{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/datasets/harishkumardatalab/medical-insurance-price-prediction Набор представляет собой данные о мед страховке.\n",
    "Пример цели: Рассчитать стоимость будущей страховки\n",
    "Входные данные: возраст, пол, индекс массы тела, дети, курит ли, регион, стоимость"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "df = pd.read_csv(\".//static//csv//Medical_insurance.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Визуализация данных - ящик с усами. Как видим - выборка относительно сбалансированна, пускай и смещена в меньшую сторону. Пустых значений нет.\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x=df[\"age\"])\n",
    "plt.title(\"Box Plot для age\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.show()\n",
    "\n",
    "#Визуализируем отношение стоимости и возраста\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df[\"age\"], df[\"charges\"])\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel(\"Charge\")\n",
    "plt.title('Scatter Plot of Age vs Charge')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Выбросы:\n",
      "      age     sex    bmi  children smoker     region     charges\n",
      "14     27    male  42.13         0    yes  southeast  39611.7577\n",
      "19     30    male  35.30         0    yes  southwest  36837.4670\n",
      "23     34  female  31.92         1    yes  northeast  37701.8768\n",
      "29     31    male  36.30         2    yes  southwest  38711.0000\n",
      "30     22    male  35.60         0    yes  southwest  35585.5760\n",
      "...   ...     ...    ...       ...    ...        ...         ...\n",
      "2735   52    male  41.80         2    yes  southeast  47269.8540\n",
      "2736   64    male  36.96         2    yes  southeast  49577.6624\n",
      "2744   32    male  33.63         1    yes  northeast  37607.5277\n",
      "2764   22  female  31.02         3    yes  southeast  35595.5898\n",
      "2765   47    male  36.08         1    yes  southeast  42211.1382\n",
      "\n",
      "[296 rows x 7 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Есть шумы, убираем\n",
    "\n",
    "\n",
    "# Статистический анализ для определения выбросов\n",
    "Q1 = df[\"charges\"].quantile(0.25)\n",
    "Q3 = df[\"charges\"].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Определение порога для выбросов\n",
    "threshold = 1.5 * IQR\n",
    "outliers = (df[\"charges\"] < (Q1 - threshold)) | (df[\"charges\"] > (Q3 + threshold))\n",
    "\n",
    "# Вывод выбросов\n",
    "print(\"Выбросы:\")\n",
    "print(df[outliers])\n",
    "\n",
    "# Обработка выбросов\n",
    "# В данном случае мы заменим выбросы на медианное значение\n",
    "median_charge = df[\"charges\"].median()\n",
    "df.loc[outliers, \"charges\"] = 0\n",
    "df = df[df.charges != 0]\n",
    "\n",
    "# Визуализация данных после обработки\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df[\"age\"], df[\"charges\"])\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Charge\")\n",
    "plt.title(\"Scatter Plot of Age vs Charge\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь создадим выборки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 1485\n",
      "Размер контрольной выборки: 495\n",
      "Размер тестовой выборки: 496\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "train_df, temp_df = train_test_split(df, test_size=0.4, random_state=42)\n",
    "\n",
    "# Разделение остатка на контрольную и тестовую выборки\n",
    "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n",
    "\n",
    "# Проверка размеров выборок\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))\n",
    "\n",
    "# Сохранение выборок в файлы\n",
    "train_df.to_csv(\".//static//csv//train_data.csv\", index=False)\n",
    "val_df.to_csv(\".//static//csv//val_data.csv\", index=False)\n",
    "test_df.to_csv(\".//static//csv//test_data.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проанализируем сбалансированность выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение Review_type в обучающей выборке:\n",
      "sex\n",
      "female    765\n",
      "male      720\n",
      "Name: count, dtype: int64\n",
      "Процент мужчин: 48.48%\n",
      "Процент женщин: 51.52%\n",
      "\n",
      "Распределение Review_type в контрольной выборке:\n",
      "sex\n",
      "male      257\n",
      "female    238\n",
      "Name: count, dtype: int64\n",
      "Процент мужчин: 51.92%\n",
      "Процент женщин: 48.08%\n",
      "\n",
      "Распределение Review_type в тестовой выборке:\n",
      "sex\n",
      "female    259\n",
      "male      237\n",
      "Name: count, dtype: int64\n",
      "Процент мужчин: 47.78%\n",
      "Процент женщин: 52.22%\n",
      "\n",
      "Аугментация данных не требуется.\n",
      "Аугментация данных не требуется.\n",
      "Аугментация данных не требуется.\n"
     ]
    }
   ],
   "source": [
    "train_df = pd.read_csv(\".//static//csv//train_data.csv\")\n",
    "val_df = pd.read_csv(\".//static//csv//val_data.csv\")\n",
    "test_df = pd.read_csv(\".//static//csv//test_data.csv\")\n",
    "\n",
    "# Оценка сбалансированности\n",
    "def check_balance(df, name):\n",
    "    counts = df['sex'].value_counts()\n",
    "    print(f\"Распределение Review_type в {name}:\")\n",
    "    print(counts)\n",
    "    print(f\"Процент мужчин: {counts['male'] / len(df) * 100:.2f}%\")\n",
    "    print(f\"Процент женщин: {counts['female'] / len(df) * 100:.2f}%\")\n",
    "    print()\n",
    "\n",
    "# Определение необходимости аугментации данных\n",
    "def need_augmentation(df):\n",
    "    counts = df['sex'].value_counts()\n",
    "    ratio = counts['male'] / counts['female']\n",
    "    if ratio > 1.5 or ratio < 0.67:\n",
    "        print(\"Необходима аугментация данных для балансировки классов.\")\n",
    "    else:\n",
    "        print(\"Аугментация данных не требуется.\")\n",
    "        \n",
    "check_balance(train_df, \"обучающей выборке\")\n",
    "check_balance(val_df, \"контрольной выборке\")\n",
    "check_balance(test_df, \"тестовой выборке\")\n",
    "\n",
    "\n",
    "\n",
    "need_augmentation(train_df)\n",
    "need_augmentation(val_df)\n",
    "need_augmentation(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По результатам анализа требуется приращение, соотношения отзывов вне допустимого диапазона"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Оверсэмплинг:\n",
      "Распределение sex в обучающей выборке:\n",
      "sex\n",
      "male      765\n",
      "female    765\n",
      "Name: count, dtype: int64\n",
      "Процент мужчин: 50.00%\n",
      "Процент женщин: 50.00%\n",
      "\n",
      "Распределение sex в контрольной выборке:\n",
      "sex\n",
      "male      257\n",
      "female    257\n",
      "Name: count, dtype: int64\n",
      "Процент мужчин: 50.00%\n",
      "Процент женщин: 50.00%\n",
      "\n",
      "Распределение sex в тестовой выборке:\n",
      "sex\n",
      "female    259\n",
      "male      259\n",
      "Name: count, dtype: int64\n",
      "Процент мужчин: 50.00%\n",
      "Процент женщин: 50.00%\n",
      "\n",
      "Андерсэмплинг:\n",
      "Распределение sex в обучающей выборке:\n",
      "sex\n",
      "female    720\n",
      "male      720\n",
      "Name: count, dtype: int64\n",
      "Процент мужчин: 50.00%\n",
      "Процент женщин: 50.00%\n",
      "\n",
      "Распределение sex в контрольной выборке:\n",
      "sex\n",
      "female    238\n",
      "male      238\n",
      "Name: count, dtype: int64\n",
      "Процент мужчин: 50.00%\n",
      "Процент женщин: 50.00%\n",
      "\n",
      "Распределение sex в тестовой выборке:\n",
      "sex\n",
      "female    237\n",
      "male      237\n",
      "Name: count, dtype: int64\n",
      "Процент мужчин: 50.00%\n",
      "Процент женщин: 50.00%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Загрузка данных\n",
    "train_df = pd.read_csv(\".//static//csv//train_data.csv\")\n",
    "val_df = pd.read_csv(\".//static//csv//val_data.csv\")\n",
    "test_df = pd.read_csv(\".//static//csv//test_data.csv\")\n",
    "\n",
    "# Преобразование категориальных признаков в числовые\n",
    "def encode(df):\n",
    "    label_encoders = {}\n",
    "    for column in df.select_dtypes(include=['object']).columns:\n",
    "        if column != 'sex':  # Пропускаем целевую переменную\n",
    "            le = LabelEncoder()\n",
    "            df[column] = le.fit_transform(df[column])\n",
    "            label_encoders[column] = le\n",
    "    return label_encoders\n",
    "\n",
    "# Преобразование целевой переменной в числовые значения\n",
    "def encode_target(df):\n",
    "    le = LabelEncoder()\n",
    "    df['sex'] = le.fit_transform(df['sex'])\n",
    "    return le\n",
    "\n",
    "# Применение кодирования\n",
    "label_encoders = encode(train_df)\n",
    "encode(val_df)\n",
    "encode(test_df)\n",
    "\n",
    "# Кодирование целевой переменной\n",
    "le_target = encode_target(train_df)\n",
    "encode_target(val_df)\n",
    "encode_target(test_df)\n",
    "\n",
    "# Проверка типов данных\n",
    "def check_data_types(df):\n",
    "    for column in df.columns:\n",
    "        if df[column].dtype == 'object':\n",
    "            print(f\"Столбец '{column}' содержит строковые данные.\")\n",
    "\n",
    "check_data_types(train_df)\n",
    "check_data_types(val_df)\n",
    "check_data_types(test_df)\n",
    "\n",
    "# Функция для выполнения oversampling\n",
    "def oversample(df):\n",
    "    if 'sex' not in df.columns:\n",
    "        print(\"Столбец 'sex' отсутствует.\")\n",
    "        return df\n",
    "    \n",
    "    X = df.drop('sex', axis=1)\n",
    "    y = df['sex']\n",
    "    \n",
    "    oversampler = RandomOverSampler(random_state=42)\n",
    "    X_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n",
    "    \n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "# Функция для выполнения undersampling\n",
    "def undersample(df):\n",
    "    if 'sex' not in df.columns:\n",
    "        print(\"Столбец 'sex' отсутствует.\")\n",
    "        return df\n",
    "    \n",
    "    X = df.drop('sex', axis=1)\n",
    "    y = df['sex']\n",
    "    \n",
    "    undersampler = RandomUnderSampler(random_state=42)\n",
    "    X_resampled, y_resampled = undersampler.fit_resample(X, y) # type: ignore\n",
    "    \n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "# Применение oversampling и undersampling к каждой выборке\n",
    "train_df_oversampled = oversample(train_df)\n",
    "val_df_oversampled = oversample(val_df)\n",
    "test_df_oversampled = oversample(test_df)\n",
    "\n",
    "train_df_undersampled = undersample(train_df)\n",
    "val_df_undersampled = undersample(val_df)\n",
    "test_df_undersampled = undersample(test_df)\n",
    "\n",
    "# Обратное преобразование целевой переменной в строковые метки\n",
