pw04/MLP_HOG-p7o8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X_wPegydbafH"
   },
   "source": [
    "# Computing features to train a MLP\n",
    "This notebook will guide you through the use of the `keras` package to train a multilayer perceptron for handwritten digits classification. You are going to computing the histogram of gradients from the images in the `mnist` dataset (LeCun et al. 1998)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "61oGWRyMbafJ"
   },
   "source": [
    "## Loading the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "duI9Ar-8bafS"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-30 23:07:42.512607: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-04-30 23:07:42.928350: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.datasets import mnist\n",
    "from keras.layers import Dropout\n",
    "from keras.layers.core import Dense\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.utils import np_utils, plot_model\n",
    "from matplotlib import pyplot as pl\n",
    "from skimage import exposure\n",
    "from skimage.feature import hog\n",
    "from sklearn import metrics as me\n",
    "\n",
    "from utils import plot_confusion_matrix\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KpPmUqiRbafz"
   },
   "source": [
    "Load the `mnist` dataset and normalize in the range [0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "id": "verKKFaFbaf6",
    "outputId": "29e842da-35fa-49b6-a7f7-f35d9e8a71b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "n_train, height, width = X_train.shape\n",
    "n_test, _, _ = X_test.shape\n",
    "\n",
    "X_train = X_train.reshape(n_train, height, width, 1).astype('float32')\n",
    "X_test = X_test.reshape(n_test, height, width, 1).astype('float32')\n",
    "\n",
    "X_train /= 255.0\n",
    "X_test /= 255.0\n",
    "\n",
    "n_classes = 10\n",
    "\n",
    "print(n_train, 'train samples')\n",
    "print(n_test, 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "Y_train = np_utils.to_categorical(y_train, n_classes)\n",
    "Y_test = np_utils.to_categorical(y_test, n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TazeB7LLbagE"
   },
   "source": [
    "### An example of how to compute the histogram of gradients\n",
    "Let's compute the HOG for one image in the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "cL-609oKbagI"
   },
   "outputs": [],
   "source": [
    "n_orientations = 8\n",
    "pix_p_cell = 7\n",
    "hog_size = int(height * width * n_orientations / (pix_p_cell * pix_p_cell))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "r2DA2Da_bage"
   },
   "outputs": [],
   "source": [
    "fd, hog_image = hog(X_test[0,:,:,0], orientations=n_orientations, pixels_per_cell=(pix_p_cell, pix_p_cell), cells_per_block=(1, 1), visualize=True)\n",
    "hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s2Ow4uJtbago"
   },
   "source": [
    "Show the image and the corresponding gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "id": "83RZeljKbagu",
    "outputId": "e1507d91-5cd9-484c-e0bc-0673b046a274"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = pl.subplots(1, 2, figsize=(12, 6), sharex=True, sharey=True)\n",
    "ax1.axis('off')\n",
    "ax1.imshow(X_test[0,:,:,0], cmap=pl.get_cmap('Greys'), interpolation='nearest')\n",
    "ax1.set_title('Input image')\n",
    "ax1.set_adjustable('box')\n",
    "\n",
    "ax2.axis('off')\n",
    "ax2.imshow(hog_image_rescaled, cmap=pl.get_cmap('Greys'), interpolation='nearest')\n",
    "ax2.set_title('Histogram of Oriented Gradients')\n",
    "ax1.set_adjustable('box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Yv-ep9ybag4"
   },
   "source": [
    "Compute the HOGs for the train and test datasets\n",
    "\n",
    "> Indented block\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "id": "y8Q3cdrebag6",
    "outputId": "4c21e6ce-56d2-4287-eab6-634cda1ad94b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train done\n",
      "X_test done\n"
     ]
    }
   ],
   "source": [
    "X_train_hog = np.zeros((X_train.shape[0], hog_size))\n",
    "X_test_hog = np.zeros((X_test.shape[0], hog_size))\n",
    "\n",
    "for i in np.arange(X_train_hog.shape[0]):\n",
    "    X_train_hog[i,:] = hog(X_train[i,:,:,0], orientations=n_orientations, pixels_per_cell=(pix_p_cell, pix_p_cell), cells_per_block=(1, 1), visualize=False)\n",
    "print('X_train done')\n",
    "\n",
    "for i in np.arange(X_test_hog.shape[0]):\n",
    "    X_test_hog[i,:] = hog(X_test[i,:,:,0], orientations=n_orientations, pixels_per_cell=(pix_p_cell, pix_p_cell), cells_per_block=(1, 1), visualize=False)\n",
    "print('X_test done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kos0aTYmbahU"
   },
   "source": [
    "Create the MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "id": "NnawtqztbahV",
    "outputId": "c99078e7-4754-41c7-dbe9-e953931c5f6c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-30 23:07:59.299392: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:07:59.309118: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:07:59.309167: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:07:59.311037: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:07:59.311078: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:07:59.311108: I tensorflow/compile"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 256)               33024     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                2570      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 35,594\n",
      "Trainable params: 35,594\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "r/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:07:59.924871: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:07:59.924930: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:07:59.924937: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1722] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.\n",
      "2023-04-30 23:07:59.924970: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:07:59.924991: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21568 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:0c:00.0, compute capability: 8.6\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([\n",
    "    Dense(256, input_shape=(hog_size,), activation='relu'),\n",
    "    Dropout(0.4),\n",
    "    Dense(n_classes, activation='softmax'),\n",
    "])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n6XDaJS8vD5GRkViyZAmmTJli6XDaxKLJcMCAAZJr1Q96rV692pLhMsbM7I033mjxeYYDBw7EuXPncOTIEVRUVFggOtMoLCxEXl4eTpw40a6eq9YgISEBa9aswZo1ayTTmj7PsPntJS3J4r8M9+3bp9O1OC4uDsD9MStNy2NiYiwcacfVli8aq1atMsqytPe/zMrKwo0bNyAIApYvX26Uth91vO3+z7Rp03TOf33d9gGge/fuOHjwINzc3Mwcoen4+Pjg9OnT6Nu3r6VDabd169a1+Iuw6fMMichqOmp1rDv0snYhPePVTGXBggV6e5Wx1vG2Y8xyLJoMDblT+5dffmnCSBhjjNkyi18mZYwxxiyNkyFjjDGbx8mQMcaYzeNkyBhjzOY90r1JY2JieEgGa1VHf4pDR8HnI7Nmj3QynDdvntU+oJRZ3kcffQQAeOuttywcyaMvJiaGz0dmNKb4UvVIJ8Nhw4Zh8uTJlg6DWal9+/YBAB8jZhATE8PnIzMaUyRD/s+QMcaYzeNkyBhjzOZZTTLcsWMHBEEQH7o5ZswYCILQ4j0JGWOMMWOxmmT46quv6ty8VfvSd7d6xhhjzJisJhlaKxcXF8kTHjZs2GDpsNrlUVoX9mjbtWuXznHa0pfiX3/9FRMmTEBFRQWKi4t15hk4cCBqa2sl8zSvJwgCBg0aZOpVMpvDhw+jV69ecHBouX/k7du3sXnzZowcORKenp5QKBTo2bMnpk6diqysLL3zNDQ0YNu2bXj66aehVqvh4eGBoKAgbNy4EXfv3tWpu3jxYiQlJeltp+kjnARBwNChQ9u/skbEybAVVVVVuHDhAgAgIiICRNRhnyzwKK0Lsw2fffZZi49wyszMxKBBgxAWFgY3Nzd4eXmBiJCRkSFOnzdvnmQ+bb309HSo1WoQEc6dO2fydTG13NxcTJgwAUuWLEFhYeED6y5cuBBz5sxBREQELl++jJKSEmzfvh2ZmZkICgpCSkqKZJ7XXnsNsbGxGDVqFK5cuYKff/4ZMTExmDNnDiZNmqRTd+bMmViyZAlWrFghaafpI5zs7e0fbqWNiJPhI8bFxQXDhw+3dBisnSy9/yy9/LaqqKjA+PHjMWnSJMyePVsyXS6XQ61WIyEhAXv37rVAhOa3YsUKPPPMMzh//jxcXV1brT9jxgzMnTsXPj4+cHZ2RnBwMPbs2YN79+5h0aJFOnXz8vKwa9cuDBw4EGvXrkXnzp2hVquxaNEiPP/88zh48KD4JQQAAgMDceDAAaxZswbJyclGX1dT4GTIGOtw1q9fj4KCAqxcuVLvdCcnJ+zevRt2dnaIi4vDtWvXzByh+W3btg2LFy9+4OVRrcTERLGzYlMajQYKhQK5ubk6z0H9/fffAQBPPPGEZJ4+ffoAAH777TdJW1FRUXj77bfR0NBg0LpYAidDxliHQkRITEzEkCFD4Ovr22K98PBwLF++HJWVlYiOjtb7/+GjRKFQPHQb1dXVqKmpQb9+/XRuU9inTx/IZDLk5ORI5snJyYEgCOjfv79k2sSJE3H9+nUcOnTooWMzNU6G7ZSSkqLzJ/Avv/yCmJgYuLu7Q61WY9y4ccjNzRXrb9iwQazbtWtXZGRkIDQ0FK6urnB2dsaIESNw5swZsf7q1avF+k0vWx09elQs9/LykrRfXV2NM2fOiHXa8i2xJQ0NDUhKSsLzzz8PHx8fKBQK9O/fH5988gkaGxsBAGVlZZLOCKtXrxbnb1oeFRUltl1UVIT4+Hh0794djo6O6NSpEyIjI3Ue+Nx8G1+9ehWTJ0+GWq0Wy4qLi9u9fu1VUlKC+fPnIzAwEI6OjvDw8MCYMWNw8uRJsY6x919HPH5MJSsrC4WFhdBoNK3WfffddxEWFobs7GzMmTOnTe23Zf8aev5rteW4tyTtXZmWLVumU+7t7Y0NGzYgKysLS5cuRVFREUpLS7F+/Xp89913WLlyJXr16iVpb8CAAQCAb7/91vTBPywyoqioKIqKijJmk+0GgJKSkozS1oULFwgARURESKZFRESI09LS0qiqqoqOHz9OCoWCBg8eLKmv0WhIqVTSsGHDxPoZGRn01FNPkaOjI506dUqnvlKppGeffVbSTlBQEKnVakl5S/Xbsi7NpaamEgBau3YtlZaWUlFREf3jH/8gOzs7WrBggU7d8PBwsrOzo59//lnSzrBhw2j37t3i+/z8fOrWrRt5e3vToUOHqLKyki5evEghISHk5OREaWlpOvNrt3FISAidPHmSqqur6ezZs2Rvb09FRUWtrkdL2nO83rx5kwICAsjb25tSU1OpvLycrl69SpGRkSQIAm3dulWnvrH3n6WPnxEjRpCnpyelp6e3WEcfQ8/HnTt3EgD67LPPWpy2du1avfNmZGSQSqUS3xcVFZG/vz8BoF27donl6enpkm1g6P415Pw39Lh/GH5+fmRvb2/QPAUFBeTt7U2xsbEt1klOTqauXbsSAAJAXl5etG3bthbrl5eXEwAKDg7WO93e3p6GDBliUJxExv18/69kToZt0JZkmJqaqlMeFRVFACQf1hqNhgDQhQsXdMqzs7MJAGk0Gp1ySyfD5557TlI+bdo0kslkVF5eLpZ9++23BIDefPNNnbqnT58mPz8/unv3rlj2yiuvEACdBEl0/4NILpdTUFCQTrl2Gx8+fLjVmA3RnuP11VdfJQC0d+9enfLa2lry9fUlhUJBBQUFYrkpkqElj5+QkBDy8PAw+IPbmMlw/fr1BIA2bdqkd97myZDofuKTyWSkVCrpypUrYlnzbWDo/jXk/Df0uH8YhibD4uJiGjBgAMXExFBDQ4NkemNjI82cOZNkMhl9+OGHVFBQQEVFRZSQkEAKhYJiYmKovr5eb9uCIFCPHj30TrOmZMiXSY1k8ODBOu/9/f0BAPn5+ZK6SqVSvHyg1b9/f/j6+iIrKws3b940XaAGGDdunM6lIS2NRoP6+npcunRJLAsLC0P//v2xY8cOlJSUiOUffPAB5syZA5lMJpalpKTAzs4O48aN02nXx8cHffv2xfnz53H9+nXJcp9++mljrNZDOXDgAABg7NixOuVyuRyhoaGoqakx+SUhSx4/p06dQmlpqUWfPqH976/pMdWaoUOHYsOGDaiurkZ0dDRqamr01mvv/m3L+d/e497UqqurER4ejieffBK7d+/WO9