Day_010_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業 : (Kaggle)房價預測"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業目標]\n",
    "- 試著模仿範例寫法, 在房價預測中, 觀察去除離群值的影響"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 觀察將極端值以上下限值取代, 對於分布與迴歸分數的影響 (In[5], Out[5])\n",
    "- 觀察將極端值資料直接刪除, 對於分布與迴歸分數的影響 (In[6], Out[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig  ... ScreenPorch PoolArea PoolQC Fence  \\\n",
       "0         Lvl    AllPub    Inside  ...           0        0    NaN   NaN   \n",
       "1         Lvl    AllPub       FR2  ...           0        0    NaN   NaN   \n",
       "2         Lvl    AllPub    Inside  ...           0        0    NaN   NaN   \n",
       "3         Lvl    AllPub    Corner  ...           0        0    NaN   NaN   \n",
       "4         Lvl    AllPub       FR2  ...           0        0    NaN   NaN   \n",
       "\n",
       "  MiscFeature MiscVal  MoSold  YrSold  SaleType  SaleCondition  \n",
       "0         NaN       0       2    2008        WD         Normal  \n",
       "1         NaN       0       5    2007        WD         Normal  \n",
       "2         NaN       0       9    2008        WD         Normal  \n",
       "3         NaN       0       2    2006        WD        Abnorml  \n",
       "4         NaN       0      12    2008        WD         Normal  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 做完特徵工程前的所有準備 (與前範例相同)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "data_path = 'C:/Users/zero0/OneDrive/cupoy/Machine Learning/教材/data/'\n",
    "df_train = pd.read_csv(data_path + 'house_train.csv.gz')\n",
    "\n",
    "train_Y = np.log1p(df_train['SalePrice'])\n",
    "df = df_train.drop(['Id', 'SalePrice'] , axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36 Numeric Features : ['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#只取 int64, float64 兩種數值型欄位, 存於 num_features 中\n",
    "num_features = []\n",
    "for dtype, feature in zip(df.dtypes, df.columns):\n",
    "    if dtype == 'float64' or dtype == 'int64':\n",
    "        num_features.append(feature)\n",
    "print(f'{len(num_features)} Numeric Features : {num_features}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>548</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>460</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>608</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>642</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>836</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0          60         65.0     8450            7            5       2003   \n",
       "1          20         80.0     9600            6            8       1976   \n",
       "2          60         68.0    11250            7            5       2001   \n",
       "3          70         60.0     9550            7            5       1915   \n",
       "4          60         84.0    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  GarageArea  \\\n",
       "0          2003       196.0         706           0  ...         548   \n",
       "1          1976         0.0         978           0  ...         460   \n",
       "2          2002       162.0         486           0  ...         608   \n",
       "3          1970         0.0         216           0  ...         642   \n",
       "4          2000       350.0         655           0  ...         836   \n",
       "\n",
       "   WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  \\\n",
       "0           0           61              0          0            0         0   \n",
       "1         298            0              0          0            0         0   \n",
       "2           0           42              0          0            0         0   \n",
       "3           0           35            272          0            0         0   \n",
       "4         192           84              0          0            0         0   \n",
       "\n",
       "   MiscVal  MoSold  YrSold  \n",
       "0        0       2    2008  \n",
       "1        0       5    2007  \n",
       "2        0       9    2008  \n",
       "3        0       2    2006  \n",
       "4        0      12    2008  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 削減文字型欄位, 只剩數值型欄位\n",
    "df = df[num_features]\n",
    "df = df.fillna(-1)\n",
    "MMEncoder = MinMaxScaler()\n",
    "train_num = train_Y.shape[0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業1\n",
    "* 試著限制 '1樓地板面積(平方英尺)' (1stFlrSF) 欄位的上下限, 看看能否再進一步提高分數?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.846643681071632"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 顯示 1stFlrSF 與目標值的散佈圖\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.regplot(x = df['1stFlrSF'][:train_num], y=train_Y)\n",
    "plt.show()\n",
    "\n",
    "# 做線性迴歸, 觀察分數\n",
    "train_X = MMEncoder.fit_transform(df)\n",
