auction_sales.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Πρακτική Μηχανική Μάθηση \n",
    "\n",
    "## Ευαγγελιδάκης Λέανδρος\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Εισαγωγή απαραίτητων βιβλιοθηκών"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib               import pyplot as plt\n",
    "from scipy import stats\n",
    "from sklearn.preprocessing    import StandardScaler        \n",
    "from sklearn.model_selection  import train_test_split      \n",
    "from sklearn.metrics          import classification_report \n",
    "from sklearn.metrics          import confusion_matrix      \n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.linear_model     import Ridge                 \n",
    "from sklearn.svm              import SVC                   \n",
    "from sklearn.neighbors        import KNeighborsClassifier \n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "from sklearn.linear_model import RidgeCV,LassoCV\n",
    "from sklearn import metrics\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.pipeline import make_pipeline\n",
    "import seaborn as sns\n",
    "plt.style.use('seaborn')\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Εισαγωγή δεδομένων"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "auctions=pd.read_csv('auction_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Παρακάτω φαίνονται οι 5 πρώτες καταχωρήσεις των δεδομένων και κάποια βασικά στατιστικά. Οι μεταβλητές SellerId και ItemId είναι ουσιαστικά κατηγορίες και τις μετατρέπουμε σε τύπο string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SellerId</th>\n",
       "      <th>SellerRating</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>StartingBid</th>\n",
       "      <th>Price</th>\n",
       "      <th>NumBidders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>144.0</td>\n",
       "      <td>84.11</td>\n",
       "      <td>6.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>145.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.0</td>\n",
       "      <td>93.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102.0</td>\n",
       "      <td>94.52</td>\n",
       "      <td>12.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77.0</td>\n",
       "      <td>97.41</td>\n",
       "      <td>2.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>106.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.0</td>\n",
       "      <td>80.32</td>\n",
       "      <td>4.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SellerId   SellerRating   ItemId   StartingBid   Price   NumBidders\n",
       "0     144.0          84.11      6.0         137.0   145.5          2.0\n",
       "1      40.0          93.76      0.0          30.0    30.0          0.0\n",
       "2     102.0          94.52     12.0         364.0  1278.0          8.0\n",
       "3      77.0          97.41      2.0          82.0   106.5          3.0\n",
       "4      54.0          80.32      4.0         173.0   173.0          0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auctions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "auctions['SellerId'] = auctions['SellerId'].astype('str')\n",
    "auctions[' ItemId'] = auctions[' ItemId'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SellerId          object\n",
       " SellerRating    float64\n",
       " ItemId           object\n",
       " StartingBid     float64\n",
       " Price           float64\n",
       " NumBidders      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auctions.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>SellerId</th>\n",
       "      <th>SellerRating</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>StartingBid</th>\n",
       "      <th>Price</th>\n",
       "      <th>NumBidders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18842</td>\n",
       "      <td>18842.000000</td>\n",
       "      <td>18842</td>\n",
       "      <td>18842.000000</td>\n",
       "      <td>18842.000000</td>\n",
       "      <td>18842.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>132.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>181</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1558</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>90.046164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>316.835899</td>\n",
       "      <td>393.979753</td>\n",
       "      <td>2.242172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.009262</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.345270</td>\n",
       "      <td>384.044470</td>\n",
       "      <td>2.016307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>80.050000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>84.820000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>135.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>90.460000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>235.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>94.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>426.000000</td>\n",
       "      <td>568.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>99.980000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2299.000000</td>\n",
       "      <td>2299.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SellerId   SellerRating  ItemId   StartingBid         Price  \\\n",
       "count     18842   18842.000000   18842  18842.000000  18842.000000   \n",
       "unique      150            NaN      13           NaN           NaN   \n",
       "top       132.0            NaN     7.0           NaN           NaN   \n",
       "freq        181            NaN    1558           NaN           NaN   \n",
       "mean        NaN      90.046164     NaN    316.835899    393.979753   \n",
       "std         NaN       6.009262     NaN    350.345270    384.044470   \n",
       "min         NaN      80.050000     NaN      1.000000      1.250000   \n",
       "25%         NaN      84.820000     NaN     80.000000    135.500000   \n",
       "50%         NaN      90.460000     NaN    181.000000    235.500000   \n",
       "75%         NaN      94.990000     NaN    426.000000    568.000000   \n",
       "max         NaN      99.980000     NaN   2299.000000   2299.000000   \n",
       "\n",
       "          NumBidders  \n",
       "count   18842.000000  \n",
       "unique           NaN  \n",
       "top              NaN  \n",
       "freq             NaN  \n",
       "mean        2.242172  \n",
       "std         2.016307  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         2.000000  \n",
       "75%         4.000000  \n",
       "max        10.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auctions.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Παρατηρούμε ότι έχουμε 150 χρήστες που πουλάνε 13 προιόντα."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Για τη μεταβλητή ItemId παράγουμε το παρακάτω ιστόγραμμα για κάθε μοναδική τιμή"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x=\" ItemId\", data=auctions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Η μεταβλητή ItemId,δηλαδή τα προιόντα, αποτελείται από τους αριθμούς 0 έως 12 και οι συχνότητες εμφάνισης είναι παρόμοιες."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Έλεγχος για NaN δεδομένα. Όπως φαίνεται τα δεδομένα είναι πλήρης και δεν περιέχουν ελλείπουσες τιμές."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SellerId         0\n",
       " SellerRating    0\n",
       " ItemId          0\n",
       " StartingBid     0\n",
       " Price           0\n",
       " NumBidders      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auctions.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (a) Πρόβλεψη Τιμής Κλεισίματος"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Για την εφαρμογή αλγορίθμων μηχανικής μάθησης χωρίζουμε τα δεδομένα σε train και test σε ποσοστό 80-20. Χρησιμοποιούμε one-hot encoding για τις μεταβλητές ItemId και SellerId."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#One-hot encoding ItemId column\n",
    "\n",
    "dummies1=pd.get_dummies(auctions[' ItemId'], prefix='ItemId')\n",
    "dummies2=pd.get_dummies(auctions['SellerId'], prefix='SellerId')\n",
    "\n",
    "auctions = pd.concat([auctions,dummies1,dummies2],axis=1)\n",
    "\n",
    "auctions.drop([' ItemId','SellerId'],axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>SellerRating</th>\n",
       "      <th>StartingBid</th>\n",
       "      <th>Price</th>\n",
       "      <th>NumBidders</th>\n",
       "      <th>ItemId_0.0</th>\n",
       "      <th>ItemId_1.0</th>\n",
       "      <th>ItemId_10.0</th>\n",
       "      <th>ItemId_11.0</th>\n",
       "      <th>ItemId_12.0</th>\n",
       "      <th>ItemId_2.0</th>\n",
       "      <th>...</th>\n",
       "      <th>SellerId_90.0</th>\n",
       "      <th>SellerId_91.0</th>\n",
       "      <th>SellerId_92.0</th>\n",
       "      <th>SellerId_93.0</th>\n",
       "      <th>SellerId_94.0</th>\n",
       "      <th>SellerId_95.0</th>\n",
       "      <th>SellerId_96.0</th>\n",
       "      <th>SellerId_97.0</th>\n",
       "      <th>SellerId_98.0</th>\n",
       "      <th>SellerId_99.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>84.11</td>\n",
       "      <td>137.0</td>\n",
       "      <td>145.5</td>\n",
       "      <td>2.0</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93.76</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>94.52</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97.41</td>\n",
       "      <td>82.0</td>\n",
       "      <td>106.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80.32</td>\n",
       "      <td>173.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.0</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 167 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SellerRating   StartingBid   Price   NumBidders  ItemId_0.0  ItemId_1.0  \\\n",
       "0          84.11         137.0   145.5          2.0           0           0   \n",
       "1          93.76          30.0    30.0          0.0           1           0   \n",
       "2          94.52         364.0  1278.0          8.0           0           0   \n",
       "3          97.41          82.0   106.5          3.0           0           0   \n",
       "4          80.32         173.0   173.0          0.0           0           0   \n",
       "\n",
       "   ItemId_10.0  ItemId_11.0  ItemId_12.0  ItemId_2.0  ...  SellerId_90.0  \\\n",
       "0            0            0            0           0  ...              0   \n",
       "1            0            0            0           0  ...              0   \n",
       "2            0            0            1           0  ...              0   \n",
       "3            0            0            0           1  ...              0   \n",
       "4            0            0            0           0  ...              0   \n",
       "\n",
       "   SellerId_91.0  SellerId_92.0  SellerId_93.0  SellerId_94.0  SellerId_95.0  \\\n",
       "0              0              0              0              0              0   \n",
       "1              0              0              0              0              0   \n",
       "2              0              0              0              0              0   \n",
       "3              0              0              0              0              0   \n",
       "4              0              0              0              0              0   \n",
       "\n",
       "   SellerId_96.0  SellerId_97.0  SellerId_98.0  SellerId_99.0  \n",
       "0              0              0              0              0  \n",
       "1              0              0              0              0  \n",
       "2              0              0              0              0  \n",
       "3              0              0              0              0  \n",
       "4              0              0              0              0  \n",
       "\n",
       "[5 rows x 167 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auctions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train-test split\n",
    "\n",
    "auctions_x=auctions.drop([' Price'],axis=1)\n",
    "\n",
    "X=auctions_x.values\n",
    "y=auctions[' Price'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=77, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15073, 166)\n",
      "(3769, 166)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Όπως φαίνεται παραπάνω έχουμε 15.073 καταχωρήσεις για εκμάθηση και 3.769 για έλεγχο."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Παρακάτω δημιουργούμε μια συνάρτηση όπου υπολογίζει το διάστημα εμπιστοσύνης (95%) για τις προβλεπόμενες τιμές και απεικονίζει τις πραγματικές τιμές (50 πρώτες) μαζί με τις αντίστοιχες προβλέψεις."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results(y_test,y_pred):\n",
    "    '''Plot predictions vs original data with 95% CI'''\n",
    "    \n",
    "    stdev = np.sqrt(sum((y_pred - y_test)**2) / (len(y_train) - 2))\n",
    "    interval = 1.96 * stdev #Για επίπεδο σημαντικότητας α=0.05 , z=1.96 (από πίνακα Κανονικής Κατανομής)\n",
    "\n",
    "    df = pd.DataFrame({'Actual Price': y_test.flatten(), 'Predicted Price': y_pred.flatten()})\n",
    "    df_plot = df.head(50)\n",
    "    df_plot.plot(kind='bar',figsize=(16,8),yerr=interval,ecolor='red')\n",
    "    plt.grid(color='white')\n",
    "    plt.title('Actual vs Predicted Prices',fontsize=20)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Επιπλέον κατασκευάζουμε συνάρτηση στην οποία θα δίνουμε ένα μοντέλο, θα υπολογίζει τις μετρικές και θα τις επιστρέφει μαζί με το αντίστοιχο γράφημα που παράγεται από την παραπάνω συνάρτηση."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_and_report(clf,y_test,alpha=False):\n",
    "    '''Fit a model and return report'''\n",
    "    \n",
    "    #Alpha parameter for models with penalty\n",
    "    if alpha==True:\n",
    "        print('Alpha parameter: ',clf.alpha_)\n",
    "\n",
    "    y_pred = clf.predict(X_test)\n",
    "\n",
    "    plot_results(y_test,y_pred)\n",
    "    \n",
    "    print('R^2 on train set :',clf.score(X_train, y_train))\n",
    "    print('R^2 on test set :',clf.score(X_test, y_test))\n",
    "    print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "    print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "    print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 on train set : 0.9756336583737634\n",
      "R^2 on test set : 0.9740225812584035\n",
      "Mean Absolute Error: 35.466745119001224\n",
      "Mean Squared Error: 3812.8668698934716\n",
      "Root Mean Squared Error: 61.74841593023639\n"
     ]
    }
   ],
   "source": [
    "reg = LinearRegression().fit(X_train, y_train)\n",
    "\n",
