Lab3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use(\"fivethirtyeight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in data from `CSV` files ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mystery = pd.read_csv(\"mystery.csv\")\n",
    "shooting = pd.read_csv(\"shooting.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can veiw the data..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.432565</td>\n",
       "      <td>0.635274</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.665584</td>\n",
       "      <td>-0.601412</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.125332</td>\n",
       "      <td>0.551185</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.287676</td>\n",
       "      <td>-1.099840</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.146471</td>\n",
       "      <td>0.085991</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.190915</td>\n",
       "      <td>-2.004563</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.189164</td>\n",
       "      <td>-0.493088</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.037633</td>\n",
       "      <td>0.462048</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.327292</td>\n",
       "      <td>-0.321005</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.174639</td>\n",
       "      <td>1.236556</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.186709</td>\n",
       "      <td>-0.631280</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.725791</td>\n",
       "      <td>-2.325211</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.588317</td>\n",
       "      <td>-1.231637</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.183186</td>\n",
       "      <td>1.055648</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.136396</td>\n",
       "      <td>-0.113224</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.113931</td>\n",
       "      <td>0.379224</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.066768</td>\n",
       "      <td>0.944200</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.059281</td>\n",
       "      <td>-2.120427</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.095648</td>\n",
       "      <td>-0.644679</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.832349</td>\n",
       "      <td>-0.704302</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.294411</td>\n",
       "      <td>-1.018137</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-1.336182</td>\n",
       "      <td>-0.182082</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.714325</td>\n",
       "      <td>1.521013</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.623562</td>\n",
       "      <td>-0.038439</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.691776</td>\n",
       "      <td>1.227448</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.857997</td>\n",
       "      <td>-0.696205</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.254001</td>\n",
       "      <td>0.007524</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-1.593730</td>\n",
       "      <td>-0.782893</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-1.440964</td>\n",
       "      <td>0.586939</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.571148</td>\n",
       "      <td>-0.251207</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>0.623690</td>\n",
       "      <td>1.491878</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>1.234523</td>\n",
       "      <td>2.358438</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>1.172422</td>\n",
       "      <td>2.274788</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>0.341753</td>\n",
       "      <td>2.805945</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>2.080053</td>\n",
       "      <td>2.731503</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>1.739756</td>\n",
       "      <td>2.458564</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>1.149115</td>\n",
       "      <td>-0.154548</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>2.392098</td>\n",
       "      <td>0.509604</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>3.078299</td>\n",
       "      <td>2.185236</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>0.391826</td>\n",
       "      <td>0.525130</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>1.474069</td>\n",
       "      <td>0.893274</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>0.389372</td>\n",
       "      <td>2.186794</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>2.250834</td>\n",
       "      <td>1.520049</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>2.000167</td>\n",
       "      <td>2.563801</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>0.982739</td>\n",
       "      <td>0.158950</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>0.940791</td>\n",
       "      <td>1.979510</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>0.746629</td>\n",
       "      <td>-0.133974</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>2.425813</td>\n",
       "      <td>0.057335</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>1.251480</td>\n",
       "      <td>1.793781</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>1.350165</td>\n",
       "      <td>1.359636</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>0.241585</td>\n",
       "      <td>0.369659</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>1.812620</td>\n",
       "      <td>1.207462</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>4.190277</td>\n",
       "      <td>0.917464</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>1.789696</td>\n",
       "      <td>0.603652</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>0.077197</td>\n",
