{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Цель работы\n",
    "Мы будем кластеризовать автомобили, основываясь на их характеристиках, с целью выделения групп автомобилей с похожими свойствами. Это может быть полезно, например, для автосалонов или производителей для сегментации рынка."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# загрузим датасет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Price</th>\n",
       "      <th>Levy</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>Prod. year</th>\n",
       "      <th>Category</th>\n",
       "      <th>Leather interior</th>\n",
       "      <th>Fuel type</th>\n",
       "      <th>Engine volume</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Gear box type</th>\n",
       "      <th>Drive wheels</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Wheel</th>\n",
       "      <th>Color</th>\n",
       "      <th>Airbags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45654403</td>\n",
       "      <td>13328</td>\n",
       "      <td>1399</td>\n",
       "      <td>LEXUS</td>\n",
       "      <td>RX 450</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>3.5</td>\n",
       "      <td>186005 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44731507</td>\n",
       "      <td>16621</td>\n",
       "      <td>1018</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Equinox</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>3</td>\n",
       "      <td>192000 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45774419</td>\n",
       "      <td>8467</td>\n",
       "      <td>-</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2006</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>200000 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Variator</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Right-hand drive</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45769185</td>\n",
       "      <td>3607</td>\n",
       "      <td>862</td>\n",
       "      <td>FORD</td>\n",
       "      <td>Escape</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.5</td>\n",
       "      <td>168966 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45809263</td>\n",
       "      <td>11726</td>\n",
       "      <td>446</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2014</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>91901 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19232</th>\n",
       "      <td>45798355</td>\n",
       "      <td>8467</td>\n",
       "      <td>-</td>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>CLK 200</td>\n",
       "      <td>1999</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>CNG</td>\n",
       "      <td>2.0 Turbo</td>\n",
       "      <td>300000 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Rear</td>\n",
       "      <td>02-Mar</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19233</th>\n",
       "      <td>45778856</td>\n",
       "      <td>15681</td>\n",
       "      <td>831</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Sonata</td>\n",
       "      <td>2011</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>2.4</td>\n",
       "      <td>161600 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Red</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>45804997</td>\n",
       "      <td>26108</td>\n",
       "      <td>836</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Tucson</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>116365 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Grey</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19235</th>\n",
       "      <td>45793526</td>\n",
       "      <td>5331</td>\n",
       "      <td>1288</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Captiva</td>\n",
       "      <td>2007</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>51258 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19236</th>\n",
       "      <td>45813273</td>\n",
       "      <td>470</td>\n",
       "      <td>753</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Sonata</td>\n",
       "      <td>2012</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.4</td>\n",
       "      <td>186923 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19237 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  Price  Levy   Manufacturer    Model  Prod. year   Category  \\\n",
       "0      45654403  13328  1399          LEXUS   RX 450        2010       Jeep   \n",
       "1      44731507  16621  1018      CHEVROLET  Equinox        2011       Jeep   \n",
       "2      45774419   8467     -          HONDA      FIT        2006  Hatchback   \n",
       "3      45769185   3607   862           FORD   Escape        2011       Jeep   \n",
       "4      45809263  11726   446          HONDA      FIT        2014  Hatchback   \n",
       "...         ...    ...   ...            ...      ...         ...        ...   \n",
       "19232  45798355   8467     -  MERCEDES-BENZ  CLK 200        1999      Coupe   \n",
       "19233  45778856  15681   831        HYUNDAI   Sonata        2011      Sedan   \n",
       "19234  45804997  26108   836        HYUNDAI   Tucson        2010       Jeep   \n",
       "19235  45793526   5331  1288      CHEVROLET  Captiva        2007       Jeep   \n",
       "19236  45813273    470   753        HYUNDAI   Sonata        2012      Sedan   \n",
       "\n",
       "      Leather interior Fuel type Engine volume    Mileage  Cylinders  \\\n",
       "0                  Yes    Hybrid           3.5  186005 km        6.0   \n",
       "1                   No    Petrol             3  192000 km        6.0   \n",
       "2                   No    Petrol           1.3  200000 km        4.0   \n",
       "3                  Yes    Hybrid           2.5  168966 km        4.0   \n",
       "4                  Yes    Petrol           1.3   91901 km        4.0   \n",
       "...                ...       ...           ...        ...        ...   \n",
       "19232              Yes       CNG     2.0 Turbo  300000 km        4.0   \n",
       "19233              Yes    Petrol           2.4  161600 km        4.0   \n",
       "19234              Yes    Diesel             2  116365 km        4.0   \n",
       "19235              Yes    Diesel             2   51258 km        4.0   \n",
       "19236              Yes    Hybrid           2.4  186923 km        4.0   \n",
       "\n",
       "      Gear box type Drive wheels   Doors             Wheel   Color  Airbags  \n",
       "0         Automatic          4x4  04-May        Left wheel  Silver       12  \n",
       "1         Tiptronic          4x4  04-May        Left wheel   Black        8  \n",
       "2          Variator        Front  04-May  Right-hand drive   Black        2  \n",
       "3         Automatic          4x4  04-May        Left wheel   White        0  \n",
       "4         Automatic        Front  04-May        Left wheel  Silver        4  \n",
       "...             ...          ...     ...               ...     ...      ...  \n",
       "19232        Manual         Rear  02-Mar        Left wheel  Silver        5  \n",
       "19233     Tiptronic        Front  04-May        Left wheel     Red        8  \n",
       "19234     Automatic        Front  04-May        Left wheel    Grey        4  \n",
       "19235     Automatic        Front  04-May        Left wheel   Black        4  \n",
       "19236     Automatic        Front  04-May        Left wheel   White       12  \n",
       "\n",
       "[19237 rows x 18 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"/mnt/d/AIMLabs/AIM-PIbd-31-Kouvshinoff-T-A/static/csv/car_price_prediction.csv\", sep=\",\")\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Предобработка данных\n",
    "Мы удалим неинформативные столбцы, такие как ID, преобразуем категориальные переменные в числовые (one-hot encoding), а также нормализуем данные для дальнейшего анализа."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnsupportedCUDAError",
     "evalue": "A GPU with NVIDIA Volta™ (Compute Capability 7.0) or newer architecture is required.\nDetected GPU 0: NVIDIA GeForce GTX 1060 6GB\u0000                                                                                                                                                                                                                                    \nDetected Compute Capability: 6.1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnsupportedCUDAError\u001b[0m                      Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 8\u001b[0m\n\u001b[1;32m      5\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mget_dummies(df, drop_first\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# Нормализация числовых данных\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m StandardScaler\n\u001b[1;32m     10\u001b[0m scaler \u001b[38;5;241m=\u001b[39m StandardScaler()\n\u001b[1;32m     11\u001b[0m df_scaled \u001b[38;5;241m=\u001b[39m scaler\u001b[38;5;241m.\u001b[39mfit_transform(df)\n",
