07_regression.ipynb

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   "source": [
    "# IGNORE THIS CELL WHICH CUSTOMIZES LAYOUT AND STYLING OF THE NOTEBOOK !\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
    "warnings.filterwarnings = lambda *a, **kw: None\n",
    "from IPython.core.display import HTML; HTML(open(\"custom.html\", \"r\").read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 7: Regression\n",
    "\n",
    "Regression belongs like classification to the field of supervised learning. \n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<i class=\"fa fa-info-circle\"></i>&nbsp; \n",
    "<strong>Regression predicts numerical values</strong> \n",
    "in contrast to classification which predicts categories.\n",
    "</div>\n",
    "\n",
    "<img src=\"./images/30416v.jpg\" title=\"made at imgflip.com\" width=35%/>\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<i class=\"fa fa-info-circle\"></i>&nbsp; \n",
    "    Other differences are:\n",
    "\n",
    "* Accuracy is measured differently\n",
    "\n",
    "\n",
    "* Other algorithms\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Salmon weight\n",
    "\n",
    "The dataset `data/salmon.csv` holds measurements of `circumference`, `length` and `weight` for  `atlantic` and `sockeye` salmons.\n",
    "\n",
    "Our goal is to predict `weight` based on the other three features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>circumference</th>\n",
       "      <th>length</th>\n",
       "      <th>kind</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>sockeye</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>sockeye</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>atlantic</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.5</td>\n",
       "      <td>89.0</td>\n",
       "      <td>atlantic</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>atlantic</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   circumference  length      kind  weight\n",
       "0           19.0    59.5   sockeye     5.1\n",
       "1           18.0    53.0   sockeye     4.1\n",
       "2           28.0    75.5  atlantic     9.1\n",
       "3           33.5    89.0  atlantic    15.6\n",
       "4           23.5    63.0  atlantic     5.2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"data/salmon.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>circumference</th>\n",
       "      <th>length</th>\n",
       "      <th>kind</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>24.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>atlantic</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>18.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>sockeye</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>18.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>sockeye</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>20.0</td>\n",
       "      <td>64.5</td>\n",
       "      <td>atlantic</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>23.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>sockeye</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    circumference  length      kind  weight\n",
       "95           24.0    76.0  atlantic     6.7\n",
       "96           18.5    67.0   sockeye     5.0\n",
       "97           18.0    59.5   sockeye     4.7\n",
       "98           20.0    64.5  atlantic     4.1\n",
       "99           23.0    75.0   sockeye     7.2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us inspect the features and their distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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yKeBfzP/bfuTIEc2bN0933HGHTNNUr169fLZbu3atdu3apcLCQk2dOtXzfI8ePbRw4UJJ8hrw2rdvn4qLi5WVlaUf/ehHnudtNpuWLFkiq9WqP/3pT2H6qQDEI6e9KrB2NZVh7gkAAAASFZkU8C3mB8ieeOIJrVq1SsOGDdPrr7+uQYMG+Wz30UcfSZIuv/zyZtdGjRqlnj17atOmTbLb7ZKkf/7zn3K5XPrmN78po8moef/+/XXhhRfq4MGD2rt3b4h/IgDxyprWPbB2qRlh7gkAAAASFZkU8C3mB8gGDRqk//zP/9Qbb7yhvLw8v+3cA1lDhgzxeT0nJ0emaerzzz/3ap+bm+v3fSVp9+7dbe47gMThMp1Kyy8KqG1afhH1HgAAABByZFLAv06R7kB7zZ07N6B25eXlkqTevXv7vO5+/vjx417tMzMzA2rfkhUrVmjlypUB9XPHjh0BtQMQWyyGVbacAiX3z22xKGpy/1yvI7sTGd+dABA8vjsBtIRMCvgX8wNkgaqtrZUkpaSk+Lzuft5d5D/Y9i05ePCgNmzYEFyHAcQdl2mq74z5fo/VTu6fq74z5stlmhREFd+dANAWfHcCaA2ZFPAtYQbI3HXELBaLz+sul8vrn8G2b8mAAQNUWFgYUD937Nih6urqgNoCiC0Ww5CRnKoBcx6Ro7RE9pJiOWsqZU3NUFp+kWw5+QSRRvjuBIDg8d0JoDVkUsC3hBkgs9lskqRTp075vF5XV+fVLtD2Xbp0afW9p02bpmnTpgXUz9mzZzPrB3Qwl+mUxbD6fRxK7qDRJfsir2Xr7voOBJFz+O4EgODx3QlEj47MmMEikwLNJcwAWWZmpnbs2KHjx4/r/PPPb3b92LFjks7VFnPXHvNXY8zd3l+NMgDRzz0zVlu2vWHmzF4la1r3szNnBWGdOWsajgghAAAA8SGSGTNYZFLgnIQZIMvNzdUHH3ygvXv36utf/7rXNZfLpX379slqtXoGz9ynV7pPs2zKfdqlv1MxAUQ3l2nKrKvxWXvBvv1DT+0FIzmVoAAAAICAkDGB2JUwv5Hjxo2TJK1evbrZtc2bN6uiokKjRo1SWlqaV/s1a9bIbHK07aFDh7Rjxw4NGDBAgwcPDnPPAYSDxTD8FiaVpLpDe3TktSUEFwAAAASMjAnEroRZQVZYWKjc3FytXbtWy5cv1/Tp0yVJFRUVWrRokSRpzpw5nvYDBw7UuHHj9NFHH+nJJ5/U3XffLanh1MoHHnhATqfTqz2A2OEynaot297i0dZSQ4BxlJaoS/YwQgxi3lX3rmrza996fGoIewIAQHwiYwKxLWEGyAzD0NKlS3XjjTdqwYIFevPNN5WZmakNGzboq6++0vTp0zVhwgSv1yxcuFAzZ87U73//e61Zs0Y5OTnavHmzjh07pvHjx2vmzJkR+mkAtIfFsMpeUhxQW3tJsVfhUgAAAMAXMiYQ2xJquDo/P19vvPGGJk2apLKyMq1du1b9+/fXokWL9NBDDzVrP3DgQL3xxhuaNm2aKioqVFxcrG7duunee+/VM888o06dEmZ8EYg7TntVYO1qKsPcEwAAAMQLMiYQu+JuhGfZsmUtXh88eLCeeuqpgO/Xr18/Pfzww+3tFoAoY03rHli71Iww9wQAAADxgowJxK6EWkEGAFJDfYi0/KKA2qblF8nV5KAOAAAAoCkyJhDbGCADkHAshlW2nAIl989tsV1y/1zZcvIpngoAAIBWkTGB2MZvJICE5DJN9Z0x32+ASe6fq74z5rd5Zs9lOlt8DAAAgPgT7ozp/V7kTSCU4q4GGQAEwmIYMpJTNWDOI3KUlsheUixnTaWsqRlKyy+SLSdfLtMMembP/Zrasu0N97RXyZrW/ew9C9p0TwAAAMSGcGXMxsibQHgwQAYgLrhMpyyG1e9jX9zBoUv2RV7HbLtn9NoyOGbW1ejIa0tUd2iP1zX79g89M4ZGciqhBQAAIMLakh8DEeqM2Rh5EwgfBsgAxLRQzKA1DUJtDRMWw/AZVtzqDu3RkdeWaMCcR9p0fwAAALRfR63AClXGbHoP8iYQHgyQAYhZ0TSD5jKdqi3b7jesuNUd2iNHaYm6ZA9jVg8AAKCDRVN+DBZ5EwgvflsAxKxAZ9A6IhhYDKvsJcUBtbWXFBNWAAAAIiCa8mOwyJtAePEbAyAmuUynHKVbA55BC8VJQa1x1nwVYLvKMPcEAAAATUVjfgyW014VWDvyJhA0BsgAxKRomEFrepR25tU/UfdvXCOjS1qLr7OmZoS8LwAAAGhZR+XHphmx6eP2sKZ1D6wdeRMIGjXIAMSsSM2g+S3sOrxIPYpmKb1gog6/ulhnKg/7fH1afhHHbwMAAERAOPNjuIv/u0yn0vKLZN/+YattyZtA8BggAxCzIjGDFlBh1+n3qd/MBTr44jyZtXavNsn9c72O+wYAAEDHCVd+7Iji/xbDKltOgZL757a4TZS8CbQNw8kAYpJ7Bi0Q7hm0UAiosOvyh5WU0UddR07yuuYORtFYzwIAACDehTM/dlTxf5dpqu+M+Urun+vzOnkTaDtWkAGISZGYQQv2aO1uhd9R3eG9strcS+vzWeoOAAAQIeHKj8FmxC7Zw9qxisyQkZyqAXMekaO0pGErZ02lrKkZ5E2gnRggAxCz3DNo/mbrGs+ghSIkBFvY1Tb1TvWb+aCnrw33IKwAAABESjjyY9AZsZ2Tt+5+dcm+yOte5E2gfRggAxCzIjGD1tbCrgQVAACAyAtXfozE4VEWw9rkMXkTaA8GyADEtI6eQeNobQAAgNgWjvxIRgRiH0PMAOJCR8ygRepgAAAAAIReqPIjGRGIDwyQAUCAGhd2bYm7sCvL3AEAAOIfGRGID/xmAkAQOFobAAAATZERgdhHDTIACAJHawMAAKApMiIQ+xggA4AgcbQ2AAAAmiIjArGN31AAaKOWCru6TKfXtaaPAQAAEB1Cnds64vAoAKHHCjIACCH30vnasu0NS+vtVbKmdT+7tL6ApfUAAABRgtwGoDEGyAAgRFymKbOuRkdeW6K6Q3u8rtm3f+gpzmokpxK2AAAAIojcBqApftMBIEQshuEzZLnVHdqjI68tIWQBAABEGLkNQFP8tgNACLhMpxylW/2GLLe6Q3vkKC3hiG8AAIAIIbcB8IUBMgBRI5YL21sMq+wlxQG1tZcUMxsJAADiWjTnOnIbAF+oQQYg4uKlQKrTXhVYu5rKMPcEAAAgMmIl15HbADTFABmAiIqnAqnWtO6BtUvNaPG6y3R6HQ/e9DEAAEA0iqVcF6rcFg5kQSAyovu/NgHEvXgpkOoynUrLLwqobVp+kc9aFu7nasu2q3zVkzr88iKVr3pStWXbva4DAABEo1jJdaHIbeFAFgQiixVkACLGZTpVW7Y94AKpXbKHRTxQ+WMxrLLlFCi5f26LP09y/1zZcvKbPR9LM64AAABNxVKua29uCweyIBB5/GYBiJiOKpDaUUViXaapvjPmK7l/rs/r7mDja/YvVmZcAQAAfOmIXBfKTNee3BYOZEEg8hJuBVl9fb2WLVumv/zlL/riiy+Unp6ugoICzZ07VyNGjGjWvrS0VE8//bQ2bdqkqqoqZWVl6brrrtOsWbNk8OUEtFs4C6R2dJFYi2HISE7VgDmPyFFa0vCeNZWypmacfc98n+8ZSzOuAAAA/oQr14Uj07U1t4UDWRCIDgk1QHb69GndfPPN2rBhg5KSkpSfn68uXbro448/VnFxsR588EHNmDHD037nzp26/vrrZbfbNXLkSA0fPlyffPKJFi9erC1btuixxx6L4E8DxIdwFUiN1DJ19726ZF/ktSTfPfvo672CnXHtqKX+AAAAwQhHrgtnpmtLbgsHsiAQHRJq2Pm3v/2tNmzYoAEDBmjFihV65ZVX9MILL+idd97R4MGD9ctf/lI7d+6UJLlcLs2bN092u12PPvqoXn31VT3zzDN69913lZeXp7feekvvvvtuhH8iILaFs0BqpJepNz1pqLX34ahxAAAQy8KV6zoi0wWb28KBLAhEXkINkL355puSpMWLF2vIkCGe5/v27avFixfL6XTq6aefliStXbtWu3btUmFhoaZOnepp26NHDy1cuFCStGzZsg7sPRB/GhdIbYm7QGqgYcVlOuUo3RrwMvVoOBEomo8aBwAAaE04cl0sZrq2IgsCkZcwA2QVFRU6duyYUlJSNHbs2GbX8/PzZbPZtHbtWjmdTn300UeSpMsvv7xZ21GjRqlnz57atGmT7HZ72PsOxLNwFEjtqOL/oRKtR40DAAAEI9S5LtYyXVuRBYHokDA1yMyzXyJdunTxWVzfYrHIYrHI4XDo0KFD2rt3ryR5rTRrLCcnRydOnNDnn3+ugoKC8HUciHPhKpAaS8vUo/GocQAAgGCFI9fFUqZrK7IgEB0SZoCsR48e6tatmyorK/X555/r/PPP97q+c+dO1dTUSJIqKytVXl4uSerdu7fP+7mfP378eKvvvWLFCq1cuTKgfu7YsSOgdkCgnKYpa6MQ0vRxNAhHgdRIL1N3mU6vehZNHzdv3zDj6q/GRuMZ11idHQ0W350AEDy+O+FLR+bBUOe6SGe6UGopH5IFgchLmAEywzD03e9+V8uWLdPPf/5z/eY3v1GfPn0kNQxyPfjgg562p0+fVm1trSQpJSXF5/3czzscjlbf++DBg9qwYUN7fwQgKE7TJath0ba9x7X60wOqrK5TRnqyJl4yUCOGZHquR5NQFUh1L1O3b/+w1bbuZeqhChptPYY8mo4ajxZ8dwJA8PjuRGORzIOhyHWRzHShFGg+JAsCkZUwA2SS9JOf/ESbNm3Stm3bNHnyZI0YMUKGYWjr1q3q3bu3xowZo/Xr16tTqASYwQAAIABJREFUp06ebZgWi++/MFwul9c/WzJgwAAVFhYG1McdO3aouro6wJ8I8M1puuQ4Va9Fz63Xrv3ey82LN3+pvKwMLbx1jGwpSVE3SBYKkVqm3t5jyKPlqPFowXcnAASP7064xUMejIeth23Jh2RBIDISaoAsLS1NL7/8sv7whz/o7bff1qeffqrMzEzNmDFDP/rRj3TnnXdKktLT02Wz2SRJp06d8nmvuro6SfK0a8m0adM0bdq0gPo4e/ZsZv3QblbD4jMMue3aX6lFz63XY3eN7+CedZxILFMP9BjyAXMeaeU+kT9qPBrw3QkAweO7E27xkgdjfethW/IhWRCIjIQaIJMaBrTuvvtu3X333c2u7du3TxaLRf369VNmZqZ27Nih48ePN6tXJknHjh2T5L9GGRApTtPUtr3H/YYht137K7Vl9zENH9wramcN26Ojl6m7TKdqy7YHfAx5l+xhhB0AABAW8ZQHY3nrIfkQiC0JNUC2Z88eHTx4UJdeeqk6d+7sda2srEyHDx/W+eefL5vNptzcXH3wwQfau3evvv71r3u1dblc2rdvn6xWq8/BMyCSrIah1Z8eCKjtmo37NWJI6Ad5gy1QHy4duUw92GPIo3UbAAAAiH2B5sF0W5Jqak/LkCkp8tnNn1jdekg+BGJLdH6ThMnvfvc73Xbbbdq4cWOza6+99pokafLkyZKkcePGSZJWr17drO3mzZtVUVGhUaNGKS0tLYw9BtqmsroupO0C5Q4ptWXbVb7qSR1+eZHKVz2p2rLtXtc7WkctU0+EY8gBAEBsaC3n9euVqifuHq9vFAyIuuzmTyxuPSQfArEjoVaQTZw4UX/961/15JNPasSIEZ76Yf/4xz/00ksvqWvXrrrpppskSYWFhcrNzdXatWu1fPlyTZ8+XZJUUVGhRYsWSZLmzJkTkZ8DaE1GenJI2wWivQXq40E8HUMOAABiW0s5L92WpF/dNkYZyaYOvviLhM1uHYF8CMSOhPqm+/a3v62ioiJt2bJF3/rWt3T77bfr+9//vv7jP/5DSUlJevrpp9W1a1dJkmEYWrp0qWw2mxYsWKDp06fr9ttv1+TJk7Vr1y5Nnz5dEyZMiPBPBDTnNE1NvGRgQG0njM6S02z9JNZABFqANF4DlvsY8kC4jyEHAAAIh9by4JRLc5TZI03ly5cmbHbrCORDILYk3Lfdk08+qR/96Efq3LmziouLdeLECV199dVasWKFxowZ49U2Pz9fb7zxhiZNmqSysjKtXbtW/fv316JFi/TQQw9F5gcAWmE1DI0Ykqm8rJZnofKyMjRiSO+QFGR1mU45SrcGXIA0Hv/yb3wMeUvcx5ATNgEAQLi0lAcNw6Irx2apZl9iZ7eOQD4EYktCbbGUpJSUFL+nWPoyePBgPfXUU2HuFRBaTtOlhbeO8Xu0d15WhhbeOkZO0xWSATIKkDaI9WPIAQBA/PCXB7P6pKtn91SVf1Ac0H3iObt1BPIhEDsSboAMSARWwyJbSpIeu2u8tuw+pjUb96uyuk4Z6cmaMDpLI4b0DtngmBsFSGP7GHIAABBf/OXBYYN6SiK7dRTyIRA7GCAD4pR78Gv44J4aMaS353l3zbFQDo5JFCB1i9VjyAEAQPzxlwclsltHIh8CsYHfRCDOWZv8hdt4YMzZpJ5E08eBogBpc7F4DDkAAIhPTfMg2S0yyIdAdGMFGZCA3Nsrt+09rtWfHvBsv5x4yUCNGJIZ9PbLxgVIWyr26i5ACgAAgMghuwFAcwyQAQnGabrkOFXvs4B/8eYvPQX8bSlJQQ2SUYAUAAAgNjhNlywuU5nXzVf562Q3AJAYIAMSjtWw+D3dUpJ27a/UoufW67G7xgd1XwqQAgAAxAarYdEj/98mzfnOhQ3Zbd9W2bd9cC67Db9MtkEFZDcACYUBMiCBOE1T2/Ye9zs45rZrf6W27D6m4YN7BbnVkgKkAAAA0cydB9eWHFbJ3uOaMjZHU8YOVubUAk+b45U1+vPfdmjEkN66cFDPkB/uBADRiAEyIEq4TKdX4c6mj0PBahha/emBgNqu2bi/2WlHgYqnAqQd8ecCAAASW0fmjcZ5sNpRr+Wrd+vN9/coq0+6bCmd5Dh1RvuPVss0XTpW5dDwwb3C0o9oQM4D0BgDZECEuZeu15Ztb9iWaK+SNa372W2JoV/a/pW9LqTt4lVH/7kAAIDEE6m8UVntnfNM06UvDp9stV28IOcB8IUBMiCCXKYps67GZ2F7+/YPPcVRjeTUNv8l3XQm7Gc3jNRfPizVO+tKVe2ob9Y+3ZakKZfm6Hvjc1q8TzzriD8XAACQ2NqbN9qz+ikjPTmk7WIJOQ+AP/zGAxFkMQy/pz5KUt2hPTry2pI2/eXsrvtVW7Zd5aue1OGXF6l81ZOyHt2l2VOG6r9+Ml79eqV6vaZfr1Q9cfd4zZ4yVMbRXV6vqy3b7nXfeBbOPxcAAACp7XnDX8YLNKs5TVMTLxkYUB8njM6S03QF1DZWkPMA+MMKMiBCXKZTtWXb/f7l7FZ3aI8cpSXqkj0s4L+oA5kZy5x+vxbPHaO7n/hQ1Y56pduS9Kvbxigj2dTBF3+RsDNq4fxzAQAAkNqeN0Kx+slqGBoxJFN5WRktHtyUl5XR5nq00YqcB6Al/LYDEWIxrLKXFAfU1l5SHNRfzoHMjJUvX6o+PdM0ZWzDVsopl+Yos0eaypcvjdoZNZfpbPFxKO5rMaxK7pOj7t+4RkaXtBZfF+yfCwAAgNT2HBiq1U9O06WFt45RXlaGz+sXD+mtJT++NGzZqz3a06dw5m8AsY8VZEAEOe1VgbWr8T+711SwM2NXXzZE+w5W6nvjc+TYtzUqZ9TCVUjV732HX6YeRbOUXjBRh19drDOVh32+Ppg/FwAAgMaCzYGhXP1kNSyypSTpsbvGa8vuY1qzcb8qq+uUkZ6syWNzNDS7uyyGIUfp1qgpYh+qPBiO/A0gPjBABkSQNa17YO1Sfc/u+RLszFjm1HwtvPVSSVL5tsBfZ8vJD7hP7RGuQqoB3Xf6feo3c4EOvjhPZq292T2C+XMBAABoLNgcGGzGay2rWQ2LJGn44J5eWyldpjPqitiHMg+GI38DiA+sGQUixGU6lZZfFFDbtPyioIrjt3VmLBpn1MJVSDWg+y5/WEkZfdR15CSfbYL9cwEAAJDO5sDhlwXUNm34ZZ68EY6sZm2SoSyGNeqK2IcqD4YzfwOIfQyQARFiMayy5RQouX9ui+2S++fKlpMfVAhp68xYtM2ouUynHKWBb/sMNMQEdd99W9X14iski/fn35Y/FwAAAOlsDhw0IrAcOKjAkzfCndXClb3aI5R9Cmf+BhD7+I0HIshlmsq8br7fv6ST++cq87r5QYWPts6MReOMWrgKqQZ1320fqFO33urc+9xx6O5l/MwqAgCAtnCZTpmnT6nv9PtazIF9p98n8/Qpnaiq0YmqGqUOLwro/m3NatFYxD7UfXKZpvrOaDl/k/OAxEQNMiBCnKapXWWV6tk1WQPmPCLHvq2yb/tAzppKWVMzlDb8MtkGFejoCbtOHK1SXnaGp1ZESxrPjLU00+aeGWvv68ItXNs+g71vynnD1TnzvLOFYPMjUpwWAADEB4thlaWzVWdO1bSYA8+cPCFrajf97R87JIs0e0r4s1o0ltwIZZ8shiEjObXhcy8taSj47/7cyXlAQmOADIgQq2HonXVfaNPOo5oyNkdTxg5W5tQCz/XjlTX689926G/rSnXJhX10YU6PgO/tnhnzV6uh8cxY47/82/q6cArXVoJg79vrijmS5JlNJDQh1rz1+FSfz19176oO7gkAQDp7KmNSZ3216V2lDh6pzKl3eq6d+eqYvtr0rmwXjNXRE3b9bV2pLBbpW4UDlTn9fpUvXxq2rBZtJTek0PfJ/dl0yb7IazCRnAckNgbIgAiqrK5TtaNey1fv1pvv71FWn3TZUjrJceqM9h+tlmm6PO2C0daZsWibUXNv+7Rv/7DVtu6tBIH0rT33JTABAIBQcOeubqMmyVFaoq82vyfXmdOydOqsLtnD1G3UJB09YdeCZ9fLXlsvSVrw7HotnjsmbFktXNmrPcLZJ4thbfKYnAckMgbIgAjKSE/2/H/TdOmLwydbbReots6MRdOMWri2fUbrdlIgEPuWXBOS+wya/+eQ3CeWtWf1nL8VeQAQDH+5q/FOAvfgmCQdPl6ju5/4UP9+bYG+MTz0WS0aM1I09glAfGKIHIgQp2lq4iUDW28oacLoLDnPriYLVltnxqJlRi1chVQp0AoAAKKFxbDKaZrac6BSdzz2vm5eulrLV+/2Ghxzq3bUK61LZ5lqvhMgFKIxI0VjnwDEn7CsIPvyyy/1wgsv6LPPPlNVVZXq6vxvD7NYLFq3bl04ugFENathaMSQTOVlZWjXfv8FRfOyMjRiSO8O7Fl0Cde2z2jbTgoAABKb1TCUOzBDyUlWT5kNX8KdDaMxI0VjnwDEn5APkB04cEDXXHONqqur5XK1vuLFYmn9VD4gXjlNlxbeOkaLnlvvc5AsLytDC28dI6dpytroL/ymj1t+j7a/NlqEa9tnNG0nBQAACDwbujynm4cj60VjRorGPgGILyEfIHv66ad18uRJ9e7dWzfeeKMGDx4sm80W6rcB4oLVsMiWkqTH7hqvLbuPac3G/aqsrlNGerImjM7SiCG95TRd2lVWoXfWfeG5NvGSgRoxJNMrHDXlvrZt73Gt/vRAUK+NVuHa9hkt20kBAEDicmezJKuhx+4ar892l+v9jQd8ZkOrYemQrBeNGSka+wQgPoR8gGzdunVKSkrSsmXLdN5554X69kDccQeX4YN7ei2Xd5qm6k6f0a/+uEFb9hzzek3x5i89M4i2lKRm4cdpuuQ4Ve9z9rG11wIAAKBjNc5uh47bNWVsjiaPzdY9s0Z52pxxmjIbDY6R9QAgtEI+3H7y5Enl5OQwOIa45WxS/LPx45autfa46VJ4q2Fo/u/WNRscc9u1v1KLnlvvM/RYDYvfpfmtvRYAAAChE0getBoWPbN8i3btr1S1o17LV+/WLUv/oTsee18/f+Yj3fHY+7r/N/+UcTa7kfUAIPRCvoKsX79+cjgcob4tEHGBLGPfVVbpZytkQy2IQJfAO01T2/Yeb7F4v9QQfrbsPqbhg3t51aFo62sBAAAQGo2z46ad5fpe0WD16Jqikr3HtaZRHpxwyUBdPCRT994wSjXPr9fWPcclSabp0heHT3rdsyG79STrAUAYhHyA7Morr9Tvf/97rV27Vt/4xjdCfXsgIgJZxr7g5q+rR9cUbdp5VNWOhiO5N+08qqd/+k117mTVoucDXwJvNQyt/vRAQH1bs3G/19bM9rw2XrlMp1e9iqaPAQAAQqnplsn/+sllshoW/fTJD/3nwVvG6MFbxuj2R9fo8AnfCw5K9h7TiCG94zrrkdsARErIt1jeeuutysvL07333qu33npLNTU1oX4LoMMFsox98QufqG/PVE0Zm+N5fsqlOerZrYvPwbHGr/W1BL6yui6gvvlq157XxhP3qUa1ZdtVvupJHX55kcpXPanasu1e1wEAAEKpcXaccmmO+vRM1eIXPmk5Dz6/Xp07WfXjawr83vd0vVNSfGY9chuASGvXCrKxY8f6fP706dNyOByaN2+eJMlms6lz584+21osFq1bt6493QDCKpgti5/tLtfksdl68/09kqTJY8/TZ7vK27QEPiM9OaD++WrXntfGC5dpyqyr0ZHXlqju0B6va/btHyq5f676zpgvIzmV048AAEDINM6OhmEJOg8OO7+XOnUydOZM8wGhzkkNK6niLeuR2wBEg3Z9u1RWVvr8X01NjVwul+d/NTU1fttWVrb8FwUQacFsWXx/4wH1zrApq0+6svqkq3f3LlqzMfAl8I3riE28ZGBAr5swOktO0+V53J7XxhOLYfgMWW51h/boyGtLCFkAACCkGmfHtuTBpE6GCof28Xk9f3DvuMx65DYA0aBdK8heeumlUPWjQ61atUovv/yydu/eLdM0lZOTo2nTpumGG26Q1eq9v720tFRPP/20Nm3apKqqKmVlZem6667TrFmzZPAFnTAaL083DIuy+qTLltJJjlNntP9otcyzwcPdzpbSyedrA30Pq2FoxJBM5WVltDjbmJeV0ayuRHteGy9cplO1Zdv9hiy3ukN75CgtUZfsYQQuAAAQMl/Z63Rev666MKeHpODzYPdGK7/c2fOC7HPZLZ6yHrkNQLRo1wBZYWFhqPrRYR599FG98MIL6ty5sy655BJZrVZt3LhRS5cu1YYNG/TMM8/IYmlYxbNz505df/31stvtGjlypIYPH65PPvlEixcv1pYtW/TYY49F+KdBR8lIT1a6LUlTLs3R5LHnqXf3Lp5rx6pq9fePv9A760o9y9gdp854vTbQ92jMabq08NYxfmufuYv7Nz0Bs72vdYvlAqkWwyp7SXFAbe0lxbLl5Ie3QwAAIKH89IZR6pp6LtsFmwerqus82fPKsVnq2T3V08ZlOuWS0e6sFy3IbQCiRchPsbzvvvuUk5OjuXPnttr2V7/6lXbt2qVly5aFuhs+7dq1S3/84x/Vo0cPvfLKK8rJaSimfvToUc2cOVP/+Mc/9N5772nSpElyuVyaN2+e7Ha7Hn30UU2dOlWSVFFRoZtuuklvvfWWrrjiCk2aNKlD+o7IcZqmplx6nm6YfIH69EzVZ7vK9dJf/3XuaO7RAzV7ylBdUZilipOndKzSof1HqyU1DJ5NGD1QxZu/bPV93Evgz51kaZEtJUmP3TVeW3Yf05qN+xu9Z5ZGDOntN/S057Uu05TFMFRbtl32kmI57VWypnVXWn6RbDkFnuvRzmmvCqxdDdu8AQBAaLjz1ecHv9KaTw/oK3udfnrDqODyoNNUv142/XjaN9W9axc59m1V+QfNM1lairVNWS8akdsARIOQD5CtXLlSo0aNCmiA7JNPPtH+/ftD3QW/1q1bJ5fLpe9+97uewTFJ6tOnj2bNmqX/9//+nz799FNNmjRJa9eu1a5du1RYWOgZHJOkHj16aOHChZo1a5aWLVvGAFkCsBqG8rJ7yO443eLR3Atu/rrysnvoH5+U6YLsDDlOndG767/QDZOHtnkJvDvUDB/c0+u6u5ZES6GnLa+NpwKp1rTugbVLzQhzTwAAQCJwmi45TtXr2ZXbVF7p8JTiWPXhPs2eEngeNE1TN155ocy6Gh188RetZrK25MRoQ24DEA3aNUD2xRdfaPny5c2eP3jwoB599FG/r3O5XDp06JD27NmjXr16tacLQXFvnTx69Giza+7DArp3b/hy/uijjyRJl19+ebO2o0aNUs+ePbVp0ybZ7XalpaWFq8uIElbD0uLR3IeO27Wu5LC+NSZLk8aep0ljz5MkHf+qVjW19Vp4yxgter7tS+CtTQajggk8Lb3WaZpe1wMtkDpgziMBv38kuEyn0vKLZN/+Yatt0/KLYmZVHAAAiF5Ww6IzTlP3Xj/K89yxqlqt2XhARyscWnDz1/3mybysDC28ZYxXNgs0kzUtgNFS1mv6OBqQ2wBEi3YNkGVnZ2v9+vXasWOH5zmLxaKjR4/qxRdfbPG1LlfDzMZVV13Vni4EZdy4cXrkkUf097//Xc8++6yuvfZaderUSe+9955eeukldevWTddcc40kae/evZKkIUOG+LxXTk6OTpw4oc8//1wFBQUd9jOg4zU+qtuXfr1StXju2Ibtl7vLtebTA+eWuF8yUBcPyZTTNKNqCbz7/bbtPa7VZ5f//+yGkbIe3RUXBVIthlW2nAIl989t8edJ7p9LHQsAANBu7oGnLw6d1B83/p9XKY7rLh+iY5UOna536rG7xmvPgSp9vO2QPv+ySt3SGufBhnuEomh906zn7s/ESwZqxJDMqNp+SW4DEC3aNUBmsVj0y1/+Ui+//LLnuZUrV6pXr14aN25ci6+z2WzKy8vTtGnT2tOFoJx//vlavHixlixZoscff1yPP/6459rFF1+shx9+WP369ZMklZeXS5J69/Z98ov7+ePHj7f6vitWrNDKlSsD6mPjwUZEh8ZHdTeVbkvS4rljlZLcqcXtlwtvHaPUlKQWl8B3VFF89/L/xkVdz+vXVempKSrfVhzQPWKhQKrLNNV3xny/s6/urQnMQkYvvjsBIHh8d3a8hmx1xmfB/OLNX+riIb11/02FSjob63IHdlfuwO6erOc0TUnnVv23t2i9r6zXuD/ubGpLSYqaQTJyG4Bo0O4