xh586d2Lp1K15//XW89dZb8Pb2hpeXF2bNmiWOKdy4caPe9h0cHFrc3taEk6GRqFQqnfeOjo4AIP631pS7u7veNjp37gwAuHXrlpGja5/y8nKsXLkS/fv3h4eHh/j/yMKFCwEAd+7c0ak/b9483LlzB59++ikA4Nq1a/j+++8xa9YssU5dXR3Ky8vR2NgIlUol+b/xhx9+AAD89NNPkniUSqWpVrVNtLE7OTnp7bru7e0NACgoKDBpHB3l+DEVJycnAEB9fb1B88XHxyMmJgYXL17UOxzjYfZva+f/wxz3ptTQ0IDo6Gj4+fnh888/b3Hc39GjRwEAo0aNkkwLDQ0FABw5cqTFZRijc4+pcTK0gJKSEp1uy1raDzHthxoA2NnZSe7uANzvuKKPMR9UO378eLz33nuYOXMmrl27hsbGRhCR+BzA5uswdepUeHt7Y+PGjairq8Pf//53vPLKK/Dw8BDryOVyuLu7w8HBAfX19XpvwUdEGDFihNHWw1jkcjlUKhVqa2tRWVkpma4d6Ozj4yOWmWL/dZTjx1S6dOkC4P6XNUMlJiaid+/e2L59O3bu3KkzrT37t62s9biPi4tDXV0dkpOTdTpL9ejRA2fPnhXfV1dXt9qWvhsjVFRUgIjEfWbNOBlaQG1trc4AVQD48ccfkZ+fD41Go3PgdOnSBTdu3NCpW1BQIBnTo+Xs7Kzz4de7d29s2bLFoPgcHBxw6dIlnDlzBj4+PoiPj0enTp3ED8qWLnnI5XK8+eabuHXrFv7+979j9+7dmDt3rqReZGQkGhoadHo/aq1btw6PPfaY1Y5LmjhxIgBIuorX1dXhxIkTUCgUCA8PF8tNsf+s/fgxtX79+gFAuy4puri44KuvvoJSqRSvYDRl6P41hLUd96tWrcKlS5fw9ddfQy6XP7DukCFDAAAnTpyQTPv+++8BQO9t1bTHnnafWTNOhhagUqmwdOlSpKeno7q6GufOncO0adPg6OiITz75RKduWFgY8vPzsXHjRlRVVSE3Nxdz587V+fbf1B/+8Adcu3YNv//+O9LT05GXl4fg4GCDY7S3t8dzzz2HgoICfPDBByguLkZNTQ1OnjyJzZs3tzjfm2++CYVCgeXLl2PUqFHo0aOHpM7f/vY3BAYGYsaMGThy5AjKy8tRWlqKhIQE/PWvf8WGDRussks/cD/2gIAAzJs3DwcPHkRlZSWuXbuGl19+GTdv3sQnn3wiXk4DTLP/LHn8jBw5Emq1WudXg7lpNBp07ty5xXtotqZv3756B5wDhu9fQxh63E+bNg2CIOA///lPu5b3IDt27MBf/vIX/Pvf/4arq6vksm3zYSFvvvkmevbsic8++wz/+Mc/cOvWLZSUlGDbtm14//334efnp/fWjtohI2FhYUZfB6MzZnecR7E3qVKpFLsRa18ffPABpaenS8qXLVsmLrvpa+zYsWJ7Go2G/Pz86PLlyxQeHk6urq6kUCgoJCSETp8+LVl+WVkZxcbGUpcuXUihUNDw4cMpIyODgoKCxPbfeecdsX5OTg4FBweTUqkkf39/nR53+talpdeVK1eoqKiI4uLiyN/fn2QyGXl7e9Orr75KixcvFuvp6wE3c+ZMAkD/8z//0+J2LSkpofnz59Pjjz9OMpmMOnXqRGFhYXT8+HGxjr5tbMxDtr3Ha3FxMc2bN48CAgJIJpORSqWi8PBwOnHihKSuMfcfkWWPHyKi4OBgi/cmJSJaunQpOTg40I0bN8SyoqIiybHyoB6ab7zxht4etW3Zv+09/9ty3GuNHDmSXFxc9Pbu1Ec7FErfq/mQkLFjx7b6GdB8+ExpaSktXLiQ+vTpQ3K5nBwdHSkwMJBmz56t08O2qejoaElv8qasqTcpJ0Mz036YPcq2b99u1G7ipmJNx2tbddTjx9jJsKysjPz8/CguLs5YIVqV27dvk0KheOCYP2uXmZlJgiBIhqk0ZU3JkC+TMqPbvHkz5s+fb+kw2CNMpVIhNTUV+/fvx6ZNmywdjlEREeLj4+Hm5ob33nvP0uG0S15eHiIjI7FkyRJMmTLF0uG0CSdD9tASExMxceJEVFVVYfPmzbh9+zYmT55s6bDYI+CNN95o8XmGAwcOxLlz53DkyBFUVFRYIDrTKCwsRF5eHk6cONGunqvWICEhAWvWrMGaNWsk05o+z/DevXsWiE4/ToZmor33Y1ZWFm7cuAFBELB8+XJLh2U0KSkp8PDwwGeffYYvv/zSajvAdFSP+vHT3LRp03SGHOjrtg8A3bt3x8GDB+Hm5mbmCE3Hx8cHp0+fRt++fS0dSrutW7euxV+ETZ9nSEQW7YzVFH9imcmCBQse2QfpxsbGIjY21tJhPNIe5eOHMWvAvwwZY4zZPE6GjDHGbB4nQ8YYYzaPkyFjjDGbZ/QONNevX0dycrKxm22X9PR0S4fArJj23pbWcrw+6vh8ZNZM+O9ofqOIjo7G/v37jdUcY4wxpldSUpIxxzPvM2oyZIwZJjk5GTExMXofycQYM5t9/J8hY4wxm8fJkDHGmM3jZMgYY8zmcTJkjDFm8zgZMsYYs3mcDBljjNk8ToaMMcZsHidDxhhjNo+TIWOMMZvHyZAxxpjN42TIGGPM5nEyZIwxZvM4GTLGGLN5nAwZY4zZPE6GjDHGbB4nQ8YYYzaPkyFjjDGbx8mQMcaYzeNkyBhjzOZxMmSMMWbzOBkyxhizeZwMGWOM2TxOhowxxmweJ0PGGGM2j5MhY4wxm8fJkDHGmM3jZMgYY8zmcTJkjDFm8zgZMsYYs3mcDBljjNk8ToaMMcZsHidDxhhjNo+TIWOMMZvnYOfoVYUAACAASURBVOkAGLMVt27dwr/+9S+dsuzsbADAunXrdMo9PT0xc+ZMs8XGmK0TiIgsHQRjtqChoQE+Pj64ffs2ZDJZi/Xq6uoQFxeHzZs3mzE6xmzaPr5MypiZODg44KWXXoK9vT3q6upafAHAyy+/bOFoGbMtnAwZM6OXXnoJ9fX1D6zj4+OD4cOHmykixhjAyZAxsxo2bBi6du3a4nRHR0dMnz4ddnZ8ajJmTnzGMWZGgiBg2rRpLf5nePfuXbz00ktmjooxxsmQMTN70KXSxx9/HAMHDjRzRIwxToaMmdlTTz2F3r17S8odHR3xyiuvWCAixhgnQ8YsYPr06ZJLpXfv3sWUKVMsFBFjto2TIWMWMG3aNDQ0NIjvBUGARqNBr169LBgVY7aLkyFjFtCtWzf84Q9/gCAIAAB7e3u+RMqYBXEyZMxC/vSnP8He3h4AcO/ePUyePNnCETFmuzgZMmYhkydPRmNjIwRBwLPPPgs/Pz9Lh8SYzeJkyJiF+Pj4ICQkBETEl0gZszCT36hb+58IY4wx1h5RUVHYt2+fKRexzyyPcJo3bx6GDRtmjkW1KD09HR9//DGSkpIsGgfrWGJiYkx6/NbU1GDLli2YO3euSdrvKPj8ZC356KOPzLIcs/wyTEpKsnjngOTkZMTExICfWMUMYY7jNz8/H76+viZrvyPg85O1JDo6GgBM/suQ/zNkzMJsPREyZg04GTLGGLN5nAwZY4zZPE6GjDHGbB4nww7kyy+/hCAIEAQBTk5Olg6HMavx66+/YsKECaioqEBxcbF4ngiCgIEDB6K2tlYyT/N6giBg0KBBFojeNA4fPoxevXrBwaHlQQO3b9/G5s2bMXLkSHh6ekKhUKBnz56YOnUqsrKy9M7T0NCAbdu24emnn4ZarYaHhweCgoKwceNG3L17V6fu4sWLO0wPYU6GHciUKVNARAgNDbV0KMxAVVVV6NmzJ8aNG2fpUB45mZmZGDRoEMLCwuDm5gYvLy8QETIyMsTp8+bNk8ynrZeeng61Wg0iwrlz58wdvtHl5uZiwoQJWLJkCQoLCx9Yd+HChZgzZw4iIiJw+fJllJSUYPv27cjMzERQUBBSUlIk87z22muIjY3FqFGjcOXKFfz888+IiYnBnDlzMGnSJJ26M2fOxJIlS7BixQqjrqMpcDJkzAyICI2NjWhsbLR0KK1ycXHB8OHDLR1Gm1RUVGD8+PGYNGkSZs+eLZkul8uhVquRkJCAvXv3WiBC81uxYgWeeeYZnD9/Hq6urq3WnzFjBubOnQsfHx84OzsjODgYe/bswb1797Bo0SKdunl5edi1axcGDhyItWvXonPnzlCr1Vi0aBGef/55HDx4UPwSAgCBgYE4cOAA1qxZg+TkZKOvqzFxMmTMDFxdXZGbm4vDhw9bOpRHyvr161FQUICVK1fqne7k5ITdu3fDzs4OcXFxuHbtmpkjNL9t27Zh8eLFD7w8qpWYmIiEhARJuUajgUKhQG5urs7Yz99//x0A8MQTT0jm6dOnDwDgt99+k7QVFRWFt99+W+exZdaGkyFjrEMiIiQmJmLIkCEPHKsZHh6O5cuXo7KyEtHR0Xr/P3yUKBSKh26juroaNTU16Nevn84tNfv06QOZTIacnBzJPDk5ORAEAf3795dMmzhxIq5fv45Dhw49dGymwsnQiuXk5ODFF1+ESqWCUqlEcHAwTp8+3WL9oqIixMfHo3v37nB0dESnTp0QGRmJzMxMsU5KSopOh4FffvkFMTExcHd3h1qtxrhx45Cbm6vTbl1dHVauXIk+ffrA2dkZnp6eGD9+PL755hvcu3fP4BhsTfNtrv0wNnRfbNiwQazbtWtXZGRkIDQ0FK6urnB2dsaIESNw5swZsf7q1avF+k0vex49elQs9/LykrRfXV2NM2fOiHXa8gvDErKyslBYWAiNRtNq3XfffRdhYWHIzs7GnDlz2tR+SUkJ5s+fj8DAQDg6OsLDwwNjxozByZMnxTrtOZ8A6z9PtHd7WbZsmU65t7c3NmzYgKysLCxduhRFRUUoLS3F+vXr8d1332HlypV6H1A9YMAAAMC3335r+uDbi0wMACUlJZl6Ma1KSkoiM6yu0fz000/k7u5Ofn5+dOzYMaqsrKTs7GwKCwuj7t27k1wu16mfn59P3bp1I29vbzp06BBVVlbSxYsXKSQkhJycnCgtLU2nfkREBAGgiIgISktLo6qqKjp+/DgpFAoaPHiwTt3Y2FhSqVR07NgxunPnDhUUFNCCBQsIAJ08ebLdMXQExjx+tdu8pqZGb3lb9gURkUajIaVSScOGDRPrZ2Rk0FNPPUWOjo506tQpnfpKpZKeffZZSTtBQUGkVqsl5S3V1xoxYgR5enpSenp6W1e9Ve05P3fu3EkAaO3atXqnZ2RkkEqlEt8XFRWRv78/AaBdu3aJ5enp6ZLtcPPmTQoICCBvb29KTU2l8vJyunr1KkVGRpIgCLR161ad+obsQ3OeJ35+fmRvb2/QPAUFBeTt7U2xsbEt1klOTqauXbsSAAJAXl5etG3bthbrl5eXEwAKDg42KBYioqioKIqKijJ4PgMlczK0UtHR0QSA9u/fr1N+48YNksvlkmT4yiuvEADavXu3TvnNmzdJLpdTUFCQTrn25E1NTdUpj4qKIgBUVFQklgUEBNAzzzwjibFXr146ydDQGDoCcybDtuwLovvJEABduHBBpzw7O5sAkEaj0Sk3djIMCQkhDw8Po35ot+f8XL9+PQGgTZs26Z3ePBkS3U98MpmMlEolXblyRSxrvh1effVVAkB79+7VKa+trSVfX19SKBRUUFAglhuyD815nhiaDIuLi2nAgAEUExNDDQ0NkumNjY00c+ZMkslk9OGHH1JBQQEVFRVRQkICKRQKiomJofr6er1tC4JAPXr0MHgdzJUM+TKplTp69CiA+/93NOXr66v3MkRKSgrs7OwkXfd9fHzQt29fnD9/HtevX5fMN3jwYJ33/v7+AO7fPFpr9OjRSEtLw6xZs3D27Fnx0ujVq1fx3HPPPXQM7L627AstpVIpXnrS6t+/P3x9fZGVlYWbN2+aLM5Tp06htLTU4k+i0V5ulslkbZ5n6NCh2LBhA6qrqxEdHY2amhq99Q4cOAAAGDt2rE65XC5HaGgoampq9F7ya8s+tNbzpLq6GuHh4XjyySexe/du2NvbS+rs3LkTW7duxeuvv4633noL3t7e8PLywqxZs8QxhRs3btTbvoODQ4vb2xpwMrRCdXV1qKyshJOTE1xcXCTTO3fuLKlfXl6OxsZGqFQqyUDiH374AQDw008/SdpSqVQ67x0dHQFAZwjApk2b8MUXXyAvLw+hoaFwc3PD6NGjxQ+Mh42B3deWfaHl7u6utw3tsXHr1i0jR2d9tDeeqK+vN2i++Ph4xMTE4OLFi3qHY2iPZScnJ71DE7y9vQEABQUFkmmt7UNrPU8aGhoQHR0NPz8/fP7553oTIfB/X9JHjRolmaYd/3zkyJEWl2GMzj2mwsnQCsnlcri6uqK2thZVVVWS6aWlpZL67u7ucHBwQH19PYhI72vEiBHtikcQBEyfPh3fffcdysrKkJKSAiJCZGQkPvzwQ7PEwHSVlJTofdyRNgk2/cJkZ2cnuTMIAJSVleltu6M8kLtLly4AgPLycoPnTUxMRO/evbF9+3bs3LlTZ5pcLodKpUJtbS0qKysl82oHsvv4+Bi8XGs9T+Li4lBXV4fk5GSdDlM9evTA2bNnxffV1dWttqXvM6uiogJEJO4za8TJ0EqNGTMGwP99E9MqLi7G1atXJfUjIyPR0NCg05tQa926dXjsscfaPcbH3d1d7Eotk8nw/PPPi73omnaVNmUMTFdtba3O4GYA+PHHH5Gfnw+NRqPzodOlSxfcuHFDp25BQYFkPJiWs7OzTvLs3bs3tmzZYsTojaNfv34A0K5Lii4uLvjqq6+gVCrx6aefSqZPnDgRACRDAerq6nDixAkoFArJXxhtZW3nyapVq3Dp0iV8/fXXkMvlD6w7ZMgQAMCJEyck077//nsA9y9FN6c9/rT7zCqZ+l9JcAeadvn555/J09NTpzfppUuXKDw8nDp37izpQFNYWEiBgYH0+OOP0+HDh6msrIxKSkpo8+bN5OzsLNkHLXXmeOeddySdM1QqFYWEhFBWVhbV1tZSYWEhrVq1igDQ6tWr2x1DR2DM47e1DjRt2RdE9zvQqFQqCg0NbVNv0tmzZxMA+uc//0mVlZX0888/0+TJk8nPz09vB5rRo0eTSqWi3377jdLS0sjBwYEuX74sTreW3qSNjY3UuXPnFjv76OtA09yuXbsIQKu9SSsqKnR6k27ZskWnviH70NDzZOrUqQSA8vLyWt0mzbXWgeZf//qX2CO0pVfT/Xz79m3q2bMnyWQy+uSTT6iwsJCKi4spMTGRnJ2dyc/Pj/Lz8yXL2bNnDwGgAwcOGLwO3JvUyDpaMiQiunr1Kr344ovk5uYmdtE+ePAghYaGigfqn//8Z7F+SUkJzZ8/nx5//HGSyWTUqVMnCgsLo+PHj4t10tPTJQf7smXLiIgk5WPHjiUioszMTIqLi6MnnniCnJ2dydPTk4YOHUpbt26lxsZGnZjbEkNHYozj98CBA5JtO3Xq1HbtC6L7ydDPz48uX75M4eHh5OrqSgqFgkJCQuj06dOS5ZeVlVFsbCx16dKFFAoFDR8+nDIyMigoKEhs/5133hHr5+TkUHBwMCmVSvL395f01gwODraK3qREREuXLiUHBwe6ceOGWFZUVCTZfg/qofnGG2/o/VJQXFxM8+bNo4CAAJLJZKRSqSg8PJxOnDgh1mnvPjTkPBk5ciS5uLjo7d2pT2pqaouJrfmQkLFjxxqUDImISktLaeHChdSnTx+Sy+Xk6OhIgYGBNHv2bJ0etk1FR0eTn58f3b17t03r0BQnQyPriMmQWZ61HL9NaZPho6S952dZWRn5+flRXFycCaKyvNu3b5NCoXjgmD9rl5mZSYIgSIaptJVNDq0YMGCApHfVg16rV6+2dMiMMQtSqVRITU3F/v37sWnTJkuHY1REhPj4eLi5ueG9996zdDjtkpeXh8jISCxZsgRTpkyxdDgPZFXJELh/GyBq0qsqLi4OwP3uuk3LY2JiLBwpY8waDBw4EOfOncORI0dQUVFh6XCMprCwEHl5eThx4kS7eq5ag4SEBKxZswZr1qyxdCitsrpkyBjTT3vv0KysLNy4cQOCIGD58uWWDssqdO/eHQcPHoSbm5ulQzEaHx8fnD59Gn379rV0KO22bt06q/9FqGVVd+A15Ca1X375pQkjYcz6LFiwAAsWLLB0GIw9kviXIWOMMZvHyZAxxpjN42TIGGPM5nEyZIwxZvOsqgONOSQnJ1s6BNbBpKenWzqER552G/P5yZq7fv06unbtavLl2Fwy5PGJzFAff/wxPv74Y0uHYRP4/GT6REVFmXwZNpcMSc9jb1jbCYKApKQkTJ482dKhmIWtra+lJCcnIyYmhs9PJhEdHW2W5fB/howxxmweJ0PGGGM2z2qT4Y4dOyAIAhISEgDcf9itIAh6n6LMGGOMPQyrTYavvvqqzo25tS8XFxdLh8YYY+wRY7XJkDHGGDMXToYP4OLiInmGop2dHTw8PKDRaPDmm2/i/Pnzlg7TZmif2iAIglnGHbGO49dff8WECRNQUVGB4uJinXN24MCBqK2tlczTvJ4gCBg0aJAFojee27dvY/PmzRg5ciQ8PT2hUCjQs2dPTJ06FVlZWXrnGT58eIvPjJ03b57eeerr6/HRRx8hKCgIrq6u6Ny5M8aMGYPU1FSdHsGLFy9GUlKSSdbV2DgZPkBVVRUuXLgAAIiIiAARob6+Hjk5OfjrX/+KnJwcDBo0CK+99hru3Llj4WgffQsWLAARQaPRWDoUZkUyMzMxaNAghIWFwc3NDV5eXiAiZGRkiNP1fahr66Wnp0OtVoOIcO7cOXOHb1QLFy7EnDlzEBERgcuXL6OkpATbt29HZmYmgoKCkJKS8tDLqK6uxsiRI7Fjxw589NFHuHXrFs6dOwcXFxdMmDABly5dEuvOnDkTS5YswYoVKx56uabGydBA9vb28Pb2RkREBL7//nssWrQIO3bswEsvvcRjpJjJubi4YPjw4Ta7/OYqKiowfvx4TJo0CbNnz5ZMl8vlUKvVSEhIwN69ey0QofnNmDEDc+fOhY+PD5ydnREcHIw9e/bg3r17WLRokd55MjIy9PbR0HeziYULFyI7OxvHjh3DH//4RygUCjz22GPYsWMH5HK5Tt3AwEAcOHAAa9assfq7C3EyfEjvv/8+hgwZgm+++YafsciYma1fvx4FBQVYuXKl3ulOTk7YvXs37OzsEBcXh2vXrpk5QvNKTEwUe+A3pdFooFAokJub+1Bf2gsLC7FlyxZMnToV3t7eOtOUSiVqa2vRr18/ybKjoqLw9ttvo6Ghod3LNjVOhg9JEATxG+mnn35q4WgYsx1EhMTERAwZMgS+vr4t1gsPD8fy5ctRWVmJ6Ohovf8fPuqqq6tRU1ODfv36QRCEdrfzzTff4N69ewZfHZg4cSKuX7+OQ4cOtXvZpsbJ0Ai0B8bZs2dRX18vlhcVFSE+Ph7du3eHo6MjOnXqhMjISGRmZop1UlJSdP6w/uWXXxATEwN3d3eo1WqMGzcOubm5Osurq6vDypUr0adPHzg7O8PT0xPjx48XD9Sm2hKDqTRft6tXr2Ly5MlQq9ViWXFx8UPHuXr1arG9pifp0aNHxXIvLy+TrWdzJSUlmD9/PgIDA+Ho6AgPDw+MGTMGJ0+ebHfM2s5D1dXVOHPmjFjHwcFBZ7q2c1FGRgZCQ0Ph6uoKZ2dnjBgxAmfOnDHZ8i0hKysLhYWFbfoP+d1330VYWBiys7MxZ86cNrXflv3YnvMXMP95uW/fPgDAsmXL9E7fuXMnBgwYAKVSCZVKJV5abe6HH34AAHh4eODtt9+Gv78/HB0d0a1bN8THx6O0tFRv+wMGDAAAfPvtt8ZYHdMgEwNASUlJpl5Mq5KSkqg9q3vhwgUCQBERES3WqampIQAEgPLz84mIKD8/n7p160be3t506NAhqqyspIsXL1JISAg5OTlRWlqaThsRERHictLS0qiqqoqOHz9OCoWCBg8erFM3NjaWVCoVHTt2jO7cuUMFBQW0YMECAkAnT54U6xkaQ1u0Z39q1y0kJIROnjxJ1dXVdPbsWbK3t6eioiKD49RoNOTn5ydZjlKppGeffVZSHhQURGq12rAV/S9D1/fmzZsUEBBA3t7elJqaSuXl5XT16lWKjIwkQRBo69atDxVzS/W1NBoNKZVKGjZsmHgcZWRk0FNPPUWOjo506tQpky5/xIgR5OnpSenp6S3W0ac95+fOnTsJAK1du1bv9IyMDFKpVOL7oqIi8vf3JwC0a9cusTw9PV2yrobuR0POX1Oclw9SUFBA3t7eFBsbq3f6s88+S9OnT6fz589TVVUV5eTk0PTp0wkAzZkzR+96+vj40NSpUyk3N5du375Nn3/+OSmVSurVqxeVlZVJllFeXk4AKDg42OD4o6KiKCoqyuD5DJTMybAVbUmGd+7ckSTDV155hQDQ7t27derevHmT5HI5BQUF6ZRrD7LU1FSd8qioKAJARUVFYllAQAA988wzkjh69eqlkwwNjaEtHiYZHj58WO90Q+O05mT46quvEgDau3evTnltbS35+vqSQqGggoKCdsfclmQIgC5cuKBTnp2dTQBIo9G0qb32Lj8kJIQ8PDwM/kBvz/m5fv16AkCbNm3SO715MiS6n/hkMhkplUq6cuWKWNZ8XQ3dj4acv6Y4L1tSXFxMAwYMoJiYGGpoaDBo3qeffpoA0NmzZ8Wy8PBwAkABAQFUX1+vU3/16tUEgFasWKG3PUEQqEePHgavg7mSIV8mNYKbN28CAGQymXhpKSUlBXZ2dhg3bpxOXR8fH/Tt2xfnz5/H9evXJW0NHjxY572/vz8AID8/XywbPXo00tLSMGvWLJw9e1a8NHr16lU899xzYr32xmAqTz/9tN5ya4vzYRw4cAAAMHbsWJ1yuVyO0NBQ1NTUmPxSkVKpFC9LafXv3x++vr7IysoSj1dTOHXqFEpLSzFs2DCTLUNL+9+fTCZr8zxDhw7Fhg0bUF1djejoaNTU1Oit19792Jbz11zHe3V1NcLDw/Hkk09i9+7dsLe3N2h+7WOTUlNTxTKlUgkAGDVqlOQS+fjx4wG0fCnUwcGhxe1tDTgZGsHp06cBAMOGDYNMJkNdXR3Ky8vR2NgIlUolGciqve7+008/SdpSqVQ67x0dHQEAjY2NYtmmTZvwxRdfIC8vD6GhoXBzc8Po0aPFExjAQ8VgKtoTqSlrjLO9tOvi5OQEV1dXyXRt77uCggKTxuHu7q63vHPnzgCAW7dumXT55uLk5AQAOv/Tt0V8fDxiYmJw8eJFvcMxHmY/tnb+mut4b2hoQHR0NPz8/PD5558bnAgBoEuXLgB0j5fu3bsDANRqtaS+9vgqKipqMSaFQmFwHObCyfAhNTY2YtOmTQCA//f//h+A+98e3d3d4eDggPr6er3jd4gII0aMaNcyBUHA9OnT8d1336GsrAwpKSkgIkRGRuLDDz80SwzGYsw47ezscPfuXUl5WVmZKUKXkMvlUKlUqK2tRWVlpWR6YWEhgPu/ALQMjbktPQFLSkr0dp/XfqhpP7RMtXxz0X5Yl5eXGzxvYmIievfuje3bt2Pnzp0609qzH9vKXOdlXFwc6urqkJycrPMLrkePHjh79myb2tD+mm16vGg7W+m7uqA9vpoPuQDujwclInGfWSNOhg9pyZIl+N///V9MnDhR5yGUkZGRaGho0OnBp7Vu3To89thj7R5z4+7ujpycHAD3LxE9//zzYq+2pl2XTRmDMRkrzi5duuDGjRs6ZQUFBfjtt9+MFmtrJk6cCACSLuR1dXU4ceIEFAoFwsPDxXJDY3Z2dtZJXr1798aWLVt06tTW1op3X9H68ccfkZ+fD41Go/OBZIrlm4t2PFt7Lim6uLjgq6++glKp1DskytD9aAhTn5erVq3CpUuX8PXXX0sGwTeXmJiIoKAgSTkRiYPktZc/AeCFF16An58fjh49Khmior2c+uKLL0ra0x5jzccgWhVT/yuJR6wDzb1796iwsJBSUlJo5MiRBIBmzJhBd+7c0ZmvsLCQAgMD6fHHH6fDhw9TWVkZlZSU0ObNm8nZ2VmyTbR/wNfU1OiUv/POO5IOESqVikJCQigrK4tqa2upsLCQVq1aRQBo9erV7Y6hLdqzP1tat/bG2VIHmtmzZxMA+uc//0mVlZX0888/0+TJk8nPz89ivUkrKip0eiFu2bLloWIePXo0qVQq+u233ygtLY0cHBzo8uXL4nSNRkMqlYpCQ0Pb1JvU2Ms3Z2/SxsZG6ty5c4sdevR1oGlu165dBKDV3qSt7UdDzl9Dj/epU6cSAMrLy2t1m/zrX/8SO/O19Gq6b7Zu3UoA6M0336SffvqJampqKCcnR1xm896kRERHjhwhBwcHioiIoGvXrtHt27fpiy++IKVSSUOGDJF8FhIR7dmzhwDQgQMHWl2H5rg3qZG152RTKpWSA0kQBFKpVNS/f39644036Pz58y3OX1JSQvPnz6fHH3+cZDIZderUicLCwuj48eNinfT0dMkyli1bRkQkKR87diwREWVmZlJcXBw98cQT5OzsTJ6enjR06FDaunUrNTY2GhyDIQzZn/rWraV90JY4P/jggxa3FRFRWVkZxcbGUpcuXUihUNDw4cMpIyODgoKCxPrvvPOOydZXq7i4mObNm0cBAQEkk8lIpVJReHg4nThxQlLX0JhzcnIoODiYlEol+fv7S3pSar8oXL58mcLDw8nV1ZUUCgWFhITQ6dOnTb784OBgs/UmJSJaunQpOTg40I0bN8SyoqIiyXHyoB6ab7zxht7E35b92J7zl8iw83LkyJHk4uLSpt6gY8eONSgZ1tbW0r59+2jixIkUGBhIcrmcVCoVPffcc7Rnz54Wl5OWlkbh4eGkUqnI0dGR+vTpQ6tWrdKbCImIoqOjyc/Pj+7evdvqOjTHydDI2nuyMV3Wsj/NpaOtb0u/mq1de8/PsrIy8vPzo7i4OBNEZXm3b98mhULR4hjBjiAzM5MEQZAMU2krHlrBGGOtUKlUSE1Nxf79+8WObI8KIkJ8fDzc3Nzw3nvvWTqcdsnLy0NkZCSWLFmCKVOmWDqcB+JkyBjr0AYOHIhz587hyJEjqKiosHQ4RlNYWIi8vDycOHGiXT1XrUFCQgLWrFmDNWvWWDqUVnEyZOwRoL13aFZWFm7cuAFBELB8+XJLh2U23bt3x8GDB+Hm5mbpUIzGx8cHp0+fRt++fS0dSrutW7fO6n8RalnuLruMMaNZsGABFixYYOkwGOuw+JchY4wxm8fJkDHGmM3jZMgYY8zmcTJkjDFm88zSgeajjz4Sn7RsKdr7Fza9fyhrH2vYn+Zka+trCXx+spacPXsWQ4cONflyBCI9t7c3Ij64GWtZYWEhLl68iNDQUEuHwpjVGjZsGObPn2/KRewzeTJkjLUsOTkZMTExeh+5xBgzm338nyFjjDGbx8mQMcaYzeNkyBhjzOZxMmSMMWbzOBkyxhizeZwMGWOM2TxOhowxxmweJ0PGGGM2j5MhY4wxm8fJkDHGmM3jZMgYY8zmcTJkjDFm8zgZMsYYs3mcDBljjNk8ToaMMcZsHidDxhhjNo+TIWOMMZvHyZAxxpjN42TIGGPM5nEyZIwxZvM4GTLGGLN5nAwZY4zZPE6GjDHGbB4nQ8YYYzaPkyFjjDGbx8mQMcaYzeNkyBhjzOZxMmSMMWbzOBkyxhizeZwMGWOM2TxOhowxxmweJ0PGGGM2j5MhY4wxm+dg6QAYsxX5+fkYN24c6uvrxbI7FdCKmgAAIABJREFUd+5ApVKhf//+OnUHDhyIL774wtwhMmazOBkyZia+vr64e/cuLl26JJlWXl6u837KlCnmCosxBr5MyphZ/elPf4KDw4O/gwqCgJdfftlMETHGAE6GjJnVSy+9hHv37rU4XRAEBAUFISAgwIxRMcY4GTJmRv7+/hg6dCjs7PSfevb29vjTn/5k5qgYY5wMGTOz6dOnQxAEvdMaGxsxefJkM0fEGONkyJiZRUdH6y23t7fHc889B29vbzNHxBjjZMiYmXl5eSE0NBT29vaSadOnT7dARIwxToaMWcC0adNARDpldnZ2mDhxooUiYsy2cTJkzAJefPFFyGQy8b2DgwPGjh0LlUplwagYs12cDBmzAFdXV4wfP15MiPfu3cO0adMsHBVjtouTIWMWMnXqVDQ0NAAAFAoFXnjhBQtHxJjt4mTImIWMGTMGSqUSABAVFQWFQmHhiBizXR3u3qTXr19HWlqapcNgzCgGDx6MkydPwt/fH8nJyZYOhzGj6IhjZQVq3qXNyiUnJyMmJsbSYTDGGGtBB0srALCvw/0y1LKGja0dPL1v3z4LR8KAjrk/GhsbsW7dOixZssTSoRhEEAQkJSV1yF8AzHQ68o8V/s+QMQuys7PDwoULLR0GYzaPkyFjFtbaI50YY6bHyZAxxpjN42TIGGPM5nEyZIwxZvM4GVqJL7/8EoIgQBAEODk5WTocxkzm119/xYQJE1BRUYHi4mLxuBcEAQMHDkRtba1knub1BEHAoEGDLBC98dy+fRubN2/GyJEj4enpCYVCgZ49e2Lq1KnIysrSO8/w4cMl20H7mjdvnt556uvr8dFHHyEoKAiurq7o3LkzxowZg9TUVJ1e+YsXL0ZSUpJJ1rUj4GRoJaZMmQIiQmhoqKVDYQCqqqrQs2dPjBs3ztKhPFIyMzMxaNAghIWFwc3NDV5eXiAiZGRkiNP1fahr66Wnp0OtVoOIcO7cOXOHb1QLFy7EnDlzEBERgcuXL6OkpATbt29HZmYmgoKCkJKS8tDLqK6uxsiRI7Fjxw589NFHuHXrFs6dOwcXFxdMmDABly5dEuvOnDkTS5YswYoVKx56uR0RJ0NmcS4uLhg+fLilw9BBRGhsbERjY6OlQ2mVNW4/fSoqKjB+/HhMmjQJs2fPlkyXy+VQq9VISEjA3r17LRCh+c2YMQNz586Fj48PnJ2dERwcjD179uDevXtYtGiR3nkyMjJARJLXxx9/LKm7cOFCZGdn49ixY/jjH/8IhUKBxx57DDt27IBcLtepGxgYiAMHDmDNmjU2eTck7tPNmB6urq7Izc21dBiPlPXr16OgoAArV67UO93JyQm7d+/GCy+8gLi4OAQFBaFXr15mjtJ8EhMT9ZZrNBooFArk5uaCiCAIQrvaLywsxJYtWzBr1ix4e3vrTFMqlXovR2s0GkRFReHtt99GZGSkTQ374V+GjDGTIyIkJiZiyJAh8PX1bbFeeHg4li9fjsrKSkRHR+v9wH7UVVdXo6amBv369Wt3IgSAb775Bvfu3TP4qsHEiRNx/fp1HDp0qN3L7og4GVpITk4OXnzxRahUKiiVSgQHB+P06dOSeikpKTp/kl+9ehWTJ0+GWq0Wy4qLiwEAJSUlmD9/PgIDA+Ho6AgPDw+MGTMGJ0+eFNvbsGGDOF/Xrl2RkZGB0NBQuLq6wtnZGSNGjMCZM2ckcbSl7dWrV4ttNz0Bjx49KpZ7eXlJYqmursaZM2fEOpb+Ntp8m2s/kJuX//LLL4iJiYG7uzvUajXGjRun82vS0G39qGw/fbKyslBYWAiNRtNq3XfffRdhYWHIzs7GnDlz2tR+W45PQ/efVlFREeLj49G9e3c4OjqiU6dOiIyMRGZmZts3gAG0txNctmyZ3uk7d+7EgAEDoFQqoVKpxEurzf3www8AAA8PD7z99tvw9/eHo6MjunXrhvj4eJSWluptf8CAAQCAb7/91hir03FQB5OUlETWEnZUVBRFRUUZPN9PP/1E7u7u5OfnR8eOHaPKykrKzs6msLAw6t69O8nlcsk8ERERBIBCQkLo5MmTVF1dTWfPniV7e3sqKiqimzdvUkBAAHl7e1NqaiqVl5fT1atXKTIykgRBoK1bt+q0p9FoSKlU0rBhwygtLY2qqqooIyODnnrqKXJ0dKRTp06JdQ1tW6lU0rPPPitZh6CgIFKr1ZLyluobqr37oyXabV5TU6O3PCIiQtx2x48fJ4VCQYMHD5a0Y8i2JjL+9hsxYgR5enpSenp6W1e9VQAoKSmpzfV37txJAGjt2rV6p2dkZJBKpRLfFxUVkb+/PwGgXbt2ieXp6emSbWDo8WnI/svPz6du3bqRt7c3HTp0iCorK+nixYsUEhJCTk5OlJaW1uZt0BYFBQXk/f/bu/e4qMp9f+CfAYZhGGBAlNuEipi2UxvdaF7ZKBRoXggCschX7rI4XSQzy0wtT2ptffXa1enY8cI2y9RAO1B4K6M85yjQRhJMDTWpTG4CcpdBkO/vD3+ztsNayMwwzIzO9/168QfPetazvutZl+/Mmmet5e9PCxculJw+efJkmj9/PhUWFlJzczOVlJTQ/PnzCQAtWrRIcj0DAgIoOTmZLly4QHV1dfTJJ5+QSqWiYcOGUX19vWgZDQ0NBIDCw8NNjt+ezs8myrjtoranzjb35JuYmEgAaO/evQblZWVlpFAobpkMDxw4INnmggULCADt3r3boFyn01FQUBAplUqqrKwUyrVaLQGgEydOGNQ/efIkASCtVmt2246SDLOzs0XLB0DV1dUG5ab0NZHl+y8iIoJ8fHwseuI2NRlu2LCBANDGjRslp3dNhkQ3Ep9cLieVSkU///yzUNa1D0zdP03Zfk888QQBoJ07dxrUraioIIVCQWFhYUb2QM9qampo9OjRlJSURB0dHSbNe//99xMAys/PF8piYmIIAIWEhFB7e7tB/bVr1xIAWrVqlWR7MpmMhg4davI62NP52UQZfJnUBg4dOgTgxu8jNwsKCupxwMD9998vWZ6ZmQkAmDlzpkG5QqFAVFQUWltbRZc9VCqVcElEb9SoUQgKCkJxcTEqKirMbtsRjBs3zuD/4OBgAEB5ebmorrF93ReOHDmCK1euYOLEiX22jJ7oLzXL5XKj55kwYQLeffddtLS0IDExEa2trZL1zN0/jdl+WVlZcHJyEt1iExAQgBEjRqCwsBCXLl0yep2609LSgpiYGNx7773YuXMnnJ2dTZo/ISEBAJCdnS2U6V8c/cADD4gunc+ePRtA95dCXVxcuu3vOxUnQytra2tDU1MT3Nzc4OHhIZru5+d3y/n1O3jXNhsaGuDm5gZPT0/RdP1IssrKSoNyb29vyWXoY7h8+bLZbTsCtVpt8L+rqysASN6OYUxf38n0D5Job283ab7U1FQkJSXh1KlTkrdj9Gb/7Gn76dvu7OyEWq0W3eSu/03u/PnzJq1TVx0dHUhMTIRGo8Enn3xiciIEgMDAQACG+9HgwYMBAL6+vqL6+v2uurq625iUSqXJcdzOOBlamUKhgKenJ3Q6HZqbm0XTu/tRu6c21Wo1dDodmpqaRNOrqqoA3Pg0e7Pa2lrJ90LqDyg/Pz+z2nZycsK1a9dEdevr6yXj782IuduFMX2tdyf2n/5k3dDQYPK8aWlpGD58OLZt24YdO3YYTDN33zeGQqGAt7c3XFxc0N7eLnlvHxFh2rRpJrd9s5SUFLS1tSEjI8PgG9zQoUORn59vVBv6b7M370f6QVhSVx30+13XWy6AG/eDEpGwzRwFJ0MbmDFjBoB/XS7Vq6mpwdmzZ81qMy4uDgBEw6Hb2tqQk5MDpVIpuiyr0+mEJ3/o/fTTTygvL4dWqxUOBlPbDgwMRFlZmUHdyspKXLx4UTJ2d3d3g5P/8OHDsWXLlh7X+XZibF8Dd2b/jRw5EgDMuqTo4eGBL774AiqVCh999JFoujn7vrHi4+PR0dEhOcJ6/fr1GDhwIDo6OsxqGwBWr16N06dP48svvxTdBN9VWloawsLCROVEJNwkr7/8CQAPPfQQNBoNDh06JLpFRX859eGHHxa1p9/39NvMYdjq10pz2dMPtOYO2Pjll1+oX79+BqNJT58+TTExMeTn53fLATRdB3PodR1R19jYaDCibsuWLQb1tVotqdVqioqKMnk0aU9tv/DCCwSAPvzwQ2pqaqJffvmF5s6dSxqNRnIAyPTp00mtVtPFixcpNzeXXFxc6MyZMyb3q7UH0HQtX7ZsmeRAGVP6msjy/WcPo0k7OzvJz8+v24E+UgNouvrss88IQI+jSXvaP03ZflVVVRQaGkpDhgyhAwcOUH19PdXW1tKmTZvI3d1d1AfJyckEgEpLS3vsk48//pgA3PLv5m22detWAkDPPfccnT9/nlpbW6mkpERYZtfRpEREBw8eJBcXF4qNjaVz585RXV0dffrpp6RSqWj8+PF09epV0Ty7du0iAJSZmdnjOnRlT+dnE/Fo0t7ozcn37Nmz9PDDD5OXl5cwpHvfvn0UFRUlHAhPPfUU5eXlSR4kUmpqamjx4sUUEhJCcrmc1Go1xcTEUE5OjqiuVqsljUZDZ86coZiYGPL09CSlUkkRERF09OjRXrVdX19PCxcupMDAQFIqlTRlyhQqKCigsLAwIf5ly5YJ9UtKSig8PJxUKhUFBwd3O+KwJ5ZKhpmZmaL+Tk5OltwWK1asICISlc+cOVNoz9S+tnT/hYeH23w0KRHR66+/Ti4uLlRWViaUVVdXi/ruViM0n332WckPBMbsn+Zuv9raWlqyZAkNGTKE5HI5DRgwgKKjo+nw4cOiOCIjI8nDw8Oo0aAzZ840KRnqdDras2cPxcXFUWhoKCkUClKr1TR16lTatWtXt8vJzc2lmJgYUqvV5OrqSvfccw+tXr1aMhES3RjtrtFo6Nq1az2uQ1f2dH42ESfD3rD0NxFr0p+g7yT2uj3uxL42JxnW19eTRqOhlJSUPorKturq6kipVHZ7j+DtoKioiGQymeg2FWPZ0/nZRHf+rRWjR4/u9pUnUn9r1661dciM3ZHUajWys7Oxd+9ebNy40dbhWBQRITU1FV5eXlizZo2twzFLaWkp4uPjsXz5csybN8/W4VjdHZ8MgRuPN6KbRn+lpKQAAA4ePGhQnpSUZONIGbuzjRkzBsePH8fBgwfR2Nho63AspqqqCqWlpcjJyTFr5Ko92Lx5M9atW4d169bZOhSbcIhkyP5F/zzL4uJilJWVQSaTYeXKlbYO647EfS1t8ODB2LdvH7y8vGwdisUEBATg6NGjGDFihK1DMdv69esd8huhnv090dfCTHmY7ueff96HkdiHpUuXYunSpbYOwyFwXzN2++BvhowxxhweJ0PGGGMOj5MhY4wxh8fJkDHGmMO74wfQ9LX8/HwkJibaOgwGCA815u1hHe+9957wVnbGAPOePWsv+JshY4wxh8ffDHtpwoQJ/OnYTui/EfL26HsymQwvvfQS5s6da+tQmB3JyMi4bR9ewt8MGWOMOTxOhowxxhyeQyXD7du3QyaTYfPmzQBuvGRXJpNJvnGeMcaY43CoZLhgwQKDB3Pr/zw8PGwdGmOMMRtyqGRoDzw8PESvjXJycoKPjw+0Wi2ee+45FBYW2jpMxvrM77//jjlz5qCxsRE1NTUGx8KYMWOg0+lE83StJ5PJMHbsWBtEbzl1dXXYtGkTIiMj0a9fPyiVStx9991ITk5GcXGx5DxTpkzp9vVzixcvlpynvb0d7733HsLCwuDp6Qk/Pz/MmDED2dnZICKh3muvvYb09PQ+WdfbASdDK2tubsaJEycAALGxsSAitLe3o6SkBG+99RZKSkowduxY/PWvf8XVq1dtHC1jllVUVISxY8ciOjoaXl5e6N+/P4gIBQUFwnSpk7q+Xl5eHnx9fUFEOH78uLXDt6hXXnkFixYtQmxsLM6cOYPa2lps27YNRUVFCAsLQ1ZWVq+X0dLSgsjISGzfvh3vvfceLl++jOPHj8PDwwNz5szB6dOnhbpPP/00li9fjlWrVvV6ubcjToZ2wNnZGf7+/oiNjcV3332HV199Fdu3b8ejjz5q8MntTufh4YEpU6bYOgyLsvU62Xr5N2tsbMTs2bPxyCOP4IUXXhBNVygU8PX1xebNm7F7924bRGh9Tz75JF588UUEBATA3d0d4eHh2LVrF65fv45XX31Vcp6CggLJn3vef/99Ud1XXnkFJ0+exDfffIO//OUvUCqVGDhwILZv3w6FQmFQNzQ0FJmZmVi3bh0yMjL6ZH3tGSdDO/S3v/0N48ePx1dffeUQr5VijmHDhg2orKzEG2+8ITndzc0NO3fuhJOTE1JSUnDu3DkrR2hdaWlpwmC+m2m1WiiVSly4cKFXH4arqqqwZcsWJCcnw9/f32CaSqWCTqfDyJEjRctOSEjAyy+/jI6ODrOXfTviZGiHZDKZ8Mn5o48+snE0jPUeESEtLQ3jx49HUFBQt/ViYmKwcuVKNDU1ITExUfL3wztdS0sLWltbMXLkSMhkMrPb+eqrr3D9+nWTrwzExcXh0qVL2L9/v9nLvh1xMrRT+h04Pz8f7e3tyMrKMvix/OzZs5g7dy58fX2FspqaGgBAbW0tlixZgtDQULi6usLHxwczZszA999/L7Svfwu7TCbDXXfdhYKCAkRFRcHT0xPu7u6YNm0ajh07JorLmLbXrl0rtH3zgXjo0CGhvH///qJYWlpacOzYMaGOi4t1HpBki3Uytf9vtz7tqri4GFVVVdBqtT3WffPNNxEdHY2TJ09i0aJFRrVvzDbsegz99ttvSEpKgre3N3x9fTFr1ixcuHBB1HZ1dTVSU1MxePBguLq6YsCAAYiPjzfpxeGm0D9BacWKFZLTd+zYgdGjR0OlUkGtVguXVrv68ccfAQA+Pj54+eWXERwcDFdXVwwaNAipqam4cuWKZPujR48GAHz99deWWJ3bB91m0tPTyV7CTkhIoISEBJPnO3HiBAGg2NjYbuu0trYSAAJA5eXlQnlsbCwBoIiICPr++++ppaWF8vPzydnZmaqrq6miooJCQkLI39+fsrOzqaGhgc6ePUvx8fEkk8lo69atBsvRarWkUqlo4sSJlJubS83NzVRQUED33Xcfubq60pEjR4S6pratUqlo8uTJonULCwsjX19fUXl39Y1lzvaw9TqZ0v99sfxp06ZRv379KC8vr9s6UgBQenq60fV37NhBAOjtt9+WnF5QUEBqtVr4v7q6moKDgwkAffbZZ0J5Xl6eaD1N3Yb6Yyg2Nlbo88OHD5NSqaRx48YZ1C0vL6dBgwaRv78/7d+/n5qamujUqVMUERFBbm5ulJuba3QfGKOyspL8/f1p4cKFktMnT55M8+fPp8LCQmpubqaSkhKaP38+AaBFixZJrmdAQAAlJyfThQsXqK6ujj755BNSqVQ0bNgwqq+vFy2joaGBAFB4eLjJ8dvT+dlEGbdd1PbU2X2ZDK9evXrLZHjgwAHJ+RYsWEAAaPfu3QblOp2OgoKCSKlUUmVlpVCu1WoJAJ04ccKg/smTJwkAabVas9u+HZKhrdfJlP7vi+VHRESQj4+PySd1U5Phhg0bCABt3LhRcnrXZEh0I/HJ5XJSqVT0888/C2Vd19PUbag/hrKzsw3qJyQkEACqrq4Wyp544gkCQDt37jSoW1FRQQqFgsLCwozsgZ7V1NTQ6NGjKSkpiTo6Okya9/777ycAlJ+fL5TFxMQQAAoJCaH29naD+mvXriUAtGrVKsn2ZDIZDR061OR1sKfzs4ky+DKpnaqoqAAAyOVyg8tfevfff7/kfJmZmQCAmTNnGpQrFApERUWhtbVVdPlDpVIJl0b0Ro0ahaCgIBQXFwuxmNO2vbOHdTK2//vCkSNHcOXKFUycOLHPlgFA+O1PLpcbPc+ECRPw7rvvoqWlBYmJiWhtbZWsZ+42HDdunMH/wcHBAIDy8nKhLCsrC05OTpg1a5ZB3YCAAIwYMQKFhYUWeW1RS0sLYmJicO+992Lnzp1wdnY2af6EhAQAQHZ2tlCmUqkAAA888IDo8vjs2bMBdH8p1MXFpdv+vlNxMrRTR48eBQBMnDhR8gSi39Fv1tbWhoaGBri5ucHT01M0XT+irLKy0qDc29tbMgY/Pz8AwOXLl81u257ZyzoZ0/+3Ozc3NwA3bgA3RWpqKpKSknDq1CnJ2zF6sw3VarXB/66urgCAzs5Og7Y7OzuhVqtFN7nrf5M7f/68SevUVUdHBxITE6HRaPDJJ5+YnAgBIDAwEIDhvjJ48GAAgK+vr6i+ft+qrq7uNialUmlyHLczToZ2qLOzExs3bgQAPP/880bPp1AooFarodPp0NTUJJpeVVUF4Man2pvV1tZKDuHWH1h+fn5mte3k5IRr166J6tbX10vG35uRc+awl3Uypv/7cvnWoD9ZNzQ0mDxvWloahg8fjm3btmHHjh0G08zd542hUCjg7e0NFxcXtLe3S97bR0SYNm2ayW3fLCUlBW1tbcjIyDD4Bjd06FDhhdU90X+bvXlf0Q+0krqyoN+3ut5yAdy4H5SIhG3mKDgZ2qHly5fjn//8J+Li4kx+a3tcXBwAiIZFt7W1IScnB0qlEjExMQbTdDqd8AQQvZ9++gnl5eXQarXCQWFq24GBgSgrKzOoW1lZiYsXL0rG7u7ubnCiHz58OLZs2dLjOveGPayTsf3fV8u3Bv39bOZcUvTw8MAXX3wBlUoleauROfu8seLj49HR0SE5snr9+vUYOHBgr+7HW716NU6fPo0vv/xSdBN8V2lpaQgLCxOVE5Fwk7z+8icAPPTQQ9BoNDh06JDoFhX95dSHH35Y1J5+/+p6D+Idz1a/VprLnn6gtdQAmuvXr1NVVRVlZWVRZGQkAaAnn3ySrl69KppX/+N/a2urZNtdR9Y1NjYajKzbsmWLQX2tVktqtZqioqJMHk3aU9svvPACAaAPP/yQmpqa6JdffqG5c+eSRqORHOwxffp0UqvVdPHiRcrNzSUXFxc6c+aM0f1qidGk1l4nU/q/L5ZvrdGknZ2d5Ofn1+1gHqkBNF199tlnBKDH0aQ9bcPujqFly5aJBjNVVVVRaGgoDRkyhA4cOED19fVUW1tLmzZtInd3d1EfJCcnEwAqLS3tsU8+/vhjYZBcd383b5etW7cSAHruuefo/Pnz1NraSiUlJcIyu44mJSI6ePAgubi4UGxsLJ07d47q6uro008/JZVKRePHj5c8x+zatYsAUGZmZo/r0JU9nZ9NxKNJe8Ock69KpRLt8DKZjNRqNY0aNYqeffZZKiwsFM2Xl5cnebBIqampocWLF1NISAjJ5XJSq9UUExNDOTk5orparZY0Gg2dOXOGYmJiyNPTk5RKJUVERNDRo0d71XZ9fT0tXLiQAgMDSalU0pQpU6igoIDCwsKE+JctWybULykpofDwcFKpVBQcHNztyMPumPvhxJbrZGr/W3r54eHhVhlNSkT0+uuvk4uLC5WVlQll1dXVon36ViM0n332Wcmkb8w2lDqGVqxYIazPzX8zZ84U5qutraUlS5bQkCFDSC6X04ABAyg6OpoOHz4siiMyMpI8PDyMGg06c+ZMk5KhTqejPXv2UFxcHIWGhpJCoSC1Wk1Tp06lXbt2dbuc3NxciomJIbVaTa6urnTPPffQ6tWrJRMhEVFiYiJpNBq6du1aj+vQlT2dn03EybA3zD352hP9yfhOcDtuj9u1/81JhvX19aTRaCglJaWPorKturo6UiqV3d4jeDsoKioimUwmuk3FWPZ0fjYR31rBGLMOtVqN7Oxs7N27VxggdqcgIqSmpsLLywtr1qyxdThmKS0tRXx8PJYvX4558+bZOhyr42TIGLOaMWPG4Pjx4zh48CAaGxttHY7FVFVVobS0FDk5OWaNXLUHmzdvxrp167Bu3Tpbh2ITnAwdlP7ZlcXFxSgrK4NMJsPKlSttHZbDcOT+Hzx4MPbt2wcvLy9bh2IxAQEBOHr0KEaMGGHrUMy2fv16h/xGqGebp/Yym1u6dCmWLl1q6zAcFvc/Y/aFvxkyxhhzeJwMGWOMOTxOhowxxhweJ0PGGGMOj5MhY4wxh3fbjia1l6fxA/YVC+PtYS1JSUlISkqydRiMWcRtlwwnTZqE9PR0W4fBmEXk5eXh/fff532aMRuTEUm8SI0xZhUZGRlISkqSfJ8hY8xq9vBvhowxxhweJ0PGGGMOj5MhY4wxh8fJkDHGmMPjZMgYY8zhcTJkjDHm8DgZMsYYc3icDBljjDk8ToaMMcYcHidDxhhjDo+TIWOMMYfHyZAxxpjD42TIGGPM4XEyZIwx5vA4GTLGGHN4nAwZY4w5PE6GjDHGHB4nQ8YYYw6PkyFjjDGHx8mQMcaYw+NkyBhjzOFxMmSMMebwOBkyxhhzeJwMGWOMOTxOhowxxhweJ0PGGGMOj5MhY4wxh8fJkDHGmMPjZMgYY8zhcTJkjDHm8DgZMsYYc3icDBljjDk8F1sHwJij0Ol0KC8vNyirqqoCAJSWlhqUOzs7Y9CgQVaLjTFHJyMisnUQjDmCuro6+Pv7o729vce6Dz30EPbv32+FqBhjAPbwZVLGrMTHxwfR0dFwcur5sJs3b54VImKM6XEyZMyKHn/8cfR0MUahUCAuLs5KETHGAE6GjFnVnDlz4Obm1u10FxcXzJkzBx4eHlaMijHGyZAxK3J3d0dcXBzkcrnk9OvXryM5OdnKUTHGOBkyZmWPPfZYt4NoVCoVpk+fbuWIGGOcDBmzsujoaKjValG5XC5HUlISFAqFDaJizLFxMmTMyuRyOebNmwdXV1eD8vb2djz22GM2iooxx8bJkDEbePTRR3Ht2jWDsv79+yMiIsJGETHm2DgZMmYD4eHh8Pf3F/6Xy+WYP38+nJ2dbRgVY46LkyFjNuDk5IT58+cLl0rb29vx6KOP2jgqxhzSDrtqAAAfvElEQVQXJ0PGbGTevHnCpdLg4GCMHTvWxhEx5rg4GTJmI2FhYRg6dCgAYMGCBZDJZDaOiDHHJXprRV5eHv7+97/bIhbGHI7+MukPP/yAxMREG0fDmGPYs2ePqEz0zfCPP/7A3r17rRJQT/Lz85Gfn2/rMBizCKn9eeDAgfD29oaXl5eNoroz7d27F5cuXbJ1GMzOXLp0qdv81u37DKUyp7XpPynbQyyM9VZ3+/O3336LBx54wBYh3bFkMhleeuklzJ0719ahMDuSkZGBpKQkyWn8myFjNsaJkDHb42TIGGPM4XEyZIwx5vA4GTLGGHN4nAztwOeffw6ZTAaZTHbLF7/eiQ4cOIBhw4bBxaXbsVyMWdXvv/+OOXPmoLGxETU1NcKxKZPJMGbMGOh0OtE8XevJZLLb/iEKdXV12LRpEyIjI9GvXz8olUrcfffdSE5ORnFxseQ8U6ZMEfWD/m/x4sWS87S3t+O9995DWFgYPD094efnhxkzZiA7OxtEJNR77bXXkJ6e3ifrCnAytAvz5s0DESEqKsrWoVjNhQsXMGfOHCxfvhxVVVW2Due209zcjLvvvhuzZs2ydSh3lKKiIowdOxbR0dHw8vJC//79QUQoKCgQpkud1PX18vLy4OvrCyLC8ePHrR2+Rb3yyitYtGgRYmNjcebMGdTW1mLbtm0oKipCWFgYsrKyer2MlpYWREZGYvv27Xjvvfdw+fJlHD9+HB4eHpgzZw5Onz4t1H366aexfPlyrFq1qtfLlcLJkNnEqlWrMGnSJBQWFsLT09PW4dx2iAidnZ3o7Oy0dSg98vDwwJQpU2wdRo8aGxsxe/ZsPPLII3jhhRdE0xUKBXx9fbF582bs3r3bBhFa35NPPokXX3wRAQEBcHd3R3h4OHbt2oXr16/j1VdflZynoKAARCT6e//990V1X3nlFZw8eRLffPMN/vKXv0CpVGLgwIHYvn276L2eoaGhyMzMxLp165CRkWHxdeVrU8wm/vGPf0CpVNo6jNuWp6cnLly4YOsw7igbNmxAZWUl3njjDcnpbm5u2LlzJx566CGkpKQgLCwMw4YNs3KU1pOWliZZrtVqoVQqceHCBRCR2Y8RrKqqwpYtW/DMM88YvMEFAFQqleTlaK1Wi4SEBLz88suIj4+36M8r/M2Q2QQnQmZPiAhpaWkYP348goKCuq0XExODlStXoqmpCYmJiZIn7DtdS0sLWltbMXLkyF49T/err77C9evXTb5qEBcXh0uXLmH//v1mL1sKJ0MbKCkpwcMPPwy1Wg2VSoXw8HAcPXq02/rV1dVITU3F4MGD4erqigEDBiA+Ph5FRUVCnaysLIMfq3/77TckJSXB29sbvr6+mDVrluibRFtbG9544w3cc889cHd3R79+/TB79mxhJzU1BmYdXbe1/oRs6j7w7rvvCnXvuusuFBQUICoqCp6ennB3d8e0adNw7Ngxof7atWuF+jefwA4dOiSU9+/fX9R+S0sLjh07JtSxx8FSxcXFqKqqglar7bHum2++iejoaJw8eRKLFi0yqv3a2losWbIEoaGhcHV1hY+PD2bMmIHvv/9eqGPOMQxY/9jUP0FpxYoVktN37NiB0aNHQ6VSQa1WC5dWu/rxxx8BAD4+Pnj55ZcRHBwMV1dXDBo0CKmpqbhy5Ypk+6NHjwYAfP3115ZYnX+hLtLT00mi2CYSEhIoISHB1mFY1Pnz58nb25s0Gg1988031NTURCdPnqTo6GgaPHgwKRQKg/rl5eU0aNAg8vf3p/3791NTUxOdOnWKIiIiyM3NjXJzcw3qx8bGEgCKjY2l3Nxcam5upsOHD5NSqaRx48YZ1F24cCGp1Wr65ptv6OrVq1RZWUlLly4lAPT999+bHYOpNBoNOTs796qN24Gl92f9tm5tbZUsN2YfICLSarWkUqlo4sSJQv2CggK67777yNXVlY4cOWJQX6VS0eTJk0XthIWFka+vr6i8u/p606ZNo379+lFeXp6xq94jAJSenm50/R07dhAAevvttyWnFxQUkFqtFv6vrq6m4OBgAkCfffaZUJ6Xlyfqg4qKCgoJCSF/f3/Kzs6mhoYGOnv2LMXHx5NMJqOtW7ca1Ddl+/X1sdlVZWUl+fv708KFCyWnT548mebPn0+FhYXU3NxMJSUlNH/+fAJAixYtklzPgIAASk5OpgsXLlBdXR198sknpFKpaNiwYVRfXy9aRkNDAwGg8PBwk+O/RX7L4GRoZYmJiQSA9u7da1BeVlZGCoVClAyfeOIJAkA7d+40KK+oqCCFQkFhYWEG5fodLDs726A8ISGBAFB1dbVQFhISQpMmTRLFOGzYMINkaGoMpuJkaJ6ekqEx+wDRjWQIgE6cOGFQfvLkSQJAWq3WoNzSyTAiIoJ8fHwseuI2NRlu2LCBANDGjRslp3dNhkQ3Ep9cLieVSkU///yzUNa1DxYsWEAAaPfu3QblOp2OgoKCSKlUUmVlpVBuyvbr62PzZjU1NTR69GhKSkqijo4Ok+a9//77CQDl5+cLZTExMQSAQkJCqL293aD+2rVrCQCtWrVKsj2ZTEZDhw41eR1ulQz5MqmVHTp0CMCN3x5uFhQUJPljfFZWFpycnERD6AMCAjBixAgUFhZKPp1/3LhxBv8HBwcDAMrLy4Wy6dOnIzc3F8888wzy8/OFS6Nnz57F1KlTex0Dsy1j9gE9lUolXH7SGzVqFIKCglBcXIyKioo+i/PIkSO4cuUKJk6c2GfL6In+UrNcLjd6ngkTJuDdd99FS0sLEhMT0draKlkvMzMTADBz5kyDcoVCgaioKLS2tkpe8jNm+1nr2GxpaUFMTAzuvfde7Ny5E87OzibNn5CQAADIzs4WylQqFYAbz+bteul89uzZALq/FOri4tJtf5uLk6EVtbW1oampCW5ubvDw8BBN9/PzE9VvaGhAZ2cn1Gq16CZW/TX38+fPi9pSq9UG/+vfm3fzUPyNGzfi008/RWlpKaKiouDl5YXp06cLB29vY2C2Zcw+oOft7S3Zhn6fvHz5soWjsy/6h120t7ebNF9qaiqSkpJw6tQpydsx9MePm5ub5C1E+lGUlZWVomk9bT9rHZsdHR1ITEyERqPBJ598YnIiBIDAwEAAhvvR4MGDAQC+vr6i+vr9rrq6utuYLD0Ij5OhFSkUCnh6ekKn06G5uVk0vesPxgqFAt7e3nBxcUF7e7vkvTtEhGnTppkVj0wmw/z58/Htt9+ivr4eWVlZICLEx8cLL3ju6xiYfaitrTV42oee/uR18wc1JycnXLt2TVS3vr5esu3ejDi0Fv3JuqGhweR509LSMHz4cGzbtg07duwwmKZQKKBWq6HT6dDU1CSaV//AiYCAAJOXa61jMyUlBW1tbcjIyDD4Bjd06FCj3zer/zZ7836kH4QlddVBv991veUCuHE/KBEJ28xSOBla2YwZMwD863KpXk1NDc6ePSuqHx8fj46ODoNRfXrr16/HwIED0dHRYVYs3t7eKCkpAXDj8tCDDz4ojGi7edhyX8bA7INOpxOesqL3008/oby8HFqt1uDEExgYiLKyMoO6lZWVuHjxomTb7u7uBslz+PDh2LJliwWj772RI0cCgFmXFD08PPDFF19ApVLho48+Ek2Pi4sDANGtAG1tbcjJyYFSqRT9bGKsvj42V69ejdOnT+PLL78U3QTfVVpaGsLCwkTlRCTcJK+//AkADz30EDQaDQ4dOiS6RUV/OfXhhx8Wtaff9/TbzFI4GVrZ22+/jX79+mHx4sU4fPgwmpubcebMGTz++OOSl07feecdhIaG4sknn8TBgwfR0NCAK1euYPPmzXjrrbfw7rvv9mqo+r/927/h5MmTaGtrw+XLl7FhwwYQESIjI60WA7M9tVqN119/HXl5eWhpacHx48fx+OOPw9XVFR988IFB3ejoaJSXl+M///M/0dzcjAsXLuDFF18UXebX+/Of/4xz587hjz/+QF5eHkpLSxEeHi5Mj4yMhK+vr9HfMvqCVquFn59ft8/c7MmIESOwefNmyWnvvPMOQkJCsHjxYuzbtw9NTU04d+4cHnvsMVRUVOCDDz6Q/AZkDFOPzccffxwymQy//vprj21v374d//7v/44ffvgBnp6eosuwUrd5/Pjjj3j++efxyy+/QKfT4ezZs5g/fz4KCwuxaNEijB8/XqirUCiQlpaG2tpazJs3D+fPn0d9fT127NiBd955B+PHj0dqaqpoGfpbRqKjo83psu6