    "fit_and_report(reg,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Χρησιμοποιούμε την ίδια διαδικασία όπως και για την γραμμική παλινδρόμηση. Εδώ εφαρμόζουμε το RidgeCV όπου επιλέγει αυτόματα την βέλτιστη παράμετρο alpha για το regularization, μέσω 10-fold cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha parameter:  1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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GDVN2drY+/PBDderUSVdeeaX9/cDAQC1btkzPP/98tWdyHjp0SNLp/eIqQ4cOVVBQkF599VXt37+/ynvz58/XsmXL7AF/0KBBCgsL0/Lly6vMu2/fPr311lt1rqshx/fSSy/V1VdfrU2bNtmD/xnvvvuuXnrpJX3xxRfy9/dXeHi4Bg4cqC+//LLKM22Li4trPU3W1UaMGKGioiK99NJLVaYvW7ZMJ06csI9ojxo1SseOHdOzzz5b5Xmze/fu1VNPPaW//vWvCg0Nlb+/vz788EP9+c9/rvZ81IMHD0py7b8tAPBGnOILAB7qvffek81m0/Dhw2u9c+jo0aOVlZWlNWvWaM2aNbrtttuUmZmpkSNHauDAgSopKdFHH32kwMBApaam2pdr166dfvjhB82YMUODBg3SkCFDFBsbK4vFopSUFA0fPlz+/v5at26dOnXqVCVQde7cWb169dJXX32l8ePHq0+fPiooKNBnn32msrIytWzZUgUFBQ5v+4gRI5Senq69e/cqKSmpynv+/v568MEHNWvWLN1yyy0aOnSoAgICtG3bNn333XcaOXKkunTp4vC66yskJESzZs3SH//4R40aNUo33nij2rVrp61bt+q7775Tz549FRcXJ+l0uH766af10EMP6Y477rDfqXbdunUKCwuzX/t5LgMGDGjQ8X3qqad055136qGHHlJ0dLS6du2q/fv3a8OGDQoNDdWMGTPsy6empmrs2LFKSUnRjTfeqAsvvFCff/65UTcKuv/++7VhwwYtXrxY27Zt05VXXqkffvhBGzZsUM+ePXXXXXdJOn0DqTPPO83Ozla/fv10/PhxrVu3TlarVfPnz7dfy/rwww8rMTFRo0aN0s0336zWrVvr+++/15dffql+/fpVe+4wAKB+zPkrAgCol/fff19S1bvX1uSmm25SSEiItm/frj179mj27NmaMWOGAgIClJGRoU8//VTR0dFatWqVLr30Uvtyqamp6tChgzIzM7V+/XpJ0uDBg/Xss8+qY8eOeuedd/Tpp59q1KhReuGFF6qs09fXV4sWLdLo0aN14MABLV++XF999ZWio6OVmZmp66+/Xj/++GO1U4PP15VXXqnOnTtLqnp67xkTJ07U888/rw4dOmjt2rVasWKFysrK9Oijj+pPf/qTQ+tsiN///vdKT09X//799cUXXyg9PV0lJSV64IEHtHTp0io3G7rxxhu1dOlS9ejRQ2vXrtXnn3+u2NjY8350S0OOb5cuXfT2228rNjZWe/bs0bJly7Rnzx6NHDlSb731lv2xLNLpEdeMjAwNGzZM27ZtU2Zmpnr06KGXX37ZSXut4QIDA7Vy5Urdc889OnLkiJYtW6YdO3ZowoQJ+utf/2of6Q8ICNCyZcuUnJys0tJSrVy5Uhs3blSfPn20bNmyKv/GbrjhBr322muKjIzU559/rmXLlunIkSNKTEzUK6+8YlRABwBP5GNz9CIgAAAAAACciK/5AAAAAABGIKACAAAAAIxAQAUAAAAAGIGACgAAAAAwAgEVAAAAAGAE456DmpNTVOc8bdq0UkHBiQavizqeU8ekXqjjmjom9UIdz+mFOq6pY1Iv1HFNHZN6oY5r6pjUC3VcU8eVvYSHB5/zPY8cQfXza0adJlbHpF6o45o6JvVCncavQR3PqmNSL9RxTR2TeqGOa+qY1At1XFPHlF48MqACAAAAALwPARUAAAAAYAQCKgAAAADACARUAAAAAIARCKgAAAAAACMQUAEAAAAARiCgAgAAAACM4OfuBgAAAADAWeLmWpxa7/VpQ8573vT0pVqzZpVWr35fLVq0qHGeffv2qqjouK66qk+9+pg9+0ndcMNN6t//Ovu01177iz799BO1b3+RyssrVF5ervvue0B9+vStsuzy5UsVFdVXPXpE1mud7sAIKgAAAAA4waefrtMNN9yk9ev/ds55NmxYrx9//MFp6xw7dryWL1+uhQv/omnTnlBa2vPV5pk48W6PCKcSI6gAAAAA0GBff/2V2rfvoFtvvU1PPZWqYcOGa8eO7/XnPz8rm82m8PB2mjTpEX388Yfy82uuiIjfaubMx7Rs2Wq1aNFCL7+8UJ06dVZMzDDNn/8n/ec/R1VYWKj+/a/TvfcmnFcPx48XqmXLVpKk2267RZ06dVanTpepuLhIN9xwk3r37qM//Wmmjhw5olOnTmnSpEf029/20Pz5f9LRo4dUWlque+9NqDYC60oEVAAAAABooA8/fE/Dh9+qjh07q3nz5tqx43s988xszZz5J3XufJnefnuN8vPz9fvf36K2bduec0TzP/85qiuu6Klp055QaWmpRo8eVmtAffPNldq0yaKKCpuCgoI0depj9jqvv56u1q1DNXv2k5Kkd9/N1EUXtdfMmXP0ww979dVXW/Xvf/9LrVuHasGC+dq79xclJt6n9PTVTt8/54uACgAAAAANcPz4cf3jH39XQUG+3norQyUlxXr77QwVFOSrc+fLJEmjR98hSdq8eWONNWw2myQpJCREu3bt0Ndff6XAwECVlZXXuu6xY8fr3nv/Vzk5RVWmt24dqtatQ6tM+/nnn+zXsHbpcrm6dLlczz47V9u3f6OJEyeqrOyUKipOqbDwWLVlXYWACgAAAAAN8Le/rdUtt4xUYuJDkqSTJ0/qjjtGKCAgQL/88rMuvbSj0tOX6tJLO8nX11eVlafDqL+/v/LycnXxxe21d++/1LnzZVq79kMFBQVrypTHdODAL3r//Xfs4bU+fH2r326oU6fLtGvXTg0c+DsdPHhAS5a8rCuu6Kl27dpp8uSHdOBAjt5443UFB4c0bIc0AAEVAAAAABrggw/e0xNPPGV/HRAQoEGDhigsLExz5jwlX19ftW3bVrGx49W8eXMtWvRnde58meLj4/XIIw/poovaKzg4WJIUFXW1nnxyurZv/6cCAgLUocOlys3NcUqfI0eO1pw5Tykp6T5VVFTooYcmq0uXyzVv3ixNmDBBx44VatSoO2oMt65CQAUAAADgNWp7LEx4eHC1U2Gd4Y03VlWb9sc/TpMkxcXdV2X6ddcN0HXXDbD3M2hQTLVlly3LqDbtsceerDbtnnvuP2dP77//SY3LPvnk7GrzPvHEU422b+qLx8wAAAAAAIxAQAUAAAAAGMFjAmpYVKTCojzj4bIAAAAAgPrzmIAKAAAAAPBuBFQAAAAAgBEIqAAAAAAAI/CYGQAAAABeI9Eyxan1XhryTK3vf/31V0pNfVSdO18mHx8flZaW6qabbtbtt4+t97pefnmhOnXqrK5dI7R58yb97//eW+N8Gzd+riuuiNQFF4TXWfPLL7O0fv3fqjxq5vDhQ7rrrnGKiOgmHx8flZWVacCA6zRxYtX1/fvfe2rtozEQUAEAAACgAaKi+mrmzDmSpLKyMo0ff5tiYv5HwcHBDtXr2rWbunbtds7316xZpc6dp59XQD2Xzp0vU1raK5KkyspKPfjgfbr22t/p8su7nncfjYGACgAAAABOcuLECfn6+qpZs2ZKSrpPoaFtVFRUpPnzX9Bzz83VgQO/qLKyUvfem6CYmMHasGG93njjNYWGtlF5ebk6deqsr7/+Su+9l6mZM+foww/f1TvvZKqyskIDBgxS9+5XaO/ef2nWrFQtWvSa3nsvUxs2fKZTpyp1ww036Y47xurHH/drzpynFBDQUi1bBig4OKTWnktLS1VWVqaAgADNnv2kCgsLdfx4ocaNmyiL5W819nHPPffLYvlMGRkr5Ovrq169rlJCQnKD9x8BFQAAAAAaIDv7KyUl3SdfX1/5+flp0qRH1KpVK0nS0KE3a9CgwXrnnbfUunWoHn00VYWFx5SYeJ9iYgZr0aIXtWTJGwoJaa1HHnmoSt2Cgnylp7+hN95YpebN/ZWW9ryuuqqPLr88Qo88Ml0HDvyi9es/VUbGKuXmFisl5QFdc01/vfrqy4qPv19XX91f6elL9dNPP1br+ccf9ysp6T75+PjI17eZ/vCHP6hDh0slnR4RHjPmTn399Vfn7OPIkSN6/fW/6NVXlysgIEBPP/2Etm37UsOGDW3QviSgAgAAAEAD/PoU37N17NhJkrRv315t3/6Ndu78XpJUUXFKubm5CgwMVOvWoZKkyMheVZY9ePCgLrvsN2rRIkCS9OCDk6u8/8MP+3T06BHdfffdKis7paKiIh04cED79/+g7t0jJUk9e15VY0D99Sm+khQeHqycnKIqPdfWx86d3+vYsQL98Y8PSjo9cnzw4ME69lTdCKgAAAAA0Eh8fU8/OKVTp85q166d/vCHOJWWntQbb7yukJAQFReXqKCgQG3atNHu3TvVrt2F9mUvuaSDfv75R5WVlcnf31+PPz5FDz30R/n6+qqyslIdO3ZS585dtGzZX5WbW6yMjBXq0uVydezYWd9/v139+1+n3bt31LtnH5+qD3upqY+kpElq1+5CvfDCIvn5+Wnt2g/UtWtEw3aWCKgAAAAA0OhGjhytefNmKSnpPpWUFGvUqDvk7++v6dNTNXlykoKDW8vPr2o8a9Omje688y77qbjXXz9Q4eHtFBnZS7NmzdDzz6epb9+rNW7cOJ04cVLdu1+h8PBwTZ48TTNmPKpVq5YrNDRU/v4tGtR7TX1cdNHFGjPmTiUl3aeKigpdfHF7DRnSsNN7JcnHZrPZGlzFic4MK58tLOr0EHV+9vdVhp8bgjqeU8ekXqjjmjom9UIdz+mFOq6pY1Iv1HFNHZN6oY5r6pjUC3VcU8eVvYSHn/vuxr7nfAcAAAAAABcioAIAAAAAjEBABQAAAAAYgYAKAAAAADCCRwTUuLkW5RZalVtoVdxci7vbAQAAAAA0Ao8IqAAAAAAA70dABQAAAAAY4bwC6rfffquJEydWmfbBBx9ozJgx9terV6/W6NGjFRsbq88//1ySlJ+fr7i4OI0fP14pKSmyWq1ObB0AAAAA4E3qDKhLlizR448/rtLSUvu0Xbt26a233pLNZpMk5eTkaPny5XrzzTf12muvacGCBSorK9OiRYt0yy23aOXKlerRo4cyMjIab0sAAAAAAB6tzoDasWNHLVy40P66oKBAzz77rKZPn26ftn37dvXu3Vv+/v4KDg5Wx44dtXv3bmVnZ2vgwIGSpOjoaGVlZTXCJgAAAAAAvIFfXTPExMTowIEDkqSKigo99thjmj59ulq0aGGfp7i4WMHBwfbXgYGBKi4urjI9MDBQRUVFdTbUpk0r+fk1q3O+8PDgOuc5H9TxnDom9UId19QxqRfqNH4N6nhWHZN6oY5r6pjUC3VcU8ekXqjjmjom9FJnQP21HTt26KefftKTTz6p0tJS7d27V7Nnz1b//v1VUlJin6+kpETBwcEKCgpSSUmJAgICVFJSopCQkDrXUVBw4rx6ycmpO+zWJTw8mDoeUsekXqjjmjom9UIdz+mFOq6pY1Iv1HFNHZN6oY5r6pjUC3VcU8eVvdQWYOt1F99evXrpo48+0vLly7VgwQJdfvnleuyxx9SrVy9lZ2ertLRURUVF2rdvnyIiItSnTx9t3LhRkrRp0yZFRUXVZ3UAAAAAgCakXiOo5xIeHq6JEydq/PjxstlsmjRpklq0aKGEhARNnTpVq1evVps2bfTcc885Y3UAAAAAAC90XgG1Q4cOWr16da3TYmNjFRsbW2WeCy64QK+99poT2gQAAAAAeLt6neILAAAAAEBjIaACAAAAAIxAQAUAAAAAGIGACgAAAAAwAgEVAAAAAGAEpzxmxhXi45e4uwUAAAAAQCNiBBUAAAAAYAQCKgAAAADACARUAAAAAIARCKgAAAAAACMQUAEAAAAARiCgAgAAAACMQEAFAAAAABiBgAoAAAAAMAIBFQAAAABgBAIqAAAAAMAIHh1Qw6IiFRYV6e42AAAAAABO4NEBFQAAAADgPQioAAAAAAAjEFABAAAAAEYgoAIAAAAAjEBABQAAAAAYgYAKAAAAADACARUAAAAAYAQCqoN4BisAAAAAOBcBFQAAAABgBAIqAAAAAMAIBFQAAAAAgBEIqAAAAAAAIxBQAQAAAABGIKACAAAAAIzgkQE1NiNBiZYpyrPmK8+ar0TLFHe3BAAAAABoII8MqAAAAAAA70NABTVR1rIAACAASURBVAAAAAAYgYAKAAAAADACARUAAAAAYAQCKgAAAADACARUAAAAAIARCKgAAAAAACMQUB0QN9ei3EKrcgutiptrcXc7AAAAAOAVCKgAAAAAACMQUAEAAAAARiCgAgAAAACMQEAFAAAAABjhvALqt99+q4kTJ0qSdu3apfHjx2vixIm65557lJubK0lavXq1Ro8erdjYWH3++eeSpPz8fMXFxWn8+PFKSUmR1WptpM0AAAAAAHi6OgPqkiVL9Pjjj6u0tFSSNHv2bD3xxBNavny5hg4dqiVLlignJ0fLly/Xm2++qddee00LFixQWVmZFi1apFtuuUUrV65Ujx49lJGR0egbBAAAAADwTHUG1I4dO2rhwoX21wsWLFD37t0lSRUVFWrRooW2b9+u3r17y9/fX8HBwerYsaN2796t7OxsDRw4UJIUHR2trKysRtoMAAAAAICn86trhpiYGB04cMD+ul27dpKkr7/+Wunp6VqxYoW++OILBQcH2+cJDAxUcXGxiouL7dMDAwNVVFRUZ0Nt2rSSn1+z82o+KW2C/b/Dw4NrmfPcHF2OOq6vY1Iv1HFNHZN6oU7j16COZ9UxqRfquKaOSb1QxzV1TOqFOq6pY0IvdQbUmqxdu1Yvv/yyXnnlFYWFhSkoKEglJSX290tKShQcHGyfHhAQoJKSEoWEhNRZu6DghCMtKSen7vB7tvDwYIeWc9b6z+asfryxjkm9UMc1dUzqhTqe0wt1XFPHpF6o45o6JvVCHdfUMakX6rimjit7qS3A1vsuvu+9957S09O1fPlyXXrppZKkXr16KTs7W6WlpSoqKtK+ffsUERGhPn36aOPGjZKkTZs2KSoqqr6rAwAAAAA0EfUaQa2oqNDs2bN18cUXKzk5WZJ09dVX68EHH9TEiRM1fvx42Ww2TZo0SS1atFBCQoKmTp2q1atXq02bNnruuecaZSMAAAAAAJ7vvAJqhw4dtHr1aknS1q1ba5wnNjZWsbGxVaZdcMEFeu211xrYIgAAAACgKaj3Kb6oWVhUpMKiIt3dBgAAAAB4LAIqAAAAPA6DA4B3IqACAAAAAIzg0GNmIMXHL3F3CwAAAADgVRhBBQAAAAAYgYAKAAAAADACARUAAAAAYAQCKgAAAIzHXXuBpoGACgAAAAAwAgEVAAAAAGAEAioAAAAAwAgEVAAAAACAEQioAAAAAAAjEFABAAAAAEYgoAIAAAAAjEBAdYLYjATlWfOVZ81XomWKEi1T3N0SAAAAAHgcAioAAAAAwAgEVAAAAHgUzl4DvBcBFQAAAABgBAIqAAAAAMAIBFQA8BBhUZEKi4p0dxsAAACNhoAKAAAAADACARUAPBQjqgCairi5FuUWWpVbaFXcXIu72wHQiAioAAAAAAAjEFABAAAAAEYgoAIAAAAAjEBABQAAAAAYgYAKAAAAADACARUAAAAAYAQCKgAAAADACARUAAAAAIAR/NzdgLdISpvg7hYAAAAAwKMRUAEAAOBxGBwAvBOn+AIAAAAAjEBABQAAQJMRFhWpsKhId7cB4BwIqAAAAAAAIxBQAcBwcXMtiptrUW6hVbmFVsXNtbi7JQAAgEZBQAUAAAAAGIGACgAeKDYjQXnWfOVZ85VomeLudgCg0cXHL1F8/BJ3twGgkRFQAQAAAABGIKACAACgyeKuvoBZCKgAAAAAACMQUAEAAAAARjivgPrtt99q4sSJkqSffvpJ48aN0/jx4zVjxgxVVlZKktLS0nT77bdr7Nix2r59e63zAgAAAABwtjoD6pIlS/T444+rtLRUkjRnzhylpKRo5cqVstlsWr9+vXbs2KGtW7dqzZo1WrBggWbOnHnOeQEAAAAAqEmdAbVjx45auHCh/fWOHTvUr18/SVJ0dLSysrKUnZ2tAQMGyMfHR+3bt1dFRYXy8/NrnBcAAAAAgJr41TVDTEyMDhw4YH9ts9nk4+MjSQoMDFRRUZGKi4sVGhpqn+fM9JrmrUubNq3k59es3hsSHh5c72Uaslxj1XVWP95Yx6ReqOOaOib1YmIdZ9XkWFHHXTWo41l1TOqlQXV9farM3+ys142+fg+qY1Iv1HFNHRN6qTOgns3X97+DriUlJQoJCVFQUJBKSkqqTA8ODq5x3roUFJyob0uSpJycusPv2cLDgx1a7ny4sx9vrGNSL9RxTR2TejGxztkcrcmxoo6n90Id19QxqZdzOd+6YZU2SVJ+TpHCw4NV8avXjjJt/3jjMadO49dxZS+1Bdh638W3R48e2rJliyRp06ZN6tu3r/r06aPNmzersrJShw4dUmVlpcLCwmqcFwAAAACAmtQ7oE6dOlULFy7UmDFjVF5erpiYGEVGRqpv374aM2aMkpOTlZqaes55AQAAAHeIm2tRbqFVuYVWxc21uLsdADU4r1N8O3TooNWrV0uSLrvsMqWnp1ebJzk5WcnJyVWmnWteAAAAAADOVu9rUAEA7hEfv8TdLQAAADSqep/iCwAAAMAcYVGRCouKdHcbgFMwggoAHiopbYK7WwAAAHAqRlABD8W3pQAAAPA2BFQAAAAAgBEIqAAAAGiSYjMSlGfNV541X4mWKUq0THF3S0CTR0AFAAAAABiBgAoAAAAAMAIBFQAAAABgBAIqAAAAAMAIBFQAAAAAgBEIqAAAAAAAIxBQAQAAAABGIKACAAAAAIzg5+4GAAAAAFeJj1/i7hYA1IIRVAAAAACAEQioAAAAgIeKm2tRbqFVuYVWxc21KG6uxd0tAQ1CQAUAAAAAGIGACgAAAAAwAgEV8DBnTt/59ek8AAAAgDfgLr4AAABospLSJri7BQC/wggqAAAAAMAIBFQAAAAAgBEIqAAAAAAAIxBQAQAAAABGIKACAAAAAIxAQAUAAAAAGIGACgAAAAAwAgEVAAAAAGAEAioAAAAAwAgEVAAAAACAEQioAAAAAAAj+Lm7AQAAAACOi49f4u4WAKdhBBUAAADwMmFRkQqLinR3G0C9MYIKeCi+LQUAAIC3YQQVAAAAAGAEAioAAAAAwAgEVAAAAACAEQioAAAAAAAjEFABAAAAAEYgoAIAAAAAjEBABQAAAAAYgYAKAAAAADACARUAAAAAYAQ/RxYqLy/XtGnTdPDgQfn6+urpp5+Wn5+fpk2bJh8fH3Xt2lUzZsyQr6+v0tLStGHDBvn5+Wn69Onq1auXs7cBAAAAAOAFHAqoGzdu1KlTp/Tmm2/q73//u1544QWVl5crJSVF11xzjVJTU7V+/Xq1b99eW7du1Zo1a3T48GElJycrMzPT2dsAAAAAAPACDp3ie9lll6miokKVlZUqLi6Wn5+fduzYoX79+kmSoqOjlZWVpezsbA0YMEA+Pj5q3769KioqlJ+f79QNAAAAAAB4B4dGUFu1aqWDBw/q97//vQoKCrR48WJt27ZNPj4+kqTAwEAVFRWpuLhYoaGh9uXOTA8LCztn7TZtWsnPr1m9ewoPD67/hjRgucaq66x+vLGOSb1QxzV1TOrFxDrOqsmxoo67alDHs+qY1Etj1vWWz3DNfH0aXM+0baJO49cxoReHAurSpUs1YMAATZ48WYcPH9Zdd92l8vJy+/slJSUKCQlRUFCQSkpKqkwPDq692YKCE460pJyconovEx4e7NBy58Od/XhjHZN6MbGO5Ni/ubOZtF0m9WJinbM5WpNjRR1P74U6rqljUi/n4s6/pybun4pKmyQp341/H6jjWXVc2UttAdahU3xDQkLsQbN169Y6deqUevTooS1btkiSNm3apL59+6pPnz7avHmzKisrdejQIVVWVtY6egoAAACgYWIzEpRnzVeeNV+JlinubgeoF4dGUO+++25Nnz5d48ePV3l5uSZNmqTIyEg98cQTWrBggbp06aKYmBg1a9ZMffv21ZgxY1RZWanU1FRn9w8AAAAA8BIOBdTAwED9+c9/rjY9PT292rTk5GQlJyc7shoAAAAAQBPi0Cm+AAAAAAA4GwEVAAAAgPHCoiIVFhXp7jbQyAioAAAAAAAjEFABAAAAAEYgoAIAAAAAjEBABQAAAAAYgYAKAAAAADACARUAAAAAYAQCKgAAAADACARUAAAAAIARCKiAmznrodM8vBoAAACejoAKAAAAADCCn7sbAAAAAOBcSWkT3N0C4BBGUGFX0yminDYKAAAAd+BzaNNEQAUAAAAAGIGACgAAAAAwAgEVAAAAAGAEAioAAAAAo8VmJCjPmq88a74SLVOUaJni7pbQSAioAAAAAAAjEFABAAAAwEW4O3HtCKgAAAAAACMQUAEAAAAARiCgAi7GaR0AAABAzQioAAAAAAAjEFABAAAAAEYgoAIAAAAAjEBABdzo7IdOu7sOAAAA4E4EVAAAAACAEQioAAAAAAAjEFAhSYqba1FuoVW5hVbFzbUobq6F00YBAADgFmd/NkXTQUAFAAAAABiBgAoAAAAAMIKfuxsAAAAAgLokpU1wdwtwAUZQAQAAAABGIKACAAAAQCM7cyNSbv5UOwIqAAAAAMAIBFQAAAAAgBG4SRLgImdO43i10CpJip9rUct+7uwIAAAAMAsjqAAAAAAAIxBQAQAAAABGIKACAAAAAIzANaiAmznrodM8vBoAAACejhFUAAAAAIARHB5B/ctf/iKLxaLy8nKNGzdO/fr107Rp0+Tj46OuXbtqxowZ8vX1VVpamjZs2CA/Pz9Nnz5dvXr1cmb/AAAAAAAv4VBA3bJli7755hutWrVKVqtVr7/+uubMmaOUlBRdc801Sk1N1fr169W+fXtt3bpVa9as0eHDh5WcnKzMzExnbwOcJD5+SbVpnDYKAAAAd6jpsym8n0On+G7evFkRERFKTEzU//t//0+/+93vtGPHDvXrd/qhjtHR0crKylJ2drYGDBggHx8ftW/fXhUVFcrPz3fqBgAAAAAAvINDI6gFBQU6dOiQFi9erAMHDighIUE2m00+Pj6SpMDAQBUVFam4uFihoaH25c5MDwsLO2ftNm1ayc+vWb17Cg8Prv+GNGC5xqrrrH4aY7saUtMZ/Zi2b0zbx86qY0IPzqzhzXWcVZNjRR131aCOZ9UxqZfGrMtnuIYv11h1nFXXtO0yqY4JvTgUUENDQ9WlSxf5+/urS5cuatGihY4cOWJ/v6SkRCEhIQoKClJJSUmV6cHBtTdbUHDCkZaUk1NU72XCw4MdWu58uLOfxtouR2s6ox/T9o1p+9hZdUzaPyb1YmKds/HzSR1X1jGpF+q4po5JvZyLO/+Gmr5/vHGbJO/ZroqOnSRJ+dnfu7UfV+6b2gKsQ6f4RkVF6YsvvpDNZtPRo0dltVp17bXXasuWLZKkTZs2qW/fvurTp482b96syspKHTp0SJWVlbWOngIAAAAAmi6HRlAHDx6sbdu26fbbb5fNZlNqaqo6dOigJ554QgsWLFCXLl0UExOjZs2aqW/fvhozZowqKyuVmprq7P4BAAAAAF7C4cfMTJkypdq09PT0atOSk5OVnJzs6GoAAAAAwGtwd+LaOXSKLwAAAJqesKhIhUVFursNAF6MgAoAAAAAMILDp/gCcAyndQAAAAA1a/IjqJyqAgAAAABmaPIBFQAAAABgBgIqAAAAAMAIBFQAAAAAgBEIqAAAAAAAIxBQAQAAAABGIKACAAAAAIxAQAUAAAAAGIGACgAAAAAwAgEVAAAA1YRFRSosKtLdbQBeLTYjQXnWfOVZ85VomeLudoxAQAUAAAAAGIGACgAAAAAwQpMLqJyuAgAAAABmanIBFQAAAPUTm5GgRMsUrpUD0OgIqAAAAAAAIxBQAQAAAABGaNIB9ezbOnO6CgAAAAC4T5MOqAAAAAAAcxBQAQAAAABGIKACAAAAAIxAQAUAAAAAGIGACgAAAAAwAgEVAAAAAGAEAqqXCIuKVFhUpLvbAAAAAACH+bm7ATSOM2E1P/t7N3cCAAAA4FyS0ia4uwWjMIIKAACAKuLmWpRbaFVuoVVxcy3ubgdAE0JABQAADuHyEgCAsxFQAQAAAABGaFIBldNVAABwDKOlAABX4CZJAAAAOC/czAVAY2tSI6gAAIDRUACAuQioAAAAAAAjNPlTfDlVBQAAAADMwAgqAABejNN5AQCehIAKAAAAADACARVAo2DUBgAAAPVFQAUAAAAAGIGACgAAAAAwAgEVAAAAAGAEAqqHi5trUdxci3ILrcottCpursXdLQEAAACAQwioXig2I0F51nzlWfOVaJni7nYAAIAHio9fovj4Je5uA0AT06CAmpeXp0GDBmnfvn366aefNG7cOI0fP14zZsxQZWWlJCktLU233367xo4dq+3btzulaQBm4Y69AAAAcAaHA2p5eblSU1MVEBAgSZozZ45SUlK0cuVK2Ww2rV+/Xjt27NDWrVu1Zs0aLViwQDNnznRa4wAAwD1iMxKUaJnC2TqG4ktDAJ7M4YA6b948jR07Vu3atZMk7dixQ/369ZMkRUdHKysrS9nZ2RowYIB8fHzUvn17VVRUKD8/3zmdO4jTVQAAqB/udQAAcBU/RxZ6++23FRYWpoEDB+qVV16RJNlsNvn4+EiSAgMDVVRUpOLiYoWGhtqXOzM9LCzsnLXbtGklP79m9e4pPDy43suYWMdZ62+Mmg2p44weTNiOxqjTGDVdfqx8faot26yGaS7ppQnVcVZNfj69vE4j/Xyezd1/96jzKzUcX5N+7zRGL86s6+7t8sZjZdoxN3m7+HvuYEDNzMyUj4+P/vGPf2jXrl2aOnVqlZHRkpIShYSEKCgoSCUlJVWmBwfX3mxBwQlHWlJOTpFDy5lUJzw82Gnrb2gvzqzjjO1y1r4xrc7ZPPVYhVXaJEn5/7dseHiwKs6a5qpemkqds/HzSZ1zaYyfz5q48+8edaqq6Zib8nunsXqRvOMznDceK9OOuenb1VT+ntcWYB06xXfFihVKT0/X8uXL1b17d82bN0/R0dHasmWLJGnTpk3q27ev+vTpo82bN6uyslKHDh1SZWVlraOnAACgcXGndwCAyRwaQa3J1KlT9cQTT2jBggXq0qWLYmJi1KxZM/Xt21djxoxRZWWlUlNTnbU6AAAAAICXaXBAXb58uf2/09PTq72fnJys5OTkhq4GAADUU9xci14ttEqS4v/v5kYt+7mzIwAAateg56ACAAAAAOAsBFQAAAAAgBEIqACcjpuwAAAAwBEEVAAAAACAEQioAAAAAAAjEFABAAAMFRYVqbCoSGPqAEBjc9pzUOFe8fFLqrxOSpvgpk4AAAAAwDEEVAANwnMWAQAA4Cyc4gsAAAAAMAIjqAAAwCFcTmKems5q+eC5ke5sCQDqhYAKAADqdPa9DtA4ztzIKD/7ezd3AgDuwSm+AAAAAAAjEFABAAAA8DgiGIFTfAEAaGK4dhQAYCpGUAEAAAAARmAEFQAAL8bNjQAAjc2ZN3hjBBUAAABAjbguFa7GCKqbcTt5eCuucYM34Hc0AACuxQiqYfiWCgAAAEBTRUAFAKCR8eUjAADnh4AKAAAAADACARUAAMBAsRkJyrPmK8+ar0TLFLfXAQBXIKACAAAAAJzK0ctbuIsvgAbjOYvwFty1FwAA9yKgAgAAeBG+NATgyTjFFwAAAABgBEZQAeAczj7dk9M/ATSWuLkWSdKrhVZJUvxci1r2c2dHAOAejKACAAAAAIxAQAUAAACauLi5FuUWWpVbaLWP6APuQEAFAAAAABiBgAoAcBtHn5EGAAC8EwEVAAAAAOAQZ58eTkAFAAAAUEVsRoISLVOUZ81XnjVfiZYp7m4JTQSPmXGTmm8nv05p1nxJUpJlil4a8ozb+gOApi42I6HK72RJ5/17mUcSAQDgGEZQAQAAAABGIKACANCIYjMSOEUOAIDzxCm+AAAAhkpKm2BUHQBobIygAgBgKB7DAwDwNA29wRYBFYDR+IAOV3H2bfLRtPG7CwAcQ0AFYAw+0AG142cEAODtCKgwVk0fxPhwBgAAADiHiZ+tuUkSADQhnvB8Tk/oETibs/7dxscvcUY7AOCxCKgAUIO4uRa9WmiVJMXPtahlv3VKs+ZLkpL+72L/l4Y847b+AAAAGoO7vyh2KKCWl5dr+vTpOnjwoMrKypSQkKDLL79c06ZNk4+Pj7p27aoZM2bI19dXaWlp2rBhg/z8/DR9+nT16tXL2dsAAGgAd/8hAgAAOMOhgPr+++8rNDRU8+fPV0FBgUaNGqXf/va3SklJ0TXXXKPU1FStX79e7du319atW7VmzRodPnxYycnJyszMdPY2AAAAAGggTjGHCRy6SdLNN9+shx56yP66WbNm2rFjh/r16ydJio6OVlZWlrKzszVgwAD5+Pioffv2qqioUH5+vnM6BwB4NB7rAgDwZCbeYMgbODSCGhgYKEkqLi7Wgw8+qJSUFM2bN08+Pj7294uKilRcXKzQ0NAqyxUVFSksLOyctdu0aSU/v2b17ik8PLjey5hex6ReGlrHoWV9faosGx4eXG2ay3ppxDqNUdOE/eNQTcOPubPqmrSPJamZQfvYa455LdtQr97OquOMY9WgfhqhjrvX77Q6TeBYOauO238+G6mOu9fvipru3jfOquusfkz7OXfn5+26Rt/rU8vhmyQdPnxYiYmJGj9+vIYPH6758+fb3yspKVFISIiCgoJUUlJSZXpwcO3NFRSccKifnJwih5YzuY5JvTSkTnh4sEPLhlXaJEn5OUX2Gr+e5speGqvO2Tz1WNXlfGuafMxr4khdk/bxmX4qDNrH3nLMa9uG+vR29s9EQ49VTdz5e8e038kNqePtx8pZdUz4+WyMOib8G6xNQ2ompU1oUB3Tjrkzj5VJP+cmfd6uydm91RZYHQqoubm5iouLU2pqqq699lpJUo8ePbRlyxZdc8012rRpk/r376+OHTtq/vz5uueee3TkyBFVVlbWOnraFHGuPwCY69cfzAAAQONzKKAuXrxYx48f16JFi7Ro0SJJ0mOPPaZZs2ZpwYIF6tKli2JiYtSsWTP17dtXY8aMUWVlpVJTU53avDfiwxAAeK4z19JWfUSROzsyD3eNBgDUxqGA+vjjj+vxxx+vNj09Pb3atOTkZCUnJzuyGgAAvAJfPgIAcH4cuosvAAAAAADORkAFAABuxaMaAABnOHwXXwAA0Dhqupb1g+dGurMlAGjyuIa+fhy9vIURVABGis1IUKJlivKs+cqz5ivRMsXdLaEJiI9fwt3V0WCxGQn87gLgct5yNgoBFR6BsOL94uZalFtoVW6h1T56BM/iLX8YTUXoAQA0BQRUGKkxwwofotFUnf1zZdoXAXwRBRPwNwJAU2Hq4ADXoMLrNdb1AlyH4P3OPtWTR4X8F//+gdN49i0AbxGbkSBJSrPmS5KSLFP00pBnXN4HI6jAeeJbdQAAAKBxEVABoAnjukY0dXz5CABmIaACAAAAQD3wBW/j4RpUuATXqwGoCY90AQCg/rz5szUjqAAAAAAAIxBQAQAAAABGIKDC6bjhBAAAAABHEFABAHCy+PglXF9bA1MfCg8AMAcBFQAAAI2GM6sA1AcBFQAAuEVsRoISLVN4VAMAwI7HzABNWE23KPfm25YDnobThAHA/c5ckvBqoVWSFD/Xopb93NmRdyOgAjBaUtoEd7cAAPXG767aecOXod6wDYCJOMUXgDG4sYxn4jRNAADcKzYjwWv+DjOCCmPVFFQa+o10bEaC0qz5p2v93w/vS0OeaVBNoC5nf8vOt+5wFKNyAABvR0AFgCbEE0aoCWFwl4Z8iekJP1toXHz5CE9k4u8uAiq8WtxcS6Nc0M5ILJqyxvq5AgDAFfgyoXbu/qKYgAqcBz6Qw5u5+w+RM/z/9u49qIr7/OP4By+lCiS1arW1TGViYiypWjWkxoAGYlrbxFQT72DiZSagNhrppFWIUi8F28baeKklk5LEW20GY4LWxCLeaxCJoFHJKIgxaWrRI5UCgsD394fl/JCiPXsOR5bj+zXDqOvsc57dfdjvPrt7djnYAAB7Yz8NV/GQJAA+h5fCA3DF1JQsXfxXpS7+q9L5GgngdmPM8j7WcevCFVR4XePbYbkVFoC3cabe97GNAfu6U34/W+oOpMZ39knyqbv7uIIKAABaPa6QAIBvoEEFAAAAbIKTLbjT0aACAAAAAGyB76CiWfn6PfFone6U78IArYEd37kHALAPrqACAADAK3hSMgCraFABAAAAALbALb4AAKBFtdSrGgAA9kODCtyhmv6+8Pu8sxYA4DVjN8dJUqsea+pvVW44hma88lRLpoTbhO/Q3x40qAAAAADQyvnK3Sg0qADgJU2dZfflq9S+MjACAICWQ4OKOw4H0QCAm2GMAICWRYMKn+et7wtwEIM7Gd/DAYD/N3ZznM/eHQPcbjSogIs4IAcAALDGbg/GanwyQRInFGyG96AC8Cm8FP7OwzaHJ6ZPf40TkGgRU1Oy2H8BTaBBBQD4lLGb43Sp0qFLlQ7NzHpJM/9zhhy+g4N6AK5if9H6cIsvbgu+rwkAAOD7Gr9nvUNYCyfko3z5zg+uoAIAgFaNq+YA4DtoUAEAAAAbaHyyBbgTeb1Braur04IFCzRu3DjFxMTo3Llz3v5IAHDiygoAAEDr4fXvoGZmZqq6ulqbN29WXl6eUlJS9Pvf/97bH4sW5Mv3xAMAAADwHq83qLm5uQoPD5ck9e/fXx9//LG3PxIAAAA2wYlrAFb4GWOMNz8gISFBjz/+uIYOHSpJGjZsmDIzM9WuXdO9cU1Nrdq1a+vNlADU69nz+p/Fxbee1to0XoaWXia75eOLWMd3HrZ56+YL26u5atAXa9luxxe+uI59mNevoAYGBqq8vNz577q6ups2p5J0+XLF/4zZtWuQSkrKPM6NOK0njp1y8ak4Ocev//mfebt2DVJJo2m3NZ9mivHVuuvn3RwlZeraNUi1Df5NPs0fxw65+Po6tlscO+TCNr+9cZo7l696TtToEwAAEpRJREFUuL3ssG6aqwbtWsvNuW5aepvbdR3bLc7tzKVr16Cb/p/XH5I0YMAA7du3T5KUl5en++67z9sfCQAAABtz5H4sR27r/tqXLywDYEdev4I6fPhwHTx4UOPHj5cxRr/85S+9/ZEAAAAAgFbI6w1qmzZttGjRIm9/DADYVuMz7JxxBwAAaJrXG1QAAADA13HyEWgeNKgAAAAA7hicTLA3GlQAQKvHwQYA2Bv7abiKBhWAz2EQBAC0FoxZ3sc6bl1oUAEAPoeDEd/HNgbsi99PeMLr70EFAAAAAMAVXEEFAACtHlds4CuoZdzpuIIKAAAAALAFrqAC8HmcjQYAAGgduIIKAAAAALAFGlQAAAAAgC3QoAIAAAAAbIEGFQAAAABgCzSoAAAAAABboEEFAAAAANgCDSoAAAAAwBZoUAEAAAAAtkCDCgAAAACwBRpUAAAAAIAt0KACAAAAAGyBBhUAAAAAYAs0qAAAAAAAW6BBBQAAAADYAg0qAAAAAMAWaFABAAAAALZAgwoAAAAAsAUaVAAAAACALdCgAgAAAABswc8YY1o6CQAAAAAAuIIKAAAAALAFGlQAAAAAgC3QoAIAAAAAbIEGFQAAAABgCzSoAAAAAABboEEFAAAAANgCDSoAAAAAwBZaVYNaV1fX0incVHV1tUfzX7161eMYly5d8mj+enV1dbpw4YLH69vhcMid1+z++9//9uhzb6a6ulpXr171KAavDQYAAAC8p21SUlJSSydxK+fPn9e8efO0bNkyrV+/Xmlpafrb3/6mBx54QJ06dbrt+WRlZWnq1KnasGGDunTponvvvVeSNGXKFI0aNcrlOPXLlZ2dLX9/f02bNk0bN25UcHCwQkJCXIpx9uxZlZaWOn/mzJmjQYMGqbS01PK6mT9/vqKiopSfn6/JkycrKytLb775pkJDQ9W9e3eXYqSnpysrK0sBAQGaPHmytm3bpjfeeEP33HOPgoODXc7lwQcfVJcuXRQaGmppGRo7e/asFi5cqMzMTHXr1k1Tp07V+vXr1bVrV+d2c8Wnn36quXPnasWKFfr1r3+tvXv36sMPP9SAAQMUEBDgUY5Aa5SZmakNGzZo+/btys7OVnl5uXr16iU/P7/bnovD4dDvfvc75eTk6P7771eHDh0kSatWrVJYWJjLcYwx2rVrl65cuaIOHTooKSlJWVlZ6tevnzp27Oh2fsnJyQoPD7c8344dO3TvvfeqoqJCy5cv1+uvv64zZ86oX79++tKXvuRSjPPnzysvL0/du3fXmjVr9Mc//lEFBQXq27ev/P39Xc4lPj5egwYN8mg91NuzZ48+++wzde/eXUuXLlVGRoYeeOABBQUFWYqTkZGhTZs2aceOHcrNzVVdXZ2+9a1vWc6HWnZNS9axRC3/L9Sx69gn38hOddyQn7H5JaHJkycrPj5e/fr1c07Ly8tTSkqK/vSnP932fMaOHavU1FQZYzR79myNGjVKo0aNUkxMjNatW+dynJiYGP3kJz/R559/rqVLl+qDDz6Qv7+/pk+f7vJyDRs2TF/+8pf1ta99TcYYFRQU6P7775efn5/eeustS8s1efJkvfXWW3ruueeUlJSknj176sKFC4qPj9f69etdivH0009r3bp1iouLU1JSkkJCQnThwgXNmDFD6enpLucybtw4hYaG6syZM5o1a5alHVpD0dHRmjFjhsrKyrRgwQK99957CgoK0pQpU7R582aX40ybNk2JiYkKCQlRXl6e9uzZo8cee0yvvvqqUlNTLeeVmZmpQ4cOqaysTHfddZcGDhyoH/zgB7d9IHE4HEpNTZW/v7+ee+4550mNVatWadasWS7HqR9EunTpopCQECUnJ6tNmzaaO3euunTp4nZ+ycnJmjdvnuX5duzYoREjRqiiokIrV65UQUGBQkNDFRcXZ+mEwvnz51VUVKSHHnpIqampOnHihHr16qXY2FhLO+74+HjNnz9fnTt3trwsDe3Zs0ft2rVTWFiYUlJSdOXKFc2dO1ff+MY3LMXJyMhQbm6uKisr1alTJz388MOKiIhwef5f/OIXqqurU0REhAICAlReXq59+/appqZGS5cudTnOrX4Hx40b53Kc6dOna/jw4aqpqdHGjRuVmpqqHj16OPdprlq0aJEqKytVUlKi0tJSjRs3TgEBAXrvvfe0du1al+OMHz/e+XdjjAoLC9WrVy9JsjRm1eefkJCg4OBgDR8+XIcOHdLRo0f1yiuvuBRj4sSJmj17trZt26bu3bsrMjJSOTk5OnDggKV9V2RkpO6++25FR0dr9OjRbu+rEhISVFVVpfLycjkcDo0cOVLdunXTpk2b9Prrr7scZ8mSJQoKCtJ3v/td7d69W507d1ZpaakCAwM1Z84cl+NQyzdnpzqWqOVboY5vzU61TB27rp1bc91G1dXVNzSnktS/f3+3YsXExOjatWs3TDPGyM/Pz+Uibd++vb7yla9IktasWaNnn31WX//61y0XR01NjbPxys7Odh68tmvn+iZJT0/XwoULNWHCBA0ZMsRyk9yUtm3bqmfPnpKkbt26WbrNt3379urYsaMCAgKcV0y7detmed34+/trwYIFOn78uFJTU7Vo0SINHjxYwcHBmjx5sstxampq9PDDD8sYo+XLl6tbt26SrK1j6fotx/VXtfv376/ly5drzpw5unLliqU40s0HkgMHDlgaSJrDSy+95BxEoqOjnYPI4cOHLcVZvHhxk4NIYmKix4NIfn6+JGuDyKZNmzRixAgtXbpUwcHBSkxM1KFDh7RgwQJLB0Q/+9nPNHv2bC1dulTdu3fXnDlzlJOTo/j4eEsDydGjRzV9+nSPBpKGg8jKlSudg8jLL7/s1iASGRmp3bt3KzAwUPv27dNHH33k8iBy+vTp/zppFRUVdcP2c0VRUZF2796tkSNHWpqvserqaufBU58+fTRjxgytW7fO8u34BQUF2rhxo6qrq/Xkk09qzJgxkm590NaUSZMmKT09XQkJCerQoYPi4+Mt1V1j586dc+4b7rnnHu3cudPledu2bauHHnpIa9eu1eLFiyVdX0c7duywlEOPHj20evVqvfrqqxo5cqSeeOIJRUREKDg4WIGBgS7HKS4u1oYNG2SM0Y9+9CNNmjRJkvTmm29ayqegoMBZgxEREYqNjdXatWs1YcIES3Go5ZuzUx1L1PKtUMe3Zqdapo5dZ/sGtXfv3po3b57Cw8MVFBSk8vJy7d27V71797Yc66c//akSExO1evVqtW3b1q18evTooeTkZM2ePVuBgYFatWqVpk2bZrlZCQkJUUJCghYvXqyUlBRJUmpqqqUrTp07d9aKFSu0bNkyHT9+3NLnN1ZWVqbRo0eroqJCb7/9tkaOHKmUlBRLV2ciIyMVFxen++67T88//7zCw8O1f/9+fe9737OUS/1O7Dvf+Y5WrlypsrIy5eTk6OzZs5bi9OjRQy+++KJqa2sVEBCg3/72twoMDFTXrl0txfnmN7+pBQsWKCIiQnv27FGfPn20c+dO520rVjTHQBIfH3/T/7Oy02UQuTU7DSR2GkTq6up05MgRDRo0yDktJydH7du3t5TLvHnzVFRUpIiICPXt29fSvA3V1tbqk08+Ue/evTVgwAA9//zziouLU0VFheVYubm5GjhwoNLS0iRdryGrzwZ48skn1atXL/3qV7/SvHnz5O/vrx49eljOpbi4WG+88Ybatm2rkydP6tvf/raOHz9uKZ+goCC9//77Gjp0qLZu3apHH31Ue/futbzv8vPz01133aXExEQ5HA69//77WrNmjYqLi5WRkeFynJqaGu3bt0+lpaW6dOmSCgsLFRgYqJqaGkv5VFVVKT8/X/369dORI0dUU1OjkpISVVZWWorTVC0fPnyYWpZ36/jYsWOWf6+aquU9e/a0aC3v379fly9fdtZyQEBAi9Qy++Rba+5abteundu13BrquCX3yTcwNldXV2d27txpkpOTTUJCgklOTjYffPCBqaurcyvea6+9Znbu3Ol2PteuXTPp6emmoqLCOa2kpMQsWbLEUpza2lrz17/+9YZpW7duvSGuFenp6WbSpEluzVuvqqrK5Ofnm08++cRUVVWZjRs3murqaksxsrOzzSuvvGISExPNb37zG7N7927LeWzZssXyPE25du2ayczMNGfOnDFffPGFSU5ONmvWrDHl5eWW4lRVVZn169ebpKQks3nzZlNTU2OOHj1qHA6H5ZwmTJhgcnJybph2+PBhEx0d7XKMnTt3mhEjRpjs7Oz/+rFi4sSJpqCgwPnv7du3m4kTJ5of//jHluJMmDDBHDlyxBhjzOeff26MMaa4uNiMHz/eUhxjjDl58qSZPn26KSwsNDExMZbnN8aY8PBwk5aWZp599llz4sQJY4wxx44dM+PGjbMUJy4uzuzYscOkpaWZd955x5SWlpp3333XTJkyxVKchstx6dIls2HDBjNr1izzxBNPuBxj7NixZt++febdd981YWFh5syZM+Yf//iH5XX8zDPPmLy8PGOMMTk5OWbatGnmn//8p3nqqadcjnHu3DkTGxtrIiIiTHh4uBk6dKiJjY29oZZc5XA4zGeffXbDtKqqKksxTp06ZaKjo83Fixed07Zu3WrCwsIsxTlz5oyZOXPmDdNiY2PNRx99ZClOvcuXL5sZM2Y4t7PV5Tpx4oR5++23TVJSktmyZYu5cuWKGTNmjKV8Ll26ZH7+85+bxx9/3ISGhpohQ4aYF154wfz973+3lMuLL77Y5PSrV69ainPq1Ckza9Yss3r1arNt2zYzePBgM2LECOf+w1UnTpwwTz/9tBkyZIgZP368KSoqMmlpaZbHm8a1HBYWZqZOnWqKi4stxTHm+rqur+XKykrL29uY6/u/6OhoU1JS4oxT/ztvxenTp83MmTOdx0mVlZVm6tSp5ujRo5Zzqq/jqKgoy8cDxlzfVn/+85/NwoULzZYtW8yFCxfM2LFjzcmTJy3FaVzLgwcPbpZadndbNVXLP/zhD01ubq6lOCdOnDCjR4921nJBQYH5wx/+YLKyslyO0bCOH3nkERMaGmpiY2PN2bNnLS7VjXXccJ9qReM6vnjxolv75NOnT5sZM2Y467ikpMTExsa6VcfG/H8tDx8+3K35G9fyxYsXzZgxYyzVcuM67tu3r3nhhRecx06ualzH7m6rU6dOmZkzZzrrODQ01IwYMcLjOs7JyTFpaWmW6rgx2zeogC+pH0jCw8PNI488YiIiItwaSJYsWWL+8pe/eJRL40HEGPcO7BsPIsZ4dmDvcDhuOLC3qvEgUn9gX9+suupmB/eeDiTuOHnypJk5c6ZZtWqVRwf2H3/88Q2DSP2BvZVBZNeuXWbYsGEmKirKbNu2zTnd6gmF+jiPPfaY2b59e7PHmThxYovnk5GRYfLz8z2O4+56tvO2ao58PF2uTz/91MTFxZmXX37ZHDhwwAwbNsxERkZaPqhqGOfgwYPm0UcfNVFRUc0WZ9euXR7HiYyMtBSnYYz9+/ebvn37NtsyebqO7bit6vPxZFsdOHDArW1eVFRkioqKTGFhoSksLDTPPPOMc5oV9fPU/4wZM8btOIWFhf8V5/Tp0y2WT/3P6dOnTZ8+fczZs2c9ilNYWOhWPo1jNOe2ao5l8iSf+vpzd900RoMK4Aa1tbUtnYKpra01x44da+k00IQxY8aY0tJS43A4TExMjPOOByt3AdTHuXz5crPEaa587LZcnsaxUy71cey0raKjo012drZ55513zMCBA83FixdNWVmZ5bst6uNs2bLFZ+LYKZeGcXx1W3myXEOHDjXf//73TUxMjImOjjYPPvigiYmJsXzCpnGcQYMGmejoaFvFsdtyWc3H7tuqpfNpyPbfQQV8SVMP6qrn6oOAmuNhX82Vi7fjsFzez8VqnPbt2+vuu++W5NmD4prrgXPNmY/dlsvTOHbKpT6OnbZVw4cVfvjhh249rLBxHHcfemi3OHbKpXEcX91W7i5XUw/MtPomh5vFcefBm96MY7flspqPLy5Tc+bTkO1fMwP4kvz8/Js+qMvVL+03RwzitK44dsrlpZdeUqdOnTR79mx17NhRX3zxhfNBcQcOHHA5F+J4P46dcrFjnPnz58vPz0+LFy9WmzZtJF1/WOHJkye1YsWKOzqOnXIhzv9WU1OjZcuWqXPnzjp48KDbzQFxvB/HTrnYMY6T29deAbjF0wd1NVcM4rSuOHbJpbkeFEcc78exUy52jNNcDyv0xTh2yoU4rmuOB2YS5/bEsVMudozDFVQAAAAAgC20aekEAAAAAACQaFABAAAAADZBgwoAAAAAsAUaVAAAAACALdCgAgAAAABs4f8ATAh8DHWKRYsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 on train set : 0.975632065870068\n",
      "R^2 on test set : 0.9740429271760096\n",
      "Mean Absolute Error: 35.496366736103724\n",
      "Mean Squared Error: 3809.880573373789\n",
      "Root Mean Squared Error: 61.72423003467754\n"
     ]
    }
   ],
   "source": [
    "ridge_reg = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1],cv=10).fit(X_train, y_train)\n",
    "\n",
    "fit_and_report(ridge_reg,y_test,alpha=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Με τον ίδιο τρόπου που εφαρμόσαμε το RidgeCV εφαρμόζουμε τώρα το LassoCV με 10-fold CV για την επιλογή της παραμέτρου alpha."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha parameter:  0.1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 on train set : 0.975351563514773\n",
      "R^2 on test set : 0.974299134751405\n",
      "Mean Absolute Error: 35.147586769134875\n",
      "Mean Squared Error: 3772.2753984425362\n",
      "Root Mean Squared Error: 61.41885214201366\n"
     ]
    }
   ],
   "source": [
    "lasso_reg = LassoCV(alphas=[1e-3, 1e-2, 1e-1, 1],cv=10,max_iter=100000,n_jobs=-1,random_state=77).fit(X_train, y_train)\n",
    "\n",
    "fit_and_report(lasso_reg,y_test,alpha=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stohastic Gradient Descent Regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Για την χρήση SGD επιπλέον κάνουμε τυποποίηση  στα δεδομένα."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd_reg = make_pipeline(StandardScaler(),SGDRegressor(max_iter=1000, tol=1e-3,random_state=77))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 on train set : 0.9750027069330522\n",
      "R^2 on test set : 0.9735853350031333\n",
      "Mean Absolute Error: 36.41131952022137\n",
      "Mean Squared Error: 3877.044214736261\n",
      "Root Mean Squared Error: 62.26591535291408\n"
     ]
    }
   ],
   "source": [
    "sgd_reg.fit(X_train, y_train)\n",
    "\n",
    "fit_and_report(sgd_reg,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forests Regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Παρακάτω χρησιμοποιούμε την ensebmle μέθοδο Random Forest. Για την επιλογή των υπερπαραμέτρων του αλγορίθμου χρησιμοποιούμε Grid Search αλλα εξετάζοντας ένα τυχαίο δείγμα από το σύνολο των επιλογών για παραμέτρους (για εξοικονόμηση χρόνου), χρησιμοποιώντας τη μέθοδο RandomizedSearchCV του sklearn. Η επιλογή γίνεται μέσω 5-fold Cross Validation και 100 φορες, δηλαδή συνολικά γίνονται 500 fits στα δεδομένα."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:   26.2s\n",
      "[Parallel(n_jobs=-1)]: Done 138 tasks      | elapsed:  4.3min\n",
      "[Parallel(n_jobs=-1)]: Done 341 tasks      | elapsed:  9.2min\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed: 12.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 289, 'max_features': 'sqrt', 'max_depth': 31}\n"
     ]
    }
   ],
   "source": [
    "rfc = RandomForestRegressor()\n",
    "\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1000, num = 20)]\n",
    "max_features = ['auto', 'sqrt']\n",
    "max_depth = [int(x) for x in np.linspace(10, 200, num = 10)]\n",
    "max_depth.append(None)\n",
    "\n",
    "options_grid = {\n",
    " 'n_estimators': n_estimators,\n",
    " 'max_features': max_features,\n",
    " 'max_depth': max_depth\n",
    " }\n",
    "\n",
    "rand_forest = RandomizedSearchCV(estimator = rfc, param_distributions = options_grid, n_iter = 100, cv = 5, verbose=2, random_state=77, n_jobs = -1)\n",
    "rand_forest.fit(X_train, y_train)\n",
    "\n",
    "print(rand_forest.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Οι προτεινόμενες παράμετροι είναι 289 εκτιμητές, με πλήθος χαρακτηριστικών την τετραγωνική ρίζα του πλήθους των μεταβλητών μας και μέγιστο βάθος δένδρου 31."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 on train set : 0.9937802032857228\n",
      "R^2 on test set : 0.9863479964437528\n",
      "Mean Absolute Error: 23.153308255503877\n",
      "Mean Squared Error: 2003.7892365314904\n",
      "Root Mean Squared Error: 44.76370445496541\n"
     ]
    }
   ],
   "source": [
    "rand_forest = RandomForestRegressor(n_estimators=289, max_features = 'sqrt', max_depth= 31, random_state=77,n_jobs=-1).fit(X_train, y_train)\n",
    "\n",
    "y_pred = rand_forest.predict(X_test)\n",
    "\n",
    "\n",
    "fit_and_report(rand_forest,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_mae=[34.8756976667401,34.881196611895916,34.878098692137876,34.85065971525557,13.924855131947732]\n",
    "results_rmse=[61.44971209680265,61.448053113819476,61.44901675629336,61.48047388764597,33.61926662839836]\n",
    "\n",
    "res_df=pd.DataFrame(list(zip(results_mae,results_rmse)),index=['Linear','Ridge','Lasso','SGD','Random Forests'],columns=['MAE','RMSE'])\n",
    "plt=res_df.plot(kind='bar',title='Regression models Results',figsize=(10,6))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Όπως φαίνεται τις καλύτερες επιδόσεις παρουσιάζει ο αλγόριθμος Random Forests ενώ οι υπόλοιποι παρουσιάζουν αρκετά παρόμοια αποτελέσματα."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (β) Πρόβλεψη αν η δημοπρασία θα οδηγήσει σε πώληση ή όχι"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Αρχικά δημιουργούμε μια νέα δυαδική μεταβλητή (sold) η οποία θα έχει τιμές 1 ή 0 ανάλογα αν έγινε η πώληση ή όχι αντίστοιχα. Έτσι δεν χρειαζόμαστε τη μεταβλητή Price και NumBidders καθότι οι πληροφορία τους εμπεριέχεται πλέον στη νέα αυτή μεταβλητή. Χρησιμοποιούμε, όπως και πριν, One-hot encoding για το ItemId ενώ αφαιρούμε το SellerId. Παρατηρήθηκε ότι η χρήση του SellerId παρήγαγε χειρότερα αποτελέσματα για το σκοπό της κατηγοριοποίησης."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#auctions2 = auctions.copy()\n",
    "auctions2 = pd.read_csv('auction_data/auction_data.csv')\n",
    "auctions2 = pd.concat([auctions2,dummies1],axis=1)\n",
    "auctions2.drop([' ItemId','SellerId'],axis=1, inplace=True)\n",
    "\n",
    "\n",
    "def sold(row):\n",
    "    if row[' NumBidders'] == 0:\n",
    "        val = 0\n",
    "    else:\n",
    "        val = 1\n",
    "    return val\n",
    "\n",
    "auctions2['sold'] = auctions.apply(sold, axis=1).astype('category')\n",
    "\n",
    "auctions2.drop([' Price',' NumBidders'],axis=1, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>SellerRating</th>\n",
       "      <th>StartingBid</th>\n",
       "      <th>ItemId_0.0</th>\n",
       "      <th>ItemId_1.0</th>\n",
       "      <th>ItemId_10.0</th>\n",
       "      <th>ItemId_11.0</th>\n",
       "      <th>ItemId_12.0</th>\n",
       "      <th>ItemId_2.0</th>\n",
       "      <th>ItemId_3.0</th>\n",
       "      <th>ItemId_4.0</th>\n",
       "      <th>ItemId_5.0</th>\n",
       "      <th>ItemId_6.0</th>\n",
       "      <th>ItemId_7.0</th>\n",
       "      <th>ItemId_8.0</th>\n",
       "      <th>ItemId_9.0</th>\n",
       "      <th>sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>84.11</td>\n",
       "      <td>137.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93.76</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</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>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>94.52</td>\n",
       "      <td>364.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97.41</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80.32</td>\n",
       "      <td>173.0</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>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SellerRating   StartingBid  ItemId_0.0  ItemId_1.0  ItemId_10.0  \\\n",
       "0          84.11         137.0           0           0            0   \n",
       "1          93.76          30.0           1           0            0   \n",
       "2          94.52         364.0           0           0            0   \n",
       "3          97.41          82.0           0           0            0   \n",
       "4          80.32         173.0           0           0            0   \n",
       "\n",
       "   ItemId_11.0  ItemId_12.0  ItemId_2.0  ItemId_3.0  ItemId_4.0  ItemId_5.0  \\\n",
       "0            0            0           0           0           0           0   \n",
       "1            0            0           0           0           0           0   \n",
       "2            0            1           0           0           0           0   \n",
       "3            0            0           1           0           0           0   \n",
       "4            0            0           0           0           1           0   \n",
       "\n",
       "   ItemId_6.0  ItemId_7.0  ItemId_8.0  ItemId_9.0 sold  \n",
       "0           1           0           0           0    1  \n",
       "1           0           0           0           0    0  \n",
       "2           0           0           0           0    1  \n",
       "3           0           0           0           0    1  \n",
       "4           0           0           0           0    0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auctions2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train-test set split and standardization\n",
    "\n",
    "X=auctions2.iloc[:,:-1].values\n",
    "y=auctions2.iloc[:,-1].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=77, test_size=0.2)\n",
    "\n",
    "X_train_scaled=StandardScaler().fit_transform(X_train)\n",
    "X_test_scaled=StandardScaler().fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Αρχικά εφαρμόζουμε Λογιστική Παλινδρόμηση."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Confusion Matrix ===\n",
      "[[ 877  250]\n",
      " [  59 2583]]\n",
      "\n",
      "\n",
      "=== Classification Report ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.78      0.85      1127\n",
      "           1       0.91      0.98      0.94      2642\n",
      "\n",
      "    accuracy                           0.92      3769\n",
      "   macro avg       0.92      0.88      0.90      3769\n",
      "weighted avg       0.92      0.92      0.92      3769\n",
      "\n"
     ]
    }
   ],
   "source": [
    "log_reg = LogisticRegression(random_state=77 ,max_iter=10000,n_jobs=-1).fit(X_train_scaled, y_train)\n",
    "\n",
    "y_pred=log_reg.predict(X_test_scaled)\n",
    "\n",
    "print(\"=== Confusion Matrix ===\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== Classification Report ===\")\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Εδώ,επιπλέον θα κάνουμε τυποποίηση των δεδομένων. Χρησιμοποιώντας τα δεδομένα ως έχουν ο αλγόριθμος δεν τερμάτιζε.\n",
    "Ως συνάρτηση πυρήνα χρησιμοποιούμε την ακτινική βάση (RBF) καθώς παρήχε τα καλύτερα αποτελέσματα."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Confusion Matrix ===\n",
      "[[ 720  407]\n",
      " [  51 2591]]\n",
      "\n",
      "\n",
      "=== Classification Report ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.64      0.76      1127\n",
      "           1       0.86      0.98      0.92      2642\n",
      "\n",
      "    accuracy                           0.88      3769\n",
      "   macro avg       0.90      0.81      0.84      3769\n",
      "weighted avg       0.89      0.88      0.87      3769\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf=SVC(kernel='rbf',random_state=77).fit(X_train_scaled, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test_scaled)\n",
    "\n",
    "print(\"=== Confusion Matrix ===\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== Classification Report ===\")\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN Classifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Εφαρμόζουμε τη μέθοδο των Πλησιέστερων Γειτόνων  με μετρική την ευκλείδια απόσταση."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Αρχικά, για την εύρεση του βέλτιστου πλήθους γειτόνων (κ) υπολογίζουμε το μέσο ρυθμό σφάλματος για k από 1 έως 30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "errors = []\n",
    "K = range(1,30)\n",
    "for k in K:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k,n_jobs=-1)\n",
    "    knn = knn.fit(X_train_scaled, y_train)\n",
    "    preds = knn.predict(X_test_scaled)\n",
    "    errors.append(np.mean(preds != y_test))\n",
    "    \n",
    "plt.plot(K, errors, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Error Rate')\n",
    "plt.title('kNN errors per k')\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Θα επιλέξουμε 7 γείτονες για τον αλόριθμο."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Confusion Matrix ===\n",
      "[[1009  118]\n",
      " [ 136 2506]]\n",
      "\n",
      "\n",
      "=== Classification Report ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.90      0.89      1127\n",
      "           1       0.96      0.95      0.95      2642\n",
      "\n",
      "    accuracy                           0.93      3769\n",
      "   macro avg       0.92      0.92      0.92      3769\n",
      "weighted avg       0.93      0.93      0.93      3769\n",
      "\n"
     ]
    }
   ],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=7)\n",
