNbxSLa9h/Si7MBzp+NgFPG7GnJc/gjJwM/t+pPYPhWELvgzIkOk1P0uN9qeP53rR4Cs+biHAOzbA/ZvQ4Cpv/89Yq+kNsR/c0TuhsjOGJ7u+QzJ1fnTPTugjF7PCvsN1frkXYhSIDchebibjckYOqHkXnrXCenbLjUa8Dm4D8ljX9/TEDrBQ3SgdjJVWd2HeomZ2Qg+8A1y/DgYPz+SB2+m8fyNjWFvpKcNR7xoFmurLNPTD4GjI63915GtWmYd74D430gb88PtXSNtHs5FPHxLIkvucqrAmge5ecnUC0lZUGkce9k7xiDbYFok/zdG/HikzcceRduLIxvt98Q+QW3IWXE59T0ldrHrlezISaSXcr1kvr0zyqg8j3rA1opE0MIeZXsUwRZUS0GOJNbUOzZXuYzjvlSB/VqpRKqyuDuyx92WaUPrdj3kZHoW6bDtZbC3ByLnMdkb8cIbCMfHZvbOu+17NnSapbpeSmB/vHNI+4FmfZAs9vXwMvWgyu3t+fPaecc8yEnxJQoEnWRQN837dAgTKstj/t3mJtebP54OAPtE5vM2DWP1c7TWnkeyaRSSMz9EsvBptObXJrCFDwgHbYkpZGD/Ilo4mRD/ehbpr+1iAkQlitlR8MtwFAR0LlGu83uEzJ+2GNNcEu9VgnccMZ73lsEWh1Ityzqchn1kJh//3uNb78Dk47/zIITcWolx3oYUqOmKe7Iy8pTd69F0XRCYMyUyKLxOvSsTA1u8PwP7t1NE0No9rnCun97VPf3/Q2MwH1Ls3p7a6GbHz4gotReoKlv9EDjpALwfHyCFYT4iUnJMum80Ukj3Ld45G1K0xiKg6UlsEyEkjE4y5rpI8dzsyOPfhsCLvCHd2si7vwMp7YmJi9JxB8cP7LdHuN1KUR4AGd//zPcX13Mk5TdEBKDT0xZERN/cyOM9BBkOdyJDwktzDEKpuAciBe1Uo6lVbMxGIaU8b1ZcGnebWfsL2/k+COjrU/TbSz69jWi+jAC5DimhvRlPiRPaUaQ6ec2WIM09ad7KzZ5XIEpdtQT2J5SOEJj4CqHgtNnv/4qSO9QjsLshBaiNKrhwLAEyLp7OL4cA7KftG8+mSE9Ghu2Hea0k+puN5prbL6Osm3uNjj2yvRdRD7GNtJGRtbcAclb9FPG7Ncv+TMQYlYro8ohHeXbWTAS4/Lt0nwMyn9o6Kw28lez6mZmuaN4Qux8yynPEs8sZr4U73NbnJtTrhDcB+0MRKDkzATB+kcb2PeSAzFHAzr88I2rP8lon0+d3kTx5BwPcSJvq2W/nx+9SjaLKBq9Hpg5Cxl8exzPs2lfUHVlH2fwN4t9Q/xJFQrZR3X+m1Vqbk3BktxEg1wcIpJgn0Uuv9F25LvXatAPsd+J39UQ84QUiVd0B/ZK2e6FyEv9OEL+UBYsTwEem4W6EM7I3igL9JZLTr1lfN0EAlBtrTyGDsdZ/FLF/GpJ1gzFZjOS0R0DeBuyEdK6Pjd4Oo1m3cEDvAhqMckLf2xLpDJ9T3wvCx+RA+6YtMP20s8fd/t+BiLK9iKL0V6JBL1d3Vie8v2lsDgZuy+9M17zcXBOwvzgCELPRvjzi+0cjsOZpu6dW7sbnhSIakAALnkKg0RDMgVfc68Clr3t34A5Ceu9tJLAJZbm9YzT3E+q63MqIdzxE2C5dkaN2GAJBN0Ay90Ui69X3v2gC9p9F+v391tdziU1QD0NrbSxyRmRgfwoUSLQtyoxZlABeTkMy6iME9HuJDdfRcoT4wiiY6Cu7Ptb6XQuqmRg6QvxgXhvza4i9R7ojMN3ly8NUbaWpiZKgr9Kw10ALehmQz1FdTz8mQPfrqfOJeZFT4zmCx92FeP+DiCf8xWhkpfSOPsT+SA+hjBYvk3Q/xZ5WSHbdmOakzcb8tuJbliQcP1dQzUjshgBstxFfJcrMer/WRHR5FfV1OyNagyOR89D3VChLVXWze39KgOUjkVMkb4b5Z3vXGq34yCTQUrlf1deIvg+k2FvC5ngEsE/T80W7vZHNORrx18VtzH+CdLjP0drKmSQO7H+CnGCNwH7R5xlR0NUz1rfhSIY5b/9pe99t7S1MkS2d7jvS+vQeVT1uRqobXh9e9LEVsP9bOhCkQOuI/YHIph5mbc3e8OyiyD55niKbyq57Foavk0nOYCXk99RIv/oaBYX+wObcA5ueQzLJgf1+BLD/MrIDmuTkASRQH2FPPyJsg3dsnhzf+RvCBg5HsvB5xM9XJaLcL8xrvnjfMgTf+XOLPvUiynU1ZsYRa/02Qu/tT9hH1xIVILoSuvSd6Xy2K7ZE63NmOqj7U5eNK9s3edbQ5Br6/+HjW+/A5OO/96C6+7ULmC+JCJ5a9BQSmG0IeMgb6LgyPSVSUL3UQsuNOI3JeGTr9RT14YmUci+9UDNoESjchkCo7tSN3amJyKqniSilJ6gqqz2M2e2IhMDmVOvsr0GU8RmGDNUfU61/2IUQem7g7IaM5mdtbI+zd1+cnsnjPD3yyo8lbQ7W3vxN4Jx3RxFSryBg0ssqXUER/Wtj5+ncLcEuBHh4O98g5fJpYtPj3QhBPBMydu8lBMNTSFDNhxT4PyGlORuUvpGN74Gwfe4PEcXbHQn7vggs/RcCfZ5EgnyJ9FwfBIh/hSLGnL72RsDSmR0dY1orUp2iRBc0sp7N4S+Q4vs1cozNWzxTAvsTXeuwaHd25LW/1P7O3hntdkK/Ml/Yg4hY8EwSN5COpwFktGu/QUDd5oQz62yqEft/RrztO03vt3m5D4HwR6Bo/fns/FAEdjkAOAexxvzIkfCdmtpI8OwpgPPT+fkJsHJDWxN/zc8hAzL3cz209r2W6rzIOLoiPdeqpvviyMheq/xGG7OrkYz5GDnytqUO5pRZXZ7CvhoBMN6MItbORbxgrLVdzruX7/rZv2PcrU1P5X8QyY+l8ruorvG/2L0HFvPWx54f0UCXTn9diTTyMUiOXkJEEL3FJBpjrcaHAJdvsN9eNqdprR1jdHYHkgXPIB59P5KxgxG4v7LdPw0CoW4t2snA/sV0MMK4nW8rnV5djc4fQkDlzLTYgNifR+DCOZ29bptoP53b3frzPkXd50R/oxAw76DdD9I3Lm+08ibhvDuNesTfHAg8egXJ855IZn9tY+HR1HMRjufP7f0lz1yd2C/pO8ipuiHimWVUrzu7BiPnWAaO3DFZ0kan7TNEVR/ZHoEMo5HTqhZFicq0tQG7dNbco5INxyN96UrgL636TBXY3zi3hfj/QRTReghwOpbWwP53kQPo70YrayKgoQuKFnyLCO4o92DJwP7mCGC6G4EoV6DMm08pItGRPrcn0ldH2HuXQMDLhonGdvNxIDJ7fpPocQARkf050gtaAfszoQy1oYiXegbbHITN8RWSJ0eTgP0Wc3igPVORPTaeT9m135NK8SB5uq2Nz0+oluWcJIcz4tMz2niul867nTcLAvvbgJuKZ6exa0e2035Zu/svxbf1QwE8v0c89QNCfv+JhsAF69NqSJaMIGREG+L/Y5HcX8loYFMsOKig778TDovcp26ofMxWiI/tg3icB5X1IGT1YkTwRgnsd0WBH9/Y9fOp8qpDqdJrCexPT+wFUO5516X42w2B4EcT4L73aX20bj7FnH/t8bdJoKXj7J230CLzF+mXq3fk/UTpyV9SDVbohfCCYYi39SzobB9kew6isEmoAuUDi2tzIkfwwwRo20ZDRkzxvkMQ4Pv9NB/9kUy7x+ixcXNTozMvf/oh1TVYAvsrEU6ktcq2Wozh6jTbo1OgbK0pjHb6UHVK9iWC/s4s6DaXm3oA6QkDOomGuhPl2Y6mKvNmQHxijI13Ds7qR8i5B2h25sxpNDOK6qbczyEsYiqkPzyH9LbZ7blNkf76JeLR30egttPICZl3FO9cFsmZo9r55sPtm86hQedMNP8cEbx6t737cgIXc5k+NcEDnkAOJ3cor4LsrTdQcGVjcEgH52pGGvZ/nHz8Z45vvQOTj//uIzGPtQlhdnG6Pi6yw/4uZ/dca7/7IsPvOaxkjAkMr8X8IvWd7rsg4fd6umeR/D7731PD92q45v1awhj2DenanCiieEnkZNjKmPMsxsAvtXafZDyeRqpR4F1ROuri1NM3S0F8tQmDZZDysQkSSIORAnp++U3p2TWQ0fEZ/4aN9ayvHvnWhhThXMLBv9fTvrZooJdsoC1l33omEbGxKhJanxA1e8uUzFltnrobPTyBAJ6/Y8YmVaWuP1KIfVPOEtj3vvUjov9fReCsp8y+QNU4OggJzI+RInw9MtQrUbIdHNesSLV0Zk3MfKX/TyM2GvuDjcMH9t72gP2RRvedWhv8v/EwGvQoia7IkG+zcTiHABmXKJ7bzGjhN0iZWouIuDibKNl1o9HTwLSenB+5on4ZiUcgRepVZAj6fiHzEOnsv0TRRs6Dt0htd2rEOFLkPUrwuJLWCKPZS8M4KOLGym/t2UVRVNNhdn0WBCi+hpRIBz2barpfifhELns0N2mDQps75yGvIwV8XPmFhu/ayubaa0+eQtVRvBSSFa+iVPGuBK+bw775DjphQ9wW4z4b4ittCPjexb813eOgyiZ2X3asdCX2YvkZDXU9i/fth4wAN2heQrQ9SRHkVOXwHFQNv/5Iro4lIoyup14aYAOb138goMadGH9GYMR6RNk73x/ku0TU/45Fe2sSUa2TLDPtO86nWl7hIALEz7Rdgs/n2LVdO4lunCZ6Ix5xHjJOd6KaddKPquPt5yjK71xkKL+L5OK0KJNgKHBzw3dPRbX0QZu1sQEhY2ciom+/i2TSZYSzckFCz/o7MqSHIIddLl/UFfGKuVCZQZ9Dl9WrUAUTPINoKHLYHYayT17srDGnGs3cjdaRmNsSm+eeR5Rm7IIAps8Q35p1Uvtk7Xot43wMJ0Xi0xrYf5fIrp2W2MPkI+oZo9+hAPbt/AloLX6T3j/U3vEdVPrrMyLj41LqWQwl+NGXyJJ6DAFyizXwmQE23h5p/XWildFEDex+iN/sheSQt+XBNr2RQ+EFCmC/6Nf0CMh5ipA5cxOA12+Qw+NTG5MjaFHKzPp+l/V5uXKeEI27nnEB4wmSYNIB/ZMJvvw04czvWvydhYiS3bngRb1SeyXNZeBzX7TuB1EtmbIQUcroBqQLzUY4nq8kAsWaymaugGThO0Sd6KuMFp5EdtfpyNbqU7ZDZF4/ZO/tRjiDjkYg4oHIZtu7RT8agX0EELfZnL5gffs9wRtdz2sP8PNAu8G0kylc0NEMyOn3tI3DF8hR/h5ak98l9J1yX4EJBvkR796I0JsWabinF/W67OPbDPUq++686XUPYo2cROje5UbruyNw+DKiREoZ+X4mlrlQvLcnCgD5Gwou6Fc8W9L1p0Z7OcNmRqTLv4GCdeZoNWdIhp5l33QfhdOjePcaTOCeZbRfiqcv0m/uR3b3OcSm3GsQwZY/px7s5+VMrynpaGIPhBk9jvi7BwBmm346IvjpjuLZ/kgPaClnke7/EpIZryG7Ipd07opsoq9R0Mbh9q7jjBbakM43BXKyOLB/YmqjlG2Z3zUFCcxGYGCH0mzTHEwEvgxBsuZJzLFBBDR2TW26A+gzG9PrjE7bgP07Y74mH9/e8a13YPLx33MkYZKFRbfEENYglPWT0z0Z/NjKrp9qzPQ+AmTdhmrNMwdXWnmrl7Pn2qhGjboCviUyKv9BGFr+11Nhv2MM6ykk+I8y5j0EKTb3IiU2GwiNwH4T4x3PeNZ2Wbf/1zfm+wDh9e1JlIloQ4BGyw38iJroi01InzraZyLq7Utj/ruQjJI0313LZ9Pv/vbcxkgJGGDnXRmfCikAGdjvVbTh75mb2FitBmYV78zAfhnV1RMZCWMR8NXP6KILUebkaQKw7IqMVafDYci4nKhNSAlFqo0ORlVMQNueRngtUWd8ahSt5pkSTcD+8sT+D+t0Zp/+G46CPpZHwPJVVB1VN6R5+UfDGK2EeMhHVPcUWYkosXEuAkTuMhpa1GirH3IefpeIWHJnnitdCyCQ0+dtBqKWeC5xk6P2v9uJY5T53/a2Jo+lDrYPREr+UJLSa9f+ALyQ7lvD+vkS4fA4ytbe54j/nkTdgDwc25yaMHQPQAb0I8hY7pHecxhVYD9HxS1LAnhtHl7BSmcV730QyZPTaSgLh4y0hyg2d5/I8W4EOpDB8CZh9JdyLRt8wxG46jTUEynsg4iSZu2W0kL8cg4EovRlEsvSFHS0PwJrH7V3OOCzts2Xy9dlijFYyb79S6xUgPXPM/NuIcp2bICArjE2dx8gHn0dtkdN6s9qdN6muB6UsH86l/WVMcjgG1A8tz9RWq7dsm0TMt5Uawzn0mf3U3W6T4lk7mgiYtpB1veJ6K5ZkcHXRlFWD+kv7gS/lCiPMtjmOmcwdkGG5+dEnfLZCT72Gzu3HwEiH1GODZFN9TICRVyefYyAqrzmDycMVz++orrfzkRFoiYa7oMi4m9FASt/Qo6j0oGzLeJbY23M70LrezDiVxPtRC9oexuk89yNQOkfEhHHN5I2pS6/nchM2w3JLwc3fkPB6wj+k4H955Ge/w2RGbgPyopxnekvyHEwBDm/nrLzx9JOzWCjn8UQbTudHUVrR+W0CJT7G5JTZ2E1qe26A6YfI7pfq+HbeiHgNQP7JT3OhOTE6vZ7OkJ//H26zx0tYxHPGNiiz4OAJ9O5cTaV/V7bxs4dBp3iCGoxhjvZHI61Mdqy4R7nOztbn07KfW+H1jJwfgCSqe8RdpDLgG4IuN6OKnjbCtjvVr7bfi+JIqZHI77hIOnj9vx5dl/Phv45sP+wvXdaqvtl+HF1w7g0RezfhGSs87LdUSmeD5D943vFbG7Xn6Od7FnCETRzR/mE/Z4d6aIPEWvqaxS5eyRyQPzD/m+3LGc77/R5PNba37y4vrTN/99tXNahKKHYot0BSGd4hQTc07rM3U5U7daD7b6bKYKykPPMaWvD4lo3qOzDcBzSd8eVc0r3HoLkWKbrrBfNgmyD/umb5kXBhiW/dSfAeIH9iZyn1RMdr23ncqm8z9P1BwnH9BbERu+PIAfapsh5Pdi+v1N0LXvf3AiHeJS688zX2gxIFrURAaQ1h1877xiIMkZqm/kiW2YI0iv7oEzvV5AOfjyyQfJ+LTlivyWw37Q+i2tLEPrZ6Ygn9rY+/AjxjtcTjXh20j4E3lJm7/RApSr/iXj8p4iv/aAjfZp8/Hcf33oHJh//HUchdPojJbUpxXGNxOTPQgLOAYSViJIUmyHAfATy5vYo30U7wH5iQEsn4XF+0ZeZiXSiP6bz6xoj/AEyQNuQsPEI3U9QpPxTRImXW6iWKhhIAPuPMgkCiqpyvmIao3Jjll42bh5x8CtSJBNVz7T3rVNB/fSuHZDS8mPkuf4EGUQDO/K9driD4mFkYPWmrgANNAGUgX1XsEtldBFkGA1DETgbpGtNwP4wazdvKrwuUa+yLNdxP4peOZ1qPUavL7opUtIntWb5OjYuE+UYaNGmlzgaRgDDeU3PRkTG3k5989xVaGdTv/+rR7H2+gFbI2XLI+JcUV8+rctrCl6wGaHs72nnDgV2sP9XT8/+jDAwvkZK8ReIzwxFwMdXdm1zFG3sfck1wt2IvyrTOIowHEsYYxt3whg5T+ltdP4b5IRzvl5GmDiguSdRnmAfBNr8gZT2TERT72+/l0BG3GikSJYbzx6ODIJXsYhxG1N3DhxMvX7+AALYfw05JRwM+oc9u6Ld65lkxxfjWiuVgqJu5rLv6YdA5Vq02cSOt/0/N6nMm52biYjQuZoGwAJFwLUBFzmd2/eOBf5p53q204eW1zpprXnGwMsIhBuQrvW3eRxsx0+Q83EO5AT26M8DcttUgf27SdH9SK6eT4Dqf6IwZjr5W1dBToTbi2/blQDfjjPa7IvW1omIH7xHJ5Q6S9/XB/Gc0UgvmNPGyqPWX6S+8fx1iBe9i4CVMygyNFDQRBtypnu07hZEKaPd7dwyCKj60M7PZN/rIMGMNl5dUYaNlxq5LL2rG+FE+NTGysuj9UfG+l2Es70Lyjr4xGhoe6rAvjuz90JlXzKtTGzJnexAcX4xGOki7lA6iToAvB1VXehkJFPaBePG05eyfv3PkA6dowuXJzb4vopqWYJSt1rOvutmJKeOYjw8AoEoRyHZ9o0db5A2iLd5dHthOAKzXM99DsnGn9AC2CdofBGidvR9NEe4lrplU5ms/mhduk3xc+q6Xlfr4w42pp/YnM1OFbyegciGWt7o9vrinUvaN/r4nNEw9tMjUH84aT+scp6IrIo25BTr1/TdnXEgZ9QX9q5fN8xHBq/asLJWhLO9F1p/hyPwduOi/Q3ResnlLJo2wK45CGgN7DcGGCGw7XWkMy1qc+D602PUHa9NwP6/kP78GcrK3ALRvmeD5H2CSmB/UUTrX6LSRO4I+QXiUy6zdiOc81cjnvJTqhG9Pr5dkE31GhMZDY3W5+7IKb4qwX/zZpcPA99vtcZatOtBBt0RAP8N0o9d1z4A0bs7u9zJtll+R0kP1m43xAc+TuedFsoydz2s3YvTXNyL+MdiRdurEjr83g3flHn/34ls7WWoYhszIZkwjCg7WdvHK/1/UOr/KOTwPYykZzIeYL8T1vo4e9Tm7ZfW/zMQj1sCZZJ8jfihb9y7FrF5rPMljxTv1D3UEE7wio2tB3TU1jsRQX/wJLyrazFHq6EgjJHAT9L5bZCuOho5wNxuaAL2j5uE/ixJlA8bhQLGXrN2ByFdbx+0jg+zax8jG3XKok+Zt02Bgp7moVpmrNPlyeTjP3d86x2YfHz7R8HA9kIA9yATML+j2FQMRSQ5E78fGX3nETUtDyHq3p1PKL6ZoTgTLoH9BdM9HQH2FyHqJv4NRRt4xPJIa3OsnfsAGTeL27PTIqHsStUdVNNG1yDqRr5Bi/pwtBCsJXNEWQy+yc6hTc8jZXhTIpLp9Mxw7Z4VCA/tLE3vngRayHPk4Ppu9v2fmPAY2N53p+fXQoqAz+1UJb3Z74EEsP+y/Z9T6jN9LkbUz7sBE/DleBtd/RgBhHlvBK9bvU5+jiqoN2VqY5IA/HbGZrxRKRPQVhfkhPscgaG9yzG2+xYmNt/5Oy2ApZJu/xcOZIh9iKLfr0vj5nyoByrp4RGenyA+6AD9CExRRCBEGwKsHXhakzAK2lBk4fXIKLzVxvs5ZOC8Z3TotRub6ltfbTTuUcye4bQUArguQCDAwp00Pj2RMj7I+n2Wn2+4d1MkG76y8bnbvuMDpynCCFra1uCd6fkNbDy8xu05yHj1kg/vERHDOfuk5sAkZMQApMR+iCKObyZKMRwDrJvWQBtweaKBWpRXGusnsShXOqHsDlVetp/RxK2ETHLDd0YC2L8dAbVe/3JVAuD6bmpvOqT8P12OT/rt9L4wKXq3k9faQWnOlmhxz7Qo2ukjIsLIs7A+pMWGeVSB/TsRmOZjNgWKgjsqnft3APpdEL/wUhtbFNd3J8oovY6Mdk9Xf4lOLHGGQI5zbPxOJaL/5iU2APc1ta1d+72duwQDR5vGyWjwn0iPWgoZtxVAv7h/IIrg7od42zPEBowORvUxmn+RiCTzPl+B+O/LNn7TIOfVakYT6xZtdUMOgo8IYL/PeMZrkmroI9l6F5IH56DMhy5IRxqKeOC51DdP34EA9vfqxPn/GQKmryL2+8i600JECberkL7r+k0Jmq2MAIorqAJV3a2dfRFoshIhj84nnHc/RAB1F6p69P1I7p2XzvWkDuw7OFvJFE7/L0zwvatpsQlrmqsy2t15Qn/kbPAo8Y2L93RBJTTfQwDXBygzxcvB1IIfiM1hN7ff7hCfHellT9j7lm3R518geV8ruZL6/T3ri2dV/ngSaacpMzvP+zYE+P3DpnVEZGafQhX4vA2tkbZ03IEFstj4XkoBfFK3mRoBUToA7Bf9/LnR4PHIyXoO4hsf2XeWa6F7epdncX6JAq2yPFqRKF1xRkM/fYwXILLHFyZ0pk9snH5EsglsXN9ADuIfU3f8e8bJFTTUCe/A3HdLfZsW6TnDEO8aYOP7M6SjPQPsW45pi3bz2OyCaP8bBEafTmyg+gGy8xYiovkfoMiYMFq6MI1dF6JG/1lEFsQp1LNKz0X8bAckJ7ZEOuHWDXPje8jkveFKB1ZfZL8P93FqMQYr0RCh37DWPBv+PaRzezDfKKQfr5bunYEA9u+ioTzQJPKCcVmaiL/cUtCj76UyhCqw3x/hEYcg/r8eDfvHTGLf3El0jn3/6Q3r2/96KdXjOuG9F6J1+RwRSNKG5IXTRAb2j6AZ2HcM5IxJ6MschDP6faPDM0nObUKP2s7uK4H9WnnqhvdMjtD/P3586x2YfPyHJ7xu5Gch4xGRXyBQ6mlCITuheG71dO1TZOCeTCi1f0VRDXPY76YyAE0R+x9QTfnrCLC/OFGy4G1kiD5MRMdcjMDgYcCq6TkXBHMQBtcf7dz8RHTRzRQbhrUYvzURCJWBcTf4rkAK0scko67FuDiw/zQS8HehjIP5UdqkKzK1iIKJoIeORF70pg7s55TG5VFZgWzI+dxmb/Wvy+vp90CiduqThMHfh3rNxcWITIVraRGFhwzuqYv58LqvDhB0pzWotz5SqBtr5v63HcjI+YCoO14aOD2Iki6jkCI5+7fd73/TWOR12ZOoye37I8xd3ouUxxmRsfC0jeVrqEb8RnbP9Aigu5HkULJraxNlIX5Fqt1NACOlMXc09YyRviiiaBSWkp2uXQC8av/X0kQnYbymRiDg59a/52hncz/jB24IfYx40nwNNDc1isxvoxp5vSySF+6Q9aiTy31ukGH5pLW/eNPcFvM3JQJrPFL6HaTUvmx9ncdo4UXE7+dutfatvcuNXpZveu9EjHHmTafY/L6F+F7eGyQD+x6R8ypygFyJwIUvsTrR6bmpiPIAezeMT37//chI7NT9AWzOnkEg7KJlHxrun9Xm7HcIqNuHakRxk3zMwP4/kFHZdN8klRLqwLduR0RxlnvorI6MsFeQbnIvci51atkMFNTwLgLR3KCcl3A4XEA4WQYRa/b3pOxDLLKyoX0vFfE6MiYrgD4N5aEQ7/sSgRZlVs2K1sZf7XfWGR62NbcJWpve77/YOpmDqG3dNb2vBPZb1vOewLFtihQ9FumRZxHBKnMSwPnnxOZ2ZcT+95Ex3ilRjGhfgrwHx0VN64YqsO9lk2ZqaO/7di3vKeQAqOvebUbT+yM9zANi/kY4uJ1/OU04eHVq7ht1YP9HyCk0K9J156bQvagC+3+lNbDfssyCXe+PdMGvEG2vRxX02Bfx5w9QQMArCJypyWu738tJbVSc/xkC7foSOu2xWJYpwZs3Q3bUV6Q9EIp79rG+bml0NpYi020CaCd/az8kp3tTl63fI0Cp421uvD8rEvLT9aM+SA8dg7KlNkVR/9fZfc8DK9i9PXJfij5tZXTzL8RHM006fXUU2O+F1sk3wI12bnqb1+FI11uh4du7I8D7AZv/z0hZsAQdL0k7wH7TuBu9DSPKsE5RjElXtMYGWR8fsvnfADmABiPanKB9vRr60xP4tdHdcen9CxABbaNs/LJO0RQlXQL676N17bKozfp9OkVAAVojT1AFl/sTQUh/Sn1blNDxRmCb4hbtHWDvvtP60oYyET7HAiFSv6dGtlCWayUtdLNxGov01lp2BMTeWePjQaiKQJu9N0flb5XG/Z/YWkk0e45de4pJyPRq6E8/pGtfgPRjX8/Zdp7OxnoIWseb0In6Y3vjZdfXINbZ4S3u+S0C2DebxL4cR+BHgwhH6t8oMuwZP7C/Kg2O0Yns15TI6dbFaHJgOQcouGVbqsD+wHR9GRRQU9MBJh//949vvQOTj//whIfALBUf9/zfTBUk9fTKNmIzJBeGaxKAba6x3xtFar1NO/Uy7V4X1FMgwT2cAHW6FO9rD9ifFwldr6k2lIiOG03sal5unOO/ZzcG+DWRNn47MsZzTcdWdfJXIaK9l0/nF7N3D0dGyHoUmzkW7fi1PlRr7LtHfwgSuvs29WMCaSErIBsjpf0qFI21RHFvT6rA/v5IEVicKOtSRr+7ArsiHdsRflp7x6Io6vAOBIj8CylBGSRtF9hPY9vfvukn9vtgInq3H+2Deo/Y+DducPbfchCRo17q5NcUkU/p9xrIEHdj6Bd00mZG/y1HsUYdeJkSKXxDkLK/43jamAql0g5MY7cOKt/0GbBKi3W0NhGx/0sM2CGiTVoZc+VaONPaOJZwjO6HgOqraDDAJ3aM0rkZUMbVR/aNO5Ai9xrGtj9SWGejuVRbjlYZjWTL1MU9cyCDemNktOSNR5e35y7M7bWzBrohYLgNAT+bIKNwjI2j81YHmQbbtROop+AfjmTBFXSi88TadqD0BlpHsvu6zRH7nxkN70W1TnTe0+Z7SNY8QhUI6VV8WxsyTnt08rc5aPvLduislL2tAP/2AhAWpgrsL5/a7ZRoI5pB7i5FP/6GAJE17XcJLvRhPNHjk9jHBZFM9mySGYmyO16aaQoEHriR+jQG6Kc5yMb7ToQO1ptqBtIODTTavfh7J2mTPKo8YzGjz5epGpr7IJ1t13RuVSKbaQxF2n2mHwLY/8j6P8HRq+m927WguZmQPvIg1c1RPSPiQgTwvYjkzLkUmzLT+bxkC+RQHYucIpXN/dL/Gdj/BvhpSeeoNF0bihJdAzlIPErxGcQ3zkV86CGUSbGJvXcMilafy99NyLzL7Pr6Df3vicrQPWXvuRFFgg61MXyYIqCGOrBf6m3jNmtF/PAExP93pxqJ3h/pvF+hNTQO2Ldnv49sgtF2HEsBRKe2jkhjt6ydOxDZQHcScn5dgpevUbRxAmG7bEe17MryCJS83X4fb2O6TTmmHaCZTBf7ITvndQTqnUPaM8juycD+A2ht/xqBtiOJTYi7IGe165V5E/rpEAg9HIFM5fjlPp1M7EHh2VsjqJa+aAL2/0joW5mfHUc43OZK56dFzrnRiP5WpC5zPBNhkNGCO/hLZ+ZSdBzY70lkJbqj9EyKcodo/WxHBEX4MQbxxUnO+EL8eIjNqdP2IkiutSHHx45IZ30V2C/PWdHXOZEud6KN1RDMsY94+SaoZG6JPWxg7zoPOVK6oLX5nNHXUGQD5gykTUm1xu1cPxvb49H6ehcFj/QmMmnaqGbJe/9zUEWTzjIlAm4fpcpDuiCd/1Ske55BlKRsD9S/ysZnKfudnTkLICykDel7GX+YHjnkD23V9kTSgeM8n1i/tm/B56ZDWbVDjAY3ZhJkbRpDn4epEC+9AAXUrVPc64EUbajE46KYgw1lCwxB8nmSbHb7zuuQrPyMcCzlShKZZ40P2J9xUvrT8L4DUTbFR8jGOQ/xINdL+hPAvu9T1J1qSdI1OpOGJh//Hce33oHJx39wsiU8byWVT7FjGmQYfEICR+2eQwiP8koNbbqS+qN0rhfhYW/0mBJK2bha3sj4rDE/qoZVBvYvKO7ribyYK6CozOmQR7KN2BDEo0S6NvTFy2pkoe+g4OE01HKz36vSTvS89XlppET+FEX1TN3wbSsjpWCe9D2bIaN6jD23Bi2cDBNIC/n7TzJhlBXHV6gbUz1RzeBXEDD6TyJD4kcmMJYgRSNSBfZabhxDGOj9iH0SBiElylOA/0bVcbAwVWC/jJzuhQTYGOAUOzcDMmBeQcb3WJpBvZOIqIxOBb4mYf22O9fIsfWBzc0haWyzMnwoAoaXQ8rP2xTlnf4vH1QB9u8hUNbLTkyBlGSn78U7MsZIYXQg9BobvxmLNVRm7DgQdiZhaLZrzBXrZQUUmT0KGVPuMKxkMk3KGCElb2nShqJEmu8IBP41Gbrl737I6LoUGXbjHBVIvvQmajs37oPRop8b2jNnl3PbcO/UNr4zEFkZX9r4HUy13va8RP3YZ6nXEj8cGaWvldc6gT59o+YnSJHs6fo01IHAmYgatH8o6LKURTOhkhhjENixW3H9QKOhl/g3rHtk5LUBp41vjpHhMQ4kpyiX0YF1uQgtgP1O/J5+Rj/LFue9TIADoXemddXoXJjUvlHlMfn/WQmdbiMEUv65ePZnBKjfhnSdXvlb7H8vi7Ux4g+5Lvq96b7S2O+LgLFLkEzxSL+mebvW2rvHxu9XSDd7jZQ5ZvSwAsFLbyU23K45hohNiNuYyD0LiHr/17S4fhEhT6YlNkf9nY8L0uGcx59FJ5cjKMcVZW+6znQaVWdJHp+FCMf/xohnltkE1xHAYRtyvpxCtSShZ6OtgnjuxoifjkVZsRk49X0ZrirfldcSktXvEID3rcRmjW0o2nfONO8LEcD+nyhKDKB1e0f6Dj8eQc75/D3H0wzsD0Ag0Qikg+7Sak0i552P7Qgiw+pDYIHimRORXPoUcwamaz+354ajtfgjO3yfkb3tPi9Ld9wk0JBnZo+2/np9+Jodg9apg9ZfIr3lDKqbcPdGdtBrVJ3zPdAaHo306amKtrPOdiwRpbwBWmPfI0r55NIbGdh3HnEjURpqCgT+nm/vHmf7pXlrF9i3ef2+zVUbDdkw6W8G9s9vZ9yntjndHdGi28q/pMhysf+nRMD6cQhk35qJiLKliCRP3/8YsefQrISO/As79x3CsfBMosFx349Kr7hd7nLmSgo8oeHblkfZq0OwfRfQmnw20ctLhM6dywZ9N9HFi8jh9Cah318NnGv39kJyxtdWLWM/r+eGPi9iz+a9+/ogeeDZpm5D/4t2QGWb/8+Q08JlbKkrLGNjPZh6gF2f8fV3IvnBhsQ+Gn9qGh/77cD+p8hxUnPWduBdu1PP1piOcOLn4+Divu0JXvWRzbtXlXiTKOk1SXvn2P8vIcfS5xTZVQ20k4H9w6hm/deyZSdhnnzz9S+RfPaxeJG0fxUB7L+KnLJ5fRzdWXQz+fjvOr71Dkw+/kMTLSXXU/N3LxjX/MYYLimeOd7uvykLFuqp5rW67ggY8TTkWhpz+v92pPhmIHhhEzC7ovp0JcjRGLFP1YOe37FnEhDHlfckhruZ3XMxErR+3b3Dr2Cpcunawmlc98htUxfUDgzeSJE2iRwDHjWega9eKMLB60O33OxsAmghCyWf438hQGA+FBU/zObllOLZnig67O9pTA5ETp/BSMn6wOauNBjHB+z3QsbcaGTcTG3zsAgB3l9GtTyTA/sjELCSa+wvjYTxaYQHuydSQF0RGxfdk57bB4F6j9HJexZMwvrN63UJtD52QvsW5PncCylmHyDFKz+3gtHYrchQ8KiqA7/t7+ukMcpr3lPr25AhlI34vIfHeKOdkDG0PmGojyTSsNsD9u+x+y9CBsB4jbninRsTEVODbM1N9Ibd1q4bjX1tLQ23d0xL8LTpUUTmGFsDy9EOryGAG+/n5dSdw7vY9fvpIMiFIqDakKI8fcP1PN5HY9kTyMgfgfjIy8TmmnkD36UJvn0vAiiORNEvbaS6/p1Mo9vTDJqshYCSwciA/RtVsHVmQuZdQdUxUxpd8xnNeZTjvxCo5jVb3/93fJu9ez17x5PtzZnR9/Ed6QfVNbYR0gl8HnMpnluRjOmsSP1uqe1hiKesUNzTn4gU3aqky04c13EZltavxk1MkW4xmtBTPChhLQS8Po8Al2dsLHPdXK8dfQHVKOHZCCfk5k3fh2RLm9HvVwTwUytHgLKfPFrMj1ebaMG+dYU0xudQ1F6nClwfQaqLPBHjvCjiGccX57Pj1WvXLofAmasbxmJUGrNTmQRjvtWzVKORN7M114bAv4Hl8zaWiyFZNgUCBb6iCKRBDp/zUDbjPNSztR5AgNRA+10C+5cggHdN68/TFBGXBT10QeDeB4jveXRvX+urA9p/JWV6obXvdHQL1X0bHkTy6zK0J8N6aAP4L4kSNq4T9iOA/ZeRvt0NOS3GIrn7EQKxWmaBIIfyWUT05J+JAJ1N8jgTencTsH8AUabPj6GkcmvW9tfAehNDR2jtf4Hkgmf5LE51M95Di+e3I8qelJuYdzVaGYxtmpv4VS0b1ua2LFW0uT1/GxFA1QvpS57d2EY1K9z54uxInzsy0bo7uh4DHmkYj1bAfqWcm/V1J5ur4cD+DWsrl+Jps7kZWL6zaNP7vjqRqZKB/UkCAFOfyuzIaZBjwAPL+qV52RzpTr8rnnFn51d2fcc09w4yfkA4wZ6w8byf1oF9m6c5OsjOOaA/blNSxE++pHlz7KUQWP8k0o/uQzS8vrWbS932JoD9J4HlJmAs+6I1+QUCTk8ispgeQ/x3VULH2r6dtnojPvMR7ZQ/JTLuTsj0WtJvZx42bp/be49t4h1pzZyGyrnNNYHvcDzmCmyfLBuTO2zd/Npo4yfEmj+e6ppc0+byDcQbn0cZDDP5+p/EcehGlJh2Z/OTNARUFf3amnBMHEMnBARStXM80/t65Pjpg/CAXxLlro4n1nMfZGs6Xb5GizJak4//jeNb78Dk4z842WKmXyCwKRttiyJBfGE6dxwB6C+ezs+EwIeV07mmmr2zEWUDDm1iHigC7iukeHuq9k+QcpAV2vuBHxTPtgL2a5vx2v+7pfZK8N2Vnx3teq32GRFp+hIJ2AfmQkr/AQ3jkQ3ankj4/4NUQ8+urUZE+u/V0E5PZITs1Mn0sCURFbWE99nmZQQyisZS7KfgfUOA+ryEwf0UUbN3FIpiK6NgM7B/XHFtI3vf5dTrjD9htHI6RbkY68fVyCCYBRms+yIB+3qirbzJ1kVIGXgWKaxLGU1dgBT3jykirL7FdZvpuGl93IsMkR727YeirBsH9E5ESuibdm4fa2tbGiIh/q8fBO+6nRRhS6zziQH2XclzIH5cKRlaA/trIfCskvnDBBpzRpfT046ROCF0hIy4R5DhdAMyRnrk/qPorAkB9mdETiQ30kYipXvLdM+fkUK6RhrTDPKWIF1XxC9HIkdbv/Jb7P/v2jsPIUoAfEWUX7uJtIFvan9BtN4HEWvpPRT5OfeEjO0EzIGXadgfrdX+aE1/QUTbOBh4XfHsTITMuzR/i13floh2mwXJs1eJ9OFX7NsmqQ5vi+/KsupOZNxvS9XRnuXhFj5nHaFZ+39/o4U/UC3tsBCKCmxDUeCdUufV6G8Z5DD6kADhLkGBEL7+N0Ey45J/E8243PL65jchgHO/4r4BCFj9giiv4328FBl08xH7+zxOrMXrCD7kmUU5Lf4Ie+acdvq5LhHRemOLOcz8cX/kYN/ZaLu/0e1sRbs9kHx7gggUaQT2W9FOB8Z4BoL/+phNQTUyuAS3L7Jv3cJp3/7Oi4CAvW0tTHQN/WLNzIPA7w1oruu+KSFPjqMB2C9o2zetfIN2In/zGBP86/dU12AG9kcjvj0Wyb9NG9rKdDAD4sVjqUbQ+r3LEnLlrKJvCyP5dFQ691O798yij1MjEGqYfUcPqjLRAe0nCZ1xNrTu9qL1vg2lQ34+o2VvwwHRxYv7jqc1sD81Wk/7oEj1XNLzYHvuARocpy3msJz/rZGMrG2WjkB0l4dbF9e2IZzFWa/pikpnvA48kM57hmG5X9V0SB891H73QZnIwwjnfA+bJ3e2fJeIhj4ltTVuo/RMO4ivuOwbRDiLahlP1p+zEKj4FvXMrH4oYv8LFJ1c28g9/V0EC1JjPMBrev8qNOiCHWljPO2vidaHO5ynMXqrle+xMbsdrcPFfV7s70b2zI+RjjKznd8W8eQnrN2hCE+Y0ubes50zL8rlfT4gHMAO6Ff2mkK6+hjqdvMaKEimKwKFp09zsJy1v3PxTC9C93+EAtinWY74HO1CAPlt9s1HUnU0uk3VbqAUESxwEimrxene/nqAxNkTO/8TSTMbIP1tJCmiuxwbo6V2yyu3aH8Zm//RSBdZBPG79xHPzutzCyKY6niqukQ3e24BxHtc/k4SoF/0dTMkc8+3PjxDkQVIvSTjjkbXB3XC+3O7UxFlBj1QyfWzaRDG9Y69e9uG+VqYpFs10frk4//+8a13YPLxH5xsRTN4hPXP0/n5kbL6ogm9H9MA6Nu9RxozXqsD71uCVPcOgVy9kQL3I2M+rxN1OD1y+Cl7z88IkLiNImWIKrD/uxZ9aAXsH1AwzBWQkG8jauqfAhyT7rmUAPZdQexOVag3RbHtamPxCha1QCgKOY14j1btFN/RGSlcUyDF4hNCie6JlOhh1tetkaI2gmp0TDZo9rf7TyEi2LZBYPIYpKDOVbx7ZaKkTlYajrNzeeOg7kSkz4lEalm5OcwCCBjoQqTiPmNzNWXDeM6OFCrfaO5r6+8oe/6/AtAv+nyy9fVZBNzvhcCYYchA2hYpOv2RQZgV0NHIIDkotVcpidJEu//XDhSV4LV4F2m43grYrwEv5TozWlyDiPA+I9FjK+Bqlhbn2zXm/l1zYWv8WqKWfC3qMPWtBPaXba9fRnu9kUHmIIxH5KyPnMqfArf5/cXzfYv3d0eG+adITuxEPXV/JXvXh5ijGTkYNkLR7ddZH24l6oRnYL8XAkNXMdqZlk7eY4Kqw2IhW5cfI+DrBYJXbWP9mQMBC8MJ5d3pYiZic8rshN/X5ukO6rWM50WydzoKA3IivqUGDha/eyDQaTiSzRtTr3m9Eiqr9j7tbPRIdb3sY3TwEbHHRL6+KALbJzoDYTy0vQySdS4v3kHO52UQEH2/nd/837Ru+xL6iTvF21BAxGyJvn5v53dB/O0C5DR6H2Ug9kCBCJcSwIYDb2cTm6yX8nIlIkp39XL+0/vXJhzKP2uiE5o3xjwABZt8haIyb0Yg2oBEV+0C+5Mwtj9H62/x9D1dCeDlyrLv9r8b+qsU7Z1kdDorLTIqJnStIb3MnZRtiIfsTz2LtSWw3zBn3Yg6++OAfVpsMG109AnSqcaVQUpz3wOt90etzc9IwTg0A2bn2L0XA/fne6mu7xWJ0ibrFN9R0b8RMPkOVSCxB6GT1krA2D39kZNpgfRNWRY5oDIY7TfTq3h+1YY2u1ibbQjIKoNVjqcFsN9iDn6MZN1EBZ0g4Pxl5Ny9puhnuVFwG8qymL2Yi60JYP/AdL4/ken2fYInnkJdBvwWgYbr2u8pkLM2R9rvZjT0OuFo/AGxBk7L/W/xf84uyPc3lV+dHdm8XyHdaMOChvohvjoEOf9bAvslr2hvbadzLYH9ieQf3RAvakM2zY4IIB2C7O9S/+pm9w2mnr1zGYqGnooA27shHjgKRW0/SkReu2PtRpJDyq4tgLJm/kDav8HGfCzSH7MOc6y1tUk6N02a1+zQ64Fo2WvE79pAE70Ih+w4YJ+o59/daGENtGdKzrBZFulpu5H22yrW10ha6DUEYL864lGvon0beufr9v8+1sfdJ4UOJpJ2NiLKV7YE9ieh/cVI8hXJt8+I/QhyluqGhOw7geDJLR0wE9GfVmWXnDcMRDLKeeJ8ZR9IGchMYonUhn78DEXn/wm4M9NrumdK4CCbs5vT+aZ9Pf7P2/qTjxa08m13YPLxb57gutG9MpHS9L10/lI754rY9dRrlK+HFMoHaSdtrHhmSVRjdawxm6cJz+sgzAhH6VajkdNhofT8NFQjRw4p2l+GMLTLUiRNGQS5reuQ4XsqAkVHY1HLhGPjCap1Xn1TtJcIBaZi2Bb9c+X4GSS0Vi2uL4AU4d3Lfv+b6aInSic82X53Qwrz50iJdoP5GJu3j5CC6N/a5/+xd9bhdlRXG//duJIgwQIkuJPgTnAIDkUKxd3dSnF3a6Fo269oW1rcixcKRYs7IUiRkACBeE6+P961716zz5yr595zk0yeZz25Z2bPzPa91rsMWVidan3UK3n/ekRrrTxgf10kmHgA/1Lrq7VDv1Ihka3VNddzAVlpjHFzLBzAqUVVL8S0nYXAy8uRUFyW9LPWhKxBJyIlV2qxElzQryYr4PZHIMth1q413L1Dbb6/QIVYt9MjIWVHicTCLCkTQCUP7H8Q1rMrl8c0dra5+w4SbM+hEWDf/67wzqoKc430z4ZIaL2HJDFbhfoGYH+87Qtlln15bba94Zdovw8KpdeRMDwFsxyzsrsShcYHbD0GRe/sSGAci4SgGxC4t7i9P8TGP7BC/QcQ48F6YN8LDVWNeZ03xu5ed6R8ewopEkP4snAOhrl5l42Tt6wJAsZ8Nl+G2O8DkSD+BeYZRWI9VKV2edBnS6Rk/RsCkrd1fdsX7UUB/L4EeYMMtrZnxowoRA8iH2wLAt9n2Fmct0ZoHYDqQ4F0sXnXL6fcvAgQCYrjSQgUvgspv6+jDRKPI/DvR+T9tigCT4Pi7C6iZf42CCCcQrRqnYb4tsXc+wKwH4DSuzEFJBXAKLRX/oCz6s4ri86c0fbeU/LKkt0rQuLqr5E3z7+svuORIB14kU7Iai4A+/X5SlrRr/2JVqOP4c4AZEH4it37i1/D9n8Ip/gQUiDOgtbyF0i51uJ5kPRPiH3+mf19FeI/xyBvqPmTZwOwPwntpbOm78VZNZIP7PtQSb+09kxEBgUL+DLJt7vb94Oi6Pq0fkn5kK9hko33OpX6gpjz6oC0j9zanQvxfM+5NjYUAmbbSv2eVw8EmnhgP/TTrmi9XZXzXFe0531LjF3uz58zSIB91+aQ92wQMTzWO7RQeUncM8YghfK8yVzzPMw9yGDEx6IPZ9DOaL+bhvPkJSaWDWN6MuWetUehfefeZG7O6+bWHMgD+AtiCKOuaE2OIvL3V1Zop7d034GY6Df1LgjjNwApFSe6+oe9dSf3TKPAfvp3Tt18uWWTe54XvJTWA/tzEb1rxls/+GS33gunK1Ep/Osw3ig++Ci75/fw3ki5+Lr9Dh5Op5FjFIgLHYl4hFly6jGcCHCHsdnR3vdL33/IiroMeLZ7m5JgHMl9D+z/G5ONrE1/Jno1j7b7e1R4j++P/a3845gFO8JbNkDncmp1fS5aQ2/ZnOrn7gcF9hhychm2B9E+wP7f0Fn+tPVDfYLZpL8ywH6V2+nHsBfi/cr4CmT8E9ZHxmIfGZjejVMwVauv0L4YjHF/wIUSo/wMHIjOh3rld0EzF9W8AgW10cBmtYad/CZJNunjYLu2AlHYfhuzoHDvWNs23p+BHZtZl8HooP8YWYz9Bwlkg12ZoKUPVjipBjzEtf8W8xIgHvqrAicn5f2BMD9ZRmpPItM22g6W6zEgEAFSryKmtt4i3z3/J8ot9ivFPe1OtLCbgDEHyfvmcH+3uUuUmwe9iSDQvMjS4jNc6Akb9xKR2b3I2vSRzZOXiIksU83xMHKAfff91GopzMuN7XcI65MB9O3e68jiyAP9ndy7l0cMSYkKlnbTExEtbtZPrgfl010k8Z4beNfJxBixLQ4N0NEI7XGBSQwKt0oAVdg7+iIL3xKmJEJhqc5DCs5TcDkuwrNIafUuEpTOJQL7TbLOoo2FuQbqEICR4I5d8TtE4WkuxMyOopHEsZQzmYMQ4Ps80TtnHNE9PQBWE21P+cZ+f0kMDTI7EjLfdmVDgrSx5IQ+S+owG1lg3zPje6DQXblxX1vQv15AGIbOmosQsLBk6FckPAwhHzheD51LDyFBYhBSVA4lC8Z1RiGHxiHF+aDGxrQV7fJz9xzKE1COR4BREI5nsTUUQvBNJu7HY4kxdHsji61PbexfIRFGEK8w1bWvqns4ZPJMXG7t+A4J9idRnqius43hMURwP4DjH9BKb4gKdXwQKeG9BeOyRED6Xtc/exFB1WDAcT1JTGKk0P4TWpf/RcrKstBFxH1gWQR+9bB1cxHaFw6g3ABkIwRITCEL7KdGJiG+7v3E2N59UAiT79A5dYwboxB/PfBU15ETiqaZfTuICPA8icCG8L3FiZ5Zf0memwsp50o2fz+2vz+nSp5+yChkMhZD1/XP5URw9lqS3D/ICOAtK7MqAuy6kLU8rveYoAKwj3i9e6wO1yDeuAey+r0QKZmOxlmt2v0tkDwxxeZYOvc8n3gScT/JC30Z5l8A926qtA9Y37wFvOuuVQoB0wcpm89J35POVQx8Ruv+MKR0+g4ZtVyKAN4xlK+D0Cdhnv8u3Xfs79Pt/lc462V3f6D10+m0IMF50t8Purmzuq9nUueQtDeEyOmM1l9vYrLkqTYX/L50GhGA2iOpx4lIhvsEGXJtVqG+u9s7jrbf3jv4JbReJ5LE/ba51ymnTdsTvQuOcHtJJ8QfvGX3/4yUNSejs2gSOls9EN6LCOx/6uoYvhussbuTKJqTORXCMqWhZdckertckNc/zRz7xZCBwFRry1buXsqvrUcMofYcMU/Hh5TvMX3R3vg5UfEaxj7Py/9ZKoTbo1ze9/0UwtAckZTZA/EUkykHnkMC+5R39+F2exCNet5DOMVL9vtZZMzyFyKPc7p71q+nhdE+8CVOeW7lxxI96x5EiojgkToQ7ePfozP4BaSAOZto+HhoXn+1FxGB/Z+Bs6rwvjSE3bJJH/twnQ0B+5dUqX1+j9jP5u1IxLeeSnmonX5EYP8N5LmxGXFPPbqNxmEltDcFuWcX30++LcQwcr9oi7oU1LGp5hUoqA0GVcLZh8iduswKA1mHP2GH4R52rReyYn8TMQDXIJfXFZFg8QnJwUoOgNJIvfrYpljn6tLJKAhHK/v7yfMhIc/xybOX2PUtKHfbHYaYs9PJMgrBjXMC2Tj26yKhbiIuFqjdaxawT2TuupFNVDKHv9/S/mxin1dyK0uZqODqd6j9DtZo3RGzcabNlyOJgl6wxrm+0jfJAvuX4dzWSFzDkYv/WJtrQcF0LuWxMs+2uvyGCLB0JolxjhRVwUXzsgbanuu621HI5s/DSBjq566fSr5FzCru70woGCIw+iZtlCyzhv1UR4ytvX+FMmFuzIdZBaI9KSgKzyZauQYGajxwYvoe5BkUgP2ziK6jHUKYq9D+EMKpoieD68v+RIZxAE2M31vhfV0Q2HgNMSfJkThAL/QbEeD4GottiwSwxZAS4K9I4fJrDPhP+zjn+x7YfwJZ/B2KQsCNoxFlRRPb6Mf4dNt7AqhaQsLbbpSHDvPPrYksvcYh68+biYm6gmB4sBuX/ZHANdh+t6nS0vo81GM44iUOJ4Zd+M6tq55oTz/fyj+LBI4Q3q6Xm+8fEJVaE1FoEW/JN3tbtI+4H/R2dXnX6vs52geewxID5ozXPAgwehtZSC1bhTplADa01zyOJd8ja+W7JNGN/T4UimdRBBisYnP8A6JHweDkWwHYLyFAYzNykrsRBcbeRF7N06ckgCwNAPtof+mBeINRlIOhQTCtB7OTPlkdCd6tilvr1tFg5AVUQsYrQ4jA/hJUBvYHWv+9i3i7f9DKZObu3Ysj3vUN4j7YHYHoPyHwIYBQ11Jusb8DAihnRyDo40i5ehLlyTNzgX271wMpBnrZ+D+RM/6vob3Nn4Ehxv5U5HGRzj0/z3/t3pVaz4c1Wg/0uvnYFfHs/cJ4Ik+GEtqXnrS/80LAXI3WxTaV9gX7ex90bi7svrE/UoSFOn9M1sM45TEXR8B/JnRp0geBn5tKAqC6Pm1x4kWyoT3ut299hFMGkzX+CgYjW7lrPW0OjUeA5KCc7wwghlItoTXxZ+L++inaiz6132VGYij8aolyz+ytrX8WJfL7Pa3vHrC6/RWBhalnbh6w39nNl9QwbFEkY4xHZ/AvkzWxmz33MtqP9iWGCeqLzo8riXPV8+IHISV2rqEEmtNPkxNCshnjHcbsVCRThVC2L5KT58L93gzxVxMRD/YQ0YtrH7J5L35r/bOCG7M8QH8Xu3diC9qxpD3rc+ftgUD0MdaXwaI8JNfd2p5Zt7E1gfamE4le+OeQXSvbEZVUG7nrvW1+TDN6FVPmEo1nPrI58Ari6T5CSvAQqnZutJcExWOgEWRxiZrFPyd6PXyD7e/NnH+DyBouzo14whDCbXmiYdXjwAr+Hcm62dTGfDQ5BjHNbJff44Nx0TjE742w3w9QntC7HzpvJ5M1bjmmCn3dkAyzOjqjpyD+1OeL8/P1PKvPJrWaMwXVjmpegYKqPKASMr93G82TyB22X1Juf3dQhbi9vZEg8DRZK68SYgoyCWZbWc+UkbjUvrNTA89sQRS6grZ7O2Ly3zS26VpEAfTgtN7E0DjBEsW7U79FZNBy4