Math's score/Math score.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predict average math exam results for students of the tutors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**original data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>years_of_experience</th>\n",
       "      <th>lesson_price</th>\n",
       "      <th>qualification</th>\n",
       "      <th>physics</th>\n",
       "      <th>chemistry</th>\n",
       "      <th>biology</th>\n",
       "      <th>english</th>\n",
       "      <th>geography</th>\n",
       "      <th>history</th>\n",
       "      <th>mean_exam_points</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  years_of_experience  lesson_price  qualification  physics  \\\n",
       "Id                                                                    \n",
       "0   40.0                  0.0        1400.0            1.0      1.0   \n",
       "1   48.0                  4.0        2850.0            3.0      1.0   \n",
       "2   39.0                  0.0        1200.0            1.0      0.0   \n",
       "3   46.0                  5.0        1400.0            1.0      0.0   \n",
       "4   43.0                  1.0        1500.0            1.0      0.0   \n",
       "\n",
       "    chemistry  biology  english  geography  history  mean_exam_points  \n",
       "Id                                                                     \n",
       "0         0.0      0.0      0.0        1.0      0.0              63.0  \n",
       "1         0.0      0.0      0.0        0.0      0.0              86.0  \n",
       "2         0.0      0.0      0.0        0.0      0.0              53.0  \n",
       "3         0.0      0.0      0.0        0.0      0.0              56.0  \n",
       "4         0.0      0.0      0.0        0.0      0.0              59.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('train.csv', index_col = 0)  \n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>lesson_price</th>\n",
       "      <th>qualification</th>\n",
       "      <th>physics</th>\n",
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       "      <th>Id</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10000</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <td>10001</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <td>10002</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>10003</td>\n",
       "      <td>57.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>10004</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3150.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "        age  years_of_experience  lesson_price  qualification  physics  \\\n",
       "Id                                                                       \n",
       "10000  46.0                  3.0        1050.0            1.0      0.0   \n",
       "10001  43.0                  3.0        1850.0            2.0      0.0   \n",
       "10002  52.0                  1.0        1550.0            1.0      1.0   \n",
       "10003  57.0                  6.0        2900.0            3.0      1.0   \n",
       "10004  44.0                  4.0        3150.0            3.0      1.0   \n",
       "\n",
       "       chemistry  biology  english  geography  history  \n",
       "Id                                                      \n",
       "10000        1.0      0.0      0.0        0.0      0.0  \n",
       "10001        0.0      0.0      0.0        0.0      0.0  \n",
       "10002        0.0      0.0      0.0        0.0      0.0  \n",
       "10003        0.0      1.0      0.0        0.0      0.0  \n",
       "10004        0.0      0.0      0.0        0.0      0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_original = pd.read_csv('test.csv', index_col = 0)  \n",
    "test_original.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**checking data for outliers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10000 entries, 0 to 9999\n",
      "Data columns (total 11 columns):\n",
      "age                    10000 non-null float64\n",
      "years_of_experience    10000 non-null float64\n",
      "lesson_price           10000 non-null float64\n",
      "qualification          10000 non-null float64\n",
      "physics                10000 non-null float64\n",
      "chemistry              10000 non-null float64\n",
      "biology                10000 non-null float64\n",
      "english                10000 non-null float64\n",
      "geography              10000 non-null float64\n",
      "history                10000 non-null float64\n",
      "mean_exam_points       10000 non-null float64\n",
      "dtypes: float64(11)\n",
      "memory usage: 937.5 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>years_of_experience</th>\n",
       "      <th>lesson_price</th>\n",
       "      <th>qualification</th>\n",
       "      <th>physics</th>\n",
       "      <th>chemistry</th>\n",
       "      <th>biology</th>\n",
       "      <th>english</th>\n",
       "      <th>geography</th>\n",
       "      <th>history</th>\n",
       "      <th>mean_exam_points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>45.878000</td>\n",
       "      <td>1.986800</td>\n",
       "      <td>1699.105000</td>\n",
       "      <td>1.719500</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.132900</td>\n",
       "      <td>0.109600</td>\n",
       "      <td>0.053700</td>\n",
       "      <td>0.032100</td>\n",
       "      <td>0.019400</td>\n",
       "      <td>64.340800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>8.043929</td>\n",
       "      <td>1.772213</td>\n",
       "      <td>524.886654</td>\n",
       "      <td>0.792264</td>\n",
       "      <td>0.484147</td>\n",
       "      <td>0.339484</td>\n",
       "      <td>0.312406</td>\n",
       "      <td>0.225436</td>\n",
       "      <td>0.176274</td>\n",
       "      <td>0.137933</td>\n",
       "      <td>13.536823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2150.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3950.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age  years_of_experience  lesson_price  qualification  \\\n",
       "count  10000.000000         10000.000000  10000.000000   10000.000000   \n",
       "mean      45.878000             1.986800   1699.105000       1.719500   \n",
       "std        8.043929             1.772213    524.886654       0.792264   \n",
       "min       23.000000             0.000000    200.000000       1.000000   \n",
       "25%       40.000000             0.000000   1300.000000       1.000000   \n",
       "50%       46.000000             2.000000   1500.000000       2.000000   \n",
       "75%       51.000000             3.000000   2150.000000       2.000000   \n",
       "max       68.000000            10.000000   3950.000000       4.000000   \n",
       "\n",
       "            physics     chemistry       biology       english     geography  \\\n",
       "count  10000.000000  10000.000000  10000.000000  10000.000000  10000.000000   \n",
       "mean       0.375000      0.132900      0.109600      0.053700      0.032100   \n",
       "std        0.484147      0.339484      0.312406      0.225436      0.176274   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        1.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "            history  mean_exam_points  \n",
       "count  10000.000000      10000.000000  \n",
       "mean       0.019400         64.340800  \n",
       "std        0.137933         13.536823  \n",
       "min        0.000000         32.000000  \n",
       "25%        0.000000         55.000000  \n",
       "50%        0.000000         63.000000  \n",
       "75%        0.000000         73.000000  \n",
       "max        1.000000        100.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a25935310>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.boxplot(train.qualification, train.lesson_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q3 = train[(train['qualification']==1)]['lesson_price'].quantile(0.75)\n",
    "Q1 = train[(train['qualification']==1)]['lesson_price'].quantile(0.25)\n",
    "IQR = Q3 - Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering Values between Q1-1.5IQR and Q3+1.5IQR\n",
    "train[(train['qualification']==1)] = train[(train['qualification']==1)].query('lesson_price <= (@Q3 + 1.5 * @IQR)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q3 = train[(train['qualification']==2)]['lesson_price'].quantile(0.75)\n",
    "Q1 = train[(train['qualification']==2)]['lesson_price'].quantile(0.25)\n",
    "IQR = Q3 - Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering Values between Q1-1.5IQR and Q3+1.5IQR\n",
    "train[(train['qualification']==2)] = train[(train['qualification']==2)].query('(@Q1 - 1.5 * @IQR) <= lesson_price <= (@Q3 + 1.5 * @IQR)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q3 = train[(train['qualification']==3)]['lesson_price'].quantile(0.75)\n",
    "Q1 = train[(train['qualification']==3)]['lesson_price'].quantile(0.25)\n",
    "IQR = Q3 - Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering Values between Q1-1.5IQR and Q3+1.5IQR\n",
    "train[(train['qualification']==3)] = train[(train['qualification']==3)].query('(@Q1 - 1.5 * @IQR) <= lesson_price <= (@Q3 + 1.5 * @IQR)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q3 = train[(train['qualification']==4)]['lesson_price'].quantile(0.75)\n",
    "Q1 = train[(train['qualification']==4)]['lesson_price'].quantile(0.25)\n",
    "IQR = Q3 - Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering Values between Q1-1.5IQR and Q3+1.5IQR\n",
    "train[(train['qualification']==4)] = train[(train['qualification']==4)].query('(@Q1 - 1.5 * @IQR) <= lesson_price <= (@Q3 + 1.5 * @IQR)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train[train['lesson_price'].notna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split train dataset on features and answers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id\n",
       "0    63.0\n",
       "1    86.0\n",
       "2    53.0\n",
       "3    56.0\n",
       "4    59.0\n",
       "Name: mean_exam_points, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = train['mean_exam_points']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pd.DataFrame.to_numpy(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_by_score = (train.groupby(pd.cut(train['mean_exam_points'], [30,40,50,60,70,80,90,100], right=False)).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>years_of_experience</th>\n",
       "      <th>lesson_price</th>\n",
       "      <th>qualification</th>\n",
       "      <th>physics</th>\n",
       "      <th>chemistry</th>\n",
       "      <th>biology</th>\n",
       "      <th>english</th>\n",
       "      <th>geography</th>\n",
       "      <th>history</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  years_of_experience  lesson_price  qualification  physics  \\\n",
       "Id                                                                    \n",
       "0   40.0                  0.0        1400.0            1.0      1.0   \n",
       "1   48.0                  4.0        2850.0            3.0      1.0   \n",
       "2   39.0                  0.0        1200.0            1.0      0.0   \n",
       "3   46.0                  5.0        1400.0            1.0      0.0   \n",
       "4   43.0                  1.0        1500.0            1.0      0.0   \n",
       "\n",
       "    chemistry  biology  english  geography  history  \n",
       "Id                                                   \n",
       "0         0.0      0.0      0.0        1.0      0.0  \n",
       "1         0.0      0.0      0.0        0.0      0.0  \n",
       "2         0.0      0.0      0.0        0.0      0.0  \n",
       "3         0.0      0.0      0.0        0.0      0.0  \n",