    "def decode_target(df, le_target):\n",
    "    df['sex'] = le_target.inverse_transform(df['sex'])\n",
    "\n",
    "decode_target(train_df_oversampled, le_target)\n",
    "decode_target(val_df_oversampled, le_target)\n",
    "decode_target(test_df_oversampled, le_target)\n",
    "\n",
    "decode_target(train_df_undersampled, le_target)\n",
    "decode_target(val_df_undersampled, le_target)\n",
    "decode_target(test_df_undersampled, le_target)\n",
    "\n",
    "# Проверка результатов\n",
    "def check_balance(df, name):\n",
    "    if 'sex' not in df.columns:\n",
    "        print(f\"Столбец 'sex' отсутствует в {name}.\")\n",
    "        return\n",
    "    \n",
    "    counts = df['sex'].value_counts()\n",
    "    print(f\"Распределение sex в {name}:\")\n",
    "    print(counts)\n",
    "    \n",
    "    if 'male' in counts and 'female' in counts:\n",
    "        print(f\"Процент мужчин: {counts['male'] / len(df) * 100:.2f}%\")\n",
    "        print(f\"Процент женщин: {counts['female'] / len(df) * 100:.2f}%\")\n",
    "    else:\n",
    "        print(\"Отсутствуют один или оба класса (male/female.\")\n",
    "    print()\n",
    "\n",
    "# Проверка сбалансированности после oversampling\n",
    "print(\"Оверсэмплинг:\")\n",
    "check_balance(train_df_oversampled, \"обучающей выборке\")\n",
    "check_balance(val_df_oversampled, \"контрольной выборке\")\n",
    "check_balance(test_df_oversampled, \"тестовой выборке\")\n",
    "\n",
    "# Проверка сбалансированности после undersampling\n",
    "print(\"Андерсэмплинг:\")\n",
    "check_balance(train_df_undersampled, \"обучающей выборке\")\n",
    "check_balance(val_df_undersampled, \"контрольной выборке\")\n",
    "check_balance(test_df_undersampled, \"тестовой выборке\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pima Indians Diabetes Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database Датасет диабетов\n",
    "Использование: предугадывание склонности к диабету\n",
    "Входные данные: \"Беременности\", \"Глюкоза\", \"Кровяное давление\", \"Толщина кожи\", Инсулин, Диабет, Возраст"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
      "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "df = pd.read_csv(\".//static//csv//diabetes.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Анализируем датафрейм при помощи \"ящика с усами\". Естьсмещение в сторону меньших значений, это можно исправить при помощи oversampling и undersampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Box plot для столбца 'Popularity'\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x=df['Age'])\n",
    "plt.title('Box Plot для Age')\n",
    "plt.xlabel('Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Выбросы:\n",
      "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "4              0      137             40             35      168  43.1   \n",
      "12            10      139             80              0        0  27.1   \n",
      "39             4      111             72             47      207  37.1   \n",
      "45             0      180             66             39        0  42.0   \n",
      "58             0      146             82              0        0  40.5   \n",
      "100            1      163             72              0        0  39.0   \n",
      "147            2      106             64             35      119  30.5   \n",
      "187            1      128             98             41       58  32.0   \n",
      "218            5       85             74             22        0  29.0   \n",
      "228            4      197             70             39      744  36.7   \n",
      "243            6      119             50             22      176  27.1   \n",
      "245            9      184             85             15        0  30.0   \n",
      "259           11      155             76             28      150  33.3   \n",
      "292            2      128             78             37      182  43.3   \n",
      "308            0      128             68             19      180  30.5   \n",
      "330            8      118             72             19        0  23.1   \n",
      "370            3      173             82             48      465  38.4   \n",
      "371            0      118             64             23       89   0.0   \n",
      "383            1       90             62             18       59  25.1   \n",
      "395            2      127             58             24      275  27.7   \n",
      "445            0      180             78             63       14  59.4   \n",
      "534            1       77             56             30       56  33.3   \n",
      "593            2       82             52             22      115  28.5   \n",
      "606            1      181             78             42      293  40.0   \n",
      "618            9      112             82             24        0  28.2   \n",
      "621            2       92             76             20        0  24.2   \n",
      "622            6      183             94              0        0  40.8   \n",
      "659            3       80             82             31       70  34.2   \n",
      "661            1      199             76             43        0  42.9   \n",
      "\n",
      "     DiabetesPedigreeFunction  Age  Outcome  \n",
      "4                       2.288   33        1  \n",
      "12                      1.441   57        0  \n",
      "39                      1.390   56        1  \n",
      "45                      1.893   25        1  \n",
      "58                      1.781   44        0  \n",
      "100                     1.222   33        1  \n",
      "147                     1.400   34        0  \n",
      "187                     1.321   33        1  \n",
      "218                     1.224   32        1  \n",
      "228                     2.329   31        0  \n",
      "243                     1.318   33        1  \n",
      "245                     1.213   49        1  \n",
      "259                     1.353   51        1  \n",
      "292                     1.224   31        1  \n",
      "308                     1.391   25        1  \n",
      "330                     1.476   46        0  \n",
      "370                     2.137   25        1  \n",
      "371                     1.731   21        0  \n",
      "383                     1.268   25        0  \n",
      "395                     1.600   25        0  \n",
      "445                     2.420   25        1  \n",
      "534                     1.251   24        0  \n",
      "593                     1.699   25        0  \n",
      "606                     1.258   22        1  \n",
      "618                     1.282   50        1  \n",
      "621                     1.698   28        0  \n",
      "622                     1.461   45        0  \n",
      "659                     1.292   27        1  \n",
      "661                     1.394   22        1  \n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Имеется смещение в меньшую сторону\n",
    "df_cleaned = df.dropna()\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df[\"Age\"], df[\"DiabetesPedigreeFunction\"])\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"DiabetesPedigreeFunction\")\n",
    "plt.title(\"Scatter Plot of Age vs DiabetesPedigreeFunction\")\n",
    "plt.show()\n",
    "\n",
    "# уберем шумы\n",
    "\n",
    "# Статистический анализ для определения выбросов\n",
    "Q1 = df[\"DiabetesPedigreeFunction\"].quantile(0.25)\n",
    "Q3 = df[\"DiabetesPedigreeFunction\"].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Определение порога для выбросов\n",
    "threshold = 1.5 * IQR\n",
    "outliers = (df[\"DiabetesPedigreeFunction\"] < (Q1 - threshold)) | (\n",
    "    df[\"DiabetesPedigreeFunction\"] > (Q3 + threshold)\n",
    ")\n",
    "\n",
    "# Вывод выбросов\n",
    "print(\"Выбросы:\")\n",
    "print(df[outliers])\n",
    "\n",
    "# Обработка выбросов\n",
    "# В данном случае мы уберем выбросы\n",
    "median_charge = df[\"DiabetesPedigreeFunction\"].median()\n",
    "df.loc[outliers, \"DiabetesPedigreeFunction\"] = 0\n",
    "df = df[df.DiabetesPedigreeFunction != 0]\n",
    "\n",
    "# Визуализация данных после обработки\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df[\"Age\"], df[\"DiabetesPedigreeFunction\"])\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"DiabetesPedigreeFunction\")\n",
    "plt.title(\"Scatter Plot of Age vs DiabetesPedigreeFunction\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разбиение набора данных на обучающую, контрольную и тестовую выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 460\n",
      "Размер контрольной выборки: 154\n",
      "Размер тестовой выборки: 154\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки\n",
    "train_df, test_df = train_test_split(df_cleaned, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим недостаток баланса:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение Age в обучающей выборке:\n",
      "Age\n",
      "22    39\n",
      "21    36\n",
      "25    31\n",
      "24    25\n",
      "26    23\n",
      "27    22\n",
      "28    21\n",
      "23    21\n",
      "41    17\n",
      "31    15\n",
      "29    15\n",
      "37    14\n",
      "30    12\n",
      "45    11\n",
      "46    11\n",
      "36    11\n",
      "42    10\n",
      "34    10\n",
      "38    10\n",
      "32     9\n",
      "35     8\n",
      "52     8\n",
      "33     7\n",
      "43     7\n",
      "40     7\n",
      "47     5\n",
      "50     5\n",
      "51     5\n",
      "39     4\n",
      "48     4\n",
      "57     4\n",
      "66     3\n",
      "49     3\n",
      "44     3\n",
      "58     2\n",
      "55     2\n",
      "53     2\n",
      "63     2\n",
      "67     2\n",
      "54     2\n",
      "56     2\n",
      "60     2\n",
      "59     2\n",
      "61     1\n",
      "65     1\n",
      "81     1\n",
      "69     1\n",
      "68     1\n",
      "70     1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение Age в контрольной выборке:\n",
      "Age\n",
      "22    20\n",
      "21    15\n",
      "24    11\n",
      "23    10\n",
      "25     9\n",
      "33     8\n",
      "26     7\n",
      "31     6\n",
      "30     5\n",
      "28     5\n",
      "27     5\n",
      "39     4\n",
      "29     4\n",
      "42     4\n",
      "40     4\n",
      "32     3\n",
      "37     2\n",
      "46     2\n",
      "51     2\n",
      "44     2\n",
      "34     2\n",
      "43     2\n",
      "41     2\n",
      "45     2\n",
      "54     2\n",
      "35     2\n",
      "58     1\n",
      "53     1\n",
      "61     1\n",
      "59     1\n",
      "64     1\n",
      "36     1\n",
      "50     1\n",
      "72     1\n",
      "49     1\n",
      "47     1\n",
      "66     1\n",
      "62     1\n",
      "69     1\n",