yQSheE3SUJ3uDJBMOzm+vVJ2gikSL7t6zoLssYfnJQJiofgAr97cj8wNMuTPZh7Iuu/kh1uaVihFNgPoM8FSLlzAWIc13F91IkYt24iSVI0K3MwYoRfQgJ1L2Qx+DASfv+CNO4hkdOKRAvRS/L6piNRui5CXRGj/LObk8FlO4+B/idmzZbzrtORQNVsa6/pgZBb+CTEoKeeIH5OBhBhaXctCNX/QoDlABQuJFjanJO8rxMS5L6w+5ch8Limwlwj/XOi1fVWEpAjzDX7f2EEcs1ThW92Sf9GINrrSLBfISl/svX5o1QI90Mjsd0rPDMbyicQ9qwSEtar2tdEa7UXkDA73NZdSLZ6JOUhCboiQfVtdO4eTwTsnkKM+t+IVnTXu2eDgNhmYZvs/UOsv97168bu+QR0n5CAtGFukT1Lf0PMxzKb3T/KvjHJ/u6fzs02aFc3BB5PtX6exa7PTQwP8iCJsJm8Y35akDQur4/s/162Vm9EfMk7uJjQyTMe2A+hCO8mWlzviiwRKwH7IRTPVBoG9nugM3uyjfUgpLTZnhje4vTkmQDsj0/rb302ieSsIu7D9+K8JBBvG/iVTmS9UVsaS9fvTXMicGEy4ieG0jRgvzsKp9CXnBwlrZgLm9j3tg91RQrhsUiBMCcC0z4lWuxnEnSiMCbBHT8YOIS+XYPyxIO5wL7d74nOxinobBiKeOyT0Rn4OTrfQoLYEGP/ZRv/v1GeBNnzqCH2fAkBpj40y6pEBUHw5OxFBOIeIsb63ti9ZyI669P99gjrjwdx4Z38//b3gVb3dyjP57Ik4jX3IYKeqQLKvyvIXGcl3/N9cCGJRXIL5k1DwJC3qA9K7jcoD4+5KlIGfk/0EOlEBL/OR/JirgHWtLjvvIiMUcYjOem3SLFfQkDnVmm/2rObWZm/YWcJUnj/0+bmMkSvgRBa6TuiAvwrxJsNSN7rgf1DrU0vojB4wZDJK01nR8qNSciQyYe37Ink5WWI5/bOyGDkVbROzqJcuXAkAiU/J4YbKsulRCs9kNx7BiAZbABxb30ZB1ZaX/q9cFakhH+A6M19pT17HHGtBbA+eGH+nfI1sA46f94jhy+oNG+J62M2tI5vtN97EgH9YCASLMqnIqXf3tb/p1sdd0Qeo9tY2Q1t7m1FNBj5DMnmvZJ+eRrtuRdSvgdcQgT1r7Vr/Ww+1IdzszlxJDIiCftk4Nu6ojNkW8QDbkBWQVjzhKboHG92nHhkpPk9cK797kNco15BMhTFiy+hHFxDkjHw62I9Im/TaiM8Yniq+4n5BYcQlddP4PI5uHYciPiuf5AN0dWi8SJ7DiyCQPwNySZJH0rk9e6kPMH6akhxNIYEFyho5qCaV6CgNhjUCJA/a4fLRGQpsXBSLjDENxPBwjok5O6PBNo/IkbFxxqt+iFDdE99gyQkCFlBdxQ6ZLshIeRcxOSlz6xHzA+QWsR6Rtsfnpsj5j1Y6O5Roa6e+QmuWF8hBnA+lGV8dQSk+295i/1HaUNgPzkgDkEMxjeI8f0LYnr8Qbmr1esaDEixA+IJ65PBFcYjADmvUA7O+fdvjA6/Fawuk5A2fB2yiSu7EK2rPkHCxXIoIdN/kIXaV0jI7k0EvsYQQyCUkOVDiMW5EtMBsJ/MlUVwgigC+UpIgDyayIQMSd6xk907K+l//+4WW3t1dEJAXLCgO8bPQ/f/uggUeYUosGxja+MhohDZ3dZJEFhKZOPHzk4U5r5H4EaHEeYq9M/CCJT9GHkalCWMs79/SxMsnRr51lzu71RgW932gDOS66cTlVXLu+u9/XtoBTOPQORbkFJy4Za+p8K717V96N+U52i4g8iMe0ByOWL4jxE257zVmBcy1yCCtzuGPmlNfzTQlhS43hQBnqf7cU3m7p+tfr/DQn6kc8DWVX9kKfsM5Z4LuxBB6KNog3A2yffWQaD0HTl1+Q9a/+eTowRri/5G55pPNB3ofqIxQ7q3/AntO9PQOf97sgn+mgvsb0oM9ZSGbfwd5SDpWzYnz6dcmbqhPTeKcou9ScAd7tpp5CiriaEKVq3Uby3o77D39rZ630pMUF1CgNYQGgH207Go8pzYncibr4v2hxFEo4W+xBBM3yKjiAWIuSFuQrzPOUgJsJ7N8xDHeBiVgf0fye7hAXS/nCw43B+dpz9aGQ+KdkNKzY9t/P2czMt5FZTOQfFwmY3N+zY3j0zWyEQUenANstaKh7j3BO/TkETzZHTWj7S/LyEnrwoC3sJ5PTjUudJ8IwLet9lY9fftQ0qAN5EyIQ3rkBdKqNlyFlm+fygCLXcga13t9+RgbPM1Ont3tr4L4a18aLvOSKkzggiAN1hHxCMNtLr0Jio27qEB0AnN1QDyfYyUL4G/P8TK9ESWvZMQvxIsf4NRwFeIV54jeff2RHngWPv7+UrtQbx4CAW6Rc79bkhB8Daa41/YvDyWGB/eewQ9Zm0ZVGnsW7FfNOh1jJR8IZRnBtgP7UcA97JE3va3RC+mv5N4+Lp7P5GE/CDK4VOxpLUV6p0bmpKY3+ZbG4ND0Pn2PeW5xYbbWI4myoWN0X727Db2++ikL3zorqDs74fOgiAHb+Tm0zlo//0i9C1x/fdDCokvELB9II3kXckbw+mJkEHpSBu/84kK6PqQeq7sELLJc4f4fkj7ohrrBimlR9g4B7mvC1L6jkV79USbe2nkhrCm+7hrLQX0/fw/FvFqwah2CtoLQ/2WRmf+VHQuXoHwlSOI3lCH1HrsC6oN1bwCBVVxMOPh0RcxOyMQiHM1ElRGofiHwZpnPiSkfIu5vKYbbc43WipANcWSMoAeNxMTv3kmNVjqX2EHxEUIILnK1x0xj8GlMte7gAQccr83IYKCt5NjRZJ8q5NtsKcgBvI1xNyUEGNxAVlQps2BfbKMXRA0vkXCz/PEJFM3uPYvSWRgnkEA/Lf2O3g5lCUiRMD+H4hAehmw774xq5UZb2OYCv8BROhCNs5eSMg1DTFDwcXxesTcnYcYvwURwx68M0a6eeQt9i+s9VrN6Uc/zw9Eh/rzxMRBa5F19buTctBwHQSufEIVwY/pjRBIGqzrTyPLHK6HFFWTsJAaNodvtHW7pl3ripirn2wstnd9f2aYr0jIeBsJEjUT5prRN72JXjkvI1AnBegOQKDHP0nCNDTjO8MQ0OMTgE/2xeIAAQAASURBVPv9NyifTnDXTiUf0FsYKW9bldCWcsVCWyhTj7Q2DE+un+zW7arJvd4oVMSvkRJzIbTPPUUWqOqKgLiJ1h/9k/dUbX3n9Y3NixJmcZ3ULZyfQ63uH6T1s/s9kOL+HgRwBXf5Lsn82JkqAvsNzR2iknQtd60z2eSa/ex6L3Lizlehv31izBuRMHklAir2IyoWb0jHiJiU+Cmr849Ey+FUcd8UYH8CpvRL7v8VgX8emO9M5BvOJgKZ3ZNn10UhSHxixtkQ8PaW/X06Oevfyh9s96qV+8IrUF60/n4UrdPL0P5XQnv2EPKB/VuqPQ9c/fISQf8eKdXCGRWsZfdBitrXrF4bED0aXkYKTL9WF7E2TqQysH8PAg18GJ57bLz8+HdB/OyUZPz9vOuGQIcF3TV/f16yYQU9sP+dzcn/IyoxeyCQd4J9s2eF9x7t3vMUUhK8QTQYedra+DEK1+Gf3Rmd/V/QhFwlSFF5MDEW9libO2uTte4OoREuoopeHWFNub9PIuanCfR7orzhywaL/alEQPQpHAiL1msIg/Jk3hpP6pLn6dMNKSa/Jxs6opKSZCUkx022sXgn1Mnqc7Zdv4So9J8PyY8lpKj9webBgGR8f4WsptcgWg6XJWl25YNCK02EmXphjrd+vAs7c8jy9t2RwrTsXhXG3+e0mBfxugOIMkQ4o2clC+xv4d5xiI3/zmive5gILt6Hs5AmqxwKYOwYe/exKN9GkDWPdu1vVmhKG+uX0Jr6zMY017sSGeYFOe9Fa8feNjZ7Iz7rSPvt45KHEDMnhPlLTnJtu7cCSly6kuvzoe67DyP+Jyhe/RzpSwT2P0eyXq+0H2YUQufrmuh8CiFNfSi+NEzZcm4uZSz226BuQcFbIubv6or4zZ9sDIcQMZTHgK1z1lvGaKyVdQrnwwhkMPkHt4ZexzwCkHfQbcRch++hffUhqhhRo6Dpj2pegYLaYFB1CO5mm+gNdi2E1ZmKgIUt7PqRtin8i3jol1nXtbI+nqnZAVkdX2GbvRcQViMC3veRjQ0eYg1PRuBCcDMfj0uEamXrUDzZinEDG6njxsQkYxdVejbpp7AZj0SM6M3EJIHPk9XmemD/IZoRp65CPXIBD6JVzP1EF9oeCPweZ+QtE9YkCkvhYDkQHXK3IpDvNqRcGeie60WMiVcJ2O+EBMkpyJqhzEIBHag+eduWiHH/s7UhJB060co8hZQPvXLeE2L+v0QEOJYnCuutTvpTzfXq/j4XHdQfI+G2f854lnCMkd3bkJi0ce/2qHdHJsR4B6XUlzav7yWJqWpl+9p6PdbNvb2RgPsR0T3f9//Z7vmaCnMtmGNzI4VlCVl6/B4BQauhfXkMEjoWa+m3iMLZu1gCYt9uJBBNAm633yF+bx6gd5rVaaWW1Cd5V5sB32hfD4CCT8ZbSVmxFi6hM1GpuQ35VmNeyAxWY31aOk5N6SOkAApu5esS99QAKKaKkj64/Dg57x6MgISJRmc2ME89sH8ELbSUR56Lo6kQ754I2Gxdoa+9QL8jLqZ3lfq7S/L/E+i89ckhVyYmYr3OXT/Krv0FKb8Wozyhoe/TxoD9BdGZOpK4V9Wh/esDBIqGsU8VHwE86op4qDXTfoIya7sQezsoBvLW/waI33uRKnrWWP2DQcIZSX8vg/byEuI7h5AF9l+xe39oZR2aYuzSGYUq+hztywPJni0X25iuA/wCAXmv2bz/gJiA2AP7gxAYOhEZcQxL5kknnMEJOiPfsff2dmXyxr8nAslzlV9k95d10X5RH9LDrv+GCCZukjy/p12/Jv2Gzb3V3NwdhoyFvkC83yvWL6+hM/saXO4O956lrF8Ghz5o4njOg/atJ4nyyQNYWEvk1fAfG5eQW6saMpbv07Cm3kUWsXsRPX/+hoXRSMY7JHr8L+Vez0EunBvxVO8SlUa51rNIgZdadC+AZIv7mtqnaN9ZCq05rxSa3/rwBaIBxcJET7Hr0Fr4H1JuHIWs/7u4fg/zNeQZuhon19i99Ew+NaeOoc3Bmn+U0V442S6nr6oGuLk6zI54uxFE5dXtxHwQHtgPoeXeRTL16Vbvn4gy05+ICp87cAmj7X8vA5+JFPVekfQhcGDob1oYmtKN67c0Ei4RKcLHIvn2sIb6nJizZmUkmz5r8zTIUpnz3565F8kUiyZ9MZToTTIKCx1HuUW6B/ZHIEVKgxb70zMhBdP/iIq2UxtqL1lg/89UCMHZgnrkea6sgYVVtXmwPTJc+JjoIbc2UWGTAfar3E/B0OlesmFhF0MyWjAw29yur4qU3ZMRprchOYY2Bc1cVPMKFNTKASy3iAoHTB8knE3CMorb5noiYuR/QtblSxCZkSuqVKeVyWccA7MZmPWvkFAVwqR0QyEhHiUyBf9ETHHYVI+0stsgJcUU2+gqAj5JHdJDvQuRwfDlNiLGIj0v70CwcnXEhEkP4WLA2v1gBXKZHRo+eW6Isf9COo7N6Ov1kSC8eFKn3tZ3oylPQhcSGt2JU5zYvbmR6+VQ5OYVlA8TiXFzxyCBdxn3nAf2X8r5ZnckSL1FNgFiHVISXI5Cl1xDEvKDqAkPcRNLSDD7FFPcuDH0SoHHrWy9qx8CE0dShYSGbbCWg4XavX4+kxXgQxieEjrQL0SWnaFfjkr7bWYlZJH4e2Is4fHIimZHVybslwOJIXEGoD3xC+Le1BUxm6OIOUsus3s1E+aa2A9+XwvhhuaxNRSAwhBjvmTrcMlWfrMPslycgAS7XZP7s6J9ewLRUvAeyhWCmxLDIs1Z6zlVoU+3d39fbG0J86ah3Bd/Qvtrv+T6uvbMGWH+kHUD9yDzmggAzs3p0so2hr3mVWQB2ZsIZp5FBDw6kfXIus3WWm7CUMRz3GXv+SKnX1JgPyjY6xMEN6MNXYmKlrfJ2feJIOFBZC3P8wT6F2zNtEoRn1OH0LfXI0ViSDZc78GAlNLBEvg6pCD5LwI6lm7k/c0B9hfAJZ134/sMWcCnkuKjN1IWnu/qvhPab/6GeKHhKBzBItaG4Eq+blKXYYhPGk8D+ZZa2OezIxDqQ6KFeWc3jxdGPHQJgZ5DiXzE0gj4GdKK7/tzfSukXLoYAdoLk+WTuln/f032bFnD5rX34NjO6vwZ4tU2SL9nvweRBfbXoZw/DuPXE+0DX6I1lYam8OPf3ebJJTlt9iDe2kQF4L45feKT54ZQFp2Jsb3T+PyHEMHpzxB/FEKyzG7t7U4MT3oe0Uq2c06bM8q2vLWU1y53bX8iOBX4uiNQ+Kp6/qHKc3pf5NVzL1kF8rlE78V7MUUw2X3hEbv/LNE7wYNEXdyY7+v6Jy+M0kvWTv/8omidv0COIiV5V5rPoYu7V4f4hyswhQ8C7MO4+rUQlHZf2Xy6Gp0n27n3rWj1/cnKzJvTnmsQj7RZXr2tzC/Qmbk/2lO+RUZR/RubK60c8zBfff6M55Cl7xPE9bB0Ur4/2TVWsucHWR8vivaFB4hhPW7EhaLKWTsDiMrFj2hlngFX1/lJZM5G+iTIipOJ3oCd7Z1dUfjHNCzTndbG/1m9T8kZu5PQfnk9zsKe7BkdZLFb3XPp3tsXhVb71uZdh5NHWzkn/T6/pY399WjtfW99O6CB55cjht/6XTpfWrpG7O80tK1XUt+N5I2QqyHscXcTZaX3SHimKvXZFfb+9UMfEvGMeZGB6USkMA440qrEqAY3kQ1dNVPL/zMr1bwCBbVi8ASGvoesx+Zz18NGNB8SAp4ka/G7PmLsSkhouQQxfO+SxA1rQZ0WJ1p5e2vFEOPyRcQQnkNkEB8ihlTpggCnC4nxTccgAWpXd3h2QollAqB2KY0kqiPLfGyPgOT7kNC/MuUC/IYkwD7ZwypYoP8FAd4rJ8+HGLR/x1ymk/vdkFB5SkP1bqA96xKta9ZI7g22OqVeDKcSQaah7noqIPVAoPhE69tFkfvtCUQrhhfJAvsHEcPkvEtWqJgXWTE86Ma5LzqIfkbMXrCifo9E2eDes5m9Z7SVPz5nbMNBuLHV/4HkHVV1e67SWl4VAcLPkVg42f3ZiAf5psR4siW0dnPB6oKmgRRVC6P90IfCqgeskvJ74BQkZC04X0bM6WTgZLtWE2GuiW33zGxIQrqR/e6DLFDPQ6G4bkAKylYnx7X390YW+5WA/S2ICpeXiTEjwx6/hq2HH7DwcB2NiG7sl9rvfez3H4mWcHm5L7ZDguOllAu4KxOtghYngqdnk2819i0VAPRmtsXvof3tuw+StRpaHwm936KzIDUqWBNZ2z1HudDsz86liEL0XzElSIV5uyc6a5qlaCKCDr2IQNp7mOBDFrz9wNr1po3LOTl9fQZa9+dQ5bwk1m8lYrzgX/gxcXX1wP799v8pJEmIK3yjIWB/UE6/pQBv8KY5nwjy1HuNuHKX273d7HdwX/c0HoGIs6MwEcGr8TqUU2EY8ogIHplH5M2jVvb5AujMfaGBubMUMRH6gwhsCPx1i+dAshbOzemfT6wfg4VtD6LQ/x+0fxyI9s3JwC+T9+/h3uXzCKVjOgjx/z/Z3F8j1C9nXE+z951INEjJrBMi6D4ZF97Cz2X7e30ioJ/mvKpkxLCR1SsoTofb95YlAtLfIyAyWAxfThZY7osApneI3k6VEr3mAfW+bksiL5KNyIIpaXiRrdBaDWfdl/b/G1go1CrN5/mQTPUOJosgJcZxSBb4hOjB+neizJVnsf9vYviQTsQ5/0tizpj1ifuTz7FwHNpDz8XlVUH78JtoTQfQzPenXxM3UZ5QuC8Cf4On0Jzu77WRTHBH0ierWb+PQHLJFLR+tk3mxT5I3hyLzoq1XNuPsesvkBOOkPxQQ/sgEDDDCyZ9XU2vwVmQvPYDcJq7Pj9SlpaQHL1UTj2GI0XriSSepGj/WwCFZnvS3nMDEXgPMnDKQ/vQlFXJM5B3rZE+CcD+OMzDwubgDVane7Bkz3ZvWbSfBsVWmvfiUHQ+v0U2x1za9hWIa71iDjc0nw/AhUuZEcjNHQ+cL2p9vzExr8jJOGA/p3+WR4qpBapRH/u7LLStu7eBjdndaN/q5ubGE8hT5QJcnpEq9ll3dKaOxhlUJGUGEc/M4931FciGLGrQwKOgGZtqXoGCWjhwOkgftoX8HbKAWt/dD5tRSEB7avL8fEQXsAlEK44/0wpXMMRcBA3r60TG8R9IEAwCdRfbpAKT+QhJdnXETCxl5fzm75mxrYjWg6eTE8c39Jf7O3gMlIjx70cjoWWh5LkNiQLnOZQD+3MgMOCZ5DkPng9x15eggosUzWDykJv8Rwik2z59H7I8/pmstcBpFeo0LxKU1nXX9iQycN4tujNi8O4mAguBwdsEHZbB8m6we64P0TrzKsQ4f2C//4WY8pWIiXcPaKDtmxOZpvvz+tL1wZdoji9AXBMdToONrDZKOGDerg9DoMuXiEG+2uZcZ8QorW5zwTNQBaBf3r/14BhyZ1yQclfrsHZCOI7Dk/vb2LxeBIGeNRfmGmmz/2ZwbR5HK5nkZtahIrCPFFXHI8HrK6R06G3rdjeb7yWyyfpqunaTPl3L+vROLBEhOv+8C/oDJIC7Pfc6Ojc2QJZMmyZlbkRn8hcIIDuTciHk1yRWY81oR6qc9r+3ReFHxgA7JM/1sbn0rc2lW6zsPEjZ+IS1e4/G+hGd7QFEupmGgf1cy84mtNMndQ+x5+uBfVeXs21tT0XAcprv5RBr8ysk4W2qOLeGE72AbvH182NGFtifBuzV1LWRjPPOCPD5GXk05e4Lrg8XJYL5JcQzpOFPDrI2PIZAt31sHj+IBPrlkbVyMOj4EJ1nqyHjjknu/VOsfrl5OarQ37MhoHMcMKxSXyHL+RLaw14nWrxWI4buofbuJ2w8NkJr/13i/rGklZ2dmNjU91EuyIAUN6HcQZX6EPFG16Fzay5klX8y8gDayZUb5ubnRHROem+CTtae7xHw9X/IwvlQokFCJ7QWbyUB9Mmu90rA/vqIh56I9sU3EA//k71zIFqr2xB5Jq+UX87e87dmrJlOOfU7HoHAoV6jsDAOFfp4ALIIf5jovXtUY99u5lxaGskEIZFsF+Td9ANaZ92Q3DeCqEwNHtX1MdLROgzKh73I7hmDiNbvz+IMrez+YehcezcZ265Wn6BcvT2ZN368N0FKvz/gEoPbWD5aoe0hnGDw6AheZMshsDhYX19VYY3XIWOzIEuWEIAWzvJPcBbkybN54Zu6Wd95XtDnVtgXKT2aZWBEjsWy1f0Q67OLXNtXcP0S9u1RxP3EeyXlrr3kG0PIAvuDkjILozO9I+UZ2JRo/LQW0RjqEesfvzd0Q7xPyJnyEMq9sCfCV6YgI8mgGEm9zzbDEouTzeHWELA/Q4ZLQWf6KOD65HpPxO9WAvbr0DkT5kJYay2aE8m8TkPbpkrroTZmtyXX17a27EsFJWQV+qs7iqxQIscj0a3TkFfy2uR+APYnIkytVZ7WBU2/VPMKFNSKwZP1zmFES76pSGDyya0WscPpZ5LYlHZ/TqSBDNYjx7SiPoH5nQUJJiUUcmVFZB1zqC9nf/cngr2PEN1CO7v3LYIsmTclEbbt/pbEJGGnUwHYt7IhMcpjSIDqjCzDgqvy5bi4rYhJKREP6CuS9/VADPOz7tpp5IPnfRAAs4/9zgArzezr4faNG92131gfdUXM+7dYbDgqKBnsucNxDLFdu9yuBbAqFVIGI0H0J2DnUAYJVT0Qg3dCMhc3QMJ8SPr3LBL4ZnVltrZ7FzTS/k2ICWQucNfrwynZ7/ds7rUq0WI7rOUg3IdkcLMgxuN7u/4ZcY3+iQrxpZs7j2YGStbZydaX3yDA9BCSOM1oPykhD5yhdm1NxHR9jCz06myM0nXRJsJchXZVZHSTNXCU1eVLomBaFtO0reYP2vc8sL+buzeQCASVEKMfFK3fYPFYrWxNhZ5kHs1CBEyG2LUggKxGBE5uSd6xOfG8PhidvROQYn4pV24jFBIgAH5pfOLDkYD5NlV0Bbb5WkLK2Y9xAIAr0x+BOZ8QFePhfJxMTIrXAxkVXINA+ysQyFIfl9fWkgf2UwVINYDTwEc0BOx3IQKNXyCL1GFI0LsWARNfYUYKbTjH/Ll2Sk4bgnA31Oo0jRiSsEmW40iwDuDDLjb3Rti49kTW91dYXy2Jy59g8+MdJDye7t7T1Z4bjQC0EG/4SqS8XyapQy+iQcerKJxGL+R9eCraI7cg6yVS9fVvbZiKFOe5ibRRfpWvkGHK9yRgVjO/l4I6f0ZAom9nN6Ssf8z653aiBWt/ZN15NeIPNkUWn1sgUGn15P17EI1W9qvUl8iitz/aq5+xPnkPeVF4g4FgJFQCjnPzqauN23fEvcDTM2TzMC1HI56FZPec05HCJyhUhiLQ9XNkRDTc6h7a+yo6559P3rkkAudeROGfKn4XnfkhrnKeZ8UnyNDnOKI3xx+T/krP1x6ID960UplWzK2+SHEWPBBWQ0qxETiFXTKG/7Q+6YT2wN7ovJlmFPigjYl70NKIBw3n9KtWJpxrXxJD4aQg+uIo/GUJKX1ST69dbK6MSfpoVptbf6rQr/+HlHNp+L4L0NwvIZmmLKQSWXl0ZRvfr+x9byBZNoTkCYB5TyRv3YGMmC4A1ku+3Q0BwoEXPAzJZXsQefkme0Uixf6ulCtSe6E14POdLE4E9EPM8PD7O+I6qs9j5tbHOoj/WMRdD+fOckRg/3rivrQy2q8eREZiNc8z4N65NeIxH7ExPZsKhovWl6sTPX8CjUbnVTjX8rzPxlkfBO+qlWgCsD8jEtojQr9cmLMuPLD/G+KedaD14yXVnAtUDm3r9/VliPv4QYjHH448YH4kWd9t0GcHIP75WiqH1R6CzuanKVeGLo+wvtEk4csKmnmo5hUoqIUD5+LAowM8JGotIevpPV3ZLW0j+DtZkLXedRIJLtu5ey1iNMkC+zcQmd8viLH904O8P/nAfifkrhcsukt2EByCCZNJG1+zMqeRA+wjwedrO3iHuOvHEBnUicBviTGRl0FW6ZOQEH2Ze66LtTMwLqsiV/hK4HkIzbBbY/3YhH5eBYHj99jvS4iMa7BuCQmgArN9b06dNkIM5n9wXgo2dpOJ8d3KYq0SAaD7yR6OnYhKnStwjCtiGtdCCZN75rz3t9bPFWNXurKbEC32z825H0I+3U6OMqgjEbIc/9nm2V2IQQ/zfUck1KxKdF+uakznmYGICr1RSHCcYnP8ZlwyJmR1HOIUfmjzuz6nB+0szFVoi3dpP83m+JU4C2m0Py2MwJmfSQD9Nujfikw4AhwOJwfYt/uLIWDtZtunDgNWbcq7azCPLkIKyd8Df/fj4fp9M6Il5+tI6H7I5ttkBAS9bHvdzUhJmgrr+9k+MAFZgx1q8+oOe+9X5ITqakL9l0RCzgMInDkeU3oSk9WH/ClHUzmfzDw25+60tXAFsIXd60N0FQ5gfwkBTJcSczvkAfuLNLdNTZ2bNA7sX0pM6hZoIvImW7za9apQ103RfjMR+HVOGwLAcrrV78/NfP9w6+cgSG9nY9mbmFAz0P+Qct7HOT6UaMH6hfVN8GZ8FxlCXIDW87NYMmTMI4Ss11QATy4gCVuS1LlNlNVo3YV1ekSFMtfZXJ6P6oUmOxrtcZ9jMZ/tuu+bIda3YynfL8NcGEDWeyLwgF6RvJddH0PDwH4PdC5OtPHon1cWgQ9TiIDu48QwlV8gXvphm1cb2jtLiD8vS+qd1qPSPRKeB52zZSE3EVAd8nKdS9YKfRbr05+x8yX5hv/7X8ha3FvTHoj243vIhq+8gOhlchM5oGFeOxtqewvnlT+HLkFy3zb2u7v9vyXige60+m4V7hMB2/vs+f9Ye19BfGiYdwvavHrF7peQAuE2xHOsSQTvf5fUcXki0P4UCmu2uft2icQADK29sSRKAvfOC+25B5E3QSekNP/M2voVZhHclHFAXjHz2BwLXibh/95EmepHohJ2JG6/dnN0D7Q+Jlh9Jlt9mhxHHclpJSQL/4JyXuFXxLCKfYmg8xWuzNLEve4bpBjz8/04YtzwwPue4e7nAfv3I0PCIAOflNSrQ4SmRCBtCXmK9ErudbE2pXkcNkZzfj/ru3BeNuZ99ikRw/DA/oVt2caOQm7NLk9MHHxRzrrYkuhpdSvCHMag9bpQFevTWGjbWd3a3tzN/y/d34dXqz4N1HMg8Rw9KrkX+nRbtKef5u557GUIiQd6QTMX1bwCBbVi8MrB8e0RQzsBMQ63YEwvEdDaxX6XJYd172kVo0kW2P8DETA/gwrWZGSB/SeQoB8Yk8/s76uQ0DjGDuf5k3cEYH8SshrzFuA9kDXHeGLc0K5IafATYji2JiaDu5yolV+GGFLoL5QngQ1uzkGwvYckcSFyGx6B3JJbHfMMMZzB8iK4at9K1uJrJWKi2/cpD/EwDDHV45BlohcEg0LgZhJ3U1dmEGIOX6ecSQsa7hIC6nOFYbJCyH6I2X+GRGnTQD8MJwL7/0AM9EoInBlp9Wt1vOn2IORC966bj8ekc8WNV6GJb17fDrC94cGwNhF49nc3d1Z25VdDFsZTbDzeRYJJuwpzFdoShKs8EK4E3OTK9kUhykJyy7YC9L3FyGrI4u5wFFaqFzF/wZFUBvYrJiOv9fxxdZmdmNg3AMOVvGYWRGD8+zZfvrT9dFsE/IQzsT5Rl//f/t6KqGAK9A06K5u9r6E9f0TOnPkXMZbnysSY2Y+TE/eZcoDZA1897PmJSPGxKlLkXmv9FRStC4V3oJAaDyKB5R6qKNTl1LmxUDwr2jy9BIFFm9DOSZqJ8YAnUQHYRzzSD0hg3bgJ7wzn+JE4MM+97xokhF9tc+A8xK/8jBRZXhGzot1/BwHTjyODhvmJeZWCdeb1uLjc9o7Af66A9sT/0MwQUlXs6+Vt3pUQ3+PPgf2RYuMWqmdRvbZ96w1bi4enayj8JhqCPJHznt6IV5qM9oMLrS9L6Fxb1JVtENi38Q9h5y4nWvyWJZC1vzdBfN0ItK89j3juc6wOXkk+C+K9g4KzDNhvyrqtcK8+WScRmDkQKRfet34YnLQ15EH5mGwoEt++EHLpZOK6WRB5T72BWXsiIPx4xJONIHpX/YEaeocioPJ5dFak7T8QGVqtBWyezMuxKKxWMAxaBJ1RY63d9cC+3e+B9qE1ETjmY5SvTDxrUmB/WQQMf088g6YZjSIagHklwjjKwcHwrW7EM+tLIjj2FeLPPqABD+7QZ3l/J9/piXjwiWifnNXm90F27Wvg7OTZbgj8/bvNubtp/hqYDcmqX1tbtqcc2A9rdpD14X3EfbYLConyCVEh+6ZdryN6n4xE596pRP721px+WJKYly8ovg/ze1dOG2oWmhLxIRPTOYD297sRLvA5Wu+p527qXdUU77M3iIpwr8T6fa32hPYkt25XoGFgf0OigmgKktEyYa6qUJfd7f2VQtt+gfaLq9H+tSFSSr9pa/aXabvasN+GEA1gTsF5hhJzjI3HFHjuXoeRkQqqLdW8AgW1YNCyh2Aa+3UxxGCMIjLRByIG7j67Xp/kpg3rGJiJfsglNYDeKzbwTH9i8rdrbHO7m8hA9yGGhRmDQIL5kndsgQTREs7S0+6dBlwe6odAt++sXv3t+rFEgfRSosX+ckiwC4CEd+Gamyi0fE05WLUeYgQnAb+q1vjbdwNY+Gno22R+7IasqSYiQWN7xLwfSUxCdxwS0N/EElIiJvoLa8/uZD1DgpAzD2Kgn0UCaF+cpQZy4wzWmr/F4uTlzT0kaH2FBOhmWUQicDYwoOOtzu/gEjBPL2RrYHFygELrzx+QAN8z7cOCMn2Vzq9lbH5skVxf1q3dv5O1Du9uzy1p1O7CXAPt64zA3vFor9wIWWuFeL0PuLIB7GhzC31kPRyUbIFeQdadwdLpCPJj7Pu4yx12bqMz9v+QoPY9UoimIEDYo7sigXxJFK+6K8pJ8pXNp26+fIU+7YTOkO2R4nNJoF8L6r0hMS7s2UhhcDJRKfx8eC8SxoKF7W1Udlevc3UMfx9GtIrzCRR7IeV2UIz9lqwrfkgK/ANtpLQknmMVgf2OQjQO7He1tV+y+Ti4gXf5+fQcOh/7uGs9kUB9OzGRYTerw7O2Xi/GgH33XG+iUspbjG2Bs8bNq4f9noMIhK5Sw75egeih8TVSMD3tfrd436b8LJob8VwBgH+MyuHQ5kQA0nfEOMNhna1u188kqyQJYNvd5AP735DjlWDlRxP3gAYNftAZNAtSdIY6/Ra42ZUJe3o/ovfmf1vTnxX6uK/16/Vo//gceSEFhWVqABSU+e8j0M0rJQ+2ufC6f876+3/EHBZdkDfoj+gs64T29hBe8o+0Ij9ZK+dYSLxYCvW166uh8/hxd60PMmja2tqRSSBsc/A4coD9nO/WJf+vQmVgf27r05MQ3/ALosfxSCxZuJVdDAfqk91rQl3mQF5s/0N7+j+Q3PYG4pPWzJvXRHlmOObVUKGPQ+6Vn5FnRvBuW5Qo4463/8+s8I4BNNNr2PVlfxuHUURgPy8c7b5Wh2OTNdgF7fu7Wp8HpfrBRO+TFdx7QgzyEi7OONk94DDE2w0PdbV+arc8A430XSd0tt1l7dgUKaIWJWINo5G309c2fsFLJAXzm+t9dhHxLF3erp3YHvtBexEVDP78PKEBYN/Ni18hXiMYoVYzr0KzQtu6tdaHLP/aLt7C1l+Bd3oH8XZXEWW7NvcaKGj6pZpXoKBWDJ606cfgLNLdvUXskP7JNtQnEFM9BoHTLUo8V6EeDW52ZBn612kAtEUAyIGI8XwDs5pCTOrR1p6RREHwWsoZ9h0wjwT73cW9Y3b7ewBier8gK/j0QQJB2FT/YIdADySo3oeE7D+RBfbXQQzlFGR1dhxKfHYGYmBKOJeqvEOwBf1+JFFIKyHrz8CQewurbYmJbQOFJHQHWT9MQozwjtZPdSgW909ICPsVWcFnJeKheJH10RtIwbBJ0i8e2J/L3euJwj69YvdfpoUgPLIc+x4JuTchBrZDh9xpQpv8GK6JhP+JmOKloCb12wC0/6yELEL62XU/l5ekArBv92sizOW8o56BRvvUm5Qnsh5EtN5/MH22jfv9DCI4vAti0s9CAuj31oe9iLlgArDfakVnO7StEy6hLAKgb7Z99EkSUBiBJ3mCTh1K4lh/HtBA2JEq1n8Dq+vbZMGSzgh4CcD+6UQBdQgR2L+ZbKzoiqC7raUJoU+S9dgZxU1/A529ayTPLkqOZ0CV+qDO14cKwD4dK9RTAPZ/Bs7Kub8wUdi7GGfk4NrpAYdTrewlft7ZfvIcsK79DqE6wnh5YH+Qe64LCZDn7vn8AJfmPWO//4b2h5p61FlfXor4tcmIr/onrQi7RBYAW5SoAJkT8W//s28dRtZwoo4IijyB+LDUgGUHZJBSn0zU/l+K6N1TD+wji/LgcfqWq0snFN4kWDXPQgPnBQJhOiX1OBHxi9cjpUJnV5/QDi8H/Le14+36qxs69wLP/iCwvd27i3gmeYC+PzH8zM/IAOR3CJCbSk5oMwQa70y0iF7H+n8E2XUX4jeXkEzQaot9GpAXkrU0NxFI3BLx9Z8jD4qjiAZPexAB2HDtObTf1889994BZIH9HXDrnhzPWrLA/if2jSsbaxMxMfJIN47L2rWzkrJlygW0juckzu9z7Nnzcp7z8/gpJBOVhXSy+4MQv/I0ZnSD5Ow/2/uvRnz6aKSA8ImTm5TvpIEx9uGjjkYg5Hs2Dmk4maC8u8L1QR/g14hPXcWVXQzJXa8TjcK6Ia+dn23c3rf3eWVd95y69URGAjULTUkFMJiYq+1LNM+/QefZP7DQhyjxbdibeiRzqiXeZy/4sak0r6Z3srV2JjlhX9w6W54YseHihtYFVea/aF5o2y/JCW1LFfCaZtZ5cYRjBKVawM68grbD8KkFdRyqeQUKasZgZRmQ5exwGYM05V7jHQ6XWdEBHwTzSUaf2mZWDWDZC+zLIDBjA8otujxD3xiwv4mV28F+d7HDfyxiqua0g/NTosV+XoiAfakQR5HI+FzgvhGEqXeR0PtvFELiRMTwjbfvl2yzvYWsm/GKKExQcOUO5V4hmxyyteGN6hDTcTQC1LcmZk6/nQg6+rHpg5j83yBvh63RQfsUYm4upNz9ewHrh58RcHAdAnoWQzFLpyGgaG8b0wmIcUzdQhsC9rdFTPLptDIWHDF58CgktHVYa99mtutXRJdZ7+I6Q7Svyn3l98hjkTD/FtFTZrsKZT2w/1eygk/NhLmc9vVG1rkHIrBviF3vTNz35yfGOPUW+20G7COPhLHIKjJ1Sw4eTn8khgHqjfb0oKTdp9Zzp9I8aqDMYgjsDkDSEnY9KDeOrPDcDmHeNPR9G9MFm1v3nHcNs+99ilMIuvnSGXlPlIB/JM8uRwLsI+XMCzildvLMfTY3QzK+FOztic4tbxnV1jF1/VofSAS+UmB/mbasRwvrvilReZ8ncC5PFP6uJVGUuHJHI37pDcRD9UB80NE2/l+RNYbwXhjr2toOwH6TFC9EZXsZIGf317I6vdnUd7ZDf8+DeNkFqBBaq4nv8fzXgTa/XiCeIXMioHUMOl/2JlrVhr5f29bSc4iH7oX2nSUROPYO0ZDDg1BLonMsAPsB1PoWnYsLJN/pjBQ3ozBQjXJL1bBfLEMEWwNg6umT9B3kA/vvYOFvWtHHq1r7lrJ1cABSIteHcES5Q8qAfbt3IQLxQt2/RkDfokm9w/9eEXaVrYdN7XdQhO1jYxrO4E2a2aZUVvEAat/knh/zYQgIOgx58XQjek0F+gmzRA7PI0+zcP9lopIoHX8P7L+KLL7rkMxzHzl7J1lgP3jweWC/UjhWD+wPR/zWBGIS9m7I+KgHkt26oDmcZ+C2LjGu+RF5dUSW6yGfWq6SHa29R8N4ovV7mb33eveuy5AM+DUVjDyaOR8CaN4f7ZcXICVUCVmY70DWsGMg2uM/Q97pB6K19gPaf2Z147I+2hNCWN6QM+VH4EO7Ng/R++R2P3bE9d3ueQYa6Kfu1oYjk/t72DdGI4XeNrj1ZG0fBdxf4f0t9j4jaxAyw4Cx1mdBDrqKHIWMmyPDibmaLkvvt3E9p7vQtmg/Wxjt60PI5iacYeZQQdWlmlegoCYOVHmG+m2QFflkpPU+kGwM+XpmFLmmXkG06C7hwK1W1CmNQfkFMWnSCGTN7t3rPUPfGLC/OxbHFjFlI4wG2rW+yMIrCCp/wAQVux9cEJ8gWuf7PjzA7h/s76EwFuORID0H0kCXUMzY3RFAfSmyeJxKEorH3rE8YrSORkzY4Lw+a+n4+2tEgS7EqEyB/YbikR5p5X9PYu3lygxCVhvBVXws0So5hHiajADT44hWTJkQGjQcimcAVXK5tPFrl4SGbU3WL/+yPvsY2Lu182hmIbduxxJzXQRgOZc5QvvqH6zcI27/qYkwV6FdwQrwFbTfhoRcqRVyJWC/rULwHGPfSsMbhcTh95CE10Dg7lF2f89az5m8PkLnwLFICDyRxD2fHGCfqDBOk8aFsVna5uWTJPH0/d8IsHiPVpzVSNh+wurzEhWsY4nA8X8QeNDZzSkP7N9OBBS2qtC+kC/gAteWFNhfDgn/j6T32mA8fd8eZetibVdfD+x/SwuSD7fDnGzwXENCa/AIfBvxKKuguNbrIQu1iegcWcDmRUjsF2gKUgoMcO/NA/bHIp5hvibW3ee9uRXxAvMghf5jdn3fWvdxG865EMbiY9tD+rt7c6C983u0n18BzGtzckPXP3shfiDkfCoh8PqDMF8pB2FTYP8UYmiJLmSVet2I/PQVlIPZPvb+wwgoD2E7nkZhwY5DIZxKdq2/r5d7xyxEIKjFXlpW7xBG5+8I+HjF5vEpZD2LGgL257S1sl5YG3njmKyHvgiUHElUYIT2HY3A8bVJ4jg3oU2eZ94wuXcWMqzpn1N2LSKvuH3y3CrIaGYvLASNW9OhPVsT9wGfHDXPYj+c9c8i75MQbuTPNAzsD3ff+F0T+iIA+x8jReKPaO95x8b5ZXRePWvz7U3koVLmGYHCkIZvn4OUQX2NziAqFudO+zaZw91d/6+B1u0dSdntibxniQTUbsk+Yv3+H+uDz63NITHz27hQPIhvOJBogFZCBljfY7lX3HtnRWs3yH/rWn+PwBlZEXOslYC7knGtSZ6BCuPTm+iV/hPaE/w6GUDl/G4n2XO/wYHwSZnpyvusPcjmTNgDrmmgf3uRzUd1bTvXsz8zQGjbjly3gmpPNa9AQU0YpKyAcKYdkj8i5uZ72yC/sEO0vyubMiY7Iib/iCrX73SrQ7DmvoeoQHgBMVDefTAA+68QrRu75tXZrv0eAcchJmJgXvZB2tfX7H0b2PVuyKXvYyIgH4D9Lq4vgmA0yK6thYSoLxE4sxYSWl6l3BV3XSLzcBsVrBaTZ1q0GSfjvwticM8DdkrK5QL76beJzNhfkeXB4PQ7yXu72mF4NWLC7kJA/xKIcZ2EGKhgsdS5wnc9sH85RZb2poz96shbwsd6LwD98n7yISaWRcL2P4jhuw4kuhGfQY4C1P5e2ub4Ecl721WYa6Cdi9va+S58gwQ4JR/Yf7aN+/8vyJrLK1ZPt2/fi0scjpSe89vffTFvg45AyVw4i6ik9nQ1AjNCv6fA/uJEZUsfsom2OqPY+mEfPC35vg8LFaxg96QVjDwwFCmlQv38WISY/uE8vLzCO5Yjni2P4axTSaxtkbfeKHQur+uu+5Aig2ydPNjSdrVgPEOuofGYNZZbKz2Rp87PJInypheyefc3olXlZGLStZ/RuT0vAiD+gqzS/g/YDvEC49C+tjtZi2QP7A9D4NdXNCN5MNm8Nz8iHvY7xKMdlH5rRiGiEvZesuEa/bycCwGlwUjiO+RV8yMKz3MoMoh52e4/hXjsMLY+LEYesH8rUaHdGQFfD1NuybqUfW8MMvioz93gxv9wxO9dhWSRd8gmmp4TF/6NysB+fxLQuoX9Ozcx1FDIP3EEUVnq53EZsE+WP/V8654oIeaDCBBeyN3rhACqt5ACYUt3b3VkJX1vUs9m8WxEhdsv7XeINX9Duu6Q8iAoXfdL7lUKceONvsL63oJ4xh1Rqe42Xw8jym4rEcM63UK5p16IOz4nUlIH3uWPTeiHkLx5Mjov/o1kvvcRaP0lWisjkAw8tFL7bRwDbzbWxu9T+/0uUQ7M9WhM+5IYenE9+x28v5ZA3h8HWz1bpSRGPNLziBf5jbu+KJKlx1q/bk+c972RkuoGpIQP3ii35/SRDyFzNTof62Vp+/9gYiz/EjGGfs1DU7q53AvtkROs3XnK6UrrfX+kLGnUY4zp0PusrQl5Kj9OBWDfjdFviYrqCTSSvLoN61uEti1ohqSaV6CgZgyWAJwSEs5WROD1vCgW5NcISD+IxAUx2cBywaxm1sPH/lwIudv+A2PmkOCwEGKIA3i/uHt+FhSTroTAt18hpnX55NDthICpzxHDNjBpy8WImRmGxQlG1h0vIcY6xOgrofA0syftCJZJn9umHg7qQ+x+cEs93n7XkWWAVkMa6slIOF4pqXtVBVRk8ZUCTNcjBUZgWioC+8m7elgfjaSRWH9Jm4NSpBdyF59AFCi+JUdQoxzYD4f6BbSR5fCMREn/z1CgRxv01SIoRuZ4YK3k3tbE/A1lwD7Oesn3d9rntJMw10AbF0XA/k9I4bhuOj+Ie/R8xHjpbSZgEEMXrWO/TyeCWUOSsvehuKu5yWU7AhE9DJ5ACWW3QFbeXxMtX4e4/l4UAftT0VmzNDoHA8B1cPL+EGKuZN9K+2J/dJ7/G+fV1IJ2hPotTbTYfxiF0PBJLN9DAOtq/rnkXUNtfofzsLutp2fIJtibAwlvJQQo1FvFuzIB7Dy5leOUvreSYvoI688viOBNCjT2bE1fdwRCSqR1kCHEXbbOLkWW/vX5dhDPcxXZ0B4nEMH2XZJ7HtjPeB82o24BDJmC+MXFyHrsdZj1X6WxWBUBYc+Rcxag/FFdrU9DOMX/IeXKtchSf0ErG3IrnOCeX53I417nrod5fQjijZe1+TDIrm9tz4zAgcBEC98xVu+LEPgewsocQwT9b0V89kXu+S7uPQ0B+01as03o3zAnF0B86GS0j4WwX119vezvPGA/3XvPo5zPfhDYLCm3l333P8iba18ieNqqPDFEL9rJRDnqL5SHjJibCKbt666nQHxZn+OUNq4vt3JtPiKvfIX3r0SUpzLAftL/T9hcHAOc2sS+OI5osb+jux7CkIa6V0rm7uu9HgoX9A4yQnsGyVUhVFMAsbsgGXYNa5tXtod5HLxg1ky+dzGShfpThXw5aO8uoTUX2hrW5ADEa41FFvs7EIF1L3cNJSpebieriAvvnB3xrSOJSu8wR36NlInL+7lNBwlNaXP4NrQeT6s0F5JneiKPsd9Z/f5HE8OBMXN6n+XxhH5te2D/9yRhbGy9vob2tsHEUJw1k2kpQtsWNINRzStQUBMHSoD2B0gYCwndPMB6iN37miSrvJVJgalqxNNfCVltjCObHDXUay6iFc2j7n4nBCTcjhjW763MwZTHY++KGK+vyYbyWQMxMTe4ayHkxiMIkN8AJe4Jm/ajlAP7f7bDfIIdOD7EyT72XEhoWG8N7MrsSbRG+BMuxn6Vx38b6+d/IsbtV8SEL3ciATkwYB7Yv4V8d7MAyJRIwijkjONawOY45hkBhb9DzGJ3ouviKMqTI6UCwCYI2OtwYQ4Kmn4JxccNQsuT7rpnPDd38/5MDMAn5tToEMJcumZy7i+KgNNJties4e6lwP5AWgDCNbO+OxDDHoR9OA/Q/4Xdu6K1fdTK+jaUeHBFBGy+SDmIsirR1fh2sonQFrZrJSxMDzofQriMQ5N3hdj6JeB+ZJm/IzqTxiMQukWJw/PaShbYf8TmRV9iPpSjK42Je8cc7tpC6OyZiBkauHtDiYko/4MStM6HeILj0Hn+CS7pagva5UHEfdB59CfkPr8MEaBZBgngPxBjiVcFWJyeyMb6CZt3zxJBzaCU7IQscAOwvzNZYL/VfYTA6bG4+Nh2vaq5RzoCIY+HEkkIFgS0X4gUTG8h4GsQ4qOOsf75L/KgmAvFgj4eeMm9I8ztFYjWsx7Yv9KtvdXcWumCwKw9bT18DuzvnpsLyRIh3OJIdF6+Y7+/QDze93bvbD9+7js9yQL7/ex6VY04EFB0DtpjQz/cR3lYujxg/xmS/cfGbCoCp4Yj6+6b7NpruPBryODofKLHRJADmg0OIUVot+Ra8PybYn0ZwgGme9chZOWWVNZLE5neg9b/jeSAmGRD8RwRvknkJ1ZDSr/DbW4Gz+klieGQbqHcIjwk7l2KbAz4tL5lIY+ISuDPcR7K5HgUVZonye/e1uc+zFxXd+9WNNdDPzyOwEifHDiEB7qSuJcGOfwekgS2rZjj+9t3dk/WfqjLAKKRXCbGflLfoWSB/eVy+uQtZHW/nru+OpKh70rKd6aDhKZEvM2PaO1n5pbdO9DGa1M3zmu6MX6YZobKYSbyPiPrsTwPCus3K+V7kQf2/4jj/9HZ9iNwYPreGrSnCG1b0AxJNa9AQU0cKAmmJczVlsiohUO7B5Hx+YQkxn4b1CcAN/fbgT+bXU9jcM6FQIMSsuyuc4fq+UR34rVyvlGHQP0riALKdta2lxEzHdxTN0QM46s4IMbe4cNQ5AH7iyHrAp/AtZN9q4SEmrnTurm/XyOGG7of5/ZXxf4+G1llDXHXliMy0XdTDuwHi4H7yM8yfwwSGC6nAatZxPB8ZWPZGTGEk5A1WT3zSLTOHQWsb9e9Z4VPPloVhregggKhMAVBCP6W6DmUsbYnC+yfjgGVdBBhjri3d7V1/EsU7mvFpNyiCMicTALsuzLtwqAiYe411293AsuHPrP/10Jg1WfA2jWeK8FNPS9XSQDb9wtlkv1wRWIitOPc9c4oBMoWZPOo7EAM45MC+8MRYBMSiJUQSP4MVQD03XfygP1/IsvWicgSLwMENPF9IQ/FVHQGeW+15dEZEQTfL4lx3z8lUZg0sz1ByOxD9BTz9AnyxgnjvAuJhf6MTOkYJvtWyIuzrrvvY6ZXBParVLdgdT4JF6KsvfaqdhyDcB7taL9nQTz693b9M6Kl/Z+Q5+2cRPDjLZvHJdtvHrH3pKDe8sT42dchYMXvJS/gwvfZMz0QPx5idB/g7vVCAOADiLcfh8DCq5Aib23i/vc2EWzOAOhkgf1/0kbyCAKZNkNKyruIioSFk3p5ADiUewQXDxuF7fiEbIiygUTe9nWyycZnRUYqNyA+Yf3mzmcEmn5j4+FB5iPcOE7Fxcqngidweo3sPhkSmY4i8jg/IEVyr+S5rd23D3fXTydaKAd6FcVl74F4kjuISv3gQb0+sub+nKwMkwL4/pxdNrnnk+du04r5ktdvPoRLmNtPWHtvRYZfJeSRGMZnKFoXJXSOBiOrL2jG2e3e14nEcAztx0E5WBaD3JVbmMhjvImSGHdP20vjwP4h6Gx4DM3nvRDPNhXYucLcqnloSqKHye6+fsgbPCj7SkiOPdXu9URrbn9aKLMzE3ifuXGeHZ1TH1h7P0A4UBpuaw2EGYTz4c9ov51q66TJYfvauF1FaNuCZjiqeQUKauJASRgvIXC6d3LPJ6wJcTe/sAOrxbHqkm+kGtkd3QE9FbOeSBiIcBjsZOXOd/d2NyYkE2+5wrfnILqgBpqCA0iQxchUYuLbTmRj+M5OZNYeIwJ5FWPt2cH/KBJqDsJZvJN1X30RARt3UoV8BXkHCxIaLrW/u7hvL0n0hkiB/eUQeHl8he8sQEwielSF7x6BrKD+D4GefeygvooImHhr5tNJgH27vqvNyQvT/i6ooGoREi5DDM8T3fU8YP8/Vu5iZF3frsJchfr7hF9/JVpMTrQ972yySstGgf127PshRMDqouTepkSAZ58az5ENEJi1WnI9jO+xVs8jG3hHSCz7Kjpbwrj1QMDPRWQBih2pDOzPj5IZHoLOmVVJFM+tbG+IaZwC+1OIXhMhUV6TAG+yytqlkFVWAPZXdvfmRoDOfQhUehJZ1g6uQrt6IIOAKcjdewFkubwNAuYmIItoD+blxkueWYgYFmkSTmll/+cB+18hYKeqXjVEYP9ncuISzwgELGjtm4RAjXBOvGn7way21t9EZ0fI+xSS5/5o8zh4+rzq1nAK/nlg/1Zi3oOgvH6R8kTlFYF9d38WW9/diSBhZwTsh/Pzd0Sjnjxg/1Urt00b9rW3Wg7JGD2w7/MChDBUN2PW5ChE1e6Iz/61b4P93Q9ZuQdgv8Hk5TQDHCKG2vmDq2d35H10IQIhg5y1e1O/QVYufMLm4dXovAkeDiWkdN2HRFYkC+wfiJSkQQbdBa3hsxC49wMC+LogueOPRA+G99x7jnDtSz0T/Hl5mJXfNykTQvH8TAUP4yb2+bw517oQQyueQ9YLb0Xr/y9w4DZSoN2K+J7PULi5Jll8E/fbNP74APv+QPu9iPXvO5ihRFI+rMvHieFg/0XW4Mr37VAqAPvIKOH3xD2nRAPeJ5QrkUL/tWtoSmQwMgntd2siT/awP71v8+YsxM+/j/PQoZVALjOw95mbowOQ3F+yPv6769/HKfdaXgF5Z4T8GRORfLWAf2+tiSK0bUEzGNW8AgU1caBkxfMGYma2TTdFovXOySg0yjvGgKxq11tsnZYwBIcTXVu3cZu2D4NTl/y/ipV5NLwPAdHfUw6sLIa07tcgDXqIjzkbcABiSk/AEvW5952FswBINutwMG1IBMk8sN8ZMZkpU9sZMfrfIMvCPYiCl7c+HY3cdPumfdCCvvZgyXAE8vwCgSV35DEKCNgPIUHuJpvEcfaG6oQYvADuXIAAmB5IEDgRMUEfISDoDyi8xnhiCKhOOfU+HTHAo2wMz7W5+D2J9U1BBVWDkj1qX6JAspu7XocT5pA7+Eco7Ei7CXMNtCHsKd5a7DEEEP/B6lFCFpReYbYIAusmIwuZdWs4DisREz4+idzw/06Meeyt/mrCRFt/lhCoE5S/XlDdwt2vV6C6ORRiYL9qbZ3f3V+ZqNRMreBygX2qLOBUel/SjmWJFvuPIgvcXB4BysPO5ZRJgf2yxPHVGm+3To6w+l+VrNnOCNT8BgFRVTFsmJ6ICKp2otwL7yrrt+8xq1nygf1DEG/wEQaEVrmOQTH2DVVUYnUkQuDGuwgk+xCB9WlIr6eQ4chAd20OZGhRQrz8GPv7ElcmTZC+PDF/SggRdiJRkfMfGgf293fzJvP+5LmuCNh/HQE2FxJD2aXAfi9csvB26PPZyAL7i7l7eyBvumHu2upEfmE0lRN69iML7G/t7rVGvuqCzpx+9ns5+787UQEReJoU2O+SvCdjwW+/z0Og4yXEeOsDiXHPp9j9fSi32N8ByZz7WZl/UW6ZewvR2yTEYh9k8+p9JDt9gp15KAzY58jTMS+k6UFoTxhFzCnh75+K1kuL+C2bq49RHnppEMoz8QxZQ6WuyMtgEpJl+ifP9UHeIrOk/deEumyI84ZA6/4L5I2wATEp8zVET5x53PNeYf0xUrAdgMUrb+C7y5MF9oe4ewvYfLwNC1nj5tfC1DA0JRV4COunP9m3gzLpIyTTDnD1D3N+05bWocL3Z1jvM5vXT1n7TnfX17D1MhEpjVMspxdS5uwJbEzETmZ4T8mCCqoV1bwCBbnBqCyMB+uNI+3QeIhs4lnP2F2B3HZDUt1/t+YQTeoRLEbuIFoBbE10xzwtrz3EMDYX2O9Zkdvxy/Y7CAF7IsFjIpHJfo4GgGD3jU0Rc3pXpf5EFiojicDK7YjBPBgBZS/aoT+UyPz2QwqDH6zOVyNBrS9KYPSEHXaru+9UI19BsKLx9ESlg5EssH8/FiuTRMFS4VvLW79Mtfn1X6LV1wgE2AQA5VUEYi6V9jFZYP8ksmFMPqEV4RYKKqgxSubiPm7uhVikZcIcStjU7sJcQ20gCnCnJWtqfeQNNBWdAUPdvYVQGK2g2KsZkImUitcR3ZJ/sL1p67yxqlEddydalwYgIoBQCyOrpCnAcLtW7yLv3vEsUhKnodlCQvPD7LcXvCsB+1URdJL5sj3iA65Ayuc0X80yZGPsL5PzPg/MXY4AsWdsfS2SlE2B/RUr1Kta4P5tCPzxIEcnxPOUkFdL/zAG1fru9ELI2+d2ZATSNbkXQhqOIYYpywP298OBom1Qx41wvOyMSAjIWpz83Ebr2P54FzKk8PvLnEixsjgK8xD4bO+BlgL7qyDgdWUUDu09ZDl9I00H9n0onrURoPwbZKAxL9EyuJvdf5NGgH33vnYBdMgC+0/YXncoMRHxQkn5I4gh0K4l8W5y5fohYH8iUqDs2ML6zZnXF0QZa6ecNRt4mgywb/cOQwDsPGQB8PkRsP5vosy2MBHcvAbJXRPQXrAn5Z7gfZAyqgRskdwLCeXvSeeV3V/M+mkqCuU1H5IhpiC5Kp0fRyLFyudEq95gsNYD8WqbIJllnvR7Tej3kAT5ZrLK+DqiMv94d70TMWzRWUQlSx+qYG2OLMqDYuUY5PXwI7Is92fmmsTwhtdSnq/gaKR08cZu2yLF4A1o7S6fPLMCFYD9dL3SAUJTurrUEZPc+vj5na0fLrGxWpRynucBJL8ObGk9GqjfDOd9ZvP/GGvTRUQsahliYuyQHPwlsrm98va36V7JUVBBHZlqXoGCbCCyB/iSRkOSMosiwDYANysmz62OGLgQ4uRJxHyu0MI6eQFjEQR8/YNyoWBLO8xKiGka7A7g1RH4UUIhWAKzHADo/VHM6D/a75+QML6ftbEEnGDvqsurm/1eAAHT9eVDvybP/Q0xoiF8RkiaVULgXQkB2ocRAfRZkXdAcD/72foilD+iCuPv67gfsjZ4FjF3p7pv/SFvztjvJWx8SsCWzfz+YAQifoyUF/9BzNFgu78gAqt+tvefW6Hufs6sj5i+YzEGvaCC2oLIUV6Rtdi/hyjMLZDM03YX5hpoR3/bZ97AQCCyioahxDwaFyfPLoosk8qS39VoTAYgi8AB5HhO1ahOKYgQrFhTgTfkp5mEs+p099dCQMgjNmbesnVFJPy+5+aNP6c9sH9wFdvm535QLASaikD51HvAx9h/GAnjw/w4IYE+uFqHkHsTkGJp5eR9KbDfIt6jCW3thMCh94jAfedkzdavHwQErdwWdemohEJmBGF7OC0A9guq+pikQN1jiEfftkJ5f04NR3z2ZHKsQd3/AXi5wMZ3TwSAhSTejQH7nyCP3DOI/F6gdxH/3C98i8rAfq0Vt7MRk3UH693PcMrLZDyOcO3MePgl7+2HeOUSsGsL6rU2Mrza3a83JBedjEDt/yEr+TRETQD2pwB72LVLgGnorNrO1xmB+tdiOctQXqzL7B3eu/pPRO+dvcl6PtUhz+qfcXw8MdRmJoQqAtzrAXkE6r5NjOU/EckEPZK6DkbrYSwx/0mYy3MgY7LP7Jvf2Tu3bka/b2799jDRA3xPLL8PMRfFea7dQUFcf57YvdWRDFsWDqcF82E48Ywej4VFy5mfw4n56d5AVvnbI9D+J7QGg1X6Oe6dYU5PRqC3V2akFvvLpt+mBnkGcvrIGxdcjGTjkYgHOJEKZzuUeYBMtHlUpmCtBjGDeZ8hg6V/I4VSmA9LEEP+BkNNf7aU5UcsqKCC2odqXoGCyhj33xjj8qMd8JeSDaeyAgLrS3bA/xYxiXvbYTqZyOz9jhzrihbUbylisj+f7NQzHB7Y/w+ykvk9smScgJi1N4mupdsQk4CV7Nn/wyVQJDJZf6EJAgIC5yYjxvSwnPvrIsZ3R6RpnoCYvAeQK9lCiFn/FDGgxxND9PRCMaOvsEPuE8TU+ZAcLRJiKAfnr0TM0FLu2spIkVACrmvg2WWAzVox1n2Q0BLCTHimaDCKmznO2r+Nu1dR4VJQQW1JydwbRDbk1N62ZqbZfhmEuc5EQaFdhTlkPb1RhXuL2550p29b0sZ1iHvt2snzHTKOJ3QcK2my5+21RGAzBfYD6DkZKXlXt+uboPwFJaSQTkGfLsBNdv9kcoBSsjlp9qty+w4iern9CimFX7B59RfKEy57YH8aOvtCWzsD1yNA50rEi+xEVMrnuV0HYH+CvXdIteeS1esZxCMtbtfLAH03HiOBy2s999p5ns9jY/8jEsqbBewX1KZj8yvED5eoEK+6wnOZMA8IdOmES+bt9pt+yEjjCfs9GxHAqwTs70kEpcIesi3inQ9FYPRU5IkVvFk7EYH9n5HM0mEALQRw3mL72MI59/2+HJIbl7Akr3njghS5ZZbpTahLF2JYpdfR+eHlqM5IufCtjUFDwH4JeXh8hfbtyQiwH5CUX4BopT/M1vrfkzLrovNoBM670d3/P7u+jv0+nQjoD0nK3oeMp7wxwkFor55qdZ7bz1X7uzsCRTP3EKD/jn3vUaSsup1o6HQCpmRqpO9DnP5D7PcB6Ez8iSjfTUbnylLE8+RsEhAYKaxH0zqg2s+70Dclsp4yqQy2FgLx02TFrxMVKYcT1+4uaK/5LdGQ4Cpc6CJijP1JyGBkeXevXfMMNNRPyLggKAw+QB5Jn9rvt3E4B+Xr9Rir52ckHobVJmYw7zNkjBmSvfcnhq+83JXZAeECE5DsNKzW9S6ooJmRal6BgtxgxERE3yDGKBzcDyGmOTA5yyG3Se8KF+gY975/2rtafIghq5wf7HsvEgXqPLBpK2Lsz7HIaudcJBT8zZiGR4nA2jBjQK40BqF/8u09jWnwQs8GiPG+GsWaH+zudbX3BWYvKER6IKvxRzHAAjGKo63clq49fRBo8R6yBjmeREBBjE4fqgxkI2b1QsxCP/QvEXz0ydAqAvutqRNZRjMvluqCyBtjktVz44bKN3S9oIKaQmQFv5RZ92twGEridh7OhRwJ9NNs3eyLGNP7gQ3tfrsJc4jhDyBaXrK2BRFjXB/iqkJbL7W9cftaj09HJtvnF7S54UPW+ZA4FxLPrBTYP48obE9GIFmwgDuGCGylcaQXQpZsT5JNWOfHcDcEyLQqz0i6/yOvlDfIWk+uigTtErJUWyl5ZikkJId18iU6JzsjgflWYjLdTsh7LShEXqIc2F/SvvMdjcT3bUL7ckPIIVCmhCz3gidBvVeNK3eZ3atpguYazf+5EAA3lsaB/W+osgKmoLLxGGDrrGR7yd7uXpP4NSKwPxWBv6eSgEiIF+6MjDBKWMJRpOgJMdDzgP2e6Nz7AZ2lyyb3r0VA6G1YvPNQdwTmhaSoNbfWTPsz3ScrlaWJwH5zxy2ZA0dYH79NPrB/FA0D+79EhjXfIl7hPJTAMhfYd8/dYe0KvE8IbTMEgbAhfOsSyXM72Lif4uZUHqD/C7t3BbLSDzLr83Z9lNFeNJDviwjk9kBA81TgN0mZYLDxKYlxQ4W2b2NtuAnJiZPReXe2+95VVuZ/dv9M7Ix37zkRWXzfSBKqqCVzFIWwfYmoKC9huS1cGc839EHW0vtZO7YiepV3QdjBs5TzjzsAL9v7LyDLA/0RJUYvYSEH7fogOkBoSptL99g8OB8B/F0RT3Wx1fu/ft7at7ex/phEGyTonZEpmXMh5NoS5CsF57B1GDwzHqJKYZ8LKqigplPNKzAzE9kYcUsikP5BDFhAyWj+zw6kJxEwEZ6Z3Z45A7nVH0c2nt5xRJf6WVtYv+4oNno47CeQE1812fy3JSbPPRZZJB5uTEZwnXyInPjqZBnr1RGT+gWWdAcJpyHuZaBncEm4UKz7PVy50SieXyh/LLIyuhNZaHxl7awjy0j+wpiAAOzPmra1ynNhcSLT+xXGwBLdT30ytDxgv0sV6hAAqe7IGvhAxHyvTpYBXBAxhQEIbRTYL6ig1pLtcztXuLcWSuZUb+nk1vPWSBiYhkC/4MJ8IO0szNk+cyFwtK9jUuYmtNfun3Mv7Achxu1xtR6XjkoIyH6SeB59hbkL2/3u7u8gGOYB+9ujEAdvojPpRnSuPYfC7yyPA5IRKNOXGNLgkHQOuL+r5gaOzurhSJkfQDwf9mgpogdBBti3Oi9nbT3fynyOzsGXMfd2soJ9N2L+hzxgf3FaEPfY1ynvt/t/WQRMBqXL+Wl/IgvR0Sj+71y1npNtNM8bjFtO04D9S60PP2EmzD/QzuO1uu0nq7przQWG70Xn2TTEx45GgGu6BpdB4PG9xLNjIA0D+wfbmtouuX4aEcxdOa277SHr0oKQNG3Y1415PpQBV/Z3s4D9FtRrDhQK5Xtkhb4zzQf2l0R7+rKIr1iNHGA/aeM/kDdFOuaXIuvn7ukeavfnJMZ0LyH5aXn/fsSD/RfJeakH4S+QHLU/kl++RfxX/4bGy9o3CgGz6dnzX3S2n0UjwDECxgcTw5OWbO1cSjYu+7pEL82nKVdqHYHk4XdwBmWtnAt9MDAanb+hfvsm9S/LeZK85zhgPXReH+Oe8zzAljbOJcyynZhn4AGStUsHCU2JjGF+xuXzc/eeQevoArJ8WJ2N90Tk3VCV8ZoZya3xkCg8JHYOmEHIk7gDOieKvi6ooBpQzStQUD1zNhQx5ikztDACswKwvw6NCACIGfzaqFVJzpC2+whiPPmLyVESuE2/DoFoPxLjPH6BLPXvIroAPo5AhDxr8E0pB+jOIoIHO1g/BIuRD3Eui1Z+bwTe/Qf4CFnY7opckscRBaIPEYifCsYpsH8MllixDefBFkTvgXvc9TQZmgf2/1Clb/uESA8RY5AG+jNm3WPlBlEA+wW1EaVziGgBVhajG3nhPE2ONS7lwtwYxOQfTzsLc5SDbb0RALuBbzNSSo5HgkruukLA8k8Ubq6V+npDomL3MQS+hz3tYlfOC7wVgX27Pwdy0+9le3UICfADCgeQgmAbEkPUzJfcazSBeTPbu6V9ayQCTPa066m1agrs+2S29bG5icB+sAjOzdFCObDf7JAUFd4bhMWeSJl2OfLOS8GovdD5PhWFB5gfAVO9kAXmGCRstlmy145Atpfs7n6n4z4XErZ/QqDPZpSDhOel+15BbTZeft9p9h4AbEzkzf5NVCD+iDxfl8MUz8T42pu55+clAvvPYYYzdi8YEi3trp1qZe/DWWcjQHmldL7lzcF26ld/RtZ7uTah7Nooxv2c7tohro93aINx74fkih/QGfErmgnsp+2hMrAf9vbLrD23YcmCkRJnJAJ1e1aaj0Rr/hJwUXJvUxTiLcODkRMOEIUP+pgE2IdyL2GkPC+R9WjpjSyCJyFFdojNPwc5ubvI5rsJ3vDTkFfJgLSeNg9etfffY2O0HzE/w1e0ELyutCaS74fkuSVyQvMh5cm8ybVw/n+L5NWDk370cz0orB5HfHWaZ6AzUQFYqzwDKf9/As7DxI2rr4tXLszhyqxMG8XQn9mIGNrxqOT6KYjXGuqutdrQsKCCCmoe1bwCMwtRARAmJrO6E1kehESynsFZkCywv3Z6WNtBPA+KYTsFCbpl1vBNrGuaeKsfSng6EgH0u5NjGZEwDlcSgfNL3PWhyBuhDNg3ZuUiBGj9CBxq13e23/eFQ4MYmzKE2hmFMZNW32fs+l52yHtL81OIlruvE8MKpKBbAPbfRsLwqThrnjaaJ5sQQxidmTMmPiFRSAy8fiu/Gd7ZEzHmUxFItR5ium+0OfUSLqEbAlDOtzF4glbmbiho5iakrFrH/c5Lejs0ecaDihnLpvC/0aoIrJ2C9tl2FeZ8vdx6O5xoEb1WUi7ErfwegfwLu3v72X74PA6EKKi+fza0Pex9DOhE5+P67rwY5sp7IKUM2Ccbr7o3UhBcY7+PJJ5nJRSmZndiWIMrbM5tmc7pNmj3Va4e9ZZ6OeUCsD8JKbvLLIZtnl6EztWpKNdPLqCUrMEPSUL7tKAdvq9fcG0qEYGcQa78vsT45CHJ+8f2+53WrtmOTmTBjaPTfnS/50F5joLicnM6aA6Ogpo07sPcuvgVsIqN60QE7t0OLIYUAF8iY4253PPzImONEjoTQx6NK+3a1vY7F9C3e39BZ2WftmxrE/vDy0z9knu7kFVgpnHKQyLQoclzAcQqAYu0Zv8me87sjjz2/k1MOPo8jVvsb19pHw7togKwb/fnQHJXyb4bvBa/oglxwJECZ5Q98yRSDP0dGWtNBo5w9agjCYVm97ohucwD+7O5+/uikC7diZbrIQ5+f6vzRBygb/fWQXu/jxfv58TeSLacRvQavxmXaN2VHe7WRqCvkXFIi8LZEg2neiEPmu0QXzprzvc9sO9D8RyI+JoLk7Z1Rzn0glfiHSTGd8RztSdSaHyIvEXS/q1JaEr3ni7+f/v7DMSHbBTuUTmHziY2N2ZIz7xaEjpzvkLnyW5oPzjH5t1zeeu9oIIKaj+qeQVmBkIg/I/ATjn3ArAz3g7rYF2TAswe2H/MNtdUm708YuRvxKwwmli/VPgrA64RUH6EbegjEPOZC+yjWHchG3rIC3CkY2oWJsYPfAgTFFAs+6+RxUgAQWYzJuMLYA271g2F0fnZ+uxYItO1n5XZDDGGgWHqixJG3YgAm/8Rw/LcQWR48oD9HezdB7bTfNnU+m0S8Ot0nIig4MpYUuQqfDOEBSkhRZNnlue3cf8CWXl4BckCREuwB2hljMmCZk5CuS9KSBj1ybKDxVBILrd8zn41GNjK/U73xW7IS6hEBPvaXJgjCxz4xLz169itna8oB/YvQYDwOAROXocA5AlIwF+y1uPW0QgB91OBd3GW80SQPZy3v0ieq2SxP9SNX1d0Fk0AfufK90Ixjv+FlL8l+3s4Epp/QMqiNhEyk7pf7ubtWnnrwa4tRUyauaW7nskTg4D9STbf1m2gDt1sTU3BxdpuTZuQd99ElOBydWRp/j4CGS4hq+haDVnzvYPAin+iRKKtiuc/vRCyaP0B8ZHH+jFMyi1CtLZ9HsUcLizqplMiC+yH9b4mkhGmIrDlWhTv+FPKk2TPRwyrcoFd28zW8RVE5XK9QY17dhu793va2NilmX3yEPJcCJa7QUlxaFpPBOgHA6CD3HUPmB6DU5ZVoX7n2R72LvAH5IH0jY3XG1SOsf8/W+O7kjUOSNd4HQKLX7FxvBxnsY94rdvRWfUu4o2a7Mlkz19n+8gUq9NfMYMfBBqfjOSq/yJ5Yr3kHd1QzrQA7B9mc3EPBBiWkBJyeXT+PEwMAVQG6Ns7/4kMnQb7vrD/97T3jkMK6hXRGilZX4TcOKn8twaKWb8nUpD1a+GY+6S/95NNdPsOOr/ScfTA/plIQTMGrekFct4dgP1xNp82Td7n5/RrSDmzGzHPwDzUMDSlryMyxLuDGIb2CKvXKUjZnwvoW9n/IM+XFoUdLqjRMToTYS9TiN6vbxMTNbe7h1ZBBRUkqnkFZgZCoR5KyKqhTIBCVgvh8D7ZXc8Dry6xcq+Sn2ixH0nMuUbq5pnHnRAD/BZi0s5A4Xe6uHcfgUCuEVQG9oPF91fIQmYkERwJTNbixKRaDxJd/5YEBrp3DUBeByeE+qK4jN8j4T242QULtE+IwH5gdi5EzPOFSOh9CDFo81k7SkgIqEv7xLWnxcmGWzhnQjK0yeQD++ncqEai3hfscO7jrtUhBmoSEkb658ybwQhwKUDGglpEKDzEDUiwfZokrAxi5ks4IK/Ce8rijSIB4WAkMLaLMOfqsWByvQ8CK4PFUR1SPlYC9vcnGwd2FBIKZ+hwIi2cQyEkxf/IWuJ7AGQfBCJsjQCxzZF1Wgr2BGB/ImZ5jpTR3yGgK7j8+3k2kGwoqB+RJeY39p5fVKGNlUIjeI+TwCOMJFrf5gH7y2FJ8RAgsAcCj3xYqk5Eb8J6xXqlOgBzt6JtXjnRDVlT3pjUZ3NkIToFxUNeOHlHWSi9GY0amANbIWF7AjnAPlGx9SBxD3yaQhE/XROyUA7nw9Z2rYvtcX+0vWcsEQTz4N4wxM/XW+EjvvghYq6KByiPn74GAo2/JAFsa9wXS1qdJiBr4iAX3EnCnyLA+C0a8PLLeX+r+GzbY4Mx01Lu+jLI63Us8jrKs9g/1va9EJI0jEUl46oU2J8zKbMMkq9aFJrEnh1o/4e9xQOuPxK9jkfi5Bgr28364x0br8+QvFOfPB6dKU/YO75Bckh9gnrX1rPs3hXEpJ5Bbt0KGXCNxYUXQ1boT1LOC1bVm87NpzmQEiWsqUOQciQYjuxEuaJiR6ISNnjUDApzIucb3YlKrBFob0hDy65lffWMzYHAX46hRqEpk/d1Bx61eXuOXZsb4QDv23emInyiX/LsWXbvbAovtKoSWWOkvdHZcj/at+ZO52RBBRXU/lTzCswshBjs/vZ3mXsacscMIIKPSZiCtwsaI3BYFerkmfuQLGcSUQtbQgza9o7hSYH9nUiEQgSCB2DjF5Rr9QMzuptjVp4DlqlQbjAx/uPCxtiMxAFmCFSeZv33PQLx6oCFiLGPL0GgwGzugJofWTCVUCzRXGC/0ni08ZwJwH5qsV/1gxMJclOAW/13yImfiIDJlXDCBDM4kFJQ2xOyxAoCydNkQ/Gca+tgUBPeU28RT35M1/YS5pa2b9zrrr2NFIt745Jg0wCwb2WWQeEVFkj304Lq+2hdysGtzgjgCvv66a5MoNeR5V//5H2X2f2jkHXSOkiRnMYpTj1DeqIYt7cRw/2UaKXAS1aIn9vmxOroTE7jo19h3/wMS56ZU0+fR+UuImiwZnLfx9j/kgaA/SqMYR+kgD/E+nqgXffeYesTwwxeSpYP8H00w+V2cXtbZ5tnsyX3tyEf2Peed+/b/DiBVoZKKKhjEFlgf/vk3ma2l91MVnE2O/IK/Z4kVw3i2wMAeQ3ZEC6bEcNilcX8rnE/dEWK+xeIVqR34wyFXNn5EKh/uLvWpnsGsqSejOXKIatwnhfJMeMQGL8L5cB+UDD3RIYOf0c8xfnAJsm3GgX2q9SmEGqnJ8qFNhF5H8yKjMIOsmtfA2cnz/ZEyvi70Fn1DFK2eGX5YgjQLdn92ZN3HGdz+CXMGw4yoW7+isLCPEx5rPllyeEF26CP+iAA9EfM+tyuL0IM/TQKeWGkwP5qiF/cn8h7lMmA5AP7nyH+JcybTRFgXsK8vOkAoSnJ7ksrIgXDeZhyASmATiCGF7opncvIu+Ab68+ZwkOvvYkG8I+8OVlQQQW1L9W8AjM6pRsdMdxCWeI5osX+dzQM7Pv4ca1mQomeBPch192FkAXPgwiA+gAB8CH2/CwI2P8cCfl7Um4NcCBiXq+mHHAIzMc61tYQiudJYDnftpy2h1A7u9rvHoh5fAWB+i/a/QPdM+sRE+9eHpgB8oH9PxEZvpoD1URgfxzOi6OV70zHqg65Xo4DnnfXKsUsnM/GvSycVEEFtYaQxb4H9kNsz8ttPylLhJY874W5y5Eg9TdqIMwhb6Qv7Bt/RQLTeCSM90jq2yiwX1CT+nxtIri1UzLmuxKt4s5C7u13IgHyGwQOdCUrYK6N4kaXUKiAL2hAYKT8vN8eWf23Ns6sV8AfTQyZUULg21UkMZGJoXhGkgD7xLOvN3JXH4/CTs2RvMMryNoc2CfyRy/Zdxbza8SVS4H9QbWee+0wt/3e9nsEcL1i4zzQjdU2RGD/N8k7jkYAzmq1bk9BVZ8fHtjfNrnnLW+7IKXO5wjofCqdY/b37kRL9jcQOP4QOocn0Y5geAv64lar9xRkrBOst9P9uaqyVCN16ozkkJ+wuO859VkQyWEl4FmcxT5ZJeyzROvqb4ig8LHJ+1Jg/xLaIAycnQ9n275zAdFQYlFk0VuyM6aE5QojnkEDUCiwELruecQjhfZ2QaHsPiHKqbshr7v7iVbpCyT91AudbcFYLayNPyFDsfbkBXdD8tWVRK+G5Ym8xSPIg3CUjXlPxD/2yOvrdIzd32GeB2B/KjoHphBl4M+xtUvtQlPOndYfhcg9CIW7/YgkFDEyaLkOnV9voJBNKyAlwDU2v76hUFQXVFBBMynVvAIzGyGLv8nIsqAssagxKgHY39tdz3MFrQagPwhZxL1HDIETmK15kZD9ox2iPtFUXyQgTvL1dPcHEi3kj0zuhff/Cgnua6HQGyUUi3hVolV4PQiBGNTAVGzu3tcZASfT7GD/PUloIgTs/4umAfu3VKNvqzhnNkWWLiVan4Qw9Gcfknj8KIbhKJRo0gP6syTlgsvopq2pS0EF5RFZYP9fKHfE5bYnrouEnUWNFkJKpnmQ1dNCdABhjqzAGvbBKeSHxSiA/erOHw/s72jXQnzaF7GQM3Z9bgRCTEFgQn+77oH9JREoEkCUTf34NTT+9ner3MDJCu0BWB+JLNqfJib9ex8Lk+HKB4v9j0mAeKTA+BsS+k8jWsVVCu/iQ/F8ivOkqeLY9UHnbxi/4b5O5AP741Dc8PlrPffaYW73Ilp2jnH99CxKEBhAnW2IINktKMzF5SiW8zvkhG4saPonssD+Nu56/foxutHKjLb51NPd93vX2kgG+BqBad8gHnwLV6ZDxVBG+UJeQjHW30Hy1uVEnj8vFFm78PvEPCYHN1BmOyK/8IGt3TB+wRp+ApJzZkcAbkjoOwk4NW0bkqmCd8W51R4zohz5NFF2W4Qor12NDMZG234dwqrMgs7dEkrg+ygxEe8fibxRV8SvBWVGoFHoDAseXR6EPh3JrhcjXnFz1wd3IYVx4ME8L3gzVUz4ieTTs5DioZ9dW4yoeDrXrnne7yT7+zZygP28eYs8Obd287w78FvEN09F2MMGZA3n+tL+oSkvRN6Rvh6diPL568hQYRaSdWnz7CxiPrxxaJ1MsucLQL9pY9Bh8I2CCiqoelTzCsyMhKzNJyKhqzFgf682rssa9q3z7HdnjPG333Mhl9EScHvy7CwkybOS+0OILrAnY9Ypdm91BBy/bL8XJIZ6GW/3Nsp5Z/AqOJ5o8bAaYibfR+DLOGOQBibPNgbsz4fiMZcw99iOQsasVSVRr41xSA42xF3f15i/wCidmjJsKNHkWMQUF4mICqoakRVQPLB/H9EDJ8T5nYgE2wlEq9QSCntyOh1AmHNt+YoohN7urnvgOA/Y/4wOFK94eiKywP7lREB/I1cmWDYvQgTG10/eE8osjjwtSsi6tYu/305tCuHq7sIEYnQGL4+AjWCFl8a8vdTujUVKjHC2r4aMC+4hycODhOdfIo+8RYlgcSeiNf3bNAA4tKKdIQRPsMAcatfzgP310Jn/LW2UiLjWRARi69Ce+DPK6zA3MBQBtOOQ4cWWbqyGEfmZQCNpRZiEgjo+0Qiwb393JibBrVd+uvmWWgPPhjxiZyUL+ncoQN/Va0WkwF8WhQ2ZjEIQhdAidbQDuJ/Tj9sTAeylknuBB1gQyTJXWdndw7vIWsMH8HxxpNT06zz10KlDxlNP0ga5r5Cy8VEsBBAKpXiZ1eV6V4fLEA/3NQKZN0VA/+lWpis6zz4hKiTTXCurImvunVA41l6+/1zZB5HS14ceW5Z4Vt5NOS/4T7t3Y0vnQoV5tQqmVEfe5SFH1G9dmd7EPHOjiV6eN5Jvse/X81o2zzOW6sTkuSWkdKk33CPxoLfrQ2jD0JQof1Ho/8fI5jhYlaioHoHljaLcs7yXjftZyGjhcnTuDahGHWdEIvF2pDxkcrPHlw669xdU0MxMNa/AjEpu8/QAuY9RdzyNA/uT7X5Fq44q1DMkF/xjA21YCCkYxhHBhFSDnrvBI/e4cFC/DlyPwLaRdi0ktQ0x/QNoF+goHNCGBJaP7d7f7H2B+TkQ2AhZ6QZgv7kW+4PI8TzoSFSNw9T19znuwO+OgM1gAbNe8syJCDz5lEYSlhZUUGPU2DxGwH4QbEsIvDsPWdz/2db+TciF+RbgKHuuJsJchTYsiISjaxAIWiKbt6ISsH+2lX2XZiQ+LyjT9x7Yf4+sAjO1SH0KnXGLJO9IY/u+Zu+7I28Mq1j3TmkdbK5PxDzmKAcxwt79ODBPcu96kvBtRCXBfu5aF3RuBu+SYDn4S7deOiGFb2ti6FbiF8I3+qKwGSUbmwzfkYzLOsyg4XdIlEe2tz1ANp/NPDYeP6Akm1sSLa8XRrzkeciat8HwZQXNGEQW2N8quVcvjxA9f77C8XvJ+moSr1+jdjbGQ3RBBkSvkAPs2/+DqRI/24T6DERx8EOM+4XSZ1E+gwmUW1UvYOv7OfKt4S9F4G4Y9zyL/bZQwnr5ob/9vQZSGN+RlN2BqGAu2V72ubsf8gwNJMbRvwUajtdN1kK/MzqjHgd2tms+J8uSxJCvKS+4PHAvSX63FvRFV8rznQQjtAHIiOBZYhjGoJh4Csmn31v7g8dpLrBvz6xJNNI6pMK4ZDz2qGFoSiRjB2/vJ4Hl3LgvjwxyUj65iNfe8v4OvEN/FGLyFqSAOw4XspFm7Otk+dNNceGUCiqooNpRzSswI1IOg+EZCs8sNwbs72eHW1UstCvUdRFkGf8GphlP7ocDIQAGw1rwjcXt+WBNW0IWZAfZ/U0Rk3c/sC1iso40xmcq8BtcEkMUc/Bl965vgP3tXjcE7L9AZYv9dR0TdAUJsO/KdRjhpYrjHRi3gUhA+AiXeApZQdzuxuhOBEgGRcinFJZ+BbWSkj1ySSSYLEWMoxnm6dxES+OnKE/o1yGEuUbaOof9vwAxRvEt7n4XV4/65KsoJEqx1lrX9+u6c2LL0MfJvNkEuW8/avNteWRRuR7JmYis1l+z9/3Nj2EV6roqOYknrb6zoPPwSwR4l1nL2vWnELi7nl3LSxQdym9h7bgf5W7Zghja5UOUDO86xKN8RJWSLOKAagSorW/j1DtZrz4Uz9M0AOzPyGT98io6h98BNkvnHAKKTrGxDxb7hTJwJibkqRH2vs0rlOmKjGyC0c0wd69Dr69kD18ByQ77IQVkHyJQ2AV5Jb1CzMERgP1VbN//S2v3t6Q+GwKHIjB2Lyz5uN3bEnl9TUHK1g3cveB1/CbZ5MSdkJHDA1hYPrLW8De4ssHTbzJVysPl61Hheqr4+aPVIZxDPRB/toTNs+MRwHwgcH8o4/sRKSsDsH+zu941+ZaPoX+i9fkxtleeV6G+KS+4KPFcLLNgb2LfhHNtVgSav4a8RNOccpvZdy+3393dvbeQom0X5Dm+EjE8URmwjwwXXqNcOR/OyDxgP+Q2aPfQlGQVaSHk7tPIQyCs16HoHCsBl+Wtr3TOpfOvoLK1MYCImYQcCyWEk+zuyjeKeZDlPXdH+8wDdIAchAUVNLNTzSswIxEGKrnf+6M45U/bIb9YzgHfGLC/dBXqVSlGbmcUozHE9jsjve/+vgmB5C22YLD2b46siIJrXXcE0n+Li9lv97ZHDGAA9mdz9xZF4P0wYBmkBFgGMb9dEFPdVGD/d8xkmmak/Lja2n8RznLVxuRolLwpMH3vIlBhwVrXvaDpm5I98jRiDNXRyMJ+cFJ+bqIL8dNEQbGzUU2Fubx2pdeJwsySSGDPAPuu7Oa0QHFaUIPj4q1Wd0zurUqM07srMa9I8Br7FDg/eWYRBLJWzWIfATbBQm3DCmWeQN4EISFgxprf6OSwpyfPes9B/9xvXd8Et/eLcAlzkbKrhIVWaOVY+GSPtxOTPJaIgIa3Xm0Q2J8ZCCk8S4hH+oGosExBjgE2/gHY3xwHGBU085BbZxvY3LkEhbT6LQIC53NluxIV59MFsJ/sYScn+0gJGaEcSbQc70oE9ktI5tnX7f0nVrE+Z6EQOb4+43CW88gaP3x7Cjp3bnXtOChvDFAM/dCmYA3/96QuO9s7Qh6uo6vU514Zu5B9fyWyHuhh3gUea83k+lVWrxEIVP4SKZCDpXqn5Fse2P8/ykOx1IcTIYZY9HS/e3f6bOAFp6KzdeGWznmy4Okr1sbXkZyZfncjopFKGMs6xLuOI5uvojMNAPsozFRqoZ8qWDyw/2+Ue+4bZETTrqEpySqi50TKn8kopOFQssB+4IcuSdtSUJP6OqyNWRG/+jNS7PRDBkbn29x/nex+09RcUXugcI+jSXI5FVRQQbWhmldgeicUvubSnOvBrdXTf5HVRq+krAf2K1nUtMhqnCwwPxAxhbMnZTYjAhknUh5vbXVjAl6jiu7byELoEeRSfpddq0sO/m2RgFoG7Lsyu1jd9/ftpnFgfxiRWRpW67lUpT7NxE3NmwvusJ8fMdX/xcIUOaYqlFkEAZ/daCXoWVBBnohxRUcgoTYI3J8BqyVlfYz9x4F17XpNhTn3ziCEdkdAymEo7MXQnLIe2PcuxrshEPlpa1eHBFSmRyIbimd7uxasNEvIFTnkbXgQKTXPIYaJ+wcuvwhZYP/2KtSvJwo1dzPleUy6GN1k3/s95Z4d4f+VQxn73VjYgt7IWvIGBEYtRrk14KO2Rge2tH32nnCm9HJr/UkUauoJYvz3J3Bxn8kC+4+TeOvMDAQMRwBeCbjNXU+9CwOwPwoJ3K1WxBQ0fRFZ4OVssh6yQXl+N7CKK5cC+2vXuh1NbOtZVudXgRNQDpB/o5CfPyOeYVYr2wWBoMGYZyoCFA9372vVmUvkaZ5BFvnrIPAr5Gy51JVdCRk1hFA0JQRwHxDGEckxZTKP3f+DPbO2/Q4htxZBIXqOtLFutfchWWXsrcSQp2FPPpKs4jjkPrsSS2COcslNQqDyfxFY/50rF+qfB+yHWPM+Br3/3o1ImXkl2iv3IypWbkjb4X4vgc77H3GKrhb2UT/EQ/xIYiCXUzaEe/w7AtZ/j/b318iJDU/DwP687u+KBnz2/0OIx1zB3WuX0JRuPHsjfORWq0vwHHgAWeyHcj4UTwHst6zPuyFF2k9orwny/RKIpy3Zmvwc23fsfhneRDmg/6XN2WXbsg0FFVRQ06nmFZheyRiJ3kRB6yJ3bwfESD+EmLqhCMD+BjF3B1EOnB9vG+8kYLsq1dFvwiciZv0jBIJtn5Tdwx2uNyFAaiCylg9M8J5V7L/ZiVYAX2LWJkRGzdfdA/u/xoXisfsrIOb1seR6U4D9jYAdaj2fqtSngRnq7PqxJwovkQI1Xcgm5TzKz+1Kc77WbSxo+iWyCsa5kfv1I1iIGZuTv3d7whrJ83MRk56+ZvtTRxDmvMD7ANm8IFNs750/eWZJ29NK6Jy4CQERP9AGIYAKKgP2jydaoB9L9Fo6j6w7/HJ2dowHdk3GexGiIuD/qlC/rkSh69fAEcn9RRDw/QMCUYLg7S0lQ5z8w8kqmvaytt1o6ySdj16R7s/egxAv81eq4IJvazy43Z9GTOraBynZg3fY07g417a2Qoz9+5kJFczoHB9jfeCtflNgfw4b609xXg8FzVyEjGBKSCm3HQIGzyby82/hzlhbmwHYfxcXFqYjEgJDxyM5Yil3fQAy9PkAgfbn4uQtxBOfghS3m7vrrQq3iTxqRqMQZkOSe3+wuvyTxNsVGdcsiQDU4IXVC3lMPYz4hL8gz4IQKrQHCo1ZSscJheT5zv4uC7/WgnZ1cnUK4dmeAE5HwGxQxv4fUe4YSjwb3yPyOlMxxQYKL7OX7VNjgZOIIXhSYH8gUlQslFwP/z9hdfFn98rE/GvXuespL7gYrVBYuzafZN+6mBg/v5Jh1fLWHq9sewNZUHuvac8zp8B+r+Sd6TmQhqbsQo1CU5L1qHgR8TCPIgX0ZUTlzmNUBvYvbO1cntkI8a8/IAA/8JbLEJU4lwG/QjLTB7j8jWT5wDxA/wcKQL+ggjoU1bwC0zvZofOtbZCX27UTkaWU14bPhoDyz5GVQx6w/2t7T1UT4yLmq2SbcIjpXELJ1Xzcxp2QBV2ItRf+H48DGGiFe6J/B7IAv82+MQFYPSmTAvuvWtmzE4ZkFqKCYBf/LPnA/rwV6jjdxtAHrkUCQ2CK65CWPjCOTyBX0DSB4tqI0f6QIn53Qe1AyPNnI2PWg5WZj38bvJzygP0A5B9jv2smzNk7vMAbkpr9w/acfYnKyMsoj8++OFIsjEPg/2vFGmzzueeB/QDoz4WSGL+Ii0OOQPan7Ww6F7P4zBnDp0lAnFbWMVjbfwHs4653QUqr7xGQcgpZwX01ZKU6FljHrvVGgrJv8xQkuK+VzuOkHkfZd0ZQvUSSs1pfv0oU9P1ZPggpuUpIgdfLrbE+KMb/TKv0QvmHfkCenb+uNH7IaGKO9qxbQR2HEAD4Pzt/lnbX61A865Bc9fnkfmeiR9yutW5HI2083s7WX7i2hb2iB7CN7V0jME9cKoDceftfC+pzgPXbNsn104jhTVZO7p0HnJ1c600Ez8eQteT3wPZxdu0SYAm7digyHLuHFnr7kSMfobPnj/a9c8gm6l7RxuELDDC262si3uwHZND2OfBW8t6eKJfLSMQP/obKwH76uzeSV69HxmrruLp6QPpDGuEFm9E3FZUkwH3WzgG+vg2U74PO8NORt1zIvTQrUnjMSTlG0GCM/bR9dJDQlKFORO+SM8jy7Mug9RE8P4aQTZ4bQP+zWluPGZXQ/pfKOHOgvWR1+z2/rZcScLFdm9PmbvDSOiB5R2GhX1BB0wnVvALTM7mDczmixf5lKOnMhXavE1mhdHcaBvZXrHIdF0Ma2PuBlezaHsgSpwRcgEsQZW3Z2Q73fyDgfyN3v7XWLBeRZcIXJbrWP0SSQyA5UH6BmL8j3LUADKyHwJfr0mfJAvs/AheSWCpOz4QY4xCL869Epng+BCw+bffGIuXHDmTBq0sRU757Nca4oIIqEXKRn4LAubdRcs/gNVIfSoSGgf3ZaWdhrpE2dbU9fxxSOIb1Nw+y3A8WatcAiybPzoW8uYYzk+X1qOEcXM/G5Aj7vQ5OKe/m4L/t+llAX7veFxe2Iox/M7+fxrxNE/91t317DAJofAK8uZCVfjAkeB6BOlchi8cSFk4CCfT/tvV2C7IEXYOYCP0lYP3k272Qwu0hBBx/QBXy+oR2oxwGJeBOu1YGSliZT4D3sVCBuFALtZ4/tSYULvFHZHRREdgvaOYlBGiXcLHiySoA5yRaa57s7yN+eZ1at6EJbQy5wAJglYbZmwXJNyXg2ip/u8wCm+hJtJK7fqpduw+n+EWh3/Yh8gYnuHvXI3nyPMTrLIg8pp+y8p8ii+75iUY7IxAgF3imJVrYrguREnhQcn2QfeMZsp5hQfE9CSm++yfPDUCGZJ8iXug8u+7Dx3RBXhcVgX37Oz03Q66Rb6y/glyZhhmtCi+I4pEfTk4CciSDfYy8Eno38p4uZAFtvy4PRoYq3yKM4D5gt+T5xpLndog8Azntnh2FnfyQmEvAe5UvTAwJ8yDZGPsr2/wogOTYn0HWmS25PjuS8Wdz8yDwr1sjnOSG5JnDrN9/svu/Sscd2BNhV4WFfkEFdVCqeQWmdyIL7IeM7aMxV8ScQzMF9g/IYwJooYCWHr5IiB9HufA+3B345+OA/dCutA4trZN7fph97xGyifgWQbEFSyi2dgrs12ugybrk+wNnfqK7Z5lAYu3ZgGjtv0Zr2tJRyDFEsxGZ+jsotzjdmwjmlJA75RF2f03gawSQlsVzLKigapDNwz2JccrHEa3OwjyudyUmAvufYgnX7F67CnPuubn9+9319ZEwehsWogSF9wlC/p1ISJmKrCAXr/VYzOxENlHkMkjheW2YH+QA+nZvcbu3mbvWZIE3ObMGJfcOA1a1v3vYnj0WAfs+X0x/BLy/gUI6TEPA/cfAvlamCwL6f0aWlWFeLoLi9odz4E0s8bTdH4ySaf6MrHkHt6KPy9YZAqNGAf+t1H/W9rutfr+o9VzpiEQB7BfUAAGH2PoJHm1lCUaRDDAWWe+WAZVWrsPOJ6JHQUXrXeMBfkJGAFVJGk0WaN6XaGEdPK2DkcHp5AD6du9qBMj/EsmLkzAFDALv/0F57rWuyAI/KHP7IuXAH5AC9hMEnC/SwnadZ+++mfLwbFvYveN9PxCVCmdhiVSRjBtCKs6GFAVfERXJAVD2Z6EH9n+wvsydk0m9fK6RW9IxIp8XvLWpfeLetyYx790+ZOWrTkgZHuL+5+YxIfKnq2H8g6tfHZHf/c7GMShxgvGdD5Hngf3rcB7a7n01D02ZvDec/S/k3Av1XoqYr+FBhKsEYL9I+l7eb+vZmgoy0OzWx2+QeP3anHgYySFD7FrIY7GZPXMSOg/SMMXbIIxgLAWgX1BBHZZqXoEZgRwDMYQI7D/u7qeHZgD2RyCN/FEkTHcL6+E1/t2Qxd+GKClRODQ9Y7AxWWDfA+1tYhGHLAC/odza0Wvp84D9Tq7Oc5EAePb3objYezn93tmYm1/Wes5UuU+DO2qwiilZX+ZZlOxg/Rtcep9Cgsl/EMN6UFuNfUEFIeFnJxRqpoTCRqXWuB7YDzkfxuFCR9FOwpx775W2tpbz77e/t0aCyGL2e35rVwm4xq5tjoTCqcjia9GW1qWgFo9hXoiZzsBCKFTFlyg01L+IYd76JuVvt3nXqmStSLF9r5szlxDBkRBnvicCEMqAfbvfj6iovZ9sOJ0QfuNeopXWYsRku9cTrVj/i4vLjID9FTGQpoXtC2dSLztf5rPfIWRCCfE96XoNAvzBVmaGOqurPJ8DsP8zRViCghwhy+4SAl0DcJMqz/ogvm/y9HAepfs38rAab/tJKk947+gRyOOoQQvqFtQnJOm9HfEqu9jve4i5f+4DhibPbYWUsFcjGS2s4xLymv0U2NLKdk7/R+FJvKdZF8Rz9KGFeU8QfzIFgX5Lu2+FfTwYUgRL+zoqK75XR9bey9vvOZHHwkh0lu1DPOPygP2PcB5nTai7zzVySs4c8HH+R1u5eZraN+59+yLFyU/2d8/kflDqXEa59bRXBL2AlOmzuWv7ufkyxF1fBfG+JZR/wPfXSsCzbg6GOP41zzNQof9ms/4bh4XDSu6HcQq5QCYgQ7Vl0rlSUP36DHvQv1Bs/A8RBnVizph2tnLfpfMfGSC9hUI/hfPC40l7IaPJ5Wrd7oIKKqgy1bwC0yMlB3RqAbOsYzCuddfzgP29kEC2b5XrdCQS8l9DVhef4cCohDHwwP65tFEcViKDFZiXu3PKpMD+Ukldz7R7byHrhME4JhbFj3sdMY+5FufJ+zqsFVIz+9Yfvl2IFiN3ES04vMtsP5Rc+H7E5I4lWkf8lSok1yqooECUC1e9kYXauzb/jiJaeeUB+1cAR+a8t72Eua62VkpIUFouef9stp4CILkPAkr+nLwnALATkLBVJLFsvzkY5lNXO/OGJveDcPS9jd0pJKA2iuE83saubyvqsggKYzAZJYYOHh23k8SLpwFgH4EH4bkl3PU6YC1kWRUsJgciy/16YR6BLcEa8BWqlBiTCCr0QVZfJeBAd38L6+c3EPhYHwfblfkzsl6tajjCGY1QjP3gtTR7retTULuOfRkv686kvkhZNxWFDKkHUcmGurvX9rQFat2enPY1FpN8DmLS7ZtwFqRkjYAmANdXsz4oRNiXSE5ZxV0PlvQllAg0Dbe3BpLLviarSN3M9vjRNh7H2/VMslPXponAQ1Xs6xB+4xD73R/xPBva7yHovHoGyWXBQj+j+La5dbe1Ywk3H+dCZ+oYBGhvTgShU2B/WySTNjlEqu2DP1Ih14irx3I005MhGfd9kHxZBuwjxcdbVocjSHLwIFD11wjU/j1S6NShMFGPWN+kOReOJSqKVs+p28rojA3zpd3zDDSzL09He9KFlCvpwvzeG3l2/APxCYPaul7TK9m6CnNkvM0tn+w2zPtOiPe90cr+2q51QQnDR9m9inOARFFVUEEFdTyqeQWmNyLLZG1ih3dwmQ+H5jJEK9JL8561332BJatcv3Psuz8TAa8y0ItyYD9o/K+kQvKdKtVvVmN8xgMb+36zvz2wfxdOM4yAivMcQ/IVAlg2sft1KGZ/CVmGzBCgfSP9GQCUnigR0tnEpDfBYj8D7LuDfhZkVfNHxLCPonCtK6iV1JR1R7TY/xgBUhWB/YbeTRsKc8l3ehOTCz5NYrFC1pLuZQTAzhuetf8vRALXY8hapoih3w5E1mr8JgSe/AeXEBDlPwjnzttYAmd3/0gExLwHLNjK+tQhHuE2JOCWkAfbILufns8psL8fEdD/KxXCOaH4uGFNbYcEvtuSevzW3jMZhQRcq5Vt84D+G+4cutOVmdPWwgQEPB6cvGM/ZG32HAVQ3ZQ+36jSHChoxqScczAvpMnOiKf7EAFlmTKIn/4OKapb7JXTBm3LJHwEtkQK1T8iINV7JK1LzBn1d2BHd29NlENqKkny2pbUyf3dG3m8TsIAfbfvrUr09PoHzrgIWeiHkCn75Xxjc6Kn9/0NjPVApFD4giopY5C34RR0Ps5DDOV5IJGXusrK/M/OizOJYd0C/3Mi4sX+SHm+uDkRsP+jnQ1bkA/sdybKLE0GmmkkJJn/Rgv6pyFg3ycNPhjJ3ROBi5GnfDck659p6+1NzLiEmLj6O+CvyTdPI8fbg/LQSD4nXk1CUzajHwcTc/8cUaHMdcjYYD5aYIQzsxHyqvgJ7XMjga3cvdQ7az10JgQvrlft7w+pEGqJmQBHKaigGYVqXoHpiZKD/SQiaL5rDnOzHC55rnsu99Bs6caZ1Gl127DvRBYhCyGgNwjWPuFeChxsiixnj2xlH1UE4lzf/BIxhVe4Mr4uCyELohJidHdAif5CHMJZrV0Pubbdi0IaLYqYzkdJQOwZjVy/9kYg1RhktXGh/Q5xLO8gYZJzDvuNqbK7ZUEzH1EujJ+GhJLDKU942yiw38Rvtpkwl3ynNzGpdxmwb2UWxBJS5/THg8jiamU6oGXkjEhuv+uN3Icn2FmxGAYouLJrunPnfZu7J7tz5kuSOKWtrFtQEk1FZ/agBsp6YP97e+4vJEYB5ITxQ2f9Y7ZGQuK7APD9AgnQtyLApsXeI5QD+uOQkv2rsB5c2YUR6BFArBdQyKr7EG/wLS1M9lhQQTMyJWfKrghA/QBZSF8I9LN7cwBn2Br7DIV7WRBZYW+IAO8SsGsHaNNa5Bv5hPB7nsaSNZbaEFmVT7H7zwD/dPvkUVWs52W2n10B3GXXvOdD8AR7xr79LYqPHizbJ+DATMr5cO99eIG73iUZ9/cQINeicDs57RpMVGoHUPp4spbo6xJD7jyNGQC5ff8E4vn0LArluWzynQFEYP91KgD7rWhHm+UaoWFg33uMH4wMA8J8fQMpzEtIPhtk5QJvsrjV+Xb3Dg/oD3HXZ0c5FDbOqV8A7Ns1NGUL+nF5ojHD2TjvBGB/JL/fUo35MCOTG8dTUVijx6xPX8TCd/ly7vdwtD9NtL5+iBgesc29NQoqqKC2o5pXYHohsqBzYDSfBIbnlG0RsN/KOg1CMSa/IYn3i8DuyVYP776fAvtVA5pQQsztyLH6NyYmMD3r5dzvhECXLZBQ8lPoQ6LAEg60nYA/ERMAvYsY6VxrmBmNkBDxV2vvKUSr4AHA2sRQPGXAfjXnYkEFkRV6znJ7TqCRJEpDyoH9I8Iab+a32yVxJAKHQ6LRZ8h6EnVCsW3/hzyRVnL39kPC8sm1HqeZjZCb+wN2Rpzp9shU2OmEwObLbfwm2Th/hkDvhatUn07IavE5pOj5p33n/0jC7yTP9bSy05BQvEZ4n/3v9/XfuL/nRsL75yTWWHYuvGp91OLQe5QD+hNRbNzZkXXqCKSM75TUa3vkuTeBqDh5hMLyvKCCyogsvx4Sa45DoP43RB54fSsTwjME69jvbI2Fve2ovHe3Z3sQoByUmxu5eyEkzFMoJMsuyBM58BW3uX1nWZQceCTiA8YjkGsX975m8wJJf3dDitQSUiB8488EokzSCVm7X4o8kf5n++91ZBOs59YHAftB2Xluzv2QBPl2mpBMtgltDOfHqgiUn2L1HmDXfejO3e28mIQMFI5FvM396FyaZnt5ULC8SJIXhXJgfzMSBXsr29NmuUbInrEpsO/DEK2NDAI+RvzDEwg3mCvnPfOgUDmfI/7yRHIAfSt7hN3bspF6tktoylb04wq2LkrIA/JBosfN11iuoYKa1JcDgGH2/6nWhy8ne00d2XyKsyIDyLnIiaFfUEEFTZ9U8wp0ZCKHybWDPMS4W7ahsnbdA/u/baN6XmKMxf3Av0J9kk38V47Rqmix31BbmlGfPYlA3pN24KTZ1ENS24utDnlJDIO1wsOUxxpM67wUAv1fJQoI/wB6tLY9HZmQMmeEtTuTVMv+XgAJeWkonuIAL6hqRFb4Pd3m27+RYm5Rt29OAE5Lng3A/vu2V/6aFgh5tIEwRxaEDABCRWA/af+n1pabrF6fUsTRb/c5icJQlFDM0DQkQF9kJboy2ZjuQ5Dn2zbIDbxqSRZdvQYiJdBiKNRcCSmol3Zl05B9m7uzdfPwPr9eiB4Ax9rvrsgTYALyyAvnxAFIsL6yGueB9eVbCGT8tTtrHkVA4lwVnuuHwIXN0HnWv9Zzp6CCOjKhOMglpKxcESn8+hINjt7GlMp2bwnbF54kAsxbu/fVNMQCCqsZlA7D7dr9CJxaKim7MjGZ6o3Jvf5IWTgfWevplgD6/uwPnk2zoHjlPyBe5VeNvGNWYF6rV+em1gdZ0wZg/x/AHigx6qUISP6GKiY3RgqLcAZ9bP/fHPbipO7D3RkTaDKSL/+MPEQGE5O/fwjsmXwvAPvfoTBCG1Z5PrU610ilMSKr5KhosW/3+yB+sc5Rnjf7762+QX69C0s07MpshID//9AEPpJ2Ck3ZijFa2ObztzZ/vkFGC4VCv3Kf1eX97a7NQcwPlQH2w9gjfjPNFzXDYiQFFTQzUc0r0BEJCdtlMSaRNdv9dgilQM58CLw+DNg5ubcsshwpYYlrqlzfkEB2LIrZ7JNMesZ0VyKw3+rkvA3UZ3FkWfO0q9e/UBidYCUZEvp8Awy2a/7A2hAxtS9QwXrRMSXe9bUvAhSeN0ZhpbZoY0ch5J1RAh6w316RE/plFQSqBGaxAPYLahNC4TzGILBhqF3rhqy5xhGFwJOS53oiS7zROG+iFny/aokj3T7a3V0LIb0aA/YvIyv0vkmV86cU1ORxPMPGYKHk+tHIgi5YXt5Iojyu0vebkmNiRQS8lwH7dn8JDMRxe34J2CkpF+bkteFctev7IVD/e2Sdfz9SfH9OFcAhBFYEEOcksgqSOxDosaS71sn/X1BBBTWNkKHGm8iYYwW7Fnjhc2wve5QkGbh7vmfyu2ZrkCxYHIx4xiD54UngoFCObJiblYjGUgf7PnDvq8u73oJ6bY/Cgexkv/siYL+EDBGG5DyfZ6TUrH5GvMwY+854ZMH8DgqX0erQZGTlrT4obMwedhaFUB63k4SWdc+sgbyxH0eGFGcTQ+ksQ/QgDvzY7snzA5C3ybu0gZU4rcg1QuT9elpbNiLJs+PKNhRjv7N7Vx0yMpvX3tvblRtIzGv3EbBpMofXRvL0OBLPh0ba0S6hKVs5TvNYHy+A83YoqOKcDHNoPVtDfZMxnZUssL+Fe8chwEsoBHLNx76gggqqLtW8Ah2NEAD6NbK2TJnfgUgwfjy5vjNyEfUgzh+TMstjDGob1dvHzj/AXa9nhO33r4gKhsOq8N2KBwMC2XdFjGGo2wPIhbAzEWy5kSRMD4qvXQJ2aEldUIKnEoo/2GVGPcCMIRpph3cAHNPEWv2s38MY/BMHVBZUUDUICYa3IGB+LbvWDblo/4yEtx3dPDwleb4XVXC7pYqJI5Hw9TXmAWXXKgH7Q5JnN0NC8mZUsFIuqE3nYx1SbIdksIcjd+OhxJA3XyFFZ0gMeEmV6+DBoaE2N3dFVmrdk7IrkAX2l3XXn7E9fm67trZbR1vZtZDv4XpyYqQit/43rcxPyOKvalZxyAKsPuQeEeC53L65YU6dhuCSSRZUUEENE/IgKpGEciN6iN2Ls/IlgkHB06yODsQPkw/s/894hlNCnV2ZAHRuhRSTf8FA/yrVx8tLafjPkPOnD1KcBq+IquVaSeqyCVJefIc8/rpQnZA7YS50wlmeu/tDiDkXPLCfKk6GIMD+TrKA/h327AVEL83Pgb2S52d3fdohjIzcepkDeeSPJZ6196OcCenZnQL7ZSFNUI6BxxEwPwKd6euFdyEPiJeI3hnrGB1u7y7RQD6GBtrTLqEpC2qXOTm7rccRNh8+sd8hV5IH9oOx57vIK/N0lHPxJyokxS2ooIKmb6p5BToSIWb3l0QwuCwEBIqDOxIlgdwcuIpo6XclArC+xoHrKbPSVocpEQj/DtjOf48so7q7lTuhld/LxGZHcYJ7VSi7A4qBGax1H0Egw2jkcjh/Mg5/sz5dLq8PG6hTJ1ef15Fly3QJYKPQDLnxn10/zUFMwnVMXj/Y/yegcCijrWxxqBdUVUKu6Y+EfcXW4L7I2uwjYE67foLbM0+p8K4OIXAgi5jAQN/trjcJ2C+o9oQ8x8bYfPsQCbg/IJBkoJVZ3cqMsjGvRtI+f+aegECNEA/6E5RscdbkmeWJCQsfQoLY8/b71KSsB/afJAL6g+x+XU49BiFQYklaF0O/sfARnjcIsbFTV/B90dl/fFPP94IKmtkJhaorAce5a6eSE4fb1vkFVAEIbuM2+f0igFEl4Fp33QP7nVDS3y+QvFV1fpYK4T+JvHV7AfvD7RujUBif1oZHDTJBL6RwvR/JWxmPB+RhHs6VemA/edeiKORaUEAvANxgz1xs1/q5M+0jEov9dGxrPA/D2M5BDFv6MJKt/4wA0ddwwL17NgD7Y+zM895qF9i7vkVKt3/Z73G2dudC/PJa9r1SQh8Ae6f1bEa72izPQEHtOiffsfnwnO09T9jvzzDvTle+P4pa4OdRWaLmggoqaMahmlegoxECadYkWg+shYsraAf3l26THI/cDNd179jY7p1dg/ofZ98eScPAfqss9Ci3QHjYGIZPUdKbNSiP29YTuXbehRQPU10/bujK1aHEhCUqhOJwB9d8WMLA8Kz93wWB2KOYDgFsBM6MQgzy2sm9VEm0mfXVD8Ae7rqP/fggUlT1ABasdfsKmnGIrLA9b5hfyIvkRQRm1luSIOujySjWZ032ySa2K+wxg5BVcwm4x92vBOwHAbdDCKozM7kxXBmFcnvLxmoDyuPffgQ8XaXv+jUR4lx/gkD644ieAY+QgOsITLmRLI9xWIV3e2D/ETcn28yQgGjl2Q1YHzgSgRzbJuUCeBSMCA5x9/ZEgNwkihi6BRXUZEKKv8nA9fb7ZHIAfbt3ip2zq9S63k1oV57FfsnvK5R7Hock3FX19qGR8J9UBvbbJMQeVfQ+tPf1cjzNJNfXf0Lx8POA/b+QzVMQysxFlJe3Qd7saa6Dk5Nv/aLW862BvumLPJt/RMnew5m6BgoZG6yf96Xcw3wvBNS/TzTICl7j95LNw7cZUgCMRt57/lzf0c7VS4GtceGWaOFZThVDUxbU7nNyFuTl8QMuJxky/HufGLZsqXSOIKXgZciIMnh6FoB+QQXNgFTzCnRkIlqUHgX0s2t9ESBwCUo2tSblQvkB9twu7VDHPAvu42kE2CfHiq8l36TcAuEVYtKbU3AJct03Z0GhCP5oh9ThOd/Y2RjA/6PcIsJ/P2iiF0/K7GtM2b+ZzuL0GfOYWmpcSxKKCOdyTFTmfIdz0bR7QRFV1dASBc2clDKE6R7k5uT+NidDXNwQlqMrsjS5yO4fX+s2NdDWsF8ORgqKpgD7b5DERC+oZuPnz4qelc4C5GFXsjlZtXBttgYmAHfj8rsgAStY7f+TRMi2OmyLwO913PW8WM3runPCJ8+tulKJCNT3tvPehyYoIYX9umTzUKxm935jv/ey82gMFfLlFFTQzEwNrV2kLA9Azt3hTMLi67tymxg/+CAdLMRVpfaRNUQJQPBEXFxod3+Y7aHPICvWqu13NCH8p9sLPbD/AQ647aiElMs/Ahcja/vNkQIj7OGLEXkfD+z/Ddg+bwztzHrExmtJuxYSDG+MrNPPQSHvOqShlZ2b+yKL/N8RQ+MsR8wR8Ii14SOUqyaVT3cmegD2Ruf7t5i3B5E/Doqrh4hJrRvzgGutl0ZVlUMFVX3+dcm5Vodi4Y9H/GkIc7UCMbRxkE1GubWXG5KMAtAvqKAZlmpegY5KCHjaE1mef4m05v0rlPVa0dWQS/lIkuz1LaxHQ8x9xkI2ueeB/W1bW48K3z+YaCE01K51A7ZAYP0nKIZ/17w62++y5Hn299zAf3FhZRwzFP5fF4VTeAVnfW51CDkOprvklIihfg0xxy8TFSUl4O/IciNVJM2GgKIArjyBFCJ3I4+I/2HW0gUV1BLCJf2y3zshS6K/IUuQ4Rg4iQS882wu7p28Z3u7PjcwW63bFepr/+fF7s0D9vNC8fRy621wrds0s1NynvRLxtKP80HIm+RdzDW5St8fhJTK7xGF9m7IWGAsAgWCteRjYU+ngtBFAwI/2eS5O7jr1QS6wnroZetgMgKADkUWjZ8QlVq7EJV4i9v1SxE/8BWKE10A+gUVlFC6/skJnYNyV4T1/iJJLhpkGPJvBNy2Cf9fjfahPGUroNjic6VtJYYVKiF5I+yjmyFwtUROOJdW1q/J4T+JwH5fZKRUAnatdR83oY0PIhnBh4hZ1todlEWLIUOwOrv3GDDNaKecd/ZF8spoElkDhV59B5cToFKf1rhf6oBrEDga+LrFiJ7j59m1ECLqVaS475Hzrs4oRNQ4ynPsBUC/Xna268HjoT7nQa37pKB2m3tnIo+N1Pujl62fN9zaWZwI6J9j18Lv74iheMryZRRUUEEzLtW8Ah2ZbDPdEfgYWZ4fSQQH6tIDF7lsPmEb674t+F76vi7u74rWsUigfx04OikTrLe/Bnauct/0RpYXX1FuIRQyrz9IBcVGTlvzLBCXI7prnkY2Vuh61teTgF+l/YJA7kVrPYda0b+HWLuvRoqivZCCqWRM4n8QiLpA8tw2Nhe+cQf88zj3zYIKaioha7gTc66fT7k3yThklTTYyuxm168ixnFcHQmH76JkoS32GGqDtva29eYto1Ngf0GkcCsB/3DlPLA/b3vUt6AGx9ID+geg0AELuGu9gaVRmJsfEKjfqpjIOWfaMARe72K/uyAA/EcE6HcxeouoiA3AfpnFVhO+70PxtBmwD1xo3zgLl3fI1vMVyMrxLWBjuz4IAWRfUQD6BRWUIbIgt+f5D0HWwe8gwGcj/wzRcOdHFOKjN1KU7+b2FB/yqubh4JJ9+VhXzxKKC/1HYMXkmZNdmVEomegkFL7rcFeuWt5VzQ3/uY79PQuwfq37uJH51RkB9Y9jMmGyhy9JtEi/G1jCyndFYZ9GIHlyEmax7/qsE/JeL9l87Gf3jkbyyLUtOddq0F+DgQ3s795EAP63ST+FBMpvoxj6ecq3Ze3su91dC4qqNP/FQOT9tli121RQxybkQRE8fX5BObD/q7D/I+VZkL+ucGWWJmIE3+CURQUVVNDMQTWvQEegPGaQrFXaTkRg/yjKY8UvbpvsWKPc+LdNrQOydB/gfl+LwIc816y1iK6Rh+bcD9bb+1S5zxax9/4uuR4YoHvJWiAEq73mJvhZAbkulpDHxIP27vF27YicMetwFiDNnYso6fDLCJQP1kILInfPp4nxKZ8DDreDPgCLCyDrkh2RYqTFCRELmnkJKcZ+IAmRY3Nwkq3FjZGwdwSyDCwhwW9BlKgpzNUXkJfJd/b74Fq3L6e9J1ndHgBWc9dTYH9RtyeVheIpqObj6IGjg2wOj8ASNdv1pYiA0j3AIi39hv32Fo/ea2x3ovfK6siz7FOie3434GwiaPUMrQiVQRbYr3rcYqA7UuZ/6trlw8DNiyzyS8AD7rlgVTuGAtAvqCCmTZsG8jY9icQLmOjlFvjcqUghtq8rMysC/kNuqrFIsV6y8+kgV7YjKM29jBNAqY+R0v8B2xtLCKwfljx7it0LCc6XJAuIVrV9ND/859JJmZr3t9XDJ8U9EcmRxyBF0XkVnkmB/VWQUvw1pPwOYPYkykOCbo28gkuIHwyeaB/SQUPuNNJv8yFv96fctZ5IgfEwkkUnIv42xND3OflmQQYsL9vvENI3L/9F8HzvsPkGCmqz+TYbUnJ+jYD97SkH9oOV/iCk3LzPzckuKATZJ8CbNo/epIqhJAsqqKCOTzWvQK2JrBVDHTkxd2kA2Ld7RyAh92mSGPYtqM+fcSA8MWb9g8BcSdl1kYDdIGhPYvnShDpUipHtmdml7bv/565VskBYBDHHLQKX7fnfEwHG8cZQ7diavu7oRBR87krnJdLcv0Q23M6ZVAgRVVBBLSFgKwTETwJOsmtXGuO5TFK2FwJISyh0Vn9gJeAfRGDiE1w4no7EcCJh9ncorMjD5AP7gYm+CFlglYBnal33mYloQGmbnFGHIuHnC6KnSFDMdEbeXjvRiqRxJPlgkBX74+TkVAAutzWwof0OsVG3t3Xxgc2n7VpaH3ufB/a3rnLfz41Am1crjQNS6AXBck+7drSdUUWuiYIKmjYNBMK8YevkBKJl8zYoD8cjtpbXJiqcM9b3Vn4V4AwUfuHvCOhf1d3vULwxCs0VAOOQVL6nnb832L3vgTWS505zfTCbu94WuUNaHP6zo5CrZ29ivHxP9xNj3ndJng3A/lTgZxRS5w8o4fxuRDl1EkkoHiSbPGb3/4d4qaDE7hAGVw3Vg6xRwJ7WjsvCPHX3vrD1e41r39VWfhME/HdDyYdDqJ4w71dKvrkB8lT5N0XoxpmK3Drtj6IrjCIC+3neH/vaPDrWfgdjvi5IWbcrOi8WqnXbCiqooPalmleg3RucdW/1fx+OtO4jkfC9bfJcHrDfz+7NBqxK1r2/JYB+F6vH+8gq55+2ed9CEj7FvhkY4P2a8t3m1gkpOfpUuDcbspx/3H4HS5o8C4QQSqYs2VUz6zM3cvEfiDGjLe3rjkzukJ8NWZN+FvqUCARtZHOxhMLrBAvo1xHguFyt21HQjEEofu2PSMA7DXgWONPu1Rl1dr+DVW6IPzo7Sow9FGcR3VHWLVkgeFGkQJxCDrDv2nkMslz7yNo63VihTc9EjDXbA4ELRyGQyFvhdwI2tfPpWyKgX1XXf2L84Svtd7CuvTWdD7YGXkVg1WJ+3iEB7EUk1LUK0HffW9/qUlWreKvjh8g6sSzUhGvTAfb9E+13NxIPx4IKmtkJyRSv2tn6G9vXTvU8nyu7My6+fIX3pR5EHUZp7up0AwKDV7HfaWjR3xK9+xZK7p0PnNxO9VyOZob/7Cjk9uE6Yoi5K1HIzv0QUF8CbgjzJmcclkYebtOAS0g8EZGXwmTyLfb72/MDiBbsHQrQR54uv0YGZ6cAW+WU2cDa+PfkHce5Pvwv4jcuJ3qpL+bKLoB4kRBeJTWGWQsZ540Hflnr/imo/YnI186CDCC+RLmYdsBhHVZmL5tLV7i11cnm8viwr/r3FlRQQTMH1bwC7dpYMWIXkLi3A+cSY0JPQoDOD5gm1JVLgf0jgFlzvtNiRtqYsO2Qm3oJWWOvkr4bxTjcDtjG3Ws1SIaA8+HIuvYVJFzcjaweZ3HlehozVCKG2LiH8niYG9oB9S+S+O+trGeZ98CMRDYPugDnWN9e7e5tagd+CYWX6IyUSg/ZtdEU1h4FVZFsT/jR9r3RwPXGSJbl/UAhs75CQGVZAjErU7N1SyPCJeXA/uppfZHy8lqkeBtc6/GZGYhoZd+bmAg90CNY7HorsxKKz7xAU8a8hfVZjiisv2b/30wOkG5r5REEhg9za2V1FGbtAftdtRByJMJgFdt9OgIhL0rr6dr1C+uPS2s9bwoqqKMRWWvgbZHF71QUIuW2wO8ZD+jL7uD2PB9ep6v7u8PwxDn7Q3+kCP8OAb6+bWF/72bn6zjMEIic0Ha0g1EALQj/WWsiAoTh/yeQorm7K7My0SjoOjfX0vj7zyHL4d7umi8Twg+VxdhP6tRR+iacrwOIXjKBpgJnJ+UHISO7EuJ5hyNvzjH2/K/t7zBH/objB90YrEnMc3YLSmS9BorF/4ldr3p+iII6Prk50h8peC4gyvIv2p7vPUQG2tz7DCkbV0G82GikCC3DpAoqqKCZg2pegXZrqLTyz9tGeS4xId1wYx4fQglJVyDGtisBv0neE4D995HV3a9xiYZaWcfA1IaQOz/aRr2H36gdY1LGELfy+ysiZjpYp3xJZF5DGJjt3SG0ttWxhCzKF0zetzaKETweFyqnoGaNybI2P3+0vzcgAvpHJWW7AptTReVJQQUFsrkV1vtT7npqHTgHMTTUKu1Zxya0IexdPZHF2lko98SQpJwH9p/EhTFBirTRwPm1bs/MRgjweRQJ4Hcii7n7UJzfT3EWrMQYpG0B6AeX59mIxgAfYsnZySq7wnm9v52FLyKr3MOJcf13rXXfNqPtg4kJ2Y6sUOZSWzvFuV9QQTmU7BHbo5BVU20vuzEp673JPLB/YK3b0UD7vHyyufv7fhT/f8mccmGvPMrad00HaMd0F/4TKb5fQUD0R8RkvvVKIpQL6SdkiZ8B9pFR0ZzIsnwsSc6ZZMz+RAT2d+yI/ZHUvQdSsP+MAPptgSORwr2ES4hr5VdAig2vAHgLWeDXEY3axgBHu+dShdZylCsSSki5so8r12H7rqCqz8WwFgeg/BM/orwVzxJxmLdxoXhs/h5IzEEScqq8RTRiKeZQQQXNhFTzCrRrYxVGIsSgvxAlFz0BWZUOScpu4g7dk5J7PZHL/2hg/zao5y4oZuFJtnF/i4Ck/m3YN+sSQfxrkeX3AGAIsDfRquNdBEYEUGM710+XIivElVDIncICoXVjEgScM60fbycC+ke6cmWuswUV1BZk++KYsN7d9UxCJmSx9D0GcnYEcuupN1HBG2gk5S7kiyKX9Z+tLQ8jAHmq7ZVFzMr2GTcPfq2IwJWLnZAzj52P39u4HOLKt+m+SIwPHQCBKyp92+r5W6LFXohX7Os7XZyPCBAKOSXOIRum6kAEgvwXmKfWdS2ooFoT5Yrvsn0JKZdD7PPnMdDb3a8E7B9V7fpWue3B2/Qs+x3ijt/q9vD6XCf2/1JW5v/au74NtGO6Cf+JLMNLdtZ8jyVfJRsmsT9Szk4jB9i3v2+x92SeT961M1IOfGNnwrB0vtaaiMYcdcBciN+7OFlTaxCV1b9Lnl8CedGcj4xB5rTrG6P8F/+1PngD+KX7Xqfk/wHI0/ok5PG2pV/nHXEuFdTmc7Ov7fcTcEakSP64ESnV3kPAfvCY6Y0iT9yAPLvOwHIuUmABBRU001LNK9CmjctxZUegVAB0zkPJf260e13s0A/Phbi0lSz2F6tyfT14EWKn74UA9W+RsNzft61K390QaYXfJ0m465iRVZCiYRwC6w9yjMumSJscNMaBRgD7pu8qqEXjM9b166FFnxZUK7I99HucoJ7cXwsB/28C89e6vkndOgM3IRD2Tyj291Vube2ZlF/A9rqRdv8nFC5lyVrUf2YlE3x2RuFqRhI97cL51BPF2P8eAfs+NEWb7ZEmWB2JDAa+sjnyez/fknrOibytfos8Ajdsj3q2UduXJ4YfGo2SI75gfMBXwFK1rmNBBdWayAKHq5ANpXA9WUXgDsjLbSpwNpa3q8K7fmlr7+hq17mK7d3Y9oZ/AKvbtbmRBfh45LEUgH0fQijkDzi+1u1pqI10IOA6p47DHZ92i7veiZgLaSCy6A/A/q1WJsifISnny0Bfu9Y5+X85ZF0c+KgP6IAhCRGgfjEySvsRUzjjDFJsfY4gH9ivI+uhsCDiOzZHRnCH2HvfQt78ndxzmWcbmlMFzVyE8uMFJWeYh93t/wFI+TMWYSw7UCHPoZUvAP2CCpqJqeYVaNPGRWvy2ZPrw4kWMeOBO3KeDZvrBkTA58QK32mRMN6U55CrVQrs+1A8gZno3sI6rI8EiHcxawy7Xm/R4a4tBlyDALGXgXXdvUWtX89H4YO2A5ZtbR/NjJTXVyg+dAn4a9GnBdWabK0Hd/RbgXWQJfK2CNwr4RR6HYVMCHsPKXO9EnV/t8/vlfPc3Ca8rY5LylpQu4xZJ6KL+7+Rm3LZeQd0R8D+GOB/ZEPxVCM8XZnQjYT1WezvxYlW+B7Y92BV7jk9ve7lyHL1Emv3eONTbiUJ11BQQTM7odxUk4CN7PdFxHwgc7ly2wGvG19+Eg0D+x3GE87qk3okHI1A/SH2O8gVuyDF32jkNd3HPbM68qj+2csYBbVoPLxn5Sl+nIhhdhZAOWECsD+PKzcL8E9i7rQ+yfs7oRA2b9vv661shwq7Zm290ur2rPGA/e1eXfJ/LrCPC7OL5PCPUG6foOwYABxLBWDfleld6/4oqOMQUfbY3X4HhZqfM2FdZWLsp3O3oIIKmrmp5hVos4bJeuByO3Q/RBr6Ldz9zVCs5EnIdW6FnHd4YD/ENzu7SvXz3gObI23sHcAxwNCkbDeywP4hyP1qCDGhyoYtqMPG9uz/MJdJu14RYEBusffSjHiXxYFT1h+p4FOXdw8pbBa2v9cAvkDMd9+89xRUUHsS8tAJAuOPwNcoAd7HZC2lO0xSXGSZ9iOwhP32gtoe5AD75CTpK6jdx3EboiX8hxhonLOXemB/JHBMtecRAkHmweUuIQrvi5ED7Nu9FVAotWG17s82GJ85EcA/GxWSYxdU0MxKxsNfZPvSxygfSFCIh7OoUvLcisC++79D8YLIE+kh+/8Gu9bZ1bcfsgL/jBg3+hpk7f0FSdjOglo1FpsazzMR+LW77kPSzIIUSSXg4eT5OZDXZQl5kWxje31X4HgkP96LAP5dcXHpa8n75fTDqiiR/QSr4245ZfKA/f9L1ua51pcfo5x6/d292ZEMnwfsr4IUe0dQ8JQzDfk9msRY0tbe7jbPrm3g2YXdvH3T1lmLDDkLKqigGZdqXoE2aZRc4oNg/TMRqPmILNi0OUrkWkJa/Dly3hU21Q2t3HFVqJ9nEM4CJrs6llAYnGOTZ7oBexIT9P6LGLO+RS6qKI5++ObWdq2e8W7gOe+9sHGtx7sjE+XhF3qE/4GVG5gXhyOQ9CT73Q94wPr8olq3q6CCpk2rtwT73hjOu5HHzvzufs3ABqLQ2gPYClnErAG8iiXwNUbbr7s9yQf2O4xwOrMSsAUxYZ3P55AqbroDv7Jy7+A821r4XT8/jjOh6nMUf/cAEs8NBOx/bd8PcYpXQTkZ6uMTF1RQQTM+kQV1DifKJM8Ay/hyNAzsz1LrtjSxvXMi45MSirH+EmaIkpTrRbTKH0+U196mSBxa7THZDAHNkxAQHeSS2ZGi+WobsyCLPpA8PwfRA3MqUk59br8/xcLtIOOzDqGQcevOe2SGMLKT7Twe2sBzK6Nwi6XAQ6C4+iWkxFgpb46imP3BYv8d4xE2Qx45HS5cVkFtNv+CzD9Pcn0AyjMy0H4vgrye3wGWz3lPCMXzuM2hybZn9myruhdUUEHTJ9W8AlVvkMD3qchK7zhgfiTg/8Wuj8AlRETW6iHG/gXkA/thc56vCvXzVtmnE90BN0NgwD7G2H4FnJM82w1ZSQTm4H3ggLSezazP2kQQa6e8elZ47mrEsO9e6zHv6GSM5DXAAPvdHwFDf8MA0IQpPAh5ZHxP1hp0ZeSu/A5FTO+COgghS7DgybQsHcAl1O3ZvZFHVtjjQj2PcWVTMMVb7B/UnvWe2SlvzpC1kh9ue2AJODmvjP3ugazkFq9i3c62745F4efCHLku3Y/tLA8Wp++7Op9Q6z4uqKCC2pfceXSd7QPjUD6KdUmsdskH9icCp+CsgjsyAcugsC0TkPHROg2U7Q6shBJBrowL3dUSmaagiv2cAvtzIGvy7xBIeCZwl8keecB+PxR25i9Ijn4WJfIMMsziSDEwDhhew3am1tA9k/urALdbG2/BKdZcmfDsUGBe+3tVZFTwHDn5YpCXWlekvOuGcu2EvpzIdJDUuqDqEtEY9HD7PYfxhSORcWQnpNy8xvGSPvyV9yT+GIW62p8qYFEFFVTQjEc1r0BVG5OND79dcm9Z5Oo6mcTNCSUq8cB+GoO/6q6uKG7maOBBzFoAWckfYYzwFGvLGTnP1gFLk+P+38K6eGDfKzwqAiwowVXVwhHNqIQElv8QvUFCYqoSsr5KBbpjkZfJF8AguxYSOPdDuSB+BuauddsKKigQsDVwYK3rkdSpK/JumYzCHVyEvAlKtsdu58qmwP5uRKu0fnl7YUFVH6/6BHxIQF4G5TJIhXIf9umU9Pkq1sfPh9WRQH8/ZqGHFOwh3vBNJII+SqR3PwJOXifr+VGAVQUVNJMRsJ+dSVci68yRyAOpS1LOKzK3RTHASyTenR2RiHLSMsi6NFg2z55TtqFQn8WZW/2xCcD+z25OHUf0auyOQqy+Sw6w794zr/FXIbb3osCf7Jkr2qMtFeoV2tEfyVe3IxD+OGBVV24V4K80Adi3vzsTw6TsmJQbhvJCfGFn/dVE2W0t4D7gWrKydXH+zwRE9BidgsIyfWDr77hkj1+T6N10LeUhmI9GxiQbu2tFUtyCCiooQzWvQNUaUjk+vE9St42V+YEEFKURYL/Kde1rDMW3wFp2rRsCdH9Gln07IBBhAg44JycWXzWYX5oI7BOBl1Os7JG1HvuOTkihFBIzj0ZWG0diSaeIQlAPlAhnCqawSQ9upAxaotZtKqigSlRLgYWsq/UcyLrlYrdvDQBOs7X4MRZ2zO6lwP7OFB4x7TpuyGrpd0SLv8+B2yhX0rcpsE9WoJ/DzscfgNWTcquiXDgl5NafAvuzoBAH87lrhUBfUEEzKbm97mQE8IxEoUDrY5znPLMrsGut696MNgaedmkisP9XcsLwFNTuY7Mp0WDhFXe9PhQhyhvzDgmwT3kSzy7ImO41K3tTOgfasV2exwuGUz8RQzu9AeznyjcK7CfvPxQH6tvZfiLyqC6h/BBf2t9/wuU+S/iJ4vyfiQh5l4b1Nj6Zg6kXashr8Qbig7cHbrB5/Cbm7V9QQQUVlEc1r0DVGtJAfHjHgAxE2vRRmItTctgGYH8qcElbbaAIwH8CA+utnvsgkOIjYpiWU5Cr5NfAWe75NmGWaATYT/rqL1bfAvRquE9D362ALIYnoxijc4W5kJSbjeju2Tl9T0EFFZQlyhVfvW3/PgkBsWE9+f3rJNvnRtAAsF9Qu4yfD5X0ko3Le8DTCPAKZ9KRyXMe2D+pjep2ORLU/wE84eaI35tXIgL7GYv9dN8u9vGCCpo5yfF4XoH5G7LAvg+3sBoOAHLXp4vziXyL/QLY7wAEHGXj8aAfK/s7gONLEsPHPJzet7+XQN5qn+PCxbb3HHVzbVYE6P8MXIG8LOcGzkBy/ae4eP9kgf0/kxNj35Vd0N47CYUpeoOYuHRH+/aqxJw7bWYYWFDHJ7IGQuNt/pVIQiYna28tBOL/4PjeEgL7F0jfW1BBBRXkqeYVqGpjKseHD8DpMGQlfWvynN9UNyRq+ddqgzoG5qM3sKD9PS+y0P4MWDinPSEeX5snSKUysO8ZuV84JqgvBVDRWJ92Rlr3KcQEitcCs9n9AGr5Pi5c6woqqAECDgYWTa7VodiwJeApE7D6271OSdmKwH6t2zazEXLl/5sJPqcBve36gsirKZxJRyfPbYrClZVIkstXqV4h1ulo4Plknnm+wQP7f6IRq7+CCipoxqSGQJf0bAF6krXY38quDwEesv1kWK3PJGA9YO8mlPN74qyYZzGFxX6HImAeBDzn8kcms3RB4Wum2Li9VOFdQ3AJPhua/23cpm4mZ/0EnEqU+5dBRmjBWvpH4FD33CooNG8J+C1JKKzkGyug0EQ/AR+ikCpLJ2WeQl6G89Z6nAuqLSHQfmtkrBKURyVg/6SM3zf7IGXZfiix+haYgogCFyiooIIaoJpXoOoNqgxK9wfeRlZ3W9g1v5H6vzcniZvXwrrkMuLpxoySD5UCo0HMdt4dhW05E1l4H1nLPrR7q9sB9TWwYa3He3ohpCzaDlgOeNX69kagn93PZYQrzaGCCpqZCYUjCG6qsyT3lkXWLmEP28PdS0GVAOx/kO51BbXZ2JUJJihB4ncoCXyPBsY7L6btVigZ43JtUNdOwHnu29v7uUQ5sB/AgzsxpW1BBRU0cxBZYHQ5HHid7BULEL01e9o5NNrkk7uI8c6P7wBtmtv25hKwZwPlfPvWRXkDhhMtv5ciAvt3pud2Qe02np2QBXvIL3Ssv5f8fxKKE/++lR3gyuaFiaqZvIIUR18B95AF9P9mdT8fhTOZgjwQjnDProkU+As04Tv9UVLgMsUUsA6ysr7L1nUhv83khIH09vf2jpfc15Xp5PbJsPZSWaWw0C+ooIIapJpXoE0alQWlN7Frb6L49MfiXFyT5/KYlBZtpGStrmdB1viDkzLBiuUCq+vuyf3t7PrywBw17MPt7doqxMSAZW7BBTU8Z8i6tf6XcmDfu17PWet2FFRQRyaUcPD0CveWBn5va+w5XCz0HGb5BCv3XyzPRUFtMl47YWHvcu4F0P4U+50BF+zvg63M/SZY+zO2dxvUN9ShC3C2fXsksL6fS2TBrJWBh3GAQUEFFTRzETFs147I6tnvY8MQ8HguMe52D9vfQvixz4CD3DM1BXSAgxBgWgL2ybmfhpB41spu7u8b7/svu7dZrcdpRiYasepFFsCldEyTufoQ8AcETM6Z3u9IBMxn62d5+z0/cD3Oyx15yN9i1z4la7Ef5PEmW0NTnuz0MeRZv22t+6Og9qdKa4NsbseQPDcXR7H9s/DyKKiggppNNa9AmzUsC0p/jAD9k4Bedr/NNOjJQX+IHfTfIGuXvwB7J0xwADSuIbpCrobi7r+DUwa0J0OV9OHxwL0kIRDash+nRyLGS+2CrDnWQ0Jcr4RZXowI7P8BZ9UJ7IYsjase/qmggqZ3InGPRiHAbqU8FM9SSGlWQjHRV3H3UmD/KBI36oKqOmYH2Dj8HeiZc38ru3+t/fZ7ZQDX50CxRb8DFvLj2JpzKD1TUSigdH50ISrf36JhYH9Of6/WfV9QQQW1LSXr/5fAWOPfl0nKrYVyhZSAX9m1+vCLKMnnqsDi7plahTNJ98W9iRb7ZcC+lVkHhR/JAFZ+j0SedL+o9ZjNyEQ0IOoP7A9cBOzueSC7fwgutB3OqAElhv0WuKTSnOhohEI+zWJ/b4Fimd+QlAn5BCaQeHK24ru/QoaDJeAwd704/2cScmuuF/IQ2c728ln9fTdf8kLxHIi8Yi7s6GutoIIK6nhU8wq0aeOyoPTN7nrFmHlV+KZn7s+3b3+L3PGeJyb2u4Gs9cq/7fozCIAKCYoO7kB9mOuqWVB9f4RDvTeKn/eN67cXUOzUHq68B/ZvBmZH4NdXwPc0wRW0oIJmRkr22UvdGlswKbck8Eeiy39FYL+gNh2vhYCXgROS60EJujICwr4BFqs0PnY2loBhVaqXF7R2RCEjHrHv7EIWXOtiwlajwH4xvwoqaOaglA8GTkThwJZNrq9j/H0m9ILdqxSqsyZ7CDIq2pnEA4oGgH1gEApx2pDldxFSou3HLiiJBgCvORlkKgqbultS/jBX5lUkqz5pvz9hOrQaRgqy+63Ny9m1nvb/MCSL/xrlU5qvFd8ZQPQ8+RiXd6KY2zMPEWX/OWze+US379h+mp4THtg/E7gM4UPfUcj+BRVUUAsogMoz7L+6urp1URxHUDLEe+163bQqNL6urq7PtGnTfsq5vj+yvH8QOG3atGkv1dXV9UAJit6yYidPmzbtUiu/JgJ910MJf0YCZ02bNu3Gata3Jf/q6urWQ94GR02bNu0Ku9Zp2rRppVrUpyP+C+NTV1fXC1lirYBCFT2CwPv1EbB1N7LQGmfPLYoUAENQAqdZEKi/ybRp095o94YU/4p/0+G/urq6O5BlzKv8P3t3HWVJdbVx+LfHcZdgQYIGggT34MFJcA0eCO7u7k6A4MElOCG4B4cPD+4ug4++3x/71EzNpWcYae/3WWtWd1fVra473V2yzz57Z238N2vrZidnGm1G/v0dLenxsq7NzqtdTUSMJ+nbiBiXvNadIunj2vqLyVlKd5NZnm9HRHcASYPKNneTTcQWlfTOGB7PkGtYRBxNlmEC+JwcYA2ygf1+ku6uXkMO1u9BPqztIOneMTkOM+v4IuIYYAqylv4Tkv5algc5m+wSckbSNpL+Uda1u/voiJiaHIDtRZbdubm6Xy3rtyAzvyciz9Pn19YdA3wo6bTyta+vbSAixiGfPasmsE+QCQ47lE3+Juns2varkT/recgeCp+Q/Yq2lPReRHSvrsEdQXnWvot8P/NLeqW27hKyN9wcZKC135i8v4hYhKyVfrWkx8qydvd3bS2j+llHxKTkAM8sZNmqW8nn+q3IJM2dgBsl/VR77brAueRzP+Tf3OqS3ulof3Nm1g609ahCa/xj+M1zxygLhgzU3sKw2XxBZmrfRTa+mrfhNXvSRNZoWTclWQt6HmoZp7SDEX9q2Qzt4Xja4z8yk7Nq0HkktSwnshRPf7LO6mbV70r5OC6ZqX8bcCXwm7Z+L/7nf+3xX+O5p34OL+fUwWRAYsaG7eoZ+9eQQeE2fz9d8R9wMUNnJ9VL1UzG0NINtwMzNbxuq3IOvZEmmtSNwfHsUb7nHWRd3PHK+bpqkPsGMF/9d5ChGfuvUfr2+J//+V/X/EfW734P+K58PLss71Hb5g/AhrWv2+VMnnL+2wt4n8xk3oBStrS2zciU4vFzQuv+3Oq/azORgfkjG7bZDBhQfm7bNaybqPweL0cGJqt+DyNdY749/SP7Wgwmn7knLcv2IGcDXkAzzthv+L9vl3/X/tdy/8oz/K1kYt7+teW/YWiflM/JUst9Gl67cDmfbkNpRN1R/+b8z//8r23/dfpM/UpELEEGDCAD+9eN4f6WJmcAPE/eHD1SWzc9OQ31Bkkb1pYfBBxCBm8PkPRsWT6dpHeH833aVaaLMxCGLyJ+RU6v/gxYUtKAsrwnmX26KBkMOk7S1w2vrTL9e0vq17pHbtb+VZkrEdGLHKj9jJz1NLg6R0bEv4A1GH7G/u7kDfRlZJbhT1iriogZyKzVxcgeMztL+rRkwc8DnFrWfUwOxLwNzA+sAwwke438r5mOZVZyYD7IOs/P1dbtSw7O3kzOtnu2tq4bma26KzlIe2lzHI+ZdTzlfLAQWQpuIeAjsjn7u2W20eD6fXx7vY+u3YeOR14n9yPrku9LZpmObMZ+u3pu6SoiYjJytlt38mc3nXJ2XC9J/cs265PX3x7UMvYjooekgQ3767A/x4iYkUyum57sxSMyc/oNYGlJH7Td0VlnEhGbAOeQSX17SOofEfOSpdjWIX8P5yXvM3ckY0M/Dmdf7fLaYGbtX7e2PoDWIulBspYewDURsdHo7qsEJc4HPiBL5DxSllf/nwPIG4j6aw5maEB//1pAfypgpzJI0NRxt6sbKl9sRui3ZImdO2oB/W7kYNLiZObnMZK+johxI2LOWnkJlY8O6Js1KA+cg0p5q4uBO4HDgalLEKIHgKS1yEzuecnz/IzVPiS9TGZvnQ0c5YB+y6tdE4eQ9BZZT/QxYD3g1IiYolxbni3LriRnru1LPiz9BXidrKXfLAH9YirynH1eQ0D/EDKgfxtwaO16PV55D1UG4JIO6Jt1beV88BjZbPQRsszmERExubKEQjSxfbtTrqUh6Vsym/koYCzy3nWNcv2ttr2APAd+BZwXEVvW99O6R24leegM4ARgWeCV8nOkBBm7lc+vJDP2BwJnRsR2ZfnAUipqiI78cywJHSuT1/DfkfXObyfvIT6onr3MxkT5PZqFnAFyYPlbm4U8N65DPvOvAJwHTEzWzl+rlIj6mfZ6bTCz9q/LBPVhSGD/D+XLX43BrmYDZgD+I+lagIjYH1ih3FgF8AOwcERMXjL0D2ZoQP+52r7WJh8ExsY6ui/IwZxJasseJqfXHUlm6H9Tls9eli3Qqkdo1sGUDP2BpU7sPcCaZCPT/asZTmX9yAT2XyB7g7zcym+jyyk/t8ER0TMiFoqIIefF8nNbj6GB/VOqwL6kD8sMt+XI4MN+ZC3qVVrg5zYNeb3+onbcBwEHMfR6/XRZPhuwYURMWd7DYEkPlXVd6l7KrCsa0d95CcY8QZb4eJostbBvRExWzoMxvNe2J80V2LfWVRKJLiPvkVYAFqwni5XfwSqwfwXDBvZ3K8s7bBC/Kcpa+n8CZgbmAtaW9KHrldvoamLgaxA5m3NDSX1LsH49YH3gTEn7lU2PJMs1Tk4mF21SZh2bmTWLLvcgKul+sj78CWOwmy/IKamTAUTEiWTW6EpAT0nvk9mF0wM3kBn6t9IQ0I+I5cnpWU8CQxr5WIf1HfA9sFhEzBsRj5JTsY8iA/rf1rbdmxxg+r71D9Os4ygZ+r3JhtLzkZloG0t6oX6DXc80awjsXx4Rv6lt179V30AX1DCz4jyygdhxETFhtc1wAvuT19bfI+lSScdIukPSZy1wqNUg64LluA9g2Bl19QH4rchg//g0cHaVWedWDVKWz5eKiG0j4pCI2CEipo2IsUr5kqfIhqTPlo/7lcC+ukBg/+8RsXVbHHNXVrvvuYksAfUfcrD6L5HlYCnrGwP7m5RVh5fZwx3i93NUSOov6Q1JX0v6sfxeO6Bvo6xcA1QSVSaulkt6nIzjQPYk2QZ4lDwvEhFjS/qeLCn5CNmEfBeyBJaZWbPockF9AEkfwhhl171HjsyuGhGvkHV1rySn8Fc1J28gT+oLk2UDzmwI6C8FHABMCJyoWu1na7+a+p2JiG7lRvE14EyybuN/yDrQR5DT776tbb8XsCrZ2POtVjlws45tTXJK+T+BI6rSOeUGu3dELB4RcwITVC8ogf3ryYDteVUmv7WshpkVd5MZS3eSwf2+9W2bCOyfWgX2m+vnVQ9UVJ/XzuP3kgPqy0TEZcBhZI39Axuu16sAW5MZuEOy+s2s84usczyofH4YWcbjbHKQ7zTyPLJfRExaMqafBLYne27tUFvXLgP7tfNifZBc5X2PbGB/D6AfcE5E/LY1j7+raXwOqWfYS7qN7EvzELAhsG1ETFtbXw/sXwWsBcwm6bvWOPa21tlmI1jrqCWqTET2ErknIlapsu1ryUILAFMDT0j6KbJPXhUXmpT8u9weWLG23MxsjHXpIMfoZNeVm9yPImIXspnfTGSQ/0RJL5b1gyU9GRF/J8vqzAGsGxHjAp8AvydvgKcCdis3Vh26KVFXUC7qA8tFfB5gHOBtZY3oys3kQM5S5FTsG+s3yxGxK9ms822y+WKXuJE2G0MLktkt/1Ct70RE7E4OkC1FzpT5v4hYR9JHAJLWjoh/AseroQmctYzy4NOHHLScDziRrEtf/7kNudYpm0muRzbNXQ8YFBF7SPp4TI+liWn2PYABtWv/t8BZ5Ey7DcgsqiMkPVNdyyNiUWB/YBBwriQH9c26kFqG/kFkMs4jZGDnc2BOYDvyHDFNROxSyjA8RgZvzgL+CvSIiMMlfdoW72F4Gs6R45dZcT0lfVC9b2Wz1QvKNvuRgX0iYkjzXEkXRsRYZFPgF1v5bXQZ1c+r/F9PDcxK/h6+Xl2bJN1exmf2J583iIizJL1X1g+uPaveWN9vG7wls3atlqgyGXAH2T/vVXKmfWMcaUD5OG9ETKjsoRdkmeUZgIck3VLbr//mzKxZdOmg/uioBQPWJ2+oPgemBXaJiL9J+qY6UUu6NCK+I5v8Vf8gLwJvAluVDBd3PG/nGrJPryYb344HfB0RRwG3SHpF0iMRcTbQhxyxPy4iHiCnJi8PrEIO7PxJ0ttt8V7MOqDPy8cZIuIdsi7lycCSZOb0jcB0wKLAuRGxDjBQ0kBJG7fFAXdxmwLLAOcCh+nnDcDnioiPgO8k/VgL7F9GZhf+FBHbjMk1sSG79m/leCaMiPuBC4EPS3DkVnJ21frkzLnpI+J1QBGxApmNOzuwo6Sby/48AG/WhZTZtbuQWfjbSXq+rHo4IuYhg/tjkT2V+pZM98fJgP/5wN+Aa8iGiu1CPagUEduQPb5mA7qXIP7NpbTEyAb2z6rt2880zawW0J8UuIi835mwrH6vzCK5S9I7JbAvchCqycB+fd8OLpo1rfzNTUCWZfwNOfv+4OFse2dE3EPeb55fzpmrkkkj/yNnpQ7Zb4sfvJl1GeHn0lFTRlx7kxk4vyKnUu1InsCvJgP13zXcLI9L1k//HVmH/14yq+LFst43vx1AyT69l6yT/zjwGRncH4fMMj1F0lNl22WBPwNbAj3LLj4iG+fuJ+n11j16s46rBFTOIW+oPwamIDPzbwD2kfRJGXB7ivx7W1gtU4PdRkJ5kNkEmENZlqxavh2wMjm4+QZ5/dxV0tdl/fTkQMBuyqbGzXEsR5BBqLqHyRIFN0nqHxGzkNf0TckgyYdk0/OpyQHZA6uAla/XZp3TiDInI2JbsuTOupKurS0/GDiY7Jt1mKQn6vuKiO7AIsB0ki5v8TcxkurnsYg4BtgL+ImcYTo52Vz0v8AZkq6svW48YHPynPod2YPkX8qa0dZCqoHkyIbzDwGzkKXi/k3OiFuCzAT+B3C6skksEbEicCA52/F4crbZO23wFsw6nNrf3X5kOd2TyGf4/o3Xi9o5f16yFO/CtV29CKxSElh8D2lmzc5B/ZHQ1I1+uVEfp2TmzwWcTmaN1gP7v3jidsZf+9YwOLMZeaE+FThcWS9veTIDa3XyZ3+CpCdrr5+bfECamHxA+lLDNsw1s5EQEWsAfyIfTh8FLgaerIIJpWzA82RgYoEmssOtlZTZSluRP6uXyKbxp5Gzlb4FXiYHZn5Nnk/3IMs2qCpz1kzHsRhwE5kddQQZqN+YHHD4EDgSuKo8oE1CBrL+BkxJzsS6CXhQ0oNlf34YM+uEImJpMknjjGqQsWH9eWQwez5J/1eWHUQTjbUj4nfkbM2nmnh2aFfnkIjYAziOHJQ4usw2nZk8L69ENvw9XtlYtXrNeMBmZA+S7sCCkl5t7WPvaso9zsXAOsBBko6srVuNnLH4Ctmg89+1Z5cVgX3JZ9T9gWPb0++gWXsQET2VPVGaWncLeT/7W0mf/dJ5vCRz7kqeHz8CrpP0uUvumFlLcfmdX9AwhX9DMiNiAPCsSi18Sc9HxI5kYH/dsu1WKvXS64H7xiC+A/rtV/Wzj2wINiEZ5HmffPCpGnXeGRGfAD+Q5RuIiOOrjH3Vmi2a2bBG5ga3CvIqa7/eGBETS/qyiU23J4PHZwGun98KGrJRu9UeiB4ly809CLxGzrDoRg587kGWU5qDbDi5QHntIIDmCOjXrrOzk31tDpD0dFn3Ftm8/sDyj4i4UlmP+Aty8PVnwbeyTwdCzDqZMqB3KjAXMDgizpTUt6yrziXfkuewWcjeLU0G9It9yXvGdcvrhmhP55CIWIQsD/Q4GSR+JrJB+Wpk5vfX5P/JIeX/4XIYUornYvLc+pUD+q1mEnLW913AsdXCyD5fR5G/a9cC95brcijdUbb5CrisPf0OmrUHEXEq8EZEnCfpx4Z1Y5H3q1+Rz/rDPY+X82f3Ev85vGHdkPtcM7Pm5qD+L6hNTz0K2Ke+LiKWAw4FPhhRYN9B/I5J2UyqF/AOWXLpaeC+ahZGbiJJ+r+IOBoIstEjEXGspGfa7ODN2rFSEuVWSY+OKOOlrBtYyp6NK+nbKqDfUD5gezI77W3gZN84t47aoOftwCURcZmknyRdEhETAWuRtaZvAy4F7q9mKkXEu2TmfAwvO2pU1AeIatfZgWRpiKcjoifZZ+HjiLiorDuUDOwrIq6SNKBhdla9oa+v3WadkKQvIuJwMhh/BFlT/jRJfWt/97cBOwDLRsTCZOPD28gBwyEB/YhYk3wGOJ+hTRPbhRja/DvIAYplyZItW5WAfk/yPR5GZpeuRJaRPArYs5wbL4Uhgf2Tq3N3e5uB0EnNRpZwvbEa/I4sO/hfsmHuIcBJylnEk5ClCD8GkHRzRPxHUj9nC5sNVWZ0/pU8X38fEZdXgf3yrB9l3czkbK47mthH9Tc1Pzkz/7bq/rE2uObzo5m1GAf1R0K5Sd8FuIes69yLDPBvCUwK7B0RrzcR2B8UEX+Vy610KFVmcLkQ94+I28hyDYuSmZxQAvoM/eL5MvADGdgfFBGnqNRXNbMUEX8k6/HuHhGLS3qqqYBAw7KdgJkj4hhJ75dlfSJiCjIbZg3yb3NNuV5sa6uyOmcnG9zeIOl7SaeWkhWTqDTna7AVef08B8asFN0IZtT9HpgsIsYrQagAkPRVRFxWXn4o2Qx3cERcUwWp/ABm1vnVAi7XRkQ10HdoWXWahpbieRn4D7Bt+frfZK+NZ2vBm0XJQcJPgCtVZnS2Bw0zjSaT9GlE3FQ+v7ecG1cD9iab+S5WtrmPnKE6N7BTeav/BKgPxvp82apmBIiI8RlaX/8QSkC/bDM3cE5ELC/pbQCVkoQO6JsNJenhiPgbWZrqVPJ0eZmkH8t57YeSCHIksFJEPKHabOGGDPxTgXEi4r/VNk4IMbPW0K2tD6CDmJ+chrq7pGskXQZsBPyLDCYdD/ymnNifJxvn3kt2O7+iZL9YB1BG2wdG1sO7ICJWlLQZ8HdyEGzNiFiyGn2vv7b87I8CLiN/9n+NrIFpZoWk24ETyNkvD0TE/CV7cMj1qCEL/69k4H4zoD4tdgHgCnIQ7T5gOUkvtc67sJqryezOQeSA9polex9JP5C166tpyZTPtwV2Bv5HNvYbowcfDTuj7p9kFu3ewHLkwMGy5dw+5LxdHrguI5tcTkieuzdtPK+bWedVzgk9y+c3kDO+niGDpNtHxMRl3QfkueqV8tKPyr9qHysDpwDzAodIuqf13sUvq86vEXEm8EhETKvsDbBPbf0mZHb3miWg313S48CT5Hn898BJkY3FrfX9HznAMk/JxH+QzNA/lGED+pD1vKckSySZWROq5w5J/yBnaX1Jnsc3iiy7U3mAHNjdHtikzESlvHZwRHSPiH3JcmUPAm4cbmatypn6DYYzhXRKMuvmuRKYGKwsuXIAOYV/nbLdnrWM/V2Bi4AH1AylBax1KMtJ9AZuBpYiay/fIWn7iBhENlG8MiJWl/RkY3Zp+dmfCPQDTpSbdZoNUU1RlbRXRIgMoNwfEUuVv6dukgZr2LI6hwJ9gcWVZRKqbe6PiJPJQP9DarrOvjWD4WXR166XZ5WHowPIwD4Rca2kfrUMpp4RMSEZPF8X+A5YezhZ/KN8XBHxZ3JA/U6yr0KQ5TQWIGfavQa82DAd+suI+CfZzOwksjyPs6rMuohyTapKyCxEzsR9nwyWHgEMjIjzJX0h6YYyYLkP2TR30Yh4gxyg/gM5sLmLpHPL/kZ79lELmp7M9F4lavWjI2IJMknpTuDV6lmnvKY3maj0IvC9pP+1+lEb5L3OA8CGwKtkn6/9gXOqgH65Dh8IrECWgHqzbQ7VrP2rEorKM8X5JafjYDKwT2Qpnh9KNv+ZZLb+ccC0EfFv8u+xN/ks8zfy7+0wZZmr9nj+N7NOKny+GSqGraX7R/Lm91NgU3Iq/waNAfqImJ0MOq1NBoL3AN4oF4pJlM332uvNvRVRSu6Uz5cgf5ZnkE1xv69tdybZWOwdYJ2mAvtlu16S+rfeOzDrGBrOs8eSN8M/AsME9iNiebJ25cfAIpLeiaGlseqZ/D63tpByfXulBMKHnCPLuqrkRP1nsQMZ2O9FBtivqwUbVgNOJgNKdwI7SHptDI6tsZHt7uTv0vJl1hQlo/RAcmbdPcDOkl6sH3/5fBJgGrmxuVmX0XAOOJLMwoTMSu8FzFS+PhD4e+1+flEyaLo1MA6ZxHE7cI2kW8s27arGfAxtaj4bcBN5zV1W0udl/ZxkKZf7JK1Ze93iwHXAsZJOqi1vV++vqyjXtHuBX5F9vharJw+VbOG9yWeUFZU9ZHyPZDYCDfexW5KB/YnJhJArlY1vq0SjHcj+FpADnRMBU5EDbSuVZxX3rTCzVuWgfhPKzf2+DYvvJ7MKv2g8WTcE9m8H9pD0ci3o4RuqDiCy4dTqZNBpS2Dm8hDUjfxbqQKRZ5FNdUYY2Dezpo1EYD/ILLRjgeMlvemb5NYVEWcA85DB92fLsnHIwNel9WBBwwPR38gHom5kYP+GWjbo7sBnwG1VMKkZjvMfwDRkaQJJ2jsiusOQmVczlOPZlBEE9mv7c7DKrAsp56XjyZKaRyoba49PJnDsCkxO9t04uwrsl9dNQDb77qahtffbxTmkqetluZftTQZ9DwLOl7R1Wfcb4BpK7Xwy8D9n2XY+siTP3a33DqxRLeFhHjLhYTLgLnIw5htgFbL58dvA0pLe9X2T2chpeC5pDOxfpdIfsST+LQVsQZYrex14FDhV0if+mzOztuCgPj/L1tmanLr/OHADMBaZedgDuEjSFmW7xsD+bGRZgTWBNSTd3JrvwcZMCQLdQU6hfhDoLmmJiOhdZcE0XPCrwP7bZGD/KQf2zUZeRPTU0LIHPwvsl+XVQ+wwWeLWsiLiUDI79QZg15J5FMCVZLm5U4BjlHWXhwnsl+0OYWjDyN2Am2uZTs0W8IqISclg/pRktuy9wJ/U0KCyicD+TnL/BbMuLyImI5NxZiT7sjzd8EywPllyYQaywft5tYz9+mBmmwfym1IyS0PSmbVlvyZnS81EzkC+uizfELiAnKXQlxxY70aWFDqttY/dfq52nZ2N/L1cFJiirP6I7C+0p6QPHVw0a9rwztcNzyXDzdgv68clE0m+r90H+2/OzNpElw/qNxGcP40M7K5XPfRHxAJkBst0wD8kbTOc184JTCfpttZ8DzbmIuuHrkvWypsK+ACYU1Lfhge3pgL735JZMc+0zdGbtW8juIGuB0+aytj3DXIri4jlyCzN54C/KnvJ9JQ0oJSdOJXM4D+LzGr9WWC/7OcJsrHiZ2Qw7LLGYPtoHFtTmfWzkYMNvyPLEaxeAhqN5YKqwP7GwMPAjspGkWbWRTRei0qA+zngCUnLl0FJyOejejmG88ryfcka5l+34mGPlshSLEeWLy8jE4/ekfRDRKwE3EgOaGyloWV41iaTk35P/r/cKOmKsq5dDlx0NbXA/njAuOSMCpE/r+8k/eh7J7OmxdBSZGORA5u/An6S9GAT2zYG9i+X9EN9P+VzJ/WZWZvq1tYH0NbqJSAi4jjyRvYSSS9