       "      <td>1.748601</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>1.746786</td>\n",
       "      <td>0.010337</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>0.064227</td>\n",
       "      <td>1.813509</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1.648573</td>\n",
       "      <td>-0.525084</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>-0.193073</td>\n",
       "      <td>2.028990</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>2.219188</td>\n",
       "      <td>1.843471</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     feature1  feature2 class\n",
       "0   -0.432565  0.635274   Yes\n",
       "1   -1.665584 -0.601412   Yes\n",
       "2    0.125332  0.551185   Yes\n",
       "3    0.287676 -1.099840   Yes\n",
       "4   -1.146471  0.085991   Yes\n",
       "5    1.190915 -2.004563   Yes\n",
       "6    1.189164 -0.493088   Yes\n",
       "7   -0.037633  0.462048   Yes\n",
       "8    0.327292 -0.321005   Yes\n",
       "9    0.174639  1.236556   Yes\n",
       "10  -0.186709 -0.631280   Yes\n",
       "11   0.725791 -2.325211   Yes\n",
       "12  -0.588317 -1.231637   Yes\n",
       "13   2.183186  1.055648   Yes\n",
       "14  -0.136396 -0.113224   Yes\n",
       "15   0.113931  0.379224   Yes\n",
       "16   1.066768  0.944200   Yes\n",
       "17   0.059281 -2.120427   Yes\n",
       "18  -0.095648 -0.644679   Yes\n",
       "19  -0.832349 -0.704302   Yes\n",
       "20   0.294411 -1.018137   Yes\n",
       "21  -1.336182 -0.182082   Yes\n",
       "22   0.714325  1.521013   Yes\n",
       "23   1.623562 -0.038439   Yes\n",
       "24  -0.691776  1.227448   Yes\n",
       "25   0.857997 -0.696205   Yes\n",
       "26   1.254001  0.007524   Yes\n",
       "27  -1.593730 -0.782893   Yes\n",
       "28  -1.440964  0.586939   Yes\n",
       "29   0.571148 -0.251207   Yes\n",
       "..        ...       ...   ...\n",
       "370  0.623690  1.491878    No\n",
       "371  1.234523  2.358438    No\n",
       "372  1.172422  2.274788    No\n",
       "373  0.341753  2.805945    No\n",
       "374  2.080053  2.731503    No\n",
       "375  1.739756  2.458564    No\n",
       "376  1.149115 -0.154548    No\n",
       "377  2.392098  0.509604    No\n",
       "378  3.078299  2.185236    No\n",
       "379  0.391826  0.525130    No\n",
       "380  1.474069  0.893274    No\n",
       "381  0.389372  2.186794    No\n",
       "382  2.250834  1.520049    No\n",
       "383  2.000167  2.563801    No\n",
       "384  0.982739  0.158950    No\n",
       "385  0.940791  1.979510    No\n",
       "386  0.746629 -0.133974    No\n",
       "387  2.425813  0.057335    No\n",
       "388  1.251480  1.793781    No\n",
       "389  1.350165  1.359636    No\n",
       "390  0.241585  0.369659    No\n",
       "391  1.812620  1.207462    No\n",
       "392  4.190277  0.917464    No\n",
       "393  1.789696  0.603652    No\n",
       "394  0.077197  1.748601    No\n",
       "395  1.746786  0.010337    No\n",
       "396  0.064227  1.813509    No\n",
       "397  1.648573 -0.525084    No\n",
       "398 -0.193073  2.028990    No\n",
       "399  2.219188  1.843471    No\n",
       "\n",
       "[400 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mystery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here's an example with `mystery` dataset and *Ordinary Least Squares* to get you started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy per fold: [0.875, 0.80000000000000004, 0.92500000000000004, 0.84999999999999998, 0.875, 0.875, 0.90000000000000002, 0.84999999999999998, 0.77500000000000002, 0.75]\n",
      "Average accuracy: 0.8475\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x106625b50>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SUoAiFTei1cL993v5+usk/vorkc8/T8mRYaOCJDkZvwKPDge8/76BV14x06+f\nk7FjzTRoEEKtWiEMGGBJVzTy/HmFmTP9vzxs2KCnWDH18lBm9tx1l5fly5PYt+8iX399jmrV7DdV\nC8hggAEDnNSpcyU5UunY0UmTJu5Mj8tvoqMVJk0y0rGjhdGjTXly/TiRd0hiJHJFmTJlmDZtGr/9\n9hvdu3dHk8XXz4SEBP73v/9RvXp1PvjgA+x2e4AiFRmKiyMi+g/KHvmRYkmnKYhfs51OOHeOm0pE\nYmMVvvhCz9NPW5gwwXj1BhsfrzB3rpH27V18/70ehwMURQUU1q/Xs3mz/xcEh0O5XCrAn9dLpmvT\nZaRECYiMdODx/HtLy/OULq2yaFEKv/12id9/T+Sdd+zZKu6ZX5w/D/37m5kyxcTvv+uYM8dIhw5W\nmWAubkgSI5GrKlSowKxZs/jll1/o2LFjlvvHxcUxatQoatSowfz583EWwBtyXqecO4f5xRcJadQI\nW4cOhDz4INq9e4MdVo6KjlYYN85I69Y2Bg0yc+xY1pfC5GR4+20jAwda2LxZz7RpJlq3thEdraDR\nqPTqlcrw4Q6qV3fTp4/v6bbmzX09MRs2pM3ZSUjwPR5ftap/CYDwcC8VKnjJ4jmGXFG8ONxxh0rF\niipZPGia71y8qLBli39i+s8/Ws6fl8RIZEwSIxEQd955J/Pnz2fbtm20adMmy/2jo6N5+eWXqVWr\nFp9//jnu6yvuiVyj+ecfDN9+e/X/Kw4H5kGDUM6dy/H3UlXfWmVnz2a89pjb7ZukfPCghn/+UYiO\n9vUA/POPhq1btURHm7Baw7lwwcCZM0q2lry4cAEGDjTzwQcmjh7VsmKFgVatrJw54UaJivLNns5A\nfLzCp5/6r3566pSGhATfjTY+XsMXXxiIjFSZP983f6hfPycVKnho2TJtzk5srIbnnjMzYYKdtm2d\nhIaqNGzoYvXqZCpUKDg9NXnFjTqrC9ocKpFz5NQQAVWlShUWLVp0dfmQrJw6dYpBgwZRt25dvvrq\nK7zyqEyu0xw+nH7bkSPpy0H/RwkJsHy5nlatrDz0kI25cw1+a5SlpsK2bVoaN7ZRt24I7drZ2LdP\nx/ff6/joIz3t2tl45x0Tu3ZF0KpVKLVqhTBqlOmGQyQejy/nsdth61b/csvnzmmI/isB24MPYu7X\nDyWD6u6q6vt3rWbNXPz8s442bWwsXWpg+nQTfftamDTJQWoqvPOOkREjHDz0UNoE5+hohbNntfTo\nYaV8eZUB1yEyAAAgAElEQVRJk+w0aODBbFaREeScV7SoSseO/hPMa9Z0pyviKcQVkhiJoKhRowZf\nf/013377LQ0aNMhy/6NHj9K3b18aNmzI2rVrb2kuhcgeT5066Ra5cDdrhprDpa0PHNDy7LMWjh3T\nEhWl4fXXzWzcmDZz+Nw5hW7drFfXLTtzRsOLL5oxGuHhhz2Ehnp5/HE3vXtbOHlSQ0qKwoIFRt55\nx4jD4f9eZ88qzJhhoGtXK3PnGvnqqxQiI/2TbKPWjeJyYVi/HtPIkb5n2a9RrJhK9+7+Q7tPP+1k\n8mST37aYGA2xsQpr1iTTpUsqDRp42LFDx8CBJpYt01O+vBebzcvFiwoffGCkf38LS5bo2b1by7Bh\nZk6fzt4QT3KyL8lKSMjW7ld733bv9lW4vn6h3IKqaFGYONHBhx+m0Lati7fftvP55ykFah6VyFmS\nGImgqlevHmvXrmXVqlXUqlUry/0PHDhAjx49aNmyJT///LMkSLnAW7o09pkzUYsUAcBdsyb26dN9\nq6Nm9zW8EBvLDW/abrfvcfXrffSRgQsXfP8dH69gt/snCbGxGsxmOHpUQ5MmbrZv16Kq/vssW6bn\nwoW0bfHxvqGz//3PzI4dOqZPN/Haa2befDMte6pR3UnpuP0ol3vF9OvWoVz33LzNBqNHpzJtWgqN\nGrkZODCV6tU96WL0xakwYoSvKvW4cSaeftrKF18Yee45C8OGmVm9OhmbzXfulinjZepUB1Onmli8\n