ZMNrG6u7E0aRERGfPnqWHH36YvLy8hOHS+/bto6ioKAJAAOipp54S6tfW1tKSJUtoyJAhJJfLacCAARQdHU2HDx8W6uTl5Qnz6v9WrFhBRCQqnzlzJhERFRUVUUpKCv3pT38id3d36tevH02YMIG2bt1KnZ2dBjEbE4MpsrOzRXHp/7oONb9TWGp/zszMFPVZcnKyWfsA0Y3RpBqNhs6cOUMxMTHk6elJSqWSIiIi6OjRo6Ll19fX08KFCykwMJCUSiVNmTKFCgoKKCwsTGh/2bJlQv2SkhIKDw8nlUpFwcHBohGb4eHhNh9NSkT0+uuvk4uLC5WVlQll1dXVor671QjNZ599VnJEbU1NDS1evJhCQkJILpeTWq2mmJgYysnJEeqYu/1MOTYjIyPJw8PDqNGgM2fO7PYY1f/dfDuMTqejPXv2UFxcHIWGhpJCoSC1Wk1Tp06lXbt2dbuc3NxciomJIbVaTa6urnTPPffQ6tWr6erVq5L1ExMTSaPR0LVr13pch6741grG7IC97s/6ZHgnMScZ1tfXk0ajoZSUlD6Kyrbq6upIqVR2e4/g7aCoqIhkMpnoNhVj8a0VjDHWA7VajezsbOzduxcbN260dTgWRURITU2Fl5cX1qxZY+twzFJaWor4+HgsX74c8+bNs3j7vU6Go0eP7vb9VVJ/a9eutUTcjDFmcWPGjMHx48dx8OBBNDY22joci6mqqkJpaSlycnLMGrlqDzZv3ox169Zh3bp1fdK+Rb4Z7tmzx2Aob0pKCgDg4MGDBuVJSUmWWByzU8Z8GFq9erWtw2T/n/7ZocXFxSgrK4NMJsPKlSttHZbNDR48GPv27YOXl5etQ7GYgIAAHD16FCNGjLB1KGZbv359n3wj1OMhgMxiSOI+NWa/li5diqVLl9o6DMbsQq+ToSlPRv/88897uzjGGGPM4ngADWOMMYfHyZAxxpjD42TIGGPM4XEyZIwx5vDsfjTp3r17b4un3jNmLN6frSMpKYlv52JGs/tkOGHCBLz00ku2DuO2lZeXh/fffx/p6em2DsXhvffeewDA+7MVJCUlYfHixTZ9YTCzP/rzoRS7T4Z33XUX5s6da+swbmvvv/8+96Ed2LNnDwDwtrCCpKQkTJw4kfuaiXSXDPk3Q8YYYw6PkyFjjDGHZ9FkuH37dshkMuEllzNmzIBMJkNzc7MlF8MYY4xZlEWT4YIFCwwezK3/k3qDO2OMMWYv7rjLpB4eHqI3JTg5OcHHxwdarRbPPfccCgsLbR2mw0pPT8fo0aOhVCqF7XPq1Clbh8VYj37//XfMmTMHjY2NqKmpMTjHjBkzBjqdTjRP13oymQxjx461QfR948CBAxg2bBhcXHoei1lUVISZM2fC29sbnp6eeOCBB3Ds2DFRvddee80mo9/vuGTY3NyMEydOAABiY2NBRGhvb0dJSQneeustlJSUYOzYsfjrX/+Kq1ev2jhax3Ls2DE8+uijiI6ORnV1NX755Rfcddddtg6LsR4VFRVh7NixiI6OhpeXF/r37w8iQkFBgTB98eLFovn09fLy8uDr6wsiwvHjx60dvsVduHABc+bMwfLly1FVVdVj/R9++AGTJk2Cp6cnfv75Z/z6668YMmQIpk6dim+++cag7tNPP43ly5dj1apVfRW+pDsuGUpxdnaGv78/YmNj8d133+HVV1/F9u3b8eijj/Jrh6xI/97LF198ER4eHggNDcUff/yBkSNHwsPDA1OmTLF1iA7H1v1u6+Ubo7GxEbNnz8YjjzyCF154QTRdoVDA19cXmzdvxu7du20QofWtWrUKkyZNQmFhITw9PW9Zt7OzE0899RS8vb3x8ccfIzAwEP3798d//dd/ITQ0FAsXLkRbW5tQPzQ0FJmZmVi3bh0yMjL6elUEDpEMu/rb3/6G8ePH46uvvuLXSlnRH3/8AQDw9fW1cSSMGW/Dhg2orKzEG2+8ITndzc0NO3fuhJOTE1JSUnDu3DkrR2h9//jHP/Daa68ZdXn0f//3f3H69GkkJCRAqVQK5c7Oznj00Ufxxx9/YN++fQbzaLVaJCQk4OWXX0ZHR4fF45fikMlQJpMJn/A++ugjG0fjOK5fv27rEBgzCREhLS0N48ePR1BQULf1YmJisHLlSjQ1NSExMVHy98M7yc1JrSffffcdAEj+Vqovy8nJEU2Li4vDpUuXsH//fjOjNI1DJkMAwqWZ/Px8tLe3C+XV1dVITU3F4MGD4erqigEDBiA+Pt7gJcZZWVkGP4j/9ttvSEpKgre3N3x9fTFr1ixcuHDBYHltbW144403cM8998Dd3R39+vXD7Nmz8dVXX4mShDEx9CVTYq2trcWSJUsQGhoKV1dX+Pj4YMaMGfj++++FOvr++vLLLwFAGDwzYcIEvPvuu5DJZGhpacGxY8eEPtV/4uza17///juSkpLg6ekJX19fzJ8/H3V1dfjtt98we/ZseHp6IjAwEE8//TSampoMYu3o6EB6ejoefPBBBAQEQKlUYtSoUfjggw/Q2dkp1JsyZYrBMh9//HEAwAMPPGBQXl9f3yf935Uxfbx27VohrpsvOx46dEgo79+/v1DeU7/rp8tkMtx1110oKChAVFQUPD094e7ujmnTphkMfrD08u1FcXExqqqqoNVqe6z75ptvIjo6GidPnsSiRYuMat+U48eU8w1g+/OIXklJCQBIjg/QaDQAIPltevTo0QCAr7/+ug+juwl1kZ6eThLFNpGQkEAJCQkmz3fixAkCQLGxsd3WaW1tJQAEgMrLy4mIqLy8nAYNGkT+/v60f/9+ampqolOnTlFERAS5ublRbm6uQRuxsbHCcnJzc6m5uZkOHz5MSqWSxo0bZ1B34cKFpFar6ZtvvqGrV69SZWUlLV26lADQ999/L9QzNYaemLM9jY21oqKCQkJCyN/fn7Kzs6mhoYHOnj1L8fHxJJPJaOvWrZL91draKlqmSqWiyZMndxuTft74+Hg6fvw4NTc306effkoAaMaMGRQbG0snTpygpqYm2rRpEwGgl156yaCN7OxsAkBvv/02Xblyhaqrq+k//uM/yMnJiZYuXWpQt6ioiFQqFWm1WmpubiYiIp1OR+PHj6fdu3eb1J965uzPpvZxd/0YFhZGvr6+ovKe+l2r1ZJKpaKJEycK+3hBQQHdd9995OrqSkeOHOnT5U+bNo369etHeXl53daRAoDS09NNmkfKjh07hH1GSkFBAanVauH/6upqCg4OJgD02WefCeV5eXmi9Tf3+DHmfGPp88itaDQacnZ27nb6gw8+SAAoPz9fNO38+fMEgP785z+LpjU0NBAACg8Pt1istzgfZjhsMrx69aooGT7xxBMEgHbu3GlQt6KighQKBYWFhRmU63fO7OxsUdwAqLq6WigLCQmhSZMmieIYNmyYQYIxNYaemLM9jY11wYIFBECUHHQ6HQUFBZFSqaTKykqh3BLJcP/+/QblI0aMIAD0P//zP6J1GD58uEFZdnY2TZ06VdT2448/TnK5nBoaGgzKMzIyhATc2dlJTzzxBL3++uvdxtgTc/ZnU/u4L5IhADpx4oRB+cmTJwkAabVao9ozd/kRERHk4+Nj8snbUslww4YNBIA2btwoOb1rMiS6kfjkcjmpVCr6+eefhbKu62/u8WPM+cbS55Fb6U0yPHfuHAHoNh6ZTEZDhw61WKy3SoYOe5m0oqICACCXy4XLN1lZWXBycsKsWbMM6gYEBGDEiBEoLCzEpUuXRG2NGzfO4P/g4GAAQHl5uVA2ffp05Obm4plnnkF+fr5wufHs2bOYOnWqUM/cGCzJ2FgzMzMBADNnzjSYX6FQICoqCq2trRa/xNH1dwf97zhdyzUajUH/A8CsWbMMLj/pabVatLe34/Tp0wbliYmJWLFiBf77v/8bU6ZMQW1tLdasWWOJ1TCaLfq4K5VKJVyy0hs1ahSCgoJQXFwsHEt94ciRI7hy5YrN3j6h/+1PLpcbPY/+8n9LSwsSExPR2toqWc/cbWvM+cYeziN63t7eAICWlhbRNH2Zvk5XLi4u3fafpTlsMjx69CgAYOLEiZDL5Whra0NDQwM6OzuhVqtFN8r++OOPAIDz58+L2lKr1Qb/u7q6AoDB71AbN27Ep59+itLSUkRFRcHLywvTp08XDggAvYrBkkyJ1c3NTXJotb+/PwCgsrLSorF5eXkZ/O/k5ARnZ2e4u7sblDs7Oxv0PwA0NDTgjTfewKhRo+Dj4yP06yuvvAIAkvedrlmzBuPHj0dubi4SExPh5GS9Q8ZWfdxVdycqPz8/AMDly5f7dPm25ObmBgAG4wqMkZqaiqSkJJw6dUrydozebNuezjf2ch7Ru+eeewBAMvmWlZUBAIYNGyY5b0dHh0mDdXrDIZNhZ2cnNm7cCAB4/vnnAdz4NObt7Q0XFxe0t7dLPlaOiDBt2jSzlimTyTB//nx8++23qK+vR1ZWFogI8fHx+Pvf/26VGCwdq1qthk6nEw1UASDciBsQEGD0Mvva7NmzsWbNGjz99NM4d+4cOjs7QUTCewZJ4p7TI0eOoKGhAaNGjcJzzz2H4uLiPo9Tz5w+dnJywrVr10R1uxvsY0y/19bWSvaNPgnqk2JfLd+WAgMDAdz4IGWqtLQ0DB8+HNu2bcOOHTsMpln6+Onatj2cR/T0y5F68pe+LCoqSjStsbERRCRsg77mkMlw+fLl+Oc//4m4uDgkJiYK5fHx8ejo6JB8RND69esxcOBAs+958fb2FkZVyeVyPPjgg8IosZuHDvdlDJaONS4uDgBEQ5/b2tqQk5MDpVKJmJgYo5bp7u5ucBIdPnw4tmzZ0ttVEVy/fh3Hjh1DQEAAUlNTMWDAAOFE3N1lmF9//RVPPfUUvvjiC3z11VdQKpWIjY1FdXW1xeLqial9HBgYKHza1qusrMTFixcl2zem33U6nfCkFb2ffvoJ5eXl0Gq1Biervli+LY0cORKA9Leannh4eOCLL76ASqWSvIXLksdPV/ZwHtGLiIjAvffei7179xrccnL9+nV8/vnnCA4OFl0qBv71rVG/DfqcCT8wWp2lBtBcv36dqqqqKCsriyIjIwkAPfnkk3T16lWD+aqqqig0NJSGDBlCBw4coPr6eqqtraVNmzaRu7u76Af57gaELFu2TDToQK1WU0REBBUXF5NOp6OqqipavXo1AaC1a9eaHUNPzNmexsbadTRcY2OjwWi4LVu2GLR7qwE006dPJ7VaTRcvXqTc3FxycXGhM2fO9DhvTEyM5I/3ERERpFKpDMr0237Dhg1UXV1NV69epe+++44GDhxIAOjw4cNC3aamJrrvvvvoyy+/FMqOHDlCcrmc/vKXv9C1a9eM7M1/scRo0p76+IUXXiAA9OGHH1JTUxP98ssvNHfuXNJoNJIDWHrqd61WS2q1mqKioowaTWrp5dt6NGlnZyf5+fl1O8hHagBNV5999hkB6HE0qbnHj9T5xtTzSHJyMgGg0tLSHvukq54G0BDdGEDk5uZG8+bNo4qKCqqpqaGUlBRycXGhQ4cOSc6za9cuAkCZmZkmx9QdhxpNqlKphFGi+j+ZTEZqtZpGjRpFzz77LBUWFnY7f21tLS1ZsoSGDBlCcrmcBgwYQNHR0QYnyry8PNEyVqxYQUQkKp85cyYR3Riqn5KSQn/605/I3d2d+vXrRxMmTKCtW7dSZ2enyTEYy5ztaUqsNTU1tHjxYgoJCSG5XE5qtZpiYmIoJydHqJOZmSnqFwAGJ7iSkhIKDw8nlUpFwcHBwui97vq6oKBAVP7OO+/Q//3f/4nK33zzTSK6Mew9JSWFgoODSS6Xk7+/Py1YsIBee+01oW5YWBg9//zzBvP/9NNPVF1dLWp3zZo1JvWruR/ujOljvfr6elq4cCEFBgaSUqmkKVOmUEFBAYWFhQlxL1u2rMd+19NqtaTRaOjMmTMUExNDnp6epFQqKSIigo4ePdrnyw8PD7fpaFIiotdff51cXFyorKxMKJPaH241QvPZZ5+V/DBgzLY153xDZNp5JDIykjw8PKijo8OoPtHfpiT11/WWEL0ff/yRZsyYQV5eXuTh4UGRkZGS+5BeYmIiaTQasz54dsehkiEzZE/b09HdjvuzPhnebiyZDOvr60mj0VBKSopF2rM3dXV1pFQqaeHChbYORVBUVEQymczse3q7w7dWMMaYmdRqNbKzs7F3715h4N2dgoiQmpoKLy8vq9821J3S0lLEx8dj+fLlmDdvntWWy8mQMcZ6MGbMGBw/fhwHDx5EY2OjrcOxmKqqKpSWliInJ8eskat9YfPmzVi3bh3WrVtn1eVyMmSMieifHVpcXIyysjLIZDKsXLnS1mHZ1ODBg7Fv3z7Rva63s4CAABw9ehQjRoywdSiC9evXW/UboZ59PRWXMWYXli5diqVLl9o6DMashr8ZMsYYc3icDBljjDk8ToaMMcYcHidDxhhjDq/bATQZGRnWjEOS/nmA9hDL7SovLw8A96E94P3ZuvT7PmN6t9onZESGj6PPyMhAUlJSnwfFGGOM2QKJ38KyR5QMGWOMMQezh38zZIwx5vA4GTLGGHN4nAwZY4w5PE6GjDHGHN7/A+IjQVoaiM5xAAAAAElFTkSuQmCC",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(\n",
    "    model,\n",
    "    show_shapes=True,\n",
    "    show_layer_names=True,\n",
    "    show_layer_activations=True,\n",
    "    show_trainable=True,\n",
    "    to_file='mlpf-model.png'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EGeviXzJbahY"
   },
   "source": [
    "Define some constants and train the MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "id": "laKFY8vcbahZ",
    "outputId": "5849045b-3563-4afb-edfa-5658aac89ec8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      " 31/422 [=>............................] - ETA: 2s - loss: 1.7234 - accuracy: 0.5454"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-30 23:08:01.363884: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:637] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "422/422 [==============================] - 3s 6ms/step - loss: 0.5106 - accuracy: 0.8615 - val_loss: 0.1838 - val_accuracy: 0.9432\n",
      "Epoch 2/10\n",
      "422/422 [==============================] - 2s 6ms/step - loss: 0.2288 - accuracy: 0.9268 - val_loss: 0.1447 - val_accuracy: 0.9577\n",
      "Epoch 3/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.1961 - accuracy: 0.9366 - val_loss: 0.1381 - val_accuracy: 0.9533\n",
      "Epoch 4/10\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.1775 - accuracy: 0.9425 - val_loss: 0.1268 - val_accuracy: 0.9595\n",
      "Epoch 5/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.1642 - accuracy: 0.9470 - val_loss: 0.1214 - val_accuracy: 0.9615\n",
      "Epoch 6/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.1538 - accuracy: 0.9497 - val_loss: 0.1157 - val_accuracy: 0.9625\n",
      "Epoch 7/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.1454 - accuracy: 0.9526 - val_loss: 0.1118 - val_accuracy: 0.9655\n",
      "Epoch 8/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.1365 - accuracy: 0.9550 - val_loss: 0.1076 - val_accuracy: 0.9653\n",
      "Epoch 9/10\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.1312 - accuracy: 0.9578 - val_loss: 0.1017 - val_accuracy: 0.9683\n",
      "Epoch 10/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.1250 - accuracy: 0.9596 - val_loss: 0.1042 - val_accuracy: 0.9667\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "n_epoch = 10\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X_train_hog, Y_train,\n",
    "                    batch_size=batch_size, epochs=n_epoch,\n",
    "                    verbose=1, validation_split=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aezTZuCMbahf"
   },
   "source": [
    "Show the performance of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "u6E66I52bahg",
    "outputId": "52139b93-823a-4fca-de59-a64f77f3b0ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.1110229343175888\n",
      "Test accuracy: 0.9628000259399414\n"
     ]
    },
    {
     "data": {
