7.ipynb

{
 "cells": [

  {
   "cell_type": "markdown",
   "metadata": {
    "id": "avs03Eqmt3rh"
   },
   "source": [
    "### Lab № 07"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.\t Загрузите заданный в индивидуальном задании набор данных с изображениями из Tensorflow Datasets с разбиением на обучающую и тестовую выборки. Оставьте в обучающей и тестовой выборках диапазон классов, указанных в индивидуальном задании. Если изображения цветные (с тремя каналами), то перекодируйте их в одноцветные (оттенки серого)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from matplotlib import rcParams\n",
    "import random\n",
    "from PIL import Image, ImageOps\n",
    "import tensorflow as tf\n",
    "import tensorflow_datasets as tfds\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = 'symmetric_solids'\n",
    "\n",
    "n = 48\n",
    "m = 48\n",
    "\n",
    "values_to_keep = [4, 5, 6, 7]\n",
    "ds = tfds.load(name, split=['train[:1%]','test[:1%]'])\n",
    "\n",
    "df_train = tfds.as_dataframe(ds[0])[['image', 'label_shape']]\n",
    "df_test = tfds.as_dataframe(ds[1])[['image', 'label_shape']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_random_sample(images):\n",
    "    n = 10\n",
    "    imgs = random.sample(list(images), n)\n",
    "    num_row = 2\n",
    "    num_col = 5 \n",
    "    fig, axes = plt.subplots(num_row, num_col, figsize=(3.5 * num_col, 3 * num_row))\n",
    "    for i in range(num_row * num_col):\n",
    "        img = imgs[i] \n",
    "        ax = axes[i // num_col, i % num_col]\n",
    "        ax.imshow(img)\n",
    "        ax.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1750x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train1 = []\n",
    "for i in df_train.values:\n",
    "    if i[1] in values_to_keep:\n",
    "        df_train1.append(i)\n",
    "df_train = pd.DataFrame(df_train1)\n",
    "\n",
    "df_test1 = []\n",
    "for i in df_test.values:\n",
    "    if i[1] in values_to_keep:\n",
    "        df_test1.append(i)\n",
    "df_test = pd.DataFrame(df_test1)\n",
    "\n",
    "\n",
    "train_labels = df_train[1].to_numpy(dtype=np.float32)\n",
    "test_labels = df_test[1].to_numpy(dtype=np.float32)\n",
    "\n",
    "n_classes = len(set(train_labels))\n",
    "\n",
    "train_labels = np.array([i % n_classes for i in range(len(train_labels))])\n",
    "test_labels = np.array([i % n_classes for i in range(len(test_labels))])\n",
    "\n",
    "print(np.unique(train_labels))\n",
    "\n",
    "train_images = np.zeros(shape=(df_train.shape[0], n, m, 3), dtype=np.float32)\n",
    "test_images = np.zeros(shape=(df_test.shape[0], n, m, 3), dtype=np.float32)\n",
    "\n",
    "for idx in range(train_labels.shape[0]):\n",
    "    img = Image.fromarray(df_train.iloc[idx][0])\n",
    "    img_resized = img.resize((n, m), Image.LANCZOS)\n",
    "    img_array = np.array(img_resized)\n",
    "    train_images[idx] = np.transpose(img_array, (1, 0, 2))\n",
    "\n",
    "for idx in range(test_labels.shape[0]):\n",
    "    img = Image.fromarray(df_test.iloc[idx][0])\n",
    "    img_resized = img.resize((n, m), Image.LANCZOS)\n",
    "    img_array = np.array(img_resized)\n",
    "    test_images[idx] = np.transpose(img_array, (1, 0, 2)) \n",
    "\n",
    "train_images /= 255.0\n",
    "test_images /= 255.0\n",
    "\n",
    "train_labels = tf.keras.utils.to_categorical(train_labels, num_classes=n_classes)\n",
    "test_labels = tf.keras.utils.to_categorical(test_labels, num_classes=n_classes)\n",
    "plot_random_sample(train_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1750x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_random_sample(images, color_mode='rgb'):\n",
    "    n = 10\n",
    "    imgs = random.sample(list(images), n)\n",
    "    num_row = 2\n",
    "    num_col = 5 \n",
    "    fig, axes = plt.subplots(num_row, num_col, figsize=(3.5 * num_col, 3 * num_row))\n",
    "    for i in range(num_row * num_col):\n",
    "        img = imgs[i]\n",
    "        ax = axes[i // num_col, i % num_col]\n",
    "        if color_mode == 'rgb':\n",
    "            ax.imshow(img)\n",
    "        elif color_mode == 'grayscale':\n",
    "            ax.imshow(img.squeeze(), cmap='gray')\n",
    "        ax.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "train_images_gray = np.zeros(shape=(df_train.shape[0], n, m, 1), dtype=np.float32)\n",
    "test_images_gray = np.zeros(shape=(df_test.shape[0], n, m, 1), dtype=np.float32)\n",
    "\n",
    "for idx in range(train_labels.shape[0]):\n",
    "    img = Image.fromarray((train_images[idx] * 255).astype(np.uint8)).convert('L')\n",
    "    img_resized = img.resize((n, m), Image.LANCZOS)\n",
    "    train_images_gray[idx] = np.expand_dims(np.array(img_resized), -1)\n",
    "\n",
    "for idx in range(test_labels.shape[0]):\n",
    "    img = Image.fromarray((test_images[idx] * 255).astype(np.uint8)).convert('L')\n",
    "    img_resized = img.resize((n, m), Image.LANCZOS)\n",
    "    test_images_gray[idx] = np.expand_dims(np.array(img_resized), -1)\n",
    "\n",
    "train_images_gray /= 255.0\n",
    "test_images_gray /= 255.0\n",
    "\n",
    "train_labels = tf.keras.utils.to_categorical(train_labels, num_classes=n_classes)\n",
    "test_labels = tf.keras.utils.to_categorical(test_labels, num_classes=n_classes)\n",
    "\n",
    "plot_random_sample(train_images_gray, color_mode='grayscale')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Постройте для набора данных график логарифмического правдоподобия профиля в зависимости от числа главных компонент и определите размерность латентного пространства."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import multivariate_normal\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\", RuntimeWarning)  \n",
    "\n",
    "def log_likelihood(evals):\n",
    "    Lmax = len(evals)\n",
    "    ll = np.arange(0.0, Lmax)\n",
    "    \n",
    "    for L in range(Lmax):\n",
    "        group1 = evals[0: L + 1]\n",
    "        group2 = evals[L + 1: Lmax]\n",
    "\n",
    "        mu1 = np.mean(group1)\n",
    "        mu2 = np.mean(group2)\n",
    "\n",
    "        sigma = (np.sum((group1 - mu1) ** 2) + np.sum((group2 - mu2) ** 2)) / Lmax\n",
    "\n",
    "        ll_group1 = np.sum(multivariate_normal.logpdf(group1, mu1, sigma))\n",
    "        ll_group2 = np.sum(multivariate_normal.logpdf(group2, mu2, sigma))\n",
    "\n",
    "        ll[L] = ll_group1 + ll_group2 \n",
    "\n",
    "    return ll\n",
    "\n",
    "n_images = train_images_gray.shape[0]\n",
    "n_samples, n_rows, n_cols, _ = train_images_gray.shape\n",
    "X = np.reshape(train_images_gray, (n_samples, n_rows * n_cols))\n",
    "\n",
    "n_train = int(n_images * 0.8)\n",
    "X_train = X[:n_train, :] \n",
    "X_test = X[n_train:, :] \n",
    "\n",
    "X_rank = np.linalg.matrix_rank(X_train)\n",