       "4         0.0      0.0      0.0        0.0      0.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop('mean_exam_points', axis=1, inplace=True)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check features' correlation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Txr7LNYUL88Rz/YmKjiYuNpY6tW9jwHO9CQzMuEkG6Z3TjeVvYMSYjwhf8CORBw4SVKI4nULb0PvxRy4vwCxQOX86AyvnH9njaeZrjLkeCLfWJruttDGmNZ4pea2AO4Ax1to075yjwXkGM8bkxjP3sAyeinkJPPePrYRnDuxePPfWPmytfcUYUxzPrZWq4JmGtNxam/SWfolk5Fs+GeVKBudZ1ZUMzrO0Sxyc+41LGJz7jau14nY15nUFg/Ms7QoG55IJNDhPxBgzDWgCFAcigIF47qaFtfYT43lb4wM8d3Q5DTxkrV3vfWsXaM55BrPWnsNza51EjDHrrbXjjDGBwGxgodP+INAlY6MUERERyXpyZKGLb2tttzSWW+CSP4txlV6K+6VBxpiNeL7s4W+cT/aLiIiISPahynkWYa3N8G9YExEREfEn2aGqnB1yFBERERHxCxqci4iIiIhkEZrWIiIiIiJ+IUfW+Tyoz6hyLiIiIiKSRahyLiIiIiJ+ITtUlbNDjiIiIiIifkGVcxERERHxC1npS4h8RZVzEREREZEsQpVzEREREfEL2aGqnB1yFBERERHxC6qci4iIiIhf0H3ORUREREQkw6hyLiIiIiJ+ITtUlbNDjiIiIiIifkGVcxERERHxC0b3ORcRERERkYyiyrmIiIiI+IXsUFXODjmKiIiIiPgFDc5FRERERLIITWsREREREb+gLyESEREREZEMo8q5iIiIiPiF7FBV1uD8KvRx5ObMDiHdPRVUI7NDSHcfLvows0PwCVOwSGaH4BM5qtTJ7BDSn83sAHwkG9wH+aoRmCuzI0h/0WczOwLxcxqci4iIiIhfyJENLr6zw7sDIiIiIiJ+QZVzEREREfEL2aGqnB1yFBERERHxC6qci4iIiIhf0H3ORUREREQkw6hyLiIiIiJ+ITtUlbNDjiIiIiIifkGVcxERERHxCzm4+iedq3IuIiIiIpJFqHIuIiIiIn5Bd2sREREREZEMo8G5iIiIiEgWoWktIiIiIuIXskNVOTvkKCIiIiLiF1Q5FxERERG/oA+EioiIiIhIhlHlXERERET8gr6ESEREREREMowq5yIiIiLiFzTnXEREREREMowq5yIiIiLiF7JDVTk75CgiIiIi4hdUORcRERERv6A55yIiIiIikmFUORcRERERv6D7nEu2smLtOlp2e5i7uvRk3OTpyZZHRUXx3OtDuatLTzo/1ps9+/YDsGfffm4OaUP7nk/SvueTDHxndPw6PXq9QMtuD8cvO3TkSIblc6l6fP4hIyL+5LXNazM7lEuyYuuftBr0ES0Gfsj4BauSLZ+4eC1tBn9C+zfH8dDor/j30NFEy0+eOUeTAaN5c8YPGRXyRVmx6Q/u7vc2LZ4fxvi5i5MtX/fHn3R49V2qP9iPBb/8nmjZyOnhtH3pHdq+9A7z1/6WUSHHW756LS06dKV5+86Mmzg52fKoqCieHfAazdt3ptODj7Fn7774ZZ9+MYnm7TvTokNXVqz5GYC/du4i9L4H439ubdyciVNnADD2089peHdo/LJlK1dncI6dGDdxUio5dqLTg496ybGTk+OF4y2kbQfadrmf0PsepEOPhzMkD2stb44YRfN2HWnbuTtbt//htd2WbX/QtnN3mrfryJsjRmGtBeDosWM89FRv7grtyENP9ebY8eOJ1tu0dRtVatXjh0VL4p8b8f5YWnfsxt0duiTalq9Ya3lz+Eiat+tA2873pZLjdtp26kbzdh14c/jI+Li+/3ERre/tQuVb72Dz1m3x7Y8cPUqPx56iZr3GDH77HZ/m4M3Vmtd5y9f8TIuO3WneoRvjvvwq2fKoqCiefXkgzTt0o9NDT8QfY6t+XkeHBx6lbbcH6fDAo6xZtyGjQ5crpMG5ABAbG8vgdz9g/MihhH81nnmLlrLj712J2nwd/gOFChZg4YyJPNilA6M+/jx+2XVlSjFn4ifMmfgJb/T7T6L13hn4UvyyYkWKZEg+l2PNxCmMbdkhs8O4JLFxcbw543s+7dWNua89yfz1W9mx70CiNlWuDWbWS48w59XHaVGzCqNmL0m0fMzcpdSueF1Ghp2m2Lg4hnz5LeP6Pcbc4f2Zt+Y3dvy7P1Gb0sWK8NbjXWldt2ai55du3Ma2nXuYPbQvMwb1YcL8pZw8czbjYo+NZfDwUXw2ZhTzZk0hfMEidvz1d6I2s8LCKVSwID/OmUnP+7owcuxHAOz462/mLVzMvJlf8dnYd3nj7ZHExsZS/vpyhE39krCpX/Lt5AnkzZOH5nc2jt9ez/u6xC9v3KBeBuU40slxago5znVynOUlx0XMmzklUY7nffnpB/F5ZoTlq9aw85/dLAybxZBXBzDorRFe2w16awSDX3mJhWGz2PnPbpavXgPAuC8mUff22iwM+5q6t9dm3BcXLlRiY2MZOfpDGtS9I/65X3/fxK+/b+K7GV8RPmsqm7du55cNv/o2x5WrnRy/8eQ4bLj3HIcNZ/CrA1gY9o0nx1WeHCtVqMDYUSOofWviYy137tz85+kn6P9cH5/Gn5KrNS9wjrER7/HZ6HeYN2MS4QsWs+OvnYnazPpunucY+3YaPbt1ZuQHnwBQ5JrCfDzqbeZO+5K3B75M/0FDMyED38lhMu4n03LMvJe+fMaYEsaYn40xvxljGmZ2PADGmHbGmJcyO47LtWm7m+uuLU3ZMqXIlTMnrZo1ZnGSCtzilWtof3dzAFo0acSaDb/5vOKTkXasWM3pw1m3su/N5p17ua5EUcoWL0KuwADuvq0aS37/b6I2d7iuJ2+unADcdEMZIo5eqOxt/Wcfh06col6V8hkad1o2/fkP15UsRtmgYuQKDKRVnZos2bA1UZsyJYriuq40OUziM+if/0ZQu3IFAgMCyJcnN66ypVmxyXtFzSexb91OubLXUvbaMuTKmZPWdzVl8bIVidosWbaCe9q0AqBF0yas+WUD1loWL1tB67uakitXLsqWKU25steyaev2ROuuWbeesmXKUKZUcIbllNSmrduS5NgshRzvBqBF0ztZ88v6BDk2S5LjNm8vkyEWL11O+zatMMZwy03VOX7iJJEHDiZqE3ngICdPnaLmzTUwxtC+TSsW/7Tcs/6yFbR39mX7Nq1YtHR5/HqTp8+iRdM7KVb0QlHCYIg6F0V0dDRRUdFEx8RQvGhR3+a4LGGONTh+4kQqOd50IcelywCoUP4Gyl9fLtl28+XNS62at5A7d26fxp+SqzUvcM4j15ahbJnSF84jy1cmarNk2Uruad0SgBYhjVmz7lestVR1VaJkieIAVCx/A1HnooiKisrwHOTyZfjg3BgTkA6baQr8Ya2taa1dkWZrHzPGBFprv7PWvp3ZsVyuiAMHKRVUIv5xcIkSRBw4lKhNZII2gYEBFMyfn6PHPAO9Pfv2c89DT3F/r+dZ//vmROu9PGwk7Xs+yUcTv7qqBvNZQcTREwQXKRT/OLhIQSKPnUix/berN9KwWgUA4uIsI775kRfuaerzOC9V5JFjBBe9Jv5xyaKFiThy7KLWrXydZzB+5lwUR06c5JftO9ifZCqPL0VEHiC4ZFD845JBQUREHkjWppTTJjAwkIIF8nPk2DFn3ZKprjtvwWLatGiW6LkpM7+hbdcHGPDGsGTTKnwheZwlUsjR0yZ5jin8fYzhkWeepcP9DzHj2zk+z+N8nAnjCQ4KIuJAklwOHCA44fkxQcyHDh0myBkIBZUozmHnAj8iMpJFPy2ja8d7Em2r5s01uKP2bTS4qw0NWrSmYd07qFD+Bp/kFh9/ZCTBwRf2V3DJICIiI5O3CQpKtU1Wc7XmBZ7/kxMfJyW89MuDXs8jCS1YsowqrorkypXL90FLukl1cG6MGWKM+U+Cx0ONMX2MMf2MMeuMMZuMMW8kWD7HGLPBGLPVGPN4gudPGmMGG2N+BuoaY942xmxz1h+ZyuuXM8YsdtotNsZcZ4y5BRgBtDLGbDTG5E1h3buMMWuMMb8aY2YZYwoYYwobY9zGGJfTZpox5rEEMY5y2i82xpRwnq9gjPnByWuFMaay8/xEY8y7xpifgOHGmJ7GmA+cZSWMMd84f6N1xpj6zvODjDETjDFLjTF/GWP6JIj3ASfP340xk1PbTgr5Pm6MWW+MWT9u0tSUd2pKvIyZTZKKpNdxtTEEFSvKkm+mMPuLj3mp1xO88MZbnDx1CoCRA19i7qRxfPXRu6z/fQthPyy69NgkRdbbjkvBdz9vZsuufTzcrC4A05avp1G1GylVtLCvwrts3vqauci3GOvXcNHo5ircN3gsz3/4FbfcWI6AgIyrQ3jbJ8mOJW9tMF73ZsJVo6KjWbJ8JS2bhcQ/163jPfw4ZyZhUycSVLwYb7/3wWXHfrG8x5k0Ry9tMKn+faZ9/gmzp0xk/JhRTJn1Let+9f3nBVLaF4naeOmQafXHoSPf54U+zxAQkLgeteuf3fz5906W/fAdy3+Yy9p1G1i3wbd5ej+eLmJ/XexBl0mu1rwghT53Mf0yQZv//fk3Iz/4hMEDXkj/ADORycCfzJLW3Vo+B74FRhtjcgBdgZfxVK5vxxP7d8aYRtba5cDD1trDzoB5nTHmG2vtISA/sMVa+7oxpqiz3crWWmuMucbbCzs+ACZZa780xjwMjLHWtjfGvA7Ustb28raSMaY48CrQzFp7yhjzItDXWjvYGNMLmGiMGQ0UsdaOd1bLD/xqrX3e2f5AoBcwDnjSWvs/Y8wdwEfA+f8ZKzmvEWuM6ZkghNHAe9balcaY64AFQBVnWWXgTqAg4DbGfOxs5xWgvrX2oPM3Sms7iVhrxzmxYg/suuTydMmg4uxLUPnaf+AAQcWLem0THFSCmJhYTpw6xTWFCmKMib8qr165EmVLl+bv3f9So/KFt9YK5MtHm+YhbNrujp8aI1cu+JpC7D9yoVK6/8gJggoXTNZu9R9/Me6HlXzZ9wFy5fQc9hv/3sOGHbuZtnwDp89FER0bS77cuejbPiTZ+hmtZNHC7D98ododcfgYQddc/EXEk6HNeDLUU11+4aOvKFeyRBprpJ/goCD2R1yozEVERsZXVhO22RcRSXDJIGJiYjhx8hTXFC5EcFAJ9kdEJFn3QuzLV62lWuVKFC924dhM+Hune9rx5LP9fJFWkviTxnnAS44l2BcR4SXHlP8+JZ1cixUtSvMmjdi0dXuy+cDpYcqMr5k5OwyAGtWqJIpnfwr7a3/C82OC/VKsWFEiDxwkqERxIg8cpKgzhWXLtu30HfAqAEeOHmPZyjUEBgSw85/d3FyjOvnz5QOgYf26bNy8hdq3pW+eU2bMYqbz7kONalXZv//C/tofkbhfXcgxMtU2WcHVmldSnmMs4XGS0jGW/DwCnjx79X+F4YNe4bpry2Ro7HLlUi0nWWt3AoeMMTWBu4DfgNoJfv8Vz2CzorNKH2PM78BaoGyC52OBb5zfjwNngc+MMR2A06mEUBc4XwaeDDS4yLzqAFWBVcaYjcCDQDknpx+BzcCHwKMJ1okDZji/fwU0MMYUAOoBs5ztfAqUSrDOLGttLMk1Az5w1vkOKGSMOT9immetPWetPQhEAiXxDPa/dp7DWnv4IraTrmpUdrFr97/s2buPqOho5i9aRkj9uonahNSvy5zvfwRgwdLl1Ln1FowxHD5yNP4DXbv/3ceuPf9StnQwMTGxHDnqeYstOiaGpavXUqn89b4IP9uqXq40uyIPs+fgEaJiYvl+w1buvKlSojbbdu/njanz+eCpLhQrmD/++XceuoclQ/uw6M3e9OvQjNA7bsoSA3OAGuXLsmv/QfZEHiIqJob5a3/jzlurXdS6sXFxHDnheefG/c9e3P/so36NSmmslX5qVK3Mzt172P3vXqKio5m3cDEhjRKfukIaNWB2+HwAFixeSp3at2GMIaRRA+YtXExUVBS7/93Lzt17uKnahevxeQt+pHWLxBe3kQcvzLFd9NMyKlbw/ecHalStkiTHRV5ybMjs8O8BWLD4pyQ5LkqSY1VOnzkT/47b6TNnWPXzLz7LpXuXjoRNn0zY9Mk0a9KYOeHzsdaycdMWChYokGwQFFSiOPnz5WPjpi1Ya5kTPp+mTRrF5znH2ZdzwufTtLHnY1BLwmezZPQzWIQAACAASURBVN4clsybQ4tmdzJwQD+a3dmY0sElWbfhV2JiYoiOjmHdht+ocMP1PsixE2EzphA2YwrN7kyY4+Y0ctx8IcfGjdI9rit1teaVlNfzSMPEb56HNKrP7Hmeu2wtWLKMOrVuxRjD8RMnePy5F+n7zOPcdnONzAjfp7LDB0Iv5j7nnwE9gWBgAp6q+VvW2k8TNjLGNMEzmKxrrT1tjFkK5HEWnz0/iLXWxhhjbne20xVPdfpiRwQXWxE2wI/W2m7JFnjeAagCnAGKAntSea0cwFFr7S0ptDmVwvM58PwdziR5bYBzCZ6KxbMPDN5z87odXwgMDOC1vr14pO/LxMXFcW/rFlQsfz1jPvuS6pUrEdKgLh3btKT/kOHc1aUnhQsV5N1BLwOw7vfNjP1sEgEBAQQE5GDQC324plAhTp85wyN9BxATG0tcbBx1a9WkU9u7fZ3KZXtk6gQqNWlAgeLFeGv3duYOHMbqCclvg5eVBAbk4JUuLXnsg2nExcVxT91bqFi6BGPnLqVaudKE3FSJkd8u4vS5aJ77zHN9XLpIIT58qksmR566wIAAXn2gA4++M464OEuHRrdT8dpgxnzzA9VvuJaQW6uz+a9/6P3+RI6fOsNPG7cx9tsFhL/dn5iYWHq8+SEA+fPmZsRT9xEYkB4fdbnI2AMDeb3fczzauy+xsbHc264NFSuUZ/Qn46lepTJNGzekY2gb+r0+hObtO1O4UCHeG+aZHVixQnnubhZCq07dCQgI4PX+feOnRZw5e5bVv6xj8Cv9E73eO6M/4o///g+MoUyp4GTLfZdjXx7t/VwaOQ6meftOTo6Dk+R4HwEBgbze/3kCAgI4dOgwz/QbAHjuVNGmRXMa1avj81waN6jHspWraR7akbx58jBs0Kvxy0K79iBsuuccMOjl/gwYOISz587RqF5dGjnFi8cfeoBnX3yFr+d8R6ngYEaPSP3OGC2ahbB23Qbadu6OMYaG9eoQ0ti39zVo3KC+J8d2HZwcX4tfFtqlO2EzpgAw6OUXGTBwsCfH+vVo5Nz558clPzFk+CgOHznCE336UsVVkc8/GgtASKtQTp46RXR0NIt+WsaEj8ZwYwZcIF7NecH5Y+xZHu3zArFxcdzbthUVK9zA6E8/p3oVF00bNaBju9b0GziU5h26UbhQQd4bOgiAr2Z+yz97/uWjzyfx0eeeuwdNGDsq0QeTJWszaX1AzxiTC0+lOSeeSnhTYAjQ1Fp70hhTBojGU+V+1Frb1pmXvRFoaa1daow5aa0t4GyvAJDPWhvpTN/YYa31+lF1Y8x3eKrTk51pI6HW2nuc31Ob1lIC2ACEWGt3GGPyAddaa/9rjHkecOGpxL+HZ/AbbYyxQDdr7XRjzKtASWttb2PMajxTS2YZz+j6Jmvt78aYiUC4tfZr5zXjYzLGTAV+s9a+4yy7xVq70RgzCDhprR3pPL8FaINnSs1sJ5ZDxpiizvQgr9tJdYdxedNasrqngq6+q/8PF32Y2SH4hCl4df4HkKOK7weKGS/rz729LDn88kZkqTNXYU5Xq+iMu3VrhitcMtNPGpMKB2XYGOeBY5GZkm+alXNrbZTzocejTvV7oTGmCrDGqQSfBO4HfgCeNMZsAtx4prZ4UxAIM8bkwfM/w3OpvHwfYIIxph9wAHjoYpKy1h5wBsvTjDHn74X0qhPvo8Dt1toTxpjleOamD8RTBa9mjNkAHAPOlxa7Ax87A/acwHQg8TeeeI/7Q+dvEQgsB55MJd6txpihwDJjTCyeKUM9L3U7IiIiIuLfLqZyngPP3PJO1tr/ZUhUmSBhdd/fqXLuH1Q59y+qnPsRVc4lM6ly7lNfXZNxlfP7j2ZO5TytWylWBXYAi6/mgbmIiIiISFaQ6rQWa+02wOefgDDGvAJ0SvL0LGttmt85azz3Tk/6NV49rLWbvbVPydVSNRcRERG5WmWH95Au5m4tPucMwtMciKew7h3pHI6IiIiISKbIEoNzEREREZG0ZPqk9wyQHd4dEBERERHxC6qci4iIiIhfyGGu/tq5KuciIiIiIlmEKuciIiIi4heu/rq5KuciIiIiIlmGBuciIiIiIlmEprWIiIiIiF/QtBYREREREckwqpyLiIiIiF9Q5VxERERERDKMKuciIiIi4heMvoRIREREREQyiirnIiIiIuIXrv66uSrnIiIiIiJZhirnIiIiIuIXskNVOTvkKCIiIiLiF1Q5FxERERG/kA1u1qLKuYiIiIhIVqHKuYiIiIj4BZMN7teiwflVyOS/JrNDSHcfLvows0NId880eyazQ/CJ9sUKZHYIPtFy19bMDiH9xcVldgQ+cvX/533ViI3O7AjSX0DOzI5A/JwG5yIiIiLiF7LDpbfmnIuIiIiIZBEanIuIiIiIZBGa1iIiIiIifkHTWkREREREJMOoci4iIiIifiFHNiidq3IuIiIiIpJFqHIuIiIiIn4hO3wJkSrnIiIiIiJZhCrnIiIiIuIXrv66uSrnIiIiIiJZhirnIiIiIuIXTDYonatyLiIiIiJyGYwxLY0xbmPMDmPMS16WX2eM+ckY85sxZpMxplVa29TgXERERET8gsnAnzRjMSYA+BC4G6gKdDPGVE3S7FVgprW2JtAV+Cit7WpwLiIiIiJy6W4Hdlhr/7LWRgHTgdAkbSxQyPm9MLA3rY1qzrmIiIiI+IUcWet+LWWA3Qke7wHuSNJmELDQGNMbyA80S2ujqpyLiIiIiCRhjHncGLM+wc/jSZt4Wc0medwNmGitvRZoBUw2xqQ6/lblXERERET8QkbWza2144BxqTTZA5RN8Phakk9beQRo6WxvjTEmD1AciExpo6qci4iIiIhcunVARWPMDcaYXHg+8Pldkjb/AE0BjDFVgDzAgdQ2qsq5iIiIiPiFrHSfc2ttjDGmF7AACAAmWGu3GmMGA+uttd8BzwPjjTHP4Zny0tNam3TqSyIanIuIiIiIXAZr7XxgfpLnXk/w+zag/qVsU9NaRERERESyCFXORURERMQvZKFZLT6jyrmIiIiISBahyrmIiIiI+AWTDWrnqpxLPGstbw4fSfN2HWjb+T62bv/Da7st27bTtlM3mrfrwJvDR3L+Q8ff/7iI1vd2ofKtd7B567Zk6+3dt5+a9Rrz+aSvfJpHalZs/ZNWgz6ixcAPGb9gVbLlExevpc3gT2j/5jgeGv0V/x46mmj5yTPnaDJgNG/O+CGjQr5iPT7/kBERf/La5rWZHcolKR7ShIZrV9Dwl1Xc0KdXsuV5ypSh9pxZ1FuykPrLFlG8WQgAOYsUofacWTTb+T+qvD00g6P2sNby5ohRNG/Xkbadu6dyLP1B287dad6uI2+OGBV/LB09doyHnurNXaEdeeip3hw7fjzRepu2bqNKrXr8sGgJAGvXbSC0a4/4nxp1GrHop2Xpntfy1Wtp0aErzdt3YtzEScmWR0VF8eyA12jevhOdHnyUPXv3xS/79ItJNG/fiRYdurJiTeK+GBsbS/v7HuSJZ1+If+6rGV/TvH0nXLXqcfho4uMwPV3YV/emsa+207bzfTRvd28K++reRPvqxImTPPmf52nXpTutO3blm7C5APy7dx8d7nuA0K7307pjV6Z9/a3PckuUow/O7UeOHqXHY09Rs15jBr/9js/zuND/OjNu4uRkyy/0v850evAxL/2vs9P/fo5//stpM2nT+X5ad+7OxKkz4p8fPvoDWt7bjbZdH+CZFwZw/MSJdM3F0+/epXloR9p2uZ+t291e223Z7pwjQjvy5oh3E/e7p/twV/tOPPR0n/h+l9Z2T548RcOWbRk8fGT8c4/0epZ2XXvQutN9vD5sOLGxsemaq1w6Dc4l3vKVq9n5z24Whn3DkFcHMGjYcK/tBg0bzuBXB7Aw7Bt2/rOb5avWAFCpQgXGjhpB7Vtrel3vrZHv0bB+XZ/Fn5bYuDjenPE9n/bqxtzXnmT++q3s2Jf4VqNVrg1m1kuPMOfVx2lRswqjZi9JtHzM3KXUrnhdRoZ9xdZMnMLYlh0yO4xLkyMHVYcPY32X7qys34RSHULJX6lioiYVnv8P+8PmsjrkLjY+9hTVRrwFQNy5s/zvrXdwDxqcGZEDsHzVGudYmuU5lt4a4bXdoLdGMPiVl1gYNstzLK32HEvjvphE3dtrszDsa+reXptxX1wYCMfGxjJy9Ic0qHvhG6Lr1L6NsOmTCZs+mS8//YC8efJQv07Sb5C+MrGxsQwePpLPxoxi3qyphC9YxI6//k7UZlbYXAoVLMiPc2bR874ujBz7EQA7/vqbeQsXMW/mFD4b+y5vvD0y0QBg0rSZVLjh+kTbuvXmGnzx0RjKlApO1zySWr7q/Hnva4a8+lIa+2oAC8O+9rKvarEw7Bvq3l4rfl9Nmfk1FcrfwHczpjB5/McMf28MUdHRlChRnOkTPyNs+lfMnDSB8V9MIuJAqrc8vvIcfXRuz507N/95+gn6P9fHp/HD+f43yul/U1Lof+FO/5vppf8tZt7MrxL1v//u+ItZs79j1qTPCJv6JUudvxNA/TtqEz5jMnOnT+L668ry6RfJLwauxPJVa9i5ezcL58xKu9+9+hIL58xi5+7dLF/tubAdN3EydWvXYuGcWdStXSv+YiWt7b7/8ThuT7IfR789lO+mTyZ85hSOHDkaf9GfVeUwGfeTaTlm3kunH2PMycyO4XIZYz4zxlTN7DgAFi9bTvs2rTDGcMtNNTh+4gSRBw4mahN54CAnT52i5s03YYyhfZtWLF7qqdBVKH8D5a8v53Xbi35ayrXXlqFihfI+zyMlm3fu5boSRSlbvAi5AgO4+7ZqLPn9v4na3OG6nry5cgJw0w1liDh6oWK59Z99HDpxinpVMi+Hy7FjxWpOHz6S2WFckmturcnpv3dyZtc/2Oho9s8Oo+TdLRI3spbAAgUByFmoEGf3RwAQe/oMR3/+hbiz5zI67HiLlyY8lqpz/MTJVI6lGheOpZ+We9ZftoL2bVoB0L5NKxYtXR6/3uTps2jR9E6KFS3i9bUXLPqJhvXrkDdvnnTNadPWbZQrey1lry1Drpw5aX1XMxYvW5GozZJlK7inzd0AtGh6J2t+WY+1lsXLVtD6rmbkypWLsmVKU67stWxyKrD7IyJZumo1Hdu3TbStqpVdXFu6VLrm4I1nX919kee98/vqbhY770x4zputAWjfpjWLnPOhMXDq9GmstZw6fYbChQoRGBBArpw5yZUrFwBRUdHE2Tjf5+ijc3u+vHmpVfMWcufO7fMcNm3dnqT/NU2h/3mOmxZNm7Dmlw0J+l/TJP1vO3/u3MnNNaqRN08eAgMDqX3rLfzoHIMN6txBYKBn5u8tNaqxPzLFL3O8LIuXLad9a6ff1ajO8ZMpnCNOnqLmTU6/a313/D5J6RyR2na3bP+DQ4cPJ7twL1AgPwAxMbFER0djstKNxLOpq2Jw7q+MMQHW2kede2BmuojISIKDS8Y/Di4ZRESSE1JEZCTBQUGptknq9JkzjP9iEr2eeDR9A75EEUdPEFykUPzj4CIFiTyW8luV367eSMNqFQCIi7OM+OZHXrinqc/jFMhdKpgzey98A/LZvfvIXSrxQG3HiFGU7tSBJpvWc9v0yWwf8EpGh5miiMgDBJdMcJwEBSWrjkYcOEBwUInEbSI9bQ4dOkxQieIABJUozmHn4ioiMpJFPy2ja8d7UnzteQt+pE2Lu9Itl/h4Iw8QXPLC+aFkUIn4eBO2KeW0CQwMpGCB/Bw5dizZ36NkglyHjXqffn2eIYfJnP+OkuaV8r5Ksj/T2Ffdu3Tiz7//pmGL1rTrfB+v9HuOHDk8Oe7bH0Hbzt1p0qotjz3Yg5IlSuBLvjq3Z6TU+lDCNqWcNsn7X8lk61aqUJ71v/3OkaPHOHP2LMtXrWF/RESy1/7mu3k0qpe+7/om73clvPe7kkn3SVrnCO/bjYuLY/h7Y+j/n+RTBAEeeeZZ6jVvRf58+WjR9M70SdJHTAb+ZJarbnBujOlnjFlnjNlkjHnDeS6/MWaeMeZ3Y8wWY0wX5/m3jTHbnLYjnefKGWMWO88tNsZc5zw/0Rgzxhiz2hjzlzGmYyoxNDHGLDfGzHa2/4kxnv95jDEnjTGDjTE/A3WNMUuNMbWcZS2NMb86cS5OEPsEJ6ffjDGhKbzm48