F6iHpCWAtsgHKVhFxbvXaapp/+fqFKqBfprlaB1GCP9eStfLeAaYGjo6sjT+4+nnWf+aSticflMYjA/tm1qCcQwdHRK+IWCYitoyI7SJibvJvBwBJe5MlEMYim+fOr6Hlr6z19CanFN9RAvoTAu9ExI6SHiGbgf1f+bh/RExeAvpRfs59yn7eJGe89WRonf3RVgIZ1QDQpNVySa8A6wMvkGUiTi/LB5bB2mq7t8gyeVcCSwC/GZPjMbOOpWHQ8Ve1a0s3YPaImFlD1e/7zgcuL9seDuwWERO1+hsYdT3Lx0/JQP1/gL0i4leS/g1cRJac/FP1AknXkk2A5wc2dEC//al+DpK+lfSRpDsl3SXpsxLQH1Iu1MyGqgX0JwWuIsvm3EE+c9waEStERO9q+3LuPxT4kpyhumEZDKD+N+aAvpm1NQdLgIiYlcwQ3YIcta2CD1VTxihZ2OvQdGC/R+M+ffPbftUysYahbGz7b7KG6ofAX4BdS5bq8AL7mwBTSXq9VQ7erAMpN9ADI2ux3wDcRmY8nkn2Kbm0ZFADww3sD64PnlqLGwQEsE1ELEWWq5mSoVP8nyTr6j9HBvb3qwX2e9Wy8aclg/q7kc3DvhmTg6oF444BzirX7WrdK+T1+QVgrYi4qixvKrB/SDme68fkeMysY6mdQ04HbgXmlvQOGdSZBFiwcfuIqALjz5L3hc+Tg5Qzt9Jhj5T6fW3t/vQwsk/AWGSz8GfIWvr3RjbAvZVsRH9MRAx575IGSPq+/hzjZ5rmVV2XIqLn8J5JRpcDjGY/VwYmq4D+Q8CqwCPA7sA/yTr5xwGbVIF7+Flg/3gyBtSncf9mZm3JQX1A0qtktko3siHWQmX5gCo7cDiB/QvKdq713EGUIKMiontETBoRs0fETNX6EpC6g2zw2BfYC9j5FwL7H7fBWzFr16pssXJzfA+wIlnWZU2yAeH9wGrAfxv+BuuB/bsiYmFnnbWeksF5DBnI/zc5tX9H4OiyfjAZrK8C+zsAh0fElGVglIjYiSzR84Cki8o1drTUZ2pExOTAwmRT+h0im9RXx/1qWf4CsM4IAvuvS/pP477NrPOLiK2ArYEBQP+y+A6gO3BwZBnNattQqa8M9CGbkZ4ObCbp8dY76hGr7murr8t1txqMOBcYDKwtaXWyCe4gsqb+YuT5clxg04iYuHWPvOsq16VJgUuBWcDXI7OWVCtZdQlZZvdAYFVJJwN/B/5HlnHcA9ioHrgvgf2DyZmsO5LXCzOzdsM3EIWkW4ANyEDuqhFxWFk+uInA/trA+8BfImKZtjtqGxWlDMigiBgb+AfwGPAi8GpEXBYR6wJI+pEMZm1PPvTtx3AC+23yRsw6gHLO7EZmuCwAnARsLukmSedIWgP4HBiHvIGOKhBRAvtHA+MD10dEn+bOZrOfq4Lfkg4gr4W9gO+AZ5TNwHqW66AYGth/mgyS3RYRu0TE+WTt5g/LNmNyPN1r2bXLASsDXwFfk7MEtmvI2P8fIxHYr23v7FOzTqyJWV7zAK8AW0p6sSy7jCzF8Bvg6ohYNCLGrZX7WhjYkDwPXiTp0rK8XTxDaWgZ0ZMi4uISuKqSjR4hs/XXi4i1JJ0B/JmcLbcr8FugB1mCZ86f7dxaRLmf2Zjs5bUJDHneHOX7nMbfw/bye2nWnpS/rfWAJcnA/gklefN3ZL38eYC7gAnJ3imNGfsXAlsCf5D0fasevJnZL+jyjXIbRTaPuorMEj1Y0tFledWYqGoIuAAwh6SL2/J4beTUfn7jAA8A85IBpyfIB5l5yGymc4D9y8+4Jzk97yxydP4I4PRa5paZ1dQCvtXXvciM/ImA39UyuXuTN8+LkY38TpL0VROvPxi4XtLzrfk+urKSnXQVsAJZamJ+cnbahpIeLwED1QJeswAnA3+s7eYtYDWVZvOjeRz1JspHkdlR35PZVL0YWirjPODEEtCvXjsL2SNlTuBfkv48usdhZh1fZNNYAdsA/5R0UFleldkclwz0rAl8BNxNJndMVF4zF7CppH+2weH/ooiYFniNPDc+C5wN3Cbpg8hmwE8ALwGrVAGpiFgN2IosOzE+sLGky5vYvbWAMivkKmB2YC1JN47GPup9IuaU9EIzH6ZZp1CC+meTiR+/KgH9WciSjOsDx0ratyR1HkDORD2b7LP4U8O+3IjazNoVB/WbEBErk830xgIOaiKwP0zDqMavrX0qQfqLyYv30cBRtYebP5FBoBeAfTS04XEV2D8VmAbYWdLpbXD4Zu1SRGwDvFvKtgwJxpYb6NnJG+MbJK1T1ncj61kuTAb0j5P0bcmIWRh4TtKXbfFeuqomrml/BiaQdEFk/em/AW8D60h6qjGwX3vNJGTg/T5JHzTTse1CzvK4gRxof74s3wz4K1ku7x9kYP/V2utmIcs9zUIGsm5vjuMxs44lIuYnkzjeLYtOlXRymX05IIY2TxyHLLGwMjBHbRcDgD0lnda6Rz5qImI6cvbU+uT96gNkksqjEbEBWTf6QElH1V4zPbA42UPsolY/6C5ieEHAiFgbuJq8Vv1tVK6bDQH9zckSIvuUciJm1qCc72aSdHc53+9BnvPPlLRj2WZ2chB0bHJW19nAuZL6tc1Rm5n9Mgf1h6MW2O/DsBn7Hp3toMqF+n4yi2nVWtZwkEHG35Pd7Y+W1Lf2ut7AWuRo/hoag/rQZp1JRKxKPow+ABwq6d6yvArsT0QG9d+StFRZ9ygZiB0S0C/Lpyez1s7yDKjWUwto9SR/Lo+VQFfv6iEmIs4GtqUhsF8GuVvsmliyZ28hZ1YtK+nJ+veLLH93IJlpejY5k+qV2utnB+aTdFlLHJ+ZtX8RMQHZD2sfYEay9OIKZTC5ulbVz4NTAMuRfUU+BV6R9EjZV7tO4imBqjmA/cleYQPJEnjPk81ylwA2qq7VTbzezzjNrPa7NQkwA/ByvXxHRJwHbEb2arhiZH4GDQH9zYDDgUmBxZRlYs2sCbW/x2nI8mRvAcuUZWNJ+jEi7iDL765AxgyWcckdM2vPXHdvOEqm9vrAT8CBEXFAWe6b3Y5rbvKm99JaQL878DCwCNmc8yhJfSNi3IiYGqAEtq4D5ndA32wYLwOnkRn2B5Yga72e/iAyKLJYRGxYC+gfRS2gXxxEBm/fac030JXFsH1GziUHZy4rGaz9YmiN/e3K+umBayLi9xpajq4lr4ljkzWfXy0B/QCG1B2WdA9wItmbYTtg+yg19suxvVwF9OPntbXNrAsoSRpXk9edN8jSXdtHxNjVrLJyHgxJAyS9r6ydf4ykCzpKQB9A0veSnpC0JrAzGcw/lBzQmIhs8Lh2DKcprp9xml/53ZqIoX1mDomIJWqbXAl8CZwUEdNVv4vD218TAf2jgQmABR3Qt65uRPd65W+nOsctR85oeroe0C/r5iRLPW4K/MkBfTNr7xzUH4ES2F+XvAk+rEzhtXaqfhM8nIt6NXVu8tr2jWVAvinbLAIcEFmnlPKg911LHbtZRyTpDXJ2y9lk86l6YH9w+Xs6nCxfcCE5G+ZI4Ih6QL+UWFkfuI3MirEWVrKVBpbMzrvJRpC3AWeQ/UWqBrPdy+d/JXuOTM/QwL5GFHxoBgJ+BGaPiPlUUwvs30I2ugTYgWxq/is1TEN0sMqsc4sRNAgt16LryQDox8BOwMYlkFMF9kc4dbm9B/Qr1f+DslTkX8hr8MLAH4DJyHP9XG11fF3UHOS1DLJHw6URsReApLvJEnJTkM8d4w/vd3E4Af2xgcXlevrWxdWy8CeKiH0jG4cfGBGrw5Bm1FV84D0y8Wi6su7Hso89gXGBWyRdpexL4qQQM2vXHNT/Bco60esC20t6sq2Px36udrHtU77uXWWfRsRmtQe9z8rH30bElOS0u+FlDe8B/Bn/jZgNVwmEvE0G9k8ma/PuGxHL1zZ7nAwGV1n7L6jWdCoi9gb2Az4Bdpf0dascfBdXzpF9yEDXvMAJwNqSHqiVt6kyWHuW12zH0MD+5RGx0C8FwkZVfZBA0mfkQMM4wMplAKJaVzUzhyyr9hZwK1kmaN3GfZlZ51WCOVWwc+aIWCIilo+IxaptyrXlerJkVzeyGeIm9cB+Wxx7c6tmUZXPn5d0KLAs2Zz+SzKre4Y2PMSu6CXgCnIm4n3l62Mi4o6IWIEs73kH8CdgAfj5INVwAvrjAEuo9Jox66pq96uTkbNOjwQ2IWcq/SsijoBhEjxeJ+8b14qI8yLijxFxBvk88iY5y4mG15iZtUuuqT+KOsL0264oIhYly0NsrWwKNgHwXzLjdAtJj5UA1tVkjbyPyWl3R9MQ0I+I/cnGOecBe9Sm45lZUUq3DCwlWmYgmwuuSgYPbibrm99Vtp2HDLZuTpY0u4cM8M9NDqy9B/xR0kut/T66ssgmx38nZ1rs3jDYEuTP5y1gcMM58gyyIeNzZAZo/9EN7g/vmlplzkb2tzmHDMJtB9yurPlfD3DsBOwL7E4OLk1IliJ4bnSOycw6joZzwV7ktaYetL6cbBJ7j6T+ETEhGTw9kpxFdgRZlvHHkcnY76gi4tfAMuT52n1GWkh1b9TE8vnJwP09wOnA7OTv3tjkYNNDZBnQ+yWtVl5TXQeH/F6WgP5ROKBvNozynH8TsBg5O/hu4NfAsUBPag1xy/bzAf8B6uXIXiafR951zMfMOgoH9a1TiIjTgb+Rneq3BU4la+IdRgbtqxr66wPHkNPtbgW2k/R+bT87ktlbnwGrSHJ9b7MGtSmu4wA3kH9rE5OZL7OXze4i//aqwP70ZJO+A8neFhOS2WoPlu3easW3YEBE/IMcaJlW0oe15dsCq5CDNP8jH4yOkvRBbZsTgUvGJHAewza9nQv4FTnY+hjwqaTPykPawcDewPtkbeh7JX1UXrcQcBbwgaTVI+I4cqbVBpKuGt1jM7OOJSKOIs8PbwK3l8XrA5OQ57HTgPOV/UImIGdjHkmWRTkeuFjSD61+4K2oYQDETXFbSMkWXpcM0L9QW74teb3aWtIFETEd+bu3LDAWEOSs4yMlHdjEfjcmZ9X1wQF9s3qCUZDldZ8gE/j2rA2ELUrOlJkWOEvSDrXXzwasST6TvA7cJOlTnx/NrCPp0dYHYNYcJO0YEf2A3YA7yazO3ckHuP7VRV/SlSW4uAt5E71bRDwHfA2sR96Efw6s44C+WdNqpVtuJzO1TyEzpL8H1iADKSsB3SMCSXeVMj1vR8RtwHjAlMALwMB6hri1jlK2bDAZRJgW+DCyyewpwIrAd8DT5GDNdsDXkQ3je0rqL2n3Mfz+3WoB/f3L95iqrP4e+G9EHCHp/jJtemxgK3JWwXMRcV1ZtikwK1mTGIY2Wp5+TI7PzDqOiFgH2Av4NznD8qWy/CxgIzLpYy/g+4j4p6S+EXEteQ48kczkfJC8JnVa9axTB6xaRkT0ImeXrQk8HRFnSbqgrL6anCVyYkQ8K+npiNiKbNq5GbB62W6RGLZxJ5EN7Zcjn2+WdEDfbEjvp8nIxI93yeD8iWWGSw9gkKRHyjXiarJJOlVgX9IrEXFsfYaWA/pm1tE4U986vPpU14j4kRys+gZYrVzIezZRrmELstbeUrVdfQM8Cuws6X+t+y7MOpZyg3wVmf2yeTUbpqybi2xauhU51fzYKmO/tk2nLXPQnjVM498UuIgMor8CzEJOUb6FHBT9CpgfuA54DVikBWroH0HWMH2RLHnWmyz7s2HZZHVJt5RBpC3J7Nqla7v4AThA0illf1eRMwxWlXRvcx6rmbVPEXECmayxkqS7Ymiz2MERMQV57tiHbMS+QTXrKCLGJ4P+AyWd1xbHbp1PyQxenpxlBnApOSD9GDlofj15/7SXsndM9bpDyRKh60l6t4n9TgN0d9KRWSoJKieTzxyPkjOBF5L0dWP5qohYkAzsT0ctY394JbPMzDoKB/WtU4hsmLgFWWfyXTIo9AawoaQnahf0emB/EmBBMkt1LLKxztuSvmqTN2HWgUTEQWRzt/UlXV3+BgfWAsa/BQ4ns9VuBs6QdGdZ54B+K/mljKOI2BXYAJiJHIC5BLhPpYZ+RExEBvxflbRkMx/bGsA1wMPALvVSPhFxLznoegvwF0lflkBdbzJYMhnQF3hP0mPlNbsAJ5H9VNaU9GlzHq+ZtT/l2nMnMA8wu6SPGs97pczJmWRZsX0kHVd/vaQB5XPXULZRVnvGaPy9W57s97I48CFwGXAQeZ3agrxO3d3wOzi2pB+a2Jd/N82aUMow7gisDfQCNpN0acM2TQX2L5W0WasfsJlZM3P5HesUSib+bcAzkh6v1di/MiLWKVNco2RtVaV4vmBo3VUzGzXflY9TQv4N1ldKejEiriCD+qsBPcpD6r8d0G8dtVqjY5FT+6cmf243V+UpJJ0cERcBY6tWM79mWzKAflbZZ3MOyCxBlhI4qCGgvz8Z0L+B7LfwZXX+Jutf39TEe90H2JUsn7aFA/pmXYbIGTvjk1nOFzcOZCqbHp5LBvV/D0PPZfVrl4OmNiqqQHvtmji4YfmdEfEaWTZn3/JvRXKAaQDZY+ju8gxTPZv8AD8vj+TfTbNUC9BXfzOPRYTIv78NgI0j4nlJz1avqWXsP15mGt8LbBIRuziZz8w6OmfqW4c0vOzTiOijUp87Is4BtgbeImvkP92QDTMH8IpvlM1GXUSsTWa7PAusK+n12rp6iZcnyYfXhcis6/XVyZsRtgdVUCGymfHd5Kykyo9kDfvrJX3XsH29nNm2ZFZhX2BFSe+N4jH0IUsFfN94bGSZn8fImv1zAt+Wh65qBshtwP5VsD8i5gG+qpcdKPuZEriY7JHyKvDnasDCzLqGiPgLcD5Z0mR3lUbaZV3V2H0W8nr1LFmT3OUWbLTVBs3HJ3sJzUr2C7oGeEnSlw3bT0z2rPkTOZgd5MyzEyTt1ZrHbtYR1c7lVVC/se/EgmRvvXXJ0qBHq9aoumxTvXYe4FNJH3r2sJl1dN3a+gDMRlW5kR4UEb0jYsOI2DIiNgNQreGmpG3JGs0zANdExPy1gP6mZCOrv7XBWzDrECIimljWvXx6C5kxPQewTmSjqkqP2rbTAfcDRwB7O6DfOkqAvif5YPN74J/k1OTTyWv/P4BtImK8avvy0t4RMXFEnE02j4QMlI9qQP83ZFbiyRGxcOOxSeoHvE8GNQaVh6yDaSKgX+wC7BjZhHDIfsqndwJHk/W0HdA363qeIMturQdsHhET1tZVwZrZycHEex3QtzFRgotVg85/kwPL+5ElQO4jG+EuVNu+Zwnybwn8hbx/6l1WzxPZBNfMhqP27D8hsG9EXAncHRF7Vn9rkh4nS1tdS2bs7xsRc9b3U8vYf7YE9Ls7oG9mHZ3L71iHUrJJB5bs09vJOpXVur+QNbwfLgEjJG1b4pJbAzdExObAvGSAaHzyBtvMGtQyYrqRPSd+BXwC/AQMAvoBV5J1jPcGBkXENZLeqpUz2B7oTgb/H/WNc8uLYRt+jQssQA5u7lrOi9dHxDNkwP3YfEmcq1JDnzynXglMQAbLd5D02igewyLk4MF8wP+AmxtmAFRZUe8DKwObRcRUZFDkZwH9iFgP2BTYT7WGzADloeyU/HTYElBm1jWUcm9nk71BDgfGjYh/SXqiDHAuDOxFlmd4qC2P1Tq28hwyKLIv1z3Ab4ALgeOAXwM7ARsDk0fEMZIerJXXGQBcGxH/Ap4iB6G2UtbQd7awWRMaBtHuIJ87fiCfLxYGXoyI0ySdV8rrnFBeukF5/TAZ+/W/s6Zm/ZuZdTQuv2MdTkT0JrNcliWDhXcB6wCLks1xDwBubZiSdxqwQ2037wGrNE7LM7NhppWPTQZIFgVmAT4CHgSulHR/RPQg/652AyYBniQfbt8A/ghsBXwJLC3p49Z/J11TRIwL7E8OwuwNzCXp84joXQ14RsS6wGFkEGxf4BwNbY67K/ANcKOkz0fxey8DXE/W7j+PbF6uemZsbfrzb8jfp3GBaqB2z3q2fUQsWfbTC1ivZGKZWRfXVBA0IrYhg/czAJ+R94f9yTrmvyIHN09t7WO1ziWyT805ZCmdo4HjJfWPiN8Cx5C9G0TOUjxI0kPldcM0u63KhzQMxptZUbtfnIgsJTkrOdP0IDLhaDvyuf994ERJp5XXLQjsQc5Q/Sdwkmo19s3MOhMH9a1DaMjynAd4gLyo719uiCcnL+w7Al8D+/DzwP5WwMxkHcuzJL3dmu/BrCNoqMV+H1m65T0yyDsVMBGZ7bippMtLYH8DYEMycFL3HvBHl0RpPaVk0mlkabG7yczBRYC+ZaBmSFChicD++SoNwxqDDyP5vX9fvudXwD6Srqod0zDZUWVZb3JQaE9gQuAwSUfWtlmBbCS4GNn89qJR+s8wsw4rGnon1b+uB/QjYgFgSkk3l69XAFYH/srQMqPPAadJurBsM8rnN7NKRCwN3Ew2c99KUr+ImIsMNP4Z+Dt5n7Q9ObB0hKQHymt/dj00s+ErZRdPIktXHQccUwbR5iTvEdchB9G+J2d0nlFetyA5M399sjn1rh48M7POyEF9a3dqo/KNGS3jkhfmAWQW6rySvi+1KgeUUfwtyODUV2Qph1vqgf36/lvtDZl1MOUG+moy2+wo4Hgy23FmsgzKnmXTTSX9s5TomZAMpMxONj99DrhZtcam1joasgUhm0I+VFvfVGB/OjLj8JRaKZ5R+Z5jk7M01gG2k3RO9b3I+EWT59yImIE8b1elmu4D/g+YhixNMBbZ+PLksr3P32adTBP3e/VEjlUk3VpbVw/oL0Get2YElpX0cm27GclB6B+BLyR90tT3MvslTQwwLU/W7Z5H0lvlOnYweX90oqQ9SwLS5eTv5r3AkfXrsJmNnHJPezfwOLB2LaB/MDmIdhw5U/hK8vn/iGpGVkQsBmwCHCXp3bY4fjOzluagvrU7ETGXpOfL50MC/GR95z8Aj5CBnkUodZRr201I1smvB/ZvlvRTbRsHhcxGICKWIktc3Qes28TA2B7kTTTAcpLuad0jtF8SEbORwfq1gYeBbRoCXvXA/jrAGWRW66zKhn6j+v0mAV4EXpG0dFnWGAiZjSyBMQ85o+o6Sd+UOqlLkYO1vyX7/fQlm1/+Q9LVjcdsZp1PZPPD8yTdXb4+icy03FjS5Q3bLgEcSfYB2VbSeb90n+f7PxtVMbS/0KTkzLLjlDXwfwN8VJKLNgfOJWe7/bX22kuBjchkpJfI6/ATbfA2zDqsiJiGnBWztaRnImJaclbMlgwdRBuH/BvcgJwlfHwtY79K/hvmntTMrLNwo1xrVyLiPGDLiFhP0jW1h7PBEXEMMAdZ3/ttoGe5ma5uuEPS1xFxftndvmRQq1dEXCfpJ/CUV7ORMCulxnkpb1WV5OkuaZCkE8oA2n7AVhHxX+AnB1xbV0PGavUzCqVXIuIgssTNasABEXGopP8BlG27SRos6ZqIGAj83+gE9IvJy7/HasdWlcqYkJzFcRwwNllDH2DniNhF0n1k88BbyXP8RMA7wJeSvqi/v9E8NjNr5yJLJK4LLBoRKwKbkwH9a4GnG7ZdgJxBtiAZKP1Hff3w7vN8/2ejqjxfTEzWx5+JrN39D0mvA0REH7Lc3RfAoWXZOJK+JzOLpwY+JZOSPHPRbBRJer/MjqkC8nOTzagvkLRn2eb7iHiSDOpPAZwWEd9KuljZoNpNcc2s0+r2y5uYtaory8erSvYoJbDfXdKd5APfR8D0ZN3o6oa7e20A4GvgfDKDa1ayiWfP1n0bZh3agPJxuvKxqgE7KCK6l2XnkINriwDjOODaumrnvG5lJtM0MDRoVc6Fr5BNI28nH3QOiYhZqn1Ugf3y+b8kvTEGh/QDMBBYJiJWqx3HysCJwEVk0P8dMkD3HvA74MyImLUcw4+SnpJ0l6TXyCbL1Xvx75dZ53Yped82DRkM3QO4imw0+kq1UUQEMCd5H7hdFdAvA38O2luzKNfWKF/uRF6/DgYubth0cvJ39lPgq1I66vuybnXyWXtnchbcp9U118xGnqSvyszO7mQPvV6UOEBk42rIe8vHycG1d8mSPWZmnZ5vLKxdKVOulyxf1gP7g8oD24NkneVPgM0j4vDa+sbA/kXkjfgGGo0a0WZd2Ftkk7e1ImLS6u8Phsl0+YgMzE5LBleslZSgwaDIOvYnkeV1Xo+IOyJibxgyGNpN0qvkwObtZE+SnwX2m+OYlL0TjiBneJweEcdHxCVknf3NyaD/ocDyZOPbBYD/kj0YThrOPlX/aGadU7l/6yfpQOB5MmDzE3BTmXVUBVer88GVwMqq9e7wwJ81l9rv03hl0XTAK2SpjwENgflvgNfJJKLfaWgviF2A3wP3SfqkzCT2ALXZmOkJTED2SukPmRBS1m0JTAKcQA6ivV9LRDIz67Qc1Ld2R9lIaqnyZT2wX5WLeIgM7H8G7B8RR5T1jYH9r4Azq3ITZjbUiLLFSjmUu8jGuGdExMRV+Z1acH8geXP9NpkRY62gnOMGlvqh95EDl1OQ9ecXAI6OiGthmHNmY2D/gIiYvQUO7wpyBsd0wO7k9OjxgcvIwdVDJX0M9Jf0KfAXsrb+tBHRuwWOx8w6gOr+LSJ+D8wFfAj0AY6PiMXqA3vlnPajpKfL1w6UWrMq184Jgc8i4jXymnZlufZ2r/++lSSiG8j7oXsi4sKIuIOcofY5Wee72tYD1GZjQFlK90mylONqkb0uql5fKwEP5mbqV7Z3yR0z6/TcKNfarcgmaPeXL9eTdE1ZXtWOXhK4BpiM7Gp/QFnvRjhmI1AyvQdGRC9gFjKz5Rngh1qW2UzAjWSN88uBner11iNiG+Dv5N/gFrXp5tZCqnr5pYbvTcASwNnA/sreB4uSwYVJgTsk/bG8rjpnzkrWtV+NLFG2fVVrtBmPcTIyG385Mjv/KuCFMshaDSZVNx7Tk1m5fYHZPKPKrGtpKsM+IpYhZ2OuCxwIfAysLemR2jbDNLx1pr41t8jm7/eRzdsBrgb+UoKK1Tb1vjaHAVsAU5GzTJ4B1pf0np9LzJpPRMxIJh5ND/wfeU85N/AGsLSkD9ru6MzMWp+D+taujWJg/3BJB7fRoZp1CLW/nXGAm8ns7nHIB9A7gCMk/VCmrC4BnEmWSHmJLGn1FlkiayOyadXipf65tYJShmJ34BAyA/BgSd9GxG/JppJbkgGxKYBbJa1WXlf93Ocg6wIfKumlMTmO2qyokbqRqEpo1IIgG5BZ/KdL2nlU9mVmHVs90BkRi5DZ0DdVpRTKNegoYE9qgf16AD8ippL0Yfnc5w9rFrXr5RRko+bFgFeB1SW9ViVGlG3rv8dzkI1xPwTeU6kB7oC+WfOKiNnIMjsrAx+Qwf2tJX3ovzkz62oc1Ld2byQD+5eT2TEHSjqyjQ7VrEMopU5uJrOpHyRrU/4OmBK4nsxG+y4iegAzkYH9ZRp28xywoaSXW+3Au6iGbMCJyOz3aYBFJPUtWUsHAZsCp5PZrc+T/Q5ul7RKeW11zuw5Jhn6DUGMCYFxydqm31czNkpAbvDwsmlLEO+8coxrKxuhm1kX0HAO2Z0sD9Yb2JGsl99NQ3u5HEU2/B4mYz8i5iP7eLwsafc2eBvWiVW/oyWwfzWZ5PAwsEypq18P7Dc5U8QzSMxaTpltPC3wBdCvzFh1QN/MuhwH9a1DGInA/rJkGYo1xyT71KwrKAHVm4ELJO1VAiezkA+ucwK3kAH772qvWQOYgSzV8zTwiKRPWv3gu5haqaQqwNCbDODfK+muEuTfC9gbuFDSluV1q5M17scC/i1p5WY6nnowbntgHfJ3ZgA52+MqSZc0vGaYwEY5Xx9A9k7ZQdJZzXFsZtb+NQxSHk2eu/6PnEF0c23grzr3NQb2tyTr7f+VLPW1t6TjW/+dWGdXu+5OTt4fLQk8QAb2B9cD+2bWtjxby8y6Kgf1rcMYQWC/uukeq5q2bWZDNRFU3YJs4jalpH5V5nZETEDWkJ2bJgL71rpqg5bjAg8B50k6s2Qn9SlT+2clgwxPAquWkjg9yED7g2RD8emBayWtO4bHUw/GHUMG2b4C/kuWcFqybHoCsE8TtbLnAlYgA/o9gf0knVZ/r2NyfGbWcUTEzsDJZB+QQyT93wi27QYcDuwAjFdbtYekk1ryOK1rc2DfzMzM2rNubX0AZiNL0oNkZifAVRHx57J8UPnogL5Zg/LAOTgiekbElBExPVkb9pkS0O9Tm0rel/wbew5YFbi8BJSraa7VPqMN3kqXU35uvciM+98B80ZEL0n9JX1TNluX7ClyWQno9ykBhleAd8jBmzvJTNcxPZ4qoL81GdC/jQxsrCJpaWAhspTTHsBm9ddGxLrAvcDxwOvA5g7om3VNETEd8BfgfeCwekA/IsaNiDkiYvWI+FUZTBxMlhXbFjgH+CewThXQL0F/s2ZXAvrdJX1KXm8fIAP795Rr18BSbs7MzMys1fVo6wMwGxWSHoyIpciM/WsiYi1JN7b1cZm1R+VBdGBkU9xLgHnJ+pOfAN0jYnJJn9YeTHuU7O8qE21V4JKI2EzSt9V+Pb21ZTVk/k0LzAMcCxwpqX/D5r3Lx8kAJP1Uvt6D7IdwnaQzm+m4gsySXRvoSzbbfa62yVJkuZ9byEGFupfI3gyfAndLeqXs0wF9s06uib/zSciSbxdKera23ZrAJsDqQHfgI2CPiLiunPuuBK6MiN6S+g1n32bNqh7YLwPUVcb+MxExr2t4m5mZWVtx+R3rkCJiGeAu4HeSXmjr4zFrryJiLPJvZRHgRTIIPAEZBL4E2EXS17VSL1Ud4/GAe4Dfk4GUjRzMbz1lIGYbcvD9UGBGSR83UUppceB24CngjPL5dsAuwNvAGpK+aMbj+jXZU+FeSWvXlh9M1sS+DTigCtRFxK8lvVM+H6aBmeufmnV+DQ2ylyEH+CYHngUeJc9V35K18quGt1cBY5M1878ElpL0Rm2fPndYq2soxXMnMBdZxvDTNj40MzMz66I8XdU6JEn3AOM6oG/2cw1TwZcBZgOOJEu4LAbsCvwP2IDMgpygBPTrGfvfAsuRtdyPdQCl9ZRSEqeQpXNWIrPevyqrG38OrwDnAQuS2YNvAseVdVs3Z0C/6E0ONNSD8/WA/v61gP7EwMER8UcYWiqt4t8ps86vFtA/AbgJ2LSU2zmFHGy+A3gC2Bm4G1hF0gZkE+7LgKmAlRv26XOHtbqGUjzLUgL6Lv9kZmZmbcXld6zDkvRDWx+DWXtTMhgHlQz9hcjA/FfAESUQ8llEXAt8DJxFZkZ2i4hjJfVtCOz3jYilHEBpeVWzYhhSS/96YBpgxbLJ6sA1jT8LSZ9HxKnkIM0OwDdktv5hkt4cg+MZJhO21kfhG7KEzuKl38IODBvQr5fj2Zism33D6B6HmXVMDRn6qwFbkM277yibHEY28l4N+IGcafSMpHfKa/tHxHvkAOJzP/sGZqOguco0lfurbpI+b879mpmZmY0Ol98xM+tkIqIHmfH4W7L/xKeStmusQ0zWQD8b+DVwMpmR37chGOMyB82sVupoEqB/1a8gIsYH/ibp6PL1csCOZNDrJmBfSS+PYL89lU2PezVRe3+Uj698PiPwTRXAKMvOJEv8/B85++Mmsr7+M7VtlgcuJGtirzcmAwxm1rE0USbsL+QMohXqNfTLunGBn2p9RKrliwGXAj8Bq0t6vaWP2zqnWtmc8chmt+9Iuqutj8vMzMxsTDlT38ys8xkLuBeYEVgLeCMiJqmXYilB5QfI4OzZZEmeQRFxoqSva9s5oN/Myv99FQw/CTgtInoCjwGzRsTTku6QdFdEiPx5rga8GxEnSXq7cZ8liDagfDmgcf3Iqte9j4itybr+AyNiTUmflM3qpZzeAs6R9Ew1ABQRSwAHAhMDezigb9a5NfbLqA0KngbMB/QD7qmV5oqynSR918T+libPIdMBWzigb6OrFtCflOwjtBJ5LV1M0gejuc/6wPd41cC8mZmZWWtzDUAzs06mPGCeCpxG1lifFtg4IsaGYQIqg8hM/u2AN4D9gR1rpVasBZT/39+TQe/dI2IHsrzEdMBBwAPVtpLuBo4G7gO2B3aOiBka91nPih3dgZgSqKgC+keR5TCmJrNl6+UFPgaOIutgzwBsW4JwS0bEzmQd7MXJmQVX1t6zmXUyEbEUcF1ETNSwfByyZv6iwJzA+JG6l2D+z85TETFTROwJ/JM8h+wp6ZKyzucQGyXVNa0E9B8kZyeeSw5IfzgG+6wC+psCu0XETM11zGZmZmajwuV3zMw6mVrG9MTAlsAewLfA3sCtkn6ql9UppXiWA44gmxi+0lbH3lWUUjsrks1wpyAH2Y8CDik/u+7A4NrP6A/AAWRQ4nTg1KYy9pvp2PYjfxduBQ6X9HgT24xFZuAeCKzQsPoN4GhJF5RtXXPYrBOKiN+Sg8d/APaRdFzD+smAi4A/At8Di0p6vqlzQtl2W7LW/vPkOaQaFPQ5xEZLKe90HTm4dARwUm1WW1PbD/d3rYmA/qlk8/rfSvqo2Q/ezMzM7Bc4qG9m1gnVAvsTkYH9fciGufsCtwwnsN/HDahbXu1nMxlZl34yslHk3sC59TIWDT+jemD/FOBsSW8087EtDtwCvARsI+mFhvUTAD3qpZwiYj1gSnKmwf3A/6qBIQfjzDqn0jfjWOBxsnfG6ZK+rHq3NJznzgdWJWf3bCDpzeEE9mcG5gBek/RSWeZziI22iFiRbB5/IXlNq2ajdSeb0S8NfA28J+npsu5nvYQaAvqbkTPoegPLaNgG8WZmZmatxjX1zcw6mVoj1pD0VURcWFbtQz6IEhHDBPbLw6oD+q2gFixYAOhFBtHnI0vvDIyIqyR9U21b+xndWypQDAR2A/pHxEGNDSbH0MzA+GTT5CEB/VInf3lgbaB7RFwEXC/pVUlXNbWjctwOxpl1MqWJ7W3Au8Blkh4sy08B5i49OPqWc8BnEbElcDFZz/zUiNhR0tuNAXtJrwGv1b6PzyE2puYqH8+sBfT7AAeTvWrmILPtn4+IoyVdNZIB/XGAxSU930rvw8zMzOxnXFPfzKwTaQiSLBJDG+SeDxwDTEQ+kK4cEWONbv11G3OSbiNrTm9O9jPoDxwOrFPK81S61V5zL9lc92bg0uYK6NfqVVe1gScoy/uUGtc3kLMEJiQz8g8FNqlqZJdth7mn8O+WWedT6pMfBfwE7F8L6M9KziJaCvhHRExQG5T8DNgMuBNYBTg9IqavBp+H9718DrFR0fi7VL7+vHy5TUTMGBFrAI+QM+N6k7PeLiP7PmxZekHU9+GAvpmZmbVbLr9jZtZJNDx87gDsAFwBHFGaxU0IbAXsTmaIby7pprY63q6scXp/CSRsSAbOe5ePV1cZ+2WbhSQ9Vj4fuyVKJUXEIsDDZBPBR8jyBAsDr5OB/FvI+tnnktmN80oarYaDZtbxRMRUZMmdzyTNW5adA3xJ9uE4kJzV8y9gi1rGflWK559l/a3ADpLecYkdG1ORDZgHRURvcuD5M0lfR8SvyZr68wHfAeOSM0yuBQ6T9E35vbwZWBCYRdLrZZ8O6JuZmVm75qC+mVkHUguOdB9B7fXtgCOBwcA8kt6vvW5CYCcya3LF6uHVWkZTtXmHtz6y+ezGZNZ+HzKwf5mkHyOb8p1F1tHfszmOqeF714MX65EzO8Ymg/tnkH0Y6uV4HiUDJ/NJ+mRMjsfMOo5yDbmCHNxbjQzQ70GW41kTmJE8Vy3DLwf2/wNsL+nN1n4f1nlERA9JAyNiYuB4YAnyd2tfSd9GxExkksMMwCvA5cArkvrX9vEsEMAC9eVl3VZkuZ7xgCUc0DczM7P2wkF9M7MOoJaFVgVGxq9ncde2247MJvsOWKxkQTYOAEwAdJf0Zeu9g66jBABek3R/+brJwH5DIH2CEvgam6EZ+2MDF5H9b9Yna90vLunZMTi2Ib8LETElmbU4CPiknvkfEdOSAYwPJX3dsI8/AP8GbiIHh35ylq1Z1xERG5GB+7GB7mSQ/0gNbW47GzkYOKLA/lVkk9I1PWPMRlcM7SE0GXAv8BvgQXKg6TXgx8ZB7IbXdy/bHk3+Tu8qaUBt/W+Ba4BZySQJB/TNzMys3XBQ38ysnatloY0F7AnMSzZ3ewR4XNLZZbuZyNqw0wKLNhXQt5YVEWsC1wP3AAdI+m9Z3lhupx7Q3xKYGzix/Mz6AOsAO5MlAwDeAlargmajeWz1gP4OZEB+VnJGx53AFZKubzzehtctQtb9XxTYSNK/Rvd4zKxjaTgvPAQsRGY37y7p1NJXI8oA9C8F9qcAFpF0Q9u8G+ssInvQ3EZeLw8HTqgH5n/htXuTJQk/B5aR9HHD+smADYC7xuT6a2ZmZtYSHNQ3M2vHahn645CB4gXIh88fgKnJLMmbgU1KbdiVgP+T9GE1GNBmB98FRTaL3IVsfvsgcJCkR8u6KphVD+hvR5YLeI/Mwv+iLO8BTA6sQc66uFfS+2NwXPXveQywF9AXuI+cCbBK+fpQSaeU7erB/G7A2mRpoLmAXSSdVn9fo3tsZtaxlBJdVwAvkeV2upHXoGsisllpOdeNMLBf259r6tsoq11TNwMuBM4h+zQMGlFCQ2Sz57nIYP5ywJvASpLebep1To4wMzOz9spBfTOzdi6y8duN5MPnKWRt157AWMDVwGLk9PAtJH1fXuOH0DYSEb8BdgO2JoPmPwvsl8//Rjaf7UeWSnq7pYNbEbErcALZpPJwSU+U5RcDm5ClePaQdGpZ3g2YHjgd+CPZYPBwSedX6x2MM+taImJqYFWyDNeKwMnkAPPGkq4dQWD/FmDTxpJeZmMiIs4C/grMWK6jI7z/iWz2fB/wa3J24/6SPvJ9k5mZmXU0DuqbmbVTtSy0dcj6w5cB20n6rrbNU8A0ZHB/3/o6a321+r4zkoH9rYD7yUD4Q2WbAP4E/J0Moi9Uyu606MyKiJiH/D3qC2wr6ZmI6EWW+TkI+BaYsmy+Wy1jf1xgI2B24GpJj9Tfa0sdr5m1XxHRsypxEhG7kM3ZhxfYnxU4D1gcWFXSbW102NaJlEHnHuRg0XJkOafHRrB9b6CnpO8iYmby3ulxSd87oG9mZmYdUbe2PgAzM2tarTzB7wEBR1RB+4joHhGPkvX1zyXrt39XArCUbaK1j7krK0H5wSVQPpDMar8fWB7YNSIWgyE/1w+BJxga0O/eCqWS5iVLZRxeAvo9gO3IgP4nkqYC/ly2PSkidi/H+x0ZkNunFtAPB/TNui5JA0pQlTIAuB85SPnPiFi7NiMpJL1Knmv+7IC+NRdJgyX1B14ui6aFIcH+IUozXIAZgCMjYhJJr0m6twT0wwF9MzMz64gc1Dcza0fqD6MlOAwZiB1YW96DrNe+EJkdeZykvmX1chGxEwwzKGAtrArKl94HtwBPksHy6csmawH7R8SiJYDwKPAntW4z4yeAIyXdXL5eiayt/zmwZFn2EFmaB+DwiNgThgRPfqp25N8tMyuDmFVG/qkMP7DfTdKLKo21G4OuZqMqivLlC+XjsRExc/m97Fa2q19fjwVWAyar78vXMzMzM+uofFNtZtZGag+dUT52Lw+jfQBKBhrAi2QN/V+Xrx8AFmZoQP/b2m73B1apZ+xbyyuN+fqQNaaXBC4mf16LkaV2HiCD6AcDi5bX/FSCXS0W0K/9bnWT9AJwXG31RsDYwHq1xsqfAR+Tgf4+ZJBknpY6PjPr2Ep5naYC+xdFxLpKgxte41k+NtJqmfZDlN8rlc/PA24gM/FPrQL7ZV3V7H1XskTPo+QsOjMzM7MOzzX1zczaUETMB8xP1ir/umR6vwU8KmmNss3KZPb300AvYE4yoH9CLUOfiDgK2IfMvj7JgZPWFRFrk70NLgO2ktSvyhKMiLmAPchmtHeQpZQeboFjaLLOfePyiJid/H16BlhO0g+1fgBnAYMpjQQlndjcx2lmnUtDE/Adyea53YBZJL3epgdnHVbtGjoxeW8zBzkY/Q5wDvC8pB9LH5sLyEH1N4E9yzYfls+3Aj4DliyD2EN+X83MzMw6qh5tfQBmZl1VeUj9JzAb8GNE3EkGfMcFXoqIXpL6S7otIs4E/lZeegFwUP2BNCK2BbYFHgcud0C/TcxcPl5VAvq9gAEAkp6PiOPJRrQrAj+UoMJDzfXN62UGImIVsvTP2MAdkv6vYfMfyZJOYwMTAD+UgP6iwOrAtZKure3bTXHNbLiqjP2SQH16RIwNfOuAvo2uqtZ9REwG3EsG9L8lewwtA2wAnBwRl0p6JSK2BE4A1gCuK7sZRDZwfg5YowT03RTXzMzMOgUH9c3M2oikLyPiNGBnshHpF8CkZC32UyT1rz18nk3WgV0XmAVYMiKeA8YhM8C3BH4CNpP0Yeu/G2No34PZgVtq5ZMAkPRCRJxCNs5dC+gdEcdJemB0vlmZ5aHS9LYe0D8S2Le26VERsR9whaT3y7KPgeuBDYHdIuI2YHxgb2AS4O6GY3dA38xGqCGwf2y13IOCNjrK71Mv4HxgGrIm/j/IcoSbkY3d9wKmiohjJb0CrFWSHOYEfkfOfHwMuEbS5w7om5mZWWfi8jtmZm2gHuQoD6AnAmORTUo3l/RFrU5xVdLgd8BOwBZlN1+RWf09yTIqG0t6uVXfiA0RESsCt5P18//hwhEiAAA4SElEQVQi6e3auiBLUXQjAwzfk/X2bwQ2qDehHcnvNRvwEtkgcFNJz5bluwAnAU8BF5KzB9YmZwicAZwm6a2y7R+BQ4H5ynFVNwS7SjptlN68mZlZMyj9XQaWvkOTA48ANwG71wPyEbEaWVpnETJD/3BJP9TW96lfWz24ZGZmZp2Ng/pmZm2k9uD6BPB7MlN/XGBHssb+N2W7YWq/ltrtS5NN4T4mp6XfJenjVn4LXU5TdXhrtegnIRvkrkj2NjhH0ndlm56SBpTPPyJr7/cDLhydgZiIGAu4hMxUfBTYSdJTEXEPmXG/uaTny7arArsDSwBnkoH9N8q6pckaxGuSgw13Srq+/r5G9djMzMzGRCm5cz7ZT+hgYPVyjetO5jpUSRGrkgH9XwN/kPTf6jpduza7fr6ZmZl1Sg7qm5m1sVJG5Q9AXzIYPDVZUuciSd+XbYI8ZzvI2kZqDfu6kbMqJgW+k/RFbZtNyADDBOTP8tpayRsiYifgcGBZ4KkxCTSUmtVnAZuSAfkDyRkfx0i6oho0KtsuBRxA1iGuAvuv1/Y1ZNChfO2AvpmZtYmI2Bs4mkxcmBRYVdJ/auvrjZmPAPYjm9RvCkNnOJqZmZl1Zq6pb2bWRkpwWJKeBp4uywaSwdkTytdDAvuNQdaqka6z0FpebVbF2MCRZOmcGYHvI+I64G5Jt0q6tGQY7gccBywXEbeQDYz/TPY++BD4YEx/ZpJ+iIjty5ebAocBvwK+LsuGZChKur9Uc4LScDkiTpH0ZllW9QOo9u2AvpmZtaiI6AMMbuxBQ2bpT0z2fekBrBcRj0nqC0Pq7VeDzxcAfwWm872QmZmZdSUO6puZtZJapncVhJ9A0lf1bSRdFBGDyenmVWD/Eknfls+3BRaXtEn1EOyH2JZVAgcDI2Ic4D6yVNI7ZAO+2YBdgC0jYl9JZ0k6KSK+BjYCVi7/Ku8Bf5b00Sgew0zAb8kSOu8BD0t6qgT2/0pezzcsm09RPqqhcWU9sL8N0CMiTpL0un+HzMystUTERsBCZPm3vhFxKdlg/mOA0tT2OPLa9hdgOWDJiPh3bVZZdUEbUD4fgJmZmVkX4vI7ZmatoJbpPRZZWmcBYBqywe0FwLNVRn7ZflMysD8VWcblRrJ0ysHAtMAMkt5p3XfRdUVEL7IO/irAUcDJZLmkhYGVyNkVkLXtzyivmRZYEFgU6A28DNws6d1R/N6LA38neyiMVRYPIDPur5b0TZlBcAqwFfAlsLKkx2v7qJcqWJLM6l+SrEF8/6gcj5mZ2eiKiNOAHcjrWH9gnLLqPOA4SW/UkiAmIe+BdgDeJhvjPlD1HCr7Owg4BDidHGR3soOZmZl1CQ7qm5m1sNrD6TjAnWQg+BuyUeqkwAfAucCZ9cz9iNiYLOPyG+Crsu1nwHKSXmjdd9G1RcQSZMO+B8lM+34N67cGzgF+AjaUdEMzfd/lgFuBwcDlZMB+JrKx7UByEOHvZdtxyMD+lsB/gb9Jeqa2r3pgfzlgPEn/ao7jNDMz+yUl+34P4G6yZv775Ay0XYE5gAMkHVW2rRrdTgzsSwb2PwH+DVxVPt+EHMz+hpzFOEqz4MzMzMw6Mgf1zcxaUBVILRn6twOLkMHfA8kA8OZkc9MvyaDt0ZK+rr1+RWCd8rqXgP0l/a9V34QREduRDWZ3k3RKNVDTsM1+wBHkzIttYMxq00fEssB/gNeAQyRdWVtXBUYAFpX037J8LOBsssb+CAP7tWVuimtmZi0qIjYga+XfA+wq6bXaurXJ2XD9gXkkvVKWV/dQVWB/S2BCcqD7Q7I8z4vAlpLeberabGZmZtZZdWvrAzAz68yqZm7k9PEFyOnh+5Zmb78iG66OBUwA7AzsExET1V5/h6StgMWBTR3QbzN9y8dJysemguA3Ax8BawFTNkNA/06y3MCuVUA/InoDSNqLHAQCWK+s6y7pR2A74BJyRsiZETFvtd+mShI4oG9mZi0pIsYjr1WDyEHq18ry7gCSriWvW73IsoOU5VVfmC+BY4ALydmNX5Nl5BYFVi0B/R4O6JuZmVlX4qC+mVnLm4rMyH8GOEjS9xHxG+BQspnqP4AVyayzrYF9q8B+GRBA0lclYGtt44vycfOImLkKNNQ3kPQ88Dw5QDPp6H6jiJgSOL58+QzwVFneTVK/KggCPFY+TlA+Di7H0RjYPzUifj+6x2NmZjaGpgNWB66S9GR1/SylCavn0ffKx8VgmPufKrD/BdnT5ipgPLKvzFRkdj+SBrbWmzEzMzNrDxzUNzNrYZLeJ2uxnyLph4iYjAy6bgJcIGkb4EmydvpEwPrAnhExsbOoW089SF99Xgs83AFcSwYQDoiIaatAQy3IDjAuOTjzyRgcyldkj4XnycGeXSNi+trvQnXt7lM+vlGOcUgWfi2wfyE5y+OS8ntnZmbWqiS9SJYdfLZ8Xb9eVde2Z8vH7g3Lmwrsnwb8lrxWLlobGDAzMzPrMnq09QGYmXVmVb1ySRuV6eeQjW+3AK4vpXWQNCAiriczz/qQ5Xr6RcRhTZVMseZVa2bcHRgbmCki3iyrvykfjySb1G4IDIyIoyS9QZYTICK2ARYC/lV7zageR5Rs/AvIngv7AjsC3SLiDEnvld+Vycm6/S8D19VeO0xgPyJ2IDP5H5f02egck5mZ2eiq3Qcd+Qub/lA+VvdKw/R8qa5vkr6MiKPLJjuQfWR2iYh7fb9kZmZmXYmD+mZmzaSphqOSBpc6rwMlfVsWb0MGWi8sr+stqR+Z3f0ucCywMXC1H1BbXvXziYixgRPIwPy8ZIPajyJiX7Lp7PNkpuERZDmlpSLiDOBTsszNRmSZnv1Ht1RSLRuxf0RUNfP3JQd7ukXEIWTg425gamBv4PXqtU3s78eIWK+qM9xUo1wzM7OW8kszDmvXpQFl0aRleU9JA8rnSwOfSnqpqrFfAvsim8YvL+melnoPZmZmZu2Rg/pmZs2gFhjuBcwP9AQ+kfRKWd4Y8BcZDKYE9CHL8fQGbiLL8vTDWlT5uQyMiHGABxgazH8cmBhYEvgPcABwAfBv4DNgP2AN4KSyq0Fk0H+jqgHg6BpOYH8fYFvyur0iOWPgUOAf5fiHG6x3QN/MzNqr2nWpLzAQqMreVQH9HciSO/+IiL3LbLUqsH8c8IykK9ri2M3MzMzakoP6ZmZjqCEwfCuwBPlQ+klEXCFpt5Kx370EWF8r6/eOiL8CP5KZ35sCzwFfOaDfOsrPpSdwMTA3GSg/opTiGYfMkt8eOIas83uqpCeAtSJibWAKMmP+ceC/kj5upuNS+b1qDOzvTA4InSDpKBhaOmhk9tkcx2ZmZtYC+pFB/d7VgojYDjiIzOI/rQr0166RnwNXlG1/NlvSzMzMrDNzUN/MbAyVwHAv4AYys/s+4G1gTbLO6zTA1pL6lpdcVtb9iQwkDwZmBj4CdpH0A9YiqhkVDYvnAJYjS9ocXwL6PSV9TzbF/QA4HDgMeJr8+SLp2mY8riZLN5WPVWC/G1k/eG6gR0RMIumLkQnom5mZtXODgV7k9S3IAfXDgP7AApLeqV/Dh3fNNDMzM+squrX1AZiZdRLzAfOQNdlXkrQFmbH/KLA2cFFETAgg6R2y2erVZJme3mSG/9KSXmn1I+8CImIjgDKjonFAe3pgfODfkn4oQYMBEdGtvOZs4DSyge6JETFxMx9b9yoYERHzRcQqEbFZRCxXGvciqT9wOXA6OdNjG3Kmx7TNeSxmZmZt5DuyyfxEZB+ZKqC/SAnod29iUN7MzMysy3KmvpnZaGgis/rX5NTxw0tmdS9JL0bElmRAeA3gwojYQtJXkl6PiC2AcciH1n6Sfmr1N9IFRMSuZDB+DUnrVoH9WnCguhYuFBFjVU1uywyM6ud8FLAWMCUwLvBlMx1bt1rN+wPITPzJa5s8EhF7A/8n6duSsS+yFM/2gCLidEnvN8fxmJmZtZEB5PXtd8Dvyabwi0h6e2TLzJmZmZl1Jc7UNzMbRSUgPDgiekbE1BExPfA92eRN5eGzf2nk9gqwI1naZQ3gglrG/g+SPpPU1wH9FnUH8BKwdkRcBUMy9nuW9Y+T5ZLmBiaDzJ4v2w0uAzQDgfeBXwGzNNeB1TL0jyCzEj8FdgW2Bq4nZ39cBqwaEeOWXguXkzX+PyAz9neNiF831zGZmZm1gQHAT2ST+q9xQN/MzMxshBzUNzMbBdX079JE9V/Ak8CbwIlkGZ1xS032HqWRW0h6lZxKXgX2z4uIidrqPXQlJRP+JbJ/wQvAOrXA/oASvP8CeASYFfh7WTcoUo9S+gay/M475ABBcx7jKsCeZK3+DSWdKul8SWuX45qO7NXQu/w+VaV4jgS+IgcBpmnOYzIzM2tNkj4FbiJL8CzhgL6ZmZnZiDmob2Y2Ckqwtw9wO7Ay8CHwGFmWZUbglLLdwPIwWgX2/0cG9u8A/gycVhrBWQuqldD5H9nboDGwP6g0Jt4HeB1YKSJujYgpgCEleiJiK+AP5fXfNfNhLkT2VjhS0gvVwojYj2zgexNwcWmKq3Lc/YErgWOBv0h6uJmPyczM7Bc1873MDsCvJb1bBtUd0DczMzMbjijxATMzG4F6tlhErABcA5wKHEzWxf89cB05bfxMSTvWX1cC+4qIOYAjgIPqAVxrWVVt/IiYBbgWmBO4RtJ6tW1mAO4kB2deILPkHwCWIgcEBgGLlwGC0T2OYbIOI6IXcCOwIDCTpK/L8oOAQ4DbgP0lPVeWLyTpscb31fi5mZlZS6vd4/QAekv6vpn2G/JDqpmZmdkIOahvZtagFoAfJkhaSu78EZgN2AD4XXmY7VlKucxBBoEnBs6StEN5XWNgv6ekAW3w1rqUelCgIfg9Kzko01Rgf1rgZGBRcvZF5UlgM0kvj8Hx1I9hfklPRkQ3sozTCsD0kj6JiEOBA2kI6JfX3Ql8LmmD0T0OMzOzMVW7t5kQOIC8N9oHeHF0A/IO5puZmZmNPJffMTP7uXlgaBNTGNI49RbgUmAZ4M2qFE9Vm73Ubl8C+BLYPiLOKPsZUmO/fO2AfguolwColT7qVn52E1bblB4H69NQigdA0nvA5sCyZLPa7cgSOCuPSUC/7LsK6J8IPB4RG5ZlD5D9GNaKiN3IgP7t/Dygv145rjfKYICZmVmrqwX0JwNuBXYCZievZaNVjqcMfFcD8XO695CZmZnZiPVo6wMwM2tPIuJIYN+I2EbSP2qrugEXkw+tSwOvlIfan8qD6KDy9csRsTjwIBnYHyRp56o2u7WcKohPzkIbFBFjA4cCvwOmioiXgLsi4lZJL0XEBsAVZGCfWsb+9yWAP0ZB/ErDjIFVgS2Ae4E3yiaPAAOAs8rXNwIHS/q/2j6WJIP97wC3uMyOmZm1hXJNGxQRE5PXsl+