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@TomAugspurger
Created April 3, 2016 15:36
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"\n",
"from ipywidgets import interact\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.grid_search import GridSearchCV\n",
"\n",
"import postlearn as pl"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fit the model as Usual"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = sns.load_dataset('titanic')\n",
"\n",
"clf = RandomForestClassifier()\n",
"param_grid = dict(max_depth=[1, 2, 5, 10, 20, 30, 40],\n",
" min_samples_split=[2, 5, 10],\n",
" min_samples_leaf=[2, 3, 5])\n",
"est = GridSearchCV(clf, param_grid=param_grid, n_jobs=4)\n",
"\n",
"y = df['survived']\n",
"X = df.drop(['survived', 'who', 'alive'], axis=1)\n",
"\n",
"X = pd.get_dummies(X, drop_first=True)\n",
"X = X.fillna(value=X.median())\n",
"est.fit(X, y);"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r = pl.ClassificationResults(est, X, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Useful Attributes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Easy acccess to commonly needed attributes."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.labels"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pclass</th>\n",
" <th>age</th>\n",
" <th>sibsp</th>\n",
" <th>parch</th>\n",
" <th>fare</th>\n",
" <th>adult_male</th>\n",
" <th>alone</th>\n",
" <th>sex_male</th>\n",
" <th>embarked_Q</th>\n",
" <th>embarked_S</th>\n",
" <th>class_Second</th>\n",
" <th>class_Third</th>\n",
" <th>deck_B</th>\n",
" <th>deck_C</th>\n",
" <th>deck_D</th>\n",
" <th>deck_E</th>\n",
" <th>deck_F</th>\n",
" <th>deck_G</th>\n",
" <th>embark_town_Queenstown</th>\n",
" <th>embark_town_Southampton</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.2500</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>71.2833</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>53.1000</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pclass age sibsp parch fare adult_male alone sex_male \\\n",
"0 3 22.0 1 0 7.2500 True False 1.0 \n",
"1 1 38.0 1 0 71.2833 False False 0.0 \n",
"2 3 26.0 0 0 7.9250 False True 0.0 \n",
"3 1 35.0 1 0 53.1000 False False 0.0 \n",
"4 3 35.0 0 0 8.0500 True True 1.0 \n",
"\n",
" embarked_Q embarked_S class_Second class_Third deck_B deck_C deck_D \\\n",
"0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 1.0 0.0 \n",
"2 0.0 1.0 0.0 1.0 0.0 0.0 0.0 \n",
"3 0.0 1.0 0.0 0.0 0.0 1.0 0.0 \n",
"4 0.0 1.0 0.0 1.0 0.0 0.0 0.0 \n",
"\n",
" deck_E deck_F deck_G embark_town_Queenstown embark_town_Southampton \n",
"0 0.0 0.0 0.0 0.0 1.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 1.0 \n",
"3 0.0 0.0 0.0 0.0 1.0 \n",
"4 0.0 0.0 0.0 0.0 1.0 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.X_train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Items that must be computed, like the predicted `y`s are cached."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 0, 1, 0, 0, 0, 0, 0, 1])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.y_pred_train[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Metrics"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>predicted</th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>actual</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>520</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>113</td>\n",
" <td>229</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"predicted 0 1\n",