      "File \u001b[0;32m/mnt/d/AIMLabs/AIM-PIbd-31-Kouvshinoff-T-A/wslenv/lib/python3.12/site-packages/cuml/__init__.py:17\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# Copyright (c) 2022-2023, NVIDIA CORPORATION.\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Base, UniversalBase\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mavailable_devices\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m is_cuda_available\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# GPU only packages\u001b[39;00m\n",
      "File \u001b[0;32m/mnt/d/AIMLabs/AIM-PIbd-31-Kouvshinoff-T-A/wslenv/lib/python3.12/site-packages/cuml/internals/__init__.py:18\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# Copyright (c) 2019-2023, NVIDIA CORPORATION.\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mavailable_devices\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m is_cuda_available\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase_helpers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BaseMetaClass, _tags_class_and_instance\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi_decorators\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     20\u001b[0m     _deprecate_pos_args,\n\u001b[1;32m     21\u001b[0m     api_base_fit_transform,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     33\u001b[0m     exit_internal_api,\n\u001b[1;32m     34\u001b[0m )\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi_context_managers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     36\u001b[0m     in_internal_api,\n\u001b[1;32m     37\u001b[0m     set_api_output_dtype,\n\u001b[1;32m     38\u001b[0m     set_api_output_type,\n\u001b[1;32m     39\u001b[0m )\n",
      "File \u001b[0;32m/mnt/d/AIMLabs/AIM-PIbd-31-Kouvshinoff-T-A/wslenv/lib/python3.12/site-packages/cuml/internals/base_helpers.py:20\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01minspect\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Parameter, signature\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi_decorators\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     21\u001b[0m     api_base_return_generic,\n\u001b[1;32m     22\u001b[0m     api_base_return_array,\n\u001b[1;32m     23\u001b[0m     api_base_return_sparse_array,\n\u001b[1;32m     24\u001b[0m     api_base_return_any,\n\u001b[1;32m     25\u001b[0m     api_return_any,\n\u001b[1;32m     26\u001b[0m     _deprecate_pos_args,\n\u001b[1;32m     27\u001b[0m )\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CumlArray\n\u001b[1;32m     29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray_sparse\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SparseCumlArray\n",
      "File \u001b[0;32m/mnt/d/AIMLabs/AIM-PIbd-31-Kouvshinoff-T-A/wslenv/lib/python3.12/site-packages/cuml/internals/api_decorators.py:24\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m# TODO: Try to resolve circular import that makes this necessary:\u001b[39;00m\n\u001b[0;32m---> 24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m input_utils \u001b[38;5;28;01mas\u001b[39;00m iu\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi_context_managers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BaseReturnAnyCM\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi_context_managers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BaseReturnArrayCM\n",
      "File \u001b[0;32m/mnt/d/AIMLabs/AIM-PIbd-31-Kouvshinoff-T-A/wslenv/lib/python3.12/site-packages/cuml/internals/input_utils.py:20\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcollections\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m namedtuple\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Literal\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CumlArray\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray_sparse\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SparseCumlArray\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mglobal_settings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GlobalSettings\n",
      "File \u001b[0;32m/mnt/d/AIMLabs/AIM-PIbd-31-Kouvshinoff-T-A/wslenv/lib/python3.12/site-packages/cuml/internals/array.py:21\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01moperator\u001b[39;00m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpickle\u001b[39;00m\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mglobal_settings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GlobalSettings\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlogger\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m debug\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmem_type\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MemoryType, MemoryTypeError\n",
      "File \u001b[0;32m/mnt/d/AIMLabs/AIM-PIbd-31-Kouvshinoff-T-A/wslenv/lib/python3.12/site-packages/cuml/internals/global_settings.py:20\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mthreading\u001b[39;00m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mavailable_devices\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m is_cuda_available\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdevice_type\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DeviceType\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmem_type\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MemoryType\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msafe_imports\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cpu_only_import, gpu_only_import\n",
      "File \u001b[0;32m/mnt/d/AIMLabs/AIM-PIbd-31-Kouvshinoff-T-A/wslenv/lib/python3.12/site-packages/cuml/internals/device_type.py:19\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# Copyright (c) 2022-2023, NVIDIA CORPORATION.\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01menum\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Enum, auto\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmem_type\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MemoryType\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mDeviceTypeError\u001b[39;00m(\u001b[38;5;167;01mException\u001b[39;00m):\n\u001b[1;32m     23\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"An exception thrown to indicate bad device type selection\"\"\"\u001b[39;00m\n",
      "File \u001b[0;32m/mnt/d/AIMLabs/AIM-PIbd-31-Kouvshinoff-T-A/wslenv/lib/python3.12/site-packages/cuml/internals/mem_type.py:22\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdevice_support\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GPU_ENABLED\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcuml\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minternals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msafe_imports\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cpu_only_import, gpu_only_import\n\u001b[0;32m---> 22\u001b[0m cudf \u001b[38;5;241m=\u001b[39m \u001b[43mgpu_only_import\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcudf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     23\u001b[0m cp \u001b[38;5;241m=\u001b[39m gpu_only_import(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcupy\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     24\u001b[0m cpx_sparse \u001b[38;5;241m=\u001b[39m gpu_only_import(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcupyx.scipy.sparse\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/mnt/d/AIMLabs/AIM-PIbd-31-Kouvshinoff-T-A/wslenv/lib/python3.12/site-packages/cuml/internals/safe_imports.py:362\u001b[0m, in \u001b[0;36mgpu_only_import\u001b[0;34m(module, alt)\u001b[0m\n\u001b[1;32m    336\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"A function used to import modules required only in GPU installs\u001b[39;00m\n\u001b[1;32m    337\u001b[0m \n\u001b[1;32m    338\u001b[0m \u001b[38;5;124;03mThis function will attempt to import a module with the given name, but it\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    359\u001b[0m \u001b[38;5;124;03m    UnavailableMeta.\u001b[39;00m\n\u001b[1;32m    360\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    361\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m GPU_ENABLED:\n\u001b[0;32m--> 362\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimportlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodule\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    363\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    364\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m safe_import(\n\u001b[1;32m    365\u001b[0m         module,\n\u001b[1;32m    366\u001b[0m         msg\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodule\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not installed in non GPU-enabled installations\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    367\u001b[0m         alt\u001b[38;5;241m=\u001b[39malt,\n\u001b[1;32m    368\u001b[0m     )\n",