    "estimator = LinearRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ioewwkktRqDpcmq9CkEY/O13C9RXZhIlINNJFQ2M0kktyaaECvj7KEGLZfZ+fK7Mrrz0ZL5zV6yAAX2kjo1C4HuSSkrRlULRdXK/1hKtprKCUQEiiVfhWJKRdz6dka4XUalOOIIRSipdmyzp09OIMU8Xasn0ShmAom+Tj73o146PtQ0dJSxuDTMIgacp1ewkrITYQMa571OcddvcluThf5eJ8BVCYhlx3MrNbtM+NYKUx3HP8FK7vYxkSwdJq3UNhBxPrVMHW25XAMkQU358u2ssS6h9/12ui57inP8XVgo3rwc2jWe6/+3DDfYfGZO9AmgtzZerne4leEQuIwlU3j2YZyCSYLFSZKthtcyNhyMpHga89ic3UZHI8X9NRax52G6PgeD7fu7DAtya0Ubi8UG14X6DDR/mUiSklCviltx7i5rHGinpTSlIJSSZhamaW7L5BaEZsIGLseGyUddScdxBCcGWhypVFm9v2D25oUm/nGWxl34V2Yzg/VyZpSDylQzoQ/Kt0GKpcc7FdDxkknEdyKaaLdkvW0N7BzDJj9IZ97Z9d6NnU3MawSlBQjlAKKQQ3DmZwPMX9d2uP54GvPEfV9cKIVIT612nLIJ8ymSs7ZJImY/kUR8aHlmi3G3zeNdenXHMp2i61VnEuYKHi8O2IdTQb5UpCjOY06+jIzUO4ruLPn7rIlcUKY/lUxGIKk8tpS4eOkubKjKPNQGwgYuxorMQ6AjqahJvzDvmURS5pslBxNpTUbGcEeqXv8r7BDJ7vM1N08NGhGU8pLFPy747eHDB+KghBFHITAi7MVdrSRTsNlTXLekQsJilwXA8fsD3FWD4VnfvYyQlKtgMIhFDUh/XDPy1DkLYkB0dyfKKLz9t2Pcq2R9F2G6qZo+srxcth6GhihucuLS7LJ7xid563BFXMNzeFjv7poZFg/EvJ5ZRpILfAS1gJsYGI0VNY68q6HevowUdOU6p5q04KJ05PshhII6dMg9F8knzKaohZh2M6M1mg5vpYhuDWXX0rjm2lSalbTKmNeiHhKn44BwtlB9vzMaU2Du//sVt5f919hAbBkILBjMVwNsFCxdlQ2CyU9XA9H0/psBJo78X1FW88MMRD997R8Cxv6E+TMG0mCzUG0walqkstmK/3Dab5+Ltes2ws633etuvpnEIbo+B6Ps9cXIiMgs7VLCFpatbRkfFh7hgfYriuSC9EmFxOBbmEbiWXu4XYQMToGaxnpddOMvvMZJG9g+kVJ4VPf/15PnPiRRzXRwHlmse5mSWmS831+fTXn+fhpy7ieB4LZQcEVBw4O13sWA5jseIwXdR6S+//0neiia4ea2XZdGNVXB8SOuMVMAPj99jELK8N5L1b5TA+8o5XrdvTaTZquYSBl7GWeTGGFA2eQ/0EP5LT7KqS7bGrP72qkVoLM62d7lGI1UJHI7lEpHX0hn0DJFsUom1FcrlbiA1EjJ7BWld6J05PtpXMBlZsfnPi9CSfOfEivtLCeTXXp346MCVcXqjyO984o8MengIBlpAIoFB12d1vriqHsVhxuLRQQSIwJJRqLqaUTBdtRvOpaP+1smy65YUcPTzGMxfmeerlOVzfJ2lIzs00Gr9uMaxaGbXFilazHc5Zy7yY8JqtJvjhbBJTOvz9/W9d9bqr0Y1X0j1SSnF+thIlmJ+7tLA8dLQrzx3jQxy5eZhDY7llE/52JJe7hdhAxOgZrLUG4djJCbIJg7myg4OK5BquFmwMAS9MFdvSVY+dnGhg8NRnOQVgGQaO5+Ep8AKKjQiKuUwpqHmqIzmM6aKNRCClwFeQMiV9aZPZkkM2aS6jv9avsPNJLeXcqn9Bq2flej5PvTzHnQ8+uizn0S4UFRpK1/dRCsqOT2XBZjBjrVvpdLVCunqjBlouYyCT4IJszfTaqIx4M9W3XHOpeYr3vHEfL8+UlxkF1/P53sUFTk3M8q0XZwJG2xKSpuS2/YMcuVmHjkaaQke9kFzuFmIDEaNnsNaJ4MxkgULVxQyqd6OaKwE3DqRXpKs2M3jqE57hArCBXVO3j+srMgmj5djCyfH5oCGQ62n9H1/p40fzSXJJEydIwNZTT4FohW0IODNZBODGgdSqiquFqsPF+SpmU8jp3Rfm21ZFP3Nhnk9948yyFbECZssO3z43y92f/OaqDXaajVpYSGcI+M7Lc/z8Fx7n0GiO6VKNbMJgYqpYFxJMUHN9vvrLd7X+UrBxGfGjh8f4j0rxe9/UYbJdfSn+9e37+Cd7+yPjUKjq0NG3Aq2jou02nGM4DB2ND/OG/QOkmrxTK5D9SG+wcrnXINbTp7RXcfvtt6snnnhiu4cRY52oD0HUTwTt+jG/9j/8LRXHw5QSz1cN1MubhjIIAVcWqihYRle95/gpzs0UmSk6CKE9g3CiTBiaTRMKognAkFoNNMTuviQJ02gYW5jT8HxF0pRYhmCh6iKBlLWUAC/X3Iid84mv/oCzdXkPpXRBmufrOLxEYBqC8dEc08UqJdujL2019EFIWwYvTBZxfcVgxqJc07LP4fE3DKQajG65pumZF+cry4xDM0wpuHEgFRnY8H7rE/eFqkvaklQcHzugfUq0oTUNiVIKKQMj7mtjHRplz1ccGsu1NBCtvCndq7qzxHiz7pHfNNedny3zWBA6+t7F5aGjW3flonxCc+hosyqXtwNCiCeVUre3ei/2IGJsO1pNBJ0wZCxDUKqpZcqWQKDfk+aWsVxLumozg6cWTA46TKV0RW7wejiboFTzUK6WTJACDo7kGrj1+aTJmckCQghMqfMhNc+nP2VScXx296ei8IbjKY6MD/FrDz/NfKBk6vkqKhZLhB6HpzAl1DytzzNd0MJ2+4cyWtMHbcwWgjj+YMZkvuLofIfQMhbVoFdAPdKWwcRUqSMhPkMIpos1xkdzUY4Dljydsu3iej7zTfUAvn6MyMDQeYEL5SkwwwsH/7ZapDbnK8LFQiuWUsN1fUWppg1Cudaoe+T5iu/VsY4uzDWGjhKm5Lb9A7zl5mHuGB9eFjpKmDqPsBOSy91CbCBibCvWOxEAjOVTzJaclu8pX3FlocLVgkAp7THUG5sGdk4Q+z4yPsTffO9ytKK/cSCJ7frk0xZjfakGjwZoGLdewYMlFULKqCrZ8RT5pLEsnHTs5ARF29UTuVINlcROIEan1FI4a6pgg9BtQMPGOAADmQRf/eW7uOf4Kb5zfi7KdwCBgVNcXVyu3Npp3MBXilowuDDnUp9LcHy14rk8pZBKkDCkrsJGYQYV2QlDsjuXXMYEgtZ9H0I9qGZv0AuNQgsxvELV4fFzczz24gzfPjdLodoUOsomuGNcF6zdtn+wIXRkSBF5CJmEuaOSy91CbCBibCs2wsYJwzGt4Cq0Xo+nSBpiGTMHWhd1vf/Hbm14HXo3rWQqGiYwpT0OVy39qIQA2/V57d6BBg/mxOlJnnp5Dtv1l1UEE7wOK4pBUycvBIlS1/M5fWUxit+HSfL77hrn57/wBEadGqpSMJy1mC07y1ueGiKa+FdCaKBgKR9UnyBPGJKS11piAnQex0cxkktxNZABHx9dkpAIw23NCK9RqDpcWahSDTwUQwomC1U+8uVn+aB9mNsODFJpMjDnZ8ucmtBewjMXloeObhnLRQVrh3blovoLIKpHSFnGsjzD9YjYQMTYVmxEPbVY85CCFePoCUNLis4UHYZzrIsG2kn1dcKQKOXh+ksic818fljymMIpqd3QA1YtAFcWbQyCHEiQwHY9xcX5KreMZqNx3jqW4+x0SXdZQ8fJ58ouScPAkoKFikMuaWJJ1VbsLkRooLTERqJBzyhsl5pJmGQTRksPIDqPgD39KUxDkEuaCOgo2bxvMBPliBxvyZB6vqJa85BS8Pt/f5bf3tOH5yuevaRDR996sX3oSBesDTOaXwod1VNQM9b2Vy73GmIDEWNbsREK477BDNNFG9fTOQNoNeEKpBA4vseVBZvLCzav+PBXo9aW661cXqw4eL4fFW2N5pNcmKuQMMAQtOTzw5LHtLs/xYW5SkPiO0Q4ORt1CeKXZ8vh7VBvXerj4PfffTjSN5op1UAplFK4vs/zk0VMKbjoq44axvjoBLUV5EPCpHp4Lx94+GkuzlWipHQzBJBPGuwZSFOqeYzlU3zkHa+KnsFq4oX33TXOfV98El/5DZ+pAcyWa9zQn+LsTJHf+OsftAwdDWUTujZhfJjbbhqMamKuJQrqViA2EDG2Fa0ojIsVB0uKZXz+VseGiV5D0rI/gOP5eD4NMX6lFGcmi7z3C48zkElwaCy/KiumOVfiej6TBS3VPJxNNkhQrMS0CT0PIQR7B+Hl2XLkAWl6q6AahJ72DqajPst+oJGkazB0/L4vZTAxXeL2//trkQTIaC7JdNHW+0uJCjwsXxGFlKpO+37HoFf9htSG9ff+7Q+1fC4KQCwZ5PAY19NhP1MKPn3PbS2PXc0oVx2P1+4bIG1JbJeIimoE16w6PhPT2mB+4/RkdNwtozmO3KwL1m7dlY9CR5Yho9DRP07M8Pt/f3ZLRBK3Apst+hgbiBjbgvovdi6heeNhCEQBjq8aePQ39KXIJc2Ik39kfIjHJmaxHU1fDCdZQ+gJwfV83KDNQL1xSAR0RE8phIKy7XYkU9GcKwmroEu2hymdjiUo6j0mpfR4qq5fF4qRnJsps3cgRT6lY/BTBVsbOd9n91CGvqBC++J8BSlokACxDInt+gxnEkyXaqvSWJuhE9u6K1smaTYU1NV7T5mEwaGxPBNTRR2uCtrCZRIGtuuTTqxtZd5K4uKmoSyXFyoY0qNoe3jQ4CKaUnBboHV0ZHyIsT79mQihadKhlxB6TCdOT/If/vL72y6S2C1shehjbCBibDlaM5d8/tUP7eWz/3CWUs2lWHVxA4qkAC7OV6OQy9npIt8+N0tfyqDqar6/oQLGjBTkUwHd01MN8hnhKtd2dcw8NESdJMZb5UpGckkWKp3JPYQ4Mj7EZ068GMlESxmElBS8NFuJqLVXF22qjs9cWddpmBI8Hy7MlTHmiQTqLEOzlmRAay1UXQwBk0EjmlZJ8HrUv58yJbv7Uw21GrD887q8UKHieCRNI2oO5HuaieWjvZ9s0lh1smolcVG0XZ44pwvWzkwWKbegMOeTBj/1+r285837otBRJ4Vq3Wwn2ws4dnJChxOLbuRV5lPt5V/Wg9hAxFgXNuLatvqhThWqUZGZKQV2IKBXH6MPOfmu5+P7irmyG8hiSIRcksQo1zwG09aybl1+UJgVsiCFWPIoVkuMb1TuAfQze/ipiwxmLCYLtg79+HocVqj3hJ6wa67P1YKtx0nYglN7Q82UWMFSrYFdN5+uZhwApBT80ltv4Q9PvUSh6nJhrowpJQlDU1PvfPBRFisO2aRBf1p3mFNKG9bws9/Tn+Z88Ox0W9ClgsDmyapS8yJKamgULs1XooK1py8sRPmkEClLYgjBnoE0P/uWA9xx8/C6VFA7JURsZa+OjeD5q4ssVt2o7iXsCOh6i127xqYZCCHE54B/DkwqpV4TbPtN4CeBGvAi8LNKqfkWx54DCoAHuO2q/GKsHxv5EWzUtW31Qy1UXVzfJ2UakXFohu35DRMgBJNpwHIJm91UHC/i+ofJ0pLjL6u2Dit5FysOpiFWnOw3KvcAS4axP51iplQjIQS250fjCO+nlbjBSqGiekNaz+rqJLokga8+e0W3yAz6QHu+T9GD+UqN4WySKwtVKjUP34e5soMhBb6nw3qXFioMZxMIIbhpMNVQb5G2DM7PlqLmOpWapwsCfcX3Ly1GRuGl2cYJ2jIEb9g3EGgdDbMrCB0lTBnVJKSstReqdWLkm7/b52aK3PfFJ8kljVUl3rca4YIiqnsRulCwE/pyp9hMD+LzwO8CX6jb9jXgQ0opVwjxIPAh4P42x/+IUmp6E8d33aKTCX4lA7KWIqZWaPVDtV2tJDqSSy4xdtYAFfwvmZC66lktEX4KzValDq6vJScGMhYfecerVrzvjCWjIrrxkSwfecfhNU0WzfUDrqeTJO2MQoh6rajVvILVaL+t9n9xqoivtH5VX9qKtJIWKy4juRRJUxe5TRVtLENiCIlSfpD7UZRrHreO5SLjq4KcUMl2GMmnuLJQpcVNgdMAACAASURBVGS7umBtYoZ/nJhhsYl1NJixePNBXZtw+02DpBNGg5xFxjIwNyhn0YmRr/9uF6oOM0UHhaLq+D2Xs0iYUhvusHe3AhQdsdQ6xaYZCKXUSSHEgaZtf1f38hTw7s26foz2WC0Wu5oBqZ/ompvad/IjavVDNaSgP2NFXcvCiXMtEAKKthfF8YO2xMtgCpBS4via4ZQ0NfsHGqujz04X+YU/fAIvyGUkDEF/2qRgezw/WeTBR05DcJ+deGT5pMkLk0WdK0GHijq5x/oJf7X9k6aB73gdGQkBIAQ118eUcGWxytXFpaI0x/MoVJ2lPENghcOw2L6BDPmU7rz3gZ94BQ985Tlcr0bClFQdn6rr8drBNB/4s6d5+sLCMkrv+EhWt90cH+bwDZp1FOYS1uslrIRWvS2aW5E+f3Ux6tUR9uOWaOZYr+UsDo3lOTdTZLGylIPoy1ocGM6tfnCH2M4cxM8Bf9LmPQX8nRBCAceUUsfbnUQIcS9wL8D+/fu7PshrEavFYlczIPUeQHNT+056OIStIx1Pc/IPjeV51+v28PBTFynXXFKm1BIOKqwp6MxU1M8/9X+Hq+7wXx8d85eByF+o17S8yU+tYVKreYqpooMpdbHa2enSqoqp9R7ZVNHG9QMZDXQ4oJsQ6JBJp6eVQjOWBJoi3Cx7Dboqed9QhuFsgqliLTKoI7kU+ZRJydbJ7IOjOX70FaP88eMvU59X/stnLkd/W4bg9fuCgrWbh9ndl9py0bv6wsdWC6Gi7UW9OkLBQ+V3nqvaSoQLrd39y2Xju4VtMRBCiF8HXOCP2uzyT5VSl4QQY8DXhBCnlVInW+0YGI/joNVcN2XA1xhWi8WuZkDqPYDmpvaFqsPkYpVzM+Vl+kfNrSPrv9BHD4/x2r0DHDs5wULFoVB1GcxajOSSfP/SYktPIERzWKWVdAWAaehEXhSjD2imrSQkpot2JNjXfL5wkpdCT3qf/YezZBLGimySYycn6E9bZBMm00Vb72dJPE+xZyDF+bnVlVVXw2qhqmaESftmpdrmfa4sVBnrS/FLb72FP3vyAqYUJEwtEljzfN6yO8//9Sff5Tvn55c9K4FW0v3J19/A7TcNkkmYG84l1KPbZImhrBX16rCkwAmeS1gQuVZiwmailUe04+sghBA/g05e/6hqozWulLoU/DsphPgL4E1ASwMRY+1YLRa7mgFp/GIuNbUXAi7NV1EoUqZsWEmDblBfrulwEiqgoCrFvV94AhEk2sZHsvz0HTdFonnTRXtF4wCdxdwtQyAQSKENhOv7oKAva7WUkKh5rRPlIVRAq3U9XydgHQ+Jvq9yzaNU85gp1jgRtO6MCuQSImpgFBbsmYZk/1CGl4L8xnrtRCespYZ7gIakfbt9fKX40N1a92isL8X//NY5rQ2lNGPsj7798rJxyCABJIPj3/6aG0h1KZcQYjPIEsPZZNSrI1qoZCzyKbNBbqRX0K1uf+2wpQZCCHE3Oin9w0qpln6aECILSKVUIfj7xyGaY2J0AautPDpJ5oVfzPqm9pfnK4TyeSO5ZBRu+sRXf0DZ8SnVdG8E2w3YF4Q5AhXN8j+4UuD0lQK7+pIcGsvxwlQR1/cxRCO9M0TKlFHMvB1MqcM5htTnkIAlJZmkwYHhXMO9h/fdSZ8UQwquLtrRCthXivoiZddX0YTVyuhOF22kgHMzJSwp6UuZLDQlb0OI4H/thiXQnc7soC6knUewGpqvkzQFh3f34fg+v/P153lsYpaz06WGY0wpkELfrxHkdzS05tN00Y6K2LqJTusa2nkZod5TQww/bXJoLB+JK544PcmDj5yOmjcdHO4N72GrsJk014eAo8CIEOIC8FE0aymJDhsBnFJKvU8IsQf4rFLq7cAu4C+C903gj5VSj2zWOK9XrLTyWKvrmk1oyQfNRBLs7k9Hq+S0ZXBmsshQ1tL8+fo8QdN5QiaGAmZKNQpVN5KFaGUcBKwqwWxJUAg8pbCkYDRIMtuez025xvsK7/vBR063lO2ohykFQugVuFKaj90Kjudx7OTEMqM7XbSZKtYYyydIGJKrBXsZs6ceKvpfawSFzFF/7fWi+TpKwQuTRd7/pe827Bf2RPCVYv9glpLtcHGhgvLDug2t4WRIWKg43Pngo1HFfMF2u1Jf0OwB6LyR3RDeBNp6GUfGh/j2udkoXFgL5FPueeNQw3W0dEo6Wiz1EpNpsxF3lIuxboTeQ831KFRdSjUvqjvwFFGSz3F9XemLYqWIxrLWn6xC6QzeX2kfy9AFaIMZk75UgksLFSQChUIKwVhfalnHunuOn+Lxc7NB8na5pLhAc8+NYNUcGrVl94M2JP0Ziyc+/M8apMMXguKzMLYNuoVq1fFJGI1Fc63uu9VjlMBwzmKq2LpHRiu0O1crHBjOcGA4y/NXFrm8aJMwBKP5JAlTBsbco+L4UW9wL3h2Y/kkSVNycb4K0LJD3Xpwz/FTkVe2WHG4tKBVXC0puGEgjeMpsgmDmudHnkz4nTwwlGEwm+TsdJFCtTF3dHAkF3kQ9dcIEVaZNzeh2qmIO8rF2BREpf6lGhIRJfUWbQ8jmCTCic5bx0JktSNWm9gEOgnt+x41V08QYUMdhZbMnlys8gt/+ETAoNF01xcDyez684TjCauepRRBrYVqOw7LkChUtKKv99rufPBRDEFUcyBZYmutVOi0Uh8HKQXTazAOsPIzNKXgdXv7o4K1y/NVPvWNM8yUa5hBT9HJQo09Ayn60ppQoJSKakWSpg7ZjOZTTEwVMYQAwbIOdes1EPVe2XQxrDrXRj8MN01Ml9jdl+TSQrWh096ZqSIDRZsb+tORrhbovFA9S2kjcvTXAmIDcZ1jIyyQ83NlCkGpvwzogOG0vp5izmYb0uxRrBVhgZevtO6PXvXr6X5JIlwF4SSPUk0xV3JWnDSV0hO49BTZpIHdPioUeUAJozEMduL0JLPFGmXHi4r5Op3WVzIebsC62ijSliRpSv7ovXcwkk9GchY//QffJmVJ3fPCBxXQYs/NlMkmDNKW5ImP/Hh0njsffDSaXGueHxmIMDG+0Ym2PhR6bqZM0tDGIVTADXWarhaWFgagn7cltJdWcbwVK6u7IbGykxEbiOsYncgKPHNhPhDQ80iakrQpWKi62gAEoRVLgvJlpMK5EdSvkDdiHMLJ2ZB6xegpLRftBsVxYV2EGxR9mVJSXUsrTrTRWSkMJgQMZxIcHMlFhvjMZEHLinhRer6ja3bKUNpowFiidaD2D2V41Z6+Bgrq+bkyRiDn0Hydquvh+ipibUHj5BpVjrNUU9CNiTb0ylqFgiqOx8HhDGemSss67e3u161OHU+tSMbohsTKTsbmVqXE6GnUs0CKtstM0cFXS7ICv/Qn3+GTXz8TUDg1fXOm7OL6NMTdHZ9VaaGdYnwkGyWeN7IaDgvRlA8IoSc+T3sLqm4fWPoRtMo1tEPYf6LdPYdqBwnT4Mj4EA985TkmC1XKthv1oDZE5/e4FZlCUwTCh0HxXHN9wr7BDFcXbVqxVH0fhrIWn/jqD7jn+CnufPBR5ss1Fiq63elILoGntNfW3KGuG7jvrvFosldKRef/4Nteya1juq1oKAS5J8iBHBrL87F3vjqitI7ll+ejjh4eW3WfaxmxB3Edoz6+2kpWYLFSito8drnoty0Gs0n6ClXmyu6GJkWBVhy1DB1XNgPPRNYxpaLKarV0TGQ0VqGUmlJgB7TO5qhPOLkq4GPvfHVkiF1PUXGWDGl9W9GVEMqUO+uJ23V4fsvQjCTTEOzOJVu2EdV9rx8PwnSNORpDavXXM1NFDvgqknEX6JxNzYVbRrMIISgG1dfdLOpajXkXesrNXkAndQSbXWvQy4gNxHWGdm0zbddv4M4/d2khmgK2yjgINJOnWPUw23SIa3VMODzNwdcTletrK+D4PpaUWqkULe0R5kqcYB+FDhfVQwqB28JChBO6r/TEqlfbS+6UQuc+bhxMM5ZPcfTwGB/+8rMYAi4tVNtWea92j6C9FuV3zjrqBElDRO1Fx0fzwBJLpxlHD49xaDTHudkyIjBW2uPQx19eqOL7ukteKPvdl7YYzCZ55Fc2n/HTbiLfjIrjnSIJvlF0bCCEEHcCh5RS/0MIMQrklFJnN29oMbqNdm0zbcdfVli1XqOw1mreekgpmCs7esUvVyZgRvpKAhJSRvTFMJRQ3ytZoUAJkqbUzCOhw02aq1+3EhYwlDZZtL22q/UorBZNkALhK5zAG7CkThY3V6Z/5/zcunMqPku9LroNIQS+52MrH6XUqjH2D77tlQ19rxU6jJdMGJRqmr0WFupdmq9yQ3+yJxg/3fQCtqKTW6+goxyEEOKj6AroDwWbLOCLmzWoGJuD+pyDbt5uIFHMltdGjVwJ7aYxiV5xm7J9WGUoY0ZS0StJQAi0rhIE/ZZRWIY2DGHdQj1zyPFURLN1XJ+q60c6RKYUHBjO8Job+zk4kuXW3f38/v9+O0lTRt3sWqknZxOShNQJXcfXhiIRrMYzCaMhTh3Gx9db3byZpUpV14+qyzuJsYcx+YMjOfpTWiSuP2Ph+oqEITClDCRNdCHh1UV7Sxk/J05PRjmQe46f4kRdz+puofl3lEmYWIbg2MmJrl9ru9GpB/EvgDcAT4HWShJC5DdtVDE2Ba1kuoXYyJp/DRAwmksyU6ohhIrabIa5AENCueaTtgzKtZXZRIqwk5r2OvpTJiNB/UJwKUBoaY3AYISNaprh+YpKzSOfsiLa5dHDY9y2f5DJQhXPV1yerxBmRELjVKn5Df5N6FH0JQ0+/Z43LEt07unXgny9CiXg4+96TUcr4Far8TsffJTBjMWlhSr4IUVZ4aqtY/xs1cr+eqqN6JTFVAuE9RREekkxdhj2DWaoBLH2MCmtV3tBN7ZNhK+0fEbClHr1HoR6pNAxe9PQYaJswliTufqlt97CEx/5cR75lR+mL6XplF6QbBUdnEkBkwWbQtWh4njkkib3HD/FmckCF+YqVB0P05CBhyA1l161D34t2h7HvvnCsu3ZhLGGu+ocYR3FeiGF9rZ8pTa0At43mME0JHv605iGiPqDHxrNbVnYZatW9vW/oxDXam1Epx7EnwohjgEDQohfQPdy+P3NG9a1gW4ksrqZDGsr0x1o/W8Faq5HypIRl97xFFXXj8TmSjVPJ2OVNirN/k3z668+eyWSCS/aHkLAnkAL6tmLCwCR9EO7CI8CXpop05c2sQJDtbsvhWXYzJYcaq6PJbW34niqre5SiMfOzvErX3qKT77ntmhbsebRl5Qs2t1MMeuxr8S4qoeuLBdRxXbCkCi1lKx//NwMb/vUyXVpJYXfLcsQHBzJRrmMD77tleu/uTViq1b2vVQbsdnJ8o48CKXUbwEPA38OvAJ4QCn137o2imsQobs7Wag2uLtriYl24xz1qOd0g06m+goWqm5XKnBXghkkLl1fx+1rnmpIBCt029FSzcMLGrSM5RJk6lbezcZBodVff+EPn+DsdJHdfUlcT3FxvsJiZcngmVKuOoEqYKHi4vsqWoGO5FIMZXWHu5pP217ZrfAX373M3Z/8ZvRZZS1J0fY35zl3OChDQsIUgeeo8zxhYyaFZo2dmSxiCNb8XeuFeoGtWtn3wr1C9+eHVuhIrE8IcRC4rJSqBq/TwC6l1LmujaQL6CWxvm6IfHXjHK1WGAAfePhp5soOUuhVfPgtsIylyuNuor75ymoS3aYEQ0h8FHv601xeqKzaiN0yBINpi8Wqqz0SQSCmt/axGlKQMiWmFCxWN1aPATCYNrXxaXe9FrUUnaK+rmOlfQAOjmT14kD5PHZ2ruU4TEMGlNfcjhOlq89B1K/sr9XCtm4JCXZDrO/PgLfUvfaCbW/seBTXGbrh7m70HJ/++vN85sSLuL5P0pB4vs8DX3mOjCXpS1tkkyZXFqpRD4dQMrq7QRCNxtadqzSpUVDz9Wr9pdnO7lUpmCrWoipspcBd56SrlMJ2PUpdehBzlRUEmzqAELC7L8nlBXvZe6FxSJoSx/Vbq7wKTfHV0uta6qOVUfIjCvD6tZLqFyT18t7dlvpuha3osNZL2IqQWqcGwlRKRT67UqomhEh0bRTXILoh8rWWczR7Crv7Evy/T19GBQVdjq+YKtQC3fvWrTQjCYpNIDXVn3I1tudaVtNhyCY0QOulktaj0w513apsXu00rxjLcqmFcYCl5+p6PkZQk1F/PoFmao3mk1Qcj5rr4/lKK80GQobhd6Ge7XX6yiKG0PmETlG/gjcEvDClWWWDGb0QAS31vZl1A9dT1fNWCAl2ymKaEkK8M3whhHgXMN21UVyDaKcNs5ZEVqfn+PTXn+e+Lz7Jt8/NMFu0+d6FOf7iu5ejid5XRDz8SAivzTW7McF2E6v0A+pY7G6tWO0xbJbsRTOkgEsLNn1pi5uG0m1zGGFdR8oyODCcIdmkIHtlocq5mRLlmosRSJWHyrb1dxLSh7WQoWKqaHcc065nEU0XaxhCYEjBTMmJ/p4u1q7puoGtxH13jbNYcThztcAPLi9w5mqBxYrT1WR5pwbifcC/F0K8LIQ4jy6au69ro7gG0Y1EVifnOHF6ks+ceBFfqYiBU6xtRpBoe7Bd9mqzk/adwhC6o1lIIx3JtXbcFUSy5vmUxa27+7hpKINlBGRfAXsH0rpRj+trGqoQWHWFiwmpi/2kFFiG5MaBNP1pq+OJ/PxcOZLYrnk6DyTEUuhqo+GrGMsRfrYiYB50++fSUYhJKfUicIcQIodObBe6PI5rEt1wd9udIwwpPfXyHLbrY0mBQKwrKRtjOXrFj3J8rSEVKqP2py1my86yor8wTOQpmC5WGc4mdS2IEOzuS9Q1xRFcnK8gUJiGxHYFCVPw747ezBdOvUTZdiMygRBrm8jbyXvX03C7KfW92eh1vaVjJyfoT1vc0J+Otm20CVMzVjQQQoj/TSn1RSHErzZtB0Ap9dtdGUWMNaE+1uv5fqRc6vmrMfR7G2tpf7lF9d/rRsYyqLheV/I5Yb/kUBlVtXCrwi0DaZOS7WFKh72DGebLNUZyyWg/3StccWXRZiibiBK5AIWq9kTrtZSGcxYHhnMdjbO+PmAkl9AtRhUMZy3myi4onWzvttT3ZmAn6C31QpI6zFDFshrbgHAF8/zVRRxPK5EeGsszV7KjWG/S1HS+zWIfrYQwNNGtibrd+MOYeP37vWwcwsh+t5L9noKLcxVqns9Mqdb2OZkC9g5mWKg4/P39bwVaUyFrnq+FC+uOPXZygsGMpQX4AqkMH8VsyeE//4vOJvJmFlG9vPcto8lNk/reDNTnU4CohWk3V+cbxVYkqVc0EEqpY0IIA1hUSn2ya1eNsSrCFUzN9VisappkpeZxbqbIZMFm74B2K3NJs6V2/1agef4Lhe2cLlkq3VcBEqak6vg97zXUo9ythxBgpboRCJ6VFMsmiOaq35mSzWShxmguwUDa4uy07iLoeFoHayBtUa551DyfhCFJWzKaEDsJuWw0rNorYZ2doLe0FRXdq+YglFJewGCKDcQWIlzBzBSXej77SrFYcbGk5GrBpi+doGi7mFJEonTbCcXGjEOzR6LQsWvH9Ru29yrq6aJrOSZphYnj9V9bsdRU6Mj4EPccP8X5uTL5pInv+1xY1Cx1KWA0p3MSixWHyYId1VKUax4VRyfE+9JWQ1+IrQi59FJYZyf0ot6Kuo9O6yC+JYT4XeBPgFK4USn1VNdGEqMBYf/fSNk0cBJq6B/4TFm3cqwE7/f65NkJdOc03dt6LXUTvYL1DNOU4Hrd6dh3YCjD2//JDTz81MWoFuHMZBHQtQgF26Pi+PjKIWkaXFmoNFw3NMhXFiqYQZ1HuBrdipBLL4V1eklvaSVsdt1HpzTXtwCvBj4G/Nfgv99a6QAhxOeEEJNCiGfrtv2mEOK0EOIZIcRfCCEG2hx7txDifwkhXhBCfLDDMV5TyCdNneRrgdmyo6WV5yv4bK1x2Cz6pwwqezVdd+kqe/uTm640u13QITQR9bCA1X+QrR5F0pT86o8d4pFf+WEem5htrEUIKERTRQc7cO9s1+fCfCUS7WuG7alllOp6CmuIbodctuIanaJX9Ja2G53SXH9kHef+PPC7wBfqtn0N+JBSyhVCPIhuQHR//UFBzuMzwD8DLgCPCyG+opT6/jrGsGMRamS1qnb2fU17lNswc3adZx2EzqRY6t8ghNZCOjiSZSCTYLJYW1WLaSchEbTpVL6PEAIplu4tXK1GEhp16quw/PmbUnBwOMNr9+q1Vn3sPOyy11xZHVZMN0MEdFQBDVo+J05PslhxuLxQIWUajOaT5FNW10MuvRbW2QlV2duq5iqEeLMQ4mkhRFEI8ZgQomPtXqXUSWC2advfKaVCYZpTwN4Wh74JeEEpNRHIe3wJeFen171WUKx53DiwvC8waDaP63NNTJpeIDXt+gqlFFLA/qEMY30p7r/7MGcmC1tWtbyZMKVmGYGeuG3Xp+bDweEMe/r15+wrPSFq0TxBNmFw6+4+koaIPAcpllhdloTDu/M4vuLXHn6at33qJFMFmxcmixSqTiDnXWdUVqtKV+FYl3YM8wKZhIEUgprnc3GuwnSx2vWQSzfUB64nbIWa62oexGeAXwNOAu8EPgX8RJeu/XPonEYzbgTO172+ALy53UmEEPcC9wLs37+/S0PbejSvBHIJg/lKd1qBbif7Z7Vr1+dPXF/vv1BxWKw4/B+ff3zTx7cVkAJ8vzWNd2K61KCm66sw56IYyekWrINBv46xfILhbJIXJou4vmLPQFqHqDzFfNmhWHXZ3Zfk4nyVC3MVhjJWYzvZ4CIrfSaGgJtHl/SXwrxAfzpF0jSYLtrYrs982eHAcIIPf/lZ9p3szsr1ehPb2yi2ImezmoGQSqmvBX//mRDiQyvu3SGEEL8OuMAftXq7xba2c4xS6jhwHLTcdzfGt9Voxd5YrDhdMxCb8VCsDuis6zVM01vUvGirMJy1mCou/ywNKSIvMGFIXN+PVvymFJRsj8lCgWzC4J2v3c2VxRpnJgvYQdHcdFEL+E0X7SCHo+hLJwDB1UKVharLobEcL0wWos/KrJNdtwytyeSppZqNwWyioclPfciqL23Rl7ZYrNS4MK/rMrrNNtoJYZ1eQS8Uyg0IIf5lu9dKqf9nrRcUQvwM8M+BH1Wtm1FcAPbVvd4LXFrrdXoNK8UKm1cCrqco2G7L0Mp6J93645KGwFU6lxFq5awVUkoMtTI1cz3j3JEWfgVIAX3pBPPlJQkLCD6Puteu72NKHfGteX4grOizdyCFaUiefHmBd992IxfnKxSrLr5SuJ7iUsBEMoQ2MoWqw3TR1uquUnL/3YcB+LWHn6Zou3i+isJUNwa1NFMFm6rrkU2Y/PQdN3Hs5IT2DAJPtuJ4DXmBqwUbS8qeYBtdz9iKnM2KDYOEEP9jhWOVUurnVjy5EAeAv1JKvSZ4fTfw28APK6Wm2hxjAs8DPwpcBB4H/o1S6rmVrgW91TCoHidOT/KBh5+mUHWjiSBpSW7oS1GseUwVbHb3JelLJ1isOFxaqGj5DE9F7B7L0GqYnq/WHJMPk4+gV62vuqGPiakirqcQEqrrKF5YrenP9Yp6Q2wIsEzJWD6laxHasNKajwuRNJaaJpmGYKpgM5pP4gUyGPpz1Sq9UgiGswnmyk60XUoRsW9gKXSTTRjMlGr0pa0GCue7b7sxosiG2xcrDgror9t3YqoU9BZXJAzJSC5JPmU2VHDH2Hx0q0HSuhsGKaV+do1jrr/oQ8BRYEQIcQH4KJq1lAS+Fug5nVJKvU8IsQf4rFLq7QHD6ReBvwUM4HOdGIdexoOPnGau7GBIgWlIPF+xUHGp1ErcuivPdMEOKK1ChwvQS7yEEcStfRVw5RWmlAxnDGbKnTehSZkGvlK6t3JAIa15PoYQUb8Iydoa7HRiHHZS5XO3UH+/noIbMhYfe+erefCR0xiiiqdaP5fm14bQlF/XV7w8WyaTMCjXPPYPZRBCsGdAr/xt10cKGMxYLFQcfSalFVx35VOYgaz2Q/fesUwFODQYuaSJJRWfOfEiAtjdn0KIJY82YUgGMonIuJiGZp4ZQf7j0kKFYTfBwZHONJtidAc9UygnhNgF/Cdgj1LqbUKIVwFHlFJ/0O4YpdQ9LTa33F8pdQl4e93rvwH+ppOx7QRMTJd0c3hPNbBKHE9TOnf3p7gwV+FqoappngBKsGcgjVI6xlx1fd50YChidKwlgevWSS/nkjockAikwQWCfNKM5Dy6iaGMuSZDdq3BEJBPJzh6eIwPf/lZ9g6mubqoJ/UQMmiNWu/EWTKsSl8yG6FM9nTRZjSfIp+yyKeWqp3vu2uc+774JArq6iBsap7PhbkKJ05PNkwc4d+f+OoPeH6ygCUlrq8QKF6aKSODtqsjuQQ11+erv3wXoLWdRqoOM0XtWYhAYXGu7PCJLWIb9YocRy+gVwrlPo9e0e8JXj8P/PJmDOhahOf7QRFY40ox/DufsrhxIBWs5gVSCPYM6EmgL22xuz/Fmw4MRavAo4fHsALqo0BPMvXUxHqEjV+kFNw6luO33v06xvIp0pb+6D3ls9CFvsutcD0bB1PqkODEtBYeCPs5HNqV5zU39pNNGBhSh/6klKSCz0MAQ9kkYVfWUI9KIBjMWMwFFfTNNNCjh8e4bf8g+4cyjOSSzFccHUIMztFMfwzDE+dmy9qTROekQtsVhq4uzlfJJZfWkefnygxnkwxmLBzPp+r4uJ4iacotmaS3gtoZYwmdGogRpdSfEkQ8glqGna0tvYVIGLo6NPyxhqj/O1TYzCUNFGC73opc8F1B45hoYq/LJYVJSFNqrX8pYSyf4oNveyVHD4/x0L138Fv/6vX0p61NaS96vSGbMJZt83wdEgQ9qc2VbM7NlIKuXzXyKRPPX/qslK/zQyHNV4a9tdG5iD0DKfYMpMmnjL4opAAAIABJREFUzLbVvWEdwdVCFaV0kjtkSTme19D4JyRGeEH+QgZGIrymr6Dm+vhBfUqIfYMZZko2c2UHy9CGzTQEtutvySRdT+gIw2Br7U534vQk9xw/xZ0PPso9x0/FxmUFdKrFVBJCDBM2MBLiDmBh00Z1jSGTNLBdL7CuSzHoMJlYr7A5kksyXbSZLTk4nuLQWH6ZC33i9CR2XQLbV9pySwG7+pKM5LQQW8hZzybMZYmrsNnIQsUhIcW6EtXXGyzZKIsRopWarkLXdfSnZJRI3DuQ5mrB5sJ8lVvHchRtB6VEpJy6O5ci7NVgSoEldfgxn9JUxnLN5dBYvqHKuR5hTPoXvvCErikRukucAqYLNYrVee45foozkwVmSrU6cUSFZS43cqBDSBfnK5H4Xy5hRDRkiUAFX5vBjLUlLKaNUjt7SRBwJ6BTA/GrwFeAm4UQ/x8wCrx700Z1jeHQWJ5zM0UWK240GSRNHfNdqDiUbC9S2AQYzafIJvVKsdVkcOzkBH1pi2zSZKpgRwnn0XySquNx5mqhIYns+opnLsxHxz5/dVEXUAUhr2uhUnkzIUVYwLZ2xdxF28MyPfrT+rPtSyco11wGMgktI9JEUyzXXG7bPxiJxen+0apjsbijh8dIJwxwvIg2C2D7Po7t8e2zMxE1WRGQEwDcJSMnAMuQQZ2GR7HmRSGdiuPhBr1JvIjFlCKfMrdEM2mj1M5eEgTcCegoxBSotv4wWrTvPuDVSqlnNnNg1xLuu2scyzDY3Z/iFbvy7O5PkUtZfPo9b+Dv738rfWmroesXrLwqCkXN8imL8dEch3f3cctYTmv4oEMD9bBdj0994wz/50NPcXa6yEzJaZRgiNEWhtB5oXzS6EiOOwwjCqGpwL5SFJoIAOFnu5K0xEbE4kKmWhgecuomf0VjaFMJLQNS/40xpA5L+krhBdXt9SGdpCURwOHdfYyP5uhLd1+XqR02KsfRS4KAOwGrtRz9l23eulUIsa5CuesRq9HR1roqard/zfUZzSeZLdUaQllhwrNoe9vWXGinwpCC/rTFcDbBfNA/YaXCQlPqpLNSmi46uVhtYC3B0me72vdivQyVW3f1cXa6SKGqPdbw6oIgVSVAhAuE4LVlSMZHssxXag2eriNYNqHuyie5MF/ZFinsjVI7e00QsNexWojpJ1d4TwGxgegQK/3Y16o9327/sGDGrwsh1ENBnJTuABIgCCulLIPffPfr+PCXn2UklyRlGbw0U27rfTk+pCTcELDQbNdjtuS0/Gw3i64Zfj9295ukLYPTVwqooNe0r5a3QjWlVs69/+7DDcdVHI8LcxXyqcZpwjQkh0ZzDGaT26KZtBFq507p89ArWLGSeqehVyupO0F94VInP7hW+x87OcFkocrZ6dKOabLTi9BGVmEIyCQMDu3q46mX56IiskrN42rBXnZcUBJAypRRyC+sUH5sYrbhswLaVsECGzYc9d+PhYqDIbUHqQLl3BCG0D0pBjIWv/Xu10XXDsd6ZHxoWXX1eqp1ewlr/a1d61ipkrpjAyGEeAe6aVCkQa2U+lhXRtgl7EQD0c1VZMjQWKzUmK9cvzUIG4GW2tYhomxSUq757B1M43p+1MDpxoEU5+eWurHpYjcRJHV9LEMylk+uOPncc/xUywS1JQVlx+9oQu70u1Pf37xQdRvCjNmEwUguiWmIqOCu+ZwQK6xey9iwgRBC/HcgA/wI8Fk0g+nbSqmf7+ZAN4qdZiA2qqXSaoIA+O/ffJF/PDsbJ6FXQSvJCwGkLUl/xmK25DCUtRjJpShUHS4HXdj0PgaO5yGljBo3+b7CVz7phElf2lpx0r7zwUcZSFuIuqZPSinOTBbZO5heZjiaGW1r/e7Ur5on67S/6q99ZbEa1RVcK95CjNWxbi2mOrxFKfVaIcQzSqn/KIT4r8T5hw2jU8pdO0PwwFeew/E8FsoOl+YrPPnSLD/1+ht5w/5BrhZszk6XWl53p2O9Gk8hXVXXB0hARQlcS4rIKGQSBgeGczhegeFskkLVCcTxBElTq+06vq/lMXwfQ+hkthsILWWTxqoc+3bJUlieFG7FslkrXbM+bh96L/UISQ796ZgCGmMJnRqISvBvORDWmwUObs6Qdj46df07KfppDg9cWajy1MtzjGQtSjWX+cpSuKDmKf70yQubd2M9AkOsTVgQIGVJBALX83F8hUJrY5mGQCAY69NJ5Uxiqf4knEinCjYioLs6nsJXOt+QNGVQsayQQUK7P2MyktNR2OYJtv57kUsYLAb9PupX6+Mj2WXy2pfmKyxUXW7+939DNmHw3jsPbqhgbCWSg+v5TEwVIxbTSC4RU