    "K_linspace = np.linspace(1, 0.75 * X_rank, 10, dtype=int)\n",
    "Ks = np.unique(np.append([1, 5, 10, 20], K_linspace))\n",
    "\n",
    "RMSE_train = np.zeros(len(Ks))\n",
    "RMSE_test = np.zeros(len(Ks))\n",
    "\n",
    "for index, K in enumerate(Ks):\n",
    "    pca = PCA(n_components=K)\n",
    "\n",
    "    Xtrain_transformed = pca.fit_transform(X_train)\n",
    "    Xtrain_proj = pca.inverse_transform(Xtrain_transformed)\n",
    "    RMSE_train[index] = mean_squared_error(X_train, Xtrain_proj, squared=False)\n",
    "\n",
    "    Xtest_transformed = pca.transform(X_test)\n",
    "    Xtest_proj = pca.inverse_transform(Xtest_transformed)\n",
    "    RMSE_test[index] = mean_squared_error(X_test, Xtest_proj, squared=False)\n",
    "\n",
    "n_samples, n_features = X_train.shape\n",
    "Kmax = min(n_samples, n_features)\n",
    "\n",
    "pca = PCA(n_components=Kmax)\n",
    "X_transformed = pca.fit_transform(X_train)\n",
    "evals = pca.explained_variance_ \n",
    "\n",
    "ll = log_likelihood(evals)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "xs = Ks\n",
    "ys = RMSE_train\n",
    "plt.title(\"Ошибка в зависимости от числа главных компонент\")\n",
    "plt.xlabel(\"Число главных компонент\")\n",
    "plt.ylabel(\"RMSE\")\n",
    "ax.plot(xs, ys, marker=\"o\")\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "xs = np.arange(1, 51)\n",
    "ys = ll[:50]\n",
    "plt.xlabel(\"Число главных компонент\")\n",
    "plt.ylabel(\"Логарифмическое правдоподобие профиля\")\n",
    "ax.plot(xs, ys, marker=\"o\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal latent space dimensionality: 79\n"
     ]
    }
   ],
   "source": [
    "latent_dim = np.argmax(np.cumsum(pca.explained_variance_ratio_) >= 0.95) + 1\n",
    "print(f'Optimal latent space dimensionality: {latent_dim}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.\tСоздайте и обучите на обучающей выборке автокодировщик архитектуры, указанной в индивидуальном задании, с размерностью скрытого представления, равной размерности латентного пространства, определенной в п.2. Подберите такие параметры, как функции активации, оптимизатор, начальная скорость обучения, размер мини-пакета и др. самостоятельно, обеспечивая обучение нейронных сетей. Визуализируйте несколько исходных и восстановленных автокодировщиком изображений. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "7/7 [==============================] - 8s 758ms/step - loss: 0.6585 - val_loss: 0.4259\n",
      "Epoch 2/10\n",
      "7/7 [==============================] - 5s 697ms/step - loss: 0.1263 - val_loss: 0.0755\n",
      "Epoch 3/10\n",
      "7/7 [==============================] - 5s 716ms/step - loss: 0.0934 - val_loss: 0.0618\n",
      "Epoch 4/10\n",
      "7/7 [==============================] - 5s 720ms/step - loss: 0.0341 - val_loss: 0.0278\n",
      "Epoch 5/10\n",
      "7/7 [==============================] - 5s 712ms/step - loss: 0.0239 - val_loss: 0.0171\n",
      "Epoch 6/10\n",
      "7/7 [==============================] - 5s 707ms/step - loss: 0.0169 - val_loss: 0.0158\n",
      "Epoch 7/10\n",
      "7/7 [==============================] - 5s 717ms/step - loss: 0.0137 - val_loss: 0.0115\n",
      "Epoch 8/10\n",
      "7/7 [==============================] - 5s 711ms/step - loss: 0.0097 - val_loss: 0.0073\n",
      "Epoch 9/10\n",
      "7/7 [==============================] - 5s 714ms/step - loss: 0.0061 - val_loss: 0.0051\n",
      "Epoch 10/10\n",
      "7/7 [==============================] - 5s 719ms/step - loss: 0.0049 - val_loss: 0.0048\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x15d3ad76150>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "train_images_gray = train_images_gray.astype('float32') / 255.0\n",
    "test_images_gray = test_images_gray.astype('float32') / 255.0\n",
    "\n",
    "input_img = layers.Input(shape=(n, m, 1))\n",
    "\n",
    "x = layers.Conv2D(32, (3,3), activation='relu', padding='same')(input_img)\n",
    "x = layers.MaxPooling2D((2,2), padding='same')(x)\n",
    "x = layers.Conv2D(64, (3,3), activation='relu', padding='same')(x)\n",
    "x = layers.MaxPooling2D((2,2), padding='same')(x)\n",
    "x = layers.Conv2D(128, (3,3), activation='relu', padding='same')(x)\n",
    "x = layers.MaxPooling2D((2,2), padding='same')(x)\n",
    "x = layers.Conv2D(latent_dim, (3,3), activation='relu', padding='same')(x)\n",
    "encoded = layers.MaxPooling2D((2,2), padding='same')(x)\n",
    "\n",
    "x = layers.Conv2D(latent_dim, (3, 3), activation='relu', padding='same')(encoded)\n",
    "x = layers.UpSampling2D((2, 2))(x)\n",
    "x = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
    "x = layers.UpSampling2D((2, 2))(x)\n",
    "x = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
    "x = layers.UpSampling2D((2, 2))(x)\n",
    "x = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
    "x = layers.UpSampling2D((2, 2))(x)\n",
    "decoded = layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
    "\n",
    "# output_img = layers.Cropping2D(((6, 6), (6, 6)))(decoded)\n",
    "\n",
    "autoencoder = Model(input_img, decoded)\n",
    "autoencoder.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy')\n",
    "\n",
    "train_images_autoencoder = np.expand_dims(train_images_gray, axis=-1)\n",
    "test_images_autoencoder = np.expand_dims(test_images_gray, axis=-1)\n",
    "\n",
    "autoencoder.fit(train_images_autoencoder, train_images_autoencoder,\n",
    "                epochs=10,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(test_images_autoencoder, test_images_autoencoder),\n",
    "                verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 29ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8QAAAGGCAYAAAANejs7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABhdklEQVR4nO3debhlVXkn4A3SymCVDMUoxewIRlojKs5RW1GjeXzEhNYHbdOPRiQRTNQ44BC7Y9KtptWOSdvdSRsnaNMhxjgQ20hs6GACMsigDDIUFBRQFEUxS1X1H2lP1vrOuWvdfc+5w1nnff/aH/vefXads7+99j6bu347bN++fXsHAAAAAAAAAI3Zcbl3AAAAAAAAAAAWgwfiAAAAAAAAADTJA3EAAAAAAAAAmuSBOAAAAAAAAABN8kAcAAAAAAAAgCZ5IA4AAAAAAABAkzwQBwAAAAAAAKBJHogDAAAAAAAA0KSd5vND27Zt69avX9+tWrWq22GHHRZ7n2BJbN++vduyZUt3wAEHdDvuuLD/N0Rv0Bp9AaPpDRimL2C0cXtDX9AiYwaMpjdgmL6A0fQGDOvTF/N6IL5+/fpu7dq1E9k5WGnWrVvXHXjggQv6Xb1Bq/QFjKY3YJi+gNEW2hv6gpYZM2A0vQHD9AWMpjdg2Hz6Yl7/G8mqVasmskOwEo1zfOsNWqUvYDS9AcP0BYy20ONbX9AyYwaMpjdgmL6A0fQGDJvPsT2vB+KmTqBl4xzfeoNW6QsYTW/AMH0Boy30+NYXtMyYAaPpDRimL2A0vQHD5nNsLyxoAAAAAAAAAABWOA/EAQAAAAAAAGiSB+IAAAAAAAAANMkDcQAAAAAAAACa5IE4AAAAAAAAAE3aabl3AGAuO+ywQ1Zv3759mfYEAAAAAACAaeQvxAEAAAAAAABokgfiAAAAAAAAADTJlOnA1Nhxx/z/4dm2bdsy7QkAAAAAAADTwF+IAwAAAAAAANAkD8QBAAAAAAAAaJIH4gAAAAAAAAA0SYY4sGLtuuuuWf3ggw9mtQxxAAAAAAAASvyFOAAAAAAAAABN8kAcAAAAAAAAgCZ5IA4AAAAAAABAk2SIA8tqxx3/+f/LiZngp59+elb/yZ/8SVafeeaZWf2whz1ssLx169ZJ7SIAAAAAAABTyl+IAwAAAAAAANAkD8QBAAAAAAAAaJIH4gAAAAAAAAA0SYY4sKTSnO