TM9z2b7zXGdnM+4aZbFsC2wJXRMSpvt6ZmZmZNc2ZfmZmw/q4fJy0vrBk118B7Eg2x50NOKIE8gc3BPZfITP2PwZ2jIjjW/H4u5yIODoijoDMhi8/h3HIDPjdyXr10wPrAOcA10XEbyS9SJZRamyeO7jUum+OY6tnHo5DDgp9C+wg6bES8HiUnBFQea4hoL8ccBQwB3C4pP82x7GZmZmNqjKA3gs4CZgZOBo4sKnkhdr1b7jZ+w0B/c2Aw4B5gdsd0DczMzMbPtfUNzNrEBFzlEzucYFlJN1UW9cHWA04g5ztdChwRi2wP7g2Lf23ZNb1n+pBWms+pT7+y4CA/SQdW5afRwbxzwZOAMYnmxnvSA64vE/+bF8vvRCuJGvsXyFpoxY4zqPJGv2TkTMB1iuNBVVrwLwzme0IcDnwCdmfYW2yGfPukk4u27rusJmZtYmSpf8M8B6wdBXQj4ieZI+YFclr6lPAPyS92tR1q4mA/tHAWMCSkp5vtTdkZmZm1gE5qG9mXV4JDL9RzzIrddifIOvrby7p4tq63sCawJnAYOBI4PThBPbdFLeFRcTKwD+BcYGDJB0TEfcDXwAbS/qhtm1P4DpgVeBpYE1J75ffgcvJ7MCLJG3RjMc3A9mYdz4yS/8eSWuVdcMEOSJiLXJ2wXxAH7Iszz3ABZKuKdsM08DZzMysNZWkheeB8yVtXe6ZJiDvi1Ynr1+DyOSHR8lr8VsN+2gqoD8OsLgD+mZmZma/zOV3zKxLi4jDyGyzFUvmNJDNbYHzgIHAhRGxeW1dP+BfwN/IhnD7k2V2utUC+4PK5q6l38Ik3QZsDPwEHBURJ5Hldi6U9EMJNlSN/QYAawH3kYHz9cryV4FNyPr2p47J8TQ2sS2BjN2BG4DxgGVLSZ2qjEFUpQkk/YsccPgtsAgwK/BnB/TNzKwtDKcx++fAY8CWEXEIOSPuKWA94HFgGbLJ/K3AwmTW/jD7bCKgPzYO6JuZmZmNNAf1zazLKkH8XmS2/enACg2B/bOBrcuX5zcE9vuTgf0dGBrY/1tj0NUlUlpHCeyvD3wP/AWYnKxfX9+m6nkwCDiGzIJfthqAkfQS8AdJz43ucTQEKiaofe/7yd+xm8gZBZuV2QGoqG37taS3JD0m6e1qpkHJ6ndA38zMWkWtb9BYEbFIRMwGIOkTcgD8f+T9z85kWbsdgdUk3S/pATKoH8AM9f3WrpObMjRDfwkH9M3MzMxGnoP6ZtZllXI7h5JNSCcl6683BvYvBqpg/ogC+wOBU4BtWuXg7Wdqgf3uQG9gqbJ8UJVpWJtB8SJZnmfuiJiu1sRvjGZW1AIVpwA3RcR0tXX3kjXz/wNsSM7umGkU9u0BIjMzaxW1MoKTABcBdwE7RMS0AJKuBDYiy+1sCKwEnC3pu9pulgC+AX7W4D0itgJOJK/XztA3MzMzG0U9fnkTM7POqWRV/xgRJ5ODnHuRgf3tIuI/VY19SReXmO+FZGAfSReWdf0j4l/kQ+lBZP1zayOSbo2IdcnGt3+MiGMl7V0yDYc0ppX0QUR8C/wAfFkFzJsjcB4R45CBjHmBUyJiF0nvlv3fHxEiMxe3K9ufLOmNMf2+ZmZmzaEW0J+MDObPRN7fnAR8UG0n6amG1/UgZz8SETuR/YfuA15t2G5s4DfARMB8DuibmZmZjTo3yjWzLqvepLQ8YO4O7Al8RQZchwT2yzabkYF9gC2rwH5Z1wvoLenb1jp+G76IWBG4mqxhf4yk/RrW/40sh3M12Qj5x2b+/lMAlwDLAzcDO1aB/bJ+SbJkwXLkQNJJkt5szmMwMzMbXaWE3L/JHi/HAUfV+gYNbti2R3W/VBrSHwFsBfQFliwN6Yd5XURMTY6lf9hKb8nMzMysU3FQ38y6pFoWWjdgEkmflcD+bmTG/sgE9jcv5XmsHYqIP5IZ++ORTWpvJMvubAisDfQBFpP0Wgt9/ymAfwLLMuLA/lJkaYMTW+pYzMzMRkaV8BARu5CZ+ScD+5aSg5RydUE2v+1XGs0TERMC65IlCeckG+muK+m9Wj8bMzMzM2smrqlvZl1OySgbFBFjAUcC90bEn0tD0hPJjLSJGHGN/QHAhRGxYeu/AxsZkm4H1iMzBdcE/k427fsj8BKZPTjaQfSI6P4L3/8TYGOyZMFqwOkNNfYfAA4HHiF7MUw9usdiZmbWHGpl6BYh6+GfWgvo9yaTH+4CngWejYgzy+u+JksR/gAcQjbMdUDfzMzMrIU4U9/MupRahv64wG3AwsBbwIHAjZL6lWD/7ow4Y39bssHuUpJeaO33YSOvlOK5ChhE/sw3B3o2V8mdiNgeuF3SW8NZPzlwObAMTWfsLwNMIOlfzXE8ZmZmYyoirieb364K/I9Mdvg7Gez/GHiOvIeagFqZu4iYEvhC0oCmSvWYmZmZWfNwUN/MupyI6APcASwEnAbsL2lAWdet1IwdmcD+BJL6tvobsFFWSvHcCnxJNufr20xNcTcALgOeBNauB+sbtpsFuIJsnnsLGdh/p4ntHAAxM7NW1dBjqIekgRGxFjljMcgZb9MA/YF/AXtK+jwi5gceBx4C1pD0Vdu8AzMzM7Oux+V3zKwr+iuwBFkb/5CSTdYDoNYE7kd+XopnuYZSPA7odxClFM+KZA39r0c2oF9qB9e/7tmwyQPANcD8wNX18joN3iADIZA19s+OiOmbOE4H9M3MrFVUZeTq18Ra8sL9ZGLDS2QPmquAPwPbS/q8bPM/4FvgKwf0zczMzFpXj1/exMys01kU+Bo4VtIPJYg/JAO/HtiPiBPL4l3J4O2fgDtb/YhtjEkapZ9bQ+biFMDntRkdx5Llmh6JiB3J0j7rk4H99epZ+LWST48DrwL9yJIGcwFvN8NbMzMzGyW1jPzxyf4zcwO9gBeA6yW9HxGXkrPRpmlqdhmwJ9mM/qHWOm4zMzMzSw7qm1mXUkrvzA58V/41mR1dAvs9S2D/JGAcYCMchO0yagH9e4CBwF+ADyPiOGAPYOqIeE7SpxGxc3nZ+sBVEbFuVYqn1iDw9+XjOsDskm5upbdiZmY2RBlsHhgRk5Gl6eYnS+tABvb3Lb2D/iPpp4h4t7xuSIm4iNgF+BvwFHBRK78FMzMzsy7P5XfMrKsRMICsDbtoUxvUSuwsFBGrSfoBOAyYT9JrrXOY1h5ExExk2YHlgCMi4h9kQP8K4GhJ35cgx2fAzsCVwILAtRExTxlEIiIWIwP+LwFvSLqpLPd12MzMWlWZPTYh8B9gTuB0YD6y8e0hZPb9FcCqJaO/Ks/TKyJmiojLgcOBL4A/S/rM1zMzMzOz1uVMfTPrUiT1i4jLgN8By0TEbQ3Nb+uleA4E5o6I30v6APixDQ7Z2pCkNyJiS+BQMlMf4HbgcEmvlBI9Vbmmz0rG/mBgQ+Ba4JGIeAfYGPg1cEJ9Zohr6JuZWWsr/WJ2JEvuHA8cUCsv9xqwDTAu2Vi+mrXWHVievB7OA9wMbCfpw6rMXGu/DzMzM7OuzEF9M+uKrgG2B3YC3omIU6vgam1a+c7AYsB1ZP1966IkvVxl3Nd8VtapfKwH9ncB3gfWJIP5g8q/nSRdCsPW6zczM2tNklRmkH0AHFQL6E9ANoCfnMzYP61k9Vf1958E/g58Cdwpqa8D+mZmZmZtIxxTMLOuKCLmAZ4ky5AdD9wBPAIEWUZlZ+AHYHlJb7bRYVobq+oHR8T9ZF+FvsAfgKuA/SS9NZztxyZLPK0CfAR8JOn++jat+kbMzMwYkqX/K+Bh4Ctg0VI3fxzgUWBWMhv/pLK8D7AucL+kd0rG/uAyMODrmZmZmVkbcVDfzLqsiJgXuBGYmsykfpkM3M4IvAusLOmltjtCawsNjQDHl/RN+XwiYCLgFGBVMrC/r6S3G183Mvs2MzNrCyUwfz8wraRfl0D//wGzUAvol20nAZ4DLgf28TXMzMzMrH1wQyMz67IkPQMsSTbBfR+YjJxSfjKwtAP6XU8pI1AF9P8EnBQR6wNI+qrM2jgYuAVYDzg6IqavauuX180bETOXz6O+fwdDzMysLZWGtt2AF4FpI+IEMmg/K9n8dkhAvzgemBR40NcwMzMzs/bDmfpmZkBEjAt0B74hz41+cO1iGjL0DwJ2B3oDR5JBju9r285DZjOuRmbs7y/pzYj4HXAm2Y/hV8Cnrp1vZmat7Zdq3UfEHGS5nfGAn8gG8Ec3bLMXOZB9D7CxpL4teMhmZmZmNgrcKNfMDJD0XfV5Q3K1dQENmfaHA/sD9wEHS3qwtl13SYMkPRsRB5fF6wHjRcT/AcsB8wOHSPqkVd+EmZkZUGtsOxGwOVlW5xXgmaq/i6SXImJr4FxgfLL8IBHRm8zkPwTYnmymu21piusScmZmZmbthDP1zczMiojYCLiQbJy8j6QXf2H7ecimyhuRA+U/kA10TyvrHQAxM7MWVcrEPSDpw9qyyYC7gLlqm34InFll5EfE+GSPmLPJjP3XyTKE05KzzV4AVqsa5I4o89/MzMzMWpeD+mZm1uWV2vcBnA9sDCwq6Yna+pmBuYGVgeeBO6qeCxExOZkFOSPwmqRHy3IH9M3MrEVFxG7ACcBZZAmdTyKiD/BvYCHgEuBBYHZgN7Ks3LGS9q3tYy5gT7Ku/mTAa2TJnQslfeqAvpmZmVn746C+mZkZEBFjkyV3JpM0Q235FsBOwO9qm98JHFAP/DfsywF9MzNrcRGxHHAAsCSZcX8omXX/EHAROXtMZdulgdtpOrDfq3w6aUPGv69nZmZmZu2Qa+qbmZml/sBHwPwRcSxZdmBFYEPge2BfsrbwqsC6wH+BJoP6DoCYmVlrkHRXRPQDDge2AwYAT5OB+xMlKSK6A4Ml3RcRSwL3A3tHBLXAviQNIEv0VL1m5OuZmZmZWfvkTH0zM+tSqkDFcNZNC9wN/KYs6gtcR5YgeLhssxDwKFmaYAUyEOKLqZmZtar69SwilgCOAJYgB5zHARaS9H21XZV1HxELkjPT+lDL2HdWvpmZmVnH4Ux9MzPrMup1gSNiXGASYKCkDwAkvRcRiwCbAT2BW4D3JH1T283U5eO/HfwwM7O2UgL1VUb9gxFxEHAQsFjZ5A/ALVXgvwT0u0l6vJTiuY/M2B9X0o6+ppmZmZl1HM7UNzOzLqEhoL8TsAGwIFl250XgZOBBSe82vK6HpIHl84WBU4CZgTUlPdh678DMzCzVsu6HmX0WEX8gy8UtB1wF7Cnp/eG8dgHgMeAHYGpJfVvxLZiZmZnZGHCmvpmZdXol6FEF9I8F9gQ+Bi4GpgHmAM4DroiI4yW9Ur22FtBfmQyULAhs74C+mZm1hWqQOiLGA9aPiMHAVZK+k3Rv+bonsB7wRUQcIenj6vW1jP0nImI+4AtJfUdUns7MzMzM2hdn6puZWZcREVsB5wK3AQdIejYiegCzAv8BugHHAWdJ6ldesyCwI7AOmc14kKQzyjrXHzYzs1ZTC+hPBlwNLEU2eV8BeLU2EL0E2Tx3SeAs4HBJnzTsa8g1rD6bzczMzMzav25tfQBmZmbNJSKiqc/L192BPwNfAQdLehaGZOKvC/wKeBp4oAroFwLmJ2sPb+yAvpmZtYVq1llETALcT16bTgHmlvSipIHVta/MJjsQeADYHjgwIqao769+DXNA38zMzKxjcaa+mZl1OrVMxnod/amAt4DrJG1Y2/Zg4GDgVuDAKtgfEZNK+rx8PiV5zfyofO2AvpmZtbqI6E3OONsE2B84UVL/hqz7IWV0GjL2TweOlfRh2xy9mZmZmTUXZ+qbmVmnEBFTRcSfIuJG4JaI6NOQeTiYzLqfqPaag8iA/pByPGX5r4ATS61hJH1cC+iHA/pmZtZGJgQWBx6TdHRjQB9Akhoy9g8C7iFLyW0fEX4GNDMzM+vgfENnZmYdXql7fylwLbAoMCkZ9Kj7kszUny4ixi4Z+oeQAf39JT1X23ZVMgty5sbv5SaCZmbWhuYBZgAeBygD2D8baC6B/R7l8weAY4DrgfM8MG1mZmbW8fVo6wMwMzMbExGxDHAdEMCpwAnA5/W6+CWLsX9EXE3WGH6IDIzcSja+fa5hfweS9fWfbq33YWZmNhK+Lx+nA5D0U+MGtcz9mSLiA0nfSborIh4o18IeVUNdMzMzM+uYnKlvZmYdVkQsDNwIfA7sKGlXSR9I6lca41bGLh+vAl4gA/pvA4dJeqa2v6WAQ4EpgBMkvdby78LMzGykvUle8xaOiIUaV1YB/YgYj5yJtkG1TlL/8tEBfTMzM7MOzkF9MzPrkCJiOjIrfxBwsKRLy/JuALUGufMAR0fEipJeAnYC3gOmB3aNiI0jYoWI2AO4AlgM2EvSleX10apvzMzMrAnl+vYpcBk5+Lx1RExdW9+nBPS7AfuQZXp6+jpmZmZm1vmESwObmVlHUhrVKiL+BFwDnCFp57JumGaBETE72Qh3XeBDYFtJt5YM/4PIuvvj1nb/KnCcpAub2p+ZmVlbi4hFgLOAuYHzgXMlPVFbvytwAHlNW03SF21yoGZmZmbWYhzUNzOzDikirgHWBBaQ9GxEdK+y88v6Ocna+OsAzwBzAR8B25fA/mTA1MBy5My1Z4B3Jb1aXu+AvpmZtUsRsRJwNBnY/xB4FHgdWBD4A/AusISk93w9MzMzM+t8HNQ3M7MOKSIeAOYEFmqsfR8R4wAXAX8GDpd0cEScAOxGBjq2B+6q6gs3se+QL5BmZtbO1K9PEbEosB6wITBJ2eQj4L/ATpI+aBzwNjMzM7POwUF9MzPrkGpB/QUlvd5E6Z1VySzFvcvXE5KZ+7uQNfU3lvRQqx+4mZnZGGgI7Pck6+vPSpaTewb4UtJ3DuibmZmZdV492voAzMzMRkWt4d+nwITApsBBDQH9kHQLcEv5urekryPiIGBiYDNgzYh42Bn5ZmbWkTRctwZJeh94v75NuQ46oG9mZmbWSXVr6wMwMzMbFSqAC4B+wB8jYoHGbRpe1r9k8n8PfA/8CFzpgL6ZmXVkw6uV7+ubmZmZWefmoL6ZmXVUDwN3A78HNo+IqZvaqCpTIGlwRPyRzOy/C3ir9Q7VzMxs+Gqz0MzMzMzMfpGD+mZm1iFJ6gvsCXwN/BXYNSKmr9ZHRM+SnV/VHV4EOAQYDJwn6YvWPmYzM7OI6F4+RvnYE+g+hvv0c52ZmZlZF+JGuWZm1qFFxHzAo0BP4DKyrM6tDdusDOwHLArsKOnMsjxcosDMzFpLRPSQNDAiJgL2AhYApgI+BM4E7pX0ddl2pK5R9UbxETE78L6kb1vqPZiZmZlZ23NQ38zMOryImAf4F/Br4AeyQe5/gT7ArGRj3H7A3pJOK6/pNrxaxGZmZs2tuu5ExGTAPcBvgXeAb4E5gAAuAs6V9Nio7LN8vhVwEHA08HcPWpuZmZl1Xg7qm5lZpxARMwA7AZsD49dW/QjcTwY4birbOqBvZmatLiLGB24H5gVOBE4A+gO/A/YHViUz9o+T9N4v7Kse0N8MOJzM+p9H0gst9ibMzMzMrM31aOsDMDMzaw6S3oqI3YHTyeDIr4DvgMeBzyR9CQ7om5lZyxvBtWY1YBHgbOBQSQPL9s+RM8u+AT4HPhnZ/ZeA/tHAODigb2ZmZtYlOKhvZmadRglwvFn+DVFrRhgO6JuZWUsrZXZ6VEH7mkUBAcfUAvrjAQ8DM5AN3U+R1D8i+kj6qXHfIwjoL+6AvpmZmVnX0K2tD8DMzKylVXWFXV/YzMxaUkRcExFPApSGuD1q67oBEwEDgQnLsnHIgP6sZED/JEk/lJdsGRErNey/qYD+2GRA//kWfGtmZmZm1o44qG9mZmZmZjaGImICsvzbfBFxOwwb2C/B+A+BnmRTXMim7rMCh5IB/Z/KvgLYD9g/InpX36MW0N+UoQH9JRzQNzMzM+taHNQ3MzMzMzMbA6W8W19gGTLzfsWIuAOGBPZ7lU3vJcvvXBQRLwKzAEdQC+gXpwKTAVeSjXTr32sj4BSgDw7om5mZmXVJDuqbmZmZmZmNAUmKiO6SPgDWBx4Flq8F9qvA/P3AbcA8wIzAkZIOrwf0S9P3vwCPAFfUS8eV2QBzAj8Bf3BA38zMzKxrCpcXNjMzMzMzG3MlsD8oIqYGrgYWAe6UtGJtm0mBB4DZgKeAzcjs/X7A3uXrD4ElJb1fr6NfXj8r8K2kD1vrfZmZmZlZ++KgvpmZmZmZ2Rgo5XdUPu8mafBIBPYvB5YjA/rfA2MB3cks/w0kvVsNErTy2zEzMzOzds5BfTMzMzMzs9FUy84fB+gl6asqyP8Lgf3xgBWApYHJga+Au4G7JX3pgL6ZmZmZDY+D+mZmZmZmZmMgIiYCniSD8vtJ+rwW2J8KuIYmAvu11zeW2BnmazMzMzOzOjfKNTMzMzMzG0UR0b325QzAd8AWwK4RMXkJ6Eepfb8uDc1zyz56ATQG8B3QNzMzM7MRcVDfzMzMzMxsFEREj1JyZ6KI2A3YkqyN3w3YF9gpIiarBfY/oInAvqT+DYMDZmZmZma/yOV3zMzMzMzMRlKtEe5kwD3Ar4FngZeBqYBVyqbHA8c3lOKp19h/UtKCrf8OzMzMzKyjc1DfzMzMzMxsFETEWMBNwLLA3pKOr63bBNgPmJUM7J8g6bOGGvt3l/VTSvq09d+BmZmZmXVkDuqbmZmZmZmNgoj4HfAw8DiwkqQBEdFT0oCyfhXgBDJwfxRwmqRPa1n+UwKS9Imb4pqZmZnZqOrR1gdgZmZmZmbWwUwMjAM8XwL63WofB0u6NSImAi4hs/YjIk6U9GVEdJf0MQwt5dOG78PMzMzMOiA3yjUzMzMzMxs1P5aPS0bEtFVgvmThR/n8n8CNZbt9gV3L8kHVThzQNzMzM7PR4aC+mZmZmZnZqHkC+DfwG2Ch+opSN79X+fI94CXgUWD/iFivVY/SzMzMzDolB/XNzMzMzMwaRET34SzvUTLs7wDGBU6JiIVr67tJ6l++nA14EjiufH14REzTgodtZmZmZl2Ag/pmZmZmZmY1JXA/KCImiIhNI2LniNg8IsaVNLBsdjpwPjAVcG1E/CkipqlK6kTEjsCCwCuSbgRuByYHerfBWzIzMzOzTsSNcs3MzMzMzIqSaT8wIiYD/gPMXVu9e0RsCzwpqV9E/BUIYAvgQuB/EXEvMAewEvAOcEV5bT9gfGBS4I3WeTdmZmZm1hmFpLY+BjMzMzMzs3YjIsYja+YvAFwN3AesBfwR+ADYAfiPpB8johuwMxnEX77soj/wNLChpLcjYhbgAeBTYCVJH7bi2zEzMzOzTsZBfTMzMzMz6/JKyZ2B5fO5gIeB04ADS/PbHsABZAD/R2A7SmC/vKYnWW5nXOAL4A1JX0XEjMBRwLrAoZIObeW3ZmZmZmadjIP6ZmZmZmZmQCm5sxHQB9gamFnS4IjoXcrt9CaD+vsCPwDbA/+W1G84+1sY2BtYA/inpE3L8pAfxMzMzMxsNDmob2ZmZmZmXV7JxL+VLKFzDzAOsCjQqwT0u5UAfy9gF2Af4CcysH+rpAG1ffUCFgeuAiYEzpX0t7KuW9VM18zMzMxsdDiob2ZmZmZmXV6pjb8ccBLZ6PYL4LeSPq0F9JsK7H8L7AVcV5XvKfubDVgW+ETStdX3cEDfzMzMzMaUg/pmZmZmZtbl1Gvo15Z1B5YATgTmBS4BdpH0dROB/Z5kKZ7jgGeBxar6+k19Dwf0zczMzKy5OKhvZmZmZmZdUkRMCWwo6aTash5k6ZwzgFmAE4BjJfUdTsb+lsDNkt5vi/dgZmZmZl2Pg/pmZmZmZtbllOD9w8ACwBGSDqqtqzL2zwZmIEvyNBnYr79G0qDWfRdmZmZm1hV1a+sDMDMzMzMza22lLM7BwEfAARFxZG3dIOABYDvgLWBXYO+ImKCpgH7tNWZmZmZmLc6Z+mZmZmZm1mVFxB+Aq4BJgaMl7V9b1w1YkszYn56stX+CpK9b/0jNzMzMzJIz9c3MzMzMrNOJiKh93q1xXbVe0r3A+sDnwL4NGfuDGZqx/zqwH/DX+r7NzMzMzFqbM/XNzMzMzKxTqoL5pWTOuMCkkt6urQ+VB6KIWAa4ApiMn9fY7wYsRwb1N5H0Xuu9CzMzMzOzYTmob2ZmZmZmnUZE/B34XNIBtWXjA08CzwAHSHqttq4e2F8WuLOsOlLSgbXtugE9JfVzU1wzMzMza0suv2NmZmZmZp1CRPwW2AbYLyLmqq2aBfgUWBPYMyJmqVZIUq0Uz93A1mXV/o2leCT1K587oG9mZmZmbcZBfTMzMzMz6xQkvQisBmwv6fmIGKssfxLYB/gPsBUjCOwDrwLfAh+TNfb3as33YGZmZmb2S3q09QGYmZmZmZmNqVq2/a3l68mBpyLifEmHSHqoFrjfsmxzvKT/lWU9gf7AW8AbwFXARsC1rfg2zMzMzMx+kYP6ZmZmZmbW4ennzcLmAKYGDoqI7yUdL+nBoXH9IYH90yQ9L6l/Cfrvx/+3d6cxklVlHMafPzAiBBUBwVGGsCguMFEQRQRcAAVlcEUByShEEjESxYiYoAKChsUBFyKKAmFfXJEloOwBN2BECUZcAAUBVwgwyDq8fri3Q03RVd09zExNdT2/pHK77jnn3vf0t3rvue+BmVV1VJJ5VbUwyUpV9cQym4wkSZLUh0l9SZIkSdNOVV2VZA5wIXBUEnok9tdJcg7wU2A/YA/gF0lWBR5ur2VCX5IkScuNPH1BiyRJkiQNr45SPJVkZ+CCtumzVfWVts8bgI/RlNgBWACsBvwVeFNV3Zkk47wBIEmSJA2USX1JkiRJ084kE/uzgG1pVujfC9wNHFJV9yRZsaoWDiB0SZIkqS+T+pIkSZKmpT6J/QOral5Hv9WqakGSGVX1uAl9SZIkLc9WGHQAkiRJkrQ0jJXOacvoXATs0jYdneTTHV0fa49PtONM6EuSJGm55Up9SZIkSdNanxX7B1TVsW2fFarqyUHFKEmSJE2WSX1JkiRJ016fxP4nq+q4wUUmSZIkTY1JfUmSJEkjoSuxPwc4H3gQ2L2qLh5ocJIkSdIkWVNfkiRJ0kjoqLG/QlVdCOwFPAd44yDjkiRJkqbCpL4kSZKkkVGLvqp8e3t87iBikSRJkhaHSX1JkiRJI6WqnkyyKjC3PXXjIOORJEmSpsKkviRJkqRRtBC4DTi5qk4cdDCSJEnSZLlRriRJkqSRlGTlqnq0/XuFqnpy0DFJkiRJEzGpL0mSJGmkJUn5w0iSJElDwqS+JEmSJEmSJElDwpr6kiRJkiRJkiQNCZP6kiRJkiRJkiQNCZP6kiRJkpaqJHslqSSnDDoWSZIkadiZ1JckSZKmkTZ5PtXPXwcd91R1xb9Vn35XtX32WobhSZIkSUvNSoMOQJIkSdIS9fNxzj0P2LRP+z1LL5xl4nBgh0EHIUmSJC0LJvUlSZKkaaSqtuk+l+TNwJW92ofcQmD7JG+qqqsHHYwkSZK0tFl+R5IkSdIwO6s9Hj7QKCRJkqRlxKS+JEmSNMKSbJDkwCRXJLkjyaNJ7k1yeZLd+ox7ZZLTkvwtyWNJHkhya5IfJfngFO6/VpIb2rr3P0jyrClO4RjgPmDbJG+dwn1XTPKuJCcluTnJfUkeTvLnJMcleXGPcaeM1ehPsm77/Z4k/0syP8n7OvrOSvLdJH9P8kiSm5LsOUFc2yf5cZJ/tP/Xe5KcnWT2ZOcmSZKk6c2kviRJkjTaPgccBbwOeAS4CVgAbAeck+Sr3QOSbAFcD8wF1gL+CNxKU7v/PcBBk7lxklnANcBrgJOB3arqsSnGfz9NYh+mtlp/JnAesBewJnA7cBvwImA/4DdJXtpn/PrAfOADwF1tHJsD30+yR5KNgeuAPYF/0Dx4mA2ckWTueBdM8hXgMuDdwIrAzcDKwO7A9UnmTGF+kiRJmqZM6kuSJEmj7YfAVsBzqmrjqnptVa0HbAbcAuyfZOuuMQcDqwKnAutU1eyq2qyq1gJeDhw30U3bpPe1bf9jquojVbVwMefwdeA/wJZJdp7kmAeBvWnin1lVm1fVJsDaNPNbGzi+z/iDgKuBmVW1RVXNBL4ABDgaOJPmgUVn+6Ht2COTLPJbLMk+wAHA34G3V9UL2pjWAPal2Q/tjCRrT3J+kiRJmqZM6kuSJEkjrKourqpfVVV1nf8tzYp1aFbkd9q4PR5bVQu6xv2xqk7od88km9EkvNcDvlBVByxu/O09F9C8bQBwWJJMYsz9VXVKVf2n6/xDVXU4zQOHHZLM7HGJ/wJ7V9X9HeeOoFm1v2776W7/MnA3zdsArxo7mWQGcBhQwK5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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\"talk\", font_scale=1.25)\n", - "fig, ax = plt.subplots(figsize=(25, 5))\n", - "df = diff_sporf_df_melt\n", - "sns.stripplot(\n", - " x=\"task\",\n", - " y=\"delta_cohen_kappa\",\n", - " data=df,\n", - " color=\"gray\",\n", - " order=order.index,\n", - " ax=ax,\n", - ")\n", - "\n", - "# distance across the \"X\" or \"Y\" stipplot column to span, in this case 40%\n", - "median_width = 0.4\n", - "for tick, text in zip(ax.get_xticks(), ax.get_xticklabels()):\n", - " sample_name = text.get_text() # \"X\" or \"Y\"\n", - "\n", - " # calculate the median value for all replicates of either X or Y\n", - " median_val = df[df[\"task\"] == sample_name][\"delta_cohen_kappa\"].median()\n", - "\n", - " # plot horizontal lines across the column, centered on the tick\n", - " ax.plot(\n", - " [tick - median_width / 2, tick + median_width / 2],\n", - " [median_val, median_val],\n", - " lw=4,\n", - " color=\"k\",\n", - " )\n", - "ax.axhline([0], color=\"red\", ls=\"--\", label=\"No Performance Difference\")\n", - "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "\n", - "ax.set(\n", - " ylabel=\"Delta Cohen Kappa \\n(Forest-RC - Forest-RI)\",\n", - " xlabel=\"Task Name\",\n", - " title=\"Cythonized Forest-RC vs RF\",\n", - ")\n", - "ax.legend()\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "markdown", - "id": "e3df0228-dc38-4e05-bcf7-57d72cd83ec5", - "metadata": {}, - "source": [ - "# Create Plots of Delta Cohen Kappa (SPORF implementations vs RF)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "99934b2a-1a64-4ba0-b375-578c3c770927", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taskdelta_cohen_kappa
0Bioresponse0.017149
1CIFAR_100.015537
2Devnagari-Script0.029432
3Fashion-MNIST0.013454
4GesturePhaseSegmentationProcessed0.013316
.........
66vehicle0.018513
67vowel-0.000282
68wall-robot-navigation0.001538
69wdbcNaN
70wilt0.022800
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71 rows × 2 columns

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" - ], - "text/plain": [ - " task delta_cohen_kappa\n", - "0 Bioresponse 0.017149\n", - "1 CIFAR_10 0.015537\n", - "2 Devnagari-Script 0.029432\n", - "3 Fashion-MNIST 0.013454\n", - "4 GesturePhaseSegmentationProcessed 0.013316\n", - ".. ... ...\n", - "66 vehicle 0.018513\n", - "67 vowel -0.000282\n", - "68 wall-robot-navigation 0.001538\n", - "69 wdbc NaN\n", - "70 wilt 0.022800\n", - "\n", - "[71 rows x 2 columns]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# df.groupby('StationID', as_index=False)['BiasTemp'].mean()\n", - "diff_sporf_df_melt.groupby(\"task\", as_index=False)[\"delta_cohen_kappa\"].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "0c7bb962-1410-455d-bce3-40822c943059", - "metadata": {}, - "outputs": [], - "source": [ - "# compute descending order by median\n", - "order = (\n", - " diff_sporf_df_melt.groupby(\"task\")\n", - " .median()\n", - " .sort_values(by=\"delta_cohen_kappa\", ascending=False)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "36de972f-98a4-4fc1-84eb-499e6c0dd4e8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\"talk\", font_scale=1.25)\n", - "fig, ax = plt.subplots(figsize=(25, 5))\n", - "df = diff_sporf_df_melt\n", - "sns.stripplot(\n", - " x=\"task\",\n", - " y=\"delta_cohen_kappa\",\n", - " data=df,\n", - " color=\"gray\",\n", - " order=order.index,\n", - " ax=ax,\n", - ")\n", - "\n", - "# distance across the \"X\" or \"Y\" stipplot column to span, in this case 40%\n", - "median_width = 0.4\n", - "for tick, text in zip(ax.get_xticks(), ax.get_xticklabels()):\n", - " sample_name = text.get_text() # \"X\" or \"Y\"\n", - "\n", - " # calculate the median value for all replicates of either X or Y\n", - " median_val = df[df[\"task\"] == sample_name][\"delta_cohen_kappa\"].median()\n", - "\n", - " # plot horizontal lines across the column, centered on the tick\n", - " ax.plot(\n", - " [tick - median_width / 2, tick + median_width / 2],\n", - " [median_val, median_val],\n", - " lw=4,\n", - " color=\"k\",\n", - " )\n", - "ax.axhline([0], color=\"red\", ls=\"--\", label=\"No Change\")\n", - "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "\n", - "ax.set(\n", - " ylabel=\"Delta Cohen Kappa (cysporf - rf)\", xlabel=\"Task Name\", title=\"Cysporf vs RF\"\n", - ")\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a0533072-7c9a-4311-aaf4-9edf66ac944c", - "metadata": {}, - "outputs": [], - "source": [ - "# compute descending order by median\n", - "order = (\n", - " diff_rerf_df_melt.groupby(\"task\")\n", - " .median()\n", - " .sort_values(by=\"delta_cohen_kappa\", ascending=False)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "e96d9007-9ec8-450d-a4b1-14930b253d60", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\"talk\", font_scale=1.25)\n", - "fig, ax = plt.subplots(figsize=(25, 5))\n", - "df = diff_rerf_df_melt\n", - "sns.stripplot(\n", - " x=\"task\",\n", - " y=\"delta_cohen_kappa\",\n", - " data=df,\n", - " color=\"gray\",\n", - " order=order.index,\n", - " ax=ax,\n", - ")\n", - "\n", - "# distance across the \"X\" or \"Y\" stipplot column to span, in this case 40%\n", - "median_width = 0.4\n", - "for tick, text in zip(ax.get_xticks(), ax.get_xticklabels()):\n", - " sample_name = text.get_text() # \"X\" or \"Y\"\n", - "\n", - " # calculate the median value for all replicates of either X or Y\n", - " median_val = df[df[\"task\"] == sample_name][\"delta_cohen_kappa\"].median()\n", - "\n", - " # plot horizontal lines across the column, centered on the tick\n", - " ax.plot(\n", - " [tick - median_width / 2, tick + median_width / 2],\n", - " [median_val, median_val],\n", - " lw=4,\n", - " color=\"k\",\n", - " )\n", - "ax.axhline([0], color=\"red\", ls=\"--\", label=\"No Change\")\n", - "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "\n", - "ax.set(ylabel=\"Delta Cohen Kappa (rerf - rf)\", xlabel=\"Task Name\", title=\"ReRF vs RF\")\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "38c6580a-ad75-4b06-bb92-7e9a6fbeaaa4", - "metadata": {}, - "outputs": [], - "source": [ - "cols = [\"n_samples\", \"n_classes\", \"n_features\"]\n", - "diff_sporf_df_melt[cols] = diff_sporf_df_melt[cols].apply(\n", - " pd.to_numeric, errors=\"coerce\"\n", - ")\n", - "\n", - "sporf_df = diff_sporf_df_melt.groupby(\"task\").median()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "8b0bfd01-64d8-4475-ad4e-c392b89e4b87", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'diff_rerf_df_melt' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdiff_rerf_df_melt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiff_rerf_df_melt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"coerce\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mrerf_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiff_rerf_df_melt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"task\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'diff_rerf_df_melt' is not defined" - ] - } - ], - "source": [ - "\n", - "diff_rerf_df_melt[cols] = diff_rerf_df_melt[cols].apply(pd.to_numeric, errors=\"coerce\")\n", - "rerf_df = diff_rerf_df_melt.groupby(\"task\").median()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40f8bd6c-eb60-40e9-9ba7-3361de19dc3e", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "display(sporf_df.head())\n", - "display(rerf_df.head())\n", - "# df.groupby('gender')['convertedcomp'].median()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "116f8a99-a709-4fb4-9c03-b44edd183339", - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'delta_cohen_kappa'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/miniforge3/envs/sklearn-dev/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3080\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3081\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3082\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'delta_cohen_kappa'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m continuous_pairplot(\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mrerf_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mvars\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"n_classes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"n_samples\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"n_features\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"delta_cohen_kappa\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"coolwarm\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mcontinuous_pairplot\u001b[0;34m(df, vars, hue, cmap, diag_kind, scale)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmpl_toolkits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes_grid1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmake_axes_locatable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mvmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1e-6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1e-6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniforge3/envs/sklearn-dev/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3022\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3023\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3025\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3026\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniforge3/envs/sklearn-dev/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3081\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3082\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3083\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3084\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3085\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'delta_cohen_kappa'" - ] - } - ], - "source": [ - "continuous_pairplot(\n", - " rerf_df,\n", - " vars=[\"n_classes\", \"n_samples\", \"n_features\"],\n", - " hue=\"delta_cohen_kappa\",\n", - " cmap=\"coolwarm\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "775ce5ed-cd81-4753-8dd0-9207dbf7e13f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "continuous_pairplot(\n", - " diff_sporf_df_melt,\n", - " vars=[\"n_classes\", \"n_samples\", \"n_features\"],\n", - " hue=\"delta_cohen_kappa\",\n", - " cmap=\"coolwarm\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "f4956632-4be1-438a-b7eb-cdefe2c62c0e", - "metadata": { - "tags": [] - }, - "source": [ - "# Runtime Performance" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "5a664ca4-9c4e-496f-8df1-42cd72a45ba2", - "metadata": {}, - "outputs": [], - "source": [ - "rf_times_df = pd.DataFrame(rf_times)\n", - "rf_df[\"clf\"] = \"rf\"\n", - "rf_df = pd.concat((rf_times_df, result_df), axis=1)\n", - "\n", - "rerf_times_df = pd.DataFrame(rerf_times)\n", - "rerf_df[\"clf\"] = \"rerfsporf\"\n", - "rerf_df = pd.concat((rerf_times_df, result_df), axis=1)\n", - "\n", - "sporf_times_df = pd.DataFrame(cysporf_times)\n", - "sporf_df[\"clf\"] = \"cysporf\"\n", - "sporf_df = pd.concat((sporf_times_df, result_df), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "439d76b0-766b-4f69-9e6a-2460b234c89b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taskn_samplesn_classestask_idn_featurescv_foldtimesclf
0Devnagari-Script9200046167121102401280.741402rf
1segment231071468221600.987333rf
2jungle_chess_2pcs_raw_endgame_complete448193167119602.116413rf
3tic-tac-toe958249900.953501rf
4pc11109239182100.896651rf
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" - ], - "text/plain": [ - " task n_samples n_classes task_id \\\n", - "0 Devnagari-Script 92000 46 167121 \n", - "1 segment 2310 7 146822 \n", - "2 jungle_chess_2pcs_raw_endgame_complete 44819 3 167119 \n", - "3 tic-tac-toe 958 2 49 \n", - "4 pc1 1109 2 3918 \n", - "\n", - " n_features cv_fold times clf \n", - "0 1024 0 1280.741402 rf \n", - "1 16 0 0.987333 rf \n", - "2 6 0 2.116413 rf \n", - "3 9 0 0.953501 rf \n", - "4 21 0 0.896651 rf " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1420, 8)\n" - ] - }, - { - "data": { - "text/html": [ - "