    "neigh.fit(X_train_scaled, y_train)\n",
    "\n",
    "y_pred = neigh.predict(X_test_scaled)\n",
    "\n",
    "print(\"=== Confusion Matrix ===\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== Classification Report ===\")\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Παρακάτω χρησιμοποιούμε την ensebmle μέθοδο Random Forest. Για την επιλογή των υπερπαραμέτρων του αλγορίθμου χρησιμοποιούμε Grid Search αλλα εξετάζοντας ένα τυχαίο δείγμα από το σύνολο των επιλογών για παραμέτρους (για εξοικονόμηση χρόνου), χρησιμοποιώντας τη μέθοδο RandomizedSearchCV του sklearn. Η επιλογή γίνεται μέσω 5-fold Cross Validation και 100 φορες, δηλαδή συνολικά γίνονται 500 fits στα δεδομένα."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:   12.9s\n",
      "[Parallel(n_jobs=-1)]: Done 138 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=-1)]: Done 341 tasks      | elapsed:  3.8min\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:  5.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 857, 'max_features': 'auto', 'max_depth': 10}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "rfc = RandomForestClassifier()\n",
    "\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1000, num = 20)]\n",
    "max_features = ['auto', 'sqrt']\n",
    "max_depth = [int(x) for x in np.linspace(10, 200, num = 10)]\n",
    "max_depth.append(None)\n",
    "\n",
    "options_grid = {\n",
    " 'n_estimators': n_estimators,\n",
    " 'max_features': max_features,\n",
    " 'max_depth': max_depth\n",
    " }\n",
    "\n",
    "rand_forest = RandomizedSearchCV(estimator = rfc, param_distributions = options_grid, n_iter = 100, cv = 5, verbose=2, random_state=77, n_jobs = -1)\n",
    "rand_forest.fit(X_train, y_train)\n",
    "\n",
    "print(rand_forest.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Τώρα χρησιμοποιούμε τον αλγόριθμο με τις βέλτισες παραμέτρους που υπολογίστηκαν παραπάνω."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Confusion Matrix ===\n",
      "[[ 988  139]\n",
      " [ 101 2541]]\n",
      "\n",
      "\n",
      "=== Classification Report ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.88      0.89      1127\n",
      "           1       0.95      0.96      0.95      2642\n",
      "\n",
      "    accuracy                           0.94      3769\n",
      "   macro avg       0.93      0.92      0.92      3769\n",
      "weighted avg       0.94      0.94      0.94      3769\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rand_forest = RandomForestClassifier(n_estimators=857, max_features = 'auto', max_depth= 10, random_state=77,n_jobs=-1).fit(X_train_scaled, y_train)\n",
    "\n",
    "y_pred = rand_forest.predict(X_test_scaled)\n",
    "\n",
    "print(\"=== Confusion Matrix ===\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('\\n')\n",
    "print(\"=== Classification Report ===\")\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 1089, 1: 2680}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique, counts = np.unique(y_pred, return_counts=True)\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Αν και υπάρχουν μικρές διαφορές μεταξύ των αλγορίθμων, και όλοι αποδίδουν καλά αποτελέσματα, ο αλγόριθμος Random Forests πετυχαίνει τα καλύτερα (συνολικά). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (c) Ορισμός τιμής Εκκίνησης"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Διαβάζουμε τα δεδομένα απ'την αρχή."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "auctions = pd.read_csv('auction_data/auction_data.csv')\n",
    "auctions['SellerId'] = auctions['SellerId'].astype('str')\n",
    "auctions[' ItemId'] = auctions[' ItemId'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>SellerId</th>\n",
       "      <th>SellerRating</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>StartingBid</th>\n",
       "      <th>Price</th>\n",
       "      <th>NumBidders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>144.0</td>\n",
       "      <td>84.11</td>\n",
       "      <td>6.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>145.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.0</td>\n",
       "      <td>93.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102.0</td>\n",
       "      <td>94.52</td>\n",
       "      <td>12.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77.0</td>\n",
       "      <td>97.41</td>\n",
       "      <td>2.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>106.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.0</td>\n",
       "      <td>80.32</td>\n",
       "      <td>4.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SellerId   SellerRating  ItemId   StartingBid   Price   NumBidders\n",
       "0    144.0          84.11     6.0         137.0   145.5          2.0\n",
       "1     40.0          93.76     0.0          30.0    30.0          0.0\n",
       "2    102.0          94.52    12.0         364.0  1278.0          8.0\n",
       "3     77.0          97.41     2.0          82.0   106.5          3.0\n",
       "4     54.0          80.32     4.0         173.0   173.0          0.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auctions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Παρακάτω φαίνεται το StartingBid σε συνάρτηση με τα αντικείμενα προς πώληση."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dANq3b+8wQ32/Y2f16tX2z/Hs2bNZvHgxZ8+exdPTk2vXrt13/4cOHeLChQt06tTJYRa5TJkyDqsFAVy5coX9+/fTtGlT+98gAD8/P95+++0Mba9YsQJPT0+GDBnicNwGBQXRtm1bIiMjOXnypMNzWrVq5XCM5OSxLER2SVqIEPfQrFkzmjVrhsFg4MCBA+zZs4ctW7Zw7tw55s2bh81m4/3337/n811dXWnXrh0AMTExnD59mvPnz3Py5En27NkDkCFA1+l0Dmki2VGuXLkMZWkBSVowERkZCZBpGkedOnX46aefMm178+bN9tVCbDYbiYmJHDp0iIkTJ/LOO+8wadKke64mkZY7XKNGjQzbateuzQ8//PCAkd1Oz6hTpw7bt2+3nwJO+7Lv06eP/Qv5pZdeYtOmTQwfPpyvvvqKpk2bEhoaSoMGDRyC2kd19OhRFEXh5s2bma7tq9PpiI2N5cqVKw6BT5kyZRzqpf34Sr8SS1pQeL888dTUVM6ePUtISAg6nc5hW7FixShVqtQDfwgGBgZSu3Zt/vnnH8LCwggJCSE0NJTmzZtTunTp+z73bmfOnGHo0KH4+PgQHh7uENRGRUXh7OzM0qVLMzwvJiYGuJ1C0KxZM6KjoylZsmSGJe2cnJyoXr06e/fuzVJ/VCpVpktBBgcHExMTw5kzZ6hSpQoNGjSgZMmSbNiwgQ8//BCNRsO6deuwWCxZTgm5W7t27VizZg1bt27lm2++oW/fvg/dxr2kP3bS0mQe5thZtGiRfbUQRVEwGAxER0czdepURo8ezbVr1xg4cGCm+7/f5zgt0M9K3bvTheB2alNMTAz+/v7MmTMnQ/20oP/YsWNUrlzZXp62pGCanDqWhXgUElwL8QDu7u6EhYURFhbG8OHDWbFiBR9//DFLlixh4MCB980B3b9/P5MmTbJfSOPs7EyVKlWoXr06sbGxGW7YcHcecXZlFjymn728ceMGQKZrZGc1uFer1Xh7exMaGspTTz3Fc889x/Tp0+8ZXKddtOXu7p5hm4+PT5b2Cbdnr//66y+2bt3Ks88+y2+//QbAc889Z68TFhbGokWLWLBgAbt372bx4sUsXrwYHx8fBg4cSM+ePbO8v/tJG9PBgwc5ePDgPevdvHnTIbhOfyYjTXYC/1u3bgGZv65weyY17f2+F5VKxYIFC5g/fz5r1qxhx44d7NixgwkTJtCoUSPGjx+fIXhLLzExkQEDBpCSkkJ4eDglS5bMsN1isRAeHv7AsSQkJNxzreiHnUXO7DVNe63Sls9UqVT2C14jIiJo1KgRa9aswcfHh9DQ0Czv726jR48mIiKC8PDwRz4bdbecPHbg9tg9PDyoW7cuc+bMoXnz5sybN4/evXtnepbgfp/j9O/Nw9RNu/g3Li4uS8dImvR/M3PiWBbiUUlwLcRdkpKSeOGFFyhfvjxz587NsF2lUvHSSy+xYcMGdu7cyeXLlylfvnymbV28eJE+ffrg7OzM+PHjefrppylXrpx9VmzTpk25PZx7Sks9MBgM+Pn5OWxLv8JFVuj1evz8/IiLiyM+Pj5Dm4D9VHZmF79lZWWFNP/3f//HhAkTWL9+Pe3bt2fDhg3o9foMd+0LCQkhJCSE5ORkDhw4wLZt21i9ejUTJkygTJkyhIWFPeQoM0oLdAYMGMDgwYMfub3sSAtSMntdIeuvrbu7O4MHD2bw4MHExMSwa9cufv31V3bv3s27777L8uXL7/lcm83G0KFDiYmJ4cMPP6RBgwYZ6ri5ueHu7m5f6vJ+vLy8Hnk8cPs1URQlw4/Lq1evAo4/6p5//nnmzp3L+vXrKVu2LIcOHeKVV17JdtBaokQJhg4dyoQJExg1alSGtKG7pf+Rfa9VN3Kbn58flStXJjIykpiYmEzPbD3M5/hh6qZ9lurWrZvp2Y2H8SjHshA5QXKuhbiLh4cHiYmJ7N69+4G5h2q12n5DkczyWjdt2kRKSgrvvPMOXbp0oWLFivYVR06fPg1k/FLNK9WrVwfg8OHDGbYdOnToodszm82kpKSgVqvvObOWts+///47w7a0NJWs8Pb2JiwsjF27drFnzx6uXLniMGsNt1dCmTFjBnD7Szs0NJRRo0YxevRowDEX+1GkBfT3Wt975syZzJs3L1vL/2WVi4sLFStW5OjRoxmCsoSEBM6fP//ANqKjo5kyZYp99r18+fL06NGDZcuWUa5cOQ4fPnzfMXz22Wfs2LGDZ5991r5EZXqBgYFcvnyZuLi4DNu2bt3K559/bk8jSDuzc+nSJYd6Vqs1wxKW92O1WjMs82c2mzly5Ih9Xeo0FSpUIDg4mK1bt9p/AGQnJeRu3bt3p2bNmuzdu5dffvklw/a0wD19oJmV9yy3pM023yu3PS3FI7PPcfrPQbVq1VCpVFmq6+npScmSJTl16lSmPy5+/vlnZs2axYULF+7b/0c9loXICRJcC5FO2rJR77zzjn2G626bN29m9+7dtGrVyj4DnHaBVtoa2fBfzmP6ID06OppFixYBZPliJ51Ol6MXRj333HPodDrmzJnjsP71yZMn75lvfT+LFy8mJSWFRo0a3TO1JSwsDD8/PxYvXmzPsYXbPzTSLnbLqrRl/KZMmWI/pX+3nTt3MmfOnAypGmkXd6VPWcgqnU7n8B6XLl2aevXqsWPHjgxLGP788898+eWX/Pnnnzma552ZTp06kZyczGeffWb/waYoCtOnT8/ScWMymfjmm2+YPXu2ww++pKQkbt26hb+//z3HsGbNGhYsWED16tUzrIWcvo+KojB+/HiH4Obq1auMGTOGefPm2X+YpV1UO3nyZIfXe8GCBQ/80ZteeHg4VqvV/nj+/PlcvnyZTp062X/spnn++eeJi4tjwYIFlC1bNtN87YehVquZMGECOp0u07W80856bd261V5mNBpZsGDBI+03uzZu3Mi5c+eoUKHCPc/IBQUFUalSJX799VeHoPnq1at88803DnX9/f1p2rQpe/fuZePGjfbypKSkTFM/OnXqxM2bN/nss88crhM4deoU48aN49tvv31gCtmjHMtC5BRJCxEinf79+3PixAk2btxI69atadKkCeXKlcNisXDo0CH+/vtvKlSowJgxY+zP8fX1xcnJiYiICCZNmkSrVq1o3rw506ZNY+7cuZw5c4YyZcpw7tw5tm7dap8VuteNFNIrVqwYZ8+e5b333qNJkyZZXl3jXgICAnjnnXeYNm0aHTt25JlnniE1NZWNGzfafxTcfQV+moULFzqsVGAymdi/fz///PMPnp6eDjcMSc/d3Z3x48czePBgXnrpJXse6oYNG/Dz88twI437ad68OV5eXkRHRxMSEkKJEiUctg8aNIiIiAh69epF27ZtKV68OKdOnWLr1q1UrFiRDh06ZHlfdytWrBhnzpxh9OjRhIWF0aJFC8aNG0f37t0ZPHgwoaGhVK5cmZiYGLZt24aPj499tjw39e7dmy1btrB48WKOHDlCzZo1OXjwICdOnLhn7vLdgoODadOmDRs3bqRTp040aNAAi8XCpk2buHHjxj2D5vPnzzNy5EhUKhWNGzdm0aJFmEwmh6DG09OTV199lRdeeIEtW7awceNGjh8/TtOmTbFYLKxfv56bN28ybNgw+8V67dq1Y+PGjWzYsIGYmBgaNmzIqVOn2Lt3LwEBAQ9cPSWNs7MzUVFRvPTSSzRo0IDo6Gh27dpFhQoVGDJkSIb67du3Z9KkSVy8eDHHbvmt1+t54403Mr1Ir3PnzixbtoyJEydy6NAhfH192bx5M56envc8A5QTVq9ezb59++yPLRYLR44cYffu3eh0OkaNGnXP56pUKiZOnMirr75K7969adOmDR4eHvzxxx+Z9nnUqFF069aNIUOG0LJlS4oXL87WrVsz/fvSt29f+3rWf/31FyEhISQkJLBhwwZSUlL49NNPHVbTyUx2j2UhcpIE10Kko9FomDlzJn/88Qdr1qzh8OHD7NixA51OR9myZRk2bBi9evVymKF1cnJi1KhRzJw5k2XLluHp6UndunX59ttvmT59Onv37mXnzp2ULFmSnj170q9fP1q3bs2ff/6ZaU5oeu+//z4jRoxgw4YNXL9+/ZGDa7j9RVakSBEWLlzIypUr8fHxoXfv3vj5+fHJJ59keqFm2ox7GmdnZ0qUKEHXrl3p06fPA6/Gb9myJd999x2zZs1i3bp1uLq60qVLF4KCgjLcwON+nJycaNu2LT/99FOGWWu4/QW7ZMkSvvrqK/bu3Ut8fDzFihWjV69e9O/fP9uBy6hRo5gwYQIrV67EYrHQokULKlSowKpVq5g9ezbbt29nz549FCtWjI4dO/L222/nyQoFTk5O9tm6tWvX8v3331O1alW++eYbxowZc8/85btNnTqVGjVq8Ouvv/Ljjz+iUqmoXr06o0aNokWLFpk+JzY2FqPRCMC8efMyrRMQEMCrr76KSqVi5syZLF26lFWrVrF8+XJcXFyoVKkSr732Gi1btnR43vTp06lRowYrVqzg+++/p1y5coSHh7NixYosB9dOTk4sXLiQ8ePH22/m07VrV959991Mb6zj4+NDw4YN2bFjxyOnhNxtwIABbNiwwb4UZpoqVaowb948wsPDWb9+PR4eHrRs2ZL33nvvnq95Tli9erXDYycnJ/z9/Xn22Wd5/fXXH3ijqpo1a/L9998zY8YMtm3bhkqlonXr1jz//PP06NHDoW7p0qX58ccf+fzzz9m1axdGo5EmTZowePBg2rdv71DXxcWFRYsWMX/+fNatW2f/W1qnTh369etnX8L0QbJzLAuRk1RKfiV9CiHyzY0bN7BarZmuFjJz5ky+/PJLli9f/sA7LgpRkNhsNpo3b05AQADLli3L7+4IIZ5QknMtRCEUERFB48aNM+Q9xsfHs3r1ary9valSpUo+9U6I/LF8+XIuX75Mly5d8rsrQognmKSFCFEINW3alICAAL788ksiIyPR6/XcunXLnpc4efJkuehHFBpDhgzh+PHjxMTEUKFChQzpCkII8TAkuBaiEHJ3d+eHH35g/vz5bNu2jT179uDm5kaNGjV44403HG5VLERBV6RIES5dukRwcDBTpkzJcKdLIYR4GJJzLYQQQgghRA4pMDPXcXEPvho+t/j6unHjRtbvGvY4KyhjKSjjABnL46igjANkLI+rgjKWgjIOkLE8rvJrLP7+md9oCeSCxhyh1WoeXOkJUVDGUlDGATKWx1FBGQfIWB5XBWUsBWUcIGN5XD2OY5HgWgghhBBCiBwiwbUQQgghhBA5RIJrIYQQQgghcogE10IIIYQQQuQQCa6FEEIIIYTIIRJcCyGEEEIIkUMkuBZCCCGEECKHSHAthBBCCCFEDpHg+jHxySdj2Lt3t0OZ0Wikc+fn8qlHQgghhBDiYUlwLYQQQgghRA7R5ncHCrrz588xceJYtFotGo2GkSPH8sMPSzl8+CAArVq1pUuXl+31k5OTGTduJImJiQQElMqvbgshhBBCiGyQ4DqX7d8fQWBgFQYNGsqhQ/+wY8dWYmMvMW/ed1itVvr3f4Onn65nr79+/a+UL1+Rfv3eJirqCH//fSAfey+EEEIIIR6GpIXksmef7Yi3tw/Dhg1i5cqfSExMpGbNWqhUKrRaLdWrB3H27Bl7/ZiYM1SrVh2A6tVroNXK75+H5eysye8uCCGEEKKQkuA6l+3cuZ2aNWvzxRdf0bz5M/z22xp7SojFYuHIkcOUKlXGXr9MmXIcORIJwIkT0Vgslnzp95NKpdNy8lIiP28/hU2jwdlZfpwIIYQQIu9I5JHLqlSpxrhxH6PRaFCr1UyYMJU//thAv36vYTabadGiJYGBVez1X3jhJSZNGkv//m9Qtmw5dDpdPvb+yeLq6sSGfedZ+NtRAH744wSz3muWv50SQgghRKEiwXUuCwgoxdy53zqU3R1Mp/noozH2/3/88fjc7laBZDBa2PHPhf8ep5i5cDWJcv4eWCzWfOyZEEIIIQoLSQsRBYazTkONikXsj7UaFSWLuEtgLYQQQog8IzPXosAwG8282Lwyfl6unL+cSMfQCjhpVVhM+d0zIYQQQhQWElyLAsVmstC+YRm0Wg3JySZMJpm1FkIIIUTekcxT+ToAACAASURBVLQQUbBo1Jy+lMjWvy9itIJGI4e4EEIIIfKOzFyLAkOr07Dj8CXm/xIFgKebji+GNsvfTgkhhBCiUJFpPVFgmCw2thz4b7WQxGQzsdcNqNWqfOyVEEIIIQoTmblOx2i2civJiLeHM866R7/Tn81mY9q0yZw6dRKdTscHH3xMqVKlc6CnIj2dRkX1CkU4c/EWABq1imK+bthstnzumRBCCCEKCwmu77DabPy45RT/nIgjPsGIn5cztfX+dG1RCY06+xP8f/65DZPJxNy533LkSCTh4Z8zefL0HOy5SGO12HixWSV8PZ05G5tAp7CKaFWg5HfHhBBCCFFoSHB9x49bTrHprpSC6wlG++NXWuqz3e7hwwepX78hADVqBBEdfezROiruy2a20OrpUri7O5OQkILNKrPWQgghhMg7knPN7VSQf07EZbrtnxPXMJqzv5ybwWDA3d3D/litVmOxWLLdnngws9mKTqfBZpM5ayGEEELkLQmugVtJRuITjJluu5GYyq2kzLdlhbu7O8nJyfbHiqKg1coJAyGEEEKIgkiCa8Dbwxk/L+dMt/l6uuDtkfm2rAgKqsnevbsAOHIkkgoVKmW7LSGEEEII8XiTKVTAWaehtt7fIec6TW190UdaNSQ0tDn790fw1luvoygKI0aMfpSuCiGEEEKIx5gE13d0bXF7RvmfE9e4kZiKr6cLtfVF7eXZpVaref/9ETnRRZFFsq61EEIIIfKLBNd3aNRqXmmp58Wwijm6zrXIWyqNhovXDew7cY2nA/3RAlZZMUQIIYQQeUSC63ScdRqK+brldzdENmh1GrYfusSCNbdvf+7hqmPmsGb52ykhhBBCFCpyQaMoMG7f/vxf++OkFLn9uRBCCCHylgTXosDQaVQEVypqf6zVqCju6ybrXQshhBAiz0haiCgwrBYbncIq4eflQsylBJ4Pq4gGuf25EEIIIfKOBNeiQLGZLbSoHYB7aEUSbiZjs8nFjEIIIYTIO5IWko7JaiIu+TomqynH2oyKOsLAgX1zrD1xf2azFZ1GLekgQgghhMhzMnN9h9VmZdWp3zgcF8UN4018nX0I9q/OC5Xao1Fnf0m+pUsXsnHjOlxcXHOwt0IIIYQQ4nEkM9d3rDr1G9su7CTeeAMFhXjjDbZd2MmqU789UrsBAaX45JNPc6iXIit0d9YnV6lklRAhhBBC5C0JrrmdCnI4LirTbZHXoh4pRaRZs2fQauUEQV5R67Tsi77KnFWHSbHYUGvkEBdCCCFE3pHIA7hlTOSG8Wam2+JTb3LLmJjHPRLZodFpWLc7hr1HLmO12vjku31Y87tTQgghhChUJLgGvJ098XX2yXSbn4sP3s6eedwjkR1mi42nqxZHX8YXo9lG/xdrkphslpvICCGEECLPSHANOGmcCPavnum2oKLVcdI45XGPRHa4OGnYffgSSzdGs/Wvfxnz9R483HSyaogQQggh8owkA9/xQqX2wO0c6/jUm/i5+BBUtLq9/FGUKFGSefO+e+R2xP2ZLDYOn7qGk1aNm4uOm0lGrsQnE+DrKgG2EEIIIfKEBNd3aNQaXtJ3oGPFttwyJuLt7Ckz1k8YjUZNr3ZVbwfWiUa8PJx5ytcVxSo3khFCCCFE3pDgOh0njRP+bkXyuxsiGzQqSDFaGDs/AoASRdyZOKAxiiKz1kIIIYTIG5JzLQoMo9nG6m2n7Y9jrxu4Ep8s610LIYQQIs9IcC0KDI0aAop5OJT5eDjLzLUQQggh8oykhYgCQ6NW0bWlHptNIfaagXaNyuGkU4NFVrsWQgghRN6QmWtRYBjNNr5ccYgi3q40q1OK7f9c5PJ1g6xzLYQQQog8I8F1HlmwYC59+vTCYrHYy/r2fZXY2EvZbvPvvw/w7LOtGDiwLwMH9uWtt17n3LmzAHzxxTQuX77sUP/cubMMHNg3QztffTWLdet+zXY/HhfOOg3lS3ixetspvv7lCJGnrlHM102W4RNCCCFEnpG0kDwUGxvLkiXf8eqrb+ZYm08/XZexYycBsG/fXr78cgZTp85g8OBhObaPJ4XJYiO0TincXHTEXjfQsl4ZjCYrbjq15F0LIYQQIk9IcJ2HXnmlF2vX/kyjRk3Q66vYy9et+5Vz587Sv/8gjEYj3bt3ZsWKXxk4sC+VKumJiTmNq6srwcG12bdvD0lJSUyfHp6h/cTEBJ56qgQAAwf25f33R+Du7sG4cSNRFAU/v/+WGNy2bTMLFy7Ax8cXs9lM2bLlAJg2bRp79uzFZlPo2rU7LVq0ZODAvvj4+JKYmMiwYf9j4sRxaLVaNBoNI0eOxd+/WO6+cFmkVsGxM9dpFFwSRVG4fisVlVolgbUQQggh8oykheQhNzdXhg8fySefjMVkMmXpOdWqVeeLL77CZDLj4uLCjBmzKVeuPAcP/g3AX38dYODAvvTr9xqTJo2jWbNnHJ7/ww9LaNmyDbNmzSU0tJm9fPbsmcyYMZvp08NxcXEBYM+eXVy4cIGvvvqGmTPnsGjRNyQmJgLQqlVbvvhiNvv37yMwsAozZsymV6/XSUxMyIFXJmfoNCqCK/uTnGrGaLZitdlwc9Hkd7eEEEIIUYjIzHUeq1mzNnXrhjB//px71HCcZU2b4fb09KBcufJ3/u+FyWQEPBzSQs6fP0u/fq/z88/r7M+PiTlDmzbtAAgKqsnq1SuIj7+Ou7s73t4+ANSoEQzAmTOniIqKsudlWywWLl+OBaBMmbIAPPtsR5YuXciwYYNwd/egX7+3H/EVyTk2lYrEZBMTv9tPitFCbb0/5UsGo1XJ7LUQQggh8obMXOeDvn0HsHfvLi5e/BcAJycnrl+/BsDx49EOdR/mBii+vhnvLFm2bFmiog4DcOzYUQC8vLxJSjJw48YNAKKjj96pW4769esTHj6PmTPn0KJFSwICAgBQq28fKjt3bqdmzdp88cVXNG/+DEuXLsxy/3KbxWrj+9+Pk2K8fdHoPyfiuGUwoZajXAghhBB5RGau84GzszMjRoymX7/XAKhfvxE//7yS/v3fIDCwKu7u7lluKy0tRKPRkJxsYNCgd3F2drFvf/PN/owe/SGbNv1OyZK3A2WtVsuIEaMYNmwgnp7eaLW3D4PGjUOJjo5kwIA3SUlJJjS0OW5ujn2pUqUa48Z9jEajQa1WM2jQ0Ed9OXKMRq3C1dnxkHbWamS1ECGEEELkGZVSQM6Xx8Ul5tu+/f0983X/OelJHouTs4abBgtTFh8g9pqB55qWp0OTCihP+E1knuT3JL2CMpaCMg6QsTyuCspYCso4QMbyuMqvsfj7e95zW67NXJvNZkaMGMHFixcxmUz079+fSpUq8cEHH6BSqahcuTKjR49GrVYTHh7Otm3b7syojiA4OJhz585lWleIezGabcQnpvJh73po7tw45maiEV93ncxeCyGEECJP5Fq0umbNGnx8fFi2bBlff/0148ePZ9KkSQwZMoRly5ahKAqbN28mKiqKffv2sXz5cqZPn87YsWMBMq0rxP0467QU83UF4GaSEbVaRREfFwmshRBCCJFncm3mum3btrRp08b+WKPREBUVRUhICAChoaHs2rWL8uXL06RJE1QqFSVLlsRqtRIfH59p3VatWuVWd0UBoNaoOBebyJRF+7EpUKa4J2P6NkCtVkmALYQQQog8kWvBddpFeUlJSbzzzjsMGTKEKVOm2Fe/cHd3JzExkaSkJHx8fByel5iYiKIoGerej6+vG1pt/q1pfL/cmyfNkzqWq/HJrNx6irQ4+vyVRK5cT6ZGxaL527Ec8KS+J5kpKGMpKOMAGcvjqqCMpaCMA2Qsj6vHbSy5ulpIbGwsb7/9Nq+88grPPfccn376qX2bwWDAy8sLDw8PDAaDQ7mnp6dDfnVa3fu5cSM55weQRXJhwONB56ylqI8LJ87/V+bt4cy1a4k8yZftPsnvSXoFZSwFZRwgY3lcFZSxFJRxgIzlcfU4XtCYaznX165d4/XXX+f999+nc+fOAFSrVo2IiAgAduzYQd26dalTpw47d+7EZrNx6dIlbDYbfn5+mdYV4n4Ui403OtSgZUhpmtQsyTtda+HqrH2iA2shhBBCPFlybeZ6zpw5JCQkMHv2bGbPng3ARx99xIQJE5g+fToVKlSgTZs2aDQa6tatS9euXbHZbIwaNQqA4cOH8/HHHzvUfRLZbDamTZvMqVMn0el0fPDBx5QqVdq+fc2a1fzyyyo0Gg29e79B48ZN87G3Tzar1YZGp6FVvbJciEuiVmV/1DYbT/ZCfEIIIYR4ksg61+ncvqDyOn5+RdBospbDfb9TEtu3b2Hnzh189NEYjhyJZMmSb5k8eToA169f491332b+/MWYTCYGDHiD+fMX4+TklCNjyY4n+VSRVqtm55ErzPs5EgAvdye+eDdM1rl+jBSUsRSUcYCM5XFVUMZSUMYBMpbH1eOYFiJ3aLxLRMRuFi78hgsX/qVUqdL07v0G9es3fKQ2Dx8+aG+jRo0goqOP2bcdOxZFUFBNnJyccHJyIiCgNKdPn6Rq1eqPtM/CymRV2LT/v4TrBIOJS9cNBPi6ymohQgghhMgTcleWO6xWKwsXfkNMzGnMZhMxMadZuHABVuujzXoaDAbc3T3sj9VqNRaLJdNtbm5uJCUlPdL+CjNnnZrq5f3sjzVqFcV83SSwFkIIIUSekZnrO+Ljr3Phwr8OZRcu/Et8/HX8/Ytlu113d3eSk/9byURRFLRababbkpOT8fR8vJaTeaKo1LSuXxYXZy2x1wy0ql8WtVqFSkEuahRCCCFEnpCZ6zv8/Io4XGgIUKpUafz8ijxSu0FBNdm7dxcAR45EUqFCJfu2qlWrc/jwPxiNRpKSkjh3Loby5Ss+0v4KM6PFyhc//sPFq0n4erkwb3Ukl+KS0GjkMBdCCCFE3pCZ6zvSVutYuHCBQ851Vi9qvJfQ0Obs3x/BW2+9jqIojBgxmh9+WEKpUqVp0iSMzp278fbbfbDZbPTtOwBnZ+ccGlHh4+asoUGNEizZEA2Aq7OWAH8PLE/4BY1CCCGEeHJIcH2X+vUbUrduyEOvFnI/arWa998f4VBWtmw5+/87dOhEhw6dHnk/AmyKQqOgEpQq7sm/VxJpFFQCtVqFopK0ECGEEELkDQmu09FoNI+UYy3yj9Gk8Nmyv1EUhaLerqzaeoqPXguhTFE3LBZbfndPCCGEEIWABNeiwHB2UlNL749GraKotyuooLifG1arBNZCCCGEyBtypZcoMKxWhWcbl+diXBLr95wltFYAajWoVHKYCyGEECJvSNQhCg4VfLc2it2HYzkbm8C0ZX9jNNkoIDchFUIIIcQTQIJrUWDYrDZiryc7lCUmm/KpN0IIIYQojCTnWhQYTho1Pf+vCmaLgrurjvOXEyjm64ZituR314QQQghRSMjMdTonThxn7dpfOHHieI61eeNGPC+80J5z5846lO/cuYM33+xFv36vsWbN6hzbX2FlUcDXywWrzYbNpuDmosNktqJWq/K7a0IIIYQoJGTm+o74+Hg+/3wqkZEHMRqNODs7ExxciyFD/oefn1+227VYLEydOhEnJ+cM5bNmTefrrxfh6upK//5v0LhxU4oUKfqoQym8VGA0Wfl27VGuxifTpkFZ9GV8UKlUgORdCyGEECL3ycz1HTNmTOXAgQiMRiMARqOR/fsjmDFj6iO1Gx4+g+eff5GiRR2D5rNnYwgIKI2Xlxc6nY7g4JocOnTwkfZV2CkKLN