assignment_knn_solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6aa681fc-8ffe-40b4-9e76-d241ad334e22",
   "metadata": {},
   "source": [
    "## Assignment 3: $k$ Nearest Neighbor\n",
    "\n",
    "**Do two questions.**\n",
    "\n",
    "`! git clone https://github.com/DS3001/knn`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afb901d4-cfb9-488f-8a95-db6a96edcc16",
   "metadata": {},
   "source": [
    "**Q1.** This question is a case study for $k$ nearest neighbor The target variable `y` is `price` and the features are `year` and `mileage`.\n",
    "\n",
    "1. Load the `./data/USA_cars_datasets.csv`. Keep the following variables and drop the rest: `price`, `year`, `mileage`. Are there any `NA`'s to handle? Look at the head and dimensions of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8a9f434d-1482-430b-b05e-ea62f6dbbb2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2499, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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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>price</th>\n",
       "      <th>year</th>\n",
       "      <th>mileage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6300</td>\n",
       "      <td>2008</td>\n",
       "      <td>274117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2899</td>\n",
       "      <td>2011</td>\n",
       "      <td>190552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5350</td>\n",
       "      <td>2018</td>\n",
       "      <td>39590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25000</td>\n",
       "      <td>2014</td>\n",
       "      <td>64146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27700</td>\n",
       "      <td>2018</td>\n",
       "      <td>6654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price  year  mileage\n",
       "0   6300  2008   274117\n",
       "1   2899  2011   190552\n",
       "2   5350  2018    39590\n",
       "3  25000  2014    64146\n",
       "4  27700  2018     6654"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('./data/USA_cars_datasets.csv')\n",
    "df = df.loc[:,['price','year','mileage'] ]\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "29a8d174-0bd4-427b-955b-133fa1e6ffb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>price</th>\n",
       "      <th>year</th>\n",
       "      <th>mileage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2499.000000</td>\n",
       "      <td>2499.000000</td>\n",
       "      <td>2.499000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18767.671469</td>\n",
       "      <td>2016.714286</td>\n",
       "      <td>5.229869e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12116.094936</td>\n",
       "      <td>3.442656</td>\n",
       "      <td>5.970552e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10200.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2.146650e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16900.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>3.536500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25555.500000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>6.347250e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>84900.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>1.017936e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price         year       mileage\n",
       "count   2499.000000  2499.000000  2.499000e+03\n",
       "mean   18767.671469  2016.714286  5.229869e+04\n",
       "std    12116.094936     3.442656  5.970552e+04\n",
       "min        0.000000  1973.000000  0.000000e+00\n",
       "25%    10200.000000  2016.000000  2.146650e+04\n",
       "50%    16900.000000  2018.000000  3.536500e+04\n",
       "75%    25555.500000  2019.000000  6.347250e+04\n",
       "max    84900.000000  2020.000000  1.017936e+06"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "464094a1-f264-4b3c-9763-76f15c1286c2",
   "metadata": {},
   "source": [
    "> There are no `nan`s, since there are 2499 observations of each variable, and 2499 rows in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87a87b63-7a6c-4472-b587-d0358c33836f",
   "metadata": {},
   "source": [
    "2. Maxmin normalize `year` and `mileage`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dfb1cf4d-a578-4b12-adb8-96acb167a7c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def maxmin(x):\n",
    "    u = (x-min(x))/(max(x)-min(x))\n",
    "    return u\n",
    "\n",
    "df['year'] = maxmin(df['year'])\n",
    "df['mileage'] = maxmin(df['mileage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5434272a-8f64-48f3-a5d5-e3d6dc0a37d2",
   "metadata": {},
   "source": [
    "3. Split the sample into ~80% for training and ~20% for evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "651b0e28-3391-4d53-923b-f2bf799b88fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "y = df['price']\n",
    "X = df.drop('price',axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=.2,random_state=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6a6a2e5-1730-4c69-9e54-c2886393dca8",
   "metadata": {},
   "source": [
    "4. Use the $k$NN algorithm for regression and the training data to predict `price` using `year` and `mileage` for the test set for $k=3,10,25,50,100,300$. For each value of $k$, compute the Sum of Squared Error and make a scatterplot showing the test value plotted against the predicted value. What patterns do you notice as you increase $k$?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "01a39758-8401-4955-8dec-83404a07e996",
   "metadata": {},
   "outputs": [
    {
     "data": {
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WYufOnfjyyy/Rtm1bPPXUUwCAAwcO4NFHH8Vdd92F22+/HTExMTh//jw+//xzXLhwAfPnz0dgYKBF35s2bbIoe8Fx22234ZZbbgHQEL0lZpxAQ0bhIUOGoH///nj22WdRUVGBf/3rX+jQoQMmTpxoanfXXXehZcuWSElJQbNmzXD27FksW7YMFy5cwJdffmnR59tvv43bbrsNd9xxB8aPH48//vgD//znP3HnnXdaZIIGGspLlJeXm6KgvvvuO/zxxx+meYyMjAQAvPDCC6iurkZycjLq6uqwcuVK7NmzB59//rnFZvrKK6/g1KlTGDx4MJo3b47Tp0/jo48+QlVVFRYsWGBxbbF9vv3229ixYweGDh2K1q1bo7S0FP/73/+wd+9ePP/88xYZjJ977jksXboUmZmZ+Otf/4rg4GD861//QlxcHF566SWb+S8oKMDBgwcxffp0weiniooKU1kULjpt4cKFiIqKQlRUFCZPnmxqy6Ud4Ct/sWLFCjRv3hx/+ctfeK/D8cMPP+Dy5cuizVOe6pMgTHjY54ggXApfhudr166xO+64gzVq1Ijt2rWLMcbYli1bGAA2a9YsUX327t2bRUdHs6CgINa8eXM2cuRIdvDgQZu2w4cPZ2FhYbyZm83ZtGkTe/rpp1mnTp1Yo0aNmFarZe3atWPPP/+8RYbn4uJiNnfuXHbHHXew+Ph4FhQUxKKjo9mgQYNsHD05h2ehP3NH4TFjxkhyis3OzmapqaksNDSUxcTEsMcff9yUeZlj4cKFrH///iw2NpYFBQWxpk2bsnvuuYfXsZkxxn755Rd22223sdDQUNa0aVM2adIkU5Zic9q0aSN4T+bjX7ZsGevevTuLiIhgjRs3ZoMHD+aNclu5ciUbMGAAa9q0KQsKCmKxsbHsgQceYHl5eTZtxfb5448/srvvvps1b96cBQcHs8aNG7N+/fqxZcuWMaPRaNP+3LlzbMSIEUyn07FGjRqxu+++m504cYJ3njhHbL7njYPLWs3316ZNG5v5tH6NMcaOHTvGALCpU6cKXodj5MiRLDg4mF2+fNluO0/3SRAcGsZ4dO0EQShCXFwcnnjiCbz//vueHgpBEARxExJ+CMJFHD58GGlpafj999+dTqhIEARBKAcJPwRBEARB+BUU7UUQBEEQhF9Bwg9BEARBEH4FCT8EQRAEQfgVJPwQBEEQBOFX+HWSQ6PRiAsXLqBx48aCycIIgiAIglAX7GaJnObNmyMgQLoex6+FnwsXLpiKFxIEQRAE4V2cO3cOLVu2lPw5vxZ+uIKB586dg06n8/BoCIIgCIIQQ2VlJVq1asVb+FcMfi38cKYunU5Hwg9BEARBeBlyXVbI4ZkgCIIgCL+ChB+CIAiCIPwKEn4IgiAIgvArSPghCIIgCMKvIOGHIAiCIAi/QrLwc/78eTz22GNo0qQJwsLC0LVrV/z666+m9xljeP311xEfH4+wsDCkp6fjxIkTFn2UlpZi9OjR0Ol0iIqKwtixY3H16lWLNgcPHsTtt9+O0NBQtGrVCu+9957NWNauXYtOnTohNDQUXbt2xcaNG6XeDkEQBEEQfoYk4aesrAz9+vVDcHAwNm3ahCNHjuCf//wnoqOjTW3ee+89/Pvf/8aSJUuwe/duREREICMjA9XV1aY2o0ePxuHDh5GdnY3vv/8e27Ztw/jx403vV1ZW4s4770SbNm2Ql5eH999/H7Nnz8bHH39sarNz506MGjUKY8eOxf79+3H//ffj/vvvR0FBgTPzQRAEQRCEr8MkMG3aNNa/f3/B941GI9Pr9ez99983vVZeXs5CQkLYqlWrGGOMHTlyhAFge/fuNbXZtGkT02g07Pz584wxxj788EMWHR3NampqLK7dsWNH078ffvhhlpmZaXH9lJQU9uyzzwqOr7q6mlVUVJj+zp07xwCwiooKkTNAEARBEISnqaiocGr/lqT5Wb9+PXr37o2HHnoIzZo1Q48ePbB06VLT+4WFhTAYDEhPTze9FhkZiZSUFOTm5gIAcnNzERUVhd69e5vapKenIyAgALt37za1GTBgALRaralNRkYGjh8/jrKyMlMb8+twbbjr8DFnzhxERkaa/qi0BUEQBEH4H5KEn99//x2LFy9G+/bt8cMPP2DixIn4v//7P3z++ecAAIPBAACIi4uz+FxcXJzpPYPBgGbNmlm8HxQUhJiYGIs2fH2YX0OoDfc+HzNmzEBFRYXp79y5c1JunyAIgiAIH0BSeQuj0YjevXvjnXfeAQD06NEDBQUFWLJkCcaMGeOSASpJSEgIQkJCPD0MgiAIgiA8iCTNT3x8PBITEy1e69y5M86ePQsA0Ov1AIDi4mKLNsXFxab39Ho9Ll68aPH+jRs3UFpaatGGrw/zawi14d4nCIIgCILgQ5Lw069fPxw/ftzitd9++w1t2rQBACQkJECv12Pz5s2m9ysrK7F7926kpaUBANLS0lBeXo68vDxTm5ycHBiNRqSkpJjabNu2DXV1daY22dnZ6NixoymyLC0tzeI6XBvuOgRBEARBELxI8Y7es2cPCwoKYm+//TY7ceIEW7FiBQsPD2dffPGFqc3cuXNZVFQUW7duHTt48CC77777WEJCArt+/bqpzdChQ1mPHj3Y7t272fbt21n79u3ZqFGjTO+Xl5ezuLg49vjjj7OCggK2evVqFh4ezj766CNTmx07drCgoCD2j3/8gx09epTNmjWLBQcHs0OHDom+H2e9xQmCIAiCcD/O7t+ShB/GGPvuu+9YUlISCwkJYZ06dWIff/yxxftGo5HNnDmTxcXFsZCQEDZ48GB2/PhxizaXL19mo0aNYo0aNWI6nY499dRT7MqVKxZtDhw4wPr3789CQkJYixYt2Ny5c23GsmbNGtahQwem1WpZly5d2IYNGyTdCwk/BEEQBOF9OLt/axhjzLO6J89RWVmJyMhIVFRUQKfTeXo4BEEQBEGIwNn9m2p7EQRBEAThV5DwQxAEQRCEX0HCD0EQBEEQfgUJPwRBEARB+BUk/BAEQRAE4VeQ8EMQBEEQhF9Bwg9BEARBEH4FCT8EQRAEQfgVJPwQBEEQBOFXkPBDEARBEIRfQcIPQRAEQRB+BQk/BEEQBEH4FST8EARBEAThV5DwQxAEQRCEX0HCD0EQBEEQfgUJPwRBEARB+BUk/BAEQRAE4VeQ8EMQBEEQhF9Bwg9BEARBEH4FCT8EQRAEQfgVJPwQBEEQBOFXkPBDEARBEIRfQcIPQRAEQRB+BQk/BEEQBEH4FST8EARBEAThV5DwQxAEQRCEX0HCD0EQBEEQfgUJPwRBEARB+BUk/BAEQRAE4VeQ8EMQBEEQhF9Bwg9BEARBEH4FCT8EQRAEQfgVJPwQBEEQBOFXkPBDEARBEIRfQcIPQRAEQRB+BQk/BEEQBEH4FST8EARBEAThV5DwQxAEQRCEX0HCD0EQBEEQfgUJPwRBEARB+BUk/BAEQRAE4VeQ8EMQBEEQhF9Bwg9BEARBEH4FCT8EQRAEQfgVJPwQBEEQBOFXkPBDEARBEIRfQcIPQRAEQRB+hSThZ/bs2dBoNBZ/nTp1Mr1fXV2NSZMmoUmTJmjUqBGGDx+O4uJiiz7Onj2LzMxMhIeHo1mzZnj55Zdx48YNizY///wzevbsiZCQELRr1w7Lly+3GcuiRYvQtm1bhIaGIiUlBXv27JFyKwRBEARB+CmSNT9dunRBUVGR6W/79u2m96ZMmYLvvvsOa9euxdatW3HhwgU8+OCDpvfr6+uRmZmJ2tpa7Ny5E59//jmWL1+O119/3dSmsLAQmZmZGDhwIPLz8/Hiiy/imWeewQ8//GBq8+WXX2Lq1KmYNWsW9u3bh+7duyMjIwMXL16UOw8EQRAEQfgLTAKzZs1i3bt3532vvLycBQcHs7Vr15peO3r0KAPAcnNzGWOMbdy4kQUEBDCDwWBqs3jxYqbT6VhNTQ1jjLFXXnmFdenSxaLvRx55hGVkZJj+3bdvXzZp0iTTv+vr61nz5s3ZnDlzpNwOq6ioYABYRUWFpM8RBEEQBOE5nN2/JWt+Tpw4gebNm+OWW27B6NGjcfbsWQBAXl4e6urqkJ6ebmrbqVMntG7dGrm5uQCA3NxcdO3aFXFxcaY2GRkZqKysxOHDh01tzPvg2nB91NbWIi8vz6JNQEAA0tPTTW2EqKmpQWVlpcUfQRAEQRD+hSThJyUlBcuXL0dWVhYWL16MwsJC3H777bhy5QoMBgO0Wi2ioqIsPhMXFweDwQAAMBgMFoIP9z73nr02lZWVuH79OkpKSlBfX8/bhutDiDlz5iAyMtL016pVKym3TxAEQRCEDxAkpfFdd91l+v9u3bohJSUFbdq0wZo1axAWFqb44JRmxowZmDp1qunflZWVJAARBEEQhJ/hVKh7VFQUOnTogJMnT0Kv16O2thbl5eUWbYqLi6HX6wEAer3eJvqL+7ejNjqdDmFhYYiNjUVgYCBvG64PIUJCQqDT6Sz+CIIgCILwL5wSfq5evYpTp04hPj4evXr1QnBwMDZv3mx6//jx4zh79izS0tIAAGlpaTh06JBFVFZ2djZ0Oh0SExNNbcz74NpwfWi1WvTq1cuijdFoxObNm01tCIIgCIIgBJHiHf3SSy+xn3/+mRUWFrIdO3aw9PR0Fhsbyy5evMgYY2zChAmsdevWLCcnh/36668sLS2NpaWlmT5/48YNlpSUxO68806Wn5/PsrKyWNOmTdmMGTNMbX7//XcWHh7OXn75ZXb06FG2aNEiFhgYyLKyskxtVq9ezUJCQtjy5cvZkSNH2Pjx41lUVJRFFJkYKNqLIAiCILwPZ/dvScLPI488wuLj45lWq2UtWrRgjzzyCDt58qTp/evXr7PnnnuORUdHs/DwcPbAAw+woqIiiz5Onz7N7rrrLhYWFsZiY2PZSy+9xOrq6izabNmyhSUnJzOtVstuueUWtmzZMpuxfPDBB6x169ZMq9Wyvn37sl27dkm5FcYYCT8EQRAE4Y04u39rGGPMs7onz1FZWYnIyEhUVFSQ/w9BEARBeAnO7t9U24sgCIIgCL+ChB+CIAiCIPwKEn4IgiAIgvArSPghCIIgCMKvIOGHIAiCIAi/goQfgiAIgiD8ChJ+CIIgCILwK0j4IQiCIAjCryDhhyAIgiAIv4KEH4IgCIIg/AoSfgiCIAiC8CtI+CEIgiAIwq8g4YcgCIIgCL+ChB+CIAiCIPwKEn4IgiAIgvArSPghCIIgCMKvIOGHIAiCIAi/goQfgiAIgiD8ChJ+CIIgCILwK0j4IQiCIAjCryDhhyAIgiAIv4KEH4IgCIIg/AoSfgiCIAiC8CtI+CEIgiAIwq8g4YcgCIIgCL8iyNMDIAiCcAX1RoY9haW4eKUazRqHom9CDAIDNB7rhyAI9UDCD0EQPkdWQRHe+O4IiiqqTa/FR4Zi1j2JGJoU7/Z+CIJQF2T2IgjCp8gqKMLEL/ZZCCwAYKioxsQv9iGroMit/RAEoT5I+CEIwmeoNzK88d0RMJ73uNfe+O4I6o18LZTvhyAIdULCD0EQPsOewlIbTY05DEBRRTX2FJa6pR+CINQJCT8EQfgMF68ICyxS2inVD0EQ6oSEH4IgfIZmjUMVaadUPwRBqBMSfgiC8Bn6JsQgPjIUQoHoGjREa/VNiHFLPwRBqBMSfgiC8BkCAzSYdU8iANgILty/Z92T6DBPj1L9EAShTkj4IQjCpxiaFI/Fj/WEPtLSJKWPDMXix3qKzs+jVD8EQagPDWPMb2M1KysrERkZiYqKCuh0Ok8PhyAIBaEMzwThuzi7f1OGZ4IgfJLAAA3Sbm2imn4IglAPJPwQBOFWSJNCEISnIeGHIAi3QbWyCIJQA+TwTBCEW6BaWYTS1BsZck9dxrr888g9dZnKjRCiIc0PQfgJnjQ3OaqVpUFDrawhiXoygRGiIC0i4Qwk/BCEH+DpjUJKrSxyLiYcwWkRrYVpTotIqQgIR5DZS4WQKpdQEjWYm6hWFqEUjrSIQIMWkdZNwh6k+VEZnj6hE76FWsxN3lwri6LT1AVpEQklIOFHRZAql1AatWwUXK0sQ0U1ryCmQUPmZLXVyqLDiPogLSKhBGT2UgmkyiVcgVo2Cm+slaUGcyFhizdrEQn1QMKPSpByQicIsahpo/CmWll0GFEvnBZRSEzWoEE7pzYtIqEuyOylEtRyQid8C7WZm4YmxWNIol71PjRqMRcStnBaxIlf7IMGsHiu1apFJNSHU5qfuXPnQqPR4MUXXzS9Vl1djUmTJqFJkyZo1KgRhg8fjuLiYovPnT17FpmZmQgPD0ezZs3w8ssv48aNGxZtfv75Z/Ts2RMhISFo164dli9fbnP9RYsWoW3btggNDUVKSgr27NnjzO14FDWd0AnfQY3mJq5W1n3JLZB2axNVblJ0GFE33qRFJNSJbM3P3r178dFHH6Fbt24Wr0+ZMgUbNmzA2rVrERkZicmTJ+PBBx/Ejh07AAD19fXIzMyEXq/Hzp07UVRUhCeeeALBwcF45513AACFhYXIzMzEhAkTsGLFCmzevBnPPPMM4uPjkZGRAQD48ssvMXXqVCxZsgQpKSmYP38+MjIycPz4cTRr1kzubXkMtZ3QCd+B2yisHXf15LgrCB1G1I+3aBEJdaJhjEk2Wl+9ehU9e/bEhx9+iL///e9ITk7G/PnzUVFRgaZNm2LlypUYMWIEAODYsWPo3LkzcnNzkZqaik2bNuHuu+/GhQsXEBcXBwBYsmQJpk2bhkuXLkGr1WLatGnYsGEDCgoKTNccOXIkysvLkZWVBQBISUlBnz59sHDhQgCA0WhEq1at8Pzzz2P69Omi7qOyshKRkZGoqKiATqeTOg2KwzlYAvyqXDrREM7gTSHbnh5rvZGh/7s5Dg8j26cNUu0cEoQv4+z+LcvsNWnSJGRmZiI9Pd3i9by8PNTV1Vm83qlTJ7Ru3Rq5ubkAgNzcXHTt2tUk+ABARkYGKisrcfjwYVMb674zMjJMfdTW1iIvL8+iTUBAANLT001t+KipqUFlZaXFn5ogVS7hSrzB3AQ0HAL6v5uDUUt34YXV+Ri1dBf6v5vj1ugqNZoLCYJQDslmr9WrV2Pfvn3Yu3evzXsGgwFarRZRUVEWr8fFxcFgMJjamAs+3Pvce/baVFZW4vr16ygrK0N9fT1vm2PHjgmOfc6cOXjjjTfE3aiHIFUu4c+oKdcVmQsJwneRJPycO3cOL7zwArKzsxEa6n227hkzZmDq1Kmmf1dWVqJVq1YeHBE/3AmdIPwJtWSjNocOIwThm0gSfvLy8nDx4kX07NnT9Fp9fT22bduGhQsX4ocffkBtbS3Ky8sttD/FxcXQ6/UAAL1ebxOVxUWDmbexjhArLi6GTqdDWFgYAgMDERgYyNuG64OPkJAQhISESLllgiDchFrDy+kwQhC+hySfn8GDB+PQoUPIz883/fXu3RujR482/X9wcDA2b95s+szx48dx9uxZpKWlAQDS0tJw6NAhXLx40dQmOzsbOp0OiYmJpjbmfXBtuD60Wi169epl0cZoNGLz5s2mNgRBeBcUXk4QhLuQpPlp3LgxkpKSLF6LiIhAkyZNTK+PHTsWU6dORUxMDHQ6HZ5//nmkpaUhNTUVAHDnnXciMTERjz/+ON577z0YDAa89tprmDRpkkkrM2HCBCxcuBCvvPIKnn76aeTk5GDNmjXYsGGD6bpTp07FmDFj0Lt3b/Tt2xfz589HVVUVnnrqKacmhCAIz0Dh5YQ1no76I3wXxTM8z5s3DwEBARg+fDhqamqQkZGBDz/80PR+YGAgvv/+e0ycOBFpaWmIiIjAmDFj8Oabb5raJCQkYMOGDZgyZQoWLFiAli1b4pNPPjHl+AGARx55BJcuXcLrr78Og8GA5ORkZGVl2ThBEwThHVCuK8IcKipLuBJZeX58BbXl+SEIf4dyXRGAcNQfPQcEh0fy/BAEQbgCynVFUFFZwh1QYVOCIFQFhZf7N2qN+iN8CxJ+CIKwQYqjqSucUim83H+hqD/CHZDwQxCEBVIcTckplVAaivoj3AH5/BAEYYJzNLU2O3DlJczra0lpSxBi4aL+hHSHGjQI2BT1RzgDCT8EQQCQ5mhKTqmEq6CisoQ7IOGH8Bj1RobcU5exLv88ck9dpo3Sw0hxNJXSliCkQlF/hKshnx/CI5CviPpwhaMpOaUScqGoP8KVkPBDuB2hBGacrwid7DyDKxxNySmVcAaK+iNcBZm9CLdCviLqRYqjKTmlEgThzZDwQ7gV8hVRL1IcTckplSAIb4aEH8KtUAIzdSPF0ZScUglCGAroUDfk80O4FUpgpn6kOJqSUypB2EIBHeqHhB/CrXC+IoaKal6/Hw0aNAfkK+JZpDiaeptTqivKcRAEBwV0eAck/BBuhfMVmfjFPmgAiwWCfEXUg68KCHQiJ1yJo4AODRoCOoYk6n3i9+TNkPBDuB3OV8R6E9LTJqQKfFVAoBM54WqoIr33QMIP4RHIV0Sd+KqAQCdywh1QQIf3QMIPIYirTR/e5ivi6/iygEAncsIdUECH90DCD8GLu00fvupj4k34soBAJ3LCHVBAhzjUsN6T8EPY4G7Th6/6mHgbviwg0ImccAcU0OEYtaz3lOSQsMDd5Sc4Qcta48AJWlkFRYpch3CMLwsIVI6DcBeU/FMYNa33pPkhLHCn6cNTPiZqULmqEV9W2dOJnHAnFNBhi9p8Ckn4ISxwp+nD3T4m9UaGhTknsGzHaZRfrzO9Tia2BjgBYcIX+2zecyQgeINASSkWCHfi7oAOtf8G1eZTSMIPYYE7TR/uFLSyCoow/etDKL9WZ/Oet4dxK01UeLDNPEWGB2Pug11550ctNnwx0Imc8EW84TeoNp9C8vkhLHCnb4S7BC3Ozswn+ACu8WXyRuzNU4XA3KnJhi8W7kR+X3ILpN3ahAQfwqvxlt+g2nwKSfghLOBMHwBsBCClfSPcIWjZszObY65y9UfEzJO1cOhu53iCICzxpt+g2oIOSPghbHBXtII7BC1HdmZrvDGMWwmk2OOd+QxBEMrhTb9Bdx6sxUA+PwQv7vKNcLUTqlRhxhvDuJVAjj1ebTZ8gvA3vO03qKagAxJ+CEHcFa3gSkFLijBjrXJVe/SEksixx6vNhk8Q/oY3/gbVEnRAwg+hClwlaDnKXWOOucrVG6InlEROjh9fzgtkD38Sigl1462/QTXUdSSfH8KCeiND7qnLWJd/HrmnLqvCUc4Z7NmZOaLCg7HEzJfJW6InlESOPV5tNnx3kFVQhP7v5mDU0l14YXU+Ri3dhf7v5vjkM0GoH3/8DSqFhjHm3bubE1RWViIyMhIVFRXQ6XSeHo7H8WVtB9+9RYUH46nbEjB5UDvT4lBvZOj/bo6gEyF3kto+bZBPLihyngFffm7MEap5xz0Fix/rqQp1PuF/+Mtv0Bxn928Sfkj4ASBuYff2H5EYc0XuqcsYtXSXw75WjUv1uNrWVcgx6/i6KUiMUBwZHozQoEAYKv1nAyLUg6//Bq1xdv8mnx9CdTVXXIUYO7NS0RPevBDJscerwYbvSsSEFDckh7RMBknZwwl34eu/QaUh4YeQVXPFmzd3eygRPeGPKmhfR26osC8dHtSAr647hPsh4YeQrO3w5c1dTHRYgAYoq6rlfU/IfEgaAO/GmVBhdxds9FV8ed0h3A9FexGStB2+HgllHj0hhJEBk1ba3qs3pZonpOEoNb8Y1JJozhvx9XWHcD8k/BCia670ahPtF5v70KR4LHq0Bxxp063v1ZtSzRPSEJMywRFqSjTnTdChgnAFJPz4MGJz9ojNFZF3pkzVm7uSOYqiI0Jg7+N89+ptqeYJaQjWvNOFICo82GMFG30tN5c1dKggXAH5/PgoUu3jYmqurMs/L+rantjclfYHkCPIeCLVPDmAuheh1PzZRwyY+MU+aAALDYWrE835gx8MHSoIV0DCjw8i1+nWUc0VtdaRcYWTsZx7dXeqeX/Y+NQIX0ixJwo2+otzvVrXHcK7IeHHx5CTs0es9kCNdWRclaNIzr1y5kN3aAC8bePzBw2VOws2+ktuLkD+uuMPzxwhHxJ+fAypOXukaA/cubmLRU6OIjHIvVd3aADkbnye2gz8SUPlrkRzrnru1Yic36I/PXOEPMjh2ceQYh+XEz4q6PQZGeoRbYNUfwApzqFy73VoUjy2TxuEFc+kYPLAdpg88Fb8Y0R3DEnUi7wr+8hxAPVUQU4KUXYN/uYHI+W3SM8cIQbS/PgYYu3esY1C8Ne1B2Spzd2p3neE1BxFUk+Dcu81+4jB4loLt5xS7OQpJymlJ0xk/mSacTf+6Acj5rdIzxwhFtL8+Bhic/aAwanwUU69f19yC6Td2sRjC4nY+y2rqpV9GpR6r64+eUrZ+DyZI4VClF2H2Ofenf537sDRb5GeOUIsJPz4GGJz9pRU1YjqzxvU5iP7tBIsRQEAMzM7460N7hEAxAobtTeMsnOzSNn4PLkZeNI04+u5b8T+zv1Nu+Fv5kBCPmT28kHEON3mnrosqi+l1eZKOt3ymbGsiQwPxomLV93mHCpW2Eid8xNKq/6sAC7FJCbFAdSTm4HYZ+d0SZXDNlKeG39xdvVEeL3a8UdzICEPSZqfxYsXo1u3btDpdNDpdEhLS8OmTZtM71dXV2PSpElo0qQJGjVqhOHDh6O4uNiij7NnzyIzMxPh4eFo1qwZXn75Zdy4ccOizc8//4yePXsiJCQE7dq1w/Lly23GsmjRIrRt2xahoaFISUnBnj17pNyKz8M53a4al4oFI5Oxalwqtk8bZFoQPaE2V9LpVsi0ZE3FtTrM++mEqD6VEADE9mEu+ADSTWJiHUDlbAZKaU24Z8wR8346Yfe+xTw33Jjf/O4wJviRs6uj37m/4a/mQEI6kjQ/LVu2xNy5c9G+fXswxvD555/jvvvuw/79+9GlSxdMmTIFGzZswNq1axEZGYnJkyfjwQcfxI4dOwAA9fX1yMzMhF6vx86dO1FUVIQnnngCwcHBeOeddwAAhYWFyMzMxIQJE7BixQps3rwZzzzzDOLj45GRkQEA+PLLLzF16lQsWbIEKSkpmD9/PjIyMnD8+HE