2MBTT1mJjc08OYqKUhg82EyDBjZ69LBw/LgZvT7zEhl//62hXr0QWrTwVbh+9VUzcXFw4oTClCkG\nxo41cuiQpkD2WkVEqHTr5mLBghT693fm6oK+Iv+TxEgEnaIoNGnShO+//54lS5Zw//33Z3nM/v37\neemll+jQoQNbt24NQJR5l9ZuR3PkCPpFi9D+/PN/nwtUpAiuLl1I3LGDi/v3k7x8OWq5ctk+PC5O\n4aOPDLRvb6NHDyu7d6e/2Wo0cNtt6W9OERFeDJfzpdBQFY3Gfx+Lxff/y5f3cuyYJsOlLkqXVtHp\n0rYnJCjpngo7dEjLPfd4ePppBx+8e5Elr/5MhVHPXf25t0IF3wJw6eJTeeYZF4sWJTNunIMTJxR6\n9PDvRbJYfOuvHTigpWJFla++8n9+ftMmPTYb7NyZyLZtiYwZ42DMGBOHDvmG9/bv15KQcOPEKC4O\neva08PXXBuLjNWzbpqd1axteb8lMj3nhBYtfVey1a/WcP6+hYcMQJkwwM2OGifr1bfz9d8G9LdxM\nKQNReBXcT4DIdxRFoWXLlmzZsoVPP/2Uu+++O8tjfv31V9q1a0eHDh3YtWtXAKLMW8xGI6atW7HV\nro3lhRewtW6NuW/f/54cGQycM5Th56iKzFxYgt9+y97Qi8cDX3yh59VXzfz9t5Zt23S0bGnj33/9\nLzUaDTz7bKpfhW2jUWXUqNSri9EWL+5bqsP32DtotSrjxtmJjlY4fFjDn3/qqFTJw513ps3a1mpV\nJk+2c+0ax8oNcgyTCWbOTOWp9ucpN2MEyuVJ16pOh/3dd1EjIjI8TlF8hRH1eoiM9CVBo0fbqVrV\nw6OPOlm0KIXJk02oqnL5SbT0AXg8vsTQYIDnn7dw4EDanCeNRkWvv3GPxqVLGvbs8U/aLlzQEBt7\n47u+06nw11/+f4Pmzd0sXGggKSktPo9H4f/+z5TtIpN5ldvtWwNu5Uod69frOHNGnkAT2ScL4og8\nR6PR0KFDB9q2bcuyZcuYNGkSx48fz/SYLVu2sGXLFlq2bMnIkSOpVq3afwvC680Xj62Eu1xYXnkF\n5ZohRf1PP5F68iSeG9zYs+PCBRg71swXX6QNd730koNXXknN9HHy2FhfPZ9rud0Ka9boGDrUv2el\nbFmVbdsSWb9eh92u0Laty28FeJsNevRw0qqVi+hoDRERKgaDlyJFfBOoW7d2Y7V6WLbsAgcOmIiO\n1tC0qYfSpf3nDoWFqbRp42LdurTfpWrVaybfhofjWLgQ5/HjKNHReKpXRy1VKlvtVKaMyk8/wYMP\nurFYVOrX99Cxo5W4ON+5c/KkhmrVPOzbl5b4VKrkoVgx9fJbe2nZ0sX69WlJTZ8+zqs/z4her2Iw\nqDid/jd7q/XGx1gsKo0aufnxR73ftivDlte6eFHJ6Xn2AffPPxpatLBdTfrKlvXy3XdJfueXEDei\nJCQkBPRMiY6OZsWKFWzcuJHDhw9z7tw5wsLCqFOnDi+99BI1a9YMZDj5ksPh4PTp05QtWxaTyZT1\nAfmcy+Xiyy+/ZPLkyURFRWXrmPbt2/P6669TuXJlYmIUtm3TsXGjlkcecVO/vueGyyYosbFod+5E\nv2IF7gYNcLdpg1ryxkMUweRwONBERVG8Vq10fRLJn36Ku0OHW37tf/5RqFkzhGt7O7RalT17Eilf\n/saXjHPnoH17GwcP+j/1NW1aCn36pF964r+68lkoV65cpsvQxMQorFun49tv9TRs6ObJJ105Ns/E\n4fDNeWrYMIQ337Szd6+WlSt9SVjx4h5WrUph9mwDO3fqqF7dw5Ahqdx7r/dqT1ZMjMLu3Vq2b9fx\n0EMuqlTxUqLEjWNLSYH33jPy9ttpn/2HH3YxefI5SpXS3fCacOKEQvfuVg4c0GIyqYwfb+eBBzy0\naGHj2r/zvHkpdO6c83+r3HblXChRoiKDBoWwdq3/HLZx4+y89FLBrotW2O4NuSXgidG4ceOYPn06\nlSpVokGDBpQoUYJ//vmHdevW4fV6mT9/Po899lggQ8p3CuvJn5qayqeffso777xDTExMlvsrikL7\n9p2Jjx/L1q33Xt3eoYOT6dPthIVdd0BiIqYxYzAuWHB1k7t6dVKWLr3hsEowORwOHGfPUnrYMPSb\nN1/drup0JO7ahVqx4i2/9h9/aGjUKH3X0K+/JnLXXZmXTPjuOx1du1q4crMND/fy449JlC2b85ea\nm/ksqKqvp8lkyrwz8Nw55erk7WLF1EyTlCtOn1Z48MEQ3G743/8cVKzoJSZG4d57vSxZoic5WeHe\nez0cOaLl6FGFRYtSKFYse7+jqvqeqktN9S1kGxGhXq7lpGXTJh01a3q4/34nycnHuO222zJth9hY\nhUuXFAwG32Kzbjfs2qXl//7PhMOhMGhQKo8+6sp2bHnJlXOhSJFKdOwYyp9/+ifnTz7pZM4c+w2H\nVguCwnpvyGkBT4zWrl1LsWLFqF+/vt/2nTt30r59e2w2G4cOHUIvs+RuqLCf/CkpKcyePZsZM2Zk\nc/FZLdAbGA2UB1R2707k9tv9T33l5ElCHngA5bpaSYnbt+O9776cCT4HORwO/v33X243GDC/8gr6\nDRtQy5Uj5b338NSuDWbzLb/2mTMKTZvaOHcuLYO4/XYP33yTnOUipZcuwbFjGj77zMBtt3np1MnF\nbbepuXJDyunPwpkzCt27W9i71zfL4IEH3HzxRUqWvUt2OyxZomfIEDOgYLV6mTs3hT/+0PL22/5/\nh3btnMyebSc71Q+cTl/i0q+fhX//1XD33R4WLEihcmWvX3v+13Y4f943ely8+I3nZOV1aW1Qnnnz\nbIwe7d/uq1cn0bhxBlVEC5DCfm/IKQGfRNG2bdt0SRFA3bp1adSoEQkJCfz111+BDkvkIxaLhYED\nB7Jq1Spee+01ilx+rPzGPMB84E5gEBCNx5PB1d/rhYwKSLpyf1hBVUlXeyc7vF4vjhIlsM+dS+Jf\nf5G0YQOeJk3+U1IEUKqUypo1ydSv78JiUWnRwsVXX2WdFIFvYnL16l7eecfB0KFOypbNnaQop3k8\n8NlnhqtJEcDvv+v48kt9hlW5r2U2Q8eOLvbsSWTlyiS2b0+iWjUPvXs7KVUq7WCrVeWNN1KzlRSB\nr/eqc2fr1cnrhw5p6drVmm5R2v8qPNy35lp++DtlzUuXLk7690/FaFQJDVWZONFOlSoFOykSOSdP\nzS69shJ7ViuyCwFgtVoZOnQo+/fv5+WXX8aa5d3GBXyAotzO7NkjiYuL8/upWrQo7uuqcXtvuy3X\nh9GioxU+/NDAM89Y+OQT/a3d9EJDUUuXRo2MzJGYFAXuvtt7eXHRRObPT6FoUZU9ezS8+66BH37Q\ncu5cgbiLXpWSAtu2pb/2/PSTLltJa0gIVKigUqaMyhtvmHjiCRvz5xvZsCGZzz5LYtasFHbsSOT2\n27NfvT02ViElxb+dT53S+D12L9KLiIA33nDwyy+JbN2aSN++znw5PCiCI88kRqdPn2bLli2ULFmS\n+/LgsIXIu4oWLcro0aPZt28fgwYNysZcEwcff/w+1apVY/z48SRcqUIYFob9/fdxDBuG5777cD71\nFEnr1qGWLp1rscfGKvTsaWbkSDPffqtnyBALw4aZMnxaKBjCwnx1gQwGX/HF5s1DGDfOTKdOvvpE\n69freOUVEytW6HK8FyMjSUm+ycrZWRPtZlks+C3dccXDD7uz3QF35ozCQw/ZWLvWwOHDWqZMMfHm\nmyaaNvXQvbuLcuXUjMoj3VDRoipcV4fcYvHVaUpOzv7rFDaxsQqzZhl49FEbnTtb2bxZR2JisKMS\n+UWeSIzcbjfPP/88TqeTcePGoRSM/lwRYMWLF2f8+PHs2bOHvn37ZjlPLTk5malTp1K1alWmTJlC\nYmIiaqlSpL7+OkmrVmGfOhW1fPlcjTk+XmHXLv84163zr9ycF5w/rzBlSlrCOWCAkzVrDHTpYuWj\nj4w884yV/v19lZRzy4kTCoMGmXnkERsjRpj499+cbaO4OKhf302jRleSI5XmzV107OjKduWGI0c0\nXLzoH9eKFfpMCzZmJixMZciQ1Gu2qIwZ42DCBBMvv2wmOjpvnSd5gapq+OILPW+9ZSY6WsOhQ1q6\ndLFw4kSeuN2JfCDoY1aqqjJgwAB27txJ79696dy5c7aOc9zKhIwCwul0+v1vblAUBUNcHJq4ONDp\n8ISH4yxaNNfe72Zl1gbFihVj/Pjx9OvXj3fffZelS5fiyWSSyKVLl5gwYQIffvghgwYNonfv3lhs\nNt8Pc/k883gy6opQ8HrVLM/xQJwHV6Smmvzq5jRt6ubJJy1++/zwg57z5xVstpxfU+L8eSNt2oRc\nnWuzcKGRP/7QsnChb1jqv7eBhi+/9C1KO3y44+pj3SVKeAgLc+BwZO8ZFZPJksE2gKz/ntfyfTlU\nMJtVBg3y0KWLk+PHtVgsKsuXG/j6a9+j6KmpMG1aIlpt4M6FYElK0pOYqEWrhbAwF3q9/2f6yu8e\nH6/h00+vX25GYckSPXffnVigF6IO5DUhL8qpCecBfyrtWqqqMnDgQBYvXkzXrl358MMPs33ssWPH\nMr3ZiVunKAoVNBrCHn8czcmTgO+x9QuffMLJAExEzmknT55k3rx5bNiwIVtrq4WHh9OnTx8ee+wx\nDIb063nlJJ2uDN26RfjV/WnY0MXMmWdxu7MuSRAoOl1JevcudbVQ4dKlyTz5ZPo5XT/+eAGr9R/A\ndx7l1Fp2SUm306zZ9fUV4JtvkihVKh63O/uVvvV6I15vSc6e1WO1qhQpYkdV3TzzTBm/QowAHTq4\nmDjxOMnJ2SsFrdOVpUuX4hw+nPY6r7+eQrduUaSmZv0aGo0GrfY2Nm82s22bkdatHdSqlQLEsGdP\nBZ57rojfgwNarcquXXF4PCez98vnQ4qioNOVZ9iwUDZv1mEywbBhdjp3Pp/hZ0Svj6Bfv9L8+qv/\n9/4JE5Jo3/5Eof5SXZBptVoqVaqUI68VtMToSk/RkiVL6Ny5M3PmzLmpIbTCfHI7nU5iYmKIjIzM\nlRu31u0mZPRov3o+ACmTJpHUu3eeWLj1ZttAURQOHjzIlClTWLt2bbbeo0yZMgwbNownn3wyV8tH\nxMWZmDPHyI4dOlq0cNGrl5NixbI+v3P7PLheTIyJUaPMbNmi5913U5g1y+i3NMVtt3lZv/4SxYs7\niYszEBOjRaNRiYz0EhaWmskrZ+3kSTN16vg/fajRqCxdmkyZMk7KlEnGZDKhqmqW5+eJE2YefdTG\nhQu+3qdGjVzMnp3C+PFmvvwyfVHAAQOSuHRJg1YLFosny9c/f97Ed9/p+f13LZ06Obn3XhdFimTv\nC0VCgoHevW3s3Jl2vnXt6mTChET+/NNAhw7+taVKlvSyefMlbLbEgJ4LgeRyaRkzJoQFC/wLeH77\nbSIPPJA22ezK56FkyZIcOBBK69a2q0lkZKSXjRsTKVGiYN83An1NyGvydY/RtUlRp06dmDt3rswr\nugm5Xqvi/HlsTzyBdt8+v82u9u1J+fjjDBfXzFVut2/tL1VFLVoUrNabagMlNhbt/v3oNm3C3bgx\nvxkMvDV7Nhs2bMjW21eqVIkRI0bQsWNHtFpt1gfcApfLN7E4JCT7zXujNoiL862d5fH4ChRGROTc\nRzwxERITfUM8KSkKU6YY+eEHPTVquBkzxkHFiiqnTyu0bWvj5Elf4nHPPR6WL08