aZMOGDdPDDz/sebxy5UplZ2d7PRdNRo4cqbFjx2r9+vXKz8+XYRjaunWrtm3bpldeeUX333+/LBaLamtrJUkpKSk+7+N+3uFwtPqeBw8e1IYNG0L3Q6DD+Tuae8qlOerTM9Xn4Jjbrv2VWvTcej1213hJ3iGkYWCsY4viWw1Ls8BkS2n4KoinAqkWw5CRnKoBcx6Ro7Sk4bOtqZQ1NePsZ5tPyIpyfHcCQPD47ux4vrKVW79eqbr9+/lKSe4kR+lWn1nPVxJpTyZrqT9S02waHchtAKJByIv0r169WsnJgR1j3NG2bNmiH/7whxowYIDeeustfe1rX5PUUJPs9ttv10svvaS0tDTdddddMtwzOBbfsyruLaLuf7ZkwIABKiwsDKiPO3bsUHV1dUBt0XF8Hc1tGBZNHnuePttV3mLBVakhiGzZfUzDB/fymqnr6KL4/raLOk6dkRR/BVLdn1mX7Iu8ZlddZ2dqCVnRje9OAAge350dq6VSHOm2JP3qtjHKSDYDKrbfOJe0NZO1VhrEzV82jSRyG4BIC/kA2YABA0J9y5BZunSpampqtGTJEs/gmNRwiuWvf/1rTZ48Wf/93/+tuXPnymazSZJOnTrl8151dQ0ritztWjJt2rSAt5LOnj2bWb8QCVVRUqdpauIlzY/mzuqTrt7du+ilv/4roPus2bi/2UmVHV0U39920f1Hq3Wiqkapw+OzQGrTraqx0u9Ex3cnAASP786O1VIpjimX5iizR5rPwTE3X1mvPUXrW+pPU76yaTQgtwGIlJAPkE2cODHwN+/UScnJyerdu7eGDh2qadOmadCgQaHukqSGga6SkhKlp6crP7957aSBAwcqJydHe/bsUVlZmTIzM7Vjxw4dP35c559/frP2x44dk+S/RhkiJ9RFSa2GoRFDMpsdze3eluhv+2VTTduFogBrW/jqr2m69LeP92v2FAqkAonmrcenRroLABCT3JnSV7YyDIuuHJulmn1bg8567S1a39ZsCgCJLuTD8QcPHgz4f2VlZdq9e7fWrl2rF154QVdffbX+8pe/hLpLkqTq6mq5XC5Zrf6Lnruv1dfXKzc3V9K50ywbc7lc2rdvn6xWq8/BM3Qcd1HTxo/PnHHqwT+s04I/fKzizV9q655jKt78pRb84WP99MkP5ThV7ymOH/j7uLTw1jHKyzq3jN29LdHX9ktfmrYLtgBrqGbP/PX3nXWlOnrCrszp9yu5f67PNo0LpAIAAMQjX/myeRuXTtX5z4JZfdLVs3uqaoI4AKlx1nMXrW9LJmtrNgWARBeWGmRLlizRmjVrNHz4cM2YMUMXXnihUlNTVVNTo927d+vNN9/Up59+quHDh+umm27SyZMn9eGHH+r999/XggULlJeXp6FDh4a0Xz179lT37t1VVVWlkpKSZqvIjh49qs8//1xJSUkaNGiQxo0bp+eff16rV6/W9ddf79V28+bNqqioUGFhodLS0kLaTwSmtVVi/35tgR589mMdPl7j9bq2FiW1GhbZUpL02F3jtWX3Ma3ZuF9f2et0sqZOE3xsv/RlwuisZqvXOroovr/topJU7ajXgmfXa/HcMRRIBaLIVfeukhS+lV77llwTsnsNmv/nVtu4f55AxMrqtlB9hoF8fgDCJ9BdCObZfy567mP97AeXaMLo5tmqvQcgtbVofUtZrylf2RRAaP3iF7/QypUr9frrr2vEiBE+26xYsUL33Xef7r33Xs2dO7fD3x/nhHyA7JNPPtH777+va665Rr/61a+aFbkfOnSopk6dqocfflgvvfSSLBaLZs6cqZkzZ+rZZ5/Vr3/9ay1btkxLly4Nab8Mw9C1116r559/XvPnz9fzzz+vPn36SJIqKir005/+VPX19ZoxY4ZSU1NVWFio3NxcrV27VsuXL9f06dM9bRctWiRJmjNnTkj7iMAEcnT1gpu/rl/OHat7nvhA1Y56rzZtLUrqbjt8cE+veg0X+9h+2VReVobPGg8dXRTf33ZRt8PHa3T3Ex9q9pShmjyGAqkAACAxBJIvF946RraUTnK5pK27jmlHWaX+/vEXmj1laLNsFYoDkNpStL61rOfmL5sC6HhDhw7V7bffrpEjR0a6Kwkv5P+l+6c//UmpqalasGCB3xMgJenee+9Venq6XnzxRc9zc+bMUdeuXcNWLPTOO+9UYWGhdu/erSuuuEI333yzbrnlFk2aNEkbNmzQiBEj9POf/1xSw4Da0qVLZbPZtGDBAk2fPl233367Jk+erF27dmn69OmaMGFCWPqJlgVydPXiFz5R356pmjI2x2ebNRv3t3m2rGmhf1/bLxtzB6qm2zrdBVgD4S7AGgqt9bd/rzT924gBMpt8PTAwBgAA4lUg+XLRc+tlNQx1shpas7GhEP4760p15ESNFtz8da9s1fgApEC0lPWCLVrf1mwKIDKGDh2qO+64Q6NHj450VxJeyFeQ7du3T7m5uUpJSWmxXefOnZWdna09e84VnkxKStLXvvY1ff7556HuliQpOTlZf/zjH/XKK69o1apV2rRpk0zT1Hnnnadbb71VN910kzp37uxpn5+frzfeeENPPfWUPvnkE+3Zs0fZ2dm655579P3vfz8sfUTLgjm6+rPd5Zo8Nltvvr9HZpMAEMqipL62X7qX5E8YnaURQ3r7XL7e3gKsHd0s0RgLAAAgAElEQVRfAACAeBRMvnTvQvjK3pAlqx31evDZj/XLuWP12F3j9dnucr2/sWF75s6yKn2jgKwHALEi5ANkXbt21cGDB2WapowWZjdM09TBgweVnOxdHPLUqVNKT08Pdbc8kpKSdOONN+rGG28MqP3gwYP11FNPha0/icJpml4rrxo/bulaU8EcXf3+xgO6Z9YoZfVJ1xeHT3pdC3VRUn/bL90zc/4CiLsA65HXlvgMTo0LsIZyBVdb+xsol+n0mu1s+hgAAKCj+MqaFsnz3yrB5Ms1G/drxJDeOv9r3fXZ7oZT7Q8fr9E9T3ygKWNzNHlstu6ZNercezmdIcl6wWarcGc9AG335ZdfatasWaqoqNAzzzyjioqKZjXIZs+erX379mnVqlV6/PHHVVxcLLvdrvPPP19z5szR1KnedVpPnz6t5557TqtWrdKRI0eUnZ2t2267LRI/XkwL+QDZiBEj9L//+7/67W9/q9tvv91vuz/84Q+qqKhQUVGR5zn3yZbDhw8PdbcQIf6KnU659DzlZfcIqBBqU8EeXe0uktrYN0cPDMvMWdOBvdbu39YCrJHqb2vcfa0t297ws9irZE3rfvZnKaDAPwAA6DD+cuiE0QN1cV6m12BRsPly7PB+enPNuQGvake9lq/erTff36OsPumypXSS49QZ/fiaAg3N7tbmrNfebBXqrAegfY4ePaqbbrpJFRUVevLJJ1VUVKQVK1b4bFtXV6frr79eLpdL3/72t+VwOPT2229r3rx56tq1q775zW9Kalh89OMf/1j//Oc/lZeXp1mzZungwYP66U9/ql69enXkjxfzQj5Adtttt2nNmjX6zW9+o927d2v69OnKy8tTly5dPKdYrlixQn//+99ltVo9o5rFxcV6/PHHZZpms9FQxCZ/xU7TbUm6YfIFsjtOa/ELn7RSCDWp2V/kwR5d7S6S6paXlaGLh2S25UcKi7YUYI1GLtOUWVfjc4bUvv1DzwypkZwaMz8TAACITYEe6pTaJUlOU+rX06at/ndBerjz5ZCsDJ+F8E3T5dm5kJeVoQtzeniuBZv1yFZAfKmoqNCcOXN05MgRPfHEE5o4cWKL7aurq1VQUKDf/e53nlJQRUVFuuOOO/Tqq696Bsj+53/+R//85z81efJkPfbYY0pKSpIkrVy5Ur/4xS/C+0PFmZB/k1500UV69NFHlZycrPfee0+33nqrxo8fr0suuURFRUWaO3eu3nnnHSUnJ+vhhx/WxRdfLEl66qmntGfPHuXl5emaa0J35Dwix1+x0ymX5qhPz1Sfg2Nu5wqheg+OuY+uDsQ3Rw/UsUqH9h+t9jznDkOn6s5EXWHSYAuwhpvLdLb4uCmLYfjdPiBJdYf26MhrSyL+cwEAgPgX6KFOnayGrIZF0745OKD7ThidpeNVtT4L8zfmqxB+sFmvtWxVX3lYNTs+ltT8ICgA0aW6ulq33HKLysrK9Otf/1qXX355QK/74Q9/6FUn/bLLLpMklZWVeZ576623JEnz5s3zDI5J0ve+9z1ddNFFoeh+wgj5CjJJuvLKKzVy5Eg9//zzKi4u1pdffum51qdPH02YMEE//OEPNXDguYGOvLw8fec739HMmTO9/gVAbPJX7NQwLJo89jx9tqvcc80wLF5L0fcfrZZpurwKoboHyoI5uvriIZn659aDyh/cSxnpyfrm6IG6eEimjlfVqltaaGuQxZO2LOV3mU7Vlm1vsQCt1DBI5igtUZfsYQyUAQCAsAj2UKeh5/VQZo9UXXRehmrqnM0yqVteVoZGDOmtd9aVauUHn/sszN84c7annEdr2apTRj/1m7VASd37yLFvq+zbKG0BRLP7779f5eXl6t69e1CnVZ533nlej5OTk5WUlKT6+nrPc//617/Us2dPDRgwoNnrR48erf/7v/9rc78TTVgGyCSpb9++euCBB/TAAw/o9OnTqqqqks1mU1pams/2Dz/8cLi6ggjwV+w0q0+6enfvopf++i+l25I05dIcXTk2Sz27p3ranKiq0d8+3q931pV6CqE25j662t+sYF5Whh68ZYzqzzj1bwUD9G8FDV8Uxyodemddqf5txIB2B5Z4LUDf1qX8FsMqe0lxQO9hLykO6UlNAAAgsbSWxYI91MldemPpv39DhvXcfRpn0v690s6uCDP1byMGaPWnB/wW5j/jNGW2s9ZtS9nK6JKmfrMWyEhK0cEXf8H2SyAGHD9+XBMmTNCaNWu0ZMkSPf744wG9ztfiIYvF+7ulurpa/fv39/n6bt26Bd/ZBBa2AbLGOnfurMzM6Kn5hI7hq9ipu2C+YVj0xN3jldkjTTX7tqr8g3OzXqnDizR7SoG+VThQr7y3u9k9Aj26eldZpTbuOKLT9U51TrIqf3BvTbk0p82DY4lQgD7QbZID5jzS7JrTXhXQezhrWp7NBQAA8CWYLBZo0X2LxeJ53an9Te57NpNOnzBYyclJngzZNIc+88YWnf+17ho7vL9yB3aXxWKREYJi+P6yVdeRk5TUvY/PwTG3ljIbgI43f/58zZw5U9dee63efvttffe73/Vsl2yvbt266eTJkz6vVVRUhOQ9EkXYBsi2b9+uLVu2yG63y+l0yuXyX++ppdMuEbt8FdN3F8z/4XculM043eKsV+b0+/XD7wz1ee9Ajq7Oy+7uVRi1PcdaJ0KR1PZuk7SmdQ/ofaypvmt1AAAA+BNsFmvtUCfDsOiC7AzNnXpRQPd1mVbPiZCB5NBQ8JmtLIa6jvyWHPu2UNoCiCHDhg2T1WrVokWLdN111+mhhx7S22+/rdTU1NZfHMC9i4uLVVpaqpycHK9r27dvb/f9E0nIB8hOnz6tu+++W2vWrGm1rcvlksViYYAsDrmL6Rdv/tLr+f1Hq1VTW69u6Sk6+OJDLc56lS9fqgFzHpFpuvzOwrV0dHUoj7Vuz8qqaNZ4S0J7tkm6TKfS8otk3/5hq69Nyy+Ki9V2AACg4wSTxUw/OVSSzxIfB19c2KaMF4qs6W+7qL9s1bn3QHXq2ksV778c0P0pbQFEl/z8fM2YMUOvvPKK/uu//ksPPPBAu+957bXXqri4WEuXLtXTTz+tlJQUSdLbb7+tLVu2tPv+iSTkA2QvvviiVq9eLUnKyspSTk6OkpMpiJ5oWiqmb5EpR+nWoGa9pNDMxLVFPBagb7pFwejSVb2+NafN2yQthlW2nAIl989t8XNK7p9LSAMAAEFpSxbzlUP79UrVr24b4ynx4eyco7oj+yKS8VrbLipZfGYrI9kmidIWQCy755579L//+796+eWXddVVV7X7fldccYWmTp2qVatW6eqrr9a4ceN0+PBh/eMf/1B2drbXiZdoWcgHyN566y1ZLBbNnz9fN9xwQ6hvjxjiq5h+Vp902bokqzzCBd2dpuk169f0cWPxVoDe1xaFzpnZktq3TdJlmuo7Y77f2d1zWxRYPQYAAM5preh+W7JY0xyabkvSr24bo4xkUwdf/IVcZ07ra7f+WvZtHwR138bc+TGYXNnw8wW6XdTWLFuZdQ5JlLYAYll6erruv/9+3X333XrggQc0e/bsdt/zP//zPzVs2DC9/vrreu2119S3b189+OCDOnHihJ555pkQ9DoxhHyAbP/+/erXrx+DY/BZxLRrasMpHJGa9XIHlpK9x7Xm03PHcU+4xH0ct+9AE0+zdL62KJw+dkBnTh5X2vDL2rxN0mIYMpJTNWDOI3KUljTMhtZUypqacXY2NJ/BMQAA4BFM0f1gs1jTHFpTe1qZPdI89W9TBg5t031fe2+Xtu87oYz0ZE28ZKBGDMlUfb2p/373Xyo9dNLreX8HQwWzXdSTrfZtlX3bB3I6quR0nFTacEpbALHgkUce0SOPNN+ifeWVV+rKK6/0PJ4+fbrX9WXLlvm957Zt25o9Z7FY9IMf/EA/+MEPml274447gulyQgv5AFlqaqrS0tJCfVvEKH9FTCMx6+U0TTlqz2jR8+ubbfss3vyl8rIytPCWMbJ16dS8pkSczNL53aLgMnVy83vqUTQr4G2SLtNsds0dvrpkX9SkRpnpdR0AACS2YIvutyWLNc6hhlxeJT7auhJrbckhfXG44bQ4d35ccPPX9e1v5OieJz5QtaP+XK68dYxsKUleg2TBbxe9SKcrDqtzz/7KnHqn57ptEKUtACDUQv5fq6NHj1ZpaSnHicJL4wEnd9HRQLhnvULVB1+DY2679ldq0fPrmw2OuUyn0oYXBfQeTfvrMp1t7m84tLRF4eTmd1VfeVR9p9+n5P65Ptsk989V3+n36UxNVZCDXf5PsQUAAIkn0FVUFsNod3a0GkazDNR49Xyg961x1Kmy+pTX87v2V2rxC5+ob89UTRmb4/X8oufWN1tBFux2UYthVfXWNdr/m3/Xl8/do0MvPaBDLz3QkNmuayWznS1tAQAITMgHyP7jP/5DkjR//nydPn061LdHXDhXdLQloZz1cpqmPttd7ndwzG3X/kpt2X1MzkZhwmJYPbN0gfS3Ztd6HX55kcpXPanasoZjdaMpnPjbSmDW2nX41cUyT5/SgDmPqO/MB5U27DJ1yclX2rDL1Hfmgw0nQ50+pYo1vpf8un/O2rLtKl/1ZFR/DgAAIDJcpjOoA5uCzY7+JvG8MtDZ1fO2QSMCvm+qLVmP3zlO/Xqlel3ftb9Sn+0u1+Sx2V4nr5/Lld4ThcFu63TVn5Zcpk6Xl+nUgR06dWCHjv3t97J0tjVktlkLvTPbrIVnt2faWL0PAEEISw2ya6+9Vq+++qrGjx+vwsJC9enTR0lJST7bWywW/exnPwt1NxDFLIahMzVV6jv9Ph1Z/rD/gu5nVyp1Sg1s6XtLrIahNZ8eCKjtmo37vbaDSgqsv9fdr/rKozr+tz/IPGWX5HuLQKS1tJXgTOVhHXxxnrqOnKRuhd+RbVDBuWtfHVNF8Ss6ueld2QaPavbaYLdKAACAxNSWovttOQyoaW3Zphno5OZ3lV4wMaBMWl95VMf+9nv1vvpuLZ47Rnc/8aGqHfWedu9vPKB7Zo1SVp90zxZMyXeuDHZbpyWps9fzRpc09f72j+Sqq9VXW9codfBIr+2XZ746pq82vau0Cy8ldwFAEEI+QHbnnXfKYmmYOamqqtJ7773nedyUy+VigCxBVaxepoxx072LjroLug+/TLZBBaqvPKrKfy5X5lWhKSpYWV3XpnYu06n6isPqlNbDd3/PFqCvrzzasALr7OCYW+NCq5Hm3qLQUlFXs9auqrV/VkrWRerce6DK//JfMk/V6PSxA5KrYQWYvyL9gRacBQAAiS3YVVTBHAbkLo6/be9xrf70gL6y1+lnN4xsVtjevXq+38wFvu/bKJMefnWxzlQeVvnypRow5xFNGZuj5at3e+7lzo+2FO//vPKVK1vLYm4NecupLucN01frVnie7zpykpK69/EcOHDCYqhz74Eykm0y6xyezGYveZ/cBQBBCPkA2dVXX+13QAzwcJmelUpdL76i2axXSyuV2iojPblN7SyGVV0GDtXhV36p5P6Dm/XXPH3K09+mg2Nu5wqtDovoLJ7FsHq2KLRa1HVQgSqKX9Gp/f9qfq3J1tfgC85G9nMAAACR1Zai+4EcBuQ0XXKcqtei57zrzr7zcZmmX948A52pPKyD//0LDfzxM0oZMMTrvo0zqTvj1R3aI8e+rZoydrDefH+PzLPbJ9350XHqjFf/feXKgLPY2b54tbcY6jryW3Ls23Lu9We3XzZF7gKA4IR8gMzXEaZAY41nzqrW/llV61b6nPUyuqSp+/jpcplOWQyr1+sbPw6E0zQ14ZKBKt78ZattJ4zOarYk32Wayrz6Jzry+lJPfzv3zVHmVXfo+N+flX3bB63e171FoKU+Nn7Ppo9DJaAtCme3i57c9G7za022L0ht2yoBAAASU/CrqJrnjsZ50F3I32JYZTUszQbHJMklS0MGuu5+HXl9qVcG6pSWIWtKqsr/5ymdPvpFs0za1P/P3p0HVFXnjR9/c0DZEVBBQVBc0wRcccndrPTJsTGXybLUynKsnJoszdQxW31scqx+LdbkZD1NluWMpmmhuC8pLpgImihKiCDrZeee+/vjeq73chfuRVSUz+ufhntWGD1++J7PokvaRsiYWItyyqG9IsjOKyE9q8hiX2NcabCaZOlKuah57GaoqsAjoBm5W7+s8WcHEncJIYQr6nyBTIiaGMwarZb/ftLmWy+PoJaETX4FD/9gStKOGNPddfm4+wVeTqOPtQqWHHFXFLp3DKFTZJDDRv2dIoOs+kQYVD1lGalWJZaKlx8A+iLHjf81WomA1efVygDyisoJ8vdkeO8IunUMsQqqrpazJQoVl37Hp31Pu+ULVt+Hi6USQoiGad3bY270LQghbrDaZFFptDik9Owxm/FheXklhSWWg8IUxY07e4VTej6FRv7WLTO8Im4HjDGdrUys6rRYRiun7BQZRPeOIazakGzKKNM+N48rtZgu+Ww+4c18Lt/HUXRJ9stFjT+vK7Fb2e+njOeSuEsIIercNV0gS0lJYfv27aSlpaHT6Vi+fDnFxcX8+OOPjB49msaNG9d8EnHLURSFgqJSQia8xMXVr1sFRoq3H2GTX8HNo5Gpt4K52jZ816sqCx/ry6JPrN8qgjGIWfhYX6vMLTfFnaLEzZT8lmizJLQ2JQJX7sl2GQBAQuJ54z093hcfr0Z1vkgGjksUvMI74B1xm81ttlzNz0EIIYQQDUttmu47MxAodOI8qyb6kaH+NA305eI2+/EcuB7LlJRV0SkyiPmP9uHCpWI27E4z7aPFcNqiWPWYz9+nESP7RTGyXztCxpjHYnrAOt7SvvZsEVWrexVCCFGza7JAVlBQwLx584iPjweuNOMHOHfuHPPmzWP58uV8/PHHdOrU6Vrcgqjn/rk+mUl3dbTZ9D5w4Hg8/INtLo5patPw3V1R8PH2YOmsQRxOzWbLgXRTttawXpF069jcblmjXpdval5vKgn18iPkj8/iFz241iUC9soANCnpeSxasZelswY5/X26onqpavXyBXvbqrvaUgkhxK3l9Gv318l52s5bUyfnEULUP6403Tc/pqaBQFlfv2bVRF/L9LIZz3n6oFaU0WLCHOdjuughFBWX8egfutKtY3PKyqtYs/Uk7SMCbcSVxt+Bqsd8RSWVrI5P5dutJ4kM9cfHy4OQIB/++qDj/rtaeanEXUIIUffqfIGsoqKCadOm8euvv+Lr60vfvn1JSkoiOzsbMC6WBQQEkJWVxeTJk1m7di1hYWF1fRuinlNVA88u2375zVl7QsbEmrbpq6ooOX3kmjR81xa/ots3rZbyrlpstzrO/C2dWUlo4YGNBA+ZVKsSAb2qknQqx2HJJxgXyQ6nZhPdvlmdZpHVpasplRBCCCFEw+RMRvuVz1wYCFStib7WON9ePAdQmLjZMqazMxnSOMjIeK9d2zUm9WwuwU28mDa6q+lc2XklbNydxoBu4fh4NQIMdmM+VTWY+pgdT8tleO/IGmM+ibuEEOLaqPMFsi+++IJff/2V3r17849//IPg4GAmTZpkWiDr3LkzW7ZsYcaMGRw4cIAVK1awcOHCur4NUY/pVZXhlxvmV39zFuTvyZxH4tAlJTh1rto2Hq2+EOaoGb6jt3SFiZvwjx1OiwlzubD6DadLBLRrxv9yzuF9+vs0YmT/KNqH+1s1d3V1UMG1VptSCSGEEEIIZ7LWXRoIVK2JfnpWEZfyi/GNtp91dSWme4nilL34tO+BR0Az0/aqwhxKTiXi27mfKZZ56f2dJJ/NQ1HcTLFsSVkV6VlFqKqB+F/OXa4CcKsx5tNsOZBu1Q/XFom7hBCi7tX5Atn69evx8PBg6dKlBAcH29zHz8+PpUuXcuedd7Jjx466vgVRz7krCt3MGuabvznrEmX8M1OfGo9aDRUwo5bqyPxqMS0fmO9SiYAmr6jc7nVbNvPl1Sf6EhLsR8npI1xMurpBBddabUolhBBCCCGc5Wp8qJVWqqqBDXvSmTzSftaVWqoje8OHtJz4EgE97qLk9BFyt355JfaKHkJAj7swqHpUICn1Isln80zn12JZc+ZVAAU6+zGfOUexoTmJu4QQou7V+QJZWloa7du3JzQ01OF+oaGhtG3bltOnT9f1LYibgF41sPDxvlb9t2ymwDvgqPFo9X5i9vqL1aSmoQJVeZnkbPyIkHEv4BXZpcYSAXNB/p42P/f3acSrT/QlyFOt00EF15orpRJCCCGEEK6oTRN9zcbdadwVF0HIxHlc/No660rx9iPkD0+jVpZxYdX8GmOvXUd+d+petIywdq0COZSaXeP+9mJDWyTuEkKIulXnT003NzdKS0ud2ldVVZlk2UC5K274eDVi6axBLH6iP0N7tqJbx+a0DQ+goqISv5ghTp1HazxqTn95vHbSqRze/vIgL3+4m7e/PEjSqRyL7a745/pkcssUwqe+SYsHFuDXdTDeUTH4dR1MiwcW0HLSAnKKqlj+zVGL4xwFJlqpqS0j+0cREuxnc0FOow0qqI/BjysN/oUQQgghaqK1vHCGFh8++oeuphizV+dQLhVW4OFlzLpqMWmhRTwXNuUtPPyDaxwCoMVeHVs7Nx1SywjrH9PSqf2H9Yp0OVaVuEsIURf27dtHp06dmDJlyo2+lRumzjPIoqKiOHHiBOfPn6dVq1Z290tPT+fUqVN07drV7j7i1qb11KreMF9VDTSuZePR6iO0zSUknjeN3PbxauRSw3tHQwVy8opZsyGZDbvT6N3FceakueqlphpFcWNUv0iKr9GgAiGEEEKIm01tGtNbD2UyYAAqKqqobNqBkDFX4khXh0T17dKR9xQ31BoWs7SMsA4RQVYxX3WdIoOc6j8mhBDi2qjz36hHjx6NXq/nxRdfJD/fdp+A/Px8nn/+eQBGjhxZ17cgbjLVyx4Vxc3UeNQzrIPNY640HtVX22IgM6eYwmLb/RtS0vNYtGKvS4tjWqaXNo770dfjeXrpVl58bwdPL93Ko6/Hszo+FV1ppctv/bRS006RV95CRob60zTQl2IXBhXI4pgQQgghbnXOx4e2p5O7K24oihtbDpynwrIAAXcPD5eGRPn7eREZ6l/jvlpsaCvmM6e9xK1NpYMQQoi6UecZZJMmTeKHH37g4MGDjBw5kv79+3P+/HkAVq5cyW+//cbmzZspKCigQ4cOPPjgg3V9C+IW4Gzj0eSzeWzcfYa8onKC/D0Z1juC7h1DeO+FYbzyyV6OnMyxOrd5w1RnFsocDRUwV5u3fualpodTs9lyIJ0AX2PZcX0aVCCEEEIIcaNdbWN6vapSWakysn8bDqVcZNWG4zRu5E7n1sEMj4t0Ofa6rXWQzZhQUz02rB7zmeLXXpF069gcvWpw6SWuEEKIulXnC2SNGzfmk08+4cUXXyQhIYEffvjBtO2tt97CYDC+Fenduzdvv/02np7ON6IUDYv9xqN6yiuqePWf+zl80rLZqamM8rG+LHisL08t2ULmpRKrczs7Qltjb6iApnvH5sybGodB1Vv0gaj+tS32Sk3rYlCBEEIIIcSt5Ooa07tRUaXn9ZX76RAZxCOjbqNpoK9pq6ux15R7byft90KbsaF5RpgW69mL+bSsMVkcE6Ju1dXQtutl27Zt/Otf/yI1NZWCggJCQ0MZMGAATzzxBC1bWvYx/M9//sPXX3/NiRMnqKqqonXr1tx777088sgjeHl5WZ376NGjfPbZZxw4cICioiLCw8O55557mDp1Kn5+fg7v6+jRo0yZMoWysjKWLl3KqFGjTNt+++03PvzwQ/bs2UN+fj4hISEMGzaMGTNm0LRpUwAqKioYOHAgBQUFxMfHEx4ebnWNMWPGkJKSwk8//UREhLFP94ULF/jggw/Yvn072dnZBAYGMmDAAGbOnGnap67V+QIZQJMmTfjwww85duwYP//8M6dPn0an0+Ht7U3r1q0ZMmQIcXFx1+LS4hZk3XjUnXkfbLfbwyElPY9Fn+xl6axBzLg/lgUf77Hax9kR2hpbmV7aW797+kXRuXUgbopCSdoR49tMbSR4zBB8omKdGrNt/rDWGtHqjm2v8d60RrRSZimEEEKIhqI2jendFTf+37dHeGp8DCHBfhSfPsLFbQnoiwsIue8v+EW7Fnt5eXrUKiPMVumnEKLuaH/3kk7lEP/LOdPfzeG9I+jWMaReZmv+9NNPPPPMM3h4eNCrVy/8/f05fvw4X331FT/99BNr166lefPmqKrK7NmzWb9+PZ6envTu3RsvLy8OHDjA3//+dzZt2sTKlSsJCAgwnXvt2rW8/PLLVFZW0q1bN0JCQjh69CjvvfceO3bsYNWqVXYTl06ePMnjjz9OaWkpS5YssVgc27lzJ0899RSlpaXcdttt9OjRg9TUVFatWsXPP//MqlWriIiIoHHjxowePZpVq1axbt06nnzySYtrpKSkcOLECeLi4kwLX8ePH2fatGnk5eURFRXF0KFDOX/+PN9//z3x8fF8+umnxMRY9iOvC9dkgUzTtWtXacIv6pReVTl6Ksdhg1O4UkbZtV0zPDwUqqosG024MkJbY++tn0HVo5YX25x6VH0kuLOLWLVpRCuEEEIIIWzTqyrHT19i2uguBHmqZHw2xyLGKti/nuAhk5yOvVSzX7AlI0yI+uNaDW271t566y0URWHt2rW0a9cOAL1ez+zZs/nhhx/497//zdNPP80XX3zB+vXradOmDZ9++qlpMKJOp+Ovf/0rCQkJLFy4kHfeeQeAzMxMFi1aBMCKFSsYNGgQAOXl5cycOZMdO3bwr3/9i+nTp1vd07lz55g6dSoFBQW88cYbjB492rQtNzeX5557jsrKSt577z1GjBgBgMFg4MMPP2TZsmXMnj2bf//73wDcf//9rFq1iv/85z9WC2Rr164FYOzYsYAx4+yZZ54hLy+P+fPn89BDD1nsOx7pKSQAACAASURBVGfOHP7yl7/w448/0rhx46v8yVu6qgWyWbNmXfUNuLm5sWzZsqs+j7jxalNe6Cp3RWHLL+ec2lcro4zrHMrupEyLbVrD1Lp4KLop7k6NBA+f+qZL59Ua0do7t3kjWskeE0IIIcTN4nrEjNW5KwoFxRVEt29utTgGUJi4Cf/Y4bSYMJcLq9+wG3uFTHiJgqJSmvh7m8q1JCNMiPrDXXGz2xYHrgxtWzpr0HW+M8eys7Px8PCgeXOzljvu7jz77LP06tXLlC21cuVKAN58803T4hiAn58fS5cuZejQoWzcuJHZs2cTFhbG2rVrKSkpYdq0aabFMQBPT0/mzJnDmTNnuHTpktX9ZGVlMWXKFHJycli8eDF//OMfLbZ/++23FBQUMGXKFNPiGBjXd2bMmEF8fDyHDh3i0KFDdO/enc6dO9O5c2eSk5M5duyYKZFKr9ezbt06fHx8uPvuuwFjNt25c+cYMWKExeIYwH333ceWLVvYtGkTmzdv5t57763Nj9uuq1og27Rp01XfgJvb9fsHpFOnTk7t9/nnn9OnTx/T12lpabz77rscPHiQ/Px8IiMjmThxIpMmTUKRhQnTAk3p2WO1Li90hbPlkdp+gdWyxWo7Qrt6qm6BrpzZD/XAPSvF6ZHg3q27upBFdnWNaIUQQggh6pPrHTNWF9OuKcWnj9iM29RSHZlfLablA/Ntx17Rg/FpG0vWJR3/tzmZZx/owemMAjpEBNXLci0hGiK9qpLkQrWRs0PbrodevXqxc+dOxo0bx5gxYxg8eDC33347ERERTJo0CTBmg2VkZNCiRQu6d+9udQ5/f38GDRrEDz/8wC+//MKYMWPYv38/AEOHDrXav3379vz8889WnxcUFDBt2jTOnz/Pvffey/jx46322bdvH4DFuom5O+64g6SkJH755RfTvd5///28+uqr/Oc//zEtkO3cuZPs7GzGjh2Lj4+PU+ceOHAgmzZtYv/+/fVrgeypp56qq/u4LsxTAqs7d+4chw8fxs/Pz6Lh24kTJ3jwwQfR6XT06NGD6Oho9u3bx+LFizl8+DBLly69HrdebxlUtc7LC2vibHmktl++2YKarYaptth6s1lVZWDRZ/s5lGocDNCmZQD+vl5cdGEkuKvlkFfXiFYIIYQQon64HjFjTZlpAX5eXIxPsHt8VV4mGZ+9QECPu2kSdy8+Y54xbcvJK2bNhmQ27E6jd5dQAJZ/fRjPRu71slxLiIbIXVGId7HaqL5YvHgxf/7zn0lOTmb58uUsX76cpk2bMnToUCZOnEhMTAwXL14EsNnkXqNlleXk5ADGzDTAqsm/I8ePHweMGWybN28mLS2NqKgoi30yM40VWjNmzHB4Lm0/MK7HLFmyhB9++IEXX3wRDw8PU3mleYaadsyrr77Kq6++avfcFy5ccPp7claDWiCzt5hVWlrK/fffD8CSJUsICwsDjPWzL7zwAjqdjiVLljBmzBjAWG87ZcoU1q1bx4gRI0ypgA2Rm6Jck/JCe/SqyrDeESQknq9x32G9IqnSq1RUVjG0ZyunRmjX9GZz5rgY5n+8l8ycYny8jH99XB0JXhu1aUQrhBBCCFFfXMuY0ZnMNBXjL881xW1qqY78XWsozzxFywcW8MGaIxxPyyU9qwj1cm+xob0iyM4rMX1WH8u1hGioXK02qi/CwsL47rvv2LdvH/Hx8ezZs4dTp07x7bffsmbNGubPn8/tt98OOK7C0+v1AKbeXFVVVbW6n2effZaqqireffddFixYwOeff25xXe06w4cPN2V+2XLbbbeZ/ndgYCDDhg3jxx9/ZPfu3fTo0YMtW7bQqlUrevfubdpPvZwM0r9/f9MkTFvat29fq+/NkWvapP9m8frrr/Pbb78xYcIEhg8fbvp8165dpKSkEBcXZ1ocAwgODmbhwoVMmjSJVatW3VILZK6MwjWoekrPHrsm5YX2uCsK3TuG0CkyyGHqrHkZ5cLH+wM1N0x15s1myISXWDy9L88u205JmfFh4+pIcCGEEEKIhuRqY0ZH8akrmWngQtzmY9zveFouZzILTZ93igyie8cQVm1INi2Y1cdyLSEaKlerjeoTRVHo168f/fr1A4x9wFatWsWKFStYunQp69evB4zVb/Zo27SFpebNm5OWlsaFCxcsKuU033zzDcHBwQwbNsz0Wc+ePXnyySepqKhg3bp17N+/n2+++YYJEyaY9gkJCeHMmTNMmzaNXr16Of09jh07lh9//JHNmzej0+koKyvjj3/8o8Xim9aH7b777rNYh7keGnwaytGjR/nmm29o2rQps2fPtti2Y8cOAO68806r43r27EnTpk05ePAgOp3uutzrtaQtHiWdyuHtLw/y8oe7efvLgySdyrHYbs5NcUd3NMGp8+uOJtRZ1pNeVVn4WF86RdpecOoUGcTCx/qiVy0nV9YUsDjzZvPi6tcJberHyH5RpGcVcSm/GN/oIU7dtzYSXAghhBCiIaltzFhTfKqqBqcz09wUBYOqxy9miFP34Rc9mJy8YtKzikyfdYoMYv6jfbhwqZgNu9Ms9t9yIF0Wx4S4wfSqyvDe1otAtmhD2+qDM2fOMHr0aKtJkqGhoTz//PMEBQVRUlKCl5cX4eHhZGVlcejQIavzFBUVsWvXLhRFMS1a9ejRA4Dt27db7Z+RkcHLL7/M3//+d4sFKi37rHHjxixYsACA//3f/zWVawKm89s6L8DcuXO5//77iY+Pt/h84MCBhIaGsmXLFjZv3oybmxv33XefxT41nXvZsmWMGTOG1atX29x+NRr8Atnrr7+OwWDg6aefJiAgwGLbqVOnAOjYsaPNY6OiolBVld9+++2a3+e1pI3Cff4f25n/0R4SEs9z5GQ2CYnnmf/RHp7/x3ZKyiptPkCuR3lhde6Kgo+3B0tnDWLxE/0Z2rMV3To2Z2jPVix+oj9LZw3Cx9vDbuabLQZVT0ma7aat5sp/P0nJ6SOM7BcJwIY96fi2jcUzrIPD47SR4FIaKYQQQoiGyNWYsab49IXl28GgOh+/pR0F3PCJcjJuaxvLifR8Yto3Y2jPVrzyRD+WzhpEaXkVCz7eg6600uKY+lauJURD5K4odLtcbeSIVm1UXxa1IyIiyM3NZceOHVZN83ft2kVeXh7h4eE0bdqURx55BIA5c+Zw/vyVtkPFxcXMnj0bnU7HXXfdRUhICADjxo2jcePGrFq1il9++cW0f1lZGa+88goAf/jDH+ze2x133MGoUaMoLCxk8eLFps8nTpyIt7c3//znP/npp58sjvnuu+/4/vvvSU1NJTY21mKboijcd999XLp0iU2bNtG7d2+LaZwA//M//0Pz5s1Zv349X375pcW2HTt28Omnn5KSkkJ0dLTd+66tBl1iuW3bNg4dOkSLFi0YN26c1XatCZ75qFVz2udaAzx7tD8gzkhOTnZqv7p0NaNwb1R5obb4Fd2+qUVzRS1rzJXFMXDxzWbSNkLGxBIZ6s/G3WncFRdByISXuLj6dbsjwVv8aZ5MnBTCRfX92SmEEPVRfX12uhoz1hSfllboUdxdy0zTJoC3+NM8u1lnV+I2PQNiwxkQa2yGnZ1XwqrLTfqrL45B/SzXEqIh0qsGFj7e1+7zw9mhbdeTu7s7ixYt4qmnnmLmzJl07dqV8PBwsrOzOXToEO7u7syfPx+AyZMnc+jQITZu3MioUaPo3bs33t7eHDhwgLy8PLp06cLf/vY307kjIiJYuHAh8+fP5+GHH6Znz540adKEo0ePcvHiReLi4nj00Ucd3t/cuXPZvn07mzZtIj4+nuHDh9OiRQveeOMNZs+ezVNPPUXHjh1p06YNZ8+eJSUlBUVRWLJkCc2aNbM639ixY/noo49QVZWxY8dabff29mbZsmU88cQTvPLKK/zrX/+iQ4cO5OTkcPjwYcC4QNi5c+er+Knb1qAXyFauXAnA1KlTadSokdX20tJSALy8vGwer31eUlLi8DoZGRmm8ar1zdWMwtXS1HXHbKc+mtPKC+t6gaj6QpirC2PmXH2z6ePlwZnMSuZ/vJfF0/vaHgkeM8QUjMnimBCuqc/PTiGEqK/q47PT9ZhRz5FTl6ziU0VxIzLUHx8vD1o2NfYUczV+c1MUFE9fp+I2vapyOqOAb+NPUlhcjq60ipJy2w2vtXKt+vILtxANlbviho9XI5bOGsTh1Gy2HEgnr6icIH9Pp4a23Sh33nknn3zyCStXriQpKYkTJ04QGBjIXXfdxfTp0+natStgzMB65513GDRoEKtXryYxMRGANm3aMH36dB566CFTiaRm3LhxtGnThk8++YRDhw5RXFxMWFgYM2fOZPr06Xh4OF4WCgkJYdasWbz22mu88sor9OnTBz8/P0aOHGk67759+0hLS6N58+bcfffdFvdcXZs2bWjWrBklJSV2+7n36tWLtWvX8tFHH7Fz5062bdtGUFAQgwYNYtq0aaY+bXWtwS6QnTp1it27d+Pv72/RbM6ccnlBw96UCIPBYPFfe8LDw4mLi3PqvpKTkykqKqp5xzpyNaNw3RR3U5q6o9R2rbzwWnA0ztuVgQPg+ptNrUl/Zk4xzy7bzuSRnbmn7+0W36vWc0wWx4RwXX1+dgohRH1VH5+dtYkZzeNTf59GjOwfxah+kTQN9DV9blD1+PcYQXnWadRSxz2BzasZtLjMu7XjuM1dUWgb3oS5U678PLPzS/lxzxk27k6jqMSYSWY+HEoIceNpi1/W1UaOh7bdaAMGDGDAgAE17ufm5sbYsWNtZl/Z06tXrxqb6ffp04eUlBSb2x5++GEefvhhq887d+7M22+/7fR9ABw4cICcnBzGjx/vcAJmREQEr776qkvnvloNdoFsw4YNAIwYMcLu/yna52VlZTa3l5eXW+xnjyt/eCdPnnzd3/pdzShc59PU6zaDyplx3iln89i4+4zpjcHw3hF06xhi842BS282bTRtDWvmx4Bu4agouJvtKwtjQtRefX92CiFEfVRfn52uxoxa3NmymS+vPtGXkGA/ik8f4eI2y7jPr3N/PFu0I/OrxVTlZdq9vq1qBvOXrMavzV+sGuPFpFM5xP9yziwDJYLJIzszIi6SBR/vIcCncb0r1xJCGFlXG8nf0RuloqICDw8P8vPzeeONNwCYNGnSDb4raw12gUxrJDdq1Ci7+4SEhJCcnExOTg7t2rWz2q5NcbDXo+xmcTWjcF1JU68rzozzDpk4j6YBnhw8kWV6u5eQeN5Uc+7j1cjiAenSm822sSQeySCmfbN6n6orhBBCCFEfuBozBgd40iUqmDmTe+LXSE/GZ3Psxn0tJr5Eywfmk/HZCzYzyVytZtAGBNjqYaTFkwse68Py54bg5ekhMaAQQtRg3759zJgxA71ej6qqjBo1ii5dutzo27LSIBfIMjMzSU1Nxd/f32HtaocOHdi2bRunTp2iT58+FtsMBgOnT5/G3d3d5uLZzUIbhZuQeL7Gfe31VnAmTd1RKaSrnBnnffHr1wif+iYj+0WxOj7VtM3RwIHaNm2t76m6QgghhBA3innMp8WE3q27OixtVFWVp8bF0LixsUewrcUxTfnvJ7nw9euET32TgB53k79rjcX22lQzODPA6pVP9pniSYkBhRDCsdatW9O8eXMKCgq48847LQYJ1CcNcoHsyJEjAMTExDhsSDdw4EA++eQT4uPjefDBBy22JSYmkpubS1xcHH5+ftf0fq8l81G4jhr1O9NbwVaaujOlkK5klxlUPaVnjzk3zvv0UUb2a8e3W0+iqlf6xNkaOKDdb22y4SQoEkIIUd+dfu3+OjlP23lrrsn5xK3HlRjQPLYyqCqKolCVkYR7aBTlF047F/elHSXwjvupKryEXpdb62oGbYBVeaWeLlHBlJRVkZ5VZBFLgv14UgghhLXIyEi2bt16o2+jRg1ygezYsWMAREdHO9wvLi6ODh06sGvXLlavXm1q5p+bm8uiRYsA4wTMm921GoXrTClkiz/NQ/H0dTpocVNcGOedlEDImBgiQ/05k1losa36wIEr53euaas9NQ0GqMtMOiGEEEKI+qi2MaD5cYaqClo9/nd0SducuqbuaAI+UTGE/OFpi/OBaz1h3RWFduEBvPv8UNNnl/KL2bAn3aIxP9iPJ4UQQtycGuQC2fnzxnLCiIgIh/spisLrr7/OI488wvz58/n2228JCQlh//79FBQUMGHCBIYNG3Y9bvmaulajcJ0phbzwb2MppCtcHeft42X9x7ymwQSOmrbavJadRq7aYABtIayuMumEEEIIIeqr2saA5sd5RXQGXI/7qp/PFVo85p6VwsWkK/Gab/QQJo+M5a64COZ/vJfMnGLA+UFXQgghbg4NcoEsNzcXgBYtWtS4b0xMDN988w3Lly9n3759nDx5ktatW/Pcc88xfvz4a32r101dj8J1qRQy7Sjerbs63xfCL9C5/S6P8y6t0Fttc3YwgTMcNXI9eCKL958fQkBjfZ1m0gkhhBBC1Ee1jQGrH6eWlwCux321v28nhkBNeInF0/vy7LLtFJVUmuJJadIvhBC3hga5QPb555+7tH/79u1Zvnz5Nbqb689RGWBdjcJ1qRTyckq8MwyqHr/oIeiOba9xX7/owcY3gQbrbfYGDtSGo0auI/tHEdTEp+bmsrXIpBNCCCGEqG9qEwNqmfbmx1Vkn6OqMAe/6MHOxX0xQ64qI9+pIVCrX7cYAjW0VwR5hWUEBXjV6ppCCCHqF0lXaUC0bLCkUzm8/eVBXv5wN29/eZCkUzkW2+vseleREm+Pm+KOT9tYPMM6ONzPM6wDPm1jcVMUvDwtyyW1gQN1sTimV1UOp160uTimKG6M6hdJ8ekjTr9F1XplCCGEEELcrFyNAStyM62PM6gUJm7Gp2035+K+qJhaL44ZVD0laU7Ga6ePMLJfJJ1bB9G9Ywjrd6ZxODW7zuNoIYQQ158skDUQWhng8//YzvyP9pCQeJ4jJ7NJSDzP/I/28Pw/tlNSVlmn/7jXNiVeX8MiUVVxPi0mzLUbLHmGdaDFhLlUFRuDrJKyKtM284EDdcFdUYj/5ZzNbZGh/jQN9KU4KcGpc+mOJkiJpRBCCCFueq7GgPnbvrJ5XGHiJirzsmqO+/40z+mXjNXjTL2qujgEahvNgnyZ/1hfLlwqZsPuNLYcSJcSSyGEuAU0yBLLhshRGSAYR1UvWrGXpbMG1cn1DKoevxgnSyFjhlBeXslrK3+hid+Vxva2SiANqp7K3Ew8/IIJn/omJaePoEvahr44zzjOO3owPm1jqczLokqXRyleBAd4EhXW6qoGDjhir0GrNhzgWmTSCSGEEELURy7FgNGDqSrIpjj1gM1ySrVUR+ZXi2n5wHzbcV/MkMslmjWXVtobqPSHQW3pEBHketZbpZ4FH+9BV1opzfqFEOIWIQtkDYBeVUk6lWN3cUyTkp7H4dRsots3q4MFJDd8ooylkI7S1bWU+OLSCo6cykFVDSQknjdlevl4NbK4FzfFHe+IzmT+3yt4hrUnoPsIQsY8Y9peVZBNbsL/UZZxkrAHFwKwaHp/oPYDB2pi3vBfUdyIDPXHx8vD9Pn1ai4rhBBCCHGjuSnuzseAbWPJTfg/UKsoTNxM8JBJVsdV5WWS8dkLBPS42yru07LGnFkcszdQ6UxmIe8+P9TleG3J5wdM0yzrcviTEEKIG0cWyG5R1Rvv2ysDrG7LgfRqUyxVq8b9znBTFFMp5IXVb9gMkMxLIX19A4kM9edMZiHgOKPNoKqE/PFZLvz7NfJ3f0/j5hEonj6o5SVUZJ/Ds2U7U6q9ecB0LVLf9arK8N4RHDyRxcj+UYzqF0nTQN8r26uqnB8qcJXNZYUQQggh6gODqtLiT/PsNr33DOtAi4kvUZmXReHBTYCxnNI/drjN2FEt1ZG/aw2lZ44RMuElymmEn48nKuBuuqax0T82vnZUSZGeVcSl/GJ8XRgClZNXzAmzc9Xl8CchhBA3jiyQ3WKqp48H+Dbm8fuinU791vZbsTaJwuIKh+WONcmNX0XQwAk1lkLm7VxNyOinTSWJGnsZbW6KguLpazxv2lF0RxPQF+fRuEkbgu+c4nSqfV1wVxS6dQzh/eeHENTEh+LTR7i4LQG9Lh93v0ACB04wDRVwJpNOCCGEEOJmZy9WMy+LrCrKJfOrxahlOsCJcsroIfi0jSHrko7/2/wrzz7Qg3NZRbQO9cdNUSg9e8x4ncsxmPE6sRhUPcln8+1WUqiqgQ170pk80vmst+17zhAZ6k96VhEdWgVavFwWQojrxWAw4OZWfxbm69v91IYskN1CbKWPt2kZADif+q3td/RUDmcyCx2WO9bIoNpNiddKIQsPbsKnfU/Aspm+pnpGm0Zb/PJufbvFwpKzqfY1qZ455yiTzqDqCWisJ+OzOVZBVclvibR67O81v0W1kfEmhBBCCHGzqilWy9uxmqq8TItjHJVT5heWsmZDMht2p9G7SygAAd6NqCor5uLX1jGW7th2U4wV3swHf59GFJVU2rzXjbvTuCsugtCJ88iycS6wjNfu6deGe/q1Ia+oDB9PD8keE0Jcd7t27eKf//wnn376qemz7777jrlz5zJu3Dhee+2163YvFy9eZMmSJUyYMIG4uDjT58OGDSMjI4Nt27bRokWL63Y/V0MWyG4httLH07OKyM4vZVivCBISz9d4jqG9IsjOKyE9q8j0WW0a+Js3aM3ftcZmKSQGY4CkpaqbX1NTU+abeSq98eurXRiz3cDVUSadm+Jud/FLLdXx+xcLCXtokcO3qLI4JoQQQohbka1YzaDq8e3cj6JDP1ntr5VTarFjs//5M6V+YUx7LZ6qKmPsOLRXBMWlFQQH+th8Qakp//0kF/79GuFT32RkvyhWx6fa3K+opJL5H+/lrZn9HcZr5eWVfLH+OGm/F1rFh0IIcb1kZWUxbdo0wsPDb/StADBnzhx27drF+PHjb/StXDVZILtF2GvEr6oGftxzhskjO9MpMshho/5OkUF07xjCqg3JqNX+oXe1gb9Vg1aDSsXFs1b7aanqa2xcE65v01NHDVztZdIZVD2lZ485TMevysvk/CfPETblrWuW8SaEEEIIcbMwODPMyaDi5tEYr7D2fLMh2bQ4psWrJaXlFJ8+4jAGA+MiWcnpo4zs145vt560GW8CZOYU89XmVP48rptVvKbq9fy4O42tl182l5RVkfRbztVVWgghRC0ZDPVrUV69/DttdStXrqSyspJmzZpd5zuqPfmt/BbhqBH/xt1pXLhUzPxH+9Ap0vakxE6RQcx/tA8XLhWzYXeazX22HEh36R9+rUGrZ1gHm9s9wzoQMmEeWZd0dq+pNT2ta/pqf4lVVXXYwBXg9xwdOw//Dly5HzfFHd3RhBqvp5bqyN+xus4z3oQQQgghbjaKolBQVErIhJdqiBNfsogTtXg1J78UH29PipMSnLqeLimBZkG+RIb6O9yvf0w4etVgite0X/o27ztHzy4teOupgbz11EDefX4on8wbwYQ7O/J7jo5FK/bK4pgQQlQTGRlJu3bt8PC4efKybp47FTWyV45YVFLJgo/38Mr0fiydNYhDqRfZeuBK+eDQXhF07xjChUvFLPh4D7pS2/0ZnG30rzE2aPVx3KC1qop3V+2zec1OkUF13vTUVgllcIAnM8fFknzGOgNP07KZL4un9yO0qS+HUi+yxWwAgl6X79y1i+1n7wkhhBBCNCT/XJ/MpLs6mhryl539FbWqHMXDE682XfGJiqGgqJT/25xM7y6hFvHq5xuSeWFyL5djsOoDocxpcefOwxn8uPcswQGePDUuFtWgck//NhxKucjnPxw3xc/DekUweWRnRsRFsuDjPS5VWgghrg9H023ro/z8fFauXElCQgLp6elUVFQQHBxMnz59mDFjBm3btuXdd9/lvffeAyAjI4NOnToRFxfHqlWr7J63tLSUL7/8ks2bN5OWlkZpaSlNmjShe/fuTJ8+nZiYKxmz+/bt4+GHH2batGmMHTuWd955hwMHDlBRUcFtt93GY489xp133gnA+fPnGT58uOnYhx9+GID4+HhatWpltwdZYWEhn332GZs2bSIjI4PAwEBiY2OZOXMmnTp1qtOfqatkgewW4qgcMTOnmOeWbWNkvyj+MKgt3SeFmLYV6MpZdbnpqb3FsZrOD7YfQBVVBnYcPku3Du0IGXPlL15OXjHbdqdxR0wYT43vxnPLtlk0TtXS1WtqeurKQ89eCWWblgF4Nvawm4Hn79OIxdP74eXpwfP/2G41AMHdL9DRj8XE3dd29p4QQgghxK2mphhNVQ3M/2gPf74/luh2t+PTNvbKNr2eiko9Tfy9efaBHgBk55WY4tVmgd6A6zFYZKg/x9NyrbabV1K8/+0RdKWVdIkKxt3DnbLSCl7+cLfd9hvzH+3DK9P78d3WkzLNUoh6QuvvbH+6bf3r/5yTk8PEiRM5f/48rVu3pn///pSWlnLs2DH++9//snXrVtatW0enTp0YMWIEP/30Ez4+PgwfPpx27drZPW9ZWRkPPvggv/76Ky1atKBnz54YDAZ+/fVXfvrpJxISEvjqq6+Ijo62OO7EiRNMmDABPz8/evbsSVZWFocOHWLmzJl88MEHDBs2DB8fH0aPHs3evXvJzs6mf//+NG3aFB8fH7v3k5mZyZQpUzhz5gyhoaEMHjyYrKwsNm3axNatW/n888/p3r17nf1cXSULZLcIvaoyvLfjRvxFJZWsjk8lun0z0xsuvaqS9nuB3aal5rRyx+oLVjU9gKLbNeXlj/fSyF3Bx8uDkrIq0rOKUFUD8b+cY+msQfx5XCyb9p69/EYukm4dmztcHKvNQ89eCaX2NtFehtzI/lGENvW1WBwD4wCES/nF+EYbhxHUxC9mSL18GAshhBBC1BVnYjQVGNm/DZNHdiIk2I/i00coTrqyr2/0EHzbTTbz3wAAIABJREFUxqKvqiI9u5i/f5loih0BSsqrXIvBoo0x2Mzx3bgjNpwtB9LNMsGMcWf1SophvSJwV9x45ZN9disMUtLzWPzpPpbOGkRHO21MhBDXl0FVUcuLbQ5RM59uq3j61qvfy95//33Onz/PlClTmDNnDm5uxt+DdTodjz/+OImJiaxdu5YZM2YQExPDTz/9RFBQEEuXLnV43i+++IJff/2Vu+++m7///e+mcseKigqef/55Nm3axNdff221QLZ7927Gjh3L3/72Nzw9jYky77zzDh9++CH/+te/GDZsGMHBwSxdupQpU6aQnZ3Nk08+SZ8+fRzez6JFizhz5gzjx49n4cKFNGrUCIDvv/+eOXPm8NJLL7Fx48Za/QzrgiyQ3SLcFYVuHUOcasRv/nartsdpnHkAhUx4iUWP9eHZZds5k2mZoaY1/+8XHcaAWOMUDq3nmKPFMVcfevaGGICx0SrYzpBTFDfu6WdMq7c1AGHDnnQmjzRrMuum2JzW6RnWwaLZqxBCCCHErcaVGK1z6yCqyoptTqHU9g2dOI82of4E+XtyJrPQtN0iBgvvhKGyzOakdNAGQsWQmVNMSLAP0e2bWsS0ZRVVVpUUiuJG7y4tOJxqHf9Vl5Kex6HUi