0a9ZM4SnyXuyF3bo7bF0pp9t6I8OuU5cx/X+H7Jq6OKRs27ERIRJa8yP3RCnHGVOMA6jUHClKak0CAzS4t3s8PtpWaLedvfsW89zg5ucdadx81dnVXeH13oAa03EQ6kTDGHPKGB4TE4P3338fI0aMQNOmTbFy5UqMGDECAHDs2DF07twZubm5SE1NxaZNm3D33XfjwoULiIuLAwAsWbIE06ZNw6VLl6DVajFt2jRs2LABBQUFpmuMHDkS5eXlyMrKAgCkpKSgT58+WLhwIQDAaDSiVatWeP755zF9+nTBsdbU1KCm5k9fl8rKSrRq1QoVFRXQ6XTOTIPX4g4TQb2Rof+7OYKbE7cBb582yO6iJMbM5Qx6XShm3+vcfXP3KiRsiGHVuFRFNzNOgAD4NwNO8BQSNKzbicXR926N9X2LeW6iwoNRdq2O932x1yF8D38xffozlZWViIyMlL1/y3Z4rq+vx+rVq1FVVYW0tDTk5eWhrq4O6enppjadOnVC69atkZubCwDIzc1F165dTYIPAGRkZKCyshKHDx82tTHvg2vD9VFbW4u8vDyLNgEBAUhPTze1EWLOnDmIjIw0/bVq1Uru7fsM7lCbK+F0K9bM5Qh7573iSudNI0pU/1ba/0aMicwVUWGOvndrrO9bzHMjVfDhuw7he5A5kHCEZIfnQ4cOIS0tDdXV1WjUqBG++eYbJCYmIj8/H1qtFlFRURbt4+LiYDAYAAAGg8FC8OHe596z16ayshLXr19HWVkZ6uvredscO3bM7thnzJiBqVOnmv7NaX78HVerzZ11urW3MUthSnoHrNpzBoZK/kg3pUwjQo6oTSK0uFxV6/DzrnDGdGQiUzpjcL2RYcfJEkljtL7v7CMGSZ+Xex3CNyFzIGEPycJPx44dkZ+fj4qKCnz11VcYM2YMtm7d6oqxKU5ISAhCQpz36yCk4WwEhlQNgjWcWW3yoHbo3TYaoz/ZLdhWqcgvc2HDUFmN0qs1iAoLxtubjqGsqtYjtdHsbQZKRoXJMU9aO6HWGxm+zb8g+vNi8ETtObVB9a4IogHJwo9Wq0W7du0AAL169cLevXuxYMECPPLII6itrUV5ebmF9qe4uBh6fUNaf71ebxOVxUWDmbexjhArLi6GTqdDWFgYAgMDERgYyNuG64NQF84WRHXGTGHt5Fhy1X35jQIDNKi4Xov3so45FAQ87YypVIiwkN+QPTSwve89haUoFaElk3IN8FzHnyA/GIL4E6eTHBqNRtTU1KBXr14IDg7G5s2bTe8dP34cZ8+eRVpaGgAgLS0Nhw4dwsWLF01tsrOzodPpkJiYaGpj3gfXhutDq9WiV69eFm2MRiM2b95sakOoC2cTsjljplAi9FsuUvyUPFUbjUOJEGE55sno8GCL++ZC1jcpHJLu6fn1NFTviiAskaT5mTFjBu666y60bt0aV65cwcqVK/Hzzz/jhx9+QGRkJMaOHYupU6ciJiYGOp0Ozz//PNLS0pCamgoAuPPOO5GYmIjHH38c7733HgwGA1577TVMmjTJZI6aMGECFi5ciFdeeQVPP/00cnJysGbNGmzYsME0jqlTp2LMmDHo3bs3+vbti/nz56OqqgpPPfWUglNDcCihKncmIZsjzZE9ZmZ2tuhbTF9R4cEwGhnqjQyBARpZ9y9GEIiJCMbMu7tAr/O8+UGJEGEp5smosGA81a8tJg9qb+rTFdF8d3SIxYQ72qFXm2jknSnDuvzzfmfuoXpXBGGLJOHn4sWLeOKJJ1BUVITIyEh069YNP/zwA4YMGQIAmDdvHgICAjB8+HDU1NQgIyMDH374oenzgYGB+P777zFx4kSkpaUhIiICY8aMwZtvvmlqk5CQgA0bNmDKlClYsGABWrZsiU8++cSU4wcAHnnkEVy6dAmvv/46DAYDkpOTkZWVZeME7au4026vpKpcbpFQexuzPTQA3tpwFBlJ8aZriOmr/FodRn+6G/GRobi3ezzWHyiSfP9iBIHSqjrodaGqccp0NmOwWFPh5IG3YsqQjhbfuxxzmRgm3NEOFddrccf7W/zW3KO0MztB+AJO5/nxZpzNE+AJ3Gm3l5v3xVXCmVzNAF9elzkbj2DpL4WQk7xYTN6bdfnn8cLqfId9LRiZjPuSW0gfBFw3z3L7zT11GaOW7nLYTmo+H2u4kUSGB6PiWp2gwBQfGYqZmYmYtFK53EV8qN2J2B3PIkG4G2f3b6rt5UUolSVZDHJV5XwCSkyEFn+/LwnDujk3NmvN0YniK1i45ZTDz1lrJLIKivDxtkLZWgYxpgJX+xa5UgiWGyIs17FdajQfp4kCYNdM56igrRLmHm9wIqZ6VwRhC1V19wK4nClCJR2UrkwOiFeVz8s+bqr/JORUWVpVi+dW7sOcjUecHhe3Md+X3AL92jUV9RnrulVK5Azi7n/X7/wFYl1ZY0is86q7K5vLdWwXay57Iq2NRbI6R8kboyNCXFrR3luciKneFUHYQpoflSPW1KO03V7shrRwyyks3HIKel0oqm/U2xUqPtpWiO4tozCsW3OnxwfI0zQ4mzPImkkr9mHu8K42p3xX1RgSq5EzGoG3NrhfIyHHb0isxuGupHibZ9ueH9m6/POi+pWT1sCbnIip3hVB2EKaHxUjp6SDUqn7parADZXVKBdRauC1dQWiNBBitBZyNA1KlzYov14neMoXW3ldCmI1cs+ttK+RcJVWqN7IEBmmxStDO2FmZmfMe7i7w9ICzmomzLWBabc2MX3fYp/h2EbSE58qUbLFnbjiWSQIb4Y0PypFrnlGKbu9M+Hl9iitqnOonZLiRyFV0+AqvwahU76cCDd7DrTOCG+cRmL614cwe/0RGCqV1QrZ+97s3a+YCDw5mgmxz/BLa/Ix+94uku5dyYzY7kJutCVB+CIk/KgUqeYZpVP3yw0vF4O9DUGOU7eURd0VQp0jk6MUB2JHgp+zwhsDbmroLLV0RU46zTvrjM8JsdO/PmSjQYwMD5Y8HkD8M1xcWSP53l3pROzK6DGqd0UQDZDZS6VIOTG6ym4vpCp3FqENwZnK4kKmD752Uquua0Q2dPaUL8aB1pGJyBkYgBlfH5JsAlOyIjyf6bTimrBp0RHcMxynE36G5QQMuMqJOKugCP3fzcGopbvwwup8jFq6C/3fzVGN8zRB+Aok/KgUKSdGV9rthybFY/u0QVg1LhWTB7Zzuj97G4K7/CikCnViM2FJPeWb+93sOFmC2esPOxQgADj0c3KGsmt1WJhzQtJnlPjeOAFK6POA/GjGoUnx+OdD3e22kfpsOVuyhQ9viR4jCF+AhB+VIuaEHxUejBVjU+w6kyoBp1WZMqSDw9NuREigYD98BSzNcacfBSfUKSHQAdJP+dYn/NGf7IahUrjoqvnmbM959cNHezitGVq247QkIUOJ783Vgm9JlfIFbZV0Iq43Msxer4z2jCAIx5DPj0oRE54698Gu6Nc+VlVj+udD3WE0Mry2rgClVX+aMMQ41Lo7GVtggAb92sVi4ZaTTvc1M7Oz6FO+M6UcuM3Znp9TQIDGKV+t8uuOndLNUeJ7c7Xg66pnSykn4oU5Jywc0K2hEhTyUHv2bcJzkPCjYpytteTJMWUkxUtedORmCHYG7prO5v6JjhAXLu1sksXTJddM/y/kvCr0HUWHB6HmBsO12nqH15EiZCjxvYkVOk6XVIkel9JjFMJZJ+KsgiLM+0mcqVFN0WNqxxuybxOeg4QflaPG8FQxY5KzIXgiGVtggAb3do/HR9sKnepH7KbkbJLF+T/9ho76Rg4X76FJ8TAacVMDVwsAKLt2A41CxP3kpWhAlPje+ibEQK8Ltav9AIBVe85aVIKXOsYJX+zjfZ+JGKMrsOfrxAeVoBCHO0sBEd4J+fx4AWIjmXxhTO5OxsbV+XIWsZuSEid3Id8PcwfqBT/9hudW7jMJPhxVNTfs9i03SsnZ7y0wQINRfVs7vI6hskY1iQOVQIowHBMRjF5tol06HneXRHEFSkYfEr4LaX4Iybjaju4ubZcSdb6kmkuUyNPD5/shpQyKEM5q16R8b3zPUNvYcFHX4RMgHT2TjjQsnipHIUUYLq2qwx3vb3GZ2cZXzERSnOfJf8p/IeHHx3C1YOKuBdIdydiUqvMlRVhQKsmi+aYp14E6JiLYwildyJdMyjMl5nsTeoZG9nGs+QFsBUgxz6RaN0TJZWRcZLbxJTORN2bfJtwPCT8+hKsFEzUtkNYbcq820cg7U8a7QQtt3s4ufhoA4wckSLpnR/4xYgUYbtN0Rns18+4u0OtC7Qo1Sj9T9p6h+T/9hqjwYFRcqxPtlCz2mVTrhihVGHZF0VRvKtIqBndHjRLeCQk/PoKrBRNXLZByNFV8G3KABjA34XMbNG6Oy3rznpmZiJIr4nK/CMEAfLytED1aR0uaW3sRczMzO+OtDUdFRyU5o73S60IFtRz1RoaFOScx76ffbN6T+0yJeYY0Zv/vyHFayjOp1g1RThkZTku1fEchYhuHOK3hVatWTC6eiBolvA8SfnwAd5zcXLFAytEqCAl51r6Lhopqwcgeruq5UsiZWzl5evgEALmaiqjwYMHFP6ugCLPXHxZMuij3mRLzDJVdq8OU9PZYvfecw/QOUp5JNW+IQsKwI97acNT0/85o49SqFZOLJ6JGCe+Dor18AHeUhVB6gZSTyl+KicddcRxS59Y8mobblK0j5qRETsnVVKR3aobvD16wiejhvhd72aYBec+U2GejbWyEqaTKgpHJWDUulTeLuZRn0hXlKJTEsozMrZI/70wJDLVqxZzB3VGjhPdBmh8fwB0nNyUXSLmaKqUclF2B0Nyam/VOl1zDqj1nLXLZCJ3YxUZOyXGg1gD4at95fLXvvMUYhiTqJfsPiblvbuxKb7JS26kxaag5nLN434QY/G/feUnfqTMaXjVrxZxBjTnSCPVAwo/KkOMDI2UTkBsNpuQCKdeEpma1O993ICb83J7/jJjIKbk+I3xjeDG9vWThUux9c35WYp6hsqoa9Ju72UL7pNeFYPa9XUxzVG9kMDKGqLBglF+3rQRv3p/5M+kNG6Kc7xSQ75vjy2Yid0SNugsq1aEsJPyoCLmRNWIFk7KqGvR/N0dW5I6SC6RcTZUa1e5CQp/Y8HMlnMVrbhjxYnoHG62StRO49b/NxwAAH2/7XfS1pd63oaIak1buw/gBCfh4W6HgM3Rv93g8t3K/zfUMlTWY8MU+LHmsJwBbJ3a+8QH8z6S7N0Q5m5ZcPyBA3iFB7Voxf8dXcjCpCQ1jzG/TXFZWViIyMhIVFRXQ6XQeHYvQpsEtkY7s1NznAf5Nhdt05PZvfh1nf4S5py5j1NJdDtutGpdqsUnVGxn6v5vjdI4cpRCaO26cUjct6/u1B9/3oNeFYFTf1mgbG2ET/l9ypcbCQdZZNJB+35zAxEW08WmGXv32EMqv8WtyACBcG4jrtfUOv3+1bAzO/l7MBSex36GU58je9Xxdu+At9+rs3uCrOLt/k+ZHBSgRrWU/fDoRb21QJhpMCbOBXBOaFHOA+ftyq5s7IjpCi/uTmyMyTGtyGt5TWIodJ0tk+SZJdRa3vqfiyhrM/+kEFj/W07T5cf9dl39e8niEENq8xZozoyNCsH3aIJtnaNepy3YFHwAOi7JGhQdj0aieSFVBGRgl0k+Ya6nqjQyfbC90qW+OL5mJ7OEtmhRfy8GkJkj4UQFKhZELCSZKh6krsUCO7NOKt5K1IxOakJBnbdLh1PWAYxOJXEqravHZjtP4bMdpRIUHA4DDzdsernQWV8pkOCW9vWBhUanRV9bPUO7vJU6Pr/xaHQICNB7fCFyxafmyb447UVOyVkf4Wg4mNUHCjwpQMlqLb1NRUx4PR07AYnwM+IQ8exmerdtevlKN51fnK6oNckbocYezuLNlNcScip2P0lJm01aDY7yrNi3yzXEOb9OkqGnt9jVI+FEBrs6zoZY8Ho6cgEf0bIF3HuwGbZDj9FN8Qp7QJsLXNiAgQNFEh3JxlbO4oeK6jU/DzMzOmLRyv2Qz4KSBt2LqkI4Ox+dsRGDarU2wcMtJCSPjRw2O8a7ctLwhYk2teJsmRS1rty9Cwo8KEHMqj3fClq+GPB5iEhR+te88tp8ssQhpdhXDusVjSUBPm0zGrvIPEkLqiV3sIjdz3WG88f0RC41UfGQoxg9IwPoDRZLMgKv2nEXXFpEujwhMvaUJosKD7WrRNBpAKERDTfloXL1p+YtvjtJ4myZFDWu3r0IZnlWAveyzHNdr67Ew5yTW5Z+3ycrrTP/u8hUQm6DQUFkjO1OtVIYmxWPH9MFYNS4VY/u1BeA+wScqPBgrxqbwZi62B7cYOvqmrtbcsBEiDBXV+HhbIWZmJmLVuFTMe7g7YiK0DvsqraoT/Z04k1k3MECDuQ92tdv/+NsTTDXAzFGbz4uj70mDPw805lm/pf62xeDq/tWEo3uVmhPN0/OmhrXbV6FQd5WEugMNZqHpX9sP9eWQE5ng7ggHc7PLieKrok0a3Glm+7RBDiuzKzVOOaHpzrDECadKsTmEhIg3m9usgiLBGmjm8H0n9nDm+2qoLXZEMBO2t0TqOEo/sVggZ5GS9+LqufJ0uLhlBvWqm7mu/tTkWt+ro3QZf6ZjaIiQVcsz5i3PvDtxdv8m4UdFwk+9kdlkthVCbo4Hdy1WYrIbO4LLV+LqH77YvENKERUejLzXhjg171kFRXj1m0MorZLnaG0+t2KEH+vPuRpHz6mnN12x2Ht2Abg0f4ur88N4ekMWs8bw3au9w4MGyuVEUxpveebdBeX58SH2FJaKEnwA58JlXb15OauZ4Lh4pdotYanutu+XX6tz2qFyaFI8rtcZMeXLfFmfN1RWm/ywpCBmrpRYpB09p97i8yLknAwA/d/NcVnUkaujmjwdLu5MBvWhSfEYPyABS38ptMmAPrZ/g0+cGqPBvOWZ9xZI+FERUjdhtUUmANIqrzsiNiIEf/3qgMsXIk9ESighcOl18se970wpTl28qkgtL3PcpQ1QyylYzDh4cxqduuzSqCNXRjUpKVjJ+R6lrjHW95pVUMSr2WEMWPpLoaS+CO+FhB8VIXcTVktkAqBM5XXO7g4NnF7ArRdXvnxAcnLgZHSJwxNpbfHDYQP+k3tG8j0qIXA5k7vnv7vOSmpvHVXCt2llHzG4RRuw8eAFvLauwMLk5wn/B+ECrp0RHRFid0N3ddSRK/tXSrCSKyjLXWMuXql2KLhJ6Yvwbkj4URFyNzM15XgQuyiktI3G7tNlNq+bRzCUXBVnAhS6Jt/iap0JmltsuRBtsfxwuBgP9GiBu5LiJQk/Soamyq3+LRcuqoS/rlgoqm/w19xSUks3Z+MRfLTN9nRe5ObsvEJml6KKapvCrHwbujfn9lJCsHLGbCZX8GjWOFSRwxnXF+HdUKi7ihAT8m6OebisWhC7KOw+XYao8GBTWQgO85BoZxZwbnG1Xuiso1W5xRZocGQUa0riNvNebaJFhZ5znwHsh6ZKDa8dmhSPRY/2QHREsN12zhBv9p0IzauhstpulKK5NkAuGw8W8Qo+5td447sjLg9Jlmp24Z4x81QBvdpEw5EMGKBpaCcHKaH25oh5/pwVrMRoX+x9j1IFD/N7dVZjo8Y1l5AHaX5UhlD6emvUmuNBivaq4uZmOSW9A9rGhtuYCMT0FRUeDKORod7ILMLixW5O5lqJ7dMGYUiiHgtzTvDWHbP+XFFFNfLOlInWvthLaFhvZFiYcwLLdpxG+XXx5pysgiK8teGohQlIG6hBbb0yAoB5LS8l/Lnkbj71RobX1hU4bOcOfwyp2gM+zVfemTIbQdwaIwPyzpTJuhc5CSfFmqGcTbznrNlMyhpjfa9SBCeqn+bbkOZHhQxNisf2aYOwalwqFoxMxpT09tDrQizaiEka5wmkaK+4hWX13rO4u1tzpFlV4hbTV/m1Ooz+dDf6v5tjOlnL2Zy4xTYwQIMX0juYkh464qcjBsHEfvGRoZiS3gELRiZj1bhUwYSGWQVF6PX3bMz76YSF4APwaw3MP8enhVFC8ImPDMWSx3rihfQOJsFn+Y5Cp00GYjcfaw3Ert8vo7SqVtRnXe2PIad/a82XOzINS0k4KajR43n+nE285+y9S1ljrO9VrEbsw0d7yErUSXgPpPlRKdYRIpMHtVcsusXVkTJitVeA41Oe2L7MfQVqbhhljXvHyRLTnAzqHIdPd5x2+Jlv8s/j1cxE2fWWHIXsCvnLSNHCPNWvLW7UG0U5OU8eeCv6tWtqMXYlcjZJ8XXiu15UmHiznqv9MZzpn9vQ3VWzScxzKSd6y5kCq0rcu9D14yNDMbJPa15NMiBeIzY0KR4ZSfGqiCgkXAMJP16CuTDkbPZcd4Qic4vuvOzfRGV2tncaHJoUj0Gd4vD5ztOYl/0brtXV27QxX6T/8VB3WWM2H6deF4JGIUG4WnPD7mdKq/7M2SM1D4dYAYZPQJSi3fp852mHJhZOOJliVcBUiZxNUswFQtez1ogJERMR7BJ/DPPfXGxECPS6UBRXSo+y4zZ0d9ZscvRcyjVDyRX4lbp3udcXK7hRXh3fhoQfL4FbfH84XIT/7TuPK9V/bspihRcxERZKVosODNCgX7tYUcKPvVOeWM0Dt0iDQXYIOEdxZY3ozzpS4wsJq1LNc+bXkWIOEev/ay2c1BsZpn99yOkoMrHFW5XwKfr7fUmKn855NVHhwSaBW8x4rTd0Z4vAKokzZig5AoKS9y5XQJErOBG+Awk/XoCjzV9MmK8Y1faMrw/ZVDl3Vivk7ClPjuahpKrG6RBwpVIN2NO0STXPmV9HSdNOgAZYOKqHzXe8MOeEqDpzQgzu1BTP3H4r76bCJxA6G4b87IAEDOvWXPbn+RB6/jhn/UgHVegB4Q3dGdORI6Roh91lgjPHlfcuFmvBifMzI2HIPyDhR+VISeNuL4+KGNV2Gc8i7myCOmdOeXI1Ac0ahyLt1ia8i6t1nh9nkCu4cXOa2U38fFqH1/ZNiEFMhFa0E7A9jAyIjrB0qK83MiwT4fNkj83HLmF4z1Y2362QQHhXkl5Uv1FhwRZmsCYRWrx1XxKGSZhPMYg5MIQGBWDFMykouVqDZo1DUVZVa1MQ096G7goNhFTTtjtNcOaoSfvi6TplhPsh4UfFSN387TkOy40aUSJBndxTnlRNgPUizbe49moTjb2nS5F76jJ+K76CH48US74fDgZ5ghv32oaDthFcQlhfJzBAg7/fl4TnVopPzGgP6+djT2GpaD8be8xcV4CMpD+fG3sC4Wciha1Fj/ZEQIDG5RummAODobIGARoN7ktuYXo9I0nahq6kb4mc5IGeNMGpwa/G03XKCM9Awo+KkWMGEBJynFFZc74087J/Q792sbI2GzmnPCkCm9Aibb24KhG5JAYx351YoXZKenvexXdYt3g8+0eC3cR/YoltFGKh8jdUKjM/l6tqTQK5GE2Kxo5mjhNuU61SIrgKub4wntrQnam5pQYzlCdwdQFYQr2Q8OMClAoll6OtERJynKkDxbFwy0ks3HJStjpY6qYQa2WKsYeYRVqpavMc9hZGJXPNtI2NEHxvxrBEdG8ZhZf/dxBVNZZRcGL8nTRocN59aU2+ha9XjIIZo7m5EKNJYezPcXk6wZwnfGGcwdnkgfYOKGopJKs0riwAS6gbEn4URknbsdRF1V6Yr5J1oNymDha5tv5tWGc83T/B7mKsZLV5DuuF0XyDKLkiri6ZGBw9B8O6NUdGUjx2nbqM3N9LcOpSFXb9fpnXh8saIV8v84zRzsKNX6xAOLZfW2wsMHhcA+EpXxi5KJE4ke+A4sv+MO5INsnhqwKktyIpw/OcOXPQp08fNG7cGM2aNcP999+P48ePW7Sprq7GpEmT0KRJEzRq1AjDhw9HcbGlX8XZs2eRmZmJ8PBwNGvWDC+//DJu3LDMp/Lzzz+jZ8+eCAkJQbt27bB8+XKb8SxatAht27ZFaGgoUlJSsGfPHim3ozhSsqSKgVt8xeIozNdeJuKo8GCxsoao+jtKILawaTNdiMNFRKmChnxcvFKNrIIi9H83B6OW7sILq/Px1oajDms3iZlvsXWEAgM06Nc+FkktIpFVYBAl+HDfuyOExjnu9rairsGNX6wwn56ot8hwbi87titxNpOxu3GFpkrpNU1tuEu7Z70+jFq6yyIrPeF+JAk/W7duxaRJk7Br1y5kZ2ejrq4Od955J6qqqkxtpkyZgu+++w5r167F1q1bceHCBTz44IOm9+vr65GZmYna2lrs3LkTn3/+OZYvX47XX3/d1KawsBCZmZkYOHAg8vPz8eKLL+KZZ57BDz/8YGrz5ZdfYurUqZg1axb27duH7t27IyMjAxcvXnRmPmTjbLE+PrjFV8zSOu72BERHhJjKAdTeMPIWKByaFI+tLw/EzMzOeCKtDWZmdsbWlwdi7oNdAYhWtihSqNIRchYmocKMrix5cLqkSlQRVQ7Nzb/xAxIc9i1lcxWj3dKFBuFfD3XHqnGp+MeI7qJC2aMjtBb/5kpf/C2zC561cw8aq/GXVdXaFQi50gLciTjt1ia4L7mFTdkTdyKlRISnkVvMVAhXrGlSkVroVypKzxkfvi5Aeisaxpjsp+nSpUto1qwZtm7digEDBqCiogJNmzbFypUrMWLECADAsWPH0LlzZ+Tm5iI1NRWbNm3C3XffjQsXLiAuLg4AsGTJEkybNg2XLl2CVqvFtGnTsGHDBhQU/FnIcOTIkSgvL0dWVhYAICUlBX369MHChQsBAEajEa1atcLzzz+P6dOnixp/ZWUlIiMjUVFRAZ1OJ3caAAC5py5j1NJdDtutGpdq13bMpxrNPmIQdNJtEqHFgz1b4PuDRXZDujk1NQBbp0ZdKEb1bY3K67X4Jv+8JJPHgpHJFpEuSlJvZOj/bo5Ds8P2aYMQGKCxq54/briKeT/9puj4uOszxiz8ZawR+i64KunTvz5kI4REhwdjzoNdJW2uUp/Bdfnn8cLqfIft5z3cHfrIMEF1/caDRXhtXYFF2L21WUSMv5UGcJlA4azJwVtMFtw8A/w+U1LmV6k1TS7uMrfJnTMxzwS3hglpna3XMEI8zu7fTvn8VFRUAABiYhqk4ry8PNTV1SE9Pd3UplOnTmjdurVJ+MnNzUXXrl1Ngg8AZGRkYOLEiTh8+DB69OiB3Nxciz64Ni+++CIAoLa2Fnl5eZgxY4bp/YCAAKSnpyM3N1dwvDU1Naip+XOTqqyslH/zVihhO7b3Y98+bRD2FJbCUHEdpVW1iGnUkGK/rKoGk1but9lUrA9IhopqTPiCPyzaUFltIRjERGiRkhCNTQWOw8Bd6ewZGKDBvd3j7UYzcZoFe+GqQvetBCP7tHYoVBkZ8Hhqa/RsHQ19ZJjFIsk5mXL+OkCDxiP1FunaDqnPoNjvrrSqFpndmiPvTBkuXmnQ9pnfw7Bu8XbDu8VopIQSLQohRRhRYhNVQ0i2GJSM2nKnP4w17gw/lzNnYp8pcqhWL7KFH6PRiBdffBH9+vVDUlISAMBgMECr1SIqKsqibVxcHAwGg6mNueDDvc+9Z69NZWUlrl+/jrKyMtTX1/O2OXbsmOCY58yZgzfeeEP6zYrgdMk1Ue2ENhw5P3buVCFGdSdFvVdWVYusgmJEhQej4lqdx5w9swqK8LEdwWf8gAQMTYoXpZ5XmiYRWrz9QJLoLM3/3XUWPx29yGvG4vx1+rWPdWpMUsyE9UYGo5HZJAzk460NR/H2xqMWArVeF4JRfVujbWxEQ1SeBqZEf9aCiBh/K75Ei0JIEWb8MYeLddRWbKMQgDVkPs89dVm01spT0W6eCD+XkopDyjPlSQGSsI9s4WfSpEkoKCjA9u3blRyPS5kxYwamTp1q+ndlZSVatWrldL9ZBUWY7+D0b09YkPtjd5UTrynnitn/uyvsuN7IsOvUZez8vQTLd562K7ysP1CEV4Z2dqkzMx8xEcHInTEY2qAA5J66LPpzjjZcqaYV6/a92kSLik4qq6qxq4rnw0aTWFmDeT+d4G1rLYhkHzGIuoaYDUDsxsM9R9P/x1+bzNdzuHCaqqyCIvx17QFZWi9PRbt5SlsiRrsnda32tnQJ/oQs4Wfy5Mn4/vvvsW3bNrRs2dL0ul6vR21tLcrLyy20P8XFxdDr9aY21lFZXDSYeRvrCLHi4mLodDqEhYUhMDAQgYGBvG24PvgICQlBSIj43DFikFKZW0hYkPtjd+VpgQuBnpLeHqv3nnNL2LGQ/4sQ3Jy4+9T0zgNdoQ1qiBWQkj/J3EnUesOVapoRan9v93h8vK1QUGC9t3s8r5lUScwFEQCiMzc72gDEbjxGI2xKTPCh5CaqRp8gZ7Vensr8rGZtidS12tvSJfgTkqK9GGOYPHkyvvnmG+Tk5CAhwTLSo1evXggODsbmzZtNrx0/fhxnz55FWloaACAtLQ2HDh2yiMrKzs6GTqdDYmKiqY15H1wbrg+tVotevXpZtDEajdi8ebOpjbsQq3UQytILyP+xu+O00DY2wi1hx1kFRZjwxT7JhTS5zcZT2AuHFqKoohoLc/7UmkiNBrHX/uNthRg/IIE3OmnRoz2x/kCRSwUfwFLIm73+sKjPiImoEbvxPLfSdm7s4ewmqsYwZqUitTwR7aZmbYnUtdrb0iX4E5I0P5MmTcLKlSuxbt06NG7c2OSjExkZibCwMERGRmLs2LGYOnUqYmJioNPp8PzzzyMtLQ2pqakAgDvvvBOJiYl4/PHH8d5778FgMOC1117DpEmTTFqZCRMmYOHChXjllVfw9NNPIycnB2vWrMGGDRtMY5k6dSrGjBmD3r17o2/fvpg/fz6qqqrw1FNPKTU3ohD7Y7CXpVfuj12JrM1irulqZ896IxO9SVrDnbJdPQ/mTP/fITQODTY5JQs5TNpj3k8n0FHfGEMS9ZLU6GK0H+sPFGHrywNNDspKVU2XAieIiEXMBuCqk74zm6hafYqUNB25uwCpmrUlctZqfy0donYkCT+LFy8GAPzlL3+xeH3ZsmV48sknAQDz5s1DQEAAhg8fjpqaGmRkZODDDz80tQ0MDMT333+PiRMnIi0tDRERERgzZgzefPNNU5uEhARs2LABU6ZMwYIFC9CyZUt88sknyMjIMLV55JFHcOnSJbz++uswGAxITk5GVlaWjRO0q1HilCL3x65k1max15SKGHPAnsJSu6Hijsbnynngo/x6HUZ/shsxEVrcn9wcQxL1pr/lOwrx1oajovp547sjaBwSLGmTErup5Z0ps9nU1OpUObZfW1EbgNInfWefcTXXhVLadOTOaDdPFlp1hNy1Wk0V7IkGJAk/YlIChYaGYtGiRVi0aJFgmzZt2mDjxo12+/nLX/6C/fv3220zefJkTJ482eGYXImcHwOfQCD3xy50qnBGCFBqgRHrxyJ1U+Yb39CkeCx6tKdNvhnrHDtKUlpVi892nMZnO06b7uvJfgn4ZHuhKK1HUUX1zfB2x1y8Uo16I8OOk+LbW6NWp8r0RGE/PXOU1PAp8YyrOYxZzaYjMahVW+KMYOYt6RL8Bart5SRSfwz2BAK5P3a+U8W23y5i8dbfHY4/pW00zpRes9C8KLHASDEHSF2A+caXVVCEtzYcsRB8GocGom/baGw+Jk5gcIYis/uadU+ihNxC4jbe0yXXJEVo8c2pO82D5gkgiytrFDFfOPqtSbknJZ5xNTvmqtl0JBa1akvUKpgR0nAqw7O3o1SG53ojw8Kck1i2o9AiZ4rYLLfmmUSV+rHvOFGC0Z/udthuxdgUpN40pyi1wEjNalpvZOg3d7ND01dUWDAWPdoTqVblDpSu1i4X8/tamHNSVEbpFc+k4K9rD9jdpCLt5FuyNwZ7OUoAacKCFA2a+fMMQLGMwxxCB4iZmZ3x1oajdoW7qLBgLBrdU1YSSWs8nQHZEUpmeyZsUWOEnz/h0QzPBP9CHBUWjKf6tcXkQe1FZbm19g9QYqFMvbUJosKD7UZPRYUHmwQJJRdnqeaAwAANZt/bxaG2ZO7wrjaJAF1RrV0u5vfVvlmEXW0EJ6Sk3tJElDZDrOAD2DflSHXO5oSFPm1jTA7Up0uuYeXuMygWqFxvfQJW4pRsvdHwOXQHBmgQEKCxq4WdO7wr+rVzLpkkh9q1K6ShcC1kxvJuSPhxAiGNQ8X1Osy/Gc3DLTDu9g8IDNBg7oNd7QoUcx/s6pKTihxzwNCkeCx5rKfkOlfuTnAoho+2nsTPvzk2tXFCir1NamSfVoLJBK0Ru6lx5gQxztnl1+sQoNFAGxRgei6zCoqg0Vg+N+bO39YnYGfNF/ZMxdZ15dy54avZMZdDraYjgvA0JPzIRGqkhyf8A4Yk6jElvQM+2/47KqpvmF7X60Iw+94uLisgWSKgEbDG2i9FTp0rNUYwORJ8GupYWZoc+DapXm2i8e/N4oqx3p/cHHd0bIbIMC3qjczh5hYYoEFsY3EJP83nWEjgL6uqxbIdpwU3VrmnZDmh5O7c8L1Bu0IaCoKwhYQfmUjV5Lg7+kKsOU5J+K7Jhz1zgNQ6V2JrqqmJhjpWWpvXzTeprIIi3PH+FtFarW/zL+Db/AsAxJcvkPpMuju025nruXPDJ+0KQXgfkjI8E38iVZPTq000HK2FAZqGdmKoNzLknrqMdfnnkXvqskWmVqEMwJw5TmytJSkIXdMaJc0BWQVFopyK5RIfGYqnbmvjkr7tPT9i51IIoczQ1vRNiEFUeLDdNlHhwSYhVYrArwTuvp4zcMLWfcktTH5sasTeukEQasLVzyppfmQi9dScd6bMYbSMkQF7T5ciQKOBobIapVdrEBOhhT4yzOIkac8HQmrGYCWQ4nSslDmAu6bS/G1YZzTThZjMTv/NPa34NQA0VELnQQkHbjHfM1f4s85BVfq6G0bs+v0yUm9p4nbTrZpCyX0hskdq/TiC8BTueFZJ+JGJ1EgPsQv0pBX7LMLlObgvHoBdH4gX0zu4PfGaWKfjmZmd8WS/BKfD6PcUlmLHyUsucXReuOUEnrqtLX6/dBUTv8jj/S4UQWAKlHLgNv+eudIW3MZdVlUrqvAnAFTV1mP0J7sRFRaM9M7isqcrZbpVS6I+XxAa1FqGgyCscdezSsKPTKRGeohdoIU2Wy6JXmR4sF2tzrKdhaKuo+RpWWxfsY1D7Ao+jk7XYn2KnKHi+g3M33xSVFtnsmiXXOV3CjdUiru3OxPj8OORYoftso8YMHVNvtNzVn69Dl/t+wMaDSCUGUzIl0uu1kQNoeS+IDSouQwHQZgj5Vl1FhJ+nEBKpIcS2XUZYDdvj6P3zbEWxpxR6ytxQnd0ulZLIkNz7AkCjuCbi6yCIrz1vbgCrykJMaKEn892nJY6NLvYE3wAW18uZ7Qmng4l9xWhQc1lOAjCHCnPapem9v0VHUHCj5OIjfRwZ/HNqLBgVFznzwjMd1oWs0HZE46cPaE7Ol0verQn3tqgjkSG5sjxvxOaC7HCHff5x9Pa4pPthXaFaVfWNbPuW6jkiLNaE0+GkvuK0KAm3ymCsIeUZ5WEHxUgNqxWaCF3lIlZKk/1a4v5P50QXWvM0QYFwK5w5MwJXczpeua6Alw2q9nlrQjNRe0NI1795pAowYf7vDYowGFmaFcG8hgZ8Hhqa2g0GrSJCcfjaW2hDfozeFRJrYmnQsl9RWhQi+8UQTjCnc8qCT9uhm8hNxqZqDpcjuC0ApMHtUdHfWOHp2UxG9T0rw/x1pWyPr3LPaGLOV17o+AzomcLbD952cKHR0g78uo3BSitciz8xkRo8fYDSabP25vzYUl6fKqwycua/+46a/r/T7YXWtyb0loTTyTq8xWhQQ2+UwQhBinPatXVK05di4QfD2C9kNcbmWh/oOjwYJRdq+M1nTEAw5IaBKshiXoLISs2IgTQNDja5p66bIoAcrRBCWmk+E7vck7oSp6aG4UE4mpNvWL9OcPtHZri3RHd7c7FxoMX8NzK/aL7fC2zs+hsxnsKS2UJP3JNstbCsC9oTXxFaHCn75QvpAQgPIc7n1USflRAYIAG93aPx0fb7EdqaQDMebArAFszFOeD8emO0/h0x2kbZ+G/fnXAxmw1LMk5j3m+07vUE7qSp2a1CD5Aw33xCbm5py7j4pVqFF6qwr9zxNXs4tBHhvG+zjfnUh3suaVk/IAEfLytULIAZC0M+4LWxNMO10riDt8pX0gJQHged/n5aRiTG6/i/VRWViIyMhIVFRXQ6XQeG4cYZ1chB+TsIwbeiB5Hm5mSTtcLRibbFJgUS72Rof+7OU5FwUnFfG5W7z2Hius37LbniAoPxjv3J+GtDUftjlevC8GO6YMVC9PnNAzbpw2StNFyzxXg+Lu2Fpb5CsyKZdW4VPRNiLH7vcq9J0/gS5u6qzQzQmsY17M3pAQg1IWjZ9XZ/Zs0Px5GTEbfmIhgbH15oIVDaWCABn0TYjB1TT7vZ7iT+NJf+E/x3GsBN8O1nRE8nDm9c6dre9XnzVFCaDM/QQzo0AyjP3HsbzWiZ0u8O6IbAgM0CAjQ2I3au1J9AwtzTmLyoHYIDNAoEqZvz2FcaIEQOkHFR4ZiZmZnREeECH5uSKIek1fmYVOB43B6azhTllBFem/UmvhK7S5X+E75SkoAQl242s+PhB8PIyajb2lVHfLOlNmYUJbvKHTos+NIr+dMRJBSPg9Dk+LxdL+2onLSyB3u5IG3on1cY5uNSyjZoDW3d4i1ESqEtCNVtfWY99NvWLazEO/c39WpMP2YiGC880BX3lOzGI2E3I07MECDJ9ISZAk/p0uuof+7OYLPppoqnouFKqML4yspAQj/goQfDyPHMVTpTMdP3tYG/8k9I0kQEnN6l6JiH5KoVzwhnzn92jXlXXjl+qYMSdRj9vojAIRNQ+XX6vDcSnEaLT6aRGiRO2OwhcaPQ0oOHbkbtxy/ocjwYMz/6TfB9lPSO5g0YoRv4AvO7YT/QcKPhxG7+ZZcqUG9kSH7iEHxTMetosMla4D0kaEY2ac1am4YTdFjzmT2FRNZExOhlRX2Hm9HOyU3omdPYanoUhRyefuBJF7BR6yZYVCnOOSdKZNtqpGSmNP8faF2GgCr957F5EHtRI+BUD+nS6pEtVOzc7s/QJF4lpDw42HEnq7f2nAUS38pRPWNetGCjwYNJRiEBBtuY49pxF9h3BrOdHS6pAqr9pzFvJ9+M70nphRFUUU1JnyxD2P7tUV6ot7ixycmsuat+5JEF+Q0x552Sm5EjytPsQEaYOGoHk7lRiqqqEbqnM0oNRMW5TjpCvkN8WV4FvLxsR4XmT98h6yCIrvfOeA9KQF8GV9y2lcKEn48jJTTtRRNA7dVj7u9IdoLEN7YI8O0ovrs164pKq7XYv5PJwQFmxcHt8Pqvefs3gdfOD4gLsQxIADinaM1wKJR8ksoxERocV9yc0SGaVFvZBYCkCtPsQtH9cSwbsJjFit4lVppyeQW4+TzG+rVJtpGq/T9wQui+pMjONKpVX1wGkgxeItzuy/iC8V5XQEJPypAaPN1BnOBoUfraLsCRb2ROSyxER0ejF5tonHH+1vsCjZiK6ID/D8+Rw66Q5Pi8eGjPTB51X6HproPHkm2K0SYY37dn44Y8E3+eVyuqsVnO07jMx5BjdPYuaLCfICtpcsCuYKXM5E3fH5D1v92VW4fOrWqEzHBGgDwYnoH+p48BEXiCeNgmSXcxdCkeGyfNggzMzs73dfMzM7Y+vJARIZpsS7/PCLDtNj68kCsGpeKBSOTseKZFPxjRHeTv86mgxdQ4SCnCwOw97S4xU4s3A/yje+OoN5MkuE22vuSWyDt1iY2P8ph3Zpj4aiedvt+dkAC7paYeygwQIOK6w0Cj3W5CU5QyyooMrWddU+ipP5jIhwX4uMWo3o7kh0neMlZqsxNT/bgEjKuyz+P3FOX7Y5H7Lg0sO9/xQd3arV+7qy/D8L9iNXgVVz3vvI0voKUSDx/gzQ/HoRPlR/bWJz/jRBNIrSIaxyKO97fwntSDgkKwF/XHpAsxJRfq0PuqctOjY0POX4g9UaG6AgtxvZri2/yz1sIKk0itHjrviTRGh9zGgqMFog+JQ1NiseU9PYOfR44Zt7dBaVXa/DWhqOCbcTMhxRTqRA7TpYImo7kalqUzohMp1Z1I1aD99mO0+ibEEPaHw9AkXjCkPDjIYQ2mJF9WjvV7+WqWkxebVsvynDTJ8c5XJeDWeyPj2/eYiK0uD+5OYZYOVFLoaHA6CG7BUb5BJP2zRqJvoZeFwqxQ3M0H5yptKEoqvST9cItJ/G/fX/wFlp1xj9AydT0lD9G3Yg1/ZKQ6jl8ocyMqyDhxwPY22Dm//QbosKDeSupAw0LSVR4MEKCAmCoFJegD1BGbIkM0yIyLEh0OQgp8P34rDVjZVU1vIVAy6pqsWzHafRsFYXlOwpxpvQa2sSE4/G0tryh4tZIzcDMCSb1RmZXi2MOZ+4Rq14WsxgNTYrH9dp6TFlzQFSf1nACzaJHeyA6IgSGymq89f1hpzUtSmVEplPrn6jR4VtsdnYSUj2HrxTndQUk/PAgtNAosQCJUeVrzP6fz3Qw58GuGNQpDqlzfrKrqVCSAA3w9kZxG72cvsuqLAU5Pg2PRmCquTmavDrf4vW3Nx7FuNsTMGOYsG+OmPIi1nCCiViHT+BPc4/Si5FQsVMxcNeftGq/w0zgXHuxm5gSGZFjI8SZgMW2s0aNAgUfanb4HpoUj7H92uJTEQlK/UFIVRu+VJxXaUj4sUJoobm3ezzWHyhyegESo8ovu1aHKentsXrvOUHTQe6py24TfADnymCI6XvSyv1YfNOPRkgTI7UEr5EBH90M8xcSgKQIMIClw67Yxfzpfm0tsi0ruRhJzcLMh9R5ddsmJnY9lrFuq1mgMMcbwpTTE/WihB9/NK2oAXdVSfc2SPgxw15yPm4TNUfOAiR242gbG4Ht0wYJnkx98RTFZSWWqolxxNJfCvHSnZ14TWBS53Fm5p+CidjFfEii3uLfSi5GSjg/S8Vdm5jYumti23F4g0ABeI/DN5lW1I8vFedVChJ+biLH/CFnAYoVmU255EoNvj94Ac0ah+Lubs1t+va1UxRnUvlv7mnFc+cYGfDKVwfwSJ/WNj94qfMYHfFnQkhnFn0lFyN71dsdZV2Wgrs3MVc4a3qLQAF4j8M3mVa8AyrOawkJPzeRav7gkLIAZRUUYfb6ww77DNDAwpGWTx0vp+gk4/l/IaLCgpF6SwyyDkuv6u0MZ0qvuaTfb/Mv4Nv8C04nKzTXFDm76Cu5GAkJUwCweu85p8xigGc2MVdoFLxFoAC8y+GbTCuEt0FJDm/i7ALi6POcql1MhJa1fw1fQjfzJHtitiJ9ZCiWPNYTSx7rCX0k/0m5UUgQwrWBAIDy63VuF3wAoE1MuEv7dzZZobWWgVv0redUHxmKF9M7mBJJikkS6Cx8ySGlPidC6CND3W4Osjd2ucKYNwkU3hamzCVq5ZKprhqXiu3TBpHgQ6gS0vzcxNkFxN7nxZrUrItFcgip44VrUgXjzXu6oEnjUF6TitEIPLfSNjz1ao3yIexi4U7xj6e1xSfbC53WVAghlKzww0d7YvKqfQ6LwIoxYTkq/Opu5JZP0YUG4Y17u0AfGeYx/wClNQreJFB4oy8NmVYIb4GEn5vIjZoRswCJNanZUw4IqeOHJsXDaAReW/dnsrvSqjq8vekYZt2TiPusSjw05KYRV4zQXZif4rVBAS534OWby2Hd4rEQPXjzCEkxYWUVFPEWfrXnTCs15FpOiLa5gGaouI63Nhx1mBxx7oPdZGXKVhol/aO8SaAgXxqCcB0k/NxETtSM2AVISRW6dV9ZBUW8WhyhzVaub5MrsT7Fy9FUdGupw8E/KiVd13ouh3VrjiUBGtlaBjnOtFJDru21F/L54RMawrSBdhM7ju2fgOiIhtpwaogMUUqj4G0CBfnSEIRr0DAmNcuH71BZWYnIyEhUVFRAp9MBcE2en9xTlzFq6S5FxrxqXKppE6g3MvT6e7ZgNXbuFLt92iDTYr4u/zxesEoG6E64Mf1jRHeUVNXY3Vj5Mjy/+f0RC78pvS4E93SPx9JfTksei/lc2ruu2I1f7PfMXVco5Jq7krXgaq89Q0Pmb/NnISq8oZCq+Wvmzyzfs85hbYJVYw4cZ/CWPD8c3pKQkSDcBd/+LQXS/FhhT8X+ytDOshagvgkxiInQyqrBxMGnjl+Yc1JQ8AH4zTue9GUwP1n3ax/rsD3faT8jKd7iOyi5WoMXeGqZORqHPdMGd11uw+FSDjj6vqU400rVEjlqD8DmWeB7Nqw1gkYj4zX1CTndqyUHjrN4W94T8qUhCGUh4YcHoYVG7gIUGKDB/cnN8ZmILKh88Knj640My3bYJl7kw3xTlhrarSRKqOrNv4OsgiI8v0q64AM4Nm3I0QxIcaYVG3K969Rl9Gsfq5i50lywGtQpTnRtMrXlwFECEigIwn+hUHc3YZ3lVwp8YcZ7CktRfl1ceQvzTVlqaLcSjO3XVvGwV04TIhUxIducecla2OBLOWBO34QYk6mJDw3+LI8hVks0aWXD9ZT0G5ObUNJck0gQBOHNkObHTcjVuExJb4+Jf2mHvDNlFs6nYjfDqLBgG/POkEQ9RvRsga/2nZc0lvjIUFyvqxesOM/XXkkfCnO/h5IrNZLmcvLAW9GvXVO7po16I8POEyX469oDsjIAZx8xODRDchonsVqi8ut1mPjFPryY3l5UeynITSiphhw4BEEQzkDCj5swjzIBxEeTLd95Gqv2nLVw8uXKFojhqX5tLTZpe06u1kSHB+PfI3ug9FqtSejKPmIQjJRhaBDW2sZGKO5DIWXc1sRHhmLKkI4OzVzTvz5kV3gBhFMOiNFERYUHmzSAUlMrrNpzFnpdKIorlct/JDehpBpy4BAEQTgDmb3ciFA2YCG4Cu/WWaENFdWY99MJRIUH283aGx0ejMmD/tQYCJlzrNHc/JvzYFfc3qGpRcZgexmNlzzWEy+kd7BorwRixy2EGP+eCV/scyj4mGOt/RDjk1N+rc5kMpJifmQADJU1GNW3NQDnMjVzn4+/mVAyPjJUUvH0eJXkwCEIgnAG0vwojKOQVPMok00FRfhP7hnJ1+DMLxx8eYk44cXcQVps4VZHjsnujJSRU3CWI0ADLBzVw67Zrd7IRNVbs8Za+yGnbAInSE7/3yFR/lttY8N5c75EhwejTILgBkhPKKnGHDgEQRByIeFHQcRECJkLR87UsWJo0CRMSe+A1XvPOoxKEhstNDOzM57sl+Bwg3NXpIwzUU4LR/V0mKF4T2GpqHprHEJh8nLLJgxNikfj0GCM/mS3qM+m3drERvDs1SYar359UJQPV0xEMN55oKtNQsnZ6w9bzIO1MERJ9QiC8CVI+FEIoQR05vlRANgIR0L1vMTSNjYc26cNcqiFEauZiG0coqqTvRznWimO1nL659N+OFM2IfWWJpI+ax3uf8f7W0QJiE0itMidMRjaID5rt+X9xOlCMapva7SNDVd9DhyCIAipkPAjgJSMqmIS1k3/+hBvlJSzxb6bNQ4VpYXxpoKO5hReuiqq3czMzohtHCJ5o5Zyv00itHj7gSReocqZsglyPyskcFvDfertB5JsBB+hPoorqzH/p9+w+LGelAuHIHwcf8wgLtnhedu2bbjnnnvQvHlzaDQafPvttxbvM8bw+uuvIz4+HmFhYUhPT8eJEycs2pSWlmL06NHQ6XSIiorC2LFjcfWq5SZ38OBB3H777QgNDUWrVq3w3nvv2Yxl7dq16NSpE0JDQ9G1a1ds3LhR6u3wklVQhP7v5mDU0l14YXU+Ri3dhf7v5gjmdxGTsK7cQXi49XPm6LmT6nzKaSaEulWbM2u9kWF+9m+Yv/mkw7bxkaF4sl+CLEfrvgkx0OtCHLaLiQhG7ozBdrVJ9pzBHeUWkvpZqT5cUvvgXnvjuyOod1ZCd0C9kSH31GWsyz+P3FOXXX49giD+ROp+5ytI1vxUVVWhe/fuePrpp/Hggw/avP/ee+/h3//+Nz7//HMkJCRg5syZyMjIwJEjRxAa2rCwjx49GkVFRcjOzkZdXR2eeuopjB8/HitXrgTQULPjzjvvRHp6OpYsWYJDhw7h6aefRlRUFMaPHw8A2LlzJ0aNGoU5c+bg7rvvxsqVK3H//fdj3759SEpKkj0hYsxX1puIEnlPjKxBe3G+/Do+23HarkZIjvOpXO2CJ04EYsPOOUb2aS17TIEBGsy+twsmfGFbHNacdx7oKmAussQZZ3Apn1XCh0tslmnrsH4l8bYaWwThS8jZ73wFpwqbajQafPPNN7j//vsBNGh9mjdvjpdeegl//etfAQAVFRWIi4vD8uXLMXLkSBw9ehSJiYnYu3cvevfuDQDIysrCsGHD8Mcff6B58+ZYvHgx/va3v8FgMECr1QIApk+fjm+//RbHjh0DADzyyCOoqqrC999/bxpPamoqkpOTsWTJElHjty6MVm9k6P9ujuCGwFcoFFCucOm8h7vjvR+OO9zU9LoQzL63i6yHUspm44mNSawpx5wFI5NxX3ILp6/LJ3BFhwdjzoNdVbcAiC1Qa29ulOjDGaQWdiUIQjnk7ndqQVWFTQsLC2EwGJCenm56LTIyEikpKcjNzcXIkSORm5uLqKgok+ADAOnp6QgICMDu3bvxwAMPIDc3FwMGDDAJPgCQkZGBd999F2VlZYiOjkZubi6mTp1qcf2MjAwbM5w5NTU1qKn5M6KlsrLS4n25J2GpCeuEKK2qFXWa/+fDyejXznFhUD7Eahc8cSKQG9Zu7rcjV1PFzcuuU5eR+3sJgAY/qtRblMtXJIScMYv1VTpdUuV0H67wA5Na2JUgCGVRg+bXkygq/BgMBgBAXFycxetxcXGm9wwGA5o1a2Y5iKAgxMTEWLRJSEiw6YN7Lzo6GgaDwe51+JgzZw7eeOMNwffl5GoBHJuUGBqy+9oz40SFByOmkWPfEwAouSo+NJsP66rl6w9cQOnVGsREaKGPDEOvNtEu35j4Nnw5Ye3mfkrOaqoCAzTo1z7WouI854/iKrOf3DGLFbjn/dTgb8eXdduZCDVn8aaF1x+dQQnfR+5+5yv4VbTXjBkzLLRFlZWVaNXqzzIRzpyEOYdV642My4+y/2wZPtomXIW9/Fodzl4WV2tJiZO4vXIRMRHBKK2yX6PKmY1JaMMfliS9+Cvnp+QKTZWrzX7OjNm6XIo9OAEIsBy/MxFqzuItCy/5JBG+irdGACuFouUt9PqGzau4uNji9eLiYtN7er0eFy9etHj/xo0bKC0ttWjD14f5NYTacO/zERISAp1OZ/FnjrMRUUOT4rH15YGYmdkZT6S1wczMztj68kAMSdRjXf4FwXFxrN57FnpdiOIRWdbRNBsPXrBbLsKe4GOOnI3JXsX0T3ecFt2PBsCLg9thSKLeoQmFAfjbNwWovWFUZJwTvtiHBT/9Jjo6iS+aSYlIq6FJ8XgxvYPoe+LGb16Z3pkINWfwhoXX3jNgPocE4Y14WwSw0iiq+UlISIBer8fmzZuRnJwMoEG7snv3bkycOBEAkJaWhvLycuTl5aFXr14AgJycHBiNRqSkpJja/O1vf0NdXR2Cg4MBANnZ2ejYsSOio6NNbTZv3owXX3zRdP3s7GykpaXJHr+zJ2G+U+In2wsxsk8rUVmEiyqqMSW9Peb/dEKxkzjfmAI04gqrOsLexsRnKgDgcMMP0ACMOR4fAzB/80l8+esfGNmnlUNz2eWqWqTO+ckiu7G9sTsap5A2xRohzcHIPq0VMfu0jZWWJZzPbOnOciUc3MLr6Hsrq3LOxCsX8kkifB1Pan7VgGTNz9WrV5Gfn4/8/HwADU7O+fn5OHv2LDQaDV588UX8/e9/x/r163Ho0CE88cQTaN68uSkirHPnzhg6dCjGjRuHPXv2YMeOHZg8eTJGjhyJ5s2bAwAeffRRaLVajB07FocPH8aXX36JBQsWWJisXnjhBWRlZeGf//wnjh07htmzZ+PXX3/F5MmTnZoQuSdhe6dE843SEW1jIxQ7iQuNydk0Ko5OBEJ5IxbmnHS42RmZbe0ye0iZ39KqOlEndqm+R0KaAPvPxG+i+nakXZOjGTEXrDg4PzCli9IKERigwcxMx4Vd39pw1CN5f6T4JBGEt+Ipza8akKz5+fXXXzFw4EDTvzmBZMyYMVi+fDleeeUVVFVVYfz48SgvL0f//v2RlZVlyvEDACtWrMDkyZMxePBgBAQEYPjw4fj3v/9tej8yMhI//vgjJk2ahF69eiE2Nhavv/66KccPANx2221YuXIlXnvtNbz66qto3749vv32W6dy/HBIPQmL0RSIRah+k9STuDMFQe3h6ERgz49F7IZ/V1Icck+VWhT7FCq8Kef+HJ3YpZrz+DQBSj0TjoQbZyINPe1PEx2hddjGU07P3uKTRBDO4gnNrxqQLPz85S9/gb3UQBqNBm+++SbefPNNwTYxMTGmhIZCdOvWDb/88ovdNg899BAeeugh+wOWiZTCnc4U3zTHXJvCd30pUSdKjckaewUuldrwNxX86csVFRaM9M5x+GrfH9IHKzAORxuqs9qUtFubOD3/YiOt7KmuHSHnPpWMfFKzgOENPkkEoRTuKlStJvwq2stVKLU4S/Unsudr4ooNY/LAWzFlSEe3ClwV1+sUE3zMsTc/SmhTpMy/s/Z2oUhDe9eTE8KudOSTmgUMV6QBoJB5glAPikZ7+StiF+cp6R0QFR5s83p0eDCWyPQnEvJhccWGkXZLrN3FWuqGLwYpwseU9A6IEWFKAezPD6dNAcSP07pf8c9Ee0Xs7UOT4rF92iCsGpeKBSOTMSW9AzSwHb8zjvNKRz6pOdrE3jMgZw79tX4SQagV0vwoQK820QjQ2HckDtAAE/9yKyYPamc3i3C9kVm8n5IQg9nrD0uOOhGjvXA0ZhscrPNiN/xGIUFoFBIoKgJODNwpvH2zCAQ72IzEntid1aaI1RxMHtQekwe1V0QjYK267qhvJJh3Sopg5arIJ7VHmzjK3SV2Dv25fhJBqBUSfhQg70yZQyHCyBrapd3axCaLMAdffamFW+z3K+TDImZjWTiqJ6IjtNh4qAj/3XXG/oXgOLN034QYhwkSAeBqzQ189HgvBGg0uHilGieKr2LhFsfV27mx893Lvd3jMWnlfruaIjnmJHNHwNMl1zD/ptO2o41a6sbuCnu7FEdGeyYZV2ZjVkrAcBXOOoNSyDxBqBMSfhRACcfNrIIih5XFpfYtZWMRI/w40uwEBmjwQHILUckKS67WmIpl5p66LEr4mZLeHqv3nrO5l5mZiXhrg+PItjgZBWGd0aaoYWMX48joyJfH1Y7Jao82ccYZ1JvKeBCEP0HCjwI467hZb2SYvf6wS8YgZmNR0rkzPVEvSvgxH68UE9HEv7TDf3NP40zpNbSJCcfjaW2Rd6bM5QVhOaRu1Grf2MWYZNzhmOyr0SZqjmgjCH+GhB8FcFZ42FNYKtv/RYxg4mhjUdL3Qs5ciL1+9hEDbwbtu0TWBHO2IKz5eKVs1Grd2MWaZLa+PNBjBVC9HTVHtBGEP0PRXgrgKDqIAZiZ2VmxpHocSjqFKpXpU26UjKPrAxCMNvpMZE0w2mAsEWuSyTtTpmjkkz+h5og2gvBnSPPDg5x8HI6ig97acBQBN+soWSN3U1bad0QpE41cXxeh6wNA/3dz7GooNA4i12iDsUWKSea+5BYe919SA0I164R+M2qPaCMIf4WEHyucSeQ2NCkeRiPw3Epbx2V7Ya19E2Kg14U4NH3FR4biHyO6o6SqxmW+I0qZaOQKUnzXzz112aGGwk7ScQAN0WC0wVgi1SSjdv8lV8O3NnB5u8wjNK3XCzU4vhMEYYmG2atV4eNUVlYiMjISFRUV0Ol0gs6f3NLuyPxTb2To/26O4EbN+UZsnzbIZsMQE+1lLxGiL7Mu/zxeWJ3vVB/xAvPuz3DPqyNfHpo3YcdwPoTWC8rwTBDKYb1/S4V8fm4ipi7VG98dsVth2plK0EOT4rHksZ4I1wbavKfRAM8OSPBLwQdQxleHKnDbonQWY19FapFgofWC02rel9wCabc28ft5JQhPQsLPTZwRXDiUCGu9Xltve20GfLyt0GdS4dcbGXJPXca6/PPIPXXZrkAJOHYaFQuFE9uilKO7mpH6vFkjp2admPWCIAjPQT4/N1FCcHEmrFXM6VJOJli1qdrl+FQ5U7ncHIr24seXfXmUKMbqjNBMAjdBqBMSfm6iRD4OZ/L9yM0Ea0+4UboKt6PrOcKZGkdSa22ZQ3loHKPWXETOoFRNLWeEZhK4CUKdkPBzEyWyHDsT1ipH82RPuAHgcOGXetq3dz1HfSlR44jTUMzLPo6FW04JjtMce/OuNq0YoRxK1tQSUyTYGhK4CULdkPBzE6XyccgNa5WqebJ3qp3wxT5EhQfbXfhnfH0Is9cftgivt6cVEnM9e+G+StU4CgzQoF+7pqKFH6F5d4VWjFAPStbUkmp2JWdxglA/5PBshlLOn0OT4rF92iCsGpeKBSOTsWpcKrZPG2T381IywYqJTDMXRPjalF2rs8krxGmFrB2r5VzPvK96I8OOk5cEx2OOGA2YGAfoqLBgrHgmhXfeOUHOenMsErh/wvtQuqaW0NoQHR5syvXD4UvO4gThq5DmxwqlnD/l1H8Sq3lylPRPLkLmALnRLkIaJnuI0YCJmau5w7vyFjF15FjObo5ZqmM5oS5cUVPLXgZyMp8ShHdBmh8ePJWPQ6zmyZURJHwhunKvJ6Rh4kNqjSO5WjoxglzZtToszDkhahyEOnFVTS2+tYHy9xCE90GaHxVg7Xi79eWByDtTJniSdEcEibnA466IFak+EnK0dGIFuWU7TmPyoPa0kXkpVFOLIAh7kPDjYew53t6X3ALAn0nauA2+V5toh5FpkeHBqLjphyMnL465wCMn2kUKARpg3O3yMlhLNS+KFeTKr9eJcoYl1AvV1CIIQggSfjyImDwkAHiFo3u7x+PjbYXCPi8PdhX87PW6elRcqxMd0q9UkkEhuAzWPVpHu3xD6psQg6iwYJRfF3YI56AEdd6PLydwJAhCPlTYlKcwmjvyv4gpghoVHowynqgtbiTjByRg/YEiG+FmZmZnREeE4OKVasQ2CgEYLCrBZx8xYOLNIqp8gpOQzwyflir65hidFYrcWURzwU8nMO+n3xy2WzUulTQ/BEEQKsTZwqak+bEiq6AIs9cfgaHSTE2uC8Xse5VVk4vJQ8In+HDvaQCsP1Bk4x9UVlWLtzbwm9G4jVyuOUDoFJ19xCBZw8R3T2LzrjjL5EHtsGxnoWA6AEpQRxAE4duQ5sdMcswqKMKEmxoRPpYomLtjXf55vLA63+l+zLUTQmY0IY2Okhouvr6ENEz2WDAy2eTr5EqEvmtH2i8pUAZpgiAI10CaH4WoNzK8tOaA3TYvrT2gWP4XpSKoOL8UOen8laznxNeXnHpc7oosG5oUjyUudIalDNIEQRDqhYSfm+w8WYKq2nq7bapq6rHzZAlu79DU6espFUHFCQtKpvNXEs5UtuvUZUxauU/Q0dgTpiZXOcMqVVCTIAiCcA2U5PAmX+/7Q1Y7Lgx9Xf555J66jHqjOFGGi6ACYLdMgz3Mk7Qpnc5fSQIDNOjXPhZzh3eFBrb368m8K0onqBNTCuSN746Ifk4IgiAI5SHNz00caX342jlr2pBjFjLHXFhwRTp/pfGHvCtq1cARBEEQf0LCz036tI3Gj0eKRbUDlDNtWJteYhuF4KU1+SiurBE0hwVogIWjelj078iMppYIJl/Pu6JmDRxBEATRAJm9bjLmtgSH5ifNzXZKmzbMTS/92sVi9r1dTNfjY+GonhjWrblNH0JmNLWl83dnLSS5Zkm5eIMGjiAIwt8h4ecm2qAApCc2s9uma0sdtEEBok0b//zxmKwNV6hoZ3xkKJY81hPDugnn4ZFT7NNVuFvwsCaroAj9383BqKW78MLqfIxaugv9381BVkGRy67pqoKaBEEQhHJQnp+beQIiGjW2m3GZ48NHe6DOyCTl6BHyA3KUB8be+3zvAQ0+J4aK6yitqkVMoxDodZ4xKykd6i01Z47UnEdKwl0bkJZBmyAIghCHs3l+SPi5OXmHL9Vh1NJdDj8TExGMD0b2xOhPd0u6lgaWmx6fcKDXhWJU39ZoGxtud4Pn+2xUeDAAWGQt9lReGaUFD6mClJjSIa4upUF5fgiCIFwHJTlUCLEOqKVVdYAGknP0MPyZZJDLfGzjLF1ZbVFzim+zFBIs+Eo1eCKvjJxki/YQut+iimpM+GIfPny0h43/kxoirnzdsZsgCMKbIZ+fm8RGhIhuu/loscm5WApFFdXYdeqyoHBgDSe8cD4q9gQLPjyRV0aK4OEIMfc7edV+bDxo6cOjlogrdzp2EwRBEOIh4YdDwr60Lv8ChiTqsfixnogKC5Z0mdzfS0Tn9LEWXhwJFkJ9iBU2hJDiuKyk4CHmfo0MeG7lPgsnZoq4IgiCIOxBZq+blFytEd32clUt9hSWYmhSPBqHBmP0J1L8f6Sd/s2FF2c0FYbKauSeuizZBCPVd0VJwUPK/Zqb0rwl5xFBEAThGUjzcxOpWoCfjhgAAKm3NLEb2mxOgAZIkbnhckKLXN76/rDkkG/O38Za+2JtjjNHyVBvKfdrrt3yppxHBEEQhPsh4ecmya2iJLX/dMdpZBUUmTZaMR41RgYEaDSihSVzOG2NnM8CNx21zbAnwADya1QpKXhw9ysWc02R2nIeEQRBEOqBhJ+brNx9RvJnuM1/aFI8nu7XVtRnSqpqJBc05TQlUouh2mvjyBnaGcdlpQQP8/sVg7WmaGhSPLZPG4RV41KxYGQyVo1LxfZpg0jwIQiC8HPI5+cmZ0qvSf6Mebj0kEQ9Pttx2uFnmjUORdqtTSQVNL23e7xJUyJUHDQ6PBgMliHvMRFaXK6qFezXXsi3s47LSoV6D02Kx4eP9sDkVfsh5Gdtz4eHi7giCIIgCA4Sfm7SJiZc1ue4zV+qk621cPDTEQO+O2jgvcbH2wrRo3W0SWMhJFgAsHjNUHEdU9YcEH0P5ijhuKyU4DGsW3MshAbPrdxn8x758BAEQRBSIbPXTR5Paws5eye3+cvxdeGEg7u7NcevZ8rtXsfaPMWXQ8b6NX1kmKR7MEdtNaqGdYvHksd62vgAkQ8PQRAEIRXS/NxEGxSAcbcn4KNthaLa85lahExSegdlDVyVkdiZkG9OmJv4xT5owF+jyt3aFsqaTBAEQSgBCT9m9GgdDUCc8MMADEtq2IjNN2AxG7R1kU5DpWsyEjsrwMgV5lwJ+fAQBEEQzkKFTSVWdQca8vWYO99KKVjJlzQwJiLYJhSdj1XjUmVt/M4W2ZRaUZ0gCIIgXInfV3VftGgR3n//fRgMBnTv3h0ffPAB+vbtK+qzcqq68yG2WrlQkU4x/TtbhZwEGIIgCMJXcFb48WqH5y+//BJTp07FrFmzsG/fPnTv3h0ZGRm4ePGi5L4ulEkPdecQU0BUbFFSV2UkpiKbBEEQBNGAVws///rXvzBu3Dg89dRTSExMxJIlSxAeHo7PPvuMt31NTQ0qKyst/jjy/yh3aiyOCoiKLUoaHaG1+DdFMxEEQRCEsnitw3NtbS3y8vIwY8YM02sBAQFIT09Hbm4u72fmzJmDN954Q6BHZTQhQk7JYp2VZ2Z2hj4yjMxTBEEQBOEivFbzU1JSgvr6esTFxVm8HhcXB4OBP1ngjBkzUFFRYfo7d+6c6b22TeQlObRGKOmf2KSB+sgwMk8RBEEQhAvxWuFHDiEhIdDpdBZ/HI+ntXVK9+Mo6Z/akgYSBEEQhL/itcJPbGwsAgMDUVxcbPF6cXEx9Hq95P60QQEYPyBBVFs5TslKVjsnCIIgCEI+Xiv8aLVa9OrVC5s3bza9ZjQasXnzZqSlpcnqc8awRDw7IIFXOxMREoglj/XEEieqlStV7ZwgCIIgCPl4dZ6fL7/8EmPGjMFHH32Evn37Yv78+VizZg2OHTtm4wvER0VFBaKionDu3DkLE1jtDSNW7j6NfWfLER4ciHuTWyDllj/9b+qNDHmny3DpajWaNgpFr7bRkjQ2zn6eIAiCIPyZyspKtGrVCuXl5YiMjJT8ea+N9gKARx55BJcuXcLrr78Og8GA5ORkZGVliRJ8AODKlSsAgFatWtltt9jpkRIEQRAEoTRXrlyRJfx4tebHWYxGIy5cuIDGjRtDo7HUvHBSpbVWiHAMzZ18aO7kQ3MnD5o3+dDcycfZuWOM4cqVK2jevDkCAqR78Hi15sdZAgIC0LJlS7ttrKPCCPHQ3MmH5k4+NHfyoHmTD82dfJyZOzkaHw6vdXgmCIIgCIKQAwk/BEEQBEH4FST8CBASEoJZs2YhJCTE00PxOmju5ENzJx+aO3nQvMmH5k4+np47v3Z4JgiCIAjC/yDND0EQBEEQfgUJPwRBEARB+BUk/BAEQRAE4VeQ8EMQBEEQhF9Bwg9BEARBEH4FCT8CLFq0CG3btkVoaChSUlKwZ88eTw/JpWzbtg333HMPmjdvDo1Gg2+//dbifcYYXn/9dcTHxyMsLAzp6ek4ceKERZvS0lKMHj0aOp0OUVFRGDt2LK5evWrR5uDBg7j99tsRGhqKVq1a4b333rMZy9q1a9GpUyeEhoaia9eu2Lhxo+L3qxRz5sxBnz590LhxYzRr1gz3338/jh8/btGmuroakyZNQpMmTdCoUSMMHz4cxcXFFm3Onj2LzMxMhIeHo1mzZnj55Zdx48YNizY///wzevbsiZCQELRr1w7Lly+3GY83PbeLFy9Gt27dTBle09LSsGnTJtP7NG/imDt3LjQaDV588UXTazR3/MyePRsajcbir1OnTqb3ad7sc/78eTz22GNo0qQJwsLC0LVrV/z666+m971qn2CEDatXr2ZarZZ99tln7PDhw2zcuHEsKiqKFRcXe3poLmPjxo3sb3/7G/v6668ZAPbNN99YvD937lwWGRnJvv32W3bgwAF27733soSEBHb9+nVTm6FDh7Lu3buzXbt2sV9++YW1a9eOjRo1yvR+RUUFi4uLY6NHj2YFBQVs1apVLCwsjH300UemNjt27GCBgYHsvffeY0eOHGGvvfYaCw4OZocOHXL5HMghIyODLVu2jBUUFLD8/Hw2bNgw1rp1a3b16lVTmwkTJrBWrVqxzZs3s19//ZWlpqay2267zfT+jRs3WFJSEktPT2f79+9nGzduZLGxsWzGjBmmNr///jsLDw9nU6dOZUeOHGEffPABCwwMZFlZWaY23vbcrl+/nm3YsIH99ttv7Pjx4+zVV19lwcHBrKCggDFG8yaGPXv2sLZt27Ju3bqxF154wfQ6zR0/s2bNYl26dGFFRUWmv0uXLpnep3kTprS0lLVp04Y9+eSTbPfu3ez3339nP/zwAzt58qSpjTftEyT88NC3b182adIk07/r6+tZ8+bN2Zw5czw4KvdhLfwYjUam1+vZ+++/b3qtvLychYSEsFWrVjHGGDty5AgDwPbu3Wtqs2nTJqbRaNj58+cZY4x9+OGHLDo6mtXU1JjaTJs2jXXs2NH074cffphlZmZajCclJYU9++yzit6jq7h48SIDwLZu3coYa5in4OBgtnbtWlObo0ePMgAsNzeXMdYgeAYEBDCDwWBqs3jxYqbT6Uxz9corr7AuXbpYXOuRRx5hGRkZpn/7wnMbHR3NPvnkE5o3EVy5coW1b9+eZWdnszvuuMMk/NDcCTNr1izWvXt33vdo3uwzbdo01r9/f8H3vW2fILOXFbW1tcjLy0N6errptYCAAKSnpyM3N9eDI/MchYWFMBgMFnMSGRmJlJQU05zk5uYiKioKvXv3NrVJT09HQEAAdu/ebWozYMAAaLVaU5uMjAwcP34cZWVlpjbm1+HaeMvcV1RUAABiYmIAAHl5eairq7O4p06dOqF169YWc9e1a1fExcWZ2mRkZKCyshKHDx82tbE3L97+3NbX12P16tWoqqpCWloazZsIJk2ahMzMTJv7o7mzz4kTJ9C8eXPccsstGD16NM6ePQuA5s0R69evR+/evfHQQw+hWbNm6NGjB5YuXWp639v2CRJ+rCgpKUF9fb3Fww0AcXFxMBgMHhqVZ+Hu296cGAwGNGvWzOL9oKAgxMTEWLTh68P8GkJtvGHujUYjXnzxRfTr1w9JSUkAGu5Hq9UiKirKoq313Mmdl8rKSly/ft1rn9tDhw6hUaNGCAkJwYQJE/DNN98gMTGR5s0Bq1evxr59+zBnzhyb92juhElJScHy5cuRlZWFxYsXo7CwELfffjuuXLlC8+aA33//HYsXL0b79u3xww8/YOLEifi///s/fP755wC8b58IEt2SIAi7TJo0CQUFBdi+fbunh+I1dOzYEfn5+aioqMBXX32FMWPGYOvWrZ4elqo5d+4cXnjhBWRnZyM0NNTTw/Eq7rrrLtP/d+vWDSkpKWjTpg3WrFmDsLAwD45M/RiNRvTu3RvvvPMOAKBHjx4oKCjAkiVLMGbMGA+PTjqk+bEiNjYWgYGBNh7+xcXF0Ov1HhqVZ+Hu296c6PV6XLx40eL9GzduoLS01KINXx/m1xBqo/a5nzx5Mr7//nts2bIFLVu2NL2u1+tRW1uL8vJyi/bWcyd3XnQ6HcLCwrz2udVqtWjXrh169eqFOXPmoHv37liwYAHNmx3y8vJw8eJF9OzZE0FBQQgKCsLWrVvx73//G0FBQYiLi6O5E0lUVBQ6dOiAkydP0jPngPj4eCQmJlq81rlzZ5PZ0Nv2CRJ+rNBqtejVqxc2b95ses1oNGLz5s1IS0vz4Mg8R0JCAvR6vcWcVFZWYvfu3aY5SUtLQ3l5OfLy8kxtcnJyYDQakZKSYmqzbds21NXVmdpkZ2ejY8eOiI6ONrUxvw7XRq1zzxjD5MmT8c033yAnJwcJCQkW7/fq1QvBwcEW93T8+HGcPXvWYu4OHTpksShkZ2dDp9OZFhtH8+Irz63RaERNTQ3Nmx0GDx6MQ4cOIT8/3/TXu3dvjB492vT/NHfiuHr1Kk6dOoX4+Hh65hzQr18/mzQev/32G9q0aQPAC/cJ0a7RfsTq1atZSEgIW758OTty5AgbP348i4qKsvDw9zWuXLnC9u/fz/bv388AsH/9619s//797MyZM4yxhhDGqKgotm7dOnbw4EF233338YYw9ujRg+3evZtt376dtW/f3iKEsby8nMXFxbHHH3+cFRQUsNWrV7Pw8HCbEMagoCD2j3/8gx09epTNmjVL1aHuEydOZJGRkeznn3+2CJ+9du2aqc2ECRNY69atWU5ODvv1119ZWloaS0tLM73Phc/eeeedLD8/n2VlZbGmTZvyhs++/PLL7OjRo2zRokW84bPe9NxOnz6dbd26lRUWFrKDBw+y6dOnM41Gw3788UfGGM2bFMyjvRijuRPipZdeYj///DMrLCxkO3bsYOnp6Sw2NpZdvHiRMUbzZo89e/awoKAg9vbbb7MTJ06wFStWsPDwcPbFF1+Y2njTPkHCjwAffPABa926NdNqtaxv375s165dnh6SS9myZQsDYPM3ZswYxlhDGOPMmTNZXFwcCwkJYYMHD2bHjx+36OPy5cts1KhRrFGjRkyn07GnnnqKXblyxaLNgQMHWP/+/VlISAhr0aIFmzt3rs1Y1qxZwzp06MC0Wi3r0qUL27Bhg8vu21n45gwAW7ZsmanN9evX2XPPPceio6NZeHg4e+CBB1hRUZFFP6dPn2Z33XUXCwsLY7Gxseyll15idXV1Fm22bNnCkpOTmVarZbfccovFNTi86bl9+umnWZs2bZhWq2VNmzZlgwcPNgk+jNG8ScFa+KG54+eRRx5h8fHxTKvVshYtWrBHHnnEIk8NzZt9vvvuO5aUlMRCQkJYp06d2Mcff2zxvjftExrGGBOvJyIIgiAIgvBuyOeHIAiCIAi/goQfgiAIgiD8ChJ+CIIgCILwK0j4IQiCIAjCryDhhyAIgiAIv4KEH4IgCIIg/AoSfgiCIAiC8CtI+CEIgiAIwq8g4YcgCIIgCL+ChB+CIAiCIPwKEn4IgiAIgvAr/h95Ly/Z1SKfNgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for k in [3,10,25,50,100,300]:\n",
