Fraud Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Fraud Detection  \n",
    "The dataset contains credit card transaction in september 2013 by european card holders. Due to cofidentiality purposes, the data has gone through PCA transformation. Often, when analyzing fraud detection we are dealing with data that are unbalanced. As a consequence, traditional accuracy evaluation may not give good insight on how well our machine learning algorithm is working. In these cases, we may get high accuracy by just predicting every output as non-fraud, but we fail to successfuly predict every fraud instances. Due to the highly skewed outputs, we will use F1 as our performance metric, and some other methods to try and accurately predict fraud activity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"C:/Users/sasan/Downloads/creditcard.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.83 of data are classified as non-fraud\n",
      "0.17 of data are classified as fraud\n"
     ]
    }
   ],
   "source": [
    "# finding missing values\n",
    "data.isnull().sum().max()\n",
    "print(round(sum(data[\"Class\"]== 0)/ len(data[\"Class\"]) * 100, 2), \"of data are classified as non-fraud\")\n",
    "print(round(sum(data[\"Class\"]==1) / len(data[\"Class\"]) * 100 , 2), \"of data are classified as fraud\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'barplot /n {not faraud : 1} , {fraud : 2}')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(\"Class\" , data= data)\n",
    "plt.title(\"barplot /n {not faraud : 1} , {fraud : 2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# skewed distribution \n",
    "\n",
    "It is visible that the dataset is very unbalanced. Most of our transaction have relatively low amount, and outliers reach to 25000 thousand indicative of possible fradulant activity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'distribution plot of/n time of transactions')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KX0REREREDqaKJxERySSnArXuvtrd9wJzgRkJ+n0NuBNojbTNAOa6e5u7rwFqw/FERERERCRNlHgSEZFMMhrYELlfF7btZ2anAGPd/dc93VdERERERAaWEk8iIpJJLEGb799olgd8G/hcT/eNjDHbzBab2eKGhoZeByoikkk+/3lYsCDdUYiIiBxKazwlM2dO4vbZswc2DhGRwaUOGBu5PwbYFLlfBpwIPG9mAEcC883skhT2BcDd5wBzAKZNm3ZIYkpEJBvNmQN79sBFF6U7EhHpypzXDv2cOfsD+owpuU0VTyIikkkWARPNbLyZFREsFj4/ttHdd7h7tbuPc/dxwCvAJe6+OOw308yKzWw8MBFYOPCnICIy8NrboUOXUhARkQykxJOIiGQMd28HbgSeApYDj7j7UjO7Laxq6mrfpcAjwDLgt8ANuqKdiAwW7e3Q2ZnuKMDMppvZCjOrNbObE2wvNrN54fZXzWxcZNsXw/YVZnZBd2OGX1K8amYrwzGLwvaPmtmfzazdzC6NO/41Yf+VZnZNfzwGIiJyME21ExGRjOLuC4AFcW23Jul7dtz924Hb+y04EZEMlQkVT2aWD9wNnEcw/XmRmc1392WRbtcBTe5+nJnNBO4ALjezyQRVrlOAUcDvzWxSuE+yMe8Avu3uc83snnDsHwLrgWuB/xMX3wjgK8A0gjUAXwvHaurrx0JERA5QxZOIiIiISBZzD5JOGVDxdCpQ6+6r3X0vMBeYEddnBnB/ePtR4BwLFu2bAcx19zZ3XwPUhuMlHDPc5+PhGIRjfhLA3de6+1tA/CNyAfA7d28Mk02/A6b31cmLiEhiSjyJiIiIiGSxWKVTuiuegNHAhsj9urAtYZ9wevUOoKqLfZO1VwHbwzGSHas38enqpyIifUyJJxERERGRLNYepl4yoOLJErTFXz00WZ++au9KSvu4+xx3n+bu02pqaroZUkREuqPEk4iIiIhIFoslnjKg4qkOGBu5PwbYlKyPmRUAw4HGLvZN1r4VqAjHSHas3sQnIiJ9TIknEREREZEslkEVT4uAieHV5ooIFgufH9dnPhC7mtylwLPu7mH7zPCqd+OBicDCZGOG+zwXjkE45q+6ie8p4HwzqzSzSuD8sE1ERPqREk8iIiIiIlksUyqewvWWbiRI5iwHHnH3pWZ2m5ldEna7F6gys1rgJuDmcN+lwCPAMuC3wA3u3pFszHCsLwA3hWNVhWNjZh80szrgM8CPzGxpeIxG4GsEyaxFwG1hm4iI9KOC7ruIiIiIiEimyqCKJ9x9AbAgru3WyO1WgoRQon1vB25PZcywfTXBVe/i2xcRTKNLdIz7gPu6PAkREelTqngSEREREclimVLxJCIikogSTyIiIiIiWSyTKp5ERETiKfEkIiIiIpLFVPEkIiKZTIknEREREZEspoonERHJZCklnsxsupmtMLNaM7s5wfZiM5sXbn/VzMZFtn0xbF9hZhdE2u8zs3ozezturHlm9kb4s9bM3gjbx5lZS2TbPb09aRERERGRXKGKJxERyWTdXtXOzPKBu4HzgDpgkZnNd/dlkW7XAU3ufpyZzQTuAC43s8nATGAKMAr4vZlNcvcO4KfAXcAD0eO5++WRY/83sCOyeZW7n9zz0xQRERERyU2xhJMSTyIikolSqXg6Fah199XuvheYC8yI6zMDuD+8/ShwjplZ2D7X3dvcfQ1QG46Hu78INCY7aLj/ZcDDPTgfEREREZFBRVPtREQkk6WSeBoNbIjcrwvbEvZx93aCKqWqFPdN5kxgi7uvjLSNN7PXzewFMzszxXFERERERHKWptqJiEgm63aqHWAJ2jzFPqnsm8wsDq52eg842t23mdkHgMfNbIq77zwoELPZwGyAo48+OsVDiYiIiIhkJ1U8iYhIJkul4qkOGBu5PwbYlKyPmRUAwwmm0aWy7yHCMf4KmBdrC6frbQtvvwasAibF7+vuc9x9mrtPq6mp6fbkRERERESymSqeREQkk6WSeFoETDSz8WZWRLBY+Py4PvOBa8LblwLPuruH7TPDq96NByYCC1M45rnAO+5eF2sws5pwoXPM7NhwrNUpjCUiIiIikrNU8SQiIpms26l27t5uZjcCTwH5wH3uvtTMbgMWu/t84F7gZ2ZWS1DpNDPcd6mZPQIsA9qBG8Ir2mFmDwNnA9VmVgd8xd3vDQ87k0MXFf8ocJuZtQMdwPXunnRxchERERGRwUAVTyIikslSWeMJd18ALIhruzVyuxX4TJJ9bwduT9A+q4vjXZug7THgsVTiFREREREZLFTxJCIimSyVqXYiIiIiIpKhVPEkIiKZTIknEREREZEspoonERHJZEo8iYiIiIhkkbVrYWHkcj2qeBIRkUymxJOIiIiISBa5/Xa48soD91XxJCIimUyJJxERyShmNt3MVphZrZndnGD79Wa2xMzeMLM/mtnksH2cmbWE7W+Y2T0DH72ISP9raYHm5gP3VfEkIiKZLKWr2omIiAwEM8sH7gbOA+qARWY2392XRbo95O73hP0vAb4FTA+3rXL3kwcyZhGRgdbRAfv2HbiviicREclkqngSEZFMcipQ6+6r3X0vMBeYEe3g7jsjd4cCPoDxiYikXWdn4sSTKp5ERCQTKfEkIiKZZDSwIXK/Lmw7iJndYGargDuBf45sGm9mr5vZC2Z2Zv+GKiKSHh0dB5JNoIonERHJbEo8iYhIJrEEbYdUNLn73e4+AfgCcEvY/B5wtLufAtwEPGRm5YccwGy2mS02s8UNDQ19GLqIyMBQxZOIiGQTJZ5ERCST1AFjI/fHAJu66D8X+CSAu7e5+7bw9mvAKmBS/A7uPsfdp7n7tJqamj4LXERkoGiNJ5Hs09jSyOd/93leWv9SukMRGXBaXFxERDLJImCimY0HNgIzgSuiHcxsoruvDO9+AlgZttcAje7eYWbHAhOB1QMWuYjIAOnoAPfg3/x8VTyJZKI5r80BwN354/o/8st3fknzvmYMo7y4nKlHTE1zhCIDRxVPIiKSMdy9HbgReApYDjzi7kvN7LbwCnYAN5rZUjN7g2BK3TVh+0eBt8zsTeBR4Hp3bxzgUxAR6XexyqZY1ZMqnkQy16JNi3hwyYOMKhvFF874AmPKx3Dv6/eyZfeWdIcmMmBU8SQiIhnF3RcAC+Labo3c/pck+z0GPNa/0YmIpF+ssmnfPigpUcWTSKZq2dfCL5b9gmOGH8NNp91EnuXxD9P+gf/643/xw8U/5Msf/TL5efnpDlOk36niSUREREQki8Qqm2IJJ1U8iWSmx1c8zq62XVwx9QryLPjoXTWkis9M/gzv7X6PtTvWpjdAkQGixJOIiIiISBaJVjyBKp5EMtG67et4Ye0LnHXMWYyrGHfQthNHnohhLKtflp7gRAaYEk8iIiIiIlkkWeJJFU8imeOx5Y8xrGgYM/5ixiHbhhUNY1zFOJY2LE1DZCIDT4knEREREZEskmxxcVU8iWSGZ1Y/w4ptK7jwuAsZUjgkYZ8pI6ewdvtadu/dPcDRiQw8JZ5ERERERLKIptqJZC5355bnbqGypJKPHvPRpP2m1EzBcZZvXT6A0YmkhxJPIiIiIiJZJFnFk6baiaTfb1b+hlfqXuGiiRdRmF+YtN+4inEMKRzC0npNt5Pcp8STiIiIiEgWSVbx5B78iEh6dHR2cMuzt3Bs5bGcMfaMLvvmWR6TayazrGEZrv+4kuMK0h2AiIiIiIikLpZ4iiWcYv9CUPWUnz/wMYkMFnNem3NI2+wPzAbg/jfv580tb/Lwpx9mZ9vObseaXDOZxZsWs6R+CScdcVKfxyqSKZR4EhERERHJIsmm2kGQlFLiSWTg7WrbxX88+x+cNuY0Lp9yOT/+84+73eeE6hMAeGHtC0o89bOuEoa96Sc9o8STiIiIiEgWSTbVDrTOk0i63PHSHWzevZnHL38cM0tpn8qSymCdpwat85QO0STTvo59tHe2U1pYmsaIcpcSTyIiIiIiWaS7iicRGVhrmtbw33/6b66YegUfGvOhlPczM44adpQST2ng7ryz9R2erH2SdTvW0dreCsDostEcX308Zx59JqPKRqU5ytyhxJOIiIiISBZRxZNI5uj0Tq791bUU5RfxjXO+0eP9R5WN4u36t3H3lCul5PA0tjTyP3/+H1Y1raKipILTx5xOeUk5nd7Jym0r+cO6P/Dcmuf40OgP8ZfH/2W6w80JKSWezGw68F0gH/gfd/9G3PZi4AHgA8A24HJ3Xxtu+yJwHdAB/LO7PxW23wdcDNS7+4mRsb4K/B3QEDZ9yd0XdDWWiIiIiMhgoYonyUXZurbOM2ue4cV1L/LTGT9l7PCxPd5/VNko/rD+D2zevZmjyo7qhwglasPODXz/1e+zt2MvV5x4BaePPZ3C/MIDHSbC7r27ear2KZ5b+xyvb36dseVjuep9V6Uv6ByQ110HM8sH7gYuBCYDs8xscly364Amdz8O+DZwR7jvZGAmMAWYDvwgHA/gp2FbIt9295PDn1jSqauxREREREQGBVU8iWSGTbs28fg7jzPj+Blc/b6rezVGbDqXptv1v9rGWr758jcxM/799H/nrHFnHZx0Cg0rGsanJ3+a2z52G2OHj+Xqx6/m6l9eTcu+ljREnRtSqXg6Fah199UAZjYXmAEsi/SZAXw1vP0ocJcFdYIzgLnu3gasMbPacLw/ufuLZjauB7EmHasHY4iIiIiIZLVY4imWcFLFk+SqTK6CatnXwo9e+xElBSX86OIf9Xqa3P7EU/1Szj323L4MMeMl+v1C//yO65vr+cGiHzC8eDj/9uF/o7K0stt9RpSO4HOnfY4tu7fwny/8J6ubVjN/1nxGlI4AMvv5mWm6rXgCRgMbIvfrwraEfdy9HdgBVKW4byI3mtlbZnafmcWeEb0dS0REREQkZ3Q11S7dFU9mNt3MVphZrZndnGB7sZnNC7e/Gv0i2sy+GLavMLMLuhvTzMaHY6wMxyzq6hhmVmhm95vZEjNbHi7jIdJjnd7Jva/fS31zPbPfP5sjhh3R67HKisqoKq1SxVPEnNfmHPJzOJpamrhr4V0A/NOp/5RS0ikmz/L4ytlfYe6lc1m0aRFn3HcG63esP6x4BqNUEk+JUreeYp9U9o33Q2ACcDLwHvDfPYgDM5ttZovNbHFDQ0OCXUREREREsldXU+3SWfHUH0t0dDPmHQRLdEwEmsKxkx4D+AxQ7O5TCdam/fsezsAQAWD+ivksqV/CZVMu4/jq4w9rLDNjysgpSjz1k47ODi5/9HK27tnK9dOup2ZoTa/GuWzKZTz92afZtGsTH7v/Y9TtrOvjSHNbKomnOiC6StoYYFOyPmZWAAwHGlPc9yDuvsXdO9y9E/gxwXS6VOPA3ee4+zR3n1ZT07snlYiIiIhIpsrgiqf9S3S4+14gtkRH1Azg/vD2o8A58Ut0uPsaILasRsIxw30+Ho5BOOYnuzmGA0PDzyulwF5gZ9+dvgwGL657kSdrn+QjR3+Es485u0/GNIzX33udHy3+UZ9U+MgBtzx7C79b/TuumHoFk6omHdZYZ407i6c/+zQNzQ18/P6Ps711ex9FmftSSTwtAiaGpaxFBN9EzI/rMx+4Jrx9KfCsu3vYPjMsdx0PTAQWdnUwM4su5f8p4O3IMXo0loiIiIhIrsnUiif6Z4mOZO1VwPZwjPhjJTvGo0AzwayK9cA33b2xd6cqg9Hj7zzOQ0se4sSRJ3LFiVf0el2neEeVHUVLe4sSGX3sl8t/yTde+gaz3z+bjxz9kT4Z80NjPsRvP/tb3tv9Ht955Tvs3ru7T8bNdd0mnsIX6xuBp4DlwCPuvtTMbjOzS8Ju9wJV4YLfNwE3h/suBR4hWIj8t8AN7t4BYGYPEywMfryZ1ZlZrDT2znDe9VvAx4B/624sEREREZHBIoOvatcfS3T0ZkmPZNtOBTqAUcB44HNmdmx8Ry3dIYk8v/Z5Zj02i3EV45j9/tnk5/XdBdZHDQsWGH9v93t9NuZg99aWt7jm8Wv44KgP8r0Lv9enY58+9nSemPUEDXsa+P7C79Pa3tqn4+eiVK5qh7svABbEtd0aud1KMGc60b63A7cnaJ+VpP9VXcSRcCwRERERkcF2f0jHAAAgAElEQVSiq6l2aa546skSHXU9WKIjUftWoMLMCsIvyqP9kx3jCuC37r4PqDezl4BpwOpogO4+B5gDMG3atO7Wp5U+1rKvhV+t+BXN+5qZUDmBE6pP6PXi3X111bGXN7zMxQ9dzLGVx3LdKddRXFDcq3iSiV3ZbtOuTUyuiV8WTdra23rUf3XTai548ALKi8t57LLH+vz3BXD2uLP5u/f/Hfcsvod7Ft/DDR+8gcL8wj4/Tq5IKfEkIiIiIiKZIZZciiWcMqjiaf8SHcBGgiU6rojrE1ui409Elugws/nAQ2b2LYKKpNiyGpZozHCf58Ix5oZj/qqbY6wHPm5mDwJDgA8D3+mHx0F6ad32dfz4zz9mW8s2hhUNY+HGheRZHpdNvoyzx53dZ1PbemLRxkVc+PMLGVU2it9f9XueePeJPj9GWXEZZUVlbNrV5XLIg0pjSyPPr32eN7e8yebdm7nzpTt535Hv47Qxp+1fIDxREvG9Xe9x/s/OZ2/HXv7w139g7PCxh/TpqWRrbp185Mlc9b6reODNB/jJGz/hb9//t4d9rFylxJOIiIiISBZJVPFkBu7prXhy93Yziy3RkQ/cF1uiA1js7vMJluj4WbhERyNBIomwX2xZjXYOXqLjkDHDQ34BmGtmXwdeD8cm2TEIro73E4I1ZA34ibu/1U8Ph3Qh0Qf5up113PnynZQVlfG50z7HhMoJbGvZxryl85i7dC51O+uYNTXhpJl+8+yaZ5kxdwY1Q2p45upnOKrsqO536qUjhx3J5t2b+238bNLa3sq3//RttrZsZVLVJE458hRWNq5kwcoFPFn7JB8e82GmT5h+0D7uziNLH+HGJ2+kZV8Lz1z9zIBUj50x9gz27N3Do8sfZeiSofz9B/4+LQnSTKfEk4iIZBQzmw58l+ADxv+4+zfitl8P3ECwTsduYLa7Lwu3fZHgMtodwD+7+1MDGbuIyEBItMZTcTG0tqa94qm/lug4ZMywfTUHroAdbU94DHffnezYkn5PvPsEhXmFfOnML1FeXA5A9ZBq/mHaPzB/xXyerH2SHW07+mWqWyK/WPoLPvvLzzKpahJPffap/dPh+kv1kGqWb13er8fIBu7Oz5f8nIY9DXzutM8xsWri/m1NLU08vfpp/rDuD7y84WUeW/4Y5084n51tO1lSv4SXN7zMB0d9kJ/M+AlTRk4ZsJjPm3Aeu/bu4qlVT3Hrc7fytY9/bcCOnS2UeBIRkYxhZvkE30ifR7BGxyIzmx9LLIUecvd7wv6XAN8CppvZZIJvtacQTNP4vZlN0oUoRCTXJEo8FRUFiac0r/Ek0isbdmzgjc1vcPGki/cnnWLyLI9P/sUnqSyt5KElD/GpeZ/ify//X0oKSvolltb2Vm7+/c1899XvcvrY0/n1rF9TWVrZ5T7JpmL1RNWQKna07mBfx75BvVbQy3Uvs3DjQi45/pKDkk4AlaWVXD7lcqZPmM7CTQvZvHsz33z5m1QNqWJ8xXjuPPdOhhUN46UNL/HShpcGNO5P/cWn2L13N1//w9epHlLNv3z4Xwb0+JlOiScREckkpwK14bfYmNlcYAbB1AsA3H1npP9QDlzFaAYw193bgDXhFItTCdb4EBHJGYmm2hUXH7xNJJv8euWvKS0o5Zzx5yTtc9YxZ5Fv+Tz41oOc/7Pz+cVnftHrRccTcXd+t/p3/Pvv/p23trzFP536T9x53p39luCKVz2kGsdpbGns0/PKJq3trcx7ex7HVx3PhcddmLTf8JLhnHfsecz+wGzaO9spyDuQ1uiLJGBvmBlXTr2SI4Ydwb8+9a+MKB3BVe9Let20QUeJJxERySSjgQ2R+3XAh+I7mdkNwE1AEfDxyL6vxO07un/CFBFJn2RT7aLbRLLF/mqniRczpHBIl30/cvRHuPC4C7lu/nV8YM4HePSyR/nwmA93uU9HZwct7S207GuhwzuobaylIK+AfMvHzHh327vcvehuFm5cSN3OOiqKK7jxgzdy4sgTeeDNB/ryVLtUVVoFwLaWbYM28fT6e6/T1tHGJcdfQp7lpbRPNOmUbvl5+fz8r37OJx76BNf+6lqKC4q5bMpl6Q4rI2TOb0lERCRY7DXeIZeydve7gbvN7ArgFoKrF6W0r5nNBmYDHH300YcVrIjIQPPIq1r0qnbl4ewkJZ4k2zy/7nmK84s559jk1U5Rs6bO4oSaE/jUvE9x2r2ncd6x5/E3p/wNb25+k/y8fHa07qBuVx0bd26kbmcdW5q30OkHSgG/8vxXEo47qmwUV7/vak4ddWpaprpVD6kGYOuerQN+7EzxysZXqB5SzYTKCekOpddKCkr41cxfcdHPL+KKx64g3/L59ORPpzustFPiSUREMkkdEL3u7Rigq2sLzwV+2JN93X0OMAdg2rRphySmREQyWTSxFL/GE2iqnWQXd+ft+reZMnJKt9VOUScfeTJ/nv1nvvfq97jvjfuY9dihV7urKq1iTPkYTj7yZMqLyyktKCU/L5+zjjmLDu+gvbOdTu9kXMU4lmxZQllxWV+eWo9VlFSQb/lsa9mW1jjSpamliRVbV3DRxIuy/qpww4qG8ZsrfsP0n09n5mMz+U3xbzh/wvkH9Uk0JXD2B2YPVIgDToknERHJJIuAiWY2HthIsFj4FdEOZjbR3VeGdz8BxG7PBx4ys28RLC4+EVg4IFGLiAyQZIknTbWTbLRx10a2t27nxJoTe7xvZWklXzn7K9zy0Vt4pe4V5r49lw7vYFjRMEaXjaa0sDThfonW3Vm7fW2Pj9/X8iyPEaUj2LZncCaeFm5ciON8aPQhKyxkpbLiMp688knO/MmZzJg7gy+c8YV+vzJiJktt4qSIiMgAcPd24EbgKWA58Ii7LzWz28Ir2AHcaGZLzewNgnWergn3XQo8QrAQ+W+BG3RFOxHJNdGKJi0uLtnu7fq3AZgyckqvx8jPy+eMo89g6hFTOfnIkzluxHFJk06ZrmpI1aCcaufuvLLxFcZXjM+p9a3Ki8t5YtYTFOUXcdfCu9jZtrP7nXKUEk8iIpJR3H2Bu09y9wnufnvYdqu7zw9v/4u7T3H3k939Y2HCKbbv7eF+x7v7k+k6BxGR/hJf8eR+8FQ7VTxJNnm7/m3Glo+loqQi3aFkhKrSqkE51a5uVx2bdm3qdqH4bHT08KO54YM3sLNtJw++9WC6w0kbJZ5ERERERLJEfMVT7L4qniTbtOxrYVXTKk4c2fNpdrmqekg1O9t2srdjb7pDGVDL6pcBcMqRp6Q5kv4xrmIcF0+6mDe3vMnS+qXd75CDtMaTiIiIiEgG6OyE88+Hm26Ciy5K3Ce+4il2ZTtVPEm2WdawjE7vVOIpoqq0CmDQrfO0qmkVI4eOZHjJ8B7tl2iB7kx1zvhzeGnDS8xbOo9bq2+lIG9wpWIG19mKiIiIiGSolhZ45hmYNCm1xFN7+4H7qniSbPN2w9sMKRzC+Irx6Q4lY1QPqQYYVNPt3J3axlpOOuKkdIfSJ5IlwwrzC7ls8mXctegunlnzDBdMuGCAI0svTbUTEREREckAe8PZNe++m7xP/FQ7VTxJtlrWsIwTqk8gPy8/3aFkjKohQcXTYFpgfEvzFpr3NXPciOPSHUq/m3rEVKaOnMqTK58cdNMplXgSEREREckAbW3Bv10lnpJNtVPFk2STHa072N66nQmVE9IdSkYpLy6nIK9gUE21q22sBRgUiSeAc489l5b2Ft7c/Ga6QxlQmmonIiIiIpIBYhVPGzbAnj0wZMihfVTxJLlg/Y71QHDFr4GWyesC5VkeVaVVbG0ZPBVPq5pWMbRwKEcMPSLdoQyISVWTGFE6gpfrXuaDoz+Y7nAGjCqeREREREQywN7IzIva2sR9VPEkuWDdjnUYxtjhY9MdSsapGlI16CqeJoyYgJmlO5QBkWd5nDbmNJY3LKeppSnd4QwYJZ5ERERERDJAbKodJJ9up6vaSS5Yv2M9Rww7gpKCknSHknGqS6sHzRpPO9t2Ut9cP+imXJ425jQc55WNr6Q7lAGjxJOIiIiISAaIVjwlSzwlm2qniifJJut3rE/LNLtsUDWkiuZ9zexq25XuUPrd6qbVwOBZ3ymmZmgNx404jj9t+BPunu5wBoTWeBIRERERyQCpJJ6iFU3t7ap4kuxT31xPU2tTrxNPmbxGU18YUToCgLqddZxQc0Kao+lftY21FOQVcMzwY9IdyoA7fczpPPDWA6zbsY5xFePSHU6/U8WTiIiIiEgGiE21KyjovuKpoEAVT5KdXtv0GsCgTDakoqKkAoCNuzamOZL+t27HOsaUj6EwvzDdoQy4k444CYB3tr6T5kgGhiqeREREREQyQKzi6fjju694Ki7WGk+SHeIrlBasXADA2HItLJ5IZUklEFQ85TJ3Z+POjZxy5CnpDiUtyorLOGrYUazYtoLpx01Pdzj9ThVPIiIiIiIZIJZ4OvFE2LYt+IkXSyyVlKjiSbLTuh3rGDl0JKWFpekOJSPtr3jamdsVT1uat9C8r5lRZaPSHUraTKqaxKrGVXR05v43Bko8iYiIiIhkgGjiCWDlykP7xBJLqniSbLV+x3pNs+tCYX4hw4qG5XzF09v1bwMwunx0miNJn0lVk2jraGP9jvXpDqXfpZR4MrPpZrbCzGrN7OYE24vNbF64/VUzGxfZ9sWwfYWZXRBpv8/M6s3s7bix/p+ZvWNmb5nZL82sImwfZ2YtZvZG+HNPb09aRERERCTTxNZ4mjo1+DfRdDtVPEk22713N40tjbqiXTcqSipyfo2nWOJpMFc8TRwxEYB3tyWZW51Duk08mVk+cDdwITAZmGVmk+O6XQc0uftxwLeBO8J9JwMzgSnAdOAH4XgAPw3b4v0OONHdTwLeBb4Y2bbK3U8Of65P7RRFRERERDJfdI2n/HxYseLQPrHEUnziSRVPkg027NgAwNjhWt+pK5UllYOi4qmsqIzy4vJ0h5I2w0uGc+SwI3m3UYkngFOBWndf7e57gbnAjLg+M4D7w9uPAueYmYXtc929zd3XALXheLj7i0Bj/MHc/Wl3D99CeQUY08NzEhERERHJOrHE09ChcOyxXVc8FRcHSSdVPEk22bx7MwCjhg3eKpdUVJRU5HziaUn9kkFd7RQzacQkahtr6fTcfvFOJfE0GtgQuV8XtiXsEyaNdgBVKe7blb8BnozcH29mr5vZC2Z2Zg/GERERERHJaLGpdkVFMHEi1NYe2if+qnb79h3YJ7pdJBNtbt5MSUHJoK5ySUVlSSUNexpoa29Ldyj9otM7WVq/lNFlg3d9p5iJVRNpbW/dXw2Yq1JJPFmCNk+xTyr7Jj6o2X8A7cDPw6b3gKPd/RTgJuAhMzvkFcvMZpvZYjNb3NDQkMqhRERERETSLlbxVFwMlZWwc+ehfaKLi8OBZJUqniQbbNm9hSOHHkkwOUaSiV3ZbtOuTWmOpH+s275u0F/RLmZS1SQg99d5SiXxVAdEJ+GOAeL/B+zvY2YFwHCCaXSp7HsIM7sGuBi40t0dIJyuty28/RqwCpgUv6+7z3H3ae4+raamJoXTExERERFJv1jiqagomG7X3Hxon+ji4gAtLQf2iW4XyUSbd2/miGFHpDuMjFdZWgmQswuM719YvFyJp4qSCqqHVLN6++p0h9KvUkk8LQImmtl4MysiWCx8flyf+cA14e1LgWfDhNF8YGZ41bvxwERgYVcHM7PpwBeAS9x9T6S9JrYwuZkdG46V278dERERERk0olPtkiWeoouLw4HEU6ziSYknyVRt