lab2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Лабораторная работа №2\n",
    "\n",
    "# Методы снижения размерности\n",
    "\n",
    "Седунов Илья, <br>\n",
    "Альперович Вадим, <br>\n",
    "МАГ-ИАД21."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from tqdm import tqdm_notebook\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выполнить первичную обработку данных. Сделать первые выводы."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Data loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|col 1 |col 2 |col 3 |col 4 |col 5 |col 6 |\n",
    "| - | - | - | - | - | -|\n",
    "| Возраст (лет)  | Количество иждивенцев     | Доход (руб./мес.)| Срок проживания в регионе (лет) | Оценка автомобиля заемщика (USD) | Размер кредита (тыс. руб.)|\n",
    "| age  | dependents | income| residence | car_valuation | loan_amount |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 56 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "columns = ['age', 'dependents', 'income', 'residence', 'car_valuation', 'loan_amount']\n",
    "df = pd.read_excel('data/HW_2_v7.xls', \n",
    "                   header=None,\n",
    "                   names=columns\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>age</th>\n",
       "      <th>dependents</th>\n",
       "      <th>income</th>\n",
       "      <th>residence</th>\n",
       "      <th>car_valuation</th>\n",
       "      <th>loan_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>16700</td>\n",
       "      <td>18</td>\n",
       "      <td>18000</td>\n",
       "      <td>177000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>33800</td>\n",
       "      <td>6</td>\n",
       "      <td>34000</td>\n",
       "      <td>337000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>12900</td>\n",
       "      <td>9</td>\n",
       "      <td>12000</td>\n",
       "      <td>126000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>17700</td>\n",
       "      <td>13</td>\n",
       "      <td>18000</td>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>20000</td>\n",
       "      <td>19</td>\n",
       "      <td>22000</td>\n",
       "      <td>211000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  dependents  income  residence  car_valuation  loan_amount\n",
       "0   54           3   16700         18          18000       177000\n",
       "1   36           2   33800          6          34000       337000\n",
       "2   37           1   12900          9          12000       126000\n",
       "3   44           2   17700         13          18000       180000\n",
       "4   54           4   20000         19          22000       211000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Simple stats**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows count: 50 Cols num: 6\n"
     ]
    }
   ],
   "source": [
    "print(f'Rows count: {len(df)} Cols num: {df.shape[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype\n",
      "---  ------         --------------  -----\n",
      " 0   age            50 non-null     int64\n",
      " 1   dependents     50 non-null     int64\n",
      " 2   income         50 non-null     int64\n",
      " 3   residence      50 non-null     int64\n",
      " 4   car_valuation  50 non-null     int64\n",
      " 5   loan_amount    50 non-null     int64\n",
      "dtypes: int64(6)\n",
      "memory usage: 2.5 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>age</th>\n",
       "      <th>dependents</th>\n",
       "      <th>income</th>\n",
       "      <th>residence</th>\n",
       "      <th>car_valuation</th>\n",
       "      <th>loan_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19992.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20060.0</td>\n",
       "      <td>199960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10002.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10096.3</td>\n",
       "      <td>100021.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>12600.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>126500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>18500.0</td>\n",
       "      <td>183000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>46.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>29075.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>29750.0</td>\n",
       "      <td>293000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>56.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>43300.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>44000.0</td>\n",
       "      <td>437000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  dependents   income  residence  car_valuation  loan_amount\n",
       "count  50.0        50.0     50.0       50.0           50.0         50.0\n",
       "mean   40.0         2.0  19992.0       10.0        20060.0     199960.0\n",
       "std     8.0         1.0  10002.1        5.0        10096.3     100021.8\n",
       "min    24.0         0.0   1500.0        0.0            0.0       4000.0\n",
       "25%    35.0         1.2  12600.0        6.0        13000.0     126500.0\n",
       "50%    40.0         2.0  17900.0       10.0        18500.0     183000.0\n",
       "75%    46.0         2.8  29075.0       14.0        29750.0     293000.0\n",
       "max    56.0         4.0  43300.0       19.0        44000.0     437000.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(df.describe(), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Charts & dists**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=3) \n",
    "axes = axes.flatten()        \n",
    "fig.set_size_inches(12, 7)\n",
    "\n",
    "for ax, col in zip(axes, df.columns):\n",
    "    sns.distplot(df[col], ax = ax, bins=10)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x576 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.pairplot(data=df, diag_kind=\"kde\")\n",
    "g.fig.set_figwidth(11)\n",
    "g.fig.set_figheight(8)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr(method='pearson')\n",
    "plt.figure(figsize=(7, 5))\n",
    "sns.heatmap(corr, cmap=\"Blues\", annot=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Dimensionality reduction via PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Выполнить центрирование данных и такую нормировку, что евклидова норма каждого признака\n",
    "равна 1 (полученная матрица обозначается через Z).\n",
    "- Найти SVD разложение матрицы Z (матрицы U, S, V). Докажите, что при выбранной\n",
    "нормировке, коэффициенты разложения столбцов матрицы Z по скрытым факторам являются\n",
    "корреляциями этих столбцов и скрытых факторов.\n",
    "- Исследуйте, сколько скрытых факторов (главные факторы) достаточно для хорошей\n",
    "аппроксимации матрицы данных Z. Заменить значения исходных признаков линейными\n",
    "комбинациями главных факторов (матрица Z k , k – число главных факторов). Оценить\n",
    "погрешность аппроксимации в евклидовой матричной норме и норме Фробениуса.\n",
    "- Сравнить матрицы корреляций признаков для матрицы Z и матрицы Z k . Оценить качество\n",
    "аппроксимации по близости матриц корреляций (можно использовать относительную\n",
    "погрешность в одной из матричных норм)\n",
    "- Используя выделенные главные факторы, найти представления клиентов векторами меньшей\n",
    "размерности (размерности k).\n",
    "- Используя представление клиентов векторами меньшей размерности, построить карту\n",
    "клиентов. Сделать выводы о клиентах банка."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html#sklearn.preprocessing.normalize\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, normalize\n",
    "from sklearn.decomposition import PCA, TruncatedSVD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data normalization**\n",
    "\n",
    "*Выполнить центрирование данных и такую нормировку, что евклидова норма каждого признака равна 1*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>age</th>\n",
       "      <th>dependents</th>\n",
       "      <th>income</th>\n",
       "      <th>residence</th>\n",
       "      <th>car_valuation</th>\n",
       "      <th>loan_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.187243</td>\n",
       "      <td>0.19245</td>\n",
       "      <td>0.105862</td>\n",
       "      <td>0.22752</td>\n",
       "      <td>0.113581</td>\n",
       "      <td>0.112182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.124829</td>\n",
       "      <td>0.12830</td>\n",
       "      <td>0.214259</td>\n",
       "      <td>0.07584</td>\n",
       "      <td>0.214542</td>\n",
       "      <td>0.213590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.128296</td>\n",
       "      <td>0.06415</td>\n",
       "      <td>0.081773</td>\n",
       "      <td>0.11376</td>\n",
       "      <td>0.075721</td>\n",
       "      <td>0.079859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.152568</td>\n",
       "      <td>0.12830</td>\n",
       "      <td>0.112201</td>\n",
       "      <td>0.16432</td>\n",
       "      <td>0.113581</td>\n",
       "      <td>0.114084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.187243</td>\n",
       "      <td>0.25660</td>\n",
       "      <td>0.126780</td>\n",
       "      <td>0.24016</td>\n",
       "      <td>0.138821</td>\n",
       "      <td>0.133732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  dependents    income  residence  car_valuation  loan_amount\n",
       "0  0.187243     0.19245  0.105862    0.22752       0.113581     0.112182\n",
       "1  0.124829     0.12830  0.214259    0.07584       0.214542     0.213590\n",
       "2  0.128296     0.06415  0.081773    0.11376       0.075721     0.079859\n",
       "3  0.152568     0.12830  0.112201    0.16432       0.113581     0.114084\n",
       "4  0.187243     0.25660  0.126780    0.24016       0.138821     0.133732"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = normalize(df, norm='l2', axis=0) \n",
    "df_normalized = pd.DataFrame(Z, columns=df.columns)\n",