0CvY3QqtfFXQogB4L8ATwJn0a1AYzRhLVox+wYzkaTyxFSR01cWeWGq2CDdcOzkBDXXY6ZUw3H1atX1fC4u2A3GoRnZhMFQxuInXjWK2emnvEOwHu/BDbi+Muh3HUpLWHJJ9woaJ9mQc191PUDh+wrPVxhS5xx8pXjF7j5uHs1y+4EhhnK641s9wvM1fy+coCI7YciGOof77z7cwPO/OFdmtuyglMKUeqX/O4++gGDJ4wjRKV2zXY3FaE53o3O9JaXWi/PVllIiMa4PrFYH8UbgvFLq48HrHPA94DTwyc0f3s7DWlz/++4a59cefpr5shNp8rieYqZUi9Q3/9eVBebKS2J6nYZXhnMJHM/nxPMzjOSSzJWdZXz8nYp2ktntoAULwVcehhQMZxIkTINswqDm+W058SHn/v1f+g7lmkfSFDh+kMT29eQOS0ZgJY59q+8FwEAmESmlhqjn+S9UXaSAZCCFIYVuLhSquq6XrtmKKvrgI6eXHtiSDgdCiFhB9TrFamvLY0ANQAhxF/CJYNsCQZvPGI1YS6Xm0cNjjOaSmIFejmVIbhzQMhuf/sYZ/vNff5/5SqPSaqeT4uSizULZwfV8CkEHsrDCdifBCGSnE4aMjCjoPELSlMsEEOsRSmlDWN0s6E+ZHBzJtVytt6rKPXp4jE+/5w3sGUizuz9FytT9PHxUVP0eGoGVqnzX+r146N47ouY7zZ+bFDqcuN5K63Yo2C43DqQwg8ZUphTcOJBiKvCCYwXV6w+r5SAMpdRs8Pe/Bo4rpf4c+HMhxHc3d2g7E2ut1FysOoyPZCNNpKLtULJdXpop89T5+XWPI2wk4we8d63pA4bQWVpvhxRK+L7CJdQG0vUJAkiYkkzQGU0iEEFP5lDiOmHKiCEU1jQM55LLOp51UpV79PAY774wz2f/4SxV19cd1lIm+ZS5XB6D5XUEx05OMFWwmS7a7Mqn6AtyB52EhLJBy896NXdf6e3d7gUQfnfHR5ca/5RrLjXv/2/v3IPkOss7/bzn9G3uGs3Fo6slGRsZU2CMYmRwWEHMxQ6FQ2KqcLIsm4USSbExYQuCt5KtULBUmS2yBAOV4PW6wAkr/6FdCrKJWcxFMRSyQTaWMVhY1kjGus790tP3c7794zunp2fmdM+tZ6Zn5n2qWt1z+nT3OV+3vvd87+1n6ND+RZuS+VYQbiABClYC9AcVzy00wL2pWEivmJLnM5ErcmUiR3sqzsXxHOeGM7w4mObyRJ7JvDetHx1ziFfReqiFwbpibOtw27e/GPjhWxIu77lx2/JPdhWwXVGN1dTwfYolHy9IzR7NFMsKZ6HsZkvSoSnhzqgWNsaQKfoMTObntHcOr9Y/c+erAfirbz03Z59jpwY4+vQFetqSvGpbO33tSbJFn8vj2apX7gYYncrzD0+8xMBkjr72ZODTzzKRLSy4h9CHbt0bGHkf3/jBvd1eb6r9dhMxpxy8PnV5IpCr9TV4vQmYb5I/AvyriAxhM5l+BCAir0DbfUcSdRV5+Lf3cvCaLkamCkzkCjzzmzGOnxnmeP8IvxmZ+58sGXO4/YY+XriSZqpQZHAyX+4k6pvpdM3FEr5mIlfin569tKzzXG1CXQPXge4gGHxxLMtoxmYCtaVifOjWvRzvHwkaEhbAt6uHUJmtrz0ZmXo6Xwvo2fGD7tbUjGynkPu/9wJfOXYGz7cCOgMTdjJvScZob0ogIlwez3F5Il/u2DrfFfg9t10HwIM/PstUwStnMYXb60m1Pkef+84pTg+kcQMRpFBI6NpelRjd6MynSf1ZEfk+sA34rpmuqnOAP1vpg1vPGGPwAh3ol0YynH3yJX5yZpifnh2ZI+/ZmoyRcB1847Nrawt//MY93HbDVZzoH+FT//dX5APJUD+IMcddh0Lg6lgq3jqLV4cdUjE2YDs4aUVrDNZgNidcjj59gbtu2sGFsSxdLYmgathHgJ7WRLkwbLZ7ZL7EgoWmI3/l2JlAN9xmABU869oanMyXJUJbkzHGs8Wqmg5R3HPbdStiEKKIclvd9+jz9kGlohCwkdr0KNHM6yYyxjwRse2FlTmc9YsxhlzR57FfXea+R0/ZNsu+4bmL4zz1m9E5+1/T08It13Rxy74urt/WTmsyRlPCpTkRww1cSm991VU4jvCn33iKbNFOdHFHyjoOSyHM/mmk/9puIHxUi8pGgxfGsrYoLcA3MDxVoKslwfH+ET797hvKV8Elf27V8OzJfbYBCMWWzg1nuPuBJ2gN4gC14kpffbyfkm/bbEgQExEvUGarsMbrsXNouuCxY0uKoXShQtwoqZ2BNwEaR1gGhZJPtuCRKZaYynv88sI4n/3n5xnJFGY0RAM7Mf/Wnk5uuaaLg/u62L7FtlNoSbo0xd0ZLRcqObS/lz1dLeUlvlWhs88txdUUC2QmG0EPwhHYvbWZ1mSMX16cIOZKTfGi8HzDfcIVhSNW3WwyV+L8aGZBVcOVk3RlYsFEtsjFcVsXmnSFgckcE9lieayqpZS+PJoh6To2BTf4KmOO7e7qyuJEfxqNasHr3rZUjVcpGwE1EIvA9w2Zoke2YG9j2QInzo1yvH+YJ/uH57iOXBFaki4tSRfPN3z+vTfSnHRpSdjVwmyq5ZqH6YeVV3C4gmeguyXOQCABuRAEobslztBUYc1bf7clrKvo/GiWRMyZI3Q0m7A4DSq0DYLtIjYYP/vqfHbV8PBUnsHJPC8NZ3jlXz3Kvu4Wbn91H0efvkCmUGIonQ/e31ZXh6uGhOuwpTlRNdtpV2cznu8znC7iY6yRECHmGPZ2tzCeLa7bRnfaInvzogZiHnKhQSh65IoeF8eyHO8f5viZYU6eH5+TLpoK1LZ831DyDUXPp+Q57O1uZXdXdddCrUBp1BXcUDrHyFSRtqb5DYRTseroao2TcB3czNIC3fVkPO+VO6FubYmTKXhMzKr7CHEEHEdwfFslJ9jjdx1wg1WR68icSasy8Hp6YJKxjDWMrjPdHG/oiZd4/8GrOd4/wrnhDEl3uvXGZK5oRX88w817tvKZO18dOcGHk2hXK4xniuQ9n5jj8JG3vGLV4gfzsdRit+WK9CjrlxXTgxCRh4B3AQPGmFcH294LfAq4HrjZGBPZelVEzgGTgAeUqnUanE09url6vk3vywaGoVDyef7SRNkonBuemXUUd4Ubd23hln1dHLymi8eeu8zXn3jJTmgCBsE38NG31p4oqrV/7m1Lccu+rXzl2BlKvk/StZ1G465b1hk4cW6knKkzGwH29bSUdQkefe4yLwykibtWRnM0Y1c9W5vjDKUL1CN2vZgq59DN1JaKM5TOcWUiWrGtskBue3uSfNBHacZkfOiaecf45y+PYvwwfdZmGVn5zDYe/fM3z/gewiZ9Bqu93NeRWnDH1EabRJfbOVjZuNSjm+tS+BrwZeDhim3PAb+Prcaej7cYY4ZW4LjmEK4SMkWPfNFjKl/ixEujHD8zzJNnRxgPmqqFbGmK84Z9W7nlmi4OXN1JcyJGIubQkojxxe+d5qr25AzZxvamGMf7R7inxjFUy5Q5fWWCC2NZOpvjTOZK5Es+I1NFPnJot81uwf7n/+DDP4vMTHIdyn3+D+3v5Xj/CHu6mmlOxOgfTNsW1TBnFRIP/OeLZT7jUPl8MmaNZ9gHaSRdwDcQEyu8ExqKmIARIe4KV7UlibkOuWyRLU0JYo6z4Mn45dGMXfFViOaEx/PCQJpjpwZmuFMGJqxxAOhuTS6qY2qjsdjur4oCK2ggjDGPi8ieWdueB6oGZFeL8iohcB15vuHyeI6fnBnmeP8wJ18emxNk3tc9nXX0yr42XEdIxl1aEzGaky7xoC/PhbFsuUdPSMJ15i0qqlaBnSn4pPM5PGOvYnd2NuE6MsPgHNrfy0ffei1f+uGLFINK4pgDHc0JPn/Xa2dMAJWGqFYWyo7OZjL5EoPpwqKC2ZVB9HiVgHgiCEaHweZTlydIuE5ZayEem47PeL7tvrovMGqVRPUxqsWuzmaG0nnyoeULrIMEx/rVx/s5cvhg2Z1ybjhDKubQ3ZosVz+vV4H75XR/VTYvjRqDMMB3RcQAXzXGVO37JCKHgcMAu3fvrvqGuaJHJjAI+cAo1HIdxZzAdRQYhb6OFCJCKu7Y7KOES8ydW4jelowtqagoKhA4ni2SK3nEKt7r4liObR3JOf+x77ntOl6zc8u8Lo4oQxTFb0Yy7N7aTJ8rXBqfK68ZhSNW3zh03cRdB2M8xBE8z65UDLborRxDkLC3kJ20Dfa7EpkOPhsTrZFw+soEdz/wxIJ96mFzxFwxWC2FQW7HrkzCMQ1XAlFuv/WYpgqLbwGjKNC4BuJNxpiLItILPCYip4wxj0ftGBiPB8DGIMLtlauETMHDD1oHTGcdjTA2y3XU0RTn4L6t3LKviwN7rOtIRGhOuMFtukahGuWYziKLiqICgQnXIZ0vYXzwPFt45xt4eTTLdREGZyEujtAQDc5K/ZyNb+D8qHVthVrLUVS6jITpiV6AYmmuBnIlrUnbEqPg+bgihMmkYc2DbwwOtghudh3CUNq2JJndQC5KnCfk0P5ePn/Xa/nTbzxFLlhFJGIOfe2psuRm1FhthOydjXQuyurRkAbCGHMxuB8QkW8CNwORBmLm62Bkyva5CVMmL0/kbFuLM8OcPD82J89+T1fzjII11xGcwCi0JGM0J6rXKERRq6hoviyS2RP8rZ/7AVe1JWfoH8N0Ydj933uB4/0jvDyaoS0ZwxhDuuDVvJqubGFdC8HWTHS1JOhuTfLry5OYCvdRSGVpfaWh6GlNMJot4orN6AqfDIV3jDFM5T1irpBwnRnFZJX4QFdLgnPDmRkxiNGMlQNdrE/90P5e/u6PXh8ZsK