7W00tTZx5LAj0x1KxotVPOXqOk/7E09a6wuA0WWjc7a6Laaguw7u3m5mNwJPAfnAfe6+1MxuAxa7+3zgXuBnZlZLUOk0M9x3qZk9AiwjmDZ3g7t3AJjZw8DZQLWZ1QFfcfd7gbuAYuB3YQnmK+EV7D4K3GZm7UAHcL27H7I4uYiIiIhINtq7F/LyoKDgQOLJHaKzkqJrPMGhFU+aaieZakvzFgCOGKqKp+5UloQVTztzp+Jpzmtz9t9+/J3HqSqtorSwNI0RZY7R5aNZUr+E1vZWSgpK0h1Ov+g28QTg7guABXFtt0ZutwKfSbLv7cDtCdpnJel/XJL2x4DHUolXRERERCTb7N0L+fkwZw4sXRokne6+O0gqzZ4d9IlPPLW2HnxfFU+SqbbsDhJPqnjqXklBCUMLh+ZsxdPGXRu1vlPE6LLRdHonyxuWc8pRp6Q7nH6RylQ7ERERERHpZ21tQeIJDlQwtcVd1Cp+cXFVPEm22Ny8GcMYOXRkukPJeGbGmPIxObnGU0dnB5t3b+aosqPSHUrGiF3db0n9kjRH0n+UeBIRkYxiZtPNbIWZ1ZrZzQm232Rmy8zsLTN7xsyOiWzrMLM3wp/49QhFRDLa3r1QWBjcjr9qXUyyxcVV8SSZbsvuLVQNqaIwvzDdoWSF0eWjc7LiaVvLNjq8g6OGKfEUM3LoSAryCliyRYknERGRfhdeROJu4EJgMjDLzCbHdXsdmObuJwGPAndGtrW4+8nhzyUDErSISB+JTbWDA4mk2JXuYpItLh5LWKniSTLV5t2btb5TD+RqxVNsyqUq3w7Iz8vnqGFHqeJJRERkgJwK1Lr7anffC8wFZkQ7uPtzkauevgKMGeAYRUT6RVtbsLA4dF/xFD/VrrAwWJhcFU+SiTq9ky3NW7S+Uw/ErnTW6bmVTa5vrge0yHy80WWjlXgSEREZIKOBDZH7dWFbMtcBT0bul5jZYjN7xcw+2R8Bioj0l717DySeYms2xVc8JZtqV1AQVEup4kky0fbW7ezt2MsRw5RsSNWY8jG0d7bvT9TkivrmekoLShlWNCzdoWSUUWWj2LRrE40tjekOpV8o8SQiIpnEErR5wo5mnwWmAf8v0ny0u08DrgC+Y2YTEuw3O0xOLW5oaOiLmEVE+kQ08ZSs4inZ4uIFBap4ksy1/4p2Q1XxlKrYgtO5ts7TluYtjBw6ErNEf/INXqPLg9/32/VvpzmS/qHEk4iIZJI6YGzk/hhgU3wnMzsX+A/gEnff/7HM3TeF/64GngcOuSatu89x92nuPq2mpqZvoxcROQzRNZ6SXdUufqpda2vwryqeJJNt3r0ZQFPtemBMebCSQK4lnuqb6zXNLoH9V7bL0QXGlXgSEZFMsgiYaGbjzawImAkcdHU6MzsF+BFB0qk+0l5pZsXh7WrgDGDZgEUuInKYEq3xlOri4qp4kky2uXkzJQUllBeXpzuUrBGrgNm4M3cWGN/XsY/GlkYtLJ5ARUkFFSUVObvOU0G6AxAREYlx93YzuxF4CsgH7nP3pWZ2G7DY3ecTTK0bBvwiLNNeH17B7gTgR2bWSfDFyjfcXYknEckaqUy1S7a4uCqeJJNt27ONmiE1ml7VAyOHjqQgryCnKp4a9jTguBJPCZgZU0dOVeJJRERkILj7AmBBXNutkdvnJtnvZWBq/0YnItJ/Ek21S2VxcbOg2kkVT5Kptu7ZqoXFeyjP8hhdNpqNu3Kn4im2ULoST4lNHTmVB5c8iLvnXJJWU+1ERERERDJAWxsUFga3CwuDhFIqi4vHqqRU8SSZyN3Z1rKNqtKqdIeSdUaXj86piiclnro2sWoiO9t2sq1lW7pD6XNKPImIiIiIZIBoxZNZUPWUbKpdtOIplnjKhIonM5tuZivMrNbMbk6wvdjM5oXbXzWzcZFtXwzbV5jZBd2NGa4H+KqZrQzHLErhGCeZ2Z/MbKmZLTGzkv55JCRm195d7O3YS/WQ6nSHknXGlI/JuYqnYUXDGFo0NN2hZKRjK48FYFXjqjRH0veUeBIRERERyQDRNZ4gqGpKZXHxTKl4MrN84G7gQmAyMMvMJsd1uw5ocvfjgG8Dd4T7Tia4oMQUYDrwAzPL72bMO4Bvu/tEoCkcu6tjFAAPAte7+xTgbGBfnz4Icohte4LqjaohqnjqqdFlQcWTu6c7lD6xpXmLqp26MKFyAgCrm1anOZK+pzWeREREREQyQFvbgYon6LriKTbVrrU1oyqeTgVq3X01gJnNBWZw8BVGZwBfDW8/CtxlwWImM4C57t4GrDGz2nA8Eo1pZsuBjwNXhH3uD8f9YRfHOB94y93fBHD33JvPkoG2tmwFoLpUFU89NaZ8DHv27WFH2w4qSirSHc5hq2+u54TqE9IdRsZ6bu1zAMxbOo9de3ftb5/9gdnpCqnPqOJJRERERCQD7N17YI0nCJJLqVzVLlMqnoDRwIbI/bqwLWEfd28HdgBVXeybrL0K2B6OEX+sZMeYBLiZPWVmfzazz/f6TCVlqnjqvdFlwVM6F9Z5amtvY3vrdlU8daEov4jy4nK27tma7lD6nBJPIiIiIiIZILrGEwQVT8mm2mVoxVOiyzDFzxFK1qev2rs6RgHwEeDK8N9Pmdk58R3NbLaZLTazxQ0NDQmGkp7YtmcbQwuHUlKg5bR6akz5GAA27sz+dZ7q92hh8VTUDKmhoTn3XneUeBIRERERyQBtbQev8VRS0v3i4u4ZVfFUB4yN3B8DbErWJ1xzaTjQ2MW+ydq3AhXhGPHH6uoYL7j7VnffAywA3h9/Eu4+x92nufu0mpqalE9eEtvaslULi/fS6PLcqXiq363EUypqhtTQsEeJJxERERER6Qfxi4t3VfFUEikeyaCKp0XAxPBqc0UEi4XPj+szH7gmvH0p8KwHKyfPB2aGV6QbD0wEFiYbM9znuXAMwjF/1c0xngJOMrMhYULqLA5ef0r6wbY92zTNrhfmvDaHBSsXAPD4iseZ89qcNEd0eGLJFCWeulY9pJrtrdvZ15Fb1z1Q4klEREREJM06O6G9/eCpdqms8QSZU/EUrqd0I0GCZznwiLsvNbPbzOySsNu9QFW4ePhNwM3hvkuBRwgSQb8FbnD3jmRjhmN9AbgpHKsqHLurYzQB3yJIZr0B/Nndf9M/j4YAdHon21q2aWHxXirIK6CsqIztrdvTHcpha9jTQFlRmaZcdqNmaA2O09jSmO5Q+pSuaiciIiIikmaxyqb4iqfuptpF98mAiifcfQHBFLZo262R263AZ5Lseztweypjhu2rOXDlu2h7V8d4EHiwy5OQPrNl9xbaO9tV8XQYKksr2d6SA4mn5gZNuUxB7DFq2NPAEcOOSHM0fUcVTyIiIiIiaZYo8VRc3P3i4gA7dsCcOcG/q1b1b5wiPbFm+xoAJRwOQ0VJRU5UPG3ds5WaIVozrTuxxyjX1nlS4klEREREJM26qnjyyHXhYhVN0X6x6XlmB/cVSbe129cCUFWqiqfeqiyppKm1Kd1hHJa9HXtpbGmkZqgST90pLy6nMK+QrXu2pjuUPqXEk4iIiIhImsUST/FrPLkHaz/FdHYGU+rMoLAwaMvLO/Bvmq9qJ3KQNU1BxZOm2vVeRUkFzfua2duxt/vOGWr9jvU4roqnFJgZNUNr2NqsxJOIiIiIiPSh2FpOsWQSHJhOF13nqaPjQHIqPvGkiifJNGu3r6W8uJyi/KJ0h5K1KksqAbJ6ut2qxmAOsKZcpqZ6SLWm2omIiIiISN9KVPFUVHTwNggST7FEUyzxFNtHFU+SadZsX6NpdoepoqQCyPLEU1OQeNJUu9RUD6lm656teA59k6DEk4iIiIhImiVbXBwOrnjq7FTFk2SPtdvXqsrlMFWWBhVPTS3Zu87T6qbVFOYVMrx4eLpDyQo1Q2po62hj195d6Q6lz6SUeDKz6Wa2wsxqzezmBNuLzWxeuP1VMxsX2fbFsH2FmV0Qab/PzOrN7O24sUaY2e/MbGX4b2XYbmb2vXCst8zs/b09aRERERGRTBJLLsUvLh7dBqp4kuzR0dnB+h3rVfF0mHKl4ql6SDVmlu5QskJsLaxcWmC828STmeUDdwMXApOBWWY2Oa7bdUCTux8HfBu4I9x3MjATmAJMB34Qjgfw07At3s3AM+4+EXgmvE94/Inhz2zgh6mdooiIiIhIZutNxVOsryqeJBNt2rWJfZ37tLD4YSopKKGkoCSrr2y3qnGVptn1QOz/zKBKPAGnArXuvtrd9wJzgRlxfWYA94e3HwXOsSCdOQOY6+5t7r4GqA3Hw91fBBoTHC861v3AJyPtD3jgFaDCzI5K5SRFRERERDJZsqvaRbdB14uLq+JJMsna7WsBLSjdFypLKrM28eTurG5aredBD8QWlM/W33kiqSSeRgMbIvfrwraEfdy9HdgBVKW4b7wj3P29cKz3gJE9iENEREREJOv0xVQ7VTxJJlmzfQ2Aptr1gYqSCra3ZOdUu/rmepr3Ne+fPibdKy0spaSgJGt/54mkknhKNBEz/i0tWZ9U9k1VSmOZ2WwzW2xmixsacusShCIiIiKSm/picXFVPEkmiVU8jSgdkd5AckBlaWXWrvG0umk1gBJPPZTNVW6JpJJ4qgPGRu6PATYl62NmBcBwgml0qewbb0tsCl34b30P4sDd57j7NHefVlOjJ7eIiIiIZL6uEk/xU+1U8STZYM32NYwqG0VhfmG6Q8l6lSWV7Gjbwb6OfekOpcdWNa0C0BpPPVRZWpnVVzKMl0riaREw0czGm1kRwWLh8+P6zAeuCW9fCjzr7h62zwyvejeeYGHwhd0cLzrWNcCvIu1Xh1e3+zCwIzYlT0REREQkmyWaaqeKJ8lma7evZXzF+HSHkRMqSipwnM27N6c7lB5b1bgKwzTlsocGXcVTuGbTjcBTwHLgEXdfama3mdklYbd7gSozqwVuIrwSnbsvBR4BlgG/BW5w9w4AM3sY+BNwvJnVmdl14VjfAM4zs5XAeeF9gAXAaoIFyn8M/ONhnbmIiGQkM5tuZivMrNbMbk6w/SYzW2Zmb5nZM2Z2TGTbNWa2Mvy5Jn5fEZFMlWhx8cLCoIop1cXFVfEkmWRN0xrGVYxLdxg5IbbY9MZdG9McSc+t3r6aMeVjVPnWQ5Ullexs20lHZ0e6Q+kTBd13AREF1NMAACAASURBVHdfQJD4ibbdGrndCnwmyb63A7cnaJ+VpP824JwE7Q7ckEq8IiKSncwsH7ib4IuHOmCRmc139//f3p3HyVXV+f9/fdKd7k46vWXfk87CkpAIJIBsicOOo4kLSuQrIuIXHdFxhlFwmVFEma+oIzP+EDUzIMIgqwtxBBEGCEtYQhLIQgjpzkL27k4v2Tvp7vP749xKKp2q7uruqrq3Ou9nHvWoqlv3nvs5VdWpup/6nHPfjlttGTDTObfPzP4O+BFwpZkNBL4LzMTPAbgk2Lb3/FwkIr1WoqF2Zn6C8WSTi8fWjSWiVPEkUdHS1sLmXZtV8ZQm5f3KAdi8a3PIkXRddX01EyomhB1GzqnoV4HD0dTcFHYoaZHKUDsREZFsOROocs6tc84dBB4C5sav4Jx7zjm3L7j7Kn7OP4BLgaedc/VBsulp4LIsxS0i0iOJhtqBTzzFVzx1NtROFU8SBZuaNtHqWlXxlCaxiqecTDw1VDOxYmLYYeSc8iKfbKzfXx9yJOmhxJOIiETJKGBT3P3NwbJkrgOe7Oa2IiKRkajiCfw8T8kqnhINtVPFk0RB7Ix2lRWqeEqH4r7F9O3TN+cST3sO7mH7nu1MGjgp7FByTuxskL1lniclnkREJEoswbKEv9+b2afxw+p+3JVtzex6M3vDzN6ora3tdqAiIumUaI4nOHaoXaKKp/ihdqp4kihY37geQBVPaWJmVBRV5FziaV3DOgAmDlTFU1fFqtwa9zeGHEl6KPEkIiJRshkYE3d/NLC1/UpmdhHwbWCOc665K9s65+Y752Y652YOGaJT+4pINCRLPBUWdm1ycVU8SRRsaNxAH+vDmNIxna8sKSnvV55ziafq+moADbXrhqL8IgrzCqk/oKF2IiIi6bYYmGxmlWZWAMwDFsSvYGanAb/CJ51q4h56CrjEzCrMrAK4JFgmIhJ5zc0+kdSn3bfzjiYXV8WTRNWGxg06k1maVRRV5NxZ7aobgsSTKp66zMyo6FehiicREZF0c861AF/GJ4xWA48451aZ2a1mNidY7cfAAOBRM3vTzBYE29YD38cnrxYDtwbLREQi7+BBn2Rqr/0cTx1NLq6KJ4mK9Y3rNcwuzcqLytmyawttLnf+yKvqqxjUb9DhibKlayqKKnrNHE/5na8iIiKSPc65J4An2i37TtztizrY9h7gnsxFJyKSGR0lntoPtYslmmITkeusdhI1Gxo3cGHlhWGH0atUFFVwqO0QtXtrGTZgWNjhpKS6oVrVTj1Q0a+Ct2veDjuMtFDFk4iIiIhIyJqbfZKpva5MLq6KJ4mC5pZmtuzaooqnNKvo5yebzqV5nqrrqzW/Uw9UFFXQ1NzEodZDYYfSY0o8iYiIiIiELNWhdh1NLq6KJ4mCTbs24XBUlleGHUqvEhuuliuJp4OtB9nYtFGJpx6oKKrA4di+Z3vYofSYEk8iIiIiIiFLlngqKPCPxRJKHU0urooniYL1DesBVPGUZhVFuVXxtLFxI22ujUkDJ4UdSs6KVblt2rUp5Eh6ToknEREREZGQJRtqV1jok0mxeZ40ubhE3YbGDQBUVqjiKZ1KCkvI75OfM2e20xntei7Xko0d0eTiIiIiIiIh62ioHcDevf62htpJ1K1vXE9+n3xGlowMO5RepY/1YWTJyJxJQlTXB4knDbXrttjwykfffpTGA41HPXb9jOvDCKnblHgSEREREQlZZ4mn+fNh4EDYutUnnObPh+XL/WMaaidRsqFxA2NKx5DfR4ea6Ta6dHTOJJ6q6qvo37c/wwcMDzuUnNW/b38K8wpp2N8Qdig9pqF2IiIiIiIh6+isdnD0ULv4Cqf216p4krBtaNygYXYZkkuJp+oGf0Y7Mws7lJxlZpQXldNwQIknERERERHpoWQVT7HhdIeCs2k75yub4EilkyqeJErWN65nfNn4sMPolUaX+MSTy4EMc3VDteZ3SoPyonKaDjSFHUaPKfEkIiIiIhKyZImnWFKptdVfx1c8xR5TxZNExf5D+9m+Z7sqnjJkdOlo9rfsj3wFTJtrY13DOs3vlAZlRWU0NSvxJCIiIiIiPZRsqF37xFOiiied1U6iYmPTRgDGl48PN5BeanTpaAA2NW0KOZKObd29lQMtB5R4SoOywjIaDzTmRJVbR5R4EhEREREJWaoVT84dW/EUn4DK8WMTyXEbGjcAUFmuiqdMGFc+DjiS4Iuq2BntJg2cFHIkua+8qJyWthb2HdoXdig9osSTiIiIiEjI0jHULlYJpaonCcv6hvWAKp4yZVyZTzy91/ReyJF0rLrBJ540x1PPlRWVAeT8cDslnkREREREQnbwYGpD7drakk8uHktAKfEkYdnQuIGCvAJGlIwIO5ReaUjxEArzCtnYGP2Kp/w++YwtGxt2KDmvvLAcgMYDjSFH0jNKPImIiIiIhKy5uftD7dpXPMXWFcm29Y3rGVc2jj6mw8xM6GN9GFs2NvJD7aoaqhhXNo78Pvlhh5LzDlc85fiZ7fROEBEREREJWWdD7Vpa/HWiiqf4s9rF1hHJtvlL5rN4y2L69e3H/CXzww6n1xpXPi76Q+3qqzW/U5qUFfaOoXZKPImIiIiIhCwdk4ur4knCVre/jtPKTgs7jF5tbOlYnqh6IuwwOrS6bjVnjDxDCcg0KMwvpF9+Pw21ExERERGR7nPOD7VLNMdTfvAzcUdzPKniSaLgQMsB9hzcw6D+g8IOpVcbVz6O7Xu2c6DlQNihJFS/v559h/YxpHhI2KH0GuVF5Tk/1E6JJxERERGRELW2+uRTV89qN3o0TJwIw4f7+6p4kjDV768HYHC/wSFH0rvFzmy3qWlTyJEkVlVfBcCQ/ko8pUtpYSmNzcdBxZOZXWZma8ysysy+keDxQjN7OHj8NTMbH/fYN4Pla8zs0s7aNLMXzezN4LLVzP4YLP+AmTXFPfadnnRcRERERCQKDh7016kOtYslmCoq4KabYMAAf18VTxKmun11AKp4yrDYmeKiOs9TdX01AEOLh4YcSe/RGyqeOp3jyczygJ8DFwObgcVmtsA593bcatcBDc65SWY2D7gduNLMpgDzgKnASOAZMzsh2CZhm8658+P2/Tvg8bj9vOic+1B3OysiIiIiEjXNzf460VC7jiqe2lPFk4Rp576dAAzur4qnTBpX7iueonpmu+oGn3jS+yB9yorKaGpuwjmHxf6jzzGpVDydCVQ559Y55w4CDwFz260zF/hNcPsx4ELzz8hc4CHnXLNzbj1QFbTXaZtmVgJcAPyxe10TEREREYm+7lY8tReFiqcsj5SoDNpYG7RZ0Nk+gsfHmtkeM/ta+p+B41fd/jr69ulLSUFJ2KH0aqNLR2MYGxujm3gqLyynIC/Bf2jSLeWF5bS0tbD30N6wQ+m2VBJPo4D4AaSbg2UJ13HOtQBNwKAOtk2lzY8C/+uc2xW37Gwze8vMnjSzqSnELiIiIiISaV1JPEW54ilupMTlwBTgU8EIiHiHR0oAd+BHStBupMRlwF1mltdJm7cDdzjnJgMNQdtJ9xHnDuDJ9PRaYur21TGo/6CcrcjIFQV5BYwsGcl7u6I51K6qvorBxap2SqfyonKAnB5ul0riKdH/HC7Fdbq6PN6ngAfj7i8Fxjnn3gf8fySphDKz683sDTN7o7a2NtEqIiIiIiKR0dWhdhGueMraSIlgmwuCNgja/Egn+8DMPgKsA1alsd8C1O2t04TSWTK2bGx0K57qqzW/U5qVFZUB0HggdycYTyXxtBkYE3d/NLA12Tpmlg+UAfUdbNthm2Y2CP8h8+fYMufcLufcnuD2E0BfMzsmleqcm++cm+mcmzlkiP7jExEREZFo66jiKZZMih9qF9WKJ7I7UmIQ0Bi00X5fCfdhZsXAzcD3OuqEfsjuOucctftqlXjKknHl4yI5x9O+Q/vYtmeb3gdpVlboE09Nzb274mkxMDkYQ12AL4Fd0G6dBcA1we0rgGedcy5YPi8YZ10JTAZeT6HNTwD/45w7EFtgZsPjfqk4M4h9Z9e6KyIiUZfC/CCzzGypmbWY2RXtHmuNO/tp+88qEZFI6ijxZOYTTTlS8ZTNkRId7SvZY9/DD83bk+DxIyvqh+wuq9lbQ3NrM0OK9Xxlw7iycWxq2kSbi9YpLNc1rANQ4inNekPFU6dntXPOtZjZl4GngDzgHufcKjO7FXjDObcAuBu438yq8JVO84JtV5nZI8DbQAtwg3OuFSBRm3G7nQf8sF0oVwB/Z2YtwH5gXpDcEhGRXiLFM6m+B3wWSDQp7H7n3KkZD1REJI06GmoHfrhdjlQ8dWWkxOYUR0qQZHkdUG5m+UFVU/z6yfZxFnCFmf0IKAfazOyAc+7O7nc5O+YvmX/MsutnXB9CJInFzmSmhEN2jC0by6G2Q2zfs52RJSPDDuewqvoqACUg06wgr4D+ffvn9BxPnSae4PDQtifaLftO3O0D+CqlRNveBtyWSptxj30gwbI7gch/KIiISI8cnssDwMxi84McTjw55zYEj0XrZz4RkW7qqOIJID8/tcnFI1DxdHhUA7AF/2PyVe3WiY2UeIW4kRJBlepvzeynwEiOjJSwRG0G2zwXtPFQ0ObjHe0DOD8WhJndAuzJhaRTLqiuDxJPSjhkxbiycQBsbNwYzcSTEpBpV1ZYRmNz7lY8pTLUTkREJFtSmR+kI0XBvByvBhPIHkNzd4hI1HSWeGpf8ZRsqF3YFU9B5VFsVMNq4JHYSAkzmxOsdjd+vqUq4EbgG8G2q4DYSIm/EIyUSNZm0NbNwI1BW4OCtpPuQzKnuqEawxjUb1DYoRwXxpX7xNN7TdE6s92aujUM7j+Y4oLisEPpdcqLynt/xZOIiEiWpDI/SEfGOue2mtkE4FkzW+Gcqz6qMefmA/MBZs6cqSHbIhK6WOKps6F2znU81K79RORhyOZIiaA69swEy5PuI26dWzp6XLqmuqGa8qJy+ub1DTuU48LYsrEAbGjcEG4g7azZuYYTB50Ydhi9UllRGTt27gg7jG5TxZOIiERJKvODJOWc2xpcrwOeB05LZ3AiIpkQm+Ops4qn2OymnVU8hTjUTo5T1fXVGmaXRaWFpQzuP/jwZN5R8e7Od5V4ypDyQl/xFLUJ5VOlxJOIiERJKmdSTcjMKsysMLg9GDiXuLmhRESiKtWhdrHEU5QrnuT4VN1QrXl9smzSwElUNVSFHcZhTQea2LF3BycOVuIpE8qKymh1rew9uDfsULpFQ+1ERCQyUjmTqpmdAfwBqAA+bGbfc85NBU4GfhVMOt4H+GG7s+GJiERSqkPtYpVMnZ3VThVPkk27m3dTs7eGc8acE3Yovd5RZzd0sGzbsvCCaWfNzjUAnDjoRHbszd0hYVFVVlgGQFNzEyWFJSFH03VKPImISKSkMD/IYvwQvPbbLQKmZTxAEZE027XLXxcnmY+3feIp2VA7VTxJGGLDvVTxlF1Diofw6pZX2X9oP/369gs7HNbUBYmnwUo8ZUJ5UTkAjQcaGV16zNfgyNNQOxERERGRENXU+GTS4MGJH091qJ0qniQM1Q3+HB5KPGXX0OKhAKxvXB9yJN6anWvIszwmVEwIO5ReqawoqHjK0TPbKfEkIiIiIhKimhoYNAjyk4xFyMuDlhZVPEk0VdcHiSdNLp5VsURfVX005nlas3MNEyomUJCXZLI66ZHYULvG5saQI+keJZ5EREREREJUUwNDhyZ/XBVPEmXVDdUM7DeQ/n37hx3KcSVW8RSZxFPdGk0snkF98/pS3LdYFU8iIiIiItJ1qSaeVPEkUVTdUM3Eiolhh3HcKS4oprhvMWt3rg07FNpcG2vr13LiICWeMqmsqEyJJxERERER6bqaGhjSwSglndVOoqy6vpqJA5V4CsOQ4iFUNYRf8fRe03scaDmgxFOGlReV5+xQO53VTkREREQkRLW1XRtqp4oniYqDrQd5r+k9rpp2VdihHJeG9h8a+lC7+Uvms6pmFeDneZq/ZH6o8fRmZYVlbNu9LewwukUVTyIiIiIiITl0COrruzbUThVPEhVV9VW0ulZOGnxS2KEcl4YUD+G9pvdobmkONY4de3cAMKx4WKhx9HZlRWU0NTfR5nLvP3klnkREREREQlJX569V8SS56J26dwA4efDJIUdyfBpaPJQ218aGxg2hxrFjzw6K8osoLSwNNY7errywnDbXxp6De8IOpcuUeBIRERERCUlNjb9WxZPkotW1qwF0NrOQROXMdjv27mB48XAsWVZc0qKsqAwgJycYV+JJRERERCQk3Uk8qeJJomJ13WrGlI5hQMGAsEM5LsUST2vrwz2z3bbd2xheMjzUGI4H5UXlADQeyL0JxpV4EhEREREJSVcST7Ghdqp4kqhYXbeak4domF1YivsWU1ZYFmrF096De2lsbmRkycjQYjhelBUGFU/NqngSEREREZEUqeJJclWba+Odunc0v1OIzIxJAyeFmnjatsefZW3kACWeMi02h5aG2omIiIiISMpqaqBvXygrS76OKp4kijY1bWLfoX1KPIXshEEnHJ7kPQxbd28FUMVTFvTN68uAggE0NmuonYiIiIiIpKimBoYMSV7FBJCfn9rk4qp4kmw6fEY7DbUL1bSh09jYtDG0Kpitu7dSmFfIwH4DQ9n/8aa8sFwVTyIiIiIikrra2o6H2cGxFU/JklSqeJJsWl3nz2iniqdwTR82HYCVNStD2f/W3VsZUTJCZ7TLktKiUiWeREREREQkdTU1qSWe2tpU8STRsrp2NQP7DWRw/8Fhh3JciyWelu9YHsr+t+7eyqiSUaHs+3hUXlSuoXYiIiIiIpK6VBNPAC0t/loVTxIFq+tWc/Lgk1XpErLRpaMpLyoPJfFUt6+O3Qd3M6JkRNb3fbwqKyxjV/MuWtty6xeG/LADEBERERE53syf76+3boXt24/cTySWeDp0yF+r4kmiYHXdaj5y4kfCDuO4Z2ZMHzad5TXZTzytqlkF6Ix22VReVE6ba6N2Xy3DBwwPO5yUqeJJRERERCQEzc3+UlLS8XrtK546Szyp4kkyrW5fHXX76jSxeERMHzqdFTtW0Oay+8e/qjZIPOmMdllTXlQOwLbd20KOpGtSSjyZ2WVmtsbMqszsGwkeLzSzh4PHXzOz8XGPfTNYvsbMLu2sTTO718zWm9mbweXUYLmZ2c+C9Zeb2ek96biIiIiISJh27/bXqSaeYhVPnQ21U8WTZNrhM9ppYvFImD5sOrsP7mZj48as7ndlzUqK8osOJ0Mk88oKywA/t1Yu6TTxZGZ5wM+By4EpwKfMbEq71a4DGpxzk4A7gNuDbacA84CpwGXAXWaWl0KbX3fOnRpc3gyWXQ5MDi7XA7/oTodFRERERKKgu4knDbWTsMXOoDZlSPvDQglDWBOMr6pdxciSkZrnK4vKinpp4gk4E6hyzq1zzh0EHgLmtltnLvCb4PZjwIXm331zgYecc83OufVAVdBeKm22Nxe4z3mvAuVmplnMRERERCQnZariSUPtJNOWbF3CwH4DGVs2NuxQBJg6dCqQ3cSTc45VNas0zC7Lem3FEzAK2BR3f3OwLOE6zrkWoAkY1MG2nbV5WzCc7g4zK+xCHCIiIiIiOUEVT5KrlmxbwowRM1TpEhEDCgYwsWJiVicYr9lbw879OzWxeJbl9cmjpKCEbXt63xxPif43cSmu09XlAN8ETgLOAAYCN3chDszsejN7w8zeqK2tTbCJiIhEWQrzCs4ys6Vm1mJmV7R77BozWxtcrsle1CIiXdfVxFNnk4ur4kmyobmlmZU1Kzl9hKbcjZLpw6ZnteJp2fZlAIwuHZ21fYpXVlTWKyueNgNj4u6PBtr38vA6ZpYPlAH1HWybtE3n3LZgOF0z8Gv8sLxU48A5N985N9M5N3PIkCEpdE9ERKIixXkF3wM+C/y23bYDge8CZ+E/O75rZhWZjllEpLt274bCQn/pSKpD7VTxJNmwsmYlh9oOMWPEjLBDkTjTh01n7c617Du0Lyv7W7ptKYCGW4agvLC8VyaeFgOTzazSzArwk4UvaLfOAiD2y/IVwLPOORcsnxec9a4SPzH46x21GZu3KZgj6iPAyrh9fCY4u937gSbnXG7Vl4mISGc6nQPQObfBObccaP+b/qXA0865eudcA/A0/sQWIiKRtHt359VOoIoniZZYwuHdne8yf8n8wxcJ1/uGvQ+H483tb3a+chos3baUSQMn0a9vv6zsT47IxYqn/M5WcM61mNmXgaeAPOAe59wqM7sVeMM5twC4G7jfzKrwlU7zgm1XmdkjwNtAC3CDc64VIFGbwS4fMLMh+KF1bwJfDJY/AXwQP0H5PuDaHvdeRESiJtF8fmf1YFvNBSgikbV7NwwY0Pl6qniSKFmybQn98vsxuP/gsEOROOeOPReAFze+yDljzsn4/pZuW8oZo87I+H7kWOVF5ezYu4PWtlby+uSFHU5KOk08ATjnnsAnfuKXfSfu9gHgE0m2vQ24LZU2g+UXJGnHATekEq+IiOSslObz68m2ZnY9cD3A2LEqDxeR8OzbB/37d76eKp4kSpZuW8rYsrGaWDxihhYP5eTBJ7Nw40JuPu/mzjfogYb9DaxvXM8XZnwho/uRxMoKy2hzbdTsrWFEyYiww0lJKkPtREREsiWl+fx6sq3mAhSRqNi/H4qLO1+vfeJJFU8SlkOth1i+Y7nm9YmoWeNm8dJ7L9Haltn/BGITi2uC+XCUFZUB5NRwOyWeREQkSlKZVzCZp4BLzKwimFT8kmCZiEgk7dsH/VKYHqX9UDtVPElYVtWuorm1WYmniJo9bja7D+7O+DxPsXm+ThtxWkb3I4mVF5UDSjyJiIh0i3OuBYjNAbgaeCQ2r6CZzQEwszPMbDN+iPevzGxVsG098H188moxcGuwTEQkcpzr+lC7zuZ4Ap+UUsWTZEos4TCubFzIkUgis8bNAuCFjS9kdD+x4Zaa5yscZYW5V/GU0hxPIiIi2ZLCvIKL8cPoEm17D3BPRgMUEUmDQ4d8giidFU/gk1KqeJJMWbJ1CSUFJQwp1lD1KBpVOoqJFRNZuHEh/3j2P2ZsP0u3LdUwuxCVFpZiGNv2bAs7lJSp4klEREREJMv27fPXqVQ85Qc/FaviScL2yuZXmDFyBn1Mh5FRNWvcLF5870XaXGYy0Lubd/Puznc5fbgST2HJ65PH0OKhOVXxpP8xRERERESyLJZ46krFU2dntQNVPEnm7Ny3kze3v8kF4xOehFwiYva42dTvr2dVzaqMtP/m9jdxOFU8hWxkyUglnkREREREJLn9+/215niSXLFw40IcjgsqlXiKstg8Tws3LsxI+0u2LQE0sXjYci3xpDmeRERERESyrCtD7VTxJO1t272NL/zpC+zcv5MpQ6YwutRPfXj9jOszts9n1z9Lcd9izhh1BqtqM1NNIz03vnw8kwZO4vE1j/PlM7+c9vZf3vQy48rGMbJkZNrbltSNLBl5OAmYC5R4EhERERHJsljFU3eG2qni6fi2bNsyfrnkl4fv/3ntn/n7s/6eiRUTM7rfZ9c/y/njzqcgryCj+5GeMTPmTZ3Hv770r2zfs53hA4anrW3nHC9sfIFLJ16atjale0YMGMGOPTtoaWshv0/00zoaaiciIiIikmXdqXjKhbPamdllZrbGzKrM7BsJHi80s4eDx18zs/Fxj30zWL7GzC7trE0zqwzaWBu0WdDRPszsYjNbYmYrguucGzN2qPUQj61+jJElI/nnWf/MrR+4ldLCUn722s+obqjO2H637d7G6rrVmt8pR8w7ZR5tro3H3n4sre2u2bmGmr01h4fzSXhGlozE4dixZ0fYoaREiScRERERkSzL1OTiYVY8mVke8HPgcmAK8Ckzm9JuteuABufcJOAO4PZg2ynAPGAqcBlwl5nlddLm7cAdzrnJQEPQdtJ9AHXAh51z04BrgPvT2f9seHb9s9Ttq+OTUz7JmNIxDBswjBvffyOlhaXc+fqdbNm1JSP7fW7DcwCa3ylHTB06lWlDp/HgygfT2u4LG18A/ATmEq7YUMdcmedJiScRERERkSzbvx/69vWXznRlcvGQK57OBKqcc+uccweBh4C57daZC/wmuP0YcKGZWbD8Iedcs3NuPVAVtJewzWCbC4I2CNr8SEf7cM4tc87FjtJWAUVmVpi23mfYruZdPFH1BNOGTuPkIScfXl7Rr4KvnPkVWtpa+PyfPo9zLu37fm79c5QXlXPq8FPT3rZkxqdO+RSLNi1iY+PGtLX5wsYXGD5gOJMGTkpbm9I9scTTtj3bQo4kNdEfDCgiIiIi0svs25faMDvo2lC7kOd4GgVsiru/GTgr2TrOuRYzawIGBctfbbftqOB2ojYHAY3OuZYE6yfbR11cOx8HljnnmrvYx9D8tfqvHGw9yBVTrjjmsaHFQ/nYSR/joVUPcfeyu/n86Z9P676f3fAss8fNJq9PXlrblZ6bv2R+wuVXnnIl33r2Wzy86mFuOvemHu/HOcfCjQuZNW4W1lH2W7JiRMkIQBVPIiIiIiKSRFcST2Y+oZQDFU+JImtffpNsnXQt7zQOM5uKH373hQTrYWbXm9kbZvZGbW1tolWyzjnHsu3LmDpkatLJomePn83fjP8bbnzqxrRWuayuXc26hnVcNOGitLUpmTehYgJnjTqL/17+32mpgtvYtJHNuzYza6zmd4qCocVD6WN9lHgSEREREZHE9u9PbX6nmLw8iB07RrjiaTMwJu7+aKD9UdHhdcwsHygD6jvYNtnyOqA8aKP9vpLtAzMbDfwB+IxzLuFs3M65+c65mc65mUOGDEmp45m2fc926vbVMX3Y9KTr9LE+3DP3HhyOzy34HG0uPRnIB1Y8QB/rk7DSSqLt+hnXs6JmBU9VP9XjthZuWAigicUjIr9PPsOKhynxJCIiIiIiiXWl4gmODLeDb1caWQAAIABJREFUzs9qF2LiaTEwOTjbXAF+svAF7dZZgJ/YG+AK4FnnyzEWAPOCM9JVApOB15O1GWzzXNAGQZuPd7QPMysH/gx80zn3clp7nmHLa5YDcMrQUzpcb3z5eH56yU95dv2z/GLxL3q8X+ccv13xWy6svDBppZVE16enf5oxpWP4wQs/6HHV0wsbX2Bgv4FMHTo1TdFJT40sGcmW3Zk5oUC6aY4nEREREZEs278fhg5Nff34xFNHQ+369AlvqF0wn9KXgaeAPOAe59wqM7sVeMM5twC4G7jfzKrwVUjzgm1XmdkjwNtAC3CDc64VIFGbwS5vBh4ysx8Ay4K2SbYP4MvAJOBfzOxfgmWXOOdqMvF8pNOKHSsYXTqagf0Gdrru50//PL9/5/fc9MxNXDrpUp5d/+wx61w/4/qU9vvq5ldZ37ie787+bpdjlvAV5BVw07k38ZUnv8ILG19g9vjunY3OOccz659hTOkY/mvpf6U5Sumu8eXjWVW7qvMVI0AVTyIiIiIiWdbdiiezzud4CrHiCefcE865E5xzE51ztwXLvhMknXDOHXDOfcI5N8k5d6Zzbl3ctrcF253onHuyozaD5euCNiYFbTZ3tA/n3A+cc8XOuVPjLpFPOtXvr6e6oZppQ6eltL6Z8Z8f/k/69unLtY9f26Mhdw+seICi/CI+evJHu92GhOu6065jWPEwbnvxts5XTuK1La/xXtN7nDbitDRGJj1VWV7JhsYNaRtWm0mqeBIRERERySLnfOKpq3M8QcdJJwi34kky46mqp2hzbR3O79Te6NLR/Ozyn3HNH69h+IDhXDzh4i7v91DrIR5Z9QhzTpxDaWFpl7eXaOjXtx83nn0jNz9zMzc9fROTBk466vFUqt8eXvkwBXkFnDrs1EyFKd1QWVHJgZYDbN+znZElI8MOp0OqeBIRERERyaJ9+3xyqDsVTx3N7wThVzxJ+v3P2v+hpKCE8eXju7Td1dOvZs6Jc/jjO39k+57tXd7vU9VPUbuvlqtOuarL20q03HDGDYwpHcNvV/yW1rau/QfR5tp49O1HuWzSZfTr24VsuWRcZXklAOsb1occSeeUeBIRERERyaLGRn/d3aF2HVHFU+/S5tr4S9VfmDp0Kn2sa4duZsavPvQrCvMK+fWyX9PS1pLyts45bl14K2NKx3D55Mu7GrZETHFBMT+7/Gds2b0l4ZxfHVm0aRFbdm/hyqlXZig66a7KiiDx1Bj9xJOG2omIiIiIZFFPEk+qeDq+vFP3DvX76zlh0And2n74gOF8evqn+dWSX/HbFb/l6ulXY51lL4Hfrf4di7cu5tdzf829b97brX1LtMw9cS7Thk7jT+/+iRkjZ6Q0UT34YXZF+UV8+IQP8+DKBzMcpXRFrAoyFyqelHgSEREREcmihgZ/rTmepDOvbHoFgIkVE7vdxukjTufySZfzZNWTjCoZxYUTLuxw/Za2Fr797LeZMmQKV0+/mruX3d3h+hJN85fMP2bZvFPmccvzt3D/8vv5yplf6bSKrrWtlcdWP8bfTv5bSgpLMhWqdFNRfhEjBozIiYonDbUTEREREckiVTxJqhZtWsTAfgMZVjysR+3MOXEOpw47lUfffpSl25Z2uO49y+7h3Z3v8q8X/Ct5ffJ6tF+JlsH9B/PJqZ/k7dq3+Wv1Xztd/+FVD7N9z3Y+dcqnshCddEdlRaUSTyIiIiIicrRY4qkrFU/5wTgFVTwdXxZtXsTZo89OaXhcR/pYH6497VoqKyqZv2Q+9yy7J+F6z294nn986h85d8y5zDlxTo/2KdF0/tjzmTFiBo+veZyq+qqk6+09uJebnr6JGSNm8NGTP5rFCKUrKssrc2KonRJPIiIiIiJZpIonSUX9/nreqXuHc8ack5b2ivKL+Iez/oGTh5zMdQuu46anb6JuX93hx5/f8Dx/+9u/ZXz5eH73yd/1ONkl0WRmXD39agb2G8j8JfN5d+e7Cde7/eXb2bJ7C/9x2X90eWJ7yZ7K8ko27drEodZDYYfSoZTmeDKzy4D/APKA/3LO/bDd44XAfcAMYCdwpXNuQ/DYN4HrgFbg751zT3XUppk9AMwEDgGvA19wzh0ysw8AjwOxdN7vnXO3dq/bIiIiIiLh6E7Fk+Z4Ov68uvlVAM4Zc07S5EB7ieb1iVeYX8gNZ9zA4q2L+fGiH3Pn63dyycRLWFu/lnfq3mFY8TCuPfVaHl/zeI/jl+jq17cfX5r5Je549Q5m3zubZz/zLCcPOfnw4+sb1vPjRT/mqmlXce7Yc0OMVDpTWVFJm2vjvab3mDiw+3PBZVqnqUszywN+DlwOTAE+ZWZT2q12HdDgnJsE3AHcHmw7BZgHTAUuA+4ys7xO2nwAOAmYBvQDPh+3nxedc6cGFyWdRERERCTnNDZCQcGR4XOpUMXT8WfRpkXkWR5njDwjre3m98nn13N/zaovreLKU65kRc0KJlZM5DuzvsM/nf1PlBaWpnV/Ek2jSkdx49k3AjD73tn8+OUfs75hPXctvouZ/zmTPMvj9otuDzlK6cyEigkAkZ/nKZWPuzOBKufcOgAzewiYC7wdt85c4Jbg9mPAneZrM+cCDznnmoH1ZlYVtEeyNp1zT8QaNbPXgdHd7JuIiOSg7lbZmtl4YDWwJlj1VefcF7MVt4hIqhobuzbMDlTxdDx6ZfMrvG/4+yguKM5I+1OGTOHXc3991LLOKqakdxlZMpKFn13INX+8hpueuYmbnrkJgA+M/wA/u+xnjC7VoXjUVZZXAkR+nqdUEk+jgE1x9zcDZyVbxznXYmZNwKBg+avtth0V3O6wTTPrC1wNfDVu8dlm9hawFfiac25V+2DN7HrgeoCxY8em0D0REYmKuIrYi/GfDYvNbIFzLv7HjsNVtmY2D19le2XwWLVz7tSsBi0i0kWNjV0bZgeqeDretLS18Nrm17j21GvDDkV6uRMGncAr171CdX01f3jnD0weOJk5J87RHF85YnTpaPL75PeKiqdE7ziX4jrJlif6yGzf5l3AC865F4P7S4Fxzrk9ZvZB4I/A5GMacW4+MB9g5syZ7dsUEZFo60mVrYhITuhJxVNniSdVPPUOK3asYO+hvWmbWFykMxMHTuRr53wt7DCki/L65DG2bGzkE0+pTE+/GRgTd380vuIo4Tpmlg+UAfUdbNthm2b2XWAIcGNsmXNul3NuT3D7CaCvmQ1OIX4REckdiapsRyVbxznXAsSqbAEqzWyZmS00s/MT7cDMrjezN8zsjdra2vRGLyKSgkwOtVPFU+/wyuZXADh7zNkhRyIiUVdZXhn5oXapJJ4WA5PNrNLMCvCThS9ot84C4Jrg9hXAs845FyyfZ2aFZlaJr1B6vaM2zezzwKXAp5xzh3+vMbPhsV+0zezMIPad3em0iIhEVk+qbLcBY51zp+F/uPitmR0zQ6pzbr5zbqZzbuaQIUN6HLCISFc1NGRuqJ0qnnqHRZsWMWLACMaVjQs7FBGJuMryyshXPHU61C6Ys+nLwFP4iV7vcc6tMrNbgTeccwuAu4H7g8nD6/GJJIL1HsEPkWgBbnDOtQIkajPY5S+BjcArQZ7p98EZ7K4A/s7MWoD9wLwguSUiIr1HV6psN8dX2QafCc0AzrklZlYNnAC8kfGoRUS6oLERujoVaVcqnlpauheXRMeiTYs4Z8w5mmdHRDo1oWICNXtr2N28m5LCkrDDSSilk7gGQ9ueaLfsO3G3DwCfSLLtbcBtqbQZLE8Yk3PuTuDOVOIVEZGcdbgiFtiC/yHjqnbrxKpsXyGuytbMhuATUK1mNgFfZbsue6GLiHTOucxOLq6Kp9y3fc921jeu54Yzbgg7FBHJAScPORmAt2vf5qzR7c8DFw0pJZ5ERESyoSdVtsAs4NagMrYV+KJzrj77vRAROdbGjfCDH/jrtjad1U6Se2WTn99JE4tLNsxfMj/sEKSHpg+bDsDyHcuVeBIREUlFd6tsnXO/A36X8QBFRLrhgQfgv/4LzjgDPvYxOPXUrm2f6lA7VTzlvkWbFlGQV8DpI07P2D6UbBDpPcaXj6e4bzEralaEHUpSSjyJiIiIiGTYunUwfDi8/rq/P7+Lx/2qeDp+LNq8iJkjZ1KYXxh2KCKSA/pYH6YNm8byHcvDDiWpVM5qJyIiIiIiPVBdDRMmdH97VTwdH5pbmlmydQlnjz477FBEJIdMHzqdFTUriOr515R4EhERERHJsHXrYOLE7m+viqfjw7Lty2hubdb8TiLSJdOGTaN+fz1bd7c/GXQ0KPEkIiIiIpJBzc2waVP2Kp6UeMpdizYtAlDFk4h0SfwE41GkxJOIiIiISAZt3AjOZa/iSUPtctdL773E+PLxjCgZEXYoIpJDpg2dBhDZCcY1ubiIiIiISIbMnw8rV/rbK1Z0fVLxGFU89X6tba08t+E5Pn7yx8MORURyTEW/CkaXjlbFk4iIiIjI8ai21l8PGdL9NlKteOrbF3btgi1bur8vCcfSbUtpPNDIRRMuCjsUEclB04dNV+JJREREROR4VFfnE0Klpd1vIz8Yp9BZxdP55/t1r7wSDh3q/v4k+55Z9wwAF1ReEHIkIpKLpg2dxjt173Cw9WDYoRxDiScRERERkQyqrfXVTp0ljTqSasXTiBF+ON/LL8O3vtX9/Un2PbP+GaYPm87Q4qFhhyIiOWj6sOkcajvEmro1YYdyDCWeREREREQyqK6uZ8PsIPU5ngCuugq+9CX4yU/giSd6tl/Jjv2H9vPyey9zUaWG2YlI98TObPfWjrdCjuRYSjyJiIiIiGSIcz7xNHhwz9pJteIp5qc/9RVP557bs/1Kdry86WWaW5s1v5OIdNtJg0+irLCM59Y/F3Yox1DiSUREREQkzZ5+Gvbvh927obk5fYmnVIfrFRbCbbdBWVnP9ivZ8cy6Z+jbpy/njzs/7FBEJEfl98nn4okX81T1Uzjnwg7nKEo8iYiIiIik0cqVcMkl8M1vpueMdtD1iifJLc+se4azx5zNgIIBYYciIjns0omXsmX3FlbVrgo7lKPoo0tEREREJI1eeMFf33UXvP22v63EkySzqWkTS7ct5eIJF4cdiojkuEsnXgrAU1VPhRzJ0fTRJSIiIiKSRi+95IfW9e0LTz7ph8cNGtSzNrs61E5yx/3L78fhuGraVWGHIiI5bkzZGKYMmcJfqv8SdihHUeJJRERERCSNXnoJLrwQvvY1aG2F8nKfhOoJVTz1Ts457n3zXmaNm8WEiglhhyMivcBlEy/jhY0vsPfg3rBDOUwfXamqrYUFC+D88+H002HfvrAjEhGRkK1dC6eeCps2hR2JiETFxo3+/4TzzoOvfx1KS2Ho0J63q4qn3unVza+ytn4tn33fZ8MORUR6iUsnXcrB1oMs3Lgw7FAOU+IpVT//OTzxBDQ2wrJl8PDDYUckIiIh++Mf4a23/MeDiBy/7rzzyFfDl17y1+edBwMGwD/9E1x9dc/3oYqn3uneN++lf9/+XDHlirBDEZFeYta4WfTL7xepeZ700ZWKhgbYtg0+/nFYvhymTIFf/jLsqEREJGSxA8wXXww3DhEJT1OTH1L32c/CT34C8+dDURG8+qq/PXx4zycWByWeeqP9h/bz8KqH+fjJH6eksCTscESklyjKL+LiiRfz4MoHIzPcTh9dqXj3XX994om+vvmLX4TXX4elS8ONS0REQtPWBi+/7G/HElAANTWwZEk4MYlI9j3+ODQ3w4ED8Oc/Q1UVTJyY/gSRhtr1Pve9dR9NzU1c875rwg5FRHqZm865idp9tfzijV+EHQqgxFNq3n0X+veH0aP9/auvhn79VPUkInIce+cd2LnTz/EUm9MF4IYb/HSATU3hxicimfHGG/C97/nkM/ghduPGwaxZ8MILsHUrTJqU/v2q4ql3qd1byzf/95ucP/Z8Lqi8IOxwRKSXOXfsuVw84WJ+9PKPIlH1pI+uVLz7Lkye7D/p58+HRx7xE4zfdx/8x3/4ZSIiclyJVTl94xv++uWX/cjsBQtg/3547LGj162uzn6MItJz69eDc/52Wxtcey3ccgv85jc++fzXv8KVV8KHP3zkzHWZTDyp4ql3+PrTX2f3wd388kO/xPSiikgGfHf2d6ndV8sv3wi/YEaJp840NPhxEyeccPTyWbN8XfW//7s/rZGIiBxXXnrJn6nq4x/3Ewi/+KL/XeLgQaiogPvv9+tt2QIXXwxXXHGkQgJ8lVTsYFZEoumhh2DCBD93E8Dvfw8rV0JJCXzlK/B//y+0tPiEU2kp/O3f+sfGj09/LKp46j0WbljIb976DV8/5+tMGTIl7HBEpJc6d+y5XDThIm5/+Xa27d4WaiwpfXSZ2WVmtsbMqszsGwkeLzSzh4PHXzOz8XGPfTNYvsbMLu2sTTOrDNpYG7RZ0Nk+Mio2v1P7xNP48fC5z/mz3P3kJ/Cxjx1ZV0REui0TnzmZ8OKL/qxV+flwzjk+EXXffTB1Ktx4IyxcCBs2wPe/7+d+efNNePRRv+2DD/qPke9//0h7773nlysZJZIdBw5Abe2R+7t3+98Vv/Utf3/vXj9puJlftngx3HorDBsGX/2qr2z84x/9/TFj/DaXXgq33w4FBemPN1cqnqJ+3JDNz4lEnt/wPB99+KNUllfyz7P+Odu7F5HjzO0X3c6+Q/v4wG8+wJZdW0KLo9PEk5nlAT8HLgemAJ8ys/ap+euABufcJOAO4PZg2ynAPGAqcBlwl5nlddLm7cAdzrnJQEPQdtJ9ZERLy5Fv/u3nd4p31ln+qGHOHF9nPXUqfOEL/uewzZt19CAi0kWZ+MzJRJybN/uk0vnn+/vnnedPerpokZ8G8NOf9su/9z24+274u7+DU06Bf/kXP/HwF7/oD0xvvRVeecVXRc2eDVdd5dcBf0B80UXwoQ/53zjAX3/ve0dPXr53r59zJr6aqrXVf5SJHA8aGvyQt5jWVv9VbOPGI8tefNH/fb39tr+/YweceaafBPyll/xXtmuv9ev9v//nZ1L4xCf83+aXvuSrmGbPhhUr/N/kmDH+79M5mDnz6GRQXkb+18mNiqeoHzdk83OivUOth7hr8V1cfP/FDB8wnGc+8wz9+/bPxq5F5Dh2+ojTeerTT7Ft9zZm3zubJVvDOQNOfgrrnAlUOefWAZjZQ8Bc4O24deYCtwS3HwPuND9YeS7wkHOuGVhvZlVBeyRq08xWAxcAVwXr/CZo9xfJ9uFcBrI7v/oV3HabP5J4550j8zslUlDg66rPOw/+9Ce4554jcz716+ePNMaNg8GD/bl021+XlPi28/L8dfxtsyOX9ve7cxGRcDjnL/H/j7S2+kvfvv7vs60N9uzx6xQX+3WamvwRVWmpH7u1dy+sWQN1dX4CkcrKzB3hhCcTnzmvpDvI2Nnszjvv6Gsz+D//x/9WMXs23Huv/yj4znf8yVDnzoWzz/Zvh9dfh498xK/fv79/WT/yEf/xc+iQnyNq61b/NjnnHJ+QuvlmP4n5D34A3/2uHwJ0003+4Pj97/cFuEuXwg9/CPv2wT/8gz/YfvhhP/Rv4kT4+7/31Vb33ecnQr7oIvjMZ/yB+GOP+YTXnDlw4YU+xr/8BcrL/cH2+PHw/PO+8uOUU/y2Bw/66q4tW3zfzjgD1q3zp5LPy/PLxo2Dt97yybkxY/w6eXmwbJnvz5QpMG2afw7eesv3f/p0v+66dbB6tf8TmDbNn6b+nXd8hVhlpT/h7N69PqGwdy+cdJLfbvNm/+fSrx+cfLL/M1q3zicjRo70H+0tLX60fEODf25Gj/YJjKoq/6c4ebLv+3vv+UTj4MF+PTPf1s6dMHas319Tk9+utdWvM3iwf07Wr4eyMr+sb18/19e2bX6bykr/OlVV+euJE2H4cP+6r13r3xcnnOD/S1i3zscxapRf7+BB37/GRn9/7Fjf7urVvgpvyhQYNMi3s3atb3fKFP9fzcqVfh8nneSfv9pan1BpbfWv64gR/jlescJ/VTntNP91Z+lSH8dJJ/lJ9Xfs8MnWXbv8+2/KFFi1Cl57zff5nHNg4ED/9/LWW/7x886D7dv9V6bNm/17aPZsv82CBf71/ehH/Xv7wQfhf/7H7/+aa/xzdNddPvH6iU/49/aDD/r3fWurH/o2Zw58/eu+vf79j0wC/q1v+XUef9xXJN15p3/vDR/u3+szZvgk8Mc+5l+zf/xH/x446yz/XiwshDvu8NVNM2f6v/cPfci/Fz7wgXT/D5NYjlQ8Rfq4oYN9pP1zomZvDdX11axvXM9rm1/jwZUPUruvlksmXsIjVzxCWVFZuncpIpLQuWPP5a9X/5UPPvBBZv7nTM4adRZXTr2SEwadwKSBk5hQMYG+eX0zGkMqiadRwKa4+5uBs5Kt45xrMbMmYFCw/NV2244KbidqcxDQ6JxrSbB+sn3UpdCHrhkzxn/ziY2J+Ju/6XybsjL/E/eVVx75KXz7dv+t7KWX/AHl3r3hV0F1JZnVmbD7Ise3RO+/dC+LF/ub6Oy6re3YS6ztvDx/9NbS4o+swf8dFhb6MRsxsSR0fMlKXp4/aopXUODPrnnttR3Hnlsy9ZmTVi+95JMBp57q7591lj/Ynz37SIHsZz7jEzJf/ao/uP3wh/3B+auv+iTQ+94H//3ffmhPfj48+aTfft48+NGP/PxRzz/v3xof/ag/yJ48GZ5+2v/GEauMmjHDJ5h+8pMjCbBZs3zC5JZb/AV828uWweWX+/t9+vgkw623+oNzODJPTWx+KvBvz4MHj7QD/u2ejY+ARPtpvyyVdVLdrk+foyvHki1rL9Xt2u+zu9t1t8+JdHe7dOjfH37+8yP3Bwzw/zXecceRZaec4pNN//7v/n5pqf+7+8EPjgxVnTHDv3f/7d/830FJiU/orlzpk1Dgzwkzd67/m/vKV/zXvK98xf+d/fu/+6TT6afDJZf4GH76U5/I+9jH/PYnnujPWDlo0JHfEAoLjzyeDbHfJiP+e0PUjxuy9jnx8Uc+zkvv+bNQFOQVMOfEOVzzvmu4fNLl5PWJ9osoIr3P+0e/n3VfXcd9b93HXYvv4sa/3nj4seVfXM60YdMyuv9UEk+JMhDtv44kWyfZ8kTlQx2tn2ocmNn1wPXB3T1mtibBdqkYTCyp9eijR5JQuS5WeQGxg9gj/ezd1M/eJTf72dp6dIIJ/BFmomX+yPNIP9snncBnAz73OX/pnnHd3TCDMvGZc/TG6fucOHwGq5j//d9j8/Y//KG/xLv6an+JOXjQV13Eq6nxiap4a9f6icrjLVnC4CVLjv57eOGFY2NduPDo+21tvgIp3qFDRw9ZAn8ejfYylJQ45u86lRxxd3PLidZJlGDqLOmUYJ3BQF2i7drvM9X9pavPiXSwXcb/n9237+j7e/Ycu87KlUff37Xr2Pf3knajBnbvhgceOHx3MFC3dKmv2Io5cAB+/OOjt1u61A+DjXfzzcnjz7LDr8eTT/pLR77whbTsszufEVE/bsj65wTAQQ7yWPCvB6L43SeKMYHi6irFlbrIxPQFjvqPvkdxTb9lenc3TflzIpXE02ZgTNz90cDWJOtsNrN8oAyo72TbRMvrgHIzyw9+vYhfP9k+juKcmw/MT6FfHTKzN5xzM3vaTtSpn72L+tm7HC/9bCdTnzmHpetzIip6y/tE/YgW9SNacqgfUT9uyNnPiSi+B6IYEyiurlJcqYtiTBDduOKlMj3hYmBycNaIAvyEfAvarbMAuCa4fQXwbDD30gJgXnBmiUpgMvB6sjaDbZ4L2iBo8/FO9iEiIr1HJj5zREQkO6J+3KDPCRGREHRa8RSMi/4y8BSQB9zjnFtlZrcCbzjnFgB3A/cHE/TV4z8QCNZ7BD+hYAtwg3OuFSBRm8EubwYeMrMfAMuCtkm2DxER6T0y9ZkjIiKZF/XjBn1OiIiEw1Q0lJiZXR+U2fZq6mfvon72LsdLP6Vnesv7RP2IFvUjWnpLP6T7ovgeiGJMoLi6SnGlLooxQXTjiqfEk4iIiIiIiIiIZEQqczyJiIiIiIiIiIh0mRJPCZjZZWa2xsyqzOwbYcfTHWa2wcxWmNmbZvZGsGygmT1tZmuD64pguZnZz4L+Ljez0+PauSZYf62ZXZNsf9liZveYWY2ZrYxblrZ+mdmM4HmrCrZNdNrdjEvSz1vMbEvwmr5pZh+Me+ybQcxrzOzSuOUJ38vBBJ2vBf1/OJisM+vMbIyZPWdmq81slZl9NVjea17TDvrY615Pya7uvIeizMy+ZmbOzAYH95P+vUeRmX0/iPNNM/urmY0MludaP35sZu8Esf7BzMrjHsuZ95WZfSL4P7fNzGa2eyxn+gG943updF+mX/8of0+xDB/PWDe+I5rZiXHPyZtmtsvM/iGM58tCOi5Kto9O4kr42WJm481sf9zz9svu7r+jPnYQV8ZfN/MnM3g4WP81MxvfSUwPx8WzwczezPZzlRHOOV3iLvhJC6uBCUAB8BYwJey4utGPDcDgdst+BHwjuP0N4Pbg9geBJwED3g+8FiwfCKwLriuC2xUh92sWcDqwMhP9wp/Z5OxgmyeByyPUz1uAryVYd0rwPi0EKoP3b15H72XgEWBecPuXwN+F1M8RwOnB7RLg3aA/veY17aCPve711CW7l66+h8KOt5O+jMFPHLyR4LMr2d97VC9Aadztvwd+maP9uATID27fHvf/b069r4CTgROB54GZcctzrR+94nupLtF9/aP8PYUMH8/Qw++IQZ+3A+PCeL4I6bgo2T46iSvZZ8v4+PXatdOl/SfrYydxZfx1A77Eke8E84CHO4qpXRz/Bnwn289VJi6qeDrWmUCVc26dc+4g8BAwN+SY0mUu8Jvg9m+Aj8Qtv895rwLlZjYCuBR42jlX75xrAJ4GLst20PGccy/gz04SLy39Ch4rdc694vxf5H1xbWVVkn4mMxd4yDnX7JxbD1Ri8+RYAAAF/klEQVTh38cJ38tBBvwC4LFg+/jnLKucc9ucc0uD27uB1cAoetFr2kEfk8nZ11MiI9l7KMruAG4C4ieeTPb3HknOuV1xd4s50pdc68dfnXMtwd1XgdHB7Zx6XznnVjvn1iR4KKf6Qe/+Xiqdy/jrn4PfU6L0HfFCoNo5t7GTeDPyfIV4XJRsH0nj6uCzJaFu7r/Dz9sQj6/i430MuDBWkdRRTME6nwQe7CjQTDxXmaDE07FGAZvi7m+m4/98o8oBfzWzJWZ2fbBsmHNuG/gPGWBosDxZn3PluUhXv0YFt9svj5IvB+WQ98SVtXa1n4OAxrj//CPRz6Ds9DTgNXrpa9quj9CLX0/Jmq68hyLJzOYAW5xzb7V7KKf6AWBmt5nZJuD/AN8JFudcP+J8Dv+LKOR2P+LlWj9yLV5Jr6y+/hH8npLJ45l0fEecx9FJgbCfL8jO85NsH6mK/2wBqDSzZWa20MzOj4u3q/vv7t9Lpl+3w9sEjzcF63fmfGCHc25t3LKwn6tuU+LpWInG9ubiqf/Odc6dDlwO3GBmszpYN1mfc/256Gq/ot7fXwATgVOBbfjSS+gF/TSzAcDvgH9oVzVwzKoJluVEXxP0sde+npI+ZvaMma1McJlL199DoemkH9/mSJLmqM0SLItyP3DOfds5NwZ4APhybLMETUW6H8E63wZa8H2BHO1Hos0SLIvy/5m5Fq+kV9Ze/4h+T8nk8UyPnlvz8/fMAR4NFkXh+epIJOJI8NmyDRjrnDsNuBH4rZmVdnP/3dkmG69bd5/LT3F0YjPs56pH8jPZeI7ajJ9rImY0sDWkWLrNObc1uK4xsz/gywJ3mNkI59y2oJSuJlg9WZ83Ax9ot/z5DIfeHenq12aOLvuM1GvvnNsRu21m/wn8T3C3o/dsouV1+HLK/CDrHmo/zawv/ovOA8653weLe9VrmqiPvfX1lPRyzl2UynpdeA+FIlk/zGwafu6Et4Kq89HAUjM7kxzqRwK/Bf4MfJcc7If5yWU/BFwYlO1DDvYjicj1oxO5Fq+kV1Ze/6h+T8nw8UxPvyNeDiyNPU9ReL4C2Xh+ku2jQ4k+W5xzzUBzcHuJmVUDJ3Rz/13+e8nS6xZra7OZ5QNldDLkL1jvY8CMuFhDfa56ShVPx1oMTDY/K30BvoRyQcgxdYmZFZtZSew2fjK3lfh+XBOsdg3weHB7AfAZ894PNAWleE8Bl5hZRVB2eEmwLGrS0q/gsd1m9n7zR0CfiWsrdHb0uNuP4l9T8P2cZ/6MCZXAZPwEcwnfy8F/9M8BVwTbxz9nWRU8z3cDq51zP417qNe8psn62BtfT8mubryHIsc5t8I5N9Q5N945Nx7/Reh059x2kv+9R5KZTY67Owd4J7ida/24DLgZmOOc2xf3UM68rzqRa/3I+e+l0iMZf/2j+j0l08czafiOeFQ1StjPV5xsPD/J9pFUss8WMxtiZnnB7QnB87Oum/vv8udtll63+HivAJ6N+1EnmYuAd5xzh4fQhf1c9ZjL8OzluXjBz/L+Ln7G+m+HHU834p+An2H/LWBVrA/4saT/C6wNrgcGyw34edDfFRx99pfP4SdTqwKujUDfHsSXGR7CH6Bcl85+ATPx/+FUA3cCFqF+3h/0Yzn+P4sRcet/O4h5DXFn5Ej2Xg7eI68H/X8UKAypn+fhyzqXA28Glw/2pte0gz72utdTl+xeuvMeivqFuDMYdfT3HsULvlpgZfB6/AkYlaP9qMLP+xD7/+qXufi+wh9AbMb/OrwDfyCVc/0I4s3p76W6RPv1j+r3FLJwPEM3vyMC/YGdQFncsqw/X4R0XJRsH53ElfCzBfh48Pq+BSwFPtzd/XfUxw7iyvjrBhQF96uCxyd0FFOw/F7gi+3iz9pzlYlLLCAREREREREREZG00lA7ERERERERERHJCCWeREREREREREQkI5R4EhERERERERGRjFDiSUREREREREREMkKJJxERERERERERyQglnkREREREREREJCOUeBIRERERERERkYxQ4klERERERERERDLi/weJzYG4fMDYfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot1 , axs1 = plt.subplots(1 , 3 , figsize=(20,5))\n",
