{ "cells": [ { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "df630e55-57dd-470a-849b-f1bc90ac719e" } }, "source": [ "# 1. Importing and Visualization of CIFAR-10 Dataset" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "nbpresent": { "id": "409a1ab7-fe1d-4430-b904-7694020a6223" } }, "outputs": [], "source": [ "import numpy as np\n", "\n", "# function to import CIFAR-10 data set\n", "def unpickle(file):\n", " import pickle\n", " with open(file, 'rb') as fo:\n", " dict = pickle.load(fo, encoding='bytes')\n", " return dict\n", "data_batch_1 = unpickle(\"./data/data_batch_1\")\n", "data_batch_2 = unpickle(\"./data/data_batch_2\")\n", "data_batch_3 = unpickle(\"./data/data_batch_3\")\n", "data_batch_4 = unpickle(\"./data/data_batch_4\")\n", "data_batch_5 = unpickle(\"./data/data_batch_5\")\n", "test_batch = unpickle(\"./data/test_batch\")" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "ce2be501-0be3-4750-8207-dfc00d7db01a" } }, "source": [ "What is the data structure of e.g. data_batch_1 ?" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "nbpresent": { "id": "f77bd9ec-de3b-4c56-b08d-4a65f0780408" } }, "outputs": [ { "data": { "text/plain": [ "dict" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(data_batch_1)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "09a5b60b-dcbb-4f97-ab57-e4611c253e2e" } }, "source": [ "What are the keys of e.g. data_batch_1 ?" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "nbpresent": { "id": "c874a7c9-de0c-4ccd-a0f1-8f8a3265a0b6" } }, "outputs": [ { "data": { "text/plain": [ "dict_keys([b'batch_label', b'labels', b'data', b'filenames'])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_batch_1.keys()" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "a7f910e7-0b11-453b-84d5-df6ac88ac6dd" } }, "source": [ "What is the data structure of data_batch_1[b'data'] ?" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "nbpresent": { "id": "fe299a35-c930-4078-97b7-c9b67f42ec42" } }, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(data_batch_1[b'data'])" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "3f978c4f-50d0-4f00-9f19-bf8744e505a3" } }, "source": [ "What is the data structure of data_batch_1[b'labels'] ?" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "nbpresent": { "id": "46a97575-36c0-4920-a8dc-762e94239b7e" } }, "outputs": [ { "data": { "text/plain": [ "list" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(data_batch_1[b'labels'])" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "2fd19982-c318-4303-8042-7a5a6998d175" } }, "source": [ "What is the shape of data_batch_1[b'data'] ?" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "nbpresent": { "id": "b012720d-81f8-455d-8ce7-bfca64a842c8" } }, "outputs": [ { "data": { "text/plain": [ "(10000, 3072)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_batch_1[b'data'].shape" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "378aeda2-a547-435e-b28b-09ceb0074a53" } }, "source": [ "What is the size of data_batch_1[b'labels'] ?\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "nbpresent": { "id": "49c776cb-c8aa-461b-a0da-4f4d38342e2e" } }, "outputs": [ { "data": { "text/plain": [ "10000" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(data_batch_1[b'labels'])" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "02ca495c-e6d2-48d4-9bc2-2295272d5f6f" } }, "source": [ "What are the first 10 elements of data_batch_1[b'labels'] ?" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "nbpresent": { "id": "438920f4-774e-4e94-9b7c-30a2106d163c" } }, "outputs": [ { "data": { "text/plain": [ "[6, 9, 9, 4, 1, 1, 2, 7, 8, 3]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_batch_1[b'labels'][:10]" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "1e599fcd-a46c-4750-94f8-1cf4ad8fb342" } }, "source": [ "What is the data type of data_batch_1[b'data'] ?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "nbpresent": { "id": "7617a699-c3d5-434f-97a5-3443489ac9db" } }, "outputs": [ { "data": { "text/plain": [ "dtype('uint8')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_batch_1[b'data'].dtype" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "067d850d-0411-4af6-8714-a79b310ca8c1" } }, "source": [ "Let us concatenate the batch training data" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "nbpresent": { "id": "942f351b-b771-4375-8df2-eec28391a576" } }, "outputs": [], "source": [ "X_train=np.concatenate([data_batch_1[b'data'], \n", " data_batch_2[b'data'], \n", " data_batch_3[b'data'], \n", " data_batch_4[b'data'], \n", " data_batch_5[b'data']], \n", " axis = 0)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "b289f0b9-b3ab-480b-9ae6-76a893980efe" } }, "source": [ "Let us concatenate the training labels" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "nbpresent": { "id": "9b85b9a0-5f2b-4c68-a74f-82f1ec212215" } }, "outputs": [], "source": [ "y_train=np.concatenate([data_batch_1[b'labels'] , \n", " data_batch_2[b'labels'],\n", " data_batch_3[b'labels'],\n", " data_batch_4[b'labels'],\n", " data_batch_5[b'labels']], \n", " axis = 0)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "d9967582-1305-4b95-948b-e75c46fc49bb" } }, "source": [ "Let us define the test data as X_test" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "nbpresent": { "id": "5c85918c-f89e-4156-8cdd-ca38d14afbb9" } }, "outputs": [ { "data": { "text/plain": [ "(10000, 3072)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test = test_batch[b'data']\n", "X_test.shape" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "48754d72-9acd-49cf-b209-737c45047284" } }, "source": [ "Let us cast the test labels as ndarray" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "nbpresent": { "id": "5f913d95-aa49-4727-8c6f-5630cbf59741" } }, "outputs": [ { "data": { "text/plain": [ "(10000,)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_test=np.array(test_batch[b'labels']) \n", "y_test.shape" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "f4c3aa97-0d97-4e9c-a6b3-f2d2b1f08632" } }, "source": [ "What is the shape of X_train ?" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "nbpresent": { "id": "a0eb7a33-19c9-46e4-b471-6f7904389177" } }, "outputs": [ { "data": { "text/plain": [ "(50000, 3072)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "81d3c33f-544e-44c8-8260-e89143cc6ef1" } }, "source": [ "What is the shape of Y_train ?" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "nbpresent": { "id": "d699e7a7-efc0-421f-bd8d-2d2b34a09516" } }, "outputs": [ { "data": { "text/plain": [ "(50000,)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train.shape" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "d0a61e44-a2a9-4dff-9849-5f6a0e232290" } }, "source": [ "Let us visualize an image. \n", "\n", "By default, NumPy arrays are created in row major order. \n", "Spatially this means, that if we have a two-dimensional \n", "array of data, the items in each row of the array are stored \n", "in adjacent memory locations. In the case of a three-dimensional \n", "array of data, the items along `axis=2` are stored in adjacent order. \n", "\n", "Since the first $32$ entries of the array $X\\_train[0]$ are the \n", "red channel values of the first row of the image, etc., we need to \n", "pass the tuple $(3,32,32)$ to reshape. \n", "\n", "By default, NumPy arrays are created in row major order, that is, when \n", "reshaping the array, higher order dimensions are traversed first \n", "(e.g. `axis=2` before advancing on `axis=1`.)\n", "\n", "\n", "`plt.imshow` needs for each inner list the values representing \n", "a pixel. Here, with an RGB image, there are 3 values. We thus \n", "need to transpose the array : the RGB values need to be located \n", "along `axis=2`. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "nbpresent": { "id": "d817d603-7d37-4ff2-b3d1-e95875b48f8f" }, "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "plt.imshow(X_train[20].reshape((3,32,32)).transpose((1,2,0)).astype('uint8'))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "38cf20c0-9404-4f32-91ed-a00e910832f8" } }, "source": [ "We visualize some examples from the dataset.\n", "We show a few examples of training images from each class." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "nbpresent": { "id": "ba3743b9-ea50-4201-ad99-5fa47e8b82fb" } }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 70 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n", "num_classes = len(classes)\n", "samples_per_class = 7\n", "\n", "\n", "\n", "for y, cls in enumerate(classes):\n", " idxs = np.flatnonzero(y_train == y)\n", " idxs = np.random.choice(idxs, samples_per_class, replace=False)\n", " for i, idx in enumerate(idxs):\n", " plt_idx = i * num_classes + y + 1\n", " plt.subplot(samples_per_class, num_classes, plt_idx)\n", " plt.imshow(X_train[idx].reshape((3,32,32)).transpose((1,2,0)).astype('uint8'))\n", " plt.axis('off')\n", " if i == 0:\n", " plt.title(cls)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "ab168c02-9867-455d-815d-c2de707e2f87" } }, "source": [ "# 2. K-Nearest-Neighbour Classifier" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "36557d31-2ba4-416c-8ead-a92fb7446e85" } }, "source": [ " We subsample the data for more efficient code execution in this exercise." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "nbpresent": { "id": "26316896-3b01-455b-9a0a-87278f088d83" } }, "outputs": [], "source": [ "num_training = 5000\n", "mask = range(num_training)\n", "X_train = X_train[mask]\n", "y_train = y_train[mask]\n", "\n", "num_test = 500\n", "mask = range(num_test)\n", "X_test = X_test[mask]\n", "y_test = y_test[mask]" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "27db2b6f-c417-4d15-bff4-8c00d58cb808" } }, "source": [ "We define Class KNearestNeighbor." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "nbpresent": { "id": "497fbf77-9a17-4b35-a0d8-375972850902" } }, "outputs": [], "source": [ "class KNearestNeighbor():\n", " \"\"\" a kNN classifier with L2 distance \"\"\"\n", "\n", " def __init__(self):\n", " pass\n", "\n", " def train(self, X, y):\n", " \"\"\"\n", " Train the classifier. For k-nearest neighbors this is just \n", " memorizing the training data.\n", "\n", " Inputs:\n", " - X: A numpy array of shape (num_train, D) containing the training data\n", " consisting of num_train samples each of dimension D.\n", " - y: A numpy array of shape (N,) containing the training labels, where\n", " y[i] is the label for X[i].\n", " \"\"\"\n", " self.X_train = X.astype('float')\n", " self.y_train = y\n", " \n", " def predict(self, X, k=1, num_loops=0):\n", " \"\"\"\n", " Predict labels for test data using this classifier.\n", "\n", " Inputs:\n", " - X: A numpy array of shape (num_test, D) containing test data consisting\n", " of num_test samples each of dimension D.\n", " - k: The number of nearest neighbors that vote for the predicted labels.\n", " - num_loops: Determines which implementation to use to compute distances\n", " between training points and testing points.\n", "\n", " Returns:\n", " - y: A numpy array of shape (num_test,) containing predicted labels for the\n", " test data, where y[i] is the predicted label for the test point X[i]. \n", " \"\"\"\n", " if num_loops == 0:\n", " dists = self.compute_distances_no_loops(X)\n", " elif num_loops == 1:\n", " dists = self.compute_distances_one_loop(X)\n", " elif num_loops == 2:\n", " dists = self.compute_distances_two_loops(X)\n", " else:\n", " raise ValueError('Invalid value %d for num_loops' % num_loops)\n", "\n", " return self.predict_labels(dists, k=k)\n", "\n", " def compute_distances_two_loops(self, X):\n", " \"\"\"\n", " Compute the distance between each test point in X and each \n", " training point in self.X_train using a nested loop over both \n", " the training data and the test data.\n", "\n", " Inputs:\n", " - X: A numpy array of shape (num_test, D) containing test data.\n", "\n", " Returns:\n", " - dists: A numpy array of shape (num_test, num_train) where \n", " dists[i, j] is the Euclidean distance between the ith test \n", " point and the jth training point.\n", " \"\"\"\n", " num_test = X.shape[0]\n", " num_train = self.X_train.shape[0]\n", " dists = np.zeros((num_test, num_train))\n", " X = X.astype('float')\n", " for i in range(num_test):\n", " for j in range(num_train):\n", " dists[i, j] = np.sqrt(np.sum(np.square(self.X_train[j,:] - X[i,:])))\n", " \n", " return dists\n", "\n", " def compute_distances_one_loop(self, X):\n", " \"\"\"\n", " Compute the distance between each test point in X and each training point\n", " in self.X_train using a single loop over the test data.\n", "\n", " Input / Output: Same as compute_distances_two_loops\n", " \"\"\"\n", " num_test = X.shape[0]\n", " num_train = self.X_train.shape[0]\n", " dists = np.zeros((num_test, num_train))\n", " X = X.astype('float')\n", " for i in range(num_test):\n", " dists[i, :] = np.sqrt(np.sum(np.square(self.X_train - X[i,:]), axis = 1))\n", " \n", " \n", " return dists\n", "\n", " def compute_distances_no_loops(self, X):\n", " \"\"\"\n", " Compute the distance between each test point in X and each training point\n", " in self.X_train using no explicit loops.\n", "\n", " Input / Output: Same as compute_distances_two_loops\n", " \"\"\"\n", " num_test = X.shape[0]\n", " num_train = self.X_train.shape[0]\n", " dists = np.zeros((num_test, num_train)) \n", " X=X.astype('float')\n", " \n", " # Most \"elegant\" solution leads however to memory issues\n", " # dists = np.sqrt(np.square((self.X_train[:, np.newaxis, :] - X)).sum(axis=2)).T\n", " # split (p-q)^2 to p^2 + q^2 - 2pq\n", " dists = np.sqrt((X**2).sum(axis=1)[:, np.newaxis] + (self.X_train**2).sum(axis=1) - 2 * X.dot(self.X_train.T))\n", " \n", " \n", " \n", " return dists\n", "\n", " def predict_labels(self, dists, k=1):\n", " \"\"\"\n", " Given a matrix of distances between test points and training points,\n", " predict a label for each test point.\n", "\n", " Inputs:\n", " - dists: A numpy array of shape (num_test, num_train) where dists[i, j]\n", " gives the distance betwen the ith test point and the jth training point.\n", "\n", " Returns:\n", " - y: A numpy array of shape (num_test,) containing predicted labels for the\n", " test data, where y[i] is the predicted label for the test point X[i]. \n", " \"\"\"\n", " num_test = dists.shape[0]\n", " y_pred = np.zeros(num_test, dtype='float64')\n", " for i in range(num_test):\n", " # A list of length k storing the labels of the k nearest neighbors to\n", " # the ith test point.\n", " closest_y = []\n", " # get the k indices with smallest distances\n", " min_indices = np.argsort(dists[i,:])[:k] \n", " closest_y = np.bincount(self.y_train[min_indices])\n", " # predict the label of the nearest example\n", " y_pred[i] = np.argmax(closest_y) \n", "\n", " return y_pred" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "91c8998c-f531-4774-98ca-6c9631050fd3" } }, "source": [ "Create an instance nn from the class KNearestNeighbor" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "nbpresent": { "id": "215be79c-8fe0-4e10-9587-6bea172bb33a" } }, "outputs": [], "source": [ "classifier = KNearestNeighbor()" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "2f886096-8250-4739-8645-37950f408d41" } }, "source": [ "We call the method `train` of the `KNearestNeighbor` class." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "nbpresent": { "id": "de24c3a8-0860-446e-b974-3e0c334feced" } }, "outputs": [], "source": [ "classifier.train(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "d058a8de-3c50-4514-8405-5aff67b26398" } }, "source": [ "We test our implementation with two_loops" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "nbpresent": { "id": "d87bb3a8-6338-4957-ac73-4c81b87821eb" } }, "outputs": [ { "data": { "text/plain": [ "(500, 5000)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dists = classifier.compute_distances_two_loops(X_test)\n", "dists.shape " ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "c1277b26-a267-4dec-ab9d-e44d31cdaa3e" } }, "source": [ "We can visualize the distance matrix: each row is a single test example and its distances to training examples" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "nbpresent": { "id": "ae3a05a2-a3e6-4e65-a59f-0204411f57f9" } }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABECAYAAAB6WXVJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAABttElEQVR4nO39eZBtWXbeh/323me8870558t886u5u6q6q7sBYiIAggRA0qQkSoZMSbSokBSWKImEZQdpRSjscPAPSxGiJdkOmjIZpkKkQQqkRBACCIJAAwQaPXdX11xvrDfkPNz5nnlv/7FOZhUpdBMUulnN7lwRL16++zLz3rPPPmuv9a1vfUs557iwC7uwC7uw7yzTH/YHuLALu7ALu7Bvvl049wu7sAu7sO9Au3DuF3ZhF3Zh34F24dwv7MIu7MK+A+3CuV/YhV3YhX0H2oVzv7ALu7AL+w60b5lzV0r9uFLqXaXUXaXUn/1Wvc+FXdiFXdiF/c9NfSt47kopA9wGfgx4AnwR+Fedc29909/swi7swi7swv5n9q2K3D8J3HXO3XfO5cDPAH/kW/ReF3ZhF3ZhF/aPmfct+r2XgMcf+PcT4FMf/Aal1L8D/DsA2gQfb8bL4MAGmjKCYGKxgQYHVQTe3GEDBXWioSuH9RXWgKrkNWfA5KCsQ1koGgplkT8VVCGYzKEc6NySLhuCkfxep8Gk8suVczitKBsKncv7B1NH5SuUdYDCBmA90AXoUt6zChS6AFV/Nmc4/7xn5gyg5OdcfbRWARBYwkNwRlHGCpMD9eeowvqaS/DmFc7TOK0o2qBK8GeOKlJUDUd4ZCmbBi+xWF9T+aCcvK8/s5SRlmstwKl6rRw4VV+bk+svY40/qygbBl3W6xHJ78KCrj6wpnl9zYHCZLIOJrOUsZb3nVSUTYNT9ftquSdFE0wK/rzC+hpVOZynsL5C5w5nFLqwZD2NtzjbOPKz1gd/WpF3jHwm3v+93ry+9ojz++GMwimo2o7gFFQh66QquSZrZG+UDYX1wVuAySzOU+Qt2Ue6oP7bgYIqkO+V+y/r6Tzwp/b8OsoIwqGlaGm8xFG0ZF8EE0cZyc+HQwtA3pUNofOz++0oY4Wu5NpMBqqUvaWsw3qKqulQuUK5+v7GGuuBl0IZyd9FE/wZlI16nQKHN5E9bNKz3ydroEu5vnwJgiFYT6EqRxUqdFn/vKJ+hhxVLPccC86rr3dUYT2N9dXZLcOp+gvAJJaq3odn982f12s0d+TLDjPVoOU9ipbCZNR7E0xS4Xwt1x9AMLVkfY1J5XtMJs/D2b3VpQPryLuaYGyxvqLog54rdH3fkMcak8v+xYGXWpypr8FC0VTvX3urhFMPZeUZoFWhR6beH3IfdAVF2+HN1D+yP7DyXt68oooMaKh82V9V0+FP5LpMJj7GhrIfnZI9iZO1ztuKfOfJsXNu5bfxwd8y5/5PNOfcXwL+EkBzedt98ul/FzPNmDzXY3JFs/x6gc4tJi05frFBNJRFP9uAzb2c2VZAsqzo3a9YrMhmWn49oWh5BKc5uz/YxGSw8rWUoukxuezRu1/gTwvK2PDgXzJs/qrCn1vm6x6No4pgUmAWJVXD4+S5iOjUMbqpWftyweSyh5dCfFSyWPWYbyqCCXTvF+jCMtsKaByWeEnFbDMAxAGUTUXWg/ZDR9GUjdI4tkyuGHQB801H2bXc+JmcxUbIbFMzeKdAWUfWNUwvG8oG+BPY+MyEsh2QLPvsf68iOtasfSHj9NmQyTMVV/9OxfSST/NAPmPekY0SDh39dxOOXm5gMoe/cDilQEH3zgJnFCarSFciosOEyY0Wzd2Mo5dievcLVOk4eCUgHIKXyIFgMsdiTdPcswSTiqxvaD1KGd+IaT/Jyfoe83XD5v+0w+5PXKLoQP/divmaJho6jl9W9N6BwesTpjfaNPYyZttykkUnJTbQNB5NufOv9Vj/nMV6ijJSmMIxuaJZ+2LGyfMhXuLQJaTLivDE0X5S4C0qnvxoTPeOxWlF3hGnOn6u5MbPlITv7LD3R6/T3qlwBpKBobVTcPxiQNmAwVuWzu0Jo+c6HH4SWg81jX2LM9C9u8AZzeHHGmRL0L0jB1AwcWR9xcavnbK43GG6bRg/7bj5Mwv2v6dF727Jzg8ZnHFs/4OK8VWf+ZZj8zMl8c6cg+/tMrnh6L0t96X9pGT/Uz69O5a8LQ6ntVsxu2QYvJWy2AjZ/f0l8YOA+MjRv51x8IkIVcnhkQ0U3fsVhx/TrH++YnTDo2hDfivh6l/RHH48ZOVrObMNHy9z5E1FMHc0DnLu/2/hxn9jSdZDdOGYXhJXoawjGjn8uZUAacmjuZtjPcVi3We6rdn6lSloxfhGow4cxGk6LQfl4K0FJx9tYDIoY7lvlz694PDjDVZfTdj5Dwr83+jgz2WfHr4C3TuKzqMSpxSNx1MW220WK4ZkTbH5Gwse/OGY/tvyPuG4YnLFIxzJIRyOKqyv2P+U4dI/LPGSknt/UtF+NaJxIM7S5I6iqQlmltmGIT6xxIcFedfDW1TYQDO+6pH1wUsg/r