/Tu0sLhfkKI68NNUWw+gzTm023rk6CgIAYOHMjTTz9tWhwD8PPz49577yUxMZHMzEyXz+vl5cXgwYP561//atELrHHjxowdO5ZNmzbZPK+npyfz5s0zLY4BPPTQQ3z44YccPXrU5fsA4/TNrVu30rx5cxYsWGBaHAP44x//yPr16ykuLiYnJ+eGNfaXBbJbiF41sPDxvnYbzdsrW9SrBuY/2ofFn9p+Q6alj9vK6HLmAXRx9esOx2tvOZBebdHOce+G2jz0HA0xSM8qIju/lGG9rDPwIkP9aR7ozec/HLd57MbdadwVF0HIhHmUpuzBp30PPAKu/GWuKsyh5FQivp37SfaYEEIIIW5prsZoF792vG/W18Z9X5oSZ1XmuOPQef44uC1hk1/Bzf3KrzRVhTkUJm6mMHETjYJamhr9f59wij+P6wZYxpn/79sjbD1oHf8FB3jx2bpfnfq+tx44R/dJITW2BhFCXFsGVU/p2WPOTbdNO4p366715vezZ555xuqz3NxcUlJSOHDgAACVlfbbIdnz0EMP8dBDD1l8VlhYSGpqKtu3GzNwKyoqrI5r3749fn5+Fp81a9YMNzc3SktLXb4PMPY3Axg4cCCNGze22v7pp5/W6rx1SRbIbiHuihs+Xo1YOmsQh1OzbaaPWy+OqaSczSM4wKvGBv4Z2cV0ah2I++WHiEsPoNNHGNmvvc3x2q40/7+ah56966iqgR/3nGHyyM5WmXQ1lV8WlVTy7jdH+NtjfQjocRclp4+Qu/XLK+UE0UMI6HHX5b4b9ePhK4QQQghR11yN0bxadaQ88zen9vWM6GIRp7q5ufHo6C74+XhSknbEspQzejDBQyYR2H8sSmMvsi7pmP/xXkKDbffEyS20jvFqiv+q0/aTxTEhbiw3xR3d0QSn9tUdTah3FT7p6el88cUXJCYmkpaWhk6nAzBllBkMBkeH23Xx4kW+/PJL9u3bR1paGvn5+RbntcXf33rqr5ubG4qioNfra3Uf2dnZABYN++sbWSC7xWj/MFdPH7dXtuiuKGzcfYaDJ7IY2S+Ke/q15rlJPU3bzRui9u4SSpeoK9tcegAlbSNkTCyRof4WKfJQc/N/c1fz0HN0nY270xgRF2mVSeeo/BKMDfyfmRCLobyUjFU3V527EEIIIURdqU2M1rh5BBUXzzq177odp+nbtQXPTeppKuXM+OxvDmMvXTG8/OEeLuSWcFtr2z3CbMV4NcV/zpxDCHFjuDrdtr5Yt24dL774Inq9njZt2jBo0CDatWtHdHQ0Fy5cYMGCBbU67969e3nyyScpLS0lLCyMuLg42rZtS9euXfHw8ODJJ5+0eZyjxbPaqu3C2vUkC2S3KHcbDertySsqNzXw/3brSSJD/a2a6Wv7VVcX47XtNf+3e65aPPRqGmJQVFLJgo/38Mr0fhYZeAW6cgqLyxlm59iR/aMICfaz2T9DU1/r3IUQQggh6pKrMZriaX/SWfV9dx7J4JP/HuPxMV25d0Bbp0s5B3Vvxer4VJvxpr340FH7DVtcjWWFENeOq9Nt64Pi4mIWLlyIoih88MEHDB482GL7qlWranVeg8HAyy+/TGlpKa+++irjx4+32L5ly5Za33NtaH3FLly4YHP7L7/8QmZmJn369CE0NPR63pqJLJAJi7deqmqwyvCytZ/G1QeQ9kZOY6/5v8Nz1eKh58wwgsycYv7ft0d45Yn+Vhl43W0cqyhujOoXSfHpIzdlnbsQQjR0694ec6NvQYhbiqsxmlpe4vS+gX6edGkTxICYls7HXqePMHpAR4pKym3Gm/biQ0ftN6qrTSwrhLg2DKoevxgnp9vGDKk3PaJ/++03iouL6datm9XiGMCuXbsAUFXjABJns7tyc3M5d+4czZs3t1ocs3Xe2nL2fnr06AHAnj17qKqqshgaALB8+XL279/PN998c8MWyG78nwZx3enN/gJob86cYXw7duVY7QHkDL/oweTkFZOeVWT6zHxogLNcuublh55GG2LQyc4Y7k6RQcye3AtVNaBgeU8GVc9rM/rT3SwAigz1p2mgL8VJCU7dj+5oQr14AAshhBBC1DVXYzS1ssw4cdKJffVVVcydEscbMwcSGODtfOyVtI3AAO/LzfktY2CNvfhw4+40LlwqZv6jfRzGjq7GskKIa8dNcccnKhbPsA4O9/MM64BPVEy9+d1M68mVmprKuXNXnot6vZ4PPviArVu3AlBebqzo0hrcFxcXO+xLFhgYiJeXFzk5ORw5csT0ucFgYM2aNXz11VcW560tbdJlUVGRw/3atGnDHXfcQWZmJm+99ZZFyeXatWvZv38/UVFRREdHX9X9XA3JIGtAtNTvpFM5xP9ibMR/T7/WDIgNd/rtmHmDffMHkKO3eMbx2rEkHskgpn0zh0MDauLSNas1XXRmiIGxmb4bJWnHLBu+xgzBJyqWRY/3JflsPj/uSSPA1/hgulnr3IUQjo3+639qfayWmVQX5xB17/Rr99fJedrOW1Mn5xHiVlCbGM2/23DKM1KNC2UG68Urbd+K3Ewu7ViN4h1As7umuhx7rVibRGFxBcN7R9Cto+W0SUfxYW5hOZ1a+7g0AEsIcWMZVJUWf5pntwxb61FYX7LHAEJCQhg1ahQbNmxg9OjRxMXF4eHhwdGjR8nOzqZ9+/acOnWKnJwcwLjwFRgYSH5+Pg888ADR0dHMmzfP6rzu7u5MnjyZFStW8OCDDxIXF4ePjw/JycmcP3+edu3acfr0adN5a6t169YALFq0iP/+97/89a9/NX1W3auvvsqDDz7I559/zpYtW7j99tvJyMjg2LFjeHt7s2zZsmvS/8xZskDWQOhVAyVllSxasddiIex0Rj7tWwVaNac31ykyiIWP9aWySqWRh+VDxPkHkJ4BseEMiA033Q/UbtqPQdU7vKZ3VCyh41+8vNjlbnGc++WvbQ0xMDZ8LbF5XvOGr51bN7EYVnAz1rkLIYQQQtQ1V+JCN8Wd5qOMzaGrCnMoTNxMYeIm1FKdxb5VhZf4/bM5qGU6GocYf+FyNfY6eiqHM5mFJCSeN2V9+Xg1slgkA9vxoasDsIQQN5aboqB4+hI+9U1K0o4akx6K83D3Dbqc9BBTrxbHNK+//jpt27blhx9+YO/evQQEBNCqVStmzJjB+PHjGTBgAIcPHyY3N5fg4GCWLFnCG2+8wbFjx8jJybG5QAbwl7/8hebNm/Ptt9+SmJiIt7c3YWFhjBs3jqlTpzJhwgRSUlI4ceIEt912W63u/YknnuDMmTPs27ePXbt2cf/999tdIAsLC2PNmjV89NFH/Pzzz2zZsgVfX1/uuecennrqKTp0cJz9d63JAlkD4a64WS2Ogf3m9NXfjlVU6fliYzJTR99ucXxtH0BXE0y4Ke4UVLgbr3n6CLqkbaZr+ncfgVerTrgpivXY78tZYAZVtTPEwM3phq+am7XOXQghhBCirjkbF5ZlpFCUuPlKjBY9hOAhkwjoeQ8F+9bh3bY7Pm1jjItjXyxELTMumlVkn6OqMAe/6MHOxV42WnykpOexaMVels4aZLW/oyFXrgzAEkLcWNrvW96tb7eoKtLa79TH38e8vb15+umnefrpp21u379/v8XXgwcPtupXNnbsWMaOHWvxmYeHB4888giPPPKIzfP+97//tfi6T58+pKSk2L3P48ePW30WHBzMRx99ZPW5vSEAwcHBzJ07l7lz59q9zo0iC2QNgF5VSTqV47A5/XPLtjGyXxTj7+xg8XasskrlUMpF3v/2CF2igm0efz0fQNr3smTVAUb2i2Jkv/aEjIm9sr2qCn1ZMVlfO84CUzx9Le7LoOopPXvM5Wb7V1PyKYQQQghxq7EfF+pRK8u5sPpNys4ctTjGPEYLHjYZVVWpyM00ZY6ZnYTCxM0ED5nkdIuPNRuSLVqEgHGR7HBqNtHtm8lClxC3MPNqIuPX9W9hTNQv8iekAXBXFOJ/cdwEtaikktXxqXywxti8b/XPKbyxcj8T5v3Ago/3kJVbYhphbc/1eABp34t2v4++Hs/TS7fy4ns7WL/zNO4eHjYXxzRaFlj1e3NT3NEdTXDqHqo329fKCew1gzSvcxdCCCGEaAis40J3Mr9YaLU4pjGP0dw9PMjfsdpyceyywsRNVOZl0WLCXIexV8iEl8i6pGPD7jSb+2w5kC6LY0IIISxIBlkDkVfk3GSK3ELjfgdPXOR4Wq7p8/o0wtr8e1FVA2cyC1EUN154qIfzY7/NssA0tW22f7PWuQshhBBCXA8uZeqfPop3m67oiwts7qOW6sj8ajEtH5hvs92GX/QQfNrGkHVJx/yP96IrrbR5HmdjYyGEEA2HLJA1EEH+ni7tV1JWZfrMfIT1tXjTZquZfvW3jrbu0VxkqD9NA325uC3BqWvqjiZYT7m8imb7N2OduxBCCCFEXagplnMpUz8pAZ+2MTRu2Y7StCM296nKyyTjsxcI6HE3TeLuxaftlXYbRboyVm1IZsPuNLuLY+B8bCyEEKLhkAWyBkCvqgzvHUFC4vka9x3WK5ICXTnBAZ5EhbW6piOstayq0rPH7DbTr76wZO978fEy/lGubRZYXTXblzp3IYQQQjQUrsRyrsZoPp36ULD7O7v7qaU68netwSuyK40iOtOoUSP0qspvvxeyOj61xutorUOkzFIIIYRGFsgaAHdFoVvHEDpFBtlt1A+WZZSLpvcHrt0Ia4OqopYX25wa6aiZvhvY/F60jLfaZoFJs30hhBBCCOe5Gsu5GqN5h7V3shF/jKkJv704sTot5q3evF8IIUTDJuktDYReNbDw8b50irQuDwTLMkpz1+qtmpui2AyoNOW/n+Ti9+/gpigYVL3pc0VR0Ov1vPpkf7qb9URLzyriUn4xvtFDnLq+lgVmTprtCyGEEEI4x5lYTmu6r2XqO8MvejC5+SXkFpQ4GZfpUS7Hq4qikFdUxvxH+ziMeec/2of8ojLTcUIIIQRIBlmD4a644ePViKWzBnE4NZstB9LJKyonyN/zmpZR2uJMo1aPoJY0G/UEgHXafvQQfNrGsujxviSfzePHPWfIKyrnxNl87oitfRaYNNsXQgghhKiZK033S8+dwCu8o/OZ+m1jKcgppmUzXwyqvsa4LPlsHht3nyHAtzGP3xfNyvXHeeCuTiydNYhDqRfZeuCcKeYd2iuC7h1DuHCpmK82p/DsAz3q+kcjhBDiJiYLZA2ItvgV3b6pxUTKa1VGaU6vqrhfXliqqVGr4u1Hy0nzURp5kfHZHLtp+6ET59ExogldonqatlVVVRE6cR5ZX9t+o2meBWa7h5g02xdCCCGEcMTZpvuKtx+NgluilhdjqKygxYS5XFj9Ro0xWkiwj+k6YCsu01NeUcWr/9zP4ZPZALRpGQAYJ5w/t2wbI/tFcU+/1jw36UqcmJ1XYmrg37tLaK2/fyGEELcmWSBrgNyrLfJc24UxY1Za0qkc4n85Z3q756hRa0CPu2kUGGpzcUxT/vtJsr5+jfCpb7Jux2l2HsmgpKyKSr3Kosf6XHUWmDTbF0IIIYSwz5mm+wE97sbDtwkZn81BX6qj5QPzjTHa6SPokrZdidGiB+PT9kpT/+qzzM3jMlU1oCjuzPtgu0WfsfSsIrLzSxnWyzjMaXV8Kt9uPUlkqD8+Xh6UlFWRnlVk6jsmTfqFEEJU1yAXyDIyMnj//ffZuXMnubm5BAUFMWTIEJ555hmaN29usW9aWhrvvvsuBw8eJD8/n8jISCZOnMikSZNQZNHEIb1qoKSskkUr9poCGO3tnt1GrW4KAT3uouT0YafS9ktOH6Ff1/Z88t9jpoDn2WXbmTyyM/f0lSwwIYQQQohrocam+6aY7ogppsv47AUCetxNQPcRhIx5xrRrVUE2FbkXaBQY4vCUetWAwWDgSEq2VRN+VTXw454zTB7Z2dSkX1UNnMkstDqP+WAqIYQQQtPgVgqSkpIYM2YMa9asoUmTJgwePBhFUVi9ejWTJk2ioKDAtO+JEycYN24cP/zwA2FhYQwcOJALFy6wePFiXnjhhRv4Xdwc3BU3i8UxqLmZfuPmEXgENEOXtM2pa+iSttEsyJfIUH/TZ2HN/BjQLRy12h9vWRgTQgghhLh6zjTdvxLTJZg+U0t15O9aQ/r7f+b8iuf4/fOXOb/iOdLf/zP5O1bXGKu5K254uCtsOXDO5vaNu9O4cKm4xib9tgZTCSGEEA0qg6yiooLnn3+eoqIiXn75ZSZPngxAeXk5s2fPZtOmTbz77ru8/PLLGAwGXnjhBXQ6HUuWLGHMmDEA5ObmMmXKFNatW8eIESO4++67b+S3VG/pVZWkUzk23+5t2JPO5JG2G7UqnsaeE86k7QPoi43nj+3QjKiwgOs+cEAIIYQQoqFxU9xrbLrvMKYzqFRcPGvxkRbT2aNXVU5nFNAhIoi8onKb+xSVVLLg4z28Mr2fzSb9EicKIYRwpEGl1GzYsIEzZ84wevRo0+IYgKenJ3PnzqVZs2akpaUBsGvXLlJSUoiLizMtjgEEBwezcOFCAFatWnV9vwEX6C+XE9r7uq6Pq85dUYj/xf7bvaxLOkImvGQ1ulstLzEeX1PavnYdX+PbwcfGRPPcpJ5Et292+foS9AghhBBCXCsGVaXFn+ZZxXIa9wBjCaOrMR1ciT/N41B3RWHP0UwAgvw97Z4nM6eY55ZtY9WGZNqGNeG5ST1Z/ET/y3Fi08vnkjhRCCGEtQaVQbZ582YApk6darWtZcuW7Nq1y/T1jh07ALjzzjut9u3ZsydNmzbl4MGD6HQ6/Pz8rtEdu656U3ztjdnw3hF06xhi941ZbY9zxNHbvfkf72Xx9L6XG7UeRZd0uZm+XzBqZTl+MUPQHdte4zX8YoZgUPWm5q0S8AghhBBCXHtuioLi6VvDYCS98zFd9BAKikr5dN1xhvUyxp+VlSorNx1HVQ08fl80p87nWzTit6eopJLV8alEt29Gu1ZNWP1TCh4e7sR2aFbruFYIIcStr0EtkB0/fpxGjRpx2223kZmZybp160hPTycwMJC77rqLmJgrDd1PnToFQMeOHW2eKyoqikuXLvHbb78RGxt7Xe6/Jraa4msSEs+bei74eDWyCApqe1xNanq79+yy7YzsF8V9gzsS0taymX5NaftgHAdu3oRfCCGEEEJcP1rPMO/W9gYj1VyKCZdjurYxrNqQzNaD59l60Bh/zn+0D/9zRxTvfJUIQBM/T6tG/PZojfhXbUhm7fbTAHy75WSt41ohhBC3vgazQFZRUUFmZiYtWrTgxx9/ZN68eZSWlpq2r1ixgkcffdTUfP/ixYsAVlMtNdrnOTk5NV77u+++4/vvv3fqPpOTk53azxZbTfHNpaTnsWjFXpbOGlQnxzmiV1WG93b+7V50e09TkOKmKBhUPS3+NI8L/37NZkDlGdaBFn+aZ5E9JoS4tVyvZ6cQQtxKbsSzs3osZt5sXyvFdBTThUx4iaxLOjbsTjN9npKex+JP97F01iCi2zUzZY797xcHGBEXyfxH+7D4030249dOkUEsfKwvFy4VW5xTO6+rca0QQoiGocEskOl0OgAKCgp48cUXueeee5g5cyZNmzZl586dLFq0iE8//ZTWrVszceJE0+KZl5eXzfNpn5eUlNR47YyMDPbv319H34lt9priV5eSnsfh1Gyi2zfDXXGr9XE1cVcUunUMcfrtXvXvJS2jkDYt/R2m7VdVVfFbRhFR4U3kDaAQt6Dr8ewUQohbTX17djosxYwejE/bWLIu6Zj/8V50pZUWx6ak53Eo9SJ3923Npr1neOiezoQ183PYiH9orwi6dwyhsLiCBR/vsTqndl5X4lohhBANQ4NZICsvN/bDKi0tZcCAASxdutS0bdSoUfj4+PDEE0/w/vvvM2HCBJTLb77c3Gz/o2kwGCz+60h4eDhxcXFO3WdycjJFRUVO7WvOUVP86rYcSDctStX2OGfoVQMLH+9rNzvNfMy2eXDirij8Z/tpUs7mMuP+WGLadbFI29dXVXEo5SLvf3uELlHBPDepp9P3JIS4eVyPZ6cQQtxq6uOz014pZk5eMWs2JLNhd5rNhSyArQfO8dykniSdyuHCpWJT5thzy7Yxsl8U9/RrbRELVulVikoqeG7ZNrJy7b/IdjWuFUIIcetrMAtk3t7epv/9wAMPWG0fMmQIoaGhZGVlcfbsWXx8jKOpy8rKbJ5PW3DT9nNk7NixjB071qn7nDx5cq3f+tlril/TfrU9ribuihs+Xo1YOmsQh1Oz2XIg3ekx23lF5WReKmHBx3vw8FCI6xxKoL8n+UXl7E/OoqrK2NuiZTNfl+5JCHHzuF7PTiGEuJXU52eneSnmu6sP8/Mv6aiq45fN5vGnrcyx9745TLtWgfSLDqNDRCAFugrm/r+dDhfHqp9XCCGEgAa0QObv70+jRo2orKykVatWNvcJCwsjKyuLvLw8QkJCSE5OJicnh3bt2lntm52dDdjvUXYjOGqK72i/2h7nDG3xK7p9U4u3dPrLwZC9tHbza1VVqfx/9u49PMr6zP/4Z54hTDKZIEFIECQYSogRkiACgm0hQkXx91O72ipqWY31sL9ut12PVSNrrcViL9t1adke1OIW3SJaLGu3dVUgpgvIUQgRhCCRcD4GyGRCgHme3x84Qw4zk0kyh2Tm/bouL515vs8z3wlyX9/vnfu5n5Wb90dsTgAAAIiv02e87SbHpPNrPc+ps9p/pMFfOXbjpGG6/I4s/zjf2nLhe59q/5GGsK8LAICP0f6QxGC32/2JroMHDwYc42u4369fP+Xl5Uk6/zTL5izL0s6dO1tcM958TfHDMWVsjn8R0dnzOspuGK1eB+/3EKs5AQAAIPY6sta7euwQHa7zqPbguVtBfQ95qtl3ssUa0Ndb98vFg8K6LmtIAEBrSVNBJkmTJk3Sp59+qnfffVeTJ09ucWznzp3au3evsrKyNGTIEH31q1/Vyy+/rKVLl+rOO+9sMXbDhg06duyYxo8fL5fLFcuvEFRnm+J3pZl+tHTHOQEAECs7Z98SsWsNK/tjxK4VTZH6zj3l+ya7jqz1Lh+RpQV/2dqi2izYGpA1JACgK5KmgkySZsyYIafTqT/96U965513/O+fOHFCTz31lEzT1J133inDMDR+/Hjl5eVpxYoVWrRokX/ssWPH9Mwzz0iSSktLY/4dQvE1xc/PyQx4vHlT/EicF03dcU4AAACIjHDWerO+faUOHG3QX1bWtHg/1BqQNSQAoLOSqoJs8ODBmj17th599FE98sgjmj9/vrKysrRx40bV1dVpwoQJ+va3vy1JMgxDzz33nO666y7NmjVLb731lrKysrRmzRqdOHFCt956q6ZMmRLnb9RSZ5vid6WZfnf7LgAAAOj+wlnrnWo6qz8ur9bwIX3DXgOyhgQAdFZSJcgk6frrr1dubq5+9atfac2aNdqxY4eGDBmie+65R6WlpUpJSfGPLSoq0ptvvqm5c+dq9erVqq6u1tChQ/XQQw/pm9/8Zhy/RXCdbYrf2fOiqTvOCQAAAJHR3lovJcXQPTeMavN+e2tA1pAAgM5IugSZJBUUFGju3LlhjR0+fHjYY7uTjjTFj8R50dQd5wQAAIDICLbW6+oakDUkAKAjkqoHGQAAAAAAANAaCTIAAAAAAAAkNRJkAAAAAAAASGokyAAAAAAAAJDUSJABAAAAAAAgqZEgAwAAAAAAQFIjQQYAAAAAAICkRoIMAAAAAAAASY0EGQAAAAAAAJIaCTIAAAAAAAAkNRJkAAAAAAAASGokyAAAAAAAAJDUSJABAAAAAAAgqZEgAwAAAAAAQFIjQZaEvKYZ8jUAAAAQbaxJAQDdSa94TwCx4zUt2Q2bNu84oqVrd6uuvkmZGQ5NHTdEo0dk+Y8DAAAA0cKaFADQHZEgSxJe05Ln1Bk989JH2lZb1+JY+YY9ys/J1NP3TZAzNYUFCQAAAKKCNSkAoLviFsskYTdsARciPttq6/TMSx+xEAEAAEDUsCYFAHRXJMiSgNc0tXH7oaALEZ9ttXXauP2wvKYVo5kBAAAgWbAmBQB0ZyTIElTzJqd2w9DStbvDOm/Zulp+YwcAAIAuCdSAnzUpAKA7owdZgvEtPip3HNGytbvVJ7237vt6oerqm8I6P9xxAAAAQGvBGvDfOGmY8oZksiYFAHRbJMgSiNc05Wk8q2dePt/X4ZKL+kiSMjMcYV0j3HEAAABAc6Ea8H++/6R+8cjVrEkBAN0Wt1gmELthtEiOSVLtwXodPt6oKWOHhHWNKWNz6PcAAACADgvVgL/2YL3cntOaOo4H76pZAAAgAElEQVQ1KQCgeyJBliC8pqmPAzQ9NU1L7676XJfnZyk/JzPkNfJzMjV6xAD6PQAAAKBDwmnAb7PZNHpE+GtSVqQAgFgiQZYg7IahZUGanv51ZY0OHG3QrG9fGXRBkp+Tqafvm8Bv6gAAANBh7TXgz8nOUHpaiurqT7W7Jp317St1vP6UDH5pCwCIIXqQJZBgzUzrPWf0L79dpR/dP1EvfH+SPt5+SMvXnW+aOmVsjkaPGOBvqgoAAAB0VKjG+s7Uc9uOV/+8RbdPyw+4Jr167BBdPiJLB4426A/vbdODt4+J1dQBACBBlkhCNTPdf6RBD734oaZPzNWNk4bp8juy/Md8VWMkxwAAANBZodainlNnJZ1r/+Fbk143cageuuMK/5jDdR4t+MtW/WVljcZdlh31+QIA0BwJsgThNU1NGTdE5Rv2BB1T7zmjRUu3q3B4fxUOv1B249wdtiTGAAAA0BVe09TUEGvR5g+OKt+wR4uWbtdby6uVk50hZ2oveU6dVe3Beplf/OLW16SfdSoAIFboQZYg7IahyzvQ9NSXHAMAAAC6ym4YIRvwB3pwlGla+nz/SW2pOabP95/0J8d4cBQAIB7IkiQQr2nq6XsnhG7Ef+8EeU0zxjMDAABAovOalp6+L/hatLq2Tme9ZsgxPDgKABAv3GKZQOyGIWdaL73w/UnauP2wlq2rDdCI36R6DAAAABFnN2xypqa0sxa1whpD9RgAINZIkCUYX/KrcPiFGj1igP99X9UYyTEAAABEiy+x1XYt2vahUOGMAQAgVsiWJKjWiTASYwAAAIiVtmvRtkmvcMYAABArSVlB9qc//Uk/+MEPgh7/h3/4Bz344IP+15s3b9a8efO0efNmeTweDR8+XH//93+vG264IRbTBQAAAAAAQBQlZYJs69atkqQvf/nL6tevX5vjBQUF/v9esWKFHnjgAZmmqXHjxiktLU2rVq3SI488oh07drRIpAEAAAAAAKDnScoE2ZYtWyRJP/nJT5SdnR103KlTp/Too49Kkn73u99pwoQJkqTa2lrNnDlTv/71r3XNNddo1KhR0Z80AAAAAAAAoiIpG1N9+umn6t+/f8jkmCQtWbJER48e1Q033OBPjklSTk6OHn74YUnSggULojpXAAAAAAAARFfSJch2796tkydPauTIke2O/dvf/iZJmjp1aptjU6ZMkd1uV0VFRcTnCAAAAAAAgNhJugSZr//YhRdeqGeffVbXXHONCgsLde2112revHlqamryj62urpYkjRgxos11XC6XsrKydOzYMR05ciQ2kwcAAAAAAEDEJV0PMl//scWLF+uCCy7QFVdcoezsbFVVVWnu3Ln629/+pldffVW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\n",
      "text/plain": [
       "<Figure size 619.85x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 525,
       "width": 612
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\")\n",
    "\n",
    "sns.pairplot(df, hue=\"kind\", diag_kind=\"hist\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In contrast to our previous examples, our data set contains a non-numerical text column `kind`.\n",
    "\n",
    "<code>sklearn.preprocessing.OneHotEncoder</code> is a preprocessor which encodes text values to according flags:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "features = df.iloc[:, :-1]\n",
    "values = df.iloc[:, -1]\n",
    "\n",
    "# needs 2d data structure, features.iloc[2] has dimension 1\n",
    "encoder = OneHotEncoder(sparse=False)\n",
    "one_hot = encoder.fit_transform(features.iloc[:, 2: 3]) \n",
    "\n",
    "one_hot[:5, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the one-hot encoder computes two columns with exclusive flags 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>circumference</th>\n",
       "      <th>length</th>\n",
       "      <th>kind</th>\n",
       "      <th>is_atlantic</th>\n",
       "      <th>is_sockeye</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>sockeye</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>sockeye</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>atlantic</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.5</td>\n",
       "      <td>89.0</td>\n",
       "      <td>atlantic</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>atlantic</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   circumference  length      kind  is_atlantic  is_sockeye\n",
       "0           19.0    59.5   sockeye          0.0         1.0\n",
       "1           18.0    53.0   sockeye          0.0         1.0\n",
       "2           28.0    75.5  atlantic          1.0         0.0\n",
       "3           33.5    89.0  atlantic          1.0         0.0\n",
       "4           23.5    63.0  atlantic          1.0         0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[\"is_atlantic\"] = one_hot[:, 0]\n",
    "features[\"is_sockeye\"] = one_hot[:, 1]\n",
    "\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we remove the categorical column now:\n",
    "del features[\"kind\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we prepare the data for training and testing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "(features_train, features_test, \n",
    " values_train, \n",
    " values_test) = train_test_split(features, values, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without further explanation we pick a regression algorithm, more about regrssion algorithms will be discussed later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.kernel_ridge import KernelRidge\n",
    "kr = KernelRidge(alpha=.001, kernel=\"rbf\", gamma=.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <i class=\"fa fa-info-circle\"></i>&nbsp; Regression methods in <code>scikit-learn</code> also have <code>fit</code> and <code>predict</code> methods. Thus cross validation, pipelines and hyperparameter-optimization will be available.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "kr.fit(features_train, values_train)\n",
    "predicted = kr.predict(features_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us plot how good given and predicted values match on the training data set (sic !)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 266,
       "width": 721
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def plot_fit_quality(values_test, predicted):\n",
    "\n",
    "   \n",
    "    \n",
    "    plt.figure(figsize=(12, 4))\n",
    "    plt.subplot(1, 2, 1)\n",
    "\n",
    "    x = np.arange(len(predicted))\n",
    "    plt.scatter(x, predicted - values_test, color='steelblue', marker='o') \n",
    "\n",
    "    plt.plot([0, len(predicted)], [0, 0], \"k:\")\n",
    "    \n",
    "    max_diff = np.max(np.abs(predicted - values_test))\n",
    "    plt.ylim([-max_diff, max_diff])\n",
    "    \n",
    "    plt.ylabel(\"error\")\n",
    "    plt.xlabel(\"sample id\")\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "\n",
    "    plt.scatter(x, (predicted - values_test) / values_test, color='steelblue', marker='o') \n",
    "    plt.plot([0, len(predicted)], [0, 0], \"k:\")\n",
    "    plt.ylim([-.5, .5])\n",
    "      \n",
    "    plt.ylabel(\"relative error\")\n",
    "    plt.xlabel(\"sample id\")\n",
    "\n",
    "    \n",
    "plot_fit_quality(values_test, predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For assessing the quality of the predictions of a regression method, we can use multiple methods which we will discuss later in this script.\n",
    "\n",
    "For our current example we compute the average absolute difference between given values $y_i$ and predicted values  $\\hat{y}_i$:\n",
    "\n",
    "$$\n",