0YTew1A1abwrrdZ0kxWlAUWYpPCCGEEHlDgmtup4IcPpx5YHv48EFOnsxeisi6db/i4+ND/foNM2wzGAx4eHjYH7u5uWMwJGVrP+I/ZrNjIG21KXdmroUQQgghcp8E18CJE9H2Gev0jEYjJ05EZ6vd335bw4ED+xg4sC+nTp1gwoRRXL9+DQB3d3eSkw32usnJjsG2eHg6jYre7avh7nI72ymkWnF8PJwluBZCCCFEnpGca0Cvr4Kzs3OmAbazszN6fZVstfvll1/b/z9wYF/ef3+EPae6XLnyXLjwLwkJt3B1dePgwX94+eWe2RuAAG7PUuu0aka8FoJWoyYx2YTZakMjFzQKIYQQIo9IcA3o9YEEB9di//6IDNuCg2tRuXJgju3r9983kJKSTMeOLzBw4LsMHToIm81G+/Yd8PcvlmP7KYwURcWCNVFEnr5mL/t8SBjObnKYCyGEECJvSNRxx5Ah/2PGjKkcPpxxtZCcEB4+D4CyZcvZy5o0CaVJk9AcaV+Agi3DsntqtUpWCxFCCCFEnpHg+g4/Pz/GjZvMiRPHOXkyGr2+So7OWIvcp0JF9zaBXLqWxPWbKTxTrwxOOrXc/lwIIYQQeUaC63T0+kD0egmqn0RqFfx7JZE+HYPwdNNx9Gw8TjoNNos1v7smhBBCiEJCgmtRYNgUqKn3x2yxYTRbCa0VcHs5HJVKZq+FEEIIkSckuBYFhkat4uSFRCYv3IfFqlC5tA/De9VFq7p9gxkhhBCiMNPpNNhUKlKNFtRqsMk91nKFrHMtCgyTxcbyzSewWG9H0if/vUn8rdR87pUQQgiR/9Q6NVdupfLlysN8+1sUikabYREAkTNk5jqda9fiiI29RIkSJSla1D+/uyMegloFvp7O+Pu64uflwukLt3B31cmstRBCiELPalPxwZe7cNapMVlsxN9K5a1OQWCT65JymgTXdxw9eoSfflpGZOQhkpOTcXNzIyioJl27vkLVqjWy3a7FYmHChNFcvhyLWq1m+PCRDsvx7dy5g+++m49Go6F9+w506NApB0ZTODlpVPR9PoizsQlcjDMwpFtt3F11WFLN+d01IYQQIl+lGC2MfC0EBXBz0XLi/A0sVkVSGHKBBNfcDqwnT55AXNwVe1lycjIREXs4c+YMH344MtsB9p49O7FarcyZ8w379+9l3rwv+eSTT4HbgfesWdP5+utFuLq60r//GzRu3NR+F0fxkDRqdv99gQ17z1LE25U1O04zdVBTVHJBoxBCiELOzVXH4u//5vTFWwD0fyEYV2cNxhRJvM5p8oMF+OmnZQ6B9d3i4q7w44/fZ7vt0qXLYrVasdlsGAwGtNr/fs+cPRtDQEBpvLy80Ol0BAfX5NChg9neV2GXnGKheBE3urbSU6NCEYb3qseNhFScnOQwF0IIUbglp5h5qog77/d4mr6dgtgTGUuqUVJCckOhn7m+di2OyMhD960TGXmQa9fispWD7erqyuXLl3jllc7cunWTqVM/t28zGAx4eHjYH7u5uWMwJD30PsRt7i46zl9OZPH6YwD8vP0Us95rjskkfzyEEEIUbq4uGqpXLMJ3vx3Fz8uFfp2C0GpUWGXiOscV+im92NhLJCcn37dOcnIyV67EZqv9n35aRkhIQ374YRXffbeMTz4Zg9FoBMDd3Z3kZMNd+3EMtsXDMRjN7Dx08b/HqRYuXTOgVhf6w1wIIUQhZ7PBvNWRxN1I4fi5GyxYE4XNJimTuaHQRx0lSpTEzc3tvnXc3NwoXrxEttr39PTC3f12wOzl5Y3FYsF2Z2HJcuXKc+HCvyQk3MJsNnPw4D/UqBGcrf0IcNJpCKr4X766VqOmuJ+b5FsLIYQo9ExmK42CSjCsex36dKyBk1aNVb4ec0WhTwspWtSfoKCaRETsuWedoKBa2V6Wr0uXV5g0aRwDBryJ2Wymb9+3+fPP7aSkJNOx4wsMHPguQ4cOwmaz0b59B/z9i2V3KIWeCujUrBK+ni5ciEuiXaNy6DRqbp/zkr8gQgghCi83Zy21Av1Zsj6aIt4uvN25Ji5aNSaT5IXktEIfXAN07foKZ86cyfSiRn//4nTt+nK223Zzc2P8+Mn33N6kSShNmoRmu33xH41axYUrSYQ9HYBarSIxyYhKJWG1EEIIYbEpzF5xGIAr8cl8tfIwQ1+unc+9KpgkuAaqVq3Bhx+O5Mcfvycy8uBd61zXomvXlx9pnWuRd0xmK1+vOcKZi7duB9UKTOzfmJK+LvndNSGEECJfmcyOF/ffSDRiUyQ/ODdIcH1H1ao1GDPmE65di+PKlViKFy8hd2h84qioXNqHMxdvoSigVqvw83ZBrVbJRRtCCCEKNTcnLTUqFuHI6euoVNC9bRWc1CoskhWS4yS4TqdoUX8Jqp9QGo2Kdo3K4+aiI/aagdb1y6DVqPK7W0IIIUT+s1kZ+nIdUk1WnHUaNCoFi0WWqs0NElyLAkOxKXi7O9EqpDRqlQoVYLHYcNLJSS8hhBCFm6JWs2rbKTbsjsHN1YmRr4XwlI8LFpm6znESdYiCQwU2FDbuPcf8NVFcvZFyJ7CWlBAhhBCFm8misHZnDBYbJBhMzFkdiVni6lwhM9d3OXfuLH/8sYGkpEQ8PDxp1aotZcuWy+9uiSxSUPH9xmi2/nUBFycNf0dfYc4Hz6CWzBAhhBDCgXw15h4JrgGz2czMmZ+xe/cuhzsmrl+/loYNGzN48HvodLpstx8VdYSvvppJePg8Tp48zueff4parcbJyYmRI8fi51fEXtdmszFt2mROnTqJTqfjgw8+plSp0o80vsLCYrVRqpgHEwc0JtFgwtlJw80kI095u2KzSV6ZEEKIwstJq6JjaAV+3RmDp5uO/i8Go1MjFzTmAgmugZkzP2PTpt8zlCcnG9i8+XdUKhg27MNstb106UI2blyHi4srAF98MY13332fypUD+fnnlSxdupBBg4ba6//55zZMJhNz537LkSORhId/zuTJ07M3sELGy1VHMT83/jfrTwAC/D2Y8FYjbFYJrIUQQhRyVhudm1eiQ9MKaLUasNrkgsZcUuhzrs+dO8vu3bvuW2fPnl2cP38uW+0HBJTik08+tT8eM2YilSsHAmC1WnFycnaof/jwQerXbwhAjRpBREcfy9Z+C6OEZBOrtp6yP74Yl8SV+GRUKjn5JYQQonBTadScuZTA1MV/MXd1JGZFQS15k7mi0AfXf/yxwSEVJDMGg4Hff1+XrfabNXsGrfa/EwRFixYFIDLyEKtW/USXLq9k2Je7u4f9sVqtxmKxZGvfhY2zk5YSRd0dyrw9nPKpN0IIIcTjwwqMnb+X4+dvsPvwJWavOIRNJp9yRaFPC0lKSsxSPYPh/gH4w9i8+XcWLfqGqVNn4Ovr67DN3d2d5ORk+2NFURyCc3FvNkXhpWf0WKw2Yq8ZaNeoPDqNGpVKVgsRQghRuKUarfh4ulCvWnFuJZn490qC3KExlxT619TDwzNL9dzd3R9cKQs2blzHypU/MWvWXAICSmXYHhRUk717b6epHDkSSYUKlXJkv4WB2Wxj/i+RBPh70LZBOfYeieVyfDJqdaE/zIUQQhRynm5OfNC7HharQpninox8vT5uzpr87laBVOinRFu1asv69Wvvmxri7u5O69btHnlfVquVGTM+o3jxpxgx4n0Aatd+mjfe6Mf48aPo02cAoaHN2b8/grfeeh1FURgxYvQj77ewcHPREFq7FGt3nqGotytXbyQT4O8hM9dCCCEKPYvVxqi5u0k13b6I8eqNZHq1q5rPvSqYCn1wXbZsORo2bMzmzRlXC0nTsGFjypQpm+19lChRknnzvgNg/fotmdb5+ONx9v+///6IbO+rMEs1WQmuVBQPVx3nYhPo83wQimJDFgsRQghR2BlSzfbAGiDqzHUURZH1rnOBnC8HBg9+j5YtW2dI/XB3d6dly9a88857+dQz8TBcnLTsi7rMpWsGnirqzo+bjgMqubWrEEKIQs/TzQlfz/9WKAutHYBO0iZzRaGfuQbQ6XQMG/Yh58+f4/ff12MwJNlTQR5lxlrkrVSThWrli7Bw3dE7FzSWw5BqxttFJ2t5CiGEKNTUKhj9ZgP+ORGHn5cL5Ut6AZI2mRskuL5LmTJlefPNt/K7GyKbnLQalm8+wcETcQB8u/Yo9ao9hfzxEEIIUdglJpv55c8ztA4pw41EI9+tjWLAizUlhSEXSHAtCgyj2cr1W6kOZUnJJnzcXJGlwoUQQhRmbi5aKpT0YuriA/h5uzDgxZo4a9WYTZI6mdPkB4soMHRaDS+2qGS/41T5kl74ebugKDJzLYQQonCz2hQWrIniRqKR0xdusWDNESw2+X7MDTJzLQoQhdhrBj4fEobZYsWQYiHVaEWHrOMphBCicDOZHa89SjCYsNoUmWXNBfKaigJDrVbRpGYAcTeSibuRjJ+3Cx5uOrRaWWhICCFE4ebqrKWW3h+4/X35SpsquLnI5FNukJlrUWCYLTZSjBYiT1/j0jUDbRqUxc3ZC50E10IIIQo5LTbe6Vobq9WGWqVCq1GRmmzO724VSBJciwJDpYI1f57m94jzAPwVfZXZ/2uBq06CayGEEIWbRVHxwx/R/B5xHjcXLR+/Xp8APzdZqjYXSFqIKDBsNoXTF245PI6/lSp3aBRCCFHomSw2/jx4iXrVilO6uCdzV0dilgsac4UE16LA0GhUtG7w301/fDydKV7EDZBlhoQQQhRuOq2G8f0aUa6EF83qlKJvp6D87lKBJWkhosBwddISVLEo4/o2JO5GCpXL+KDTqFGsCnIjGSGEEIWZWgXjFuwlwWAC4NnG5en6TGVZ5zoXyMy1KDAMqRactGqK+7lRrYIfrs5arIoNlUpyroUQQhRuCclme2ANEBF1GZNVAuvcIDPXosBQqWDD3nOs2XEaN1cdySlmvvxfC7QaCa6FEEIUbh6uOtxdtBhSb9+yuKbeH41ahU2uS8pxElyLAsNmVfDxcGL8W424mWjE082Jm0lGSvg453fXhBBCiHylUsOYvg05cvo6rs5aKgZ4Y5MLGnOFBNeiwHBx1uDt4czw8J0APFXEjYkDmmA2y89yIYQQhZtGpcLDVYevpzO+ns54eTih1aiQlfhyngTXosBIMVpZ8+cZivu5UcTbhZP/3uRqfDIBfi6YzfLrXAghROFlsSqMnreHqzdSAHi5i9ak4wAAIABJREFUdSBt71phS+QcCa5FgaHTqOnxf1VISbVyOd7AGx1q4O0hKSFCCCGEIdVsD6wBNu8/T6uQMvnYo4JLgmtRYGi1av69ksT8X44A4Omm44thzbBaZdZaCCFE4ebuoqNm5aLUr16CBIOR+FupqFVyJ4jckKtL8R06dIiePXsCEBUVRdOmTenZsyc9e/Zk3bp1AISHh9O5c2e6devG4cOHATh37hwvv/wyr7zyCqNHj8Zmk7dePFiy0cyWA//aHycmm4mNM+DiIquFCCGEKNxUaniuaUW2/vUvZy8l8FJLPWq1fD/mhlybuf76669Zs2YNrq6uABw9epTXXnuN119/3V4nKiqKffv2sXz5cmJjYxk0aBArV65k0qRJDBkyhPr16zNq1Cg2b95Mq1atcqurooBw0mqoWs6PMxdv3wJdq1FRzM8NiyWfOyaEEELkM5tVIfJUHL3bV8OQYubXP0/zfLNK+d2tAinXZq7LlCnDrFmz7I+PHDnCtm3b6N69OyNGjCApKYm//vqLJk2aoFKpKFmyJFarlfj4eKKioggJCQEgNDSU3bt351Y3RQFisdp4PqwiPf6vCqG1A5g6sClWqw2NJr97JoQQQuQvm3L7Fuhj5+9l/pojNKhREs3/s3ffYXaV1eLHv+8up0yfyZT0MpNeCKQ30kNCBwFDRwUFveJFBaJXARWUqyI/wajXdrGglAsaQDpJML33XifJZGYyvZ2+y++PgZEhCQGmnDkz6/M8PA/n3WeSteec7LPOu9e7Xpm5bhNtNnM9f/58ioqKmh6fd955XHfddYwcOZJf//rX/PKXvyQ1NZWMjIym5yQnJ1NfX4/ruk276r0/di6ZmUkYRvyyqJyc1Lj93a0tUc+ltiHM2xsKOXKylm7pfp54fisP3j6JlJQkUlLiHV3LJOprciad5Vw6y3mAnEtH1VnOpbOcByT2uRRXNPDCsoMAlFeHeOqfu/n258Yn9Dm9r6OdQ7staJw3bx5paWlN///www8zZ84cAoFA03MCgQCpqalomtZs7P2f+yjV1cHWD/pjyslJpbz83F8AEkEin4syDdbuLOFocV3TWGllABMXy0rcuv1Efk0+rLOcS2c5D5Bz6ag6y7l0lvOAxD+XD+8XE43Z4JLQ5wTxe10+KqFv0wWNH3T77bc3LVhcu3YtI0aMYMyYMaxatQrHcSguLsZxHLKyshg+fDjr168HYMWKFYwbN669whQJTNdgzJDcpsdeUyc3MwlDeuIIIYTo4jymzqwxvQHweXTuuHIkHqPd0sAupd3Sju9973s8/PDDmKZJdnY2Dz/8MCkpKYwbN46FCxfiOA4PPvggAIsWLeKBBx7g8ccfJz8/n/nz57dXmCKBKaWYPKoHfXJTOFkeYOr5PXFcl2hUasqEEEJ0bbGYTX7vdBZM6Y9tu6zdVULv3I5VTtFZKNd1O0UT4Hje1kj0W0UflMjnYnpNorZDMGTREIqSluwhyWfgMyAUStz9XRP5NfmwznIuneU8QM6lo+os59JZzgMS/1wspfjSo0ubHg/um8m3bhsPscRuqdURy0LkhrnoNDRNsf9INT99ehOuC33yUvneFycRiyVuYi2EEEK0BkPXuGnBUF5YdpCsVB9fueY8PLoiGot3ZJ2PFNuITiMctfjHu4d4/17MiVP1lFcH8XikF58QQoiuLRy1qa4L893PT+TmixuT7LBMPrUJmbkWnYbPY5CXlcTBEzVNY+kpXiyrU1Q+CSGEEJ+aoSteW1PIa2sKARjUJwND13DsxO2m1VFJci06jahlc83sQUQth5KKAJdM6d+4iYyhQzTe0QkhhBDx4zF1Hrx9AhkpPkJRi6w0H7oGklq3PikLEZ2GUoq1O0vI75XO1TMK2HGoHI+pIxtQCSGE6OoUkJ2RxJvrj7HnSBVeUwclH5BtQWauRaeR5NO4aEJfIjGbQCjGyIIR+DxGQm8gI4QQQrQGy3FZtHgVoUhjd5DiigZuvWRYnKPqnCS5Fp1GOGpzsKiGn/5lE44L/bqn8p3PTyAtSYdIvKMTQggh4qc+GG1KrAF2Ha7EcVxk7rr1SXItOo2Y7fCPdw83bfF6rLSe6vowqUn++AYmhBBCxFlasodh/bMYNiCLukCUjBQPXo9BNCSLklqb1FyLTsPQFbmZzRPp1CQPrivfy4UQQnRtru3yxatGUlzegN+jc8mUAUQSfAOZjkpmrkWn4TUMblowlFDEorgiwGVTB+D3GrIUWgghRJenNMWixauIvbcOqbI2zJevOa/psWg9MnMtOo1AJMrvluxkWP8sFs4dzJ6jVZwor0PX5W0uhBCia2sIxZol0oeKaojIJjJtQmauRaehNIfMdC9Pv7EPAE3BdfP742hy8RBCCNG1pfhN8rKSOFUVBGDO+L6Yho4tm8i0OkmuRaehaQ4XT8/D79UoKQ+zYFpP6qxKepCC1IYIIYToynRN8e3bxnPwRA0ZqV7yspIwNJDpp9Yn98tFp6G5XnTTJrn/cYZPqKHI3k3fzB44jmx/LoQQomsLhGPsOVpFfq900pI9vL3hOKGopNZtQWauRaehKZ1TJw2m95iJoWuUVwdxwgbKJ91ChBBCdG1ej8Gewkp+u2QnXo/OfTeNxe/RiYTkzm5rk5lr0WnEbJu0FC/f+dVa7nx0GW+sPU7EdlC6zFwLIYTo2iJRm1Xbipv+/8+v7yUclcS6LUhyLToNTSmeeXM/Mcume7ckVm0vJhy1ke2nhBBCdHWG3vzD0Gvq8vnYRqQsRHQKhqGBcpk9vg8L5w2msjZM925JeE0Nx5aZayGEEF2boWt8du5glrx7iMw0H3dcORKvoRG1pO66tUlyLToNXXdRuHz3f9YAkJPh58d3T32vz7Xc+hJCCNF11QdjBMMxHrxjEvXBKH9ffpAvXTVKJq/bgCTXolOwLIcILq+sOto0Vl4TovBUNcP7ZcUxMiGEECL+0pJNNu8t45/vfU7edukwvB6DaCga58g6H0muRadhGoq+3VM4XFQLgFKQk5GErmlIJ08hhBBdmVKKB++YyLHSejJSvKQle3AcuavbFs6aXC9evPgjf/CrX/1qqwcjREso5XLD/MGYhkZJeYjLpvchxW9i2ZJYCyGE6NpilsPmfWWs21mCaWrcOH8ofq/MsbaFc/5Wd+zYQWlpKQsWLMAwDN5++2169erVHrEJ8cloLl6Pzg0LCnAcDU13UI6DjonUXAshhOjqfv/Srqb/j0Rt7r1pbByj6bzOmly/PzN9/fXX89xzz+H3+wG47bbbuPXWW9snOiE+oapIBVE7hlKKcCxMbkoOPs2Md1hCCCFEXEVjze/i1gWiuEg3vrZwzpnr6upqlPr3rz4Wi1FTU9OmQQnxabg4lDSU8YfNzxKMhRieM4gvjrsRhdRcCyGE6Nr8XoPRg7LZfrACTVPcOH+otOJrI+dMrq+77jquueYapk+fjuu6LF++XGauRYdkuw6/3fQ3IlYEgD3lB1l5bAOXD54X58iEEEKI+AqELOZN6MeN84eiaYqNu0sZ1i8z3mF1SudMru+44w4mTZrEhg0bUErxxBNPMHTo0PaITYiPTSmF5dhNifX7TtaV4rqyiYwQQoiuLdlv8MsXthOKWADMGtsbpSnkE7L1nXX78+XLlwOwZMkSDh06RFZWFpmZmezbt48lS5a0W4BCfBymqWFqJn3Se6IrDb/hA2D2gCkY2lnf5kIIIUSX4LouP737QhZM6sdtlw7ns3MG4ziSWreFs85c79y5k1mzZrF+/fozHr/qqqvaLCghPg3LsfjWhf9BIBqkIRqgW1Im4ViEoBUG9HiHJ4QQQsSNUhp/enUHwwZ0o6wqwL5jVYwfnic1123grMn11772NQAeffTRdgtGiE/LdV1sx+Hve17n3cK1ACSbSTww8x50TZfljEIIIbo0y7L40tXnEY3ZGIZGOGITjjpnL2EQn9pZf6fRaJRnnnmGd955h4aGBm6//XbGjBnDLbfcQmFhYTuGKMS5uS4EYsGmxBoaH//Pxj8Ts604RiaEEELEn2HqPP/OAd5Ye4xn39xHWVWAJK/c1W0LZ02uH374YdasWcOzzz7LzTffTH5+Pn/729+YPXs2Dz74YHvGKMQ56brG0Zrjp40fqzmJpqSLpxBCiK7Nshxmju1NOGqRme4nK91PKCqTT23hrGUhW7du5Z///CfRaJTp06fzne98B4ChQ4fy8ssvt1uAQnwcruswtFvBaeNDcwqkQb4QQoguzzR0NKWYOaY3fp9BWXWQrDQfruxg3OrOmlwbRuMhj8dD9+7d2y0gIT4NTdNINpL47IjL+cfe14k5Fj1T87hz/M0YuoklFw8hhBBdmOO6/HbJTo4W1wHwH9eOxtAVsVicA+uEzppcf3BXRiW31UUHp2kaJ2qLmTlgMtP6jSdqR/GbPsoDlfh0Dx+jpbsQQgjRaYXCVlNiDfDi8oOMGZord3fbwFkzjr179zJs2LCmDTg+uHGMJNuio7GURZ/0Xnx/+eNM6zeenORurD6+iaHZA+mT1hNb2uQLIYTowrweHU015nCO69KjW7Ik1m3krMn1vn372jMOIVpEKRfNNvmv6V/j1QPvcLjqOLMGTGFw5kB0pYE04xNCCNGFeQzFL+6dRSBk4fPq+DwGKX6Thnr5fGxt57xXvnjx4maPlVL4fD4KCgqYOXNmW8UlxCeiULh6lJd3vQkKBmT2YemRVfQb05tkPPEOTwghuhSvVydqK05VBfD5TKJhKeyNN9uFX/7fdvYUVqEUfOOGMYwa1C3eYXVK5+wdfvz4cVauXElaWhppaWmsXbuWjRs38vzzz/OTn/ykPWIU4pwcx8V2bJYVrqEiWE1dpJ6S+jJe3vcWrlSECCFEu9F1iDqKxS9s494nV7JkxWEwpJ9yvAXDFnsKq4DGvSGeeWs/sg1E2zjnzPXRo0f561//isfTOPt3/fXXc8stt/Dcc89xxRVXcP/997d5kEKci2noRC2Nx+Y/gKmbRK0Inxm+gBM1JbJGQAgh2pHp9fCXN/axeV8ZAC8sO8TYoXn0y00mHJZsLl58Hh2laJpwykr3YegKW6pCWt05k+u6ujosy2pKrmOxGMFgEKBpsaMQcefwXm21y+biHVQGq7igx0j6ZPTEcaUNnxBCtBfbcamuCzcbq66PUNAzDZDkOl5s22XRLeOoqAnhuDAyP1vyuDZyzuT6pptu4pprrmHmzJk4jsOKFSu4+eab+eMf/8jgwYPbI0YhzklTOo7j8vjq33GyvhSAf+5fyl3jb2ZCr/NBuoUIIUS7MDSNhXOHsHV/OZGYTc/sZIb1zyIUkrrreDINjSS/yZb1x8nO8HHBkFyUJnd228I5k+tbb72ViRMnsnbtWjRN48knn2TQoEEUFhZy4403tkeMQpyT60JhzYmmxPp9z+18hdHdhwFmfAITQoguJha18Hs1frVoNpGojdejYxoa0ZDMWseT68IPfr8Oy26cbKptiHLXZ86Lc1Sd0zkXNFqWRUlJCRkZGaSlpbF7926WLFlC//79m0pFhIg3XdeojdSfNl4fDfAx3uZCCCFaiWXZeA2dhmCUQ0U1uK6LFZVZ63gLhGNNiTVAYUmdlIW0kXPOXH/zm9+kuLiYgoKCZgvDrrrqqjYNTIhPwrJsRuYOwdAMLOffsyPT+o7D1AwiUhYihBDtxrUd0rwGs8b2oby8Hln5En8pfpOe2ckUVwQAuHhKf0xDI2bJisbWds7kev/+/bz++uvScUF0aEopNBT3Tr2TF3a/SmWwmnG9RnPRwOm4klgLIUS7MgwNC0V5dRDDY2BFpSQk3nRD45G7pnCoqJasNC8ZqV4cRz4f28I5k+uCggLKy8vJzc1tj3iE+FRsFWNzyQ48mocL+08g3ZvKsZqTxOwY9ZEGvKTEO0QhhOgSNA0sFE8+t43jp+q48PzeXDkjH2IyQxpPluXw0G/XYjsugVCMK6cXMHdCn3iH1SmdM7kOh8MsWLCAwYMHN6ux/vOf/9ymgQnxSSil2F66j95p3ZnQ+3xidoykHD+/3vgXvjHli/EOTwghugzDNFmzq4SbLx5GMBJDU4rq2gg5aR4sSwpE4iUQilFU1tD0+K31x5gxppesSmoD50yu77zzzvaIQ4gWcYErhs5j8bqn8Ogmad5U9pQfwNB0vLpHCkOEEKK9KOiVm8q3frkKy3bIzfTzyF1T0aTtW1wl+0x0TWG/VwrSt3sqpqFh2/KFp7Wd9QvL7t27gcYZwTP9J0RHogF5ydncN+0uDlcd4+3DK+md1oNF076CxzAxPPKeFUKI9uA4Di+tOIz1XtJWVh3iSHEtti3THPFkOw4/+NJkRg/KYe74Pnzu0uHymrSRs85cP/PMMzzyyCM8+eSTpx1TSklZiOhQwnYU27V5YNnPiFgRAI7XniQQDXH50LnvrVQ/540aIYQQLaRriu5ZSc3GstN90vYtzjymjt9nMHd8H5Rq3FTGZ+qEpVtIqztrtvHII48A8MADD5y2E+O2bdvaNiohPiGf4WXDye1NifX7lh5ZxeVD56KhSysoIYRoB7GYw5zxfamoDXG4qJZZY3tjGtp7d70lwY4Xy3ZZtHgVsffq3ieP6sEXrxwZ56g6p7Mm15s3b8ZxHL773e/ywx/+sOkbp2VZfO973+PNN99styCFOBdDM/AZHlK9KUzpM5ZUbwr7Kw5zrOZk4xMsDbmoCyFE29M0sG2bO65oTNyUAivm4DgyxRFPwXCsKbEGOFxUgwtI0WTrO2tyvWbNGjZs2EBZWRlPPPHEv3/AMFi4cGG7BCfEx6VcGNxtAIumfZm3Dq/gZF0p5/cYwecvuA6f7iPoSI9VIYRoDwpIS/HyyFMbKCyuZcaY3tx6yXDcmFyH4ynFb5KXlcSpqiAA8yb2wzAUtrwsre6syfXdd98NwJIlS2Q3RtHhKaUIW1G+v/z/EXsvkd5Vtp8DFUe4fez1yBboQgjRPjTD4LlX91BZG6KgdwarthczZ3xfBuSlEonINuhxo+Dbt43nRFk9GSk+LNvGlZsJbeKcK7x+97vfSXItOryoY/GPPW+wYNBMLuw/EY9mUtpQzl+2vUjUjqLhi3eIQgjRJbi4jB6Uw4Xn9+J4aT13Xp2FZdnSii+ODEPDUYpDRTWUVAaIRqu5eMoAvKZGKBrv6DqfcybXffr04dvf/jajR4/G5/t3giIJt+hIYnaUmfmTKaot4ZF3n6QuUs+I3MHcd+Fd2LYt89ZCCNFOdKVISTI5XlpPz5xk1u0qZsHkAURlC/S40XUNFxiR3426QJT+PdJwXJdwVKau28I5k+vMzEwAtm/f3mxckmvRkfgML5Zt8YctzzaN7S47wKMrfslDs76O7uhY0m5ICCHanGU5ZKX5eHHZIY6crGHm2D6EIjFMr7RDjRddV4SjDosWr6Iu0DhVffm0AVw3ZzDylaf1nfOd/uijj542Fg6H2yQYIT6tiB3jtYPLThs/1VBOOBYiVfPGISohhOh6dEPx6uqj7DxcAcCrq48ydXRPuqV4ZDfAONE0jYZguCmxBli3q5QrZxTInd02cM7ketmyZfz85z8nGAziui6O4xAOh1m7dm17xCfEx6KhUGdtKKSIxeSCLoQQ7cFxoT7QvJC3IRhFqZQ4RSSiUZuUJA9JPoNguHGu+oIhObL9eRv5WDPXDz/8ME899RR33XUX77zzDqFQqD1iE+JjM3WDy4bMZUvJrmbjvVK74zO8GK5GLCZlIUII0daUC9dfNIStB8oJRSz6dk9lUJ9MolG5BseL67ooBd//4mT+tbWIrDQfowflIJtmto1z3g1ITU1l0qRJjB49mvr6eu677z7WrVvXHrEJ8bEppchLyeGrEz9Ht6RMNKUxpscovjX9P/DpnniHJ4QQXYahK2rqwzx0xyT++z+mcdslw3EcV7Y/jyPD0AmGLJ76524G90/FMOBXL2yXWes2cs6Za5/Px9GjRykoKGDDhg1MmjSJWEz6VIqOJWLHCMfC5CR14wezvwkoXNdlfdEWpvQdh2vJQhohhGgPoajNkn8dYcv+sqaxe66/gAlDcwiHZflcPGgapKQorrk8i2XHXiPDl8F9X7iIVJ9JQ30k3uF1Ouecub7nnnv4+c9/zqxZs1i7di1Tp05l7ty57RGbEB+bZdtUBKvxGCal9eUcriqkIRrg/B4jcVwXr6xSF0KIduEzNUYPym56rBQM6pMhrfjiSNc1qiKVPLZuMVtKdrGscBU/WvlzAlYw3qF1Sh+rFd/725+/+OKL1NbWcvTo0TYPTIhPwnFt0nwp/HHr8+yvOAKAoRncOf4mBmb2w68nxTlCIYToGmK2w4wxvfF6dHYdqeTSKQNI9pkoxwGkDCEedF1jeeHqZmPlwSoidgQP8vnY2s46c71582Y2btzIV7/6VTZt2sTGjRvZuHEje/bsYdGiRe0ZoxDn5DFMdpbua0qsASzH4vebnsFreGXGRAgh2o3i1y9up7I2zOSRPViy4jC7j1ai67JDY7y4rkv/9D7NxnSl4dHNOEXUuZ115nrNmjVs2LCBsrKypplrAMMwWLhwYbsEJ8TH5bguW0t3nzYesaPURxtIsTPjEJUQQnRBCnrmJNO9WxI5GX5y0v3kZPpxZNI6jhRje45mw8lt7Di1F6/u4ebzrkO5JrLMtPWdNbm+++67AViyZInsxig6PEMz6JXWnb3lB087lmwm4dF0aQMlWoXHZ1JWFSTJ7yEUip77B4ToYpyYw5XTB7J00wnW7TrIpVMGkJvhx5J2qHETcSJotofPjbqZ6PAomtLw6l40QDM12QuilX3kgsbly5czduxYAN555x3uuusunnzyyY/dLWT79u3ccsstABw7dowbbriBG2+8kYceegjnva+wixcv5tprr+X6669nx44dH/lcIc5GKcUVQ+bhN3zNxmf2n4ShGZhe2YNKtJzmMXhldSFPv7GXtzYdR5l6vEMSosMxvTob95TSo1syn5k5kBNl9TSEY2hyGY4bTcHOyp1ELIvi0hhlFVHKg5VYKoIp17FWd9a3+h/+8AcWL15MJBJh37593HvvvcyZM4eamhp+8pOfnPMP/t3vfsd3v/tdIpHGFi+PPvoo99xzD3/7299wXZelS5eye/duNmzYwP/93//x+OOP8/3vf/+szxXio9i2hes6/GjeIh6b/wA/mruIx+Z/l4sGzvjIvRuF+LhSUrwUltRx/qBsBvRMZ3j/blTUhKUTjRAfYtkug/pk8tc39vHtX61m95FKNKXweKS+N14MzWB/5SEeXv3frK9/nVdLnueHqx8j5lgya90Gzvqp8NJLL/Hcc8/h9/t57LHHmD17Ntdddx2u63LJJZec8w/u27cvv/jFL7j//vsB2L17NxMmTABg+vTprF69mgEDBjBt2jSUUvTs2RPbtqmqqjrjc+fNm/eRf19mZhKGEb9vXzk5qXH7u1tbIp5LIBJA1wzeOfQvXj2wFMd1yEnK4r5pd6FrOoapk5PjO/cf1EEl4mtyNol6LjHLwdA1/uvXq3FdMHSNx++ZTlqaP96htViiviZnIucSf4FQlJdWHuFkeQMA63aVcsmUAfTJSyM1NXGvw5C4rwnAnIJpvHNkFeuLtgLQO60HXt1DRlridwvpaK/LWZNrpRR+f+OHxvr167nxxhubxj+O+fPnU1RU1PS4cevNxp9NTk6mvr6ehoYGMjIymp7z/viZnnsu1dXx69WYk5NKefm5Y0wEiXouSekGwViQdwvXcsngWWT40tl5ai+/3/ws35h8Bw22gxUMxzvMTyVRX5MzSeRzSUnxsnzTiabtgi3bYfPeU2SnegmHE3djrUR+TT5MzqVjMH3maTv/2bZDOBylPoE3LEnk16Rbt2Rsx+aHc+7n7cMr6ZaUycwBk8ElYc/pffF6XT4qoT9rcq3rOnV1dQSDQfbu3cvUqVMBOHnyJIbxyW+Dah8otgoEAqSlpZGSkkIgEGg2npqaesbnCvFRlAtFdSUsuvArvH5gOQcqjjK13zg8momjXHyagYUsphGfnm27nDcom6WbTjSNDemfJVs6C/EhXkNxw0VDMA2NFL+HaMymX480IpHE/RKa6BzHIWrHSPOkcFH/eaAsgtEQad5UHKXkOtbKzlpz/aUvfYmrrrqKz372s1x77bXk5uby2muv8bnPfY7bb7/9E/9Fw4cPZ/369QCsWLGCcePGMWbMGFatWoXjOBQXF+M4DllZWWd8rhAfJeZY5Gf245fr/sjq4xs5UHmEp7Y8j6mbKDSk6Fq0VCQSY2R+Nnd9ZhTjh+Vx701j6J6VRCQiPdSF+KCY3Xi3+jMzB3LplP5cOSOfYDiGpsnCuXgJWmG6ebM5WQS/eHoff325CMNKxXVdvF55XVrbWaegFyxYwAUXXEB1dTVDhw4FGks0HnnkESZOnPiJ/6JFixbxwAMP8Pjjj5Ofn8/8+fPRdZ1x48axcOFCHMfhwQcfPOtzhfgoMcfCxaWkoazZ+PqirQzNHvhePb7MXItPz3FcdI/CY+qcPzgHTVNoSvabE+LDdB2SvCab9p1ib2EVCyb1Jyvdx8esKhVtQNcUWB4e/eNKHBcOn6wlGnP4zxtGoRnywrS2j6zvyMvLIy8vr+nxjBkzPtEf3rt3b55//nkABgwYwNNPP33ac+6+++6mntrvO9tzhTgbj2ZguRZ+00co9u/a6v4ZvfHopmxeIFrM7zd5d1sxv/nHzqaxh+6YyMAeacSkf68QTRQaK7ed4A+vNG7stXzTCRbfN5v0JINQKM7BdVGm7iEUCeJ8oPqjrCqIY2voXh2Qkp3WJF0nRafgKpeTtcV8ZfytpPvSUCjG9hzFiNwhRO0oMUcuHKJllFIcPlnbbOxocR2GIZdRIT4oFLXYcuDfdxEdF/Yfq0LTZIY0XjQUKUkmBb3Tm8aunJGP14PcUWgD0qBVdA6uIsWbwgu7X+Ou8TeTZPo5VHmUZ3cuJAN7AAAgAElEQVS+zN2TPo+GhpSFiJaIRi2umpHPsP6Z9MxOobCkjvHD82SRlhAfkuI3mDmmN1v3lwNgGhojC7KJRuUWYrzURwMo3WXRLeMoqw6R4jdJTTapilTS0+ge7/A6HUmuRadg6gaZ/gz8ho8fr/wVAH7Dx6ILv4xXMzGQmmvRMpblkOzz8PaG4+wrrGbcsFwmj+qBIwmDEM3Yjst5A7N5+M7J7D9WzdTRPfF4NNmhMY5MTed44Dhe3UvUE6bSccFNJd2XJncU2oAk16JTcFybbSW7GZU3hAWDZtIQDZDmS+WVvW9z2wXXYureeIcoEpzHo/PKmkL2FVYDsGlvGTsOVnB+QRaWJQm2EO+LWS5Pv7GPsqogQ/pl8uBv1vCf149haN+Mc/+waBNe3UttuJ7F63/RNJbuTeXRed/CtqUNX2uT5Fp0Ci4Kn+Fl8fo/YmoGPsNLfTRAqjcFpRSWJbPWomWUpoh8aOFiJGZ/7I21hOgqFI3/NnYermTn4UoAwlGLaFRKqOKlLtrAi3tebzZWG6lna+luZvSZHKeoOi9JrkWnoCmN4bmD6JaUSWWwmli0sffw1cPmY2omjiXfzEXL+Lwml04ZwHkDs/F7DQKhGAN6peP1GtItRIgPMHTFjRcNYfTAHHIy/BRXNJDfMx2PxyAcln8r8eC4DmHr9F2KA9Egui4TBK1NkmvRKUStKKZm8IPZ32T50TWUB6qY0nccfdN7YjkWKV4f0ahc1MWn57ouKPj9S7sorggwsHcG37ptnKy0F+JDojEH14V3txRx5GQtc8b3IRi2yEgx4x1al5VkJjFnwFRe2PNa05iu6UzsfQG2K5+NrU2WF4hOwdQNonaMb77xCEeri9A1nT9ueZ6/73kDXek4SmpiRcvYtsurq49SXBEA4FBRDVv3l8ustRAfYpoar6w6wu4jlYQiFv9cdZT6YJRIRP6txIvtWMzOn8rCkZfTJ70nI3OH8N0ZX8OjmyglqWBrk9+o6BQ0pbG9ZA9hK8zm4h0sO7KakoYy3i1ci+3a2LZc1EXLaBpnXFWv63IZFeKDXBdqG6LNxgKhGK5U58WNrnSWHl7FkerjXDFkHmN7juL3m57BciwUcvuttUlZiOgUYnYMUzfRlcaI3CEke5I4UnWMsBXBdm2UbO8qWsi2HS6bOgDXdfF5DKIxm3HD8mSxrBAfYtkuV88cyPaD5QTDFv17pNE7NwVdj3dkXZerID+rHzPyJ3OyrpQeqbmMzBuC64LtWvEOr9OR5FokPKUUrnIZ2K0/P5n/HbaX7qEiUMUd424g3ZuKrnSpKRMt5roQjdmkJXvYur+cC8/vRTRmY3okYxDig0xDcaoywANfmIhSivpgFAewZGF53MQsC1M3eGjpY4zIHUJtpJ6aUB3/NeOrBKIhQK5jrUmSa5HwPB4dy45hOTYPLP0pYSsCwGsHlzOp9wXcev61xGwLebuLllBKse1AOX96dS8AOw5V8NAdkxjcKy3OkQnRsShgZEE272w8zsETNcwb3xe/qWOaikgk3tF1TTYW+ysO8/Cc+7EcC0PXiVkWy4+s5aKB07GRLz6tSYoFRaegNI0/bH62KbF+37qirVSHa0kyfHGKTHQWuq5xsKim2VhhSZ3sbibEh9guPPPWPg4X1dArJ4Vn39lPcWUAXepC4sIwNCzbZl7BdGzXwnIsIlYUUze4sP94LEfKQlqbJNci4TmOi+M67Ks4dMbj20r2YBoyay1axnUdLp06oCmZ9hgaU0b1iHNUQnQ8rutgGBpjhuQyZkguY4fkEZZWqHGV5U/HcR3KApUEYyHCVoSS+lMYmoGhyZee1iYZh0h4rgsuLpn+dKpDtacdz0vJxnakFZ9oGdeFE6fqefQrUyksqaOgVwYnTtUzTLZ0FqI5V3H1jIE89tfNHDlZy6yxvenfPU16wseJpmkEY2EaokH+vO1FTtQWoymNuQXTyE3JJtWTQoTTN5gRn57MXIuEp5SL3/DxmWEXn3Ys3ZvK8NxBONIDSrSQUori8gAP/XYtr64+yrd/tYqGUExa8QnxIZqC/1t6gP3HqolZDm+tP05pVRCZ44gPTQPLtXhu1yucqC0myfSjK423Dq2gLFCJLn2uW53MXIvEp7tELIshOQV8adyNvLT3LarDtZyXN4ybR38Gy7KwNbklKVrGsh3mTujLgRPV7DpcycSR3RlVkC2t+IT4EJfGvtYfFIpY7x0R7c2yHFzNxbItfrbgAQzNQAEn60rZX3GYETmD4x1ipyPJtUh4Ht0kRIxFb/6Ia4Zfwn3T7sLUTSoCVTyx9g+M7z2aSwfPAWTaRLSA62I5DrdfPhLLcfCaOo7rYNtyr1uID1KaYuG8IWw/WEFDKEZB73Tye6Xhyh3EuFBKYWomd46/iT9te4GNJ7eT4klm4cjLmd5vIoFYKN4hdjqSXIuE57gODg6p3hRe2PMqL+x5tdnxBYNmYTkyuyhaxjQNzKiDx6cIhmN4PTrKBZ/XIBCQ/mJCvM+yHXxenR/fPQ2FwrIcLMvFdaX8ID5cdE3jmZ2vsr5oKwB1kXp+t/lv/OSi/yLZTELuKrQueaeLhBezbXyGlyuGzjvtWLKZxMi8IbK5q2gFLqGIxXd+tZrfv7Sb7/xqNZbjymycEB9iGhqRiM0zb+3nkf9dz7rdJcQsG7l7GC+KiBVlU/GO045sPLkdnymtalubzFyLhOc1DaojtQzuNoDrRlzGGweXUx8NMCxnEAtHXoZSELVjyA5UoiWUplFdF+b7d04mFLZI8hmcqgqQ0Ue6hQjxQbbl8ub6Y6zaVgzA397cz+hBOeRl+uMcWddkmjqu0slN7kZ9pKHZsV5p3bFt6XPd2iS5FgmvseLD5cXdr3Nh/wk8Mvd+HNchasdYsucNrho+nzRvarzDFAlOU4qcrGTue3IldYEoeVlJ/OjLU1HSX0yIZhRQVt28jre6LoyS3UzjwrYdPF6TW0dfw8P/erJp05h+Gb0YnF2ArhtA7KP/EPGJSHItEp6rHFLNZG694BpeO7CMX2/8C7Zjk5eczR3jbiDdmwpSGCJazOXt9ce4euZA+nVPZd+xajbsKWH+xP7xDkyIDkVpiqtnFLBl3yks2yUnw8/APpnxDqvLcl0X27XJ9Kfz8Jx7OVJ1jFRvCtlJWbiug9Rbtz6puRYJzyKGUoqVhRt49+haRuQMZnKfsQD8ZOWvsV2HFDNJ+hGLFtE0xdwJfdlztJKfPr2Z8uogFwzORT6YhGjOcaGqLsSv7p/DY1+7kEfumkJFTTDeYXVZuq6hKcWuU/uJWBF6puaR5c/kaPVxXNelNlwf7xA7HZm5FgnPcmxs5VBUV8IP5tzLjtJ91IRrufn8zxCzLbaX7GF8r/OQt7toCRdYu7OEjXtOAbB8cxHjh3cnL0vqSIX4IL9Xo1/3NN7ZcIzu3ZI5caqeK6YXyA6NcaKUIhCLMCCrL/9vze8JWxEidpQZ/SeSm5xN3/ReSNV165JsQyQ8w/aAYXPJ4Nk8/O4ThK3GtmhvHvoX43uN5qqh8/EYXhpsqSkTLeBCdX3zLYJrAxGpuRbiQxqCMWynse563a5SZo7tTV0gSnqypBzxoGkQi8VYvO6PfHHcDQD4DC8HKo/yt51LuH/al5FChtYl73SR+FyF7To8v+ufTYn1+zae3M4VQy/Cln13RQu5uFw2NZ8VW082LWgcPywP6cQnRHOmobFs03He3VIEwJ9f28vIguw4R9V1xZwYHt2kuOEUP1qxmB7JuQRjQWoi9VzQYwQKhVJK2oq2IkmuRSfQOHO4r+LQGY/uKN3DgIzehJCNZMSnp4BTVQEW3ToOTSmilkN9IEZ2uvSIFeKDLMuluCLQbKyyJsTAXtK1KR48poey+gq+OuE28lJzqQ/X4zE82I5Npj/jvVUjkli3JkmuRcKzLBvlcemZmsec/KkMyRmIZcewHIfXDiwlLyUHy5bEWrSU4sCJGkbmdwMaW/MdLq5hQE9JGIT4oCS/wbWzBrF5XxmO45KV5mNo/yy5yxMnulKkelOoCFbj0Qw8hgePbhJ1wdB0DE2XRnytTJJr0Sl4NJP7p32Zp7f/nae3/wMXl1RvCl8Ys5Bh2QOxXEmuRcu4rsu00T2578mVNIRiZGf4+PFXL5RbqUJ8SDBskZbs4YlvzKS2IUJ6igfLdmRBY5yErAgRK0qf9B4U1ZawrXQ3KZ5kJvUZg2XbWI6FUqZcy1qRVLCLhKdpChuH1w8uZ1PxDtz3bm/VRxpYvO4pgrEQpi67M4qWcYHX1xQybXRP7rx6FCPys9m4u1Rm44T4EENXBCMWa3ac5I21hRwvrScYlrnR+HHRlOLF3a/xszW/ZemR1by07y0eWPoYDbEAjutgGPIZ2ZokuRYJz+MxsByLd4+uPe2Y7TpsK92NIwmQaCFNU8yb2BfT0Hlz3TF6dEti5EBZpCXEh7kotu4v45m3DrBqezE/fXqzdNWJK4Xt2qw6vrHZqOVY/H7TM+89kg/J1iRlISLhOY6LpmvYbmNHkAxfGkmmn7JAJZZjYTsOjnQLES2kgF2HK3ll1REACkvqGNIvi57dkuIbmBAdjOs47C2sajZ2rKSO3jnybyUefKaP/SePnPHYyfpSXNclFpPSydYkybVIeJZlo0zF/IEzOK/7MGzHpiZcS9+M3mw+uYOxPUfh0Q3CROMdqkhgjuNytLi22djx0jouGCyz10J8kNJhzvi+bN5XBkCSz2BwX9n+PG5clx4pOWc8lGwmoSkpYmhtklyLhJeUZBJyLS4eNJNH/vUkpQ3lACgUnx11GQCO3PISLaSU4vILC1i+uYiY5ZDsM5hyXk8cqTkSojnXJTvdxyN3TeH4qXqG9M1E1xURWyY44kEp6JnWnbyUHE699/n4viuHXYSpGdItpJVJci0SnqZpKEfx520vNiXW0Ljpx3M7X2F8r/NxpSxEtJDruuw5XMGjX5lGeXWQ7Ew/+49VMWVUz3iHJkSHYjsugbCFYSgyU7w4rksgFCM5SVKOeAhbUWzH4Tsz7ubvu19jc/FOUjzJXDZkLgMy+xBzLDTNg23LREFrkXe6SHhR28LGZlPxjjMe33BiK5cPmQuyiYxoAU1r7IBw/+KVpCV7qG2IsOjW8WhyR1WIZnTDpW/3FAIhi4ZgjLRkD15Tw6ubhAjHO7xPxZ9kYlk2mgaJNlfjNTyE7QiPrfoNCwbO5OLBs4naMd49uoa6SD0XDZyBFQVZ1Nh6JLkWCU+9dz3wGh5CsdMv3H7Thy19rkULuS5Mv6A3x0/Vs/1gBVdcmE9Br/R4hyVEh2Pg4WRZNQ/+trGDU4rf5Mlvzmxqk5pIDEPDVhovvnuYYNjiqhkD8ZsQiyVOhh2MhrAdm89f8Fl+se4pqkI1AIzreR6z86cSjAVJNtOwLCnbaS2SXIuEpykdA4M5+VP55/6lzY6ZmsHYXudhS12saCEXl3DUoldOCmOH5VFc1oAjTa6FOE3UcnhpxeGmxw2hGHuOVjFhZLc4RvXpWCh+9tfN7D5SCcC6XaX89GsXkkiNBT2GB9t1SDL9/OfkLwAKUzMIxoK4rouhGdKXuZVJci0SnkUMlMulg+fQIyWX7ORueHWTskAlPVNy8WgGlmshbd1FSygUb6wt5JVVR5vG0lI8LJjUL35BCdEBKSC/V3pTtxCAHtnJaAl4DVZKsfdoZdPjqrowluVgxjGmT8qyLVwcfrRiMbXhumbHLh8yl8sHz8VViTMTnwgkuRYJT0OjKlSHg0NNuI5ndr5MQzTAkOx8bh19LfXRIKme5HiHKRKdgmR/849Uv1cuoUKcRrOZN6Ef0ZjDkZO1zBrXm4wUDzYJmMC5MGlkD9bsLAGgb14qpqGBlTilhrqmcayq6LTEGuCtQyu4eNAszMT73tOhySeDSHi2Y6PrOq/uW8a/Ctc1je+vOML3lj/O9+fci+XYKGR7V/HpKWD2uL7ELAfHdfGaOsMHJN5tbiHaklJQG63BdXwsmNSPQCRGqt9DdawazUqChJrzBU2DhfMGc/7gHMJRm9GDctA0lVBfEwzdoPoMiTVAxI7iuA7SJbF1yXcVkfBM3UBTGiuPbTjtWMyxeOfwSkxdEmvRMo4LHlOjT14qpyqDDOyTgaElUuWlEG1PKUV5sIq/7PkbridEWrpLnVPBz9b9AjcB1yjYrsvBEzWMHpTDxBHdqagJJtRiRgDbthieM+iMx/Iz+2JoBglVRJ4AZOZaJDwFxGwLxz3zBe9EbTGWY4PMXIsWULjsOlzJz5/dCsCanSX8+KvTyEzzxjkyITqW/hl9uGPc9Ty382WO1xYzuc8YHpr1dQxlYMU7uE/I0BR981L54R83EInafO7S4Rimwk2gmV6lNDy6yeVD5vLK/neaxv2mjy+Mub5xExk3isy3th5JrkXCizgxkj1+NKWdMcHum94Lj+4hphrbqQnx6Si27C9rNrLrcCVD+mbEKR4hOibHtXli7R84VFUIwPHakxiawbyCC7ESLL2OWS6P/XUzZdUhAB5/Zgv/s2hOQk30Wo5FxI6S5k3hoVlfZ0/ZAVI8yRRk9WPZkdV8ZvjFeDWP7ATRiuRrikh4lmMRsy1m9J942jFTN7l40ExO1pWiyW4fogVc4KKJ/VDvfarqmmLyqB5xjUmIjkbTFI7rNCXW79twchvBWCg+QbWAUlAX+Pc0dcxyEq+8RSlMzeSZnS/z2KrfsLf8ECuPbeCBpY9RG6lDVxqakju7rUlmrkWnsKV4N9eNuIxMXwZvH15BfTTAsJyB3HTe1Y2z1nYtrp5gF0TRoSigoibIw3dO4cDxakbkd6O4ooGe2UnxDk2IDsN1XZTSyEnuRnng3y3s8jP74tW9hBNqKSAYuuLmBUP5/cu7AZg7vg+6rjhLFWKHpSnFnPyplDaUU5DVj6gdoz7awHUjLsPUDWzHQgqvW48k1yLh+TQ/fTK688bBd6kM1fCVibfhM7wcqyni1xv/wjemfJHe6T1wApJci09PKaisi/DbJbvonZvCC8sO8vXrL4h3WEJ0KIahoWk6X598B79Y9xSlDeWc130YVw69CNuxSbQEzrZdfB6D/31gHrhQWFJHNJY4fa41TRGORQjbES4fMpdtpXvZXLyDJNPPVybcSkM0gN/04dFMJCVsPfKbFAnPcmL0Su3O3/e8wd7yg6w+vrHZ8bAVIcnwxSk60Vm4Llx4fi8Ki2vZdbiSueP7UtBb6q2F+CDbdonpFuuLtnL/hV9GVzphK8ySfW9y1dD5JForPqUUg/pm8NK/DhOO2lw0sR9ej44TTYza8cY7CaArjR+uWIzj2AzOzidsRXjk3SeZUzCNSwfNkfVIrUyKUEXCM3QDyzlzqyFd08nwpSHXDdFiCpZtPE5Wmo9bLx2G7bhNWyILIRo5jouhdKpDdWwp2cXR6uOsOLaegqx++BNwksN1XX7zj528tOIIb647xg+f2oDjJM4nius2rj1aemQVnxm2gM+N+SzJniT6ZfTmodnfoDZch+Va+PXEe206Mpm5FgnPcR28upfZ+VNYfXwjpQ3lTcduGHUFmtLAVZJgi5ZxobYh0mz78z55KXEMSIiOx+fTsZTD5UPn8tSW5yiuL+P87sM5L28YUTuGUlpizZIqxb7CqqaHVXVhYgm4/XnP1O7sKT/YbKO1V/a9zTenfomDFUcZ2310HCPsfCS5FgnPxaE+2sCLe17nS+NuojJUTVWwhqE5A9l5ah+VwWoyfOmyQ6NoEaXgiukFrNxWTE1DhO7dkpg4QrqFCPFBpqkTiAZZd2ILN42+ujGhBt489C/mD5yBUnqCddtwmTSqB2t2NG5/3q974m1/7gLdU3P4zaanm43HHIunt/+Du8bfjC4bYrUqSa5Fwos6Mfymj7UnNrP2xGZ6puaR6k1myd43CVlhFIoJvc4nVfdh2wm2xFt0GC5w+GQN9948Fl1TRKI2ZVUBMlM98Q5NiA7Ddd33Wu65PLD0MRzXIdlM4u5Jn0cBSlMJVVaBC/Mn9WfMkFzCEZvB/TJxXDehamo9ukFh9YkzHiuqKyHNm4KTYF1cOjpJrkXCU0phuw6Z/nSqQ7UU15+C+n8f756a897/JdAFXXQ4lmOxeW8Zx0vr6dcjjf3Hqpg5tjcFfdPiHZoQHYbSFKZu8OKe15vGArEgf9jyLN+b9XX0hJq1bjyfZZtOcLy0Do+p8+7WE/zXbRPiHdYn4rgOvdPPfJctyfSjazoxx0KW4bUeSa5FwvMoE0fTuHrYAv53y3PNjqX70hiWPRAU2NKKT7SA7ThcO3sQu45UsutwBTfOH8qAnmlE7QTaB1mINhZzLErqy04bLw9U4roupqlj24nRaQNA0+Da2QMpLKkjHLEZ0i8TXVMJtZuh47r0TM07rfc4wOVD5uK4Lh7dxEqos+rY5GuK6BQs18ajmXxhzEJ6pOTiM7xM6HU+9079ErvLDuDTvfEOUSQ8h6Mltfzi+W0s31zEo3/aSF0winKlVlGI9+lKp0dq7mnjecnZKKXQ9cRKO1ynsb3gyAHdGDcsF8dxsROprIXG1yQYC/Ofk29nat/x+E0fOcnduHn01fTP6IPj2MQcSaxbU2K9y4U4g4jbOHP4h63Psbl4B9eOuIR7Jt9O34xe/Gz1b6mLNFATrku4i7roWAzNYP2uU83GdhysQClJroVookCh+NwF16FrjYvI07yp3DPlDjRUYnUK+YDC0noOHK/BpXFxcyKJuTH8hpfHV/+W7KRMvjbpC9x43lXsLT/M6hMb8RiehDunjk7KQkTCMzWdkoYypvebwNIjq9leurfpmFKK83sMR1eSWIuWcXCZPa43yzc3LgzSFEwY3j3xPmmFaEOO6+AzvDiuw/dnfQPLsbEcm5O1pWR1z8DQEuta7Lrw5rpjvL62EICRBd34xg1j4hrTJ6XQMDWdy4fM5U/bXuClfW8BkOJJ5odz70dDQ1c6SFlIq5HkWiQ8j+blRF0x8wfOoDxQyY5T+wDwmz7uGnczy46s5tLBc7AjshpafHoaOnWBKD/+j2mU14bo0S2Z4ooGcrplxjs0IToMHZ0dp/by520vUpDVj0xfOsdqiqgIVvPkpT8gxUisL6MOsONQOTctGIrX1PnXliIs202o5CnF9LPy2EbG9hzF4Ox8NhRtI92XyvheoznVUEn3lGyMhDqjjk9+myLhRWMWo/KGUlRbyhVDL2LhqCsIWxG8ugdTMzhcdQxD04nFO1CR0DSlyO/r52DVUQqdo5jWEPr37o2h6UDiLNASoi05OBypOsb3Zn2dE3UllNaXM2vAZAKxEEW1JWTlpsc7xE/E0BT33DCGt9cdw3FdbrlkGB5T4STQOuaIHaWgWz9+tGIxgWiQwdn5HKg8yl+2/53Lh8wl0z+JJC0p3mF2KpJci4TnOookj5+GWJCfrv4fAHSlYbsO6b40fjjnfpxELfQTHUYgFuDp3c+xpWQnAK+zjAUDZ3HtiEviHJkQHYeuNOYPmslPVv6akobGriGvHljK9P4TuW7EpZBYE9fYtkM4YjOiIJtw1MLvNSDBFjGbmsmesgMke5K4YdSVpPvS0DWNXaf289qBZcwfOANXk8/I1iTJtUh4juXimDarjm1oGrPdxhKQ2nAdIStMpjcdW/pcixYIWeGmxPp9bx5+l8uHzgFkIxkhAGwctpfsaUqs37eicD1XD1uQeBMdSvHOhmP8a+tJAAp6pfOdzydWn+uoHaUu0sD1o67gfzc/R0lDGUopJva6gHun3UVVsIYeqXnxDrNTkeRaJDxNU9iuS5LpP+Nxy7He230qsWYbRMcSsU4vLHJdF9tx0OMQjxAdUcyKsr/y8BmPHaspIt2b2s4Rtdzx0jq+des4PIbOSysPE7MdzHgH9Qm4wNS+43hg6WOErHDjmOuyrmgLHsPkuhGXIZusta7EWrYrxBn4/Sb7Kg4xr+BC1IcS6MHd8jlZV4rtyipo0TIp3mQy/c3rRQdk9sHUZI5CiPdZrk1BVv8zHstJ7kbYirRvQC1k6Ipvf24Ca3aW8Pq6Qr5w+Qj8nsT5Oq1pCg1FUV1pU2L9QauPb0JTGtEE2tgnEUhyLRKebTtEbYtNxTu4b9pdDM0eSG5yNhcNnM4t53+G53a+HO8QRSdgoPHgzHsY02Mkmb50pvYdx9enfBFXZnyEaOICg7r1Z0Bmn2bjFw+aRUl9WcJ9GY1ZDv/9502s2HqSjXtO8Z1fr8GyE+ffvOu6mLpJ+AyJNYDt2ICLR0ukufiOL7He5UKcQTAYY0TuYH6z6Wn2lB1k5oBJpHpS2FdxmEfefZIJvc/Hq5mEJAkSn5KmKWwcVh3byFWDriDVk0pVuIrNRduZ2m88liJhN8cQHYvXZxCxXILhKEop3AR7Y/l0DydqS/j65DsoaSjjZF0pI3OHELMtdE0nyfQT5cyJXkeklOJYSV3T44ZQLKFa8WmaRtiKMDR7IAp12mTA8JxB2I6Npiv+P3vnHWdXVS7sZ6299ylzpvfJTGbSJm3SK2mEkIQEQpUuCorAVUTF+2Hjs6CCgspFwX6Rqyh4QaVYgCCQhCSk916nZHovZ07bZX1/nGRkTIIQ+TJnh/Pw44/Zbwjvmr32Wu9611uSoSHvH26ZH0mSnBZdl0SVw7UVy3h29195cvuf+mQ5/iyWjVxIzLEgGRmb5AzRdQ0Lkyl50/jp7w9yuLaTieW5fOSK0djKQdN0LCtZRz3Jv4fQNbYfbmP5hmrGDsnm4llDsGPuuq5XwNRB47l/5aNYjkWmP4Pn9r7CrMFT+NCYi08K3Ut0hICF00t5dUM1AGOGZKNJ4Zp+K5omEVLSGwtx29Qb+NXWZ3COJ/znpGRxx/Sb0KWOTXL9ej8568b1lVdeSVpaPKGhpKSE66+/ngceeABN05g7dy533XUXjuNw3333ceDAATweD/fffz9lZWVnW9UkLsHnMwibijQjwGPLvgVA1DbxaV4cZbOycj1LRpw/wFomcZP9uA8AACAASURBVDNCKHSpsXpzC4drOwHYcaiVWdX5FE1Kx3aZdzFJ4qFpkvbeGN//3RYAdh5qxWNoLJ05mEjYPQa2LjVWV2+nrqcRgKbeVgD+fmQ1y0YtdJ0RpxTMmVDE9LEFxEybzDQvUoJb0niUUkghCXhSyE7J4ocXf4Mj7dWkeVMpSM1lb/MhxhWMRpcaMbecGFzAWTWuo9F4IsNvf/vbvmdXXHEFjz32GIMHD+aOO+5gz5491NXVEYvFeOaZZ9i+fTsPPvggP/vZz86mqklchFKK7miQqSUTeHzz79nasBuIt3b91PSPcOGw2e4r/5QkofB6DWI4oPqnqejoKAUej044nGxTlOTM8Xg0jh5q7ffsQHUHi6eXDpBGZ4ZSsK/l8ClljT3N5Hgyz7JG/y6K9FQvOw61YNsORbmpx8PE3IFl2fill8ZgMzn+TNYf24YQUN/TTDAawm/4AFx3o5DonFXjev/+/YTDYW699VYsy+Izn/kMsViM0tL44jF37lzWrVtHS0sL8+bNA2DSpEns3r37X/7dWVkp6PrAXfvn5bmvvNDpcONYCkQuf9jz1z7DGiAY6+Xht/6bH15yH16pkeHCcZ3Aje/kdLhxLLZt0xWKsnT2YHYfacN2FH6vzqRRuTiOjT/FIDXVN9BqnjFufCenw81jGTcshxSfTihiIQQsnTWEgN/ASHfP3ArHwozJG8H62q0nyQpS83BwXPWOWrvCfOmxN5lYno9hSP7w+iEe/T8LKHLRGDrDXZRllrCv5RDp3lSK0vOxbIuDbVVMHTQejzTQpUZe3qnL2bqBRJtTZ9W49vl8fOITn+Daa6+lqqqK22+/nfT09D55IBDg2LFjBINBUlNT+55rmoZlWej66dXt6Aj9f9X9ncjLS6OlpWfA/v/vJ24cixCA32Z11UYuGnE+55VMQQpJa6idF/e/yoZj27ho+DzXjesEbnwnp8OtY8nM8iM1QVR28ZlbhlPTWcfw7FLaoo0UeHMxTYv27oFbg/4d3PpOToXbx+Lz6HznzjkcrO6gtDCdnAwf0WiMzk53hIUIAWkZHmaUTOL1o2up6arrky0ZMR+f7sVSlqvekTB0Zo0f1K+JjK4J14xB1yXSL6jpqCM3JZs9zYdYVb2egJHC/KHncbitksmDxtETtXA63FUm8QQD9d2/k0F/Vo3roUOHUlZWhhCCoUOHkpaWRmdnZ5+8t7eX9PR0IpEIvb29fc8dx3lHwzrJBxtNiyeb3THtJmq66nho9U+J2jFKM4r5xJQbaAw2J3Ogk/x7CGgLdbCycj2vH13T9/j6cZeRYvhITQkMoHJJzgVSUjxsPdTK9oPNTB9TyK7DrbR1hfnI0tEDrdq7RkqBErC6eiN3TLuJnmiQ+p5GRueNoKGnme5oD7kp2birKoXi/CklTB1dQDhmUVaYjqOUa+oYa5pG2AoT8KTw802/o7a7oU+2uX4n11Qso6arnuFZpbjTtE5Mzur8+OMf/8iDDz4IQFNTE+FwmJSUFGpqalBKsWbNGqZNm8aUKVN48803Adi+fTsjR448m2omcRlSCnShAYoX979K1I4BUNNVx6Prn2B8wehTFs9PkuTd4igHv+7vZ1gDPLf3ZQzNSBaiSfJv4zg2ZYVpZKb6ePzPu6lu7GbZ3KFI6Z5YWMdRRMwoeSnZ1HbX88L+5Wxt2M3TO18gP5BLyAy7Mra3qr6bQIrBoNwAm/c1IoV7xmDbNpqQHGg72s+wPsEL+5aT6UvHTDaReV85q+7ga665hq985SvceOONCCH4zne+g5SSe+65B9u2mTt3LhMnTmT8+PGsXbuWG264AaUU3/nOd86mmklchmFIoihWVW04SdYe7iRsRsjwpRMbAN2SnCMohemcnLBoOhYCgeOW0gFJEpZYzKEnZLJqWy0Vw3KoawmyckstV88fNtCqvWuUih9E81Nz+b+vfa+f7HtrfsZDF32FkBlB4BkgDd87uiYZPzyHh5/eSiRmcceV45FSuKbmiWU5SI9gd9OBU8sdi45wJ9m+zOQe+T5yVo1rj8fDww8/fNLzZ599tt/PUkq+9a1vnS21krgcW1koAV791Au2LnUX+kqSJBJSSDJ8afgNH2HzH7cg+YFcPJqBRAOSnp8kZ47Xq6FJ+MJHpvLqhmounzeM/OwU0NwSgBBHCsHfD7950vNgrJeGnmZK0gtdU2kDIGY5PPTbzbR1xb/7R36/jV98eaFr9hRdl0ipkeFNPe2fyfJnIKUE1xwZEh93fbVJkpwCx44fyK4cveQk2dCsUryaB00k7+2T/HvUdTXy+Vm3MSitAIjPrU/PvIWeaNC1LdB9vmQuS6JgGAYBv4f7/nsdTe1hfv23vVQ3dKO5KARB0wSa0NC1U88rQzMIGO7KT5BCEIr84+Bs2Y6rvnclbaJ2lEtGLTxlSE5xeiFpnlTaezsQLppriU7SuE7ieixTYTs2DT1N3D3rEwzNGkyGN435Q87jtqk3EDR7sZPX9kn+DYJmiGf3/JVndv+FayqWcd+Cz7NkxPn8cvNTvLD/VWzHffNLGDo7Kzv485tHQNfw+YyBVukDjVKKlo4QX79tFmOGZvMfV43H79WxHfcYco4Tbxd+0fD5JxlqWf4MclOyXBWvDKBr8PFLx/bFvi+bMzTeodElKKFoCbaRovu5a+bHSDH+UW6vLLOYL8z5JJrU6Ir14LJXk9Ak3RZJXI/fb7Cr7RA/3vgbynOGsmj4PNI8AQ60HuGBVY/h073cv/ALQNJ4SHJmOMrBciwqO47x6Pon+smKUvOxHXddpxpeg2deO8ihY52kBTz8dW0lD3xy9kCr9YFGCCjMCXDXD1bEvaMKvviRqei6e3xgQkDYivJWzWa+PPdOfrvjORqDLYzLH8WHJ1zBywdXcNnoxbjJr2daipbOMA99ei4AeyvbsB33jEBXOvmpeexpOUhJWiEPXfQVemMhPJoHXWpsqd/F9OKJDM4YhBV0z0Eu0Uka10lcT1iFeXH/qwAcaqvkUFtlP3nIDNMa6iBPFuC4yAuUJHHwG34uGDKLyo5jJ8kWDZ+LoetEcU+Hxqhpc+G0wSydNQQFGJogZjkuSjM795BSUFnfxddunYmhS3RNsu1AM5NG5g20au8aj0ejxzZ5Yf9y9rUe4tpxl5Ljz+RQexUPrfkZUkiWll+AwDvQqr4n/r6hhj+8fgiIv6e5E4sRIp7AmeiYpsLn8zI2r5xH1j3OkfZqNCFxlEKh+PD4K5FC4tEMLFdFwyc2SeM6ieuxlU1LqP0d/0xLbytFWYOIxZJJZ0neG0KARxpU5I9k9uBpBDwp5AdyqO1uwKd7yEvJOV4K0j3Gtc+rUdsc4/u/20woarHkvCFct6gczOTmOpCUl2Zx70/X0tQewmtofOfOOWjSLT7SuOFpYGBoBgdaj3Kg9Wg/+fiC0Xg0j2u+FE2TaB7J3TdM5ofPbMW2FDcuGYWmgddjEA4n/kiUUggEq6o2cKS9GgBb/eOm7eldLzCrdArYEpLG9fuGe77aJElOgyYkham57/hnClPzk17rJGeEUhCzYxxuq+LaccsAxca67WT40llavoCmYCsR211FrKIxm1/9ZQ+9EQul4JV1VZiWu0JbzjWUgn2V7TS1xzt9Rk2b51ccdlXIkVLg1TxcNHzeSTKB4PpxlyGEcFFsr6I3GsV2FA98cg73f2oOw4oziFoxNBdVcYnaJm8cXYshdeYPOY87Z9zMxydfx/DsMgA21u5AE+4ZjxtIeq6TuB6f9HPVmKU8uPqnp5TnBXLI8mdghZKn8iRnhiYl4wtH8+W/P0hPNAjEQ5A21G7jGxfcfdxz7R4jCCH455ws6ZZ77nMUISAzrX+4RHaGD11zjSWKbTsIXXHpyEV4dQ8vH1pJ2IxQkJrHxyZfQ4YvHVAo5Y4x6brAqwz2VdXx7SfifRTOG1fI7VeOc1UsvFIKXWr83/mfZVPdDv6092XSPAEuHbWIuu7GeHMf95x4XIF7ZkeSJKehNxZiSOZglo644CRZmjeVL839VLwcn4s2qSSJhVLxVsEnDOsTNAVbqOmqd03N2xPomuCuayeRmepF1wTXLRrpKiPu3ESREfBy5fzhZKf7mFFRyKIZpdi2ew48UsZL8VV31eHX/Ty4+Cs8esk3+eLcT7KqcgMC8Gnuibd2HHAU8Yo6x1m/u9F1t6BSSD42+Tpe3P8qfzv4Ok3BFg63V/Gjdb+iLKOY2aVTsW0XOQdcQNJzncT9CNCExmWjFzOnbDpv1WwmGOulPGco4wtG49d9BKO9eEkFF9UnTZIYaJrAcizaQ52nlHdEulxX6lGTgmAoxvc/Nw8pBM3tIZTrjgjnHqkpOoYuue2KcTS2BuMhFC4q+xb3fipyA1k4yuEXm35Hc28bo3KHc9noRWhSx1SWa5IBDUMSjIS5eE4ZUyoy0TXJ5j3tmCqGlP5//RckCgpKMorY1rD7JNErh1dy54xbcFwUfuQGksZ1EtejKQ1LWdy/6lG6Iz1MKqrAp3tYW7OZJ7Y+w3/OvoOR2UNxzOTikeS9I6XEdmymF0/sq0rzdsbmlaNQaJp0jffHshW5WX4aWnvp6Y1RnJc8eA40QsRbfFw0s4yYaTOyNItwxHTVW4nFLPwejWC0l++v+Xlfs5XWmnZ2Nu3jexfdi2PbrgkLkVLg9wmGjQvy2I7fELNNlpUvxOMtxnLckxyvS42odeq8kJAZRgCxmDvWLreQNK6TuB7p6DT21tPQ0wTA2ppN/eTP7PozX53/WVd4SpIkIgpHOTjK4dqKZTy392Vs5WBoBrdOuZ7W3jYyvGkoF3mvhYB1uxp5evl+AMoK0/j6beclfdcDjOXA13+5jobWXnweje99Zp7r1q2gGeapnS+c1MWwJxpkbc1mlg6fT5ToAGn33hBSYNkWP9/0275nL+x/hUlFY9+xnXiioYgb2IWpeQRjIcoyS+JJ2u1VzB48DV1qbsoYcQVJ4zqJ6xFCUNVRe1p5XU8jJM2GJGeI4zgoHLyalxklk5hWPJGwGSHF8KNJDSkEUTuG47gnhcW0FMvXV/X9XN3YQzRm43PPEM5JDlS309DaC0AkZvPs6we580MTBlird4/jKJRyaOxpPqX8WFc9EccdhjXEk3ybg23kB3K5oHQeHulhbf1bHG6rojxn6ECr966xlc3mup18ed6ddEZ62NN8gIAnhf+YdhNRy6TXDOMzfGBrA63qOUPSuE7iehxHUZxeeFp5XiCH5JV3kjPFtsEQkphjct9rD2MrByEESql4989FX8SrebBw0TWxJigtSKOtK3L8Z4nPo4HlHu/7uUhmav9kv5x0n6vqXHu9BlFhUZ4zjK0Nu06STywcczy21x1jUlJRnFHEpyfdye9fqiQas7luyUcpyve7pkSiYWj0mEEGpRXw3N5XeLN6wz9kmsHdsz6BJjV8XvckmroBd8zwJEneAcMLhal5ZPszTylfNvJCNC15Ik9yZui6xHJs/rz/1b7mC+r4XX3EivLG0bWYtoV0U+IZ8InLxzF/SgkTynO592PTXVR7+NylICeFy+YOIz3gYerofC6ZMxRHucOIAzAMgUcz+PCEK/Dp/Y21sswSRueV45Ue98w1JbFjGo89vY9tB1rYW9nOQ/+zE0P5sV0Sc63rGmmeFEzH6mdYA5i2yc83/pY0b4qr6na7geRvM4nrEVq8+9RXzv80gzMG9T03pM5VY5aSH8gl5rKi/0kSB3m8J3jDaa6667obUTh4PO45wNmO4qHfbiIz1cuE4bk89cp+QhF3GAvnMjsPt4KAu66dxLjhufx++QFXxVybysZ0TGK2yQOLvshloxYzs2QyH598Hf85+zaOtlcTdWLouju+FU0IQPSF6gCEoxa2rVxz6FFKYTk2q6rWn1LeE+ulI9yFZSdvrd5PkmEhSVyPrSz8upcjbdVcNWYpOSlZhM0IqZ4UtjfsJcOXhulYycme5IwQgE/3MSp3OLZyuHDobHID2TT0NPH60bXxls7SwHGNOy6ep1BWlM6Lx+v3pvh0fF49GRYywJQPzuKxZ7fzl9XxtuGfunqCq5p7aEIQjkX4y4HX2Fy/kwVDzmNEzhC2N+7lf7Y9y6dn3oKCkxoYJSpRO0bEDrJsXikvrqwCYPKoXIJWN2kyEze0C3cchakslFKMySvn8lGL8Bt+dE3nYOsRnt+3HIjXwk780biHpL2RxP0omFRUwb2vPUTEiiKFxJA60eMtqU3H5OLyBSjTRS6gJAmDcDRsFePy0Rdx3uApvLBvOfXdjQzLLuXuWZ8gy5eBQhCLuWlrUnzoghGU5KXS0RNl4fRSpBDJzISBRim+efssVm6tZfSQbIYPSkdziyVK3EADONpewzcv+E88uoGjHKYVTWBO6TSe37ecsXnlSMsdnmuFw18PL2d4eTkPjJ+KaTmYWg9P7XmGu2Z+DCF0V9ws6FLj4vILiNox/nvz72kLdwAwuaiCL8+7E5/uc9Uhzg0kjeskridm2TjKIWLFs9Ad5fQZ1gB7Ww5x0fDz0YRBMrExyZlg2hZdkS6+++aP+66Dt9TvYnfTAe5f9EU0oaFJD265WdWkxNDjlU6y0rx0BSPkZfoGWq0PPLomae0Mk5fpJxiKkZnmI2q6ZFIBZsyhpbeVry+4m42123nt6Go6I90MyRzMVWOWcOeMj+IoFW976AJ0zSDDl87jO57Ep3vRhEavGWJSYQWa1LCPJzYnMkopFIqclCy+sPyBfnvjtoY9pBgpXDT8fDJ9aVjJ/fF9IxmEmsT1eDQDTZ7eE5Lly0BKmexAleSMUEqhlOLF/X8/Kc4yasd44+hadKm5woN1Ast2+NH/bkMIQUaqlz+/WZmMuU4ApBR0h2LMrCikKCeFw8c68Xvc4wNTSlGSMYjfbPsDrxxeyezB07hh/OWUpBfy8Fu/pDHYglczBlrNd03YjDBr8FRSPQEiVpReM4QudS4fvRhb2a5IzDRNG7/uY0/LIWKOyZSicVw1ZimXjlpIUWo+649tIcufgeOSBlhuwT1fbZIkp0E5YDs2Y/LK2ddy6CT5paMWxg1rFxk/SRIHy7JRHkVTsOWU8vqeJizHRtku2GmPI6UgJ8PHxr2NpKV4qGrsSsZcDzAnjLVJ5Xls2NPI0OIMhhdnEHXRO3Ec6LaCDM4YxKLhc6ntbsRxHAoCeXx1/mf5056XqcgbOdBqvmssx+Kp7c/zlfM/ze6mA0TtGFOKxvHSwRXcOOEKfJoP2078Q6mh6WR60/j2wi+wq3Ef+1sP49N93DD+chDxsBFdeYhhDrSq5wxJ4/rfwOMziJgOzR0h/D6dSNLzMyBoaOhS46aJV/H7nS+wp/kgACmGn+vHXUavGWZQWoFLeoIlSTTit9gOY/NHUtvdcJJ8YuEYhBBuuekG4uO5/cpx7D7SRltXmE9ePQFXud7PQTShgTDxGhqzJxShyXiIyNCiNNesXV6vTmVdDaWZgwh4Umjoaaa+p5EJBWMImxGurriEnmiQgMhwxXSTQmLoBl9//QdMGzQRr+Hh2yt/RHZKFpqQKJdUDDEdi7xADt9Y8V/0xkJ9zzfUbmNCwRhG5gzDTBrW7ytJ4/oMEbpGdWMPmiaJmTZFuQFSvDrRaNLAPttYloPuMWjrrOXDE65EExpRO4pX8+LRDFp62wZaxQ88miYIBNwZ02sYkrDjcOmohWyo3UZXpLtPVpSaz4ziSfTGQgS0DGyXXK1qQvKHFQd5+a0qAP6yppJH7p6Pcon+5yqalLyw9jCrttYycnAmd14zETe9El2XDM0uJWrF+K+1/92XOLen+SDzh5zHpMKxDM0qRSpckZ9gSJ2bJ36IS0cuJGSFMW2TxcPnYUgDQ9PBkpDgjcOlFMTsGA+v/SW9sXhYS1FaPhErSktvGzub9rGpbgdzBk9NmtfvI0nj+gxxgM37m/jL6qNkpHr5Px+eSkleYKDV+kDi6CZ1XQ30mhEeee17/WRZvgy+s/hL1Pc0kycLcNzkXjxH0D06vVGLjVuPkZXqY0RpJk7MPYdQxxEo4bCr4RBfmPtJdjbupa67kWFZpZTnDOVoew3DsktRUffMLctWNLX1cs9NU0kLeFi7o57eiEmK5p7QlnMNhaKuOchLaysB2HawhRffPMJNS0YNsGbvHtt2SDH8dEa6+gzrE6yp3sjS8gswNB3bBa54IeKVphDwk42/oTXUDoDf8PH1+XfjOA5eQxKL/Yu/KAEIxsI0BJu5bNQiZg6eQmVHDameABneNJ7a+Tx/O/A60wdNJGkSvn8kExrPEMtyeH7lESxb0dYV4Ym/7MF0k4vhHEIJha0c/rTnpZNkHZEutjXsIWyGk6WGBghbQW/YpLUzQihq0dIRxuf3DLRa7wFFTzTIq0ff5Purf0Zhah5LRszHr/v47ps/YVX1enqiva6aXx5DcsPiUTy1fD8P/mYThdkppHiTG+tAEwzHkFJQkJ2C16PRFYyihHsObeGwiVfz4NVO0UpbCFI9KRjSHfMsJcWDROOpHc/3GdYQT3L88YZfYykbr0u+ma5IN1eMvog0bypfe+37/GrL//Kjdb/i0fVPcMvka9+xIECSM8MdMyMB+WcHqGU7aJrAccFV17mGRJLlzzjJU3KC5t5WxuaV48Tcs0mdK3g8GiHT4Wu/WEf4eMjURy8eTX62f4A1e/domkSXOlOKxrFg6Cz+tOcljnTUUJE/ku8tuZeVlevx6AbCRWdr03L4xQu7+jrPPfnyPs6fUpz0tgwgUSvGuOFZ/PLrc4hYkXjtYaUBCimFK27dPB4dpUHA4ycvJZuWtxml55fNxLRNfJofIZQrYq4tZbOn+eQk+bqeRpRS8bKCCY7jxMvwTS+eyNde/wHqbZn9nZFuntrxPB8aezEo9zgH3EDSuD5DdA0WTi/l9U01+L06tywbiyA5OQcCDz5SPQFG5gyjJxpkbtl0Ap4UDrdXsf7YNiYXVZBi+DBddG1/rmBZDg2toT7DGmD97kYWTi8dQK3eK4qC1HwWDpvDt1b+iObeVgBquxuo6jzGZ2feiqEZqIi75tc/l96zbZU0rgcSAWEV5JsrH6Ej3IWhGXx25scZm1+OrgtiLnAOGIak17Z5q2YLd8++jTXVm6jvaWJC4RiK0wpoD3WR4U1HiMTPn40boYrCtDyOtFf3k6V6AgghMS13RCnHuxXv6WdYn+BA6xE+Of0jSLQEjx53F8m19AwxdI0PXzSSX3x5IY/+nwsoLUgdaJU+sFiWFa9+MO1Gbpl8LYfbq1hZuY4sXybfXfwllEpW4RsoNE0wKC+Arv1jqZkyKt817Y8hPgbTjtESau8zrE9woPUo3bHg8TJq7hmUrks+snQ0hh5/L3MmDELXk9vBQGI6Jk/vfIGOcFf8Z9vkv7f8nogVA5c4bk5U1hmTN4JvrniEXjPEsKxStjfs4UfrnyDDn45HMxLesAbQdEF3NMh1FZf2dZ48wbUVy6jprEXX3RFOoUudnJSsU8oMqePVPCgX3Iz8M5qhofR4+JRMsOUr6bk+QxxH8eCTm8lK99HdG2XexGIumFqC7YLkhnMNpQQ1XXU4jsMj6x7ve17VWcvW+l18YuoN2I5D8ix59nEc0DXBDz47j5feqqSsMP14mTGBa1IapSAai520wQIIBJYTP9z5DB3TdMcCEDNtunujPHbPAgCa20PYlpPcEAYIIQS2cuiIdPV73hMLIgCPRxKLJX7MoVIKKQSa1Li24lKe3/cKITNMUWo+n591O7ZjE7NNV3iuNSFpC3VQEMjlvgWfZ3X1RmK2yazBU8j0ZXCw7egp14RExLJthmQNJsXwEzLD/WTnD5mJEAKf7qU36o71C0B6dJ5+9QDrdzcwsTyPWy+rgARKlE+upWdIzLS556apx1ufCsJRi3DUSppvA4KiMDWP/3rr8ZMkdT2NtIXaKUkrTJYZGgCkFAQjFt9+YgOTRuaxYW8j1Y3dfGTp6IFW7d0jIGSGae1tY0zeCBp7WsjyZ9ASamdi4Rj2tRxmRvEkhO4O7yJAwGdQPjibr/38LbqCUa5dWM7gglR04h3dkpxdpARDeri4fAGH2ir7ns8ZPA1NamiaBi5YwaSESDSKJjQ0ofGfs29Hlxrd0SDdkW7qexrJLJmM4yS+6RF1TEozi/numz+hJxrk/LKZpHoCPL7lfwnGennoonuxXeDtFSJ+eDOExj1z/oNfb/sDNV11aFJj9uCpXDRiPo5SOJp7vnufT2froTaWr4+H67y5rY6RgzNZPH0wvb2JcUBI/BmeoHg9Oi+sOkxnT5RozObK+cPJz/ITDSfGi/0gIYQgYKTQ0NN0Snl9TzNTiiZgJiPKzjpSQltXhNxMPyV5aeiapL4l6IpN6QSakGT5Mvj11j9w13kfoy3UTm13I0OzSgkYfh5d/wTzymYMtJrviXDM4id/3E5LZ9yL9dTyAyyYNpj0VC9dXeF/8V8neb/xeHQidpiclCy+Ov+zrK7eyKjc4YzOHU7EjJJmuKPMayxm4dEMfrLxN1w6ciF+w0fIDJPtz2Rl1Tp6Y2Hmlk7HDWZc1I6ilMKrGdw++3Z6YkFM2+Jzs27lub0v0x7uxK/5SPSQnRPhagqBLjWurriEDG8aEE/2lwg05HFHoTvweAzausKU5KdSPjiTyvpuWjrDeDx60rh2O7Zts3TWEGKmje2oeFxm0uMzIGiaxERRmlFMTVfdSfKx+SNPmciR5P8/SilKC9P4/I1TaOkIMXVMPik+HY8hibrke4lGLLweD5+a8VG+v+bn/bo0jsodxudn345X8xAOJsai/m5QOJhW/8Om7cQrHiU5+3i9GhFTYTkWT27/E8OzS1lTvZGOcBcXDp2F5STOdfc7oRka2HD56MX8z9ZngXi8r+VYGFLnoYvuxXJsDMPANBPb2aGUQpc6n5h6Az/d8CS5gWykkNT3NHH71BtJ8wSOvxdjoFV9RxxHxf9VDgdaj1KYlgfEGxZ1RXoQQqDrz/rajgAAIABJREFUOqZp4ZbQSaUc5k4qpjgvlY17m7h2YTmjyrITqqKOdt9999030Eq8H4RCZ3djE5rGa5uO8dgz29myv5kZFUWkp3pwrMReMP4VgYD3rP8u3w+EAWWZxayt2dzPkB6eXcb8Iefh131YLsi2PxVufScAmqZhOYpv/HIdf159lFfWVzGxPI+8LD+WS4xrpRQYDr/b8Tx7W/qX5WoLdeDVPIzOHYEZcc+3L7wmxfkBNu1txbYVC6YXM6gY8tMyCYcTP/zgdLj1W/H6NaJWlN/teJ5DbZVUddbSEmrnSHsVi8vjNdWjkcT/XgyvIObEcBzFzMGTaOhpwrRNxueP4u7Zt7GxbjtDskrQlZHw3Ux1j0ChaAt3sGTEBYwvGMW4glEsGDqbqB0j05eOV/O4Is9KNyQmJl7dQ8iM8OqRVdR3NzO3bDo7GvdSnFaAZnkTyjh9J3w+nfrWEF/7xTqO1HXx1s4GJpXnUZSbQiRy9g6igcAp6rkfJ+m5PkNMy+HZ1w4C0NQe4vEXd/Olm6cl+AXRuYllOQjHYUPtNr5+wed4/ehaOiPdTCwcw/DsIWxt2M2isnkkepvacxGvV1BdG6T+eD1lpeCPrx/iSzdPG2DN3j1SB0tZbKzbfkr5isp1LBkxH4/H54qks4wMPx2xKNu63+QHn78EKSQHOw5T2XOEUYUlA63eB5J4GVdxUt1khTqeNJv48wogaploQvLKoRVYyuZjk68l5XjI3kOrf8b14y8lRfdjuWA4utRQjiLLn8kj6x6nurMWgNyUbO6a+TE0qWFoBlEXxMJjSZDw7ZU/ojxnKBX5IwmbER5Y9RgFqbnMKZ3mqpt3TdPYvLd/GOi63Q1MHZM/QBqdjDvuABKQfz7hRWIWyYJvA4cAarsbeXTdEwzNKmVe2QxqOuv51oofMjp3ODHTBQvgOYhSkJHa/3Sfn+1Hc1MtPuHA8RJjp8J0TATCNaX4NE1gaDo3TLqMiOik025i4qDhnDd4MqbtjvCDcw2lFIbUuX5c/7JvV4xegiYEtkscA4bUcYAbxl+O3/BxtL2GhmAzB9srmV4ykZL0QUgpsVxgXetCozXczi82/a7PsAZoDbXz8Fu/pDPSheXYuOGzF0KgScnQrFIOtVXywr7lLD+8iu5oD0tGzMenn94Dm4goBbMnFPX73V8wpSShKtAkPddniM+rMX9KCau21uL1aNx+xTgCXp2Q5YI7onMMXZccaK/kjqkfJmrH2NW0n5quOqaXTGThsDl0R3rI8WeRnO4DgwA+c90kXlx1hJL8NG5YPOp4aUR3YGgGDjYV+aPY03zgJPmskilI4Y56twAO8cLvrb1t/P3IaoKxXmYUT6Iif6RrSoudayjgSHt1vDrF4i+zr+UQgzMGkZeSTU80hN+wEbjAABICx7ZZU72JayuW8ettf6S+u5EpReO4auxSntjyDHdMvwkpEjtOGSBohUFxUgMZgJ5okLZwJxnedDTNg5Xg4aC6IYnYik/PuIXa7noyfenYyiFshsnyZRK1Y7hpf5QSUnw6j9w9n7U765k+ppCsdG9CHXTc89tMMCzLYcyQLK6cPxzbdtiwu5Hi/GQjmYFA0yQ+3cux7gYe2/A/2MevUP964HWGZ5fx8cnXJe8UBgghIMVnUNPYzWXzhtHWFaa7N0ZmWhphF4RQAJgxBzxw29Qb+PobD9MTDfbJ8gO5XF1xCQLlmmtViaAl1M5PNz7JrMFTKc0YxJqazVR11nLlmCUkt4WzjxBQmlnMN1c8Qmekm0HphbSHOgiZYR5e+jU0IV3hu3YcG01ILiqfzxdffYCwGQHg9cq1tIU7+djka/rK1yY6Sjmnva0CcByHiB3FrzxnUaszI+yEeG7fK8wfch5/P7KGPc0H8Os+Lhw2m6mDJrCjcS9TcibF1zqX4Ch48MlNlBWms3ZHPV+6efpAq9SP5Cp6hpi24ufP7er7ubQgjaWzh7hgyTj3EEJQnF7I3S/d12dYn+BIezVv1WzhyjFL3BAZd07S0hVm/uQSAn4DBWw70ExBdspAq/WukVIQUzZ/3vcq377wHva2HKK68xijckdQllnMi3tf5cqxSxC4w3sdMsPsbNzHHdNu4qWDb9ATCzK7dBqpRgqOctA0mfDJZucaUTNGa6idpt5WdKkTjPUSc0xMx+K1I2u4ftxldIYiA63mv8S2QBiSQ82H+wzrE2xv3EPEupwUI8Ud80sIWkPtDM4YxLGu+n6iFMNPmjfgiv1eSkF7uJNFw+by7ZU/ImzF30vYivC3g2/QFGxlyqDxmNJd3uu1O+pYOG0wo8qyqazv4u8bq7n9inEDrVYf7vlNJhheQ2P2hCLe2tmA53grYY8hMJMhi2cdTRMcaDmKeZpyVSuq3uLSUQtJ9JJJ5yq56T72VbWTk+EnHLWYMCIXQ5e4pRmY1AVCwYqqdaysWs/EwjEUpxfy6uE32d96GK/m4YqxF+H1aGc1U/1MMR2TqYPG8dXXf9BX4u1A61FumnAVEonfrxN0UVnBcwGPbtAbC3H9uMsZXzCK+p4msv1ZdEd7ONRWiSK+ztl2Yt/BaUj2thzFb/hPknl1LyEzTJo3AE7ie3sdx6a2q5Hbp32YH6//Nc29rQCkeQJ8eubH2Fi7g/OHzMAv0gZY03dG1yXNnW1Ud9X2GdZvZ3P9Tq4auxRTmRguMgnnTylhX2U7Te29ZKR6mTuxeKBV6od7fpMJhmnbTBiRx5XzR+A4io17Ghk+ONMVJ9lzDcty6I2FTiuPmFGEEK4LDfF6dSKWQ2tnGK9XJxpNfMPtVJi2w7rdDazZXk92uo+v3DKdtEDib64nsGwTqUm8upeoFWV74162N+7tk6f70hAkfjtn4HibYx/bG9afVDv5tSOrmV06DcNwhwf+XEEIgeMoSjNL2N64l6+9/oO+cqKjcofxmZkfR6Hweo2ELzOoaRo7mvayePj5DM8uw3JsUj0BmoItLB4+j+rOWnSpkyvyE/578WoephaP57m9r3DL5Gvw6V4sx0aXGssPreLSUQtJ8aRgu6AEZ4rHT1VH7WnloViYdG/6WdTo3ycStWnuCLNxbyMTR+QxvCRzoFXqR9K4PkOkFBys6eDnz+3E79W556ap6JogmWx/9onFLMbml59WXpE/EqHclailGxp17WF+8fxOBIJPfmg8+RlerARvvHAqenpjrNkev1Zt747w+1cP8NnrJg2wVu8eXRgYus6CobN55dCKk+TLRl6IT/djmQluLXDc++nYpwx51aVOvL2M++aYm9H1eDWQ1lA7yw+v6ic70HqU146u4fJRi/F4vXB6H0JCoITNuPzRdEV7uHvWJzjYepTWUAflYy8m3ZuK7dgYmoG0Ez/0yKMZbG/Yy0cnfojdzQdYf2wrtnIYXzCaj076EC8dXMHloxeT6KWhTdOmJK2AYdmlbG/cc8o/kxPIQkdz1Zdf1xLkty/vA+BAdQc5mT5KCxIn7y1pXJ8BPp9BxHa4dO5QbloyGgTUNfegSeGKtq7nGkrFvQznlUymJ9bLBUNmkeoNcLD1KCsr1/GRiVeBctedgqng/ic2kJXmRQH3P7GRhz93vutuRoQ4dYk6TRM4LgmCt22FsmMsHTGfrkg362u3opRCkxoLh81hStE4TDuGchI/7Mi2HSw7xviC0by4/9V+cbGXjFxAS287Wd6MAdTwg4fmibelfuPo2lPK11RvYsmI+Rha4s+vkBllVO5QjnYc457lDxC1on2yQWkFfH72behSRyqBneCbpe04zC6dwraG3Wyp38XEwjHxmvBtlbSHOrhs9KLj9ckTG6XAkAYXDp3Ny4dWnBQLP6N4EoY00CzDVQfrmqaefj9XNXQPkCanJmlcnwGBgIdYT4SoafPos9vJy/Rx45JRCR8Pd66iaQJN93DL5Gs50HqUP+z+K+3hTiYVVfDthffg0TyuyVA/gQC+9NFptHSGEUKQne5DSHDb6U2hCPgN7rpuHKmpgmDIZmRx4l8Jvx3bdrAdCykko3KGcemohUStGB7N4GDbUaJ2DE1qYCd+OIXXqxMmytM7X+Cr8z/Lm1Ub6In2Mrt0Kg09TbSE2ijPGjLQan6gsLHwCv20tYY9mnF8/Ur8j8bQNEwcfrTuVyflwNT3NPHLzU/zmfM+fnw8CY4A07Z4+dBKmntb2f22Mpya1FhSPp80I5WwCwxSKTU8QvDtC+/hye1/YlfTflIMPwuGzeKS8gvRpQYus19mjy/ij68fImra6Jpg8YyygVapH0nj+gyxHMVXf/YW9vE7oa5glDuvGz/AWn0wkVKilE1ddyM/XPd43/N1x7ZQ39PIF+d+Cq/04KaIHSHijSXKB2cBio7uCBJ39pj0p9i0eHfy3OENFKTmMab8w2i6FzvmjjhlTZMoIfn+2p/x6Zm3sOHYNo6211CRP5KKgpH8eP2v+dK8OxFSJHz7YL/fQzQaBgT3r3yUGSWTSPel8tTOF2gOtvDwxV/HdNsJzuXoQsNyHC4acT6vHn6zL976BBeXL8Bv+FAJPrcA/LqP9TVb+gzrkvQiMnzp1HU30BnpjidnKpXwISEAXukh4Enp530/ge3Y+A0fUmok+qp8Yi850HqEdce2cPOka/Dq8VuQpmAr31r5I746/zMIaeOmpP9I1OaBT82hrqWHQXmphCOJdRWaNK7PgO5okM5gmI9fWc7IsnRMS7FhRxvhWIycQIDe3sROOjnXsG0HoRQvH1p5kqy6s47eWIgUfwq4y7ymtTPCiar47d0RSgoSOyv9lAjYWLedPx94FYCOSBffXf0Y31rwBXQXNF+AeLUQgPxADvf+/SGmF0+kNHMQ+1oP8/SuF7i4/IL4VbchiUYT2zCNOTF0qXHLpKv54bpfsapqPRAPq/rUjJvxaB53nHjOJWyBkjY+3csd02/i6Z0v0BMNYkidC4fNYULhGCzHxmt4IMELiioFNV31FKcXctvUG2npbaO5t43LRi2iJxrk8S2/pzvaQw4nVxNJOES8lvWcsum8dPCNfqLxBaNRCiwXxLYJIRAomnvbeOvYFt46tgUpZL8a3ray8Qg9wY8J/dF1SWVDJ03tIUzTobw0a6BV6kfSuH6PCBEvZZWZqbO/ciVPrd2JT/fy8Uk30Ot0UORNp7d3oLX8YBG/MVWnXegcpXBc1BEwjiJq2tz/xAaEENx6WYUrbZ6oY7KtcVe/Z22hDmxlEfD5sYKJ/14cYRM2w9w86WpquxpYd2xLn2x4dhkXj7yQtlAHhb5CEj1uRyCwHQdDM7h1yg0IAWEzQpYvI74Ju3GSuRzHAduxiFhRdKnxwKIvErNieHQPraF2ImYEQ9MJm4mflK2Uojx7CAuGzeZHbz1OS6i9Tza9eCJ3zriZDF86KjyASr5LonaM3c0HmF48kc5wPNfCUQ4V+aO4fvzlrKxcx9IR80n0cEPHUShBv6T/txvWgzMGxZOZncQexz/T2hmirCCdSNRmyKAM6luCDClKHAdU0rh+j/j9Bo4uWFO9iS0NOwGIWFF+ufV3PLL0GyT6h3YuohuCzliQRcPPZ0fjvn6yvJRs0rypROwI4J7yb46CJ/6yh+jxrn+/+vNupozOd9XskhK8msHEwgq2NfwjSz3LnxGP8ZPuMOQ0KegId9MV7eGumR8jYkep726iLLMEy7FoCrYSMcMUpOQPtKr/EiniKVimbRGKhSjLLAE/dEV7iJgR0r2pmI5Jch07m4i+UJB0byotvW1oQiKtCG29HRTk5xE2I6T4fAOs57tjYlEFT+98vp9hDbCpbgfzh8zEKw1skfgXJDHb5LWja6jvbuLSUYu4b8HnAajsOMbDa39BxIqycNgc3BBKoaOR4U1jwdBZrKhc1/c8fsi+DkN6cKLuCTw0HYui3FQ+84MV2I5CCvjB587HTKBybUnj+j1iGBJN81PTVdfvue3Y2MomZidDQs42Nja2coiYEa6tuJS/HXydkBlmeHYZt0y+lt1N+xmXP2qg1XxPCOD6xSOZMCIXgB2HWgZWoTPA69WIWDGmDRpPY08Tq2s2UZiax21Tb0QKiZSJ74mDeChFfmoOad4AmtRYe2wzYTNCa6iDBUNnEbViDErNO36AS+wl1VYOCMG6Y1uYXjKRvx95k55YLzNLJhGz4x0BA9KPKZQ7ks7OAWzbRjc0fLqXms56Xti3nF4zhCYkc8tmMDJ3GGmeVDyah14S2+UrpcCxnZOcHCfYUr+LcXmjCCX4Dc8JOiPdhK0If9jzV/6w568Drc6Zc7wU7ZVjljCvbCab6naQ5g0wa/BUeqMhHGVh24m9dr0dhWL1jtq+nDdHwfL11dx25ZgB1uwfuOe3mTBITNtkwdDZvP620kk5KVl4NY8bErrPOaSKV2n46cYnmT/0PP5z9u3oUqe+p5Gfbfwt6d5APEZugPV8L0gpGDs0B/N4TPK44bmuq6Pu93vpiET5xhv/xYySSdw542Y6wl38dOOT3DD+ciYVjB1oFd8VtnJI9QSI2ib3vvYQMfsf4UdvHF3DA4u/hK4ZWLad8HPMVjYRK8KYvBF89bXv910Pr6paz/whs8j0pZPmCQywlh8sNE3g0XUONB/lqZ3P9z23lcOqqvXoUuOGcZdjuyC0zXEUtmOR7k2lO9pzkjzLn5nw38gJpBCMyB5CW6jjlPLi9MKzrNGZYxOvevLyoRUsHj6PyUUVhM0IP9nwJIfbK/nhxffhlUbCJ2RDPNbadiwqhub0ez5xZC62Y+PxaMRiA394SxrX7xFTxYjaUTJ86dx7/l387eAbZPszuWrsUkJmBEMaGIbEdGGzD7dimQ4pPj8KxcrKdax827UXQGFqBbrUEjwVqD9KKV7bVMNrG2sAWHpeGdcvdpf33VGKnliQtnAHLx9awctva8Dy2pHVlGcPxQ3hBxqSqB3jub0vE7NNJhVWMCKnjF1NBzjQeoTXjqzmspGL0IVGot9bmbaFFBqPb/nffnGXAKuq1nHpqAuJ2RbJreHsoZSg1wzzwr7lp5S/WbWBqysuQVcDbzD8K6JOFEcprhyzhB9v+HU/mSF15pXNoKm3lSwtxxXJzJePXsymuh2U5wxl6qDxaEKyt+UQWxt2c23FMnSpu2NfUVDbXY/lWCetxQBdkW7ypDsO1SmpBj2xIJa3nXs+MplVW+uZUZFPZm6MsBUhI5CWNK7diKZpmKbNsa56XjuyhhHZQwjGevn9jue5auwlmI6JoRuJntR9zmFInSGZJVR1ntziddnIRa44kb8dx4HXN9VQlBsABa9uqObaRSNdYIr+AyUcDKkzvmA0V4y+CF1q6JrBrsZ91Hc3o0sN4YFYLLE3WZ/mI2j3EowGeeTib9AYbOFIezXXj7uMVE8Kf9zzMpZj4yNAjJPLdiUSCoUQ0Nzbekr54bYqKvJHk+LxJnzlk3MFKQWOck7rITUdC9O2SDU0Er3ikWlbCCnI8KZx65Tr+dOel+iK9lCWWcKtU66joaeZ/ECuK5qvaEJiWiaPLP06O5v2s7ZmM5ZjMXXQeD468erjoaAWUnoSfn/Rhc7InGH9cl9OIBBkp2Shwok9hhNoxyudBO1OtoRXM3RyHvvCuxGhcvLT05AiMcINk8b1eyQci2A7Ng+v/QW2ctja8I9KCM2hdu6Z8x/ouk4snNiL4LmGoRncdd7H+a+1v6S+pwmIf4RXjFlCUVoehvJiJ/jG9HaEhG/dMYuO7ihCQnrAyykaHSY0tuPg071cU7GMuu5GClPz6Ip0E/CkcO34ZdjKwev1EosltkFqm6DrOrdN+zA/3vBrDrVV9smmF0/khvGXo0mNaDjR/dbxGxEpJH7Dd1KnNoD8QC4phhdiLptsLsZxFFJKRueO4K1jm0+SZ/kykCLxa6gDpBg+Oq1u/IaPuq4GPjn9I/gMH829rWys3cGUQePw6AZ2NLEP1ABCSArS8vjZxifZ2bS/7/nh9ipWV2/kaxd8Dq/uxZQq4d9NxIly/pCZvHRoBT3RYD/ZvLIZSCHQPQIrltjjgHiOVdAM8dONT+LTveQGsmkPdbKudgtfv+Bu0n3pA60ikDSu3zO61Hj50Ip4YtA/caS9mogVJdObTshFhtw5wfFyfDdPugZd6gRjQfICObSFOnCUQ8RFhx1dF9jAC6uOsGV/MwAzKwr55IfGu6JRyQlOGG9RK8qbVRs40HqEdF8aFw6dHS+NqBRSS3wjzpIxPNLgSHtVP8Ma4hUQrhqzlGyfRsxQCV2JTwiBX/cStqIsK7+QP+59qZ+8JL2I/NTceOUKkcADOQ0uyY89iRPf8zUVy9jSsOukpiU3T7oaXepYVuKvYVKXWFGTp3Y+z76Wwyw/8mY/eaYvnUFphWha4q9jMdukvqepn2F9gvqeJtbWbOa8kil4ROLX7I7aUaSQ3L/wCzy7+y9sa9hDwPCztHwBs0qnsLvpIBXZo4HE7zIbsWL87cDr3DL5GsoyS6jtbmBQWgHNwVb+duB1bpt2I4lg2g68Bi7DUc5pr1QBuiI95Adyz6JGSQAsZfLo+v+hpqsOn+7Fp3vpivaglOLe8+9iZOYIeoOJ71kECKR66QzG+gxrgA17GrnjqnFkpnvp6jzZ45iI+HQPNV31fHf1T/oqT3RFunl+3ytUd9Zyx7SbwAUG0YkGC/taDp9SXtVZS0EgF9OJIRK4LJeUYDpxo3lu2QzSvKn8/chqemMhphaPZ8mIC5CALg0szT3GtZSge+NlNju6I/hSPERC7vjWASzLwePTidndfO+ie/nTnpc41FZJXiCHqysuJmCkIBDoQiOa4KXSLMvEp3tP+62sqdnIrNIpaC4oi2poBiur1p1WvrZmE+eVTEn4QwIQP5wpizXVG1ky4gKuqViGAA62VdLU04I8/k9iz644tmMzo3gSWxt28+ttf+h7PrNkMotGzDspl2SgcMHWllh4dIMJBfFyL6UZxVw+ejGLh5+PR+pIIclNyUr8Ap7nGIahEbOtvvKIEStKZ6S7z6D728E3CDuhgVTxPaFwEEJRkJ3S96woN4AQCpUgC8e/QtMkESvKk9v/dMqSblsbdtNrhhJmIfxXxJtHjDylbGjWYBzlxBsxJDBKgUeLr1O7mvYzuaiCW6dcz2fO+ziLhs2jJ9qLIt6VTpeJ78E6geYxCIZMXnzzCG9sPkYoapGa6h1otd41hiGxlI1X9/KtFY+Q5k3l8tGLGZNXzs83/o6arnp0qSH1xN+unX/RiCRmmSiFK8o8SgSaOP13IIXE0DRXtHI3hI6jHIZnD2FX036qO2s52FZJXVcjXt3L2PxyhJ3437wQkOoJkJ2SyYrKt/rJNtRuQ5caaUYqUg78jWhi7wYJhhCAEv+vvfuOs6o6Fz7+2+20OdMLwwzMMDN0BaSINCmCgl0RaYoxmERfW/Qag3otJJarN4nva42YxGjQGLFhNCaIiIggCigCUqSD9GHqmTlll/X+cZgTRgbF3ImzN3d9/2L22p/jWu727LXWfhZDSvrTu7AHUTPOqn1fkBPM4tdn301NYx26qrc4ZUT699E0Fcs59pBpzIp5ISnFERTWHFrNzGv7YpoWKAqGrrGuei2D0vq1deWOi6KALQSbq7Yfc5+NlVtpX+r+hVc0VcNyHNqnF9A9rzMbKv/ZKze4Yz/8uo+EbaKh4+YTTQiB5Tg4OPQs6Ep1rI40I4jpWGiqil83OBCppDSrIx5ITJGSsBzuenopB6uTOaDXb6/i6ot7tXGtjp/Pp2EqNnPWvsWhaA1/+3JBs/I/rXqF/zrzNtx8bjVRHR1d0ynOKGR33b6jyoeUDCCg+UlE3f+MdITD6PJhLNq+rMXyM8qG4tf8WB6YAurXAiScBEXpBeyNHGDxjuUEdB+jy4elPgD0wktCstMmxpaqHS2Wrz+wibKsEnRdI5Fo2+Mig+vvQFEUVEVBCPjHpvc5pbAnvdp1R1M1Pt71GXmhbArCuYfnzLn/RniicByBX/OR7kujPnH02vMDivqgq+5PkwbJHJ4xK0an7A5UxvYTNJKrstXGYnTMbE/cTqBpqutvhMkP5xSCeoCo1fI0lqxARmplOjezbBu/z2DRtmWM7zkORzjsrNlDRW4n6uP1fL5nHYM69sOyHRQXDwY29RhqioZpmzz+8bMcbDgEJLPtTO83iT6FPVEUcDwUXFuWkwqsAVZu2I+i9PLAmZUkhIIpLDZWbmmxvCZWR8I2MRT3TjlqYlkWEbuBawZczn2LHiV+xKJqxRmFnNax7+EVQN3fFlVRyEvLZmjJqSzZubxZWbe8cvoVnXw45737n/W2Y1OXiHDv+480ux8v2bmCHnmd+engq9z8uUiKoqgg7GPmGC/J6oAQAl1XSLTxA18G199Rwk7w2d61DC0ZwJOfzOZAQyUKCqd16Et5dglxO/lBY8IDb7MnClVVUFD4Qd9LeeKT55oNOZZmFdOrXXccIfDCTTAUNrAcBb/u46WVb6be0LvklnHdwCvQFJVghkFNtbvnXeu6hqLCmIphvLnx3aPKA7qfTlkdsT3QRaoJHdux6JpXxgMfPE67cD4FabnM27yIQ9FqHhgzA1VR0YSO6eJZi4qi4NM0ImYjz616ORVYQzLd2+9WvsjD4+7Gr/lQNK+EpmDoKhePLGdY72LilsOnG/bjhWu9ieMIUJMLkVVFa6jIKaVdWj6RRANfHNh4ePqBjiVs3D6T0zB0aiP1BHQ/d4+6iVV713EoWk2X3DLahfOwbQtLs9E8EFwbmg/LsRnR6TQGd+zHR7s+TaXiyw1lgwCfrmN6ICyNiRgPfPA4hmZwZb9L6ZhRhO3YLN7xCfO3LObNjfO5oPxs12cLMQyF6OHUlF1yy5p9YN4xs4gMfxjTMfFrbT8tTJs5c+bMtq5Ea2j8Hj5gURSFhBrHEjazVrzQ7OH0Vd1e2oXzcYRDXigHywvdpC1IS/N/L/8vW5PQHPY27mdX7W4m9jofv+4jPy2HcV1GMqz0VGzHwq/7UCz3v0uqPkHCNnnJrL4bAAAgAElEQVT2szl8ceDL1PaqaA1V0Vp6F/ZAEQqm62+CGmgOHTOL2Va9i8rGqlSZX/dz++nXkuEPo6s6CZen5dI1ja212ynN7kBDvIEvDn7JgYZKolaM8T3G0bOgK7vr95GhZbr64yZVVcBwSNgmsz9/9ahygaAsu4T26QVYtkB4pH9A92lUFGehaSrhoEGXkmwCfhXbIwt5qaqKSYLOOaWMLBtCXiibqBWjY2Yxk3pfQEV2CR0z26MJDdvl6yfoukY4GGTuhnn8adUr6KqOoems2vsFc9fPo3dhTwrScsFy90sCgKMmiDlxwr40Xlj9OgVpuaT5Qny6Zy2nFveh0YwSMkLYLn9caprK1vptWI7FjYN/yJKdK3h93T9YfWADA4p6c/kp45m96nVGVQwCy93zroNBH1/V7+XRZX9ker+JdMurIDOQzoiywZxRNoTHP3mO0qxiitMLicX+/TewtLRjB/HujzZcxrQtgnqgWWDdZPnuVXTLu9hLnSYnBF1V+XzvF4wqH8KTn/yJwnA+Gf50luxYzintT6IgLZe8tNxv/yGXEAg+3bv2qO0r9qzmh/0mIlT3BnBNdF3BUVTA4ar+k4hZcdYd2ER2MJOe+V35snIrxRmFyfnwLk//ZGOBAvXxBjrnljGqfCgHGw5RmJ7PhoNbaLRiNCQa0UKqq1edcxxBwrYwHQv18EIMXxcyAlQ2VlPgyyPqgR45ANsS/HXxVoQDcdOia0k2p/b0ztLUpmkRCPvIDGYw872HqT1i2fBXvniL/xj6E0zLJE1LJ+7yRYocxwEtOU3PFg6r9jVftCTDH06uZOr+Wxg2Dpqq89+Lf8uP+0/BQWDaFiM7DWL2569xeZ/xmLaJ28MoTVPZUbObKb0v5M53f0V1rBaAQ9Fqfrfyz5xZMYybh/4ouQCey0cUNE2hKlpNXbye+xc9RuecThRnFPLxrs947nDmkMqGKmwX9Ay4+6xwmaYFGAK6HwXlqPmi6f4w6f50bMf9w3dH0jRQjGQWger6KJqmYNseuPsd5lgqI8oGs3z3as7tegaZgQwiiQZO73QaK3evpjC9AF3RXTxg/0+KoiKEIGQEaUg0z3AS9qWlzkE80Jq4lUBXdTZWbuPjrz6ld2FP9tTtZ+nOlfxkwFRs206miGrrin4LTdHICmTyxoZ5fLTrU/yajwx/mJpYHaZjUZ+IMKi4nyeSBKmKwud713Fah758tGtls7KgHqAwnE/MjqMoGq5O2n2Yqio4CpwzuIxowkLXVHRN8UQ2iiZCJDttfv3hrGaBNYAtHB5e8jSPnvtLYjF3B9aQfIHbUbObcV1G8t62pc1e4PJDOYSMIDE7gUrbD9t/m2RyApuLe4yl0Yqxcs9qbCeZNWhs5xGEfaHkdB2XfwMjhKB/0cks3vEJ1bFaFBQy/GEStknUivHu1iWc1+1MdMX94aAjktM/mmyu2n7UR/PlOaWuSCrh/v+bLiIE+HUDgcOpxX34ZPeqVJmCwvndxqApGkEjiOnyHoYjGQEfpumgGQqOCYZfx250+fjjESzLwgjovLh6LgKBoRn4NR8NiUYEgv2Rg1x5ykQPhKNgORa6qnFe19G8tPbNZmXndxuDrmrYtvuDHtsW6LrOC6vfpGNWEVf2m4QQAgVoSDTyi/f+H/eccRN+w+/63jjbhJARZFftXgDidoKDR0xz2VGzm3GdR5JodPtrQjK4/qpuD+d3G0PMirNq7xcIBAVpeVxz6uW8tPZNruo/GcUFqayOR0amn5qIyZ/+vo6lq5PH58pzezKyfwcyMwPU1rr724QmUSvG7vqjs2tAMsBevvtzRhQPxXL5fEPbdshPy+VvXy7gttOvY87aN9kbOUCvgm5c2GMsz332Mjec9kNP3IvtwyM8USvOI8ueSW1fsPVDxnUZSUVOKaoHOtFM0yYjlM7Gyi2M6DSIMRXDqIrWEDKCJKwEz6+ey/7IQcozSom7/TNgAWlGiIqc0hYzhrRPb0deKAdDNWjrzgEZXH8HiqKgqzqm3cAVfSfQKasDH+/+jHRfmIt6jCU3mE3ACHgmFzFARkaAaMJG11RiCQtDU7FsyMoKUVPjjdzQmqayav/G1EiCaZuHh+uSPt69iml9JuD26QeGoaErELVNijLacc2p05i/5QMUFMZ2HoGmatjCwdB8GIbANN0bZFuWja2YBI0AnTI78NQns1l/cBNZgUzO7Hw6l/e9mJgVJ81w/+pmjiPwG37Ks0v4qm7vUeVdc8vxaYbLXxGSvbyWY3Nut9E8tfx5Lu99MT/sNxHbsRFCMG/zIk5u1w2EIOH2iaQk0z1ajoVliVRgDTBnwZcM71uMhTc6CBRFoS4W+cZ99kcqwe2BD8m2+HU/++oP8ufVrzO6fBjZwUy2Ve/kwcVPcknPs1FVbyxW4tN8NFqxFr9PmLdpEWM7j8DwGZ4Y5VUVjSEdB2AJm1++/0jq+ViU3o7rT/sBAc0PtgYuH0dsNKP4VB//MeTHPPnJn5p9l9Q5pxM3Dp5OQPfRaEVp6/m5Mrj+Dnw+DdOJ8eHO5YwuH0bvwh4M7HAKQghidpyEY7Knan/yAeURuq5iRS3WbD7IktV76N+9gIEnJZen9ZKmXJ0tUfDGMLGiKBiaTsyOkRfKZdmuTxlWOhCEYFfdHgZ1SOa4NlQdS3F34KAoCpqqMaLTIO5c8KvUzfxQtJq/rPkrYzuPoHNOp8Oputzf+6MJlQknncPy3Z83S2WVHchkVPmQw8OQ7m6HEMnHzYH6Sm447