kNN_binary_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-NN Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler, scale\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, accuracy_score, roc_curve,classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Wine Quality Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        1  \n",
       "1      9.5        1  \n",
       "2     10.1        1  \n",
       "3      9.9        1  \n",
       "4      9.9        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['fixed_acidity', 'volatile-acidity', \n",
    "                'citric-acid', 'residual-sugar', \n",
    "                'chlorides', 'free-sulphur-dioxide', \n",
    "                'total-sulpher-dioxide', 'density', \n",
    "                'pH', 'sulphates', 'alcohol', 'quality']\n",
    "data = pd.read_csv(\"datasets/wine_quality/winequality-white.csv\", sep=\";\")\n",
    "\n",
    "data['quality'] = np.where(data['quality'] > 5, 1, 0)\n",
    "\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: quality, dtype: int32"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = data['quality']\n",
    "# target = (target > 5).astype(np.int)  # 1 if Good Wine, else 0 (Bad Wine)\n",
    "target.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002E1236C4948>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E123787F08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E1237C5E48>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002E123A3EF48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E123A780C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E123AB11C8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002E123AE3908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E123B21388>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E123B27F08>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002E123B67108>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E123BC86C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E123C02748>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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OHrrLPklVnQScBLBu3bpav379orPNzMywK/OthIWybth09tDyLcfMP88k7E7rdFr0JSf0J2tfci7WKEcUkWQvut76RwDvAv4Le+53yca1O1i1z113HsbxOibZ+9/HdQ39zN3HzNDP3H3MvJuZ7wv8SF/sYfQv9yvViM/3ZXuYLces78WXjT5kBHOOmznHpw8Zp9iPA1+sqq8AJPkg8MPAfkn2bvsmBwHXtvpbgYOBre1UtfsDNyx/bEnql5E6iqrqNuBxSfYDPgR8/7Bq7a899zuxYdPZbFy7g7deeudVP44e+kn2/vdxXUM/c/cxM/Qzdx8z99R1SQ5sRxMdCFzfyme/wM+a/XK/lTtOVZstn1mGnJKk6XYVcHiSe9OdenYE8Gng48DzgNOAY4EPt/pntcf/1qZ/zLMcJGlhi7rqWVXdRPdl/XBaz32bNKznHnvuJUnc8UUd7voF/qXt6meHAze3U9T+AfiJJA9opxn8RCuTJO3BquoCuqEtLgIupduXOYlu3LvXJNlMdybDyW2Wk4EHtvLXcMfFFCRJO7HgEUVJHgR8p6puSrIP3SGfb8Gee0nSHEneT3c00AFJttJdvewE4Iwkx9H9Gvz8Vv0c4EhgM3AL8DKAqrohye8A/97q/fbswNaSpD1bVb2Rrm0ZdCXdVTLn1v0Wd7Q5kqQRjXLq2YHAKW2corsBZ1TVR5J8Fjgtye8C/8Gde+7f23rubwCOnkBuSdIUqqoXzjPpiCF1C3jFPMt5N/DuMUaTJEmSNIJRrnp2CfD4IeX23EuSJEmSJO1GFjVGkSRJkiRJknZfdhRJkiRJkiQJsKNIkiRJkiRJjR1FkiRJkiRJAka76pn2cGs2nc3GtTvYsOnsO5VvOeFZY1n2MONYtiRJkiRJWhw7iiRJkiRpDzPfD7aS5KlnkiRJkiRJAuwokiRJkiRJUmNHkSRJkiRJkgA7iiRJkiRJktTYUSRJkiRJkiTAjiJJkiRJkiQ1dhRJkiRJkiQJGKGjKMnBST6e5Ioklyf5xVb+piTXJLm43Y4cmOd1STYn+XySp0/yBUiSJEmSJGk89h6hzg5gY1VdlOS+wIVJzm3T3lZVfzBYOcmjgKOBRwMPAf4xyfdW1W3jDC5JkiRJkqTxWvCIoqraVlUXtfvfAK4AVu9klqOA06rq1qr6IrAZOGwcYSVJkiRJkjQ5oxxRdLska4DHAxcATwZemeSlwKfpjjq6ka4T6fyB2bYypGMpyfHA8QCrVq1iZmZm0eG3b9++S/OtpI1rd7Bqn+7voHG8jrnLHNey+5h5Vh/fI33MDP3M3cfMkiRJkjRJI3cUJbkP8NfAL1XV15OcCPwOUO3vW4GfBTJk9rpLQdVJwEkA69atq/Xr1y86/MzMDLsy30rasOlsNq7dwVsvvfOq33LM+rEse5ilLruPmWf18T3Sx8zQz9x9zNxnSX4Z+Dm6NuFS4GXAgcBpwP7ARcBLqurbSe4JnAo8Efga8IKq2rISuSVJkqQ9yUhXPUtyd7pOovdV1QcBquq6qrqtqr4L/Bl3nF62FTh4YPaDgGvHF1mS1DdJVgOvBtZV1WOAvejGs3sL3Xh3hwI3Ase1WY4DbqyqRwBva/UkSZIkTdgoVz0LcDJwRVX94UD5gQPVfgq4rN0/Czg6yT2THAIcCnxqfJElST21N7BPkr2BewPbgKcBZ7bppwDPbfePao9p049o7ZEkSZKkCRrl1LMnAy8BLk1ycSt7PfDCJI+jO4VgC/DzAFV1eZIzgM/SXTHtFV7xTJL2bFV1TZI/AK4C/hv4KHAhcFNVzQ5WNjim3Wrg6jbvjiQ3Aw8Evjq43FHHu1up8ajmG4dtmJmZmV6Mm9WHjGDOcTPn+PQhoyRpz7ZgR1FVfZLh4w6ds5N53gy8eQm5JEm7kSQPoDtK6BDgJuADwDOHVJ0d026s492t1HhU843DNsyWY9b3YtysPmQEc46bOcenDxklSXu2kcYokiRpiX4c+GJVfaWqvgN8EPhhYL92KhrceUy728e7a9PvD9ywvJElSdMmyX5JzkzyuSRXJPmhJPsnOTfJF9rfB7S6SfKOJJuTXJLkCSudX5L6wI4iSdJyuAo4PMm921hDR9Cdovxx4HmtzrHAh9v9s9pj2vSPVdVdjiiSJO1x3g78fVV9H/BY4ApgE3BeuzDCee0xdEeuHtpuxwMnLn9cSeqfUcYokiRpSarqgiRnAhfRjV/3H3SnjJ0NnJbkd1vZyW2Wk4H3JtlMdyTR0cufenmt2XQ2G9fuuMvpaltOeNYKJZKk6ZLkfsBTgQ0AVfVt4NtJjgLWt2qnADPAa+lOeT61/dBwfjsa6cCq2rbM0SWpV+wo6qE1ixjzQpKmRVW9EXjjnOIrgcOG1P0W8PzlyCVJ6o2HA18B/iLJY+kuivCLwKrZzp+q2pbkwa3+7RdGaGYvmnCnjqJRL4ywM30apHw262IuuAAs++vr4zqddn3JCf3J2peci2VHkSRJkqQ+2Bt4AvCqdqTq27njNLNhxnphhJ3p0yDls1kXc8EF6C66sJz6uE6nXV9yQn+y9iXnYjlGkSRJkqQ+2ApsraoL2uMz6TqOrktyIED7e/1A/YMH5h+8aIIkaR4eUSRJ0hJ5SrAkTV5VfTnJ1UkeWVWf544LI3yW7gIIJ3DXCyO8MslpwJOAmx2fSJIWZkeRJEmSpL54FfC+JPegG+fuZXRnSZyR5Di6q2zOjnF3DnAksBm4pdWVJC3AjiJJkiRJvVBVFwPrhkw6YkjdAl4x8VCStJtxjCJJkiRJkiQBdhRJkiRJkiSpsaNIkiRJkiRJgB1FkiRJkiRJahbsKEpycJKPJ7kiyeVJfrGV75/k3CRfaH8f0MqT5B1JNie5JMkTJv0iJEmSJEmStHSjXPVsB7Cxqi5Kcl/gwiTnAhuA86rqhCSbgE3Aa4FnAoe225OAE9tfSZIkSdIyW7Pp7Nvvb1y7gw0Dj3dlGbO2nPCsJeWSNJ0WPKKoqrZV1UXt/jeAK4DVwFHAKa3aKcBz2/2jgFOrcz6wX5IDx55ckiRJkiRJYzXKEUW3S7IGeDxwAbCqqrZB15mU5MGt2mrg6oHZtraybXOWdTxwPMCqVauYmZlZdPjt27fv0nwraePaHazap/s76P++78ND669dff+hy1iMpa6j+TKPY93P91rG9X/t43ukj5mhn7n7mFmSJEmSJmnkjqIk9wH+Gvilqvp6knmrDimruxRUnQScBLBu3bpav379qFFuNzMzw67Mt5I2bDqbjWt38NZLR1v1W45ZP3QZizFsGYsxX+alLnd22cOMY9nQz/dIHzNDP3P3MbMkSZIkTdJIvRVJ7k7XSfS+qvpgK74uyYHtaKIDgetb+Vbg4IHZDwKuHVdgaSmGnVsNnl8tSZIkSRKMdtWzACcDV1TVHw5MOgs4tt0/FvjwQPlL29XPDgdunj1FTZIkSZIkSdNrlCOKngy8BLg0ycWt7PXACcAZSY4DrgKe36adAxwJbAZuAV421sSSJEmSJEmaiAU7iqrqkwwfdwjgiCH1C3jFEnNJknYzSfYD/hx4DN3YdT8LfB44HVgDbAF+pqpubEezvp3uh4dbgA2zV+CUJEmSNDkLnnomSdKYvB34+6r6PuCxwBXAJuC8qjoUOK89BngmcGi7HQ+cuPxxJUmSpD2PHUWSpIlLcj/gqXRj3lFV366qm4CjgFNatVOA57b7RwGnVud8YL924QRJkiRJEzTaNdolSVqahwNfAf4iyWOBC4FfBFbNXvCgXUXzwa3+auDqgfm3trI7XRwhyfF0RxyxatUqZmZmhj759u3b5502DhvX7hjLclbtc9dlTTL3rpj0uhwXc46XOcenDxklSXs2O4okScthb+AJwKuq6oIkb+eO08yGGTY2Xt2loOok4CSAdevW1fr164cubGZmhvmmjcOGTWePZTkb1+7grZfeuWnecsz6sSx7XCa9LsfFnONlzvHpQ0ZJ0p7NU88kScthK7C1qi5oj8+k6zi6bvaUsvb3+oH6Bw/MfxBw7TJllSRJkvZYdhRJkiauqr4MXJ3kka3oCOCzwFnAsa3sWODD7f5ZwEvTORy4efYUNUmSJEmT46lnkqTl8irgfUnuAVwJvIzuB4szkhwHXAU8v9U9BzgS2Azc0upKkiRJmjA7iiRJy6KqLgbWDZl0xJC6Bbxi4qEkSZIk3YmnnkmSJEmSJAmwo0iSJEmSJEmNHUWSJEmSJEkCHKNIu7E1m85m49odbNh09kpHkSRJkiSpFzyiSJIkSVJvJNkryX8k+Uh7fEiSC5J8Icnp7eqaJLlne7y5TV+zkrklqS8W7ChK8u4k1ye5bKDsTUmuSXJxux05MO11bWP8+SRPn1RwSZIkSXukXwSuGHj8FuBtVXUocCNwXCs/Drixqh4BvK3VkyQtYJRTz94DvBM4dU7526rqDwYLkjwKOBp4NPAQ4B+TfG9V3TaGrOqxNZ7+JUmSpCVKchDwLODNwGuSBHga8KJW5RTgTcCJwFHtPsCZwDuTpKpqOTNLUt8s2FFUVZ9YxGGaRwGnVdWtwBeTbAYOA/5tlxNKkiRJUuePgF8D7tsePxC4qap2tMdbgdXt/mrgaoCq2pHk5lb/q4MLTHI8cDzAqlWrmJmZWXSo7du379J8y2Xj2h2331+1z50fL8UkX/O0r9NBfcnal5zQn6x9yblYSxnM+pVJXgp8GthYVTfSbYzPH6gzuKG+kz1hgzzMxrU7FrVxHvb6FrthX+o6mi/zYpa73Jlnn3PUdT1N76M+vq+hn7n7mFmSpD1VkmcD11fVhUnWzxYPqVojTLujoOok4CSAdevW1fr16+dWWdDMzAy7Mt9yGby4y8a1O3jrpeO5ptGWY9aPZTnDTPs6HdSXrH3JCf3J2peci7WrW4gTgd+h29D+DvBW4GcZcWMMe8YGeZgN7Upco26ch218F3sVr6VuwOfLvJjlLnfm2eccdV1PspFbrD6+r6GfufuYWZKkPdiTgee08VHvBdyP7gij/ZLs3Y4qOgi4ttXfChwMbE2yN3B/4Ibljy1J/bJLVz2rquuq6raq+i7wZ3Snl8EdG+NZgxtqSZIkSdolVfW6qjqoqtbQjYv6sao6Bvg48LxW7Vjgw+3+We0xbfrHHJ9Ikha2Sx1FSQ4cePhTwOwV0c4Cjm6XojwEOBT41NIiSpIkSdK8Xks3sPVmujGITm7lJwMPbOWvATatUD5J6pUFz8lJ8n5gPXBAkq3AG4H1SR5Hd1rZFuDnAarq8iRnAJ8FdgCv8IpnkiRJksapqmaAmXb/Su44w2GwzreA5y9rMEnaDYxy1bMXDik+eUjZbP03012uUtIYrJlnfKctJzxrmZNIkiRJknZ34xnuXr1j54MkSZKkpXCfQto92VE05ebb+E6D5W4YbIgkSZIkSZqsXRrMWpIkSZIkSbsfjyiS5uERTJIkSZKkPY0dRZKkZZNkL+DTwDVV9ewkhwCnAfsDFwEvqapvJ7kncCrwROBrwAuqassKxZYkSYvgD65Sv3nqmSRpOf0icMXA47cAb6uqQ4EbgeNa+XHAjVX1COBtrZ4kSZKkCfOIIknSskhyEPAs4M3Aa5IEeBrwolblFOBNwInAUe0+wJnAO5Okqmo5M0uS1CfTfCEcSf1hR5Ekabn8EfBrwH3b4wcCN1XVjvZ4K7C63V8NXA1QVTuS3Nzqf3VwgUmOB44HWLVqFTMzM0OfePv27fNOG4eNa3csXGkEq/a567ImmXtXTHpdjos5x8uc49OHjJKkPZsdRdIebNivTp47rklI8mzg+qq6MMn62eIhVWuEaXcUVJ0EnASwbt26Wr9+/dwqQNfZMt+0cdgwpl9wN67dwVsvvXPTvOWY9WNZ9rhMel2OiznHy5zj04eMkqQ9mx1FkqTl8GTgOUmOBO4F3I/uCKP9kuzdjio6CLi21d8KHAxsTbI3cH/ghuWPLUmSJO1ZHMxakjRxVfW6qjqoqtYARwMfq6pjgI8Dz2vVjgU+3O6f1R7Tpn/M8YkkSZKkyfOIIknSSnotcFqS3wX+Azi5lZ8MvDfJZrojiY5eoXySJGmC5huA+z3P2HeZk0iaZUfRHmAxVz/wSgmSJq2qZoCZdv9K4LAhdb4FPH9Zgxq3MHcAACAASURBVI3I7aQkSZJ2Zwt2FCV5NzA7COljWtn+wOnAGmAL8DNVdWO71PHbgSOBW4ANVXXRZKJPj/l2GhwUeM/iwNCSJEmSpL4b5Yii9wDvBE4dKNsEnFdVJyTZ1B6/FngmcGi7PQk4sf2Vptq0HCGwZtPZbFy7Y2xXUJIkSZIkaTEWHMy6qj7BXa80cxRwSrt/CvDcgfJTq3M+3dVsDhxXWEmSJEmSJE3Oro5RtKqqtgFU1bYkD27lq4GrB+ptbWXb5i4gyfHA8QCrVq1iZmZm0SG2b9++S/ON28a1O4aWD8u2ce0OVu0z/zzTajGZ53vdi7GYZcz3HpjUut7Z841adz6LyTyO9/44Ms+als/jYvQxsyRJkiRN0rgHs86QsqGXM66qk4CTANatW1fr169f9JPNzMywK/ON23ynCW05Zv3QuhvX7uCtl/ZrHPHFZJ7vdS/Kpd8cUjj8+Yc93+xzTmJd7+z5Rq07n8VkXuyy53u+cS13Wj6Pi9HHzJIkSZI0Sbu6B31dkgPb0UQHAte38q3AwQP1DgKuXUrAPpuWcW8kSZIkSZJGsasdRWcBxwIntL8fHih/ZZLT6Aaxvnn2FDVpUuyQkyRJkiRpPBbsKEryfmA9cECSrcAb6TqIzkhyHHAV8PxW/RzgSGAzcAvwsglkliRJkiRJ0gQs2FFUVS+cZ9IRQ+oW8IqlhpKmmUcwSZIkSZJ2V/0aUVm9YEeKJEmSJEn9ZEeRtAew806SJPVdkoOBU4HvAb4LnFRVb0+yP3A6sAbYAvxMVd2YJMDb6YbGuAXYUFUXrUR2SeqTu610AEmSJEkawQ5gY1V9P3A48IokjwI2AedV1aHAee0xwDOBQ9vteODE5Y8sSf1jR5EkSZKkqVdV22aPCKqqbwBXAKuBo4BTWrVTgOe2+0cBp1bnfGC/JAcuc2xJ6h1PPVsET9+RJEmSVl6SNcDjgQuAVVW1DbrOpCQPbtVWA1cPzLa1lW2bs6zj6Y44YtWqVczMzCw6z/bt23dpvnHbuHbHgnVW7TNavUkYto7myzIt63QUfcnal5zQn6x9yblYdhRJkjTFhv1IseWEZ61AEkmaDknuA/w18EtV9fVuKKLhVYeU1V0Kqk4CTgJYt25drV+/ftGZZmZm2JX5xm3DCD9sb1y7g7deujK7gVuOWX+Xsvkyv+cZ+07FOh3FtPz/F9KXnNCfrH3JuVieeiZJkiSpF5Lcna6T6H1V9cFWfN3sKWXt7/WtfCtw8MDsBwHXLldWSeorO4okSZIkTb12FbOTgSuq6g8HJp0FHNvuHwt8eKD8pekcDtw8e4qaJGl+nnom7WYcS0vTyEsaS5LG4MnAS4BLk1zcyl4PnACckeQ44Crg+W3aOXTtyGa6tuRlyxtXkvrJjiJJ0nKYvaTxRUnuC1yY5FxgA90ljU9Isonuksav5c6XNH4S3SWNn7QiySVJU6GqPsnwcYcAjhhSv4BXTDSUJO2G7CiSJmi+o3vGMRCtRw6pT9qh/rNXpPlGksFLGq9v1U4BZug6im6/pDFwfpL9khzoKQOSJEnSZNlRJElaVitxSeNxXrp0kpcUHvWSxSt5Gda+XAbWnONlzvHpQ0ZpUvyhU+oHO4okSctmpS5pPM5Ll45y6eFdNeoli4ddXni59OUysOYcL3OOTx8ySpL2bEvqKEqyBfgGcBuwo6rWzTcw6dJiSruXaf41ZZKny2nPtrNLGrejibyksSRJkrTC7jaGZfxYVT2uqta1x5voBiY9FDivPZYk7cG8pLEkSZLUD5M49Wy+gUklSXsuL2ksSZIk9cBSO4oK+GiSAv60jRUx38CkdzLqAKQ7s9yDAY5rANNRByudJn3MDP3MPa2ZF/qs9XFwzj5m7isvaSxJkiT1w1I7ip5cVde2zqBzk3xu1BlHHYB0Z5Z7MMBxDWA66mCl06SPmaGfuac180KD5/ZxcM4+ZpYkSZrm8S4l9d+Sxiiqqmvb3+uBDwGH0QYmBZgzMKkkSZIkSZKm2C53FCXZN8l9Z+8DPwFcxvwDk0qSJEmSJGmKLeX8llXAh7oL2bA38FdV9fdJ/p3hA5NK6rH5DnHecsKzljmJJEmSJGlSdrmjqKquBB47pPxrDBmYVJIkSZKkUVx6zc13GSPWHyil5TF9I+ZK6pXZI402rt1xe2NuIy5JkiRJ/WRHkaSx8zQ1SZIkSeonO4rm4SUnJUnTys5YSdpzuF8iabnt8lXPJEmSJEmStHuxo0iSJEmSJEmAp55JkjSUh/pLkiRpT+QRRZIkSZIkSQI8okjSFBh25IaD8kqSpD2JR7IuzIs5SMvDI4okSZIkSZIEeESRpJ7xlyRJkiQNWu7vh3Ofb+PaHWzYdLbfR7XbsKNI0lTy8GtJktQXl15zMxs8lX7qOLyBtGvsKJIkaTfhEXeS1A/+ICZpmtlRJEmSJEkTYIfQ9Fnsjyr+D7UnmlhHUZJnAG8H9gL+vKpOmNRzSeqHaWloZ3PMnk8OHnExrWxLxmMcnz0/I5L6yrZEo5iW76nSNJhIR1GSvYB3Af8T2Ar8e5Kzquqzk3i+pXCDIO2+/Hz323K1Jb5PlkdfT4tzfAup3/q0XyJJ02JSRxQdBmyuqisBkpwGHAWMdYPsoHGSZi11Z7+vO7G7uWVpSzSaYUfijXO54zbunIPGmXmUnJPcDrnt0x7AtkTLZjHtw2K2s+PYVq/ZdPbQNsftvYZJVY1/ocnzgGdU1c+1xy8BnlRVrxyoczxwfHv4SODzu/BUBwBfXWLcldDH3H3MDP3M3cfM0M/ck8z8sKp60ISWvUcYc1vSl/dnH3L2ISOYc9zMOT6LyWhbskTulwzVl6x9yQn9ydqXnNCfrH3Iuei2ZFJHFGVI2Z16pKrqJOCkJT1J8umqWreUZayEPubuY2boZ+4+ZoZ+5u5j5j3M2NqSvvyv+5CzDxnBnONmzvHpQ8bdjPslc/Qla19yQn+y9iUn9CdrX3Iu1t0mtNytwMEDjw8Crp3Qc0mSdk+2JZKkpbItkaRFmlRH0b8DhyY5JMk9gKOBsyb0XJKk3ZNtiSRpqWxLJGmRJnLqWVXtSPJK4B/oLkP57qq6fAJPtaRDRFdQH3P3MTP0M3cfM0M/c/cx8x5jzG1JX/7XfcjZh4xgznEz5/j0IeNuw/2SofqStS85oT9Z+5IT+pO1LzkXZSKDWUuSJEmSJKl/JnXqmSRJkiRJknrGjiJJkiRJkiQBPe4oSvLLSS5PclmS9ye510pnWkiSX2x5L0/ySyudZz5J3p3k+iSXDZTtn+TcJF9ofx+wkhnnmifz89u6/m6Sqbxk4Ty5/0+SzyW5JMmHkuy3khnnmifz77S8Fyf5aJKHrGTGYYblHpj2K0kqyQErkU27Lskzknw+yeYkm4ZMv2eS09v0C5KsGZj2ulb++SRPn8acSdYk+e/22bo4yZ+scM6nJrkoyY4kz5sz7djWRnwhybFTnPO2gfU50QFtR8j5miSfbdvP85I8bGDasqzPJWacpnX58iSXtiyfTPKogWnT9FkfmnO5P+tanN2orZmKz/OIWTck+cpApp8bmDZN7c3bBjL+Z5KbBqYt5zZy3u+5bXqSvKO9jkuSPGFg2nKuz4VyHtPyXZLkX5M8dmDaloHt56cnmXPErOuT3DzwP/7NgWk7fd/0QlX17gasBr4I7NMenwFsWOlcC2R+DHAZcG+6QcT/ETh0pXPNk/WpwBOAywbKfh/Y1O5vAt6y0jlHyPz9wCOBGWDdSmdcRO6fAPZu99/Sk3V9v4H7rwb+ZKVzjpK7lR9MN8Dll4ADVjqnt0X9T/cC/gt4OHAP4DPAo+bU+d+z70e6K92c3u4/qtW/J3BIW85eU5hzzdz37AqvzzXADwCnAs8bKN8fuLL9fUC7/4Bpy9mmbZ+i9fljwL3b/V8Y+L8vy/pcSsYpXJeD7dBzgL9v96ftsz5fzmX7rHubyP+1L23Nin+eF5F1A/DOIfNOVXszp/6r6AZLX4l1OvR77sD0I4G/AwIcDlyw3OtzxJw/PPv8wDNnc7bHW1jG7+ojZF0PfGSp75tpvfX2iCK6zpZ9kuxN1/ly7QrnWcj3A+dX1S1VtQP4J+CnVjjTUFX1CeCGOcVHAae0+6cAz13WUAsYlrmqrqiqz69QpJHMk/uj7T0CcD5w0LIH24l5Mn994OG+wNSNkj/P+xrgbcCvMYWZtaDDgM1VdWVVfRs4jW5bNWhw23UmcESStPLTqurWqvoisLktb9pyLqcFc1bVlqq6BPjunHmfDpxbVTdU1Y3AucAzpjDnchol58er6pb2cHB7v1zrcykZl9MoOedrh6bqs96H9lJ3sdu0NVPyeYbR1ul8pqq9meOFwPsnlGWndvI9d9ZRwKnVOR/YL8mBLO/6XDBnVf1rywErvB80wjqdz1Le31Ojlx1FVXUN8AfAVcA24Oaq+ujKplrQZcBTkzwwyb3penUPXuFMi7GqqrYBtL8PXuE8e4qfpev9n3pJ3pzkauAY4DcXqj8NkjwHuKaqPrPSWbRLVgNXDzze2sqG1mkdsDcDDxxx3mnICXBIkv9I8k9JfmRCGUfNOYl5F2upz3WvJJ9Ocn6SSf7osdicx3HH9n651udSMsKUrcskr0jyX3RHQb96MfNOQU5Yvs+6Fmd3amsGrdTnGUbP+r/aKUhnJpndb5rKddpO4zsE+NhA8XKu04XM91qWc30u1tz3aAEfTXJhkuNXKNNcP5TkM0n+LsmjW9k0r9OR7b3SAXZFuvFxjqL7MN4EfCDJi6vqL1c22fyq6ookb6Hrpd1Odwjajp3PpT1ZkjfQvUfet9JZRlFVbwDekOR1wCuBN65wpJ1qHbZvoDvVT/007Iibub/Oz1dnlHnHZSk5twEPraqvJXki8DdJHj3nqIRxWco6mbb1uTMPraprkzwc+FiSS6vqv8aUbdDIOZO8GFgH/Ohi512ipWSEKVuXVfUu4F1JXgT8OnDsqPOOyVJyLudnXYuzO7U1XcWV/TzDaFn/Fnh/Vd2a5OV0R2w9bcR5x2Uxz3U0cGZV3TZQtpzrdCHT8B4dWZIfo+soespA8ZPb+nwwcG6Sz7WjflbKRcDDqmp7kiOBvwEOZUrX6WL18ogi4MeBL1bVV6rqO8AH6c5nnGpVdXJVPaGqnkp3GNsXVjrTIlzXDk+k/b1+hfPs1tpAcs8Gjqmqvm1Y/gr4XysdYgT/g66z+TNJttAd2npRku9Z0VRajK3c+cjMg7jraci312mnKt+fbvs7yrwrnrOdrvA1gKq6kO6c9+9dwZyTmHexlvRcVXVt+3sl3Rh2jx9nuAEj5Uzy43Sd1s+pqlsXM+8KZ5y6dTngNO44RX6a35u351zmz7oWZ3dqa6bh8zxS1qr62kC+PwOeOOq8y5lzwNHMOe1smdfpQuZ7Lcu5PkeS5AeAPweOmt0uwp3W5/XAh5jcaZwjqaqvV9X2dv8c4O7pLowzdet0V/S1o+gq4PAk927n/x4BXLHCmRbUej9J8lDgp1mhc1h30Vl0v3jR/n54BbPs1pI8A3gtXQN+y0L1p0GSQwcePgf43EplGVVVXVpVD66qNVW1hm6j/oSq+vIKR9Po/h04NMkhSe5B9yVt7lVFBrddzwM+1jpfzwKOTnelmkPofgH61LTlTPKgJHsBtF8kD6UbaHKlcs7nH4CfSPKAdtTvT7SyqcrZ8t2z3T8AeDLw2ZXKmeTxwJ/Sbe8Hf4BZrvW5yxmncF0OtkPP4o4f46bqsz5fzmX+rGtxdpu2Zko+z6NmPXDg4XO4Y19v6tqbJI+kGwj63wbKlnudLuQs4KXpHE43dMs2lnd9LqjtJ38QeElV/edA+b5J7jt7ny7n0KuRLZck39P6IkhyGF3fytdY2vep6VFTMKL2rtyA36LbGb0MeC9wz5XONELmf6bbQHwGOGKl8+wk5/vpDoH+Dt3O83F051mfR/eF5jxg/5XOOULmn2r3bwWuA/5hpXOOmHsz3XmtF7fbVF1BbJ7Mf90+i5fQHSq8eqVzjpJ7zvQteNWz3t3oxnv7T7pf39/Qyn6b7kswwL2AD7TP1aeAhw/M+4Y23+eBZ05jTrqj8y5v7cZFwE+ucM4fbJ+fb9J9Gbp8YN6fbfk3Ay+bxpx0Rx9f2tbnpXO3AyuQ8x9b+zS7vT9rudfnrmacwnX59vZZuRj4OPDogXmn6bM+NOdyf9a9jf3/2pe2Zio+zyNm/f8HPhMfB75vYN6paW/a4zcBJ8yZb7m3kcO+n78ceHmbHuBd7XVcysAVoZd5fS6U88+BGwfeo59u5Q9v6/Iz7X3xhmV4jy6U9ZUD79HzgR/e2fumb7e0FyJJkiRJkqQ9XF9PPZMkSZIkSdKY2VEkSZIkSZIkwI4iSZIkSZIkNXYUSZIkSZIkCbCjSJIkSZIkSY0dRZIkSZIkSQLsKJIkSZIkSVJjR5EkSZIkSZIAO4okSZIkSZLU2FEkSZIkSZIkwI4iSZIkSZIkNXYUSZIkSZIkCbCjSJIkSZIkSY0dRZIkSZIkSQLsKJIkSZIkSVJjR5EkSZIkSZIAO4okSZIkSZLU2FEkSZIkSZIkwI4i9UyS9yT53Qku//Ik6ye1fEnS4iV5ZJL/SPKNJK9O8idJfmMCz/OmJH855mX+SJLP72T67e3aQnUlaU80tw1Y6TzzSVJJHtHu75Pkb5PcnOQDE3q+sbcfSV6f5M93Mn1Lkh9f6vNo+u290gGkaVJVj569n+RNwCOq6sUrl0iSBPwaMFNVj1/pIItVVf8MPHJX6ibZAvxcVf3jZNJJUi/0sQ14HrAKeGBV7Zj0ky2mrVlgOb83hjjaDXhEkSRJmnYPAy5f6RCSpBWx0zYgyV7LmGVUDwP+c1c6idJxP10ryjegplqSxye5qB1qejpwr4Fpz05ycZKbkvxrkh8YmLYlya8kuaQd8nl6knu1aQck+Uib74Yk/zy7MZ49nDLJM4DXAy9Isj3JZ5I8P8mFc/JtTPI3y7IyJGkPlORjwI8B72zb4++dc7j9a5Ocn2Tv9vgX2mnEs9v8w1sbcVPblq8fWPYhSf6ptTHnAgfsJMcDWtvxlSQ3tvsHDUzfP8lfJLm2Tf+bVr4+ydaBejtr126vm+S9wEOBv22v+9eSnJ3kVXNyXZLkubu8giVpiu2kDTgxyTlJvgn8WJJ7JvmDJFclua6dorzPwHLm3W+Y83xJ8rYk17d9iEuSPKZNm0nycwN1NyT55JBl/Bbwm9yxH3Fc5pzanGRNulPV9h5Y9puT/AtwC/DwIcsdqf1oj7+/LfOm1iY+p5Xfo62HV7XHeyX5lyS/2R7PzfmSJF9K8rUkb5iT525JNiX5rzb9jCT7D1uv6h87ijS1ktwD+BvgvcD+wAeA/9WmPQF4N/DzwAOBPwXOSnLPgUX8DPAM4BDgB4ANrXwjsBV4EN0hoa8HavC5q+rvgd8DTq+q+1TVY4GzgEOSfP9A1Re3fJKkCaiqpwH/DLyybY//c06V/wN8G/j1JIfSbbtfXFXfSrIaOBv4Xbp25FeAv07yoDbvXwEX0nUQ/Q5w7E6i3A34C7pfiR8K/DfwzoHp7wXuDTwaeDDwtrkL2Fm7NuR1vwS4CvjJ9rp/HziFrt2ZXd5jgdXAOTvJLUm9tZM24EXAm4H7Ap8E3gJ8L/A44BF028bZzo9R9htm/QTw1Las/YAXAF9bZOY3cuf9iJNHnPUlwPHtNX1pcMJi2o8kdwf+FvgoXXv0KuB9SR5ZVd+ma0d+u+3TbAL2oluXc5fzKODElushdOvuoIEqrwaeC/xom34j8K4RX6umnB1FmmaHA3cH/qiqvlNVZwL/3qb9f8CfVtUFVXVbVZ0C3NrmmfWOqrq2qm6g21g+rpV/BzgQeFhb7j9X1Z06ioapqluB02lf0pM8GlgDfGSpL1SStGuq6rvAS+m+sJ4F/H5V/Ueb/GLgnKo6p6q+W1XnAp8GjkzyUOAHgd+oqlur6hN0bcV8z/O1qvrrqrqlqr5B96X6RwGSHAg8E3h5Vd3Y2pZ/GrKYnbVro/gwcGjrEIPuy/vp7Yu/JO1JPlxV/9LagFvp9g1+uapuaNvo3wOObnVH2W+Y9R26jprvA1JVV1TVtom/ms57quryqtpRVd+ZM20x7cfhwH2AE6rq21X1Mbr9lRcCVNVldD+gfIjuB5SXVNVtQ5bzPOAjVfWJth/0G8B3B6b/PPCGqtrapr8JeN7sUVLqNzuKNM0eAlwzpxNntnf9YcDGdjjlTUluAg5u88z68sD9W+g2mND9+rwZ+GiSK5NsWkSmU4AXJQndF/Qz2oZRkrRCqmoL8HG6zvvBXzMfBjx/TlvxFLofCx4C3FhV3xyof6dfcAcluXeSP22H4H8d+ASwX7qxMQ4GbqiqGxeIurN2bUGtvTkDeHG6U6ZfiEe1StozXT1w/0F0R3ReOLCt//tWDqPtNwDQOlXeSdeWXJfkpCT3m+QLGXD1TqYtpv14CHB160QbrLt64PEpdG3mOVX1hZ0tZ/ZBay8Hj656GPChgXV6BXAb3Rkb6jk7ijTNtgGrW6fMrIe2v1cDb66q/QZu966q9y+00Kr6RlVtrKqHAz8JvCbJEcOqDpn3fLpTHH6E7pBXv6BL0gpLciTwQ8B5dD8GzLoaeO+ctmLfqjqBro15QJJ9B+o/lPltpLuizJOq6n50pyYApD3P/kn2WyDqztq1YYYd7XoKcAxwBHBLVf3bAs8pSbujwe3jV+lOB370wLb+/lU1+yPxovYbquodVfVEulOJvxf41Tbpm3QdUrO+ZxF5R5l3Z2c4LKb9uBY4OHceEPuhwDUDj/+Y7iijpyd5yk6e8+DZB0nuTXf62ayrgWfOWa/3qqpr5i5I/WNHkabZvwE7gFcn2TvJTwOHtWl/Brw8yZPaoHP7JnlWkvsutNB0g9k9om1ov07X8z3scMvrgDW561UHTqX7pWFHVd1lADtJ0vJJcgBwMvBzdGMM/WTrOAL4y/b46W3Aznu1AT8Pqqov0Z2G9lttcM+n0P14MJ/70u2I3NQG63zj7IR2WsLfAX+cbtDruyd56pBl7KxdG+Y65gxo2jqGvgu8FX+skKTZU5D/DHhbkgcDJFmd5Omtysj7DUl+sNW7O13nzre4Yz/hYuCn2xGmjwCOW0TMi4GnJnlokvsDr1vky1xM+3FBy/5rrT1aT9e+nQbdANXAE+nGb301cEqS+wxZzpnAs5M8pY2R9Nvcuf/gT4A3J3lYW+6Dkhy1yNelKWVHkaZWG3Php+k2YjfSDSb3wTbt03TnG7+zTdvMHYNVL+RQ4B+B7XQb3T+uqpkh9T7Q/v4/9u4+zLKyvPP99xcQXwiKgFaQJtMkEhMTIiEdJOOcTEWi8uLYzkQTlCg45OpJRMeMZEKTnJOYTDKDZ8ZRHHNIOmJsHBUIEwaOGiNBa5ycIyqggoiGFltpaSHKizac6LTe54/1lG6Kqq5dXbVf6/u5rrpqrWc9e+/7rr1rr72f9bx8LclNPeXvAH4CP6BL0jjYRjdXxfuq6mt0H9zfmuTwqroT2Ey3aMHf0139/Ld87/PPS4FnAvfSNfxcuo/HeRPwWLor19fTDWvo9TK6uS0+C9wD/MbCO9jXeW0J/4Fuku77k/xmT/mlwHF0DWGSJDif7vvA9W148N/Q9QJd6feGx9M1LN1HN1zra8B/asfeSDey4G663p3v7De4Nkfe5cDNdIsorGiO05WcP1rdF9DNnfdVut5DL6+qz7b5+d7U9vdU1bvoLpo8YgGGqroVOJdu4Yfd7XF39VS5iG5uwA8k+QbdufGZK8lL4yt9zOErqUe6pTbvAU7Yx5heSZIGIsnLgS1VtdRwAUmSpP1mjyJp5X4d+LiNRJKkYWtzRLySrieVJEnSmnPpOmkFkuykm7j0hSMORZK0zrT5Nv6SbkjFu0YcjiRJmlIOPZMkSZIkSRLg0DNJkiRJkiQ1YzH07IgjjqiNGzcO/XEffPBBDj744KE/7qBNa14wvbmZ1+RZLrcbb7zxq1X1pCGGtO6t9blkWl+/5jVZzGvyrGVunkuGb6Xnkml4LU9DDjAdeZjDeJi2HPbnXDIWDUUbN27khhtuGPrjzs3NMTs7O/THHbRpzQumNzfzmjzL5Zbki8OLRrD255Jpff2a12Qxr8mzlrl5Lhm+lZ5LpuG1PA05wHTkYQ7jYdpy2J9ziUPPJEmSJEmSBNhQJEkagiRPS/LJnp+vJ/mNJIcluTbJ7e33E1v9JHlzkh1Jbk5ywqhzkCRJktYDG4okSQNXVZ+rquOr6njgp4GHgKuArcB1VXUscF3bBzgVOLb9bAEuHn7UkiRJ0vpjQ5EkadhOBj5fVV8ENgPbW/l24IVtezNwaXWuBw5NcuTwQ5UkSZLWl7GYzFqStK6cAby7bc9U1W6Aqtqd5Mmt/Cjgzp7b7Gplu3vvKMkWuh5HzMzMMDc3t2ZB7tmzZ03vb1yY12Qxr8kzzblJktYHG4okSUOT5CDgBcAFy1VdpKweUVC1DdgGsGnTplrLFSqmYcWLxZjXZDGvyTPNuUmS1gcbilZg49b3Llq+88LThxyJJE2sU4Gbqurutn93kiNbb6IjgXta+S7g6J7bbQDuGmKc65bnOkkaf75XSxok5yiSJA3TS/jesDOAa4Cz2vZZwNU95S9vq5+dBDwwP0RNkiRJ0uDYo0iSNBRJHgc8B/hXPcUXAlckOQf4EvDiVv4+4DRgB90Kaa8YYqiSJEnSumVD0RKW6s4pSdo/VfUQcPiCsq/RrYK2sG4B5w4pNEmSJEmNQ88kSZIkSZIE2KNIkiRJ0gRI8jTg8p6iHwJ+F7i0lW8EdgK/VFX3JQlwEd1Q5oeAs6vqpmHGvBYc6SBp2OxRJEmSJGnsVdXnqur4qjoe+Gm6xp+rgK3AdVV1LHBd24dupc1j288W4OLhRy1Jk6evhqIkhya5Mslnk9yW5GeTHJbk2iS3t99PbHWT5M1JdiS5PRwCdgAAIABJREFUOckJg01BkiRJ0jpzMvD5qvoisBnY3sq3Ay9s25uBS6tzPXBokiOHH6okTZZ+h55dBLy/ql6U5CDgccBv07XcX5hkK13L/fk8vOX+mXQt989c88glSZIkrVdnAO9u2zNVtRugqnYneXIrPwq4s+c2u1rZ7t47SrKFrscRMzMzzM3N9R3Enj17VlR/f5x33N6+6+5PLMPIYRimIQ9zGA/m0EdDUZLHAz8HnA1QVd8CvpVkMzDbqm0H5ugair7bcg9c33ojHTn/5i1JkiRJ+6tduH4BcMFyVRcpq0cUVG0DtgFs2rSpZmdn+45lbm6OldTfH2evYI6inWfOrvj+h5HDMExDHuYwHsyhvx5FPwT8PfDnSZ4B3Ai8hhG23K+VfbWyDbrlfpCmoQV0KdOam3lNnmnOTZKkMXcqcFNV3d32756/MN2Glt3TyncBR/fcbgNw1xDjlKSJ1E9D0YHACcCrq+qjSS7iexPELWbgLfdrZV+tbINuuR+kaWgBXcq05mZek2eac5Mkacy9hO8NOwO4BjgLuLD9vrqn/FVJLqObCuMBRzlI0vL6mcx6F7Crqj7a9q+kazi6e34yOFvuJUmSJA1akscBzwH+sqf4QuA5SW5vxy5s5e8D7gB2AH8GvHKIoUrSxFq2R1FVfSXJnUmeVlWfo1th4DPtx5Z7SZIkSUNRVQ8Bhy8o+xrdd5SFdQs4d0ihSdLU6HfVs1cD72wTx90BvIKuN9IVSc4BvgS8uNV9H3AaXcv9Q62uJEmSJEmSxlxfDUVV9Ulg0yKHbLmXJEmSJEmaEv3MUSRJkiRJkqR1wIYiSZIkSZIkATYUSZKGJMmhSa5M8tkktyX52SSHJbk2ye3t9xNb3SR5c5IdSW5OcsKo45ckSZLWAxuKJEnDchHw/qr6UeAZwG3AVuC6qjoWuK7tA5wKHNt+tgAXDz9cSZIkaf2xoUiSNHBJHg/8HHAJQFV9q6ruBzYD21u17cAL2/Zm4NLqXA8cmuTIIYctSZIkrTt9rXqm/bNx63sfUbbzwtNHEIkkjdwPAX8P/HmSZwA3Aq8BZqpqN0BV7U7y5Fb/KODOntvvamW7e+80yRa6HkfMzMwwNze3ZgHv2bNnTe9vXCyX13nH7V20fNz/Fuv1+ZpU05oXTHdukqT1wYYiSdIwHAicALy6qj6a5CK+N8xsMVmkrB5RULUN2AawadOmmp2dXYNQO3Nzc6zl/Y2L5fI6e5GLHAA7z1z6NuNgvT5fk2pa84Lpzk2StD7YULQGFus5JEl6mF3Arqr6aNu/kq6h6O4kR7beREcC9/TUP7rn9huAu4YWrSRJkrROOUeRJGngquorwJ1JntaKTgY+A1wDnNXKzgKubtvXAC9vq5+dBDwwP0RNkiRJ0uDYo0iSNCyvBt6Z5CDgDuAVdBcsrkhyDvAl4MWt7vuA04AdwEOtriRJkqQBs6FIkjQUVfVJYNMih05epG4B5w48KEnSRElyKPBW4Cfo5q77l8DngMuBjcBO4Jeq6r4kAS6iu/DwEHB2Vd00grAlaaI49EySJEnSpLgIeH9V/SjwDOA2ujnvrquqY4Hr+N5iCacCx7afLcDFww9XkiaPDUWSJEmSxl6SxwM/B1wCUFXfqqr7gc3A9lZtO/DCtr0ZuLQ61wOHtoUTJEn74NAzSZIkSZPgh4C/B/48yTOAG4HXADPzCx60VTSf3OofBdzZc/tdrexhiyMk2ULX44iZmRnm5ub6DmjPnj0rqr8/zjtub991/8s7r160/LijnrDkbYaRwzBMQx7mMB7MwYYiSZIkSZPhQOAE4NVV9dEkF/G9YWaLySJl9YiCqm3ANoBNmzbV7Oxs3wHNzc2xkvr74+yt7131few8c3bJY8PIYRimIQ9zGA/m0OfQsyQ7k9yS5JNJbmhlhyW5Nsnt7fcTW3mSvDnJjiQ3Jzlhv6OTJEmSpM4uYFdVfbTtX0nXcHT3/JCy9vuenvpH99x+A3DXkGKVpIm1kjmKfr6qjq+q+RVrnDROkiRJ0lBU1VeAO5M8rRWdDHwGuAY4q5WdBcyPv7oGeHm7kH0S8MD8EDVJ0tJWM/RsMzDbtrcDc8D59EwaB1yf5NAkR/qmLEnSdNm4xHCInReePuRIJK0jrwbemeQg4A7gFXQXv69Icg7wJeDFre77gNOAHcBDra4kaRn9NhQV8IEkBfxpG8e7qknjJEmSJGklquqTwKZFDp28SN0Czh14UJI0ZfptKHpWVd3VGoOuTfLZfdTta9K41awusFb2NRP4SlYXWIlh5DkNs7QvZVpzM6/JM825SZIkSVq/+mooqqq72u97klwFnEibNK71JlrxpHGrWV1grczNzXH2+x9c4uhgFoTb14oDa2UaZmlfyrTmZl6TZ5pzkyRJkrR+LTuZdZKDkxwyvw08F/g0ThonSZIkSZI0VfrpNjMDXJVkvv67qur9ST6Ok8ZJkrQuLDVxtSRJkqbLsg1FVXUH8IxFyr+Gk8ZJkiRJkiRNjWWHnkmSJEmSJGl9sKFIkjQUSXYmuSXJJ5Pc0MoOS3Jtktvb7ye28iR5c5IdSW5OcsJoo5ckSZLWBxuKJEnD9PNVdXxVbWr7W4HrqupY4Lq2D3AqcGz72QJcPPRIJUmSpHVoMGvAa0lLTQa688LThxyJJI2FzcBs294OzAHnt/JL27x31yc5NMmRrqIpSZIkDZYNRZKkYSngA