Breast Cancer Wisconsin Kaggle Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "      ...       texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0     ...               17.33           184.60      2019.0            0.1622   \n",
       "1     ...               23.41           158.80      1956.0            0.1238   \n",
       "2     ...               25.53           152.50      1709.0            0.1444   \n",
       "3     ...               26.50            98.87       567.7            0.2098   \n",
       "4     ...               16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/thakkar_/anaconda3/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
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    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d58509438>"
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     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
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yJP/tyOe+8gSfW8PW5ydJXn6C453oc2v5zRMc+zeSfN6pHriqvjLJrZN8alX9yt6Xzkzy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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d584a2438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15,9))\n",
    "sns.heatmap(data = df.isnull(),cmap = 'plasma',yticklabels=False,cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.drop('diagnosis',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def stringToInt(a):\n",
    "    if a == 'M':\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "    \n",
    "df['diagnosis_int'] = df['diagnosis'].apply(lambda x : stringToInt(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 31 columns):\n",
      "radius_mean                569 non-null float64\n",
      "texture_mean               569 non-null float64\n",
      "perimeter_mean             569 non-null float64\n",
      "area_mean                  569 non-null float64\n",
      "smoothness_mean            569 non-null float64\n",
      "compactness_mean           569 non-null float64\n",
      "concavity_mean             569 non-null float64\n",
      "concave points_mean        569 non-null float64\n",
      "symmetry_mean              569 non-null float64\n",
      "fractal_dimension_mean     569 non-null float64\n",
      "radius_se                  569 non-null float64\n",
      "texture_se                 569 non-null float64\n",
      "perimeter_se               569 non-null float64\n",
      "area_se                    569 non-null float64\n",
      "smoothness_se              569 non-null float64\n",
      "compactness_se             569 non-null float64\n",
      "concavity_se               569 non-null float64\n",
      "concave points_se          569 non-null float64\n",
      "symmetry_se                569 non-null float64\n",
      "fractal_dimension_se       569 non-null float64\n",
      "radius_worst               569 non-null float64\n",
      "texture_worst              569 non-null float64\n",
      "perimeter_worst            569 non-null float64\n",
      "area_worst                 569 non-null float64\n",
      "smoothness_worst           569 non-null float64\n",
      "compactness_worst          569 non-null float64\n",
      "concavity_worst            569 non-null float64\n",
      "concave points_worst       569 non-null float64\n",
      "symmetry_worst             569 non-null float64\n",
      "fractal_dimension_worst    569 non-null float64\n",
      "diagnosis_int              569 non-null int64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 137.9 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaled_data = scaler.transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA( n_components = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_pca = pca.transform(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 2)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6d50041e80>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/thakkar_/anaconda3/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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Hs3npvtGUFlUPmfvdtKUUbS5K2wx4QojkasscubUsa64Zp1lxLI3fupygdUKT\nT/IBdSJhuiVcFmJA4o1MECoHbDJBvM7H+Jy36t2zwPF/Q374Rto4v6CN8wvyw7ficr6r1z7SacbY\n2bz+8NusWfwT4VCY4u0lzP5oLk/e/FxVX/YFkxaxbIYNgOMo1tiJ5wvYtM7DvGn5BMqDLPsmwfSi\njcwYw4TnJ8UkeQB/qZ9Jr0whFAylPSYhRKzaskeF1nr/XQujZfFTG4kWQznb8ThfYDkr6l65aqNs\nytVVOLSKKQ7RhzJ1Y0yZ1xlHq/C1tHFOp7VzIXnhu2ntXEK++QN55u+0NpeR7TyZ0mEtZx3seAAv\nc7Eox6IULzPJN79HOUV17yADpr39Nf6y+D73y2faLJwcuUD5adk6nJBTtezDFztQtDW2iWYwAJPe\nbUtZcaS8Q7f012QUbytm3fJ1CZetW76edcukKY8QmVZb1f0/gHFa6zuA2dGyocA/iTyjFy2Ncchx\n/oGXSbjYiiGLYHgwJep3KbWa91vnEnL64TPjsCgmpLrjV5dgVPX0iV7nE3LNY1iUR0u24SZ2nHUX\n28k2owk7PQhYZ9R6zCzzNpj4eY5c/EQWb1LOzXW/7zRLNnGNE3L4aek6Dj7xILr26YyyFMaJ3OHP\nntSaR26xOOPKrezTPcDO7W6+Gt+a8aM7ANBzUA8OP3NI2t5DJW+2F19O4mlNfdle8trlpT0mIUSs\npInetu3XtdbZwItA5RRX64E/27b9WjqCE+mVZV4gi7eobO6nqMDLDPLMnyjm6Vq3Vc5OfLyFy/yM\no9pRytUJLw585sMaSb6W/RHCayYToPZEb5G8mt8yTXNwofad27J2afxdsOW22Ldf5E/t0JGD0Yf1\nYfms6ur4BV/ls2ROO/of3Y/Vi9awY8tOLJdFr8E9ufrRy7Fc6X+8k5WbRb8j+jBzbPzsf/qIvhmp\nZRBCxKq1H71t288Dz2utC6Kvm+xENmLPec00ErXp9zAPl7OIcGWf7l1YzgryzQO4WRUpMOBjLKXh\nPxF0DYtZ10XqVbkWdc+Hbmpp6GfIJst5FYMPvzqzyQzcM+yCo1j6jU2gPLb6Xh/el4NPipxjpRS3\nPHM9Lz/wOsu+sSkvLqdbv66ccPlwRl5/MkWbdzB/4kI6dGvPgOH9Mzp87RX/dwlFm4pYPmtFVbvL\n/Q/uyRV/urjW7RZMWsScT+cRDoU54Mh+HHPBkRm5WBGipatz9rrmqEnNXmcC+MwY3MbGkEOFOrNe\nU582GlOe/9lSAAAgAElEQVRBlnkTt1kKeAmoYeSYp3CxMeHqxep3BKxfJFyWH74TL1Pjyh1yKOJN\njKu6kV6r8GV4WJ5SiOWcR5nrwVrXyQ9fhzfBZDIGC0MOFiUAhOlCqbqZoHVaSsdubJ+/+AWTX5vG\n2mXryG2dwwFHaa5+5HLadoq0uq8o9fPT0rW079oOt9tFcWEJnXvtg8vdNEbC2/Vz7jgOsz/+lrXL\n19GpR0eGnXtErbG++rs3mPjyl4SD1b0JDj11MLc/dzNuTyoDdjYdqxat5svXp1NSWEqnnh059YaT\naNW+Vd0b1kNzm0mtJWhu57y22esk0Tewmh8O5RSTb36Dh/lVyx3yKFO34LcuylSIYMpp5fwaD/Oq\ni7BwaI+L+Eobh1bsUKNxrASt6k05bZyzcLE94aECHESx66Wq19nOKLLN/xLWHNQUphs71RM4Vo+k\n6yhTTBvnLCx2xocFcccI04Ed6jWMVVDH0dPDCTtsWbuVnFY55EefZRtjeO9vY/j6g1lsXrMFX66X\nA48+gOv/cQ2tC6qTx7YN29myZivdB+xLdl522mPfky/B5TO/55EL/0HQH4xbdsX/XcypN4zY0/DS\nZupbX/H6H9+mpLC69qlr387c8cKtdO3bcKNYNrek0xI0t3O+p9PUit2UbZ6NSfIAFiVkm5cJOKdi\nrIa96k89rldikjxEpkW1KMTBi7XLLGwBhidO8kAkpSbu4w3gYTmWswbH6g5AuboeZXZEG/xtwyGb\nEAcRpgtulqEIEKJvpAV/LUm+MmqTZEKYRJ94F1vJ4gPKuTHB0vSzXBadenSMKft01ETG/Pvjqipw\nf2mAeRMW8n8//o3Hv/oLJUUlvHD3q3w3bSllO8po360dR5x5GJf8/gIsq+GrvYP+IO888gGLpy+r\nenxw6vUnc9y5uz+j3pxPvk2Y5AGWz/i+2ST6UCDER0+Oj0nyAOu/38AHj4/jtv/dlKHIhIglib4R\nuUncl9vFZrx8jJ9L0xxRhNsknqpAEcKhNUH2w6IQQ2uC6ijK1K+S70zlEKIfLmYl2WcAi004dI8W\nWJS57qXc+SVu5hOmO46VYMi3FERmjBuAi+n12Khkt46VLrPGzU44vtDPKzbw/uNjWf3dT3z7WfXF\n47Z12/nkmQlk52dx7l1nN3g8T93yHLM/nlv1evOaLfw4fxUdOubTSSe7+Nt9zamGcd7Ehfy8IvGj\nrpXzf8QYI1P/iiahtrHu36WWIc1s276wUSJqUZxaliW/C25sFj8nXeZiB5DNTp7DsfZLacjdcm7C\nw/y4mgCAMF0JqYExZR5nGj7zHm5W49CKoDOMcnXDbs3EVqZuxmf9DOHq+dwdcrB2mf8dIo8nwmpQ\nvY+RTpt/2pp02fR3v2Hn1sRViXPGz2vwRP/9nBXMn7Qwrrxo8w4+fPIzbvzvL3drv4eOPJhJr0xJ\neFevD++7W/vMhNpqUCTBi6aktjv6j9MWRQsVYgAelsWVh2lPgNMzEBH4wq/hSjINbiUXG8nifcrU\nnSntM+w6iNLwbeTxHxTVX94GF351OqjqZ8geZzp55o9Vk9+4WI/HLMMyWyh1/b7e78exNKr9O5Rs\neQ6X2YCj2uM3x5HPH+P65wc5koBq2kOyenO8SZf5ywJUlCQeq2rH5p04YadBW63P+XQewYrEVew/\nr4wfuyBV/YcdwLGXHM3k0VNxwtUXw4NPPohTrmva/z81HXzSQLrprqxLML9XnyG9JNmLJqO2fvSv\npDOQlqhc/RK3WYKHpVVlhiwq1CUYq13a43E7M8jhqTobwkHt/dMTCbgupdjphM+Mw8XPOLQjoE7C\nb10Qs57PvBc3wx2Al8mUO9fgWPvW67gAysqnwro+pqzY+SdZ5mXcLAc8BNUhlKsbm+ykQJWGjBzM\np6MmJlzWtU9nwsFQ3DNhgA77tm+wJB/0B3nuzpeZM/7bpOsUbd7JqNtfoKvuwohrTsSbnfwCJZFr\nHr2cgcf259sJCwgHw/Q7oi/HXXpMk+lVkAq3x805d57J6N+9wY4t1Q1Cux+4L+ffe04GIxMiVp2t\n7rXWbuBa4GCgasBt27avbdzQdl9TaXUPgCkly7yB26zEkItfnULIysxUs/nhW/EyM6V1y7mUMtdd\nDR5Dm/A5uFibcFmJune3eiM0t9axtTHGcPew37Lhh9hnv94cLzf+6xoWfPEd09/5JmaZ2+vi8j9e\nzIh63A2vmLuS+ZMW4c32cvxlw2ndobph6Av3vMIXr8Z3l0ym1+Ce3D369pheAXuTDT9sZNIrUygp\nKqVzr06cct2JDd4ToiV9xpuL5nbO97TV/ajoescDzwCXAnGz2okkVC4V6vq612tsxo+L1CY9CdOR\nCtU4TTAcWiVsJx/p3tfwjbuaG6UUD314Ly/d/zrLvllOeXEF3fp14cQrjuPIcw7nsNMOJTsvi4Vf\nLmbntmI6di9g+AVHpZzkHcfhf795iRkfzq6qlv/8hclceP8vOO7SYwiUB1g4uX7TCv8wfxXv/m0M\nv3z8qnq/36bMcRzmfb6QHZt3MOTUwbQuaJ1wvc699qlzcCAhMimVRD/Utu2BWutFtm0/orV+Ghjb\n2IGJhuYCklevmug6IQZSpq7brSr0VATVMDwJWv2HGEhQHdUox2xu2nRsw29evJWSwhLKdpbToVt1\ntbzb6+bqRy4nFAxRUVJBTuucenWrm/D8JKa99XVMWdGmIt55dAyDTz4IJ+ywc2v8uAR1WTH3h7pX\nakaWz/ye0b9/i1WLVoOB9/7+IcMvHMbFvztfnr2LZieVb4jKgcnDWusc27Z3AB1r20A0QcpNkEMS\nLnLIoZzz2cmz7LSeJ2Q1XsItV7+kgnOrZrkzuAgyiBL1YJN/fp5ueW3z6Ni9IOGzd7fHTV7bvHr3\nnf9uytKE5UWbivjytWm06tAqrm9/SppRt7i6BP1BXrxvNKsWrq7qd7Rj804+eWYCk+vxSEOIpiKV\nO/rtWuu2wGfAp1rrrVCPActFk1Gm7sAyP+NhQVWDPIOFRRk5vIfD51SYiyhXjTjQh3JR6vot5c7V\nePgm2v3uyBaR5I0xfDNmFvM+X0CgPMB+/ffl9FtOISe/aYyxD1C4OfnUvTu27sTldjHsvCN5528f\nxEyTW5deh/RsiPCahGnvfM265fFfcU7YYc6n8zjxquPSH5QQeyCVRH+6bdthrfVviTyfbwO82rhh\niToZg9d8icvMB5VDBedirE61b2K1p9j8D6+ZgMfMwMsErBr9+S12km1eIegMImQd2ajhO1ZX/FxQ\n94rpYMIotmPIi+kKWF+jH3qTz1+aXJUgv/1sAYu+XMy9b/6G/LYNP12rMYav35/BwsmLCYfD9D2s\nDydddVytY8WHAsnHbyiLTjV75m2n4vK6mPHBLLau30agPEBFqT/pdt0H7Me5dzf8YD2ZsmNzfK+Q\nSqVFdU+0JJL7fs5KJo+eyvYNhbTp1IbjLzuGA45sAnN/tHB1JvpokvcAGlgALLdtO9TokYkYbmc6\nPvMFinJC9MbNd3iZgcKJzhb3AWXOrwlYZ9a+I+UiZAaQzbMxSb5qMQF8ZiIhooneOHicqbiZiSKE\nUZ2o4ByMlaB61wTJMi/jMfNQhAmpAyjnWoyVuBFTU5DlvInXjMPNGhzaEGQopdZ99U74a5asZcqb\nX8XdBf8wfxVjn/iEy/+YvDfBrI/mMnPsbEqLStln/06MvP5kuvTpXOvxjDE895uXmfLW9Krq5Zkf\nzmHhF4u465Vf4/Ym/tNu36UN6xP0+wbwRLdRSnH6Tadw+k2nEAqGUEox48NZ/DB/NTn5WfQ4qAff\nz1jOtk076dqnM6feeDI5rZpOrcWe0kP74va6CQXiv+b22b/2i2mR3OyP5/LivaPZua26JfuCiQu5\n6pHLGHbuERmMrOWrM9FrrY8G3qT6WX2W1vpi27a/qWWzOmmt9yVSM9CJyFfV/2zbfmKXdY4j0vAv\nOv8pH9i2/ac9OW5zlO08RbZ5DRUdec7HpLh1XGwnxzxL0ByPUcnvHl3hBeRzT9JJaCAyDz2Ay1lO\nrvkrbpZU97034ONdyp1b8Vs1+gobhzznXnw1OmR4zLe4WcBO5ylcrCTbvIGLHzHkElRHUK6uB5W5\nUZh94ffJ4YmqQX5cbMLFRyinlBLX3+u1rzmffJt0MJsfF6xKWA7w4b8+Zsy/PqoaJW7x9GUsmrKE\nXz93E/sPSl4dvujLxUx/75u4sSsXTl7MhBe+4PSbT0m4XZfenVn0ZeIhkDv1jL94q6wdOPr8ozj6\n/Oq2G6dddWyz6npUHwcecwCDThjAt5/FzorYtnObenVhFNWMMXzy7OcxSR6gpKiUT0dN5MhzhjbK\nXA0iIpUz+xRwuW3bfW3b7gtcRqSb3Z4KAXfZtt0fOAK4VWvdP8F6023bPjj6b69L8pazlizzXlWS\nr42LjXjNuMQLjSEn/DitubHWJA8QUhqMQ655BE/NJF91nO1km8fJDj+B5awGwGsm4U0w5ryHxeSY\nf5BvHsDHF7hZFS17njznj3W+p8bgchaTF76LXP4eM5JfJQ8zsBy79p04JeSG/0Lr8Hm0CZ/JiHPe\n4YAhicfRTzYITOmOUia9MjluKNjNa7bw0ZOf1nr4+ZMWxkzxWtP3s1ck3W7k9SdTsG+HuPL9DtyX\nk68+odZj1rTkm+V8+K+PmPLG9IR3vs3dbaNu5tQbTma//t3o2L2AQ0cO5lfP3EifQ3tlOrRmqXBj\nET8t/inhstXfrWHT6voN0CXqJ6XbKdu2p9b4fbrWe/5MxbbtDcCG6O/FWutlQFcgcbPgFsxy1uFm\nDmF6EbYOilnm5bOE07AmU3k3viuvmUgW76DqGGM/yCAq1IV4zFe4a/mvcFFODq+SZT6gwjkPZcpR\nSaZG8DIj4fS3HqZgOctxrH6x78HswDJbCauuoLKigw69izLbCateBNTpu10TYDnryDcP4qqlPalF\nOR4W4SfJ59w45Jt78DK7qqhnL/jts15+d0V3Vi+Lrcbud0Ti8dtnfTSX7RsSN45b/V3iL8VKqrZx\n1q34ho1lO8uwXC46di/g1meu58MnPmHV/NVYbos+Q3px0W/PJyvXV+sxITJj21O3/I/5kxYSKI9c\noIwf9TnX/u2KpO+zOfJmebji/y7JdBgthsfnwePz4C+Pv2Hx+Dxk5dT92RO7L5Vvy4la68ts234d\nQGt9KTChIYPQWvcABkPCKdCO0lovItLS/27bthPXOzZD7vBMcvkrLjaiCEe6moWHUKJ+j7H2ia6V\n+pCgDvkEiK9aVM4GsszzdSb5ED0pVk+AysYyP0ee/9chMu3uGwRI3iUv2cWHRTleZlJBNNGbcnKd\nv+JhBi4KCZtuBBmIh+9wsS66DgTNWIrV3zFW+zrj21WWebPWJB85RBYhElUuRXjNZDzMjSsv6BLg\n3OsL+eed0USvYPDJgzj716dTuqOUT/83kQ0/bCS3VS7HXjKM3Fqea3u8nlpjHHrGoUx+dWrCiWEO\nHHZA1e+Lpi7ho/+OZ/V3P+H2uOh7WG8ufugC7n3tDgLlAZSl8PhqP1ZN7zw6hlkfxb73dcvX8+pD\nb/Lnzx5q0LH2RcuR3y6PvkN7M+/z+EmS9NA+tN2nbQai2nukkuivAu7UWj8ffe0DtmmtrwGMbdt7\n1Kdea50HvA/cYdv2rreu84D9bNsu0VqfBnwI9Klrn23b5uDO4JjZBQX5da7jlH8EOx6AGlXyijBe\nZtHO8yhWuxci64Uugm1vgqm9uh3cWDnn077VgNjjlI6G4ieBwto3V61xt/0/Oni7RI87AraNAlN3\nbYIiiM9rQSAf2PW5rQvLygEn8fPc3LzO5OdGzpdT+Fvwf1Zjy3XVCb4GDwtp53saq81jVWXt2wWg\nYjyoHFT2GSiV+A7B2b6Rup6CKKsLbbJngNdg+eJ7HzjFP0Bp4ougffaLNGXJzs/ipMuH86v/XsfG\n1Vt45ILH+XHhmqr1Zo6dzTV/uYR9dRfW2vGzCR58/IG1fo4KzhzC0ltOYdxTnxGMVp0rBcPPP5KL\n7j4Dl8vFD4vW8NxvXmLb+urPzpzx8yjcUMgTX/8Zbwqf013ZM5YnLF+9aA3218sYfp40qqopHA4z\n65N5lBaVcfS5Q/doaNxUvleasl89cQ1/vfQJVs6vbrPS86D9uPXfVzXZ99ZU46qvVBL9kMY6eLQ1\n//vA67Ztf7Dr8pqJ37bt8Vrrp7XWHWzbTj6XJ1BYGD9FabqkND6yMbRyXsKTJOOYwGy2bZofnac9\nlyyuIJvnYqZeDXAwYfrhYg2GbALqWAIVp4O/+tiWs57W5j8JJ5GpOhYQpjPl3ExgxwFUJ+p25JoT\nyOLDlN63P6AIqevJMi/hil5UOOTg52zczmw8bEp47JLiEIGyYixnLa3NlJQajQCEK2azbfMOUBbt\ns14mXPpa1XHLtzxJIOvXBK2T4rbLDedWT9iwCwcX4MZyfoTSZzClL1DOcEqsP4Oqvuv1Obkka+5Y\nXBS5wCwvrmDaezMZccMI3nnkg5gkD5FGSG/97UOME/+4w5vl4djLh9f5OTrv/nPpe1Q/5nzyLeFQ\nmAHHHEBB9wJe+N3bZLfKZs3in2KSfKWV81fx9r8+YeQv489PXUqKkv9t/bRiQ8Yb6DmOw4KJiyja\nsoNDRw6OGcM/3RZO/o63/vIeaxZH5nZ47oG2HHHWEC7740X1bnjW3MZdTySrXSseGns/X74+jc1r\nttKhW3tOuOJYvFmeJvnemts5r+2iJJXudWvqWmd3aK0V8AKwzLbtfyZZZx9gk23bRms9lEjjwW2N\nEU86WWzATfLGXgo/Llbj0BuACutKgs6h+Mz4SPc6NQC/OjMm+STiY1ytSR7AoQM71BgUZWQ5L6Eo\nJ8hRhKyDKbUexDGd8JipWKwHglhUJJz9LqT6UmFdht85CR/jUITwcyKO1ZdW4cRjoCvApSJfgi6z\nFCtJFX9iQcDgDY/DlD6Jq0b7gCzfevzbHiaYNwBP9j4xW/nVGXjNFCxi+0OH6QC4cVE9mYwigI9J\nhM3+lKsbMcYwZ/w8Vs4Nc/nNbWnbPraWpKICpo1rU/V6x5adTH51Ciu/jZ0ut9KWJHPPByqCTHnj\nKy77Q93zDQwc3p+Bw/tXjWE/a9xc/GWRPu/e7OSfj42r4i+8UtG1X1c2/Bi/bX77PA477dDd2ueu\nSopK+PBfn/DjwlVYLou+Q/twzu1n4M2q/fNuz17B6Ife5MfoiHbv/m1MxoatLSkq5cX7Rsf8Hxdu\nKOTTUROZ+9l8zr/nHI65YO8b8tnj8zDiWum5kG5JE73WerRt21dorecQ14kHbNseuofHHgZcAXyn\nta7sx/IgsF90/88C5wM3a61DRLr3XWzbdrMfa9OQjSErYYtvgDDtCO0yXG3YOpAyDqzngZIPclIp\nwPF4mEiu+S+u6NS0htEEwidSYj1MuXUD5dwQ3V+YfOd2vMyI2UeQgVSoyyKrWJ2oIHYSHyfJiMkG\nCJv9ALASNNarTYh+oFzk8EzCRoCt25Wx6PM/0f/Up2O3s4ZS5twWmVGQnzAoQmhCDCSbdxMey2Nm\nUxy4jv/c8AzfTliAcQw/zW/LTX8K0POAUiwLNq3z8Onr7Zg6LvZZY3lpxW4N+rdlTf3OxyfPTIgb\nw76ysVwibTu1SbqsNqfdeDI/zP+RwhqNCJWlGHbeEbTrvOfPWStK/Tx26b9jLo6WfrWcVQtWcc9r\ndyRtAxAKhHjx3tGsXVb9qKdy2NqC/Tpw0lXH73Fs9THp5S+TXshtWbOV0Q+9SYdu7WWwGJEWtd3R\n/zv68+7GOLBt219B7VOj27b9JPBkYxw/k4xqS4iDE3ZHM0CAEQ0yX31QHYkxbyftmmfwEuQocs2j\nVUkeKu9kPyVk+lChatyNKxfF1j/INi/iNvNROITUgZRzLajkDcv86iw8ZjYW8d3PfHxEwByPUk6C\ny8nEwnSiQl2JcjajnK1JO4mWbv+ZUCAUN3iM37oAvzkbj/kaQzYhNZQs80otx69g3H/HM/fT+VUl\ni2bkc+uIPI49J8zAY7vwwu82UFayS7sQBX0O7UXZjnJ+XrmRXeW0zqFsR+Kq8Pz29RtJb9Hk71Je\nd59e+zDi2tS70tXU7wjNHS/cypTRU1m9dC25rXM5ZMQgTtmNxwCJjB81IWENyMLJi/n6g5lJ74Kn\nvftNTJKv5IQd5n46P+2Jvnh74q6WlUoKS5ny5nRJ9CItkiZ627a/jf6UWRwaQam6A8tsxc2yqjIH\nHxX8gnKrYeaBD6mhBMwJ+PgsbpnBR7m6CBercZnE1bheMwF32MZiIw7t8avTCFrHU65uTj0IEybM\nvpRzOTk8F9PyXwFeZpNtnsPPWWTzQsKLgUi8XoL0J8z+VKiLcKzeuJz5WFbiRnHGwMrFHg44rYI8\nb4KkqbwEVfWXf8AcRzYvJzx+mL4s+yb+UYsxiilj3OR3G0K7bgsp22V89IOOG8DhZx1Gn8N6s3bp\nOlZ9V/0UzOW26Na3MxtXb2bnLs8B89rkctylxyR8X8n4y5K3MGzdsTU7Nu/A7XHR+9BeXPTb8/ao\nUVifQ3tx1MiDG+X55Zokfa0hMj5AskRftDF5Y9NMDFu734F1z/5YtKn2x2qpCAVDfDd1KZalGDC8\nf9IxG8TeLZWR8b4CzrRtuzD6uh3woW3bwxs7uJbMsXqww7yEz3yMxVocuqX03B2IVskrUMmnncUE\nsdhAqbqTIAfiMTOw2ApYhDiQgDqdkDWIbGdU0l24sVE12hJ4zAzKnF/jt1Kbq97nfIDPvIubFYAn\nafe+LPMGPj7H4MYQX80Tph1l6g4C1ukx5Y7qTSjcDrcV3+CsrESxfOEALm6Tm1KsjtUTf/hUsngv\n5lFAiO6UqysJBd9Kuq3H5+auV29j3BPjWbVoNW6vh35H9OH8e87Bsiw6dG3PQ2Pv45lfv8D8CQsJ\nBUOEQw7fz/mB/Pb5dOrZkU2rIjUqXXUXzrhlJL0G759S3JW6HdCVlfPi74R9OV7ufOlWLJcLb46X\nbn27NOlpVn3ZyT/T3qzkfa2b2rC1x5x/JFPfmM7ymd8nXad91z2rtfvqvRmM++/4qgl49u3fjXPv\nPJPDzzxsj/YrWp5UWt3nVSZ5ANu2t2utW0afg0xTHvzqFymv7nKWkG2ex80SQBHiIMrUzThWbFLI\ncl7HZ8ZGh5vNJ8gQStUfMFb8iGgBhpHNqwn7uu+aDizKyTLv4TeRmD1mCoowAY7D4mfAi2N1iyxz\nppBj/lWjp0DyO06LINGxk6K9AApwyAU8BDmMCuvGhMP6GpVP0BqJy7wR8xw8HIJ3n+7M8IuPrVdS\nK7PuI2z2x2O+wqKMEPtToa7Asfal50HdsWfFjziXlZfFYacdSqfuHbn+n1cn3bfb62bDio2EgrGJ\nqHhbMb0O7sG1j11JKBBi4LH9a52UJpnTbzqF5TO+Z+MuDeUOP3MIfYb0rvf+MuWw0w5lxtg5caP+\nZedncfSFySdaSjZsbZtObXb7McWesFwWv3n5V7zxx7f5+oNZcRcgbTq23qPHCWuWrGX0Q2/GPCJY\nu3QdLz/4Bt0P3E/G5BcxUvlGsaLz0JdBVb/31EfYEA1COZuiI7pVP4d0MRmXWc1O81JVIvQ6H5Nj\nnqx6Lq/YiY/JKFNMsXkmbjrYsDUAf3hEtKV83dz8QJbzH3x8g5vVADh4o8fzEAwfRLm6JVpTUf9u\njpEYQhSrZxJPnLOLMus3ZGe1ofCn93C5i9iy3suCWb1p3etahl80rJ4HV/jVhfiJr7E4+/YzsGet\nYNWi6up3y2Ux/MKj6J5CNa09eyXrkkwms2rhGvTQPnW2Kq9N175duOuV2/jkmQmsXb6erBwvA48b\nwBm3jNztfWbCYacfysjrT2bya1Mp3xkZkyC/fR5n/uo0eg7sXuu2t426mbf+/C5LvlqOv6yCfQ/Y\nl1NvODljFzr5bfO48YnrOOv2M3jzT+/w/ZyVBP1Begzszhm3jGT/QT12e9+TX5uasB3Ajs07mPTK\nFC5/OPkkSmLvk0qif5PI6HiV49vfDLzWeCGJRLLNywkHj3HzIz7zNhXqOgB8ZlzCxnce5uM2MyNz\nv++i1HqIkOmNz4zFww91xpLDWzGj5llVxwvi5Vtc5h4iUxnsHheFeJhBgFSmPlWgPOR1aBUZlrdN\nAZ0HHEp5gve5J1oXtOL+d+5i/DMTWLNkLd5sL4ecPIhjLkyti5TX58FyWTjh+DYFLq8LK8GwtfXV\ntW8XbvjXNXu8n0y77A8XcuwlRzNz7GxcbhfDLzyK9l3rHgXRm+Xhyj9fmoYI66fz/p248+XbKCsu\nJ+QP0qoB+vaXFCZv7Fe8vfn0/RbpkUo/+ke01j8DZ0WLRtm2LfPRp5EKb8BXy8A1LhNtwGSC0Wr9\nBPsghJsV1dPPxiy08KvLCDkH09pcXefQt3Utr23SnETP4OPX8eCwL25nNlnmfSzWYWhDQJ2A3zov\nZt1sMwpKX8ATjcmiGJf5EUWAMnVHHUeqn/y2eVz04Hl1r5hA70P3p+eg7vwwL34muz5DeuH2uvlx\n4Sq+encG/jI/vYf0ZviFR8U1rtr283YmPD+Jwg1FtN2nNaf88qSUkmBz061vF86/55y6V2xGcvKz\nIX/3G0HW1LF78tquTj32aLBS0QKlOqnNK8ArjRyLSMBy1tKK62rcNcdzEalO9pmxSQedMViRvue1\nCFsH4oRbV40w1xhC9MfFiuhz+cSCHIQypeTycEwsHjMHy9lEuXVLpMAE8ZqJsMuFhyIyHn2ZubHe\n88rvLsdxmDl2Dstm2Li9bo76xeExM50ppbjowfN47q5XYvrI9xjYnYt/dz4fPf0pY/7xUdVUt1++\nPp0ZH87m7lduwxttoLb06+WMuv0FtqytHjNq5ri53PjEdRx4dO3/t/VljGnSjfb2diOvP4k5n3zL\nzys2xJR369eVkTecnKGoRFOVSqv7jsBtQK+a69u2nVrT672ccjaSbV7H4icM+fjVKYSs1LtOZZvn\ncZe0E/IAACAASURBVNU5GGB0PnWTfLQ9h1xyzBO4wpGR6ML0oUQ9hGP1qFrH7XyLonG6Ijm4CTKc\nUnUX2eZ/ZDE27s7eEGnlXqoeJNf8Pe6CQxHGZz6mwlyBUflYbMNF/DjxAC7W4zIbCKv6tV5Xzk6y\nzXO4WQxASA2kjBvASt6vPRQM8Z/rn2HuZ/Or+uJPeX06p99ySsxd6YBj+vPnz37H5y9OZufWnezT\nsxMnXnkcxYUlfPzkp3Hz2S+euoQx//6Yix44F4AP/jEuJskDbF23jTH/HNcgib6i1M/rD7/Nsm+W\n/z975x0eVZn24fucqemFhCQQehl6kw4KSEcRsaIURRT7Ytm1rP1b1+66ujZEmigiINIEpPfe+9Br\nGul9MjPn/f6YtGHOmUxCUMS5rysXnP7OZDLPW57n96Mwv4g6zWoz+NEBtOlVSaEmP1edsKhQJnz7\nBD9/tICTu08hAY1vaMTdLw53zRz48VMOX0b0PwNHgJVQgf2ZHzdk5RQh4h+lSWsAJrGGfGU8hbK6\nLOzllK+zrxApWFP0RSIfA2WGJDJ7CRVjyVJ+LhXncSXQVex7XxWcxJMnPY+QY8kXryIrORhZXRrs\nXUG+BQU8hkIsOo1cAR3JGMRmiqSBKISjUAMdiR7nOYlCkaIr10hRSIh4BgNlDlsGsR89h8kWX2qW\nMy77ZoWbmA6ALd/G0onL6XJrR+o0jy/dHxIZwp1/d8892PD1ZnLS1NdcS7zl0xMzOLFL/T05sesk\naQnp1KhV9XItIQSfPfIle1eVCe+kX0zn7IFzPDP5CZp2rtBLqtKkXkhj/U+bUBSFbsO6ULtpXLU/\n43qmTrPaPPPtEyhK8bJVJfXz/fx18CXQR1it1vFXvSXXIQFisluQB5dlq1nMxibuVC0Zuxzhg02t\nQixmZSqKCMRJODrcPc4FErJKH01HNgHif+TzRnHbUjzO0W6XhJMYJFLR+ZB4Z+AMYWIUhcoYCuUx\n5MrvYxTLMYitSGSg4xx6rITyNxyiDlrJfAI9TopLhyQzRfRUla610w0hVa4K1CxmuwX5srbvwSR+\nxiap+5Mf2aI+k1KQU8jGn7dy36t3eX1uyRe1GlmpOWxdsJ0GFWRoX+k0+/41Bzmw/rDH/syULJZP\nWV3tgX7R50tZ9MVScoszx5d+s4J+Y3pz32t3V+tz/gr8WQO8EIJV09eyd9UBbAWFxFviGfrUICLj\nrlwV1I87vnxCDlosllpXvSXXIVqjcR1JGMRvPt3DQVuvxwVgZD1B4nOC+BrQ4aTsD8VJTRS0p/IM\n7AdAUi5WavZAQlAoPYiDdj5foyODADEJnXIIJJkieRB58ovouIiec6WCOnrOI6Fuj+ugFU6p7D3J\nl5+HgLtKX7NCADZ6kCe/5L0xwolB2YBR+RWEa7lC72XpQy/U7VkB1Uz60mNegngJ3W7vTFCYuoRw\nwrEEPhv/Ne+P+IRIjRF74xsaVVln3ulwsnHuZhZ9sdSjdr2ElErq7lfEqX2n+eW/i0qDPEBBdgFL\nv1nOzqW7q/VZfq5dprwwgykvzWD38r0c2nCU375dyfv3/ZfUC39637JrDp9G9LiMZzZBWaaXf43e\nF7y9vZ7BV1YuYhCb0LMfHSnFCXQtsNMOA3tV7lGSwV4WTHSk4SSaAobjpD5F0lBCxUjQqGkXuKaj\nA8R36CpwunO/TsJJbfRUbrpVJh+T+LXUoMck5qHHMxNdxomTKGQykHAicM1c5PJ3dy0AyYAU+hqO\ngiQkdiBTgJFdhCgvkSu9hZDDPO6tV7YSKD4rVv4Dp4jDxm3InPfyerXMbaFRh4bsW33QY7/BbKDT\n4Pal2xnJmSAEEbHuQTmuUSx9H+jNkq+Xqyq7gcttzhRgJLRmKNkpZZ2gqPgaDH/uNtVrKuL80QtM\nnDCFU3vPeD0vOMI3dUFf2ThnC4U5nkmjjiInO5bspuPgDipX+bmeOHPgLJt+3uKx1Hj+yAUWfb6U\nse+N+mMadp3iS6CfWfzjp5LYaYdeZa3ZQX2KpHKZscJOkPIORtYg414Da2QHNrqTy8uYWIhBo3yu\nPDouEcAvOIlDpgAHTdCrrGMLoIAxAMUytb7joA0OqRsShZjEYg8HOW9ldOVV+GShPVp0LVtIxdeA\nTBKBTCRXfOgmFSyy/4WJjW73N7KBIPEOubzv/myRS7B49zLhoUQCmKjZXoUAiqQhmu0c+uRgjmy2\nuunhS7JErxE9adqpCdZtx5n74XyXWYsQNGzfgDueG0rLG1uUnj/ilbto2qkx2xbv4sSuUySe8Px9\n2QqK6DCwHVHxNUhPzCAyLoIB4/p6XZsvzCskPTGDiNhwD3376f+cWWGQ1xt1dL61o9dzKoutQDsP\nxNsxP9cPu5fvpTBP3V3zjBe/Az9Vw5c6en9ZXRUpkJ5EL05hoCxRy0lN8qXH3QJVoPgCMws172Nk\nM0X0wy71wCAqDvQl6EgkQExSXed3ueT1xCF1JkD5GglPdzU1XCPrUCCPUOUBBBIKEcUj77Jg723F\n2EGZR7xTaoIQ6ufrSPUwwTGxAYf4kUJpTHGDCsC2SeVqMLAdSaQjpLJAaBJzVYWHtNrrJIJCaTQO\nWXsJxRRo4sWZz7Js8kpO7j6N0aSnXb+2dL+jC5kpWXz19Ldu099HNlv5+uwlXpv/IjXrliUMdhjQ\njg4D2jH5H9NVAz247Fh9Wcd2Opx8/8Ysdi3bS+qFNCJrRdJhQFvGvH0feoOeMwfOcWzHCa/3qFE7\nkl739ay0wU55Th84y45fd6HT67jxnu7UrBtN006NWfP9etXzDSY97434D8mnkwmOCKZ9/7bc/uyt\nf9p1aD/qGM1ePA1MfuHV6sabH/0Eq9X6qcVi+RB1P/oXrmrLrjFk5Txm8T16TqEQhF3qiU2600NS\ntjxCDiNbfIVJLEYnjiOkYAq5y13aVQgMQj1QlSABAcwgh/cwM1PT4U39WqeqmYxCJDb6EyZGovPR\nC971IdChIxudyhq6QKdpXFOeICYS4FxENp9QJA/CLuZhvGxpwkkAOgpUr9eLPVA8EyGRC4p63b9M\nNrK4hLNcoJdEpuq5ajicwew59G/qtOiAQdtPBQBjgJHbnvIc9S+btEJ1jTvtYjoLP1vCwx95Vl/U\naqK9HFKznm+VBDPfms1v364q3U5PSGfltDUAPPT+aDJTMjWXCQJDAxn9fyPodOsNVS7VEkLw3asz\nWffjxtKR2/LJqxj69BAGj+/Plvnb2b/GfbkjrlEsu5fvpSDbNeOTfOYSJ/ecJjMlk4feH1Oldvi5\nNul9/40sm7SS9ETPv92WPZv/AS26vvHWTS6ZX80F8lR+/jLIymlCxAQCmIuB3ZjYQJB4n0Dl44ov\nlgzY5OHk616gQH5CRb/dgeTD2rhMCgg7TuojfFKl946OdIJ51+cgX4K3QO5LkHedBzoSCOVxoIhc\n6QMKGYKTOJxEUkR3HHiTlhXl/hcJ+gaqZzmpi1Ny10d3Sk19fv8yL9l4fcjXvNTnDRZ9uRSA5LMp\nLJu0ku2Ld3pNwishI0m7Y7F+9ia2Ldrhsb/vmD40bFffY39cwxgGPzqgwmcWFRSx8zf1nI7N87ZS\nkFtA824WatZX7zQ0aFOXXvf1rDDI2/JtrP5+HcunriY/2z0HZPO8rayYusZtejYnPZdfPlnEBWsC\nz017mtufvZUWPZvRrFtTbnliEOGxYaVBvjzbFu1SDQh+/rwERwRzz8t3EF6zLIdGb9TR5bZODH16\n8B/YsusTb370Ey0Wiw5ItFqt3/yObbrmCBDT0XPWbZ+EgolfKVTuRZErNjXRRDKgEF+hKI5EHmE8\nglw8ynWVmcUhk4KM+lqXNwRoquhpt6F60ZGOWcylUB5NHv8C4QAUkIzolb0YxUYkldfmkNqUa5QO\nzMMRuSfdNP4FMjZpEEjuSXRF0kAcYj4GdlXYvuP7A1AUicSTScx9bz77Vh3g7MFz5GW6glqDNvV4\n8L1Rbgp4lxMRE655zGFzMOf9+XQY0A5DuelKo9nAs1Of4qd353F8+3GcToVG7Rtw+zO3+pRdn5Wa\nTfpFdRni/OwC1s7cwODxA+g6rBOLPl+KcJZ1nIIjghkwrl+Fz1g+fS3T35pNyhlXR3HhZ0sY/Gh/\nbnlsIAC7V+xT7QgVZBewYc5mRr15L/e8dIfbsb91+Lvqs3LScti3+gB9Rvqdsa8nbrq3B236tGL1\njHXYCmy06tmcVr1a+hUZrwJe1+itVqvTYrGMB/7SgV6nkagmk42R1RTim/iNFkV0Qs8+r4FUAqRy\nU9kSDnRcRCEEvAR6Ueosd20ii3IZ91LZx9Eht6PQORwzc9xmCoroSqHknpErBz9Idh6YxFJkklCI\nokjqT6GkYnAi6ciRPiJQfIqevUjYyEhRCApKJqBccnnSeQOzvygb8dptdg5vdC+xO73/LFNfnMHb\nv72OrFOfHBv4SD+2LthOyrlU1eMJxxPZtmgnPe9y9yCoUSuSJ/73MEK4gvAFawKLvlhKQU4BMfVr\nMnBcX6LreNoOA4RFhxEQGuBWvlaeQxuPUqNWJBvnbHEL8iGRwYz7+EE6DfGe9Z5wIpFv/vEdWall\niaPpCenMfX8+9VvWpeWNzbEXeJE4LlT/PAZFBJOq0kHRG3V/StvVhBNJrJi2mozETCLjwhkwri+x\nDf58r+NqEl4zjDuer1rViB/f8SXrfo3FYrnLarXOveqtuWbRXqAVXHnpkSI1RNJQtPOGhKJaEifQ\noxCIk+YUcgcmlmFkCxKFCFzB/2op4FUWE79idK4DjChEYpd6UiCNB0lHvvx37OIGjGIdEg7sUhts\n0h1uiYwl2OQ7seGb4YyQQ8njNcBVI/7KgLdo3j6Qm+/IIKyGg6SzRhZMieLc8YrXp88cOMfWhdvp\nPryr695CsHneVnYt30dRvo26LevwwLsj+XTcVxRpBDhbvnZHTZIkvnl2qktBrtwIeeuC7Tz51Xia\nd7V4XGM0G4ipH60Z6PMy8pjz/i+kJ7hPh+ek53JgzUE6VxDo13y/3i3Il38dG3/eQssbm1OvdV2X\nJLDHC4Jm3TzbDNC2TyvOqmRcN+nYmGZdm3ptUwlCCHYu20PSyWSadGzk83XVzd5V+5n03DS3pZvt\nv+7i0U/H0fqmFl6u9OOn+vEl0D8IPG+xWApwrc1LgLBarX8ZiyS71BGD8FRMc1IHm3TLld+fzjiJ\nQof6qM/3+9THTn8KpNEuudbigGinHzrlIHqxBzPzPdT6qgNfXOnUkHFAsZKfjhQM4iiyyCBP90+Q\nJOzSzdi5uTqb6saq79aSl5nPzjWh7FxTNfvQ8gFzxuuzWD55VWlQ3r18Hw3b1adN31bs/NVTDKZG\n7Ui6De/iti8/Ox8kicCQANb8sJ61MzeoPnP+J4tp/pN60Bw0vj9fPKYxESdLXDymntVv3V5xmWV+\njnqSJFDqIT/ksQHsW33AVVJYjg4D2tFlqHq53t0vDSfzUha7lu4hLysfWS/TpGMjxn0wxqfp3KTT\nKXz9t285vvMkQhEYTAZa3tSCv018FHOQtg5CdSOEYP5/f/XIz0hPyGD+fxf5A72f3x1fAn31FtH+\nCSmQHkEnTmFkI1KxgYyTWPKkp13uaMKBQWwDFISovHOUkCMoUgZgFjOvaB1czxn0fIde7CWfx3BK\nZap1TqkJAeIbdFchyEP1rt8bWI2kPKySuFh95OfkM/kfM9ilNupUQaeXcTo815zNwWZa924FwLnD\n51k7c4PH2vSpvWeo1aQWtRrHknCirIzRGGBgwLi+pUlvx3eeYN7HCzmx5zT2Qjt6g57CfO08imPb\nj5OTnktIpKeUcrdhnVkzYz2HN7kvN0TEhXtoHpTHoaGOV5665bT7Lye2sat0MiA4gBdmPsPCz5Zw\nau8ZZL1M864Whj41WLNUTqfX8din40j8WxIH1h0irlEsrW5q4fOa7bSXZnBse1nJoN1mZ++KfXz3\n2o+M/89Yn+5RHaSeT+PUXk8RKIBTe86QkZThIZrkx8/VxGugt1gstwIWYLfVal3z+zTp2sRJPZyc\nRiILhRjyeAGn3BaDspxAMRk9ri8YkdYUgzIGu1y5zNF86Tn0YicGjlW5ja6vQxtGdqATiWQp0xCy\n6wslSPk/THgv47taKJgqlTCoIwMDOyjiymdLtJj4zFR2LPaekGcONiHLMvGW2rS4sRlrv99AZor7\nUknHwe2p19KVjLn9110eDnQlpJ5P5eU5f2fJxOUkn0omMDSQ7nd0oV1fV2JhemIGXzwxya0Ur6gC\n8RhFEUiyehCUZZlnpjzBzLfmcHSLlaJCO3Vb1iEvM4+jW7VH7Q3b1NM8VsLNo3ux49edHN7s/lmt\n0zyeWx4rqwoIDg/m/tcrL6AZ1yiWuEaxFZ9YjovHEzi6Tf1v59CGIzgdTnT6in0jqgNZJ2v+XiRZ\n8msC+Pnd8VZH/y5wL7ADeNZisbxntVo//91adq0gBMHKS5hYW7pLRwYhvEGO8+8E8YG7narjGEF8\nTLbSBEVu7PtzJIki6WYMouqBvjw6LmAWMylUbkMWqRjxnP6tLCXjwMqO3iVslZraFxhwUr+ST/Gd\nhOOJHFjrXXioeXcLz0x+EkmCoPAgJEmiVc8WLJu0kovHEwgICaBtn1ZuiUTeAolOr6NGrUhGvzVC\n9bhWvb036raoQ3C4do5IcHgw4z8ZixACIQTbFu3k88cmap4f1yiG2yaUda5Szl5i+6+7CAoPoued\nXUsrAwwmA/+34EW+en4Gx3eeQFEUGrVrwO3PDSUksnJGQtVFRmImRRoJgAU5Bdht9t8t0NeoHUmT\nGxpyeJOnd0KTGxoRVtNTltmPn6uJtxH97UA7q9WaXWxqMx/4ywV6vdiKsZy8agk6zhPEZx6e6a5j\nGZjFL+Tzj0o9q0j0IJCvKwyITqKQyCxe39YmgCkEiClA9UytV/Uelb3OTgecsu8e6EIUYlJmI5GF\nne5ery0qtDP/08WaI29ZLzPyjXvp90Bvt5I3gBY9mqE36blw9CItejTzyKDufV9Plk9eRdYlTzGh\nZt28J4WpZZt7wxhgZOy7I306V5IkJEnCuvUYQlGfto+uG8XLPz1PVJ0ohBDMeO1HNszZXFpK+OuX\nyxj5xr207++agQirEcq4D68dEZsmHRtRs160ameptqUWpsAKFI+qmbteGM7Xf5vs1p6YBjHc9eLt\nv2s7/PgB74G+wGq1ZgNYrdYEi8Xiy3r+dYeB3UgaAVVG+8tZxncFthL00hmv2fdOwsmXnqNIGkCo\nMgoZ7xKm13o1qqsCwICEQMKBwIidduRKr/p8D4OyGZH6H4KLy/QE0yhy9iFXftOtXA8g8VQynz3y\nlWpmdwmKQ6Egp8AjyKclpDNxwhSs245jt9kJCg2k/cC2jP9kLHqD6zkRsREMm3ALcz9cQH6WK0BK\nskS7fm1o2LY+n4z7gkvnUgmtEULX2zq5ScuGRfuWCGgwuTLqn532FHENKze9bQzQlh1t1L4BUcXl\neiunr+G3yavcOgUJxxOZ/uoPNO/e9HdNbPMVU6CJm+7twS+fLHJz4QsIDaDfA31+99rsZl2b8taS\nV1j+7UrSk7KIrBXBoIf7/mEzHn7+2ngL3tEWi+UJrW2r1frl1WvWtYNA+w/TdUxdscspaScsaeGg\nNYqX0jeJAhQiXY5tXhKqrjW0pu2d1CdbngYiBwPbcYr6gA2zmIreeQZBCIXcj0OnkQ8qbASKj8F5\npnSXRCEmluIQ9SiUHnE7fdbbc70G+RI2/byVoU8Ndgv2k56fzsFyfu152flsnLOFkBohbtPxgx7p\nT+terVg3ayNFBTaadbMg6SS+enoyuRll5W6HNx0hPTGjdOq//4N92LZwB5nJ2iqJPe7qyth3RxEY\nqm5pWxE3jejJ6hnrPVTsZJ1MhwFliZu7f9unOvJPOXOJNd+v90md74/gjudvIyw6lC3zd3DRegFF\nQGRcOBePJ1KYZ8Mc9PuO6sOiQrn7MlEgP37+CLxlhawEOpX7Kb/9l8nEt0l34FSxYhXI2LgLB56q\naA4aUIiKWIvbDRTMynRCnI8R6hxDkPMtEJnYvby1MjaCxf8hKydwqjz3WsSl4FdL45iMThxAyLUQ\nBBHEm4TxBIHMw8huTKwjlMcIdj5frJrnjlEs0SwVNIqtbtu2fFuFJi4lJJ5MchO4OXfkAke3qPvR\n7199oFTUpgSDSY9OL6Mz6DEYdayYstotyIPLknX9rDId+FqN43jogzE0at+gLJGr+B9zsJmut3fm\nkY/HVjnIA8Q3rcWdf7+NkBplWfrmYDP9x95Mjzu7lu4ryNUun8vNuLbVr/uMvAlzkIms1Bxy0nI4\ne/A88/+ziA9H/bfC5EY/fq5XvEng/n71KNcwQgomT/p7sX+5SwZXIRQbt1Ao3+9aVxdfo+cAADpz\ne3JsD6n6oJcnSPkXJhaWjnQNHMLE4uL7m5Eo1HB0S8Es5lDACIysuKZH9go68vgnDqkdQeJtDOxF\noqz0zMApQsQLFDjvxswi1XwHCYGJtTjFZAqkR8vurSgUZicRojnh4j5qdToVnHbvOQ0lhEaFEBZV\nduPkU8maiV656Xk47U70Rtef0srpa5nz/jxy0lyBfdmkFZpJYCnnUjm86UjpaLrjoPbUbhLHL/9Z\nSF5OIeFRodS2xNGmT2vim6p3lirL4EcH0HloR9bN2ojT7qTzrR1LqwZKqN2klluZWgl6o57mPdTr\n9q8ExamQnphBYGjAFXVkADb/spXdyz11/o9stvLlk5N4ZsqTV3R/P37+jPyh6+4Wi2UQ8CmgA761\nWq3vXXZcKj4+BNc394NWq9VTdeQqY5d7kyW6YRRLkMjFzs0ocm0AFKk+ubwHwhXAosPDUC55qoaV\nR+9cjomlHoG8JAi6FOwkVEwDAZBJxsAGzeNXi8plzoOddhTphgGQI74hRHkEI+516zIFBPJjqT6B\nFgaxnQIeBSFYP3szy6esBsdxPvpZR1CIZ+23E/eKh8CQAOq3qcfBdYc9zr2clje2IDiibNTbrFtT\nwmPCVKfVYxrWLA3yWZey+Pmj+aVBHkAoQtMlTm/Uu2nXb1u0g2kvzyTrUtlz6rSIp33/dmqXV5ka\ntSK54zlt2dHBjw7g8KYjJJ9xT2xr378trW6sXrGXldPXsnrGWi5YEwgMDaRFdwsPvjuS0KiqiRcd\n8VI6uH3JLma/N89DY9+Pn+udP6ygs9gw5wtgMNACuM9isVz+LTIYaFL8Mx746ndtZHkkE0XycGzy\n6NIg735cdv14u4WSTbDzOUJ4rcLA5m2krlADPYd/92Q7J7Ww0xg7jXCg8h6UQwIMHEFWir94JQkd\nyRrnen8vAGTOEua8l8C8AdSLeY3o6L2cOqhn7YIwlMt0bJzUplDyzEgf9vQQImK1TWYAwqJCeOiD\n0W77QiJD6DK0k8e5piATN4/qVbq9duZGslI8M+61aNqpMfVbu+rWnQ4n8z5e6BbkAc4fvsDcD+b7\nfM/qIN5Si2emPEnPu7tRp3k8jW9oyKDx/bnv1bs8linUOH3gLF8+NYmX+77BW0PfZd7HC3E6PDtj\nm3/Zxg9vzuLMgXM4ihxkp2azdeEOPn/8G5+eo4bB6GXsImD9rI3kZl7byw9+/FQ3f+SIvjNwwmq1\nngKwWCyzgGFA+SHXMOA7q9UqgK0WiyXcYrHEWa1Wdf3Oa5wg8W9MrPP5fDV/dycR2KThBInXvVxX\n/Rn3AtCT4LZtJxY9SZrPksknWLxJvvICDrktAk+Nel9xTetnoA+ADjeCpW0W/36sPv97MZ4LJ0x0\n7ptDg9Y1MIe3oUC6H0X2LGdreWMLXvrpOf736EQuHL2o+pyYhrEEqUwfj/7XCEJrhLDrtz1kp+ZQ\ns140ve+/0c2Mxl6k3WEJCgvEVmDDUeT6fTZq34AH3inL49i35iDnj6i36fjOEwghPDLHN8zdwsbZ\nm0lLSCciJozuw7vSZ1T1OLzVa1mXJz5/hKTTKXz/+o+s/2kTK6eupl6rugx8uB/DH1dPyDt3+Dyf\njvvSrazMuv04CSeSeOqr8e7tn70ZW77nuvmRLVYOrj9M616+l1iW0OOOLqybuQGbxnp8emImu5fv\n5aZ7elT63n78/Fn5IwN9beB8ue0LQBcfzqkN/OkCvaSkYmBbpa5x0AhBCAb2A04cNKNAGoNTaopO\nqL8FFY2Dqi5647mtJ6n4f9pPNXCUEDGBPOVZHLT1sPv1BbWOS1AoPPPheUZ3asm8b2oy75ua3PXC\nMO54fpjXe9VpFs+oN+/lw9GfupVhlVC7qWfiJbiU5oY/N5Thzw3VvHenwR1Y8tVvbh7sJfS4qxs3\nDGzHyd2nqFkvmq7DOrs53onLpyXKowiPQL9i6mp+eHN2qVFOwvFErDtOkJuVx9Anq8fP2+lw8vnj\nEzm1p0zO9eSe00z/5w/UaxJLnbYNPK5Z8vVy1Vr2nUt3cWL3SRp3KEsiTU9QL0912p2cPnC2SoG+\nScfGDH16MD9/tFC1ckDWyRXO6vjxc73hTRlvB16+wa1Wa+er0qJqICIiEP3vpIKlRnS0K5FLKVgB\nBXNBSQSdGRze1+7dkTGE3IccNBLFcQ6EDaO+ESZJRik6BOlaJXjeqc6RvuteFU+xyuQQIs+EiCmQ\ntgGEekmiOgbNqf3oWk5uGprJ+kWude70C2ml7703br67K+tnbmDzwh1u++MaxjDy5eE+3UO1PX1a\nMvDBPiz4Ypnb/sYdGjDuX/cSGRvBzXd1Vb227z3dmPveL5w9fMHjWIvuFmJiypI7nU4nG37a6OGG\n57A52DRnM6P/ORyDseLZE3uRnfzsAkIig1VlWZdNWe0W5EvIy8pn6ZTVvDLzGY9jyafVl2eKCuyc\n3H6CbgPL8g1i6kVzXmVmRaeXadOtaZV/D4++N4q086ms+dFT8tnSqRF97ujyp/Q8r+r74afqXC/v\nubcR/d+L/70FaAZMLt4eC3hqO1aei0D5dN/44n2VPceDjIz8ik65akRHh3DpUg5GZSFB4kPk+qcK\nBwAAIABJREFUctnf2vXkoUg4kckrd66CLWcLSm4CskhBkWIoZARCDkN2FhKuca9rFucJstP2Ionn\nCebVSiT1KZrnShKM/nsSLTvnsfHXMNbO3kx8izoMeqRiY6FHP3+YyDpRHN58lKKCIuq1iGfIE4MI\njArjUgXJlFooToW0lGz0Jj0OmysBL6RGMMOeuRWnTl96X6fDybaFO8m6lEX7AW1LFfYGPzaQ79+c\nRW562ecgJCqYWs3iSUrMLM3eT0tI56zGNP+5Ixc5sO0EdZpp6zjYbXZmvDGL/asPkpOeQ0z9mvS8\nqztDHnOfjj++X3v2Je1imur7pDdpdzBko8Htms63dWL/usMeHRZL16bU69Coyr8HgDHvjCLjUg4H\n1x8qXS6p2yKeEa/fQ2qqun3vlZKbkUvKuVRiG8aUGhVVFyXfK35+P/5s77m3Tom38rp1ABaL5QOg\na/E6ORaLZTGwuRratQNoYrFYGuAK3iPAo/h8IfBU8fp9FyDrT7E+LwRmMdctyEPZJHf5wFUyHi4f\n5EvONbG8TClPgJFl5Cr/wik3R1Ei0XlR5rvWcKngGbHLgyhU9mFmIVKx0Y2CEQmHW+ldCZfnKFxO\n3SZF1G2SxuD701k4NYo187b6FOgNJgP3v353lV6LFr/8ZyEb57j/aeSk5TL3/fm069sGnV7HkS1W\nvnt1JmcPulak5n28iG7DO/Hgu6O46d4e1G0Rz6oZa9m36iCZyZnkpOYy619z2Dp/G2PfH02TGxoR\nGBpAUFgQmYWe6ouBYYGEVZCxPum5aWycu6V0+8yBc1ywXkRv0DFgXN/S/ZdL/JanRq0aqvvb3dya\nQxuOeOyPbRRLr/t6uu3reVc3cjNyWfPDBs4fvUBQaCAtejbjwXdHXfGI2xRo4h/fT+DgukMc33WK\n8Nhwbrq7e2mFRHVSVGhnygvT2bv6INmXsomsFUHHQR0Y8/Z9bsszfvz8UfjyqY8EzECJioapeN8V\nYbVaHRaL5SngN1zldVOsVushi8XyWPHxr4EluErrTuAqr/tT1PZLpKHjpMYxz20d6pnal5+r5xyB\n4kty5C8pYBRBfK4aHK9NZHTiNA65E/m6lyhSBmEUa0GSKGQQgeJLTCqeAuBbcqEpQHDb2FQO7jyF\n4lT+kC/YfRpGOWcPnWfLgu10HdqJaS//wPkjZdPzeVl5rJy2lpr1anLrE4Oo37oeIZEhpF5Ic7vH\nmQPnmPbyD/xr6asEBAfQsmczNv289fJH0bJnc6+laSnnLrFn5T6P/Y4iJxvnbXUL9D3v7sbK6Ws9\nLFcDwwIY9FAf1fsPfmwAiaeS2DJ/OwU5Lj+BuMaxjP6/EaoSvIMe6U//sTeTci6VoLBAQmtU31Sp\nJEm07t2q1Eb4ajHlhe9Y/1NZBy89IYPlU1bhcDhx2p2c2H0SBDTu0JA7XxhGVG31TpIfP1cLXwL9\nT8AWi8XyU/H2PcCs6ni41WpdgiuYl9/3dbn/C+APUbiQlCQCxcRSIRwHLcmXHkPI6sla5REiAK5S\nANZzgCDnqxjYioRyVTLsvVHV50koBDCNIjEEIQXjkNvhoGy9Ns/5EgZuVzXqkfDN6tYUIOh1W7Zq\nkHcUOZBk6ao6mGkZ5QBkpWSxcd5WtyBfnj0r9nHrE4MA2K/RYTi9/ww7l+6m860defDdkeRl5XNo\nwxHsNjt6o45mXS089P5o1WtLOL7zZKlRzeWknk916yTpDXqe/Go8378xi6Nbj1GUX1Scdd+XG/q3\nVZ3WlGWZhz96kEHjB7B7+V6Cw4O58e5uHt4B5dHpdcQ11J49uJbJzchl76oDqsfWz9ropqFw8VgC\nZw6e442FL12TfgF+rl8qDPRWq/VVi8WyDehdvOtVq9X661Vt1R+NyCdUPI+eMtlTPafRi2Nki0kI\nKVj7UiWfEPE4koZe/ZUiYcPM0nLbvy9qzyvMh2P7A6jdwE5RoZ7IWB0mk2etso5kjGIxNsnTqlXI\n0ShKPLKKpK2CjkxmEshUdJxAJhUdqR7nAcQ1qum2fXznCeZ/+iun951Bp9fRtFNjRrxyF9F1o3x6\nvZWhdpM41bK9wLAA2vdvy44l2lpP5fXnC3I0JGiFqzwMICgsiBd+eAbrtmOc3HuGei3r0KJHswqn\nvOu3qos5yKRaGRBWM8yjkxTXMIZ/zJhA1qVsbPk2ourU8MlPPb5prUqr+dnybdgKigiJDP7TJMsl\nn0khO1V9Rk5NKOnswXMsm7SS25+59Wo3zY+fUnxasLJarYuARVe5LdcMZjHLLciXoOcYJjGTQmm8\nylUuRPabGNH2OvdmWuMb195UvTkQ5nwRw651oShO+GrVaRo00zpbY81d0uOkpqp2vYwTo7SJPPkt\nAEzK9wSLTzzv7JSo1XpE6ZxA0ulkPn/sGy6dL+sUbLm4naTTybyx8J8YzVWv61dj0Pj+HN95kvRE\n96qCLrd2olbjONr0bsn8/yxSrfGu1bhspqh2kzgSTyR5nBMUHkj7AW3c9lm6NMXSxbsFbnlqN61F\nyxtbsGuZu0IhEnQecoPmdb6661WF7NRsvnv1R45ssVKYZyO+WW0GjutL9+GXV9tWP4V5haReSKNG\n7UgCgiufQBfXKJaI2HAyknx3q0w4llDxSX78VCMVBnqLxWIBXgUalT//Wi6vu1J04ozmMZNYik25\nDyF7riVKIhts6uvMJTiph8RpTevb8qhNk18tbXutKXlfpuoVBex2CafDdeah7QbVQO8kiiJu8dgv\nK2cxsRCZFM1nGMSR0ol7m3QvRrEVI2UJZUKATRqIQy4TjPlt0kq3IF/C6X1nWTVjLYN9SNqrDJbO\nTZjw7eMs/WYFF48nEhgSSLu+rRn6tKuuvUGb+nQc3J5N89z1FCLjIhg0vqwtgx7pz4ldp8hMcVfJ\n6zqsMzH13GcsqsKjn45l8j/0HFh3iPysfKLia9B1WCduf7ZslOkocrD6h3Wc3H0ag9lAt9s60/LG\n5lf87MsRQvDZ+K85vKmsY318xwkSjiUQFB5I2z6tq/2Z4Kp8mPHaj+z6bS9pF9OJjAunw8D2jHn7\nvlLbYV8IDA2k4+D2rJi6xudrAsKuTM/fj5/K4ssnehYwB5iK5nDs+kKgPTWv5xwh4lmyxZcguScX\nSSINhPqUsuu+LhEcmRQkcgAdAiM61CU5BUEU0RwjO6/6FL1mCZsP1548ZGbfxrKOz/cfx9J1kJmo\nmmUjF4UACqX7EbK7WIlZ+ZEA8Q2yRkJi2fXl1jQlAznyJxiVn0g+spS0hEy2/mZi10Y77QfMYuQb\n96DT61SDfAlJJz1HzNVBk46NadKxsebxx/73MDXr1+TA2kMU5BYQ36w2gx7pT5MbyoRkWvRoxtPf\nPMbyyatIOJFIYGgQ7fq1ZuhT1SOEExwezIRJj5OZkknK2VTqtoh3WzO25dv4eMxnHCyXPb/hp83c\n+uQg7n5xeLW0oYSdS/dwZItntW5eVj5rvl9/1QL9D2/+5PJLKCY9MZOV01zBuqI8h8sZ8/b9GExG\ndv22l8zkTKLia2Dp0oStC7aTn+2+DBMUFkjvy6oP/Pi52vgS6GWr1frOVW/JNUShNAyTWKoZfAzs\nwSTmY5PucduvSHEg1QVF2/PczLJywVMB7JqjZgdNgfBqC/JXI3EvJcHAjI9iUZSyO9sd4aQ53iZA\nWo5enEAQhE0ajEN214uXlGTMYnKFQd4lt3vZNK5kYMb7ASz41ASUJHKlsuybFSgOJw++O4qQSO0M\n7urM7q4MOr2Ou18cXmHAbN7NQvNu1e8UV57wmuGE1/RUiZv/6WK3IA+u2vvFXywlvlltug2rvsm8\n80fOqyrYAaReuDrlo0UFRexa7ll5ALB7+T7ue+2uSk3j6/Q6Rr11LyNeuZO8zDyCI4PR6XU0aFuP\nRf9bWqoUWLNuFEOfHkKDNvWr42X48eMzvgT6LRaLpY3Vat1/1VtzjaDIFvKVCQSKj5BRT4zSi6Oe\nOeCSGUxDEPlfqwbUygRZhWAEoRg0Ss7+SIQAJJnsvI68+3QgR7enYzAq2ItkZL1Mr/t6ElWnHoU8\n4vU+JhaqWtNejgTIkntnwFHkYPviXarn71y2h3tfuZNe9/Vgx9Jd5Ge5/w6j69Sg/0N9Va/9M2Hd\ndpwVU1aReDqFoPBAOg3pQL8H+ngksimKwv41B8lMyaLjoPZuznyXs3L6WpZ9s1L1mN3m4IsnvmHH\nr7t4Y/ZzVWqz4lTYunA76QkZtLqpJTH1tbPtw2O8Wz1XlazUbDIS1T936QnpZCZnVWm9Xm/UE1az\nrM19R/em553d2PyLa6mm+/AumAJNVWu0Hz9XgC+Bvgsw1mKxWIHS+qHreY0ewCbfjt65GzPqBQYC\n9XU2KeRZ7PlrMagk82khAQ7qIDAik4+DFkgkV8oAx9fnXCm2Qti+KpROfbIJDMrlrRkBiKLTIOwk\nnosiOf0uWvW718f2+OYPL4TE4m/2c2hnMrUtcQx5dAC2/CLSLqapnp+ekEHK2Us062ph5Bv3smTi\nci5aXcsI5mAz0XWiOHPgLG2ucn311eTghsN8+cQkt3X8wxuPculcKve/7ppp2r54J4u/XMaZg+dK\nlfpmx8yjz8ibVGcUdvy6ix/e+glbvnYZo+JQ2LpgB9Pf+InbntW2ulXjxO5TTHnhO84ccM14mYMW\n0mFgOxq2b+AhtWsMMNLjTnW54CslLDqMGrUjSD7tqckfFV+DiNgIlauqhinQRJ+R1WM05MdPVfEl\n0HsKWv9FsEm3YBKrkHCvj1YIoVBSNzeRJAkH7SsV6IuvJFs3GwC9solQoT1i+r1r58uTdN7IO4/W\nZ+w/E7j7CSuhgYcp6fOERVygCT+RrdyEIter8F6KiEQgVyj6Y90TyNQ3zwHnYTFsX7SLRh0auDRw\nVQiPCSe6jqt8rs/ImwiOCGLihKnkZ+dTmFvI4c1WTh88x6g3763yl/Cx7cdZNWMdaRfTCa8ZSq8R\nPatVmGX7r7tY/f06kk+nEFIjhBsGtOO2vw0pHa0v/WaFR7Ke4lTYMGcztzwxiANrDzH1xRkUXFbb\nn5mcxcL/LSG6ThS977/R7diGOZuxqZTdqbHzt71kpOZi3XYMu81B/dZ1ue1vQzSldxVFYdrL35cG\neYDCPBub522j75heBIcHYd16DFtBEbUttbh5VK9qXSIoj9FsoNOQG1h8mScBQMfB7TEH+Ufdfq4v\nfKmjL5HCDSre/suYOTvkLuQrD2EWs0rlZp3UpEB6EEXWXj8tlIZiFnN8HrGCuye7niOa1zoxoatA\nOOZqUrdxEe/NPknKBb2qPK2ORMxiFvm8qHkPSbmEWUzCzJIKg3xqkoHPX6lF+a7NxWMJXPRSotSh\nfxsCy1nNLpu00q1OHaAgu4DfJq+i14ielVbR27l0N988N9VNk37PqgOMfmuER/CsClsXbmfS89Mp\nKE7kSj6dwoldJ8lMyeKBf9+PEILzh9SFd7JSstkyfzvbFuzwCPIlOO1Otv+6y6OtGclZquerce5o\nAif2nCndvngsgZN7TvPij88SU9+zMmD3b3s5te+Mx35wVUG8vfx1Us5dIicth3qt6lYq870qjHj1\nLgB2LNlDWkIakXER3DCoPSNfv6eCK/34+fPhS3ldQ2Am0A4QFotlDzCqxEf+eqdQHodNuR0jSwCZ\nIoYiZO81xYpswe5si4FdHrr2WiNxJ7WQlHRkTiOEt/v/sdrZkgRtu+eRkqCtMCcX+9bLyll0nMVB\nm9Jse5PyCwHiK3SoT7uXx2Yz8dTAhmRc8pROVSO8ZhgdBrbjwXdGlu7Lzcjl7EH15Mhzh85z5uA5\nGrat79P9ARJPJTPxmankZbr3d0s6Djfd2+OK5XdXTV9bGuRLEbB1wXbCokPYuWQPaYnaiWpHt1pJ\nOOndEuLy9gPUqBXBSW1NHzeKVLQAkk4ls+Tr5Yx9b5THsbSEdE2jw/wcVyesZt1oataNRgjB2UPn\nKcwrpHGHhldFzVCWZe5//R7ufnE42ak5hNQIqXZdBT9+rhV86TZPBL7BVV4H8GDxvuotQr6GEXIN\nbFSi5EbkoyNBVdfeiRndZUsBotiKNUzcg44MnESiEIaM5whLp5Ec+HsT5sXtQKKAYOezGNiBTIGr\nft7ZlwLGEIBvQV5gxHr4JjIuadfWX85tzwxh0Dj3j6XeqC+WX/V83/QmfaVcxhSnwmePfKkaJMGl\nenbBepG6LeqoHvfpGYpCgopYDkDWpWzmfrAAxel9FiQnI5fA0EBy0rRd2mIaeI66e913IwfWH/bs\nZFyGMcBAUYG6dXCiRtli+/5t+fnDBeRmeL53tcsp6Fm3H2fW23M5sfsUTruTOs3jGfRIv6u2zm0w\nGahR+4qtO/z4uabxJdBHW63WKeW2p1oslglXq0HXAwaxFx1aIyozhfQvHu1n46QOAj1GyooaypYJ\nopDJqNDB7Y/AFTzV2yVzCn25bHodqZj5CQPrfArydppSID1ChKUbNeJfI83HMquQCM+SOXOQGUuX\nJqoZ+k07NSa2Ehrrq79fX+o6p4bBbMAcfGUa5rIsExQWpKm0VlGQB0g6kUyHAe1IPq3eSYqIC2eA\nStVB+35tePCdkayYuprzhy9gCjLTrGsT4i21sW47Rm5WPrUaxXJ810lSz6v/HgPD1DtONetG03VY\n59Ja9RLCaoYy8GFX56wwr5Bvnp3qpgp4/sgFvn/zJ6LrRmEOMrNs4nIuHE8kIMhMm5tbMexvt/gd\n4vz4qQBfAr1isVgsVqvVCmCxWJryFxHOqSpOYhAYNfTug8mTXwOcSOQjKcmEMUb1PhJpKERU2Y7W\nSSCgIFNY7cl7QgoD1NeA1dz4JEBPxSI1DhqQLU8BKYDAELh9wq3M+vdcTSOWEmpbatHl1o6qx0a+\ncS9pF9M5WS6zO75ZbUa+6Vt1QAnrf/Je6mjp3ISadaMrdU812vRpxQWrp2a+rJdRHBUH+uy0HLoN\n70x2ajb71h6kKN/1OdSb9DTt3IQ7nh1K4w4NVa+98e7u9LyrGxlJmZiDTORn5bPoi2UU5tnIy8jj\nzIGzpF/U/jyqzRSU8OC7I4muU4M9K/aTl5VHXKNYBjzUlxY9XDKKK6auUZX+LcguYMlXv3H+6EXS\nyj3buv04yWdSeOzTcRW+J378/JXxJdD/E9hgsVj2Fm+3hcrMY//1UORG2J0dMOJpI2qnE0g6QIcQ\nBkJ4UtWxDUBGQBWDvEuFrxkGTrlVDZQsk15J4HcSSyH9COJ71ed6m4HwJrXroBl50ssglY0K+47p\nTV5WHrPfnYfiVF/kjawVwd0vDtf0Go+uG8Ubi15mw+zNJJxIokatSG4edZOqbao38rK0Oxt6o46R\nb95D8tkUzuw7S4N29X0O+vvXHGTD3C3kpOUQHV+Dvg/0Jislk90r9lGQU4isl2lyQyMykzNJPuNZ\nEnY5TruTQxuO8OzUpzi55xTW7Seo1TiWtje39sksRpIkIuMiSDl7iQ9Hf1pamugLBdnaDn6yLDP0\nqSEMfWqI6vHMFG29+DMHznlUGQBsX7yLweMHUK9l1ZdL/Pi53vEl636ZxWJpCaXSZFutVqu2tqgf\nAPKkF5DEW+g5UGwna6SIruTJz5eeYxDr0XPsqjxfAozsVs0TqCxOjIAJmVwkBDJJmFlGERaMuMuX\nCkwIioo7KertUtfwB5lUZLI9ugmHN1s1gzxATN0oOg5u7/U16A36K17nDYsOVR1xAnQc3IEFn/7K\n/rUHyc8qIDAskHY3t2b8J2O9dihWTlvDj2/PdXOs27fmAI9//gjDnh3KoQ2HiWsYS+veLfnxX3NU\nS8LUMJpdz2zUviGN2quP3iti0RdLKxXkAdr2a1PxSRrENozVPOawq3eGC3ML2bf6gD/Q+/HjhQoX\ntywWSysg32q1LrZarYuBwuLA78cLilyPbHkyOdKH5EkTyJa+Ilf3idtoVceZCsvLroTqmK53EkYR\nfZHJKTXUkXCtuxtUOikyNs0gDyDQUcDt7tr1xehIxSw8ZwkyK3AGO7rtOJsvM4opwW6zs+jzpXwy\n9nM+ffhLVs9Yh6Kov+c56TnsX3uQ1Avq689NO6lr2JuDTShOJ1sX7ChV4cvPymfzL9uY9soPmu22\n2+wsnbTCw5Y29UI6iz5fQnzTWgwc1482fVohSRIjXrmLfg/0JqymqypDp7E2LckSSaeTOXdYO5/A\nF84dVi/h00LWy3S4gkDf5/4badiuvsf+iNhwouJraF4XehWd9fz4uR7wJYtlOrgtNhcV7/NTEZKE\nXe5NoTwGh9zO47CDpgjfnIJ/d1xT6U3JkT7ByBYNSd/KO+k5qYedgcia6/tWEO6BL7KW96xoIVyl\ncpdjt9n5aPSn/PivOexYsptti3by7d+n89WTkxCirO1Oh5MpL37HC71e4717/8NLfV7n03FfkHdZ\n7f3wZ4fS6qYWbvvMwWZueXwQ1m0nVNu2f/VBVe93gMMbj2jOEJzZf87Dz1zWyTz0wRjeX/svXpj5\nDMERQarXCkWw7seNvHP3R2xduF31HF+obLmZ4lQ8NPIrg96oZ8KkJ+hyW0ciYsMJjgiiXuu6xDWK\n0dQEiLfUoucdV0dBz4+f6wVfAr3OarWW1tJYrdYifPSx9+OOrJwh0PkJQc63MSmzcNABO9oe4H8k\nghDstEZgQtJw16sKTmIQUphqB8dhh6XfhzPj9Xks+mIphXmuL/deI3pgqkCtLETFpGbZpJUcWHfY\nY/+WBTvYvXxv6fZP7/zMymlryUpxJRHmZxewbfEuvn1umtt1pkATL8x8hofeH0234V3o90Bv/jnn\neboP70LWJXVjnuzUbHLSc1SPmUMCNDPG9Qad5rHQGiHUrBtNVqr6fcuencOCz5b4lKmvxuWdmgoR\nMHHCZI7vVO/0+EJ03SgmTHqCT7a+x61PDibpVDKHN1nLKgjK9Tjjm9XmgX+P1MzN8OPHjwtf/kLs\nFoulYYlAjsViaYQ/6947QmASCzGIdcjk4SAGgYyZtcgUfzkLMPEbubyFwkQMbHZzcdOadlcw4aQ+\nBjytPavUVI1nyeQQwM+YxWJAvWa6KsgU4pSa4hCtMbCndH/KRT1vj6+PdU8QsAqAtTM3MP6TsXQZ\n2om8rHwW/m8JKSrJaLUax9Lvwd4e+0/sVtd0UpwK+1cf5IaB7VGcCrs1nMwOrDtE6oW00mljIQSL\nPl/Kll+2c+l8KqHRocg6mXtevpPYBjEknU72uEdMg5pExHo6xIFrKaBhu/qc2OXZTkuXph6BPjcz\nl4WfLeH0vrOa9eqXc/bAOY7vPIEp0MQFawIN29XHaXcWj5i1zW0Ahj45mPOHL7D9110eswtapCdk\nsGTiCiZ4ser1ldXfr/OU5BVQu2kctz45mB53dr3qCnp+/FwP+PJX8hawyWKxlLi7DIEKbMn+4gSK\nTzGLmaXZ51oToAb2Y+YH8nT/BlHgKrcTNgLF/zCxXPUaGRuCdPJ4Cj1HkElG5hQ6vJefaVHROr5U\nzXK7CjVBksiTniVYvIkeV5D79u1axUG+jMQTScx6ey6vL3iJm0f1ovf9NzL73V/Y9PMWV5mVBA1a\n1+Oxj8eouo1prWED6AwutTVbgY3sVPXReH52AQknEksD/cLPljDn/V9KSxcunb3E8imrKSq00+32\nTsz/9Fc3y1VZJ9N9uHYwKll3n/T8NLea90YdGtJ/bB/WztxAnea1adS+Ifk5+bx/3385qdF58cZn\nj3xNdkYOziInkiwhFEFwZBBt+rRm3AejNZ3aZJ3MU18/ypEtVvatOUhGQjoBIQEYTAZ2/7abxFPq\nFQAXjlZubb8EIQTbFu1k9/J9JJ9OVu3UARTkFnLjPd2RZfXfr9PhZOPcLRzbcQJjgJGed3WjUbsG\nVWqTHz/XA75k3S+2WCy9gH7Fu96zWq1Vn5u7zlEcCZjEIp9FbgysRVLGIeSaCAIQEuSKtxCKDhPL\nNfTkL4GkkCt/AECE8+ZqfQ0VIQBBGAKjqy2VuE7BFWCdckuyxAxMYj5OWxIHtx9HTb3uxO5TnDlw\njgZt6iHLMiNeuZPbn7mFXcv2EhQeRJs+LYmJCePSJc9p7NZ9WrF14Q6P/cYAA11u6wS4BHWi60Sp\nKraFx4TRoI3LnCfheAK/fLJIVcZ18y/beHv5qxgDTGxbvJOMpAwiYyPoOrwztz4+yOt70qJHM/69\n4g1WTFlNVmo2cQ1jOL7zJB+M+pT8rHwMJgPNuzWlVpO4KgV5gIzksmTGko5Ibnoem3/eirPIwYRv\nn/B6ffNuFpp3c/d2aNPTwrsjP1M939fR/+VM/+cPrJy+tsKlBr1Rr1kmWFRo5z8P/o/9aw6W7lv3\n40Zuf+ZWbntavazveibpVDIH1h2iVpM4WvRo5lN5pZ/rD1/nvZJwldX5qIT9F8a2DBnvWeLl0ZNK\nmLgfmzKUAulvQCFmMQOJXBQC0aG+Dluybi6JdKQqjuarigScSnyLbYsv0OqG7TRtk4hZf9yn64xs\np0BJRyYRk1jsep1SPSSd+heQ0+7k04e/IN5Smz6jenHDwHaYg8w+WZj2GtGDI5uPsvmXbaVCM8YA\nA4Me7o+lcxNXmySJ7nd25dyRCzjt7p2qjoM7EBLpWvuf/MIMVX13cOm+v3v3Jzz/3VMMm3ALDruj\nUlPKgSEBDJtwCwAz3pjFxrlbSo/ZbXb2rz3k5vpWnexfd4ikM8nEFvvC56TnsGLaGvKz8mnYrgFd\nh3VSHTn3urs7U1+bRdIpT/W9zJQskk4nE9vAd9XBYztPsPbHDT7lE1g6N9EMWAs+W+wW5MFVgrf4\ni6V0u71zqavh9Y7D7mDSc9PYvWwvedn56Aw6mnZqzCP/GUusF1EjP9cnvpjaDMGlbe8E6lsslo7A\nG1arVd2n9a+O5H3dUw0dGQSIH1BEHEaWYyy3dq2GQIdThBKgTEIIAwJJcwq+Mpa2vp6bnRXC67f+\nQGqiK/BF1YnlP/MVYuJOVnitjlSCxPsY2IqMS4vdbIB3fojkhTtiyUzzXOhIOZtKytlbZ/55AAAg\nAElEQVRUjmw9xkPvj6aHj1nWsizz+P8epvvtXdi7aj86vY6uwzrR5LL141seG4iExKaft3DpfCph\n0WG079+WEa/cCcDhLUc5ssV7TkRGUgYTJ0yhRY9mBIcH0W/szYRFVa7sSwjBvlUHVI/laujrX44p\n0OTVT/5yCrILOHfoArH1Y9i5dDfTX51ZJjksufIknpv2FOYg93JInV5HvCVeNdDb8otYMXUNo/9v\nhM/t2Llkj6Z+fnmadGrE/V4c5qzb1DucuRl5bJi9mTuev83nNv2Zmf3uPDbM3ly67bQ7ObLZyuR/\nTOeVuf/4A1vm54/A1zX6TsBSAKvVurM4Ic/P5QgFlCwUzJrlY1pIODHxIwYqHrkpRBDMRCRRVLyt\n7e6lYMJBa4zsqrAczmW6E4yOMjMUteC/eq65NMgDpJ7P5tmhUfx3VQdqhK1CJt1rh0HPjtIgX0K9\nJuk8/IaJj/6mPdooyC5g5bQ1dB/execpSEmSaNevDe0qqO8e8tgABj/aH1u+DaPZ6JYIN++DBZrO\na+U5f+Qi54+4pGvXztrI2HdH0WGAZ1mlFo4iB7kZ6kY0vmbOF9nUZx20CI4IomHb+jiKHPz0zs/u\nvgICDq4/zE///pkHyjkClmAwa399aL0OLSQv9T9xjWJo27cNdZrHc9M93b262ZXPkbgcLf2E65F9\nq9U7jNZtxzmx+5SmBLKf6xOf5hetVmuSxeK2RvfHGaJfwwQp70DuL15rFr2Nmr1N+TuJQKEuLq+7\n/W7HZC/5ADI2DBz0uebdwQ0USi3RiYsoRCAQGNmEjiQUotjyWygT3/C8Li2hkNlfNuK+V15EL7YR\nKD5TrQxwEoUOdWHF00dD0Rv1Xtd4j+86yUXrReKbxfv0eiqDJEmlI9eM5EyWTlzOpXOpHN9T+bXx\ntAvpzH73F9re3Npnm1WDyUBMgxiyVcrmwmPCad27BdsW7igd+QaFBxJdL5ozB85SorskvCgIqtG2\nbxui4muwYc5mLh5TN2LaunAniaeTQUCTGxox9KnBANSsp90pU/Ok90b327uwYuoaClXq5XvdfyO3\nacjmXk6j9g04stnzcxcQYqbrsM6VatOfFSGEpjeEo8hB4skkf6D/i+FLHX2OxWKJoXhMY7FYekMl\nFqH/IsjKMYz8pnrMSSAKYdhpQiGDERojcEGg5v2LpKFk66ZQ2cpGl7Ss77MLDqkVhfI48nSvU6B7\nmkLd38iWZ5IpLyJL/oklP7ZDUdS7KrY8G0gSDrkr2fJkiuiGKPcRc1KL/2/vvsOjqtIHjn/vTDLp\nCS2hV4FDBwlI76iAUiwoFgQL9rLs2tG1rK7urvpzXV1XRUUBlV0rVgQEadKlw6GXJJRAIL1N5v7+\nmElIMjOpk0wS3s/z+JC59cxNnHfuuee8bw7DPe678rtIvpkdVOpALofdwfuPzi2S8MaXTNPk189W\nMWv0c3z31k+s+3YjuWXoUvbk6K5jbFq0pfQNCxl+4xCCQt1T5vYb34d73riDp796jAkPXsHVD0/g\nxcXPEB4VRlmTKwYEBWALsWENsNKoRUNG3TKMGa9OByixNG1yYjLbftnBtmU7+OKVb3jlljfIzcll\nzIzRNOvgnrbWFmLzOqXQm9bdWjFmxmgCiyXp6X15L8bddVmZjzPxD1fSoVgGwwCblZFTh9OiUDnc\nuswwDJpe5Hl8RFR0JN2HSWLTC01Z7ugfx9lt31YptRzoAFwYD7rKwcZKLF4GxTloQ4rlHSAYMLA4\n0ghiZZFt8mhONmOxMtvtjt9BOFk472iqcuCdg3CyTffypRhWTJzPm1t0au553rkBHV0D3JyvQ0i1\n/Aub+QtWdmISSbZxDWBgc6zEStE556t+jMKeU7bu+H2bDrBl6XYurkS6VU+O7DzKR7M+Yc/avWXq\nqi+L7b/u5Ex8ErFjepWpyM2IG4dgtVr49bNVnDqSSGTDCGLHXMykmVcC7rnrz5ZQCKa4K+4Zw7WP\nTCQ5MYXwemFFcvD3m9iXr//5HedOuheOKW7Hil388N5SBl43iAffu5eXrnuV5EIFZ3Iyc5j37IKC\ntpfVdY9fTfdh3Vi3cD252XY6D+zIwKv6l6sMbVhkKE/+90/89N4SDm0/gi3ERt+xvek7rneZj1EX\njLxlOAe3HXH7AnfJ+D7Ui4nyU6uEv5Rlet16pdQIYCDOG8Q1Wmu5oy/GpKRBeFYwzt+tp1n+hsP8\nNwHmRgyyyaMjmcZU8gxFgCMBGz9juCraOQgj05iKw+IMonm0J4BDHs9SWRbSqMc08vKakEc7V8sP\nYCELOx3ING7minvHsG3ZDrdR4D1HdKP/xL5FD2gY5BijgKJfHrKMWwgx3y7ynD4jrewfPg67g4R9\nCT4N9I48B+/NnMPBrYfLtL1hMWjWviktOzfn8PajnDjonizHGmhl6UfLAfjqtYUMuqY/t7xwY6nj\nC4ZeP4ih1w8qY8PL/o0kIMCCNcBKg6b13dZFNYpk5NRhLPzXD9izS58et+s3zcDrBpF+LoMMD1X9\nMlMyWTZ/RbkCPUDnAR3pPKBjufYpLig0qGAWw4VqwMRLsFgMls79lVNHEgmvF07s5b2Y+IcL+7pc\nqMr6jD5ZKfUbMBxoRSW77pVS/wDG48ybfwC41dOXB6XUYSAVZ3+1XWvtueB4DZBr9nKNfvf0wVus\nb9UIIsOY6fE4GczATjMsnAYjkizG4rCc/+DLNG4m0FyDpZJpaUvKiGchlUCKjl62EkeAuRsz6lUe\nmf8Hvn79Ow5tPYI10Eqn/h25auZ4rwlMisuyTMHu6IrN/A4LadiNi2isgmHxkjLtHxgUSPtY344H\nXfPVujIHeXAO+jodd5pRtwzjivvG8vL1r5JebC5+4el6aWfT+fmDX2jctjFj7hhd/HBsWrSF375a\nS3pyJk3axjDu7suJblX6VLDIRpFen60XFtEgnKFTBpe4zbWPTKJ5h2as/24jGSmZJJ0467V6XX51\nvAObD5Cb7fnRRuIxKXLpT/3G96Xf+L6lbyjqPK+BXik1D/iH1nqrUqoBsBVIARoppWZprWdX4ryL\ngSe01nal1N+AJ4DHvGw7ojaUxbUZqzAq8dzYcKQSZj5HIOuxkE4e9ck1h+KwFM3o5aAlJjYoR6D3\nVIO+ImkzrJwgxPyM+o2f5daXbq7AEc6zW7pjp3vB6yvuTWHbMs0RD8Vpius2rEvBPHhfOV2BoJSd\nkcPSj5cTpxPcgrwnpsNk86ItboF+4b9+4MtXFpKT5RwxvxXY+ssOHnr/Hlp3bVXiMZt3aOpx8Flh\nQWE2rrx/bJnmkA+YdAkDJjkHrW3/dSevTH3DLZAH2KwMvtq5TeN2jQuy7RUXFS1dxELUBCXdgvXW\nWuc/jJ0K7NZadwVigfsrc1Kt9c9a6/z+wbWA74dQVzOTkoqulFyQBSDMfIEglhXcqVs5SzDfEOoo\nmn0s0FyJlbNe2pD/r7Xg5zyc6U19lQ/LSuVKn3oT2TCSh+c+xOhpw2nbsw31GnsOEg1bNOCB/9zl\n8/O373NRycVRvFzA+L3HWfOV5xK5nmS4npnac+0kHT9LyukUVxrdotPiThw6ycI3fij1eEOuG0hw\nhHvJX3CmsO08UPHMN08w/r6xZW5jvu7DujL2rksJDj9//OCwIC6/YzT9xjmLMcVe3osOfdx7V6yB\nVvpNqLEdcEJcUErqui88VHsw8BWA1jpOKeXLIc+3AQu8rDOBJUqpPOAdrfW7ZTlg/fqhBJRxSpOv\nOPJuhDOfgsO92Ehg2ACiI9yrqxXsaz8BZ9Z5HAAWYl1DaKNZGIZzNLLjzCKvNWYMowVEPolh6w9Z\n30L2z1jtx8Dhu6xqgbb6RDfw/l4qIzo6gk4f3ofD4eCObjM9DgzLTMkk0HQQHR3htm9ljLi6H0tG\n92D9D0WTPwbaAug/PpbtqzXnTrg/sbIEWDxOCfOmXfeWfP+v71nx+VpOHUkkODyIZA/pewGO7Dha\n6vuKHnsxt71wA/97ZSGJx84AEBQWRKe+7bn7tWm079WWPHseuTl2gkNL/8JZ3AOv38oVt41k2Wer\nAZOhkwfS4WJnL1PaybMkHj3NH9+5k9mPzWf7yt1kZ+bQpF0Ml00bzpQ/Sk4tX6rs37gov7pyzUt8\nRq+UagacxflsvvDsac+3EEX3XQK4z72BWVrrb1zbzALswHwvhxmstY5XSsUAi5VSe7TWK0o799mz\n1ZsS1smCzbyTCOPfYDq7gU0s5DCYnPQG2NLvdD6PpjVZxo04LG0K9rQ69lDP9Pxh78g7w7nEU5hG\nJBbHfqLMLV67YTLNQWSk9SPI8QWh5hvnK+X5iEkAablDyfESmHzl8I6jxHup056RksmOdfuxhp8f\n3BgdHeEx1703pmlyJj6JQFsAUYVGIN/95gwinl3AztV7yErNpGXnFlx+xyh6X9aLN+99lzVfrHU7\nVrP2TYjb4/k5dnENmzfAnudg/gtfFCzL9pJWF5x35GV5X4OnDKH3uFhWLFiNYTEYct0gQiNCSD2b\nxvM3/B97fttLdkY2LTu14PIZo+lTzgFyEU0bMGHm+aC9ff0B5j3lDOy52XYatWjIoGsHcN1Tkzl7\n8hwd+1xEUGhQuX4nomTl/RsXlVfbrnlJX0pKCvQvAVtwDphbpbXeBaCU6g+lp2/TWruPOCpEKTUd\nuBIYpbX22EOgtY53/XtKKfUVcAlQaqD3lxzLRKg/iowzc8HMwG70JsDcSTgvFYyiD2QjNnMdqY6/\nkWfpBIDD6EieGYMV93SieTQvGNEfyMYSpvCFkGF5CMxMQsw55Q7yedQHQrCS4DpeKCY2LJxzZcxr\nRLYxgRxL1c6sNE2Tj56cX5Cbvrj6jevR7uKKVyLb+ONmvn3rJw5tO0xAYAAd+7Tn+qeuoW331gSH\nBXH7P27BNE0ceY4iiW6mPjeFswln2b1WF/S8tOneijtfv433Zn7IoW1H3M7Voc9F5OU5yE7PokWn\nFgydMpA373ynzG1V/co2+nzzz1v4afYS4vYkEBLuLEd70zPX8caM/7Bz5a6C7Xau2s2x3XEEBds4\ncegkx/bEE14vjEtvG0n9xmWb926aJu889H6RVLOn487w7Zs/UC8mistv9zA9swY7dyqZfRv206xD\nU5pfIPPsxYXHa6DXWv9PKbUS51154YnTR6lkmVql1BjgUWCY1tpj5FJKhQEWrXWq6+fLgOcrc97q\nYAloSqblHgAMxxnC+GtBkM9nJY4Q82PS+CsAphFBDqMI5tMij4JNAskxxhXkB3UOxLNgeMiQkkcH\nMIKwOb7BSnyJbTQBOx2AECCTPNqTZdxMntEOm/k9BmnkMhKH0YRAcwUGSeQyCtNSviQoFbFjxS72\nbfKeM7/PFecLzZTXoW2Hef+Rj0lOdJaltWfb2bZ8B0nHk3j+x6cJDnN2bRuG4ZbNLio6kllfPsL6\n7zZxbFccjVo1YsjkAQQEBjD9pZv44LF5HNnh/P4bHBZE7JiLufuN24sc56/X/oPMMnbzdxrQkSlP\nXVPqdjtW7uKdP3xA6hnnVMVzJ+H4gZMc2nKYwzvcv4+nnEnlX3e/UyRF7YoFq5j+0s30GVvyXPOz\nJ8/xwWNzPeaTd9gdbPh+U60J9I48B3OemMf6HzaTkphCcFgQnQd14s7XbiUqunw1CoSo6Ursutda\nn8BZua7wsrL1U5bsTZwj1Ba7Uuuu1Vrf7XpUMFtrPQ5oDHzlWh8AfKK1/skH5642NpZgJcnjOmux\n9LAZlj9immEEmsuxkISDpuQYY8my3FCwTa4xELvZzS0FrolBjjHU9ar0YXcmNtJ5ijxrN7d1OcZV\nRV7nGiNKPV5FWBz7CDHnEMAeTIKw04sMywMcP3DC6918aFQo0164scLnXPLxrwVBvrA4ncDiOb+U\nOmDNYrHQf0Jf+k8oOmWpQ5/2vLDoaX77ej1nT5yj+7AutOneusg2+zYdQG/wXt05vEEYw28aij3L\nTquuLRgyueSc7gXvac6ygiBf2OEdR73mxy+ehz7p+Dn+9/JX9Brdw2vVveMHTvDa9H+VOJUv+bT7\nta2p/vvyVyxx5TgAyErP5veft/LuzA95ZN5D/muYEFWg7LU0fUhr3d7L8gRwpoDTWh8EelZnu3wt\nP5uc53XFhjkYFjKNe8g078Y52i4QiidWMQzSjD8TZr5EIFsxsJNHA3K4jCxjOgA5xqXkme9jJc7r\nuS3kEMQSMnAP9NXB4ogj0nykyAj+QDRWx2G6DZ1FaFQIGcnuKVnbx7YrV5a04s4e9zxbAeCMayBb\nRVkDrAy+doDX9Qd/P0RulvdUun3GXMyNT00u93lPHkn0uLysRXDyHdsTz+aft3LJFbEe13/1+nel\nztePKcO8/5rANE1+/9lzauLda/YQpxNooaQbX9QdFf/UFKVymJE4cM9bDmDHSzepYYBhcw/y+ce0\ntCXV8g4pxtukGs+QwtuYho0IxwOE5/2JIPMHMpmGg9LmMPuvklewOc/jNL1ANtCm3U5iL3cfLBYa\nFcLIm4dV6rwl5V9v2LJhpY5dmnYXt3XL454vKiaSW1+eWqHjRjTwnpGxUYvyvaeSytse3na4xH1D\nIkMYcdPQErepKfLseaQkeR7DkpWeTfw+X3RaClFzSKCvKmYGYfwDC0VHVZs4B845CAGzgkUADQO7\npTe5DCKCZwk1P8bGbwSxnDDzJQLYRbLxNrmFEtIUbUMgOVXQJW+aJlnpWaWWA7V6Gctp4MBq7mLG\na9MZf/9YWnVpQYPmDeg6tDMzXr3V691mWTVuG+Nxrnxz1YxLp4+s1LFL0yH2IroN7uy2PMAWwORH\nJxEY5PlLQGn6XdkHS4D7/8Zte7Zm5pz76TxQFbznxm1jaNnZc8qKmDbRJV5fi9X7Y4Qm7Rpz+z9u\nKfUZf00REBjgte5AZKMIOvb12OEoRK3ll677C0Gw+SUBHu5aDcAgkzDeJ9Cxk1TL62BU7EM+xPyI\nAHYXO76JjZ/I4ipSLO8S4ZiJjfPTwkwsZDERu6V8U6xKYpomC9/4gbULN5CUkES9xlH0vSKWax6e\n6DGve0l1AUwjgoDAAG54ejI3PD0Z0zTLXHu+JPOfW8BP7y0pkpbWMAw6DezIzc9NKRiIV5Xu/fcM\nPnx8PjtX7iLtbDpN2zdh2JRBjJw6vMLHHHXLcJISkljx3zWciU/CGmilQ+xFTPvrjbTu2oqnv3qM\nwzuOkXI6mU79OnL84An+ecfbRXLzB4cFMeaO0QSVMM++4yXtOeoha2Hj1tG8/MtzRQrk1AZDpwzi\nyPaj5BTL+tdnbO8yz0AAZ+Kjb17/np2rnVMNW3dtyfj7x5a7TK8QVUkCfRUxylAOwMZagsxvyDau\nrdA5AtjjcbmFTGwsJ9PoSqrldYLMzwk0t2MSQK4xmBzj0gqdz5uvXlvIF68sLEiDmpqUxrE98diz\nc5ni4blzjjEKm7kSo1hvRx5NyKbotfBFkI/bm8Avc38tEuTB+QWlbY82tC02cK6qhEWFcf/bd5KR\nkkFqUhoNmzfwOvitPCY/fjWXz7iU//71S7Kzsuk8QBWZKtamW0ugJQCtu7biyc8f5sd3FnPy0EnC\n64cx+NoBdBtacunS65+8hrg9Cez57fwg0qiYSCY/cXWtC/IAo1xfrlZ8tppThxOJaBjBxZf24Pon\nS5/pkM80Td68513Wf7uxYNmBzQfR6/fx2Pw/0KgMKYeFqA4S6H3I6tiFI30ngY4Y8sxOXqfCFRZg\nbnULbmVl4r0nwMwfG2AEkm3cQDY3eN22Muy5dn77er17rnMT1i7cyFV/nOB2p5hjuZxMxyGCzK+w\n4kwulJ3bikVfxrJ5xadENYrksttG0aSd55ra5bVu4QYyUz1Pazu4+aBPzlEeoZGhhEaGlr5hGcXv\nTeDf971XMJd/9edr+WXuCu57+06aeriGjZo3ZOrzU8p1jrDIUGZ9/jArFqzmyPajNGwSxcDJA2nY\nvGrHNgDkZOXy62crSU5MRfXrQLchnX3yBXDU1OGMmjoce44da6C13MfctmwHmxf97rY8Xifw3duL\nmP7XmyrdRl+y59rZtUYTYAugU78OZS5CJWo/CfS+YGYT7niKQH6D1EwisGCnG3bcp8K57VqGPPje\n5Bp9sJnu2dryaEA2Eyt83PJIPpXCKS8jv08dSeR03BmPiUgyLXeT5bgOG0uI35fCi7fs5uThg4Az\n8K77bhMzXp1Gr1EVK0WblZ5FenIG9RvXK3GkvqWaUyVXhfnPLXBL2HNwyyHmP7uAhz9+0GfnsQZY\nCwbcVVfWsF2r9/DB43NJcI34D7AF0HNkNx545x5sXgY3FpaZlsmvn64iKyObvmNjad6xqds2JdY4\nKKltazT2nDyP6+L2lJzLorqtWLCa7976iTgdDwa07d6aax+7yqelnkXNJYHeB0IdrxPELwWvDRwE\nso0cepPJZGyswsJxtxnuJjZXvfaKyTKmEmDuwcbygqQ8edQj05iBaameZ4Th9cOIio7idJz79LSo\n6EjqlfC807Q0IJvrePepVzh5uOjUt7PHz/LlqwvpMqhTQUnUsshIzWDOE/PZsWIX6efSadq+KZdc\n0ZvIRhGknHYPTKqfb6vgFbb++00sm7eCxKOniWwUQd8rYhkzY7RP7kbzJZ9KZs9a9wQ2AHvX7SM1\nKY19G/ezeM4yThw8SVi9MC4e3cNZVrgSUxWrgyPPwbxnPisI8gD2HDubftrCf1/6gpufK7lXYs3X\n61jw4hckHnX2Gn375o8MuXYA0/56k09+ByFh3jOBB1XDmI+y2r/5IPP+/Blp51wVFk04tO0IHzz6\nMc//+FS5xiSI2kkCfWWZJoGs97gqkO1kGDPJsDxOiOMNgs3/FaSwdRBKljEZu8X73OtSGQGkWV4m\nwFxNoLkBDBvZTMJhaV7xY5ZTUGgQPUd2Y+nHv7qt6zGyO2FRJXdRp5xO4cDvnrvP9286yIN9HqXL\nAMWNf55cpmee/753NpsLzZE+uvMYx/efoPeYnmxbtpPMlPPz83uM6MbEh64s9ZgVsfrLtXzw2NyC\n8yXsP87eDftJOZ1SrufApcnKyCbHS7787MxsNv64mU+e/x/p586X0T34+yHOnjjHHa9M81k7qsLG\nnzZzeLvnGRo7V3ken5IvNSmVT577H0kJ5xNWZaZksnjOMlp2bsGoW4ZXun0jbxnGko+WkVQsP4Nh\nMbi4gj1RVWH5JyvPB/lCzsQnsfiDX7juiav90CpRnWr2V/paIQ/DS155g1wsOO9GMi0PkmzMJsOY\nToYxnWRjNpkWH3SrGgZ2y2AyrTPJtNxXrUE+3y0v3MjQ6wcS0dA5mj68fhgDr+7H7X8rw9xww/Ca\nMwAgJTGFtQs38Pod/8aeY/e6HcCBLYfYsXKn2/Lc7FxSTqfS78o+RXoHkhKS2LV6t9v2lWWaJks/\nWl7kSwU471BXffEbGanuyYAqKrpVI1p3belxXeturVj/3cYiQT7f+u82cuqo50cuNUXyKe+Z9rLS\nS04lvPTjX4sE+Xymw2Tz4q0e9ii/yIYR3PDnyUXyFYREBDN62ghGTRvuk3P4QsoZ749YalM2Q1Fx\nckdfWUYAebTFinvXdR6NsRuXFLx2WBSZqOpsXbUIDArk7jfu4OypcxzZfpSWnVvQsFmDMu0b2TCC\n9r3bsX25e4Au7OCWwyz/ZCWjp3uf/39g00FyMj1nn4vXCexdv7/IyPs4ncCcJ+fz0tIOhISHlKm9\nZZGbbSfhgOcscmfiktjzm6b3Zb2cbdibwPL5K8lIzaRl5+aMmjq8TM+e81ksFsbceSkfzZpfJJtg\neP0wxsy4lP++9IXH/dLOprP1l+1Vnj+gMvpeEcuXry70mLa4VRfPX27ylVRToLQvCeUx6Or+9L6s\nJ8s/WUV2ehZ9xsXWuKx60SX0hPlqwKuo2STQ+0CWMZkAUxepGGdiIdsYg2nUjXrGZVE/ph71R5X/\ned+1j07i1JFETh5yr95XWML+klOwtu7RGltQoNvcaHDeTRefXgdw6nAiSz9ezpX3lpznvjwCbFZC\nw0NI8TBYLTA4sOARxLJPVvLp8/8l7ez5O+61X6/n4bkPlqtwz5DJA6kfE8XyT1dyOj6JrLQsQiJC\n2LxoC6bHupBgCbDQpG3N/pCvFxPFkOsG8sN/fi6S0rdB8waMu/uyEvftMlDxw38Wefydt1C+7fUK\nCQ9h7J2+nbLqS2PvHM2mRb9z6nDRHpxW3Vpy2a0194ue8B3rs88+6+82+FxGRs6z1Xk+h9GOPNoC\nGQRYbeSabcgybiDLuL3Ebmnh1LBZAwZM7EtgUCBJx8+Snuy5FG/suIvp5KF0a1hYEBkZOTRq3gC9\nfr/bFwZroJV6MVEeu7AB2vZsQ7ehXSr/RlwMwyB+XwIHtxx2W9d1cGeuvGcMWenZvHnPO5w7UTTf\nQlLCWTJSMgirF0ZyYjIZKZlYrJYSByTac+3s3XgAi9VK3O54juw8xpn4JI7tiScjxfO17ND3Iq57\n/OoKD0rLv+ZVrdvQLtSLicKR5yCyQThdB3di6vNT6BB7UYn7NW4Tw5GdR0nYV/TLYfOOzbjt5ZsJ\nLWXsSE1TmesdGhlKhz7tSTmdQmZqJqFRofQc2Z3b/zGNejGlpcq+cFXX37ivhIUFPedtnWF6+8pf\niyUmpvrtTVXXtKO6asfKXfzfbW+5Pd9u0q4xLy7+s8cu9sLXPDUplQ8encvOlbtJO+fMPjfkuoEk\nHj3Nsnkr3Pa1WC384YP76DPGd5kCAXIyc3jrvvfYtmw72Rk5WAIsdOzTnrv+eRuN28SwdO5y3n/4\nY4/7WgOtRe5Ew6JCufjSHtz292luGfx2/6b56Mn5HN1VQhEjqwVbiI2stCwsVgvtY9txxyvTPXYx\nnzxyCr12H626tHCrwFdYbfg7t+fa+fr179i5ag+5mTm07taK8Q+Mo0nbys1ISTuXzpI5y0g7m0bb\nnm0YMOmSKp+T7qvrbc+1eyzDLNzVhr/xwqKjI7x+a5eue1GjdBvShZueuY6f3tgjiaYAABfOSURB\nVFtC3J54rIFWLrq4LVNmXVum5+gRDSJ4aPa9pJxJJTkxmSZtGxMYFEicTmDnyt1uc/67D+tK7OW9\nfP4+bCE2Zn5wHwe3HmLPb/to1qEJPUd2L7iDzvNSihdw625OT85g1edryc2289DsewuW23PtpQZ5\ncD62GHRNf1p2ak5Mmxh6jujmdidvz7Hz3h8/ZNPPW8lIzsAWEkjngZ24+5+3e6zPnpeXx+ov1qLX\n7yMoxMawKYNp0clzl7hpmmSmZREUYqvWABMQGMC1j0zi2kd8d8zfF2/lwyfmcTq/2qEBy+av4I9z\n7ic0oub3EvgiE6OofeSO3sdq27fAmsqea2ffxgMEhwXTpnurEruYy3rND207zLdv/ciRHcewBdvo\nPEBx/ZNXl5jjvaqkJ2fw2PA/exwZ7k1IZAgv/vzngmfrK/+3hrfvn12mfa/643gmP3aV1/UfzfqE\nRbOXuC2PvbwXfyqWdCc3O5e37voP63/63VmlCWfmvGsenciYGUWfVS+d+yvLP1nJiYPOdLvdh3Xl\n5uemlGvAYU1hz7Uza/RzHPOQDGf09BHcVpZZJhUknyvVr7Zdc7mjF7VOQGAAnQf4doZC2x5tePCd\ne3x6zIoKiwpl7F2X8sXfvyYrvWxVDDNTMjm87UhBoPc0Gt3jueqFMvT6QV7X59nz2PbLdo/rdq7e\nw8kjp2jc+nx399evf8f6H4umfk1PyeDrf35Pvwl9CxKwrFiwmrlPf1owzz/9XDonD50iPTmDB/5z\nV5naXpOs/36TxyAPsGft3mpujRBlJ/PohfCTK+6+nIfnPsjwG4dwyfg+tPIyHz5faGQo7Xq1LXjd\na1R3QiK8Z2cDCA4PZvx940qsppaTlUPq2TSP67LSsjhZbLT2nnWeg1pKYgq/frqq4PWKBas9JvPZ\nsmQr8XtLnkFRExUfN1KY3cNMDyFqCrmjF6IKOBwOfv95K6eOJtJ9eDdaeMj3D9BlUGe6DHLWqT+4\n9TCvTH2Dcyc9Vz7sOao7Ma3P11FvoZpzyZV9igRXcN7Bdx7UiejmjRg8eQBte5RcnS84LJjGbWNI\nO3vIbV2DZvVpf3HbIsscJY0vsJ8fX5Cfera4zNQsdv+2x2Pe+Zqs34S+fPXaQpKOu/9+2vSsngqI\nQlSEBHohfOzo7jhm/2kOB34/hOkwCYkIJnZMb+56/dYSB6O169mGu/95K9++9RN7NziT+5gOk6iY\nSHqN7sH0F92roc14dTqNW0ezddkOMlIzad6