2VAdWIgefFsJHORVk9Gs5AiEgL4BhjJoPHb8c23ZyXguczMpXn1KXJsuuof5bsZswRXruzY0bBGlg3w0IK1+ZjV2czZ4fSM7blSz6dzYma/X5qvVflnCzYXk0xV/jKsTM2JiFwesB+5o4tqXnf+9D+Xtqb4ogxjOej4xA2s8i2hfjMna/mE0dPMpkrkauoVYg7QCAss6OziYvjOZpj0ymiUwWPoudjDKSC7qphN9Z8yYZ/Q9dZrQK5RMxh55YUVybynB/Lcm1PK22pWFkOdHAyT8HziTsyJ6EgioVOlvXSQGgULYVGDqIrjcmKGQgROQIcArpF5Dzw11ip0i8BPcA/i8gzxph3BDKmDxpj7gCuAr4ZTNAx4H8ZY76zkM+8PJ7jvX9/vNzALaQ9FeMN++wq4bf2dNKStKcdc5ygkC1GKl4f1axb9m3lp+dGymmuBc9nMF0gFshkDqdtdlN4xf3hf3yqauO28L3Cq/bQndPRFGc8U8TzTTkbyQ1acAylC+zraZ33anpXZzM/T+cj21wkYw7tTXEyhRI7O5s5tL+X9x+8utzuOlxdAIEsZYydnc3snOXn7mlLcn40G1RAm3Jxm8h0FXTJM1VdUCHh+7U3JcgUSnS2JOlsSXJuOM1wumjjFY5Q9A2TuVJZbKkW802W8zXwWyj1eh9FWQtWMovp7ipPfTNi34vAHcHjfuC1S/nMiVyxbByu7mrmln1dvPGaadcR2MBp6Dqql1Go5Hj/CL1tiTlprpcn84gIDtZPkw8uxF0xVTuMPvjjszN8OuGjwXQegfLxFzw7aRc9Q96D/sH0vFrCH37zPj748AlcR4g7NqsoLPorlHyG0lZ6Mu4Ir//Md0nnPba2xMkWvaAmYVqqcyJb4OnfjNKWijGZK9HZHKe7NYnrCJ3NcZKucGkyT1yEvo4k58eydsUWc5iosoIJqVzHhUVr54YzXNvTwlCQmusgmGA8O5vjdUndrFdaqKaXKuuZhnMxLYfmhMtH3nINB/d1sSNwHYE1Cq1Jm46ajK2svu7Lo5nINFcvyO83zKwvMESL29uAovXRG8/MiUEAJGJ2+nRFyq4fRyhrCb+ip6XqcR7a38t1va2cHZqiUPJnvL/BrkRSMYeib5sQ+sYwMJEvtxwXMVway2I64MJYjpgj9LWn8PwMVybzDEzmaU3Gyq2pK4vIWhLWQI9l5ncHhYdVWbQWHlcp0CoIU4C7W1O0pWJ1Sd2sV1qoppcq65kNZSB2djbzBzftBCAZt5rDYRHbatGacHlxcApXZIbwe3iFP7uMzDdw+sokV7VPp1mGV53JmBN0LZ3rgmmOO2QKflncJcQNNTjN/PUmn3znfj5x9CSDs4rknMAXFE7CmYI35wiMgbxnuBQ0tuvrSDGZK5HO27TcuCP0tCU5+vQFXrNzywyXzrFTA3zi6MkZ8YxaVCtaS8YcENje3sRQOs/F8SzupLBn6/JTN+uVFqrppcp6ZvVmzlXAEehqSbJrazM7tjSxpTmxqsYB7KTs+4aC75Mr+RR8v1wsVo18yefCWI6WoIFfqF/c3ZrEx8xpPRETKPqGhGurpEvGxg2SQQO/ome1IvqHpmrqBh/a30tXy3QbbEdsO/G441D0IVe0WhZRhx6anqJn2BG4mobSeRxszUbBs8WHF8Yy3PPIz+eouHW1JJgnYxhXYGdHkt62FHnPrhK2dzSVi9auak+SL/pcGMsGwXC72nlhIM07v/Cvy9JMXogy4HwcOzXA6FSec8NTnL4yuSglOUVpBDaUgYiX+xSt3WkNTOaQypxPU1t/YUYdQXDFv6uzmWzRo70pzvaOpjmv8YKW1+mCvQK/rreVnZ1N9HU0BTEFBzdoaDefuHy64NEcd6yBidniP3+emo3gtOhIuiDWxdQ/mLbxCbE1KF7QrDDmCJmCN+c4bDru3HOrpLMlwX99z2s4cvggN+/ZSl/HtJ4zQMy18qIxx7bs8HybpRZzhHMjmXnPvRaH9vfy6XffQG9bivFssaq0aDVCN2HRN+zc0gQC58dyJFxH+x8p64YN5WJqBIqe1UZOxKeNVCg6Hw+urCunX4O9ct+xJUU6bxvnVRY1taVic3QfyrbHwJNnh8sN52wNB4HVEfo6UriObSHx7PmxGbKVv7O/h8sTBQYn85igAA/HZhdVa/xXiSO2G2vClXKxW+V9WE/i+7bnUtjKIpwYWxMu52bJrUrwjyPQkojNaBNSrWFhc8Klrz3F2aGpsi61MdZAzf7MxbKctNDZwekwA2tLc0KNg7Ju2FAriEYgEXMgqAI2mBlX430dTcRcme5Mig1g97QmuTJhA7t3P2AF/CqvXmtd0Idzue1OausJYs50hlFT3OWXF8f54g9eJFv0iDkwlS/xzWcu8bNzI+XX2CZ2tj15eGzVPECV7cO9wBi4IkH3WoK0U4JGgIbu1uSMwOyxUwMMTxUoBZKcIYLVjd6xpZn73/e6GTGLo09foLM5TirmUvAMI1NF7rppB9f2tpEtejaTK3gvY+y4rmUwOHQTVqLBaWW9oSuIOnNtbxvnhtMz0lxbmm3RWMwVtnekuDiWpegT5MbHGJ6yQeKwyO3jR0/S05pkMl9iV2czF8eyVqmMua6qchy64olK3Yhs0WOq4JX7JAF4xqaWlnybEVQKHnvGcPOeLus3H8lQKvmUIoxTZfO9eLCCMAZ2djYxFbRIzxQ8kjGhuzU1o6YC7NV1e1OclmSMwck8+ZKN07iusKerdU5NSHg13tGUoqfNbssUShzvHymvttwg9iMQGKXUmgaDNTitbAR0BVFnPvzmfcRdl76OFK+8qo2+jhStqTgfOXQNvW0pfAMH9nTxn267ltdfvZXxXImYK+zY0kR7U4KSZxjLFDk7NFUurPKMvWqvmpRkpq/aDcwJrPq+qRoQLnh2crYCaMKRwwe59/braU3GQGTGFT5AT2ucZMwJOsYKgnUlicCViTzX9rZx//tex/YtTfR12LTT2YHZ8Oq6LRVnX08r129r54bt7fS1pzhy+OAcF0ytq/EwVrC3u8UaUQe2d6TK8qXLDQYfOzXA3Q88wa2f+wF3P/DEgmMa9QhyK8paoyuIOlOrjUOlHsSxUwMc7x8pX8WHDKXzOAKeMUGjwBgJ18YuIg1EcCkfcxwMhqa4bTpX+dn3PPLzchxkzssNBLV7FD1TrkL+/F2v5b5Hn+fscIYY0NuWpCXhMlXwmMh5dLfGGcuW7FW72GaEJWPK51qrlcVir67n2z+MFVTWWlRqYCyV5VRBa+8jZSOwYopya0E9FOVWg8qJ59JYthwU3t7RxMXxbKBP4JRdRZfHMwymqxeVhT2RqinX3f+9F/jiD14st//IV/iNKj1UCVd4/dVbOXL4YM3jD9tglzzDUNr2QXIdW3/wnY/9m0Wd/0LUzdZKDa2Wyt98Y6Qo64VainLqYloDKjNcettThGHroXQeN0jZ7GlLlvefzHvEHWhJuMRdoSXh0teeZGdHkvZUDB87cVYah0rXyPH+Ed79mj6a4m5ZUCgkNBWuI2zrSC0oiBq6T2KusLe7hd1bm+ltS3Hv7dcv6PwXm0K63JTTpaKBZmWzoy6mNaCy/UJbKs72LTAwkSNX8rm2p4XhqQKuY9M1w6vlnUGMIsQYw3i2yLOfum3O+0e5Ri6MZfnQrXs5+vQF4q5wfiRTTrlNxhyuqqKJEEU93CeLTSFdi06kGmhWNjtqINaA2RNPWyqO60jZdVHpSw+Fg2ZSsAscAAANfUlEQVTrJdSaqKo1iHvwx2fpabNtKvoCdxYESnCLDOpuhtbRKrKjbHbUQKwB8008syff6eZ9C5uoqjWImyp47A5cJmFF8lA6T67k1yWou9HQQLOy2VEDsUrMFo2566YdHO8fWdDEs1iBm8HJPEOT+SDN1BqCbNFWUFdKd7Y3xctuJQ26RrMZVkqKUg01EKtAVEzg6NMXFhVoXYzATV97kgtjOc6PZtmxxRBzHYqeKccgluIyWStVtEZRY1OUzYhmMa0ClTGBsLYh7BO0XMJspQ//41MMTOTwfEN7U4IdW2xbj8sT+XLWzz23XbekbKDQ+AxM5mbUAyynW+pCz20tPldRFIuuIFaBlRKNqVw1hJKeF8dybN9i3UehpnSl+2gpLpO1UkVTNTZFWVvUQKwCK5UuWTmBJtxAXEhgcDJPWypet5TM5Rq4pbqJVI1NUdYWdTGtAivVl6eykCsUFzLGkC95de39E+pTVLJQ47McN9FyPldRlOWjBmIVWKlK4MoJNBQXchzBdZy6Vht/+M37mMgWOX1lkucvjQfqaMUFGZ/lxF+04Z2irC3qYlolViJdcnY9RZiyuhJtKAyABKp3Mt2iYz730XLcRFqHoChry4oZCBF5CHgXMGCMeXWw7b3Ap4DrgZuNMZGd9UTkncAXARd40Bhz30od53JY6xTMek2g853HVx/vp6MpzrYK+dNMocTnvnOKqYJXs9vpcuMvq12HsNbfqaI0EivpYvoa8M5Z254Dfh94vNqLRMQFvgLcDrwKuFtEXrVCx7hkGiUF89D+Xo4cPsiPPvnWSC2F+VjIeVRrWtc/NDWv+2g9uYka5TtVlEZhxQyEMeZxYGTWtueNMb+e56U3Ay8aY/qNMQXgEeDOFTrMJbOStQ2ryULOY1dnM0PpPP2DaU5dnqB/MM1QOg8wb7fTterEuhQ2yneqKPWiEWMQO4CXK/4+D7yh2s4ichg4DLB79+6VPbIKNkoK5kLO45Z9W/npuZGynkTB8xlMF9jWlpjRugOi3UfrpV3FRvlOFaVeNGIWUw3dtIgnjHnAGHPAGHOgp6dnBQ9rJhslBXMh53G8f4TetgQJ18E3kHAdetsStDUl1o37aCFslO9UUepFIxqI88