      "File \u001b[0;32m/usr/lib/python3.12/importlib/__init__.py:90\u001b[0m, in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m     88\u001b[0m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m     89\u001b[0m         level \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/mnt/d/AIMLabs/AIM-PIbd-31-Kouvshinoff-T-A/wslenv/lib/python3.12/site-packages/cudf/__init__.py:20\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcudf\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgpu_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m validate_setup\n\u001b[1;32m     19\u001b[0m _setup_numba()\n\u001b[0;32m---> 20\u001b[0m \u001b[43mvalidate_setup\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcupy\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumba\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config \u001b[38;5;28;01mas\u001b[39;00m numba_config, cuda\n",
      "File \u001b[0;32m/mnt/d/AIMLabs/AIM-PIbd-31-Kouvshinoff-T-A/wslenv/lib/python3.12/site-packages/cudf/utils/gpu_utils.py:89\u001b[0m, in \u001b[0;36mvalidate_setup\u001b[0;34m()\u001b[0m\n\u001b[1;32m     85\u001b[0m     device_name \u001b[38;5;241m=\u001b[39m deviceGetName(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     86\u001b[0m     minor_version \u001b[38;5;241m=\u001b[39m getDeviceAttribute(\n\u001b[1;32m     87\u001b[0m         cudaDeviceAttr\u001b[38;5;241m.\u001b[39mcudaDevAttrComputeCapabilityMinor, \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m     88\u001b[0m     )\n\u001b[0;32m---> 89\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m UnsupportedCUDAError(\n\u001b[1;32m     90\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mA GPU with NVIDIA Volta™ (Compute Capability 7.0) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     91\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor newer architecture is required.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     92\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDetected GPU 0: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdevice_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     93\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDetected Compute Capability: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmajor_version\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mminor_version\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     94\u001b[0m     )\n\u001b[1;32m     96\u001b[0m cuda_runtime_version \u001b[38;5;241m=\u001b[39m runtimeGetVersion()\n\u001b[1;32m     98\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cuda_runtime_version \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m11000\u001b[39m:\n\u001b[1;32m     99\u001b[0m     \u001b[38;5;66;03m# Require CUDA Runtime version 11.0 or greater.\u001b[39;00m\n",
      "\u001b[0;31mUnsupportedCUDAError\u001b[0m: A GPU with NVIDIA Volta™ (Compute Capability 7.0) or newer architecture is required.\nDetected GPU 0: NVIDIA GeForce GTX 1060 6GB\u0000                                                                                                                                                                                                                                    \nDetected Compute Capability: 6.1"
     ]
    }
   ],
   "source": [
    "# Удаляем неинформативный столбец ID\n",
    "df = df.drop(columns=[\"ID\"])\n",
    "\n",
    "# Преобразование категориальных данных в числовые с помощью one-hot encoding\n",
    "df = pd.get_dummies(df, drop_first=True)\n",
    "\n",
    "# Нормализация числовых данных\n",
    "from cuml.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "df_scaled = scaler.fit_transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Визуализация данных с помощью PCA (снижение размерности)\n",
    "Для визуализации мы применим метод PCA, который уменьшит количество измерений до двух, сохраняя при этом максимальное количество информации.  \n",
    "Ключевые термины:\n",
    "- PCA (Principal Component Analysis) — метод снижения размерности, который находит новые оси в данных, вдоль которых разброс максимален, и проецирует данные на эти оси.\n",
    "- Снижение размерности — процесс упрощения данных за счёт уменьшения числа признаков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Импортируем PCA и визуализируем данные\n",
    "from cuml.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Применяем PCA для снижения размерности до 2\n",
    "pca = PCA(n_components=2)\n",
    "df_pca = pca.fit_transform(df_scaled)\n",
    "\n",
    "# Визуализация\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(df_pca[:, 0], df_pca[:, 1], c='blue', edgecolor='k', alpha=0.6)\n",
    "plt.title(\"PCA: Визуализация данных после снижения размерности\")\n",
    "plt.xlabel(\"Главная компонента 1\")\n",
    "plt.ylabel(\"Главная компонента 2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Количество кластеров\n",
    "Количество кластеров напрямую влияет на результаты кластеризации, так как оно определяет, сколько групп или сегментов будет выделено в данных. Оптимальный выбор количества кластеров важен, чтобы обеспечить баланс между точностью кластеризации и интерпретируемостью результатов.  \n",
    "# Зачем выбирать количество кластеров?\n",
    "## Оптимальная сегментация данных\n",
    "Разное количество кластеров может приводить к слишком мелкому делению (много мелких кластеров) или слишком крупному (слишком обобщённые кластеры).\n",
    "-Слишком мало кластеров: важные различия в данных могут быть упущены.\n",
    "-Слишком много кластеров: анализ становится сложным, и кластеры могут быть избыточно раздроблены.\n",
    "## Интерпретируемость результатов\n",
    "Оптимальное количество кластеров делает результаты понятными и полезными. Например, выделение 3-5 кластеров может быть удобно для анализа, тогда как 15-20 кластеров усложнят интерпретацию.\n",
    "## Избежание переобучения или недообучения\n",
    "Количество кластеров влияет на обобщающую способность модели. Слишком большое количество кластеров может привести к переобучению (модель подстраивается под шум), а слишком малое — к упрощению и игнорированию важных данных.\n",
    "## Практическая применимость\n",
    "В бизнес-задачах обычно требуется понятное разделение данных. Например, если мы сегментируем клиентов, 3-5 кластеров проще использовать для таргетинга, чем 20."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Определение оптимального количества кластеров\n",
    "Для выбора количества кластеров мы применим:  \n",
    "- Метод локтя — измеряет инерцию (размерность ошибок внутри кластеров).\n",
    "- Коэффициент силуэта — показывает, насколько хорошо объекты распределены между кластерами.\n",
    "  \n",
    "Ключевые термины:  \n",
    "- Инерция — сумма квадратов расстояний от точек до центроидов их кластеров. Чем меньше, тем лучше.\n",
    "- Коэффициент силуэта — оценивает плотность внутри кластеров и разницу между ними (от -1 до 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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09nFxcRg6dCjmzJmDXbt2ITMzE7Nnz8Y777xTqSs7VLqxY8di6tSpSE1NxYABA2Bubl5qO4VCgUaNGmHXrl2lrre0tKzGKv8XWsoaU1wRqampaNq06esp6F937tzBX3/9hZEjR2Lu3LnYsmVLlfYze/ZsREVFYcmSJVi8eHGlty8oKMCDBw+UY2/Lk5qaCmNjYxgZGZXZpmnTptiyZQs++eQTdOzYUWVdaWGrNJ999hn69u0LuVyOmzdv4uuvv4YgCCX6ae3atWjevDmkUinOnTuH77//HgDw888/l9jn81ftKqKqVzNL880332DBggWYNGkSlixZAgsLC4jFYsyaNavCv0yEhIRg6tSpMDIywn//+1+MGDGi1F/UKlp38VXwis54s2nTJsTHx+PEiROlrp82bRpOnDiB2bNnlzl+vfhYP/30U/Tr16/UNi+Gq9ep+F6G3Nxc7N+/HyNHjsTRo0fRp08fyOVy9OnTB+np6Zg3bx5atGgBIyMjpKSkYMKECRX+OYWHh+OPP/7Ahx9+iGnTpuHMmTOltktNTS2zDwC8tnoqo3ify5cvh5ubW6ltygv6Zb3/Kvq+7Nu3Lz777DMAQHJyMr799lv4+PggLCys0p9h0gwMzTVgwIAByhvNSiOVSjF//nzs3r0bGRkZaNOmDb799lvl3dtXrlyBXC7Hf//7X+WfRT/99FMMHToUMpkMEomkJg6j1nr77bcxffp0XL58GXv37i2znZOTEwIDA9GlS5fXcsJr2rQpAgMDkZ2drXK1+c6dO8r1zwsLC4OVlRVsbW2r9HoymQyxsbHo37//S2sCgKioqBLr7ty5g4YNG5YImYcPH0a3bt3g7++PDz/8EO+9916V/gRpaGiIjRs34urVqzAzM8PChQtx7do1fPrppxXa/tq1a5DJZHB3d69Q+1u3bimv6r/MuHHjkJiYiMWLF2PHjh2oX78+3nvvvQq9BgC0atUKvXv3BgD069cPUqkUX375JZYuXapy05yHh4ey9oEDB+LatWs4fvx4qft0dHTEyZMnS7x3oqOjoVAolL9YNW3aFAqFAjExMSrH+vDhQ2RkZJR4jxVfPX0+FERHRwP43y9rv//+O3x8fLBp0yaVbTMyMpQ3VJanT58+WLduHfLz83Ho0CFMmzZNOYNEVeq+desWLC0tKzSMJi8vD4sXL8aMGTPK/AXSwMAAf/75J6Kjo5GUlARBEPDw4UOVn3uzZs0AABKJRPnzLYuTkxMiIyPLra0ynn/NoUOHIjg4GN9//z369OmDGzduIDo6Gtu2bcP48eOV7Sp703BAQACGDBkCHR0dDBo0CJs2bcLkyZNV2iQnJyM7O/uln6WK1lPcp6+jr5ycnAAUDTEq7+dTLCYmBo6OjsrnsbGxr/R5sra2VnltV1dXeHt749ChQxgzZkxVD43UiMMzNMCHH36IoKAg7NmzB9evX8eIESPQv39/5Z+XOnXqpLzrVy6XIzMzEzt27EDv3r0ZmF8DY2NjrFu3DosWLcLgwYPLbDdy5EjI5fJSZz0oLCxU/om4ot566y3I5XL89NNPKstXrlwJkUik8ovWkydPcPbsWQwZMqRSr/G8P/74A8+ePcObb75ZZhtra2u4ublh27ZtKscTGRmJkydP4q233iqxTbdu3QAAM2bMgLe3N6ZPn16haa1K4+fnh8TEROzcuRO9e/dGp06dKrztvn37lP+5lycpKQn//PPPS/uiWHh4OBYuXIhly5ZhxIgR6N279yuNuy7um+ennSuNQqGAjo5OqevKeu+sWLECQFHoLm4HAKtWrXppu2L3799XmQEgKysL27dvh5ubm/Iqro6OTolhOPv27VOO360Ib29v6OjowMjICL/88gsuXLiAjRs3qhxfRevOzs7GsWPHKvSzBIAff/wRubm5ypkeXqZ58+bo1asXevfujS5duqisa9SoEXr27In169fjwYMHJbZ9/Pix8t/vvvsurl27VuqsH68ypKmYXC5HQUGBctqz4vfN8/sWBAE//vhjpfZb/NkeOHAgRo8ejc8++wwPHz5UabNnzx4AeGn/V7QeS0tLdO/eHZs3by4x80ll+6lTp05wcnLC999/r5zN6HnP/3yKFc/6U2zNmjUAoDwXV/bz9KLiz35p0yaSduCVZjVLTEzEli1bkJiYqLzq9Omnn+L48ePYsmULvvnmG+VVpZEjR2L69OmQy+Xw8vJ6pamSSJWvr2+5bXr06IHp06fD398fERER6Nu3LyQSCWJiYrBv3z78+OOPZY6HLs3gwYPh4+OD+fPnIz4+Hu3bt8fJkyfxxx9/YNasWcorJUFBQfjiiy/w7NkzWFpaYufOncp9FF8F3LlzJ95+++1Shxrk5eVh4cKF+Pnnn+Ht7Y2+ffu+tK7ly5djwIAB8PLywuTJk5VTzpmZmZU633ExkUiEgIAAuLm5YeHChfjuu+8q3BdA0VR3K1euxI4dOyo1hCQ3Nxdr167F6tWr0bx5c5U5Wov/s7x+/TqCgoLg5eWFdevWwd/fH/Xq1StxE+aL8vLyMHbsWPTs2VM5BreygoKCoKurqxyesWbNGnTo0KHEMJugoCCkpaUph2ecPn26zKvsb731Fnr37o358+cjLi4Obm5uOHPmDPbv34///Oc/yjGh7du3h6+vLzZs2ICMjAz06NEDISEh2LZtG4YNGwYfHx+V/TZv3hyTJ09GaGgoGjdujM2bN+Phw4cqQ0kGDRqEr7/+GhMnToS3tzdu3LiBXbt2Ka8SVla/fv3w3nvv4fPPP8fgwYNhbW1d4bp/++03LF68GE+fPsUXX3xRodc7efIkli5d+ko3dxZbu3YtunbtirZt22Lq1Klo1qwZHj58iKCgICQnJyvnrv7ss8/w+++/Y8SIEcopAtPT03H48GH88ssvaN++faVfu/g8kJubi0OHDiE+Ph6zZs0CUDT9m5OTEz799FOkpKTA1NQU+/fvLzEuuzJ+/PFHtGzZEh999BF+++03PHz4EAsXLkRAQABGjx6NFi1alLltZepZvXo1unbtio4dO2LatGlwdHREfHw8/vzzT0RERFS4XrFYjICAAAwYMACtW7fGxIkT0aRJE6SkpODs2bMwNTXFkSNHVLaJi4vDkCFD0L9/fwQFBWHnzp0YO3as8udT2c/TvXv3lD+nlJQU/PTTTzA1NeXNgNpMDTN21GkAhIMHDyqfHz16VAAgGBkZqTx0dXWFkSNHCoIgCA8ePBBcXFyEzz77TAgPDxfOnz8v9OjRQ+jVq1eZ0/BQ2SoyxZcglD2t2IYNG4ROnToJhoaGgomJidC2bVvh888/F+7fv1+i7cumnBMEQcjOzhZmz54t2NjYCBKJRHBxcRGWL1+u8nP19fUVAJT7iIuLK/U1kpOTBTs7O2HWrFlCZmZmifUoZUq2wMBAoUuXLoKhoaFgamoqDB48WLh165ZKm9KmvhIEQVi8eLGgq6srhIeHl1hX1vRQaWlpgo2NjTBmzBiV5RWZcq54eqvyHsWv6+HhIYwYMUK4c+dOiX29OLXbtGnThAYNGggpKSkljqOiU84VP8RisWBrayv4+voKycnJynbF78fih56enuDs7Cx89dVXglQqLbUuQRCEnJwclfeOs7OzsGzZshLTwclkMmHx4sWCo6OjIJFIBDs7O8HPz09lmrTnj+nEiRNCu3btBH19faFFixYl+j4/P1+YO3euYG1tLRgaGgpdunQRgoKCSq2xNKW939LS0gRLS0vh7bffrlTdb7/9tjBgwAAhODi4xOuUNeWctbW1kJubW25NLyprCsG7d+8K48ePF6ysrASJRCI0adJEGDRokMrUkIIgCE+ePBE+/PBDoUmTJoKenp7yvVDaNG4VmXKu+GFoaCi0atVKWLlypcp549atW0Lv3r0FY2NjoWHDhsLUqVOFa9eulXoML3p+yrnnbdu2TQAgHD58WPjnn38EZ2dnYdGiRcr36cv6qjL1REZGCm+//bZgbm4uGBgYCK6ursKCBQtKrfVl01gKgiBcvXpVeOedd4QGDRoI+vr6QtOmTYWRI0cKp0+fLtGnt27dEoYPHy6YmJgI9evXFz788MMSU2hW5vP0/M+pYcOGQt++fYWgoKAyayXNJxKE1/C3IaowkUiEgwcPYtiwYQCKvtBh3LhxuHnzZok/xRobG8PKygoLFizA8ePHERoaqlyXnJwMOzs7BAUFqUwzRLXPhAkTAKDEN5E9TyQSIS4u7pVuEtRW8fHxcHR0fOnxL1q0CPHx8S/tw7rOwcEBbdq0wdGjR9VdClGNWrRoERYvXozHjx9XeFw+1U0cnqFmHTp0gFwux6NHj5Tjx16Ul5envAGwWHHAro67jomIiIhIFUNzDcjJyUFsbKzyeVxcHCIiImBhYYHmzZtj3LhxGD9+PH744Qd06NABjx8/xunTp9GuXTsMHDgQAwcOxMqVK/H1119jzJgxyM7OxpdffommTZuiQ4cOajwyqgne3t7lthk3blyF56itbYyNjcs9/nbt2lX6652JiIiex+EZNeDcuXMlbhAAim4+27p1K2QyGf773/9i+/btSElJQcOGDeHp6YnFixejbdu2AIruUP7uu+8QHR2NevXqwcvLC99+++1Lb74gIqooDs+guorDM6iiGJqJiIiIiMrBeZqJiIiIiMrB0ExEREREVA7eCFiNFAoF7t+/DxMTkwp/Vz0RERER1RxBEJCdnQ0bG5sSs5U9j6G5Gt2/fx92dnbqLoOIiIiIypGUlARbW9sy1zM0VyMTExMART8EU1NTNVdDRERERC/KysqCnZ2dMreVhaG5GhUPyTA1NWVoJiIiItJg5Q2l5Y2ARERERETlYGgmIiIiIioHQzMRERERUTkYmomIiIiIysHQTERERERUDoZmIiIiIqJyMDQTEREREZWDoZmIiIiIqBwMzURERERE5WBoJiIiIiIqB0MzEREREVE5GJqJiIiIiMrB0ExEREREVA5ddRdAr4dcISAkLh2PsvPRyMQAHo4W0BGL1F0WERERUa3A0FwLHI98gMVHbuFBZr5ymbWZARYOboX+bazVWBkRERFR7cDhGVrueOQDfLAzXCUwA0BqZj4+2BmO45EP1FQZERERUe3B0KzF5AoBi4/cglDKuuJli4/cglxRWgsiIiIiqiiGZi0WEpde4grz8wQADzLzERKXXnNFEREREdVCDM1a7FF22YG5Ku2IiIiIqHQMzVqskYnBa21HRERERKVjaNZiHo4WsDYzwMsmljMzlMDD0aLGaiIiIiKqjRiatZiOWISFg1sBQJnBOfOZDL8GJ9RcUURERES1EEOzluvfxhrr3usIKzPVIRjWZgbo3bIRAGDBHzex5Z84dZRHREREVCvwy01qgf5trNGnlVWJbwQUi4Bvj0fhl/N3lVPPTenWTN3lEhEREWkdhuZaQkcsgpdTgxLL5/V3ha5YhJ/OxuK/f96GXCFgeg8nNVRIREREpL04PKOWE4lEmNu3OT7p5QIA8P/rDtaejVVzVURERETahaG5DhCJRJjdpznm9GkOAFh+IgqrT8eouSoiIiIi7cHQXId83MsFn/VzBQCsOBWNFaeiIQj8im0iIiKi8jA01zEzfZzhN6AFAGD16Rj8cJLBmYiIiKg8DM110PQeTvi/gS0BAD+djcW3x6MYnImIiIhegqG5jprSrRkW/fvFKL+cv4tvjt1mcCYiIiIqA0NzHTahiyOWDG0NANj4dxy+PnqLwZmIiIioFAzNddz7Xg745u22AIAt/8Rj4eGbDM5EREREL2BoJoztbI/v3m0HkQjYHpSA/zsUCYWCwZmIiIioGEMzAQBGvmGH5cPbQyQCdgUn4suDNxiciYiIiP7F0ExKwzvZYsXI9hCLgD2hSfh8/3XIGZyJiIiIGJpJ1dsdbLFqdAfoiEX4/UoyPtt3jcGZiIiI6jyGZiphSHsbrP43OB+4moI5v0WgUK5Qd1lEREREasPQTKUa2M4aa8d2gK5YhD8i7uOTvRGQMTgTERFRHcXQTGXq38YaP4/rCImOCH9ef4CPd19lcCYiIqI6iaGZXqpvayv88l4n6OmI8VdkKmbuCkdBIYMzERER1S0MzVSuXi0bY/34TtDTFePkrYeYsesKpIVydZdFREREVGPUGpovXLiAwYMHw8bGBiKRCIcOHSp3m7Vr16Jly5YwNDSEq6srtm/frrL+wIEDcHd3h7m5OYyMjODm5oYdO3Yo18tkMsybNw9t27aFkZERbGxsMH78eNy/f19lPw4ODhCJRCqPZcuWvZbj1kY+ro0QMN4d+rpiBN5+hP/suIJ8GYMzERER1Q1qDc25ublo37491q5dW6H269atg5+fHxYtWoSbN29i8eLFmDlzJo4cOaJsY2Fhgfnz5yMoKAjXr1/HxIkTMXHiRJw4cQIAkJeXh/DwcCxYsADh4eE4cOAAoqKiMGTIkBKv9/XXX+PBgwfKx0cfffR6DlxLdW9uic0T3oCBRIyzUY8xjcGZiIiI6giRIAgaMQmvSCTCwYMHMWzYsDLbeHt7o0uXLli+fLly2dy5cxEcHIyLFy+WuV3Hjh0xcOBALFmypNT1oaGh8PDwQEJCAuzt7QEUXWmeNWsWZs2aVeFjkEqlkEqlyudZWVmws7NDZmYmTE1NK7wfTRd09wkmbQ3FM5kcXZ0bYuN4dxjq6ai7LCIiIqJKy8rKgpmZWbl5TavGNEulUhgYGKgsMzQ0REhICGQyWYn2giDg9OnTiIqKQvfu3cvcb2ZmJkQiEczNzVWWL1u2DA0aNECHDh2wfPlyFBYWvrQ+f39/mJmZKR92dnYVPzgt4uXUANsmeaCeng4uxqZh0tZQ5BW8vG+IiIiItJlWheZ+/fohICAAV65cgSAICAsLQ0BAAGQyGdLS0pTtMjMzYWxsDD09PQwcOBBr1qxBnz59St1nfn4+5s2bhzFjxqj8dvHxxx9jz549OHv2LKZPn45vvvkGn3/++Uvr8/PzQ2ZmpvKRlJT0eg5cA3k4WmD7JA8Y6+si6N4TTNgSilwpgzMRERHVTrrqLqAyFixYgNTUVHh6ekIQBDRu3Bi+vr747rvvIBb/L/+bmJggIiICOTk5OH36NObMmYNmzZqhZ8+eKvuTyWQYOXIkBEHAunXrVNbNmTNH+e927dpBT08P06dPh7+/P/T19UutT19fv8x1tZG7gwW2T/aA76YQhMSlw3dzCLb+G6SJiIiIahOtutJsaGiIzZs3Iy8vD/Hx8UhMTISDgwNMTExgaWmpbCcWi+Hs7Aw3NzfMnTsXw4cPh7+/v8q+igNzQkICTp06Ve6Y486dO6OwsBDx8fHVcWhaq6N9feyc0hmmBroIS3iK8ZuCkZVfcqgMERERkTbTqtBcTCKRwNbWFjo6OtizZw8GDRqkcqX5RQqFQuUGveLAHBMTg8DAQDRo0KDc14yIiIBYLEajRo1eyzHUJu3tzLFriifMDCUIT8zA+5tCkPmMwZmIiIhqD7X+HT0nJwexsbHK53FxcYiIiICFhQXs7e3h5+eHlJQU5VzM0dHRCAkJQefOnfH06VOsWLECkZGR2LZtm3If/v7+cHd3h5OTE6RSKY4dO4YdO3Yoh1/IZDIMHz4c4eHhOHr0KORyOVJTUwEUTVenp6eHoKAgBAcHw8fHByYmJggKCsLs2bPx3nvvoX79+jXYQ9qjra0Zfp3aGe8FBONaUgbeCwjGjskeMK+np+7SiIiIiF6ZWkNzWFgYfHx8lM+LxxH7+vpi69atePDgARITE5Xr5XI5fvjhB0RFRUEikcDHxweXLl2Cg4ODsk1ubi5mzJiB5ORkGBoaokWLFti5cydGjRoFAEhJScHhw4cBAG5ubir1nD17Fj179oS+vj727NmDRYsWQSqVwtHREbNnz1YZ50wltbYxw69TPTEuIBg3UjIxLiAYOyd3Rn0jBmciIiLSbhozT3NtVNF5/2qbqNRsjAu4jLScArSwMsGuKZ3RwLju3CBJRERE2qNWztNM2sHVygR7pnnC0kQfd1KzMXZjMNJypOVvSERERKShGJqpWjg3KgrOjUz0EfUwG2M2XMaj7Hx1l0VERERUJQzNVG2cLI2xd7oXrEwNEPMoB6M3XMbDLAZnIiIi0j4MzVStHBsaYe90T9iYGeDe41yM3nAZqZkMzkRERKRdGJqp2jVtYIS9073QxNwQcWm5GLUhCPcznqm7LCIiIqIKY2imGmFnUQ97p3vCzsIQCU/yMGpDEJKf5qm7LCIiIqIKYWimGmNbvx72TvNC0wb1kJT+DKPWX0ZSOoMzERERaT6GZqpRNuaG2DvNC44NjZCS8Qyj1gch4UmuussiIiIieimGZqpxVmYG2DvNE06WRrifmY9R6y8jLo3BmYiIiDQXQzOpRSNTA+ye5gmXRsZIzcrHqPVBuPs4R91lEREREZWKoZnUppFJUXB2bWyCR9lSjFp/GTEPs9VdFhEREVEJDM2kVg2N9bF7midaWpsiLUeKMRsvIyqVwZmIiIg0C0MzqZ2FkR5+ndIZrW1MkZZTgDEbL+PW/Sx1l0VERESkxNBMGqG+kR5+neKJdrZmSM8twNiAy4hMyVR3WUREREQAGJpJg5jVk2DH5M5wszNHRp4MYzdexvXkDHWXRURERMTQTJrFzFCCHZM90KlpfWTlF2JcQDAikjLUXRYRERHVcQzNpHFMDCTYNskDbzjUR3Z+Id4PCMaVhKfqLouIiIjqMIZm0kjG+rrYOtEDnR0tkC0txPhNwQiNT1d3WURERFRHMTSTxjLS18WWiW/A26kBcgvk8N0cguB7T9RdFhEREdVBDM2k0erp6WKT7xvo5tIQeQVyTNgSikt309RdFhEREdUxDM2k8Qz1dLBxvDt6NLfEM5kck7aG4mIMgzMRERHVHIZm0goGEh2sf78T3mzRCPkyBSZvC8X56MfqLouIiIjqCIZm0hoGEh2se68jerdsDGmhAlO3h+HsnUfqLouIiIjqAIZm0ir6ujr4eVxH9GvdGAWFCkzfcQWBtx6quywiIiKq5RiaSevo6Yrx09iOeKutFQrkCnyw6wpO3ExVd1lERERUizE0k1aS6IixenQHDG5vA5lcwMxd4fjrxgN1l0VERES1FEMzaS1dHTFWjmyPYW42KFQI+HD3VRy9fl/dZREREVEtxNBMWk1XR4wfRrrhnY5NIFcI+Hj3VfwRkaLusoiIiKiWYWgmracjFmH58PYY6W4LhQDM3huBA+HJ6i6LiIiIahGGZqoVdMQiLHunHcZ42EEhAHP3XcO+sCR1l0VERES1BEMz1RpisQhLh7XFe572EATg8/3XsSckUd1lERERUS3A0Ey1ilgswpKhbTDB2wGCAHxx4AZ2BSeouywiIiLScgzNVOuIRCIsHNwKk7o4AgDmH4zE9qB49RZFREREWo2hmWolkUiEBYNaYlr3ZgCAr/64ic0X49RcFREREWkrhmaqtUQiEfwGtMAHPZ0AAF8fvYWAv++puSoiIiLSRgzNVKuJRCJ83s8VH73pDAD475+38cv5u2quioiIiLQNQzPVeiKRCHP7umJWbxcAwLK/7mDt2Vg1V0VERETahKGZ6oxZvZtjbp/mAIDlJ6LwY2CMmisiIiIibcHQTHXKR71c8Hl/VwDAysBorDgZBUEQ1FwVERERaTqGZqpzZvR0xpdvtQAArD4Ti+8ZnImIiKgcDM1UJ03r7oQFg1oBANaevYtlx+8wOBMREVGZGJqpzprc1RGLh7QGAKw/fw9L/7zN4ExERESlYmimOs3X2wFLhrUBAARcjMPiI7cYnImIiKgEhmaq8973bAr/d9oCALZeisdXf9yEQsHgTERERP/D0EwEYIyHPb57tx1EImDH5QT83x+RDM5ERESkxNBM9K+Rb9jh++HtIRIBvwYnwu/ADQZnIiIiAsDQTKTi3U62WDnSDWIRsDcsCZ/vvw45gzMREVGdx9BM9IJhHZrgx9EdoCMW4fcryfh03zUGZyIiojqOoZmoFIPb22DNmA7QFYtw8GoKZu+NQKFcoe6yiIiISE0YmonK8FZba/w0tiN0xSIcvnYfn+yJgIzBmYiIqE5iaCZ6if5trLDuvU6Q6Ijw540H+OjXqygoZHAmIiKqaxiaicrRp1VjrH+/E/R0xDh+MxUzfw1ncCYiIqpj1BqaL1y4gMGDB8PGxgYikQiHDh0qd5u1a9eiZcuWMDQ0hKurK7Zv366y/sCBA3B3d4e5uTmMjIzg5uaGHTt2KNfLZDLMmzcPbdu2hZGREWxsbDB+/Hjcv3+/1NeTSqVwc3ODSCRCRETEqxwuabE3WzTGhvGdoKcrxqlbD/HBziuQFsrVXRYRERHVELWG5tzcXLRv3x5r166tUPt169bBz88PixYtws2bN7F48WLMnDkTR44cUbaxsLDA/PnzERQUhOvXr2PixImYOHEiTpw4AQDIy8tDeHg4FixYgPDwcBw4cABRUVEYMmRIqa/5+eefw8bG5tUPlrReT9dG2OTrDn1dMU7feYTpO64gX8bgTEREVBeIBEHQiLm0RCIRDh48iGHDhpXZxtvbG126dMHy5cuVy+bOnYvg4GBcvHixzO06duyIgQMHYsmSJaWuDw0NhYeHBxISEmBvb69c/tdff2HOnDnYv38/WrdujatXr8LNza3Cx5SVlQUzMzNkZmbC1NS0wtuRZrsUm4ZJ20KRL1Ogm0tDbBzvDgOJjrrLIiIioiqoaF7TqjHNUqkUBgYGKssMDQ0REhICmUxWor0gCDh9+jSioqLQvXv3MvebmZkJkUgEc3Nz5bKHDx9i6tSp2LFjB+rVq1fh+rKyslQeVPt4OzfE1okeqKeng79j0jB5WyieFfCKMxERUW2mVaG5X79+CAgIwJUrVyAIAsLCwhAQEACZTIa0tDRlu8zMTBgbG0NPTw8DBw7EmjVr0KdPn1L3mZ+fj3nz5mHMmDHK3y4EQcCECRPwn//8B+7u7hWuz9/fH2ZmZsqHnZ3dqx0waSzPZg2wdaIHjPR08E/sE0zcGoK8gkJ1l0VERETVRKtC84IFCzBgwAB4enpCIpFg6NCh8PX1BQCIxf87FBMTE0RERCA0NBRLly7FnDlzcO7cuRL7k8lkGDlyJARBwLp165TL16xZg+zsbPj5+VWqPj8/P2RmZiofSUlJVTtQ0goejhbYPtkDxvq6uHwvHRM2hyJHyuBMRERUG2lVaDY0NMTmzZuRl5eH+Ph4JCYmwsHBASYmJrC0tFS2E4vFcHZ2hpubG+bOnYvhw4fD399fZV/FgTkhIQGnTp1SGcNy5swZBAUFQV9fH7q6unB2dgYAuLu7K0N6afT19WFqaqryoNqtU1ML7JjsARN9XYTEp2PC5hBk55ccKkRERETaTatCczGJRAJbW1vo6Ohgz549GDRokMqV5hcpFApIpVLl8+LAHBMTg8DAQDRo0ECl/erVq3Ht2jVEREQgIiICx44dAwDs3bsXS5curZ6DIq3Vwb4+dk7pDFMDXYQlPMX4zSHIYnAmIiKqVXTV+eI5OTmIjY1VPo+Li0NERAQsLCxgb28PPz8/pKSkKOdijo6