      "image/png": 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BA8jIyMALL7yAzz//3KENLztfKxKJrJs1ms1m67mOLOedgYGlH+LD/PD2N8Uo5AJyRESuRyQa8LCMM82YMQNnzpzBpEmTur3G09MTP/nJT/CTn/wEDz30EGJiYnDixAnMmDEDMpkMRuPA1gqbOHEipFIpvv76a4SFhQEAtFotzp07hxtvvHFAZfcXA0s/JISPAQB8f6UORpMZEvHAJlgRERFZPPXUU7j99tsRFhaGn/3sZxCLxfj+++9x4sQJPPvss8jJyYHRaMScOXOgVCrxxhtvwNPT07qnX2RkJA4dOoSf//znkMvlCAwMdLgOPj4+uP/++/G73/0O/v7+CA4OxtNPPw2xWGzX6zJcOIelHyYFe8NLJkGT3oiz5fW930BERNRHixYtwocffgiNRoNZs2YhKSkJ27ZtswYSPz8//Pd//zdSUlIwffp0fPLJJ/jggw8QEBAAANi8eTMuXbqEiRMnIigoqN/12LZtG5KTk3H77bdj4cKFSElJwZQpU6BQOOdhE5HZmQNSg0ir1cLX1xd1dXVQqVSDVq7BYMD+/fuxZMkSm/G+e/77Sxw9X43n7pqGe+aED9r3o5511x7kPGwT1+KO7dHS0oKLFy8iKirKaf+YdsdkMkGr1UKlUkEsHtl9BI2NjRg3bhxeeuklrFy50qF7e2qjvv77PbJ/ek7UvoAc57EQEdHoU1BQgL179+L8+fP47rvv8Itf/AIAsHTpUqfUh3NY+okr3hIR0Wj34osv4syZM5DJZEhMTMThw4f7NSdmMDCw9FN824q35yoaUN9igI/CPbpeiYjIPSQkJCA/P9/Z1bDikFA/BfsoMM7PE2az8LQQERERDR0GlgGw7CvEYSEiIqKhxcAyAJZ5LNwIkYjI+UbJQ6+j0mC0DQPLAHTsYeFfFCIi55BIJAAAvV7v5JpQd5qahN2zB/KoPSfdDsDUsb6QSkSoatDjSk0zwvwHvocEERE5xsPDA0qlEpWVlZBKpS613onJZIJer0dLS4tL1Wu4mM1mNDU1oaKiAn5+ftZw2R8MLAOgkEowJVSF76/UoaC4loGFiMgJRCIRQkNDcfHiRVy+fNnZ1bFhNpvR3NwMT09Ppy1p7wr8/PwQEjKwzYIZWAYoPswP31+pQ2FRLX5y/VhnV4eIyC3JZDJER0e73LCQwWDAoUOHMH/+fLdZebgzqVQ6oJ4VCwaWAUoI98OeY5dRwBVviYicSiwWu9zS/BKJBK2trVAoFG4bWAaL+w2oDbL4MGHn5h+vaqFrHdh23kRERNQ1BpYBigxQwk8phb7VhFOl3LmZiIhoKDCwDJBIJGrfV4jrsRAREQ0JBpZBwI0QiYiIhhYDyyBICBfmsRQwsBAREQ0JBpZBED/eDwBwuboJ1xpd65E6IiKi0aBfgWXnzp2IioqCQqFAYmIiDh8+3Kf7vvjiC3h4eCA+Pt7mfE5ODkQikd3R0tLSn+oNO1+lFBOCvAAAx9nLQkRENOgcDiy5ublYu3Yt1q9fj4KCAsybNw+LFy9GUVFRj/fV1dXhvvvuw4IFC7p8X6VSobS01OZwtefpe8KNEImIiIaOw4Fl27ZtWLlyJVatWoUpU6YgOzsbYWFh2LVrV4/3Pfjgg7jnnnuQnJzc5fsikQghISE2x0iSYAks7GEhIiIadA6tdKvX65Gfn49169bZnE9LS8PRo0e7ve/111/H+fPn8eabb+LZZ5/t8pqGhgZERETAaDQiPj4ezzzzDBISErotU6fTQafTWV9rtVoAwjLIBoPBkY/VI0tZvZU5bawPAOFJIZ1OD7HYffeMGEp9bQ8aPmwT18L2cC1sj9719WfjUGCpqqqC0WiEWq22Oa9Wq1FWVtblPefOncO6detw+PBheHh0/e1iYmKQk5ODadOmQavV4uWXX0ZKSgqOHz+O6OjoLu/ZunUrNm3aZHc+Ly8PSuXgb0Ko0Wh6fN9oAqQiCepbWvHX9z6C2nPQq0Ad9NYeNPzYJq6F7eFa2B7da2pq6tN1/dpLqPOOk2azuctdKI1GI+655x5s2rQJ1113XbflJSUlISkpyfo6JSUFM2bMwCuvvIIdO3Z0eU9WVhYyMzOtr7VaLcLCwpCWlgaVSuXoR+qWwWCARqNBampqr/tAvFX2Nb69XAvfCddjScK4QasDtXOkPWh4sE1cC9vDtbA9emcZIemNQ4ElMDAQEonErjeloqLCrtcFAOrr6/Htt9+ioKAADz/8MADAZDLBbDbDw8MDeXl5uOWWW+zuE4vFmDVrFs6dO9dtXeRyOeRyud15qVQ6JL8UfSk3IXwMvr1ci+9L6rFiNn8xh9JQtTP1H9vEtbA9XAvbo3t9/bk4NOlWJpMhMTHRrmtLo9Fg7ty5dterVCqcOHEChYWF1mP16tWYPHkyCgsLMWfOnC6/j9lsRmFhIUJDQx2pntNZNkLkirdERESDy+EhoczMTKSnp2PmzJlITk7G7t27UVRUhNWrVwMQhmpKSkqwZ88eiMVixMXF2dwfHBwMhUJhc37Tpk1ISkpCdHQ0tFotduzYgcLCQrz66qsD/HjDKyHcDwBwuqwezXojPGUS51aIiIholHA4sKxYsQLV1dXYvHkzSktLERcXh/379yMiIgIAUFpa2uuaLJ3V1tbigQceQFlZGXx9fZGQkIBDhw5h9uzZjlbPqUJ9FQj2kaOiXocTJXWYHeXv7CoRERGNCv2adJuRkYGMjIwu38vJyenx3o0bN2Ljxo0257Zv347t27f3pyouRSQSISHcDx//WI7C4hoGFiIiokHCvYQGmWUeS0FRrXMrQkRENIowsAwyyxL9nHhLREQ0eBhYBtn08b4Qi4DSuhaU1Y2MzRuJiIhcHQPLIPOSe+A6tWWZfm6ESERENBgYWIaA5fFmboRIREQ0OBhYhkACJ94SERENKgaWIRDf1sNy4kodWo0m51aGiIhoFGBgGQKTgrzhI/dAs8GIs+UNzq4OERHRiMfAMgTEYhGmh/kCAAo48ZaIiGjAGFiGiHU9Fs5jISIiGjAGliFinXjLJ4WIiIgGjIFliFgm3p6vbIC2xeDcyhAREY1wDCxDJNBbjjB/T5jNwPfFdc6uDhER0YjGwDKE2jdC5MRbIiKigWBgGULcCJGIiGhwMLAMoY5L9JvNZudWhoiIaARjYBlCsaEqSCUiXGvUo/has7OrQ0RENGIxsAwhhVSC2LFcQI6IiGigGFiGWELbPBZuhEhERNR/DCxDjBNviYiIBo6BZYhZJt6evKqFrtXo3MoQERGNUAwsQyzcXwl/Lxn0RhNOXtU6uzpEREQjEgPLEBOJRBwWIiIiGiAGlmEQz4m3REREA8LAMgzYw0JERDQwDCzD4Pq2wFJ0rQnVDTrnVoaIiGgEYmAZBr6eUkwM8gLAXhYiIqL+YGAZJpadmxlYiIiIHMfAMkysGyFy4i0REZHDGFiGiWXi7fHiWphM3LmZiIjIEf0KLDt37kRUVBQUCgUSExNx+PDhPt33xRdfwMPDA/Hx8XbvvfPOO4iNjYVcLkdsbCzee++9/lTNZcWE+EAhFaNe14oLVQ3Org4REdGI4nBgyc3Nxdq1a7F+/XoUFBRg3rx5WLx4MYqKinq8r66uDvfddx8WLFhg996xY8ewYsUKpKen4/jx40hPT8fy5cvx1VdfOVo9l+UhEWP6OD8AwHccFiIiInKIw4Fl27ZtWLlyJVatWoUpU6YgOzsbYWFh2LVrV4/3Pfjgg7jnnnuQnJxs9152djZSU1ORlZWFmJgYZGVlYcGCBcjOzna0ei4tvm0eCyfeEhEROcbDkYv1ej3y8/Oxbt06m/NpaWk4evRot/e9/vrrOH/+PN588008++yzdu8fO3YMjz32mM25RYsW9RhYdDoddLr2NU20WmGfHoPBAIPB0JeP0yeWsgajzGljfQAABZdrBrWO7mQw24MGB9vEtbA9XAvbo3d9/dk4FFiqqqpgNBqhVqttzqvVapSVlXV5z7lz57Bu3TocPnwYHh5df7uysjKHygSArVu3YtOmTXbn8/LyoFQqe/soDtNoNAMuo1YHAB44XabFex/sh1wy4CLd1mC0Bw0utolrYXu4FrZH95qamvp0nUOBxUIkEtm8NpvNducAwGg04p577sGmTZtw3XXXDUqZFllZWcjMzLS+1mq1CAsLQ1paGlQqVV8+Rp8YDAZoNBqkpqZCKpUOuLyd//4c5Vodxk1LwuxI/0GooXsZ7PaggWObuBa2h2the/TOMkLSG4cCS2BgICQSiV3PR0VFhV0PCQDU19fj22+/RUFBAR5++GEAgMlkgtlshoeHB/Ly8nDLLbcgJCSkz2VayOVyyOVyu/NSqXRIfikGq9yEsDH4149lOHG1ASnR3X8+6tlQtTP1H9vEtbA9XAvbo3t9/bk4NOlWJpMhMTHRrmtLo9Fg7ty5dterVCqcOHEChYWF1mP16tWYPHkyCgsLMWfOHABAcnKyXZl5eXldljnSWSfe8kkhIiKiPnN4SCgzMxPp6emYOXMmkpOTsXv3bhQVFWH16tUAhKGakpIS7NmzB2KxGHFxcTb3BwcHQ6FQ2Jxfs2YN5s+fj+effx5Lly7Fvn37cODAARw5cmSAH8/1JHDnZiIiIoc5HFhWrFiB6upqbN68GaWlpYiLi8P+/fsREREBACgtLe11TZbO5s6di7fffhsbNmzAk08+iYkTJyI3N9faAzOaTBvvC4lYhDJtC0rrmhHq6+nsKhEREbm8fk26zcjIQEZGRpfv5eTk9Hjvxo0bsXHjRrvzy5Ytw7Jly/pTnRFFKfPAZLUPTpZqUVhUi9BpDCxERES94V5CTmCZx1LAYSEiIqI+YWBxAstGiJx4S0RE1DcMLE4wo62H5fuSWrQaTc6tDBER0QjAwOIEEwK94aPwQIvBhNNl9c6uDhERkctjYHECsVjUPizEeSxERES9YmBxEktgKeA8FiIiol4xsDhJew9LjXMrQkRENAIwsDiJJbCcr2xEXTO3HSciIuoJA4uTBHjLEe6vBAAc5zwWIiKiHjGwOBEn3hIREfUNA4sTJVh2bmZgISIi6hEDixO1PylUA7PZ7NzKEBERuTAGFieKHauCTCJGTZMBRdeanF0dIiIil8XA4kRyDwlix6oAcD0WIiKinjCwOBkn3hIREfWOgcXJLBNvCxhYiIiIusXA4mQJYWMAACev1qHFYHRybYiIiFwTA4uThfl7IsBLBoPRjJOlWmdXh4iIyCUxsDiZSCTiRohERES9YGBxAZx4S0RE1DMGFheQEC7MY+HOzURERF1jYHEB08N8IRIBxdeaUdWgc3Z1iIiIXA4DiwtQKaSYFOQNACjkPBYiIiI7DCwugvNYiIiIusfA4iLirQvIcR4LERFRZwwsLsKygNzx4joYTdy5mYiIqCMGFhdxndobnlIJGnStOF/Z4OzqEBERuRQGFhfhIRFj+nhfAJx4S0RE1BkDiwuJ50aIREREXWJgcSEJ1iX6OfGWiIioo34Flp07dyIqKgoKhQKJiYk4fPhwt9ceOXIEKSkpCAgIgKenJ2JiYrB9+3aba3JyciASieyOlpaW/lRvxLKseHu2vB6NulYn14aIiMh1eDh6Q25uLtauXYudO3ciJSUFr732GhYvXoyTJ08iPDzc7novLy88/PDDmD59Ory8vHDkyBE8+OCD8PLywgMPPGC9TqVS4cyZMzb3KhSKfnykkUutUiDUV4HSuhZ8f6UOyRMDnF0lIiIil+BwD8u2bduwcuVKrFq1ClOmTEF2djbCwsKwa9euLq9PSEjA3XffjalTpyIyMhL33nsvFi1aZNcrIxKJEBISYnO4Iy4gR0REZM+hHha9Xo/8/HysW7fO5nxaWhqOHj3apzIKCgpw9OhRPPvsszbnGxoaEBERAaPRiPj4eDzzzDNISEjothydTgedrn3fHa1WCwAwGAwwGAx9/Ui9spQ1mGX2ZPo4FT76oQzfXb4Gg8G+x8rdDXd7UO/YJq6F7eFa2B696+vPxqHAUlVVBaPRCLVabXNerVajrKysx3vHjx+PyspKtLa2YuPGjVi1apX1vZiYGOTk5GDatGnQarV4+eWXkZKSguPHjyM6OrrL8rZu3YpNmzbZnc/Ly4NSqXTkY/WJRqMZ9DK70qwFAA989e9y/POf+yESDcu3HXGGqz2o79gmroXt4VrYHt1ramrq03UOz2EBhOGbjsxms925zg4fPoyGhgZ8+eWXWLduHSZNmoS7774bAJCUlISkpCTrtSkpKZgxYwZeeeUV7Nixo8vysrKykJmZaX2t1WoRFhaGtLQ0qFSq/nysLhkMBmg0GqSmpkIqlQ5aud1p1hvx6qlPoTUAM264BaG+7jWPpzfD3R7UO7aJa2F7uBa2R+8sIyS9cSiwBAYGQiKR2PWmVFRU2PW6dBYVFQUAmDZtGsrLy7Fx40ZrYOlMLBZj1qxZOHfuXLflyeVyyOVyu/NSqXRIfimGqtyuvk9MiA9+vKrFiasNCA/0GfLvORINV3tQ37FNXAvbw7WwPbrX15+LQ5NuZTIZEhMT7bq2NBoN5s6d2+dyzGazzfyTrt4vLCxEaGioI9UbNdon3nI9FiIiIqAfQ0KZmZlIT0/HzJkzkZycjN27d6OoqAirV68GIAzVlJSUYM+ePQCAV199FeHh4YiJiQEgrMvy4osv4pFHHrGWuWnTJiQlJSE6OhparRY7duxAYWEhXn311cH4jCNOQvgY/O2rIj4pRERE1MbhwLJixQpUV1dj8+bNKC0tRVxcHPbv34+IiAgAQGlpKYqKiqzXm0wmZGVl4eLFi/Dw8MDEiRPxhz/8AQ8++KD1mtraWjzwwAMoKyuDr68vEhIScOjQIcyePXsQPuLIY+lh+f5KHQxGE6QSLkhMRETurV+TbjMyMpCRkdHlezk5OTavH3nkEZvelK5s377dbvVbdzYh0AsqhQe0La04U1aPuHG+zq4SERGRU/F/3V2QWCzC9ZZ9hTgsRERExMDiqrgRIhERUTsGFhdl2QiRE2+JiIgYWFyWZUjoQmUj6pq4pDMREbk3BhYX5e8lQ2SAsMVA4ZVa51aGiIjIyRhYXJh1AbmiWqfWg4iIyNkYWFxYvPVJIU68JSIi98bA4sI6Trw1m81Org0REZHzMLC4sCmhKsg8xKhtMuBSdd+23yYiIhqNGFhcmMxDjLixKgDcCJGIiNwbA4uLiw9rGxbixFsiInJjDCwuLj7cDwCX6CciIvfGwOLiLEv0nyrVosVgdG5liIiInISBxcWNH+OJQG8ZDEYzfryqdXZ1iIiInIKBxcWJRKL29Vi4ESIREbkpBpYRgBshEhGRu2NgGQHae1hqnVoPIiIiZ2FgGQGmj/eFSASU1Dajsl7n7OoQERENOwaWEcBHIUV0sDcADgsREZF7YmAZITjxloiI3BkDywjBibdEROTOGFhGCEsPy/HiWhhN3LmZiIjcCwPLCHGd2gdKmQSNeiP+XdHg7OoQERENKwaWEUIiFmH6eF8A3LmZiIjcDwPLCGLZuZnrsRARkbthYBlBEtp2bubEWyIicjcMLCOIZefmM+X1aNC1OrcyREREw4iBZQQJVikwzs8TZjPw/ZVaZ1eHiIho2DCwjDCWx5s5LERERO6EgWWE4UaIRETkjvoVWHbu3ImoqCgoFAokJibi8OHD3V575MgRpKSkICAgAJ6enoiJicH27dvtrnvnnXcQGxsLuVyO2NhYvPfee/2p2qjXceKt2cwF5IiIyD04HFhyc3Oxdu1arF+/HgUFBZg3bx4WL16MoqKiLq/38vLCww8/jEOHDuHUqVPYsGEDNmzYgN27d1uvOXbsGFasWIH09HQcP34c6enpWL58Ob766qv+f7JRKm6cLzzEIlTW63C1rsXZ1SEiIhoWDgeWbdu2YeXKlVi1ahWmTJmC7OxshIWFYdeuXV1en5CQgLvvvhtTp05FZGQk7r33XixatMimVyY7OxupqanIyspCTEwMsrKysGDBAmRnZ/f7g41WCqkEU0JVALgRIhERuQ+HAoter0d+fj7S0tJszqelpeHo0aN9KqOgoABHjx7FjTfeaD137NgxuzIXLVrU5zLdjXXiLeexEBGRm/Bw5OKqqioYjUao1Wqb82q1GmVlZT3eO378eFRWVqK1tRUbN27EqlWrrO+VlZU5XKZOp4NOp7O+1mq1AACDwQCDwdDnz9QbS1mDWeZATRvrAwD4rqjGpeo1HFyxPdwd28S1sD1cC9ujd3392TgUWCxEIpHNa7PZbHeus8OHD6OhoQFffvkl1q1bh0mTJuHuu+/ud5lbt27Fpk2b7M7n5eVBqVT25WM4RKPRDHqZ/VXXDAAeOFFcgw8+3A+JGz7r5UrtQQK2iWthe7gWtkf3mpqa+nSdQ4ElMDAQEonEruejoqLCroeks6ioKADAtGnTUF5ejo0bN1oDS0hIiMNlZmVlITMz0/paq9UiLCwMaWlpUKlUjnysHhkMBmg0GqSmpkIqlQ5auQNhNpvxpzOfoa65FVEJNyBu3OB9Xlfniu3h7tgmroXt4VrYHr2zjJD0xqHAIpPJkJiYCI1Gg7vuust6XqPRYOnSpX0ux2w22wznJCcnQ6PR4LHHHrOey8vLw9y5c7stQy6XQy6X252XSqVD8ksxVOX21/VhY3DobCVOlNYjITLA2dUZdq7WHsQ2cTVsD9fC9uheX38uDg8JZWZmIj09HTNnzkRycjJ2796NoqIirF69GoDQ81FSUoI9e/YAAF599VWEh4cjJiYGgLAuy4svvohHHnnEWuaaNWswf/58PP/881i6dCn27duHAwcO4MiRI45Wz20khPnh0NlKFBbV4r5kZ9eGiIhoaDkcWFasWIHq6mps3rwZpaWliIuLw/79+xEREQEAKC0ttVmTxWQyISsrCxcvXoSHhwcmTpyIP/zhD3jwwQet18ydOxdvv/02NmzYgCeffBITJ05Ebm4u5syZMwgfcXSKb1tAroBL9BMRkRvo16TbjIwMZGRkdPleTk6OzetHHnnEpjelO8uWLcOyZcv6Ux23FD/eDwBwsaoRtU16+Cllzq0QERHREHLD50tGhzFeMkQFegHgRohERDT6MbCMYNwIkYiI3AUDywjWcSNEIiKi0YyBZQSzLtHPnZuJiGiUY2AZwWJCVJB7iFHXbMDFqkZnV4eIiGjIMLCMYDIPMeLG+QLgsBAREY1uDCwjHCfeEhGRO2BgGeE48ZaIiNwBA8sIZ+lhOVWqRYvB6NzKEBERDREGlhFunJ8ngnzkaDWZ8UNJnbOrQ0RENCQYWEY4kUhk83gzERHRaMTAMgpw4i0REY12DCyjACfeEhHRaMfAMgpMH+8HkQgoqW1GhbbF2dUhIiIadAwso4C33AOT1T4AgAL2shAR0SjEwDJKcOItERGNZgwso4Q1sHDiLRERjUIMLKNEQvgYAMD3V2phNHHnZiIiGl0YWEaJScHe8JJJ0Kg34lxFvbOrQ0RENKgYWEYJiViE6eP9AHA9FiIiGn0YWEYR63osDCxERDTKMLCMInxSiIiIRisGllEkvq2H5WxFPepbDM6tDBER0SBiYBlFgn0UGOfnCbMZOHGFOzcTEdHowcAyylh6WbjiLRERjSYMLKNMAnduJiKiUYiBZZTpuHOz2cwF5IiIaHRgYBllpo71hVQiQlWDDldqmp1dHSIiokHBwDLKKKQSTAlVAeDjzURENHowsIxC8ZzHQkREo0y/AsvOnTsRFRUFhUKBxMREHD58uNtr3333XaSmpiIoKAgqlQrJycn4+OOPba7JycmBSCSyO1paWvpTPbfXPo+lxrkVISIiGiQOB5bc3FysXbsW69evR0FBAebNm4fFixejqKioy+sPHTqE1NRU7N+/H/n5+bj55ptxxx13oKCgwOY6lUqF0tJSm0OhUPTvU7m5+DBh5+YfrmqhbzU5uTZEREQD5+HoDdu2bcPKlSuxatUqAEB2djY+/vhj7Nq1C1u3brW7Pjs72+b1c889h3379uGDDz5AQkKC9bxIJEJISIij1aEuRAYo4aeUorbJgFOlWlzfNkREREQ0UjnUw6LX65Gfn4+0tDSb82lpaTh69GifyjCZTKivr4e/v7/N+YaGBkRERGD8+PG4/fbb7XpgqO9EIhH3FSIiolHFoR6WqqoqGI1GqNVqm/NqtRplZWV9KuOll15CY2Mjli9fbj0XExODnJwcTJs2DVqtFi+//DJSUlJw/PhxREdHd1mOTqeDTqezvtZqtQAAg8EAg2Hw9tGxlDWYZQ6H6WNVOHimEt9dvoZ7Zo1zdnUGzUhtj9GMbeJa2B6uhe3Ru77+bBweEgKE/4PvyGw2253ryt69e7Fx40bs27cPwcHB1vNJSUlISkqyvk5JScGMGTPwyiuvYMeOHV2WtXXrVmzatMnufF5eHpRKZV8/Sp9pNJpBL3Mo6WtFACT44sxV7N9f7OzqDLqR1h7ugG3iWtgeroXt0b2mpqY+XedQYAkMDIREIrHrTamoqLDrdeksNzcXK1euxD/+8Q8sXLiwx2vFYjFmzZqFc+fOdXtNVlYWMjMzra+1Wi3CwsKQlpYGlUrVh0/TNwaDARqNBqmpqZBKpYNW7lBLaTbgv059hqoWEZJvWogxSpmzqzQoRmp7jGZsE9fC9nAtbI/eWUZIeuNQYJHJZEhMTIRGo8Fdd91lPa/RaLB06dJu79u7dy9+9atfYe/evbjtttt6/T5msxmFhYWYNm1at9fI5XLI5XK781KpdEh+KYaq3KESKJViQpAXLlQ24sfSRtwc4+XsKg2qkdYe7oBt4lrYHq6F7dG9vv5cHB4SyszMRHp6OmbOnInk5GTs3r0bRUVFWL16NQCh56OkpAR79uwBIISV++67Dy+//DKSkpKsvTOenp7w9fUFAGzatAlJSUmIjo6GVqvFjh07UFhYiFdffdXR6lEH8WF+uFDZiILiWtwcE9z7DURERC7K4cCyYsUKVFdXY/PmzSgtLUVcXBz279+PiIgIAEBpaanNmiyvvfYaWltb8dBDD+Ghhx6ynr///vuRk5MDAKitrcUDDzyAsrIy+Pr6IiEhAYcOHcLs2bMH+PHcW0KYH979roRPChER0YjXr0m3GRkZyMjI6PI9SwixOHjwYK/lbd++Hdu3b+9PVagHCeHCAnKFRTUwmcwQi3ufGE1EROSKuJfQKDY5xAdyDzG0La24WN3o7OoQERH1GwPLKCaViDFtnDBPiBshEhHRSMbAMspxI0QiIhoNGFhGOctGiJx4S0REIxkDyyhn6WE5VVqPZr3RuZUhIiLqJwaWUS7UV4FgHzmMJjN+uFrn7OoQERH1CwPLKGezczMn3hIR0QjFwOIGLOuxFHDiLRERjVAMLG6APSxERDTSMbC4genjfSEWAVfrWlCubXF2dYiIiBzGwOIGvOQeuE7tAwDIPnAWdU0GJ9eIiIjIMQwsbuKnM8YBAPZ+XYx5f/wUuw6eR4uBjzkTEdHIwMDiJn49bwL+fP9MTFb7QNvSiuf/dRo3vXAQb39dhFajydnVIyIi6hEDi5sQiURYMEWN/Wvm4aWfXY9xfp4o07Zg3bsnsCj7EP71QynMZrOzq0lERNQlBhY3IxGL8B+J4/HJb2/EhtumYIxSivOVjVj95ne4a+dRfHmh2tlVJCIissPA4qYUUglWzZuAz5+4GY/cMgmeUgkKi2vx891f4pevf42TV7XOriIREZEVA4ubUymk+G3aZHz+u5twb1I4PMQiHDxTidteOYzHcgtRfK3J2VUkIiJiYCFBsEqBZ++chgOZN+L26aEwm4H3Ckpwy0sHsfH9H1HdoHN2FYmIyI0xsJCNyEAv/OmeGfjg4Rtww6RAGIxm5By9hBtfOIiXD5xDo67V2VUkIiI3xMBCXZo23hdvrpqDN1fOwbRxvmjQtWL7gbO48YXP8Nejl6Bv5aPQREQ0fBhYqEc3RAdi30MpeOXuBEQGKFHVoMfT7/+Ihds+x77CEphMfBSaiIiGHgML9UosFuGO68dCk3kjnrkzDoHechRda8Katwtxx5+O4POzlVzDhYiIhhQDC/WZVCJGelIEPv/dTfht6nXwlnvgx6ta3P+Xr/GL//kKx4trnV1FIiIapRhYyGFecg88siAah564GStviIJMIsbR89VY+uoXyPhbPi5UNji7ikRENMowsFC/+XvJ8OTtsfj08Rvx0xnjIBIB+0+UIXX7IWS9ewLl2hZnV5GIiEYJBhYasPFjlNi2PB4frZmHBTHBMJrM2Pt1EW584TP88V+nUddscHYViYhohGNgoUETE6LCn385C39/MBkzwv3QYjBh58HzuPGFz7D70Hm0GIzOriIREY1QDCw06GZH+eOd38zF7vRERAd7o7bJgOf2n8YtLx7E378thpGPQhMRkYMYWGhIiEQipE0Nwb/Wzscfl01HqK8CV+ta8MT/fo9bsw8h78cyPgpNRER9xsBCQ0oiFmH5zDB89vhN+M8lMfD1lOJcRQMeeCMfy/7rGL65dM3ZVSQiohGAgYWGhUIqwQPzJ+LQEzcj46aJUEjFyL9cg5/91zGszPkGZ8rqnV1FIiJyYf0KLDt37kRUVBQUCgUSExNx+PDhbq999913kZqaiqCgIKhUKiQnJ+Pjjz+2u+6dd95BbGws5HI5YmNj8d577/WnauTifD2leOLWGHz+u5tx9+xwSMQifHK6Are+fAi//ftxXKlpcnYViYjIBTkcWHJzc7F27VqsX78eBQUFmDdvHhYvXoyioqIurz906BBSU1Oxf/9+5Ofn4+abb8Ydd9yBgoIC6zXHjh3DihUrkJ6ejuPHjyM9PR3Lly/HV1991f9PRi5NrVJg60+nIe+x+VgyLQRmM/DOd1dwy4uf49kPT6KmUe/sKhIRkQtxOLBs27YNK1euxKpVqzBlyhRkZ2cjLCwMu3bt6vL67OxsPPHEE5g1axaio6Px3HPPITo6Gh988IHNNampqcjKykJMTAyysrKwYMECZGdn9/uD0cgwMcgbO3+RiP97KAXJEwKgN5rwP0cuYv4fP8OfPj2HJn2rs6tIREQuwMORi/V6PfLz87Fu3Tqb82lpaTh69GifyjCZTKivr4e/v7/13LFjx/DYY4/ZXLdo0aIeA4tOp4NOp7O+1mq1AACDwQCDYfAWKrOUNZhlkr2pIV746y9n4Mi/q/FC3jmcKqvHi3ln8dejl/DwzRPxs8RxkErEbA8XxDZxLWwP18L26F1ffzYOBZaqqioYjUao1Wqb82q1GmVlZX0q46WXXkJjYyOWL19uPVdWVuZwmVu3bsWmTZvszufl5UGpVPapLo7QaDSDXiZ17YFIoMBHhH8WiVHZoMfTH5zCnzQncVuYCdcHmCEWsT1cEdvEtbA9XAvbo3tNTX2bu+hQYLEQiUQ2r81ms925ruzduxcbN27Evn37EBwcPKAys7KykJmZaX2t1WoRFhaGtLQ0qFSqvnyMPjEYDNBoNEhNTYVUKh20cqlntwP4fasJud9ewasHL6CyUY+ccxJMbfDBjX61eGjZAshkMmdXk8C/I66G7eFa2B69s4yQ9MahwBIYGAiJRGLX81FRUWHXQ9JZbm4uVq5ciX/84x9YuHChzXshISEOlymXyyGXy+3OS6XSIfmlGKpyqXtSKfCreROxfHYE/nz4InYfOo8fS+vxY6kE77/yJRbGhiA1Vo3ZUf6QSviEvrPx74hrYXu4FrZH9/r6c3Hov/IymQyJiYl2XVsajQZz587t9r69e/fil7/8Jd566y3cdtttdu8nJyfblZmXl9djmeQ+vOUeWLMwGp8/cTPuSwqHVGzGldoW5By9hF/8z1eY8YwGj+wtwL7CEm60SEQ0Sjk8JJSZmYn09HTMnDkTycnJ2L17N4qKirB69WoAwlBNSUkJ9uzZA0AIK/fddx9efvllJCUlWXtSPD094evrCwBYs2YN5s+fj+effx5Lly7Fvn37cODAARw5cmSwPieNAoHecjx5WwymmS7AZ9JMfHa2Gp+cLkdVgx4fHL+KD45fhYdYhNlR/lg4RY3UWDXC/Ad/PhMREQ0/hwPLihUrUF1djc2bN6O0tBRxcXHYv38/IiIiAAClpaU2a7K89tpraG1txUMPPYSHHnrIev7+++9HTk4OAGDu3Ll4++23sWHDBjz55JOYOHEicnNzMWfOnAF+PBqNZBJgwZRg3Dp9HEwmMwqv1OLAyXJoTpbjXEUDjp6vxtHz1dj84UnEhPhg4RQ1FsaqMX2cL8Ti3udaERGR6+nXpNuMjAxkZGR0+Z4lhFgcPHiwT2UuW7YMy5Yt6091yI2JxSLMCB+DGeFj8MStMbhU1YgDp8px4FQ5vrlUg9Nl9ThdVo8/ffZvBPnIsXBKMBZOUSNlUiAUUomzq09ERH3Ur8BC5KoiA72wat4ErJo3AbVNehw8UwnNqXJ8fqYSlfU67P26GHu/LoZCKsa86CCkTlHjlinBCPS2n8BNRESug4GFRi0/pQx3JozDnQnjoG814auL1ThwshwHTlWgpLYZmrZhJJEImBE+pm3eSzAmBnn36TF9IiIaPgws5BZkHkKPyrzoIGz8iRmnSuuhOSkMHZ0oqUP+5RrkX67B8/86jcgApXXey8yIMfDgI9NERE7HwEJuRyQSIXasCrFjVVizMBqldc345FQFDpwqx9F/V+NSdRP+58hF/M+Ri/D1lOKWGGHey/zrAuGj4DoKRETOwMDSm6ZqeOqrnF0LGkKhvp64NykC9yZFoEHXiiPnKqE5WYFPT5ejpsmA9wpK8F5BCaQSEZImBCAtVo0FU9QY6+fp7KoTEbkNBpZeSDQbcMup9yEapwOSHgTEfLJkNPOWe+DWuFDcGhcKo8mM/Ms1OHBKmOtysaoRh89V4fC5Kjy570dMHauyrvcydayK816IiIYQA0tPDM1AXTE8TDogLws4+S7wk1eA4CnOrhkNA0nbInSzo/zxn0um4HxlQ9uk3XLkX67Bj1e1+PGqFi9/cg6hvgosaHtkOnliAOQeDLZERIOJgaUnUk8Y09/HiT2/w/SKdyC68g3wX/OAeb8F5mUCHnwU1p1MDPLGxBu98eCNE1HdoMNnZypx4GQ5Dp2rRGldC978sghvflkEL5kEN04OwsIpatw8ORhjvLhJIxHRQDGw9EYkxqWgBYi967eQfvx74OxHwOd/AE7+n9DbEjbb2TUkJwjwlmNZ4ngsSxyPFoMRxy5UQ3OyHJ+cKke5Vof9J8qw/0QZxCJgZqQ/UtueOooK9HJ21YmIRiQGlr5SjQXu3gv8+B7w0RNA5Wngz2nA7F8DC54C5D7OriE5iUIqwc2Tg3Hz5GCYlsbhh6t1wlYBpypwqlSLry9ew9cXr2HL/lOYGOSFhbFqpE5RIyF8DCTcKoCIqE8YWBwhEgFxPwUm3ATkbQAK/wZ8vRs4vR+4fTtwXZqza0hOJhaLMH28H6aP90Nm2mRcqWmyPjJ97Hw1zlc24vznF/Da5xfg7yXD7Eh/JIT7ISF8DKaN84WnjHNfiIi6wsDSH0p/4M6dwLSfAR+sAWovA2/9DIhbBix+HvAKdHYNyUWMH6PE/XMjcf/cSGhbDDh0Vpj38unpClxr1ONfP5bhXz8KO5hLxCLEhPgIASZsDBLC/RAV6MWnj4iIwMAyMBNvBjKOAZ89B3y5E/jhf4HznwK3bgWmrxB6ZIjaqBRS3D59LG6fPhYGowmFxbUoKKpBQVEtviuqQblWZ33y6M0vhR3P/ZRSxIe1B5jrw/zg68nF64jI/TCwDJTMC1i0BYj7D+D9R4DyH4D3HgS+/7swTDQmwtk1JBcklYgxK9IfsyL9redK65pRUNQeYk6U1KG2yYCDZypx8Eyl9bpJwd5ICBOGkRLC/XCd2odzYYho1GNgGSzjZgAPHASO7gAOPg+c/wTYmQTc8iQwhwvOUe9CfT0ROs0TS6aFAgD0rSacLtO2h5jiWlyubsK/Kxrw74oG/CP/CgBAKZPg+vF+SAj3E3pjwscgyIeP3BPR6MLAMpgkUmGNlik/Eea2XP4C+DhLGCr6ySuAeqqza0gjiMxDbJ3Ae//cSABAdYOubSipFgXFNTheXIcGXSuOXajGsQvV1nvHj/EUemDChCATO1bFxeyIaERjYBkKgdHA/R8C3+UAmqeBknzgtfnADZnA/Me54Bz1W4C3HAumCHsZAYDRZMb5ygbrMFJBUS3OVtTjSk0zrtQ044PjVwEAMokYU8eprHNhEsL9MM7PkxN6iWjEYGAZKmIxMPNXwHW3Av98HDjzT+DQH4UF5+7YAUQkO7uGNApIxCJcp/bBdWofrJgVDgCobzHg+yt17SGmuBbXGvXWQIMvhHuDfOQ2c2Gmj/eFUsb/JBCRa+J/nYaaaizw878BJ/cB+38HVJ0FXr8VmLUKWPA0oFA5u4Y0yvgopEiZFIiUScLj9WazGUXXmmzmwpy8qkVlvQ55J8uRd7IcACAWATEhKpu5MBMCvSDmhF4icgEMLMNBJAKm3glMuFFYcK7gTeCb/wHOfATctg2YfKuza0ijmEgkQkSAFyICvHBnwjgAQIvBiB9K6qxzYQqKalFa14KTpVqcLNXib18Jj1WrFB6I7zAXJj7MD35K7o1ERMOPgWU4eY4Blr7avuBczSVg7wpg6k+BxX8EvIOcXUNyEwqpBDMj/TGz02PVhW1DSAVFNfj+Sh20La04dLYSh862P1Y9IcjLZi7MBH+FMz4CEbkZBhZnmHAT8JtjwMGtwLE/AT++C1z4DFj0HHD93VxwjpzC8lj14rbHqg1GE86U1dvMhblY1YgLlcLxznfCY9WeUjGC5RIc1v2ImFAVJocIc2qCfeSc1EtEg4aBxVlkSiDtGWFvovcfAcpOAP/3G2HBuTuygTGRzq4huTmpRIy4cb6IG+eL9LY54tca9The3D4XprCoFvW6Vlw2iHD5uxIAJdb7fT2luE7tjevUPpgc4oPoYOFPfy8OKRGR4xhYnG1sAvDrz4SeloN/EHpadiYDN68Hkn7DBefIpfh7yXBzTDBujgkGAJhMZpwprUXux4fhPTYa/65swtmKelyqakRdswHfXKrBN5dqbMoI9JZZn2wSwow3otU+UCm45QARdY+BxRVIpMANj7UvOHfpMJC3HvjhHWHBuZA4Z9eQqEtisUjYKiDAjCULJkEqFUJHi8GI85UNOFfegDPl9ThXXo8z5fUovtaMqgY9qhqqcfR8tU1Zob6KthDT3iszKdibj1oTEQAGFtcSMBG4732g4A0g70ng6nfA7huBlDXA/CcAKSc30sigkEowdawvpo71tTnfqGvFvys6hpgGnC2rR5m2BaV1wvF5hwm+IhEQNkZpN7Q0MdiLK/cSuRkGFlcjFgOJ9wPRacBHvwNOfQAcfklYx+WOHUBkirNrSNRvXnIPXB8m7DrdUV2zAefK63G2vAFny+txpqwe5yrqUdWgR9G1JhRda8KBUxXW6yViESIDlHZDSxEBXpBKxMP8qYhoODCwuCpVKLDiTeDk+8D+x4HqfwM5S4TVcxduBBS+vRZBNFL4ekrtHrMGhL2TLCHGcpwpq4e2pRXnKxtxvrIRH/1QZr1eJhFjQpCX3dBS2BglF8AjGuEYWFxd7E+AqPmA5ingu78C3/6lbcG5l4CY25xdO6IhFeAtR7K3HMkTA6znzGYzKup1OFPWIcSUN+BceT2a9EacLqvH6bJ6m3IUUjGig30QrfbGZLUPrmt79Hqsr4KPXhONEAwsI4GnH/CTHcC0ZcKk3GsXgLfvAWKXAotfAHzUzq4h0bARiURQqxRQqxSYf137Yosmkxkltc04V1GPM2XtvTLnKhrQYjDhREkdTpTU2ZTlLfdoDzFqH0QFeSHCX4nxY5SQeXBoiciV9Cuw7Ny5Ey+88AJKS0sxdepUZGdnY968eV1eW1pait/+9rfIz8/HuXPn8OijjyI7O9vmmpycHPy///f/7O5tbm6GQsGJplZR84HfHBUefz76ijCv5cJBIG0LkHAvF5wjtyYWixDmr0SYvxK3xLSHeKPJjMvVjXZDSxcqG9Gga23fFLJjWSJgrJ8nIgKUCPf3QmSA0vp1RIASXnL+vx7RcHP4b11ubi7Wrl2LnTt3IiUlBa+99hoWL16MkydPIjw83O56nU6HoKAgrF+/Htu3b++2XJVKhTNnzticY1jpgtQTSN3UvuBc6XHg/YeBE38H7ngZ8J/g7BoSuRSJWIQJQd6YEOSNW+NCrOf1rSZcqm4UJvi2PXZ9uboJl6ub0Gww4kpNM67UNOMLVNuVGegtR0SAEhH+SoQHKBEZ4IXwttf+XjIOMxENAYcDy7Zt27By5UqsWrUKAJCdnY2PP/4Yu3btwtatW+2uj4yMxMsvvwwA+Mtf/tJtuSKRCCEhId2+T52EXg+s+hT48lXgs+eAi4eAnXOBm7OApIcACf8PkKgnMg+x9QmjjsxmMyrrdbh8TQgvRdWNuFTdhMvXhK9rmgyoatChqkGH/Ms1duV6yz0Q7q9EZGB7j0yEvxIRgV4IUSkg4eRfon5x6F81vV6P/Px8rFu3zuZ8Wloajh49OqCKNDQ0ICIiAkajEfHx8XjmmWeQkJDQ7fU6nQ46nc76WqvVAgAMBgMMBsOA6tKRpazBLHNQzc4AJt0KyUe/hfjSYUDzFMwn/hett70MhExzdu0Gncu3hxsajW0yxlOCMeN8ED/Ox+49bbMBRdearY9bF9U0C8HmWhPKtDo06Fqtu153JpWIEDbGE2H+bb0z/p4I91ci3F+J8WM8IR+EeTOjsT1GMrZH7/r6s3EosFRVVcFoNEKttp3kqVarUVZW1s1dvYuJiUFOTg6mTZsGrVaLl19+GSkpKTh+/Diio6O7vGfr1q3YtGmT3fm8vDwolcp+16U7Go1m0MscVH6rEB5+HaaW7IWs7HtI/rwA/1YvwZmQO2ESj769W1y+PdyQO7ZJOIBwGXBDKIBQwGACqluAqhYRqnRtf7YA1S0iVOsAgxG4UNWEC1VNdmWJYIafDAhUmBGo6PSnHFA42Gnqju3hytge3Wtqsv/70JV+jRt0Hp81m80DGrNNSkpCUlKS9XVKSgpmzJiBV155BTt27OjynqysLGRmZlpfa7VahIWFIS0tDSqVqt916cxgMECj0SA1NdW67Ljrug2ofwymvP+E+PT7uK78Q0QbTsK4ZDvMEaNjwbmR1R7ugW3SN0aTGaV1LW09M83CEFPb18XXmtCoN6JGD9ToRThn3zkDfy+p0Bszpr1nRpgI7ImADvNm2B6uhe3RO8sISW8cCiyBgYGQSCR2vSkVFRV2vS4DIRaLMWvWLJw7d67ba+RyOeRyud15qVQ6JL8UQ1XuoPMPA37+BnDqQ2D/4xBduwCPN5cCM+4HUjcLj0iPAiOmPdwI26RnUgBRwTJEBdv/D5XZbG5b1bfROvH3cnVj27yZJlQ36nGt0YBrjXUoLK6zu99LJkGYvzD5d/wYBerLRQi4Uo9Jal+oVXJOAnYB/PvRvb7+XBwKLDKZDImJidBoNLjrrrus5zUaDZYuXepYDXtgNptRWFiIadNG3xyMYTPldiBqHqB5Gsh/XVh07vSHwrwWn7GAqtPhMxZQBghbAxDRsBKJRAjykSPIR47ECH+79+tbDNZ5MsKfjbhUJby+WteMRrsF8yTIvfAtAMBTKkFkoBeiApWICvRCZIAXogKFg0800Uji8JBQZmYm0tPTMXPmTCQnJ2P37t0oKirC6tWrAQhDNSUlJdizZ4/1nsLCQgDCxNrKykoUFhZCJpMhNjYWALBp0yYkJSUhOjoaWq0WO3bsQGFhIV599dVB+IhuTOEL3JEtLDj3/qPAtfPCui3dkcgAn1D7INPxtXcIn0AiGmY+Cinixvkibpz9lhy6VuER7KLqJlyqbsTFygZ8c/oSmsTeuFLbjGaDEadKtTjVxSRgH4UHJgR6tQUaL2ugiQz0gq8newPItTj8L8+KFStQXV2NzZs3o7S0FHFxcdi/fz8iIiIACAvFFRUV2dzT8Wmf/Px8vPXWW4iIiMClS5cAALW1tXjggQdQVlYGX19fJCQk4NChQ5g9e/YAPhpZRd4AZBwDir8GtCVtRymgvSp8XV8KNFQARj1Qe1k4uiMSA17BXffQdHwt9Ry+z0fkxuQeEkwM8sbEIG8AwpyJ/aILWLLkBkAsQfE1IchcqGwUAk2V0Dtzta4Z9S2tOH6lDsev2A8zBXjJhABjF2aUUMr4Py00/Pr1W5eRkYGMjIwu38vJybE7Zzabeyxv+/btPS4qR4PAQy4MEXXHaADqy2xDjOVrS7ipLwVMBqChTDiuftd9eZ5juh566hhuFL5cnZdoCEklYuuiebfE2L7XYjDicnUTLlZZQkwjLrYFmsp6Haob9ahu1OPbLtaaCVEpEBmoRFSgd9tQk/BnmL8Scg/JMH06cjeMySSQSAG/MOHojskENFV1CDEl7UGm4zlDE9BcIxwVP3ZfnlTZfQ+N5ZxXEOfVEA0BhVSCySHCbtadNehahQBjCTIdwkxtkwFl2haUaVvw5YVrNveJRcC4MZ6IDPCyDjVFBgpfj/PzhIeEf5ep/xhYqO/EYsA7WDjGdrOon9kMtNR1CDFX24NMx16b5hoh2FT/Wzi6/Z7Stnk1wtwasZcaEyq0EF1QAuPiAe+g7u8lon7xlnt0O2emtklv0ytzoaptqKmyEY16I4qvNaP4WjMOn6uyuU8qEfZ6igqwHWaKalsBWMwVgKkXDCw0uEQi4dFpTz8geEr31xma28JLFz00lnP1ZcIQVF2RcACQAJgGAHvfEsrxVgPqOOHpp5BpwtcBkzgxmGiI+CllSAiXISF8jM15s9mMygYdLlU14WJVAy62/XmpSphDo2s14UKlMJemM7mH2Pr0kqVHJjLQC+PGCGvMKKQcZiIGFnIWqScQMFE4umM0AA3lNj00xtpilJ/+GqHiaoiuXRTebygHzn/Sfp+HAgiKsQ0x6qmjZg0aIlckEokQ7KNAsI8Cs6NsH802mcwo1ba098h0GGoqutYEXasJZ9o2oOyKj9wDgT5yBHjJEOgtR4C3DAHecgS1/Wk5F+gth0rhwUe1RykGFnJdEingO144MAsAYDIY8I1uP5YsWQKpSQdUnALKTwBlJ4CyH4DyHwFDI1BaKBwd+Ya3hZi4tl6ZOMAvknNkiIaYWCzCOD9PjPPzRMqkQJv3Wo0mXKlpxsXqDvNl2o5ybQsMRjPqda2o17XiYpV970xnUokIAV5yBPrIhD+95Qj0tg06ltf+XjJIOa9mxGBgoZFL7g2EzRIOC5MJqLkIlP/QIcT8ANQVtw8tnfln+/UyH6H3xRpipgHBsYBs8PejIiJ7HhKxdXIuJtu+ZzaboW1uRVWjDtUNelQ16FDdoENlgx7VbTtmVzcITzNV1etQr2uFwWi2TgruCz+lVAgzbb03gXa9NpawI4eXTMLeGydiYKHRRSxuH2qK7bD6cnNNe3gp+0Holak4DejrgeIvhcNCJAb8J3YIMdOFr31C+Rg20TASiUTwVUrhq5RiYh/m17cYjMLj2G1hpqpBbxN0qtq+rmrQ41qjDiYzUNtkQG2TAT1M/bdSSMVtvTdyBHrJrMNQHXttLEFnjFIGCScSDyoGFnIPnmOEdWg6rkVjNABV59p7Yyx/NlYC1eeE48f3OpTh3xZiprUPLQVOBjxG327YRCORQiqxDj31xmQyo7bZ0BZgLOGmvdfGEm6qG3Woqtej2WBEi8GEktpmlNQ291q+WAT4e8ngr5QBOjEONp9AiJ8Sah851CoFglUKBPvIEaySc+2aPmJgIfclkQLqWOGYvrz9fH1527yYH9pDTNU5oPkacPGQcFiIpUDQ5A5PKrUFGq+A4f88RNRnYrFICBReMlyntl+LprMmfSuq6vV2w1PtvTbtw1M1TXqYzGh7Tw9AjLOFpd2WPUYptYYYS6BRq+TC67avA73lbj/fhoGFqDMftXBMWth+ztACVJ4SQoy1N+YHQFcnfF3+A/D92x3KCG1/QskSYgImAmL+nxTRSKSUeSA8wAPhAb3Pb2s1mnCtSY+qej3K6hrx6RffICQqBlWNBlTUt6Bcq0O5tgUV9TroW02oaTKgpsnQYfNKeyKRsF1CsI8QYKwBRyWH2qc92AR4y0ftUBQDC1FfSBXCYnkdF8wzm4XJvNYQ09YrU3OxbR2ZUuBcXvv1Hp5tPTptvTHqqYBqnLAQH/deIho1PCRi6yPe0UGeqD9rxpL5UZBKbTeUNJvNqGs2WAOMJcRYvi7X6lDRdq7VZLb22JzsvrMGYhEQ6N2pl8Ym5Ah/+itlI26xPgYWov4SiQC/cOGIWdJ+XlcvPF7dsSem4qSwsm9JvnB0JvMBvAKF8OIVJByWr63ngoWVfeUqTv4lGgVEIhH8lDL4KWVdbpFgYTKZUdOkF4JNfQsqtC0dQo4OFfUtqNDqUNmgg9FkRkW9DhX1Opwo6f57e4hFCPaRI6iHYSi1jwJ+SqnLPBnFwEI02OQ+QHiScFiYjMC1C51CzClhE0mjXnhaSV8v9M70RiJvCzJtIaa7r72DhcnGHIYiGtHEYhEC2p5GioWq2+uMJjOqG3Wo6BBmhF6b9mBTrhXm27SazLha14KrdT0//i2TiBGskiO4LdSsWRiNmJDu6zCUGFiIhoNYAgRGC0fcT9vPW/ZeaqwCGiuAhgrhKaXGyq6/1jcARh2gvSIcvRGJAaWl5yawPch07sWxHHziiWjEkojbVxvuah8oi1ajCVUN+vahp3pdW6+NJdgIr6sb9dC3Lex3pUZ4Mmr1jT2sTj7EGFiInKnj3kuBk3q/Xt/URaCpABra/mysajtfIaw9Yza1na/oW30Uft0HGu/gtl6ctgAk8xrAByciZ/GQiBHiq0CIr6LH6/StJlQ2tPXStIWZyADn/b1nYCEaSWRKQBYBjIno/VqjoUPPTWWHcNOx56ZD0DEbgZZa4ag623v5Ui/AKxASryDMbjBD/NGngGps247e6raj7Wv23BCNODIPcZ/XtRkODCxEo5VECqhChaM3JpPQI9OXYamGCmFYytAI1DZCXHsZoQDwXReTiS08x9gGGJuj7ZxPiHCdi0zwIyLXwsBCRMKWBl4BwhE8pedrzWbhSai2INNadxU/fvUZ4iKDIGlqG5JqKGv7sxwwtQphqLkGqDzdSz2kbQGmc7Dp8NpHLQxNcb8nIrfCwEJEjhGJAIVKOAImwmww4NIFCWJvXAJJp3UmYDIJQ0wN5UB9hxDTUG4fbJprAJMB0JYIR2/kqt6DjbcaUAbwSSmiUYCBhYiGjlgMKP2Fo7eem1Zd25BTubA9gjXUlHc6KoDWFkCnFY7qXratE4nbJw17qwHvkA5fdxiO8g4GZN4ckiJyUQwsROQaPOSA73jh6InZLAQVS5ix6bnpFHAaq4QnpSyvcaLnssVSQOErPLWl8G07/Lo/p/CzPS+R9lQ6EQ0AAwsRjSwiUXtACIzu+VpjK9BU1anXprxTwGkLPPoGYUiqqUo4+kPq1UO46Xi+i3MyH6FHioi6xMBCRKOXxEMY7vEJAXp7WErX0PZYdx3Q3PZnS13fzunbNq0zNApH/VXH6yoSC/Ny+tSb42cfeDwUHM6iUY2BhYgIAOTewtHbkFRXjK3CMJVduKltDzndnqsVtmcwm9rXwam97HgdJDJA4QcPhQrzms2QaPcIc4c8x7QtTjimLfR0eG05x3VyaARgYCEiGiiJR/vk4v4wtPTQk1Pbde9Ox2thFkJPYwVEjRXwB4Dz5/v+/aVenUKNX+8hx3OM0CPEYSwaJgwsRETOJlUA0rahK0eZTMKQVFuIaW2owndffIoZsRPhoW/r9bGsg9Pc9rXlXIsWgLl9KKsvj5N3JBJ3GK7qoSfH+rrDOalrrJ5KIwcDCxHRSCYWt89nAWA2GFD6oxbm+CVA53VxOjMZ23ttugo0zbXtw1ad3zc0CcNYljDUl53GO5LIew85XgFtj6G3rasj93Hse9CowsBCROSuxJL+D2W16roJOH14bTYK2zs0lAlHX0m9hPVyLOvmeIe0LxDY8WtlIIeqRiEGFiIicpyHXAgIPmrH7jObhUfI+xJwGqva19nR1wvDVjUXe+/NEUmExQJ9OiwU6BPSYRXkDoFH2vOOxeQ6GFiIiGj4iETC0I7cB/AL7/t9+sYO6+m0hZj6sva1dSznLTuPW3tvjvdcrsLXdnsHm2DT4fxo2ZjTZBSG8wzNws/U0Nz2uo/nblrnWLsNon4Flp07d+KFF15AaWkppk6diuzsbMybN6/La0tLS/Hb3/4W+fn5OHfuHB599FFkZ2fbXffOO+/gySefxPnz5zFx4kRs2bIFd911V3+qR0REo43MC/CfIBw9Mba2b/FgsxJyWaevy4VhKcsTV1Vney5XIuu6h6Zz7413cP9XPDabhaE2S1jQN3UIDpbw0Nu5XkKHUde/ulkk/nLkBJbc3FysXbsWO3fuREpKCl577TUsXrwYJ0+eRHi4/YfQ6XQICgrC+vXrsX379i7LPHbsGFasWIFnnnkGd911F9577z0sX74cR44cwZw5cxz/VERE5J4kHoAqVDh6YjYLQaVzD01XvTiWtXLqioWjN8oA65waiTII00srIflgP9Da3ClgNLc9odXhHMyD8VPoG6my/ZAphSe3rOc8hZDY+Zxq7PDVrxOHA8u2bduwcuVKrFq1CgCQnZ2Njz/+GLt27cLWrVvtro+MjMTLL78MAPjLX/7SZZnZ2dlITU1FVlYWACArKwuff/45srOzsXfvXkerSERE1DORqH29maDJPV9raAEaKzps71Bmu9VDx/2szEagqVo4Kn6EGEAUADi624NE1hYWOoQGa6hoOyfrECTsznUMHl2ck3qOuCEuhwKLXq9Hfn4+1q1bZ3M+LS0NR48e7Xcljh07hscee8zm3KJFi7ocOrLQ6XTQ6dq7trRaLQDAYDDAYDD0uy6dWcoazDKp/9gerodt4lrYHkNBAniFCkdPzCYhqDRUQNRQDjRWwFR3FRfOnsSEyXGQyL1htoQOj/YgYZZ2PNcWPMRDPMW0tXVoy3dAX39XHfqJVFVVwWg0Qq22nRWuVqtRVubAo2mdlJWVOVzm1q1bsWnTJrvzeXl5UCqV/a5LdzQazaCXSf3H9nA9bBPXwvZwBSrhCI3BGW3n90wAGtoO99bU1NSn6/oV4USdupHMZrPduaEuMysrC5mZmdbXWq0WYWFhSEtLg0qlGlBdOjIYDNBoNEhNTYW0t0WYaMixPVwP28S1sD1cC9ujd5YRkt44FFgCAwMhkUjsej4qKirsekgcERIS4nCZcrkccrnc7rxUKh2SX4qhKpf6h+3hetgmroXt4VrYHt3r68/FoaUAZTIZEhMT7boaNRoN5s6d60hRNpKTk+3KzMvLG1CZRERENHo4PCSUmZmJ9PR0zJw5E8nJydi9ezeKioqwevVqAMJQTUlJCfbs2WO9p7CwEADQ0NCAyspKFBYWQiaTITY2FgCwZs0azJ8/H88//zyWLl2Kffv24cCBAzhy5MggfEQiIiIa6RwOLCtWrEB1dTU2b96M0tJSxMXFYf/+/YiIiAAgLBRXVFRkc09CQoL16/z8fLz11luIiIjApUuXAABz587F22+/jQ0bNuDJJ5/ExIkTkZubyzVYiIiICEA/J91mZGQgIyOjy/dycnLszpnNvS+Es2zZMixbtqw/1SEiIqJRjttZEhERkctjYCEiIiKXx8BCRERELo+BhYiIiFweAwsRERG5PAYWIiIicnkMLEREROTyhnj/6uFjWeulr5so9ZXBYEBTUxO0Wi33gXABbA/XwzZxLWwP18L26J3l3+3e1mwbNYGlvr4eABAWFubkmhAREZGj6uvr4evr2+37InNflqEdAUwmE65evQofHx+IRKJBK1er1SIsLAzFxcVQqVSDVi71D9vD9bBNXAvbw7WwPXpnNptRX1+PsWPHQizufqbKqOlhEYvFGD9+/JCVr1Kp+MvmQtgerodt4lrYHq6F7dGznnpWLDjploiIiFweAwsRERG5PAaWXsjlcjz99NOQy+XOrgqB7eGK2Cauhe3hWtgeg2fUTLolIiKi0Ys9LEREROTyGFiIiIjI5TGwEBERkctjYCEiIiKXx8DSi507dyIqKgoKhQKJiYk4fPiws6vklrZu3YpZs2bBx8cHwcHBuPPOO3HmzBlnV4vabN26FSKRCGvXrnV2VdxWSUkJ7r33XgQEBECpVCI+Ph75+fnOrpbbam1txYYNGxAVFQVPT09MmDABmzdvhslkcnbVRiwGlh7k5uZi7dq1WL9+PQoKCjBv3jwsXrwYRUVFzq6a2/n888/x0EMP4csvv4RGo0FrayvS0tLQ2Njo7Kq5vW+++Qa7d+/G9OnTnV0Vt1VTU4OUlBRIpVJ89NFHOHnyJF566SX4+fk5u2pu6/nnn8d//dd/4U9/+hNOnTqFP/7xj3jhhRfwyiuvOLtqIxYfa+7BnDlzMGPGDOzatct6bsqUKbjzzjuxdetWJ9aMKisrERwcjM8//xzz5893dnXcVkNDA2bMmIGdO3fi2WefRXx8PLKzs51dLbezbt06fPHFF+wBdiG333471Go1/vznP1vP/cd//AeUSiXeeOMNJ9Zs5GIPSzf0ej3y8/ORlpZmcz4tLQ1Hjx51Uq3Ioq6uDgDg7+/v5Jq4t4ceegi33XYbFi5c6OyquLX3338fM2fOxM9+9jMEBwcjISEB//3f/+3sarm1G264AZ988gnOnj0LADh+/DiOHDmCJUuWOLlmI9eo2fxwsFVVVcFoNEKtVtucV6vVKCsrc1KtCBB29szMzMQNN9yAuLg4Z1fHbb399tv47rvv8M033zi7Km7vwoUL2LVrFzIzM/Gf//mf+Prrr/Hoo49CLpfjvvvuc3b13NLvf/971NXVISYmBhKJBEajEVu2bMHdd9/t7KqNWAwsvRCJRDavzWaz3TkaXg8//DC+//57HDlyxNlVcVvFxcVYs2YN8vLyoFAonF0dt2cymTBz5kw899xzAICEhAT8+OOP2LVrFwOLk+Tm5uLNN9/EW2+9halTp6KwsBBr167F2LFjcf/99zu7eiMSA0s3AgMDIZFI7HpTKioq7HpdaPg88sgjeP/993Ho0CGMHz/e2dVxW/n5+aioqEBiYqL1nNFoxKFDh/CnP/0JOp0OEonEiTV0L6GhoYiNjbU5N2XKFLzzzjtOqhH97ne/w7p16/Dzn/8cADBt2jRcvnwZW7duZWDpJ85h6YZMJkNiYiI0Go3NeY1Gg7lz5zqpVu7LbDbj4YcfxrvvvotPP/0UUVFRzq6SW1uwYAFOnDiBwsJC6zFz5kz84he/QGFhIcPKMEtJSbF7zP/s2bOIiIhwUo2oqakJYrHtP7ESiYSPNQ8Ae1h6kJmZifT0dMycORPJycnYvXs3ioqKsHr1amdXze089NBDeOutt7Bv3z74+PhYe758fX3h6enp5Nq5Hx8fH7v5Q15eXggICOC8Iid47LHHMHfuXDz33HNYvnw5vv76a+zevRu7d+92dtXc1h133IEtW7YgPDwcU6dORUFBAbZt24Zf/epXzq7ayGWmHr366qvmiIgIs0wmM8+YMcP8+eefO7tKbglAl8frr7/u7KpRmxtvvNG8Zs0aZ1fDbX3wwQfmuLg4s1wuN8fExJh3797t7Cq5Na1Wa16zZo05PDzcrFAozBMmTDCvX7/erNPpnF21EYvrsBAREZHL4xwWIiIicnkMLEREROTyGFiIiIjI5TGwEBERkctjYCEiIiKXx8BCRERELo+BhYiIiFweAwsRERG5PAYWIiIicnkMLEREROTyGFiIiIjI5TGwEBERkcv7/wBk0SZSTZKEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(history.history['loss'], label='Training')\n",
    "pl.plot(history.history['val_loss'], label='Testing')\n",
    "pl.legend()\n",
    "pl.grid()\n",
    "\n",
    "score = model.evaluate(X_test_hog, Y_test, verbose=0)\n",
    "print('Test score:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "axdxLHYMbahu"
   },
   "source": [
    "Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "id": "Mhm1wjNfbahv",
    "outputId": "8366db2a-2162-4c03-d1d3-de0a4114c3cb",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-score 0: 0.9810353664787289\n",
      "F-score 1: 0.984182776801406\n",
      "F-score 2: 0.9702495201535508\n",
      "F-score 3: 0.9491358024691358\n",
      "F-score 4: 0.9631147540983607\n",
      "F-score 5: 0.9630044843049327\n",
      "F-score 6: 0.9747899159663866\n",
      "F-score 7: 0.9549461312438786\n",
      "F-score 8: 0.9431643625192012\n",
      "F-score 9: 0.9423361261705273\n"
     ]
    }
   ],
   "source": [
    "pred = np.argmax(model.predict(X_test_hog), axis=-1)\n",
    "cm = me.confusion_matrix(y_test, pred)\n",
    "\n",
    "plot_confusion_matrix(cm, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}