    "\n",
    "sns.distplot(data[\"Amount\"].values, ax = axs1[0] ,color = \"r\")\n",
    "axs1[0].set_title(\"distribution plot of/n Amount of transactions\")\n",
    "\n",
    "sns.distplot(data[\"V1\"].values, ax = axs1[1], color = \"b\")\n",
    "axs1[1].set_title(\"distribution plot of/n V1\")\n",
    "\n",
    "sns.distplot(data[\"Time\"].values, ax = axs1[2], color = \"green\")\n",
    "axs1[2].set_title(\"distribution plot of/n time of transactions\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalizing Time and Amont\n",
    "\n",
    "Since all of the PCA transformed Variables are normalized, we will normalize Time and Amount variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "      <th>Amount_norm</th>\n",
       "      <th>Time_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "      <td>1.783274</td>\n",
       "      <td>-0.994983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.269825</td>\n",
       "      <td>-0.994983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "      <td>4.983721</td>\n",
       "      <td>-0.994972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "      <td>1.418291</td>\n",
       "      <td>-0.994972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "      <td>0.670579</td>\n",
       "      <td>-0.994960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.425966</td>\n",
       "      <td>0.960523</td>\n",
       "      <td>1.141109</td>\n",
       "      <td>-0.168252</td>\n",
       "      <td>0.420987</td>\n",
       "      <td>-0.029728</td>\n",
       "      <td>0.476201</td>\n",
       "      <td>0.260314</td>\n",
       "      <td>-0.568671</td>\n",
       "      <td>-0.371407</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.559825</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>-0.371427</td>\n",
       "      <td>-0.232794</td>\n",
       "      <td>0.105915</td>\n",
       "      <td>0.253844</td>\n",
       "      <td>0.081080</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.256131</td>\n",
       "      <td>-0.994960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.229658</td>\n",
       "      <td>0.141004</td>\n",
       "      <td>0.045371</td>\n",
       "      <td>1.202613</td>\n",
       "      <td>0.191881</td>\n",
       "      <td>0.272708</td>\n",
       "      <td>-0.005159</td>\n",
       "      <td>0.081213</td>\n",
       "      <td>0.464960</td>\n",
       "      <td>-0.099254</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.270710</td>\n",
       "      <td>-0.154104</td>\n",
       "      <td>-0.780055</td>\n",
       "      <td>0.750137</td>\n",
       "      <td>-0.257237</td>\n",
       "      <td>0.034507</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.237686</td>\n",
       "      <td>-0.994937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.644269</td>\n",
       "      <td>1.417964</td>\n",
       "      <td>1.074380</td>\n",
       "      <td>-0.492199</td>\n",
       "      <td>0.948934</td>\n",
       "      <td>0.428118</td>\n",
       "      <td>1.120631</td>\n",
       "      <td>-3.807864</td>\n",
       "      <td>0.615375</td>\n",
       "      <td>1.249376</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.015455</td>\n",
       "      <td>0.057504</td>\n",
       "      <td>-0.649709</td>\n",
       "      <td>-0.415267</td>\n",
       "      <td>-0.051634</td>\n",
       "      <td>-1.206921</td>\n",
       "      <td>-1.085339</td>\n",
       "      <td>0</td>\n",
       "      <td>0.262698</td>\n",
       "      <td>-0.994901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.894286</td>\n",
       "      <td>0.286157</td>\n",
       "      <td>-0.113192</td>\n",
       "      <td>-0.271526</td>\n",
       "      <td>2.669599</td>\n",
       "      <td>3.721818</td>\n",
       "      <td>0.370145</td>\n",
       "      <td>0.851084</td>\n",
       "      <td>-0.392048</td>\n",
       "      <td>-0.410430</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.268092</td>\n",
       "      <td>-0.204233</td>\n",
       "      <td>1.011592</td>\n",
       "      <td>0.373205</td>\n",
       "      <td>-0.384157</td>\n",
       "      <td>0.011747</td>\n",
       "      <td>0.142404</td>\n",
       "      <td>0</td>\n",
       "      <td>0.994900</td>\n",
       "      <td>-0.994901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.338262</td>\n",
       "      <td>1.119593</td>\n",
       "      <td>1.044367</td>\n",
       "      <td>-0.222187</td>\n",
       "      <td>0.499361</td>\n",
       "      <td>-0.246761</td>\n",
       "      <td>0.651583</td>\n",
       "      <td>0.069539</td>\n",
       "      <td>-0.736727</td>\n",
       "      <td>-0.366846</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.633753</td>\n",
       "      <td>-0.120794</td>\n",
       "      <td>-0.385050</td>\n",
       "      <td>-0.069733</td>\n",
       "      <td>0.094199</td>\n",
       "      <td>0.246219</td>\n",
       "      <td>0.083076</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.255991</td>\n",
       "      <td>-0.994878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.449044</td>\n",
       "      <td>-1.176339</td>\n",
       "      <td>0.913860</td>\n",
       "      <td>-1.375667</td>\n",
       "      <td>-1.971383</td>\n",
       "      <td>-0.629152</td>\n",
       "      <td>-1.423236</td>\n",
       "      <td>0.048456</td>\n",
       "      <td>-1.720408</td>\n",
       "      <td>1.626659</td>\n",
       "      <td>...</td>\n",
       "      <td>0.313894</td>\n",
       "      <td>0.027740</td>\n",
       "      <td>0.500512</td>\n",
       "      <td>0.251367</td>\n",
       "      <td>-0.129478</td>\n",
       "      <td>0.042850</td>\n",
       "      <td>0.016253</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.198421</td>\n",
       "      <td>-0.994866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.384978</td>\n",
       "      <td>0.616109</td>\n",
       "      <td>-0.874300</td>\n",
       "      <td>-0.094019</td>\n",
       "      <td>2.924584</td>\n",
       "      <td>3.317027</td>\n",
       "      <td>0.470455</td>\n",
       "      <td>0.538247</td>\n",
       "      <td>-0.558895</td>\n",
       "      <td>0.309755</td>\n",
       "      <td>...</td>\n",
       "      <td>0.238422</td>\n",
       "      <td>0.009130</td>\n",
       "      <td>0.996710</td>\n",
       "      <td>-0.767315</td>\n",
       "      <td>-0.492208</td>\n",
       "      <td>0.042472</td>\n",
       "      <td>-0.054337</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.167819</td>\n",
       "      <td>-0.994866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.249999</td>\n",
       "      <td>-1.221637</td>\n",
       "      <td>0.383930</td>\n",
       "      <td>-1.234899</td>\n",
       "      <td>-1.485419</td>\n",
       "      <td>-0.753230</td>\n",
       "      <td>-0.689405</td>\n",
       "      <td>-0.227487</td>\n",
       "      <td>-2.094011</td>\n",
       "      <td>1.323729</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.483285</td>\n",
       "      <td>0.084668</td>\n",
       "      <td>0.392831</td>\n",
       "      <td>0.161135</td>\n",
       "      <td>-0.354990</td>\n",
       "      <td>0.026416</td>\n",
       "      <td>0.042422</td>\n",
       "      <td>0</td>\n",
       "      <td>1.390344</td>\n",
       "      <td>-0.994866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.069374</td>\n",
       "      <td>0.287722</td>\n",
       "      <td>0.828613</td>\n",
       "      <td>2.712520</td>\n",
       "      <td>-0.178398</td>\n",
       "      <td>0.337544</td>\n",
       "      <td>-0.096717</td>\n",
       "      <td>0.115982</td>\n",
       "      <td>-0.221083</td>\n",
       "      <td>0.460230</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.071407</td>\n",
       "      <td>0.104744</td>\n",
       "      <td>0.548265</td>\n",
       "      <td>0.104094</td>\n",
       "      <td>0.021491</td>\n",
       "      <td>0.021293</td>\n",
       "      <td>0</td>\n",
       "      <td>0.076853</td>\n",
       "      <td>-0.994854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-2.791855</td>\n",
       "      <td>-0.327771</td>\n",
       "      <td>1.641750</td>\n",
       "      <td>1.767473</td>\n",
       "      <td>-0.136588</td>\n",
       "      <td>0.807596</td>\n",
       "      <td>-0.422911</td>\n",
       "      <td>-1.907107</td>\n",
       "      <td>0.755713</td>\n",
       "      <td>1.151087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.222182</td>\n",
       "      <td>1.020586</td>\n",
       "      <td>0.028317</td>\n",
       "      <td>-0.232746</td>\n",
       "      <td>-0.235557</td>\n",
       "      <td>-0.164778</td>\n",
       "      <td>-0.030154</td>\n",
       "      <td>0</td>\n",
       "      <td>0.514218</td>\n",
       "      <td>-0.994843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.752417</td>\n",
       "      <td>0.345485</td>\n",
       "      <td>2.057323</td>\n",
       "      <td>-1.468643</td>\n",
       "      <td>-1.158394</td>\n",
       "      <td>-0.077850</td>\n",
       "      <td>-0.608581</td>\n",
       "      <td>0.003603</td>\n",
       "      <td>-0.436167</td>\n",
       "      <td>0.747731</td>\n",
       "      <td>...</td>\n",
       "      <td>1.353650</td>\n",
       "      <td>-0.256573</td>\n",
       "      <td>-0.065084</td>\n",
       "      <td>-0.039124</td>\n",
       "      <td>-0.087086</td>\n",
       "      <td>-0.180998</td>\n",
       "      <td>0.129394</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.083980</td>\n",
       "      <td>-0.994843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.103215</td>\n",
       "      <td>-0.040296</td>\n",
       "      <td>1.267332</td>\n",
       "      <td>1.289091</td>\n",
       "      <td>-0.735997</td>\n",
       "      <td>0.288069</td>\n",
       "      <td>-0.586057</td>\n",
       "      <td>0.189380</td>\n",
       "      <td>0.782333</td>\n",
       "      <td>-0.267975</td>\n",
       "      <td>...</td>\n",
       "      <td>0.196002</td>\n",
       "      <td>0.013802</td>\n",
       "      <td>0.103758</td>\n",
       "      <td>0.364298</td>\n",
       "      <td>-0.382261</td>\n",
       "      <td>0.092809</td>\n",
       "      <td>0.037051</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.125900</td>\n",
       "      <td>-0.994843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.436905</td>\n",
       "      <td>0.918966</td>\n",
       "      <td>0.924591</td>\n",
       "      <td>-0.727219</td>\n",
       "      <td>0.915679</td>\n",
       "      <td>-0.127867</td>\n",
       "      <td>0.707642</td>\n",
       "      <td>0.087962</td>\n",
       "      <td>-0.665271</td>\n",
       "      <td>-0.737980</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.672638</td>\n",
       "      <td>-0.156858</td>\n",
       "      <td>-0.888386</td>\n",
       "      <td>-0.342413</td>\n",
       "      <td>-0.049027</td>\n",
       "      <td>0.079692</td>\n",
       "      <td>0.131024</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.294977</td>\n",
       "      <td>-0.994831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-5.401258</td>\n",
       "      <td>-5.450148</td>\n",
       "      <td>1.186305</td>\n",
       "      <td>1.736239</td>\n",
       "      <td>3.049106</td>\n",
       "      <td>-1.763406</td>\n",
       "      <td>-1.559738</td>\n",
       "      <td>0.160842</td>\n",
       "      <td>1.233090</td>\n",
       "      <td>0.345173</td>\n",
       "      <td>...</td>\n",
       "      <td>0.984460</td>\n",
       "      <td>2.458589</td>\n",
       "      <td>0.042119</td>\n",
       "      <td>-0.481631</td>\n",
       "      <td>-0.621272</td>\n",
       "      <td>0.392053</td>\n",
       "      <td>0.949594</td>\n",
       "      <td>0</td>\n",
       "      <td>0.346538</td>\n",
       "      <td>-0.994819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.492936</td>\n",
       "      <td>-1.029346</td>\n",
       "      <td>0.454795</td>\n",
       "      <td>-1.438026</td>\n",
       "      <td>-1.555434</td>\n",
       "      <td>-0.720961</td>\n",
       "      <td>-1.080664</td>\n",
       "      <td>-0.053127</td>\n",
       "      <td>-1.978682</td>\n",
       "      <td>1.638076</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.175074</td>\n",