0kK+NOq2gbMzDf+VNXuJE9udY8C7uy57a5W9rCGoiRb6HocMTMzw9zc3JoFu2fPnjW9v3GxXF7nHbd31Y8xir/ben2+JtW05gXTnZskaX2woUiSNCzPqqq7WmPQtUk+u4+6WaSsHlHQNTZtA9i0aVPNzs6uSaDQNXas5f2Ni+XyOnuJnq8rsfPMpe9/UNbr8zWppjUvmO7cNFhLjTyQpGFzjiJJ0lBU1V3t9z3AVcCJwN1JjgRov+9p1XcBR/fcfANw1/CilSRJktYnG4okSQOX5OAkh8xvA88FPg1cA5zVqp0FXN22rwFe3lY/Owl4wPmJJEmSpMFz6JkkaRhmgKuSQHfueVdVvT/Jx4ErkpwDfAl4cav/PuA0YAfwEPCK4YcsSRo3SXYC3wC+Deytqk1JDgMuBzYCO4Ffqqr70p10LqI7nzwEnF1VN40ibkmaJDYUSZIGrqruAJ6xSPnXgJMXKS/g3CGEJkmaPD9fVV/t2d8KXFdVFybZ2vbPB04Fjm0/zwQubr8lSfvg0DNJkiRJk2wzsL1tbwde2FN+aXWuBw6dnxdPkrQ0exRJkiRJmhQFfCBJAX/aVr+cmZ/Hrqp2t9U1AY4C7uy57a5W9rA575JsAbYAzMzMMDc313cwe/bsWVH9fTnvuL1rcj+L2VeMa5nDKE1DHuYwHszBhiJJkiRJk+NZVXVXawy6Nsln91E3i5TVIwq6xqZtAJs2barZ2dm+g5mbm2Ml9ffl7K3vXZP7WczOM2eXPLaWOYzSNORhDuPBHBx6JkmSJGlCVNVd7fc9wFXAicDd80PK2u97WvVdwNE9N98A3DW8aCVpMtlQJEmSJGnsJTk4ySHz28BzgU8D1wBntWpnAVe37WuAl6dzEvDA/BA1SdLSHHomSZIkaRLMAFd1q95zIPCuqnp/ko8DVyQ5B/gS8OJW/33AacAO4CHgFcMPWZImT98NRUkOAG4AvlxVz09yDHAZcBhwE/CyqvpWkkcDlwI/DXwN+OWq2rnmkUuSJElaN6rqDuAZi5R/DTh5kfICzh1CaJI0VVYy9Ow1wG09+68H3lhVxwL3Aee08nOA+6rqqcAbWz1JkiRJkiSNub4aipJsAE4H3tr2AzwbuLJV2Q68sG1vbvu04ye3+pIkSZIkSRpj/Q49exPwW8Ahbf9w4P6q2tv2dwFHte2jgDsBqmpvkgda/a/23mGSLcAWgJmZGebm5vYzhf23Z88ezjvu20N/3MWsZf579uwZyd9zGKY1N/OaPNOcm7RaGxdZ4nnnhaePIBJJkiSt1LINRUmeD9xTVTcmmZ0vXqRq9XHsewVV24BtAJs2barZ2dmFVQZubm6ON/ztg0N/3MXsPHN20fL9+bA9NzfHKP6ewzCtuZnX5Jnm3CRJ0mAt9hlfksZFPz2KngW8IMlpwGOAx9P1MDo0yYGtV9EG4K5WfxdwNLAryYHAE4B71zxySZIkSZIkrall5yiqqguqakNVbQTOAD5YVWcCHwJe1KqdBVzdtq9p+7TjH2wrDkiSJEmSJGmMrWTVs4XOB16bZAfdHESXtPJLgMNb+WuBrasLUZIkSZIkScPQ72TWAFTVHDDXtu8ATlykzj8AL16D2CRJUybJAcANwJer6vlJjgEuAw4DbgJeVlXfSvJo4FLgp4GvAb9cVTtHFLYkSZK0bqymR5EkSSv1GuC2nv3XA2+sqmOB+4BzWvk5wH1V9VTgja2eJEmSpAGzoUiSNBRJNgCnA29t+wGeDVzZqmwHXti2N7d92vGTW31JkiRJA7SioWeSJK3Cm4DfAg5p+4cD97fVM6FbNfOotn0UcCdAVe1N8kCr/9XeO0yyBdgCMDMzw9zc3JoFu2fPnjW9v3GxXF7nHbd3yWOrMei/5Xp9vibVtOYF052bJGl9sKFIkjRwSZ4P3FNVNyaZnS9epGr1cex7BVXbgG0AmzZtqtnZ2YVV9tvc3BxreX/jYrm8zt763oE87s4zl37MtbBen69JNa15wXTnpumysb3fn3fc3oe99++88PRRhSRpTDj0TJI0DM8CXpBkJ93k1c+m62F0aJL5ixYbgLva9i7gaIB2/AnAvcMMWJI0npIckOQTSd7T9o9J8tEktye5PMlBrfzRbX9HO75xlHFL0qSwR5EkaeCq6gLgAoDWo+g3q+rMJH8BvIiu8egs4Op2k2va/kfa8Q9W1SN6FGlybFyip5JXriXth/mFER7f9ucXRrgsyZ/QLYhwMT0LIyQ5o9X75VEELEmTxIaiMbHUB2hJmnLnA5cl+UPgE8AlrfwS4B1JdtD1JDpjRPFJksZIz8IIfwS8tmdhhJe2KtuB19E1FG1u29AtjPCWJPHCgyTtmw1FkqShqqo5YK5t3wGcuEidfwBePNTAJEmTYKwWRtjfycsHtXDAUv7LO69+RNl5x3W/Zx778HgmdTL2aZhI3hzGgzmss4aihb12ujfEdfUnkCRJkibSOC6MsL+Tlw9q4YD9cd5xe3nDLd/7TjToxQcGZRomkjeH8WAOtpJIkiRJmgzzCyOcBjyGbo6i7y6M0HoVLbYwwi4XRpCk/rnqmSRJkqSxV1UXVNWGqtpIN3fdB6vqTOBDdAsfwOILI4ALI0hS32wokiRJkjTJzqeb2HoH3RxEvQsjHN7KXwtsHVF8kjRRHHomSZIkaaK4MIIkDY4NRZIkrUMLF3iQJEmSwKFnkiRJkiRJamwokiRJkiRJEtBHQ1GSxyT5WJJPJbk1ye+38mOSfDTJ7UkuT3JQK39029/Rjm8cbAqSJEmSJElaC/30KPom8OyqegZwPHBKkpOA1wNvrKpjgfuAc1r9c4D7quqpwBtbPUmSJEmSJI25ZRuKqrOn7T6q/RTwbODKVr4deGHb3tz2acdPTpI1i1iSJEmSJEkD0deqZ0kOAG4Engr8MfB54P6q2tuq7AKOattHAXcCVNXeJA8AhwNfXXCfW4AtADMzM8zNza0qkX6cd9zeh+3PPPaRZZNgub/Vnj17hvL3HIVpzc28Js805yZJkiRp/eqroaiqvg0cn+RQ4Crgxxar1n4v1nuoHlFQtQ3YBrBp06aanZ3tJ5RVOXvBUsDnHbeXN9zS159grOw8c3afx+fm5hjG33MUpjU385o805ybJEmSpPVrRa0kVXV/kjngJODQJAe2XkUbgLtatV3A0cCuJAcCTwDuXbuQJUmTJsljgA8Dj6Y791xZVb+X5BjgMuAw4CbgZVX1rSSPBi4Ffhr4GvDLVbVzJMFroDYuuIgzb+eFpw85EkmSJEF/q549qfUkIsljgV8AbgM+BLyoVTsLuLptX9P2acc/WFWP6FEkSVpXXBhBkiRJmgD9rHp2JPChJDcDHweurar3AOcDr02yg24Ookta/UuAw1v5a4Gtax+2JGmSuDCCJEmSNBmWHXpWVTcDP7VI+R3AiYuU/wPw4jWJTpI0NQaxMIIkSZKktTV5MzlLkibSIBZGGOQKmtO6st18XuO+6udK//bT/nxNm2nNC6Y7N0nS+mBDkSRpqNZyYYRBrqA5rSvbzee1cCXQcbPcCp8LTfvzNW2mNS+Y7txGzYURJGk4+pmjSJKkVXFhBEnSGnBhBEkaAnsUSZKG4Uhge5un6PuAK6rqPUk+A1yW5A+BT/DwhRHe0RZGuBc4YxRBT4veJejPO27v2PcmkqTFtAsGSy2M8NJWvh14HXAx3cIIr2vlVwJvSRIvPEjSvtlQJEkaOBdGkCSthUEsjLCa+e72d06qcZonbuaxD49nUufYmob5wcxhPJiDDUWSJEmSJsQgFkZYzXx3+zsn1Tj17DzvuL284ZbvfS1c6Rxx42Ia5gczh/FgDs5RJEmSJGnCVNX9wBw9CyO0Q4stjMC+FkaQJD2cDUWSJEmSxp4LI0jScDj0TJIkSdIkcGGEIdi4xLC4nReePuRIJI2KDUWSJEmSxp4LI0jScNhQJEmSJEkDsFTvHEkaZ85RJEmSJEmSJMAeRZIkaQwtdhXe+TEkSZIGzx5FkiRJkiRJAmwokiRJkiRJUrNsQ1GSo5N8KMltSW5N8ppWfliSa5Pc3n4/sZUnyZuT7Ehyc5ITBp2EJEmSJEmSVq+fOYr2AudV1U1JDgFuTHItcDZwXVVdmGQrsBU4HzgVOLb9PBO4uP3WGllq9QTnbpAkSZIkSauxbI+iqtpdVTe17W8AtwFHAZuB7a3aduCFbXszcGl1rgcOTXLkmkcuSZIkSZKkNbWiOYqSbAR+CvgoMFNVu6FrTAKe3KodBdzZc7NdrUySJEmSJEljrJ+hZwAk+X7gvwG/UVVfT7Jk1UXKapH72wJsAZiZmWFubq7fUPbbecftfdj+zGMfWTbJ5v+Ge/bsGcrfcxSmNTfzmjzTnNsgJDkauBT4AeA7wLaquijJYcDlwEZgJ/BLVXVfupPMRcBpwEPA2fO9WyVJkiQNTl8NRUkeRddI9M6q+stWfHeSI6tqdxtadk8r3wUc3XPzDcBdC++zqrYB2wA2bdpUs7Oz+5fBCpy9YG6f847byxtu6butbOztPHMW6BqMhvH3HIVpzc28Js805zYgznc3BEvNYSdJkiT1q59VzwJcAtxWVf+559A1wFlt+yzg6p7yl7fVz04CHpgfoiZJWp+c706SJEmaDP10p3kW8DLgliSfbGW/DVwIXJHkHOBLwIvbsffRDRXYQTdc4BVrGrEkaaLta767JMvNd/ewCw+DHMY8icML+xlOPcnDrvf1fEzi89UP85o805zbqDmMWZKGY9mGoqr6Wxafdwjg5EXqF3DuKuOSJE2htZ7vbpDDmCdxeOHCIdaLmeRh1/NDrBczic9XP8xr8kxzbmPAYcySNAQrWvVMkqT9ta/57trxFc93J0laPxzGLEnDMZmXFCVJE6WP+e4u5JHz3b0qyWV0V3+d706S9F3jMox5uaGGkzDUt98hyeM+pHIahn2aw3gwBxuKJEnD4Xx3kqQ1MU7DmJcbatjPkOBR63dI8r6G/46DaRj2aQ7jwRxsKJIkDYHz3UmS1sK+hjG33kQOY5akVbKhSJIkSdLYcxjzaG1cpHfUzgtPH0EkkgbNhiJJkiRJk8BhzJI0BDYUSZKkibDY1Wzwira0XjiMWZKG4/tGHYAkSZIkSZLGgw1FkiRJkiRJAmwokiRJkiRJUmNDkSRJkiRJkgAbiiRJkiRJktTYUCRJkiRJkiQADhx1AJIkSauxcet7Oe+4vZy99b0PK9954ekjikiSJGly2VA0RTa2D8gLPyz7QVmSJEmSJPXDoWeSJEmSJEkC+mgoSvK2JPck+XRP2WFJrk1ye/v9xFaeJG9OsiPJzUlOGGTwkiRJkiRJWjv99Ch6O3DKgrKtwHVVdSxwXdsHOBU4tv1sAS5emzAlSZIkSZI0aMs2FFXVh4F7FxRvBra37e3AC3vKL63O9cChSY5cq2AlSZPJ3qmSJEnSZNjfyaxnqmo3QFXtTvLkVn4UcGdPvV2tbPfCO0iyha7XETMzM8zNze1nKP0777i9D9ufeewjy6bBwryG8bcdlj179kxVPvPMa/JMc24D8nbgLcClPWXzvVMvTLK17Z/Pw3unPpOud+ozhxqtpsLGBauggQs8SJMsyduA5wP3VNVPtLLDgMuBjcBO4Jeq6r4kAS4CTgMeAs6uqptGEbckTZq1XvUsi5TVYhWrahuwDWDTpk01Ozu7xqE80sJlc887bi9vuGX6Fn5bmNfOM2dHF8wam5ubYxivlWEzr8kzzbkNQlV9OMnGBcWbgdm2vR2Yo2so+m7vVOD6JIcmOXL+AoUkad16O150kKSB299WkrvnP7S3oWX3tPJdwNE99TYAd60mwP2x2BVESdLYGeveqePca+yWLz+waPl5xy1/2/XSm3Yp4/qcLmWcX4erMa15wXTnNmpedJCk4djfhqJrgLOAC9vvq3vKX5XkMroW+wd8M5YkrdBY9E4d515jC3vIrsR66U27lEnrZTvOr8PVmNa8YLpzG1OrvuiwFrxQLWmaLPuJKsm76Vrpj0iyC/g9ugaiK5KcA3wJeHGr/j66ccA76MYCv2IAMUuSpsNY906VJE20vi86rKZ36nwPsknuqbmanqbj1HtuGnrzmcN4MIc+Goqq6iVLHDp5kboFnLvf0UiS1hN7p0qSVmvVFx1W0zt1vgfZanp6jtqqepre8uCixaNYOGAaevOZw3gwB/i+tQtFkqTFtd6pHwGelmRX65F6IfCcJLcDz2n70PVOvYOud+qfAa8cQciSpMkwf9EBHnnR4eXpnIQXHSSpb9M3SYEkaezYO1WStFpOiSFJw2FDkSRJkqSx50UHSRoOh55JkiRJkiQJsEeRJElaR5ZawnoUE69KkiSNI3sUSZIkSZIkCbChSJIkSZIkSY1DzyRJGgNLDYmSJEmShskeRZIkSZIkSQLsUSRJkrQiTogtSZKmmQ1FkiRp3bPxR5IkqWNDkSRJ0hKcO0qSVs7Gd2my2VAkSdIQ2fAgSZKkceZk1pIkSZIkSQLsUSRJkiRJGoLFetU6HE0aPzYUrQO+IUuSJEmSpH4MrKEoySnARcABwFur6sK1fgzneZCk6TaMc8kgeZ5aX1ZyYcaJXqXhmfRzybTz/VAaPwNpKEpyAPDHwHOAXcDHk1xTVZ8ZxONJkqaP5xJJ0mp5LpkuNipJwzGoHkUnAjuq6g6AJJcBmwHfkMeEb7KSJsBYnksczquVWGmvsoX1zztuL7NrGI+0Do3luURra+PW93LecXs5u4/3XM/Z0vJSVWt/p8mLgFOq6lfb/suAZ1bVq3rqbAG2tN2nAZ9b80CWdwTw1RE87qBNa14wvbmZ1+RZLrd/VFVPGlYw02gMziXT+vo1r8liXpNnLXPzXLJKQziXTMNreRpygOnIwxzGw7TlsOJzyaB6FGWRsoe1SFXVNmDbgB6/L0luqKpNo4xhEKY1L5je3Mxr8kxzbmNkpOeSaX2OzWuymNfkmebcJtRAzyXT8HxPQw4wHXmYw3gwB/i+tQymxy7g6J79DcBdA3osSdJ08lwiSVotzyWStEKDaij6OHBskmOSHAScAVwzoMeSJE0nzyWSpNXyXCJJKzSQoWdVtTfJq4C/pluG8m1VdesgHmuVRjr0bYCmNS+Y3tzMa/JMc25jYQzOJdP6HJvXZDGvyTPNuU2cIZxLpuH5noYcYDryMIfxsO5zGMhk1pIkSZIkSZo8gxp6JkmSJEmSpAljQ5EkSZIkSZKAKW0oSnJKks8l2ZFk6yLH/1GS65LcnGQuyYaeY69P8un288vDjXzfkrwtyT1JPr3E8SR5c8v75iQn9Bw7K8nt7ees4UXdn1Xm9v4k9yd5z/Ai7s/+5pXk+CQfSXJrK5+K12L737sxySdbbr823MiXt5rXYjv++CRfTvKW4USsQUhyQJJPjOP7yv5KcmiSK5N8NsltSX521DGtlST/pr2nfDrJu5M8ZtQx7Y/F3n+SHJbk2nb+vjbJE0cZ4/5YIq//2F6LNye5Ksmho4xxf+3rnJHkN5NUkiNGEZsGb7nvHOMqyc4kt7TPYze0srF+r1nJ++Nyn9VGZYkcXtc+N36y/ZzWc+yClsPnkjxvNFE/XJKjk3yofY64NclrWvnEPBf7yGFinoskj0nysSSfajn8fis/JslH2/NweboJ/Eny6La/ox3fuOyDVNVU/dBNUvd54IeAg4BPAU9fUOcvgLPa9rOBd7Tt04Fr6Sb5Phi4AXj8qHPqifvngBOATy9x/DTgr4AAJwEfbeWHAXe0309s208cdT5rkVs7djLwz4D3jDqPNXzOfgQ4tm0/BdgNHDrqfNYgr4OAR7ft7wd2Ak8ZdT5r9Vpsxy8C3gW8ZdS5+LOq18Fr2/M4du8rq8hpO/CrbfugcXpPWWVeRwFfAB7b9q8Azh51XPuZyyPef4D/E9jatrcCrx91nGuU13OBA9v26ycxr6Vya+VH002e/EXgiFHH6c9Anvtlv3OM60/7/HXEgrKxfq9Zyfvjcp/VxiyH1wG/uUjdp7fX1KOBY9pr7YAxyOFI4IS2fQjwdy3WiXku9pHDxDwX7e/5/W37UcBH29/3CuCMVv4nwK+37VcCf9K2zwAuX+4xprFH0YnAjqq6o6q+BVwGbF5Q5+nAdW37Qz3Hnw78j6raW1UP0r0gThlCzH2pqg8D9+6jymbg0upcDxya5EjgecC1VXVvVd1H1xg2NnnBqnKjqq4DvjGEMFdsf/Oqqr+rqtvbfdwF3AM8afAR92cVeX2rqr7Z6jyaMexupOz2AAAgAElEQVTVuJrXYpKfBmaADww+Ug1Kul6mpwNvHXUsayXJ4+k+oF4C0P4X7x9tVGvqQOCxSQ4EHgfcNeJ49ssS7z+b6Rr5aL9fONSg1sBieVXVB6pqb9u9HtjwiBtOgH2cM94I/BbgqjHTq5/vHJNkrN9rVvj+uORntVHq4zNmr83AZVX1zar6ArCD7jU3UlW1u6puatvfAG6ju2AzMc/FPnJYytg9F+3vuaftPqr9FF0nmCtb+cLnYf75uRI4OUn29Rhj9yVtDRwF3Nmzv4tHPvGfAn6xbf9z4JAkh7fyU5M8rnUT/nm6K0KTYqnc+/mbjLtpyGExy+aV5ES6K1WfH2Jcq7VkXq27583t+OtbQ9gkWTS3JN8HvAH4tyOJSmvpTXRf8L4z6kDW0A8Bfw/8ebohdW9NcvCog1oLVfVl4D8BX6LrfflAVU1TY+1MVe2G7sMt8OQRxzMI/5LuivNUSPIC4MtV9alRx6KBmuTPpgV8IN10AFta2SS+1ywV86Q9N69qw7Le1jPkb+xzaMOXfoquN8tEPhcLcoAJei7STZPwSboOBdfSfVe8v+ciTG+c382hHX8AOHxf9z+NDUWLtYwtvJrzm8A/TfIJ4J8CXwb2tg+W7wP+X+DdwEeAvUyOpXLv528y7qYhh8XsM6/W4v4O4BVVNUlfWpfMq6rurKqfBJ4KnJVkZqiRrd5Sub0SeF9V3bnIcU2IJM8H7qmqG0cdyxo7kK67+8VV9VPAg3Rdwyde+yC3ma47+FOAg5P8ymijUr+S/A7dZ613jjqWtZDkccDvAL876lg0cJP82fRZVXUCcCpwbpKfG3VAa2ySnpuLgR8Gjqe72PGGVj7WOST5fuC/Ab9RVV/fV9VFysYij0VymKjnoqq+XVXH0/XIPRH4scWqtd8rzmEaG4p28fBeQBtY0AW9qu6qqn/RPiz/Tit7oP3+o6o6vqqeQ/cHvX04Ya+JpXJf9m8yAaYhh8UsmVcbKvJe4H9vXTUnSV//h8CtwP82xLjWwlK5/SzdVYiddL0bXp7kwuGHp1V6FvCC9jxeBjw7yX8dbUhrYhewq6rmr5hdSddwNA1+AfhCVf19Vf0v4C+BfzzimNbS3T3DW4+ku3I4FdItrvF84MxqEydMgR+ma7T8VHsf2QDclOQHRhqVBmFiP5vO9+auqnuAq+i+ZE7ie81SMU/Mc1NVd7cv/N8B/ozvDWka2xySPIqugeWdVfWXrXiinovFcpjE5wKgTSUwRzdH0aFtGD48PM7v5tCOP4FlhkFOY0PRx4Fj24zfB9FN1nRNb4UkR7RhIgAXAG9r5Qe0IWgk+UngJ5msuUauoftymiQn0XW/3003meJzkzyxXXl9biubJEvlNukWzau9dq+iG9P7F6MNcb8sldeGJI+F7/YCeBbwuVEGuh8Wza2qzqyqH6yqjXS9Fi+tqqnosbGeVNUFVbWhPY9nAB+sqonvnVJVXwHuTPK0VnQy8JkRhrSWvgSc1IaNhy6320Yc01q6BphfrfQs4OoRxrJmkpwCnA+8oKoeGnU8a6WqbqmqJ1fVxvY+sotu0tSvjDg0rb1lv3OMoyQHJzlkfpvue8Gnmcz3mqVinpjvDQvm6/nndM8FdDmc0VarOgY4FvjYsONbqJ1nLwFuq6r/3HNoYp6LpXKYpOciyZPSVgtt361+ge6zz4eAF7VqC5+H+efnRXSfb/d5gebAfR2cRFW1N8mr6BpCDgDeVlW3JvkD4IaqugaYBf5DkgI+DJzbbv4o4H92rx2+DvxKzxi/kUvybrrYj0iyC/g9upipqj+hGzZ3Gt0EWw8Br2jH7k3y7+hOaAB/UFX9TqQ2FPubW7vt/wR+FPj+dttzqmosGsJWkdcv0U08e3iSs1vZ2VX1yaEFvw+ryOvHgDe0/70A/6mqbhlu9Pu2mteiNOZeDbyzfaG5gyl57VbVR5NcCdxEN4TpE8C20Ua1f5Z4/7kQuCLJOXSNYi8eXYT7Z4m8LqBb1ODa9rnr+qr6tZEFuZ8Wy62qLhltVBqGpb5zjDisfswAV7X/uwOBd1XV+5N8nDF+r1nh++NYflZbIofZJMfTDQPaCfwrgPb99Qq6izp7gXOr6tujiHuBZwEvA25JNz8OwG8zWc/FUjm8ZIKeiyOB7UkOoOv8c0VVvSfJZ4DLkvwh3eeh+fPRJcA7kuyg60l0xnIPkOnp6StJkiRJkqTVmMahZ5IkSZIkSdoPNhRJkiRJkiQJsKFIkiRJkiRJjQ1FkiRJkiRJAmwokiRJkiRJUmNDkSRJkiRJkgAbiiRJkiRJktTYUCRJkiRJkiTAhiJJkiRJkiQ1NhRJkiRJkiQJsKFIkiRJkiRJjQ1FkiRJkiRJAmwokiRJkiRJUmNDkSRJkiRJkgAbiiRJkiRJktTYUCRJkiRJkiTAhiJJkiRJkiQ1NhRJkiRJkiQJsKFIkiRpvyTZmKSSHNj2/yrJWaOOS5K0ekl+MMmeJAcscfx1Sf7rGj1WJXnqWtyXtBZsKNK6kWQ2ya5FyueS/OooYpIkTY+qOrWqtgMkOTvJ3446JknS/qmqL1XV91fVt0cdizRsNhRJkiRJkqbOfI9Prc5Svao0vWwo0tRJsjPJBUk+k+S+JH+e5DGjjkuSNDpJfirJTUm+keTyJJcl+cPFev70DgFIcnqSTyT5epI7k7xuH48xl+RXk/wY8CfAz7ZhC/cn+Zkkd/d+aUnyi0