aY9caY9eMmTLysv52376tKegXt7Sut0rrKHvvxOB68vxv58+W7rLjSi/UavXff/byZLbv28XAzzwl52vL1NKp2I6WKFvZVeJKQtz6VpIOW6tCef6fPZOlNtdjQtQc3fTQ2y3x1nLe+lvRDTN6+IC6t8IeOfJ8X+jxDQECA1+WRBw7y3x1/0qBunYsP9iJdzLHvtQ3G+9/DGH5asYqiRYtQvUrl9AnyMnjfV0naeN1Xqe+slWvWUqVSJVYsmMecaZMZPHwkJ0963uQtFVySuTOnsDDsG2aHz+fgoUOXHf/F8NW5PSOl1IfSbINJITeocMP1PPpAdx5+5lke7d0XV8Ubkx1bH3/+JQEBAbS7O30veC9qn1xGv0tpu1NnfUOj+vUoleAiLaHPP3yflQvmEhUdzdp1G1J9DfG9q+puLcaYu4CKwO14zq/fGWMaASWAvdba1k67wsaYosA9QGVrrTXGXONs5gNgkrX2S2PMw8AYoL2zrBTQAKgMfAd8nUo4twNVgV3AD0AHp31+YMv5b486f6AZY0oA44FG1tq/nfgAXgGWWGsfdmL8xRizyFp7KuGLWWvHAeMAOH3sokehU2bMYua3cwCoUa0q+/dfqBrsj4gkKElFJzgoKNHbe97aJPX7li0sWLSEke9/wPETJ8iRIwe5c+Xi/q6dLzbMdBF8TSH2H7kwTWX/kRMEFS6YrN3qP/5i3A8r+bLvA+TK6TlENv69hw07djNt+QZOn4siOjaWfLlz0bd9SIbFn52c27uPvKVLxz/OU7oU5/bvT9SmTPdubOjcHYCj6zeQI3duchUrStRB3w50UjJlxtfMnB0GQI1qVdgfkeA4iYyMr3Kd5zmWDiRp4zmWihUrSuSBgwSVKE7kgYMUdaawbNm2nb4DXgXgyNFjLFu5hsCAAJrd2RiA739cTPM7G5MzZ/qf2oODSiSqKkZEHvCSUwn2RUQQXDKImJgYTpw8xTWFC3lyTfD3iHD+HkuWr2TJ8pUsX7WGc1FRnDx5ihdeG8TIIYPSPf6EpsyYlWBfVU2UV8L9cCGvJOe9BPszpX317XfhPN7zAYwxlLuuLNeWLs1fO3dxU/Vq8dspWaIEFcvfwPrfNtKyWfq+K5cR5/aMlFIfStpmX0Skl/6XtO9eyK1T+7Z0cqZUvfvhJ5RM8O7B7PD5LF25iokfj0mXC5UpM79m5uzvAKhRtUqSfneAoOJezhERSfdJ6v0uuGQJr9v9bdMWNvz2O9NmfcOp02eIjokmX958vNDn6fi2uXPnJqRRAxYvW079Ordfcb6+knUuGX3naquc3+X8/Ab8imcQXRHYDDQzxgw3xjS01h4DjgNngc+MMR2A08426gJTnd8n4xmMnzfHWhvnTEPxfvl5wS/W2r+stbHAtATbiQW+8dK+DrDcWvs3gLX2cIKcXjLGbASWAnmAdPtEYvcunQibMYWwGVNodmdj5oTPx1rLxk2bKVigQLKTX1CJ4uTPl4+NmzZjrWVO+HyaNm6U6mtMnTCeJfPDWDI/jAe7d+WJR3pm+MAcoHq50uyKPMyeg0eIionl+w1bufOmSonabNu9nzemzueDp7pQrGD++Offeegelgztw6I3e9OvQzNC77hJA3MfOvbbRvKVv4G815XF5MxJ8D2hRP6wMFGbs3v+pVgjz2GVv+KN5MiTO9MG5gDdu3SM/1BmsyYJj6UtaRxLWy4cS008x1JIo4bMCfd8G7TnGGsIwJLw2SyZN4cl8+bQotmdDBzQL35gDjDvh4W0bpn+U1rAM5jYuXsPu//dS1R0NPMWLiKkUYNEbUIaNWR2+PcALFj8E3Vq34YxhpBGDZi3cBFRUVHs/ncvO3fv4aZqVXm+11Msnx/Gkrnf8u7QwdSpfZvPB+bgnPemf0XY9K9o1qQRc8K/v8Tz3vdJ9tU8AOaEz4s/H5YKDmbNL+sBOHjoEH/v+odry5Rhf0QEZ8+eBeDY8eP8+vsmbijn/bM6V5yjj8/tGalG1cpJ+t9iL/2vAbOd42bB4qVJ+t/iJP2vCgCHnOkge/fvZ+GSZbRp0Qzw3Blm/JdT+Pjd4eTNkz6f3+jeuSNh0yYRNm2Sp9/Nc/rd5i0ULJDf+z7Jn5+Nm51zxLzv4/dJSKMGXs8RIY0aet3uqKFvsHT+HJaEz+bFZ3vTvvXdvNDnaU6dPh0/Jz0mJoZlq9ak+NkxyThXVeUczwXVW9baT5MtMOY2oBXwljFmobV2sDHmdqAp0BXoBXgbbSWsQif8hFlaF29Jq9fnH591BuzeYvf+rjDca631fp+ldNS4QX2WrVxN83YdyJsnD8MGvRa/LLRLd8JmTAFg0MsvMmDgYM6eO0ej+vVo1KAeAD8u+Ykhw0dx+MgRnujTlyquinz+0Vhfh33RAgNy8EqXljz2wTTi4uK4p+4tVCxdgrFzl1KtXGlCbqrEyG8XcfpcNM995rl+Kl2kEB8+1SWTI78yj0ydQKUmDShQvBhv7d7O3IHDWD0hfe88kN5sbCzbXnqFWrOmYnIEsGfqdE66/8uNL/Xj2MbfOfDDQv54/Q2qvzeSck8+BhY293oufv3Gv/5MQMEC5MiZi5KtWrCuYzdO/fd/GRZ/4wb1PMdSaEfnWHo1fllo1x6ETff8/Qe93J8BA4d4jqV6dWnk3M3o8Yce4NkXX+HrOd9RKjiY0SPSviXknr172RcRye23eb9b0pUKDAzk9X59ebT3c8TGxnJvuzZUrFCe0Z+Mp3qVyjRt3JCOoW3o9/pgmrfvROFChXhvmOeOORUrlOfuZiG06nQfAQGBvN7/+RSn5pw3afpMPps0hYOHDtOu6wM0rl+Xoa8NSPe84s97ofcmP+91vZ+w6Z5bvyY679WrS6P6nvPe4w89yLMvvpxgXw0D4OnHHmbAwMG07Xwf1lpe6PMMRYtcw6q1P/P2u2MwxjMF4eEe3XFVvDHd8/Kaow/O7SGtQjl56hTR0dEs+mkZEz4aw40++OC/p/89x6O9+6bR/4bQvH1np/+9ASTsf90JCAjg9f594/tf7/4vc/TYcQIDAxn44vMULuT5XNKQEe8SFR3NQ888C8DN1asx+OX+6ZZP4wb1WLZqNc1DO5E3T+7E54huDxA2zXPXn0ED+jFg0JucPXuORvXrXDhH9HyAZ196ha/D5lIquCSjhw9Nc7venDlzlqf69icqKoq4uDjq1L6Nrvem/JmWrCA73OfceJvT5G+MMSettQWcaS1DgKbW2pPGmDJANJ6LkMPW2rPGmPZAT+B+IJ+1NtKZQrLDWlvUGPMdMMtaO9kY0xMItdbeY4yZCIRba79O+JopxNME+J4L01q+B8ZZa79Jup4xZinwgtPuVxJMa7HWHjbGDAMKAb2d6Tc1rbW/pfoHuYRpLf4ids13mR1Cunum2TOZHYJPtC/m9bDwey13bc3sENJfnO/vFJIpcqQ+8PdLWWj+d7qKjc7sCNJfJn24OUMUKJrpHXFVybIZNsapH7E7U/K9qirn1tqFxpgqwBpnfthJPIPwG4F3jDFxeAbrTwEFgTBjTB481enzZbc+wARjTD/gAPDQZYazBngbqAEsB2anEfsBY8zjwLfOh0cjgeZ4LjbeBzYZT1I7gTaXGZOIiIiI37par1MTuioG5wkr0dba0cDoJE3+BBZ4WTXZJx6stTvxMr3FWtszpddMwWlrbbL5EEnXs9Y2SfD793iq7AmXnwGeSOO1REREROQqcFUMzkVERETk6ncVTxqKp8H5FTDG1MBzR5eEzllr78BzZxURERERkYumwfkVsNZuBm7J7DhERERE5OqgwbmIiIiI+IVs8HnQbDF1R0RERETEL6hyLiIiIiJ+wWSDeymqci4iIiIikkWoci4iIiIifuHqr5urci4iIiIikmWoci4iIiIifkGVcxERERERyTCqnIuIiIiIX9DdWkREREREJMOoci4iIiIifiHH1V84V+VcRERERCSrUOVcRERERPyCyQalc1XORURERESyCFXORURERMQvZIObtahyLiIiIiKSVWhwLiIiIiKSRWhai4iIiIj4BU1rERERERGRDKPKuYiIiIj4BZMNSucanF+NbFxmR5DuTMEimR1CumtfrEBmh+ATcw6dzOwQfKJlXGxmh5D+cgRkdgRysazN7Ah85CocaGWDwaP4lgbnIiIiIuIXssO1j+aci4iIiIhkEaqci4iIiIhfyA5zzlU5FxERERHJIlQ5FxERERG/kA0K56qci4iIiIhkFaqci4iIiIhfyJENSueqnIuIiIiIZBGqnIuIiIiIX8gGhXNVzkVEREREsgpVzkVERETEL+g+5yIiIiIikmE0OBcRERERySI0rUVERERE/ILJBmXlbJCiiIiIiIh/UOVcRERERPyCPhAqIiIiIiIZRpVzEREREfEL2aBwrsq5iIiIiEhWocq5iIiIiPgFzTkXEREREZEMo8q5iIiIiPiFbFA4V+VcRERERCSrUOVcRERERPxCjmxQOlflXEREREQki9DgPJuz1vLmiFE0b9eRtp27s3X7H17bbdn2B207d6d5u468OWIU1loAjh47xkNP9eau0I489FRvjh0/DsCipctp27k7oV170KF7T9b/tjF+W3v37efhp/twd4cutLq3K3v27vV9oo4Vm/7g7n5v0+L5YYyfuzjZ8nV//EmHV9+l+oP9WPDL74mWjZweTtuX3qHtS+8wf+1vGRVymoqHNKHh2hU0/GUVN/TplWx5njJlqD1nFvWWLKT+skUUbxYCQM4iRag9ZxbNdv6PKm8PzeCor0yPlvYmuQAAIABJREFUzz9kRMSfvLZ5bWaHkqblq9fSokNXmrfvzLiJk5Mtj4qK4tkBr9G8fWc6PfgYe/bui1/26ReTaN6+My06dGXFmp/jnz9+4gR9+r9Cy3u7cXfH+/ht05YMySUzzhdVatUjtGsPQrv24MlnX/BhTvemkdN22na+j+bt7k0hp3sT5QTw8/oNhHa9n9Ydu3L/o0/GP//l1Om06dSN1h27MnHKtHTPyZd5ffblZEK73k9o1/tp06kbVWrV5eixYwBM/GoarTt2pU2nbvQd8Crnzp1L15wuHEudGDdxUrLlF46lTnR68FEvx1In51jynDf27Y+gxxO9uLtjN1p37s6X02bEt392wGuE3vcgofc9SEjbDoTe92C65pKQr46r8zZt3UaVWvX4YdGSRM+fPHmKhi3aMvjtkb5JzEeMybifzKLBucMYc70xZovzey1jzBjn99zGmEXGmI3GmC7GmM+MMVUvY/u3GGNaJXjczhjzUvplcHmWr1rDzn/+z959h0dRPH4cfw+hSm8hCgiCqCgoKKB0CIQiJRhCryp2QURBEaQqKF8QASs/RHoLJcEEJRBKQFBBRaoFEQGRhN4MhIT5/XHHmYQLQcglXPi8nicPyd7s7nxud/bm5maP/USGhTBy8ECGjR7jttyw0WMYMeh1IsNC2LtvP9EbNgIw+fMZ1KxRnciwhdSsUZ3JnzsumDVrVGPp/FmEzZvJqKGDGDxytGtbrw0ZzpPdu/Dl4vmEzJxK0cJFPB8USLx4kZHTFzO5/1N88e4AIjb+yO6/DiUrc1vRwox+uiMtalZNtnzNlp3s3HuAJW/3Y/6wPkxdtoYzcecypN5XlC0b9747is0durC+dgNuDQok710VkhUp/8pLHAr7gg3+Tdjy1HPcN8ZxLC6eP8dvo//HL8NGZEbNr8vGabOZ1Cwos6uRpsTEREa8O44pE8cRETKb8OUr2b3nj2RlQsLCKZA/PytCF9CzcwfGTvoIgN17/iAiMoqIBbOYMuk9hr8zlsTERADeHvs+dWs9zFeL5hI2dzrl7yiTIXky43qRO1cuwubNJGzeTD55P/07EdFfb3BmWsjIwa+nkWkgkWEL3WSqRmTYImrWqObKdOr0aYaPHsPH48cSsXAeE8aMAuDX3b8TsiSMkBmfEzZvFmvWfc3effu8JlevHt0ImzeLsHmz6Pfi81R/sCqFChYkJjaWGfPms2jWNMJD5pJ48SIRy1ekWx5HWxrrbEtzUmlLXzjbUoibtrSSiAWzk7Uln+w+vP5yb75cOJf5n09mTshi1zbfHz2SsDnTCZsznSb+DQhoWD/dsqTkqXYFjudt7IQPqVPz4cu29/7Hn1LjoaqXLZfMp865G9bazdbaPs4/qwI5rLVVrLXzrbW9rLU7r2GzVQBX59xau9Ra+0561Pd6RK2Jpk3LRzHGUOX+Spw6fYbYw0eSlYk9fIQzZ89S9YHKGGNo0/JRolZHO9Zfu442LR2x2rR8lJVrHMvz3nKL67tI4+LOcekN6O49f5CQmEjtRx52lcuTJ3cGJIWtv+/j9hJFKe1blJzZs/PoI1VZ9f2OZGVKFi/C3bffdtmctt//iqH6PeXJ7uPDLblzcXfp21i31f3oRkYq9GBV/vljL3F/7sNeuMChJWGUaN40eSFryZ4vPwA5ChTg3KEYABL/iePEt99x8Vz6jm5lhN3rNvDPseOZXY00bd2xizKlS1G6VEly5shBiyaNiFq7LlmZVWvX8ZizDTVt1ICN332PtZaoteto0aQROXPmpHTJ2yhTuhRbd+zizJmzbPrxJ4IDWwGQM0cOCuTPnyF5Mvp6kXGZmjszVebU6dNXkak5UavXOjNF06ZlC2emFqxc41j+xZfLCfBvyG23+gFQtIhjEOL3P/byQOVK5MmTm+zZs1P9oaqsWLXWa3IlFbE8kpbNmrj+TkxM5Nz58yQkJHAu7hy+xYulW56tO3amaEuNU2lLzQFo2qghG7/bnKQtNU7RlnbiW6wY991zNwD58ualXNkyxMQeTrZNay1frlxFy6YB6ZYlJU+1K4CZ80Jo2qghRYsUTra97Tt/5ujRY9R+pIbHcnmKMSbDfjJLluicG2MGGWN+cY5wzzXGvGqMWWOMqeZ8vJgxZq/z97LGmHXGmB+cP7XcbK+BMSbcGOMLzAKqOEfOy6fYbjPnNn4yxkQ5l9Uwxmwwxvzo/PduY0xOYATQIckIfE9jzAfOdcoYY6KMMVud/97uXD7NGDPRuZ09xpjg9H7uYmIP41fC1/W3n68vMYeTX5xiDh/Gz7d48jLOC9jRo8dcF2Df4sU4lqTDtGLVGpoFdeCZl15h1NDBAOz9cx8F8uXnxVdeo02n7rw7fpJrNNDTYo+fxK9IIdffJYoUJOb4yata957bHZ3xuPPxHD99hu927ebQ0ROequpVy3WrH3FJpgWdO/g3uW69NVmZ3WPGcVu7IBps3cxD82aya+CgjK7mTStl+yqRpO0kLXOrs0z27NnJny8vx0+edK5b4rJ19//1F0UKFWLg8Ldp07kng0aO5p+4uEzJ4+nrBcD5+HiCuvSkffcnWbk6/TuxKZ/n1DOlyJ1Gpr1/7uPUqVN0e+o5gjp3JzR8GQB3lS/H5h9+5PiJk8TFnSN6/QYOxcR4Ta5L4uLOsW7DNzRp1BBwnJ9PdOtCw0cDqdOkBfny56NOzUc8lqeEb/FU2pKjzOVt6crt8MDBv9n1y288UOm+ZMs3/7iFokWKUPb20umWJSVPtauY2FhWrl5Lx+DHkm3r4sWLvDt+AgP69vZIHrl+Xt85N8Y8BHTEMcIdBFRPY5VYIMBa+yDQAZiYWkFrbSzQC1jnHDn/Pcl+iwP/B7S11j4AtHM+9DNQz1pbFRgCjLLWxjt/n39pBD7Frj4AZlhr7wdmp6jTrUAdoCWQ7iPtFnvZMpNi3OrSvLZkZa7iDWWAfwO+WjyfD8e9y4SPPwUgITGRzVu28NrLfVg4cyoH/vqLxV9EXFPd/ys3Ma56TlntyndT74GKdB4xiVc+nEWVO8vg43MDNB93AVIEvTWoDX/NW8Ca+6vxfcdu3P/RpJvji2JvAG7bV4rnPrU26OZ0xRhHG9r5y690Cn6M0DnTyJMnj9u57J6Q0dcLgNXLQlk8exrjRo1g1Njx7Nt/4L9W+4rcZ0pRxm2mK4dKTExkx66f+XTie0z5cCIf/d9n/PHnPsqXu4NePbvzxPO96fXiS9x9VwV8fHyuJ4Jbnsp1yerodTz4wP0UKlgQgJOnThG1Jpqo8CWsWx5BXFwcYRFf/ud6p8Z9e0jZltyUwaTZDs/+8w99BrzBG6+8RL58eZOVC1++kpZNG19Lla+ap9rV22Pf59U+L1x2fs1ZsIh6tWtxq1+JVNaUzJYVvkqxLrDEWvsPgDFmaRrlcwAfGGOqAInAXde430eAaGvtHwDW2mPO5QWB6caYCjiuFTmuYls1cbyxAJgJJJ1wFmqtvQjsNMak2pKMMU8DTwN8OvE9nn6iZ6o7mz1/IQuWhAFQ+b6KHIqJdT12KDb2so8i/Xx9OZRklMFRxvEOvmjRIsQePoJv8WLEHj5CkRQfnQFUf6gq+4b+xbHjJ/Dz9eXeu++idKmSADRqUJ+ftm2HNqlWN92UKFKQQ8f+He2OOXYS30IFr3r9ZwMb82yg4yL96kezKFOieBpreN75g3+T57bbXH/nvu1Wzh9KPo++ZJdOfN++CwAnNn9Ptly5yFm0CPFHjmZoXW9Gfr6+ydpXTCrt6++YWPxK+JKQkMDpM2cpVLAAfr7Fk42oxjjbnZ+vL36+xV0jfM0aNWDytFkey5CZ14sihQtRwrlu6VIlqVHtQXb+8iu3ly51nZlCkmS6N9nznLS+yTO5z51aJr8SvhQuVIhb8uThljx5qPZgVX7+9TfuKHM77dq0pl2b1gC8N+kjSiQZNb3Rc10SEbmCFkmmtGz4dhOlSt5GkcKOck38G/Lj1m0EtmieLtkubw+H3Zx7xfk7JsZNW0q9HV5ISKDPgDdo1awJTfwbJNteQkICK1avYfHMz9MlQ1IZ0a6279xFv4GOT6GOnzjJ2vUbye7jw4/btvH9jz8xN2QRZ+PiuHDhArfckodX+7yQ7jk94WYYW7oBhv7Shbs3zAn8my/ppOaXgRjgAaAakPMa92lS2e9IYLW1thLQKsW+r1bS7SadEJzqKWmtnWytrWatrXaljjlAlw7BrhusGjeoT2j4Mqy1bNm6nfz58l12UfAtXoy8t9zClq3bsdYSGr6MRg3qAeBfr67r49rQ8GU0ql8XgD/37Xe909+x62cuXEigcKGCVL6vIidPnebYccfHbt9u2syd5e64mufkulUuV5o/Dx3hQOxR4hMSWPbNjzR88L60V8RxM+nx02cB+GXfQX7Z9ze1K1/r+7r0c/LHLdxS7g7y3F4akyMHfo8FEvtVZLIy5w78RdF6dQDIW+FOsuXOpY55Bql87z3s3X+A/X8dJP7CBSIio/B3HotL/OvVYYmzDS2PWsMj1R/CGIN/vTpEREYRHx/P/r8Osnf/Ae6/ryLFixXFr4Qve/b+CcDG776nfLmyHsuQmdeLk6dOER8fD8Cx4yf4YcvWdLledOnQznVTY+MG9QgN/9KZaVsambY5M32ZIlOEM1MEjeo7ljeqX4/NP24hISGBuLhzbN2+g/J3lAXg6DHHWM7Bvw8RuXpNsnnbN3ougNOnz7Dp+x9dZQFu8yvBT9u2Exd3DmstG7/b5MqbHirfWzFFW1rppi3VZUm4Y7R+edTqFG1pZYq2dC/WWgaNGEW5O8ryeNdOl+1zw3ebKVe2TLIpJ+klI9rVqvAlrIoIZVVEKE0bN2TowP40blifcW+PYM2yMFZFhPJa3960afGo13TMbxZZYeQ8GphmjHkHR55WwKfAXuAh4Dsg6VztgsABa+1FY0wP4Fo/T9wIfGiMucNa+4cxpohz9Lwg8JezTM8k5U8Dqd21tQHH1JyZQBdg/TXW6T+rX6cWa9dvICAwmDy5czNq2L9zPQM7diNsnuPj8mFvDGDg0JGcO3+eerVqUq92TQCefrw7fV8bxMLQpdzq58eEMY6v5Fu+ajVh4V+SPXt2cufKxfh3RmKMwcfHh9de7k2PZxxf+XdfxbtpFxSYIVmz+/gwuHsQvf43mYsXLUH1alChlB8TF31FpTtK4f9gJbbt2Ufv96dx6mwcq7fsZNLi5YS/M4CEhES6vfUhAHnz5GLMc53J7oGPov8rm5jIztcHUS1kDiabDwfmzOPML79y5+v9ObnlJw5/FcnPQ4ZTafxYyjz7FFjY9uLLrvXr//AtPvnzkS1HTko82pRNwZ04++tvmZjo6jw5Zyp3NahDvmJFGb1/F18MHcWGqRkzteO/yJ49O0P6v0yv3v1ITEykbeuWVChfjgmf/B+VKt5Do/p1CQ5sSf8hIwlo056CBQowftRwACqUL0fzxv482q4LPj4+DBnQz/Xx9Jv9X+bVN4dz4UICpUvexuihb2RInoy+Xvz+x16Gvv0uxhistTz1ePd0fzNfv05tZ6a2zkxvJsnUlbB5s5yZXmPg0BFJMtVyZupB39feSJLJ8a0s5cvdQd1aj9C6QxeyZctGcJvW3HVneQB6v/o6J06eJHv27Ax9rT8FCxRI10yezAWwYvUaaj9Sg1vy5HEte6ByJZo28uexLt3J7uNDxbvvokNQ+n0k6mhL/ejV++U02tIIAtq0c7YlxzdR/duWOuPjk50hA17Bx8eHzVt+ImzZV9x1Z3nXVyX2e/4Z6tdxPAfLIlfSoonnbgS9xFPtKqu6GUbOjbt5TN7GGDMI6A78CRwAdgLhwALgDLAK6GqtLeucbrII+AdYDfS21uYzxpQFwq21lYwxDYBXrbUtk/7u3Nca59+bjTHNgVE4RuhjrbUBxpiawHTgsHO/3Zz7LQIsxzHNZTSQB6hmrX3Rue+pQDHneo9ba/cZY6Y567TQue8z1tp8aT4hZ497/0FN4eKOrzO7Cuku8tGnM7sKHhF69ExmV8EjPjm1J7OrkP6yZf4bTM+4CV69s4qLGfOFABkqW1aZlOBG3sKZ3rj+qnpPhvVxSv74c6bkzRKd86SMMcOAM9Za7/pW/fSkzrlXUOfcu6hz7k0yvf8gV0udc+9yA3TODz5UMcP6OLd9vytT8mbhM0hERERExLtkhTnnyVhrh2V2HUREREQk/d0Mc841ci4iIiIicoPIciPnIiIiIpI1ZbsJhs41ci4iIiIicoPQyLmIiIiIeIWbYOBcI+ciIiIiIjcKjZyLiIiIiFcwN8HQuUbORURERERuEBo5FxERERGvcBMMnGvkXERERETkRqHOuYiIiIjIDULTWkRERETEK+iGUBERERERyTAaORcRERERr3ATDJxr5FxERERE5EahkXMRERER8Qqacy4iIiIiIhlGI+ciIiIi4hXMTTCsfBNEFBERERHxDho5FxERERGvoDnnIiIiIiKSYTRyLiIiIiLeIZtGzkVEREREJIMYa21m10HS2+mjWfCgZsFIANl8MrsG6e9iYmbXwCOeLVAus6uQ7j458VtmV8EzfHJkdg3Sn16rvUwWPV75i2X6sPXJhlUy7MktuHpLpuTVyLlIZsmKHXMRkZteFu2Yi1vGmGbGmF+MMbuNMa+nUqa9MWanMWaHMWZOWtvUnHMRERER8Qo30re1GGN8gA+BAOAAsMkYs9RauzNJmQrAQKC2tfa4McY3re1q5FxERERE5L+rAey21u6x1sYD84DAFGWeAj601h4HsNbGprVRdc5FRERERFIwxjxtjNmc5OfpFEVKAvuT/H3AuSypu4C7jDFfG2O+McY0S2u/mtYiIiIiIt4hA79K0Vo7GZh8hSLuKpPypoPsQAWgAVAKWGeMqWStPZHaRjVyLiIiIiLy3x0ASif5uxRw0E2ZMGvtBWvtH8AvODrrqVLnXERERES8gzEZ95O2TUAFY8wdxpicQEdgaYoyoUBDR9VNMRzTXPZcaaPqnIuIiIiI/EfW2gTgRWA5sAtYYK3dYYwZYYxp7Sy2HDhqjNkJrAb6W2uPXmm7mnMuIiIiIl7BZOCc86thrV0GLEuxbEiS3y3Qz/lzVTRyLiIiIiJyg9DIuYiIiIh4hxvoPyHyFI2ci4iIiIjcIDRyLiIiIiJe4Uabc+4JGjkXEREREblBaORcRERERLyD5pyLiIiIiEhG0ci5iIiIiHgHzTkXEREREZGMopFzEREREfEKRnPORUREREQko6hzLiIiIiJyg9C0FhERERHxDrohVEREREREMopGzkVERETEO+iGUBERERERySgaORcRERERr2BugmHlmyCiXEn0hm9oGtSRgDbtmDxtxmWPx8fH03fgmwS0aUe7Hr04cPBv12Offj6DgDbtaBrUkXUbvwHg/PnzBHd/ktadutOifRcmfjrFVf71YW/h37otgZ17ENi5B7t++dXDmdozedrMK2RqT7seT7nJ1N6Z6VsA9uz901XnwM49eLB+ANPmzAdg0qefUbd5oOuxtes3eCQTgLWWt8aMI6B1MK3ad2HHrp/dltu+82date9CQOtg3hozDmstACdOnuTx53rTJDCYx5/rzclTp5Ktt3XHTipWq8VXK1cB8M2m7wns2M31U/mReqxcvdZj+SD9jx3AqdOn6TNgEM3adqJ5cGd+3LrdoxmuR7fPPmRMzO+8ue2bzK5KmqI3fkvT4C4EBHVi8vRZlz0eHx9P3zeGEhDUiXaPP+M6VsdPnKTbcy9RtX5TRvxvfLJ1lq2IolXnnrTo0J0xEz/2WN0dbek9AgKDadWhKzt2/eK23PZdzrYUGMxbY95L3pae70OTNu14/Pk+rraU2nZ3/fIrHXo+RYt2nWnVoSvLIle69vHKoKE0DepAy/ZdGDj8LS5cSEiXjOl9bb8kMTGRNp178EzfV13LOvd6znUNrNOsNc+/8lq6ZPB0pr8PxdDtmRdpHtyJFu27MH3ufFf5n3/9jQ6PP0WrDl159uX+nDlz1iOZkudKv+uef6u2tOrQjcDOPQjq9kSy7c2cF0LToI60aN+FMRM+9FguuXbqnF8HY8xeY0yx69xGNWPMxPSq03+RmJjIiHfHMmXiOCJC5hC+fCW79/yRrExI2BcUyJ+fFaEh9OzcgbGTPgJg954/iIhcScSC2UyZ9B7D3xlLYmIiOXPmZPonk1g6dwahc6azbsM3bNn2b2doQJ8XCJsznbA506l4910eyjTOmWl2KpnCnZkWuMkURcSCWckylStbxlXnxTOnkid3bgIa1ndtr2fnDq7H69eple6ZLon+eiN79+0nMiyEkYMHMmz0GLflho0ew4hBrxMZFsLeffuJ3rARgMmfz6BmjepEhi2kZo3qTP783xe3xMRExk74kDo1H3Yte6T6Q4TNm0nYvJlM//QD8uTOTe1HHr5sf+nFE8cO4O2x71O31sN8tWguYXOnU/6OMh7LcL02TpvNpGZBmV2NNCUmJjJizHimTPgfEfNnEL48it179iYrE7I0wnGsFs+lZ6f2jP3gEwBy5crJS888yYA+zycrf/zEScZM/JjpH75PxPwZHD12jI3ffe+R+kd/vZG9+/cTGRrCyMGvX7ktDX6dyNAQ9u7fT/QGR6du8rSZ1KxejcjQEGpWr+bqUKW23dy5c/PuiCFEhMxhygfjGTX2fU6dPg1A6+ZN+WrRPL6YP4vz5+MJCV163fk8cW2/ZMbcBZS/o2yybc2Z8rHrGli1ciWaNGxw3RkyIpNPdh9ef7k3Xy6cy/zPJzMnZLFrm4PeGs0rLz7PF/Nn0bhBfabMnJ3umf7Nlf7XPYDpn05yvW5d8s3m74mKXs8X82YQsWA2T3br7JFcHmVMxv1kEnXOM5m1drO1tk9m7Hvrjp2UKV2K0qVKkjNHDlo0aUzU2nXJyqxau47HWjYHoGmjhmz8bjPWWqLWrqNFk8bkzJmT0iVvo0zpUmzdsRNjDHlvuQWAhIQEEhISMvR/89q6Y1eKTI1SyfSoM1MDNn73fZJMjVJk2pVs3Y2bNlO6ZElK3uqXYZkuiVoTTZuWj2KMocr9lTh1+gyxh48kKxN7+Ahnzp6l6gOVMcbQpuWjRK2Odqy/dh1tnLnbtHyUlWuiXevNnBdC00YNKVqksNt9L1+5mrq1HyFPntweSueZY3fmzFk2/fgTwYGtAMiZIwcF8uf3WIbrtXvdBv45djyzq5GmrTt2UaZUSUqXvO3fYxW9PlmZVWvX81iLZgA09a/Pxk0/YK3lljx5qFblfnLlypms/P6DByl7e2mKFC4EQM0a1VjuoU9qotZG06ZFc0dbqlyJU2dSaUtnzlL1fmdbatGcqDVrneu7b0upbfeOMrdT9vbSAJQoXpwiRQpz7PgJAOrXqYUxBmMM999XkZjY2OvO54lrO8ChmFjWfL2B4Dat3O73zNmzfLP5exo3qHfdGTIik2+xYtx3z90A5Mubl3JlyxATexiAP/7cR/UHqwBQ++HqRK5ak+6ZHLk8+5qV0tyFoTzdoys5czraX2rXfMlc6pxfBWNMWWPMz8aY6caYrcaYhcaYW5wP9zbG/GCM2WaMuccYk80Y85sxprhz3WzGmN3GmGLGmHbGmO3GmJ+MMdHOxxsYY8Kdv+czxnzu3NZWY0xbY4yPMWaac71txpiX0ytXTOxh/EqUcP1dwre468KUtMytzjLZs2cnf768HD950rmub5J1fV3rJiYmEti5B7UCWlDr4eo8UOk+V7nxH02mVcdujBo3gfj4+PSKkiKT+3olz+SbSqYSV1w3YnkULZs2TrZs9oJFtOrYnYHDR102VSQ9pczm5+tLzOEU2Q4fxs+3ePIyzgxHjx7Dt7jjgx7f4sU45uwExsTGsnL1WjoGP5bqviOWr6Bl0ybplsUdTxy7/X/9RZFChRg4/G3adO7JoJGj+ScuzqM5bgYxh4+kOFbF3ZyLR9weq9SUKVWKPX/u48DBv0lISCBq7ToOxVx/R9Vt/VOcL35u65+ivZW4mraU9na3bt/BhQsXuL1UyWTLL1xIICziK+rWeiTd86XXtX3UuPfp3+cFsqUy6Xfl6mhqVn+IfPnyXneGlDyV6ZIDB/9m1y+/uV6v7ipfztVJ/mrlKv726LnogdcsY3jyhZcJ6voE8xeHucrs3bePzVt+ol2Pp+j69AtpduZvRCabybCfzKLO+dW7G5hsrb0fOAVc+kz2iLX2QeBj4FVr7UVgFtDF+Xhj4Cdr7RFgCNDUWvsA0NrNPt4ETlprKzv3swqoApS01lay1lYGPndXOWPM08aYzcaYzZM/n35Vgaz77aRdBoN188ildX18fAibM521y0LZumMXv+7+HYB+Lz7LV4vmsmjGZ5w8dcrtPNXrdaV6XbEMJpXn49/f4y9cYFX0epo19nct6xT8GCtCFxA2Zxq+xYryzvgPrrnuaUmt3snKWHf5r7zdt8e+z6t9XsDHx8ft47GHj/Dr7t+pU/P6Ow1X4oljl5CYyM5ffqVT8GOEzplGnjx53M7plP/G7Xl2NeciqZ+MBQvkZ9hr/Xh50DC6PN2bkrf6pXpOXi83Vbv8XHPblq7cmNLabuzhI/QfMoLRwwaTLVvyl9/h7/yPag9WoVrVKlfcx9XwxLV99bqvKVKkMJUq3pPqfsMjV9CiacB/rO3V8dTrFcDZf/6hz4A3eOOVl1xvLN4e8gZzQhYR1PVxzv7zDzlzeOb7Mzz1mjX3s49ZMvtz/m/iOGaHLGbTD1sASExI5NSp0yyYNpkBfV6g78A33Z7rkrn0bS1Xb7+19mvn77OAS1NRFjv//R64NFl0KhAGvA88wb8d6q+BacaYBUnWS6ox0PHSH9ba48aYPUA5Y8wkIAKIdFc5a+1kYDIAp49eVUvz8y3OoZgY198xsYddo0FJy/wdE4NfCV8SEhI4feYshQoWwM/XN9moVkxs7GXrFsifn4cfqsq6jd+ha2gVAAAgAElEQVRy153l8S3meDxnzpwEtWrB1Flzrqaa/8nV1MvP15e/Y2LdZEr5fMTiW/zfUejor7/hvnvuoljRIq5lSX9v91hrnu3bP13zzJ6/kAVLHKMele+rmCzboVSyHUoy6nIoSYaiRYsQe/gIvsWLEXv4CEWcH2du37mLfgMHA455v2vXbyS7jw+NnfPqv1wRRUDD+uTw0ItTsrqn87Hz8/XFz7e4azSsWaMGTJ6W/m8KbzaO5zvpsUrt2nH5sboS/7q18a9bG4D5S5aSLVv6dc5nL1jIgiWO+dyV762Y7Hw5FHvYdX36t/7Jz8dDMf+ej6m1Jb8SxVPd7pkzZ3nmpVfo+9zTVKlcKdm+Ppj8GceOn+CDQaPTJasnru2rotezKno90V9v5Hx8PGfOnOXVN4cxduQwwHHt2LZjJx/+L30yZEQmgAsJCfQZ8AatmjWhiX8DV5nyZcsy9cMJgGOKyxoP3ezvqdesEpeu+0UKE9CgHlt37KT6g1UoUcKXgIb1HdOoKt1LNmM4fuIERQp70fQWfc+5JJGyw3vp7/POfxNxvtmx1u4HYowx/sDDwJfO5c8Cg4HSwBZjTNEU2zQp92OtPQ48AKwBXgCmkE4q31uRvfsPsP+vg8RfuEBE5Er869VJVsa/Xl2WhH8JwPKo1TxS/SGMMfjXq0NE5Eri4+PZ/9dB9u4/wP333cux48ddNzqdO3eeDd9tplxZxw14sUeOXMrEyrXRVChfLr2iJMl0T4pMUW4y1WFJ+DJnpjUpMkWlyFTRtV7E8stHhS5lAli5em26Z+rSIdh1U2bjBvUJDV+GtZYtW7eTP1++yy7ivsWLkfeWW9iydTvWWkLDl9HIOf/Tv15dQp25Q8OX0ah+XQBWhS9hVUQoqyJCadq4IUMH9nd1zAEivoqkRTPPTmkBzxy74sWK4lfClz17/wRg43ffU75cWY9nyercHitnp/oS/3q1WRLxFQDLV63lkWoPpjnyfNQ5PeTkqdPMWRhKu8CW6VbnLu2DCZs7g7C5M2jcoB6hEV862tK27eTPl9d9W8qbly3bnG0p4ksa1b/Uluq4bUv+9eq63W78hQu88OprBLZsTvOARsn2E7JkKes3fsN7o4ZfNpp+rTxxbX/lxeeIXhbGqi8W897bI3ik+kOujjk4pn40qFObXLlypUuGjMhkrWXQiFGUu6Msj3ftlGxbR48dA+DixYt8/Nk0OrZNfdrf9eVK/+veP3FxnDnr+HaZf+Li+Prb71yvTY3r1+WbzY4brf/4cx8XEhIoXKiQR7LJtdPI+dW73RhT01q7EegErAeqXqH8FBwj7DOttYkAxpjy1tpvgW+NMa1wdNKTigReBPo6yxcGfIB4a+0iY8zvwLT0CpQ9e3aG9O9Hr94vk5iYSNvWLalQvhwTPvk/KlW8h0b16xIc2JL+Q0YQ0KYdBQsUYPyoEQBUKF+O5o39ebRdZ3x8sjNkwCv4+PgQe+Qorw8dSeLFi9iLF2kW0IiGzhftVwcP4/jxE1hruefuCgwfOCC9oqTI9DK9evdLI9NIAtq0d2YaniJTF3x8fBgyoJ/rY/W4c+fY8N0mRgxKXuf/TfiIn3/9DYyh5K1+lz2enurXqcXa9RsICAwmT+7cjBo22PVYYMduhM1zTNcY9sYABg4dybnz56lXqyb1atcE4OnHu9P3tUEsDF3KrX5+TBjzdpr7PHDwIH/HxFLjoSud6unDU8fuzf4v8+qbw7lwIYHSJW9j9NA3PJ7lWj05Zyp3NahDvmJFGb1/F18MHcWGqTfeNBzHsepLrz6vknjxIm1bPUqF8ncw4dPPqFTxbhrVq0Nw6xb0H/o2AUGdKFggP+PfHuZa3z+wPWfOnuXChQRWrl3P1InjuLNcWd5+byI//7YbgBee7MkdZVJeItNH/Tq1WPv1BgIC25End67kbalTd8LmOr7JaNjA/gwc9hbnzp2nXu1H/m1LPbvT9/VBLAz7glv9SjDh3bevuN0vV0Sx+YctnDh5iiVfODpZ7wwbTMW772Lo6DHc5udHh8efBiCgYX1efPrJ68rniWt7WpZFruSpnt2uq94ZnWnzlp8IW/YVd91ZnsDOPQDo9/wz1K9Ti/DlK5gT4viAO6Bhfdq2buHBXOl73Tt69Bgv9Hdc5xITE2jZtAn1nPcytA1syRsjRtGyfVdy5MjBO8MGZ+iXNqSLTJwLnlGM5hqlzRhTFlgGRAO1gN+AbsBOoJq19ogxphow1lrbwLlODuAoUMNa+7Nz2WKgAo4R8igcnfD6OOaqtzTG5AM+BB7CMRI/HPgdx7SYS0MqA621X16xwlc5rcW7ZMFI6fiR/Q3lYmLaZbzQswXS/5OezPbJid8yuwqe4ZMjs2uQ/vRa7UWy8LHKXyzTe8Zxnepn2BOcZ+7aTMmrkfOrd9E5LSWpspd+sdZuBhokeewBHDeC/pykjLsvMF7j/MFaewbo4abMg9dSYREREZGsxOtG+q+BOuceYIx5HXiOf7+xRUREREQkTboh9CpYa/daayulXdJV/h1rbRlr7fq0S4uIiIiIOGjkXERERES8w01wQ6hGzkVEREREbhAaORcRERER73AT3BCqkXMRERERkRuERs5FRERExCvcDF+lqJFzEREREZEbhEbORURERMQ76NtaREREREQko2jkXERERES8guaci4iIiIhIhtHIuYiIiIh4B805FxERERGRjKKRcxERERHxDppzLiIiIiIiGUUj5yIiIiLiFYzmnIuIiIiISEZR51xERERE5AahaS0iIiIi4h10Q6iIiIiIiGQUjZyLiIiIiHfQDaEiIiIiIpJRNHIuIiIiIl7B3ARzztU5z4qy4olrM7sCHnDxYmbXwDOy+WR2DTzikxO/ZXYV0t2zhSpkdhU84pNTezK7CunPZsWLIOCTFbshWfA1WDJUVmwVIiIiIpIVac65iIiIiIhkFI2ci4iIiIh3yIpTd1PQyLmIiIiIyA1CI+ciIiIi4h00ci4iIiIiIhlFI+ciIiIi4h00ci4iIiIiIhlFI+ciIiIi4h2yZf1x5ayfUERERETES2jkXERERES8g+aci4iIiIhIRlHnXERERETkBqFpLSIiIiLiHTStRUREREREMopGzkVERETEO2jkXEREREREMopGzkVERETEO+g/IRIRERERkYyikXMRERER8Q6acy4iIiIiIhlFI+ciIiIi4h00ci4iIiIiIhlFI+ciIiIi4h00ci4iIiIiIhlFnfObXPSGjTQN6kBAYDCTP59x2ePx8fH0fX0wAYHBtOv+JAcO/u167NOp0wkIDKZpUAfWbfjGtXzg8Leo2fhRWrbvkmxb73/0Ka06dCWwU3eeeP4lYg4f9lywJKI3fEPToI4EtGnH5GmpZBz4JgFt2tGuR6/kGT+fQUCbdjQN6si6jf9m9G8V5MjSuQdB3Z7w2hwAiYmJtOncg2f6vupaNmv+QgLatOPuarU4duKE50IB1lreGjOOgNbBtGrfhR27fnZbbvvOn2nVvgsBrYN5a8w4rLUAnDh5ksef602TwGAef643J0+dAmDlmmhate9CYMduBHXpyeYft7i2VbFaLQI7diOwYzeeTZLbE6I3fkvT4C4EBHVi8vRZlz0eHx9P3zeGEhDUiXaPP+M6bsdPnKTbcy9RtX5TRvxvfLJ1lq2IolXnnrTo0J0xEz/2aP2vV7fPPmRMzO+8ue2btAtngn/bVXsmT5t52eP/tqv2tOvxlJt21d7Zrr51LZ82ex4t2nehZfuu9HtjKOfPnwfg9WFv4d86mMDOPQjs3INdv/zq+YApRG/4hqZtOxHwWIcr5B1CwGMdaNfzqeTn47O9qVovgBFj3svoagOeu1Z8u/l7HqrXyHVN+GDyZ65tnTp9mj79B9IsqAPNgzrw40/bPJirbRq5dtGqfWcCWrdNJVfbZLmmTJ9JYMeuBHbsSst2nahYrSYnTp4EYNqsubQI7kjLdp3oN3Cw6xz1CtmyZdxPZkXMtD3fYIwx04wxwR7YbmtjzOtXeLyKMebR9N7v1UhMTGTEO+OYMvE9IhbOJXz5Cnbv+SNZmZDQLyhQID8rwhbSs0tHxk78EIDde/4gInIlESFzmDJpPMPfGUtiYiIAQa1aMGXS+Mv216t7V76YP4uwuTNoULc2H/7f1IzJ+O5YpkwcR0TIHMKXr7w8Y9gXFMifnxWhIfTs3IGxkz5KnnHBbKZMei9ZRoDpn35A2JzpLJ7p3TlmzF1A+TvKJtvWgw9U5vOPJlLyVj9PRyP6643s3befyLAQRg4eyLDRY9yWGzZ6DCMGvU5kWAh79+0nesNGACZ/PoOaNaoTGbaQmjWqu95k1qxRjaXzZxE2byajhg5i8MjRrm3lzpWLsHkzCZs3k0/eH+uxbImJiYwYM54pE/5HxPwZhC+PYveevcnKhCyNcBy3xXPp2ak9Yz/4BIBcuXLy0jNPMqDP88nKHz9xkjETP2b6h+8TMX8GR48dY+N333ssw/XaOG02k5oFZXY13HK0q3HOdjU7lXYV7mxXC9y0qygiFsxK1q5iYg8zY/5CFs2YSviCWSRevEhE5ErX9gb0eYGwOdMJmzOdinfflfF5x7zHlAljiVgwi/DIVPIWyM+KJfOdeR1v/nLlyslLz/ZiwEsvZGidk/LUtQKgWpUqrmvCi08/6Vr+9v/GU7fWI3y1eD5h82dRvlxZD+Ta4My1kJGDX08j10Aiwxa6yVWNyLBF1KxRzZWrV49uhM2bRdi8WfR78XmqP1iVQgULEhMby4x581k0axrhIXMd5+jyFemeS66dOuceZq1daq195wpFqgBuO+fGGI/eE7B1x07KlC5F6VIlyZkjBy2aNCZqTXSyMqvWruOxlo7qNW3UkI3fbcZaS9SaaFo0aUzOnDkpXfI2ypQuxdYdOwGo/mBVChYscNn+8uXL6/o9Li4Og+fnjbnNuHZdsjKOjM2BFBnXrks1Y0bzVI5DMbGs+XoDwW1aJdvWvffcTanbbs2QbFFromnT8lGMMVS5vxKnTp8h9vCRZGViDx/hzNmzVH2gMsYY2rR8lKjVjnM1au062jjP0TYtH2Wl8xzOe8stGOfcxLi4cxlwtl1u645dlClVktIlb3Met0ZERa9PVmbV2vU81qIZAE3967Nx0w9Ya7klTx6qVbmfXLlyJiu//+BByt5emiKFCwGONyHLV6/NmEDXYPe6Dfxz7HhmV8OtrTt2pWhXjVJpV5eugQ3Y+N33SdpVoxTtahfg6ASfO3+ehIQEzp07h2/xYhmezZ3L8gY0JmptivMxej2PtXBeR/wbsHHT90nOxwfIlTOnmy1nDE9dK1Jz5sxZNv3wI8FtWgOQM0cOCuTP76FczZ25KnPq9OmryNWcKGe7j1obTZuWLZy5WrByzeXXg4jlkbRs1sT1d7JzNO7GOUevijEZ95NJbtrOuTGmuzFmqzHmJ2PMpc/26hljNhhj9iQdRTfG9DfGbHKWH+5cVtYY87MxZooxZrsxZrYxprEx5mtjzG/GmBrOcj2NMR84f2/nLPuTMSbaGJMTGAF0MMZsMcZ0MMYMM8ZMNsZEAjOMMeuMMVWS1OVrY8z96fEcxMQexq+Er+vvEiV8L5tqEnP4MLeWKAFA9uzZyZ8vH8dPnCTm8GH8/EokWbc4MbFpT1MZ/+En1H80kC++iuSl555KjxhX5MiYpJ6+l9czJjZlxrwcP3ny8ufH1/ffdY3hyRf6EtT1ceYvDvXaHKPGvU//Pi+QzWTepSBl/fx83Z+Hfr7Fk5dxZjh69JjrhcW3eDGOJekIrli1hmZBHXjmpVcYNXSwa/n5+HiCuvSkffcnWenBjm3M4SMpnvvibrId4VZnmaTHLTVlSpViz5/7OHDwbxISEohau45DMbGeCZDFXbGNJynj7vhc3iYd65bwLc4TXTvRsGUQdZoFki9fXuo88rCr3PiPPqVVx+6MGjeB+Ph4DydMLuZwymu+m/Mxlbw3Ak9eK7Zs20brDl3p9WJffvt9DwD7//qLIoULM3DYSNp06s6gEW/zT1ych3L9ey6lnitF9qvIBY7BiXUbvqFJo4aA41x9olsXGj4aSJ0mLciXPx91aj6S7rnk2t2UnXNjzH3AIMDfWvsA8JLzoVuBOkBL4B1n2SZABaAGjlHuh4wx9Zzl7wQmAPcD9wCdneu/CrzhZtdDgKbOfba21sY7l8231lax1s53lnsICLTWdgamAD2ddbkLyGWt3Zoez8Ol+WpJmRTvFFMrczXruvPyC8+ydlkYrZo1Ydb8hf+httfm8lq6yeiuDAbr5pFL68797BOWzJ7G/00cx+yQxWz64cfrr+wVeCLH6nVfU6RIYSpVvCd9KnmN3NaPqzkP0952gH8Dvlo8nw/HvcuEjz91LV+9LJTFs6cxbtQIRo0dz779B/5rta+K23pfTbYrjPMXLJCfYa/14+VBw+jydG9K3uqHj4/P9Vf2JnSlNn7FMphU2iScPHWKqLXriFoawrqvwoiLO0fYsuUA9HvxWb5aNJdFM6Zw8tQpt/cgeNI1X/Mz5XOny3nqWnHfPfewKiKUpfNn0a1je17oNwCAhMREdv78C52CgwidO4M8efK4vTfrernPlaLMNb7mAqyOXseDD9xPoYIFAec5uiaaqPAlrFseQVxcHGERX/7neovn3JSdc8AfWGitPQJgrT3mXB5qrb1ord0JXHob28T58yPwA45OeAXnY39Ya7dZay8CO4Ao62hB24Cybvb7NTDNGPMUcKVX06XW2ktvz0OAlsaYHMATwDR3KxhjnjbGbDbGbJ48dfqV0zv5lfBNNuIWExOLb7HkH235+fryd0wMAAkJCZw+c4ZCBQvg5+vLoUMxSdY9/J8+FmvZvAmRq9Zcdflr5edbnEMxSeoZe3k9/XyLp8h49t+MSZ+f2FjXuiWKO0ZmihYpQkCDeq6Ps70pxw8/bWVV9Hr8WwXRb9AQvtn0Pa++OcyjOS6ZPX+h6+Yr3+LFktXvUJLn+d9svhxKMqLpKOM8BkWLuD4Cjj18hCJFCl+2v+oPVWXfgb84dtxxc+ul41e6VElqVHuQnR66Mc9x3JI+96kdN0eZpMftSvzr1ibk80+ZP/Vj7ihzO2VKl0r/yt8ErtTGk5Zxd3wub5OOc3LDd5spddttFClcmBzZs9OkYX1+3Oq4idC3WDGMMeTMmZOgVi3Y5uHrRkqX5Y05fPk1v4T7vJklI64V+fLlJe8ttwBQv04tEhISOHb8BH6+vvj5FueBypUAaNbIn50//5JOuUJcN2v6Fk9+LiWtc/Jc7rOndQ2MiFxBiyRTWjZ8u4lSJZ3naI7sNPFv6DpHvYKmtWRZBvcDjedTlLn072jnyHYVa+2d1trP3JS/mOTvi7j5Dnlr7bPAYKA0sMUYUzSV+p1Nss4/wAogEGgPzHG3grV2srW2mrW22tNP9Ehls8lVvrcie/fvZ/9fB4m/cIGIyJX416+brIx//TosCV8GwPKo1TxS/SGMMfjXr0tE5Eri4+PZ/9dB9u7fz/333XvF/e3dt9/1+6q16ylXtsxV1fN6ODIeSJ6xXp1kZfzr1WVJuGPUIFnGenVSZDzA/ffdyz9xcZw56zhE/8TF8fW331GhfDmvy/HKi88RvSyMVV8s5r23R/BI9YcYO3KYR3Nc0qVDsOvmq8YN6hMavgxrLVu2bid/vnyXveD6Fi9G3ltuYcvW7VhrCQ1fRqMG9Vy5Q53naGj4Mho5z+E/9+13jTbt2PUzFy4kULhQQU6eOuWaTnDs+Al+2LKVO8vd4ZGcle+9J8Vxi8K/bu1kZfzr1WZJxFcALF+1lkeqPZjmiNhR58fWJ0+dZs7CUNoFtvRI/bM6t8fnsnaV9Bq4JkW7ikrRripym18Jftq+nbhz57DWsnHTZso7r3WxRxwdKGstK9dGe/y64TbvviTX/BUr8a+X4nysW5slzlHU5avW8Ej1tM9HT8qIa8XhI0dd14qt23dw0VoKFypI8WJF8StRgj17/wRg43ebKH9H+lwrunRo57pZs3GDeoSGf+nMtS2NXNucub5MkSvCmSuCRvXrudY7ffoMm77/0VUWcJyj27YTF+c8R7/bdNmXAkjmuln/E6IoYIkxZry19qgxpsgVyi4HRhpjZltrzxhjSgIXrmWnxpjy1tpvgW+NMa1wdNJPA2ndYTIF+AJYl2SU/7plz56dIQNeodeLfUlMvEjbwJZUKF+OCR9PptK9FWlUvy7Bga3o/+ZwAgKDKViwAONHjQSgQvlyNA9oxKPBnfHJ7sOQ1151fbTe740hfLf5B46fOEG95q3p/Uwv2rVpzbhJH/HHn/swxlDyVj+GvzEgvaJcOWP/fvTq/TKJiYm0be3M+Mn/UaniPc6MLek/ZAQBbdpRsEABxo8a8W/Gxv482q4zPj6O58rHx4ejR4/xQv+BgOOmmpZNA6hXy7Pz9TyR40pmzFvAlBmzOXL0GK07dqd+7Zq8/eZAj2SrX6cWa9dvICAwmDy5czNq2L9zwwM7diNsnuOWkGFvDGDg0JGcO3+eerVqUq92TQCefrw7fV8bxMLQpdzq58eEMW8DsHzVasLCvyR79uzkzpWL8e+MxBjD73/sZejb77qmZz31eHePdc4dx60vvfq8SuLFi7Rt9SgVyt/BhE8/o1LFu2lUrw7BrVvQf+jbBAR1omCB/Ix/e5hrff/A9pw5e5YLFxJYuXY9UyeO485yZXn7vYn8/NtuAF54sid3lCntkfqnhyfnTOWuBnXIV6woo/fv4ouho9gw9fKv8MsMjuPzMr1690ujXY0koE17Z7saDiRtV13w8fFhyIB++Pj48ECl+2jaqCGPdXmc7D4+VLz7LjoEBQLw6uDhHD9+Amst99xdgeED+2d83gH96NWnn+Oa37qFM+8UZ946jrxDRxLwWAdH3qTnY+vgJOfjOqZOes9jbccdj10rVq5i7sLF+Pj4kDtXLt4bPdL1huTN117h1UFDuXDhAqVLlWR0kn2mX67azlxtnbneTJKrK2HzZjlzvcbAoSOS5KrlzNWDvq+9kSTXKNf6K1avofYjNbglTx7XsgcqV6JpI38e69I9yTnaJt1zecxN8J8QGXfzmG4GxpgeQH8gEceUFYBwa+1C5+NnrLX5nL+/BPRyljkDdHWuF26treQsM+3S+saYspceM8b0BKpZa180xizGMSXG4HiD0BcojOMNQA5gNFAROGOtTfb9bsaYn4G+1tqv0gx35ljWO6g36XnqlTLxu2E9KiFjb97LCM8WqpB2IS/0yak9mV2F9JdVr4E+WXGMMAt3HvMWyvRwCUO6Z1hjyD5iRqbkvWk7597EGHMbsAa4xzm//crUOZfMpM6511Dn3Itk1WugOufe5QbonCcO65lhjcFn2LRMyZtFX0WzDmNMd+BbYNBVdcxFRERExGtlxbesWYq1dgaQ/t/dJCIiIuJtboI55xo5FxERERG5QWjkXERERES8g0bORUREREQko2jkXERERES8g0bORUREREQko2jkXERERES8Q1b9vzSSyPoJRURERES8hEbORURERMQ7aM65iIiIiIhkFHXORURERESugTGmmTHmF2PMbmPM61coF2yMscaYamltU9NaRERERMQ73EDTWowxPsCHQABwANhkjFlqrd2Zolx+oA/w7dVsVyPnIiIiIiL/XQ1gt7V2j7U2HpgHBLopNxIYA5y7mo2qcy4iIiIi3sGYDPsxxjxtjNmc5OfpFLUpCexP8vcB57Ik1TVVgdLW2vCrjahpLSIiIiIiKVhrJwOTr1DE3Rwb63rQmGzAeKDnf9mvOuciIiIi4h1urP+E6ABQOsnfpYCDSf7OD1QC1hjHXHk/YKkxprW1dnNqG72hEoqIiIiIeIlNQAVjzB3GmJxAR2DppQettSettcWstWWttWWBb4ArdsxBI+ciIiIi4i1uoG9rsdYmGGNeBJYDPsBUa+0OY8wIYLO1dumVt+CeOuciIiIiItfAWrsMWJZi2ZBUyja4mm2qcy4iIiIi3uEGGjn3FM05FxERERG5QWjkXERERES8w431bS0ekfUTioiIiIh4CY2cZ0VZcT5WVszk9v8ukBuWT47MrkG6++TUnsyugkc8W6BcZlch3X1y8vfMroJnmCw4RpgQn9k1yNqyZH8guSzYKkREREREvJNGzkVERETEO2jkXEREREREMoo65yIiIiIiNwhNaxERERER76BpLSIiIiIiklE0ci4iIiIi3kH/CZGIiIiIiGQUjZyLiIiIiHfQnHMREREREckoGjkXEREREe+gkXMREREREckoGjkXEREREe9gsv64ctZPKCIiIiLiJTRyLiIiIiLeIZvmnIuIiIiISAbRyLmIiIiIeAfNORcRERERkYyikXMRERER8Q76nnMREREREcko6pyLiIiIiNwgNK1FRERERLxDtqw/rpz1E4qIiIiIeAmNnIuIiIiId9ANoSIiIiIiklE0ci4iIiIi3kH/CZFkddZa3hozjoDWwbRq34Udu352W277zp9p1b4LAa2DeWvMOKy1AJw4eZLHn+tNk8BgHn+uNydPnQLg283f81C9RgR27EZgx258MPkz17amzZpLi+BOtGzXmX4D3+T8+fNek+uSrTt2UrFaLb5aucq1bMz7k2gR3InmQR2SbSv9M7VNI9MuWrXvTEDrtqlkapss0+nTZ3j2pVdo3aELLYI7sijsCwD+Ovg3QZ27E9ixKy2COzJ34eJ0zePpXOA4Dy/Vv2uvZ13Lp8+ZR8t2nWgR3JFps+emY473CAgMplWHruzY9Yv7HLuc51xgMG+NeS95juf70KRNOx5/vo8rR2rb3fXLr3To+RQt2nWmVYeuLItc6drHK4OG0jSoAy3bd2Hg8Le4cCEhXTJGb/iGpkEdCWjTnsnTZl72eHx8PH0HvklAm/a06/EUBw7+7Xrs089nENCmPU2DOrJu47eu5dNmz6DBJ3kAACAASURBVKNF+y60bN+Vfm8MdV0PXh/2Fv6tgwns3IPAzj3Y9cuv6ZIhvXT77EPGxPzOm9u+yeyq/CfRG76hadtOBDzW4QrHcAgBj3WgXc9/j+HxEyfp9mxvqtYLYMSY9zK62m5Za3nr3bEEtA6iVfvOV752tOtEQOsg3np3rKvNfbliJS3aduCeBx9m246drvJff/MtQZ2706pdJ4I6d2fjd5syJA+k//GJO3eOp/v2p1lwZ1q078rYSR9nWBa5duqcXyVjTFljzHY3y6cYY+5NY929xphinqvdtYv+eiN79+0nMiyEkYMHMmz0GLflho0ew4hBrxMZFsLeffuJ3rARgMmfz6BmjepEhi2kZo3qTP58hmudalWqEDZvJmHzZvLi008CEBMby4x5C1g063PCQ+aQePEiEctXeFWuxMRExk74kDo1H3Yt++Gnrfzw01aWzp9FeMgctu3YxXff/5DOmTY4My1k5ODX08g0kMiwhW4yVSMybBE1a1RzZZq9YCHly93B0vmzmfl/H/Pu+InEX7hA8eLFmDdtCmHzZrFgxlT+7/MZxBw+nK6ZPJnr1OnTDB89ho/HjyVi4TwmjBkFwK+7fydkSRghMz4nbN4s1qz7mr379qVDjo3s3b+fyNCQtHMMfp3I0BD27t9P9AZH527ytJnUrF6NyNAQalav5nphTm27uXPn5t0RQ4gImcOUD8Yzauz7nDp9GoDWzZvy1aJ5fDF/FufPxxMSuvS68yUmJjLi3XFMmTiOiJDZhC9fye49fyQrExIWToH8+VkRuoCenTswdtJHAOze8wcRkVFELJjFlEnvMfydsSQmJhITe5gZ8xeyaMZUwhfMclwPkrzJGNDnBcLmTCdsznQq3n3XdWdITxunzWZSs6DMrsZ/kpiYyIgx7zFlwlgiFswiPDKVY1ggPyuWzHceQ0dnLleunLz0bC8GvPRCZlTdrej1l64dixzX+VHvui03bNS7jBg8kMiwRY5rx9f/z959h0dVJW4c/x4TQToiJKigCMuMoAhIWUA6BJQEiPQqFuRnAUUsK1YWFYUFXMSKqPRiVIgSlBJKqAooUs3aWHGVJNJBIIXz+2NuwiQZSIDMpPh+nicPM3fO3Dkv596bM+eee+M5drhq1GDyhHE0url+pvKXly/PW/+ewGdRc3ll9PM88cwof0cB/Nc+dw/oyxcfzWHB7A/4ett2Vjv5Cy1jAveTT9Q5v0jW2sHW2l05lyyYYlfFERnRCWMM9W66kSNHj5GY9EemMolJf3Ds+HHq162DMYbIiE7ErozzvH/1GiIjOgEQGdGJ5avicvzMtLQ0Tp46RWpqKidPnCSkUqVClWvmvCg6tmvDFRUuz1hmMCSfSiYlJYXk5BRSUlOpWKGCHzLd5mSqw5GjR3OR6TZiV652MsURGRHuZApn+SrPcmPg+J9/Yq3l+J8nKFe2LMFBQRS79FKKFSsGQHJyCqft6TzN4+9cn32+hLC2bbjqysoAXOG0x48/76FunRspUeIygoODadSgPstWrL74HKvjiAx3ctS5kSPHzrLNHTtO/ZucHOG3EbsqPYfvbe5s673u2muodk1VAEIrVaJChcs5cPAQAK2aN8MYgzGGm26oRUJi4kXn27ZzN9dWrULVKldT7NJLCe/QjtjVazKVWbF6Dbc7GTq2a82Gr7ZgrSV29RrCO7SjWLFiVL36Kq6tWoVtO3cDWY4HJ08SUqlAjmNk88Oa9fx54GB+V+O8ZGvDsPbErl6bqcyKuLXcHn4bAB3btmbDJk8blixRgob16lLcOSYUBJ59v1Mujx03nTnOO/tcjerXUb3atdnWW/t6N6Ehnt9LNWtUJzn5FMnJyX7P44/2KXHZZTRpeDMAxS69lNpuFwmJeT/IInlLnfPzE2yMmW6M2WaM+cgYU9IYs8oY0xDAGNPXGLPdGLPDGOPzK7wxZoTz+g5jzHCv5c8aY74zxiwzxsw1xjxmjKlhjPnaq0xNY8yWvAyUkJhE5dCQjOeVQ0KyjY4mJCVROaRS5jLOzr1//4GMX6YhlSpywOuX1dbt2+nSewCDhw7n+x9/AiA0JIS7B/anTadImneIoHSZUplGoAt6roTERJavXE2fHrdnWlf9unX4e6MGNO8QQfOO4bRo+ndqVL/OD5lCc5EpS+4cMvXv3ZMff/6ZFh3D6dKrH08//giXOPeR/X1fAp179ad1p87cO2ggoX74IuWvXHv++wtHjhxh4L33063fHSxctBgAV43qbP76Gw4eOsyJEyeJW7uefQkJfshRyXcO7+0yNDfbXM7r3bZjJykpKVxT5epMy1NSUomO+YIWzZrkUb4zdQ/1agPvMlc6ZYKDgylTuhQHDx/OliH9vaEhlbh7QF/aRHSj+a1dKV26FM2bnDkevPrmO3TucwdjJkwKSOeoqMu6/YWG+thGz9KGBVFCYiKVK3vtG6Eh2b6IJiQmZj52+ChzLkuWr6CW250xUOFP/m6fI0ePsnLNOpo2apB3lc4Pl1wSuJ/8iphvn1w4uYEp1tqbgCPAA+kvGGOuAsYCbYF6QCNjTKT3m40xDYC7gL8DTYB7jTH1nc59d6A+0A1oCGCt/RE4bIyp56ziLmCar4oZY4YYYzYbYzZPed9nEZ8s2edFGzKfyvE1dzqnsz03XH89K2IW8un8WQzs04sHRzwBwOEjR4hdFUfsok9Ys2QRJ06cJDrm81zXN7f8leul8f/msYceJCgoKNPy//6ylx9/3sPqLz4l7ovP2LhpC5u2fHP+FT8H35mylPGZ6dyh1m7YSC2XizVLYlg4dyajx47n2LFjAFxZOZTPPpzN0uiPWbBoMX/s33/B9T8bf+VKS0tj5+7veOe1iUx94zXefPc9fv7vL9Sofh2D77yDux8YxuChD+N21czWnhfC1yUGWet4ITlyWm9i0h88/txoXh71TMaXqnT/fOVfNLy5Hg3r18u6ivPms52y5jvLfufr6gtjnOPB6jXEfhrFmi+iPceDxUsAGDH0Pr74eC4fz5jK4SNHmDJ91kVn+KvLzfbns0y2PbJgyNU+5+N9Oe1z6b7/8UfGv/Y6o58ZeQG1O3/+bJ/U1FRGPD2Kgb17UjXLl3gpeHS3lvOz11q7znk8C3jI67VGwCprbRKAMWY20BJY6FWmObDAWnvcKfMJ0ALPl6Roa+0JZ/lnXu+ZCtxljBkB9AYa+6qYtXYKMAWA4wfPeSXi7Pkf8eGCaADq3FCLfQlnRhH2JSZmO61cOSSEfV4jZJ4ynhHUK66oQGLSH4RUqkhi0h9UcKZ6lC5dKqN8q+bN+OfL4zhw8BBfbt5ClauvosLlnnId2rbmm23b6eqcprsYgci1Y9duRox8BvBcgLN67QaCg4LY88te6ta5kVIlSwLQ4pambN2+g0YNMs9lPP9MUV6Zamca4fWub+ZMvnOfLdMnny5iyJ13YIzh2muqUuWqq/hpz3+56cYbMtYTWqkSNatfx+ZvtnJr+3YXlSlQuSqHhnB5+fKULFHCc8r35vp895/vue7aa+gZ2YWekV0AmDj5TUK9RqvOK8eHH/HhAs987jq1a2XJkURIRR/bnPd2mZCbHJXOut5jx47zfw8/yvD7h1Cvzo2ZPuv1Ke9x4OAhXn/65QvKllXWuiecZZ/6PSGRyqEhpKamcvTYccqXK0vlkMwZEpw2Xv/VZqpc5XU8aNPKczzo1DEjY7FixejWOZz3Z+XNhbt/ZdnaMMHHNhrquw0Litnzo/jwE8+v1To31GbfPq99IyEXxw4fZXzZl5DA0BFPMPaFUVxTtUoe1f7c/Nk+z44ZR7VrqnJnv155Xu+A033OJYusnV7v57nZWs5W5lzv/Ri4DYgAtlhrL3rosn/vHhkXarZv3YqFixZjrWXrth2UKV062y/ckEoVKVWyJFu37cBay8JFi2nXuiUAbVu2yJgusHDRYtq1agFA0h/7M77hb9uxk9PWcnn5clxVOZRvt+/gxImTWGvZ8NVmalxX7WIjBSzXikULWBGzkBUxC+nYvg3Pj3yc9m1acVXlUDZt+ZrU1FRSUlLZtOWbPMnVv3dPoufNInreLNq3bsnCRZ87mbbnkGm7k+nzLJlinEwxtGvlWX5l5cps+GozAH/s38/P//2FKldfzb6EBE6ePAl4Rji//nYb112bfX5mQc3VrlVLNn+zldTUVE6cOMm2HTsz2mT/gQMA/Pb7PpauXEXErR0uLEevHkTPnUH03BmeHDFOju07KFO6lO8cpUqxdbuzzcV8nlHfti2b+9zm2rZs4XO9ySkpPPjYP+gacRu3hWX+whS14FPWbtjIxDH/zDaafqHq1L6ePXt/Ze//fiM5JYWYpbG0bdk8U5m2LZuzwMmwJHYVTRo1wBhD25bNiVkaS3JyMnv/9xt79v7KTTfU8hwPduzgxEnneLBpMzWcOcCJf3jmDltrWb46jpo1qudJjr+yOrWvZ88ve8+04bLltG15S6YybVvcwgLnbOaSFato0ujmXI80B0L/3j2Jnj+b6Pmzad/G+zif22PH4ox97myOHD3KkGGPMGLYgzSoV9efcTLxV/u8+tYUjh07zlMjHjpnOSk4TF7f7q2oMsZUA34GmllrNxhj3gW+AzoDjwH/AzYCDYCDwBJgsrU22hizB89UlWvwTEtpgqdD/iUwEM8ZjHeAZs7jLcC71trxzmdPxjPt5R5rbc5zQHIYOfdmrWX0K+NZs2EjJS67jDGjnqFO7VoAdO0zkOh5njtGbN+1m5HPv8DJU6do2awpz/7jUYwxHDx0mOH/eJrf9+3jysqVmTTuJcqXK8eseVHM/egTgoKCuKx4cZ589GFurnsTAK+99S6Lly0nOCiIWm4XLz33VJ7P5/NXLm9PPj+a1i2ac2v7tqSlpfHPl//Fpq+/wRhDi2ZNGPno8Gz1yuz8fuF5Mv3LK9OzXpkGED1vllem0V6ZHvPK9JRXpjGUL1eOhKQkRj4/OuML1b133kHX8NtYt/FLXpn4GsZ4Th8P6N2D3t1vP1cVL4i/cgFMnT6TTz5dxCWXXEKPyC7c2b8vAP3uHsKhw4cJDg5m5IjhNP17o1xU9NwXxFprGT12PGvWf0mJy4pn3ub63kH03Blncox6kZMnT9HyliY8+4TXNvfk0/y+L4ErK4cyaaxnmzvbeqMXf8FTo17kb16d1ldGPUMtt4vajZtzVeXKlCrlOZMT1qZVxh2TzidTVqvXrmfMxNdIS0uje5cI7r9nEJPefpcba11Pu1YtOHXqFI8/9wK74/9DubJleXXMPzNOob/13nQ+/nQRQUFBPPXow7S6pSkAr70zlcVLY88cD559kmLFinHHfcM4ePAQ1lqud9fknyMfzzgzlZP7yvq/I3/PnPdxtW5O6YpXcCQhkc+eH8P697Pf+i6vvH34xzxZz+p1GxgzcRJpaafp3iWc++8exKS3pzpt2NzThs+/wO747z1t+NKojDZs26UHx44fJyUllTJlSvP+5In87WKvrQm+8GN/xrFj/YYzx44bPDdP69q7P9HzZwOwfeeuM8eOW5plHDuWrVjJC2MncODgQcqWKUMtd03ee3Myb777HlPen861zgXXAO+/NTnjovIcpV749RF53T6lS5WiVUQ3qle7lmKXXgrAgF7d6RnZ+cIqWLZSvn9TS5s5JmAd16CBT+VLXnXOc8npnC8G4vB0or/H07FeDDxmrd1sjOkHjMTT61psrX3Cee8eoKG19g9nesrdzmqnWmv/7ZQZBfQF/gsk4Zki867zWhM8I+jXWGvTcqzseXTOJT/l+zFOzoef7laTr4piJgLTOQ+0vOqcFzgX0TkvsC6ic17gqXMeEJpznkvW2j2Ar/uZt/YqMweY4+O91bweTwR8/QWH8dbaUcaYkni+AEzweq058H6uOuYiIiIiRdUl+f79wO/UOS84pjh/zOgyYLq19msAY8wCoAaeu8CIiIiISBGmznkBYa3td5bleT/JV0REREQKJHXORURERKRwMEX/RoNFP6GIiIiISCGhkXMRERERKRwK0H33/UUj5yIiIiIiBYRGzkVERESkcNCccxERERERCRSNnIuIiIhI4fAX+CNEGjkXERERESkgNHIuIiIiIoWD7tYiIiIiIiKBopFzERERESkcdLcWEREREREJFI2ci4iIiEjhoLu1iIiIiIhIoGjkXEREREQKB805FxERERGRQFHnXERERESkgNC0FhEREREpHPRHiEREREREJFA0ci4iIiIihYMuCBURERERkUDRyLmIiIiIFA5/gT9CpM55UfQXOOUjBZi1+V0D/yiKuYpiJuDtwz/mdxXy3H3lauR3Ffzi7WO/5HcV8l6QulZycbQFiYiIiEjh8BcYgCz6CUVERERECgmNnIuIiIhI4aD7nIuIiIiISKBo5FxERERECodLiv64ctFPKCIiIiJSSGjkXEREREQKB805FxERERGRQNHIuYiIiIgUDrrPuYiIiIiIBIo65yIiIiIiBYSmtYiIiIhI4aALQkVEREREJFA0ci4iIiIihYP+CJGIiIiIiASKRs5FREREpHDQnHMREREREQkUjZyLiIiISOGgP0IkIiIiIiKBopFzERERESkcNOdcREREREQCRSPnIiIiIlI4aM65iIiIiIgEikbORURERKRwuERzzkVEREREJEDUOZcM1lpeHDuesC7d6NyrHzt3f+ez3I5du+ncsy9hXbrx4tjxWGsB+HzZcsK79+b6m//O9p27MsofPHSIgffeT/1mrRj9yr8CksVbUcxVlDJZa3lx3ATCunSnc6/+587Sqx9hXbrz4rgJGVkOHT7MXfcPo0PX7tx1/zAOHzkCwNTpM+naZwBd+wwgomdfajVsyqHDhwGYNmsu4T36ENGzLyNGPsOpU6fyNFPc+o107NaHsMieTJk2I9vrycnJDB/5LGGRPek5aDC//vZ7xmvvfDCDsMiedOzWhzUbNmZ6X1paGpH9BvF/wx/LWNZv8P107TeIrv0G0fzWLjzw6D/yNEtuxK3fSMfufQm7vTdTps3M9ron73OE3d6bnnfem5H34KHDDLxvGPVbhjF63MRAVztHRTXXuQx87w3GJfzIs9s35lw4wPLjWDF9zjwievYlvEcfps2e6+dcPXLI9R2de/UnrEuPs+TqkSlXum07d1GrYTO+WL4i0/Jjx47TomNnRr8y3i+5/MZcEriffKLOeYAZY6oZY3Y4jxsaY147R9nWxphFgapb3Nr17PllL0ujP+aFZ0YyasxYn+VGjRnL6GdGsjT6Y/b8spe4dRsAcNWoweQJ42h0c/1M5YsXL87DD/wfTzzykN8z+FIUcxWlTHHr0rN8xAvPPMmol8f5LDfq5XGMfnokS6M/8mRZ78ky5YMZNG3ckKXRH9O0cUOmfODpDA8eNJDoebOInjeLEUMfoNHN9SlfrhwJiYnMmDefj2dNY1HUXNJOnyZmybI8y5OWlsboseOZ+toEYqLmsGjJcn746edMZaKiP6NsmTIsWxjFnf16M37ymwD88NPPxCxdTsyHs5k6eSL/fGU8aWlpGe+bMfdDalxXLdO65kx9i+g504meM536dW6kQ5vWeZYlN9LS0hg9biJTJ40n5sNZLFrqK+8iypYtw7IF8528bwFQvHgxHr5vME88/GBA65wbRTVXTjZMm83kW7vldzV8CvSx4j8//EjUgmiiZnxA9LxZrFqzjj2//OKHXBucXFGe4/k5cz3J0ugoH7kasTT6I5o2bpSRCzzb8fhJb9C86d+zre/fb71D4wb1sy2X/KfOeT6y1m621uZPj9WH2NVxREZ0whhDvZvqcOToURKT/shUJjHpD44dP079ujdhjCEyohOxq1YDUKP6dVSvdm229ZYsUYKG9etRvHjxgOTIqijmKkqZYlfFERlxWy6z1HGy3EbsSk8Wz/9FOACREeEsdzJ6i1mylIhbO2Q8T0tL4+SpU6SmpnLyxElCKlXMszzbdu7i2qpVqFrlaopdeinhHdoTu3pNpjIrVq/h9ojbAOjYrg0bvtqMtZbY1WsI79CeYsWKUfXqq7i2ahW2OWc29iUksmrdenpEdvb5uceOH2fj5i20b90yz7LkxraduzPnDWtP7Oq1mcqsiFvL7eFO3rat2bBpC9Zaz/ZWry7FixULaJ1zo6jmyskPa9bz54GD+V0NnwJ9rPjx5z3UrXMjJUpcRnBwMI0a1GfZiuzvyZtc6cfzGzly9FgucnUidmWck2sNkRGdnFydWL4qLuN9M+dF0bFdG66ocHmm9e3Y9R379x/gliaN8zyPXDx1zs+DMWaAMeYrY8xWY8w7xpggY8wxY8xLxphvjTEbjTGhTtkazvNNxpjRxphjPtaXMTJujGnlrHerMeYbY0wZp1hpY8xHxpjvjDGzjfHf3fcTEhOpXDk043nl0BASEhOzlwkJOWeZgqYo5ipKmRISk6gc6pUlJISEpKTMZZKSMmcJCSEh0VNm//4DGZ3rkEoVOZClY3HixEnWrN9Ih3ZtAAgNCeHugf1p06krzTuEU7pMaZo3beK3PKEhlTLq6l3mSqdMcHAwZUqX4uDhw857Q7zeeybnmAn/5vGHHuSSs5xqXb4yjqaNGlC6dKk8y5IbCUlZ6hxaKXv7JSZxpVPGO29BVlRzFWaBPla4alRn89ffcPDQYU6cOEnc2vXsS0jwU64sdfaZq9J55UpITGT5ytX06XF7pnWdPn2asa9O4onhw/I8S0AYE7iffKLOeS4ZY2oBvYFbrLX1gDSgP1AK2GitrQvEAfc6b5kETLLWNgJ+y8VHPAY86Ky7BXDCWV4fGA7UBqoDt5ylfkOMMZuNMZunvD/tAhKCM30t63ozl/H92Rf0eYFSFHMVpUzWR02z1tL6CJzbLCvj1nBz3ZsoX64cAIePHCF2VRyxixawZkkMJ06cIDrm8/Ou99nk5v/dZxmM7/8LY1i5Zh0VKlzOjbWuP+vnLlq6jPCOYedZ24uXm7bxWSZbKxcsRTVXYRboY0WN6tcx+M47uPuBYQwe+jBuV02CgoLOu9458Z0rF9taDrFeGv9vHnvowWx1nvPhx7S8pRlXeg3wSMGiWynmXjugAbDJ2dFLAIlAMpA+L3wLkP7bsSkQ6TyeA+R0xcU6YKIxZjbwibX2V+dzvrLW/gpgjNkKVAPWZn2ztXYKMAWAPw/7+t3v0+z5UXz4yUIA6txQm337zowK7EtIJKRSpUzlK4eEsM9r9NVXmYKgKOYqSplmz4/iwwXRgJPFazRqX2IusiQmZowUXXFFBRKT/iCkUkUSk/6gQpbTtzFLlxHuNaVl/ZebqHL1VVS43FOuQ9s2fLNtO12d6QkXq3JIpUx5EhKTsk2bqRxSid8TEqgcGkJqaipHjx2nfLmynpwJiV7v9eRcEbeWFXFriVu3gVPJyRw7dpzHnh3F+BdGAZ4LELfv3MUb/3o5TzKcj2x1TkgipGKWvKEh/J6QmC1vQVZUcxU2+XmsAOgZ2YWekV0AmDj5TUK9RrgvLtdHXrlqZdrWvOucOVdSljKVzplrx67djBj5DOA5Rqxeu4HgoCC+2b6dLd98y9yojzl+4gQpKSmULFmCxx4qJNdI6I8QiRcDTLfW1nN+3NbaUUCKPfOVNo0L/MJjrX0FGIyn07/RGJM+ROZ9G4kLXv/Z9O/dk+j5s4meP5v2bVqxcNFirLVs3badMqVLZztAhFSqSKmSJdm6bTvWWhYuWky7VoGd45obRTFXUcrUv3fPjAuw2rduycJFn59nls9p58ytbtuyBQsXxQCwcFFMpoxHjx5j05ZvMsoCXFU5lG+37+DEiZNYa9nw1aZsF1lejDq1a7Fn76/s/d9vJKekELN0OW1bNs9Upm3LFixY5BmtXxK7kiaNGmCMoW3L5sQsXU5ycjJ7//cbe/b+yk031ObRofcTtziaFZ99wsSXRtOkUYOMjjnAF8tX0Lr5Lfly/UOd2tez55e9Z/IuW07blplP8LVtcQsLnLMTS1asokmjmwvkWRxvRTVXYZOfxwqA/QcOAPDb7/tYunJVpmtXLi5XD6LnzSR63kzat/Y+nu/IIdeOM8fzTLkWO7kW065VCwBWLFrAipiFrIhZSMf2bXh+5OO0b9OKCS+NZtXiaFbELOQfw4cRGd6p8HTM/yI0cp57sUC0MeZVa22iMaYCUOYc5TcC3YH5QJ+cVm6MqWGt3Q5sN8Y0Ba4HDuVBvXOtVfNbWL12PWFdulHisssYM+rZjNe69u5P9PzZAIx66h+MfH40J0+douUtzWjZvBkAy1as5IWxEzhw8CD/99AIarlr8t6bkwFo26krx44fJyUlheUrV/P+m6/xtxrVlUuZzmTp2j17lj4DiJ43K3uWZk1peYsny5C7BjH8H0/x0cJPubJyZSaNG5Px/mUrV3FLk8aULFEiY1ndOjfSsV1bbu9/B8FBQdRyu+jdLZK8EhwczHOPj2DwsEdIS0uje5cIataozqS33+XGWtfTrlULenSN4PHnRhMW2ZNyZcvy6pjRANSsUZ3b2relU89+BAUF89wTj+bqNPripcu5986BeZbhfAQHB/PcEyMY/NAI0tJO071LuJN3qpO3uSfv8y8QdntvT96XRmW8v22XHs72lsry1Wt4f/JE/lb9unzJ4q2o5srJPXPex9W6OaUrXsHLe3fz2fNjWP9+9ttI5odAHysAhj32JIcOHyY4OJjn//E45crm/ZmRVs2bObl6OLme8co1kOh5M51cTzDy+Re8cjV1ct3B8H887ZXrpTyvY0FS0L4AG2NuxTOVOQiY6gy2er8+As/gayqQBNxtrf3vOdfpax6T+GaM6Q2MxHPGIQV4EFhurS3tvN4DiLDW3mmMqQnMwjPiHgMMsdZebYypBiyy1t5ojGkNPGatjTDGTAba4Bkd3wXciWdqzGPW2ghn/a8Dm621085Z0fOY1iKS54rqMeV0Ws5lCht7Or9rILl0X7kazz/SrQAAIABJREFU+V0Fv3j7WN7fmjD/FdFjIECpy/O9Z3x6/YKA/Qdf0uz2c+Y1xgQB/8EzpflXYBPQ11q7y6tMG+BLa+2fxpj7gdbW2t7nXK865/5hjCkJnLDWWmNMHzyN1TUgH67OueSnonpMUedc8pE654VJET0GQsHonG+IDlznvGnXnDrnTYFR1tqOzvORANZanxcAGWPqA69ba33e3COdprX4TwPgdefWh4eAu/O5PiIiIiKSS8aYIcAQr0VTnBtwpLsa2Ov1/Fcg+198OuMeIMfbg6lz7ifW2jVA3fyuh4iIiEiREcC7tWS6E55vvkbWfY7sG2MGAA2BVjl9rjrnIiIiIiLn71egqtfzKvj42zbGmPbA00Ara+2prK9npc65iIiIiBQOl+T7tHdvm4CaxpjrgP/huTtfP+8Czjzzd4BbrbW5+jPdus+5iIiIiMh5stamAkOBJcBu4ENr7U5jzGhjTBen2L+A0kCUMWarMebTnNarkXMRERERKRwK2F8ItdYuBhZnWfac1+P257vOgpVQREREROQvTCPnIiIiIlI4FLC/EOoPGjkXERERESkg1DkXERERESkgNK1FRERERAqHAnZBqD8U/YQiIiIiIoWERs5FREREpHDQBaEiIiIiIhIoGjkXERERkcJBc85FRERERCRQNHIuIiIiIoXDJZpzLiIiIiIiAaKRcxEREREpHDTnXEREREREAkUj5yIiIiJSOOg+5yIiIiIiEigaORcRERGRwkFzzkVEREREJFA0cl4UpSbndw3yXnCx/K5B3ktLye8a+EnRnw9YZAQV0V8BRXBk7e1jv+R3FfzivtLX5HcV8tzbx/6b31Uo2jTnXEREREREAqWIDpuIiIiISJFTBM+MZVX0E4qIiIiIFBLqnIuIiIiIFBCa1iIiIiIihcMlRX9cuegnFBEREREpJDRyLiIiIiKFgtGtFEVEREREJFA0ci4iIiIihYNupSgiIiIiIoGikXMRERERKRw051xERERERAJFI+ciIiIiUjhozrmIiIiIiASKRs5FREREpHDQnHMREREREQkUjZyLiIiISOFwSdEfVy76CUVERERECgmNnIuIiIhI4aA55yIiIiIiEijqnIuIiIiIFBCa1iIiIiIihYP+CJGIiIiIiASKRs5FREREpHDQBaEiIiIiIhIo6pxLhrj1G+nYvS9ht/dmyrSZ2V5PTk5m+MjnCLu9Nz3vvJdff/sdgIOHDjPwvmHUbxnG6HETM73nnmEj6NJvEOG9BvDcy/8iLS0tIFm8WWt5cex4wrp0o3Ovfuzc/Z3Pcjt27aZzz76EdenGi2PHY60F4PNlywnv3pvrb/4723fuyih/8NAhBt57P/WbtWL0K//ye4649Rvp2K0PYZG9ztE+zxIW2Yueg860D8A7H8wgLLIXHbv1Yc2GLzOWT5/7IRG9BhDeqz/T5szPWD520uvc2r0vnfvcwYOPjeTI0aMByNWTKdNmnCNXT3oOGuwjV08n10YAft+XwMD/G8ptPfoS3qs/0+eeyTV85LN07TeIrv0G0bZzN7r2G1ToM333n+/pfde9dO49gPseeZxjx477JRM4+9K4CYR16UHnXv3PsS99R+de/Qnr0oMXx03I2JcOHT7MXfcPo0PXHtx1/zAOHzkCwJebt9CgZTu69hlI1z4DeX3KexnrOnL0KA89PpJbu/Xmtm69+ebb7f7N54djxbqNX9Kt3x107tmXbv3uYMNXm/yWIVOWcRMI69I9h7baTede/Qjr0v0sbdU9U1tNnT6Trn0G0LXPACJ69qVWw6YcOnwYgOlz5hHRsy/hPfowbfZcv2fMrYHvvcG4hB95dvvG/K6KT/mxX40c9SJN291GRM9+/g+Y50wAf/KHOucFhDFmlTGmYX59flpaGqPHTWTqpPHEfDiLRUuX88NPP2cqExW9iLJly7BswXzu7Neb8ZPfAqB48WI8fN9gnnj4wWzrnfTyC3w6ZzqL5s/k4MFDfBG7MiB5vMWtXc+eX/ayNPpjXnhmJKPGjPVZbtSYsYx+ZiRLoz9mzy97iVu3AQBXjRpMnjCORjfXz1S+ePHiPPzA//HEIw/5PUNaWhqjx05g6msTiImazaIlZ2mfMmVYtvBDp33eBOCHn34mZmksMR/OYurkifzzlfGkpaXxnx9+ImrBp0TNmEr0nOmscv6fAG75eyMWzZ/JZ/NmUO2aqrzzQfYvA3mXa7yTa85Zcn3m5IrykWs5MR/OzpQrKDiIJx8ZxucfzWX+B1OYE/VJxjr//fILRM+ZTvSc6XRo25qwNq0KfaanX3yZR4c+wGfzZ9G+dSumzpyd55nSxa3b4OxLUZ596eVxPsuNenkco59+kqXRUZ59ab1nX5rywQyaNm7E0uiPaNq4EVM+OPPFpWG9ekTPm0n0vJkMHXJPxvKX/vUqLZo14YtP5hM9fxY1qlfzXz4/HSsuL1+et/49gc+i5vLK6Od54plRfsuQLm5depaPeOGZJ3Noq5Esjf7IR1s1ZGn0xzRt3DCjrQYPGkj0vFlEz5vFiKEP0Ojm+pQvV47//PAjUQuiiZrxAdHzZrFqzTr2/PKL33PmxoZps5l8a7f8rsZZ5cd+1a1zOFNff9W/weSCqXOex4wxQfldhwuxbedurq1ahapVrqbYpZcSHtae2NVrM5VZEbeW28NvA6Bj29Zs2LQFay0lS5SgYb26FC9WLNt6S5cuBUBqWhopKSmYfPgmGrs6jsiIThhjqHdTHY4cPUpi0h+ZyiQm/cGx48epX/cmjDFERnQidtVqAGpUv47q1a7Ntt6SJUrQsH49ihcv7vcM2dqnQztiV6/JVGbF6jXcHtEJgI7tWrPhK0/7xK5eQ3iHdhQrVoyqV1/FtVWrsG3nbn7cs4e6dW6gxGWXERwcTKOb67FsZRwAzZv8neBgzyUp9ercwL7ERD/l2pUlV/uz5HK2u3Zt2PDVZq9c7bPk2kVIxYrccL0bgNKlSlG92rUkJCZlWqe1ls+XryCiY1ihz/Tzf3+h0c31AM+XqqUrVuV5pnSxq7z3pRs5cvTYOfalOmf2JWe7il29hkhnG42M6MTyVXHn/Lxjx46z6etv6BHZBYBil15K2TJl/JAMp37+OVbUvt5NaEglAGrWqE5y8imSk5P9lgPS2+q2XGZJb6vbiF3pyeL5vwgHIDIinOVORm8xS5YScWsHAH78eQ9169xIiRLO8aRBfZatyP6e/PDDmvX8eeBgflfjrAK9XwE0alCfcuXK5n2YQDAmcD/55C/TOTfGPGuM+c4Ys8wYM9cY85gxpoYx5gtjzBZjzBpjzPVO2WuNMbHGmG3Ov9c4y2sYYzYaYzYZY0YbY445y1sbY1YaY+YA251lC5317jTGDPGqxzFjzARjzNfOuit5VbOnMeYrY8x/jDEtnPJrjDH1vN6/zhhzU17//yQkJVE5NCTjeWhoJRKSMndoEhKTuNIpExwcTJnSpTjonM48l3uGjaBZhwhKlSpJx3at87TeuZGQmEjlyqEZzyuHhpCQpbOZkJhI5ZCQc5bJTwmJWdonJCRbh/Ns7eN5b2i297pqVGfzN99y8NBhTpw8Sdy6DexLSMj22R9/GkPLZk39mMu7bpXOkiv0LLnO/X/y62+/szv+e+reeEOm5Zu/2coVFSpQ7ZqqeR0p4JlcNapndP6/WL6C3xP8t91mrV/lkJDsx4mkJCqHVMpcxsmwf/8BQipVBCCkUkUOeHWYtm7fTpfeAxg8dDjf//gTAHv/9z8qXH45I0e9QGTfO3h69Ev8eeKEH/P5/1ixZPkKarndFPMxmJGXsm6HZ2+rLO2Zi7YCOHHiJGvWb6RDuzaAZzvc/PU3nuPJiZPErV3v83gi2QV6v5KC7y/ROXemi3QH6gPdgPTpI1OAYdbaBsBjwJvO8teBGdbam4DZwGvO8knAJGttI+C3LB/TGHjaWlvbeX63s96GwEPGmCuc5aWAr621NwOrgee91hFsrW0MDPdaPhW408nhAopba7dd0H/EOaTPXfNmsnxr9FkmFyPh702eyNrPo0lOTmHj5q8vvJIXyEe1s2fz8b6sZfKT9VHD7Bl8t4/vbFDjumoMvqM/dz84nMHDRuCu+TeCgjKf+HnrvekEBQXR5bYOF1P9s8rN/7vPMpgc/0+O//knDz3xFE89+nDGGZx0i5YsJ6Jj+wupco4Cneml555iTtTHdBtwF8f//JNil/rvJlxn28YylfF5LDn3em+4/npWxCzk0/mzGNinFw+OeALwnHHb9V08fXt0Y+HcGZQoUSLTKfu85u9jxfc//sj4115n9DMjL6B258d3W2Upk4vj/tmsjFvDzXVvony5coDnrMHgO+/g7geGMXjow7hdNbMdT8S3QO9XhZ5GzouM5kC0tfaEtfYo8BlwGdAMiDLGbAXeAa50yjcF5jiPZzrvT18e5TxOfz3dV9Za74mlDxljvgU2AlWBms7y00D61VyzvNYN8Inz7xagmvM4CogwxlwK3A1M8xXQGDPEGLPZGLP5Qn55VQ4JYZ/XiFtCQhIhFStmLhMakjEql5qaytFjxymfy9NixYsXp23L5tlO7/vL7PlRdO3dn669+xNSqSL79p0ZwdmXkEhIpUqZylcOCck0dcNXmfyUrX0SEzNGSrzL+GqfyiGVMo1ged7rydYzsjMLZn/A7HffpHy5slzrNZK8YNFiVq1dx/gXn/fbF5XsdUvykasSvztlMuc6+/9JSmoqDz3xFJ1v7UCHtq0zrS81NZVlK1fRKcw/nfNAZ6pRrRrvvzGJT2Z9QHjHMKpefXWe5pk9/6OMC8pCKlXMVL99Z9kO93mN9u/z2t6uuKJCxun6xKQ/qFDhcsAz/a1UyZIAtGrejNTUVA4cPETlkBAqh1Sibp0bAbi1XVt2fRefx/kCc6zYl5DA0BFPMPaFUVxTtUreBfAye35UxsWaIZUyb4fe7ZAuWxav9jxbW6WLWbqM8Fszf2nvGdmFBXNmMPu9dyhfNvPxRDLLz/1KCr6/SufcV8/iEuCQtbae10+ts7zf12BJVhm3SDDGtAbaA02ttXWBb/B8Gchp3aecf9Nw7kFvrf0TWAZ0BXqR/UsBTrkp1tqG1tqGQ+66IxfVzaxO7evZ88te9v7vN5JTUohZtpy2LW/JVKZti1tYEPM5AEtWrKJJo5vP2Wk7/uefJP7hOWCkpqayet0Gn/Mx/aF/755Ez59N9PzZtG/TioWLFmOtZeu27ZQpXTrbgS+kUkVKlSzJ1m3bsdaycNFi2rVqGZC65kad2tezZ++vZ9pnaSxtWzbPVKZty+YsWLQYgCWxq2jSqAHGGNq2bE7M0liSk5PZ+7/f2LP3V266wbOp73dOf/62bx9LV6zOGE2OW7+Rd6fP5q2JYylx2dk23bzIVStLruU+crVgwSJnu4tdmSXX8iy5amOt5enRY6h+XTXuGtA322eu/2oz1atdm+k0cmHOtP/AAQBOnz7NW+9No0/32/M0T//ePTIuKGvf2ntf2pHDvrTjzL7UumVG7oXONurZx1oAkPTH/oyRwW07dnLaWi4vX45KFa+gcmgoP+35LwAbvtpEjeuuy+N8/j9WHDl6lCHDHmHEsAdpUK9untY/WxbnYs32rVuycNHn55nl8yxtFQPAwkUxmTIePXqMTVu+ySibLn1b/O33fSxduSpjPrpkl5/7VeFX9O/WYnydKilqjDGN8IyMN8PT6d0CvItnisur1too4+ll3mSt/dYY8ykQZa2daYy5E+hqrb3dGBODZ7rLfGce+URrbWmnM/6YtTbC+byuwGBrbWdnHvtW4FZr7SpjjAX6WmvnGWOeAUKttcOMMaucdWw2xlQENltrqznra4BntH+NtbZ3joGPJF1Qo65et4ExEyeRlnaa7l3Cuf/uQUx6eyo31rqedq2ac+rUKR5//gV2x39PubJlefWlUVSt4hmla9ulB8eOHyclJZUyZUrz/uSJlC9Xjv975AmSU1I4nZZGk0YNGPnIsIwLDc9L8IXPz7TWMvqVf7Fm/QZKXHYZY0Y9S50bPLOPuvbuT/R8z90ttu/cxcjnR3Py1Cla3tKMZ//xGMYYlq1YyQtjJ3Dg4EHKlilDLXdN3ntzsid3p65O7hTKlCnD+2++xt9qVM9dxdJSzivH6rXrGTPxNdLS0ujeJYL77xnEpLffddqnhad9nnuB3fH/8bTPmH9mtM9b703n408XERQUxFOPPkyrWzxzyPsNvp9Dh48QHBzMyEeG0bSxZ8ZXWGQvklNSMs6M1L3xBkY/ldtToud3QPPkmuSV604fuUZ75RrtlWuakys4I9fmrd/Sf/D9uP5Wg0su8Yw/jHjg/2jVvBkAT456kbo33kDfHnnbic2vTNPnzmdOlOekW1ibVjw69P7cn+m45PzGZzz70njWbNjo7EvPUKe254te1z4DiZ7nuavP9l27Gfn8C559qVlTnv3HoxhjOHjoMMP/8TS/79vHlZUrM2ncS5QvV45Z86KY+9EnBAUFcVnx4jz56MPcXNdzac3u+P/w9OgxpKSkULXK1bw86hnKlc3hjN0F/nlvfx0r3nz3Paa8Pz3TSPL7b03migoVzqdyF5Ylo62e9WqrAUTPm+XJsmv3mSzNmmZk8bTVU15tNSZjCssnny5izfoNvPrKS5k+s9/dQzh0+LDneDJiOE3/3ijHet5X+przynUh7pnzPq7WzSld8QqOJCTy2fNjWP++f+5ABfD2sf+eV/n82K9GjHyWr7Z8zcFDh7iiQgWG3XcvPZ0Lr8+p1OX5Pt/T/vpdwDqupsr1+ZL3L9E5BzDGjAL6Av8FkoBVwHLgLTzTWS4F5llrRxtjqgHvAxWdsndZa38xxtTEMxXFADHAEGvt1T4658WBhcDVQDxQCRjldM6PAa8CnYDDQG9rbdK5OufOOr8Dhltrv8gx7AV2zgu0i+icF1jn2TkvPPL92C25dZ6d80LjAjvnBVoR/V0diM55oJ1v57xQKQid8//FB65zfrVbnXN/MsaUttYeM8aUBOLwdKzP6+pE570nrLXWGNMHzwh41/NcxzFrbenzfM9VeL5MXG+tPZ3jG9Q5LxzUOZf8ps554VFEf1erc17IqHMeEP67rL/gmWKMqY1n7vf08+2YOxoArztTYA7huUDTr4wxdwAvASNy1TEXERERKary/euB//1lOufW2ov+G7XW2jXARV3Nc76j5tbaGYD/7h0mIiIiIgVGETz3JyIiIiJSOP1lRs5FREREpLAr+vNaNHIuIiIiIlJAaORcRERERAoHP/3F6oJEI+ciIiIiIgWERs5FREREpHDQyLmIiIiIiASKRs5FREREpJDQyLmIiIiIiASIRs5FREREpHDQnHMREREREQkUjZyLiIiISCGhkXMREREREQkQjZyLiIiISOGgOeciIiIiIhIoGjkXERERkcJBI+ciIiIiIhIo6pyLiIiIiBQQmtYiIiIiIoWEprWIiIiIiEiAaORcRERERAoFowtCRUREREQkUIy1Nr/rIHntSFLRa1R7Or9r4B9Bl+Z3DfJeUR3VOJ2W3zXIe5cU0ZOnaSn5XYO8F1RE26qIuq/0tfldBb942x7J/wP8/l8D18e5okq+5NXIuUh+KYodcxGRv7ii2jGXwNFXcREREREpJPJ/8N7fNHIuIiIiIlJAaORcRERERAqHonpdkxeNnIuIiIiIFBAaORcRERGRwkEj5yIiIiIiEigaORcRERGRQkIj5yIiIiIiEiAaORcRERGRwkFzzkVEREREJFDUORcRERERKSA0rUVERERECoeiP6tFI+ciIiIiIgWFRs5FREREpJAo+kPnGjkXERERESkgNHIuIiIiIoWDbqUoIiIiIiKBopFzERERESkcNHIuIiIiIiK+GGNuNcbEG2N+MMY86eP14saY+c7rXxpjquW0TnXORURERKSQMAH8yaEmxgQBbwC3AbWBvsaY2lmK3QMctNb+DXgVGJvTetU5FxERERE5f42BH6y1P1lrk4F5QNcsZboC053HHwHtjDn33Bx1zkVERESkcDAmYD/GmCHGmM1eP0Oy1OZqYK/X81+dZT7LWGtTgcPAFeeKqAtCRURERESysNZOAaaco4ivEXB7AWUyUedcRERERAqHgnW3ll+Bql7PqwC/naXMr8aYYKAccOBcK9W0FhERERGR87cJqGmMuc4YUwzoA3yapcynwCDncQ9ghbX2nCPn6pxLhrj1G+nYvS9ht/dmyrSZ2V5PTk5m+MjnCLu9Nz3vvJdff/sdgIOHDjPwvmHUbxnG6HETM73n1TffoVV4N+q3DAtIhpzEbfiSjj36E9atL1Omz8r2enJyMsOfep6wbn3pedf/ZWRc9+Umut0xmM59B9HtjsFs2LQlIPW11vLiuImEde1B594D2Lk73me5Hbu/o3Ov/oR17cGL4yaSvt8fOnyYux54iA6RPbnrgYc4fORIrtZ77NhxWtzamdFjx2csu2focLr0GUh4z348N2YsaWlpeZhxAmFdetC5V3927v7Od8ZdTsYuPXhx3ITMGe8fRoeuPbjr/mEZGdNt27mLWg2b8cXyFdkzduzM6FfGk9fi1m+kY7c+hEX2Ose+9Cxhkb3oOejMvgTwzgczCIvsRcdufViz4cuM5W07d6dz74F07TeIbgPvzrS+mfOi6NitD+G9+jNu0ht5nifdmbbqnkNb7aZzr36Edel+lrbqnqmtpk6fSdc+A+jaZwARPftSq2FTDh0+DMC0WXMJ79GHiJ59GTHyGU6dOuW3fHl9DDxx8iRDhj/OrT36Ed5rAOMnv+W3umdVFPcrf2X6cvMWGrRsR9c+A+naZyCvT3kvY10jR71I03a3EdGzX57nuVgD33uDcQk/8uz2jfldlQArOHdrceaQDwWWALuBD621O40xo40xXZxi7wFXGGN+AEYA2W63mJU65+fJGFPNGLPDx/LRxpj253hfpI/b6xQYaWlpjB43kamTxhPz4SwWLV3ODz/9nKlMVPQiypYtw7IF87mzX++MXzTFixfj4fsG88TDD2Zbb5sWtxA1/VzTtQLHk/FVpk76FzHzZ7BoSSw//LQnU5moT2MoW6YMyz6Zy519ezH+9bcBuLx8Od6a8AqfzZ3OK88/xROjXgpInePWbWDP3r0sXRjFC888yaiXx/ksN+rlcYx+5kmWLoxiz969xK33HKynTJtJ00YNWbowiqaNGmZ0OHJa77/fmkLjm+tnWjbplZf4dN5MFn04m4MHD2X7pXxRGX/Zy9LoKF54ZuS5Mz79JEujo9jzy17i1m/wZPxgBk0bN2Jp9Ec0bdyIKR/MyHhPWloa4ye9QfOmf8+2vn+/9Q6NG9TPtvxipaWlMXrsBKa+NoGYqNksWnKWfalMGZYt/NDZl94E4IeffiZmaSwxH85i6uSJ/POV8Zm+BE1/ZzLRc6bzycz3M5Zt3LyF2Li1fDZvBjEfzuaegf7rRMStW++01Uc5b49Pj2Rp9Ec+2qohS6M/pmnjhhltNXjQQKLnzSJ63ixGDH2ARjfXp3y5ciQkJjJj3nw+njWNRVFzSTt9mpgly/ySzV/HwLsH9OWLj+awYPYHfL1tO6vXbfBL/bMqavuVvzM1rFeP6HkziZ43k6FD7slY3q1zOFNff9UveS7WhmmzmXxrt/yuxl+etXaxtdZlra1hrX3JWfactfZT5/FJa21Pa+3frLWNrbU/5bROdc7ziNMQy89RJBLPPTBzzZmbFBDbdu7m2qpVqFrlaopdeinhYe2JXb02U5kVcWu5Pfw2ADq2bc2GTVuw1lKyRAka1qtL8WLFsq23Xp0bCalYMRARcrRt526urXI1Va++ypOxQzti47JkXL2W28NvBaBj21Zs2PQ11lpqu12EVvLkqFn9OpJPJZOcnOz3OseujiMy/DaMMdSrcyNHjh0jMemPTGUSk/7g2LHj1L+pDsYYIsNvI3bVauf9a4iM6ARAZEQnlq+Ky3G9O3Z/x/4DB7ilSeZfvKVLlwIgNTWNlJQUcrgTVO4zroojMqKTpy433ciRo2fJePw49es6GSM6EbsyPYvvjOCMKLdrwxUVLs+0vh27vmP//gPc0qRxnmTwlm1f6tCO2NVrMpVZsXoNtzt17tiuNRu+8uxLsavXEN6hHcWKFaPq1VdxbdUqbNu5+5yfN/ejhQwZNIBizv6XNWte8rSVs93cVIcjR4/moq1uI3Zl+vYYR2REOACREeEsd7ZTbzFLlhJxa4eM52lpaZw8dYrU1FROnjhJSCX/HE/8cQwscdllNGl4MwDFLr2U2m4XCYlJfql/VkVtv/J3prNp1KA+5cqVzfsweeCHNev588DB/K6G+IE65xcmyBjzrjFmpzFmqTGmhDFmmjGmB4Ax5hVjzC5jzDZjzHhjTDOgC/AvY8xWY0wNY0w9Y8xGp8wCY8zlzntXGWPGGGNWA08bY342xlzqvFbWGLMn/XleSkhKonJoSMbz0NBKJCRl/iWSkJjElU6Z4OBgypQuxUHn1HNhkJD0R+aMIT4yJv2RY8YlK1ZTy10zozPk1zonJlE5NDTjeWWfdc7cdpVDQzI6APv3H8jozIRUqsgB50B+tvWePn2asa++xhMPD/VZn3seHE6zsE6UKlmSju3a5GFGr/qHhPjOGFIpc5kcMyayfOVq+vS4PdO6PBkn8cTwYXlS/5zyhHrV1buMr+0sa7tkeq8x3PPgI3QbcDfzP4nOKLPnl1/YvPVbeg66lwFDHsyxM3/x2by3m7O1VZb2zKGt0p04cZI16zfSwdm2QkNCuHtgf9p06krzDuGULlOa5k2b+Cebn4+BR44eZeWadTRt1CDvKn0ORW2/8ny2fzIBbN2+nS69BzB46HC+/zHHgU3JTwG8lWK+sdbq5zx+gGpAKlDPef4hMACYhmeifwUgHjDO6+Wdf6cBPbzWsw1o5TweDfzbebwKeNOr3AdApPN4CDDhLPUEeqWSAAAgAElEQVQaAmx2foacby6Xy9XT5XJN9Xo+0OVyTc5SZqfL5ari9fzHYsWKPeL1/E6Xy/X6WdZ/LL/bLrcZL7vssie8M7pcriu8nt/gLKsRoDrHuFyu5l7PY10uV4MsZRq5XK7lXs9buFyuz5zHh7y3EZfLdfBc63W5XENdLtcT52pPl8t1mcvl+tjlcoXld0Yn06EsZdMzRrlcribO42kul6uH8zjHjIHYzrLuSy6X6wqXy/WGy+UakL4Pu1yu91wuV3fn8VXOvyEul+tbl8vV0nm+w+VyveZyuYzL5Wrscrl+drlcpqBtj+dqK6/nvdO3Xef55S6Xa4XL5arkcrkudblcC10u1wA/ZfPbMdDlcgW7XK7PXS7XcH/UPa/bylpLzZo1