       "      <td>0.040002</td>\n",
       "      <td>0.295814</td>\n",
       "      <td>0.332931</td>\n",
       "      <td>-0.220385</td>\n",
       "      <td>0.022298</td>\n",
       "      <td>0.007602</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.237546</td>\n",
       "      <td>-0.994807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.694885</td>\n",
       "      <td>-1.361819</td>\n",
       "      <td>1.029221</td>\n",
       "      <td>0.834159</td>\n",
       "      <td>-1.191209</td>\n",
       "      <td>1.309109</td>\n",
       "      <td>-0.878586</td>\n",
       "      <td>0.445290</td>\n",
       "      <td>-0.446196</td>\n",
       "      <td>0.568521</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.571955</td>\n",
       "      <td>-0.050881</td>\n",
       "      <td>-0.304215</td>\n",
       "      <td>0.072001</td>\n",
       "      <td>-0.422234</td>\n",
       "      <td>0.086553</td>\n",
       "      <td>0.063499</td>\n",
       "      <td>0</td>\n",
       "      <td>2.930343</td>\n",
       "      <td>-0.994796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.962496</td>\n",
       "      <td>0.328461</td>\n",
       "      <td>-0.171479</td>\n",
       "      <td>2.109204</td>\n",
       "      <td>1.129566</td>\n",
       "      <td>1.696038</td>\n",
       "      <td>0.107712</td>\n",
       "      <td>0.521502</td>\n",
       "      <td>-1.191311</td>\n",
       "      <td>0.724396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.402492</td>\n",
       "      <td>-0.048508</td>\n",
       "      <td>-1.371866</td>\n",
       "      <td>0.390814</td>\n",
       "      <td>0.199964</td>\n",
       "      <td>0.016371</td>\n",
       "      <td>-0.014605</td>\n",
       "      <td>0</td>\n",
       "      <td>0.168937</td>\n",
       "      <td>-0.994784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.166616</td>\n",
       "      <td>0.502120</td>\n",
       "      <td>-0.067300</td>\n",
       "      <td>2.261569</td>\n",
       "      <td>0.428804</td>\n",
       "      <td>0.089474</td>\n",
       "      <td>0.241147</td>\n",
       "      <td>0.138082</td>\n",
       "      <td>-0.989162</td>\n",
       "      <td>0.922175</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.061972</td>\n",
       "      <td>-0.103855</td>\n",
       "      <td>-0.370415</td>\n",
       "      <td>0.603200</td>\n",
       "      <td>0.108556</td>\n",
       "      <td>-0.040521</td>\n",
       "      <td>-0.011418</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.275554</td>\n",
       "      <td>-0.994772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.247491</td>\n",
       "      <td>0.277666</td>\n",
       "      <td>1.185471</td>\n",
       "      <td>-0.092603</td>\n",
       "      <td>-1.314394</td>\n",
       "      <td>-0.150116</td>\n",
       "      <td>-0.946365</td>\n",
       "      <td>-1.617935</td>\n",
       "      <td>1.544071</td>\n",
       "      <td>-0.829881</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200454</td>\n",
       "      <td>-0.185353</td>\n",
       "      <td>0.423073</td>\n",
       "      <td>0.820591</td>\n",
       "      <td>-0.227632</td>\n",
       "      <td>0.336634</td>\n",
       "      <td>0.250475</td>\n",
       "      <td>0</td>\n",
       "      <td>0.010480</td>\n",
       "      <td>-0.994772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-1.946525</td>\n",
       "      <td>-0.044901</td>\n",
       "      <td>-0.405570</td>\n",
       "      <td>-1.013057</td>\n",
       "      <td>2.941968</td>\n",
       "      <td>2.955053</td>\n",
       "      <td>-0.063063</td>\n",
       "      <td>0.855546</td>\n",
       "      <td>0.049967</td>\n",
       "      <td>0.573743</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.799229</td>\n",
       "      <td>0.870300</td>\n",
       "      <td>0.983421</td>\n",
       "      <td>0.321201</td>\n",
       "      <td>0.149650</td>\n",
       "      <td>0.707519</td>\n",
       "      <td>0.014600</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.294977</td>\n",
       "      <td>-0.994725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-2.074295</td>\n",
       "      <td>-0.121482</td>\n",
       "      <td>1.322021</td>\n",
       "      <td>0.410008</td>\n",
       "      <td>0.295198</td>\n",
       "      <td>-0.959537</td>\n",
       "      <td>0.543985</td>\n",
       "      <td>-0.104627</td>\n",
       "      <td>0.475664</td>\n",
       "      <td>0.149451</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.227404</td>\n",
       "      <td>0.742435</td>\n",
       "      <td>0.398535</td>\n",
       "      <td>0.249212</td>\n",
       "      <td>0.274404</td>\n",
       "      <td>0.359969</td>\n",
       "      <td>0.243232</td>\n",
       "      <td>0</td>\n",
       "      <td>0.061902</td>\n",
       "      <td>-0.994725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.173285</td>\n",
       "      <td>0.353498</td>\n",
       "      <td>0.283905</td>\n",
       "      <td>1.133563</td>\n",
       "      <td>-0.172577</td>\n",
       "      <td>-0.916054</td>\n",
       "      <td>0.369025</td>\n",
       "      <td>-0.327260</td>\n",
       "      <td>-0.246651</td>\n",
       "      <td>-0.046139</td>\n",
       "      <td>...</td>\n",
       "      <td>0.227812</td>\n",
       "      <td>-0.150487</td>\n",
       "      <td>0.435045</td>\n",
       "      <td>0.724825</td>\n",
       "      <td>-0.337082</td>\n",
       "      <td>0.016368</td>\n",
       "      <td>0.030041</td>\n",
       "      <td>0</td>\n",
       "      <td>0.277789</td>\n",
       "      <td>-0.994713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.322707</td>\n",
       "      <td>-0.174041</td>\n",
       "      <td>0.434555</td>\n",
       "      <td>0.576038</td>\n",
       "      <td>-0.836758</td>\n",
       "      <td>-0.831083</td>\n",
       "      <td>-0.264905</td>\n",
       "      <td>-0.220982</td>\n",
       "      <td>-1.071425</td>\n",
       "      <td>0.868559</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.323357</td>\n",
       "      <td>-0.037710</td>\n",
       "      <td>0.347151</td>\n",
       "      <td>0.559639</td>\n",
       "      <td>-0.280158</td>\n",
       "      <td>0.042335</td>\n",
       "      <td>0.028822</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.083840</td>\n",
       "      <td>-0.994713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.414289</td>\n",
       "      <td>0.905437</td>\n",
       "      <td>1.727453</td>\n",
       "      <td>1.473471</td>\n",
       "      <td>0.007443</td>\n",
       "      <td>-0.200331</td>\n",
       "      <td>0.740228</td>\n",
       "      <td>-0.029247</td>\n",
       "      <td>-0.593392</td>\n",
       "      <td>-0.346188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.457331</td>\n",
       "      <td>-0.038500</td>\n",
       "      <td>0.642522</td>\n",
       "      <td>-0.183891</td>\n",
       "      <td>-0.277464</td>\n",
       "      <td>0.182687</td>\n",
       "      <td>0.152665</td>\n",
       "      <td>0</td>\n",
       "      <td>0.153706</td>\n",
       "      <td>-0.994713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.059387</td>\n",
       "      <td>-0.175319</td>\n",
       "      <td>1.266130</td>\n",
       "      <td>1.186110</td>\n",
       "      <td>-0.786002</td>\n",
       "      <td>0.578435</td>\n",
       "      <td>-0.767084</td>\n",
       "      <td>0.401046</td>\n",
       "      <td>0.699500</td>\n",
       "      <td>-0.064738</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213734</td>\n",
       "      <td>0.014462</td>\n",
       "      <td>0.002951</td>\n",
       "      <td>0.294638</td>\n",
       "      <td>-0.395070</td>\n",
       "      <td>0.081461</td>\n",
       "      <td>0.024220</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.125900</td>\n",
       "      <td>-0.994713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284777</th>\n",
       "      <td>2.079137</td>\n",
       "      <td>-0.028723</td>\n",
       "      <td>-1.343392</td>\n",
       "      <td>0.358000</td>\n",
       "      <td>-0.045791</td>\n",
       "      <td>-1.345452</td>\n",
       "      <td>0.227476</td>\n",
       "      <td>-0.378355</td>\n",
       "      <td>0.665911</td>\n",
       "      <td>0.028351</td>\n",
       "      <td>...</td>\n",
       "      <td>0.829758</td>\n",
       "      <td>-0.002063</td>\n",
       "      <td>0.001344</td>\n",
       "      <td>0.262183</td>\n",
       "      <td>-0.105327</td>\n",
       "      <td>-0.022363</td>\n",
       "      <td>-0.060283</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.293440</td>\n",
       "      <td>1.034693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284778</th>\n",
       "      <td>-0.764523</td>\n",
       "      <td>0.588379</td>\n",
       "      <td>-0.907599</td>\n",
       "      <td>-0.418847</td>\n",
       "      <td>0.901528</td>\n",
       "      <td>-0.760802</td>\n",
       "      <td>0.758545</td>\n",
       "      <td>0.414698</td>\n",
       "      <td>-0.730854</td>\n",
       "      <td>-1.245088</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.431876</td>\n",
       "      <td>0.141759</td>\n",
       "      <td>0.587119</td>\n",
       "      <td>-0.200998</td>\n",
       "      <td>0.267337</td>\n",
       "      <td>-0.152951</td>\n",
       "      <td>-0.065285</td>\n",
       "      <td>0</td>\n",
       "      <td>0.810452</td>\n",
       "      <td>1.034693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284779</th>\n",
       "      <td>1.975178</td>\n",
       "      <td>-0.616244</td>\n",
       "      <td>-2.628295</td>\n",
       "      <td>-0.406246</td>\n",
       "      <td>2.327804</td>\n",
       "      <td>3.664740</td>\n",
       "      <td>-0.533297</td>\n",
       "      <td>0.842937</td>\n",
       "      <td>1.128798</td>\n",
       "      <td>-0.220744</td>\n",
       "      <td>...</td>\n",
       "      <td>0.543613</td>\n",
       "      <td>-0.032129</td>\n",
       "      <td>0.768379</td>\n",
       "      <td>0.477688</td>\n",
       "      <td>-0.031833</td>\n",
       "      <td>0.014151</td>\n",
       "      <td>-0.066542</td>\n",
       "      <td>0</td>\n",
       "      <td>0.041920</td>\n",
       "      <td>1.034716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284780</th>\n",
       "      <td>-1.727503</td>\n",
       "      <td>1.108356</td>\n",
       "      <td>2.219561</td>\n",
       "      <td>1.148583</td>\n",
       "      <td>-0.884199</td>\n",
       "      <td>0.793083</td>\n",
       "      <td>-0.527298</td>\n",
       "      <td>0.866429</td>\n",
       "      <td>0.853819</td>\n",
       "      <td>-0.195152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.236818</td>\n",
       "      <td>-0.204280</td>\n",
       "      <td>1.158185</td>\n",
       "      <td>0.627801</td>\n",
       "      <td>-0.399981</td>\n",
       "      <td>0.510818</td>\n",
       "      <td>0.233265</td>\n",
       "      <td>0</td>\n",
       "      <td>0.111786</td>\n",
       "      <td>1.034716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284781</th>\n",
       "      <td>-1.139015</td>\n",
       "      <td>-0.155510</td>\n",
       "      <td>1.894478</td>\n",
       "      <td>-1.138957</td>\n",
       "      <td>1.451777</td>\n",
       "      <td>0.093598</td>\n",
       "      <td>0.191353</td>\n",
       "      <td>0.092211</td>\n",
       "      <td>-0.062621</td>\n",
       "      <td>-0.792066</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.631658</td>\n",
       "      <td>-0.147249</td>\n",
       "      <td>0.212931</td>\n",
       "      <td>0.354257</td>\n",
       "      <td>-0.241068</td>\n",
       "      <td>-0.161717</td>\n",
       "      <td>-0.149188</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.125760</td>\n",
       "      <td>1.034716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284782</th>\n",
       "      <td>-0.268061</td>\n",
       "      <td>2.540315</td>\n",
       "      <td>-1.400915</td>\n",
       "      <td>4.846661</td>\n",
       "      <td>0.639105</td>\n",
       "      <td>0.186479</td>\n",
       "      <td>-0.045911</td>\n",
       "      <td>0.936448</td>\n",
       "      <td>-2.419986</td>\n",
       "      <td>0.525012</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.857904</td>\n",
       "      <td>0.235172</td>\n",
       "      <td>-0.681794</td>\n",
       "      <td>-0.668894</td>\n",
       "      <td>0.044657</td>\n",
       "      <td>-0.066751</td>\n",
       "      <td>-0.072447</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.128275</td>\n",
       "      <td>1.034728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284783</th>\n",
       "      <td>-1.796092</td>\n",
       "      <td>1.929178</td>\n",
       "      <td>-2.828417</td>\n",
       "      <td>-1.689844</td>\n",
       "      <td>2.199572</td>\n",
       "      <td>3.123732</td>\n",
       "      <td>-0.270714</td>\n",
       "      <td>1.657495</td>\n",
       "      <td>0.465804</td>\n",
       "      <td>0.832931</td>\n",
       "      <td>...</td>\n",
       "      <td>1.145750</td>\n",
       "      <td>0.084783</td>\n",
       "      <td>0.721269</td>\n",
       "      <td>-0.529906</td>\n",
       "      <td>-0.240117</td>\n",
       "      <td>0.129126</td>\n",
       "      <td>-0.080620</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.147279</td>\n",
       "      <td>1.034740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284784</th>\n",
       "      <td>-0.669662</td>\n",
       "      <td>0.923769</td>\n",
       "      <td>-1.543167</td>\n",
       "      <td>-1.560729</td>\n",