    "    model = KNeighborsRegressor(n_neighbors=k).fit(X_train,y_train)\n",
    "    y_hat = model.predict(X_test)\n",
    "    SSE = np.sum( (y_test-y_hat)**2 )\n",
    "    #\n",
    "    plot, axes = plt.subplots()\n",
    "    plt.scatter(y_test,y_hat)\n",
    "    plt.title('k: '+str(k)+', SSE: '+str(SSE))\n",
    "    axes.set_ylim(-1000, 62000)\n",
    "    axes.set_xlim(-1000, 62000)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a232c6c-983f-426e-a1e1-a2c1f0c03e7b",
   "metadata": {},
   "source": [
    "5. Determine the optimal $k$ for these data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b116cc55-c2fd-49e2-8278-b5904c2eee15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[77]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Determine the optimal k:\n",
    "k_bar = 200\n",
    "k_grid = np.arange(1,k_bar) # The range of k's to consider\n",
    "SSE = np.zeros(k_bar) \n",
    "\n",
    "for k in range(k_bar):\n",
    "    fitted_model = KNeighborsRegressor(n_neighbors=k+1).fit(X_train,y_train) \n",
    "    y_hat = fitted_model.predict(X_test) # Predict values for test set\n",
    "    SSE[k] = np.sum( (y_test-y_hat)**2 ) # Save the computed SSE\n",
    " \n",
    "SSE_min = np.min(SSE) # Lowest recorded SSE\n",
    "min_index = np.where(SSE==SSE_min) # Find the indices of y that equal the minimum\n",
    "k_star = k_grid[min_index] # Find the optimal value of k\n",
    "print(k_star)\n",
    "\n",
    "plt.plot(np.arange(0,k_bar),SSE) # Plot SSE by k\n",
    "plt.xlabel(\"k\")\n",
    "plt.title(\"optimal k:\"+str(k_star))\n",
    "plt.ylabel('SSE')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5499e892-df09-438e-a8af-3ca28044b505",
   "metadata": {},
   "source": [
    "6. Describe what happened in the plots of predicted versus actual prices as $k$ varied, taking your answer into part 6 into account. (Hint: Use the words \"underfitting\" and \"overfitting\".)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c6273c6-613a-4bfd-8e8d-1da13ec81410",
   "metadata": {},
   "source": [
    "> The optimal $k$ is around 77, so for 300, the model is probably overfitting, and for 3, 10, and 25, the model is underfitting. For $k$ equal to 50 and 100, the answer is pretty close. What do we notice about $k=300$? There is a bunch of horizontal bunching, where common answers start to exert a lot of influence on the predictions and organize them into horizontal groups. Since the data are fairly evenly distributed, this is an unnatural artifact of over-fitting. On the other hand, for very small $k$, like 3, the predictions are high variance and low precision.\n",
    "> The other thing to notice is that as $k$ increases, the range of predictions shrinks: The high, outlier values become less influential, and predictions shrink towards average values. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47338226-79f4-4109-ab76-07187db9b5a2",
   "metadata": {},
   "source": [
    "**Q2.** This question is a case study for $k$ nearest neighbor, particularly variable selection. The data for the question include (summaries reproduced here directly from the codebook):\n",
    "\n",
    "- age: age of the patient (years)\n",
    "- anaemia: decrease of red blood cells or hemoglobin (boolean)\n",
    "- high blood pressure: if the patient has hypertension (boolean)\n",
    "- creatinine phosphokinase (CPK): level of the CPK enzyme in the blood (mcg/L)\n",
    "- diabetes: if the patient has diabetes (boolean)\n",
    "- ejection fraction: percentage of blood leaving the heart at each contraction (percentage)\n",
    "- platelets: platelets in the blood (kiloplatelets/mL)\n",
    "- sex: woman or man (binary)\n",
    "- serum creatinine: level of serum creatinine in the blood (mg/dL)\n",
    "- serum sodium: level of serum sodium in the blood (mEq/L)\n",
    "- smoking: if the patient smokes or not (boolean)\n",
    "- time: follow-up period (days)\n",
    "- death event: if the patient deceased during the follow-up period (boolean)\n",
    "\n",
    "1. Load the `./data/heart_failure_clinical_records_dataset.csv`. Are there any `NA`'s to handle? use `.drop()` to remove `time` from the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b8281bef-5f9b-432f-90ac-af0afb590d29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(299, 13)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60.833893</td>\n",
       "      <td>0.431438</td>\n",
       "      <td>581.839465</td>\n",
       "      <td>0.418060</td>\n",
       "      <td>38.083612</td>\n",
       "      <td>0.351171</td>\n",
       "      <td>263358.029264</td>\n",
       "      <td>1.39388</td>\n",
       "      <td>136.625418</td>\n",
       "      <td>0.648829</td>\n",
       "      <td>0.32107</td>\n",
       "      <td>130.260870</td>\n",
       "      <td>0.32107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.894809</td>\n",
       "      <td>0.496107</td>\n",
       "      <td>970.287881</td>\n",
       "      <td>0.494067</td>\n",
       "      <td>11.834841</td>\n",
       "      <td>0.478136</td>\n",
       "      <td>97804.236869</td>\n",
       "      <td>1.03451</td>\n",
       "      <td>4.412477</td>\n",
       "      <td>0.478136</td>\n",
       "      <td>0.46767</td>\n",
       "      <td>77.614208</td>\n",
       "      <td>0.46767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25100.000000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>212500.000000</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262000.000000</td>\n",
       "      <td>1.10000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>582.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>303500.000000</td>\n",
       "      <td>1.40000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7861.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>850000.000000</td>\n",
       "      <td>9.40000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>285.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age     anaemia  creatinine_phosphokinase    diabetes  \\\n",
       "count  299.000000  299.000000                299.000000  299.000000   \n",
       "mean    60.833893    0.431438                581.839465    0.418060   \n",
       "std     11.894809    0.496107                970.287881    0.494067   \n",
       "min     40.000000    0.000000                 23.000000    0.000000   \n",
       "25%     51.000000    0.000000                116.500000    0.000000   \n",
       "50%     60.000000    0.000000                250.000000    0.000000   \n",
       "75%     70.000000    1.000000                582.000000    1.000000   \n",
       "max     95.000000    1.000000               7861.000000    1.000000   \n",
       "\n",
       "       ejection_fraction  high_blood_pressure      platelets  \\\n",
       "count         299.000000           299.000000     299.000000   \n",
       "mean           38.083612             0.351171  263358.029264   \n",
       "std            11.834841             0.478136   97804.236869   \n",
       "min            14.000000             0.000000   25100.000000   \n",
       "25%            30.000000             0.000000  212500.000000   \n",
       "50%            38.000000             0.000000  262000.000000   \n",
       "75%            45.000000             1.000000  303500.000000   \n",
       "max            80.000000             1.000000  850000.000000   \n",
       "\n",
       "       serum_creatinine  serum_sodium         sex    smoking        time  \\\n",
       "count         299.00000    299.000000  299.000000  299.00000  299.000000   \n",
       "mean            1.39388    136.625418    0.648829    0.32107  130.260870   \n",
       "std             1.03451      4.412477    0.478136    0.46767   77.614208   \n",
       "min             0.50000    113.000000    0.000000    0.00000    4.000000   \n",
       "25%             0.90000    134.000000    0.000000    0.00000   73.000000   \n",
       "50%             1.10000    137.000000    1.000000    0.00000  115.000000   \n",
       "75%             1.40000    140.000000    1.000000    1.00000  203.000000   \n",
       "max             9.40000    148.000000    1.000000    1.00000  285.000000   \n",
       "\n",
       "       DEATH_EVENT  \n",
       "count    299.00000  \n",
       "mean       0.32107  \n",
       "std        0.46767  \n",
       "min        0.00000  \n",
       "25%        0.00000  \n",
       "50%        0.00000  \n",
       "75%        1.00000  \n",
       "max        1.00000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('./data/heart_failure_clinical_records_dataset.csv')\n",
    "\n",
    "print(df.shape)\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01dbfbff-661e-4ac8-a9bd-99736e66cf4b",
   "metadata": {},
   "source": [
    "> No missing values, since 299 values for every variable and 299 observations in total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "edc75d7f-5f81-44e3-964c-b63b2a6842da",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('time',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b72bf9ee-de68-4ac2-8953-dee31f5fb338",
   "metadata": {},
   "source": [
    "2. Make a correlation matrix. What variables are strongly associated with a death event?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c8ba761b-a696-4233-9ee6-0a1a00c74e1d",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>anaemia</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.088006</td>\n",
       "      <td>-0.081584</td>\n",
       "      <td>-0.101012</td>\n",
       "      <td>0.060098</td>\n",
       "      <td>0.093289</td>\n",
       "      <td>-0.052354</td>\n",
       "      <td>0.159187</td>\n",
       "      <td>-0.045966</td>\n",
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       "      <td>0.018668</td>\n",
       "      <td>0.253729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anaemia</th>\n",
       "      <td>0.088006</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.190741</td>\n",
       "      <td>-0.012729</td>\n",
       "      <td>0.031557</td>\n",
       "      <td>0.038182</td>\n",
       "      <td>-0.043786</td>\n",
       "      <td>0.052174</td>\n",
       "      <td>0.041882</td>\n",
       "      <td>-0.094769</td>\n",
       "      <td>-0.107290</td>\n",
       "      <td>0.066270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <td>-0.081584</td>\n",
       "      <td>-0.190741</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009639</td>\n",
       "      <td>-0.044080</td>\n",
       "      <td>-0.070590</td>\n",
       "      <td>0.024463</td>\n",
       "      <td>-0.016408</td>\n",
       "      <td>0.059550</td>\n",
       "      <td>0.079791</td>\n",
       "      <td>0.002421</td>\n",
       "      <td>0.062728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <td>-0.101012</td>\n",
       "      <td>-0.012729</td>\n",
       "      <td>-0.009639</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004850</td>\n",
       "      <td>-0.012732</td>\n",
       "      <td>0.092193</td>\n",
       "      <td>-0.046975</td>\n",
       "      <td>-0.089551</td>\n",
       "      <td>-0.157730</td>\n",
       "      <td>-0.147173</td>\n",
       "      <td>-0.001943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ejection_fraction</th>\n",
       "      <td>0.060098</td>\n",
       "      <td>0.031557</td>\n",
       "      <td>-0.044080</td>\n",
       "      <td>-0.004850</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024445</td>\n",
       "      <td>0.072177</td>\n",
       "      <td>-0.011302</td>\n",
       "      <td>0.175902</td>\n",
       "      <td>-0.148386</td>\n",
       "      <td>-0.067315</td>\n",
       "      <td>-0.268603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <td>0.093289</td>\n",
       "      <td>0.038182</td>\n",
       "      <td>-0.070590</td>\n",
       "      <td>-0.012732</td>\n",
       "      <td>0.024445</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.049963</td>\n",
       "      <td>-0.004935</td>\n",
       "      <td>0.037109</td>\n",
       "      <td>-0.104615</td>\n",
       "      <td>-0.055711</td>\n",
       "      <td>0.079351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platelets</th>\n",
       "      <td>-0.052354</td>\n",
       "      <td>-0.043786</td>\n",
       "      <td>0.024463</td>\n",
       "      <td>0.092193</td>\n",
       "      <td>0.072177</td>\n",
       "      <td>0.049963</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.041198</td>\n",
       "      <td>0.062125</td>\n",
       "      <td>-0.125120</td>\n",
       "      <td>0.028234</td>\n",
       "      <td>-0.049139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serum_creatinine</th>\n",
       "      <td>0.159187</td>\n",
       "      <td>0.052174</td>\n",
       "      <td>-0.016408</td>\n",
       "      <td>-0.046975</td>\n",
       "      <td>-0.011302</td>\n",
       "      <td>-0.004935</td>\n",
       "      <td>-0.041198</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.189095</td>\n",
       "      <td>0.006970</td>\n",
       "      <td>-0.027414</td>\n",
       "      <td>0.294278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serum_sodium</th>\n",
       "      <td>-0.045966</td>\n",
       "      <td>0.041882</td>\n",
       "      <td>0.059550</td>\n",
       "      <td>-0.089551</td>\n",
       "      <td>0.175902</td>\n",
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       "      <td>0.062125</td>\n",
       "      <td>-0.189095</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027566</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>-0.195204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>0.065430</td>\n",
       "      <td>-0.094769</td>\n",
       "      <td>0.079791</td>\n",
       "      <td>-0.157730</td>\n",
       "      <td>-0.148386</td>\n",
       "      <td>-0.104615</td>\n",
       "      <td>-0.125120</td>\n",
       "      <td>0.006970</td>\n",
       "      <td>-0.027566</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.445892</td>\n",
       "      <td>-0.004316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking</th>\n",
       "      <td>0.018668</td>\n",
       "      <td>-0.107290</td>\n",
       "      <td>0.002421</td>\n",
       "      <td>-0.147173</td>\n",
       "      <td>-0.067315</td>\n",
       "      <td>-0.055711</td>\n",
       "      <td>0.028234</td>\n",
       "      <td>-0.027414</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>0.445892</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEATH_EVENT</th>\n",
       "      <td>0.253729</td>\n",
       "      <td>0.066270</td>\n",
       "      <td>0.062728</td>\n",
       "      <td>-0.001943</td>\n",
       "      <td>-0.268603</td>\n",
       "      <td>0.079351</td>\n",
       "      <td>-0.049139</td>\n",
       "      <td>0.294278</td>\n",
       "      <td>-0.195204</td>\n",
       "      <td>-0.004316</td>\n",
       "      <td>-0.012623</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               age   anaemia  creatinine_phosphokinase  \\\n",
       "age                       1.000000  0.088006                 -0.081584   \n",
       "anaemia                   0.088006  1.000000                 -0.190741   \n",
       "creatinine_phosphokinase -0.081584 -0.190741                  1.000000   \n",
       "diabetes                 -0.101012 -0.012729                 -0.009639   \n",
       "ejection_fraction         0.060098  0.031557                 -0.044080   \n",
       "high_blood_pressure       0.093289  0.038182                 -0.070590   \n",
       "platelets                -0.052354 -0.043786                  0.024463   \n",
       "serum_creatinine          0.159187  0.052174                 -0.016408   \n",
       "serum_sodium             -0.045966  0.041882                  0.059550   \n",
       "sex                       0.065430 -0.094769                  0.079791   \n",
       "smoking                   0.018668 -0.107290                  0.002421   \n",