mPFwlk+LUmYqL\ng+ees/DDD2kJw1NPOQkLU+n0hJ3qpr8xrluHt3x53A0b3nDCfEIC9O5t8VsaA2DRomSqVnXTrFnI\n1eU8KlXysGpVMgcOaJg+3YTBAKNGObjvPk+2H7e/WYcOaahTxz/5URSV339PxGSCrl0t15QTUFm4\nMIW2bd04nTd/TfB6ffWLdDrSFzvNQ86eVWje3MaZM/5zhF58MZX/+7+0ROfaz4OqmoiK0rBunY7w\ncJXmzd2FYjkQqWOUMwI+x+jK8NmSJUt44oknbrqnSARAkSK4HnkkfWLUoUPgk6KEBPSrV2MaOxYl\nMRFXx444xo2D7M53SkjANG4chs8/B8A4axb1OnRg6ezZ/HLkCBMmTOCnn37K9CWOHTtGv379mDZt\nGq+//jrt2rVDk8PrqOn1//3m5HT6JnNPmmS8+u36vvs8l3tVcuamEBICISG+1woLU5k40cGlS6nY\nbCpWqy+G9983Xk2KAA4e1LJwoQGvF2rV8lCrlvumH50uXhzee8/Oxo0ufv9dR506blwumDDByMDu\niRSpX//qohaeihVJvsHThHa74rdg6xW7d2tp08bNli1JHDumwWCAcuW8HDqkoUsX29X9fvrJyk8/\nJXH//Tc/T+X8eV/CqtOpFC2qZngKp2bQsaaqCh6PQsmSXhYvTmH/fi3Hjmlo1sxN6dLeW1rSLy4O\nVq/WM2+ekaJFVd5808H993uwpJ8iFXRms8rdd3vSJUY1a954QTezGe6808uQIYVzro34bwI+Tf/t\nt99m8eLFhISEULFiRSZPnszbb7/t9+/PP/8MdFjiWno9zmefxdWiBSqgajQ4e/TA3ahRQN7e6fT9\nA9AcO4Zl8GA0Fy6guN0YlizBOHMmmmyucqlcuID+clJ0hX7VKjTx8dSpU4fVq1ezevVq6tSpk+Vr\nHTx4kF69etG0aVPWr1+fJ4YUwXfjPHZMw6uvmnj+eQuVK3uZOtWOoqj89ZeWDz4w4nDAxYs5X6vS\nbPatMH+lByU5GfbsSZ947N2rJSZGw5NPWlm2zHBLi5SWLavyyCMuSpXyMGOGkUWLDKxakUilcc/7\nrRWnPX4c7e7dGb5GaKjKQw+5r/73sGEOvvwymSefdKGqvkVhGzXyUKeOB7NZZdIk/2/dqqowe7Yh\nwzqgmTlzRqFHDwu1aoVQvXoIw4ebM6wDpdNBhQr+cyfr1XNhNPrOtZIlVR55xE3//k7uvtub6YK+\nN+L1wvLlBoYNs/D331p27NDx6KNWTp/Om09thYbC2287CA1N+7zVquWmbl2ZYypyR8B7jE6fPo2i\nKCQlJTFt2rQM9ylfvjxVqlQJcGTiWmrJkqTMm4dy8SJoNKhhYb6SxrkoOdm3KvZ77xlRFF9X+T2H\nT3P9tV+/ciW6F14AQGe3o8TE+IbZQkPTdbsoTme6BVYVSMu8gMaNG9OoUSM2btzIhAkT2Lt3b6Zx\n7t+/ny5duvDggw/yxhtv0Lhx46D2esbGGmnWzHb1MfEtW3QMG+agXTs3q1fr2b5dx/btvpo6tWu7\nef55Z64NKxQpAm3auNi1y//S0rixmyVLfHMepkwx0r79rS3iWqYMDBzopFcvFyaTSpgzBsPP29Lt\np5w9m+HxFotvOCwuDl54wcnMmQbefddImTIq776bQoMGab0mWi1kNE3jZouKO50wZ46BHTuuDN8p\nLFtmoFUrNx07+meqhw9rmDnTzqJFBv74Q0u9em7atnWh1ebc3ysuTmHuXP9fzOv1PbXVt6+TtWt1\nlCypUquWh9Kl80byf+edXrZvT+TkSQ02m0qpUjk7RCzEtQL+FWHWrFnEx8dn+q9bt26BDktkpFgx\n1IoVfbV8cjkpAl8NmKZNbXz1lYFlyww0bWrjYMVH8RYv7refp3x5VIOBMno9trFjCXngAWxNmuBc\nuo5zJx3X5jyoYWF47rnH73hv+fKo143lKIrCww8/zA8//MBnn31G5cqVs4x3165ddOjQgXbt2rFz\n585b/8X/A71ez86dunS1c+bPN9Kli68h6tZ1s3ChkZ07dbz3nokOHXJ+SYkrtFro1s1Fu3ZOQEWj\nUenc2UmJEurVJ77cbt+Q0bZtWn7/XXPTFbRDQ31FJ4sVA7fVSmrXrn4/V7VaPM2b3/D4MmVU3nvP\nzgcfGNi40YCqKkRFaejSxcrZs2mxhITAyJEOFCXtBmw0qjz/vPOmhq8SE2Hz5vST9/ft03Dhgm9u\n2RXVqnl46ikLWi08/riLuDiF5cv1hIZm//2yotWq2Gzpk4oiRVSee87Mq69a6NnTSps2Vs6cyRvT\nHDQauO02lQYNPFSr5r2aFNntcOqUwrZtWk6dMqPTlQhypKIgyJt9p6LQsdvh3XeNeL1pF2KvV2Ha\nh6Fc6jPg6jbVZMIxcSLukBCsu3djXLgQz913s/+zHfTb+gytOxbn//7PeLXwnRoRQfLSpTg7d8Zb\npgzOxx4jadUq1JIlM4xDURTatWvHtm3b+Oijj7j99tuzjH3btm20atWKTp068fvvv//Hlrh5GXVW\naTQqt9/upWFDF48+6uKbb9J6cI4e1RIXl3s3vMhIlZkz7ezdm8iuXUmULetl4MC0bpaBA1N5+20T\nbdvaaNYshM6dLbdcqFAfH4+7Y0dSn3sOb0QEnqpVSV6zBm8W1co9HoXvv/dPVrxehd9+8x8GrFLF\nw08/JdG9eyqvv25nx45EPB7fzTi71betVt/Qz7XGjHFQpYqXrl2t9OljZu9eDXa7b426pUt9i89+\n8IGB4sVVRo5MzdG5P+HhMHasg2srahct6qVuXQ87dqSdJ8ePa9m+Peil7m7I4/Et11KjRght29qo\nXTuEjz6KICkp78Ys8oeg1jESt6YgPnlgt0PfvmbWrfPv4m/b1sm8aTHY/v4d5cIFPDVqoJYqhdPj\nIbR/fwwrV3Loy59o+GKDq08TATzyiJO5c+1pE1yTk1ESE1FtNrDZyC63283ixYuZNGkSp0+fztYx\nbdq0YeTIkbm+tE1qaipxcXF4PGVo3DiEhIS033/4cN+cjMcec9K4sY34eP8b/s8/J3LvvTlT6C45\n2TdclNHkcY8Hjh3TMHWqkVOnNDz9tJOwMC/du1tR1bRkaM6cFLp0ubkJUEpMDNa2bdGcOIGrQwfc\nTZuC04mrXTvfiqiZOHtWoUUL29WCkVd8+20S9eqln7vi8fiGeTt3tnLypAa9XmXUKAe9ejmzNWn+\n9GmF9u2tHD+upVYtN127unjllbRkUatV+fnnJO6+2/c3iYsDl0uhaFE1W0N3V64J5cqV4+JF3zWh\neHH1hj1bSUlw/LiGzz83EBHh5YknXLz4oplt2/yTxdGj7bz8ct6cwHzmjELDhjbi46/9JVV+/TWR\nu+4qnLe1gnhvCAbpMRJ5gtnM5SdIrr2gqQwZ4sQcUQRPkya4H3sMtVw50OtRFQVn3bqoViunUkv6\nJUUAGzbo/YeXrFZfL9FNJEXgW9C4R48e7N69m3feeYdSpUplecy6deto2LAhzz77LEeOHLmp98sO\nVfXdaBcssDF/fgWMRli+PJmePZ089JCLd9+1U7y4yuef61EUaNzY/0Z///1uihf/7zcOpxMOHNDQ\nr5+ZLl2sLF2q5/x5/320Wt/8kOnT7SxenEzLli5GjLD4JUWQ8YTtrCgXLqA9cgTF5cLw1VdYBg3C\nMmyYr1p7FiIjVd59145Gk9YOjRq5brjAa0IC9O9vvvq0ncul8OabviUnsqNsWZVvv01m+/ZE5sxJ\n4ZNP/L8AeDwKn32Wtq14cV/v0c3MZ9LpIlixwsqjj1pp1crKokV6zp3z/Y3eesvIp5/qry6jYrPB\n/fd7mTTJwcsvOyldWqV6df/fXVFUWrdOP0v+3DmFo0c1nD6dO2vWZZfDwXVJEYAiy6SI/0z6HEWe\ncc89HtavT+add3w3iFdecXLPPRk/eaKqKvZWrTB+/TUmY/qbmcHALT3GfCMGg4Fnn32W7t278/HH\nH/Puu+8Sl8kNWFVVvv76a1asWEG7dt0YNWo4d91V4dbePD4eJSEBtFrUsDCiLobSrJntajK4erWb\nN990kJQEFSt6mTPHwMGDWoYNcxARAZMmOWjWzM3atXrq1/f1VuTExNV//1Vo1sxGaqrvRvTrrzom\nT7bz7LNOri/3ZDb7/jkc0KqVizlz/AsatW9/84/LqXo9KvhNrlcBNYMZ06rqW3zW5QKr1Tc/qUED\nN7/9lsiuXTrKlfNSqZL3hu1ityt+xSyv2LdPm+2et5IlVUqWVDl3DgyG9O+j06m4XL7SDTdLURSO\nHg2lf/+0Aksvvmjh00+TeestI4cO+WL3FeFMSjf53mj0PezgcsGXXxoID/cycaKDsmX9f7fjxxW6\ndbNy8KAWo9HXa+brBbz5mP8rqxVuv93DP/+knWxarUr58gV3yQ8RGNJjJPKMkBCoU8fDggV2Fiyw\nU7u2J9MOntMeD0kLF1LyLhvVqvl/sx04MJXw8JzvTjebzbzwwgvs3buXMWPGEJrFrFiv18uqVYuo\nX81cBEcAACAASURBVP9Bhg59mTNnzvjvcPEimr17MY0ahX7+fJR///X7sRIdjaVXL0Jq1CCkenUM\nc+eybo0Wq9U3/LRkSTKTJqUSEeG76S5YYODgQQ2tWjnp18+JXu/rHenVy8XChSm89JLzlp4Gy8ia\nNfqrSdEVH3xgzHT+kskEQ4ak8sgjvt5Bk0ll5Ej7DRPgzKjh4bg6dfLb5nr8cdQSJTh7VuHkSYVz\n5xTsdtiyRUuLFjbuvz+Erl2tnDihYLVCpUoqXbq4qFfPQ2SkitvtS/j++EPD8eMKFy/6XtdoVLn3\n3vQxVq7s2+bx+IbnoqOVLEsi2Gy+JOTa3lGLRaVtW/ctlTEAcDo1fPRR+olICxYYaNEiLe6oKA1b\nt2b8fTgyUmXcOAe//JLId98l06qV2+/zd+ECvPSS+eryNampCmPGmImKCs5tJDJS5fPPU6hUyff7\nhYV5WbAgkWLF8t+yRSJvkR4jkedkt6qw1+vFGRZGRCkTixensGmTjp07tTz+uJtq1dy5WqzOZrMx\nbNgw+vTpw6xZs5g1axZJ1z5edB2328WCBfP54ovPefbZZxk6dCglwsPRb9yI+dlnr/Z6eKdPJ2n9\nel9xQpcLw9y56Ldu9f1QVdHt2EHSg15mzkxh8GAzJ05oURSVp59OZcSIVPr3T8XrJcMCgjf7mHlW\nTKb0CZZer6abDK6qvuGXK5W4S5VSmTvXzqVLDjQaCA+/uSGjq4oWJXn8eOyPP45xyxY8zZrhqFmX\nPQfDeO45CydParj3XjcLF9rp1MmK253Ws9Wnj4Vly5IJD/d/yT//1PDYY1YSEjQoisrgwam89FIq\nJUrA7NkptG9vvTyXy/d0WtmyXi5c8CWJEyeaSE2F/v1T6dPHdcPhSovF17O3aFEKK1fqsdlUnnjC\n90j+rf6NfEuveAD/7qZixVQSE/3/IJk9BWgyccPE2W5X2LYt/S1j1y7tLRW8zMylS3DhgoKq+upN\n3ahHqnJlL99+m0xSkoLB4MHrPYPBEM717SDEzZDJ1/mQTLDLe21w/vx53nvvPebOnYvdnvXq8haL\nhf49evD6mjUUj472+1nywoW427eHuDhsjz2G9pqCp6rNxr4N//DyG2HpHgH/5psk6tcPXNG7U6cU\nGjUK8ZvL9fHHyTz+uPtqcpSY6KurNGqUmfh4haeecjJ0aCqRkTlz2XE4HJw9e5bSpUtjMBiIilKo\nUyeE5GRfACVKeBk3zsHAgemz5H37LlG+fFoc584ptG1r9VsAFmD79kTuu897tVfo/HkFm02lWDFf\n8rl1q5Z27fy7NmfMSKFnT1eGTwz+P3tnHmBT+cbxzzl3v3c2M5gxGEuUpbKEsmcJyZYoUqJUoki2\nkkqWokRkiWSXQpYkKcsPw9jJUmRtjBljzD5z567n/P44Zsade2djxlD38xfvnHuW955z3uc+y/cB\nJWfp4EEV+/ersVqheXMHjz7qvOWyfIvFQmSkjlat/LOu3WCQ2bgxnWeeMWV9RyqVzL59aVSvXnhD\n5vp16N7dh+PHXedny5Y0Hn206O67a9cExo/X8d13ipDmk0/amT7dQkhI3vfM3fZOKAm8c1A0eENp\nXrwUAUFBQXz88cccO3aM3r0HAnk3cDSbzUybP5/7YmIYD9zcd11ISFD+YTLheOQRl88JaWloZCuH\nDrn/co+IUBaszHyamBjBRdOpqKlQQWbnzlTefNPK00/b+OWXNFq3drgYA//8I/LCC4r3JjVV4Ouv\ndXw9V4s9NlFZaYsAu92OdEOKOj5eID1dQBAkPv88g3Xr0jyG6QIDJbdcHqsVN6MI4PRp5TWpUika\nSA8/LFG1qmIUOZ1KuConS5ZoSUxUcqrOnhV5/309Y8boOXtWxGJROto88YSTN96wMmyYlXbtbt0o\nAkhLU3PihIoffkhn5EgLo0ZZ2LEjDatV5sUXbVSoIFG3roONG9MpX97VKMo0+GJjFQ9NbpQuDbNm\nmfHzy96oZ09bViirqNi5U83y5ZnSHQKbN2tZsUKD0yt07eUO4TWMvJQMSUmIJ0+i+eYbVOHhSpPY\nfwHBwcFMnTqZKVNOoFYPIL9odQrwEVAF+AxI02hwtGyp/NFgwDp8OFKlSlnbOxo0wLeUyk0XB5T8\nrJQU2LRJTYcOJpo08eHTT3WFFlAsKKIIlSvLjB9vYf78DJo0cbqE7+x2RWV59GgrYWHZi/EPq7Qk\n/3ZYaVR8+DBFab0pfdwk1q83AzKvvWbi7FmRrl2zjyEISkVaTq+VXi/z4IPuq2/t2rl7V0QRqlZ1\n/3v58hI6nVIS36SJD7Nn65gzR0eTJj5cuKC8dtPSlDydzBDf7XDtmoZXXvGha1cTu3ap2b1bzbvv\n6gkPVxMZKbBmTTrffZdO48ZOl6KE69dh7lwtbdr40LGjiQ0b1CQl5X6cWrUk9u5NZceOVA4eTGXK\nlIz8lBEKhdUK69e7h8E2bdKQkuKtNvNyZ/AaRl7uPFYr2jVr8G3WDOPIkfh06oRhyJAi8yCUNCYT\n9OsXzLFjX/Djj4fo0qVXvi1DEoDRwH0+PszdtAmLRekaLoeFkfbrr6Tu3k1qRATmlSvxDzXx+ecW\nKlTIXJAVr0CNGk4uXVI8NBcvqkhMFJk+Xc/SpZpbTuotCKIIZjMuC+q1awKffaZj6FAju3Yp1Wpt\n2ihJscHBEvprUaiOH8f01FO5tu/IydWrAmfOiHmKKwYGykyblkFkpMjIkUb+/FPFW28ZadrUwZYt\naSxcmM7hw6m0betwq5wrUwYWLDBneVQ0Gpnx4zMICcndMBIE6NvXRunS2dsYjTLvv29FrVZES+32\n7O/ebheYM0fLlSsCw4YZqFfPl44dTezeraIAEdhcOXBABSjNZvfvVxMRoWbnTjXt2jl46y0bc+dq\nuXBBxfTpWvr1M7J8uYaEBNi8WcPYsQauXBE5f15Fv35GN69ZWpryaDqdSi+3ChVk6tWTqF5dKnQz\n4PzQauHRR91v1kcecWI0lmzWhyRR6B55Xu5NvDlG9yD3ehxZiIrC99FHEdLTXcZT9+xBKqAo4r02\nB6dPn2by5MmsX7++QNuXL1+ekSNH0qdPHzS51G9fuSITHy9hMqkIDFQEFj/8UM/Mma6l8JUrKzII\nRZXXczOpqUrJ+uTJepxOGD7cSv36DiZP1jN/fvZ5aLUyq1en07OnkU0LL9Hqo3aozp8HIH3VKhzt\n2uV5nLNnRXr0cBdXNBjc74PoaOjd2yerBUkmAwZYee89i1vC9c1khiGTkgSMRpnSpeV8k/hlWalk\n27NHTUYGtGrloHx5GZsNXn7ZwK+/uobapk8387//qdmwIXtcrZY5eDCVKlVu7Ts6cECkXTvXroI1\nazqZPDmDZ581MW1aBvPm6VzygxYsSGfWLB3Hjrl6NXv1sjJnjgVJgnPnRMaP1xEZqaJXLxvPPls0\nUg95ER0t0KOHiT//VM41LExi06Y0KlYsmRwjsxkuXRKZN0+LWg2vvWajUiWJu/G1c6+9F+9WvB4j\nL3ceh8PNKAJFsO9WsduVBe02dlGs1KhRg8WLF7Nr1y46dOiQ7/ZXrlzh7bffpmHDhqxcuRKnhwSL\noCArBsMFype3ZFXthIW5bxccLHvUzSkKzpxR0amTifBwxUvRo4eRa9dcxQoBbDaBs3+LHPnlPI+u\nHJllFAFuvfByEh8Pr79ecHFFozEzpOaKTiejzqcOVxAUvaEaNSTCwhSjSMnXggsXBBYs0LBihYao\nqOx8HEFQvCjPPWenXz87lSopxzEa4c033UVLmzZ1snGjq7HrcAgcOHDrRcKVKjno3t2a9X+TSWbm\nTDOjRimVcsHBslvS9LFjKsqWdXeBVKigKGZHRyvq4L/8ouXkSRVjxxqYMUPHDWdmsREaKrNuXTq7\ndqXyv/+lsmVL/kZRcXL2rEizZj4sWaLj2291NGvmw8WL3qXz34z32/Vyx5F9fd2SimWjEaly5Xw/\nm5CgvLBTUjSob6xy164JTJ2qo107H3r1MnHkiIjVms+OSoiHH36Y77//nq1bt9KqVat8t7906RJv\nvPEGjRs3Zu3atVlJxrnx5JMOl+RatVpm4kRLsQjwWa0we7YWV4lFgYsXVfj7uy9k5ctLVLq8B59N\na7PG7E2aIFeokOdxLBbBozL2sWMqjyHKgAD44AOLi6q1r69Mv362QiU4m82wY4eKXr2M/PWXiiZN\nfBkxwsjgwUZatvTh8uX8c14eesjJqlVmGjRw8MgjDn74wYzRKHv03uVMii4M/v42PvjgGvv2pfDb\nb2ns359KWJhEQoKIIHgOAa1erWHMGKuL0VyqlETfvko+1r596qwKt0wWLtSSkFD8uT7BwUqSe926\nUpHpbt0KZjN89plrD0e7XWDmTF2xFjZ4KVm8hpGXO09QEOZFi7C1a4es0eB88EHSfv4518auoIQr\n/v5b6YD+4IO+9OnjR0ZGJRwONV9+qWPKFD3//COyf7+a9u197pqu4LnRoEED1q1bx88//0zjxo3z\n3f7vv//m5Zdfpnnz5mzatAk5l/Kh8uVlfvstjW++MTN5spkDB9J46KHiKecRRTzmfRw8KDJ+vGuT\n0kqVJOrUlXC2bk3anj1kTJxI+o8/krFoEXLZsnkeR6uVPapL16rlzHUeHnrIyZ49aQwerDR//d//\n0goVpnI6FU+R2SwwaJCVRYu0WCzZ91R8vMjSpXlXHgL4+0O7dg5Wr05n9ep02rd3EBoq8/nnGQhC\n9vk88ohi0OZsqVIYHI5YKldWhFErVJApXRomT85AliEpSeC++1zvg4cflggLk9i3L40JEzKYNs3M\nrl3Z3hkfH/f5Mhjcdar+zUiSot+UE7OZPCv4vNzbeHOM7kH+NXHklBSEtDSlhUM+4ZSYGIHWrX1c\nwiehoRK//JJG8+a+biJ2X35ppl+/e0MBV5ZlduzYwcSJEzly5EiBPlOrVn1GjBhDs3r3UVYQEFNT\nkUuVUowMDy0xiovTp0WaN/fJSjAWRZnt29OoUkXi4kWRH3/UUq2akyeeUAyCW+X4cdFFXPG112yM\nHm3BZCr6ZyEpCX79VcMHH+hJThbo2VPJrXnmGZNLBVmPHkqj4qwqr5QUhPh4hIQE5LJlkcuUIbdE\nlLQ0iI4W2b5djZ+fjE4H77xj4Ikn7EyenJHf4+BGbu+ElBRF7XrfPhXNmjlZskTLwYMq2re306eP\nPU9toCtXBNq1c220O3VqBv362VxCksnJkJAgkJYmEBioKLDnTGwvCHY7xMUpIcpSpfLP7cpJcb0X\nd+1S0aWLiWzPqMzvv6fTsOHdpx/wr1kbShivYXQP8l+8+f/8U6RJE1+38UMHk+n2tK9bW4KlS9Pp\n0qUYS7GKAVmW2bx5M5MmTeLUqVMF+kyDh+oz9dQxWkoSso8P6d9/j7NxY2RRRWysgCSBn59c2N65\nBcZigchIkfnztdjt8PrrNipXljwualeuCGzZoubCBRU9eyrbFTS0lZu4YnE8C8eOiTz+uA83hwgH\nD7YSHS2wbl220ekiqJmcjHbBAvQTJyLIMrJWi3n5chytW5NbYtO1a4oxdPSo2sX42LYtlUceKVxY\nraDzYLNBejr4+VEg4yUqSmDNGg1//SXy4ot2atVyulSiJSQolXezZumQZYHAQIkNG9ILrYSdkAAr\nVmj5/HM9Fgu89JKNkSOthUr0Lq73YnIyHD6sYupUPSoVjB5toU4dJ77ur6MS57+4NhQHXsPoHuRf\nc/MnJSFGRqLevh2pZk2cdesiBwd73PTSJYF69XxdurILgsxf288ScTaY/q/5kbmQhYVJbN7s3ijT\nheRkJQFcrc43lFMs2GzKgumh063SX20Dn3zyCWfPni3Q7p4AJgANy5bl6o4DhJ8szYgRBmJiBLp0\nsTNpkqVYczUyc1hya9wbHS3QoYMPkZGZG8gsXGh2Ucm+FYrjWfjoIx0zZrjuq2JFiVmzzAwYYESv\nh/fft9C+vT0rd0u4cAHf+vVdsq2kUqVICw9HLl/e43GuXBGoXdsX1xwtmDPHzPPPF87bWdzvBFnG\n4/d04oRI8+auFkLt2k7Wr08rlL5RRITIggU64uJEwsNVSJLAhAkZDBrk3pA4N4p7DpKSlDm4HSHO\n4uZfszaUMN4cIy8lg92OZsMGfFq0wDBuHKbnnsPQt2+uQo+BgTKDB7tmVL/ZP5FSaxbR8dQ0dm5P\n4a23rEyfbs7XKBIiIzG+/DK+tWtj6twZ1cGDRSoymCfx8ai3bMH48svoJk1CuHzZbRNRFHn66aeJ\niIhg7ty5VMonORngd+AxoFtcHP87fIlevYxcvizicAisXavlww/1eCgELDJEMXejCODQIdVNRhGA\n0oA0NvbuS1i5/35PlVoSVctnsHPBMbZ/vpvnWkW7JLSLcXHkvBIxMREhN8EllPysJk1yejVl6te/\n+0I0NxtFqalKhV5EhOhW6QZw6pQKm63g32tCgpKzpVIpuVbr1qVTubKTVau0RVZlmqmB9c8/Aqmp\nt7aPgIC72yjyUnR4m8h6KRGEa9cwfPihy2Ki2b8fS2SkRw+Onx8MG2blmS5mju1Mp15NM/ed+50y\nH09BkGX0zz9FnQkP5H/g+HiML7+M+tAhAFRnzmDq3JnUvXuR1WoIDKTY4k52O9plyzCMGweA5uef\n0S5fTtr27R69Cmq1mt6dO/Pili3Mrf4QH+w9jsV6Jc9DbJRlNvZ9HOgJfAzUBGDDBg3j34rCV3UV\nKTSUYilTy4P4ePeFMjk57xYUJUWrVg7uu8/J+fPKoq/TyUwecYWaz7ZGdeECoCiQp//wA5miSFK5\ncsgqFcJNsgpShQrIecRbSpWC2bMzeOEFkVOnVJhMMpMmZVCuXLZhlp6uqIcnJwuUKiVTpoycp36O\nJIlERyvzGhAgF7ghc0HJyIC1azUMHWrAYIAlS9wNvzp1nOh0BftiHQ5Yu1bLiBHZ3XN/+EFi+vQM\nvvtOUySNj8+fF3nmGSOXLqlQqWSGDrXy5pvWIhen9PLvwesx8lIyOJ1K8D4HYh5lOUFBUC/kCm9u\n6kKLwY9SYdxbSj6H0UhBVwAhNTXLKMoas1hQHzyIb+vW6MePR4iLK9y1FBAhLg79tGkuY2JsLKrj\nx/M8X+P27bxzNII9K9YRGjod8BxudGU18CDQFzivtKg4tA/fpk3RLl5MrtLRxcTjjzvctJRefNFG\nYODdZxmFhsr8/LNSRTZvnpkDu67TYNZrWUYRgPrQIcSb7l+5dGnMixYh+/kBiqGUvny5W2hYiI1F\nvXEj+mHDUG/aRBXfONavV9S49+9PoWNHO5cvi/z+u5qLFwXCw1XUr+9Lixa+NGjgy44d6lydm2p1\nWZYvN9G4sS916vgycmTRe+SUFi8GQCAjQ+DAARVDh1pQq+Ubcycxf765wMnjcXECkya5CpJGR4vE\nxAiMG2e5bcMuKQmGDtVz6ZJi5DqdAtOm6bM0sbx48YT37vBSIsh+fjjatnUd0+tx1qyZ9weDgrC9\n8opS1gPIGg3mWbOUCqCCoNFkLV4uxw4IQEhJQTd/vhJaKy7sHnJHPI1lnpdWi7N6dcSkJOq+2YWI\noX5ELPmRdwa9T0BAfl4fCVgG1KBahRfhm4kA6D/5BMFD+xXh2jVUu3ejnTUL8ehRilItMzRU5tdf\n02jc2EGlShLvvmvh7bet6G6siVevCpw6JXL+vMCff4p8+aWWdevU+couZGSoUanKIghF+yorV07m\niSccPPecncrOcxh3/Obyd1mnQ75ZkdxoxNGhA6l795J68CBp27cj1anjGoNKSEA/fDimF19Et2gR\npj59MLz7LmXUCdx3n4TBAGPHGmjWzJeePU00aOBLUpJIgwaKF8pqFXj9dWOuve+uXPFj1CgjyclK\n/7XvvtNmJcUXFWaz4CJbMHWqjqQkkb17U9m3L5Vt29I8hiJzQ5Zx2V8mej2Ehd2+0Ww2Ky1ScuJp\nzIuXTLyGkZeSISCAjJkzsT33nGIk1a1L2i+/IJcrl/fnTCbs3buTcvgwib//TsqhQzg6dCBrhc0H\nuUwZMiZNctEitrdti/jPPwg3fopr1qwpFpESOTAQ66uvuo75++OsVy/3DwUFkTF9OrKPD+LVq4SN\nfoVHvh3DR0P7cvDgAV577TV88y2PcfD77pXU+OsvhgCxdjtCTvni+Hj0I0bg07kzhrFj8W3VCu3C\nhUXmWdLpoH59ie++S+e339IYPjy74ujyZYGOHU28+aaB/fvVNG3qw7hxBvr3N9Gli4mYGPeF025X\nKhXfeMOXfv3Ks2SJibi44slXksuWxd6mjcuYZfRo5JxuEa0WuUIFpOrVlfs4R7aykJiI5uefXcY0\na9Yg3Ggyd+WKyKpV2VVvTqfARx/pefnlbBdRSorgJroISl7a+vXuMba1azVFKsjo6ysTHHyz4aNU\nrRmNUKOGIsZYmGT6wEDZ5fpAUe1u0sS9l92toNfL1K3rnrN1N+Zxebl7KBGzedWqVURERHDs2DH+\n/PNPbDYbc+bMoXfv3iVxOl5KCDk0lIxp07B89BGyTkeeTaxuxmTColJx2W6nYnBw4aovtFrsXbrg\nbNQIVUQEcnCwku80enTWJo4mTTyX4BQGux1y9jjT67G99RZSlSpov/sOqWpVrKNG5Vq1lIlUqxap\n+/YhXriAHBCAXK4ccpky+FosvPrqqwwePJwZM75mxYp5WK25GzI24CtggSDw6rx5DB0zhqAbcy7G\nx6P96SfX050yBXvPnshhYbcyAx5RUpuyjU6LRSn3vnBBxbRpGXz+uc6l8vDcORVHj6ooV841STmz\nXUWm+N6oUUbi4y0MH24tehmn0qXJmDMH24kTiH/8gePxx5GqVKGwCTCCzeaWoC3cGJeBy5fdf6de\nvSq6qIgHBkpu7U4yMiAlRUvNmu6emipVJAyGojPyQ0Jk1qxJp29fpVFx+fISCxaYb7kPn14PQ4da\nKVdO4ocftISFOfngA+ttaV7dTGAgfPVVBl26mLh2TdHA6t/fRtWqXsPIS+6UiGE0ceJEoqKiCAoK\nIiQkhMseKnO8/AtITlYMhKCg3A0Nkwm5qDNE88PfH8nfH+mBByAyEt933snyoDgeegjHU08Vbn/J\nycobXqdDiItDFR6Odu1aHA0bYu/RAzk0NGtTuUwZ7P36Ye/WTflMQRZXtRq5QgWcHqrTtFodFy6E\nsnTpFzgco4HJiOJcJCn3nigZsszMb79l4Q8/8MYbbzB48GACPfWus9kQ7HaKMwsoLQ0OHlReQwEB\nMnFx7saBp3Dajh1qN0Xi+fO19OtnKxZZAjk4GEdwMOQI/xZqH6VK4bzvPpc+cc6aNZFvJMLXquVE\nFGWX9hP16zuy+nKVKiWxYoXZRdvn+nWYN0/HV19pWb7cTLVqTs6dU1wtJpPMhAkWPESObxlRhIce\nkvj113TMZuUWDgm5PTXssmVl3njDRq9eNvT6oq99eOABif/9L43ERAGDIVsDy4uX3CiRUNpXX33F\n8ePHOXv2LP379y+JU/BSnJjNqA4dwvTCC/g88wyaZcvgSt7VVCVGWBhp27aRtnUrqbt2YV6zJv9w\n3g2EuDg0y5Zh6t0b/YgRCJGR6KZMwdS/P5qNGzF8+CGmp59GiI3N8UFBcZ0UQcmN01maoUONNxSZ\ng4HpSNI5OnV6JauXXG6kpaXx+eefU7duXaZu2EBqjhXU+dBDHvOxCsz16wgXLyJcuZJrHpWfH7Rq\npfxtxw413bq5bqdSybRq5f7r3pMtbTDcsL+TkhBPn0b900+Ip08rGbh3AXJICOnr1mHr1Qtn5crY\n+vQhfdWqrCrMsmVlli0zExSkeH7q1HGyYIGZrl1t7NuXSnh4Gg0bOl30Io8eVd8QRRQZPNjIhx9a\n+O67dJYtS2ffvlQeeCDbi2S1KoaULcOh1MjfhkRFcLBMlSpyoUNnuaFSKeL3xVEQKghKjlvt2hJV\nq3qNIi/5UyIeo5YtW5bEYb3cIcTLlzG1b59VumwcMoSM8eNx1K+P9OCD3G1vJjk0FOdNXp0CYbGg\nnTUL/YwZAKj37sXetSvaRYtcNlOdOYMQHZ2rcOXtIstqIiNzrkwV6Np1BhMnvsVnn33G999/n2fz\n2aSkJMZ/+SVzAgIYXaECg65fR92yJZbPPit4UnsOhH/+wdivH+qjR5H9/cn49FPsnTuTUy5Yq4WB\nA20cOKDihx80LF1qRquVWb9eQ0iIzGefuZavZ9KkiYOyZaUb4RGFsWMtlPU1o523EMP48VnjGWPH\nYhs4sPhkGAqBHBZGxrRpCKmpSin/TRLhJhN06OBg9+40bDYlnyczuhwa6j4Hsqw0gs0kNlakb18T\nzZrZWbrU7FKOfvmywIwZOvbvV9OqqZlBXVMJ+9887C+95OLR9OLFizf52ksxoFm1ykXPBUC7bBnq\n48dRFbDVxd2OkJCAbv581zFJ8tjGXHAUX2sSUTTTvLnr/gVBpn59icqVKzNnzhz279/PM888k+++\nriclMTIqimp+fsxq0QJrAT1nbiQlYRg8GPXRo8r5JCdjGDQIMSbG4+ahoTLLl5uJiEilRg0HY8da\n2LMnjQ0b0mjWzOmxvUj58jLbtqXx3nsZPPeclV9+SeXJJ+2orl9DP2mSy7b6Tz9FuJ3urEWN0agY\nyh4uTKVS5qNyZTnflDtBgHr13L1pwcGuWkdXrwp0725iwQIdJ06omPm1L30/rMn1UtUx9uoFCQlF\nWrnmxcu9jtcw8lLkeBS1MxoRLBa08+bdlgtfiI1FPHIE46FDVFKp3JJZ7yg5ymZU+/dj79TJZUwK\nDUXKJ7n6dpDlBGbNMtO4sR2QKV1aYvFis0vlUPXq1fn2228JDw/nqQLkT8Vcu8bw99+nQYMGLF++\nHEchDTshPR11eLjrGKA6fBhZVlphHDqk4tQpMav0vHRpuO8+mcqVlYTZcuXkfFtKVKwoM2xYGh99\ndImHH7ZgsQjE2kopXsmbj+10ItyQd8gLm01pupob8fFw5ozIgQMqoqIEitHeLTBdu9qpVi3buPam\nQAAAIABJREFUOCpVSmLMGKuLzZWYKHD2rOu9euCghuvVGvHPyC9YuqE0/fsbWbhQw9Wrd58S+Z3g\n+nWlyjHzu3V6c7P/03gNIy9Fjr17d+SbtPNlwDpoEJo1a5BDQvLuHZEHQkwMxh498G3dGt9OnQhq\n2xZ9CSXuy6VLYxkyxGVMs3o1lk8+IeODD3DUrYu1Xz/SNm8u1lCF0+kkONjCihVmjh9PZdeuNDp1\ncniMGj344IOsWLGCHTt20LYAScSRkZG8+eabPProo6xZsybPcJwLajVS1aru51qjBhcuiLRs6UPb\ntj40bepL376G21qMJUnCbg/giy98adbMh059QtkweCMJL7yevU1QUFaCc27884/AmDF6nn/exNKl\nGrfS/7g4gZEjDTz6qC/t2vnw2GO+nDhR8q/PTDHKX39N5ccfk9m1K5WqVV2/J8+Pm4wcFMigRU0Z\nMsyXn3/W8M47RgYONHA3OdfuBLGxij5UkybKd9u0qS+nT5f8d+ul5CjxJrJffvkl48ePZ/bs2YUq\n17fk1GH5D2Gz2YiNjSU4OBhtkdcm3z6CLKOLjUW3fDlCbCyOp55C/euvaFevJnXXLjJyeFAEQUDO\nRzdIEAR8vv8e49ChLuO2Ll1InTkTRwk0TNQmJ6Pdvx/tihU4a9TA+vLLWENCECQJVWoqktGIM58E\n6Nvhdu+D/fv3M3nyZCIiIgq0fY0aNRg1ahRPPvkkQh4Zt4IgYDh8GJ+nn0awKtVxts6dif1sLgOH\nl+GXX1zPdcGCdDp3Ts/3HvCE1SoxY4Y/06Zlu0gEQWbf+gs0fL4OUmgo5gULyLj/fuRczjkuTk/r\n1r4uFXFDh2YwdGgGVquAr6+DU6f0tG/v3ix17doU/P3vUJ+9PLDZbMTFxVGmTBm3eyE5Wcsrr/gQ\nHp6dj/R0p3TGjUqkTovy5Gxie+BACmFhGXfitIuUW30ewsON9Ojh+t0+9pidZctS8fW9t2KMhZ0D\nQRCw20U0GumWnr+7jaJqnHvPyn9GR0fj/I/7O2NzVjvdZQQOGECA04l2zRocYWGk79xJtEqF5YaX\np6xKhV9cHJrdu3HUqYO5WjWinU6PD6iPjw/+OVp5AKj+/JOM69e5WkIPtbZOHYx162IXBNItFoiM\nzP6jh5YnxcGt3gehoaHMmDGDgwcPMnfuXE6ePJnn9qdPn+bll1+mZs2aDBw4kMaNG+dqIJlCQym7\ndy/qS5eQSpcm3c+POLPAyZOeVIhVNG+eRHq6L1arFq3WiVp9HZvNXZNJo9EgSUHIsg5BsKFWq1i5\n0vVlKMsC63eXoebGjThKlSJSELBHRblso9PpkCQfNBoHf/+tdzGKqld30ratk5EjfThxQsULL1gw\nmVyvs1QpicaNHWRkyNhscXfNDzVP94IoisyaVYHt243s2qXlqTaptKp4lkRHGDmNIgCbzXFPS6gU\n5nnQ6/UcPVrFbfzPP9WkpNhJSro35yG/ORBFEZWqIr/8YmD3bh1t21po29aM3R51zxpIKpWKqh48\n1bfCPWsYhf6HKynudo/RzdgB5xtvIMsysiyTmTaistkwzZuH4aZEWWP79ui/+gqbhxJxQRCwdeuG\nbtkyl3Fbx47oypWjYlHI5N4md7onZVHdB2FhYXTv3p2tW7cyZcqUfA2kv/76i6FDh9KoUSNGjx5N\n06ZNPW7nABw3vIMaINih4ok2Nr5d5GrI9OhhIyEhhOefN3HxooqAAIlZs4w0b25Bp7v5x4/AhQt6\nXn/dxIkTKmrWdDJ3rplmzRysXu16/RV8kzG+/TbmyZMJqV/f5W/JyVq2b9fy3Xc67rvPycCBVmrX\ndnLqlHIPffihhVdfNXL1qmIsTZ5s5Pvv01GCwgKPPebg3XctLFig5eWX/XjxRT0dO1pL1HOU/70g\n06ePmT59MhAEEOxhONMM1K/v4MiR7GXgoYeclC4tEBBQ8c6dfBFxq89D69YSOfL1ad3aTmCgGoPh\n3pqHgs5BcrKWl17yYd8+xYu4ebOGDh10fPWV5p7zkhUH96xhVFQus3sZrVZ7z86DEBeH/vPPXcY0\nW7agTkhAvKHrkhOpTh0sw4ejmzULbDbsHTpgGzgQ7Z0WiLzLyPU+cDoVaWmjsUBK3p07d+app55i\n48aNfPLJJ5w5cybP7Q8cOMAzzzxDy5Ytef/992nUqFHeB0hIYMTzcZw5ex/h4Ro0Ghg8IIXQEJlu\n3X25eFExTJKSRF56ycSRIxKVKmX/eo2JEXjmGR9iYhSD5a+/VHTvbmLz5jTWrtXgdCrXWLGiRJsH\nYxA/Oo5gt7vMjdUK33yjY+pUZWz3bjUbNmhYvNhM164mtFolJyfTKAKl39bvv6uZPNnChAk63nvP\nwvPPm7Jacxw5oubqVZHhw8WiV90uJAV+J2i1BJtg+XIz8+Zp2bVLTbNmDt54w0ZIiAjcm+8VKPx7\nsWpViY8+ymDKFD0Wi0CDBg4mTLBQqpSae3WJzG8OIiPFLKMok19/1ZCSoqZMmZL/kVnS3Jvfupd7\nHsFuz+pN5jLuQYE5i9KlsQ4fjq1fP2SnkyRAHxh4D7/Ciw8hOhrNypWo9+zB3qEDjq5dC6SlJIoi\nXbt2pVOnTvz44498+umnXLx4Mc/P7Ny5k507d9K+fXvGjBlDnTp1PG6XmKLGmWxm7rjLqGU7UnwS\n5X5dxqWM8Zw/7/oydjoF/vlHpFKlbI9RUpKQZRRlkpAgYrFI7Nubwq/rHYT4ptG8egzVhz2HXLo0\nUrVqLtvHxwvMmePaVy8xUSQpSenmHhGhclGWzmTOHB3Hj6fQrZuN337TuPUr+/prXbGpbhcnoaEy\nY8daiYuzodHI3FQz8Z+hVClFS6tHDzsOB/j55S+VcK/juY5CKI4WkfckJZJ6v3TpUgYNGsSgQYNY\nv349siy7jC1durQkTsvLHUS60Tj2ZmR/f6SQkLw/aDQiV6yIJSSEuH+h+Ipw5Qra2bPRDxqEatcu\nRaG4sPu4dg1j794YJkxAs307xlGj0L/9NiQmFngfKpWKZ599lgMHDjBz5kwqeGhHkpMtW7bQsmVL\n+vbty19//eXyt8uXBfoMCuHBHvVp9kxVdhz0o8z2Hym1eA56jSNL7Tkb2c3IMBplVCrXMUGQMRic\nVKxk4a3XUunt9zNVPxmEo3lz0n7/3WMfOk9VWhoNvPKKjS++sBASItG9u6vRPmCADX9/CAlRWm3k\nRKcrGgXoO43VCidOiPTrZ6RVK18++URHbOw9eCG3icGgyD9UqfLvN4pAad5bq5Zrjm7jxnYCAryW\nEYDq3XffHXenDzpv3jy+//57Tp48SWxsLIIgcOXKFU6ePMnJkycpVapUgfRW/qs4HA5SUlLw9/fP\nt+3DXYvRiKNNG4SoKMSrV3HUq4d52TLkKlUKFPb5V8xBDoSYGHyefBLt2rWoT5xAu3IlcnAwzrp1\n3TSTIPc5EC9fdsndAhDPncP20kuZXVwLjEqlok6dOrzyyiuULVuW48ePk56XVw84c+YMCxcu5MKF\nC9SqVQu1OpAhQwzs2KEFBKxWgU3bfOjxXkWCz+5F0+dpatewsW6T6UafMJkhA5Jo08KMwS/b3a9W\ng8Egs2tX9tjbb1upXz8eX18Nal9fpNq1cXTtiv2pp/AkhKTTgSzL7N6dvY8yZSSGD7cyZoyBUaMM\nbN2q4d13rXTtqnhRxo618swz9qyp8/eXWb1ag9mcfZ9OmGChSRPnrSpR3Da3+jxERQm0bOlLZKSK\nlBSBffvUmM3QvLnDrQfy3c6/8Z1QWAo6B4rKuh21WsbphD59bIwbZ6WYBPrvOUq8XN9L4bFYLFy+\nfJmKFSveszlGWaSmKu0RDIZCLdp3cg5iYxUxP5OpePssqbdtw5RDoVr29yc1IsKjFlJucyD+9Re+\njRu77gdIPXIE+TarNsxmM99++y3Tp08noQDeLJVKRbduz7Nu3XgkqbLL36ZPiKP/s4kIVivymAlE\nDRjD5XgTQVV9EWSJE2eNPPyIiuBgKauTSGKikv9z6pRIzZoSZcvaSUq6UKj74Pp12LNHzbJlWu6/\nX+K116zMmKFl0U1J4VqtzMGDqS45TjcTGSmwbJmWc+dE+vWz8fDDznxvX0lSVKiTkwWMRpkyZWSP\nqt63wq0+DytWaBg82PUk9HqZI0dSi6zD/Z3iX/VevEUKOwd2u9LI2ceHe84QLk68KlZeigenU3ni\n8hMF9PVVFv1CejLuBDYb7NunomNHE7Vr+/LSS0b++Sd3b9b163DhgkhkpEA+ThXPmN3L08nIoLCB\nfzkoCGetWi5jjjZtkIvAqjMajbz11lv88ccfvPfeGPzyaTLrdDr58cdlSNL9wJtAdNbfqtfVk/kT\n1e/XddTqVo+HYn7ny68MNGwdysuvB9CggQ+bNmmyxNJLlYKaNSV69HBQu7Z0SxU0pUtD164Oli41\nM368BbVaZvFi17wjm03g+PHck1DDwmTGjLHyzTcZtGyZv1EEcPKkIm7ZuLEvDRr4smSJ9k4pOuRK\nYKD7vRUQIJeY58vLnUWjUZ4pr1Hkivf2vxtITESIjs67H8E9hBATg3bmTEzPP4921iyEq1dL+pQK\nhs2GeOEC2unT0cyaRWyUk65dTTcSgwV27tTQv7/RozJwVJRA794m6tf35ZFHfJk6Vcf160BCAuLF\niwiRkeRnLTnr1nVrp2J74YV8VZtzIpctS/qaNVhGjMDRtCkZ48eTMXs2Ll1FbwOHA+Li/ElI+JBm\nzf7muedGYjTmVxloB2YD9wEjaNYsmvsfUBZlOSgIW79+oFIRXbMV3629OQNYYNQoQ1brkKLEaFTC\nc6IoeDQQPCVh34wgQEEjNrGxAv37G7P0kux2gffe07s0wS1O4uMVde+YGNcE27p1nVStenOuiczE\niZZ8r92Ll38z/81A7N2CJCGePo1h2DBUp05hb94cy5QpyGFhJX1mt05cHIb+/dHs2weAetcu1L/9\nhnnxYuWn+l2MeO4cPq1aIVitSAEBXCzbC6vV1Zg4ckRNaqpAUFD2wmE2w6RJeg4eVB4nu11g+nQ9\nbVvZqPjtMDQbNoBaje3VV7EMH57rPMihoaRt3Ypu/HhUFy9i69kTe58+HpuN5occGor13Xexms1K\nQkER6jxFRQm0aOFDWpoABAOfMWvWIE6f/oIFCxbkI3ZoAb7gyOG5zP96IG8OGUJAQADW0aNxNG9O\nnNm9l0lKinA77fVcSE+Hy5dFNmzQEBIi0batg5AQmcmTLbz6qoFMwcNGjexUrlzAFigFwGLBrfIO\nBM6dE6leveiOczM2myJxsHOncl+WLSszbZqODz6w0KiRE51O6Un388/p7Nih5tw5kW7dlOv2eoy8\n/JfxGkYliBATg6ljR8SkJAC0mzejiooife1a5Pw6aN6liImJWUZRJprwcISkJOSSMoycTiUkZTLl\nnthtNqObPDmrhYVgNlPa3z1M4+cnu7mdk5MFtm93f5S2boEnUlKUpdbhQDd3Lo42bXDk1qdMpUJ6\n4AEy5s1Tzjcw8PYMGrUaPIW6rl9HvH4dMjKQg4ORy5UrUMJ7Jj/9pLlhFGUze3ZFfvppIoMHD2ba\ntGksXrwYex5Vg+YMM1OnTWP+ggW89dZbDBw4EN+uXakYqVR4Wa3Z+69d25mVY0RSEmi1LsaiIAgk\nJmo5f17FxYsijz3mpFw5CR8fJafHYgG9HkJCZA4dUtGtmwlZVvZfrpzE1q1ptG9vJyLCyc6daqpX\nl3jwQSfBwUXnNdHroXJlJ5cu3fx9ym59zYqSM2dEnnjCB4tFudYKFSRmzTLTs6eRgwfTqFhRub7Q\nUJk+ff59FZ5evNwq3t8FJYhw7VqWUZSJ6sQJhJJOPCgGhBISyBCio9FNnYqpTx+0X3+NkJtUvt2u\nGAuZn7PZqHBxD53a35z3I/PppxluYQaTLZGHaru7NBo+kIT4zz8uY+qNG/M/aR8fpaKqiNW8k5Lg\n0jmJi2ccJP92GJ9u3fBp0QLx3LlC7eeG7eiC3a604ihXrhyff/45hw8f5sUXX0SVzzWkpKQwadIk\n6tSpw1dffYWvr5m1a9OpVEkCZBo2dLBsmZky6gQ0a9Zg6t0bw8CBiH/9RaYbSaUK5eWXfejUyYe3\n3jLSqJEPR4+qOHxYpH17H+rW9aNDBxN//y2wZInWpSN9TIzItm1q/PyU3KWBA220aeMoUqMIIDhY\n5ttvzfj7K/sVRZmxYy0EB2cbRgkJcOmSwOXLAhm32aosORk++kifZRQBREWJRESoqVdPctOD8uLF\nSzbep6Mk8aDYLGu1yCUtn1tQZBnh8mU0q1ahWbkSITISKSAAew4FZHvjxkglkFwtxMZi6tkT/aef\not65E8N776EfNsyzno+/P9bXX3cZqvDRYKaPu8pvv6Uxe7aZ/fvT6NzZ7uoxSkmh7MSRTB5yidKl\nsxe5xx+30TAsBtWFCy77dOaoFrtTxMYq3eHrNQyg3lP303vLAM4u/g2Sk9G/+26h+rp17+5Aq3U1\nHIYOtVKmTPZYWFgYX331FQcOHODZnj09dORyJSEhgQ8++IDHHqvHqVNz2bgxnhMnUvn++3SqVrKj\nXbMG44ABqCMi0P70kxLyvHIFgOhoAxER2V+KLAuYzQLPPGPin3+UV1x8vIjdLtCggRO9Ht5808qs\nWWbUatlF5bo4qVNHYu/eVMLDUzlyJJXXX7dlVTlGRwv07Wukbl1fGjb0ZdYs7a1IWGVhtSqGUE6i\no0WCgiSPOVVevHhR8IbSShApKAhb585ob/IiWEaMKLmQUyERIiPxadUK8cYbXPb1JXXnTjKWLMHx\n3Xdodu7E3qoV9t69SyS/SIiPR3XqlMuY5pdfsCQleUxodjRvjnnmTHRffglqNZb33qNMORVlajpp\n1Mhzw2IhPR3Nzp3U+bMH4TNmc1Uoh1EnUVqXQlB5HVJwMOINL5W9USMcjz9epNeo1WoRPSWEpKcj\nxMcr+VJBQezaFezSTyx8j5Y5jzzE61tPU3rtIrRmMwWVPa5QQWLHjjQmT9YRHy/y+utWmjd3IAgQ\nFwcnT6rZtEnNY485adasGvO/+YYRjz/OlMGD+TGffcfGxjJq1ChmzpzJqFGj6N27N0JcPLqZM122\nEywWNJs2YXn1Va5ccb9+WVZai2Ty0UcWRozQZxlQ33+vpVcvG4MGWena9c6EkdRqKF9epnx5V6Mk\nM0ctPFw5N4sFJk0y0KKFk0cfvbVG2UFB0KuXjfHjDS7jTzxh58gRlQdBTc+kpio5XqKo5CjdBS0J\nvXgpdryGUUkSFITliy+wDRiA6uhRHC1aIFWufEvJtnccpxPd/PlZRhGAkJqK/rPPyPjyS2xvv43t\ntdeUa7lX3qaBgdhfeAFH+/bIouhRIDAnsp8fjqZN0a5bxwN9Hud+jQYcDtJ/+glnleak7dihVOXp\ndMhlyxZp7pguIYEqe/eiDQ/H8eSTOBs1Qi5bFhIS0M2fj27qVASHA/PQd/j50mS3z+/4nxaTTzkC\nSg2jj85BTv+l06l4mpKTBUwmmdKlFd0dnQ5q15aYNy8Duz3bnkpLg+nT9VktNxYsgCZN7CxZYuaB\nFi1Y9PvvDD1xgvHfLmLrqRN5XltUVBRDhgxh+vTpvDtwIP1UKnL6UWWdcpx69RyoVHJWrzSAgADp\npjGZKlUkF68SwKpVGvbsSaNCheLL8ykIyclKL7acbNmivmXDSKWCF16wEx8vsnixFh8fmTFjLNSq\n5aRZM0eB5AViYgTef1/Phg0aypSRmD8/gwYNnPfE68mLl9vBG0orYeSyZXG2bInt7beR6tcvspLq\n28ZuV8IruekQ2e2IHnpoiZGRih9fpQJf36I3ipKTES5cwHDmDJVUKoQ8dJI86fnYO3TIW89HFJWe\nYgU1YEwmLB9/nHUcWRCwTJqE86GHlP+HhiLVr49Uu3bRJtRfv45p8GB833gD3YoVmF54Af24cZCk\n5DXpJ09GcDgA0EaE06Kee/jw4YednD+vYszEUlxPdRWDk2U4elRF06bZujs//KAhNTV7G6PR1cmU\nkCAwb56r+bJ3r4brMU5MHTrg06ULIWGPUqlBBH5+4fj4tM73Mi9evMjro0dTx2plDZD5bct+fooB\nK8uo1XH8+GM6lSs7EUWZDh3sGAwyw4ZZAaVVx42pcEGSBLRa2VNE+45iMMjUrOluANWvf2tGUSZl\ny8p88IGFgwdT2bkzjRdesFOtmlygV0xGBkyZomPtWi0PPeRkwYIMNm9W8+67eo4dE13uAy9e/m14\nDSMvbgiXL6MfNw5T795o583znLCs1yvaMzmwvfRSgUMyhSYhAd3kyfjWr49fy5aUbtUKw+nTuW4u\nBwcrej6jRuFo0YKMSZOwfPmlu5hkairCpUuIR44gXL5MYTNf5bAw0tevJ+XYMdL37gWHA8333yva\nRc7bW9zcMJsRrl5FvH4dzY4dLn/SrFyJkJiIev9+l3HVgQN0rnmGhg2zQ0ZVqjh59lkbGzZocDgE\nUlNds4CuXhV46SUjiYnKK8JmE3jnHQPXr+eeLeRwCB4v13o1CTE2loS+bzB2RR2+XWIiJaUpaWnb\nUKu3UafOo/le9umYGHoC9f38WNexIyk7d2b1QXM4Umnc2Mzq1eksX27m/vslOnf2ISND4Kef0vnu\nOzPVqzupWNHViM7sDRUXJ3D4sMi6dWouXLhFcc7bICAApkyxZCVmg+Jpa9Dg9u8dvV6pOgsJKVwY\nLClJ4KefNKhUMhMmWOjTx8TcuXqWLtXx+OM+HDhwj3iBvXi5BbyhNC8uCDEx+HTqlFVNpd67F9Wh\nQ2RMn+5W+u145BEyJk9GN3UqOBxY33wTR5s2xXZu4pUr6OfOzT7X5GSMr79O+qZNSgjJA3JoKNbR\no7P1fHLm46SkoF2yBP2HHyLIMrJWi3nZMhytWxdKDlYuWxbx9Gl8Hn8c4YaOjzxpEmm7diFVqVL4\ni7VaEeLiwOFQRB+DghCuXEH36adotm3DPG+e20cEWUZwOHDm6G4vAJXnfMB3i9dwLUnH1asC8fEi\ngwYZsVgE/PxkSpVyz3vJmbsjywJRUSJVqnhesP39JZo0cbB3b/a8BQdLlLNFIjgcXG/ZlbV9XeMw\nDkdrBg/eSmDgFiZOnMixY8fynJY/UlLo/ssvNLh2jbFjx/Loo4/eODdl4X/+eSOZWkTbt6tp08bG\nrFk6YmNF5s83s2iRlqNHVbRp42DIECuyDEOG6Nm8WfF0CYLM8uVmOnRw3NEI8P33S+zZk8rlyyIm\nk9JA9+Zk9juNVisTFqZIHuzYoSY5+WaDWGD8eAN166b9JxquFjeyrCTfX7okYjAoOWhFXRXppXB4\nPUZeXBBiYtxKzDVr1yJ4quQKCsI2YABp4eGkRURgGzq0WBPHxb//dhtTnT3ruX7cZaMbYT0PScpC\nfHyWUQRKmb7xtdcQrl0r3MmZzeimTMkyikDJudJ+/XXhvUZJSWiXLMH30Ufxq1sX03PPIVy6hP7t\nt9EtX44YE4MYG4tUqZLLxxz16iEFBOCsVg1b795kvlqlwEAsn35KmfIaHnhAws9P5uOP9URFiZQr\nJ7FqVbrbi9hoVDR+XJEpXz730GVQECxYkMGAAVYqVZLo3NnG5p9TqLJoIgCq1BQ3AwyUriBt27Zl\nx44dLF++nFo5wp+eOHToEN26daN79+5ZxlRQkMzrr2fLJgwfbmXwYBN79mg4d07Fs8+aCAqSWb06\nnXHjLISGykRFiVlGESjG39tvF4/Sdl6IIlSoINO4sZOHH5ZK1ChKTYXERIGPP7ZgMslkZLjPhdlc\ntM7QxES4cEHg7FmRuLg7O/clzfnzIs2b+/DUUz60bu1Ljx5GYmL+W3Nwt+E1jLy44knoTxByFwBU\nq5FDQhSRwGJuuCPVru025qxTR2lAe4uI16+7aSwJyckInvqW5YXdjujBmBKioz0nuOR1TlFRGEaN\nQrgR01EfOoT+vfeQbjIY9BMmYJ41C1uvXjirVcM6YADm5cuV3KgyZciYNInUw4dJOXyY1N27kWrU\nUPalhgYNJLZuTeXEiRR27kylUSOnW2uL4GCZRYvM+Pkpc6NSyXz6qYXSpfNYsM1mylvO82mXnfy2\n/AJzP4ujanUBy/vvI/v7U3HFF3w6Nh7I3ketWk4eeEAxtgRBoFOnToSHh7NgwQKqVauW71xFRETw\n6quv0rt3b86fP8KoURbCw1NYsiSd++93uOj1pKYKzJ2rIzVVILO/pqeKtrg4gTy0KV1JTlaa5OXX\nE7AocTgQoqJQ7duH+OefyvGLCFmG3bvVNGjgy8yZOt5808Kzz9pQqVy/90GDrAVOw8uPa9cERoww\nUL++IlXQo4eJK1f+G4ZBWhqMG6cjISH7PjxxQs22bd5gTkniNYy8uCCXK4fzvvtcxuy9exe6X1dx\nIIWEkDFxYpbOkxQWRvq8ebclBSAFByPnsAqkcuWQfdxbU+SJvz/WAQPchm2vvqqUcRUCdXi425hm\n2zacjzyS9X8xMhJT796YR4wgZeNGLBMnZuXcCJcvo//4Y0y9eqGbNUsx/G7yllmtYLEorSKOHVMT\nG+u+CIkiNGjgdNHdefFFm0ch7UxUBw7g26ABpbu05/7m1Qnu9RTC1atIDz9M6t69WKdNpUPZg+xd\n/Tdj37rG0umX+fG7BEJCXBddURTp0aMH+/btY/bs2YQVoEXOjh07aN26Na+91ofjx/8ClMjpk0+6\nCm8KgoyPT/bxatd2ui36jRs7XLbxiMWCePQoxhdewKdLFzQLFyIUoYGSF+Kff+L72GP4dOiAb5Mm\nGAcPRuvJo3sLXLsmMGaMHhDYtk1Dly4+LFum5eef02nZ0k6dOk7mzDHTpYu9MGLpeRIRoeLHH7Vk\nhkD/+EPFtGk68uws8y/BbIa//nKP2XpzuEoWr1nqxQU5JIT0n35Cu2wZqoMHsT/9NI527SBHc9MS\nISAAW//+2Lt0AbMZs1aLWL48hTM7XJFLl8a8eDHGQYMQUlKQQkJIX7FCqUwrJI4WLTCunCz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BqE16CoKPbvClu3buXUqVN069bNFxQBmM1mXn/9dZxOJ9/kVvu6zRBPnvQLigA033/vqzPxkZGB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106625550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf = SVC()\n",
    "\n",
    "# Get labels\n",
    "mystery_labels = mystery[\"class\"]\n",
    "mystery_data = mystery[[\"feature1\", \"feature2\"]]\n",
    "\n",
    "\n",
    "# Do Stratified 10-folds CV\n",
    "k = 10\n",
    "skf = StratifiedKFold(n_splits=k)\n",
    "\n",
    "acc_per_fold = []\n",
    "\n",
    "for train_index, test_index in skf.split(mystery_data, mystery_labels):\n",
    "    clf = SVC(kernel=\"linear\")\n",
    "    clf = clf.fit(mystery_data.loc[train_index], mystery_labels.loc[train_index])\n",
    "\n",
    "    predicted = clf.predict(mystery_data.loc[test_index])\n",
    "    \n",
    "    # Compate predicted values with ground truth (accuracy)\n",
    "    acc_per_fold.append( accuracy_score(mystery_labels.loc[test_index], predicted) )\n",
    "\n",
    "print \"Accuracy per fold:\", acc_per_fold\n",
    "print \"Average accuracy:\", np.mean(acc_per_fold)\n",
    "\n",
    "# To visualise the data\n",
    "colourmap = []\n",
    "for i in mystery_labels.values:\n",
    "    if i == \"Yes\":\n",
    "        colourmap.append(\"red\")\n",
    "    else:\n",
    "        colourmap.append(\"blue\")\n",
    "        \n",
    "# Plot the points\n",
    "plt.scatter(mystery_data[\"feature1\"], mystery_data[\"feature2\"], c=colourmap)\n",
    "\n",
    "# Decision boundary\n",
    "# Train the classifier on the whole dataset\n",
    "clf = SVC(kernel=\"linear\")\n",
    "clf = clf.fit(mystery_data, mystery_labels)\n",
    "# Plot the decision boundary\n",
    "w = clf.coef_[0]\n",
    "a = -w[0] / w[1]\n",
    "xx = np.linspace(-2.5, 4.5)\n",
    "yy = a * xx - (clf.intercept_[0]) / w[1]\n",
    "plt.plot(xx, yy, \"k-\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can put your code below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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