      "55     1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение Age в тестовой выборке:\n",
      "Age\n",
      "22    13\n",
      "21    12\n",
      "24    10\n",
      "29    10\n",
      "28     9\n",
      "25     8\n",
      "23     7\n",
      "38     6\n",
      "27     5\n",
      "39     4\n",
      "30     4\n",
      "43     4\n",
      "42     4\n",
      "32     4\n",
      "36     4\n",
      "58     4\n",
      "31     3\n",
      "62     3\n",
      "44     3\n",
      "26     3\n",
      "41     3\n",
      "37     3\n",
      "60     3\n",
      "54     2\n",
      "50     2\n",
      "34     2\n",
      "65     2\n",
      "63     2\n",
      "45     2\n",
      "40     2\n",
      "33     2\n",
      "53     2\n",
      "55     1\n",
      "48     1\n",
      "67     1\n",
      "56     1\n",
      "51     1\n",
      "49     1\n",
      "57     1\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def check_balance(df, name):\n",
    "    counts = df['Age'].value_counts()\n",
    "    print(f\"Распределение Age в {name}:\")\n",
    "    print(counts)\n",
    "    print()\n",
    "\n",
    "check_balance(train_df, \"обучающей выборке\")\n",
    "check_balance(val_df, \"контрольной выборке\")\n",
    "check_balance(test_df, \"тестовой выборке\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Используем oversample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение Age в обучающей выборке после oversampling:\n",
      "Age\n",
      "26    39\n",
      "25    39\n",
      "33    39\n",
      "23    39\n",
      "35    39\n",
      "39    39\n",
      "29    39\n",
      "27    39\n",
      "21    39\n",
      "28    39\n",
      "41    39\n",
      "31    39\n",
      "24    39\n",
      "22    39\n",
      "42    39\n",
      "32    39\n",
      "36    39\n",
      "51    39\n",
      "34    39\n",
      "30    39\n",
      "45    39\n",
      "49    39\n",
      "57    39\n",
      "53    39\n",
      "48    39\n",
      "55    39\n",
      "46    39\n",
      "47    39\n",
      "52    39\n",
      "50    39\n",
      "37    39\n",
      "58    39\n",
      "61    39\n",
      "40    39\n",
      "65    39\n",
      "67    39\n",
      "38    39\n",
      "66    39\n",
      "81    39\n",
      "44    39\n",
      "43    39\n",
      "56    39\n",
      "54    39\n",
      "60    39\n",
      "69    39\n",
      "70    39\n",
      "68    39\n",
      "63    39\n",
      "59    39\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение Age в контрольной выборке после oversampling:\n",
      "Age\n",
      "25    20\n",
      "58    20\n",
      "27    20\n",
      "26    20\n",
      "31    20\n",
      "22    20\n",
      "28    20\n",
      "39    20\n",
      "21    20\n",
      "45    20\n",
      "41    20\n",
      "61    20\n",
      "51    20\n",
      "64    20\n",
      "36    20\n",
      "30    20\n",
      "53    20\n",
      "40    20\n",
      "23    20\n",
      "59    20\n",
      "29    20\n",
      "42    20\n",
      "46    20\n",
      "43    20\n",
      "44    20\n",
      "34    20\n",
      "24    20\n",
      "32    20\n",
      "33    20\n",
      "72    20\n",
      "35    20\n",
      "50    20\n",
      "54    20\n",
      "47    20\n",
      "49    20\n",
      "66    20\n",
      "62    20\n",
      "69    20\n",
      "55    20\n",
      "37    20\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение Age в тестовой выборке после oversampling:\n",
      "Age\n",
      "43    13\n",
      "21    13\n",
      "34    13\n",
      "50    13\n",
      "55    13\n",
      "22    13\n",
      "44    13\n",
      "37    13\n",
      "65    13\n",
      "40    13\n",
      "60    13\n",
      "29    13\n",
      "28    13\n",
      "24    13\n",
      "36    13\n",
      "48    13\n",
      "45    13\n",
      "39    13\n",
      "41    13\n",
      "27    13\n",
      "23    13\n",
      "38    13\n",
      "25    13\n",
      "67    13\n",
      "31    13\n",
      "30    13\n",
      "54    13\n",
      "33    13\n",
      "62    13\n",
      "26    13\n",
      "56    13\n",
      "58    13\n",
      "53    13\n",
      "63    13\n",
      "42    13\n",
      "32    13\n",
      "51    13\n",
      "49    13\n",
      "57    13\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "def oversample(df):\n",
    "    X = df.drop('Age', axis=1)\n",
    "    y = df['Age']\n",
    "    \n",
    "    oversampler = RandomOverSampler(random_state=42)\n",
    "    X_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n",
    "    \n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "train_df_oversampled = oversample(train_df)\n",
    "val_df_oversampled = oversample(val_df)\n",
    "test_df_oversampled = oversample(test_df)\n",
    "\n",
    "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n",
    "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n",
    "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diamonds Prices2022"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В данном наборе данных представлена цена на алмазы. Входные данные: цвет, вес (в каратах), цена, чистота, габариты. Цель: узнать, насколько дорого обходятся девушкам их друзья (Мэрилин Монро)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'carat', 'cut', 'color', 'clarity', 'depth', 'table',\n",
      "       'price', 'x', 'y', 'z'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "df = pd.read_csv(\".//static//csv//Diamonds Prices2022.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x=df['carat'])\n",
    "plt.title('Box Plot для carat')\n",
    "plt.xlabel('carat')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NFEXU1NR4PM6ECROQmpqKd955B++88w5GjBhhN9SssbERzc3Nds/p27cvEhMT7c6hnAULFkAURcycOVP22tq9ezfeeOMNAJ3XuCAIdiXfKyoq8MEHH3h8DRI1+0hISFB0zRAR+YKZICKKOPfffz8aGxtx3XXXYcCAAWhtbcXnn3+Od955Bzk5ObjjjjsAdA4beuKJJ7B48WKMHTsW119/PWJjY/Hll18iKysLS5YswWWXXYaUlBTcdttt+O1vfwtBELBq1SrZIGvYsGF455138PDDD2P48OHo1q0bpk2bhr59+yI5ORkvv/wyEhMTkZCQgEsvvRS5ubl49dVXMXnyZFx44YW444470LNnTxw/fhxbt25FUlIS1q1bF+jTh2effRaTJ0/GqFGjcNddd1lLZBuNRixcuNDu9QKd5cFvvvlmREdHY9q0abLzhaKjo7F06VLccccduPzyyzF9+nRrieycnBw89NBDgXp5Hl1xxRWYOXMm/t//+384dOgQJk2aBIvFgu3bt+OKK67A3Llz0bdvX/zpT3/CvHnzUFFRgWuvvRaJiYkoLy/H+++/j7vvvhu/+93v3B4nOjoa119/Pd5++200NDTgL3/5i93fv/vuO1x55ZW48cYbkZeXh6ioKLz//vs4efIkbr75Zrf7vuyyy/DSSy/hvvvuw4ABAzBz5kz0798f9fX1+PTTT/Hhhx/iT3/6E4DO4hR/+9vfMGnSJNxyyy04deoUXnrpJfTr1w/79+9XdM7U7GPYsGHYtGkT/va3vyErKwu5ubm49NJLFR2HiEix4BSlIyIKno8//li88847xQEDBojdunUTY2JixH79+on333+/ePLkSaftV6xYIV588cVibGysmJKSIl5++eXixo0brX/fuXOnOHLkSDEuLk7MysqyltwGIG7dutW63blz58RbbrlFTE5OFgHYlV1eu3atmJeXJ0ZFRTmVpN6zZ494/fXXi2lpaWJsbKyYnZ0t3njjjeLmzZut20glsk+fPq3oHEglsv/zn/+43U6uRLYoiuKmTZvE0aNHi3FxcWJSUpI4bdo0sbS01On5ixcvFnv27CnqdDpF5bLfeecd67lOTU0Vb731VmsZa4k3JbI9bXvbbbeJCQkJLv/mWNq8vb1dfPbZZ8UBAwaIMTExYvfu3cXJkyeLu3fvttvu3XffFceMGSMmJCSICQkJ4oABA8Q5c+aI3377rce2i6Iobty4UQQgCoIgHjt2zO5v1dXV4pw5c8QBAwaICQkJotFoFC+99FLx3//+t6J9i6Io7t69W7zlllvErKwsMTo6WkxJSRGvvPJK8Y033rArC//aa6+J/fv3F2NjY8UBAwaIK1eutF5ztgCIc+bMkT2W0n1888034rhx48S4uDgRAMtlE5FfCKLoxWp6REREREREYYpzgoiIiIiIKKIwCCIiIiIioojCIIiIiIiIiCIKgyAiIiIiIoooDIKIiIiIiCiiMAgiIiIiIqKIEtaLpVosFpw4cQKJiYkQBCHYzSEiIiIioiARRRH19fXIysqCTuc+1xPWQdCJEyfQq1evYDeDiIiIiIhCxLFjx3Deeee53Sasg6DExEQAnS80KSkpyK0hIiIiIqJgqaurQ69evawxgjthHQRJQ+CSkpIYBBERERERkaJpMiyMQEREREREEYVBEBERERERRRQGQUREREREFFEYBBERERERUURhEERERERERBGFQRAREREREUUUBkFERERERBRRGAQREREREVFEYRBEREREREQRhUEQERERERFFFAZBREREREQUURgEERERERFRRGEQREREREREESUq2A0gIiIiIiLvdFhEFJXX4lR9MzISDRiRmwq9Tgh2s0IegyAiIiIiojBUUFKJRetKUWlutj6WaTRgwbQ8TMrPDGLLQl9Qh8MtXLgQgiDY/TdgwIBgNomIiIiIKOQVlFRi9upiuwAIAKrMzZi9uhgFJZVBall4CHom6MILL8SmTZus/46KCnqTiIiIiIhCVodFxKJ1pRBl/iYCEAAsWleKiXkmDo1zIegRR1RUFEwmU7CbQUREREQUForKa50yQLZEAJXmZhSV12JU37TANSyMBL063KFDh5CVlYU+ffrg1ltvxdGjR11u29LSgrq6Orv/iIiIiIgiyal61wGQN9tFoqAGQZdeeilef/11FBQUYNmyZSgvL8fYsWNRX18vu/2SJUtgNBqt//Xq1SvALSYiIiIiCq6MRIOm20UiQRRFueGEQXH27FlkZ2fjb3/7G+666y6nv7e0tKClpcX677q6OvTq1QtmsxlJSUmBbCoRERERUVB0WESMWboFVeZm2XlBAgCT0YAdj46PqDlBdXV1MBqNimKDoA+Hs5WcnIzzzz8fhw8flv17bGwskpKS7P4jIiIiIookep2ABdPyAHQGPLakfy+YlhdRAZBaIRUEnTt3DmVlZcjMZF1zIiIiIiJXJuVnYtmMoTAZ7Ye8mYwGLJsxlOsEeRDU6nC/+93vMG3aNGRnZ+PEiRNYsGAB9Ho9pk+fHsxmERERERGFvEn5mZiYZ0JReS1O1TcjI9GAEbmpzAApENQg6IcffsD06dNRU1OD7t27Y8yYMdi1axe6d+8ezGYREREREYUFvU5gGWwvBDUIevvtt4N5eCIiIiIiikAhNSeIiIiIiIjI3xgEERERERFRRGEQREREREREEYVBEBERERERRRQGQUREREREFFEYBBERERERUURhEERERERERBGFQRAREREREUUUBkFERERERBRRGAQREREREVFEYRBEREREREQRhUEQERERERFFFAZBREREREQUURgEERERERFRRGEQREREREREEYVBEBERERERRRQGQUREREREFFEYBBERERERUURhEERERERERBGFQRAREREREUUUBkFERERERBRRGAQREREREVFEYRBEREREREQRhUEQERERERFFFAZBREREREQUURgEERERERFRRGEQREREREREEYVBEBERERERRRQGQUREREREFFEYBBERERERUURhEERERERERBElKtgNICIiIiKi0NBhEVFUXotT9c3ISDRgRG4q9Doh2M3SHIMgIiIiIiJCQUklFq0rRaW52fpYptGABdPyMCk/M4gt0x6HwxERERERRbiCkkrMXl1sFwABQJW5GbNXF6OgpDJILfMPBkFERERERBGswyJi0bpSiDJ/kx5btK4UHRa5LcITgyAiIiIioghWVF7rlAGyJQKoNDejqLw2cI3yMwZBREREREQR7FS96wDIm+3CAYMgIiIiIqIIlpFo0HS7cMAgiIiIiIgogo3ITUWm0QBXhbAFdFaJG5GbGshm+RWDICIiIiKiCKbXCVgwLQ8AnAIh6d8LpuV1qfWCGAQREREREUW4SfmZWDZjKExG+yFvJqMBy2YM7XLrBHGxVCIiIiIiwqT8TEzMM6GovBan6puRkdg5BK4rZYAkDIKIiIiIiAhA59C4UX3Tgt0Mv+NwOCIiIiIiiigMgoiIiIiIKKIwCCIiIiIioojCIIiIiIiIiCIKgyAiIiIiIoooDIKIiIiIiCiiMAgiIiIiIqKIwiCIiIiIiIgiCoMgIiIiIiKKKAyCiIiIiIgoojAIIiIiIiKiiMIgiIiIiIiIIgqDICIiIiIiiigMgoiIiIiIKKIwCCIiIiIioojCIIiIiIiIiCIKgyAiIiIiIoooDIKIiIiIiCiiMAgiIiIiIqKIwiCIiIiIiIgiCoMgIiIiIiKKKFHBbgARERERUVfTYRFRVF6LU/XNyEg0YERuKvQ6IdjNoh8xCCIiIiIi0lBBSSUWrStFpbnZ+lim0YAF0/IwKT8ziC0jCYfDERERERFppKCkErNXF9sFQABQZW7G7NXFKCipDFLLyBaDICIiIiIiDXRYRCxaVwpR5m/SY4vWlaLDIrcFBRKDICIiIiIiDRSV1zplgGyJACrNzSgqrw1co0gWgyAiIiIiIg2cqncdAHmzHfkPgyAiIiIiIg1kJBo03Y78h0EQEREREZEGRuSmItNogKtC2AI6q8SNyE0NZLNIBoMgIiIiIiIN6HUCFkzLAwCnQEj694JpeVwvKAQwCCIiIiIi0sik/EwsmzEUJqP9kDeT0YBlM4ZynaAQwcVSiYiIiMhJa7sFqworcKS2Edmp8Zg5KgcxUbx/rsSk/ExMzDOhqLwWp+qbkZHYOQSOGaDQIYiiGLaFyuvq6mA0GmE2m5GUlBTs5hARERF1CUs2lGL59nLYLmejE4BZY3Mxb0qe347bYREZOJDX1MQGIRPOP/PMMxAEAQ8++GCwm0JEREQUsZZsKMUr2+wDIACwiMAr28qxZEOpX45bUFKJMUu3YPryXXjg7b2YvnwXxizdgoKSSr8cjyJbSARBX375JV555RUMGjQo2E0hIiIiilit7RYs317udpvl28vR2m7R9LgFJZWYvbrYaaHRKnMzZq8uZiBEmgt6EHTu3DnceuutWL58OVJSUoLdHCIiIqKItaqwwikD5Mgidm6nlQ6LiEXrSiF3WOmxRetK0eGpYUQqBD0ImjNnDqZOnYoJEyZ43LalpQV1dXV2/xERERGRNo7UNmq6nRJF5bVOGSBbIoBKczOKyms1OyZRUKvDvf322yguLsaXX36paPslS5Zg0aJFfm4VERERUWTKTo3XdDslTtW7DoC82Y5IiaBlgo4dO4YHHngAb775JgwGg+cnAJg3bx7MZrP1v2PHjvm5lURERESRY+aoHHgqxqYTOrfTSkaisn6g0u2IlAhaELR7926cOnUKQ4cORVRUFKKiovDZZ5/h//2//4eoqCh0dHQ4PSc2NhZJSUl2/xERERGRNmKidJg1NtftNrPG5mq6XtCI3FRkGg1wFXsJADKNneWyibQStCDoyiuvxIEDB7B3717rf5dccgluvfVW7N27F3q9PlhNIyIiIopY86bk4Z5xuU4ZIZ0A3DNO+3WC9DoBC6Z17tMxEJL+vWBaHtcLIk2F1GKpP/vZzzBkyBA8//zzirbnYqlERERE/tHabsGqwgocqW1Edmo8Zo7K0TQD5KigpBKL1pXaFUnINBqwYFoeJuVn+u241HWoiQ2CWhiBiIiIiEJTTJQOd43tE7DjTcrPxMQ8E4rKa3GqvhkZiZ1D4JgBIn8IqSDo008/DXYTiIiIiChI9DoBo/qmBbsZFAGCvk4QERERERFRIDEIIiIiIiKiiMIgiIiIiIiIIgqDICIiIiIiiigMgoiIiIiIKKIwCCIiIiIioojCIIiIiIiIiCIKgyAiIiIiIoooDIKIiIiIiCiiMAgiIiIiIqKIwiCIiIiIiIgiCoMgIiIiIiKKKAyCiIiIiIgoojAIIiIiIiKiiMIgiIiIiIiIIgqDICIiIiIiiigMgoiIiIiIKKJEBbsBRERERERKdFhEFJXX4lR9MzISDRiRmwq9Tgh2sygMMQgiIiKKUOxQUjgpKKnEonWlqDQ3Wx/LNBqwYFoeJuVnBrFlFI4YBBEREUUgdigpnBSUVGL26mKIDo9XmZsxe3Uxls0YyuuWVOGcICIioggjdShtAyDgpw5lQUllkFpG5KzDImLRulKnAAiA9bFF60rRYZHbgkgegyAiIqIIwg4lhZui8lqngN2WCKDS3Iyi8trANYrCHoMgIiKiCMIOJYWbU/Wur1dvtiMCGAQRERFFFHYoKdxkJBo03Y4IYBBEREQUUdihpHAzIjcVmUYDXNUtFNBZ1GNEbmogm0VhjkEQERFRBGGHksKNXidgwbQ8AHC6bqV/L5iWx/LupAqDICIiogjCDiWFo0n5mVg2YyhMRvsMpcloYHls8oogimLYln+pq6uD0WiE2WxGUlJSsJtDREQUNrhOEIUjLvBL7qiJDRgEERERRSh2KImoK1ETG0QFqE1EREQUYvQ6AaP6pgW7GUREAcc5QUREREREFFGYCSIiIiIiIre62vBZBkFEREQUdF2tg0XUlXTFQioMgoiIiCioumIHi6irKCipxOzVxXCspFZlbsbs1cVhW6Kcc4KIiIgoaKQOlm0ABPzUwSooqQxSy4iowyJi0bpSpwAIgPWxRetK0WEJv2LTDIKIiIgoKLpyB4uoKygqr3W6QWFLBFBpbkZReW3gGqURBkFEREQUFF25g0XUFZyqd/359Ga7UMIgiIiIiIKiK3ewiLqCjESDptuFEgZBREREFBRduYNF1BWMyE1FptEAV3UaBXQWMRmRmxrIZmmCQRAREREFRVfuYBF1BXqdgAXT8gDA6XMq/XvBtLywLGfPIIiIiIiCoit3sEi9DouIwrIarN17HIVlNSyIESIm5Wdi2YyhMBntM7ImoyFsy2MDgCCKYtheYXV1dTAajTCbzUhKSgp2c4iIiMgLXCeIeA2EvnBY0FhNbMAgiIiIiIIuHDpY5B+uFuOU3v1wzjZQYKmJDaIC1CYiIiIil/Q6AaP6pgW7GRRgntaKEtC5VtTEPBODYtIU5wQRERERUVBwrSgKFgZBRERERBQUXCuKgoVBEBEREREFBdeKomBhEEREREREQcG1oihYGAQRERERUVBwrSgKFgZBRERERBQ0XXUxTgptLJFNREREREE1KT8TE/NMXCuKAoZBEBEREREFHdeKokDicDgiIiIiIoooDIKIiIiIiCiicDgcERER+UWHReQcDyIKSQyCiIiISHMFJZVYtK4UleZm62OZRgMWTMtjtS8iCjoOhyMiIiJNFZRUYvbqYrsACACqzM2YvboYBSWVdo93WEQUltVg7d7jKCyrQYdFDGRziSgCMRNEREREmumwiFi0rhRyYYyIzgUwF60rxcQ8E/Q6gRkjIgoKZoKIiIhIM0XltU4ZIFsigEpzM4rKa1VnjIiItMIgiIiIiDRzqt51AGSrytzkNmMEdGaMODSOiPyBQRARERFpJiPRoGi72oZWxRkjIiKtMQgiIiIizYzITUWm0QBXhbAFdM75Se0Wq2h/SjNLRERqMAgiIiIizeh1AhZMywMAp0BI+veCaXkwJSnLGCnNLBERqcEgiIiIiDQ1KT8Ty2YMhcloH8CYjAYsmzEUk/IzFWeMRuSm+r29RBR5WCKbiIiINDcpPxMT80woKq/FqfpmZCR2BjR6XWfYI2WMZq8uhgDYFUiwzRhJ2xMRaUkQRTFsy67U1dXBaDTCbDYjKSkp2M0hIiIilbhOEBFpRU1swEwQERF1eR0W0WVGgoLLU8aIiMgfGAQREVGXxkxD6NPrBIzqmxbsZhBRBGFhBCIi6rIKSioxe3Wx03o0VeZmzF5djIKSyiC1jIiIgolBEBERdUkdFhGL1pVCbuKr9NiidaXosITt1FgiIvISgyAiIuqSisprnTJAtkQAleZmFJXXBq5RREQUEjgniIiIuqRT9a4DIG+2c4eFF4iIwguDICIi6pIyEg2eN1KxnSssvECRhAF/ZIiE95lBEBERdUkjclORaTSgytwsOy9IAGAydv64e0sqvOC4/ypzM+5dXYyHJvRHTnpCl+1EUGRhwB8ZIuV95mKpRERdUCTcxVNCClIAyAZCd43OwYQ8k1fnp8MiYszSLW7nHdmy7UTw/SFb4XA9uAr4pVYumzFU0w6yluckHM6vv5kb23Dn60U4YW5GltGAFbePgDE+2mk7V++z5MWbh+BkfQuO1DYiOzUeM0flICYqdEoMqIkNGAQRUUSJhB/DgpJKLPzwa1TVtVgfMyXFYuHVF3apu3hKyd3V1AmAbVE4b+5yFpbVYPryXYq3l66yu8fl4sN9lXbtMSXFYvqI3n7PGkXC9e9KqL72QN519/YceAr4pazqjkfHa3JOtTwnWuyrtd2CVYUVIdvx9+TyZ7fgSE2T0+PZaXH47Pfjrf9We2MH6PwunTU2F/Om5GnSVl+FTRC0bNkyLFu2DBUVFQCACy+8EE8++SQmT56s6PkMgohIjUhI8ReUVOLeHzMfcl7W+G5tuJA6fxtLq7BiZ4XT3725m71273E88PZezdpoyx/XZSRc/66E6msPZHbFl3OgNOB/a9ZInxe91fKcaLGvJRtKsXx7ud1Nk1Dr+LvjKgCS2AZCam/s2LpnXGicDzWxQVDD2PPOOw/PPPMMdu/eja+++grjx4/HNddcg6+//jqYzSKiLigSFs3ssIh47L0Dbrd57L0DEbkujl4nYERuKj4uqZL9uzfrBvlaUMEdra/LSLj+XQnV1x7Idax8PQeBqrSo5TnRYl9LNpTilW32ARDQmUV+ZVs5lmwo9diOYDI3trkNgADgSE0TzI1tAHx7/5ZvL0dru8Xr5wdDUIOgadOmYcqUKejfvz/OP/98PPXUU+jWrRt27fIuCiUikhMpi2buKqvB2R9/zFw529iGXWU1AWpRaNF63SCp8II/BlRpeV1GyvUvJ5Rfe6DWsdLiHASq0qKW58TXfbW2W7B8e7nbY4R6x//O14tUbefL+2cRgVWFFV4/PxhCZkBjR0cH3n77bTQ0NGDUqFGy27S0tKCurs7uPyIiTyJl0czC76s13a6r0fputl4nYMG0zuEf/gqEtLguI+X6lxPKrz1Q2RUtzoGngF9A59A6XyotAtqeE1/3taqwwikD5CjUO/4nFM7tkbYbkZuK5DjnYglKHalt9Pq5wRD0IOjAgQPo1q0bYmNjce+99+L9999HXp78mMIlS5bAaDRa/+vVq1eAW0tE4SiQi2YGl9KuePAngweDP+5mT8rPxLIZQ2Ey+m9onK/XZeRc/85C+bUHKruixTlwF/BL/14wLc/noghanhNf96W0Qx/KHf8shd9L0nZ6nYA7Rud4fbzs1HivnxsMQQ+CLrjgAuzduxdffPEFZs+ejdtuuw2lpfJjLOfNmwez2Wz979ixYwFuLRGFo0B1NoJN6YRkXycuhyt/3c2elJ+JHY+Ox1uzRuKFm4fgoQnnQ4B2oaav12WkXP9yQvm1Byq7otU5cBXwm4wGzQo4aHlOfN2X0g59KHf8V9w+QvV2c8f3R7JM6WxPdAIwc1SO6ucFU9CDoJiYGPTr1w/Dhg3DkiVLMHjwYLzwwguy28bGxiIpKcnuPyIiTwLV2Qi2kX3SPP54pcRHY2SfyAyC/Hk3W68TMKpvGq4Z0hMPTOgv21nMNBpwz7hcxQGSVtdlpFz/ckL5tQcqu6LlOXAM+N+aNRI7Hh2vWQU7Lc+Jr/uaOSoHng4T6h1/Y3w0stPi3G6TnRZnt16QXifgmesvUn0TZ9bY3LAqGw6EQBDkyGKxoKWlxfOGREQKBaqzEWzSj5c7S66/KOxfpy8CcTdbOo5cZ3HelDxFw+e0vC4j5fqXE+qvPRDXo9bnwDbgH9U3TfNzp+U58WVfMVE6zBqb63b/4dDx/+z3410GQo7rBEmk85bpcN7iY/QQHN5unRA65bHVCuo6QfPmzcPkyZPRu3dv1NfXY82aNVi6dCk++eQTTJw40ePzuU4QEakRqmuFaK1zsdRSVNV17dfpi2AvnGl7/IrqRrxVdNTv71ekXP9yQv21B+J6DPVz4EjLc+LLvsJ9nSCJubENd75ehBPmZmQZDVhx+wi7DJAcufPWYRFDeuHYsFks9a677sLmzZtRWVkJo9GIQYMG4dFHH1UUAAEMgohIvWB3fgMlUl5nVxGo9yuSr4tIfu0SngPvtLZbQrrjTz8JmyDIVwyCiIiIiIgIUBcbMIwlIiIiIqKIwiCIiIiIiIgiCoMgIiIiIiKKKAyCiIiIiIgoojAIIiIiIiKiiMIgiIiIiIiIIkpUsBtARERERIHHdYPIF+F+/TAIIiIiIoowBSWVWLSuFJXmZutjmUYDFkzLw6T8zCC2jMJBV7h+OByOiIjIBx0WEYVlNVi79zgKy2rQYQnbNcgpQhSUVGL26mK7DiwAVJmbMXt1MQpKKoPUMgoHXeX6YSaIiIjIS13hbihFlg6LiEXrSiEXqosABACL1pViYp4prIY2UWB0peuHmSAiIiIvdJW7oV0dM3X2isprna5ZWyKASnMzispr3e6H5zUyaXX9hAJmgoiISBPhPklWja50N7QrY6bO2al61x1YpdvxvNprbbdgVWEFjtQ2Ijs1HjNH5SAmqmvmGbS4fkKFV0HQ+PHj8d577yE5Odnu8bq6Olx77bXYsmWLFm0jIqIwEWmdIjV3Q0f1TQtcw8hKytQ5BqpSpm7ZjKFd8tr0JCPR4NN2PK/2lmwoxfLt5bBNhD214SBmjc3FvCl5wWuYn/h6/YQSr8LUTz/9FK2trU6PNzc3Y/v27T43iogo1HDoh2uROCws3O+GdvXr2VOmDujM1HW1163EiNxUZBoNcJWfFNB5A2NEbqrT33he7S3ZUIpXttkHQABgEYFXtpVjyYZSTY4TSp9XX66fUKMqE7R//37r/5eWlqKqqsr6746ODhQUFKBnz57atY6IKAREWpZDjUgdFhbOd0Mj4XoO90ydP4eW6nUCFkzLw+zVxRAAu8+udIQF0/Jkjxfu51VLre0WLN9e7nab5dvL8cjPB/g0NC7UPq++XD+hRlUQNGTIEAiCAEEQMH78eKe/x8XF4e9//7tmjSMiCjYO/XAvUjtF0t3QKnOzbAAoADCF4N3QSLmewzlTF4hO76T8TCybMdTpOCYPxwnn86q1VYUVThkgRxaxc7u7xvbx6hih+nn19voJNaqCoPLycoiiiD59+qCoqAjdu3e3/i0mJgYZGRnQ6/WaN5KIKBgiNcuh5i50qHWKAlWcIRzvhkbS9RyumbpAdnon5WdiYp5J1eclXM+rP2w/dFrRdkdqG73af6h/Xr25fkKNqiAoOzsbAGCxWPzSGCKiUBKJWQ61d6FDqVMU6GEj4XY3NJKu53DM1AWj06vXCare63A8r/7QYRGx++gZRdtmp8Z7dYxQ/LzK3WQK5+8Kn0pkl5aW4ujRo05FEq6++mqfGkVEFApCLcvhb97chQ6VTlGwho2E093QSLqewzFTF4qdXkehdF6DWZa6qLwW9c0dHrfTCcDMUTleHSMYn1d3mfRQm5ukBa+CoO+//x7XXXcdDhw4AEEQIIqdHwNB6DxRHR2eLwwiolAXSlkOf/P2LnQodIqCPWxE7d30YImk6xkIv0xduASpoXBe5cpS/2n9QVw1yITnbx7q9yBM6Xvwswu6ex2YBfrz6i7IARCSc5N85VUQ9MADDyA3NxebN29Gbm4uioqKUFNTg0ceeQR/+ctftG4jEVFQhEqWIxB8uQsd7E5RONxBDwWRdD1LwilTF05BajDPq1SW2pEIYN3+Kmz55hP89cbBfv3eUfoezBrb1/r/aucrBvLz6i6Tfu/qYiTHR4fs3CRfeBUEFRYWYsuWLUhPT4dOp4NOp8OYMWOwZMkS/Pa3v8WePXu0bicRUcCFQpYjUHy9Cx3MTlEo3EF318EJVLEGTyLperYVLpm6cAtSg3FelZSlbmjt0Cw74eqz6+m9AuzXyvFmKFmgPq9K1n4629jm8vnhfJPJqyCoo6MDiYmJAID09HScOHECF1xwAbKzs/Htt99q2kAiomAKdpYjULS4Cx2szmaw76B7GkYSSuPoI+V6DiStgtxIDVLVUFKWGug8d75mJzwFLq7eK8n8qQOh1wk+zVcMxOfVUyZdqWAP0/SGV0FQfn4+9u3bh9zcXFx66aX485//jJiYGPzzn/9Enz7e1UInIgpV4TSkxlvhdhfaVjDb7mkYiZxgj6OPhOs5ULSeLM4g1T015aZ9yU54+lw/NKE/ctIT8OCE8/FW0VFU1TkHAH/86CAAYPH6gz4NJfPn57XDImLn4Wqf9wMAFdXelQIPJkGUqhqo8Mknn6ChoQHXX389Dh8+jKuuugrfffcd0tLS8M4778gupOoPdXV1MBqNMJvNSEpKCsgxiYi6KumHH5C/Cx3Kk1+D0fYOi4gxS7d4dRdVCsx2PDqewUeYctVR1uKaC5UhlKHmte3fY/H6g4q3n3tFX/TvkajqHKr9XCfHReFsU7viNsl5a9bIgGfR5QJ4XwgIjd8INbGBV0GQnNraWqSkpFgrxAUCgyAiIm2FcxnUQLe9sKwG05fv8mkfwej8hJJw7ex76igHO8gN1/PqSWu7BQPmf6xoSJwjpd8FWnyu1Xrh5iG4ZkjPgB3PVQAvRwBgjI92Oy9I2i4UbuyoiQ1UD4dra2tDXFwc9u7di/z8fOvjqamhN0SCiIjUCeehUoFuuxZj4MNxHL1WwjngDuWKhOF8Xj3Z8s1JGKL1aGxVvxSL0mGowfhMejNf0dtA110hBEfS3p65/iJ8W1WP5zYdcrltOBZIUB0ERUdHo3fv3lwLiIioiwqXalpyAtl2LQothEK542AI1uK2WgmFioRytD6voZRRUpO9kKN0Dk4gP5Oe5iu6Ov++BLpqCiEIAjBrbC4m5Weipd2i6DnhdGPHq8IITzzxBB5//HGsWrWKGSAiIopISsrkuhLKhSbUUttRVrq47fgBPbD7yJmQ6IA7CkRFQum8VpmbUNvQitRusTAluT4PSkodq6mY5q6jHehssZrshTtKshW+fK7V8FTxz9X5v3pwJv65rdypbZU/Fm34xy0XY8qgLJfHVROkWETgn9vKcXHvlKBX4fQHr4KgF198EYcPH0ZWVhays7ORkJBg9/fiYvmKOERERF2Fp5LGosz/S/8Guka5Y2/uSCsdSjZyyWbUNrQq3m8g+bsiobtJ63LnocMi4vWd5R7v8CsZrtRhEfHilsN4btN3Tn+zXTzTdo6Ilu+NXFCtVRlniRQIuArgPZW/1oJtxT/HdpxpaMWcNc5Zr0pzs+xCsbbmvrUHL0LAlEH274V0jEMn61W3ddG6Unz2+ys8BofJ8dFhdWPHq8IIixYtcvv3BQsWeN0gNVgYgYiItOTN8J9wWidIS95WR1u79zgeeHuv6uOFWpVCVxUJJZ7uyHvar7vOmW0lLrVVvu4cnYMnp13o8tgLP/waVXUtqtqs5r1x9xlz9Vmakm/CazsrVLXJnbdmjYS5qdXtZ7OgpBKPvXfAY0EAbzw0oT9m/6wfdh85g42lVfhg7wm7gF8QAF/Llr08Y6g1Yyd3DLWkc+aq9L/tcYP5+QxKdbhgYBBERERa8WWcvbuOXSjNq9CKL9XRfKm+FSoVqCRqMzZybIe9VZ9rwYtby2Bu8tzxTjToMbR3Kj777rSqNqclxKDoiQlO58/XOTdK3htPNwxcBdVadVSlNs6fmiebaZE8NKE/zE1tWKFh4GWrW6weep1O0fvsrbhoHeJionwKfGy9cPMQXDUoC8P+tNFlYBgKn0+/VocjIqLQ4M/OtdJ9d5UOvq8Tyt0VZAjnQhOu+FIdzZc5F1pVoGptt2BVYQWO1DYiOzUeM0flICZKp3o/k/IzYbEA961xvjuu5NrpzLyUyi626Ul9c4fqAAgAahpanc6fFnNuPL03nhYgTY6PdjufSfdjdsTXgGj+1IFYvN79a3VXBU0L51o6APi3wFhTmwVNbdoEQEDnXJ+i8lq3mbFwqxDnVRDU0dGB5557Dv/+979x9OhRtLban+Ta2lpNGkdEpFZX6ZR74s8yuEr33VVK8SqdqK90Qnkk8KU6mhZzLnypQLVkQymWby+3W2vmqQ0HMWtsLuZNyVO1rw6LiMXrS2X/5unaKSip9Di0yF8cz5+Wc27k3hslRRs8DTuT3i9fMkN3j8tFSkKspvOLujrb+W0f7T+h6DnhUiFO/W0PdM4J+tvf/oabbroJZrMZDz/8MK6//nrodDosXLhQ4yYSESlTUFKJMUu3YPryXXjg7b2YvnwXxizdgoKSymA3TVPSHVXHH3LpzrMvr1fpvv3ZhkBTk9WgThXVDYq2c1UpalJ+JpbNGAqT0f7vqQnRivfbYRFRWFaDtXuPo7CsBh0KVtBcsqEUr2wrd1ps0yICr2wrx5IN8gGNK95eOx0WEY+9d0DVsbQkvS/SOfxYw8+r3HuuVZB15+gc9EiK9fr5H+6r9CrrFslE/FTEJb2bsnMfLhXivMoEvfnmm1i+fDmmTp2KhQsXYvr06ejbty8GDRqEXbt24be//a3W7SQicivc1x1Ryp9ZCzWli7tS5kSrNV8iJQvZYRHxVtFRj9tleqiOJre47bDsFFz+7FaPVdfONLQ6zUnylIVsbbdg+Xb3lbWWby/HIz8foHhonLfXzq7va/wy4d4T6fwNy07BC5sOYeXOcpzVcF6KKSlW9j3XKjNw5cAeuHJgD9z66hdePb/S3IziI+F/MyMuWoemNmXr9vgqOT4aE/NM1qIZ7oRb6X+vgqCqqipcdNFFAIBu3brBbDYDAK666irMnz9fu9YRESkQScOZ/LlSvdJ9ryqs8FsbgkGL9S+6ytBAW67mzRSV1yqqHnbz8N4eP29y86XclR0HgKsHZ8pOavd0w2NVYYVTBsiRRezc7q6xfdxv+CNvr53CshpFz/OHqwdnYsTTm/wShDW3W7CxtMrp/CvNHHokAtUN6irXOVq1y3MAH+oCFQABncMUX9xyGM9v+s5jxUIgvEr/ezUc7rzzzkNlZWfqtG/fvvjf//4HAPjyyy8RG+t9mpKIyBuRNJzJnyvVK33OkdpGv7UhGKSJ+q5+tgW4z2p0paGBkiUbSjFg/sdYvP4g/lV4BIvXH8SA+R9jyYZSxe9rTnq8V8d2NVTOZDTgpVsuxof7Kl3e8BABPP7+Aby/x3mInNLrVul2gC/XTuAL82YaDbh7XC5e2VbutyyUubHN6ZrvsIhYsdN9Bk6p6oaWsBlq5Q/Bii1W7nRenNWRyWgIuxEXXmWCrrvuOmzevBmXXnop7r//fsyYMQOvvfYajh49ioceekjrNhIRueXPwCDU+HPVbqXPyU5V1rkNZmdFzdA0T4ueAq7vbnbFLKQ0b8aRNG9m2iCTov348v7LDZVTumhmbUMbHnpnLwD7bJzS61bpdoD3186oPul4cWuZomMkROvQ4MOd//su74ux53e3DjX0J7lrftf3NTA3tWuy//SEWFgsIpLjojUdxhcuFEx78wsl5/ovNwzG6P7pAWiNdrwKgp555hnr/990003Izs7G559/jv79+2PatGmaNY6ISAl/Bgahxp8r1Svd98xROXh1R7lf2qAFb4amSdkHx+eZPDzPn8MTg0HJvJmP9lfBlGTAyTr/vv9yQ+XU3siwHSI3c1QOntpw0G1HUicAM0flqDqGN9fOyL5pSI6P9piREQA8+6vBWLz+oFclxQHgvT3HMaiXEbuPnFFVnCA+Ro/GVvVlnB2veS2G/gkAjPHReOQ/+8KqsEFCjA7RUXqfM2/J8dEY1jsZm79RXxLdV8a4KEVBrK/DFIPBq+FwS5YswYoVK6z/HjlyJB5++GGcPn0aS5cu1axxRERK+DqcKZxId54BOL1eX8dkK913TJTOb23wlS9D0yblZ2LHo+Px1qyReOHmIXhr1kjseHS82+EdXS0LqWTejAhgTL/O4CTQ77/aGxnSS1m0rhR6nYBZY3Pdbj9rbK5TUQQlVejUXjt6nYBnrr/IbVtS4qOxbMZQTBmU5fLzpsTJus5rf1Nplarn3TOur1fHk/x0zfuevhDROTfFUwCUHKesumCgNLRqM3fn9lE5QQmAAKBdYfopHG8yehUEvfLKKxgwYIDT4xdeeCFefvllnxtFRKSGPwODUORuzoSvY7KV7tufbfCWkrVIFq0rdVtKWco+XDOkJ0b1TfN4zXS1LKTS+TB1ze24Y3QOUhJi7B5X8/57U+La0w0PObaZiXlT8nDPuFynuRU6AbhnnPM6QWrK7qu9diblZ+LlGUNhcij5bIyLwkMT+uOrP0z0+HmLj9ZjVG4qeiS6no8tndX39vzgtj22Mo0GzB3fD3fLnCulpGt+VB9lQ6Tio/Wyj5uSYpEc7zm4MSXF4r6f9VXewADxNQtkSorF218e06g16jW0eM4G6gRgWHZKAFqjLUEURdUhusFgwMGDB5Gba39H5fvvv0deXh6amwNzx6uurg5GoxFmsxlJSUkBOSYRha6uWKHLHX+WZFa671AqC11YVoPpy3d53O6tWSM1G5rWYRExZukWj0MDdzw6HnqdEFLnS85r27/H4vUHVT0nNSEa1w3piQl5Jrevx/a1V1Q34q2io3Z39pV+VqVsH6Aux/DCzUNwzZCeAFxXvpM7juMxpFenVbCv5ppwte32705j5ooin9sieXnGUACQff2eyF3zw/60UXUwcMUF6bh7XD9YRFFVSWxBANT3bEPXtEEmrNuvLovnzkMTznf67GlBy+9VX6iJDbyaE9SrVy/s3LnTKQjauXMnsrKyvNklEZHPXE2mDqVOppbk5kwEet/+bINawRiapmZifDgE6UrmzTg609CGFTsrMNzNZ03utTtSuqaXqzk4nthm42KidG7LYAey4IWaz5DctgUllXjsXe0WXr1zdA4m5pkwZukWrwIgwD7zLg39u/fHwFWprd9WQwBwTkEmwlZXCoAAaBYAScHp3PH90D+jG+5bo+798CRchvza8mo43KxZs/Dggw9i5cqVOHLkCI4cOYIVK1bgoYcewqxZs7RuIxGRYmqHpFDXEayhaUqGBvqrjLY3Q8rciYnSeZw348jdUMMOi4gXNh3CvTKvXc1+HNnOwXnuxsFITYjRdE5gIMrua/HeSdeVlpXSpBtJagJMiavhkNLQvx6JMS6eKW/Lt9Uoqjijuh2Ad/Onuirb4BQAFq8v1fwY4TLk15ZXmaDf//73qKmpwX333YfW1lYAnUPkHn30UcybN0/TBhIRESnhz8p5nrjLQvorq+CvzJI0L2b59nLFGSG5KnjSCvNKFlZ1tx9XbLMicTF6r0qcu+LvrKLce5eaEI0/XZOPKYN+GlHjbqicu+vKGwKAHkmxsIgiPvlaXfbh16OyMTk/023mfVJ+JhIN0aqGtvlCBHDD0J74b/HxgBwvlNlWKiwsq/EqwHVHJwBnGlo13WcgeBUECYKApUuXYv78+Th48CDi4uLQv39/LpRKRNSFhPr8FUe+rPej1fHlOu5aldF2nFMjt4K70iFlnsybkodHfj7AOm+mvqkN7+894fF5UlDgaj6NUmqDi4l5Jjw44Xys3FlulxXxVOLcFX9mFV2dm9qGNty3Zg/u+eEs5k3J8xjkeputkSN9XprbLV4FKZPzMxUN6as+F9gyymPP744JeT1UD53sCgafZ8SdY3Kdvrv9MWzNIgJz1hRjmS4CFkuVdOvWDcOHD9eqLUREFABKgptwmL8ix9v1fvxJi6yCkjk1gLrMkqfrwHbeTGFZjaIgKCPRoEmGQk1wIXdukuOiccfoHMwd39/uNSkN7IdlpyA1IQa1Lu5ue5tVVHJuXtnWmYF7dXu52yC3pV2b8stAZ0W6s03tqosXqD0PgR4yVVHdiAcm9MfEPBOe2/gdXtx6OKDHD6Z9P5ix/9hZTMizX9zYn+9BuC0M7VMQREQUCXzJiIRaNkVJcOPqTrVWWQZ/C7UCGWqyCnLXy8bSKlVZFSWZJbVBrpqhhr5kKNR2ql1dq+amNjy/6RAuMCXaXddKXrO0nasACOg8xzcP72X3mJLPutJz89oO5wBIOq4U5P7lV4M97seTX4/KRkp8DP6+5ZBXzxehLrt6JsALar795VHMHd8Pep2A0f3SIyoIAoDXdlbgtZ0Vdte5p8+yt8JtYWiAQRARkVu+ZEQ27K/EH9aW2HWmgplNURLcTMwzBawqlj+FUtU6pQHEmYZWjFm6xT6DlRSL5naLV50VV5klb4JcNUMNfRluo6ZTrWaulatA0vE1qxnG99ymQ3j7y2PWyeaevida2y3495dHFewZbudiSZ1NiIApyeBTqeO0hFg8t+k7r59vjIvCt1X1aGm3KCrvrbb8uq9sO+XS59Bfw+Iyk2Jwqr4VHSFYna7S3Ix7VxfjhqE9MapvOkb3TfPbXKlwqhLHIIiIyAVfMiJLNpTilW3lTo9XBimborTDmGiI1mT+Cv1ESQBx9eBMzFkjc62pKCrgyDYDJWUpqsxNWLz+oFdBrtKhhhXVDV632dOimLbZlur6FkXX6q6yGkXX/vgBPVQP45M6l3Jsvyf2HD2jqtCEEqu/qEBzm7ry0baS46LwVpGyoMwVc1M7ntv0UxbJ3U0eLecwqSF1yqXPodpS3UpV1oV+YYD/Fh/3e6GI9ITwqQ/AIIiISIYvFb027D8hGwDZPj/Q2RSlk/MLy2oU7c+Xu32hNkQwENwFEPOnDnQZmHgrOT7aOqRM6XwiwHOQ62moYYdF9KljbW5sk71J0GER8eKWw06FD5Qo/L5a0bW/qrBC00669H7e/9YetPkhPfBxyUmfnt/cbsHZJm2DEnc3iIKVITh08hwKy2owLDsFibHRmHRhBj75+pSmnzf6yZy3ivHM9ReF9JBpCYMgIgqKUO8Ie1vRq7Xdgkff87xwYaCzKco7IMq6Bt5Org3XggtacBVA+OMO+R2X5VoXaPWmSpu768XdUMOi8lqfsldyNxgKSirx2HsHVE/al5SdVpaZOlLb6NX+PfFHAKSF5jbtCitIXN0g6rCIqK4P7HwgyYtbD+PFrYedsrDkH2cb23Dv6mK8HOJzRwEGQUQUBOHQEfamoldBSSUef/8A6pvbNT2GFpQGLaP6pOPd4uN+WWsn3AsuaEEugND6OkiOj8bc8f18qtLmbZCrxWuxvcFgbmr1afhSYqwe2747rWjb7NR4r49DP5Hev9d3liM9MRYV1Y14q+ioT3OXtGoXBU44zB3VBbsBRBRZpI6w451vqSNcUFIZ8DbJrdyutqLXC5sO4d7VxahtUH63OpDlYqVJwa5+jgR0BqIj+6ZZJ3rLbQN4t9aOp+GF0hDBDi0nTYQJra+Dmy45D3qd4FWGSboOvF1QVsvXUmVuwqJ1vq1sX9/SgYZWz/Nm0hJiMHNUDjKN4bfqvT/pBODBK/t79dzF6w/igbf34rlN3wU9AKLAk25khDJmgogoYHyZZ+MvrrJS86cO9FjRq0dSLL74vgb3rvoKZoXZH0laQozXHU1vqF1I1Bgf7TT8KDk+Gku8HOutpEMeqQUXtC5Z+89t5bi4d4rqdWSUBLmehrFqWYGr+OiZgE2kv2ZIFmKidH6dOB+OXpx+MQBwKBl5JdQrxTETREQB0WER8frOcsXzbALRHil7I5eVmrNmD64e3NnZd+wOSh2CuuZ2PL/5kOoACAAWX5Mf8GEC0uR8k8PdbpPR4FQiWG7+xRkv52QAyn8MN5ZWeX2MUCaXbZRIAaqWncxF60qR3k1dlSbb60BOQUklxizdgunLd+GBt/di+vJdGLN0i132VnotvtIJwKpdvlUuU2PijwtKTsrPxD9uuRghPIInIExJsXhoQn98deQM7luzhwEQeSXQi+OqxUwQEfmdmupUgP/vHm3YX4knPjjgslMvZaU+3FeJl24ZisXr7dsuZUkaFQyzkXPPuFxMGRScuS/uqnt5mkPiS6ZO6Y/h2r0n8MRU9cPtQlmg58DZriPjKcOUmhCN+VddCFOS++IkauZzTcwz4aEJ5+OVbWVef0YCOSrSlBQLiyhi7d7jyEg04Bf5mXhxuoD71kRmRqhbrB7N7Ra70tdEavkyrDZQGAQRkV95U53Kn3ePXK3f40jqSKYkxGDHo+OtQUN6Qiwe+c8+AOqzIkmGKDxz/UWYMihLfcM9UFNtz1V1L28r4ikxIjcVqQnRHudM1TS0dqkhcWoWqNVadUOLxyGQT1/neXij2kVJPd3wEAAMy07GV0fOqnk5mpPOSXO7Bbe++oX18UyjwZoFjkTnWjoAeL/+EHnWr3sCDiusWhiuvJk7GmgMgojIb9RWp/Kl8pgSntbvkXOqvtkuaCgsq/Fqkm9qQjR2zZuAmCjtRyFrlWnwpiKeUnqdgOuG9MRrOyv8sn9/8aWUu1YL1HorI9GAUX3TZNcnSkmIxp+uyXe6PuRer9Lg+MUth/H8pu88ft5FIOgBEPBTRtdx6GeluVn19wSRGl05AErxYe5ooDEIIiK/UVOdypfKY0p0WET8YW2J6uc5ZqW86aAL6Lzj7q8ASKuy02oq4nljQp5JURAUKuPI3QWX7hYMlWi9QK1SjjcTJuVnwmIB/rC2BLUNnava1za04fH3S3Do1DnMHd/fuh6P3Oudkm9SdNxXtpWFxdyRuGgdHpxwPv7xaVmwm0LUZVw3JAs3XNILI/ukhXwGSMIgiIj8Rk3AYPLzOkFF5bWqyldLHclh2SkoLKuxdnbVTjb359wPravteapS5mumzt/715K74PLe1cVIdqieJ/c+a71ArRJyNxMKSioxZ43zaznb1IbnNh3Cys8rcNMl5+Gf28plX6+SwBWA1/N/Ak2vE7Dk42+C3QyiLuVnAzIwul96sJuhCoMgItKM41AapQHD/KkDcfvoXE3vHjm2xZshbFcPzsTlz261uzNuSopFcnw0zI1tHruuD03ob73L7g9az+FRW0YbUD8Xaf7UPJcTzkWZ/QeDp+ASgNMQKrnMm1YL1KqRmhCDp677aZhbh0XEwg/dD0k929jmcviX9DydAIhi1yiT3DnnhYi0tO2707hqUFbQv7/VYBBERJrYsL/SbrgN4DlgkO78ax0AybUlNSFa8fOlidFyd8ZP1rVYH3O1dkagxkT7Yw6PVEbbcViUXKZO7VykgpJKPP7BAcVtCRZvFhmVy7wpzXxJC9TKBZ9q/WHqQLtz/+KWQ5osVBmBa9gSkQrvFh/HzsM1WHi1/0Z0aI1BEBH5zFXFtaq6Fuv/K80s+KstSofCPXhlf9x3RT9c/uxWt8PMjPHRMETp7TqYyXHRuGN0jl+zP7b8NYfHXRltidq5SAUllR4XoQzGYrlyvC3M4Jh5U5NZcxV8qmUyxln/v6CkkmWOiShgqurUz0UNJgZBROQTJRXXkmUCBn/MAfKm+pute8bl4sGJ56OwrMbjMLOzjW14866h0OkEryqHOfKmCpk/59i4KqMttVXNXCRpe098KcGtJV8LM9gGUWoya47Bp1SO/WSd52Fyju+10nNORKS1ULiZpQSDICLymtKKa1oHDL60BeicN+E4VO5P1+Rb1+9RM8zM9s67t7wpcS0FTZPzTVixsyJgmTZA/VwktcPLgl0i21Nw6YljEKUksyZxDD4XXu15mJzce+3NkD4iIl+Fys0sJRgEEZHX1FRcq25owTVDeoZEW+ZPHQiTMQ4nzjRi7w9nAQioNDejtd2CmCid4kzA4vUH7YIpbyrBeVPiWi5oEn6cuC7xZ7U9tXOR1AY1wS6R7W4YmyfdYvWwiCI6LKJdkOMus+aOkmFycu91sANJIops4fAdxCCIiLym5kvO3x1bdeW44/DptyexfHu53YTvpzYcxKyxufi/SQMVZQJsAyBA/do83pS4dhU0Sa/jrtE5mJBn0jzTZkvtXCQ1731mkEpkOw5HnJhnwrIZQ7Hww6/t5rZ5cq6lA7e++oWmpdGlTNKushoUfl8NiwikxMcgPTEWpiT5rFKwA0kKDzoAlmA3grqkcPgOYhBERF5T+iWXlhCjecfW23LcqfHReHPXEXx0oNLpbxYR1jlF3mQC1K7No3ZYmbugCT8ee0NJFR6f6t8y02rnIikdXiYgOCWyXQ1HvHpwJn4abKaON4vVurOxtAoLPyy1n1eXZMDCq386X7afifSEzgBJyXwiilwMgEhrobTemycMgohIEbmJ+1Ln1tPcg8XX5GvWse2wiHhxy2Gs3FmOs00/DX+TynE7rt/iqN1ikQ2AbC3fXo5vFg+QHYaUmhDtdtidmvHQaoeVab0ukLfUriekZHiZP8uKuys6sWF/pey6RZXmZp+KbLgLiNUUwZCu9+c2fef0t6q6zoVbbxjaE4lx0Vi794RddrJbrJ4BEBEFVKis96YEgyAi8sjdxH2pc+uqs3XPuFxMGaRNx7agpBKPvXdANtCxXb/HnbpmzwslWkRgVWEF7hrbx2lCe1VdMx56Z6/HfSgJcNQOK/PHukDeUlP1zN32/i4rLnftxugFjOyThusu7olH/rNP82NK5IJSNUUwCkoqFQ3H+2/xcdnHuSgoEZFrDIKIyC0lE/ddZUxsK65p0Q5368y4W78nJT4KIgSPWSJbR2obAThPaC8sq1H0fE8BTodFhMUiIjku2i6jZctxWIGW6wK5ykaoyVKoqXrmzfZq2+64jasMSmuHiG2HqrHtULWq43pLCkrVFMFwtS0RUahjiWwiCntKJ+7veHS8Jp1bV5paO/D7/+73uJ3j+j1S9bcqcws2Hjyp6piiKGLt3uNOr0XpEMAzDkUTbMllAxzJDSvTal0gd3NgPtxXqapUt9qqZ95WSQNcD4W0baO0zYod38Pc3O7VcbSWkWjw+FkCgMffP4CmNgsyusVi4Yeu534REYWycCmRLYiiGLbfs3V1dTAajTCbzUhKSgp2c4i6nMKyGkxfvsvjdm/NGum3L7slG0rxz23lqjqEL9w8BPt/MGPFDnXPc8UxENiw/wTuW7PH43N2PDreKRBUeoff3RCp2T9mxOTm4niaiK82wyC3X28WdnWkdh/uhkJKz7p7XC7e+eoHVRk/f0tNiMb8qy5EdX0LntpwMNjNISIKiOduGoLrLvbfshiuqIkNmAkiIpf8NQeltd2CVYUVOFLbiOzUeMwclYOYKJ3Tdks2lHo1Of2ZDd+gsk67eTGOw5VSEjxXonO8E9ZhEbGrrAaPvXvAYwDy4JX9cf+V8nNk3M3FmT91IIxxMU4ZLOl8l9c0YO3eE6oCQ8cJ/htLq2SzSPOnDkRKQqxsUOMY8JxpaMXi9coXh1UyFBKAT4UM/KW2oU3RHDIioq6kul750gLBwiCIiOzYdliVfompWQ/gqfWleHVHud3CntL6PPOm5Fnb8PmhavzTy06tlgEQ8FMn+9F39yPREI1TCs/LzsOnUWVuws7D1dh48BTMLub+OFq16wjuv7K/y7/Lza2RCyxS4qOQHBeDippGnzJi0gT/F7ccxvObvnPaV6W52SkzZkqKxfQRvWFuasMHDlXL5FSaOyudSescDctOwe4jZ1BlbsLi9cygEBGFkzONoR8EBXU43JIlS/Dee+/hm2++QVxcHC677DIsXboUF1xwgaLnczgckbbk5oroBNgtKGpLmoMiN+xLzqx/fYmNpadc/v2ecbm4uHeKx/kyweapTLYW1AwxDNQkendFHLTm7rojIqLQNveKfvjdL5T157UUNsPhPvvsM8yZMwfDhw9He3s7Hn/8cfz85z9HaWkpEhISgtk0oojjqiPtLgAClK8H8NHe424DIAA/Zn68m8cjCECgbun4OwAClA8x9LSAqpYCFQABDICIiMJZVIhXhgOCHAQVFBTY/fv1119HRkYGdu/ejXHjxgWpVUSRR0lH2vHOvKv1YOT2vausBr9TWN1NrWi9gFF90gJW7jhQlA4x9LSAqhYEAMYAZoGIiCi8vf3lMZdzW0NFSM0JMpvNAIDUVPnyri0tLWhp+WmMYV1dXUDaRdTVKelIW0Rg/tSBSE+MVVwRTEkpaF+1/bjmS1eSqaDMtcTfC6NK7/Ado3Pw3KZDfj0WERF1DVV1oV8m27kcU5BYLBY8+OCDGD16NPLz82W3WbJkCYxGo/W/Xr16BbiVRF2T0o50emIsrhnSE6P6pikKgGavLg7puT2hSIDyIYaAuqIU7mQaDbhnXC4yjfb7MxkNWDZjKOaO749MowGhe0+PiIhCib9v0vkqZDJBc+bMQUlJCXbs2OFym3nz5uHhhx+2/ruuro6BEJEGlHaklW4XyHkqXYmnhUnleFpA1R0BwF9/NRiZyXHWzN7/TRrocv2eBdPyMHt1MQR4N3SRiIgih1Y36fwlJIKguXPn4qOPPsK2bdtw3nnnudwuNjYWsbGe1+cgInWGZad4rMalEzq3U8LbeSoJMXo0tna47GDH6nVo6bCo3q8/+RoQPDShP3LSE7xedFSvE6zBiVp3j8vF9cPsv3P1OsHl8AVXaxRFom4xOpxrDa1rkYgoVKgZ1h0sQR0OJ4oi5s6di/fffx9btmxBbm5uMJtDFLF2HznjsRqXRezcTglvUuAT8zLw1xsHA4DTkCvhx/9mjOyter/+IqCzpLfJYfhYcny0ouenxEfj5RlD8cCE8xUPMXRFCk4ch7K5IgidbZfWZVKqwyLCGBeD//vFBZg/dSDuGJ3jRWvtxcfoO9vk857Uu21Uttu/d+8WI/t4dlocSv44GYPO49IMRERy1AzrDpagZoLmzJmDNWvWYO3atUhMTERVVRUAwGg0Ii4uLphNI4ooSoMWpdupSYEnGvRYcu1FuGpITwCQzTRIleiMcTF4bWeFx33eMLQnNh08ZVfNLNNowNWDM/HOVz/gbKOyKmfJ8dGy29oOW5MbPraxtMpltiQ5Lhp3jM7B3PHaVs2xXUB1Y2kV/v3VDzjX0m63TaxewLTBWXj6+kGIiVJ3D0yuyIU0j+jDfZV2j3eL1SNKr7M7d91i9TjX0uG036bWzseMLs61P03Kz8SovmlY+OHXqKr7qeiOMS4Kd47Oxdzx/XGuuR13vl6EE+ZmZBkNWHH7CBh/DHQ/nDsWiz8qxYod3pV1D0VROqDdiwSXDgDzYtRVxOoFtHaIXeZzHUiGaB2ev2mIqmHdwRLUxVIFQb4DsHLlStx+++0en8/FUom0UVhWg+nLd3ncTukCnh0WEWOWbnE7TyU5Lhov3ToUI/s4Z0A6LKLsvBRP+7VdvBWAy33sKqvBzrLT+FfhEdmOubSvHkmx+OuNQ3Cqrhm1Da1I7RYLU5KyYWvSa6gyN6l+rhY6LCI+P1SNd/f8gMbWDgzPScVtl+WoDn4A12tISa/ipVsuRkpCrN25Bn46/+kJsXjkP/tQVScfRNue6+pzLTh08hxe3HpYdTslD1zZD+98+QNO1rm+/jJtFvntsIh4ccthrNxZ7hQ4K5mj1dpuwarCCpRVn8MpczMsoohT9a0/DgdJw4yR2dh77KzdtZDRLRYQgOpzLThcVY+/f1rm8XXFRQu4/uLzAEFAu6UDR0434NtTDYjRC7gwy4hJ+SacaWxF6Yk6FHx9Ei1uopkkgx73jz8fqQkxONtof322tlvw9IZSVNQ0IictHr/7+QD8d/cxlNc0oK3jp+PGRulwxQUZGNk3DQ++vVdRh9FTkGU06NEtNgqnzrWirUN99yRWD7j4SHvlwQl9san0NM42tiIjyYCbh/dGZpIB35ysx7EzjeiZbIAAAUfPNEIAoBcEvF54xOX+LspKRA9jHLrF6JGXZUT3JAOSY6Lw5ldHcexME3qnxOEvvxqC/cfO4u9bD6GoQln2Xc7Mkb0hCAKyU+Mxc1QO9DrB+p3Q0NKOHkkGDOmVjM/LqrGhpArNbT+9MVrP+0uK1WNI7xT0TIlDdX0LeiQZ0DPFgO9Pn8O276rR2m5BZnIcxvRPhzEuGp8froG5qRXdu8Wgqc2Ck/UtiNXrcLK+BWdsbpYkxAjIy0pGSnwMhuekYsbIbBQfOYPth09h39GzqG5ogSAIuCAjEd0MUag+14ZusXpclZ+JrYdOo7z6HJrbLBhgSkSf7t0wc1Tnd6TS30RXbhnRC2uKjnn9/EtzUnDt0J54c2cZDp5qgih2fkcumHYh6pvb8FbRERQf81wh+Y3bhiMqSof/7D6GD/ae8Lh9nikRWckG9OneDafqW9AzJQ4jc9Og0wn45Osq/MvNtS157sbBuG6o66kt/qYmNghqEOQrBkFE2lATXCjtwEsdZ8D+x1R69rIZQ726U6TVfrUO/EKNq8yN2sIL0rXhag6QkmtD7bn2pQMitWf+1DzMWeMcuEnuGZdrzeJtLK3CCjcZxocm9Nc8cyfxdH4dPTFlIDKS3JepD+S1rbb97kjv3U2X9MLzm9WXY39iykDkZSXh1le/8LktkvlTB6JnSpyiz5IWnxVpP9INhIrqRrz+ebldx98Tb76vbY9ZXd+CxesPKj6e0jYB3n/vSwJ1c2nt3uN44O29Xj//16OyFQUM7rw8Y6g1u+94M09p+164eQiuGdJT9fZywuU3U01sEBKFEYgouGwn1zveAZR+VtSO73U1iV7pIqv+3q/WQwBDiavMTZW5GbNXF6vqiHgqciECqDS7Xw9C7bn2peKd1J6UhBjcPS4Xr2wrl93ulW3liodGPrfpEN4qOoaFV3deX64ylY48bddhEfH6znJVAcRTG37qnLoKapWe7ypzk+LjuqLlYr3Se7d8x/dePd/c1Ibqcy2eN1Rh26FqbPvutKLPkhafFbmbF8lxehiidGhWME7R2+9r24Ioa/ceV/w8pcQf27ZoXSkm5pm8Dlj0OgHmplb8+ZNvfb7B446vVc16pcT73IbH3juAhR+W2mXQpdeptqKrFhVgPX0vS8F3qBdDsMUgiIgA+CdosZ2n4jhUqrCsxmMnUu1+1exD67LgocJdeXJvOiJaBItqz7W7oFypKnMTPtxX6XYbNXOQquo6O713y8yBkuuAecrEabGQsKugVun5Xrz+IOJi9D51HP1xk6DB6/Fsouaf16LyGsWfJV8/Kxv2V+K+Nc5VHs82KT8fvt5kAvz3nackCPREyxs87nh7I0ZA5/zGV7d7F8jb6vx+sv+Okl7nS7dcrCog0SKA8cfN0mBjEEREVrbBhe1wA2NcDDosoldfbo4ll7UapuWqlLPSu/RqfxSU7jdYpPbtPHza57vRttQGMHLnyZsfYFdBeWpCNGobPAcvtQ2tmpfxFgHZzJJjB8xdR+3e1cW4alAmPtrvPkBT2h65oPZMg7JsyJmGVtmOo+N7OCw7BbuPnJG99kPpJsGoPuk+ZRHlNLW5zr5In6UVO75HXpYRh07WK9qn3DnbsP8E5r61R3X7TEmxmD6it09l9uXeby3PoSNvA2etb/C4482NGGk7fxZ4kV7n4vUHrUN+lQQknl6PiM6hn9L2rn7v/DXCI1gYBBGRHX8ON/DmLp6a4ENNgKXmrpZWgZu/eJNVUNoRURPAuDtP3txBlMv4DctOweXPbvXYnpR4+fLW/mDbARs/oIfbjhoATQIg233aBrUdFhGPf1Ci+LmOHUe599BxDbHUhGhcN6QnJuSZMKRXMlITYlDb0KrJ6/E285ccH42RP5aZ97RuVmyUzm3RCLWe2vCN4m3l1k4pKKnEfWvUBUDx0To8NPECpCfKz4lx971p+7fy0w34164jdu+fKSkWl+Skanqd2spINHh1U0mL4YZquOrwS5VGHTPCPZJi0dxu8RgEuao6qpTtkF81AYmndd4Wrz8I3Y/vgbvfOy1GYoQKFkYgIjueKoF5O9xAyaRh2wphGYkGnGloxeL1yoIPb9utZNiSP86HVly1zxPHyavuOiVKilEA8HieAPc/rkopac/xM02