+uy7O+n/WsZ2XrXvGKV2T1Pvvsk9Vf+9rXsvqhhx4aLO+www7Zuu3bt/ffWQAAAFih4n1vjftiAABmlb8QBwAAAAAAAKBJHogDAAAAAAAA0CQPxAEAAAAAAABokgzxRRJznHbcccc518UMpz6ZTul2R9m2bVuxhqVWOgb/3b/7d8XfPeaYY7L6TW96U1Z/9rOfHSzHrPI0XxwAAACmQfodUvw+qe93POl3SL4fgtFq39sCANPJX4gDAAAAAAAA0CQPxAEAAAAAAABokinTFyhOnxOnZ966dWuxnpS+U1zF/Uwt1j4y22q9cfzxxw+Wn//852fr4jTnO+2Un7I+/OEPZ/Vf/uVfDpZvvfXWbJ0prwAAYDbEa/9JcQ/BUojReOn3PvEY3HnnnbO6dv99//33D5ZrfeJ4Z5rFXqjFDZT6LNI7ALPNvcb08hfiAAAAAAAAADTJA3EAAAAAAAAAmuSBOAAAAAAAAABNkiE+TzHDKaplHR999NGD5ec85znZuqc85SlZfeCBB2Z1zLXZsmXLYPlHP/pRtu7iiy/O6rPPPjurb7755m4uMV8nvq4MAxailnP/W7/1W3Oui30X+2y//fbL6g996EOD5ZNOOilbF4/vuC2oifkwpXEhni9rNQDMV5+8sjhWxSxZmBZ981qX6lpLjiyTUMoM77qu22uvvQbLr3jFK7J1Rx11VFY/4hGPyOp4jJ511lmD5b/+678u7lf8XcczK1nsoyOPPDKrd95556yO10Tpd62bNm3K1t15551Z/dOf/nTe+6WPmBWxB0vfmcX+i30R+yZ+p5v+vOcXTIJ7jdnhL8QBAAAAAAAAaJIH4gAAAAAAAAA0yQNxAAAAAAAAAJokQ7wgzaeI2RYPf/jDs/p1r3tdVr/97W/P6jS7JuaLj+NVr3pVcf1tt92W1d/61rey+mMf+9hg+ZJLLsnWlfI5uq6eDc1sisd3zOp+61vfmtXHHHPMYDn2WTwGa7kZb3rTmwbL/+2//bds3Q9+8IOsruW0QRTPgePksMZjuVan5CMBzJZa9uQuu+wyWI6ZlvE6LG5LxjgrVd/M1fjzaV/E3NiYsRw98MADWX3vvfcOlu+///6x9qvP79KueFzE6/ujjz46q9/4xjcOlvfcc89sXTzvx3voWL/5zW8eLO++++7Zuv/1v/5XVt93331dSfrvcDyzFEpjw+rVq7N1hx12WFbfcccdWf3ggw9m9f777z9Y3nvvvbN1cVyImeLxu9e77rpr5D6OImOcaRHvG+KxG+8jxvmeNfZBvKcp7Ucc93yHxs+Mc10efze9v0jvO+K6UduOY0p6veVeY/H5C3EAAAAAAAAAmuSBOAAAAAAAAABN8kAcAAAAAAAAgCbJEE/EjIk0++KII47I1n3pS1/K6qc97WlZXcoGiFkAfaXbruVgxAyD17/+9Vl9/PHHD5Zj5vJ73vOerL777ruzupSxzuyIWRUx1+WRj3xkVv/Wb/3WvLdVWx+PuzQP8N//+3+frTvuuOOyWo4GUS1X/pd/+ZezOp73zz333MHytddem61bt25dVsfMsUmeQ2v5sKnYB7UaFlstlyxVOz4dz0yrPpnhXdd1Z5555mD54Q9/eLbuve99b1afd955WR3Hn/S15Yuz2ErHejzud9ttt6w+5JBDsnrt2rVZvccee8z5u//iX/yL4n7FY33Lli2D5ZhBe+ONN2b1ddddl9X33HPPnK8jN3Z21O4zXvCCF2T16173uqxOc8LjMbXTTvnXerXXSn//ZS97WbYufid2wQUXZPWPfvSjrE7v/Wv38o5vJiEeR+lxF7972n333bP6mGOOyervfve7WZ2e3+O1VvpdU9d13b777pvVa9asyer0+9OYL75x48asLmUjd13+b9RHLLZ4Li8df3F8iddir3zlK7P6SU960mA5PmdZvXp1Vt98881ZffXVV2f12WefPVj+27/922xdHCfj2FZ6rkJb+lxrxzHkoIMOyup4fO+5555z/m68Nov63GvE75Ovv/76rHav0Z+/EAcAAAAAAACgSR6IAwAAAAAAANCkmZ4yvTRFetd13Utf+tLB8he+8IVs3V577ZXV9913X1aXpqytTdHWZ4rP2nQlcf3mzZvn3M+TTz45W/fMZz4zq+N0wddcc81gufZe0q742cfpnv7tv/23WX3YYYdldXqsxG31fe10W2n/dl0eD9B1XfeVr3xl3tuCruu6Aw44IKt/8zd/c876rrvuytbdfvvtWX3DDTdk9aWXXprVV1555WD5qquuytb95Cc/yeqbbropq++9996sXqzp2GvTIkalaVBpW3qsxHNtvG5ZzCnL4vVXOvVhfN0Yb2MqNRZT3ynSzzjjjKx+yUteMliO5/x0OsGu67qvf/3rWf0f/+N/zOp0SvXSdOpdZ0p1+qsd6+mU/0984hOzdUcddVRWx+lqV61aldU777zzYDlOWxjHoloEVDpddRwfYqzYLbfcktWXXXZZVl9xxRWD5TRWbdR+uF6abuk5Ml5HPOUpT8nqE044Iavj9Xx6zMbrmXgurtWpGLPx5Cc/OavT6UC7bniK23/8x38cLMd7ksjxzWJIr5Hi8ZpGZ3Rd1z3hCU/I6iOPPDKr02uk888/P1uXjildNzyuxGugdPrcOBX0/vvvn9UbNmzI6ltvvbW47ZS+ombcYyQdv+KU0R/60Iey+tWvfnVWx9iC1IMPPpjV8dornV6964a/433rW986WL788suzdX/4h3+Y1V/+8pezOr2Wq8WMMF1qx3v6HVAcE8a516hdm9XuNdI63mvE/YpjRvw+2b1Gnb8QBwAAAAAAAKBJHogDAAAAAAAA0CQPxAEAAAAAAABo0kxliNdy7o4++uisPvPMMwfLacZA1w3Pwd8nF7w2X39tfWl+/1r2Rcy5Sbe1cePGbF3MtfrWt76V1Wl+R5onPp/9YLqlx2jMvYiZMqeddlpxW6VMs3H2K/qd3/mdrP7GN76R1ffcc8+c25rVTI1ZUztPxXHgm9/8ZlY/61nPGiynWZNdN5xntmbNmqz++Z//+Tn3JWYrxXzymDG2bt26rE7zlK6++upsXcwjj78bt52OffqCuZRywuOYER1yyCFZ/XM/93NZnWZb7rXXXtm62CvxeI+5rmlv3Hbbbdm6mKkZzw9btmzJav1AX6XrjJgZ/qUvfSmr/9W/+ldZneaIxYzLOB7F/L0XvOAFWZ1e73/yk5/M1n3/+9/P6j4Z4/LFZ1PtvnbvvffO6mOPPXawHLOKYw5lzPHbbbfdsrqUIV7L9Yvn/FKGeMx6jvsZs2IPPfTQwfK5556brYvXXXL+pkvpOHr0ox+drXvd616X1bWMx/QYjsdvvO7qsz7+bOzJuL50fF922WXZugsvvDCr47VT5P6bUWrnwX322WewHL+LOuyww4q/G4/33/zN3xwsn3POOdm6r3zlK1kdz9dpZnjXle9/4rVaOi503XBubXqdF+9n+n6fTJtKn3vtGIjfc8VroNe//vWD5T/4gz/I1j3qUY/K6nhNtGnTpjn3K4qvG+t4L5HW8TuEP/qjP8rqmG1+0kknDZbjd2BxzHQPs7KNc6/xmMc8JlsXj+fVq1dn9a677prVae/E54LxOIricZXea8Rrwvvuuy+r47XYfvvtl9UHH3zwYPn//t//m62L33vN6pjhL8QBAAAAAAAAaJIH4gAAAAAAAAA0yQNxAAAAAAAAAJrUdIZ4LQsszv3/Z3/2Z1md5rrE/L2YDVCbY3+58pBiZkF87fQ9ibkhd9xxR1YffvjhWX3GGWcMlp/97Gdn62K22qxmErQqPa5iHtLJJ5+c1TE7Of58zPQbRymr8vGPf3xWv/vd787qD3zgA1ld+jcym2LW10033ZTV69evHywfdNBB2bqYUV/Ki+m6/Nwcf7aUm9Z1w5ljxxxzzJzbihlPMdsvzSvruq678cYbB8s33HBDtu6qq67K6muvvTar05+PWVJMl1JOcNcNH2dp78TMzF/91V/N6qc85SlZHa9NxhF75+abbx4sf/vb387WnX766Vl93nnnZXXMk0p73JjBKKVr4ZgZ/rnPfS6rY853zI+M2cmpeC9w5513ZnXs1xe/+MWD5Xh9/7//9//O6s985jNZff7558+57b7nDaZT7Z7vgAMOyOoXvehFWZ1eP8V7iJiXF/Na43VaKdcv7mes4/GYntdjrl+87+2TbR7/Dd/5zneyOr22jPvpfnrlKX0mr3nNa7I6HhfxuHr4wx+e1elnXzt+o3i+Tev4u/H6Ju5H/A4tHb9ij6YZll3XdT/4wQ+y+sc//nFWp31W+zc5/mdH/Kzjdc2TnvSkwfLTnva0bF3Mg43Hb/wuKn2t448/Plv3whe+MKs///nPZ/XXv/71rE7v72O/x++m+2SMxzzcmH9cus/2vWw7ap9lep6Px1+8voq/e+qpp2b1KaecMlh+8MEHs3Xx+6O4X/H6Kz32Yx/UxrbSvUQcQ+N+Pfe5z83qr371q4PlmC9+3XXXzfk6o/abpdX3XiOeu9Nrk9q9RuydeM+e3mvE8SQeN1G810jr2GcxQzzeP5TuNeI4GO814vfas3Kv4S/EAQAAAAAAAGiSB+IAAAAAAAAANMkDcQAAAAAAAACa1HSGeMyWibksv/7rv57VafZM/PlaZvg4WSx9f7c0n3+tjtJMg5iDUcsUf+pTnzpYPu2007J173vf++Z8na6TEzht4ueX9saRRx6ZrTvppJOK24p9uVhqeR2x/2Ne5zXXXDPntmTGtKN0Po1ZK7GOud8XXnjhYHn//ffP1tWyK6P0GIs/G8eyWMe8mfR8W8qp6brh/oz5O49+9KMHy09/+tOzdfH9iM4555zB8rvf/e5snTyzla1v1uprX/varP693/u9wXKagdd19eupuO1xrq/iuTw9vt/whjdk62LW+ZlnnpnVv/u7v5vVaQ5mfN1ab9Cm2nktvc7+7Gc/m617/vOfn9W33nprVsf8srRPatdZcX28ptm8efNgOR67Mcv8Wc96VlbHTLL033XRRRfNuc9dVx4XjQkrW+laKmadxhy/OCasWbNmsBxz/WK2ccyCjZmrafZxPO7jeBCPv1K+a7zOihnipSzzrsuvCeN+/MIv/EJWn3XWWVl92223zbnP+mTp1e4R03P5E5/4xGzdPffck9XxOCnlfteO51qOZel8G/sq5lTGTPF0PIpjU9xW2t9d13WHH354Vp9//vmD5fXr18+5j13n+G9Z7bON99npOBLv1+MxWTs/p/W9996brdtnn32y+v3vf39W/9Iv/VJWf/GLXxwsf+9738vWxbzjmP8apeNO7KvHPe5xWX377bdn9Q033DByO12nj1ay2mcT1++1115ZnWYhx76I1y3HHXdcVqeZ4fHn436UMsJH1el+j3v8pb8fx7k4Vm3cuDGr165dO1g+/fTTs3Xx/di0adOcr9t1+mYplO414rn5RS96UVYfcsghWZ3em8R7jVWrVmV1KZu76/LjrG+GeJ97jZghXhvb0r6M14zxXuxb3/pWVqffO7R8rPsLcQAAAAAAAACa5IE4AAAAAAAAAE3yQBwAAAAAAACAJjWXIZ7Obx9zVWM2wMknn1zcVimDr5b/Osl59pdqzv5aBlbM4LjrrrsGyzFj5C/+4i+y+oILLuj1WqwspeM9ZgHHnLFaRvFiqeXQppk6Xdd1H/nIR7L6X//rfz3ntpgNtXNvPI+leV1XXHFFtu7oo4/O6rvvvjurY1+krxX3o/SzXTec41TLrknV8snTPNladnnMtYm5N6xspUyueD79/d///ax+17veldXpGB+Pm1rO92KOGel+1fr7+OOPz+rnPve5Wf1rv/Zrg+WvfvWr2br4b4jvH23okxnedV33x3/8x4Pl5z3vedm6DRs2ZHXMi4zbTutaxmBNerzGYzW99u+64Yzx5zznOVl9zDHHDJZjfub/+B//I6svueSSXvvJypEec/E4P/bYY7P64IMPzuqYKZzWMfMy5vrF64xYl/LzYl/UMsTTXojZr6U8wa7rlykYey6+f2nOX9wPFl/tOInH4Ate8ILBcrxO7pvzXcpdre1n6V4irqtlhsdeSnu+lC8+qo7ZnPvtt99g+bLLLsvWXXzxxVkd76VK70lLmZd03WMe85isTr/njcdUvH6KY1TpfB3XxeMoXgMdddRRWf3Rj350sHz55Zdn6z7/+c9ndbxGihnP8d+RiuehfffdN6vT772uu+66bF3MG285L3ba1L7jiddT8Zop/nzqwAMPzOrf/u3fzurFzP3uozaW9Xnt+H5s3rx5sPy4xz0uW/eBD3wgq0899dSJ7QcLU7rXeOYzn5nVsTfSzPCuy+814nOBeA0Uz71xXEj3JV4fjZMhHr8zi//mWE/yXuOb3/zmYLnlew1/IQ4AAAAAAABAkzwQBwAAAAAAAKBJzU2Znk5REKcYeOUrX5nVcZqQxZzaebmmGBlHbVrz9P1dvXp1tu5tb3tbVr/pTW/KalNQr2y16XGf/OQnD5Z/5Vd+pbitpZoivSbuRzyeTzjhhKz+0z/908Hyt7/97eK2TH/bpjiNZ5yWpjT1YZzm76CDDsrqOJ1b3FZpzOhbp2rTrcfe7zN9deyDOJ3QLbfcMufvTsu4OEvSzz5+tnGav9e//vVZHacQTI+bOJ3TchonTiBOR/ilL31psBzHE1Oot6k23X/8XD/84Q9n9fOf//zBcpyyMk4rG8+J8RomreO6SV6H1caIe+65J6vTadae9axnZeue+tSnZvW5556b1V/84hcHy3FMZXmVxuwjjzwyW3fEEUdkdYwwi3U6dWG8v4zXTrFP4tTlpSnT+05jmPZzvB6MU9vG1ypN116bgje+f+n7+4Mf/GDO7Y7aNuOrvcfPeMYzsjq9Vrj33nuzdXH61tqYktbxZ+O1Ve3eoHT8x+lBY1/FbaX/jrgftSk+S1Oqx+l/473UhRdemNVXX331YLkWz6M3Vpba57PHHntk9eGHH57V6RS3cZyIx288BmMfpsdw7Vwej/dSDOQTn/jErE6nU++6rrv00kuz+owzzsjqv//7vx8sb9y4MVsX/41xP9P1j3/847N169evz+prr712aN9ZHvHYfOxjH5vVMUam9LnH65STTjopq/faa6+sjtMkp2PGYsadLuW5On1/77jjjmzdL//yL2f1l7/85az+h3/4h6xeqvdnlpSOhSc84QnZunitHMeMce41arEbk7zXKE2ZHsebcaZIj9uOY2r6/l500UXZupaup/yFOAAAAAAAAABN8kAcAAAAAAAAgCZ5IA4AAAAAAABAk1ZOkOOElOavP+6445ZwT+avlqcd/03Llb8dMwnSrIT77rsvW/cLv/ALWR0zHDZt2pTVpSw1Vp408yhm28R8ipWSId7XRz7ykcHyd7/73WxdzNygTTEDO4rHeprjEnMCL7nkkqx+7nOfm9UxMzI939Zyv/tkDsacmj6/W9tW/Nm47XXr1nWsXKVc65h9XMsMj+NCC2o5gWnu5emnn56te9WrXpXVf/M3f5PV6XsvT3xlS89rtczwU089Natf/vKXZ/WGDRsGy30zw0vr437U8suicbLvSmPKXXfdla2L+/n0pz89q9Ms3k9+8pPZuq9+9atZXTp/Mb5aXlya3xpz/dKcvlF1zO5Lt5Uud91wrl/M8VuqDPEHH3ywuK3a9VC6rTh+xtzkeI+dvr9XXnlltu7uu+8uvq577PHF4yJeGzzrWc/K6vTz7Zv/WDqf9s0IL+XYx/2Ix2DtPiRdH382Xg/GOt5rpT0d+zmOkzHz9tBDDx0sx8zLm2++Oav1xnSJ+aZ77713VqdjRcx7jcdYKTM81rVze1RaX7u2Ouqoo4r11VdfPVj++te/nq37P//n/2T1TTfdlNXpeSge+wcccEBWx/cjjjMsnXjcr1mzJqvjeSteE6XfWcYM+/h9fTw+a+f9kj7PK/qei0vbro2LpZ+PrxvPE7/6q7+a1TFDnPH1udc48sgjs3W1e41Vq1bNua14rxHHkHgt0udeo69x7jWitKfj84sHHnggq+P9Q/r+pmPPqJ+d5uspfyEOAAAAAAAAQJM8EAcAAAAAAACgSR6IAwAAAAAAANCkqc8QL2VyxXUxN6O2reVSy75Yrjn5S68b8w0OPvjgrD722GOzOubepPkHcv+WXi178cUvfnFWH3fccYPlWt7MSlXL/kyzLE866aRs3ac+9amsjllLMsanRyk7aJ999in+bjxm0s89HhPXXnttVh922GFZHfO70gzyvjmBpWzv+LO1nNrSa/cdqzZv3tyxctTOgem4fdppp2Xrahmas6D0/sVsqT/+4z/O6qc+9alZvWnTpsHySrnmY7TS9erb3/72rD7xxBOz+o477sjq9Dip5X7Ha6tSpnjfY2apjrFaHuGWLVvm/PlrrrmmuO1xcs8ZX3rft99++2XrShnhXTec1ZfWMTM45mPW1qdjUxynavf98fhMe7TWn7VtpXmu8R46ZobH9yt9f+P99mWXXVbcDxYm/bzjuSZ+v7T//vtndfpZl3K8R9WlTPG+2ypdW8QejD0bsybjz6f7Evc59l3c77g+zeKM2c+1XM/ddtttsBzvqy6++OKsvuCCCzqWV+kePJ7bH/OYx2R1PEbTY6OW91rLFE+P0b59VlLLe03PFV033HfpMR3zjF/60pdm9UUXXZTV//iP/zhYjtdT6T1IfJ2u67oNGzZkdXo/756lv9p7tueeew6W43gSxfNnKUP8RS96UfF34/FX+s5o3M+91Pt9TWpbsddjTvJzn/vcrD7ooIOy+oYbbhgs176bY2EOOeSQwXLsjdq9Rnp90HXle404ZtTuNUoZ4rUxIx4bac/GbfXJDO+6ch55fD9ixnr6/rZ8r+EvxAEAAAAAAABokgfiAAAAAAAAADTJA3EAAAAAAAAAmtR06GPMg3nUox61THsyWcuVdV7Ktarlfj/jGc/I6pghvlLy22dVLdfk3e9+95zrWskKKmVyvPOd78zqL33pS1l9++23Z/Ukc3FYPjG3JZ7nSnUt7zVme8W88tLxWMslKp2r+/5unwzxmEUVs2rWr1/fzUWfrDy//du/PViu5R3VMo1mQdrzaf5T13XdoYcemtUf/OAHs/qUU04ZLNey3Vla8dhPP4+3ve1t2boTTjghq+O1QcwkS897sadKOWCj9is9/mrbWinivynmmaXj5KWXXlrcljFkcdXe3wMPPHCwHHPoarm/pTpmvcafjTl+8efT65Jxx6n09/tmAsY63e/a+xHfv/T9Td/3rhvO9dMXi+/JT35yVsfr//R6oJZj3ydDvPaztTo9Jvfaa69sXczLjMdRn3uD2n7E9ySt47pa9nPaO/HckObydt3wdVq8L3Mvv7xiPu9+++2X1aV82FK+66g69mxal3pwXLVxIh53aaZxzDeO+c+HH354VqfH//XXX5+tu+qqq7L6nHPOyeo0MzzSG5OXZrjHYzW+3/EcGY/X9PnH0572tOLrjnNsL2aW/FLl1MfXiT0Vx8mnPvWpWZ1miHu2sTC1z/bRj370YDmOAfFauZYLnl5PxDFjKe814s+Xxp8ovl9xDEm/i63dW8R78LReu3Zttq6lew3fYAIAAAAAAADQJA/EAQAAAAAAAGiSB+IAAAAAAAAANKnpDPFZyG5Yyn9jKXuqth8xw4HlVctGPvHEE7P6hS984Zw/H7c1reIxnGaLxYy+D3zgA1n9G7/xG1ldypJlZSllnsSsoJglVMptiZ97zIDasGFDVl955ZVZ/cQnPnGwHHPC+uR8x7p2ru6T/VfLtYn7vWXLluLPs7hq+fHHHHNMVv/iL/7iYDke662c9xdL7f154xvfmNUf+9jHBss33nhjtq72ubG44rH/5je/ebB8/PHHZ+tuu+22rI55XaW8yFoOWLxOK13HTTKHvu/x1idHLG47jpPnnnvunL9bu45lPLXMxnhfl+aT1jKxa1l86XFQywwu5Q933fi54XNtK+5H7ViOdfrvKuUg1+o1a9Zk62Im4L333pvVS5XF2Zr0843H2MEHH5zV8V6hlD1fyo4c9fN9so1r69Pjbt999y3uRzxG+3z/1PeeJd3v2n7EzyJdH3/2vvvuy+r43UbMUb7nnnvm3Ed9Mxnp+xg/68c+9rFZvccee2R1zDst5cfHcaSUGd515Z4d57vXeNzE65Z47oh1mgcb18Xje+PGjVl96623zrkuXrteccUVQ/ueSt8DvdBffM/i8Rm/f0qVznldN3wtsv/++w+W0/zlUeKYMcnPtsV72ac85SlZfeaZZy7TnkyvxbzXKF13d10+TvS5thi1fpL3Gul7El8nvj+175vTf2P898e69H7G8Td+LnH8maZrJn8hDgAAAAAAAECTPBAHAAAAAAAAoElNT5lemyqqRZP8N9amNugzDW+ccrrvazFZcYqmOB3HO97xjuLvz0Ivpe9JPD5PPvnkrP7CF76Q1f/wD/8wWG5xiqBZEaediX0T6z5TrMXp2X74wx9mdXrOjFO/pVOmjdp2acqrPj/bdcPHa6n343nkrrvuyuo777xzzt81Biy+2rno1a9+9Zy/WxszyMU+ie/fox71qKx+2cteNlj+7Gc/m60zhiyu2tTbr3jFK7L6V37lVwbLt99+e7YuTiFWmx6zFEFRm3Y/Hgfpa5XWTVqf4zHuRxxj4/tZmjJdHyyvOLXeIx/5yMFy36kGY50e+6WpbEf97iSnLSyJ5/i4H3Eaw/jvSH++7/uT1nHa4HgOilOmMz+l6R732WefbF2ctr4UMTNO5FGs+0Qcjdqv1atXD5bTaUi7bnisqt3DlO6Za/r8G2v/5lJfxdimVatWZfXjHve4rP7BD34w5+u6Z1mY0vu43377ZesOOuigrE7HmK4bjocoTQ1bO8f2mTK9r/TfGMeFWh3v99P6/vvvz9bFc32s03vyTZs2ZesuvPDCrI7rHf/jqb1/pWO5b8xGnEo//T4p9oXr6H7i+xWjRlJ6ZDLivUZ6zVu714jHe+launRNM2r9Ut1r1Pq9tp9p3efeouvy9zOOv7Up06eJvxAHAAAAAAAAoEkeiAMAAAAAAADQJA/EAQAAAAAAAGhS0yGQMYdlmue2X6haZkm6vrSu7+tEGzZsGOv3GV+aIRGzwX791389q5/85Cdndfz5WrZla2JmTPz3f+hDH8rqNA9WhszKVvp8Yl5KzGWKx0Vax3W1Hrrnnnuy+uKLLx4sH3vsscX96JMxXMsYrGWGl87VMet8/fr1WZ3utzyypVfLCnva0562RHtClOZUxwxxvbG4an0Rc6zT8X3t2rXZunhujuf5+FmWxoxaXdpWaV3X1c/7pWOub+Zg6d8Yx4x03Ou6rrvtttsGy8aMlSXm+pWy+GrXHaW6b8bycqntR+nfWHs/SpmAcV3MTGRhSueb/fffP1sXe+GBBx7I6kkez33ytWMdz7+PfvSjB8u1HNpaXma6PuZQxnN1zPnso09ueq2v4rYe85jHZHWaIc7iO+KII7J6r732yuo0O7brhvsuvZ6Ix1jpHNp19WOjJB7fpeuv+F1A/K469l3MEE9zw+P32vF7hDQzvOu67s477xwsX3PNNdm6H/3oR12J663FVToex/l+vuuGz8fMX3yvS/nskXz2yYj3iX3uNWrXC6V1tXq5jHOvUXs/SuNkvLeIGeLTbGV8sgAAAAAAAAAwYR6IAwAAAAAAANAkD8QBAAAAAAAAaNLUh0rEXI10LvyY83DhhRdmdcwKmoV8lFIm1mLmsMUcGxZf/DzT3KI99tgjW/eOd7yjuK2VkpuxXGKmRsyFOe6447L6hBNOGCx/+ctfLm4r5kmxfOJnE3Nr4pgS9ckJjLlgMZvl2muvHSwffvjh2bq99947q2OOWOm1x8kr7Lp+WTSl87482KWRvs+1/N5DDz103tsdN9Ns1tTG0H/5L//lYDnmJMZcQL0zWbVjedOmTVn98Y9/fLD8+7//+9m6eB6P43spUzzuR/zdPnmocd1SXmeUcvNqx+q3v/3tSe8OU24Wx5Y+/+ZZvz9bDo985COzOp7XS9fZtczG+Lsx/zX9/Vp+Zjzfrlq1KqvTLPR4f1PKQR6ldJ9R+zfHbaf5z+Nc39R6I+ZMr1mzZsGvxWi169Xdd999sHzYYYdl6+K18K677prVMUM8vf6Kn23so3EyxGuZ4aVruVpm+AMPPFCs0/v9e++9N1t39913Z3WaGd51Xbdx48bBcvxOPM0m7zr3Gcttku+3z27xuP5afKXrqdqx3fc7zlS8Lmmhj8a5n2rx/fgZXQwAAAAAAABAkzwQBwAAAAAAAKBJHogDAAAAAAAA0KSpzxCPSnPjn3POOVn92te+drF3Z8WZZBZbmh1Qy9C4+OKLJ/a6zE/MR0pzi9785jdn6w488MA5f7brhrOXKDvttNMGy2eeeWa2Tk7T8iq93zGvLGaQxb4o5eDFrJVaT8WM13S/LrroomzdC1/4wqyu5YKn5+e4X7UxoU8WbXw/1q1bN+fPzmIu6EpTy71b6M92nc+3rzQbMeZQxwxxFlc8tuN57Zprrhksf/rTn87Wve9978vqmPFYyq2MPVPLWS3lecV1tSzzKP39Wq/3WR+P7RtuuCGrS/cKro1Wlphtmh5j8XiLde1YLl1LRSvluKjtR59/Y5/3L55jjBeLL83e7rr6NXgpX3ucupbVHY+rQw45JKvTcSD2c80k8yRLY128D+tzLVq7f3nEIx6R1XF8YvGlueF77713ti5mhu+yyy5ZHT+/9FiZZGZ4VDsGY2+kOeHxu4AHH3xwzp/tuuHvjNIM8ZgZftddd2V1zBC/4oorBss/+clPupKVMq7Oivg5p8dFPAfG81g81vt8dvFnx7l373sNNA3ivym+1xs2bJjzd2vjMfMTr03Sc2jt3NvnWnoW7zVqdfp+xbErHYumnb8QBwAAAAAAAKBJHogDAAAAAAAA0CQPxAEAAAAAAABoUnPBwKV8029/+9tZfeutt2b1Pvvsk9XpHP2t5mGm/65xshFivknMrTnvvPOKvy9XY3wxqyRmPaQ5Te9973uL26rlS8662nv9hCc8YbD8rne9K1v3O7/zO1ldynpnacU8svjZxGyvqDRO1DKg4u+mP79+/fpsXZph23Vd99jHPjarY65YKXMwqmXu9dnW7bffPue6lZLFw8L0/fxavYaalN12222w/KhHPSpbt2nTpqyO76VeWlyl/O2/+7u/y9YdcMABWf2Wt7wlq7ds2TLntuLnWBsj4rk6rfvmjy+m9N+RHuddN/z+xUyy9N9Rur9j6cXPKj2247VSrON1bqxLeeSln+26cl9MUuzX2n6V/h219yPW6fsZzym1DHHjxfj63iOn5+5adnHcdilDvHa9HjOZ16xZk9V98uZruclpPe740icnPWa6zrVPXTd8j7fzzjsXt8344mcQ3/MjjjhisLxq1apsXbxeiJ9fKUN8KTPDa9duaR0zw2Md83LjOHvvvfcOluO9fvzu9ZZbbsnqiy66aM59dl+xuGrvZ/yc07o2JtSujWO2fCr2xUr93Gs9t1SvEz+LUoa47z0m4/7778/q9BwYz5+1e43Subl2rxHrWl9OSu1eo8/9VJ97i7g+jjfxc5lmrvwAAAAAAAAAaJIH4gAAAAAAAAA0qbkp01MPf/jDs/qmm27K6i996UtZfcopp2R1OsVAaUqmVvSd6iGdEitOgXTWWWdl9Y033pjVcYoWU6aPr/aevv3tbx8sr169OlsXp8yYheN9kkrT573