"actual \n",
"0 520 29\n",
"1 113 229"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.confusion_matrix()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Grid Search Results"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x119b62588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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GkRgaT7gubEQBrdPlJK8un53V+6jpqEWn1jEjJps16SvFz80FQFEU/v5Wkd+AFeBkZRuP\nv13ED2+dd94MiJwPOuydFHtmU481y3TYOwc8V61SE6dJRW1OpLEigupWA71hbRSKw4g29RRqQ29Q\n6uoKw14h4WpNwAFwejrqiGY0sTVoTHWodHbvuQ7FSWFjMYWNxejUWmbFTmdBwlzmxM1Ar9EH4+ML\n5xkx0yqcdzbvLeP1bSWDnmPUa3j42ysx6Mdur9z+4jqeevcYTpfvj+P0jGgeuHkuBp3Yu9dDURQc\nra0oNhvaGBNq3bl/KYmZ1vHjcDn4330PeWfqsk1T+e68+wa8wazvbOSPeY/Q5XDf4MQaTfwk97sX\n7f4mh8tBS3cbWrXmnGcqO+ydVJirqLRUe2dR6zsbURj/H3ujxkhCaCwJofG9Qa0nsA3Vhfp9jd3l\n4B8Fz3OsWfY5plapuWP6TSxLXjSi9oiZ1onhVFUb/+eFg0Oe98svLSQrNWoMWnR+6LB3YrF3EKkP\nJ0Q79NJaRVGo7qilqLGYoqbjlLaVDdovGFVhGLqSaauJpqspGlxDzXUpqMNbQWtHsRtQOiKBAX7F\nVC7UkU1oYmrdAazW4fc0nVpHTtxMFibOZWaMhG4E2cstXXY6uuxEhukJMZz7fJ2YaR0fImgVzju/\nfmYflQ3+i1j3pdOqCTVqMeo0GPVaDHoNxj5/DDpt7//3fc6g8bym53n3eYPNlNY2d/Krp/f5DVh7\nrJ6Xwl1XTh/RZ76QKIpC++6dtHz0IbaqSgDURiORy1cQe+31aCJGvidZBK3jZ1vFLl47+Q4AKlT8\nNPcB0iNSBn1NUWMxjxc85308Iyabb86956LaK9lp72TLmU/ZU5PnTYCUGJrAZekrWZGyZMjgtc1q\npsJcSYW5mkqLO0Bt6m4J+P3VKrV3ljvEGcPHR4vQxg+8LFBRVBjLVvDATfNp6GykrquRhs5G6rsa\nqe9sHDSJ01DCdKGepcY9Aa07uN1dvZ8dVXsG/Qw/XfRd0ob4efNHBK0Tw38+PclHByqGPG99bjq3\nXT5tDFo0sZ1uO8OW0k+9AzkqVOTEzeSqKet8+t1uhxW55ZR72W/TcVqt/mez8VzJYI+lqyEWW3Ms\nSmcEAwadQFJMKFPTosg/2Yilyz7geWnxYazPTae83kJ5rZmyegtWm7PP27pQRzX2BrAap9/raNAx\nKWQai5PmsSRj1pDlt+TyFjbtOkNxmbtP1KhVzJ8WxxdWTiEtPnzQ1w5GBK3jQwStQsC6Hd2cbD2N\n1WElITSe9IjUMVmm09TWjVzRwomKVuSKNuqaB16+EkwatapP0KvtE+hqqGnqpNbTLoPTRnp3HTqX\ngyZ9FPWGGAC0GjUPfXsF4SEX754cRVFoeOVlWj/5yO9xXUIi6T/9Odqo6BFdXwSt46PT3sVv9/7B\nu7RsSdJC7pp5a0Cvfb/0YzaXfux9vH7SGq7Luioo7ZxoLPYOHj70BLUddX6Pr0hZwu3SjahUKhRF\nobm7hYo+s6eV5irabOaA30+n1pISnkx6RCoZ4amkRaSQEpZEq9lBQUkTuwprOFPbhi6rAG1src/r\nFZcKe+kcdOY0/v6D1b7HFYUOe6cngG3oF8zWdzViGySRy7lamryIL824ZdivE0HrxPDs5mJ2FtQM\ned7KnGTuuXrGGLRo4jrSUMTTRS/6XdqvU+v45ty7iTZEe4PUky0lOBT/QSCA2qXH0RKHozUOZ1sc\nOPyvelIBqfHhSOnRZGdEk50eTVSY+9yqBgt/fiWfNovv73hSTCg/vn0+pgiD9zmXolDf0kV5nZmy\nWjNlnv92dDtA5UQd3YgmpgZNdMOAAazi0BFuTWNSiEROgsSUpCiS40LRqN2DnnuP1fLUu8dQcKGO\naEaltaHYjLgsJgx6LT+8ZR5T00Y2ay+C1vER1D2tkiSpgL8Dc4Fu4KuyLJ/uc/wG4BeAC3hOluUn\nJEnSAs8CkwE98L+yLL8bzHYKg3O6nLx7+kN2VO3ulz0uLTyFm7OvY2r06O0dVRSFupYud4Ba3sqJ\nilaa2v1v2h9rTpdCR7fD3animwBKozi5tPEQ89pPold6l7nUGGL5KH4xNcZ45PJWFkrxY9jqiaXz\n2NEBA1YAe30d9S+/RMrXvzmGrRLO1Ydln3kDVp1ax7WZVwT82qsmX06FudJboP6jsq1kRKQxP2FO\nUNo6kbx18v0BA1aAXdX7MNssWJ1WKsxVdA5jFtOoMZAankJGRCrpnj+JofFo1BocThcnK9vYm9dI\nQclBapr6DgSqsZfMxdmcjDa+AnWoGcWlxtUeh6MuA6UrAtQKnxdUs2J2cr+MriqVinB9GOH6MDKj\nJvVrj6IotNvM1Hc2UN/VSENnU29w29WEw+V/aWCgAq0VKUxMsZGBJd2pbeqky+oYlSWe56NOexf/\nOvbKgHvR7S47jx5+CtcQWwFcnRE4W+NxtcbjskTjbzZVrVIxKSmc7PRopHQTU9OiBhx0T40P53f3\nLGZbfjX7i+uwdNmJDjewfHYSq3KSMeq1PtdOigklKSaUxTMSAXcf0dxu9QawZXVmyk60YNFVoYmp\nRR3dgErd+7lVWjsd2lKOUcrRmk9xHktC1ZpCSkg6KbHh7DtWizrhDLqU06h0vfeuru4Q7BUST27S\n8f++vtQb5AoTX7B/668HDLIsL5ckaQnwkOe5Hg8B84BO4JgkSS8DNwCNsizfJUmSCcgHRNA6ThRF\n4Z/HXuZQfYHPsUpLNY8e/gffnvdVppmyRnR9l6JQ3dCBXOEOUE9UtNI2QIKl4YgO1/OtG+dgsznp\ntjux2px0e/84sNrd/2/181zf8wJeiKAoXF+znWmdlT6Hkq1N3FH1ES+lrufA8TqyUiOJDjf4uciF\nr3Xrp0OeYzmUh6O1FW30yGZbhbHV1NXMtoqd3seXZ1yCyej+t3PZbVgOHcRaXoZKoyVk+gxCp89A\n1ecmQa1S8+WZt/HHA49698O+UPwKSWEJJIclju2HGUMd9k7y6vOHPK+g8eiQ54TpQkkP7wlOU0iP\nSCUuJLbfMus2i5XdRXUUlDRxtLSZbtvAMy+gwtWSiK3F/9+/06Xw3ObjfLi/go2XZDJvWtyQq25U\nKhVRhkiiDJE+3xcuxUWrtc09I9vZSIMnmK3vaqSxs2nIG3DgnINeYXylJQS2VPNUVRu/+Mdeblqd\nxbLZSRddlYD9tYfodvoOmvfl7/dFcWpwtcXibIvH2RoPdt9BAq1GxZTkSCTPLGpWStSwBgciQvVc\nkRXC8pZ6nBYL2uhoIqdORasP7BoqlYrYKCOxUUYWZPcO7rdZrJTVWSipbaK4pZhaVwmO0DpU6t7P\nqdLZ0SZUQEIFtbZ8qpoTUU9y+N3qoDZ2YZiWT2uJk4KSacyfdvFOJJxvgh20rgQ+AJBleZ8kSWdn\nSrABJvD+hinAq8BrnsdqYOBF8kLQHWuW/QasPRyKk/+ceJv/WvyDgJYKO10uyuss3gD1REWrZ+Zy\ncDGRBveSlPRoslKjeG5zMaU1Ay+Lu3NdNlkp55asQVEU7A5Xv6DX6glmewJbd6DroHzbLr8Baw+d\n4mR9w36eL44n73gDOVmxrJqbTE5W7EU1ytd9evAEWgC4XHSXnSE8el7wGyScs3dKtniXnkXow1mX\ncSkAloIj1D33NE5zn9/Tze+hT00j5RvfQp+U7H06RBvCfXPu4k8H/4bNacPqtPGPwuf5yaLvBJRc\n5HxUZakeUaAVpY/0zpz2BKgmQ7RP/+tSFEqq2yg41UTB6SbKagdfRhwfbSQnKw5TuIE3d5QwyPZ8\nr+rGDh59s5Cs1EhuujQLKcM07M8D7oGLGKOJGKOJ6TH99yvWdTbwu71/GvIaKWFJI3pvYfwdO9PM\n0+8dC/j8tg4bz7xfzNbDVdy5LpspyZFBbN3EIjeVDn2Sh6s7FFerO0h1mWNA6X+vodepmZoa5ZlJ\njWZKciT6ESaLdNlt1L/wPO27d/V7vvGN1zCtW0/cxlv6DVYOR1S4gZxwAzlZsdxANgANlnZ2lB6i\nsLmIBkcFqPoEsHor2qTyIa+rm1TMyapcEbSeR4IdtEYCfXd8OyRJUsuy3DO//yBwELAAb8qy3N5z\noiRJEbiD118GuY3CIHZV7x/ynNqOOj4q20q2aSpRhggi9RFo1e4fLbvDxZnadu9y35NVbf033w8g\n0RRCtidIlTKiiYvqf+P6g1vn8dR7BRxtL0QTXQ9qJ0p3GPr2KXxx5WIWSgkj+8B9qFQq9DqNuxP3\nn9TSq/izl4e8XrK1iQRrM/WGGPJPNZJ/qpGoMD0r5iSzKieZxJgh3uQ857RYcFkHHyH2urgGz89b\npW3l/eqtbpiyHqPWSOcJmerHHgGn7++6raqSij/9gUm//m2/vcsp4Ul8acYtPFP0IuDOLvyvY69y\n35wvXZCJmVQB/pCrUXNN5npvkBqpHzhRWUe3naOlzRw51URRaRPmzoHHfDVqFdnp0eRkxZKTFUtS\nTKg38E1LCOefW4ppPWt/2oxJJq5bOYVPD1Zy4HhvqZuSqnb+8NJhcrJi2XhpFukBzpoFIjE0numm\naRxvGXz578rUJaP2nsLYOSjX8+Smozic7qBDBahU9Bs00WpUXLYgDbVKxcd5Fd6Eh6er2/n983ms\nnJPMxksziboIVjDVtwS2RcDRkIK9NKffcyEGDdPSor0TAJOSIkatFF/tM09jyfNzv+hy0fLhBwDE\n33zbqLwXQHx4JBvnrGYjq7HYOshvKORAbT4lbaUBZ0lXaR3UcxoQCTLPF8EOWtuBvt+w3oBVkqR0\n4DvAJKAD+LckSRtlWX7Dc+xN4G+yLL8y1JuYTKFotaKUSDA0dDUEdN6m0x/0e6xXGcFuxNqpxWnT\no9gMKHYDhBtQ2w29jz3p0yclRTArM5bZWXHMyowlZoj9LebmchpStqCPae99MrIFV0Il1aEKsbG3\nox6jGUyX3Y6hqYZA5kwmaTqoJ8b7uK3Dxua9ZWzeW8bsrFjWL5nE8pyUC6o0jq21jep3NlGz+QOU\nAIJWlVZL2qIcdFEjzyI8FNFnnDtFUXi0YIv3cVpkMl/IuQyNWkPhQ2/7DVh7ONtase7eTvJdX+z3\n/BXxK2hw1LPpuHvfc0HjUXY27GLjrKuD8yHGUViUhPaIBodr8EG8uckz+FLudX6PKYpCWa2ZvOI6\n8orrKD7TjGuQKVJThIFFMxJZNCORednxhBr970+7PD6CS3MzOHCsjrLadvRaNfOyE8j0lBpZsSCd\nkxUt/Ov9YvJP9n5HFJQ0UXi6iUsXpHHnFdNJih2d8kVfW3oHv/r0z1hs/rPGL0qdy/qZK4Le54t+\nY3R9uLeMx98u8gaoOq2an35pEdMnx7C7sIZWs5XoCAPL5yR7A9IbLpvG05uKOHCsdy/4zsIaDp5o\n4LZ1EteuykSnvfAGuQCq2mtpcda51yAOwdmaQGSY3n1flRnLrMxYJqdEoVGP/oiw+eQp/wFrHy0f\nf0TWrRsxxMYMet5IxBPBlNQkbmAdrd3t7K04xIuH38GmDJ0PJTLWTnx88O41hNEV1OzBkiTdCGyQ\nZfkeSZKWAr+SZfkaz7FpuJcCL5Zl2S5J0l+AImATsA34lizLWwN5H5HRb/R1O7rZU5PHW6fexzlI\n1rlzpVXpiDJEEm2IJMoQQZQ+ksiz/6uPIEzXOwtgsXXw+31/xmIfuOzN1ZPXck3m+qC1W3E66Txe\njPnAfiyHDuLqHLoED4Bx6jRal1/J9gYdR0414fLz+xdi0LJ0ZiKXzE1hUtL525naW1po+XALbTu2\nodgC36ccsWw5yffeP6L3FNmDx05+fSFPFb3gffzNufcwK3Y69sYGSn/24yFfr4mKJuvBv/g873Q5\neezIM8gtpwD3jOQ35t7NrNgLZzRcURQ+rdjBW6feH/Lcnr/XHlabk+KyFgpKGik43URz+8ADQSog\nMyXSM5saR3pi+KjvATx6ppnXt5X4LD/WqFWsnp/KtcsnExl27jWY6zrqef3ku/1qtYZoQ1iVupQN\nU9ajUY8smBTZg8fHlr1lvNan3nqIQcN3N+YEvMS88HQTL39y0pu1v0diTCi3Xz6NnKzYUW3veLK7\nHHxQ+ikflW3DxdD3Yy6rkUXcyj1XzhyTCg/1L71I62efDHlezHU3EHet/wG40faPIy9ypGngrW09\nrp68jmu5Tm7IAAAgAElEQVQy1w37+iJ78PgIdtDakz24Z43C3cBCIEyW5aclSfo+cAfQBZQA9wF/\nBm4BjuP+zlWAq2RZHvCbWXyRjJ7Grma2V+5id/UBup2BZe1VFFCsIah0tgFTk58rjUpDpD6CSEME\nVoeV2s76Qc/Xq/V8Y+7d6NSjV15GcblQTpeh5BfhKjwGlsACVX+MmVnoV67hkDaFzwvrqBtgyU9G\nQjir5qawdFYiYQPMikw09qZGmrdspn3nDhRH//lndUgI+rR0uk+e8PtafWoa6T/+GZrwkS0vFEHr\n2HC4HPzPvgdp6GoCYLppGt+e91VUKhVdJ09S8Yf/Deg60/7xrN99ThZbB3/Ie4RmT73REG0IP130\nXeJDz/8bUZfi4vWTm9heuXvIc6fpF/C9lbdR39JJQUkTBSVNHC9vxeH0nzkUINSgZXZmDHOz4piV\nGUNk6LkHjENxKQoH5Qbe3F7i05cZ9BquyE3nisUZo5LxtamrhdrOOnRqLZMjM9Brzu3ziaB1bCmK\nwmvbSvhgX++ew4hQHT+4Zd6wB2kdThef5FWyaVepT2KxnKxYbr982nm/7aag7gQvHnudDqW13/OK\n4l5GfTbFocUqL+In160Z8R7z4ap67BE6Dh8K6FxdfDyGjEkYMiZhnDQJQ8ZktJGjvyd5f+0hnj/2\nnyHP++mi75IRmTbs64ugdXyIOq0CiqJwqrWUrZU7KWg4GvB+gB6O+jTsZ2a7H6gd6ENspCbriI9X\nERWtoAuxY7FbaLe1024z02410+EYn1qrw6YoJDc6mFbWzbQKK+Fd/m8WnSp3HoDhLkrSmmKIWnMZ\njVnz+PxkO3nH67E5/NRe06pZKMVzSU4KUoZv0pWJwFZXS/Pm92nfu9tnaag6LAzT2vVEX74WTWgY\n5kMHafnoA7pPufepaSIjibrkUkzrr0ITOvKbDBG0jo2tFTt5/eQmwD0T+rPcB0jzFLS31dZw5r9+\nPuQ11GFhTP3rYwMeLzdX8tDBv2P3JCtKCUviR4u+jeEcg5TxZHPaeO7oy/0yAis2A47mBLQx9aj0\n7rFZlyUKR+1knM1JxEQaB51NBUiLD/fuTc1KjRy35G4Op4udBTW8s6vUp15jRKiODcsns3pe6oRa\nvimC1rHjdLl4/gO5Xz3W2EgjP7xtHknnEFy2ddh4Y3uJT51XjVrF+tx0NiyffN6VyDlV28CLhe/Q\noOk/wKvY9NjLZ+DqCkWbUorG5M6iq7jUOJuScdRMYbIphf+6a2HQ7xMURaHjSD51zz/bP+HeMGlN\npt5ANmMShkmT0Jpizqn9dqed3+37s3vgU8Fvnoxp0Zl8b8HXR3R9EbSODxG0XsTsLgeH6o6wteJz\nKiy+acH1Gj00p2FpDEU3udjvLKqzLRbbyQWkxkaydGYiUrqJyclDb+63uxy0W82029pps5lpt/b+\nt91m7vP/lmEH0edMUUhsdgeq2eVWIjr9B6ouFVQk6jkxyUBJuoHkBjvX7GxD62eyuTRZx8lJRtaU\nG9FV++4TVun1RC5bgXHVGg61aNhxpHrAbJ8J0SGszElmxZzkfsW6x4u1qormze9i3r+Ps2sEaSIi\nMV1xJdGr16A2+maBdXV34bLb0YSFjzizYF8iaA2+Tnsnv93zR+/A09LkRXxpxi2AezVC84dbaHrj\ntcEuAUDIrFmkf3/wZcR7a/J4ofhV7+OFCXO5e9YdE3LQZihmm4XHC56jrL2i98muCLqOL/SUn1BA\naweX2rvXfyB6nZqZk2K8gepQOQDGmtXu5JO8CjbvLafL2n+1RVyUketXTWHpzKR+NV7Hiwhax4bd\n4eTJTcc4dKL3+y8lLowf3jpv1L7HSmvaeenjE5RUt/d7PipMf16UyHEpCoUljbxduIu6kDxU+v4D\nP876dOaELCc3O41Xt55yD2apnKBxgFMHipqkmFB+dNu8oPYJisuF5VAeze+/i7WiYugXjIAmPAJD\nRoZnRnYyhoxJ6OLjh3WfUG2p5W/5T2Hpaie91obR6sISqqEqQUdyRDLfmX/foIntBiOC1vEhgtaL\nkNlm4fOqPeyo2oPZZvE5HmM0sTptBcuSc3niTZmi0mZU+i40CeVoTL2Zep0NaTibEwE1P7xtHrMm\nj/4Ge5fiwmzrcAe3noD2gzOf0dTdPORr9Ro9mkCzjioKsS12Ms90knWmk0iL/7RKClCdZOD05FBK\nM0LpNrr3UVkdNly4iLQ4yTnZxZQqK1qHQkuklqKpRk6lG0ClQoOKNa5McorNOAuO+gR5AKGz52Ba\nu57GuEnsLKhlz9FaOq2+7VGpICczlkvmpjAnK3bUsgAGqru8jOb338VyMM/nmNZkwnTF1UStugS1\nYewCaxG0Bt+bp97j0/IdAOjVOn6z7CdEG6JwtLVS+8xTdB4buq4oACoVCV+8i+hL1wx62ivy2+yo\n6l1Ku3HqBi7LuGTE7R8PdR31PHbk2X79VkboFOQdWUMGqD0SokO8QaqUEY3uPEgIZOmys3lvGZ/k\nVfosaU6LD2PjpVnkZMWO6yCECFqDr8vq4NE3Cjhe3rvENTMlku/dPJfwkNHd9uJSFPYereW1bSU+\ns/2ZKZHcsTabzJSJVSLHanOyq6iGD/NP0BZ9EE30WQPb3eHMD7mMGxcu8gajnd12dhypYd+xOsxd\nNqLCDCyfncSKOUkYA6yLOlyK04l5/z6aN7+HrcZ3omMw4UuWEnftdXSXl2EtK8NaXkZ3WVnA+UAA\n1EbjWUuLJ6FPSkal8d8XKopC3ZZNtGzZjKard7WKMzqcxJtuI2bpymF9hr5E0Do+RNB6Eak0V7O1\ncid5tYe9dRX7yoqawmXpK5kTN9Ob1GJ/cR1PvDP4TWhspJE/fH3ZmI2aB7JXIS4klt8s/fGQpTKs\nVVWYD+zDfGA/9rraAc8LmZZNeO5iIhYu6lemo8eemjxe7DMjFIg5mlRWlWkx5B3F1em7XFqfnEL0\n2nUYFy0h/0w7O45U9/vS7ysyTM+KOUlckpPis4dHURROVLRSeLoZm8NJSmwYS2Ymjni5VNfpEprf\n20RHwRGfY9q4OGKuuobI5StR6wa+GVEUBbm8lcLSJuwOF6lxYSyeMfI29RBBa3A1djXz+71/8vYf\nV01ey4bM9XQUFVL7zFM4zb0zHOqQEHeJI9fA+y8BYm/YSMzVGwYMXBwuB389/A9Ot51xX1el5jvz\nvkq2aerofKggO9Vayj8Knu+3JWJZci5zjat5+JXCIV+vUav43b2L+5WkOd80t3fzzs5SdhbW+IzT\nZadFcdPqqUxNO7e62iMlgtbgau+08fCrR/qtHJo12cS3bpwTtOAK3IHye3vO8PGBCm85nR4r5iRx\n06VZ414ip6mtm08PVbI9vxK7qQRt6qn+K9oUNbNCFvPlhdcQNoaDv2dTHA7a9+yiefP72Bt884mE\nzp5D9GVraflgM10nZJ/j4QsXkfTV+1Hr+m