aT+5WYv7UgUE5rjSh+fmN33lVVOCtWSNhbmpVfS15WmQ5UBwXHf5zwUGX88GCaWJeBpb/erj1363tFoxcstnnINLTUEvp74DzZ1ANaR0020680mvAtmiJ0u91W1pM7veGu2qlto9bRFFRUY4377oU5qZWzH1rj0+fHel1qg0oXQ27dHcDIlR+75RgdTgi8opW1Y3k+Fpy1LYNgP2Xsa/t9lSS2x/nQwvedKDk2izXaXIcZuMuIJ2YZ1J8ngD50uWeOL5HngLk94t/wEP/3qf4vGhFi6pQ3nhr1kjU1Ldg7tvqh1QBnWVtHbO/voqP1qHDIqLFi1LXgLKskIDOBXcbWn+aN2NSeEc+WP5xy1BMGdQZrDz+/gFFwzs9kT5Bd4/LxT+3+WfdKqlKIgCXwyUrqhvx/KbvAMjfoHDVPiWdbKW/IdcNycINl/SyLr+gNIjxNZuhNkhzFYwo5U0VNl+qOQb7904pVocjIq/4c7iBVhOo5Ybw+NpuV/OLAj38Qg1vOlByQ89cDlGsa8Fzm37KKGQaDZg/daDTekB6nYDCshpV50k6V0rnnri6s+2qPQBgMgZnwe3sVN+rQgFAoiEK55rbFQUBJqMBW745ieXbvc98FB89o/kcqkY382mU6GaIQn1zu9ttRMAuAAJgt/BtKJq/tgQdooj7vZgD5Ip0nSzf7p8ASADw9pfHrKXiHb/vbP99gamb7BAtKUvq7ZwepfO93t97Au/vPYHk+GjcdMl5soVM5Iaz+TrEWen8uPSEWADAlEGZeFknM5ztxyDe3NimeRU2X6o5BvP3zl8YBBGRlT/LRms5gdrxy9hf7Q7VMtreDoFzHCvubqKxoypzM+as2YNlM4Y6DTVRW5LZ1eKkjsNqpM6K3J1jd+0Bfuowad2xd0XqmMwclYNXd6grey23H1eTnh23BYApF5l8CoAAYNWuoz493x/qm9uRmhCNawZnYe2+Ss3mHQVbTUMrfutlxs4Tfw1NdNcBdryZMTHPhPEDemBVYQWO1DZaF2vdfcR9oK3kZpWaYgVnG9tkh0RWmpsVFThRa0RuqqL5Po/8Z5+13L6r+TUbS6t8qsLmKsulxW9VOC4b4QqDICKy8mfZaK2rNgE/fRn7q92hWEa7td2Cx98vUXUO7/tZX6QlxNhV+gOgao0ad3dq1ZRk/qaqzuX6PI4dOFedFak9kGmP7WKKo/um4b/F2q954si2YxITpcPVgzN9mo8iBarLZO4S2zLGRWN0vzSs2FHh9bFC3ZmGNqz8PPDDC/0tXCcifFzSWSzBXZZWbt7QqzvKMSXfpOgY7jrZnib3+0LrCnOunKyzD7bkMmu+VGFzNydMi9+qUKoI6SvOCSIiK2m8sKfKW96OCXY1od1b0phof7Vbq/3K3ZUDPM+N6bCI2FVWg8LvqwEIiNIJeKOwQvHq8dKaFYYovd2Ce3KdFDXkiiq4O0/+NnFgBkxGA/SCgLX7Tng8PylxUTjT5H6YlRq286fSE2LxyH/22Z1vNaYNMmFCnkn2OkmNi8EH+47j45IqNLZ6u5YOueJtZbpIpLaCnJpza/v94iqj0dpuweKPvvZbBnNMvzTMGtMHUVE6a6Ee6fNo+508qm8aRvbpbOuKHeV2ixl7kpoQjV3zJiAmSudyG6W/HdJjG0ursGJnhcv9vXjzEDz18TdefVd3xTlBDIKIuihvJ30qqbzla5lsX+/iuZrc7492u6uko2S/Su+UOo5HLyipxGPvHQjJyd0v3DwEVw3KsnYGLGLn3c13A5B18dXQ3skoPnrW5/1cOyQLl1+QgaM1jXir6KjXQY870rwnY1wMVn9Rga3fnEazhqWdiQLNUyU5U1Istv3feOw+cgYbS6vwwd4TsuW7t393GmYP88W0lhwfjdZ2i9MNiPgYPQB4dWMiNSEGT1+Xj0n5mYp+s+V+TwzROhii9HZDi13RCcBdY3Lxqsq5Y6wOF4IYBBHJ83VdG3+vi2P7ZS/dOT9Zp+7O1MsyX8Zat9tdwJYQo8e487tjxshsaxUiuecrnbvjWEr6XjfrnCghVzVLKw9NOB8rPy8PyQAtkHxd74OI7MVG6RAXo4+oz5WAzqp5ngo1eDsXVM6ssbl4c9dRNLYp+30IpXXxPGEQRBTBtFrXJlBlRW3bDCgbMpEcH41nrr9I9nVo1T41PzjJ8dF4+tp8u0pl0qKeajNeKfFR6LAAdQG+y6mENLwukjooRETBIP1qvXTLxTDGxWDOmmJF2R4ldAIwPDsFX1Sc8bjtmH5pmP2zfh5LjocKBkFEEcrf69r4M0PkatiYXIfbU0Dn65e0L2spSFITojVZ/yPUxEYJaGkP258NIiLyQmpCNH459Dx8tN8+Y6VkVEQgMQgiilBKF5PzZpE1XzNMSgITuWFyruZauAro5IKp1IRoXDekJ8YP6AEIsJvoKveFrdXCrkREFDwxegFxMXqYNSyEQq65G6URKFwslShC+WtdG3dlmV2VKrblLoPkuEaCtGDmzsOn3U42l1tTwlWgVtvQhtd2VuA1h6o5rtpQebbJ80khIqKQ1tohopUBUMCcbWzDvauLZefshiIGQURdiD/WtSkoqcTj7x/wOLTL1SJ3rgKTKnMz7l1d7DTkzVP1IEdSQKdm4U9PbQid0c1EREThxd9rLWnFdXFyIgo70oKkrr52BHRmP6R1BTyRAhilc1s2llbZ/dtdYCI95jjnR+2K54dOnkNhWQ12fV+jeg6PqzaE7RhhIiKiIJNuioY6BkFEXYheJ2DBtDwAztkM21Xtldyd8Saz8u+vfkCHTRRTVF6r+arejl7cehjTl+/CfW/u9utxiIiISBm1w+6DgUEQURczKT8Ty2YMhcloP+TNZDRYixd0WEQUltVg7d7jKCyrsQtcJN4EMOda2vHilsPWfwfyS5ATX4mIiEKDmmH3wcI5QURd0KT8TKfJ/lIlNKVlrr0NYFZ+Xo654/tBrxPC4kuQiIiItKNm2H0wMRNE1EXpdQJG9U3DNUN6YlTfNGsANHt1sVOGp8rcjNmri1FQUml9zNsA5mxjm3Us8IjcVCTHR3v/IoiIiCisKB12H2wMgogihJIiBYvWlVqHxvkSwITDWGAiIiLSTkp8dNiUxwYYBBFFDE9zfGzX3QE6K705Vk1TSsoiFZXXer2PQEiI0QNgSWwiolAUCsmEQT3dL7gZbDoB+MctQ/HyjKHINPp3CHp8jM7lzdGU+Gg8dW1+2ARAAIMgorClpLiBLTULqUpZI7UcS3CHckbowSv7Y//CX+BlmSISREQUPD0SYwCoXzLBH06fa8Xfp18MQ1RodpktImCMi8ak/Ex89vsrMHNkb78dq7HVgl8NO0/2b2cb2zBnzR67YfWhjoURiMKQ0uIGttQspOpNZTi5EtxKjzl/6kCkJsRg8fqDONPQ6vd1errFRmFAZiL0OgET80xIjI1G4ffV1h+Tf3xaBnNT6GawiIi6sub2EIh+flRpbkZqfAyMcdForm8JdnNkzVlTjJuGn4cP91X6fVmK13aUyz4uorMfEC4LpQIMgojCjlTcwPEnQipusMzFeFxpIdUqc7NskCGgs4z2iNxUfLT/hOp2mWSCMCXH7JEUiwGZSag+14LbRuXg+U3fQYB/Fyw919KO2auLcfe43ID8aBARkXKhdhOq8PtqnAzRAAgAzja14ZVt8sGJ1txl52yH1Y/qmxaQ9viCQRBRGPFU3MDdXRhpIdXZq4tlgwwRwJT8zrLa6d1iFbXnhqHnYez56XYluJUeU/p3c7sFt776hfVxabyxv+cSiUDAfjSIiCichX5WI5SE8lB4W6E5wJGIZKktbuDI1UKqUuzy2s4KTF++C795owiGKM9f+jvLqnHVoCxrCW41xzS6CHbMjW0429iGyfkmj8cnIiLyp4QYPX440xiUY0/O74H4Hwv4hJNwWSNQEEUxdAZeqlRXVwej0Qiz2YykpNCu3kHB19puwarCChypbUR2ajxmjspBjMKJjh0WEUXltagyN6G2oRWp3WJhSnLOfijdTi1pvx+XVOJfhUc8bv/XGwbhbFOby9cq7W9jaRVW7Kzwul1A53ye20fnyr4+6Tin6puRHBuNjd+cxJHaRvROjcPmg6dQVed6eIEhWofmNotPbSMiIiLt6ARAFOWHrEvD6nc8Oj5oc4LUxAYMgigiLNlQiuXby+3GsuoEYNbYXMybkuf2uQUllVj4YSmq6pwzMLbFCNbtO4F57+3HuZYOt9u5Yxs0SEPMNpZWORVBUEvutXZYRIxZukWT+TDJcdG4Y3QO5o7vb/3ikyveQEREROFHCml+MzYXy7e7Hkoe7HWCGARRxLMNJjaWnsRH+12XbBzVJwVv3DlSNitUUFKJe1cXezzeRT2TcOB4ncft/nHLxZgyKAut7Ra88Xk5vqw4g/hoHfKyjPjhbBP+u/sHNLb+FEQlx0XhbFO7x/0qNSk/Ay/dcgn0OgGFZTWYvnyXZvsGAL0O6JeegDaLiO+rgzN8gIiIiHzjOI8302jAVYMy8W7xcdQ2tMo+JyFGj7/eOJhBUCAwCCI53mQgXGVKBi38BA2tzpkdb+kEYPyADGw+eMrvZaBdiY3S4YWbh6Cl3YIH3t4bpFYQERFRuIiJEtCqsHR5MLNBamIDFkagLkUqH612CJZF7KwUtmTDTwuE/n3zd5oGQNJxNgUxAAKAlnYL7l1djH8VVgSxFURERBQulAZAQGeVWk8LuIcCBkHUZbgrH63U8u3laG23oMMiYrmLBcG6it1Hzga7CURERNTFuKtSG0q4ThB1GZ7KRythEYFVhRXIyzKiQabAARERERG5Fw5rBTEIoi5Dqw/ckdpGpCcqWyyUiIiIiOyFw1pBHA5HXYZWH7js1Piw+PASERFRZEmOi4aWK/D4YzmfTGPnEh+hjpkg6jJG5KYi02hAlbnZ63lBOgGYOSoHep2ATKOBa9wQERFRSJg1NhfDslMwe3WxUwlrNVITojH/qgthSjJgWHYKdh85g1P1zUhPiMUj/9knuy6iGgum5QVtsVQ1mAmiLkOvE7BgWmeJa8ePnvTv8zO6ud3HrLG5iInSWfcV+h9hIiIi6upmjc3BE1M7F11fNmMoTEb7ESuZRgPuGZeLTKPrkSzCj/89fd1FuO7inhjVNw0xUTqM6puGa4b0xOj+6Vh4tfsF5N1JiY8O+mKpanCdIOpy5NYJyjQasGBa55fHkg2lWL69HLbVG+XWCXK1r26xekTpdTjb2Gb3fCXVIDONBmQlG4JamS1aJ6AtDEpXEhERuSMAuGqQCd0TDXh/z3GcsfldtqUXgA6Hn734aB0m55uQnhSLT785jcOnz6HDYr9NclwUbhreC+989YPdbz7Q2eEflp2CTQdPeWxnclw07hidg/4ZifjD2gOobZBvp5zUhGj86Zp8TBmUZfe47aLwGYmdw8/0OsH6+MbSKnyw94Tdwqa2fSF3Ckoq8dh7B5xesyuT802YMTIbI/ukBT0DxMVSKeK5+nKQtLZbsKqwAkdqG5GdGo+Zo3IQEyWfGJXbFwC7xxzTyRCA6nMt1v8/VdeM2oZWpHaLxeGT9Xjp07KAnAdJr5Q4ZKfFY9bYPhiRm4bRS7e4XPE5FAztlYTiY3XBbgZFCB06O1MxUQKaVKyFQaGrR1IsUuOjEaMXcK65HbVN7ejeLQZXD8lCjF6Po2caIYoiEmOjceJsI6rPtaK5vQOGKD26JxpgSo6FubEN31bW43RDC9LjoyEIAlraLWho7YBOEPDDmUa0WTy3RTKmbxr6Zybgs4OnUFnXCr0ADMtOAQTgs+9q7LaN1gEXmhKw90SDx/1e1icVLe0WxEbp0D3RgKwUA5IM0fimsg4lJ+pQdtrzPgCgb/d4HD/bjGY3L+q2Ub3ROzUBqd1i7YZSVZmbUH2uBWeb2iBAwODMJMz99160tLveV3JcNJ771WBs/vYUjtQ2IictHo9OGog9R87gv8XH8MOZJhii9Rh8XjJG9U2DTieg6mwT9v5wFoCAnDT7327pt/rEmUbrNk2t7Xi3+LjLYWO2WYsN+yvxh7Uldr+NpiQDFl6dh4l5Juwqq0Hh99UABIzqm2bt8Le2WzDvvf14t/i4y9f6j1sutgYx7+85jofe2etyW8mvR2Vjcn6mU/9FDU99IU/PfXHLYazY8T3Mze0utxMAmIwG7Hh0fNADIIBBEFFIkcsmBdr8qQORnhiLiupGvFV01OfxvrZSE2JwpqFVswVgE2J0+Metw3Dbyi812iORs0yZH+21e4/jgbf3Bq9R5BeOd7996RgCPy3KrfY774Wbh+CaIT0V7UvNfA+5/QKdr3PM0i2a/PakxEdjyfUXKR7mVFhWg+nLd3nc7q1ZIzGqb5r1376+N7Y8vX7bzvvG0iqX7wMALHMzxEvNcfQ6wetzEyw7D1fj1le/8LhdqLRXTWzAwghEfuTtj6WWBAFYvP6gX/adEKPDiNwUFJSc1GyfDa0WBkDkd0N6JaOovNauk8WqkOoY46JR19QW1O83JarMzZi9uhjLZgwFALfDpT0NMaoyN2Hx+oNevWbH68vdAt9q9u/qutVi7TxbTW0WFJbVyI6GcAxWlC5ZYbudp6Hsanl6/SI6F/XcVVbj9n0Q0HnNTMwzyQZkSo9TVF6LUX3TFBVx0gnAmRAZrVF9rkXRduGwLpAjBkFEfuLuBy6Q/JnrbWi1aBoAEQXKxyVV+LikCplGA+ZPHYiUhFhUmZs0z2x2ZeamzvkCvlSpCgSpI/vYewdgbnQO2qQg6e5xufhwX6VTJ/zqwZlOj6shZQIcSwb7GqRI+x2WnYLCshqngETLTumZxjbrEK7k+GgAsJsv4hisKL2hIG3n6oahbQCrNhBS+voLv69WFcR4exxpO6nw0uzVxS63tYjAnDXFWKYLfpEBte9lOGEQROQnWt6FS02ICek5PEThqtLcjPvW7Al2M8JafIweDa0dwW6GWyLgcpK31PF+ZVu5098qzc2yjysl5Q3kSgarCVIcA01pT1cNysTIJZtlJ7/7q1Mqdx4dgxVP2Q7bwNBTRsxTJsYV5a9f2T5dvV/eBAmT8jPx0i0XY+5be1wWVRIBPP7+AYwf0MPlnOVAUPNehhuWyCbyE63uwumEzh/Q6BCYcEhE5KixtQOPTbog2M0ISSajwWUWIz0hVtE+Hryyv1M5ZJPRgAl5GVi+vdzpBlnljwHJmYYWZBoNAVnqQeocL1pXig6LqGjJCikwVDOcTA2p8+7q9QvoDBiVzmNxFewoPY5jkJCSEOuxqmxtQxtGLtmMgpJKRW30VodFRGFZDdbuPY7Cshp02DRMzXsZbpgJIvITre7CWURwsjYRhSwRwFdH1HVQuzLbhSjdTuxX2GccnpuK+6/sbzcHp6a+BXPfdp3BFNE5F3T+1DzMWeN62JWWHIeNSevZOM7zMf04BNUYF4O1e4/j0Mlzivav9sai7bAzV5m0BdPyMLJPmk+ZDqXH8TYTWNvQ6vWQQCWUzMVy9156O2crFDAIItKA3GRaJZMfiYi6gk0HTwf8mKE2F0nq4j59nbIqakonnFefa4FeJ1gzFh0WEcOf2ujxeZXmZhw6dQ7G+GjF671owbZzPyk/ExPzTHa/j2caWrF4vfqKqd7cWFTaefcmiPHmOJIOi4jqemXvv8SbIYGeqJmLJfde+lK9LxSwRDaFHNsqPNLaOh7vqAWRu7soAKyTH8P2g0ZERB4pqWRme8Osur5FUeVOx9LDSkssB4u0JINcJ9mbiqlarEOjpPS2FtXpvD2OUlqWoVZb2jtcsEQ2hS13Xw6uVk1WSun6A2rWKXD1hV5pbsa9q4vx0IT++H/TL8aCD79mYQMioi7M0z1lud83QXBdwdPVMKxAlSJONEThXHO7qoBF57Akg2P5cbUVU7Wac2KbSXNFi0yHp+P4umyG7Xvv65pKakt7a3HMUMMgiEKGpy+H2oY23LdmD+754SzmTcmzPq7lHR65FaNd3QlS8oX+3KZDITdkg4goUnSLjcK5Fter3Wupqq7FevNr7vj+ijIg7uImEfKd/0CVIv7VsPOwcmeFqt8wx4n+tsOqjHExqrMfgZ5zoiRY8pYWy2bYlhT3NWultrS31us4hQJWh6OQoObL4ZVt5diwv7NSSkFJJcYs3YLpy3fhgbf3YvryXRizdItdJRXpx8fxy1f6cpa2XbKhFPetKXZZacexOovSEtgMgIiIgiNQAZCt5zYdwuhnfvod8rbzGx+jR6IhGh0W0a56l8UiwpTk/0BoYp4Jy2YMRY8k+yp28TE6CApv/ttWjauqUxcApSZE47PfXxG2HWxHviybYVthTmmfxhM1pb21OmaoYSaIQoLaL4f5a0sAiJizZo/bCX0T80yK1h9oabO4XQtCBPDYuweQaIjGyD5p0OsEbCqtUtxeIiKKHFV1Pw2JbuuweNX5bWztwK2vfiG7OKn0mCuGKB2a2y2qjwnYD8PbWFoFxzJ2ja3q9isNq6pWOYyvtqENu4+c8VtmxhV/Dfnydhij7ZBAAJqtqaR0/Z9h2Sm4/Nmtmq/jFAoYBFFIUPvlUNPQij+sLXH5oQSAR/+7HzsGK1sJ+oEfV8J252xTW+cPUlw0brssG+/tOa6qzUREFFme23TI533IVXYz//hYfIwejTIL1foSAAGdHe6NpVU+zV9x5E2FukDNf5L4c8iX0syL4+LotkMCC8tqVM/jcUVpae/dR85odsxQwyCIQoI3Y5xrG9x/oZqb27H6i6PeNsmls01teGHzYc33S0REpITUYW1ucw6AfCEIwKyxuZiYZ8KYpVs0Hc4tKB1DZ8Nd30DrjI2actHeUJp5+ez3V2D3kTOyr0vtPB5PlJT2XrtX2Q3fQAesWmAQRCFB+nLwdrwsERFRpHEsRKDF/v65rRzxMVGa/R5LnftRfdPw4lblNxAz3SxQqnXGxt28La2GfCnNvMRE6VxmVNTM41HKU1U8fxwzVDAIoqA519yOB98uxrcnz8EYF43rL+6Jf3xaxkICREREQbRip+s5smpInf2bh/fCqbpmpCbE4ExDq6Lf+asHZ1o74rZZn4rqRjy/6TvZpSm8zdh4Uy7akavMlOPjL90y1GmxWKVV8JRmk1wFj664q4rnr2OGAgZBFBRXv7gd+3+os/772JkmlJyog14HJBoCu7o1ERERdRIBmJu0qapn/LGAgzdzoz7cV4n/mzQQG0urFC8uKsK7jI2vw8xcZaauHpyJD/dVOj0+f+pApCTEqh7KpzSbpGWBgmAcM1BYIpsCzjEAstVh6Zw8+dCE/rjigu4BbhkREVH4EATH2m2h5Wxjm9c3NSvNzXhxy2HZ0syenldUXqvqWL4M+XJVPrrS3IxXtpXLlpWes2YPzE2tuGZIT4zqm6YqgJDm8ZiM9m0xGQ0+z1sKpWMGAjNBFFDnmttdBkC21vihoAEREVFXEqvXocXLSnDhYOXOcq+GyFeZm1Rt7+2QL2/WgNJijpGneTz+EIxj+huDIAqoh97Zo2i7k/Utfm4JERFReGtut+ChCefjraIjqKoLzO9mWkIMFl9zISrNzVi8/qBfj3W2ybsskuOi5554O+TL2wVQtSgr7W4ej78E45j+xOFwFFBfn/CcBSIiIiJlctLjsfOxK/HQhPMDcrw/TB2IKYOykJ4Y67djCACS49wvCOtOajf1bfNmyJevZaHDsax0V8JMEAVMh0VE9TlmeIiIiLSSkWiAXifggQn9cYGpm+IiAt4yGeOsx/UHKddyx+gcrxebNSV51za1Q758PQeHTp5DYVmN5sPKtF5DqatiEEQBU1Rei9YOZSNnMxJjIEDgsDgiIiIXUhOiMSw7xfpvqRP/t/99i5c+LdP0WI7zYjzNo/GWVC56Yp4Jb395TPX+3a0vpISaIV++noMXtx7Gi1sP+7TGkSOt11DqyjgcjgJGTdr31ktzsOiaC/3YGiIiIm3cMy4XmUbvsgK+3J+vbWjDiKc24YVN36G13YLCshp8tP8EUhNifNirc5vk5sVI82g8PV/p60uOi8abv7kUOx4dj0n5mXb7V7IP4cf/AlmuWW0bXan6cY2jgpJKn9rjqlKdVvvvahgEUcCkqxija25qxaT8TLw8YyiS470fF0xERORPd47Owbwpedjx6Hi8NWskXrh5iMf5OSnx0Xh5xlC8LDMHRW3//WxTG57bdAgXzP8Y05fvwgNv78Xi9QdV70cKIu4Zl6tqXoxR5jc62c3rc3XcZ355EUb3S7cLYFzN00mOj3bqGwSrXLOrNmYaDYqDYymLtGhdKTos3uXV3FWq02L/XZEgimLYno26ujoYjUaYzWYkJSUFuznkwc5D1bj1tS8UbZuWEIOiJyZYV1veVVaDwu+r0SGKWFV4BOdaOvzcWiIiIs/emjXSbvhUh0XEmKVb3M7LMSXFYudjV1p/42znbwzLTsGX5bWYs6bY6+po7kjVz5Lj7Rcmtx0yZfu7C3QODxvZx349Gynr4KoT+fKPAYnt66uobsRbRUdRVaduqJbcHBcAsvNegjUfxtVxpcd3Hj6NF7d6HqLoeD0pVVhWg+nLd3ncbv7Ugbh9dG6XnSOkJjbgnCAKmOoG5fN7ahparaUj9ToBo/uno76lDYvWlTIAIiIizegEwJub467WjlFSNrmqrsXuN86x06vTCZoFQI6vz3bOjatgYWNpld28Esd5K57Wx3FcB8f29c0d3091kOJqno7jY8GcD+OqjdLjSqcEeFsxTunzFq8/iFd3lHOOEBgEUQCpGQ4H2H+gPd1xIiIiUmruFX3Rv0ciqutbvFrrxt3aMUo7oxtLq1ze8deydLJF7Lz7n54Y6xR0yB3f1e+tNK9k2YyhMMbFuA303K2D46+1ZpS0W4tOv7eZJqWV5JRsJ9cGNZXqKs3NuHd1Mf5xy8WYMihL8fO6GgZBpClXXw4b9lfiiQ8OqNqX9IH2ZkVmIiIiV0b3645RfdPw/p7jXj3f5Ca7oLQzumJnBUbkpvq