    "\\frac{1}{n} \\left(\\, |y_1 - \\hat{y}_1| \\, + \\, |y_2 - \\hat{y}_2| \\, + \\, \\ldots \\,+ \\,|y_n - \\hat{y}_n| \\,\\right)\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7122581321318484\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "error = np.sum(np.abs(predicted - values_test)) / len(values_test)\n",
    "print(error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics / error measures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we used classification metrics (like accuracy, precision, recall, F1) high values indicated good classification performance. \n",
    "\n",
    "Most regression metrics turn this upside down. E.g. smaller values indicate a better regression model.\n",
    "\n",
    "The hyperparameter optimization functions from `scikit-learn` select configurations which yield a large score. To make regression functions work in this framework, we have to flip the sign of the error value to achieva a usable score.\n",
    "\n",
    "E.g.\n",
    "\n",
    "- an average absolute error of 0.1 is scored as -0.1\n",
    "- an average absolute error of 0.2 is scored as -0.2\n",
    "\n",
    "In this situation the first case would be prefered: higher score indicates lower error.\n",
    "   \n",
    "\n",
    "`scikit-learn` offers the following metrics for measuring regression quality:\n",
    "\n",
    "### 1. Mean absolute error\n",
    "\n",
    "This is the metric we used before. Taking absolute values before adding up the deviatons assures that deviations with different signs can not cancel out.\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>mean absolute error</strong> is defined as \n",
    "\n",
    "\n",
    "$$\n",
    "\\frac{1}{n} \\left(\\, |y_1 - \\hat{y}_1| \\, + \\, |y_2 - \\hat{y}_2| \\, + \\, \\ldots \\,+ \\,|y_n - \\hat{y}_n| \\,\\right)\n",
    "$$\n",
    "\n",
    "\n",
    "</div>\n",
    "\n",
    "\n",
    "The name of the corresponding score in `scikit-learn` is `neg_mean_absolute_error`.\n",
    "\n",
    "\n",
    "### 2. Mean squared error\n",
    "\n",
    "Here we replace the absolute difference by its squared difference. Squaring also insures positive differeces.\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>mean squared error</strong> is defined as \n",
    "\n",
    "\n",
    "\n",
    "$$\n",
    "\\frac{1}{n} \\left(\\, (y_1 - \\hat{y}_1)^2 \\, + \\, (y_2 - \\hat{y}_2)^2 \\, \\, \\ldots \\,+ \\,(y_n - \\hat{y}_n)^2 \\,\\right)\n",
    "$$\n",
    "\n",
    "\n",
    "</div>\n",
    "\n",
    "\n",
    "\n",
    "This measure is more sensitive to outliers: A few larger differences contribute more significantly to a larger mean squared error. The name of the corresponding score in `scikit-learn` is `neg_mean_squared_error`.\n",
    "\n",
    "\n",
    "### 3. Median absolute error\n",
    "\n",
    "Here we replace mean calculation by median. \n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>median absolute error</strong> is defined as \n",
    "\n",
    "\n",
    "\n",
    "$$\n",
    "\\text{median}\\left(\\,|y_1 - \\hat{y}_1|, \\,|y_2 - \\hat{y}_2|, \\,\\ldots, \\,|y_n - \\hat{y}_n| \\, \\right)\n",
    "$$\n",
    "\n",
    "\n",
    "</div>\n",
    "\n",
    "\n",
    "This measure is less sensitive to outliers than the metrics we discussed before: A few larger differences will not contribute significantly to a larger error value. The name of the corresponding score in `scikit-learn` is `neg_median_absolute_error`.\n",
    "\n",
    "### 4. Mean squared log error\n",
    "\n",
    "The formula for this metric can be found [here](https://scikit-learn.org/stable/modules/model_evaluation.html#mean-squared-log-error). \n",
    "\n",
    "This metric is recommended when your target values are distributed over a huge range of values, like popoluation numbers. \n",
    "The previous error metrics would put a larger weight on large target values. One could consider relative deviations to compensate such effects but relative deviations come with other problems like division by zero.\n",
    "\n",
    "\n",
    "The name is `neg_mean_squared_log_error`\n",
    "\n",
    "\n",
    "### 5. Explained variance and $r^2$-score\n",
    "\n",
    "Two other scores to mention are *explained variance* and $r^2$-score. For both larger values indicate better regression results.\n",
    "\n",
    "The formula for [r2 can be found here](https://scikit-learn.org/stable/modules/model_evaluation.html#r2-score), the score takes values in the range $0 .. 1$. The name within `scikit-learn` is `r2`.\n",
    "\n",
    "The formula for [explained variance](https://scikit-learn.org/stable/modules/model_evaluation.html#explained-variance-score), the score takes values up to $1$. The name within `scikit-learn` is `explained_variance`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some algorithms from sklearn\n",
    "\n",
    "- `sklearn.linear_model.LinearRegression` is a linear regression method, which only works well for target values which can be described as a linear combination of feature values.\n",
    "\n",
    "\n",
    "- `sklearn.kernel_ridge.KernelRidge` is [documented here](https://scikit-learn.org/stable/modules/kernel_ridge.html#kernel-ridge). It combines the kernel trick from SVMs with classical least squares regression.\n",
    "\n",
    "\n",
    "- `sklearn.svm.SVR` is an extension of support vector classification concept to regression, [you find examples here](https://scikit-learn.org/stable/modules/svm.html#svm-regression)\n",
    "\n",
    "\n",
    "- `sklearn.neighbors.KNeighborsRegressor` extends the idea of nearest neighbour classification to regression: Search for similar data points in the learning data set and compute the predicted value from the values from the neighbourhood, e.g. by averaging or by linear interpolation. [Documentation is available here](https://scikit-learn.org/stable/modules/neighbors.html#regression)\n",
    "\n",
    "\n",
    "- `sklearn.tree.DecisionTreeRegressor` expands the concept of decision trees to regression [is documented here](https://scikit-learn.org/stable/modules/tree.html#regression).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A full pipeline\n",
    "\n",
    "Let us now try to find a good regressor using `scikit-learn`s hyper-parameter tuning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross val score: -0.7568859642342609\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 266,
       "width": 730
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "\n",
    "def eval_regression(p, features, values):\n",
    "    score = cross_val_score(p, features, values, scoring=\"neg_median_absolute_error\", cv=4).mean()\n",
    "    print(\"cross val score:\", score)\n",
    "  \n",
    "    predicted = p.fit(features_train, values_train).predict(features_test)\n",
    "    plot_fit_quality(values_test, predicted)\n",
    "\n",
    "    \n",
    "p = make_pipeline(PolynomialFeatures(2), PCA(2), LinearRegression())\n",
    "eval_regression(p, features, values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = make_pipeline(PolynomialFeatures(), PCA(), LinearRegression())\n",
    "\n",
    "param_grid = {'polynomialfeatures__degree': range(3, 6),\n",
    "              'pca__n_components': range(3, 11),\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'pca__n_components': 10, 'polynomialfeatures__degree': 3}\n",
      "cross val score: -0.22752607270361702\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 266,
       "width": 730
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "search = GridSearchCV(p, param_grid, scoring=\"neg_median_absolute_error\", cv=4, n_jobs=4)\n",
    "\n",
    "search.fit(features, values)\n",
    "\n",
    "\n",
    "print(search.best_params_)\n",
    "eval_regression(search, features, values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise section\n",
    "\n",
    "- Play with the examples above and try different algorithms, metrics and pipelines.\n",
    "\n",
    "\n",
    "### Optional exercise: Timeseries prediction\n",
    "\n",
    "The file  `data/sales.csv` holds sales data of a swiss sports shop selling skiing equipment. The time axis is in units of months, starting with January.\n",
    "\n",
    "* Load the data and plot sales value over months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "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>month</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.085941</td>\n",
       "    </tr>\n",
       "    <tr>\n",