SVM Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "98466bf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC #Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb7dacbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('SVM_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a8daa7a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\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>Med_1_mL</th>\n",
       "      <th>Med_2_mL</th>\n",
       "      <th>Virus Present</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.508231</td>\n",
       "      <td>8.582531</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.126116</td>\n",
       "      <td>3.073459</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.427870</td>\n",
       "      <td>6.369758</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.672953</td>\n",
       "      <td>4.905215</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.580321</td>\n",
       "      <td>2.440562</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Med_1_mL  Med_2_mL  Virus Present\n",
       "0  6.508231  8.582531              0\n",
       "1  4.126116  3.073459              1\n",
       "2  6.427870  6.369758              0\n",
       "3  3.672953  4.905215              1\n",
       "4  1.580321  2.440562              1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "72302469",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Med_1_mL       400 non-null    float64\n",
      " 1   Med_2_mL       400 non-null    float64\n",
      " 2   Virus Present  400 non-null    int64  \n",
      "dtypes: float64(2), int64(1)\n",
      "memory usage: 9.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d1f0390",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Med_1_mL', ylabel='Med_2_mL'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = 'Med_1_mL', y = 'Med_2_mL', hue = 'Virus Present', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "36ee2cae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cee69504d0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = 'Med_1_mL', y = 'Med_2_mL', hue = 'Virus Present', data = df)\n",
    "x = np.linspace(1,10, 100)\n",
    "b = 11\n",
    "m = -1\n",
    "y = m * x + b\n",
    "\n",
    "plt.plot(x, y, color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "08393ce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split data into features, label\n",
    "x = df.drop(['Virus Present'], axis = 1)\n",
    "y = df['Virus Present']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1402c40c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(kernel = 'linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "54ae5bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit model \n",
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5a5a873f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.plotting import plot_decision_regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2ab83c5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array(x)\n",
    "y = np.array(y)\n",
    "plot_decision_regions(x,y, clf = model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "df841302",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(degree=2, kernel=&#x27;poly&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(degree=2, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(degree=2, kernel='poly')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_model = SVC(kernel = 'poly', degree = 2)\n",
    "poly_model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b03eaddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array(x)\n",
    "y = np.array(y)\n",
    "plot_decision_regions(x,y, clf = poly_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "db8ee489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(gamma=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(gamma=0.1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(gamma=0.1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_model = SVC(kernel = 'rbf', gamma = 0.1)\n",
    "rbf_model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4fa86742",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x8MxJ3HLbRZx+y39wJiXHOySBIO6YpomhB9ADLXScSBJZ+f2biZSOoLuLI/5sGzp6dRmmJqOoKnkDhrTbELAvzqixgboAJDsCSIqzzdf3VCQs9ICfaj34p11FiA9BwnH4/nsgSXDH3//EaTcvjnc4AkGHMA29WX1ECF9FEabRspdDNNzlJVRXliP9/BVqSfPjGtVlVFeWU11ZsUvxGnXV2KaO+71lEY/bponpr0XPzMbl1Nq9vdKZupeejUStpaB5AzgVo1fWrdm2jWXbVOvgtTrntCrEhyAhOWy/Pbh9xQeUbN9K/sCh8Q5HIGgX7rISPOXF1DXy4GiMbRkY1WW4y0raP1bABtsMUPPZK0hRrLVt08Q2AyBFX+xDGY1QJsPnkDA0B3pVCRJQ++07JDtV7PptHMuywLZBlVC0VE444wKyCwa0u1unM3UvPR0biUpdRe6YvuxBSPVbLZ0XVkJ8CBKWx687iVl3XMPx195D/qDd4h2OQNAmesAHioPMKXPRcprPQ9HLt1H2+gPNrMxbIzM3n/ScAnIPOw9Xdv9mz/sqiih751HSMnOAhi6UgKc8IqPROJMRGhSXnZPDGVfeSmZucJiZu7yE5ffOwwhlLWSZNc88GnG+9mYtOmv/3nOROlUL0VcQ4kOQsAzKz+K5W37Hqbdfy3HX/p2CQcPiHZJA0C7k1By0jOZW5KZ/1+72FVXDld2/xcVcUTU0p6tZq27jjAaqipqcyZnXzCMzJyg2omYxFAf5h/a9rIWgexHiQ5DQDMzL5PlbTuWk2/7Ehfc+H+9wBIJW0ZwuJMCscxNw78SoqcA2Gvw0DE8ZmCbV7op2bWF0hMyc/Ji5rPbdrIWguxDiQ5DwDMjLYK+h2Xy94U32nHxkvMMRCFokMyeftKwccvL7I2tOfl79KI2NQm3LBEXhlRUPoahqzAsve1cBp6A3I8SHoEfwwJXHMeeOR5GwGDd5erzDEdA32ynbg6IoqJoDsLEti8wpZ6DWb8FYpoFZW0lGdh5mTQWVH6wQWxiCMH3p35QQH4IegdOh8cwts5l9+zJs22bPg4+Od0h9mr7bTtl+Ap5y9NoqTBRkqf5Xraogp+VRZ6noukV1ZTnu8vZ1vrRkZ56oNuc9Ld5409f+TQnxIegxODSVZ26Zw9Sr/82o8YeiOXuvkU+i0xvbKWN51+mv2Im/qgSwUZLTUTOCBZ42Nlgmqqph6T5sQPe33vnS0syXxiSSzXmixdtTsgm98d9UawjxIehRODSVuUfuy9sr7mX6edf3SiOfnkRvKUyM1V1n44XXX1uNbVsgScEfQEICRUbWHMhK+379tjTzpel5Y7mAdiZrEY94W6InZhN6y7+pthDiQ9DjuPLkSVQtf5d1j9zN0ecLASLoPLG662y88P7w5cc8veQ+FEWtrwEJEvzr2vzvbCLcoccqa5EoC3lfyyb0JIT4EPRIbjnzUP7vP2v534dvsM8UUYAqiA2xuOsMLbylOwpBCg5oa0sgJ8odeiyzFokgpkL0lWxCT0KID0GP5eyj9uHiJU8yZsI0HE5XvMMRJBiJsvgZVcVIUQa9GVUNg9wS6Q49Fp9JS2LKNPSw6ZmqSJx5zYKw4RkkTv2FoOsR4kPQY9lr+ADmnboPt91xCaff/IAQIIIwiZBJ0BwuMAO4P1jR4pwXzEDwdfV09R16dwmyaGIq4Cnn19X3gySFrd4fu2dB2OodEq/+QtB1CPEh6NFMnzASSZKYf9vFnH7LAzgTpOK/r5Co7ZSxyCT43cVYenCEva+iCCPgD26l1BOqe2jpGHrAR0pmLlkTTsKRkdc8xqpSPJ+vDs9V6Wp2VZB1RrA0FVOSopEx9UzU1Gz06jJy8vuH62FE/UWQRP03FWuE+BD0eI4aP4Iydw2vvvwYh8y+ON7h9AkSrZ2yJdqbSXCXlWAE/PgqgiPrG9+lA9iWhemr4cn77w7fqcu2iW3b2FGyGhDcYqhzl6F8sQYlyjRaAJfTgcOV1C0LbsDnxecPkL7/8a2KocaxdEUGScssCM69UVRcuYPRHKJlHnrOv6lYIcSHoFcwfcJI/nHLSsZNPZ7c/s2niQpiSyK1U3aW8uIdrLhvAdWeKurWPIikKFhGANPvJWPy6ShpOWDbyLJETsEAVM2Bv2Inpe88im2Z5B9xXovZFd55hDMuvY68AdEFUOgz2rxpY4T4aUrAU97p63SXleApL6buo1UtbgMZ1WW4yxpMzxKpFqUz9IRsQm/6N9UehPgQ9ApyMlJ49qZTmHXnlZz4p/ta/GUviB295ZdgwOfFQCLz4HqhQXAAnOezl3H0G46aUYBZU4GiKCTnD0VpUjzaWnZFUTXyBgxp1cG0sfjxrlsaXRiYOsnOzv261gM+UBxkTpmLltNcoOvl2yh7/YHg65rQFbUoRk0FvjIJo37bpenWViwW2p6WTegt/6bagxAfgl7D4IKsegFyNSf88V7yBw6Nd0iCHoKiKOTvsTfOnIEA+Mp+o/a/61CdSWjOJPCq5BT0byY8YkHA58WwIePg03AW7NHMfMyoKqbyvcfDXSKdRc0owJEzuNnjtqHH5PjtwfCUUv7mEtyKFm5Dti0Do84T3tqKRfFpX8sm9CSE+BD0KgYXZPHczacw645rOOn6f4tfKoJ2o6hquP7A0Bxhfw5JkkCiS4RHGElCS89DdjTv2LJlBdNfi5qcmTB36O2l8baGr6II2zKwDR3TNACJzKlnomUFt3NsQw8XoZrVFTHbzhG/AxITIT4EvY5B+VlcO3N/Xnx/DYfN+kO8wxHEkZ6w1w/BzEtO/oBw5qUxPoeEnpnNmdfM6zELabTtDiPgx6jz4N3xHdiAbaKkZgeLTwHL0MNFqEYjR1hB70SID0Gv5MQpe/L4gqf5dvAwxk46It7hCLqZ9uz1y7aJu6wEPeDDNAwMPYAeCLbWGnogOASuG2mceWmMoTlQVDXCjKsz7a+WaWAbgaiPx4po2x3ushJW3LcA45vXMfUAtr8Oo6Y8woBNkiRkWY5ZHILERYgPQa/E6dB4bt4cZt32MJZls+fkI+MdkqAbaWuv310eXAiX/+tOjICfak8VWkkRWiAoOHT3TkzDRHfvDG8H+BwNxZGNMyfdkV0xTTNciBlexBtrI0mKaOeNVi+hOV1IgFnnJuBuHptZ50aqf11Tml6LaRjYtoW/shjTMCjdURgWcqFzNRZLAPmDhnLN3x4h4PNSuqOQJ++/m8zsPJw5DV00kiRhWRaGHsA0DNzlJa0W6wp6LkJ8CHotTofGs7fOZvbtj+BKSWGPfQ6Kd0iCbqStLQpbVsmeehY2drDLxDKh/u7ftmxsM0DlO8uwbQvLV4svPR2lUTGobFvIqtLlnRSmYVBdWc6Kfy1EdTjDYinj4NNQ03KB4KKdkz8ARVVbbH/NzMknLSuHVE3G6Wg+a8avyehZOc3szptmkEzTpLqyvCEvZAZ44h+3U1tVAUpQnElAWlZOVEEUFhOSRFVlGarV3ANFd+/E465g+b3zuPZvy3rMdpOg/QjxIejVOB0a886cxg0vPi/Eh6AZzux+yJoTWVGp2vB0+HHLNLC81diSBEhgBcBKqf9zEFlVOO/6RSSnpkccs2kGIODzsvnzjVTs3IGh+1EdDtIyc8KvrywpIuCtpabwu6gx+iqKsIGM8SeSPmxvfBVFeNctJWnAaBw5g7AMHaOmHGfOwFYNuxyuJFwOFc/GlS2+xuVQI8RStAxS6Y5CVvxrIRnjT8SRkYesOrCMAIXrlpI5ZS5yag5mnbtt91LbxrZt1NQc5KbFvKaB4kzBMO2E9xAR7BpCfAh6PfuPGszkAT/yxrK/cuQ5f2pzwqigb+HMLGDk3PlhK3WA2pJCfn3pPgomnUjqoFHIqgNHeoNgCC2myanpEdsC5cU7eHrxwghH0FCmwDSN4FwTPYCsOZDkhjt+2zSoXbuYlMwcZDkyE2CZJrbuRVI0ZM2JK7s/kqwiqRqS6qC9FRK72nYaLeugOpykD9s77P3hLS1EklW0nEFoGQUE3Dvb5V5qVJdFHbxn1FQgteAK2x4SZaigoGWE+BD0CeadfTi3Pf4Orz/6V6af939CgAgicGYWRPzZ0ANIikLqoFFkjZoY9T2moUfMeoFgVsDn18maeiau7P4oqoqhB9CKd2DVb+VUbXia7Klz0TKDtQ6WaeDf+ROB/65GAWgiPrBtJNVF0YfPUfzRSww55sJdvs5EWXAdriRUCSo+XEltcnrYWM3GJrSfY9sWliU1m6fT1jUkwlBBQdsI8SHoM9x69mHMf+xtPnrlcQ468Zx4hyNIIBoPkQPwV+7ENk0CVaV4SwuRNWeEQAl4yvGUF0fMegEaild1i0DAoiCvP6plofktLDuY4ZAUFUfu4LDRl20EMOvcGLKMLUnkHxa0aw94yoM274aOWl6KZJlUf7kWX/l2LCOAUVMR1SysJ5BTMIC5V89j2d9vJe+I3+PK7o9pGJSX7MC2g6LBrKvC/f4T4VoXaJ9o6C2W8L0dIT4EfYrLTzqQGfOfZ/zRp6E5xUArQVB4/LBiPnajG2XLNLAlKPrwBYo/eRVJgpFz54cFiGUEQHGQNfVMUvIbBICvooi6tUtQnKnYlkXAH6z9sG0b2w66eGKZrcYTqkMpfG1J8D22XW/KBZa3hqIPX8Dw1VL2xoP0nzUfNSVzl6473lsTmbn5qI7gNlJS3hD0gB/Vb4ZrQALl21CT08PipKOioSss4QWxQ4gPQZ8iNzOV20+fxJ13XsJpNy3GEaWtUNB38FfsxMbGMg0yp8xFDRlemTq6pxwtPRfL66Fq/fKIzEgIZ1ZBxAIX8JRjeGvQvdWomouKkmCLqlVvjW7WujHrqjCqW89aWLof2yY4fj4jH1PXARujugItNRNf0U9Uf/4KgbKtWL6aiFbg9rT4dmRrAmhWcNrYFyXWvhxyfS2LpGpIshoWJ4LehRAfgj7HMRNHIcsSt91xCaff/IAQIH2Qxi2kIedNW1agvpVWltXgDJTs/pg17Z8oa9WbdynJGagZ+aj1hZSSrmM3et42m5t8RUPLLEDLHhQ0PbMtJEVDS81Cr6nE8tVQ8dZDSJKC4fXgS8tAri/SVBUJd3lJxPU2zmK0d2uiZNvWZgW0oa0l6eevUEuKkCSJVE3Ctgz08m2Yfi9mnbtFX5Ro54JgnY3u3gmmgaRq6O7idn1Ggp6JEB+CPsn0CSORJIkFt1/CaTcvxtnDZmb0RWK5TdC48yNkeJWT3x9XbjAbYegBKkqKgu2gho5tGRHj7gNVpa0eX1JUJCV4Bw8g2cFCSknp+K/ccG20ZWJbJmZtJXJw3AwuRUKSoNYykGQl3EFjIrH8n3eGj9FSrURbWxN6wNdMpAQ85XhX30/tF2uwTRPTX4s3NQ2jupyy1x8IxgzoUXw+HK6k8PfoLitBMg1K33k0GLNpUOupQklKQw59bhLImtge7Y0I8SHosxw1fgRF5VW8seZJpp3y+3iHI2iFruhgaPw6RVVRNUe4NVSWZSRZxqgpR68uw6jzUPrWw+HCRyPgRwIkKfqWg1FVAkhYWjDzYeoBbMCov5vXK4vwF28BgvUlRnVZK5FKaA4Hlg62opKd3w/DGTQEO+Oy6wF4cvFfyDv03C4rsGwsUpLyhjD6nDuxdD++iiJK33qYuVfciOZwtepwGvIPafw9mrbdMK3XsrAtC9vUGXrC5TjSc5oV+gp6D0J8CPo0Mw4cxeJ5zzD2oOnk9u+ZnQN9ge7uYFBUjYKBQ7EsKzzY7YzLridvQHABLt1RyJOL/xLR6QIgqw5sI4D7gxXB4ygqSEGXUgDbMrECXirXP0WV1mimCZCeno4ty+E6FNsysPRAuNbEMpuPvA8t8oqihAWCaehYloVp6MFC17paAn4fP3z5MaU7CtGcLnS/D9NofjwIFuD6KoowAn4qS4L/3zjr01ImIjM30hk1mggs2vpzi9+joQco/vkrar9YgyM9p9N1HtG2ekwjmMEKWcI3pTv8P+Jd6JsoCPEh6NPkZaXxzI0nM3vhVZz4p/vCi4sgMenODgZF1VBoGOyWN2BIhKGYoijNFjjLCKClZpJ+8GnImjPs8hkq0PRXFlH+7uOceOaFZOX3D79Pc7pwOF08cc+tEXUoAXcxFo08aWybipIi9KrisP34mdcsCD9tGjrF27diW1ZQ8EhgVBZT7a7kqcV/CW7LWBbOpCR8tVWkfPdRRPwBTzm/vroYAKPOw+qVj1Bb7Qnaz4e8OCwj6D2iaNiWgVHnadZy3FYWKtr3qAf8qCVF2KYZIXZCtHdWTktDBSNs4c0AT95/F0oTc7Ou9v8QHiQNCPEh6PMM6ZddL0CuFgKkD9PeAXHlxTuCxZxmgJK3lkY8Z+oBTF8dsubEkdWf5PyhEQucNymZ2pRURu47MerAtMZ1KE/8806sOjdSSma4EBZJAstEssyw/bhe384Lwa4a27ZRkjOxaiqDC62iIMkykjMZJAmr1o0/oIPqYsf6Zyn94s36d9tBXxG/l5wjLkDSnKSlZ+HyVKKkZqM4XOjuYtzvLcO2bTKnnomamo1eXda2lXo7kGUZSZIw/bURW1yNac+snJacXBvbwrtyBkY41nYm7o4gPEgaEOJDICAoQJ696RRm3Xk1J//532Tn9+67DkEDLd0pN6ZxsWToztVEQZIj72AlWUGSJJJli2SHTM3Wr8MdLhAsVDUC/nDKv2mKPfTfDlcSTlWlYsNK1EYOoJHnCtalREOq3+4BCSU5EzkpncxDzgHbxv3+42RMOSMoULzVaCkZ6HWeYHw7f6L6izXYioaSlodXVpDT8kBWUNNyg8W3poltm6ip2cHWZFkJdgY5Xc0yCR1BUTVy8gcQaLLF1Zj2bkm09JqmtvDxQniQCPEhEIQZXJDF5cftzWsfvM4hp5wb73AE3URHZp60VrMAUFP4HbWvPUDVhpVUYeMpLw5Peg0hESwQVRSlxRR7NAfQpgQ85ZS/80jbF6ioSIoatHO3bSRZwZE9CDk1G9NTiiTLqKoTJTUb2/AjSTJyUrBVWFFUjIAPs7YSf2UxelUphrca27bwV5ViIWPbFhUlO5FkiYKBQ9uOp9VQ1ahbXILehxAfAkEjZh22LysXPM23AwYzdtIR8Q5H0E10dH+9tTvX9JyChi6U++8ma+qZOLMaOjYkSUZR1TZT7E0dQNvCX7Ez7JVhBXzoNRWAhG1bUV8vISErCplZuVRVlqM5k9AdLpBAUZWwRwkQ7EQBJGcSksOFZNsoKZko6bkAKFJwGFzITK09NLW0h6BDbKCulh++/Bh3eUmzjhnoOwWZvR0hPgSCRjgdGs/Pm8Os2x4GEAIkwWhvXUa8CHa1RA4tdGYVhP1DZFludWuicSdE6Y7CqJ0mTVtPNYcLTZEofXcZpqFT66lCdiZj+mqxAdmViiTJmL4aFGdKwxuloABRNC3435KEVVeFbZnoNW5QQyZfdthsxPZ7sXU/suZCdSahuVKQJLBb6Jxpiru8BNMwcP/yFcUbX4AmM3kt3Y9R6+bpJfchSZDWxCsEYluQ2VgAhTp8OjrITrBrCPEhEDTB6dB49tbZzL79YRzJaeyx94HxDqnP05G6jHhhGjrlJTvwuCtY8a+FAMEhcyVFaIFgbYgkBbcmogmQpp0QpmFQ7amK6DRpOmMGghmSs669jUfuuh4z7JVhNmQ8LBNUmco3HyTzkHMbTmgHp8iauh78b9vGMnSCdSIZqKFz2DZ2/WwZu1H9iiRJHZoOXV68gxX3LcBTvpOqNx7FliBj8mmoqQ2Fn0Z1GTVfrCFr6lykRt1CIWJZkNl0pk+0zp2+0nkSD4T4EAii4HRo3Hjawcx79QUhPhKAjtRltEVX+SyEO02cKeQdETSt865bipaWiyOzH5ahY9SUY1kWSpT3N+2EMPQAWklRsGBUUTGqinF/sIK64l+xdH9Etic5NR0UB7mHn41Lt1CcqeEiUiU9F7O6gqoPlmP567AhKFCwsUwTd0UplmUhGTpWvR+JUV2O7EwOHty2sc2gKDFrKsLnbGx/bht6eL6MWd3wmqbXZ8sqQ465BMsMUPThCyQNGIUje2DwGKaBb8d3KJoj+Jii4sodHDZ+ixWhz81XURQx08cyDcw6d1jwdGXnSaJn8LoDIT4EghaYOG43Jn22hTeW/ZUjz/lTh+7yBLEnFnef3eGzIClKuEBUktXggDTVQXvHrzU2C6vWCY+Yt2WlmdNqKNsTWiC1jDy0+veoqgOQgoWkpoFlBAgU/4RtBAhU/IYa8GL5PGhJ6RjeaszqMoyqndhGgKoPVkRYwduNJvHaRgDZkUTV+uWNng9mDfTMbBRVbTULlTpkNADFn7yK4kxCcaWEjxvc3umaf2dNs2dNZ/rIioribN4e3ZUxRCPeGbzuIubiwzAM5s+fz4oVK9i5cyf9+/fn3HPP5eabb4759EOBoKuZf87hLHj8bd5Y9jeOOlcIkJ5OrHwWot2hGnqgDZv0jtHYZRWI6rTauAvHNE3c5SWo6QVIkoSsqMG7+dpKrJoKLG81NV+sQcKm8o0H69t0JdSkVPTaKjD1oJ+IrJK637Go6XnBQGwbbIugaYiFZ8NTDDv+sgifjLrS3yh96xFmzDqbrPz+aA4XAZ+Xoq0/h+OMN02zZ9Fm+rRVkxPrGKLRV+pMYi4+7r77bv7zn//w2GOPMW7cOD777DPOO+88MjIyuOqqq2J9OoGgy5l39uHc/sS7vP7o3Uw/73ohQHoQTbdYQuPglbSgR0VHF5vW7lxNw8DjrkBNSkPWnOFCxtD2ROOtCaM+rd8WIZdVaNlpNYxtBzMeaTnI9ddkmfVupJaBmpSKS4HDT5zD2y8/TfZBp+LKCW55bHnxXkzdR+o+R1P73Qc4+u2BI3tQOAlhmwZmTQWS7gsWpjb2LvH7cJeVUFtTw+onl6Joka3FSBIuh8qcS29sHrJphOtILEMPCh2C1yHZwTqaWG67NF3Um8706Q76grBoDzEXHxs3buSkk07iuOOOA2C33Xbjqaee4rPPPov1qQSCbuOWsw7l9uXvsu7huzj6938WAqQHEG2LJTQOPlQEGioANWoqwoPSmnY8QMPdaGt3riEHzYKjL8GZWYDfXYwkEd6eaLo1AZ1PsYfEVemOQkzTQAtNzlWC4iMorCQUZxKyqqGoEoP2GIMzJS3CbGv3k6/h11cX4yrYHe9PnyArKporCeoH51mGH7MGTF91xNaPaZp4KsuwLAtZc6EdcDJKckYwuPqP3awuo+6rtZRs+yWie8cyA/VeISF5FaxBMU0T0zSQkCgv3kG/wcO6NBshiA8xFx9TpkzhP//5Dz/88AMjR47kv//9Lx988AH33Xdf1Nf7/X78/sheb6c/gBiiLEg0bjnzUE68ZQVVFaVR/QcEiUW0LRZfRVG4CFRNzcGoKcdXuZMtzyys311oe1ZJa3euqsMZ3o5wZhYwcu78iFbO0rcejrptsis0FldGwE+tpwqHaWAYOlKjkhbN4Wj5IPU40nPC9SkAhqeUgOYIi2zb0LGqy0hSLNIbbf1s+fYLnltyD37dInPSqbgKghkZ27YxTQNkBck0qPG4Wf3kUmpra/CuW4ptWcE6E08ZSnJGfTdPcJgegFFVjKQ4sGSJOqccYd0u6B3EXHxcf/31VFVVMXr0aBRFwTRN7rzzTk4//fSor1+0aBELFiyIeGzeVeczf/a+sQ5NIOg0V82cyLz7ruf0mx/A4XTFOxxBO2hqCBZaZMNbE7of24aMGM0qibZABjzlBKpKmz9eXxfROPvR0vt95dsjsjKlOwrxen1kjD8RSdGoe/9pzFo3hnsnkqIFt2EsE0vTMKpK2hW7pDoAG/f65fX+GvXiwzIx6zykpKfjcmgM3H0UAGufXIo/oGMDVZtexfPlaw0Hs21sIG3/41FcqeQefh4uPTh3Ri/ZQvnbj1D14VPIyenBgXcEvwurzk3lW0sBGwnQs3IihKBsm7jLWr6ejgo60XkSH2IuPp5++mmWL1/Ok08+ybhx4/jyyy+5+uqrGTBgAOecc06z199www1ce+21EY85d34OZV/FOjSBoNMcccAemJbFbXdcIgRIL0PLLEDLKNjlFs8Wp6kaethmvbG1esS5FYmzrr2tQ+8P+4B8tCr4QtuiasNK5KR0JEUJiw9FUYMZjHpr9RCNF9fqws1Yuh9JcZB5yLnYAT+S0tAgYHjKqPrwSQxfHTpOSrZvRff7CBgmGQefjpyag5qWG86cAOjunVStX45tGPUdQAMIBILtyHJqNkgK2Cbp+x+HWu+UKkkSaUlOJNtCr3Hj/vj5iEyRu7yEFfctYPm/7mzxe2hvt5LoPIkvMRcf//d//8ef//xnTjvtNAD22msvtm7dyqJFi6KKD6fTidPZ5B95ZdtpQoEgXkyfMBJJklhw+yWcdvNinOKXU49DdxeHC0D9moRtGcGthXY6dTYlVHsx57IbI6bMAlSWFPHqykfJOfQcXNn9I+7ioSGzkpya3uI01pBNe+P3h3xAJMukasPT9D/kVHasf4asw85By+wXvr5QFic0C0ZzBh1Rd77xILZpYpkGddVVoDrRayqDw+JS5GDSw7YACds0QZIJWOD31PDoX29FkqC2uoqs1GzUzH6omf3qMydBQi3CIRRVpSCvP5Zl4XNI+DKzQJYp2GOvqN0m3tJCar5wNiuwtWU1ardSKDtU9dnLbN/yfbPPMdoQP9F5Ej9iLj7q6uqatdQqitIhz3+BINE5avwIZEliwcLLOaveil2Q+MiaM1wEGqrv8KWkYtTVoHtKoT4zYDYSIYYeCFqCl5dE7TJpyzvECPipra5iYHZ/Uvvv3mp8LS10iqqSkj84YvtID/iDzqn17qNaaiZyvbdHNEIW6Jk5+Zx07hU8dOd1oDiCWzPIqI4kLCOApQfFk11/JNu2MWorkRzJpI4/CTU1C0VRcforqH7nyVavp/l1BLt3DM2BrChIsrJL3SZNt9L87mIKX1uCZUav2YHoGREhLOJHzMXHCSecwJ133smQIUMYN24cX3zxBffccw/nn39+rE8lEMSVIw7Yg/te/Jiyot/I7T843uEI2kHjItBQAeixc87j1ZWPItFgcFVe0jBPRXfvxOOuYPm987j2b8uaLVhteYdUfPMh1RtfxO+rQ6mtbva831vXqrhpL7LqaHd3jaJqoDrIPuJCLF815e88guWrxv1OdCFtWyayK5WkoXsjJ6WB14NaFylygi6okX+2LQOrrnKXr6m9hOp2MqfMxZaVLrVlF8SGmIuPf/3rX9xyyy1ceumllJSUMGDAAC666CJuvfXWWJ9KIIg7918+g9kLr+LEP90X3pcWJBYtFQ5KSKgOJ1n5/ZEVBVsiwiMjjGmgOFMwTLvVxSvapFu/u5iSTWuxLIuq8lJqzebG6kblTjyV5S2Km44w5JgLwx4cgapSKjY+F2H8lT9oKDkFA9i+5XsAHNkDMGrKkTUX6VPmoqZm15uK2WFXU6OmHM+GlaRPmoWWPQAr4KVZjse2MavLIx4yPWVYddVUf7EGR2omshY9uxHLgk+1EzU7gu4l5uIjLS2N++67r8XWWoGgNzGkXzbP3nQKs+68mhP+eC/5A4fGOyRBPe0tKNTqi4aN6rJgi2cT8WHUVAQLOHcBS/eDJCG7UlBSc1Cz+tdnWILYpo7hKUV2JkeIm6bTbU3DwNAD6IFg227jrW2r3jp9y0v3Rizwtmli+r2sffbxsOX5FQsfbDFWZ78ROAt2D9Zq2FZ4mJxesY1qWWnw72hM/TRcWW6SBbFtsKxgxkSRydt/OnXFv1JX/CsQFEaWaaDa1i4XfDadSGtbBpZpIMsKhh7ocrdSQecQs10Egk4yuCCL524+hVPvuEYIkASivQWFAKoEFR+upDY5PTxBNoImnSIdRZJkJEUN1mRIcuMnAAmp0WNNa0iaGqNhBzeHMnLzghmH2kpMrwfLMEgffyhqRj5KcgZmXdAyPTMnH6OmMqIQs7KkKGJeS2dIVmySnDKmYYSLTC3LpLa8ENsMYMsuij54rtH11v8nkJGRyZlX3ERmbnTfnJYKPgOecgpfWxKeSGsZAQxvTbBgNj2PipKdSHLLE4QF8UeID4EgBgzKDwqQWXdcw/HX3kP+oN3iHZKA9hcUzr16Hsv+fit5R/w+PBSuMaFOEWjISrjLStADPipLivDXVuP55X/4KoqQVQeO9Jxm2wyGpyToPtpoAbYNHbPJPJimNSQhYzTJMrENHSMQ9MIIuIsACaO6HJCQZAXvDxuRFYXBMy7mtw9WYAO1SM3M0wJ+H5YewKiJPoE2KvXbMSFk1QFmgKoNK6mSJKoryyO2Y2zTRFIdZB58Bmp6blBs5OSF60/0qhLcHz5FZm5+h2tdfOXbIybS6u6dVKxfgZKWg5qRjyyBWVPR4gRhQfwR4kMgiBGD8rN44roTuXTZPcz68z/jHU5c6aqx9V1FZm4+qsOJK7t/s7qNxrjLS3j6/oX4/IGw9wY2WLZJ7VuPEypY1VIykBWFIcdciCTJWJaFe/3yYPaksfgwTSyvB9WZVD/orYFQDYmsOZEVlaoNTwedQ3U/lq8WNSk1vB2kJKeRNuFkVFcqVRueBsMPSGROPRMts6BZ262naAvVq/+NbQQatpnqt28aaj6C2y6hLhnDXUygdGuwLbmuCqdDJj2ngDMuux6AFf9aSMb4E3FkBAfSBapKKdrwIkmDRqNmFGDUlJPSb0i4FsOrOXB38HsKbaVVffZyxERa6ifwSkhIihYUHx08tqB7EeJDIIghuw/IIclfxjcb32TcQUfGO5y40B1j6+OF7vehmzbp+x9P3UeryJwyFyU9P2wljmlg1lUhmTqejSvxlW8HJDInnYqSnImW3tyIq/KdRxkwdTY1X6yOes7GHTqGHqD4p6+o/uwlhh57UThLYyHhrvWF225DaJkFOHIGB4e3NSrE9NXVBs9fVYqsBQWUWedGcu8Egls72MHl26qtxLZMajatou6r14Otuf5a9MxsXE4HA3cfRcDnRXU4I+bFeEsLKf7k1Qg32c4S2krbvuX7iIm0PodElaJi11ZgOzR0w+jwED9B9yLEh0AQQ2RZ5plbZjP79kcAm3EHHRXvkLqdWI2tjwft7bxwZOQhySpaziAcOYPQA8EOE9vUkVQHVnUpRp2Hio3PYfq9KMkZaJn9cGRFGnFBcPtCS81sNS5nZgEQ9PZQS4JbO42zNHrAD7WFrR0i8pyKgmTbVG18Jhg3BC3YTbP+z0HhKMkyVm0lsqyQ5FBRNAlQUZMzOfOaeeQPDHbPFG39ud3n7iw5BQMI+LwRE2mt5DRkWaLqgxXB+LtgiJ8gtgjxIRDEGKdD49lb5zD79mVg24ybPD3eIcWFaK2niUp7OmNk26TaXY5pGJiGjl0/wt62Qa2/s7clsBWVtOw89MxsZsw6m7XPPk6yYlNXtRNL9yErDb92japibMuIOvelK3E4XaRl5XLcnHNRHQ5eefwBzG/XEah/3rIsJAh7ZWRn5zD36nnhwtCObJvZphF0jrVtDD0Qfrwt87aO0NVD/ASxR4gPgaALcGgqz9wymzl3PIZt2+x58NHxDqlH09U1JG11xoRmirz8xINUe6qQyksxTQNT1yMWVKXep0zVNBRVJX/QMFxOjaqNTzcryAxjBvB8vhqX04HDldSpjJBlGmExE7KMt41AVNt4zeFg5L4T6T90OKP3n9wln69tmfirSrGQsS2TipKdYQPWkHnbY3+5iXP+786oHS8dOW8oOxRCdTS3ZhckDkJ8CARdhENTefrm2Zwy/1Ey+w1l0PDR8Q6pR9JdNSRtvdeWVTLGH4f3o1WoKZlIsoKkqMEOlkamXI3JzM0Pixp3eUmzuS8AmsNFZm5+eKENbWFE2wIy9ABGk+4YCG73SZKEWecOb/cYXi96dVlDMaYkNRt90d5rb0pTMVi6oxAjEHSNBRp1+tiYnjKUpHSQJGzs4JA7CG7xaC6qqqpY9vdbUaOYgrX1vYqJtD0XIT4Egi7Eoan84Zj9eHjdSgZeMq/hF6+g3XRXDUlr2ZXSHYWYphmu9ZBULdi1Ikn1vh3RZ1e5y0vIzAne0Yf+vzHR7uxb2wIyDQOPuwLFkUTAE+komu6Q8WlyxHZPqibjdEiYRrAQta5kKwD+ymJMw6B0R2GLcbRENDEYmrBbt+bB8ETdgYeegW3oVG14KmjJjtQwYZeGwXOKMzlqi3Nr36uYSNvzEeJDIOhiZk7dk6+2vsu6R+7m6POvFwJkF+nKGpL2DIer8VSRXd9NYnjKsE0TvWJ7fStqMPMhS2DWVeHXZEzTZPm980BxBGtEogzXVBWJM69ZEC7chNa3gNzlJSy/dx6mLYV9R5ricmoM2mMMLqeGZ+NKTNOMvuVjBnjy/rsAUCSbM69Z0C6BFE0MmoaBo2RHcAhddRlVH66k4sOVuFSZGq9F+v7Ho2XkkZM/IGIqb8mv31Hz2ao2W5ybIibS9nyE+BAIuoFbzjyUhU++x2tLF3HMH27oEwKkJ6XE28queH75H9VvP4GkaEgSVH++GrO6nIrXH6h/hY1tW8iSgiSBnpWDio1hS2TudwI7Pngmwt8DQvbntTx2zwJcTi1ie6GlRbP/0OFc+7dl7Vp0Q4tz6Y7CZh4cUG8SBvyy6h+YAS+P3bOg2SRYaHnro7EYNA2dgox8bNvGV7YN43/pHDvnPABWr3yE3N3GkDZgeITbqB7wozQa4NdRuktY9DTPmp6CEB8CQTdx4xmHcNdT77P2oTuZccFNvVaA9OSUeEvZlVAtg5aWHe6qCHjKmw1xO+HMi8gfPIzMnHzcZSUs/9edqKkZSIpGRr3hV4iQ8VeqJuPZuLLd20btXegav66pB0cIb2khkqqRMeEkCobvFTEJFtq3pWUaOjt/+yW8jaK7S6j1VLHq8aAw8/l8uMtLSBvQ8wo/e7NnTbwR4kMg6Eb+fPo07l65nrVL7mTGhb1TgPT2lHioq6LxQu4tLaT2f+vC3SPR0DILkFQntuFv8oyNEfBTuqMwbp+Lmpa7y5Ngfd5aTEOH+pk4IRd2nxHcZrJsG8sw8HlrSUnLjGXYXU5P9qxJdIT4EAi6metPm8pfnl7Puyvu47Azr4l3OF1CTxUWbeGvLG6WHYCWt5JM0wx7gujV5VS882jDk/UdMm5JwvQG56403X7pCppNgzXNCA+Ojk6DtU0LJAklLQdJ0bAtAzk5ncxpZwNQ+e6jGLUV+Ep+Q/Z5GuLw1gVrZ2wiJvaGCBXJJgI9ybOmpyDEh0AQB66bM5X9LnmQyadejDMBtx8SkXjWkISGqFWuX44nSl0ENN9KcpeXUF1Z3uAJ4q0D2yZj6pmoGQXh+SkyYNZWtLr90tm6A9M0MfQAlT9/ydZX/hmeD2cbOpbuwzJMyot3IEkSkizv0jRYSVGDc2hUB5KiomX1R9KcSJJE1YcrMdLScSWnhOPxVJZhWRay5qLk1++a1X+Y1eVYppjQ0lsR4kMgiBN/v+AIbrntIk6/5T84k5LjHU7Ckgg1JI70nPAQtZBjZrPXNBEAut+HDchJacGBcooCSGgZBWg5g4PuqKaOIoGhOXA6om/B7WrdQUiwbPn2SzxlJVhfbaDmf69j6wEkVzKSpGBL9a5osoyNhG3ZyFjtmgYbEn1+TxWGuwzbtpFVB4a7OPwaNT2frEPPo/TVezhkxkwmHH48EGxdfuK+26jz+ZFVjZrPVkUcO1SMm52VTV2Np1X79p68hdeXEeJDIIgTh++/B5IEN99+sRAgrdCdNSStZVcUVdslx0y71h1sszVNQlNTQv/bHnal7qCxYAn4fVgS1PzvdSzdT8bkOTj6jURSFExPGe71T6Ck56Kk5mDWlAcn5xp6i/UfTcWgr66W2moPSkqwsDaEVG80pqRmI8kKGdl5EZ+dIymF/sdegSM9p9k5QvboJ5x7GU/cc6so+OyFCPEhEMSRw/bbgzsliZtuv5jTbn4gnJYWRNJVC0soO+AuLwEzQMlbSyOelxrVP3Q0u6I5XGAGqNr0ClbAS6D0FywjgBXwYflDQsHGbmfNcUfqDhoLFpLScRQXYdRWUv3ZKtS0PBy5g+vN0QBZwayuAMD0lIFlsbOqmNx+gzA8zd1Um4rBT99ezatPP0rmwXPRcgcDQeGhpjf3DGmKIz2nxWtSHU6SklNFwWcvRYgPgSDOHLrvcBYCN9RnQIQA6R6abmeYKEhy5B22KsHcK26KsD9vL5m5+aRk5iKPmEr1V29R+83b2HoAq7YSM2Q/bpmYkoSqqkhSdOvzzuDM7oeaUUCtKaNl5FLT2JkVkJ0pSID7g+VgmVheD9hBC3Q9KwdFVaOKrsafQ0Z2XnBbSZbrDdeCtSQBbw1A0IjNMqn1uMNir3RHIaZhNCs0banYNd4Fnz3Js6anIMSHQJAAHLLvcO6SJP4sBEi30d7tjMzc/F0eTqYoKlr+MPqdfBN65XYq1q9ATc9Fy+yHbeoY1WXIkkR2fn/s2ooOHTvUteKrKAq36oYI2cGHkaT6abqRaRY1I5/ck2/E8tViVpdR+fbDSLZOksMRrm9pS3SpmhPbNKl8aylEK1K1LCRJ4e1VT7HhjZexZRUj4KfaU4VWUoQWaBB8kiRRMHBohz6HriQR6o16K0J8CAQJwrR9duduSeLGu67g7NuiW2cLYk933FU7svsjKTKSomLWVCKpDmwjJD5AdzVsfbQHv7uYH1bMD3pqWAZGbRVP/PNOlPohcqYeoK62Br+3DpIDrZaXqOl52MlZyIqKpDqQLFA0R4v1LU07b1SnA9kysHQfGQfORE1tXsNhVJdjfr0W3TDJP+I8bGy865aipeXiyAwKP8vQMWrKsaLY0MeLpltM7rIS9EDkcEDN6SLg81K09ed2ZceEY2oQIT4EggRi6t7DyHh6A8W//ULB4GHxDkcQA4zqMgLl2zB91dimTuV7jwWfsE0sXw0StLrFEQ1L92PbkDH1TOTkDHR3KbKqhhMbVtGPmF+swV1egmYG58zIjRSIbRo0zoLYpoFt6mAZ2DZYpknpjkLqajwkp6aHXxeaLWPUb1WFamKSMrLxeb1o6Xk4MvuRkZsfFkIAgcoiSr5/C9syw1kmSVYxaiqCQ/pocHz1OaQOCbGuJiQEyot38PTihZ0qfhWOqQ0I8SEQJBgPXXM8v7v9j8y46m/0G7J7vMMR7CIOVxKqBBUfrqQ2OR1JVpElCez6O3tJQnK4SHKo7driaFxf4KsowrYM1NRs5ORMbGS09Bzk+m4TS/cjIaO40pBdaZi1bmzLQJKCWQh++yYYR6jOxLYwaiqx/LWgqNRU1/LEvbdRV11JWm5/FCXYeBucqutGcabUT6812O2oi7CMAIXrlqKmZoMs43QlR3bL1FVGXIusOZEkqFq/PPyYbRkYdR70zOywENMcrs5+DTEjFm6nwjG1ASE+BIIEo39uBs/fciqn3PYnZlz1112uNxDEl5yCAcy9eh7L/n5r1JHxAAFPOeXvPNJqC2+0ugMj4A8u1J5SZCvoMGrUVhHKZhi11di2ha/kF5Q6D1gmdqAOywhQ/dmqYHFpwBsUQPXvkZNSkV2pZE78HdWfvUTKuKnUfvoqWVPmkpIf7GIx9ECwTiMtF6Omgqr1y8OtspKs1teVBDENPbyFYugBTNMAy8LQA7gyChg+52Yku6EuJdRe21iIhRbhRCr4jMU2XbwLaBMBIT4EggSkf24GL86bxSm3/R/HXPEX+u+2R7xDEuwCmbn5qA5nxMj4xvbmlhFoVizaNPsRzeekdEchT95/NxnZedRaClJSBrLmwraDVulqWjaYOp5PXsTWgzUKyalpJKsKlm3i9dUhKxrO3fZDS8tC0lwoSWlBe3RTD/qSQHCbJKsgHLse8KMFbByZ/cLbJdEwDZ3S0p3hYXO+Hd9T467AtiyKf/oKtaQISZLIyR+AogadUV3Z/VEdzgghVl68QxR89lKE+BAIEpR+Oem8OG8WJy+4jqOvuJsBu42Id0i9kljcVbdURFi6oxAj4Kem8DsgmOn4dfX94VbXkJPnk/ffHR5nrykSZ117W0StRVM0hwtFVVE1DclvIylqMPtRVe/LIUmgqNiWiW1bSIAsSTicKied+0dWLfsnPp+f6u/WI2tOmnbBAJR+vg4z4EPfhfoLv7uEQGURSnImlteDe+OzIMlIiozns5dBDm7juBU1aOkuwZBjLmx2nN4+pLAvI8SHQJDAFGSHBMj1QoDEmPa2UbZl711X42nRhdM0TWo8VVS/9gDpOQXYloXhrSbj4NNQ03IBIjIA/oqdlL7zCI/edQO20vKvZ8k0MO0m56v/s5qag20aKClZZB48FxQFWYJUh4Jn40oG7zGGa/+2jI9ff4lVjz+AkpQeFgMRWCaSJOGvKmkxjqYYVcUYvhpKP3kBy7JBVrANHTvgJX3ynGAnTH2diZycjqo5MT0luD9Yga98e9RjCmHROxHiQyBIcAqy03lp/mxOnn8d0y+/mwHDRsY7pB5N4yzFnMtuRPc3aZ10uMjMDbpztiYsQkimgWHb5B12ftQiQm/ZNsrfe4IzLrsegCfvv5uC4XvhqncDbWqsZVsWhqyQ10pRYuk7j2JbFv7KYvSAhRTwIskKRnU5tmlg1VQgKSpqZkFw4JsETmeDiVlOwQBS0jORNAeZ085GyxnU7DyB8t+oWLc4bBzWGqECUvcHK9CrK5FkmbSDZqOm52N4yqn+/GUc/UaiZQ8Ey8CsqQiamSkatqxg1Hmo+uxlkpJc4S0U0ZLauxHiQyDoAeRnpfHS/DmcvODPHHnpIgbuPireIcWNzixKHW11LNr6c5vdCSEh0FoRoaKq4YF0we0SR4uzU0K0dLyApxzT0JExKXv7EWo8VUgOF0gylr8W2ZWGrAW7RCTVAZYZ3uaJhpqRj5YzuNnjlhFo8T2WoQczGpaBr6IIV3Z/hhxzIb7y7ZR9sBILCVmS0VKzkG0TSVaQFQVZUbElkCQZSYbs/H4YTgk9M5szLruegbuPIqdgQEK3pMZimy6RCmjjhRAfAkEPIS8rjRfnzebkBTdwxCWLGDS87wmQzi5Ku9rqmCjdCX53Mb+uvh/DW01qWgZINpIsN2ybSDLYNplTz0TN6o+SkolRVQIoLdq329DQ/hvxRPAz1mvceEuDBbGmYWBUFaO7d2JUl2HUeSh962HUeiFlGjq+uhpQHOibXgmaqZk6lq8m2OKraICNRHC7SdUcoDnC4iz0nSViS2os3E6FY2oDQnwIBD2IvPoMyMz5N3DidfeTld+8fbM3E6tFKVHEREexdD9IEhkHn0bB8L0AKC8pQknOAMBX9DPVX7yKlpaNIyMPy9RRFJWcgub27arDGSwztcx607GmJwsKkppv38Es/Dz8sGmaYWGSnZXN3KvnAaAHfFSWFPHyk0tx7HM8Wv5wZM2JXlWM+/3HUdJyUVNzMGrK66f6tk0ifU+xKH4VBbQNCPEhEPQwcjNTOeOwcXy84XWmzDwn3uHEhURalOKBmpaLK3cwsixTrRNsabVtlJRMsEyM6rJgB0wjgl4bDb4aaZnBUfeKEtwGaoqpqsiyzElnXcLIfSdGjSN0hx7KRhkBP96aalzJmajZA5AUDcsyQA5uuRCOySZah02iEwtR0BeERXsQ4kMg6IFceNwE1t/9PJ+/lcH+R8yMSwyiIHDXidWev6JqFAwcimVZ+H11lJRvx7YtDE85jRf3nVXFGNVlVFeW4y4viTA0sy0TzOZFpXZ9rUhWfv9Wje4a18XY2NSteRBkBUlWkRQVSQpuCelVxVimjukpA8vCUhX8LhXDU9ahaxb0DoT4EAh6IKqqsPzPp3LWXc/zOTb7H3Fyt54/kQsC40V7BEV79vxVRcJEino8X0URduNptQQFiAIE/F6wwfZ78Xz2ErIamc2wLRPbbBgypzldSIB/50+Yte5m5zKqy5DqX9cewjNbFAWQwDbBCg7Tw7apWr8c2zKx6jxIEqSmZ1LiDNaK9JU6B0EDQnwIBD0URZF54s+/4+y7n+dz22b/I0/ptnMnYkFgV9KqsJAkVEWi9N1l4VqIpqhSsG23/9Dhbe75h9p7owkUI+DH9NciSRKy3FBAaho67rISkEByJpE+YSZqem7Ee83qMvT/rQm3ETucLmwjQNWHT7V84aaOo53iowEJJBoNh7PJOuQcJNvCqC7DvX4FaelpnPt/t5GZUx9LazNtGjnChvBVFIWdYbsqwyYye12LEB8CQQ9GUWQev/53nPOXF9hkWRww/dRuPX9Prb1o77ZHezIVLofKSedew3NL/oZhRRcfhizzxD23tjsT1JJACdmqB03JGrxBwmPoJRlJ0UgaOBpHI+8Oy9DxbvsGq1EdSHJqOum5BaTvfzyOjLyI81imgd9diufz1dR63M1M1lpbeCVZwZmRh6NelFqGjoRNZnYeelUxxldZnHjWJWHh0Ro1hd+x44NnmrUKN3aGdTm1mGfYRGav6xHiQyDo4SiKzGPXncK5f32Bz4Dx3SxA4sGu1kx0tNWxvd0JAZ8XFAf5h8YmE9TagqaowToJr9rw69vQA+junUHzLiQkVQt6fNQTvck2uGWTPmzvCAFpGjrF27cSCFjU1lSz4l8Lw620IdpaeI3q8nDBqxnwobtLKasqwagpo7baw8vL/xNxzKbHC31PlR8/18wRNoQkSaQ5ZNwfPhX1c+1M5qKvZfbigRAfAkEvQFFklv3fKZz31xf5zLYYf/TseIfUJXTWJ2FXWh3bc2cbygx0dSaopes3DQNPZTk2ICelIamtG5i1hmVZ2LaNkpyJmpzebCJvawtvyOm0av3yhuPpfnSvBzUpHVlRUJPSKDj6kvA03GjHC31P27d838wRNnwuWSZQWYQ7yjXEKnPRUzN7PQEhPgR9Atu22fzrTsbs1g+pFbfHnoyiyCy77mTO/9tLfGrbTDhmTrxDijkdEQ+9cc++pesv3VHI4/fMx2fa5Bx1EVpG21sabSErKpKsRkzkbQ1/xU6c2f0YcsyFEe6oNdu+p/ijlxk8/XxS8ocga06cmQVtHi+nYAABn7fdjrCNEZmLxEeID0GfYO3Gb7l1ySpuu/Akjp08Lt7hdBmyLPPIn2by+7+9xCe2zYEzTot3SDGnPYKhN+/ZtxSvojmQJRvb0AmU/xbxnG3oGNVd09LaVjbKCPiRFQVX9oBuzyKIzEXiIsSHoNdjmhZLV60HXxVLV63n6IljUJSWdsF7PrIs8/CfZvKHv6/iE8vkwOPmdtm5EnVGRV+885VkGWwjYssjhG0ZGHUesrOyY97S2jQb4y4rQQ80DOurLCni1ZWPYtsWesDfbJCeoG8ixIeg17Pu480Ubt/BTUdkc+d7Raz7eHOvzn5AUIAs/eNJXHjPKj55lZgLkJ4yo6Iv3fkqqsaACaegpmY0ey5QVUrVZy8z9+p5zTInTYViqHhVsiL9RFojdMzy4h08vXhhRMbJCPipqa6ivKQILWAjSRIFA4f2SAESavtt3OrbmJ64lRcvhPgQ9GpCWY9pQxRO3iuN9b/4+kT2A4ICZMm1J3HhvS/z8WqbicefGbNjixkV0YlXJigkBqu/eKXF1yQlucgfNLTZe6IWr7orUJPTkRUVWetcrYWvogjvuqVojWa7WJaF0oHri9fn2vj4AU85v66+HyQpotVXadR11FO38uKBEB+ChKErikJDWY+Fs7IAuGBiOmc92zeyH1AvQK45kUv+8QofvWIz6YSzYnbsRPgF21JRaemOQoyAn4CnvN2Zj84UqMY7ExQSgyXbtkZseUScv4lZWGvFqyv+tZCM8SfiyhmIpfvDU22hfQt+04yTJKsYNUHTMb26DJ9DwqifJ9Pa8brqcw14yqNmLkLU1XiandcI+CPafiVJqvdbUcPX0du28roSIT4ECUOsi0IbZz1GFwTv3sYUOJk2RO4z2Q8ICpD/XH0il9z3ChtXWRx0Uu8YRtdaUalpGFR7qvCuvp/R59zZZndFZwtUEyUT1HTLoylNryFaPA5XEklJLmr/t47aVo7T3gW/cfttqPZEz8xuljGIdrzOfq7RhE3AU84vq/6BGfA2y1w0juesa28jOTU9/FjI4C3U9itqVzqHEB+ChKArikKbZj1C9LXsBwQNmR64+gQu/ccrbFhlM/mkc+MdUqdprajU0ANIP39F7Rdrmllzd/RY0L672nhngmJVZBtrIeXMLGDk3PnhWonStx7mjMuuJ29AQ2aktePtyufaWsbECPgxA14yDz6dgj32biY+Qp9Tcmp6s4F6u9L2K4iOEB+ChCDWRaEhMTN5oMzuOQ4CRsPd4PAcB5MHSn0q+wFBAbL4qhO47J+r+fAlm4NnnhfvkGJCtKJSPeBHLQkOYfNVFDV7T0up/t5QoBqLa4i1kGqceVIdTvIGDGl1Um5naU1AhTMYe+xNav/duywGQesI8SGIO11RFPrlj9so3FlGoWEy+YEW9pTVMr78cRsHjO7Zi01HkCSJ+688nsv/tZoPXrSZcvL58Q6pS5BlGUmSMP21lL71cDN7cEiMbpyuxjT08NwXQw9gGkZEnUNvLgpuy6I+2naLoPsQn74g7nRFUehewwdw1+WzCOhGi69xaCp7De+dv3hbQ5Ik/n3F8Vz571f54AWLKaf8Id4hxRxF1cjJH0AgM7tZij9Eb154oWFGi10/aVd378TjroiY1dJV3Rld0Z3SGx1r+zJCfAjiSlcVhTo0lSMnjIp1uL0GSZL45+XHcdX9a1j/3ENMPfWCeIcUc0J3t12d4g8tik3NtUJoTheZOfndvjiGZrSoqTnIqgamETGrpSu6M7qqOyWRHGsT1VivpyHEhyCuiKLQ+CFJEv+47FiuXryG959dwrRZF8Y7pB5HaFH0+QN4yotBcTR7jQSkZeXgcqhx8YCQ6yfcSqrWoVktu0JXdf0kgmNtvNupextCfAjihigKjT+SJHHfpcdyzeK1rH/2QabOuijeIXWYWN6JdvRYoUUxff/jqftoFZlT5qJmNBRXWqaBWecmVZPxbFzZLR4QoVhDTqWYBpKqobuLu/zcEL3WovGWScDnDU8BDtFeQRLLguCOfteJ0k7dWxDiQxA3RFFoYiBJEvdeOoM//uc13nv6AQ6Zc0m8Q2oXsbwT7eyxHBl5SLKKljMIR07D6HfbCBBw78Tp6PpJyk2vobFTqSQHf9VLEh1yLI0FibRlAp37roWwiB1CfAjihigKTRwkSeLvFx/Dnx5cx7srF3PoaZfGO6Q2ieWdaFvD0SBYuxG6a0/EO9ym1xByKg3VeADtHmffHtpbAJoIWyaNERmMxECID0HcEEWhiYUkSfztoqO5bsnrvPvU/Rx6+mXxDqlNYrlAtDYcrSmhO/Xuor0LfdPPQ3U4u6TGo73ZjLOuvY1ajxsj4Mem+WtlzRlVkHQ1QljEHyE+BAJBGEmS+MuF0/nz0jd458l/c9gZl8c7pG4n0e7UO7tt0RXdGe35jErfeYRH77oBwyZodb9uaXj7J4QkwZBjRKFzX0SID4FAEIEkSdz1h6O44eE3eHvFPzl87pXxDikuJIrb6a6Koe7ozmjtM7ItC0NWyJp6JppuoaXlIjWahaK7i6lavxzLCLT7fKLNtffQJeJj+/btXH/99axduxav18vIkSN5+OGHOeCAA7ridAKBIMZIksSi3x/FHcvf46GrZ3LwaVcwdtIR8Q4rYQlUlWJbBnr5NmxDDz8e6nbxa53v1uqoGEqU2gZnVgH+gI0jsx+S2rwVuT2INtfeR8zFR2VlJQcffDCHHXYYa9euJT8/n59//pnMzMxYn0ogEHQhkiRxy1mHct2cg5l128PYts24g46Md1gJRWhR9Hy+GqO6jLLXH2j2GgnQ630+untx7C21DYkipASxI+bi4+6772bw4ME8+uij4cd22223WJ9GIBB0E06HxrO3zmb27Y9g27DnZCFAQjReFBPN4TTRsAydxvkf29CxLYNAVWm73t+XP7veSMzFx8svv8zRRx/NrFmzeO+99xg4cCCXXnopF1wQ3b7Z7/fj90eOvHb6A4iBxQJB4hAUIHOYffujSFiMmzw93iElDKFFsSst3HsykiQjSRZGdVnE47qnFKO2ioqNz+F0Oamr8cQpQkE8iLlt5JYtW3jggQcYMWIE69at4+KLL+bKK6/k8ccfj/r6RYsWkZGREfGzaHHL+3oCgSA+ODSVZ26Zzda3HuOrD16Ldzhdjr9iJ97SwmY/orixgfZ8RoqqklMwAAkJovxIsoKJwhP33Ep58Y74XIig24l55sOyLMaPH8/ChQsB2G+//fjmm2944IEHOPvss5u9/oYbbuDaa6+NeMy583Mo+yrWoQkEgk7i0FSevnk2p92xHIC9phwT54hiT3uKGyXLwF1e0uoxesKk2F2lPZ+RqkiYSPgrdqLoAQJVO1GSM5GVerdV20RNSadg+h+QkLq1fVkQf2IuPvr378/YsWMjHhszZgzPP/981Nc7nU6cziabLJW7VhEtEPQEbNtm8687GbNbPySp6223Y41DU3n6lqAAsW2bvafOiHdIMaWt4kZ3WQlP/vM2lv/zzhaPEUu78ETs9GhPAWhdjYcn7rmVivVPRLV6B5AVleSC3bB0f4vHEfROYi4+Dj74YL7//vuIx3744QeGDh0a61MJBD2StRu/5dYlq7jtwpN67MReTVVYefNsTrtjBbUeN2MnHUlGTl68w4oZbYkGS1K6zYQsUTs92nO+UNzRrN6hwe7dW1rYlaEKEpCYi49rrrmGyZMns3DhQmbPns0nn3zCkiVLWLJkSaxPJRD0OEKTfPFV9fiJvSEB8u8XP+LZhc8y/bJFDNy979jld6cJWU/t9Ggcd1dZvQt6JjH/rTdhwgRefPFFnnrqKfbcc09uv/127rvvPubOnRvrUwkEPY51H2+mcPsObjoim8LtRaz7eHO8Q+oUmqpwzayDWTV/Nm8uvoFtP3/f9psEAkGfp0scTo8//niOP/74rji0IAo9vYagtxP6fkYOzmfpqvVMG6Jw8l5prP/F1+OzHyHystJ4cd5sTl5wA0dcspBBw0fHO6RmtHc4m0Ag6HrEbJdeQG+oIejNhL6fE6buR+H2HSyclQXABRPTOevZYPajN3xveVlpvDR/DicvuInDL74zoQRIZ4ez9QYSQXwlUseOIL4I8dHD6U01BL2R0Pdje9088vJ6ThnrYHRBsLtrTIGTaUPkXvW95Wam1mdAbuKwi+5g8B5j4h0SkHiTarubeIuvph07pmmCHRmLKgU7iUKv760iUBBEiI9upCu2RxrXENz5Xu+5i+4thL6fGaNTePxTN2fsnR7xfG/LfkBQgLw0fw4z59/EtPPnM2TkOGRFiXdYQGyLRHvKXXx58Q62b/ker9dHxvgTcWREdiXJqgPb0LtUfEXY0JeXsPzeeRhWpPgwZJnl/wq2L/f2LJRAiI9uJdbbI6G76t5YQ9AbCH0/UwbLfLvTx+HDFFJkH349jZD2HJ7jYPJAqdd9bzkZKaxacBrXPPAP3ni4mtNu/g9pmdnxDismJKLvRkuEMh4+v061pwrvR6sifDYAJAmGHHNhu47VmW2biOcUB/mH9s0slCCIEB/dRFdsj4TuqntrDUFHSMSi29D3M3dqMot+rsEwLY57vIrUZD9OR5N/emoZX/64jQNG9542xOz0FB67/hS++nkHf7jjYubc/ABpmTnxDqvTJKrvRjRC201ZU89E0y20tFwkVQs/r7uLqVq/HMsItHqcrti26c5WZUHiIcRHNxHr7ZHGWY/eXEPQXhKt6Lbx93PCuDQykhQCps1DH1VRbGVywzkzUOQGkeTQVPYaHv/FqivYa/gAHr7yKH5/xyXMvmkx6Vm58Q6p0ySCsOgIzqwC/AEbR2Y/JLXjDtJ9vWZGEHv6zuoUR5puj4QEgmlau3zMkJi5cGLzGoLe4B/REZpmlTrzucaKxt+PQ5U4cmQKx45JZeGxuQS8NSiyxLGTx4V/jpwwCofWe+8F9tx9AI9cOZ1n7rwUT2VZ228QJCShbEXTn2iCRCBoDSE+uoGmQqGzAiG02E4eKLN7joOAYYd/GtcQJMIi3B0kmnGX+H6iM273/jx61XSeueNiNq5ZiWHo3R6DmFQrECQGvfdWK0Hoiu2RL3/cRuHOMgoNk8kPtPBLsxfWEEQjEYtuxffTMmOH9ee5G2fy8oaveHrRe8z58z9Qta4fJNmTikS7EsvQI+44bUPHtgwCVaVxi0nQNxHio4tpWhQaojPFoXsNH8Bdl88ioBstviaRawhiWRyaiEW3Pf376WoGF2Rx2cmTGZD7Df9YdCVzbvhnlwuQnlQk2hVIkowkWRg15RGP69VlGHUeqj57maQkV7eKr57SqizoGoT46EKipd9DdKbF0qGpHDmh5w7wilVxaKIW3fb076e7OHnqOCRJ4t6FV3Dajf/qFgHSVzE8ZaSn52LbkVt9fk1Gz8zmjMuuZ+Duo7rlMxJZKAEI8dGliPR7c2LZctwVWaW+RrxblGdOGYskwZ03ns0+h57AgceJAZSxpD0LvcuptVt4xCJb0dezUIIgQnx0ISL93pxYtRx3VVapr5EILconHTyWiaMH8e9VH/PGsiKOOvdPcYmjNxKrhT7W2QohLASSbdstu8bEi60bYPumeEchiDGmaTHrxiXsphRzz4l5XPtyKb+aBTy78MIOC4RN3xVy0aLHwfC3/CLVyYM3nN1nskodJfR9FG79hSFDh+3S9xBr5j/2Nt/q/TjynD8ljFmcIEgiDKYTJD4XTNu9Xa8TmQ9BtxHL4lCRVeo8iTgXaP45h3Pb4+/w3F//yL5HncqI/SbHNR5BA0JYCGKJEB+CbiHWxaGiqLNzJGKLcohbzz6ML3/Yxm0r7sdbU8XeU2fENZ6WEJkAgWDXEeJD0C2I4tDEIhFblBuz78hBPH3LHE6/80ls22afacfGO6QI4j2iXiDo6QjxIehyOlMcGu9ujN5IorYoN0VTFZ66aRZnLHyKN3/bwl6HHEfBoGHxDgsQs04Egs4ixIegy+lMy3EidGP0NnpSFkpTFZ66cRZrP/qWu+/7I0dcsohBwxNnu01MZhUIdg0hPgRdzq4Wh8bSE0QQpCe2KKuqwglT9mLSuN04ecENHHHJQgYNHx3vsAQCQScQ4qOH0xO2JXa1ODQRuzF6Oj3Z+C4vK42X5s/h5AU3MfqYc9nzwENITktv+40CgSDhEOKjh9OTtyVaE06J3I3Rk+npLcq5mam8NH8OT761keW3Lee0m/9DakZW228UCAQJhRAfPZievi3RmnBK9G6MnkpvaFHOyUjhilMOZtpeu3Hh7Rcx5+b/kJaZHe+wBAJBB+g5K5WgGY23JQq3BxfmnkJT4WSaVrPnWurGaPxaQd9lnxEDeejyI3n6jovZ8s0XmEbL2Zyuwl+xE29pYbMfMZlVIGgdkfnoofT0bYnW6jkSsRujJ9TW9EX23mMgy64+miVrHubZVTDruvtQ1K7/tSYmswoEnUOIjx5KT96WaE04AQnZjdGTa2t6O2N268e9l87gmXf+x3/uvopZ19+Hqmpdek4xmVUg6BxCfPRAeopJVEu0JpwKstPa1Y3xxQ/bSHZpEZmIrspO9PTamr7C7MP2RlFk/n79HHY/8EgOmXNpl55PCAuBYNcR4qMHkojbEu2lLeG0YsH57erG2F7q5vZHVkdkIroqO9EdLb+dEU5iS6iB303bk1OmjuP6h17n7Sf/xeFnXBHvkAQCQRTE7VsPI5pJVOin8bZEohZlhhbyCydG+jNcMDGdwu1FvPP5jxw5YRTHTh7HjIPGslv/bGYcNJZjJ48L/xy2/wgee3VDRLFqawWsnaHpFlFXFb2u3fgtZy94lLUbv+3W9/ZGJEni7gumMzTwE0/efjE/bFof75AEAkEThPjoYYRMojZsC25LNP3ZsM2kcGfQJCrR6KhwamlRjdbl0xWdP7Zt89DLG9i6bXtYLIVEUiw7izojnDrzXtu2+faXImy75eFoPRVJklj0+6N44rIpfP/KA3z70VvxDkkgEDRCbLv0MHqySVQ0d03btvHpFi5NDm4Z1Ltr7jtiUNQ6i2jFqg+99D5IUsw7f1Z/+A3zlqxi8hBHl9bWdGZbpzPv7e1FtJIkMSg/i2dvnc3s2x/GXVbCmImHkZXXfBCcQCDoXoT46GH0ZJOoaMLp082FPLXuI048YhITxgwJC6fIRXUHD738IRfNnBK1WPXEx37DqcCiM/PCj3Wk9iVUMzF6aAHfbS1mzG79sCybux9/jRyHTq1fxrRsFFnapeO3RmdapmPx3r5QROt0aDxzy2weXruJ5Xc9y4yr/ka/IbvHOyyBoE/TO3/bCBKSkHAK1W4cPXEM//1hK6myj//+sJWjJ47hyAmjUGQ5YlE9sJ/NrUteZtX6r5oVq47Mc6BYfsYXGIzKj8xOPPTS+3z18442txVC2zu3P7ouvM2zZuO3bC8q4uqDnJTXGqz+tiZmtTWNtzua1sB0ZFun8XttG87aL5nC7Ts69N6eaFC3KzgdGpeeNInnbzmVtf/4EzsLt8Q7JIGgTyPEhyButLQANl1UTxwBLnRufGBVuP7CtsHr13ntu1pcis2pYxQ8tb7wsS+YmM7XP/3GrBsebLUQM5wB8FbxyMvrsb1uHnrpfe596g2mDJaYMkRjbK7EBc8Wc9DiopjU1oTEzuoPv9llJ9emXUOeWh9Oo5oD+9kdem9XFtEmIv1zM3jh1ll8+ugtLLvxbOpqquMdkkDQJxHiQxAXWloAAwEjYlGtqvUyKNXiuJEaRSVlDEgy2D3HQVmVl2+3lvLAB+VMHaoyOldme7kHvx7MTuyWpSGbfhS9modaWVhDQufqKanYupcZo1P4+qffKCkp4appuQzMz+aKabnkp7s48YiDue2y08I/d10+q8O1NY23O+5+/LWIYtYQ7cl+NBVope5qsA1OHAGF21rPfnQm29Ib6JeTzsu3n8E9505i5e0X8unrL2BZvV94CQSJhKj5EMSFlozGFj3xRvhx24aiMg+mZXHOvg7W/RTg3S1eJv17O546P3V+A78J3xRLrP4+AJJEekoATVWoqtPx6yZ/muxi6Ve/Ra3PCAmBqYNlJvW3OGp3hW+KvCiWzvgBCgcMTUeS4MBUF9NH+PnvD1u5+dyjO1UbEbruGw/P5o+vFLFXP2eHnVybdg2VVXmp8Qbon6YQMC0OKGj7vV1tUNcTvEcmjh3K8muSefb9jbx4zwZOvvYvyLK4HxMIugMhPgTdTksL4NQhMo+8vJ4TRmnsnuOgtKqOGq8fTYF0p81hwzSe/jpAWnYB1YGdnLNPGo9/XsfkCeMYOTgfVZXZc/f+yJLE3Y+vY+90N+celM0vtZ6oC2tICNxwXDKBQA2XT3Qx+9laVAlOHePAU+sjI9UFxKbItPF1Hz3SyYuDJdb97OOgxUXRF+j6zp8DRg+JeDiia2hxEe4aLwoWaU6Zar+FiUxmZvT3dpdBXU/ppBk+KI8/n3EoeWs38cw9/8cxF9xMakZW228UCASdQogPQbfT0gJ45r5JPP+lm3U/WkxeXESFpw7TMklzSNTqNhISSarE59/9wnkHZrDouHz8Zim/er0RGYk1G77BV+fhyuNySE9xcuEkqdnC2jjrkaX5cUoSo9IUFMlm/AAlvI3jcjrxBXR2z+78XJnG153k1LhyWi5flHo48YiDmTBmSLPXt9Qy3bhr6NPNhSx/9X2unZLK4CyNwkqdv39Qy8mHT2j23mg+KyFiOTenJ3bS/H7GAaS4vuLhuy5g4MTjOHjmefEOSSDo1QjxIehWWlsAx/VPZsboVL6pyeCw8aP55zNvcsgQhYvHJ3H7e3VsqbS44AAHj33pxzKD72t6x97ebYWmWY+B2Qpf7jRxyPBBocGJT9Xh1cHpqMOv66QmuXA61BazEe297sZxTdgtfZe2c0JdQ6FjnjDaxUWTG4TcD2UGmzb/gtJkCyGaz0ozdvH6GtMddvRdwWmH7cWcQ/fksn+u5rm//I/9jp3L8D3HxzssgaBXIsSHoFtp1wKo+Fj1/n/JS5K466hURuTI3LMBDhyocNUkjaIai1e+reaek/KbCYvG2QXbBl9Ax+XQIkTK0RPHsHTVeg4aKJMi+3AQzKqMyJG566gUHvjMR5lXZliuyqfbTTI0hbTMbG44ZwZJTm2XDNy6Yrujo8fsDoO6zniPJAKSJHH/lcfz42+lXH7/X7BmX8uIfSbFOyyBoNchxIegW4m2ANq2zW8lbgbnZyJJElt2lLHgoZc5ZncFbIuHP/Pzc6WFS5F45mudk0ervLXFxwMb3Fw1LTu82K7Z+A2PvvJhRCHm9jI3A3MzGZ7jCm8r5GamULizjJ99AVZ/44UoPiC1usm2agmXpHPb0bn8fWMNiiztksFbV2x37Moxu8OgrrWJxT0h+wFBATJySD4vzj+Nk+ffQ2XpXMaMn0paZna8QxMIeg1CfAi6lWgL4JoN37B01fvh4sRV6/9HvwwnV05Lp1+Oxuo3i3EqEskOWPW9wSMzUzh+lMVbP9Zy4aTM8GJ7z1NvUl1dS6HZUIhpmiaKUkdmahLU27dbls1dl8+i1ufn6y1FGEZDm6Vt25S6ayjISmPdR1+zb4aHOftn8un2sl2+g++K7Y7u2kLpCN3VSdNdpCQ5eWnB6SxetZFnbnucmdf9k5x+A+MdlkDQKxDiQxBXmhYnHjl+FI+t3sBhwxzsMziNtd/V8F2pTooGN05xcvv7flZ952fGHiqXrfGy973byEjWALCUGv4w81B265cdLsScu5+Lp7/yc0J9UadDU9l/1GAcWvCv/klT946IZ82Gb7h1ySpOmLoffm81VxyfjSxJnbqD74rtjkSc8dNdnTTdSbLLwZ/mTOXUaWOZfcel5A4dxbGXLsDpSop3aAJBj0aIjz5OvP0YmhYnPvDSB+E7+oMWF1FYVkuqZjFtiMqMESrv/mqw6juTpbOymTFa4ZuaTK4/+2gUWcahqUzbd3jYnv34UU7O2C+d2kBVu4o6m7qdnjw2NgPlomV7Ovu5J9qMn+7qpIkXu/XP4eN/X8Bbn//EvNsvYcbF88gftFvCepgIBIlOz/stIIgpLY2t72ps2+arn7fz0Kr1HDQAZu4ZdDl957PN3Hnp77jtstM4e+aRuFxODEti+h4av7jhqOEa35dbHLXMzaZiCZ/fx4DcDI6dPI4jJ4zCoalhQTM6T+PsJ3cwKk9rl4NnU7fTMXlaxPOxdAKN1+feVYS2gTZsM8MW9I1/OmNHnyhIksSRB4zgrrnj2fzUbax+YEGbc4MEAkF0ROajDxNPP4a1G7/lqnufQTJ8/P4oFU+trz49vxPTtDh28ji8vgDPv72JPVMrmTY6WOw3qABO2FrJ/zwZXH/20SQ5HRFbC6FrmjJYZt33NWCbvP59DVMGO1u9xmhup2u/q+Hs8RnhabaxuoOP9rnLstQtGaiuynQl4jZQVzF172FM3XsY9zz3IasXz+f4S+eLDIhA0EFE5qMPE4/JpuGMx0vvU1VVxYEFBrtn2JS6qxmdHzlU7Z3Pf6Supoorp+aQkeoK/1wxNRtfXTWKLIezHU2vaUyeRmFlgJumuSisDDC6jexH6H1n7Z9MQDeYOVrj823BmpJod/Bf/PBbeDJtR4n2uXdXJqSrztN0YnG0n6bfVU/n2lMP5sjBFkv/+Ds+f/OFeIcjEPQoes9vgj5CrO5cY+nH0N6YbNvmwZc+5G8rXkf3+8h0Scwaq1KQplBcozfKfjRvm21PDUHomiYNkFj7XQ1ThyicNMbJ+1sNXvuuhkkDHFGvsfFnse+gFGrqVAbmwfG/NmRYGht2OTSV7WVV3P7w6g7bh0f73B966f1gJ04XZ6B6ovNoonPlKZO47KQDOfcvL/BORRl7TjmGvAHd010kEPRkxG+eHkas7lxjOdm0vTGt/vAb5i1ZRUm5G8UOMHGgzJg8GU22caqwvdwTtjG/56k3KSwq5f2tASY/UNSuGoIvf9zG1qJSXt7s5fNtXo4YpvBdic7huyl8vs3Luh/9UesOGn8WsiSRnuIiPcXFBRPT8dV5UGQ54g7+sP1H8NjqDeFFPDQx17btNrMh0T73r3/6jR+3FHZ5Bioema6+gKLILLvuFI7OLuK1+65h63f/i3dIAkHCIzIfPYhY3bnG0o+hvTGZpsXdj79GtkOnyA+2ZfPBVoNZz5pIgI2Exw+pyX6cDhVLqWHS3qN44+NvOPOoSe2afbLX8AHMPGwC9z31BqeMTWLqqDwABuY3ZDFu+f1xUWtEmmZYqmp8SH4PB+Tbza6rJfvwtoapRfvcR+Y5UCw/4wcozNyz9QxUZ7JePd15NNFRFJlzjhnPSQePZdbtd/ChK5Pjr1hIelZuvEMTCBIS8VunBxGrO9fGd9+2DV6/jm3vWvajvTGt2fgt24uKuPgAB7nJEpoCexUoLDwyhWsOcnHFQcnsMyiZtMxs5l0yh0WXncqOknIyFB///WErR08cE7WGQFOVcLZBkWXWbviKFNXk1LEa6SnN60RM04qoO4japbG4iIMW7+Dkp6p56ycvhUUN2ZKmi3hIsAUCRoQIC2VDWvrcw499X4tLsTl1jBLedircXsRrH33bLIvSmaxXLDNdgpbJTEvmjb+cw/3nHcgzd1zMz19vwjD0eIclECQcQnz0EFpa9KItcu05TmML8u9/K6OsyhtRS9Ge47Y3plDW4+CBcORwhSN2V3H7bOZNc3D0Hiqz93IyeaDEjYemEfBWs720EsM02bajqE1R03hBXrPxG376rYhj9lApSDIpq/ISMGwCht3itYW6NG677LTwzwlHHIykOpg7PgtJdUZMiG1pEV/0xButirBoGRZvwGLJxkoOG6ayd4ESUXR757K1nD2/QWg0zTB15HtvK9PV0b9DgrYZO6w/j197LIGNj/L0wisw9EC8QxIIEgqx7dJDiNXMjAhb7lYsyNtjy93emEJZj+uma6Q5JE4erfHWFoNfqmz2yDVwqhoOxSYVH/vnW9zy4CpGDiloc4ug8YL80Kr1VFbXkaTYfF5kM+fZOkx8DdcUosm1NTXrajwp9o4ZedTppeEJsS0t4lOHyGFTspbijWaHXlWn4/f7+MNxSdQEAMmgzh/g/AlpLH9wOwPSmg/M25VJsb3RebQnMGpoAfdcMoOXPviWBX+cw76HHc/kk38f77AEgoRAiI8eQCxrNBr7MbRmQd6WH0O0mEbnOzloQGSNRDjrMQiGZ8t4/Db9UiUmDlT4w8te0p0gyzISgCQhSRKBQDDrsfS4wUDLi2TjBfmW1wvxGhbzpufRL12lsFLn7x/Uhq8pNLxu+MDcVq+tNUEFRF3ET9/LyfNfuhmTl9JivE19MEzL4u7H1zEu1c3E4cHjSRIoisrP5bWkaxYXHuDkka92sGbjtzz6yge7VK/R251HewIzp4zl2IkjuX35u7y+7O8cNPM8MaRO0OcR4qMHEMs719CdfrQ7/PZYkLcWk6fWx/ShJje8WxiOKZT1WHCCg0EZKqYNPt3mgvESnxZ5kSQZOSmD2UeO55cd5Xzw+dcMyZTYt0BiQEpwoYwmtBqLn5l7prHqv+V8tM3knAkNpmA/lBnha1r38ebw8LqWvCZaE3kPrVoPtt1sEbdtSJV9HD5MYd33NfxhUmbUeJtmWDZ9V4jP72NTrcSMJ9zhx23b5rfyOo7eXeHQoQrfVsDdj7+Gt7aKhbOzO/y9J+IAur6IQ1O5/bwjWbzqE1be+Qf2P/lixk2eHu+wBIK4IcRHgtNVd66d2caJFpNtB1tlB6db7Jdn8lD9kLj7Vr7JQQMlcpJkfqk0w8fIdEmMH6Dw5i8WgzMkjps8loWPriE/ycYw4Pz9HZS6q0lPcSFJzeNrHL+n1sfvRit8WWSw7vtajh2TGnFNIc+QtjpyWhN5c578Da8p85uTiEXcHzCoqfOS4ZJRFZ0vt/s5YLArIt4ZB41t1qXSkiPop5sLefa19Vw5LZ1B+Q4uOQiOf2Q7+w5w7VLWqy85j/YELj3pQP5w7P7MueMxfvz4TSaechH9hw6Pd1gCQbfT5eJj0aJF3HjjjVx11VXcd999XX26XkdX3Ll2dBunaYvnFz9s46ffdlIoGeGYQotwikOmNmDjStnOM29/jttTTalP46yXoxU1auRkqPx+5qEUlXv4+JstVNcFmDXOwf4DVH6pCFBW5SUj1RUhtI4cPyoc/6h8Jz9vL2WffgqHDVODBZzDk1FkKfyeRY+9RqCuutV6ibZE3iG7afy3Ko3/O3N62HDMtGwWPbaWYZmV/H5iBg5FYlSeo3mBq2WxYOkrES240QbDGYbJPU+9yVF7OJgwLFjQOsj2MWWQRLHXwLTscFanvWIx0QbQCYLfyXO3nsZ3W4u54B//x1GXLmLAsBHIstj2EvQdulR8fPrppyxZsoS999677RcLotIVd64d3cZp6l+xvdSNz7D53dHBeorQIrxHtpsLJmXw0EdVlJLBCQfvSb+cjDZjP3ivYZx+y1LqfAFcqsTbv+gcsayWar/VvGhULeOBF9fz89Zt3Hl6DnX+AAHdIGDD4bspXLYmaImekRwcCmfbNr9VFDN776RW6yUai7yDFhfh0y1cmhzpp6F6GZCbERZ5m74rJBDws8NQuP2dmvoXVUdeoFLGPU+92S5vljsfe53PN//KrGNz62OHUnc15+6rcd2bAVZ/W8OM0cGsTm+u14j3pOXuQFFkxu3enxfnzeLqBxaxriLAlNOvYsTeE4QIEfQJukx81NTUMHfuXB566CHuuOOOrjpNryfWd64d3cZp2uJ55PhRPPbqBtJlX0Q9hb/Ow+8PT2fG6BSGZWuc9aybD7/6pV21KGs2fMMn3/5CugPO2Fvlkc91Rg13MXVYckTRKAR/ad/28GrGZfrIdVooipP87ExsG/KyYcboSr6pyQxbon+6uZDn1q3n0oNb317aa/gAFl12Kj9vL2NnuYeVr3/MiUdEmptFMzULCUMbm9+KKxlckIVEw4L5xQ/beP6ND7iljS6VQMDgkZfXMzDNDg+08wV0anw6Ockyo3IkLni2mME51U0EUdfVa8RLBLRl1tabKMhO56mbZvHfn7az/M0neOHNZzjlmr8gK0q8QxMIupQuEx+XXXYZxx13HEceeWSr4sPv9+P3+yMec/oDOLsqsD5OR7dxmrZ4NvazuPO9hnqKIakW89eVIcsSx45JDW/hTD9wND/8VtLiAmaaFktefA+fP8ApozXuOjKZsro63vy5jgd/149vi/WIQthPvt3KL9uK2SEZTLp/O1lpyZGttEig+hiQm8G+IwaxdNV6jtrDydgCF9Dy9pJDU9ENk6UvvY/DoZHaSFy1lFVoLAzXbPiGpavWRyyYIeF26FC1zS6VB176AFv3csFkJ3/bUJ+9SVIJGCZBn7HgFtXZM49gxKC8iBi6ql4jHiKgr86f2WePgeyzx0AeeW0T/7rmVCYcM4sJx54R77AEgi6jS8THypUr+fzzz/n000/bfO2iRYtYsGBBxGPzrjqf+bP37YrQ+jwd2cZpaiL2/i++Zn4Wix57DX+thxyHAbbJ0o8qOXpUSjjDcOdjr/PK+i9aXMC+/HEbn2wuJFmFCw/Q8Ok2F+6vse4nLyP/UsiALFeEGCoqqyI1SePEkS4e+6yGSQeMYO708VHj78j2UuhajbpKCotNFp+cz13rO1aA23TBbG9Rr2lavPPZZk4Ym8o5k7L4zt18oJ2Nzc5yD2cePQGnQ2s1nlgQLxHQGT+T3sD5xxzAeUfvz6X/eIV3n6lm3yNOJjMnP95hCQQxJ+a/TX777Teuuuoqli9fjsvlavP1N9xwA1VVVRE/N1x6VqzDEtTTkdHnTd08T9/Lia17GZMXXPzOn5DGT7/tJN8VoLzW4IZpLrZWBGsThuc4mDQAHnl5PXhbduUcNTgf27Y5eg+N/Qc60TSFAwY5OXoPDQuJmy86lbsunxUWQ4+9uoHjRjq5+ACNY/ZQ+OCL7zly/Khm8Suy3Gx7qTW303Ufb6Zw2w4uPEAjQ7NJdsjtdgCNZjHfEVfRdR9vZtuOIq6Ymh1hBd94oJ2ExAPPv8tbn/0Qk78HbRGPIXSxcvHt6UiSxOKrTmCS6zdeuetifvhyY7xDEghiTszFx6ZNmygpKeGAAw5AVVVUVeW9997jn//8J6qqYppmxOudTifp6ekRP06nI9ZhCTpI08XTtiFb83PU7kE/C9Oy8Rvgkk0+2upjbK7EmByZsbnB2oSDFhex7kc/tu7lj9NaniPy95XvoNkBrpjkQpXl8M/lE1040PnvjzuaiaEz9nYRCOhcdqALW/ey6Ik3mh036syWKFNxv/hhG1/9vIOHXnqfA/tbHDpE5pgRKks/quT8CWktzlmJ9jk1XjDXbPy22RwXaD5TpT0ipTPW6rtCvESAmD/TgCRJXHHKQaxdeCb/ffZenv/rtZQX74h3WAJBzIj5tssRRxzBV199FfHYeeedx+jRo7n++utRRCFVj6DploGn1kcgoHP5RBcXvOJl3fe1HDkihZP3zuD5/1ZxwaQMBuancMW0AF+UeTj+sMm89em37JNexZz9M/l0e1mz9H2oyPLwYQqjc1V8jYpfx+SpHL6bwiMvr+eGs45CUYIL4NTBMlmaH6ckMTZf46jd/eHXOBzBv862baMoMosuOxXdMJtfXD0OTWV7qZvr//08ph7g4hkaaU6bSye4OOvFOgrdBlOHyNy85GVk4PaLmm8dRdtaOfOZHdz9+GvtKuptz9YQ0K1bEbGy8u8IsXTx7U2kJDlZu/Asvi8s5vL7rkZOy+fkP/4Nh7PtrLJAkMjEXHykpaWx5557RjyWkpJCTk5Os8cFiUnTjhi/brO93INDsdk9W+OgQQpLNlYybVgSWysCHD1cZUCKRXqKiwNTXUwf4eeNT74hUFfNFcdlI0tS1AXsmbc/x+vz8t6vNmP+5Y4ai9cweebtz8lOT6Fw+w5uOC6ZQKCGgdlBEXv5RBdvbKll0RNvMO/3M4D2F0qapsWpNy5B0auxLBiQLJObojI4Q2baEIWlH1Vy7ZQ0Hv1oJ0OytGaLYEsL5uhsm3d+3omvxtVqUe+m7wvb7DwKOavuirX6rhAvESDmz7SMosiMHdaft/96Dm989iMLbr+ECSf9njHjD+61rciC3o9wOBU0o2lHjG6YeGq90GjboVaHMX/7DckKsPi4JAJ6cChaissRHox26p5JrS5gM6ftzY4yD15/yyPHk5waJxy8J+fd8TgHDZRJkX04JVCQCBg24/JUDh/WPEPSnkLJdR9v5scthVx0gMaSTQE2FRmM6+fAAn5/gJOzX6hj5RduspMkzttHYfn3OyIWwZYWzOsOy+LTHWZEi3BTHJqKZdltdh7V6tvBNll0ZrDDpasX43iIADF/pv0cNX4EOWkuVrz9FGs2vc2xF90iBIigR9It4uPdd9/tjtMIYkTTjhjdNPl6SxGG0bDnL0sSr238ij3Tqpg4PCs8FC1g2PxcrpOuWZw0Usa2Gzphmy5gqckurjvzyDbj2fRdIYU7y/ixzs/qr+utzOWGX7iGZeP1enngpQ8YMSiPwu07+NO0dP62YUeLi6VpWjy0aj3jCwxOGuXgx3KT5f/V2SvfF+4w2SPLZtnnPmaOVjl0N4XNlUZ4EQRaXDDH5Ds5Zg9Hm626Ad1otfPItGzufuI1Dsiq6ZYsRLxEgJg/0zH2HzWY/UcN5u/PfsjSP85iwvFns+/hJ8Y7LIGgQ0h2tCq6eLN1A2zfFO8o+iTtNZba9F0hFy16HIxIj5bQYLQjh8lcN8XF0H45pLgaCoj/9Eop2+jHswsvbPcCFtAN3vn8RxYsXU0uQSvzpjz6qQc7fRCyBP2t7Vx+gMy/N9nsVAZGPdeaDd/w5388ycJDbHbPlPmp3OCyNT78JmSnONBUmVKPD0WyefzkZHKSYFuNwvwPZW67bA4F2WlRrz8C1cmDN5zNAaOH7JJh15oN33Dr4qdZPisrLD4ANhf7OetZN7ddOjumWYjWvtOw46vmCl9TrAjoBu9/+XOb7d/T9h3e4lDAvoppWvzxwdd473+FHH/lXQwZ2Te3pgSJwwXTdm/X64T4EESwZsM37aqXaGnB+HFbKf957i1U20CSwKEqjUzAbAzTQnUk8+CNHVvAWloYG1OrA5bJfdNVBqcaFFYr3Py+xG2XzWnmqzHrxiUMlXey4Mh0bDvoo3HlCztZ/UOAUbsN5MgDx/Lvp99k+u4KdxzuRLdsAqbEkv/J7FQGsWLB+Xz41S9RF0zbtvmtxM3wgbkcst8eODS13Z9r0xgHsZO/nZDX7PldEXFt0dJ3+unmQp5a9xGnHz2Jg/feXYiABKTCU8vM+U8zcL/DmTDjdNIys+MdkqCP0l7xIX6DCMJ0xFiqJdv3gG4wblj/qIvyp5sLWf7aR5x7+IQOu3K2ZY4W2qIYl1LJ4FSL/mkqYHFgf5pdS+O6hvSUYEbBXePl1DEKm4oUysoree7tz0lS4etSk7d+MZizp8a2Kotjd4f5H+7gnc9/bFFEBN1O3+e2C0/Coaktfq6tZUPisRUR7TsNxd4ex1dB/MhOT+HV28/g3S9+ZN4t5zBk7HiOvuBGVLXrDekEgl1BiA9BmFi4S7YkSkKLWIbiY9PmX8J1FZ09bog1G77BV+PhlAMU0pw2WUkKNQGbE0fAze831H6E4jiwn82w7GBdg23D1uIqBqTBlMEKb//q47edtQxIk3Eq8NJ3Oufso5GkQUGSyf4FzQVN0+tsLDRa+lxb68rpioGCu0JfdxztSaSluDhhyl4cN3kcz733Nf+56yqm/+EmcvoNjHdoAkEzhPjoJXR2CFhTY6lYt3R25SIWiv2AAouCJJt0l0LAsEl3SvRLNjmgoKFQ8ssft7H5l+18UVfD+q3bcDpU/AGdqhovigSKLGHaNhLgNWwumeDgno0B9n6glhQHVPpsFFkhOzt61qHpda7Z+A3/euZtpjb5XI8cP6rVLFOsBwruCl39d0LQNciyzOzD9iYl2clDS67DzBvN3oedxG6jxXRxQeIgfoP0EtZu/JazFzzK2o3f7tL7u9JdMhaOmbZtt+gy+uWP2ygsKuOtn7zMebaOI5bVMvnhag5fVsOsZ2p544c6CouCYmHsbv3IycpAVRTSMrO55aJZGEoyhiUxdx8nSDBjD5U0ZzALcuweKseOUEl3wYUHOBiepVDug3NPnNos6xDtOm9c/BKbf/yFM/YKbu+EPtfGA/oS1cVTOI72bI6bOIoXF5zOlROT+fLJhWz++J14hyQQhBHioxfQWfvtjswh6QghwfDaR9+2axFrTWC0Jq72Gj6Akw+fgKQ6uPbQXG47toAFMwq4ZGISZ+ylUavbnHTYePYaPoB3Pv8RX62HvxyfR8BbzbpPNlPudnPqOI07jkjhhFEONhVLpLsUTttTY0C6yuUTk7CRyctK567j8xmS7WJwQVazosumi/XvJ6RRWVHOiGyJLM2PbQc/16lDZB55eT1TBsvM3DONgwbYCTfDpKv+Tgi6F0mSOOrA0Tw/7zR2vLOMZX+ey/Yt32OZLTv/CgTdgRAfvYDODgFrumiG6Oyd7tqN33L2/Ee5c9nadi1iLQmM1sSVbdt8X1jCZ99uYcYIJ+cdmMmRI1I4cIDC+H5w6YEujhimsG7j12ATkZkYX2Cx7JUNpKsWF09IwgLO28/BL+U6Bw6Q2bufgirbjMvXOGQ3lRVf1rLXwFQOG+bgsdUbIuKItlhnaX4OGSoDEl5fAE+tD4gc0FdV6+WoIQY/bClMqIxCV/2dEMQHl1Nj5c2zefb64/jp+bt57OZz+e7zDViWEJGC+CDERw+ns1sahmFy38o3OaiN6a+GYbaYlWgtLqOukp9+K+L3E9Iinm9pwFpIYDQ+X2viau3Gbzn95of4esuOhiFyi4s4aPEO5jxbx3Er6ti0w2TLtp3c/8L74QXVtmFMtoki2UwaJINt8V2JzoatOskazBytUu23+bHcZHNJgMN3U/h8m5e97v2Nt7YE2FpUypc/bgvH0XSxtiybMncN5+7noMpv88l2g21lVXzxm5cUycvhw4ID+raXVLF7ps3+eToPJUhGIZrZWGsTgQU9hwF5GSy/4Xfcf8EU5M+fZNU/bxICRBAXRMFpD6ezQ8Duf2E9X36/laJ0Z6stnfe/sJ4n1mxot09F4xH193xo8FO5zoi8BqOslgas3Xh4Ngve3sYdy9ax+oMvmfeHE3n0lQ+iFj1CMJPhMGvIzu3H/505HUWWgy29r77PtVNSGZylUVipc88HNTz95mfhzERVjQ/ZtshNljhljEZGWgpJTgevvlXCMSM0hmbKOFxJJDk0khwa+TkSM0ZXsmGnk4Cuc8oRB4ZrPqIt1sWV1fh1k7xkGJUj8ec3fUj4CFhunAr0T1OQpQBf7bQ4bJjCKaMVbl5f2OXdJO0pTBaOo72f/UYOYr+Rg/jPy5/w0B9nMf6YORxw9Ox4hyXoQwjx0YPp7BAw07R459PNZCbJpGVmc8M5M1Dk5guSosg88Ny77fL/aBzXpIE2hw5V2LhV4YJnixmcU918wWs0YG3aEIUjdtd48Us//3nhXQamwd2Pv4a3toqFs4OmSU2nvf6wpZAL9lF46L/lKLLM0RPHsHTVek4Y7eKiyQ3zSf63w8dzX+/k4eMGYduws8LDmh91ZozQ2C1DoszjpU52sKPapMhj8/YvPiwCZKYmNZik2VDuriLDJbHp24Z24WaLtW3jrvGCbZKkBV+Tm6JQ6bXolwx1usSt0/Mpq6xibJ5NmkNidK7EPrnB7EdXdpO0Z+heorT5Crqei088kAuPH8/1S9/gP1cs56DTrmDEPpNITk1r+80CQScQ4qOH0fjOtb1DwFq621338Wa2FRVx93F53PleDYosRV2Q1mz4hm1FRdx0RDZ3vLuDh17ewEUzW56oGYrrjllZDEqFK6al8EWZhxObDFqzsSkq8/DxN1sp3L6DO2dlUer2MGuMwttbvEwfmcXKL4vYd4Crmbh66KX3sYH983SO2E1l0476bQvLavaZ2DacOAJe/97mp3Kd/CSbTdt8FNfYlNaavLnFwGd4SUvxo1tacGtJAUnROGvmEYwYFHQY/XRzISvWvM+CozL4ywcNn2/TxTqUeflTfeYF4IvtPlZ8Vskf9lO59yOda1eXY5sBMpwSDkXCb1iUe23S07c1yyg0/f52ta26vSZyidDmK+g+ZFnmrxcejafWy73Pv8aK5x9k1k2LyczJj3dogl6MEB89jNCda2g7oj1DwNZ9vLnZ3W57PRyavu7xzzzMW7KKgXmZnDBlz2bxNX792AIXAAcOczF9hL+ZQ+aaDd/w1ydeo9Rdy2n7JDEgxWZ7ic6kQQqH7abwzo/VjMuxqfHpmJYdzspcMDGdEx/7Dck2+OthCnkpEqeMVrhp/VYWPVbd7DOp9QXol2Jz4MBgBibNAQoWSFL9oF4JnwmZSencddFJ4fM0nicSuq7jRrk4YUwSGwv94c+r8WJtGCb3PPUmx+zh4LwDM4OfiWWzZGMl04drnDha48tiePVnm71zZa6clIRTkdjwm86Dn/rxGhJjd+sX9TsPfX/tyV5EQxiGCVojPSWJeWcfzumHlnDGggsYOHo/jv7DDWgOZ9tvFgg6iBAfPYjGd673rXwTt6eaQrP1ffnQlkbTu9321oo0fp1p2dT6DXIcOnc//hrHHjS22Z1z49fbts3m4gBjChzNjh+aKuut9ZCk2Jw0UqbUXU2KBmkOOHsfB2e9UEeRJqEpNqu/rWHG6FQAdsvSkE0/4/vDXgUqOUkyexcEsyDrthTjr3VFfia2TcAwsW2NlCQImCY3H5YRzkoAFHkM7v/E12L2J3RdNxyXzC87yjl9r2QufbX553X/C+v5/LutbE11sLE+hqo6Hb/fxx+OSyJgSvxujMJbW7xMH+7g5DFOTMtm6SYfgzIkfnV7WPfJd8yctnez77w95mRt/d0RhmGCthg5JJ+N//w9r370PX+98QwG7TWZSTPPJzUjq+03CwTtRIiPHkTjO9c73q3glOkHs9+IQS2+3qGpFJV7mt3thuoi2qoVaVpTsmZzDRV1Btcc5GLhh0Ws2fhtRPajaeHly9/UcNvrpdw6PY8Zo1ObZWN++HkruUkwKlchx6FTXWcwKF2i0meT47JJ1iSSNAmfYfGHZ3YyJDcFSZKoqtPxBkxmjXGSmyyhyhK5yRK/G63wSZHdbHsnHF/9/Jd90qvDWYnG/HdHoNXsz9TBMlman0DAINvhZ2qUz+udzzYzNFMhJSuX688+GoC7H1/H3ulupo7KxsbGsCo5ZKjCy98bXD3J4vWfdbZWWdw01cl1b/i48YFVnHDwnhEiMfT9NTYn60j2orOFyYK+haYqzJwylplTxvLAy5/y5O3nM2jicew95Why+w+Od3iCXoAQHwlCW/v40e5cN337C38+86gW71xDk1Gb3u1Gq4uA5gtS06zH0o8qmTZE4cx9nby/1eDux19jaL9MFFlh7LB+EYWXBy0u4rfyOnJdVmSxqVrGpu9/46FV69k9Xec3N3xeZHHK03U4FEh1SFQHbGr8NpoiceX+Gg99YWDKGicecTD7jxrEXY+/xlC5mNF5Mg4FAqaNQ4HRuRLj+5l82cIAtE3fFeLz+fi4xupQF0fjrEcgUEP/NJWiap3T90qNyH6s+3gz23YUcev0XO58rzpckOqr83DlcTlkpDqp8QYwTJPfjdG4Yq2PPRdXUe23OXoPlbF5CpMHK7zyQxmfbN7KgWOGRnzn7//i45GX13PyWEeHshedLUwW9G0uOXECfzh2f15497/8429XcfQVdzNgtxHxDkvQwxHiI0Foax9/V+5co77nmR0seuy1NmtFQun90OtWf1vD1ooAdxyaglOVOXdfjfNf3sExVy8mPVnjnqtnc+SEUeHCy083F/Lsa+v587Qk7nrfG85GODSVorIqftxSyO1TVRyqg7U/6Kz82s/Z+zio8tms/dEgN0Vmv34yhw9T+aEC3v7V5Mvvf2XGQWOpcHvYVqMz+1kDh9Ig1AKmTZXfJjl1e9Q20L2GD2DRZafy8/YyBudnRhV5Tbs4Qgv3QQNlUmQf/9/encdHWV2PH/88s2af7AkBAgmyBMIetsgiCkFELNWqVUGsVn9WcCm2daGthRYiVvm2lYoFFavWqq0LiCzixmIQKIsiO4KEJezZk9mf3x9xQvZMJrORnPfrldfLTDKZm2cw9zzn3nOuUYFwvQajzgmqmRF1rlfNQG/pBxtAUWpd64ulZuwOlW4xCiNTtaw6bMdkVLi5tx6HqnBzHwNfHLOwefdRCksqar1/t/U18u6uIjISwt3+N+D6d3DsxEnuvjoCVVWrf+97h0Ux9Z3mNxALoddpuXXcIMYO7s5tubNRQ2K4/qFcIkwx8u9GeESCjyDQXBWCJ3eujT1neEd455sG9kXUpDvPO5/tqJfFmJDuasblpEOklis7wcqDZUTrtNUlouOG9Kx+7ZzuRqYOiWNHwbnqzaYAP3lyCVlJdrI6GkmJ1PLSdgvX99Dzox5aHvvUSkaChlNlKncPNKAoMD5dw0eHbHx75BQ2ux2tzkhatJYZw4wYapQGW50qf99i4TzGeps2oSqwsNkd1cfdu7Pc4MrmHKqwsPLbSkwhGrQKlNtUzDaVyPBQjGHnWfzBpnqB3o1vnABFw3E91SW450vKUZ1OIn/I8Og0CtldwxiTUVVRk5oMPzpWyPodB9i480D1+6eqEKu3MD69qjnZz4dHu5W9cL0XHcMcPLX2HA6V6r0z3eIMdAyzN7mBWIiaEmMi+fSZ6fxvfz6/X/gA9ogUhk25h9QesnQnWkaCjyDQXBWCuyW1Df3Mus/5RXY067+3N7ovAqom6eF9upAcZ6qVxXhodBQdEwwAlJst3JJpZ2O+g1GpWr48kt/gck3dcQIcOpLP01dpiQ/XsPO0g/xiBw4nXP9vGzoNdI3WkN1Zy6AOWvKLnVwRp+Panlr2V0bjcKpcLCrCqlNZsMlab+wlFpUyexG7jxQwtHeXWl9rKMjTaJQml7v6dkth3gM3MeellVwRW8S9w01sO17Jv3eU0CFahxIey+/unsQ/3ltfL9DL6aZne2EEj027Fq1G4Y21/2PfF9sZm67jgSGhPL+lko3H7DyUHYkpIqT6NWeOiuXmN45jcajkTk9EVeFcURlWq42Zw0K498NK1h4o57qMiGazH7sOneBYwTmOnzHXWwJTVZXjF8zEh6r85a1PGtxALERDsnqlsmr+NNZuPcCy9xawMyyV/uNvomvGwEAPTVwm5C9NgDXXHt2TVtdNPad7vJGxaQa+PniMCcMyuC67T72PcUN6EhEWwrghPZkwLIOvDx7j6nQD/TtHEhpiJMRopKjcQpcohWvSdOQXOxmUWNVnw2q1N5qlWbp8I0s+2MDARDu94jUoKHSLgd+NNjB7lAGjTqF7nIYyK9w7OISIEB2mEC0GvY6HR8dTUVbC//blExEehqo1UmzX1/tQtUYiwsMabBndUJv25k4DdpXZWitLyb0ungk9w/n6pJkIvUqkASwVpWzde4wTBQX1zkG5f4QJc3kpWo3CuKyefLJ1D8mRCvPGGpnYw8DDww0kRSjsKSjDYrv0HqXHGhiYYEfjsNIlRs+54gqOnSlEVZ1kJOgY0UnLks2FVFqdzbY779sthRuvGUpiVAhPjosjMSqEG665krkzfsoN11xJYlQIs6+JpajwopzXIlpswtCe/Hv2LfxhYke2/+tp/jXnXi6cORXoYYnLgGQ+Aqy5vRyetLr2Znvshn6WxWqnpLwSBZXoEAWtRiGnm8LLu/OrqzEaytLc+uZxii0qGruN8a9VZS2cqooCVNpVdApYHQrZnbV0NYHDqRAdpqHwop2OJpXsjgobdh7g+V/d1uS5Iga9jkE9a+/Ir967kQJTMn/Yk7F8Y1UHsibKVusGcq69L0+ODmH+BgsdTFpeWbGRG3oZmtxDs/nbo1gtZib209M9TkNJpZ3UKA0jOml5fG0Zi7YdJ8RYVfprszsoKbdRblUZ9NeTWKwWnA4HThW0mgoAym0w6G+nCDdqm3w/tRoN2/cerbcE9sS08dVLY3cMiWN7wTnZfCo8oigKWRmprHn6To6eusAvnp+FRW9i7PTfkJyaLntCRIMk+PAST7pOurOXw5NW195sj133ZzmcKvNfXU1h2SmGpGi5d3AIBi2kxSh8fdbKy8s3MDJVS1qsvt5EPKarnl1FEeQMz8TpVDl4/Cyfb/uWKRkhbDxSQVeTSn6xky0nHYxaVo5Wo0GjKJRYnDgxV7U6118g3hTe4jNF1m7Zx8Ej+dw9VqGk3My9w6K4+Y18LA6VZyY2XrbaUAXPhHQNGXEaescrrDpciUZR2fA9jQZ6quYcX+w6TFyYws8H6kEFreIkIVzh/2UZ2JjvwK4L46lf3IhWo2BzOPj2SAGoYLba+ffqTdyUqeWdb61kD+5Dj86J6LQaMtM7oNdpm3w/Gwtu6waJUnorvCEtJY41udNYv+sIS9/5E19qEug/7id07z800EMTQUaCDy/xpOuku3s5Wtrq2tP22A0FUHV/1qq8PZy/eJH4MIXcceH0Sqia/CrtTm7sqWHl/gpW7VPIXHgCU5i+/ovozFw3ojcDunfi5ieX8JPMcG4fGMmW/EpOlqpU2qu6jlbYVJyoRITqQatUtzrvk9ahxWeKuBqaZSXZSY/Wca6olJ4p8WQl2fnqhIMb+kQ0WrZaM/iqu/flwdFWdpwvZtiATH40ui96rbbB11+x6VsOrv6SoV11OFQ4eMGB44e4LFIPIzppWfd9EUmxEQzt3RWAH43qV10qfUNGCH++IQGHco7vKysbLCNu7Pd29SdxqlXvb0aSkZGdFV5ZsZEbexvoEq2pOuFXSm+FF40ZkM6YAels+vo7/rb8eXauMpF9y0w6desV6KGJICHBhxe4e2ZGQ89xpz16ayaClmRkmgugHA4nSz/YgMZhZVQ3HV1jNJh/GLeCQs94DVFGhRC9QkxCHI/feW11rwsX1x16zYzCpuOFlNkvnakCEKYDtHpm3lJ1tkrNVucttXbLvupNrq4eHacvlnBTLy27CuysPVDe6J2/K/iqWcEzJK1qb8fQiBAmdLfwfUkp12dnVr9PNa+506nyzOtriTRq2HLCya3/rSRUr1BhVbE6ITpEQ6VNRafRYrM7mzy7p6XZCdfzJwwycuebp5g7MYHrMiLolaBHtRXz416hHD11gY4J0ZgiQiT7IbxuZP9uXNkvnfwzhfzib3P4nFAm/mIucckdZTmmnZPgwws8OTPDX8eWNxdQuCa7Hp0Tmw2gdh06wf5jpymsdLDygIOVB2pXm1TYVPQaeHiEkZd3F6HVaFp9cqqnAYeLK+sxMNFORoKhukdHwYVSMhI0jOqi4x95F3n3rs5N3vm3pOKo5jUHKCkp5Jb+UXy0r5RfjQglJUrh8Hkb8zZaGH1FGKPSwlj4ZTnni8rqnd3TmhOLX1q+keEpCqv3l4HqYMnmQkanhbJmfxlXp2kJpxKrTeXkhRJCjEavBr1CuCiKQpfkWFbNn8o3h0/yp1dnc94ZSdb10+nef0ighycCRIKPVvL0zAx/HFvuTkbGNdlNHjWw2QCqb7cUnnv4FtZu3UdMZBgKl+5cnE6V9z7fzvDECu4ansD35SVBcXLqrkMn2H/0JOZyKzedtqPTKNgcTorMTsINGkJ1CjaHypfflTR659+SLBVQfc1dG1qzO2r4vtDK9T303JsVgsVmY3yakW/POvnkSAXLbu3AwfN2Xl35JSgKmItZ8NoaKsuLmX9LbK3fx93shCu43V1iwWIx86tsI8/mVZLx7HEsVithevjsiI1wgwaz3UxEmAWj4Yc/B14IeoVoSL8rOvLO725h49ffsezjF9m15l8Mmngb3foNC/TQhJ9J8NFKnqbG/TH5NpeRcU2qamVR9R6ApgIoVwbiy10H62VSVuXt4aP1X/Ho2ERMEUbuG64ERQq/d9dkYqOjCAm1MWtMDKBQcKGYd76ppNSuZVo/PRrsmHRm0mOjGrzzb0mW6szF0upr/tQnx6l0aPjWcelguX1nrTicTrQamNJLz7ojlSzOK6o+qdeohbnjY3n0wwL6Jhs9XpJz9Sf548sf0S+qiOnDY9lfVMiuoigmZGfy1sfbiNWW85uxsby6rYQzzmiemD4RrUZpddArRHNG9e/GyH7pHMw/y29e/htfvqPnuhl/lHNj2hEJPlohmM/McCcj4wpOJvYK57VtRdzer2ovQ3MZgLqZlGC+Dp/vOMTZ8xcJUWwoisKYNAPHQjUkhRp54KNK5nxuJSFcy4JNJUSEWavu/uvc+bubperdNZl5y1ZdOovlSCX/uxiB06mSGVnM0G7RFFwoQadRSQjXkhAOE7s7+PRQOfcMNVWd1JuiZUJ3I+93Vlj7nZkRLxQ0vDbeTHbC1Z+k5rkyD46KZdp/ikCFcJ2dl29OpleSkR4JBqb9p6jRE32F8AVFUejZJYnlc28n//RFZv79cS7a9Iy5YxZdevWVPSFtnAQfreBJ59FAja2hI+1fWr6RkZ017D1tJiddS4ze0mTlQ2OZFH9cB09LmV0bZI0GlSWbCxmTnkJibDTxMSqju5xh03EHudcn8crWS3f/oUZ9rTt/d7NUq/L21LoO9w038emb56l0aNheoTB+2UVKyiureovUUG6zkfHscRSng5/0NqICD42OZ+e5kkY70TaXnWgsIByVqqk+nK6hQDFnaC8OHj/boussRGulJsey4o+389XeY7y6dhFffRBC/3E3kTFkTKCHJnxEgg8P+atapTVjayoTUbMSYtN3pfz+hlCsVhsl5eYGKx8ay6TUPYDOV9fBk1LmXYdO8O2RU1hsDmYMqdrzMOAvJzGF6bFY7ZRV2DCFaEmJ0jP/uvjqu39PlsMau+bXpOvZXhjJr6fm4FRVvj1SgN1eu0GaRoH31+9keEIFo3vGEhaiZ0hXAzndLdVn4rT02jUWENY9nE5VVfadsfLzoZHc+d8C5v3zYz7cuLNF11kIbxneuwvDe3dh58HjPPfuq2xb8SpXTZtFao++gR6a8DIJPjzkr2oVTzSXiVi1eQ/LPvySkZ01rD1QRnYnLb3jdRwvsVdXPqTHGhiarNYLVupmUhZ/sMnn18GTUmao2u/RIT6azAi1es/DNyUmfjU1h2deX8sVsUXMuDKazGQD312wMaqz54FSY9f8vuGmH4IaDZOz+/CjUf3qPXfOy6s5f+4cP7s2iajwS2e8eJo5aiwwVlWI0Ji5uquW1fvLuDPLxMcHy5n