zne/M6jPOOCOrf/zjH2e13lg+taljdt111+L6PuIU6vFzTvclHhOXXHJJVu+///7FbadTCvWZdnfUfqW/H4/7OK1PnK6Nla007VrpOBiXacXyKcBK09t13cqd0m5WpH0Sj90vf/nLWX3IIYdk9Ute8pKsTqesrU1zHj/30jTotenW4/rSMRV/tnZNUppiME7R+93vfre4Ldc/y6fvFJ933HHHYDlOpRenge0zbWycFjfen8Q6Huvp+nGnRE6Px75TD5b+zfFn4/sV6/T9jeOFaIHFF2OMasdketyVpkCfT51uK/Zo/N4rRnik1xnx98e9Dku3VZt2Np7X+4xHfe5hap9LPLdcf/31835dRqtNvX3QQQdldXqMPvKRj8zW7bLLLlkdv2ssfb5LOWV66f696/Lz+zhTpHddPm3t5s2bs3XpGNx1XffDH/4wq9PpnU2Rvrzi+x+PoTRyLkYHxOOrdmyn57V4zMSIrtp9Rx+13y2NGeO8Vt/p1cd57fh9HP3V3v94Dkx7I95rxLp2r5Gem+N1ePwutdZ3y3WvUbv3KI0/sS69n3F8iWNVNE1jir8QBwAAAAAAAKBJHogDAAAAAAAA0CQPxAEAAAAAAABoUtNBwXFe/Jib/N//+3/P6le/+tVZnebcxEyNcbMBWpC+BzH/6WMf+9hS787MicdgzJB4/OMfn9VvetOb5txWKQObulI+Z8zEeu9735vVb3jDG4rbYrJipkn6fm/atClbF/NgjzvuuKyOWWhpFlgt06mW8ZqK29qyZUtWX3rppVl9zDHHZHU6FvbJCB8lXR/z29Jss67ruhtvvHHO7UxTtsw0S9/n+NnHa6Srr746qx/zmMcMlse9BuqTVTmL58A04ymO5UyvT3/601kdM8Uf97jHDZZjdlcpG7XrhvskvY6L40u8xptkpl4py7zr8tzP8847L1t3zTXXTGw/WFy1zNF0vI+51jEDMGbPxWM/zfbt2xdRup+1XNmolHVcy/3uk2VYez/uueeerE6vAW+66aaR+/4zsmInL94rlO4rYt33+qfPtvbdd9+sjsdvvEbvk20elXonvm5Uy2BO1/fJF49KY+SobV955ZVzbkvfzE98n+Jxcvjhh2d1+t3srrvumq2LGeIPf/jDszpmvKafb9/rp6hPJnGfTNdahng898exMz3333nnndm6devWZXXMEGflqJ1P0nF9zZo12brYB/H8mV4/dV2euXzttddm644++ujitkp9spjnxJVyvo3fc8Wx/5xzzpnzd1fKv2Ha1K5Z096I1zR97zXSOn7W44wZ8Vqjz+92XX5t0jf3u7Q+/mx8f+L7l76/Ld9reKoLAAAAAAAAQJM8EAcAAAAAAACgSR6IAwAAAAAAANCkpjPEo5iLcdttt2X1aaedltWf+9zn5txWLS9qGtX+DTGTIM0FjDmJ559/flbXcmvpr/Z5/c7v/E5Wp1nWtXxJxpO+n/FcceKJJ2b1F7/4xaz+m7/5mzm3FT83xlfKOLnllluy+s/+7M+y+kUvelFWP/3pT59zWzFvss85MfZ6zLmJOayHHnpoVu++++6D5ZgXU8sNjPuZHoNxXdx2zPZhedWOuXPPPTerjzvuuMHyUo4ZffI4p0V8r+P7d8kllwyWY/6u66eVq3asxnPgRz/60az++Mc/PlhOz9Nd1z8HvHRujtuKdZ+equWCxW2n9wpnn3128XdrGa+sXNdff/1g+dZbb83W7bnnnlmd3o90XX6MdF2ekRmvd2pjTynfNW6rppR1HK/pStmEXVfO6osZ4fG8EceE9No0fd+ZnNJ5LmZNx1zRNBc5bquWax2Vrn9WrVo15+uM2q/4Wuk4EceMWgZmaczoe41Wymiu5TeX8sfjunieufrqq7P68ssvn/c+8s/Szzu+T3vttVdWH3TQQVm92267DZZjhngtM7w0NvTNf41K1x6la6+uGx4b0u9Pa/mvpczwruu6zZs3D5Y3btyYrbvooouKv1v6nFhe8fhMrxdiNvzBBx+c1TGzvvTdzHe+851sXcwQr93TlI6bFo6p2MvxWvV73/teVl933XVZnb5f7tUXR/qex+9p4310vEbaZZddsjodY0rXR6OUxoHadV3te510W7V7jQceeCCrS/capYzwrivfa8RjvSX+QhwAAAAAAACAJnkgDgAAAAAAAECTPBAHAAAAAAAAoEkzlSF+5513ZnXMsYnZve94xzsGy5/4xCeydbX8mBYymWNmQcxe+u53vztYfte73lXcVgu5IitBmjkRj7ljjjkmq1/zmtfMuZ0Wjs9pUcvj+chHPpLV8Twkg2b5xM8qfhZnnXVWVl977bWD5Ze//OXZupihGc+vUZqNFns9Zs/E/KiLL744q5/3vOfN+bvjZLTGPLc77rgjq+OYmzImLL3aueTMM8/M6ve+972D5ZjVF7cVj6vaeW8cpTzOmuXKH68d79/4xjfmXCdDfHrEzzlea8UMrv/wH/7DYDnmi9dyv0uZ4qU8slF1rb9L4r855n6mGWQxfy9ybK9ctXN6mkV3xRVXZOv222+/rI5ZsfH+Ml5blF63dqyn24rHeS0jsE+uX8zxi1l9pZzwUk7sqDp9f2MGYJ/MT+YW37f0WImf7dlnn53VJ554YlbHz36u7Xbd8PkzHoPpcRc/+5gt2SdzubRuVF3KFK/1VZ/rsNrxW8oQjz0a82C//e1vZ3W8l0r/zePcK82ymNka74XTsSCOA7UM8dIxWjsGoz7ZyH0yw2MdezTmvcaejmPDpk2bBsvXXHNNtu7HP/7x0L6njAUrV+mzWb9+fVbH436fffbJ6nhOTH++NlbtvffeWR2P5T5q1/Ppv7l2bMb1tbq0H316PZ5HPve5zxX3s/Q9PfMzzr3G/vvvn9W77bZbVj/iEY/I6vS6p+/9QKzTbdXuNWrHb3otUrvXiGNKHENK9xrxe9pYX3rppXNut6V7DX8hDgAAAAAAAECTPBAHAAAAAAAAoEkeiAMAAAAAAADQpJnKEI9z/d96661ZvXbt2qz+/Oc/P+e2YqZ4zPOIuRF9spWWUrqf8f2J2T1/93d/l9W/+Iu/OFiO+QUt5QpMi/e9731ZHT+D9LOWIb64Shm3MaMsZr+/9a1vzeo/+qM/GizHTJK4LSardt6K5/Irr7xysBwzn9LzZdd13VFHHZXVpUzxeAzFn425ajfeeGNWp9nmhx56aLYuZiHGf1NpvIrH4+233z7nz3Zd/u8wJiy9Uq5Y13Xd5ZdfntVnnHHGYPnf/Jt/k62LWUKlzNeaxcwbXyniex0zoL7+9a/P+btyladXLff7+9///mD5s5/9bLbuN37jN7I65tDGbaXn6thD8TxeukeJarlpcVsxM/SCCy4YLN9xxx3F1zUuTI/SZ3XZZZdl9UEHHZTV8RiJ95t97lHidXDcVlrHa5baWFM69uM5PGZtxmurOGamWX2xL2J91VVXZXV8f1N6aHGUxuG/+qu/yup4DL7kJS8ZLK9evbq43dtuuy2r43GT9kbMXI75mLGO12lp3SefuevKGZlxvOmTPx71HSPS9zPuY3ov1HVd95WvfGXe22Jh4vEcP5M017107h71u6XjqnaMxeOo9FnHdXHMief+UuZr/L40XtfVMl7T9/PCCy8svq7rqzbEz23dunVZHXPoYw74qlWrBsvXXXddtu4LX/hCVp966qlZXbqHWczjaZxt9/3dtG/22GOPbN03v/nNrI736qXv2pmMPvcaBx54YFbHa6TadU6qdq8bz/vp9Vbp/rzr6sdoOsb0vdeIY8rmzZsHyxs3bszWbdq0KauvvvrqrI4Z7amWxpOV82QWAAAAAAAAACbIA3EAAAAAAAAAmjRTU6ZHcYqBDRs2ZPV+++03WD799NOzdXHKkd/7vd/L6sc+9rFZnU4rUJt+sDaFW59pZ+P60hQOcd2f/umfZvXb3va2rE6naIlTQZhmajJKU/G//OUvz9a98pWvzOo4dVL6GdWmdFmp02Cs1Kl0a9OTpmpTQX7wgx/M6q9+9auD5TgNt75bXqVzd5zm8Mtf/nJWP+MZz8jql770pVmdfrZxupw4DWJtDLn00ksHy+m41nXDx2OfYyhOXxfH0MiU6StL7TNIo2FOOOGE4s/GMWWS0TClKdX7Tre+VNOz16bx/dznPpfVadyC83q7StMPxvuMGG/xS7/0S1ldmkq3Nh1obSr3cc7P8dozTjlIm9JzabxmOffcc7M6nSa368rjRTxWa9PVlqZIjNMl9h0v0teOrxunwo3fMdx1111ZnU6LHuNm4rSFMbIsfX9Ni7v84mfw53/+51l91llnDZb32WefbF2MD9hzzz2z+oADDsjqNWvWDJbTqXC7rut23XXXrI73CqUp1uM1SvzdOEbEXkrvB2rTrfeZUr3P9Opdl491u+++e7buM5/5TFbHaALXXguTnnPiexinc7744ouzOsbGpfpO418aR+J5sc/U+3EMitc4cUyK4186NsT3o++U6T/60Y8Gy9dff/2oXR8wFrQpfq4xliBea6RjShxf4r3osccem9VPf/rTszq974jfAfV9JtHn+Bznd6PYr+kYHN/L0047rbgt119Lr3Sv8fd///dZHa8BSt/Bj3uvUYpnqk2ZXorliONN7V4jjiHptOjx+I7PM+P7Nyv3Gv5CHAAAAAAAAIAmeSAOAAAAAAAAQJM8EAcAAAAAAACgSTOdIR7FnJZ0zv6Yu/r9738/q2Oe8/HHH5/Vb3nLWwbLBx98cLaubz7SOOJ8/2ku2Yc//OFs3dlnn53VMf8g3U85S5MRP/tSzuOHPvSh4rb6ZCsxnnj8x0yTVOzBmA2y7777ZvXb3/72wfK73/3ubN1KzVSfVelnW/tszjvvvKyOOS7pGLJ27dpsXen46rrhLMA0PybNH+u6rjv66KOzOh6PpX9HPMf85Cc/Ke6X43Vlieet+Hmm2fPvf//7s3Uf+9jHsnrz5s1ZHbP/0vFnMXO8x80U7/O7UTpexx689tprs/q9733vgvaJtsRrvNTHP/7xrD788MOz+qijjsrqNFesdL0+an3pGr6WPx6zZq+44oqsPv/88+fctmO9HaXrn5hb953vfCer48+nx1y8JonXPzGPPGYyTzLXLz32Y55gzPGL2bDxO4Y0vzhmwcbM8Pi76fulh5Zf/AzicZVmOsZ8x5p4LbHXXnsNlvfff/9sXcwb33vvvbM65mmmvRNfp5SP2XXD5/30mi/+bOy7eK1ZWh/PDfF343iU/pu/+MUvZuu+9rWvZbXM8MmrnY++8Y1vZPVzn/vcwfJznvOcbF38bONxUrpGr+Wd1s71fTLE45gU6zQ3vG9m+Pr167M6ZrCnWs54ZW7xc4/H38033zxYTr8P6rrh66eTTz45q2PG+BOf+MTBcswqr33PXDoex80IT38+/m4pMzyuP+mkk7J18bs5Y8byG+de44UvfGFWl67pF/NeIypliMf9iBnid999d1bHMSTt+Xg8x/fn1ltvzepZudfwhAwAAAAAAACAJnkgDgAAAAAAAECTPBAHAAAAAAAAoEkyxAvSnJc4537MYYo+85nPZPUZZ5wxWH7qU5+arTvmmGOy+ud+7ueyOuY0pXmyMbM1ZidceOGFWX3WWWdl9SWXXDK07z8Tc5pivkHLWQLLpZb/84xnPGOwHDMj/uqv/iqrS3kVMd8r5tfdcMMNWR3zYBfrs4///pjTFI/XuH6pxGyQmMmT5sXWMgprSllqpfxRllftc47HxS233JLV6Rhy3HHHZevSzLWuGz43xxyctH+vuuqqbF3MJ1+1alVWx2MsPR7jujQTcxRjxsoWP8/0GI15xvG4efvb357VMVM8PW7itUVUywEv5Uf13Vaf343i+5X2WezJN77xjVm9cePGrC71FbMhHpvxPP6e97wnq//rf/2vWZ1mp8Zro1qGeMzYK4l9Ee9RYv5xejzX8l9pQ+28m2Zadl3Xfetb38rqY489drB8xBFHZOtiBmu8Ztltt92yOs1CruX61foi7auY6xczxGO+Zsz1u+aaawbL55xzTrbu9ttvz2rZsNOllCva95oljgNp78Q+it/5xN7YZ599sjrNHN93332zdWlW+ahtxYzxtI7fMdTq2JdpXevRmJN++umnD5Y/+clPZuviey//dfJq5/54Tv3Qhz40WP70pz+drXve856X1fHcX7quGSczvOvya5PSONB1wz0aM15LGeJxnIjfJ/3whz/M6nRsMC7Qdf0+93hsxmM3Xnu8/OUvz+r/+T//52D5aU97WrYuXgPFa6TS8Vr7N9T6Ne3RuK04lq1bty6r3/KWtwyW47WYe5aVrTbe3HTTTVldutc4/PDDs3XxeF69enVWx3uN9F44XtPE46j2fX46xsSerd1rxO/frr766sHyueeem62Lzw1ndUzxF+IAAAAAAAAANMkDcQAAAAAAAACa5IE4AAAAAAAAAE3aYfs8Joe/6667ukc96lFLsT9TK865H/ORUjFTo++205zKmGPTd67/NG9nVvOJN2/ePJQLMV9L3RvpZzQruQ4sj2nqixaUcgVjrz/+8Y/P6te85jVZveeee2Z1zIhKxSzoZz/72VmdZp91XT7mxAzB448/PqsvuuiirE7Hm2nO7puV3kiPyVr24h/8wR9k9SmnnJLVaeZRvG6J2Uq1jPFSr/TJCK/9fC2zbJdddsnq9N914oknZuvOOOOMrG4xl2xW+mKp1I6Ro48+Oqtj/mZpW/EeJb2v6Lr8XB37NdYxX/N1r3tdVl933XWD5VnNJ1tob7TaF7XjIM3ie8ITnpCtO/LII7N6v/32y+qYdZxuq3Scj9qvPrl+d999d1bfcsstWX3ZZZdl9RVXXDFY7pO12RJjRn997hX6iL0Rc1djn8U