Cuir93AhfX6Fiqsp5867VYyHm8PJqhqyXJvp5W9ve1s6+nla6WJOl8qe7uo6VqQNeDjfKdKkq9aEQX08+Aa0VkL3ABeB/wh6t5AAvJZNkoKZiH9vdy1/kxHvzxWaYKtuPrh27dO+M8amUi1dN9tBwN6HqwUb5TRakXK6ZJLSJHgENAN3AF+Gts0PpLQA8wBjxjjHmHiGzHprPeEbz2DuBvsWmuDxljPruQz6yHJvVa6R+vFQs539UaE9WAVpTVp5Ym9YqtIIwxd1d56psR+14E7qj4+1+Af1mhQ6vJZmsQt5DzXa0raw0SK0pj0YgupjVlvU5SUW4xYF5X2ULPdzUykVQDWlEai0YMUq8p6zGTJarA6xNHT/LxoyfnLfpqpPPVILGiNBZqIGaxHiepqAKvyVyJdL40b9FXI53veiqqU5TNgLqYZrEeM1mi3EQl37eN9Sqo5jpqpPNdL0V1irIZUAMRwXqbpKJ89zHHmVNyWM11tN7OV1GU1UFdTBuAKDdRWypGazLWEK4jRVHWJ7qC2ABEuYn+y+/aBriN4jpSFGX9oQZig1DNTaQGQVGUpaIuJkVRFCUSNRCKoihKJGogFEVRlEjUQCiKoiiRqIFQFEVRIlEDoSiKokSiBkJRFEWJRA2EoiiKEokaCEVRFCUSraReZyxEL1tRFKUebHoDsZ4m3Ept6EoRoE+jLTUURak/m9rFFKXEFqW61ihECQNFiQApiqLUg01tINbbhPvyaIamuDtj23rQy1YUZX2yqV1MUUpsjTDhVnN7RQkDNbpetqIo65dNvYLY1dlMtujN2LbWE24tt1cj6UcrirLxWTEDISIPiciAiDxXsW2riDwmIqeD+84qr/VE5Jng9u2VOsZGnHBrub0O7e/l0+++gd62FOPZIr1tKT797hs0QK0oyoqwki6mrwFfBh6u2HYv8H1jzH0icm/w9ycjXps1xty4gscGRCuxrXUW03xuL9WPVhRltVgxA2GMeVxE9szafCdwKHj8deAY0QZi1aj3hLvctFmNMyiK0iisdgziKmPMJYDgvtrMmRKREyLyhIj8Xq03FJHDwb4nBgcH6328i6IeabP1cnsdOzXA3Q88wa2f+wF3P/BEw6buKorSuDRqkHq3MeYA8IfA34rINdV2NMY8YIw5YIw50NPTs3pHGEE90mbrEWdYb/UdiqI0Jqud5npFRLYZYy6JyDYgcsYyxlwM7vtF5BjwOuDM6h3m0qhX2uxy3V6VhgqgOREjUyiVA92KoigLYbVXEN8GPhA8/gDwrdk7iEiniCSDx93Am4BfrdoRLoNGSZvVgjpFUerBSqa5HgGOA68UkfMi8kHgPuBtInIaeFvwNyJyQEQeDF56PXBCRE4CPwTuM8asCwPRKGmzjWKoFEVZ34gxZq2PoW4cOHDAnDhxYk2PIcxiWsu02cqmfk1xl2zRo+gZrZlQFGUOIvJUEPOdw6ZutbESNEKdQiPWdyiKsv5QA7FBaQRDpSjK+qZR01wVRVGUNUYNhKIoihKJGghFURQlEjUQiqIoSiRqIBRFUZRI1EAoiqIokWyoQjkRGQReWuvjiKAbGFrrg2hAdFyqo2MTjY5LNMsZl6uNMZGdTjeUgWhUROREtUrFzYyOS3V0bKLRcYlmpcZFXUyKoihKJGogFEVRlEjUQKwOD6z1ATQoOi7V0bGJRsclmhUZF41BKIqiKJHoCkJRFEWJRA2EoiiKEokaiDohIudE5Bci8oyInAi2bRWRx0TkdHDfGWwXEblfRF4UkWdF5Ka1Pfr6ISIPiciAiDxXsW3R4yAiHwj2Py0iH4j6rPVElXH5lIhcCH4zz4jIHRXP/edgXH4tIu+o2P7OYNuLInLvap9HvRGRXSLyQxF5XkR+KSIfDbZv6t9MjXFZ3d+MMUZvdbgB54DuWdv+G3Bv8Phe4HPB4zuARwEBDgJPrvXx13Ec3gzcBDy31HEAtgL9wX1n8Lhzrc9tBcblU8DHI/Z9FXASSAJ7gTOAG9zOAPuARLDPq9b63JY5LtuAm4LHbcALwflv6t9MjXFZ1d+MriBWljuBrwePvw78XsX2h43lCWCLiGxbiwOsN8aYx4GRWZsXOw7vAB4zxowYY0aBx4B3rvzRrxxVxqUadwKPGGPyxpizwIvAzcHtRWNMvzGmADwS7LtuMcZcMsY8HTyeBJ4HdrDJfzM1xqUaK/KbUQNRPwzwXRF5SkQOB9uuMsZcAvuFA6HE2w7g5YrXnqf2l7/eWew4bKbx+Y+Bq+Sh0I3CJh0XEdkDvA54Ev3NlJk1LrCKvxk1EPXjTcaYm4DbgY+IyJtr7CsR2zZjvnG1cdgs4/N3wDXAjcAl4G+C7ZtuXESkFfjfwJ8bYyZq7RqxbcOOTcS4rOpvRg1EnTDGXAzuB4BvYpd2V0LXUXA/EOx+HthV8fKdwMXVO9pVZ7HjsCnGxxhzxRjjGWN84H9gfzOwycZFROLYSfAbxpj/E2ze9L+ZqHFZ7d+MGog6ICItItIWPgbeDjwHfBsIsyk+AHwrePxt4N8FGRkHgfFwOb1BWew4/D/g7SLSGSyh3x5s21DMiju9B/ubATsu7xORpIjsBa4Ffgr8DLhWRPaKSAJ4X7DvukVEBPifwPPGmP9e8dSm/s1UG5dV/82sdbR+I9ywGQIng9svgb8MtncB3wdOB/dbg+0CfAWbXfAL4MBan0Mdx+IIdulbxF69fHAp4wD8B2yg7UXgj9f6vFZoXP4hOO9ng/+02yr2/8tgXH4N3F6x/Q5sRsuZ8He2nm/ArViXx7PAM8Htjs3+m6kxLqv6m9FWG4qiKEok6mJSFEVRIlEDoSiKokSiBkJRFEWJRA2EoiiKEokaCEVRFCUSNRCKUoWoDqxV9jskIm+s+Ht2x837gu3HRCRSWF5E3iUiPxeRkyLyKxH5cK33UpTVILbWB6AoDczXgC8DD8+z3yEgDfykYtsXjDGfX8iHiEgSKxl5szHmfPD3nqW8l6LUE11BKEoVTEQHVhG5J7jCf1ZEHgkaqf0J8LHgCv+3F/LeIpIWkU+LyJPAG7AXa8PB5+aNMb+u57koylJQA6Eoi+Ne4HXGmNcAf2KMOQf8PfYq/0ZjzI+C/T5W4RZ6R8T7tGC1Id4QGKJvAy+JyBER+SMRqfy/Od97KcqKoAZCURbHs8A3ROTfAqUa+4UG40ZjTFRPIA/biA0AY8yHgN/B9s/5OPDQIt5LUVYENRCKsjh+F9sL6PXAUyKy1DhezhjjVW4wxvzCGPMF4G3AHyzvMBVl+aiBUJQFErh9dhljfgj8BbAFaAUmsbKQS33fVhE5VLHpRuClZRyqotQFzWJSlCqIyBFshlK3iJwHPgO8X0Q6sF1Fv2CMGRORfwKOisidwJ8t5aOAvxCRrwJZYAr493U4BUVZFtrNVVEURYlEXUyKoihKJGogFEVRlEjUQCiKoiiRqIFQFEVRIlEDoSiKokSiBkJRFEWJRA2EoiiKEsn/B/YhlvJ1SZOtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8886877546783782"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 將 1stFlrSF 限制在你覺得適合的範圍內, 調整離群值\n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "df['1stFlrSF'] = df['1stFlrSF'].clip(300, 2500)\n",
    "sns.regplot(x = df['1stFlrSF'], y=train_Y)\n",
    "plt.show()\n",
    "\n",
    "# 做線性迴歸, 觀察分數\n",
    "train_X = MMEncoder.fit_transform(df)\n",
    "estimator = LinearRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業2\n",
    "* 續前題, 去除離群值有兩類方式 :  捨棄離群值(刪除離群的資料) 以及調整離群值,  \n",
    "請試著用同樣的上下限, 改為 '捨棄離群值' 的方法, 看看結果會變好還是變差? 並試著解釋原因。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8935105961259268"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 將 1stFlrSF 限制在你覺得適合的範圍內, 捨棄離群值\n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "keep_indexs = (df['1stFlrSF']> 300) & (df['1stFlrSF']< 2500)\n",
    "df = df[keep_indexs]\n",
    "train_Y = train_Y[keep_indexs]\n",
    "sns.regplot(x = df['1stFlrSF'], y=train_Y)\n",
    "plt.show()\n",
    "\n",
    "# 做線性迴歸, 觀察分數\n",
    "train_X = MMEncoder.fit_transform(df)\n",
    "estimator = LinearRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "捨棄離群值的方法比較好，因為不會將捨棄的值調整至最大最小值，進而影響DATA的分配形狀\n"
     ]
    }
   ],
   "source": [
    "print('捨棄離群值的方法比較好，因為不會將捨棄的值調整至最大最小值，進而影響DATA的分配形狀')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}