k+OaCUJWldat8Fzk9yM/BgkgOTPCXJf0vy90m+kORf99Q/MckN7X3+7iT/uZUvHF58TJL/0c4j1wJH9NzHI0YutDh+oecxPtLOB7uTvCXJQX3mc3aSO9rjfiHJma38YUPfloj3w+12f5PkjxfU/4skX0nyQKv34z3H3p7k4iTvS/Ig8PMreAo0BWwo0rQ6E3ge8MPAjwD/+2jDkSSNSvsw/t+BdwCHAX8B/GKfN38QeDlwKHA68OtJXrivG1TVbcCvAR9pwxYOraqPA18DntNT9VdaTJKktfUSuvfsQ4HvAP838CngKOBk4DeSPK/VvQi4qKoeT/fd4Yol7vNdwI10DUT/DljJnHTfBv5Nu+3PthheudyNkhwMvBk4taoOAf4x0O8FhncBHwMOB14HvGzB8b8CjgWeDNwEvHPB8ZcCfwQcAjiUep2xoUjT6i1VdWdV3Uv3BveSVv6U1pL/3R/gn4wuTEnSEJwEPAp4U1X9r6q6Evh4PzesqrmquqWqvlNVNwPvBv7pfsaxna5xiCSH0V3QeNd+3pckaWlvbt8F/j/gZ4AnVdUfVNW3quoO4M+AM1rd/wU8NckRVbWnqq5feGdJfrDdz/9RVd+sqg/TNT71papurKrrq2pvVe0E/pT+zyXfAX4iyWOrandV3brcDXri/d2W898C1yyI6W1V9Y2q+iZdQ9Izkjyhp8rVVfX/tPPfP/QZq6aEDUWaVnf2bH8ReErbvqtd2f3uD7aQS9K0ewrw5aqqnrIv9nPDJM9M8qE2XOEBup5CRyx3uyX8V+CfJfl+4JeA/1lVu/fzviRJS+v9LvCPWHCxGPhtYKYdP4duBMJnk3w8yfMXub+nAPdV1YM9ZX2dRwCS/EiS97ShXl8H/j19nEva4/0y3blnd5L3JvnRPh7yKcC9VfVQT9l3/yZJDkhyYZLPt3h2tkNHLFZf648NRZpWR/ds/yBw16gCkSSN3G7gqCTpKfvB9vtB4HHzhUl+YMFt30V3FfboqnoC3dxDYXn1iIKqLwMfAf453RAAh51J0mD0vgffCXxhwcXiQ6rqNICqur2qXkI3BOv1wJVtyFev3cATF5T/YM/2wnPJAcCTeo5fDHwWOLYNcftt+juXUFV/XVXPAY5s9/Fniz0m0Hv+2g0clqT3eO/3o5cCm4FfAJ4AbJwPvfeh+4lP08mGIk2rc5NsaF37fxu4fNQBSZJG5iPAXuBft0lN/wVwYjv2KeDHkxyfbuGD1y247SF0V2X/IcmJdB+u+3E3sGGRyUovBX4LOA64auWpSJJW6GPA19sE149tvWl+IsnPACT5lSRPqqrvAPe323y79w6q6ovADcDvJzkoyT8B/llPlb8DHpNuAYRH0c2P+uie44cAXwf2tB5Bv95P4ElmkrygNVB9E9jTE9sngZ9L8oNtyNgFi8T7uhbvzy6I95B2f1+ja2z69/3Eo/XDhiJNq3cBHwDuaD9/ONpwJEmjUlXfAv4FcDZwH103/r9sx/4O+APgb4DbeeRw5FcCf5DkG8DvsvQkpwt9ELgV+EqSr/aUX0U3DOKqBUMYJEkDUFXfpmskOR74AvBV4K10PWkATgFuTbKHbmLrM5aYk+elwDOBe4Hfo2v4n3+MB+jOF28FvkzX26d3FbTfbLf/Bl2PoH4vYn8fcB7d6Ih76eY1emV7zGvb/dxMN8n2exbc9ky6ibO/Rvdd6HK6xiFa7F9ssX4GeMS8TFrf8vDh+tLkS7IT+NWq+ptRxyJJGk9J3g7sqqqhr4qZ5PPAv/I8JUkaliSXA5+tqt8bdSwaf/YokiRJGpIkv0g378MHRx2LJGl6JfmZJD+c5PuSnEI3J9F/H3VcmgwHjjoASZKk9SDJHPB04GVtLgxJkgblB+iGWR9ONwzu16vqE6MNSZPCoWeSJEmSJEkCHHomSZIkSZKkZiyGnh1xxBG1cePGUYcBwIMPPsjBBx886jDWhLmMr2nKx1wWd+ONN361qp60JnemvuzvuWSaXsNLmfYczW/yTXuO+5uf55LhW+m5ZJJeu8Y6GJMUK0xWvMa6NvbnXDIWDUUbN27khhtuGHUYAMzNzTE7OzvqMNaEuYyvacrHXBaX5Itrckfq2/6eS6bpNbyUac/R/CbftOe4v/l5Lhm+lZ5LJum1a6yDMUmxwmTFa6xrY3/OJQ49kyRJkiRJEmBDkSRJkiRJkhobiiRJkiRJkgTYUCRJkiRJkqTGhiJJkiRJkiQBNhRJkiRJGiNJ3pbkniSfXuTYbyapJEe0/SR5c5IdSW5OckJP3bOS3N5+zhpmDpI0yQ4cdQBa3zZufe+i5TsvPH3IkUiSJK3OUp9r3n7KwUOOZOK9HXgLcGlvYZKjgecAX+opPhU4tv08E7gYeGaSw4DfAzYBBdyY5Jqqum8QAfuZVtI0sUeRJEmSpLFRVR8G7l3k0BuB36Jr+Jm3Gbi0OtcDhyY5EngecG1V3dsah64FThlw6JI0FexRJEmSJGmsJXkB8OWq+lSS3kNHAXf27O9qZUuVL3bfW4AtADMzM8zNzfUd1549e5ibm+O84/Yuenwl9zVo87FOAmMdnEmK11hHx4YiSZIkSWMryeOA3wGeu9jhRcpqH+WPLKzaBmwD2LRpU83OzvYd29zcHLOzs5y91NCzM/u/r0Gbj3USGOvgTFK8xjo6Dj2TJEmSNM5+GDgG+FSSncAG4KYkP0DXU+jonrobgLv2US5JWoYNRZIkSZLGVlXdUlVPrqqNVbWRrhHohKr6CnAN8PK2+tlJwANVtRv4a+C5SZ6Y5Il0vZH+elQ5SNIk6buhKMkBST6R5D1t/5gkH23LTV6e5KBW/ui2v6Md3ziY0CVJkiRNmyTvBj4CPC3JriTn7KP6+4A7gB3AnwGvBKiqe4F/B3y8/fxBK5MkLWMlcxS9BrgNeHzbfz3wxqq6LMmfAOfQLUd5DnBfVT01yRmt3i+vYcySJEmSplRVvWSZ4xt7tgs4d4l6bwPetqbBSdI60FePoiQbgNOBt7b9AM8GrmxVtgMvbNub2z7t+MlZsDSBJEmSJEmSxk+/Q8/eBPwW8J22fzhwf1XNrwPZu9zkd5eibMcfaPUlSVMuyWOSfCzJp5LcmuT3W/mKhysnuaCVfy7J80aTkSRJkrS+LDv0LMnzgXuq6sYks/PFi1StPo713u8WYAvAzMwMc3Nz/cQ7cHv27BmbWFZrEnI577i9i5YvjHsSclmJacrHXLTAN4FnV9WeJI8C/jbJXwGvZQXDlZM8HTgD+HHgKcDfJPmRqvr2KJKSJEmS1ot+5ih6FvCCJKcBj6Gbo+hNwKFJDmy9hnqXm5xfinJXkgOBJwCPmDiuqrYB2wA2bdpUs7Ozq0xlbczNzTEusazWJORy9tb3Llq+88zZh+1PQi4rMU35mIt6tbki9rTdR7Wfohuu/NJWvh14HV1D0ea2Dd1w5be04cqbgcuq6pvAF5LsAE6km9xUkiRJ0oAsO/Ssqi6oqg1t0rgzgA9W1ZnAh4AXtWpnAVe37WvaPu34B9sXB0nSOtBWyfwkcA9wLfB5Vj5c+bvli9xGkiRJ0oCsZNWzhc4HLkvyh8AngEta+SXAO9rV33vpGpckSetEGx52fJJDgauAH1usWvu91HDloQ1jXg9DDqc9R/ObfNOS41JD6qclP0nS+rCihqKqmgPm2vYddMMAFtb5B+DFaxCbJGmCVdX9SeaAk1j5cOX58nm9t+l9jFUPY14PQw6nPUfzm3zTkuNSQ+rffsrBU5GfJGl96HfVM0mSlpXkSa0nEUkeC/wCcBsrH658DXBGWxXtGOBY4GPDyUKSJElav1Yz9EySpIWOBLYnOYDuYsQVVfWeJJ9hBcOVq+rWJFcAnwH2Aue64pkkSZI0eDYUSZLWTFXdDPzUIuUrHq5cVX8E/NFaxyhJkiRpaQ49kyRJkiRJEmBDkSRJkiRJkhobiiRJkiRJkgTYUCRJkiRpjCR5W5J7kny6p+w/JvlskpuTXDW/wmY7dkGSHUk+l+R5PeWntLIdSbYOOw9JmlQ2FEmSJEkaJ28HTllQdi3wE1X1k8DfARcAJHk63YqZP95u838lOaCtvvnHwKnA04GXtLqSpGXYUCRJkiRpbFTVh4F7F5R9oKr2tt3rgQ1tezNwWVV9s6q+AOygW2XzRGBHVd1RVd8CLmt1JUnLOHDUAUiSJEnSCvxL4PK2fRRdw9G8Xa0M4M4F5c9c7M6SbAG2AMzMzDA3N9d3IHv27GFubo7zjtu76PGV3Negzcc6CYx1cCYpXmMdHRuKJEmSJE2EJL8D7AXeOV+0SLVi8ZETtdh9VtU2YBvApk2banZ2tu945ubmmJ2d5eyt7130+M4z+7+vQZuPdRIY6+BMUrzGOjo2FEmSJEkae0nOAp4PnFxV840+u4Cje6ptAO5q20uVS5L2wTmKJEmSJI21JKcA5wMvqKqHeg5dA5yR5NFJjgGOBT4GfBw4NskxSQ6im/D6mmHHLUmTyB5FkiRJksZGkncDs8ARSXYBv0e3ytmjgWuTAFxfVb9WVbcmuQL4DN2QtHOr6tvtfl4F/DVwAPC2qrp16MlI0gSyoUj/P3v3H2ZZVd/5/v0REBVRfmkHu4lNBuJoZETtABnm5taAUQRjm7mgKEEwJJ3JSEbHNoqZzAPxRwZnxqBGx6QjBDAqIGroKIlh0BqvMwERJCIQLy220kJEpEFbR02b7/1jr4Ki+pyqU12/zql6v56nntp77XX2+a5Tp/Y+57vXWluSJEkaGlX18h7FF05T/23A23qUXw1cPY+hSdKK4NAzSZIkSZIkASaKJEmSJEmS1JgokiRJkiRJEmCiSJIkSZIkSY2JIknSvElySJLPJLk9ya1JXtPKz0vyzSQ3t58TJz3mTUm2JPlKkhdMKj+hlW1Jcs5StEeSJElaabzrmSRpPu0ENlbVTUn2BW5Mck3bdkFV/bfJlZM8AzgV+DngKcD/SPKzbfN7gV8CtgE3JNlcVbctSiskSZKkFcpEkSRp3lTVPcA9bfl7SW4HVk/zkPXAZVX1I+BrSbYAR7VtW6rqToAkl7W6JookSZKkBWSiSJK0IJKsBZ4NXA8cC5yd5JXAF+h6HW2nSyJdN+lh23g4sXTXlPKjezzHBmADwKpVqxgfH591nDt27Nitx42S5d5G2zf6lksbNx6xs2f5cmmfJGllMFEkSZp3SR4PfBR4bVV9N8n7gLcA1X6/A/g1ID0eXvSeQ692KajaBGwCWLduXY2Njc061vHxcXbncaNkubfR9o2+5dLGM8/5ZM/yi0/YZ1m0T5K0MpgokiTNqyR70SWJPlhVHwOoqm9N2v6nwCfa6jbgkEkPXwPc3Zb7lUuSJElaIN71TJI0b5IEuBC4var+cFL5wZOq/Qrw5ba8GTg1yd5JDgUOBz4P3AAcnuTQJI+mm/B682K0QZIkSVrJ7FEkSZpPxwKnA7ckubmV/S7w8iRH0g0f2wr8JkBV3ZrkCrpJqncCr66qnwAkORv4FLAHcFFV3bqYDZEkSZJWIhNFkqR5U1Wfo/e8Q1dP85i3AW/rUX71dI+TJEmSNP8ceiZJkiRJkiRggB5FSR4DfBbYu9W/sqrObXNJXAYcANwEnF5VP06yN3Ap8FzgO8DLqmrrAsWvEbG2z11AJEmSpMmSXAS8CLi3qp7Zyg4ALgfW0g1hfmlVbW9z470LOBH4AXBmVd3UHnMG8Httt2+tqksWsx2SNKoG6VH0I+C4qnoWcCRwQpJjgLcDF1TV4cB24KxW/yxge1UdBlzQ6kmSJEnSIC4GTphSdg5wbfvucW1bB3gh3Y0QDgc2AO+DhxJL5wJHA0cB5ybZf8Ejl6RlYMZEUXV2tNW92k8BxwFXtvJLgJe05fVtnbb9+JbplyRJkqRpVdVngfunFE/+jjH1u8el7TvLdcB+7U6bLwCuqar7q2o7cA27Jp8kST0MNJl1kj2AG4HDgPcCXwUeqKqdrco2YHVbXg3cBVBVO5M8CBwI3Ddlnxvosv6sWrWK8fHxOTVkvuzYsWNoYpmrYWrLxiN2zlxpkqlxD1Nb5sNyao9tkSRJi2BVVd0DUFX3JHlyK3/ou0cz8b2kX/ku5vK9ZOKzQ7/PusP0uWKUPucY68IZpXiNdekMlChqtyo+Msl+wMeBp/eq1n736j1UuxRUbQI2Aaxbt67GxsYGCWXBjY+PMyyxzNUwteXMWc5RtPW0sUesD1Nb5sNyao9tkSRJS6jfd4+BvpPA3L6XTHx26PdZd+pn2qU0Sp9zjHXhjFK8xrp0ZnXXs6p6ABgHjqHr1jmRaFoD3N2WtwGHALTtT2TXrqOSJEmSNKhvtSFltN/3tvKHvns0E99L+pVLkmYwY6IoyZNaTyKSPBZ4HnA78Bng5FbtDOCqtry5rdO2f7qqembvJUmSJGkAk79jTP3u8cp0jgEebEPUPgU8P8n+bRLr57cySdIMBhl6djBwSZun6FHAFVX1iSS3AZcleSvwReDCVv9C4ANJttD1JDp1AeKWJEmStAwl+TAwBhyUZBvd3cvOB65IchbwDeCUVv1q4ERgC/AD4FUAVXV/krcAN7R6b64qRzlI0gBmTBRV1ZeAZ/cov5PuVpNTy3/IwwduSZIkSRpYVb28z6bje9Qt4NV99nMRcNE8hiZJK8JAk1lr5VjbYyK+reeftASRSJIkSZKkxWaiSENpasJq4xE7GVuaUCRJkiRJWjFmddczSZIkSZIkLV8miiRJkiRJkgSYKJIkSZIkSVJjokiSNG+SHJLkM0luT3Jrkte08gOSXJPkjvZ7/1aeJO9OsiXJl5I8Z9K+zmj170hyxlK1SZIkSVpJTBRJkubTTmBjVT0dOAZ4dZJnAOcA11bV4cC1bR3ghcDh7WcD8D7oEkvAucDRwFHAuRPJJUmSJEkLx0SRJGneVNU9VXVTW/4ecDuwGlgPXNKqXQK8pC2vBy6tznXAfkkOBl4AXFNV91fVduAa4IRFbIokSZK0Iu251AFodE29hf2EreeftMiRSBpGSdYCzwauB1ZV1T3QJZOSPLlVWw3cNelh21pZv/Kpz7GBricSq1atYnx8fNZx7tixY7ceN0qWextt3+hbLm3ceMTOnuXLpX2SpJXBRJEkad4leTzwUeC1VfXdJH2r9iiracofWVC1CdgEsG7duhobG5t1rOPj4+zO40bJcm+j7Rt9y6WNZ/a5iHbxCfssi/ZJklYGE0VaUewFJS28JHvRJYk+WFUfa8XfSnJw6010MHBvK98GHDLp4WuAu1v52JTy8YWMW5IkSZJzFEmS5lG6rkMXArdX1R9O2rQZmLhz2RnAVZPKX9nufnYM8GAbovYp4PlJ9m+TWD+/lUmSJElaQPYo0rzr12tH0opwLHA6cEuSm1vZ7wLnA1ckOQv4BnBK23Y1cCKwBfgB8CqAqro/yVuAG1q9N1fV/YvTBEmSJGnlMlGkkedwMml4VNXn6D2/EMDxPeoX8Oo++7oIuGj+opMkjbok/wH4dbp5626hu8BwMHAZcABwE3B6Vf04yd7ApcBzge8AL6uqrUsRtySNEoeeSZIkSRp6SVYD/x5YV1XPBPYATgXeDlxQVYcD24Gz2kPOArZX1WHABa2eJGkGJookSZIkjYo9gccm2RN4HHAPcBxwZdt+CfCStry+rdO2H59pbsMpSeo49Ewjw7mPJEmSVq6q+maS/0Y3193/Af4GuBF4oKp2tmrbgNVteTVwV3vsziQPAgcC903eb5INwAaAVatWMT4+PnBMO3bsYHx8nI1H7Oy5fTb7WmgTsY4CY104oxSvsS4dE0WSJEmShl67C+Z64FDgAeAjwAt7VK2Jh0yz7eGCqk3AJoB169bV2NjYwDGNj48zNjbGmf3mzDxt8H0ttIlYR4GxLpxRitdYl45DzyRJkiSNgucBX6uqb1fVPwIfA/4lsF8bigawBri7LW8DDgFo258IeAdNSZqBiSJJkiRJo+AbwDFJHtfmGjoeuA34DHByq3MGcFVb3tzWads/3e62KUmahokiSZIkSUOvqq6nm5T6JuAWuu8ym4A3Aq9LsoVuDqIL20MuBA5s5a8Dzln0oCVpBDlHkWbkJNKSJEkaBlV1LnDulOI7gaN61P0hcMpixCVJy4k9iiRJkiRJkgSYKJIkSZIkSVJjokiSJEmSJEmAiSJJkiRJkiQ1TmYtSVrRbvnmg5zZY9L+reeftATRSJIkSUvLRJGWLe/WJkmSJEnS7Mw49CzJIUk+k+T2JGAssd8AACAASURBVLcmeU0rPyDJNUnuaL/3b+VJ8u4kW5J8KclzFroRkiRJkiRJmrtB5ijaCWysqqcDxwCvTvIM4Bzg2qo6HLi2rQO8EDi8/WwA3jfvUUuShlKSi5Lcm+TLk8rOS/LNJDe3nxMnbXtTu7DwlSQvmFR+QivbkuScqc8jSZIkaWHMmCiqqnuq6qa2/D3gdmA1sB64pFW7BHhJW14PXFqd64D9khw875FLkobRxcAJPcovqKoj28/VAO2iw6nAz7XH/PckeyTZA3gv3YWHZwAvb3UlSZIkLbBZzVGUZC3wbOB6YFVV3QNdMinJk1u11cBdkx62rZXdM2VfG+h6HLFq1SrGx8dnH/0C2LFjx9DEMle705aNR+xcmGDmaNVjFza2xf6br/T32bBaTm1ZKlX12XauGMR64LKq+hHwtSRbgKPati1VdSdAksta3dvmOVxJkiRJUwycKEryeOCjwGur6rtJ+lbtUVa7FFRtAjYBrFu3rsbGxgYNZUGNj48zLLHM1e60pdedf4bBxiN28o5bFm7u9a2njS3YvntZ6e+zYbWc2jKEzk7ySuALdMOZt9NdRLhuUp2JCwuw6wWHoxclSkmSJGmFG+ibd5K96JJEH6yqj7XibyU5uPUmOhi4t5VvAw6Z9PA1wN3zFbAkaeS8D3gL3UWDtwDvAH6N/hcWeg2L3uWCA8xP79R+PRaXU++y5d5bzvaNvuXSxn69n5dL+yRJK8OMiaJ0XYcuBG6vqj+ctGkzcAZwfvt91aTys9tQgaOBByeGqEmSVp6q+tbEcpI/BT7RVqe7sDDQBYf56J36Rx+8qmePxcXuabiQlntvOds3+pZLG/v1zL74hH2WRfskSSvDIHc9OxY4HThuyh1rzgd+KckdwC+1dYCrgTuBLcCfAv9u/sOWJI2KKTc0+BVg4o5om4FTk+yd5FC6u2V+HrgBODzJoUkeTTfh9ebFjFmSNJyS7JfkyiR/n+T2JL+Q5IAk1yS5o/3ev9VNkne3O2h+Kclzljp+SRoFM/YoqqrP0Xt4AMDxPeoX8Oo5xiVJGkFJPgyMAQcl2QacC4wlOZJu+NhW4DcBqurWJFfQTVK9E3h1Vf2k7eds4FPAHsBFVXXrIjdFkjSc3gX8dVWd3C4mPA74XeDaqjo/yTnAOcAb6e6eeXj7OZpuKLRz3knSDBZudmBJ0opTVS/vUXzhNPXfBrytR/nVdD1UJUkCIMkTgF8EzgSoqh8DP06ynu4iBcAlwDhdomg9cGm7kH1d6410sNNiSNL0TBRJkiRJGgU/A3wb+LMkzwJuBF4DrJpI/rQb7Ty51V/NrnfRXA08IlE0lxsjTExU3m8i82GaxHyUJlU31oUzSvEa69IxUSRJkiRpFOwJPAf47aq6Psm76IaZ9dPv7pqPLJjDjREmJmLvN5H5MN0YYZQmjTfWhTNK8Rrr0jFRtEKt7XMykyRJkobUNmBbVV3f1q+kSxR9a2JIWbuBwr2T6g90F01J0sNMFEn0TpxtPf+kJYhEkiRJvVTVPyS5K8nTquordDfWua39nEF3F+YzgKvaQzYDZye5jG4S6wedn0iSZmaiSJIkSdKo+G3gg+2OZ3cCrwIeBVyR5CzgG8Apre7VwInAFuAHra4kaQYmiiRJkiSNhKq6GVjXY9PxPeoW8OoFD0qSlplHLXUAkiRJkiRJGg4miiRJkiRJkgSYKJIkSZIkSVJjokiSJEmSJEmAk1lLkiRJ0oJYe84ne5ZvPf+kRY5EkgZnjyJJkiRJkiQBJookSZIkSZLUmCiSJEmSJEkSYKJIkiRJkiRJjZNZLyNTJ8vbeMROzjznk06WJ0mSJEmSBmKPIknSvElyUZJ7k3x5UtkBSa5Jckf7vX8rT5J3J9mS5EtJnjPpMWe0+nckOWMp2iJJkiStRPYokvqYj9uZ9tvHxSfss1sxSSPgYuA9wKWTys4Brq2q85Oc09bfCLwQOLz9HA28Dzg6yQHAucA6oIAbk2yuqu2L1gpJkiRphTJRJM2TfkkhaSWpqs8mWTuleD0w1pYvAcbpEkXrgUurqoDrkuyX5OBW95qquh8gyTXACcCHFzh8SZIkacUzUSRJWmirquoegKq6J8mTW/lq4K5J9ba1sn7lu0iyAdgAsGrVKsbHx2cf3GO7Od2m2p19DasdO3Ysq/ZMZftG33JpY69jCSyf9kmSVgYTRdIs2XNImjfpUVbTlO9aWLUJ2ASwbt26Ghsbm3UQf/TBq3jHLbueDreeNvt9Davx8XF257UZFbZv9C2XNp45zZDz5dC+YZFkD+ALwDer6kVJDgUuAw4AbgJOr6ofJ9mbbij0c4HvAC+rqq1LFLYkjQwTRUOuV1LCu5hJGjHfSnJw6010MHBvK98GHDKp3hrg7lY+NqV8fBHilCSNhtcAtwNPaOtvBy6oqsuS/DFwFt28d2cB26vqsCSntnovW4qA59t8zKUpSf141zNJ0kLbDEzcuewM4KpJ5a9sdz87BniwDVH7FPD8JPu3O6Q9v5VJkla4JGuAk4D3t/UAxwFXtiqXAC9py+vbOm378a2+JGka9iiSJM2bJB+m6w10UJJtdHcvOx+4IslZwDeAU1r1q4ETgS3AD4BXAVTV/UneAtzQ6r15YmJrSdKK907gDcC+bf1A4IGqmpggavK8dg/NeVdVO5M82OrfN3mHc5nvbmL+qX7zU/Uz1zmr+j3fdPsdpbmyjHXhjFK8xrp0TBStAM6pI2mxVNXL+2w6vkfdAl7dZz8XARfNY2iSpBGX5EXAvVV1Y5KxieIeVWuAbQ8XzGG+u4n5tfrNT9XPXOfB6/d80+13lOYCM9aFM0rxGuvSMVEkSZIkaRQcC7w4yYnAY+jmKHonsF+SPVuvoon57uDhufC2JdkTeCIwtD1UvbgraVg4R5EkSZKkoVdVb6qqNVW1FjgV+HRVnQZ8Bji5VZs6F97EHHknt/o976IpSXrYjImiJBcluTfJlyeVHZDkmiR3tN/7t/IkeXeSLUm+lOQ5Cxm8JEmSpBXvjcDrkmyhm4PowlZ+IXBgK38dcM4SxSdJI2WQHkUXAydMKTsHuLaqDgeu5eGD7guBw9vPBrrbUkqSJEnSvKmq8ap6UVu+s6qOqqrDquqUqvpRK/9hWz+sbb9zaaOWpNEwY6Koqj7LrmN5J99qcuotKC+tznV044UPnq9gJUmSJEmStHB2dzLrVVV1D0BV3ZPkya38oVtQNhO3p7xn6g7mchvKhTRst7XrdevLfvFNrbvqsf1vnTlqllNbYPjeZ3NhWyRJkiRp+Zjvu54NdAtKmNttKBfSsN3WrtetL/vd9nJq3Y1H7OQdtyyPG9stp7YAXHzCPkP1PpuLYfufmYvl1BZJkiRJ2h27e9ezb00MKWu/723lE7egnDD59pSSJEmSJEkaYrubKJp8q8mpt6B8Zbv72THAgxND1CRJkiRJkjTcZhzLk+TDwBhwUJJtwLnA+cAVSc4CvgGc0qpfDZwIbAF+ALxqAWKWJEmSJEnSApgxUVRVL++z6fgedQt49VyDkiRJkiRJ0uJbPrMDS5IkSdIIWNvjhjWSNCx2d44iSZIkSZIkLTP2KJKWwC3ffJAze1xJ2nr+SUsQjSRJkiRJHRNFkiRJkrQM9BvS5sVISbNhomgEOaZZ0ihKshX4HvATYGdVrUtyAHA5sBbYCry0qrYnCfAuujtp/gA4s6puWoq4JUkadWvP+SQbj9j5iB7tJo8k9eMcRZKkxfSvq+rIqlrX1s8Brq2qw4Fr2zrAC4HD288G4H2LHqkkSZK0AtmjSJK0lNYDY235EmAceGMrv7SqCrguyX5JDq6qe5YkSknSkktyCHAp8FPAPwGbqupd9k7dPbMdpWAPJGnlMFEkSVosBfxNkgL+pKo2Aasmkj9VdU+SJ7e6q4G7Jj12Wyt7RKIoyQa6HkesWrWK8fHxWQe16rGw8Yidu5Tvzr6G1Y4dO5ZVe6ayfaNvubSx17EElk/7hsBOYGNV3ZRkX+DGJNcAZ9L1Tj0/yTl0vVPfyCN7px5N1zv16CWJXJJGiIkiSdJiObaq7m7JoGuS/P00ddOjrHYp6JJNmwDWrVtXY2Njsw7qjz54Fe+4ZdfT4dbTZr+vYTU+Ps7uvDajwvaNvuXSxl53NAW4+IR9lkX7llq7sDBxceF7SW6nu4hg79RF0KsHkr2MpOXJRJEkaVFU1d3t971JPg4cBXxr4kN7koOBe1v1bcAhkx6+Brh7UQOWJA2tJGuBZwPXs4S9Uyd6i/XrTTZM+vWgnYuF6ik3Sr3wRilWGK14jXXpmCiSJC24JPsAj2pXgPcBng+8GdgMnAGc335f1R6yGTg7yWV0wwQe9AqwJAkgyeOBjwKvrarvdlMR9a7ao2xee6dO9Ibr15tsmGw8YmfPHrRzsVC9b0epl+EoxQqjFa+xLh0TRUPCW95LWuZWAR9vH+b3BD5UVX+d5AbgiiRnAd8ATmn1r6abfHQL3QSkr1r8kCVJwybJXnRJog9W1cdasb1TJWkemSiShsh8jP12/LiGUVXdCTyrR/l3gON7lBfw6kUITZI0ItpdzC4Ebq+qP5y0yd6pkjSPTBRJkiRJGgXHAqcDtyS5uZX9Ll2CyN6pkjRPTBQtMoeYSZIkSbNXVZ+j97xDYO9USZo3JoqkIdcvuehwMkmSJEnSfDNRJI0oe6dJkiRJkuabiSJJkiRJ0qzZ811anh611AFIkiRJkiRpOJgokiRJkiRJEuDQM2lFsFuwJEmSJGkQJooWkJMNS5IkSVppvEgpjTaHnkmSJEmSJAmwR5G0ovW62uOVHkmSJElauUwUSZIkSZIWnBcppdFgomgeOBeRJEmSJElaDkwUzYIJIUmSJEmStJyZKJL0CLNNiG48YidnTnmMXYglSZIkaTQtWKIoyQnAu4A9gPdX1fkL9VyShstsk00mltSP5xJJ0lx5LhluvT43bjxiJ2OLH4qkZkESRUn2AN4L/BKwDbghyeaqum0hnm8uph6YJnpH+MVVkpbWKJ1LJEnDyXOJJM3eQvUoOgrYUlV3AiS5DFgPeECWtIvFnv+rVyJ47TmfdBjd8PFcIkmaK88lkjRLqar532lyMnBCVf16Wz8dOLqqzp5UZwOwoa0+DfjKvAeyew4C7lvqIOaJbRley6k9tqW3p1bVk+ZpXyvSIp5LltN7uJ/l3kbbN/qWext3t32eS+ZoEc4lo/TeNdaFMUqxwmjFa6zzY9bnkoXqUZQeZY/ISFXVJmDTAj3/bkvyhapat9RxzAfbMryWU3tsixbQopxLVsLffbm30faNvuXexuXeviG3oOeSUfrbGuvCGKVYYbTiNdal86gF2u824JBJ62uAuxfouSRJy5PnEknSXHkukaRZWqhE0Q3A4UkOTfJo4FRg8wI9lyRpefJcIkmaK88lkjRLCzL0rKp2Jjkb+BTdbSgvqqpbF+K5FsDQDYebA9syvJZTe2yLFsQinktWwt99ubfR9o2+5d7G5d6+obUI55JR+tsa68IYpVhhtOI11iWyIJNZS5IkSZIkafQs1NAzSZIkSZIkjRgTRZIkSZIkSQJWaKIoySFJPpPk9iS3JnlNjzpPTPKXSf6u1XnVUsQ6kySPSfL5SXH+fo86eye5PMmWJNcnWbv4kc5swLa8LsltSb6U5NokT12KWGcySFsm1T05SSUZ2tspDtqeJC9tf59bk3xoseMcxIDvs59ux4gvtvfaiUsRq+ZXkhOSfKUdC8/psX0kjpX9DNC+kTh+TmemNk6qN/TH1V4Gad8oHGf7GeA9OtLH3iQXJbk3yZf7bE+Sd7f2fynJcxY7Rs2fQY9HS6XX+zHJAUmuSXJH+73/UsY4od93s2GMt9/nyHSTpV/fYr083cTpQyHJHu24+om2PpSxJtma5JYkNyf5QisbuvcAQJL9klyZ5O/b+/YXhjXW3VZVK+4HOBh4TlveF/j/gGdMqfO7wNvb8pOA+4FHL3XsPdoS4PFteS/geuCYKXX+HfDHbflU4PKljnsObfnXwOPa8m+Nclvatn2BzwLXAeuWOu45/m0OB74I7N/Wn7zUcc+hLZuA32rLzwC2LnXc/sz5774H8FXgZ4BHA3/X47g/EsfKObRvJI6fc2ljqzcSx9Xd/BuOxHF2Du0b6WMv8IvAc4Av99l+IvBX7Tx0DHD9Usfsz27/rQc6Hi1xjLu8H4H/ApzTls+hfddZ6h/6fDcbxnj7fY4ErgBObeV/PHEsG4Yf4HXAh4BPtPWhjBXYChw0pWzo3gMtlkuAX2/Ljwb2G9ZYd/dnRfYoqqp7quqmtvw94HZg9dRqwL5JAjyeLlG0c1EDHUB1drTVvdrP1BnK19O9mQGuBI5v7Roqg7Slqj5TVT9oq9cBaxYxxIEN+HcBeAvdQeWHixXb7hiwPb8BvLeqtrfH3LuIIQ5swLYU8IS2/ETg7kUKTwvnKGBLVd1ZVT8GLqM7Nk42EsfKPmZs36gcP6cxyN8QRuS42sMg7RuJ42wfg7RvpI+9VfVZus+L/awHLm3noeuA/ZIcvDjRaZ4NejxaMn3ej5PPc5cAL1nUoPqY5rvZ0MU7zefI4+g+O8CQxAqQZA1wEvD+th6GNNY+hu49kOQJdInYCwGq6sdV9QBDGOtcrMhE0WRtaMGz6bLBk70HeDrdh5RbgNdU1T8tanADat0JbwbuBa6pqqltWQ3cBd0tQoEHgQMXN8rBDNCWyc6iuzI3lGZqS5JnA4dU1SeWJMBZGuBv87PAzyb5X0muS3LC4kc5mAHach7wq0m2AVcDv73IIWr+PXQcbLax6wWCkTlW9jBI+yYb6uNnHzO2cdSOq1MM8jccmeNsD4O07zyW97F3tv+nGl6j+rdcVVX3QJecAZ68xPHsYsp3s6GMd+rnSLreZQ+0zw4wXO+HdwJvACa+xx7I8MZawN8kuTHJhlY2jO+BnwG+DfxZG9L3/iT7MJyx7rYVnShK8njgo8Brq+q7Uza/ALgZeApwJPCelj0cOlX1k6o6ku7q8FFJnjmlSq8r4r16tyy5AdoCQJJfBdYB/3Ux45uN6dqS5FHABcDGpYpvtgb42+xJNyxiDHg58P4k+y1ulIMZoC0vBy6uqjV0QwU+0P5mGl2DHAdH5ljZw8Cxj8Lxs49p2ziKx9UpBvkbjsxxtodB2rfcj72jfIzRI/m3XAAzfDcbGlM/R9J1Ltil2uJGtaskLwLuraobJxf3qLrksTbHVtVzgBcCr07yi0sdUB970g3rfF9VPRv4Pt1Qs2VlOZ18ZyXJXnQHog9W1cd6VHkV8LHWvXAL8DXgny9mjLPVuryNA1OvMG4DDgFIsiddd+7pukYvuWnaQpLnAf8ReHFV/WiRQ5u1Pm3ZF3gmMJ5kK93Y5s0ZgYlXZ3ifXVVV/1hVXwO+QveFZmhN05az6MZvU1V/CzwGOGhRg9N8e+g42Kxh12EtI3esnGSQ9o3c8XOKmdo4ssfVZtD36EgdZycZpH3L/dg70P+pRsKo/i2/NTHcsf0emuGrfb6bDW288IjPkcfQDSXds20alvfDscCL2znxMrohZ+9kOGOlqu5uv+8FPk6XhBvG98A2YNukEQlX0iWOhjHW3bYiE0VtbOaFwO1V9Yd9qn0DOL7VXwU8DbhzcSIcXJInTVxNTPJY4HnA30+pthk4oy2fDHy6qoYlc/yQQdrShhX8Cd2XnKH955upLVX1YFUdVFVrq2ot3XwhL66qLyxJwDMY8H32F3ST5ZLkILohEqP6PzP5///pdF9Wvr2YcWre3QAc3u708Wi6yao3T6kzEsfKPmZs36gcP6cxbRtH7bjawyDv0ZE4zvYxSPuW+7F3M/DKdI4BHpwYpqCRM8j7eRhNPs+dAVy1hLE8ZJrvZkMXb5/PkbcDn6H77ABDEmtVvamq1rRz4ql0n2tOYwhjTbJPkn0nloHnA19mCN8DVfUPwF1JntaKjgduYwhjnYs9Z66yLB0LnA7c0saXQneXs58GqKo/ppsM8+Ikt9B10XtjVd23FMHO4GDgkiR70CX+rqiqTyR5M/CFqtpMd+D9QJItdFfHT126cKc1SFv+K93k4h/pzil8o6pevGQR9zdIW0bJIO35FPD8JLcBPwF+p6q+s3Qh9zVIWzYCf5rkP9B1xz1zhBIG6qGqdiY5m+59ugdwUVXdOqLHyl0M2L5ROX72NGAbR9aA7RuV4+wuBmzfSB97k3yYbljgQenmWTqXbqLbic+WV9MNqdsC/ICu97pGUL/38xKH9Qh93o/nA1ckOYsuMXvK0kX4CP2+mw1jvP0+R94GXJbkrXR3p7xwKYOcwRsZvlhXAR9vn0/2BD5UVX+d5AaG7z0A3Rx6H2yJ4jvpjuePYjhj3S0ZofOvJEmSJEmSFtCKHHomSZIkSZKkXZkokiRJkiRJEmCiSJIkSZIkSY2JIkmSJEmSJAEmiiRJkiRJktSYKJIkSZIkSRJgokiSJEmSJEmNiSJJkiRJkiQBJookSZIkSZLUmCiSJEmSJEkSYKJIkiRJkiRJjYkiSZIkSZIkASaKJEmSJEmS1JgokiRJkiRJEmCiSJIkSZIkSY2JIkmSJEmSJAEmiiRJkiRJktSYKJIkSZIkSRJgokgjKsnWJM+b77qSpNGQ5OIkb53nfZ6Z5HOT1o9NckeSHUleMp/PNek5KslhbfmPk/ynedjnrUnG+mwbS7Jtrs8hSctJkvOS/PkcHv/QcXeu+5rr8/fY9ojj/nR1pQl7LnUA0jBLMg78eVW9f6ljkaRRkmQr8OtV9T8Wov4ieTPwnqp612I8WVX923naz8/Nx34kSbtKcjGwrap+b6JsqY+7s3n+yXWTnAccVlW/uhBxaXTZo0iSJKm3pwK37s4Dk3gxTpIkjSQTRVpySd6Y5JtJvpfkK0mOnzqkYLqu8q1755VJLm/7uCnJs6ZUOzLJl5I82Oo9pj12/ySfSPLtJNvb8pq27W3A/wW8pw07eE8r/+dJrklyf4v3pZNiOTHJbS2ObyZ5/Ty/XJI09JJ8APhp4C/b8fMNrfzFrcv7A0nGkzx9hvofSfIP7dj92SQDXTFNcliS/9ked1+Sy1v52jbUa89JdceT/HqPfXwV+JlJMe09dSjz5OEFk/Z9VpJvAJ/uE9vvJLknyd1Jfm3Ktqnnvt9IsqWdbzYneUor/5etXYe09We11/Sft/WH4kzy2Lbf7UluA35+ynM+JclH23nwa0n+/SCvsSQNmyTnJLlyStm7kry7LT+lHUvvb8fW35hmXz3PP0k2AKcBb2jnhr9s5X2nukhyTJL/3Y7Tf5dphn21Nny1fZe4LcmvTNn+G0lun7T9OVOff4Dj/tYkz0tyAvC7wMtaW/4uySlJbpxSf2OSv+gXs5YnE0VaUkmeBpwN/HxV7Qu8ANi6G7taD3wEOAD4EPAXSfaatP2lwAnAocC/AM5s5Y8C/ozuqvFPA/8HeA9AVf1H4P8Fzq6qx1fV2Un2Aa5pz/Fk4OXAf5/05eVC4DdbW55Jny8KkrScVdXpwDeAX27Hz/+S5GeBDwOvBZ4EXE2XhHl0r/ptV38FHE53vL0J+OCAIbwF+Btgf2AN8Ee70YZ/NiWmHw340P8beDrd+ewR2ofy1wO/RNeuvvPnJTkO+M9056+Dga8Dl7XY/jfwJ8AlSR4LfAD4var6+x67Ohf4Z+3nBcAZk57jUcBfAn8HrAaOB16bZJfYJWkEfBg4MckTAJLsQXcM/dCk7duApwAnA3+Q5Pg+++p5/qmqTW35v7Rzwy9PF1CS1cAngbfSfU95PfDRJE/q85Cv0l2ofiLw+8CfJzm47esU4DzglcATgBcD3+mxj77H/cmq6q+BPwAub215FrAZODTtQk7zq3TnGa0gJoq01H4C7A08I8leVbW1qr66G/u5saqurKp/BP4QeAxwzKTt766qu6vqfroPxUcCVNV3quqjVfWDqvoe8Da6D/n9vAjYWlV/VlU7q+om4KN0JxuAf2xteUJVbW/bJUnwMuCTVXVNO1b/N+CxwL/s94CquqiqvteSNOcBz0ryxAGe6x/pLgA8pap+WFWfm+kB8+i8qvp+Vf2fHtteCvxZVX25qr5P16Z+TgMuqqqbWvvfBPxCkrUTz0P3ReLzwN3Ae/vs56XA26rq/qq6C3j3pG0/Dzypqt5cVT+uqjuBPwVOHaCdkjRUqurrdEmdiZsPHAf8oKquaz0w/xXwxnZeuBl4P3B6n33t7vlnql8Frq6qq6vqn6rqGuALwIl9nvcj7TvLP1XV5cAdwFFt86/TJahuqM6W1uappjvuT6u19/IWN+1i+FrgE4PuQ8uDiSItqaraQnd1+Tzg3iSXTXStn6W7Ju3zn3j4asGEf5i0/APg8QBJHpfkT5J8Pcl3gc8C+7UrEL08FTi6dR19IMkDdB/mf6pt/3/oDvxfTzfs4Rd2oy2StBw9ha5XDPDQsfouup4su0iyR5LzWxf87/Jwb9ODBniuNwABPp9uqNuvzfSAeXTXNNueMmV7rw/4k+tOfr120F05Xt3W/xG4mK736juqqnbjOZ8KPGXKOe13gVXTxCVJw+xDdD3+AV7Bw72JngLc3y4MT/g6Pc5Bczz/TPVU4JQpx9l/RddTdBdJXpnk5kl1nznpeQ+h63E0k9mca3q5BHhFktAl0q6YRa9aLRMmirTkqupDVfWv6A6kBbwd+D7wuEnVfqrXYyc5ZGKhdaVfQ3eFdSYbgacBR1fVE4BfnNjNRHhT6t8F/M+q2m/Sz+Or6rdaW26oqvV03VT/ArhigBgkaTmaevy8m+44D0D7AHoI8M0+9V9BN6z4eXQ9Z9ZOPHTGJ676h6r6jap6CvCbdEOED6M7t8Dszi+TDXJu6pewAbiHSecruiHP/Ux9vfYBDqS9Xm04w7l0w6ffkWTv3XjOu4CvTTmn7VtVPa90S9II+Agwlm7O0V/h4UTR3cABSfadVPenefgcNNlM55/pjvNT3QV8ee2kjQAAIABJREFUYMpxdp+qOn9qxSRPpevVeTZwYFXtB3x50vPeRTecbCazOdfs0paqug74Md0QuFfgsLMVyUSRllSSpyU5rn3A/SHdHEE/AW6mG2N8QJKfout1NJ3nJvk36SYofS3wI+C6AULYtz3nA0kOoPvQPdm36CYznfAJ4GeTnJ5kr/bz80menuTRSU5L8sR2pfe7rS2StBJNPX5eAZyU7oYFe9El6n8E/O8+9fdt279Dl5z5g0GfuE3Guaatbqf7IPyTqvo23ZeCX21XjH+NwT50T7gZOLUd+9fx8LDjQV0BnJnkGUkex67nnMk+BLwqyZHtHPkHwPVVtbUl2S6mmxfvLLovBW+Z5jnflO7mDWuA35607fPAd9PdVOKx7TV5ZpKf770rSRpu7Tg/TpdE/1pV3d7K76I73/znJI9J8i/ojp+95r6b6fwz9Xw1nT8HfjnJC9ox9jHpbtKzpkfdfejOV98GSPIquh5FE94PvD7Jc9M5rCWXppruuD/Vt4C17UL7ZJfSzdu6c5GHb2tImCjSUtsbOB+4j2542JPpur1/gG5yza10E5JePsN+rqKb/2I7XRfJf9OSNTN5J90cGffRJZb+esr2dwEnp7trwLtbd9Xn083fcHeL+e2tHbTn3tq6qf5b2vheSVqB/jPwe637/Our6it0x8Q/ojvm/jLdRNE/7lWf7kPq1+kSO7cxWPJ/ws8D1yfZQTcx52uq6mtt228Av0P3BeDneDhRNYj/RJdY2k43yeiHpq/+SFX1V3TnnU8DW5jmhgdVdW17vo/SJYL+GQ/PHfTv6YaH/ac25OxVdEml/6vHrn6f7nX8Gt359KErw1X1E7q/w5Ft+310X0R2Zx4OSRoWH6LrDTT1GP1yut5BdwMfB85tcwZNNdP550K6OUkfyAx3A2sJqvV032++Tdcr6Hfo8T28qm4D3gH8LV0C5wjgf03a/hG6+VQ/BHyPbvTCAT2etu9xv4ePtN/fSTJ5btUP0CWp7E20QqX/kHZpNCQ5DzisqkzKSJIkSdIcpLuj5r3Ac6rqjqWOR4vPHkWSJEmSJGnCbwE3mCRaufZc6gAkSZIkSdLSS7KVbgLtlyxxKFpCDj2TJEmSJEkS4NAzSZIkSZIkNUMx9Oyggw6qtWvX9t3+/e9/n3322WfxAhpCvgYdXwdfgwnD/jrceOON91XVk5Y6jpXEc8lgfB18DSb4Ogz/a+C5ZPHNdC4ZZsP+fp6r5dw+2zaaRqVtu3MuGYpE0dq1a/nCF77Qd/v4+DhjY2OLF9AQ8jXo+Dr4GkwY9tchydeXOoaVxnPJYHwdfA0m+DoM/2vguWTxzXQuGWbD/n6eq+XcPts2mkalbbtzLnHomSRJkqSRkGRrkluS3JzkC63sgCTXJLmj/d6/lSfJu5NsSfKlJM9Z2uglaTSYKJIkSZI0Sv51VR1ZVeva+jnAtVV1OHBtWwd4IXB4+9kAvG/RI5WkEWSiSJIkSdIoWw9c0pYv4eHbeq8HLq3OdcB+SQ5eigAlaZQMxRxFkiRJkjSAAv4mSQF/UlWbgFVVdQ9AVd2T5Mmt7mrgrkmP3dbK7pm8wyQb6HocsWrVKsbHxxe2BQtkx44dIxv7IJZz+2zbaFrObRs4UZRkD+ALwDer6kVJDgUuAw4AbgJOr6ofJ9kbuBR4LvAd4GVVtXXeI5ckSZK00hxbVXe3ZNA1Sf5+mrrpUVa7FHTJpk0A69atq1GYnLaXUZlYd3ct5/bZttG0nNs2m6FnrwFun7T+duCCNhZ4O3BWKz8L2F5VhwEXtHqSJEmSNCdVdXf7fS/wceAo4FsTQ8ra73tb9W3AIZMevga4e/GilaTRNFCPoiRrgJOAtwGvSxLgOOAVrcolwHl0E8Stb8sAVwLvSZKq2iV7r8Wx9pxP7lK29fyTliASSdKw63XOAM8bkpZekn2AR1XV99ry84E3A5uBM4Dz2++r2kM2A2cnuQw4GnhwYoia+vM8IGnQoWfvBN4A7NvWDwQeqKqdbX1ivC9MGgtcVTuTPNjq3zd5h7MZC7ycx/4Nai6vwcYjdu5SNqqvp+8FX4MJvg6SJK04q4CPd9es2RP4UFX9dZIbgCuSnAV8Azil1b8aOBHYAvwAeNXihyxJo2fGRFGSFwH3VtWNScYmintUrQG2PVwwi7HAy3ns36Dm8hqc2atH0Wm7t6+l5nvB12CCr4MkSStLVd0JPKtH+XeA43uUF/DqRQhtJPXrOSRJg/QoOhZ4cZITgccAT6DrYbRfkj1br6LJ430nxgJvS7In8ETg/nmPXJIkSZIkSfNqxkRRVb0JeBNA61H0+qo6LclHgJPp7nw2dSzwGcDftu2fdn4iSZIkSVoa9h6SNBuzuevZVG+km9h6C90cRBe28guBA1v564Bz5haiJEmSJEmSFsOgk1kDUFXjwHhbvpPudpRT6/yQhyeQ0zLgnQ8kSZIkSVoZ5tKjSJIkSZIkScuIiSJJkiRJkiQBJookSZIkSZLUmCiSJEmSJEkSMMvJrDXcvO2lJEmSJEmaC3sUSZIkSZIkCTBRJElaREn2SPLFJJ9o64cmuT7JHUkuT/LoVr53W9/Stq9dyrglSZKklcJEkSRpMb0GuH3S+tuBC6rqcGA7cFYrPwvYXlWHARe0epIkSZIWmHMUDYl+8wttPf+kRY5EkhZGkjXAScDbgNclCXAc8IpW5RLgPOB9wPq2DHAl8J4kqapazJglSZKklcYeRZKkxfJO4A3AP7X1A4EHqmpnW98GrG7Lq4G7ANr2B1t9SZIkSQvIHkWSpAWX5EXAvVV1Y5KxieIeVWuAbZP3uwHYALBq1SrGx8f7xrBjx45pt68UM70OG4/Y2bN8Ob12vhc6vg6+BpIk9WKiSJK0GI4FXpzkROAxwBPoehjtl2TP1mtoDXB3q78NOATYlmRP4InA/VN3WlWbgE0A69atq7Gxsb4BjI+PM932lWKm1+HMfkOhT+v/mFHje6Hj6+BrIElSLw49kyQtuKp6U1Wtqaq1wKnAp6vqNOAzwMmt2hnAVW15c1unbf+08xNJkiRJC89EkSRpKb2RbmLrLXRzEF3Yyi8EDmzlrwPOWaL4JEmSpBXFoWeSpEVVVePAeFu+EziqR50fAqcsamCaNe/YKUnDpd9xWZJmwx5FkiRJkiRJAuxRpCm8CiFJkiRJ0splokiSpBXICwOSJEnqxaFnkiRJkiRJAkwUSZIkSRohSfZI8sUkn2jrhya5PskdSS5P8uhWvndb39K2r13KuCVpVJgokiRJkjRKXgPcPmn97cAFVXU4sB04q5WfBWyvqsOAC1o9SdIMTBRJkiRJGglJ1gAnAe9v6wGOA65sVS4BXtKW17d12vbjW31J0jSczFqSJM3Iya8lDYl3Am8A9m3rBwIPVNXOtr4NWN2WVwN3AVTVziQPtvr3Td5hkg3ABoBVq1YxPj6+kPEvmB07drDxiJ8s2P6X+nXZsWPHksewUGzbaFrObTNRJEmSJGnoJXkRcG9V3ZhkbKK4R9UaYNvDBVWbgE0A69atq7GxsalVRsL4+Djv+Nz3F2z/W08bW7B9D2J8fJxR/dvMxLaNpuXcNhNFkiRJkkbBscCLk5wIPAZ4Al0Po/2S7Nl6Fa0B7m71twGHANuS7Ak8Ebh/8cOWpNFiokiSJC24fkPXtp5/0iJHImlUVdWbgDcBtB5Fr6+q05J8BDgZuAw4A7iqPWRzW//btv3TVbVLjyJJ0iOZKBpBzhMhSZIkPeSNwGVJ3gp8EbiwlV8IfCDJFrqeRKcuUXzzrtf3gY1H7MSvd5Lmg0cSSZIkSSOlqsaB8bZ8J3BUjzo/BE5Z1MAkaRl41FIHIEmSJEmSpOFgokiSJEmSJEmAiSJJkiRJkiQ1MyaKkjwmyeeT/F2SW5P8fis/NMn1Se5IcnmSR7fyvdv6lrZ97cI2QZIkSZIkSfNhkB5FPwKOq6pnAUcCJyQ5Bng7cEFVHQ5sB85q9c8CtlfVYcAFrZ4kSZIkSZKG3Ix3PauqAna01b3aTwHHAa9o5ZcA5wHvA9a3ZYArgfckSduPVoBet+vcev5JSxCJJEmSpPngZ3xp5ZgxUQSQZA/gRuAw4L3AV4EHqmpnq7INWN2WVwN3AVTVziQPAgcC903Z5wZgA8CqVasYHx/v+/w7duyYdvtysPGInT3LJ9o9+TXoV3c2+r2es9n3bPYxX3+/lfBemImvQcfXQZIkSZLm30CJoqr6CXBkkv2AjwNP71Wt/c402ybvcxOwCWDdunU1NjbW9/nHx8eZbvtycGaPDD3A1tPGgEe+Bv3qzsbEfgeNY6776Fd3tlbCe2EmvgYdXwdJkiRJmn+zuutZVT0AjAPHAPslmUg0rQHubsvbgEMA2vYnAvfPR7CSJEmSJElaOIPc9exJrScRSR4LPA+4HfgMcHKrdgZwVVve3NZp2z/t/ESSJEmSJEnDb5ChZwcDl7R5ih4FXFFVn0hyG3BZkrcCXwQubPUvBD6QZAtdT6JTFyBuSZIkSZIkzbNB7nr2JeDZPcrvBI7qUf5D4JR5iU6SJC1rve6iA95JR5IkaanMao4iSZIkSZIkLV8miiRJkiRJkgQMNkeRJElzkuQxwGeBvenOPVdW1blJDgUuAw4AbgJOr6ofJ9kbuBR4LvAd4GVVtXVJgpckST05fFhankwUSZIWw4+A46pqR5K9gM8l+SvgdcAFVXVZkj8GzgLe135vr6rDkpwKvB142VIFP+omf5DfeMROzuzzwV6SJEly6JkkacFVZ0db3av9FHAccGUrvwR4SVte39Zp249PkkUKV5IkSVqx7FEkSVoUSfYAbgQOA94LfBV4oKp2tirbgNVteTVwF0BV7UzyIHAgcN+UfW4ANgCsWrWK8fHxvs+/Y8eOabcvZxuP2PnQ8qrHPnJ9IfR6nWf7nAv5t1rJ74XJfB18DSRJ6sVEkSRpUVTVT4Ajk+wHfBx4eq9q7Xev3kO1S0HVJmATwLp162psbKzv84+PjzPd9uXszClDz95xy8Ke/reeNjZtDLu7j/mykt8Lk/k6+BpIktSLiSJJ0qKqqgeSjAPHAPsl2bP1KloD3N2qbYP/v727j7GsrA84/v3xpjRYVgTGze7WtXFtIG4qZgObkDRTwAbBsPwBBIOwS2g3UWhqnbRu7R/W1j+2bXyhqdGuLmEx6kLVlg3QGINMiI3gGwgCsa50A1M2UMuLbojW0V//OM/Qy3DvzJ2de+6959zvJ7mZe57zzJnn/Obee+7znOeFDcBcRBwHnAw8O4ryauV6TW4qSZKk8WdDkSSpdhFxGvDL0kh0InAB1QTV9wCXUa18th24vfzKgbL9zbL/65n5ih5FkiS1nY3vkobNhiJJ0jCsBfaVeYqOAW7LzDsi4lFgf0R8BHgA2Fvy7wU+FxEHqXoSXTmKQkuSJEmTxoYiSVLtMvMh4Kwu6Y8DZ3dJ/zlw+RCKJkmSJKnDMaMugCRJkiQtJyJeHRHfiojvR8QjEfHhkv7GiLg/In4UEbdGxAkl/VVl+2DZv3GU5ZekprChSJIkSVIT/AI4LzN/F3grcGFEbKWa8+7jmbkJeA64ruS/DnguM98EfLzkkyQtw4YiSZIkSWMvK0fK5vHlkcB5wJdK+j7g0vJ8W9mm7D8/ImJIxZWkxnKOoiFz1QJJkiTp6JRFEb4LvAn4JPBj4PnMnC9Z5oB15fk64EmAzJyPiBeA1wE/GWqhJalhbCiSJEmS1AiZ+SvgrRGxBvgX4Ixu2crPbr2HcnFCROwEdgJMTU0xOzs7mMIOyMzm+eUzAVMn9p+3bnXE8MiRI2P3vxkUz62Z2nxuNhRJkiRJapTMfD4iZoGtwJqIOK70KloPPFWyzQEbgLmIOA44GXi2y7H2AHsAtmzZktPT0/WfwArs6HNEwszmeT768HhU7w5dNT3wY87OzjJu/5tB8dyaqc3n5hxFkiRJksZeRJxWehIREScCFwCPAfcAl5Vs24Hby/MDZZuy/+uZ+YoeRZKklxuPJmdJkrRqzoMnqeXWAvvKPEXHALdl5h0R8SiwPyI+AjwA7C359wKfi4iDVD2JrhxFoSWpaWwokiRJkjT2MvMh4Kwu6Y8DZ3dJ/zlw+RCKpkV63bg4tPviIZdE0tFw6JkkSZIkSZIAG4okSZIkSZJU2FAkSZIkSZIkwDmKJEnSGFrJxNzOeSFJkjQ49iiSJEmSJEkSYEORJEmSJEmSChuKJEmSJEmSBNhQJEmSJEmSpMKGIkmSJEmSJAGueqYh6bV6jSvVSJIkSZI0PpZtKIqIDcAtwOuBXwN7MvPGiDgFuBXYCBwCrsjM5yIigBuBi4AXgR2Z+b16ii9JkiadNyMkSZIGp5+hZ/PATGaeAWwFro+IM4FdwN2ZuQm4u2wDvAPYVB47gU8NvNSSJEmSJEkauGUbijLz8EKPoMz8GfAYsA7YBuwr2fYBl5bn24BbsnIfsCYi1g685JIkSZIkSRqoFc1RFBEbgbOA+4GpzDwMVWNSRJxesq0Dnuz4tbmSdnjRsXZS9ThiamqK2dnZnn/3yJEjS+5vkpnN8yvKv3DenTFY6TGWOu5iKzl2ncfopU2vhaNlDCrGQZIkSZIGr++Goog4Cfgy8L7M/Gk1FVH3rF3S8hUJmXuAPQBbtmzJ6enpnn97dnaWpfY3yY4e8yj0cuiqaeDlMVjpMZY67mIrOXadx+ilTa+Fo2UMKsZBkiRJkgavr4aiiDieqpHo85n5lZL8dESsLb2J1gLPlPQ5YEPHr68HnhpUgSVJkiSpbXpNzC9Jw7bsHEVlFbO9wGOZ+bGOXQeA7eX5duD2jvRrorIVeGFhiJokSZIkSZLGVz89is4FrgYejogHS9oHgd3AbRFxHfAEcHnZdxdwEXAQeBG4dqAlliRJkiRJUi2WbSjKzG/Qfd4hgPO75E/g+lWWS5LUIhGxAbgFeD3wa2BPZt4YEacAtwIbgUPAFZn5XOnNeiPVjYcXgR0LK3BKkiRJqs+yQ88kSRqAeWAmM88AtgLXR8SZwC7g7szcBNxdtgHeAWwqj53Ap4ZfZEmSJGny9L3qmVSHXpP2Hdp98ZBLIqlOZa66w+X5zyLiMWAdsA2YLtn2AbPAB0r6LaWX6n0RsWZhAYVhl12SJEmaJDYUSZKGKiI2AmcB9wNTC40/ZRXN00u2dcCTHb82V9Je1lAUETupehwxNTXF7Oxsz7975MiRJfe3wczm+WXzTJ3YX7426PX/noTXQj+MgzGQJKkbG4okSUMTEScBXwbel5k/raYi6p61S1q+IiFzD7AHYMuWLTk9Pd3zb8/OzrLU/jbY0cfSyjOb5/now5Nx+T901XTX9El4LfTDOBgDSZK6cY4iSdJQRMTxVI1En8/Mr5TkpyNibdm/FnimpM8BGzp+fT3w1LDKKkmSJE2qybilqFfoNTeQJNWhrGK2F3gsMz/WsesAsB3YXX7e3pF+Q0TsB84BXnB+IkmabK6gKUnDYY8iSdIwnAtcDZwXEQ+Wx0VUDURvj4gfAW8v2wB3AY8DB4HPAO8dQZklSePFFTQlaQjsUTTmFnr+zGye72vuCUkaR5n5DbrPOwRwfpf8CVxfa6EkSY3iCpqSNBw2FEmS1DAOH5Y06Ua1gmadVrsiZRNWtVxNbNu8SqHn1kxtPjcbiiRJkiQ1xihX0KzTakcPNGFVy16rUfajzasUem7N1OZzG+9PEkmSJEkqllpBs/QmcgXNMdatR+yh3RePoCSSlmJDkY6aQx8kSZI0LK6gKUnDYUORJEmSpCZYWEHz4Yh4sKR9kKqB6LaIuA54Ari87LsLuIhqBc0XgWuHW1xJaiYbiiRJkiSNPVfQlKThOGbUBZAkSZIkSdJ4sKFIkiRJkiRJgEPPJElSS7m6jiRJ0srZUDQAvVb/8suoJEmSJElqEoeeSZIkSZIkCbChSJIkSZIkSYVDz9QYG3fdyczmeXYsGurnED9JkiRJkgbDHkWSJEmSJEkCbCiSJEmSJElSYUORJEmSJEmSABuKJEmSJEmSVNhQJEmSJEmSJMCGIkmSJEmSJBXHjboA0mpt3HVn1/RDuy8eckkkSZIkSWo2exRJkiRJkiQJsEeRJEljrVevSUmSJKkOyzYURcRNwDuBZzLzLSXtFOBWYCNwCLgiM5+LiABuBC4CXgR2ZOb36im6JEmSJKnJnEZCGj/9DD27GbhwUdou4O7M3ATcXbYB3gFsKo+dwKcGU0xJkiRJkiTVbdmGosy8F3h2UfI2YF95vg+4tCP9lqzcB6yJiLWDKqwkSZIkSZLqc7RzFE1l5mGAzDwcEaeX9HXAkx355kra4cUHiIidVL2OmJqaYnZ2tucfO3LkyJL7R21m83zX9G5l7pV3OVMnHv3vNlGv2K0kDuP8mlmNcX8/DItxkHQ0Nu66k5nN8+xYNNTBIQ6SNF4WD0mb2TzP9GiKIk2cQU9mHV3SslvGzNwD7AHYsmVLTk9P9zzo7OwsS+0ftcVfNhccumq677zLmdk8z0cfnpy5x3vFbiVx6HaMNhj398OwGIdmcb47SZIkqRmOtuXh6YhYW3oTrQWeKelzwIaOfOuBp1ZTQE2mQazy0+0Y3jGWRuZm4B+BWzrSFua72x0Ru8r2B3j5fHfnUM13d85QS6uJ42SqkiRJlX4ms+7mALC9PN8O3N6Rfk1UtgIvLAxRkyRNLue7kyRJkpph2R5FEfFFYBo4NSLmgA8Bu4HbIuI64Ang8pL9LqqhAgephgtcW0OZJUnt4Hx3fRj0/HSTNuddN853V2nqe2KQjEGzOIxZkoZj2YaizHxXj13nd8mbwPWrLdS4GsRwKEnSsiZyvrtejnZuu14mbc67bpzvrtLU98QgGYPGuRmHMUtS7Sb7m2LNbFiSpCU5350kqW+ZeW9EbFyUvA1eWgxrHzBL1VD00jBm4L6IWLNwzRlOaZdmPUHSOLOhSJI0Kgvz3e3mlfPd3RAR+6nu/jrfnSSpl6EOYx6UOoYAt31o8dSJ7R0O3OZhsJ5bM9lQJEmqnfPdSZKGrJZhzIMy6GHF0P6hxTOb57mipUNF2zwM1nNrpvZ+kkgr0K37r0siS4PjfHeSpJo4jHmC9Bqy5/d2abCOGXUBJEmSJOkoLQxjhlcOY74mKltxGLMk9c0eRZIkSZLGnsOYJWk4bCiSJGkMuAKOJC3NYczqxSFp0mA59EySJEmSJEmAPYokSZJ6crEDSZI0aexRJEmSJEmSJMCGIkmSJEmSJBUOPZN6cFI8SZIkSdKksUeRJEmSJEmSABuKJEmSJEmSVNhQJEmSJEmSJMA5inrqNT+NJEmSJGn8davTOd+otDwbiqQB8UIkSZKkTt58ltRENhRJkiStgKtiSpKkNrOhSKqRlQlJkiRJUpM4mbUkSZIkSZIAexRpwozLOHF7GkmSJEmSxpENRZIkDdG4NFhLkjSJvGErLc+hZ5IkSZIkSQLsUSRJkjQQ3e5Se4dakiQ1jQ1FkiRJkqSJ5pA06f/ZUCRJklQTKx6SJKlpbCiSxkg/k9zObJ5nx647rWRIkiRJY8SbA2qLiWoocu4ADcK4rFjkhUgaf+PyeSFJko5OXXXIzuMu3Age1LGl1ZqohiJJkiRJklbDG0Fqu4lvKPJNLkmSJEmSVJn4hiJJklbCGwwaBIcPS+3itUFLWcnrYyVD3byWqC61NRRFxIXAjcCxwGczc3ddf0vS0lZyERmXC864lEOjNcpriV/6JakdrJeo6fxOomGrpaEoIo4FPgm8HZgDvh0RBzLz0Tr+3mK+kaT+rPbuBqys4cb3plZi1NcSSVLzeS3RJFrJd25vwqqbunoUnQ0czMzHASJiP7ANGOgHspVO6ZWG/b4Y1d/rXB0C6muw8uI5Ul5LNHEG8Xrst7fo4s/RozluL3XeXBjnz+UmlnkCDOVaIjVVnd+DVjJ6od/fX8ogjr2SoX8P/9cLXa+jq/3MH4drSWTm4A8acRlwYWb+Ydm+GjgnM2/oyLMT2Fk2fwf44RKHPBX4ycAL2izGoGIcjMGCcY/DGzLztFEXosm8ltTGOBiDBcZh/GPgtWSVariWjLNxfz2vVpvPz3Nrpqac24qvJXX1KIouaS9rkcrMPcCevg4W8Z3M3DKIgjWVMagYB2OwwDhMBK8lNTAOxmCBcTAGE2Kg15Jx1vbXc5vPz3Nrpjaf2zE1HXcO2NCxvR54qqa/JUlqJ68lkqTV8loiSStUV0PRt4FNEfHGiDgBuBI4UNPfkiS1k9cSSdJqeS2RpBWqZehZZs5HxA3AV6mWobwpMx9ZxSEb3xV0AIxBxTgYgwXGoeW8ltTGOBiDBcbBGLReDdeScdb213Obz89za6bWnlstk1lLkiRJkiSpeeoaeiZJkiRJkqSGsaFIkiRJkiRJwBg1FEXEhRHxw4g4GBG7uux/f0Q8GhEPRcTdEfGGUZSzbsvFoSPfZRGREdHK5fj6iUNEXFFeE49ExBeGXca69fGe+K2IuCciHijvi4tGUc46RcRNEfFMRPygx/6IiH8oMXooIt427DKqGfr9bG26bu+ZiDglIr4WET8qP19b0lv5/omIDeWz8bFyffiTkj5pcXh1RHwrIr5f4vDhkv7GiLi/xOHWMrkvEfGqsn2w7N84yvIPUkQcW66Vd5TtiYuB2qXN9aa214XaXMdpc91lIuskmTnyB9XEcj8Gfhs4Afg+cOaiPL8P/EZ5/h7g1lGXexRxKPleA9wL3AdsGXW5R/R62AQ8ALy2bJ8+6nKPIAZ7gPeU52cCh0Zd7hri8HvA24Af9Nh/EfBvQABbgftHXWYf4/fo97O1DY9u7xng74Bd5fku4G/L81a+f4C1wNvK89cA/1E+IyctDgGcVJ4fD9xfzu824MqS/umO68h7gU+X51e26XsW8H7gC8AdZXviYuCjPY8+vyM2st7U7/WahtaF+vzfNbKO0+e5Nbbu0u371aL9rfsuMS49is4GDmbm45n5v8Dkpdh0AAAEn0lEQVR+YFtnhsy8JzNfLJv3AeuHXMZhWDYOxd9QfeH9+TALN0T9xOGPgE9m5nMAmfnMkMtYt35ikMBvlucnA08NsXxDkZn3As8ukWUbcEtW7gPWRMTa4ZRODdLvZ2vj9XjPbAP2lef7gEs70lv3/snMw5n5vfL8Z8BjwDomLw6ZmUfK5vHlkcB5wJdK+uI4LMTnS8D5ERFDKm5tImI9cDHw2bIdTFgM1Dptrje1vS7U5jpOq+suk1gnGZeGonXAkx3bcyWtl+uoWuzaZtk4RMRZwIbMvGOYBRuyfl4PbwbeHBH/HhH3RcSFQyvdcPQTg78C3h0Rc8BdwB8Pp2hjZaWfHZpMk/46mcrMw1A1ogCnl/TWx6UMHTqLqjfNxMWhDLl6EHgG+BrV3d7nM3O+ZOk815fiUPa/ALxuuCWuxSeAPwd+XbZfx+TFQO3S5npT2+tCba7jTHrdpXXfJcaloajb3ZrsmjHi3cAW4O9rLdFoLBmHiDgG+DgwM7QSjUY/r4fjqLpmTgPvAj4bEWtqLtcw9RODdwE3Z+Z6qu6OnyuvkUnS92eHJpqvk+5aHZeIOAn4MvC+zPzpUlm7pLUiDpn5q8x8K1VvgrOBM7plKz9bF4eIeCfwTGZ+tzO5S9bWxkCt1OZ6U9vrQm2u40x63aV1149x+cfMARs6ttfTpStaRFwA/CVwSWb+YkhlG6bl4vAa4C3AbEQcohr/eKBpk7j1oZ/Xwxxwe2b+MjP/E/gh1YdqW/QTg+uo5lkgM78JvBo4dSilGx99fXZo4k366+Tphe7P5edCN/bWxiUijqdqJPp8Zn6lJE9cHBZk5vPALNX3hjURcVzZ1XmuL8Wh7D+ZpbvZN8G5wCXlO9N+qiFnn2CyYqD2aXO9qe11oTbXcSa97tK67xLj0lD0bWBTWYXiBKoJBA90ZijdDP+J6sOuKWM1V2rJOGTmC5l5amZuzMyNVGOOL8nM74ymuLVZ9vUA/CvVRH1ExKlU3TQfH2op69VPDJ4AzgeIiDOoPmz/e6ilHL0DwDVlpYGtwAsLQ0ukDv28n9rsALC9PN8O3N6R3rr3T5lTZi/wWGZ+rGPXpMXhtIW70BFxInAB1XxN9wCXlWyL47AQn8uAr2dmo++GZuZfZOb68p3pSqpzuooJioFaqc31prbXhdpcx5n0ukvrvksct3yW+mXmfETcAHyVasb0mzLzkYj4a+A7mXmAqsvkScA/l3kFn8jMS0ZW6Br0GYfW6zMOXwX+ICIeBX4F/Flm/s/oSj1YfcZgBvhMRPwpVdfGHW37QhsRX6TqentqGc/8IaoJWcnMT1ONb74IOAi8CFw7mpJqnPV6P424WLXo8Z7ZDdwWEddRfUm7vGRv6/vnXOBq4OEyPw/AB5m8OKwF9kXEsVQ3Bm/LzDvKdXN/RHyEamWdvSX/XqphAAepetFcOYpCD8kHMAZqqDbXm9peF2pzHaftdZdJrJNEQ/43kiRJkiRJqtm4DD2TJEmSJEnSiNlQJEmSJEmSJMCGIkmSJEmSJBU2FEmSJEmSJAmwoUiSJEmSJEmFDUWSJEmSJEkCbCiSJEmSJElS8X8doFuvnDgvvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1080 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "data.hist(bins=50, figsize=(20,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002E123829B88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E124953148>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E124BF8AC8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002E1242FF4C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E124319E88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E12434E8C8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002E124381D08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E1243C2748>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E1243CC308>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002E124404508>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E12446BAC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002E1244A5B48>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaler = StandardScaler().fit(data)\n",
    "features_std = scaler.transform(data)\n",
    "df = pd.DataFrame(data=features_std[:],\n",
    "                  index=list(range(0,len(features_std))),\n",