       "      <td>2.833960</td>\n",
       "      <td>3.240843</td>\n",
       "      <td>0.181576</td>\n",
       "      <td>1.282746</td>\n",
       "      <td>-0.893890</td>\n",
       "      <td>-1.453432</td>\n",
       "      <td>...</td>\n",
       "      <td>0.202670</td>\n",
       "      <td>-0.373023</td>\n",
       "      <td>0.651122</td>\n",
       "      <td>1.073823</td>\n",
       "      <td>0.844590</td>\n",
       "      <td>-0.286676</td>\n",
       "      <td>-0.187719</td>\n",
       "      <td>0</td>\n",
       "      <td>0.251520</td>\n",
       "      <td>1.034740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284785</th>\n",
       "      <td>0.032887</td>\n",
       "      <td>0.545338</td>\n",
       "      <td>-1.185844</td>\n",
       "      <td>-1.729828</td>\n",
       "      <td>2.932315</td>\n",
       "      <td>3.401529</td>\n",
       "      <td>0.337434</td>\n",
       "      <td>0.925377</td>\n",
       "      <td>-0.165663</td>\n",
       "      <td>-0.386953</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.716336</td>\n",
       "      <td>0.108519</td>\n",
       "      <td>0.688519</td>\n",
       "      <td>-0.460220</td>\n",
       "      <td>0.161939</td>\n",
       "      <td>0.265368</td>\n",
       "      <td>0.090245</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.282401</td>\n",
       "      <td>1.034740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284786</th>\n",
       "      <td>-2.076175</td>\n",
       "      <td>2.142238</td>\n",
       "      <td>-2.522704</td>\n",
       "      <td>-1.888063</td>\n",
       "      <td>1.982785</td>\n",
       "      <td>3.732950</td>\n",
       "      <td>-1.217430</td>\n",
       "      <td>-0.536644</td>\n",
       "      <td>0.272867</td>\n",
       "      <td>0.300342</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.588269</td>\n",
       "      <td>0.588482</td>\n",
       "      <td>0.632444</td>\n",
       "      <td>-0.201064</td>\n",
       "      <td>0.199251</td>\n",
       "      <td>0.438657</td>\n",
       "      <td>0.172923</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.182352</td>\n",
       "      <td>1.034740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284787</th>\n",
       "      <td>-1.029719</td>\n",
       "      <td>-1.110670</td>\n",
       "      <td>-0.636179</td>\n",
       "      <td>-0.840816</td>\n",
       "      <td>2.424360</td>\n",
       "      <td>-2.956733</td>\n",
       "      <td>0.283610</td>\n",
       "      <td>-0.332656</td>\n",
       "      <td>-0.247488</td>\n",
       "      <td>-0.328271</td>\n",
       "      <td>...</td>\n",
       "      <td>0.488487</td>\n",
       "      <td>0.293632</td>\n",
       "      <td>0.107812</td>\n",
       "      <td>-0.935586</td>\n",
       "      <td>1.138216</td>\n",
       "      <td>0.025271</td>\n",
       "      <td>0.255347</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.167819</td>\n",
       "      <td>1.034751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284788</th>\n",
       "      <td>2.007418</td>\n",
       "      <td>-0.280235</td>\n",
       "      <td>-0.208113</td>\n",
       "      <td>0.335261</td>\n",
       "      <td>-0.715798</td>\n",
       "      <td>-0.751373</td>\n",
       "      <td>-0.458972</td>\n",
       "      <td>-0.140140</td>\n",
       "      <td>0.959971</td>\n",
       "      <td>-0.028284</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.430347</td>\n",
       "      <td>0.416765</td>\n",
       "      <td>0.064819</td>\n",
       "      <td>-0.608337</td>\n",
       "      <td>0.268436</td>\n",
       "      <td>-0.028069</td>\n",
       "      <td>-0.041367</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.251659</td>\n",
       "      <td>1.034763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284789</th>\n",
       "      <td>-0.446951</td>\n",
       "      <td>1.302212</td>\n",
       "      <td>-0.168583</td>\n",
       "      <td>0.981577</td>\n",
       "      <td>0.578957</td>\n",
       "      <td>-0.605641</td>\n",
       "      <td>1.253430</td>\n",
       "      <td>-1.042610</td>\n",
       "      <td>-0.417116</td>\n",
       "      <td>0.076605</td>\n",
       "      <td>...</td>\n",
       "      <td>0.305268</td>\n",
       "      <td>-0.148093</td>\n",
       "      <td>-0.038712</td>\n",
       "      <td>0.010209</td>\n",
       "      <td>-0.362666</td>\n",
       "      <td>0.503092</td>\n",
       "      <td>0.229921</td>\n",
       "      <td>0</td>\n",
       "      <td>0.537972</td>\n",
       "      <td>1.034763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284790</th>\n",
       "      <td>-0.515513</td>\n",
       "      <td>0.971950</td>\n",
       "      <td>-1.014580</td>\n",
       "      <td>-0.677037</td>\n",
       "      <td>0.912430</td>\n",
       "      <td>-0.316187</td>\n",
       "      <td>0.396137</td>\n",
       "      <td>0.532364</td>\n",
       "      <td>-0.224606</td>\n",
       "      <td>-0.753365</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.849919</td>\n",
       "      <td>0.300245</td>\n",
       "      <td>0.000607</td>\n",
       "      <td>-0.376379</td>\n",
       "      <td>0.128660</td>\n",
       "      <td>-0.015205</td>\n",
       "      <td>-0.021486</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.170335</td>\n",
       "      <td>1.034775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284791</th>\n",
       "      <td>-0.863506</td>\n",
       "      <td>0.874701</td>\n",
       "      <td>0.420358</td>\n",
       "      <td>-0.530365</td>\n",
       "      <td>0.356561</td>\n",
       "      <td>-1.046238</td>\n",
       "      <td>0.757051</td>\n",
       "      <td>0.230473</td>\n",
       "      <td>-0.506856</td>\n",
       "      <td>-1.032990</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.480820</td>\n",
       "      <td>-0.074513</td>\n",
       "      <td>-0.003988</td>\n",
       "      <td>-0.113149</td>\n",
       "      <td>0.280378</td>\n",
       "      <td>-0.077310</td>\n",
       "      <td>0.023079</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.023475</td>\n",
       "      <td>1.034810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284792</th>\n",
       "      <td>-0.724123</td>\n",
       "      <td>1.485216</td>\n",
       "      <td>-1.132218</td>\n",
       "      <td>-0.607190</td>\n",
       "      <td>0.709499</td>\n",
       "      <td>-0.482638</td>\n",
       "      <td>0.548393</td>\n",
       "      <td>0.343003</td>\n",
       "      <td>-0.226323</td>\n",
       "      <td>-0.348134</td>\n",
       "      <td>...</td>\n",
       "      <td>1.307511</td>\n",
       "      <td>-0.059545</td>\n",
       "      <td>0.242669</td>\n",
       "      <td>-0.665424</td>\n",
       "      <td>-0.269869</td>\n",
       "      <td>-0.170579</td>\n",
       "      <td>-0.030692</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.251659</td>\n",
       "      <td>1.034810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284793</th>\n",
       "      <td>1.971002</td>\n",
       "      <td>-0.699067</td>\n",
       "      <td>-1.697541</td>\n",
       "      <td>-0.617643</td>\n",
       "      <td>1.718797</td>\n",
       "      <td>3.911336</td>\n",
       "      <td>-1.259306</td>\n",
       "      <td>1.056209</td>\n",
       "      <td>1.315006</td>\n",
       "      <td>-0.146827</td>\n",
       "      <td>...</td>\n",
       "      <td>0.694418</td>\n",
       "      <td>0.163002</td>\n",
       "      <td>0.726365</td>\n",
       "      <td>-0.058282</td>\n",
       "      <td>-0.191813</td>\n",
       "      <td>0.061858</td>\n",
       "      <td>-0.043716</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.237686</td>\n",
       "      <td>1.034822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284794</th>\n",
       "      <td>-1.266580</td>\n",
       "      <td>-0.400461</td>\n",
       "      <td>0.956221</td>\n",
       "      <td>-0.723919</td>\n",
       "      <td>1.531993</td>\n",
       "      <td>-1.788600</td>\n",
       "      <td>0.314741</td>\n",
       "      <td>0.004704</td>\n",
       "      <td>0.013857</td>\n",
       "      <td>-0.815911</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.883365</td>\n",
       "      <td>0.088485</td>\n",
       "      <td>-0.076790</td>\n",
       "      <td>-0.095833</td>\n",
       "      <td>0.132720</td>\n",
       "      <td>-0.028468</td>\n",
       "      <td>0.126494</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.294977</td>\n",
       "      <td>1.034845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284795</th>\n",
       "      <td>-12.516732</td>\n",
       "      <td>10.187818</td>\n",
       "      <td>-8.476671</td>\n",
       "      <td>-2.510473</td>\n",
       "      <td>-4.586669</td>\n",
       "      <td>-1.394465</td>\n",
       "      <td>-3.632516</td>\n",
       "      <td>5.498583</td>\n",
       "      <td>4.893089</td>\n",
       "      <td>8.655320</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.565026</td>\n",
       "      <td>0.890675</td>\n",
       "      <td>-1.253276</td>\n",
       "      <td>1.786717</td>\n",
       "      <td>0.320763</td>\n",
       "      <td>2.090712</td>\n",
       "      <td>1.232864</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.169496</td>\n",
       "      <td>1.034857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284796</th>\n",
       "      <td>1.884849</td>\n",
       "      <td>-0.143540</td>\n",
       "      <td>-0.999943</td>\n",
       "      <td>1.506772</td>\n",
       "      <td>-0.035300</td>\n",
       "      <td>-0.613638</td>\n",
       "      <td>0.190241</td>\n",
       "      <td>-0.249058</td>\n",
       "      <td>0.666458</td>\n",
       "      <td>0.120908</td>\n",
       "      <td>...</td>\n",
       "      <td>0.634646</td>\n",
       "      <td>-0.042114</td>\n",
       "      <td>-0.053206</td>\n",
       "      <td>0.316403</td>\n",
       "      <td>-0.461441</td>\n",
       "      <td>0.018265</td>\n",
       "      <td>-0.041068</td>\n",
       "      <td>0</td>\n",
       "      <td>0.530986</td>\n",
       "      <td>1.034881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284797</th>\n",
       "      <td>-0.241923</td>\n",
       "      <td>0.712247</td>\n",
       "      <td>0.399806</td>\n",
       "      <td>-0.463406</td>\n",
       "      <td>0.244531</td>\n",
       "      <td>-1.343668</td>\n",
       "      <td>0.929369</td>\n",
       "      <td>-0.206210</td>\n",
       "      <td>0.106234</td>\n",
       "      <td>-0.284708</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.514376</td>\n",
       "      <td>0.279598</td>\n",
       "      <td>0.371441</td>\n",
       "      <td>-0.559238</td>\n",
       "      <td>0.113144</td>\n",
       "      <td>0.131507</td>\n",
       "      <td>0.081265</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.230699</td>\n",
       "      <td>1.034904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284798</th>\n",
       "      <td>0.219529</td>\n",
       "      <td>0.881246</td>\n",
       "      <td>-0.635891</td>\n",
       "      <td>0.960928</td>\n",
       "      <td>-0.152971</td>\n",
       "      <td>-1.014307</td>\n",
       "      <td>0.427126</td>\n",
       "      <td>0.121340</td>\n",
       "      <td>-0.285670</td>\n",
       "      <td>-0.111640</td>\n",
       "      <td>...</td>\n",
       "      <td>0.337120</td>\n",
       "      <td>0.251791</td>\n",
       "      <td>0.057688</td>\n",
       "      <td>-1.508368</td>\n",
       "      <td>0.144023</td>\n",
       "      <td>0.181205</td>\n",
       "      <td>0.215243</td>\n",
       "      <td>0</td>\n",
       "      <td>0.028645</td>\n",
       "      <td>1.034904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284799</th>\n",
       "      <td>-1.775135</td>\n",
       "      <td>-0.004235</td>\n",
       "      <td>1.189786</td>\n",
       "      <td>0.331096</td>\n",
       "      <td>1.196063</td>\n",
       "      <td>5.519980</td>\n",
       "      <td>-1.518185</td>\n",
       "      <td>2.080825</td>\n",
       "      <td>1.159498</td>\n",
       "      <td>-0.594242</td>\n",
       "      <td>...</td>\n",
       "      <td>0.654850</td>\n",
       "      <td>-0.348929</td>\n",
       "      <td>0.745323</td>\n",
       "      <td>0.704545</td>\n",
       "      <td>-0.127579</td>\n",
       "      <td>0.454379</td>\n",
       "      <td>0.130308</td>\n",
       "      <td>0</td>\n",
       "      <td>0.810312</td>\n",
       "      <td>1.034916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284800</th>\n",
       "      <td>2.039560</td>\n",
       "      <td>-0.175233</td>\n",
       "      <td>-1.196825</td>\n",
       "      <td>0.234580</td>\n",
       "      <td>-0.008713</td>\n",
       "      <td>-0.726571</td>\n",
       "      <td>0.017050</td>\n",
       "      <td>-0.118228</td>\n",
       "      <td>0.435402</td>\n",
       "      <td>0.267772</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.717211</td>\n",
       "      <td>0.297930</td>\n",
       "      <td>-0.359769</td>\n",
       "      <td>-0.315610</td>\n",
       "      <td>0.201114</td>\n",
       "      <td>-0.080826</td>\n",
       "      <td>-0.075071</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.269964</td>\n",
       "      <td>1.034928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284801</th>\n",
       "      <td>0.120316</td>\n",
       "      <td>0.931005</td>\n",
       "      <td>-0.546012</td>\n",
       "      <td>-0.745097</td>\n",
       "      <td>1.130314</td>\n",
       "      <td>-0.235973</td>\n",
       "      <td>0.812722</td>\n",
       "      <td>0.115093</td>\n",
       "      <td>-0.204064</td>\n",