6zSfvk7U56rXTa6v4u/Hf/7d/+7dZ/ed//ueD5dr7sZL7qtXeiOfc9Lo67vPv/u7vZvVrX/varI5Zqql4jVO7NyhlEsdrnlqm65YtW7I6zXSNma0333xzVl9++eVZ/bWvfW3O15qVcSLVal8sl1pfxGMq/d7ngx/8YLYuzeLuuq7bY489sjpe56R1vI+I115xP2NGc9rv8Tuv2EPvec97sjrNmZ7me3O9MWyce42jjjoqq/fdd9+sLt1rxPEnjntxP+L4U7rXuOeee7I63mtceumlWT3r9xrz6Qt/IQ4AAAAAAABAkzwQBwAAAAAAAKBJO9V/hPmIUwyUpkWP0xPUplGI0qlA+k4tWpsClJUtPTZqn30ffaeZXS7TMpXHYu2nfm1X6ZiJ/fmjH/0oqz/1qU9l9atf/eqsfspTnjJYjlPvrFu3LqtvuOGGrH70ox89537GKUI3btw4tO9z/S4rX+nziuPPqaeemtU//vGPs/qjH/3oYHn33XfP1sVp+eN0Z6Vrpnj9VNPnHBojAeIUbT/84Q+zOp0mPcYFTPM0bCyPeIzEYygeY5/4xCcGyx/4wAeydXfddVdxW7GP0jruR+yL733ve1mdTpHedaJ+GBaPg3iOT++h43F+9dVXZ/XBBx+c1QceeGBWpxEyj3zkI7N1cVrD0rS5XZdf89xxxx3Zungtdf3112d1nOYwNQvTFjIZfY6NPvf2cVroOA1nrOO2097ae++9s3VxevW4Pk7Vmk6hfuutt2brLrjggqxev359VpeuCfXV8ovn2PQ4itcpJ598claff/75WX3aaadl9Zo1awbL6TTl8xGPjbQf4j1J/I63NqVtOoX6nXfema2L40gc7+J07K6nmKTaMRTPp2kvxKnHP/OZz2T1r/3ar2X1i1/84qxOr9XifUWMJNu0aVNWx6iBc845Z7D8la98JVt33nnnZXXpOwX35m0Z517jqquuyupDDjkkq+P3o2nMTLzXiN8fRfH6Kx1D4ner6RT/XTd8z+1eoz9/IQ4AAAAAAABAkzwQBwAAAAAAAKBJHogDAAAAAAAA0KQdts9j8vi77rprKN+H5SM/ZrI2b97crV69ekG/qzdolb5YuWKmUymTreu67thjjx0sv+pVr8rWxSzZmKn5ohe9KKvTbL8bb7wxW/fyl788q2OOTStjl94oZ3B13XAOV5q1dMopp2Tr3vCGN2R1zJeM0mOnljce6/T4rfnJT36S1TEf7Q//8A+zOs0RnMXMcH2xtEpZYKeeemq2Ls2377o8C7nrhvP60j6J+Zi77rprVsccz7/8y7/M6rQXZqEPRllob8xqX5Syj2vXDvF302zKeJzHOm47Hvv33XffnOv67lef322VMWPlquWPj3PMxuujeF320EMPjVwepdVMzFnsjb73FUcccURWf+ADHxgsH3fccdm6mFEcs7zj+Ty9t4iZ4ek40HXD11Mx7/i2224bLG/YsCFbd+GFF2b1WWedldW143/WzGJfrCRpj9b6M4o5ymnmcrzvj59xvB+/4447sjp+F5CK+xnF79Cmld4YT99rifjz6f3EIx7xiDnXjdp2aYxxrzGe+fSFvxAHAAAAAAAAoEkeiAMAAAAAAADQJA/EAQAAAAAAAGjSTvUfYaWZxfn/AfgntczwWJ977rmD5euvvz5bd8IJJ2T1AQcckNWXX355Vj/zmc8cLK9fvz5bV8oM7zpjV0viZxmzw2JGZHqsvPOd78zWffzjH8/ql7zkJVn9whe+MKsPP/zwwfLatWuzdfGYi7li1113XVanx/e3vvWtbN3ZZ5+d1fH4jrlkaT2rWcksndiD6bH/n/7Tf8rWHXbYYVn97Gc/O6tjJmZ6LMd815tuuimrv/vd7xb3s5V8PpZO6Vqhb7ZxemzH43ySFjNzGZZa3+O1dvyn4vVR6XopXmfF/dJX7eh7X3HNNddk9YknnjhY/vmf//k513Vd173gBS/I6jVr1mR1mulau56P9xmxTreV5ol3Xdf94Ac/yOqYGe4+mpUkPf5iX5Tuibtu+NjesGHDyOWFiOeGlPtx5qN0Tz2fn7/33ntHLndd123atGnMvVv4flHnL8QBAAAAAAAAaJIH4gAAAAAAAAA0yQNxAAAAAAAAAJokQxwAplgtUy/NcbrxxhuzdZ/61Key+mUve1lWP//5z8/qNIPzzjvvLO6X7LPZVcoWi7lit9xyS1Z/7nOfK9ZpVtiqVauydfGYi5llW7ZsKe12cVsxoyxmIzu+WSnisfjBD34wq//kT/4kqw888MCsvv/++wfLe+yxR7YuZoZv3rw5q2OfyO9jkhYz23ic/XD+Z5Yt1vEfr7OYXX0yi88///xsXaxjZvgzn/nMrD7qqKMGywcffHC2Lo4pcb9uuummrL7yyisHy9/73veydTE72X0z0yqeq2vn7ti/JbXrLfcZTJp7jdnhL8QBAAAAAAAAaJIH4gAAAAAAAAA0yQNxAAAAAAAAAJokQxwAGpbmOMWMmwcffDCrzzzzzKy+7LLLsnr//fcfLG/cuLH4uouVp8P0KWWJlXIAu66cn1TLse/zWvF14j7LKGMlS4/fmON9xx13ZPX73//+rP4v/+W/ZHX6+2meeNd13Xe+853ifsh8ZSWRtwfQntK1RrwGim6//fas/trXvlasS3bbbbesvvfee7O6NAbJDGdWuVegJc7d08tfiAMAAAAAAADQJA/EAQAAAAAAAGiSKdMBYEbUpvSJU0pfeeWVWX3KKacMllevXl3clumwmI94nPQ5bvpOyz/Oa8G0iNP7x+lDL7300qz+6Ec/mtWf+MQnBsvnn39+ti7WkWnjAIDlUos4ivcO8d43Fe8T4jXOPffc0+u1StsCAJaOvxAHAAAAAAAAoEkeiAMAAAAAAADQJA/EAQAAAAAAAGiSDHEAoOu64ay0mH22fv36kcujyEZjsTnGoK6WKf7Xf/3XWf2EJzxhsHzfffcVtx23VcvuBABYLvHeYZzrlnifHLftPgUAViZ/IQ4AAAAAAABAkzwQBwAAAAAAAKBJHogDAAAAAAAA0CQZ4gDASDH7LGallX4WgJWnlpf5n//zfx4sx4zwvtsCAGiRe18AmE7+QhwAAAAAAACAJnkgDgAAAAAAAECTPBAHAAAAAAAAoEkyxAGAeZGVBtC2+++/f7DsnA8AAAC0wl+IAwAAAAAAANAkD8QBAAAAAAAAaJIH4gAAAAAAAAA0SYY4AAAAcsMBAACAJvkLcQAAAAAAAACa5IE4AAAAAAAAAE3yQBwAAAAAAACAJnkgDgAAAAAAAECTPBAHAAAAAAAAoEkeiAMAAAAAAADQJA/EAQAAAAAAAGiSB+IAAAAAAAAANMkDcQAAAAAAAACaNK8H4tu3b1/s/YBlM87xrTdolb6A0fQGDNMXMNpCj299QcuMGTCa3oBh+gJG0xswbD7H9rweiG/ZsmXsnYGVapzjW2/QKn0Bo+kNGKYvYLSFHt/6gpYZM2A0vQHD9AWMpjdg2HyO7R22z+Ox+bZt27r169d3q1at6nbYYYeJ7Bwst+3bt3dbtmzpDjjggG7HHReWHqA3aI2+gNH0BgzTFzDauL2hL2iRMQNG0xswTF/AaHoDhvXpi3k9EAcAAAAAAACAabOw/40EAAAAAAAAAFY4D8QBAAAAAAAAaNJO8/khuQK0SOYGDNMXMJregGH6AkaTIQ7DjBkwmt6AYfoCRtMbMKxPX8zrgfj69eu7tWvXTmTnYKVZt25dd+CBBy7od/UGrdIXMJregGH6AkZbaG/oC1pmzIDR9AYM0xcwmt6AYfPpi3k9EF+1alXXdV138MEHD56w33LLLdnPPPDAA4Plbdu29dpR6CP+n0ux3nXXXQfLu+yyS7bu6quvHizfdddd3dq1awfH90LoDVaSSfSGvqA1xgwYzZgBw1bKmKEvWElWSl90nd5gZdEbMJr7DBhmzIDRlnrMmNcD8Z/txI477jhoktKO1qZa2L59+3xelhlVO35qTZLWcYqE1atX9369+eyL3mApLGVv6AumhTEDRjNmwLBpGTP0BUtpWvoi/V29wVLQGzCa+wwYZsyA0VbamLGwoAEAAAAAAAAAWOHm9RfiP7Nhw4bBU/aHHnooW5dOneD/CmEcteOntv6ee+4ZLKfTe3Rd1z3pSU8aLG/dunUBezea3mApLEVv6AumjTEDRjNmwLBpGzP0BUth2vqi6/QGS0NvwGjuM2CYMQNGW2ljhr8QBwAAAAAAAKBJHogDAAAAAAAA0CQPxAEAAAAAAABoUq8M8fvvv3+QKzDJvAKYpDTj4qc//Wm2bt26dYPlSeZf6A2mwXx6Q18wa4wZMJoxA4Yt9ZihL5gGrqVgNL0Bo7nPgGHGDBht0mOGvxAHAAAAAAAAoEkeiAMAAAAAAADQpF5Tpm/btm0wjQJMgzhdwn333TfnunHoDabNXL2hL5hlxgwYzZgBw5ZizNAXTBvXUjCa3oDR3GfAMGMGjDaJMcNfiAMAAAAAAADQJA/EAQAAAAAAAGiSB+IAAAAAAAAANKlXhjhMu23btg2WJ5m5AdPuZ72hL+CfGTNgNGMGDDNmwDB9AaPpDRjNfQYMM2bAaAsZM/yFOAAAAAAAAABN8kAcAAAAAAAAgCZ5IA4AAAAAAABAk3pniMspYJqlx++kj2W9wTT72fGrL+CfGTNgNGMGDFusMUNfMM1cS8FoegNGc58Bw4wZMNpCxgx/IQ4AAAAAAABAkzwQBwAAAAAAAKBJHogDAAAAAAAA0KTeGeIwzRYzcwOm2WLlNME0M2bAaMYMGGbMgGH6AkbTGzCa+wwYZsyA0WSIAwAAAAAAAMD/54E4AAAAAAAAAE3yQBwAAAAAAACAJskQZ6bI3IDR5DTBMGMGjGbMgGHGDBimL2A0vQGjuc+AYcYMGE2GOAAAAAAAAAD8fx6IAwAAAAAAANAkD8QBAAAAAAAAaJIH4gAAAAAAAAA0yQNxAAAAAAAAAJrkgTgAAAAAAAAATdppuXeA/nbYYYdled3t27cvy+vCfOkNWL4+iPQFK50xA4wZMIq+gNH0BsyP+wwYpi/gn7ieWl7+QhwAAAAAAACAJnkgDgAAAAAAAECTPBAHAAAAAAAAoEnNZYj3mYN/uebrX8zXHWfb8Xe3bduW1TFXIK1nNXNgmuiNyW271Aux1hsrm74wZjCa3jBmMExfGDMYpi/0BaPpDb3BaHpDbzBMX+gLRtMbemPS/IU4AAAAAAAAAE3yQBwAAAAAAACAJnkgDgAAAAAAAECTmssQT9Xm2I/rYz3OXPml1x43V2CSuQR9thVzBkpazhlogd4Yb1uxF/RGG/TFZLelL9qhN8bbljGjTfpistvSF23QF5Pdlr5oh96Y7Lb0Rjv0xmS3pTfaoC8muy190Q69MdltzWpv+AtxAAAAAAAAAJrkgTgAAAAAAAAATZr6KdNLUyHsuOOO8/7ZUXU6FUDfqQtKPx/3K0450Hf6h3H2q/RvfOihh4q/u3Xr1pHbWcmmZT8nYZK9EX8+nVKjld7os129Mb30RT/GjOnYz0lwPTU5eqMdxozJ0Rft0Bf9uJaajv2cBL3Rj96Yjv2cBPcZk6M32qEv+jFmTMd+ToLrqX70Rv/99BfiAAAAAAAAADTJA3EAAAAAAAAAmuSBOAAAAAAAAABNmvoM8aiUK9C3Hmeu/FKmQd9cgcXKnolKmQOjpOvjz05LzsAsabE3otL6vtsq/azeaEeLfdHn2NYXzKXF3oiMGfTVYl8YMxiXvtAXjKY39AajtdgbkfsM+mqxLzzPYBJmvTeMGZPnL8QBAAAAAAAAaJIH4gAAAAAAAAA0yQNxAAAAAAAAAJo00xniD3vYw7J6p53yt2Pbtm3zep1RSq9dyxWI6xdT+trxdR966KE5f7brum7r1q2Lt2NM3Kz3Rt/MDb0xG2a9L/rSF7Nj1nvDmMEo+kJfMGzW+6IvfTE7VkpvxG33ybw0ZrAYVkpvuJ5iJZn1vuhLX8wOvdGP3qjzF+IAAAAAAAAANMkDcQAAAAAAAACa5IE4AAAAAAAAAE1qOkM8zv0f61rOQCk7YJycprhuMbNo+vx8LYeh9n6yso3TGzFzo5QpoTf0xjTRF/qC0abxeirmQcX1fYzTG7X3R29ML2OGMYNh+kJfMJre0BuM5j5DbzBsGvvCmMFScD2lNybNX4gDAAAAAAAA0CQPxAEAAAAAAABokgfiAAAAAAAAADSpuQzxdJ78OMd+rGMGTKzTXIHanPq1+fxLc/DX9mucbdWkP197P2Ld97VYXuP0RszYKB2jk+yN+Dq1zA29QV+T7IulGjP0BUthGq+nJnnMxd/ts5/xZ2tjqN6YHq6ljBkMcy2lLxhtGscM302xFGbtPqPvfkR6Yza00Beup1gM7jX0xqT5C3EAAAAAAAAAmuSBOAAAAAAAAABNam7K9EkqTVcwSbVpOWs/P99181mfqk2bUJqyAn6mTx+NexxNsjfSOk5tojcYpc+xvlTjS9fpC5bfYh3vi3kcTbI3atN66Q1qjBn6Yla4ltIXjG+c3ljO76b0BgsxDfcZtXuBPj+vN5iPldoXnmew3Nxr6I2u8xfiAAAAAAAAADTKA3EAAAAAAAAAmuSBOAAAAAAAAABNkiGeWK7M8En+/FJmXjI7JplDNklL1Rt9MjaYHUvVFyt1zOiTiwzLYSmz/4wZ1Mz6fYYxg1FcS+kLRjNm6A1GW8pM1z58NwXDPM9gubnXcD01ir8QBwAAAAAAAKBJHogDAAAAAAAA0CQPxAEAAAAAAABo0kxliMfcgL71OL+b5lnEbIuYAVPb1lLN79/n/WC66Y0yvTCb9EU/+mR2LWVvpHWtN2pZYotFL8ymxRwzonhs9+kLYwZLybVUP/pidhgz+tEbs8O4UaYX6Dp9UaNPZocxox+98U/8hTgAAAAAAAAATfJAHAAAAAAAAIAmeSAOAAAAAAAAQJOayxBP577vM9f/qLo0f39tjv2HPexhc/58fJ2otr60XzGjoI9ZzQ2YFS32RtyPdFuTzN+o/Zv0zvRqsS8iYwYLUeqNWi/E43m+rzPKYo4Z813XlzGjXStlzCjlwdb6z5jBpK2UvnAtxUqzUnrDmMFKs1J6w3dTrCQt3n9HxgwWosUxI9IbS8tfiAMAAAAAAADQJA/EAQAAAAAAAGiSB+IAAAAAAAAANGnqMsRrWQGl+ftrGRs77TT321F73TjXf9xWKadp69atxf2M207rvlk08efTHILav5GVTW+Ue6OWuaE32qQvjBmMpjeMGQxrsS9quWrGDGpa7AvXUkxCi71hzGASWuwN9xmMS18YMxhNb+iNpeYvxAEAAAAAAABokgfiAAAAAAAAADRp6qZMr0n/9L823VNtWoXSFAW1KQZKr9V3v0rTKMSpdmr71XfaBdqhNybXG/qoHfpi4X1hqp226Q1jBsP0hfsMhukLfcFoekNvMJrecJ/BMH2x8O+XfDfVNr3hemrS/IU4AAAAAAAAAE3yQBwAAAAAAACAJnkgDgAAAAAAAECTmssQT8U59vvO51/KKIh1nK+/tK2ddsrf9oceeqj4u6XsgEnmZMjcmB3j9kasS9tart6oqe1Xabt6pR19zvOT7IvImMFKZswwZvBPjBkLpw/apS8WTl+0TW8snN5om95YOPcZs2EW7r/7HrtxW6X8ZtpizFg4vTGavxAHAAAAAAAAoEkeiAMAAAAAAADQJA/EAQAAAAAAAGjS1GeIl7ID+uYGxPn+Y45G6XXjzz7sYQ+b87Xiuvi7ffLK++Zhyg6YHdPYG/F148/26Y2otI7ZUToHrtS+iOtqjBksxDSOGZO8nopq24q9RJuMGcYMhukLfcFoekNvMFoLveF7WyZt1u+/9QVzaWHMqNEby8tfiAMAAAAAAADQJA/EAQAAAAAAAGiSB+IAAAAAAAAANGnqM8SjdN78lTTHfpoFsHXr1gX/btf1y62MvytHeXYtV2/UtlXKyagp9UbfY12vzKZp6IvasTnJ/dIH/Mw09kbNUl1P6Zt2TWNf1MaMcY5XYwZdpy9KrzvutphuemPu1x13W0y3aeyNGt/bMq5Z6ItxfldfzK5p7A3XUyubvxAHAAAAAAAAoEkeiAMAAAAAAADQJA/EAQAAAAAAAGhScxniqTg/f9+cgbTuOx9/zIvZccd//n8P4uvETPH0Z0dtq5RRUPrZUevTepJ5Bqxstd7oW5fUjsHF6o3SsT6f/ZIPO3tWal/EdX37orTOmMF8tNobrqcYh77QFwzTF/qC0fSG3mC05eyNEr3BcjJm6AtG0xt6YxL8hTgAAAAAAAAATfJAHAAAAAAAAIAmNTdlejoVQG1ahDhdQaxL2yq9btd13cMe9rDi+tLP1pSmcu+7n8yOPr1R65U+x9E4vdH3dfv0bOl3mR3T2Be1MaM0Fc+4USKldXqoLbPWG66nmA99Mbm+0EPt0Bf6gtH0hmspRlspvdHnflZvsNhWSl8s1ZhR24++6xf6s6x8s9Yb7jUWn78QBwAAAAAAAKBJHogDAAAAAAAA0CQPxAEAAAAAAABoUnMZ4qk4536sY3bF1q1b51xfm7+/9lqlOfrjz9bWp3X8N8S69m8uZcvW/k21/WblmmRvjPta4/RGaT/0Bn0tZ1+UxGyZ+Lq1bY8zZpTeg9r7oy/aoTeMGQzTF/3eg9gXxow26Qt9wWh6Y7zecC3VLr3hPoNhK/U722kZM1xPtcuYoTcmwV+IAwAAAAAAANAkD8QBAAAAAAAAaJIH4gAAAAAAAAA0qbkM8dKc+3He/Dif/0MPPVSs5/u6XTecHZC+dm3+/tq203rcDNc+WTRMN72xdBlmTI8+x0ysJ9kXpZ6c1r5guo3TG3EMSeu+x4neYCXRF/qCYfpCXzCa+wy9wWhLNW7UjiO9wUrS55jxna2+mCXuNfTGpPkLcQAAAAAAAACa5IE4AAAAAAAAAE3yQBwAAAAAAACAJjWXIZ4aN6epT65ALatmqXIFxqnlJM8OvdG/XujPMj1qn2stp2mcvog5Tanl7IuoTxaNvmhH39746U9/mtVpr7TSG66n0Bf6gmH6Ql8wWil3sutm8z5Db9B1xo2+td6YDbUxYzG/s12pfRH5bmo2GTOMGZPgL8QBAAAAAAAAaJIH4gAAAAAAAAA0yQNxAAAAAAAAAJrUdIb4uCY5j36fPOLa+klmboyTkzyrOQO02RvjZG7U9ovpMO7nNs7v9/ndxeyLPtvq+7P6Ynot52e3Unujz2vrjTbpi/FeW1+0SV+M99r6grm0eJ/R57X1BotBbzAtJnnMTPJ3fTfFUnOvMd5r641/4i/EAQAAAAAAAGiSB+IAAAAAAAAANGmmp0zftm1bsS5NVxDF9XGK5T6/W1tf+vn4bxhnW8yuFnpjMaffYTbUjoml6ou4ru95vrRu3JrZ1Lc30nol9cZi9gqzR1/oC4a10heldfqChWjlPsOYwbj6HhfTMm7oDRaTMUNf8E9aGTNK6/TG5PkLcQAAAAAAAACa5IE4AAAAAAAAAE3yQBwAAAAAAACAJk1dhngtC+CnP/3pnOuiHXfM/3+A+++/f85tL+b8+5Pcdt9tpT8f8w62bt1a3HafTBIWn96Y3O/G39cb02sx+yLWpd9fzvP8JLelL9qhNya7Lb3RBn0x2W3pizboi8luS1+0Q29Mdlt6ox16Y7Lb0htt0BeT3Za+aIfemOy29EadvxAHAAAAAAAAoEkeiAMAAAAAAADQJA/EAQAAAAAAAGjS1GWI1/TJNo7z6Jcs57z5y/XatddtOUugRS30xko55vRGO1roi5Xy2vqiLXpj6V5Xb0wPfbF0r6svpoe+WLrX1RfTRW8s3evqjemiN5budfXG9NAXS/e6+mK66I2le91Z6Q1/IQ4AAAAAAABAkzwQBwAAAAAAAKBJHogDAAAAAAAA0KSpzxCPc9unWQGTnPe+1Tn0039Xn5wFVj69MR690SZ9MR590S69MR690SZ9MR590SZ9MR590S69MR690S69MR690SZ9MR590S69MR69UecvxAEAAAAAAABokgfiAAAAAAAAADRp6qdMj7Zt2zZYrk0LYJqFXAv/BuamNxauhX8Do+mLhWvh38Dc9MbCtfBvYDR9sXAt/BsYTV8sXAv/BuamNxauhX8Dc9MbC9fCv4HR9MXCtfBvYG56Y+Fa+DcsBn8hDgAAAAAAAECTPBAHAAAAAAAAoEkeiAMAAAAAAADQpOYyxFPmyYfR9AYM0xcwmt6AYfoChukLGE1vwGh6A4bpCxhNbzAJ/kIcAAAAAAAAgCZ5IA4AAAAAAABAkzwQBwAAAAAAAKBJHogDAAAAAAAA0CQPxAEAAAAAAABokgfiAAAAAAAAADTJA3EAAAAAAAAAmuSBOAAAAAAAAABN8kAcAAAAAAAAgCZ5IA4AAAAAAABAkzwQBwAAAAAAAKBJHogDAAAAAAAA0CQPxAEAAAAAAABokgfiAAAAAAAAADTJA3EAAAAAAAAAmuSBOAAAAAAAAABN8kAcAAAAAAAAgCZ5IA4AAAAAAABAkzwQBwAAAAAAAKBJHogDAAAAAAAA0CQPxAEAAAAAAABokgfiAAAAAAAAADTJA3EAAAAAAAAAmuSBOAAAAAAAAABN8kAcAAAAAAAAgCZ5IA4AAAAAAABAk3Za7h2ApbTDDjsMlrdv376MewIry896Q1/APzNmwGjGDBhmzIBh+gJG0xswmvsMGGbMgNEWMmb4C3EAAAAAAAAAmuSBOAAAAAAAAABN8kAcAAAAAAAAgCb1yhDfYYcdBvOyb9u2bVF2CBZTmrnRdZPL3dAbTLvFyKPRF0w7YwaMZsyAYYsxZugLpp1rKRhNb8Bo7jNgmDEDRlvImOEvxAEAAAAAAABokgfiAAAAAAAAADSp15TpO++88+DP0O+7775s3aSmaoBJilOK7LzzzoPl7du3Dx3HC6U3mDZz9Ya+YJYZM2A0YwYMW4oxQ18wbVxLwWh6A0ZznwHDjBkw2iTGDH8hDgAAAAAAAECTPBAHAAAAAAAAoEkeiAMAAAAAAADQpF4Z4n/xF3/R7bbbbl3Xdd0rX/nKbN299947WP7pT3+arZM5wGKK2QE77fTPh/Wuu+6arXvc4x43WN66dWt3wQUXTGQf9AYr0UJ6Q1/QOmMGjGbMgGHLPWboC1ai5e6LrtMbrEx6A0ZznwHDjBkw2mKPGf5CHAAAAAAAAIAmeSAOAAAAAAAAQJPmNWX6z6ZBuOeee4b+26jatAkspT7H4tatW4eWxzle9QYr2UJ6Q1/QOmMGjGbMgGHLNWboC1Yy11Iwmt6A0dxnwDBjBoy22GPGDtvn8VM33nhjt3bt2vrewhRat25dd+CBBy7od/UGrdIXMJregGH6AkZbaG/oC1pmzIDR9AYM0xcwmt6AYfPpi3k9EN+2bVu3fv36btWqVUOh5jCttm/f3m3ZsqU74IADuh13XFh6gN6gNfoCRtMbMExfwGjj9oa+oEXGDBhNb8AwfQGj6Q0Y1qcv5vVAHAAAAAAAAACmzcL+NxIAAAAAAAAAWOE8EAcAAAAAAACgSR6IAwAAAAAAANAkD8QBAAAAAAAAaJIH4gAAAAAAAAA0yQNxAAAAAAAAAJrkgTgAAAAAAAAATfp/ypzZT0ITsfYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x400 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def image_show(orig_imgs, dec_imgs, n, m, k=10, fname=None):\n",
    "    fig = plt.figure(figsize=(20, 4))\n",
    "    for i in range(k):\n",
    "        ax = plt.subplot(2, k, i + 1)\n",
    "        plt.imshow(orig_imgs[i].reshape(n, m), cmap='gray')\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "\n",
    "        ax = plt.subplot(2, k, i + 1 + k)\n",
    "        plt.imshow(dec_imgs[i].reshape(n, m), cmap='gray')\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    if fname:\n",
    "        fig.savefig(fname)\n",
    "\n",
    "decoded_imgs = autoencoder.predict(test_images_autoencoder)\n",
    "        \n",
    "image_show(test_images_autoencoder, decoded_imgs, n, m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.\tОцените качество модели автокодировщика на тестовой выборке по показателю, указанному в индивидуальном задании."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(MAPE): 129804.7266\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "def mean_absolute_percentage_error(y_true, y_pred):\n",
    "    mape_loss = tf.keras.losses.MeanAbsolutePercentageError()\n",
    "    mape = mape_loss(y_true, y_pred)\n",
    "    return mape\n",
    "\n",
    "mape = mean_absolute_percentage_error(test_images_autoencoder.reshape(-1), decoded_imgs.reshape(-1))\n",
    "print(f\"(MAPE): {mape:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.\tОставьте в наборах изображения первых двух классов диапазона, указанного в индивидуальном задании первыми. Визуализируйте набор данных на плоскости, соответствующей двум первым латентным признакам, отображая точки различных классов разными цветами. Подпишите оси и рисунок, создайте легенду для классов набора данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "class_indices_to_keep = values_to_keep[:2]\n",
    "filtered_train_images_gray = train_images_gray[df_train[1].isin(class_indices_to_keep)]\n",