    "                  columns=column_names)\n",
    "\n",
    "df.hist(bins=50, figsize=(20, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop Redundent Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using Pearson Correlation\n",
    "plt.figure(figsize=(13,13))\n",
    "cor = df.corr()\n",
    "sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fixed_acidity            0.089749\n",
       "volatile-acidity         0.225440\n",
       "residual-sugar           0.092756\n",
       "chlorides                0.183939\n",
       "total-sulpher-dioxide    0.170924\n",
       "density                  0.268696\n",
       "pH                       0.083687\n",
       "sulphates                0.051858\n",
       "alcohol                  0.383280\n",
       "quality                  1.000000\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlation with output variable\n",
    "cor_target = abs(cor[\"quality\"])\n",
    "#Selecting highly correlated features\n",
    "relevant_features = cor_target[cor_target>0.05]\n",
    "relevant_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>fixed_acidity</th>\n",
       "      <th>volatile-acidity</th>\n",
       "      <th>residual-sugar</th>\n",
       "      <th>free-sulphur-dioxide</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.172097</td>\n",
       "      <td>-0.081770</td>\n",
       "      <td>2.821349</td>\n",
       "      <td>0.569932</td>\n",
       "      <td>-1.393152</td>\n",
       "      <td>0.70949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.657501</td>\n",
       "      <td>0.215896</td>\n",
       "      <td>-0.944765</td>\n",
       "      <td>-1.253019</td>\n",
       "      <td>-0.824276</td>\n",
       "      <td>0.70949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.475751</td>\n",
       "      <td>0.017452</td>\n",
       "      <td>0.100282</td>\n",
       "      <td>-0.312141</td>\n",
       "      <td>-0.336667</td>\n",
       "      <td>0.70949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.409125</td>\n",
       "      <td>-0.478657</td>\n",
       "      <td>0.415768</td>\n",
       "      <td>0.687541</td>\n",
       "      <td>-0.499203</td>\n",
       "      <td>0.70949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.409125</td>\n",
       "      <td>-0.478657</td>\n",
       "      <td>0.415768</td>\n",
       "      <td>0.687541</td>\n",
       "      <td>-0.499203</td>\n",
       "      <td>0.70949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed_acidity  volatile-acidity  residual-sugar  free-sulphur-dioxide  \\\n",
       "0       0.172097         -0.081770        2.821349              0.569932   \n",
       "1      -0.657501          0.215896       -0.944765             -1.253019   \n",
       "2       1.475751          0.017452        0.100282             -0.312141   \n",
       "3       0.409125         -0.478657        0.415768              0.687541   \n",
       "4       0.409125         -0.478657        0.415768              0.687541   \n",
       "\n",
       "    alcohol  quality  \n",
       "0 -1.393152  0.70949  \n",
       "1 -0.824276  0.70949  \n",
       "2 -0.336667  0.70949  \n",
       "3 -0.499203  0.70949  \n",
       "4 -0.499203  0.70949  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['citric-acid', 'chlorides', 'total-sulpher-dioxide', 'density', 'pH', 'sulphates'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  fixed_acidity  volatile-acidity\n",
      "fixed_acidity          1.000000         -0.022697\n",
      "volatile-acidity      -0.022697          1.000000\n",
      "                residual-sugar  fixed_acidity\n",
      "residual-sugar        1.000000       0.089021\n",
      "fixed_acidity         0.089021       1.000000\n"
     ]
    },
    {
     "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>volatile-acidity</th>\n",
       "      <th>residual-sugar</th>\n",
       "      <th>free-sulphur-dioxide</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.081770</td>\n",
       "      <td>2.821349</td>\n",
       "      <td>0.569932</td>\n",
       "      <td>-1.393152</td>\n",
       "      <td>0.70949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.215896</td>\n",
       "      <td>-0.944765</td>\n",
       "      <td>-1.253019</td>\n",
       "      <td>-0.824276</td>\n",
       "      <td>0.70949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.017452</td>\n",
       "      <td>0.100282</td>\n",
       "      <td>-0.312141</td>\n",
       "      <td>-0.336667</td>\n",
       "      <td>0.70949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.478657</td>\n",
       "      <td>0.415768</td>\n",
       "      <td>0.687541</td>\n",
       "      <td>-0.499203</td>\n",
       "      <td>0.70949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.478657</td>\n",
       "      <td>0.415768</td>\n",
       "      <td>0.687541</td>\n",
       "      <td>-0.499203</td>\n",
       "      <td>0.70949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volatile-acidity  residual-sugar  free-sulphur-dioxide   alcohol  quality\n",
       "0         -0.081770        2.821349              0.569932 -1.393152  0.70949\n",
       "1          0.215896       -0.944765             -1.253019 -0.824276  0.70949\n",
       "2          0.017452        0.100282             -0.312141 -0.336667  0.70949\n",
       "3         -0.478657        0.415768              0.687541 -0.499203  0.70949\n",
       "4         -0.478657        0.415768              0.687541 -0.499203  0.70949"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df[['fixed_acidity', 'volatile-acidity']].corr())\n",
    "print(df[['residual-sugar', 'fixed_acidity']].corr())\n",
    "\n",
    "df.drop('fixed_acidity', axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      residual-sugar  free-sulphur-dioxide\n",
      "residual-sugar              1.000000              0.299098\n",
      "free-sulphur-dioxide        0.299098              1.000000\n",
      "                      free-sulphur-dioxide   alcohol\n",
      "free-sulphur-dioxide              1.000000 -0.250104\n",
      "alcohol                          -0.250104  1.000000\n"
     ]
    },
    {
     "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>volatile-acidity</th>\n",
       "      <th>residual-sugar</th>\n",
       "      <th>free-sulphur-dioxide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.081770</td>\n",
       "      <td>2.821349</td>\n",
       "      <td>0.569932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.215896</td>\n",
       "      <td>-0.944765</td>\n",
       "      <td>-1.253019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.017452</td>\n",
       "      <td>0.100282</td>\n",
       "      <td>-0.312141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.478657</td>\n",
       "      <td>0.415768</td>\n",
       "      <td>0.687541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.478657</td>\n",
       "      <td>0.415768</td>\n",
       "      <td>0.687541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volatile-acidity  residual-sugar  free-sulphur-dioxide\n",
       "0         -0.081770        2.821349              0.569932\n",
       "1          0.215896       -0.944765             -1.253019\n",
       "2          0.017452        0.100282             -0.312141\n",
       "3         -0.478657        0.415768              0.687541\n",
       "4         -0.478657        0.415768              0.687541"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df[['residual-sugar', 'free-sulphur-dioxide']].corr())\n",
    "print(df[['free-sulphur-dioxide', 'alcohol']].corr())\n",
    "\n",
    "df.drop(['alcohol', 'quality'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (3918, 3) <class 'pandas.core.frame.DataFrame'>\n",
      "X_test:  (980, 3) <class 'pandas.core.frame.DataFrame'>\n",
      "Y_test:  (980,) <class 'pandas.core.series.Series'>\n",
      "Y_train:  (3918,) <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "# mask = np.random.rand(len(df)) < 0.8\n",
    "# train_X = df[mask]\n",
    "# train_Y = target[mask]\n",
    "\n",
    "# print(\"Number of train examples: {}\".format(len(train_X)))\n",
    "# print(\"Number of train labels: {}\".format(len(train_Y)))\n",
    "\n",
    "# test_X = df[~mask]\n",
    "# test_Y = target[~mask]\n",
    "\n",
    "# print(\"Number of test examples: {}\".format(len(test_X)))\n",
    "# print(\"Number of test labels: {}\".format(len(test_Y)))\n",
    "\n",
    "train_X, test_X, train_Y, test_Y = train_test_split(df, target, test_size=0.2, random_state=0)\n",
    "\n",
    "print(\"X_train: \", train_X.shape, type(train_X))\n",
    "print(\"X_test: \", test_X.shape, type(test_X))\n",
    "print(\"Y_test: \", test_Y.shape, type(test_Y))\n",
    "print(\"Y_train: \", train_Y.shape, type(train_Y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-NN Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class kNN:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def euclidean_distance(self, vector1, vector2):\n",
    "        diff = np.subtract(vector1, vector2)\n",
    "        \n",
    "        return np.sqrt(np.sum(np.power(diff, 2)))\n",
    "    \n",
    "    def manhatten_distance(self, vector1, vector2):\n",
    "        return np.sum(np.absolute(vector1-vector2))\n",
    "    \n",
    "    def fit(self, X, Y, n_neighbors, weights='uniform', **kwargs):\n",
    "        \n",
    "        self.train_X = X\n",
    "        self.Y = Y\n",
    "        self.k = n_neighbors\n",
    "        \n",
    "    \n",
    "    def predict(self, X, threshold=0.0, train=True):\n",
    "        \n",
    "        preds = []\n",
    "        pred_idx = 0\n",
    "        progress_count = 0\n",
    "        total = len(X)\n",
    "        probability_estimates = []\n",
    "        print(\"No. of examples: {}\".format(len(X)))\n",
    "        \n",
    "        # loop through each row\n",
    "        for row in X:\n",
    "            dist_arr = []\n",
    "            class_arr = []\n",
    "            if train:\n",
    "                print(\"Training done {} %\".format(round(progress_count/total*100)), end=\"\\r\")\n",
    "            else:\n",
    "                print(\"Testing done {} %\".format(round(progress_count/total*100)), end=\"\\r\")\n",
    "            progress_count += 1\n",
    "            \n",
    "            # loop through all the other training examples\n",
    "            for ex in range(len(self.train_X)):\n",
    "                # get the current row vector\n",
    "                example = self.train_X[ex]\n",
    "                # label for the current row\n",
    "                label = self.Y[ex]\n",
    "                if not np.array_equal(example, row):\n",
    "                    # calculate the euclidean distance \n",
    "                    dist = self.manhatten_distance(example, row)\n",