0D6XSE2NxzZCeHrfzNK9EyvD836QBio7rKpjTesia0nZLmSlv+ZJp8lRJzlVmUWkb5k8diEyjQVV59Llv7cGLEDBlUGRmhBgEkWY27K/EH9aW2K3UbIyLRr+MBOw+clbVvmxLXHq7IjMREZEt245mQUklFn/0tVf7qTI3Y8/RM7Id3xG5qTAlxaKqzvMQcFcdc63LTysNqjz93koZnlqFa/5lJBqcAp4zDa1YvF7bIWtK2y2XmVLK10yTVElu9upi67wsibvMotI2zFmzBxPyMlT1lywicN+aYryss297pKwzxCCoCwrGxbtkQyle2Vbu9Li5qU11AAQAT0weaH0Nh06e06CFREQUyWw7mhtLq3waYi0C1t+8eVPsy0TrdQKmj+itaKFPd0PGXHWY1VCaXZAoHYaXmhCjKKtxpqHVY5EIwPcha/6eb6NVpkmqJOeYyemRFIvpI3qj5ccy53L9NiXV3zYfPKX2pQEObY+kdYYYBHUxwbh4N+w/IRsA+eJ37+5Dc5tF030SEVHkSo6PxpLrL8LEPBPGLN2iSYZl+fZyPPLzAYiJsl9xJCc9QfE+XHXMXXWY1VKzbo7SjFHx0TOYlG/Cyp0VskGaCCAvMxH3rSlWtD9fh6xpOd9GjpaZJmlBW8eKebZBs1y/TcmoGG+rX0ttNze1BmReVajgOkFdSDAWyeqwiHjigxLN98sAiIiIPLmoZxIEhf1liyhaO59KgopYvecdW0RgVWGF0+NqOtvutp2Un4nPfn+FV4uf6gTgpVuUdVpb2y14bfv3WH/gBBINUR4X/ly16yhW7qwAAKfzL8Uvm785raq9toGErQ6LiMKyGqzdexyFZTXosIhOjw3LTkGm0eCy3QLsh9mrpabYhRJScYjYKB2e3/SdXclwQL7fpmWxDDlV5qaIW2eImaAuQstJga7WLHBcL6DDImLxR1/jTKP26xgQERG5IpV9PnC8DgAQpQPaPdw7Mze1Y1dZjeLlGobnpmLH4RqP2x2pbXR6TOm8ICUd891HztjNtVXKIgIpCoKnJRtKsXx7uddZBOl5Vw7ojs3fnPZ6P5KPf+z4j8hNxSclVU5zjaVFUh3XOLp6cCb+ua3cZWZq/tSBXk8N0KLYhSO1/Tati2U4qm1o9fu8qlAT1CBo27ZtePbZZ7F7925UVlbi/fffx7XXXhvMJoUtrVK1csPpHL249TCiflwArOvcDyAiolA3Od+Ej0uqnDrangIgSeH31YjWKxsEk5ueoCgIyk6Nd3pMrxOw8OoLce9q98PBlAxV8yUD4Om5rubzemPrt+oyP678q/AI/lV4BPExejS2Oq8LeFbmxmuVuRn/3FaOu8fl4sN9lbJ9mMXrD0KnE7waziUVqvCUQVRzw1ltv01JsQydAIiiujlk0vytVIXLmPg7IxVIQR0O19DQgMGDB+Oll14KZjO6BG8nBdqmlF/YdEh2OJ2cdgZARETko4QYvaLtBACmpFjsOXrWp+NZROCtoqMet8s0GvD4lDx4ShzoBGDmqBzZv03Kz8TLM4ZaMxe2UuKj8bLC+RW+ZADcPbe13YLl27Wbz6v1KCm5AMgV6dAf7qvE41MGym7jy9QAqVCFknbIDemTo7bfZtsGx8tS+PG/WWNzZf/uim2xEFOSf+dVhaKgZoImT56MyZMnB7MJXYY3kwKVZH2IiIjUiInSodVDaka6+3zTJb3w/GbPVdREQHHFNXdS4mMUla6+eXhvxMXoMWtsrttMyayxuU5FEWxJk+B3ldU4DSlXOjRrWHaKdfifUkqqwq0qrNA8cAkmKQBZ8KF82XNfiy9Mys/EXaNz8NqPc6HcURLgeNNvc1Usw3bdqot7p8gWyLp6cKZTlsz2eR0WUZN1jMJJWM0JamlpQUvLT19edXV1QWxNaFG7CJerWvNERES+uHpQJt4tPu7x92XBtDy0KBzHdufoHFUV1+SkxEcjPVHZkJ+c9M4hblL5a8c5MzqhMwByLI8tR68TMLp/Okb3T1ffaHTOCVIbAAGeh9rJzWXqCtzNn/J1XsuEPJOiIEhJgOPt4qmO1eUcl0Jx9/f/mzTQ5fO0WMco3IRVELRkyRIsWrQo2M0ISWouXneT8YiIiLylE4Cnrx+ECXk9XI40sC3/W1jmec4NAEzMM/nctiXXXwRjnLIqa7ad2HlT8vDIzwdgVWEFjtQ2Ijs1HjNH5bjNAGlJ7RwMk8JlMeTmMvlC6XyUTKMB86cOREpCLD4uqcS/Co9o2g4lvJ3X4m3gIseXoEOqLudu33J/9/Q8JZmmriSsgqB58+bh4Ycftv67rq4OvXr1CmKLQovSi1dpeVAiIiI1pOFhtnejq8xNqG1oRWq3WJiS7O8+q+1UepoYLkcnAC9OH+rTkJ+YKB3uGttHxVG1o3TY1Nwr+mF0v3TFC6TPHJWDpzYc1GRInDQfxVV1NgC4a3QOJuSZnNqnVRAkAEhJiEZtg+eKtd7Oa9E6WxKKQYenTFNXElZBUGxsLGJjlaWyI5WSi7crVfYgIqLgkxse5umus7SNmk6lq23deXH6xZgyKNOr44UCpYHiQxPPV9XumCidxzlPSmQqmI/iqkOvpOKZEtKr/tM1+Vi8/qBf57VoHbiEYtCh5LPbFQiiKIbEqChBEFSXyK6rq4PRaITZbEZSUpL/GtfFFJbVYPryXcFuBhERaUjqMk3Iy8Dmg6fc3uGPjxYwwJSE706dw7mWn6pwOU7AdzWhukdiDMb274742ChNhofJFepx1XmW29bV2jGuOqVqjhcKpHm8gHzgtkxhpTk5cusE6QTgyoEZOPCD2a6QREp8NK4dkoXzUuJlM3uA81qDnjr0rl6bOwkxejTYVI+zfe/8ea5sqX2dFBhqYoOgBkHnzp3D4cOHAQAXX3wx/va3v+GKK65Aamoqevfu7fH5DII8k/uQAsCYpVt8vvNCRBRo0TqgQ3SulBUfo0O7RURru3++1aJ0wNDeKZjzs37Yc+wsVn5eAXPTTx1uJZmJpFgdBvdKQWllHWpkhuwMNMXjXLMFJ+qa0WFTLyA+WocLs5LQMyUePVPiMDI3DbuPnMHrn1fgbJN8p7+13YJVhRUor2mAKIpIjI1GVV0zeqbE4bK+6dbqZHKLY+8+csapYxeIDp+aY7j6bVPTxnDrxPozcJOuF8c5T4E6R0oD29SEaPzpmnz8Ij/TbbvCLcgl7YRNEPTpp5/iiiuucHr8tttuw+uvv+7x+QyC3HP3JQDA4yJuRBT6BACC0HlnNDctDvGx0fih9hzONlsQo9ehd2ocfjYgA9u+PY0TZxvQ2CbCYhHR0Gqx67QnROvQp3s8LKIAQ7QePZMNSDREQ6fTISctHjcN7413vjyKsupzOGVuxqn6Fuw/7rpC5z9uuRhXDOiBP35Ugo1fV6Gmod3ueHHROozIScXIvqn4/HANdpXXoq3jpy1i9QIuOs+InslxEATBrvMOwK7kcJROwAubD7kMQgafZ8SUizKRl5mEU3XNKD52BlVnm3GyvgWZRgNG5Kbhtsty0GER8fSGUpRXN8AQpUO/jERE6XWyJY09BQ/DslPwZXmty7LIrjqdcvt21fEMt048+a4rv+daBLae9tdVzhW5FjZBkK8YBLnmqgS29PF/6ZaL8fgHJbIrLxNReFG66CLg+btByVCRDouIMUu3uCywIo273/HoeGwsrXJ7vLvHdU6m9rY9atrCDhARUdemJjYITH1HCih3JbClx/6wlgEQUVexaF0pOhSUeFLy3aBkX54qTEprcewqq3F7PBGd66/40h6lbVGygjsREUUOBkFd0OeHqz12CpSUkCSi8KC0k69VwKC0wmTh9+6/iwDnuT1q26O0LayKSUREtsKqRDZ5tmRDKf7pY7lLIgo/Sjr5WgUMytfY0Gb4mbv2KG2Lt+uCEBFR18RMUBeyZEMpXpEZW+9KooExMFFXcejkORSW1bgdOqZVwCCt7eEqxBHQWYRFq3Um3LVHaVt8WReEiIi6HgZBXURruwXLtyvPAOmEzkXFiCh0mZJiYYxTdrPixa2HMX35LoxZugUFJZWy22gVMEgLTkrPcdwH0Lng5Mg+aW6PB3R+F/nSHqVtYVEEIiKyxSCoi1hVWOF2bL0jiwhkJBlwz7hc/zWKKAxMGJih2b6S46OREOPd16rURX9oQn+8cPMQvDVrJHY+diWW/nKQqv1UmZsxe3WxbCCkZcAgrZpuMtpnaUxGg7Wim6fjCQBmjc31uT1K2kJERGSLJbK7iCfXluBfhUdUPeeFm4fgmiE9sejDr7Hy8wr/NIwohD004Xw8MKE/nlpfqiqTCnRmMO4ak4PxA0x261B8uO8EHnpnr8fnx8fo0ehixXNHBSWVeOy9A4orOnoqC63lQoJK1uLwdDyt2sN1QYiIIpua2ICTQrqIxpZ21c+Rxtn//EITgyAKKTE6YHhuGupb2rD/B9cLct41OgfjB/SARRSxs+w0Xv/8CJrbLIqPk5Mej4KSSryqMgACgP930xBcNaSn0+OmJGXzbpbPvAQ6naCowz4pPxMT80zWBUJ/ONOED/aecLlv26pqcvNypP1pETDodYLHuT+ejqdVe5S0hYiICGAQ1CV0WERsP3Ra1XN0AjAsOwVA5zyB1IRols0mv7phaE9sOngKZ5s8X2etFuC+n/XD7/67z+U2AoANJVV4fGrncKkovQ4vf6YumElPiMXv/rtPcTER22M/9fE3mDwoy6mjLs27qTI3y+5XytKM7JumqpOv1wkY3T8do/unY+3e426DIIm7qmqBDhg8HY8BDBERBRLnBIW5DouI13eW42R9q6rnWURg95EzADo7H9fJ3NEm0tLY87tj9/yJmHtFP0Xbe1pjxnENGTXrwEgT7iHA4zo2So5tKxAT9VkWmoiIyDfMBIUZ2zHvFdWNeKvoKKrqvFsEUOo0dlhEfF3pesgRkRYyEg2d2Yx+6Xhx62EFz1AWJEjXsdoO/4Jpeag+16LqOa6O7UiaqO84z8Xk5bwbR0qzTSwLTUREJI9BUBiRmzzsi4xEAwpKKvHou/thblI/p4hICccOuacOPACkJkTj0txUvLjV8/6l4EfJfgH7CfeFZTWqXourY8vRct6NIynbNHt1MQTA7vWyLDQREZFnHA4XBjosIl7YdAj3ri7WLADSCcCWb07i3tXFDIDI72w75O6Gi0lqG9rw+//uQ3J8tOI1ZJTs96EJ/bHj0fHWTIyndXNcUbOezqi+abhmSE+MUjkHyBOWhSYiIvIeS2SHuIKSSiz88GtU1fk2bIcoGDyVfXaX2bTNcLjKdsh19tWWWy4oqcTs1cWAwzFccXfsYGBZaCIiok5qYgMGQSFM6pyF7RtEEWnmyN4Ymp0KU5IBF/RIxKx/fYkT5mZkGQ1YcfsIGOOjrdu2tlswcslm1DbIF/YQABjjo2GI0tvNffO0hozawMBV4HT14Ex8uK9Sk/V0iIiIyL8YBHUBHRYRY5Zu0Wz4G1GgjB/QHStuH4HLn92CIzVNTn/PTovDZ78fDwAoLKvB9OW7PO7zzbsuVbymjrdcBU7MtBAREYUHLpbaBRSV1zIAorC05ZvTGLTwE9Q1y881O1LThMuf3YLPfj9ecVnr6oYWXOPnMu6u1qnh+jVERERdDwsjhKjvT7NkNYUvVwGQ5EhNE8yNbVzvhoiIiIKCQVCIWvbp98FuApFf3fl6kcfqbEqrsBERERGpwSAoRJ1r6Qh2E4j86oS52W1Za653Q0RERP7COUEhKiMxBmeb2oLdDCK/yfpxfRtpvRvH6mwmP1Zhsy12kJ4QCwhA9bkWFj4gIiKKEKwOF6Jqz7Vi6J82BrsZRHZ+ObQnFl2dj0GLPoHFx2+OfU/+3K5cdqCqsHlan4glsImIiMKTmtiAw+FCVGq3GHTvFhPsZhDZuWFYL3QzRGHW2Fy32yUZ3CeZs9Pi7AIg4KcqbNcM6YlRfdP8FgDNXl3stvJilbkZs1cXo6CkUvPjExERUWhgEBRiOiwidh6qxl8++QbnpcQHuzkURgxRAl68eQhenjEUyQ4BhlakktbzpuThnnG5cIxTdAJwz7hc7F/4C2Snxcnuw3adoEDqsIhYtK7U4+LD0t8XrStFh6/pLiIiIgpJnBMUQgpKKvHYewdwtpFzgUi5a4dk4VeX9MLIPj9lTybmmbCrrAaF31fjhzNN+GDvCU2OZVuqet6UPDzy8wFYVViBI7WNyE6Nx8xROYiJ6ry38tnvx8Pc2IY7Xy/CCXMzsowGrLh9hFMGKFDUrL0lAqg0N6OovJZrBBEREXVBDIJCREFJJe5dXRzsZlCYyTQa8NcbhzgNHdPrBIzun47R/dOxdu9xn4MgAZ2FChxLVcdE6XDX2D4un2eMj8a794326dhaUbowq6/PISIiotDHICgEdFhELPzw62A3g8LQlHwTispr7YoIOBYYSO8W69Mxukqpam8WXOUirURERF0Tg6AQUFRei6q6lmA3g8LQazsr8NrOCmtFMwDOpaaTYpEcHw1zY5vH+TBy/FmqOpCkhVmrzM0ez4OrzBcRERF1DQyCQgCH3JCvqszNLodTnqxrsXb6BUBxIDT3in4Y3S+9y6ybIy3MOnt1sdvz0FUyX0REROQaq8OFAA65IV+5C2xEdHbsk+Oj0SNJ+bXWv0c3v5WqDhZpYVaT0fV5MBkNWDZjaNhnvoiIiMg1ZoJCwIjcVJiSYjkkjvxGBHC2sQ1v3jUU31TVYfH6gx6f01WD80n5mZiYZ7LOm0pPiAUEoPpci18XaSUiIqLQwSAoBOh1AhZefSGrw5HfVTe04PbRuXh1R7nLuTGRMB9GWpiViIiIIhODoADqsIj4/FA1/rP7KL45eQ5Jhij87ILuOFbbiF3f1wS7eRQBMhINbufGcD4MERERRQIGQQFSUFKJh/+9D42tHXaPf3XkbHAaRBHFMbsjzY1xqiTXRSrBEREREbnDICgAuBBq13fFgO749JvTXpWg1oJtRkdpdsdxbgznwxAREVGkYBDkZx0WEQvWlgS7GeRnd4/tizsvy8XMFUU+7UcAYIyPhiFKj6o65aXTUxNi8NR1+QBk1glyk93h3BgiIiKKRAyC/KyovBYn61uD3Qzyo0ybYWa+VPmT8i/PXH+RNUOz4cAJrNp11ONz/zB1oDXIYXaHiIiIyD0GQX7GhVC7NgH2w8x8qfLnmLGRMjRKgiCTMc76/8zuEBEREbnHIMhPOiwiispr8W1VfbCbQn6SKTPMbFJ+Jl6eMRSPvXcAZxvbPO4jNSEa86+6EKYk+YzNiNxUZBoNEV3OmoiIiEhrDIL8oKCk0mleBnUt0waZ8PzNQ2WHmUkFB3aV1WD1FxX4uOSk0zbSs56+7iK3ldhYzpqIiIhIe7pgN6CrKSipxOzVxQyAQtzFvZJhiFJ/+QsA7hmXi7/fMsxt4KHXCRjdPx3LZlyCl2cMRabRYPd3k9GAZTOGKipFLZWzNvmwDyIiIiL6iSCKYrCq+vqsrq4ORqMRZrMZSUlJwW4OOiwixizdwgAowHonx6DdIqCyrsVpyFhCjB4NNmsz2Q5hkxavfXfPD2hs7cDwnFTcdlkOnv3kG7y6oxyOn4xRfVLwxp0jEeNF8CQNj/SlWIEW+yAiIiLqqtTEBgyCNFRYVoPpy3cFuxldll4AOmyuVlNSLBZefaE1E9LabsGqwgocqW1Edmo8Zo7KgV4neBU4yO3Lm+CHiIiIiAJDTWzAOUEaYiU4//rXnZdCpxNcBjQxUTrcNbaP0/O8qZTmal9EREREFP4YBGkovVtssJvQJUkV0Eb2TePwLyIiIiLyGcf3aClsBxb6R0p8FOZc0ReT800et71yQHcI+KnimYQV0IiIiIhIa8wEaeiVz74LdhNCypnGdry0tUzRtlu/PY0JeRkoOV5nV1jCcQFRIiIiIiJfMQjSyNq9x7Ht8JlgNyNsWURgY+kpzBqbg/EDTKyARkRERER+wyBIA0s2lOKVbeXBbkaX8NqOCvz+FwNZiY2IiIiI/IY9TR9t2H+CAZCGLCKwqrAi2M0gIiIioi6MQZAPOiwi7luzJ9jN6HKO1DYGuwlERERE1IUxCPLBtm9OBbsJmhnXPz3YTbBqbGkPdhOIiIiIqAtjEOSDP39yMNhN0IQA4JWZlyA5LlqT/aUmxDiVulZjZ1kNOiysN05ERERE/sEgyAffnWoIdhM08ZuxuYiL0eOO0Tma7O/aIVkAnNf8UarS3Iyi8lpN2kJERERE5IhBkA+6Qq5iwsAMPDE1DwAwd3x/JMf7ng2amGfCshlDYTIa7B6Pj9Er3sep+mbPGxEREREReYElsn2QFqfH6caOYDfDa3eMzsaCafnWf+t1Ap65/iLMXl3sVYAnoHNxU2ltn4l5JhSV19qt+fP54WrMXFHkcV8ZiQaP2xAREREReYOZIB/M0Gj4WDDMGptjFwBJJuVnYtmMoUhNUJcRkoa+LZiWZ13cVK8TMKpvGq4Z0hOj+qZBrxNwWb90ZBoNLofKCQAyfwykiIiIiIj8gUGQD3ZXnAl2E1QzROvwj1suxhNTL3S5zaT8TOyaNwGpCTGK92syGrBsxlBMys90u51eJ2DBtM7hd46BkFwgRURERESkNQ6H81JruwXbDoXH5P3zUgwYnp2K64eeh8v6pSsKMGKidHj6unzMXl0MQH7+00MT+iMnPcE61E1p4CJlmxatK0Wl+ae5PyajAQum5XkMpIiIiIiIfMEgyEurCiuC3QTF7rgsF3eN7aP6ea6ClUwNgpVJ+Zmyc4aYASIiIiIif2MQ5KWKmvAoj60TgJmjcrx+vj+DFWnOEBERERFRIDEI6uJmjc1FTJRvU78YrBARERFRV8LCCF4a0islIMfRe0i4pMRHY2JeBhwTMzoBuGdcLuZNyfNf44iIiIiIwhAzQV7KSo7z6/4TYvWYNSYX9195PjaWVjnNy0mOi8Ydo3Mwd3x/6HUCWtstWFVYgSO1jchOjcfMUTk+Z4CIiIiIiLoiQRRFb9bFDAl1dXUwGo0wm81ISkoK6LE7LCKGP7URtQ1tmu5XB+DNWSOd5t10WEQWESAiIiIickFNbMBMkJf0OgF/uiYf963Z4/U+esQLqG4WIVqAbgY9Pv7t5eiZKp9h4rwcIiIiIiJtMAjywZRBWbjnh7N4ZVu5x23fksnuEBERERFR4DEI8tG8KXkYfF4K7ltT7HKbimemBrBFRERERETkDoMgDUwZlImy/Cn46KtjePiDA+iwALF6AQUPXI7cjIRgN4+IiIiIiGwwCNKIXifgmhG9cc2I3sFuChERERERucEaykREREREFFEYBBERERERUURhEERERERERBGFQRAREREREUUUBkFERERERBRRGAQREREREVFECYkg6KWXXkJOTg4MBgMuvfRSFBUVBbtJRERERETURQU9CHrnnXfw8MMPY8GCBSguLsbgwYPxi1/8AqdOnQp204iIiIiIqAsKehD0t7/9DbNmzcIdd9yBvLw8vPzyy4iPj8eKFSuC3TQiIiIiIuqCghoEtba2Yvfu3ZgwYYL1MZ1OhwkTJqCwsNBp+5aWFtTV1dn9R0REREREpEZQg6Dq6mp0dHSgR48edo/36NEDVVVVTtsvWbIERqPR+l+vXr0C1VQiIiIiIuoigj4cTo158+bBbDZb/zt27Fiwm0RERERERGEmKpgHT09Ph16vx8mTJ+0eP3nyJEwmk9P2sbGxiI2NDVTziIiIiIioCwpqEBQTE4Nhw4Zh8+bNuPbaawEAFosFmzdvxty5cz0+XxRFAODcICIiIiKiCCfFBFKM4E5QgyAAePjhh3HbbbfhkksuwYgRI/D888+joaEBd9xxh8fn1tfXAwDnBhEREREREYDOGMFoNLrdJuhB0E033YTTp0/jySefRFVVFYYMGYKCggKnYglysrKycOzYMSQmJkIQBE3bVVdXh169euHYsWNISkrSdN/kHs998PDcBxfPf/Dw3AcPz33w8NwHF8+/9kRRRH19PbKysjxuK4hK8kURqK6uDkajEWazmRdmgPHcBw/PfXDx/AcPz33w8NwHD899cPH8B1dYVYcjIiIiIiLyFYMgIiIiIiKKKAyCXIiNjcWCBQtYkjsIeO6Dh+c+uHj+g4fnPnh47oOH5z64eP6Di3OCiIiIiIgoojATREREREREEYVBEBERERERRRQGQUREREREFFEYBBERERERUURhECTjpZdeQk5ODgwGAy699FIUFRUFu0lhZ8mSJRg+fDgSExORkZGBa6+9Ft9++63dNj/72c8gCILdf/fee6/dNkePHsXUqVMRHx+PjIwM/P73v0d7e7vdNp9++imGDh2K2NhY9OvXD6+//rq/X15IW7hwodN5HTBggPXvzc3NmDNnDtLS0tCtWzf88pe/xMmTJ+32wfPunZycHKdzLwgC5syZA4DXvJa2bduGadOmISsrC4Ig4IMPPrD7uyiKePLJJ5GZmYm4uDhMmDABhw4dstumtrYWt956K5KSkpCcnIy77roL586ds9tm//79GDt2LAwGA3r16oU///nPTm35z3/+gwEDBsBgMOCiiy7Chg0bNH+9ocbd+W9ra8Ojjz6Kiy66CAkJCcjKysKvf/1rnDhxwm4fcp+XZ555xm4bnn9nnq7922+/3em8Tpo0yW4bXvve8XTu5b7/BUHAs88+a92G130IEcnO22+/LcbExIgrVqwQv/76a3HWrFlicnKyePLkyWA3Laz84he/EFeuXCmWlJSIe/fuFadMmSL27t1bPHfunHWbyy+/XJw1a5ZYWVlp/c9sNlv/3t7eLubn54sTJkwQ9+zZI27YsEFMT08X582bZ93m+++/F+Pj48WHH35YLC0tFf/+97+Ler1eLCgoCOjrDSULFiwQL7zwQrvzevr0aevf7733XrFXr17i5s2bxa+++kocOXKkeNlll1n/zvPuvVOnTtmd940bN4oAxK1bt4qiyGteSxs2bBCfeOIJ8b333hMBiO+//77