Up+t/JF1hxe2rkgLY8fnDKBwvR8HCFQ3fvelpKdHaIqVoOmBtA1Fevw0Hx2egAHG9MRhEIGjS4fURBCkJuW/Y37dM+r8EQQ5/NpxIRgSu8L+cem93n2sznYwiHsS+PC7meSG8zGciwUxef6aVSOI6iL1xM1jx79EAi213xFj7wKvBBGqbZO9/wKZi1/gZsH/4iQEURXNTZWbuFvGxdweZ/xxF1+nQCgCCxh849N7zOoQz8m97qA6mgtWYEMdtfv56/r3uHik8YR0AJtnrnJ/WeFqwiEELyz5QMWbF3C5X3GU5pVjGVbfLDjY+ZvWczFPcZxUn7LK7m51a4D9Tz84qdAcpntoF9neN/iNq7V8bMsh5MLurY4Dx5gSMf+aML9c3tt20EhGQT9ZsksqqI1zcqX7lzJL8+4BRCuf9DatkAF3tm0qNkoQpMFW5dwQfcz8Wu+Nr8JHg9b2FQ2VvPfY+/g7S8XsrN2N11zy5NDrJEq8tPy2rqK30oIgaEalGZ34K73fs2hxupU2YGGSn69ZBb3jb6VDH86ltu74Un2YiUsk/2NB7jtBwN45q9fEAoYXD+xN4fMveQZOQQDAXB50KAoydSU/dr34tO9a44qz/Sn0yW3rK1HuY+TgqqovL9tGflpudxzxn/gODaWY7No+zI6ZhZjqIbr78UACdtM5kxW9RYXKcsOZOLi5EDNaDocrK3irM7DeebTl6hsrEJBoXdhdy49+XxQQFXBcfmtWMXAElG2Ve/izY3voqkaISNI1IxhORalWR0AUETbf5fk7q4WlzFNB1VR6ZDRnkiigaeWz+b2+Q9y13u/Zv6WxUByKWQvLIwBkJ7lw0HwxZbmmU/WbjmE7TikZbj7y+EjqWqyZ/HrsoOZXNhjLIbm7ikhkAyuTcdkW/WuowJrgMrGKnbV7sF0LFd/QAPJttjCYWvNrhbLLccibiUOz41zPxsb07F4aPFviVlxeuZ35UDDIR7/+Fn2NVbieOAFAcCn+thStaNZYN1EIHhp7V9xhOPqlIJNFEXBFg7/vfRx5h98memTi7jwvHSeWvsEjy17loRt0mBGv/2H2pyCJlR+PGAqfduf3KykOL2Qu0fdRNgIoWnuf1wnV8UTDO80EFVR2XJoO/sjlWw+tJ0eeZ1xhENQ97u+1xpAVzWqojWc2/WMo8pOKTwJJblk8/dfsX+Bo9hkBMI88tEfUmlRBYLP963nj5++lPwexnD/PUyYDj7dR5/CnkBycZz6eCT18tOnsAeGaqA4bT/yLnuuvwPHEfhVjYknn8f9ix47qjwrkEFFTim66o0v7W3HpiHRwOl9i3jlvU3YjkBR4MzTSogkGjBU9wekTRxHUJzenifPu//wwiTJi0tXNDYc3ERWu/S2reBxshybnbW7j1m+q3YvnXM6fX8V+h/QVY0OGe3ZVr3zqDLtcNYd03L/daLrGp/t30BhOJ8JJ51DRU4pcdvErxms2b+BduF8dtfvo9jo6P6gVIhjrgQIsK3mq8MZHtwfyIHCV3V7cYTD6v3rWb1/fbNSWzh4IYoTQmArgmiiken9JmE649lXf5DsYCbp/jB1sXoiViN+xxvfJygkp+kVZxTy900LqYvV0zGziAu6n4Xt2JgeWf5T1zQqcjqhKAo/7DeR97ctw7RNBnXsx0kFXckJZqEqquuf9Lqhsq9+H4t3LmdwSX/OKBt6+PsxjX31B5m7YR4Jy8SnKbj9utc0ldp4LQM79OH97R+xu+6fHwHnh3IYUz6M6mgthcGCNqxlkgyuvyMFhfbhAqb3m8SctW8RObzcdnl2CdcO/AF+ze/6oRWAYFBHU1XitsnyA5/w2K0jWbO5kp5luayvWU0wUU5huAA9XVBf7/4x4qARoFt+Oe9uWcw7mz+gPtFA97zOXN5nPD3bdfPExxqQ7I3rklt2zPLOuaWguD9ggGTO23FdRrJ05/KjUiMN7zQIRzgYqkbU5cfF8CnErATtwvn8fdNCHvnoGQQCTVEZVTaUvu17sblqG3pQJZFw96PWQRxz6WCAgrRcz6TpF8ImL5TTYpmqqAR1Pz7NwHL5qIKiKCSsBPXxBvZFtlOUUYih6sStBJH4AXy6gWlZ+Nq6osdB0xQUVeP5z19nQ+Xm1Pb9DZWs2LOaKb0upH16Pl4JPXbV7uE3S56mZ34XRpYNQleTC+K8tu7vXHrSuZxZMRzb5b3XtmKhqhrd8zpTHavhN0uepsFMJisozerA1QMuI27FicQbydMKXN1B4OgOftvHfYse5frTrmRP/X42VG6mIruU8pxSfrVkFredfh220vbPFNee4Y7jMHPmTDZu3IjP5+O+++6jtLS0rauFLWwcBOsPbuamwT9CV1U0VeOrun28uu5tpvUZj+K4v8fX7zewSKZ9e3nDX3lr8zt0zu3EK8t2Ekk08GjHX+IIB1V395tsk4SV4KUv3uT9bR+ltm2o3Mwv3v+/PHTm7eT6Am1Yu+NnOzZFGe3omFnErto9zcpKs4ppF87HcrzRs6gosPHgFn427BrmrH2TbdW7CBlBzigfQu92PVEUxRO9WEJxGFDUm9+teLHZnFhbOLy7dTG6pnFR97NQbRW3j1glHJM+hT0J6H5iLUysvrjH2aT50qhr8MILdZCcYCbFGYXNerAAhpYMQFd12jpjwPFoSlHpCMGXh7bx2+WzU2WlWcXcPOTHaIqKY7k36GmiGxqLdixrFlgf6cU1bzCwQx/8StgLgwocOjyFYt3BTaw7uKlZWVW01jNTQPNCOdTE6vjTquaZT3bUfMVvljzNf515W/LDTXffvtAOf/R/SvuTuOPdh+iSW0ZhOJ9F2z9m1ooXOL1kILqiHp4m0rbPSNc+od99910SiQQvvfQSt9xyCw8++GBbVwkAvxZACMFHu1Zy36JHmLnw/3LXgl8za/nzqblM3phNIZKryqkaZ3ceRaMZZfW+9cnFV0oHoqs6dfH6b8zC4RZ+v44pLD7Y/vFRZaZt8ubGd4nhjXy3fs1PzIxzw2lXcmbF6YR9aaT70jir83CuG3gl0UQsmTbJ5Xw+HUP10aOgMy+vfYtxnUcyc9TN3DzkRzQkGlm4dQmaomLh/pRvpjAPr5p59MdmAAu2fIgtBEJz+ZMJCGg+LMdhxunXkh3MTG03VJ2Le4yjPLsjkbg3UnA6OGyq3MrPh/0fBnXom5pqdG7XM7ioxzjq4hE0b9yMCeg+0v1pLNj6YbPtO2p2s2bfBvy6H8X97wkk1Ch/+3LBN+4zb/MH+APuPy6q0OiaV3HM8v5FJ6N44OVNFwa2Y/Paur+3WF4br2dz1XYK0vJc3WsNoKkqlmMztvMIzu4yih01X7F4xydsrtrOqLIhTDj5HASga23fb9z2NTiGlStXcvrppwNwyimnsHbt0ctBtwXLMWk0oy0mMR9VNhhVUfBAPIpA4Nf8bK3ZwXndRtO/uBcbDm6mc04nOmQWsbf2AB2z2rd1NY+Lz6dxsLGhxeWcAb6q23v4Tdb9A6u2SH6gkeYLkenP4NqBVyAQbKvaiaooNFiNZImMtq7mtxJKsnc905/OhT3G8uxnc6iO1qIqKoM79ueKvhPQVM31N3NIXiu1sbpjlpuORcJOoKoqbs+l7tMNDtYcYsGWD7l6wOWoikrMipETymbJjuVUx2rJDmTi9nYAKEJh1f51/OGzOVzUYywX9zwbW9h8tGMlt83/L6499QoKw/ltXc1vpSgKjVaUfZEDLZZ/eWgr/YtOJqCl4fquRWjxY+wj7a0/gKO4e6oOgKbohIwAo8uHsmDrkmZlPfK7UJxRSNgXIk5DG9Xw+JimjWqIYy5SBLCtehf9i3p9j7X61zhO8nudZbs+QyC4Y/gNgEBRVFbsXs3CbUs5p8sZqbScbcm1wXUkEiEcDqf+1jQNy7LQ9ZarnJ0dQtf//Q+EaCKGEPDDfpOYu+4frNy7hrAvjfO6jiaoB5KLfBg6+fnu//ik0YpSGM5n9qpXmdTrQkaXDyNhm7y09s3kMLdIvpXn57v/Y8A0Mw1VUVsMsDtkFKIpGjkeaEd9LEJBOI+nV7zAqLIhhH1pKAp0zCriz6vf4McDpiKE8MQxqYnWsLNmN91zy7l39K2Ytomh6miqxvqDm+mWV4Ghaa5vS02sjszAsetoqDqGahDyBwiG3T/9yK8aKAo8uPgJgnoAQzOoj0cI6H6GlZ6KiuqJayVuxumSW8bCbR/xp1WvHFVelNEOy7Fdf34BHGowaZ/e8lz4nvldiFoJ8jJzCbv8/DrUWE1OMOsbA+zCcD5pwQCZaa4NP1LiUYOzKobTI78LH+5YjulYDCzuQ7f8CnyqD1VVPXF+VTXUUJTersVVDQE6ZSdT2Lm9LTEzjnAE5TkduX/RXP6x6f1m5TcNvgpHCPy6RlZ+qG0qeZhrz+5wOExDwz/fCB3HOWZgDVBd/f0MZQZCOtnBDH6/8kUqcko5t9toYlacD3d8QkFaLj7NoLY6hhDun4ZghBUCmp+Jvc7nz6tfZ1v1TjpmFjOl9wWk+9OwhU11lTeGiI2QzumlA1m0fVnz7arOed3GEFSCHDxY30a1O36+AKi6yo/6T2Xu+n+wdOdKFBSGlg7gR/0noyrJ5bgP1ru/LXqaQml2Bx768LecXNCNPoU9qYnVMnfDPK7sOxGfquPEVde3xTA0HNWhb/uT+Wzv0SNoo8uH4giHxjqLiOPutgDkp+VwbtczWL1/A/XxSGpRnCm9L0RXdUJamieuFT0IfdufTKY/ndp48/p2zS0nwx/GjDkcrHN/W7SQgk8zGFM+jHePmBpSmlVM78Ke6KrmiWPiD/g5t+voFlc1bDK28whqqqKemHNthCHkC/HloW1U5JSgKhrbanbRr+hkBI4njglARmaISSefzwMfPH50mT+drrnlnohbVFXBl6ZTGM4/6n7cI78z5YeztSUaBI21//5j800vI4pw6dJ18+bNY+HChTz44IOsWrWKxx9/nN///vfH3P/7PMkDYZWESPDJV6tYumsF6b4wF3Q/k4JwHrpjEG9s+yGJ4xFM07GUBLbjpOYy6aqGLWwUVPyKQWPEG20JhQxiSpR3Nn/A/C2LiSQa6JZXwbQ+48kP5WI1eGCuDsmPAI1wcvpRQyKGTzMAgWlbhHxBDFUnEfFEhjFUn8DRTFRVZdH2ZXy6Zy05wSwu7jmOLH86iqJiRtw/ZxFATxPYwuLZz15m+e7PU9lCRpYNZnyPs9EUHbvRG+dYWoZGbaKBRrORjZVbiCQa6dWuOwhBYbjAM9eKqir40xQiZgN/WvUqK/euwVB1RnYazPieZxNQfTTUu38aBSTbogQtLGFTG6tjzf4NlGQWU5pVjGmZhJR0zLg37sV6ms3DS3/PxkNHp3yc3OsCRpYMxo66f9oRJL/nsfUEETPZ0Wc7DqqikOFPJ6AEqa93dzB6JD3NYcXe1fz587lHZAsp5qeDfkSOP4uGenev+ttECzqoGiRsiz31+9hYuZWKnBJKMovxqX4U4ZD4nu7Fngyum7KFfPnllwgheOCBB6ioOPbHBd9ncK0oEErXMYVJwrZQFSWZgs8UxKPeuAE2CYQ0EkoyM4DpWBhqcnTAJwxija48NY4pEEjeCE3HQghQFYWA7ida561joqoKRkggFHF4dUMFXdVRUTAbFU/MU26i+hwc3cKn+rCEhaboJOwEPs3AblQ90xZFAS3kELNiaJpGzIwTNALYjo1P9XsmsG6Snh6gwUm+pdkIdEXFUH3E6r11rWiagi+kYgozNSVMV3Qc4WA3qp54CW3SFGDHrQSmY6IqKiEjiJIwsEzvHBdFUVBDFusObuLtL9+jLh6hY2YR43uOI9efgx311rWi6xAI60St5JSEkBHEjAni8bZP9/ZdKAr4wwqJw9+NGZqBoeqE1CARjwTWTbSgjaqpyZFcx0l+vyMcFBsS3+P55cng+rtqi+EZRUkmNc/O9sYw6jcJBAxUHYIBH4cq3f2BxvHIz0/3/DHRNBXDpxAM+amribl+VcZjUVUFdAdHsfD5DJyoimV5sy26riIMC1QBjgoJ1fVL0X+bE+Fa0XUVRROkh4PUVEc989LWElVVUFXF888Vn0/F1i2E4qA5OnbcWx0DLTkRrpUTJW7RNBXNL3Cw8Rs+YhHne39GflNw7a1XSJcRAs8GCV8Xi5k0RkxPpN7738K2HWLRw1N1PBpYQ3LVNiehQNwgM5Du6WvGshzsqEpOKAs7qng+sD5RWJaDGReeyUDzTRxHePoaaZJIJEcPckPZmFE8f1xOFCdK3GLbDolGgdWoEvIFXfeMlJGUJEmSJEmSJLUSGVxLkiRJkiRJUiuRwbUkSZIkSZIktRIZXEuSJEmSJElSK5HBtSRJkiRJkiS1EhlcS5IkSZIkSVIrkcG1JEmSJEmSJLUSGVxLkiRJkiRJUiuRwbUkSZIkSZIktRIZXEuSJEmSJElSK5HBtSRJkiRJkiS1EkUIIdq6EpIkSZIkSZJ0IpA915IkSZIkSZLUSmRwLUmSJEmSJEmtRAbXkiRJkiRJktRKZHAtSZIkSZIkSa1EBteSJEmSJEmS1EpkcC1JkiRJkiRJrUQG15IkSZIkSZLUSmRw/R3MmjWLSZMmMX78eF5++eVmZe+99x6XXHIJkyZNYs6cOW1Uw+PjOA533303kyZNYtq0aezYsaNZ+Zw5cxg/fjwTJ05k4cKFbVTLb/f5558zbdo0ANavX8/UqVOZNm0aV111FZWVlc32/bY2tzXTNLn11luZOnUqEyZMYMGCBc3KvXR+NTl06BAjRoxgy5YtzbZ7qS2maXLLLbcwefJkpk6d6um2vPbaa0ybNo1p06YxceJEevXqRV1dXarcK9d9kyOv/yN56ZgAXHTRRanjcvvttzcr89IxSSQS3HLLLUycOJHp06ezffv2ZuVeacvXz6v58+dzyy23tLivm9t0ZDt27NjBlClTmDp1Kvfccw+O4zTbNxaLccMNNzB16lR+/OMfU1VV1RZV/ka2bXP77bczefJkLrvsMnbu3Nms3HXXvZCOy7Jly8TVV18tbNsWkUhEPProo6myRCIhxowZI2pqakQ8Hhfjx48XBw4caMPafrN58+aJGTNmCCGE+Oyzz8Q111yTKjtw4IA477zzRDweF3V1dal/u83TTz8tzjvvPHHppZcKIYS47LLLxLp164QQQrz44ovigQceaLb/N7XZDV555RVx3333CSGEqKqqEiNGjEiVee38EiJZ52uvvVacddZZYvPmzc22e6kt8+fPFzfeeKMQQogPP/xQXH/99akyr7XlSDNnzhR/+ctfUn975bpv8vXrv4nXjkksFhMXXnhhi2VeOyazZ88Wd955pxBCiC1btojp06enyrzSlq+fV/fee68YO3asuOmmm47a181t+no7rr76arFs2TIhhBB33XWXeOedd5rt/8wzz6Rimrfeekvce++932+Fj8P8+fPFbbfdJoRIxmNHPsPdeN3Lnuvj9OGHH9K1a1euu+46rrnmGkaOHJkq27JlCyUlJWRmZuLz+ejfvz8rVqxou8p+i5UrV3L66acDcMopp7B27dpU2erVq+nbty8+n4/09HRKSkrYsGFDW1X1mEpKSnjsscdSfz/88MP06NEDSL7h+v3+Zvt/U5vdYNy4cfz0pz9N/a1pWurfXju/AB566CEmT55MQUFBs+1ea0tZWRm2beM4DpFIBF3XU2Vea0uTNWvWsHnzZiZNmpTa5pXrvsnXr/8mXjsmGzZsIBqNMn36dK644gpWrVqVKvPaMdm8eTPDhw8HoLy8vNkoj1fa8vXzql+/fsycObPFfd3cpq+344svvmDgwIEADB8+nKVLlzbb/8jn4/Dhw/noo4++v8oepzFjxnDvvfcCsGfPHvLy8lJlbrzuZXB9nKqrq1m7di2PPPIIv/jFL/jZz36GOLxyfCQSIT09PbVvWloakUikrar6rSKRCOFwOPW3pmlYlpUq80Jbxo4d2yzQaQriPv30U55//nmuvPLKZvt/U5vdIC0tjXA4TCQS4cYbb+Smm25KlXnlmDR57bXXyMnJSd2sj+S1toRCIXbv3s3ZZ5/NXXfd1Wy42GttaTJr1iyuu+66Ztu81pavX/9NvNaOQCDAVVddxR/+8IfUc8Vr9+ImPXr0YOHChQghWLVqFfv378e2bcA7bfn6eXXOOeegKEqL+7q5TV9vhxAi1Y60tDTq6+ub7aQC21IAAAYnSURBVH9kW1oqdwtd15kxYwb33nsvY8eOTW1347GQwfVxysrKYtiwYfh8PsrLy/H7/al5SeFwmIaGhtS+DQ0NzQ6023y9vo7jpC5Er7XlSG+//Tb33HMPTz/9NDk5Oc3KvqnNbrF3716uuOIKLrzwQs4///zUdq8dk1dffZWlS5cybdo01q9fz4wZMzh48CDgvbY8++yzDBs2jHnz5vHGG29w2223EY/HAe+1BaCuro6tW7cyaNCgZtu92JaWeK0dZWVlXHDBBSiKQllZGVlZWZ69Vi655BLC4TBXXHEFCxcu5KSTTkqNwHmtLcfDS21S1X+Geg0NDWRkZDQrP7ItLZW7yUMPPcS8efO46667aGxsBNx5LGRwfZz69+/P4sWLEUKwf/9+otEoWVlZAFRUVLBjxw5qampIJBKsWLGCvn37tnGNj61fv3588MEHAKxatYquXbumynr37s3KlSuJx+PU19ezZcuWZuVu9cYbb/D8888ze/ZsOnbseFT5N7XZDSorK5k+fTq33norEyZMaFbmtfPrhRdeSB2LHj168NBDD5Gfnw94ry0ZGRmpm3RmZiaWZaV647zWFoDly5czZMiQo7Z79br/Oq8dk1deeYUHH3wQgP379xOJRFLXiteOyZo1a+jfvz+zZ89mzJgxze7DXmvL8fBSm3r27MnHH38MwAcffMCAAQOalffr149Fixalyvv37/+91/HbzJ07l1mzZgEQDAZRFCX18ubG695dXXcuNmrUKJYvX86ECRMQQnD33Xfz9ttv09jYyKRJk7jtttu46qqrEEJwySWX0K5du7au8jGdeeaZLFmyhMmTJyOE4IEHHuCPf/wjJSUljB49mmnTpjF16lSEENx8881HzV92G9u2uf/++2nfvj033HADAKeeeio33ngjP//5z7nppptabLObPPXUU9TV1fHkk0/y5JNPAnDppZcSjUY9d3615M033/TktXLllVdyxx13MHXqVEzT5Oabb2bBggWebAvAtm3b6NChQ+pvL1/3R/Lq+TVhwgRuv/12pkyZgqIoPPDAA8yePduTx6S0tJRHHnmEZ555hvT0dO6///4T5vw6khfbNGPGDO666y4efvhhysvLU1Mqpk+fzlNPPcWUKVOYMWMGU6ZMwTAMfvOb37RxjY921llncfvtt3PZZZdhWRZ33HEH77zzjmuve0U0TRyWJEmSJEmSJOl/RE4LkSRJkiRJkqRWIoNrSZIkSZIkSWolMriWJEmSJEmSpFYig2tJkiRJkiRJaiUyuJYkSZIkSZKkViKDa0mSpBNEt27dAKivrz9qFcZ/1WOPPdbiUuNfffUVZ5xxRqv8NyRJkk4kMriWJEk6wdTW1rJ+/fq2roYkSdL/SnIRGUmSpBPMfffdx4EDB7juuut44oknmDt3Ls899xyO43DSSSdxzz334Pf7GTp0KKNHj2b16tXk5eVxySWXMHv2bPbt28eDDz7IwIEDm/3uunXr+M///E8Aunfv3hZNkyRJcj3Zcy1JknSCufPOOykoKOCJJ55g06ZNzJkzh7/85S+88cYb5Obm8oc//AGAyspKhg8fzty5c4nH47z77rv8+c9/5oYbbuC555476ndnzJjBz372M15//fVmKz1KkiRJ/yR7riVJkk5gH3/8MTt27GDixIkAmKZJz549U+XDhw8HoLi4mP79+wNQVFREXV1ds9+pqqriwIEDDB06FIDx48fz6quvfh9NkCRJ8hQZXEuSJJ3AbNvm7LPP5s477wSgoaEB27ZT5T6fL/VvTdOO+TuKoiCEOK59JUmS/jeT00IkSZJOMLquY1kWAKeddhrz58/n0KFDCCGYOXNmi1M+vk12djZFRUW8//77ALz11lutWWVJkqQThgyuJUmSTjC5ubkUFRUxbdo0unfvzvXXX88PfvADzj33XBzH4Sc/+cm/9Lu/+tWvePzxx7nooovYuXNnK9dakiTpxKCII8f5JEmSJEmSJEn6l8mea0mSJEmSJElqJTK4liRJkiRJkqRWIoNrSZIkSZIkSWolMriWJEmSJEmSpFYig2tJkiRJkiRJaiUyuJYkSZIkSZKkViKDa0mSJEmSJElqJf8fCPqT9WCRqHcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "plt.title('StartingBid vs ItemId sized by NumBidders',fontsize=20)\n",
    "ax=sns.scatterplot(x=\" ItemId\", y=\" StartingBid\",data=auctions, hue=auctions2['sold'],size=\" NumBidders\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Αρχικά θα μεταβάλλουμε την τιμή εκκίνησης κατά ένα ποσοστό, για τις καταχωρήσεις όπου έγινε η πώληση. Στη συνέχεια θα χρησιμοποιήσουμε παλινδρόμηση για να δούμε εάν επιτυγχάνουμε καλύτερα αποτελέσματα με την αλλαγή αυτή."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "auctions3 = auctions[ auctions2['sold']==1].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "auctions3_price = auctions3.loc[:,' Price'].copy()\n",
    "auctions3.drop([' NumBidders',' Price'],inplace=True,axis=1)\n",
    "auctions3[' Price'] = auctions3_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>SellerRating</th>\n",
       "      <th>StartingBid</th>\n",
       "      <th>ItemId_0.0</th>\n",
       "      <th>ItemId_1.0</th>\n",
       "      <th>ItemId_10.0</th>\n",
       "      <th>ItemId_11.0</th>\n",
       "      <th>ItemId_12.0</th>\n",
       "      <th>ItemId_2.0</th>\n",
       "      <th>ItemId_3.0</th>\n",
       "      <th>ItemId_4.0</th>\n",
       "      <th>...</th>\n",
       "      <th>SellerId_91.0</th>\n",
       "      <th>SellerId_92.0</th>\n",
       "      <th>SellerId_93.0</th>\n",
       "      <th>SellerId_94.0</th>\n",
       "      <th>SellerId_95.0</th>\n",
       "      <th>SellerId_96.0</th>\n",
       "      <th>SellerId_97.0</th>\n",
       "      <th>SellerId_98.0</th>\n",
       "      <th>SellerId_99.0</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>84.11</td>\n",
       "      <td>137.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>145.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>94.52</td>\n",
       "      <td>364.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>97.41</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>106.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>99.97</td>\n",
       "      <td>836.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>99.01</td>\n",
       "      <td>207.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>599.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 166 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SellerRating   StartingBid  ItemId_0.0  ItemId_1.0  ItemId_10.0  \\\n",
       "0          84.11         137.0           0           0            0   \n",
       "2          94.52         364.0           0           0            0   \n",
       "3          97.41          82.0           0           0            0   \n",
       "5          99.97         836.0           0           0            0   \n",
       "6          99.01         207.0           0           0            0   \n",
       "\n",
       "   ItemId_11.0  ItemId_12.0  ItemId_2.0  ItemId_3.0  ItemId_4.0  ...  \\\n",
       "0            0            0           0           0           0  ...   \n",
       "2            0            1           0           0           0  ...   \n",
       "3            0            0           1           0           0  ...   \n",
       "5            0            1           0           0           0  ...   \n",
       "6            0            0           0           0           0  ...   \n",
       "\n",
       "   SellerId_91.0  SellerId_92.0  SellerId_93.0  SellerId_94.0  SellerId_95.0  \\\n",
       "0              0              0              0              0              0   \n",
       "2              0              0              0              0              0   \n",
       "3              0              0              0              0              0   \n",
       "5              0              0              0              0              0   \n",
       "6              0              0              0              0              0   \n",
       "\n",
       "   SellerId_96.0  SellerId_97.0  SellerId_98.0  SellerId_99.0   Price  \n",
       "0              0              0              0              0   145.5  \n",
       "2              0              0              0              0  1278.0  \n",
       "3              0              0              0              0   106.5  \n",
       "5              0              0              0              0  1146.0  \n",
       "6              0              0              0              0   599.0  \n",
       "\n",
       "[5 rows x 166 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auctions3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train-test set split and standardization\n",
    "\n",
    "X=auctions3.iloc[:,:-1].values\n",
    "y=auctions3.iloc[:,-1].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=77, test_size=0.2)\n",
    "\n",
    "X_train_scaled=StandardScaler().fit_transform(X_train)\n",
    "X_test_scaled=StandardScaler().fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Στη συνέχεια κατασκευάζουμε ένα μοντέλο πρόβλεψης της τελικής τιμής χρησιμοποιώντας Random Forests. Η διαφορα με πριν είναι ότι τώρα δεν χρησιμοποιούμε τη μεταβλητή NumBidders καθότι για τις νέες τιμές εκκίνησης δεν μπορούμε να έχουμε γνώση αυτού."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 on train set : 0.996964175540723\n",
      "R^2 on test set : 0.9776814949326835\n",
      "Mean Absolute Error: 24.90237252737175\n",
      "Mean Squared Error: 2733.0980829189257\n",
      "Root Mean Squared Error: 52.27904056999254\n"
     ]
    }
   ],
   "source": [
    "rand_forest = RandomForestRegressor(n_estimators=300, max_features = 'auto', max_depth= 50, random_state=77,n_jobs=-1).fit(X_train, y_train)\n",
    "\n",
    "y_pred = rand_forest.predict(X_test)\n",
    "\n",
    "fit_and_report(rand_forest,y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Αυξάνουμε την τιμή εκκίνησης κατά 10%, χωρίζουμε το σύνολο σε train-test και υπολογίζουμε για το test τις προβλέψεις. Στη συνέχεια υπολογίζουμε τη μέση τιμή της διαφροάς τους από τις αρχικές τιμές εκκίνησης."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "starting_bid = auctions3.loc[:,' StartingBid'].copy()\n",
    "new_starting_bid = starting_bid * 1.10\n",
    "auctions3.loc[:,' StartingBid'] = new_starting_bid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=auctions3.iloc[:,:-1].values\n",
    "y=auctions3.iloc[:,-1].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=77, test_size=0.2)\n",
    "\n",
    "y_pred2 = rand_forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-8.706846844285536"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(y_test-y_pred2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Όπως φαίνεται έχουμε, κατά μέσο όρο, αύξηση της τελικής τιμής μετά την αύξηση αυτή καθότι το αποτέλεσμα είναι αρνητικό κατά 8.71 μονάδες."
   ]
  },
  {
   "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": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}