    "                    dist_arr.append(dist)\n",
    "                    class_arr.append(label)\n",
    "                    \n",
    "            dist_arr = np.asarray(dist_arr)\n",
    "            # get the sorted index from the distances\n",
    "            dist_arr_sort_idx = np.argsort(dist_arr)\n",
    "            \n",
    "            # pick the top n closest neighbors\n",
    "            dist_arr_sort_idx = dist_arr_sort_idx[:self.k]\n",
    "            zeros = 0\n",
    "            ones = 0\n",
    "            \n",
    "            # find the majority class\n",
    "            for idx in dist_arr_sort_idx:\n",
    "                if class_arr[idx] == 0:\n",
    "                    zeros += 1\n",
    "                else:\n",
    "                    ones += 1\n",
    "            \n",
    "            # Using the threshold value to predict class\n",
    "            zeros_frac = zeros / (zeros + ones)\n",
    "            ones_frac = ones / (zeros + ones)\n",
    "            probability_estimates.append(ones_frac)\n",
    "            if zeros <= ones:\n",
    "                if ones_frac >= threshold:\n",
    "                    preds.append(1)\n",
    "                else:\n",
    "                    preds.append(0)\n",
    "            else:\n",
    "                if zeros_frac >= threshold:\n",
    "                    preds.append(0)\n",
    "                else:\n",
    "                    preds.append(1)\n",
    "                \n",
    "            pred_idx += 1\n",
    "        \n",
    "        print(\"\\nNumber of predictions: {}\".format(len(preds)))\n",
    "        return (np.asarray(preds), np.asarray(probability_estimates))\n",
    "    \n",
    "    \n",
    "    def accuracy(self, preds, labels):\n",
    "        #calculate accuracy\n",
    "        error = 0\n",
    "        for i in range(len(labels)):\n",
    "            if preds[i] != labels[i]:\n",
    "                  error += 1\n",
    "        \n",
    "        matches = len(labels) - error\n",
    "        \n",
    "        print(\"Matching Predictions: {} out of {}\".format(matches, len(labels)))\n",
    "        \n",
    "        accuracy = round(matches/len(labels)*100,2)\n",
    "        loss = round(error/len(labels), 5)\n",
    "        \n",
    "        return [accuracy, loss] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of examples: 3918\n",
      "Training done 100 %\n",
      "Number of predictions: 3918\n",
      "Matching Predictions: 2641 out of 3918\n",
      "Train Accuracy: 67.41%\n",
      "Train Loss: 0.32593\n"
     ]
    }
   ],
   "source": [
    "knn_classifier = kNN()\n",
    "\n",
    "\n",
    "knn_classifier.fit(train_X.to_numpy(), train_Y.to_numpy(), 5)\n",
    "predictions_train, prob_estimates_train = knn_classifier.predict(train_X.to_numpy())\n",
    "train_accuracy, train_loss = knn_classifier.accuracy(predictions_train, train_Y.to_numpy())\n",
    "print(\"Train Accuracy: {}%\".format(train_accuracy))\n",
    "print(\"Train Loss: {}\".format(train_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of examples: 980\n",
      "Testing done 100 %\n",
      "Number of predictions: 980\n",
      "Matching Predictions: 658 out of 980\n",
      "Test Accuracy: 67.14%\n",
      "Test Loss: 0.32857\n",
      "\n",
      "No. of correct predictions (Test): 658/980\n",
      "\n",
      "Confusion Matrix (Test Data):\n",
      " [[119 236]\n",
      " [ 86 539]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      0.34      0.42       355\n",
      "           1       0.70      0.86      0.77       625\n",
      "\n",
      "    accuracy                           0.67       980\n",
      "   macro avg       0.64      0.60      0.60       980\n",
      "weighted avg       0.65      0.67      0.65       980\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn_classifier = kNN()\n",
    "knn_classifier.fit(train_X.to_numpy(), train_Y.to_numpy(), 31)\n",
    "\n",
    "predictions_test, probability_estimates = knn_classifier.predict(test_X.to_numpy(), train=False)\n",
    "test_accuracy, test_loss = knn_classifier.accuracy(predictions_test, test_Y.to_numpy())\n",
    "# print(\"Threshold: {}\".format(threshold))\n",
    "print(\"Test Accuracy: {}%\".format(test_accuracy))\n",
    "print(\"Test Loss: {}\".format(test_loss))\n",
    "\n",
    "\n",
    "print(\"\\nNo. of correct predictions (Test): %d/%d\" % (np.sum(predictions_test == test_Y.to_numpy()), len(test_Y.to_numpy())))\n",
    "\n",
    "\n",
    "\n",
    "# Confusion Matrix\n",
    "print(\"\\nConfusion Matrix (Test Data):\\n\", confusion_matrix(test_Y.to_numpy(), predictions_test))\n",
    "\n",
    "print(classification_report(test_Y.to_numpy(),predictions_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing with SK-Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK-Learn Test Accuracy: 0.6877551020408164\n"
     ]
    }
   ],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=31)\n",
    "neigh.fit(train_X, train_Y)\n",
    "predictions = neigh.predict(test_X)\n",
    "\n",
    "print(\"SK-Learn Test Accuracy: {}\".format(accuracy_score(predictions, test_Y.to_numpy())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Confusion Matrix Order\n",
      "\n",
      "[True Positives        False Negatives]\n",
      "\n",
      "[True Negatives        False Positives]\n",
      "\n",
      "Confusion Matrix Train Predictions\n",
      "\n",
      "[[2117  516]\n",
      " [ 761  524]]\n",
      "\n",
      "Confusion Matrix Test Predictions\n",
      "\n",
      "[[539  86]\n",
      " [236 119]]\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n Confusion Matrix Order\\n\")\n",
    "print(\"[True Positives        False Negatives]\\n\\n[True Negatives        False Positives]\\n\")\n",
    "\n",
    "print('Confusion Matrix Train Predictions\\n')\n",
    "conf_train = confusion_matrix(train_Y.to_numpy(), predictions_train, labels=[1,0])\n",
    "print(conf_train)\n",
    "\n",
    "print('\\nConfusion Matrix Test Predictions\\n')\n",
    "conf_test = confusion_matrix(test_Y.to_numpy(), predictions_test, labels=[1,0])\n",
    "print(conf_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision, Recall and F1 Score  (Train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN DATA\n",
      "\n",
      "Scores\n",
      "\n",
      "Precision: 0.735580\n",
      "Recall = 0.804026\n",
      "F1 Score = 0.768282\n",
      "\n",
      "SK Learn Scores\n",
      "\n",
      "Precision SK-Learn = 0.735580\n",
      "Recall SK = 0.804026\n",
      "F1 Score = 0.768282\n"
     ]
    }
   ],
   "source": [
    "print(\"TRAIN DATA\\n\")\n",
    "\n",
    "precision_tr = conf_train[0][0]/(conf_train[0][0] + conf_train[1][0])\n",
    "recall_tr = conf_train[0][0]/(conf_train[0][0] + conf_train[0][1])\n",
    "f1_tr = 2 * precision_tr * recall_tr / (precision_tr + recall_tr)\n",
    "print(\"Scores\\n\")\n",
    "print(\"Precision: %f\" % precision_tr)\n",
    "print(\"Recall = %f\" % recall_tr)\n",
    "print(\"F1 Score = %f\" % f1_tr)\n",
    "\n",
    "print(\"\\nSK Learn Scores\\n\")\n",
    "precision_tr_sk = precision_score(train_Y, predictions_train) \n",
    "recall_tr_sk = recall_score(train_Y, predictions_train)\n",
    "f1_tr_sk = f1_score(train_Y, predictions_train)\n",
    "\n",
    "print(\"Precision SK-Learn = %f\" % precision_tr_sk)\n",
    "print(\"Recall SK = %f\" % recall_tr_sk)\n",
    "print(\"F1 Score = %f\" % f1_tr_sk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision, Recall and F1 Score (Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEST DATA\n",
      "\n",
      "Precision = 0.695484\n",
      "Recall = 0.862400\n",
      "F1 Score = 0.770000\n",
      "=======================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"TEST DATA\\n\")\n",
    "precision_te = precision_score(test_Y, predictions_test) \n",
    "print(\"Precision = %f\" % precision_te)\n",
    "\n",
    "recall_te = recall_score(test_Y, predictions_test) \n",
    "print(\"Recall = %f\" % recall_te)\n",
    "\n",
    "\n",
    "f1_te = f1_score(test_Y, predictions_test) \n",
    "print(\"F1 Score = %f\" % f1_te)\n",
    "print(\"=======================\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perf_measure(y_actual, y_hat):\n",
    "    TP = 0\n",
    "    FP = 0\n",
    "    TN = 0\n",
    "    FN = 0\n",
    "    for i in range(len(y_hat)): \n",
    "        if y_actual[i]==1 and y_hat[i]==1:\n",
    "            TP += 1\n",
    "        if y_hat[i]==1 and y_actual[i]!=y_hat[i]:\n",
    "            FP += 1\n",
    "        if y_actual[i]==0 and y_hat[i]==0:\n",
    "            TN += 1\n",
    "        if y_hat[i]==0 and y_actual[i]!=y_hat[i]:\n",
    "            FN += 1\n",
    "\n",
    "    return(TP, FP, TN, FN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TPR: 0.8624\n",
      "FPR: 0.6647887323943662\n"
     ]
    }
   ],
   "source": [
    "tp, fp, tn, fn = perf_measure(test_Y.to_numpy(), predictions_test)\n",
    "tpr_0 = tp/(tp+fn)\n",
    "fpr_0 = fp/(tn + fp)\n",
    "    \n",
    "print(\"TPR: {}\\nFPR: {}\".format(tpr_0, fpr_0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(980,)\n",
      "TPR: [0.         0.         0.0028169  0.01690141 0.04507042 0.07605634\n",
      " 0.12394366 0.16056338 0.22816901 0.29577465 0.35492958 0.4084507\n",
      " 0.46478873 0.51267606 0.57183099 0.61126761 0.66478873 0.70985915\n",
      " 0.74929577 0.8084507  0.83380282 0.87887324 0.92112676 0.96056338\n",
      " 0.98028169 0.9971831  1.        ]\n",
      "FPR: [0.     0.0016 0.0272 0.0608 0.1264 0.2224 0.2928 0.3776 0.4688 0.544\n",
      " 0.6368 0.6864 0.7504 0.7888 0.8176 0.8352 0.8624 0.888  0.9216 0.9456\n",
      " 0.968  0.9744 0.984  0.9904 0.9952 1.     1.    ]\n",
      "Threshold: [2.         1.         0.96774194 0.93548387 0.90322581 0.87096774\n",
      " 0.83870968 0.80645161 0.77419355 0.74193548 0.70967742 0.67741935\n",
      " 0.64516129 0.61290323 0.58064516 0.5483871  0.51612903 0.48387097\n",
      " 0.4516129  0.41935484 0.38709677 0.35483871 0.32258065 0.29032258\n",
      " 0.25806452 0.22580645 0.19354839]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(probability_estimates.shape)\n",
    "tpr, fpr, thresholds = roc_curve(test_Y.to_numpy(), probability_estimates)\n",
    "print(\"TPR: {}\".format(tpr))\n",
    "print(\"FPR: {}\".format(fpr))\n",
    "print(\"Threshold: {}\".format(thresholds))\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(tpr, fpr, color='darkorange', linewidth=8, label=label)\n",
    "    plt.plot([0,1], [0,1], color='navy', lw=2, linestyle='--')\n",
    "    plt.axis([0,1,0,1])\n",
    "    plt.title('ROC Curve')\n",
    "    plt.ylabel('False Positive Rate')\n",
    "    plt.xlabel('True Positive Rate')\n",
    "    \n",
    "plot_roc_curve(fpr, tpr)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}