//TVVvm1X/k5U1mXy1XaedzJ5XJ9n6VcNZfLtSNQbXeeP9WSkpL+VwDqoZ88/NHI+YX52Vq71Xm8BU+HPd0R4CQw1RjTDfgz65uNMeXwdNrTz+lOB1p6FZnv9XgqcJfz+C48nfVsrLVTrLUNnZ8LmeR9PrcDwu12BwPlkpOT+1/AZ+WXXGW85JJL7oIzGXFueeR2u6sAC4A74uPjf/RXJd1u94Nut3ur2+3e6tQvN+1S5SxlEtxu95UAwcHBDwKJXu/xtd6mwFC3270HGA/c4Xa7X/H+sPj4+JN4rj7P+lfQci0PMw7BK6Pzb3rGhsA8J0sP4E232x2Zm4wX6YL2JTzbWfryIVnfGx8fn/5vIp7tsLHXuj6Jj4+38fHxXwGngTyb+xGgtkrXB5jr9bw98HN8fHxSfHx8CvAJ0OwiI52NP4+BU4Dv4+Pj/50XFT2bvDx2pKamXlIQ9qtAZIqPjz8SHx9/zHm8GLjU7XYXjPmYuXDw4MEK+V0HyVu6z/mF8b5dQBpQIv2JtTbVGNMYaIfnF81QoO15rv+41/rWORehtgKCrLXZLkbNI5uAmm63+zrgf3jqnvXKsvTbAW3AuR0QUN1P9fGHXGUsW7bs887jHsCK+Ph463a7ywMxwMj4+Ph1/qxkfHz8G8AbAG63OxzPL7x5wN+Bw/Hx8b9nKf+72+0+6na7mwBfAncAk9Pz4GmzV8qVK3cFMMdrua/1ZnQ03G73nUDD+Pj4J91ud2mgjPNZwUAnIPNE6vzJ2MA7o/NvtPOe67yyTAMWxcfHLwQW+sp4oVl8uKB9ydnOPgXmGGNSnffXBL5yu92lgEvi4+OPOo874DnjhpOnLbDK7Xa7gGLAH+SRQLSVs+5yQCs8ZyLT/QI0cbvdJYETeI6rm/MqWxZ+OQa63e4X8Xz5GpzXFc4qL48dx48fP1S+fPl8368CkcntdlcGEpx9sDGeKb/7L7buIhdKI+d5zBhTGihnrV0MDAfqOS8dBcoAWGsPAweNMS2c1wYC2a+MOmMGntEkn6PmeSE+Pj7b7YDi4+N3ut3u0W63O9PtgNxud7bbATmjJROBO91u969ut7u2s3yc2+3+FSjpLB/lrww5yW3GoKCgYB8ZhwJ/A55NH8Vxu90hWT/DDxYDPwE/AO8CD6S/4Iwkpbsfz1mWH4Afgc+d5a8AYW63+/uSJUuWdZ6fc71nUQr41O12bwO+xTPi9PaFx8okzzICYZzJmC8uZl+Kj4/fCXxYrVq1G4AvgAfj4+PTgFBgrdvt/hb4CoiJj4//wlnX+0B1t9u9A5gHDIqPjz/nPXQvgj/b6nZgaXx8fMbgRHx8/JfAR8DXwHY8v7P8cvsnfxwDnbNtT+O5GcDXznHD7510x0W11f79+3+nAO1XDn9l6gHscPav14A+6fuQ2+2e+//t3XmUXVWd9vHvE8QBJICCA2BQlEFEZgQEQVDQtoEWBRsFtQGlFYcozmPE1tZGcIJXHN+IA8igjCqIiNJMQsKooELDiyKvAwokQhAIT/+xzzU3RSUkdW+y65z7fNa6q+qcm1p5zqqqW/vus/fvR3kztmHzfT2YyeME4JKnPe1pj6LcMZhM2WIAvXXRsYQkPRU4y/YmzfE7gcdSlracBVxEeTf+aEqRzCNtHydpB8qLyd8pLwSrUAY3K1FebA60fYeknwLvtD2r7/98EnAz8GTbdy77q1xykg6Z4DKaSauL1wTdvK4uXhN087q6eE2Q62qTLl4TdPe6RlkG5y3QVIH5F9uvrp0lIiIiIpadrDmf5CQdDfwTZY1vRERERHRYZs4jIiIiIiaJbAiNiIiIiJgkMjiPCZG0cu0My0JXryuiBkmb1M4Qo62rP4OSVqidIZadDM5jqUh6rqTrKKXGkLSZpC9UjjWwrl4XgKTHSNqwdo5YPEnTJU1V8TVJV0javXauAX1R0mWSDpW0Wu0wwyJplqQ3SVq9dpZhal4HXyXpNb1H7UxD0MmfQeBGSZ+StHHtIDF8GZzH0voM8CKaBg22r2bh7qZt1cnrkrQncBWlZjaSNpd0Rt1Ug5H0Mkk3SLpL0hxJcyXNqZ1rCA6yPYfSYGhNSkfgyVBbesJs70hpbPUUYJak4yXtVjnWMOwHrAVcLuk7kl4kSbVDDULSNymdPXcEtmkeW1cNNQQd/hncFPgNpRv5pZIOkTS1dqgYjmwIjaUi6ee2t5V0pe0tmnNX296sdrZBdPi6ZtN0j+y7rmtsb1o32cRJuhHY0/b1tbMMU+/7IulzlO/Xqf0/j23W3IJ/KaXByxxKD4j32/5e1WADkjQF2AM4FniQ0hDqc7b/WjXYBEi6HtjYHR0UdPVnEEDSTpSGRKtRmnb9h+0b66aKQWTmPJbW7yQ9F7CkRzZNmLowSOrqdT3QdKTtkj92bWDemC3pR5SyqedIWoUy4GstSZtK+gzld2lXypuqZzaff6ZquAFJ2hQ4CvgU8F1Kc7k5wE9q5hrAL4An1Q4xbF39GZS0gqS9JJ0KfI7ys7gecCalk2q0WOqcx9J6A+WFYG1Ku+AfAW+qmmg4unpdv5D0KmAFSesDbwUurpxpULMknQicRum4C0AHZsAOBjYHbrJ9j6THU5a2tNkxlM7I77c9r3fS9m2SPlgv1mCaO1J3Al8D3mu793P486YbdGtIOhMwpWv1dZIuY+Hfq71qZRuSY4Cv0rGfQeAG4HzgU7b7X9NPaWbSo8WyrCWiwyStBHyAso4Z4BzgY7bvrZdqMJJmjnPatg9a7mGGSNLewE96dzqazWvPt31a3WQT0ywj+Ibt/WtnGTZJ69m+qXaOYZC08+Ket/2z5ZUllkzzu/UB2x+tnSWWjQzOY6lI+vw4p+8CZtk+fXnnGRZJxwHTbd/ZHK8OHNX2AV+0h6SrbG8+5lyr15xLOhvYy/Z9tbMMk6TDxjl9FzDb9lXLO88wNGVk59l+UNIGwEbAD23fXznaQJo7GR8B1qWsFhDlzfx6NXMNStL5tnepnSOWjaw5j6X1aMqt9xuax6bA44CDJX22ZrABbdobmAPYvgNo7aCoR9K5/eXDJK0u6ZyamQYlaR1Jp0r6k6Q/SvqupHVq5xqC8V6P27708BbgIkkfknRY71E71BBsTVkKt3bzOAR4PvAVSe+umGsQFwCPlrQ2cB5lSdXXqyYajq8Bn2ZBFZqtm49td7GkYyQ9T9KWvUftUDEcbX/hj+XvGcCuth8AkHQsZX32bsC1NYMNaIqk1ZtBOZIeRzd+P9YY+6ZD0hNqBhqCmcDxwL7N8QHNubaXR5sl6dPA/6GsAX4LMLtupIHd1jymUNY0Q7m2tns8sKXtvwFImkGpkrET5Xt2RMVsE6Vmr8PBwNG2j5DUyrsAY9xl+4e1QywDz20+9i9tMWWja7RcFwYfsXytDaxMuYVL8/latudL+vuiv2zSO4oyE3FKc7wv8PGKeYblQUnTbP8WQNK6tH9wtKbt/nXnX5f0tmpphuctwIeAEym33ruwKfk62yf3n5C076L+cYtMA/qX6twPrGt7XotfByVpe0pN8IObc63tQtk3i3y+pE8B32Phja5XVAk2JFnS0m0ZnMfSOgK4StJPKQOInYD/bNYr/rhmsEHY/kZTgWEXynW9zPZ1lWMNwweACyX1NnXtRLkF32a3SzqAUtcX4JU0zaPazPbdwHtr5xiy9wEnL8G5tjkeuFTS6ZTXiz2AE5rXwba+bryN8r051fYvJa1HqQbSVkeNOe5vqNT6GWZJqwIzWNAs72fARztYOnckZUNoLDVJawGvBn5FmTm/1fYFdVMNrtkB/0T63rT2ZpzbTNIawHaUQcQltm+vHGkgkqZRyqNtT/kjezFlM+8tVYNNkKTP2n5bX0m7hbSxlJ2kf6LUa38F5U5Az1RKo5vnVAk2RJK2oqxjFnCh7VmVI8UIkfRdSm3645pTrwY2s/2yeqliWDJzHktF0uuA6cA6lLbw2wGX0P5ZiLdQZiH+CMyn2dFP2fDaOpI2sv2rvlu7tzUfpzXLXFp7S7d5w9S6AetifLP5eGTVFMN1GzCL8n3qXzc/F3h7lUTD9wClSZQpy1paqYtvDvs1/QJmUN5IGbiQMsPc9rttT7f98r7jwzuyRyDIzHksJUnXUna6X2p7c0kbAYfb/tfK0QbStITftgMv2ABI+rLtQySNd1vatlv3ZkrSu5tNakcz/iDirRViLRNNKc+n2L6mdpZBSFqxV4qvK9cEIGk68HpKZ1ABewNftn101WATIGkr27MXVe+87XXOJZ1LqUTzrebU/pT+AS+sl2pwki4B3mX7wuZ4B+BI29vXTRbDkMF5LBVJl9vepnmHvq3tv49Xn7ltmkHsbr0qNF0gaQqwve2LamcZBkl72j5T0mvHe972ceOdb4tmH8delDuaVwF/Bn5mu7WlB7t4TQCSrqH8bt3dHK9MWTLWyjttXSZptu2txpybZXvrRX1NG0jaDPgGsGpz6g7gtV148xtZ1hJL79ambvZpwLmS7mDBkok2uwn4qaTvs/CO/k/XizSYppnIkZS12a1n+8zm03s6WgFkVdtzmqVjM23PaAaBbdbFa4IyWz6/77i3FK51mruhi5yl68AbjvMl7Qec1BzvA3y/Yp5hmWN7M0lTAZrfs6fVDhXDkZnzmLDmNuiqwNlt7wDY1Cl+CNuHL+8swyTpcOAa4HvuyC+7pCtsb/lw59qmGSTtTtng9QHbl0u6ps2Doy5eE/yjQ+hrgVObUy8Fvm67dY3YmvKqi9TWjdY9kuZSChc82JyaAtzdfG7bU6sEG9AiXgcfcpcg2ikz5zFhbV+L2K83CJe0cu9WdUccRvnDNF/SPBa0rm7dH6S+CiBrS/p831NTKZvz2u6jwDmUyh+XN6XsbqicaVBdvCZsf7pZstOr1nKg7SvrppqYtg++H47tVR7+X7VHs8/rWcCqkvors0yldPCODsjMeQTQNN/4GvBY29Oa9Xz/bvvQytGi0XxPNqcM+D7c99Rc4Pxed9e2kvQ423+tnSMWrekcvEht/v41M8xjBwR3UaruvMP2Tcs/1XA0m5HXp2/w2tbyv5L+hXKnZi/gjL6n5gLfsX1xlWAxVBmcRwCSfk5Zi3iG7S2ac7+wvUndZINrZld6ZcT+2/ZplSMNpL8CSJdIuoGyaXIm8MM2L0PqamUdSTdTrme89eW2vd5yjjQ0zRK42ygNlgTsBzwJ+DXwRtvPr5du4hZV/reNFav6Sdre9iW1c8SykWUtEQ3bv5MW+ps7f1H/ti0kfQF4Bgu6ab5B0m6229wW/qmSPgFszMIzYa0dGDU2AF4IHAQcLelEyjrm39SNNSHXNx871ZjHdpc33L3Y9rZ9x1+WdKntj0p6f7VUg5vOgvK/u/TK/1bONAx7S/olMA84G9gMeJvtby3+y6INMjiPKH4n6bmAJT0SeCsLBhhttjOwSW8WVtJxwLV1Iw1sJqWpyGeAXYADaWmljH7N9+hcShWkXSh1mQ+VdDXw3jbNkvUq67S9vOXiSNqLBa3Tf2r7rJp5huBBSa8ATmmO9+l7rrV3cYB7bd8rCUmPapqzbVg71BDsbvvdkvYGbgX2Bc5nQT33aLEptQNETBJvAN4ErE15odu8OW67XwPT+o6fQqne0maPsX0eZVneLbY/Qss71ELpZChpuqRZwDuBtwBrAO+gLDVoHUlbSzpV0hWSruk9aucalKRPUmZkr2se05u7OW22P6UF/J8onZJfDRwg6THAm2sGG9DY8r+n043yvys2H18CnNDm/Q7xUFlzHtFhkn5GuaV7WXNqG+AS4B5oZ2tuSRcBz6PM8P0E+D3wSdutng2T9Bvgm5R64LeOee49tv+rTrKJk/Rr4F2UuzW9UnatrxDSvMHY3PaDzfEKwJVtLxHZdR0r//tJysbQecBzgNWAs8YsTYqWyuA8RlpXN671LKold08by2FK2oay5Gg14D8oJcQ+ZfvSqsEGJElt3gQ6HkkX2t6xdo5hawbnz+/NVjZVXH7a5sG5pDWB1wNPpW/Jq+2DamUaVNMl+ZoubOwfT1OFZo7t+ZJWAqba/kPtXDG4rDmPUdfJjWs9Dzf4lnSJ7VZ1ELV9efPp3yjrzbtifUnv5KGDozYv2Zkh6avAeSzcefd79SINxSeAKyWdT9nvsBPwvrqRBnY68N/Aj+nAZnj4R5fkqyVNs/3b2nmGQdKutn/SX+N8TCGDtv9uBRmcx4izfWZzS3oT2++qnaeC1jWtkHQusK/tO5vj1Sn1fV9UN9nATga+CHyVjgyOKG+eNqKsj+0tazEtH0DYPqFpQrQNZXD+ng7MWK5k+z21QywDTwZ+KekyFnQGbeWSvsbOlOV8ezbHvbttogO/W1FkcB4jr7klOKotj9u4jGKN3sAcwPYdkp5QM9CQPGD72Nohhmwz28+uHWJYJG3UVPvotU3v7Q1YS9Jatq+olW0IzpL0Ets/qB1kyLpQNvEfbM9oPn0j8HIWvtPWxtfzGEcG5xHFlZLOoMxe9s+uZBZi8nmw/za1pHVp8R+lvq6TZ0p6E2Xmq38JSJurMFwqaWPb19UOMiSHAYcAR7Hwz1xv1rLNS5CmA++TdB9wP8012Z5aN9Zg2rivZgmdBtwJXAHc25xr7etgLCwbQiMASTPHOe02b4ZaEpKu7HVEbQtJLwa+DPT+6O4EHGL7nHqpJm6crpMLvSi3ubmSpOuBpwM3U95w9AZ8rd04CdCUFzyUvs67wLG2713sF05izebJ/YGnNY2HpgFPtv3zytEGImkuDx203kXZZ/QO2zct/1SD60oH6xhfBucRI0zSJrZ/UTvH0pK0BqUNtyituG+vHGlgixjwfdH2vKrBBtDc1XiIDpRSPAmYA3y7OfVKYDXbr6iXajCSjqXsC9jV9jObvRw/sr1N5WgDkXQ4pa758ZTXi/2AJ1F6QLzR9vPrpZs4SV8Gjrbd9qZyMY4MziMASRsAxwJPtL2JpE2BvWx/rHK0gTQ7+v8LeALlD1Nrb1WPs953IS1f79vJAR+ApB2B9W3PbMr1Pdb2zbVzDULS1bY3e7hzbSLpCttb9t9Na/s1AUj6+dja35Iutb1dG69P0rWUN++PANYHbqJDd6WiyJrziOIrlGYpXwKwfY2k44FWD86BI4A9bV//sP9y8utf7ztW29f7Amw4ZqBwvqSrq6UZAkkzgK2BDYGZlKot3wJ2qJlrCK6UtF2vtr6kbYGLKmca1P1N5SrDP+qeP7j4L2mFByW9gtK0DGCfvufaODu5R+0AsexlcB5RrGT7sjH1Yh+oFWaI/tiRgTnAuc3Hg9u6TvRhdHHAtzewBWXTGrZvk7RK3UgT1zdruSLwGkm/bY7XBdq+6fXzwKnAEyR9nDKI/WDdSEOxP/A54AvN8SXAAc0ysjdXSzVBbV8SFksmg/OI4nZJT2fBrNE+wP+vG2koZkk6kbKzv+1NYN5HqaZzCjDu0paW25YFAz6AacD1vQFhS29X32fbknq/VyvXDjSgzs5a2v62pNnACyhLJF7ahTf2zRv5PRfx9IXLM0vEksqa8whA0nqUCiDPBe6gVJfYv+2zFF2qQtM0H3oEsDlls+RCWtxUBFj05smeNv4sNh1P1wd2o3TVPAg43vbRVYPFyJC0DnA0ZSmVKQPy6bZvXewXRlSUwXlEn2Zmb4rtubWzxMIkPZIyY/5N4HVjn+9wPeNWk7QbsDtlNvYc2+c+zJdEDE3zpv54yusGwAGUiZfd6qWKWLwMziMASY8HZrCgjN2FwEdt/6VqsAF1cdZI0pq2/1w7R0RMfpKusr35w52LmEym1A4QMUl8B/gzpR3yPs3nJ1ZNNBwzgTOAtYC1gTObc60j6bPNp/9X0hljH1XDxbgkvUzSDZLukjRH0lxJc2rnipFyu6QDJK3QPA4AWj3pEt2XmfMIQNJs21uNOTfL9ta1Mg1Dl2aNJG1le7akncd7PstaJh9JN9KdUp7RQk2n02OA7Sl3Dy+m3D1s3R6OGB2p1hJRnC9pP+Ck5ngf4PsV8wzL7c1M0QnN8Stp6ayR7dnNxwzC26NLpTyjhWz/Fmj1ZvEYPZk5jwAkzQVWBuY3p1YA7m4+b2VHTXjIrBGUutmtnDXqqzE9rpaWGuykpjMtwM6UVuldKOUZLdTV7s/RbRmcR0QrdLHUYFf1lfA0pUpLv1aW8ox2kvQzmu7Ptrdozv3C9iZ1k0UsWjaERgCSDh5zvELTerzVJB0haaqkFSWdJ6m3zKV1bN+yuEftfLGA7QNtH0i5A/X2vuPDKkeL0bOS7cvGnOtC9+fosAzOI4oXSPqBpCdLejZwKdDaNuN9drc9h9LZ8FZgA8osUmtJ2k7S5ZL+Juk+SfNTAWTS2tT2nb0D23cAW1TME6Onq92fo8OyITQCsP0qSf8KXAvcA7zS9kWVYw3Dis3HlwAn2P6rNHaVQescA+wHnAxsDbwGeEbVRLEoUySt3gzKkfQ48ncnlq83Ubo/byTp9zTdn+tGili8vEhGAJLWB6YD3wWeCbxa0pW276mbbGBnSvoVMA84VNKawL2VMw3M9o2SVrA9H5gp6eLamWJcRwEXSzqFMnP5CuDjdSPFiHkp8APgfMpqgbuBFzblc6+qmixiEbIhNAJoBrBvtv1jlanlw4CDbD+rcrSBSVodmGN7vqSVgKm2/1A710RJugB4IfBV4A+UW9T/ZnuzqsFiXJI2BnalbAw9z/Z1lSPFCJF0POUO2xmUn8F/Bi4HNgJOtn1ExXgR48rgPAKQNLVZm91/bn3bN9TKNAyS9gXOtj1X0geBLYGP2b6icrQJa6q2/ImyZOftwKrAF2zfWDVYREw6ks4BXm77b83xY4FTgL2B2bY3rpkvYjzZEBpRPCDpQ5K+Av9Y5rJh5UzD8KFmYL4j8CLgOErN39ZqqrPMsz3H9uG2D8vAPCIWYRpwX9/x/cC6tufRV3s/YjLJmvOIYiYwmwXNem6lbDg8q1qi4eg1Vfpn4Fjbp0v6SMU8A5N0M+M0I7K9XoU4ETG5HQ9cKun05nhP4ARJKwNZYhWTUpa1RACSZtneutkE2mtUcXXb1zFLOgv4PWWN9laUjaGXtfm6JD2+7/DRwL7A42x/uFKkiJjEJG0F7EhZc36h7VmVI0UsVgbnEUBT7eMFwEW2t2zq4p5g+zmVow2k2QD6YuBa2zdIejLwbNs/qhxtqCRdaHvH2jkiIiIGlWUtEcUM4GzgKZK+DewA/FvVRENg+x5Jf6LMGt1A6YzX9k2uW/YdTqFUYuhCw6iIiIjMnEf0NMsltqPc+rzU9u19zz3L9i+rhZsgSTMog9cNbW8gaS1K+bAdKkebMEnns2DN+QPA/wOOtP2baqEiIiKGJIPziCUg6QrbWz78v5xcJF1FaZd+Rd9a+mtsb1o32cRJegdlcN5rdbrQi5jtTy/3UBEREUOSZS0RS6atPe/vs21JBmgqFLTdVsA2wOmU78uewAXA72qGioiIGIYMziOWTFtvMZ0k6UvAapJeDxwEfKVypkGtAWxpey5AUxryZNuvq5oqIiJiCDI4j+gw20dK2g2YQ2mq9GHb51aONaixTUXuA55aJ0pERMRwZXAeI03SDrYvkvQo24vrFnffYp6b1JrBeNsH5P2+CVwm6VTKHY29KZ1PIyIiWi8bQmOkSZpte6u2bvhcFElzGX8pjgDbnrqcIw1VU07xec3hBbavrJknIiJiWDI4j5Em6VLgeuAlwIljn7f91uUeKiIiIkZWlrXEqNuD0tp+V2B25SwREREx4jJzHgFI2sz21bVzRERExGibUjtAxCTxF0mnSvqTpD9K+q6kdWqHioiIiNGSwXlEMRM4A1gLWBs4szkXERERsdxkWUsEIOlq25uNOXeV7c1rZYqIiIjRk5nziOLPkg6QtELzOAD4S+1QERERMVoycx4BSJoGHANsT6kPfjEw3fYtVYNFRETESMngPGIJSHqf7U/UzhERERHdlmUtEUtm39oBIiIiovsyOI9YMqodICIiIrovg/OIJZP1XxEREbHMZXAesWQycx4RERHLXAbnEUvm5NoBIiIiovsyOI8AJB0haaqkFSWdJ+n2ptY5ALb/s2a+iIiIGA0ZnEcUu9ueA+wB3ApsALyrbqSIiIgYNRmcRxQrNh9fApxg+681w0RERMRoekTtABGTxBmSfgXMAw6VtCZwb+VMERERMWLSITRGnqQpwHbA9cAc2/MlrQysYvsPddNFRETEKMngPAKQdInt7WvniIiIiNGWNecRxY8kvVxS6plHRERENZk5jwAkzQVWBh6grDUXYNtTqwaLiIiIkZLBeURERETEJJFqLRENSasD6wOP7p2zfUG9RBERETFqMjiPACS9DpgOrANcRanecgmwa81cERERMVqyITSimA5sA9xiexdgC+DPdSNFRETEqMngPKK41/a9AJIeZftXwIaVM0VERMSIybKWiOJWSasBpwHnSroDuK1ypoiIiBgxqdYSMYaknYFVgbNt31c7T0RERIyODM4jGpJ2BNa3PVPSmsBjbd9cO1dERESMjgzOIwBJM4CtgQ1tbyBpLeBk2ztUjhYREREjJBtCI4q9gb2AuwFs36oDuTEAAAEJSURBVAasUjVRREREjJwMziOK+1xuIxlA0sqV80RERMQIyuA8ojhJ0peA1SS9Hvgx8JXKmSIiImLEpJRiRPF3yoB8DqW++Ydtn1s3UkRERIyazJxHFE8EPgGsSxmk/7hunIiIiBhFqdYS0ZAkYHfgQErllpOAr9n+n6rBIiIiYmRk5jyi0WwI/UPzeABYHThF0hFVg0VERMTIyMx5BCDprcBrgduBrwKn2b5f0hTgBttPrxowIiIiRkI2hEYUawAvs31L/0nbD0rao1KmiIiIGDGZOY+IiIiImCSy5jwiIiIiYpLI4DwiIiIiYpLI4DwiIiIiYpLI4DwiIiIiYpLI4DwiIiIiYpL4X/9vlrIvUdE5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,10))\n",
    "cor = train.corr()\n",
    "sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = grouped_by_score.plot.bar(rot=0, y=[\"lesson_price\", \"qualification\"], title=\"Expected grade vs\", subplots=True, figsize=(10,5))\n",
    "plt.ylabel('price')\n",
    "plt.title('qualification')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train[['lesson_price', 'years_of_experience', 'qualification','physics', 'english']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.DataFrame.to_numpy(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Features' standardization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Получим средние значения и стандартное отклонение по столбцам\n",
    "\n",
    "means = np.mean(train, axis=0)\n",
    "stds = np.std(train, axis=0)\n",
    "# параметр axis указывается для вычисления значений по столбцам, а не по всему массиву\n",
    "\n",
    "# вычтем каждое значение признака из среднего и поделим на стандартное отклонение\n",
    "for i in range(train.shape[0]):\n",
    "    for j in range(train.shape[1]):\n",
    "        train[i][j] = (train[i][j] - means[j])/stds[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.56634002, -1.11816688, -0.90969954,  1.2917606 , -0.23819494],\n",