ORkhICDp37oynT59ixYoViIyMxLZt25T78Pf3h7u7O5ycnCCVSnHs2DHs2LFDOfxCJpNh+PDhCA8Px9GjRyGXy5GamgqgaLo6PT09lRk0AMDY2BgA4OTkBFtb22rtE9JO7e3M8etUT4wLCMbVxAy8HxCM7ZM7w8xQou7SiIiI6DVQa2gOCwuDj4+P8vmcOXMAAL6+vti6dSsePHiAxMRE5Xq5XI4ffvgBUVFRkEgk8PHxwaVLl+Dg4KBsk5ubixkzZiA5ORmGhoZo0aIFdu7ciVGjRgEAUlJScPjwYQAoMX3c2bNn0bNnz+o5WKr12jQxw69TO+O9gGBcS87EuIDL2Dm5M8zr6am7NCIiInpFGjNPc23EeZrrpjupWRi3MRhPcgvQytoUO6d0hoURgzMREZEmqpXzNBNpgxZWptg9zRMNjfVw60EWxm68jCc50vI3JCIiIo3F0ExUDZo3NsGeaZ6wNNHHndRsjNl4GY+zGZyJiIi0FUMzUTVxblQUnBub6iP6YQ5GbwjCo6x8dZdFREREVcDQTFSNnCyNsXeaF6zNDHD3cS5Gb7iM1EwGZyIiIm3D0ExUzRwaGmHvNC80MTfEvbRcjN4QhAeZz9RdFhEREVUCQzNRDbBvUA97pnnCtr4h4p/kYdT6y0jJYHAmIiLSFgzNRDXEzqIe9k73gr1FPSSm52HU+iAkpeepuywiIiKqAIZmohrUxNwQe6d7wqFBPSQ/fYbRGy4j8QmDMxERkaZjaCaqYdZmhtg73QvNGhohJeMZRm0IQnxarrrLIiIiopdgaCZSg8amBtgzzRPOjYzxIDMfozYE4d7jHHWXRURERGVgaCZSk0amBtg91RPNGxvjYZYUozZcRuwjBmciIiJNxNBMpEaWJvrYPdUTLaxM8DhbitEbLiP6Yba6yyIiIqIXMDQTqVkDY338OtUTraxNkZYjxZgNl3EnNUvdZREREdFzGJqJNICFkR5+ndoZbZqY4kluAcZsuIxb9xmciYiINAVDM5GGMK+nh12TPdHe1gxP82QYG3AZkSmZ6i6LiIiIwNBMpFHM6kmwY0pndLA3R0aeDGM3Xsb15Ax1l0VERFTnMTQTaRhTAwm2T/JAp6b1kZVfiHEBwbia+FTdZREREdVpDM1EGsjEQIJtkzzg4WCB7PxCvL8pBFcS0tVdFhERUZ3F0EykoYz1dbF10hvwbGaBHGkhxm8KQWg8gzMREZE6MDQTabB6errYMsEDXZwbILdADt/NIbh874m6yyIiIqpzGJqJNJyhng42+b6Bbi4NkVcgx4QtIbgUm6busoiIiOoUhmYiLWAg0cHG8e7o0dwS+TIFJm4Nxd8xj9VdFhERUZ3B0EykJQwkOtgwvhPebNEI0kIFJm8Lw7moR+oui4iIqE5gaCbSIvq6Olj3Xkf0adUYBYUKTNt+BWfvMDgTERFVN4ZmIi2jr6uDtWM7on9rKxTIFZi2IwyBtx6quywiIqJajaGZSAvp6YqxZmwHDGxrDZlcwAe7ruB4ZKq6yyIiIqq1GJqJtJRER4wfR7thcHsbyOQCPvw1HMduPFB3WURERLUSQzORFtPVEWPlyPZ4u0MTFCoEfLT7Ko5cu6/usoiIiGodhmYiLaerI8b3I9rj3Y62kCsEfLLnKv6ISFF3WURERLUKQzNRLaAjFmH58HYY5W4HhQDM3huB/VeS1V0WERFRrcHQTFRLiMUi+L/TFmM87KEQgE9/v4bfwpLUXRYREVGtwNBMVIuIxSIsHdYG73s2hSAAn/9+HbtDEtVdFhERkdZjaCaqZcRiEb4e2hoTvB0AAH4HbmDn5QT1FkVERKTlGJqJaiGRSISFg1thcldHAMD/HYrEtkvx6i2KiIhIizE0E9VSIpEI/zewJaZ3bwYAWHj4JjZdjFNzVURERNqJoZmoFhOJRPhiQAvM6OkEAFhy9BY2Xrin5qqIiIi0D0MzUS0nEonwWT9XfPymMwBg6bHbWHfurpqrIiIi0i4MzUR1gEgkwpy+rpjduzkA4Nvjd/DTmRg1V0VERKQ9GJqJ6pBPervg075Fwfn7k9FYFRit5oqIiIi0A0MzUR3z4ZsumNe/BQBgVWAMVpyMgiAIaq6KiIhIszE0E9VBH/R0wvy3WgIAVp+JxfITDM5EREQvw9BMVEdN7d4MXw1qBQD4+dxdLPvrDoMzERFRGRiaieqwSV0d8fXQ1gCA9Rfu4b9/3mZwJiIiKgVDM1EdN97LAf8d1gYAsOliHBYfucXgTERE9AKGZiLCe55NseydthCJgK2X4rHgj0goFAzORERExRiaiQgAMNrDHt++2w4iEbDzciLmH2JwJiIiKsbQTERKI93t8MOI9hCLgN0hifjiwHXIGZyJiIgYmolI1TsdbbFylBvEIuC3sGR89vs1BmciIqrzGJqJqIShbk3w4+gO0BGLcCA8BXN+i0ChXKHusoiIiNSGoZmISjW4vQ1+GtMBumIR/oi4j1l7GZyJiKjuYmgmojINaGuNteM6QqIjwtHrD/DxnquQMTgTEVEdxNBMRC/Vr7UV1o3rBD0dMY7dSMWHv4ajoJDBmYiI6ha1huYLFy5g8ODBsLGxgUgkwqFDh8rdZu3atWjZsiUMDQ3h6uqK7du3q6w/cOAA3N3dYW5uDiMjI7i5uWHHjh3K9TKZDPPmzUPbtm1hZGQEGxsbjB8/Hvfv31fZz5AhQ2Bvbw8DAwNYW1vj/fffL9GGqK7o3aox1r/fCXq6Ypy4+RAzdl2BtFCu7rKIiIhqjFpDc25uLtq3b4+1a9dWqP26devg5+eHRYsW4ebNm1i8eDFmzpyJI0eOKNtYWFhg/vz5CAoKwvXr1zFx4kRMnDgRJ06cAADk5eUhPDwcCxYsQHh4OA4cOICoqCgMGTJE5bV8fHzw22+/ISoqCvv378fdu3cxfPjw13fwRFrGp0UjbBzvDj1dMQJvP8J/dlxBvozBmYiI6gaRoCHflysSiXDw4EEMGzaszDbe3t7o0qULli9frlw2d+5cBAcH4+LFi2Vu17FjRwwcOBBLliwpdX1oaCg8PDyQkJAAe3v7UtscPnwYw4YNg1QqhUQiqdAxZWVlwczMDJmZmTA1Na3QNkSa7mJMGiZvC4W0UIEezS2x/v1OMJDoqLssIiKiKqloXtOqMc1SqRQGBgYqywwNDRESEgKZTFaivSAIOH36NKKiotC9e/cy95uZmQmRSARzc/NS16enp2PXrl3w9vZ+aWCWSqXIyspSeRDVNl1dGmLLhDdgKNHB+ejHmLo9DM8KeMWZiIhqN60Kzf369UNAQACuXLkCQRAQFhaGgIAAyGQypKWlKdtlZmbC2NgYenp6GDhwINasWYM+ffqUus/8/HzMmzcPY8aMKfHbxbx582BkZIQGDRogMTERf/zxx0vr8/f3h5mZmfJhZ2f36gdNpIG8nRtiy8Q3UE9PB3//e+U5r6BQ3WURERFVG60KzQsWLMCAAQPg6ekJiUSCoUOHwtfXFwAgFv/vUExMTBAREYHQ0FAsXboUc+bMwblz50rsTyaTYeTIkRAEAevWrSux/rPPPsPVq1dx8uRJ6OjoYPz48XjZaBY/Pz9kZmYqH0lJSa9+0EQayrNZA2yb5AEjPR1cuvsEE7eEIlfK4ExERLWTVo1pLiaTyfDw4UNYW1tjw4YNmDdvHjIyMlSC8/OmTJmCpKQk5c2AxfsYOXIk7t27hzNnzqBBgwYvfc3k5GTY2dnh0qVL8PLyqtAxcUwz1QVXEp5iwuYQZEsL8YZDfWyZ6AFjfV11l0VERFQhtXJMczGJRAJbW1vo6Ohgz549GDRoUJmBGQAUCgWkUqnyeXFgjomJQWBgYLmBuXgfAFT2Q0RAp6b1sWNKZ5gY6CI0/inGbwpGdn7JewyIiIi0mVovB+Xk5CA2Nlb5PC4uDhEREbCwsIC9vT38/PyQkpKinIs5OjoaISEh6Ny5M54+fYoVK1YgMjIS27ZtU+7D398f7u7ucHJyglQqxbFjx7Bjxw7l8AuZTIbhw4cjPDwcR48ehVwuR2pqKoCi6er09PQQHByM0NBQdO3aFfXr18fdu3exYMECODk5VfgqM1Fd4mZnjl+neOK9TcEIT8zA+5tCsG2SB8wMKzbTDBERkaZT65XmsLAwdOjQAR06dAAAzJkzBx06dMBXX30FAHjw4AESExOV7eVyOX744Qe0b98effr0QX5+Pi5dugQHBwdlm9zcXMyYMQOtW7dGly5dsH//fuzcuRNTpkwBAKSkpODw4cNITk6Gm5sbrK2tlY9Lly4BAOrVq4cDBw6gV69ecHV1xeTJk9GuXTucP38e+vr6NdQ7RNqlra0Zdk3pDPN6EkQkZeD9TcHIzOMVZyIiqh00ZkxzbcQxzVQX3bqfhfc2BSM9twCtbUyxc3Jn1DfSU3dZREREparVY5qJSHO1sjHF7qmeaGCkh5v3szA2oChAExERaTOGZiJ67VytTLBnmicaGuvj9oMsjN14GWk5vImWiIi0F0MzEVULl8ZFwbmRiT7upGZjzIbLeJzN4ExERNqJoZmIqo1zI2Psne4FK1MDxDzKwegNQXiUla/usoiIiCqNoZmIqpVjQyPsne4JGzMD3H2ci9EbLiM1k8GZiIi0C0MzEVW7pg2MsHe6F5qYG+JeWi5GbQjC/Yxn6i6LiIiowhiaiahG2FnUw55pnrCzMETCkzyM2hCE5Kd56i6LiIioQhiaiajGFAVnLzRtUA9J6c8wav1lJKUzOBMRkeZjaCaiGtXE3BB7pnnCsaERUjKeYfSGy0h4kqvusoiIiF6KoZmIapy1WVFwbmb5v+Acl8bgTEREmouhmYjUorGpAfZM84RzI2M8yMzH6A1BuPs4R91lERERlYqhmYjUppFJUXB2bWyCh1lSjN5wGbGPstVdFhERUQkMzUSkVg2N9fHr1M5oYWWCx9lFwTn6IYMzERFpFoZmIlK7Bsb62D3VE62sTZGWU4DRGy7j9oMsdZdFRESkxNBMRBqhvpEefp3aGW2bmCE9twBjN17GzfuZ6i6LiIgIAEMzEWkQ83p62DmlM9rbmeNpngxjNwYjMoXBmYiI1I+hmYg0ipmhBDsme6CDvTkyn8kwduNlXEvKUHdZRERUxzE0E5HGMTWQYPskD7g3rY+s/EK8FxCM8MSn6i6LiIjqMIZmItJIJgYSbJvkAQ9HC2RLCzF+UwiuJKSruywiIqqjGJqJSGMZ6eti68Q34NWsAXL+Dc4hcQzORERU8xiaiUij1dPTxeYJb6Crc0PkFsjhuzkEQXefqLssIiKqY0SCIAiV3WjOnDkvXb9ixYoqF1SbZGVlwczMDJmZmTA1NVV3OURaLV8mx9TtYfg7Jg0GEjE2+b6BLs4N1V0WERFpuYrmtSqFZrFYDC8vL+jp6QEALl68iE6dOsHQ0BAikQhnzpypeuW1CEMz0euVL5PjPzuv4FzUY+jrirFxvDu6N7dUd1lERKTFqj00p6amolGjRgAAExMTXLt2Dc2aNat6xbUQQzPR6yctlGPGznCcvvMIerpibHi/E3q6NlJ3WUREpKUqmteqNKZZIpGgoKBA+Vwmk2H//v1V2RURUaXo6+pg3Xud0KdVYxQUKjBt+xWcufNQ3WUREVEtV6XQ7OjoiD179gAA9u/fD4lEgo0bN2LMmDHIy8t7rQUSEb1IT1eMn8d1xIA2ViiQKzB9xxWcusXgTERE1adKoXnevHn44osvYGBggJEjR+KLL75AWFgY8vLy4OHh8bprJCIqQaIjxuoxHTCwnTVkcgEf7LyC45Gp6i6LiIhqqSqNaQaAqKgoXL9+HY6OjnB3d1cuX7ZsGb744ovXVqA245hmoupXKFdgzm/XcPjafeiIRVg9uihIExERVUS13ghIFcPQTFQz5AoBn+27hgNXU6AjFmHlKDcMaW+j7rKIiEgLVDSv6VZ15y/DgEhENUlHLMLyEe0hFovw+5VkzNpzFQqFgGEdmqi7NCIiqiWqFJrr169f6nJBECASiSCXy1+pKCKiytIRi/Ddu+2gKxZhT2gSZv8WgUKFgOGdbNVdGhER1QJVCs2Ojo549OgRvvjiC3Tp0uV110REVCVisQjfvN0WYrEIvwYn4rPfr0GhEDDyDTt1l0ZERFquSqH59u3bWLNmDZYuXYqrV6/iu+++g6Oj4+uujYio0sRiEZYOawNdsQjbgxLw+f7rKFQIGNvZXt2lERGRFqvyl5vMmTMHMTExaNKkCdq1a4e5c+ciIyPjNZdHRFR5IpEIi4e0xsQuDgCALw/ewI6geLXWRERE2q1KobmYhYUFVq1ahatXryI+Ph7Ozs5YtWrVayqNiKjqRCIRvhrUClO6Fv0VbMEfN7Hlnzg1V0VERNqqSlPOdejQASKRSGWZIAiIjY1FXl4ebwT8F6ecI1I/QRCw7PgdrD9/DwDwfwNbYkq3ZmquioiINEW1Tjk3bNiwqtZFRFSjRCIRvujfArpiEdaevYv//nkbcoWA6T2c1F0aERFpEX65STXilWYizSEIAlYGxmD16RgAwGf9XDHTx1nNVRERkbpV65XmssjlckydOhVA0c2C69evf527JyKqMpFIhDl9mkNHJMLKwGgsPxEFuULAx71c1F0aERFpgSqF5nfeeafU5QqFAkeOHMGBAwego6PzSoUREVWHT3q7QFdHhOUnorDiVDQKFQJm93YpcZ8GERHR86oUms3MzEpdXnwD4NChQ6teERFRNZvp4wwdsQjL/rqD1adjoFAImNu3OYMzERGV6bWOac7Pz4eRkRFnz/gXxzQTabaAv+/hv3/eBgD8p4cT5vV3ZXAmIqpjKprXXmme5hfxPxsi0iZTujXDwsGtAAC/nL+Lb47dBu+NJiKi0rzW0ExEpG0mdnHE10NbAwA2/h2Hr4/eYnAmIqISqjSmefXq1aUuLywsfKViiIjUYbyXA3TEIsw/GIkt/8RDrhCweEhr/vWMiIiUqhSaV65cWeY6e3v7KhdDRKQu4zo3ha5YhC8O3MD2oATIFQKWDG0DsZjBmYiIqhia4+LiXncdRERqN+oNe4hFIny+/zp2BSdCrhDwzdttGZyJiOjVxzQLgsDxf0RUa4xwt8OKke0hFgF7QpPw+f7rkCt4jiMiquuqHJo3bdqENm3awMDAAAYGBmjTpg0CAgJeZ21ERGrxdgdbrBzlBh2xCL9fScZn+64xOBMR1XFVGp7x1VdfYcWKFfjoo4/g5eUFAAgKCsLs2bORmJiIr7/++rUWSURU04a6NYGuWIyP91zFgaspkAsCfhjRHro6nHSIiKguqtKXm1haWmL16tUYM2aMyvLdu3fjo48+Qlpa2msrUJvxy02ItN/xyAf48NerKFQIGNjOGqtGuUHC4ExEVGtU65ebyGQyuLu7l1jeqVMnTjtHRLVK/zbW+HlcR0h0RPjz+gN8vPsqZHKFussiIqIaVqXQ/P7772PdunUllm/YsAHjxo2r8H4uXLiAwYMHw8bGBiKRCIcOHSp3m7Vr16Jly5YwNDSEq6srtm/frrL+wIEDcHd3h7m5OYyMjODm5oYdO3Yo18tkMsybNw9t27aFkZERbGxsMH78eNy/f1/ZJj4+HpMnT4ajoyMMDQ3h5OSEhQsXoqCgoMLHRkS1R9/WVvjlvU7Q0xHjr8hUzNwVjoJCBmciorqkSmOagaIbAU+ePAlPT08AQHBwMBITEzF+/HjMmTNH2W7FihVl7iM3Nxft27fHpEmT8M4775T7muvWrYOfnx82btyIN954AyEhIZg6dSrq16+PwYMHAwAsLCwwf/58tGjRAnp6ejh69CgmTpyIRo0aoV+/fsjLy0N4eDgWLFiA9u3b4+nTp/jkk08wZMgQhIWFAQDu3LkDhUKB9evXw9nZGZGRkZg6dSpyc3Px/fffV7XLiEiL9WrZGOvHd8L0HVdw8tZDzNh1BWvHdYS+ro66SyMiohpQpTHNPj4+Fdu5SIQzZ85UuO3BgwcxbNiwMtt4e3ujS5cuWL58uXLZ3LlzERwcjIsXL5a5XceOHTFw4EAsWbKk1PWhoaHw8PBAQkJCmV/Osnz5cqxbtw737t2r0PEAHNNMVBtdiH6MqdvDIC1UwMfVEuve6wQDCYMzEZG2qmheq9KV5rNnz1a5sFchlUphYGCgsszQ0BAhISGQyWSQSCQq6wRBwJkzZxAVFYVvv/22zP1mZmZCJBLB3Nz8pW0sLCzKrU8qlSqfZ2VlvbQ9EWmf7s0tsXnCG5i8LRRnox5j2o4r2PA+gzMRUW33SreAx8bG4sSJE3j27BkAVPuXnPTr1w8BAQG4cuUKBEFAWFgYAgICIJPJVGbsyMzMhLGxMfT09DBw4ECsWbMGffr0KXWf+fn5mDdvHsaMGVPmbxexsbFYs2YNpk+f/tL6/P39YWZmpnzY2dlV/WCJSGN1cW6ILRM8YCjRwYXox5iyLQzPCuTqLouIiKpRlULzkydP0KtXLzRv3hxvvfUWHjx4AACYPHky5s6d+1oLfN6CBQswYMAAeHp6QiKRYOjQofD19QUAiMX/OxQTExNEREQgNDQUS5cuxZw5c3Du3LkS+5PJZBg5ciQEQSj1xkYASElJQf/+/TFixAhMnTr1pfX5+fkhMzNT+UhKSqr6wRKRRvNyaoBtkzxQT08HF2PTMGlrKPIKOHsQEVFtVaXQPHv2bEgkEiQmJqJevXrK5aNGjcLx48dfW3EvMjQ0xObNm5GXl4f4+HgkJibCwcEBJiYmsLS0VLYTi8VwdnaGm5sb5s6di+HDh8Pf319lX8WBOSEhAadOnSr1KvP9+/fh4+MDb29vbNiwodz69PX1YWpqqvIgotrLw9EC2yd5wFhfF0H3nmDCllDkShmciYhqoyqF5pMnT+Lbb7+Fra2tynIXFxckJCS8lsJeRiKRwNbWFjo6OtizZw8GDRqkcqX5RQqFQmWscXFgjomJQWBgIBo0aFBim5SUFPTs2ROdOnXCli1bXrp/Iqq73B0ssH2yB0z0dRESlw7fzSHIYXAmIqp1qnQjYG5ursoV5mLp6enQ19ev8H5ycnIQGxurfB4XF4eIiAhYWFjA3t4efn5+SElJUc7FHB0djZCQEHTu3BlPnz7FihUrEBkZiW3btin34e/vD3d3dzg5OUEqleLYsWPYsWOHcviFTCbD8OHDER4