       "DEATH_EVENT               0.253729  0.066270                  0.062728   \n",
       "\n",
       "                          diabetes  ejection_fraction  high_blood_pressure  \\\n",
       "age                      -0.101012           0.060098             0.093289   \n",
       "anaemia                  -0.012729           0.031557             0.038182   \n",
       "creatinine_phosphokinase -0.009639          -0.044080            -0.070590   \n",
       "diabetes                  1.000000          -0.004850            -0.012732   \n",
       "ejection_fraction        -0.004850           1.000000             0.024445   \n",
       "high_blood_pressure      -0.012732           0.024445             1.000000   \n",
       "platelets                 0.092193           0.072177             0.049963   \n",
       "serum_creatinine         -0.046975          -0.011302            -0.004935   \n",
       "serum_sodium             -0.089551           0.175902             0.037109   \n",
       "sex                      -0.157730          -0.148386            -0.104615   \n",
       "smoking                  -0.147173          -0.067315            -0.055711   \n",
       "DEATH_EVENT              -0.001943          -0.268603             0.079351   \n",
       "\n",
       "                          platelets  serum_creatinine  serum_sodium       sex  \\\n",
       "age                       -0.052354          0.159187     -0.045966  0.065430   \n",
       "anaemia                   -0.043786          0.052174      0.041882 -0.094769   \n",
       "creatinine_phosphokinase   0.024463         -0.016408      0.059550  0.079791   \n",
       "diabetes                   0.092193         -0.046975     -0.089551 -0.157730   \n",
       "ejection_fraction          0.072177         -0.011302      0.175902 -0.148386   \n",
       "high_blood_pressure        0.049963         -0.004935      0.037109 -0.104615   \n",
       "platelets                  1.000000         -0.041198      0.062125 -0.125120   \n",
       "serum_creatinine          -0.041198          1.000000     -0.189095  0.006970   \n",
       "serum_sodium               0.062125         -0.189095      1.000000 -0.027566   \n",
       "sex                       -0.125120          0.006970     -0.027566  1.000000   \n",
       "smoking                    0.028234         -0.027414      0.004813  0.445892   \n",
       "DEATH_EVENT               -0.049139          0.294278     -0.195204 -0.004316   \n",
       "\n",
       "                           smoking  DEATH_EVENT  \n",
       "age                       0.018668     0.253729  \n",
       "anaemia                  -0.107290     0.066270  \n",
       "creatinine_phosphokinase  0.002421     0.062728  \n",
       "diabetes                 -0.147173    -0.001943  \n",
       "ejection_fraction        -0.067315    -0.268603  \n",
       "high_blood_pressure      -0.055711     0.079351  \n",
       "platelets                 0.028234    -0.049139  \n",
       "serum_creatinine         -0.027414     0.294278  \n",
       "serum_sodium              0.004813    -0.195204  \n",
       "sex                       0.445892    -0.004316  \n",
       "smoking                   1.000000    -0.012623  \n",
       "DEATH_EVENT              -0.012623     1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3799ada9-bebc-4dae-92f5-6054e995612a",
   "metadata": {},
   "source": [
    "> The variables with the strongest correlation with `DEATH_EVENT` are age (.254), ejection_fraction (-.269), and serum_creatine (.294). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be18bc4-bc1b-47bd-9fec-0218305808b9",
   "metadata": {},
   "source": [
    "3. For the dummy variables `anaemia`, `diabetes`, `high_blood_pressure`, `sex`, and `smoking`, compute a summary table of `DEATH_EVENT` grouped by the variable. For which variables does a higher proportion of the population die when the variable takes the value 1 rather than 0?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c3f436f2-07a5-4f87-9ba7-8747480ee8ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        DEATH_EVENT                                             \n",
      "              count      mean       std  min  25%  50%  75%  max\n",
      "anaemia                                                         \n",
      "0             170.0  0.294118  0.456991  0.0  0.0  0.0  1.0  1.0\n",
      "1             129.0  0.356589  0.480859  0.0  0.0  0.0  1.0  1.0\n",
      "         DEATH_EVENT                                             \n",
      "               count      mean       std  min  25%  50%  75%  max\n",
      "diabetes                                                         \n",
      "0              174.0  0.321839  0.468530  0.0  0.0  0.0  1.0  1.0\n",
      "1              125.0  0.320000  0.468353  0.0  0.0  0.0  1.0  1.0\n",
      "                    DEATH_EVENT                                             \n",
      "                          count      mean       std  min  25%  50%  75%  max\n",
      "high_blood_pressure                                                         \n",
      "0                         194.0  0.293814  0.456687  0.0  0.0  0.0  1.0  1.0\n",
      "1                         105.0  0.371429  0.485504  0.0  0.0  0.0  1.0  1.0\n",
      "    DEATH_EVENT                                             \n",
      "          count      mean       std  min  25%  50%  75%  max\n",
      "sex                                                         \n",
      "0         105.0  0.323810  0.470173  0.0  0.0  0.0  1.0  1.0\n",
      "1         194.0  0.319588  0.467523  0.0  0.0  0.0  1.0  1.0\n",
      "        DEATH_EVENT                                             \n",
      "              count      mean       std  min  25%  50%  75%  max\n",
      "smoking                                                         \n",
      "0             203.0  0.325123  0.469579  0.0  0.0  0.0  1.0  1.0\n",
      "1              96.0  0.312500  0.465946  0.0  0.0  0.0  1.0  1.0\n"
     ]
    }
   ],
   "source": [
    "vars = ['anaemia','diabetes','high_blood_pressure','sex','smoking']\n",
    "\n",
    "for var in vars:\n",
    "    print(df.loc[:,[var,'DEATH_EVENT']].groupby(var).describe())\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14177a9f-1356-44fe-815a-9bb402b1945a",
   "metadata": {},
   "source": [
    "> Let's look at the means for high_blood_pressure. For the proportion of the population that has HBP, the DEATH_EVENT average is .371, while for the proportion of the population that does not have HBP, the DEATH_EVENT average is only .294. That's a 27% increase in the frequency of death events. So HPB seems highly predictive. On the other hand, for sex, the mean values are almost the same for men and women, at .32, so the sex variable isn't a very powerful predictor of death events. Anaemia and high blood pressure seem like the strongest predictors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd07408e-0a85-42b6-a65f-a0cf8b03ce9f",
   "metadata": {},
   "source": [
    "4. On the basis of your answers from 2 and 3, build a matrix $X$ of the variables you think are most predictive of a death, and a variable $y$ equal to `DEATH_EVENT`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "653d6dcb-ee47-486b-b1ef-d22b5e625b29",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['DEATH_EVENT']\n",
    "vars = ['age','ejection_fraction','serum_creatinine','high_blood_pressure','anaemia']\n",
    "X = df.loc[:,vars]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04a33c8c-461e-48a3-8c5a-70765258605d",
   "metadata": {},
   "source": [
    "5. Maxmin normalize all of the variables in `X`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9c344fa3-6756-4db2-9fa2-47d7d0f95b10",
   "metadata": {},
   "outputs": [],
   "source": [
    "def maxmin(x):\n",
    "    u = (x-min(x))/(max(x)-min(x))\n",
    "    return u\n",
    "X = X.apply(maxmin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9413a9-77ee-44ec-ad4b-95f5af467560",
   "metadata": {},
   "source": [
    "6. Split the sample into ~80% for training and ~20% for evaluation. (Try to use the same train/test split for the whole question, so that you're comparing apples to apples in the questions below.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0625ea9d-0caf-4b35-8742-0418224b0c2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.model_selection import train_test_split\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=.2,random_state=100)\n",
    "np.random.seed(100) \n",
    "N = X.shape[0]\n",
    "all = np.arange(1,N)\n",
    "train = np.random.choice(N,int(.8*N) ) # Generate random indices for training set\n",
    "test = [item for item in all if item not in train] # Find test indices\n",
    "\n",
    "#test = np.where( train not in np.linspace(1,N) )\n",
    "X_train = X.iloc[train,:]\n",
    "y_train = y.iloc[train]\n",
    "X_test = X.iloc[test,:]\n",
    "y_test = y.iloc[test]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f34abd0-5809-4bd0-b279-95b444ec2f23",
   "metadata": {},
   "source": [
    "7. Determine the optimal number of neighbors for a $k$NN regression for the variables you selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "07393968-a46e-4672-849f-8e07e7b6378a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "# Determine the optimal k:\n",
    "k_bar = 25\n",
    "k_grid = np.arange(1,k_bar) # The range of k's to consider\n",
    "SSE = np.zeros(k_bar) \n",
    "\n",
    "for k in range(k_bar):\n",
    "    knn = KNeighborsRegressor(n_neighbors=k+1)\n",
    "    predictor = knn.fit(X_train,y_train) \n",
    "    y_hat = knn.predict(X_test)\n",
    "    SSE[k] = np.sum( (y_test-y_hat)**2 ) # Bug in sklearn requires .values\n",
    "\n",
    "SSE_min = np.min(SSE) # highest recorded accuracy\n",
    "min_index = np.where(SSE==SSE_min) \n",
    "k_star = k_grid[min_index] # Find the optimal value of k\n",
    "print(k_star)\n",
    "\n",
    "plt.plot(np.arange(0,k_bar),SSE) # Plot accuracy by k\n",
    "plt.xlabel(\"k\")\n",
    "plt.title(\"optimal k:\"+str(k_star)+', SSE:'+str(SSE_min))\n",
    "plt.ylabel('SSE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd21ce05-1f34-4698-9c82-8953bca1d356",
   "metadata": {},
   "source": [
    "8. OK, do steps 5 through 7 again, but use all of the variables (except `time`). Which model has a lower Sum of Squared Error? Which would you prefer to use in practice, if you had to predict `DEATH_EVENT`s? If you play with the selection of variables, how much does the SSE change for your fitted model on the test data? Are more variables better, in this case? Explain your findings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "43032a0f-e088-4441-9394-cb8e1b6c1a1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[83]\n"
     ]
    },
    {
     "data": {
      "image/png": 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VGhJkhKgIrjNnzhR+fn6WX+oqlUq8/vrrVX4ua9asEQMGDBByudyyb48ePcTp06ernIcZM2aI7du3V3nU9XxWMplMYuTIkUKlUom4uLgq2+tzXm/20EMPCaVSKf7zn/+IhIQEsWfPHtGtWzfLF1xycrIQQojHH39cABCdOnWyGv+wdu1aAUB8+umn9T7m8ePHhZOTk+jbt6/Yt2+fuHDhgnj77beFWq0WAKqEoZutXr1aABCLFi2qcZ/CwkLRvXt34eHhIa5evWq1TS6Xi6eeeqrKa3bu3CkAiM2bNwshKkK1Xq8XERERYtu2beLSpUvi448/FjqdTgAQQ4cOrfH9o6KihI+PjygrK6v1swhRMQZmxIgRQqlUWs6REELEx8cLJycnsXHjRktbdUHm9ddfFwCEt7e3yM/Pt7RX/sPm5nD77rvvijFjxogvvvhCrF+/XowbN04olUrL565ObUGmdevWAoCYOXOmVfuTTz4pAIhz587d6hRQPTDIkBDiz3/1VxcM3nvvPatBlJVflrt27aqyb3BwsBgxYoTleX2DTGRkZJV9Q0NDxciRI6u03/wv1aKiIvHKK6+IoKAgIZPJrP5VPm3atFrfuzqVQabyX9o3/kK8ldqCzNq1a4WLi4vVL2ghhPjLX/4iNBpNrQNIhRBi3759AoDYunVrlW0NDTKVzGaziI+PF0uXLhWtWrWq8gV9o7y8PLFt2zbx8MMPCwCiTZs2loHBDR3se7Onn366Sg9bpYae19zcXHHvvfda/bl55JFHxAMPPCAAiGvXrgkhhJg1a5YAIBYuXGj1+vLycqFUKq3+rNX1mEIIsWHDBuHl5WXZz9/fXyxfvlwAEHPnzr3lufH3968xSJSXl4uxY8cKlUplNaC4Ul17ZIQQIiYmRoSEhFjq1Ol04osvvhAAxH333Vft+yckJFT5+3orP//8swAgVq1aZWkbOXJkld8n1QWZJUuWVPl7Xyk8PFzcddddlueLFi0S/v7+Vf5+R0dHi8DAwBqDV21BplOnTgKAiImJsWqPiYkRAMQXX3xR7evo9nCMDDUpN08hvVW7uGGg45w5c/DWW29h4sSJ+Prrr7Ft2zZs374dXl5eDRpoN378eCQkJGD16tW3fYwbLVu2DD169EBQUJBV+7333ouioiIcO3as1tffcccdCAgIsFk91ZHJZGjXrh3mzJmDPXv2QC6X1/h+Op0Ow4cPx+rVqzF16lQkJCRUO6aqoRYuXIhly5Zh8eLFePTRR6tsb+h51ev12LJlCy5fvoyYmBgkJiZi1apVSE1NtaxhAwCBgYEAqg4qVigU8PLyshrwXtdjAsCECRMsU7j379+Py5cvo3Xr1gBqn4ZcKTg4uMYxS0888QS+//57rFy50mpAcaWAgACkpqZWaa9sq/zMQMXSCRcvXsSxY8fw22+/4erVq5YB4jXVuWbNGgDAlClTbvk5bvw8ACyfadeuXfj5558xd+5cJCYmWh7l5eUoLi5GYmKiZcB3TT8joGKM140/o2XLlmHIkCFWU9mBij83KSkptzVZoKb3rxwIf+P7U8NxHRkCUDFDQaPRID4+vsq2s2fPQi6XW36xVDp//jzuuusuy/OCggKkpqZarRdy42wNe9u4cSOmTp2K//73v5a2kpKSBs/uWbJkCZRKJZ5++mm4ublVu6ZHfaSnp8PDw6NKe+XMlxsHwNakpKTEanaPPbVu3RoeHh7VftHdrHfv3vjiiy/qtG99fPjhh3jttdcwb948zJ8/v9p9bHFeASAkJAQhISEAKma/xMbGYvz48ZbtvXr1AgBcvXrV6nWlpaXIysqCj49PvY9ZSaVSoU+fPpbnlQOLhw0bVmvNQggkJiZWuy7P888/jxUrVuC9997D5MmTq3199+7dsXfvXpjNZqsBvwcPHoRGo6kSUBQKBbp3717nOtesWYM2bdpUOyOuJpUz0irPZ1JSEgBYzayqdPXqVYSHh+Pdd9/FvHnzavwZAUBKSgoiIyMtz9PT06ssfAfU/8/NjXr16oXt27dbBvve+N43fiayDfbIEICKX0x33303tmzZYvUvkPT0dKxZswYDBgyATqezes0nn3xiNe10+fLlKC8vx6hRoyxtrq6uNp8mXBOFQlFlKuoHH3xQ7S+p+pDJZPjkk08wYcIETJ06Fd99953V9vpOv27Xrh2OHTtmmQlSae3atZDL5ejatSuAisXDqptt9c033+DatWvo3bv3bX6i6h08eLDaqa+HDh1Cdna25RdyUVER9u/fX+0xfvrpJwCw+uVdV9VNvwYqVlN95plnMGXKFLzzzjs1vr6u5xWo+FI8e/bsLWtasGABysvL8eyzz1raoqOj4evri9WrV1vNQFm5ciVMJhOGDx9e72NW5/z58/joo48wZswYqyBx86rOQMXfvczMzCoLES5ZsgT/+c9/8NJLL2Hu3Lk1vteECROQnp6OTZs2WdqysrKwYcMGjB07Fmq1usbXZmZm4l//+he6du1abZA5duwYzpw5U+M/AHJycqr8HS0rK8PixYuhUqks/1gaMmQINm/eXOXh4+OD3r17Y/PmzRg7diyAij9/3bp1w5YtW6z+Xm7btg3JyclWP6N27dph+/btVgvcmUwmfP3113Bzc0ObNm1q/Ow1mThxIgDgs88+s2r/3//+B6VSafMZhy0de2TI4s0338T27dsxYMAAPP3001Aqlfj4449hNBrx73//u8r+paWlGDp0KCZOnIj4+HgsW7YMAwYMwL333mvZp1evXli+fDnefPNNtG3bFr6+vtV2bdvCmDFjsGrVKuj1enTs2BH79+/Hjh074OXl1eBjy+VyfPXVVxg3bhwmTpyIH3/80fI5Dh06hLvuuguvvvoqXnvttVse6/nnn8dPP/2EgQMHYvbs2fDy8sL333+Pn376CX/9618t3dLnz5/HsGHD8NBDDyEyMhJyuRxHjhzBV199hbCwsFq/mG60cuVKTJs2DStWrKiywvKNVq1ahdWrV+P+++9Hr169oFKpcObMGXz++edwdna2rNpaVFSEO+64A/3798fIkSMRHByM3NxcfPvtt9i7dy/GjRtXpWfg6NGj+Oqrr6q8Z5s2bRAVFQWgYvr1zXUeOnQIjz32GLy8vDB06NAql7fuuOMOy+WXup5XAHjssccQExNjFXwXL16MU6dOoV+/flAqlfj222+xbds2vPnmm1a9JGq1GkuWLMHUqVMxaNAgPProo0hKSsL777+PgQMHWvUY1PWYANCxY0c8+OCDCAkJwaVLl7B8+XJ4enrio48+stovNDQUDz30ELp06QJnZ2f89ttvWLduHbp3744nn3zSst/mzZvxwgsvICIiAh06dKhy/ocPH2659DFhwgT0798f06ZNw+nTpy0r+5pMJqvVkwFg8ODBiIqKQtu2bZGWloZPPvkEBQUF+P7776tdY6XyZ1bTZaXvvvsOb775JiZMmIDw8HDk5ORgzZo1OHXqFN5++23LFOsbe7VuNG/ePPj5+WHcuHFW7e+++y6GDx+OAQMG4Mknn0ReXh7eeecdtGvXDk899ZRlvxdffBGPPPII+vXrhxkzZsDFxQVr165FbGws3nzzTavbKezZswd79uwBUBHgCgsL8eabbwKouOQ2aNAgABUrVj/++OP4/PPPUV5ejsGDB+PXX3/Fhg0bsGDBAqs/i2QD0g7Roabm6NGjYsSIEUKr1QqNRiPuuusu8fvvv1vtUzmgNCYmRsyYMUN4eHgIrVYrpkyZUmV1zLS0NDF69Gjh5uYmAFgGx9U02Pfm2UlC1LzqJwAxa9Ysy/Nr166JadOmCW9vb6HVasWIESPE2bNnRWhoqJg6daplv4as7FtUVGSZXVU5Hfx2pl8fPHhQjBo1Svj7+wsnJyfRrl078dZbb1kNLMzMzBQzZswQkZGRwtXVVahUKhERESHmzZtX46qu1Q32rZz2euOAzeqcOHFCPP/886Jnz57C09NTKJVKERAQIB588EFx9OhRy35lZWXi008/FePGjROhoaFCrVYLjUYjevToIZYsWWK1Cu2tpl/f+HOpbvp1ZVtNj5vPb13OqxB/TvW/0ffffy/69u0r3NzchEajEf379xdff/11jedr7dq1olu3bkKtVgs/Pz8xe/ZsYTAYbvuYkyZNEsHBwUKlUonAwEAxc+bMalfK/etf/yo6duwo3NzchJOTk2jbtq2YP39+lfeu/PNb0+PmP/85OTli+vTpwsvLS2g0GjF48OBqB40/++yzonXr1kKtVgsfHx/x8MMPWy3ZcCOTySRatWolevbsWeN5PHLkiBg7dqxo1aqVUKlUQqvVigEDBtR67m9U26rA27dvF/379xfOzs7C09NTPProoyI1NbXKfj///LMYPHiw8Pb2FiqVSnTp0kV89NFHVfar7Zze+PdfiIqZk6+99poIDQ21/JzefffdOn0mqh+ZEDUsC0lUg8p/4R8+fNjmlzeai8TERISHh+ODDz7ApEmToNPp7HpfqdLSUhgMBqxbtw5z5syx+tlMnDgRiYmJVe4DRETUHHCMDJEdzZkzBz4+PlXG1djajz/+CB8fH8yZM8eqXQiBX3/91dL9TUTU3HCMDJEd+Pv7Y/v27ZbnNw40tYc777zT6v0qB9vKZDJkZGTY9b2JiKTEIENkB87OzrecMmtLPj4+jfp+RERNBcfIEBERkcPiGBkiIiJyWAwyRERE5LCa/RgZs9mMlJQUuLm5Nepy+URERHT7hBDIz89HYGBgtYstVmr2QSYlJaXKPYKIiIjIMSQnJ1e5GeyNmn2QcXNzA1BxIm6+VxARERE1TQaDAcHBwZbv8Zo0+yBTeTlJp9MxyBARETmYWw0L4WBfIiIiclgMMkREROSwGGSIiIjIYTHIEBERkcNikCEiIiKHxSBDREREDotBhoiIiBwWgwwRERE5LAYZIiIiclgMMkREROSwGGSIiIjIYTHIEBERkcNikLlNJWUmXMjIR35JmdSlEBERtVgMMrfpoU8OYNg7e/B7QrbUpRAREbVYDDK3KcRTAwC4nF0ocSVEREQtF4PMbQq1BJkiiSshIiJquRhkblOoF4MMERGR1BhkblOolysA4HIOLy0RERFJhUHmNoVd75G5eq0YpeVmiashIiJqmRhkbpOPmxouTgqYBXA1t1jqcoiIiFokBpnbJJPJOHOJiIhIYgwyDVA54DcphwN+iYiIpMAg0wCVQSYxi0GGiIhICgwyDRByfeZSEmcuERERSYJBpgEqZy4lci0ZIiIiSTDINECoZ2WPTBHMZiFxNURERC0Pg0wDBLo7QymXobTcjPT8EqnLISIianEYZBpAqZAjyMMFAAf8EhERSYFBpoE44JeIiEg6DDINFMabRxIREUmGQaaB/lzdl0GGiIiosTHINBDvgk1ERCQdBpkGslxayiqCEJyCTURE1JgYZBoo+PqlpXxjOa4VlUlcDRERUcsiaZB57bXXIJPJrB6RkZGW7dHR0VW2z5w5U8KKq3J2UsBf5wyAd8EmIiJqbEqpC+jUqRN27Nhhea5UWpf0xBNP4PXXX7c812g0jVZbXYV6aZBmKMHl7CL0CPGQuhwiIqIWQ/Igo1Qq4e/vX+N2jUZT6/amINRLg4OXcjhziYiIqJFJPkbm/PnzCAwMROvWrTFlyhQkJSVZbV+9ejW8vb3RuXNnLFiwAEVFtYcFo9EIg8Fg9bA3zlwiIiKShqQ9Mv369cPKlSvRvn17pKamYuHChRg4cCBOnToFNzc3PPzwwwgNDUVgYCBOnDiB+fPnIz4+Hps2barxmIsWLcLChQsb8VNU9MgAXEuGiIiosclEE5oznJubi9DQULzzzjuYPn16le27du3C0KFDceHCBbRp06baYxiNRhiNRstzg8GA4OBg5OXlQafT2aXuk1fyMPb/foO3Vo0j/xhml/cgIiJqSQwGA/R6/S2/vyUfI3Mjd3d3tGvXDhcuXKh2e79+/QCg1iCjVquhVqvtVmN1Qq73yGQVGFFgLIdW3aROKxERUbMl+RiZGxUUFCAhIQEBAQHVbo+LiwOAGrdLRe/iBA+NEwAgiZeXiIiIGo2kQebvf/87YmJikJiYiN9//x33338/FAoFJk+ejISEBLzxxhuIjY1FYmIivvvuOzz22GMYNGgQunbtKmXZ1eJdsImIiBqfpNdArly5gsmTJyM7Oxs+Pj4YMGAADhw4AB8fH5SUlGDHjh147733UFhYiODgYIwfPx7/+Mc/pCy5RmFeGhxPzkUie2SIiIgajaRBZt26dTVuCw4ORkxMTCNW0zChvAs2ERFRo2tSY2QcGS8tERERNT4GGRupvAt2YhZ7ZIiIiBoLg4yN+F2/cWR2ofEWexIREZGtMMjYiP769OuSMjNKykwSV0NERNQyMMjYiJtaCYVcBgDIKy6TuBoiIqKWgUHGRmQyGfQuFb0yuUUMMkRERI2BQcaGKoMMe2SIiIgaB4OMDf3ZI1MqcSVEREQtA4OMDblfH/Cbyx4ZIiKiRsEgY0PulZeWOEaGiIioUTDI2JC7RgUAyC3mpSUiIqLGwCBjQzoO9iUiImpUDDI25M7p10RERI2KQcaGKgf7skeGiIiocTDI2JBl1hJ7ZIiIiBoFg4wN6V0qBvuyR4aIiKhxMMjYEBfEIyIialwMMjZUeWnJUFIOk1lIXA0REVHzxyBjQ5U9MgBg4OUlIiIiu2OQsSEnhRxatRIAb1NARETUGBhkbIx3wCYiImo8DDI2xgG/REREjYdBxsa4KB4REVHjYZCxMS6KR0RE1HgYZGyMi+IRERE1HgYZG9PzxpFERESNhkHGxiyXloo52JeIiMjeGGRszL1y+jV7ZIiIiOyOQcbG/uyRYZAhIiKyNwYZG+NgXyIiosbDIGNjHOxLRETUeBhkbOzPBfFKIQTvgE1ERGRPDDI2VhlkykwCRaUmiashIiJq3hhkbMzFSQGVouK0cpwMERGRfTHI2JhMJoOO42SIiIgaBYOMHXBRPCIiosbBIGMHXBSPiIiocTDI2MGfM5cYZIiIiOyJQcYOKhfF4+q+RERE9sUgYwdcFI+IiKhxMMjYwY2L4hEREZH9MMjYgWXWEntkiIiI7IpBxg4qLy1xsC8REZF9McjYgbvm+mBf9sgQERHZFYOMHbBHhoiIqHEwyNiBu2XWEgf7EhER2RODjB1UDvYtLDWhzGSWuBoiIqLmi0HGDtycnSCTVfw/Ly8RERHZD4OMHSjkMriplQA44JeIiMieJA0yr732GmQymdUjMjLSsr2kpASzZs2Cl5cXtFotxo8fj/T0dAkrrrvKmUtcFI+IiMh+JO+R6dSpE1JTUy2P3377zbLt2WefxdatW7FhwwbExMQgJSUFDzzwgITV1h0XxSMiIrI/peQFKJXw9/ev0p6Xl4fPPvsMa9aswZAhQwAAK1asQIcOHXDgwAH079+/sUutF07BJiIisj/Je2TOnz+PwMBAtG7dGlOmTEFSUhIAIDY2FmVlZRg2bJhl38jISISEhGD//v01Hs9oNMJgMFg9pMBF8YiIiOxP0iDTr18/rFy5Ej///DOWL1+OS5cuYeDAgcjPz0daWhpUKhXc3d2tXuPn54e0tLQaj7lo0SLo9XrLIzg42M6fonp6l+uDfdkjQ0REZDeSXloaNWqU5f+7du2Kfv36ITQ0FF9//TVcXFxu65gLFizAc889Z3luMBgkCTPuLtcH+3JRPCIiIruR/NLSjdzd3dGuXTtcuHAB/v7+KC0tRW5urtU+6enp1Y6pqaRWq6HT6aweUqgc7MsxMkRERPbTpIJMQUEBEhISEBAQgF69esHJyQk7d+60bI+Pj0dSUhKioqIkrLJuKgf78tISERGR/Uh6aenvf/87xo4di9DQUKSkpODVV1+FQqHA5MmTodfrMX36dDz33HPw9PSETqfDnDlzEBUV1eRnLAEc7EtERNQYJA0yV65cweTJk5GdnQ0fHx8MGDAABw4cgI+PDwDg3XffhVwux/jx42E0GjFixAgsW7ZMypLrjNOviYiI7E8mhBBSF2FPBoMBer0eeXl5jTpe5lx6Pu5+dw88NE449s+7G+19iYiImoO6fn83qTEyzYn7DT0yZnOzzopERESSYZCxE931IGMWQEFpucTVEBERNU8MMnbi7KSAs1PF6c3jgF8iIiK7YJCxo8pF8ThziYiIyD4YZOyIi+IRERHZF4OMHf25KB5vU0BERGQPDDJ2VNkjw0tLRERE9sEgY0eerhVjZNLySiSuhIiIqHlikLGjzq30AIAjl3MkroSIiKh5YpCxo37hngCAY0m5KC03S1wNERFR88MgY0dtfLTwdFXBWG7Gyat5UpdDRETU7DDI2JFMJkPvUA8AwOFEXl4iIiKyNQYZO+t7/fLSoUsMMkRERLbGIGNnlUHmSGIObx5JRERkYwwydtYxQAdXlQKGknLEp+dLXQ4REVGzwiBjZ0qFHD05ToaIiMguGGQaQZ8wjpMhIiKyBwaZRlAZZA4n5kAIjpMhIiKyFQaZRtAjxB1OChnSDUYk5RRJXQ4REVGzwSDTCJydFOga5A6Al5eIiIhsiUGmkdx4eYmIiIhsg0GmkfQNr5y5dE3iSoiIiJoPBplG0ivUEzIZcCmrEBn5JVKXQ0RE1CwwyDQSvYsTIv11AIDDl9grQ0REZAsMMo2obxgXxiMiIrIlBplG1Ic3kCQiIrIpBplG1Pf6zKUzaQbkl5RJXA0REZHjY5BpRL46Z7Ryd4EQwMmreVKXQ0RE5PAYZBpZ92B3AEBccq6kdRARETUHDDKNrDLIHGeQISIiajAGmUbWzRJkeGmJiIiooRhkGlnnVjoo5DKkGUqQlseF8YiIiBqCQaaRaVRKtPNzA8BxMkRERA3FICOB7sF6AAwyREREDcUgI4FuQe4AOOCXiIiooRhkJNA9xB1AxVoyJrOQthgiIiIHxiAjgQhfN2hUChQYy5GQWSB1OURERA6LQUYCCrkMXVpxnAwREVFDMchIhCv8EhERNRyDjES6cYVfIiKiBmOQkUhlj8zZtHyUlJmkLYaIiMhBMchIJEDvDB83NUxmgVO8EzYREdFtYZCRiEwms6wnw3EyREREt4dBRkI9rq8nwyBDRER0exhkJGRZ4fdKrqR1EBEROSoGGQl1vX7PpeScYmQXGCWuhoiIyPEwyEhI5+yENj6uANgrQ0REdDsYZCTWzbIwHmcuERER1VeTCTKLFy+GTCbDvHnzLG3R0dGQyWRWj5kzZ0pXpB108NcBAC7ynktERET1ppS6AAA4fPgwPv74Y3Tt2rXKtieeeAKvv/665blGo2nM0uwuyMMFAHDlWrHElRARETkeyXtkCgoKMGXKFHz66afw8PCosl2j0cDf39/y0Ol0ElRpP0EeFcHsai6DDBERUX1JHmRmzZqF0aNHY9iwYdVuX716Nby9vdG5c2csWLAARUVFjVyhfVX2yGTmG3mrAiIionqS9NLSunXrcPToURw+fLja7Q8//DBCQ0MRGBiIEydOYP78+YiPj8emTZtqPKbRaITR+OdUZoPBYPO6bcld4wRXlQKFpSZczS1GGx+t1CURERE5DMmCTHJyMubOnYvt27fD2dm52n1mzJhh+f8uXbogICAAQ4cORUJCAtq0aVPtaxYtWoSFCxfapWZ7kMlkaOXhgnPpBbh6jUGGiIioPiS7tBQbG4uMjAz07NkTSqUSSqUSMTExWLp0KZRKJUymqpdZ+vXrBwC4cOFCjcddsGAB8vLyLI/k5GS7fQZbqRwnwwG/RERE9SNZj8zQoUNx8uRJq7Zp06YhMjIS8+fPh0KhqPKauLg4AEBAQECNx1Wr1VCr1Tat1d7+nLnUvMb/EBER2ZtkQcbNzQ2dO3e2anN1dYWXlxc6d+6MhIQErFmzBvfccw+8vLxw4sQJPPvssxg0aFC107QdWSv3iiDDmUtERET10yTWkamOSqXCjh078N5776GwsBDBwcEYP348/vGPf0hdms3x0hIREdHtaVJB5tdff7X8f3BwMGJiYqQrphHx0hIREdHtkXwdGfozyKQbjDCWcy0ZIiKiumKQaQI8XVVwdqr4UaTmlkhcDRERkeNgkGkCZDIZx8kQERHdBgaZJoLjZIiIiOqPQaaJqAwynIJNRERUdwwyTUQrd15aIiIiqi8GmSaCl5aIiIjqr15B5tChQ9XeA6mS0WjE119/3eCiWqI/gwx7ZIiIiOqqXkEmKioK2dnZluc6nQ4XL160PM/NzcXkyZNtV10L0sqylkwJSsvNEldDRETkGOoVZIQQtT6vqY1uzUerhloph1kAaXlcS4aIiKgubD5GRiaT2fqQLYJMJrP0ynCcDBERUd1wsG8TYlkUj1OwiYiI6qTeN408ffo00tLSAFRcRjp79iwKCgoAAFlZWbatroVp5c4Bv0RERPVR7yAzdOhQq3EwY8aMAVBxaUQIwUtLDcAp2ERERPVTryBz6dIle9VB4BRsIiKi+qpXkAkNDbVXHYQ/x8hcZZAhIiKqk3oN9s3KysLly5et2v744w9MmzYNEydOxJo1a2xaXEtT2SOTZihBuYlryRAREd1KvYLMnDlzsHTpUsvzjIwMDBw4EIcPH4bRaMRf/vIXrFq1yuZFthQ+WjVUCjlMZoFUriVDRER0S/UKMgcOHMC9995ref7ll1/C09MTcXFx2LJlC95++218+OGHNi+ypZDL/1xLhnfBJiIiurV6BZm0tDSEhYVZnu/atQsPPPAAlMqKoTb33nsvzp8/b9MCWxpOwSYiIqq7egUZnU6H3Nxcy/NDhw6hX79+lucymQxGo9FmxbVEnIJNRERUd/UKMv3798fSpUthNpuxceNG5OfnY8iQIZbt586dQ3BwsM2LbEkqgwxnLhEREd1avaZfv/766xg2bBi++uorlJeX46WXXoKHh4dl+7p16zB48GCbF9mSWG5TwCBDRER0S/UKMt26dcOZM2ewb98++Pv7W11WAoBJkyahY8eONi2wpbHcODKXl5aIiIhupV6Xlvbv348DBw7gvvvus4SYL7/8EuHh4fD19cWWLVsQGBhol0JbispLS6m5JTCZxS32JiIiatnqFWRef/11/PHHH5bnJ0+exPTp0zFs2DC8+OKL2Lp1KxYtWmTzIlsSXzdnOClkKDcLpObx8hIREVFt6hVk4uLiMHToUMvzdevWoV+/fvj000/x3HPPYenSpfj6669tXmRLopDL0NpbCwCIT8uXuBoiIqKmrV5B5tq1a/Dz87M8j4mJwahRoyzP+/Tpg+TkZNtV10J1DNQBAM6kGiSuhIiIqGmrV5Dx8/Oz3AG7tLQUR48eRf/+/S3b8/Pz4eTkZNsKW6AOAW4AgNMMMkRERLWqV5C555578OKLL2Lv3r1YsGABNBoNBg4caNl+4sQJtGnTxuZFtjQdA/QAgDOpvLRERERUm3pNv37jjTfwwAMPYPDgwdBqtfjiiy+gUqks2z///HPcfffdNi+ypanskUnMLkShsRyu6nr9mIiIiFqMen1Dent7Y8+ePcjLy4NWq4VCobDavmHDBmi1WpsW2BJ5adXw06mRbjDibFo+eoV63PpFRERELVC9Li1V0uv1VUIMAHh6elr10NDt6xBQMeCX42SIiIhqdltBhuyvYwBnLhEREd0Kg0wTZemRSWGQISIiqgmDTBNVuZZMfFo+b1VARERUAwaZJirMyxXOTnIUl5mQmF0odTlERERNEoNME6WQyxDpz3EyREREtWGQacI4ToaIiKh2DDJNWMfrC+OxR4aIiKh6DDJNWOWAX64lQ0REVD0GmSas/fUxMukGI7ILjBJXQ0RE1PQwyDRhWrUSYV4aALyBJBERUXUYZJq4Dlzhl4iIqEYMMk1cR95ziYiIqEYMMk0ce2SIiIhqxiDTxFXOXLqQUQBjuUniaoiIiJoWBpkmLkDvDL2LE8rNAufTC6Quh4iIqElhkGniZDIZx8kQERHVoMkEmcWLF0Mmk2HevHmWtpKSEsyaNQteXl7QarUYP3480tPTpStSIhwnQ0REVL0mEWQOHz6Mjz/+GF27drVqf/bZZ7F161Zs2LABMTExSElJwQMPPCBRldLpdH2czMkreRJXQkRE1LRIHmQKCgowZcoUfPrpp/Dw8LC05+Xl4bPPPsM777yDIUOGoFevXlixYgV+//13HDhwQMKKG1/3EHcAwMmreSgzmaUthoiIqAmRPMjMmjULo0ePxrBhw6zaY2NjUVZWZtUeGRmJkJAQ7N+/v7HLlFS4lyv0Lk4wlptxliv8EhERWSilfPN169bh6NGjOHz4cJVtaWlpUKlUcHd3t2r38/NDWlpajcc0Go0wGv+8L5HB4PjjSuRyGboHuyPmXCaOJV9DlyC91CURERE1CZL1yCQnJ2Pu3LlYvXo1nJ2dbXbcRYsWQa/XWx7BwcE2O7aUely/vHQsKVfSOoiIiJoSyYJMbGwsMjIy0LNnTyiVSiiVSsTExGDp0qVQKpXw8/NDaWkpcnNzrV6Xnp4Of3//Go+7YMEC5OXlWR7Jycl2/iSNo0dIxfihY0nXJK6EiIio6ZDs0tLQoUNx8uRJq7Zp06YhMjIS8+fPR3BwMJycnLBz506MHz8eABAfH4+kpCRERUXVeFy1Wg21Wm3X2qXQPcgdAJCYXYScwlJ4uqqkLYiIiKgJkCzIuLm5oXPnzlZtrq6u8PLysrRPnz4dzz33HDw9PaHT6TBnzhxERUWhf//+UpQsKb3GCW18XJGQWYi45GsYEukndUlERESSk3zWUm3effddjBkzBuPHj8egQYPg7++PTZs2SV2WZP68vJQrbSFERERNhEwIIaQuwp4MBgP0ej3y8vKg0+mkLqdBVh+8jJc3n8KAtt746q/9pC6HiIjIbur6/d2ke2TIWo/gih6ZuORcmMzNOn8SERHVCYOMA2nnp4VGpUCBsRwJmbwTNhEREYOMA1Eq5Oh6fTE8TsMmIiJikHE4HPBLRET0JwYZB9Mj2B0AgwwRERHAIONwKu+EfS4jH/klZdIWQ0REJDEGGQfj6+aMVu4uEAI4cSVP6nKIiIgkxSDjgP68gSQH/BIRUcvGIOOAOOCXiIioAoOMA7L0yCTnopkvzExERFQrBhkH1ClQB5VCjpzCUrz87SnEnMuEsdwkdVlERESNTrK7X9PtUysVGBjhjZ1nM7DmYBLWHEyCq0qBQe188Nzwdojwc5O6RCIiokbBm0Y6KGO5CXvPZWHn2XTsPJOBjHwjACDUS4Nf/x4NmUwmcYVERES3r67f3+yRcVBqpQLDOvphWEc/mM0CJ6/mYcr/DuJydhGOXL6GPmGeUpdIRERkdxwj0wzI5TJ0C3bHyM7+AIBNR69KXBEREVHjYJBpRh7o0QoA8MOJFJSUcfAvERE1fwwyzUj/1l4I1DvDUFKOXWczpC6HiIjI7hhkmhG5XIb7rvfK8PISERG1BAwyzUzl5aVf4zOQU1gqcTVERET2xSDTzET4uaFLKz3KzQJbj6dIXQ4REZFdMcg0Q/dXXl46xstLRETUvDHINEP3dg+EQi7D8eRcJGQWSF0OERGR3TDINEPeWjUGRXgDADZz0C8RETVjDDLN1AM9gwAAm49dhdncrO9CQURELRiDTDM1vKMf3NRKXM0txoGL2VKXQ0REZBcMMs2Us5MC9/UIBAC8t/M8mvm9QYmIqIVikGnGZt3VFiqlHIcu5WDv+SypyyEiIrI5BplmLEDvgkf7hwIA/rMtnr0yRETU7DDINHNPRbeBRqXAiSt5+OWPdKnLISIisikGmWbOW6vG9AHhAID/bouHiTOYiIioGWGQaQH+OrA1dM5KnM8owHfHua4MERE1HwwyLYDexQkzo9sAAN7dfh5lJrPEFREREdkGg0wL8Zc7wuCtVSMppwhfH0mWuhwiIiKbYJBpITQqJWbfVdErs2x3Alf7JSKiZoFBpgWZ1DcE2uur/cZdyZW6HCIiogZjkGlBnJ0UGNrBFwDw08lUiashIiJqOAaZFmZU5wAAwI8n07hAHhEROTwGmRYmur0PNCoFruYW48SVPKnLISIiahAGmRbG2UmBuyIrLi/9eIqXl4iIyLExyLRAo7tUXl5K5eUlIiJyaAwyLVB0ex84O8mRnFOMP1IMUpdDRER02xhkWiCNSom72l+/vMTZS0RE5MAYZFqoUby8REREzQCDTAs1JNIXaqUcidlFOJOaL3U5REREt4VBpoXSqpUY3M4HAPATZy8REZGDUkpdAEnnni4B2HY6HT+cTMVzw9vhYlYhdp5Jx+6zmSgqMyHMS4MwL1eEe7uira8WnQJ1kMlkUpdNRERkwSDTgg3t4AuVQo6LmYUYtGQ3knOKrbYfT8613j/SF+9O6g6ds1MjVklERFQzBpkWzM3ZCYPa+WDHmXQk5xTDSSFD/9ZeGNbBD346NRKzi3ApsxCXsgsRl5yLnWczcP+H+/DpY73R2kcrdflEREQMMi3dq2M7op2fFl1a6TGwnQ+06ur/SJy4kosnV8UiIbMQ9324D0sn97BM4SYiIpKKpIN9ly9fjq5du0Kn00Gn0yEqKgo//fSTZXt0dDRkMpnVY+bMmRJW3PwEe2rwwshIjOoSUGOIAYCuQe7YMvtO9Ar1QH5JOR5feRgf7r6A0nJzI1ZLRERkTSYkXERk69atUCgUiIiIgBACX3zxBZYsWYJjx46hU6dOiI6ORrt27fD6669bXqPRaKDT6er8HgaDAXq9Hnl5efV6HVXPWG7Cq1v+wLrDyQCAEE8N/nZ3O4ztGgi5nAOBiYjINur6/S1pkKmOp6cnlixZgunTpyM6Ohrdu3fHe++9d9vHY5CxPSEENhy5gn//chZZBaUAgE6BOrwwMhKDIrw5s4mIiBqsrt/fTWYdGZPJhHXr1qGwsBBRUVGW9tWrV8Pb2xudO3fGggULUFRUJGGVBAAymQwT+wQj5vm78Lfh7aBVK/FHigFTPz+EhVtPS10eERG1IJIP9j158iSioqJQUlICrVaLzZs3o2PHjgCAhx9+GKGhoQgMDMSJEycwf/58xMfHY9OmTTUez2g0wmg0Wp4bDLwpor24qpWYMzQCU/qH4sPdF/D5vktY+Xsi2vhq8Wj/UKnLIyKiFkDyS0ulpaVISkpCXl4eNm7ciP/973+IiYmxhJkb7dq1C0OHDsWFCxfQpk2bao/32muvYeHChVXaeWnJ/j6KScDin85CKZfhy+l9cUcbb6lLIiIiB+WwY2SGDRuGNm3a4OOPP66yrbCwEFqtFj///DNGjBhR7eur65EJDg5mkGkEQgg8uz4O38alwEPjhC2zBiDESyN1WURE5IAcboxMJbPZbBVEbhQXFwcACAgIqPH1arXaMp278kGNQyaTYfH4rugWpMe1ojI88eURFBjLpS6LiIiaMUmDzIIFC7Bnzx4kJibi5MmTWLBgAX799VdMmTIFCQkJeOONNxAbG4vExER89913eOyxxzBo0CB07dpVyrKpFs5OCnz8aG/4uqkRn56PZ9fHwWxuUp1+RETUjEgaZDIyMvDYY4+hffv2GDp0KA4fPoxffvkFw4cPh0qlwo4dO3D33XcjMjISf/vb3zB+/Hhs3bpVypKpDvz1zvj40V5QKeXYfjodi38+K3VJRETUTDW5MTK2xnVkpLMl7irmrosDALxxXyc8GhUmaT1EROQ4HHaMDDUf93VvhedHtAcAvPrdH9h5Jl3iioiIqLlhkCG7ejq6DSb1CYZZALPXHMPJK3lSl0RERM0IgwzZlUwmwxvjOmNghDeKy0x4/IvDuHKNqzMTEZFtMMiQ3Tkp5Fg2pSci/d2QmW/EX1YcRl5RmdRlERFRM8AgQ43CzdkJK6b1gb/OGRcyCjBj1REYy01Sl0VERA6OQYYaTYDeBSum9YGbWomDl3Lw9w0nuMYMERE1CIMMNaoOATp89GgvKOUybD2egn9xjRkiImoABhlqdHe29ca/J1Sszvzxnov44vdEaQsiIiKHxSBDknigZ5BljZnXtnKNGSIiuj0MMiSZp6PbYHLfYAgBzF0Xh/Pp+VKXREREDoZBhiQjk8mw8N7O6BfuiQJjOf765RHkFpVKXRYRETkQBhmSlEpZscZMkIcLLmcXYdaaoyg3maUuS1LN/PZnREQ2xSBDkvPSqvHpY72hUSmw70I23vzhjNQlSeLKtSK8vPkkury2DW98f5pT04mI6kApdQFEQMW07HcmdsfMr2Kx8vdE6Fyc8FCfYLRyd5G6NLtLyi7Csl8vYGPsFZRfDy+f/XYJhcZyvH1/F8jlMokrJCJqumSimfdj1/U24NQ0fLDzPP67/ZzleXs/N9wV6YuhHXzRO9QDMpn9vtSFEEjMLsLhxBwEe2jQL9zTriEiOacI7+88j83HrsJ0PcAMaOuNfuGeeHfHOZgFML5nEP49oSsUDDNE1MLU9fubPTLUpMwe0hZ6jRO+i0vB0aRriE/PR3x6Pj6KScDITv5YPL4L3DUqm71fcakJ+y5kIeZcJmLOZSIp588bWvrrnDG2WwDu7dYKnVvpYCw3IzmnCJezi3DlWhFaeWgwqJ031EpFvd4zLa8EH+w6j/WHky09MNHtfTBnSAR6hXoAAMK8XTFvfRy+OXoFZSYz3pnYDUoFrwQTEd2MPTLUZF0rLMWe85nYfTYDP5xMRZlJIFDvjPcn90CfMM8GHftCRgG+OnAZ3xy9gvyScku7k0KGrkHuOJ+eD8MN7TpnpdXzG9tHdQ7Avd0D0b+1l1XPSbnJjLziMmQVlCK7wIjMAiPiknOx+mASSssrBjQPjPDGc8PboUeIR5Vj/3wqFXPWHkOZSWBEJz8sebAbdM5ODfrcRESOoq7f3wwy5BBOXsnDnLVHkZhdBLkMmDesHWbd1bbaSy4Z+SWIia/oYckuKIWfTg0/vTP8dc5wcVJgS1wK9l/Mtuzfyt0FQyJ9MbidD6LaeMFVrYSx3ISY+ExsOZ6CnWfSUVJWETzc1EqEeGkQ6O6CE1dykW4wWo7j5qyEk0KOkjITjOVmy+Wi6vQJ88Df726Pfq29av3cO06n4+nVR1FqMsNbq8Y/RnfAfd0D7XqJjYioKWCQuY5BpvkoMJbjn9+ewqZjVwEAgXpntPJwgbdWDR83NVQKOfZfzMYfKYZbHksuA4ZE+uHRqFAMbOtd61iYAmM5LmcXIlDvAneNkyVEmMwChy7l4LvjKfjxZCryisuqfb27xgneWjW8XFXw0zljfK8gDIrwrnMYOXQpBy9uOoGLmYUAgP6tPfHGfZ0R4edWp9cTETkiBpnrGGSan01Hr+CVb0+hsNRU4z5dg/SIbueDcB9XZBiMSDOUIC2vBDmFpegT5onJ/UJsOiOqtNyMhMwCKOUyqJUKqJ3kUCvlcFVX9NI0lLHchP/tvYQPdp1HSZkZSrkM9/dohQm9gtAnzL6DkomIpMAgcx2DTPOUW1SK+LR8ZBYYkZVvRFZBKfJLytAt2B2D2vnAW6uWukS7SM4pwsKtp7HjhntTBXm44IEerfBAzyCEebtKWB0Rke0wyFzHIEPN0eHEHGw8cgU/nExFgbFiELJcBiy8rzMe7R8qcXVERA3HIHMdgww1Z8WlJmw/k471h5Ow70LFAOa/DW+H2UPackAwETm0un5/c2EKIgfmolLg3m6B+Gp6PzwzpC0A4L/bz+GN78/wFgdE1CIwyBA1AzKZDM/d3R6vjOkIAPh83yX8fePxFn8DTiJq/riyL1EzMn1AONxdnPDCNyew6ehVnE8vwMTeQRjdNRCerrZbEZmIqKngGBmiZmjH6XTMXnvUspCfUi7D4HY+GNHJHwICecVlyCsug6G4HJ0CdZjYO5hTuImoSeFg3+sYZKilyjCU4LvjKfg27ipOXa19kcA723rhnYnd4adzbqTqiIhqxyBzHYMMEXAhIx/fHkvB4cQcaFQK6F2coHdxgkIux9pDSSguM8FD44R/T+iG4R39pC6XiIhBphKDDFHtLmQU4Jm1x3A6taLX5tH+oXh5dAc4O9Xvrt5ERLbE6ddEVCdtfbXYPOsOPDEwHACw6sBl3PP+XsReviZxZUREt8YgQ0RQKxV4eXRHfPl4X/jp1LiYVYgHP/odb/94BiVlNd/TiohIagwyRGQxqJ0Pts0bjPE9g2AWwCd7LmL00r3Ycy7TcisEIqKmhGNkiKhaO8+kY8Gmk8jIN1ragjxcEOnvhkh/HfqEe6JvmCdcVBxLQ0S2x8G+1zHIEN2+3KJS/OvneOw6m450g7HKdpVSjj5hHhjQ1gd9wjzg6+YMbzcVNCqutUlEDcMgcx2DDJFtXCssxdm0fMSnGXDyqgG/J2QhNa+k2n01KgW8tWqEebuig78bIgMqenFCvTQoNwsYy8woNZlRWm6Gu4sTPLjqMBHdhEHmOgYZIvsQQiAhsxC/nc/EbxeycCY1H1kFRhjL639/J72LE8K8NAj1ckWolwY+bmp4a9XwclXBS6tGsKcL1EpewiJqSRhkrmOQIWo8QggUGMuRVVCKDEMJLmQW4GxqPs6mGXA2LR/5JX8OGFYp5VAp5HUaROymVmJUF3+M69EK/cO9eDsFohaAQeY6BhmipkEIgcJSE5wUMqgUcshkFWGkqLQcSTlFSMwqQmJ2IZJzipBdUIqsAiOyCysCUWHpn1PAA/XOGNejFZ4c1AZ6jZNUH4eI7IxB5joGGSLHZjYLHE7MweZjV/HDyVRLr07XID1W/7Uf3JwZZoiaIwaZ6xhkiJqPkjITdp7JwCtbTiGnsBT9W3ti5bS+vJ0CUTPEWxQQUbPj7KTA6K4B+GJaX2jVShy4mIPZa46h3FT/AcZE1DwwyBCRw+kSpMf/pvaGSinHjjPpeGHjCZjNzbpzmYhqwCBDRA6pf2svLHu4JxRyGTYdu4qXvz2F/JIyqcsiokbGIENEDmtYRz/858GuAIC1h5Jw5+Jd+O+2eOQUlkpcGRE1Fg72JSKH9/OpNCz55SwSMgsBAC5OCkzqG4z+rb3gr3OGn84Z3loVlAr+243IUXDW0nUMMkQtg9kssO10Gj7cnYCTV/OqbJfJgBBPDYZG+mFEJz/0DvOE4oaF9cxmgau5xUg3lECtVEDtJIezUgFnJzm8tGqrfYnI/hhkrmOQIWpZhBD47UIW1h1OxpVrxcgwlCAj3wjTTYOBvVxVGBLpC5NZ4HxGAS5kFKC4zFTtMb21Kgzv6Ie7O/njjjZevF0CUSNgkLmOQYaITGaB7EIjjiXl4pc/0rDzTAbyiqsODFYp5PDTq1FuEigpM6GkzIySchNu/C3pplYiOtIXgyK8cWdbbwS6uzTiJyFqORwiyCxfvhzLly9HYmIiAKBTp0745z//iVGjRgEASkpK8Le//Q3r1q2D0WjEiBEjsGzZMvj5+dX5PRhkiOhmZSYzDl3KwZ7zmXBTK9HW1w3t/LQI8dRUGUdTZjLj4MUc/PxHKrb9kY6MfKPV9tberrizrTeGdvDFwAgfXoIishGHCDJbt26FQqFAREQEhBD44osvsGTJEhw7dgydOnXCU089hR9++AErV66EXq/H7NmzIZfLsW/fvjq/B4MMEdmK2SxwLDkXu86mY9+FbJy4kosbr1i1cnfBpD7BmNgnGH46Z+kKJWoGHCLIVMfT0xNLlizBhAkT4OPjgzVr1mDChAkAgLNnz6JDhw7Yv38/+vfvX6fjMcgQkb3kFZfhwMVs7DmXie9PpFouVynkMtzV3gedW+kR5KFBK3cXBHm4IEDvzJlTRHVU1+9vZSPWVCuTyYQNGzagsLAQUVFRiI2NRVlZGYYNG2bZJzIyEiEhIfUKMkRE9qJ3ccKITv4Y0ckfr4zpiJ9OpWLNwSQcTryGHWcysONMhtX+gXpn/OfBbrijrbdEFRM1P5IHmZMnTyIqKgolJSXQarXYvHkzOnbsiLi4OKhUKri7u1vt7+fnh7S0tBqPZzQaYTT+eQ3bYDDYq3QiIgtnJwXu7xGE+3sE4Xx6PradTkdyThGuXCvG1dyKR0peCaZ8dhAzBrXG34a3h0rJ3hmihpI8yLRv3x5xcXHIy8vDxo0bMXXqVMTExNz28RYtWoSFCxfasEIiovqJ8HNDhJ+bVVtRaTne+P4M1h5KwscxF/H7hWy8P6k7WvtoJaqSqHlocmNkhg0bhjZt2uChhx7C0KFDce3aNatemdDQUMybNw/PPvtsta+vrkcmODiYY2SIqEn4+VQaXtx0ArlFZXBxUuCVMR0xuW8wZDL7znZKN5QgIbMA1wrLcK2oFNcKS1FUZkKkvxt6hXqglbuL3Wsgqg+HGyNTyWw2w2g0olevXnBycsLOnTsxfvx4AEB8fDySkpIQFRVV4+vVajXUanVjlUtEVC8jO/ujW7Aez60/jv0Xs/HS5pP46VQqFo/vilZ2WJPm1NU8fLznIn44kYLabhDup1Ojd6gnugXr0SFAhw4BOnhr+bvUUZjMAqdTDPg9IQuHLuWg3CwQeH2QeSt3F/jq1DCZBUrKzCguM6GkzAQIQKWUQ62UQ6WUw0khR1GpCfklZSgwliO/pBwA4Oumhp/OGb46Nfx1zvB0VTWp0Ctpj8yCBQswatQohISEID8/H2vWrMG//vUv/PLLLxg+fDieeuop/Pjjj1i5ciV0Oh3mzJkDAPj999/r/B6ctURETZHJLLBi3yUs+SUexnIztGolXhnTARN71793xmQWKDOZUW4WMJkEys1mnEnNx8d7ErD3fJZlv9Y+rvB2VcNd4wRPVxWUChlOXsnDHykGlFeTcry1arTz00KllP/5HiYBgYqZWUq5DAq5DE4KOUI8NegUqEOXID3a+mg5O+s2lZnM+CPFgCOJOTicmIOrucVwd1HBw1UFT40T3DUqyGUyGMsrFmw0lpuQbijBoUs5MFwPHvYW6e+Gh/uFYFyPVtA5O9ntfRxi+vX06dOxc+dOpKamQq/Xo2vXrpg/fz6GDx8O4M8F8dauXWu1IJ6/v3+d34NBhoiasoTMAvx9w3EcS8oFAPQK9UCPYHeE+7gi3MsVwZ4a5BWX4VJWIRKzCnEpuxCpuSXILS6D4foj31jzF5hCLsOYrgF4clAbdAys/ndgcakJx6/kIvbyNZy6moezaflIzC7E7X47qJVytPd3QxsfLVp7u6K1jxatfVwRoHeGm7MTFw28SZnJjF/+SMP6w8k4nJiDkjLzbR3HTa1E33BPRLXxgpuzElevFeNqbgmu5hYhI98IlUIOZycFXJwq7iEmk8lQWl4Rhir+a4arWgmtWgk354qH2Qxk5Jcg3WBERn4JsgtLLX8uXJwUGNstAA/3C0W3IL3Ne2kcIsg0BgYZImrqTGaBz367iP9sO4fS8tv7EruZq0qBCb2C8NeBrRHsqan364tKyxGflo+EzEKYhYCTQgalXA4nhQyADGYhKnqAzGaUlJlxIaMAp67m4XSKodZgJZMBOmcnuGuc4K1VI9LfDZ0C9ejcSod2fm5wdmo597HKKSzF2kNJWLX/MtIMJZZ2vYsT+oR5oHeYJ9r6aJFvLENOYRmuFZYip6gUQEVYVCsVUCvl0Lk4oXeoBzoF6uzeE5ZbVIpNR69izaEkXMgosLS/dX9nTOkXatP3YpC5jkGGiBxFUnYRYs5l4FJWERKzK3pgknKK4K5xQpiXK0K9XBHurUGwpwZ6Fyerh7OTAorrl3oUMhnkEvV6mM0CSTlFOJtmQEJmIS5mFuJiVgEuZhZWe3+rGynlMgR7ahCgd4a/3hkBemcEurugrY8W7fzc4OGqaqRPYTtCCBSWmpBhKMHlnCJczipEYnYRLmUV4sDFbBivB1dvrQoP9wvFmK4BaOujleznV1dCCBy5fA1rDybhlz/SsPv5aPi62XY1awaZ6xhkiMiRCSGa1MDKhigzmZFbVIa84lLkFpUhJa8Ep1MM+COlYpxOTmFpra/3cVOjvZ8bIvwqgk2ErxYRfm7Qu9hvnEZdFRjLcepqHk5eycOJq3lIyilCVr4R2YXGWi8VdWmlx7Q7wzC6a4DD3lW9uNQEF5Xta2eQuY5Bhoio6RNCIDWvBEk5RUjLK0FKXjHS8kqQnFOE8xkFuHKtuMbX+uuc0SfcE4MivDEwwgf+evvf58pkFjiSmINtp9MRcy4TCZkFtY4p0qgUCPHUINRLgzAvV4R5u1YMjm5l+7ElzQWDzHUMMkREjq/QWI7zGQWITzPgfHoBzmUU4Hx6PlLzSqrs285Pi6jWXogM0KG9vxva+7nBVd2w1UZKy81IzC5EfFo+9pzLxM6zGVV6kAL1zugSpEfXIHe09dXCW6uGj1YNbzcVNKomt9pJk+ew68gQERHdzFWtRPdgd3QPdrdqzy8pw+kUA/ZdyMKe81k4cSUX59ILcC69wGq/EE8NQq6PvwnQOyPA3QU+WjU0KgVcKh9OiopLXtdvJ5GSW4zknCJcyCzA5ewimG6aoq53ccLQDr4Y3sEPvcM84ePGdXekwB4ZIiJqNnKLSrHvQjbikq/hbFo+zqblIzPfeOsX1oGbWok2vlp0D3bH3Z380DfMk+vl2BF7ZIiIqMVx16gwumsARncNsLTlFJYiPi0fKbnFSM2r6G1JzS1GdmEpiktNKC4zWf6rc3ZCgHvFbKnAyllTvlpE+LrBT6fmeJYmiEGGiIiaNU9XFaLaeEldBtkJ+8SIiIjIYTHIEBERkcNikCEiIiKHxSBDREREDotBhoiIiBwWgwwRERE5LAYZIiIiclgMMkREROSwGGSIiIjIYTHIEBERkcNikCEiIiKHxSBDREREDotBhoiIiBwWgwwRERE5LKXUBdibEAIAYDAYJK6EiIiI6qrye7vye7wmzT7I5OfnAwCCg4MlroSIiIjqKz8/H3q9vsbtMnGrqOPgzGYzUlJS4ObmBplMZrPjGgwGBAcHIzk5GTqdzmbHpap4rhsXz3fj4bluPDzXjcdW51oIgfz8fAQGBkIur3kkTLPvkZHL5QgKCrLb8XU6Hf9SNBKe68bF8914eK4bD89147HFua6tJ6YSB/sSERGRw2KQISIiIofFIHOb1Go1Xn31VajVaqlLafZ4rhsXz3fj4bluPDzXjaexz3WzH+xLREREzRd7ZIiIiMhhMcgQERGRw2KQISIiIofFIENEREQOi0HmNn344YcICwuDs7Mz+vXrh0OHDkldksNbtGgR+vTpAzc3N/j6+mLcuHGIj4+32qekpASzZs2Cl5cXtFotxo8fj/T0dIkqbh4WL14MmUyGefPmWdp4nm3r6tWreOSRR+Dl5QUXFxd06dIFR44csWwXQuCf//wnAgIC4OLigmHDhuH8+fMSVuyYTCYTXnnlFYSHh8PFxQVt2rTBG2+8YXWvHp7r27Nnzx6MHTsWgYGBkMlk+Pbbb6221+W85uTkYMqUKdDpdHB3d8f06dNRUFDQ8OIE1du6deuESqUSn3/+ufjjjz/EE088Idzd3UV6errUpTm0ESNGiBUrVohTp06JuLg4cc8994iQkBBRUFBg2WfmzJkiODhY7Ny5Uxw5ckT0799f3HHHHRJW7dgOHTokwsLCRNeuXcXcuXMt7TzPtpOTkyNCQ0PFX/7yF3Hw4EFx8eJF8csvv4gLFy5Y9lm8eLHQ6/Xi22+/FcePHxf33nuvCA8PF8XFxRJW7njeeust4eXlJb7//ntx6dIlsWHDBqHVasX7779v2Yfn+vb8+OOP4uWXXxabNm0SAMTmzZutttflvI4cOVJ069ZNHDhwQOzdu1e0bdtWTJ48ucG1Mcjchr59+4pZs2ZZnptMJhEYGCgWLVokYVXNT0ZGhgAgYmJihBBC5ObmCicnJ7FhwwbLPmfOnBEAxP79+6Uq02Hl5+eLiIgIsX37djF48GBLkOF5tq358+eLAQMG1LjdbDYLf39/sWTJEktbbm6uUKvVYu3atY1RYrMxevRo8fjjj1u1PfDAA2LKlClCCJ5rW7k5yNTlvJ4+fVoAEIcPH7bs89NPPwmZTCauXr3aoHp4aameSktLERsbi2HDhlna5HI5hg0bhv3790tYWfOTl5cHAPD09AQAxMbGoqyszOrcR0ZGIiQkhOf+NsyaNQujR4+2Op8Az7Otfffdd+jduzcefPBB+Pr6okePHvj0008t2y9duoS0tDSr863X69GvXz+e73q64447sHPnTpw7dw4AcPz4cfz2228YNWoUAJ5re6nLed2/fz/c3d3Ru3dvyz7Dhg2DXC7HwYMHG/T+zf6mkbaWlZUFk8kEPz8/q3Y/Pz+cPXtWoqqaH7PZjHnz5uHOO+9E586dAQBpaWlQqVRwd3e32tfPzw9paWkSVOm41q1bh6NHj+Lw4cNVtvE829bFixexfPlyPPfcc3jppZdw+PBhPPPMM1CpVJg6darlnFb3O4Xnu35efPFFGAwGREZGQqFQwGQy4a233sKUKVMAgOfaTupyXtPS0uDr62u1XalUwtPTs8HnnkGGmqRZs2bh1KlT+O2336QupdlJTk7G3LlzsX37djg7O0tdTrNnNpvRu3dvvP322wCAHj164NSpU/joo48wdepUiatrXr7++musXr0aa9asQadOnRAXF4d58+YhMDCQ57oZ46WlevL29oZCoagygyM9PR3+/v4SVdW8zJ49G99//z12796NoKAgS7u/vz9KS0uRm5trtT/Pff3ExsYiIyMDPXv2hFKphFKpRExMDJYuXQqlUgk/Pz+eZxsKCAhAx44drdo6dOiApKQkALCcU/5Oabjnn38eL774IiZNmoQuXbrg0UcfxbPPPotFixYB4Lm2l7qcV39/f2RkZFhtLy8vR05OToPPPYNMPalUKvTq1Qs7d+60tJnNZuzcuRNRUVESVub4hBCYPXs2Nm/ejF27diE8PNxqe69eveDk5GR17uPj45GUlMRzXw9Dhw7FyZMnERcXZ3n07t0bU6ZMsfw/z7Pt3HnnnVWWETh37hxCQ0MBAOHh4fD397c63waDAQcPHuT5rqeioiLI5dZfawqFAmazGQDPtb3U5bxGRUUhNzcXsbGxln127doFs9mMfv36NayABg0VbqHWrVsn1Gq1WLlypTh9+rSYMWOGcHd3F2lpaVKX5tCeeuopodfrxa+//ipSU1Mtj6KiIss+M2fOFCEhIWLXrl3iyJEjIioqSkRFRUlYdfNw46wlIXiebenQoUNCqVSKt956S5w/f16sXr1aaDQa8dVXX1n2Wbx4sXB3dxdbtmwRJ06cEPfddx+nBN+GqVOnilatWlmmX2/atEl4e3uLF154wbIPz/Xtyc/PF8eOHRPHjh0TAMQ777wjjh07Ji5fviyEqNt5HTlypOjRo4c4ePCg+O2330RERASnX0vpgw8+ECEhIUKlUom+ffuKAwcOSF2SwwNQ7WPFihWWfYqLi8XTTz8tPDw8hEajEffff79ITU2Vruhm4uYgw/NsW1u3bhWdO3cWarVaREZGik8++cRqu9lsFq+88orw8/MTarVaDB06VMTHx0tUreMyGAxi7ty5IiQkRDg7O4vWrVuLl19+WRiNRss+PNe3Z/fu3dX+fp46daoQom7nNTs7W0yePFlotVqh0+nEtGnTRH5+foNrkwlxw5KHRERERA6EY2SIiIjIYTHIEBERkcNikCEiIiKHxSBDREREDotBhoiIiBwWgwwRERE5LAYZIiIiclgMMkTkcKKjozFv3jypyyCiJoBBhoiIiBwWgwwRERE5LAYZInJ4P/zwA/R6PVavXi11KUTUyJRSF0BE1BBr1qzBzJkzsWbNGowZM0bqcoiokbFHhogc1ocffoinn34aW7duZYghaqHYI0NEDmnjxo3IyMjAvn370KdPH6nLISKJsEeGiBxSjx494OPjg88//xxCCKnLISKJMMgQkUNq06YNdu/ejS1btmDOnDlSl0NEEuGlJSJyWO3atcPu3bsRHR0NpVKJ9957T+qSiKiRMcgQkUNr3749du3ahejoaCgUCvz3v/+VuiQiakQywYvLRERE5KA4RoaIiIgcFoMMEREROSwGGSIiInJYDDJERETksBhkiIiIyGExyBAREZHDYpAhIiIih8UgQ0RERA6LQYaIiIgcFoMMEREROSwGGSIiInJYDDJERETksP4fKNiEisBjB2QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df.drop('DEATH_EVENT',axis=1)\n",
    "\n",
    "X_train = X.iloc[train,:]\n",
    "y_train = y.iloc[train]\n",
    "X_test = X.iloc[test,:]\n",
    "y_test = y.iloc[test]\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "# Determine the optimal k:\n",
    "k_bar = 100\n",
    "k_grid = np.arange(1,k_bar) # The range of k's to consider\n",
    "SSE = np.zeros(k_bar) \n",
    "\n",
    "for k in range(k_bar):\n",
    "    knn = KNeighborsRegressor(n_neighbors=k+1)\n",
    "    predictor = knn.fit(X_train,y_train) \n",
    "    y_hat = knn.predict(X_test)\n",
    "    SSE[k] = np.sum( (y_test-y_hat)**2 ) # Bug in sklearn requires .values\n",
    "\n",
    "SSE_min = np.min(SSE) # highest recorded accuracy\n",
    "min_index = np.where(SSE==SSE_min) \n",
    "k_star = k_grid[min_index] # Find the optimal value of k\n",
    "print(k_star)\n",
    "\n",
    "plt.plot(np.arange(0,k_bar),SSE) # Plot accuracy by k\n",
    "plt.xlabel(\"k\")\n",
    "plt.title(\"optimal k:\"+str(k_star)+', SSE:'+str(SSE_min))\n",
    "plt.ylabel('SSE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de6734bd-8981-406b-b2d4-b5eb98c4a1fa",
   "metadata": {},
   "source": [
    "> With more variables, the algorithm selects a higher optimal $k^* = 83$ instead of $k^*=6$, and it has a higher SSE of 29 rather than 27. The simpler model (fewer variables, fewer neighbors) does a better job predicting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d836efde-556c-41ce-aef8-33044c192f69",
   "metadata": {},
   "source": [
    "**Q4.** Let's do some very basic computer vision. We're going to import the MNIST handwritten digits data and $k$NN to predict values (i.e. \"see/read\").\n",
    "\n",
    "1. To load the data, run the following code in a chunk:\n",
    "```\n",
    "from keras.datasets import mnist\n",
    "df = mnist.load_data('minst.db')\n",
    "train,test = df\n",
    "X_train, y_train = train\n",
    "X_test, y_test = test\n",
    "```\n",
    "The `y_test` and `y_train` vectors, for each index `i`, tell you want number is written in the corresponding index in `X_train[i]` and `X_test[i]`. The value of `X_train[i]` and `X_test[i]`, however, is a 28$\\times$28 array whose entries contain values between 0 and 256. Each element of the matrix is essentially a \"pixel\" and the matrix encodes a representation of a number. To visualize this, run the following code to see the first ten numbers:\n",
    "```\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "np.set_printoptions(edgeitems=30, linewidth=100000)\n",
    "for i in range(5): \n",
    "    print(y_test[i],'\\n') # Print the label\n",
    "    print(X_test[i],'\\n') # Print the matrix of values\n",
    "    plt.contourf(np.rot90(X_test[i].transpose())) # Make a contour plot of the matrix values\n",
    "    plt.show()\n",
    "```\n",
    "OK, those are the data: Labels attached to handwritten digits encoded as a matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2351a60c-2f62-4030-8b82-de62bb072a0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 \n",
      "\n",
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  84 185 159 151  60  36   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0 222 254 254 254 254 241 198 198 198 198 198 198 198 198 170  52   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  67 114  72 114 163 227 254 225 254 254 254 250 229 254 254 140   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0  17  66  14  67  67  67  59  21 236 254 106   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  83 253 209  18   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  22 233 255  83   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 129 254 238  44   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  59 249 254  62   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 133 254 187   5   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   9 205 248  58   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 126 254 182   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  75 251 240  57   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0  19 221 254 166   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   3 203 254 219  35   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0  38 254 254  77   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0  31 224 254 115   1   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0 133 254 254  52   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0  61 242 254 254  52   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0 121 254 254 219  40   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0 121 254 207  18   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]] \n",
      "\n"
     ]
    },
    {
     "data": {
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WzEb9IV4tkMIuKuaNpbpa/3u7pyBh1oGkaREq8o34AHKm/E3K5MLYKOfUi7oHjClhXnv3OpBytQJkXFZChPUe2UN8ADlmYvfZOH6wm9qWvyjKXyu/cVntCiK/V8CEDRGvuxZ7LTb1e8ql2lSM8Mgm4gMogDAREvcP9aJsy2/a+Ovl/t8nypqZm2b/1TMYytmYiBAexUF8AAXi9w3f5g91IiScIK+X6S3s3aFSK0b8Tj3cCI/8Ij6AAqr0hp/kD3ROxZgTZtM2P9OPSsL+vnLuqQfhkW/EB1BgafshnoUpiInXzPTfL+opF9ui3M8nbd+zCIf4AJA6eX+DSXLKU22xaZTpRyV+djX1O/XI+/dFkbCnMQAkwNQbqXvq4T7lEnS9R9yTEPfUg/AoJuIDABKS9zdUU/dyyfvrVETEBwAkKI1vrHFNP4JOPWYd/FcqXx9ER3wAQMLCvsGaPuViUtSpB9GRb8QHAKSAjTdbvzubSuanH2HXeiCfiA8ASIkgAZLluwD7Od2CfCM+ACBF/LzxeoVHrVMuXlOPuNZ2uE+5RNnXA/lEfABAitTa/yPr4cHUAxKbjAFAZtRaYCplOzxQHMQHAKREtalHmCtbgiwwdQu606mJ8GDqURycdgGAjIkSHnFMPWpdVsvEA25MPgAgBfxOPcKs7xhnKzzCLDBl6lEsTD4AIGF5Dg+mHvBCfABASiUVHn7We5gMD6YexUN8AECCKk090jzx8MLEA0EQHwCQMtV2Lw0SHnExeUktU49iChwfe/bs0dq1a9Xe3q66ujo999xzkz5/8803q66ubtLH6tWrTR0vAOSG19Sj2iW1QcPDxjoPJh4II3B8jIyMaMmSJdq+fXvF56xevVrHjh2b+HjyyScjHSQAFEEawqPaeg/T4cHUo7gCX2q7Zs0arVmzpupzGhoa1NraGvqgACDv3FOPNIRHNUHDA6gmlu+WXbt2ae7cuVq4cKHuvPNOnThxouJzR0dHNTw8POkDAIokj+HB1APVGI+P1atX67HHHtPOnTv1ox/9SLt379aaNWv04Ycfej6/r69PpVJp4qOjo8P0IQFAqlTb18Prfi3j4g4Pr1MucYQHYHyH0xtuuGHiv1922WVavHixFixYoF27dmnFihVTnr9582b19vZO/Hp4eJgAAZBb1U63+Nk2vRJbl9TW4ic8mHog9pN0F110kebMmaNDhw55fr6hoUFNTU2TPgAgj2qt8wjL1l1qAVNiv7fLW2+9pRMnTqitrS3uPwoAUqvaqRYp3EZitqcdYe7Z4sbUA1KI+Dh9+vSkKcbhw4e1f/9+NTc3q7m5WVu3btW6devU2tqqgYEB3X333br44ou1atUqowcOAFlRaz+PMKdb0nCahUtrEVbg+Hjttdd0zTXXTPx6fL3G+vXr9dBDD+nAgQP65S9/qZMnT6q9vV0rV67UD37wAzU0NJg7agDICD8bidWSxC6mQJwCx8fVV18tx3Eqfv73v/99pAMCgLwIuoOpFGyRaZYw9UA5doUBgBjUWuMhVb+sFsiz2BecAkDRhD3V4jX1sHXKxb3HBzePQ5yYfACAQX7DI8rUIw2LTYEomHwAgAGVTrP4DQ/WeqBImHwAQERBwiOIrF/lQnigEuIDACIIGh55mHoE3d8DcOO0CwCEFGd4VJp6mF7v4XUzObcwO5sy9UA1TD4AwKA8hEeQe7p4TT0ID9TC5AMAQvCzj8e4LIdH+dTDz+kWwgN+EB8AEFCUK1uSjg7JOzxM7OtBeMAvTrsAgAFR9vIgPFA0TD4AIIAgp1vcguxgmsbwqHRVC+GBoIgPAIgozOkWm9EhBQ8Pv5fTEh4Ig/gAAJ/Cbp2e9vAIe98WwgNhER8A4IPf0y1pDw9TN4wjPBAF8QEAIbmnHmHCIy2nWSTCA/YQHwBQhd/LavMUHtW2Syc8YALxAQAVmLphXJrCI+y0QyI8YA7xAQAeouxgWutGcVlb3wGYRnwAQJla0RH1dEtWw4OpB0wiPgAgpDyEh59pB+EB04gPAPh/QaceaRB14lEN0YG4cG8XAPAh6OmWLGBxKZJCfACAot2zJS1MTT0ID8SN0y4AUEOtqYeXSjuZmuJ1Sa0JhAdsYPIBoPCiTj1sn3KJa/dSwgO2EB8AUEWYqUecmHggDzjtAqDQqk09/FzdYmvqUS06ok49CA/YxuQDADx4hYffqYfp9R5xTTuApBAfAArL5hUuYTcYCxoeTD2QBZx2AQAXv1OPOE+5+I0O9ykXIAuYfAAoJFN3rHUzccqF0yzIO+IDQOEEDQ+bU48g4cFCU2QVp10AoApbl9ZGiQ4ga5h8AICCnW6pNPUIe8rFRHgw9UCWEB8AUEGQ0y1xb6cumQsPIGnEB4BCSdsN5KJe1cI26sgi4gNA4UW9tNbG1MML4YGsIj4AwIe0nW7hVAuyjPgAABf31CNqeITd3VQyGx5MPZAWxAeAQgu7qViSC0zLucOjEsIDaUJ8ACgMP4tN/U49TAizk6l76uHG6RZkAfEBAAFlbZ0HUw+kDfEBoLBqnXLxmnoQHkB0xAcA/L9aW6lnLTyAtCI+AMCDe+phOjz8rveIEh5MPZBWxAeAQnAvNnWfcrF1A7lqot6lthzhgTQjPgDAJe6phxeT4QGkHfEBIPeyMPUoFzU8mHog7WYkfQAAYFOt8Ihj6lG+vuOm2X+dst7Dz2ZifhAdyAomHwByrXzqETQ84hB0YzEuqUUeER8AcqvajqZ+TrWkYa2HH4QHsobTLgByxys6wmwoljbuqQfRgaxi8gEgV/yEh5/TLWmYevi9aRyQNXxnA8iNMOHhxUZ4AEVGfADIhbDhkdTplqj7enDKBVnGmg8AmVZpUWnY8GDqAcQv8ORjz549Wrt2rdrb21VXV6fnnntu0ucdx9F9992ntrY2zZo1S93d3XrjjTdMHS8ATPAbHibcNPuvxr4WUw8UXeD4GBkZ0ZIlS7R9+3bPzz/wwAP66U9/qocfflivvvqqzj77bK1atUrvvfde5IMFgHFBwiPq1GM8PEwGCFBkgU+7rFmzRmvWrPH8nOM42rZtm+655x596UtfkiQ99thjamlp0XPPPacbbrgh2tECgOIJD7+8dij1y2snU6YeKCKjC04PHz6swcFBdXd3TzxWKpXU1dWlvXv3ev6e0dFRDQ8PT/oAAC/vLrqg4sJSv+FRSa2pR63HajG1hTqQB0bjY3BwUJLU0tIy6fGWlpaJz7n19fWpVCpNfHR0dJg8JAA5EXR9R6XwSGKRaaXwCLObKZAHiV9qu3nzZg0NDU18vPnmm0kfEoCMCBIe53QOpSo8vHDKBUVhND5aW1slScePH5/0+PHjxyc+59bQ0KCmpqZJHwBQrto9WtyCrPFIMjxY64EiMxofnZ2dam1t1c6dOyceGx4e1quvvqply5aZ/KMAFERcV7UkuZ9H0PAA8ibw1S6nT5/WoUOHJn59+PBh7d+/X83NzZo3b542bdqkH/7wh/rEJz6hzs5O3XvvvWpvb9e1115r8rgBFIDNy2mD8HO1i991Hn7Cg6kH8iZwfLz22mu65pprJn7d29srSVq/fr0effRR3X333RoZGdHtt9+ukydP6nOf+5xefPFFfexjHzN31AByL63h4QfhAVRX5ziOk/RBlBseHlapVFJ329c1Y5r5XQoBZIOfe7VI/sIjanS4L62tNvkwGR4S8YHs+GDsjF469gsNDQ3VXL+Z+NUuAODmd4GpjfAwgfAAJuPGcgBSo1p0+Llfi43wCDr1IDyAqZh8AEiFoOHhnnokPfEwGR5A3hEfABKX9fDwEiU8mHog7zjtAiAxtdZ2ZCU8uG8LEAzxASARQXYtHRc0PMqvUgl7J9pyXl/D9OkWph4oAuIDgHV+wsM99YgSHl6/DuLxt6/0/P2EBxAOaz4AWGUjPEzzGy4sMAX8YfIBwBpb4RFlylGJ+5SLe+rBxAPwj8kHgNRI28RjnIn1IpUQHigiJh8ArAhyZUuUnUtNTj0qRYepqQfhgaIiPgAkzlR4mMS0A4gP8QEgdn43Eat1mkWqHh6mph5BtlBnkSkQHPEBIDFBwiMP6zskph6AxIJTADGrNPWodqO4MOFhYupRKzyYegBmEB8AYuM3PMqnHmmdeNQKDz+YegAfIT4AWBVHeESdegQNDy/uqQeAyljzASAWXlOPtE08/Kzv8LOFuhf3KRemHsD/kOoArMhLeHhh6gEEw/9jABjnnnrEGR5hTrlECQ+mHkB0nHYBYJTN8AjDxPqOckw9gOCIDwDGVAuPNOzjETU8mHoAZhAfAIwIGx5Ro+Pxt6+M5S62brX29JAID8Av4gNAZOXhEeTOtKamHeMTjWoREmXqQXgAZnGyEkBo7y66wHd4nNM5FEt4lAu7NTrhAdhFfAAIJa0LSx9/+8opERI2SggPIB7EB4DAaq3vSPqKFul/wRH2dAvhAcSHNR8AAkl6fUcQhAeQTkw+APhmKzxsXL1SCeEBxI/JB4CaktixdPw/w67XqMXPPVvc4eGODonwAMJg8gGgKpvrO7wmHnFMQQgPIFlMPgBUZHN9R7XIuGn2X2ObgHghPIB4MfkA4Ckt4RHkOX74mXrUQngA0TD5ADCJ7f07gkRF1HUgJk63EB5AdEw+AFRUa+JRLu7wiAPhASSD+AAwwT31qMY99QjKdni4px6EB5Ac4gOAL9WmHkERHkCxseYDQGR+T7mYiI6g6z2ihgcA84gPAJ7c6z3KhTnlkvS0Q2IvDyAtiA8AkoKt9wjKVHj4nXqYmnYQHkA8iA8ANYW9yiWJq1k4zQKkHwtOAUxR7ZSLX6bDw8/Uw2R4MPUA4kN8AAjEz3qPrE88CA8gXpx2AVB1vUeQUy5JbRrGqRYgW5h8AEi9aqdcTIcHUw8gfsQHgEnCXmKb9VMtEuEB2EJ8AAglzL1cwqg09eBUC5BdxAeAikxuqW5SHOHB1AOwhwWnQIG5F5qWn3Jxh0f5KRdbC029ph6EB5B9TD6AgjIVHnEhPID8YvIBFJDf8HAvMPUKjzimHoQHkG/EB1Aw5eHhvrKlKOFBdADJIj6Agqg27ZCCh0cc3OER5s60tRAeQPKID6AAwoaHrZvG+Zl2SP7Cg23TgfQjPoCcy8PCUilaeBAdQLoQH0BOBZl2SMHCw9TUI8z6DonwALKO+AByKM7wMCHsrqUS4QHkAfEB5EzY0yySv/CIOvUIe5pFIjyAvDC+ydj999+vurq6SR+XXHKJ6T8GgAf3ZbSmwyOqKOs7CA8gP2KZfFx66aV66aWX/veHzGDAAsTJ1mmWsFMPv9Eh+b+UlhvFAdkVSxXMmDFDra2tcXxpAC5p37/D9GkWictpgayL5d4ub7zxhtrb23XRRRfpa1/7mo4cOVLxuaOjoxoeHp70AcCfWus7TIZHmKkH4QHAi/H46Orq0qOPPqoXX3xRDz30kA4fPqzPf/7zOnXqlOfz+/r6VCqVJj46OjpMHxKQS7UmHiYFDY/H376S8ABQUZ3jOE6cf8DJkyc1f/58/eQnP9Gtt9465fOjo6MaHR2d+PXw8LA6OjrU3fZ1zZgW3w9TIMuirPGQ/E89TK7xkOKLD8IDSN4HY2f00rFfaGhoSE1NTVWfG/tK0HPPPVef/OQndejQIc/PNzQ0qKGhIe7DAHIjanj4ZfqmcXFOPQBkSyxrPsqdPn1aAwMDamtri/uPAnLPHR5h2NjLwzROtwD5Ynzy8e1vf1tr167V/PnzdfToUW3ZskXTp0/XjTfeaPqPAgrFKzxMTz3SFh0A8sl4fLz11lu68cYbdeLECZ1//vn63Oc+p1deeUXnn3++6T8KKIww4eHF1l1qw+IutUAxGI+Pp556yvSXBAotbHgEmXokER5e6z3cWOcB5FPsaz4AhOcnPLykPTy8uKcetcKDqQeQXcQHkFJ+w8PP6RbJzm6mlVTaSj0swgPINuIDSKEo4ZGFqUetUy6cbgHyjfgAUibsqZZqkpx6+FFpbw8vTD2A7CM+gBQJEh5Rpx5pxdQDyD/iA0iJuMKj0tQjrQtNq2HqAeQD8QGkQNTwyBI/l9gCyLfY7+0CoLogW6bnaeLhhQ3FgGJg8gGkUNQrW7IYHgCKg/gAEhTlyhbCA0BWER9AQqKcbiE8AGQZ8QGkiJ/TLYQHgKxjwSmQAJNbpxMdALKGyQeQUn4WmBIeALKI+AAsM3WnWsIDQFYRH4BFYU+3EB4A8oT4ACzxe3UL4QEg71hwCsSsWnS4px5+r2wpR3QAyBomH0CMgoSHH+6pB+EBIIuIDyAmQTYRk4KfbiE8AGQV8QHEoFZ4mDjdAgBZRXwAGVBpkWkW/OHIwqQPAUDKEB+AYVGnHn6k+ZTL429fmfQhAEg54gNImSKdcmkc8PcjaNbBf8V8JABsIj4Ag2xMPbLs9OFS0ocAIAWIDyDlWO8BIG+ID8CQoFMPL1k/5RLHeg9OuQD5Q3wAFvi5f0vWsdAUgF9srw4YEHRDsTwhOgAExeQDiJnfqYfXKZe0r/cwGR6z+89MeYxTLkA+ER9AjMLcv6WWtOzxUSs83ItN3Ve6+L3MFkD+cNoFiCjqPVyyhtMsAKLinx5ATIJMPbJylQvhAcAE4gOIoNLUo1J4BJl6pG29h+3wYL0HkF+cdgFQVZjoqLXeA0CxER8AKoojPNwLTd1XuTDxAPKP+AAwRVzTDsIDgER8ACgTdl0H0w4AQRAfACItJi0PD6YdAPwgPoCCY9oBwDbiAygoU9MOifAAEAzxARQQ0w4ASWKTMaBgCA8ASWPyARQE0QEgLZh8AAVAeABIEyYfQM6ZCA+iA4BJxAeQU0w7AKQV8QHkUBzhQXQAMIX4ACKYdfBfenfRBVMen91/Rm8vrJ/yeOPANJ1aMDbl8dOHSzqnc2jSY+MhsHJe/6THH3/7St00+69THouCaQcAm4gPIKIwATKuPETK3/DLQ8QrQkzc+K2SauFBdAAwgfgADBh/E3ZHyPibtVeESLVDxCtCpKnTEPfnw2DaAcAW4gMwKOgUpNz4m32QaUgUXjeBcx/LuPLwIDoAREV8AIZFCRAp2DSkkmph4ffPlph2AIgH8QHEoNZpmHFRpyEmMe0AYAvxAcSo0hRknN8YqTQN8csdFtUw7QAQN+IDiFmtAClX/sbvJ0RMIzwA2EB8ABZUOg1TTdBTNFEQHQBsii0+tm/frh//+McaHBzUkiVL9OCDD2rp0qVx/XFAJpS/qQcJEWlqIMSF8AAQt1ji41e/+pV6e3v18MMPq6urS9u2bdOqVavU39+vuXPnxvFHAplT7U0+aJiYQHQAsKXOcRzH9Bft6urSFVdcoZ/97GeSpLGxMXV0dGjjxo363ve+V/X3Dg8Pq1Qqqbvt65oxLb4xM5BlleKEgACQlA/GzuilY7/Q0NCQmpqaqj7X+OTjzJkz2rdvnzZv3jzx2LRp09Td3a29e/dOef7o6KhGR0cnfj009NEeBh+M2RkxA1k088Bhz8c/sHwcADBu/H3bz0zDeHz897//1YcffqiWlpZJj7e0tOj111+f8vy+vj5t3bp1yuO7jj9i+tAAAEDMTp06pVKp+n5EiV/tsnnzZvX29k78+uTJk5o/f76OHDlS8+AR3fDwsDo6OvTmm2/WHJMhOl5vu3i97eL1tittr7fjODp16pTa29trPtd4fMyZM0fTp0/X8ePHJz1+/Phxtba2Tnl+Q0ODGhoapjxeKpVS8WIWRVNTE6+3RbzedvF628XrbVeaXm+/QwPjuxXV19fr8ssv186dOyceGxsb086dO7Vs2TLTfxwAAMiYWE679Pb2av369frMZz6jpUuXatu2bRoZGdEtt9wSxx8HAAAyJJb4uP766/Wf//xH9913nwYHB/XpT39aL7744pRFqF4aGhq0ZcsWz1MxMI/X2y5eb7t4ve3i9bYry693LPt8AAAAVBLfHaoAAAA8EB8AAMAq4gMAAFhFfAAAAKtSFx/bt2/Xxz/+cX3sYx9TV1eX/va3vyV9SLl0//33q66ubtLHJZdckvRh5caePXu0du1atbe3q66uTs8999ykzzuOo/vuu09tbW2aNWuWuru79cYbbyRzsDlQ6/W++eabp3y/r169OpmDzbi+vj5dccUVamxs1Ny5c3Xttdeqv79/0nPee+899fT06LzzztM555yjdevWTdl4Ev74eb2vvvrqKd/fd9xxR0JH7E+q4uNXv/qVent7tWXLFv3973/XkiVLtGrVKv373/9O+tBy6dJLL9WxY8cmPl5++eWkDyk3RkZGtGTJEm3fvt3z8w888IB++tOf6uGHH9arr76qs88+W6tWrdJ7771n+UjzodbrLUmrV6+e9P3+5JNPWjzC/Ni9e7d6enr0yiuv6I9//KPef/99rVy5UiMjIxPPueuuu/Sb3/xGTz/9tHbv3q2jR4/quuuuS/Cos8vP6y1Jt91226Tv7wceeCChI/bJSZGlS5c6PT09E7/+8MMPnfb2dqevry/Bo8qnLVu2OEuWLEn6MApBkvPss89O/HpsbMxpbW11fvzjH088dvLkSaehocF58sknEzjCfHG/3o7jOOvXr3e+9KUvJXI8effvf//bkeTs3r3bcZyPvpdnzpzpPP300xPP+ec//+lIcvbu3ZvUYeaG+/V2HMf5whe+4Hzzm99M7qBCSM3k48yZM9q3b5+6u7snHps2bZq6u7u1d+/eBI8sv9544w21t7froosu0te+9jUdOXIk6UMqhMOHD2twcHDS93qpVFJXVxff6zHatWuX5s6dq4ULF+rOO+/UiRMnkj6kXBgaGpIkNTc3S5L27dun999/f9L39yWXXKJ58+bx/W2A+/Ue98QTT2jOnDlatGiRNm/erHfeeSeJw/Mt8bvajvvvf/+rDz/8cMouqC0tLXr99dcTOqr86urq0qOPPqqFCxfq2LFj2rp1qz7/+c/r4MGDamxsTPrwcm1wcFCSPL/Xxz8Hs1avXq3rrrtOnZ2dGhgY0Pe//32tWbNGe/fu1fTp05M+vMwaGxvTpk2bdNVVV2nRokWSPvr+rq+v17nnnjvpuXx/R+f1ekvSV7/6Vc2fP1/t7e06cOCAvvvd76q/v1/PPPNMgkdbXWriA3atWbNm4r8vXrxYXV1dmj9/vn7961/r1ltvTfDIAPNuuOGGif9+2WWXafHixVqwYIF27dqlFStWJHhk2dbT06ODBw+yXsySSq/37bffPvHfL7vsMrW1tWnFihUaGBjQggULbB+mL6k57TJnzhxNnz59yoro48ePq7W1NaGjKo5zzz1Xn/zkJ3Xo0KGkDyX3xr+f+V5PzkUXXaQ5c+bw/R7Bhg0b9MILL+jPf/6zLrzwwonHW1tbdebMGZ08eXLS8/n+jqbS6+2lq6tLklL9/Z2a+Kivr9fll1+unTt3Tjw2NjamnTt3atmyZQkeWTGcPn1aAwMDamtrS/pQcq+zs1Otra2TvteHh4f16quv8r1uyVtvvaUTJ07w/R6C4zjasGGDnn32Wf3pT39SZ2fnpM9ffvnlmjlz5qTv7/7+fh05coTv7xBqvd5e9u/fL0mp/v5O1WmX3t5erV+/Xp/5zGe0dOlSbdu2TSMjI7rllluSPrTc+fa3v621a9dq/vz5Onr0qLZs2aLp06frxhtvTPrQcuH06dOT/tVx+PBh7d+/X83NzZo3b542bdqkH/7wh/rEJz6hzs5O3XvvvWpvb9e1116b3EFnWLXXu7m5WVu3btW6devU2tqqgYEB3X333br44ou1atWqBI86m3p6erRjxw49//zzamxsnFjHUSqVNGvWLJVKJd16663q7e1Vc3OzmpqatHHjRi1btkyf/exnEz767Kn1eg8MDGjHjh364he/qPPOO08HDhzQXXfdpeXLl2vx4sUJH30VSV9u4/bggw868+bNc+rr652lS5c6r7zyStKHlEvXX3+909bW5tTX1zsXXHCBc/311zuHDh1K+rBy489//rMjacrH+vXrHcf56HLbe++912lpaXEaGhqcFStWOP39/ckedIZVe73feecdZ+XKlc7555/vzJw505k/f75z2223OYODg0kfdiZ5vc6SnEceeWTiOe+++67zjW98w5k9e7Zz1llnOV/+8pedY8eOJXfQGVbr9T5y5IizfPlyp7m52WloaHAuvvhi5zvf+Y4zNDSU7IHXUOc4jmMzdgAAQLGlZs0HAAAoBuIDAABYRXwAAACriA8AAGAV8QEAAKwiPgAAgFXEBwAAsIr4AAAAVhEfAADAKuIDAABYRXwAAACriA8AAGDV/wGHSN1sCTxJNwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 \n",