    "df_normalized.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.around(df_normalized.describe(), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age l2-norm: 1.0\n",
      "dependents l2-norm: 1.0\n",
      "income l2-norm: 1.0\n",
      "residence l2-norm: 1.0\n",
      "car_valuation l2-norm: 1.0\n",
      "loan_amount l2-norm: 1.0\n"
     ]
    }
   ],
   "source": [
    "for col in df_normalized.columns:\n",
    "    print(col, f'l2-norm: {round(np.linalg.norm(df_normalized[col], ord=2), 1)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SVD**\n",
    "\n",
    "- *Найти SVD разложение матрицы Z (матрицы U, S, V).*\n",
    "- *Докажите, что при выбранной нормировке, коэффициенты разложения столбцов матрицы Z по скрытым факторам являются корреляциями этих столбцов и скрытых факторов.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.linalg import svd \n",
    "\n",
    "U, S, VT = np.linalg.svd(df_normalized,  full_matrices=False,  compute_uv=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Z_0 and Component U_0\n",
      "\tEqual: -0.96 == -0.96\n",
      "Feature Z_0 and Component U_1\n",
      "\tEqual: -0.22 == -0.22\n",
      "Feature Z_0 and Component U_2\n",
      "\tEqual: 0.14 == 0.14\n",
      "Feature Z_0 and Component U_3\n",
      "\tEqual: -0.04 == -0.04\n",
      "Feature Z_0 and Component U_4\n",
      "\tEqual: -0.00 == -0.00\n",
      "Feature Z_0 and Component U_5\n",
      "\tEqual: 0.00 == 0.00\n",
      "Feature Z_1 and Component U_0\n",
      "\tEqual: -0.96 == -0.96\n",
      "Feature Z_1 and Component U_1\n",
      "\tEqual: -0.20 == -0.20\n",
      "Feature Z_1 and Component U_2\n",
      "\tEqual: -0.17 == -0.17\n",
      "Feature Z_1 and Component U_3\n",
      "\tEqual: -0.03 == -0.03\n",
      "Feature Z_1 and Component U_4\n",
      "\tEqual: 0.00 == 0.00\n",
      "Feature Z_1 and Component U_5\n",
      "\tEqual: -0.00 == -0.00\n",
      "Feature Z_2 and Component U_0\n",
      "\tEqual: -0.96 == -0.96\n",
      "Feature Z_2 and Component U_1\n",
      "\tEqual: 0.29 == 0.29\n",
      "Feature Z_2 and Component U_2\n",
      "\tEqual: 0.02 == 0.02\n",
      "Feature Z_2 and Component U_3\n",
      "\tEqual: -0.00 == -0.00\n",
      "Feature Z_2 and Component U_4\n",
      "\tEqual: 0.00 == 0.00\n",
      "Feature Z_2 and Component U_5\n",
      "\tEqual: -0.00 == -0.00\n",
      "Feature Z_3 and Component U_0\n",
      "\tEqual: -0.90 == -0.90\n",
      "Feature Z_3 and Component U_1\n",
      "\tEqual: -0.44 == -0.44\n",
      "Feature Z_3 and Component U_2\n",
      "\tEqual: 0.02 == 0.02\n",
      "Feature Z_3 and Component U_3\n",
      "\tEqual: 0.05 == 0.05\n",
      "Feature Z_3 and Component U_4\n",
      "\tEqual: 0.00 == 0.00\n",
      "Feature Z_3 and Component U_5\n",
      "\tEqual: -0.00 == -0.00\n",
      "Feature Z_4 and Component U_0\n",
      "\tEqual: -0.96 == -0.96\n",
      "Feature Z_4 and Component U_1\n",
      "\tEqual: 0.27 == 0.27\n",
      "Feature Z_4 and Component U_2\n",
      "\tEqual: -0.01 == -0.01\n",
      "Feature Z_4 and Component U_3\n",
      "\tEqual: 0.01 == 0.01\n",
      "Feature Z_4 and Component U_4\n",
      "\tEqual: -0.01 == -0.01\n",
      "Feature Z_4 and Component U_5\n",
      "\tEqual: -0.00 == -0.00\n",
      "Feature Z_5 and Component U_0\n",
      "\tEqual: -0.96 == -0.96\n",
      "Feature Z_5 and Component U_1\n",
      "\tEqual: 0.28 == 0.28\n",
      "Feature Z_5 and Component U_2\n",
      "\tEqual: 0.00 == 0.00\n",
      "Feature Z_5 and Component U_3\n",
      "\tEqual: 0.01 == 0.01\n",
      "Feature Z_5 and Component U_4\n",
      "\tEqual: 0.00 == 0.00\n",
      "Feature Z_5 and Component U_5\n",
      "\tEqual: 0.00 == 0.00\n"
     ]
    }
   ],
   "source": [
    "from math import isclose\n",
    "\n",
    "for i in range(Z.shape[1]):\n",
    "    for j in range(U.shape[1]):\n",
    "        corr_Zi_Uj = np.correlate(Z[:, i], U[:, j])[0]\n",
    "        svd_coef = VT[j, i] * S[j]\n",
    "        print(f'Feature Z_{i} and Component U_{j}')\n",
    "        if isclose(corr_Zi_Uj, svd_coef):\n",
    "            print(f'\\tEqual: {corr_Zi_Uj:.2f} == {svd_coef:.2f}')\n",
    "        else:\n",
    "            print('\\tNot equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- *Исследуйте, сколько скрытых факторов (главные факторы) достаточно для хорошей аппроксимации матрицы данных Z. Заменить значения исходных признаков линейными комбинациями главных факторов (матрица Z k , k – число главных факторов).*\n",
    "- *Оценить погрешность аппроксимации в евклидовой матричной норме и норме Фробениуса.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Singular values: [2.33 0.72 0.22 0.07 0.01 0.  ]\n",
      "Sqr singular values: [5.43 0.52 0.05 0.   0.   0.  ]\n",
      "Total variance: 6.00\n",
      "Explained variances: [0.91 0.09 0.01 0.   0.   0.  ]\n"
     ]
    }
   ],
   "source": [
    "print(f'Singular values: {np.around(S, 2)}')\n",
    "sqr_singular_values = S ** 2\n",
    "print(f'Sqr singular values: {np.around(sqr_singular_values, 2)}')\n",
    "total_variance = np.sum(sqr_singular_values)\n",
    "print(f'Total variance: {total_variance:.2f}')\n",
    "explained_variances = S ** 2 / total_variance\n",
    "print(f'Explained variances: {np.around(explained_variances, 2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variance (via 2 components): 99.1%\n"
     ]
    }
   ],
   "source": [
    "explained_var_2comp = np.sum(explained_variances[:2])\n",
    "print(f'Explained variance (via 2 components): {explained_var_2comp * 100:.1f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Z matrix:\n",
      "[[0.19 0.19 0.11 0.23 0.11 0.11]\n",
      " [0.12 0.13 0.21 0.08 0.21 0.21]\n",
      " [0.13 0.06 0.08 0.11 0.08 0.08]\n",
      " [0.15 0.13 0.11 0.16 0.11 0.11]\n",
      " [0.19 0.26 0.13 0.24 0.14 0.13]]\n",
      "\n",
      "Approximated Z matrix by 2 components:\n",
      "[[0.2  0.19 0.11 0.22 0.11 0.11]\n",
      " [0.12 0.13 0.22 0.08 0.21 0.21]\n",
      " [0.1  0.1  0.08 0.11 0.08 0.08]\n",
      " [0.15 0.15 0.11 0.15 0.11 0.11]\n",
      " [0.22 0.22 0.13 0.24 0.14 0.13]]\n"
     ]
    }
   ],
   "source": [
    "K = 2\n",
    "Z_K = U.dot(np.diag(S)[:,:K].dot(VT[:K, :]))\n",
    "print('Original Z matrix:')\n",
    "print(np.around(Z[:5], 2))\n",
    "\n",
    "print('\\nApproximated Z matrix by 2 components:')\n",
    "print(np.around(Z_K[:5], 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approx error in Fro norm: 0.054 (sigma3^2+...sigmaN^2=0.054)\n",
      "Approx error in Euclidian norm: 0.223 (sigma3=0.223)\n"
     ]
    }
   ],
   "source": [
    "Z_errors = Z - Z_K\n",
    "enorm_F = np.linalg.norm(Z_errors, ord='fro') ** 2\n",
    "enorm_2 = np.linalg.norm(Z_errors, ord=2)\n",
    "\n",
    "print(f'Approx error in Fro norm: {enorm_F:.3f} (sigma{K+1}^2+...sigmaN^2={np.sum(S[2:]**2):.3f})')\n",
    "print(f'Approx error in Euclidian norm: {enorm_2:.3f} (sigma{K+1}={S[K]:.3f})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approx relative error in Frob norm: 0.907%\n",
      "Approx relative error in Frob norm: 0.009%\n"
     ]
    }
   ],
   "source": [
    "relative_enorm_F = enorm_F /  np.linalg.norm(Z, ord='fro') ** 2\n",
    "print(f'Approx relative error in Frob norm: {(1 - explained_var_2comp) * 100:.3f}%')\n",
    "print(f'Approx relative error in Frob norm: {(relative_enorm_F):.3f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Сравнить матрицы корреляций признаков для матрицы Z и матрицы Z k . Оценить качество аппроксимации по близости матриц корреляций*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_approx_K = pd.DataFrame(Z_K, columns=df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>age</th>\n",
       "      <th>dependents</th>\n",
       "      <th>income</th>\n",
       "      <th>residence</th>\n",
       "      <th>car_valuation</th>\n",
       "      <th>loan_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.819</td>\n",
       "      <td>-0.225</td>\n",
       "      <td>0.987</td>\n",
       "      <td>-0.132</td>\n",
       "      <td>-0.168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dependents</th>\n",
       "      <td>0.819</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>-0.225</td>\n",
       "      <td>0.285</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.363</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residence</th>\n",
       "      <td>0.987</td>\n",
       "      <td>0.738</td>\n",
       "      <td>-0.363</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.272</td>\n",
       "      <td>-0.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>car_valuation</th>\n",
       "      <td>-0.132</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.995</td>\n",
       "      <td>-0.272</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_amount</th>\n",
       "      <td>-0.168</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.998</td>\n",
       "      <td>-0.307</td>\n",
       "      <td>0.999</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 age  dependents  income  residence  car_valuation  \\\n",
       "age            1.000       0.819  -0.225      0.987         -0.132   \n",
       "dependents     0.819       1.000   0.285      0.738          0.372   \n",
       "income        -0.225       0.285   1.000     -0.363          0.995   \n",
       "residence      0.987       0.738  -0.363      1.000         -0.272   \n",
       "car_valuation -0.132       0.372   0.995     -0.272          1.000   \n",
       "loan_amount   -0.168       0.338   0.998     -0.307          0.999   \n",
       "\n",
       "               loan_amount  \n",
       "age                 -0.168  \n",
       "dependents           0.338  \n",
       "income               0.998  \n",
       "residence           -0.307  \n",
       "car_valuation        0.999  \n",
       "loan_amount          1.000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z_corr = df_normalized.corr()\n",