ejqNHj0IulyM1NRVA0XR1enp6ysDctGlTfP/993j8+LFy/1ZWVpXrLCKq9Tra18fOKZ3x/qZghCU8xfhNwdg6yQOmBpLyNyYiIq1QpdDcrVs3bN++XTmFm0gkgkKhwHfffVfh6egAICwsTKV98fzOvr6+2Lp1Kx48eIDExETlerlcjh9++AFRUVGQSCTw8fHBpUuX4ODgoGyTm5uLGTNmIDk5GYaGhmjRogV27tyJUaNGASi6gnz48GEAgJubm0o9Z8+eRc+ePXHq1CnExsYiNja2xNX06r7ZkYi0U3s7c+ya4on3NgUjPDED728KwfZJHjAzZHAmIqoNqjRPc2RkJHr16oWOHTvizJkzGDJkCG7evIn09HT8888/cHJyqo5atQ7naSaqe27ez8R7AcF4midD2yZm2DHZA+b19NRdFhERlaGiea1KA3XbtGmD6OhodO3aFUOHDkVubi7eeecdXL16lYGZiOq01jZm+HWqJyyM9HAjJRPjAoLxNLdA3WUREdErqtKVZqoYXmkmqruiUrMxLuAy0nIK0MLKBLumdEYD44rf80FERDWjonmtSqH5+vXrL13frl27yu6yVmJoJqrbYh9lY8zGYDzOlsK1sQl2Te2MhgzOREQapVpDs1gshkgkgiAIEIlEAP53g5xIJIJczm/GAhiaiQi4+zgHYzZcxqNsKVwaGWPX1M5oZGKg7rKIiOhf1TqmOS4uDvfu3cO9e/dgaGiIs2fPIi4uTrmciIiKOFkaY+90L1iZGiDmUQ5Gb7iMh1n56i6LiIgq6ZXHNJuYmODatWto1qzZ66qp1uCVZiIqlvAkF2M2XMb9zHw4NjTC7qmesDLjFWciInWr1ivNRERUOU0bGGHvdC80MTdEXFouRm0Iwv2MZ+oui4iIKuiVQ7NIJFKOayYiorLZWdTD3umesLMwRMKTPIzaEITkp3nqLouIiCqgSqG5fv36sLCwgIWFBXJyctChQwflcwsLi9ddIxFRrWFbvx72TvNC0wb1kJT+DKPWX0ZSOoMzEZGmq9LXaK9cuZJXl4mIqsjG3BB7p3lhzMbLRUM11gdh9zRPNG1gpO7SiIioDPxyk2rEGwGJ6GUeZeVjzMbLuPs4F1amBtg9zROODRmciYhqUrXcCKijo1OhBxERla/Rv0HZpZExUrPyMWp9EO4+zlF3WUREVIpKDc+QSCTQ0dHBRx99BC8vr+qqiYiozmhkUhScx20MRtTDbIxafxm7p3aGS2MTdZdGRETPqdTwjMTERPzf//0ffv31VwwdOhT+/v5o3rx5ddan1Tg8g4gqKj23AOMCgnH7QRYaGuth1xRPuFoxOBMRVbdqGZ5hb2+P7du34+rVq8jPz0ebNm0wbdo0PHjw4JULJiKqyyyM9PDrlM5obWOKtJwCjNl4GbcfZKm7LCIi+leVppxr27Yt/vzzTwQGBiIyMhLOzs7w8/NDZmbm666PiKjOqG+kh1+neKKdrRnSc4uCc2QKz6tERJrglb7cpHv37rh06RJ27dqFw4cPo1mzZli+fPnrqo2IqM4xqyfBjsmd0d7OHBl5MowLCMaNZAZnIiJ1q9SY5nfeeafMdYWFhQgMDIRUKoVcLn8txWk7jmkmoqrKypdhwuYQhCdmwMRAFzsmd4abnbm6yyIiqnUqmtcqNXuGmZnZS9ePGjWqMrsjIqIymBpIsH1yZ0zcEoLQ+Kd4PyAYWyd5oFPT+uoujYioTuKXm1QjXmkmoleVKy3ExK2hCIlLh5GeDrZO8sAbDhbqLouIqNaoltkziIioZhnp62LrxDfg1awBcgvk8N0cguB7T9RdFhFRncPQTESk4erp6WLzhDfQ1bkh8grkmLAlFJfupqm7LCKiOoWhmYhICxjq6SDA1x3dm1vimUyOSVtDcTGGwZmIqKYwNBMRaQkDiQ42vN8JPq6WyJcpMHlbKM5HP1Z3WUREdQJDMxGRFjGQ6OCX9zuhd8tGkBYqMHV7GM7eeaTusoiIaj2GZiIiLaOvq4Ofx3VC31aNUVCowPQdVxB466G6yyIiqtUYmomItJCerhhrx3XEgDZWKJAr8MGuKzhxM1XdZRER1VoMzUREWkqiI8bqMR0wqJ01ZHIBM3eF468bD9RdFhFRrcTQTESkxSQ6Yqwa5YahbjYoVAj4cPdVHL1+X91lERHVOgzNRERaTldHjBUj3fBOhyaQKwR8vPsq/ohIUXdZRES1CkMzEVEtoCMWYfmI9hjRyRYKAZi9NwIHwpPVXRYRUa3B0ExEVEvoiEX49t12GP2GHRQCMHffNewLS1J3WUREtQJDMxFRLSIWi/DN220xrrM9BAH4fP917AlJVHdZRERaj6GZiKiWEYtF+O+wNvD1agpBAL44cAO7ghPUXRYRkVZjaCYiqoVEIhEWDWmNiV0cAADzD0Zie1C8WmsiItJmDM1ERLWUSCTCV4NaYWo3RwDAV3/cxOaLcWquiohIOzE0ExHVYiKRCF++1RL/6eEEAPj66C0E/H1PzVUREWkfhmYiolpOJBJhXn9XfOjjDAD475+38cv5u2quiohIuzA0ExHVASKRCHP7Nses3i4AgGV/3cHas7FqroqISHswNBMR1REikQizejfH3D7NAQDLT0Thx8AYNVdFRKQdGJqJiOqYj3q54PP+rgCAlYHRWHEyCoIgqLkqIiLNxtBMRFQHzejpjC/fagEAWH0mFt8zOBMRvRRDMxFRHTWtuxMWDGoFAFh79i6WHb/D4ExEVAaGZiKiOmxyV0csHtIaALD+/D0s/fM2gzMRUSkYmomI6jhfbwcsGdYGABBwMQ6Lj9xicCYiegFDMxER4X3PpvB/py0AYOuleHz1x00oFAzORETFGJqJiAgAMMbDHt+92w4iEbDjcgL+749IBmcion8xNBMRkdLIN+zw/fD2EImAX4MT4XfgBoMzEREAXXUXQEREmuXdTrbQEYsw57cI7A1LglwQ8O277QAAIXHpeJSdj0YmBvBwtICOWKTmaomIagZDMxERlTCsQxPoiEWYtTcCv19JRmJ6HhKf5CE1K1/ZxtrMAAsHt0L/NtZqrJSIqGZweAYREZVqcHsbrBnTAWJR0RXm5wMzAKRm5uODneE4HvlATRUSEdUchmYiIipTv9ZWMDWUlLqueKTz4iO3IOe4ZyKq5dQami9cuIDBgwfDxsYGIpEIhw4dKnebtWvXomXLljA0NISrqyu2b9+usv7AgQNwd3eHubk5jIyM4Obmhh07dijXy2QyzJs3D23btoWRkRFsbGwwfvx43L9/X2U/S5cuhbe3N+rVqwdzc/PXcbhERFonJC4dGXmyMtcLAB5k5iMkLr3miiIiUgO1hubc3Fy0b98ea9eurVD7devWwc/PD4sWLcLNmzexePFizJw5E0eOHFG2sbCwwPz58xEUFITr169j4sSJmDhxIk6cOAEAyMvLQ3h4OBYsWIDw8HAcOHAAUVFRGDJkiMprFRQUYMSIEfjggw9e3wETEWmZR9n55TeqRDsiIm0lEjTka59EIhEOHjyIYcOGldnG29sbXbp0wfLly5XL5s6di+DgYFy8eLHM7Tp27IiBAwdiyZIlpa4PDQ2Fh4cHEhISYG9vr7Ju69atmDVrFjIyMip1PACQlZUFMzMzZGZmwtTUtNLbExGpW9DdJxiz8XK57b4f3g7D3e1qoCIioteronlNq8Y0S6VSGBgYqCwzNDRESEgIZLKSfz4UBAGnT59GVFQUunfvXuZ+MzMzIRKJXnkYhlQqRVZWlsqDiEibeThawNrMAOVNLPfp79fx8e6riH2UUyN1ERHVNK0Kzf369UNAQACuXLkCQRAQFhaGgIAAyGQypKWlKdtlZmbC2NgYenp6GDhwINasWYM+ffqUus/8/HzMmzcPY8aMeeWrwf7+/jAzM1M+7Ox41YWItJuOWISFg1sBQIngXPzczc4cAHD42n30XXkes/dGIC4tt8ZqJCKqCVoVmhcsWIABAwbA09MTEokEQ4cOha+vLwBALP7foZiYmCAiIgKhoaFYunQp5syZg3PnzpXYn0wmw8iRIyEIAtatW/fK9fn5+SEzM1P5SEpKeuV9EhGpW/821lj3XkdYman+pc/KzAC/vNcRh2Z2wZ8fd0WfVo2hEICDV1PQ64dzmPvbNSQ8YXgmotpBq8Y0F5PJZHj48CGsra2xYcMGzJs3DxkZGSrB+XlTpkxBUlKS8mbA4n2MHDkS9+7dw5kzZ9CgQYNSt+WYZiKiInKFUO43At5IzsSqwGicvvMIQNGV6nc7NsFHb7rAzqKeOsomInqpiuY1rfxGQIlEAltbWwDAnj17MGjQoDIDMwAoFApIpVLl8+LAHBMTg7Nnz5YZmImI6H90xCJ4Ob38fNnW1gybJryBiKQMrAqMxrmox/gtLBkHwlMwwt0WM32cYVuf4ZmItI9aQ3NOTg5iY2OVz+Pi4hAREQELCwvY29vDz88PKSkpyrmYo6OjERISgs6dO+Pp06dYsWIFIiMjsW3bNuU+/P394e7uDicnJ0ilUhw7dgw7duxQDr+QyWQYPnw4wsPDcfToUcjlcqSmpgIomq5OT08PAJCYmIj09HQkJiZCLpcjIiICAODs7AxjY+Oa6B4iIq3lZmeOrRM9EJ74FCtPRePvmDTsDknC71eSMcLdDh/6OMPG3FDdZRIRVZhah2ecO3cOPj4+JZb7+vpi69atmDBhAuLj45XjkW/fvo2xY8ciKioKEokEPj4++Pbbb+Hq6qrc9v/+7/+wd+9eJCcnw9DQEC1atMAnn3yCUaNGAQDi4+Ph6OhYaj1nz55Fz549AQATJkxQCeOltSkPh2cQERUJi0/HysBo/BP7BACgpyPGaA87zOjpXGKsNBFRTapoXtOYMc21EUMzEZGq4HtPsDIwGpfvFX2DoJ6uGGM97DGjpxMamTI8E1HNY2jWAAzNRESlu3Q3DatOxSAkvig86+uK8Z5nU/ynhxMsTfTVXB0R1SUMzRqAoZmIqGyCIODS3SdYcSoaVxKeAgAMJGKM93LAtO7N0NCY4ZmIqh9DswZgaCYiKp8gCPg7Jg0rTkUjIikDAGAo0cF476aY3t0JFkZ66i2QiGo1hmYNwNBMRFRxgiDgXPRjrDwVjevJmQAAIz0d+Ho7YGq3ZqjP8ExE1YChWQMwNBMRVZ4gCDhz5xFWnIrGzftZAABjfV1M7OKAKV2bwayeRM0VElFtwtCsARiaiYiqThAEnLr1ECsDY3D7QVF4NtHXxaSujpjU1RFmhgzPRPTqGJo1AEMzEdGrUygEnLyVilWBMbiTmg0AMDXQxZRuzTCxiwNMDBieiajqGJo1AEMzEdHro1AI+CsyFasCoxHzKAcAYF5PgqndmsHX2wHG+mr9klsi0lIMzRqAoZmI6PWTKwT8eeMBfgyMxt3HuQCA+vUkmNbdCeO9msKI4ZmIKoGhWQMwNBMRVR+5QsCRa/ex+nQM7qUVhecGRnqY3qMZ3vd0gKGejporJCJtwNCsARiaiYiqX6FcgT8i7mP1mRgkPMkDADQ01sN/ejjhPc+mMJAwPBNR2RiaNQBDMxFRzSmUK3DgagrWnIlBUvozAICliT5m9HTCGA97hmciKhVDswZgaCYiqnkyuQL7ryRjzZlYpGQUhefGpvqY6eOMUW/YQV+X4ZmI/oehWQMwNBMRqU9BoQL7riRh7ZlY3M/MBwBYmxlgpo8zRrrbQU9XrOYKiUgTMDRrAIZmIiL1kxbK8VtYMtaeiUVqVlF4bmJuiA/fdMbwTraQ6DA8E9VlDM0agKGZiEhz5Mvk2BOSiJ/P3cWjbCkAwM7CEB/5uODtjk0YnonqKIZmDcDQTESkefJlcuwKTsS6c3eRllMUnps2qIeP3nTBMDcb6DI8E9UpDM0agKGZiEhzPSuQY+flBPxy/i6e5BYAABwbGuHjXs4Y0r4JdMQiNVdIRDWBoVkDMDQTEWm+vIJCbA9KwPrzd/E0TwYAaGZphE96uWBQOxuGZ6JajqFZAzA0ExFpjxxpIbZdisfGv+8h49/w7NzIGLN6u+CtNtYQMzwT1UoMzRqAoZmISPtk58uw9Z+i8JyVXwgAcG1sglm9XdCvtRXDM1Etw9CsARiaiYi0V1a+DFsuxiPg4j1k/xueW1iZYHaf5ujbqjFEIoZnotqAoVkDMDQTEWm/zGcybLoYh80X45AjLQrPrW1MMat3c/Ru2YjhmUjLMTRrAIZmIqLaIyOvAAF/x2HLP3HILZADANrZmmFWbxf4uDI8E2krhmYNwNBMRFT7pOcWYOPf97DtUjzy/g3P7e3MMbu3C3o0t2R4JtIyDM0agKGZiKj2epIjxYYL97AtKB75MgUAoKO9OWb3aY6uzg0Znom0BEOzBmBoJiKq/R5nS7H+/F3suJwAaWFReH7DoT5m92kOb6eGaq6OiMrD0KwBGJqJiOqOR1n5WHf+LnYFJ6Lg3/Dc2dECs/s0h2ezBmqujojKwtCsARiaiYjqntTMfKw7F4vdIUkokBeFZ2+nBpjdpznecLBQc3VE9CKGZg3A0ExEVHfdz3iGn8/FYm9oEmTyov9qu7k0xKzezdGpaX01V0dExRiaNQBDMxERJT/Nw9qzd7EvLAmFiqL/cns0t8TsPs3hZmeu3uKIiKFZEzA0ExFRsaT0PPx0Jha/hydD/m94frNFI8zu3Rxtbc3UXB1R3cXQrAEYmomI6EUJT3Kx5kwsDoQn49/sjN4tG2NWbxe0acLwTFTTGJo1AEMzERGVJS4tF2tOx+BQRIoyPPdr3RizejdHS2v+n0FUUxiaNQBDMxERlefu4xysPh2Dw9fuo/h/5LfaWuGTXs3hamWi3uKI6gCGZg3A0ExERBUV8zAbP56OwZ83HkAQAJEIeKutNWb1coFLY4ZnourC0KwBGJqJiKiyolKz8ePpaBy7kQqgKDwPbmeDj3u5wLmRsZqrI6p9GJo1AEMzERFV1e0HWVgVGI0TNx8CAMQiYKhbE3zcywWODY3UXB1R7cHQrAEYmomI6FVFpmRiVWAMAm8XhWcdsQjD3Jrg417OaNqA4ZnoVTE0awCGZiIiel2uJ2dgVWAMztx5BKAoPL/bsQk+etMFdhb11FwdkfZiaNYADM1ERPS6RSRlYOWpaJyPfgwA0BWLMMLdFjN9nGFbn+GZqLIYmjUAQzMREVWXKwlPsSowGn/HpAEAJDoijHrDDjN9nGFtZqjm6oi0B0OzBmBoJiKi6hYWn46VgdH4J/YJAEBPR4zRHnaY0dMZVmYGaq6OSPMxNGsAhmYiIqopwfeeYMWpaATHpQMA9HTFGOthjxk9ndDIlOGZqCwMzRqAoZmIiGrapbtpWHkqGqHxTwEA+rpivOfZFP/p4QRLE301V0ekeRiaNQBDMxERqYMgCPgn9glWnIpCeGIGAMBAIsZ4LwdM794MDYwZnomKMTRrAIZmIiJSJ0EQcCEmDStOReNaUgYAoJ6eDsZ7OWBa92awMNJTb4FEGoChWQMwNBMRkSYQBAHnoh5jZWA0ridnAgCM9HQwoYsDpnZrBvN6DM9UdzE0awCGZiIi0iSCIOD07UdYGRiNm/ezAADG+rqY1MUBk7s2g1k9iZorJKp5DM0agKGZiIg0kSAIOHnrIVYFxuD2g6LwbGKgi8ldHTGpqyNMDRieqe5gaNYADM1ERKTJFAoBJ26mYlVgDKIeZgMATA10MbVbM0zo4gAThmeqAxiaNQBDMxERaQOFQsCxyAf4MTAGMY9yAADm9SSY2q0ZfL0dYKyvq+YKiapPRfOauAZrKuHChQsYPHgwbGxsIBKJcOjQoXK3Wbt2LVq2bAlDQ0O4urpi+/btKusPHDgAd3d3mJubw8jICG5ubtixY4dyvUwmw7x589C2bVsYGRnBxsYG48ePx/3791X2k56ejnHjxsHU1BTm5uaYPHkycnJyXstxExERaRKxWIRB7WxwfFZ3/DjaDc0sjZCRJ8PyE1Ho9u0Z/HL+LvIKCtVdJpFaqTU05+bmon379li7dm2F2q9btw5+fn5YtGgRbt68icWLF2PmzJk4cuSIso2FhQXmz5+PoKAgXL9+HRMnTsTEiRNx4sQJAEBeXh7Cw8OxYMEChIeH48CBA4iKisKQIUNUXmvcuHG4efMmTp06haNHj+LChQuYNm3a6zt4IiIiDaMjFmGoWxOcmt0DK0e1h2NDIzzNk2HZX3fQ7duz2HjhHp4VyNVdJpFaaMzwDJFIhIMHD2LYsGFltvH29kaXLl2wfPly5bK5c+ciODgYFy9eLHO7jh07YuDAgViyZEmp60NDQ+Hh4YGEhATY29vj9u3baNWqFUJDQ+Hu7g4AOH78ON566y0kJyfDxsamQsfE4RlERKTNCuUKHIq4jzVnYpDwJA8A0NBYHx/0dMK4zvYwkOiouUKiV6cVwzMqSyqVwsDAQGWZoaEhQkJCIJPJSrQXBAGnT59GVFQUunfvXuZ+MzMzIRKJYG5uDgAICgqCubm5MjADQO/evSEWixEcHPzS+rKyslQeRERE2kpXR4zhnWwROKcHvhveDrb1DZGWI8WSo7fQ/buz2PpPHPJlvPJMdYNWheZ+/fohICAAV65cgSAICAsLQ0BAAGQyGdLS0pTtMjMzYWxsDD09PQwcOBBr1qxBnz59St1nfn4+5s2bhzFjxih/u0hNTUWjRo1U2unq6sLCwgKpqall1ufv7w8zMzPlw87O7jUcNRERkXpJdMQY6W6Hs5/2xLJ32qKJuSEeZUux6Mgt9Fx+DjuC4iEtZHim2k2rQvOCBQswYMAAeHp6QiKRYOjQofD19QUAiMX/OxQTExNEREQgNDQUS5cuxZw5c3Du3LkS+5PJZBg5ciQEQcC6deteuT4/Pz9kZmYqH0lJSa+8TyIiIk0h0RFjtIc9zn7aE0vfbgNrMwOkZuVjwR834bP8HHYFJ6CgUKHuMomqhVaFZkNDQ2zevBl5eXmIj49HYmIiHBwcYGJiAktLS2U7sVgMZ2dnuLm5Ye7cuRg+fDj8/f1V9lUcmBMSEnDq1CmVMSxWVlZ49OiRSvvCwkKkp6fDysqqzPr09fVhamqq8iAiIqpt9HTFGNe5Kc591hNLhrZGY1N93M/Mx/yDkfD5/hz2hCRCJmd4ptpFq0JzMYlEAltbW+jo6GDPnj0YNGiQypXmFykUCkilUuXz4sAcExODwMBANGjQQKW9l5cXMjIycOXKFeWyM2fOQKFQoHPnzq//gIiIiLSQvq4O3vdywPnPfLBwcCtYmugjJeMZvjhwA2/+cA6/hSWhkOGZagm1zlaek5OD2NhY5fO4uDhERETAwsIC9vb28PPzQ0pKinIu5ujoaISEhKBz5854+vQpVqxYgcjISGzbtk25D39/f7i7u8PJyQlSqRTHjh3Djh07lMMvZDIZhg8fjvDwcBw9ehRyuVw5TtnCwgJ6enpo2bIl+vfvj6lTp+KXX36BTCbDhx9+iNGjR1d45gwiIqK6wkCig4ldHDHGwx47Lyfgl/N3kZT+DJ//fh1rz8bi4zddMNTNBro6WnmtjgiAmqecO3fuHHx8fEos9/X1xdatWzFhwgTEx8crxyPfvn0bY8eORVRUFCQSCXx8fPDtt9/C1dVVue3//d//Ye/evUhOToahoSFatGiBTz75BKNGjQIAxMfHw9HRsdR6zp49i549ewIo+nKTDz/8EEeOHIFYLMa7776L1atXw9jYuMLHxynniIioLnpWIFeG5ye5BQAAx4ZG+KSXCwa3t4GOWKTmCon+h1+jrQEYmomIqC7LKyjE9qAErD9/F0/ziqaGdbI0wse9XDCoHcMzaQaGZg3A0ExERATkSAux7VI8Nly4h8xnReHZpZExPuntgrfaWEPM8ExqxNCsARiaiYiI/ic7X4at/8Rj49/3kJVfCABwbWyCWb1d0K+1FcMzqQVDswZgaCYiIiop85kMW/6Jw6a/45AtLQrPLa1NMau3C/q2agyRiOGZag5DswZgaCYiIipbZp4Mmy7ew+Z/4pHzb3hu08QUs3o1R6+WjRieqUYwNGsAhmYiIqLyPc0tQMDFe9jyTzzyCoq+jrudrRlm926Onq6WDM9UrRiaNQBDMxERUcWl5xZgw4V72HYpHs9kReHZzc4cs/s0R3eXhgzPVC0YmjUAQzMREVHlpeVIseHCPWwPike+rOgbBTvam2NOH1d0cW7A8EyvFUOzBmBoJiIiqrpH2flYf/4edl5OgLSwKDy/4VAfs/s0h7dTQzVXR7UFQ7MGYGgmIiJ6dY+y8vHzubv4NSQRBf+GZ89mFpjduzk6N2ug5upI2zE0awCGZiIiotcnNTMfP5+LxZ6QJBTIi8JzF+cGmN27OdwdLNRcHWkrhmYNwNBMRET0+t3PeIa1Z2PxW1gSZPKiGNPNpSFm92mOjvb11VwdaRuGZg3A0ExERFR9kp/mYe3ZWOwLS0ahoijO9HS1xOzezdHezly9xZHWYGjWAAzNRERE1S8pPQ9rzsRgf3gK5P+G5zdbNMLs3s3R1tZMzdWRpmNo1gAMzURERDUn4UkuVp+OxcGryfg3O6N3y8aY1dsFbZowPFPpGJo1AEMzERFRzbv3OAdrzsTij4gUZXju17oxZvVujpbW/P+YVDE0awCGZiIiIvWJfZSD1adjcOT6fRSnnbfaWuGTXs3hamWi3uJIYzA0awCGZiIiIvWLeZiNVadj8Of1BwAAkQgY2NYas3q7wLkRw3Ndx9CsARiaiYiINMed1Cz8GBiDvyJTARSF5yHtbfBxLxc4WRqruTpSF4ZmDcDQTEREpHlu3c/CqsBonLz1EAAgFgHD3Jrg414ucGhopObqqKYxNGsAhmYiIiLNFZmSiVWBMQi8XRSedcQivN2hCT5+0wX2DeqpuTqqKQzNGoChmYiISPNdT87AqsAYnLnzCEBReB7e0RYfvukMOwuG59qOoVkDMDQTERFpj6uJT7EqMAbnox8DAHTFIoxwt8OHbzqjibmhmquj6sLQrAEYmomIiLTPlYSnWBUYjb9j0gAAEh0RRr1hh5k+zrA2Y3iubRiaNQBDMxERkfYKjU/HylPRuHT3CQBAT0eMMR52mOHjjMamBmqujl4XhmYNwNBMRESk/YLuPsHKwGiExKUDAPR0xRjX2R4f9HRCIxOGZ23H0KwBGJqJiIhqB0EQlOE5NP4pAEBfV4z3PZtieg8nWJroq7lCqiqGZg3A0ExERFS7CIKAi7FpWHkqGuGJGQAAA4kYvl4OmNa9GRoYMzxrG4ZmDcDQTEREVDsJgoDz0Y+xMjAG15IyAAD19HTg6+2Aad2aob6RnnoLpApjaNYADM1ERES1myAIOBv1CCtPxeBGSiYAwEhPBxO7OGJKN0eY12N41nQMzRqAoZmIiKhuEAQBgbcfYeWpaNx6kAUAMNHXxcSujpjc1RFmhhI1V0hlYWjWAAzNREREdYsgCDhx8yFWBUbjTmo2AMDEQBdTujbDxK4OMDVgeNY0DM0agKGZiIioblIoBBy/mYpVgdGIfpgDADAzlGBqN0dM6OIIY31dNVdIxRiaNQBDMxERUd2mUAj488YD/Hg6BrGPisKzeT0JpnZrhgneDjBieFY7hmYNwNBMREREACBXCDh6/T5+PB2De49zAQAWRnqY1r0Zxns1RT09hmd1YWjWAAzNRERE9Dy5QsDhayn4MTAG8U/yAAANjfUwvbsT3vNsCkM9HTVXWPcwNGsAhmYiIiIqTaFcgUMR97H6dAwS04vDsz4+6OmEcZ3tYSBheK4pDM0agKGZiIiIXkYmV+BgeApWn4lB8tNnAIBGJvqY0dMJoz0YnmsCQ7MGYGgmIiKiiigoVGB/eDJ+OhOLlIyi8GxlaoCZbzpjpLst9HUZnqsLQ7MGYGgmIiKiypAWyrEvLBlrz8biQWY+AMDGzAAfvumC4Z1soacrVnOFtQ9DswZgaCYiIqKqkBbKsTc0CWvPxuJhlhQA0MTcEB+96Yx3O9lCosPw/LowNGsAhmYiIiJ6FfkyOXaHJOLnc3fxOLsoPNtZGOKjN13wTocm0GV4fmUMzRqAoZmIiIheh3yZHDsvJ+CX83eRllMAAHBoUA8fvemCoW42DM+vgKFZAzA0ExER0euUV1D4b3i+h/TcovDcrKERPu7lgsHtbaAjFqm5Qu3D0KwBGJqJiIioOuRKC7E9KAHrL9xFRp4MAOBkaYRPejfHoLbWEDM8VxhDswZgaCYiIqLqlCMtxLZL8dhw4R4ynxWF5+aNjfFJr+YY0MaK4bkCGJo1AEMzERER1YSsfBm2/hOPgL/vISu/EADQwsoEs3q7oG8rhueXYWjWAAzNREREVJMyn8mw+WIcNl+MQ7a0KDy3tDbF7N4u6NOqMUQihucXMTRrAIZmIiIiUofMPBkCLt7Dln/ikfNveG7TxBSzezfHmy0aMTw/h6FZAzA0ExERkTo9zS3Axr/vYeuleOQVyAEA7W3NMKtPc/RsbsnwDIZmjcDQTERERJrgSY4UG/6+h+2XEvBMVhSe3ezMMadPc3RzaVinwzNDswZgaCYiIiJNkpYjxfrzd7HjcgLyZQoAQKem9TGnT3N4OzWok+GZoVkDMDQTERGRJnqUnY9fzt3DruAESAuLwrOHowVm924OL6cGaq6uZlU0r6n1OxcvXLiAwYMHw8bGBiKRCIcOHSp3m7Vr16Jly5YwNDSEq6srtm/frrL+wIEDcHd3h7m5OYyMjODm5oYdO3aUaNO3b180aFD0G1VERESJ17l79y7efvttWFpawtTUFCNHjsTDhw9f5XCJiIiINEIjEwN8NbgVLnzugwneDtDTFSMkLh1jNl7G6A1BCL73RN0lahy1hubc3Fy0b98ea9eurVD7devWwc/PD4sWLcLNmzexePFizJw5E0eOHFG2sbCwwPz58xEUFITr169j4sSJmDhxIk6cOKHyul27dsW3335bZl19+/aFSCTCmTNn8M8//6CgoACDBw+GQqF4tYMmIiIi0hCNTQ2waEhrXPjMB+O9mkJPR4zL99IxasNljAu4jLD4dHWXqDE0ZniGSCTCwYMHMWzYsDLbeHt7o0uXLli+fLly2dy5cxEcHIyLFy+WuV3Hjh0xcOBALFmyRGV5fHw8HB0dcfXqVbi5uSmXnzx5EgMGDMDTp0+Vl+kzMzNRv359nDx5Er17967QMXF4BhEREWmTlIxnWHs2FvvCkiCTF0XEbi4NMbtPc3S0r6/m6qqHVgzPqCypVAoDAwOVZYaGhggJCYFMJivRXhAEnD59GlFRUejevXulXkckEkFfX1+5zMDAAGKx+KXhXCqVIisrS+VBREREpC2amBvim7fb4szcnhj9hh10xSL8HZOGd36+hAlbQnAtKUPdJaqNVoXmfv36ISAgAFeuXIEgCAgLC0NAQABkMhnS0tKU7TIzM2FsbAw9PT0MHDgQa9asQZ8+fSr8Op6enjAyMsK8efOQl5eH3NxcfPrpp5DL5Xjw4EGZ2/n7+8PMzEz5sLOze6XjJSIiIlIHO4t6WPZuO5yZ2xMjOtlCRyzCuajHGLr2H0zeGorIlEx1l1jjtCo0L1iwAAMGDICnpyckEgmGDh0KX19fAIBY/L9DMTExQUREBEJDQ7F06VLMmTMH586dq/DrWFpaYt++fThy5AiMjY1hZmaGjIwMdOzYUeV1XuTn54fMzEzlIykpqcrHSkRERKRu9g3qYfmI9jg9pwfe6dgEYhFw+s4jDFpzEVO3h+Hm/boTnnXVXUBlGBoaYvPmzVi/fj0ePnwIa2trbNiwASYmJrC0tFS2E4vFcHZ2BgC4ubnh9u3b8Pf3R8+ePSv8Wn379sXdu3eRlpYGXV1dmJubw8rKCs2aNStzG319fZUhHURERES1gUNDI6wY6YYPfZyx+nQM/rh2H6duPcSpWw/Rv7UVZvVxQQur2n3/llZdaS4mkUhga2sLHR0d7NmzB4MGDXrpFWCFQgGpVFql12rYsCHMzc1x5swZPHr0CEOGDKlq2URERERarZmlMVaN7oBTs3tgSHsbiETA8Zup6L/qb8zcFY7oh9nqLrHaqPVKc05ODmJjY5XP4+LiEBERAQsLC9jb28PPzw8pKSnKuZijo6MREhKCzp074+nTp1ixYgUiIyOxbds25T78/f3h7u4OJycnSKVSHDt2DDt27MC6deuUbdLT05GYmIj79+8DAKKiogAAVlZWsLKyAgBs2bIFLVu2hKWlJYKCgvDJJ59g9uzZcHV1rfZ+ISIiItJkzo2MsXpMB3z4pjN+PB2DP68/wJ83HuBY5AMMameDT3o5w7mRibrLfK3UGprDwsLg4+OjfD5nzhwAgK+vL7Zu3YoHDx4gMTFRuV4ul+OHH35AVFQUJBIJfHx8cOnSJTg4OCjb5ObmYsaMGUhOToahoSFatGiBnTt3YtSoUco2hw8fxsSJE5XPR48eDQBYuHAhFi1aBKAoSPv5+SE9PR0ODg6YP38+Zs+eXR3dQERERKSVmjc2wdqxHfHRm1n4MTAGf0Wm4si1+zh6/T6GtrfBx71c0MzSWN1lvhYaM09zbcR5momIiKguuXk/Ez8GxuDkraJvURaLgGEdmuDjN13g0NBIzdWVrqJ5jaG5GjE0ExERUV0UmZKJVYHRCLz9CACgIxbhnQ5N8NGbLrBvUE/N1aliaNYADM1ERERUl11LysCqwGicjXoMANAVizC8ky1m+jjDzkIzwjNDswZgaCYiIiICwhOfYlVgDC5EF4VniY4II9ztMNPHGU3MDZXt5AoBIXHpeJSdj0YmBvBwtICOWFSttTE0awCGZiIiIqL/uZKQjpWnYnAxtuibnCU6Iox+wx4zfJxwLSkDi4/cwoPMfGV7azMDLBzcCv3bWFdbTQzNGoChmYiIiKikkLh0rDwVjaB7TwAUDdsoVJSMpMXXmNe917HagnNF85pWfrkJEREREWkvD0cL7J7mid1TPfGGQ/1SAzMAFC9dfOQW5GW0qSkMzURERESkFl5ODTCnT/OXthEAPMjMR0hces0UVQaGZiIiIiJSm0fZ0gq2yy+/UTViaCYiIiIitWlkYvBa21UXhmYiIiIiUhsPRwtYmxmgrInlRCiaRcPD0aImyyqBoZmIiIiI1EZHLMLCwa0AoERwLn6+cHCrap+vuTwMzURERESkVv3bWGPdex1hZaY6BMPKzKBap5urDF11F0BERERE1L+NNfq0sqrxbwSsKIZmIiIiItIIOmIRvJwaqLuMUnF4BhERERFRORiaiYiIiIjKwdBMRERERFQOhmYiIiIionIwNBMRERERlYOhmYiIiIioHAzNRERERETlYGgmIiIiIioHQzMRERERUTkYmomIiIiIysHQTERERERUDoZmIiIiIqJyMDQTEREREZVDV90F1GaCIAAAsrKy1FwJEREREZWmOKcV57ayMDRXo+zsbACAnZ2dmishIiIiopfJzs6GmZlZmetFQnmxmqpMoVDg/v37MDExgUgkqvbXy8rKgp2dHZKSkmBqalrtr6dN2DelY7+Ujv1SNvZN6dgvZWPflI79Uraa7htBEJCdnQ0bGxuIxWWPXOaV5mokFotha2tb469ramrKD2AZ2DelY7+Ujv1SNvZN6dgvZWPflI79Uraa7JuXXWEuxhsBiYiIiIjKwdBMRERERFQOhuZaRF9fHwsXLoS+vr66S9E47JvSsV9Kx34pG/umdOyXsrFvSsd+KZum9g1vBCQiIiIiKgevNBMRERERlYOhmYiIiIioHAzNRERERETlYGgmIiIiIioHQ7OG8vf3xxtvvAETExM0atQIw4YNQ1RUVLnb7du3Dy1atICBgQHatm2LY8eOqawXBAFfffUVrK2tYWhoiN69eyMmJqa6DuO1q0q/bNy4Ed26dUP9+vVRv3599O7dGyEhISptJkyYAJFIpPLo379/dR7Ka1eVvtm6dWuJ4zYwMFBpUxffMz179izRLyKRCAMHDlS2qQ3vmXXr1qFdu3bKLxDw8vLCX3/99dJtavs5Bqh8v9SVcwxQ+b6pC+cYoPL9UlfOMS9atmwZRCIRZs2a9dJ2mnqeYWjWUOfPn8fMmTNx+fJlnDp1CjKZDH379kVubm6Z21y6dAljxozB5MmTcfXqVQwbNgzDhg1DZGSkss13332H1atX45dffkFwcDCMjIzQr18/5Ofn18RhvbKq9Mu5c+cwZswYnD17FkFBQbCzs0Pfvn2RkpKi0q5///548OCB8rF79+7qPpzXqip9AxR949Lzx52QkKCyvi6+Zw4cOKDSJ5GRkdDR0cGIESNU2mn7e8bW1hbLli3DlStXEBYWhjfffBNDhw7FzZs3S21fF84xQOX7pa6cY4DK9w1Q+88xQOX7pa6cY54XGhqK9evXo127di9tp9HnGYG0wqNHjwQAwvnz58tsM3LkSGHgwIEqyzp37ixMnz5dEARBUCgUgpWVlbB8+XLl+oyMDEFfX1/YvXt39RRezSrSLy8qLCwUTExMhG3btimX+fr6CkOHDq2GCtWnIn2zZcsWwczMrMz1fM8UWblypWBiYiLk5OQol9XG94wgCEL9+vWFgICAUtfVxXNMsZf1y4vqyjmm2Mv6pi6eY4pV5j1T288x2dnZgouLi3Dq1CmhR48ewieffFJmW00+z/BKs5bIzMwEAFhYWJTZJigoCL1791ZZ1q9fPwQFBQEA4uLikJqaqtLGzMwMnTt3VrbRNhXplxfl5eVBJpOV2ObcuXNo1KgRXF1d8cEHH+DJkyevtdaaVtG+ycnJQdOmTWFnZ1fiygjfM0U2bdqE0aNHw8jISGV5bXrPyOVy7NmzB7m5ufDy8iq1TV08x1SkX15UV84xFe2bunaOqcp7prafY2bOnImBAweWOH+URpPPM7rVund6LRQKBWbNmoUuXbqgTZs2ZbZLTU1F48aNVZY1btwYqampyvXFy8pqo00q2i8vmjdvHmxsbFQ+cP3798c777wDR0dH3L17F19++SUGDBiAoKAg6OjoVEf51aqifePq6orNmzejXbt2yMzMxPfffw9vb2/cvHkTtra2fM8ACAkJQWRkJDZt2qSyvLa8Z27cuAEvLy/k5+fD2NgYBw8eRKtWrUptW5fOMZXplxfV9nNMZfqmLp1jqvqeqe3nmD179iA8PByhoaEVaq/J5xmGZi0wc+ZMREZG4uLFi+ouRaNUpV+WLVuGPXv24Ny5cyo3o4wePVr577Zt26Jdu3ZwcnLCuXPn0KtXr9dad02oaN94eXmpXAnx9vZGy5YtsX79eixZsqS6y6xxVXnPbNq0CW3btoWHh4fK8trynnF1dUVERAQyMzPx+++/w9fXF+fPn69wQKytqtovdeEcU5m+qUvnmKq+Z2rzOSYpKQmffPIJTp06VeIGUG3E4Rka7sMPP8TRo0dx9uxZ2NravrStlZUVHj58qLLs4cOHsLKyUq4vXlZWG21RmX4p9v3332PZsmU4efJkuTciNGvWDA0bNkRsbOzrKLdGVaVvikkkEnTo0EF53HX9PZObm4s9e/Zg8uTJ5bbV1veMnp4enJ2d0alTJ/j7+6N9+/b48ccfS21bl84xlemXYnXlHFOVvilWm88xVemX2n6OuXLlCh49eoSOHTtCV1cXurq6OH/+PFavXg1dXV3I5fIS22jyeYahWUMJgoAPP/wQBw8exJkzZ+Do6FjuNl5eXjh9+rTKslOnTil/y3d0dISVlZVKm6ysLAQHB1d43JW6VaVfgKI7bZcsWYLjx4/D3d293PbJycl48uQJrK2tX7XkGlPVvnmeXC7HjRs3lMddl98zQNG0R1KpFO+99165bbXxPVMahUIBqVRa6rq6cI4py8v6Bagb55iylNc3z6uN55iyVKRfavs5plevXrhx4wYiIiKUD3d3d4wbNw4RERGlDjPR6PNMtd5mSFX2wQcfCGZmZsK5c+eEBw8eKB95eXnKNu+//77wxRdfKJ//888/gq6urvD9998Lt2/fFhYuXChIJBLhxo0byjbLli0TzM3NhT/++EO4fv26MHToUMHR0VF49uxZjR5fVVWlX5YtWybo6ekJv//+u8o22dnZgiAU3dX76aefCkFBQUJcXJwQGBgodOzYUXBxcRHy8/Nr/Birqip9s3jxYuHEiRPC3bt3hStXrgijR48WDAwMhJs3byrb1MX3TLGuXbsKo0aNKrG8trxnvvjiC+H8+fNCXFyccP36deGLL74QRCKRcPLkSUEQ6uY5RhAq3y915RwjCJXvm7pwjhGEyvdLsdp+jinNi7NnaNN5hqFZQwEo9bFlyxZlmx49egi+vr4q2/32229C8+bNBT09PaF169bCn3/+qbJeoVAICxYsEBo3bizo6+sLvXr1EqKiomrgiF6PqvRL06ZNS91m4cKFgiAIQl5entC3b1/B0tJSkEgkQtOmTYWpU6cKqampNXtwr6gqfTNr1izB3t5e0NPTExo3biy89dZbQnh4uMp+6+J7RhAE4c6dOwIA5X96z/v/du4/Jur6jwP48/ghXX6OX50C2k3GjgEqRmqTfqxLoBSanWbSTD2QSVmiVrOxySrLNZczJSGLTcXmmptb4ezH4tcQjcKD4bEkhgdhR/OQn0expSC8vn80P+MCObQf6pfnY/v8ce/3+/P+8Rp89rr33p/7f/mbycjIkFmzZsmUKVNk2rRpkpiY6LbeyfiMEbn5uEyWZ4zIzcdmMjxjRG7tf2kyPGPG8tek+W56zmhERP7dvWwiIiIiorsbzzQTEREREXnApJmIiIiIyAMmzUREREREHjBpJiIiIiLygEkzEREREZEHTJqJiIiIiDxg0kxERERE5AGTZiIiIiIiD5g0ExERERF5wKSZiCaN9PR0LF++3K2ss7MTc+fOxaJFi9DX13d7JkZERHc8Js1ENGl1dnYiISEBWq0WJSUlCAgIuN1TIiKiOxSTZiKalLq6upCYmAg/Pz+Ulpa6JcwOhwNmsxmKosDf3x+pqam4fPmy2/0XL16ERqMZdblcLgDAjh07EBcXp7YfGBiA0Wh0azPWzrdGo8GJEyfUz21tbUhNTUVgYCCCg4NhNptx8eJFt3sOHz6MOXPmwM/PD2FhYcjKygIAhIeHjzlHjUaDI0eOqONdv/z9/fHkk0+ipaVF7bu3txcWiwVBQUG49957kZycDLvdPm5sR65BRGCxWDBv3jz09vZOOH4tLS0wm80ICQmBoih46KGHUFZW5jbO1atXkZ2dDYPBAD8/PxiNRhw6dOiGfV+/rsfv/PnzSE5OhqIoCAkJwbp169DV1aX2/8QTTyArKwtZWVkICAiAXq/Hm2++CRGZcHyOHDmijuvt7Y0ZM2YgOzsbw8PD48aQiO48TJqJaNLp7u5GUlISfHx8UFpaisDAQLVueHgYZrMZPT09qKysRGlpKX7++Wc8//zzbn1cT5zKysrgdDrx+eefjztmfn7+qMTbk8HBQSxZsgQ6nQ5nzpxBVVUVFEXB0qVLMTAwAAD4+OOPsWnTJrz44ov48ccfcfLkSRiNRgBATU0NnE4nnE4n7r//fuTm5qqfR66nsLAQTqcTp0+fRkdHB7Zv367Wpaeno7a2FidPnsQPP/wAEUFKSgoGBwcntIYtW7bg+++/R0lJCYKCgtRyT/Hr7+9HSkoKysvLce7cOSxduhTLli2Dw+FQ21gsFhw7dgz79+9HY2MjCgoKoCgKDAaDuk6r1QoAsFqtapnBYIDL5UJCQgIefPBB1NbW4ttvv8Xly5eRmprqNo9PP/0UPj4+sFqt+PDDD7F3714cPHjwpuLj7+8Pp9MJh8OBffv2Yffu3SguLp5Q/IjoDiJERJNEWlqaPP744xIXFye+vr4SHx8v165dc2tTUlIi3t7e4nA41LKGhgYBIFarVS1ramoSAHL+/HkREamoqBAA0tvbKyIib7/9tjzwwAMiItLd3S1BQUGyc+dOtzYbN26Up556ym18AFJUVCQiIkePHpWoqCgZHh5W669evSparVaKi4tFRGTGjBmSk5Pjce2zZs2SwsLCUeUjx3O5XPLoo49KZmamiIhcuHBBAEhVVZXavqurS7RarRw/fvyGY13vMycnR2bOnCmtra2j2niK31jmzJkjeXl5bveXlpaOu+7W1lYBMGoOO3fuHBX7trY2ASBNTU0iImIymSQmJsYt/tnZ2RITEyMiE4tPYWGhBAQEqPVnz54VLy8vt3uI6O7AnWYimlROnz6N4eFh2Gw2NDc3Y/fu3W71jY2NMBgMMBgMatns2bMRGBiIxsZGtey3334DAEydOtXjmO+++y4WL16Mxx57zK187ty5qK6uRmtr65j31dfXo7m5GTqdDoqiQFEUBAcH48qVK2hpaUFHRwcuXbqExMTECa9/LKtXr4aiKAgKCsLvv/+OXbt2AfgzFj4+Pli0aJHa9r777kNUVJRbLMaSn5+P9957D1FRUQgPDx9V7yl+/f392LZtG2JiYhAYGAhFUdDY2KjuNNtsNnh7e8NkMt3KklFfX4+Kigo1roqiIDo6GgDcjqfEx8dDo9Gonx9++GHY7XYMDQ1NOD59fX1QFAVarRbx8fHIzs7GI488ckvzJqLbx+d2T4CI6L8UERGB8vJy6PV6HDhwAGvXrsXTTz+NefPm3VQ/ly5dgpeXF0JDQ8dtZ7fbcfDgQdhsNvz6669udRkZGSgqKkJERMSYyWN/fz8WLFiAzz77bFTdtGnT4OX1z+x77Nu3D0lJSXC5XMjJyUF6ejq+/PLLv9Wn1WrFN998g/T0dBQUFOCll15yq/cUv23btqG0tBR79uyB0WiEVqvFc889px5L0Wq1f2t+/f39WLZsGd5///1RdWFhYX+r77/S6XSoq6uDiKChoQEZGRlYsGABVq5c+Y+OQ0T/Lu40E9GkEhsbC71eDwBYtWoVnn32WVgsFjUZi4mJQVtbG9ra2tR7fvrpJ7hcLsyePVstq6mpQXR0NO65555xx8vOzsaGDRvUc8YjabValJWVob29HTabDTabza1+/vz5sNvtmD59OoxGo9sVEBAAnU6H8PBwlJeX32o4AAChoaEwGo1YuHAhNm/ejK+//hqDg4OIiYnBtWvXcPbsWbVtd3c3mpqa3GIxltzcXCQnJ+PAgQN444033M4iA57jV1VVhfT0dKxYsQKxsbEIDQ11ewEyNjYWw8PDqKysvKU1z58/Hw0NDQgPDx8V25FfYEauHQCqq6sRGRkJb2/vCcfHy8sLRqMRkZGRWL58ORISElBUVHRL8yai24dJMxFNah999BE6OjrwzjvvAACSkpIQGxuLNWvWoK6uDlarFRaLBSaTCQsXLsTAwACOHj2KvXv3Yv369eP23dzcjFOnTuGtt94at11ISIiasI20Zs0a6PV6mM1mnDlzBq2trTh16hS2bNmi7lrv2LEDH3zwAfbv3w+73Y66ujrk5eXdVAxcLhfa29vR1NSEQ4cOISIiAr6+voiMjITZbEZmZia+++471NfXY+3atZg5cybMZvO4fQYHBwMAVq5ciZSUFGzYsAEAJhy/yMhIfPHFF7DZbKivr8cLL7zg9osT4eHhSEtLQ0ZGBk6cOKHG5vjx4xNa86ZNm9DT04PVq1ejpqYGLS0tKC4uxvr16zE0NKS2czgceP3119HU1IRjx44hLy8PW7duVec4kfiICNrb2+F0OlFRUYHKykr1KAgR3UVu96FqIqL/SlpampjN5lHlX331lXh7e0t1dbWIiPzyyy/yzDPPyNSpU0Wn08mqVaukvb1dRERqa2slIiJCdu3aJUNDQ2ofY70ICED27NlzwzZjwYgX80REnE6nWCwW0ev14ufnJxEREZKZmSl9fX1qm08++USioqLE19dXwsLCZPPmzaP6He9FwOuXTqcTk8kk586dU+t7enpk3bp1EhAQIFqtVpYsWSIXLly44fzHWkNnZ6dMnz5dCgoKJhy/1tZWWbx4sWi1WjEYDJKfny8mk0m2bt2q3vPHH3/Ia6+9JmFhYTJlyhQxGo1y+PBht7nc6EVAkT9f5FuxYoUEBgaKVquV6OhoefXVV9UX/0wmk7zyyiuyceNG8ff3l6CgINm+fbvbi4Ge4lNYWKjGV6PRSGhoqLz88sty5cqVcWNIRHcejciIH5wkIiIiAH/+TnNcXBxyc3Nv91SI6A7A4xlERERERB4waSYiIiIi8oDHM4iIiIiIPOBOMxERERGRB0yaiYiIiIg8YNJMREREROQBk2YiIiIiIg+YNBMRERERecCkmYiIiIjIAybNREREREQeMGkmIiIiIvLgfxHQPGhLNStXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Метод локтя\n",
    "from cuml.cluster import KMeans\n",
    "\n",
    "border_l = 2\n",
    "border_r = 5\n",
    "\n",
    "inertia = []\n",
    "for k in range(border_l, border_r):\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(df_scaled)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "\n",
    "# Визуализация метода локтя\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(range(border_l, border_r), inertia, marker='o')\n",
    "plt.title('Метод локтя для выбора количества кластеров')\n",
    "plt.xlabel('Количество кластеров')\n",
    "plt.ylabel('Инерция')\n",
    "plt.show()\n",
    "\n",
    "# Коэффициент силуэта\n",
    "from cuml.metrics import silhouette_score\n",
    "\n",
    "silhouette_scores = []\n",
    "for k in range(border_l, border_r):\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(df_scaled)\n",
    "    score = silhouette_score(df_scaled, kmeans.labels_)\n",
    "    silhouette_scores.append(score)\n",
    "\n",
    "# Визуализация коэффициента силуэта\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(range(border_l, border_r), silhouette_scores, marker='o')\n",
    "plt.title('Коэффициент силуэта для различных кластеров')\n",
    "plt.xlabel('Количество кластеров')\n",
    "plt.ylabel('Коэффициент силуэта')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Кластеризация с помощью K-means\n",
    "После выбора оптимального числа кластеров (например, 3), мы применим K-means для кластеризации и визуализируем результаты.  \n",
    "Ключевой термин:\n",
    "- K-means — алгоритм кластеризации, который группирует данные вокруг центров (центроидов) кластеров."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Кластеризация с помощью K-means\n",
    "optimal_clusters = 3\n",
    "kmeans = KMeans(n_clusters=optimal_clusters, random_state=42)\n",
    "df['Cluster'] = kmeans.fit_predict(df_scaled)\n",
    "\n",
    "# Визуализация кластеров с использованием PCA\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(df_pca[:, 0], df_pca[:, 1], c=df['Cluster'], cmap='viridis', edgecolor='k', alpha=0.6)\n",
    "plt.title(\"Кластеры, определенные K-means (PCA)\")\n",
    "plt.xlabel(\"Главная компонента 1\")\n",
    "plt.ylabel(\"Главная компонента 2\")\n",
    "plt.colorbar(label='Кластер')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Иерархическая кластеризация\n",
    "Применяем иерархическую кластеризацию для сравнения. Также строим дендрограмму.  \n",
    "Ключевой термин:\n",
    "- Иерархическая кластеризация — метод, который строит древовидную структуру кластеров (дендрограмму)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cuml.cluster import AgglomerativeClustering\n",
    "from scipy.cluster.hierarchy import dendrogram\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Применение иерархической кластеризации\n",
    "hierarchical = AgglomerativeClustering(n_clusters=optimal_clusters)\n",
    "df['Hierarchical Cluster'] = hierarchical.fit_predict(df_scaled)\n",
    "\n",
    "# Функция для получения матрицы linkage\n",
    "def get_linkage_matrix(model: AgglomerativeClustering) -> np.ndarray:\n",
    "    counts = np.zeros(model.children_.shape[0])  # type: ignore\n",
    "    n_samples = len(model.labels_)\n",
    "    for i, merge in enumerate(model.children_):  # type: ignore\n",
    "        current_count = 0\n",
    "        for child_idx in merge:\n",
    "            if child_idx < n_samples:\n",
    "                current_count += 1\n",
    "            else:\n",
    "                current_count += counts[child_idx - n_samples]\n",
    "        counts[i] = current_count\n",
    "\n",
    "    return np.column_stack([model.children_, model.distances_, counts]).astype(float)\n",
    "\n",
    "# Построение дендрограммы\n",
    "linkage_matrix = get_linkage_matrix(hierarchical)\n",
    "plt.figure(figsize=(12, 8))\n",
    "dendrogram(linkage_matrix)\n",
    "plt.title(\"Дендограмма, восстановленная из модели AgglomerativeClustering\")\n",
    "plt.xlabel(\"Индексы объектов\")\n",
    "plt.ylabel(\"Евклидово расстояние\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Визуализация кластеров\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(df_pca[:, 0], df_pca[:, 1], c=df['Hierarchical Cluster'], cmap='viridis', edgecolor='k', alpha=0.6)\n",
    "plt.title(\"Кластеры, определенные иерархической кластеризацией (PCA)\")\n",
    "plt.xlabel(\"Главная компонента 1\")\n",
    "plt.ylabel(\"Главная компонента 2\")\n",
    "plt.colorbar(label='Кластер')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Оценка качества кластеризации\n",
    "Оценим качество кластеров, сравнив коэффициенты силуэта для двух методов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Оценка качества\n",
    "silhouette_kmeans = silhouette_score(df_scaled, df['Cluster'])\n",
    "silhouette_hierarchical = silhouette_score(df_scaled, df['Hierarchical Cluster'])\n",
    "\n",
    "print(f\"Коэффициент силуэта для K-means: {silhouette_kmeans:.4f}\")\n",
    "print(f\"Коэффициент силуэта для иерархической кластеризации: {silhouette_hierarchical:.4f}\")"
   ]
  }
 ],
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