hKWPvvJQOfdqXub2GYTBsyhCO7YwrmoPAgP4TLnGr\ntNe2Rxv0Ovf8+iGRIfSfcD5BVFRMJInH3IN9YFAgrbuV3HtRE4XXC2PUtBF8/X/fkVvoOjVuE834\n+8f5sWVClEwCvRA+ZJomHzzyMfs3nc/fn5maxar/raFBk3pMearkksQ9RnSnVdeWzH16Afs27Sc3\n207rbq0YNmWwx7rvFquFSTPHM2nm+Eq1e/S04VgDLKxYsJrEY6eJahRJn7G9mTTTvRbAVTOv5NDW\nQ+j1+wuWWQOdle0K36X3Gde74MtOYZ0HdKRDbNm/iNQkV80cT7P2TVm7cAMZyRk0bhfD2Dsv81gO\nWIiaQkbd+1htG6lZUVbHDkLM+Vg5iEkYuUY/Mo3bwaj+74416ZpvW7aDv9/0epFMbvnadG/FX5c8\nW+L+efY8np/0N/Zt2F9keaMWDXns05keS/76mmmapSbSCbFZmPfil84ZDCE2YsdezICJlxTZxjRN\nPnvhc9Z8vY4zcUkEhwfTZaDi9lemScW0cqpJf+MXitp2zWXUvfApq2MXEeajWDlZsCzQ3IrVjCPN\n+hc/tsz/Eo+d9hjkAa8Z/wpbsWC1W5AHOB13hkXvL63SKVwAOVm5ZKVnEdEgvMRgHx4VxjWPTCrx\nWIZhcMPTk5k080oObD5EdOtGRUbvCyGqhwR6UW7B5vwiQT5fIMuwOPbgsHTyQ6tqhl6juhPRIJzU\nJPdR7M3aNyl1/2N7vCe/KZ7sx5ey0rP4aNYn7Fixm4yUDJpe1IQRNw9l1NRhlT52SHiIT1MMCyHK\nRwK9KDcrhz0ut5CJjd/I4sIN9A2bN6TfhL4smbOsyPLwBuFcdtvoUvePbOiehS5fRP3wSrfPm7fu\nfZdNP20peH1wyyHi98YTHGpj0DUDquy8QoiqJ4FelJuJ91SfDupXY0tqpukv3UTDZg34/ectpCVn\n0KRtDJfeOpKeI7uXuu+lt45g+acr3SqNBYcFMWhy1QTc/ZsPsM1DmeDsjBxWLFgjgV6IWk4CvSi3\nXGMwgeZmij/BtdOOHOMKv7SpJrFYLEx86AomPlT+axEWFcYdr0zjsxc+5+DWw2BCk7aNuez2kfQq\nwxeFiti38QC5WZ4TvpyOO1Ml5xRCVB8J9KLcsoypWM04bPyMBeez6Fzak2E8DEbty2Fe03Qb0oXn\nf3yKXav2kJmaSc9RPao0N3zLTi0IsFmx57jXbq/XWMqYClHb+SXQK6WeBWYA+f2TT2qtf/Cw3Rjg\nn4AVmK21frnaGim8MyykW2eR6bgJGytw0IAc43IJ8j5ksViqbQBb1yGdUf06snPl7iLLrYFW+k/o\nWy1tEEJUHX/e0f+f1voVbyuVUlbgLeBSIA7YoJRaqLXeVV0NFCVzWNqQRRt/N0NUkmEYXPfEVcw+\nncLJQ4nkZOXQuG0MQyYP5NJbR/q7eT6TcjqFhP0naKGaEV6FAxuFqGlqctf9JcB+rfVBAKXUZ8BE\nQAK9ED7icDj48PF5rFu4gbSz6QA069iUO1+bTse+HfzcOt/Iycrlg0c/YsvS7aScTiUqJpLel/Xi\n1pdvlvrs4oLgz+p1DyiltimlPlBKeRqq3Rw4Vuh1nGuZEMJHFr7xA0s/Wl4Q5AES9h7n46c+xeHw\nXrymNvnw8bmsWLCGlNPOLGfJp1JYNm8F8/78mZ9bJkT1qLKvs0qpJYCnDCGzgLeBvwCm699Xgdt8\nde769UMJKKF4SFWLjo7w27kvVHLNK2bnrzs8Lj+09TB65S6GXut9al1tuObpyelsX+75PW77ZQeR\n4TaCQoKquVUVUxuud11TV655lQV6rXXp2UEApdR7wHceVsUDhUtctXAtK9XZs6WnGq0qtS0/cl0g\n17zizp32fN1MEw7ujqezl+taW6752oXrPZaVBThzPIlD+gQNmzeo5laVX2253nVJbbvmJX0p8deo\n+6Za6+Oul1cBnr5ybwA6KKXa4gzwU4Abq6mJQlwQml7UhPi9x92Wh0QE03N4Nz+0yHd+X7yNj578\nxOv6Ri0bERXtPRNhdTmTkMT3//6JhP0nCAkP5pIrYhlwVT9/N0vUIf4aifJ3pVQvnF33h4G7AJRS\nzXBOoxuntbYrpe4HFuGcXveB1to9fZcQosIuu30UezceICUxpcjy2DG9adGpdg+J+fatH0gu9r4K\n6z+hLwE2/w7GO37gBK9Nf5P4vQkFyzYt+p2ju+O4/slr/NgyUZdImVofq23dPXWBXPPK2fbrTn5+\nfykJ+xIIjQylx4huXPPwRKwljHOp6dc85XQKM/s/TmZqlsf1o6eP4NaXby61HG9V+8+D77NiwWq3\n5ZGNInhp6bPUb+Icp1zTr3ddVNuuuZSpFUJ41WNYV3oM6+rvZvhUgC3A6916YFAAV947xu9BHuDw\n9qMel6ecTmXN1+u54u7Lq7lFoi7y5/Q6IYSoEqGRoV7zAHTo256Y1tHV3CLPAm3ee02CQmvHbABR\n80mgF0LUSTc+PZk23VoVWdasY1NueHqyn1rkTvVXHpc3bhvDkBKmNgpRHtJ1L4Sok5q2b8Kz38/i\nl3m/curIKRo0bcDoaSMIDqs5d8qTH5tE3J44tv260zk0GajXpB6TH7tK7uiFz0igF0LUKonHTrNo\n9hLOnjxH/Sb1ufz2UUS3bORxW1twIGPuKFNKD78ICg3i0U9nsm7hBvZvPkRIRDCjbhlO/cb1/N00\nUYdIoBdC1Bo7V+7iP3/4gDNxSQXL1n27gbtfv42uQ6qn2p+vWSwWBkzqx4BJMndeVA15Ri+EqDW+\nfO3bIkEe4ExcEl++9q2fWiREzSeBXghRKyQdP8uBzQc9rtu/6SBnEpI8rhPiQieBXghRKxgGXue+\nWyxGjZgXL0RNJIFeCFEr1G9Sn4ti23lcd1HvdjRo6qnatRBCAr0Qota45pGJRLdsWGRZdKtGXPPw\nBD+1SIiaT0bdCyFqjc79Fc9+P4tF7y3m7Ilz1G9Sj8vvGF2QE14I4U4CvRCiVqnfuB5Tnqo52e2E\nqOmk614IIYSowyTQCyGEEHWYdN0LIXxqy9Jt/DJvBafjzlAvOpIBV/djyLUD/d0sIS5YEuiFED6z\n5ut1fPjoXNKTMwqW7Vy1h3Mnkxl/31g/tkyIC5d03QshfMI0TRa//0uRIA+Qm53L8nkryM3O9VPL\nhLiwSaAXQvhERkomcTre47rjB09y4PdD1dwiIQRIoBdC+IgtOJDg8GCv66JiIqu5RUIIkEAvhPCR\nwKBAugzs5HGd6t+Rpu2aVHOLhBAggV4I4UM3/2UK3YZ2wRLg+mgxoH1sO6a/eJN/GybEBUxG3Qsh\nfCaifjhP/PdPbF26nUPbj9CkbQz9JvTFYpF7CiH8RQK9EMKnDMOg1+ge9Brdw99NEdXk1JFEPv/7\n1+zffBBMk/axF3HNoxNp3DrG300TSKAXQghRCRmpmbx265sc3XmsYNmJQ6c4susYzyx8gtCIED+2\nToAEeiGEEJWwaPaSIkE+37Fdcfz07mKu/pOUEM63Y8Uuls1fwZmEJOrFRDFk8kBix1xc5eeVQC+E\nEKLCEvYf97ru+IET1diSmm3N1+uZ8/hc0s6mFyzbtnwnNz1zHaNuGV6l55YRMkIIISosLCrM67rQ\nqNBqbEnNZZomi2YvKRLkAbLSslj84S/k2fOq9Px+uaNXSi0AlOtlPeCc1rqXh+0OA6lAHmDXWvep\nrjYKIYQo3YibhrLmq7WkJRUNYmH1whhx41A/tapmSUlM4egu98cbAEd3xRG/N4FWXVpW2fn9Eui1\n1tfn/6yUehVILmHzEVrr01XfKiGEEOXVumtLbnrmeha+8UNBV33Tdo0Z/8A42nRv5efW1Qy2UBvB\noUFkp2e7rQsKDaryng+/PqNXShnAdcBIf7ZDCCFExQ2bMpgBk/qx4YdNYELfK2KxBQf6u1k1Rkh4\nCJ36d2Tdtxvd1ql+HWjUvGGVnt/fg/GGACe11vu8rDeBJUqpPOAdrfW71dc0IYQQZWULDmTQ1f39\n3Ywa6+bnp3D25Dn2btjvjGxA256tmfr8lCo/t2GaZpUcWCm1BPCU3HqW1vob1zZvA/u11q96OUZz\nrXW8UioGWAw8oLVeUdq57fY8MyDAWonWCyGEEL6Vl5fH8gVrOLYnnqZtYxh181ACAn12v214XVFV\ngb40SqkAIB6I1VrHlWH7Z4E0rfUrpW2bmJjqnzcFREdHkJiY6q/TX5Dkmlc/uebVS6539att1zw6\nOsJroPfn9LrRwB5vQV4pFaaUisj/GbgM2FGN7RNCCCFqPX8G+inAp4UXKKWaKaV+cL1sDKxSSm0F\n1gPfa61/quY2CiGEELWa3wbjaa2ne1iWAIxz/XwQ6FnNzRJCCCHqFMmMJ4QQQtRhEuiFEEKIOkwC\nvRBCCFGHSaAXQggh6jAJ9EIIIUQdJoFeCCGEqMMk0AshhBB1mN9S4AohhBCi6skdvRBCCFGHSaAX\nQggh6jAJ9EIIIUQdJoFeCCGEqMMk0AshhBB1mAR6IYQQog7zW5naukwp9SwwA0h0LXpSa/2D/1pU\nNymlxgD/BKzAbK31y35uUp2nlDoMpAJ5gF1r3cevDaqDlFIfAFcCp7TW3VzLGgALgDbAYeA6rfVZ\nf7WxrvFyzZ+ljnyOyx191fk/rXUv13+18o+jJlNKWYG3gLFAF+AGpVQX/7bqgjHC9XctQb5qzAHG\nFFv2OLBUa90BWOp6LXxnDu7XHOrI57gEelFbXQLs11of1FrnAJ8BE/3cJiEqTWu9Akgqtngi8JHr\n54+ASdXaqDrOyzWvMyTQV50HlFLblFIfKKXq+7sxdVBz4Fih13GuZaJqmcASpdQmpdSd/m7MBaSx\n1vq46+cTQGN/NuYCUic+x+UZfQUppZYATTysmgW8DfwF54fiX4BXgduqr3VCVJnBWut4pVQMsFgp\ntcd1NySqidbaVEpJ7vKqV2c+xyXQV5DWenRZtlNKvQd8V8XNuRDFAy0LvW7hWiaqkNY63vXvKaXU\nVzgfoUigr3onlVJNtdbHlVJNgVP+blBdp7U+mf9zbf8cl677KuD6HzHfVcAOf7WlDtsAdFBKtVVK\n2YApwEI/t6lOU0qFKaUi8n8GLkP+tqvLQmCa6+dpwDd+bMsFoS59jkv1uiqglJoL9MLZ5XMYuKvQ\n8zXhI0qpccDrOKfXfaC1ftHPTarTlFLtgK9cLwOAT+Sa+55S6lNgONAIOAk8A3wN/BdoBRzBOb2u\nzg4eq25ervlw6sjnuAR6IYQQog6TrnshhBCiDpNAL4QQQtRhEuiFEEKIOkwCvRBCCFGHSaAXQggh\n6jBJmCNEBbiquGW5/gNYprWeqZR6HtiptV5QjmPVA+7UWv/dR20rdxuK7T8BGKK1fqQSbXgWCNda\nP1zRY9R2SqlJQILWer2/2yIubBLohai4a7XWRZJoaK3/7GlDpZQFMLXWnuaz1gMeBSod6JVSVm9t\nKCut9UIk+ZAvTAI2AhLohV9JoBfCh5RSc4CNWus3XXe1XYEonIlOBiml/gKMBLKBNK31IJzldusp\npbYAGVrrgcWOORz4J7AViAXSgela612udW8Am4CLgaeUUtcWa4NytaEdcACYrLXOcGUU/CvO8px5\nwEGt9VVKqenAlVrra0s5dxPgUyASCAa+11o/WoZr1Nl1zCaAAbyitf5IKdUeeAeIBuw463//5NrH\nBJ7CGTwb4qwTPtrV9kDXe9pdUntdx3kMmOpqygbgAa11Whmu04vAMCAI2Abc49pvDs5enY44UzL/\nhjNz3WXABGC0UuoO4DWt9celXRshqoI8oxei4j5XSm1x/Xe5l236ATdqrTvhDPYjgC5a657Ala5t\n7gPOuWpeD/RynB7A+1rrrji/GBQOGl2Bd137e8rH3Qe4EeiMMyje5Fr+BM6g1tvVnhnlPPc5YLzW\nOhZnBrE+SilPNb0LKKUCcKZvfU9r3UNr3Z3zOcTn48y21wO4GZinlIoutPs5rXVf4DHXMVZrrS92\ntWdWae1VSo3FGeQHAt1xZlR8ugzX6VEgWWt9ies6JbiuXb5uwDicv4dYYLTWehHOXpGXXb8XCfLC\nb+SOXoiKc+u69+AHrfVp188HcQaQ95VSv1C+Ihn7tda/un6eC7yrlIp0vd6ntf6thH0Xaa3PASil\n1gEXuZZfCfxJa50DUKidZT13HvAPpdRAnHfmTXAG/J9KaIsCArTW/8tfoLU+48qh3wv40LVsl6uH\noz/wrWvT/DEHm3E+Bsm/fpuAq8vQ3tHAZ1rrFNe1eBfn3X9p12kCEOnqKQHnXf3WQvt9rbXOcu23\n2bXf4hKugRDVSgK9EFUrLf8HrXWyUqorzhzao4G/KaV6+/IcXmQV+jkPCPHBOQH+CNQH+mmts1yB\nM9hHx/Yk/33k4Xz0QaHXvvgs83adDOBerfUvZdxPPldFjSJd90JUE1c3dKirW/dxIBln13kKEOrq\n1vbmIqXUENfPNwLb8+9MK+E74A+uZ9AopRqV89z1gOOuIN8cmFiGc2rArpSanL9AKdVQa50KbMFV\noc31HL8nsLYC78tbe5cA1yulIpRSBnAHZbvzXgj8USkV4mpbhKt9pUnB+cxfCL+SQC9E9WkJLFFK\nbcU5oOtHYK2rCtl8YLtSao2XfbcDdyildgAPArf4oD0v46zKtcXVTf6fcp77DZwDDHcA7wNLSzuh\n1tqO8wvB3Uqp7a5rMc61+ibgZqXUNpzXY6rWOrEC78tje7XWPwLzcA6Y2+7a9oUyHO9lnF31G1xt\nW4XzOX5p5gI3usZw+OL3JUSFSPU6IWo410jyV7TWfS6kc1dEbWuvENVB7uiFEEKIOkzu6IUQQog6\nTO7ohRBCiDpMAr0QQghRh0mgF0IIIeowCfRCCCFEHSaBXgghhKjDJNALIYQQddj/A3Sze372UYii\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d511cfc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(x_pca[:,0],x_pca[:,1],c = df['diagnosis_int'],cmap = 'viridis')\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second Principal Component')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svc = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_comp = pd.DataFrame(pca.components_,columns=['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',\n",
    "       'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
    "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
    "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
    "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
    "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
    "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
    "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
    "       'symmetry_worst', 'fractal_dimension_worst', 'diagnosis_int'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>diagnosis_int</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.216399</td>\n",
       "      <td>0.103599</td>\n",
       "      <td>0.224548</td>\n",
       "      <td>0.217965</td>\n",
       "      <td>0.137645</td>\n",
       "      <td>0.231504</td>\n",
       "      <td>0.251222</td>\n",
       "      <td>0.255266</td>\n",
       "      <td>0.133013</td>\n",
       "      <td>0.057972</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105019</td>\n",
       "      <td>0.233642</td>\n",
       "      <td>0.221970</td>\n",
       "      <td>0.125301</td>\n",
       "      <td>0.204476</td>\n",
       "      <td>0.223075</td>\n",
       "      <td>0.246288</td>\n",
       "      <td>0.120663</td>\n",
       "      <td>0.126721</td>\n",
       "      <td>-0.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.226545</td>\n",
       "      <td>-0.058262</td>\n",
       "      <td>-0.207630</td>\n",
       "      <td>-0.223224</td>\n",
       "      <td>0.188760</td>\n",
       "      <td>0.158473</td>\n",
       "      <td>0.067868</td>\n",
       "      <td>-0.027220</td>\n",
       "      <td>0.193220</td>\n",
       "      <td>0.367620</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045135</td>\n",
       "      <td>-0.192592</td>\n",
       "      <td>-0.211887</td>\n",
       "      <td>0.172476</td>\n",
       "      <td>0.147663</td>\n",
       "      <td>0.103088</td>\n",
       "      <td>-0.002433</td>\n",
       "      <td>0.142062</td>\n",
       "      <td>0.276449</td>\n",
       "      <td>0.077610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "0     0.216399      0.103599        0.224548   0.217965         0.137645   \n",
       "1    -0.226545     -0.058262       -0.207630  -0.223224         0.188760   \n",
       "\n",
       "   compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "0          0.231504        0.251222             0.255266       0.133013   \n",
       "1          0.158473        0.067868            -0.027220       0.193220   \n",
       "\n",
       "   fractal_dimension_mean      ...        texture_worst  perimeter_worst  \\\n",
       "0                0.057972      ...             0.105019         0.233642   \n",
       "1                0.367620      ...            -0.045135        -0.192592   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0    0.221970          0.125301           0.204476         0.223075   \n",
       "1   -0.211887          0.172476           0.147663         0.103088   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
       "0              0.246288        0.120663                 0.126721   \n",
       "1             -0.002433        0.142062                 0.276449   \n",
       "\n",
       "   diagnosis_int  \n",
       "0      -0.216917  \n",
       "1       0.077610  \n",
       "\n",
       "[2 rows x 31 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = df.drop('diagnosis_int',axis = 1)\n",
    "y = df['diagnosis_int']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = svc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0  67]\n",
      " [  0 121]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.00      0.00      0.00        67\n",
      "          1       0.64      1.00      0.78       121\n",
      "\n",
      "avg / total       0.41      0.64      0.50       188\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.grid_search import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ......................................... C=0.1, gamma=1 -   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ......................................... C=0.1, gamma=1 -   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ......................................... C=0.1, gamma=1 -   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=0.1, gamma=0.1 -   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=0.1, gamma=0.1 -   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=0.1, gamma=0.1 -   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=0.1, gamma=0.01 -   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=0.1, gamma=0.01 -   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=0.1, gamma=0.01 -   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=0.1, gamma=0.001 -   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=0.1, gamma=0.001 -   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=0.1, gamma=0.001 -   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................................... C=1, gamma=1 -   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................................... C=1, gamma=1 -   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................................... C=1, gamma=1 -   0.0s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ......................................... C=1, gamma=0.1 -   0.0s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ......................................... C=1, gamma=0.1 -   0.0s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ......................................... C=1, gamma=0.1 -   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ........................................ C=1, gamma=0.01 -   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ........................................ C=1, gamma=0.01 -   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ........................................ C=1, gamma=0.01 -   0.0s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] ....................................... C=1, gamma=0.001 -   0.0s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] ....................................... C=1, gamma=0.001 -   0.0s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] ....................................... C=1, gamma=0.001 -   0.0s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] .......................................... C=10, gamma=1 -   0.0s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] .......................................... C=10, gamma=1 -   0.0s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] .......................................... C=10, gamma=1 -   0.0s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] ........................................ C=10, gamma=0.1 -   0.0s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] ........................................ C=10, gamma=0.1 -   0.0s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] ........................................ C=10, gamma=0.1 -   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ....................................... C=10, gamma=0.01 -   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ....................................... C=10, gamma=0.01 -   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ....................................... C=10, gamma=0.01 -   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ...................................... C=10, gamma=0.001 -   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ...................................... C=10, gamma=0.001 -   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ...................................... C=10, gamma=0.001 -   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ......................................... C=100, gamma=1 -   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ......................................... C=100, gamma=1 -   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ......................................... C=100, gamma=1 -   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=100, gamma=0.1 -   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=100, gamma=0.1 -   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ....................................... C=100, gamma=0.1 -   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=100, gamma=0.01 -   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=100, gamma=0.01 -   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ...................................... C=100, gamma=0.01 -   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=100, gamma=0.001 -   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=100, gamma=0.001 -   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..................................... C=100, gamma=0.001 -   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'C': [0.1,1, 10, 100], 'gamma': [1,0.1,0.01,0.001]} \n",
    "grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=2)\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 65   2]\n",
      " [ 13 108]]\n"
     ]
    }
   ],
   "source": [
    "grid_predictions = grid.predict(X_test)\n",
    "print(confusion_matrix(y_test,grid_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.83      0.97      0.90        67\n",
      "          1       0.98      0.89      0.94       121\n",
      "\n",
      "avg / total       0.93      0.92      0.92       188\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,grid_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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