78Tl+n5PA8BR4ZcVGEkIcAV8uE+3bwB6def3xTlwsqeCehQvIMzvQmZIZO/3XxCXJfqS2QIIPD/mjWsUT7mRkcv+5BmtFKXeMDiP3uzLyL6pkv1yK3alSbFaJCLMAUFZpJiTsJNsPHG80k/L5tn3Me+CmZtud9+2W4tHSCXhWygxV+z0qyop5aGLtPQ9fHzpZdVbLzfH0SjKyal8Zv199jp8Pj2bjDu9N9tB89sdqtfPKio2MTNUQrjFjsUXiujSeZo4aC4yr2rZXoqoqFTY7g/56kgtlluoD56LC9Kg2K4/mxPPc5sBvFhbtm6IoxJnC+WDObQB8tOUAS//xaxwJveh/9RS69JRsyOVMgg8P+atUtKWay8ioqsrxi2e4KTOUCT0jCDNosDouTZYvbynmrBpNpcWO1mIlLsbEibNFTWRSTuNwON2apFbl7aleOpk4ordbgYinpczN7XmY3FNPepyBSquTJZsLQXWwZn8Zw1MMXpvsa2kk+7P4g01UVlbyzRmFSa8V1y55bea5jWksMLY5HLz7xdd8tm0f1w7LoEtyLO+v+5LHR4eSu74CjSGMCd2s3Doomm0nz0v2QwSVScN6ct3QHny8dT9/+9c8dsakkn3zAyR26hrooQkPSPDRxjSXkTl04hz/ePcztp1yctXSMw18h5YzpRcpLTczc2QMqw+XsPDNda3e01F36cThVJnz0opmAxFPS5kbWwaZOiCUd3cVsfaQk+zFpymusNXqg7HnnIopyjuTfU0NZcEcDiefb9tHdtcQ7h0ew0tbimuVvDb13KY0Fhg7HE7+uTKP5DAHpeXlfHu4rPrAuXe/NbPrVBEzR3VCoyhBsWlaiLoURWHCsAzGZfVk/7EzPLjo1zi1BmJSezHurl9jMIY0/0NEUJDgo41pLiNjtdnpk9ah0YnSandw/4I36BwFe0+b6RGjZf2R05jLQlq1p6Pm0smfvjjFgtfW1AhEnMx56cN6mxw9LeFtahmkT4cwJvaKYE+ZiV9NHc+zb6yr1QfjmxITv7tnklcm++as3bKPw98f5+mxGkam6kiPi/dqyWvdZa6a78HvPq46VC93eiIOp0q5xc7Izgop4VXXyhel0p4uuwlRl1aroU96Bz579i4AXlmzg1ee/Cmdh15LUlpvuvTsS4QppukfIgJKgo92prmJcs7LqzGoNmaPCeXZPCt3DY1he4GTyY2UfLp+prtlnz/uG8lr/yth16kCnpucyPz1p8j955oGN5N6WsLr3jKImWOnCxvsg+FwOFvV0t0dDR1216tjglcn/JoVQhOGZVS/B1MyI1n+9QW+OuGge4KBtQfKuVhh54krjZwrKiUqPARF8X7JuCcVS0K44+5rB3FXzgBe+3gHhSeP8e+3/kr3YeMYeeM96PSGQA9PNECCj3au5t2ozebglRUbyUnX8tPMELaecPDZoXKu62H0uOQTagcRNe+yr0nX88lBlf9+W8ALP07k6Y31S3s9We5paBlEReX4mUI6J8WgoKDValj83y8C1hitocPuSsrNXpvwG1rmcr0HJeXm6kP1Vu0rY9nWIrI7aRmUouPoRSvniysxRYR4tVTa04olIdyl0Wi469osAKaO689HWw7wt0dvISwikiE3/Izew68O8AhFTRJ8tHM170a37ctHtVUyc3hVD4h7BxuZ9n4FOT0j2ORBFQjUXzpZta+s1l32Dd1h3QGVMIOm1sTfmk2cDWV3VuXt4aXlG6vvulfl7eFEQQFPB6BBXGOH3Z28UEK3lASvTPgNLXONTtXSM9HIdyfP0T9Zy9g0HQvXX6S40k7+Rbjm1XJKLE6cmImOCKW67MYLJeOeViwJ4YmEmEjuujaLu67NwmK18cDz/+adlUuoVI2MmfYooRGRJHVKC/Qw2zUJPtqxmnejSz7YwM4DxxmfrqVfsh4n0DNBx6hUrcdVIFA/6/HSV4WMTtUyvLOew+etdI6Aa7vreOmrQuZem8Bd71ZNTOOG9GxVKXPdY+1r3nX7qzFaYxo67K5mmXNDLd5borFlrnsndaLCYsVqs2NV4equWt7dW4lD0RNm0FY9WQuKVs+0KdfQvVMC4L0DDltasSSENxgNel5+dAoA2/bl8/7mZWz57jS6zv3pP/ZHdOyWEdgBtlMSfLRjNe9Gf/nh9zhsFn6cEcr+s7bq77m6q5YZqzyrAqm7dLJybxnHLlr501XhGLUKCiphevjFkBDufL+C/CJ7rexHa0qZ6x5rX/Ou2x+N0ZrSu2sycTEmEmKc3DvcVP340q+KG23x3hINLXON6KgQb3Si1RpJjI1GVSEhFib2KmRPWTSP3TkBraYqEDDodYwe0M1r+148rVgSwtuGZKQyJCMVp9PJFzsP86elv0OJiEdBoXPmELKn3B3oIbYbEnxcZrxVMVDzbvSGPhE88dFZBnfVM7RbHAqXfm7NCapmFYg746i5dDLihQKOX6hgXJoGq93BrlN2VNVJYriG5EgN2Z209bIfnk5MNTM6rmPta951N9QYTVVVjp8tonNiNIri25NdP99xCHN5SXWTM5e0WH2rWrxD/WWu7cfNXKxwcOyigxEvnKq9nAKAAjozKfEmnwRawXzooGi/NBoNVw/uwZgBV1BpqbrZWvjfPN588jYqbU6uuWc20fFJmOISpTLLDaVFFzBXlLN2yR+597NP3XqOBB+XGW9VDNS8G9110oJBC3n5dq55+UL9Jlc/TFDxpvDqu2F3xuHa+Gmx2dj09RE++Hwbuy/YmbbCQXmlFZ3iJMKoQVEsKICqOCmzqK1e9mispBQab4xWtSdkg88rMXx9JlDdLEPfDkaevj6Jw+etPLepvMGqJV8GWsF8+KIQWq2GiLCqoPj3d47l90DB+WL+8PoLHC43c0ETS7ch4wDoOXAYkdFxARxtcCkpPM/BXVu5ePwgF/duokuHWP58x0C3ny/Bx2XEWxUDde9GrXaVhT9K5u9fFjXY5ApqT1DujsO18XNV3h7WfrWbqdeNZGCPThw6cY5F73yK6rBR6rj0/Yqi8MCKQjSK4vGyR83frW5JKTR81+3PSgxfngnUWGAzOj2M0elh7D1ja1XVUksF8+GLQjSmQ7yJf/xyMgDrdx7i+9NbsDmcLJn7Eh2vyCS+S0+GTrojwKMMnC0r3+BC/kFOHv6W/zchE1OakRvvmo5G07L/hyX4uIx4q2Kg7t2oQacwrkc4HU06t5pctWQcrglIYylh+76jPD5tPOOG9Gyy0Rm4v5m0qY6oNUtK1x4o57qMCKD+Xbc7v4+3lrt8eSZQsB12GGzjEaKlxgzszpgf/vvmq/pzobicVz/ezquPfYher0cbEsnkh3MJCa2qEFQ0mhZPwsHK6XCgqirmynI+/OsTOMyl2Gw2xvVN4cmb+hNn6ocpItTjny/Bx2XCWxUDrb0bbck4VFVl6Yo8jp04yW/rTOze2kzaWEfUuiWlSzYXMrZbGFqNUuv3dFW+NPf7eGu5qyXdUFsa8ATbYYfBNh4hWsMUEYopIpS5P7uGuT889tWe7/nTcw+gqlV/R8+WWhl5+y/R6qqm1qTOaUTHJQZoxC1zOv8IxRfPAVB68Rzb3nuRDnFRKIrCvNuyGd6nq1dfT4KPy4S3KgZaezfaknGs/HIPTy1ZTnaqgR/3jWTDUTN/eesTxg/pyaET5zzKIjS1RFJzbHVLSmesqqTf/53AFKav9Xsu/mBTs79PoBpktTTgCbbDDoNtPEJ42/A+XVn5p67Vnx8+cY53N62AH+7p3nvtEF0HX1Xj75zCoAm3YopL8PtYa8o/uJvv/vdF9eeWynLKj+5gbP+q3icpWg3PPvczIsN9d1aOBB+XAW9WDLTmbrQl43A4nCx4bQ1xBhvlFg0Op8ptfY385PXvuWvevzjw/UmPsgiNLZHUzOikxerZe8ZCpxgToDRaUqrValj8bvNdTgPRIEs6ggpx+bmiUwKP/fRSYHHfpCwO5F86wLO43MyTf7iHsKjo+k9WNIy+45d0zejvlbE4nU4+efXPnP5uT72vGZ2VzL1zdNX+OkBRIhl49x0+P1aiJgk+LgOeVgw0lLZvzd1oS8axavNeThYU8OSVIfxjh401+8vpGWVhVKrCyo276B7f8qZlTS351MzoZC48wYVSM3GRITUyHfVLSlfl7eHEqaa7nNY8E8WfDbKaCnjkgDYhLg8xUWEMz6zdSXXCsIabmhWXVXLPwufY+mbjN4Yt4XQ4uW1MBj+/71av/Dxvk+AjyLVmj4Y3D/JqyTgAFry2hpGdFaYOMLLvvJMXv7zA/LE6+ibp+OJ7C/cM0vPy7pZlEZpa8nF1RK20WFnw2lrCdWcIj47j9muH0TU5tl7vDnd/H4fT6fcGWc3tq5ED2oRoe0wRofz398EZKPiCBB9BztM9Gt5O27dkHKfOl3CyoIC5kw0YdRruGWTk9v+WsfmEymdHbOSkaxnewcGBIp3bY3NnycdV1muuKOGpnHh+s7qQ59/+lIWP3FJvknbr99GeZ+G/P/F7g6ymgixXJkaWY4QQlzMJPoKcp3s0vL1Pwd1x9O6azG8WvcfIzgrJEdqqVu2qSmaCwqNrzZiMCg8ODUFRYfIV8Njn7o3NnSWfmkskrq6tWpuZpQ1M0u78PjsPnuDddZu4Lye20df0duahuSDLdTptS95XWaYRQgQbCT6CnCd7NHxxkJe741iVt4eiwossmBxPx3gDKioF50u4vZ+ejw7ZuTpNy+guOs6UO9FobYzqrG92bO4vkVw6Nn7tgXJCtCp3D9Sz5Ov8epN0c7+P6zVHdtL6tUFWU0HW1HcunU7bkvdVlmmEEMFGgo82KFAHebkm7Cs7aejXKRKA4jIzFpudC5UQG6pwW189USEKeq2WwxcdTOmp4dFPmh6bO0skqvYcf3mraomke4KBJ1ed5eo0HT/OMLDjtKXB7EdTAtEgq7kgq2OYnd0FVafTQv33taEMh1TNCCGCkQQfbUwgD/KqN2GrKkVllSiqg/MVTq69QkeHCIUis0qYXiFEB+EOMyM6Nl354tYSyaETvPfxl9w3virrkV9oZf5V4cSHK9zUS8sT6+tnP6DxJYlANMhqKuBRVZXjF8xM6KYh1VQ1zrrv69ot++plOAJRJiyEEM2R4KONCeRBXnUnbNcZLkWlFYCTnQVO7njPjE4DCmC2Q7lNJdRoJcrUeBbB3SWSKztp6BKj5/GPzjIqVUuPBC0A/ZO1ZCVZG8x+NLYkEYgGWU0FPNv25fOfNRt5eHQUocZLjdJc7+uqzXtZ9uGmWhkOICBlwkII0RwJPtqQQB/kVXfCttrs9OqSxJylK4lRLzB1QET115yqyl83FVNpt1Fk0fDX+2/0OItQM2PQ7/9OYLGY+fmk0KrNrj+4KUPH7A3Hvdq51NsbORsLeFzjvDrdQL9Okdgd4Gqh6HpfF7y2hsry4lpt7IGALL8JIURzvB585Obm8t5777F//35CQ0PJzs5mwYIF9OwpbZZ9LdgO8jLodcSbwrHaLJyx63nuK0v114orbFgsdn6VHcKzeVYOHj/LxBG9PXodV8bA1eOjT0QRw7rVzvwkxsGY/FKvdi7110bOZt9XVeVU0WnGdgutznAsXb4RVDUgy29CCNEcrwcf69evZ8aMGQwZMgS73c7s2bPJyclh7969hIeHe/vlRA3BeJBXQ2NyOJ0seG0t/aKKmD48lgNFhXz+v33MvGl09YTYkqyCK2OwfX8+ZouZ7eUKE18vavibfwi+BnTvxEvLNzIqVcu1PULY0MIlCX9u5Gzufd22L59/rdrAY2MvZThufiMfi0Mld3rtQ60k+yGECAZeDz7WrFlT6/Nly5aRmJjI9u3bGT16tLdfTtQQjAd5NTQmVyOwhybFYYowMnNULNP+c7rWhOhJVqElwZcr6/HEpDCOnrrAbX3DeOAj9ydlf27kbOp9dQVB1/cMoV9K1SFQvRKNZCXZ2XzcQZcYvd+X34QQojk+3/NRXFwMQGxsbINft1gsWCyWWo8ZLVaMvh6YCAh3qnEAj7IK7gZfrjGM6qwhRm/BarUTa7Awys0lCV/0UfFUQxuMKyxWbsrQ8dkRa/2TfF38uPwmhBB1+TT4UFWVWbNmMXLkSDIzMxv8ntzcXObMmVPrsacevps/3DLAl0MTAeJONQ7g06xCzayH1VpGh0gdBaU2busb4Vb2I1B9VOpyBUEjUhQsdhWLzYmiKGi1OkZcEcfEXpp6J/m6+Hv5TQghavJp8DFz5ky++eYbNm3a1Oj3PPHEE8yaNavWY8bTO+D8bl8OTQSAO9U4NTdK+iKrUD1hd9QQrjFjVCBcr8Goc4JqZkQzSxKB7KNSl2sj6u4SC//akV/nFF9o6CRfIYQIBj4LPh588EFWrFjBhg0b6NSpU6PfZzQaMRrrLLIUGnw1LBFA7lTjlNtOguogd2oC4P2sgmsMhyosrPy2ElOIBp1Gwe5UKTarRISFYAxrfEkikH1U6urbLYV5D9zEH1/+iHDdGSJi4utlOSTDIYQIRl4PPlRV5cEHH+T999/niy++IC0tzdsvIS5TzW0IdThVFry+hsExZT7LKrgm7DkvreSK2CLuHW6q/trSr4o544zmqZ9f3+CE7W4flc5J0WSmp/j8EDeDXofD4aw+xXfe+lK0Go1UsQghgp7Xg48ZM2bw5ptvsnz5ciIjIzl9uuoO12QyERoa6u2XE5eR5jaErsrbg7m8hF9c57usgmvCtlaWkntzfHWQA5AWq2faf4pwOJwY9PX/13A3c3PL7Jd47qGf+DwICKaNr0II0RJeDz4WL14MwFVXXVXr8WXLlnHXXXd5++WEH/nyaHZ/dWdtzeu4m7mpKDrrlyAgWDa+CiFES/lk2UW0Tb7s6Omv7qyteR13Mze/Gxfn894fwbTxVQghWkrOdhFu8XVHT391Z/XV6/h7CSSYNr4KIURLSfAh3OLrjp7uNAjzxrKPr7rA+nMJJNAHCAohRGtJ8CGaFSwbGz1Z9vHlPhUXfy+BBNsBgkII0VISfIhmBcPGRk+Xffxx8qy/l0CC8QBBIYRoCQk+RJOCZWOjJ8s+/jh5NhBLIHWXjvyR3RFCCG+SBWHRJNekf9+wqFqP3zssivyTl85i8aW6yz6uwMfhcDb5vJoBi6/G6loCyTtRtQRS9yPvhIP801VLIL6yevNe7pyzjNWb9/rsNYQQwpsk8yEaFSwbGz1Z9vHXPpVAL4H4I7sjhBDeJsGHaFQwbGz0dNnHX/tUfFU94y5fVyEJIYQvSPAhGhXou3rwbDNnsOxT8bVgqUISQoiWkuBDNCrQd/WeLvu0lwZcwVCFJIQQnpDgo50L5koJT5Z9gmWfiq+1l+yOEKJtkuCjnfNHH4zmNBYAebLsEwz7VPyhvWR3hBBtkwQf7ViwVEo0FgB5suwTDPtUfK29ZHeEEG2XBB/tWDBUSng7AAr0PhV/aC/ZHSFE2yXBRzsVLJUSwRAAXW7aQ3ZHCNG2SfDRTgVDpUSwBECXm/aQ3RFCtG3yF74daq5Sorm25d5St3W7P1u2CyGECBwJPtqhYDuvJZABkBBCCP+T4KOdaahSwvVRs1LC15N/MARAQgghAkP2fLQzwVApIaWiQgjRvknw0c4EQ6VEMARAQgghAkdRVVVt/tv87FgenNwe6FEIH7Ha7GzY9V2zAdDoAd0w6NtufBzMre2FEMIj2Q+69W1t9y+78Dt3J1MpFa0SDK3thRAiEGRBXXjN6s17uXPOMlZv3hvooQS9up1dpbpHCNGeSPAhvEIm05ap2dlVqnuEEO2NBB/CK2QydV/dzq7S20QI0d5I8CFaTSbTlpHOrkKI9k6CD9FqMpm6Tzq7CiGEBB+ilWQybRnp7CqEEBJ8iFaSydR9wdLaXgghAk36fAiPSZv0lpHOrkIIUUWCD+ExmUxbJhha2wshRDCQ9urCY9ImXQghRC3SXl34mrRJF0II4QlZiBdCCCGEX0nwIYQQQgi/kuBDCCGEEH4lwYcQQggh/EqCDyGEEEL4lQQfQgghhPArCT6EEEII4VcSfAghhBDCryT4EEIIIYRfSfAhhBBCCL+S4EMIIYQQfiXBhxBCCCH8SoIPIYQQQviVBB9CCCGE8CsJPoQQQgjhVxJ8CCGEEMKvJPgQQgghhF9J8CGEEEIIv5LgQwghhBB+JcGHEEIIIfxKgg8hhBBC+JUEH0IIIYTwK58FHy+88AJpaWmEhIQwePBgNm7c6KuXEkIIIcRlxCfBx9tvv80jjzzC7Nmz2blzJ6NGjWLixInk5+f74uWEEEIIcRlRVFVVvf1Dhw0bxqBBg1i8eHH1YxkZGUyZMoXc3Nzmf8CxPDi53dvDEkIIIYQvZT/o1rd5PfNhtVrZvn07OTk5tR7PyckhLy+v3vdbLBZKSkpqfVgsVm8PSwghhBBBQuftH3j+/HkcDgdJSUm1Hk9KSuL06dP1vj83N5c5c+bUemzMmDGsXbsWo9Ho7eGJGiwWC7m5uTzxxBNyrX1IrrN/yHX2D7nO/tHWr7PXl11OnTpFx44dycvLY8SIEdWPz5s3j9dff539+/fX+n6LxYLFYqn+vKSkhM6dO1NcXExUVJQ3hybqKCkpwWQyybX2MbnO/iHX2T/kOvtHW7/OXs98xMfHo9Vq62U5zp49Wy8bAmA0GttkVCeEEEKIhnl9z4fBYGDw4MGsW7eu1uPr1q0jOzvb2y8nhBBCiMuM1zMfALNmzWLatGlkZWUxYsQIlixZQn5+Pvfff78vXk4IIYQQlxGfBB+33norFy5cYO7cuRQUFJCZmcmqVavo0qVLs881Go089dRTshTjB3Kt/UOus3/IdfYPuc7+0davs0/6fAghhBBCNEbOdhFCCCGEX0nwIYQQQgi/kuBDCCGEEH4lwYcQQggh/Crogo8XXniBtLQ0QkJCGDx4MBs3bgz0kNqU3NxchgwZQmRkJImJiUyZMoUDBw4EelhtXm5uLoqi8MgjjwR6KG3SyZMnmTp1KnFxcYSFhTFgwAC2b5fDKb3Jbrfz29/+lrS0NEJDQ0lPT2fu3Lk4nc5AD+2ytmHDBiZPnkxKSgqKovDBBx/U+rqqqvzhD38gJSWF0NBQrrrqKvbs2ROYwXpRUAUfb7/9No888gizZ89m586djBo1iokTJ5Kfnx/oobUZ69evZ8aMGXz11VesW7cOu91OTk4O5eXlgR5am7Vt2zaWLFlCv379Aj2UNqmwsJArr7wSvV7P6tWr2bt3L8899xzR0dGBHlqbsmDBAl588UUWLVrEvn37eOaZZ/jzn//M888/H+ihXdbKy8vp378/ixYtavDrzzzzDAsXLmTRokVs27aN5ORkxo8fT2lpqZ9H6mVqEBk6dKh6//3313qsV69e6uOPPx6gEbV9Z8+eVQF1/fr1gR5Km1RaWqp2795dXbdunTpmzBj14YcfDvSQ2pzHHntMHTlyZKCH0eZNmjRJvfvuu2s9duONN6pTp04N0IjaHkB9//33qz93Op1qcnKy+vTTT1c/ZjabVZPJpL744osBGKH3BE3mw2q1sn37dnJycmo9npOTQ15eXoBG1fYVFxcDEBsbG+CRtE0zZsxg0qRJjBs3LtBDabNWrFhBVlYWN998M4mJiQwcOJClS5cGelhtzsiRI/n00085ePAgAF9//TWbNm3iuuuuC/DI2q6jR49y+vTpWvOi0WhkzJgxl/286JMOp544f/48Doej3uFzSUlJ9Q6pE96hqiqzZs1i5MiRZGZmBno4bc5bb73Fjh072LZtW6CH0qYdOXKExYsXM2vWLJ588km2bt3KQw89hNFo5M477wz08NqMxx57jOLiYnr16oVWq8XhcDBv3jxuu+22QA+tzXLNfQ3Ni8eOHQvEkLwmaIIPF0VRan2uqmq9x4R3zJw5k2+++YZNmzYFeihtzvHjx3n44Yf5+OOPCQkJCfRw2jSn00lWVhbz588HYODAgezZs4fFixdL8OFFb7/9Nm+88QZvvvkmffr0YdeuXTzyyCOkpKQwffr0QA+vTWuL82LQBB/x8fFotdp6WY6zZ8/Wi/pE6z344IOsWLGCDRs20KlTp0APp83Zvn07Z8+eZfDgwdWPORwONmzYwKJFi7BYLGi12gCOsO3o0KEDvXv3rvVYRkYG7777boBG1Db9+te/5vHHH+enP/0pAH379uXYsWPk5uZK8OEjycnJQFUGpEOHDtWPt4V5MWj2fBgMBgYPHsy6detqPb5u3Tqys7MDNKq2R1VVZs6cyXvvvcdnn31GWlpaoIfUJl1zzTXs3r2bXbt2VX9kZWVxxx13sGvXLgk8vOjKK6+sVy5+8OBBtw6yFO6rqKhAo6k9ZWi1Wim19aG0tDSSk5NrzYtWq5X169df9vNi0GQ+AGbNmsW0adPIyspixIgRLFmyhPz8fO6///5AD63NmDFjBm+++SbLly8nMjKyOtNkMpkIDQ0N8OjajsjIyHr7aMLDw4mLi5P9NV72y1/+kuzsbObPn88tt9zC1q1bWbJkCUuWLAn00NqUyZMnM2/ePFJTU+nTpw87d+5k4cKF3H333YEe2mWtrKyMw4cPV39+9OhRdu3aRWxsLKmpqTzyyCPMnz+f7t270717d+bPn09YWBi33357AEftBYEttqnv73//u9qlSxfVYDCogwYNkhJQLwMa/Fi2bFmgh9bmSamt73z44YdqZmamajQa1V69eqlLliwJ9JDanJKSEvXhhx9WU1NT1ZCQEDU9PV2dPXu2arFYAj20y9rnn3/e4N/k6dOnq6paVW771FNPqcnJyarRaFRHjx6t7t69O7CD9gJFVVU1QHGPEEIIIdqhoNnzIYQQQoj2QYIPIYQQQviVBB9CCCGE8CsJPoQQQgjhVxJ8CCGEEMKvJPgQQgghhF9J8CGEEEIIv5LgQwghhBB+JcGHEEIIIfxKgg8hhBBC+JUEH0IIIYTwKwk+hBBCCOFX/x8MD12Ma9MW+wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_regions(x,y, clf = rbf_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0c6f9a",
   "metadata": {},
   "source": [
    "### **Grid Search**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "67f53ac9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6357c18b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ae8bec55",
   "metadata": {},
   "outputs": [],
   "source": [
    "#svc model parameters\n",
    "params = {\n",
    "    'kernel' : ['linear', 'poly', 'rbf'],\n",
    "    'C' :[0.1, 0.01, 1],\n",
    "    'gamma' : [0.1, 0.01, 1]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0ca13da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#grid search model (model name, parameters)\n",
    "gs_model = GridSearchCV(model, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7819a687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 0.01, 1], &#x27;gamma&#x27;: [0.1, 0.01, 1],\n",
       "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 0.01, 1], &#x27;gamma&#x27;: [0.1, 0.01, 1],\n",
       "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 0.01, 1], 'gamma': [0.1, 0.01, 1],\n",
       "                         'kernel': ['linear', 'poly', 'rbf']})"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d5c622d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.1, gamma=0.1, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.1, gamma=0.1, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=0.1, gamma=0.1, kernel='linear')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "214ce984",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(C=0.1, gamma=0.1, kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e493c28f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.1, gamma=0.1, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.1, gamma=0.1, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=0.1, gamma=0.1, kernel='linear')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "25a08d96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DbPRR8fYDaC53zHObWsci6f+Ex6yITX6/FB+SHpGblc6ym05g5o03w2nXscXEHZO9JIkk6WxqG2x76RhV09B0F6qmA1A8bBS2HfaCCLgERm4+B886hdefezQqUkI1PWudFEKgqhpZ+YMwcvM58fyrGDQ09pqkw2kzDmA5NgKHgZ6wEtjYjoPd+UvbRYoPSY/JyUzj5XmzOWruLdgnX8OW2+yc7CVJJAMKy7IwjRBGqJ2OEyHIKxrSRqR0B8NbFvNnxzQw6iqxdAVV0xg0dCRDRo3p0rE2xxk1DtAYgnRXCKG6O319f0VgY4SC1BnhP20qUnxIEkJEgMy8cQFIASLZjLFMo019RIRAdSmW2b6XQzy8VeXU1VQhfvkarbztcc26Supqqqirqd6k9ZqNdTiWgff9h2MedywLK9iAkZuPx613Ob3Sk7qX/o2gwVbR/SHcqhmtzRlIOI6D7TjUGeC3e+a0KsWHJGFkZ7QQICddzZbb7pLsJUkkfYq3shxfVRmNLTw4WuLYJmZdJd7K8i5HEXDAsULUf/YqIo61tmNZOFYIRPzNPhLRiEQyAi6BqbswassRQMO375Lu1nCa0ji2bYPjgCZQ9UwOP/FM8ouHdrlbpyd1L/0dB0GNoaF0T1/2I0RTqqXnwkqKD0lCyc5IY9m8E5g591acOVcxZttJyV6SRNJnGKEAqC5y95yDXtB2HopRtY7KN5e0sTLviNzCIrILiinc53Q8+UPaPB+oLqXy3YfIyi0AmrtQQr6qmIhGy0hGZFBcfkEBJ150A7mF4WFm3qpyHr/zRsxI1EJReO3Zh2LO19WoRU/t3/svoke1EJsLUnxIEk5WhoeX587mqLmL4MQrGbPd5GQvSSLpU5TMAvSctlbkVnDT7vZVTceTP6TdzVzVdHS3p02rbsuIBpqGlp7LSZfeSG5BWGzEjWKoLor23vyiFpK+RYoPSa+QleHh5XknMHPubTjOFWy1/a7JXpJE0uvobg8CsBq9hLwbMeurccxmPw3TVwmWRZ23ukspjO6QW1CUMJfVzTdqIekrpPiQ9BqZ6W5ennsCR829Hcf5P8buMCXZS5JIepXcgiKy8gooKBqCorv5ZflDtDQKdWwLVJVXn7gfVdMSXng5sAo4JQMZKT4kvUpmurupCPUvwGWM3WG3ZC9JkiA2x3bKrqCqKpruAhwc2yZ3zxPRmlIwtmViNdSQkz8Iq76amg+fkCkMSZTN6d+UFB+SXicjzd3kA3IHjn0p43baPdlLkvSQzbedsuuEfFUYDbVYqCii6VetpqJkDaLR1jAMm7qaKrxVXet8ac/OPFVtzvvbepPN5vZvSooPSZ8QFiAncNTcO3Ech/E7T032kiQ9YCC2UybyrjNYvZFgbTngoKZno+WECzwdHLAtNE3HNgI4gBHsuPOlvZkvLUklm/NUW29/iSYMxH9THSHFh6TPSPe4eGnuCZyw4B4+eeFeDrlgPoVD2rYjSvoPA6UwMVF3nS033mBDHY5jgxDhH0AgQFVQdBeK2rVfv+3NfGl93kRuoD2JWiRjve3RH6MJA+XfVGdI8SHpU9I9LpbddCJrN1Zz/IKLOfKKv0oBIkk6ibrrbLnx/vjVJzxz312oqtZUAxImrEPamjSlwh16oqIWqbKRb27RhP6EFB+SpDBqcD7PXDOT4xdc1CRARiR7SRJJQu46IxtvxYYSEOEBbZ1ZbafKHXoioxapIKYibC7RhP6EFB+SpDFqcD7PXnMUxy24mCMuv6vNxEyJpCekyuZn1pYh4gx6M2ubB7ml0h16Ij6T9sSUZRpR0zNNFZx06byo4RmkTv2FpPeR4kOSVEYOzue5a49m1vxLOPz/7qRo2KhkL0kyAEiFSILu8oAVwvvhE+3OecEKhV/XRG/fofeVIIsnpkK+Kn5bfg8IEbV6f+SOeVGrd0i9+gtJ7yHFhyTpjCjOaxIgl0oB0s9I1XbKREQSgt4ybCM8wj5QXYoZCoZTKU1E6h7aO4YRCpCRW0je5CNx5Qxqu8baCnxfLI/OVeltNlWQ9USwtBZTQtXJmXYSWmY+Rl0lBUVDovUwsv4iTKr+m0o0UnxIUoIRxXk8f93RzLrlUg677A6Khm+R7CVJOiDV2inbo6uRBG9lOWYoSKA6PLK+5V06gGPbWIF6nrxnUfROXXEsHMfBiRPVgHCKodFbifrla6hxptECeNwuXJ60PtlwQwE/gWCI7J0P61AMtVxLb0SQ9Nzi8NwbVcNTOALd5e7+xQxA+su/qUQhxYckZRhelMfz1x/DsTdfxqGX/YXi4aOTvSRJO6RSO2VPqSrbwBN3zaPOV0vja/ciVBXbDGEF/eRMPQE1qwAcB0URFBQPRdNdBKs3UvHuQzi2RdGM09uNrvDug5x43pXt1jNFPqPvPl8dI35aE/JV9fg6vZXl+KrKaPx4WbtpILOuEm9ls+lZKtWi9IT+EE0YSP+muoIUH5KUYtigXF64/liOufn/OPTSv1A8QgqQVGWg/BIMBfyYCHL3aBIahAfA+T57BdfgMWg5xVj11aiqSnrRKNRWxaMdRVdUTWfQ0JEdOpi2FD/+FUvjCwPLIN3ds1/XRigAqovcPeegF7Rtbzeq1lH55pLw61rRG7UoZn01gUqB2ZR2aZ3aSsRG29+iCQPl31RXkOJDknIMHZTDC9cfy7G3/B8HX3w7g0dumewlSQY4qqpStNX2uAuGARCo/J2G/6xAc6ehu9PAr1FQPKSN8EgEoYAf04GcPWbjLt6qjfmYWVtGzfuPRrtEeoqWU4yroG1ru2MaCTl+VzB9FVS9dR9eVY+2ITu2idnoi6a2ElF8urlFE/oTUnxIUpKIADnm5sulAJH0CaqmResPTN0V9ecQQoCgV4RHFCHQswehtOh8ieAoKlawAS09N2Xu0LtKy7RGoLoUxzZxTAPLMgFB7rST0PPC6RzHNKJFqFZddcLSOVJYpCZSfEhSliGFObx4wyyOvulyDrroz10aviWRtKQ/5PohHHkpKBoajby0JOASGLn5nHTpjf1mI42X7jBDQcxGH/4N34MDOBZqZn64+BSwTSNahGq2cISVDEyk+JCkNIMLspsEyBUcdOFtDNliq2QvSdIP6EquX3EsvJXlGKEAlmliGiGMULi11jRC4SFwfUjLyEtLTN2FqmkxZlw9aX+1LRPHDMV9PFHES3d4K8t54q55mN+8iWWEcIKNmPVVMQZsQggURUnYOiSpixQfkpRncEE2L904i6PmXcmBFy5i6BZjk70kSYrTWa7fWxXeCB+/ez5mKEidrxa9vBQ9FBYchncjlmlheDdG0wEBV3NxZMvISV9EVyzLihZiRjfxltpIiJh23nj1ErrbgwCsRi8hb9u1WY1eRNPrWtP6WizTxHFsgjVlWKZJxYaSqJCLnKulWAIoGj6KS29/kFDAT8WGEp68ZxG5+YNwFzR30QghsG0b0whhmSbeqnIZ8RygSPEh6RcU52fz8tzjmDn3Kqaffi0jx26L7pb+AJL26SxF4Sga+dNOxsEJd5nYFjTd/Tu2g2OFqHn3YRzHxg40EMjORm1RDKo4Noqm9nonhWWa1NVU8cTdC9Bc7qhYytljNlpWIRDetAuKhqJqWrvtr7kFRWTlFZCpK7hdbWfNBHUFI6+gjd156wiSZVnU1VQ1x4WsEI/99WYaaqtBDYszAWTlFcQVRFExIQS1NZVodlsPFMO7EZ+3msfvvJHLbn+436SbJF1Hig9Jv6EoL4uX5x7HzU88yKOPVjP7+n+QkZWT7GVJ+jHu/MEouhtF1ahd9Uz0cdsysf11OEIAAuwQ2BlNfw6jaCqnX7WQ9MzsmGO2jgCEAn6++2I11Rs3YBpBNJeLrNyC6OtryksJ+RuoL/k+7hoD1aU4QM6kI8gevT2B6lL8K5aSNnQCroLh2KaBWV+Fu2BYh4ZdLk8aHpeGb/XT7b7G49JixFK8CFLFhhKeuHsBOZOOwJUzCEVzYZshSlYsJXfPOSiZBViN3s7dSx0Hx3HQMgtQWhfzWiaqOwPTclLeQ0SyaUjxIelXFOVlcfcFh/D5D79z3k1nc8IN90oBIukR7txixs2ZG7VSB2goL+G3l++ieLcjyBw+HkVz4cpuFgyRzTQ9MzsmLVBVtoFnFi+IcQSNRAosywzPNTFCKLoLoTTf8TuWScPri8nILUBRYiMBtmXhGH6EqqPobjz5QxCKhtB0hOaiqxUSm9p2Gi/qoLncZI/ePur94a8oQSgaesFw9JxiQt6NXXIvNesq4w7eM+urEe24wnaFVBkqKGkfKT4k/ZJdxo9g8Tl7c/5NZ4cjINm5yV6SpB/jzi2O+bNphBCqSubw8eSNnxL3PZZpxMx6gXBUIBA0yJt2Ep78IaiahmmE0Ms2YDelcmpXPUP+tDnoueFaB9syCW78mdB/lqMCtBIfOA5C81D60fOUffwyIw86a5OvM1U2XJcnDU1A9UdP05CeHTVWc3CI5HMcx8a2RZt5Op1dQyoMFZR0jhQfkn7LLuNHsOTcvTn35nOkAJH0iJZD5ACCNRtxLItQbQX+ihIU3R0jUEK+KnxVZTGzXoDm4lXDJhSyKR40BM220YM2thOOcAhVw1U4Imr05ZghrEYvpqLgCEHRPmG79pCvKmzzbhpoVRUI26Luq9cJVK3HNkOY9dVxzcL6AwXFQ5lzyY08/JcbGDTjD3jyh2CZJlXlG3CcsGiwGmvxfvBYtNYFuiYaBool/EBHig9Jv2ancSP4x7n7cPbNZzP7un+QmZOX7CVJ+hlBbxk/PjEXp8WNsm2ZOAJKP3qRsn//EyFg3Jy5UQFimyFQXeRNO4mMomYBEKgupfH1+1DdmTi2TSgYrv1wHAfHCbt4YlsdridSh1Lyxn3h9zhOkykX2P56Sj96ETPQQOW/7mXIrLloGbmbdN3JTk3kFhahucJppLRBIzFCQbSgFa0BCVWtQ0vPjoqT7oqG3rCElyQOKT4k/Z4dxw3nvvNmcNYt53D8tUvIys1P9pIk/YRg9UYcHGzLJHfPOWgRwyvLwPBVoWcXYvt91K58PCYyEsGdVxyzwYV8VZj+egx/HZruobo83KJqN1mjWw1erMZazLqOoxa2EcRxCI+fzynCMgzAwayrRs/MJVD6M3VfvEqoci12oD6mFbgrLb7dSU0AbQpOW/qiJNqXQ2mqZRGajlC0qDiRDCyk+JAMCHYYO4z7z5/Bmbecw/HX/UMKEEmHtGwhjThvOooKTa20iqKFZ6DkD8Gq7/pEWbvJvEtNz0HLKUJrKqQUhoHT4nnHamvyFQ89txg9f3jY9MyxEaqOnpmHUV+DHain+u37EULF9PsIZOWgNBVpaqrAW1Uec70toxhdTU2Ur1vbpoA2kloSv3yNVl6KEIJMXeDYJkbVOqygH6vR264vSrxzQbjOxvBuBMtEaDqGt6xLn5GkfyLFh2TAsP1Ww1h64f788ZZzOP66JTHtjJL+TyLTBC07PyKGVwVFQ/AUhqMRphGiurw03A5qGji2GTPuPlRb0eHxhaoh1PAdPIBwwoWUQu3+r9xod69t4dgWVkMNSnjcDB5VIAQ02CZCUaMdNBaCx/82P3qM9molOktNGKFAG5ES8lXhX34PDV++hmNZWMEG/JlZmHVVVL65JLxmwIjj8+HypEW/R29lOcIyqXj3ofCaLZMGXy1qWhZK5HMToOjSz2cgIsWHZECx3ZihPHDR/vzhlnM57trFZOcVJntJkgTQGx0MLV+nahqa7oq2hiqKglAUzPoqjLpKzEYfFW8/EC18NENBBCBE/JSDWVsOCGw9HPmwjBAOYDbdzRs1pQTL1gDh+hKzrrKDlQp0lwvbAEfVyC8ajOkOG4KdeP5VADy5+DYG7X1arxVYthQpaYNGMuHU+dhGkEB1KRVvP8CcC69Bd3k6dDiN+Ie0/B4tx2me1mvbOLaNYxmMOvwCXNkFbQp9JQMHKT4kA45ttxzKgxcdwBm3nMtx1y2RAmQA0NcdDKqmUzxsFLZtRwe7nXj+VQwaGt6AKzaU8OTi22I6XQAUzYVjhvB++ET4OKoGIuxSCuDYFnbIT83Kp6jVW8w0AbKzs3EUJVqH4tgmthGK1prYVtuR95FNXlXVqECwTAPbtrFMI1zo2thAKBjgx68+oWJDCbrbgxEMYJltjwfhAtxAdSlmKEhNefj/W0Z92otE5BbGOqPGE4Gla39p93s0jRBlv3xNw5ev4cou6HGdR7xUj2WGI1gRS/jW9IX/R7ILfVMFKT4kA5JtthzCQ5ccyOm3nMtx1y4hO18KkIFAX3YwqJqOSvNgt0FDR8YYiqmq2maDs80QemYu2XvMRtHdUZfPSIFmsKaUqvce5YiTziKvaEj0fbrbg8vt4bE7boipQwl5y7BpYYXuOFSXl2LUlkXtx0+6dF70acs0KFu/Fse2w4JHgFlTRp23hqcW3xZOy9g27rQ0Ag21ZHz/ccz6Q74qfvvnYgDMRh/Ln36Qhjpf2H4+4sVhm2HvEVXHsU3MRl+bluPOolDxvkcjFEQrL8WxrBixE6Grs3LaGyoYYwtvhXjynltRW5mb9bb/h/QgaUaKD8mAZevRQ3j40oM4bf45HHftP6QAkXRIVwfEVZVtCBdzWiHK314a85xlhLACjSi6G1feENKLRsVscP60dBoyMhm345S4A9Na1qE89rf52I1eREZutBAWIcC2ELYVtR83mtp5IdxV4zgOanoudn1NeKNVVYSiINzpIAR2g5dgyADNw4aVz1Hx5VtN73bCviJBPwUzzkTobrKy8/D4alAz81FdHgxvGd73H8ZxHHKnnYSWmY9RV9m5lXoXUBQFIQRWsCEmxdWSrszKac/JtaUtvKdgWIxjbU/W3R2kB0kzUnxIBjQTtxjMI5cdwqnzz2HWNUvIKRiU7CVJUoz27pRb0rJYMnLnaqEilNg7WKGoCCFIV2zSXQr1a/8X7XCBcKGqGQpGQ/6tQ+yR/3Z50nBrGtWrnkZr4QAae65wXUo8RFO6BwRqei5KWja5e50KjoP3g0fJ2fPEsEDx16Fn5GA0+sLr2/gzdV++hqPqqFmD8CsqStYgUFS0rMJw8a1l4TgWWmZ+uDVZUcOdQW5Pm0hCd1A1nYKioYRapbha0tWURHuvaW0LnyykB4kUH5LNgAmjinnkskM4Zf65zLp2cZtCOMnmTXdmnnRUswBQX/I9DW8soXbV09Ti4Ksqi056jSAIF4iqqtpuiD2eA2hrQr4qqt59sPMLVDWEqoXt3B0Hoai48oejZOZj+SoQioKmuVEz83HMIEIoKGnhVmFV1TBDAayGGoI1ZRi1FZj+OhzHJlhbgY2C49hUl29EKILiYaM6X0+HS9XiprgkAw8pPiSbBRNGFfPY/x3CyfPPkwJE0obu5tc7unPNLihu7kK5ZxF5007CndfcsSGEgqppnYbYWzuAdkawemPUK8MOBTDqqwGB49hxXy8QKKpKbl4htTVV6O40DJcHBKiaGvUoAcKdKIBwpyFcHoTjoGbkomaHU5mqCA+Di5ipdYXWlvYQdogNNTbw41ef4K0qj/vvdHMpyBzoSPEh2WwYP6qYxy8/lBPm/ZEhW23LIefc2OnUTUlq0dW6jGQR7moRMY+584qj/iGKonSYmmjZCVGxoSRup0nr1lPd5UFXBRXvPYxlGjT4alHc6ViBBhxA8WQihIIVqEd1ZzS/UYQFiKrr4f8WAruxFse2MOq9oEVMvpyo2YgT9OMYQRTdg+ZOQ/dkIAQ47XTOtMZbVY5lmnh//Zqy1S9Cq5m8thHEbPDyzH13IQRktfIKgcQWZLYUQJEOn+4OspNsGlJ8SDYrxo0s4rN7zmL56u/584ILmH3N36UA6Qd0py4jWVimQVX5Bnzeap64ewFAeMhceSl6KFwbIkQ4NRFPgLTuhLBMkzpfbUynSesZMxCOkJx82U08eOtVWFGvDKs54mFboCnUvHUvuXud1nxCJzxF1jKM8H87DrZpEK4TyUGLnMNxcJpmyzgt6leEEAgRK7Q6oqpsA0/cNQ9f1UZq//UQjoCcqbPRMpsLP826Suq/fI28aXMQLbqFIiSyILP1TJ94nTubS+dJMpDiQ7LZIYTg8KkTEUJw2/zzmX3NPehuKUBSme7UZXRGb/ksRDtN3BkMmvEHAPwrlqJnFeLKHYxtGpj1Vdi2jRrn/a07IUwjhF5eGi4YVTXM2jK8Hz5BY9lv2EYwJtqTnpkNqovCfU/BY9io7sxoEamaXYhVV03th49jBxtxICxQcLAtC291BbZtI0wDu8mPxKyrQnGnhw/uODhWWJRY9dXRc7a0P3dMIzpfxqprfk3r63MUjZEHnYtthSj96EXSho7HlT8sfAzLJLDhe1TdFX5M1fAUjkj4zUHkcwtUl8bM9LEtE6vRGxU8vdl5kuoRvL5Aig/JZsthu09ACFg0/zxmX7tYCpAUJxF3n33hsyBUNVogKhQtPCBNc9HV8WstzcLqDKIj5h1FbeO0Gon2RDZIPWcQetN7NM0FiHAhqWVimyFCZT/jmCFC1b+jhfzYAR96Wjamvw6rrhKzdiOOGaL2wydirOCdFpN4HTOE4kqjduXjLZ4PRw2M3HxUTeswCpU5cgIAZf/+J6o7DdWTET1uOL3T9WhKd2gdPWs900dRNVR32/bo3lxDPJIdwesrEi4+TNNk7ty5PPHEE2zcuJEhQ4Zw2mmncd111yV8+qFE0lMO3W0CihAsuOVcTrhuiRQgA5xE+SzEu0M1jVAnNundo6XLKhDXabVlF45lWXirytGyixFCoKha+G6+oQa7vhrbX0f9l68hcKj5171NbboCLS0To6EWLCPsJ6JoZO50CFp2U1u644BjEzYNsfGteorRh50f45PRWPE7FW8/yMGzTiGvaAi6y0Mo4Kd07S/RdSab1tGzeDN9OqvJSfQa4rG51JkkXHwsWrSIf/zjHzzyyCNss802fPbZZ5x++unk5ORw8cUXJ/p0EkmPOXjKeAQwf/65zL52MS63J9lLkiSI1imWyDh4NSvsUdHdzaajO1fLNPF5q9HSslB0d7SQMZKeaJmaMJvC+p0RcVmF9p1WozhOOOKRVYDSdE221eRGaptoaZl4VNj3iON555VnyN/9WDwF4ZTHmpfuxDICZO5wIA3ff4hr8Fa48odHgxCOZWLVVyOMQLgwtaV3STCAt7Kchvp6lj+5FFWPbS1GCDwujePPu6btki0zWkdim0ZY6BC+DuGE62gSmXZpvam3nunTF2wOwqIrJFx8rF69miOPPJJDDz0UgC222IKnnnqKzz77LNGnkkgSxkFTxqMogpuaIiBSgPR/4qVYIuPgI0WgkQJQs746OiitdccDNN+NdnTnGnHQLD7wXNy5xQS9ZQhBND3ROjUBPQ+xR8RVxYYSLMtEj0zOVcPiIyysBKo7DUXTUTXB8K0m4s7IijHb2vKoS/ntn4vxFG+J/+d/o6gauicNmgbn2WYQqx6sQF1M6seyLHw1ldi2jaJ70Hc5CjU9J7y4po/dqquk8evXKV/3a0z3jm2FmrxCIvIqXINiWRaWZSIQVJVtYPCI0b0ajZAkh4SLjz333JN//OMf/Pjjj4wbN47//Oc/fPjhh9x1111xXx8MBgkGY3u93cEQMvgt6WsOmDwOIQTzbj6XE66XAqS/Ey/FEqgujRaBapkFmPVVBGo2subZBU3Zhc5nlXR056q53NF0hDu3mHFz5sa0cla8/UDctMmm0FJcmaEgDb5aXJaJaRqIFiUtusvV/kGacGUXROtTAExfBSHdFe1mcUwDu66SNNUmu0XqZ823X/L8fXcQNGxydzsWT3E4IuM4DpZlgqIiLJN6n5flTy6loaEe/4qlOLYdrjPxVaKm5zR184SH6QGYtWUI1YWtCBrdSox1u2RgkHDxcdVVV1FbW8uECRNQVRXLspg/fz4nnHBC3NcvXLiQefPmxTx248VnMPe4HRO9NImkU/afNBYh4MabzuGE65fgToFctaRntDYEi2yy0dSEEcRxICdBs0ribZAhXxWh2oq2jzfVRbSMfrT3/kDV+pioTMWGEvz+ADmTjkCoOo0fPIPV4MX0bkSoejgNY1vYuo5ZW96ltQvNBTh4Vz7e5K/RJD5sC6vRR0Z2Nh6XzrAtxwPw+pNLCYYMHKD283/i++qN5oM5Dg6QtfNhqJ5MCvc9HY8RnjtjlK+h6p0Hqf3oKZT07PDAO8Lfhd3opebtpYCDAIy8ghghqDgW3sr2r6e7gk52niSHhIuPZ555hscff5wnn3ySbbbZhq+++opLLrmEoUOHcuqpp7Z5/dVXX81ll10W85h74xdQ+XWilyaRdIn9dhmLQHBDUwRECpDNAz23GD2neJNbPNudpmoaUZv1ltbqMedWBSdfdlO33h/1Afl4WfiFjk3tqqdR0rIRqhoVH6qqhSMYTdbqEVpurnUl32EbQYTqInev03BCQYTa3CBg+iqp/ehJzEAjBm7K16/FCAYImRY5e5yAklmAllUYjZwAGN6N1K58HMc0mzqAhhIKhduRlcx8ECo4Ftk7H4rW5JQqhCArzY1wbIx6L95PXoiJFHmrynnirnk8fvf8dr+HrnYryc6T5JJw8XHFFVfwpz/9idmzZwOw3XbbsXbtWhYuXBhXfLjdbtytOwxqOg8TSiS9yYxdtkIIuP6msznh+n/gTktP9pIkCcTwlkULQIO6wLHNcGqhi06drYnUXhx//jUxU2YBaspL+efTD1Gw96l48ofE3MVDc2QlPTO73WmsEZv2lu+P+IAI26J21TMM2etYNqx8lrx9TkXPHRy9vkgUJzILRneHHVE3/uteHMvCtkwa62pBc2PU14SHxWUo4aCHYwMCx7JAKIRsCPrqeejPNyAENNTVkpeZj5Y7GC13cFPkJEykRTiCqmkUDxqCbdsEXIJAbh4oCsVbbRe328RfUUL9l+42BbaOosXtVopEh2o/e4X1a35o8znGG+InO0+SR8LFR2NjY5uWWlVVu+X5L5GkAvvuHBYg1958DidKATIgUHR3tAg0Ut8RyMjEbKzH8FVAU2TAaiFCTCMUtgSvKo/bZdKZd4gZCtJQV8uw/CFkDtmyw/W1t9GpmkZG0YiY9JERCoadU5vcR/XMXJQmb494RCzQcwuKOPK0C7l//pWgusKpGRQ0Vxq2GcI2wuLJaTqS4ziYDTUIVzqZk45Ey8xDVTXcwWrq3n2yw+tpex3h7h1Td6GoKkJRN6nbpHUqLegto+SN+7Ct+DU7ED8iIoVF8ki4+Dj88MOZP38+I0eOZJtttuHLL7/kjjvu4Iwzzkj0qSSSXmefnbZigRBce/M5MgIyAGhZBBopAD3k+NP559MPIWg2uKoqb56nYng34vNW8/idN3LZ7Q+32bA68w6p/uYj6la/RDDQiNpQ1+b5oL+xQ3HTVRTN1eXuGlXTQXORP+Ms7EAdVe8+iB2ow/vuA3GP7dgWiieTtFHbo6Rlgd+H1hgrcsIuqLF/dmwTu7Fmk6+pq0TqdnL3nIOjqL1qyy5JDAkXH3fffTfXX3895513HuXl5QwdOpSzzz6bG264IdGnkkj6hL13HMMC4Jqbz2H2dUvwpGd0+h5J6tBe4aBAoLnc5BUNQVFVHEGMR0YUy0R1Z2BaToebV7xJt0FvGeWfv45t29RWVdBgtTVWN2s24qupalfcdIeRB50V9eAI1VZQvfr5GOOvouGjKCgeyvo1PwDgyh+KWV+FonvI3nMOWmZ+k6mYE3U1Neur8K16muzdZqHnD8UO+WkT43EcrLqqmIcsXyV2Yx11X76GKzMXRY8f3UhkwafWg5odSd+ScPGRlZXFXXfd1W5rrUTSH9lrxzEsFII/3XwOx119NxnZuclekqQTulpQqDe1VJt1leEWz1biw6yvDhdwbgK2EQQhUDwZqJkFaHlDmiIsYRzLwPRVoLjTY8RN6+m2lmliGiGMULhtt2Vq226yTl/z8p0xG7xjWVhBP68/92jU8vzCBfe2u1b34LG4i7cM12o4dnSYnFG9jjpFbfbvaEnTNFxFaRUFcRyw7XDERFUYtPMBNJb9RmPZb0BYGNmWiebYm1zw2XoirWOb2JaJoqiYRqjX3UolPUPOdpFIusj0HbbkNkUw/8/noA7aiiMuvLlbUz0lfUtXCwoBNAHVHz1NQ3p2dIJsDK06RbqLEApC1cI1GUJp+QQgEC0ea11D0toYDSecHMopHBSOODTUYPl92KZJ9qS90XKKUNNzsBrDlum5BUWY9TUxhZg15aUx81p6QrrqkOZWsEwzWmRq2xYNVSU4VghH8VD64fMtrrfpP4GcnFxOuvBacguL4h67vYLPkK+Kkjfui06ktc0Qpr8+XDCbPYjq8o0Ipf0JwpLkI8WHRNIN9txuNK8vHM3iZZ/w8l+v4ciL5suZRSlMV1MYcy65kYf/cgODZvwhOhSuJZFOEWiOSngryzFCAWrKSwk21OH79b8EqktRNBeu7II2aQbTVx52H22xATumgdVqHkzrGpKIMZqwLRzTwAyFvTBC3lJAYNZVAQKhqPh/XI2iqow4+Bx+//AJHKAB0cY8LRQMYBshzPr4E2jj0pSOiaBoLrBC1K56mlohqKupiknHOJaF0Fzk7nEiWnZhWGwUDIrWnxi15Xg/eorcwqJu17oEqtbHTKQ1vBupXvkEalYBWk4RigCrvrrdCcKS5CPFh0SyCZx35BSUV/7Ny3+9hpkXL5ACpBW9Nba+t8gtLEJzufHkD2lTt9ESb1U5z9yzgEAwFPXewAHbsWh4+1EiBat6Rg6KqjLyoLMQQsG2bbwrHw9HT1qKD8vC9vvQ3GlNg96aidSQKLobRdWoXfVM2DnUCGIHGtDSMqPpIDU9i6zJR6F5Mqld9QyYQUCQO+0k9NziNm23vtI11C3/O44Zak4zNaVvmms+wmmXSJeM6S0jVLE23JbcWIvbpZBdUMyJ518FwBN3LyBn0hG4csID6UK1FZSueom04RPQcoox66vIGDwyWovh1114u/k9RVJptZ+9EjORlqYJvAKBUPWw+OjmsSV9ixQfEskmcs4Ru6Is/5SX/no1My9eKAVIE30xtj5ZGMEAhuWQvfNhNH68jNw956BmF0WtxLFMrMZahGXgW/00gar1gCB3t2NR03PRs9sacdW8+xBDpx1H/ZfL456zZYeOaYQo+/lr6j57mVGHnB2N0tgIvA2BaNttBD23GFfBiPDwthaFmIHGhvD5aytQ9LCAshq9CO9GIJzawQlv33ZDDY5tUf/5Mhq/fjPcmhtswMjNx+N2MWzL8YQCfjSXO2ZejL+ihLJ//zPGTbanRFJp69f8EDORNuAS1KoaTkM1jkvHMM1uD/GT9C1SfEgkPeCswyYjxKe8eNefmHnJrVKAkLix9cmgq50XrpxBCEVDLxiOq2A4RijcYeJYBkJzYddVYDb6qF79PFbQj5qeg547GFderBEXhNMXemZuh+ty5xYDYW8PrTyc2mkZpTFCQWgo6egQsedUVYTjULv62fC6IWzBbllNfw4LR6Eo2A01KIpKmktD1QWgoaXnctKlN1I0LNw9U7r2ly6fu6cUFA8lFPDHTKS107NQFEHth0+E198LQ/wkiUWKD4mkh5x56GQEn/H8nVdx1KWLpABpIl7raarSlc4YxbGo81ZhmSaWaeA0jbB3HNCa7uwdAY6qkZU/CCM3n4NnncLrzz1KuurQWLsR2wigqM2/ds3aMhzbjDv3pTdxuT1k5RVy6PGnoblcvProEqxvVxBqet62bQREvTLy8wuYc8mN0cLQ7qTNHMsMO8c6DqYRij7emXlbd+jtIX6SxCPFh0SSAP546CSE+Izn7riSoy67TQqQBNPbNSSddcZEZoq88ti91PlqEVUVWJaJZRgxG6ra5FOm6TqqplE0fDQet07t6mfaFGRGsUL4vliOx+3C5UnrUUTItsyomIlYxjtmKK5tvO5yMW7HKQwZNYYJO0/tlc/XsS2CtRXYKDi2RXX5xqgBa8S87ZHbruXUK+bH7Xjpznkj0aEImqutNbskdZDiQyJJEH84ZBJC+YJn7riCoy+9DWUTvSEksfRVDUln73UUjZxJh+L/eBlaRi5CURGqFu5gaWHK1ZLcwqKoqPFWlbeZ+wKguzzkFhZFN9pICiNeCsg0QpitumMg7PshhMBq9EbTPabfj1FX2VyMKUS7ori7n1trMVixoQQzFHaNBVp0+jhYvkrUtGwQAgenuT3dskD3UFtby8N/uQEtjilYZ9+rnEjbf5HiQyJJIGcctDOCL3jqL1dwzP/9WQqQBNBXNSQdRVcqNpRgWVa01kNoerhrRYgm3474s6u8VeXkFoTv6CP/35J4d/YdpYAs08TnrUZ1pRHyxTqKZrsUAroSk+7J1BXcLoFlhgtRG8vXAhCsKcMyTSo2lLS7jvaIJwYjE3YbX7s3OlF32N4n4pgGtaueCluyI5on7NI8eE51p8dtce7oe5UTafs/UnxIJAnm9IN2RogveOL2yzn28tulAEkQvVlD0pXhcPW+WvKbuklMXyWOZWFUr29qRQ1HPhQBVmMtQV3Bsiwev/NGUF3hGpE4wzU1VXDSpfOihZvQcQrIW1XO43feiOWIqO9IazxuneFbTcTj1vGtfhrLsuKnfKwQT95zKwCqcDjp0nldEkjxxKBlmrjKN4SH0NVVUvvR01R/9DQeTaHeb5O982HoOYMoKBoaM5W3/Lfvqf9sWactzq2RE2n7P1J8SCS9wGkH7oyifMljt/8fx17+l81SgPSnkHhn0RXfr/+l7p3HEKqOEFD3xXKsuiqq31zS9AoHx7FRhIoQYOQVoOFgOoLcnQ5nw4fPxvh7QMT+vIFH7piHx63HpBfa2zSHjBrDZbc/3KVNN7I5V2woaePBAU0mYcCvy/6KFfLzyB3z2kyChfZTHy3FoGUaFOcU4TgOgcp1mP/N5pDjTwdg+dMPUrjFRLKGjolxGzVCQdQWA/y6S18Ji/7mWdNfkOJDIuklTtl/JwRf8cifL2PWFXdsNgKkP4fE24uuRGoZ9Kz8aFdFyFfVZojb4SedTdGI0eQWFOGtLOfxu+ejZeYgVJ2cJsOvCBHjr0xdwbf66S6njbq60bV8XWsPjgj+ihKEppMz+UiKx2wXMwkWupbSskyDjb//Gk2jGN5yGny1LHs0LMwCgQDeqnKyhva/ws+B7FmTbKT4kEh6kZP33xFFfMVDt13KsVfcEffOcqAx0EPika6Klhu5v6KEhv+uiHaPxEPPLUZobhwz2OoZBzMUpGJDSdI+Fy2rcJMnwQb8DVimAU0zcSIu7AEznGayHQfbNAn4G8jIyk3ksnud/uxZk+oM/N+EEkmSmbPfjijKf1l626XMuvLOzUaADESCNWVtogPQfirJsqyoJ4hRV0X1uw81P9nUIeMVAssfnrvSOv3SG7SZBmtZMR4c3Z0G61g2CIGaVYBQdRzbREnPJnf6KQDUvPcQZkM1gfLfUQK+5nX4G8O1Mw4xE3sjRIpkU4H+5FnTXxj4vwUlkhTghH23B+DeBRew40EnsM2ueyV5Rf2PZNaQRIao1ax8HF874rF1KslbVU5dTVWzJ4i/ERyHnGknoeUUR+enKIDVUN1h+qWndQeWZWEaIWp++Yq1r/4tOh/OMQ1sI4BtWlSVbUAIgVCUTZoGK1QtPIdGcyFUDT1vCEJ3I4Sg9qOnMbOy8aRnRNfjq6nEtm0U3UP5b9+3qf+w6qqwLTmhZaAixYdE0kecsO/2jB2SywNvPMH7v37DXsefl+wl9QtSoYbElV0QHaIWccxs85pWAsAIBnAAJS0rPFBOVQGBnlOMXjAi7I5qGagCTN2F2yXiHndT6w4igmXNt1/hqyzH/noV9f99E8cIITzpCKHiiCZXNEXBQeDYDgp2l6bBRkRf0FeL6a3EcRwUzYXpLYu+RssuIm/v06n45x3sdfBMJu97GBBuXX7srptoDARRNJ36z5bFHDtSjJufl09jva9D+/b+nMLbnJHiQyLpQyZNHMkuE0Zwxb0reO/pxew9WwqQzujLGpKOoiuqpm+SY6bT4A232VoWkakpkf/tCptSd9BSsISCAWwB9f99E9sIkjP1eFyDxyFUFctXiXflY6jZhaiZBVj1VeHJuabRbv1HazEYaGygoc6HmhEurI0gmozG1Mx8hKKSkz8o5rNzpWUw5JALcWUXtDlHxB798NPO57E7bpAFnwMQKT4kkj5GCMGfzz6QK+97k3ef/Dv7nHhBspeU8vTWxhKJDnirysEKUf720pjnRYv6h+5GV3SXB6wQtZ+/ih3yE6r4FdsMYYcC2MGIUHBw4gc82tCduoOWgoW0bFxlpZgNNdR9tgwtaxCuwhFN5miAomLVVQNg+SrBttlYW0bh4OGYvrZuqq3F4KfvLOefzzxE7h5z0AtHAGHhoWW39QxpjSu7oN1r0lxu0tIzZcHnAEWKD4kkCQghuO2sA/jT0n/xzpN3s++JFyZ7SZsdrdMZFipCib3D1gTMufDaGPvzrpJbWERGbiHK2GnUff02Dd+8g2OEsBtqsCL247aFJQSapiFE4ucBufMHo+UU02Ap6DmF1Ld0ZgUUdwYC8H74ONgWtt8HTtgC3cgrQNW0uKKr5eeQkz8onFZSlCbDtXAtSchfDxA2YrMtGnzeqNir2FCCZZptCk3bK3ZNdsFnf/Ks6S9I8SGRJAkhBLf+cX+uefAt3nn8r+x70sXJXtJmRVfTGbmFRZs8nExVNfSi0Qw+6lqMmvVUr3wCLbsQPXcwjmVg1lWiCEF+0RCchupuHTvStRKoLo226kaI2MFHEaJpmm5smEXLKaLwqGuwAw1YdZXUvPMAwjFIc7mi9S2diS5Nd+NYFjVvL4V4Raq2jRAq7yx7ilX/egVH0TBDQep8tejlpeihZsEnhKB42KhufQ69SSrUGw1UpPiQSJKIEIIFZ+zHtQ+9xduP3cWMky9J9pI2O/rirtqVPwShKghVw6qvQWguHDMiPsDwNKc+ukLQW8aPT8wNe2rYJmZDLY/9bT5q0xA5ywjR2FBP0N8I6aEOy0u07EE46XkoqobQXAgbVN3Vbn1L684bze1CsU1sI0DOrjPRMtvWcJh1VVj/ex3DtCiacToODv4VS9GzCnHlhoWfbRqY9VXYcWzok0XrFJO3shwjFDscUHd7CAX8lK79pUvRMemYGkaKD4kkyYQFyP5c/9BbvPXoHex3ymXJXpIkgZh1lYSq1mEF6nAsg5r3Hwk/4VjYgXoEdJjiiIdtBHEcyJl2Ekp6Doa3AkXTooENu/QnrC9fw1tVjm6F58woLRSIY5m0jII4loljGWCbOA7YlkXFhhIa632kZ2ZHXxeZLWM2paoiNTFpOfkE/H707EG4cgeTU1gUFUIAoZpSyn94G8e2olEmoWiY9dXhIX00O74GXKJbQqy3iQiBqrINPLN4QY+KX6VjajNSfEgkKcLNp+/HDQ+9zb8euYP9T5UCpL/j8qShCaj+6Gka0rMRioYiBDhNd/ZCIFwe0lxal1IcLesLAtWlOLaJlpmPkp6Lg4KeXYDS1G1iG0EECqonC8WThdXgxbFNhAhHIfj9m/A6InUmjo1ZX4MdbABVo76ugcfuvInGuhqyCoegNo0GCE/V9aK6M5qm15pssf/Z2GaIkhVL0TLzQVFwe9Jju2Uaa2KuRdHdCAG1Kx+PPubYJmajDyM3PyrEdJenp19DwkiE26l0TG1Gig+JJIW46fQZ3Pjw2/zr4dvZ/7TLk70cSQ8oKB7KnEtu5OG/3BB3ZDxAyFdF1bsPdtjCG6/uwAwFwxu1rwLFDjuMmg21RKIZZkMdjmMTKP8VtdEHtoUTasQ2Q9R9tixcXBryhwVQ03uUtEwUTya5U46h7rOXydhmGg2f/pO8PeeQURTuYjGNULhOI6sQs76a2pWPR1tlhaI11ZWEsUwjmkIxjRCWZYJtYxohPDnFjDn+OoTTXJcSaa9tKcQim3AqFXwmIk2X7ALaVECKD4kkxZh32gzmPfoObz70Z/Y/7XKE6GIvpiTlyC0sQnO5Y0bGt7Q3t81Qm2LR1tGPeD4nFRtKePKeReTkD6LBVhFpOSi6B8cJW6VrWflgGfj+/RKOEa5RSM/MIl1TsR0Lf6ARRdVxb7ETelYeQvegpmWF7dEtI+xLAuE0SV5xdO1GKIgecnDlDo6mS+JhmQYVFRujw+YCG36g3luNY9uU/fw1WnkpQggKioaiamFnVE/+EDSXO0aIVZVtkAWfAxQpPiSSFOTGU/bl5sfe480Hb+OAM66UAqQXScRddXtFhBUbSjBDQepLvgfCkY7flt8TbXWNOHk+ec+i6MwfXRWcfNlNMbUWrdFdHlRNQ9N1RNBBqFo4+lHb5MshBKgajm3hODYCUITA5dY48rT/Y9nDfyMQCFL3/UoU3U3rLhiAii9WYIUCGJtQfxH0lhOqKUVNz8X2+/Cufg6EglAVfJ+9Ako4jeNVtbClu4CRB53V5jgDfUjh5owUHxJJinL9yXtz8+PvseLBRRx4xlVSgCSYrrZRdmbv3Vjva9eF07Is6n211L2xhOyCYhzbxvTXkbPHbLSsQoCYCECweiMV7z7IQ7dejaO2/+tZWCaW0+p8TX/WMgtwLBM1I4/cPeaAqqIIyHSp+FY/zYitJnLZ7Q/zyZsvs+zRJahp2VExEINtIYQgWFve7jpaY9aWYQbqqfj3i9i2A4qKYxo4IT/ZU48Pd8I01Zko6dlouhvLV473wycIVK2Pe0wpLAYmUnxIJCnM9SftzS1PvM+KB27lwD/8SQqQBNAySnH8+ddgBFu1Tro85BaG3Tk7EhYRhGViOg6D9jkjbhGhv3IdVe8/xonnXwXAk/csonjMdnia3EBbG2s5to2pqAzqoCix4t2HcGybYE0ZRshGhPwIRcWsq8KxTOz6aoSqoeUWhwe+CXC7m03MCoqHkpGdi9Bd5E4/Bb1geJvzhKp+p3rF4qhxWEdECki9Hz6BUVeDUBSydj8OLbsI01dF3Rev4Bo8Dj1/GNgmVn112MxM1XEUFbPRR+1nr5CW5ommUGRL6sBGig+JJMW5bs5eLHzyA95YuoCD/njNZi9AerIpdbfVsXTtL512J0SEQEdFhKqmRQfShdMlrnZnp0Ro73ghXxWWaaBgUfnOg9T7ahEuDwgFO9iA4slC0cNdIkJzgW1F0zzx0HKK0AtGtHncNkPtvsc2jXBEwzYJVJfiyR/CyIPOIlC1nsoPn8ZGoAgFPTMPxbEQioqiqiiqhiNACAWhQH7RYEy3wMjN58Tzr2LYluMpKB6a0i2piUjTpVIBbbKQ4kMi6QdcfeJ0bn3qA+67bBZTjzqD7aYfkuwlJYWebkqb2uqYKt0JQW8Zvy2/B9NfR2ZWDggHoSjNaROhgOOQO+0ktLwhqBm5mLXlgNqufbsDze2/MU+EP2Oj3ou/IlwQa5kmZm0ZhncjZl0lZqOPircfQGsSUpZpEGisB9WF8fmrYTM1y8AO1IdbfFUdcBCE002a7gLdFRVnke8sFVtSE+F2Kh1Tm5HiQyLpJ/zphOlcPmsP5ix8Gssy2XGfI5K9pD4nUZtSqoiJ7mIbQRCCnD1mUzxmOwCqyktR03MACJT+Qt2X/0TPyseVMwjbMlBVjYLitvbtmssdLjO1rSbTsdYnCwuS+m/fxSr5IvqwZVlRYZKfl8+cS24EwAgFqCkv5ZUnl+La4TD0ojEouhujtgzvB4+iZhWiZRZg1lc1TfXtnFT6nhJR/CoLaJuR4kMi6UdomsoTVx/LnIXP85UDO+67+QkQSK1NKRloWYV4CkegKAp1BuGWVsdBzcgF28Ksqwx3wLQg7LXR7KuRlRseda+q4TRQayxNQ1EUjjz5XMbtOCXuOiJ36JFolBkK4q+vw5Oei5Y/FKHq2LYJSjjlQnRNDvE6bFKdRIiCzUFYdAUpPiSSfkZEgJy86AW+cGx2njEzKeuQBYGbTqJy/qqmUzxsFLZtEww0Ul61HsexMX1VtNzcN9aWYdZVUldThbeqPMbQzLEtsNoWlTpNtSJ5RUM6HKzXsi7GwaHxtXtBURGKhlA1hAinhIzaMmzLwPJVgm1jaypBj4bpq+zWNUsGBlJ8SCT9EE1TefxPx3LyrS/wBQ47zziqT8+fygWByaIrgqIrOX9NFViIuMcLVJfitJxWS1iAqEAo6AcHnKAf32cvo2ix0QzHtnCs5iFzutuDAIIbf8Zq8LY5l1lXiWh6XVeIzmxRVUCAY4EdHqaH41C78nEc28Ju9CEEZGbnUu4O14psLnUOkmak+JBI+imqqvDYn47hlEUv8IXjsPN+R/fZuVOxILA36VBYCIGmCireezhaC9EaTYTbdoeMGtNpzj/S3htPoJihIFawASEEitJcQGqZBt7KchAg3GlkT56Jll0Y816rrhLjv69F24hdbg+OGaL2o6fav3DLwNVF8dGMAEGL4XAOeXudinBszLpKvCufICs7i9OuuIncgqa1dDTTpoUjbIRAdWnUGba3Imwyste7SPEhkfRjVFXh0auO4dTbXuRz22aXA47t0/P319qLrqY9uhKp8Lg0jjztUp6/73ZMO774MBWFx+64ocuRoPYESsRWPWxK1uwNEh1DLxSEqpM2bAKuFt4dtmngX/cNdos6kPTMbLILi8ne+TBcOYNizmNbJkFvBb4vltPg87YxWeto4xWKijtnEK4mUWqbBgKH3PxBGLVlmF/nccTJ50aFR0fUl3zPhg+fbdMq3NIZ1uPWEx5hk5G93keKD4mkn6OqCo9ceTSn/flFPgMm9bEASQabWjPR3VbHrnYnhAJ+UF0U7Z2YSFBHG5qqhesk/Frzr2/TCGF4N4bNuxAITQ97fDQRv8k2nLLJHr19jIC0TIOy9WsJhWwa6ut44u4F0VbaCJ1tvGZdVbTg1QoFMLwVVNaWY9ZX0lDn45XH/xFzzNbHi3xPNZ8838YRNoIQgiyXgvejp+J+rj2JXGxukb1kIMWHRDIAUFWFh684mtP//BKfOTaTDjwu2UvqFXrqk7AprY5dubONRAZ6OxLU3vVbpomvpgoHUNKyEFrHBmYdYds2juOgpueipWe3mcjb0cYbcTqtXfl48/GMIIbfh5aWjaKqaGlZFB94bnQabrzjRb6n9Wt+aOMIGz2XohCqKcUb5xoSFbnor5G9/oAUH5LNAsdx+O63jUzcYvCAdQhVVYWHrzyKM25/mU8dh8kHHZ/sJSWc7oiHgZizb+/6KzaU8OgdcwlYDgX7n42e03lKozMUVUMoWsxE3o4IVm/EnT+YkQedFeOOWr/uB8o+foURB5xBRtFIFN2NO7e40+MVFA8lFPB32RG2JTJykfpI8SHZLHh99bfccN8ybjrrSA6Zuk2yl9NrKIrCg5fP5A+3v8y/HYddD56d7CUlnK4IhoGcs29vvaruQhEOjmkQqvo95jnHNDDreqeltbNolBkKoqgqnvyhfR5FkJGL1EWKD8mAx7Jsli5bCYFali5byYFTJqKq7WXB+z+KovDA5TP541+W8W/bYtdD5/TauVJ1RsXmeOcrFAUcMyblEcGxTcxGH/l5+QlvaW0djfFWlmOEmof11ZSX8s+nH8JxbIxQsM0gPcnmiRQfkgHPik++o2T9Bq6dkc/890tZ8cl3Azr6AWEBsvT/juSsO5bx73+ScAHSX2ZUbE53vqqmM3Ty0WiZOW2eC9VWUPvZK8y55MY2kZPWQjFSvCrsWD+Rjogcs6psA88sXhATcTJDQerraqkqL0UPOQghKB42ql8KkEjbb8tW35b0x1RespDiQzKgiUQ9po9UOWq7LFb+Gtgsoh8QFiD3XXYkZ935Cp8sd5hy2EkJO7acURGfZEWCImKw7stX231NWpqHouGj2rwnbvGqtxotPRtF1VD0ntVaBKpL8a9Yit5itott26jduL5kfa4tjx/yVfHb8ntAiJhWX7VF11F/TeUlAyk+JClDbxSFRqIeC2blAXDmlGxOfm7ziH5AkwC59AjO/eurrF5mMeng2d0q3OuIVPgF215RacWGEsxQkJCvqsuRj54UqCY7EhQRg+Xr1sakPGLO38osrKPi1SfuXkDOpCPwFAzDNoLRqbbQtQ2/dcRJKBpmfdh0zKirJOASmE3zZDo6Xm99riFfVdzIRYTGel+b85qhYEzbrxCiyW9Fi17HQEvl9SZSfEhShkQXhbaMekwoDm+4E4vdTB+pbDbRDwgLkH9ccgTzHnuPBy9/nhl/vJ6ttt812cvqMR0VlVqmSZ2vFv/ye5hw6vxOuyt6WqCaKpGg1imP1rS+hnjrcXnSSEvz0PDfFTR0cJyubvgt228jtSdGbn6biEG84/X0c40nbEK+Kn5d9leskL9N5KLlek6+7CbSM7Ojj0UM3iJtv7J2pWdI8SFJCXqjKLR11CPC5hb9gLAh09xT9uHyY6cyc+6fcZzLGbtD/Eml/YWOikpNI4T45WsavnytjTV3d48FXburTXYkKFFFtokWUu7cYsbNmRutlah4+wFOPP8qBg1tjox0dLxN+Vw7ipiYoSBWyE/uHidQvNX2bcRH5HNKz8xuM1BvU9p+JfGR4kOSEiS6KDQiZqYOU9iywEXIbL4bHFPgYuowsVlFPyJkprt5ee4JHDX3L8D/9XsBAvGLSo1QEK08PIQtUF3a5j3thfoHQoFqIq4h0UKqZeRJc7kZNHRkh5Nye0pHAioawdhqezKHbNlra5B0jBQfkqTTG0WhX/20jpKNlZSYFlOXtJNT1ir56qd17DKhf2823SUz3c3L82Yz88a/4DiXMm7H3ZO9pISjKApCCKxgAxVvP9DGHhxSoxunt7FMIzr3xTRCWKYZU+cwkIuCO7Ooj5dukfQd8tOXJJ3eKArdbsxQbr1gFiHDbPc1Ll1juzED8xdvZ2SkhQXIUXPvBAfG7TSwBIiq6RQUDSWUm98mxB9hIG+80DyjxWmatGt4N+LzVsfMaumt7oze6E4ZiI61mzNSfEiSSm8Vhbp0jf0mj0/0cgcUGWluXpobFiACh7E7TU32khJK5O62t0P8kU2xtblWBN3tIbegqM83x8iMFi2zAEXTwTJjZrX0RndGb3WnpJJjbaoa6/U3pPiQJBVZFJpcwhGQEzhq7l04jsO4nfdI9pL6FZFNMRAM4asqA9XV5jUCyMorwOPSkuIBoTRNuBWa3q1ZLZtCb3X9pIJjbbLbqQcaUnxIkoYsCk0N0j0uXpp7AsfM/RsODuN33jPZS+oWibwT7e6xIpti9s6H0fjxMnL3nIOW01xcaVsmVqOXTF3Bt/rpPvGAiKw14lSKZSI0HcNb1uvnhvi1Fi1TJqGAPzoFOEJXBUkiC4K7+12nSjv1QEGKD0nSkEWhqUO6x8WL82Zz9I13g+MwfpdpyV5SpyTyTrSnx3LlDEIoGnrBcFwFzaPfHTNEyLsRt6v3Jym3voaWTqVCCf+qF4JuOZYmglRKmUDPvmspLBKHFB+SpCGLQlOLNHdYgBwz9+84jsOESdOTvaQOSeSdaGfD0SBcuxG5a0/FO9zW1xBxKo3UeABdHmffFbpaAJoKKZOWyAhGaiDFhyRpyKLQ1CPN7eLFuSdwzLzFOI7DxMl7JXtJHZLIDaKj4Witidyp9xVd3ehbfx6ay90rNR5djWacfNlNNPi8mKEgDm1fq+juuIKkt5HCIvlI8SGRSGLwuHVeuHE2x960BOE4TNh172QvqU9JtTv1nqYteqM7oyufUcW7D/LQrVdjOoSt7lcsjaZ/IggBIw86a5PXIem/SPEhkUja4HHrPH/DbI696V5s22br3fZN9pL6nFRxO91UMdQX3RkdfUaObWMqKnnTTkI3bPSsQkSLWSiGt4zalY9jm6Eun0+2uQ4cekV8rF+/nquuuorXX38dv9/PuHHjeOCBB9hll11643QSiaQXCEdAjufYefcDDlvvNiPZS0pZQrUVOLaJUbUOxzSij0e6XYJ6z7u1uiuGUqW2wZ1XTDDk4ModjNDatiJ3BdnmOvBIuPioqalhjz32YJ999uH111+nqKiIX375hdzc3ESfSiKR9DJul87zNx7PrJseoPS3n9hhr0MpHDKi8zduJkQ2Rd8XyzHrKql8c0mb1wjAaPL56OvNcaDUNqSKkJIkjoSLj0WLFjFixAgeeuih6GNbbLFFok8jkUj6CLdL57kbjmPFJ99z6+0Xc/DFtzN4pBzIBbGbYqo5nKYatmnQMv7jmAaObRKqrejS+zfnz24gknDx8corr3DggQcya9Ys3n//fYYNG8Z5553HmWeeGff1wWCQYDB25LU7GEIOLJZIUge3S+eIadsxeeJIjrn5cilAWhDZFHvTwr0/I4SCEDZmXWXM44avArOhlurVz+P2uGms9yVphZJkkHDbyDVr1rBkyRLGjh3LihUrOOecc7jooot49NFH475+4cKF5OTkxPwsXNx+Xk8ikSSPIYU5vHjDLF7/6+VtXCoHGsHqjfgrStr8yOLGZrryGamaRkHxUAQC4vwIRcVC5bE7bqCqbENyLkTS5yQ88mHbNpMmTWLBggUA7LTTTnzzzTcsWbKEU045pc3rr776ai677LKYx9wbv4DKrxO9NIlEkgAGF2Tz4g2zOPqmKzjowtsYssVWyV5SQulKcaOwTbxV5R0eoz9Mit1UuvIZaarAQhCs3ohqhAjVbkRNz0VRm9xWHQstI5viA/6IQPRp+7Ik+SRcfAwZMoStt9465rGJEyfywgsvxH292+3G7W6VZKnZtIpoiaQ/4DgO3/22kYlbDEaI3rfd7g0GF2Tz0o2zOGrelRx44SKGbjE22UtKGJ0VN3ory3nybzfx+N/mt3uMRNqFp2KnR1cKQBvrfTx2xw1Ur3wsrtU7gKJqpBdvgW0E2z2OZGCScPGxxx578MMPP8Q89uOPPzJq1KhEn0oi6Ze8vvpbbrhvGTeddWS/nthbnJ/Ny3OPY+bcqzjwglsZOnpcspeUMDoTDbZQ+8yELFU7Pbpyvsi641m9Q7Pdu7+ipDeXKklBEi4+Lr30UqZOncqCBQs47rjj+Pe//819993Hfffdl+hTSST9jsgkXwK1A2Jib1FeFi/PPY6j5l7F/uffyrAtNx+7/L40IeuvnR4t191bVu+S/knCf+tNnjyZl156iaeeeoptt92Wm2++mbvuuos5c+Yk+lQSSb9jxSffUbJ+A9fOyKdkfSkrPvku2UvqMWEBcjxvLb6adb/80PkbJBLJZk+vOJwedthhHHbYYb1xaEkcBkINwUAm8v2MG1HE0mUrmT5S5ajtslj5a2BARD8ABuVl8dKNx3HUvKuZce4Cho+ZkOwltaGrw9kkEknvI2e7DAAGSg3BQCXy/Rw+bSdK1m9gwaw8AM6cks3Jz4WjHwPhexvUFAE5at617HvO/JQSID0dzjYQSAXxlUodO5LkIsVHP2eg1RAMNCLfj+P38uArKzl6axcTisPdXROL3UwfqQyo760wN7MpAnIt+5x9CyO2mpjsJQGpN6m2r0m2+GrdsWNZFjixa9FEuJMo8vqBKgIlYaT46EN6Iz3SsoZg/vsD5y56oBD5fg6ekMGjn3o5cfvsmOcHWvQDwgLk5bnHM3Putexz1i2MGLt152/qIxJZJNpf7uKryjawfs0P+P0BciYdgStnUMzziubCMY1eFV8xNvRV5Tx+542Ydqz4MBWFx+8Oty8P9CiURIqPPiXR6ZHIXfVArCEYCES+nz1HKHy7McC+o1UylABBI4uI9hxT4GLqMDHgvreCnAyWzZvNzLnXs9cf5zFy3LbJXlLCSEXfjfaIRDwCQYM6Xy3+j5fF+GwACAEjDzqrS8fqSdom5jnVRdHem2cUShJGio8+ojfSI5G76oFaQ9AdUrHoNvL9zJmWzsJf6jEtm0MfrSUzPYjb1eqfnlbJVz+tY5cJA6cNMT87oykCciN7/XEuI8dtl+wlJYRU9d2IRyTdlDftJHTDRs8qRGh69HnDW0btysexzVCHx+mNtE1ftipLUg8pPvqIRKdHWkY9BnINQVdJtaLblt/P4dtkkZOmErIc7v+4ljI7l6tPPRhVaRZJLl1juzHJ36wSTX52BsvmzubYm27iw4wCDrvgFrLzCpO9rB6TCsKiO7jzigmGHFy5gxFa9x2kN/eaGUnikeKjD+iN9EjrqEeEzTH6kYpFty2/H5cm2G9cBgCj83VOfs6LqojN5vvJy07n7dtP45s1pZwx/zyOu3bxgBAgmyMyWiFJFJvPrXESiWxEZ00JFxueOSW7RwZTkc126jCFLQtchEwn+tOyhsCy7EReRsqSasZd8vuJzzZbDuGhiw/g2VvOxVdd2fkbegE5qVYiSQ1k5KOX6Y30yFc/raNkYyUlpsXUJe380hyANQTxSMWiW/n9tM/Wo4fw8KUHcdr8czju2n+Qnd83EZD+VCTam9imEXPH6ZgGjm0Sqq1I2pokmydSfPQyvZEe2W7MUG69YBYhw2z3NalcQ5DI4tBULLrt799PbzNxi8E8ctkhnDr/HGZds4ScgkGdv6mH9Kci0d5ACAUhbMz6qpjHjbpKzEYftZ+9Qlqap0/FV39pVZb0DlJ89CLxwu8RetJi6dI19pvcfwd4Jao4NFWLbvv799MXTBhVzKOXHcIpC87l2GsWk1tQ1OvnHKjCoiuYvkqyswtxnNhUX1BXMHLzOfH8qxi25fg++YxkFEoCUnz0KjL83pZEFofKotuek8wW5fFRAXJenwmQzY2ubPQet95l4ZGIaMXmHoWShJHioxeR4fe2JKrluLeiSpsbyW5RHj+qmMf+71BOmn8us65ZTG5hcZ+vYSCTqI0+0dEKKSwkUnz0IjL8Hksii0NlVKnnpEqL8riRRTxxxWGctOA8jrn6HvIGtfWRkGw6idjoZbRCkmik+JD0GYksDpVRpZ6TSnOBxo4o4okrD2fOwvOlAElRpLCQJBIpPiR9QqKLQ2VUqWekYovyVsMHNQuQP/2dvKIhSVlHV0mFEfUSSX9Fig9JnyCLQ1OLVGxRhrAAefLKwznx1gs4+k93k1+Umpt3skfUSyT9HSk+JL1OT4pDU3FgXH8nVVuUI4wZPoinrjqCE269kKOuujslN28560Qi6RlSfEh6nZ4Uhya7G2Mg0h+iUFsOK+TpPx3JCbdeyMwr/0bB4GFJXU97yFknEsmmIcWHpNfZ1OLQVOnGGEj0pxbl0UMLePrqI5m98KKUFiASiaT7SPHRz+kPaYlNLQ5NpW6MgUJ/a1HeYkhYgBy/4EJ2Ofoctp48Dbd0vpRI+j1SfPRz+nNaoiPhlIrdGAOB/tiivMWQAl64/miefPstnrr5SU64fokUIBJJP0eKj35Mf09LdCScUrUbo7/TX1uUhxflceUJezFp/M9cf/M5nHDdEtxp6clelkQi2UT6z04laUPLtETJ+vDG3F9oLZwsy27zXHvdGC1fK9m82Hfnrbhl9s48dfM5BP2NyV4OweqN+CtK2vzIyawSScfIyEc/pb+nJTqq50jFboz+UFuzubDPTlsxXwiuvfkcTrj+H0mJgMjJrBJJz5Dio5/Sn9MSHQknICW7Mfpzbc1AZO8dx7AAuPqmsznh+n/gSc/o0/PLWScSSc+Q4qMfkuomUZ3RkXAqzs/qUjfGlz+uI92jx0Qieis60d9rawYqe+04hluF4E9NEZBkCBCJRLJpSPHRD0nFtERX6Uw4PTHvjC51Y6yv8HLzg8tjIhG9FZ3oi5bfnginzTklNH2HLVkkBFfedDYn3vAPPOmZyV6SRCLpAvL2rZ8RzyQq8tMyLZGqRZmRjfysKdkxj585JZuS9aW8+8VP7Dd5PIdM3YaDd9+aLYbkc/DuW3PI1G2iP/vsPJZH/rkqpli1owLWntA6RdRbRa+vr/6WU+Y9xOurv+3T9w4Epm0/mj+fuhtP3XQOgcb6ZC9HIpF0ASk++hkRk6hV68JpidY/q9ZZlGwMm0SlGt0VTu1tqvG6fHqj88dxHO5/ZRVr162PiqWISEpkZ1FPhFNP3us4Dt/+WorjtD8crb+w53ajuf203XjyprPxN9QlezkSiaQTZNqln9EfTaIixHPXdByHgGHj0ZVwyqDJXXPHscPj1lnEK1a9/+UPQIiEd/4s/+gbbrxvGVNHunq1tqYnaZ2evHegFdFO3XY0d5wGl918DrOv+wfpmVnJXpJEImkHKT76Gf3VJAriC6dPvyvhqRUfc8SM3Zg8cWRUOMVuqhu4/5WPOHvmnnGLVY945HfcKiw8aVD0se7UvkRqJiaMKub7tWVM3GIwtu2w6NE3KHAZNAQVLNtBVcQmHb8jetIynYj3DrQi2t23Hc1dZyhccvPZzL7+XilAJJIUpf//tpH0GyLCKVK7ceCUifznx7VkKgH+8+NaDpwykf0mj0dVlJhNddfBDjfc9wrLVn7dplh13CAXqh1kUrHJ+KLY6MT9L3/A179s6DStEEnv3PzQimia57XV37K+tJRLdndT1WCy/Nv6hNXWtEx3tK6B6U5ap+V7HQdO3imdkvUbuvXe/mhQ1xlTth7FXX/Yk6dvPpvGOl+ylyORSOIgxYckabS3AbbeVI8YCx4MrlmyLFp/4TjgDxq88X0DHtXh2IkqvoZA9NhnTsnmfz//zqyr7+2wEDMaAfDX8uArK3H8Xu5/+QPufOpf7DlCsOdIna0LBWc+V8bui0sTUlsTETvLP/pmk51cW3cN+RoCuM06dh3sdOu9vVlEm0ymbD2Kv/5xT56SAkQiSUmk+JAkhfY2wFDIjNlUaxv8DM+0OXScTml5JUPTTLYscFFZ6+fbtRUs+bCKaaM0JhQqrK/yETTC0Ykt8nQUK4hq1HF/BxtrROhcsmcmjuHn4AkZ/O/n3ykvL+fi6YUMK8rnwumFFGV7OGLGHtx0/uzoz60XzOp2bU3LdMeiR9+IKWaN0JXoR2uBVuGtA8fkiLFQsq7j6EdPoi39iV0njuLuM6dLASKRpCCy5kOSFNozGlv42L+ijzsOlFb6sGybU3d0seLnEO+t8bPb39fjawzSGDQJWvBNmWD5DyEQguyMELqmUttoEDQsLp/qYenXv8etz4gIgWkjFHYbYrP/lirflPpRbYNJQ1V2GZWNELBrpocDxgb5z49rue60A3tUGxG57mv2zef/Xi1lu8Hubju5tu4aqqz1U+8PMSRLJWTZ7FLc+Xt726AuVbxHJk8cyd/Pms5Z157CmJ2mst8p/4eiqklbj0QiCSPFh6TPaW8DnDZS4cFXVnL4eJ0tC1xU1DZS7w+iq5DtdthntM4z/wuRlV9MXWgjp+6QxaNfNDJ18jaMG1GEpilsu+UQFCFY9OgKts/2ctru+fza4Iu7sUaEwNWHphMK1XPBFA/HPdeAJuDYiS58DQFyMj1AYopMW173gePcvDRCsOKXALsvLo2/QTd1/uwyYWTMwzFdQ4tL8db7UbHJcivUBW0sFHJz47+3rwzqUqmTZtKEkay68zSefPe/PHX75Rx7+e1SgEgkSUaKD0mf094GeNKOabzwlZcVP9lMXVxKta8Ry7bIcgkaDAeBIE0TfPH9r5y+aw4LDy0iaFXwm98fE5F4bdU3BBp9XHRoAdkZbs7aTbTZWFtGPfL0IG4hGJ+logqHSUPVaBrH43YTCBlsmd/zuTItrzvNrXPR9EK+rPBxxIw9mDxxZJvXt9cy3bJr6NPvSnj8nx9w2Z6ZjMjTKakx+MuHDRy17+Q2743nsxIhkXNzUrGTxuPWOeOgXdDUL3ns9v/j2Mv/IgWIRJJEpPiQ9CkdbYDbDEnn4AmZfFOfwz6TJvC3Z99ir5Eq50xK4+b3G1lTY3PmLi4e+SqIbYXf1/qOvatphdZRj2H5Kl9ttHAp8GGJyRFPNeI3wO1qJGgYZKZ5cLu0dqMRXb3uluuavEX2JqVzIl1DkWMePsHD2VObhdyPlSaff/crqhJ7vHg+K23YxOtrSV/Y0W8qp+y/Ewpf8fCfL+PYy/+CqslfgRJJMpD/8iR9Spc2QDXAsg/+w6A0wa37ZzK2QOGOVbDrMJWLd9Mprbd59ds67jiyqI2waBldcBwIhAw8Lj1GpBw4ZSJLl61k92EKGUoAF+GoytgChVv3z2DJZwEq/QqjCzU+XW+Ro6tk5eZz9akHk+bWN8nArTfSHd09Zl8Y1PXEe6SvOGn/HRHiKx7682Uce8UdUoBIJElA/quT9CnxNkDHcfi93MuIolyEEKzZUMm8+1/hoC1VcGwe+CzILzU2HlXw7P8Mjpqg8faaAEtWebl4en50s31t9Tc89OpHMYWY6yu9DCvMZUyBJ5pWKMzNoGRjJb8EQiz/xg9xfEAaDIt1dQKPMLjpwEL+sroeVRGbZPDWG+mOTTlmXxjUdTSxOFWiHwBz9tsRIf7DA7ddyqwr75QCRCLpY+S/OEmfEm8DfG3VNyxd9kG0OHHZyv8yOMfNRdOzGVygs/ytMtyqIN0Fy34weXBmBoeNt3n7pwbO2i03utne8dRb1NU1UGI1F2JaloWqNpKbmQZN9u227XDrBbNoCAT535pSTLO5DddxHCq89RTnZbHi4/+xY46P43fO5dP1lZt8B98b6Y6+SqF0h77qpEkUJ87YASEES2+7hFlX3iUFiETSh8h/bZKk0ro4cb9J43lk+Sr2Ge1ihxFZvP59Pd9XGGTocM2ebm7+IMiy74McvJXG+a/52f7OdeSk6wDYaj1/nLk3WwzOjxZiztnJwzNfBzm8qajTpWvsPH4ELj38V//IadvHrOe1Vd9ww33LOHzaTgT9dVx4WD6KED26g++NdEcqzvjpq06aRHLCvtujCvjHoks47iopQCSSvkL+S9vMSbYfQ+vixCUvfxi9o999cSkllQ1k6jbTR2ocPFbjvd9Mln1vsXRWPgdPUPmmPperTjkQVVFw6RrTdxwTtWc/bLybE3fKpiFU26WiztZup0dtnZiBcvGiPT393FNtxk9fddL0Bsftsz1CCJbcehGz/vRXNE1P9pIkkgFPav0WkPQ57Y2t720cx+HrX9Zz/7KV7D4UZm4bdjl997PvmH/eMdx0/mxOmbkfHo8b0xYcsJXOr17Yf4zOD1U2+z/s5fMyQSAYYGhhDodM3Yb9Jo/HpWtRQTNhkM4pT25g/CC9Sw6erd1OJw6K3YQS6QSarM+9t4ikgVats6IW9C1/emJH3xfM2ns7zttnJM/dejGmaSR7ORLJgEdGPjZjkunH8Prqb7n4zmcRZoA/7K/hawg0hec3Ylk2h0zdBn8gxAvvfM62mTVMn5APwPBiOHxtDf/15XDVKQeS5nbFpBYi17TnCIUVP9SDY/HmD/XsOcLd4TXGczt9/ft6TpmUE51mm6g7+Hifu6KIPolA9VakKxXTQN3l2L22QwjB3Qsv4vg//RVNdyV7SRLJgEWKj82YZPgxOI7D/9Zs4P6XP6C2tpYDtlTZMkelwlvHhGGDYlIb737xE431tVx0cAE5me7oMS6cls/Jz3lRFaVN6iFyTQfu7ObDX+q4drqH+SuDHDA+kw+/aP8aI++77shMQkYdMyfobWpKomiVfPnj76R7XJu0icf73IE+cQTtLefRVEsDbSrHTN8WRQj+uvAijr/6b1KASCS9hBQf/YxE3bkm0o+hq2tyHId7X/6I2594EyMYINcjmLW1RnGWSlm90SL60bZttis1BJFr2m2o4PXv65k2UuXIiW4+WGvyxvf17DbUFfcaW34WOw7PoL5RY9ggOOy35ghLS8Mul66xvrKWmx9Y3u1NPN7nfv/LH4Q7cXo5ApWKzqOpyFHTtkEIwV1SgEgkvYb8zdPPSFStQCInm3Z1Tcs/+oYb71tGeZUX1QkxZZjCxEEKuuLg1mB9lS9qY37HU29RUlrBB2tDTF1S2qUagq9+Wsfa0gpe+c7PF+v8zBit8n25wb5bqHyxzs+Kn4Jx6w5afhaKEGRneMjO8HDmlGwCjT5UReGQqdtEf/bZeSyPLF8V3cQjE3Mdx+HbX0tx4viGdPS5/+/n3/lpTQnXzsjv1emyLSMuA3GKbSKZuefWXHLgGJ5ecCGmEUr2ciSSAYeMfPQjEnXnmkg/hq6uybJsFj36Bvkug9IgOLbDh2tNZj1nIQAHgS8ImelB3C4NW61nt+3H869PvuGk/Xfr0uyT7cYMZeY+k7nrqX9x9NZpTBs/CIBhRc1RjOv/cGjcGpHWEZba+gAi6GOXIqfNdbWXruospRHvcx83yIVqB5k0VGXmth1HoHoS9eoPzqOpxsw9tkYAd8y/gOOvuRvd5e70PRKJpGvI3zr9iETduba8+3Yc8AcNHGfToh9dXdNrq79lfWkp5+ziojBdoKuwXbHKgv0yuHR3Dxfuns4Ow9PJys3nxnOPZ+H5x7KhvIocNcB/flzLgVMmxkQfIt0tuqZGow2qovD6qq/J0CyO3VonO8NDTmb458Jp+QQa67AsO+rxAe10aSwuZffFGzjqqTre/tlPSWlztKT1Jh4RbKGQGSPCItGQ9j736GM/NOBRHY6dqEbTTiXrS3nj42/bRFF6EvVKZKRrc+LIPbbmikPH8/S1J/Cvh2/vMKolkUi6jhQf/YT2Nr14m1xXjtPSgvyH3yuprPXH1FJ05bhdXVMk6rHHMNhvjMqMLTW8AYcbp7s4cCuN47ZzM3WY4Jq9swj561hfUYNpWazbUNqpqGm5Ib+2+ht+/r2Ug7bSKE6zqKz1EzIdQqbT7rVFujRuOn929OfwGXsgNBdzJuUhNHfMhNj2NvGFj/2rQxEWL8LiD9nct7qGfUZrbF/cVHRbFI5AzX/4dU6Z2yw0WkeYuvO9dxbp6u7foc2Nw3afwAd3/oGt9VIpQCSSBCHTLv2ERM3MiLHl7sCCvCu23F1dUyTqceUBOlkuwVETdN5eY/JrrcNWhSZuTcelOmQSYOcim+vvXca4kcWdpghabsj3L1tJTV0jaarDF6UOxz/XiEWg+ZoitLq21l0aLSfF3nLwIBqNiuiE2PY28WkjlagpWXvrjWeHXttoEAwG+OOhadSHAGHSGAxxxuQsHr93PUOz2g7M25TOpP7oPJqKzD11X2569F3efGgRB5x+VVJM+SSSgYIUH/2ARNZotPRj6MiCvDM/hnhrmlDkZvehsTUS0ajHcBiTr+ALOgzOFEwZpvLHV/xku0FRFASAEAghCIXCUY+lh44A2t8kW27I179Zgt+0ufGAQQzO1iipMfjLhw3Ra4oMrxszrLDDa+tIUAFxN/ETtnPzwldeJg7KaHe9rX0wLNtm0aMr2CbTy5Qx4eMJAaqq8UtVA9m6zVm7uHnw6w28tvpbHnr1w02q1+jPzqOpyA2n7MPNj7/HigcXceAZUoBIJJuK/G3TD4hXKwCblreP3OkfOGUi//lxbfQO//AJ7mhtRcQptLtr8jUEOGCUxY9rSqJrikQ9Tt/JxfAcjaE5OkWZGmdO8lCUqZLm0knLyueUow5g2q47g6ozMldj+giFoRnhjTJeiqCl+Jm5bRaTik10x+DUyTkcMjGTc6bmxVyTEIKlyz7AMK12r60jkXf/spXc//IHMZt4yHQIGg6ZSoB9R6us+KEey3birjfyuUfqVYYW5hAIBvi8THDwY14OfszLQY962WPJRs58rozdh6vsPUplt2Gw6NE3WLtu/SbVa/R359FU5PqT9manzGpWPHCrTMFIJJuIjHykOL1159qTNE68NTlOuFV2RLbNToMs7m8aEnfX02+x+zBBQZrCrzVW9Bi5HsGkoSpv/WozIkdw6NStWfDQaxSlOZgmnLGziwpvHdkZHoRou76W6/c1BDhmgspXpSYrfmjgkImZMdcU8QzprCOno/TE8U/+jt9S+N1NzBTZYMikvtFPjkdBUw2+Wh9klxGemPUevPvWbbpU2nME/fS7Ep57YyUXTc9meJGLc3eHwx5cz45DPZsU9RoIzqOpyHVz9mLhkx/wxtIFHPTHa2QERCLpJr0uPhYuXMg111zDxRdfzF133dXbpxtw9Mbo9O6mcVq3eH754zp+/n0jJcKMrimyCWe4FBpCDp6M9Tz7zhd4fXVUBHROfiVeUaNOQY7GH2buTWmVj0++WUNdY4hZ27jYeajGr9UhKmv95GR6YoTWfpPGR9c/vsjNL+sr2GGwyj6jtXAB55h0VEVE37PwkTcINdZ1WC/Rmcjbawud/9RmccVJB0QNxyzbYeEjrzM6t4Y/TMnBpQrGD3K1LXC1beYtfTWmBTeeI6hpWtzx1Fvsv5WLyaPDUY7hToA9hwvK/CaW7USt3rsqFgeK82gqcvWJ07n1qQ94/f75HHzmtVKASCTdoFfFx6effsp9993H9ttv3/mLJXHpjTvX7hYgtvavWF/hJWA6HHNguJ4isglvle/lzN1yuP/jWirI4fA9tmVwQU6na99ju9GccP1SGgMhPJrgnV8NZjzcQF3Qbls0qlWy5KWV/LJ2HfNPKKAxGCJkmIQc2HcLtY0luuM4/F5dxnHbp3VYL9FS5O2+uJSAYePRldgNRfMztDAnKvI+/76EUCjIBlPl5nfrm15UF3uBaiV3PPVWl7xZ5j/yJl989xuzDilsWjtUeOs4bUedK98Ksfzbeg6eEI7qDOR6jWRPWu4OfzphOrc9s5LX7r2FQ86+LuXXK5GkCr0mPurr65kzZw73338/t9xyS2+dZsCT6DvX7qZxWrd47jdpPI/8cxXZSiA6pn7FJ98RbPTxh32zOXhCBqPzdU5+zstHX//apS6K11Z9w7+//ZVsF5y4vcaDXxiMH+Nh2uj0mKJRAFVVuOmB5WyTG6DQbaOqboryc3EcGJQPB0+o4Zv63Kgl+qfflfD8ipWct0fH6aXtxgxl4fnH8sv6SjZW+Xj6zU84YkasuVk8U7OIMHRw+L2shhHFeQiaN6Avf1zHC//6kOs76VIJhUwefGUlw7Kc6EC7QMigPmBQkK4wvkBw5nNljCioayWIuhf16g7JEgG9NX+mt7jy+Gn8+ZmVvHbvzRxy9vVSgEgkXaDXxMf555/PoYceyn777deh+AgGgwSDwZjH3MEQ0kuwd+huGqd1i2dLP4v57zfXU4zMtJm7ohJFERwyMTOawjlg1wn8+Ht5uxuYZdnc99L7BIIhjp6gc+t+6VQ2NvLWL43ce8xgvi0zoiJHVRX+/e1afl1XxgZhsts968nLSo9tpUWAFmBoYQ47jh3O0mUr2X8rN1sXe4D200suXcMwLZa+/AEul05mC3HVXlShpTB8bdU3LF22MmbDjAi3vUdpnXapLHn5QxzDz5lT3dy+qil6k6YRMi3CNY3hFNUpM2cwdvigmDX0Vr1GMkRAf50/c8Xx0xDPfsjyJfM47NwbpQCRSDqhV8TH008/zRdffMGnn37a6WsXLlzIvHnzYh678eIzmHvcjr2xtM2e7qRxWpuIffBroI2fxcJH3iDY4KPAZYJjsfTjGg4cnxGNMMx/5E1eXflluxvYVz+t49/flZCuwVm76AQMh7N21lnxs59xt5UwNM8TI4ZKK2vJTNM5YpyHRz6rZ7ddxjLngElx19+d9FLkWs3GGkrKLBYfVcStK7tXgNt6w+xqUa9l2bz72XccvnUmp+6Wx/fetgPtHBw2Vvk46cDJuF163HUkkmSJgGRMWk4Ulx+3J8rzH7F88VwOO2+uFCASSQck/LfJ77//zsUXX8zjjz+Ox+Pp9PVXX301tbW1MT9Xn3dyopclaaJ1y2e8n0irbet22hO2c+MYfiYOCm9+Z0zO4uffN1LkCVHVYHL1dA9rq8O1CWMKXOw2FB58ZSX423flHD+iCMdxOHArnZ2HudF1lV2GuzlwKx0bwXVnH8utF8yKiqFH/rmKQ8e5OWcXnYO2Uvnwyx/Yb9L4NutXFaVNeqkjt9MVn3xHyboNnLWLTo7ukO5SuuwAGs9ivjuuois++Y51G0q5cFp+jBV8y4F2AsGSF97j7c9+TMjfg85IxhC6RLn4JpPLjt2DfYebvHrPjbINVyLpgISLj88//5zy8nJ22WUXNE1D0zTef/99/va3v6FpGpZlxbze7XaTnZ0d8+N2yxHWyab15uk4kK8H2X/LZj+LoAkexeLjtQG2LhRMLFDYujBcm7D74lJW/BTEMfz83/T2fSn+8vS76E6IC3fzoClK9OeCKR5cGPznpw1txNCJ23sIhQzO39WDY/hZ+Ni/2hy3q/4WX/64jq9/2cD9L3/ArkNs9h6pcNBYjaUf13DG5Kx256zE+5xabpivrf62S94sXREpPbFW3xSSJQIGyvyZS46Zyn4jbV695wYpQCSSdkh42mXGjBl8/fXXMY+dfvrpTJgwgauuugpVVRN9Skkv0Dpl4GsIEAoZXDDFw5mv+lnxQwP7jc3gqO1zeOE/tZy5Ww7DijK4cHqILyt9HLbPVN7+9Ft2yK7l+J1z+XR9ZZvwfaTIct/RKhMKNQItil8nDtLYdwuVB19ZydUn74+qhjfAaSMU8vQgbiHYukhn/y2D0de4XOG/zo7joKoKC88/FsO02l5cEy5dY32Fl6v+/gKWEeKcg3Wy3A7nTfZw8kuNlHhNpo1UuO6+V1CAm89umzqKl1o56dkNLHr0jS4V9XYlNQT0aSoiUVb+3SGRLr6pwMVH74548WNe+fsNHHHBTTIFI5G0IuHiIysri2233TbmsYyMDAoKCto8LklNWnfEBA2H9VU+XKrDlvk6uw9XuW91DdNHp7G2OsSBYzSGZthkZ3jYNdPDAWOD/Ovf3xBqrOPCQ/NRhIi7gT37zhf4A37e/81h4t3euGvxmxbPvvMF+dkZlKzfwNWHphMK1TMsPyxiL5ji4V9rGlj42L+48Q8HA10vlLQsm2OvuQ/VqMO2YWi6QmGGxogchekjVZZ+XMNle2bx0McbGZmnt9kE29swJ+Q7vPvLRgL1ng6Lej//oaTTzqP7l60Ex9kka/VNIVkiYCDOn7no6N1QX/6YF+64ip0PPpEtt94x2UuSSFIG6XAqaUPrjhjDtPA1+KFFCLnBgIm3/46wQyw+NI2QER6KluFxRQejHbttWocb2Mzp27Oh0oc/aLS7ljS3zuF7bMvptzzK7sMUMpQAbgEqgpDpsM0gjX1Ht42QdKVQcsUn3/HTmhLO3kXnvs9DfF5qss1gFzbwh13cnPJiI09/6SU/TXD6DiqP/7AhZhNsb8O8cp88Pt1gxbQIt8ala9i202nnUYOxHhyLhSeFO1x6ezNOhggYyPNnzp+5G+M/+4H7l91B1dpDmHzw7GQvSSJJCfpEfLz33nt9cRpJgmjdEWNYFv9bU4ppNuf8FSF4Y/XXbJtVy5QxedGhaCHT4Zcqg2zd5shxCo7T3AnbegPLTPdw5Un7dbqez78voWRjJT81Bln+vyYrc6U5jG3aDn6/nyUvf8jY4YMoWb+By6dnc/uqDe1ulpZlc/+ylUwqNjlyvIufqiwe/4/BdkWBaIfJVnkOD38RYOYEjb23UPmuxoxugkC7G+bEIjcHbeXqtFU3ZJgddh5ZtsOix95gl7z6PolCJEsE9IaLbyqx36Tx7LvzWP5w+8v8G9hVChCJREY+JLE4jsPP6yqYMWlcTJ76yGmxLrWff1/Csvc/iw5Ga/n+36sa2W+0SnG6jbchSIYnXEC8qRvYdmOGMv+8Y5i3dHnUyrw1D33q451Pv+O9z75j18E22+cF2XWw0+65VnzyHb/89jsL9tJoMGD/LTVe/j7AwU/4yc9woWsKFT6bHI/g9J3cCOCIsQpzPwoLmuL8rG5tmPEMuzozkHtt1TcEGnyce0jfRCHaEwGO4zQ7vuqJFwGbw/wZRVF44PKZnHnHMj51bCYfcmKylySRJBXhpGI59tpVsP7zZK9is+S1Vd90qV4iZJh88NUvbTaMn9ZV8I/n30ZzTIQAl6a2MAFzMC0bzZXOvdec0q0N7PPvSzh74aNgBtt9TYMB2BZ3HaAxItOkpE7lug8EN51/fBtfjVnX3McoZSPz9svGccI+Ghe9uJHlP4YYv8Uw9tt1a/7+zFscsKXKLfu6MWyHkCW4778KG9XhPDHvDD76+te4G6bjOPxe7mXMsEL22mkrXLrW5c+19RqHs5HbDx/U5vnLX61gHYN5bsFZCYtCtPedfvpdCU+t+JgTDtyNPbbfkuk7jul06rEkPrZtc9Ydy2CrvZly2EnJXo5EknDOnL5ll14nf4NIonTHWKq9u/aQYbLN6CFxN+VPvyvh8Tc+5rR9J3f7Lrazu+NIimKbjBpGZNoMydIAm12H0OZaWtY1ZGeE0xneej/HTlT5vFSlsqqG59/5gjQN/ldh8favJsdvq7Ou1uaQLWHuRxt494uf2hURYbfTD7jprCNx6Vq7n2tH9uXJSEXE+04ja++K46ukcxRF4b7LjuSsO1/hk+VIASLZbJHiQxIlEe6S7YmSyCaWowb4/Ltfo3UVPT1uhNdWfUOg3sfRu6hkuR3y0lTqQw5HjIXrPmiu/YisY9fBDqPzw3UNjgNry2oZmgV7jlB557cAv29sYGiWgluFl783OHUHnTQditMsdi5uK2haX2dLodHe59pRV06qpCL6s+NoqqIoCvddegTn3PUqq1+x2f2IU5K9JImkz5HiY4DQ0yFgrY2lEt3S2ZubWGTtuxTbFKc5ZHtUQqZDtlswON1il+LmOpOvflrHd7+u58vGelauXYfbpREMGdTW+1EFqIrAchwE4Dcdzp3s4o7VIbZf0kCGC2oCDqqikp8fP+rQ+jpfW/0Ndz/7DtNafa77TRrfYZQp0QMFN4Xe/juxOaMoCvdeegTn3vUqq5c57H7kqclekkTSp8jfIAOE11d/yynzHuL11d9u0vt7010yEY6ZjuO06zL61U/rKCmt5O2f/Rz/XCMzHm5g6gN17PtwPbOebeBfPzZSUhoWC1tvMZiCvBw0VSUrN5/rz56FqaZj2oI5O7hBwMFbaWS5w1GQQ7bSOGSsRrYHztrFxZg8laoAnHbEtDZRh3jXec3il/nup185cbtweifyubYc0JeqLp4DxXE0VRFCsOSSw3H/vopVyx5O9nIkkj5Fio8BQE/tt7szh6Q7RATDGx9/26VNrCOB0ZG42m7MUI7adzJCc3HZ3oXcdEgx8w4u5twpaZy4nU6D4XDkPpPYbsxQ3v3iJwINPm47bBAhfx0r/v0dVV4vx26jc8uMDA4f7+LzMkG2R2X2tjpDszUumJKGg8KgvGxuPayIkfkeRhTntSm6bL1Z/2FyFjXVVYzNF+TpQRwn/LlOG6nw4Csr2XOEwsxts9h9qJNyM0x66++EJBYhBIsvPpy0dR/z0csPJXs5EkmfIcXHAKCnQ8Bab5oRenqn+/rqbzll7kPMf/j1Lm1i7QmMjsSV4zj8UFLOZ9+u4eCxbk7fNZf9xmaw61CVSYPhvF09zBitsmL1/8AhJjIxqdjm4VdXka3ZnDM5DRs4fScXv1YZ7DpUYfvBKprisE2Rzl5baDzxVQPbDctkn9EuHlm+KmYd8TbrPD3IXqMUQOAPhPA1BIDYAX21DX72H2ny45qSlIoo9NbfCUlbhBDcc9FhZGz4hA9fejDZy5FI+gQpPvo5PU1pmKbFXU+/xe6dTH81TavdqERH6zIba/j591L+MDkr5vn2BqxFBEbL83Ukrl5f/S0nXHc//1uzoXmI3OJSdl+8geOfa+TQJxr5fIPFmnUbuefFD6IbquPAxHwLVTjsNlwBx+b7coNVaw3SdZg5QaMu6PBTlcV35SH23ULli3V+trvzd95eE2JtaQVf/bQuuo7Wm7VtO1R66zltJxe1QYd/rzdZV1nLl7/7yRB+9h0dHtC3vryWLXMddh5kcH+KRBTimY11NBFY0nOEEPz9wsPI3vgpH764NNnLkUh6HVlw2s/p6RCwe15cyVc/rKU0291hS+c9L67ksddWddmnouWI+js+Mvm5ymDsIHf0+fYGrF2zbz7z3lnHLQ+vYPmHX3HjH4/goVc/jFv0COFIhsuqJ79wMFecdACqooRbev/5AZftmcmIPJ2SGoM7Pqznmbc+i0YmausDKI5NYbrg6Ik6OVkZpLld/PPtcg4aqzMqV8HlSSPNpZPm0ikqEBw8oYZVG92EDIOjZ+warfmIt1mX1dQRNCwGpcP4AsGf3gogCBCyvbhVGJKloogQX2+02We0ytETVK5bWdLr3SRdKUwe6I6jqYoQgr9dcCgX3/MaK5+/n2nHnpnsJUkkvYYUH/2Yng4Bsyybdz/9jtw0hazcfK4+9WBUpe2GpKoKS55/r0v+Hy3Xtdswh71Hqaxeq3Lmc2WMKKhru+G1GLA2faTKjC11XvoqyD9efI9hWbDo0TfwN9Sy4Lh8oO201x/XlHDmDir3/6cKVVE4cMpEli5byeETPJw9tdkZ9L8bAjz/v408cOhwHAc2Vvt47SeDg8fqbJEjqPT5aVRcbKizKPU5vPNrAJsQuZlpzSZpDlR5a8nxCD7/trlduM1m7Th46/3gWKTp4dcUZqjU+G0Gp0OjIbjhgCIqa2rZepBDlkswoVCwQ2E4+tGb3SRdGbqXKm2+myNCCP56/iFcsvg1PnjuPqbPOivZS5JIegUpPvoZLe9cuzoErL273RWffMe60lIWHTqI+e/Xoyoi7ob02qpvWFdayrUz8rnlvQ3c/8oqzp65R7t3zpF13TIrj+GZcOH0DL6s9HFEq0FrDg6llT4++WYtJes3MH9WHhVeH7Mmqryzxs8B4/J4+qtSdhzqaSOu7n/5Axxg50EGM7bQ+HxDU9rCttt8Jo4DR4yFN39w+LnKoCjN4fN1AcrqHSoaLN5aYxIw/WRlBDFsPZxaUkGoOifPnMHY4WGH0U+/K+GJ1z5g3v453PZh8+fberOORF4ub4q8AHy5PsATn9Xwx5007vzY4LLlVThWiBy3wKUKgqZNld8hO3tdm4hC6+9vU9uqu2oilwptvpszQgjuOu8QLlvyBh88+w+mH3dOspckkSQcWfPRz4gUZS7/6Jsu5+XjFXJ2tVak9etGZlrceN8yln/0Tdz1tXz91sUesjM87Do6mwPGuvnPj2s5cMpEDpm6DYdM3QaB4M+PvcGN973CroNthmY4hEIGuw1X2WcLlXd/qmObAof6gIFlN9eanDklm//9/Dvf/vQrx0xQGZQhOHqCyo9r1rLwkTfafCbehiCDMxx2HRaOwOx2z3rmvhug3oDaIDgIApZATcvmL5fN4a9XnMxfrziZJVedxLlH7ckhU7fhwCkT+c+Pazl0vIfDJ6YxrcXnFdmsD5m6DQfsOoGVX/3IQVu5osWv+4xJ59MSPweM0Tligot9RuvUhRy2zFOYt286N+2bzpET3RiWwG8Ktt5icNzvPPL9bWpbdU8LkyV9hxCCO849iCLfNzx87an8+MVHyV6SRJJQZOSjH9HyzvWup9/C66ujxOo4Lx9JabS+2+1qrUjL11m2Q0PQpMBlsOjRNzhk963jDmyLvN5xHL4rCzGx2NXm+JGpsv4GH2mqw5HjFCq8dWTokOWCU3ZwcfKLjZTqAl11WP5tPQdPyARgizwdxQoyaQhsV6xRkKawfXE4CrJiTRnBBk/sZ+I4hEwLx9HJSIOQZXHdPjnRqARAqc/knn8H2o3+RK7r6kPT+XVDFSdsl855/2z7ed3z4kq++H4tazNdrG5aQ22jQTAY4I+HphGyBMdMVHl7jZ8Dxrg4aqIby3ZY+nmA4TmC37w+Vvz7e2ZO377Nd94Vc7LO/u5Iw7D+gxCCv5xzEN66RuYsvBvLMpk4ea9kL0siSQhSfPQjWt653vJeNUcfsAc7jR3e7utdukZpla+Ns2ikLqKzWpHWNSWvfVdPdaPJpbt7WPBRKa+t/pbD99w2er7WhZevfFPPTW9WcMMBgzh4QmabAtMff1lLYRqML1QpcBnUNZoMzxbUBBwKPA7puiBNFwRMmz8+u5GRhRkIIahtNPCHLGZNdFOYLtAUQWG64JgJKv8uddqkd6Lra5r/skN2Hafvmtvm+f9sCMXdkCPXNW2EQp4eJBQyyXcFo9GPlp/Xu599x6hclYy8Qq465UAAFj26gu2zvUwbn4+Dg2nXsNcolVd+MLlkN5s3fzFYW2tz7TQ3V/4rwDVLlnH4HtvGiMTI99fSnKw7TrE9LUyWJI/crHReuHE2x8xbAiAFiGRAIMVHitBZHj/enevn3/7Kn07av90718hk1NZ3u/HqIqDthtQ66rH04xqmj1Q5aUc3H6w1WfToG4wanIuqqGw9enBM4eXui0v5vaqRQo8dW2yqVfL5D79z/7KVbJlt8LsXvii1OfqZRlwqZLoEdSGH+qCDrgou2lnn/i9NLEXniBl7sPP44dz66BuMUsqYMEjBpULIcnCpMKFQMGmwxVftDED7/PsSAoEAn9Tb3eriaBn1CIXqGZKlUVpncMJ2mTHRjxWffMe6DaXccEAh89+vixakBhp9XHRoATmZbur9IUzL4piJOhe+HmDbxbXUBR0O3Epj60EqU0eovPpjJf/+bi27ThwV851/8GuAB19ZyVFbu7oVvehpYbIk+XjcOi/cOJtjb1qCcBwm7Lp3spckkfQIKT5ShM66EDblzjXue57d0KYuIkLLWpFIeD/yuuXf1rO2OsQte2fg1hRO21HnjFc2cNAli8lO17njkuPYb/L4aOHlp9+V8NwbK/nT9DRu/cAfjUa4dI3Sylp+WlPCzdM0XJqL1380ePp/QU7ZwUVtwOH1n0wKMxR2Gqyw72iNH6vhnd8svvrhNw7efWuqvT7W1Rsc95yJS20WaiHLoTbokJ65Pm4b6HZjhrLw/GP5ZX0lI4py44q81l0ckY1792EKGUoAt4AMXcGt2eAE2L3V59VS6N3/8gcgRMxnXV0XwLQcxuQJ9hyp8trPJjluwaytdSxHMGsbF++tDbL661+p8TXGfH8nbOfmha+8TByU0eW/A5G/B2vXreeMfTNxHCd63WdOyeakZzsvIJakBh63zvM3zObYm+7FcRwmTtkn2UuSSDYZKT5SgM66EDblzrW99+w2DJ79b5y6iJZolTz7zhdtohgHbhkx47IZkqWyx3BY/mM9uZoabRHdb/L46LkPGOvmpMkFfFFaER3H/v/t3Xl8k1XWwPHfk6Xp3lK6UaDQIrIVVDahQFFZFBFFlEUFdFQUBQSZeUcBEUGh4CiOiqK4MKMOiooCKoi4sVhUpoKygwNSlrLTlm5JkzzvH7Wle9OQ5EnT8/18+gdp05w80d7z3HvPuQC3T19C1xgrXZuaiAvR82a6mZsuN3LL5Xoe+8ZCuygdx3NV7r3KD0WBAYk6vjhQxM6DxymyWtEbTCSE65lwtQm/MqXBFrvKKz+ZOYOp0qZNKE4siqy20uPuHVluKJnNOZBv5vOdBYT569ArkFekUlikEhIUgCnwDItXbq6U6A177ygoOo4YKS3BPZOTh2q3E/LnDI9Bp5DcMpC+7YorauJj4ZbD59nwyz42bdtX+vmpKkQYzQxILG5Odn+PcIdmL0o+i6aBNmatO41NpXTvTKvGfjQNtDJrySqaRoWXW0IT3ql4BmQkw+e8garaad+jn9YhCeEUST68QG0nvjpaUlvV76z4nIeSw9nwh7XafRFQPEj36NCC2MZh5WYxHkkJpWmUHwB5hWZGJFnZlGGjT7yeHw5mVLlcUzFOgAMHM5h/jZ7IIB3bTtjIyLZhs8NN7xdh0EHLcB3JzfV0bqInI9vOZY0N3NBGz96CcGx2lXNZWVgMKgs2WyrFnmNWybVmseNgJt3btyj3vaqSPJ1OqXG5q2OrOOY+fBuz3/ycyyKyGNcjjK1HCnj/lxyahBtQgiKYee9gXv9kQ6VEb2ArI+nng3lszA3odQrvrfsve75P59pEAw93C+DlnwrYdNjKI8khhAX7l77mxD4RDH/vCGabSurd0agqnM7KxWIpYuLV/oz7rIB1+/K4sV1wrbMf2w8c5XDmaY6cLKy0BKaqKkfOFhIZoPLPD76ucgOx8D4mPyMfPTmCEU+/haqqdOjZX+uQhKgz+UujsdpKXp1pdV3Tc1pHmrg2wa9S2WvZr/7d2hAc6E//bm1KS0yvS/TjiuYhBPib8DeZyMoz0yJUoV+CgYxsO52ji/tsWCzWamdp3li1iSUrN3JVtJW2kToUFFo1gpkpfszo44fJoNC6sY5cC4zr4k+wv4Ewfz1+RgOTUyLJz83hv3syCA4KRNWbyLYaK32pehPBQYHY7ZVbf1dValpb2aqf0YDNZsdScIHUGyO5vk0Qvx4rJNioEuIH5vwL/Lz7MEczMyudgzK+ZxiFeRfQ6xT6d23D1z/vIjZEYe61JgZd7sfkHn7EBCvsyszFXHTxM0qM8OOqKCs6m4UWjYyczs7n8MnzqKqddlEGejbTs2TLeQos9lrbnXdsFcewft2JDvVnev/GRIf6c3O/XsyZMIqb+/UiOtSfGf0iyDp/Tkpv6xGTn5EPZ47g0FdvszPta63DEaLOZOZDY7Xt5XCm1bUr22NX9bvMFis5eQUoqIT7K+h1CgNbKby1I6O0GqOqWZqRy46QbVbRWYsY8E7xrIVdVVGAAquKQQGLTSG5uZ6WYWCzK4QH6jh/zkrTMJXkpgobt+3j5b/dUeO5In5GA53bNC/3WOnejTgYmvTnnoxVm4o7kNVQtloxkSvZ+zI9xZ95G800CdPz9upN3NzWr8Y9NFt2HsJiLmRQJyOtG+vIKbASH6qjZzM9j6/LZdHWI/ibikt/i6w2cvKKyLOodH7xGGaLGbvNhl0FvS4fgLwi6PzScYJM+ho/T71OR/ruQ5WWwKaNGVC6NHZXt8akZ56Wzaf1TPEMyEhGPL0UVBtJva7XOiQhHCbJh4s403XSkb0czrS6dmV77Iq/y2ZXmfevtZzPPU63OD3juvjjp4eERgq/nrLw1qqN9I7XkxBhrDQQ921pZHtWMAN7JGG3q+w/corvtu5kaDt/Nh3Mp2WYSka2nZ+O2eizNA+9TodOUcgx27FTWNzq3HiWyLCgOp8psu6nPew/mMG91yrk5BUy7upQhr+Xgdmm8uyg6stWq6rguT5RR7vGOtpHKqz5vQCdorLxD6pN9FTdab7f/juNAxXuv8oIKugVO1FBCg929WNThg2rIZBZDw1Dr1MostnYeTATVCi0WHl/7WZuS9Lz4U4LyV06cHnzaAx6HUmJTTAa9DV+ntUltxWTRCm9rZ/8jAY+nDmCkc+8g6qqdOx9g9YhCeEQST5cxJEzMypydC9HXVtdO9seu6oEquLvWpO2izPnzhEZqJDaP4i2UcWDX4HVzrA2Oj7fm8+aPQpJC48SFmis/CKGQm7s2Z4rWzdj+PQl3J4UxJ1XhfBTRgHHLqgUWBVUFfKLVOyoBAcYQa+UtjrvkNCkzmeKlDQ06xpjJTHcwOmsC7SJi6RrjJUfj9q4uUNwtWWrZZOvintfJqVY+OVMNldfmcQtKR0x6vVVvv7qzTvZv/YHurc0YFNh/1kbtj/zshAj9GymZ/0fWcREBNO9fUsAbunTqbRU+uZ2/vzj5ihsymn+KCiosoy4uvdd0p/ErhZ/vu1iTPRurvD26k0Ma+9Hi3Bd8Qm/Unpbb/kZDSx/YgSjnnkPQBIQUS9I8uECjp6ZUdVzait5vdSBoC4zMrUlUDabnTdWbkRns9CnlYGWjXQU/hm3gkKbSB2hJgV/o0KjqMY8PvaG0l4XJUru0MvOKGw+cp5c68UzVQACDYDeyMQRxWer+BkNpFzZCj9j3f+TXffTntJNriU9Ok6cy+G2tnq2Z1pZty+v2jv/kuSrbAVPt4TivR3dg/25vrWZP3IucFNyUunnVPaa2+0qz767jhCTjp+O2hn5cQEBRoV8i4rFDuH+OgqKVAw6PUVWe41n99R1dqLk+dd3NjF22XHmDIrixnbBtI0yohZlc2vbAA4dP0vTqHDCgv1l9qMe8zMaWD6zOAFRVZVOfQZpHZIQNZLkwwVqq1apiqeOLa8toSgZ7C5vHl1rArX9wFH2Hj7B+QIbn++z8fm+8tUm+UUqRh1M7mnirR1Z6HW6Sz451dmEo0TJrMdV0VbaRfmV9ujIPHuBdlE6+rQw8HraOVbc07zGO/+6VByVveYAOTnnGXFFKF/sucDfegYQF6rw+5ki5m4yk3JZIH0SAln4Qx5nsnJLnzvr/ptZ+tnmSzqx+M1Vm+gRp7B2by6oNpZsOU9KQgBf7s3lugQ9QRRgKVI5djYHf5PJpUmv8DyjQc8HT4xg1DP/QVVVrki5UeuQhKiWJB+XyNkzMzxxbLkjMzIlg92QPlfVmkB1bBXH85NHsO7nPTQKCUTh4kyK3a7yyXfp9IjO554eUfyRl+MVJ6duP3CUvYeOUZhn4bYTVgw6hSKbnaxCO0F+OgIMCkU2lR/+l1PtnX9dZqmA0mtesqE1uamOP85buOlyI+O6+mMuKmJAgomdp+x8fTCfpSObsP+MlX99/gMoChRms+CdLynIy2beiIhy78fR2YmS5HZHjhmzuZC/JZt4Lq2Ads8dwWyxEGiEbw8WEeSno9BaSHCgGZPfn38OXJD0Cm2UJCB3znuf7aqdK/vepHVIQlRJko9L5OzUuCcG39pmZEoGVbUgq3QPQE0JVMkMxA/b91eaSVmTtosvNvzIX6+NJizYxAM9FK+Ywm/fMpaI8FD8A4qY2rcRoJB5NpsPfyvgglXPmE5GdFgJMxSSGBFa5Z1/XWapTp67UHrNZ319hAKbjp22iwfL7TllwWa3o9fB0LZG1h8sYHFaFuOuDuXmfx/BpIc5AyL462eZdIw1Ob0kV9Kf5Om3vqBTaBZ394hgb9Z5tmeFcn1yEh98tZUIfR5/vzaCf23N4aQ9nGl3D0KvUy456RXaMhr0LJs+nLtSP+RXVeWKa4ZoHZIQlUjycQm8+cwMR2ZkSpKTQW2DeGdrFnd2Kt7LUNsMQMWZFG++Dt/9coBTZ87hrxShKAp9E/w4HKAjJsDEw18UMPs7C1FBehZsziE40FJ891/hzt/RWar2LWOZu3TNxbNYDhbw33PB2O0qSSHZdG8VTubZHAw6laggPVFBMKi1jW8O5HFf97Dik3rj9Fzf2sSnzRXW/a+Qnq9mVr1Xp5bZiZL+JGXPlZnUJ4IxH2WBCkEGK28Nj6VtjInLo/wY81FWtSf6ivrHaNCzbNrtTFm8lsUrljBkyj9o1qqt1mEJUUqSj0vgTOdRrWKr6kj7N1dtondzHbtPFDIwUU8jo7nGyofqZlI8cR2cLWUu2SBr8lNZsuU8fRPjiI4IJ7KRSkqLk2w+YiP1phje/vni3X+AyVjuzt/RWao1abvKXYcHeoTxzbIzFNh0pOcrDFh6jpy8guLeImXkFRXR7rkjKHYbt7c3oQKPpESy7XROtZ1oa5udqC4h7BOvKz2crqpEcWD3tuw/cqpO11l4J4NBz6JJgzmTlcuts2dw3fi5koAIryHJh5M8Va1yKbHVNBNRthJi8/8u8OTNAVgsReTkFVZZ+VDdTErFA+jcdR2cKWXefuAoOw8ex1xkY0K34j0PV/7zGGGBRswWK7n5RYT564kLNTLvxsjSu39nlsOqu+b9Eo2knw/h/0YPxK6q7DyYidVavkGaToFPN2yjR1Q+KW0iCPQ30q2lHwNbm0vPxKnrtasuIax4OJ2qquw5aeH+7iGM/TiTuf/+is82bavTdRbeLTI8mJVPjWToUzO49sFnaH5ZO61DEkKSD2d5qlrFGbXNRKzZsouln/1A7+Y61u3LJbmZnvaRBo7kWEsrHxIj/Ogeq1ZKVirOpCxeudnt18GZUmYo3u/RJDKcpGC1dM/Dbzlh/G30QJ59dx2XRWQxoVc4SbF+/O9sEX2aO58oVXfNH+gR9mdSo2NIcgdu6dOp0nNnv7WWM6dP85cbYggNunjGi7MzR9UlxqoKwbpCrmupZ+3eXMZ2DeOr/XnM+eo0Tw6MokccvL16E1H+Ns2Xy4RrNQ4LupiAPPAMzVu31zok0cBJ8uEkT1SrOMORGZnUf3+JJf8Cd6UEkvq/XDLOqSS/dQGrXSW7UCU40AxAbkEh/oHHSN93pNqZlO+27mHuw7fV2u68Y6s4p5ZOwLlSZije75Gfm80jg8rvefj1wLHis1qGR9I2xsSaPbk8ufY09/cIZ9MvrhvsofbZH4vFyturN9E7XkeQrhBzUQgll8bZmaPqEuPitu0FqKpKfpGVzi8e42yuufTAudBAI2qRhb8OjOT5LdpvFhau1TgsiFWzRzH0qZn0vX828ZfLKcZCO5J8OMlTpaJ1VduMjKqqHDl3ktuSAri+TTCBfjostouD5Vs/ZXNKDafAbEVvttC4URhHT2XVMJNyApvN7tAgtSZtV+nSyaCe7R1KRJwtZa5tz8OQNkYSG/tRYLGzZMt5UG18uTeXHnF+Lhvsy6lm9mfxys0UFBTw20mFwe9kly95reW51akuMS6y2Vjx/a98u3UPN1zdjhaxEXy6/gceTwkgdUM+Or9Arm9lYWTncLYeOyOzHz4oIjSIVU+N4panZtH3/qeIv7yj1iGJBkqSDx9T24zMgaOneX3Ft2w9bueaN05W8RN6Tl44x4W8Qib2bsTa33NYuGz9Je/pqLh0YrOrzH5zda2JiLOlzNUtg4y+MoAV27NYd8BO8uITZOcXleuDseu0Slioawb7sqqaBbPZ7Hy3dQ/JLf0Z16MRb/6UXa7ktabn1qS6xNhms/Pvz9OIDbRxIS+Pnb/nlh44t2JnIduPZzGxTzN0iuIVm6aFezQKDWTVU6MY+tRTpNz3FPFtJAERnifJh4+pbUbGUmSlQ0KTagdKi9XG+AXv0TwUdp8o5PJGejYcPEFhrv8l7ekou3TyzPfHWfDOl2USETuz3/ys0iZHZ0t4a1oG6dAkkEFtg9mVG8bfRg/guffWl+uD8VtOGDPvG+ySwb42637aw+9/HGH+tTp6xxtIbBzp0pLXistcZT+DmV8VH6qXenc0NrtKntlK7+YKcUHF18odpdLOLrsJ12sUGli8BDPrKXrfO4sWbSvvRRLCnST5aGBqGyhnv7UWP7WIGX0DeC7Nwj3dG5GeaWdINSWfJb/T0bLPWzuG8M5/c9h+PJPnh0Qzb8NxUv/9ZZWbSZ0t4XVsGaSQwyfOV9kHw2azX1JLd0dUddhd26ZRLh3wy1YIXX91u9LPYGhSCKt+PcuPR220jvJj3b48zuVbmdbLxOmsC4QG+aMori8Zd6ZiSbhPeEggK2ePYuis2ah/eZKW7a7QOiTRgEjy0cCVvRstKrLx9upNDEzUMyrJn5+P2vj2QB43Xm5yuuQTyicRZe+y+yUa+Xq/ysc7M3n11mjmb6pc2uvMck9VyyAqKkdOnqd5TCMUFPR6HYs//l6zxmhVHXaXk1fosgG/qmWuks8gJ6+w9FC9NXtyWfpzFsnN9HSOM3DonIUz2QWEBfu7tFTa2Yol4V7hIYGsmnMHQ2c9Dfc8Qct2V2odkmgg5P/+Bm7tlt2Mnb2UtVt2k/ruetSiAib2KC73HNfFRMZ5C22jjGQcKx4Q66ri0knJXfa9V/lxOusCN7eGMKNKoJ+udOC32eylsxdpR4tnLyp+pR21kXGieLmnopLZnRuTO5R+KRQPogpKaXJzNDOTB64OLffccVeHOv1e63JNLh52p//zsDs4djaHxIiLA35NFUS1KbvEcvho8TJXSryeNtHFsxtXxOq5NsHAwg3n+OOchbQjNvr9K48RH+XT89XjJL+aWet1djYed19fUTdhwQGsnD2KtH89w6Hd27QORzQQMvPRgJW9G12yciPb9h1hQKKeTrFG7ECbKAN94vVOV4FA5VmPN388T0q8nh7Njfx+xkLzYLihtYE3fzzPnBuiuGdF8cDUv1ubSyplrnisfdm7bk81RqtOVYfdlS1zrqrFe11Ut8w1bnAz8s0WLEVWLCpc11LPit0F2BQjgX764ifrQdEbGTO0H62bRQGuO+CwrhVLwnPCggNYNWcUQ2fNRR0zncQOnbUOSfg4ST4asLJ3o49+9ge2IjO3tgtg76mi0p+5rqWeCWucqwKpuHTy+e5cDp+z8Mw1QZj0CgoqgUZ4qJs/Yz/NJyPLWm7Z41JKmSsea1+2T4gnGqPVpH3LWBo3CiOqkZ1xPcJKH3/jx+xqW7zXRVXLXD2bKkSa7Oj1JqIjwlFViIqAQW3Psys3nMfGXo9eV5wI+BkNpFzZymX7XpytWBKeFRoU8OcekHkwehqJSV20Dkn4MEk+6hlXVQyUvRu9uUMw0744RZeWRrq3aozCxd9bdoAqWwXiSBxlN372fDWTI2fz6Z+gw2K1sf24FVW1Ex2kIzZER3IzfaXZD2cHprIzOiXH2pe9666qMZqqqhw5lUXz6HAUxb0nu373ywEK83JKm5yVSIgwXlKLd6i8zJV+pJBz+TYOn7PR89XjhAcHQLnPSwFDIXGRYW5JtLz50EFRWWhQAKtm38HQp+aj3vUYrZK6ah2S8FGSfNQzrqoYKHs3uv2YGT89pGVY6ffW2cpNrv4coCLDgkrvhh2Jo2Tjp7moiM2/HmTld1vZcdbKmNU28gosGBQ7wSYdimJGAVTFTq5ZveRlj+pKSqH6xmhr0nbx5qqNbq/EcPeZQBVnGTo2MTH/phh+P2Ph+c15VVYtuTPR8ubDF0XVQoL8WfnUKIbOms/xXrdxRa8BhEZEah2W8DGSfNQjrqoYqHg3arGqLLwllld+yKqyyRWUH6AcjaNk4+eatF2s+3EHo2/szVWXN+PA0dMs+vAbVFsRF2wXf15RFB5efR6doji97FH2vVUsKYWq77o9WYnhzjOBqktsUhIDSUkMZPfJokuqWqorbz58UdQsJMifVXPu5OPvt7P4mY+57fFFNIpuonVYwodI8lGPOHvGSXW/p+Ru1M+g0P/yIJqGGRxqclWXOEoGIJ05h/Q9h3h8zAD6d2tTY6MzcHwzaU0dUcuWlK7bl8eN7YKBynfdjrwfVy13ufNMIG877NDb4hF1Exxo4p4br6ZPp0TunD9REhDhUpJ81BOuqhi41LvRusShqipvrE7j8NFjPFFhYHfVZtLqOqK2iTbxv2OnS0tKl2w5z7WtAtHrlHLvs6Typbb346rlrrp0Q61rwuNthx16WzzCOa2aRbHs70O4c/5Ehj3+MhHR8nmJSyfJRz3hqoqBS70brUscn/+wi1lLVpEc78etHUPYeKiQf37wNQO6teHA0dNOzSLUtERSNraKJaUT1hTQ6YWjhAUay73PxSs31/p+tGqQVdeEx9sOO/S2eITzWjWL4v3HbuaO+ZO49bGXaRwjCYi4NJJ81AOurBi4lLvRusRhs9lZ8M6XNPYrIs+sw2ZXuaOjidvf/YN75v6HfX8cc2oWobolkrIzOgkRRnafNNOsURigVFtSqtfrWLyi9i6nrlruqgvpCCq8TWLTSD54/BbumD+JoX9/icaxTbUOSdRj8tesHigZ/OrajVNVVXYfykRVLy6tVNX9s+JX/25tquzxUJc41mzZzbHMTB7t6c+5fCtf7s0jwmimT7zC55u2Q0F2nbt4Vlzyqa4jatLCo/R77QjJr59h0LtZDHo3i/STCoXm4pLSkvdps9k5erzmLqc1vaY71dQRtKrPVQhPSIhrzAfTbmHls49w9sQxrcMR9ZgkH16uqj0aJV9l9y5UNRiWbZ3uyThKZj16N1cYfaWJlHg9r/1wloJCCx1jDAQb7dzXue4t2ysmP2WThJIZnVkPjSAmKpL4RkaiIxvzl2EDmP3wSOZMGMX8icMrVezU9n7WbNlV7Wu6S20Jjys/VyHqqmWTiwnImcxLa7svGi5ZdvFyzu7RcPW0fV3iOH4mh2OZmcwZ4ofJoOO+zibu/DiXLUdVvj1YxMBEPT2a2NiXZXA4NkeWfErKegvzc5g1MJK/rz3Py8u/YeGUEZWWSRx6P/ozLHz/a483yKppX03J6bSyHCO01LJJY5ZPH8rIeZO55f9eJLJJM61DEvWMJB9eztk9Gq7ep+BoHO1bxvL3RZ/Qu7lCbLC+uFW7qpIUpfDXdYWEmRQmdfdHUWHIZfDYd47F5kizqrLHxpd0bdUXFfJGFYO0I+9n2/6jrFi/mQcGRlT7mq7e+1FbklVyOm1dPldXlQkLUVaL2Ag+nD6UEfMe+TMBaa51SKIekeTDyzlTMeCOg7wcjWNN2i6yzp9jwZBImkb6oaKSeSaHOzsZ+eKAlesS9KS0MHAyz45OX0Sf5sZaY3O0PLjssfHr9uXhr1e59yojS37NqDRI1/Z+Sl6zdzO9Rxtk1ZRkjf7w4um0dflcXVUmLERF8bERfDj9VkbMm8yQv75AdNMWWock6gmZr/VBNe2NcKeSAbtXMx2dmoUQ4G+iyArmIitnCyAiQOGOjkZC/RWahepBtTO0ja7W2MpuJk1efKLSV9pRG4czT/PPD4qXSFpH+fHmj+e5LsHAre386Bpj5Y06bhJ15DVdcdR8WbXtQ2kaaOVYZibjqvlcq9qIWnH5zd0bZUXDEx8bwUczhvHZ849y6thhrcMR9YTMfPgYLQ/yqrSPQlXJyi1AUW2cybdzw2UGmgQrZBWqBBoV/A0QZCukZ1O/GmNzaInkwFE++eoHHhhQPOuRcd7CvGuCiAxSuK2tnmkbKs9+QPVLElo0yKppH4qqqhw5W8j1rXTEhxXHWfFzXffTnkozHFqUCYuGp3lMIz5+YhjDn3mUm6YuJLpZS61DEl5Okg8fo+VBXhUH7JIzXLIu5AN2tmXaueuTQgw6UIBCK+QVqQSYLISGVd/YzNElkl7NdLRoZOTxL07RJ17P5VF6AK6I1dM1xlLl3o/qliS0aJBVU8KzdU8GH325ickpoQSYLjZKK/lc12zZzdLPNpfbiAq4fPlNiOo0i27ExzNv4/anpzJ46vPENEvQOiThxST58CFaH+RVccC2FFlp2yKG2W98TiP1LKOvDC79nl1VeXFzNgXWIrLMOl4cP8zpWYSyMwadXjiK2VzI/YMDije7/um2dgZmbDzi0s6lrt7IWV3CUxLndYl+dGoWgtUGUPzZlnyuC975koK87HJt7AGXdMUVwlFNo8L/TED+yuBHnyemuSQgomouTz5SU1P55JNP2Lt3LwEBASQnJ7NgwQLatJE2y+7mbQd5+RkNRIYFYSkyc9Jq5PkfzaXfy84vwmy28rdkf55Ls7D/yCkG9Wzv1OuUzBgUmC0seGcdHYKzuLpV+Zmf6MbQN+OCSzuXemojZ62fq6pyPOsE17YKKJ3heGPVJlBVTZbfRMPWNCqcFTNv5/Zn/sqgyc8RG5+odUjCCymqi9sk3nDDDYwaNYpu3bphtVqZMWMGO3bsYPfu3QQFBTn2Sw6nwbF0V4bVIFiKrGzc/r9a9ymkXNmqyg6mnorJZrez4J11dArNIvWGCKZ/eZ5jujhWpD5YOiA6M6uQvjeDB1PfAau5+h8ymHh92liubN2M4dOX0EJ/knnXhzN9XRaHbTF8NO8BhwZlm83O8OlLyDh8iPgWCQ4/zxm1fa5b92TwnzUb+fiuKDrF+bPnpJnh753GbFP57O7o0uQDYM9JM2M+ymLOw5V7nwjhSplnsrlv4WfkEsjtj/2TgKAQrUMSHjAuxbFk0+Uj0Jdfflnu30uXLiU6Opr09HRSUlJc/XKiDG88yKuqmEoagT0yuDFhwSYm9olgzEcnys08ODOrUJdNoiWzHtMGB3Lo+Fnu6BjIw184PvvhyY2cNX2uJUsyN7Xxp1OcPwBto010jbGy5YiNFo2MHl9+EwKgSWQYa+aNZsvOQ0x9ejx3zHxNEhBRyu23v9nZ2QBERERU+X2z2YzZXP5O1WS2YKryp0V950g1DuDUXgxHk6+SGPo019HIaMZisRLhZ6aPg0sS7uij4qyqNhjnmy3c1s7AtwctlU/yLeHB5TfRsPVMSuCFvyg8OudBRs18ncBgSUCEm/t8qKrK1KlT6d27N0lJSVX+TGpqKmFhYeW+Ul99151hCQ05cjhdTYequTKGOzv5Y7EU0STEgMVSxB0dTQ69nlZ9VCoqSYJ6ximYrSrmIjsWq4peb6DnZY0Z1DaYmKhIZj00gjkTRpX7KnvOjRDu1qNDS168vzfvz3mA/As5WocjvIBbZz4mTpzIb7/9xubNm6v9mWnTpjF16tRyj5lO/AJndrgzNKEBR6pxym6UdMesQumA3VRHkK4QkwJBRh0mgx3UQnrWsiShZR+Viko2ou7IMfOfXzJoHOJfYZZDAUPxSb4ywyG01r1dC14epzDp6Qe5Y+brBIaE1v4k4bPclnxMmjSJ1atXs3HjRpo1q/7QIZPJhMlUYZHlvJ+7whIacqQaJ6/oGKg2UkdHAa4vDy2J4UC+mc93FhDmr8OgU7DaVbILVYID/TEFVr8koWUflYo6topj7sO38fRbXxBkOElwo0geG3s9et3F5MfVjdCEuBTd2sXz8rgUJj39IKNmvkZQSJjWIQmNuDz5UFWVSZMm8emnn/L999+TkCB13qJYbRtCbXaVBe9+SZdGuW6bVSgZsGe/+TmXRWQxrsfFP35v/JjNSXs4s+6/qcoB29E+Ks1jwklKjHP7IW5+RgM2m730FN+5Gy6g1+mkikV4tW7t4nnlwb48POdB7njydUlAGiiXJx8TJkxg2bJlrFq1ipCQEE6cKL7DDQsLIyAgwNUvJ+qR2jaErknbRWFeDg/d6L5ZhZIB21JwgdThkeXKUBMijIz5KAubzV5lKbKjMzcjZrzJ84/c7vYkwJs2vgpRF13aNOfV8dcwYc6fMyCh4VqHJDzM5X0+qrvbW7p0Kffcc49jv0T6fHgldx7NXtI3oxkneG5IVKXv/+2z0xwl9pL7aVzK69TWb6Nk5iY/6xQtWya6tfcHFCdrT766nPeGN6JtjEl6eIh655d9R3j4te8lAfEhmvX5cHEuI7yIOzt6eqo766W8jqMzNzP7N3Z77w9v2vgqhLM6t2nOaw9dy4NPP8ioJ14jOKxR7U8SPkHOdhEOudRzUGrjqVNk3fU6nl4C8aaNr0Jciisvb8aSh/vxwDPjJQFpQCT5EA5xd0dPRxqEuWLZx11dYCsmA+5MArQ+QFAIV7uidVPemNCPcc+MZ+SMxYSEV92UUvgO+cskalXxrr5kat9ms3s0jrVbdjN29lLWbtnt8HNUVWX3oUy3LgfWtgTi6utUsnSUdrR46ajiV9pRGxknipeOhKgvOl3WlDcn9mf5M+O5kHVW63CEm8nMh6iVJ+/qq+Psso8nTp719BKIp5aohPC0jq3ieOuRAdz3zEOMfGIxIeGNtQ5JuIkkH6JG3rKx0ZllH3fvUyn7Gp5cAqm4dOTOKiQhPC0p8WIC0mf0X2mV1BmDoYrziUS9JssuokaOnMXibs4u+7j7jBjwjiUQZ5ajhPBmSYlxvPPoDRi3LePD+ZOxWou0Dkm4mMx8iGp5y8ZGZ5Z9PFV9ovUSiCdmd4TQQpsWMaSOG8iKjTt5OfURRj7+IgajHL3hKyT5ENXyVO+Nmji77OOpfSruqp5xlLurkITQ2m0pSegUhRdTH2HktJckAfERknyIaml9Vw/Obeb0ln0q7ibt1UVDcWufDiiKwgvzJjFq+suSgPgAST5EtbS+q3d22aehNODyhiokITxlaO/2oCAJiI+Q5KOB8+ZKCWeWfbxln4q7NZTZHSHKGtqrPQqwcO5ERk5/GaOfqdbnCO8kyUcD54k+GLWpLgFyZtnHG/apeEJDmd0RoqJberVHURSemzeRUdMXSQJST0ny0YB5S6VEdQmQM8s+3rBPxd0ayuyOENW5ObkdCvCPuRMYNf0VjCZJQOobST4aMG+olHB1AqT1PhVPaCizO0LUZEhyO3Q6hQVzH2bUjFclAalnJPlooLylUsIbEqD6piHM7gjhiME92qIoMF8SkHpHko8GyhsqJbwlAapvGsLsjhCOuvHqtuhQmDv3Ie6YsVgSkHpC/sI3QJ4+hbU6FVu3e7JluxDCd9xwdRueGJrEsmfGYzEXah2OcIAkHw2Qt53XomUCJITwDdd3b8OTwzrx/jMPSQJSD0jy0cBUVSlR8lW2UsLdg783JEBCCN8ysNvlzLrtCt5/ejzmwgKtwxE1kD0fDYw3VEpIqagQwl0GdG2NTlF4bPpYOvS6nl7D7vO6BooCFFVV1dp/zMMOp8GxdK2j8EmWIisbt/+v1kqJlCtb4Wd0T26avjeDB1PfAau5+h8ymHh92lgpFRVCOOVcTh5vrEkn7XQgg8c/KQmIh4xLSXTo5yT5EB7nDQmQN/Dm1vZC+IqFH//A98eM3PTwU/L/mQdI8iE8TgbTulmTtkvz1vZCNAQvfJzGt0f1DJkwW/42uZmjyYcsqAuXWbtlN2NnL2Xtlt1ah+L1KnZ2leoeIdzn0duT6R9v57NXnsQb77cbIkk+hEvIYFo3ZTu7SnWPEO43eVhPBsarrH55piQgXkCSD+ESMpg6rmJnV+ltIoRnTBrWk0GJCqteekISEI1J8iEumQymdSOdXYXQzoShPRh8mZ5VL83Abpe/UVqR5ENcMhlMHSedXYXQ3sO3XM2Q1kZJQDQkyYe4JDKY1o10dhXCO4y/uTu3tDGx6qXpkoBoQJIPcUlkMHWct7S2F0IUe3BIN4a29WflPx+XBMTDfLeDk3A7aZNeN97Q2l4IUd4DN3VDp/yXj194jFsfXYBOJ3+rPEGSD+E0GUzrpmOrOOZPHF5rZ9eOreI8GJUQ4v7BXVGU//LRwr9z69RnJQHxAOlwKpwmbdKFEL7krbXpfPhrNsMefRadXq91OPWStFcXQggh6ujtL9P5YFsWt039hyQgTpD26kIIIUQd3XtDF0Z3jWDp327nh5VLtQ7HZ0nyIYQQQpQxdsBVbHnpPoKO/8TmT9/WOhyfJMmHEEIIUYGiKCyadBOhJ7ayacWbWofjcyT5EEIIIaqgKAovTRxM+Olf2PTxG1qH41Mk+RBCCCGqoSgKL064kYhz29n40RKtw/EZknwIIYQQNVAUhRceGkTk+d/Y+OFrWofjEyT5EEIIIWqhKAoLH7qB6JxdbFi+WOtw6j1JPoQQQggHKIrCcw9eT8yF3Xz/watah1OvSfIhhBBCOKgkAYnL28v377+idTj1liQfQgghRB0oisKzDwykWeF+vlu2SOtw6iVJPoQQQog6UhSF+fcPIN5ygG//85LW4dQ7knwIIYQQTlAUhdT7BtDSepBv3v2n1uHUK5J8CCGEEE4qSUBaqYf5+t0XtA6n3pDkQwghhLhEc+/tz+Vk8PU7C7UOpV6Q5EMIIYRwgaf/0p+2uqN88fpcMv/4XetwvJokH0IIIYSLzL6nH3e21/HjWzPY+cM6rcPxWpJ8CCGEEC404tpOrJxzJ0e+fYcdm7/UOhyvJMmHEEII4WJ+RgPLZ47k6Hfv8tumtVqH43Uk+RBCCCHcwGjQs3zmSDI3LuPXjWu0DserSPIhhBBCuInRoOf9GcM5ufl9ft3whdbheA1JPoQQQgg3Mhr0LJs+nJM/fMCv33+mdTheQZIPIYQQws2MBj3vTx/O6S0fsf271VqHozm3JR+vvvoqCQkJ+Pv706VLFzZt2uSulxJCCCG8nsGg5z/TbufMjx+z/duGnYC4JflYvnw5U6ZMYcaMGWzbto0+ffowaNAgMjIy3PFyQgghRL1QkoCc/XkFv3yzUutwNKOoqqq6+pdeffXVdO7cmcWLF5c+1q5dO4YOHUpqamrtv+BwGhxLd3VYQgghhFewWm2MXfAJYV1voXO/W7UOx2XGpSQ69HMun/mwWCykp6czcODAco8PHDiQtLS0Sj9vNpvJyckp92U2W1wdlhBCCOE1DAY97z5+Gznpq/jl60+0DsfjDK7+hWfOnMFmsxETE1Pu8ZiYGE6cOFHp51NTU5k9e3a5x/r27cu6deswmUyuDk+UYTabSU1NZdq0aXKt3Uius2fIdfYMuc6uowf+02dyld/z9evs8mWX48eP07RpU9LS0ujZs2fp43PnzuXdd99l79695X7ebDZjNptL/52Tk0Pz5s3Jzs4mNDTUlaGJCnJycggLC5Nr7WZynT1DrrNnyHX2DF+/zi6f+YiMjESv11ea5Th16lSl2RAAk8nkk1mdEEIIIarm8j0ffn5+dOnShfXr15d7fP369SQnJ7v65YQQQghRz7h85gNg6tSpjBkzhq5du9KzZ0+WLFlCRkYG48ePd8fLCSGEEKIecUvyMXLkSM6ePcucOXPIzMwkKSmJNWvW0KJFi1qfazKZmDVrlizFeIBca8+Q6+wZcp09Q66zZ/j6dXZLnw8hhBBCiOrI2S5CCCGE8ChJPoQQQgjhUZJ8CCGEEMKjJPkQQgghhEd5XfLx6quvkpCQgL+/P126dGHTpk1ah+RTUlNT6datGyEhIURHRzN06FD27dundVg+LzU1FUVRmDJlitah+KRjx44xevRoGjduTGBgIFdeeSXp6XI4pStZrVaeeOIJEhISCAgIIDExkTlz5mC327UOrV7buHEjQ4YMIS4uDkVRWLlyZbnvq6rKU089RVxcHAEBAVxzzTXs2rVLm2BdyKuSj+XLlzNlyhRmzJjBtm3b6NOnD4MGDSIjI0Pr0HzGhg0bmDBhAj/++CPr16/HarUycOBA8vLytA7NZ23dupUlS5bQqVMnrUPxSefPn6dXr14YjUbWrl3L7t27ef755wkPD9c6NJ+yYMECXnvtNRYtWsSePXt49tln+cc//sHLL7+sdWj1Wl5eHldccQWLFi2q8vvPPvssCxcuZNGiRWzdupXY2FgGDBjAhQsXPBypi6lepHv37ur48ePLPda2bVv18ccf1ygi33fq1CkVUDds2KB1KD7pwoULauvWrdX169erffv2VSdPnqx1SD7nscceU3v37q11GD5v8ODB6r333lvusWHDhqmjR4/WKCLfA6iffvpp6b/tdrsaGxurzp8/v/SxwsJCNSwsTH3ttdc0iNB1vGbmw2KxkJ6ezsCBA8s9PnDgQNLS0jSKyvdlZ2cDEBERoXEkvmnChAkMHjyY/v37ax2Kz1q9ejVdu3Zl+PDhREdHc9VVV/HGG29oHZbP6d27N9988w379+8H4Ndff2Xz5s3ceOONGkfmuw4dOsSJEyfKjYsmk4m+ffvW+3HRLR1OnXHmzBlsNlulw+diYmIqHVInXENVVaZOnUrv3r1JSkrSOhyf88EHH/DLL7+wdetWrUPxaQcPHmTx4sVMnTqV6dOn8/PPP/PII49gMpkYO3as1uH5jMcee4zs7Gzatm2LXq/HZrMxd+5c7rjjDq1D81klY19V4+Lhw4e1CMllvCb5KKEoSrl/q6pa6THhGhMnTuS3335j8+bNWofic44cOcLkyZP56quv8Pf31zocn2a32+natSvz5s0D4KqrrmLXrl0sXrxYkg8XWr58Oe+99x7Lli2jQ4cObN++nSlTphAXF8fdd9+tdXg+zRfHRa9JPiIjI9Hr9ZVmOU6dOlUp6xOXbtKkSaxevZqNGzfSrFkzrcPxOenp6Zw6dYouXbqUPmaz2di4cSOLFi3CbDaj1+s1jNB3NGnShPbt25d7rF27dqxYsUKjiHzT//3f//H4448zatQoADp27Mjhw4dJTU2V5MNNYmNjgeIZkCZNmpQ+7gvjotfs+fDz86NLly6sX7++3OPr168nOTlZo6h8j6qqTJw4kU8++YRvv/2WhIQErUPySf369WPHjh1s37699Ktr167cddddbN++XRIPF+rVq1elcvH9+/c7dJClcFx+fj46XfkhQ6/XS6mtGyUkJBAbG1tuXLRYLGzYsKHej4teM/MBMHXqVMaMGUPXrl3p2bMnS5YsISMjg/Hjx2sdms+YMGECy5YtY9WqVYSEhJTONIWFhREQEKBxdL4jJCSk0j6aoKAgGjduLPtrXOzRRx8lOTmZefPmMWLECH7++WeWLFnCkiVLtA7NpwwZMoS5c+cSHx9Phw4d2LZtGwsXLuTee+/VOrR6LTc3l99//73034cOHWL79u1EREQQHx/PlClTmDdvHq1bt6Z169bMmzePwMBA7rzzTg2jdgFti20qe+WVV9QWLVqofn5+aufOnaUE1MWAKr+WLl2qdWg+T0pt3eezzz5Tk5KSVJPJpLZt21ZdsmSJ1iH5nJycHHXy5MlqfHy86u/vryYmJqozZsxQzWaz1qHVa999912Vf5PvvvtuVVWLy21nzZqlxsbGqiaTSU1JSVF37NihbdAuoKiqqmqU9wghhBCiAfKaPR9CCCGEaBgk+RBCCCGER0nyIYQQQgiPkuRDCCGEEB4lyYcQQgghPEqSDyGEEEJ4lCQfQgghhPAoST6EEEII4VGSfAghhBDCoyT5EEIIIYRHSfIhhBBCCI+S5EMIIYQQHvX/AsNL1Yy/R7IAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_regions(x,y, clf = model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6cfe75b",
   "metadata": {},
   "source": [
    "## **Example 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b6db91b",
   "metadata": {},
   "outputs": [],
   "source": [
    "1- read data\n",
    "2- explore data\n",
    "3- data preprocessing:\n",
    "    - split data x,y (class)\n",
    "    - encoding\n",
    "    - split data into train, test\n",
    "4- logistic regression\n",
    "5- evaluate logistic regression\n",
    "6- svm model\n",
    "7- evaluate svm\n",
    "8- grid search on svm (kernel, c, gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7af7fc0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('mushrooms.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c9ecd507",
   "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>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "0            c         n          k  ...                        s   \n",
       "1            c         b          k  ...                        s   \n",
       "2            c         b          n  ...                        s   \n",
       "3            c         n          n  ...                        s   \n",
       "4            w         b          k  ...                        s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "1           o         p                 n          n       g  \n",
       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d01e663a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8124 entries, 0 to 8123\n",
      "Data columns (total 23 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   class                     8124 non-null   object\n",
      " 1   cap-shape                 8124 non-null   object\n",
      " 2   cap-surface               8124 non-null   object\n",
      " 3   cap-color                 8124 non-null   object\n",
      " 4   bruises                   8124 non-null   object\n",
      " 5   odor                      8124 non-null   object\n",
      " 6   gill-attachment           8124 non-null   object\n",
      " 7   gill-spacing              8124 non-null   object\n",
      " 8   gill-size                 8124 non-null   object\n",
      " 9   gill-color                8124 non-null   object\n",
      " 10  stalk-shape               8124 non-null   object\n",
      " 11  stalk-root                8124 non-null   object\n",
      " 12  stalk-surface-above-ring  8124 non-null   object\n",
      " 13  stalk-surface-below-ring  8124 non-null   object\n",
      " 14  stalk-color-above-ring    8124 non-null   object\n",
      " 15  stalk-color-below-ring    8124 non-null   object\n",
      " 16  veil-type                 8124 non-null   object\n",
      " 17  veil-color                8124 non-null   object\n",
      " 18  ring-number               8124 non-null   object\n",
      " 19  ring-type                 8124 non-null   object\n",
      " 20  spore-print-color         8124 non-null   object\n",
      " 21  population                8124 non-null   object\n",
      " 22  habitat                   8124 non-null   object\n",
      "dtypes: object(23)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "017e3ce4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class                       0\n",
       "cap-shape                   0\n",
       "cap-surface                 0\n",
       "cap-color                   0\n",
       "bruises                     0\n",
       "odor                        0\n",
       "gill-attachment             0\n",
       "gill-spacing                0\n",
       "gill-size                   0\n",
       "gill-color                  0\n",
       "stalk-shape                 0\n",
       "stalk-root                  0\n",
       "stalk-surface-above-ring    0\n",
       "stalk-surface-below-ring    0\n",
       "stalk-color-above-ring      0\n",
       "stalk-color-below-ring      0\n",
       "veil-type                   0\n",
       "veil-color                  0\n",
       "ring-number                 0\n",
       "ring-type                   0\n",
       "spore-print-color           0\n",
       "population                  0\n",
       "habitat                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "71493ec3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dfd21c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "e    4208\n",
       "p    3916\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b75bcae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split data into features and label\n",
    "x = df.drop(['class'], axis = 1) #features\n",
    "y = df['class'] #label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ca12f1e1",
   "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>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>stalk-shape</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>k</td>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>t</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  cap-shape cap-surface cap-color bruises odor gill-attachment gill-spacing  \\\n",
       "0         x           s         n       t    p               f            c   \n",
       "1         x           s         y       t    a               f            c   \n",
       "2         b           s         w       t    l               f            c   \n",
       "3         x           y         w       t    p               f            c   \n",
       "4         x           s         g       f    n               f            w   \n",
       "\n",
       "  gill-size gill-color stalk-shape  ... stalk-surface-below-ring  \\\n",
       "0         n          k           e  ...                        s   \n",
       "1         b          k           e  ...                        s   \n",
       "2         b          n           e  ...                        s   \n",
       "3         n          n           e  ...                        s   \n",
       "4         b          k           t  ...                        s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "1           o         p                 n          n       g  \n",
       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d97d1b7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    p\n",
       "1    e\n",
       "2    e\n",
       "3    p\n",
       "4    e\n",
       "Name: class, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "eb7de70a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1b09ca68",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6bc27e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fit LabelEncoder on y\n",
    "y = encoder.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a850ee36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 1, 0])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7d0156fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',\n",
       "       'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',\n",
       "       'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',\n",
       "       'stalk-surface-below-ring', 'stalk-color-above-ring',\n",
       "       'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',\n",
       "       'ring-type', 'spore-print-color', 'population', 'habitat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3516d64f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fit LabelEncoder on x\n",
    "for col in x.columns:\n",
    "    x[col] = encoder.fit_transform(x[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e6864e42",
   "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>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>stalk-shape</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cap-shape  cap-surface  cap-color  bruises  odor  gill-attachment  \\\n",
       "0          5            2          4        1     6                1   \n",
       "1          5            2          9        1     0                1   \n",
       "2          0            2          8        1     3                1   \n",
       "3          5            3          8        1     6                1   \n",
       "4          5            2          3        0     5                1   \n",
       "\n",
       "   gill-spacing  gill-size  gill-color  stalk-shape  ...  \\\n",
       "0             0          1           4            0  ...   \n",
       "1             0          0           4            0  ...   \n",
       "2             0          0           5            0  ...   \n",
       "3             0          1           5            0  ...   \n",
       "4             1          0           4            1  ...   \n",
       "\n",
       "   stalk-surface-below-ring  stalk-color-above-ring  stalk-color-below-ring  \\\n",
       "0                         2                       7                       7   \n",
       "1                         2                       7                       7   \n",
       "2                         2                       7                       7   \n",
       "3                         2                       7                       7   \n",
       "4                         2                       7                       7   \n",
       "\n",
       "   veil-type  veil-color  ring-number  ring-type  spore-print-color  \\\n",
       "0          0           2            1          4                  2   \n",
       "1          0           2            1          4                  3   \n",
       "2          0           2            1          4                  3   \n",
       "3          0           2            1          4                  2   \n",
       "4          0           2            1          0                  3   \n",
       "\n",
       "   population  habitat  \n",
       "0           3        5  \n",
       "1           2        1  \n",
       "2           2        3  \n",
       "3           3        5  \n",
       "4           0        1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "85b2d24b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into train, test\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.3, shuffle = True,\n",
    "                                                   random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ce5f063b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape (5686, 22)\n",
      "y_train shape (5686,)\n"
     ]
    }
   ],
   "source": [
    "print('x_train shape', x_train.shape)\n",
    "print('y_train shape', y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f0fe2454",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_test shape (2438, 22)\n",
      "y_test shape (2438,)\n"
     ]
    }
   ],
   "source": [
    "print('x_test shape', x_test.shape)\n",
    "print('y_test shape', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c47406a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "148e5cc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b2a52914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit LogisticRegression on train data (x_train, y_train)\n",
    "lr_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "beee1fee",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_pred = lr_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cddac654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 0, 1])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f7dfe916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 1, 0, 1])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1fa23d5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9499589827727646"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, lr_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "415d51e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9864643150123051"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model = SVC()\n",
    "#fit svm_model on train data (x_train, y_train)\n",
    "svm_model.fit(x_train, y_train)\n",
    "#predict\n",
    "svm_pred = svm_model.predict(x_test)\n",
    "accuracy_score(y_test, svm_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d52c7e2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c82c8226",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'kernel' : ['linear', 'poly', 'rbf'],\n",
    "    'C' : [0.1, 0.01, 1],\n",
    "    'gamma' : [0.1, 0.01, 1]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c94a1ec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_model = GridSearchCV(svm_model, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d6c22b6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 0.01, 1], &#x27;gamma&#x27;: [0.1, 0.01, 1],\n",
       "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 0.01, 1], &#x27;gamma&#x27;: [0.1, 0.01, 1],\n",
       "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 0.01, 1], 'gamma': [0.1, 0.01, 1],\n",
       "                         'kernel': ['linear', 'poly', 'rbf']})"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8b665b81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.1, gamma=0.1, kernel=&#x27;poly&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.1, gamma=0.1, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=0.1, gamma=0.1, kernel='poly')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "61e41253",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(C = 0.1, gamma = 0.1, kernel = 'poly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9e6ed85e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.1, gamma=0.1, kernel=&#x27;poly&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.1, gamma=0.1, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=0.1, gamma=0.1, kernel='poly')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1db54162",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5d41cc76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e67c9260",
   "metadata": {},
   "outputs": [],
   "source": [
    "1- decision tree\n",
    "2- random forest \n",
    "3- xgboost "
   ]
  }
 ],
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