      "\n",
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0 116 125 171 255 255 150  93   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0 169 253 253 253 253 253 253 218  30   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0 169 253 253 253 213 142 176 253 253 122   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  52 250 253 210  32  12   0   6 206 253 140   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  77 251 210  25   0   0   0 122 248 253  65   0   0   0   0   0   0   0   0   0   0]\n",
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    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 \n",
      "\n",
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  38 254 109   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  87 252  82   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 135 241   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  45 244 150   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  84 254  63   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 202 223  11   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  32 254 216   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  95 254 195   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 140 254  77   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0  57 237 205   8   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0 124 255 165   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0 171 254  81   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0  24 232 215   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0 120 254 159   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0 151 254 142   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0 228 254  66   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0  61 251 254  66   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0 141 254 205   3   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0  10 215 254 121   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   5 198 176  10   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]] \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \n",
      "\n",
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0  11 150 253 202  31   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0  37 251 251 253 107   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0  21 197 251 251 253 107   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0 110 190 251 251 251 253 169 109  62   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0 253 251 251 251 251 253 251 251 220  51   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0 182 255 253 253 253 253 234 222 253 253 253   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  63 221 253 251 251 251 147  77  62 128 251 251 105   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  32 231 251 253 251 220 137  10   0   0  31 230 251 243 113   5   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  37 251 251 253 188  20   0   0   0   0   0 109 251 253 251  35   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  37 251 251 201  30   0   0   0   0   0   0  31 200 253 251  35   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  37 253 253   0   0   0   0   0   0   0   0  32 202 255 253 164   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0 140 251 251   0   0   0   0   0   0   0   0 109 251 253 251  35   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0 217 251 251   0   0   0   0   0   0  21  63 231 251 253 230  30   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0 217 251 251   0   0   0   0   0   0 144 251 251 251 221  61   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0 217 251 251   0   0   0   0   0 182 221 251 251 251 180   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0 218 253 253  73  73 228 253 253 255 253 253 253 253   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0 113 251 251 253 251 251 251 251 253 251 251 251 147   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  31 230 251 253 251 251 251 251 253 230 189  35  10   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  62 142 253 251 251 251 251 253 107   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0  72 174 251 173  71  72  30   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]] \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 \n",
      "\n",
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0  50 224   0   0   0   0   0   0   0  70  29   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0 121 231   0   0   0   0   0   0   0 148 168   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   4 195 231   0   0   0   0   0   0   0  96 210  11   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0  69 252 134   0   0   0   0   0   0   0 114 252  21   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  45 236 217  12   0   0   0   0   0   0   0 192 252  21   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0 168 247  53   0   0   0   0   0   0   0  18 255 253  21   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  84 242 211   0   0   0   0   0   0   0   0 141 253 189   5   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0 169 252 106   0   0   0   0   0   0   0  32 232 250  66   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  15 225 252   0   0   0   0   0   0   0   0 134 252 211   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  22 252 164   0   0   0   0   0   0   0   0 169 252 167   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   9 204 209  18   0   0   0   0   0   0  22 253 253 107   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0 169 252 199  85  85  85  85 129 164 195 252 252 106   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  41 170 245 252 252 252 252 232 231 251 252 252   9   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0  49  84  84  84  84   0   0 161 252 252   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 127 252 252  45   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 128 253 253   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 127 252 252   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 135 252 244   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 232 236 111   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 179  66   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]] \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from keras.datasets import mnist\n",
    "df = mnist.load_data('minst.db')\n",
    "train,test = df\n",
    "X_train, y_train = train\n",
    "X_test, y_test = test\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "np.set_printoptions(edgeitems=30, linewidth=100000)\n",
    "for i in range(5): \n",
    "    print(y_test[i],'\\n') # Print the label\n",
    "    print(X_test[i],'\\n') # Print the matrix of values\n",
    "    plt.contourf(np.rot90(X_test[i].transpose())) # Make a contour plot of the matrix values\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ed38e4-a229-4974-9cbc-41c79c7f1e86",
   "metadata": {},
   "source": [
    "2. What is the shape of `X_train` and `X_test`? What is the shape of `X_train[i]` and `X_test[i]` for each index `i`? What is the shape of `y_train` and `y_test`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4cc585d8-615c-4f94-94fe-422ec8aa84cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(10000, 28, 28)\n",
      "(28, 28)\n",
      "(28, 28)\n",
      "(60000,)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(X_train[0].shape)\n",
    "print(X_test[0].shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af49dd19-20b3-4535-ad5b-b43c532e93ba",
   "metadata": {},
   "source": [
    "> There are 60,000 matrices of size 28 by 28 in the training set, and 10,000 matrices of size 28 by 28 in the test set. The y_train vector has 60,000 numeral assignments, and the y_test vector has 10,000 numeral assignments. Basically, each `X_train[i]` is a matrix of values in two-dimensional space, associated with a numeral in `y_train[i]`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e312859c-7c8f-4c09-8d7f-30203e9ab48d",
   "metadata": {},
   "source": [
    "3. Use Numpy's `.reshape()` method to covert the training and testing data from a matrix into an vector of features. So, `X_test[index].reshape((1,784))` will convert the $index$-th element of `X_test` into a $28\\times 28=784$-length row vector of values, rather than a matrix. Turn `X_train` into an $N \\times 784$ matrix $X$ that is suitable for scikit-learn's kNN classifier where $N$ is the number of observations and $784=28*28$ (you could use, for example, a `for` loop)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4f135e1c-2083-4fd1-aa56-34401804281f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# To save on reloading cost, I save the reshaped data and reload it rather than run the\n",
    "# code that loops over appending the rows \n",
    "\n",
    "reload = 0 # Control the way data loads\n",
    "\n",
    "if reload == 1:  # If reload is 1, do the reshaping process\n",
    "    Z_train = []\n",
    "    for i in range(60000):\n",
    "        row = X_train[i].reshape((1,784)) # Turn the matrix for i into a row vector of features\n",
    "        Z_train.append(row[0]) # Append the row vector to the list\n",
    "    Z_train = pd.DataFrame(Z_train)\n",
    "    Z_train.to_csv('./data/Z_train.csv')\n",
    "    #\n",
    "    Z_test = []\n",
    "    for i in range(len(y_test)):\n",
    "        row = X_test[i].reshape((1,784)) # Turn the matrix for i into a row vector of features\n",
    "        Z_test.append(row[0]) # Append the row vector to the list\n",
    "    Z_test = pd.DataFrame(Z_test)\n",
    "    Z_test.to_csv('./data/Z_test.csv')\n",
    "else: # If reload is not 1, just load the reshaped data\n",
    "    Z_train = pd.read_csv('./data/Z_train.csv')\n",
    "    Z_test = pd.read_csv('./data/Z_test.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "448e9f4d-682b-41f2-b2f9-c37d015a6115",
   "metadata": {},
   "source": [
    "4. Use the reshaped `X_test` and `y_test` data to create a $k$-nearest neighbor classifier of digit. What is the optimal number of neighbors $k$? If you can't determine this, play around with different values of $k$ for your classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c603952f-ccb1-4f1c-849e-36cb22085042",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Determine the optimal k:\n",
    "k_bar = 50\n",
    "k_grid = np.arange(2,k_bar) # The range of k's to consider\n",
    "accuracy = np.zeros(k_bar) \n",
    "\n",
    "for k in range(k_bar):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k+1)\n",
    "    predictor = knn.fit(Z_train.values,y_train) \n",
    "    #y_hat = predictor.predict(Z_test.values) \n",
    "    accuracy[k] = knn.score(Z_test.values,y_test) # Bug in sklearn requires .values\n",
    "\n",
    "accuracy_max = np.max(accuracy) # highest recorded accuracy\n",
    "max_index = np.where(accuracy==accuracy_max) \n",
    "k_star = k_grid[max_index] # Find the optimal value of k\n",
    "print(k_star)\n",
    "\n",
    "plt.plot(np.arange(0,k_bar),accuracy) # Plot accuracy by k\n",
    "plt.xlabel(\"k\")\n",
    "plt.title(\"optimal k:\"+str(k_star))\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2c9140f-0cf3-46f2-b8d2-f00a8ceef4d8",
   "metadata": {},
   "source": [
    "5. For the optimal number of neighbors, how well does your predictor perform on the test set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e601ea9e-ba5f-4546-abeb-31f7c09137bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.8953\n"
     ]
    },
    {
     "data": {
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       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>10</td>\n",
       "      <td>750</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>920</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>933</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>45</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>773</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>51</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "      <td>877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0    0     1    2    3    4    5    6    7    8    9\n",
       "row_0                                                   \n",
       "0      953     1    0    0    0    6   16    2    1    1\n",
       "1        0  1126    3    2    0    0    3    1    0    0\n",
       "2       25    41  883   22    2    5    9   31   11    3\n",
       "3        3     8   16  880    1   48    5   14   23   12\n",
       "4        2    19    1    0  858    1    6   10    4   81\n",
       "5       11     9    0   45   10  750   26    5   19   17\n",
       "6       18     5    1    0    7    3  920    2    2    0\n",
       "7        0    39    7    3    7    1    0  933    0   38\n",
       "8       18    11   13   50    7   45   16    8  773   33\n",
       "9        6     8    1    7   51    8    1   40   10  877"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "predictor = knn.fit(Z_train.values,y_train) \n",
    "y_hat = predictor.predict(Z_test.values) \n",
    "\n",
    "accuracy = knn.score(Z_test.values,y_test) # Bug in sklearn requires .values\n",
    "print('Accuracy: ', accuracy)\n",
    "\n",
    "pd.crosstab(y_test, y_hat)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3857e40-a079-4459-a982-7a6b1013477d",
   "metadata": {},
   "source": [
    "> With k=3, the rule is 90% accurate on the test set. When it does make mistakes, it tends to be things like confusing 4 for 9 or 8 for 3 or 7 for 1, which is understandable. It is remarkable that a simple algorithm like kNN does this well at classifying such complex data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79d91425-b18f-4842-9d15-6535faade902",
   "metadata": {},
   "source": [
    "6. So, this is how computers \"see.\" They convert an image into a matrix of values, that matrix becomes a vector in a dataset, and then we deploy ML tools on it as if it was any other kind of tabular data. To make sure you follow this, invent a way to represent a color photo in matrix form, and then describe how you could convert it into tabular data. (Hint: RGB color codes provide a method of encoding a numeric value that represents a color.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba65f2bc-dcd2-4894-8e4b-c8aac78094f0",
   "metadata": {},
   "source": [
    "> The current data include an \"intensity\" for each pixel in the 28$\\times$28 grid. To add color, we could have three $28 \\times 28$ matrices that each capture the Red, Green, or Blue color intensity. Then we would reshape the three matrices and put them side by side into one long row to create tabular data, like we did above."
   ]
  }
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