d35955hnRaDSKH3zwgbhv3z7x6quvFnNzc8WmpibrNpMmTRIHDx4s7tq1S9y+fbvYr18/cfr06da/m81msUePHuKtt94qlpSUiG+99ZYY9//bu/uYKsv/D+Dvw8PhSQ9PR550ICiiKQjiODETWTCR2XL5h2gsqBScycJpRLrU+bBg6bBlyVz5tHSabZktraYClYoYjJMP6AlOPCwHUtgRTQuEz++Pftxf7kBoBp5D5/3a2M65rus+XPfn/nCf+8O5uXBzk927dytjzp07J46OjvL2229LTU2NvPnmm+Ls7CyXL18e9hhY00Dxt1gskpycLB9//LFcv35dysvLJS4uTmJjY1WvERISIps3b1b9PPR+j2D8+zdY7mdmZsq8efNUcb1165ZqDHP/0QwW+94xb25ulr1794pGoxGz2ayMYd7bDhZBfxMXFycrV65Unnd1dUlQUJAUFBRYcVYjX2trqwCQb775RmmbM2eO5ObmPnSbkydPioODg7S0tChtxcXFotPp5M8//xQRkddff12mTp2q2i4tLU1SUlKGdgdGkI0bN8r06dP77bNYLOLs7CyffPKJ0nbt2jUBIOXl5SLCuA+l3NxcmTBhgnR3d4sIc364/P1ipLu7WwICAmTbtm1Km8ViERcXFzl8+LCIiNTU1AgA+f7775UxX375pWg0Grlx44aIiOzatUu8vb2V2IuI5OfnS0REhPJ80aJFMn/+fNV8DAaDLF++fEj30Zb1dzH4dxcvXhQA0tjYqLSFhITIjh07HroN4z+4hxVBCxYseOg2zP2h8U/yfsGCBfL000+r2pj3toO3w/XS0dGBqqoqJCcnK20ODg5ITk5GeXm5FWc28t2+fRsA4OPjo2o/dOgQ9Ho9pk2bhrVr1+LevXtKX3l5OSIjI+Hv76+0paSkoL29HVevXlXG9D5ePWPs/XjV1tYiKCgIYWFhSE9PR1NTEwCgqqoKnZ2dqphNnjwZwcHBSswY96HR0dGBgwcP4uWXX4ZGo1HamfPDr76+Hi0tLao4eXp6wmAwqPLcy8sLM2fOVMYkJyfDwcEBFRUVypiEhARotVplTEpKCkwmE3777TdlDI/H4G7fvg2NRgMvLy9Ve2FhIXx9fRETE4Nt27apbv1k/B9dWVkZ/Pz8EBERgRUrVqCtrU3pY+4/Hjdv3sSJEyewdOnSPn3Me9vgZO0J2JJff/0VXV1dqgsQAPD398f169etNKuRr7u7G6tWrcKsWbMwbdo0pf35559HSEgIgoKCcOnSJeTn58NkMuHTTz8FALS0tPR7LHr6BhrT3t6O+/fvw83NbTh3zSYZDAbs378fERERaG5uxqZNmzB79mxcuXIFLS0t0Gq1fS5E/P39B41pT99AY+w57n/32WefwWKx4MUXX1TamPOPR0+s+otT7zj6+fmp+p2cnODj46MaExoa2uc1evq8vb0fejx6XoP++jvE/Px8LFmyBDqdTml/9dVXMWPGDPj4+OD8+fNYu3YtmpubUVRUBIDxf1Tz5s3DwoULERoaCrPZjHXr1iE1NRXl5eVwdHRk7j8mBw4cwOjRo7Fw4UJVO/PedrAIomG3cuVKXLlyBWfPnlW1Z2dnK48jIyMRGBiIpKQkmM1mTJgw4XFP8z8jNTVVeRwVFQWDwYCQkBAcPXqUF8iP0Z49e5CamoqgoCCljTlP9qazsxOLFi2CiKC4uFjVt3r1auVxVFQUtFotli9fjoKCAri4uDzuqf5nLF68WHkcGRmJqKgoTJgwAWVlZUhKSrLizOzL3r17kZ6eDldXV1U789528Ha4XvR6PRwdHfuslHXz5k0EBARYaVYjW05ODr744guUlpZi3LhxA441GAwAgLq6OgBAQEBAv8eip2+gMTqdjhf8/8/LywuTJk1CXV0dAgIC0NHRAYvFohrTO8cZ93+vsbERp0+fxrJlywYcx5wfHj2xGuhcHhAQgNbWVlX/gwcPcOvWrSH5WeB7xv8KoMbGRpw6dUr1KVB/DAYDHjx4gIaGBgCM/1AJCwuDXq9XnWeY+8Pru+++g8lkGvQ9AGDeWxOLoF60Wi1iY2Nx5swZpa27uxtnzpxBfHy8FWc28ogIcnJycOzYMZSUlPT5aLc/RqMRABAYGAgAiI+Px+XLl1Un65430ieeeEIZ0/t49Yzh8fqfu3fvwmw2IzAwELGxsXB2dlbFzGQyoampSYkZ4/7v7du3D35+fpg/f/6A45jzwyM0NBQBAQGqOLW3t6OiokKV5xaLBVVVVcqYkpISdHd3K8VpfHw8vv32W3R2dipjTp06hYiICHh7eytjeDz66imAamtrcfr0afj6+g66jdFohIODg3KrFuM/NH7++We0tbWpzjPM/eG1Z88exMbGYvr06YOOZd5bkbVXZrA1R44cERcXF9m/f7/U1NRIdna2eHl5qVZrosGtWLFCPD09paysTLUM5L1790REpK6uTjZv3iyVlZVSX18vx48fl7CwMElISFBeo2e54Llz54rRaJSvvvpKxowZ0+9ywXl5eXLt2jV5//337XK54N7WrFkjZWVlUl9fL+fOnZPk5GTR6/XS2toqIn8tkR0cHCwlJSVSWVkp8fHxEh8fr2zPuP87XV1dEhwcLPn5+ap25vzQunPnjlRXV0t1dbUAkKKiIqmurlZWHyssLBQvLy85fvy4XLp0SRYsWNDvEtkxMTFSUVEhZ8+elfDwcNUywRaLRfz9/eWFF16QK1euyJEjR8Td3b3PUrVOTk6yfft2uXbtmmzcuNEulqodKP4dHR3y7LPPyrhx48RoNKreA3pWvDp//rzs2LFDjEajmM1mOXjwoIwZM0YyMjKU78H492+g2N+5c0dee+01KS8vl/r6ejl9+rTMmDFDwsPD5Y8//lBeg7n/aAY774j8tcS1u7u7FBcX99meeW9bWAT1Y+fOnRIcHCxarVbi4uLkwoUL1p7SiAOg3699+/aJiEhTU5MkJCSIj4+PuLi4yMSJEyUvL0/1P1NERBoaGiQ1NVXc3NxEr9fLmjVrpLOzUzWmtLRUoqOjRavVSlhYmPI97FVaWpoEBgaKVquVsWPHSlpamtTV1Sn99+/fl1deeUW8vb3F3d1dnnvuOWlubla9BuP+6L7++msBICaTSdXOnB9apaWl/Z5jMjMzReSvZbLXr18v/v7+4uLiIklJSX2OSVtbmyxZskRGjRolOp1OXnrpJblz545qzA8//CBPPfWUuLi4yNixY6WwsLDPXI4ePSqTJk0SrVYrU6dOlRMnTgzbftuKgeJfX1//0PeAnv+ZVVVVJQaDQTw9PcXV1VWmTJkib731lupCXYTx789Asb93757MnTtXxowZI87OzhISEiJZWVl9fpHL3H80g513RER2794tbm5uYrFY+mzPvLctGhGRYf2oiYiIiIiIyIbwb4KIiIiIiMiusAgiIiIiIiK7wiKIiIiIiIjsCosgIiIiIiKyKyyCiIiIiIjIrrAIIiIiIiIiu8IiiIiIiIiI7AqLICIiIiIisissgoiIaMRqaGiARqOB0Wi09lSIiGgE0YiIWHsSREREj6Krqwu//PIL9Ho9nJycrD0dIiIaIVgEERHRiNTR0QGtVmvtaRAR0QjE2+GIiMgmJCYmIicnBzk5OfD09IRer8f69evR87u68ePHY8uWLcjIyIBOp0N2dna/t8NdvXoVzzzzDHQ6HUaPHo3Zs2fDbDYr/R9++CGmTJkCV1dXTJ48Gbt27Xrcu0pERFbGeweIiMhmHDhwAEuXLsXFixdRWVmJ7OxsBAcHIysrCwCwfft2bNiwARs3bux3+xs3biAhIQGJiYkoKSmBTqfDuXPn8ODBAwDAoUOHsGHDBrz33nuIiYlBdXU1srKy4OHhgczMzMe2n0REZF28HY6IiGxCYmIiWltbcfXqVWg0GgDAG2+8gc8//xw1NTUYP348YmJicOzYMWWbhoYGhIaGorq6GtHR0Vi3bh2OHDkCk8kEZ2fnPt9j4sSJ2LJlC5YsWaK0bd26FSdPnsT58+eHfyeJiMgm8HY4IiKyGU8++aRSAAFAfHw8amtr0dXVBQCYOXPmgNsbjUbMnj273wLo999/h9lsxtKlSzFq1Cjla+vWrarb5YiI6L+Pt8MREdGI4eHhMWC/m5vbQ/vu3r0LAPjggw9gMBhUfY6Ojv9+ckRENGKwCCIiIptRUVGhen7hwgWEh4f/4yIlKioKBw4cQGdnZ59Pg/z9/REUFISffvoJ6enpQzZnIiIaeXg7HBER2YympiasXr0aJpMJhw8fxs6dO5Gbm/uPt8/JyUF7ezsWL16MyspK1NbW4qOPPoLJZAIAbNq0CQUFBXj33Xfx448/4vLly9i3bx+KioqGa5eIiMgG8ZMgIiKyGRkZGbh//z7i4uLg6OiI3NxcZGdn/+PtfX19UVJSgry8PMyZMweOjo6Ijo7GrFmzAADLli2Du7s7tm3bhry8PHh4eCAyMhKrVq0apj0iIiJbxNXhiIjIJiQmJiI6OhrvvPOOtadCRET/cbwdjoiIiIiI7AqLICIiIiIisiu8HY6IiIiIiOwKPwkiIiIiIiK7wiKIiIiIiIjsCosgIiIiIiKyKyyCiIiIiIjIrrAIIiIiIiIiu8IiiIiIiIiI7AqLICIiIiIisissgoiIiIiIyK78HwzEEBG+oA0EAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# Визуализация данных после обработки\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df[\"price\"], df[\"carat\"])\n",
    "plt.xlabel(\"price\")\n",
    "plt.ylabel(\"carat\")\n",
    "plt.title(\"Scatter Plot of Price vs Carat\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удаление строк с пустыми значениями"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cleaned = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разбиение набора данных на обучающую, контрольную и тестовую выборки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применение методов приращения данных (аугментации)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 32365\n",
      "Размер контрольной выборки: 10789\n",
      "Размер тестовой выборки: 10789\n",
      "Распределение price в обучающей выборке:\n",
      "price\n",
      "789      80\n",
      "605      79\n",
      "544      72\n",
      "552      72\n",
      "828      71\n",
      "         ..\n",
      "9942      1\n",
      "7787      1\n",
      "18663     1\n",
      "7979      1\n",
      "8164      1\n",
      "Name: count, Length: 9496, dtype: int64\n",
      "\n",
      "Распределение price в контрольной выборке:\n",
      "price\n",
      "625      34\n",
      "828      31\n",
      "605      30\n",
      "789      26\n",
      "544      26\n",
      "         ..\n",
      "4188      1\n",
      "7541      1\n",
      "3498      1\n",
      "3314      1\n",
      "12196     1\n",
      "Name: count, Length: 5383, dtype: int64\n",
      "\n",
      "Распределение price в тестовой выборке:\n",
      "price\n",
      "802     33\n",
      "844     29\n",
      "776     29\n",
      "675     26\n",
      "645     25\n",
      "        ..\n",
      "1567     1\n",
      "5529     1\n",
      "2031     1\n",
      "417      1\n",
      "5431     1\n",
      "Name: count, Length: 5338, dtype: int64\n",
      "\n",
      "Распределение price в обучающей выборке после oversampling:\n",
      "price\n",
      "5076    80\n",
      "1789    80\n",
      "3931    80\n",
      "1263    80\n",
      "2026    80\n",
      "        ..\n",
      "3678    80\n",
      "4592    80\n",
      "516     80\n",
      "7152    80\n",
      "2353    80\n",
      "Name: count, Length: 9496, dtype: int64\n",
      "\n",
      "Распределение price в контрольной выборке после oversampling:\n",
      "price\n",
      "966      34\n",
      "13638    34\n",
      "3669     34\n",
      "1052     34\n",
      "2818     34\n",
      "         ..\n",
      "4032     34\n",
      "544      34\n",
      "3362     34\n",
      "6559     34\n",
      "792      34\n",
      "Name: count, Length: 5383, dtype: int64\n",
      "\n",
      "Распределение price в тестовой выборке после oversampling:\n",
      "price\n",
      "3742     33\n",
      "559      33\n",
      "8403     33\n",
      "1238     33\n",
      "1243     33\n",
      "         ..\n",
      "1149     33\n",
      "2401     33\n",
      "958      33\n",
      "702      33\n",
      "14618    33\n",
      "Name: count, Length: 5338, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки\n",
    "train_df, test_df = train_test_split(df_cleaned, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))\n",
    "\n",
    "def check_balance(df, name):\n",
    "    counts = df[\"price\"].value_counts()\n",
    "    print(f\"Распределение price в {name}:\")\n",
    "    print(counts)\n",
    "    print()\n",
    "\n",
    "\n",
    "check_balance(train_df, \"обучающей выборке\")\n",
    "check_balance(val_df, \"контрольной выборке\")\n",
    "check_balance(test_df, \"тестовой выборке\")\n",
    "\n",
    "def oversample(df):\n",
    "    X = df.drop(\"price\", axis=1)\n",
    "    y = df[\"price\"]\n",
    "\n",
    "    oversampler = RandomOverSampler(random_state=42)\n",
    "    X_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n",
    "\n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "\n",
    "train_df_oversampled = oversample(train_df)\n",
    "val_df_oversampled = oversample(val_df)\n",
    "test_df_oversampled = oversample(test_df)\n",
    "\n",
    "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n",
    "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n",
    "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение price в обучающей выборке после oversampling:\n",
      "price\n",
      "5076    80\n",
      "1789    80\n",
      "3931    80\n",
      "1263    80\n",
      "2026    80\n",
      "        ..\n",
      "3678    80\n",
      "4592    80\n",
      "516     80\n",
      "7152    80\n",
      "2353    80\n",
      "Name: count, Length: 9496, dtype: int64\n",
      "\n",
      "Распределение price в контрольной выборке после oversampling:\n",
      "price\n",
      "966      34\n",
      "13638    34\n",
      "3669     34\n",
      "1052     34\n",
      "2818     34\n",
      "         ..\n",
      "4032     34\n",
      "544      34\n",
      "3362     34\n",
      "6559     34\n",
      "792      34\n",
      "Name: count, Length: 5383, dtype: int64\n",
      "\n",
      "Распределение price в тестовой выборке после oversampling:\n",
      "price\n",
      "3742     33\n",
      "559      33\n",
      "8403     33\n",
      "1238     33\n",
      "1243     33\n",
      "         ..\n",
      "1149     33\n",
      "2401     33\n",
      "958      33\n",
      "702      33\n",
      "14618    33\n",
      "Name: count, Length: 5338, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "\n",
    "def oversample(df):\n",
    "    X = df.drop(\"price\", axis=1)\n",
    "    y = df[\"price\"]\n",
    "\n",
    "    oversampler = RandomOverSampler(random_state=42)\n",
    "    X_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n",
    "\n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "\n",
    "train_df_oversampled = oversample(train_df)\n",
    "val_df_oversampled = oversample(val_df)\n",
    "test_df_oversampled = oversample(test_df)\n",
    "\n",
    "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n",
    "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n",
    "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")"
   ]
  }
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