       "      <td>-0.657422</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.808520</td>\n",
       "      <td>0.050343</td>\n",
       "      <td>0.102800</td>\n",
       "      <td>-0.435870</td>\n",
       "      <td>0.124079</td>\n",
       "      <td>0.217940</td>\n",
       "      <td>0.068803</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.269825</td>\n",
       "      <td>1.034939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>-11.881118</td>\n",
       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>4.356170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.296653</td>\n",
       "      <td>1.034951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>-0.975926</td>\n",
       "      <td>...</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>0</td>\n",
       "      <td>0.038986</td>\n",
       "      <td>1.034963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>-0.484782</td>\n",
       "      <td>...</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>0</td>\n",
       "      <td>0.641096</td>\n",
       "      <td>1.034975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>-0.399126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.167680</td>\n",
       "      <td>1.034975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>-0.915427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>0</td>\n",
       "      <td>2.724796</td>\n",
       "      <td>1.035022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               V1         V2        V3        V4        V5        V6  \\\n",
       "0       -1.359807  -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
       "1        1.191857   0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
       "2       -1.358354  -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
       "3       -0.966272  -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
       "4       -1.158233   0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
       "5       -0.425966   0.960523  1.141109 -0.168252  0.420987 -0.029728   \n",
       "6        1.229658   0.141004  0.045371  1.202613  0.191881  0.272708   \n",
       "7       -0.644269   1.417964  1.074380 -0.492199  0.948934  0.428118   \n",
       "8       -0.894286   0.286157 -0.113192 -0.271526  2.669599  3.721818   \n",
       "9       -0.338262   1.119593  1.044367 -0.222187  0.499361 -0.246761   \n",
       "10       1.449044  -1.176339  0.913860 -1.375667 -1.971383 -0.629152   \n",
       "11       0.384978   0.616109 -0.874300 -0.094019  2.924584  3.317027   \n",
       "12       1.249999  -1.221637  0.383930 -1.234899 -1.485419 -0.753230   \n",
       "13       1.069374   0.287722  0.828613  2.712520 -0.178398  0.337544   \n",
       "14      -2.791855  -0.327771  1.641750  1.767473 -0.136588  0.807596   \n",
       "15      -0.752417   0.345485  2.057323 -1.468643 -1.158394 -0.077850   \n",
       "16       1.103215  -0.040296  1.267332  1.289091 -0.735997  0.288069   \n",
       "17      -0.436905   0.918966  0.924591 -0.727219  0.915679 -0.127867   \n",
       "18      -5.401258  -5.450148  1.186305  1.736239  3.049106 -1.763406   \n",
       "19       1.492936  -1.029346  0.454795 -1.438026 -1.555434 -0.720961   \n",
       "20       0.694885  -1.361819  1.029221  0.834159 -1.191209  1.309109   \n",
       "21       0.962496   0.328461 -0.171479  2.109204  1.129566  1.696038   \n",
       "22       1.166616   0.502120 -0.067300  2.261569  0.428804  0.089474   \n",
       "23       0.247491   0.277666  1.185471 -0.092603 -1.314394 -0.150116   \n",
       "24      -1.946525  -0.044901 -0.405570 -1.013057  2.941968  2.955053   \n",
       "25      -2.074295  -0.121482  1.322021  0.410008  0.295198 -0.959537   \n",
       "26       1.173285   0.353498  0.283905  1.133563 -0.172577 -0.916054   \n",
       "27       1.322707  -0.174041  0.434555  0.576038 -0.836758 -0.831083   \n",
       "28      -0.414289   0.905437  1.727453  1.473471  0.007443 -0.200331   \n",
       "29       1.059387  -0.175319  1.266130  1.186110 -0.786002  0.578435   \n",
       "...           ...        ...       ...       ...       ...       ...   \n",
       "284777   2.079137  -0.028723 -1.343392  0.358000 -0.045791 -1.345452   \n",
       "284778  -0.764523   0.588379 -0.907599 -0.418847  0.901528 -0.760802   \n",
       "284779   1.975178  -0.616244 -2.628295 -0.406246  2.327804  3.664740   \n",
       "284780  -1.727503   1.108356  2.219561  1.148583 -0.884199  0.793083   \n",
       "284781  -1.139015  -0.155510  1.894478 -1.138957  1.451777  0.093598   \n",
       "284782  -0.268061   2.540315 -1.400915  4.846661  0.639105  0.186479   \n",
       "284783  -1.796092   1.929178 -2.828417 -1.689844  2.199572  3.123732   \n",
       "284784  -0.669662   0.923769 -1.543167 -1.560729  2.833960  3.240843   \n",
       "284785   0.032887   0.545338 -1.185844 -1.729828  2.932315  3.401529   \n",
       "284786  -2.076175   2.142238 -2.522704 -1.888063  1.982785  3.732950   \n",
       "284787  -1.029719  -1.110670 -0.636179 -0.840816  2.424360 -2.956733   \n",
       "284788   2.007418  -0.280235 -0.208113  0.335261 -0.715798 -0.751373   \n",
       "284789  -0.446951   1.302212 -0.168583  0.981577  0.578957 -0.605641   \n",
       "284790  -0.515513   0.971950 -1.014580 -0.677037  0.912430 -0.316187   \n",
       "284791  -0.863506   0.874701  0.420358 -0.530365  0.356561 -1.046238   \n",
       "284792  -0.724123   1.485216 -1.132218 -0.607190  0.709499 -0.482638   \n",
       "284793   1.971002  -0.699067 -1.697541 -0.617643  1.718797  3.911336   \n",
       "284794  -1.266580  -0.400461  0.956221 -0.723919  1.531993 -1.788600   \n",
       "284795 -12.516732  10.187818 -8.476671 -2.510473 -4.586669 -1.394465   \n",
       "284796   1.884849  -0.143540 -0.999943  1.506772 -0.035300 -0.613638   \n",
       "284797  -0.241923   0.712247  0.399806 -0.463406  0.244531 -1.343668   \n",
       "284798   0.219529   0.881246 -0.635891  0.960928 -0.152971 -1.014307   \n",
       "284799  -1.775135  -0.004235  1.189786  0.331096  1.196063  5.519980   \n",
       "284800   2.039560  -0.175233 -1.196825  0.234580 -0.008713 -0.726571   \n",
       "284801   0.120316   0.931005 -0.546012 -0.745097  1.130314 -0.235973   \n",
       "284802 -11.881118  10.071785 -9.834783 -2.066656 -5.364473 -2.606837   \n",
       "284803  -0.732789  -0.055080  2.035030 -0.738589  0.868229  1.058415   \n",
       "284804   1.919565  -0.301254 -3.249640 -0.557828  2.630515  3.031260   \n",
       "284805  -0.240440   0.530483  0.702510  0.689799 -0.377961  0.623708   \n",
       "284806  -0.533413  -0.189733  0.703337 -0.506271 -0.012546 -0.649617   \n",
       "\n",
       "              V7        V8        V9       V10  ...       V22       V23  \\\n",
       "0       0.239599  0.098698  0.363787  0.090794  ...  0.277838 -0.110474   \n",
       "1      -0.078803  0.085102 -0.255425 -0.166974  ... -0.638672  0.101288   \n",
       "2       0.791461  0.247676 -1.514654  0.207643  ...  0.771679  0.909412   \n",
       "3       0.237609  0.377436 -1.387024 -0.054952  ...  0.005274 -0.190321   \n",
       "4       0.592941 -0.270533  0.817739  0.753074  ...  0.798278 -0.137458   \n",
       "5       0.476201  0.260314 -0.568671 -0.371407  ... -0.559825 -0.026398   \n",
       "6      -0.005159  0.081213  0.464960 -0.099254  ... -0.270710 -0.154104   \n",
       "7       1.120631 -3.807864  0.615375  1.249376  ... -1.015455  0.057504   \n",
       "8       0.370145  0.851084 -0.392048 -0.410430  ... -0.268092 -0.204233   \n",
       "9       0.651583  0.069539 -0.736727 -0.366846  ... -0.633753 -0.120794   \n",
       "10     -1.423236  0.048456 -1.720408  1.626659  ...  0.313894  0.027740   \n",
       "11      0.470455  0.538247 -0.558895  0.309755  ...  0.238422  0.009130   \n",
       "12     -0.689405 -0.227487 -2.094011  1.323729  ... -0.483285  0.084668   \n",
       "13     -0.096717  0.115982 -0.221083  0.460230  ...  0.074412 -0.071407   \n",
       "14     -0.422911 -1.907107  0.755713  1.151087  ...  0.222182  1.020586   \n",
       "15     -0.608581  0.003603 -0.436167  0.747731  ...  1.353650 -0.256573   \n",
       "16     -0.586057  0.189380  0.782333 -0.267975  ...  0.196002  0.013802   \n",
       "17      0.707642  0.087962 -0.665271 -0.737980  ... -0.672638 -0.156858   \n",
       "18     -1.559738  0.160842  1.233090  0.345173  ...  0.984460  2.458589   \n",
       "19     -1.080664 -0.053127 -1.978682  1.638076  ... -0.175074  0.040002   \n",
       "20     -0.878586  0.445290 -0.446196  0.568521  ... -0.571955 -0.050881   \n",
       "21      0.107712  0.521502 -1.191311  0.724396  ...  0.402492 -0.048508   \n",
       "22      0.241147  0.138082 -0.989162  0.922175  ... -0.061972 -0.103855   \n",
       "23     -0.946365 -1.617935  1.544071 -0.829881  ...  0.200454 -0.185353   \n",
       "24     -0.063063  0.855546  0.049967  0.573743  ... -0.799229  0.870300   \n",
       "25      0.543985 -0.104627  0.475664  0.149451  ... -0.227404  0.742435   \n",
       "26      0.369025 -0.327260 -0.246651 -0.046139  ...  0.227812 -0.150487   \n",
       "27     -0.264905 -0.220982 -1.071425  0.868559  ... -0.323357 -0.037710   \n",
       "28      0.740228 -0.029247 -0.593392 -0.346188  ...  0.457331 -0.038500   \n",
       "29     -0.767084  0.401046  0.699500 -0.064738  ...  0.213734  0.014462   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \n",
       "284777  0.227476 -0.378355  0.665911  0.028351  ...  0.829758 -0.002063   \n",
       "284778  0.758545  0.414698 -0.730854 -1.245088  ... -0.431876  0.141759   \n",
       "284779 -0.533297  0.842937  1.128798 -0.220744  ...  0.543613 -0.032129   \n",
       "284780 -0.527298  0.866429  0.853819 -0.195152  ...  0.236818 -0.204280   \n",
       "284781  0.191353  0.092211 -0.062621 -0.792066  ... -0.631658 -0.147249   \n",
       "284782 -0.045911  0.936448 -2.419986  0.525012  ... -0.857904  0.235172   \n",
       "284783 -0.270714  1.657495  0.465804  0.832931  ...  1.145750  0.084783   \n",
       "284784  0.181576  1.282746 -0.893890 -1.453432  ...  0.202670 -0.373023   \n",
       "284785  0.337434  0.925377 -0.165663 -0.386953  ... -0.716336  0.108519   \n",
       "284786 -1.217430 -0.536644  0.272867  0.300342  ... -1.588269  0.588482   \n",
       "284787  0.283610 -0.332656 -0.247488 -0.328271  ...  0.488487  0.293632   \n",
       "284788 -0.458972 -0.140140  0.959971 -0.028284  ... -0.430347  0.416765   \n",
       "284789  1.253430 -1.042610 -0.417116  0.076605  ...  0.305268 -0.148093   \n",
       "284790  0.396137  0.532364 -0.224606 -0.753365  ... -0.849919  0.300245   \n",
       "284791  0.757051  0.230473 -0.506856 -1.032990  ... -0.480820 -0.074513   \n",
       "284792  0.548393  0.343003 -0.226323 -0.348134  ...  1.307511 -0.059545   \n",
       "284793 -1.259306  1.056209  1.315006 -0.146827  ...  0.694418  0.163002   \n",
       "284794  0.314741  0.004704  0.013857 -0.815911  ... -0.883365  0.088485   \n",
       "284795 -3.632516  5.498583  4.893089  8.655320  ... -1.565026  0.890675   \n",
       "284796  0.190241 -0.249058  0.666458  0.120908  ...  0.634646 -0.042114   \n",
       "284797  0.929369 -0.206210  0.106234 -0.284708  ... -0.514376  0.279598   \n",
       "284798  0.427126  0.121340 -0.285670 -0.111640  ...  0.337120  0.251791   \n",
       "284799 -1.518185  2.080825  1.159498 -0.594242  ...  0.654850 -0.348929   \n",
       "284800  0.017050 -0.118228  0.435402  0.267772  ... -0.717211  0.297930   \n",
       "284801  0.812722  0.115093 -0.204064 -0.657422  ... -0.808520  0.050343   \n",
       "284802 -4.918215  7.305334  1.914428  4.356170  ...  0.111864  1.014480   \n",
       "284803  0.024330  0.294869  0.584800 -0.975926  ...  0.924384  0.012463   \n",
       "284804 -0.296827  0.708417  0.432454 -0.484782  ...  0.578229 -0.037501   \n",
       "284805 -0.686180  0.679145  0.392087 -0.399126  ...  0.800049 -0.163298   \n",
       "284806  1.577006 -0.414650  0.486180 -0.915427  ...  0.643078  0.376777   \n",
       "\n",
       "             V24       V25       V26       V27       V28  Class  Amount_norm  \\\n",
       "0       0.066928  0.128539 -0.189115  0.133558 -0.021053      0     1.783274   \n",
       "1      -0.339846  0.167170  0.125895 -0.008983  0.014724      0    -0.269825   \n",
       "2      -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0     4.983721   \n",
       "3      -1.175575  0.647376 -0.221929  0.062723  0.061458      0     1.418291   \n",
       "4       0.141267 -0.206010  0.502292  0.219422  0.215153      0     0.670579   \n",
       "5      -0.371427 -0.232794  0.105915  0.253844  0.081080      0    -0.256131   \n",
       "6      -0.780055  0.750137 -0.257237  0.034507  0.005168      0    -0.237686   \n",
       "7      -0.649709 -0.415267 -0.051634 -1.206921 -1.085339      0     0.262698   \n",
       "8       1.011592  0.373205 -0.384157  0.011747  0.142404      0     0.994900   \n",
       "9      -0.385050 -0.069733  0.094199  0.246219  0.083076      0    -0.255991   \n",
       "10      0.500512  0.251367 -0.129478  0.042850  0.016253      0    -0.198421   \n",
       "11      0.996710 -0.767315 -0.492208  0.042472 -0.054337      0    -0.167819   \n",
       "12      0.392831  0.161135 -0.354990  0.026416  0.042422      0     1.390344   \n",
       "13      0.104744  0.548265  0.104094  0.021491  0.021293      0     0.076853   \n",
       "14      0.028317 -0.232746 -0.235557 -0.164778 -0.030154      0     0.514218   \n",
       "15     -0.065084 -0.039124 -0.087086 -0.180998  0.129394      0    -0.083980   \n",
       "16      0.103758  0.364298 -0.382261  0.092809  0.037051      0    -0.125900   \n",
       "17     -0.888386 -0.342413 -0.049027  0.079692  0.131024      0    -0.294977   \n",
       "18      0.042119 -0.481631 -0.621272  0.392053  0.949594      0     0.346538   \n",
       "19      0.295814  0.332931 -0.220385  0.022298  0.007602      0    -0.237546   \n",
       "20     -0.304215  0.072001 -0.422234  0.086553  0.063499      0     2.930343   \n",
       "21     -1.371866  0.390814  0.199964  0.016371 -0.014605      0     0.168937   \n",
       "22     -0.370415  0.603200  0.108556 -0.040521 -0.011418      0    -0.275554   \n",
       "23      0.423073  0.820591 -0.227632  0.336634  0.250475      0     0.010480   \n",
       "24      0.983421  0.321201  0.149650  0.707519  0.014600      0    -0.294977   \n",
       "25      0.398535  0.249212  0.274404  0.359969  0.243232      0     0.061902   \n",
       "26      0.435045  0.724825 -0.337082  0.016368  0.030041      0     0.277789   \n",
       "27      0.347151  0.559639 -0.280158  0.042335  0.028822      0    -0.083840   \n",
       "28      0.642522 -0.183891 -0.277464  0.182687  0.152665      0     0.153706   \n",
       "29      0.002951  0.294638 -0.395070  0.081461  0.024220      0    -0.125900   \n",
       "...          ...       ...       ...       ...       ...    ...          ...   \n",
       "284777  0.001344  0.262183 -0.105327 -0.022363 -0.060283      0    -0.293440   \n",
       "284778  0.587119 -0.200998  0.267337 -0.152951 -0.065285      0     0.810452   \n",
       "284779  0.768379  0.477688 -0.031833  0.014151 -0.066542      0     0.041920   \n",
       "284780  1.158185  0.627801 -0.399981  0.510818  0.233265      0     0.111786   \n",
       "284781  0.212931  0.354257 -0.241068 -0.161717 -0.149188      0    -0.125760   \n",
       "284782 -0.681794 -0.668894  0.044657 -0.066751 -0.072447      0    -0.128275   \n",
       "284783  0.721269 -0.529906 -0.240117  0.129126 -0.080620      0    -0.147279   \n",
       "284784  0.651122  1.073823  0.844590 -0.286676 -0.187719      0     0.251520   \n",
       "284785  0.688519 -0.460220  0.161939  0.265368  0.090245      0    -0.282401   \n",
       "284786  0.632444 -0.201064  0.199251  0.438657  0.172923      0    -0.182352   \n",
       "284787  0.107812 -0.935586  1.138216  0.025271  0.255347      0    -0.167819   \n",
       "284788  0.064819 -0.608337  0.268436 -0.028069 -0.041367      0    -0.251659   \n",
       "284789 -0.038712  0.010209 -0.362666  0.503092  0.229921      0     0.537972   \n",
       "284790  0.000607 -0.376379  0.128660 -0.015205 -0.021486      0    -0.170335   \n",
       "284791 -0.003988 -0.113149  0.280378 -0.077310  0.023079      0    -0.023475   \n",
       "284792  0.242669 -0.665424 -0.269869 -0.170579 -0.030692      0    -0.251659   \n",
       "284793  0.726365 -0.058282 -0.191813  0.061858 -0.043716      0    -0.237686   \n",
       "284794 -0.076790 -0.095833  0.132720 -0.028468  0.126494      0    -0.294977   \n",
       "284795 -1.253276  1.786717  0.320763  2.090712  1.232864      0    -0.169496   \n",
       "284796 -0.053206  0.316403 -0.461441  0.018265 -0.041068      0     0.530986   \n",
       "284797  0.371441 -0.559238  0.113144  0.131507  0.081265      0    -0.230699   \n",
       "284798  0.057688 -1.508368  0.144023  0.181205  0.215243      0     0.028645   \n",
       "284799  0.745323  0.704545 -0.127579  0.454379  0.130308      0     0.810312   \n",
       "284800 -0.359769 -0.315610  0.201114 -0.080826 -0.075071      0    -0.269964   \n",
       "284801  0.102800 -0.435870  0.124079  0.217940  0.068803      0    -0.269825   \n",
       "284802 -0.509348  1.436807  0.250034  0.943651  0.823731      0    -0.296653   \n",
       "284803 -1.016226 -0.606624 -0.395255  0.068472 -0.053527      0     0.038986   \n",
       "284804  0.640134  0.265745 -0.087371  0.004455 -0.026561      0     0.641096   \n",
       "284805  0.123205 -0.569159  0.546668  0.108821  0.104533      0    -0.167680   \n",
       "284806  0.008797 -0.473649 -0.818267 -0.002415  0.013649      0     2.724796   \n",
       "\n",
       "        Time_norm  \n",
       "0       -0.994983  \n",
       "1       -0.994983  \n",
       "2       -0.994972  \n",
       "3       -0.994972  \n",
       "4       -0.994960  \n",
       "5       -0.994960  \n",
       "6       -0.994937  \n",
       "7       -0.994901  \n",
       "8       -0.994901  \n",
       "9       -0.994878  \n",
       "10      -0.994866  \n",
       "11      -0.994866  \n",
       "12      -0.994866  \n",
       "13      -0.994854  \n",
       "14      -0.994843  \n",
       "15      -0.994843  \n",
       "16      -0.994843  \n",
       "17      -0.994831  \n",
       "18      -0.994819  \n",
       "19      -0.994807  \n",
       "20      -0.994796  \n",
       "21      -0.994784  \n",
       "22      -0.994772  \n",
       "23      -0.994772  \n",
       "24      -0.994725  \n",
       "25      -0.994725  \n",
       "26      -0.994713  \n",
       "27      -0.994713  \n",
       "28      -0.994713  \n",
       "29      -0.994713  \n",
       "...           ...  \n",
       "284777   1.034693  \n",
       "284778   1.034693  \n",
       "284779   1.034716  \n",
       "284780   1.034716  \n",
       "284781   1.034716  \n",
       "284782   1.034728  \n",
       "284783   1.034740  \n",
       "284784   1.034740  \n",
       "284785   1.034740  \n",
       "284786   1.034740  \n",
       "284787   1.034751  \n",
       "284788   1.034763  \n",
       "284789   1.034763  \n",
       "284790   1.034775  \n",
       "284791   1.034810  \n",
       "284792   1.034810  \n",
       "284793   1.034822  \n",
       "284794   1.034845  \n",
       "284795   1.034857  \n",
       "284796   1.034881  \n",
       "284797   1.034904  \n",
       "284798   1.034904  \n",
       "284799   1.034916  \n",
       "284800   1.034928  \n",
       "284801   1.034939  \n",
       "284802   1.034951  \n",
       "284803   1.034963  \n",
       "284804   1.034975  \n",
       "284805   1.034975  \n",
       "284806   1.035022  \n",
       "\n",
       "[284807 rows x 31 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import RobustScaler , StandardScaler\n",
    "\n",
    "# robust scaler are less prones to outliers\n",
    "\n",
    "data[\"Amount_norm\"] = RobustScaler().fit_transform(np.array(data[\"Amount\"]).reshape(-1,1))\n",
    "data[\"Time_norm\"] = RobustScaler().fit_transform(np.array(data[\"Time\"]).reshape(-1,1))\n",
    "\n",
    "\n",
    "\n",
    "data.drop(columns= ['Time' , 'Amount'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split Data \n",
    "Now that we have constructed a normalized dataset we want to split our data into train and test sets. The reason for this is due to preventing our model to overfit the dataset, and evaluate the accuracy of the model by training the model on the train set and testing the performance on the test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "X = data.loc[:, data.columns != 'Class']\n",
    "y = data.loc[:, data.columns == 'Class']\n",
    "\n",
    "\n",
    "sss = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n",
    "\n",
    "for train_index , test_index in sss.split(X ,y):\n",
    "    original_Xtrain, original_Xtest = X.iloc[train_index], X.iloc[test_index]\n",
    "    original_ytrain, original_ytest = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "# Array form \n",
    "original_Xtrain = original_Xtrain.values\n",
    "original_Xtest = original_Xtest.values\n",
    "original_ytrain = original_ytrain.values\n",
    "original_ytest = original_ytest.values\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Under Sampling Method\n",
    "\n",
    "Under sampling is a method used to adress highly skewed data by resampling the data so that we end up with 0.5 ratio for both fraud and non-fraud observations. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of fraud transactions: 492\n",
      "Number of non-fraud transactions: 284315\n",
      "Percentage of non fraud transactions:  0.5\n",
      "Percentage of fraud transactions:  0.5\n",
      "Total number of transactions in resampled data:  984\n"
     ]
    }
   ],
   "source": [
    "# shuffling data before creating the subsample\n",
    "\n",
    "data = data.sample(frac=1)\n",
    "\n",
    "\n",
    "\n",
    "number_fraud = len(data[data.Class == 1])\n",
    "print(\"Number of fraud transactions:\", number_fraud )\n",
    "print(\"Number of non-fraud transactions:\", sum(data['Class'] != 1) )\n",
    "\n",
    "\n",
    "fraud_idx = np.array(data[data.Class == 1].index)\n",
    "non_fraud = data[data.Class == 0].index\n",
    "\n",
    "non_fraud_idx = np.random.choice(non_fraud , number_fraud, replace = False)\n",
    "non_fraud_idx = np.array(non_fraud_idx)\n",
    "\n",
    "under_sample_idx = np.concatenate([non_fraud_idx , fraud_idx])\n",
    "under_sample_data = data.iloc[under_sample_idx,:]\n",
    "\n",
    "print(\"Percentage of non fraud transactions: \", len(under_sample_data[under_sample_data.Class == 0])/len(under_sample_data))\n",
    "print(\"Percentage of fraud transactions: \", len(under_sample_data[under_sample_data.Class == 1])/len(under_sample_data))\n",
    "print(\"Total number of transactions in resampled data: \", len(under_sample_data))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing Algorithms  \n",
    "\n",
    "In this section we will use two popular ML algorithms, Support Vector Machines, and Logistic Regression. \n",
    "\n",
    " * ## Logistic Regression \n",
    "   Logistic regression is among the simpler classification models. The way this model makes predictions is by classifing the values depicted in the following plot with negetive values on the x-axis as non-fraud, and the values on the positive side as fraud. \n",
    "   ![logistic-regression](data:image/png;base64,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)\n",
    " * ## Support Vector Machines \n",
    "   SVM is a relatively new model which has a convoluted mathematical process, yet it is easy to get an intuition of what it does. In the case of support vector machines, we aim to find the best hyperplane that seperates different classes. As depicted in the plot below, we have three lines(hyperplanes), and we want to find the best one to correctly classify our data points. First, the green line completely fails tocorrectly classify our linearly seperable(able to seperate by one line) data. Second, Although the blue line seperates the data correctly, it the ands of the lines are near to our data. However, the red line is the hyperplane that has the largest distance to the nearest training-data point of any class (so-called functional margin).\n",
    "   ![svm](https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/Svm_separating_hyperplanes_%28SVG%29.svg/330px-Svm_separating_hyperplanes_%28SVG%29.svg.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifiers:  LogisticRegression Has a training score of 94.0 % accuracy score\n",
      "Classifiers:  SVC Has a training score of 54.0 % accuracy score\n"
     ]
    }
   ],
   "source": [
    "X = under_sample_data.drop('Class', axis=1)\n",
    "y = under_sample_data['Class']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Turn the values into an array \n",
    "X_train = X_train.values\n",
    "X_test = X_test.values\n",
    "y_train = y_train.values\n",
    "y_test = y_test.values\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "classifiers = {\n",
    "    \"LogisiticRegression\": LogisticRegression(),\n",
    "    \"Support Vector Classifier\": SVC()\n",
    "}\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "\n",
    "for key, classifier in classifiers.items():\n",
    "    classifier.fit(X_train, y_train)\n",
    "    training_score = cross_val_score(classifier, X_train, y_train, cv=5)\n",
    "    print(\"Classifiers: \", classifier.__class__.__name__, \"Has a training score of\", round(training_score.mean(), 2) * 100, \"% accuracy score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regularization and Kernels\n",
    "\n",
    " ##  Regularization \n",
    " \n",
    " By adding a regularization term to our cost function(a function used to find the error score of our model), we are able to use more sophisticated algorithms without risking our model to overfit. The plot below gives an intuition on how the model parameters differ when using a regulization term compared to when we don't use one. \n",
    " ![regulization](https://upload.wikimedia.org/wikipedia/commons/thumb/0/02/Regularization.svg/354px-Regularization.svg.png)\n",
    "\n",
    " ## Kernel Function \n",
    " \n",
    " Kernels are basically a transformation that depicts the data on higher dimensions making it easier to separate data with a hyper plane. \n",
    " ![kernel](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Logistic Regression \n",
    "log_reg_params = {\"penalty\": ['l1'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "\n",
    "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n",
    "grid_log_reg.fit(X_train, y_train)\n",
    "\n",
    "# logistic regression best estimetor\n",
    "log_reg = grid_log_reg.best_estimator_\n",
    "\n",
    "# Support Vector Classifier\n",
    "svc_params = {'C': [0.5, 0.7, 1], 'kernel': ['sigmoid', 'linear']}\n",
    "grid_svc = GridSearchCV(SVC(), svc_params)\n",
    "grid_svc.fit(X_train, y_train)\n",
    "\n",
    "# SVC best estimator\n",
    "svc = grid_svc.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg_score = cross_val_score(log_reg, X_train, y_train, cv=5)\n",
    "print('Logistic Regression Cross Validation Score: ', round(log_reg_score.mean() * 100, 2).astype(str) + '%')\n",
    "\n",
    "\n",
    "svc_score = cross_val_score(svc, X_train, y_train, cv=5)\n",
    "print('Support Vector Classifier Cross Validation Score', round(svc_score.mean() * 100, 2).astype(str) + '%')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
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}