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taskn_samplesn_classestask_idn_featurescv_foldtimesclf
0Devnagari-Script9200046167121102402121.569089cysporf
1segment231071468221601.020177cysporf
2jungle_chess_2pcs_raw_endgame_complete448193167119602.746897cysporf
3tic-tac-toe958249901.016986cysporf
4pc11109239182100.940076cysporf
\n", - "
" - ], - "text/plain": [ - " task n_samples n_classes task_id \\\n", - "0 Devnagari-Script 92000 46 167121 \n", - "1 segment 2310 7 146822 \n", - "2 jungle_chess_2pcs_raw_endgame_complete 44819 3 167119 \n", - "3 tic-tac-toe 958 2 49 \n", - "4 pc1 1109 2 3918 \n", - "\n", - " n_features cv_fold times clf \n", - "0 1024 0 2121.569089 cysporf \n", - "1 16 0 1.020177 cysporf \n", - "2 6 0 2.746897 cysporf \n", - "3 9 0 1.016986 cysporf \n", - "4 21 0 0.940076 cysporf " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# melt the dataframe\n", - "sporf_df_melt = pd.melt(\n", - " sporf_df,\n", - " id_vars=[\"task\", \"n_samples\", \"n_classes\", \"task_id\", \"n_features\"],\n", - " value_name=\"times\",\n", - " var_name=\"cv_fold\",\n", - ")\n", - "sporf_df_melt[\"clf\"] = \"cysporf\"\n", - "\n", - "# melt the dataframe\n", - "rf_df_melt = pd.melt(\n", - " rf_df,\n", - " id_vars=[\"task\", \"n_samples\", \"n_classes\", \"task_id\", \"n_features\"],\n", - " value_name=\"times\",\n", - " var_name=\"cv_fold\",\n", - ")\n", - "rf_df_melt[\"clf\"] = \"rf\"\n", - "\n", - "# melt the dataframe\n", - "rerf_df_melt = pd.melt(\n", - " rerf_df,\n", - " id_vars=[\"task\", \"n_samples\", \"n_classes\", \"task_id\", \"n_features\"],\n", - " value_name=\"times\",\n", - " var_name=\"cv_fold\",\n", - ")\n", - "rerf_df_melt[\"clf\"] = \"rerf\"\n", - "\n", - "times_df = pd.concat(\n", - " (\n", - " rf_df_melt,\n", - " # rerf_df_melt,\n", - " sporf_df_melt,\n", - " ),\n", - " axis=0,\n", - ")\n", - "\n", - "display(times_df.head())\n", - "print(times_df.shape)\n", - "display(sporf_df_melt.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "e5bc618c-eceb-437e-9046-e54563f2698f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\"talk\", font_scale=1.25)\n", - "fig, ax = plt.subplots(figsize=(25, 5))\n", - "df = times_df\n", - "sns.stripplot(\n", - " x=\"clf\",\n", - " y=\"times\",\n", - " data=times_df,\n", - " color=\"gray\",\n", - " # order=order.index,\n", - " ax=ax,\n", - ")\n", - "\n", - "# distance across the \"X\" or \"Y\" stipplot column to span, in this case 40%\n", - "# median_width = 0.4\n", - "# for tick, text in zip(ax.get_xticks(), ax.get_xticklabels()):\n", - "# sample_name = text.get_text() # \"X\" or \"Y\"\n", - "\n", - "# # calculate the median value for all replicates of either X or Y\n", - "# median_val = df[df[\"task\"] == sample_name][\"delta_cohen_kappa\"].median()\n", - "\n", - "# # plot horizontal lines across the column, centered on the tick\n", - "# ax.plot(\n", - "# [tick - median_width / 2, tick + median_width / 2],\n", - "# [median_val, median_val],\n", - "# lw=4,\n", - "# color=\"k\",\n", - "# )\n", - "# ax.axhline([0], color=\"red\", ls=\"--\", label=\"No Change\")\n", - "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "\n", - "ax.set(\n", - " ylabel=\"Runtime for Training (secs)\",\n", - " xlabel=\"Classifier Implementation\",\n", - " title=\"Runtime Performance\",\n", - ")\n", - "# ax.set_yscale(\"log\")\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "88ec23be-b927-4a9e-8ab5-783f8cf4879b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\"talk\", font_scale=1.25)\n", - "fig, ax = plt.subplots(figsize=(25, 5))\n", - "df = times_df\n", - "sns.lineplot(\n", - " x=\"n_features\",\n", - " y=\"times\",\n", - " data=times_df,\n", - " hue=\"clf\",\n", - " # color=\"gray\",\n", - " # order=order.index,\n", - " ax=ax,\n", - ")\n", - "\n", - "ax.set(\n", - " ylabel=\"Runtime for Training (secs)\",\n", - " xlabel=\"Number of Features\",\n", - " title=\"Runtime Performance\",\n", - ")\n", - "legend = ax.legend()\n", - "legend.texts[0].set_text(None)\n", - "# ax.set_yscale(\"log\")\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "1a12fab6-1fbe-438a-9916-38b8141e17b2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "done\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.set_context(\"talk\", font_scale=1.25)\n", - "fig, ax = plt.subplots(figsize=(25, 5))\n", - "df = times_df\n", - "df[\"clf\"] = df[\"clf\"].replace({\"cysporf\": \"Forest-RC\", \"rf\": \"RF (Forest-RI)\"})\n", - "sns.lineplot(\n", - " x=\"n_samples\",\n", - " y=\"times\",\n", - " data=times_df,\n", - " hue=\"clf\",\n", - " # color=\"gray\",\n", - " # order=order.index,\n", - " ax=ax,\n", - ")\n", - "\n", - "ax.set(\n", - " ylabel=\"Runtime for Training (secs)\",\n", - " xlabel=\"Number of Features\",\n", - " title=\"Runtime Performance\",\n", - ")\n", - "legend = ax.legend()\n", - "# legend.texts[0].set_text(\"\")\n", - "# ax.set_yscale(\"log\")\n", - "# fig.tight_layout()\n", - "print(\"done\")" - ] - }, - { - "cell_type": "markdown", - "id": "cf171bc1-7828-4b8c-9933-efe4fc277132", - "metadata": {}, - "source": [ - "# Statistical Testing\n", - "\n", - "Here, we compare performances in terms of cohens' kappa and runtime." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "2d0de4b4-3823-4f6f-85a8-28a6c1de7c19", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taskn_samplesn_classestask_idn_featuresclfcv_foldcohen_kappa
0Devnagari-Script92000461671211024cysporf00.909444
1Devnagari-Script92000461671211024cysporf00.934343
2segment2310714682216cysporf00.678524
3jungle_chess_2pcs_raw_endgame_complete4481931671196cysporf00.523605
4pc111092391821cysporf00.717043
\n", - "
" - ], - "text/plain": [ - " task n_samples n_classes task_id \\\n", - "0 Devnagari-Script 92000 46 167121 \n", - "1 Devnagari-Script 92000 46 167121 \n", - "2 segment 2310 7 146822 \n", - "3 jungle_chess_2pcs_raw_endgame_complete 44819 3 167119 \n", - "4 pc1 1109 2 3918 \n", - "\n", - " n_features clf cv_fold cohen_kappa \n", - "0 1024 cysporf 0 0.909444 \n", - "1 1024 cysporf 0 0.934343 \n", - "2 16 cysporf 0 0.678524 \n", - "3 6 cysporf 0 0.523605 \n", - "4 21 cysporf 0 0.717043 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# melt the dataframe\n", - "sporf_df_melt = pd.melt(\n", - " sporf_df,\n", - " id_vars=[\"task\", \"n_samples\", \"n_classes\", \"task_id\", \"n_features\", \"clf\"],\n", - " value_name=\"cohen_kappa\",\n", - " var_name=\"cv_fold\",\n", - ")\n", - "\n", - "display(sporf_df_melt.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "4bda7c89-f8bf-4acc-a1cc-c6edd70fc3ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taskn_samplesn_classestask_idn_featuresclfcv_foldcohen_kappa
0Devnagari-Script92000461671211024rf00.917444
1Devnagari-Script92000461671211024rf00.944444
2segment2310714682216rf00.628019
3jungle_chess_2pcs_raw_endgame_complete4481931671196rf00.523605
4pc111092391821rf00.717043
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" - ], - "text/plain": [ - " task n_samples n_classes task_id \\\n", - "0 Devnagari-Script 92000 46 167121 \n", - "1 Devnagari-Script 92000 46 167121 \n", - "2 segment 2310 7 146822 \n", - "3 jungle_chess_2pcs_raw_endgame_complete 44819 3 167119 \n", - "4 pc1 1109 2 3918 \n", - "\n", - " n_features clf cv_fold cohen_kappa \n", - "0 1024 rf 0 0.917444 \n", - "1 1024 rf 0 0.944444 \n", - "2 16 rf 0 0.628019 \n", - "3 6 rf 0 0.523605 \n", - "4 21 rf 0 0.717043 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# melt the dataframe\n", - "rf_df_melt = pd.melt(\n", - " rf_df,\n", - " id_vars=[\"task\", \"n_samples\", \"n_classes\", \"task_id\", \"n_features\", \"clf\"],\n", - " value_name=\"cohen_kappa\",\n", - " var_name=\"cv_fold\",\n", - ")\n", - "\n", - "display(rf_df_melt.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "0c805317-404b-434f-a742-f7972dcf66ee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9942803646351084\n" - ] - } - ], - "source": [ - "rf_vals = rf_df_melt[\"cohen_kappa\"]\n", - "sporf_vals = sporf_df_melt[\"cohen_kappa\"]\n", - "\n", - "# perform paired wilcoxon test\n", - "stat, pval = wilcoxon(sporf_vals, rf_vals, alternative=\"greater\")\n", - "print(pval)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "976bf175-556f-4d37-907c-8ded4653eea7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1300, 9) (650, 8)\n" - ] - }, - { - "data": { - "text/html": [ - "
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indextaskn_samplesn_classestask_idn_featuresclfcv_foldcohen_kappa
00Devnagari-Script92000461671211024cysporf00.909444
11Devnagari-Script92000461671211024cysporf00.934343
\n", - "
" - ], - "text/plain": [ - " index task n_samples n_classes task_id n_features clf \\\n", - "0 0 Devnagari-Script 92000 46 167121 1024 cysporf \n", - "1 1 Devnagari-Script 92000 46 167121 1024 cysporf \n", - "\n", - " cv_fold cohen_kappa \n", - "0 0 0.909444 \n", - "1 0 0.934343 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_1 = sporf_df_melt.reset_index()\n", - "df_2 = rf_df_melt.reset_index()\n", - "\n", - "joint_df = pd.concat((df_1, df_2), axis=0)\n", - "\n", - "print(joint_df.shape, sporf_df_melt.shape)\n", - "\n", - "display(joint_df.head(2))" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "febbd5b2-c60d-41a2-966f-eeee600129dd", - "metadata": {}, - "outputs": [], - "source": [ - "ck_dabest = dabest.load(\n", - " joint_df,\n", - " idx=(\n", - " \"cysporf\",\n", - " \"rf\",\n", - " ),\n", - " x=\"clf\",\n", - " y=\"cohen_kappa\",\n", - " id_col=\"index\",\n", - " paired=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "8e54bcdf-c23e-4b8b-ad1f-a2d4e0504316", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v0.3.1\n", - "=============\n", - " \n", - "Good evening!\n", - "The current time is Mon Aug 23 12:40:48 2021.\n", - "\n", - "The paired mean difference between cysporf and rf is -0.0109 [95%CI -0.0145, -0.00677].\n", - "The p-value of the two-sided permutation t-test is 0.0. \n", - "\n", - "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n", - "The p-value(s) reported are the likelihood(s) of observing the effect size(s),\n", - "if the null hypothesis of zero difference is true.\n", - "For each p-value, 5000 reshuffles of the control and test labels were performed.\n", - "\n", - "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ck_dabest.mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "5473599c-18a9-49a1-a6a9-56ea41a1a14c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/adam2392/miniforge3/envs/sklearn-dev/lib/python3.9/site-packages/IPython/core/pylabtools.py:134: UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ck_dabest.mean_diff.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9852695a-0a73-4327-9311-096b0c347fc1", - "metadata": {}, - "outputs": [], - "source": [ - "mean_diff" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "sklearn", - "language": "python", - "name": "sklearn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/validation/plot_orthant.py b/validation/plot_orthant.py deleted file mode 100644 index 7a9765219..000000000 --- a/validation/plot_orthant.py +++ /dev/null @@ -1,61 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd - -# Load true values -# test_df = pd.read_csv("orthant_test.csv", header=None).to_numpy() -test_df = np.load("data/orthant_test.npy") -y = test_df[:, -1] -print(y.shape) - -ofpreds400 = np.load("output/of_orthant_preds_400.npy") -ofpreds2k = np.load("output/of_orthant_preds_2000.npy") -ofpreds4k = np.load("output/of_orthant_preds_4000.npy") - -reps = ofpreds400.shape[0] -print(ofpreds400.shape) - -rfpreds400 = np.load("output/rf_orthant_preds_400.npy") -rfpreds2k = np.load("output/rf_orthant_preds_2000.npy") -rfpreds4k = np.load("output/rf_orthant_preds_4000.npy") - -rerfpreds400 = np.load("output/rerf_orthant_preds_400.npy") -rerfpreds2k = np.load("output/rerf_orthant_preds_2000.npy") -rerfpreds4k = np.load("output/rerf_orthant_preds_4000.npy") - -oferr400 = 1 - np.sum(y == ofpreds400, axis=1) / 10000 -oferr2k = 1 - np.sum(y == ofpreds2k, axis=1) / 10000 -oferr4k = 1 - np.sum(y == ofpreds4k, axis=1) / 10000 - -rferr400 = 1 - np.sum(y == rfpreds400, axis=1) / 10000 -rferr2k = 1 - np.sum(y == rfpreds2k, axis=1) / 10000 -rferr4k = 1 - np.sum(y == rfpreds4k, axis=1) / 10000 - -rerferr400 = 1 - np.sum(y == rerfpreds400, axis=1) / 10000 -rerferr2k = 1 - np.sum(y == rerfpreds2k, axis=1) / 10000 -rerferr4k = 1 - np.sum(y == rerfpreds4k, axis=1) / 10000 - -ofmeans = [np.mean(oferr400), np.mean(oferr2k), np.mean(oferr4k)] -ofsterr = [np.std(oferr400), np.std(oferr2k), np.std(oferr4k)] / np.sqrt(reps) - -rfmeans = [np.mean(rferr400), np.mean(rferr2k), np.mean(rferr4k)] -rfsterr = [np.std(rferr400), np.std(rferr2k), np.std(rferr4k)] / np.sqrt(reps) - -rerfmeans = [np.mean(rerferr400), np.mean(rerferr2k), np.mean(rerferr4k)] -rerfsterr = [np.std(rerferr400), np.std(rerferr2k), np.std(rerferr4k)] / np.sqrt(reps) - - -n = [400, 2000, 4000] -logn = np.log(n) -plt.figure(1) -plt.errorbar(logn, ofmeans, yerr=ofsterr) -plt.errorbar(logn, rfmeans, yerr=rfsterr) -plt.errorbar(logn, rerfmeans, yerr=rerfsterr) -plt.ylim(ymax=0.1, ymin=0) -plt.xticks(logn, n) -plt.title("Orthant") -plt.xlabel("Number of training samples") -plt.ylabel("Error") -plt.legend(["PySPORF", "RF", "RerF"]) - -plt.savefig("figures/orthant_experiment") diff --git a/validation/plot_orthant_diff.py b/validation/plot_orthant_diff.py deleted file mode 100644 index d1feefa04..000000000 --- a/validation/plot_orthant_diff.py +++ /dev/null @@ -1,45 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd - -# Load true values -# test_df = pd.read_csv("orthant_test.csv", header=None).to_numpy() -test_df = np.load("data/orthant_test.npy") -y = test_df[:, -1] -print(y.shape) - -ofpreds400 = np.load("output/of_orthant_preds_400.npy") -ofpreds2k = np.load("output/of_orthant_preds_2000.npy") -ofpreds4k = np.load("output/of_orthant_preds_4000.npy") - -reps = ofpreds400.shape[0] -print(ofpreds400.shape) - -rerfpreds400 = np.load("output/rerf_orthant_preds_400.npy") -rerfpreds2k = np.load("output/rerf_orthant_preds_2000.npy") -rerfpreds4k = np.load("output/rerf_orthant_preds_4000.npy") - -oferr400 = np.sum(y == ofpreds400, axis=1) / 10000 -oferr2k = np.sum(y == ofpreds2k, axis=1) / 10000 -oferr4k = np.sum(y == ofpreds4k, axis=1) / 10000 - -rerferr400 = np.sum(y == rerfpreds400, axis=1) / 10000 -rerferr2k = np.sum(y == rerfpreds2k, axis=1) / 10000 -rerferr4k = np.sum(y == rerfpreds4k, axis=1) / 10000 - -ofmeans = np.array([np.mean(oferr400), np.mean(oferr2k), np.mean(oferr4k)]) -rerfmeans = np.array([np.mean(rerferr400), np.mean(rerferr2k), np.mean(rerferr4k)]) - -diff_means = ofmeans - rerfmeans - -n = [400, 2000, 4000] -logn = np.log(n) -plt.figure(1) -plt.plot(logn, diff_means) -plt.xticks(logn, n) -plt.title("PySPORF vs RerF: Orthant") -plt.xlabel("Number of training samples") -plt.ylabel("Difference in Accuracy") -plt.legend(["PySPORF - RerF"]) - -plt.savefig("figures/orthant_diff") diff --git a/validation/plot_sparse_parity.py b/validation/plot_sparse_parity.py deleted file mode 100644 index aa1a52d54..000000000 --- a/validation/plot_sparse_parity.py +++ /dev/null @@ -1,71 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd - -# Load true values -# test_df = pd.read_csv("Sparse_parity_test.csv", header=None).to_numpy() -test_df = np.load("data/sparse_parity_test.npy") -y = test_df[:, -1] - -pysporfpreds1k = np.load("output/of_sparse_parity_preds_1000.npy") -pysporfpreds5k = np.load("output/of_sparse_parity_preds_5000.npy") -pysporfpreds10k = np.load("output/of_sparse_parity_preds_10000.npy") - -reps = pysporfpreds1k.shape[0] - -sporfpreds1k = np.load("output/sporf_sparse_parity_preds_1000.npy") -sporfpreds5k = np.load("output/sporf_sparse_parity_preds_5000.npy") -sporfpreds10k = np.load("output/sporf_sparse_parity_preds_10000.npy") - -rfpreds1k = np.load("output/rf_sparse_parity_preds_1000.npy") -rfpreds5k = np.load("output/rf_sparse_parity_preds_5000.npy") -rfpreds10k = np.load("output/rf_sparse_parity_preds_10000.npy") - -rerfpreds1k = np.load("output/rerf_sparse_parity_preds_1000.npy") -rerfpreds5k = np.load("output/rerf_sparse_parity_preds_5000.npy") -rerfpreds10k = np.load("output/rerf_sparse_parity_preds_10000.npy") - -oferr1k = 1 - np.sum(y == pysporfpreds1k, axis=1) / 10000 -oferr5k = 1 - np.sum(y == pysporfpreds5k, axis=1) / 10000 -oferr10k = 1 - np.sum(y == pysporfpreds10k, axis=1) / 10000 - -sporferr1k = 1 - np.sum(y == sporfpreds1k, axis=1) / 10000 -sporferr5k = 1 - np.sum(y == sporfpreds5k, axis=1) / 10000 -sporferr10k = 1 - np.sum(y == sporfpreds10k, axis=1) / 10000 - -rferr1k = 1 - np.sum(y == rfpreds1k, axis=1) / 10000 -rferr5k = 1 - np.sum(y == rfpreds5k, axis=1) / 10000 -rferr10k = 1 - np.sum(y == rfpreds10k, axis=1) / 10000 - -rerferr1k = 1 - np.sum(y == rerfpreds1k, axis=1) / 10000 -rerferr5k = 1 - np.sum(y == rerfpreds5k, axis=1) / 10000 -rerferr10k = 1 - np.sum(y == rerfpreds10k, axis=1) / 10000 - -ofmeans = [np.mean(oferr1k), np.mean(oferr5k), np.mean(oferr10k)] -ofsterr = [np.std(oferr1k), np.std(oferr5k), np.std(oferr10k)] / np.sqrt(reps) - -sporfmeans = [np.mean(sporferr1k), np.mean(sporferr5k), np.mean(sporferr10k)] -sporfsterr = [np.std(sporferr1k), np.std(sporferr5k), np.std(sporferr10k)] / np.sqrt(reps) - -rfmeans = [np.mean(rferr1k), np.mean(rferr5k), np.mean(rferr10k)] -rfsterr = [np.std(rferr1k), np.std(rferr5k), np.std(rferr10k)] / np.sqrt(reps) - -rerfmeans = [np.mean(rerferr1k), np.mean(rerferr5k), np.mean(rerferr10k)] -rerfsterr = [np.std(rerferr1k), np.std(rerferr5k), np.std(rerferr10k)] / np.sqrt(reps) - - -n = [1000, 5000, 10000] -logn = np.log(n) -plt.figure(1) -plt.errorbar(logn, ofmeans, yerr=ofsterr) -plt.errorbar(logn, sporfmeans, yerr=sporfsterr) -plt.errorbar(logn, rfmeans, yerr=rfsterr) -plt.errorbar(logn, rerfmeans, yerr=rerfsterr) -plt.ylim(ymax=0.5, ymin=0) -plt.xticks(logn, n) -plt.title("Sparse Parity") -plt.xlabel("Number of training samples") -plt.ylabel("Error") -plt.legend(["PySPORF", "CySPORF", "RF", "RerF"]) - -plt.savefig("figures/sparse_parity_experiment2") diff --git a/validation/plot_sparse_parity_diff.py b/validation/plot_sparse_parity_diff.py deleted file mode 100644 index ac6a4048e..000000000 --- a/validation/plot_sparse_parity_diff.py +++ /dev/null @@ -1,42 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd - -# Load true values -# test_df = pd.read_csv("Sparse_parity_test.csv", header=None).to_numpy() -test_df = np.load("data/sparse_parity_test.npy") -y = test_df[:, -1] - -ofpreds1k = np.load("output/of_sparse_parity_preds_1000.npy") -ofpreds5k = np.load("output/of_sparse_parity_preds_5000.npy") -ofpreds10k = np.load("output/of_sparse_parity_preds_10000.npy") - -reps = ofpreds1k.shape[0] - -rerfpreds1k = np.load("output/rerf_sparse_parity_preds_1000.npy") -rerfpreds5k = np.load("output/rerf_sparse_parity_preds_5000.npy") -rerfpreds10k = np.load("output/rerf_sparse_parity_preds_10000.npy") - -oferr1k = np.sum(y == ofpreds1k, axis=1) / 10000 -oferr5k = np.sum(y == ofpreds5k, axis=1) / 10000 -oferr10k = np.sum(y == ofpreds10k, axis=1) / 10000 - -rerferr1k = np.sum(y == rerfpreds1k, axis=1) / 10000 -rerferr5k = np.sum(y == rerfpreds5k, axis=1) / 10000 -rerferr10k = np.sum(y == rerfpreds10k, axis=1) / 10000 - -ofmeans = np.array([np.mean(oferr1k), np.mean(oferr5k), np.mean(oferr10k)]) -rerfmeans = np.array([np.mean(rerferr1k), np.mean(rerferr5k), np.mean(rerferr10k)]) -diff_means = ofmeans - rerfmeans - -n = [1000, 5000, 10000] -logn = np.log(n) -plt.figure(1) -plt.plot(logn, diff_means) -plt.xticks(logn, n) -plt.title("PySPORF vs RerF: Sparse Parity") -plt.xlabel("Number of training samples") -plt.ylabel("Difference in Accuracy") -plt.legend(["PySPORF - RerF"]) - -plt.savefig("figures/sparse_parity_diff") diff --git a/validation/run_cc18.ipynb b/validation/run_cc18.ipynb deleted file mode 100644 index 5fdab2d20..000000000 --- a/validation/run_cc18.ipynb +++ /dev/null @@ -1,3051 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "86b13932", - "metadata": {}, - "source": [ - "# CC-18 Performance Evaluation\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "544af671", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext lab_black" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "737d7f18", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "from pathlib import Path\n", - "import numpy as np\n", - "import collections\n", - "from tqdm import tqdm\n", - "from pathlib import Path\n", - "import time\n", - "import logging\n", - "import json\n", - "\n", - "sys.path.append(\"../\")\n", - "\n", - "from oblique_forests.sporf import ObliqueForestClassifier\n", - "\n", - "# from oblique_forests.ensemble import RandomForestClassifier\n", - "from rerf.rerfClassifier import rerfClassifier\n", - "\n", - "import sklearn\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.model_selection import cross_val_score\n", - "from sklearn.ensemble import RandomForestClassifier as skrf\n", - "import openml\n", - "\n", - "from joblib import Parallel, delayed\n", - "\n", - "from sklearn.calibration import CalibratedClassifierCV\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "from sklearn.impute import SimpleImputer\n", - "from sklearn.metrics import cohen_kappa_score\n", - "\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "efaed243", - "metadata": {}, - "outputs": [], - "source": [ - "openml.config.apikey = \"e5bef9acec2f51e174bf75ac6c8c3fcb\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "240a29b4", - "metadata": {}, - "outputs": [], - "source": [ - "benchmark_suite = openml.study.get_suite(\"OpenML-CC18\") # obtain the benchmark suite" - ] - }, - { - "cell_type": "markdown", - "id": "c5afa7b8-197f-43d0-8d3b-0bc899169887", - "metadata": {}, - "source": [ - "## Helper Functions" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "304cec1f-1ecd-4427-be2b-1e50593f7496", - "metadata": {}, - "outputs": [], - "source": [ - "def _run_task_helper(clfs, task_id, save_path, overwrite):\n", - " task = openml.tasks.get_task(task_id) # download the OpenML task\n", - " task_name = task.get_dataset().name\n", - "\n", - " if not overwrite and os.path.isfile(save_path):\n", - " logging.info(f\"APPEND MODE: Skipping {task_name} (already exists)\")\n", - " return\n", - "\n", - " print(f\"Running {task_name} ({task_id})\")\n", - " logging.info(f\"Running {task_name} ({task_id})\")\n", - "\n", - " X, y = task.get_X_and_y() # get the data\n", - "\n", - " # TODO: what does this do?\n", - " nominal_indices = task.get_dataset().get_features_by_type(\n", - " \"nominal\", [task.target_name]\n", - " )\n", - " try:\n", - " print(\"Running Train Test now...\")\n", - " train_test(X, y, task_name, task_id, nominal_indices, clfs, save_path)\n", - " except Exception as e:\n", - " print(\n", - " f\"Test {task_name} ({task_id}) Failed | X.shape={X.shape} | {len(nominal_indices)} nominal indices\"\n", - " )\n", - " print(e)\n", - " logging.error(\n", - " f\"Test {task_name} ({task_id}) Failed | X.shape={X.shape} | {len(nominal_indices)} nominal indices\"\n", - " )\n", - " import traceback\n", - "\n", - " logging.error(e)\n", - " traceback.sprint_exc()" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "11253df4-805d-49e9-b201-bafa54fed07d", - "metadata": {}, - "outputs": [], - "source": [ - "def stratify_samplesizes(y, block_lengths):\n", - " \"\"\"\n", - " Sort data and labels into blocks that preserve class balance\n", - "\n", - " Parameters\n", - " ----------\n", - " X: data matrix\n", - " y : 1D class labels\n", - " block_lengths : Block sizes to sort X,y into that preserve class balance\n", - " \"\"\"\n", - " clss, counts = np.unique(y, return_counts=True)\n", - " ratios = counts / sum(counts)\n", - " class_idxs = [np.where(y == i)[0] for i in clss]\n", - "\n", - " sort_idxs = []\n", - "\n", - " prior_idxs = np.zeros(len(clss)).astype(int)\n", - " for n in block_lengths:\n", - " get_idxs = np.rint((n - len(clss)) * ratios).astype(int) + 1\n", - " for idxs, prior_idx, next_idx in zip(class_idxs, prior_idxs, get_idxs):\n", - " sort_idxs.append(idxs[prior_idx:next_idx])\n", - " prior_idxs = get_idxs\n", - "\n", - " sort_idxs = np.hstack(sort_idxs)\n", - "\n", - " return sort_idxs" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "4b266bfb-df88-48d8-aee7-613579653dcf", - "metadata": {}, - "outputs": [], - "source": [ - "def _check_nested_equality(lst1, lst2):\n", - " if isinstance(lst1, list) and isinstance(lst2, list):\n", - " for l1, l2 in zip(lst1, lst2):\n", - " if not _check_nested_equality(l1, l2):\n", - " return False\n", - " elif isinstance(lst1, np.ndarray) and isinstance(lst2, np.ndarray):\n", - " return np.all(lst1 == lst2)\n", - " else:\n", - " return lst1 == lst2\n", - "\n", - " return True" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "3ecab472-3e51-4f77-85bd-3285b0d99d9b", - "metadata": {}, - "outputs": [], - "source": [ - "class NumpyEncoder(json.JSONEncoder):\n", - " \"\"\"Special json encoder for numpy types.