    "filtered_train_labels = train_labels[df_train[1].isin(class_indices_to_keep)]\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "latent_features = pca.fit_transform(filtered_train_images_gray.reshape(filtered_train_images_gray.shape[0], -1))\n",
    "\n",
    "# Построение графика\n",
    "plt.figure(figsize=(8, 6))\n",
    "for i in range(len(class_indices_to_keep)):\n",
    "    indices = np.where(filtered_train_labels[:, i] == 1)[0]\n",
    "    plt.scatter(latent_features[indices, 0], latent_features[indices, 1], label=f'Класс {class_indices_to_keep[i]}')\n",
    "\n",
    "plt.title('Визуализация набора данных в латентном пространстве')\n",
    "plt.xlabel('Латентный признак 1')\n",
    "plt.ylabel('Латентный признак 2')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.\tВыполните бинарную классификацию изображений по латентным (скрытым) признакам и всем признакам при помощи классификатора метода ближайших соседей (kNN). Оцените долю верных ответов (accuracy) для двух построенных классификаторов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (all features): 1.0000\n",
      "Accuracy (latent features): 0.8378\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "filtered_train_indices = [i for i, label in enumerate(df_train[1]) if label in class_indices_to_keep]\n",
    "filtered_test_indices = [i for i, label in enumerate(df_test[1]) if label in class_indices_to_keep]\n",
    "\n",
    "filtered_train_images = train_images_gray[filtered_train_indices]\n",
    "filtered_test_images = test_images_gray[filtered_test_indices]\n",
    "\n",
    "filtered_train_labels = train_labels[filtered_train_indices]\n",
    "filtered_test_labels = test_labels[filtered_test_indices]\n",
    "\n",
    "binary_train_labels = np.array([1 if label == class_indices_to_keep[1] else 0 for label in df_train[1][filtered_train_indices]])\n",
    "binary_test_labels = np.array([1 if label == class_indices_to_keep[1] else 0 for label in df_test[1][filtered_test_indices]])\n",
    "\n",
    "flatten_train_images = filtered_train_images.reshape((filtered_train_images.shape[0], -1))\n",
    "flatten_test_images = filtered_test_images.reshape((filtered_test_images.shape[0], -1))\n",
    "\n",
    "knn_all_features = KNeighborsClassifier(n_neighbors=5)\n",
    "knn_all_features.fit(flatten_train_images, binary_train_labels)\n",
    "all_features_predictions = knn_all_features.predict(flatten_test_images)\n",
    "accuracy_all_features = accuracy_score(binary_test_labels, all_features_predictions)\n",
    "print(f'Accuracy (all features): {accuracy_all_features:.4f}')\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "latent_train_features = pca.fit_transform(flatten_train_images)\n",
    "latent_test_features = pca.transform(flatten_test_images)\n",
    "\n",
    "knn_latent_features = KNeighborsClassifier(n_neighbors=5)\n",
    "knn_latent_features.fit(latent_train_features, binary_train_labels)\n",
    "latent_features_predictions = knn_latent_features.predict(latent_test_features)\n",
    "accuracy_latent_features = accuracy_score(binary_test_labels, latent_features_predictions)\n",
    "print(f'Accuracy (latent features): {accuracy_latent_features:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.\tВизуализируйте ROC-кривые для построенных классификаторов на одном рисунке (с легендой) (Указание: используйте метод predict_proba() класса KNeighborsClassifier)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "all_features_probabilities = knn_all_features.predict_proba(flatten_test_images)[:, 1]\n",
    "\n",
    "latent_features_probabilities = knn_latent_features.predict_proba(latent_test_features)[:, 1]\n",
    "\n",
    "fpr_all_features, tpr_all_features, _ = roc_curve(binary_test_labels, all_features_probabilities)\n",
    "fpr_latent_features, tpr_latent_features, _ = roc_curve(binary_test_labels, latent_features_probabilities)\n",
    "\n",
    "auc_all_features = roc_auc_score(binary_test_labels, all_features_probabilities)\n",
    "auc_latent_features = roc_auc_score(binary_test_labels, latent_features_probabilities)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(fpr_all_features, tpr_all_features, label=f'Все признаки (AUC = {auc_all_features:.4f})')\n",
    "plt.plot(fpr_latent_features, tpr_latent_features, label=f'Латентные признаки (AUC = {auc_latent_features:.4f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n",
    "plt.xlabel('Доля ложноположительных')\n",
    "plt.ylabel('Доля истинноположительных')\n",
    "plt.title('ROC-кривая')\n",
    "plt.legend(loc='lower right')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8.\tВизуализируйте границы принятия решений классификатора kNN для латентных признаков на плоскости, соответствующей двум первым латентным признакам (для прочих латентных признаков задайте средние/медианные значения). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "\n",
    "x_min, x_max = latent_train_features[:, 0].min() - 1, latent_train_features[:, 0].max() + 1\n",
    "y_min, y_max = latent_train_features[:, 1].min() - 1, latent_train_features[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "Z = knn_latent_features.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.contourf(xx, yy, Z, alpha=0.4, cmap=plt.cm.coolwarm)\n",
    "\n",
    "scatter = plt.scatter(latent_train_features[:, 0], latent_train_features[:, 1], c=binary_train_labels, edgecolor='k', cmap=plt.cm.coolwarm)\n",
    "plt.xlabel('Латентный признак 1')\n",
    "plt.ylabel('Латентный признак 2')\n",
    "plt.title('Границы решений классификатора kNN в латентном пространстве')\n",
    "plt.legend(handles=scatter.legend_elements()[0], labels=['Класс 0', 'Класс 1'])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.\tОпределите на первоначальной тестовой выборке изображение, имеющее наибольшую ошибку реконструкции. Выведите для этого изображения первоначальное и реконструированное изображения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 27ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "reconstructed_images = autoencoder.predict(np.expand_dims(test_images_gray, axis=-1))\n",
    "\n",
    "reconstruction_errors = np.mean((test_images_gray - reconstructed_images) ** 2, axis=(1, 2, 3))\n",
    "\n",
    "max_error_index = np.argmax(reconstruction_errors)\n",
    "max_error_original = test_images_gray[max_error_index]\n",
    "max_error_reconstructed = reconstructed_images[max_error_index]\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(max_error_original.reshape(n, m), cmap='gray')\n",
    "plt.title('Оригинальное изображение')\n",
    "plt.axis('off')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(max_error_reconstructed.reshape(n, m), cmap='gray')\n",
    "plt.title('Реконструированное изображение')\n",
    "plt.axis('off')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Lab_report.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "tensorflow",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}