    "np.around(df_normalized.corr(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "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>age</th>\n",
       "      <th>dependents</th>\n",
       "      <th>income</th>\n",
       "      <th>residence</th>\n",
       "      <th>car_valuation</th>\n",
       "      <th>loan_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.997</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dependents</th>\n",
       "      <td>0.997</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>0.132</td>\n",
       "      <td>0.206</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>0.999</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residence</th>\n",
       "      <td>0.891</td>\n",
       "      <td>0.854</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.301</td>\n",
       "      <td>-0.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>car_valuation</th>\n",
       "      <td>0.166</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.999</td>\n",
       "      <td>-0.301</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_amount</th>\n",
       "      <td>0.154</td>\n",
       "      <td>0.227</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.312</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 age  dependents  income  residence  car_valuation  \\\n",
       "age            1.000       0.997   0.132      0.891          0.166   \n",
       "dependents     0.997       1.000   0.206      0.854          0.239   \n",
       "income         0.132       0.206   1.000     -0.333          0.999   \n",
       "residence      0.891       0.854  -0.333      1.000         -0.301   \n",
       "car_valuation  0.166       0.239   0.999     -0.301          1.000   \n",
       "loan_amount    0.154       0.227   1.000     -0.312          1.000   \n",
       "\n",
       "               loan_amount  \n",
       "age                  0.154  \n",
       "dependents           0.227  \n",
       "income               1.000  \n",
       "residence           -0.312  \n",
       "car_valuation        1.000  \n",
       "loan_amount          1.000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z_K_corr = df_approx_K.corr()\n",
    "np.around(df_approx_K.corr(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approx error in Fro norm: 0.826\n",
      "Approx error in Euclidian norm: 0.679\n"
     ]
    }
   ],
   "source": [
    "corr_errors = Z_corr - Z_K_corr\n",
    "ecorr_norm_F = np.linalg.norm(corr_errors, ord='fro') ** 2\n",
    "ecorr_norm_2 = np.linalg.norm(corr_errors, ord=2)\n",
    "\n",
    "print(f'Approx error in Fro norm: {ecorr_norm_F:.3f}')\n",
    "print(f'Approx error in Euclidian norm: {ecorr_norm_2:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualization**\n",
    "\n",
    "- *Используя выделенные главные факторы, найти представления клиентов векторами меньшей размерности (размерности k).*\n",
    "- *Используя представление клиентов векторами меньшей размерности, построить карту клиентов. Сделать выводы о клиентах банка.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.16365566, -0.17058968],\n",
       "       [-0.17106441,  0.17860289],\n",
       "       [-0.09492935, -0.04908803],\n",
       "       [-0.13711726, -0.07227935],\n",
       "       [-0.18911208, -0.16968201]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_K = U[:, :K]\n",
    "comp_K[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 6))\n",
    "plt.scatter(comp_K[:, 0], comp_K[:, 1], label='Clients', edgecolors='black')\n",
    "plt.legend()\n",
    "plt.ylabel('Component 2')\n",
    "plt.xlabel('Component 1')\n",
    "plt.title('Client map', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Latent factors analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Выписать матрицу коэффициентов разложения признаков z j по главным факторам. Исследовать\n",
    "структуру этой матрицы с целью выделения групп признаков тесно связанных с каждым из\n",
    "главных факторов.\n",
    "- На основе анализа структуры матрицы коэффициентов разложения, предложить интерпретацию\n",
    "выделенным главным факторам.\n",
    "- Дать интерпретацию построенной выше карте клиентов.\n",
    "- Выполнить вращения в пространстве главных факторов с целью улучшения структуры матрицы\n",
    "коэффициентов разложения признаков z j по главным факторам. В случае улучшения структуры,\n",
    "найти новое представление клиентов векторами меньшей размерности (размерности k).\n",
    "Построить улучшенную карту клиентов и дать ее интерпретацию."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Выписать матрицу коэффициентов разложения признаков z j по главным факторам.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading matrix:\n",
      "\n",
      "(z1) age           = -0.96*u1 + -0.22*u2\n",
      "(z2) dependents    = -0.96*u1 + -0.20*u2\n",
      "(z3) income        = -0.96*u1 + 0.29*u2\n",
      "(z4) residence     = -0.90*u1 + -0.44*u2\n",
      "(z5) car_valuation = -0.96*u1 + 0.27*u2\n",
      "(z6) loan_amount   = -0.96*u1 + 0.28*u2\n"
     ]
    }
   ],
   "source": [
    "K = 2\n",
    "loading_matrix = VT.T[:, :2].dot(np.diag(S[:K]))\n",
    "print('Loading matrix:\\n')\n",
    "for i, feature in enumerate(df_normalized.columns):\n",
    "    pad = 14 - len(feature)\n",
    "    print(f'(z{i+1}) {feature}' + ' '*pad + \\\n",
    "          f'= {loading_matrix[i, 0]:.2f}*u1 + {loading_matrix[i, 1]:.2f}*u2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Комментарий:** \n",
    "\n",
    "Исходя из значений данной матрицы, сложно предложить интерпретации выделенным факторам, так как не образуются группы по коэффициентам (вклад первой компоненты в каждый из признаков больше, чем второй компоненты).\n",
    "\n",
    "*Требуется улучшение матрицы коэффициентов.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Дать интерпретацию построенной выше карте клиентов.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 6))\n",
    "plt.scatter(comp_K[:, 0], comp_K[:, 1], label='Clients', edgecolors='black')\n",
    "plt.legend()\n",
    "plt.ylabel('Component 2')\n",
    "plt.xlabel('Component 1')\n",
    "plt.title('Client map', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Комментарий:** \n",
    "\n",
    "Можно предположить, что в сторону увеличения значений ```Component1``` уменьшаются значения всех признаков,  а по увеличению ```Component2``` увеличивается ```income```, ```car_valuation``` и ```loan_amount```  (признаки, связанные с платежеспособнотью клиента) и уменьшаются ```age```, ```dependents``` и ```residense``` (признаки, связанные с социо-демографическими характеристиками или оседлостью клиента)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Выполнить вращения в пространстве главных факторов с целью улучшения структуры матрицы коэффициентов разложения признаков z j по главным факторам. В случае улучшения структуры, найти новое представление клиентов векторами меньшей размерности (размерности k). Построить улучшенную карту клиентов и дать ее интерпретацию.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для выполнения данного пункта воспользуемся библиотекой https://github.com/EducationalTestingService/factor_analyzer, которая имеет функционал по вращению матрицы нагрузок."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install factor_analyzer\n",
    "from factor_analyzer import Rotator\n",
    "from factor_analyzer.rotator import ORTHOGONAL_ROTATIONS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method: varimax\n",
      "[[-0.55 -0.82]\n",
      " [-0.57 -0.81]\n",
      " [-0.9  -0.44]\n",
      " [-0.35 -0.93]\n",
      " [-0.88 -0.47]\n",
      " [-0.89 -0.46]] \n",
      "\n",
      "method: quartimax\n",
      "[[-0.96 -0.26]\n",
      " [-0.96 -0.23]\n",
      " [-0.97  0.26]\n",
      " [-0.88 -0.47]\n",
      " [-0.97  0.24]\n",
      " [-0.97  0.24]] \n",
      "\n",
      "method: equamax\n",
      "[[-0.96 -0.26]\n",
      " [-0.96 -0.23]\n",
      " [-0.97  0.26]\n",
      " [-0.88 -0.47]\n",
      " [-0.97  0.24]\n",
      " [-0.97  0.24]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "rotations = ['varimax', 'quartimax', 'equamax'] \n",
    "rotated_loadings = {}\n",
    "for rotation_method in rotations:\n",
    "    rotator = Rotator(method=rotation_method, max_iter=100)\n",
    "    rotated = rotator.fit_transform(loading_matrix)\n",
    "    rotated_loadings[rotation_method] = np.around(rotated, 5)\n",
    "    print(f\"method: {rotation_method}\")\n",
    "    print(np.around(rotated_loadings[rotation_method], 2), '\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age           = -0.55*u1 + -0.82*u2\n",
      "dependents    = -0.57*u1 + -0.81*u2\n",
      "income        = -0.90*u1 + -0.44*u2\n",
      "residence     = -0.35*u1 + -0.93*u2\n",
      "car_valuation = -0.88*u1 + -0.47*u2\n",
      "loan_amount   = -0.89*u1 + -0.46*u2\n"
     ]
    }
   ],
   "source": [
    "rotated_loading = rotated_loadings[\"varimax\"]\n",
    "# print(np.around(rotated_loading, 2))\n",
    "for i, feature in enumerate(df_normalized.columns):\n",
    "    pad = 14 - len(feature)\n",
    "    print(f'{feature}' + ' '*pad + \\\n",
    "          f'= {rotated_loading[i, 0]:.2f}*u1 + {rotated_loading[i, 1]:.2f}*u2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Комментарий:** \n",
    "\n",
    "Получив наиболее подходящее вращение матрицы коэффициентов с помощью метода ```varimax```, можно заметить, что в новой матрице первой компоненте соответствуют признаки, связанные с платежеспособностью (```income```, ```car_valuation``` и ```loan_amount```), а второй компоненте признаки относящиеся к оседлости клиента (```age```, ```dependents``` и ```residense```)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.54753, -0.82421],\n",
       "       [-0.56507, -0.80651],\n",
       "       [-0.89588, -0.44373],\n",
       "       [-0.35263, -0.93451],\n",
       "       [-0.88479, -0.46561],\n",
       "       [-0.88891, -0.45795]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotated_loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ???\n",
    "# comp_K_rotated = U.dot(rotated_loading)[:, :K] \n",
    "comp_K_rotated = df_normalized.to_numpy() @ rotated_loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGICAYAAABbdQrXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABMU0lEQVR4nO3deViU9fo/8Pcw4JK4A2Kco2guoOAWieZSmEdFRRFSc0E9uYVLakpI4YJShJhpKh4tTQ8qJqGYBdTXtTRFyDIrl9CAAtlxwY1h5vn9wY85jsMwMMzyDPN+XVfXJY+feZ575g65+awSQRAEEBEREZkhK1MHQERERKQrFjJERERktljIEBERkdliIUNERERmi4UMERERmS0WMkQWxJIXKYr1vesSl1jfC5EpsJAhMqBjx45h5syZ6N+/P3r37g1fX1/s3bsXMplM2ebQoUPo2rUriouLAQABAQGYO3euXuO4e/culi5dit9++02v9zWGffv2Yfny5bV6zc6dO9GvXz/06tULX3/9NY4dO4ZVq1YZKELd6JqTp9/L5s2b0bt3b73GVlBQgFdeeUX5/ySRmLGQITKQsLAwLFy4EA4ODli7di22bt2Kl19+GevWrcNbb70FuVxe5etWrVqF4OBgvcZy5coVfPXVV2b3m3xOTg42b96MJUuW1Pg19+7dQ1RUFAYMGIBPP/0U/fv3x549e5CXl2fASGtP15w8/V7Gjx+PPXv26DU2e3t7+Pr64r333tPrfYkMwdrUARDVRwkJCdi/fz/WrFmDiRMnKq+/+OKL6NKlC5YsWYKjR4/C19dX7bWdOnUyYqTitnnzZgwdOhRt2rSp8Wvu3r0LQRAwdOhQeHh4GDA6cXB0dISjo6Pe7zt9+nQMGDAAv//+O7p166b3+xPpC3tkiAxg586d6Nq1q0oRU2nkyJF4/fXX0bJlyypf+/TQ0oMHD7B27Vq8+OKL6NGjBwICAvD7778r//7QoUPw9PTEuXPnMHbsWLi5uWHkyJE4fvw4ACAlJQXTpk0DALz66qsah2k2b94MPz8/JCQk4F//+hd69OiBGTNmID8/HwcOHMDLL7+M559/HsuWLcPDhw+Vr7t58ybefPNN9OvXD25ubhgyZAi2bt2q7GlISUlB165dcebMGYwbNw49evSAn58fzp07V+1nWFBQgKNHj2LUqFEq17///ntMnToVvXv3hru7O8aOHYtvv/1W+VkMGTIEALB48WIMGTIEAQEBuHDhAk6dOoWuXbvi77//BgBkZmZi3rx56N27Nzw8PBAUFKQylLJ8+XLMmzcPS5cuRa9evRAYGKgxXytWrMDMmTPRo0cPrF27FgDw999/Y9GiRcphxcDAQGRkZFSbk9LSUoSHh8PLywtubm7o168fgoODcffuXeWznn4vTw8tyWQy7NixA8OHD4e7uzt8fHxw9OhR5d///fff6Nq1K06cOIGZM2eiZ8+eGDRoELZt26byvpo1a4aBAwdi586d1eaJyNRYyBDpWX5+Pq5fv46XXnpJY5vg4OBq/76SIAgIDAzE119/jcWLF2PTpk1o0KABAgICkJWVpWx3//59vPPOO5gyZQq2b9+Oli1bYsmSJbh9+za6d++OlStXAgAiIiIwb948jc/7888/8cknn+Dtt99GeHg4Ll26hICAAMTHx2P16tVYuHAhvvrqK/z3v/9VPnfatGm4ffs2IiMjsX37dnh6euLjjz/GyZMnVe69dOlSvPLKK9i8eTNatWqF2bNn4/r16xpj+fbbb9GkSRP07dtXee2XX37BnDlz0LlzZ0RHR+Ojjz5C48aNsXTpUhQXF+Pll1/Gli1bAABvvfUWtmzZglWrVqFbt27o06cPPv/8czg4OKCwsBCTJ09GTk4O1q1bh7CwMPz888+YOXMmysrKlM87ffo0FAoFtm3bhunTp2uM9dChQ+jQoQOio6MxduxY5ObmYvz48cjMzMTq1asRERGBv//+G5MnT0ZeXp7GnCxduhQnTpzA0qVLsXPnTrz++uv46quvEB0dDQBVvpenBQcHIzo6GhMmTMC2bdvQu3dvLFu2DHFxcSrtQkJC0LNnT/znP/+Bl5cXNm7ciNOnT6u0GTZsGI4dO6bymRCJDYeWiPQsNzcXAPDss8/W+V5nzpzB+fPn8dlnn+HFF18EAAwaNAijRo3Ctm3bEBERAaDit/CgoCCMHDkSANC6dWuMHTsWKSkpGD58uHK4qnPnzmjXrp3G5z148ADvv/8+evbsCQA4deoUvv76a5w4cQJOTk4AKgqMS5cuAagofNq1a4eNGzeiVatWAID+/fvj2LFjSE1NVfaOABVzORYsWKBsM3ToUHz22WfK9/C0lJQUdOnSBVKpVHntjz/+wL/+9S+Vya7PPvssxo0bh0uXLsHLywuurq4AgPbt2yuHRGxtbfHMM8+gV69eACrmmTx+/Bi7du1Sxt2jRw8MHz4ciYmJyiG/8vJyrF69Gs2bN9f4mQFAkyZN8M4778DKquJ3ww8++ACPHj1SuX/fvn2V73n58uVqOXn8+DFkMhlWr16NwYMHAwA8PT3x008/4cKFCwAqhh2ffi9PunbtGr7++muEhYXhtddeAwAMHDgQpaWl2LBhA/z8/JRtvb298eabbyqf88033+C7775TKbC7deuGR48e4dKlS3jhhReq/QyITIWFDJGeVf7gVSgUdb5XSkoKGjdujBdeeAHl5eXK6wMHDsSJEydU2j75g61yzsSTQ0A1IZFI4Obmpvy6devWaNWqlbKIAYAWLVrg3r17AAA3Nzfs378fMpkM6enpyMjIwO+//47y8nK13+KfHCJq0KABBg0apPwBXZXs7Gw899xzKtf8/f3h7++PBw8e4MaNG8jIyMD58+cBoFa9BikpKejVqxeaNWum/Fzbtm2L5557DufOnVMWMq1atdJaxABAu3btlEUMAKSmpsLT01NZxFTeq3///hrfc8OGDbFr1y4AFcM/GRkZ+OOPP3Djxg00bNiwRu8rLS0NADBixAiV6yNHjsTXX3+NGzdu4JlnngGg+v+LlZUVHBwc8ODBA5XXVeY9OzubhQyJFgsZIj1r27YtAODWrVsa2+Tn58POzk7lh19Vbt++jYcPH6oUF5VsbGxUvm7UqJHyz5X3rW0x1bhxY5UekMpr1dm2bRt27tyJe/fuwcnJCb1794a1tbXaahx7e3uVr1u1aoU7d+5ovG9paanKewIqeoxWrlyJpKQkAECHDh3g4uICoHZ7q9y+fRuXLl1C9+7d1f7uyThbt25do/s93e7u3bvKnqGn26Wnp2u8z/HjxxEREYG//voLLVu2hJubGxo1alTjPN65cwfW1tZo0aKFynU7OzsAFZ9pZSHz9GdrZWWl9hlWtqksXInEiIUMkZ61atUK3bp1w5kzZ7Bs2bIq2/z73/+GnZ2d1mWzTZs2RevWrbF9+3ZDhFpnCQkJ2LRpE1atWoXRo0ejadOmACqGjp52+/Zt5Q9UACgqKlLpsXhaixYtUFpaqnJt7dq1OHv2LHbs2IEXXngBDRo0QHp6uspk1pqwtbXF4MGDlUMrT2rSpEmt7lWV5s2bo7CwUO16YWGhWpFRKSMjA4sWLcK4ceOwd+9eZa/aokWLcOPGjRo/t7y8HLdv31Z5TmUsmp6tSeUk49q+jsiYONmXyACmT5+OK1euqE2wBIAjR44gPT0dPj4+Wu/z/PPPo7i4GM888wzc3d2V/x09ehRffvlljeN5updFX3766Sc4Ojpi0qRJyiLmt99+Q3Fxsdpv909O/i0rK8N3330HT09Pjfd2dHRUzjeq9PPPP2PQoEEYMGAAGjRoAKBiFRNQfY/M0z1fzz//PG7evImuXbsqP9MuXbpgy5Yt+PHHH2vwzqv3/PPPIyUlRWUVVHFxMc6dO4c+ffoAUM/J77//DplMhjlz5iiLmAcPHuDHH39UeW/V9eI9//zzAIDk5GSV64mJiWjdujWcnZ1r9T4q96up7GUkEiP2yBAZwNixY3Hq1CmsXLkSv/zyC1555RVIJBKcOXMGsbGx8Pb2hr+/v9b7eHl5wd3dHXPmzMGCBQvQtm1bfPPNN9i/fz/CwsJqHE9lkXH69Gk888wzanNPdOXu7o4DBw5gy5Yt6Nu3L27cuIGtW7dCIpHg0aNHKm2jo6NhY2ODDh064L///S8ePHiAWbNmabx3//79ERERAZlMphxGc3d3x4kTJ3D48GG0bdsW58+fVy4Pfvp5T2rWrBmuXLmClJQU9OzZE//+979x5MgRzJo1C9OmTYONjQ127dqFn3/+GYsXL67z5zJjxgwcPnwYr7/+unLZ9rZt29CgQQPl6qenc+Lq6gqpVIqoqChMmjQJJSUl2LVrFwoLC5VFW1Xv5UkuLi4YPnw4PvjgA9y/fx9du3bF8ePH8fXXX2PlypVahzKf9tNPP8HW1lbtOURiwh4ZIgOQSCTYsGEDVq1ahWvXriE4OBhLlizBjz/+iNDQUKxfvx4SiUTrfaRSKXbu3IkBAwYgKioKc+bMQVpaGiIiIpSrUmqic+fOGDt2LLZv346oqKi6vDUVfn5+mD17Ng4cOIA5c+Zg7969mDlzJl599VX8/PPPKm3ffvttxMXF4c0330RZWRn27duHf/7znxrv7eXlhfLycuUEVqBib5cXX3wR77//PhYuXIjz589jy5YtcHZ2xk8//aTxXjNmzEBZWRlmzZqF33//Hc8++yz279+Pxo0bIygoCEuWLIFCocBnn31W5dyW2mrbti327dsHBwcHLF++HO+++y6cnJxw4MABZW/L0znp0KEDIiMjce3aNcyZMwfr16+Hu7s7Vq1ahVu3bil7R55+L09bv349pkyZgt27dyMwMBAXL15EVFQUpkyZUuv3cfbsWbz88stq87GIxEQimNue5URkVio3f/viiy/g7u5eq9cuX74c5eXlWL9+vYGiI00KCwvx8ssvIy4uTi/FHZGhsEeGiERr3rx5OH36NHJyckwdisWJiYnBK6+8wiKGRI+FDBGJVrt27bBgwQJs3LjR1KFYlPz8fBw9elS5+zCRmHFoiYiIiMwWe2SIiIjIbLGQISIiIrPFQoaIiIjMVr3dEK+k5D4UCsNM/2nd2hZFRaXaG5JRMB/iwVyIC/MhLsyHbqysJGjZUvPRIfW2kFEoBIMVMpX3J/FgPsSDuRAX5kNcmA/949ASERERmS0WMkRERGS2WMgQERGR2TL6HJmcnBwEBQWhqKgIHTp0wPr169GkSdWTeM6ePYsdO3Zgz549dX6uXF6OkpIClJeX1fle+flWUCgUdb6PmFhbN0DLlvaQSuvttCkiIqqHjP5TKywsDJMnT8aoUaOwdetWREdHIygoSKWNQqHA7t27sX37dnTp0kUvzy0pKUCjRs+gSRPHGp06XB1rayuUl9efQkYQBNy/fxclJQWws2tr6nCIiIhqzKhDSzKZDKmpqRg+fDgAwM/PD8nJyWrtbty4gRs3bmDt2rV6e3Z5eRmaNGlW5yKmPpJIJGjSpJleequIiIiMyaiFTElJCWxtbWFtXdERZG9vj7y8PLV2nTt3xnvvvYfmzZvr9fksYjTjZ0NERObIYENLSUlJiIiIULnm7Oys1s5QP0Bbt7ZV+To/3wrW1vqr2/Rxr/v3SxEdvRk//XQRUqkUTZs2w5tvLsH9+/fx6afbsW3bJ3jvvTXw83sVrq7dan3/tWtXYdasuWjb9tkatbeysoK9fdNaP0cMzDXu+oi5EBfmQ1yYD/0zWCHj7e0Nb29vlWsymQyenp6Qy+WQSqUoKCiAg4ODQZ5fVFSqsvGQQqHQ27wWfcyRUSgUWLx4Ifr08cCuXftgbW2NixfTsGTJQixdGgxBEFBerkBwcCgA6PS8H39Mw4wZs2v8WoVCgYKCe7V+jqnZ2zc1y7jrI+ZCXJgPcWE+dGNlJVHrnFD5eyPGAhsbG3h4eCAxMREAkJCQgMGDBxszhFqLj49D334eaOPYAn37eSA+Pk4v9714MQ2FhYWYOXOucqitTx8PvPPOSsjl/ys8FiyYg4sX0wAAMTG78frrUzB9+iRER2+CIAi4dSsH//73ZKxduwIBAROwaFEg7t69g5iY3SgsLEBQ0CLcuXMbW7ZsxPTpk/D661Owa9cOvbwHIiKybIb6GVkbRt9HZtWqVTh48CBGjhyJtLQ0LF68GAAQGxuLTZs2GTucasXHxyEkNBSOfQLgvfAgHPsEICQ0FHFfHKzzva9fvwZX126wslJNQf/+A9GyZUu19ufP/4Br167gk0/+i88+24eCggJ8+20SACA9/Q9MnDgFMTEHYWtri2+/TUJAwAzY2dkjKmoTHj58iPPnf8CePbHYtm0X/v77Lzx+/LjO74GIiCyXpp+Rxi5mjL782snJCTExMWrXJ02apHbN09MTnp6exgirSpFRkXDxmge7du4AALt27nDxmoeIDz7AON9X63RvKysJBKHmZ26kpV3A77//ipkzAwAAjx8/Qps2jujRoxdatmyFLl1cAAAdO3bC3bt3VV5rZ2ePhg0bIjDwdbz44iDMnh2Ihg0b1il+IiKybJp+RkZGRcLff7zR4uDOvtXIzEhHKydXlWutnFyRmZFe53u7uHTD9etX1YqZ7du3VtleoZBjwoRJ2L17P3bv3o8dO/Zg2rTXAQANGjRQafv0Pa2trbFjx27MmhWIO3fu4I03/o2srMw6vwciIrJchvwZWRssZKrR3rkTirOvqFwrzr6C9s6d6nzvnj17o2XLVti1awfkcjkAICXlHBITv0RJSbFa+z59XsA33yTiwYMHKC8vR0jIUpw6dbzaZ0ilUsjlcly/fhULFsxBz569sWDBYjg7d2QhQ0REdWLIn5G1wUKmGsFBwbh6MhqFWZehkJejMOsyrp6MRsjy5XW+t0QiwQcfbEBOzt+YNm0ipk9/Dfv27UFU1Ca0bNlKrf3AgYPx0ktDMGfODEybNhGdO3eFt/foap/x4ouDsGzZItjaNoWbWw9MmzYRr78+BY6ObdGv34t1fg9ERGS5NP2MDA4KNmocEqE2EzXMyNPLr3NzM+Ho2L7W94mPj0NkVCQyM9LR3rkTgoOCMXHixHp1REElXT8jU+OSRvFgLsSF+RCX+piPqn5G6nt+jLbl1yxkdFDfzlqqxEKG6oq5EBfmQ1yYD92Iah8ZIiIiIn1iIUNERERmy6IKmXo6iqYX/GyIiMgcWUwhY23dAPfv3+UP7CoIgoD79+/C2rqB9sZEREQiYvSdfU2lZUt7lJQUoLT0dp3vZWVlBYWifk32tbZugJYt7U0dBhERUa1YTCEjlVrDzq6tXu7FmedERETiYDFDS0RERKR/pj4B22J6ZIiIiEi/Kk/AdvGah24+rijOvoKQ0FAAMNrBkeyRISIiIp08eQK2ldRa5QRsY2EhQ0RERDoRwwnYLGSIiIhIJ2I4AZuFDBEREelEDCdgc7IvERER6aRyQm9kVCRSDlWcgB0RHm60ib4ACxkiIiKqA3//8UYtXJ7GoSUiIiIyWyxkiIiIyGyxkCEiIiKzxUKGiIiIzBYLGSIiIjJbLGSIiIjIbLGQISIiIrPFQoaIiIjMFgsZIiIiMlssZIiIiMhssZAhIiIis8VChoiIiMwWCxkiIiIyWyxkiIiIyGwZvZDJycnBlClTMGLECAQGBuL+/ftqbfLz8zFz5kyMHTsW48aNw7lz54wdJhEREZkBoxcyYWFhmDx5MpKTk+Hm5obo6Gi1NuvWrYOXlxeOHDmCDz/8EMuWLYNcLjd2qERERCRyRi1kZDIZUlNTMXz4cACAn58fkpOT1doNGzYMPj4+AID27dvj8ePHePDggTFDJSIiIjNgbcyHlZSUwNbWFtbWFY+1t7dHXl6eWrthw4Yp/7xz5064urqiadOmtXpW69a2dQtWC3v72sVDhsV8iAdzIS7Mh7gwH/pnsEImKSkJERERKtecnZ3V2kkkEo332L17Nz7//HPs3bu31s8vKiqFQiHU+nU1YW/fFAUF9wxyb6o95kM8mAtxYT7EhfnQjZWVpNrOCYMVMt7e3vD29la5JpPJ4OnpCblcDqlUioKCAjg4OFT5+nXr1uH06dPYt28fHB0dDRUmERERmTGjzpGxsbGBh4cHEhMTAQAJCQkYPHiwWrvdu3cjJSUFsbGxLGKIiIhII6OvWlq1ahUOHjyIkSNHIi0tDYsXLwYAxMbGYtOmTRAEAVu3bkVxcTECAgIwduxYjB07tsq5NERERPVdfHwc+vbzQBvHFujbzwPx8XGmDklUjDrZFwCcnJwQExOjdn3SpEnKP6emphozJCIiIlGKj49DSGgoXLzmoZuPK4qzryAkNBQA4O8/3sTRiQN39iUiIhKpyKhIuHjNg107d1hJrWHXzh0uXvMQGRVp6tBEg4UMERGRSGVmpKOVk6vKtVZOrsjMSDdRROLDQoaIiEik2jt3QnH2FZVrxdlX0N65k4kiEh8WMkRERCIVHBSMqyejUZh1GQp5OQqzLuPqyWgEBwWbOjTRMPpkXyIiIqqZygm9kVGRSDmUjvbOnRARHs6Jvk9gIUNERCRi/v7jWbhUg0NLREREZLZYyBAREZHZYiFDREREZouFDBEREZktFjJERERktljIEBERkdliIUNERERmi4UMERERmS0WMkRERGS2WMgQERGR2WIhQ0RERGaLhQwRERGZLRYyREREZLZYyBAREZHZYiFDREREZouFDBEREZktFjJERERktljIEBEZSXx8HPr280Abxxbo288D8fFxpg6JyOxZmzoAIiJLEB8fh5DQULh4zUM3H1cUZ19BSGgoAMDff7yJoyMyX+yRISIygsioSLh4zYNdO3dYSa1h184dLl7zEBkVaerQiMwaCxkiIiPIzEhHKydXlWutnFyRmZFuooiI6gcWMkRERtDeuROKs6+oXCvOvoL2zp1MFBFR/cBChojICIKDgnH1ZDQKsy5DIS9HYdZlXD0ZjeCgYFOHRmTWONmXiMgIKif0RkZFIuVQOto7d0JEeDgn+hLVEQsZIiIj8fcfz8KFSM84tERERERmy+iFTE5ODqZMmYIRI0YgMDAQ9+/fV2uTn5+PGTNmYMyYMZgwYQKuXLlSxZ2IiIjI0hm9kAkLC8PkyZORnJwMNzc3REdHq7X56KOPMHz4cHz55ZdYsGABwsLCjB0mERERmQGjFjIymQypqakYPnw4AMDPzw/Jyclq7d577z1MnDgRAPD333+jWbNmxgyTiIiIzIRRJ/uWlJTA1tYW1tYVj7W3t0deXp5aOyurivpqxIgRyM7OrrLXhoiIiMhghUxSUhIiIiJUrjk7O6u1k0gkGu+RnJyMK1eu4PXXX0dSUhJatGhR4+e3bm1b47a6sLdvatD7U+0wH+LBXIgL86Fq3779WLl6Df68+Qc6dOyMNatXYsqUyUZ7PvOhfwYrZLy9veHt7a1yTSaTwdPTE3K5HFKpFAUFBXBwcFB77alTp/DCCy+gSZMmcHV1xbPPPou//vqrVoVMUVEpFAqhrm+jSvb2TVFQcM8g96baYz7Eg7kQF+ZD1ZMHd7qMqji4c8GiZbh375FRlsUzH7qxspJU2zlh1DkyNjY28PDwQGJiIgAgISEBgwcPVmt3+PBhHDx4EACQnp6OwsJCdOzY0ZihEhFRPcODO+sno69aWrVqFQ4ePIiRI0ciLS0NixcvBgDExsZi06ZNAIB33nkHZ86cwZgxYxASEoIPP/wQTZo0MXaoRERUj/DgzvrJ6Dv7Ojk5ISYmRu36pEmTlH9u06YNdu7cacywiIionqs8uNOunbvyGg/uNH/c2ZeIiCwCD+6sn3jWEhERWQQe3Fk/sZAhIiKLwYM76x8OLREREZHZYiFDREREZouFDBEREZktFjJERERktljIEBERkdliIUNERERmq9pC5sGDB8jOzla7/scffxgsICIiIqKa0ljInDlzBi+99BLGjh2L8ePHIy8vT/l3b7/9tlGCIyIiIqqOxkJmw4YN2Lt3L1JSUtC/f39MmzYNd+/eBQAIgmC0AImIiIg00VjICIKArl27QiqV4q233sKgQYOwaNEiKBQKY8ZHREREpJHGQsbKygo3btxQfh0SEgJBELBq1SoWM0RERCQKGguZxYsXY/LkyUhKSgIASKVSfPzxx/j999852ZeIiIhEQeOhkYMGDcLx48chk8mU15o1a4bY2Fh88803RgmOiIiIqDrVnn5ta2urdq1Bgwbw8fExWEBERERENcUN8YiIyCzFx8ehbz8PtHFsgb79PBAfH2fqkMgEqu2RISIiEqP4+DiEhIbCxWseuvm4ojj7CkJCQwEA/v7jTRwdGZPWHpn9+/erXduxY4dBgiEiIqqJyKhIuHjNg107d1hJrWHXzh0uXvMQGRVp6tDIyDT2yMTGxuLRo0fYvXs3Hj9+rLwuk8kQExODOXPmGCVAIiKip2VmpKObj6vKtVZOrkg5lG6iiMhUNBYy1tbWuH79Oh49eoTr168rr0ulUqxYscIowREREVWlvXMnFGdfgV07d+W14uwraO/cyYRRkSloLGTGjx+P8ePH49ixYxg6dKgxYyIiIpGJj49DZFQkMjPS0d65E4KDgk06FyU4KFg5R6aVU8UcmasnoxERHm6ymMg0tE727dmzJ7Zs2YLbt2+rXA/9/5OqiIiofjPkxFpdC6TKNpFRkUg5VPHaiPBwTvS1QBJBywmQU6dORdOmTdGtWzdIJBLl9QULFhg8uLooKiqFQmGYwy3t7ZuioOCeQe5Ntcd8iAdzIS76ykfffh5w7BOgMoxTmHUZuRdjcOF8ms73fbJAerpXpT4WJPz+0I2VlQStW6vva1dJa49MUVER9u7dq9egiIhMRWxDJObAUBNrn1x5BEBl5RFzQjWldfn1s88+iwcPHhgjFiIig6rsAXDsEwDvhQfh2CcAIaGhWjdSs/SN1yon1j5JHxNrMzPS0cpJvUDKzODKI6o5rT0yDg4O8PX1Rd++fdGoUSPldc6RISJzo0sPADdeM9zEWq48In3Q2iPj5OSEMWPGwNHRES1atFD+R0RkbnTpAeDGaxUFW0R4OHIvxiBp8wTkXozRyzyW4KBgXD0ZjcKsy1DIy1GYdRlXT0YjOChYT5GTJdDaI7NgwQI8evQImZmZ6Ny5M8rKylR6ZoiIzIUuPQDceK2Cv/94vfdAceUR6YPWHplLly5h6NChmDt3LvLz8/HSSy/h4sWLxoiNiETMHOeN6NIDYKj5IVTB3388LpxPQ17ubVw4n8YihmpNayETGRmJ3bt3o0WLFnB0dMS6devw3nvvGSM2IhIpTZNm9+1TP5tNTHQZIuHwB5G4aR1aevToETp1+t9vHi+99BI++ugjgwZFROKmadLsytVrMGyYj4mjq15th0g4/EEkblp7ZKytrXHnzh3lZng3b96s0wNzcnIwZcoUjBgxAoGBgbh//77GtqWlpRg6dChSUlLq9Ewi0i9Nk2b/vPmHiSIyLA5/EImX1kImMDAQU6dORW5uLt566y1MmjQJgYGBOj8wLCwMkydPRnJyMtzc3BAdHa2x7dq1a3H37l2dn0VEhqFp3kiHjp1NFBERWSqthYyXlxe2bNmChQsXok+fPti3bx+GDx+u08NkMhlSU1OVr/fz80NycnKVbRMTE9GkSRN07dpVp2cRkeFomjeyZvVKU4dGRBZG6xwZoGJ4qVevXhAEAY8fP8Zvv/2G7t271/phJSUlsLW1hbV1xWPt7e2Rl5en1i4nJwd79uzBnj17MHv27Fo/h4gMS9O8kSlTJvMsGSIyKq2FTFRUFPbu3YvWrVsrr0kkEhw/frza1yUlJSEiIkLlmrOzs1q7Jw+iBACFQoF3330XK1asqNN+NdUdMKUP9vZNDXp/qh3mw/jeeON1vPHG62rXmQtxYT7EhfnQP62nXw8ZMgSxsbFo06ZNnR8mk8ng6emJ1NRUSKVS3Lp1C1OnTlUpitLT0zFz5kzl7sFZWVmws7PD2rVr0a9fvxo/i6dfWw7mQzyYC3FhPsSF+dBNnU+/btu2rV6KGACwsbGBh4cHEhMT4ePjg4SEBAwePFilTadOnXD69Gnl1wEBAViwYAE8PT31EgMRERHVH1on+/bv3x/r1q3Djz/+iN9++035n65WrVqFgwcPYuTIkUhLS8PixYsBALGxsdi0aZPO9yUiIiLLU6OhJbUX1WCOjKlxaMlyMB/GFx8fh8ioSGRmVEz0DQ4Khr//eOZCZJgPcWE+dFPnoaUTJ07oNSAiMm+VxxO4eM1DNx9XFGdfQUhoKABUOfmXiMiQtA4tPXjwAKtXr8aQIUMwePBghISEoLS01BixEZEIPXk8gZXUWnk8QWRUpKlDIyILpLWQiYiIQFlZGbZu3Yro6GhIJBKsXbvWGLERkQhpOp4gMyPdRBERkSXTOrR06dIlfPnll8qvw8PDMWrUKIMGRUTiVXk8QeWBkUDF8QTtnTtV8yoiIsPQ2iMjl8uhUCiUXysUCkilUoMGRUTipel4guCgYFOHRkQWSGuPTP/+/bF48WJMmjQJQMUyae7pQmS5NB1PwBOhicgUtC6/Li8vR3R0NL7//nsoFAoMHDgQ8+bNQ8OGDY0Vo064/NpyMB/iwVyIC/MhLsyHbuq8/Nra2hrz58/HK6+8AqlUiq5du6qdj0RE9ZemPWOIiMRAayGTlpaGJUuWwNraGnK5HDY2NoiOjkbXrl2NER8RmVB1e8awmCEiMdA6tOTr64u33npLeSbSiRMnsGPHDhw4cMAoAeqKQ0uWg/kwnL79PODYJ0BlhVJh1mXkXozBhfNpau2ZC3FhPsSF+dCNtqElrauWAKgc7DhkyBA8fPiw7pERkehxzxgiEjuthYyLiwsSExOVX585cwZdunQxaFBEJA6Ve8Y8iXvGiEd8fBz69vNAG8cW6NvPA/HxcaYOicjotM6RuXDhAhISEhAWFgZra2sUFRWhYcOGOHbsGCQSCS5evGiMOInIBIKDgpVzZFo5VcyRuXoyGhHh4aYOzeJx/hJRBa1zZLKzs6u9gZOTk14D0hfOkbEczIdh1WbVEnNhPDWZv8R8iAvzoZs6z5FxcnJCQUEB0tPTVf5zcnISbRFDRPrj7z8eF86nIS/3Ni6cT+Nv+yJR3fylyiEnK6mUQ05U72kdWlqyZAnS0tLg4OCgvCaRSPDSSy8ZNDAiItJM05lX9vZtOeREFkVrIfPrr7/i+PHjaNCggTHiISKiGtA0f8laCrh4zVMWOHbt3OHiNQ+RUZEsZKhe0jq01LFjR5SXlxsjFiIii1fTlUj+/uMRER6O3IsxSNo8AbkXYxARHo6iwjwumSeLorVHZuLEiRgzZgx69+4Na+v/NY+IiDBoYERElqa2K5H8/cerXY+MiqxyyIlL5qm+0tojs27dOgwcOBCurq7o3Lmz8j8iItKvyKhI5bCQldRaZViopoKDgnH1ZDQKsy5DIS9HYdZlXD0ZjeCgYANGTmQ6NTo0cvXq1UYIhYjIsmVmpKObj/qwUMqhmg8LVfbQREZFIuVQxZL5iPBwzo+hektrj0yvXr1w6tQpI4RCRGTZ9LWTcuWSeYVcziXzVO9pLWTOnz+PN954A+7u7ujTpw969+6NPn36GCM2IiKLIqZhIR5/QOZC69DSnj17jBEHEZHFE8uwEI8/IHOi9YgCAEhMTMT3338PmUyGgQMHwtfX1wih1Q2PKLAczId4MBe6qc0xELWhaz5qcvwB1R6/P3RT5yMKdu7cie3bt6Nr167o3r07PvvsM2zbtk2vQRIRGZKYh0kqez8c+wTAe+FBOPYJQEhoqEljrO74AyKx0Tq0lJCQgNjYWNjaVlRDr776KiZMmIDAwECDB0dEVFdiHyZ5csk1II6deDUdf8C9aEiMtPbIAFAWMQDQtGlTlY3xiIi0MWWPiD72ZjEkMfZ+iGnSMZE2NTr9es+ePZDJZJDJZNi9ezeeffZZY8RGRPWAqYdOxFgoPKm2S66NURRqOv5ADD1YRE/TOtk3Ly8Py5Ytw8WLFwEAPXv2RFRUFJycnIwSoK442ddyMB/iUVUuTD1x1NTP1+bJoa8nD3+sqnCoTVuA3xtiw3zoRttk3xqtWvrrr79gZ2cHuVyOrKwsdOvWTa9BGgILGcvBfIhHVblo49gC3gsPwkr6vyFphbwcSZsnIC/3tsFjqu0Pf1Oo6aql2hZl/N4QF+ZDN3VetRQTE4N58+ahcePGKCkpwcKFCxEXJ54Z/0QkbvrarVZX5jBMUrkTb17u7Wp34tX3MJmYV3MR1ZTWQubzzz9HbGwsAOCf//wnEhIS8N///tfggRFR/SCGiaM1LRTEQlOBoc+i0NRzl4j0RevyI7lcrrZqSSKR6PzAnJwcBAUFoaioCB06dMD69evRpEkTtTajRo1Cu3btAAB2dnbYuXOnzs8kItMRy2615qK65eLBQcEah8lqS4zLvol0oXWOzPz589GhQwdMnDgRAHDo0CHcuHEDH3/8sU4PnDt3LsaMGYNRo0Zh69atePDgAYKCglTafPPNNzh79izWrFmj0zMAzpGxJMyHeDAXdadtHkxtdgGuLh+mnrtkifj9oZs6z5EJCwtDZmYmfH198eqrryIjIwOrV6/WKRiZTIbU1FQMHz4cAODn54fk5GS1dpcvX8b169fh5+eHadOm4dq1azo9j4jI3GibB6OvYTJTz10i0hetQ0t2dnbYvHmzXh5WUlICW1tb5YZ69vb2yMvLU2vXsGFD+Pr64rXXXsPp06cxf/58JCYmokGDBnqJg4hIrIy1q64+h6mITKlGy691kZSUhIiICJVrzs7OyMjIwHfffQcAKC8vR+/evXH58uVq7zVmzBisW7cOLi4uhgiViEg09u3bj4WLg9D15UBlgXHt1DZs3hiFKVMm6/1ZK1evwZ83/0CHjp2xZvVKvT+DyNAMVshURSaTwdPTE6mpqZBKpbh16xamTp2K48ePq7SLiYnB6NGj0bJlSwCAj48PPvroI3TqVPPfSDhHxnIwH+LBXOiHvk7DZj7EhfnQjbY5MkY9NMnGxgYeHh5ITEyEj48PEhISMHjwYLV2qampePToEWbPno0LFy5AoVCgY8eOxgyViMhk/P3Hc+UQUQ1p7JFJTU2t9oUvvPCCTg/Mzs7G8uXLUVRUhLZt22LDhg1o3rw5YmNjkZ+fj0WLFiEvLw/Lly9HQUEBGjZsiPfee6/Ww0rskbEczId4MBfiwnyIC/OhG52PKPDx8QEAPHz4EDk5OejUqROsra1x/fp1PPfcczhy5IhhItYTFjKWg/kQD+ZCXJgPcWE+dKPz8uujR4/i6NGjcHNzw969e/Hll1/i0KFD+Pzzz5Ub1RERGYO5b6Vv7vETiZnWOTJ//vkn+vTpo/y6e/fuyMzMNGhQRESVqtvp1hzmkZh7/ERip3VDvEaNGuHQoUOQy+UoLy9HbGwsmjVrZozYiIhUttK3klqrbKVvDsw9fiKx01rIvP/++4iJiYG7uzt69uyJw4cPq+0PQ0TiUB+HMPR94rOxaYo/488/6l2uiExB69DSc889h8OHD+P27duQSCRo3ry5MeIiolqqr0MYxtrp1lCqiv/6uc/R6JkWcOwTUK9yRWQKWntkCgoKMGfOHEycOBHl5eWYOXMm8vPzjREbEVXj6d6XVatX1sshjOCgYFw9GY3CrMtQyMtRmHUZV09GIzgo2NSh1UhV8WdeSkQv77fqXa6ITKFGh0YOHToUDRs2RLNmzeDi4oLQ//+bAxGZRmXvi2OfAHgvPAjHPgHIz8sx6yEYTfz9xyMiPBy5F2OQtHkCci/GICI83Og9F7oO21UVf3nZg3qZKyJT0FrIZGdnY8KECbCysoKNjQ2CgoJw69YtY8RGRBpUNYH0meZt6u1pxvo68VnXYqSqwjEkNLRWxcyT8bfv0Lne5orI2LQWMhKJBAqFQvl1aWmpytdEZHxVTSDt0v81XEz80GyHYAytLsWIvlcemftwGZGYaJ3sO2zYMCxbtgz37t3DgQMHEBcXhxEjRhgjNiLSoKoJpI1sW6OZbWPkXoxByqGKwwZNMQQjVk8WIwBUihFtn1FmRjq6+agPBaUc0m0oqPJ5kVGRzBVRHWktZN544w0kJCRAoVDghx9+wMSJEzFhwgRjxEZEGgQHBStXKLVyqlj1cvVkNH8YVqMuxYghVk7xYEgi/dBayOzfvx+TJ0+Gr6+v8tqOHTswZ84cQ8ZFRNXgb/S1V5dipLrCkYhMS2MhExsbi0ePHmH37t14/Pix8rpMJkNMTAwLGSIT42/0tVOXYoSFI5F4aSxkKk+6fvToEa5fv668LpVKsWLFCqMER0T/Ex8fh8ioSGRmVPwgDQ4K5g/SWqhrMcLCkUicJIIgCNU1OHbsGIYOHWqsePSmqKgUCkW1b01nPIpdXCwhH0/u2ivmOTGWkAtzwnyIC/OhGysrCVq3ttX491oLmeLiYnz55Ze4f/8+BEGAQqFAZmYmPvzwQ70Hq08sZCyHJeSjbz8POPYJUJnfUZh1GbkXY3DhfJoJI1NlCbkwJ8yHuDAfutFWyGjdR2bx4sX44YcfEB8fj9zcXCQkJMDKSuvLiEiPzP3gRCIiQ9FakeTk5GDHjh0YPHgwpk6ditjYWGRlZRkjNiKzYsiTpytX3DyJO8ESEdWgkLGzswMAODs74/r162jTpg3Ky8sNHhiROanrFvbacCdYIqKqaS1kWrdujU8//RRubm6Ij4/HiRMnUFpaaozYiMyGti3s69pbI5aDE4mIxEbrhnhr1qzB119/DQ8PD7i5ueHjjz9GUFCQMWIjMhvV7Rr75Iqjbj4VK45C/v8J8rUpRLj8l4hIndYemW+++QbTpk0DAAQFBSEhIQE3b940eGBEYlVV70p1c1j0feAgERH9D3f2JaoFTb0r48b44IvDm9F96ELlPi+/HduMdRERmDd/tl4OHOSGeERE6rizL1EtaDpB+ejX0ZCXl+GX/9uKB3fz8UwzB0BeBkA/Bw7qa3iKiKi+4c6+OuCmRuJizHy0cWwB74UHYSX93+8ACnk5vomeihfGvlvlhnX6OKmaG+KRLpgPcWE+dKNtQzytk3379++P1atX47vvvkN5eTkGDBiAd999F7a2mm9KVF9p6l2Ryx5XuWFdyqF0vRw4WN1kYiIiS6Z1su8HH3yAsrIybN26FdHR0ZBIJFi7dq0xYiMSHU37uTi0ebbaDev8/cfjwvk05OXexoXzabUeDuKGeEREVdNayFy6dAnvv/8+XF1d4ebmhvDwcPzyyy/GiI1Iq/j4ODzX2cUgu+lWRdN+LmGr1xh0wzpuiEdEVDWtQ0tyuRwKhUJ5vpJCoYBUKjV4YETaPDkB1mWU8SbAVrefS12Gj7Q905D3JyIyV1on+4aHhyM/Px+TJk0CULEs287ODitXrjRKgLriZN/6z1wmwFoSS/neMJel8JaSD3PBfOimzpN9ly9fjujoaGzYsAEKhQIDBw7E/Pnz9RokkS44AZZMgUvhicRFa49MVc6ePYsBAwYYIh69YY9M/cceGfGxhO8Nc/r/zhLyYU6YD91o65HRONn3119/xWuvvYY33ngDxcXFAICcnBzMnz8fgYGBOgeUk5ODKVOmYMSIEQgMDMT9+/fV2pSVlSE8PBy+vr4YNWoUzpw5o/PzqP7iBFgC6n4gZ21lZqRXudQ+M0O3nkBjx09U32gsZMLCwjBs2DD84x//wLZt25CYmIhRo0bh0aNHOHLkiM4PDAsLw+TJk5GcnAw3NzdER0ertfn0009RUlKCw4cPY+PGjQgJCYEOHUdUz1WuIMr/eS9PhLZQlcM8jn0C4L3wIBz7BCAkNNSgxYA+l8KbIn6i+kbj0NKIESOQnJwMuVyO4cOH49GjRwgJCcGoUaN0fphMJoOnpycuXLgAa2tr3Lp1C1OnTsXx48dV2vn4+CAqKgouLi4AgPT0dHTs2FG5cqomOLRkOZgP8TB2LkwxzPPkHBldd2quZOj4+b0hLsyHbnQeWmrcuDGAirOVHj9+jE8++aRORQwAlJSUwNbWFtbWFXOM7e3tkZeXp9YuMzMTqamp8PPzw8SJE1FYWFirIoaILIO+h3lqQtNeQrr0BJoifqL6RuOqpSc7alq1agVXV1dNTauUlJSEiIgIlWvOzs5q7SQSido1uVyO3NxcxMfH49q1a5g1axaSkpLQtGnTGj+/uupNH+ztax4LGR7zIR7GzEWHjp2rPDKiQ8fOBo3jjTdexxtvvF7n+xgjfn5viAvzoX8aCxmFQoE7d+5AEAQIgqD8c6UWLVpUe2Nvb294e3urXKscWpLL5ZBKpSgoKICDg4Paa+3s7DBq1ChIJBK4uLjA0dERf/75J3r06FHjN8ahJcvBfIiHsXMRtPRtjcM85vD/hKHj5/eGuDAfutF5H5nr16+jX79+yuLF09NT+XcSiQRXrlzR9FKNbGxs4OHhgcTERPj4+CAhIQGDBw9Wa+fl5YXExER069YNf/31F27duoUOHTrU+nlEVL+Z+47H5h4/kRjotI9MXWRnZ2P58uUoKipC27ZtsWHDBjRv3hyxsbHIz8/HokWLUFpaijVr1uC3334DACxbtgxeXl61eg57ZCwH8yEetc2FueyQa674vSEuzIdutPXIGL2QMRYWMpaD+RCP2uRCn6t/qGr83hAX5kM3Oq9aIiIypMioSLh4zYNdO3dYSa1h184dLl7zEBkVaerQiMiMsJAhIpPgDrlEpA9aD40kIjKEyh1yn156XJcdcnmQI5HlYY8MEZmEPs/K4jAVkeVijwwRmYQ+lx5nZqSjm4/6MFXKIe6QS1TfsZAhIpPx9x+vl6EffQ5TEZF54dASEZk9fQ5TEZF5YY8MEZk97pBLZLnYI0NEJqePpdP+/uNx4Xwa8nJv48L5NBYxRBaCPTJEZFJcOk1EdcEeGSIyKS6dJqK6YCFDRCal7x1+iciysJAhIpOqXDr9JC6dJqKaYiFDRCbFpdNEVBec7EtEJsWl00RUFyxkiMjk9LXDLxFZHg4tERERkdliIUNERERmi4UMERERmS0WMkRERGS2WMgQERGR2WIhQ0RERGaLhQwRERGZLRYyRGRQ8fFx6NvPA20cW6BvPw/Ex8eZOiQiqke4IR4RGUx8fBxCQkPh4jUP3XxcUZx9BSGhoQDADfCISC/YI0NkQJbeGxEZFQkXr3mwa+cOK6k17Nq5w8VrHiKjIk0dGhHVEyxkiAyksjfCsU8AvBcehGOfAISEhlpUMZOZkY5WTq4q11o5uSIzI91EERFRfcNChshA2BsBtHfuhOLsKyrXirOvoL1zJxNFRET1DQsZIgNhbwQQHBSMqyejUZh1GQp5OQqzLuPqyWgEBwWbOjQiqic42ZfIQCp7I+zauSuvWVpvROWE3sioSKQcSkd7506ICA/nRF8i0hsWMkQGEhwUrFyx08qpYsXO1ZPRiAgPN3VoRuXvP56FCxEZDAsZIgNhbwQRkeGxkCEyIPZGEBEZltELmZycHAQFBaGoqAgdOnTA+vXr0aRJE5U2b7zxBm7dugUAUCgUuH79Or744gu4u7tXdUsiIiKyUEZftRQWFobJkycjOTkZbm5uiI6OVmvzn//8B0eOHMGRI0cwdOhQTJgwgUUMERERqTFqISOTyZCamorhw4cDAPz8/JCcnKyx/Y0bN5CQkIDgYC7VJCIiInVGLWRKSkpga2sLa+uKES17e3vk5eVpbL9t2zbMnDkTtra2xgqRiIiIzIjB5sgkJSUhIiJC5Zqzs7NaO4lEUuXr79y5g7Nnz+K9997T6fmtWxu2+LG3b2rQ+1PtMB/iwVyIC/MhLsyH/hmskPH29oa3t7fKNZlMBk9PT8jlckilUhQUFMDBwaHK158+fRqDBw9Gw4YNdXp+UVEpFApBp9dqY2/fFAUF9wxyb6o9fecjOHgZ9h+IxeOHpWjY2BaTX5uEyMj1ert/fcbvDXFhPsSF+dCNlZWk2s4Jo65asrGxgYeHBxITE+Hj44OEhAQMHjy4yrY///wzPDw8jBkeUUURczAOvUe9rdzEbv/BDQDAYoaISISMvmpp1apVOHjwIEaOHIm0tDQsXrwYABAbG4tNmzYp2/31119o06aNscMjC7f/QCx6e7+lctBjb++3sP9ArKlDIyKiKhh9HxknJyfExMSoXZ80aZLK15988omxQiJSevywtMqDHh8/ZHcwEZEY8fRroic0bGyL4uwrKteKs6+gYWNO0CMiEiMWMkRPmPzaJPyUtAGFWZehkJejMOsyfkragMmvTdL+YiIiMjqetUT0hMoJvfsPrMPjh/fQsHFTrloiIhIxiSAIhlmjbGJcfm05mA/xYC7EhfkQF+ZDN9qWX3NoiYiIiMwWCxkiIiIyWyxkiIiIyGyxkCEiIiKzxUKGiIiIzBYLGSIiIjJbLGSIiIjIbLGQISIiIrPFQoaIiIjMFgsZIiIiMlssZIiIiMhssZAhIiIis8VChoiIiMwWCxkiIiIyWyxkiIiIyGyxkCEiIiKzxUKGSKTi4+PQt58H2ji2QN9+HoiPjzN1SEREomNt6gCISF18fBxCQkPh4jUP3XxcUZx9BSGhoQAAf//xJo6OiEg82CNDJEKRUZFw8ZoHu3busJJaw66dO1y85iEyKtLUoRERiQoLGSIRysxIRysnV5VrrZxckZmRbqKIiIjEiYUMkQi1d+6E4uwrKteKs6+gvXMnE0VERCROLGSIRCg4KBhXT0ajMOsyFPJyFGZdxtWT0QgOCjZ1aEREosLJvkQiVDmhNzIqEimH0tHeuRMiwsM50ZeI6CksZIhEyt9/PAsXIiItOLREREREZouFDBEREZktFjJERERktljIEBERkdliIUNERERmy+iFTE5ODqZMmYIRI0YgMDAQ9+/fV2tTVlaGpUuXwsfHB2PHjsUPP/xg7DCJiIjIDBi9kAkLC8PkyZORnJwMNzc3REdHq7U5cuQIFAoFjh49inXr1mH58uXGDpOIiIjMgFELGZlMhtTUVAwfPhwA4Ofnh+TkZLV2CoUCDx8+hFwux8OHD9GoUSNjhklERERmwqgb4pWUlMDW1hbW1hWPtbe3R15enlq7cePG4fDhwxg0aBDu3r2LDRs2GDNMIiIiMhMGK2SSkpIQERGhcs3Z2VmtnUQiUbu2ZcsW9OrVC7GxscjIyMCMGTPQvXt3ODk51fj5rVvb1jrm2rC3b2rQ+1PtMB/iwVyIC/MhLsyH/hmskPH29oa3t7fKNZlMBk9PT8jlckilUhQUFMDBwUHttcePH8dHH30EiUSCDh06oGfPnvjll19qVcgUFZVCoRDq/D4AID4+DpFRkcjMqDjzJnzNKgwb5qOXe1Pd2ds3RUHBPVOHQWAuxIb5EBfmQzdWVpJqOyeMOkfGxsYGHh4eSExMBAAkJCRg8ODBau1cXFxw7NgxAEBxcTF+/fVXuLq6GjNUpfj4OISEhsKxTwC8Fx6EY58ALFwchPj4OJPEQ0RERP8jEQRBP90WNZSdnY3ly5ejqKgIbdu2xYYNG9C8eXPExsYiPz8fixYtQmFhIVasWIGsrCxYWVlh7ty5GD16dK2eo68emb79PODYJwB27dyV1wqzLiP3YgwunE+r8/2p7vhbjngwF+LCfIgL86EbbT0yRi9kjEVfhUwbxxbwXngQVtL/jcIp5OVI2jwBebm363x/qjv+4yAezIW4MB/iwnzoRlRDS+aovXMnFGdfUblWnH0F7Z07mSgiIiIiqsRCRovgoGBcPRmNwqzLUMjLUZh1GddObUNwULCpQyMN4uPj0LefB9o4tkDffh6cz0REVI8ZdR8Zc+TvPx4AEBkViZRDFauWNm+M4qolkaqcnO3iNQ/dfFxRnH0FIaGhAP6XSyIiqj84R0YHHOcUlyfzwcnZpsXvDXFhPsSF+dAN58iQRcnMSEcrJ9Wl+q2cXJGZkW6iiIiIyJBYyFC9wsnZRESWhYUM1StVTc6+ejKak7OJiOopTvaleqWqydkR4eGc6EtEVE+xkKF6x99/PAsXIiILwaElIiIiMlssZIiIiMhssZAhIiIis8VChoiIiMwWCxkiIiIyWyxkiIiIyGyxkCEiIiKzxUKGiIiIzBYLGSIiIjJb9XZnXysriVnfn2qH+RAP5kJcmA9xYT5qT9tnJhEEQTBSLERERER6xaElIiIiMlssZIiIiMhssZAhIiIis8VChoiIiMwWCxkiIiIyWyxkiIiIyGyxkCEiIiKzxUKGiIiIzBYLGSIiIjJbLGRqYdOmTdi8eXOVf1dWVoagoCB4e3tj3LhxuHHjhpGjsww5OTmYMmUKRowYgcDAQNy/f1+tTVlZGZYuXQofHx+MHTsWP/zwgwkitQw1zUd4eDh8fX0xatQonDlzxgSRWoaa5KNSaWkphg4dipSUFCNGaFlqko/8/HzMnDkTY8eOxbhx43Du3DkTRGreWMjUwL179/DOO+9g165dGtvExMSgcePGSEpKwjvvvIPly5cbMULLERYWhsmTJyM5ORlubm6Ijo5Wa3PkyBEoFAocPXoU69atYy4MqCb5+PTTT1FSUoLDhw9j48aNCAkJAU9GMYya5KPS2rVrcffuXSNGZ3lqko9169bBy8sLR44cwYcffohly5ZBLpebIFrzxUKmBo4fPw5nZ2f8+9//1tjm1KlTGDNmDADghRdeQElJCXJycowVokWQyWRITU3F8OHDAQB+fn5ITk5Wa6dQKPDw4UPI5XI8fPgQjRo1MnaoFqGm+UhKSsLs2bMhkUjQuXNnfPbZZyxkDKCm+QCAxMRENGnSBF27djVmiBalpvkYNmwYfHx8AADt27fH48eP8eDBA6PGau5YyNSAr68v5syZA6lUqrFNfn4+7O3tlV/b29sjNzfXGOFZjJKSEtja2sLauuLQdnt7e+Tl5am1GzduHG7fvo1BgwZh6tSpWLZsmbFDtQg1zUdmZiZSU1Ph5+eHiRMnorCwEFZW/KdH32qaj5ycHOzZswdvv/22sUO0KDXNx7Bhw9C8eXMAwM6dO+Hq6oqmTZsaNVZzZ23qAMQkKSkJERERKtc6duyI3bt363Q//mOtu6py4ezsrNZOIlE/3n3Lli3o1asXYmNjkZGRgRkzZqB79+5wcnIyVLj1Xl3yIZfLkZubi/j4eFy7dg2zZs1CUlIS/7GuA13zoVAo8O6772LFihXsqdSjunx/VNq9ezc+//xz7N27V9/h1XssZJ7g7e0Nb29vnV7r4OCAgoICtG/fHgBQUFAABwcHfYZnUarKhUwmg6enJ+RyOaRSqcbP+Pjx4/joo48gkUjQoUMH9OzZE7/88gsLmTqoSz7s7OwwatQoSCQSuLi4wNHREX/++Sd69OhhrPDrHV3zcfPmTdy8eRPvvvsuACArKwuhoaFYu3Yt+vXrZ7T465u6fH8AFfNkTp8+jX379sHR0dEYIdcr7DLQk5deeglHjhwBAKSlpaFhw4Z49tlnTRxV/WJjYwMPDw8kJiYCABISEjB48GC1di4uLjh27BgAoLi4GL/++itcXV2NGqslqGk+vLy8lG3++usv3Lp1Cx06dDBqrJagJvno1KkTTp8+jSNHjuDIkSNwc3NDeHg4ixgDqOn3x+7du5GSkoLY2FgWMTqSCJx1V2OVS68XLlwIAIiNjUV+fj4WLVqEx48fY+XKlfj111/RoEEDhIeHo3v37qYMt17Kzs7G8uXLUVRUhLZt22LDhg1o3ry5Si4KCwuxYsUKZGVlwcrKCnPnzsXo0aNNHXq9VJN8lJaWYs2aNfjtt98AAMuWLYOXl5eJI6+fapKPJwUEBGDBggXw9PQ0UcT1m7Z8vPnmm+jbty9sbW3RrFkz5et27NiBNm3amDBy88JChoiIiMwWh5aIiIjIbLGQISIiIrPFQoaIiIjMFgsZIiIiMlssZIiIiMhscUM8onqqa9eu6NKlC6ysrCCRSPDw4UPY2tpi9erVcHd3r/a1cXFxKCsrw5QpU/TS7mmjR4/GihUrLG7Zb00+r02bNuHOnTtYuXKlESMjMl/skSGqx/bs2YMjR44gISEB33zzDUaOHInw8HCtr/vxxx/x6NEjvbWjCtV9Xrm5uXjzzTexa9cuI0dFZN7YI0NkIcrLy3Hr1i3lAXUymQwffPABzp07B6lUih49eiAkJATnzp3DiRMncPbsWTRq1AjDhw/HypUrUVRUhIKCAjg5OWHjxo24ePGiSrspU6Zg27Zt+Pbbb6FQKODk5IRVq1ahTZs2SE9PxzvvvIOHDx+iY8eOGk/3LSgowKpVq3Dz5k1YWVnhtddew7Rp05Cbm4vVq1cjOzsbgiDA19cXs2bNwt9//43p06ejX79++Pnnn1FeXo63334bn3/+OW7evAk3Nzds2LABOTk5CAgIQN++fXH16lUIgoCVK1fCw8ND4+dga2uLIUOGYNy4cTh37hxu3boFb29v5WGLJ06cwLZt2yCTydCoUSMEBwejd+/e2Lx5M7Kzs1FQUIDs7Gy0atUKH330EX755Re1z+tJX3zxBZ5//nl07NgRd+/eNez/DET1iUBE9VKXLl2E0aNHCz4+PsKAAQOEIUOGCGvXrhUKCwsFQRCETZs2CQsWLBDKysoEuVwuLF++XFixYoUgCIIQHBwsfPrpp4IgCMLu3buF7du3C4IgCAqFQpg1a5awc+dOtXaHDx8WFi9eLMhkMkEQBOHAgQPCrFmzBEEQhLFjxwoHDx4UBEEQ0tLShK5duwrnz59Xi3n+/PlCZGSkIAiCcPfuXWHUqFFCRkaGMGXKFGHXrl3K6z4+PsJXX30l/PXXX0KXLl2EY8eOCYIgCCtXrhS8vLyEe/fuCY8ePRIGDBgg/Pjjj8p2X375pSAIgnDq1ClhwIABQllZWbWfg5eXl/DBBx8IgiAIubm5gru7u5CVlSX8+eefwujRo4Xi4mJBEATh+vXrwoABA4T79+8LH3/8sfDKK68I9+7dEwRBEObOnSts2rRJ7fPS5OOPPxbCwsK0J5iIBEEQBPbIENVje/bsQatWrfD7779j9uzZ6N27N1q3bg0A+O6777BkyRLY2NgAqNiufv78+Wr3mD59OtLS0vDZZ58hIyMDf/zxB3r27KnW7uTJk7h8+TL8/f0BVJy0/PDhQ5SUlODatWvw9fUFADz//PPo3LlzlfH+8MMPCAoKAgA0bdoUX331FR48eICLFy8qh1yaNm0KPz8/fPfdd+jZsydsbGwwZMgQAEC7du3Qu3dv2NraAqg4zPXOnTtwcHBA8+bN4ePjA6DibDSpVIpr165p/RxeeeUVAECbNm3QunVr3LlzB5cuXUJ+fj5mzJihbCeRSJCVlQUAym3nAaBbt264c+eO5iQRUZ2wkCGyAN26dUNISAhCQ0PRs2dP/OMf/4BCoVBpo1AoIJPJ1F4bFRWFX375Bf7+/vD09ER5eTmEKk42USgUmDVrFiZPngwAKCsrw507dyCRSABA5TXW1lX/02Ntba1sD1QcMtmiRQu15ykUCpSXlwOoOJzvyddUFiRPk0qlaveQSqVaP4eGDRsq/yyRSCAIAhQKBfr374+NGzcq/+7WrVtwcHDA//3f/6FRo0ZqryEiw+BkXyILMXr0aPTq1Qvvv/8+AGDQoEE4cOAAZDIZFAoF9u3bhwEDBgCo+KFfWSicOXMG06dPh6+vL1q3bo0ffvgBcrlcrd3AgQPxxRdfoLS0FEDF6pu3334bLVq0QPfu3REXFwcA+O2333D9+vUqY+zfvz/i4+MBAPfu3cP06dORmZmJnj17Yt++fcrrCQkJePHFF2v1/ouLi/Hdd98BqJjfYmNjgy5dulT7OWjSr18/nD17Fjdu3AAAnD59GmPGjMHjx4+rfd2TnxcR6Qd7ZIgsyIoVKzBmzBh8//33CAwMRGRkJHx9fVFeXo4ePXpgxYoVAIDBgwdj7dq1AID58+dj3bp1iI6OhlQqRZ8+fZRDKE+2mz17NvLy8jBhwgRIJBK0bdsWH3zwAQBgw4YNCAkJwYEDB9CuXTt07NixyvhWrlyJ1atXw8fHB4IgYO7cuXBzc8P69euxZs0aHDp0CGVlZfDx8YGfnx+ys7Nr/N4bNmyII0eOYP369WjUqBG2bt0KqVRa7eegSefOnbFmzRq89dZbEAQB1tbW2LZtG5555plqX/fk5zV37twax05EmvH0ayKq9/7++2/4+Pjgp59+MnUoRKRnHFoiIiIis8UeGSIiIjJb7JEhIiIis8VChoiIiMwWCxkiIiIyWyxkiIiIyGyxkCEiIiKzxUKGiIiIzNb/AzA87ygh4L5wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 6))\n",
    "plt.scatter(comp_K_rotated[:, 0], comp_K_rotated[:, 1], label='Clients', edgecolors='black')\n",
    "plt.legend()\n",
    "plt.ylabel('Rotated component 2')\n",
    "plt.xlabel('Rotated component 1')\n",
    "plt.title('Client map (after rotation)', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Комментарий:** \n",
    "\n",
    "С ростом ```Rotated component1``` снижается платежеспособность клиента,  ```Rotated component2``` уменьшается оседлость клиента. Таким, образом можно наблюдать практически прямую линейную зависисмость между двумя этими факторами."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Dimensionality reduction with MDS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Предложите способ вычисления расстояния между клиентами и вычислите матрицу\n",
    "расстояний.\n",
    "- Проверьте, можно ли клиентов банка поместить в евклидово пространство размерности 2 с\n",
    "сохранением расстояний между клиентами.\n",
    "- используйте любой из алгоритмов MDS для размещения объектов в евклидовом пространстве\n",
    "размерности 2. Оцените погрешность такого размещения. Постройте карту клиентов.\n",
    "- Сравните с картами клиентов из предыдущих пунктов и сделайте выводы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.  , 0.25, 0.19, 0.1 , 0.08, 0.13, 0.24, 0.29, 0.11, 0.32],\n",
       "       [0.25, 0.  , 0.25, 0.2 , 0.26, 0.25, 0.15, 0.09, 0.25, 0.11],\n",
       "       [0.19, 0.25, 0.  , 0.1 , 0.26, 0.07, 0.13, 0.22, 0.09, 0.24],\n",
       "       [0.1 , 0.2 , 0.1 , 0.  , 0.16, 0.07, 0.15, 0.21, 0.06, 0.24],\n",
       "       [0.08, 0.26, 0.26, 0.16, 0.  , 0.2 , 0.28, 0.32, 0.18, 0.34]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import euclidean_distances\n",
    "# distance_df = df_normalized @ df_normalized.T\n",
    "distances = euclidean_distances(df_normalized)\n",
    "np.around(distances[:5, :10], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = distances.shape[0]\n",
    "C = np.eye(n) - 1 / n * np.ones((n, n))\n",
    "B = - 1/2 * C @ (distances ** 2) @ C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.  , -0.  , -0.  , -0.  , -0.  , -0.  , -0.  , -0.  , -0.  ,\n",
       "       -0.  , -0.  , -0.  , -0.  , -0.  , -0.  , -0.  , -0.  , -0.  ,\n",
       "       -0.  , -0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,\n",
       "        0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,\n",
       "        0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,\n",
       "        0.  ,  0.  ,  0.01,  0.38,  0.63])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigenvals = np.linalg.eigvalsh(B)\n",
    "np.around(eigenvals, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Комментарий:**\n",
    "\n",
    "Поскольку матрица B неотрицательно определена, то возможно помещение векторов, соотвествующих клиентам банка  в евклидово пространство размерности 2 с сохранением расстояний между клиентами. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import MDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stress value: 0.02\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(50, 2)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mds = MDS(n_components=2, max_iter=300, dissimilarity='euclidean')\n",
    "embeddings = mds.fit_transform(df_normalized)\n",
    "print(f'Stress value: {round(mds.stress_, 2)}')\n",
    "embeddings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Комментарий:**\n",
    "\n",
    "Погрешность размещения можно оценить с помощью финального значения ```stress``` функции (что позволяет оценить сумму квадратов разницы изначальных и полученных расстояний), которое в данном случае равно **0.02**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjEAAAFRCAYAAACFc/wzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAA1NElEQVR4nO3de1yUdfr/8fcA4iEtRTmUfcHKA5qYkSmZWmx5QCNT1CzDWk1LV90sjTA8JcUiHbUs3SwLD6lBFpWuW2lHRY12c0syM3TXQlDURFRO8/vDH5OEMBxm7pkbXs/Ho8dD7rmZ+5q5nOby87k+n9titVqtAgAAMBkPVwcAAABQGxQxAADAlChiAACAKVHEAAAAU6KIAQAApkQRA8C0WFwJNGwUMQCq7aOPPtL48eN1ww036Nprr9Udd9yhlStXqqioyHZOamqqOnXqpLy8PElSdHS0HnjgAYfG8dtvv+mRRx7Rd99959DnBWAuXq4OAIA5zJ8/X2+99ZbuuOMO3XXXXWrWrJl27NihhQsXKj09Xc8//7w8PT0r/N7cuXPl4eHYfy/t2bNH77//vu677z6HPi8Ac6GIAWDXhg0btHr1aj3xxBO68847bcd79+6tjh07avr06UpLS9Mdd9xR4Xfbt29vYKQAGhKmkwDYtXz5cnXq1KlcAVNm8ODBGjdunFq1anXB3/3jdFJBQYEWLFig3r17q1u3boqOjtb3339vezw1NVW9evXStm3bNHToUHXt2lWDBw/Wxx9/LElKT0/X2LFjJUkjRozQY489dsHrLl68WMOHD9eGDRvUv39/devWTffdd59ycnL01ltv6eabb9Z1112nGTNm6PTp07bf279/v6ZNm6awsDB17dpVf/rTn/TSSy/Z+m/S09PVqVMnffHFFxo2bJi6deum4cOHa9u2bTV8VwHUFUUMgCrl5ORo7969uummmyo9JyYmpsrHy1itVk2aNEkffPCBHnroIb3wwgvy9vZWdHS0Dh48aDvv1KlTmjVrlsaMGaOlS5eqVatWmj59uo4fP66rr75ac+bMkSQlJCRo8uTJlV7v559/1t///nc9+uijio+P17///W9FR0crJSVF8+bN09SpU/X+++/rzTfftF137NixOn78uBITE7V06VL16tVLixYt0pYtW8o99yOPPKJbbrlFixcvlo+PjyZMmKC9e/fafQ8AOA7TSQCqlJ2dLUm67LLL6vxcX3zxhbZv367XX39dvXv3liT17dtXQ4YM0csvv6yEhARJUlFRkWbOnKnBgwdLklq3bq2hQ4cqPT1dAwcOtE1RdejQQYGBgZVer6CgQE899ZSuueYaSdLWrVv1wQcf6JNPPlHbtm0lSZs3b9a///1vSeeKnsDAQD3//PPy8fGRJN1www366KOPtHPnTv3pT3+yPffIkSM1ZcoU2zm33nqrXn/9ddtrAOB8FDEAqlTWrFtaWlrn50pPT1fTpk11/fXXq7i42Ha8T58++uSTT8qd2717d9ufAwICJKnctE91WCwWde3a1fZz69at5ePjYytgJKlly5Y6efKkJKlr165avXq1ioqKtG/fPmVlZen7779XcXGxCgsLyz33kCFDbH/29vZW3759tWPHjhrFB6BuKGIAVOnSSy+VJP3666+VnpOTk6M2bdrYXYV0/PhxnT59ulxhUaZRo0blfm7SpIntz2XPW9NCqmnTphVWTDVt2rTK33n55Ze1fPlynTx5Um3bttW1114rLy+vCnvS+Pr6lvvZx8dHJ06cqFF8AOqGIgZAlXx8fNSlSxd98cUXmjFjxgXP+fOf/6w2bdrojTfeqPK5WrRoodatW2vp0qXOCLXONmzYoBdeeEFz587VbbfdphYtWkg6N130R8ePH1ebNm1sPx89etQ2BQXAGDT2ArDr3nvv1Z49e7R+/foKj7377rvat2+fIiMj7T7Pddddp7y8PDVr1kwhISG2/9LS0vTee+9VO54L7UfjCN98840CAgJ011132QqY7777Tnl5eRVGYs5v9C0sLNRnn32mXr16OSUuABfGSAwAu4YOHaqtW7dqzpw5+vbbb3XLLbfIYrHoiy++0Jo1axQREaGoqCi7zxMeHq6QkBBNnDhRU6ZM0aWXXqp//OMfWr16tebPn1/teMoKjE8//VTNmjXTVVddVevXdr6QkBC99dZbevHFF9WzZ0/99NNPeumll2SxWHTmzJly5y5ZskSNGjXSFVdcoTfffFMFBQW6//77HRIHgOqhiAFgl8Vi0bPPPqt169YpNTVVmzdvVlFRkdq1a6e4uDiNGDFCFovF7vN4enpq+fLlevrpp5WUlKT8/HwFBQUpISFBw4cPr3Y8HTp00NChQ7V06VL95z//0SuvvFKXl2czfPhwZWVl6a233tKrr76qtm3bavz48frpp5/09ddflzv30Ucf1cqVK/W///1P3bp106pVq/R///d/DokDQPVYrNxBDQCqrWyzvbffflshISGuDgdo0OiJAQAApkQRAwAATInpJAAAYEqMxAAAAFOiiAEAAKZUL5dYHzt2SqWlzJK5m9atm+vo0XxXh4EaIGfmQr7Mh5zZ5+FhUatWF13wsXpZxJSWWili3BR5MR9yZi7ky3zIWe0xnQQAAEyJIgYAAJgSRQwAADCletkTAwCAuykpKdaxY7kqLi60HcvJ8VBpaakLo3IfXl7eatXKV56e1S9NKGIAADDAsWO5atKkmS66KMB2w1QvLw8VF1PEWK1WnTr1m44dy1WbNpdW+/eYTgIAwADFxYW66KKLq3XH94bGYrHooosuLjdKVR0UMQAAGIQCpnK1eW8oYgCYVkrKevUM6yH/gJbqGdZDKSnrXR0SAANRxAAwpZSU9YqNi1NAaLQipq5TQGi0YuPiKGSAGjh1Kl/PPJOo6OhRuu++uzV16gP64YdMZWTs0pQpEyVJf/vbAmVmfl+r53/qqfnKzv7VkSGXQxEDwJQSkxIVHD5ZbQJD5OHppTaBIQoOn6zEpERXhwY4jDNHG0tLSzVjxl918cUX6/XXV2vFitX6858naMaMafrttxO28x57bLaCg7vU6hoZGbtktTpvR2JWJwEwpQNZ+9QlsnO5Yz5tOys9dZ+LIgIcq2y0MTh8srpEdlbeoT2KjYuTJEVFjazz82dk7NKRI0c0fvwD8vA4N6YRGtpDs2bNUUHBadt5U6ZM1LhxExUa2kPJySu0Zcs/VVJSql69wjRp0jRlZ/+qWbNm6Morr9LevT/Ix6e1Fiz4m9599x0dOZKrmTP/qpde+ruSk1do5850eXp6qE+fmzRu3MQ6vwZGYgCYUlC79so7tKfcsbxDexTUrr2LIgIcy9mjjXv3/qDOnbvYCpgyN9zQR61atapw/vbtX+mHH/bo739/U6+/vkq5ubnavHmjJGnfvh91551jlJy8Ts2bN9fmzRsVHX2f2rTxVVLSCzp9+rS2b/9Kb7yxRi+//Jr+97//6uzZs3V+DRQxAEwpZmaMMrcs0ZGDu1VaUqwjB3crc8sSxcyMcXVogEMcyNonn7YVRxsPZDlmtNHDw1KjqZ5du3bo++//o/HjozVu3D3KzPxeP/+8X5LUqpWPOnYMliRdeWV7/fbbb+V+t00bXzVu3FiTJo3TunWrNWHCJDVu3LjOr4HpJACmVDacnpiUqPTUfQpq114J8fEOGWYH3EHZaGObwBDbMUeONgYHd9E777wtq9Vabnnz0qUv6frre1U4v7S0RKNG3aXRo++RJJ08eVKenp46ceK4vL29y537x+LIy8tLy5at0L/+laFt277Ugw/+WYsXL1NgYFCdXgMjMQBMKypqpHZs36XD2ce1Y/suChjUK84ebbzmmmvVqpWPXnttmUpKSiRJ6enb9OGH7+nYsbwK54eGXq9//ONDFRQUqLi4WLGxj2jr1o+rvIanp6dKSkq0d2+mpkyZqGuuuVZTpjykdu2u1MGDB+r8GhiJAQDADTl7tNFisehvf3tWixc/o7Fj75SXl5cuuaSlkpJeUH5+foXz+/Tpp3379mrixPtUWlqiXr16KyLitiqXUPfu3VczZvxVzz67WF27dtPYsXeqSZMm6tChk8LCetf9NVidufbJRY4ezVdpab17Wabn69tCubknXR1GpVJS1isxKVEHss79zyJmZkyD/5e9u+cM5ZEv95adfUABAeWnT7h3UnkXeo88PCxq3br5Bc9nJAaQ85cyAgAcj54YQGycBgBmRBEDyPlLGQEAjkcRA4iN0wAYox62oTpMbd4bihhAbJwGwPm8vLx16tRvFDIXYLVaderUb/Ly8rZ/8nlo7AXExmkAnK9VK18dO5ar/PzjtmMeHh4qLWV1knSuyGvVyrdGv8MSaxiG5Z/m4+qcsey9ZlydL9QcObOPJdYATIdl7wDsoScGgFti2TsAeyhiALgllr0DsIciBoBbYtk7AHsoYgC4JZa9O15Kynr1DOsh/4CW6hnWQykp610dElAnFDEAXKaqL9WoqJFKiI9XdkayNi4epeyMZJa910FZo3RAaLQipq5TQGi0YuPiKGRgaiyxhmFYSmg+zszZ+auPfNqeW32UuWUJhUodVJWvnmE9FBAarTaBIbZjRw7uVnZGsnZs32VUiPgD/r9oX1VLrBmJAeASrD4yFo3SqI8oYgC4BF+qxqJRGvWRS4qYtLQ0DR48WP3799eqVasqPP7RRx9p6NChuv322zV58mSdOHHCBVECcCa+VI1FozTqI8OLmMOHD+u5557T6tWr9e6772rt2rXat+/3f3nl5+dr3rx5WrZsmd577z116tRJixcvNjpMAE7Gl6qxaJRGfWT4bQe++uorhYWFqWXLlpKkgQMHatOmTZoyZYokqaioSPPmzZO/v78kqVOnTkpLSzM6TABOxk03jRcVNZL3F/WK4UVMTk6OfH1/v0uln5+fvv32W9vPrVq10q233ipJOnPmjJYtW6bo6GijwwRgAL5UAdSF4UXMhVZ0WyyWCsdOnjypyZMnKzg4WMOGDavRNSpbigXX8/Vt4eoQUEPkzFzIl/mQs9ozvIjx9/fXrl2/70mQk5MjPz+/cufk5ORo/PjxCgsL06xZs2p8DfaJcU/sh2A+5MxcyJf5kDP73GqfmN69e2vbtm3Ky8vT6dOntXnzZvXr18/2eElJiR588EFFRETo8ccfv+AojdHYqhsAAPfjkpGY6dOna+zYsSoqKtKIESPUrVs3TZgwQdOmTVN2dra+//57lZSU6B//+IckqWvXrnryySeNDlVS+V1Fu0Se21U0Ni5OkpjLBwDAhbjtgB1s1e04DJuaDzkzF/JlPuTMPreaTjIbdhUFAMA9UcTYwa6iAAC4J4oYO9hVFHVBUzgAOI/hjb1mw66iqC2awgHAuWjshWEaWgNbVU3hMTNjlJiUqANZ5wrjmJkxblnYNLScmR35Mh9yZh+NvYALVNYUnvXzj4qNi1NAaLQipq5TQGi0YuPimGoCgBqiiAGcpLKm8MZNmis4fLLaBIbIw9NLbQJDFBw+WYlJiS6KFADMiSIGcJLKmsILz+SzbB8AHIAiBnCSqKiRSoiPV3ZGsjYuHqXsjGQlxMcr6IoOLNsHAAdgdRLgRFFRIy/YsFu2asmn7blVS5lblighPt4FEQKAeVHEAAZj2T4AOAZFDOAClY3QAACqj54YwADs3AsAjsdIDOBk7NwLAM7BSAzgZIlJiewLAwBOQBEDOFllO/eyLwwA1A1FDOBkle3cW7YvDP0y5kPOAPdAEQM4WWU798bMjLH1y3AfJfMgZ4D74C7WMExDvltrSsr6C961uqo7Xe/YvsuFEZ/TkHNWGXfOGfkyH3JmX1V3sWZ1EmCAyvaFOZC1T10iK/bLpKfSL+OuyBngPphOAlzIXr9MbdGz4TzOyhmAmqOIAVyoqn6Z2qJnw7mckTMAtUNPDAzD3O+FVdYvU1uO7NkgZxfm6Jw5CvkyH3JmX1U9MRQxMAwfVmP4B7RUxNR18vD8veWttKRYGxeP0uHs4zV6LnJmLuTLfMiZfVUVMUwnAfXMhXo29m5bK+/GF9EjA6BeoYgB6pk/9mxkfrFK//3uI1075FF6ZADUK0wnwTAMmxrn/J4N78YX6dohj9aqR4acmQv5Mh9yZh/TSUADExU1Uju279Lh7OMqPHuKezcBqJcoYgCTqu5eMOxrAqC+oogBTKgme8GwrwmA+oqeGBiGuV/HqeleMLXd14ScmQv5Mh9yZh/3TgLqmZrev6eyezcBgJkxnQSYEH0uAEARA5gSfS4AQBEDmFJU1EglxMcrOyNZGxePUnZGshLi45kycgPcQRwwDj0xgEnR5+J+ylaNBYdPVpfIzso7tEexcXGSRK4AJ2AkBgAcJDEpUcHhk9UmMEQenl5qExii4PDJSkxKdHVoQL1EEQMADnIgax+7IwMGoogBAAdh1RhgLIoYAHAQVo0BxnJJEZOWlqbBgwerf//+WrVqVaXnxcTEKDU11cDIAKD2WDUGGMvw1UmHDx/Wc889p9TUVHl7e2v06NHq1auX2rdvX+6cuXPnatu2berVq5fRIQJArbFqDDCO4SMxX331lcLCwtSyZUs1a9ZMAwcO1KZNm8qdk5aWpltuuUURERFGhwcAAEzC8JGYnJwc+fr62n728/PTt99+W+6c+++/X5L09ddf1+oald0oCq7n69vC1SGYzqpVqzVn3hP6ef+PuuLKDnpi3hyNGXO3YdcnZ+ZCvsyHnNWe4UXMhW6abbFYHHoN7mLtnrhba82dv3la8JBzm6dN+esMnTx5xpApC0fnrLZ300b18BkzH3JmX1V3sTZ8Osnf319Hjhyx/ZyTkyM/Pz+jwwBMoT5tnlZWkAWERiti6joFhEYrNi6ObfkB1JrhRUzv3r21bds25eXl6fTp09q8ebP69etndBiAKdSnzdPqU0EGwD24ZCRm+vTpGjt2rO644w7ddttt6tatmyZMmKDdu3cbHQ7g1urT5mn1qSAD4B4s1gs1qZgcPTHuibnfmju/J8an7bmemMwtSwzbe8SROesZ1kMBodFqExhiO3bk4G5lZyRrx/ZdDrlGQ8dnzHzImX1u1RMDoPrq0+Zp7GYLwNEYiYFh+BeH+bA6yVz4jJkPObOvqpEYw5dYA2i42M0WgCMxnQQAAEyJIgYAAJgSRQwAADAlihgAAGBKFDEAAMCUqixiDh48qB07dqiwsLDc8S+//NKpQQEAANhTaRHz3nvvafTo0Zo/f74GDRqkvXv32h57+umnDQkOAACgMpUWMa+++qreeecdffDBB3rggQc0fvx4/frrr5Kkerg/HgAAMJlKixgPDw/5+/tLku68805FR0dr0qRJOnPmjGHBAQAAVKbSIqZp06bavn277eeJEyfqqquu0kMPPaSzZ88aEhwAAEBlKi1iYmNjFRsbq5SUFNuxhIQESVJWVpbTAwMAAKhKpfdO6tatm7Zs2VJuZZK3t7deeeUVZWRkGBIcAABAZezuE+Pt7V3hWGhoqFOCAQAAqC42uwMAAKZEEQMAAEzJbhHz/PPPVzgWHx/vjFgAAACqrdLG3kWLFum3337Thx9+qPz8fNvxoqIiffLJJ4qLizMkQAAAgAuptIi55pprtHv3bnl4eKhly5a2456enlq8eLERsQEAAFSq0iLmpptu0k033aR+/fqpW7duRsYEAABgV6VFTJlmzZopNjZWx48fL3fPpFdeecWpgQEAAFTFbhHz6KOPKjQ0VNdff70sFosRMQEAANhlt4gpKiqiiRcAALgdu0usg4KClJOTY0QsAAAA1WZ3JKa0tFS33Xabrr76ajVu3Nh2nJ4YAADgSnaLmP79+6t///5GxAIAAFBtdouYYcOGKTs7Wz/88IP69OmjnJwcXXrppUbEBgAAUCm7PTGffvqpRo8erfnz5+vo0aMaPHiwPvroIyNiAwAAqJTdImbx4sVat26dLr74Yvn5+Wn16tVatGiREbEBAABUym4RU1paKj8/P9vPnTt3Zr8YAADgcnaLmKZNm+qXX36xFS67du0qt0oJAFwpJWW9eob1kH9AS/UM66GUlPWuDgmAQew29s6YMUPjxo1Tbm6u7rzzTmVlZXEDSABuISVlvWLj4hQcPlldIjsr79Aexf7/zTmjoka6ODoAzmaxnn9DpEr89ttv+uabb1RaWqprrrlGPj4+RsRWa0eP5qu01O7LgsF8fVsoN/ekq8NADbh7znqG9VBAaLTaBIbYjh05uFvZGcnasX2XCyNzDXfPFyoiZ/Z5eFjUunXzCz9WnSfIzMxUYWGhioqKtGvXLm3evNmhAQJAbRzI2ieftp3LHfNp21kHsva5KCIARqrWDSC3b9+uoKAg2zGLxaIBAwY4NTAAsCeoXXvlHdpTbiQm79AeBbVr78KoABjFbhGza9cubdy4URdddJER8QBAtcXMjLH1xPi0PdcTk7lliRLi410dGgAD2J1Ouuyyyyhg4FZiYmbo/4Lays/vEv1fUFvFxMxwdUhwkaiokUqIj1d2RrI2Lh6l7IxkJcTH09QLNBB2R2JCQ0M1ffp0hYeHq0mTJrbjTCfBFWJiZmj1uvW6dsijtn95r173rCQpMfFpF0cHV4iKGknRAjRQdkdivvnmGx05ckTr169XcnKykpOTtXLlyjpdNC0tTYMHD1b//v21atWqCo/v2bNHUVFRGjhwoB5//HEVFxfX6XqoP1a/tUbXRjysNoEh8vD0UpvAEF0b8bBWv7XG1aGhGtjTBYAj2R2JSU5OliQVFxfLarWqUaNGdbrg4cOH9dxzzyk1NVXe3t4aPXq0evXqpfbtf2/EmzlzpuLj49W9e3fNmjVL69at0913312n66J+OHs6/4KrUc6erv0SxZSU9UpMStSBrH0KatdeMTNj+Je9E7CnCwBHszsSc/ToUd1///3q3r27unXrprFjx+rw4cO1vuBXX32lsLAwtWzZUs2aNdPAgQO1adMm2+OHDh3SmTNn1L17d0nS8OHDyz2Ohq1x0+bKO7Sn3LG8Q3vUuGmLWj1f2RdrQGi0IqauU0BotGLj4hghcILEpEQFh08uN4oWHD5ZiUmJrg4NgEnZHYl54okn1L17dz377LMqKSlRcnKy5s2bp5dffrlWF8zJyZGvr6/tZz8/P3377beVPu7r61vjoqmyTXHger6+tSs2yoz/81gtf+NZXRvxsK0n5puNz2r8n8fW6rmTnllo+2KVZPtiTXpmoR58cFydYq0v6pqzMgey9qlLZMVRtPTUfQ67BhyXLxiHnNWe3SImKytLL7zwgu3nadOmaciQIbW+4IU2CD7/hpL2Hq8Odux1T47YmXLevASdPl2k1W8t1NnTJ9W4aQvdPfouzZuXUKvn/nn/jwoecqEv1h/ZRVOO3U20qj1deK8dg91fzYec2VenHXuLi4t19uxZ28+nT5+u012s/f39deTIEdvPOTk55e6S/cfHc3Nzyz0OJCY+rf8eOKScnN/03wOH6rQqqeyL9XxsluYcMTNjlLlliY4c3K3SkmIdObhbmVuWKGZmjKtDA2BSdouYwYMH67777tP69eu1fv16jRs3TgMHDqz1BXv37q1t27YpLy9Pp0+f1ubNm9WvXz/b423btlXjxo319ddfS5I2bNhQ7nHAkfhiNQ57ugBwtGrdADIlJUWff/65SktL1bdvX40YMaJOozFpaWlaunSpioqKNGLECE2YMEETJkzQtGnTFBISoszMTMXFxenUqVPq0qWLEhIS5O3tXe3nZzrJPbnrsCmrkyrnrjnDhZEv8yFn9lU1nVStIubYsWPauXOnPD091bNnT7Vo4d5NSBQx7okPq/mQM3MhX+ZDzuyrU0/MP//5Tw0YMEBvvvmmXn31VfXv31/bt293eJAAAAA1YXd10nPPPaeVK1eqU6dOkqTvvvtOcXFxeuedd5weHAAAQGXsjsQ0adLEVsBI0tVXX12nfhgAAABHsFvE9OrVS8uWLVNBQYHOnj2rtWvXqkOHDjpx4oSOHz9uQIgAAAAV2W3svfrqq1VSUnLhX7ZYtGfPngs+5ko09ronGtjMh5yZC/kyH3JmX1WNvXZ7Yr777juHBwQAAFBXdouYM2fO6OOPP64wdTRmzBhnxQQAAGCX3SLmgQce0MmTJ3X55ZfbjlksFooYAADgUnaLmJycHG3cuNGIWAAAAKrN7uqkjh07Kjc314hYAAAAqs3uSMygQYMUERGhjh07ysvr99PffPNNpwYGAADcnyvvP2e3iElKStIDDzygwMBAI+IBAAAmkZKyXrFxcQoOn6wukZ2Vd2iPYuPiJMmQQsbuPjGRkZFKS0tzeiCOxD4x7on9EMyHnJkL+TIfs+esZ1gPBYRGq01giO3YkYO7lZ2RrB3bdznkGnW6AWR4eLhWrVqlnJwcHT9+3PYfAABo2A5k7ZNP287ljvm07awDWfsMub7d6aQVK1aosLBQCxYssB1z1516AQCAcYLatVfeoT3lRmLyDu1RULv2hlzf7kjMt99+q8zMzHL/UcAAgDmlpKxXz7Ae8g9oqZ5hPZSSst7VIcHEYmbGKHPLEh05uFulJcU6cnC3MrcsUczMGEOub3ckprS0VMuXL9dnn32m4uJi3XjjjXrwwQfLrVQCALg/Vzdhov4p+3uTmJSo9NRzq5MS4uMN+/tkt7E3KSlJmZmZuuuuu1RaWqq1a9fqyiuv1OOPP25IgLVBY697MnsDW0NEzszFXr6MaMJEzfAZs69ON4D8/PPPlZKSokaNGkmSbr75Zt1+++2OjRAA4HQHsvapS2TFJsz0VGOaMAFHs9sTY7VabQWMJHl7e5f7GQBgDmVNmOczsgkTcDS7RUxwcLCeeuopHTx4UAcPHlRCQoI6duxoRGwAAAdydRMm4Gh2p5Pmzp2r+Ph4jR49WqWlperbt69mz55tRGwAAAdydRMm4Gh2G3vPd/bsWTVu3NiZ8TgEjb3uiQY28yFn5kK+zIec2VerHXsLCwsVExOjjz76yHZs2rRpio2NVXFxseOjBAAAqIFKi5hFixYpPz9f1157re3YE088oRMnTmjx4sWGBAcAAFCZSouYrVu36plnnlHr1q1tx/z9/bVw4cJyozMA0BCx8y3gepUWMY0aNVKTJk0qHG/evLm8vb2dGhSAhsVsBUHZzrcBodGKmLpOAaHRio2Lc/u4gfqm0iLGw8ND+fn5FY7n5+fTEwPAYcxYECQmJSo4fLLaBIbIw9NLbQJDFBw+WYlJia4ODWhQKi1ibrvtNsXFxamgoMB2rKCgQHFxcRowYIAhwQGo/8xYEBzI2iefthV3vj2Qxc63gJEqLWLuvfdetWjRQjfeeKNGjRqlESNG6MYbb9TFF1+sv/zlL0bGCKAeM2NBwM63gHuocjppwYIFSktL0/33368HH3xQmzZt0hNPPCEPD7sb/QJAtZixIGDnWzhCSsp6XdUh2DS9YO7I7o69l19+uS6//HIjYgHQAMXMjFFsXJyCwyfLp21n5R3ao8wtS5QQH+/q0CpVk51vU1LWKzEpUQeyzp0XMzOGHXJh6wULDp+s4CHn/t7HxsVJEn8/aqBGO/aaBTv2uid2pjQfo3JWX7/oz/+i+mOB5ozXx2fMPHqG9VBAaLTaBIbYjh05uFvZGcnasX2XCyNzP1Xt2EsRA8PwP1jzIWd1K7CM/qIiX+bhH9BSEVPXycPz9wmR0pJibVw8Soezj7suMDdUq9sOAEBDV9fl32ZsWoYxzNgL5o4oYgCgEnVd/s0XlWOZbVPEqpilOdzd33O7jb0A0FAdyNqnLpEVR1LSU6s3kmLGpmV3dX5/UZdI8zfClsWc9MxCpaf+WGVzuKuY4T2nJwaGYb7efBp6zhzR02Jk03J9zld9bYR155y5y3tOTwwA1IIjhvyjokZqx/ZdOpx9XDu273Kbf8GaDf1FxjPDe850EgBUoib7wcC5yvqLzh8VoL/Iuczwnhs+EvPLL79ozJgxGjRokCZNmqRTp05Veu6XX36pe++918DoAKA8RlLcg1kaYesTM7znho/EzJ8/X3fffbeGDBmil156SUuWLNHMmTPLnVNaWqoVK1Zo6dKl6tixo9EhAgDcDKNixjPDe25oY29RUZF69eqlHTt2yMvLS7/++qvuueceffzxx+XO+/HHH7VixQrddNNNSk5OVnJyco2uQ2Ove3LnBraGpCaNpuTMXMiX+ZAz+6pq7DV0JObYsWNq3ry5vLzOXdbX11eHDx+ucF6HDh305JNPKj09vVbXqezFwvV8fVu4OoQGbdWq1Zo1e7Y63TzJtmRy1uzZatGiicaMufuCv0PO6m7VqtWaM+8J/bz/R11xZQc9MW9Ope93XTk6X0bG3lDxGas9pxUxGzduVEJCQrlj7dq1q3CexWJx+LUZiXFP/IvD9eLmzFenmyfZGvXaBIZIN09S3Jz5GjAgssL57pQzs95f6UI3+pvy1xk6efKMw+N3dL6MjL2hcqfPmLtyyUhMRESEIiIiyh0rm04qKSmRp6encnNz5efn56wQAPxBXTdvcxUzbLpVmfN3/ZVUbtdfYgfqxtDVSY0aNVKPHj304YcfSpI2bNigfv36GRkC7HD3LaZRN2bdBr+u2/+7khn22qiMmWNHw2D4Euu5c+dq3bp1Gjx4sHbt2qWHHnpIkrRmzRq98MILRoeD89T1Zndwf2ZYMnkhZv4yNWvhKJk7djQM3HYANs7eYpq5X/dgxtVJ7rL9eW2cPxX2x/snmaknxtmxN1Tu8hlzZ26zOgnuzaz9EqiZqKiRpvsCMvONFM2w10ZlzBw7GgZGYmDDSAz+yJ1yZtbVSUZyp3yhesiZfdwAEtVi1n4JGMPVTd9s/w/gjyhiYBMVNVIJ8fHKzkjWxsWjlJ2RzNAxJDWspm9XF2sAqo/pJBiGYVPzKcuZWRtrazoFZfZGVj5j5kPO7GM6CUCdmHGJc21Gj8y8H407YTQLRqGIAWCXGfcLqU1BYq9Y48vZvoY09QjXo4gBYJcZm75rM3pUVbHGl3P1MJoFI1HEALDLjE3ftRk9qqpY48u5esw49QjzoogBUC1mW+Jcm9Gjqoo1vpyrx4xTjzAvduwFUC/VdrfZynY0LvtyPn+FFl/OFZl5d2WYD0UMgHrLkbdY4Mu5erhVAYzEPjEwDPshGMsR2/STs/Lc/dYH5Mt8yJl93AASaGDO37StS+S5UYPYuDhJcqsvXbMx480zgfqMxl6gHmIlDYCGgCIGqIdYSfM7NqgD6i+mk4B6iJU05zCtBtRvjMQA9ZAZd9h1BqbVgPqNkRigHmKZ6zkHsvapS2TFabX01IY3rQbURxQxQD3FShqm1YD6jukkAPUW02pA/cZIDIB6i2k1oH5jJAZABWXLkj08PU2/LNlsN64EUH2MxAAoh2XJAMyCeyfBMNwjxBx6hvVQQGh0uWbYIwd3KzsjWTu273JhZLCHz5j5kDP7qrp3EtNJML36vCOrK14bu/0CMAumk2Bq9Xnqw1WvjWXJAMyC6SQYxhnDpvV56sNVr+384smn7bniKXPLElb1mABTE+ZDzuyrajqJkRiYWn3ekdVVr41lyQDMgp4YmFrZ1Mf56svUhytfW9my5NKSEpYlO0l97uUCjEIRA1Orzzuy1ufX1tCVTdkFhEYrYuo6BYRGKzYujkIGqCF6YmAYZ839pqSsV2JSog5knZv6iJkZU29GDlz92pivdw5n9TuRL/MhZ/ZV1RNDEQPD8GE1H3LmHP4BLRUxdZ08PH9vSywtKdbGxaN0OPt4rZ+XfJkPObOPfWIAwI3U514uwEgUMQDgQNVp2KXfCXAMllgDgINUd4NClrEDjkFPDAzD3K/5kLOacfXmi+TLfMiZffTEAHAr9XWPFO47BRiL6SQAhqrP97vivlOAsQwfifnll180ZswYDRo0SJMmTdKpU6cqnJOTk6Px48dr6NChGjZsmLZt22Z0mACcJDEpUcHhk9UmMEQenl5qExii4PDJSkxKdHVodUbDLmAsw4uY+fPn6+6779amTZvUtWtXLVmypMI5CxcuVHh4uN59910988wzmjFjhkpKSowOFYAT1Ocpl6iokUqIj1d2RrI2Lh6l7IxkGnYBJzK0iCkqKtLOnTs1cOBASdLw4cO1adOmCucNGDBAkZGRkqSgoCCdPXtWBQUFRoYKwEnq+x4pZfedOpx9nPtOAU5maE/MsWPH1Lx5c3l5nbusr6+vDh8+XOG8AQMG2P68fPlyde7cWS1atKj2dSrrYobr+fpWP49wD47OWfwTczX1oZnSzZPk0/ZcT8wPW1/W4ueT+PvhALyH5kPOas9pRczGjRuVkJBQ7li7du0qnGexWCp9jhUrVmjt2rVauXJlja7NEmv3xFJC83FGzgYMiNRTC86U2yPlqQULNGBAJH8/6ojPmPmQM/uqWmLttCImIiJCERER5Y4VFRWpV69eKikpkaenp3Jzc+Xn53fB31+4cKE+/fRTrVq1SgEBAc4KE4ALREWNZJoFQJ0Z2hPTqFEj9ejRQx9++KEkacOGDerXr1+F81asWKH09HStWbOGAgYAAFyQ4Tv2Hjp0SI899piOHj2qSy+9VM8++6wuueQSrVmzRjk5OZo2bZp69uyp5s2b6+KLL7b93rJly+Tv71+tazCd5J4YNjUfcmYu5Mt8yJl9VU0ncdsBGIYPq/mQM3MhX+ZDzuzjtgMAAKDeoYgBAACmRBEDAABMiSIGAACYEkUMAAAwJYoYAABgShQxAADAlChiAACAKVHEAAAAU6KIAQAApkQRAwAATIkiBgAAmBJFDAAAMCWKGAAAYEoUMQAAwJQoYgAAgClRxAAAAFOiiAHgllJS1qtnWA/5B7RUz7AeSklZ7+qQALgZL1cHAAB/lJKyXrFxcQoOn6wukZ2Vd2iPYuPiJElRUSNdHB0Ad8FIDAC3k5iUqODwyWoTGCIPTy+1CQxRcPhkJSYlujo0AG6EIgaA2zmQtU8+bTuXO+bTtrMOZO1zUUQA3BFFDAC3E9SuvfIO7Sl3LO/QHgW1a++iiAC4I4oYAG4nZmaMMrcs0ZGDu1VaUqwjB3crc8sSxcyMcXVoANwIjb0A3E5Z825iUqLSU/cpqF17JcTH09QLoByKGABuKSpqJEULgCoxnQQAAEyJIgYAAJgSRQwAADAlihgAAGBKFDEAAMCUKGIAAIAp1csl1h4eFleHgEqQG/MhZ+ZCvsyHnFWtqvfHYrVarQbGAgAA4BBMJwEAAFOiiAEAAKZEEQMAAEyJIgYAAJgSRQwAADAlihgAAGBKFDEAAMCUKGIAAIApUcQAAABToogBAACmRBEDh/rll180ZswYDRo0SJMmTdKpU6cqnJOTk6Px48dr6NChGjZsmLZt2yZJslqtSkxM1KBBgzR48GB9/fXXRoff4FQnX2W+/PJL3Xvvvbafi4qKFBoaqqFDh9r+KykpMSLsBq0uOeMzZrzq5KuwsFAzZ85URESEhg0bpp9++kkSn7FqsQIONHHiROv7779vtVqt1hdffNG6cOHCCuc88sgj1uTkZKvVarX+9NNP1t69e1uLi4utGzdutE6YMMFaUlJi3b9/v/XWW2+1FhUVGRp/Q1OdfJWUlFiXL19u7dmzp/Wee+6xHd+9e7d13LhxhsWKc+qSMz5jxqtOvl599VXr7NmzrVar1bpjxw7riBEjrFYrn7HqYCQGDlNUVKSdO3dq4MCBkqThw4dr06ZNFc4bMGCAIiMjJUlBQUE6e/asCgoK9Omnn2rw4MHy8PDQFVdcocsuu0zffPONoa+hIaluvn766Sf99NNPWrBgQbnju3fvVl5enkaNGqVRo0Zpx44dhsTdkNU1Z3zGjFXdfG3dulW33367JOn666/XsWPH9Msvv/AZqwaKGDjMsWPH1Lx5c3l5eUmSfH19dfjw4QrnDRgwQJdccokkafny5ercubNatGihnJwc+fn52c7z9fVVdna2McE3QNXNV4cOHfTkk0/aclbGYrHolltu0dq1azVv3jxNnz5deXl5hsTeUNU1Z3zGjFXdfOXk5MjX19f2c1le+IzZ5+XqAGBOGzduVEJCQrlj7dq1q3CexWKp9DlWrFihtWvXauXKlZLOzdf/kYcHdbYjOCJffzR69Gjbn7t06aJu3bopIyNDt956a63jxO+ckTM+Y87j6Hx5eHjwGasGihjUSkREhCIiIsodKyoqUq9evVRSUiJPT0/l5uaW+1ff+RYuXKhPP/1Uq1atUkBAgCTJ399fubm5tnOq+n3UTF3zdSEbNmxQaGioAgMDJZ37gmzUqJFD427InJEzPmPOU5d8+fn5KTc3V0FBQZJ+zwufMfsoweEwjRo1Uo8ePfThhx9KOvcl169fvwrnrVixQunp6VqzZo2tgJGkfv36KS0tTSUlJTpw4ICysrIUEhJiWPwNTXXzVZkffvhBr732miRp//792rNnj6677jqnxIpz6pozPmPGqm6+brrpJr377ruSpF27dqlx48a67LLL+IxVg8V6ofFFoJYOHTqkxx57TEePHtWll16qZ599VpdcconWrFmjnJwcTZs2TT179lTz5s118cUX235v2bJl8vPz08KFC/XZZ59JkmJjY9WnTx9XvZQGwV6+/vrXv9rOTU9P14svvqjk5GRJUn5+vmbNmqX9+/fLYrHo8ccfV1hYmKteSoNRl5xZrVY+YwarTr7Onj2rOXPm6D//+Y+8vb0VHx+vq6++ms9YNVDEAAAAU2I6CQAAmBJFDAAAMCWKGAAAYEoUMQAAwJQoYgAAgClRxACokZKSEr3++usaPny4hg4dqsGDByspKUmFhYWuDs2htm7dqhdeeKHKc95++209+OCDBkUE4I8oYgDUyLx58/TNN9/ojTfe0Lvvvqu3335bP//8sx5//HFXh+ZQu3fv1okTJy742PHjxzVnzhzFx8dfcCt/AMbgtgMAqu2///2v0tLS9MUXX6h58+aSpGbNmmn+/Pm2uyGfPHlS8+fPV2ZmpiwWi/r27auHH35YXl5eCgkJ0X333aetW7cqPz9fM2fO1KZNm7R37175+fnplVdeUbNmzdSlSxfde++9Sk9PV0FBgR5++GENGDBAkvTSSy/pgw8+kKenp6644grNnj1bvr6+io6OVvfu3ZWRkaFff/1V1113nRITE+Xh4aGMjAw9/fTTOn36tCwWi6ZOnarw8HClpqbqn//8pzw8PHTgwAE1atRIiYmJOn36tN566y2VlJSoRYsWmj59ern3YePGjfLz89Ojjz6qTz/91NgkALChiAFQbd9//73at29vK2DK+Pr62oqM+Ph4tWzZUmlpaSoqKtKkSZP02muvaeLEiSosLJSvr6/S0tK0bNkyxcXFaePGjfL19dWIESP08ccfKzIyUiUlJbrkkkuUmpqqzMxM3XPPPerRo4e2bNmizz//XG+//baaNWumxYsX67HHHtPy5cslSQcPHlRycrIKCgoUERGhHTt2qHPnzoqNjdXy5ct1+eWX6/Dhwxo1apQ6deokSdq5c6fef/99BQQEaMGCBVq+fLkSExM1evRoHTt2rEIBI0l33XWXJCk1NdWZbzcAO5hOAlBtHh4eKi0trfKczz77TPfcc48sFou8vb01evRo2zb3kjRw4EBJUmBgoDp27Ch/f395eHjo8ssvLzd9c88990iSgoOD1bFjR+3cuVOfffaZhg8frmbNmkmSxo4dq+3bt9v6ccLDw+Xh4aHmzZsrKChIJ06c0L/+9S/l5ubqL3/5i4YOHaqJEyfKYrHohx9+kCRdffXVtnt4denSpdIpJADuh5EYANXWrVs37d+/X/n5+eVGYw4fPqzZs2dr0aJFFYqc0tJSFRcX234+/y68Vd2R19PTs9xzeHp6Vug/+eNzN2nSxPZni8Uiq9WqkpISXXXVVVq/fn25eH18fJSWlnbB3wFgDozEAKg2f39/RUZGatasWcrPz5d07kaQ8+bNU8uWLdWkSRP16dNHq1atktVqVWFhodatW6fevXvX+FobNmyQJH333Xf6+eefdf3116tPnz5KTU1VQUGBJCk5OVnXX3+9vL29K32e7t2768CBA9q5c6ckac+ePRo4cKBycnKqvL6np2e5AgmA+2EkBkCNzJ07V0uWLNHo0aPl6empwsJC3XrrrZo6daokKS4uTvHx8YqMjFRRUZH69u1bq2XIGRkZWrdunUpLS/Xcc8/pkksu0YgRI/Trr79q5MiRKi0tVVBQkJ5++ukqn8fHx0eLFi3SwoULdfbsWdudnNu2bVvl791www2aOnWqGjVqpNmzZ9c4fgDOx12sAbidTp06adu2bfLx8XF1KADcGNNJAADAlBiJAQAApsRIDAAAMCWKGAAAYEoUMQAAwJQoYgAAgClRxAAAAFP6fz+tcs5pXCaIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 5))\n",
    "plt.scatter(comp_K[:, 0], comp_K[:, 1], label='Clients', edgecolors='black')\n",
    "plt.legend()\n",
    "plt.ylabel('Component 2')\n",
    "plt.xlabel('Component 1')\n",
    "plt.title('Client map', fontsize=16)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(9, 5))\n",
    "plt.scatter(comp_K_rotated[:, 0], comp_K_rotated[:, 1], label='Clients', edgecolors='black')\n",
    "plt.legend()\n",
    "plt.ylabel('Component 2')\n",
    "plt.xlabel('Component 1')\n",
    "plt.title('Client map (after rotation)', fontsize=16)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(9, 5))\n",
    "plt.scatter(embeddings[:, 0], embeddings[:, 1], label='Clients', edgecolors='black')\n",
    "plt.legend()\n",
    "plt.ylabel('Component 2')\n",
    "plt.xlabel('Component 1')\n",
    "plt.title('Client map (via MDS)', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Комментарий**: MDS позволяет оценить расстояния между объектами и визуализировать объекты в пространстве меньшей размерности, это полезно для извлечения локальной информации об объектах (Клиент 1 \"близок\" к Клиенту 2). Для извлечения более глобальной структуры в данных стоит использвоать инструменты изложенные в пункту 3 (так как больше возможностей для инттерпретации полученных результатов)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод:** На приере данных о клиентах банка было выполнено снижение размерности с помощью PCA и MDS, а также интерпретация скрытых факторов, которая позволила выявить новые закономерности в данных"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}