\n", - "\n", - " Pass to json.dump(), or json.load().\n", - " \"\"\"\n", - "\n", - " def default(self, obj): # noqa\n", - " if isinstance(\n", - " obj,\n", - " (\n", - " np.int_,\n", - " np.intc,\n", - " np.intp,\n", - " np.int8,\n", - " np.int16,\n", - " np.int32,\n", - " np.int64,\n", - " np.uint8,\n", - " np.uint16,\n", - " np.uint32,\n", - " np.uint64,\n", - " ),\n", - " ):\n", - " return int(obj)\n", - " elif isinstance(obj, (np.float_, np.float16, np.float32, np.float64)):\n", - " return float(obj)\n", - " elif isinstance(obj, (np.ndarray,)): # This is the fix\n", - " return obj.tolist()\n", - " elif isinstance(obj, (datetime, date)):\n", - " return obj.isoformat()\n", - " return json.JSONEncoder.default(self, obj)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "30724c68-d4ce-4f22-9301-a14fcf121d60", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def train_test(X, y, task_name, task_id, nominal_indices, clfs, save_path):\n", - " # Set up Cross validation\n", - " skf = StratifiedKFold(n_splits=cv, shuffle=True, random_state=0)\n", - " n_samples, n_features = X.shape\n", - " n_classes = len(np.unique(y))\n", - "\n", - " if vary_samples:\n", - " sample_sizes = np.logspace(\n", - " np.log10(n_classes * 2),\n", - " np.log10(np.floor(len(y) * (cv - 1.1) / cv)),\n", - " num=10,\n", - " endpoint=True,\n", - " dtype=int,\n", - " )\n", - " else:\n", - " sample_sizes = [len(y)]\n", - "\n", - " # Check if existing experiments\n", - " results_dict = {\n", - " \"task\": task_name,\n", - " \"task_id\": task_id,\n", - " \"n_samples\": n_samples,\n", - " \"n_features\": n_features,\n", - " \"n_classes\": n_classes,\n", - " \"y\": y,\n", - " \"test_indices\": [],\n", - " \"n_estimators\": n_estimators,\n", - " \"cv\": cv,\n", - " \"nominal_features\": len(nominal_indices),\n", - " \"sample_sizes\": sample_sizes,\n", - " }\n", - "\n", - " # Get numeric indices first\n", - " numeric_indices = np.delete(np.arange(X.shape[1]), nominal_indices)\n", - "\n", - " # Numeric Preprocessing\n", - " numeric_transformer = SimpleImputer(strategy=\"median\")\n", - "\n", - " # Nominal preprocessing\n", - " nominal_transformer = Pipeline(\n", - " steps=[\n", - " (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n", - " (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\", sparse=False)),\n", - " ]\n", - " )\n", - "\n", - " transformers = []\n", - " if len(numeric_indices) > 0:\n", - " transformers += [(\"numeric\", numeric_transformer, numeric_indices)]\n", - " if len(nominal_indices) > 0:\n", - " transformers += [(\"nominal\", nominal_transformer, nominal_indices)]\n", - " preprocessor = ColumnTransformer(transformers=transformers)\n", - "\n", - " _, n_features_fitted = preprocessor.fit_transform(X, y).shape\n", - " results_dict[\"n_features_fitted\"] = n_features_fitted\n", - " print(\n", - " f\"Features={n_features}, nominal={len(nominal_indices)} (After transforming={n_features_fitted})\"\n", - " )\n", - "\n", - " # Store training indices (random state insures consistent across clfs)\n", - " for train_index, test_index in skf.split(X, y):\n", - " \n", - "\n", - " # split X, y data based on cross-validation\n", - " for train_index, test_index in skf.split(X, y):\n", - " # Store training indices (random state insures consistent across clfs)\n", - " results_dict[\"test_indices\"].append(test_index)\n", - " results_dict[f\"{clf_name}_metadata\"][\"train_times\"] = []\n", - " results_dict[f\"{clf_name}_metadata\"][\"test_times\"] = []\n", - " \n", - " X_train, X_test = X[train_index], X[test_index]\n", - " y_train, y_test = y[train_index], y[test_index]\n", - "\n", - " # vary sample sizes\n", - " if vary_samples:\n", - " stratified_sort = stratify_samplesizes(y_train, sample_sizes)\n", - " X_train = X_train[stratified_sort]\n", - " y_train = y_train[stratified_sort]\n", - "\n", - " for clf_name, clf in clfs:\n", - " pipeline = Pipeline(\n", - " steps=[(\"Preprocessor\", preprocessor), (\"Estimator\", clf)]\n", - " )\n", - "\n", - " fold_probas = []\n", - " oob_fold_probas = []\n", - " if not f\"{clf_name}_metadata\" in results_dict.keys():\n", - " results_dict[f\"{clf_name}_metadata\"] = {}\n", - "\n", - " probas_vs_sample_sizes = []\n", - " oob_probas_vs_sample_sizes = []\n", - "\n", - " # loop over all sample sizes\n", - " for n_samples in sample_sizes:\n", - " start_time = time.time()\n", - " # Fix too few samples for internal CV of these methods\n", - " if (\n", - " clf_name in [\"IRF\", \"SigRF\"]\n", - " and np.min(np.unique(y_train[:n_samples], return_counts=True)[1])\n", - " < 5\n", - " ):\n", - " print(\n", - " f\"{clf_name} requires more samples of minimum class. Skipping n={n_samples}\"\n", - " )\n", - " y_proba = np.repeat(\n", - " np.bincount(y_train[:n_samples]).reshape(1, -1)\n", - " / len(y_train[:n_samples]),\n", - " X_test.shape[0],\n", - " axis=0,\n", - " )\n", - " # y_proba_oob = y_proba\n", - " train_time = time.time() - start_time\n", - " else:\n", - " pipeline = pipeline.fit(X_train[:n_samples], y_train[:n_samples])\n", - " train_time = time.time() - start_time\n", - " y_proba = pipeline.predict_proba(X_test)\n", - " # y_proba_oob = predict_proba_oob(pipeline['Estimator'], pipeline['Preprocessor'].transform(X_train[:n_samples]))\n", - "\n", - " test_time = time.time() - (train_time + start_time)\n", - "\n", - " probas_vs_sample_sizes.append(y_proba)\n", - " # oob_probas_vs_sample_sizes.append(y_proba_oob)\n", - " results_dict[f\"{clf_name}_metadata\"][\"train_times\"].append(train_time)\n", - " results_dict[f\"{clf_name}_metadata\"][\"test_times\"].append(test_time)\n", - " # results_dict[clf_name + '_oob'] = oob_fold_probas\n", - " print(\n", - " f\"{clf_name} Time: train_time={train_time:.3f}, test_time={test_time:.3f}, Cohen Kappa={cohen_kappa_score(y_test, y_proba.argmax(1)):.3f}\"\n", - " )\n", - " \n", - " fold_probas.append(probas_vs_sample_sizes)\n", - " # oob_fold_probas.append(oob_probas_vs_sample_sizes)\n", - "\n", - " results_dict[clf_name] = fold_probas\n", - "\n", - "\n", - " # If existing data, load and append to. Else save\n", - " if not os.path.isfile(save_path) or overwrite:\n", - " with open(save_path, \"w\") as fout:\n", - " json.dump(results_dict, fout, cls=NumpyEncoder)" - ] - }, - { - "cell_type": "markdown", - "id": "7eb38063-e327-4a87-bbc6-df8c740aecf5", - "metadata": {}, - "source": [ - "## Set Hyperparameters on Which Tasks to Run" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "79db6527-e501-4e5d-9def-df6e0ca6ed2c", - "metadata": {}, - "outputs": [], - "source": [ - "n_jobs = -1\n", - "n_estimators = 500\n", - "feature_combinations = 1.5\n", - "max_features = 1.0" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "fa915fec-40af-4397-bf61-fdfdb02c3371", - "metadata": {}, - "outputs": [], - "source": [ - "# rerf_rf_clf = rerfClassifier(\n", - "# n_estimators=n_estimators, projection_matrix=\"Base\", n_jobs=n_jobs\n", - "# )\n", - "# rerf_clf = rerfClassifier(\n", - "# n_estimators=n_estimators, projection_matrix=\"RerF\", n_jobs=n_jobs\n", - "# )\n", - "cy_of_clf = ObliqueForestClassifier(\n", - " n_estimators=n_estimators,\n", - " feature_combinations=feature_combinations,\n", - " max_features=max_features,\n", - " n_jobs=n_jobs,\n", - ")\n", - "# rf_clf = RandomForestClassifier(n_estimators=n_estimators, n_jobs=n_jobs)\n", - "skrf_clf = skrf(n_estimators=n_estimators, n_jobs=n_jobs)\n", - "\n", - "clfs = [(\"RF\", skrf_clf), (\"CYSPORF\", cy_of_clf)]" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "0dc6a295-c58e-4fc3-b64e-d198d43ae47c", - "metadata": {}, - "outputs": [], - "source": [ - "# if both start/stop are None, then run on all tasks\n", - "start_id = None\n", - "stop_id = None\n", - "\n", - "name = \"hackerman_master\"\n", - "overwrite = True\n", - "\n", - "# cross validation\n", - "cv = 10\n", - "\n", - "vary_samples = False\n", - "\n", - "# hyperparameters of forest\n", - "max_features = None\n", - "\n", - "# directory to save the output\n", - "data_dir = Path(\"/home/adam2392/Downloads\")\n", - "\n", - "# folder to save results\n", - "folder = data_dir / f\"sporf_benchmarks/results_cv{cv}_features={max_features}_{name}\"\n", - "folder.mkdir(exist_ok=True, parents=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "61fe7b19-d5f3-4cc3-af99-78fd38d5277c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analyzing [3, 6, 11, 12, 14, 15, 16, 18, 22, 23, 28, 29, 31, 32, 37, 43, 45, 49, 53, 219, 2074, 2079, 3021, 3022, 3481, 3549, 3560, 3573, 3902, 3903, 3904, 3913, 3917, 3918, 7592, 9910, 9946, 9952, 9957, 9960, 9964, 9971, 9976, 9977, 9978, 9981, 9985, 10093, 10101, 14952, 14954, 14965, 14969, 14970, 125920, 125922, 146195, 146800, 146817, 146819, 146820, 146821, 146822, 146824, 146825, 167119, 167120, 167121, 167124, 167125, 167140, 167141] tasks.\n" - ] - } - ], - "source": [ - "# get the task IDs to run\n", - "task_ids_to_run = []\n", - "for task_id in benchmark_suite.tasks:\n", - " if start_id is not None and task_id < start_id:\n", - " print(f\"Skipping task_id={task_id}\")\n", - " # logging.info(f'Skipping task_id={task_id}')\n", - " continue\n", - " if stop_id is not None and task_id >= stop_id:\n", - " print(f\"Stopping at task_id={task_id}\")\n", - " # logging.info(f'Stopping at task_id={task_id}')\n", - " break\n", - " task_ids_to_run.append(task_id)\n", - "\n", - "print(f\"Analyzing {task_ids_to_run} tasks.\")" - ] - }, - { - "cell_type": "markdown", - "id": "a5a70fdb-82d2-4fd5-b812-209161da9ff1", - "metadata": {}, - "source": [ - "## Run Benchmarks" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce7cc95f-584b-4d5e-8483-f904bff92048", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/72 [00:00 top: - base = top - add /= 2 - top += add - - # at this point, base < x < top - if x < (base + top) / 2: - return 0 - - return 1 - - -def consistency(n): - # np.random.seed(1231761) - - X = np.zeros((n, 2)) - y = np.zeros(n) - - box = 0 - for i in range(0, n): - # box = np.random.randint(3) - box = (box + 1) % 3 - - if box == 0: - X[i, 0] = np.random.uniform(0, 1) - X[i, 1] = np.random.uniform(0, 1) - - y[i] = find_label(X[i, 0]) - - elif box == 1: - X[i, 0] = np.random.uniform(1, 2) - X[i, 1] = np.random.uniform(1, 2) - - if (X[i, 0] < 1.5 and X[i, 1] > 1.5) or (X[i, 0] > 1.5 and X[i, 1] < 1.5): - y[i] = 1 - else: - y[i] = 0 - - else: - X[i, 0] = np.random.uniform(0, 1) - X[i, 1] = np.random.uniform(0, 1) - - y[i] = find_label(1 - X[i, 1]) - - X[i] += 2 - - # np.random.seed(None) - return X, y - - -def sparse_parity(n, p=20, p_star=3): - # np.random.seed(12763123) - - X = np.random.uniform(-1, 1, (n, p)) - y = np.zeros(n) - - for i in range(0, n): - y[i] = sum(X[i, :p_star] > 0) % 2 - - # np.random.seed(None) - return X, y - - -def orthant(n, p=6, rec=1): - if rec == 10: - print("sample size too small") - sys.exit(0) - - orth_labels = np.asarray([2**i for i in range(0, p)][::-1]) - - X = np.random.uniform(-1, 1, (n, p)) - y = np.zeros(n) - - for i in range(0, n): - idx = np.where(X[i, :] > 0)[0] - y[i] = sum(orth_labels[idx]) - - # Careful not to stack overflow! - if len(np.unique(y)) < 2**p: - X, y = orthant(n, p, rec + 1) - - return X, y - - -def trunk(n, p=10): - mu_1 = np.array([1 / i for i in range(1, p + 1)]) - mu_0 = -1 * mu_1 - - cov = np.identity(p) - - X = np.vstack( - ( - np.random.multivariate_normal(mu_0, cov, int(n / 2)), - np.random.multivariate_normal(mu_1, cov, int(n / 2)), - ) - ) - - y = np.concatenate((np.zeros(int(n / 2)), np.ones(int(n / 2)))) - - return X, y diff --git a/validation/test_runtime_performance_sporf.ipynb b/validation/test_runtime_performance_sporf.ipynb deleted file mode 100644 index be003a253..000000000 --- a/validation/test_runtime_performance_sporf.ipynb +++ /dev/null @@ -1,729 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis of Rerf-sporf, Py-sporf and Cythonized-sporf Performance\n", - "\n", - "Here, we are interested in looking at the runtime of each implementation of SPORF on a fixed classification task.\n", - "\n", - "Namely, we will utilize the orthant and sparse-parity tasks in the original SPORF paper." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext lab_black" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "import numpy as np\n", - "import collections\n", - "\n", - "from sklearn.ensemble import RandomForestClassifier as rfc\n", - "\n", - "sys.path.append(\"../\")\n", - "\n", - "from oblique_forests.sporf import (\n", - " ObliqueForestClassifier,\n", - ") # , PythonObliqueForestClassifier\n", - "from rerf.rerfClassifier import rerfClassifier\n", - "\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def load_data(n, data_path, exp_name):\n", - " \"\"\"Function to load in data as a function of sample size.\"\"\"\n", - " ftrain = data_path / f\"{exp_name}_train_{n}.npy\"\n", - " ftest = data_path / f\"{exp_name}_test.npy\"\n", - "\n", - " dftrain = np.load(ftrain)\n", - " dftest = np.load(ftest)\n", - "\n", - " X_train = dftrain[:, :-1]\n", - " y_train = dftrain[:, -1]\n", - "\n", - " X_test = dftest[:, :-1]\n", - " y_test = dftest[:, -1]\n", - "\n", - " return X_train, y_train, X_test, y_test" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "def test_rf(n, reps, n_estimators, exp_name):\n", - " \"\"\"Test traditional RF classifier\"\"\"\n", - "\n", - " preds = np.zeros((reps, 10000))\n", - " acc = np.zeros(reps)\n", - " for i in range(reps):\n", - "\n", - " X_train, y_train, X_test, y_test = load_data(n, data_path, exp_name)\n", - "\n", - " clf = rerfClassifier(\n", - " n_estimators=n_estimators, projection_matrix=\"Base\", n_jobs=8\n", - " )\n", - "\n", - " clf.fit(X_train, y_train)\n", - "\n", - " preds[i] = clf.predict(X_test)\n", - " acc[i] = np.sum(preds[i] == y_test) / len(y_test)\n", - "\n", - " np.save(f\"output/rf_{exp_name}_preds_{n}.npy\", preds)\n", - " return acc\n", - "\n", - "\n", - "def test_rerf(n, reps, n_estimators, feature_combinations, max_features, exp_name):\n", - " \"\"\"Test SPORF rerf implemnetation.\"\"\"\n", - " preds = np.zeros((reps, 10000))\n", - " acc = np.zeros(reps)\n", - " for i in range(reps):\n", - "\n", - " X_train, y_train, X_test, y_test = load_data(n, data_path, exp_name)\n", - "\n", - " clf = rerfClassifier(\n", - " n_estimators=n_estimators,\n", - " projection_matrix=\"RerF\",\n", - " feature_combinations=feature_combinations,\n", - " max_features=max_features,\n", - " n_jobs=8,\n", - " )\n", - "\n", - " clf.fit(X_train, y_train)\n", - "\n", - " preds[i] = clf.predict(X_test)\n", - " acc[i] = np.sum(preds[i] == y_test) / len(y_test)\n", - "\n", - " np.save(f\"output/rerf_{exp_name}_preds_\" + str(n) + \".npy\", preds)\n", - " return acc\n", - "\n", - "\n", - "def test_cython_of(n, reps, n_estimators, feature_combinations, max_features, exp_name):\n", - " \"\"\"Test SPORF rerf implemnetation.\"\"\"\n", - " preds = np.zeros((reps, 10000))\n", - " acc = np.zeros(reps)\n", - " for i in range(reps):\n", - "\n", - " X_train, y_train, X_test, y_test = load_data(n, data_path, exp_name)\n", - "\n", - " clf = ObliqueForestClassifier(\n", - " n_estimators=n_estimators,\n", - " feature_combinations=feature_combinations,\n", - " max_features=max_features,\n", - " n_jobs=8,\n", - " )\n", - "\n", - " clf.fit(X_train, y_train)\n", - "\n", - " preds[i] = clf.predict(X_test)\n", - " acc[i] = np.sum(preds[i] == y_test) / len(y_test)\n", - "\n", - " np.save(f\"output/cythonof_{exp_name}_preds_\" + str(n) + \".npy\", preds)\n", - " return acc\n", - "\n", - "\n", - "def test_python_of(n, reps, n_estimators, feature_combinations, max_features, exp_name):\n", - " \"\"\"Test PySporf.\"\"\"\n", - " preds = np.zeros((reps, 10000))\n", - " acc = np.zeros(reps)\n", - " for i in range(reps):\n", - "\n", - " X_train, y_train, X_test, y_test = load_data(n, data_path, exp_name)\n", - "\n", - " clf = PythonObliqueForestClassifier(\n", - " n_estimators=n_estimators,\n", - " feature_combinations=feature_combinations,\n", - " max_features=max_features,\n", - " n_jobs=8,\n", - " )\n", - "\n", - " clf.fit(X_train, y_train)\n", - " preds[i] = clf.predict(X_test)\n", - " acc[i] = np.sum(preds[i] == y_test) / len(y_test)\n", - "\n", - " np.save(f\"output/of_{exp_name}_preds_\" + str(n) + \".npy\", preds)\n", - " return acc" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "data_path = Path(\"./data/\")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# How many samples to train on\n", - "n = 1000\n", - "\n", - "# How many repetitions\n", - "reps = 3\n", - "\n", - "# experiment name\n", - "exp_name = \"sparse_parity\"\n", - "# exp_name = 'orthant'\n", - "\n", - "# Tree parameters\n", - "n_estimators = 100\n", - "feature_combinations = 2\n", - "max_features = \"auto\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test Classification Performance" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# acc = test_python_of(n, reps, n_estimators, feature_combinations, max_features, exp_name)\n", - "# print(acc)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.7043 0.6796 0.6941]\n" - ] - } - ], - "source": [ - "acc = test_rerf(n, reps, n_estimators, feature_combinations, max_features, exp_name)\n", - "print(acc)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.6396 0.5934 0.6273]\n" - ] - } - ], - "source": [ - "acc = test_rf(n, reps, n_estimators, exp_name)\n", - "print(acc)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.7191 0.673 0.6911]\n" - ] - } - ], - "source": [ - "acc = test_cython_of(\n", - " n, reps, n_estimators, feature_combinations, max_features, exp_name\n", - ")\n", - "print(acc)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test Actual Runtime" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# keep track of a list of runtimes\n", - "n_list = collections.defaultdict(list)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "clf = rfc(n_estimators=n_estimators, n_jobs=8)\n", - "rerf_clf = rerfClassifier(n_estimators=n_estimators, projection_matrix=\"RerF\", n_jobs=8)\n", - "# py_of_clf = PythonObliqueForestClassifier(\n", - "# n_estimators=n_estimators,\n", - "# feature_combinations=feature_combinations,\n", - "# max_features=max_features,\n", - "# n_jobs=8,\n", - "# )\n", - "cy_of_clf = ObliqueForestClassifier(\n", - " n_estimators=n_estimators,\n", - " feature_combinations=feature_combinations,\n", - " max_features=max_features,\n", - " n_jobs=8,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1000 sample size" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# run on higher sample size now\n", - "# How many samples to train on\n", - "n = 1000\n", - "X_train, y_train, X_test, y_test = load_data(n, data_path, exp_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "87.8 ms ± 7.58 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "time = %timeit -n 1 -o clf.fit(X_train, y_train)\n", - "n_list[\"BaseRF\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "39.2 ms ± 4.21 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "time = %timeit -n 1 -o rerf_clf.fit(X_train, y_train)\n", - "n_list[\"ReRF-Sporf\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "19.4 s ± 319 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "time = %timeit -n 1 -o py_of_clf.fit(X_train, y_train)\n", - "n_list[\"Py-Sporf\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "124 ms ± 4.61 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "# with pointer arrays (old)\n", - "time = %timeit -n 1 -o cy_of_clf.fit(X_train, y_train)\n", - "n_list[\"Cy-Sporf\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "209 ms ± 4.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "# with pointer arrays\n", - "time = %timeit -n 1 -o cy_of_clf.fit(X_train, y_train)\n", - "n_list[\"Cy-Sporf\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "212 ms ± 9.02 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "# with std::vectors\n", - "time = %timeit -n 1 -o cy_of_clf.fit(X_train, y_train)\n", - "n_list[\"Cy-Sporf\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5000 sample size" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# run on higher sample size now\n", - "# How many samples to train on\n", - "n = 5000" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "X_train, y_train, X_test, y_test = load_data(n, data_path, exp_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "245 ms ± 12.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "time = %timeit -n 1 -o clf.fit(X_train, y_train)\n", - "n_list[\"BaseRF\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "199 ms ± 7.27 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "time = %timeit -n 1 -o rerf_clf.fit(X_train, y_train)\n", - "n_list[\"ReRF-Sporf\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "508 ms ± 8.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "# with pointer arrays\n", - "time = %timeit -n 1 -o cy_of_clf.fit(X_train, y_train)\n", - "n_list[\"Cy-Sporf\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "680 ms ± 6.55 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "# with pointer arrays \n", - "time = %timeit -n 1 -o cy_of_clf.fit(X_train, y_train)\n", - "n_list[\"Cy-Sporf\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "579 ms ± 8.19 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "# with std::vectors\n", - "time = %timeit -n 1 -o cy_of_clf.fit(X_train, y_train)\n", - "n_list[\"Cy-Sporf\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 10000 sample size" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# run on higher sample size now\n", - "# How many samples to train on\n", - "n = 10000" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "X_train, y_train, X_test, y_test = load_data(n, data_path, exp_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "456 ms ± 11.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "time = %timeit -n 1 -o clf.fit(X_train, y_train)\n", - "n_list[\"BaseRF\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "407 ms ± 15.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "time = %timeit -n 1 -o rerf_clf.fit(X_train, y_train)\n", - "n_list[\"ReRF-Sporf\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "971 ms ± 13.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "# with pointer arrays\n", - "time = %timeit -n 1 -o cy_of_clf.fit(X_train, y_train)\n", - "n_list[\"Cy-Sporf\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.18 s ± 12.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "# with pointer arrays\n", - "time = %timeit -n 1 -o cy_of_clf.fit(X_train, y_train)\n", - "n_list[\"Cy-Sporf\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.04 s ± 15.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "# with pointer arrays (c++ std vector)\n", - "time = %timeit -n 1 -o cy_of_clf.fit(X_train, y_train)\n", - "n_list[\"Cy-Sporf\"].append(np.mean(time.timings))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "sklearn", - "language": "python", - "name": "sklearn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}