       "       [ 2.23851537,  1.16020973,  1.62108202,  1.2917606 , -0.23819494],\n",
       "       [-0.95321662, -1.11816688, -0.90969954, -0.77413725, -0.23819494],\n",
       "       ...,\n",
       "       [-0.85649747, -0.54857273, -0.90969954,  1.2917606 , -0.23819494],\n",
       "       [-1.14665493,  0.59061557, -0.90969954, -0.77413725, -0.23819494],\n",
       "       [-0.46962087, -1.11816688, -0.90969954, -0.77413725, -0.23819494]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Split the dataset on training and testing subsets for 75 to 25%**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(train, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training algorithyms for regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use $L_{2}$ loss $L(y, z) = (y-z)^{2},$ it's derevative by $z$ will look like $L'(y, z) = 2(z-y)$. So will implement it as function (can get rid of 2*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bias(y, z):\n",
    "    return (y - z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean squared error will be used as a quality indicator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_squared_error(y_real, prediction):\n",
    "    return (sum((y_real - prediction)**2)) / len(y_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def r2_score(y_real, prediction):\n",
    "    mean_number = sum(y) / len(y)\n",
    "    ssreg = (sum((prediction - mean_number)**2)) \n",
    "    sstot = (sum((y_real - mean_number)**2))\n",
    "    \n",
    "    return round(ssreg / sstot, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gb_predict(X, trees_list, coef_list, eta):\n",
    "    # Реализуемый алгоритм градиентного бустинга будет инициализироваться нулевыми значениями,\n",
    "    # поэтому все деревья из списка trees_list уже являются дополнительными и при предсказании прибавляются с шагом eta\n",
    "    return np.array([sum([eta* coef * alg.predict([x])[0] for alg, coef in zip(trees_list, coef_list)]) for x in X])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gb_fit(n_trees, max_depth, X_train, X_test, y_train, y_test, coefs, eta):\n",
    "    \n",
    "    # Деревья будем записывать в список\n",
    "    trees = []\n",
    "    \n",
    "    # Будем записывать ошибки на обучающей и тестовой выборке на каждой итерации в список\n",
    "    train_errors = []\n",
    "    test_errors = []\n",
    "    \n",
    "    for i in range(n_trees):\n",
    "        tree = DecisionTreeRegressor(max_depth=max_depth, random_state=42)\n",
    "\n",
    "        # инициализируем бустинг начальным алгоритмом, возвращающим ноль, \n",
    "        # поэтому первый алгоритм просто обучаем на выборке и добавляем в список\n",
    "        if len(trees) == 0:\n",
    "            # обучаем первое дерево на обучающей выборке\n",
    "            tree.fit(X_train, y_train)\n",
    "            \n",
    "            train_errors.append(mean_squared_error(y_train, gb_predict(X_train, trees, coefs, eta)))\n",
    "            test_errors.append(mean_squared_error(y_test, gb_predict(X_test, trees, coefs, eta)))\n",
    "        else:\n",
    "            # Получим ответы на текущей композиции\n",
    "            target = gb_predict(X_train, trees, coefs, eta)\n",
    "            \n",
    "            # алгоритмы начиная со второго обучаем на сдвиг\n",
    "            tree.fit(X_train, bias(y_train, target))\n",
    "            \n",
    "            train_errors.append(mean_squared_error(y_train, gb_predict(X_train, trees, coefs, eta)))\n",
    "            test_errors.append(mean_squared_error(y_test, gb_predict(X_test, trees, coefs, eta)))\n",
    "\n",
    "        trees.append(tree)\n",
    "        \n",
    "    return trees, train_errors, test_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_alg(X_train, X_test, y_train, y_test, trees, coefs, eta):\n",
    "    train_prediction = gb_predict(X_train, trees, coefs, eta)\n",
    "\n",
    "    print(f'Ошибка алгоритма из {n_trees} деревьев глубиной {max_depth} \\\n",
    "    с шагом {eta} на тренировочной выборке: {mean_squared_error(y_train, train_prediction)} \\\n",
    "    r2 = {r2_score(y_train, train_prediction)}')\n",
    "\n",
    "    test_prediction = gb_predict(X_test, trees, coefs, eta)\n",
    "\n",
    "    print(f'Ошибка алгоритма из {n_trees} деревьев глубиной {max_depth} \\\n",
    "    с шагом {eta} на тестовой выборке: {mean_squared_error(y_test, test_prediction)} \\\n",
    "    r2 = {r2_score(y_test, test_prediction)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Число деревьев в ансамбле\n",
    "n_trees = 30\n",
    "\n",
    "# для простоты примем коэффициенты равными 1\n",
    "coefs = [1.1] * n_trees\n",
    "\n",
    "# Максимальная глубина деревьев\n",
    "max_depth = 3\n",
    "\n",
    "# Шаг\n",
    "eta = 0.2\n",
    "\n",
    "trees, train_errors, test_errors = gb_fit(n_trees, max_depth, X_train, X_test, y_train, y_test, coefs, eta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_error_plot(n_trees, train_err, test_err):\n",
    "    plt.xlabel('Iteration number')\n",
    "    plt.ylabel('MSE')\n",
    "    plt.xlim(0, n_trees)\n",
    "    plt.plot(list(range(n_trees)), train_err, label='train error')\n",
    "    plt.plot(list(range(n_trees)), test_err, label='test error')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ошибка алгоритма из 30 деревьев глубиной 3     с шагом 0.2 на тренировочной выборке: 39.71702972482438     r2 = 0.757\n",
      "Ошибка алгоритма из 30 деревьев глубиной 3     с шагом 0.2 на тестовой выборке: 40.579725667565036     r2 = 0.755\n"
     ]
    }
   ],
   "source": [
    "evaluate_alg(X_train, X_test, y_train, y_test, trees, coefs, eta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_error_plot(n_trees, train_errors, test_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Make final prediction on test dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>years_of_experience</th>\n",
       "      <th>lesson_price</th>\n",
       "      <th>qualification</th>\n",
       "      <th>physics</th>\n",
       "      <th>chemistry</th>\n",
       "      <th>biology</th>\n",
       "      <th>english</th>\n",
       "      <th>geography</th>\n",
       "      <th>history</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>45.97280</td>\n",
       "      <td>1.980000</td>\n",
       "      <td>1697.095000</td>\n",
       "      <td>1.709400</td>\n",
       "      <td>0.38130</td>\n",
       "      <td>0.123500</td>\n",
       "      <td>0.120100</td>\n",
       "      <td>0.056000</td>\n",
       "      <td>0.031400</td>\n",
       "      <td>0.0184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>7.95628</td>\n",
       "      <td>1.783289</td>\n",
       "      <td>524.262621</td>\n",
       "      <td>0.793483</td>\n",
       "      <td>0.48573</td>\n",
       "      <td>0.329027</td>\n",
       "      <td>0.325095</td>\n",
       "      <td>0.229933</td>\n",
       "      <td>0.174405</td>\n",
       "      <td>0.1344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>46.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>51.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2150.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>68.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4050.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age  years_of_experience  lesson_price  qualification  \\\n",
       "count  10000.00000         10000.000000  10000.000000   10000.000000   \n",
       "mean      45.97280             1.980000   1697.095000       1.709400   \n",
       "std        7.95628             1.783289    524.262621       0.793483   \n",
       "min       23.00000             0.000000    200.000000       1.000000   \n",
       "25%       41.00000             0.000000   1300.000000       1.000000   \n",
       "50%       46.00000             2.000000   1500.000000       2.000000   \n",
       "75%       51.00000             3.000000   2150.000000       2.000000   \n",
       "max       68.00000            10.000000   4050.000000       4.000000   \n",
       "\n",
       "           physics     chemistry       biology       english     geography  \\\n",
       "count  10000.00000  10000.000000  10000.000000  10000.000000  10000.000000   \n",
       "mean       0.38130      0.123500      0.120100      0.056000      0.031400   \n",
       "std        0.48573      0.329027      0.325095      0.229933      0.174405   \n",
       "min        0.00000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.00000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.00000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        1.00000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.00000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "          history  \n",
       "count  10000.0000  \n",
       "mean       0.0184  \n",
       "std        0.1344  \n",
       "min        0.0000  \n",
       "25%        0.0000  \n",
       "50%        0.0000  \n",
       "75%        0.0000  \n",
       "max        1.0000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_original.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_original = test_original[['lesson_price', 'years_of_experience','qualification','physics', 'english']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.DataFrame.to_numpy(test_original)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Standardization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Получим средние значения и стандартное отклонение по столбцам\n",
    "\n",
    "# мастабируем тест к тому же масштабу, что трейн, поэтому тест надо преобразовать, используя те же means и stds\n",
    "# параметр axis указывается для вычисления значений по столбцам, а не по всему массиву\n",
    "\n",
    "# вычтем каждое значение признака из среднего и поделим на стандартное отклонение\n",
    "for i in range(test.shape[0]):\n",
    "    for j in range(test.shape[1]):\n",
    "        test[i][j] = (test[i][j] - means[j])/stds[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_prediction_score = gb_predict(test, trees, coefs, eta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>mean_exam_points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>54.588823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10001</td>\n",
       "      <td>63.651147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10002</td>\n",
       "      <td>50.375537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10003</td>\n",
       "      <td>89.794834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10004</td>\n",
       "      <td>89.794834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9995</td>\n",
       "      <td>19995</td>\n",
       "      <td>44.506529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9996</td>\n",
       "      <td>19996</td>\n",
       "      <td>80.009960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9997</td>\n",
       "      <td>19997</td>\n",
       "      <td>54.773343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9998</td>\n",
       "      <td>19998</td>\n",
       "      <td>63.564653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9999</td>\n",
       "      <td>19999</td>\n",
       "      <td>72.542828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  mean_exam_points\n",
       "0     10000         54.588823\n",
       "1     10001         63.651147\n",
       "2     10002         50.375537\n",
       "3     10003         89.794834\n",
       "4     10004         89.794834\n",
       "...     ...               ...\n",
       "9995  19995         44.506529\n",
       "9996  19996         80.009960\n",
       "9997  19997         54.773343\n",
       "9998  19998         63.564653\n",
       "9999  19999         72.542828\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.DataFrame({'Id': test_original.index,\n",
    "                       'mean_exam_points': final_prediction_score.flatten()})\n",
    "\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.loc[:, ['Id', 'mean_exam_points']].to_csv('mean_math_exam_points.csv', float_format='%.1f', index = False)"
   ]
  }
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