1HtP5CBxtoRjd81O87pfv/blPGmubjBYefbBMfWfa/z7H6eUUws2QdQ9ZTNA4teUfRu5ORrPikffFdrb2KJz/muPE3S/a+J6J3z5IMFOOnHdu/XJEODK2dHJ1WZMshB6943PlPf/rh1/Ox3yrnvgNsf+DfW/Vrv60pC7PLMdHQB+dYerPAmxUUbR9lDb17BeHBnKIfk/V9TGaZXg6ITypaTyoWa75E/Qam2yHtxxmLzYjlNwrGV33yrkd4nBP0DVWkCA8K8vWQxiNDYz9hvikOxZ+W5B0fOj7eoiKYOuabmuauY7HiEUwdfmLJuwYvczR3IRpVOE8RPp7jjCLveixWPDoPM0Y3Q8KJo7lfUjbkBjbeq6giiUxVZRi8nYENqU4MVWxo35+hqiZVqKgCTfvujNlml8FbFelAY32Dso7O22PKuI/TjsmVgN69Auf56LwgGlmJVjV0Hpbo3JEuGYq2T/PAoktHdJJTNDxsoNBFxfhqi7yjCCaOxr2U6CQkWQuIjy1Z16ALx9LbFU5BGSu81DFfM7QfV/izirzr0X6YMroZE59U+JMcnVVYL2L+7Crd9wqSJY+jlzXhiSI6yencCwgnlmylQXyUk3d9Wo8zHv5ERPNJQPdhwei5LkuvwfSyYfBWDn0P60H/ToVJK1q7FcFEIvjGocOflWQDn6zn0XokTr/9YMbkRpO0r2k89FBVQfLCFu0nFXlH09rJOH7BIz7RNPYcWU9hPZjcaqMsRIea9iO5zslVn7wbkKx4eAtHfCwHXTBxtJ9k6Cog2W7jz0pWvpzgVJv5pZilt3JU5ei9Y1BOMb7uU8aw9IZkSWU7RFXixHr3M5Jln/BowdKbLdKeJj6R+9Z4PEW5FmXDUMSK5t0AfwrtnRJvmrH0huHoJZ/24wJnfPxpReeeprGzoApbOA3jPCZZsQzeKRnd8Bm8k/Hox0Iu/3JG1vcpI0PjLZ+iW+DPLOFxgs5jqkgz2zRY49C5JTxJyTtNRjdCvMzhzx39OxXz7QZ5W9M4LBlf92ntVuRNjZdavBRMUtB9IPshGlqq0GNyLab7sMQ/mlO9s8SlVxOOn48Jpo72e5r+7Yys5xEf5pLRGujfToAYVVQsve4wBZSRYnzNp3c3JzpYcPJSF5NZ0r7P6pctTkHR8mi87dO9X+IllmRZfq/dDMFBugIrr6YkayFpVxN40H3tBG/RZbHq4yeWveVlgq4lHWiW3sp4vNGnGVt05aiaPr07Od6ion0/ZrEOwV2Lco7ug4pglOPPA7xxRjOvsH4MznH0kqb3hmK+oVn9akHR0vRvFzgd4k9SDj/u07lXkQ8C/GnJ0huKO9/ACX+rMPcvAreUUteUUgHwU8DPfb1vdhqik+I8/ZuvyZmjKzl5s64h3WgxuxRQxpLuNo6qOqoN8BPJy60Ey1ijURaSgUfWg3BYkC0FlLGkmVUrIByWZMsWNMRHBWWsKGODPyvx5hX+OMNfOHQmziw+KbG+wiTykDkFeVdRNDRVqCiWGxRtg6oczf1CosyGoogVRUtTNAR2SAeGtGfQuSVvQ7Lsk/dgsSEbr2wHLFY0Onf4M0sxiFBOPkPRUjhP4+9NsA2fZEWRLilaeyWqcuRtgXWsryibBpNJ6j7b9CgjhfPU+XqXkRwSeVtTRR7NvZzuvVzS83bEfCPAm1vSvkYX4iTna1oyJKPIWxpv4Uj7msWqTzAuWayHNA5LkmVDFXnY0DDb1ATDnCrUzDcV8b6iigGtSFYUZahQpaVoeujCki4HtB5JduCUwk8syarCWzi5xqYimEp0ZQND3tKg5LXplmGxHgj8Esp6WQ9mVxoULYVyjsW2ZATh0YJk2eAvLEXDE/gvUJQxFO16T56Wck8ask6LNR/rK6KdmXw+D+YbmspXlA1xGkVT4S0q0iWfdC0mWZO9PV/3Ze+Fivklid6qACZXNf60QlWWKpR7l/V8kiWNM5rJFQNA1pH3yZcasn8Khy4dWc+RrsgzglLMtjx0BcmKh6qgbBgW64oq9lksa2abmnTFEkwrpluGYOKoAk04UhRNj6KhMLkl2ZBMSVkne3LVk8/nIVBhrLGhR9HQNI4rvKR+dlcN8WFO46gkbxviY3sO9aGUrEHskyzLe83XDdaDYG7J2oay3yBfLZmvh1hf4LfZFcv4aoDJncBtzZAy0mSDgLwnkNT0siZrK3ThCEeWdOCRL8U0jkqcUYQTy3Tb4KUVwahgfrVksWLAnsFjmmBaUQWKKnQULQ+dO6KxRTnItrpkfY/FuibpG8rVnObjBd7CkSz72KsJ3sLilARlZcNgA0265Gjuvp91Fk1N2fRI+wYXGrKBHCizLY0/kQy/9Sgl7RvJhgee3IOWT3giz7dJ5aBYrJpv6IS/JZG7c65USv0p4JcAA/wV59yb3+hn5puBYJ5hjf8FRiLGymAKV2808FL5umhq8o4iOrVkbX2OSZahbLy8rYmGFbMtj7zjn+OKAFWoKdoeOpMNnfYNzoANFEnLR5fyUECN42eSsuGgaBmKWOMvLP7M1Y5SUYXy8J3hjF5qcRq82sE6Iw7nrD6QLvkoK+lgMAbrafKOwV8owUubNf5aWMFAW/KzRdtDF02Klo8/k9+dtw3BtEIXkPXEkZsc8o4iHFuJjjuaxbIhnFr8mUAVyjqKpqKKDEXHI29qWWtfcPZkxcMU7vzB9efy2Ysm+HPBcP0FoGRdlIXFmjiVbODLtSpYbEZ4qUWXhnS1xuoDjTeXz+il8pmtZwinFemSvJfJdf1QIDhp26PoKMq54Nd5z8NPHHnH/CP33yj5jE5D0VL4h07Wrq/wJoa8q6iCVr2+jirWlG25xjMYIetqnApqbFThlMMGkKxAeqlF3tY1vgtZT+5NGUm9JF3yqXyFruTeZn0fXTiKlsGfO8qmIllSFF1HdCTZHsi6SE1HoMeqKXukigSS0YWWfRYIbJP1NP5c6i66cBRdgagW62ffDya1mEwcTdEGfwomVcw2BC9OVjXxMaTLgu9nHU001JikDhKU1DvOsGjr19i9MpjEQ1eO2aZB51J78RJHtuSTDAzRSA7hcGzlvmSWsqHI+4HUR+r6TRXCfFUw6qwfYMYGXVlsINdoUgkuFiuGZuWwvk8ZK5QVmLZoB3gL8BfiqPOOor1ToUvLouMTnZbkLX3+XKtA441NXZ+T51vnFfNNX/xLQL2n5Hqtp/Dm8nyrUmpw5tQn7yuyviY+tlQznyrWZG0JhqwHOAjG4rDDkTwLZawIR3Vdq+FTRXVtDlhsWjr3NIvNCF2BretE/kL2QNYH52myvoeXWPz5N2Y6fst47s65X3DOPeWcu+Gc+/Pf6HutgeEzMLukmW8p+rcTptsBk2uGylc0Hs9JBxJlZR1N2tecPgfxkaW5m6McjJ63FE2YXoXZhsfoljjmvO9YrEiEVzYksqhiw+ErmvhQMbrpUwWKdAlmm4bh04bxdUM6MHgLy/j5ku79nOOXNOmyYnzDMHwOFiuaaGgJZhLd5l2PcFwx21IcflyWdb5lWazocyc+X9c0Dgraj3LKWJF3ZROY1NHYd4xuGapAE59UJEuao5e0FCK1orVfkbdhfM1nsRGDVgzeTLABHL0sRa/mrmN62dB+nDG5oklWFc6DZEnT2i1ZrCuSgSZZMiQrHnnXI1kVrPT0GcPJR+HwE5CsR/gLy+kLkA4Uh694nD5r6N5Z4M8tJhPnOb0G0WnFfENx8oIhGJecvuCoQshbmvA0Ixw6Tp8z4mQSaL1yTLpsyTuCuysLJy8Y0oEm6ykWSwbvE0MmH81JB5qTlxxbvzpnfr1gum0Ih475mhRqR9flEGrupHiJpbVb0HmUcfyiYr4F/TsF6UBRNJRALQYa+7IGw6d9Gkclxx/x8SclxaBkfENLTacSB3b6PEwvSyGxdyfBelC0JY0fPQ3zLei+V2ByR7Lu0BUEY8fopqFoKcLjnO57gnn3Xz1hsaJpP85Z/0LC8KWSKrasfTFldENz+lxA87Ci+15JOCykkOZrVl5NcRrCkeDuVaSpQlis+aTLEB05KS7PLfN1n2hYScARK7KBwD/xscMsStqPLcHM0X4ojri1UzG9Kgd9+5lT4qOCbACjGz5rX7DM1w1lpAW2OCrIelLELyOp45RNg/UUo2ctsytyUPfupxx+zHDykmWybZhelSL9+Kn3g5vZJTnM02UhIBQdx+gjlu69nKyn6dyD8LQkGzgW61KQbByX52SE+brPbFvhzyxVDOmSx/pnpyTLsucnzxWEwxIsLNY1w6cDjl9WDN7OOP6Iz2LVp/VYcPCyaRjfkKy6aCgmVwx2JaexnzN8WjO6JRlnsuLRf/WE3v2C3p2c/puK0Y2A6VVL5+6MwZc8rIGsrxhf9wgmFbqyNA4cw08UJCuaZFmhS4c3L4mGFePrAU4rkmVNY9/RvTmke79gdknT2Ms4fVYOd+vBYtlQPL/g5PmIk+cNedvQfZB+Qx/8oRVUP2gmh/47EE5Kykgz24poP84J5nJCTW+06dxPqBoefqjxjyqqyCeYlDXjwdG5Y9C5o/3IEU4s/kKgkO5tCMcWb2ExuSGYWIJRTvd2TLoE7ScVaU/TfuhoHFZ4icFfOExmSZYNnXclNe7cgyp0hKd19JNB2tOY3NHeqYiOpBjaeiJF0zLWtN/T+FPBZPNYEZ068q53zkppPRImRNFS5F1Yfr3Cn1dUgaa1W6EqQzApwXnM1wzNXUfnYS7ZgXWcvBBjUkfnPuKoVxRLbxbkHY+lt0pmm4aioaV2sOHRv10JjBQIrFU0NZ0HluZegdMBwURYCY2dOdlSRPddiE8rioak0+laSNHQOAXNA4vTBudB+5GkRE5D87EmmFoahznZUkg0tHiJqg8/y8mbS7T3FNFpweimT+OgonHkUCWkfUM0qhjd6dF5omgeWEymOHq5SfsdaO4LlBGdSmbU2pWMarYVySEZakxuWXpDMprxVZ/2I0t0KulS0VIs1hW9u1JEdVrR3JF73X43pP2oYr4uYVT7saVxoChjx2JNMbkW096psL5HOClpPvaoIsg6UhRvPQScFAj771bCalCK6SVD575j+syA+NRx+myIyRytuxJdzy4p+rcrlIOkr5mvG/q3HfGpw2QVi7VQoJNQ0Ti2xIcZugzQuSXqBaQrinAIRVPT3CtIl32CEcQnwgbr3SkZPuVRNr1z1s9iXbH0RsVixdC9o7CBZf76gHa7Ij5ydN4rOP5IQP+u0GOiw4zZdkx8bHFG09yX+21SiwkV7XuGaOjw5xXJSsDgLUeyZIhO5fuDeUXroUc4qjC5JhgLVKKsRzCzBCND+EAz3/Tp3p5z719u0buv6NyTyDrrS1ajc/m781DWAORwCyYVw+daUkBF0X3Tx3o53rwiPHU0D0r8qcfoRiBrXTlGTxma+5IRth868n4g9ZlHFWUUkfdKlt6UOpcqoXM/YfZ0n8WKZCnjWzB406LuKWZXmswuQzSE7nulsLi6Hrp0LDYU8YOAcOSITmRfCpxo6N7PqWJNc18OzOk7A6pnFP7MMd8MiY8kMl+sK3p3KszdmO79gsaRwZ9WjK9F8Gtf369+W8gPOC1wSxmJ41is6prmKHjxfE1j5lKAyZtK8EVgvu4xW/eYXpHUaHZZ6Eq6cFijBHIIFdMtI1BNS5EODIuNkPi0YrFpiY9ysr7gsyg5CKyRyHN8Q6MLiVC9miYZTiwmhbKpmG0rsp4m7WmKjkfWqalSVn7emroO4CSqcwaSZc3wKXEMVVxDAAPIlqxU5Y1icsVjvippY9bzmV0W+MR51JisR9H2yAZKHhIHeVORrDm8pGL4lJzZAi8oTO4Y36yx9gaSGnc0ZaQEcok0ZQw4iI8teT9ketkjPrXyvrpmmlzxyHqKdEWRdoVyN77moWqK2nwzOE+Ni5YcSNNtia5n64asrwnG8rvytiYdIJh+7gjGBc4I1c9byAYvYoWXQLoq6xhM5GETWEARHmcsVjXpQJGsaqkJLBuC6Rm1UKJJauisaCryrmDbZaSZbRiqEGbbIUW7hvx8yHoCg3lJfa9b8pCWsTwui2WDrqSoN9vSZH1FPLQky5q8V/P9FOQ9n2TN4ScCXVgP0mWYb8l1VRFMr5xBipbRs07+f80n6yrSpYDJFc3sknx2k1mqyMie3vbRlSPrW4q27CtlHWlPkffkI1hfnP5ZpJ935fekayWTq4a8zmYWKwaTCv6tKoEkFpuWItYkA1MXkAXWqAJYrEmdqQo18w2ByKr6vSZX5H77M4eXCRw2uSyObnTTI60x8rPnPutowqEcZsmyxgaGqlUxXxfoxWSOvAPzNY90WTHd8qhCjZcJnGYDCRxmW0IZrmLwFo7ZJZ/5pZgqlOegcVSRdxT+rGK+Zig6jtmGIWvL3p9ue1Ija9T3eMWc02eVlSLwdMtjsSawT7mZkbfr/dnT5JsF4xuCNJSxIe1pZpcC0iVHMHqfjmt9GN+Q9TRJSRVosq6SDDGDZFUYV3lLncPRZ5RRnSn8RSnU4VDqbd/Ivi0i9zPusD8X1kM4dviTgrJh6DwsWax4VK2AaCiUpjMsu/LFIZsnMF/XNJ8gaamCcFJRZZq0B609SzAumV4Kae8UBKc5RTcgGGuw0DgQeMSflRQtqeAr66hC2WDNHcHlGkfyfV7mCKcWXWiiU4vJHd6iIj4Rh+cniuikoAoCGocSIZ/h3l4iUWpQ42VOQzgU3qrzpFgSTBzRqCJvaYJJSeMgxCTCJQ6GGV5a4ZTCW0jWEkwF3vAWGlVaWk/koDC5ZCEoRf9twXH9mUNXDpM5gomlbGj8cUHUNJSRHEDR/pwqbtf1AYW/sPV90qgKwrHAALoUh2IyuZb4qCAdCJYbnmRUQcTCk0je+qquDyhMIpxsf2YIpnIgVbERGmlaYTJJR4O5xRpFMJaNXzTlENel8IJtZGjuS22jiJH6QOqkGL1wRCfCYjGJJZiL4zaZwp8L00aXPnlL03qcsViNUZXDpBCdKKkRVEiNYmyID6SIripo7RZkfe+8h8Fk4qTiY4vOdc3bV+jcEow8/FmFLhzBtCLr+pQNcVomlfqKlzh0IawQ60F8WlLGmvC0IBzoc0zWegqdVSjr0X2QM7oR4M8UwQjCseDGrd2KdFmcaXgK/kL2affegrzdxBmFPzE0d+WQc0r2YrKqiIYC0cRHBf40wl+UhBOHvyiJTuRDNPdkHYKpJT5IqYKYMlL4iWRAqtJ1P0XNCLOS5RUNRTSUzNGf5CRrESYXDnrR0lgjB4JyoBOpl+VtKQ57C7kPXia05fA0wwaxFBprhtnsUgd/4c7rPNHQ4k9LrO/jL0oWqwHBRG5Y+3HJ9LpH41CeXdA0jkqBn05K5ps+8WkFDvy5HCTOaMKRxZ/WReW5RzSUAMRPHGph6N6TDNGfl/gLTXxYML0cStBihSCiKoEy845BFxVeYomPwWktzjqD5o49h3JBqJfRSYmyBirJ+sOTAnXlG7vvb4vI3Xow3TZMt3yml+Qhz/s1M8ZT9N6akvf8mh4okeT0ksH6wmgIZlZOaAPzDUMVaSZXPBoHGYtNhT8pyTvCe826hqrhMbrpYzKYb4b4c0u6rEhWAhYrhvm6RAeDt+akS9B9UDJfM+RNTd7SzNeluNI8qDBF/TsDLbSqJU1aR2/JqiIZmPPGHmWh9SSlvVOc/7/JJV1r7Clm9fsGc0sVSJTlTXOiYyeYe1cxudHETHLMoqB3T9LbZEnTuT2he9cxvRrR3MsZXwvI24rGfk7ly2E32zDyfqclJrXCzb+kKbo+VSD4+uSKoWyHOKU4fdYj7WvSgRSo249y/KSOituaxZoiPhHeetqXAuPkmiFva5L1iPbdCSBRUe+tCVlXsbhSMrsqjqBxKCyC0Y2QZNljvu6RLPsk6xXjp6CINdPLwqNfXJLIJ+2LA3RGMboeEoxL2vfn9O6nBDOLN7cMn/aZXBVMP13SpMs+eVvYSyZV5B1DshqgS2Gc2EAzu2oZ3fCFBaOlkDe5bJheMngpNPeFUTXfdpjcMr2sKVqK+FT2XtaXQ6MKYXrJJxl4BOOcxoFjum1o7Gckyx6DdzLajy2LdcXikiU+tiRLhnTg0TioGLydEx6lpD2NN87w53LgmEzWvGxK4VtZadRpPRTYIj4uKVu+NODVmWcwlSwqHAvTSFnw547wWLBfk0vxtfPumMXNnMZOymxDk6z4NPbcedHYeppwXJG1FbNLknllXU3Z8gUD31Syjxqa6LRictljtqVJB0JUUJUTmu1UnNZsO6aMpFmxDKVQOL6hae0IFTk+EIZV3hEWzPssOAiGOfOtBuOrUrg3OeS9kM7DiiJWdTakiA8zor0Z1oP5esh0W7jri1UPk1u8mQQtjb2EKoTmWweE44r5msfiSkl0mLFYMRRNyWiSzZj+GxOho55UdN8RyDhZUzR3c4ITQ3RaUjQ18/WA1uOU4HhO+5E7r1Mky5L9xLszlHXMt5vSfBgKRJfeSln9SoGy0L07l8O8cHQeVmR9j2xJ+g6mWx5Fy2PwbvEN/eq3RFvmn9aaS9vu2r/507Qfy8l39LJm8KaTSv6R5eQFRfs9zhuZktX32QBFSxp00iXF6lcLvKTi9JmQ9uOKo5c8Vr8s2K4uHOmyYuOzGWXDkCwZZlsKbwHJuqOxo4iPLfMNTXQq0cn0KvRuw+gZiA9k4+Rd+d6zZoaiBatfKcm6mvEtjT+Va3JaMoKiCbPLgtcHI43zHDoTiGX9czkHnwwoWo6yW7H6GUPelgfP1lmJ00hB6uWcwed9uvdyjl4KqWJIruRETwLifcf4KYftlCx/xidZU6x+KePkhZDFuiM6UYQjcRKzrbOuSKH86Vzw5dFN2XiNfUd8WlHGmmSgpOjpIBkosr4UwNIVx9Jrwq4AiEaWtKcFiugowlOJoCdXJeX2EphtC7Y5vqlY+6LgwMm6Iz5QDN4uGD7t40+FmXP0Cqx8uWYppI7DP5wx+OWI4TPQfg+Kjtz/5q4lWdVMr1c442i951FGsPYlodXu/LBPdKToPpC6wXRbky1bBq/JGpeROHwvlajRTyz7nxKH1HyiMKk4yskNhyoVzZ26W9FJBjS9LPunbEF0JKyPZNWx9LocRNMrirLhaD5WpMvQfuQ4+p4Kb2QIh3VKvwTNXYfJBPIYfn9G842I9kNL2pcGuGDiGD0lWcPgLaRx717J8Qseyc2MlV8PmG0pwlOBJZJVR/c2nP5Aztov+Rx+CvpvKE4/ajELTdmt6L3hkXcgHDkWG+BPhc7Z3HWEY8fuj1Zs/T3N6IYRiKwjsEdzR54jnclBYXLZU429+lDpKFo7lukVTXjqmF6D5mNZs3DsGF/TdB7KcwaSWQ2fd3TfVYyes6x8UXP4AyXtd3y8ucCZWVfR3LdMt7XU526XzNcNi1VFPrCsfAmOX1KsfNUxvSxQqs4BJTDY8msVi2XN6DnH4GtSID9+2bH0mtyj/ruWxapmtu24/Es5yYrP6QuKla9aRjcN0ZFw6E+fq9kvy5buU6fMv7KMP6+ZRr2S3qtyuPbuFex/KiA8gfGzFapS9N6SzGjvB6D7rsEGEJ2I39KFFOIn1xT5UwnRmzHJRkW8Lyyw8BOnlL8xIO86Bm+fwZ6KydMVD/+9/8OXnXOv/HZ+9dvCuUeb2+7mH//pOpJ2XP77GaNbIToXHLT1JOXk+RgUdTERyiZ4c9lgWV+x2HBUkcOfKXrvwvgmtB7DyacKNv++cGqTFXGYrSeWyRXN4nrB8mc9ug8ydn4wIhwKrcxkjvhU0v3dH4HlL2gWGwp/JphwuqzovCddieFIsNbGgaX9MGHv+5sALL1e8OT3GZqPNd0HJVnXUMbSVJQODOG44uEfs1z+24bTpz1pPY6he1+i6fBU3qe1Yzl9VrPx2YKdH/Ro7ii696V7Ne9I96r1YP3zKfufivCnsiaT6wrrOzZ/o2T4lC8deC3Z2N37JZPLksnMthS9e+JI8rawIQZvSRq92FDkHYcuwEsU8aE4bZM5JlcN2VJN5etIZLn0VsnwlsAywcTRv52y/6kYbwbNg4qDT2rKJYk2Vn7Tpwrls862NMFYDrQqgPm2Bc/RvmvIetC7Yzn4gYrtX1CMr8laFW0wiaSua78xJF9rcvJcyNoXZzz4I01w0H4o2UgwhsWGk3b2QtG5J46ojIVV0r9TsvNTBd7dGC+RTNJLxFkLFc5x5RcXHL3cZHrdsvLlGmsfyPXHh+J8l18TWGO+rvEXjvYjKUzqApbezhneDITC2VXMtyw2dLTvGYoWtJ5I4TY+dDSOKo5e9Lj0DxMOPx6Td+DSb6SMbob4c8d0W+PP5NlJ1hx513HlF0rBylcNeVdggN7divE1Q+PA0TgsqULFYtmIk3tNnoXT52Hr10oe/a8rbvxlx6Mfi4gPpcAfjGDwbi4snNOK2aZhfAuCkWLwboU/s4yveqRLtZRBBvGhXMeZJEPRPqN7vi/T0NwViNKGoDOYXRaq47W/M2P4bIujVyxLX9WcfsQRTDTpekn7tkfnoTSSzTd85puKzc8kPPyJiKXXpEaSdxXBWAKd/pvChkmWNUVb9lXnPcms/bljchPCE8Xal1Ie/2jI1qelF2Pv+zzM01O2/gvDw59o4C3kEA7GjvbjnNPnQpbeyHj04wGb/1Du09WfPeTtPzNg5XMSnNlAAianpRaSLkNjT7Kk9c/lOCOsp7wl8NPpM9KYt7iRs/RZH11B60nO5EpANBL4zOSOnT9YsvSbwu1v7QhF+rM/+23u3Nu9LffKJ/4U/iQnXY2kM/OdU8pBEzNNSTdaRHszaV5oevWmEf0UV9cUkhXBn1pPcvxRSj6IKRtCaey9M0PnFbPrbRpPFniPDkmf3+LwYyEbn5mTDUKyrqZ3e04VeeiiQpWWxaWGFFjqFuayoc+jOdGu0XiZJd5PMSczsu1+rSkjjSyzrYBwJC3rRUPjJ8LasUY45otVoeNNtz2SZcXWp2eooiLZaBKeZBQdn/jRmONPLQumnli6rx4B4BohR5/soUopFunCcvpcwMavjSl7IWZeML/cEO2QQPDPYJSTroSYzOLPhQmR9X1abxyRXRlIE1YJjU+/iX3+OtlSRBVr/El5vuYgzT7RaUk68CTVLiXd9mY5yWaMzh2NByNsM2R6rUnr4YKiE5Au+8w2NeHIsfzlEUef6NE8qIifzHGhwYYGVTr2v6dBc9fSepJRNj3GVz38ObR2coq2sKLKpqb1YEa62kBX0uUpdYfqXKNovuYRTi3xXkq2JPd4fENz9e8MUXlJttGR7uLdGYff12fwVspsKyRZ1gzezqR5pe8xvm7o3xYWxGwzYPDVIfObHRbLUtPQxRkNsmS+7tN7a4qNPJRzHL7SYvBWdl40HT4VCXc+dRIcDC3tRxneMGHybJdkWbPy5Rl5P6Tx7iGn37uJvxBNoGBa0XzniOT6EtHjMdPnlzh9xhAdO7r3c+J7x8yfXWVyxaN/W6C5pTfmHH6sxcanjzj5xDJOweQGbP9KymI1QDmID3IOPhmz+uWUou3RvD/m0R9eYuuXxlSdAP9wxuJajyrUZB1N47jEJJbwYEZ6qU3W86QRywrEFZ7mJKsh4bA4l+TIO4bGbkreDWg8mpCvNMkG0lk+2zS0d0oqX9H73A53/91ttv9BxmI9OA8kll7PqGJDvLfAHIxIn1pDl5bhzYjVX9/n+PvXhS008ES64b0Eb7hgcb1HvDNnfqVF2jf035qhCst7/0KH9c9KjadoeueSBv685OjFiLXPS1NHuhZiUkf8YEhytcdizcdkjsNXFFd+MSfveXgLy5Mf9rj2cwlVZAifjEmv9Igej3nyEysEY0fncSEkj5am8+6YZKtN4+4p5XKLohNIQ+YV0cfp3SuwvsKfCK315PmQtS/MOfpYk41fPaLqxuhZzugjPb7w1//jb2/nHm1uuxv/+k8TjiTVn21Lum59+f9syRIfaqkmrzuCsSJZtTSfaJJ1KVJ4c0XZdLQeQhVLR+Ppx0pad32BUvakmcBknKfD6eWc6HFA0ba0H4jQVBkJjlc2IL1U0LrrM38hxX8YUgws3lQTjBTpsj0Xqmo9EsGj6XWpaDsDRcfSf0MxflpSxHyjIHwSUDYtJnm/yq0rRbpegm9Z/ozPYq2+7lpoyaSQrFpsvyB8HNDcgdmWpLn55Rz/cYANoexUmE5O/GqDZNXSuaeY3HBUnQrv1MMGjt47itOPl6hcE+8a0jVL66EWiOGqfB5vpohO3he4OrsHAOmyJRxq8o7Dnyph+CSK5iNFukLNInI0nwikkg2kecN6ML8k8EzZcIQnmqLtKJZKoh2f6Fgi8bMms9lTBe23fWwgglfjT6REdyMRIZvC7GqFP9E0dhTj56taSMnhD03NtBGhudEPpMRvxuhcUueiX6fIb2qq6Kw5Su53FUF4CuNnSlShaexK80664ihvJrjDiP4bivmmOv8ci0tWdFzGmipw6EqRDSq67xi8RKLDYjsjuhNRNB3hSKE+NWK+20ZlivhQk6xawlOJxOdblsbNMelbPfyp1GiKjtQAvLmiih3RsaJofCAqfm5E/rW+wB4nML1m0YUiHCqSFxI6n4sZP10RHoumTRVC/PSI6vN9iq5D51C0nDRq+RLNWh/KF2e0fqXJfAv8iaLoSHOP9R3+RD6v01LnKhuSMVtPYMtwKJGyP4fpjRJ/bPAn7z/PjQPHbBuiY7nG8XMSmSerjt674P0rhxy9uYI/03gzyU4a+4qsJ0yq+NCRLQnTp2w6gokiWXE0a7jUpLI24Ugx36poPhZtpnSjpPnAE278lQzvKMAk0jCX9R1Ft6Lx2JOGw9jR2FXMLkvm4c8d41sCfeqpx82PPuH+l7bxJ4qy5eDaHPu4Sfs9KZaPnpWCub2WUE4CWvc8TCbXGh4Iwyw6FARisWmJDqW2YF4ck97rULUr9EITjDXdTx0y+a1VirZ8JoC8D1nf8vA//F04d6XUNvDfAmsIP+AvOef+S6XU/xn4t4Gj+lv/T865X6h/5s8B/xZQAf+hc+6XvtF7NNa23c2f+mmhM25pencrkQOIFM2DUooNA4kak2VNdCIpTnPX1iqKQlc8c07de5bZtiY6dpy+4Bi8LmnNYk3wuO57JaMbgvn23obGccnJ8z7hsG4qygRHTgZa8MDbgs2eKTBmfcEAkyVN737J9JKhtV9hUsfohsANvXslJ897BGMRgMrbUoQNp5Jt+HPLo5/QbP4aUsRsSErbOLYslrUcQANF+0nF6TOGpbcrjj9q6N4V3nYwLhjdiikjKcyuvFZy/LxHY1+gk3Qgh1VzV7oCQShh1hf641klPh1oOo9K5quGvCMP4MprBVnHsFgV2qFywljwEjlIg6llfN2cH5rOEwe+9GbB8ClfNFdOLeGwFF53Im3cw1uG5FKFWWh6b1MXo0TLxVvI5y5jxemLFm+i6d6FdEnRfmw5/ASsf9aRdnVdhKKGcqB3v6AKNItVQ+e9nINPhKJumAvEZjIpslnfoaxi5dWKoikNNdMrmqU3Sh7/hKLxyGDSWuHRE4hOVUKpXHqrJOsILLHyai1/4Ne860clRy97rH6ppGgJ5dbkwtwaPuVhUhi8LSJRyZIUQ5N1RdGSAEVZiE4syYpABsFE9mr/diFU4EuK1S8XzDY8modSsPQSwWuLlnyG5Tckck67huk1RXQoTJSzjtve3RynYXQzYL4NK1+x5C3N5AasfNXy5MctV34Ohrd8TCqiY/GR9H5YXxzWfNUwuyzYfv9uIRDUqqnlIeqO2MKJyJmS+3NWa5lviMie0/L8zdcMKIHlhs8o/Jli/XMp2cBn7/colr6mOH0e4iNFuiROP5xaUZSMNZNtw/rnZzz5kRYrr8o6lQ1FOHSMb0HnPsTHFZOrHtRc+f5t6WMIxwKjNfYV3XsFRy/5rLxWMF+TXpH5zYKn/nLG7g+0zruQ2zvSuzDb9Gjtljz6/Yb1z8L4umbtSznv/SHDyhdrSrESFVldOIZPG4q2o3NP9nLnPYsppKanS1FEPX0mQOeO8dOOxo4mPpK61/iqCB2Or4k+z84Pw9JXNWUsImNlpPjCX/v6zv13wpYpgf+9c+454HuAf18p9Vz9f3/BOfdS/efMsT+HaMk8D/w48P+qh3d8XVOlREjJsijrWV8q2elACQ0ykip8Y7+g+6Cs+b3inHrvzKQ9OhGFQWnllwe3//aibtKQrsNw6GjuV3gLSXd674hTOnxZRJyCqVT1844i6WuJ8ueKxpEVed8K8p4UnvKWondfCqn+HEkvNzzhkddNTCId68g6Bqekgabyhb968pxP/w1przdpjfFGwmVeejORLKIhqX587JhcMbQeyjUXLcNsKxKtjUActzXSzAJCIYxO6zBYyWcIx+5crkFoVu9L5xax0LDSFVfLEYuyXt4XnNKfyDotf2VCNLJMrhq6D4QWtvx6SjASJgbq/QjdGlWn60KzCyYVg3crovU5wUgOuc4D6SrWheD6i1XppA1WF9hAsoeiXTsLq5humzp6hN59gZW675XMNnwmV+Whm2/4eEld0D60zLegcVSy8rWS+EjhT4VJkg408UmJSeo1mgruH0wd2ZI4xHRZ5ACCsTQrnUWeaU8z33aUDemCPXnOI953JMuG+bo8fNYItbb/bilt4xrytqLzuKT7XknetVgfll/PUKX8X3PfCjX3MKe1U2E9RXOvJDpynDzvi7xwbmkcVkQjS9mE+OCMyisc7e79FGsc7Z2SZE2YMmVTpAQOXwlBSYFzuiUd3yJZ7PDaBXlbCnhectaIdcaBr5kzinMnPr7q1yqKjnTNUjbk8O+/k9b7QNrjnZF7i5ZDWBgrhmhoa8kJhaoUWd9x8kKEU7D0mqJ7b0F0Is9a555AcifPGrKeOZdKnm3HeHOoImHahCMpShYtx+D1Cf6sxHoiqqZLaVrzUoeyggwsv7pgseqJ+Fv1Aec7NoxuNc+DhyqWfXCmaTW+6tN7RzHd0pSxyEosvarPmUrB1BFMLc3djPhQyBJVJE2M6ZImGJWizxMoFqtyUAdTR+vamPYTy/yS9FSI7If4kfmqxoVW9KICYRnp4ncpP+Cc23POfaX+egq8jei1fz37I8DPOOcy59wD4C4yvOPrm4L+O47+7Rydi0MziaV/p2Tw5gx/Up5rQZexxp9berelmDa91kQ5J6yXZYlKW7s58ZFjdiUm6wllMRhLNoBS+KOU5p5lelXR3ilZ+2JO+z2RMe28Z+nfLhi8tZBmjGmtt3HsiE4tzV1L976ltV+R9kU3IxpWREcZvTsJzT1Lc8fR3MtoPqn51qOKYGbp36kIxxWVr9j47IJkRREfF6Ixb6Ud3B8XTK9EtHakXVw5ibY7D4UK2dwtaOxldO7PqXxFfGRp7Aulr4qheSgdrrqShzSY2XMd7t69iioUESV/Xgl3/8DSvTun/djSvQ3xkSM4StClo3tXCqsmc8RHjtGzbXThGLxdSgPUAqaXRREwOnHEO3OWvybX29zNaOxnmNyx8ppgiPM1TfW2KC3iJAtrHFoaRyLjMHhHBNfcnRbNHU37SUX3jnxvdKLov1sQjhyNQ8tiydA8EL5wa6+g/USK1r3bc8KRfN68I4wHibjMuZ55+0nG4B0poPlzR7y3QBW19nlL0XrkCMaO3h0rdMIGNA9EbdOfKVo7BWuftwQjOdBXvlZgA0Xv9oLWrqW9U9Hclwd4um3OO1a7D3JmG4bptkfziSbe14xuBAQzR+9+Qd6STV5G4sDinTl512B9xepXMuGcTwsRobOO+MCRDaQGEo5KWo9SklXhvpexpvlE9kV8ZImfTFl+vSCYSNbQv1vgpZLmOw36QUzzSSr3f2YZPiVF//6dFJNWtB+meIkTUoFzLL+R0H1ngilEori1I81ai/WAxr5l8LowkoKJiL+1HzqSJY+sq1n+0kQkNfYsjYMSk0L3Diy9kRIf5pw+B8mawHDth450RVHFho3PZbQep3TfntI4cLTvz6giaDwRJ+2ljpXXSlqPNNPrLVRpCWuGXXQiTt2fOeHbt2B6NaoPQ/AnOZ2Hst91qeg8SNCFsInaDy3hsEBnjqKh6d0rGD/lGLxT0tgXhdPh8/KsxycOfyF1trLpUUWKxkOP1k6FzoVQoaz0hDR3szo4rbvYX+0z39D0363wp3K4mlSgv8E7Gf6xR3OvoLlriU5LUN/EJial1FXgZeDzwPcBf0op9W8AX0Ki+yHi+D/3gR97wm9zGHxwWIfp99n/QYvKPFxc4r7os1hXZH1LdNSm+8AyuqFxnqTaZUORL1c0H3oiPtWD8JVTRqdN5sYx34rIbyS0vhSjPzni1PWoQsHnptcUoxtdFlcLzESx+/0e0ZFi8kJBcOAJfuYM0WGT1hNH4wePKP/uMsOXS4JDwePslRRzP8KfK/KOx+iFkmi/SXgCk6cqnG+xfoT9ySFH7/Rp7nioEhaXHO0HwtMe3WzgXpxyULZrnQ7H5Jaj91aDogXDZxTZRknzbiisjFPF5NmC+ZZP9450cFofjj/ucL4l/hWBp3Z+SNN/U3H8CWmzxnks1gV/nF2viPYUSWaoQinEDV+qGD7dqpXvCvy4IFnuUjYdxXaGyw0UkmYvf9Fw9KKsQdFyVEs59q2A2c0KM9cUjR7jT2aYwwB/4tHcdQyfc1RNxeCrhuFHKl5+4QFPpj3G1QpVAMmqIVsvCA88bKBRBVz73kcsioDdN9bwtmcs/80G3r94wkG4StmUSLCKHKcfFWy8d9cIB31DM19vM//UAq0cnV9psPMj4I8CioFFZQrbqsgGIc6D6Egx367I+h0Grxywvz7AG3qUXcHdXbNETT1cM8ef+YyedUSXJpwWHaYvp7jU0n7HZ3rNI7+cgWviDExvVXhTj/6b0jE9/Jhl9Tcihs/B2hcsk8uG9JU5QVhS/nqX0TOWY+XhTxQrX6uYr3ucfNxSxh2yviJZs6TLIcrBbKtJ0XLMCo90vaJ13zD/3gXOa1DGAV4C8ysleVe6TocvKPyxpgoGJCsS1aIdhy/7Us95OcGfR7z8e9/lzsOnOf1YhT8yBCPZS80nMTaQiH2x7igGJf7IsFiP8acxyZpDby0o5j7Bnk98qBm/UGBaJfZUUmanDYtnpW5SRY4H/1IHXSiygcWfeRTtiurlBbpsMb2iQEkWnzyfkLwgh/upjaXwue9YbMRkqxVFs8P8RsGua6MzOPpUhXKKcHVK8ist0kFMOlDkfU25ktN5NWR6syLe9UnWLcpqsk4sezZvUDQVk1sVt57fYfzGFsffU6JKjQss0U7M2hcL6UJuK6JDxcEnFcW1BC+JwEnH6/SaxXmalS/CYsmI2OCLMw76TcpuSftdn/jYMLskNNlgEpP3BA7+wR96jbf/wgscv6hZ/Ypj+DzMhz7xoWPnhyLaHznmMUsUSyWN9wLC4T8BUv+dFlSVUi3g14E/75z720qpNeBYlp7/K7DhnPuTSqn/B/A559x/V//cXwZ+0Tn3s1/vdzdWt90PbP1JstUG83Wf45ccm79ZY8JdzeS66F5PL0uiYVLoPBbcPF2SAmtz13HykmPly+9jWbvf5+HXBUKTCde6uSN4c/fugsf/sWX5v2uwWDGkA0X3gaX5JCHvBRRNzeErmt47cPypims/W3H80VAoSKWIQ82vSXGk/Z78ztm2pn+7wqTSSAPSeAE1F/pYsL+iJRjcYl04rosbOb2VGcv/ecT4RszpCwIZNQ8q5huG0xeFnxwdKVa+lpP2hdo5/H0paidi61cKDj8WYD82ZfMvBuz9npDl1wUjnlyTWkHec1z+5YKjFwOqUAqdqnLn4l0ms4SnBVWoyXsew1uGy3/3lCc/OaD1WK557wcFvvAS6TKNjx2nz0PvXWHsnD7rsfkbC/Z+T4POQ+ElZwPH9b8x5L1/cUAVOq78vZSjF2P8meP4+wtabwds/OacZCMSBtGSIT61RKc5kysR/dcnvPsfxKx+WgZKTK5KFjW9Ctt/P+HRj8f4M4lg0hVLMNSsfTEn7xj2vl/RfVei3KIta61/+JTob/RoP0h4/AeadO/WtDwf+rcLDj7hk26WLH/BsPzlEacv9jj+sRR1GNJ+INRXkwkf/+QFj/nVktY9j+hE4Key4bjy8zOm15siQ/uxGVf/a839Pxqz/Krj4EdL/Ljg8l8y7H1PxOKpjCs/K13C9/7VEBdVNO7WLJZDx/EnKykCRhLFeqk4PlPrp8//lTHzB12CsWbtC0KXNbn0Zcy3hWAw/FhJ72syGKRsWS4/dcD4f9xkftmx+eslJx/x6b9bcvxRj/ZDidzLf/sY/VeWmW0ZwlPH6GloPRLZDWmqkkh1fF2z9ekF2SBgfNVj8nTFzb+eYhYFR690iUaW8VUj67Mm+6Z/u2B62RPqbUeKudv/0yn3/rU+G5+piP70Lo9/7TLBWKDUkx9P8e7GRCcCbw3ezqkiLcNHOtB713L4BzPiN2JUBetfSHjywzHBUGpkUv8puPdTHtf+tmVy2Sf6YwccvLpGfPQBpce0hmbrmkH/3YKyqaXvxCgOX9EUvQp/bPjhH3mVV//rl2jt5uz8YMgnf/8bvPrfv0B06mgclRx+zMefQP79U9b+vxFpX2iSWS2/UMaw+Zmc8TWRhVisWzZ/w3Hyx+e0fr7N8BmEt39FM7tesvabMkjEm1UEo4zxUy0Ovs99Q5777yhyV0r5wN8C/ppz7m8DOOcOPvD//w3w8/U//6kGdYBgqNObbRYrotPhljLma+G5VG7RrxhfNxLBnoiGSnNfCi1FryI69hjfVFhfxL5wwssuVkrKliY6kW7Koi0cY6cV1mvSbR4yvNlBF5CsSzu99eJauleyhKItvHqRm4WpFkeQ9x2EFdkKeIlXa6g75quaaCiFruRSiX9qKFsWVaoaZ4OqaUmXDFXDkbUtfrPgWv+EBy/eomxC1Swp2h4zz7DYUDhPmiTKhc/o+llnraLbmTPVFl1KxrHSnjO60UJVMLwpU6PKhqPsl5ixx+SKTzYQfHG2BdGp1AH8KXQeiRzq6TMioJT3HKcf67PYtOhMWuBdI6cILdXMECIOxoaO6TWNyQxZ35Gshcy3pOU97zrypYrxsz2yvsXFFZPLEYt1J5rtUUm67DO+2SBZkWxkdgWCueL4IzFlA6qwQ29pSLI6kEk+XdHqyLYKjl9qULYsupSmGrtUkMSGk+flAMNUzLekjqAqKf6+sv6Yz24P8Och2UCm25SxBATjaz7ZcsXy1ojszjLDF7pMrilWlyfsz5YYP+Vo7BhaO4Ibzy+XqLhkvqUpY03ek3R8fKvJfFOT9Ry9dsLo5jJVv2B6JQDlWOrNmK+vUDYdcTtjsdym3PbAWnRQUTacOJtMES8v0Hc6zC/n+LMAkH3ffihqn5udCY8va5InbSZXPMp+if9ING3KpmW+pRmsj0nvL8OVBXFQst6cMK02KNZkQEreESpp1hdO9dLrilaQc7gpa1NuKoqVghneOb9+sY4wp7qO8fVYunqXwEUV2VJIuRUzuQHhV6Q4vdiQxrMqhvF1n3QJstUKF1e4PZ+TV/oUSyWnT/u80hxzt2/xFprZtuLa+jF3pxtQl+4mV6VbfX7Z4gY5i2HEUn/G6bJowM+ehJQNhzUQn4iuTxkpBpdOma8t4TR87+oD/lZrhazQOF9kkE0mMxryjlzjfF0kr9OBIW9DFVm8bk5hfHxlKeNas2ep4qnmIV+rRdmq0CPvOEyiaEQZVdhgvi41i2wgzKK8XzG8GbDYkBqV2UgYX2/SaaRkDWHLFE3NYtOi4pLpdkTjQGFXDSYLqCLwlxe/nTt932//DtgyCvirwKlz7k9/4PUN59xe/fWfAT7lnPsppdTzwF9HcPZN4FeAW8656uu9R3Np2z31L/8ZaeUOFOG4Oi84SIRSokrp+ptdCohGFcOnfJq7lsZBzvRySN4R2Vt/jrRKd0X69vSZkMaRJTopSJZFs6T5eMHw2RaLDcXy61Jpt0ZG353pyetS8LXZlqJx4M67RXXd8RsN5bPqUrRaGk8WLC41SAaGKqrHoW0YgplEW3lLRNHi00qKNwNDsqwYvFMwvu7XHa2WYCaYuTNSuFv9rRN2f3QZbyHFmu57wlIwqfDonVZ1IUgaVnr3hS9scsfwlkfvnhSPRc9asD0vlUr+2aCR9iOZGpX1hG2w9PoM52lOXmgQjkUK1UulSKQqR94V7fbJFU3vXs0i2PDo3cs4eimi/aiqVTitDDLwRE8jWfY4/rgjOtAM3hHtHJPL+nkL+R2dhzkP/5BP85EUOEVPH+aXFIO3K5KBfObWXlVTRWUeZxGLdr0/L1msBTLYoiP7p3s/l8aXS6LwN3hbIrIzBkj3vZydHxI1wzM9cG9eHwoWkhVF715FeFpy+DFpdZ+vi5SDPxetj6IpnykZGIK5FK2jo5zRrQgc9O6nJCsByUCfd+xWkWPlq/acOz5b96hixcpXF8y2IhqHOemSMGz8mTynnQcJybpkOeNrEpsFY0fnPRm86jTs/kDIyqsVi1VN+1HJ9LJH/3bGdCtEl47JNZGYjUaW0Q3D6pczHvwLHld+vmK26ctgiy1D/3aBDRXxbsJiq0ERK6ZXNJ33LMFUahnWKIbPSHexv5CMJhmINMPS2zn7nwikBb8lBe68JROaUFDWCqOnzxqiU5E17t6e8+gn21z69ZSjlyJ53qzs+3RgaO4W9eHi09gvWKz5NA4Lsp4nkh0tRbKsWP9Chj/JOfhUm+59ecbaj0V1VlmRxu48FIZb0dI0DvLzJrGTjyo2f70k6xuSZRlKs/TGnGwpJFmSNR/fUPRuW9IlzeCdnP1PBXTv2lriweI80X85fjEmXYbOPaGhhmNLYz8nW5IpWdZTzNd9/MSx/z3Qe1ey0PjEitz5QcHJCyG9OwVPfsTj8i/LtXqJ+J9v1MT0O2HLfB/wrwM/opR6tf7zk8B/ppR6XSn1GvDDwJ8BqIdy/E3gLeDvAf/+N3LsZ9Y4sucDqqeXPIJRIY4oqRjeCmWYRM+jcVCQ9kTKtIwV42syycQpcEq60vyZFDBHN8LzgbllwzC5ZvBSi4094hOJkOD9wcfxQS5Mk2FF80lad7MKth2fWOITSziWtDzta1HY64mqYboaYz0REYpOHSa15wNC8nrwBkoGhUyueIQTGeNXtIwo861L8Utnlqwr046CmWP2VA9lJR32EvBmsrnTJZlXmawolr+WkXU0ec/hzapz9cLGoaunCykZg5Y40p5s7qwjkYQuz8SJHK0dK5NnKstiIyI+FQ3xaCTMm+mWFANnm3JQtHbkYU37htZeSdkwdB+UohJpnUzD6Sii04rptkfRhMauNDF5iej5oCA+yiljTWtPIvnmI2FNtfZEuGr8lHRL5i2hDXYelyQDaa5arHpkbU3W08zXDONrYT0E3dU1B86HVUfH7rxTtbEna+YnQksLT0WUKuspiqYcgNFIpgjlPVmv0U0Z3mFyR2u3EjXRhiJZEdbIGbwjapIyVWq+oYjGltH1CG9hKZuKyXVp14/3lRQup8KSKRvSi7FYl7GPyooujcjeyv2qIo8ilolg7UcV80vCKptvSICwWPdp7p514CryrlBcnZIJUIt1ibaDqVBuoxPH+EZA+56RARUeNPYz6fiORZ43XY0pQzng249EaqIKFSapyLuise+lso5FQ9G7l4nk8ECYKLqSQqMoSUJ4kpJ1DVlH9GjCoTj2rKfJBxHZkjSLhaeO5l5Fsi7FWKcVyaqPN8txCpJVn6NXwCQlw6cMwUx6T+JDR7Lis7gU0zio0KWj/bjk5DmP5m6GP7PML9nzZ6MMpXYWTJzQjIeKrC+HdzBxNA8qik7AbN0j68o1ZJsFuoTo1IqW0JX8XB3V5JaioSlaHnlbulPjk5LGcSWYfdenaGgZQxloonE9V/hYM9uS7u6zwGex7pMN5CAMRjIwXpeOYCTzDb6R/RNhGefcb3LOVflH7Be+wc/8eeAbDuj4R0xJlBrMRanw6OOa1p4vME0nYHoVoqHQBqdbHiZxLNY84iOp/OuiVlecO/xJed62u1hTbP5Wyv4nI1pPxEHpwpEOgppvrETk6Ao0dhWL9YB0oKVoe8Nnet2y/FWkEakQR6pLKS6VTST1yhThVCQAxtc17UeOrKsYXw/oPLRkHRmakPUd8aEiHQg182jFY/m1ipNnDWXLUawVgOH0OZmlWTTVObxUNmH+fMryr4aULZ/xNY+irVjcyIkeBZw+GzLbdpTLBdnAZ7GqaO3IQOR0FaLDWhdkz5H3YP+yId6rtUT2HIu1gMW6iER1HlXSzdeT7MGfi+JlGUubuSnkuotDRTrQtZyqPHxlJIdQfCij66bb4siTFU8GW9yV5rF4AZMrHpOnS8AjOjUUTU3WrTf0hqP7QDjF/sKRr5a0H3jMtuWAK9oeJpWNny4p5peEftPYFXnb5oFI9R6/KHIN83Vf5H47AnmlPRl2UDTrsW9+cD4oOV1yOF+EsdTIUkVQ9CqGz2v6b8hem68Lg6WKJA1XFoqpSCFnfUfzQMYQTi9rshXLdGoomoDymDxTSNE2ln1QtCSLWqx55D1IruaMs4DmrhVoKgadS2Nf2dDoUobLZH2P6WVN1S3w7yrGtzROB4LfrglLZfKRnOa+x/xyxWkSkvfqw24rZb4TyzrfgekVoRAPnzG0HjsW6yFlpwKn5NnZkfqJlwK1WuF0yzBfEyVN4ZfL67MtLXNkVzTxoTyDRVN4//GRON/RM616GAlUTWl2C6aK6VVL+4lCL2Us1hs090Q+wCQKXdlaN0Zhskb9XgZ/AuMbDfK+I+1JNtx64kiWNLoUjHvwrmO66ZGsW6ZXQqxR2H7OYiUUquVDy+imNFGtf076bEa3jLDCYhFsM4E0JGIdzne0lucMn+3R3BE5bR1VzNdEaiI+tIyva8KhZnGtIFsyOC1d1uOnHEXTw3lg8pD5qjlvWrQ+lFdTprOYdNUS70svgPnomPlxV6ZRrQXgYHQrYr5lv6Fb/bZQhXRGCnL+tCJZNoQnMlDCn4uOiTevH8JA8G8QipKXSVR9pnc825JNH07ej7iOX4gIh1IkUiUsVrzzUXjZcoVJRSQqOhFdkGAi3+slDn+qhU+an434k/cVlUd5T11ItKILJ9o0tQSvl8jrZUOJBs5U1ePKBMtv7DrG10RxEEctL2qJTgTL8xJ5H+eJM/Ufh2QD0bj3FhCeOFQqN99L5GHGyfxLXUjbvEmlY7aSQJDmfikH0kJgG+nWdXipJTx1hEN3PibMeQI9Jaua2SVxjs6vlfkmtTqnEgy18kVOQVWysVEy8iwYS0YUnVboTDHdVgRTwTV1AXqhMYnwh4OZFc3tlhJ+cyyZi7KgZ+Y8Igvq9c879UT5vqO5o4mOxLGHQxn6kXdEDz86FSZC2RB4B0/2TTCWlNh5kr3kHUUVqXMFwsaxNIs5oyCqCI/l4BE6oCM6sVRBPRx7XI/zm0oGIIXqSno2mhXBWOQJZLMrnC/RbHQiWUTRFE6z9R3k0oKet2VPWk+07Bv7iuhIDgKUqAyGQ4eOSuZborMUn5bkben8LFpKRsG1FN5M1jk+EFVM41U0Di3eog4gFkI6OOtncBqIJLI9GxhhsjrDrZt6grGT8XKx/Kxo4avzsZSNA0vWl05Wkbl2dUAFzd38vGdA9rnMSYgPtGQkzfQ8C2gcWbIlS94S3Zhg4s6HfXhzRxW78+wz6wluLrNWpX/Gn0LWFnhUl3IN1gPtCTffn8scXGWhsadYrOjzRkmRF38fpo2OlMxcPtAEXnlesI2OFWEkkXx8amX+ak0/xiriA004lmw1PhCITecyge1sj5exRPi2lPsfDGsBtAzmpzFerQ56puYpsh/fmAr5beHclYXoOCU6XBANq1oQzBIfV8RHRZ22CcaqK5m01NwvydpyM4OJ3HTlEB77THjczV1xbPGJOBxnpHAWTCp6dzMAug8yWjsVRUsRDUtauxnB1NK7l+JP5TTVGTQOK8KhiDq1dkpaTxy6VAQjOQyCqcxUPYd19mQcXXzkiE8s0YkMwjgbNt15VIozOq01qDNNMMrkELJy2LV3SuITi0kV0XGtwHicCq97p8SbiQplfCoTZ1SuhY9biV56MJPRe439WvxrVtB+T5Qfmwf2PGX2ZxWt3YLWXklrryQY5qgS2g/mmNQRji3NfakV+DPwJ+580AdOuPThyIpY1sNUDslRKY02kaLxaCKNIo7zKT6C+4vqZHiSoQvJEJqHluhImqj8uaX1MBHxphRae2U9Uk4eiHg3ITqSAzk+cufa6M29Ai+15wemQGlS2KMSrf348VTggiP7AVG1utV8rMibivAkpbVToSb+ea1FWZH/bT1O6b8tr/l1XSU+tvX6CF/fS0DPDZ2HmQQR+yX+0NTZjsj+SrOTpXN3ii4U/shIo42jrnEAVuQUzvFcI2uuKrCJhzeXYrQ/KfFn0lHrJVJADqZS2ItPLSYRp5LP5AQLRvJ7GvtnGu3CLAkmFUpL30cwlUPOnwn3H1c3iNXPpj8Vdkg4eh95jY5z2g9TGoeWcCTBTjSsezEiRbg/F0eV189kLlx1uYaCNPdl1mz8/qAKkKlp8YklOJwTjCthwRwrGoeF3OuZo/3YEk5kr0YjmR8cDSs5bEeiz9M4rrBzISY0DoULH9d/RyPpQD93xscWL7EEE+HjezNh/Bjt6N0v5VmbOnxPDnN/LrWmYFT7pFwOP10IbBeMJfCTQyElPinP1TX9hYNEah3BWD5bMHUE+z6dRzn+zNHYTYlOc6n3TP85cO44KBs+RTeSiH3h8JJSxJYKSzSyMsShjjZBMKhobM8LOWct9mWo8GZSdPBn7nzQgSol6talw6QVZcPgTbVEAIkUhqyvqHwNzkHl8Kdyeocjdb7RVAnpkkyf9ycQTgTX1rklPHsYgWAqjRLS8efq9norzmzkzgXP/IXoZehUU8UewbiUpgYrDAZdiPMPR/JgVg2PYJgRnKb4Y4mAy1DTPJSW/aLhEQ5FJx0nnxfEIRZt/zwCw4nGu3TIVpjcUkWidW6yCpNDuhpL5EodKZ6eUeDq22Ykgisj+R4/Eb1pGeBRScReQtkXtUWTyuxZgXvk0AIoOoFkC3V3bNFRzNckw0rWI6JjUaz05lU94EH+lK2g1qe3xKe2lu1158W1YCxa2WcOwiSgClkX26g7WY0MtKgCqdmcHRzKQdnyJRJN5ADRVX0/pznpalhP9uG8JuFl0pVpQ4PJLdGoEu2Rpoycw0lkH55KRijrJ2tQNQJMIlH32fCVcJjj1x2huoBoLENG/Lk4C5M7vJH3gXb3Sgrfk/fXyOSuzmBygpl8rWYy8k46lKWmkvWlua0MZWiMPpbQ2ktlmIq/kEDgLHL3FhWqJmPkbUPRkubCYCzNO+nymV6+7LWzqUXhyFG1pfFNl9Td2Y50La7lpR3pSXwuA1HEGlWKT6gCGWbjIg8bKMLTUoKWeSEDXSqZ01o064E/c3vekVo031fS9OaiMWRS2YdnAYpfT5SyBqJjkbRwWkT/VOlqZyvP0vFJ+3yYeTBzTCcxfmLP/U3jWA4Vb6aphCRVU44lSzkLFmSojvi1ZEUTHXpUsUT6ykoBVgaEyF62gaGMZPxmOP7GbvXbwrkrC6fPhCSr/nk7vvU0s0se6SCgjKQg1DgoaByJtzobnhGf2nrGp6TtVQDZsvxMdGqJjh1ZrUeSd+oRXwqSJY/WI1E6PHnex3ly0yfXAuYbPtmycI0l1RetFi91pH3BarOeaECcCeqfKebNNgzja15Nt1TnI/e8zLFYFZpmFYqKZXzgMIU4+/BUk6wE2EDTPLAkA4/FqgzuPde8yR1VaJhea7K41JDCbe5IB9JFG56cDVCuHUYgG6+5XxJMhdKlbD1JaiTRZRkq8q7P0YsRp8/IGEPrCad2uuXR3BdtkayraRzVhdVtdc4Waj+RB79oSko9vWTw544qEkXOaCjFRFU5mvuWyS2JIK2niI/Oxt8Zsq6RYtmiYnqtEnw/s/XIRalxDJ8OsYaaRSAaI8FE0v3xVUNrV3TbnZbxfUF98HZuT2gcSrdvdGBYrAdMbjQJ667lM8rtmT69ySVNHj4VkvY1/kz2TdEQZtL4qSbJQB6d9qOK0U2Dt+BcRmC6HVC0RL0yPlBML4lMsTPqfJbm5Ko4jujEkXc0s+1QGDFOoL1wZMk7fq2fJLIXixUjhWIrejLJsiYYChxkPUW6HNHaLcl7kp3mbSnKCtyiWayJlHT7vmZ8VWbazi7Jvk027Ln2kPXkABKaqKZs1BO9OKtZyTVaI1j6+LoMa3FG0dqtyNsyStIa2Su6QogNudSjqoaU+vK2yFCUDcXouhQr815IvOPR3JfnXJeO1mNF60nOfM2Q9g1lW4bqpEuejFLsBgLZTQW+GV+X4SJeIppJQmpQ+FOpW2R9j9Z9w+Dd7LxID5AsKeLjEufBbCsQ2etVfb4vrEc9BxW83ZDxFZ/xNakFBg8iso6mjDRVqOt7JJDN/JJcv5dKEKpLgQaLlk868EVGO3FMr1f4EyEueIkl68q9sB4kyx6zK0IMmW775B0Pb/G7lB/4Z2GVXxcTQsX4hqb9OGO6HQh+llmaeyXjGwGzzYDFiiesBE846FlbC8tg4JhtiYhW3tIsVmVC0VlbcBELJjvbMOQdj3SgGL5YkXUMvXslVQCnz4aCxxUOk0q0nS2/zzyofClCWqNo7le18p2tDw9N925S8+iF5VO0agc7F1qTjAeUUzs+Kjn9qLBqqlARjmC6pcl6HrMNOf7LOpLO+sJUmF9SJEsyQu1MdElZcRrBTHRCkoE4w9PnPKbXIZhVNdVTyVSiJUV0UnL6jDiubCAb9yyqSJcU6WooEUtLMbxlGF/TorczkCHNy2+W8rNLjtENj9FNw+SqZBkmcyxWtLAdxsIOOGMRjJ7SOCOKeYsVGXHopcJDXqwK++j06RCTaLK+Y3LZq2fiijJmNHR1d6zM8Zxe8s5pkfGxk+HSby8Y3TRkA3U+zWr8TJfxdRnG4CXvM4QWK9LZ6k8rkq2SyRVN1hPOfBVJtpD1Bfrrv5NKf0PP4bQU1rO+TBBrP7JMrktk1Ti0lJEcBFUoe04yr5KsZpkUTWneKdqC9U+uyvjCKlLnWcNi1WAyYcLkXVj9UiKDalYCxtdFJA1ENTLvitR1FSpmGx46g5MXfHr3LJPLHtHIoktL5z3JpBYbrsamIVuq4ZKGpbmTMr0sk4eqUPZuYy8j7RnCYVFPE1KkywJ9mUIOBF3rskyu1AHOlpH3WTGUbZnrmg2ERpkuSz/FYrVuLosVi03Ryem8V5AsG7KBZb7hMdsWAbXJTcvpMyH92wJbZj1p/AnHFUXXUjakJpN1NY1jS951LFYMyUpA2YDhU/75M7RYER1168PxCyGNg4L5psxWDaaOo5cD0qsZ8UnJ6ClN2ZRnY3o5xBSiGtu7m1N0KroPC9GoupOQrYnA4XxdM70kM1TTgaiDnkl+zzc1rR1RMW3uCc01HJbvi9CVwh77YOep0xBMJbOo1jOZC9GQGcBnc52/nn1bOHdVn+qmcLWAlqk1UeRBApni4mXCuT6LIuLjsxmICBboy0YLZnIUmxxMLtGpyaUgoSpJaZUFfyQj8CSqkBRRMMW6uOa9r6aIksMkmDpsKA7V1umW0OMszhcOsy7A+jLZyBmhxunK0TiSoq6XuPMCpM6FWlk0pXbgJfZ8LYQKauvrtzL93UqGER0Jr1mVdbEwrR/Yok5154IPFk2DKaQDT9QgIet5RKfC0fbrgo6X1OuoQOdCWzsrQHlpvT4ZZLW6pTPUCpoCF3mL98WVvLSe3ZnZ899rMofOAKvwapnVKpbfoSoRNjNZPaQ6EDw+mLrzza5LVc/oPCvuyfc7JWvoLwRXF+EzcajKCraJqr+voG5YcudFX+tLtKpKceK6FGVDXdRKmAv5jGXTEzik/oxVwPnEH1PUa5DY8/c3OfhTgRadB15ayecqXV2UV+eKg8FEPrf16gO7jnR18f412UATjmWMYHTy/kPtzVRN95Qxj2cPvD91Eq3OZNawU4r4MK/3qlx7GSp0Jr0AKLChwUveP+jPdPHDscwU9hIRrQrGUmgsI3FKrv6dwVj27Bk06S9kXSRrkX0Zjmoxtsn799ufyuHmT4rzQqHo5FOz02Qv5D1PIBUrvzc4SgiGGlMHTFUggaJJ5ef9aVWPKZTC9dl+VrbuSM0FBpLB37rOmoBCOob9Kee/NxzLLAanhc5sEk3eMgLVeQKrnu23s/U1uUTqqnp/opozcj+znk8wq9CVPO9n8KfJ5HvCcYU1MjfBZLUfnPh1b4fo15w/s1/Hvi2cO0ihTTC7WgHPU4TDuktrzRMdkoGqueIS4SRLou4nQ2dVLTrmKCNN+7GV1D5R5xu+tWvr0XyGxqGlWJICXRVKc8sZn715IHhb3lUEQ+nibO1Y8raoQeoMcNJOnbVlCZ1WDG+FtTCRZbrtYX2ZrTm9JBBLGSpml6QxYrrlY3LF6GbA9Aqkq5aw5rdmPUnFgonAHVUEoxui6WIKh/U16UrA8GkZORgfOOZrhtlV4feXjTMMFrKORFuLNYGpgrFI7J5JKoOIsUWnVnjuJ/KgZx1Fc08Kaf5UsphkTQ60M8XL9kOBC/yZ4N7TyyHh2LFYldRzviHwWHO/FG50LNo6qhRHVrSo55DKYScFUEe8r2nuSJt952HF9KoiPFHn7KGiqc4jsawvVMcqEDbO5HpMdOoITx3Jau2Yw7pHYKFILkkjS3xckiwJtJYsewQnhvhYoqzh88LuaBxL01ayUXHynE8ZyrB0XbiaDfS+cJfMOBVVyOahOKDZpYC8L5z4oxdjUffbkilh4Yk4knSZ+r5UVDHMNwQCK9pQdDyJKicwvCVrWbSl29Krh8bkfUvel85siTBFgwmQcXilyB3PNwIOX4mZbyrKGyllJGvY3KnVDh95DG8JrcpbOBY3cto7JdPtgCpUHL4Uk6zo8zmseUdmFZexovXECoEgh+llGVcpA7ih9RCo4SdVvl8Dsr442Pm6oYqkEHryEVF286/OSPtKArMG5GuFSFgHirKGu6KhZfRcW2YPNEWipGhLVtR+KDBqOpA5q14K0ZGM+JPipSO9VJAOJBMyGfW8V4Fu/BOP6dWI9mMpNnuJBACzTUO6ohhf8/G25/gLKf4Pb0VE21PJ+kupweQ1zTZZdTR35RBTlUTwWVfm8zqlmFyOyDuiQx8faGaX5fMNnwoIx5ZkpZ505Sva9wzRSUn7ccli2TB8+hvz3L8tnLsuYXTLsFgRvLbzXkV0XDC7VE9xH1u6dxesfn5C724uMEtLpDw3f/WEcCSp8ODNs3RTxm6tf3ZO77ataZUCjbR2Sxp7Gcmy5tIvCd1odENGfrWflCzWDONrMjBa6JGw+tWS6ZbQo2QQhwxQXv9cgcmFDRNMCilathTzTU18Ys/nk3qJVL6TZU1zv6J3r6BsKFa+4mgeWvrvQPORpmjI5PmN35rjlNQIWo9SOg/EufZui7MpY4liB285GRqxLrTBMwnh9uOK+LisMwFH/07J8usV6UBLdrSA+KQiqJtnTG4ZXzMcf1TeMzxOWX5tweSqof1EmAdVCNt/b0R0Ktzl/h0Z27b986c0DyrKhqLzIGV0C7oPKskuRhWtXekmbu+UXPq1nOnHUuIDRedRTv9di7eQyCgaVpQxNHczkhcSFhuCqw9vidRxFcH4mtA7vYVj7QtSWV9+PUeXguVe/vvTOisS57H8mnyu3p0FK1/LiI4d7bseqnJMt3xWvzwnbykaR2UdtTtajy3Rgcg9Ty7X1L1HhuaeMC+KjjiQyVU5/Ld+dc5sU8YsmjNYatmgS0f/7RnLrwqHevm1FJPD8tcsl/9+TrIu2ci1/2Ek0q9rHoO3K5o7jt7tBatfzTGp5dJvJETDGqstoLmTsvRmJtiwgs49xeD1GpbzFFv/YIpT0L+dU0UyODs+crQep6hKZqT2Ph3J4fykIlmRwdWL6znNQ2FdBTPL5i95ZF1p5AlHJd2HZV0veF9/RVWicz58ToY/xycVl39xTN5SnHxUoMv5ljBPipZi+fWExqGlCjXd+7mMQtwpCYaK0U1dz6ataP1Ci7XPDIkPHN4c1n/FY+MfDpmvy/5GyYEqTUvybG9+pqT9qCIbKMY3YONXj+m9NcYGSiZB9WQ04BlK0Puaz9X/8YQyFGXV5n5B0ZAGOZPVAaESmDfrKeKDlM7DguXXSsKJpfHpFsOnRGs/Glqaf7cjBetMhr+3nxR078zo3hVue95WdU0IuncX8lw5mdvbu1sxeKcieT5h6TXR0Go/KaXYfVqd062n16TONLvk0Tiq6Dz454DnrqzIyHYf5PX4Nk14tKB5IG3sWUdjTucUgwjl5CHs3heIY3azW8vlOmkBf5ITHWZ0HubML4nmeTitCE8zlHWEwwyzyBm8kzG+bmgc5AJRTB3haUb/dk77UUl0Io6h+0BasQfvFgQz6bRr7jqw1FPIlYyhy0Vat/ugoPOerfnDnLfXnw17rkJN1pOhw6oS/YtkRZFsODrvZZjCkqxFhJOK9mOJAPN2PR9yVtF4NCc+LlAVTK7K9Jb+HaGJzi5D+94U50G8MyOs55JmHYkW2o+EMgZ1tFkIrdSflPTuVfRuiz65Gc7J+wGDt8WBt3cK2k8qxk93yNsabw7BpKDzsGT6dBddWFpPSrKBT++2QFXevDxnGvRvF9KR2PdovhEJxDbNhffsHL23p0QHCa3dumPvrZjeHYuXOfp3KpJVRWNfYC1TOOKhJe95dO5OyXoSppoMhs+0KBqaznsZ7R05zHThqCL5nvjUsthwxMcFvXsp6WpIa6+SKH3f0b09k+iw6YiHFYN3SuJjS96XbMfkjuaOpXFYylDrFEa3GlJDOS6lE9lB/90Fwbik6ARMrhnpJN0MMJllvm44/mhI/23RyDn+WJf2o5zug1Qy0RVFFXlUgcakFcNbEf5C4KjGUYmZyR4xSUX7iWW2LVBT3tSEJxnJRkw4ArMoBTLKBbevItGJSZaVzEGdSrdy7650UnfeDOpRkI7GkznDW5rW40Qi9UnOYkmg0mBSc/inQl2tfJlZ3HkoIfnomTb92xmDN9y5bLSyQjWsQk00LIlOckxS4s8h6RuCsWPpLaEjh3tTRs9CutGSw2ZkWaxrZtc7UkB0EO0taD/K34dsj1NOa6E+VYp8eLbRJl9q0NyriJ5MGbyVM7li6NybE45KJtcts1tdoQInlqIlPSfBaU7ziQQInXtzWjtChzTzXIqam4aspxk9I/TbwTsVRUNz+jz181QR78wp2gbrGxbriu67isaxzJzVpbDDioYm2l2gCynQpn1NcDdmckWE8ZKBof1YBNKqABqHJd13Nc33ZgzeTghPMhar39h9f1uM2Ys3tt31P/HThEPHfFOx8jWpcutCpq903hqy9yPLIiMQCTslG8hkl3AkvPDDT1CPJhM97sWaaEwf/kDJ5Z9TUmWvOyjbj6QDbbFd0X3LsPLqgsc/2iCYCqfdS+q0e1Lx8A8r1n9TM92W9m+TycPUeU8q8+HEsVjVNPeE0jS6IcyI9pOK/e/VRIeiTSO0SElZg1lF3jLs/VjJym/6zDfUOdbW3JU27GgoE+K7D4SN0b1fcfCKpvVY0X83Q1eO6VbI9LJEMhufzdj7nhB/Bt2H0mpdRY7lr4k2d2tHWA7+VKYiZR0pyIyvaboPJLtJ607a1a8IP31yTVM2HDqvKX+nQi/zF5ajj/pUsXTdFk2BP/q35bOaXJhLg9fHPP4DvXpYds7jHwsoloUDtvxbkh05o86HXZ8NdRg9V6JKRWNHOn/7dyx7P+RY+01p9a9C6uEqwv9e/a0heJq9H+ix9FbGkx8OhEN/UtcjEqQ7tSEj6wZvuPMJXtZXNA4q9v5gSXg/JBjLSL5gKhz0s2Hc658rmFzxmF6Fzc9UnDwno9JMKlnZ4Suw/jnZn1mvps4diY68KAzmTK749UB3Ke6W/ZLeqz55D4FHPGEyRcOKoxd91j+XcfKCiGF13hOF0GDsWGzIFLLpZSEW5D3L5V8qKdqG+aphsSlKqa0nIqdQBYqlNzPmGwHpsgyTX/mKvN/pc4rr/8OMO/+Rx9bP+By9LNPDsj40n8jhf1Z7mm+IkFX7gWSmwugwTC+r8wanYCSD3c/qCWVDmnPm24rGrmSdYQ0NopAg6akKf6jZ/E2Rkzh+2bH8VcXRxwVatLGjfdvQeVwR76dMrsdMtzVLb5cMb3p0H1RkXREHjE5kEtPS65INjq8KnJn1of+uPFOtHcvRS4rGgWLl1ZTd3xOx/SszplcajK9rihdnXP2vFDs/1JSh3pXAvf13E06fj2kcWfY/qdn6dMHe7wm48osz7vxvYi7/kmV40xdFzyPh2w9v+QLN7Ijg4fLXZEJZsuLXGbgwp/wZjF+UAdnhVA7GtCuD1lGAg/0fdAxe1TUJwBHMHZ/7G19/EtM/lZ77t8qEqyqO2ilNeJyjSsH64oMMG/t0HxR4i0oq5S3hSjcOKxqHOclKQHQk0qS6cjKaD4/GYUnjvo8uCuITEWbqPKxoPpyR9jtYY4iPLclaSDCR1vusraUR6bgQfvK+R3RakPakQ1EKZoL5Nw+l6NG7VxCeZNjQEExMTcvMiI4atHalYAqaKnLERwU6F3Gw5p2A5l5OGftSAKykQSaYmRqfU0SHGd56A6cV7fckjVUWVGHxE0s4NKClgBtM6tb7xNJ+JIeEt7A0d2uxJudJYWxRSzGUohPfepQyuhVLh92honVvDEqR9vtEx9IhqXNH40Cis6Itk5jm65rOw7qdv6No7qTMLjVlOMRxCaWl89Bijeh/9N4JOH7F0HhsaO0W5B2DspZiKsVAYQBYJtdldmTnYU2F9CA40ejS0jgQVpD1zopuDhf7pMsR3Qcl/jijuRNItHhsWazIZ8x6hrQvmU5c31v5f4/mkxR/p0HriaTV1tc094QFJVxmRTDKaUZCCzwrCBYtSbWrQNF+oPFnBbqUvRFMLeFJRt5qCJ99mNEKREuluSdqlN7Cp/OoZOoMjWMZ7pH2DdFhSu+uITie037sSft7LvTI1k6OrgLiw4IyDEhWFa2HGm9eoiqJOKvY0Ni3FE0JJoqmJtqdki4PatlchZdU6NzhT32cUbhhQHiaEh0ZmocWVRrioa054WesLB+UdFgG04roMEUXEcmyf95p2tqVZ69sKJp70oYfjS1l09A4tjX0WBLM5H6EI0veN3XhWtHczZlvhrSeZKRLIsVRBdCps1iA5p5cezAq0ZVHfJBh8gCTacpI4c8V7ccpJimZbTSJRg5daoEhEZpodOrTu1vW99phJinKxrSeOE6WG6hyTnwkxAIvcbSfyHMbH0th1Z8aeebGYKYZjd0WqrI094U8YTJHdJjibXl1s54MelEWguM5JgnJewG6sFRBQDi1TG94mAKKWJrOwqklGJWMbgYy5CM3NOshQc3dnLL5jTH3b4vIXSk1Bd79sD/Ht4ktIzr5F3axFh+0i7V43y7W4n274pxb+e3+49sicgfe/XqpxXebKaW+dLEWYhdr8b5drMX7drEWvzP7tiioXtiFXdiFXdg31y6c+4Vd2IVd2Hegfbs497/0YX+AbyO7WIv37WIt3reLtXjfLtbid2DfFgXVC7uwC7uwC/vm2rdL5H5hF3ZhF3Zh30S7cO4XdmEXdmHfgfahO3el1I8rpd5VSt1VSv3ZD/vzfCtMKfVXlFKHSqk3PvDaQCn1y0qpO/Xf/fp1pZT6r+r1eE0p9bEP/MyfqL//jlLqT3wY1/K7MaXUtlLq00qpt5RSbyql/qP69e/GtYiUUl9QSn2tXov/S/36NaXU5+tr/htKqaB+Paz/fbf+/6sf+F1/rn79XaXUH/iQLul3bUopo5T6qlLq5+t/f9euxTfFnHMf2h/AAPeA60AAfA147sP8TN+i6/xB4GPAGx947T8D/mz99Z8F/m/11z8J/CLSdPw9wOfr1wfA/frvfv11/8O+tn/KddgAPlZ/3QZuA899l66FAlr11z7w+foa/ybwU/XrfxH439Vf/3vAX6y//ingb9RfP1c/NyFwrX6ezId9ff8L1+Sngb8O/Hz97+/atfhm/PmwI/dPAnedc/edcznwM8Af+ZA/0zfdnHP/EDj9x17+I8Bfrb/+q8Af/cDr/60T+xzQU0ptAH8A+GXn3Klzbgj8MvDj3/IP/00059yec+4r9ddT4G3gEt+da+Gcc/XgN/z6jwN+BPjZ+vV/fC3O1uhngR9VSqn69Z9xzmXOuQfAXeS5+ufKlFJbwB8E/j/1vxXfpWvxzbIP27lfAh5/4N9P6te+G2zNObdXf70PrNVff701+Y5aqzqVfhmJWL8r16KGIV4FDpED6h4wcs7Vquf/yHWdX3P9/2Ngie+QtQD+78D/ETjTsV3iu3ctvin2YTv3C0OiOM7FeL/zTSnVAv4W8Kedc5MP/t9301o45yrn3EvAFhJhPvPhfqIPx5RSfwg4dM59+cP+LN9J9mE79x1g+wP/3qpf+26wgxpioP77sH79663Jd8RaKaV8xLH/Nefc365f/q5cizNzzo2ATwPfi0BPZ5pPH7yu82uu/78LnPCdsRbfB/yvlFLvIdDsjwD/Jd+da/FNsw/buX8RuFVXxQOkOPJzH/Jn+mdlPwecsTz+BPB3PvD6v1EzRb4HGNeQxS8Bv18p1a/ZJL+/fu2fG6tx0b8MvO2c+y8+8F/fjWuxopTq1V/HwI8hNYhPA3+s/rZ/fC3O1uiPAb9aZzk/B/xUzSC5BtwCvvDP5CK+Seac+3POuS3n3FXEB/yqc+6P8124Ft9U+7Aruggj4jaCN/4nH/bn+RZd4/8P2AMKBAf8txCM8FeAO8A/AAb19yrg/1mvx+vAKx/4PX8SKRLdBf7ND/u6/hesw/cjkMtrwKv1n5/8Ll2LjwJfrdfiDeA/rV+/jjiku8B/D4T161H977v1/1//wO/6T+o1ehf4iQ/72n6X6/J7eZ8t8129Fr/bPxfyAxd2YRd2Yd+B9mHDMhd2YRd2YRf2LbAL535hF3ZhF/YdaBfO/cIu7MIu7DvQLpz7hV3YhV3Yd6BdOPcLu7ALu7DvQLtw7hd2YRd2Yd+BduHcL+zCLuzCvgPt/w+LLP3cpU1azgAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.imshow(dists, interpolation='none')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "7855beeb-d7e2-4ea7-994f-1adfa5a2c886" } }, "source": [ "Let us now predict labels and run the code below: We use $k = 1$ (which is Nearest Neighbor)." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "nbpresent": { "id": "219d7522-e633-4136-aa98-9abe80ca7bf3" } }, "outputs": [], "source": [ "y_test_pred = classifier.predict_labels(dists, k=1)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "f083926f-4bd0-488f-8ba9-e77dc946fac8" } }, "source": [ "We compute and print the fraction of correctly predicted examples." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "nbpresent": { "id": "f1ac90b4-5005-4940-9663-0bfd9574dc8c" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Got 137 / 500 correct => accuracy: 0.274000\n" ] } ], "source": [ "num_correct = np.sum(y_test_pred == y_test)\n", "accuracy = float(num_correct) / num_test\n", "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))\n" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "7a33b48c-c106-4903-ba68-769ce91ccb8b" } }, "source": [ " Let us now predict labels and run the code below: We use k = 10" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "nbpresent": { "id": "7a4433f3-d7d4-4b7c-bd21-6f6d5272c837" } }, "outputs": [], "source": [ "y_test_pred = classifier.predict_labels(dists, k=10)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "8cede653-c157-4396-a534-b4a8741251e2" } }, "source": [ "We compute and print the fraction of correctly predicted examples." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "nbpresent": { "id": "445220c9-4974-41a0-a36c-a309d395490b" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Got 141 / 500 correct => accuracy: 0.282000\n" ] } ], "source": [ "num_correct = np.sum(y_test_pred == y_test)\n", "accuracy = float(num_correct) / len(y_test_pred)\n", "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 648x648 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# utility function for plotting confusion matrix\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.metrics import confusion_matrix\n", "\n", "def plot_confmat(y_true, y_pred):\n", " \"\"\"\n", " Plot the confusion matrix and save to user_files dir\n", " \"\"\"\n", " conf_matrix = confusion_matrix(y_true, y_pred)\n", " fig = plt.figure(figsize=(9,9))\n", " ax = fig.add_subplot(111)\n", " sns.heatmap(conf_matrix,\n", " annot=True,\n", " fmt='.0f')\n", " plt.title('Confusion matrix')\n", " ax.set_xticklabels( classes)\n", " ax.set_yticklabels( classes)\n", " plt.ylabel('True')\n", " plt.xlabel('Predicted')\n", " \n", "plot_confmat(y_test, y_test_pred) " ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "df615e0b-aeeb-4074-abef-075af4118640" } }, "source": [ "## Algebra and Performance of Distance Matrix Computation\n" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "04f92811-3067-4a08-8227-ed55c42fed50" } }, "source": [ "To ensure that our vectorized implementation is correct, we make sure that it\n", "agrees with the naive implementation. There are many ways to decide whether\n", "two matrices are similar; one of the simplest is the Frobenius norm. In case\n", "you haven't seen it before, the Frobenius norm of two matrices is the square\n", "root of the squared sum of differences of all elements; in other words, reshape\n", "the matrices into vectors and compute the Euclidean distance between them." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "nbpresent": { "id": "edecc2dc-bbf4-47bb-8902-6910fef3eae0" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference was: 0.000000\n", "Good! The distance matrices are the same\n", "Difference was: 0.000000\n", "Good! The distance matrices are the same\n" ] } ], "source": [ "dists_two = classifier.compute_distances_two_loops(X_test)\n", "dists_one = classifier.compute_distances_one_loop(X_test)\n", "dists_zero = classifier.compute_distances_no_loops(X_test)\n", "\n", "\n", "difference_two_2_one = np.linalg.norm(dists_two - dists_one, ord='fro')\n", "print('Difference was: %f' % (difference_two_2_one, ))\n", "if difference_two_2_one < 0.001:\n", " print('Good! The distance matrices are the same')\n", "else:\n", " print('Uh-oh! The distance matrices are different')\n", "\n", "difference_one_2_zero = np.linalg.norm(dists_one - dists_zero, ord='fro')\n", "print('Difference was: %f' % (difference_one_2_zero, ))\n", "if difference_one_2_zero < 0.001:\n", " print('Good! The distance matrices are the same')\n", "else:\n", " print('Uh-oh! The distance matrices are different')" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "94c6dacb-929f-4378-b80f-4859256bd7f4" } }, "source": [ "Let's compare how fast the implementations are" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbpresent": { "id": "1d3c6b0c-9a33-4f71-b283-0b1eb8061e77" } }, "outputs": [], "source": [ "def time_function(f, *args):\n", " \"\"\"\n", " Call a function f with args and return the time (in seconds) that it took to execute.\n", " \"\"\"\n", " import time\n", " tic = time.time()\n", " f(*args)\n", " toc = time.time()\n", " return toc - tic\n", "\n", "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n", "print('Two loop version took %f seconds' % two_loop_time)\n", "\n", "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n", "print('One loop version took %f seconds' % one_loop_time)\n", "\n", "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n", "print('No loop version took %f seconds' % no_loop_time)\n" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "e55a0c49-3d30-47b3-bbfc-2ba53025a0eb" } }, "source": [ "# 3. k-fold cross validation\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbpresent": { "id": "48a7d639-21bd-4b58-892d-c54a818111aa" } }, "outputs": [], "source": [ "num_folds = 5\n", "\n", "k_choices = [1, 3, 5, 7, 9, 10, 12, 15, 18, 20, 50, 100]\n", "\n", "X_train_folds = []\n", "y_train_folds = []" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "8b1aa44f-7099-4511-8b20-168c0f37edb9" } }, "source": [ "Split up the training data into folds. After splitting, `X_train_folds` and \n", "`y_train_folds` should each be lists of length `num_folds`, where \n", "`y_train_folds[i]` is the label vector for the points in `X_train_folds[i]`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbpresent": { "id": "ee7f2e26-fa37-45b0-af4c-c225369eedc2" } }, "outputs": [], "source": [ "num_train = X_train.shape[0]\n", "fold_size = np.ceil(num_train/num_folds).astype('int')" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "235c4927-a8f9-475f-83c4-54fb4b1de699" } }, "source": [ "In the case of `num_train = 5000` and 5 folds, we obtain \n", "`X_train_folds = np.split(X_train, [1000, 2000, 3000, 4000])`\n", "`y_train_folds = np.split(y_train, [1000, 2000, 3000, 4000])`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbpresent": { "id": "dd9d3e91-fb0d-4ea1-8e37-6282e1eea5f5" } }, "outputs": [], "source": [ "X_train_folds = np.split(X_train, [(i + 1)*fold_size for i in np.arange(num_folds)])\n", "y_train_folds = np.split(y_train, [(i + 1)*fold_size for i in np.arange(num_folds)])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train_folds[1].shape" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "99d20b22-bc30-49c6-85a1-86f153b21fe0" } }, "source": [ "A dictionary holding the accuracies for different values of $k$ that we find\n", "when running cross-validation. After running cross-validation,\n", "`k_to_accuracies[k]` should be a list of length `num_folds` giving the different\n", "accuracy values that we found when using that value of $k$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbpresent": { "id": "a14b3164-b63a-49eb-980e-57c74b2304db" } }, "outputs": [], "source": [ "k_to_accuracies = {}" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "369cc408-fb92-4899-9e37-92c02a9ef4c1" } }, "source": [ "We perform $k$-fold cross validation to find the best value of $k$. For each \n", "possible value of $k$, run the $k$-nearest-neighbor algorithm `num_folds` times, \n", "where in each case you use all but one of the folds as training data and the \n", "last fold as a validation set. Store the accuracies for all fold and all \n", "values of k in the `k_to_accuracies` dictionary. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbpresent": { "id": "6c869757-5e74-48cc-b7ef-14246b832a99" } }, "outputs": [], "source": [ "for k in k_choices:\n", " \n", " k_to_accuracies[k] = []\n", " classifier = KNearestNeighbor()\n", " for i in range(num_folds):\n", " X_cv_training = np.concatenate([x for k, x in enumerate(X_train_folds) if k!=i], axis=0)\n", " y_cv_training = np.concatenate([x for k, x in enumerate(y_train_folds) if k!=i], axis=0)\n", " classifier.train(X_cv_training, y_cv_training)\n", " dists = classifier.compute_distances_no_loops(X_train_folds[i])\n", " y_test_pred = classifier.predict_labels(dists, k=k)\n", " k_to_accuracies[k].append(np.mean(y_train_folds[i] == y_test_pred))\n", " \n" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "c10d6b24-607c-470b-bffd-614c8fa0be2c" } }, "source": [ "We print out the computed accuracies." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbpresent": { "id": "d7c42393-850e-4329-91db-5c052fe247e0" } }, "outputs": [], "source": [ "for k in sorted(k_to_accuracies):\n", " for accuracy in k_to_accuracies[k]:\n", " print('k = %d, accuracy = %f' % (k, accuracy))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We plot the raw observations." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbpresent": { "id": "e81573f1-9d05-44e2-a581-ffa01100b7af" } }, "outputs": [], "source": [ "for k in k_choices:\n", " accuracies = k_to_accuracies[k]\n", " plt.scatter([k] * len(accuracies), accuracies)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "21f79bed-12f0-4e15-abdd-1105b4467cf0" } }, "source": [ " We plot the trend line with error bars that correspond to standard deviation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbpresent": { "id": "c9af79e8-2cfa-42ed-84fe-efbdadcf65fd" } }, "outputs": [], "source": [ "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n", "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n", "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n", "plt.title('Cross-validation on k')\n", "plt.xlabel('k')\n", "plt.ylabel('Cross-validation accuracy')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "301c698f-4817-4bc5-8e35-ee37caebacba" } }, "source": [ " # K-Nearest Neighbor with L1 distance" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbpresent": { "id": "ce60718f-a584-4026-8071-292b5943eca4" } }, "outputs": [], "source": [ "class KNearestNeighbor_L1(KNearestNeighbor):\n", " \"\"\" a kNN classifier with L1 distance \"\"\"\n", "\n", " def __init__(self):\n", " super().__init__()\n", " \n", "\n", " def compute_distances_one_loop(self, X):\n", " \"\"\"\n", " We overwrite the compute_distance_one_loop method of the parent class \n", " KNearestNeighbor. \n", " Compute the distance between each test point in X and each training point\n", " in self.X_train using one loop and the L1 distance measure.\n", "\n", " Input / Output: Same as compute_distances_two_loops\n", " \"\"\"\n", " num_test = X.shape[0]\n", " num_train = self.X_train.shape[0]\n", " dists = np.zeros((num_test, num_train))\n", " X = X.astype('float')\n", " for i in range(num_test):\n", " dists[i, :] = (np.sum(np.abs(self.X_train - X[i,:]), axis = 1))\n", " \n", " \n", " return dists\n", " " ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "d5745a61-1071-4704-8b71-6c0d175de9fc" } }, "source": [ "We create an instance nn form the class `KNearestNeighbor_L1`" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "nbpresent": { "id": "235c3d13-a428-4dae-a286-6ea912f8a0b2" } }, "outputs": [], "source": [ "classifier = KNearestNeighbor_L1()" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "94df5594-5eff-4354-bc83-889aca850336" } }, "source": [ "Call the method train of the `KNearestNeighbor` class" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "nbpresent": { "id": "627b4ca8-b0df-473d-8e53-3bcc2e31acd8" } }, "outputs": [], "source": [ "classifier.train(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "b96d32ad-0526-4a52-a91e-dffb4a9e634a" } }, "source": [ "We test our implementation with one loop." ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "nbpresent": { "id": "f6ecd69e-e8b4-44a5-8ec1-8aeb47fbc5b5" } }, "outputs": [ { "data": { "text/plain": [ "(500, 5000)" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dists = classifier.compute_distances_one_loop(X_test)\n", "dists.shape " ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "cd4c75ed-d9f1-4f3f-8990-2259f4f2f0d5" } }, "source": [ " Let us now predict labels and run the code below: We use $k = 10$" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "nbpresent": { "id": "606e2720-6672-45f3-ae46-761df5c2066d" } }, "outputs": [], "source": [ "y_test_pred = classifier.predict_labels(dists, k=10)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "3408b28c-0781-4186-b1cc-f8d0040ecf8f" } }, "source": [ "We compute and print the fraction of correctly predicted examples." ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "nbpresent": { "id": "1919eb5a-988f-4bee-a646-d110372bbca6" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Got 139 / 500 correct => accuracy: 0.278000\n" ] } ], "source": [ "num_correct = np.sum(y_test_pred == y_test)\n", "accuracy = float(num_correct) / len(y_test_pred)\n", "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The confusion matrix looks as follows:" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 648x648 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# utility function for plotting confusion matrix\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.metrics import confusion_matrix\n", "\n", "def plot_confmat(y_true, y_pred):\n", " \"\"\"\n", " Plot the confusion matrix and save to user_files dir\n", " \"\"\"\n", " conf_matrix = confusion_matrix(y_true, y_pred)\n", " fig = plt.figure(figsize=(9,9))\n", " ax = fig.add_subplot(111)\n", " sns.heatmap(conf_matrix,\n", " annot=True,\n", " fmt='.0f')\n", " plt.title('Confusion matrix')\n", " ax.set_xticklabels( classes)\n", " ax.set_yticklabels( classes)\n", " plt.ylabel('True')\n", " plt.xlabel('Predicted')\n", " \n", "plot_confmat(y_test, y_test_pred) " ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "09892b80-b73f-41f3-8671-04ebc8f58ece" } }, "source": [ "# k-fold cross validation" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "nbpresent": { "id": "4d4d5599-4959-4aa9-8250-7ccd99c0eef6" } }, "outputs": [], "source": [ "num_folds = 5\n", "\n", "k_choices = [1, 3, 5, 7, 9, 10, 12, 15, 18, 20, 50, 100]\n", "\n", "X_train_folds = []\n", "y_train_folds = []" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "eeb5aeda-0ce5-4581-9fbe-e7605376384a" } }, "source": [ "We Split up the training data into folds. After splitting, `X_train_folds` and \n", "`y_train_folds` should each be lists of length `num_folds`, where \n", "`y_train_folds[i]` is the label vector for the points in `X_train_folds[i]` " ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "nbpresent": { "id": "50f9138b-3378-411f-96a1-5e3fe013e396" } }, "outputs": [], "source": [ "num_train = X_train.shape[0]\n", "fold_size = np.ceil(num_train/num_folds).astype('int')" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "267d72f8-6485-4abd-a1d7-d26b4be5a6cc" } }, "source": [ " In the case of `num_train = 5000` and 5 folds, we obtain \n", "`X_train_folds = np.split(X_train, [1000, 2000, 3000, 4000])`\n", "`y_train_folds = np.split(y_train, [1000, 2000, 3000, 4000])`" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "nbpresent": { "id": "ca3a1d8c-4b8a-42d6-94e7-793e87cebdea" } }, "outputs": [], "source": [ "X_train_folds = np.split(X_train, [(i + 1)*fold_size for i in np.arange(num_folds)])\n", "y_train_folds = np.split(y_train, [(i + 1)*fold_size for i in np.arange(num_folds)])\n" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "e1be1d21-0776-4587-9b88-d38e804eab71" } }, "source": [ "A dictionary holding the accuracies for different values of $k$ that we find\n", "when running cross-validation. After running cross-validation,\n", "`k_to_accuracies[k]` should be a list of length num_folds giving the different\n", "accuracy values that we found when using that value of $k$." ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "nbpresent": { "id": "05e1ac10-1a25-4740-a21b-8b067116fd69" } }, "outputs": [], "source": [ "k_to_accuracies = {}" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "f97b560b-929b-4a1f-90ee-3cf17ecef7e6" } }, "source": [ "We perform $k$-fold cross validation to find the best value of $k$. For each \n", "possible value of $k$, run the $k$-nearest-neighbor algorithm `num_folds` times, \n", "where in each case you use all but one of the folds as training data and the \n", "last fold as a validation set. Store the accuracies for all fold and all \n", "values of $k$ in the `k_to_accuracies` dictionary. " ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "nbpresent": { "id": "bc2a21b5-4387-4bfc-8851-abcf62acaafc" } }, "outputs": [], "source": [ "for k in k_choices:\n", " \n", " k_to_accuracies[k] = []\n", " classifier = KNearestNeighbor_L1()\n", " for i in range(num_folds):\n", " X_cv_training = np.concatenate([x for k, x in enumerate(X_train_folds) if k!=i], axis=0)\n", " y_cv_training = np.concatenate([x for k, x in enumerate(y_train_folds) if k!=i], axis=0)\n", " classifier.train(X_cv_training, y_cv_training)\n", " dists = classifier.compute_distances_one_loop(X_train_folds[i])\n", " y_test_pred = classifier.predict_labels(dists, k=k)\n", " k_to_accuracies[k].append(np.mean(y_train_folds[i] == y_test_pred))\n", " \n" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "c24db8cd-04a8-45a6-b15e-24194bb42248" } }, "source": [ "We print out the computed accuracies." ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "nbpresent": { "id": "972c66f2-03ea-4de0-8ac6-a564c3365f50" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "k = 1, accuracy = 0.291000\n", "k = 1, accuracy = 0.313000\n", "k = 1, accuracy = 0.294000\n", "k = 1, accuracy = 0.275000\n", "k = 1, accuracy = 0.308000\n", "k = 3, accuracy = 0.269000\n", "k = 3, accuracy = 0.299000\n", "k = 3, accuracy = 0.290000\n", "k = 3, accuracy = 0.278000\n", "k = 3, accuracy = 0.296000\n", "k = 5, accuracy = 0.275000\n", "k = 5, accuracy = 0.311000\n", "k = 5, accuracy = 0.301000\n", "k = 5, accuracy = 0.314000\n", "k = 5, accuracy = 0.309000\n", "k = 7, accuracy = 0.280000\n", "k = 7, accuracy = 0.329000\n", "k = 7, accuracy = 0.313000\n", "k = 7, accuracy = 0.320000\n", "k = 7, accuracy = 0.313000\n", "k = 9, accuracy = 0.291000\n", "k = 9, accuracy = 0.314000\n", "k = 9, accuracy = 0.310000\n", "k = 9, accuracy = 0.322000\n", "k = 9, accuracy = 0.315000\n", "k = 10, accuracy = 0.289000\n", "k = 10, accuracy = 0.312000\n", "k = 10, accuracy = 0.320000\n", "k = 10, accuracy = 0.323000\n", "k = 10, accuracy = 0.313000\n", "k = 12, accuracy = 0.295000\n", "k = 12, accuracy = 0.320000\n", "k = 12, accuracy = 0.324000\n", "k = 12, accuracy = 0.332000\n", "k = 12, accuracy = 0.318000\n", "k = 15, accuracy = 0.287000\n", "k = 15, accuracy = 0.324000\n", "k = 15, accuracy = 0.317000\n", "k = 15, accuracy = 0.319000\n", "k = 15, accuracy = 0.321000\n", "k = 18, accuracy = 0.289000\n", "k = 18, accuracy = 0.321000\n", "k = 18, accuracy = 0.307000\n", "k = 18, accuracy = 0.319000\n", "k = 18, accuracy = 0.306000\n", "k = 20, accuracy = 0.287000\n", "k = 20, accuracy = 0.327000\n", "k = 20, accuracy = 0.309000\n", "k = 20, accuracy = 0.307000\n", "k = 20, accuracy = 0.306000\n", "k = 50, accuracy = 0.285000\n", "k = 50, accuracy = 0.301000\n", "k = 50, accuracy = 0.294000\n", "k = 50, accuracy = 0.290000\n", "k = 50, accuracy = 0.293000\n", "k = 100, accuracy = 0.283000\n", "k = 100, accuracy = 0.285000\n", "k = 100, accuracy = 0.279000\n", "k = 100, accuracy = 0.285000\n", "k = 100, accuracy = 0.277000\n" ] } ], "source": [ "for k in sorted(k_to_accuracies):\n", " for accuracy in k_to_accuracies[k]:\n", " print('k = %d, accuracy = %f' % (k, accuracy))\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "57f5291f-1e32-456f-b84f-76eba7b40d44" } }, "source": [ "We plot the raw observations." ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "nbpresent": { "id": "a028040f-a7a6-4b61-904d-48090dcbbe8d" } }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "for k in k_choices:\n", " accuracies = k_to_accuracies[k]\n", " plt.scatter([k] * len(accuracies), accuracies)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "6a867f1e-9207-4d0d-adf9-7884532ed06e" } }, "source": [ "We plot the trend line with error bars that correspond to standard deviation." ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "nbpresent": { "id": "caf9f446-5155-42db-a69b-46f1d7f06322" } }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n", "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n", "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n", "plt.title('Cross-validation on k')\n", "plt.xlabel('k')\n", "plt.ylabel('Cross-validation accuracy')\n", "plt.show()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }