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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# hospital readmissions reduction\n",
+    "\n",
+    "- 20% readmitted within 30 days, 34% within 90\n",
+    "- $17.4 B annual cost\n",
+    "- symptomatic of quality problems within hospitals, lack of coordination in follow up care, and misalighed financial incentives\n",
+    "\n",
+    "- oct 2012 - HRRP links Medicare reimbursements to hospital's risk-adjusted readmission rate\n",
+    "- exceeding risk-adjusted, 3-year rolling readmission rate for AMI, HF, and pneumonia(3 conditions) => penalized portion of Medicare reimbursements\n",
+    "- 2012, penalty up to 1% of total\n",
+    "- 2,225 hospitals subject to reduced payment penalties in 2012, worth 225 M nationwide\n",
+    "- max penalty increased to 3% in 2014\n",
+    "\n",
+    "- 18% of Tahoe's total revenues were Medicare reimbursements for the 3 conditions\n",
+    "- fines for exceeding readmission rates in 2012 were over $750k\n",
+    "\n",
+    "- estimated loss would rise to 8k per readmitted patient if readmissions rates not recuded\n",
+    "\n",
+    "- CareTracker program incorporates patient education and periodic at-home monitoring\n",
+    "- pilot study reduced readmission 40%; however cost was $1.2k/patient\n",
+    "\n",
+    "- cost and benefit analysis for rollout to entire hospital system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 716,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "from time import time\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from scipy.stats import randint as sp_randint\n",
+    "from seaborn import pairplot\n",
+    "\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.ensemble import GradientBoostingRegressor\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "from sklearn.linear_model import ElasticNet\n",
+    "from sklearn.linear_model import ElasticNet, ElasticNetCV\n",
+    "from sklearn.linear_model import Lasso\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.metrics import r2_score\n",
+    "from sklearn.model_selection import RandomizedSearchCV\n",
+    "from sklearn.utils import shuffle\n",
+    "\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import LinearSVC\n",
+    "\n",
+    "import mglearn\n",
+    "\n",
+    "from sklearn.datasets import load_iris\n",
+    "import random\n",
+    "random.seed(15)\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from collections import Counter\n",
+    "\n",
+    "from imblearn.over_sampling import RandomOverSampler\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 717,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>female</th>\n",
+       "      <th>flu_season</th>\n",
+       "      <th>ed_admit</th>\n",
+       "      <th>severity score</th>\n",
+       "      <th>comorbidity score</th>\n",
+       "      <th>readmit30</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4377</th>\n",
+       "      <td>88</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4378</th>\n",
+       "      <td>98</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>51</td>\n",
+       "      <td>136</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4379</th>\n",
+       "      <td>84</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4380</th>\n",
+       "      <td>67</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>73</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4381</th>\n",
+       "      <td>79</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>92</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      age  female  flu_season  ed_admit  severity score  comorbidity score  \\\n",
+       "4377   88       1           0         1              34                 94   \n",
+       "4378   98       0           0         1              51                136   \n",
+       "4379   84       1           0         1              10                 32   \n",
+       "4380   67       1           1         1              10                 73   \n",
+       "4381   79       1           0         1              20                 92   \n",
+       "\n",
+       "      readmit30  \n",
+       "4377          0  \n",
+       "4378          0  \n",
+       "4379          0  \n",
+       "4380          0  \n",
+       "4381          0  "
+      ]
+     },
+     "execution_count": 717,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# admissions over last year for patients with AMI\n",
+    "\n",
+    "# severity score - generic physiologic severity of illness score based on lab tests and vital signs\n",
+    "# comorbidity score - severity score based on patients' pre-existing diagnoses\n",
+    "# readmit30 - indicator for hospital readmission within 30 day\n",
+    "\n",
+    "df = pd.read_csv('Final Project Data_Case.csv').dropna()\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 718,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>female</th>\n",
+       "      <th>flu_season</th>\n",
+       "      <th>ed_admit</th>\n",
+       "      <th>severity score</th>\n",
+       "      <th>comorbidity score</th>\n",
+       "      <th>readmit30</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4377</th>\n",
+       "      <td>69</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>44</td>\n",
+       "      <td>213</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4378</th>\n",
+       "      <td>86</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>113</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4379</th>\n",
+       "      <td>69</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>144</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4380</th>\n",
+       "      <td>90</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>26</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4381</th>\n",
+       "      <td>77</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>26</td>\n",
+       "      <td>77</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      age  female  flu_season  ed_admit  severity score  comorbidity score  \\\n",
+       "4377   69       0           0         1              44                213   \n",
+       "4378   86       0           0         1              25                113   \n",
+       "4379   69       0           0         0              22                144   \n",
+       "4380   90       1           0         1              26                 54   \n",
+       "4381   77       1           1         1              26                 77   \n",
+       "\n",
+       "      readmit30  \n",
+       "4377          0  \n",
+       "4378          0  \n",
+       "4379          0  \n",
+       "4380          0  \n",
+       "4381          0  "
+      ]
+     },
+     "execution_count": 718,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# shuffle the data\n",
+    "df = df.sample(frac=1).reset_index(drop=True)\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ">first train a classificiation model to predict whether a patient will readmit in 30 days\n",
+    "since there's class imbalance\n",
+    "\n",
+    ">use SVM classifier, then rank feature importance\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 719,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    4382.000000\n",
+       "mean       94.735509\n",
+       "std        57.145155\n",
+       "min         1.000000\n",
+       "25%        51.000000\n",
+       "50%        86.000000\n",
+       "75%       131.000000\n",
+       "max       322.000000\n",
+       "Name: comorbidity score, dtype: float64"
+      ]
+     },
+     "execution_count": 719,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['comorbidity score'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 347,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x113e378d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['readmit30'].hist();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OPxq0ctY0GltjEPK50FSKIRUJ8OEnsdUztRopLn1ihMsbTMQj5HPR16nBt98Y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Z47daduddWtmbof5nbf6unu+TBzP3XV1K2Zul13ppdJJt09ncRJnDhx4tiyZFm2VlvctHAFQRIL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fa3Wgd3geg+OJPJqqrfAHw5hf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KCnpwcXL15EMBiE2+3G6Ogo2traNqtIhBBCCCGEEEIIIYTk3KascItGo/jWt76F6upqfP7z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\nmG4P7HSxqcXCxHSM66NBHFY9Xa4ahibDuGyCrnhxgWwpC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k+jVspRKgXjO2e9nksD09gs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c/jgalYIdXY0CJVmp4CfHhPeUFv+qVYCnPFEe3t9exhgo/n82myeWzEgWmqQo4EUDhClv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\n3uCybq+M8oJkKpNDXydN7nA06jl1aZ5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Cbngml+fAoBNfOEEmWyAYTZEvgKNJh6NRLyl5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YDu70oFaBx2FiOpyWtvvJ9TCHd3pZKpa5MZloSUw1FpT9Hgs2k47rE/LSKuH5HF/a7+fa3XjT\ndMaZ0RB+j4WfnplsSSwe3OUhNJ/lyIiHqXBGJj7X8NRuH9PhNF892k84nsNo0HKjzRqmw2np83R3\nGvnlcvF6tSvsvaYpPovsiYKCwvplsLuD8eBCU3wbTWSVCY7HgC9a6um+Cbf/8B/+A3/+539OZ2cn\nIGq6qVQq3nnnnXsvtLeX//Jf/gv/4l/8CwCuXbvGoUOHADhx4gQffvgharWavXv3otPp0Ol09Pb2\nMjY2xp49e+65bbvdhFYrP/Mx0G3ntZ/cwOMw0tVpkoIRr9PEkKFT9gZhS08H318enTy2p5u/+uV4\n04X6wo0oLxzpYzFb5M3376IT1KhQtdWo0AsakosFRoacbR1HNRq1NPrq7DCgVauYmF0klszjdhjR\nC1qO7PI22Z9PR9J8OhbjpeMDshXOydAixXKNpVIFr8PMpVsx+e/GY2sZFdULMV4+McRf/OI2ekFD\ntVqlx91eK+RvPrgLwEtPDzIeTHF3dgFUK58pQP9y507rd2Tj49Fwy+OheBanU0yU7RpwMBVqHUvY\nOeCQnlPH5bKyf5dP9r2uJ+513C62a13NFdHr9bhc+oexxHWDy7X+x9K+KKxGHbdnGrUO4e5siq2B\nznXx3d/ruAWkm4ZSpcp8Sjwf6QQ1arVqXaz/YfI4HbdnRyMcGPZQLJWJJPJ4HEZ0gpazoxG+89Wd\n6+J7v9ex6+0yy3bo+brMGAx6DIZHv/6HxeN03ALklkTF49XnrNxSeV2cs+513AbcFll3YF9Xc8x3\ndMQnK2lSTxTNRDNs77fz/Z/elEwKAh4rt2dSxJJ5etwWDgx7pIQZiDFJtVbjskwn3csnhpqLhsux\n8POHeoml8lLCTC9oUKHiwytzDPc72k5r/NUv77Ts4+k9PpydRj5aNZFRKFWoVmt0d5nRatWMzy3I\nxlPVWo1+n42PR0Mk0wVOnRhkftUIbZ1G4y79KoOHQkl0hbWaBJ49GLjn72fXoENW9qQxlt0sv7/7\nxQqNrOf3vJ7XBut7fet5be2413GbK7S5VhTWx7XiYbMRv9+14m0jN+Zxmtb03d834fbOO+9w+vRp\nzOZfzTHvhRdeIBgMSv+uJ+oAzGYz6XSaTCaD1bry5ZnNZjKZ+zt/JJPyrZwGg5brd8UEW3apxHDn\nitlDcrFAfCHHgWEPtVqNYDRDwGPB12Xm+mRCGuOst7ivbmWPJnLodOIHv623s61TVGg+KwnLGnTy\nhgDODgMmg1ZKTO0adHL64mxLYPTisX7ZoGEmkpECq6YKp9fKVn8npUoVZ4ce3/INRSNWk8BkWF4b\nYy6W4ek93dSAM1fDPHcowKdjrZ10PS4LKpWKpWKZDy7P4fdYePGpPibCC/R7V0wiypUqJ57s5oMr\nKwGbXtDQ322VTbhtC3QSi4nrfWafn3fOzbTs+9AOt/ScL4KHefJod9zabLq2x9NMNEOhUGBxceN0\nO31eXC7rF/odr2dsNh0L2WJbx7x23/16OG5BXH+5XJNd/9efHnisjt3H7bj1dImdbBq1mq5OIxq1\n2LXs7Wp/3ML6OHZtNh1+t4XLt1qvz363RTluNzFrPWeth+PWbNY1yabU0QsaetwWPrkWlv7d1lyr\nWJaO88m5tBTvntjbIxuH1hN0dYKRTFMnXZ25eXkJkWy+jFGnZTqSZu82F71eKz87M0W+UEbQauRv\naBwm+aRaDUbvyGsYByMZTp0Y5PZMqq2GXDCS4YXDvdLnNB5cYLjfIcXQjWvw/v/svWlwm3ee3/nB\nTZwkAOIiwZsiRVGSdR+WJVu+j3a7Z9qxx9Pl6Z5JbU22UpmtVGpq0tU7VVszqSSVF0lqq1J5sdlJ\ntjPZ3vF0uqftvmy3L0m2bOu0REkUKd4gQFwEiZO49wWIhwDxQAfdahHU83llg3ge/AE9eP4//I7v\n16pDr1Wh0yh578vZmnMFl1J8/80D2Azq235/Dm233zaWvd/fv81w3a5nM99zNvPaYHOv77e5ts1w\n3UrxbTWb+dr7bWMyqWtMOMvNTedvBPiDp/vvOU64Y8JtcHCQTCZzzwm39cjlayOEiUQCk8mEwWAg\nkUhUPV6ZgNsIC6Ekrz65DW8wzo2ZCLv6reib1Ix7Iuia1Hw+4sOgU/LN4738+MNbTMwvo1auCSKG\nl1Y4trutxmhgPhDH0lxK4HlDcYa6xbvXuttMXLpZ0iY7O+Lj6E4X6UxO0IYz6dUkV7JkczlB96je\n6Kc3mKgJRqA6qVdZ4bS1aDnzlZd0Ns8XIwv88ctDXF51aSqzrcOMp46dsScQp7VFy8WbAY7tdjE6\nHeaNZwcZm4vg8ZcSlNs6WvBHUjUisxduBPjOc4NCp2D5cY1KwWtPbePzkQX63c24rDquTSyyd8CG\nQiGvGjdQKRX83Ue3ODrsZP+wa0uMit4tPS6TuGNeg+ghSWycelqHjWI77gmIr79eElliazDYaebv\n3h8DqsWE/+CZgQe5rLtGqYBXTvSyEE4ST2UxaFU4rTqQSfOkW51GvWcpFBBfyXJgyEE+XyCTK6BW\nylEo5EBRiBetLU11JU2CkRQOi45Oh5ELowGeOdRJLJGpG4eWE3Tlv63vpIPS979eXFnu7srk8oxM\nlEZEmw1qUukcZ0d8PLbbRbFY0knraTOhVSu5NCo+nTG7EGN7VwvjntoxTbfdwAfn50isZIXu1fW4\nWvX0tpl49cl+ikW4Nb/MmcteXjzWTTCSYnZVhsbWouXkXjfJlQw/OzMtOjI61GXBadaKrrOSrSJ7\nIiHxMNKoe4XE18du0VWZcJYLM4eHnRs63x0Tbq+88grPPvssAwMDKCqsu374wx/e0wvt2LGDL774\ngsOHD3Pq1CmOHDnC7t27+Y//8T+STqfJZDJMTEwwMLDxYL1QKHJiXxs/em9srUXebiSeyqCUy8nl\nC3z3pSFuzkaYnC+5imayBWH0MxJN89IxF7/4tNaK/KVj3YSWU0IyzqBViVbmettMyGUyYsmSWGvp\nuWp2WXW02wyMTIbx+OMcGnbws1OTmE0aIeG3nsrEWiWVSb3K1650VU1n84xMLvLYIy6W4xkCq9nZ\nFoMGjVped1T05uwiGpUCvVaFXCbjR+/dRK2S0+0yceVWiMn5Jfrd5pobEMDobET0xhSJpvnTbw3z\n7hdzXJ+OCJlitUrOc0e6CEWSqFVK3v1ihkKhyMcX5/mrPz36UNmf97Y38/nIgsj1JOkEbHUa3aW0\n0dcvsTHWi7eXi1PTIuNTm418Hlr0GkZnIlWj3AvhBNu7zGvaXhJbkka9ZyWTOVxWHf5wkpVM9YhT\nMJISvo8uqw5fOFk3zmu365HJ4JFtrbxzeuq2cWgwkhLiUI1KQZtNL3SIlYlE0+zbbrvtaGY5tnE7\nDBTyRdTKkqO9TCbHqFeiCsm5PBbk+SPduGw60fuI3azFbNKKxt4mvZp0Ns+uvlYyuVozh9J0hp5M\nNs+29uYq59CycdeJvW5+8+UsO/usfHTRw7dP9PL43nbRDrh7MeZ6mGJZCYmtRKPuFRJfj3weOh1r\nUxCVJpwdDsOGYsQ7Jtz+9b/+1/zgBz+4o0nCnfiLv/gL/vIv/5J//+//Pb29vTz33HMoFArefPNN\n/vAP/5Biscg//+f/HI1m4/PQuVyBSW/0ti3y52/4efpQB75QUtiQtZq18c7Qkrg7Zji6QqfDyN9/\ncEtI5lV2r7ltBtpsBk5d8pJMZ4lE0zXjnldvhfEvJnFYtEyvrvN2Wm89bSYuiiTW+t3N2M06AotJ\npheiuKx6lKvdYpXM+mI0G9SMzS7x9KFOTl3yEEtmefXJbWhUwZoAottlokiRwU4LnkCM+EqWdDZP\nOpvn6kSpjb/bZWJGpBPrdhXOW/NL9Hc0c2HUX6PJ8crjvZy5HCGTLWA3a4UR3k8uenj1RC/wcNif\nx5IZDg87hHHcstlHLPnwtCs/rPS2i2sdNoooa73197Y3xvolNoZWreSjqyXZiPJeBzSE0YtCAZGY\n+Ci3w6JHcXcyRBINSqPeszQaJSqVQvS6PbjDwadXvLisWnQaZV1JE62mpKH28zNTDHaa7xiHuu0G\n/ItJju9pR9+kxGLUiE5eDHZauDhaG1dWFoI1KgVqpYJJ/zJyGeiblMRTGaa8y9jMWpxWPZ5gjA6H\nSfRcGrWSuUCMZw51sBhL4wnEcdsMuO0GPrviYzmRRgY0aZSi8VR4OcWXNwLk8oWa9ceSWRbCCdQq\nOU1qJdemFvlHT/Sxs8vM99/cz+fX/YzOfL0OtYchlpWQ2Eo06l4h8fWQy8GoV4vuI0a9GvkGHM3v\nmHAzGo1861vf2sh6cbvdvPXWWwD09PTwt3/7tzXPee2113jttdc2dP71NDWpmF2IcWx3GzJAIeIA\nlM7micTSLMdLAYZBq8akV3Fstwu1SlHXNcnjj1PIF4VNulAoCq2GT+xv5/wNP59d9XFst4vzN2qT\nZLv6rKRWcnQ6jNgtWuE56Wy+bmDksOjY3W+rmR++eivElVthDDolbzwzyLWpCB9dmKtZs9tu4OpE\niHQ2z6/OTnN0p4t8vsCMb5nvPDfIzdkIc/44bnspWfjWh+PkcgUujgZ56VgPY3O1bfvTvig7+1pr\nbkCRaJoDQ/aqx8tJyUKxyDunpxjoNFe5VqWzeWZ8MZ480IEnEK/qkhidjiB/4uGpBs7543xxbeG3\n1roq0TjUFea03HlcZTPQ2iLecdBaoaEpsfWIpTKiHW6xVGMUCcqGCev1WsWCa4mthb5JfEJB33TH\nkPiBolDATEVRufK6zeYKPPZIO11OA6cve/EE4xzd6aqKH20tWrpdRq5PRzix183VWyHh3NbmJtHP\nRKmUk1jJks3mGQ/GhJHW9XHp9akQ33ishxlfVJio6Gtvxr+YpNNppL+9GZtZx3ywVJjdP+Tg52em\nahKHJ/e7uTkd5uR+N6GlFIFICrtZi6bsUmozEEtkmPZFMRubUCkV/OzUJCf3u8nlC4zNLgnvXaUo\n6UuqFHJ0TSqu3ArhbNUTXqo/bntir5tfnZ3mmYOdQvxZr0NN6liTkNjaSPHtw4lMBv5wAqdVz0I4\nSbGlFDc4rTr84QSyDRhM3zG62L9/P//sn/0zTpw4gUqlEh7faBLufiKTwcEd9tV2+zzXp2I11uRQ\nSp7l8gWcVn1Vdc1h0eGyiWs/bOtoEU3GpbN5bkxFBPHXoR4LGpWC5EqOTK7AQKcZa3MTV8fDPPqI\nk1ueZb4aD+F2rDmAlrXeygFMqW2+iS+v+ZleiNbavzuNPHeki/lgnLdPT9HTZhI1J+jvaOazq6Wu\nN7lchqtVjzcYxxtKoVJG6G030e0y8fbpSeF55ffkX0zgttda4saSWQY7W2rMFAC6nCbO31h7/OhO\nV43W23oRXl8oQWgpxcT8ctVzXn2q/6EKZMrtyeudx6S25a1PPJXllRO9eALxUsXeXqrYx1PZOx+8\nCbg8GuKNZwcZnysl8Etaj2bOXPLyB0/3P+jlSdwnjFoNH12Yq/3BfKDjAa/s7vCHk3X1WiW2Nlpt\nqQMquZITEjq6JiU67eZOuOXzMB9I1L1uj+52UiwWhfhyvf7MwR0OsoUCOo2SU5c8bO8y0+EwspLJ\noZDLRav5xWKRhXAStbJkJDC7EBMcSKviUocRm1nP1Ymw8PiF0QBHhp2oFHJ62kz87a9vCsnCaZ+4\neVc0kcG3mGRbp5mlWBqXVY/ZpCGVLj23ckQ1lsxiW9VRCy6lGJuNMNBprnnvkWia/UN2wssrq+vU\nik5quO0G4sls3ZHRckw6G4hz9toCozNLbO9q4eiwU9Jkk5DYgqykc6LxeXKlMeJziY2zFM/wy89m\nMOpUdLtMXBqLEEtmObbbtaHz3TG6SKVSGAwGLl68WPX4Zky4AVCU3dYKHaCtVY/drOWji56aEcfX\nn9nG5Zu1rewGvQqbRdw+3NWqR69VYW1u4sNzHh4ZaGUlmycUKunCUCxyfG8b/2+FoYCrVS9kzcvd\nckadiueOdPHu5zMA7OxrFRxFK5MwbpuBxeUVmtRKPMG4sPbff6KPL6/5hY61KW9UeI0ay3Z/jAuj\nAU7ud9e4TUHJHXNPf6toZl8ul1UFZuXq41sfjnN4h5N0JkcknqZYLIoGVJUivGJ6dCVXWPEK5Fal\n02kUvba6nA+PBfPDiq5Jxc8+mRS0Eq9OhLgwGuCVx3sf9NLuisf2tvGj924CpdHC8zcCnL8R4I1n\nBx/wyiTuJ8lVyYH1nTaNEoge3OHg7XV7okal4JvHG+N7J7FxgpEUpy57hUD65mwpkH5iX/uDXtpt\nUSjgwA4775yu7Qx7+XgPPz8zRSZb4I1nB7lwo7ooqlbJabPpaVIpiSYzxJJZulzNvHN6EoD9Qwo+\nu+KrSaQ9utuFRqXA1aovaRerFcwuxGri0i6nkQ/Oz9U87g0lgCJjc8vCeswmTV1TB08gzkuP9vD/\nvT/GvkE7K5kc16cWsZm1vHqyH28oUXXfWYqlhfPptaqqiZHyWjQqBS6rHrWqZDChUMjFOxy1Kj4f\n8fG//v6uugm02UC8Sv9tZiHKxxfn+f6b+6Wkm4TEFkOnbez4XGLjlI0xYsmsIKsFpfzIRrhjwu3f\n/Jt/QzabZWpqinw+z7Zt21AqN2cVMJXKCmMilZSTPFqNkoNDdvIFuDweEh1xvOVZ5thuF8uJTFWV\nbyGUpNfVzMitWvHUnX1W3vrN2Grms41fipgu2C26mnWJVRMTqSwDnWacVj35fEHQlqt0gTPp1Vwe\nDxKJpoVEYqkrLcVQt5nPrvq4MBrA2tLEkdUxUm9Q3GklmsyI6nG0teqZDyU4stNFPJUREmsmvZpb\nnmUhWD087OT0Za9wfLmq+HtP9PHZlWpNuTJlEd5INI1OoxK1mB+fW3qo2vW3dbRw7rofqP637u9o\neZDLkvgdMLsQqxrRKd+XxFxrNyPjc2tmKZU/tMY9EaDzAa1K4n5Tlm9Y32kz62uM6zawlBTdEwNL\nyTpHSGwVZrzV1275njvt3dzXbqFQ6uQSu25Dq2OS6Wye61Mh/vD5QWKJDLP+OAuhBNsGWigW4Rdn\npikUS46mU95loTutrMG7PmHmWZUdUSrkfHB+jhN72sRHrMxaVtK5mjXbzFqWYmlmfGsmCLfTjHNY\ndUx4ltk3aK+ZkBiZCHNibxvPHOpkMbpSikstWlQKPal0jiu3wpyN+Hj2UCeLsRUWQklhlHZmIcqJ\nvW4+OD+HxaThuSOdhJZWSl0rDgMOi45Lo0EGOs0EIknkPRbR+PPstQXRz//sNb+UcJOQ2GI0enwu\nsXHcNkNVN3e5qOze4H3+jpmzkZER/uzP/oyWlhYKhQKhUIj/9J/+E4888siGXvB+olYr6o7gBSMp\nvn2yTzA9APHuN48/zq7+VpIrOVrNJe2HfAEu3gywb7tNVLtidGaRTLaUHFvJ5EQ34zl/XAhSNCoF\nqXSOC6OBmmri/u12RibCjM1G2L/dxsvHe5kPxpgPJNi33Ua7zcjswrLwD1/ZLTbjizLY1VKl+xZP\nZehwGLl8U9xm3ROI47DoqgIfjUqB227gy2ulBJBRp6bf3cyML4atRcu4Z1moBo7NLom+33PX/bTV\nGc91teqRAX3tchIrGSG5VMmOOsHOVmXKtyz6bz3lW37QS5O4z7QYNHx0wSOqZdMIzNUxS5lbkEbz\ntjL7huz8QkSD6RuP9Tzgld0dU/Pibqr1HpfYOuwfsovqh232a1cur399Ts5HOba7jVgyy3wgTpst\nza8+qy3+Hh52kEjnyOTyFV1mRZythrrxmtOq450zUwCcueLjW4/3Mbuq1dbtMgFFgpGUaCKuSa0k\nm0tWnf922sVD3RbOXvFhMqhFY8tMrsDF0UDNdMrLx3s5t6qNXB4v1WtVjEyE2dlnRaOSE0ukGe6x\nEIykiMTStBg1WJqb+OSih89Wzze9EGVkIkxoKU2xWKgaF5XLZYzO1GobA9ycjTxURWIJiYeBRo/P\nJTZOs0HNiT1tQmPUzj4rlA/gjAAAIABJREFU+iYleq3qzgeLcMeE27/6V/+K//Af/oOQYLt8+TJ/\n/dd/zY9//OMNveD9JJPJ13f8a29h2le/+6288bta9axkcnS5SqN8v/xsmky2wHCvhbmFtfHN9doV\nZlPJXbVem7wvlBCs1Svb6ddXE4ORFA6LDv9ikl53C//jVzfXfdGD/KOntvHVeFh4fvm8tlXL9FOX\nJmqqgi8f7xG1We9xmVDIZdjM2uqOvlVL+fL7feFoF00aJRPeZUJLKcFwIp7KiFcpLTrUKkVd+/ZT\nl+YB+Kev7ubLa/6qDDLA4/serptZk0pVM/KrUQU5ub8x9JAkNk4smRG9LzWK+Hx3m/g9t6fd9ABW\nI/G7IhgR77SptwduNpyt9QtCElub0LL4tRte3tzXbrEI3S5x+YmeNhPXp8L4F1N864levIGE6HtM\nrOQwaNUkUlnaO0tJMP9iiv1DDtF4rd2m5xefTguJpEKhyPnrfvYMtjLYZebSWJDd/a1cuBHgxWPd\nhJdXmPZGhYL02REfZqOagY4Wvhpbk2s5O+Ljsd0uQMa0b+35f//BOL9/so/Tl7yin8HUfLRmKiKd\nzTPlXRYmQprUSmLJLLFVPTZ9kxJXq4GzV334F5OCOYpGpeClY901ExbpbJ7gUpKRiXDVuGihUGR7\nVwszIrH0YKf5npNtchFjN7HnSEk8+JN/++E9Pf9v/uWT92klEg8TjR6fS2yMfB4MWhUfnq9Ntn7z\nRC/5/B1OIMIdE27JZLKqm23Pnj2k07UdSZsBpbI0Y61RLdQEDd1tRj44V+vkCdUjjq5WPflCHplM\nRpECLzzaw5w/SiFfP0B32w1cGC1V1uq1yfe0m7i4+pzKdvr1rYquVj0atYJDOx1cn1gU/aKPzy2x\nd8CGQiEHWZEr4yE0KgXNejXTvmXRYwKRFEZddZBSclrR8j8/uiXaaVepgxFeXuHyeFC0qlg+pvK8\nbTYDP/n4Fo/tdlEslnQv3DY99tW2/XKm2KRV8U9f3c0X1/zM+Uutu4eHHezosRIMPjwtu3X1kNKN\noYcksXHq6QHU6xzbbNjN4i5OtpbGcFmV2BhiPzoB0cLOZsRtN/DVWO11u9FxAYnG4XZdYpsZmQzs\nFnFXa5tZy/XpMAeGHMSSWVFTACjFu75wgke22XBZ9UL8FoykRGVOyiOslbFJT5uJ8FKayzdDdLmM\nxBIZLM0awkspzt8M8vpT/YzPLuMJxHn1ZD/+xSSj04t847EefOEEHn9cMDuQy2VkcvmqOHI+EKer\nTmLR1arn8ljtxEYwkuL4njZajE14AjE6nUZcVj2dTgNGrZrR2QhAlZFaOptnPpgQlVWpLGZXjose\nHXby8cX5ms9fzGShHpWmC8O9Fg5tt9eMo0rGDF+Pe03QgZSkk6il0eNziY0hl5ckcsTyKQuhBHL5\nvZ/zjgm35uZmfvOb3/D0008D8P7779PSsjl1pTQaBaMzEfGxz+mS6OqdRhznAzGu3Cp1j33zRC9v\nn5oASh1bh4YdNKlq2wv73M1cWHXtrNcmXygUee5IN4HFJPFUlv6O5pJm27pzFYpw6tI8nY76Yvm+\nUIJMLk8kmubbJ/uxmrSoVQqi8ZJmhRjT3ign97mZDyWqgqmywLVYp1052ACYWYiJVhU9gRi/93gf\n3nCCaW8Ut8NAv7uZM5d97BuwAaUfNl1OA3//YemzLCf2ALZ3WfjR+zerEnnnb/hxWPXYDOrb/Gtv\nLRpdD0li4zitelE9iDZrY3TanL8eEL3nnr8ekFxKtzBdzloXa2B1xGzzo5CL66huJJCSaCwaubvx\ndvfbpw928ZOPbgH1i79uu4GrEyHOfOWl2aDm2yf7mZhfxuOPs2+7DZZXKBZBpSh9ERRyamITh0XH\njz+6RaFQFIqvB4YcfHHNz+FhB2Ozy5y/4efxfe2EllKolHIisTRFojTr1Rzd5WQuEEfXpOK9L2dr\nOrgm56PsqmPa1WYrJdycVp1QmITSv90X1xaIJbMcGXbS4zKhVSsJRlZ4+1Tt+HBZSqZy+qSSshsq\nVI+LdtoNfP/N/Zy95ufmbITBTjNHhx13nQwTM1344NxclemCZMwgIbE5cDV4fC6xMWQymBHZP6H0\nuOzOzck13DHh9td//df8+Z//OT/4wQ8A6Ojo4N/9u39376/0OyCVyuENJepalg92mUU3cIdFx0cX\n5shkCxwYcgjVPG8oXhXYAKIOqNs6W4TA3RdM8Poz27jlWRbEZttsBn7+2RTfeLSHXL5AaClFX7tJ\n9FwHd5SqZP7FJPu220QDpkpb9OmFKI/0t/I371xHrZIz1G0RD7IcBt79Yhag2oFql0u0ulcZbED9\nqqI3mKCtVU8hX2RbZwvpdJ4ZX5yVbI6rEyWjhicPuJldWDNtCERSHN3pYiWT49efz4iaV3xy0cOr\nJx4eF5hG10OS2DidjjqdNo7GCKzb7HrBLGW9u53E1mVHt4UvK/YwWNVg6jI/wFXdPRZjE/5wEpVC\nTmuLVkgwWIxND3hlEvebHd3mqvFGKOuHbe5rN5+/zf12l4u5hTXZlHrFX5NezeFhJ2OzS9jMpWLt\nLU+EvQN2fvHpNEBVl/2rT/bXuKKOTISrtI/T2TzpTMkwIVcocvFmEIVSTlurnqsT4arE4NmRBb5x\nrIebM4t0u5pFxyVtZi0fXfTw8vFevMG4YGzQ224inSmws89aVZi8eDNAs0FNLJlFq1HidhiZD8ZK\nGsXWWsOySikZt8PA1Vuhqr+XtefKx60fF+20G+i0GzY07nk3pguSMYOExOZgqNvMZZG9Yvsm3ysk\nvh75fKk4JZZsddsN92ektLu7m//8n/8zOp2OQqFAOBymq6vr3l/pd4BcLqNj9QNa37FlM2s585WX\nw8MOkMmY9kbpaTdha9Fy/nqAnX1W2m1G/kHoaNPS3mrgV2enBR2IKW9UdBMcmVhERklc0W038Hfv\njwOloOXCaICbs4v8yUs7+L/fviYk8ybrnCuVzgk6FIOdFi6O1n7RKwMBTyBOs14tJAYtpibxIEu3\nJkBb5UBVxzSh8jU0KgUdDgNfXFuo+cy7nEa8wQS6JiWtzU38wyeTNe9rwhPl0A47tzwlsdmjO101\n7lPrzSuuTy0if6LvodGuCNVxHgsubW5NGYmvTyKVretY3Ah0O01cuBGouudqVAq6nY3R6SSxMa5N\nhUU7ba5Nh3nm0ObX4BzzRLBb9PgXE2RyBVQqOXaLnjFPpCHWL7Fxpn1R0Wt32re5R0oVCtjVZxW9\n33a5jHxa4Qx/dsQnFDaDkRTOVh0qhYLwcorRmTVDgUtjQf7ohe1VWjWRaBqzSUMilWXOL+5wX05Y\nwWqCLlY6xuOP02JQ8+zhTn703lhNnHdgyMGsP4atRVc3Xm1SK9m7zcY7pyeF81+4EUApl4kWql9/\nZhuR6Ar7t9vpaWsW9HCdVp3gvrqesl6yXCarMhpzWLSoVaUCcHk99cZFN6LZdifTBUAyZpCQ2CRM\nzC+L7hWT85Kh3VZGoaj+bVOm/NtGobj3c94x4fbDH/6Qn/70p/z0pz9lfn6ef/JP/gnf+973eP31\n1+/91e4zhUKRLqeJ8yIf0GCnGYVCRr4AgcUEh3Y4ePeLGWGUcXohikYV5Miwk2IR0tkcn48sCN1X\nU97l2xoi7OpvJbyUIp7KCq8dWl7hWyf68C8mhJFToMo0YT3BSIqnD3WyEE7w8QUPLx7rJrCYYs4f\nqxKhLdNhN6BSyoUEllwu4+hOF+lMjsBSCqdVh8WoIRIX192zmbUYtGrBNKGn3USPq5nxuQidDiNu\nuwG33YC2SdwAwWbR8f7q51ivo6XDYWBXr4W3T5eu0HpOrpXmFQ+bS2m9QH+z/wCQ+PpEkxkoyqo6\nbfKF1ccbgPlQrLoTYbWr1xuWNC62MrP+uHg3ubO+HMJmQqOsNqoBVt3HJKOarc7kfLSuAdZmJpeD\nYgFeOdGLJ7B2v3XbDWRyuaox70KhKHTCHd/TxhfXFshkC7xyopeRyUXhnCqFnEwuT4fDiCcYr0rS\n7Ry2MjYrnvypjFWDkRR2ixaVQk8qncNu1jLlrW9SthRLs6vfyuWbIV56rIdQJMX0QpQelwlLcxO/\nPjvDjh5LVZFYo1KQWBGPHSfnoygVcjLZPN5gvCpxeLvRWpVSwemv5ikUimhUCoZ7rfR3mIlEV+hr\nb2bPNhs7e8x02H47XWV3a7rw2zRmkJCQ2DgzvlhD7hUSX49CAeZDUd58cYjrU+HSxKLDwI4eK+Oe\nMIXCvZ/zjgm3t956i7feeguA9vZ2fvKTn/Daa69tyoSbWq1kPhivyUbrm5Qk0lnmFqL4wike293G\nXCAu6kxkM2v55ad1rNRXcjUbt0alYM+AjU+vzKNSKlAr19Ke3zrRxzunJzGbNFWP3zYIcBg4dclT\nlQg06lS8+uQ2fvzheI3pQbNBQyS65qJSGWQ9c6iTC6ML3JjK8fozA1y+Wdstp9Uo+eSSB6NOxQtH\nuzHqVIzPLZHPF2lt0VIoFFkIJ3BYdTVdOE1qJRdvBARtt3rdcgadmk67ke+/uZ9r0xHRTjmoNq94\n2FxK6+ohSV1CW54uh4n/uaq7U+nU++2TjaF/1tfewv/49U0MOiU7e1sZmQxxYTTAd54ffNBLk7iP\nuOt0kzeK6UDZqKYSyajm4aBn1Vl5/bW72Z2VlUoYmQrz2RUf1maNcL/97KqP33uiD7u51lABQKmQ\ns6PHilIh550zU7x0rJsvr/nZP2RnoMOMfzFBl9OIQl4tm+JfTNaNVbucJj4f8RJeLu1X5Vj5zReH\niERTXJ1YrDkGSnHern4rv/psRpBFMepUPHmggw/Pz/HCoz3YWrQ1Rel6hWqNSkFyJYdRp8JiamLK\nu5aoup2uslIh45NLnqrnKpUyYok0R3Y4KBbhy+t+ook0R3c66fwtJd3uxnTht2HMICEh8fUpm7es\n3yu62zb3XiHx9ZDLoa/NzH//5Q1grcv6wo0A33lu8P6YJmSzWdTqNfF6lUp176/yO2JlJcuUd61y\naW1pwqBVk87kmFuI09Nu5vgeN+lcjrmFWmcijUqBNyhupb6SzdOsVwvHlDvJVjI5Lo8H6XY1Y9Sp\nSaWzzPpjGHUqodK2PsF2uyDAZdXzWcVYAEAsmWVkIsTeAZuQ8LKbtWjUSpRymVAJW+9weeVWiL3b\nHavvKy46tqbXqjiw3Y5GrWTWH8NtN1YFXOXzfuOxHqZ9UfyLqRo307LWm9tuQKVQCN1y5aQcxaIg\nNttpNxBNZOpWHG0tTezdZnvoXErFAuWy85jE1mZmoTTelM7kCERS7OqzolEr67pAbjbG5iK8fLyX\n+WCMKW+UgU4z7TYjY3MRXjza+aCXJ3GfMOnUovcsk27zm90UCrfpKvZGN1S9lGgc7BZxZ2V7A+y3\n3kCCY7vbyOcLxJJZtrnNbO+S4wslWAglRQvOoaUUkViaidUxqClvFP9ikll/jAs3Avze430kVjLk\nC1R9JreLVfVaJd2uZrZ3VevvXh4LMOVdZqDTLBrn2c1aEqnqTrVYMkswkuLFR3vwBKPs7reyGEtX\nHb8+jq6MwYORFFpNqUNNpZJXHVcerS0Wi3gCJXfU4V4Lf//Brap1aVQKdBoVA51mUcOCb5/sY8Dd\nUldD7W5HPdebLuzoKbmUdjuNwvFf15hBQkLit4NRKx7nGJo2by5E4uuTzRYZnY2ISnGNzkZ45uC9\nT0LcMeH29NNP893vfpcXXngBgPfee4+nnnrqnl/od4FarRDcp9LZPL1tzRSLa5FzIV9gIZyg1awl\ntJSqsgcvFIqYTRp8oYTouRdCSbZ3tfDGs4OMzUUw6dR8dGFN86I8WvPtk/1cHgvR7TLhqbATXq9V\n8cX1BX7/8T48wbhgrtDf0cypi96a19aoFORXfyBEYmlO7HXzmy9LBggvn+jBYdHS4TDWOFxmsjk+\n+HIOgBePdfPLz2aq2mIBnjnUSTqb4+pEmGcOdTDnFx8D8ATiNBvVzPrjVbohZa03a7OGfQN2fvT+\nKPFkruo1/vfv7UetVpDJlM57dNjBxxc9tT/U9BpWMhtQItwCJFfEdbzKLrISW5f5YIJ2mwHl6kip\nclW8fT4ofi/abBia1FWjeaWCR5CT+x+uLtWHjVgqIzpKvLC4+UeJ5fKSEVDlqEi5UNXWqpecSrc4\n8aT4fhtPbu79tlCAg8N2/OFklWizQg4dDiNqlYJTl+ZFDWwq49HyNMFyPMMT+9qJpjKEIqkap04o\nJayePdTJYizNQighFHvL7qLr9XcDiylUSgXtNiMalZgxhYUPLszV/Igtj8h2OIz8/NNpTuxpq3pO\nOptH37SW/BPTAj5/I8A//uYwpy+V1lL+Xp+/4efITheZXJ6x2QgGrZJ9g3YhZrabtezsa6XHaeDU\nVz7RGPjm7BLvnJniX/zB3qrE12wgztlrC4zOLLG96+4SY5WmC4FYmg++nOW//nKU7V0tHB12Cn/f\nqDGDhITEbwedVim6V+i0d0yfSDQwCoWsrv6nxx9Hobh3m9I7XjF//ud/zq9//WvOnTuHUqnkj/7o\nj3j66afv+YV+F+RyBcHxD0pVzFAkRXbVGbTkyCRHsSpMWk4IlYOFRCrL4KB4Vc5m1vLJ6ib+2CNt\nLMZW6mzKEY7sdJHPFzDq1UIibHx2iRePdeNfTOLxxzk87ORnpyoEYUcDXJ0IsW/QzvRqZ8v6Cl6H\nw8gT+92cueQTkmrnrwU4utvJTz6aqBmDffl4L+duBICSm2hZ4FYul3FkVefNG0pgbdbxxrNOzlz2\nksmtGSVU/gjxhRLsGbCxzW3m4s0g7TY9DouOK+Nh/ujF7dzyLPP26UkGuyz0u5s5c9nHvoM2ul3N\n/OqLOTz+G7gdBvYP2tm/rVWo3o3ORLC1NK0GcDMUCkU+vjjPX/3pUWyGjXdKNFqQEk1k+PSKryZQ\nPvaI5PS41Tm4w8HbpyarRjLjyRyvPN4YLr3RZEb0XtgoGnQSG2NXr5XrU4voNEpO7nczH4gTWEyw\ns8f6oJd2VzzSb0WjkguB9M4+K/omJdsbxGVVYuMsJzJ8dsWHUaei22VibDZCLJnl2CZ3VpbLQUYp\nfl0f1xYKoNeqhDivjEalQC6TVZlgbe9qIV8o0m43cG1ykWAkRVurHrfDUBP/FgpFQksprM1NmHQq\nTl/21nTBVervlh3u/+HUBN860Sck5LtdJgY6W7g+vYhJp+boTidLsQwfXfRQKBSF42zmUvfhmSul\nzjQoMueP09NuYqDDDDIZmUyeXL4guu98NR7kf3t9D59e8THnj7F/yF6KSS/5cNsM9LQ14wsnBC25\nHT0WtE1Kzl71MtAxxPhcfc06vVZV5RQ6G4iLdMN5+Kev7mbnXdxHphdiot10339zv/AajRTHSkhs\nNYJLKU5drnWFfnxf+4NemsR9RCZD2A/LccK0L0osmcXtMCC793zbnRNuAM8//zzPP//8vZ/9AVB2\n/NM1KZFR7WjkCcZ5bLdLqOKVA2y9VsUzBzsw6tUk0/m6rknlx2SyUsebGMFIivlgnEg0zXdfGuL/\n+cUN4biyVsWffHOYL66u2X6X15PO5rE0N92xgndibzunLpX0HY4MO5ldEHeRmvIuC+fyh5Oc2NvO\nYnSF8NIKlmYN/sU8oVAKmQwm55fYv8OOLxQX7ZZDVuTdz2cA+MPnBomnMgQWV3j0ESd/9/541Rov\n3Ajw+jPbMOk1/F//MFLzt2+f7Ce8nOLRnU6aDSp+dmqqJoD75KKHV0/ce8Khutq4Vi3c7MytVp/X\n6wTUy7BLbB3C0aTQOTvljTLYZWGgw8xsoDFckCo7J+7mcYmtwXIyTW97C2NzET664MHtMDDQYWY5\nKW7Qs9nI5Ap8cc0PUNWR3dve/CCXJfE7wBssjWWW45yyOdZ8cHPfs/J5yBeKotftNx7rYc63LIz3\nzwcS7B+y47YZ+MknE1UF3NHZJTrsRqa9US6NBSkUisz6YzVdZVCKfzVqJTKZjLGKEZtKKvV3K2Pl\nH384jlGn4rnDnTRplPzo/TFeONpNsVjk3PUA7XY9f/TCEH//4bhwXPlcC+Ekn17x0tfezMuP9bAY\nXeHSzQDnRwPs326vazw254/zm3NzXLxZKjaX487H97XzycV5isDYbASzUQPIWIqluT4VZu+AjU+v\neLGZtXVHYa9OhKucQs9eWxCNvU9dmqdZp7qj2UK94yuTehISEg+OSk3Iu3lcYmtQKMDuXitum4G5\nQGk/3dlnpcNuxGLU3B/ThEZCLpeRSmexW/SUq2KVm9nRnS5RS/GT+93EklmS6TxfXF/g8A4nuXwB\nXygh6gzqCcQFIcX1dLlMXB4rbfRXxkM1m2kmW2DeHyO4JC7+OrcQ4/VntjEfTBBPios6L0ZXhOfb\nzVouj4dEP4/KwOXADjvvnC4lto7tbhM1hnj5uI4dXRb+a0WSsPy3N54d5NOvSp/BLc8yl8cCqFVy\n9LraSyidzTO7ECebi4quf9oX5epEiI8vznNyf4doAHd9ahH5E333VN0TrzZWVws3K2XThPWdhV0u\nSZhzq9PtbOZ//PpmTWK6UUwH6l273dK1u6XRN6n5778crblu33xx+wNe2d0xPrdUpXdVLi6VOly6\nHvTyJO4jh4Yd/OyT9WPwik3fVaxQUGMMVr5u54NxBrusIuP9Cr75WA/JlZyoDErlOOiZKz5+7/E+\nZnxRAhU6vGdHfPS4THQ4xePetlY9arUCt93ATz+eqPl7s7GJm9MRXnuq2vxr1h/j4miQN54b5Ier\n4tTlTrcyDouOr8ZDGHRKAqtJthaDBvU6rbYyboeBq7eqY+J0Nk88VXrNYCTF4WEnY7NL2Mxahnos\nhJdXSKzkUCpKRmJiSUezqYl0Ni84hcrlMkZnxLvhApEUI1OR2ybcbnd8ZVJPQkLiwdHbZqLDXtuE\nolJtoMVJomFQKGAlk+dnp6r304uqIG88M4BCcYcTiLClEm6ZTJ7uthZ+9O5NBrqaUSnWNk6NSsFK\nRtxSPLiUEjb4I8NOznzlxahT8dieNj4856k5pttlRF9HSNFu1mI2asjkCngr9OAqq4tfXg/Q4TDg\ntOqFRF75b4FICtlUBJtZy0RYvMMlGElxYk8byXSer26FBN269ZQDF6NOhTeUuOPnMOeP06SudbhK\nZ0u6F0ZdyY10biHGkZ0uYskMI7fCNVp4UBqRDIkkFQFhvODqRJh4KiPqqrWjxyKc624Dj0auFjos\nOk7saasZb2oEEWeJr8etuWXR6/aWpzE63KRr9+FkbGapzl6xxPOH711Q9neNcb0Oa0UBTmJrsxBO\nil67Yhpmm4l8vpRsErtunzrYwXxQfNrBv1iaZBA1BKsYBy0Uipy77geKZHIFYXwKVk2xVHLRuNft\nMFAswOdXF3j+aBezCzHCSysc2GFnIZxgPhCnCHxwbk7oJqw0WhibiaBvUpLJFqo65DSqki5zoVDE\nFyoZHvgXkyzF0/S5W7g4WqsRt73TzOcjCzWfnccfx2HRYTdrhbFY/2KSYCTFoZ0OfMEkF2/62L/d\nXmM80ek08uuzM1VOoYVCke1dZlFzI5tZy+XxIC8d6awbu5aObxE9vpzUk/jd8if/9sN7PuZv/uWT\n92ElEpuFbpeJH703JtKEMvCAVyZxP8lmi4zN1Ylx55bIZu/dEG5LJdxMJjWj04scGHKQzRdYCCWE\nZNCUb7luC3plJ1ihWBKBjSWzFEVaBjUqBWqVkgs3AjWbcpO69DjISKSy9PY3C4kwsfHQcnUREP3b\nN0/0CnpulfS0mygUYSWTQ6mQC7p16wMPW4uWwzud9Lub+c2qeUI9a3UAXyiBUkQIsJSky7Oto4WL\nN4NV3XLr30u5UmrQqdBqlHXdSK9OlCqQc6tBUOXzNCoFj+9z39N4aKNXC9UqhWj35bef7H/AK5O4\n39zOLbERkK7dh5NGv24l7cGHl3rXaL1rerOgUECsznWbWMmKTk4ApNK5ugXQyvgX1jrEYhUGEhqV\nAodVR7NeXdJPKxTx+ONC3PuTjydQKeScPODmwo0A/sUkJ/e7eef0FAeGHKIJwsp40ROI8+zhTmQy\nOTO+ZTodRtx2A0qFjH/4pHTubzzWw0I4icuqx9aiZWp+iZP73UQTGcF9tEldGlutPHcZV6ue0FIK\njVpJNl+oGimeD8TRqJTs325nJV0yEYNSvDw2G8Fm1nFiT3uVIcJsII61WVNXgqa1WXvHmPPosJOP\nL87XHF9O6klISDxYbnnEC+ITDVIQl9gYKpXstnI5KpWMlPiWWpctlXDLZApC9Q+q9S2O7HQRT2VE\nXckqW9g9FQmg8HKKw8MOUukc/rKTUW8rc4EY7Q4Dn35VK6R4/JE2isWSY5RMhiBgW6+rTOy/y//v\nX0wKXWVlNCoF9hYdb59eM1wIRpIcHnaQyRbI5AqolXKaNErabHqmvVGuTy4KXXDrrdUr6Wk3kc+t\nBQjrTRvsFi1P7HPjXxSvDpcrpQD97c00aZRVicTy+ttsBj67Wurs295l5tGdDj4bqbY/B+5pPLTR\nq4XTvqjQgVh5bW72HwASXx9nHbdEV6v+QS/trihfu+uFRaVrd2tTr7O6Ua5bSXvw4aWuGPIm74SH\nNb3X9UzNRzn2SBueQLwqlkiksnS5jHULoM5WHTemFoFSfKZUyNndb6ty8NwzYGMhHMcXSvLlNT97\nB2xkcvmqDrh0IU8skaWtVU9gKSUkruvFvpWddW67gY8veggvp0vJPYsOlVLBJ5c8wrmDkRQjkyGe\nO9LNzz5ZMwkrP9+gVa89v+Lc5ed0OAzYWpr45dmZugXwF491c/VWmG8e7+XcDb8Qj3Y7jVUx5Fww\nzt/84gbBpVRV4d3tMCBDxsWbAf7iO/vu+G/ZaTfwV396lA/PzVXFv5t9IkNC4mFhdqF0z1y/V8ws\n1N5LJbYOmUyBDhETIYAOp4FM5t5F3LZUwk2tlhNLZUT1LdLZHD1tJvRNyprRp0JxLeHlatXjC8XR\nqBS0tmgx6tUEIyka2UaEAAAgAElEQVTyBdA3qVCr5PS1GwktpYUNvVwZ1KgU2Mxa5gNxLo+HsDY3\ncXjYgVqlYGxWvPtqzh+jtUV89GraG+XVJ7dxczaCxx/HbTfQZjPwszOTHKx4j+lcnu2dVsbmIoRC\nKdx2A3azluVEmk+v+ATdtvJ6m9TiGhVatZJoLsOJPW2CQ5RYUFJv5CYYSfHUwQ4MWhWW5iauTYZ4\n+Xiv4FJVXv8/nJoQXvPosIMOm4HXT1bbn/+4Ym66zJ3GQxu5WugNVIs4C7os0o+/LU9Pm5EmEbdE\nl60xEhcLwSSvPrlNEOre2Wel3Wbk4qpDssTWpNtl5Kux2pGuLpfxAa7q7nFZ9UIwXUmbtTG+dxIb\np8Wg5rWnttWIIcdTm7+7sd5167Yb+PL6Arv7rXS5mpnxLWNtLsWBF0YDdDuNQmxXjrM0KgUWYxND\nPVa6HEaCyylyuQK5fIGlWJrBzhYyuSK/+XKOkwfbuXLLg9mkwRtKiI7fzvljPHWwA7tFx7XJMAOd\nLXecLIlE0wx0mkln8+i1KVxWPUqFnNNfzVc9f9oX5Yl9bjz+WE2xetYfE9xN09k8gUiK43vaBJ02\nfZOS8PIK0UQGlUJeNwnoCyY4sN3OswfcVeOglcm22UCc98+XEns7eiwAXJtapMWgRoaMFoOav/jO\nvrtOmu3osWIzqDf9FIaExMNIm13PoWFnTXw7H5QSblsZuVzOto4Wzt8I1MS429wtyOXyez7nlkq4\nrazkMWrr67LEkznR0aeDO0oJGY2qJPoqA/rdLdjNOn7y8S0y2YIQGHw+ssAzh9xEk1kODDlIr+qu\n2c1aNGoli7EVDDoVO/usBCMpEis5mtQKetpMoplSh1VHk1pcfc/tMPD26QniyRxmk4YLowEujAY4\nOOTg0yslnblnD3eynMjwo/du1ryvbzzWIzx2dqSUQEtncswH43zjsR4Ci0lm/DHsLaW1v/flLIVC\naaT295/oZ25dYANrIzdiumudTiPJVI5EKodBp+L61BIzq86sPe3NaDVKprzLtFn17OixcGRHdSWv\nUrPt2mrFdT2T3mWUSjm5XG12udNu4Ptv7ufsNX/DVQsP7WxMEWeJr49WoxS9L73xXGNoRJzY386P\n3l1//ykJYUtsXRLJkiN4OVFc/mGbqOjI3qwUCtDlMnJZJGHY6TJuyIFKonGwW/Q196yLDXDPWlkp\nSXuIXbcymYwJzzLO3W28c3qydpRz1STh1Sf7+XxkoSruUynkLIQS9LQ1c+YrL4/vbWe418q7n8+Q\nzRf41uN9TMxFcbbq+WosWHdKwm03sBRPoWtSYbdoCSymhG7C9bha9WhUCnrbm/nbd0dRyGQ4LDqU\nCrkwDlrZ9W0za7l0M1j3s6kcje1pN2HQKXFaS51vapWc976c5ehOFw6Lrm4S0BtK8NTBkv6kWPJr\nvTFXea8+sBqTN6mV/C/fOCh67J0SalKyTUJi87Gzt5X//sv1RoJB3nxx6AGvTOJ+UiwWOXXJK9o0\ndOqSl+cOPeQaboVCsa6+RTKdI7Ui7vqZzRU4MuxErZLTpC6NYy4nMty64GFXfysOi45Lo0F29hlo\nUpecknb1t/K3v7oJlMY6y5oP33l+sMZxUKNS8N2Xhvh8ZKEmSNrVb8UbSIh2nJl0alRKBelsuqqa\nmMnmVk0TclweC+Gw6kTflycQrzmvUiHHYmrCF05i0inZ1WflN1/O1VQM/eHEbU0PxHTXdE1KVtI5\nEis5Prk4j9tuYGah1H57ZdVJVaNS8M3jvbxwqL6odqFQZLjHwkzFSFp5vBXgL//Ll3U13TrtBjrt\nhrpJuc3KQqgxRZwlvj4TdUwTJuYaQyNifDYiuv7x2Qg8eu+bkkRjkFx1BA9EkhQBvVaF3awjHN38\n9yyZDMwGjWjC0GzQIJMMyLY0jXrPUioVxFNpXj7ew5Q3WhpjtBuQyWScHfEJplhQf5TTE4izZ6CV\nd8/Oks0XqmRDMrkcT+xzY2nW4A2UTL8e2+3iF2fWHO6BulMSrlY9Rq2SH70/LvzNadWLPrenzcS5\n6wskVrIoZDKhU23/kB2tRsmhHU5UShlT3ijtNgM9bc1cm1pkuMdyW5MwjUpBsVBkJV0AmYwmjYLr\nk4sUCkXOjvg4/kg7mVyu7jn+z7cu8y/+YK9oobaeMVd5PFZMvuRetIglJCQ2FzemwqLf+RtTYZ4/\nLBksbWVsFi2+UAK5TMa2jhbSmTy+UAK7ZWOGcFsq4aZUyuvqW8RTWUK3MQvYs82GWq1gMZrh7FVf\n3QpWuVvuyniI15/ZxoRnmTl/nANDdnZ0W7gp4moBcG0qLGqycHMmAshE/7YUTxOJpmvOZW3W8dGF\nUpLMadXh8Yu/Z18oIVT81o+HAnQ6jCDiXAUw448x1GVmYr72R7/Lqsdi0mAzawWND41aSXIlx7nr\n/tsGWgDD3WbR9Vby+D43H5xbSwSuX389TbdGDW4aXYBcYuPMiAT+t3t8szFX5/5T73GJrcFAp5n/\n9vMbAEIXCsD3vrH5K78yGVydDPPZFV+NDmuuUOSZQ1IgvZVp5HuWzazn5vQiY7MRul0m/ItJIU4z\nmzVCp1e9Li6PP85KOi8k0MRlQzrI5Ao8f6SDImujM+VJiUw2VzIsSGbw+OPYLVp621sw6ZTcnK0u\nIJWPKRaLVeYGE54lPIEE2VxRiFM1KgWhpRSvPz3A1YlQVTz8i0+n2D9ox9Wqr9KOgzWTsLIUx5kr\nPvYO2ARt5qO7XEwvRCkUinxyycMT+9x1zQ5iyayodMntjLmCkRQOi65GvmR9R9ydtIglJCQ2F428\nV0hsnGw2z1CXucqhFhAcarMieZM7saUSbnK5HGcdfQutWomrjsizs1XPRxdLLp57Bux3FHiNJjKM\nzkQ4dyOAtVnDKyf6mfZG0agVogkSs0nD3EK8Shi9HDB0Oowc2uHgnTNTwnPLQcKLx7pr7M01KgWJ\nik6925kgdDmNXB4PYtSp0Gpqx1b9i0n2D9nF9UBsBlRKcQv4NpueX346jVol59Untwljrzv7rKKB\nlkxW6oq7lxHPHT1WYTx00rtc19K+MjBq5OCm3tiF27G51y3x9Wm3Gep+BxsB6dp9OKn80VvZiTsy\nGeaZg5s7YVUoIBSq1ncSe/xxaaR0i9Oo96xcLs+1yTCfjyxw/JF2tBo5kVhGSLiV48GRiXDduNBu\n1qJUyIVuOFHZkERJv823mGTSsywkss6O+ITC83CvleRKlqcOuglGUgQjCVYymqrJBChNLHx6xUtf\nezPNBrUQ3x4YcpDO5ulymVgIJWjfbqCvvRlkiEqklCVc5gOxqgJ1aTRVzpVbIcLLK8JxlSOmcjk8\nsc/N5PwynU4j+wdtnNzXxtkRP+NzS7QYNcL7A3Fn+9sZc3U6jTxzwE3Huj27Xkfc7bSIHwZe/hc/\ne9BLkJC4Kxp1r5D4+kx5o6L37ylfFPa23/P5tlTCLZnM0Okw8NVYbZLIbtGiUsjFNVscRr68toDT\nqqsJFspUbt6eQBy9tuQeGl5OMzG/xMhEiFueCM7W2i9nJJrm0LCD2VVNtMrgvrvNxPWpRV4+3kNo\naYXJ+bXgJhhJ1SS83HZD1RpvZ4JgM2t54Wg388EEY7NLVUFToVAknc1j0qlFj5XJZPz6i1m+daJv\nbX7ZYWCgw0xwKSGc66PzHo7tKo23rjeGKAda2zpa+Kt/fOieRzwrx0P/8r98KfqcysCokYOb7Z1m\nLoiIM27vvHM3oERj06zXiI+U6zUPcFV3z65eq+i1u6vX+gBXJXG/qVv5Xdj8lV+5/PaB9Ab0cCUa\niO1ddfbbrs293zY1KfAGEhzd6SKeyjDlTXF42CnEteV4EOqPfWpW/347LTPPasxX/n6Uk15Hd7r4\n9IqXdDaPTFb6QWJt1nLhZoCVdA6jTsXu/ta6esX+cLIqDtWoFMgoMtRj4aMLc4xMhDm0w1m36L0U\nS9Ns0HDx5oLgTioDTl321rxeecQUYHI+yv/xxweRy2XkcgVmA3E+u7rAjekIzlUd5UoziXrO9vWM\nuZ7eX5tsu11HnFhCT6Ix+ZN/++E9Pf9v/uWT92klEveDRt0rJL4eKpWCqToTZlPzUVQqxT3nNLZU\nwk2tVmDSq3jlRC+ewJrIXafDiEIJnkCiZnRT36RkOb6CUaciEk3zyIDttvoQ6/8bSh++vkmNfzHJ\noWFnTcJPrZILArHrv7QOsw6VQs67n89g0CrZu93BB6uaamXdsvJ6HRYtbruB+VCyqlvu4s0AB4Yc\nVS37+iYlWo2SH394q6ZSWA6aAGKpDN99aYiRyXDJCXXV1rzcnfbO6Umg1Hl34UaACzcCvPb0Nk5d\n8hJLZnl0t4t3v5jFoFOyd8Au+tn1tjV/LT21XK5Qt7JYDowaPbi55V3ijWcHGZ+LMOeP0+EwsK3D\nzKR3Cdi8mjISXx+TQSWqJdVsVD3opd0VuUKeN54dZGwuItxDBjrM5Ar33nIt0Th0uYxV+1Akmiad\nzdPdZnrQS7sjmUyBHd0W0UB6R7dlQ5bvEo1DYiUjes9KrGxul9JkMseBHXbeOT0lXLeeYJzHdrvI\nFYp4/HEy2RwvH+/FF45XjX2WRzPLXVy/f7KP+YqkWiUddgPnK1ymy9/xfL6ARlWalmhSK0ln8yVn\n0gMd/OLTKWLJLO12g2is2+9uZrCrhRuTEeb8MfYO2ITxz4NDDoZ7rFhbtFy9FRJ978FIikf6W4mu\nmrKUNd86HMa646HlxwZXC5eFQrGu8UE5Lr6ds/29GHPdriOuXkJPQkJic5FMZ0X3ilRm85tDSWyc\nYrGIc3Uqcn2M62rVUyze+/17SyXcFAo5C6EUH5wvjYdaW5pQKRWMe5YILqVwWvWoFHKuTS1iMWkw\naNXEUxmmfTGGui2olApcrTrRDrny5r1+I4eS25IvVKrqy2Syqh/PboeBfncLpy7Oi+q0nbvup1As\nEktmyWQLuFsNqFVy0tm80CFmbdbwxrPbuTG1iC+UpMNhoEnVJrzGjh4LCjm02Yzk8gXkMhlKhZxx\nT62eXOV4LIDDrOPXZ6fJ5vL88cs7+bv3x5iYX64ZN6jsypvwLPNIvw25HIx6NU/scwsByunL3prP\nrl7wci/UqyyWz93owU1bq57kShaFQk5rixaFQk5yJYuzVf+glyZxn/EGE5z5ylszbn78kbYHvbS7\nYmQiwhfXFrA2a9jZ28rIZIjPrvg4POzkhSNdD3p5EvcJl1XPiT1r+9DOPiv6JiVOi+5BL+2OyOVy\n4itp3nxxiOtTa8WmHT1W4qmVDVm+SzQO3mCSQiGJnDUx5PG5JRSb/J9dLpcRWkpVxUGFQpFTl70c\nGXYCcOVWmHM3AvS1NxNtyuCylr6PlSPgGpWCZCpHW6t4csyoU4sWfQvA7z3RhycQ57OrpcSdzawl\nvLQ2jfHlNX8p0ZfI4AnE6XQasZm1nLroxW7V0aSSk8sXqtbjCyXI5PJcmwqzp07R227WkljJkltN\n+pWPPTvi47HdLnQaJcHlFVQKOQqFXEgsalQKmg1q3j47w9XxEPY6JmO5fIGXHu3m4Hb7bSciypMX\nd1PEvVPcKiEhsbmZDyQoFGi4vULi61EoFOlyGmlSyclkC2RyBbqdJtSqknTZRnIKWyrhlk7nWEqk\nhc2tx9XM5yMVBggLa1oQQPXfVrOY33tpqKpDrtRp1MKpi14e3e3CbTPw3rlZdvVZmfZFS0kyu4Em\ntYL9Qw4uXA+wZ7AVllfoa2/mV2enuXAjwM4+q1A9q/xR/ehuFxdvBjm2u42VTI5ffz7DvkE7TquO\nyflletqa8YUTvH1qUkjSyWUyvrhWK3T7h8+1YG1p4sNzHswmDWplrW4blCqFx/e0kcnmKQJtrQYU\nCjkfnZ/DYdExMb98e9HdQJxmg5qx2SV+8N0DuCuSQndb/btX7qay2MjBTbNew3/7+Y2atTeCALnE\nxikWEfTb1o+bzyzE2EAR5XeOL1Ryswsvp/nk0nzN4xJbk2gyK7oPnTxQ34F6M9Gib+K/Vpg+lDu4\n/1i652559E0qPrrgqdlvT+7f7NqDRSbrjLl4V5NW5ffUYizFmdemFkvSIKE43lACt82ASa8mHF2h\nSSXn4A4HqXROMMDSNSkJL5div/VmVbP+GF+NBTkw5KBQKAoF6Dl/TJBcabPpyWQLdDmNuO0GfnV2\nmthqV9r0QlSIwb0V+0N5aqRkBCY+DdLd1sxPPr6FWqXg6UOdJFNZpnxRtneZ2dFj4avxkslCh8OI\n3aKl026ktaWJ7rZmfvzRLVQKOU8f6qzbQedfTPK/vjJc9UPqdkm1u/nBdS8dcRISEpuPRt0rJL4e\nuVwBg1aJH8jmC4SWSo1SapUcvVa5oam9LZVwUyrleFZdSm8nCJvPF8jlC+JWvzMRLt0MkMkWMJs0\nnL8R4PyNACf2tvPpVR9um4Ed3RY8/ji7+lsZ7DRTpIg/nGQ5lmZHr4VffDqNQaekWLAKgUalnkb5\nR7VGpUAuk7F/0C4ENXK5jA6HkVueZWwtWn52arIq2DHqVBQpiq79+tQiO3pKrfO3M1Nwtur44toC\nsWSWw8NOZIBCDia9pqTHMRq47fFuu4GrEyHS2Ty/ODtDa7OGg9sdQuXvbqt/98qdzt2owU2xCFcn\nxK2nr06EefqAdGPfqshka6N56+luMyGTPYBF3SO3W7/E1iVaUdwqUxZc3+zkcnmu1DF9uDIR5rFd\nrge1NInfAdFkRvTajSU390ipUinHba9jsrMal0HtSOX5G34ODjvwBhNcGF0boxZ+OBZhV7+V36zK\nmRzb3YZRp6obQ+fyBY4MO4VOsrIjqEalwKRT47LpyGXyzPrjQgxceXylCdn6tZ677ufPXnuEK7fC\n3JyN0O9uoVgs8PnVUidbYiXH1VshtnW08L0Xt0MR0RHRE3vbOXVpniKgUpSmRsKrP5rE9qvKSYjf\nptv9/YyJJSQk7i+NuldIfD3kchnRxP/P3p1Ht3Ge9+L/YjAY7CBBEAD3VaI2SpYly7bsyEsS2fWW\n1FFuZTm1c5vb9tecJO1p3Zw0beO6jmP7NL2nS5qmbs69t4l7kjjVTRPL9ZLrVbZjx1osS5REbdxX\ngCRIEABJrL8/QAyxE6QAEiC/n3N0RMy8M/O8My8GLx7MzJv+R2WrWbes8/maSrgJggINdiP6Rqaz\nXqHlD0azlen0DLnnB0TwJXTCJ9yzuPfmZvzirSsJO//EeQcO3L4BM/4gBEGEWhKwe4sNZqMm4Ze0\n2DPRYpfmt9SaYDPr0D86jXBkYQTO2C+KAORRP+PvH9ZrVfLoasmGx7xortHjobs241zPRMYBEVRK\npdwJil3K73LP4b59zXjhV924b18LHBM+VJRr0g6/3lJbhhOdDnn53hE3Xj02kDAaaCE7FtnWXYqd\nG4UCGY/pwKinJJIutDyRCNBaW4YP4k7qwPz7rMZUEle46TXpzzM6dWk8g46WJ+M5y1H8gyYolULW\nc65SKQDgMwjXqkxttL/I264oChn7dSa9hI31ZigUSHhWGxB9ZEXvkDsl0RRNkPtRYdLg9OWxhNs0\n79rblPFqsNjVy6MTPtRZDai2RJ9FbCnXYtYfxLDTC4NWlXF/Ol0z2NZqgQKpsW5qMGNLfTm21JfD\najXi5LlhvNsxguu22vHSr3oS+t/vfDSEA7e3pv1CPOGelbcVu/puaMyLTY3mtPsvdidEoUa7L5X+\nKBEtKNXPCro6gqDAgMOT9rNlwOGBICz9i/maSrgFg2HUWKOXo2e7QsugU0EtKdPOq67U49RFZ8r0\nyek5DDnT7/yeYTcu9rngD4Rx78eagQgwMxtAlUUnbyP2PDa1SolP7KlHOBKGTqNClUUvJ6/ir8qr\nsugwPjkr32oae0aOQSthxh9MG3tzrQlatYRnX+qEpBLQWluG+29tRd/otDyYQnLnJv5S/v5RD8p1\nktyZOtXplJNvPcNuNFabYK/Q4p1TQ/LzetSSEu93jCw6GuhKJ8BKrXPDoafXrxl/IO2AGTP+0vgF\nbdI7i/v2tSyMZmwzoMZqwKAztT3T2pHxnFXkVxQDQCgUznrODYU4aMJalu0qsWLm94cwPeNP+zxg\nz2wAW5rMeP7troSrytQqJWxmLU5dSp88G3B4MDrhS7jyKxyO4NUP+rLe5VBhUmPcrcew0wetWgWl\ncg5nLo+hucYEs1GLjy6OZdzPzTUmbKwvw3OvXpJjjY06elP7wiNAznWP440Ph+APhDDlSX+lyZVB\nd0oCDVhItCUPePbOR0O4bos9+kOnw5NyJ0Sm0e7fOT2MBz+5Mf2BIaI1qVQ/K+jqhMORjI/FGR7z\n8hluwWAYZr0an76lBSPjPlSUpb9Cq8IoQa9Vo+PKWErHpLHKiF+fHUlZ94b6MpzvdqXd7oDDg6Zq\nE85cGceAw4PhMQ9c03M4cPtGfDTfyYldoQZED+TMXAi+2QBGJrzySBh1NgMiEcgJw7tvrsaL7/ak\nXM54cH8bPpofBj4+9gabAT3D7oTO2KWBSRi0EnZsqMRbHw6k1LfCpJFfD495E24rAKLP3DDqVPj/\nfnM7nvn5mZRncdy+uw5tDWZoJBGX+idTEmv5uDQ/W7KulK5ky2ZzQ4ahpxs49PRaFokAZkP0WVIG\nnSgPOnB8/llSxX6FWyQSbbs//uVFSCph/jw4hhOdDhy6o63o46fla6oypT1nNVWVxq3E7S2WtPG3\nt1hWMSpaCbHPWyCxb1YKn7dGrYQ3TgzI59ue4SnoNRL2tNsx4Z7Bjg1WzAWCcLhm0FRlQpVFh+Ex\nL5pqTPLdDPFtPpaQSn522lwgFP1RNc3VYG2NZvz4lQvy7afPv92V0k+NPSs53fLNNWU42zWBm3fU\nwDcXhE6jwrTXj37HNH7VMYK97VVoqTbhQt8E3jsznPWZxMNjXvkKtnhWsxYX+1wpA57NzAVx/Pwo\n/vzh3Wi0G1Oe2ZZptPuL/ZMYcc2gyqxdyuEiohLW3mxJ+1nR3sx+wloWDkeyPi5n3SfcAGBqZg4j\n4154Z4PoG5nG3Tc3wTHhQ/+oB1UWLbY2V0aH970whp1tVtgrdDh+3oEaqx7bmi2Y9Mym7SAEghHU\n2vRZn2kGRD/8d260YnDMi3c+HJJv7xwY9WD3Fhva6s340f+7AL8/JHdK6qv1aKoyygM1xK5kG53w\npf2lrW/Ejb3bqzHt88u/cOo1IiIKQK0S8avTAymdnz1b7dixwSon4mIPlv2w04n2Vsv8M+YETPsC\nKduc9gXwwblR+APhlFickzPyr4cHbt+Qkmy7mkvzsyXr8vmMjdUWDgO9o1NpR8zrHnYhzIst1qxQ\nKILzvS45Sd495EZTdRk0kojOXhdu3Vm72iFmpVBEk+/xSf5YAr5nxM3bodcony+IofFpHLpjU/Tz\ndP6c1VZvRt/oFHy+4GqHmJUgKOCZnU0/Suns7LJuF6DSEIkAl4dcCW031je7PDSJSKRhtUPMKByO\nwDPrx337WjDonMagw4tNjRUw6SQMjHrQYDPCpFcjjAgqy7RwTs7g/Y4R1NkNqDHr0Dc8jfZWg3yn\ng0opyAmp5MeeNNWYoEAkZVCFazdZca57Qk5iZXrO26w/CKNOwt03N2HI6cXwmBeN8yOWemf9MOhU\n8M0GUGsz4GdvxD2qZWQab54cxH+/dwtOX4ze5pr1mcJ2Q8qtr9FHMpShucYkP4PZWq7FifMO3Lqr\nFrfvrEW91ZDypSnbaPdWsxbvnhnGgVtarvYw0jr2hadfX/Iy//vPPl6ASCgXnf3jaT8rLvSP444b\n+HzttSzfj8spioRbOBzGY489hgsXLkCSJDzxxBNobGxc8npEUUAoiISH3MWu0Lr/tg2Y9Yfw419e\nSElG/bdPbMR/vHYJJ8478OCdm3DDNju8s8GEZJZaJaDBbsbJztQry2qsBnmI9OZaE944Gb2S7LMf\n34hnX+pMeebbp/a14PDrl+QBHDSSEs/9v0sJ5Rrsxoz17Bp0y79Uxo94emOgCoKgSNv5mZkLyomx\nba0WjIx78e7pIXkfqVVK/O6n23Hk7a602+wZdsNeoUvp8MQ/H8MxkfgLY6ZL87Pdehpzrns8Y7IO\nQEGesbFaBAForTXjB/91fuEqoctjOHHegc/fswUCh59es0RRAa0k4o0zqUny23fXQRSL/4u/RiXi\nvTRJfo7itHZpNCI21FXg2RdTz1kP3b0FGo0Ij6e4b4mWRAnPvpg6SumhOzetcmRUSAoFoJOklL7g\nifMO3L67rqh/JFCrRTTYTPi/b1xOe0XZ8293Ye/2aphN6oSR9eLLxB5t8pnbWqGAAi+82w1g4bEn\nRp0Kn7y+AVMeP14/3h/d7vxzhM9cGYfZqEb//PMPsz0r2emagdM1g76T0/Lypy45sWODFcfPj+J3\nP70NP3zxPLyzqQk7AOgf9cAxv+65QChh4DF5f6iUqK00QAEFIpEIBhwe2MxaVJZHk43vnRmGvSL6\nfLlj50YAKHCy04Hbs/yQtbc9/Wj3GknE2e4J/LfbWtfEXRVEtLhsnxW0dkV/lI0+vmHOH71i3GbW\nQi2J8M36S3fQhFdffRV+vx/PPfccTp06haeffhrf+973lryecDiCvtHptFdo9Q5PIxAKpU0AdQ1O\nQVIJ8AfCuNDrwq/ODMsdhFgya+/2avSPuNM+q+jnR68AiH4oW8u1mPYFYNSpMj7zbcjpgVGnwrQv\nAKUg4HL/VEq50Qkfdm22ZX3OXPyIp0D2wSDiE2MaSYmzSQ98nAuEcLZrHNWV6a/iq67UY3gs9SGR\n8c/HuDw4JTfCbJfmX+hzLdpY3zo5kHbfHet0ZBxhNpdEXjGKRICOrmg7i41MGtPRNY79e3hiX6sU\nisyjILl9/qL+8heTLX5amxQK4FyGc9a57nHceX1xn7P8/hAu9rrSjlJ6sdeF23ZwlNK1rFTPWV6v\nH70j7oxXlAHAzFwQgYn0faT40UGHnF50dI9j1yZbQrJKLYn49dkRiHG/9MX3NbuG3PLziRcbzT72\nfOL45efm49dylMsAACAASURBVPzo0hjMxvQJO7NJjQu9roTnyiVfgWev0EJSiegZnsLpy+OosxlQ\nZpDkEd93b7YBiH5Btpq1GJ2YWRgUIktfscFqwIHbW3GhbzLhGXnvdQxj/54GJtuI1pFS/aygqxMO\nR1BVocORt6M/SMV+cAKA+/Y1l+4tpSdOnMC+ffsAADt37kRHR0fW8mazDmKW5zmkIyoV6B7KPKpa\nU7UJzskZeUSS5GTWoMODtoZyHH79Eow6FZpry6BVi+gemkKNRQ+bWQuzSYPj56IdjKZqU8bRTeKf\n+VZr0+P9jtRnxs0FQijTp7+cscaqT/ucOUkUYK1IP+R5LDEWG7Y93S+KXYNT+NjOGjmZF7/NBrsR\n57rHE8onD+e+tbkCFstCJ2ZbS0XaS/OTy6Vztnsi7fTRCR+cU7Np513oc8FqzXxl4GrL1m6zjZin\n0aih0agLGVrRKebjmG/ZRntUq9WwWlf32Gdrt0Dxx7+S1lW7zfT5VkTnrKzn3Cyfz4t9Pq0166nd\nAsV/zsrWbrON/Gk2qTE85kVlefrnjMX/8Drg8KDRbsS7p4fQWluWkKxSq5S4ps2ati9ZblRDIynl\nvmmmK89M+vT9TMd8DNEHkSsSkmoxLvccWmvL5XXNBUIJV+Dde3Mz3j09hL5RDxrsRphNapQb1QnP\nbI6va/zfwOJ9xWvabDjyTjf0WpW8TrVKiY/vqS/Ie2WtvP8W6yvQ8iS3j2JuL8UcWybL+m5WJJ8V\nK60Uj+9yHT/nSHhcTuzxW8fPO/D5e7YteX1FkXDzeDwwGBY6uEqlEsFgEKKYPjyXy5d2OoCMD7kD\nso+qdubKGPyBMHZtTt/JsJq1CAQjUKuUmPYFcHp+MASjToVbrq3Dqx/0AQB2bbahZ8SNnmF31l/+\nzlwZg1qlxPjUbMa4pmf82Lu9Gh6fH464X9qcrpm0HRylUoBSKaSdZy3Xor3VgnKDhOmZ9Jn5Olt0\ndLZ0t9SGI2Fc22aNTp+cga08+ktobMRTtUqJ6zfb4IwbmfD6zTa8dqw/JZbkculsa65A73Bqss5e\noYOlTJN23qYG86LrTbaSJ49s7TbbiHlLrVOps1qN66rO2c5LmfZDsbRbAKjPEH/9Omu7bLdRix33\nYmm7y41/rVlv7RYo7XNuvc2YdtS82I+q21st0GnTP2Mm/o6E+GcPJyer5gIhNNgN+Ohial9SI4l4\n5/TC1WaDDg/u29eM4TEv+kc9aK41wV6hQ2+aGAHAZtbizJVx7N5iw4nzDtTbjWlHGa21GXDk7a6E\nLzw2sxZNNWW4PDCJ0YkZuU7xgyOkq2v838DifUWrQcIjD1yL986O4kKfSx7J1GqQ8v5eKfT7r1ja\nLS1ffPso5vN1PmMrlna7nM+KtaqY214h1Nj08iMY4u94vGlH9bL6CUWRcDMYDPB6F65MC4fDGZNt\ni9nWUokP4p7hBiz8QtZWn34kyNgz2NQqJWqtRqhVqVd4aSQRb380iI/tqEYwHMHA6MLl9y+914Nw\nOJqM29JoxslOB6Z9gYzraqo2QRQFVFXo0D00hY0Z4trWbMEP/iv6jJlPXt+Ao/OjjAqCAnvbq6P3\nFU/OoM5mgCgo8M7paPIrfl69zYAqix69w26olAICwTCu2WDF8XPp98Og0wtBAaiUAirLtVApo7cV\njE7M4J2Por8ufv2h6xAIhvCrjlE02I0pw6rHNNgM+PpDu1M6Lbnc9nnrrrq0ybo987cJpHvGxt5t\n9pT1lIodG6xp28CODdZVjIpWwjUZjv01JXLst2+w4nia+Le3lkb8tDyZzlnbS6Tdlnr8tHyl/Hm7\nfUMljp9P7eNqpGifWacR5f5oujKxq7Xi+716jZhSVqFQpPz42lpbhiPvdMtXm6lVStgrdHC6ZnH6\n8hgevnsr/v3l87j7pmaYDeq0Majn49y1KZpwi90qGkG0Xx37Yflnb17GZ25tRdfgFCan59DWUI5A\nMIL/ercbuzbZ5Ho015ig14hy/zddXeOTcbn2FRtsBjTYDMt6Vg9RPnGghdVTyp8VdHV2blw49rGr\no9UqJXZuXN6xVz722GOP5TG+ZfH5fHjrrbfwyU9+EqdOncKVK1fwqU99Kkv5zPdO11p0sFUaoFYJ\nUECBazfZcO0mKzp7XAghhDtuaII0P6+91YJbd9fh/dMj2L6xEtdtsePEOQc+saceljINIhFg5yYr\nrt1kw4UeF7Y0V6C91YoJ9ywkUUBrXRlm/SGEQhFcu8mG3VtsuNDrwk07amAp0+BCjwv3fqwZep0o\nb++23XXo7JmAVi2iocqEKY8foxNe3HljEzRqJRRQYFurBZ+8vh5vnxjEXR9rhkZS4srAVHSkJbMW\n4VAEZQYJ7a2V8HoD0GtEbGm2QBIFIAKYDBJ2tlkx4wuivsoI1/QcfHMh6LUq7N5sw84WC5pqy6Cc\nT6a1b6jErdfW4v0zIzDoVdjeasWUJzp0vEkvocqiR2ePC9dvrcJvfXwDaiv1KNNLaG+uwO27arGt\nyYwyvZT2eORaLllTbTlaq41QSyJC4Qhu2FaFQ5/ciAabAWV6CTs2VKadt1R6/cpdDpxLu421zW2t\nFvzG3iZc31a5YvEVC71enXVfrTXLOfbF0m4Btt0YttvcjnuxtF2226j11m6B0j7n1lp0qKo0QKcV\nEYkA7S0WbGo0Y2YuiD1b7aiy6DE5PYu2BjOqLHooFMC1bdF+8IUeF7a1WnDrrmi/d8fGSuzeYoNa\nEmExaaBQKHDNRiv2X1+PvtFpaNUqCAoFRFGAThJRZdWjucYEg1aCUlBgx4ZK1NoM8Mz4cf3WKjTa\nDNjYYMbFvgloNSLaWyph1EXLXtNmRVu9GcFQCL95ayuuaa7Ajg2VkFQiuofdaKwyYv8NjZicnkXX\noBttDWaUG9TY2mKBQSvhQp8LFUY1rttix/luF7a3WnDLrjqMjHuhkVSwV+igUCiwZ7MNe7YulLlx\nezWmfX4ooMCNy+grRgqcayv0+69Y2m28X7zTXeBI6NMfa17xbeazLRdLu2U/YcF66yvUWHSoshqg\nijv2d9/UhOs2Lq+foIhECv1xsrjYKKUXL15EJBLBk08+idbW1ozlc72k0WCQ5JHSdDopoaHEv9Zo\nRMzORh/kKklK+P3RTLbJJMHtjpbRalWYmQmkXV4UBQSD4ZTlc5me/Do+lvjLN+OXz7auXMul23Z8\n+eR5K/0rX3zds237auNaycuWc2236+2y3WTruf651r0Y2y3AY8e651Z2pfCcu7j1XHegdM+5VqsR\nXu8cIpEIAvPPOBMEBYLBMERRgCSJmJ0NpPTjgsEwJEkJUVQiGIwuJ4qC3I/y+0PyMqK4MHBCrJzf\nH4IkKVP6hsn9sFh/Mrbu2PLp+qHJ/ThBUEAQFDCb9Wn7gfGDc6X7P1uZYrOWbinNtR7LuWKLlmY1\nrnAr1VtK2U/IzXqufz76CUVxS6kgCHj88cfzvt5Ysg1IzWDHv44luAAkJJxiyTYACcm25OXjOxDx\ny+cyPfl1fCzx4pfPtq5cyy0WY/K81eyoZNt2MXagiIiIiAopuV8b6w8Fg2EEg9F58f26+KRaLn3S\nTP3LdOtMFisTv45M/dDkdaRL4MW/jv2d6f/F5hEREa0kYfEiRERERERERERElKuiuMKNiIiIiIiI\niJZvqbftcpAFosLiFW5ERERERERERER5xCvciIiIiIiIiNaZ5QxkwaviiHJXFKOUEhERERERERER\nrRW8pZSIiIiIiIiIiCiPmHAjIiIiIiIiIiLKIybciIiIiIiIiIiI8ogJNyIiIiIiIiIiojxiwo2I\niIiIiIiIiCiPmHAjIiIiIiIiIiLKIybciIiIiIiIiIiI8ogJNyIiIiIiIiIiojxiwo2IiIiIiIiI\niCiPmHAjIiIiIiIiIiLKIybciIiIiIiIiIiI8ogJNyIiIiIiIiIiojxiwo2IiIiIiIiIiCiPmHAj\nIiIiIiIiIiLKIybciIiIiIiIiIiI8ogJNyIiIiIiIiIiojxiwo2IiIiIiIiIiCiPmHAjIiIiIiIi\nIiLKIybciIiIiIiIiIiI8ogJNyIiIiIiIiIiojxiwo2IiIiIiIiIiCiPxNUOYDmczunVDmFFmM06\nuFy+1Q5jVaxU3a1WY8G3EZNru13Pxx1Y3/XPte7F2G4BHjvWfXHF2HZ57NZn3YHSPeeuh+PGOl69\nYmu3QHEf12KODSju+PIZG9tt8VnP9c9HP4FXuBUxUVSudgirhnVfv9Zz/Uu97qUe/9Vg3UtXqcd/\nNdZz3YHSrX+pxr0UrOPaVMx1LubYgOKOr5hjy4e1Xr/FrOf656PuTLgRERERERERERHlERNuRERE\nREREREREecSEGxERERERERERUR4x4UZERERERERERJRHTLgRERERERERERHlkbjaARSSySTB7fan\n/J1tXi5/X+3yua4rl7qsRlxGo4Tp6ezL6/USvN6FdRkMEjye6GuNRsTsbBAAIElK+P0hAIAoCggG\nwyl1jy8DAIKgQDgcSSlHREREtB6YTBIAIBwGhPmfz91uPzQaESqVAIUCaefHlktnteZHIoBCkTpf\np5MgisDsbAgajTJlfiAQgUqlQCgEhEJhzM4GodGICAbDUKujX3Hm5oIQBAWUSgEzMwHodBKCwVBC\nXzIcjkClUmJmJgBRFBAOR+T5gqCQ/w4GwynTY9Nif8fEr18QFPJ20snUr13q9FIhitEGGQyG5T5+\n7LjF5guCAL8/+l1BpYqOEqhQKBAOhyFJQsKxVyoBvz9xOrDQrmL/JwsGATHpm3By+4yVCQaj20le\nT2zd8e+zZJnKJMcViUT/ZSuj10tQKhfafjgMeb9JkoBgdJdBEKLbi0QiCARC0OlEeDwBaDQqeb8m\nt1kACW1bEBQJ37+0WhVmZgIAIL9PYuWTxb9P4t8HpdxuiZZrxRJuH330Ef72b/8Wzz77bML0119/\nHd/97nchiiIOHDiA3/qt37rqbX1wcQynLzsxMOpBc60J1nItjp9zoK5Kj/YWqzyvzm7A1mYLfnVy\nADftqsO57nGMjc/gtj116Ogax5DDi+vb7Rge86J3eBr1VQZsb7XizGUn+kc9aKk1oXJ+3TU2PXZs\nsODosQGYzVpsrC/Hr04N46adNbjY74rbXgXePDYIW6UWOzZY0dHlxIgjus1z3dFt7tlmx8i4F4Oj\nXtyyqzZpeQuOHh/Ajk2VcLpm0D3kRp3dgB0brDjX48TQyMK64usyMOpBvd0QF1c1LvVPon9+vW0N\nZhw9MYgqqw47Nlhx5ooTg6OJ9Y/F/86JQdx4TXT5YacPt+yqwaX+SQw6vLhuqw1jkzPoGnTL+yv5\nWJzqHMPHrl3YfjQuM46eHES1VY8bttlxu9WIk1fGcfKCIxp7lQHXbLDiysAkOnsnsbmxHHu3VaHB\nZrjq9lIs4ttt7Jhe31a52mHRCij1Y1/q8dPylPpxL/X4aflK+dh/cHEMp188Hxe7Be8cH0BFhQ6b\nGitwoW8CPUPTqLbqsaWxAlcGo32tPVvtcLp86Bpyo7HKCKtZh5PnHWisNqKyXIsPLzixe7MVDpcP\nWrUE76wfvcPTqKrUo6nagHKDRu5P1tkNaKoyIRgOYWTMh57hadRXGVFr1UOhAI51OHDtZivGJqP9\n1KpKPZprjNCpVbjUP4m+0WnUWg1orjahd9SNYYcPt+yqxaV+l9wv3dxgxsUBF/pGPGiuNqK5pgw9\nw+608VvNWhw/70BjVbTc+d4JjI75sGebHY4JH3pHpnH9NjsiEaB3ZBojY14015pQWabFiU4Haqx6\nmHQSPLN+NNhM6B1xo9/hQbVFj+0bLDjfM4H+kWhcW5rM8M4E0DUUXU9jtRF6jQSXZxb1NiOqLTpc\n6nfhQu8U9m63Y3jchysDU6iq1GNDrQltdeVy3/Vc9zheP9aX0q/tc3jw3tmRnKeXguOXxtDRNQad\nWgW3zw+TTsK0z49+hwd1NgNMOgne2QBaa8twvteFYacXTdVGtMy/dk7M4JZra+TvDrU2A7a1WHCp\nfwLdg9Pyd6Q3jw+gqlKLBrsJPSNuDMjrV6PMqMKw04ue4ej7Y1tzBTwzAfQMT8Mx4cN1W2wYGfeh\nb3Qa9TYDjDoJLs8ctjSZoRaV6Ogeh1GrTnpvGKHXiPDMBOW21VhtxJamCpztHpfbTVO1CcFQCKPj\nPug0Kui1KvSNejAy5kV9lRFVFTqIogKRSARuTxCe2Tk02k2YC4Tkck3VRrTWlaNneApqlYgprx+D\nTg+aqoyoqtTj+NnE9121VY8tTRW4MjApf49rb7HgYt8Eugaj8TfYDbCYNLjQPwGNSoVpXwD9juj7\ns0wvwTcXwNbmSpy54kT/SPQ725ZmC852j6FMp5bLt7dYcP1mGwDg/XMjAIT54zuNDXXlqKrQ4r0z\no2hrKCupdkuUD4pIJFLwVPP3v/99PP/889BqtfjpT38qTw8EArj77rtx+PBhaLVaHDp0CM888wwq\nK7N3epzO6YzzPrg4hv9z5CzmAgsZebVKieu22FFdqceRt7tS5v33e7fg3144j7lACL/76XY8+2L0\n75t31OD4+VG5fPLr+HW/e3pIXtcz/9kBtUqJh+7eIq8rvvyhOzbh3/7rHNQqJe7b1wKDVoUf//JC\nyjY/+/GNaePNtN6H7t6CYDAsryu+Lrksf9++Fhx+/ZJcJwBp6/v5e7bgB/91PiXGXPfXoTs2yTFm\n2v7/+NQ2/K/nzy66rq8/tPuqTtpWq3HZyy7Vctrt79y3rWS+BOSL1WrMuq/WmuUc+2JptwDbbgzb\nbW7HvVjaLttt1Hprt0Bpn3Mzx74F//KzjoQ+aXy/KZf+6337WnDk7S5ct8WeUva3PrERvzia2h/9\n9C0t+OlrlxKm3bDNDltFan/7lp01+PXZ9DFk6p/H+oW5xJ/8N4CE+isFZNx+8j7IVCZbvWP77YZt\ndoTmLxpKF/MN2+z4+K46AMBTz55Imf+lz+7Adw+fznl6tn5wsbTb45fG8L+ePyvvo3RtLH4fJu/v\nXNpI7PWhOzbhUv9kxn1/9FR0vcltIlsbiz+umdYLQF53/LLx9YitI1NbvGGbHVUWPX5xtAv37WuB\nY8Kbtlymdpo8PVOdkvfZDdvsaKktT/vdLNMxyfRdLtt+in+vlUK7jbcePyvjref651r3bO12RZ7h\n1tDQgO985zsp069cuYKGhgaUlZVBkiTs3r0bx44du6ptnb7sTHiDA8BcIIRQKIwhpyftvDNXxmHQ\niai2aHGuexxzgRDUKiVm/UG5fPLr+OVn/UGoVUp5XRtqozs8tq7k8hf7XbCUqTEXCMHtmcPFflfK\nNo06Vdp4s633XPc4HC4v5gKhhLrEk1RCxuWHnB4YdSrMBUKY8wcRCoXTluvoGgeAhBiXsr8u9rtS\n6hS/fUkl4NQlZ07reu/saMq6SlGmdnv6snOVIqKVUurHvtTjp+Up9eNe6vHT8pXysc8c+zi2NJbJ\nfVKjTiX3m3LpvwLAkNMTnZ5U1qhTYcCRvv884Ij22+Kn+WaDcEz4EsqqVUp4Z9PHoJjfdqZ+qaVM\nnVP/O/7v+D6sWqVEKBTOuP25pH2w2H7KVO+5+dv0vLNBKAD4A+m3550N4linA8c6R1PmA8AH51Kn\nzwVC+OBcan+3VPrBH12Kvrdm5/dRtuMZmx+/vxdrI7FjMRcIoWtoCoFgKOO+V6uUKW1isfcIAPgD\n4azH1B8IyzHHps8l1cMfCGdti77ZIEbGfTDoRDgmfGnLYZF9EZOtTsn7zDsbRNfQFCSVkFI23TEB\nIH93TVeHTN8f49+jpdBuifJlRW4pvfPOOzEwMJAy3ePxwGhcyAbq9Xp4PJ6UcsnMZh1EUZl23sBo\n+uX9wTDGJmcyLtPeUokygxqnLkY/FMwmNZyuhfLJr+M5XTMwm9QYGfdhYNSDu/Y24RdvX8kYS2x7\nb304CINOhc5eV8o2mqpNGHCkLm82qbOud2Nd9FezPVur5brEa6o2ZV7e4UFTtQlnrozD4ZpBZbk2\n43bMJjWs5Vo5xqXsr9jyI+OJHbLY9mNlclnXhT7Xiv4ScjWW024HRj0lU798Wk91LvZjn63dAsUf\n/0paT/UthePOc25u1lt9i/3YL7fdHrh9A873fginawZN1Sa535RL/xWI9sHMJjUcSWUz9Udjy8T6\njTHR/iMS+nnZYlBLSlzqn8y4/vaWSnQPubPGPzLuS/g7vg9rNqmzfgdwJO2DxbaTrd6xem6sL0ff\naPorIpyuGSgApLu9yGxSo28k/XJ9I9Np+87F0g/O1m775/v9zrh9lE78/Pi6LtZG4o+FxxfIeKzj\n23t8m8glpmxtyDnf3pKPjyOpHv7556Vla4uV5UB7SyWmM9TDbFJnfT/Gtpf1e1jSPnO6ZhCJIKVN\nx9c//phk+y6a7ftj/DpKod0mK4Z4V9N6rv/V1n1VB00wGAzwer3ya6/Xm5CAy8Tl8mWcV2c3pP2Q\nk0QBdbb08+rsBnR0jUESBTTXRj8kXe45tLda5PLJr+NZzVp0zJ+g6uwGvHasBy73HHZvsWXdHhD9\nYIjFHL+NnmF32u0ttl7nZHTfHDs3LNclXs+wG9s3VKZf3mbAmSvRuGxmLURl+gsg6+wGnDjvgHcm\nIMe4lP0VWz7b9uNjzLauTQ3mq7rEdSVPHstpt3V2w7q7hHe9Xba8nGNfLO0WYNuNYbtdmJ5tPxRL\n22W7jVpv7RYo7XNutthffq8LQLRPerHPhbYGc9r+Wbz4/uuuzVac7HSmlM3UHwUS+20xNrMWOo0K\nLvfcQp2yxDDnD6HWps+4/o6uMTRVly0af/zf8X1Yl3sOTVUmWM3atOuwmbVykmHXZuui28lW7zNX\nxtHeasGcPwRbRfrtWc1a2Ct0SJdyc7nncN0We9rlGqqMOJbmKrds/eBiabf1dgOOn3egvdWCjvl9\nlG0/x8rFLNZG4o+FQaeCUlAsehzj20Qu75GmKlPWY6pSCgltHkhsW0D0u2isfKa2qNOo0NE1hq3N\nFigUqeVc7rmM7bTOZsCJTodcLtf3rdWshV6rwocXUu9ASndMsn0Xzfb9MX7/l0K7jbcePyvjref6\nl8wtpZm0trait7cXk5OT8Pv9OH78OK699tqrWueODdaES16B6GW1SqWAGqsh7bztrRZ4fEEMj89g\na7NFvtxVI4kJlwLHv45fXiOJ8iXJ21stuDwYPSixdSWXb6s3Y3xqDmqVEiaDGm315pRtTvsCqLUa\nU5bPtt6tzRbYzHqoVcqEusTzB8IZl6+xGjDtC0Qvt5ZEKJVC2nLtLRYASIhxKfurrd6cUqf47fsD\nYezcaM1pXXvnn5tQ6jK12x0brKsUEa2UUj/2pR4/LU+pH/dSj5+Wr5SPfebYLTjfOyX3Sad9Abnf\nlEv/FQBq578sJJed9gVQb0vtj6pVStTZov22+Gk6jQhbhS6h7FwgBL0mfQyR+W1n6peOT83l1P+O\n/zu+DzsXCEGpFDJuX520DxbbT5nqrZai1zDoNSIiANSq9NvTa0Ts2WzDns32tH38G7alTlerlLh+\na2p/t1T6wddsjL63NPP7KNvxjM2P39+LtZHYsVCrlGipKYM0f9toclm9Jrre5Dax2HsEiD6SJ9sx\nlVRCyjPL1En1kFRC1rao04iosujg8QVhr9CnLYdF9kVMtjol7zO9RkRLTRn8gXBK2XTHBID83TVd\nHTJ9f4x/j5ZCuyXKlxUZNAEABgYG8Cd/8if46U9/iiNHjsDn8+HgwYPyKKWRSAQHDhzA5z73uUXX\nlctDvGOjT7XUlqGyXLPoKKU376rD2QyjlI6M+9AztDDqZmyklta6MljKNKmjlFZosbFufjTQa2tw\nqW9h1KVcRimNbXNgxINbdtcmLR83SunkDLoHlzBKaZURG+vKFkYpHZhC/8h0dJTQLKOUxuqfMkrp\nwOT8yFI1uDQwicHR2Cils+ganMo4qqs8Smls+1UGbKyLjlJaY9Pj+q123H5dA155vyfNKKVTuNDn\nwqYGM/Zus1/1KDfF9GDOUh41LZ/W468oSz32xdRuAbZdgO021+NeTG2X7XZ9tlugtM+5qbFHRym1\nVOrRVm/GxX4XugfdqLHqsbmxAl1DU+ibH6XTOTGDK0NT8tU9J8470FhjRGXZ/CilW6xwumagkVTw\nzQbQM+xGdaUejelGKZVHXYyOiNhYZUR18iilU9F+anWlHs01JmjVIi73T6J3dBp1tuhIp32jbgw5\n40YpHVkYpfTSgAu9I9H+Y1OVCb3D09H4q6Oj3sfit5ZHRyltqjaiqaYMnT0TGBnz4fptdoy6fOgd\nnsYN7fOjlA5PY3jMG/1+UKbB8XSjlI660T/qQY1Fj/YNFnT2TKBvfoTGzU1meGcD6B6MrqepxgSd\nWoXJ+VFKqyw6XOqfxMW+SdzYXoWRCS8uD0yh2qJHa20Z2urK5L6r0+PH68f6U/q10dFIR3Oenkkx\ntdvjl8ZwtnscWknEtM8Po07C9Iwf/aMeeURQ32wALbVl6Ox1YcgZHUm2udqEzl4XHLFRSgcmo23E\nZsDWFgsuD7jQNeCWvyO9eXwAVVYtGmzpRykdGfOheyj6/tg6P0pp7/A0RudHKR0d96F3dBr1diOM\nWhUm50cpleJGKfXNBdAzFHtvREcp9c6PUhprE5sbzWlHKXVMzECrFqHXqtA/6sHwmBeNVUbYsoxS\nGivXXGNCS20ZekamoBZFuL1+DDg9aK4xwV6hWxildP59V2PVY3NTBboGp+TvcdtbLLjY78KVgWj8\n9fOjlF4ccEEtivDMBNA3Oo06qwEmvYRZfxCbmywLo5RWGbClyYJz3WMw6dTwzATQP+rBtpaKuFFK\nRwGFAh5f9PhurCuDrUKH9ztGsLG+vKTabcwXnn59yev+33/28SUvU6zWa18ByM8VbiuWcMunXA+4\nySTB7fan/J1tXi5/X+3yua4r/gAXU1xGo4Tp6ezL6/USvN6FdRkMEjye6GuNRsTsbPQhnJKkhN8f\n/cVENgFC5gAAIABJREFUFAUE559vEF/3+DIAIAgKhMP5abbFeFJfzyc1YH3XPx8n9XxbyrHgsWPd\ncym7UnjOXdx6rjtQuudcq9WIubno7WvhMCDM36/idvuh0YhQqQQoFEg732SSMq53teZHIoBCkThf\nrVbD652DKAKzsyFoNMqU5QOBCFQqBUIhIBQKY3Y2CI1GRDAYhlodvTJnbi4IQVBAqRQwMxOATich\nGAwl9CXD4QhUKiVmZgIQRQHhcESeLwgK+e9gMJwyPTYt9ndM/PoFQSFvJ16s/WXq1y51erJia7dA\nNCaXK/o4oWAwLPfxY8cNiH4fEAQB/vkH9qvmr5ZSKBQIh8OQJCHh2CuVgN+fOB1YaFex/5MFg4AY\n93AltVotv6+SywSD0e0krye27vj3WbJMZZLjikSi/zKVUavV8PnmoFQutP1wGPJ+kyQBwegugyBE\ntxeJRBAIhKDTifB4AtBoVPJ+TW6zABLatiAoEr5/abUqzMxEr4yLvU9i5S2WxFvy498n8e+DUm23\nTLit375CPvoJq/oMt0KLTx7F/51tXi5/X+3yua5rqdtbqbhiybZsZeKTbQDkZBsAOdkGIOFEnnzS\nT1cGSO2wEBEREa0nmfqLs7NBzM4ufblim2+1quHzLZSN9QWTl59Jei58rI8ZDCZvJ7p8/Drjxfqg\nsf/jk3Hx4qen+zu5XLp1JMs0f6nTS0V8fz92XOO/GyR/H0h+HWvfqcc+/fRcWa3qRdvnarJa1Qnf\nrzLVP51YvTK1/3jp2nN0ewu3Uccfk3Rl071/Sr3dEi3Xqj7DjYiIiIiIiIiIaK1hwo2IiIiIiIiI\niCiPmHAjIiIiIiIiIiLKIybciIiIiIiIiIiI8ogJNyIiIiIiIiIiojxiwo2IiIiIiIiIiCiPmHAj\nIiIiIiIiIiLKIybciIiIiIiIiIiI8ogJNyIiIiIiIiIiojxiwo2IiIiIiIiIiCiPmHAjIiIiIiIi\nIiLKIybciIiIiIiIiIiI8ogJNyIiIiIiIiIiojxiwo2IiIiIiIiIiCiPmHAjIiIiIiIiIiLKIybc\niIiIiIiIiIiI8ogJNyIiIiIiIiIiojwqeMItHA7j0UcfxcGDB/HQQw+ht7c3Yf7zzz+P+++/HwcO\nHMCPfvSjQodDRERERERERERUUGKhN/Dqq6/C7/fjueeew6lTp/D000/je9/7njz/b/7mb/DCCy9A\np9PhnnvuwT333IOysrJCh0VERERERERERFQQBU+4nThxAvv27QMA7Ny5Ex0dHQnzN23ahOnpaYii\niEgkAoVCUeiQiIiIiIiIiIiICqbgCTePxwODwSC/ViqVCAaDEMXopjdu3IgDBw5Aq9Vi//79MJlM\ni67TbNZBFJUFi7mYWK3G1Q5h1ay1ui+l3a61ui/Veq5/sdV9qefbYot/JbHuxYXn3Nys57oDxVf/\nXNttscVdCKxj6Vgr59tijg0o7viKObZMCplTKMX9kc1aq89SXG3dC55wMxgM8Hq98utwOCwn2zo7\nO/Hmm2/itddeg06nw1e/+lW89NJLuOuuu7Ku0+XyFTTmYmG1GuF0Tq92GKtipeq+kiePXNvtej7u\nwPquf651L8Z2C/DYse65lV0pPOcubj3XHSjdc+56OG6sY37Wv1LWwvm2mGMDiju+fMZWjO12OYr1\nWC1HMbe9QstHP6Hggybs2rULR48eBQCcOnUKbW1t8jyj0QiNRgO1Wg2lUomKigq43e5Ch0RERERE\nRERERFQwBb/Cbf/+/Xj33XfxwAMPIBKJ4Mknn8SRI0fg8/lw8OBBHDx4EA8++CBUKhUaGhpw//33\nFzokIiIiIiIiIiKigil4wk0QBDz++OMJ01pbW+W/Dx06hEOHDhU6DCIiIiIiIiIiohVR8FtKiYiI\niIiIiIiI1hMm3IiIiIiIiIiIiPKICTciIiIiIiIiIqI8YsKNiIiIiIiIiIgoj5hwIyIiIiIiIiIi\nyiMm3IiIiIiIiIiIiPKICTciIiIiIiIiIqI8YsKNiIiIiIiIiIgoj5hwIyIiIiIiIiIiyiMm3IiI\niIiIiIiIiPKICTciIiIiIiIiIqI8YsKNiIiIiIiIiIgoj5hwIyIiIiIiIiIiyiNxKYXffvttvPzy\nyxgZGYEgCLDZbLjllltw5513Fio+IiIiIiIiIiKikpJzwu0f/uEfcPr0aXzqU5+CzWYDADgcDhw+\nfBinTp3C1772tYIFSUREREREREREVCpyTri9+OKLeOmllyAIiXeh3nvvvbj33nuZcCMiIiIiIiIi\nIsISnuGmVqsxMjKSMn1oaAiSJOU1KCIiIiIiIiIiolKV8xVuf/Znf4bPfe5zaGpqgtVqBQA4nU70\n9PTgqaeeKliAREREREREREREpSTnhNtNN92El19+GadPn4bD4UAkEoHdbsc111zDK9yIiIiIiIiI\niIjm5XxLKQAMDw+joaEB99xzD3w+H1555RW89tprWZcJh8N49NFHcfDgQTz00EPo7e1NmH/69Gk8\n+OCDOHToEP7wD/8Qc3NzS68FERERERERERFRkcj5Crd/+7d/w7PPPotwOIwbb7wRw8PD2L9/Pw4f\nPoyuri586UtfSrvcq6++Cr/fj+eeew6nTp3C008/je9973sAgEgkgm984xv4x3/8RzQ2NuI//uM/\nMDg4iJaWlvzUjoiIiIiIiIiIaIUpIpFIJJeC9913Hw4fPoyxsTHce++9eP/996FWq+H3+/HZz34W\nzz//fNrlnnrqKezYsQP33HMPAGDfvn14++23AQBdXV3467/+a7S0tODSpUu49dZb8Xu/93uLxhIM\nhiCKylzrSFQU2G6pFLHdUqli26VSxHZLpYjtlkpRru32vkd+seR1H/mfn15OSLQG5XyFWygUgiRJ\nqK2txRe+8AWo1eqEeZl4PB4YDAb5tVKpRDAYhCiKcLlc+PDDD/Hoo4+ioaEBf/AHf4D29nbs3bs3\naywuly/XsEua1WqE0zm92mGsipWqu9VqLPg2YnJtt+v5uAPru/651r0Y2y3AY8e651Z2pfCcu7j1\nXHegdM+56+G4sY75Wf9KWQvn22KODSju+PIZWzG22+Uo1mO1HMXc9gotH/2EnJ/hduedd+Jzn/sc\nQqEQvvKVrwAAOjs78eCDD+I3fuM3Mi5nMBjg9Xrl1+FwGKIYzfOVl5ejsbERra2tUKlU2LdvHzo6\nOnINiYiIiIiIiIiIqOjknHD7oz/6I/zxH/8xlMqFyy4lScJXvvIVOQGXzq5du3D06FEAwKlTp9DW\n1ibPq6+vh9frlQdSOH78ODZu3LjkShARERERERERERWLJY1SumfPHnzzm9+UX7e0tODWW2/F1772\ntYzL7N+/H5Ik4YEHHsBTTz2Fr3/96zhy5Aiee+45SJKEb33rW3jkkUdw4MABVFVV4bbbblt2ZYiI\niIiIiIiIiFZbzs9w+4u/+Av09/ejo6MDly5dkqeHQiG43e6MywmCgMcffzxhWmtrq/z33r17cfjw\n4aXETEREREREREREVLRyTrh98YtfxODgIL71rW/hy1/+sjxdqVQmJNCIiIiIiIiIiIjWs5wTbmq1\nGjfccAP+5V/+JWWez+dDeXl5XgMjIiIiIiIiIiIqRTkn3P7yL/8SzzzzDH77t387ZZ5CocBrr72W\n18CIiIiIiIiIiIhKUc4Jt2eeeQYA8PrrrxcsGCIiIiIiIiIiolKXc8ItpqurCz/96U8xNTWVMP2p\np57KW1BERERERERERESlaskJty9/+cu4++67sWnTpkLEQ0REREREREREVNKWnHAzmUwJo5QSERER\nERERERHRgiUn3O6//3783d/9HW688UaI4sLie/bsyWtgREREREREREREpWjJCbcPPvgAZ86cwcmT\nJ+VpCoUCP/zhD/MaGBERERERERERUSlacsKto6MDv/zlLwsRCxERERERERERUckTlrpAW1sbOjs7\nCxELERERERERERFRyVvyFW79/f24//77YbVaoVKpEIlEoFAo8NprrxUiPiIiIiIiIiIiopKy5ITb\nd7/73ULEQUREREREREREtCbknHD7+c9/nnV+bW3tVQdDRERERERERERU6nJOuP36178GAPT19aG3\ntxe33norlEol3nnnHWzYsAG/+Zu/WbAgiYiIiIiIiIiISkXOCbennnoKAPDQQw/h+eefR0VFBQBg\namoKX/rSlwoTHRERERERERERUYlZ8iilDocD5eXl8mutVgun05nXoIiIiIiIiIiIiErVkgdNuO22\n2/A7v/M7uOOOOxAOh/Hyyy/jrrvuKkRsREREREREREREJWfJCbevf/3reOWVV/DBBx9AoVDgC1/4\nAj7xiU9kLB8Oh/HYY4/hwoULkCQJTzzxBBobG1PKfeMb30BZWRn+9E//dKkhERERERERERERFY0l\n31IKAPX19bjrrrtw5513Qq/X4/DhwxnLvvrqq/D7/XjuuefwyCOP4Omnn04p85Of/AQXL15cTihE\nRERERERERERFZclXuH3ta1/Dhx9+iKmpKbS0tKCzsxO7du3CZz/72bTlT5w4gX379gEAdu7ciY6O\njoT5J0+exEcffYSDBw+iq6trGVUgIiIiIiIiIiIqHktOuB07dgyvvPIKvvnNb+Lhhx9GJBLB448/\nnrG8x+OBwWCQXyuVSgSDQYiiCIfDge9+97v4p3/6J7z00ks5x2A26yCKyqWGXpKsVuNqh7Bq1lrd\nl9Ju11rdl2o917/Y6r7U822xxb+SWPfiwnNubtZz3YHiq3+u7bbY4i4E1rF0rJXzbTHHBhR3fMUc\nWyaFzCmU4v7IZq3VZymutu5LTrjZbDaoVCq0trbiwoULuOeee+D1ejOWNxgMCfPD4TBEMbrZl19+\nGS6XC7//+78Pp9OJ2dlZtLS04DOf+UzWGFwu31LDLklWqxFO5/Rqh7EqVqruK3nyyLXdrufjDqzv\n+uda92JstwCPHeueW9mVwnPu4tZz3YHSPeeuh+PGOuZn/StlLZxvizk2oLjjy2dsxdhul6NYj9Vy\nFHPbK7R89BOWnHCz2+145plnsHfvXnz7298GAPh8mRvrrl278MYbb+Duu+/GqVOn0NbWJs97+OGH\n8fDDDwMAfvazn6Grq2vRZBsREREREREREVExW3LC7Vvf+hbeeust7NixA3fccQdeeOEFPPbYYxnL\n79+/H++++y4eeOABRCIRPPnkkzhy5Ah8Ph8OHjx4NbETEREREREREREVnSUn3AwGA6655hq8+eab\nOHToEG677TbU19dnLC8IQsoz3lpbW1PK8co2IiIiIiIiIiJaC4SlLvDiiy/ii1/8Ip544glMTU3h\ngQcewC9+8YtCxEZERERERERERFRylpxw+/73v48f//jHMBgMsFgs+M///E/867/+ayFiIyIiIiIi\nIiIiKjlLTrgJggCDwSC/ttlsEIQlr4aIiIiIiIiIiGhNWvIz3DZu3Ih///d/RzAYxPnz5/GjH/0I\nmzdvLkRsREREREREREREJWfJl6b5fD6Mjo5CrVbjz//8z2EwGPBXf/VXhYiNiIiIiIiIiIio5Cz5\nCrfBwUE8+eSTeOSRRwoRDxERERERERERUUlbcsJNEAR8/OMfR3NzM9RqtTz9hz/8YV4DIyIiIiIi\nIiIiKkVLTrh99atfLUQcREREREREREREa8KSE27XX399IeIgIiIiIiIiIiJaE5Y8aAIRERERERER\nERFlxoQbERERERERERFRHjHhRkRERERERERElEdMuBEREREREREREeURE25ERERERERERER5xIQb\nERERERERERFRHjHhRkRERERERERElEdMuBEREREREREREeWRuNoBEBERERERERERFcIXnn59ycsc\n+Z+fvurtFjzhFg6H8dhjj+HChQuQJAlPPPEEGhsb5fkvvPACfvCDH0CpVKKtrQ2PPfYYBIEX3hER\nERERERERUWkqeGbr1Vdfhd/vx3PPPYdHHnkETz/9tDxvdnYWf//3f48f/vCH+MlPfgKPx4M33nij\n0CEREREREREREREVTMETbidOnMC+ffsAADt37kRHR4c8T5Ik/OQnP4FWqwUABINBqNXqQodERERE\nRERERERUMAW/pdTj8cBgMMivlUolgsEgRFGEIAiorKwEADz77LPw+Xy4+eabF12n2ayDKCoLFnMx\nsVqNqx3CqllrdV9Ku11rdV+q9Vz/Yqv7Us+3xRb/SmLdiwvPublZz3UHiq/+ubbbYou7EFjH0rFW\nzrfFHBtQ3PEVc2yZFDKnUIr7I5u1Vp+luNq6FzzhZjAY4PV65dfhcBiiKCa8/va3v43u7m585zvf\ngUKhWHSdLpevILEWG6vVCKdzerXDWBUrVfeVPHnk2m7X83EH1nf9c617MbZbgMeOdc+t7ErhOXdx\n67nuQOmec9fDcWMd87P+lbIWzrfFHBtQ3PHlM7ZibLfLUazHajmKue2thKvtJxT8ltJdu3bh6NGj\nAIBTp06hra0tYf6jjz6Kubk5/PM//7N8aykREREREREREVGpKvgVbvv378e7776LBx54AJFIBE8+\n+SSOHDkCn8+H9vZ2HD58GNdddx0+//nPAwAefvhh7N+/v9BhERERERERERERFUTBE26CIODxxx9P\nmNba2ir/3dnZWegQiIiIiIiIiIiIVkzBbyklIiIiIiIiIiJaT5hwIyIiIiIiIiIiyiMm3IiIiIiI\niIiIiPKICTciIiIiIiIiIqI8YsKNiIiIiIiIiIgoj5hwIyIiIiIiIiIiyiMm3IiIiIiIiIiIiPKI\nCTciIiIiIiIiIqI8YsKNiIiIiIiIiIgoj5hwIyIiIiIiIiIiyiMm3IiIiIiIiIiIiPKICTciIiIi\nIiIiIqI8YsKNiIiIiIiIiIgoj5hwIyIiIiIiIiIiyiMm3IiIiIiIiIiIiPKICTciIiIiIiIiIqI8\nYsKNiIiIiIiIiIgoj8TVDqCQTCYJbrc/5e9s83L5+2qXz3VdudRlNeK62nVptSrMzAQAABqNiNnZ\nIABAp5Pg86XWXRQFBIPhlOkAIAgKhMORlL+zLUNERERUykwmKWWa2+1PO71U5yeXdbv9MBolhMOA\nUgkEAhEIggKRSPT19LQfen10mXA4gkgkAlEUEA4DkUgEMzMBiKIAURTkMgCgVAoIBEIIBsMQRQGC\noJC36feHAKT2N2PLx09fivhtrDfZ2sBqW43YAoEIVCoFwmFAmL8UZnY2BI1GiXAYCAbDkKToDIMh\n1r6BcDgMhUIBlUoBjycASVIiHI4gGAxDrRYxNxeEICgQDIZTviMJggJ+fwiSpEQwGEY4HEn73SlT\nu1/Mct8XRGvRmky4fXBxDKcvOzEw6kGd3YC2BjOOnhhEXbUOW5usCfO2NlfgzWODsFVq5XIN1QZU\nlmtx/JwDNTY9trda8NaxAdjtWmxpjC4/Nj6D2/bUoaNrXF5Xe4sFvzo5gBuvrcO57uj0ersBG+vL\n8f5Hw/jY7rjpVQZsb42ua8jhxZ5tdoyMe9E7PB2Nud6MoydPoL7aAOt8LI3VRljNWpw878TurTa5\nfPy6BkY9aKw2orpSjw86RhOWuW6bDcNjXvSPeHDdVhuckzPoHnQnlK+x6bFjgxUdXU44nAt1TBdj\nbN/V2vXyNnZtsWJscgZdg240VBnQniau42cdifHP76OjJ4dQZdWhrcGMd06exK4tVgzLZYy4YZsd\n7Y1mAECfw4P3zo7gYt8U9m63Y2QiWpcb2u0YcnrQk2aZYpfcbndssOL6tsrVDotWQKkf+1KPn5an\n1I97qcdPy1fKx/6Di2M4/eL5uNgt6OwdR9eAB43VBmxpsuBs9zga7Sb0jLjlck1VJvQ53GiwmxAO\nhzEy7oNGEuH2+THi9OGW3bW42OdKWO+57gno1CpMef0YdHrQWGVEa22Z3F9rqjKh3+HG5sYKzMwF\n0TXkxoDDgzqbASadhEnvHDY3VODK4CR6h6fRWG2EvUKP4+dG0VRjlPva1VY9NjWUQxCAd04O46ad\n1bjUP4n+UQ9qbQa01ZdDFBW43D+F7iE3qq16bGmsQM/wFDSSmBDflqYKnO0eR/9ItA++rcWy8LrK\ngG1NFnTM98VrbQZsajDDM+vH8bMOtNQZsbHOjDNxffutzRb4Zv147/QoWupMaLQb0dE9gZExL5qq\njdBrJUQiYdy4tQoNNsOix6/P4cHFgUlcHnRjZMyLDfXluGVHdU7LlrIPLo7hbNcYeoenUVWpR1O1\nEUplBDq1JB/Xqko9GuzGaPLUG4B3JiBPb6wyIhyJ4MQ5B2rtBmysK0P3sBvdQ240V5tgrdBCEpXo\nGXan/c5i0kmYnvHDqJUw5Z3DpgYzeobd6BqMrr/OZoCoVKBML+HSgAsa1UK7r7cZYNRJ8M0F0Fpb\nDrfXjwGnB2aDGm6fP+U91lZnRvfIJNrqzHB5/OgdmV4o6/CgwW7EhroynO91YXjMm7B+a7kOvtn/\nv707j2vqSvsA/iMJCfsiAoKAAiOKUkVxrQvFhUrZRhFBFLHb6Eyrti6vW1VsHWuLrePa1rGt1plP\nP/WtWl/HttSq1Y4KqK0iUkUFBWRHtiSQ9bx/YGKAJEQNksDz/Svknnvy3HuenJx7uMmRoU7UXNbb\nzQ5B/i5QKBS4ll8DZzsBhI1S2Fnz0SCW4n6lCMMGuKGyRox7ZQ3w83RocQ07wMcZt+/XwNZaAM+e\nNqgTylBY3oCKajFGDHJHSZUIRWUN8O3tAFsrS9SLpBg10B321pYtrrNKq8W4U1yHXj1t8afeDgjw\nctKas6rrsxv3ajGgjxPGDDLsfUFIV2bBGOvQ6WelUonU1FTcvHkTfD4fGzduRJ8+fdTbT506hV27\ndoHH4yEuLg4zZ85st87Kygad27LyqvDlseuQyBTq5wSWXESP94OTvQAHvv+jzbZZ4f2x73iuuty3\np25BYMnF8EB3nMsugcCSi5TIQADA/uPN+78WG6S1rpejA/HlsbbPp0QGqvcFgLGDPXHpj3JIZIoW\nj1vHrC2W6PF+OPZrvta6NPfXtY+h5TXPl659Wp87Q+JqXU7b8c4K74+vf7rZpswbMwbDwdoS7x+4\n3CYuXa/3xozBOifdXF3ttT7fEZ4kb1+OHmQ2FwHG4upqr/dcdTVP0vamkrcA5a4K5a1h7W4quUt5\n26y75S1g3n2urthTIgOx57sczJjYD8d+zdc7zjr2az5GDXKHQgn1mEm135OM16LH+6HigQiZ1w0f\n72mONzUfx07wg4Od9rF67AQ/HDx5C8Cj8eXwQHe949nWf+saJ8ZO8MP9ShE8etpqjXdWeH/cKqoF\nAJ2vd+mPcqxKDtE7uVBYIcSp34q1nqv29n0Spp63usb6sRP8cPRs23ZQ5W3r3AGACcGe6vOq7zrn\n0h/lOvNy1CB3AIBfbyetcQ0PdAeXA2Re1517qrqTXwpE3r0Hestqy9NLf5Rj1CB3nL1S0qJs8kuB\n+KPgQZu8N+SablZ4f+Tfb85fQ86R6hj1lRs1yB0Th3mpc9bV1R6Xr5eqr880yz5ubptK3mp6ZfOp\nx677i5UTH3sfU9VVxgpP0o7HPoo16Nj15W2H/4bbzz//DKlUim+++QZLly7F5s2b1dtkMhnef/99\nfPHFFzhw4AC++eYbVFVVPdXrZd+ubPFGBwCJTAFxoxS5BdVat+UV1cDFUQCJTIGSSiHsbSwhkSnQ\nJJVDYMmFRKbA9fxq5BXWQiJTwMPFWmdd2berwbdse1pz8h+VF1hy0SSVQyJTtHjcui5tsQBASaVQ\na12t99eMv6RS+FjlS6tEyCusaTdG1bkzNK7W5XQdb15RTZvzKJEpcDG3HBdvlLeJS1+MWbnlbWIw\nNbryNvt2ZSdFRJ4Vc297c4+fPBlzb3dzj588OXNue12x5+RXI7CPI0oqheBbcvSOs/iWHIib5FAo\nlJDIFLC3sdRbvrXW47WKB2I0SnSPY7Xtrzne1HxcVi3GH3cfaK2ruKL5NVXjPQDtjmc1/7a3sdRZ\nvrhCCGs+V+d5yCuqgTWfC4mO/SUP47lwXf948+KNcoiatNfR3r7m7JqOvM0rqtFavrhCezuIHuat\nKl8kD9tZYMlVn9f2rnP0vT9ETXJIZUrkl9S1iUn1elJZ81cu9V278S05yCusgbjp8fMUAERNj55X\nbcstqIY1n9uiPkOv6fKKasDnGXaOZAoFpDJlu+VETXJcvFHR4vkL18u6XW4TYogO/0rp5cuXMX78\neABAcHAwcnJy1Nvu3LkDHx8fODo6AgBCQkJw8eJFRERE6K3T2dkGPB5X67bi8rYf7gDg6myDnPwH\nOvcJ8uuJM7/fR3GFEH09HHDtTjUqaxrh7CBAWbUYReVC9HSyBgCMGOiBK3naB2bF5Y/2V8frIGgR\nl7ODAJU1jW0et6lLSyyq57XV1Zpm/MUVQvX+hpQvqmhAT0fr9mN8eO5uFtYYFJezg6BFOV3Hq+08\nAsC9sga4OreNS1+MhWUNz/S/Jbo8Sd4WlwtNIvZnrTsds6m3vb68BUw//mepOx2vObQ79bmG6W7H\na+pt/6R5Gxf2Jxw6fRt9PRzaHWdV1DSqx7TtlVeNC7XVc+1ONYSNMlTV6h7Hattfc7yp+bi9uvp6\nOKCytlG9jyHjWdXffT0c9I63w0K8cPpysfbt5c3b8x7e5dZaxcPXu1lYozeHyh406oyhvX1Nnb68\nLdKTt63zQ991QuXDvFXtU6FxbWTI9YAqD9qrXypXas3bCo3Xb+/aTShuzuXHzVNV+davr8pBQ699\nWlwDtspfffuVVYsNuv6rrGmEBVp+ftwo1P7+MOXcbm+M+zRM9ZifVFc7nsfxtMfe4RNuQqEQdnaP\nbiPlcrmQy+Xg8XgQCoWwt390ALa2thAKtXeCmmpqxDq3ebnbobC87W1/lTVindu83O2Qk998Z52X\nmx2u3Wl+7OpsjZyHEz7e7nbgcZvfkBdzS+Hb20lnXddut7xLr6ZegpBAN3X5mnoJgvxdUFje0OJ2\nw8RCAAAgAElEQVRxm7p0xDJsgKvWulrT3MfLzQ6XH/4nwpDy3m724HIt2n0N1bkTiuUGxVVTL2lR\nTtfxajuPANCnlz0cbC3bvIa+GH166b4N9ll2Hk+St17udl3iFt7H0VVuWzbUk7S9qeQtQLmrQnn7\n6Hl958FUcpfytll3y1vAvPtcfbH/eCEfvd1skXOnut1xZX8fZ/C4zd8iuFtar7f85VZ3sWjWAwB2\n1pbgcPBY+2uONzUf21lbwlrA0xu7VKZEkL+L3uPUrFP1d15hDQJ8nHXWfb9CiN5utjrP7/0KIVyd\nrbVud3O2xrU71XhhmKve91OvHs0TGdrq6O/jbPT3oqnkrbeevL38R8v8qKmXIGSAm852teRyUFMv\nAfDovAMw6HpAlQfP+ffUWz+fz1W/hiY3Z2vwHr5+e++xof3dYGGBx85TVfmcVjccqHLQ0GufFteA\nrfJX3369XGzAQfvXf67O1nDvYaPOWVdXewzwccK90vo2ZR83t00lb59WV/ps7Y5jBU0m/5VSOzs7\niEQi9d9KpRI8Hk/rNpFI1GIC7kkM/pNri9twgeavN9pY8zHQ10XrtgBvZ1TXSSCw5MLT1Q4NYhkE\nllxY8XnqW2oH+bmgfx8nCCy5KK1u1FnX4D+5qG831hTk59LitmErPk99q6/qceu6tMUCAL1d7bXW\n1Xp/zfg9Xe0eq7xHT1sE+Di3G6Pq3BkaV+tyuo43wNu5zXkUWHIxYqA7RgxwbxOXvhhHDnRvE4Op\n0ZW3g//k2kkRkWfF3Nve3OMnT8bc293c4ydPzpzbXlfsQX4u+ONeHXq72kMqU+odZ0llSthY8cDl\nciCw5KJBLNNbvrXW4zW3HjawEegex2rbX3O8qfm4l4sNAn17aK3Ly635NVXjPQDtjmc1/24Qy3SW\n93KzQ6NUofM8BHg7o1Gqe5wpeBjPmEH6x5sjBrjD1kp7He3ta86e05G3Ad7af1/Zy81Oa3nbh3mr\nyhfBw3aWyBTq89redY5UpoSnq+76+ZYc+Hk6tolJ9Xqqn7vRd+0mlSkR4OMMW6vHz1MAsLXitfhq\npsCSi4G+Lmh8uGKuIdc+mu+rAG9nSOWGnSNLLhd8S0675WyteBgxwK3F82MG9ep2uU2IITp80YT0\n9HScPn0amzdvxpUrV7Bz507s3bsXQPNvuEVGRuLgwYOwsbFBYmIiPvnkE7i7639jGvIj3qrVp7zd\n7dDPgFVK3Xtaq8v5eNijp5OVcVYp7WWPfl6OOlcpvXa7EvcrRBgZ5I6yajHultS3iFkzlj6e9nB1\nerTiqLq8Rl1F5UL09XRALxeb5lVKtexTWNqA4QPd1KuJNpe3RVZOmd5VSjVjbLFKaS9b9WsMC3RF\ndV0T7hTXwcfDDkF+j853X08HeLjY4uL18jbx9/NqXqXUw9UG/Xyc8d/fSjBsoCvKq8UoKKmHTy97\njBzYepXSctwqqsXooF6oeCBGfkk9Rg5yR2mVSOs+2pjSD3Oa86ppxtQd/4vyuG1vSnkLUO4ClLeG\ntrsp5S7lbffMW8C8+9y2sbvgRmE18ouE6ONph8A+LsgtqIZPe6uUPhDDyvLhKqVVYkwY1hu3CmtQ\npFHvHwUPYC2wVK/K2NfDAX6eDurxWp9eDiiqrMcAn+ZVSgtK6lFU8WhVx+bVIHsgv6QOd0uax5vu\nzja4mFuOvp4Oj8barrYI0FyldKgHbhXVoaisAV5uzSvZ83gWuF1ch4L79fB0tcWAPj1wt6yu+Rg0\n4hvQx7nNKqW5BdUo1Fil9HpBdfNxutkhoI8zRI1SXLxeAX8vB/zJy6nNKqWNkuZVSv17O8Db3R65\nBQ9QUi2Cr4cDbK0toVQyjB7o/hirlNbhzv06lFaLEODlhHEdtEqpqeVtbkE17pbUw6OnLfp6OIDD\nVTavUvqwXT1Uq5TygHqhDKImmfr5Ph72UCoZLuVWwNvdDv5ejrhb1rzKqK+nA9ycrWGpsUqp+pql\ntF69aq5qldJ6kQQBPs64W9qA/Pt18NBYpdTBlo/b92sh4PHUq4p6u9vB3pqPRqkcfp6OqH+4eqmT\nnQANYimKHq7Mq7lK6d3yOvTr7YQaoQyFZfXqssUVQvg8XO33xr0alFSJWtTf09Ea4iYZ6kXN9Xo/\nvL5UKBTIya+Bk50AwiYp7KwerVIaMsANlbWNuFdaDz9PR7hoXMMO8HHG7ZIa2FkJ4PFwldKi8gaU\nV4sxMsgdpZWi5tVNezvCxoqHBrEUIwNVq5Q+us4qeyDC7eI6eLjYwr+3IwK8HFvkrOqzRHV9drOw\nBv19nDFmkGHvC02mlLcqtGhC1xgrdNaiCc9sldK8vDwwxrBp0ybk5uZCLBYjISFBvUopYwxxcXGY\nPXt2u3Ua2uAODnzU10vbPNa3zZDHT7u/oXVpJrcpxfW0dVlbW6KxUQYAsLLioenhj4ra2PAhFrc9\ndh6PA7m87V2DAMDhWECpZG0e69tHkyl26l2lU3tS3fn4DT12U8xbgNqOjt2wss8K9bnt687HDphv\nn+vqag+JpO1X3urrpXBw4Ovcz5y2CwSCNsdYXy+FvT0fSiXA5QIyGQOHYwHGmv9uaJDC1rZ5f6WS\ngTEGHo8DpRJgjKGxUQYejwMej6MuAwBcLgcymQJyuRI8HgccjoX6NaUP7ypqPd5U7a/5/OPgcCzg\n4tKxX2E3tbwFdOeuKdCWc8+CTMZgaWkBpRLgPPzuWVOTAlZWXCiVgFyuBJ/PgUAgQGNjc3xKZfM1\ntoWFBSwtLSAUysDnc6FUMsjlSggEPEgkcnA4FpDLlW2ukTgcC0ilCvD5XMjlSiiVTOu1k668b611\nX/qk7wtVXc8KTbgZpquMFTprwq3Df8ONw+Hg3XffbfGcv7+/+vHEiRMxcWLHJKTmhI/mY33bDHn8\ntPsbWtfjvt6ziutp61JNtgFQT7YBUE+2taZv4kyzM9d8bMhkGyGEEEKIOTJkvGjO211dBVrLNjTo\n318k0r9dLldqGSMqWmzXRtd480knFZ50v66gvRzoLLpy7llo1LI2gWqyFwCamprjEwrbxqfaV/Oa\nSnWtpcozXddImq+hLfefNNe7c34T0lqH/4YbIYQQQgghhBBCCCHdCU24EUIIIYQQQgghhBBiRDTh\nRgghhBBCCCGEEEKIEdGEGyGEEEIIIYQQQgghRkQTboQQQgghhBBCCCGEGJEFY4yWESGEEEIIIYQQ\nQgghxEjoDjdCCCGEEEIIIYQQQoyIJtwIIYQQQgghhBBCCDEimnAjhBBCCCGEEEIIIcSIaMKNEEII\nIYQQQgghhBAjogk3QgghhBBCCCGEEEKMiCbcCCGEEEIIIYQQQggxIppwI4QQQgghhBBCCCHEiGjC\nzUTIZDIsXboUiYmJSEpKwp07d3Dv3j3MmjULSUlJWL9+PZRKZWeHaXRXr15FcnIyAOg83oMHD2L6\n9OmYOXMmTp8+3ZnhGpVSqcS6deuQkJCA5ORk3Lt3r8X2U6dOIS4uDgkJCTh48GAnRdkx2jv2ffv2\nITIyEsnJyUhOTkZ+fn4nRdpxNHNfk6m3O+Vt985bwDxztzvnLUC5C5hn3mpqrw3N3bRp09T5t2rV\nqi41Bu7OY13A9HLXFNtDJpNh+fLlSEpKwowZM3Dy5EmTiQ0AFAoFVq1ahcTERMyaNQt5eXkmFZ+x\ndOexAo0TOnCcwIhJOHHiBFu0aBFjjLH//ve/7M0332Tz589nGRkZjDHG1q5dy3766afODNHo9uzZ\nw6Kiolh8fDxjjGk93oqKChYVFcUkEgmrr69XP+4K0tPT2YoVKxhjjP3+++9swYIF6m1SqZRNnjyZ\n1dbWMolEwqZPn84qKys7K1Sj03fsjDG2dOlSdu3atc4I7Zlonfsq5tDulLfdN28ZM9/c7c55yxjl\nrrnmrab22tCcNTU1sdjY2BbPdZUxcHcf6zJmWrlrqu3x7bffso0bNzLGGKupqWGhoaEmExtjzdep\nK1euZIwxlpGRwRYsWGBS8RlLdx4r0Dih48YJdIebifD19YVCoYBSqYRQKASPx8P169cxcuRIAMCE\nCRNw/vz5To7SuHx8fLBjxw7139qONzs7G0OHDgWfz4e9vT18fHxw48aNzgrZqC5fvozx48cDAIKD\ng5GTk6PedufOHfj4+MDR0RF8Ph8hISG4ePFiZ4VqdPqOHWjOhT179mDWrFn47LPPOiPEDtU691XM\nod0pb7tv3gLmm7vdOW8Byl1zzVtN7bWhObtx4wYaGxvxyiuvYO7cubhy5UqXGQN397EuYFq5a6rt\nMXXqVCxevBgAwBgDl8s1mdgAYPLkyXjvvfcAACUlJXBwcDCp+IylO48VaJzQceMEmnAzETY2Nrh/\n/z4iIiKwdu1aJCcngzEGCwsLAICtrS0aGho6OUrjevHFF8Hj8dR/azteoVAIe3t7dRlbW1sIhcJn\nHmtHEAqFsLOzU//N5XIhl8vV27rqcQP6jx0AIiMjkZqaiv379+Py5ctmd0t6e1rnvoo5tDvlbffN\nW8B8c7c75y1AuWuueaupvTY0Z1ZWVnj11Vfx+eefY8OGDVi2bFmXGQN397EuYFq5a6rtYWtrCzs7\nOwiFQixatAhvvfWWycSmwuPxsGLFCrz33nuIjo42ufiMoTuPFWic0HHjBJpwMxH79u3DuHHjkJ6e\njqNHj2LlypWQyWTq7SKRCA4ODp0YYcfjcB6lo+p47ezsIBKJWjyvmfTmrPWxKZVK9Ru9Kx83oP/Y\nGWNISUlBjx49wOfzERoaitzc3M4K9Zkyh3anvKW81cbU27475y1AuauLObW9vjY0d76+voiJiYGF\nhQV8fX3h5OSE6upq9fauNAbubmNdwLRz15Tao7S0FHPnzkVsbCyio6NNKjaVDz74AOnp6Vi7di0k\nEonJxfe0uvNYgcYJ2hmj3WnCzUQ4ODioG8/R0RFyuRwDBw5EZmYmAODs2bMYPnx4Z4bY4bQd7+DB\ng3H58mVIJBI0NDTgzp07CAgI6ORIjWPYsGE4e/YsAODKlSstjsvf3x/37t1DbW0tpFIpLl26hKFD\nh3ZWqEan79iFQiGioqIgEonAGENmZiaCgoI6K9RnyhzanfKW8lYbU2/77py3AOWuLubU9vra0Nx9\n++232Lx5MwCgvLwcQqEQY8eO7ZJj4O421gVMO3dNpT2qqqrwyiuvYPny5ZgxY4ZJxQYA3333nfpr\nhNbW1rCwsEBQUJDJxGcs3XmsQOME7YzR7qbx7wWCefPmYfXq1UhKSoJMJsPbb7+NoKAgrF27Fh9/\n/DH8/Pzw4osvdnaYHWrFihVtjpfL5SI5ORlJSUlgjOHtt9+GQCDo7FCNYsqUKTh37hwSExPBGMOm\nTZtw7NgxiMViJCQkYOXKlXj11VfBGENcXBzc3d07O2Sjae/Y3377bcydOxd8Ph9jxoxBaGhoZ4fc\nocyp3SlvKW81mUvbd+e8BSh3WzPHttfWhl3FjBkzsGrVKsyaNQsWFhbYtGkTnJ2du+QYuLuNdQHT\nzl1TaY9PP/0U9fX12L17N3bv3g0AWLNmDTZu3NjpsQFAeHg4Vq1ahdmzZ0Mul2P16tXw9/c3iXNn\nTN15rEDjhJaM2e4WjDHWQXESQgghhBBCCCGEENLt0FdKCSGEEEIIIYQQQggxIppwI4QQQgghhBBC\nCCHEiGjCjRBCCCGEEEIIIYQQI6IJN0IIIYQQQgghhBBCjIgm3AghhBBCCCGEEEIIMSKacCOEGNWq\nVavw4osv4j//+Y/R6165ciUOHz5s9HpJ17Zq1SoEBQUhKCios0MhpMPs2LEDO3bseOp6iouLMXHi\nRIPLnzx5Etu2bQMAbN++HZcuXXrqGAihvCKmTlefe+3aNaxZs6bN85p967Zt23Dy5EkAQHJycscG\nSshjeNxrrddffx3l5eUoKirC6tWr1c9v27YNERERiIyMxJdffql+/vz584iOjkZ4eDi2bt1q1NhN\nFa+zAyCEdC1HjhxBdnY2+Hx+Z4dCCIDmnPzhhx/w6quvdnYohHQ5kyZNwqRJkwAAFy9exKhRozo5\nItIVUF4Rc/Xcc8/hueee01tm8eLF6sdZWVkdHRIhHeaf//wnACAzMxNFRUUAmnP6woULOHbsGORy\nOV566SWEhobC09MTq1evxoEDB+Dh4YH58+fjzJkzCA0N7cxD6HA04Wam5HI5UlNTcevWLVRVVcHX\n1xc7d+7EwYMH8a9//Qv29vbw8/ODj48PFi5ciLNnz2L79u2Qy+Xw8vLCe++9B2dn584+DNLFLFiw\nAIwxxMfH4+WXX8b+/fuhVCoxaNAgrF+/HgKBAGPHjkVYWBguXboEV1dXJCUl4cCBAygrK8PmzZsx\ncuRIZGVlYevWrWhqakJdXR2WL1+OiIiIFq/13Xffaa2fEE2aOSmTyQA0//du5MiRmD59OgCgf//+\nuHnzps46bty4gXXr1kEul0MgEOD9999H3759dfarP/zwA7788ks0NTVBIpFg48aNGDFiBL788ksc\nOXIEHA4HgwcPxrvvvgulUolNmzbhwoULsLCwQExMDP7yl78gMzMTn332GaysrHDnzh30798fW7Zs\noYnsbmzPnj344YcfoFAoMG7cOCxfvhyff/45Dh48CGdnZzg4OGDw4MF669CVm7m5ueo7MgYMGKAu\nv3LlSlhbW+Py5ctoaGjA6tWrcfToUdy4cQOTJ09W/yc8KysLo0ePRk5ODt555x3s3LkT/fv379Dz\nQZ69srIyLFu2DGKxGBwOB++88w6Cg4ORnZ2N999/H01NTXB2dsaGDRsgEomwbNky9d3up0+fxjff\nfINPP/1Uay7fv38fr732GpydnSEQCBATE6M1r+bPn49Tp06Bw+EgKysLe/bswd69e9uN8fz589i8\neTMYY/D09MRHH30EGxsbnf1vWloalEol+vXrh3Xr1uHdd9/FrVu3oFAo8PrrryMqKqqzmoFoYIxh\ny5Yt+Pnnn8HlcpGQkICUlBQUFBRg3bp1qK2thY2NDdasWYPBgwcb1Kfp+1zWzAsvLy9kZ2cjPj4e\nYrEYM2fOREpKCjIzM7Fz504cOHBAb986cuRI5ObmAgDi4+Mxc+ZMZGRk4KOPPgIA7Ny5E3w+H3/5\ny1/U+124cAFpaWkAAEdHR3z00Ufo0aMH9u3bh6+//hpcLhdhYWFYvnw5qqqqsGbNGpSUlIDH4+Ht\nt9/GhAkTsGPHDly5cgWlpaWYPXs2xo0bh9TUVNTW1sLKygpr167FwIEDn1UTEiPQzM3evXvDxsam\nTX8lFAqxevVqlJeXo6KiAsOHD8eHH34IANi8eTN++eUXuLm5QaFQYOTIkSguLsYbb7wBb29v5OXl\nISgoCCNHjsSRI0dQV1eHXbt2wd/fHxMnTsRXX32FjRs3ori4GBs2bMD69etx4MAB8Hg8lJeXQ6FQ\nwMbGBtnZ2ejTpw+8vb0BANHR0fjxxx+7/IQbGDFLWVlZLDU1lTHGmEKhYHPmzGE7duxg4eHhrKGh\ngTU1NbH4+Hi2fft2Vl1dzWJiYlhtbS1jjLGvv/6arV69ujPDJ11YQEAAy8vLY7NmzWJNTU2MMca2\nbNnCdu3apd5+4sQJxhhjc+bMYUuWLGGMMXb48GH2t7/9jTHG2MKFC9nt27cZY4ydP3+eRUVFMcYY\nW7FiBTt06JDe+glpLSAggBUVFbGwsDDG2KM80tyuz8qVK9n333/PGGPs+PHj7MiRIzr7VYVCwebO\nncuqq6sZY4z97//+L5s/fz6TyWRs1KhRTCqVMoVCwdatW8fKysrYv/71L/a3v/2NyeVyJhaLWVxc\nHDt9+jTLyMhgwcHBrLS0lCkUChYXF8dOnjzZEaeHmIEzZ86whQsXMrlczhQKBVuyZAnbtWsXmzp1\nKhMKhUwkErGoqCi2fft2nXXoyk3GGIuKimLnzp1jjDG2c+fOFu8VVb98+PBhFhISwqqqqlhDQwMb\nOnQoq6+vZ4cOHWIrVqxgjDX36RkZGR12Hkjn2rFjB/vnP//JGGMsIyOD7d27l0kkEhYdHc3u37/P\nGGPs7NmzLCUlhTHGWHR0NLt58yZjjLElS5aw48ePa83l7777jhUVFan7asaYzryaPXs2O3/+PGOs\nuW8+fvy4QTGOGTOG5ebmMsYY++ijj9hXX32lt/8NCQlh9fX1jDHG0tLS2P79+xljjDU0NLDIyEhW\nWFho/BNMHtv333/PEhMTmUQiYUKhkMXExLCKigoWFxfH0tPTGWOM/f777+yFF15gEonEoD7N0LzY\nvn07i42NZSKRiDU0NLApU6aw3NxclpGRwebMmcMY09+3qsYhqjGIUChkY8aMYUKhkCmVShYeHs7K\nyspaHO+cOXPY1atXGWOM7d+/n/3666/s6tWrbMqUKay+vp7JZDKWkpLCrl27xhYtWsS++OILxhhj\nhYWFbOzYsayyspJt375dHR9jjCUkJLDr168zxhi7desWCw8PN3IrkY6mmZu6+qtjx46x3bt3M8YY\nk0gkbPLkyezatWvshx9+YHPmzGFSqZRVV1ezsWPHskOHDrGioiLWv39/dv36daZQKNjkyZPZli1b\nGGPN/ezf//53xhhjYWFhrKioqEXeq2zbto0NGTKErVixgimVSnbs2DG2dOlS9fZz586xl19++Vmc\nok5Fd7iZqREjRsDJyQn//ve/kZ+fj7t372LUqFEICwuDnZ0dACAyMhL19fW4evUqSktLMXfuXACA\nUqmEo6NjZ4ZPurjMzEzcu3cPM2fOBADIZLIW/y2bMGECAKB3794ICQkBAHh6eqK+vh4AkJaWhtOn\nT+PHH3/E1atXIRKJHqt+QowpNDQU7777Ln799VeEhYXhxRdfxNmzZ7X2qxwOB7t27cKpU6dQUFCA\nrKwscDgc8Hg8DB06FDNmzMCkSZMwe/ZsuLu7IzMzE9OmTQOXy4W1tTWio6Nx4cIFTJw4Ef369UOv\nXr0AAP7+/qirq+vM00A60YULF5Cdna2+K7OpqQknTpxAUlISbG1tAQBTp06FUqnUWYeu3Hzw4AEq\nKirw/PPPAwCmT5+OQ4cOqfdT9deenp7o168fXFxcAABOTk6Uk93MmDFjsHDhQvzxxx8IDQ3FnDlz\ncPfuXRQVFeGvf/2rupxQKAQAxMbG4vjx4/D29kZWVhY2bdqEf/zjH21y2dPTEyEhIXBxcYGXl5fe\nGOLi4vB///d/CA4ORkZGBjZs2NBujDdv3oS7uzsCAwMBAEuWLAEALFq0SGf/6+vrC3t7ewDNvznU\n1NSkfl+IxWLcunVLfZcG6TwXL15EREQE+Hw++Hw+jh49CpFIhMLCQoSHhwMAgoOD4ejoiPz8fADt\n92n6Ppc18wIAXnrpJdjY2AAAwsLCkJWVpb6Trb2+tTVbW1uEhobip59+gre3N7y9veHu7t6izKRJ\nk/Dmm29i8uTJmDRpEsaOHYvPP/8cYWFh6rj27dsHAMjIyMDGjRsBAN7e3hgyZAiuXr0KAOq7oUUi\nEXJycrBq1Sr1a4jFYtTU1NA3ocyMKjd19VdRUVHIzs7Gvn37kJ+fj9raWojFYmRlZSE8PByWlpbo\n0aOH+v0BAD179lRfX/Xq1QtjxowB0PzeKS4ubjemRYsW4fXXX8eCBQtw8OBB9XtFk4WFhTEO36TR\nhJuZOnnyJLZv3465c+di+vTpqKmpgb29vXrCQpNCocCwYcPw6aefAgAkEkmbCQxCjEmhUCAiIgLv\nvPMOgOYPdIVCod6u+bU4LpfbZv+kpCSMGjUKo0aNwpgxY7Bs2bLHqp8QfSwsLMAYAwD110z1mTp1\nKoYOHYrTp09j//79OHPmDF544QWt/apIJEJcXBxiY2MxYsQI9O/fH//+978BALt378aVK1dw9uxZ\nvPbaa9iyZUubCRLGmDqXNb8irRkz6X4UCgVSUlLw8ssvAwDq6+vx1VdftfjM5/F4kEqlOuvQlZut\nc6t1n2xpadniNUj3FRISguPHj+OXX37B999/jyNHjmDFihXw8vLC0aNHATTnalVVFQAgKioKKSkp\nGDBgAMaNGweBQKA1l7lcLmpqamBlZdVuDFOnTsXWrVuRnp6OCRMmtPmava4YNTU0NEAkEuntfzVj\nUSqVSEtLw6BBgwAAVVVV9I9rE9G6TyouLoajo2Obz0vNtm2vTzM0L1rvzxhr8Xd7fas2cXFx+OST\nT+Dl5aWelNY0b948hIWF4fTp00hLS0N2dnabSYzy8nJYW1vrPQeq41AqleqJSpWysjI4OTm1Gysx\nLZptqq2/OnDgANLT0zFz5kw8//zzyMvLA2MMFhYWLXJeM4db96+G5DAA3LlzB1KpFIGBgbC2tkZ4\neDhu3ryJqVOnqj8fAKCiogJubm5PfMzmglYpNVMXLlxAREQE4uLi0LNnT1y8eBEAcObMGQiFQkil\nUvz000+wsLDAkCFDcOXKFRQUFABovuhTfWebkI4watQonDhxAtXV1WCMITU1Ffv37zdo39raWty9\nexeLFy9GaGgozp0712Yy7WnqJ8TJyQm3b98GAPz888/tln/rrbeQnZ2NxMRELF68GLm5uTr71bt3\n74LD4WDBggUYPXo0zp49C4VCgQcPHiAiIgIBAQFYvHgxxo4di5s3b2L06NH47rvvoFAo0NjYiGPH\njtGPg5M2Ro8erb5zQy6X44033oCdnR1++eUXNDQ0QCKR4MSJE3rr0JWbzs7O8PT0xC+//AIAT7XC\nNJfLpX9+dGEffvghjh49imnTpmHdunXIzc2Fn58f6urq1KuIHjp0SP1PMnd3d3h4eGDPnj2IiYkB\noD2X09PT9b6uZl5ZW1tjwoQJ+Pjjj7VOSGiL0dfXFw8ePFD3+3v37sXXX39tcP87evRofP311wCa\nLxBjYmJQWlr6hGeRGNOIESNw4sQJyGQyNDY24rXXXkNVVRW8vb3x008/AQCuXLmCqqoq9OvXz6A6\nH+dzOT09HVKpFHV1dTh9+jRGjx6t3mZo38rlciGXywEAw4cPR1lZGTIzMzF58uQ2ZePj40XTtNUA\nAARvSURBVCESiTBv3jzMmzcPubm5GD58OM6ePat+Ty1duhQ5OTkYPXo0vv32WwBAUVERfvvtNwQH\nB7eoz97eHn379lVPuJ07dw6zZ8826DwR06Srvzp37hwSEhIQExMDCwsL3LhxA0qlEmPGjMGPP/6o\nzuNff/31iV5XM4+Li4vxzjvvQCqVQiqV4uTJkwgJCcGQIUNQUFCAe/fuQaFQ4D//+U+LO+q6KvpX\npZmKj4/HsmXL8OOPP4LP5yM4OBgPHjzA3LlzkZCQABsbG/UPz7q6umLTpk146623oFQq4e7urv7B\nTUI6woABA/Dmm28iJSUFSqUSgYGBLX70VR8nJyfEx8cjMjISdnZ2CA4ORlNTE8RisVHqJyQpKQlv\nvfUWoqOjMXr0aLi6uuotv2DBAqxZswa7d+8Gl8vFypUrdfarDg4OCAwMREREBKysrDBixAiUlJSg\nR48eSExMxIwZM2BtbQ0PDw9MmzYNAoEAd+/eRWxsLGQyGWJiYjBlyhRkZmY+o7NBzMHEiRNx48YN\nzJw5EwqFAuPHj0dKSgosLS0xY8YMODg4wNPTU28dAwYM0JqbQPPX+FetWoV//OMfbS7IHsf48eOx\nfv16fPDBBxg2bNgT10NMU3JyMpYuXYojR46Ay+Vi/fr14PP52LZtG/7+979DIpHAzs4OH3zwgXqf\n2NhYbN26VT1hoS2Xp02bhvv37+t83dZ59dJLL+G3337DkCFDDIpRIBAgLS0N//M//wOZTAYfHx98\n+OGH4PP5BvW/b775JlJTUxEVFQWFQoHly5fDx8fHSGeVPI0pU6YgJycH06dPh1KpxNy5c+Hr64u0\ntDSkpqZix44dsLS0xI4dOwxedCghIcHgz2VPT08kJiZCIpFg/vz58Pf3b3EHjyF966RJkxAbG4vD\nhw9DIBBg8uTJqKur0xrvkiVLsHLlSvB4PAgEAmzYsAEBAQGYM2cOEhMToVQqMWXKFDz//PPw9/fH\nunXrcPjwYQDAxo0btd5NpDpXe/fuhaWlJbZu3dotvubXVenqr1JSUpCamoovvvgCtra2GDp0KIqL\nixEfH49r164hKioKPXv2hL+//xO9rr+/PxoaGrB8+XKkpaXh6tWr+POf/wwul4vw8HBERkYCaF6g\nYeHChZBIJAgNDcXUqVONefgmyYLRd1S6jIKCApw5cwbz5s0DAPz1r39FfHw8Jk6c2LmBEUIIIYQQ\n8pQUCgU+/vhj9OzZU/21VEK6AsYYZDIZ5s2bhzVr1qi/EkgIMW90h1sX0rt3b/UMtYWFBcaNG4ew\nsLDODosQQkze999/j88++0zrNs3fNiHE1CUnJ2v9PdfExETMmjWrEyIixHji4uLg7OyMTz75pLND\nIcSoKisrERkZifj4eJpsI6QLoTvcCCGEEEIIIYQQQggxIlo0gRBCCCGEEEIIIYQQI6IJN0IIIYQQ\nQgghhBBCjIgm3AghhBBCCCGEEEIIMSKacCOEEEIIIYQQQgghxIhowo0QQgghhBBCCCGEECP6fytE\nXIr5+qT9AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x113eb0080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check for relationships\n",
+    "pairplot(df);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x113d7c7f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(x='comorbidity score', y='readmit30', style='o');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 720,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Counter({0: 3384, 1: 998})\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>female</th>\n",
+       "      <th>flu_season</th>\n",
+       "      <th>ed_admit</th>\n",
+       "      <th>severity score</th>\n",
+       "      <th>comorbidity score</th>\n",
+       "      <th>readmit30</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4365</th>\n",
+       "      <td>86</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>33</td>\n",
+       "      <td>77</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4366</th>\n",
+       "      <td>83</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>19</td>\n",
+       "      <td>98</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4367</th>\n",
+       "      <td>66</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>24</td>\n",
+       "      <td>180</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4368</th>\n",
+       "      <td>69</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4376</th>\n",
+       "      <td>99</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>28</td>\n",
+       "      <td>140</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      age  female  flu_season  ed_admit  severity score  comorbidity score  \\\n",
+       "4365   86       0           1         1              33                 77   \n",
+       "4366   83       0           0         1              19                 98   \n",
+       "4367   66       0           0         1              24                180   \n",
+       "4368   69       1           1         0              35                 53   \n",
+       "4376   99       1           1         1              28                140   \n",
+       "\n",
+       "      readmit30  \n",
+       "4365          1  \n",
+       "4366          1  \n",
+       "4367          1  \n",
+       "4368          1  \n",
+       "4376          1  "
+      ]
+     },
+     "execution_count": 720,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# there's class imbalance, and readmits are the minority class\n",
+    "print(Counter(df['readmit30']))\n",
+    "\n",
+    "readmits = df[df['readmit30'] == 1]\n",
+    "readmits.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 349,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "X_holdout = df.ix[:599,:6]\n",
+    "\n",
+    "y_holdout = df['readmit30'][:600]\n",
+    "df = df.ix[599:,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 350,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3783, 7)"
+      ]
+     },
+     "execution_count": 350,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 351,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(600,)"
+      ]
+     },
+     "execution_count": 351,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_holdout.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 352,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(600, 6)"
+      ]
+     },
+     "execution_count": 352,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_holdout.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 721,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(6768, 6)\n",
+      "X_train shape: (5076, 6)\n",
+      "y_train shape: (5076,)\n",
+      "X_test shape: (1692, 6)\n",
+      "y_test shape: (1692,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = df.ix[:,:6]\n",
+    "y = df['readmit30']\n",
+    "\n",
+    "# up-sample: resample the minority class to deal with class imbalance\n",
+    "\n",
+    "# !pip install -U imbalanced-learn\n",
+    "# from imblearn.over_sampling import RandomOverSampler\n",
+    "\n",
+    "ros = RandomOverSampler()\n",
+    "X_resampled, y_resampled = ros.fit_sample(X, y)\n",
+    "\n",
+    "print(X_resampled.shape)\n",
+    "\n",
+    "# default 75-25 train test split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X_resampled, y_resampled, random_state=0)\n",
+    "\n",
+    "print(\"X_train shape: {}\".format(X_train.shape))\n",
+    "print(\"y_train shape: {}\".format(y_train.shape))\n",
+    "print(\"X_test shape: {}\".format(X_test.shape))\n",
+    "print(\"y_test shape: {}\".format(y_test.shape))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 354,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 27,  98],\n",
+       "       [ 14, 110],\n",
+       "       [ 25, 222],\n",
+       "       ..., \n",
+       "       [ 14,  64],\n",
+       "       [ 12,  67],\n",
+       "       [ 46, 149]])"
+      ]
+     },
+     "execution_count": 354,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train[:,4:6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 355,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 81,   0,   0,   0,  27,  98],\n",
+       "       [ 76,   0,   0,   1,  14, 110],\n",
+       "       [ 72,   1,   0,   1,  25, 222],\n",
+       "       ..., \n",
+       "       [ 77,   1,   1,   1,  14,  64],\n",
+       "       [ 70,   0,   0,   1,  12,  67],\n",
+       "       [ 75,   0,   1,   1,  46, 149]])"
+      ]
+     },
+     "execution_count": 355,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 722,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x11f89df98>"
+      ]
+     },
+     "execution_count": 722,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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18dBDD6f9/IVAIfoL4u9xedxbEP6KvY7ReV1/DR5/FQIFV+stG+n1jYaWD7edSEskaBUJ\npCtKSDSfbfT+Xty23dYwfKFV8s7G551LUomsrTh69AhPPfVJ9uzZTVvbGZYuvY/y8jFpO/9QJVvl\nQYzu8erD71u+Jl/8FXuu2PO6/kr8nEt6yastFt/Y/TwAX7n7GdNjspFe3+gL+Kp8nZ6+WyxflygS\nNIvGNNIRlZmtH9Cf0+j97b6wja07enl5c61lHp5sVPJOV36WRJmLB0NUlmxkbcVLL/2cN96opqRk\nGOvXr0VRFB55ZHXK5x3s5Iu/wPgeD1VKaL/L9DX54C+j68SeN9/9Belx2FDwV6GQVyNKpzvbON3Z\nxpbmbaaL1TKdXt9MBucCp/CUXUj4eqtIMFOZdBO9Xv+Y2fvzjmzHU3bBcEFpptfAxJLsIk2j86Tj\nmHwlU4tqJ02azDe/+R06O6/R0tLMXXctprLyupTPO9jJB39B4nvcilz7y+wc2mPJ+Et7XaE5bLD7\nq5DImxElLRoDeLWuimHeEsOespZe32iuXSOV9PpWXzzfpDp6a8Zavt4qEnz6jk9zor2eHxx4AYDP\n3/6pqIgj1Z0MdtYP2H1/1buaWD5/MsOKfVlNLJfO/Czfvffr6WpWXmL2ByXVf6f77ruf1tZTXLx4\nkTFjxrJ69WMpnW8okC/+gtQclkt/QWKHJeMvyG5yzHQ5bLD7q5DImxGl051tUb9bbYFctXg6jy+d\nEReZFfu9PL50RtLlGxINLSeKyOxEglZz76lGIYnms528P20YPlNrYKzaafSzSzSJ/qCkSn19Hd3d\n3dx22x1MnWpd98slP/wFqTks1/4yOqee35xY79hfkLl1fGa4Dht85MWIkj4a01PduMl0/jUT5Rvs\nfKmtIrJEkaDV3HuqmVftrB/4Te36hOfRv7+Oa73sz8AaGDPsrK9yUUnUKU71M9u8+Q0AHn3091I6\nz1AgX/ylXTMRZg7Lpb+Mzh/LqWunE54j1l+QmXV8ZrgOG5zkrKOkT99/uqfN8JhEi9XSXb7h6Ts+\nnfCYqp0nqa4fKBTpKbuA3+dl5S13JIwEraZKYm9mp6KxI8jhvmGsWfYcWw+28uLrNQmP7ylqy+pN\nn6mppMFGphfVNjY2UFNznGHDhjF79g3JNnNQk4/+gsQOq9p5kuoDTUB++cvs/LHMHj2DO7yrbflr\ndGlR1jsursMGJznpKOmrUhfdvA1vqfmxyd50mSI2EtwfOEzZ8CJWLZhu+TqrqZJXatYaZl518r7t\ndPI0FogKXt5ca5nPpdjvpYn47cSZuumzlZ9lMGB3kWeyn1lV1e/w+/08+6zxSMlQx/XXAOnyF9h3\nWGd3wJa/Fsyp4IWjP417znWYi1Oy3lGKTdroGX7N8vhUt0BqKenTmUBMiwRPtNez8cApzl1OvGjP\n6o+bUd4OTbCZKJw5vMTHzEkjOXay3fSYidO7aLjcGPd4pm76TE8lDSacdIqd0tp6ir173+P662cy\nd+6tGbtOoZJtf0H6HZZNf0H6i//a8dfMSSNp6WzKasfFddjgJasdpdj0/UU3b8NOMfJUojLtJrYr\nGSc3td2570RTJUaZYruC3fzw3XUc3zc6KnJKlCfEDp3dAepPXbY85rTvA8z+aVK56Y0+3w+7jIWm\nYSS2Fw6+CMCn5v1JUu1wMWbDhnWEQiFWrXoMxc7NOYTIhb/AmcPyyV9bmrdx7cMpkdE3jVQdZsdf\n9acuU1VvnjgyWYeZfb5Op8NdfxUWWe0oxabvTxSNaSQblenT1dtJS68fUtcwu6mdzH0nu/Ohpuc9\nevqWRz1mVm7AyY2XqIyCp+wCStlF0+eTjcjMPt+xdxxI+NpYsR26cMzRtV0Sc/78ebZv38aECZNY\nuPDOXDcn78i2v8CZw/LNX+vq3uDKnmVxjxs5LJ3+AugrOWs4Iq6RjMOsPt/6EmfT4a6/CousdpS0\n9P2+qUcB6N67AoCSOzaj+BKnzncqGv2Q8PbW3VGSiY0Mzl/qZoNBYUizjomTRXtmUyVbmrfxal2V\nafsVXwD/rP301c2Pv1ZMnhAnN16iMgq+SXUJz+E0IrOqk9f63q08vvQx2xGmJlXtZzcqSw/V1VX0\n9wdZtepRdzTJgGz7C8wdVgj+CtCLf9Z++q+MIdh2ffz1dA5Lp78g/Q5LVOfz8aUreHrZdFvncv1V\neGS1o6Sl7/dVtgAQaL4ZgO73o0dNPvHQnJR3g8QWP9QXOzSKDBKhv6mdLNqzWl9gJ1Lzlp+jz+Bx\nfbkBpzdeojIKvTVqmYOVy0t5+/KrUc/FJpmzg92K6fqOnxV6qbqRWXq4fPkSb7/9FmPHjmPx4o/k\nujl5STb9BeYOG37x9qz5S2sHxDvMrr+8I9sNO0qaw47xZuSxdPgLVId94qE5VE7tiiTI1HDqMNdf\nLllNOLlAVFB8/TEUBRRlIDLTU+z38va1X5nmJjHjhYMvRnUYjBYYbm/dHYkMnEgGohOYOSlUuL11\nt2FbOrsDrB79aVYUf4Ynxz7NdxZ/izXLnmPNsudYUfwZepsEoH5O3krjIWQtT4jTG+/8JfP6QRpW\nu96c4qRieiL0/8ZWj7k4Y+PGavr6enn44VX4fHmRXi3vyKa/wNhhW1t3Z9VfWjti22LlL73DFEUd\nGbdyWKb8tWBORcKSTnZw/eWSVSMOL/HhGd8ycPHKFjzDL9Nbsyjy2MpF09jY/VrCc8VGOfobLDYS\n0+gnRHXLBuCmpNrfca3X0aI9s/UFidYSjBpRjH/SQAZZ/6R6w4hsdGmR6Y1nFJW9cPBFTl/spPm9\nOQnf6113+tibpl1v6ayYbiRSNypLjWvXrrF58xuUlY3kvvvuz3Vz8pZs+Ut73shhEMI39WhkNMsJ\nTv0V2w6rEfnYtVB2HXYouDHusXT4a+WiaWnb9eb6yyWrI0qqHAZufkUBT9klYCB9/0HPwFSPFpW9\nUrM2IhYNfZQTO/VkFIlFrjmuOen2Dy/yOipUGLu+ADAd0dIXc+wsOxG15sEoItMiJic33qELxzjb\nfzJh+x9ZPJ2O0sOmz+s/A6sCoBrpqphuFXm5UVnybNmyic7OTh566GGKilKrWj+YSdZf2mv1DrPy\nl/a8GdrUn1Oc+iu2HVYj8rHFaO06rLUvviOTDn+tWjzd1siZ6y8XO9geURJClEspzRNX2MDo5lcU\nmLzoIP/77v/FsGIfG7cMZLnV6ifFbo+NjXKcDN1qQ+bJRGQoiu0cNkbrC/7j6Kvs2DXO8nXVu5oY\nvmBL3OOxEdmNCzp4/sAPTM8TG5VpN6KiYLpAPNLW4edsR52acKwWqtpNcJmozpTVv60blSVHb28v\n1dUbGDZsOPff/7FcNyejpOqwZP2lf+2Tcx5P6C/z0aSBayblMAf+0toW67BEI/LaWp3NLdYO81Y2\nUjbtLGb7BlPx1/hRJbZHzlx/udgh4YiSEOI2IUQNcFAIMUkIUSeEuMPphaxu/gvB0wwr9hnO63/h\n3a/STyiykBGihbW1dZfTpiQdkXX2WO9sAXXb7Yn2ekOp7m7bQ09fEN/Uo4brGwACY+roDsbPwWsR\nmRa51gZ3xRXi1BN74+l/95afsyzu+8GVxJ9pdeOmSH2nrmA3Lxx80TQy0yqmW5GozpSdiMuNypzz\n7rtbuHLlMg888CClpRYppguYdDgsWX99Y/fzkddqDkvkLztOS8ZhTvwFJh3Dcc2W/urpC/IfBzZG\nZeqOvFbnsKJpJ7iGeZ81FX91XOu1NXL20vFfu/5ysYWdEaUfAo8Dv5JSnhJCfAb4CeAo0YrVUDLA\n8/vWGP7h199w2jmsoq1Y1ix7LvJzZ3eAZ9bscLwQ0j9rPwCjSxPPjVc3bqLt2jnDNobC6wtid81E\nXUs3rx9L6bRG/vEP/phdbbvor+tP2BYtKou9AdWo7AA9B5Ybvu7BMb8f2bXz/fd/EonMZo+eERWR\nfuHdr0Z+PnThGLUdDZZFQIG4tQ3Ffq+t5HN2Ii43KnNGIBBgw4b1+P1FrFixMtfNySQpOyxZf53u\nbKOtc2CRr93Abs2y5/jsli9Gfobk/QWqww4FT7AK6xElrYNxoO2QocMUJX7XXyxHOs0/q9Jpjay4\ndyZVJxM7PFl/jS4t4ul5A+/TzGGuv1zsYqejNFxKeVwIdReWlHKTEOK7Ti6SaCgZoOGy9fZLUDtI\nTkeQ9EO4w0t8PLBwimG+ESu85ecAEg6tJhruBVUyWoqa2OFzb2WjZT6WPnrZ1baLtXWJF4vCwI1n\ndAN6/AE8ZRfovxJdRVw/hGy3WriGUWI9LUpbNnVJXJ2pnqI2xNRybqmYnvC96Du8Lulh587tXLhw\nno997CFGjhyV6+ZkkpQclqq/nAR2Gn+z7euRnzWHJesvUB3W2nfO8hj9/V7XYZ6s0cxfoDosgPmi\n5j562XCy2labD104xgsHX0zaX2DusA+vtNryFww4bNXiJa6/hih2OkoXhRDzCK9iFEL8IWCettmA\nRNFYJtHfZFU7T7Jp78CQtTZ8239lLF6PQrA/Xmj+WfsjYvhFzX8wO7xTwijysDPcq8/j56tsiRKN\n1WiSxrr61+knejRpxshpPLPgs1GPadGo1XCuUVSmH0K2Wy089nn9ZxO7BkBfMf1b763lRCPcUuFW\nqM82oVCIqqp1eDxeVq5clevmZJqUHJYLf13puxr5WXNYMv6CaId9Y/fzLJ54Z0J/hWx07mL9BfYc\nFntmI3+B6jCrUZZE/gJzh314pdXwnEaJQfUOc/01NLGz6+0zwBrgZiFEB/A08CknF/nRsm+zZtlz\njCsZk0QTU2fNgZ9F7dbwVjbirWzEN6kuksH1obun8fjSGRT7vVGv1UaTQBVWdeOmqJvv+X1reH7f\nGlujSbEoChTdNnCuvlMz6W0SdO1ZwYqSv2DpxEVxrwmE4kecGi43sfVgK53d6nP6zpGVaLSoDAZ2\n7WhDyE6qhevRojIgag1T7Pz/ifZ6Tl07zalrpyNrIlyyx969ezh9+hT33PMRxo2z3mAwCEjJYbn2\nF8DfbVmTlL+K/V58YwYcdrqzLWP+8lY20ndqZsRfa5Y9Z+iwWGL9BfbW61j5C6wdZsdfYO4w119D\nCzsdpQeklB8BxgBTpZQLpZQnnF7oRHs957sdDUSljaMXj7N++8BQsn9SPf7JtXhHtuMd2Y6n7AJv\n7m1mWJGXBxZM5p651/HI4unMvOdEXNFL/U3T2R2g4XITDZeb+NXhDXHXtSNWj39gvts/qT4SkVXv\namKbg0j2V40/45k1O6jaedLRXHfpjYdYdF8njz4eSljiQCNRhK29Nrbopp7fnFhv+LNTzLb3fmP3\n846T/g0V1NGk36EoCqtWPZbr5mSDlB2WS38BnAsNTO3Z9dcnHprD3GXxHQW7/po9egZPzLIebbTy\nV1dPwPZonN5fYH+9jpm/IPkadWbe0v+caX+B67B8ws7U21PAT6SU9ipAmvDS8V+n8vKUUBTwzNhH\nsG6+4Tog36Q6emvG8qvNtZHHiv1ePLebR1jr6t7g1x9swzNC/f1c4FTcMXbEqkVlwdOzIu3yVjYS\nLO7E42Btg6fsEt19QTa0/hqfg8A3QA/HuncjWzysmHGfmq7/yH5qLzurFq5H20Wij9r08/9aNKah\nRWXJVPM2295rtSNwqHPkyGEaG+tZuPAuJk6clOvmZIOUHZYNf3lQGKtM41zoZNxz2rb4/itjbPtr\n5aJpHOs+bngtO/6q7WigviO+LbHtMvJXT9v1/Mu+/7S9Pkvz19qtDezvsbeGCeL9BdhymBX60SMj\nh00um5hxf4HrsHzCTkepRQixBXgP6NIelFJ+3fwl0WQiGvOg8KNl34783tkd4As7/jZuBEjDW34O\nZu3HOzJ+S6oWlekXBvZP22M53BagF6U0cSZWO3j8QTwxWWzxJt7Kq0dRoGjOrkgCvFhmj55hOrTe\n26++j69t+E/O1FwHs3bhHeno8nEYRYTa/L9RBPabE+v527v+0tE19AvK9Ysw9VHYN3Y/z1fufsbR\neQc7VVW/Axgqo0mQosOc+mvpxEVsb92dsJOQjMO8o+IXZBv5qydB0GTXX7HrIY0w8lew7Xrqe8yT\n1sai+au3ZhFn+0+afgZGaP76t90bGNt7E5v2thCauTMlh1mtwRxTUh73eDr9Ba7D8g07HSX92GlS\nJcWdRmNOgCftAAAgAElEQVSxArHD9/b+1PLmUhRVNGbH+CbVEWy/DECw7fqotUlW50wHigLEZLFN\nBk/ZJdM22Vl/cLboEL2BCggXxdXzyOLpzL0lFCkwuXTiIsc7ELuC3bxSszYqGtM4de10JFqzG5nF\nDotrotFHYm5UFk1dXS3Hjh3hllvmMWOG8wi4QEnJYU79tb11t2N/gT2H4TV+LtZfQEKHZdJfRXN2\nEb9s2xpP2aWohedOOXB5J93vD1d/MXDYPXf7eb+/CkjsL6s1TJn2F7gOyzcSrlGSUv498M/AfuAg\n6hD23zu5yLW+TkeN0ieXtItRKvxYrG5A78h2/JNr8U+uS+lmzSWptlnxBSi62Xi+fMPOk/z8wMBI\nULI7gbZZyKmqYaPtdQWx6Qm0YXGzpH8uKuvW/RaA1asfT3Dk4CFVh2XDX2DPYWbo/QXk3GFmI9tW\naMFsslj5C2Bvx/bIz5nYyZgOf4Gxr1yH5RY7mbkfBD4APgH8MXBICPGIk4t8996vR1WW1rDaEeH0\ni/zk2Kfp2rOCkEEQY/SYEYq3H8UbTOlmDfb4k36tGUsnLop8dk+Ofdr2+4nl1rE3sWbZc3xn8bfo\nPzfV8BjP8PhlHEXz3qLotk10MBBJ9RNiUukEx22wanpvf18kz0kizLb9miX9c4Hm5iYOHNjP7NmC\nOXNuzHVzskaqDsuGvwAeH/m/UnJYxF+VjUk7LHh5FH1npiT12qi2KOq2fw3NYYn8lahzd+vYmxz7\nC9R1VPplF8n6y4p0+AuMfeU6LLfY2fX2TeAjUsonpJSPA4uAbyR7wef2/Djys9XQpxaVdXYH2Hqw\nlaodjXFbSGFge+v5S934ph41vNGyGVl5ivrSfs5trbsGygpceDPp96OtG9onz5oWB1YU4qIyT1Ff\n1O4WDaMh6HSQKCozSnYJ5sPl4EZkAFVV6wBYvfoxlEIcMk2etDnMqb8AS4dp/qraeZLfvFufFof5\npzjelBzBU3Yp6RJPseiTcGoOS8VfoDrMqb+AgvGXPtFoLK7DcoedNUp+KWVkb72UskEIYaeDFUfV\nzpOc7Gq2faNsbd3NO6+NiUoZ//Lm2qiU8drNeHTPSUoWpucGT+VGzsTfnxDw4tGXWeT5OB+GjiW8\nRigE3XtXAPCJh+ZEEqRp7GzfZHkOfVRWNO+tpN9TbMkTfSkBK/QZwI1IZtvvUI/I2trOsHv3DqZM\nmcpttzku1VjopMVhTv21vXU3wy/eHlf2Qu+wAX+p90VJGjopiifJIWcyF1SGgJ8c+CXdoc6U/fXs\nOz+37S9QR5OSeV+58Jc+0WgsQ91hucROR6lZCPE08H/Dv/8ZkLjeSAxVO0/y2tmXHO5ECBGccAh0\n2V97wltIAU4UDZTySOUPuu3WhP2Ti2D8Uu9lquQ+fDdEi8RbqfpfW8CpoT3ecW1G3Lla+o0LWmpo\nUVnv0SUJR8iMdtOZpes3q1xuJCAtA3gsZtGYHf7ynS8zp3x2VFVy7ZxgXUG80NmwYT2hUIjVqx8f\naqNJkAaHJeOvfkJUnawi0BedvVpz2K7uVyOPFc3ZRX/nyKw4LFf//D10RtqQyF/acwc6rrCER6Me\nd+IvMB5N0igkf4HqsO/d9824c8Lg9leusRNV/SnqUHUD0Bj++ZNOLtLZHaB6V1NSC/zMhoGrdzVF\nDe1mYsorFkWB/iujCHaVpHSeYd4S1ix7Do+tj38A36x9KEq4MGW4erc+yZse7fEzniNRCc3sLjL1\nDL9mq/NpFGE5qYJtlT3XaK4/2SRyoK4hOHThWNw0SGy29cFGe/tFtm59h8rK67jzzrtz3ZxckJLD\nMuEvgI7QwAhBOqe8rFAUCDqvqRvHMG+JY39p17fjL+25huDeqKSMTvwFiUeTCslfoDpsqPkrH0g4\noiSlPCuEeFZK+aQQYhQwX0rpaHJ3nzxLaOZ2PElEMsH28YaPh2Zuj/pdUdSFiL016gJL/6z9Uan7\n00UysoxFS8hoJ0eJHsU7cLyvsoVQz/DoJJXatmBdUs3DXTuQjX6WTV3CKzVrbW/pV5TkO59mWXWN\nIh+rG/xnR17mtvE38+QcdYdWZ3eA1aM/zaWrPYwaUcwCUcHwEjuDokQV13yp9iX66uYD8PLBN/BM\nNs5lMlh4/fXXCAYDPPzwarxek/3lg5hUHZYJfxXN2RX1BzzWX5BBhyW1cCKaVEZFwNpf2u+KL0Af\nsK6+Gr/Hz7nO84785Z+133I0yYx0+au6cRMH2g4B8OScx11/FTB2dr09C2hJQYYDXxVCfM3JRS5d\n7Um6g2G2e8PofPrHUtm5pke/QyN4WU00lo6haydlRoxQFPBPlpHf/bEJK8MEQ8HI1lOneY/M3meZ\nfwS3jr2JMv8I09caRWVVDRupatgY+T1RfanLfZcjZVyqdp7kmTU7ePH1GtZua+TF12uiSh4kQv95\ne8vPRSJa5bqBbMaDMSq7evUKb721idGjy1my5N5cNycnpOqwXPjL6rVOCAWViLcgt1Nveqz8Fft7\nIOwwp/6yypvnwZNxf9V2NLCtdTfbW3e7/ipw7HRnHwHmAUgpTwshlgMHgK/Zvch7fb9NeHMGL4+y\nTJioJzYa09Bnd9XmwM1e7x1pT3z66xhl9c4lim5wQPEFIvP9Rgkr19a9FvdYslzpu5qwoxf7/Jbm\nbfT290V+XjZ1ia0bO0SIZ9/5BSd2T457Tr9eLbbOk541B34W9buixEe0EF1mZbDw5psb6enp5okn\nfh+/P/2pKwqElByWC38Bpg5z5C9vKMpb+dBJ0jDyV7DtesMyU0md3+K99tNvuXA6nf4KgeVaNXD9\nle/Y6Sj5gGGA9q0qwmHK1fb+xKv1zSSjzWcHdAu6raI7T9mlKNmYHZMqoRD0XymPSCgfIjX/pHrw\nGE/nOZ3mSwcvHHwxsnBaH4lVNWxk2dQlposjAf7Xli9FSkA0B48A8R0ljepdTSyfP5lhxcZf52Pt\n8fWu1Ig2fhu1PkOutsYgmRpO+UB3dzdvvvk6I0aMYNmy5bluTi5JyWGuv5LDp/gIhOx3eLRRJP+U\n2gRHZodU/AXRDvNVtkR9B/Rkyl9Q+A7LF+zMVr8A7BdCfFcI8TywF/gXJxfxklokq1/kaBaNaSjK\ngGzUNPoqnrILeMouJHy9XRQFW5Fa4KLxGoVMoPgCKCYdpVygRWX6aAzUBYkbG94xfZ26fmvg75h+\n8acRPX1B9tWcNT2XGYo3/m+lPkNuoS+S3LJlM1evXuXBB1dSUpLaBoQCJyWH5cJf2rGxDiu6eVtW\n/dXbJOhtEkldw0knCVR/+SfLvHHYoQvHIgvJnfgLnDksU/6CwndYvmCnhMn3gI8Dp1G31P6hlNJR\nRykYSrwoOJE8tC+ZnWhKk43+WN+kOnyT6tISjTkhXWulckEo4KNrz4qILLv2rKC/oyLh656YtYph\n3hKGeUvY0ryN3xpM+62v32g6P280rZdoR1DHNeMCn8msBatu3BRZf2A3026+0dfXR3V1FcXFJTzw\nwIO5bk5OSdVhufCXVuA61mFmmaczhX9yremutEygpHGvgeYvvcPsBK5PzFrFmmXPMcxbQnXjJsf+\nAucOS7e/gIJ3WD5hZzH3GGCUlPJ5YATwZSHETU4ucr/vU5Zp6+2k59e+ZN17Vwx8+fc9RODiePr7\n4iM+bRuqKpwLeEe24x3ZnvXpsVxPx6WCtm5Av4VXGWUc+eh5ta6KrmA3XcFuXq2rImQw7ad4+1kv\n34qTjVkElWhUaXRpUdxjTrb56ukKdvPS8d9Efi/EiGzbtnfp6Gjn/vsfYMSIslw3J6ek6rBc+Eub\nyot1WNb95e1Py3qhXKBft6k5zE7g+tu6DZF8R13Bbkf+guQclm5/bWneFld01yV57KxRehmoEkKE\ngCeA7wM/AZbavchJ3y4Ui12a2s3f2ySitohqXyqzud1gfwi/xc4GCOcnmVRnt6l5SX9AweNLPttu\nKvgn10bSEqRr2F9/7upds6Lm560iKLN5/mK/lwVz4ke6UtlZeL77QuTnRJl2841gMMhrr63H6/Xx\n0EMP57o5+UBKDkvWX2DtMCt/6R8bDA7LFZEAz0FnL0SIdfWvJz63gb/AucMy4a+qho1R04WF5rB8\nw05HqVxK+WMhxI+An0spfymE+LyTizT0HLZ1nH9SfXRHKRyFmXWU7FTIjp2LL0Sy1UnqOb6Q/itj\nox4ruWMzhCOqdA/7K95++sbUsa9mNkvmTUwYQRktjAVYuWga6xurACI5lyA6w65+YaXBmVmz7NuR\n3/5u57ejOkpgnmk3H9mzZzdtbWf46EeXU14+JtfNyQdScliy/gJrh9nxFwwOh2UDI3+B5jBn2Flf\nFesvSDwKZOSwdPsLCt9h+YadxdweIcR84DFggxDiNux1sCL8eNm3WVHyFwmHqPVDpVpxSKvhynxd\n/9PfNSzXTUiKohv2UXTLO/imHsU39WjcNt1MDPv7J9dG5uftRFD6ef5iv5fHl85g1eLpbA/nK4nl\nhYMv8n92/KOFZABdAdMT7fVxggHzTLv5RigUYv36tSiKwiOPPJr4BUODlByWjL8gscPy1V/By+VJ\nL+DOJZq/AFOHpRu9v8CZw+z4C3DkLyh8h+UjdjpKXwK+A3xXStmAOmT9l04v1Fr6jq0/tNpQqf4P\notEiOLvRmBk9xxfSd2ZK8iewQCkayFprZ/1CvqB4Q3hKuvFVtuCrbMnKIs7+a6MYXVpk+waeVDSD\nx5fO4BMPzeGfnrqHVYun80rNWvoJRVVs1zh04RgXexJH45qk9GuTYimEef4PPniflpZm7r77Hior\nK3PdnHwhZYc59RdYOyxVf0HmdtQG2yui3kehOEzzF5A1h2n+Amw7rMIz3ba/AEf+gsJ3WD5ip4TJ\nW8Bbut+TKhZld75V8QUM0/vHDlemGo2pO+AyM5yt37aZycWXmch9EnW+DERises4iv1eFjxUwfc+\n+JGt17f2NfDlJdOjHtNLYnvr7sjwtX4YXEEJp35T61R9996vx53bLBLTyPd5/lAoxLp1qmhXrXJH\nkzTS4TAn/vJWNqIUd1o6LB2jSZkakfJPkSi6EDpTDsuUv4puf3PgvBl2mOYvgN+cWG/r9edCJyNT\ndWDuLzCeyjPzFxS+w/KVNFT9SYzT1ftGW2j1EVk6orFc7CBJN8m0PxSCrj0rCF4eFXkseHlU1qLG\n2CRpKxdNo6WziVPXrEtvff72T7Fm2XNxlb21aExDH5Xp/7iFdMfE5hrRsIrENPI5IqupOU5d3Qlu\nv30+U6dOy3VzBg1O/eWfVG84Cq49lg5/QeY6MEpW/iok3/5QCEt/eXyZzcOkH6VauWgaw4p9nGiv\nT8phVv4C4w66mb+g8B2Wr2TllnC6et8qwy3k79x+IaB9jvrOqN3SC2m5vjeEt7Ixan7eTiQWe8zz\n+9bw/L41hvP621t3J/zjZiSLa33xi9WHeUtYs+w5PCh4UBJm480l69b9FoBHH/29HLdkcOHYX76A\npcNcf6WGPiknxPsr0y5TfAGKJzZF/AX2RpP0xyTyF1h30M06O2YO0/y1Ztlzee2wfMVOHqW/FkJc\nl8pFvrP4W1ERQLL4KlscR2N9Z6ZE8pZo/2VqbVKh4KtsyapYYimaJnn08VBEMsP9iRe/xx7TcLmJ\nhstNhosc+wkl/OMWG5VpeVOMjnt+3xrLNQT5QENDPUeOHOLGG29m1qzZuW5OXpGqw9LlL4i/9+yg\nJXt1/TVALv0F4J9yIqo+m1OHJfLXKzVrLR1mNKpk5bB891e+Y2fnxzDgXSFEHfAi8DspZeJUtTp2\nHjmdMCNtKOAjcH4C/uvMs5cqivPRJC1vhadMnbftvzI2YZbnQiUUAoK+uF0eoYCP7veXRz6Dojl7\nc9C6aDa3bGHFjPvo7A5wh3c1M4t7GDWimAWiguEl1l/L5/etSUsb9HWRrIajGy43RX6OXUOQL1RV\n/Q6A1asfy3FL8pKUHGbXX93vL8c39WhChzlFSzug3b+D2V/de1dQcsdmQ4f11t4OqP7K9bKJIo+a\nJLSzO8A+eZaZXQ9yx9j0+Wtr666Ex8TWdbMzpZav/sp3lJDNxSlCiI8AfwDcB2wBfiql/MDo2HPn\nrkSd9K82PkdP0fmE17CzuC9yMy3caPtm6TszBc9wtR5mb81dABTNeS+SmyR4uZzemrsiU3vJRH35\nTm+TwDvuFErJNTwGNYJygfDew/F9o+npG8jmV+z3snLRNMtq2p/d8sW0teGJWasANZu4XZZOXJRX\nsmltPcWXvvRXTJ8+g69//VsoOfryjh9fltd3TbIOs+uv3iaBf6q05TBw1mnqbRJ4y9Ws+L01d1n6\nSynqxpsgEW++ErxWirfUOF9bf08R+Ppcf+l4YtYqlk1dwpbmbbYdlm/+yhes/GVrjZIQYjhwPTAD\nNftgO/BDIcQ/2nl9jz+xZAAIqrV5AhfHE9JlwtXX7OneuyJyrF18lS2REiZacVx9AjftcW1LaSEK\nJhH+ybV4S6/mjWQAanrei5IMqAUi125tMK2jlK7RJI1kikaa5TvJNp3dAbYebOXZH/475y918bEH\nH8lZJynfScVhdv3ln1RP917VX4GL4wkFBhwV5zAH/oLw/Rt2mLey0dJfhdpJAuuktp7iXtdfMWju\ncuKwfPNX1Y5Gth5spbM7f0vlJLxbhRAvAcuAauAbUsrt4ceLUYtM/u9E5/CcuYn+CYkXRGpba2Nv\ndMUXwD9rP3118yOPdb+/nKI5u/COtFdkUsOsFIB/5qGsyEW/tdTJqFiqaGVI0kWwqwTvsPj5cCdo\n/96x2YwBqnc1xZUGgOhpMD3aThK70Vrs7jkjXqlZazgErs315zIqq9p5kupdTVy+dJFD775LSekY\nXtrdR4fnpGU0OxRJ1WHJ+AuwdJgTf0H0/WuUGyhb/oIBh2XCX4XUwcuEvyA5hxmlCygEf+k7mi9v\nrk04Gpcr7IQ1bwGflFJGuvpCiCIpZY/dwpL9FScSHxTGP9l46Npbfo7YRQV2KnHHncekFICnqMfx\nuZJBLcx4BmLyrOQLdqcFtMRuqeKffMJQND19QfbVnI3KN2IVjT2/bw3PLPgsw7wlACysVNczpCID\nq8grl3P9VTtPsnZrAwBn6nYTCvUz4YbF9Ab6I4/no2xySEoOS4e/IN5hyfgLjOuWZctfoOZZ8l7X\nkLf+cro0IxXS7S8gbQ4rBH/p0UbjIP/8ZWfq7c9jBOMB9gNIKc/YuYiv+c6Ex/QcX0jXnhWmLVIU\nNf+IRmxSykJB8QXwlF3CU+RoPXzaCfZ6DR/vaxZ0711hWMJAmz7obRJp++y1dAFG6EsDgHk0pj2n\nr/i9tXUX21p3Jz20G5vfJJZc7SDp7A5QvUv9HPp6rnHu5AGKh41izOSBZKzVu5ro6snfYewckJLD\n0uEviHZYofoL1DxL+ewvwNJh6SzPkk5/AWlzWCH4y4x89JfpiJIQYgvqokeEEPp5mwBgLwVpmP4J\nRxI3ZOpxKLIeZdFHZMlGY07JRISSD4L0FhmXQ9d22BgN72tDzekuC2AWlZ292EnVjkbOeI/S1Hco\n4Xl+W7ch6vcQIf7j6KuRbMhOhnbtzOPnIirbJ89Ghqvb6vfQ3x/gutmL8HgG/nAYRbNDkXQ5LF3+\nggGHFbK/IPcOS+Qv7edYFF8gLultqpj5a3iRl60HW7l0tYetvf+R8DzP71vD6WttUY8l67BC8JcZ\n+egv046SlHIZgBDiB1JK25W2jej3X014jKekEzzW62i0iKyvbj79QQWvz3phXygIinHgYRvtmv29\nfrzFuY2isoG2lsKskKR/cm3a1ztpUVmsbHYcUYP9kjt2gtc4iZ+ekEEEpaWHAGdDuz9a9m0+//bf\nmlYRjy0j8JfvfBmA7933TetG6tAiOieyunRVnWIJ9HXT1rAPf1Ep46fNizsuNpodiqTLYenyF+h8\nYsNfwR4/9IwwXS5gh6HoL22Ex8xhSpoXhBv5y+tR+PW79fT2qd+JkoVXEvrLbMQpGYf9aNm3+cK7\nXzXMqwS591ci8s1fViNKj0gpNwDvCyH+R+zzUspf2L1IyNNPwgDE028rSolEZDa+7Kl2kvTXHEpY\nvd90d5I0/FOkYVSmr/7d2yRYLe43FISZFIzqBJottNSzpXmbaScJBhK+aXlMevud/xHSoj4nohk1\nohiAs437CQZ6mHDTPXi8/rjjtEKdQ5l0OSyd/gL7PvEU9aEUp16Pcqj5yz+pHq9XIbOFTGKuGeOv\nYH+IYL/6N8pb2Rj5bmTLYWbJJzVy7a9E5Ju/rNYoLQz//z7gowb/2ab/wErLWmJO6owpSvT8vpZ5\nOxQaqGPWtWcF/b3Ott8mumauh5qzScKRm4CPvjNT6Dszhd4mQSiYjsJVxg/rh8/9k+oN568TSSE2\nQZ82tGvFuvrXEzR4YEuuFo3F/mxFoorhZiwQFfg9/bTV7cHnL6Hi+jvijin2e1kwp8L2OQcxaXFY\nOv0F0T4x81fXnhWE+tLjsCHnL1+AfsX6D38oSFb8BblxmJ10AbnyV7HfegQjH/1lNfX2d+H/fyLV\ni3inHLH8Ijm9ifXz+9oXSDuH1vNW/IHI/DyoCSYBAqdmUXxj7jNTFzKKLzBw4/Z70jKUrZ9W1dCP\nJmnX7RtTx76a2SyZNzGSFXfdhTcSnjs2Iks0tOv3+AgE1WtbVeuG6GjMbmRmVTHciuElPqYUn2JH\n7zUm3nAPPn9J3DFaoc6hTroclm5/6THzF2DsMG8f3tLEU4Eu1ije8GefQX+BtcPmiwr2ybNcutrD\nWz3pddh37/161AiVlcOy7a+Vi6YZ7nrTyEd/WU29NYLpsvmQlHKm7auMa3bYLGti6/zoe9u+yhaU\nou7IMf5Z+wm2TY/M8yvDDBPxmhLs9ZouHBzKRP4N0jgVF7t92jBfzGTJjnY/O96BprbLBDqHUTQt\n8Xy2fp4frId2Y6O72GFqPUYR2F++82XLuX6ziuF2ZBMIBDjX8B6TK0Yxec6iqOkFO1mBhxJpc1ia\n/aXHyF+B5pujalrGOswJrr/MURQy6i8wd9iGliC/qumlp/EmvJWNOXNYtv0FA2urYvMo5bO/rLpt\n96HGUV8FGlBrJAWAP0TNcGsb5UoloZFtiQ9MktiOk35O3lt+Dk/xwBfG43c2F+vxu5KJxU6pmWTQ\nR2WxkVjkGC+0BI8QApRx4LeZ4VgfkSUa2jUato6tq6RhFIElisrMKobbEc3Ondu5cOE8f/BfVvNf\nn/wY+2rO0nGtl9GlRSyYU5F3kViOuY80OCzb/vJNPWrpMCe4/soesaNKVg67MqwOZRjQeJPtXcSZ\ncFi2/aWxavF0ls+fXDD+Ml2jJKVsklKeBG6VUn5DSvmhlPKMlPJ5YJGTi/SXpSYZLUdJf6+9BV6x\n4vGUXkn62orifA3CYMdOJyl2vcWKkr+IJFKzQvsDYSkPZWDdReD8hMgaj0RokbvV0K5VBe7Yat1W\n8/lmz5nlN7Ez1x8KhaiqWofH42XlylUMK/axZN5EVi2ezpJ5E/NWMrkiXQ5L1l/672TP8YW2/RVb\nRikVh7n+So5U/QXWDtP85Zt6lO73l+fEYdn2VyyF5C87CScVIURk4aMQ4iHUqMw2/e3jnbYrCt+k\nOrVGW1FutgwOpYWQ6UKTgMbo0iK+e+/X+c7ib/Hk2KcR3sWmr7NKTxCLJo7uvSuixFbRGb/IOXTq\nRh5fOsNyaNdqEWTsc1aRl9lzibLlWrF37x5Onz7FRz6yhHHjxlke6xJFSg5L1l9RpZOmHrftr6Fc\nFiRfSMVf3spG09GkWPTTrnqH/bexTyO898Qdn06HZdtfhYydjtKfAT8QQpwTQlwAvgE4WhyZ6vZU\n78h2/LOcrS3KRwoxstO3OdTvzLiaBLRh4qqdJ3lmzQ5efL2Gmp49pq9z8n2JFZrGxeHxSQKHT2u0\nFIzdLbVgb3dI7DGpZMtVR5PWoigKq1Y9lvDaLlGk5LB0bK93F2DnhuC1Usfe0kjWX/5J9Y6n02Lp\nuNZLM/vjHk+Xw7Ltr0InYUdJSnlASnkrMAeYLaWcL6VMXCFSh793bLLti+B0bVEm0LbwJtvhyURk\nFwqpw/qZImoKwOPsjWsSWLloGpv3f8jarQ309AUTRluKouYbidoqbXHp2K2z3spGAsRH790G02d6\nnGyptbM7JPYYu9lyjThy5BCNjQ0sWHAnEybkT8baQiBVh+n9FQqFuFjXRn/Aeu1PsCvxNE0uSMVf\nmSDT/vIMv+bYWxpJ+8sXoO/UzKT9BXDGc8Sww5Muh2XbX4WOEjL5FxRC/KuU8pNCiLcx2DmiZb01\n4ty5K1HHf/atL1pur3VJjVDAi+LL10WbCt9Z/E2eWbMjssOh5I7NCYelQwEf3e8vB1RZ+a+Ll4me\nvjNTIjtCrM6faKt/vvLNb/49x48f5R/+4Vmuv35GrptjyPjxZXl1l6fLYXp/XTndwcFf7sJX7GPc\njROonDuZERNGobjzW0nj+ivaX8V+L8MXvEW3zazaLunByl9Wq6deCP//a6k2YFjPRLpKWlM9jYsJ\nmZBM+na2hfjJ/v+kp29gFEQTiB69fPSSAeOIKxZt62yiaM9qq3+qaHmdLl3tYdSIYhaICoaXpL5A\nsbb2BMePH2Xu3FvztpOUp6TFYXp/FZUWM/muGZw9eoozH7Rw5oMWho0ppXLuJMbfPInisvwcScpn\nMuKvgM9W2SMbZ8qqvwBuXNCBdJBVO51kymGFjumIkh4hxK3AvUAf8LaUUlod744oZYZQEPqvjcI7\nMjsFNU3boetE2etQKXTtedD0WTWPSPRXqrdJEGy73lY0ptF/bipF488YTrvpKfL4+cy8/8kN5TPT\nJoaqnSczlhfk+ee/zYED+/nyl7/GjTfelNK5Mkm+jSjpScVhRv4K9YfoaDrP2SOnuFDbRn+gHxQo\nnz6OirmTGDu7Eo8vTTWUBhGZSi0SS/BaKd7Sa2lqT/b8tXLKI7zb9++W64xAddj37vtmWjs2mXRY\nISSUS6sAACAASURBVJDsiBIAQojPAU8BVai6+CshxDellD9PXxPtE7w2YsgujFS82as6btmOmK3L\nfWemsGr6KtOb6acbjrGTM6bnM0zKFq4Cbica0/COb+EHy76d8Ljvv/8Tqhs3IY9748Rgtzq3nqqd\nJw0zzTopwmtGc3MTBw7sZ/ZswZw5NyZ1jqFOJhymeBTKrx9P+fXjCXT3cb7mNG2HT9HeeJ72xvPq\n1NycCVTMnUTZxNHu1FyYbH0MnuGJO0mQf/5atXg6q7CeVvv++z8BjDs2yfhLO1emHDYYsNP1/HNg\nvpTyCoAQ4h+ArUBWOkqxoxcE4wuADiUyKZqe4wvxlJ+xHQFp+CpbLLPEjikzL4RompTNF2DRfZ18\n/PZv8cLRn1LbYZ7yHmDpxEW2Ep6daK+PnOvI8b309w0s1PWUXaAPWLtVFY8dMXR2B6je1WT6vLey\nkeq6JpbP/6Ok8oRUVa0DYPXqx9w/tsmTUYf5Svxcd9tUrrttKl0Xr9J25BRnj5zizMGBqbmKuROp\nuHkSRWXD3O36GaLn+EL6r4x1NIoDmfPXbYuucPvop9nTt46Gy42WbciVvyDzDhsM2EkPcA2iMrNf\nAxyliS0+vjotO8UUhaTS+LvYwzepzjICMvs3VBRo8u0yfd240cNMn7PaRit79tDS2ZSwkwT2d1vo\nd4X4JtVFPeebVBd5zKhwpRH75NmoiC4W/6R6lOtqExbhNaKt7Qy7d+9gypSp3HZbfF4oF9uk5DAn\n/ho2ZgTTlwoWfvqj3PxfFzLupgn0XOmiaVste194h6P/bw/njrUStPjOuCSHdu86GcWBzPmrmf1U\nTu1K2EmC3PkLMuuwwYJpR0kI8VUhxFeBC8AOIcRfCyH+EngbqHVykZ4bNrhRVAHgHdlu+e9k9dyu\nNvO8ImYVo+0svH7p+K/NL6rDTg4PfTQG6vv1lF0A1GjMO7I98lii6twal672mD6nvT/FF+BAh/NC\nzBs2rCcUCrF69ePuaFISpMthyfhLnZobx5xVt3HnXyxj1sdupmzCaDqaLiA3HGTPmi3UbjzM5VPt\n2Fkn6pIYNd/e/qT+1gxVf0FmHTZYsBpH075u2jdoePj/bzq+ShoLDw5VsrUIMnlCPPvOL7hzhlpC\na3KZukvkhvKZphWj7SRlu9bXyZplz0V+f37fGlqvnqG7P/7mTlRvyCjHiG9SHb01Y6OiM+2x9463\nsWSedc6iUSPMh+X1768huBd41PJcetrbL7J16ztUVl7HXXc5qhjkMkB6HJaiv2Kn5s4eaeXs0VO0\nHfqQtkMfUjJ6OJW3TKLi5kkUjzQfvSgkcuWr5JODRvtr2dQlnGhX798bymcOWn9B5hw2mDDtKEkp\n/x5ACPEzKaWjTNyxFB9fRfeNVXn+hz6/0RYdaltIjXZa5Loz1Rw8wun6GgCuHzUVUCUDA/Pl67c3\nEuxXI2htC61ZXpInZq3i7opF/NvuDXT1BLht9J00XDafS+8nxPP71nB7xa1xW2djozEN78h2tQip\nbkpXi8qOnVQXOVrN9XeWnaB4YhM9rdOizxsTbfbR62hL7+uvv0YwGODhh1fj8diZIXeJJV0OS6e/\nho0ZwbSlNzD1I7O51HyBtiOnuHDiDE3bamnaXsvoqWPVXXM3XIfXYBSjUDD7rDSHZcpfqbxe769l\nU5dEOiY3lM9Myl8AK69/gM7uAC99sJGungANQWt/vVKzlvHDx0XaoJEpf21p3kZvWZBivzdu+i1V\nhw0m7Bh4rhBiRCoX6Rx30O0kpQH93Lt/cvzu5lx9xqGAj74zU+i/MopAKEAgFKC2o4HajgZ+s+89\nOrvVm+3+Oybj9UQ30mr4em1dNc+s2cGByzup6dnDrxp/lrAtDZebeM2kcrYZRmsa7M71b27ZQtFk\n410vTtqg5+rVK7z11iZGjy5nyZJ7bb0m02xp3maZETjPSclhmfCX4lEYPX0c4pF53PnZZcx6cC4j\nJ5bT0XSBE68dYs+at6h9/TCXP7w4qKbmtHst3/31H0dfjXOYU38BrKt7Y8BhgZ0J27KtdTevNW6K\nc0Wm/FXduInNLVtYuWha3HOpOCzfSNVfdpaw9wPNQggJdGkPWmW1jcU7vjmJprnEoqXUD/UMR8li\nsBkKQV+ziIsAI+3yBUwXUG7+cAtvvdvNykXTGFVaRG8gehrDavi6nyD90/bgC4vIbmqE7mA3P3x3\nHZ+7Vx0mNovGIu03EHRkrv/KWPbVnDUcwtbXVbpt0RWO7xttWeLAbqK4N9/cSE9PN0888fv4/fmx\ny1MTZIFGkyk5LNP+8hX7uW7eFK6bN4Wui9c4e1TdNdd2+EPaDqtTcxVz1am5klGFPTWnKFA0Z1dW\n/QXWo1VG/tp55r3I8ZrDbppe7shfAAF6oxyWsJ2EIhm5NVdkw1+l17fw+NIZkXQDqTos30jVX3Y6\nSl9M6sw5INdTT6kQuDgeb/m5hO33Vbao2s8iigL+KZb5+Uzb7R3ZTt+EQ6zdGuTGaeXRz9mosK1f\nc+Dk37am5z2qds5j1eLpSUdB2lx/xzXjBJb68zazn3966qvsqznLby5sMS1NX924yfJm7e7u5o03\nqhkxYgTLlsVnAM4FeqEWoiQpEIeFQjBsTCnTltzAlHtmc7nlAmePnOK8PEPz9lqad9QyasoYKm+Z\nzNjZlXiL8merduDiePrq5lOycGPC+zQXueAStSn2ef3vmsMO1N4cdYwdf0Hy66bW1b0RNQXoFCf+\nqm7cxHfv/TrL509O2WH5Rjr8lfBOk1K+K4R4CLg/fPzbUsp1jq7Sr6aTzzSF2kkCbHWSIPwe0xyN\n2elgKiksk9HS89d+2AEQ2alhZzFksv+mii9Add3bLJ//Rzx9x6cNj/m33Rv4oHOr6Tm0qGx06Zy4\n52KrdHcFu9nVtgvKIXDBPDN4oohsy5bNXLt2jSee+H1KSvKjHEasUAtJkpAGh+XAXx6Pwuhp4xg9\nbRwzlt/EBXmGtiOnuNR8kUvNF/H6vYwT11Fxy2RGTi7P+a5Ib/k5QlOP2ndYGklfuRJz9CVGnPgL\nkn+/AXrZ2PBO1vyleamvvD4lh+Ub6fCXnczcXwSeAF5C3UXyZSHEzVLKb9m9SLCnGO/wzIumkMnk\nTZ6oI6QoAyn3jbBTBNIKbcow0HwzPq+CJzx/rq+HlOo1DK8bzv1htvOjpsd8S7BG0eR6FhjsRDGK\n8uxGfmY3a19fH9XVVRQXl/DAA+YlE7KJlVALhVQdlmt/+Yr9VN46hcpbp9DVrk3NtdJ25BRtR05R\nMnoYFTeHp+ZGD098wgygKM7zF9klob98gYz6C2IcZuCvdF0nlo0nN7Nixn2Gz2XCX3ZHsAolYEqX\nv+yM3X4cuEtK2QUghPg3YD9gu6PkGWYvnbxLZrDTCdNS7sdid3g5EVpENmVGD2fCOzQ8ZRfovzI2\nbdeIRcv9scRgS+uJ9nrT6txahl+Ax5fOiMtGG3vzaXQFu3li1qqkBbJt2zt0dLSzcuUqRowoS+oc\n6cZKqAVESg7LJ38NKy9l2kduYOo9s7nUfJGzRz7kvGyjeUcdzTvqGDVlDBVzJzFOXJf1qblMBXv5\n4C9QHdbffl1kh5nmr3RfR4/ZTrNM+WtL8za+e691CZVCIl3+sjOh4tEEE6YbTKcvjQnmz1z6UCEU\ngq49K2xnFA6cn2D4uN3h5URoEVlw/MBaJy0yS9c1jFBzf8RjuYtkUh3Ffi+PL51huLXW6rXJricI\nBoO89loVXq+Phx56OKlzpJtEQi0gUnNYHvpLURRGTxvLDQ/P466nljH7oVsYNWUMl1ouUvv6Yfas\n2cKJ6kNcainMXXP55i8Ir9WceTDyuz53USYd5nT0Ohf+ykfS6S87BnhLCPEq8GL49z8Gtji5SPf7\ny20t8nNRCfUrKB5ncosdnlYUKLrlHdufuRYtAZFoBAaGlz1lFyi+MbXMrL7KFs7p/jx5R7az9J5i\nZo58igVzKti8r4W12xoNc6wkSx+9/PDd33Hb6DsjlbUT7SLxjmzn0380gVsqpsc990rNWsvK3skO\n7e7Zs5u2tjN89KPLKS8f4+i1mSKRUAtoVCklh+W7v7xFPipvmUzlLZPp7uhUk1keVnfOnT1yiuKR\nw6icO4mKudmZmgsFfATOT3BUb60Q/AXgKRpYuzPgr+tZMGcpw4p9VO1odOywRFOLXcHuKId92GVd\n1ikX/spH0ukvOx2lp4HPAP8DdQRqC/CC7SugjiSkIpnAxfEovl68I7O/WyJdBLtK8JR021vs6LCT\nBMY3mqfEfkk+RQH/jIOEekrprRkb93xsXaFkMGpjR+lhlsy7BxjIEGsnOgsFfOofsPkbE241runZ\nwwevj+Tlg29w47Qx9JedTnj+tz7cwi0VN8Q9bqcmk9ObMBQKsX79WhRF4ZFHVkce16KeXEjLLBrT\nKDChpuSwQvJXyejhTL1nNlMWz+Jyy0U1oaU8Q/POOpp3pjY1Z3dXsVW6ENPXFIC/jND7C5w7LHi5\nHN+YxLvianr2cPj9C7x80MPYKYm/R9n0lxW5cli6/WVn11tICPFLYIPu4YmA7eQiqS7ySz4tff7g\nKemmt2ZhWqIauyiKs5QJnuJeKO6NmnvX6K25K+r3dEXYtR0NnGiv54bymSwQFby8uTZukaR1o+Mf\nCoWge++KuMeV62qp6YGH/J/k6vBXOd3ZBsCE4ZV85e5n4o5/4eCLAHxq3p8A6rqAftRO7Odv/1Qk\n63iqfPDB+7S0NLN48RIqK6+LPJ7L3EWDaUFnqg4rRH8pisKoqWMZNXUsweU3cV6e4eyRU1xqucil\nlovUbzrGOFFJxdzJjJo6Ju275tJxukLzF+DYYSULN8Y9FgpBWe1jnOuI/kNfcsdmAFrfW864O7u4\nhrpWysxfEO2wTPnLilw5LN3+srPr7bvAn6MWlgR110gImGHrCgz80Ur2y5mvQ95OUBQouiH7RQWT\n+ey0/BtmeCsb0/pvUt24ybImnBlmBTAVRX2ur25+5DH9YsvqurfxTG6LPKd1mGI5dOEYAJ3dAfbJ\ns7x5sSquzakSCoVYt04thhk7mpTL3EWDaUFnqg4rdH/FTc0da+Xs4Q85e7SVs0dbKR45jIq5E6m4\neRLDyktNz5OL91FI/gIcOczKXx3jdkCHsb+8lY1cDQ0UMDfzFww4bOvB1oz4y4pcOizd/rIz9voo\nMElKeTWVC1lVdS7kRJFOyHY22mTR8m/ERmUa6V64WNvRwIvbtjNj5AwW33wdJ09f5lD9hUhNJQCf\nVyEQjJ6StIrUveXn6DNpszLxeNzx39j9fFRUpkViAM9Uf5++09MovrE1qs36SDJZamqOU1d3gttv\nn8/UqQNlBAo9d1GekbLDBou/SkYPZ+riWUxZNJPLH7Zz9vCHnJdnaNlZT8vOekZOLqdy7iTGzZmQ\nVwktnZBLfy0QFdx/x2RDh8W1M0l/+afIuO9brL8g2mG/avxZ1NRvuvxlxWBymJ1db4cA8/LCNrH6\nUlhJJpTlLNSDlWCXs+SFvkl1eMouRJKraTjdBhsKQfByecLjdl/Yxouv1/DX/7KTQ5f3wng1IvN6\nFO6YPY5nP7WIYl2RUKs/XDAwqmTUZqPkmbFRmRaJASijzhqucfjFB1Vxjzll3brfAvDoo78Xecws\n94dL0qTssMHmL0VRGDVlDLNX3sqdn13GDQ/fyqipY7j8YTu1G4/w3o+3cOK1g3Q0nS/IXXPa/Rrr\nsEz76/M/3Mbnf7gtzmHzZoyhyD8gnkz7C6IdZpQNPR3+MmOwOcxOyPBLoE4IcRjdllontd4SfSms\nsJMRWr8mJZ93p2ST2Gy1ThZGghqVKd4aQkFf1DC202hMUaC/c0TUGgFvZSOTKkppPlxh+BrtGsG2\n6wn2h3i/9jzTJoyMGtK2s+5Di8rstlmLyvSRmPYe9NW5NdpDrRw+e8Jw0aQdGhrqOXLkEDfeeDOz\nZs2OPD5IchflEyk5LNv+guyOUHmLfJGEld2XuiK15gam5krU5+daT82lk8hmjSR9ro0qaR0mzWHp\n8hdgmLdJG0EqiXHYwYaL3DS9nGMnVY9k0l8Aaw5EFxA3+gxT9ZcVg81hdjpK3wM+DzQle5FsLWZM\ndXfKYCI2akrmc/GUXlH/rxvG1hYpFs3ZZXsXjz79P6g3/lkA4hc8xs7FazJ6bddJvvfURwC1IrbR\nYm0jnESQWlSmj8QS8WrN60mLpqrqdwCsXj2QOTdR7o9CFU2OSclhQ8lfJaOGRabmrpxqp+3wKc7L\n07Tsquf/t3fm4VHcd55+q0/dQreQBJJAUBgE2ICMwQFsIAnGgxMnk2MyyWYm2c3kmtnJk9lMZmYn\nzzPZGe8mO8418SSZncOJk0mc2CExMb4AYwyY0xwWoOLQBRK6kQTo7GP/aFWruruqu6q7ummJep/H\nT4K6jl+3uj76fe+rb10hr3IOpfVVFC8px+FO3sBmweEx1B5ADcf8C9izA9FWWcPM0i9Q3yiBtoZd\n7hhix/oaXj1+Nan6BXD+RmR6gRqJ6JcWs1HD9GyUhiRJ+kkiN/HdLFC1yM1CnkidymGLssXjrDup\nq7xzJuNa9PaUVbUu+DM9n7V3OD/kHNAWEQhY7vb86c9S2W13YtLHv754nj/9wAq2rq7icGMXv9h7\nKWoOgHwNI3xp/98YOr7X0xFXrL+zs4MTJ45RW7uQ+vrlwZ/Pot5F6URCGjYr9WvKi6WlX4IgkFdV\nSF5VIQu23EP/pW56GjsYbO9nuGOQ5r3nKVpcTll9JfnzixBs5u/wjHrBw5E3SRCpYcnSLwj1Podr\n2LW+W3zriw8mTb/+/siTlGRqJ7KHE69+RWM2apiejdLBqWZtLwHBbltGhGeiaW3SQ2K23KGUWmOC\nwxP4wz4LWhfEQnB4Q4TFteQtXZ91uBg5605in6O+EYLIwcCCwxMiRqcu9rHrcCs71tewZXUVI+Me\nzeoSe1kLttwBw2MFJnyTsQ8KI54Kkl27foPf72fHjvcHS7NnWe+idCIhDZuV+jXV0FHPZkQZmhsf\nHqVnasZc7/lOes934s6VQ3MVZBbmmLpG066l0LBk6hcQU8P2lF9jx/qaqPol38+ofl0f6Y5aBaeG\nmRVws1XD9GyUsoFh4EHFz/yA7o1SIjH+ya55ujq8Grm+WVUq4X/Y9d57JuZTyVbvRNM63ZavIIRa\nXdE2QlrfkXAx2v1WG1tXV5HpdgTb879wsCXCMnNWXtE18d3vcfDkpq+T6XbwozNPxwy7eQZKgm0H\n3E473/rigxGzlGLR29vLoUMHmTu3koaG+6ff2yzqXZRmJKRh6aZfZqG3Aa4Sd14mVevqKLJ/gcnK\nXwRmzTVd5+qRK1w9ogjNieU4MpIXmosHo547M/QLtDVM1q/db7UxPumNOE+PEa7UL8CQhsWrX9GY\nrRqmp+HkH4ui6ATEqeMbJUkytM1NxOuSrInUZhBvTxXX8v1xn28G8mbN6EbNljuk2xqTcc67pGsj\npPUdCbfI/AsP8r+PvM3XN30JgC2rqnjxrdaQjZLSPR5tqrjMibIeNqys0JWbpCzb3b6uOi6R2b17\nFz6fl8cem/YmwezqXZROJKphln5Fnude8wpZ9gLyqwpYsGUp/Re7IkNziwINLedUmx+a8w7nx+WF\nM3pOovoFoRrmWvIWfuBE0yI2rKxgx/oaHqwv56s/eiuk/Ymy11MsDZP1C/TlV8oaFq9+RWO2apie\nhpOrgecJNGuzAWWiKD4uSdJRvTfx3cyPu31/MjYTd9qLk2jsPVHk0lNlQqFj/rmYlq8g6Ivth5xj\n82Eva4m6EXItezPq70Rpkdlyh+j3Tq/hhNTDxKQv4ni1c7UYvB2Ixjy1+ZsRr+063Bph8bmddrav\nq1YdOBmLoaFB9u/fR3FxCevWPRj7BIuESVTD0k2/0gGboprP7rSHhubOBSrmei9cp/fCdVw5bkqX\nVVK2vNK00Jwtd8iwfoHx34cZ+gXTOiTrp6w5AI0tAxE94oxomPJa4RqmpV+Pb4xPv+5W9Gwnvwd8\nRBYVURQfAP4JuD/qWQpSmaSYjoxfaMC1JNCVe+z4NlzL92PPvLObJaVnxDH/nCHLd/TYtuB5esTJ\nOT+yQZoSW9btqOfLFpm9oCt4nSdPPMWX13yBoVvjIcdG9BwJ80ipMSfbpfnajvU1bF1dxYmmHgZv\nTzAn28WaJaVkuh3Bjt1Dt8bJz3EHh+5G4+WXdzM5OcGjj+7A4ZiZDf1mIAlpWKL6NZMaUqohJzXL\nrQt8Yxma+uXOy2TeujqqHljIzc5Beho76Gu6zrWjzVw72kzu3DmULQ80tDQrNGdUv2SPeqr0CyKr\n+I5P7mQHAa94ohqWSv26W9HzqeQoLS9Jko6Iomise+FdjmvxieADojeBMtkox3w4yq7qFnJlrpJZ\nYQU99w7POWoeDlR6y0MoQ45TOVdLZNxOO2uWqPdzksl0O4KubRk1S+3ney5F9TTdvn2bPXteJTc3\nj02bdLchs0icO6phM3mTBFMhd8UfeT36JQgCeZUF5FUWhFTN3Wjt4+b1QZr3XqBwUSll9VXMqSk2\nHJqLV7/kc82sMtQ9i07xufV7pwdzJ6JhqdSvuxk9nbkHRFF8n/wPURQfZ3pmkoUebNNu1XgSKCE5\nHX7tBb1x9W6x5Q6pnjfZNS/43+ixbYwe2xaYnB7nHwq/n+B1fCPZEdd58sRTrBFLgx27tXqOyBaZ\nGgsr8jje1MPImP60u12HW9l5oDkiAXN80svOA83sOtyqet7eva8yOjrC9u2/h8ulbQVamI6lYQkg\nCKF/5A3rhcNOyT0VLPtQAw2ffZiajYtx52fS19TFuedOcPyHr9P6hsRIn7EJM/HqF6jnKiVTv7zD\n+ar6BSSkYanUr7sZPRulzwB/LYpinyiK/cBfAZ9N7rJmFyGVEgYfOs9ACZ6BksAYT5MRhPiSTbXO\nc5RdDf4nEy3JMdZkBGUbfzXrr3m4LTiEEqL3HNF67XzbDZ5+qYkvP3VIl0CMjHnY/Vb0voW732pj\ndDxUuMbHx3nppRfJyspi8+Z3x7yPhalYGpYg8W4WvMP5jB7bFnzW3bkZVD2wkFWf3sDKj6+jfOU8\nfJNerh1t5u1/f5Mzzxzm+qk2PGOx23TEq1/yueHcCf0CEtKwVOnX3U7MjZIkSZeAx4BqoBb4mCRJ\nUrIXZhHAXtAbfFiD1sm4eWW3iVS+qP1M/s8x/5yueUaxsBf0UrRa+zpPnniKHetruHfdzag9R6J5\nlUC/NXVC6lEt5Q2/1ommnpCfvfHGPm7eHGbr1veSnZ2aMRAWAWaqhs3AEWsRKDcIsofFO+5EEARy\nK+ZQ99567v/CZsQd91JQW8zNriGuvHaeY0/to+m3pxi40oPfp+1ON7XXUpL0q+C+E1H1C0hYw5Kt\nX3c7MTdKoij+GfCSJEm3gQJglyiKn0n6ygwwGwRFC9/N/ODDG7ROXMabIiqRBSuZn5uj7KopzTj9\nHfcwYte+TvNwGyNjHpq9x2NeS0+X21jWVHjipRbKShSPx8OLL+7C6XSxbdt2XedHY2TMw4Eznew6\n1MKBM52G3O5KLt64wsUb5k5ST0fSXcO0nsOZntsE0/lASg0L169AaG4uyz7UwP2fU4TmpC7OP3+S\n4z/YT8v+pmBobqbp15izT/N1Wb8OnOnkigkalgz9MpuZqF96krk/A6wFkCSpbarU9ijwL8lcWCy8\n404mzmzBteQottzkjReQSXRIY7woLTJ7QS/Cyr0J318Q0FXSqkTu5WGkDNc7Ycfuim69xMJWFXtm\n0Zdf/Sbj581JjpatqfDkR5nwxEstlJUohw8fpL+/j/e85xHy8vITWp+ZSZhyczgzxxekKWmpYRNt\nIvaCntTol9fG2Mn33JEmtxEaFuX+rpxAaK5y7QJuXR+iu/EafReu03GshY5jLeSU51NWX0nBPbsQ\nBGOe9Yk2EcE9kqb69QAQ0DCbTcAXY7SJFsnQLzOZqfqlJ0fJCSi3oRMEutrqZux4ICnOTGyuSWy5\n/YEp90l+8P1+mOxYGNIELFWE5zcl6k2S0VPSqsQ5LxCpMJITYHPGFhm/PyBg4Xjal7At4/O67iNk\nD+KsOxn0uCVKNGtKmXiphbISxe/3s2vXb7HZ7GzfviOhdZmZhHnxxhUuDTZzabD5bvAqJaRhydAv\nAGfVpZToF4B/wp1Qh/FEiCdHMxiae08gNLfksXspWFDCre4hruw5z8kf7dUVmlPinCfNCP2Kd5Mk\nY6Z+mclM1i89HqXfAPtEUfzl1L8/APzW6I3MnokmCOBcfMLUa0a917yL4Iv+BUvVWozgvZ2NPTty\nU6TnOsoO3sFkcq8DdM4fEoRAJYn/Wj2uNS+FvPbU5m8GrQvn8lcizs2pnZrpNvYEX37qUMy4utzj\nxYxtZDRrSk68jDajSdnx9vjxY1y/3sHGjQ9RXFwc95r0JmHK411ioRw1YOaspzQlYQ1LxkxHP/5k\n1GioYsscRcgYTdHdzEPWr+IlcyleMpeJW2P0nO+k551r9Eld9ElduLLdlCytoLS+kuyS3JDzIzTM\n0i9D+mUWM12/9CRz/yWBhm0isAD4niRJf2vkJkYsGc9AieoOXQ2b3fjOO964tmDzGx5QmA4Y9Rwp\nEQRwrX45mF/gWr7f8GfgKLtKXv2ZiJ//6MzT7Fhfw/seV/9cx6aGJ+pJPpS/X8o8rnjRY03tWF/D\n4xsXRFhmgY63C4Iu5IA3aSeCILBjx/sTWpeZSZiyNSYz271KiWqYUf0aPbZNl4bFo1+JMBNznsL1\ny5WTQdX9C7jvUxtY+Yl1zL1vPj6vj47jLZz6j4Oc/vEhOk+2MTka8KqEa5ilX/r1y0xmun7p2jZK\nkvQc8Fy8NzFijdkLerHnJS9mPxPFIhESfb/KUQXxNMoUBBh1d0ZYzvJMoj1X92meu7vlNTY5qmLe\nQ/n9UnYcjwe91lS0jrcyjY1naWlppqFhLXPnqucM6MXMJEy1wZWz3auUiIYZ1a9J9BUOWMRGJBVl\nFQAAIABJREFUS78EQSB37hxy586h9uElDFzppafxGjea+2jee56W/RcoXFhK2fJAQ0t9WSbq979b\n9ctMZrp+pVW/cu9wARNNa7Hl9uO+J3YFwJ1AmdQMYMsdCPxv1u1ZuQlLpAdUrPP+/siTjHq1N1+j\n3jG63OeArIjX5DJZW+5AxBrljr0hx9uEkMG5EdezCaxYWMSWVbGFTUat462SF174DQCPPfa47mtq\nYVYSZrg1JiNbZbN5s5RsZP0CmLh0X1prmOAeAab1y9tXFXNUhx7uxLgWm8NOsVhOsVjOxK1xes93\n0t14jf6L3fRf7MaZ5aJUDs2V5hm+fjroVyySoV9mMtP1K602Sva8G9hy+3HWNN7ppWjirApMk45I\nCvQBUVKYzBYQvx8m20Vc1enbDmaiTYwqvtdHumNeo2n8GG7nlgi3bdBit0e6vdWsso9ursPltAet\np2W1hfxsz0XOXu7H6/Pj9fk5damPLz91iOrVbdSW5/KRJfFvcC5dusiFC+dYseJeamsXxH0dmTVi\nKT/fcymq+1qP213NGlO+Zm2U4kfWLyFrGEdp+51ejibOqktgC02AtlUlvkkCYxqXDE105bipvL+W\nioYabncP09PYQc/5TjpOtNJxopXs0lzKlldRck8Fzqzof5TTSb8AHqwvp7QwK6Z+bV9XzUjhKYCE\nNMxMZrp+xeePNIpX/37MtfgkgnMSv8eR9F4Z8SDYfSF9QYL/xcjzNtvKmmhqCAie3uPbxKRU7kTD\nOe9izPe9omgpmfbIsVsZ9gw+UvTnPOT4Y6rrxrHlTk+ckNv8Cw6PZuNLZazf7bSzfvlcNqysYMf6\nGjasrOBQYxenLvZFeJnGJ720exs50HnE2JsN4zvPfIe+0YGEc5NklN17tdi+rpoXWnbxbNNO1de1\nrDGZ2Z6rFDcG9ctZeQXBOZmW+gUBDTOqX0nBF1sUlZ+f93b0Rq2hnhmBnPJ8Fmxdyv2ff5gl77uP\nwoWljPTeonnvBY79YB8XfvM2/Ze68XnVq+YS0a9Mewb/d/0TvG/OZ1lYkYcttz+oYUb1CwIa9rF3\nL9alXzsPNHOg8wgHE9Swfe1vsq/9zYSuIaNXvzLdDp5t2qmqYXdSv1LiURp7eyuArh4egt1HwD1D\n1E7KdxKzhikmgrO2ceqz0nl82FDZZCFXmcB0JUc05Fh/OGPeMZ55+yW83bW4lpzEWSXgkYrx+vy6\n8j+UVll43D5aBcb07Cg/Pz33PB9f9sGY9wqnvb2Nt44fIr+ykDfGj3LgzDH+ZOUfGb5OOHKSZXgf\nErfTHuxD8qf7fgCoW5LRrDHlMZZXKZSZql93IgxmBEFHMrty/fEWpoSE5m4HQnM9jR0hobmSpRWU\n1VeSVZJnin6Nesf48i+fYbwzsDlwLbkMwERTkWH9glANi1VBFkgL8eMDnm3aGbdXSdaLS1Obj0Q1\nTI9+AcENXvi676R+pSz0Fs/wQmfllenSTtIn1GTUvSw/eDKJNH0LlruuftXQeYLDo8u6DTTB68Ke\nF99mUG7/72lfFlKKKzfslPnHz6+nMC+Dv3jja5pxfmflFfwjecHkfn92H0Wl44zoqFwRBHAvfpvt\n5b8fUcURrQJDGVI90n0sro3St3/ybbx+H+Vrq3lnIHbDOSNES8J8tmknvqn2QGoC+eerrPFm8ZKI\nfsH0H91o1zB7Y2PGtczWL/ma+GyGDD3Q937kHFLXkrdUNcyV7aayoZaKNTXc7rlJT+M1es930nmi\nlc6p0FxR1S3muB+B49vIWLUnWNWmpmFPnHpCU7+E8kvQWR3s9wdT3yOd+uWsO4mt7f6IZoyxKsiU\nGnaw80hcG6V97W8G35fWZjAeYiWRR9OwO6lfqQm9EefwVYcntP19VexNknc0I61c3nIXbCVjx7cx\n2TUvrut5ugPn+W4b7/CsR2ic8y4m7DFzlF0NlLwqBCF8TtHOA80hD6Pqeh0enLXT+WqOysvcztf/\n0Nrm9KiWumpVYIT/MfTj1wxjadHd3cUbh/aTXZpLwYLpUOePzjxt6DrRkJMwZTe8LDJKV3uibneL\nUOLVL3tZS/B7FdMbJUzrV7pomNn6BQENi0e/9OCcdxGI7fUXBIGcsjwWbFlKw+c3c8/776OwrpSR\nvlu0v/0mp1/+Ls2XvsNAS0cwNBeuYT88vCumftnLWnBUXg7+zMj3yFHYy7e++GCEhkWrIAvXMF8c\nGgbq3huzNExLvyB9NSwlHqV4rDEZ5RdeTxzdljFmeDyHXrSaN8Zck4rLON6p146yq3jalwWra+xl\nLeZ62QR/hAUJ+qzIWGE3Z+UVvN21APQNj3H2cmSjtnBsiiZ5RtpGVDgX8Dcb1C0QrQoMtd+JUYvs\nW898G6/fS9XaBQiKD8xMq0wNpSUG0wKZLsmcM5lE9MtoyFtuwZEsDYsHM/VLPlfWiWToFxjzgtns\nNooWl1O4qJybBzfQf7WRPt9ZbnQ0cmNnC85M51RDyyqyFRrWYT8VcznOqkshnjO9v9N49AvM0TAt\nA/Zu1rDUbJQSeKiMioUgJNZkMRrxXneyPbT5XCKjBEJCW8Tfr8UzUGKoBFVt8wTTSYfKa2m9P9nC\n8nbXIrUPQvvmkNlxk13z8LQvC26yfDcL4u6p1TmpnfSnVoGh9cfQyMM6MNDPntdfJWNOFsXi3IjX\nf3TmaVNyldRQs77idbtbhJKQfhlscCg3do2nZ1myMFO/IFTD0k2/nG4or1vLvG0Oxibzp0NzJ9vo\nPNlGdkkuhWUjFORugZNbVfULpg1F3+38uDTMqH6BORoG0XOB7lYNS0nobez4tpgVC3rR445OliWW\nkFWpINFxCErhnrh0H+MXGgKueo/+fa9ZIxnsBb0h17Ll9ke9dvhnoXwvjrKrIWGKRBuParmK1Sow\nov0x1OMCfrZpJ3/+gy/j8XoC3iRb5JclWRZZuCUmE6/b3SIUM/VLD7aMsbTxJoH5+gXTz1s66pes\nYSGhucdXUbSojJH+W1x9Zx+nX/4ul478kqGh48HQnPyezNIwI/qlvL8asTRMrjSLlQ5xt2pYynKU\nzPLypJOAxMLvh9Fj20ISAG25/cH3MH6hIa5Yv2yRQSBvx1F5OVhyauQaibbLV2u976w7HfV3pIzz\nh1tAgpCY9R5OtIda2cY/VmhFz8P6+pUDNB45jSvHTeky7SZuZuYqyUQTwXSK889kkuWlViNdNM7v\nnwqnm6xfMK1h6aZfjsrLERpms9soWlTGPY+v4v7PP8yCrUvILnczOHSSpt+e4thT+2jec57bPcPY\n5zWapmF69Qtih4djadjBqXYCeirL7kYNS0noLZ3i7VrEauDo9wq6ylmVhIfJgNDEvsrL2HLjszgc\nZVfx3SgPWiy27OHINXscYFfv1QGJt8sPb73vze3H5ox9RTlXSU1Q9H5PJtpEPrrivbgctmD1xG8G\nf8jYlDWUac/gHzd9Peo15AqM/3Fod8xR8koX8MiYhxNSD0O3xsnPcdPueIuOk614J73M37AIm0M7\nmc5si0zLEpNJpzj/TGU26Fc8yO9ZqWFm6RcENEy+R7roVywPkDPLTcXqGuaudHB7YIDeqYaWnW+3\n0fl2G1nFZylbXknJ0gpc2dG7UY9faGBd9bJgE8k1S0r52yNfD3p01PozKVFWkP2iP3a+p6xhavol\na0g0b5LM3ahhKdkopdIa0yJW6a0ggHOetsgY3STJyMnXQEiZKCTmlvXdLAgRLcEeWS4ay0KLt10+\nROYpCAI4F2tbeHLZrkwiCbIQ2Gytry8PVkzsa3+Tsf7ph3x0aijl5vkbol4n0+3g+5u/ofu+8sRw\nZX6Ac+UhOt9uw5HhpHxFbAvbzDi/HmsrXeL8M5V00K9YxNKvRJA1zEz98nsc+EZyg9dIC/2qi56c\nrdQwx/xz5JR7yNmcR/UmkcGWXrobOxi40kPL60207pcoWFBMaX0VhQtLVI0nV9UVPvbux0M0TLlR\n0aNhcgXZBvRpmJp+ZTQcNazFd5uGpc0Ik/CKKbMtOD3XE2IEIqNttuT1K5P75PvKFplyY5MoZg0O\njtcqU8sRsEXpi6KseANjIbbJrnn4bpSHzM4SHB6ujrSx2B1oLqbmMn6x5TUcNxYGLac1YilZGfF/\n5XcdbmXngdAkS8f8c3SfuYpnbJL571qE3RX7+mZaZP9kYJNnkTyU+uWsO4m9oDflXqhY+hUN5fq1\nNMyWdSvRJU5f0+ExRcNM1S9n9I2ZUsOU+mWz2yisK6OwrozJ0YlgQ8uBK70MXOnFkeGkqHoxc9fm\nk12WF6yGvb9wQ0hpfLI1TEu/4vme3m0aljYbJQjtZBve6CwZmyejxPRITQlkOOFhsmh4hwsA8zZC\nEOnNSZR4ql6UFW9GH05H2VV8KiItd2HVSkBUdvcG+PmeSxHN2/Si1Q3XVtRKx69bsDvtzL1vfuBn\nCAk9/HIOQLKqSyySg6xfsgaMHjOmX3eyk7bSO6NmxCjDZNGQBwO7lhw1TcPSQb9gWsME94jm+c5M\nFxWra6hYXcPtnmF6znXSc66Dbukc3RJkFedQVl9FydIKBqveAR4EtEvyzdIwLf1S+10nql8w+zQs\nZVVv0arV5E2Rs/JKcL6OMsHOltsPvthLlefDaTVrS3YDNy0rUhDAseC0vmvk3cB7I/pgQKPEW4Kr\nRbwVJ/I6jCY7alWPyLN9oiUgKt+7PAdp1+FWQ/cH9W64jvnn6D3XwcStccrvnY8zMzBkM9FKjbP9\n55Pes8RCP4nqF6BLv/BO69edaDhpL+jVNGL0bizkwcCejjrT1pUu+gWBtejVr+zSPGofXkLD5x5m\n6QdXUyyWM3rjNi37mzj+g9f57b/9kuf2PM/k5GTSNUxLv6K1E0iE2aZhadFwUhAIdOAOi0nLblVH\n5WV8t/ODTRaVyP0qlN6naA+7dzg/rvEcQTGsO4mjUP1Bi/Ye7W79DuJEhEG26IJrgpiJykYwao2F\nrwfMHYnwswu/0tUdV2mR7n6rja2rq0Lc3rFQ64ZrL2nj2gvNCHaByoaakNfijakrK0qS2bPEQj+J\n6hdMd9IPfxbU9EvPPePB7wfvjZKo+mVGxVYiKQYzQb/UiNXQsnBhKYULS6dCc9fpORcIzX3ru9/k\np0//B2NVfkqXV5JTnh/SqBbM0TA1/YrVTiDenKDZqGFp03BSreW8LEDyxsaW24/vZtH0dRViElKZ\nEeV+0Vrb+73gm8jAnhn5h1eO05vVvyMagsPD+IUGfDeLsOX2h+TmxEK26OTPyeW0R50LZBSj7z98\nPWqoNYNzOW18aNNCro62c3zyt5rn9o0NxFxDeH7U+KSXE009bFipXcYfTng3XMf8c/RLXYwNjlK+\nch6unNAKlXgrNZRW2GyyyGYyieiXs+4k3u6a6epUxbOgpV967xmO3wveoRL8ExkhOUbK9cR6fs3Y\nnNnzbjB+oYGJpsT1y+mwMeExNhMu6vWToF8QqWFup51/+G9r2XfxDK8PPx/8eSA0V03F6mpG+m7S\n3djB9fNdjJ0e5frpdrKKsildXkXp0ooQTUlUw9T0S087gXg2S7NRw1KyUfLdjO3F0dyNKwTIUXmZ\niSbFRimsWaGnfZku60/zNTtRO+LqjdNrIXul9MTv5fcaj3Wm/JwS3SS5HDa8Pj9en/poAL3vpbCn\niq6B0ajHKZmY9OFy2hm0vQOD0Y9dkFdLg/N9DN0ap7XrJqcu9cW8/uDtCd1rgchuuPbSdq7uvoIg\nCFStXaB6jlGrTK0/yWyxyGYyieiXvaA3xNOkfDbV9Avi9yYJ9uRvhPx+fR3zzdKveDZJNgF8Gm6o\nePUr73olfUP6u6WPT3o51zLANZt2JV1WcS61Dy3h4UffS1FbHcePHuRq8zluvTFG82/OkVe6gJLq\nlcwpX4TN7ow434iGheuXno14PF6l2aphqWkPkMCQVeWDrdzdqzYrnH8uYddxvJssJSEVfKv2AKFN\n29Q60Gq15A93++qx0BLND3iwvjykt8eek9ciqiW01qfFI49UM3h7ghcPtwbFT05+1UrUPH25j09s\n/SPOtQ5oVn3sOtzK7jfaODfZZOQtMifbZeh4uRuu/Dl07VrIzcsnKapqwH/x/YwCj29cEFeiuIya\n9TVbLLKZTKL6FV5Sb8vtx1bQpapfnvZlKR35pEZEIU2Yhqk1ekwn/fqDLYsCRtZUf7W+oTHNnB69\n+rXjkRrD+nXkfDdrlz7GJ1YXamrYrsOt7D7YxoVJL5Q8TGH+AwxeP0+W/wxDPVcY6rmCw5lBYdUy\niuevIHtORTA0Z0TDwvVLzYufqH7B7NWwlGyUxo5vM234YdBS0ajM0JrpI6MM5ZlNuFgou80qY8xq\nVp/eMlc9Flq4580opYVZIQ/MjvU1XOkY4uyV/riu53baWbOklEy3g62rq/jP1y5yqLErmIulJTSn\nLvVFeId++qrEoqo5rF1aRt/QGL+LIzFbXo9R5M/kxcOtXL94GIC5i9fjdtrjrqaTidbtdjZYZDMZ\nM/ULtBs1yl6laBqWSv0CdQ1Ld/0amfCyZU1oP7O27psp168LbTe40Bb5e37mFYkVC4uoLMmJ0C+H\nK5Pi6tUUV69m9GYvfe1n6b/aSE/LSXpaTpKZW0zRvBVULFhpWMNkfQrvo2SGfsHs1rCUtQcwq3LB\nnndDe+iqwirT2u0nYh3GXFuYWCjfsxxjjrZ2Pc3TJprWhiSUGx0OqQc1S6W+tjBuodm+rjqYdJjp\nduCuvYDbJ6luImPh8fo1BSie9Rhlx/oa5mUP81cHBlnxwAN84iMbgyKaCNGsrtlgkc10zKy80gr1\nhHfyV9MwvfoVT9GK2mYnXMNsuQOWfiWgXwBen1/VCAwnM7eEecu2UHXPQwz1ttDXfpbB6xe5dn4f\n3u6jfO87Z9m06WFWrVqDy6XPu6Ts5i173MzQL5jdGpaSjZLROT4xrxclDi9bZWq7fdeSt5Lap8R7\no2R6jWHvWa5ciLZ2vVZZeOv9RNr4h6PlbRkdN/7707JUjnYfw5YznUQQnqiYLNTWE97OX09Dt5df\neoGivAy+8sU/pq5Of0K4FnpmJ810i2wmY7Z+RUOZqxSuYUb0Kx6DUKlfoKFhln6lVL8cdgEPduaU\n1TGnrA67f4LanF7G+5p4550znD59mlGvndpFK7l/3QYe27KW7MzIfCYlcjdvM5ntGpaSjZLZfTCM\nlOoqd/vJ9CZB6EOv9p6d8yRdTStlC8vlsDHp9YX0U1FvvR9fG381tLwtctWEPIxXWaGjpK4yj3uq\nCynOz1C1VNTm+qiVv5rFqkXFVM/NU7Wc1Nr5x2ro1tx8mcbGsyxdWk9d3SJT1qjH2prpFtlMxmz9\niobsVfKPZ0VomBH9iscgjOYR13PdcC2qKMpibNLLjZvjQQ2z9MsYj6ytpjg/Q9X78+MXjvDsr3fT\n1XqGy+2v8dre1/jO94p577u38t8/9UGKiuIPXxpltmtYSjZKk6e24vWrVxio9alItKurUlCUu321\n2H+iHb/VusZqWaB6Rgwoxeqe6gI+/h6R860DHG7sQro6mFB+QDRixanlqglhKjdMTWjcTjtf+vC9\nUd24WnN9kmWVrawrVrWe1Nr5w3RDt0mPjw9sjKxm27Ur0Kpgx473m7bGpzZ/07RrWZiPUf2CxDTM\nUXYVvNPPkPxspEq/IH4vmlKLcrNcvKe+nGU1hTzzShNnmwdmrX655zUzkgT9Ks7P0NSvN86PUL7k\nIcrEjQz3BEJzN65L/Pr5X3D49V1s3biWTZseZvXqBt2huXiZ7RqWko2SYANblvpkZrU+FdEqEdSq\nyEKuF5Z0GW23H28r+5BrVF0EQkN80SxQv8ehufZwzlzp550fHmZlXTGrxRKa3XsTyg9Qw24T+Ojm\nOtYvn6sqEMrQVME9FxnS6PsCsXN/ok2JToZVpuWG12rnr+R3h1sRCFSCyHR0XOPEiWPU1i6kvn65\naeu0SG+M6hdoa1gs/QL9GpYs/QJzNEy6Ooh0dRCbAFUlOQnnZ6oRS79gWsPyFkvcSpJ++W2Td0y/\nBMFGftlC8ssW4pkcY+Daefraz/DaG0dobDxLZmYWDzywjg0bHmLRosURDS0tYpOSjZLfH73aQW+V\ng1YVmRLVkJeGt8KM5pGC3Y+z6lLIVOlolpjRDYHPP139ldGQeH5AOI+9q5a+nBM8f9nH6E03o+Me\n7p1zP2vEUva+fS0kNJXRcAn5EVPmUthtAisWFrF+WTkHznQG832W1YSWxb7ZH31KtNleJS3hU2vn\nr8auw604HLaglfq73/0Wv9/PY4+93xKbu4hU6hfo17Bk6FewdYHJGtbec4uM6tTqV1aGIyS8ntFw\n2ZB+rREDmxTZUHxzPP31y+HMoLR2FaW1qxi92c+8rOvc7r7A66/v5fXX91JWNpeNGzfxrndtSmlo\nbqaTmqq3bHVrTEZv91O1KrKQ62iFvDQebK0yXKPubMHumx74qqMHSjwPVCzrUcsqe7C+nL7hMaT2\nyK6NS2sK2LG+hi/s+wF+vz/o7j/9Uh7PvCIFm0xCZBM8ZYWOniqOwETy6MMIzPIqxXLDq7Xz10Ie\nFXBr+AaHDh2koqKSNWvuT2h9FjOMFOkXGNOwaG0EjGhYiH5ptC4Ix6iGxatf9y0upqt/hOv9IxHn\nRNOvn++5xMLKPM63Bt5LPPr1zCsBr57X55+R+pWZW8Sgs5R//MZnab7cxIED+zlx4hi/+tUveO65\nZ1m2rJ6NGwOhObfbHfuCdzGpGWFiQu8MrSoyvSEvIw92rF5MMhmr9gTX5Ky8gn8kL/gwyiNIzEKP\n9RhulbmddgrzMjjU2KV6/PnWG/zl7/4FsvyBdUexdrX6VmklRaodq4d4NpG/t76GEo2ERzXC2/lH\nQx4V0Hz6ZXw+Lzt2WN6ku41U6RekVsPU9EveEKaLflUV53DqoroBFk2/xrtrg5skiE+/QgzFGaxf\nb1/sY8PKFSxfvoLbt29z7NgRDhzYT2PjOzQ2vkNGRmYwNLd4sWjpmwqpCb2hpw199GNiuaNjJR+a\nnQOjJnyOmneC/060aVo4ejdvSt7dMI9Xj0d/wG9mTrujZcIf9mhDhtVi/eHoGckw2TVP96ZLxuW0\n8ei6GsON0sLb+ceis6ef/fv3UVxcwrp1Dxq6l8XMJxX6BanVMEu/7rx+QaCv0eMqRSPRMKpfylEn\n2dnZPPzwFh5+eAvXr3fy5ptvcPDgAfbv38f+/fsoKytnw4ZAaK64uNjQumYzOuqwTLiJjrLWaMfE\nckeDvhJeM8t81a6lHKYru+PvFKsWFVOcnxH1YdIUEMXnCtGtKT2WllnHhPOhTQvj6iYrt/PXy+V3\nDjM5OcGjj+7A4UhZj1aLNCEV+gWp1TBLv2K/ZvYxahTnZ8Q+KAyj+qU16mTu3Ao+/OE/4Lvf/We+\n+tW/Zf36DQwMDPDcc8/ypS99ge9//7uG1zZbSY1H6fQjjE36Q1y9wdd0VFDocUePvb11Ko6s/oWN\nd8evht7SWbOtMiNUz82LGcuO9nDLn6ueIcOxrLJ4rEk9jEzEP/B3x/oaJj2+mCNQ3E47n//Uxzi6\ndB6bNm2O+34WM5dU6BeQMg2z9GuaO6lfYHw4t4wR/Yo16kQQBOrrl1Nfv5yRkU8HQ3P5+flxrW02\nkhKP0tyiHN1WVTh63dGQuOWgF71W3Z20yuZku6LGsmMKyNTnmkxrKlGMDrYN5wM6hkBuX1dNwZxc\ntm3bnvReJBbpSar0C1KjYZZ+hV3rDukXJKZhevXLyHiSrKwsHnpoM1/72tf5wz/8L3GvbbaREo/S\np7bfwxOnd2q+Hi0BTq87WnCPJGw56MFoI7Y7YZXJVoTfj2YsW291XjKtqUSId7BtOI9vXIDDYUva\noEiLmU8q9Mss70csLP1KH8zQsGTql82WEj/KjCAlG6WLY6fiTlLU25wxo+HlmMcYqdLSwmiOgN7S\nYTNRWhHb11VHdKDWk5wIgd+Ls7yVya6aJKwyMRIZbBtOMgdFWsx8UqFfoD8XJhENu9v0q+Kebrql\n8pAKtnTBLA2z9Cv5CH6/+V+g3t6bIRf9ize+xqh3TOtwwFjH6nRCz6gCecyB3RZ4upUPrdy1dmVd\nMR29t2hsGWDCo6fKJhItKyJ8ppmRHisZ9gzeP+ezHL3QzcWrg3i88X9f1N6/USxPT/pSUpI7a+qK\nlRo2k/Ur1jNnRL/UUOpXcX4GfUNjvHK8nYlJ4xqWDP3KtGfwvx74Gv/2u/OcudJvSHvi0Ss951ga\nlp5E06+UbJSUdPbd4umXmugZvM2tES82m4DDJlC/oJDBWxP4/XBzZIKcLBc2IVAVcK5lAK8vkO3/\nwNIybo1OclLqweP147ALrBJLWTA3j4WVebx0pJ2ugRF6boxgswmMT3jJcDtwOWysWFhMWUEmCAId\nvbc4KfUw6fWBH4rzM5nweMnNciEIsLByDhVFWSyrLeRcywCDtyfIctlBEOgeGOHslT48Pj8Om8CC\nijyaO4eZ9PrxeLwU5WfisAvUlOcxOu6hIC+Dkqkhi0DUnf/ouCfk9cqSbH6x9xJDtyfIyXRyb10x\nCAJzsl0ha4tlRYRfN9Z91a4Vfkz4/WP9W+39x3MNy1JKT2brRkmJXv3Ky3FTnOtmfnkuPTdGA3o1\npRd1lXlc7hjW1K8bt8ZxO+0MDI8xPDLB6JgnqGFLawqZ9PgQBDhzuS+mfimfud6hMW4Mj+F02Djf\nOqCpXyUFWZTkZ1BdnsvouJeB4bGghul5HrU0bPDWBDYBViwsZl5pTtrrVzx6peccS8PSk5RvlCws\nLCwsLCwsZgNWtpaFhYWFhYWFhQbWRsnCwsLCwsLCQgNro2RhYWFhYWFhoYG1UbIwjCiKj4mi+PWp\n//93oihuuNNrsrCwsNCDpV8WRrFS7y0MI0nSC8ALU//cBLx+B5djYWFhoRtLvyyMYlW9zRJEUawC\nfgZkExhl/meSJB0RRbEB+DaQBfQBfwLkAv8pSVL91Lm/B3xGkqTHRFH8KvBhwA68AvwkO69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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11f3624a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# train classifiers using severity score comorbidity score\n",
+    "\n",
+    "# X = np.array(X_train[['severity score', 'comorbidity score']])\n",
+    "X = np.array(X_train[:,4:6])\n",
+    "y = np.array(y_train)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(10, 3))\n",
+    "for model, ax in zip([LinearSVC(), LogisticRegression()], axes):\n",
+    "    clf = model.fit(X, y)\n",
+    "    \n",
+    "    # visualize decision boundary found by linear model\n",
+    "    mglearn.plots.plot_2d_separator(clf, X, fill=False, eps=0.5,\n",
+    "    ax=ax, alpha=.7)\n",
+    "    mglearn.discrete_scatter(X[:, 0], X[:, 1], y, ax=ax)\n",
+    "    ax.set_title(\"{}\".format(clf.__class__.__name__))\n",
+    "    ax.set_xlabel(\"severity score\")\n",
+    "    ax.set_ylabel(\"comorbidity score\")\n",
+    "axes[0].legend()\n",
+    "\n",
+    "# Decision boundaries of a linear SVM and logistic regression on the dataset with the default parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "linear methods won't work, use nonlinear methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 357,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def decision_boundary(clf, X, Y, h=.02):\n",
+    "    \"\"\"Inputs:\n",
+    "        clf - a trained classifier, with a predict method\n",
+    "    \"\"\"\n",
+    "    # Plot the decision boundary. For that, we will assign a color to each\n",
+    "    # point in the mesh [x_min, m_max]x[y_min, y_max].\n",
+    "    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
+    "    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
+    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "    # Put the result into a color plot\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.figure(1, figsize=(12, 9))\n",
+    "    plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
+    "\n",
+    "    # Plot also the training points\n",
+    "    plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)\n",
+    "\n",
+    "    plt.xlim(xx.min(), xx.max())\n",
+    "    plt.ylim(yy.min(), yy.max())\n",
+    "    plt.xticks(())\n",
+    "    plt.yticks(())\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 358,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x11926b208>"
+      ]
+     },
+     "execution_count": 358,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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VpFQpP6pXV/Pvv1pu3bpFZOQ7BAcXt5tpL/cbQ14CA63n3rVrBz//vBKlUkmx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\nO1RsI4fvv/+OgABvFAoF27ZtIy0tDb1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m8sEHIwG4c+cWgwYNolKlSpQuXZp27dqJxhwA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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118fac198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# X = X_train.as_matrix()\n",
+    "# y = y_train.as_matrix()\n",
+    "\n",
+    "# severity and comorbidity\n",
+    "plt.scatter(X[:,0],X[:,1],c = y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 359,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "177"
+      ]
+     },
+     "execution_count": 359,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# make a mini set for illustration purposes\n",
+    "len(X[::25])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 360,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "177"
+      ]
+     },
+     "execution_count": 360,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(y[::25])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape=None, degree=3, gamma='auto', kernel='poly',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf = SVC(kernel='poly')\n",
+    "\n",
+    "clf.fit(X[::25], y[::25])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KQcYUi/artgSd6JtNMWElIrIRNmq9gBvjGvY0+xest4VcmNR0CyIiouehyhKSupnz2lAk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BJtHiuusD4d6PMoq69F27fAueAIWCgiIqK3wbJKtLmsWFhXeoZPCuP7RomIyLhYVok2nxW3BNjj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\nkTAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1109ea320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "decision_boundary(clf, X[::25], y[::25])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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41rqRVWG2jacHhEgifff63tKQLhqLHKc5+fb/wnbmc4yGofMV7bsuWuHl7MHQ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\nG5BdGKB8XXCmg2jH3e1z6j4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EFqTexO2386BnMcXJd8aVDi7SuTxdv4e1ucdC+kC2u7Lp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jDYqRqOXO3mfWR9aj/TWfo+bIRtWW9Q07uV9QYlqPvkNTzxS6mz/0uy9r5f/dfIoLLIM2i5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\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HZYYHJHIK7XN584pCQanuai4P9vGqez9AIGiSXWBwvVEhPCWCEB9FnKblop+UdJ3z7wbI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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x14070cf98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "decision_boundary(clf, X[::3], y[::3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CglVgYeBSZeYzDM6Woyi9R3O5zORCRKkoZh5WItq8ggHg6td2DPWaUF4dwMyk\niCtf2eF160x6u44Yzc97rPxrPBxuxLXeM7jRfwotvc3wh2zYkfsgYXUMKSZYpXnVdXnOAYx7CwyV\nz7zZRJRKYrawJko8N74ZnwuPRxp+7CsrwWUbx668uyt+j60ARMlx66IdjW96YbYs/H/r7gAqdwZw\n7Q92HH3Hu7aVWyV68waLgoKm0gvJqNISkxjEfMiuuu7F5G5UcvY3ItpAEh6w6vlVPuHNQ9toHY6U\nfbuUBH7UXYTrfW/iUOl5zdvjjZbImGAAsFiVpWB1kSgCuUUhTI+LyMzRn9ZuvdJyb1mNFsoM61TE\nq39PwIHp+RxkGJgEhPdQIko1MQdd/fpuX9wFGbk5X+05i6Pl30YsfzJaj+K0brjs47rK5U2XSJ+Z\nSRFDPSZU7/dHrBsfkjA7JaJyZ0Dlm6SHkfvn45GDmPZlQfoxk4sohNBQcgGiEPXWHhXvmUS0lnQP\nuopHIloR1NK/AMCu3Du40X9a96u1laYJpfUjnvPs6VgtpuZzIEAGIGBfwXXYzZ6Y3+H5oS4tQ8b0\nhHoX9/5OE6r2RQaypJ+RKYtr8m8B+HEQFqcpJqINalX6sK5EgfpdNqSYIAnGXjsyaF1/9PwIutbb\njG3Zj7Ez9x6AhfyUl7vfRUPJBaRZZuPeFs+VBaIEmC3A5KiIrLxX16BnToDXLcLu1N56R/GL9zx8\n/fxdHqzyXCaijSQlAtaQLCEkixFTvd4fOoya/JuGyuZNe30w0lI/4i5Gtn04bEY2kxjEqcrP0NKr\n3t0knnps9nOn9ug87rdY0fFAgNWuwD8vQBCBhjc2x4Cr9WCzn6NEtHkYDlgXb5hGAo4DxZdxoetD\nHCo9jzTLDBQFeDJ6EFbT/IqvdOOpG6U2o91KOid3orHk+4jlghC9u0k8mI8S2H/EB0VZGIRlMmt/\n5UxERJQIMQPWxQe2L2jF1HwusuyjEUmqE8Fm8uJ05ad4PNIAb9ABBSKqsh8ixzGiuazNHFysR8ke\nSR2UpaSWvxkIAiKyBRAREa2mmAFrSJZwve8MbCYPchxD6B0+BF/QhkOl30W8vjdKEmXsK2yN+/MM\nTGlRrqMf94cOobboetjykCyhf2bLGtWKiIiIEiVmwHqtrxkNxRdgNfkAAJWuDngDDrT2v6l7PmwG\nmpRoY54SmEQ/Xk7swtbsNgCA25+Ga71nUZzevca1I1ofVnrb4Q+a8M3zP4MgALIiIss2ihOVX8f8\nTqLu9/H47RBhAAAgAElEQVS8iRmYrUD3VBVEIQRJkLGv8NqKbwT5PCJaP2IGrA7z7FKwushu9sAi\n+uEPWXR1D2C/QEqG+qIr6J+txPW+NyFAhlXy4fSWT3Fz4NRaV40o5WjtiuPx2/Fp+1/i/ep/Qrp1\nIetG30wlfvP4r/Gzmn+vaTsr3fv1dBN6MNQEu9mNw2XfAVjoxnal5x00llxAmmUmofUjorURM2At\ncParLs92DGN6Pgd5zsGkVIo2D/fH5wz3Y92e/RBtY/XYnXcHpRldS8tDsghZUc8lSrTZGLnOvn7+\nEf7V7n8Ia6QozehCXeEV3Bk4gvrillWphxpPwImgbEZVzqOlZVaTD6crP0VL71s4Wv4HTeWxUYUo\nNcV8mo+4i1WXT3jzkWnTN/sU0XLuj88ZejjkOEbh9megZ3rb0jJf0IaLXT9BXeHVNasXUSpwftJs\nOEh0mmdU36htz2lD11S1obKNejJyEPsKr0csX8gSon82tkTsNyJKnJgtrHP+zIhX//NBO3xBm6Fs\nAQwCSM3r54XWB0VDyUV0Tu7A1Z5myIoIh9mDExVfwixpe2Dx3CRSESOd2VqnOlMg/Di7HRFtZDED\n1sNl53C97wwc5lnkOQYx6i6GN+jEoVIOuKLk0nquuGcFPG+xwWZXYLYqmJ4V8aQwiO17ON89kVGz\nPheCsikir3H31DaUpHetTaV+tCvvDh6ONKG28FrYckVZmPGOiDYGQVGUqHMsfvbRLgCAN+DA5Hwe\nsu0jsJm0zXLDIJWSTVGAHz534MQHnrDWnpdPzDCZgPJqBq1ERl5vz/oy8HXHL/Dhzv+wNBB3zJ2P\n717+FH+29/9OVBV1uzNwDLnOQZRnvgCwkNLuas9b2Fd4HS7bhK4y+ewiWn2/qCuNui6ugDUWXtS0\n1trvWVBQGoQrN/K14I3zdjS+yalEiV6nJ3j1+B345sXPYZXmISsSLNI8mrf+FqLGcY3JyBIAAC8n\nd2J4rhSiEIICEfsKrsNhdmsuh880orWjO2D99d2+pFSIKJFuX7LhwMl5zeuIaEEyBxclIgBMVv0Y\nnBKlllgBKzv40JKVHgqKAjyf2IMJbx4AATty7634uk3vA+FpaxAvPn8KUQmi4ORO1J+1Rf2sxarA\nMyfAkRb52ysUVPkCEYVJ9cAt1eu3mcTznGgfr8X0fBYAAbvy7iDDOhX18zy2FC+2sG5SWlssQrKE\ni10fYG/BDeQ7ByArAh4ON0ESQ9iTfzOhdfv9k7/Cjry72JV7D4IAdE9V4cr83+Ann9Sqvn4MBoAb\n39tx5K3wV/+jAxLGhyTsrNef0YKIaLPS+pwIhMy41P0+agtbkOsYhqwIuD90GDaTB7vy7mkqi4Hs\n5sQuAQTA2Gu11r7TqC1siZj57M7gUVTnPECaZdZo9QAAt/qPw2Ubw/actrDlE55cXOs9g/d2/Jel\nZa/f0IZ7Jbx4YkF5VQDpmTJePLZAlIDao+wOQEQULyPPiWu9Z3Cw+FJEOsGb/aewt6BV86DtRQxe\nNw92CYjDShfpjM+FttE6AEBxejfKMl9G/WyqXVyJ6P+lQIwIVgFgf8E13B48gcaSi5rLDMkiHo80\nwBt0wG7yoCb/JnpntuFgyeWIz2Y7xqAsm+fi9b9r64//+lqqMPonv8KepnlYrJqrRES0KSXiOSEI\nimru69rCq7g/dAQHSy7pKtf5SXPKPVdp9W3KgFXrhflwuAEh2YyGkosQBQU909txset9nKz4QjVp\ndrTy1/MFFy0xtyTKUHRMfzruycfD4SYcLLmINMss5vwZuNz9HqK39wOSGFqx3NK0DpR+cxL4Jnz5\net73RETrQbTnhEkMIKRIhspm0EqbJmDV++txypsDBQJqi17NlV2e+RzZ9hHcHTqK+qL4p/58vQ7r\n7cILKeqnStdkNUozorc2R/N49CBObfl86f9plhmc2vI5/v2t/xFT3iy47JNhnw/KJrj96Zq3s4g3\nOyKi5Ir2nOiY2IutWW2q6+LF+zdt+IDV6GuO9vH9aCz5PmJ5mmUG/lD0kesrWc0AanE7RvbFrty7\naOltxuHSc0utynP+DPRMb8eJyq80lTXjcyHHPqy6rqnsPL7o+Av8ac3fL03/KysCPnv63+Lstt/q\nrr/WfR1rX8mKgLbRA3AH0pBumcLOHweHJWrb612sfecPWfBouBEB2Yyi9J6lRO9qNtt+I1pLiXhO\nVOc8QGvfG2gs+X7pnjg9n4URdwmqcx4arhttbht60FUi+uS09p1GU+kFzevitRYXou7W5vlsPBk9\nAEkIQoEIizSPusKrmucSH54rhjeQhsqsZxHruiarIQoB3Oh/A2mWWYiCjGmfC2e2/g5Zdu0z1iQ6\nSfliV4b64svIsE5hwpuHe4MLfbNipW4xUsdUpXXfvZzYhWF3KeqLfoDV5EPP9Da8mKjBiYov4+ru\nAazffUW03uh9Tkx48/B0rBaSEISsSLCb3dhfcE3zcwLg9b4ZbeosAUaD1o7xGmTaJpHvHAhbrijA\n9b5mHC4zdkGlygWZzMThy8mKgNa+M6r77lrvGTSVnocoxOjMGodY+9XI3/pD17sRLcqKAlzueRcn\nKqK3NHeM12DUU7zQl0s2w26ei5j7XE2qnB+Asf0WCJlxc+AUjiw75r6gFfeGjqKpNPItRjxSaf8Q\nbXSpPsEErX+bOmAFjF1kigJ83/lHaCy5gI7xvQjIZmzPfoSnY/XYW9Cqu1UNWJ8XaKJuWI9HDiLd\nOoXyzOdLy3qmt2PW50JN/i3d5SYrUAUWWlfHvQWqr7buDR7Bjtx7sJs9EevaRmthN3nCWpQnvHno\nGN+jq4V+tc6blfaXL2jDo5EGBGUTqnMeIss+FvWzdwePYFfeXdW0Ntd6z+Bw2XcY8xTgxcRumCU/\n9uTfXOoSEo/1eC2RupXOu4HZCvROb/0xs8itqK3zPCeI1p9Nn9bK/fE53cGKIABFaV24PXgMR8rO\nwSz68XC4Ef6QZdO9AgbU665n39bk30LHeA2u9TZDFIKQFRNyHQO6gtVV6wscyEC6ZVp1nd08h/mg\nQzVgnfDm42j5t2HLsu2jMIlBeAPq34llcX8n8u/WegzbRmsx63ehrvAqTGIAbWP1eDpWi8Nl36l+\n3h+yRs3BKAohXO5+B/nOATSWXIA/ZMPtgRPIdQyhKueRpvqv52trM9Jy3oVkCT90v4ctrqdoKr0A\ntz8NLb1nUelqV00zuBGztRBtZpuihRXQ37o268tEx/he1BdfCVs+6c1F7/Q27Cts1Vymnhume1bA\nw+s2mMwKFAWQQwL2H5mHzWHs1XkyJLt7gd4HjtF6BWUTbg8cV20VvdbbjEOvDUhb5Ata0TZ6ICzL\nxKIZnwv9M5WaZ4B5nZGHr979MT2fhc7JXRF/07gnH4NzFaoznw3NlcLtz8C27CcR675o/wgnKr+K\n+AHY2ncaewta4dAY0C9iYJJ6jCalry+6HJEP+ofud3G07BtIonpKpVh4jhCllk3dwmo0SHkyegAH\niy9GLM+yj+HpWK2hsuM17xVw55Idx97zLAVEsgz88LkDx971wGRelWrELVUfAkZa2gHAJAZhlXwY\nmitFYdqrH3M909uRYZ1QHVRgEgPwhdRnMJiazzXUpUQvo9fE07E6NJREBu05jhE8G9+n+p3CtD5c\n6noPRendcJjdS8vvDx2CSQwgwzqFSV8unvnfhFWZwp6086gvuoJ7Q0fRoGNSCmDzpDKLdTyDsgmP\nRw7CF7KhOucBXDb1gYursZ8S8UNWbfKSusKraBs7oGuKaLbME60fGzpgTUxLnxL1l7soxDey2ahH\nrVYcfssTFhCJInCo2YtHN2ycflSD1x9Mes6P2qIWtI3WoXNyF6QfB1DlOgZRk39b9fOSKMMfsiEk\nixHnUddkteaUYEYl6pqINigu1jVxvOIr3Bk4gYBshiiEEJTN2Jr1FN6AHZf9fwv78TPYfdyJeY+A\ny599hPIXfwdZ0TG0+DUbKWjVk5VhcK4MdUVXYTN50DZ6AE9GD4Slplup7ETtu0Scd9HOrTTLNNz+\nNENlb6TzhGij2tABq9EWNQBLqYuy7aMR64Ky9qZNPTdFQYBqK6rNoSAU1FxcSor3OPXNbEH3VBVM\nYgCyYkKGdTLufq/L973e4HVX3t24PwsADSUXcanrA+zKu4ui9B5MzWfj/tBh7M67o6mc1+l9uCbi\nmkizzGJqPjuitU5RYl8ToqCoTs14eeovcOyvTyAzf2EmHEeagsaPitH6//0PyJ/+d7rruRECEL3H\naj5ox6inKKzvdE3+LUx6c3F/+HBcGSrUtr+W+zTaufVsfD+T0hNtApuiD6vRLAHnO/8Ypyo/h0l8\nFR3e7D+FrVmPkeOIDGSj0XtTvPm9DQ1vqLeixlqXyvQck67JaniDzrCAcdRdhK6pHSu+Np7xufBs\nfB/SLdOozrkfV05Ab8CBtrE6WCQ/duXeiTtXaDSdkzsw6imC0zyLXXl3NKfuWstBVq+TFQHfd/4R\nTld+FrZPWvtOr5gtAIj8Oy5/YcXx9yNf9Qb8wMPrNtSfWH/nt1FGf1Tc6j+J/YUtqvO6L2Zl0Gut\n+pB3TVXBG3CG9fn2BJy41X/S0JsKBqtEqWNT92EFjM3gIQjAiYovcaP/NAQoEAUZIUXC9uzVCVYB\nILsghMEeE4rKw5tTu5+ZUVS5/ppY9T64+mcrI0bb5zkH0TO9Pepoe0UBrvU1I80yjdrCFsz4snC5\n+11szX6C0oyuqNu6M3AMCgTsLWiFP2RDa/8byHMMoCrnsa66A8CWrHZsyWqP+Zn1MPuZKCg4Xv4V\nWvvfWLomFlJbPQgLVuP9Wxzp6r8ezJaF47fZJOL1eUgRVYNVwHhXJr2vz412x6l0daBnejuu9pxd\nesMiiQEcq/hac1nL60NEqW9TtLBGk6qj2dXcvmSDM13Gjjo/FAVou21F0A/sPxrZMpVK5j0CHlyz\nQpQWgg9RBPYfmUfm/6Ft38uKgFsDJ9Go0pLqDTjwbHwf9hdej1h3Z/Aotmc/jhjcdLn7HRwp+1a1\n1fTZ+F6kWWZQnN4dtvz2wHFU5TzEmLsIQ+5SmMQAgrIZpRmdYflkY0nUOfHklhVz0wIkExAMCNi+\nx4+cwsT0qTZyXej5+1q/s6PpTGTKq7FBCVNjErbvjT8f60ZheErpsX3IcQwj1xE5BfLVnrMRP/y0\nSnSwtxoTlzBAJUp9m37igI1iclTEi8cWCAJQtc+PjCztaVxWk88roPU7O46964G40D0Rfh/Q8o0D\nJz7wQBQXlsXzsFqYWUz9VeaouxBT8zmqrZ/RXn+6/Wl4PrFHNciN9p2QLOLLZ3+OfYXXUenqWFre\nMb4HCgRNc2UbeXjevGDDtho/svNfHf97V20oqgigoHR1BgIm0nCvhNEBE/Y0vfrxFQwAV75y4ORP\nPLqmdNwIEtGVaXm3jftDh1CQ1heW5UIrBn5ElCybvkvARpGVJ+PgqfXTn+9hqxVH3n4VrAKAxQrU\nn5hH+10Ldh1YaDmL51WhICwMupAVIaLvZ9tYPY6Xq/dhi/b602mZgy9k0/QdAHBYZsOCVQCoynmE\nqz1nUZX9MO7gSu9r1ZlJEc50JSxYBYDao/No/c6OglL15PyprKAshFBo4ceNyaxADgGyLODI25s3\nWAWMd2U6Xv4Vrve9CVGQl7IyVLqe6QpWGaQS0VpjwEpJEy27QUaWjI45UfU7sR7SB4p/wPedf4T6\noivIto/CF7Th1sAJVLraowY2IUX9FO+fqUSeYzDqdxQFEWW+mNiFnTnqSf5zHCOY9kWOnE+0jgcW\n1B1X/9EimdZvh8/iyiCK12F/7NWwPFiMN4C1mny6X/0zQCWiVMOAlZJGjtFjIdY6QL3V1WF2480t\nv0fbWD2eT9RAEkJoKLkYc875kvQutI/tx47c+0vLgrIJ7WP78cbWT1W/syPnPu4OHgub3UxRFiaR\n2BMlhZY/aIVFSn5/YrNFgd8nwGaPDE6V1O4hsi7ECgYHZisw589AReYz1QT2qz1gLh6plJaKiMgI\nBqyUNI40BdPjIjJzwiOpnmdmFFfE35q2PHjVkr+0wtWBl5M7XxtZLEFWpJgji3McI/CF7D9+JwgF\nAoKyGWe2/QvuDh7F9pzI6UWnfdlhMzgly64DPty7EpnKTA4BodAmfn8eB719QofnitE+VosKVwdy\nHUN4MHwYgiDjYPEPcZe/VoEiA1Qi2ig46IqSRlGAa3+wo6I6gJKtQSgK8OKxGTMTUkJya67GyOLl\nBmfL8GKyBg3FF2A1+eAJOHCj/w3szb+BHMeI5vJiBRRz0wqeftEH02QnZEsaMup2Y3uDDc8fmTE7\nKWHf4XlIJmB6XMTdyzYcfssLq0rL60YTz3EfnivBi8ndkIQgQooJW1ztKErv0bytkCzhas9bEXk+\nR9zFGHUXxz1pxXIMJImIIjFLAK2pvhcmDPUu9Aut3BFAXvHqjmRPdGDrD1nwYPgQZEWESQxgX0Fr\n2KQSiTDhzUPbaD0Ol327NMhsYLYC/TNb0FByESP/5ju03bZCEABHmoyd9X7dA5ScnzRDloF7Q0cA\nALWFLUsZHBatVYCl59i9nNgFTzAtbG75ttE6SGJQUyYHAHgw1ISt2U+QZpmNWHettxmHy2Lvl6n5\nbEzN56AorRdWU/QfaYnYv9H21ai7CN6gEyXpnapp3Bg8E1GqYMBKpNFatN6+7mrPWzha/oeI5Q+G\nmlCZ1R6RV9aIH7rehjuQgZq82wCAx6MHkW6ZxLGK+AbspFpOzmh5Rq/2nMWRsm81BfatfafRVHpB\n87o5fzpuDZxEvnMAOfZh9E5vR1AxoankvKbtG0m2PzxXjPbxWhSnd8FpnkP3dBUcpjnsK2zVtX0i\nomRjwEq0ihIR7Lb2vYGm0u8jlodkEbcHT6hOoKBH+9gezPkzcaD4atjyG/0nkW0bwvac2DNzqTES\n5BjddxPePIx5ClVbUrumqmAzeTWldWof249cx6Bqd49YLawXOn+CU5WfhQWni9MDL+/7mgxB2YSW\nnrMRXRn6ZrbA7U/HjtwHmstk8EpEyRYrYFXPLUREa0qBejOcrEgQhcSlA3g03IT6oqsRyxuKL+H+\n8FEAgC9oxeBsOTwBR1xlOj9pXrMWakkIIiSrjyUNymZIgrauG9U593F/6HDEFLEvJ3ahIK1X9Tv9\nM5XYmtUW0ZKaYZ1CIGTVtH29Hg034qDKj5rSjE5MeAt0lbmWx5WIiFkCiFJQSDapTpJwf+gI9hTc\nSNh27OY51VfUggDYTF609DbDIvqRn9aHp2N1mPNn4nDpuajz1L/u9eBmtVrnMm2TeDTSqLpuaK4c\n27MjMzzEIghAU+l5XO55F3aTB1bJi1l/JnIdw2Gp0l43OFeO2sIW1XWixoBZL1/ICofZk5Q66J30\ngojICAasRAlmZIaiRXVFV3Ch80McLvsWDrMHirIwo5fN7IHNlLjZrDyBdNVJEmRFwLC7BKe3fLaU\nrqs88wUCITOu9TXjRIX6zGLRLO6LlQKdROy7LVlPca23GQ0lF2ASgwjJEm4NnERF5jNd5TktczhR\n8RWCsgmBkAX2KIHgorKM5+ieqsY2leBYjjKRRaLZzW7M+jKRbp2OWBdtMg0iolTGPqxEq0BPABaU\nTXgwdAgB2QxZkbAt+wnynQMJrVfn5Db0z27HsWUDvC51v4e5eQfe2/GbiO88GG7EFle7ajAULyPJ\n7+PhCTjxcLgJgiBDViTsyb+hOtI/WS52foDjFV+FjcofdRdiYLYS+wuvJ337IVnED93vR/SjfTmx\nC4IgY0uW9r7Ji9i6SkTJwkFXRCkokf0B9QaA9+fexsiev8H85e9QZr4LBQJ6A7Wwn2yGfOULvFX8\nDxHfH3UXYtaXha3ZbatSXzXJ6EtpZET+cr6gFa39b8JhnkO6ZQoT3gJYTR7V/sLJMuXNwYORJmRY\npmAzezDuyUeOYwQ7c9WnF14JA1UiSjYGrESk6tZFGw6eWsgPOty/MAazoGRhUNe5f9eOPzL9bcR3\nHgw3YmvWU6RZZnRvdz0GP3qCWH/IAo8/HenWSUiitsFyavtITx28AQd8IRsyrRO6U2oREa2GWAEr\nOzMREYBXgeoiOX8b5obSw16lB0JmTM3nbrpgFYiv3ssDSovkh8U+rqssPXVQC2jtZo9qv9v1ehyI\naHNiCyvRJvao1YrKnX6kZYbfBmYmRfQ+N8H7rAO2598jP3AX49IuzJadQf1/VwWTWXtrHwMkIiKK\nRXeXgM8+2oVxTy4udH4Iq2kevqANZ7b9Di7bZNQC+VAiWj9kGbj8hQMHTnqXgtaZSRH3rthw7D0P\nRBHw+4DJUQmZOTJs9qi3C6J1I54fWy8mdmPMUwgFAgrTelDp6oj5eT77iIzTHbD+r6f+Nca9BThZ\n+SXMUgCBkBkXun6C4rQu7Cm4rakSvJhps1F7KHr8dthMXoivTdmx1teGLANPblox7xWgKIDDqWDX\nQV9YHWltLD+HQrIIQAjLPgCs/TmUyrS+CVAU4FL3+9iRcx9F6QuTQ/ROb0XPdJXqdMmx8LgQaaM7\nYP3rA/8bPtjxnyKWf/b0L/CTnf+ku0K8iGkj0PIg/KL9I5glP1y2ccz6XJicz8WHO/4RFtPKSdx5\nvWxMWs6fMU8Bno7VwSLNQwDgD1mxJespitO74/r+ZjuHjGR5eDxyEMXpXciyj4UtH5wtw5w/A1U5\nj3WVu9mOAZEeugPW//2tP1Wdk/vBcCNy7MMoyYjvZqmGFy9ptdJDaNKbi/bx/RAgI9M2iR0592KO\nitZ6Dup9CH7R/hEaSi6hIK1/aZk34MBn7f8Nfr7n73WVmerXT7R9NeNz4cnoAQiQkWaZwe6826rH\nKNX/Pj30nj/egAM3B05FTNZws/8UqrIfwqUyqGsliT73fUEbHgwfgqwIMEt+7CtohUmM/mMsmcfX\naEqy631v4lDpec3r4rERz2uiRNKdJcAizkdd7g3YjdWKIkS70W62m5yeB87D4QYoENBUch6CsJAr\n9HznH+NU5edRH5xq20lUKqFFsgyYJX9YsAosjNzenv0Ig7OlKErXPrhxLaY9XakeK2kbrcV80IHG\nku8hCgomvbk4//KnOFH5BSySP65y1+O1YDSAejDchMOlkX/3weKLuNbXjCNl2vdJtJnH9NS1b6YS\n3VPVaCi5CIvkhyfgwJXud7C34DpyHKMxt/+6RBzbROTnFRC9n3asdfHgtLZE+sUMWNvH96MyK7Kj\neefULry17b8mrVIbTbw30aAs4tOnf4V0yxTs5jnM+V0QoOBtDTfh9Xwz1PuwmfVlIiibUVf0av72\nPOcQTlR8iVsDJzW1iCQ6If2c34Ucx7Dquqqcx7jWewZF6b9VXX+p6x3M+rKQYZuEP2jDhDcX71f/\nExyW8KlZk/XwV6N3/8wH7ZjxudBUenFpWZZ9DKe2fIab/adxOM6gK1UC9XglJIASFJilgMpyQBJW\n7lISSyLq1zm5C8crvl76v8Pswaktn+Ny9zthy7XUxUjaL6N/kyQGMR+0R0yBPONzwW6eM1Q2EekX\nM2A1i348GanD7vy7S8seDjfAYZ41NCBjPTxojNB7w/zdk1/i3ar/jDTrq5ti/0w5vur4Od6tiv4D\nwRuw4L8++reQhBD8v/gDfrb775Fhiz4NZartf6MPmCej9ThYfCli+fJWu7XgsMxgxudSXTc4W47y\nzOeq6650n0VhWj9OVr564AdCZvzzk1/iz/b+Xytud/k+XeuZpR6NNKjO8mQSg/AFLYbqlGrnc6KF\nZDMUBapdJ2RlbVNpD86WoyzKOZxmmVYN/OJh5NgaDVprC1twsesDnKj4cukeMh+040b/abyx5V90\nl7vRz1OiZIt5t3tj62e41X8cv2/7SzjMc3D707HF9RQnK7+K9TVVm+FiNXKTHJ4rRGVWe1iwCgAl\nGT14OHwIsgzVHwn3hw7i+cR+/Kvd/4B06ww8AQfOv/xjZNsGcLTiQtz1XKvjk5gWTSHqLEJGX+EZ\nZRJljLmL4AtaYTX5lpYrCvBwuBF/UvMr1e9NzOfjWMW3YcvMUgBNpd/j9sBRHCjWNsWnkQAgEcdo\nzF0AkxjZSggAIYNBVyq/Zk1Ei9+O3Hu4P3wYtYXXwpY/n9iN0owXhso2yhe0wW52q66zSH4EZTMA\n7QGrUYvng559bxKDOFnxBe4OHYWiiFAgwCQEcbryM4iC9vtJqp6bROvNik+KgyWXcbDksuaCeZFq\nc3/oCA6Vfa+6Ltc5iDm/Cxm2qYh1T0Ya8Yt9f7f0f4fZgw92/Cf89vEvAagHrGrW6lVrIh7opRkv\n0Tm5A1uy2iPWBeW1n8ztj3b+B/y+7V9jR949VGU/Qv9MBe4PH8GJii+jfifDqp7reEtWO24PHNMc\nsC5aq+DOLPnwdKwWu/Ii57Gf82caLj/Vg1ZAf+CfbR/FuKcAV3vOYmfuPUhiEE/H6pBumcb27CeJ\nrKpmZZkvcKP/dEQfbQCY9mUZmhEtEfT2STdLATSWXEzI9ogoMTjTVQJ0tZsxPiRBMgG7D84j6//U\n/mB6PFIPs+hDdW5kypQLnR/gaNnXESmQeqcrMTRXjoaSHyK+0z62F1PzWWgqjVwXr9W8+RoNWn/o\nehf7C1uQ+WNQryhAS+9bqMm7pWsU9aJo+0BPfXumt+LRcBMqXO2oyb8T87PfdPwMb1f9JmL5lDcL\n1/rO4p0YXUTisVoZEhY9HG7AmKcQ+wuvI9u+MBBHUYAb/acx48tC87bfGSpfixmfC22jdQAEbMl6\ninznQNTPplJf4JAs4sVkDUKyhKqcRzFH4atJ5Ln8uofDDciwTqHitcT6T8dqYRL9hgJqBn9Em4/u\ntFYMWGMLBoCWbxyo3u9DYXkI/nng3lUbCstCKK9+9foz3gfCPz/+JX66+1dhfdUCITP+5elf4Wc1\n/0/E5x+P1EEAwvoYL+qfqcDdwUN4f4f+wGa1HxhGHpxPR/fjyWg9CtL6YBKDmPDmQVAUnK36bdJe\n42tiUeUAACAASURBVCV6gNbrft/2V3hzy++QYZsOW/55+5/jnapfwxSlC0Q89B5XI39vSBZxtedt\nuGzjcAfSIQohBGUzyjKeY8xTjPriK7rL1uL+0CEoELA3vxWiIKNjYi9G3UU4Wv7tyl9+TaqmZVqr\nvsovJnZj2F0CSQgipJhQnvkcpRmdmsthkEq0uTFgTZIb522oPzEPkzl8+fVzdjSc9kKK8jY62kOh\nf6Yc1/qa0VB8ESUZ3Xg2vhcPh5vwQfV/jBgZDgChEPD184/w/o7/HLHu2+c/xfGKL2A36x94tF5a\nWD0BJx6PNKBh2Ss8T8CBxyONEcuTZc6fgftDhyGJQQhQEJTNqMm/CZdtQnNZsgz89snfYHvOY+zN\nv4EJbx5a+97E1qwnmmeZW5So46n3WA3OluPZ+F7szrsNl20cbaP1mPVn4kjZtzHz5SbK8FwxJufz\nsTM3vFvCuCcfQ3PlqMm/pavcjRxkJfNHGbCx9x0RaceANUlufm9DwxuRuWo9swI62yyoafSpfCvS\n8ofCjb4T6J3Zjj35N7Aj91HM7/7m0S+xv+g6qnNefa57ajuu9JzFn7/Wt1Wr1QhuFAV4Nr4fU/PZ\ncSX6j+Zm/ynUFV1RfUV6rfcMDpd9p71QjfwhC670vIPTlZ8u/Q2KAlzufhcHSy7CYfboKrd3uhI3\n+08jxzGM4+Vfac7OsRoBgZagRlGA5xM1mPVlYXvOI2RYI/tlJ8v1vjeX8vQuZ/Q8YeBFRJuVogDP\nH1owMyki3SWjap9fdyOE7okDKDZRUl9uT1Mw743/aC1/2DV+0oxGxNf39Gd7foXPn36Ee4NHkGmd\nwKzfBUEI6QpWjTx0tbbETM9n4fbgCewvuIYdufcx7snHxa6foL7oMjJt6gOOopEVIWp/PlEIqS5P\ntAfDh3C07Juwi1QQgKPlf8CtgZNoKlUfULeSsswulGX+Y1yfXaugaaXtvn5uCAKiTm0Zq5zEJISX\no95EjZ4nqTzoi4goWWanRNy9YkNNgw9V+/yYHBVx5UsH9h2ZR2a2/q5rahiwGhAKqj/9nt23YOvu\nxLyKj+dB/cHOyC4BWrejh5Eg4v7Q4bDWyBzHCE5VfobLPe9GTEG5ksK0PvROb0VZ5suIdQtpdZIv\nJEthaasWSWIISFBqrZVGPK90PB6NNGDW54L4Yz/DfGd/3INijJwrqRLI2c1ujLoL8HxiHwRBhgAF\nIUXCjpw7a57PlIhoPXp43Yrj73mWnuVZeTKOvefBtT/YceTtxKa0413agJKtATy5ZcXug68Cldkp\nEZMjEnbUJiZpfao87F9ntLVr1peJbMdoRGuXICyk8Jn1ZSLdOq3+ZRUVrg583/kh8pyDYUnKb/af\nQnXOA0N1jZesRGluX2EdsDCw7vFIA/yyBdUx5oY3st9vDZxAReYz7Mm/ubSsa7IaT8dqI/p0xrPt\nZAzcmvTm4tn4XlglH2ryb6rO7mTErtzb+F3bL/Enu3/14w+JBV93/Cnqi7Sn7ntdKl6nRETJ5JkV\nkJkd+eZKEIDs/BBmJkVkZCWulZUBqwElW4IQJaD1OztMZgVyCLBYFTQ1r36i7PXEE0hDmkW972K6\nZQruQLqmgBUATlZ8jlsDJyEr0tII9Oqch8hzDiWiyiuqcHWgY3wPqnLC+xz3TG9DQVr0vuAd4zUY\n8xSjrugKrJIXT0YPoG2sPu6pSuPhC1oBRYjYF5VZz9DS2xx1FqVYVpqIQGu/1pbes8iyj6Gx5ALm\ngw7cGjiFfGc/tiUwz2jb2AG8W/XrsGAVAN7a/hu09r2JgrTBhG2LiGij87pFODPUA9J0lwz3DAPW\nlCIKSni/RQNT1i4X70P/5cQuDLtLIQpByIoJ2fYR7Mi9v+L31qpVKNcxiBv9b6DytbyNiwZmK9BY\nor2/pyTKaCpVnyhhxF2M5xM1kIQgZEWCWfLjQNEPCR2ZXpzejftDh3Bn4Bj2FV6HABkPR5oQkk04\nUKzeeucJODE1nxcWnNbk38akNxcPhpqwr7A1IXXrma7C1iiBX7plCt6gE44osxWtJBF9S+8PHUFN\n/q2lbAp2sweHy86hte8NlGR06praU4034ES6NTKRvSgo7MNKRKRRVn4ILx6bUbkz8m3YQLcJ9ccj\nB6UbETNgXXwY9Uxvw9BcGYrTu1Ca0aX62c14sx7oMmFyRELjm68eqDOTIm6ct6PpjL6HrNYAoG20\nFjaTNyzo6Z+pxP2hQ9hfeF3ztuI5jkZnp5JEGXazG4Oz5ShK71laPjhbDod5Luo0q3oMzxWjb2Yb\njry2f+b86bjc/S5O6JhieFHP9HYMzZWiNKMTxendAID9hdfhCThxb/AoFAjYlXcn5kw/j0YaUF8U\nmX80yz6Gp2N1uuu2XJplGjPzWUsJ+1/nCzpgFhPTfUUvb9Chmvqrvugy7g0dTVhaMgVi1Nbklbpt\nEBFROFEEnOkKBrtNKKp4NfB5uE+Cza5ETe2pV8zi3P403Oh/A9uzH6Gh+CJ6Z7bjQudP8P+z997B\ncaTpneaTmeWrABS89yRBECQI0IDek+17rGZGZqUZjU67Eet0Wp2k0Cn2TncRq5NWil3dmtFJWmk1\nI62knVGP6e5hWza9Bz0BggThvXeF8pV5f1QTRLGyCuUAAmQ+ER1NZFb6L7/vl+/3mt0ln2J+Jk1P\nNAHzoorZ/g59iFgFSM+UycgOMD0uYs+JT3jFKwIVBSZdeWFJz4vTu+mdWUdAlsKmP2M9h6WeWbLl\nJhsKLvNgrJHu6ZoFy7DdNL6kyI6Xjqm6ELEKYDPMUZzezbCjhIIo0/VqOLzpXB84zPrsu+wsOkPP\nzHpOd32BPaWfYNK5sOjn2VF8NqZ9KVGyG0hi6vw3C9P6ONfzBhWZj0KWy4qA029Nua9ovESybuol\nX4rC1YJUZ7bSNt5IbW5ooY2x+QIyEsiVu5gXtY/T0NDQiEZdk4dHdwz0PTYj6RQC/qAO2rI7trSe\n8RBVsN4YPMjhincXLBLB6iUdXOh5PS7r1POqU7/cSDr14bSmwcvNcya2H0qtOfxZZtzZ5FhGVNdV\n2B8xMFdBWUZHQvuOdYoz0VrdQJhweIIvoOfm0AEUBAQUZEVia8HFhHKZSoK6IKzOauVq/5G4BeuN\nwQMh70SFvZ3S9A4u9r0Wd3aDdOMUY/N5dAlH8RTvQNQbkIc6qfd/D1/AENe+lqI25xZnut9iW+EF\n0o3TjM0XcndkF7tLlj9H7VKMzReqLh92FGPVz6XsOLnWIYYcpdwc2sfW/CuIQoC28YYwt4x4eZH6\nNA0NDY142bB1ZWbpogpWtUhuUVBIM07j9FkSEhCxWvDWAnIEA6prXsBoSqVtSB2DzoXLb1Fd5/Cm\nk6kyBRwPifrlJeMyICsCZ7vf5lDFewuWP1kRONP9BfaWfhy3P6OCulOxN2CIuxb7tDuLXOtQ2Dsh\niTI2/SwevwmjLvaPlI05t/mbmb/kjd8pxWwL7lRRajn71/vY6Po/4zq3pci1DnHQ8lNax3Yw77WR\nZR7jaOWPl63C1KzHTufkJjLNYyE15tXQSz5uD+2lofDSwjK/rOPu8C4yzeMpPa/6/Gs4vGncHDqA\nrAisy2qhNnfpLAmLeRH6Lg0NDY21RlTBajepC54M0yTz3oyEq/e8KCiygN9HWGnWe1dM7Di8/JkC\nLHon0+5sVb+8/tmKsIj1eIl3YE5FAE7L6A6aSj4LmaYWBYWD5T+lefAQu0tOxbU/AQWP3xiWI/XW\n0H4aCi5F2EqdGXc2WeZR1XVpxmB2g2iC9dn72fNIzx6Dgtn2VDgLAhz6lp1rp/6cggT9oBez+JmI\nghKS1mo5UBS42PcadtMEtbk3mHAVcLb7TerymiPOBthN4+TZBrjY+wo60YesSCgIHKl8j1vD+1J+\njjbDXMx+sZo41dDQ0FgdRBWsg3MVqkFWo/PFlGdEt5q8DDQecHHxAwu12z3kFQfweuDORRP5pf6U\nOxtHwu0z8VnXF9lZfJZ04zQObxrNA4eY96UhKwK3hvbjkw2fT62LNBRcCvM/jkQ0C2us4nTWbeej\njq+RZphGRsTts/LWhr/FoFOfQnD60lTLdepEPwLxB2PtKDrL2e632Jh7m6K0HnwBPbeG95FpHovL\nGgpQmNbD3ZHdqm4E484CqjLVI/Ej3cOxQYkdh8PPQRBAp099sYHuNj2jAxKSLlj0omy9l4Ky6D7O\n8X6EXB88TGPBBawGBxDMnlCU1sOZ7rc4VP6+qkU3oOgWfreYcWd+Skq3aqJTQ0NDY+0TVVYJwKQr\nNyS6eGy+AIPoiTuYZzErNYBMjoqMDugoqvCnNBfYE/QGOPQFJ52tevoe6xElqN/jwWiOT2wkapn0\n+I3kWEfZVnh+YarXpHNxoPwDHow18vHjn+FgxU8XLOEBWeRsz9vsKfkkLtGaKHOedD7q+Bm+UvtX\nC+3F7Tfzg9Z/ys9t/i+IKrP1CpHnqCNN70dDEgMcrfoJXVM1XO0/8rmV8VpCswMGyUtAlph2ZYck\n9x+dL8Kkc6pmN1iqrcsyqvchkrtJorQ2G0mzB2g69lQgt9004HaJVNSkLugqIOsWxOpiNuddp31y\nCxuy74Wtq7A/4t7ITrbkP7X+BmSR28N7OVb5o4TPRROqGhoaGi8OgqIoEdXVD77eyE/afoks8whW\ng4N5bxpT7ly+tPGvE44sXolBxO0UaD5joqA0QH6pn4FOHRMjEruOu8Km7583yQjCSVcuE8581an/\nK/1HqLI/JM82GLI8IEtcGzjCntLlD7b5x5Zf4YsbvxfWVqZc2VztP8pr638Qtk3/bCXegJGqzLZn\ntsmhf7YyRNQ8DxQFbg3vw+O3IIk+ArIeq2GG+vxrIb+LpZ3PTIr0P9ZT1xTqruCaF2i7aaQxRTns\n/D64dcHEziPh+7v66dIp2OJpo1f7j6jmw1UUuDagvg6CqfP6ZqoXXAJkRWJH0dm4reCgCVUNDQ2N\ntcrPNZZEXBfVwnq1/yhf3fSXIdZUv6zjct8JDpR/kLozTDHNZ0zsfc21YLnauM2LzwvNZ8zsfoGq\nUGUYJ3g0Uc96wgXriKOE3SriQBIDiELqrc1qWPUO1Q+bTPMEnoBJdZuS9C6aBw7hDZioyb6NIEDP\n9Hp6ZtZzoCzxvKmQmqpMggDbCi/GfQw1MrJk+gVouWZk004PggDDvRKP7hrZ/0bq/MN72/VUbVL/\nwLSmybidAiZL5FmBeILoAop6l9I5tYkK+yPVdQBlGR0JZ7TQBKqGhobGi88SQVcTYVP/OtGPVT8X\nd0T0E5Y7S8DMpEhOQSBsmlVvAL1Bwe8DSQeDHQKKolC8Lv6ylKkkmYh6SZTRi14mnHlkW54GA816\n7PjlyGmRok27p5JAlGTs0RK17yg+y9h8IdcGjgJQlNYdd8ooiL2NPU/BU7fTw8yEyI2zJgQBsgsC\nHHwrtcGMBpOCx6X+zAN+EKWlXVhibafZ5hF6pteHZAbwBfT0zVRzuPK9qNv6ZR19M9VY9A7ybQNL\nno+GhobGaket31QU6JutRkChJL1TVYNofVw4UV0C/vSt1ylWCbrqnNpImmGGXGvitbeX62F0t+lJ\ns8tkF4T72D66Y8A7OYP3yikq3e+DItNlfgv7saNUNdmW5XziIVHhemtoLy6/Fd3nU9Q60UdxWifz\nvjTWZ7eE/NbhTePR+Fa2FYVXWEo17z38BQ6UncRungpZ3jW1gbH5IppKzqTsWNrLHRlFgaufmNn9\nSvjswuWPzOx5NfZZh1jaaNt4AxPOPPSSl4CsQ1ZEdpV8FjWN2O3hPXj8Zirsbcz70umfrWJD9t24\n8uRqbUBDQ+N5EM/Y3T6xmTFnIeUZ7SgES2fnWQdYF6F8thovcl8XzSUgqmD9Pw7/M3YUnQtbfm3g\nMI0FF5OqkLNcN9zpEHh8z0D9nvAqC5c/MpF16d+zzRZq6bnrfI2M/+XXKf+7V+M+3nJdRyLiVVYE\nROHp42weOESGaWLBx3XEUUzL2A4OV7wb8rvlQpbhH+7/Sw5XvEdRejACvH1iE3eHd/PVur9KeL8v\n8su6XPR36Bjq0dF4wI1O/7mf92kzdU1uMnPjdxGJpX1GKoP6LA/Ht2IzzIR9HJ/rfoO9ZR/HnS/3\nCfFWY5v12GkZ3fm5b7KO4vRuyjIex3QMDQ2Nl4dEjUuj80WMzheHpRe8M7ybkvTOkJnSWHnR+qCE\nBevvHPjXbMm/gs3wtNrMrMfOg7HGiMETsbDcN/jKJ2a27nVjtj69tOlxkUt/cImfyfu3YYOoosCV\n/hOrutpNoi/IsKOE7ukaBBSyLcNUZ7auuAvEhd5XGJsvQlZEqjNbaCi8Gvc+XrSXMhXMTok4pgXy\nSgIxBRO65gVam41AMG1W3U5P0kGIqci9e6nvRFj5XACnz8LD8QYaC+PLl5sII44iuqZraSr+bOFj\nrnNqIw5velhA3VJobVVD48Uk2f7uct8Jdpd8siY1yEqRcNDV7pJPuDZwBFmRMOvncfls6EQfTcWJ\nidXlvKGLG9IRReDytX+Kf90+TLl23EPjWLpOUeodVBVrggBihBKe8Rx/Oa8v1n0/+0IV2PpjmlZN\n5tyXeon3l30c875elJcuXuLpCOc8Gdwa2ke2ZZQM0zhts5XoRD/bi86r/v7JPTVblWUvF5wIkcrn\nWvROvAHjipzD48nN7HumnVZltnF94DDegAGDFHvpwRe1FLWGxstMKj7ORcG/ZjXIaiB6HlYBdpWc\nRlHAGzBhkNxxWeeW4+bF0mhEQWGf4c+Qe/4cX6cxeN5GuMzxsGlzCE6lRwsCWu3MTom03TQEE8Jv\nO8+6LV6y8lYmEwBog3IiJNP5XR88zJGKnyy8iyXp3YzNF3B3eBf1BeHW62jHWg3PLlJmgVmPHYs+\nPKfrchDJ7aAu7xoPxraxteBKQvt9GQYRDQ2N2JAVnaqrVEAW17QGWSliqsckCIRlBFgLnbAoKCHn\nXZfXTPPgIZqeKct4c/Agtbk3kzpWKu+HxyXgmBHJyF56qnegU8fogI4dR9yIYnBqoeWakZkJmcra\n1CWEf5FxOwXmZ0XsOYFlr1CW7Fd652Qtm3JvhHV4udZh2ie3JHU+ibThZLJcPKHQ1svjyU0hQQeK\nEvTBPlz5blL7jkQwk4Y+pCiKGrIiIQqJF0kBTbRqaLwIpKKvq829wY3BQ+woPhuyvHnwEHV5zUnt\n+2XoY6L6sP79rdgjdFeaRBvOwGwF3dM1pBmmQVCY82RSltFOaUZnQvtbqpHEc56+gJ4r/cex6ufI\nsM0yqm/Ev/EIjV8rjmjZvvKJem7ZK58EE8I/z5Rdqx23U+DmeRO2dJn0TJmxIQmDQWHrvvCAvVSR\nbId3beBw2AfXEyIl7Y+H5XQNicbD8XrGnQXYTcEcvQ5vBlvzL5Nhmlp64zgYmiulY6qOTNMYesnL\nuLOAPOsAI45S9pV9FPb7K/3H2FF0NuHAL3g5BhINjZeJZPq6/tlKeqbXk2acBkVgzmunwv5QNSNT\nLLxo/UvCQVerWbAuJpHG4/RZAbDo5+PaLpbGkWhjPtv9JntLPw7JvjDrsXNv03+i4SuFYb93zAj0\nPdZTuz3cv67vsQ6dAQrLkvOLeZE5/76FfW84Q3L2TgxLDPXo2LxreURrsoK1a6oGo85NUVpP2LpL\nfSfYVXyK+6NNuP1mFEQKrH1UZEZO2K9GqjrAeK9VUcDhzcAgeRLK8bwUbr+ZG4MHwnxVH4w1IiAz\nOl/MntJP0Es+FAVax3YgCn5qc28nfMwXbTDR0NAIZ7VpkLVMwkFXax2H18bAbCVlGQ8x60NFXbRG\nkmiDSEaMTLuyybUOhaUKSzdOI575GyyPP8H5W7Gfl2ZZjc5Qj47yGm9YgYnsggAdLausfu8iKjMf\n8lnnFymw9Yb4Yo84irHo5zjd9SV2l36KzTALBEueXu0/yq6Sz2I+RqqKe8Q7+yAIkGacSeqYiwnI\nEvO+NKz6OSQxwL2RXaoBo7W5t7jcd4KmktPcHt6HAgRkHeuy7pNrHY77uC/6gKKhoRFKIjOtkTSI\n1n9EZk1ZWGMVhC6fgX+496+oyHxIrmWYYUcpfTOV/NLW/4AUo19zvI0mWcvZg7FGitO7SDdOh627\nM7ybjTm3F6xOi89NcwlIjBtnTWw7qB5EeOOsadmj6ZNpL/NeGzcGD5JumiLdMM3IfDFmnRNPwMS2\nwvNhHz3tE3WkGWfiSsIfcrzn3IEmYql9UiUt3TjJrCdzwRc1kstENFeLeHje90pDQ0NjLbNmLayJ\nDur/cO9f8fP1/3VB4G3Ob2bGbedv7v4632r8j3EfeyUGoXxbPwOzFaSrTD86fWnopadT1IstYKXV\nPm5dMNGwLyi+ngRd5Zeop8/QCFJQ6mewS0dxVbjLRGAFvCgWt6l427nV4OBgxUmcPivz3nTK7Q+R\nRJkr/cdUi3msz27hSv+xhAXr8woaSvT9vzpwjM1510LyRzt9Fk4++jm2F51X9UcNyMlb1TWxqqGh\nobF8iEv/ZOWx/tGJhAernukq6vKaw3zgMkzTlKR34/IZEjqfaCgKTP2rT5j7N4kPWFnmMUbmSwjI\noSZghzcNUfCrVqay/tEJNrxzhKpNXppPm7hx1sT1z0wUVfq0DAFLUFzlp7PVwLPzC9PjIkbz8lcB\nW0yiQseinyfXOoQkBlOYCUQ+72jrYiEVOQjjOVaix/MF9EhCIESsQjCna7m9nUu94ft9OF5P6RIV\nrZZCE6saGhoay8uqs7AmOzDeGDjEgYoPVNcVpPXSO1NDTc69uPYZbTB6eNvAzISE2SrjcQv4t15g\n20EXBtPT38R6TXtKPuVi76vYTRNkW0YYcpThCxjZXRJ9MCz6y2MULXGeGuFsP+Ti8kdmMnMD2HNk\nBrt1CAJsO7j6kuvHgqyIBGQJSQxNw9QzvZ6itO6k9r1SbSvZ93/KnUu2Rd3vtDCtlxFHMRd6XyPf\n2odB8jI4V0GWeZSS9K6Ejqe9cxoaGhorQ1Qf1nd/vhaP38i9kd0EFBFJDFCffyVq1ZdkO/BkB6zW\n0Xq8soUGlUTfZ7rfZEfRWWyG2JKRL3Ut7XcNmG0yJYumlQN+uPShhQNvOZfcf6RrdXjTmHbnkGsZ\nxKiLLVpdGzgTZ25aZHZKJK/Yjz5+A3xKSab9e/wmLvS+xoHykwvv6IQzj9ax7RwoV/+Ii8ZSbWps\nUKL7oR5BgKy8AJW1viXdUGYmRR7dMSCKYLHJ1DSGB75B4vfB4zfRMrqDbUUXwtbdHt7Dhuy7WPTz\njM0X4JcNFNh6E3Kd0d43DQ0NjdSTcFqrP3v7VTqnatlZfBqD5MXjN3J14Bg12bfJtw3GdRLxdPDJ\nitbv3v5f+Sf1/znE0uTxm/gfd/8l3972x0tuH+u5XjtloulYuDWuv0OHIEJxZfKl1hI9N421Szzt\n/9mqKd6AgTvDe5EVEQWBdOMUtTk3Q36jVmkF4mtbty8G89dWb/YiCEHx+uCmkf3PpAlbzMPbBrwe\ngbqdHkQxKF5vnTOx9zVnyIzEs8TbH1zofY1dxadC/Hn9so5Lfa9wsPxkXPt6Fu3909DQ0Fg+Ehas\nv7X/19lf9mHY8vM9rydksVlMKhPuP8uYI5+Tj3+OrQVXKEnvomuqhnujTfxc3X/FbAiPqI/1nJ4l\nWjT5SkSaa7w4xFtg4trAEURBRhL9+GU92eYRanLuhv32SZtWFLh5zkTAT7CEr1/AmiZT1xR/vtmx\nQYmZCYl1W0JnWpwOgUd3jDTsC2/3bpdA63VjmLtFwB98V9Q+/NSI5T49Ead24wRF6d0MO8qYdOYu\n5FiNF02kamhoaKwMCWcJyLMOqC7PMo8x67GrpmBaihl3Jm3jjfBrv0+uZZiqrAdx72Mpcm0jfLPh\nT2gdredM15tsLzrHtxvVLavJDEZyhIqNk6MiGVlywvvVePGJRXjJMpzq+jIOTwYBReJQxfvkWke4\n0Ps6+8s+CBFfXVM1tE9sZn32fSC8XV87ZWbLLjeWtKffp2ODEveuGtkSZ5GE7od6dhwOF5gWm4Iv\ngrfQgxtG6veEbyPpQFgi9NPjEmi9YUQOgPnIOTZu84RYcZ+9lzrRz8Hyk8x5MhidL6Yso53NedeX\nvK7FaCJVQ0NDY3URVbAaJXWrh17y4AvE7+x3f3QnAVliZ/FpREFhaK6Uz7q+yKHy9xYinVPJpry7\nbMqLbHVKFoNRYXZKJD0z9Nxbm43sez2yJVfj5SPeGQOH18Z7D3+RE1XvkGMdJSBLXO4/xtX+bOry\nboZZCiszH3Kx95UFwboYp0PAYpNDxCpAblGArgeGiC4CkRDFyL+PtFwOBMVpPNsA9HfqGOzS07Df\nhcEY9Dc+/76F7Ydd2NKD1xPpfRaBgs//HV8tGQ0NDQ2N1UZUwXp/7gjl9vaw5W3zB3k767/GdaBZ\njx1/QE9D4aWFZYVpfWSax7kxdDDhpN3P0xJSv9dD82kTRrNCZa2PqTGRvsd6Nu3wvJA5UJ+IB0mn\nIMsgBwQa97ui+h++7CTq2nLy0c/ztbq/WMgZKokB9pd9zKedXyTf2qe6jU5Un+4e7NZRuk59XZo9\ngMshhInZaGTmBhgdkMgrDp1ikGVQInx3Vm3y8uiOgZqGcBOsHICBB26GP72JfvwRss6Ct2Qb9d9Y\nT2+7nr2vPv34S7PLHHzbyZWPzex5Vfso1NDQ0HhZiCpYxW3Hae+9y3rz04j7NtdBhK2HGe59l8I0\n9YFTjQdjjewoOhu23KRzISsxlp9SIVVlJBNBEGDnUTdOh0Bvu56MbDnEsrp4KjM7P0DFxrWbG3Wo\nR8fclEjTsafXF/DDhZMWDr7tJOCHlutG/D6B9EyZdVu8UUW79Y9OEJBFWse24/TZMOvnqcttjmhp\nX2tTtMkGDmaax1QT3B8o+4gfPfgmX679LvdGmwjIEjmWEaqzWpEV9dc5M1dmYkTCnhN+b52O+PPO\nVm3yce59C7YMFxZbcFtZhksfWNh2SF1EZubKtN8VmRoTycwNnoeiQPMZExlp87h+8OccNL8H3rlb\nvAAAIABJREFUtuDvA6Mi7/7h79PwT5vC9iUIoDcGP5oiBXhpaGhoaLxYRBWslfuyCez8t5y72Ipu\nfhS/rYCCtzdS7tEz9ziDQmIXrEBEMSKwtv09LTaFjY2hlqPuNj1jQxIN+9zoDTDSJ3HuPQt7X3Oi\nW72l6iPS91gfIlYhOMW7aYeH658ZkWWRhv1uTGaFyVGRc+9Z2HXchckSKoaeCLkJZy73Rnazvegc\nacYZ5jwZnO95ky35V8i2jIUdP5IAXGtCNlYiWUsNkpthRwk3h/azvegcBsnLiKOYjx5/jQzj5MLv\nFlenys4P8OCGgapNoWmnfF7weYSIU/XR2P+Gk3uXjXi9QrDCmgzbDj0VsGo0HXPT8X/9MV15BxHN\nZuThXrb6/47WkQb2lX0c8ltJlNkn/Wc6H/4ZecXGsH1Jus+tuZpg1dDQ0HgpiDpUdV6a5NA37ZTW\nNYQsP/0Xkxy09nKx95UFK5Bf1lGbe4ssc7jYgOD0f99MFaUZnSHLFQUCESxDaxW/D0YHdCECL780\nQFa+k5vnYo+IXk1IOnUhklMY4PZFIye+9jTvbFaezIG3nFz71MzuV57eg8Wis2VsJ4cr31v4O804\nw+HK9zjX80ZcqYdWuoTuSjHjzlZdfm+kierMVvaUfrqwLN82wNHKH3Nt4HDIbxfPPjTsd3PxpIWy\nDV4Kyvz0PjIw3Cex+0Ri0+qiCFv3hQdrLWVZrrcAjo/AAUjB/yKJ8zzzAOdujLHlaHjUqNedmNDW\n0NDQ0FibRHcJuH+WnruvU17/NMCq44YX3cPPaHYe4UjFT0IsNhd6X6Oh4GJYWUSAsozHnOl6myzz\nKNbPE/crClzuP8GmnJtJXcTzFir9bT5G70+gT7dSeyQtYkS03rB0RPRqJVJGhPY7ehpUhIsogqRX\nVAN6Zj12ss0jqvvLNo8knIHiebqHPMuTc0jUNWBd1n0u9r4SYnmccmXTPHCQn63/07Df6yWfavne\nJ+dgBV4F+pvX0/bKX1Jc5Q9LSxUv0a6tZ3o9w44SjJKHzfnXVN0bnhDJlcEbMCAM3GOkK5/8yqfT\nEm23DBSUJZfjWENDQ0NjbRFVsL6W9184971RHuYdw5RlxTU+T/7Eh+QGxtlcej1MiOwp+ZhrA0dD\nrD+LOVjxPreG9uOTDQjIBBQdm3JvYDdNqv4+GqtBlPh9cPVP77Nx8i84YG3F5bPQfPkXmar5Opt3\nqYuHtepzZ7IqzEyIZGQ/kxHhponXfla9cpgoPk1Sv1jcuHwWrPrwjxoAq2EWl8+akGBdjcz/5icJ\nidbN+Td4OL6Zd1q+TYZpCm/AiKxIbMprxqxTj3kXhQhfFYsosbRTcuEgPFMIKtr7FG+O2PM9b7Ah\n5w67Sk7j9Fm42n+Msoz2sNmVhf3rZ5l05YbNztwYPMhbRd+h43u3uJl3AsGWjjI+TOXcO5SYn6bD\nWw19gYaGhobG8rLkpNrB/O8D34cpglN4eXC1/whGnUpORVGOOmiKgsL2ovMJnehyDEqxDsS+gJ7m\nwcMIgrwgtNdn3ePBht/nsPO3MViDliqz3skB/Z/x03t22je9wYb6cAtWwL820wdsbvJw/TMT1nSF\n2m0e5udEWq4Z2bzTzaM7Bur3hFtZA35BVaDnWoe42n+cisxHYeuG5srZVXJqOS7huZGotbUm5z41\nOaFpqiZduTwY38am3PBZCb+cuHN0skFiT7g+eJgD5ScX0m5Z9E72lX3E+Z7XKU7vUrUC1xdc5Ur/\nMSw6B3V513H60rg7spvitC6MOjebdKfBcTroRgBgDv5vxp3J/dGd6H7t/0FBICBLNBZewKJfuiyy\nJnI1NDQ01hZLClZFgceTdUy68hYS/UeL6k8m4n8xz1OgLkZR4GzPWxwqfz8k9+XV/iN4z72LoTpc\nlB6yfIePfpBNYdl20uxPLZL3rhopXb+ymQKiXfPAbAWd2/4AS66FDXvNUa2/ghAMmpmbFrl90YTZ\nKtN0zIUoBSsVTYxIZOc//Vh5dMdAXon6tK0oKKQZp+mdqaYso2Nhed9MFWnGmYhT27GwmoVIsm4C\nECza8WBsW5jbxN3hXZTbwz8AVhoBRbWa1NaCyzwY205dXrPqdrtLTjHnyeDm0EHM+nl2l3wSNTez\n02fl9vBeDpb/dGGmR1YETnd9kUMV70d1QYDwZ7Ca242GhoaGxhKlWf/2q3toHjzElrxr5FqHGHEU\n0zK6g4rMB3gDFjbm3A75ff9sJR6/meqs1oRPaLUI1Se0jTeQaxkIi1xXFPjRg1/mK5v+e9g2ihIU\ntLqcHFzFexDMVuSRPtY7/p5C01OBlqprjff6/LKO8z1vUGlvoyLzEbMeO7eH9rI++z6Fab0hv431\nHFubjczPCohS0LJaVOmjpOqpaFA7x/aJzYw7CxEFP7KiI8cypJr4finWsthI9CPq9vBe3H4LohDA\nL+upymwLe3bPg6v9R9hVcjpsuaIEra+J5lt+lst9x2kqPo0khs7oOH0W2sa2sa3oQoQto7OW25KG\nhobGWidaadaogvV/2/sbHCg/GeKrqihwvucNMs1jePwWthZcQhTkz3NC6hKe8ofVJ1Yh8gAM8HHH\nV3ml+p2w5UGRO0i2ZTTu48V6D5K5rst9J9hRdCbMEnau+42w572YUU8pLXv+fqFwgChC4343YoxG\n9VRMO7/ogiJVU/ORSJWvaiQu9Z1gb2n4MVrHtlFo6yXTPJ70MSD6exltXay86O3seRBr+/LLOm4M\nHkRBWHDBqs25FVPb0Z6bhsbaJppgjeoSkG0ZCRMvggAZpkmqs1oRBZm7I7uRFYna3Buq2QGeJSBL\ntIxtx+O3UJNzO2Rac3VO0wnIiqA6Ta0T/LSMbqcu78bCMoc3jbH5wjDrc6wszp8ZaX3yqE/b1uVd\n59HEVmpy7oStG/WU8vDg39F08GkaJLdT4OIHFg68Fd1ncHJM5OJ3x5AMP+BNxzexGCL/PpZnPjog\n0fdYjz0nEJZbdK3zPNt8ogFiiylO6+bBWCO1ubcWls15Mphy5ar63SbKSrglPQ+i3X9vwMD90Z3I\nikRdbjPmCL66q6PfTMIFq/stDpSfxCA9dbe61HcipgDd1TmGaGhopIKoFtbvvPUGJeldYcs7J2tJ\nM06Rax2O62BdUzUMzpWzteAyFr2DltEdOLwZEbMKqJFIB5TMIDzrsdMxuYnGRSVlAabdWfRMbyDb\nMkLvzDokwY+sSOglL9sLzyUkomK5tlQI1kgWKFkRuDF4iJ3PTNvO/+YnXDtlDiscADDQqUNRoKRa\n3Wfw4z94TOHgD9hRfI6ArONy3zH8soHj1T9S/X20e+D3wZWPLRRX+ajY6GNyROLBTQO1270h/rMa\nqSHRttY7U03fTDV6yUtA1mGQPGwrPJ/SD4tHE1uwGWYpSusJW55mmEnKPWIlRE6897ZldDvzvjS2\n5l8OzmiN7EIQ5LB+KRorJd6S6aMejteTZR4NG1sUBS71vRJWYCIeNPGqobH6Sdgl4HcP/gt2lXwW\ntvxy3wmaik9FDYp4Fo/fxM2h/WHidMqVQ99MNfUFV2Pe12Li6YQS7UgfjDUw57XTWHARnejj0cRW\nxp0F7C39KOlBON5OdDmnbVtGt1Oc1o3dPBF2bjfOmth+SL3gQaR1zf/iP1Bhf0S5vSNk+a2hvaQZ\npliX/SBsm2j34+qnZrYddKE3hC6/cNLM/jciJ8CfGJHouG8IujIEwGBSqN/jifvZLXXvHV4bH7b/\nLOnGKQBmPFkcr/ohmZ/fz2ishcF0ud0V4uXawGGsege1uTdQFJG7I7sQBIWGgssJ73O5nkMy9250\nvoix+cKQmRwI5rqVFZHKzIdx73M1Xidorh4aGi87CbsE6CUPo/NF5FkHF5YNzZVi1jviEqsA90ab\n2FF0Nmx5pnmctvEGlS1iY6kp9MUs/l08HWtt7m3cfjN3RvYQkCWqs1pVp80TQe08+maqGJsvosze\nTo5FPcF+MuRbB2ifqGN9dsvCMoc3jUlXHnV5N1Tvpxzhcft94YUBnlzTtPsXKbd/ELZNQ8Elfvjg\n26qCNdrzFAQlTKwCVNf56Huso3RduJV3fEiitz20rKxjRuDyx2b2vhpc1vtIz9SYSMVGX1ie2cXX\nEw2v38BPH/0CX930lwsR6gFZ5Mdtv8yr6/4nNoN6rtpIx1iNA+tynlMiQqep+AzT7iyuDxxGFJSY\n3ZKeJdmZDYc3jUcT9dgMs6zPupdyF5WOyU3sLgmfhSq3t3O573hCgnU5imyk4oNGQYzogrWWXT00\nNDSSJ6pg3VZ4kXsjO+mYrFuI5E43TiZkwQjIkqrfJIC0RAqapYhHtD4h3hRDJp2LHUXn4j63eJj1\n2LkxeJB1WfepL7hCx+Qm7o/uZF/pRxHvXSJUZ7XSObWRS30n0Ik+ArIO+ci3aPzf85kX/o3qNpIE\nrnkBszV0ILl3xUTt9mAO1mfvpVFSt8gKQuR1kZBl0EVIM5qVF+DRXYOqYO1s1YeVwrVlKBSU+nl0\nV89ov451m73UNfl4fN9AS3OwXGm8BR4+fPwN3q7525B0SpIo83bN3/DTh7/Al1WySURjNVXtWgkS\n9du2myZjtroth8vNlf5jmHRONuddZ85j53zvG1RltlKS3h3XfqIhCoGIIvh59J2RSIUP9MacW9wb\n2cXWgishy2c9dky6pfPrRuNleZc0NF5UlszDuiX/ekoOZDdNMu7MV7UYJpPw/AmxdpROr4WT7b+A\n7fNgrznPr/L6+r9f0gIGy+9jenNoP4cr3l0YnGpy7lKV+YBrA0fZV/ZRwvtVoyqzjarMtpg78Yb9\nbi59YKFio5eSaj9+H9y9bCLNLpPzneOq2zi8GaqlWd1+c9zPXBTB51UftR/fN1C+IcLHUIQWXlnr\n44d/buOr/+zpc9/Y6MU1L3DznIkdh5+K3FgGYlHwY9GHV6AySF6MMSSyj8TLJlzVWO5rT/SdvTO8\nm5rsOwvR69mWUQ6Wn+RC72sU2PqXzAUbK5Ig4/EbMepCi3PIikAgBX1nKkk217DdNMmA6Ofm0D62\n5l9BEgN0Tm2kf7aKA2Unkz4vDQ2NtcuSgjVVbMi+w6muL3Ok4t2Q3Il3R5qotMc/pZUIXr+Bdx9+\nk69u+m8LFktfQM87rb/CF2q+i8UQ2Q8Sok/bJmtZGHEUUZreGSbu9JIPSfRHnCaLh2Q6bVGE/W86\nGejUceOsCePpP2W74cdRqwrtKvmU091f4GjluwvLFAU+aP8Gr1b/z7jPITM3QH+HjowsP/2tHrJK\n9KRlScxNiaRnqvsseF3q98zvg7zi8EAts1VBUdnVUqLVL6v4KiysW12iQiNIsu+s02dTTbW0rfAc\n90eaaIgjICoa9QWXudj7WsjHLMCV/uNsybuW1L6XS8gl0zfW5TXj8KZxY+gAiiJSlvGYg+Xxi1VN\npGpovFhEFaypTK8kCHCg7CRX+o8hiYGF/HplGY9XLOH5B4+/wRc2fi9kel0v+fhS7Xf5sP3rfKn2\nu3HtL5VBKJOuPIojTCOadE58AaNqOdxYSVXnXVzlZ8M7R8C69G9LM7pxeDP4cdsvYTdOIiMy5cpl\nb+lHS34cqLFui5dP/7iL0qkfstF2kWFXNefkb7D3N7YD6tbX0ftj+I6mh/m+3vypk6p6RXW7SHll\no4nWfGs/PdPrKLc/Dlk+4ijCqovfr/LZ42qkllS8u5Gm4y16J94oHzDxYpC8bC86x6W+V5FEPwIK\nflnHhuy7pBln4t7fSrenSMeL9gxshrmYi0xo74eGxsvBkhbW2xcM9HfqsdgUnA6B8hofW3Z5E+qE\njDpPUmlJkkWKMG1r0rnQSx6VLVaO0owOOibrwny3IGjJMcTp87mcxOOrVpt7h9rc+ALUIu37Sv8x\nXsu/jDkraNVdZ7hNtXKbs//2bQ5Xvqe6TUXvMa7+v8ep+cY2cstEAn64//EMBfe/Q+vQr1L8z3PC\ntgn4I0fNRJry3FN2ih8++BbzPhu1ObcRBGifqOPW0F6+vvkvYrruaMfTWH1Emo4fcRSRZY6/aEg0\n0o3TCbsFrdY2tFrPS0NDY3USVbCefc9CfrGft37pqchruW7g4gcm9r2uLqBWa7oUiDzAwPOftrUZ\n5nB4M5j32rAu8qcddpRgM8wkFXm8HM9kqX0uRwokRRHDkqULAlRmtjEwW6FqoRZFeEX5HR7/VRN3\nTdsRfPNsEX+ESe/m1qNjeN05GExPf9/focOes3ROVzXR/pXav6ZtrJ4ftn4bBFifdTchsaoN5OFE\nak9+WeRq3zF0kp+dRadVg+WW634Wp3fxcDy00EZAlrg/2sSxqh8vyzEX86K3k0jP3OM30TtTjd00\nSa51SPU3L/q90dB4GYkqWAM+2LjNG7KsbqeXT//RulCacy0x77MxNFdCYVp/yPIRRxGzbvtzOqun\n7Cn9mOsDh5EVHZLowy/rSTdOU58fv5/a8+6wnz1+sgJWVgR0onpgVYGtjwfjjaqCVVFEAoqO9dZr\nrOcafD7d3zxwiC/m/AG3/8MvIVftRLCmIY8OkDv4Lo2mT+Hjpe+hmrV1Y+5dNubejemanvczWo3E\n004+6fgSILIl7yoBRcdHHV8nzTDN/vLQWZzlyh9blvF4IduGJPhREAnIOvaXfZjQ/l7G9hDPs1EU\nuDZwFEn0U2l/wJQ7l5ax7TTkX17IHR3Lfl/G+6yh8SIQtXDAH35/mLL14X5a7Xf16AwKlRtTEwUb\nC6mp8HSUnulqdhafW8hd2DW1gWsDR6mwP1ItkhAPibhJREItuj7ScR7f09HabMKSJuOaF6io9bKl\nKXVpsFLBchY8eDDWSL6tnyzzWNg6X0DP2Z632FZ4gSzzGLIicHdkNwbJE1IqNNr9Bm2QWy4SbRe3\nBvdg0HnDkulf7jtGcXoXZRmdqtspCtwb2YXTb0NAQRICbC24lHTauKXajxova5tK9JnfHNpHlb0t\nTJye7vpCWEDacjI2X8DjyS1Ioo+ArKfC/nDJOIyX9VlraMRLwoUDvG71HsDrFrCmJxex/oTFnZdf\n1jE0V4bNMKsafZssCgJf3/wX3Bzcy82hbyMIMmXpHXxj8//Hlf5jKT/eE5azs7r+mQlJD29/y7HQ\nYbdcN3D+fTMH3oo/sGm5SEWOxoAsMjBbRnH608HB4zfSNr41pHb9YvSSj2OVP6JtvJH2ic2AQE3O\n7bCa5KtZrEa6b9PuLGY9mRTaesME1/M+56VIti10TdfylU1/FbZ8V8lnvNP6K6qCVVHgTPcX2F50\njvTP09p5/EbOdr/NoYr3lhStk65c5r1pFKX1hGQ6gdg/Ll9mkn3mbr8lTKwCbM67zuPJzazPvp/U\n/mOhe2oDM54sdpd8svDMH4w1MuuxU5MTeWZF7dq1tqGhER9RBevj+wbWbQnvxHsf66lrCg9eWopo\nHdbNoX34AgZKMzoZdpRwb6SJ+vyrqh1UomSaxhmbL2Rb0SW2FT1NOTPhzFsop5koz6vzmRyVePVn\nQ59F3U4vp96xrCq3jVRYWEUBJlwF9MzUYNI58fjNKAgU2PrwBfQRBYcgEFHQRmKln2c892fWY+fW\n0D7yrIPYTRPcHNqPJMjsKH5aSW6p/T3PwTIVbcESoaKVKCgRE8w/ntxMXV7zgliFYCDooYr3uDW8\nL2JU+oQzj5bRHeTbBrAZZrg+eBiTzhlTARUtj26QVDzzSC5BudYhOqc2Jr3/WBiYqwgLHK7NvcWl\nvhNxW9kX35OXvX1oaMRCVMFaWO7j3Htm9r7mQqcP5q48976F8vXeaJuFEEtH1TK6g+K0LvJtwRKw\nRWk91ObeTvlUz4bsO1zofR1vwLjg7zg0V0b7xGYOJJDn7wnPq7MZ6pEoqVbvxGsavdy9bKBhX+zP\nKhpLPcfBuXJ6ptcjCgFkRWJL/jVshtmUHBuC1jGd6F3w5/UF9OhEH4IADm86Dyca2JyXXJGLlXyO\niQ7gigLNg4c4UvGThfeiMK2XcWc+d4Z3q2aZWOr4a3GwdHjUi1L4ZR0ev0V1m0lXnqoVTi/5UBT1\nLztZEbg3siskC0VxejdDc2U8GGugNvd2TOe71u/3aiBSYGzvTPWKpEac9dgjzvyVpncwOKce+BkL\nqaw4pqHxohJVsG7d62VyTOSnf2vDYFDw+wQOfsFJRlaEwvKfE+9gPOvJpC6vOfz4BZd5ONHAxpzY\nBoWlEAQ4UP4BXVM1XOk/hoBCrmWIgxXLn5T62QFLUWBiWMJoVkizR7+fkRB1SsQUTAGfgF6fnNtG\nrM+xZXQ7OtHHntJgvfOALHJt4BhVmS0LHyGJMO3OIiDrFqUIenqti62pAVlCElbOnzoZkrU0dU5t\noi63OUyo5VhGeDRRn9Q5rdSAmQr3kIaC81zpP8ae0lMhy890vc2RSvUIfSVCrt5o61rHdrCt6HzY\n8sK0Xrqna+I446csde1znnR6Z9ZTbn8YVoFvrYqaVDzzTNM4/bMVIWVvA7LI48nNHK38SZJnuDSi\nICMr6kmaA7IOfRIV7UATrRoaS7FkHtasXJkv/vLSZUuT6YykCFM9WeYxHk/WJbzfSFRmPlwIulqK\neDqQWO9B72/9FtPuHPKt/Uz4LTxy5VObc5Nc63Bcx8svlrl5Rkf9nvAcsg9vGzj+tfjdNiC+Z+mX\ndcx57ewueSocJFFmT+knXOx9JSHBOjBbQdf0RrLNw+hEH23jDeTb+vFFSMbeMraDXcXLEzC32phw\n5VGd1aq6LtKUaays5ICZbJW4ddkPmXLn8/6jn6PQ1kdA1jHsKKU66z7ZFnUrWIGtl76ZKkqf8W91\n+izoRfWZCKfPGuJCsJhI/VaieP0GftT2LUrTuyhM6+H20F4G5yr40sa/wqALfpCtZlePpUi2bGtt\n7i3uj+6kd2Y9GcYpXD4rLr+FvaUrk9vbZphl2p2tum5grpK9pcmVz17Nz05DYzWQktKsyQfTqE/1\njM0XkmkaZ8hZQaf5SwgWG+JED42GH2DUuQnIInedb+LM3gJ+P/kzn7HOFrTUrobp3QHnerotbyGY\nrEiTHTQa36F/tgqjzsWeZ6Ldz3a/yT7zR1HLv6pRut7LuffM7HvdhaQDWYZrp0xk5vkRRehs1TMx\nIiEIYDIrbNrhiVjJKZHn+HB8K5tyb6iu04m+uP265r02emfWhaQGWp/dQsvoDnIsA5zrfoM9pZ98\nPo0bjPjONo8giQF6ptcz5ChDQEYv+tiSfzXp6O/niTdg4JbnqwSyqlHc85Q73yfPMhgx5+zzziWc\nKImK153F5wCYcOYgiQGaSs5E/X2FvZ2Lva8giQGK0nqAoBX/iYuFGjbDLNOubFVf+mh5nRPhR22/\nzJc2/jVGXfADtDLzES6fhZ88/BZfq/tvMe1jLbgeJJPybnPedRQF5n3pGCXXir/fVZkPuNx3nKbi\n00hiAFkRuDl0gGKVstqx8ryeUyz33emzcG9kFwCyIrEuqyVi7tsnrNZ2p7H2iZrW6u9v9UdaFUKy\ngrVtvAGbYTpkqkdR4HT3F8kuCKCc+FXW7wn6pfm8cP1vemno/12ubX2HHUdd2D7PWDDUq6OvXUfT\nsedTFWrxfbjt+TLia9+mekcwK73XDde/14nu9knV6Sunz8qj8a1L1h9X6wzGh0QufmjBaFbwuASa\njrooLA9w9VMzFTVe8kuDEc2OGYHm02b2v+lEF2GsjfdZto5toyS9U9UKdbX/KE3Fn8XVkV/tP8qO\norNhUdgQTGu1vfA8d0d2IysCsiKxIfsO2ZYxmgcOkWMdpMLeDoDLZ+Fi36vsL/sQky712RJ8AT3z\n3nTSjNMh55psZ/3k/s95M7iU80c0fbNyobBBR7ObwAf/nanBAIcr30UUnr66w44SJpwFqq41sbLa\nB5pU5VPtnKxl1FmEgILNMMOm3BsR2+iTfmixzzBA78w6PH5TyiLTp1zZPBzfyu7S8JmC8z2v01h4\nPsw9IFZW+3NdilifezJuWvEw77Vxf3QngqAgKxKbcm9EtMJH3c8qDuwEGHfm0zbeyO6ST9GJfhQl\n6CIjif643fTWehvUWDmipbVKiWB9QjIDyr2RnTi8dnIsg8wZqpjJO8j6r22ls83M9kOhAlRR4Kd/\nY+OVbzgwGEP30/NIj06vUFz5/HwaXfMCj24b2LovdKpeUeCD32vla7ZfU93uav8RdpWcXnL/sbz8\n/R1B43lJdeh98LgE7l8zht3TZ4n1WfplHc2Dh0JcAp5wsfeVuEvxRrsH1wYOq0ZyT7pyGZorDxNr\nAVnk6sAx1dytifJknzrRR4Zx8vMpQiGqME+ks77yiZldx11h+7x90UTZOg/t79zFPnyWNO9jxp1F\nmPWOmKLWU3V+q5XlKBIw67Fze3gvGcZJLHoHY85CMoyTYTlgk6F5cD+Ftn5Vy3nHZC2+gIGNcZY4\nXsyL9IyXm1S3obWakeNi76uq5YAXW5gTQWuLGtFIOA9rvCTTEKsITmfPTIhkWhTMVoU7F41s2RUu\nrAQB0rMCYWIVoHyDj+unTc9VsLbdNLJlt/p5p60rhuHwbZw+CwYp3Bc1UYZ6dew8En4ORrOCLAct\n1a0fT6KM9RMwZlB1rJzM/Ke/i9XfTCf6yTBO8mCscSF1VECWuNJ/nA3Z0Ss+jc3nc67nLQySG4/f\nzKGK9z6vFiSpdoaRprzbJ7bQpOLDKokyopBYQFskLve/wo6isyFWW4c3nWsDRyMWnkjEN1TSKaoC\neHOTm3tXTOz651twO+txOgQ258iIIiTmsfxiEct9jncQTzdOc7D8JE6fFZfPwrqs+ylPUl9pb6N9\nsl5VsA7NldFQeDG1B9SIyIsgqJIV3cGsLOruFptyb/Booj7uVIFP0ILLNBIlpYI1WQQBzDZlIbpd\nQYjobxltwFipiieRiOa3KWXn0tW9nUpTqHWmeeAwe5ewRsbzkke7B26HTPO//5T90n/CpHOhKHDn\nO28wfuJXWL8/PeZjPKE29xYjjmIu9x1HFAKAQGPhBSz6yBLq5uAept25fGnjXyOJAQLS2hB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ZyQPc50VytN2y0D87MiW3a5kXTQct2ILAsh6cNSwc6jLu5dMeJ2CgtFKYqrfJRUra3cnxoa/z97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EI/e+JrsDj6fqUJnV67WdMWDBmRbSc4ezTjIhhOx0lBkVgpIqNzqvyqujeoBZa9Xl5NZNJ4j8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\nQgghhJCY9v9Wwe+KQ+IoOwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11078ff28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "decision_boundary(clf, X[::10], y[::10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NmScLh5tcnNkmIsIWzKwu66m2o8dk+1aifFFlCXHNML3t2XwMx1vdWzyi7WFvgwN3p8I4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ybYyRQAcsur8eizH6Ls9AW3wWrYWEf304eoffPDAz3OndAMf7uTZsCneeWqMBDYW38g12Om5Q5q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\nEAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117816048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "decision_boundary(clf, X[::10], y[::10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 361,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# grid search for parameters C and gamma\n",
+    "\n",
+    "# C = 1\n",
+    "# gamma = [1e-1, 1, 1e1]\n",
+    "# classifiers = []\n",
+    "# for gamma in gamma:\n",
+    "#     clf = SVC(C=1, gamma=gamma)\n",
+    "#     clf.fit(X, y)\n",
+    "#     decision_boundary(clf,X,y)\n",
+    "    \n",
+    "# C = [1E-1,1,10,100]\n",
+    "# gamma = 250\n",
+    "# classifiers = []\n",
+    "# for C in C:\n",
+    "#     clf = SVC(C=C, gamma=gamma)\n",
+    "#     clf.fit(X, y)\n",
+    "#     decision_boundary(clf,X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 362,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# default kernel is kernel='rbf', other nonlinear kernels are poly and sigmoid\n",
+    "\n",
+    "# clf = SVC(kernel = 'poly')\n",
+    "# clf.fit(X, y)\n",
+    "# decision_boundary(clf,X,y)\n",
+    "\n",
+    "# clf = SVC(kernel = 'sigmoid')\n",
+    "# clf.fit(X_small, y_small)\n",
+    "# decision_boundary(clf,X_small,y_small)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 723,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set score: 0.71\n",
+      "Test set score: 0.71\n"
+     ]
+    }
+   ],
+   "source": [
+    "# now use all the features\n",
+    "\n",
+    "clf = LogisticRegression()\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = clf.predict(X_test)\n",
+    "\n",
+    "# Returns the mean accuracy on the given test data and labels.\n",
+    "\n",
+    "print(\"Training set score: {:.2f}\".format(clf.score(X_train, y_train)))\n",
+    "print(\"Test set score: {:.2f}\".format(clf.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 724,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set score: 0.71\n",
+      "Test set score: 0.74\n"
+     ]
+    }
+   ],
+   "source": [
+    "# now use all the features\n",
+    "\n",
+    "# clf = SVC() # use default values for C and gamma\n",
+    "# clf.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = clf.predict(X_holdout)\n",
+    "\n",
+    "# Returns the mean accuracy on the given test data and labels.\n",
+    "\n",
+    "print(\"Training set score: {:.2f}\".format(clf.score(X_train, y_train)))\n",
+    "print(\"Test set score: {:.2f}\".format(clf.score(X_holdout, y_holdout)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 725,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set score: 0.71\n",
+      "Test set score: 0.70\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
+    "\n",
+    "clf = QuadraticDiscriminantAnalysis()\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = clf.predict(X_test)\n",
+    "print(\"Training set score: {:.2f}\".format(clf.score(X_train, y_train)))\n",
+    "print(\"Test set score: {:.2f}\".format(clf.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 728,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set score: 0.99\n",
+      "Test set score: 0.96\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "# now use all the features\n",
+    "\n",
+    "clf = RandomForestClassifier() # use default values for C and gamma\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = clf.predict(X_test)\n",
+    "\n",
+    "# Returns the mean accuracy on the given test data and labels.\n",
+    "\n",
+    "print(\"Training set score: {:.2f}\".format(clf.score(X_train, y_train)))\n",
+    "print(\"Test set score: {:.2f}\".format(clf.score(X_holdout, y_holdout)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 367,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1u4BO1DvQS3//nHaXMWn09HTbj8JeDGc/1C7OHCVJqhiOkiRVDEdJkiqGoyRJFcNRkqSK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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119235748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot feature importance\n",
+    "\n",
+    "feature_importance = clf.feature_importances_\n",
+    "# make importances relative to max importance\n",
+    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
+    "sorted_idx = np.argsort(feature_importance)\n",
+    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
+    "\n",
+    "fig = plt.figure(figsize=(14,7))\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.barh(pos, feature_importance[sorted_idx], align='center', color='m')\n",
+    "\n",
+    "plt.yticks(pos, np.array(list(df))[sorted_idx])\n",
+    "plt.xlabel('Relative Importance')\n",
+    "plt.title('Variable Importance')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 729,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set score: 0.99\n",
+      "Test set score: 0.90\n"
+     ]
+    }
+   ],
+   "source": [
+    "# now use all the features\n",
+    "\n",
+    "clf = SVC() # use default values for C and gamma\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = clf.predict(X_test)\n",
+    "\n",
+    "# Returns the mean accuracy on the given test data and labels.\n",
+    "\n",
+    "print(\"Training set score: {:.2f}\".format(clf.score(X_train, y_train)))\n",
+    "print(\"Test set score: {:.2f}\".format(clf.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 730,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set score: 0.99\n",
+      "Test set score: 0.95\n"
+     ]
+    }
+   ],
+   "source": [
+    "# now use all the features\n",
+    "\n",
+    "# clf = SVC() # use default values for C and gamma\n",
+    "# clf.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = clf.predict(X_holdout)\n",
+    "\n",
+    "# Returns the mean accuracy on the given test data and labels.\n",
+    "\n",
+    "print(\"Training set score: {:.2f}\".format(clf.score(X_train, y_train)))\n",
+    "print(\"Test set score: {:.2f}\".format(clf.score(X_holdout, y_holdout)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 731,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf = SVC(random_state=0)\n",
+    "\n",
+    "# specify parameters and distributions to sample from\n",
+    "# set the parameters by cross-validation\n",
+    "# C = [1E-1,1,10,100]\n",
+    "# gamma = [1e-1, 1, 1e1]\n",
+    "\n",
+    "# default C is 1 and default gamma is 1/n_features = 1/6\n",
+    "param_dist = {\"C\": sp_randint(1, 100),\n",
+    "              \"gamma\": [.1, 'auto', 1],\n",
+    "             }\n",
+    "            \n",
+    "# run randomized search\n",
+    "# n_iter_search = 20\n",
+    "n_iter_search = 10\n",
+    "random_search = RandomizedSearchCV(clf,\n",
+    "                    param_distributions=param_dist,\n",
+    "                    cv=5,\n",
+    "                    n_iter=n_iter_search,\n",
+    "#                     n_jobs=-1\n",
+    "                   )\n",
+    "# 5-fold cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 732,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RandomizedSearchCV took 45.07 seconds for 10 candidates parameter settings.\n"
+     ]
+    }
+   ],
+   "source": [
+    "start = time()\n",
+    "random_search.fit(X_train, y_train)\n",
+    "print(\"RandomizedSearchCV took %.2f seconds for %d candidates\"\n",
+    "      \" parameter settings.\" % ((time() - start), n_iter_search))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 733,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model with rank: 1\n",
+      "Mean validation score: 0.933 (std: 0.009)\n",
+      "Parameters: {'gamma': 1, 'C': 32}\n",
+      "\n",
+      "Model with rank: 1\n",
+      "Mean validation score: 0.933 (std: 0.009)\n",
+      "Parameters: {'gamma': 1, 'C': 91}\n",
+      "\n",
+      "Model with rank: 1\n",
+      "Mean validation score: 0.933 (std: 0.009)\n",
+      "Parameters: {'gamma': 1, 'C': 45}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# random_search.cv_results_\n",
+    "\n",
+    "# Utility function to report best scores\n",
+    "def report(results, n_top=3):\n",
+    "    for i in range(1, n_top + 1):\n",
+    "        candidates = np.flatnonzero(results['rank_test_score'] == i)\n",
+    "        for candidate in candidates:\n",
+    "            print(\"Model with rank: {0}\".format(i))\n",
+    "            print(\"Mean validation score: {0:.3f} (std: {1:.3f})\".format(\n",
+    "                  results['mean_test_score'][candidate],\n",
+    "                  results['std_test_score'][candidate]))\n",
+    "            print(\"Parameters: {0}\".format(results['params'][candidate]))\n",
+    "            print(\"\")\n",
+    "            \n",
+    "\n",
+    "# report(random_search.cv_results_)\n",
+    "\n",
+    "\n",
+    "report(random_search.cv_results_, n_top=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 737,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set score: 0.9994\n",
+      "Test set score: 0.99\n"
+     ]
+    }
+   ],
+   "source": [
+    "# now use all the features\n",
+    "\n",
+    "clf = SVC(C=15, gamma=1) # use default values for C and gamma\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = clf.predict(X_test)\n",
+    "\n",
+    "# Returns the mean accuracy on the given test data and labels.\n",
+    "\n",
+    "print(\"Training set score: {:.4f}\".format(clf.score(X_train, y_train)))\n",
+    "print(\"Test set score: {:.2f}\".format(clf.score(X_holdout, y_holdout)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 374,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# personalized intervention, identify subgroups that would benefit from CareTracker, let sleeping dogs lie\n",
+    "# let's construct an uplift model to directly model the incremental impact of intervention\n",
+    "# maximizes return on investment by targeting the persuadables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "first, calculate the costs if without any CareTracker intervention\n",
+    "\n",
+    "assumptions:\n",
+    "\n",
+    "$8k loss in Medicare reimbursements per readmitted patient (2014), based off CFO Leila Houssein's estimated figure\n",
+    "\n",
+    "so with our given dataset, we have 998 readmits, 998*8000 = 7,984,000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 375,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "998"
+      ]
+     },
+     "execution_count": 375,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('Final Project Data_Case.csv').dropna()\n",
+    "readmits = df[df['readmit30'] == 1]\n",
+    "len(readmits)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "now suppose we introduce CareTracker to all 4382 patients\n",
+    "\n",
+    "our cost would be 4382*1200 = 5,258,400 for CareTracker, but we'd expect to recoup $ through preventing readmission.\n",
+    "\n",
+    "CareTracker has shown to reduce readmission by % in the past, that means instead of 998 we'd expect x readmits, reducing penalties by $xx.  So the net loss is x."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 706,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10048800.0"
+      ]
+     },
+     "execution_count": 706,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "4382*1200 + (8000*.6*998)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 376,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4382"
+      ]
+     },
+     "execution_count": 376,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 377,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5258400"
+      ]
+     },
+     "execution_count": 377,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "4382*1200"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 378,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2196,    3],\n",
+       "       [   0, 2212]])"
+      ]
+     },
+     "execution_count": 378,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import precision_score\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "cm_train = confusion_matrix(y_train,\n",
+    "                            clf.predict(X_train))\n",
+    "cm_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 379,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[741,   1],\n",
+       "       [ 68, 661]])"
+      ]
+     },
+     "execution_count": 379,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import precision_score\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "cm_train = confusion_matrix(y_test,\n",
+    "                            clf.predict(X_test))\n",
+    "cm_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 380,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[443,   1],\n",
+       "       [155,   1]])"
+      ]
+     },
+     "execution_count": 380,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import precision_score\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "cm_holdout = confusion_matrix(y_holdout,\n",
+    "                            clf.predict(X_holdout))\n",
+    "cm_holdout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 381,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "true negatives:  443\n",
+      "false negatives:  155\n",
+      "true positives:  1\n",
+      "false positives:  1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Thus in binary classification, the count of true negatives is\n",
+    "# :math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is\n",
+    "# :math:`C_{1,1}` and false positives is :math:`C_{0,1}`.\n",
+    "\n",
+    "# true negatives is quadrant II, true positibes is quadrant I\n",
+    "m = cm_holdout\n",
+    "print('true negatives: ', m[0][0])\n",
+    "print('false negatives: ', m[1][0])\n",
+    "print('true positives: ', m[1][1])\n",
+    "print('false positives: ', m[0][1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 382,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "def show_confusion_matrix(C,class_labels=['0','1']):\n",
+    "    \"\"\"\n",
+    "    C: ndarray, shape (2,2) as given by scikit-learn confusion_matrix function\n",
+    "    class_labels: list of strings, default simply labels 0 and 1.\n",
+    "\n",
+    "    Draws confusion matrix with associated metrics.\n",
+    "    Source: http://notmatthancock.github.io/2015/10/28/confusion-matrix.html\n",
+    "    \"\"\"\n",
+    "    assert C.shape == (2,2), \"Confusion matrix should be from binary classification only.\"\n",
+    "    \n",
+    "    # true negative, false positive, etc...\n",
+    "    tn = C[0,0]; fp = C[0,1]; fn = C[1,0]; tp = C[1,1];\n",
+    "\n",
+    "    NP = fn+tp # Num positive examples\n",
+    "    NN = tn+fp # Num negative examples\n",
+    "    N  = NP+NN\n",
+    "\n",
+    "    fig = plt.figure(figsize=(8,8))\n",
+    "    ax  = fig.add_subplot(111)\n",
+    "    ax.imshow(C, interpolation='nearest', cmap=plt.cm.gray)\n",
+    "\n",
+    "    # Draw the grid boxes\n",
+    "    ax.set_xlim(-0.5,2.5)\n",
+    "    ax.set_ylim(2.5,-0.5)\n",
+    "    ax.plot([-0.5,2.5],[0.5,0.5], '-k', lw=2)\n",
+    "    ax.plot([-0.5,2.5],[1.5,1.5], '-k', lw=2)\n",
+    "    ax.plot([0.5,0.5],[-0.5,2.5], '-k', lw=2)\n",
+    "    ax.plot([1.5,1.5],[-0.5,2.5], '-k', lw=2)\n",
+    "\n",
+    "    # Set xlabels\n",
+    "    ax.set_xlabel('Predicted Label', fontsize=16)\n",
+    "    ax.set_xticks([0,1,2])\n",
+    "    ax.set_xticklabels(class_labels + [''])\n",
+    "    ax.xaxis.set_label_position('top')\n",
+    "    ax.xaxis.tick_top()\n",
+    "    # These coordinate might require some tinkering. Ditto for y, below.\n",
+    "    ax.xaxis.set_label_coords(0.34,1.06)\n",
+    "\n",
+    "    # Set ylabels\n",
+    "    ax.set_ylabel('True Label', fontsize=16, rotation=90)\n",
+    "    ax.set_yticklabels(class_labels + [''],rotation=90)\n",
+    "    ax.set_yticks([0,1,2])\n",
+    "    ax.yaxis.set_label_coords(-0.09,0.65)\n",
+    "    \n",
+    "    # Fill in initial metrics: tp, tn, etc...\n",
+    "    ax.text(0,0,\n",
+    "            'True Neg: %d\\n(Num Neg: %d)'%(tn,NN),\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "    ax.text(0,1,\n",
+    "            'False Neg: %d'%fn,\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "    ax.text(1,0,\n",
+    "            'False Pos: %d'%fp,\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "    \n",
+    "    ax.text(1,1,\n",
+    "            'True Pos: %d\\n(Num Pos: %d)'%(tp,NP),\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "    # Fill in secondary metrics: accuracy, true pos rate, etc...\n",
+    "    ax.text(2,0,\n",
+    "            'False Pos Rate: %.2f'%(fp / (fp+tn+0.)),\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "    ax.text(2,1,\n",
+    "            'True Pos Rate: %.2f'%(tp / (tp+fn+0.)),\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "    ax.text(2,2,\n",
+    "            'Accuracy: %.2f'%((tp+tn+0.)/N),\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "    ax.text(0,2,\n",
+    "            'Neg Pre Val: %.2f'%(1-fn/(fn+tn+0.)),\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "    ax.text(1,2,\n",
+    "            'Pos Pred Val: %.2f'%(tp/(tp+fp+0.)),\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "def show_confusion_matrix(C,class_labels=['0','1']):\n",
+    "    \"\"\"\n",
+    "    C: ndarray, shape (2,2) as given by scikit-learn confusion_matrix function\n",
+    "    class_labels: list of strings, default simply labels 0 and 1.\n",
+    "\n",
+    "    Draws confusion matrix with associated metrics.\n",
+    "    Source: http://notmatthancock.github.io/2015/10/28/confusion-matrix.html\n",
+    "    \"\"\"\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    import numpy as np\n",
+    "    \n",
+    "    assert C.shape == (2,2), \"Confusion matrix should be from binary classification only.\"\n",
+    "    \n",
+    "    # true negative, false positive, etc...\n",
+    "    tn = C[0,0]; fp = C[0,1]; fn = C[1,0]; tp = C[1,1];\n",
+    "\n",
+    "    NP = fn+tp # Num positive examples\n",
+    "    NN = tn+fp # Num negative examples\n",
+    "    N  = NP+NN\n",
+    "\n",
+    "    fig = plt.figure(figsize=(8,8))\n",
+    "    ax  = fig.add_subplot(111)\n",
+    "    ax.imshow(C, interpolation='nearest', cmap=plt.cm.gray)\n",
+    "\n",
+    "    # Draw the grid boxes\n",
+    "    ax.set_xlim(-0.5,2.5)\n",
+    "    ax.set_ylim(2.5,-0.5)\n",
+    "    ax.plot([-0.5,2.5],[0.5,0.5], '-k', lw=2)\n",
+    "    ax.plot([-0.5,2.5],[1.5,1.5], '-k', lw=2)\n",
+    "    ax.plot([0.5,0.5],[-0.5,2.5], '-k', lw=2)\n",
+    "    ax.plot([1.5,1.5],[-0.5,2.5], '-k', lw=2)\n",
+    "\n",
+    "    # Set xlabels\n",
+    "    ax.set_xlabel('Predicted Label', fontsize=16)\n",
+    "    ax.set_xticks([0,1,2])\n",
+    "    ax.set_xticklabels(class_labels + [''])\n",
+    "    ax.xaxis.set_label_position('top')\n",
+    "    ax.xaxis.tick_top()\n",
+    "    # These coordinate might require some tinkering. Ditto for y, below.\n",
+    "    ax.xaxis.set_label_coords(0.34,1.06)\n",
+    "\n",
+    "    # Set ylabels\n",
+    "    ax.set_ylabel('True Label', fontsize=16, rotation=90)\n",
+    "    ax.set_yticklabels(class_labels + [''],rotation=90)\n",
+    "    ax.set_yticks([0,1,2])\n",
+    "    ax.yaxis.set_label_coords(-0.09,0.65)\n",
+    "\n",
+    "    # Fill in initial metrics: tp, tn, etc...\n",
+    "    ax.text(0,0,\n",
+    "            'True Neg: %d\\n(Num Neg: %d)'%(tn,NN),\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "    ax.text(0,1,\n",
+    "            'False Neg: %d'%fn,\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "    ax.text(1,0,\n",
+    "            'False Pos: %d'%fp,\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "\n",
+    "    ax.text(1,1,\n",
+    "            'True Pos: %d\\n(Num Pos: %d)'%(tp,NP),\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "    # Fill in secondary metrics: accuracy, true pos rate, etc...\n",
+    "    ax.text(2,0,\n",
+    "            'False Pos Rate: %.2f'%(fp / (fp+tn+0.)),\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "    ax.text(2,1,\n",
+    "            'True Pos Rate: %.2f'%(tp / (tp+fn+0.)),\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "    ax.text(2,2,\n",
+    "            'Accuracy: %.2f'%((tp+tn+0.)/N),\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "    ax.text(0,2,\n",
+    "            'Neg Pre Val: %.2f'%(1-fn/(fn+tn+0.)),\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "\n",
+    "    ax.text(1,2,\n",
+    "            'Pos Pred Val: %.2f'%(tp/(tp+fp+0.)),\n",
+    "            va='center',\n",
+    "            ha='center',\n",
+    "            bbox=dict(fc='w',boxstyle='round,pad=1'))\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 383,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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5SR555BF69OhB/fr1S7mq4tu0aRPr16+nbdu2wUt3fPLJJ6XaWzJr1iySk5O5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\nAOP9HyLsg3ica7EKAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119448898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_confusion_matrix(cm_holdout,\n",
+    "                     class_labels=[\"no readmit\", \"readmit\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 384,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[443,   1],\n",
+       "       [155,   1]])"
+      ]
+     },
+     "execution_count": 384,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cm_holdout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 385,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([598,   2])"
+      ]
+     },
+     "execution_count": 385,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(cm_holdout)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 388,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0016666666666666668"
+      ]
+     },
+     "execution_count": 388,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1/600"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 389,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.25833333333333336"
+      ]
+     },
+     "execution_count": 389,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "155/600"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 387,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "201"
+      ]
+     },
+     "execution_count": 387,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "157+42+1+1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 594,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6000.0"
+      ]
+     },
+     "execution_count": 594,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1200*.4 + 9200*.6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 708,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6000.0"
+      ]
+     },
+     "execution_count": 708,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1200+.6*8000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 625,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[   0, 1200],\n",
+       "       [8000, 6000]])"
+      ]
+     },
+     "execution_count": 625,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cost = np.array([[0, 1200], [8000, 6000]])\n",
+    "cost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 626,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.73833333,  0.00166667],\n",
+       "       [ 0.25833333,  0.00166667]])"
+      ]
+     },
+     "execution_count": 626,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cm_percentages = cm_holdout/600\n",
+    "cm_percentages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 627,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ppl = cm_percentages*4382"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 773,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 3235.37666667,     7.30333333],\n",
+       "       [ 1132.01666667,     7.30333333]])"
+      ]
+     },
+     "execution_count": 773,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ppl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 774,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  0.00000000e+00,   8.76400000e+03],\n",
+       "       [  9.05613333e+06,   4.38200000e+04]])"
+      ]
+     },
+     "execution_count": 774,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ppl*cost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 775,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9108717.3333333358"
+      ]
+     },
+     "execution_count": 775,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(sum(ppl*cost))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 776,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "no_care_tracker = 998*8000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 777,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quadratic_discriminant_analysis = 6037465.6934306575"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 802,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10058400"
+      ]
+     },
+     "execution_count": 802,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "4382 * 1200 + 600*8000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 940,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>female</th>\n",
+       "      <th>flu_season</th>\n",
+       "      <th>ed_admit</th>\n",
+       "      <th>severity score</th>\n",
+       "      <th>comorbidity score</th>\n",
+       "      <th>readmit30</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4377</th>\n",
+       "      <td>88</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4378</th>\n",
+       "      <td>98</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>51</td>\n",
+       "      <td>136</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4379</th>\n",
+       "      <td>84</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4380</th>\n",
+       "      <td>67</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>73</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4381</th>\n",
+       "      <td>79</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>92</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      age  female  flu_season  ed_admit  severity score  comorbidity score  \\\n",
+       "4377   88       1           0         1              34                 94   \n",
+       "4378   98       0           0         1              51                136   \n",
+       "4379   84       1           0         1              10                 32   \n",
+       "4380   67       1           1         1              10                 73   \n",
+       "4381   79       1           0         1              20                 92   \n",
+       "\n",
+       "      readmit30  \n",
+       "4377          0  \n",
+       "4378          0  \n",
+       "4379          0  \n",
+       "4380          0  \n",
+       "4381          0  "
+      ]
+     },
+     "execution_count": 940,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# admissions over last year for patients with AMI\n",
+    "# severity score - generic physiologic severity of illness score based on lab tests and vital signs\n",
+    "# comorbidity score - severity score based on patients' pre-existing diagnoses\n",
+    "# readmit30 - indicator for hospital readmission within 30 day\n",
+    "\n",
+    "df = pd.read_csv('Final Project Data_Case.csv').dropna()\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 941,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({0: 3384, 1: 998})"
+      ]
+     },
+     "execution_count": 941,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Counter(df['readmit30'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 942,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: (3286, 6)\n",
+      "y_train shape: (3286,)\n",
+      "X_test shape: (1096, 6)\n",
+      "y_test shape: (1096,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = df.ix[:,:6]\n",
+    "y = df['readmit30']\n",
+    "\n",
+    "# default 75-25 train test split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, random_state=171)\n",
+    "\n",
+    "print(\"X_train shape: {}\".format(X_train.shape))\n",
+    "print(\"y_train shape: {}\".format(y_train.shape))\n",
+    "print(\"X_test shape: {}\".format(X_test.shape))\n",
+    "print(\"y_test shape: {}\".format(y_test.shape))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 943,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set score: 0.787\n",
+      "Test set score: 0.83\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Quadratic Discriminant Analysis\n",
+    "# A classifier with a quadratic decision boundary, generated by fitting class conditional densities \n",
+    "# to the data and using Bayes’ rule.\n",
+    "# The model fits a Gaussian density to each class.\n",
+    "\n",
+    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
+    "\n",
+    "clf = QuadraticDiscriminantAnalysis()\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = clf.predict(X_test)\n",
+    "print(\"Training set score: {:.3f}\".format(clf.score(X_train, y_train)))\n",
+    "print(\"Test set score: {:.2f}\".format(clf.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 944,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[816,  52],\n",
+       "       [136,  92]])"
+      ]
+     },
+     "execution_count": 944,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cm_train = confusion_matrix(y_test,\n",
+    "                            clf.predict(X_test))\n",
+    "cm_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 945,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZvHgxxhhGjRpF9erVMcb4JoisVq0aGRkZZGZmFhkcKOXvt99+01pcZ+jIkSNO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\niAAAgPEIRAAAwHj/H6CDY62nDLw7AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1121174a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_confusion_matrix(cm_train,\n",
+    "                     class_labels=[\"no readmit\", \"readmit\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 946,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6804940.1459854022"
+      ]
+     },
+     "execution_count": 946,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_population = sum(sum(cm_train))\n",
+    "cm_percentages = cm_train/train_population\n",
+    "all_patients = cm_percentages * 4381\n",
+    "total_cost_with_model = sum(sum(all_patients * cost))\n",
+    "total_cost_with_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 947,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 3261.76642336,   207.85766423],\n",
+       "       [  543.62773723,   367.74817518]])"
+      ]
+     },
+     "execution_count": 947,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_patients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 948,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7984000"
+      ]
+     },
+     "execution_count": 948,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# no care tracker at all\n",
+    "8000*998"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 949,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1179059.8540145978"
+      ]
+     },
+     "execution_count": 949,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# savings\n",
+    "savings = 7984000 - total_cost_with_model\n",
+    "savings\n",
+    "\n",
+    "# depending on the random state of the train-test split, we could have 80k or 1.17M"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 883,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# 4382-998\n",
+    "\n",
+    "# perfect = np.array([[ 3384,  0],\n",
+    "#        [ 0,  998]])\n",
+    "\n",
+    "# cost\n",
+    "# .6*998\n",
+    "# 998*1200 + 598.8*8000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 908,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def try_different_random_states():\n",
+    "    X = df.ix[:,:6]\n",
+    "    y = df['readmit30']\n",
+    "\n",
+    "    # default 75-25 train test split\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(\n",
+    "        X, y)\n",
+    "\n",
+    "#     print(\"X_train shape: {}\".format(X_train.shape))\n",
+    "#     print(\"y_train shape: {}\".format(y_train.shape))\n",
+    "#     print(\"X_test shape: {}\".format(X_test.shape))\n",
+    "#     print(\"y_test shape: {}\".format(y_test.shape))\n",
+    "\n",
+    "    # Quadratic Discriminant Analysis\n",
+    "    # A classifier with a quadratic decision boundary, generated by fitting class conditional densities \n",
+    "    # to the data and using Bayes’ rule.\n",
+    "    # The model fits a Gaussian density to each class.\n",
+    "\n",
+    "    from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
+    "\n",
+    "    clf = QuadraticDiscriminantAnalysis()\n",
+    "    clf.fit(X_train, y_train)\n",
+    "\n",
+    "    y_pred = clf.predict(X_test)\n",
+    "#     print(\"Training set score: {:.3f}\".format(clf.score(X_train, y_train)))\n",
+    "#     print(\"Test set score: {:.2f}\".format(clf.score(X_test, y_test)))\n",
+    "\n",
+    "    cm_train = confusion_matrix(y_test,\n",
+    "                                clf.predict(X_test))\n",
+    "\n",
+    "    train_population = sum(sum(cm_train))\n",
+    "    cm_percentages = cm_train/train_population\n",
+    "    all_patients = cm_percentages * 4381\n",
+    "    total_cost_with_model = sum(sum(all_patients * cost))\n",
+    "    total_cost_with_model\n",
+    "\n",
+    "    # savings\n",
+    "    savings = 7984000 - total_cost_with_model\n",
+    "    return savings\n",
+    "\n",
+    "# depending on the random state of the train-test split, we could have 80k or 1.17M\n",
+    "\n",
+    "v=[]\n",
+    "for i in range(10000):\n",
+    "    v.append(try_different_random_states())\n",
+    "    \n",
+    "# print(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 909,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "count    1.000000e+04\n",
+      "mean     4.094681e+05\n",
+      "std      3.531484e+05\n",
+      "min     -1.037023e+06\n",
+      "25%      1.701507e+05\n",
+      "50%      4.179810e+05\n",
+      "75%      6.418277e+05\n",
+      "max      1.829814e+06\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pprint\n",
+    "pprint.pprint(pd.core.series.Series(v).describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 910,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "possible_savings = pd.core.series.Series(v).describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 911,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1037022.627737226"
+      ]
+     },
+     "execution_count": 911,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(pd.core.series.Series(v).describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 912,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1829814.2335766423"
+      ]
+     },
+     "execution_count": 912,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(pd.core.series.Series(v).describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 913,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "349420.95641262305"
+      ]
+     },
+     "execution_count": 913,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(pd.core.series.Series(v).describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 914,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "737814.0206160117"
+      ]
+     },
+     "execution_count": 914,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.std(pd.core.series.Series(v).describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 921,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x11a54bd30>"
+      ]
+     },
+     "execution_count": 921,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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wDnozs8I56M3MCuegNzMrnIPezKxwDnozs8I56M3MCuegNzMrnIPezKxwDnozs8I56M3M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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1120e22b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(pd.core.series.Series(v), color = 'g')\n",
+    "plt.title('Estimated Savings from 10k different splits')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 950,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ROC AUC: 0.78\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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JCYlORxMJC760HROAG4DbgRrAY8BLwJXAe8D5/gwo4nG0Q7G1nYRI6YuKiuL3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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11fa4b400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_curve, auc\n",
+    "\n",
+    "probabilities = clf.predict_proba(X_test)[:, 1]\n",
+    "\n",
+    "fpr,tpr,threshold = roc_curve(y_test, probabilities)\n",
+    "roc_auc = auc(fpr, tpr)\n",
+    "print (\"ROC AUC: %0.2f\" % roc_auc)\n",
+    "\n",
+    "plt.figure(figsize=(10, 7))\n",
+    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
+    "plt.xlabel(\"False Positive Rate (1 - Specificity)\")\n",
+    "plt.ylabel(\"True Positive Rate (Sensitivity, Recall)\")\n",
+    "plt.plot([0, 1], [0, 1], 'k--')\n",
+    "plt.title(\"ROC Curve\")\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test set score: 0.80\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Test set score: {:.2f}\".format(.80))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/.ipynb_checkpoints/Modeling Customer Relationships as Markov Chains-checkpoint.ipynb b/.ipynb_checkpoints/Modeling Customer Relationships as Markov Chains-checkpoint.ipynb
index ca8ae10..87b8c2c 100644
--- a/.ipynb_checkpoints/Modeling Customer Relationships as Markov Chains-checkpoint.ipynb	
+++ b/.ipynb_checkpoints/Modeling Customer Relationships as Markov Chains-checkpoint.ipynb	
@@ -407,7 +407,7 @@
     "    \"\"\"\n",
     "\n",
     "    new_matrix = []\n",
-    "\n",
+    "    \n",
     "    for row in continuous_tuple_matrix:\n",
     "        new_row = []\n",
     "\n",
@@ -2268,9 +2268,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda root]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-root-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2282,7 +2282,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/.ipynb_checkpoints/price optimization of financial services-checkpoint.ipynb b/.ipynb_checkpoints/price optimization of financial services-checkpoint.ipynb
new file mode 100644
index 0000000..d6aab85
--- /dev/null
+++ b/.ipynb_checkpoints/price optimization of financial services-checkpoint.ipynb	
@@ -0,0 +1,413 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Tier</th>\n",
+       "      <th>FICO</th>\n",
+       "      <th>Approve Date</th>\n",
+       "      <th>Term</th>\n",
+       "      <th>Amount</th>\n",
+       "      <th>Previous Rate</th>\n",
+       "      <th>Car  Type</th>\n",
+       "      <th>Competition rate</th>\n",
+       "      <th>Outcome</th>\n",
+       "      <th>Rate</th>\n",
+       "      <th>Cost of Funds</th>\n",
+       "      <th>Partner Bin</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>695.0</td>\n",
+       "      <td>7/1/02</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>35000.0</td>\n",
+       "      <td></td>\n",
+       "      <td>N</td>\n",
+       "      <td>6.25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.49</td>\n",
+       "      <td>1.8388</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>751.0</td>\n",
+       "      <td>7/1/02</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>40000.0</td>\n",
+       "      <td></td>\n",
+       "      <td>N</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.49</td>\n",
+       "      <td>1.8388</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>731.0</td>\n",
+       "      <td>7/1/02</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>18064.0</td>\n",
+       "      <td></td>\n",
+       "      <td>N</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.49</td>\n",
+       "      <td>1.8388</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>652.0</td>\n",
+       "      <td>7/1/02</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>15415.0</td>\n",
+       "      <td></td>\n",
+       "      <td>N</td>\n",
+       "      <td>6.25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.99</td>\n",
+       "      <td>1.8388</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>730.0</td>\n",
+       "      <td>7/1/02</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>32000.0</td>\n",
+       "      <td></td>\n",
+       "      <td>N</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.49</td>\n",
+       "      <td>1.8388</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Tier   FICO Approve Date  Term   Amount Previous Rate Car  Type  \\\n",
+       "0   3.0  695.0       7/1/02  72.0  35000.0                       N   \n",
+       "1   1.0  751.0       7/1/02  60.0  40000.0                       N   \n",
+       "2   1.0  731.0       7/1/02  60.0  18064.0                       N   \n",
+       "3   4.0  652.0       7/1/02  72.0  15415.0                       N   \n",
+       "4   1.0  730.0       7/1/02  48.0  32000.0                       N   \n",
+       "\n",
+       "   Competition rate  Outcome  Rate  Cost of Funds  Partner Bin  \n",
+       "0              6.25      0.0  7.49         1.8388          1.0  \n",
+       "1              5.65      0.0  5.49         1.8388          3.0  \n",
+       "2              5.65      0.0  5.49         1.8388          3.0  \n",
+       "3              6.25      0.0  8.99         1.8388          3.0  \n",
+       "4              5.65      0.0  5.49         1.8388          1.0  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = pd.read_csv('nomis_data/Nomis Solutions_Data Part I.csv')\n",
+    "df1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0    162298\n",
+       "1.0     45787\n",
+       "Name: Outcome, dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1['Outcome'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Tier</th>\n",
+       "      <th>FICO</th>\n",
+       "      <th>Approve Date</th>\n",
+       "      <th>Term</th>\n",
+       "      <th>Amount</th>\n",
+       "      <th>Previous Rate</th>\n",
+       "      <th>Car  Type</th>\n",
+       "      <th>Competition Rate</th>\n",
+       "      <th>Outcome</th>\n",
+       "      <th>Rate</th>\n",
+       "      <th>Cost of Funds</th>\n",
+       "      <th>Partner Bin</th>\n",
+       "      <th>Unnamed: 12</th>\n",
+       "      <th>Unnamed: 13</th>\n",
+       "      <th>Unnamed: 14</th>\n",
+       "      <th>Unnamed: 15</th>\n",
+       "      <th>Unnamed: 16</th>\n",
+       "      <th>Unnamed: 17</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>702</td>\n",
+       "      <td>7/2/02</td>\n",
+       "      <td>60</td>\n",
+       "      <td>22000</td>\n",
+       "      <td></td>\n",
+       "      <td>U</td>\n",
+       "      <td>5.85</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.19</td>\n",
+       "      <td>1.84</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Term</td>\n",
+       "      <td>60</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>710</td>\n",
+       "      <td>7/3/02</td>\n",
+       "      <td>60</td>\n",
+       "      <td>21000</td>\n",
+       "      <td></td>\n",
+       "      <td>U</td>\n",
+       "      <td>5.85</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.19</td>\n",
+       "      <td>1.84</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Car Type</td>\n",
+       "      <td>U</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>693</td>\n",
+       "      <td>7/6/02</td>\n",
+       "      <td>60</td>\n",
+       "      <td>19598</td>\n",
+       "      <td></td>\n",
+       "      <td>U</td>\n",
+       "      <td>5.85</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.29</td>\n",
+       "      <td>1.84</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Min FICO</td>\n",
+       "      <td>684</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>696</td>\n",
+       "      <td>7/6/02</td>\n",
+       "      <td>60</td>\n",
+       "      <td>23071</td>\n",
+       "      <td></td>\n",
+       "      <td>U</td>\n",
+       "      <td>5.85</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.29</td>\n",
+       "      <td>1.84</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Max FICO</td>\n",
+       "      <td>712</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>697</td>\n",
+       "      <td>7/8/02</td>\n",
+       "      <td>60</td>\n",
+       "      <td>21578</td>\n",
+       "      <td></td>\n",
+       "      <td>U</td>\n",
+       "      <td>5.80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.29</td>\n",
+       "      <td>1.84</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Min Amnt</td>\n",
+       "      <td>17800</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Tier  FICO Approve Date  Term  Amount Previous Rate Car  Type  \\\n",
+       "0     2   702       7/2/02    60   22000                       U   \n",
+       "1     2   710       7/3/02    60   21000                       U   \n",
+       "2     3   693       7/6/02    60   19598                       U   \n",
+       "3     3   696       7/6/02    60   23071                       U   \n",
+       "4     3   697       7/8/02    60   21578                       U   \n",
+       "\n",
+       "   Competition Rate  Outcome  Rate  Cost of Funds  Partner Bin  Unnamed: 12  \\\n",
+       "0              5.85        0  6.19           1.84            3          NaN   \n",
+       "1              5.85        0  6.19           1.84            1          NaN   \n",
+       "2              5.85        1  7.29           1.84            1          NaN   \n",
+       "3              5.85        0  7.29           1.84            3          NaN   \n",
+       "4              5.80        1  7.29           1.84            2          NaN   \n",
+       "\n",
+       "  Unnamed: 13 Unnamed: 14  Unnamed: 15  Unnamed: 16  Unnamed: 17  \n",
+       "0        Term          60          NaN          NaN          NaN  \n",
+       "1    Car Type           U          NaN          NaN          NaN  \n",
+       "2    Min FICO         684          NaN          NaN          NaN  \n",
+       "3    Max FICO         712          NaN          NaN          NaN  \n",
+       "4    Min Amnt       17800          NaN          NaN          NaN  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = pd.read_csv('nomis_data/Nomis Solutions Part II.csv', header=1)\n",
+    "\n",
+    "# Term\t60\n",
+    "# Car Type\tU\n",
+    "# Min FICO\t684\n",
+    "# Max FICO\t712\n",
+    "# Min Amnt\t17800\n",
+    "# Max Amnt\t25000\n",
+    "\t\n",
+    "# Total good obs\t1540\n",
+    "\n",
+    "df2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    996\n",
+       "1    544\n",
+       "Name: Outcome, dtype: int64"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2['Outcome'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/.ipynb_checkpoints/purchases-checkpoint.ipynb b/.ipynb_checkpoints/purchases-checkpoint.ipynb
new file mode 100644
index 0000000..fdc8f4d
--- /dev/null
+++ b/.ipynb_checkpoints/purchases-checkpoint.ipynb
@@ -0,0 +1,2603 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>purchase_amount</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>customer_id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>71.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>115.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>50.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>68.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>50.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>16.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>15.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>77.857143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>15.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>34.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>27.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>29.444444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>60.833333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>31.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>75.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>183.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>690</th>\n",
+       "      <td>20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>710</th>\n",
+       "      <td>27.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>760</th>\n",
+       "      <td>38.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>780</th>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>800</th>\n",
+       "      <td>101.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>820</th>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>830</th>\n",
+       "      <td>48.571429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263290</th>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263300</th>\n",
+       "      <td>45.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263310</th>\n",
+       "      <td>60.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263320</th>\n",
+       "      <td>45.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263340</th>\n",
+       "      <td>500.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263350</th>\n",
+       "      <td>200.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263360</th>\n",
+       "      <td>20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263370</th>\n",
+       "      <td>15.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263380</th>\n",
+       "      <td>45.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263390</th>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263400</th>\n",
+       "      <td>15.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263410</th>\n",
+       "      <td>22.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263420</th>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263430</th>\n",
+       "      <td>50.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263460</th>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263470</th>\n",
+       "      <td>1000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263490</th>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263500</th>\n",
+       "      <td>20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263510</th>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263540</th>\n",
+       "      <td>10.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263590</th>\n",
+       "      <td>60.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263600</th>\n",
+       "      <td>16.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263610</th>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263630</th>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263660</th>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263820</th>\n",
+       "      <td>10.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263870</th>\n",
+       "      <td>50.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263880</th>\n",
+       "      <td>20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263890</th>\n",
+       "      <td>54.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264200</th>\n",
+       "      <td>500.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>18417 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             purchase_amount\n",
+       "customer_id                 \n",
+       "10                 30.000000\n",
+       "80                 71.428571\n",
+       "90                115.800000\n",
+       "120                20.000000\n",
+       "130                50.000000\n",
+       "160                30.000000\n",
+       "190                68.000000\n",
+       "220                25.000000\n",
+       "230                50.000000\n",
+       "240                16.250000\n",
+       "260                15.000000\n",
+       "280                20.000000\n",
+       "290                77.857143\n",
+       "320                15.000000\n",
+       "330                34.000000\n",
+       "400                27.500000\n",
+       "410                29.444444\n",
+       "480                60.833333\n",
+       "490                31.000000\n",
+       "500                75.000000\n",
+       "510                30.000000\n",
+       "520               183.333333\n",
+       "690                20.000000\n",
+       "710                27.500000\n",
+       "760                38.000000\n",
+       "770                30.000000\n",
+       "780                25.000000\n",
+       "800               101.666667\n",
+       "820                30.000000\n",
+       "830                48.571429\n",
+       "...                      ...\n",
+       "263290             30.000000\n",
+       "263300             45.000000\n",
+       "263310             60.000000\n",
+       "263320             45.000000\n",
+       "263340            500.000000\n",
+       "263350            200.000000\n",
+       "263360             20.000000\n",
+       "263370             15.000000\n",
+       "263380             45.000000\n",
+       "263390             25.000000\n",
+       "263400             15.000000\n",
+       "263410             22.000000\n",
+       "263420             30.000000\n",
+       "263430             50.000000\n",
+       "263460             30.000000\n",
+       "263470           1000.000000\n",
+       "263490             30.000000\n",
+       "263500             20.000000\n",
+       "263510             30.000000\n",
+       "263540             10.000000\n",
+       "263590             60.000000\n",
+       "263600             16.000000\n",
+       "263610            100.000000\n",
+       "263630             30.000000\n",
+       "263660             30.000000\n",
+       "263820             10.000000\n",
+       "263870             50.000000\n",
+       "263880             20.000000\n",
+       "263890             54.000000\n",
+       "264200            500.000000\n",
+       "\n",
+       "[18417 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('purchases.txt', '\\t', names=['customer_id','purchase_amount','date_of_purchase', 'purchase_year'])\n",
+    "df['purchase_year'] = df.date_of_purchase.apply(lambda x: (x[:4]))\n",
+    "\n",
+    "# pd.pivot_table(df.customer_id, df.purchase_amount) #, rownames=['customer_id'], colnames=['purchase_amount'])\n",
+    "pd.pivot_table(df, values='purchase_amount', index='customer_id').to_frame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>purchase_year</th>\n",
+       "      <th>2005</th>\n",
+       "      <th>2006</th>\n",
+       "      <th>2007</th>\n",
+       "      <th>2008</th>\n",
+       "      <th>2009</th>\n",
+       "      <th>2010</th>\n",
+       "      <th>2011</th>\n",
+       "      <th>2012</th>\n",
+       "      <th>2013</th>\n",
+       "      <th>2014</th>\n",
+       "      <th>2015</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>customer_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>690</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>710</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>760</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>780</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>800</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>820</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>830</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263290</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263300</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263310</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263320</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263340</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263350</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263360</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263370</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263380</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263390</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263400</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263410</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263420</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263430</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263460</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263470</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263490</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263500</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263510</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263540</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263590</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263600</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263610</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263630</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263660</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263820</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263870</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263880</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263890</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264200</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>18417 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "purchase_year  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  \\\n",
+       "customer_id                                                                 \n",
+       "10                1     0     0     0     0     0     0     0     0     0   \n",
+       "80                1     0     1     0     1     0     1     0     1     1   \n",
+       "90                1     1     1     1     1     1     1     1     1     0   \n",
+       "120               0     0     0     0     0     0     0     1     0     0   \n",
+       "130               1     0     1     0     0     0     0     0     0     0   \n",
+       "160               0     1     1     0     0     0     0     0     0     0   \n",
+       "190               1     1     1     1     1     0     0     0     0     0   \n",
+       "220               1     0     0     0     0     1     0     0     0     0   \n",
+       "230               1     0     0     0     0     0     0     0     0     0   \n",
+       "240               1     0     1     0     0     0     0     0     1     1   \n",
+       "260               1     0     0     0     0     0     0     0     0     0   \n",
+       "280               1     0     0     0     0     1     0     0     0     0   \n",
+       "290               1     0     1     1     1     1     1     0     1     0   \n",
+       "320               1     0     0     0     0     0     0     0     0     0   \n",
+       "330               1     1     1     0     1     0     0     1     0     0   \n",
+       "400               1     0     0     0     0     1     0     0     0     0   \n",
+       "410               1     1     1     1     1     1     1     1     0     0   \n",
+       "480               1     1     1     1     1     1     1     1     1     1   \n",
+       "490               1     1     0     0     0     0     0     0     0     0   \n",
+       "500               1     0     1     0     0     0     0     0     0     0   \n",
+       "510               1     0     0     0     0     0     0     0     0     0   \n",
+       "520               0     0     0     1     1     1     0     0     0     0   \n",
+       "690               1     0     1     0     1     1     0     0     0     0   \n",
+       "710               1     0     1     1     1     1     0     0     0     0   \n",
+       "760               0     1     1     1     1     0     0     1     0     0   \n",
+       "770               1     0     0     0     1     0     0     0     0     0   \n",
+       "780               1     0     0     0     0     0     0     0     0     0   \n",
+       "800               1     0     0     0     0     0     1     0     0     0   \n",
+       "820               1     0     0     0     0     0     0     0     0     0   \n",
+       "830               1     0     1     0     1     0     0     1     1     1   \n",
+       "...             ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
+       "263290            0     0     0     0     0     0     0     0     0     0   \n",
+       "263300            0     0     0     0     0     0     0     0     0     0   \n",
+       "263310            0     0     0     0     0     0     0     0     0     0   \n",
+       "263320            0     0     0     0     0     0     0     0     0     0   \n",
+       "263340            0     0     0     0     0     0     0     0     0     0   \n",
+       "263350            0     0     0     0     0     0     0     0     0     0   \n",
+       "263360            0     0     0     0     0     0     0     0     0     0   \n",
+       "263370            0     0     0     0     0     0     0     0     0     0   \n",
+       "263380            0     0     0     0     0     0     0     0     0     0   \n",
+       "263390            0     0     0     0     0     0     0     0     0     0   \n",
+       "263400            0     0     0     0     0     0     0     0     0     0   \n",
+       "263410            0     0     0     0     0     0     0     0     0     0   \n",
+       "263420            0     0     0     0     0     0     0     0     0     0   \n",
+       "263430            0     0     0     0     0     0     0     0     0     0   \n",
+       "263460            0     0     0     0     0     0     0     0     0     0   \n",
+       "263470            0     0     0     0     0     0     0     0     0     0   \n",
+       "263490            0     0     0     0     0     0     0     0     0     0   \n",
+       "263500            0     0     0     0     0     0     0     0     0     0   \n",
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+       "263630            0     0     0     0     0     0     0     0     0     0   \n",
+       "263660            0     0     0     0     0     0     0     0     0     0   \n",
+       "263820            0     0     0     0     0     0     0     0     0     0   \n",
+       "263870            0     0     0     0     0     0     0     0     0     0   \n",
+       "263880            0     0     0     0     0     0     0     0     0     0   \n",
+       "263890            0     0     0     0     0     0     0     0     0     0   \n",
+       "264200            0     0     0     0     0     0     0     0     0     0   \n",
+       "\n",
+       "purchase_year  2015  \n",
+       "customer_id          \n",
+       "10                0  \n",
+       "80                1  \n",
+       "90                0  \n",
+       "120               0  \n",
+       "130               0  \n",
+       "160               0  \n",
+       "190               0  \n",
+       "220               0  \n",
+       "230               0  \n",
+       "240               0  \n",
+       "260               0  \n",
+       "280               0  \n",
+       "290               0  \n",
+       "320               0  \n",
+       "330               0  \n",
+       "400               0  \n",
+       "410               0  \n",
+       "480               1  \n",
+       "490               0  \n",
+       "500               0  \n",
+       "510               0  \n",
+       "520               0  \n",
+       "690               0  \n",
+       "710               0  \n",
+       "760               0  \n",
+       "770               0  \n",
+       "780               0  \n",
+       "800               0  \n",
+       "820               0  \n",
+       "830               1  \n",
+       "...             ...  \n",
+       "263290            1  \n",
+       "263300            1  \n",
+       "263310            1  \n",
+       "263320            1  \n",
+       "263340            1  \n",
+       "263350            1  \n",
+       "263360            1  \n",
+       "263370            1  \n",
+       "263380            1  \n",
+       "263390            1  \n",
+       "263400            1  \n",
+       "263410            1  \n",
+       "263420            1  \n",
+       "263430            1  \n",
+       "263460            1  \n",
+       "263470            1  \n",
+       "263490            1  \n",
+       "263500            1  \n",
+       "263510            1  \n",
+       "263540            1  \n",
+       "263590            1  \n",
+       "263600            1  \n",
+       "263610            1  \n",
+       "263630            1  \n",
+       "263660            1  \n",
+       "263820            1  \n",
+       "263870            1  \n",
+       "263880            1  \n",
+       "263890            1  \n",
+       "264200            1  \n",
+       "\n",
+       "[18417 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "purchases = pd.crosstab(df.customer_id, [df.purchase_year], rownames=['customer_id'], colnames=['purchase_year'])\n",
+    "purchases = purchases.applymap(lambda x: 1 if x>0 else 0)\n",
+    "purchases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>purchase_year</th>\n",
+       "      <th>2005</th>\n",
+       "      <th>2006</th>\n",
+       "      <th>2007</th>\n",
+       "      <th>2008</th>\n",
+       "      <th>2009</th>\n",
+       "      <th>2010</th>\n",
+       "      <th>2011</th>\n",
+       "      <th>2012</th>\n",
+       "      <th>2013</th>\n",
+       "      <th>2014</th>\n",
+       "      <th>2015</th>\n",
+       "      <th>last_purchase_year</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>customer_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>510</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>690</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>710</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>760</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>780</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>800</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>820</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>830</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263290</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263300</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263310</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263320</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263340</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263350</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263360</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263370</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263380</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263390</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263400</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263410</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263420</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263430</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263460</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263470</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263490</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263500</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263510</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263540</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263590</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263600</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263610</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263630</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263660</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263820</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263870</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263880</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263890</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264200</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>18417 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "purchase_year  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  \\\n",
+       "customer_id                                                                 \n",
+       "10                1     0     0     0     0     0     0     0     0     0   \n",
+       "80                1     0     1     0     1     0     1     0     1     1   \n",
+       "90                1     1     1     1     1     1     1     1     1     0   \n",
+       "120               0     0     0     0     0     0     0     1     0     0   \n",
+       "130               1     0     1     0     0     0     0     0     0     0   \n",
+       "160               0     1     1     0     0     0     0     0     0     0   \n",
+       "190               1     1     1     1     1     0     0     0     0     0   \n",
+       "220               1     0     0     0     0     1     0     0     0     0   \n",
+       "230               1     0     0     0     0     0     0     0     0     0   \n",
+       "240               1     0     1     0     0     0     0     0     1     1   \n",
+       "260               1     0     0     0     0     0     0     0     0     0   \n",
+       "280               1     0     0     0     0     1     0     0     0     0   \n",
+       "290               1     0     1     1     1     1     1     0     1     0   \n",
+       "320               1     0     0     0     0     0     0     0     0     0   \n",
+       "330               1     1     1     0     1     0     0     1     0     0   \n",
+       "400               1     0     0     0     0     1     0     0     0     0   \n",
+       "410               1     1     1     1     1     1     1     1     0     0   \n",
+       "480               1     1     1     1     1     1     1     1     1     1   \n",
+       "490               1     1     0     0     0     0     0     0     0     0   \n",
+       "500               1     0     1     0     0     0     0     0     0     0   \n",
+       "510               1     0     0     0     0     0     0     0     0     0   \n",
+       "520               0     0     0     1     1     1     0     0     0     0   \n",
+       "690               1     0     1     0     1     1     0     0     0     0   \n",
+       "710               1     0     1     1     1     1     0     0     0     0   \n",
+       "760               0     1     1     1     1     0     0     1     0     0   \n",
+       "770               1     0     0     0     1     0     0     0     0     0   \n",
+       "780               1     0     0     0     0     0     0     0     0     0   \n",
+       "800               1     0     0     0     0     0     1     0     0     0   \n",
+       "820               1     0     0     0     0     0     0     0     0     0   \n",
+       "830               1     0     1     0     1     0     0     1     1     1   \n",
+       "...             ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
+       "263290            0     0     0     0     0     0     0     0     0     0   \n",
+       "263300            0     0     0     0     0     0     0     0     0     0   \n",
+       "263310            0     0     0     0     0     0     0     0     0     0   \n",
+       "263320            0     0     0     0     0     0     0     0     0     0   \n",
+       "263340            0     0     0     0     0     0     0     0     0     0   \n",
+       "263350            0     0     0     0     0     0     0     0     0     0   \n",
+       "263360            0     0     0     0     0     0     0     0     0     0   \n",
+       "263370            0     0     0     0     0     0     0     0     0     0   \n",
+       "263380            0     0     0     0     0     0     0     0     0     0   \n",
+       "263390            0     0     0     0     0     0     0     0     0     0   \n",
+       "263400            0     0     0     0     0     0     0     0     0     0   \n",
+       "263410            0     0     0     0     0     0     0     0     0     0   \n",
+       "263420            0     0     0     0     0     0     0     0     0     0   \n",
+       "263430            0     0     0     0     0     0     0     0     0     0   \n",
+       "263460            0     0     0     0     0     0     0     0     0     0   \n",
+       "263470            0     0     0     0     0     0     0     0     0     0   \n",
+       "263490            0     0     0     0     0     0     0     0     0     0   \n",
+       "263500            0     0     0     0     0     0     0     0     0     0   \n",
+       "263510            0     0     0     0     0     0     0     0     0     0   \n",
+       "263540            0     0     0     0     0     0     0     0     0     0   \n",
+       "263590            0     0     0     0     0     0     0     0     0     0   \n",
+       "263600            0     0     0     0     0     0     0     0     0     0   \n",
+       "263610            0     0     0     0     0     0     0     0     0     0   \n",
+       "263630            0     0     0     0     0     0     0     0     0     0   \n",
+       "263660            0     0     0     0     0     0     0     0     0     0   \n",
+       "263820            0     0     0     0     0     0     0     0     0     0   \n",
+       "263870            0     0     0     0     0     0     0     0     0     0   \n",
+       "263880            0     0     0     0     0     0     0     0     0     0   \n",
+       "263890            0     0     0     0     0     0     0     0     0     0   \n",
+       "264200            0     0     0     0     0     0     0     0     0     0   \n",
+       "\n",
+       "purchase_year  2015  last_purchase_year  \n",
+       "customer_id                              \n",
+       "10                0                   0  \n",
+       "80                1                   1  \n",
+       "90                0                   0  \n",
+       "120               0                   0  \n",
+       "130               0                   0  \n",
+       "160               0                   0  \n",
+       "190               0                   0  \n",
+       "220               0                   0  \n",
+       "230               0                   0  \n",
+       "240               0                   0  \n",
+       "260               0                   0  \n",
+       "280               0                   0  \n",
+       "290               0                   0  \n",
+       "320               0                   0  \n",
+       "330               0                   0  \n",
+       "400               0                   0  \n",
+       "410               0                   0  \n",
+       "480               1                   1  \n",
+       "490               0                   0  \n",
+       "500               0                   0  \n",
+       "510               0                   0  \n",
+       "520               0                   0  \n",
+       "690               0                   0  \n",
+       "710               0                   0  \n",
+       "760               0                   0  \n",
+       "770               0                   0  \n",
+       "780               0                   0  \n",
+       "800               0                   0  \n",
+       "820               0                   0  \n",
+       "830               1                   1  \n",
+       "...             ...                 ...  \n",
+       "263290            1                   1  \n",
+       "263300            1                   1  \n",
+       "263310            1                   1  \n",
+       "263320            1                   1  \n",
+       "263340            1                   1  \n",
+       "263350            1                   1  \n",
+       "263360            1                   1  \n",
+       "263370            1                   1  \n",
+       "263380            1                   1  \n",
+       "263390            1                   1  \n",
+       "263400            1                   1  \n",
+       "263410            1                   1  \n",
+       "263420            1                   1  \n",
+       "263430            1                   1  \n",
+       "263460            1                   1  \n",
+       "263470            1                   1  \n",
+       "263490            1                   1  \n",
+       "263500            1                   1  \n",
+       "263510            1                   1  \n",
+       "263540            1                   1  \n",
+       "263590            1                   1  \n",
+       "263600            1                   1  \n",
+       "263610            1                   1  \n",
+       "263630            1                   1  \n",
+       "263660            1                   1  \n",
+       "263820            1                   1  \n",
+       "263870            1                   1  \n",
+       "263880            1                   1  \n",
+       "263890            1                   1  \n",
+       "264200            1                   1  \n",
+       "\n",
+       "[18417 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# purchases['last_purchase_year'] = purchases['2015']\n",
+    "\n",
+    "# year = 2015\n",
+    "\n",
+    "# purchases['last_purchase_year']\n",
+    "purchases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# for customer in purchases.iterrows():\n",
+    "#     print(type(customer))\n",
+    "#     print(customer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# recency - 1, 2, 3, frequency - 1, 2, 3, monetary 1, 2\n",
+    "\n",
+    "RFM_states = {(1, 1, 1): 1,\n",
+    "              (1, 1, 2): 2,\n",
+    "              (1, 2, 1): 3,\n",
+    "              (1, 2, 2): 4,\n",
+    "              (1, 3, 1): 5,\n",
+    "              (1, 3, 2): 6,\n",
+    "              (2, 1, 1): 7,\n",
+    "              (2, 1, 2): 8,\n",
+    "              (2, 2, 1): 9,\n",
+    "              (2, 2, 2): 10,\n",
+    "              (2, 3, 1): 11,\n",
+    "              (2, 3, 2): 12,\n",
+    "              (3, 1, 1): 13,\n",
+    "              (3, 1, 2): 14,\n",
+    "              (3, 2, 1): 15,\n",
+    "              (3, 2, 2): 16,\n",
+    "              (3, 3, 1): 17,\n",
+    "              (3, 3, 2): 18}\n",
+    "\n",
+    "RFM_states[(1, 1, 2)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Hospital Readmissions - binary classification.ipynb b/Hospital Readmissions - binary classification.ipynb
index 4aeb33a..bb2a1bb 100644
--- a/Hospital Readmissions - binary classification.ipynb	
+++ b/Hospital Readmissions - binary classification.ipynb	
@@ -30,9 +30,7 @@
   {
    "cell_type": "code",
    "execution_count": 716,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -82,7 +80,6 @@
    "cell_type": "code",
    "execution_count": 717,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -193,9 +190,7 @@
   {
    "cell_type": "code",
    "execution_count": 718,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -311,7 +306,6 @@
    "cell_type": "code",
    "execution_count": 719,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -342,7 +336,6 @@
    "cell_type": "code",
    "execution_count": 347,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -364,9 +357,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -387,9 +378,7 @@
   {
    "cell_type": "code",
    "execution_count": 126,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -410,7 +399,6 @@
    "cell_type": "code",
    "execution_count": 720,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -526,7 +514,6 @@
    "cell_type": "code",
    "execution_count": 349,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
@@ -541,7 +528,6 @@
    "cell_type": "code",
    "execution_count": 350,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -564,7 +550,6 @@
    "cell_type": "code",
    "execution_count": 351,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -586,9 +571,7 @@
   {
    "cell_type": "code",
    "execution_count": 352,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -609,7 +592,6 @@
    "cell_type": "code",
    "execution_count": 721,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -652,9 +634,7 @@
   {
    "cell_type": "code",
    "execution_count": 354,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -681,7 +661,6 @@
    "cell_type": "code",
    "execution_count": 355,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -710,7 +689,6 @@
    "cell_type": "code",
    "execution_count": 722,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -803,7 +781,6 @@
    "cell_type": "code",
    "execution_count": 358,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -840,7 +817,6 @@
    "cell_type": "code",
    "execution_count": 359,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -863,9 +839,7 @@
   {
    "cell_type": "code",
    "execution_count": 360,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -886,7 +860,6 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -913,9 +886,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -935,9 +906,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -955,7 +924,6 @@
    "cell_type": "code",
    "execution_count": 55,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -977,9 +945,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -997,7 +963,6 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1020,7 +985,6 @@
    "cell_type": "code",
    "execution_count": 60,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1088,9 +1052,7 @@
   {
    "cell_type": "code",
    "execution_count": 723,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1119,7 +1081,6 @@
    "cell_type": "code",
    "execution_count": 724,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1149,9 +1110,7 @@
   {
    "cell_type": "code",
    "execution_count": 725,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1176,9 +1135,7 @@
   {
    "cell_type": "code",
    "execution_count": 728,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1208,9 +1165,7 @@
   {
    "cell_type": "code",
    "execution_count": 367,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1246,7 +1201,6 @@
    "cell_type": "code",
    "execution_count": 729,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1276,9 +1230,7 @@
   {
    "cell_type": "code",
    "execution_count": 730,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1306,9 +1258,7 @@
   {
    "cell_type": "code",
    "execution_count": 731,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "clf = SVC(random_state=0)\n",
@@ -1339,7 +1289,6 @@
    "cell_type": "code",
    "execution_count": 732,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1362,7 +1311,6 @@
    "cell_type": "code",
    "execution_count": 733,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1411,7 +1359,6 @@
    "cell_type": "code",
    "execution_count": 737,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1467,9 +1414,7 @@
   {
    "cell_type": "code",
    "execution_count": 375,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1502,9 +1447,7 @@
   {
    "cell_type": "code",
    "execution_count": 706,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1524,9 +1467,7 @@
   {
    "cell_type": "code",
    "execution_count": 376,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1546,9 +1487,7 @@
   {
    "cell_type": "code",
    "execution_count": 377,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1568,9 +1507,7 @@
   {
    "cell_type": "code",
    "execution_count": 378,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1596,9 +1533,7 @@
   {
    "cell_type": "code",
    "execution_count": 379,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1624,9 +1559,7 @@
   {
    "cell_type": "code",
    "execution_count": 380,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1652,9 +1585,7 @@
   {
    "cell_type": "code",
    "execution_count": 381,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1905,9 +1836,7 @@
   {
    "cell_type": "code",
    "execution_count": 383,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1929,7 +1858,6 @@
    "cell_type": "code",
    "execution_count": 384,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1953,7 +1881,6 @@
    "cell_type": "code",
    "execution_count": 385,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1976,7 +1903,6 @@
    "cell_type": "code",
    "execution_count": 388,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1998,9 +1924,7 @@
   {
    "cell_type": "code",
    "execution_count": 389,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2021,7 +1945,6 @@
    "cell_type": "code",
    "execution_count": 387,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -2043,9 +1966,7 @@
   {
    "cell_type": "code",
    "execution_count": 594,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2065,9 +1986,7 @@
   {
    "cell_type": "code",
    "execution_count": 708,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2088,7 +2007,6 @@
    "cell_type": "code",
    "execution_count": 625,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -2113,7 +2031,6 @@
    "cell_type": "code",
    "execution_count": 626,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2137,9 +2054,7 @@
   {
    "cell_type": "code",
    "execution_count": 627,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ppl = cm_percentages*4382"
@@ -2148,9 +2063,7 @@
   {
    "cell_type": "code",
    "execution_count": 773,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2171,9 +2084,7 @@
   {
    "cell_type": "code",
    "execution_count": 774,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2194,9 +2105,7 @@
   {
    "cell_type": "code",
    "execution_count": 775,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2217,7 +2126,6 @@
    "cell_type": "code",
    "execution_count": 776,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -2228,9 +2136,7 @@
   {
    "cell_type": "code",
    "execution_count": 777,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "quadratic_discriminant_analysis = 6037465.6934306575"
@@ -2239,9 +2145,7 @@
   {
    "cell_type": "code",
    "execution_count": 802,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2262,7 +2166,6 @@
    "cell_type": "code",
    "execution_count": 940,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2372,9 +2275,7 @@
   {
    "cell_type": "code",
    "execution_count": 941,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2395,7 +2296,6 @@
    "cell_type": "code",
    "execution_count": 942,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2427,9 +2327,7 @@
   {
    "cell_type": "code",
    "execution_count": 943,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2460,7 +2358,6 @@
    "cell_type": "code",
    "execution_count": 944,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -2485,9 +2382,7 @@
   {
    "cell_type": "code",
    "execution_count": 945,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2509,7 +2404,6 @@
    "cell_type": "code",
    "execution_count": 946,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2535,9 +2429,7 @@
   {
    "cell_type": "code",
    "execution_count": 947,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2559,7 +2451,6 @@
    "cell_type": "code",
    "execution_count": 948,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2583,7 +2474,6 @@
    "cell_type": "code",
    "execution_count": 949,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2610,7 +2500,6 @@
    "cell_type": "code",
    "execution_count": 883,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
@@ -2628,9 +2517,7 @@
   {
    "cell_type": "code",
    "execution_count": 908,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def try_different_random_states():\n",
@@ -2686,7 +2573,6 @@
    "cell_type": "code",
    "execution_count": 909,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2725,9 +2611,7 @@
   {
    "cell_type": "code",
    "execution_count": 911,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2747,9 +2631,7 @@
   {
    "cell_type": "code",
    "execution_count": 912,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2769,9 +2651,7 @@
   {
    "cell_type": "code",
    "execution_count": 913,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2791,9 +2671,7 @@
   {
    "cell_type": "code",
    "execution_count": 914,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2813,9 +2691,7 @@
   {
    "cell_type": "code",
    "execution_count": 921,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2846,9 +2722,7 @@
   {
    "cell_type": "code",
    "execution_count": 950,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2891,9 +2765,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2920,9 +2792,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda root]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-root-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2934,7 +2806,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Modeling Customer Relationships as Markov Chains.ipynb b/Modeling Customer Relationships as Markov Chains.ipynb
index ca8ae10..87b8c2c 100644
--- a/Modeling Customer Relationships as Markov Chains.ipynb	
+++ b/Modeling Customer Relationships as Markov Chains.ipynb	
@@ -407,7 +407,7 @@
     "    \"\"\"\n",
     "\n",
     "    new_matrix = []\n",
-    "\n",
+    "    \n",
     "    for row in continuous_tuple_matrix:\n",
     "        new_row = []\n",
     "\n",
@@ -2268,9 +2268,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda root]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-root-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2282,7 +2282,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/price optimization of financial services.ipynb b/price optimization of financial services.ipynb
index 6c4f896..d6aab85 100644
--- a/price optimization of financial services.ipynb	
+++ b/price optimization of financial services.ipynb	
@@ -15,7 +15,6 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -150,9 +149,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -175,7 +172,6 @@
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -363,9 +359,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -397,9 +391,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda root]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-root-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -411,7 +405,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/purchases.ipynb b/purchases.ipynb
index 6537bd2..fdc8f4d 100644
--- a/purchases.ipynb
+++ b/purchases.ipynb
@@ -3,9 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd"
@@ -14,9 +12,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -367,9 +363,7 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1413,7 +1407,6 @@
    "cell_type": "code",
    "execution_count": 98,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2525,7 +2518,6 @@
    "cell_type": "code",
    "execution_count": 102,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -2538,9 +2530,7 @@
   {
    "cell_type": "code",
    "execution_count": 106,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2591,9 +2581,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda root]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-root-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2605,7 +2595,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,