/tUBQFR3MT3WVlWMvPYC0ro7u8HGeb//scf1Q6HYb0\ndAwZkzFkZGDMmIw+NRW1TkfD66/S8sHmAV+bePe9RK1YFfB79SWC1vEhgtYLnEtxUdRYzNaKnZxo\nLfE5rlFpWJg4lzVpK/1uRne6XPzp5XxOVAzciXzz+tksmn7udVED5XQ5+cvhJzjdVub3uAoV9825\ni7nxs/wet9XWegNVW3XVgO9jzMwkYtESwhfloosZfBbZ7nLw8KHH+y/9O/t6GgPdTt+9aUmaaK5o\niiPuYAmOujqf4+rQMKIuuZToyy6nRRXC5wU17Cqs8amh2CM7PZpVOcksmp5AR5edx94qpLSm/1Jj\ng17DHWunsSonZdDP1VfnCZnmdzfRWew7iKFLTCLm6g1ELlmKSjv4jUhjWxePvVXks/zZqNfwxfXZ\nLJ+dHHCbziaC1uB6tujf3rqsEfpwfrPoh1jefddbh69H2JwcEu/5KrhctO38HGt5GSqNhpDpMzCk\nplH92CM423sD3Oh1VxB/860DLv1qs7bzhwN/pc3m/pkJ14Xxs9wHMBl9B5AmkkP1BTx/7D84XL0r\nJTZMWc8lSZfy+vYStucPPVuRlRLJL+9aFMxmjpnqxg7e3HG63xLRHvOmxrHx0kxS40evxmsgRNAa\nPE1t3Tz4Sn6/DL+Lpidw34aZY7avua6lk1c+PUX+qcZ+zxv1Gr6wYgprF6WN6SolRVEoqWrno7wK\nDskNKCGt6KccRR3Wf0lzsiGde+fdSnLY2N1bnc1lt9H++Q6aP9iMo9l3dVvYvPnEbvgCxslTAPdn\n6zpejPnAPpxmC5roaKKWr8A4JXNY7+tobXXPyHr+dJeX4WhsHPqFPTQa9AkJ2GpqBj1NHRJC5p8e\nRm0c/lJqEbSODxG0XqC6HN3srcljW8VOGv0spQ3XhbEqdSmrUpcRZRh8qUxZnZn/fu6Az/MRoTru\nXJfN4hmJo9buQHU5ung5/z84DhxkSpUVnQNaIjWUTo9h9crbWJDQv6i2vaHBG6haK8oHuCoYMiYR\nkbuEiNxcdHHxw2pTp72TF4tf40hj/6AuUh/BbdINzIyROFCXz9aKz6nu8J3VNar1rO/OYFpRI47j\nfrLrqtVELFxE9Nr16KdkUni6mc+PVFNQ0uQttt7venoNKqDLNnBG569fN2vQfz9FUeg8dpTm9zbR\n5Sfjrz4llZgN1xKxaHFAe006ux389z/309A6cGbqb90wh4XS8P7ue4igNXhK28r488HexElfjFtL\nyju7sZad6T1JoyH+pluIXrt+0FlBW10dlQ//qd+NSOTyFSR++Z4Bl3qdbjvDXw496S3BlRGRxg8W\nfAOdZuJl1fZX0katUnOHdBP2hhTe3F5Ce6c9oGvdc/UMVuaMfCBnIiqpbuONbSU+K0dUKlg+O4nr\nV2YSGzU2+3RF0Boc1Y0dPPhKPi3m3oHa1fNS+OJ6aVz2MhedbuKlAUvkTCUnKy6o7+9wusg7Xs/H\neRXuQWS1A23qKbRJZ/plATZqjGyctoGlyYuGXCkWLC6rlbbtW2n+cAvOtrb+B1UqIhblEnP1tRjS\n08esTU6LBWtFuXd5cXf5Gex1dX63WA1H4lfuJWrl8GdbRdA6PkTQeoFp7GpiW+Uu9lQf8DurlxKW\nxJr0VeQmzgv4Zu+Vz07y4X73DGJ4iI51i9JIjg1j7tS4ccsC2X2mlKq/PtxvOWKPyJWrSLzrbhyt\nLZgP7HcHqmdKB7yWPjWNiNzFROQuRp+YdM5tq+9s4GiTjM1pIzEsgTmxM/rVEFQUhRMtJWyt/Jyi\nxuM+iaZUqFiqmsTiUw5UhwpR7L43t8bMTKLXridiwSLau53sLqplx5HqAUvnDCQ+ysjvvrrEJzmF\noih0FR6hdfO7fv/u9OkZRF99LaFz5w8rMcKHB8p5c/vpQc9Jignlf+9bMqKlkCJoDQ5FUXjo0N+9\nqxuWVhtYuqcexdrbx+gSE0m+/xsYJ00O6JqO1hYqH34QW1Wl97mwefNJvv8bqPX+MwTvqNzDKyfe\n8j5elpzLndNvmlDLZv2VtDFqjFyTfCOf77b7rDBQqQa+78rJiuU7G+eMWzbgYFIUhaJSd43Xivr+\nuRW0GjWXLUjlmmWTiAhyyR4RtI6+0pp2Hn71CJau3u+ua5ZN4sZLMsf1d9XhdPHpQXeJnC7r2JTI\nMXfa2J5fzWeHKr2ro9RRDegmH0Vt6D94uyhxHhunXTvi5EDnytnVRdvWT2n56EOclrMSQarVRC5Z\nRszV16BPDnyFVjC5uruxVlTQ7V1aXObea+sMvPSi6cqrib/plmG/twhax4cIWi8A7pI1p9lasZOC\nxmN+g6DZcdNZk7aKbFPWsL40bHYnP3xsFx3d7uVtt6yZypVLMka1/cPlMLdz5le/wGXxTSLVQ2sy\n4WhpGfC4LinJM6O6GENKajCaGZD6zka2V+5iT80BrE7f5b6Z2gQurw4nIu84zlbfJdpak4noNZcT\ndclq1GFhnKxsY8eR6gFL5wxJUZA6ylneXECizffvr8oQx66YHE6HpvovEjdK/uuuRSNKliGC1uA4\nXF/I00UvoLO7WJ1nYWZp/5utyOUrSLjjS8NeZuXs6KDqkYfpLjnlfS4kWyLl2w/4LX2kKAovFr/G\n3tre5F+3STeyKnXpMD9RcFidNp47+hKFjce8z0XqIkluW01+Uf/fb61GzZVL0lk6I5E3Py/l8MkG\nb/AaatCyen4q16+aMuYJ1saaS1HYf6yOtz4/7bMCI8Sg4crFGazPzUCjUbGzoIbt+dVUNXag06qZ\nPSWG9bnpZKWOfD+sCFpH17EzzTz6ZiHWPit8br1sKlcsHt/7hr7aOmy86SmR0/cfVKNWsS43nWtH\noUROVYOFj/Mq2XO0FnvPd7HOii6jGG1s/5VWsUYTt0o3MitWOqf3HCmnxULLpx/T+unHvvk1NBqi\nVqzEdNU16OPHb6lyoFx2G7aqKupe/NegkxU9Yq+7gdhrrxv2+4igdXyIoHWCUhSFpu4WrE4rJkM0\noTrfciF2l4ODdflsrdhJ5QAla5Yl57I6bTkJoSNbbrmrsIZn3i8G3DdZD317xahn+xuupvc20fT2\nm8N+nS4+3huo6tPSJ9TsTJejiz3VB9hWuYumbt9gMVITxpXtyWTkV2Iv813e7C6Zs5zoy9djSEmh\ns9vBj/6+q7fguqIQ7bCgdTlo14VjU/f/N1QpLmZazrCsuZA4e5vP9ctCEtltyqEsJCmowWqPb984\nhwXZw/+ZFUHr6HO4HPx+34Ooq2q5clc7JnPvzajaaCThi3cRuXT5iK/vslqpfvxvdBb1JiUyZEwi\n9Xs/9Ft03u6089Chv1Nudu9H16g0fG/B18mMmjTiNoyGdpuZJ478kzJz7772CFUsbYXzsHb2/31b\nkB3PLZdNJSG6t19vMVupbupAq1YxOTkSg27iZwceTQ6ni+351by7q9Rn6XRkqI4Qg5a6li50LjtR\ndgsOlYZWXQQqlYovXSGxev7IBh9F0Dp6Dsr1PLnpqDfxkVql4u6rp7NizsRc3l5a085Ln5ygpCqw\nEjmtFivmTjuRYXqiwnxXALgUhaLTTXx8oIKjZ/p+jyto4ivRpcuotL3721WouCxjFddMWT8u9acd\n7e20fPQBrVs/Q7H2HzBSabXu2ulXXo0uJnbM23au2vfupvbpfwx5Xsavfhvw6qC+RNA6PkTQOgEd\nqD3MR2VbvfseNSoN8xPmsGHKFcSHxtJuM/N51V4+r9yD2e472xhrNHGpp2SNv2B3OP7nX3mc9tQ8\nWzE7iXs3zDyn642Gst/9Bmu5/yRMZ9PGxBKRm0tE7hIMkyZPqEDVH5fioqDxGFsrPudUq+8ooRY1\nl7omM/d4h7tkjp/MrKGzZmNat54/7O2ksqGDue0nWdx6jFi7+9/RrtJQHD6ZHbHz6dQYmG0+zbKW\nIkx237qwp0NT2G2aQ2XI2O5b/vkXFzAtbfhJdkTQOvo+K9/B6XdfZcURC5o+P26GyVNIvv8b6BPO\nffRdcTioffYpd61fD11iImnf/5HfveXN3S384cAjWOzucglR+kh+mvsAUYbxWVbnr6SNpiMBS3FO\nv5I2KXFh3L52WlDKg10oum0OPjpQwQf7ynsH3YAwRxermg8zy1yKzrOvuUUXwf7oGeRHSvzq7lwm\nJwVvdQaIfmMwO45U8/wHx72rBbQaNd+4fhbzp41swHysKIrC3qN1vLrtlE+JnCnJkdy5Lhu7w8k7\nO0v77cGeNdnEdasymZoaRbfNwe6iWj7Oq6TurD2zKqMFY+YxCO+fWyQjIpXbp28kI8I3AWaw2Vta\naPlwC207tqHY+n9mlV5P9OrLMK2/Em30xE50NxiX3c6ZX/180AROIdNnkP6jn47o+iJoHR8iaJ1g\ntpR+wnulH/k9FqI1Mi06i2NNx/2WrJkaPYU16avIiZs5Khv4y2rN/Pc/exMw/fKuhWSljE9ZAgBn\nZycd+Yepe/F5n47WH9M11xJ33Q3D2nM5kZSbK9lWsYu8unxv8pm+hiqZY42Ko9aqYVK3b0ZigC61\nDqdaR7jD97W6WXMJWXsl2ozJ5/w5+vr4QAUf51UOes65lFASQevoam+qY/8jvyGjqv8ovOmKq4i7\nYeOQmaKHQ3G5qH/5Rdq2fuZ9Tmsykfq9H2FI9Z1Fk5tP8Wj+U97tEFlRU3hg/v399o+PBX8lbRz1\nadjLZoLi7ntCDVquWzWFNfNTL/jlvqOlvdPG+7vL2Hq4EqO1gy9VbiHK4b+m4+HIbMxrruOr1/rP\nGD8YEbSeuy17y3htW291AqNewwM35SBlmMaxVcPTbXPw/p4yPtxf7lMiZyBqFczPjqf4TItvLXWV\ni+iscmwxJ1DoHe3Ta/Rcm3kFl6YuH/O+yt7USPOWzbTv3IHi6N9edUgI0ZetxbR2PZqI8Rn8G23W\n6iqqHv6z361ihvR0Ur//Y7+reQIhgtbxIYLWCaTCXMX/O/DXYb1Go9KwKHEeq9NXjPqI3T+3FLPj\niDtleEZiOL/5Su6Yz1S6uruxHMnHfGAfnUWFPh3tYNJ+8nNCs8dnj8hoarOa+bxqD59X7fHOLPWV\noInmyqY44gcomeOPAvj8S6pUhC/MJfaaDRjSg7P/qL3Dxq+f3U97x8CDDndfPX1YpXj6EkHr6Ok4\ndpQzTz6KrqM3YFVFhJPy1a8TNmt2UN5TURSaNr1N87vveJ9Th4WR+sAPCMnM8jn/k/Lt/TL0Xpq2\ngluyh78/aaQO1h3hX8Wv9CtpY6+chqM6E/fiP7hkXgo3XJJJZJATCl2odhfW0PT040gd5f77LY/N\nmVfwvV/cPuzri6B15BRF4bVtJXywr3fLSkSojh/cMo9JSedn4FPf0sl//JTIGY6p2Q464g/Rau8/\nuzo7djq3ZN9AbMjYBvO2ulqaN79P+97dPkmK1GFhmNauJ/rytWhCw8a0XWPB2dFB284dmA/sx2Wx\noDWZiFy+goily3zqxg6HCFrHhwhaJ5CXjr/Orur9AZ3rLlmzjFWpS4csWTMSnd0OfvDYTmx29wjh\nV66aziVzxyZjnMtmo6PgCOYD++goLAhoVvVsuoREJv/P/z1vZ1n9sTvt5NXls7VyJ1UW3/pjQ5bM\nGYhaTcSSpcRctQFDSvD/jSvrLfz1jSO0KFWooxpRqV24usJRmlO4adX0c0r0JYLWc6c4HDS+/SYt\nH27pl9a2Y0oSc779c7RRwV9t0fLJxzT859/exyqDgZRvfscnWFYUhWeP/ptD9QXe5+6acStLkhcG\ntX2KovBJ+XbeLuktXK+4VNhLZ+Nscs8KT0uL4o612eftzftEceRwCYbHfs9QPfmZiHTWP/z7YV9f\nBK0j43S5+NcHMp8X9H4XxUYa+OFt80ka5Qy846GotImn3ysedIC1L71OzZLZMTgSj5LffLjfsQh9\nODdPu44FCTmjOvBvra721EQ1o42KImLpsn7JkqxVVTRvfte97eKse31NRCSm9VcSvWYNauO5bSO7\nGImgdXyM3tou4ZyVtQ++bLLHkqQF3C5tDGp9wt1FNd6ANcSgZUmQa7G67HY6jxZhPrAPS36+T1KA\nHuqQEMLmzcdaVoatusr/xVQq4m+9/YIKWAF0Gh3LUnJZmryIk55s0YV9skV3u2xs0p9CtUDFkoWL\nWfLaYbANXQsy/rY7MV12ebCb72WMsGFasJ/Ojv6Btz7zFFHpJmDiZJm82NgbGqh56nG6T/eWJXKq\n4NDCGDbe+1u0urGpnWlauw5NeBi1zz4NLheK1UrVIw+TfN/XiFi02HueSqXizuk3U9tR780B8LL8\nBinhSaRHBCcruEtx8dqJd9hRtcf7nOLQYjs1H1d7LKYIA7esmcriGQkTfg/9RKI4HNgb6rHV1WGv\nr8NWV4utro7QigoCiRSTukc+MyYMj93h5MlNxzh0osH7XEpcGD+4ZS4xkWPTRwTb7CmxzJpiYk/R\n0KuXpqREsGaNmvfK3sbc3D/PyIqUJVyfdRWhutEL5F02G3XPP4t5395+zzdtepuoS1YTuWIVLR9u\nxnIwz+e1WpMJ0xVXE7XqEtQGw6i1SRDGgghaJ5BAb3CmRmcGNWBVFIWth3sDwuWzkzDoR3/vheJw\n0Fl8zB2oHj6Eq8t/jVGVwUj4vHlE5C4hdNZs1Dodru4u6p5/DnPegX4jiJroaBLv/BLhc+eNensn\nCpVKRbYpi2xTFg2dTWyv3MXumv3ekjkKCnuVM8zQOokMYJBYZxq7pUqd9i4eOfyk3wzJNpeNF4+/\nhlFrZH7CnDFrk+DWvn8v9S883+/3sDVcwwcrIlm74jaMIwhYy+vMbM+vprzOjFqtYsYkE5fOS8UU\nMfTNUuTS5ahDQ6l5/DF3rWKnk5onH8fZ0UH0pWu85xm1Bu6bcxd/zHuELkc3dpeDpwr/xU9yv0u4\nbnSXu1mdNv5x5AWOt8re51xWI7YTC9HYorhmeQZXL50UlP5yrHWXnqZ1xzZsVZWotDpCZ84iatWl\n5zTTrjid2JuasNe7A1J7nTs4tdfXYW9sHLhgbQD0enE7Mxa6rA7+9mYhxWW9fXhmSiTfu3nuuFcW\nGG3e+sgqF5qYWjSxNai0NhS7HmdTCs6WRFS6brpTi3j5ZP9Jh6TQBG6fvpGp0VNGvV21zz6FJe+A\n7wFFoW37Vtq2b/U5pI2LI+aqa4hcvhK17sL6dxIuHqKXn0CmRWdSYR5g9rCPrCB0gn3J5a3UNPUm\nFVkzwlIC/ihOJ53ycXegeuggrg7/iTVUej1hOXOJyF1M2Jy5qPX99x6ojSEkf+2bxG1soKOwAJfV\nij4pmbDZc0Y1OcxEFx8ay03ZX+CazHXsqcljW8UubwbTqgQ9kWf8z1j3cKqBSWl+a8QGw7bKnX4D\n1r42lWxhbvysUUkmJgzNZbVS/9KLtO/6vN/zxycb2JobQVx0CkuTFw3rmoqi8M7OUjbtOtPv+ZOV\nbXywr5z7vzAroJJG4TnzSP3+j6h+9C/uYFpRqH/heZwWCzFXb/AO9CWExvGVmbfzeMFzADR1t/Bc\n0dV6wMUAACAASURBVEt8a969o/Zz1NzZyp/3PUWb0ju75OqIwHpiIQunpHPLZVOJjz7/l9kpikLD\nKy/T+kn/hIBdJ2RaPthM8je+PeieZsXlwtHa0huQev5rq6/D3tDgs6dutETOnBGU6wq92jtt/OXV\nI5yp7c00P2uyiW/dOAdjEAYNXC4X6nFcMSWlR7OzuBRD9kHUYf2z62tMDbisRlRaG62q3kRLWpWG\nKyZfxrpJa9CpR//vpPtMqf+AdQC6xERirt5A5JJlF9W9kXBhEj/BE8iq1KVsq9yFS/EtY9JjZoxE\n4ghrrgbqsz6zrNMzokmJO7fZCsXlouvkCcwH9mM5mIfT3O73PJVWS+icHCJyFxOeMw+1ceiZHV1c\nPNFrxm5p60QVog3hsvRVrE5bQWHjMbZW7ORItsyMIYLWExlG/pb/5zFqZWDquxopa69kSpRYJhxs\n3eVl1Pzjcey1vcXuFb2OjxcYKZ5iBJWKG6duGHbgt7Owxidg7WFzuHjinSJ+9eVc0hPCh7xWaLZE\n2o9/RtVfHsTZ7u47mt56A6fFQvzNt3q3AcyOm8HVU9axufRjAI63nOTd0x9yXdZVw2r72RRFYdsx\nmTcq/4Oi6x3Mc7bGEdeynDs2zmTmBVTCpvWTj3wC1h6u7m6qH3uEjF//Dk2I0TNbWttnSa/7v4p9\n6G0JA9GaYtAlJqJPTESXkIg+MYm23TvpOHRw0NeZLls34vcUhtbU1s2Dr+RT26eky6LpCdy3YSY6\n7egFlo01Zzix6d+EFp0mpMtJV4iGztmZZH/hTuKSJ4/a+wRikRTPy2VHUEJ9y8EBqA39v1+nRk/h\nDmkjiWHnXgZsIO179wx9Eu49q/G33UFE7uILbquUcPESQesEkhAazy3Truc/J970ezw+JJY7Z9wU\n1Da0Wqwc7rNPZc2C3ozEiqJgr6vDZe1GFxuHJnzgG07F5aL7dAnmA/sx5x3A2dbq/0SNhrBZs90z\nqvMWoAk5/2cqxpNapWZu/Gzmxs/mJ5bfsmO+nUsO+9byBWiI1rJ90dBBw3hot/kf2BBGh6IotH76\nCY2vv9IvI7c+PYPXFms5Y3D/zMyIyWZGbPawru1SFDbvGbyOssOp8NH+8oDrPhszJpH+01/+f/bu\nOzyK61z8+HeLdrVNKwn1AkiABgRI9GaMccEdtzg4jhPbiUvi9MS5SW6/vzz3Jvcm105unNgpThzH\niXuwcYntuNExYIoKZQBR1XvZoq3z+2PErhZUVqtdSeDzeR4eaWbPzBxha5h3zjnvS+3Pfhqqu9f5\n7jsEnQ6y7/kiGp06Hfe6qVdypqeWqtZDAPz91IdMthXEPN28rtXJU5u2Um/bjCapXyDWVshtU9dy\n5S2Tw1MILwKK30/7228N3cbr5dS///OoRkx1KSkYsnPU4DQruy9IzSEpM2vAdXamEonatjY8p04O\neL6M227HNGNGzP0RhtbQ5uR/n99PR48ntG/1vDw+d7UUU2mywZw+vIeOx35Fhif84t7sDmDefZS6\nyh/i+vpXmTwzsUnW+qvpqUExDz0zCMCoM3L7jLUsy12U0BlCvrY23IcPRdXWumgRKUuXJawvgjAe\nRNA6wegHqNtlM1hZnruYqyZfhiWOi/kHsqWinkBQXVdktxiYPyMDRVHo3raVjrf/hrexL3mOTodt\nwUIm3Xo7hiz1raKiKHhOnaRn9056du/C394+8EW0WsyzStUR1fkL0VkuvjTrE0Fqsp19s1y02XUs\nOOxicqMPDdBj1lI9zcS+mSZ8SRPzgdtmmJjB9MUg0NND4x9/j7Nif8T+1DXXULU4h5Mn1aBFgzrK\nOlL1rU6aOgZen97fRwebyM+0kp1uIifdTGaqacgapobsbCb/4J+p/dkjeOvU9WPd27cRcLnIffAh\ntAYDWo2We0o/w092P0azWw1unzn0AjmWLHIt0SeTc/X6eHXrCTae+Bh9UQUabXitZUFgIV+9/hZS\nLBdfEpPeEycGf8HYXxQBq9ZiUUdLs3MiA9Os7BG/nNSZzRR+7x/peO/vdG3aiL+9DTQaTNJM0q6+\nFmtZ+YjOJ0TvREM3P3uxAoc7/NLmhuVTuG1VcVwTjfm9Hlp//WusnoFnmpk8QVp//WvyfvJL9Ibz\nf/cURcEX9OEJeOn1e/AEPPQGPHgCXjwBDx7/OdsBDx6/t2/f2T/hYz0BL75gdDMGluQsYEXekuEb\nxsDf2UHPxx/Ts3snvTXHoj5On5L4LO+CMNZE0DqBKIrCptptoe2lOQtZV3IzRp1xTLJQBoJBNu6v\nD21fWp6HXqeldf3LtP/tjXMaB+jZvQvnwYNk3/MFPCdP0LN7p7pmaSAaDaYSSQ1UFy5Cb4t/mR4h\n0qKsedQ5GjidZ+R0nhFtQEEXVPDpNdD3/5PdkMLDC78yZkXOPzi9hffPbB6yzaTkdKamiKnBieA6\nfIiGJ39DoDMcmOisNrK/eD+aWTN4a8f/hPYvz11MnjVnxNfo9UQ3AhcIKrz4YfghTKOBSSnJ5KSb\nyU4zk5VuIjvNTE66iUn2ZHRaLfrUNAr/4QfUPfbz0AOcc/8+6n7+CHlf+yY6sxmT3sQDc+/mp3t+\niTfgVZMnVT3N9xZ9HZPehKIonGzs4UyzA51Ww8zJaUyyq0sRgkGFLZX1vLypBo/9CEnT+pWOUrTc\nWLCW66RLRvx3MpEpioK3thZnVUXUUw/P0iYn903h7QtO+03pHWomTiy0RiOTblhL+vU3ong8aPR6\nsUYvwQ6ebOex9VV4vOHf6TuumM41S+J/fz6w5XWsjqGDRKvDx7Ov/DftM7LPCTLVQFSJKs90/ClD\nLOmKhb+7G8ceNVB1Hz0SU4Iy2+Klce2TIEwE4o4/gdR0neSMIxw0ri68hGT92KWPrzzWFpr+o9Go\n03/cx2vOD1j7CTodNDz+2KCfJ0+fgW3xEmwLF6NPTY17n4XBXZK/lE112+n0dAEQ1GkI6iJfftxY\nfDWTTGO3Hm/NlNV83LSPLu/Aa4QAbihaI5IwxZkSCND2+qu0v/lGxAOQaeYscu9/EH1qGi8ffQ23\nXx0hNegM3Fh8dUzX8gVie4BTFGjt6qW1q5fqE5GzNHRaDRmpJnLSTGSnm8m54rPk8CLamsOAmiSo\n9n//h/xvPYw+JYU8aw6fn7WO31f/GYBmVytPH3yBazNv4+l3ZE71SySj0cBCKYuVc3NYv/k4p5u6\nSZpyiKTsM6E2SRojD827Byl9ekw/20QT9HhwHTqIs7ICZ1Ul/o5BZsUMIikri8Lv/xO6FPuYl/XR\naDRoosh3IIzOHrmZ37x2AH9AvV9oNRruvW4mK8tyE3I9x6Fqohl/zz5Qx77UHrwTaJZQPP4NDTgc\nOPbuoWf3LlyHDw4aqOrTJ4FWE1omMRDb8hUYckb+wlEQJjoRtE4gG/uNshbbpzLZVjBE6/jrX+Zm\n3vQM0lOSaXzpgxGfJ7moWB1RXbSYpPRJ8eyiMAKWJDPfmPcAv678Y2iq5FlajZabp12XsClNg7EZ\nrHx9/oP8uvKPtLrbIj7TaXTcMv16luaO3Zqli0mwt5fuXR/hOXUKjU6LSZqJtXw+/q5OGn7768ip\nZVotGbfcRtq116PRaml2tbK5NjzKtmbyZdiNI5sN4fMHeHPHKd4cZj3rWVNzbJiMepo6XLR3e4Zs\nGwgqNLW7aGp3QY36/42Whdxo7aXUcRIAz+lTHPr3/6D7tvvImJJHcbrEVZMv473TmwCoaj1IVZUf\nd2NxxLkVBT4+3MzHh5tB68cwowJdWnjGSKrRzlfL74tp1Hki8TY14ayqxFlVgVs+HLGWeaRSr1iD\n3i5eQl4MGtqcfHSgiS6nhxSLgaWzsqmp7+bptw+H4ia9TstDN89mfhQZv2MW5RrponovX3q5lZY0\nPXWZSdRlGajPSqLXGBnE6jU6jHojRp2RZJ0Ro86AUWfs22fo29f3R28YcJ/b18sv9v92yP5oNVqW\n5CyI7Ud2uXDs26sGqocODPp3oEtNxbZoMbbFS0kunobi6aXhN0/grKo8r61tyVKy7743pv4IwkQn\ngtYJoqO3k4qW6tD26oKxnYLW1OGKGN24fEE+AZdLfeMXBX36JFIvvwLboiUkZSY2u7EQvWxLFv+y\n9GGqWg9yoE3GF/SRY8liWe4iUo3js+Yl15LNvy39LhWtBzjUJuML+sm1ZLMsd9GIAyVB1bN3D01P\nPRlZY/WD99FarSg+H4onHBTqJ00i98GHME0LjxpuqHmLgKI+MNkNKVw5+bIRXV8+3cHTb8sRmUWH\nkp1u5rufmYc5Wa0X6PUFaO50q4FphzsUoDZ1uOlyDlyOKajR8Xr2Stw6Iwu71Lqpxp52jH/+FU/m\nX0WbIRWjwYxRysJnagZAyTmCtttGsGuAe1SSB+OMPWit4SRghdY8vlz+hXH7XRkNxe/HdURWA9XK\nCnxNjUO2NxYWYplbjn7SJJqf/fOgD9DGwsnYL12ViC4LY8gfCPL024fZVhX5/8Ub2yNfOiUbdHzz\n9jKkyYmr5+32u2m0a4j2yUGrQHa7n+x2Pwtk9Z6nzc3BMH0a5hIJq1RKcnpGXPrW/8XXQK6deuWI\n7g/B3l4cFfvp2b0TV3XVoC+PdLYUrIsWYVu8FNP0GREZgDXJJvK+8W16TxynZ+dHBBw96O2p2JYt\nJ3nylOh/OEG4wGiUURTznihaWnou+B/itZq3eeeUOqqZarTzw+U/GLN1hgAvfnCMt3eeIsPbRZnS\nxCpzpzoyE+Xbz5wvPkDKiotrrZdw4cnMtEU1V/FiuGec5Tp8iNpHfwrB4aflWhctIfvue9CZw8nP\najpP8ujex0Pbn5v5aZbnLY7q2g63j5c+PMaWyoaI/XaLgasWFbC9ujGi5jPA7KJ07rthFqnW6BIZ\nuT1+mjvcNHWogWxju5vmDjWgdbh9oCisbK9gZUd41MGtNfBi3pU0JGeC3otx9vZQeQoloCPQmYk2\n2YUS1BLsnkSgKx3DtGq0xnDQX5oucd+cu8Z0icZo+Ts7cFZW4qiqwHXwIIpn8JJXGqMR86xSLGXl\nWOaUkZQenuLokg/T9PRT+JqbIo6xzJtPzr33xX296niK9p4BF9d94w9vHmJrVcOQbWzmJL6zbh5T\ncmwJ6YOiKFS0VPPS4VdZvKWW0hNDz7gIakBnMEa8hBtMUlY2ppISzCUzMZWUoJ+UEdNU9qAS5O2T\n7/Pu6U14+9UzT9YZuXbqlVw1+bJhzxv0eHBWVdKzeyfOqkoU78Av4rQWC7aFfYFqiRTKii5MPCO5\nbwjxI0ZaJwBvwMfW+o9C25fmLx+zgDXo8dB94ADat//OQ91nsPudAAxd3fMcGg2mkpGVxRAEIT7a\nNrwyfMCq1ZL9+XtIWbkq4gFLURTWHwuvWc+35kY1PVtRFHYdaua5947Q7YpMnrJ6fj63X1aMOTmJ\n65dN4ciZTk43OdDpNEiT08gfYd1nk1HPlBzbgA/ODrePpg4Xze2zadiWS+7ud9Rjgl7urHuX9bmr\nOWnOw3t0PsbSnWi0QTS6APpJ4dElna0TfV4N/Z87V+Qu4TPSrWP64hDUUmFoNFE/XCvBIL0njqtr\nUysr8Jw5PWT7pKxsLGVlWOaWYyqR0CYlDdjOLM1k6n/+GLd8GE/tGTRJSZhnzcaQHX0GZmFwwaAS\n11IxI9XQ5hw2YAW459qZCQtYO3o7efHIBg41VHH91m6mNgwcyJ2lAL23X0P5VevwnDmN+4iM64iM\n++gRgk7nee19zWrN4O6tWwDQp6djmiFhKpEwSxJJ2TlR/Z5pNVquL1rD5YWXUtV6EEdvNynJduZk\nlJKsH/zFW9Dnw1Vdpdanr9g3aKCtNZmwzl+IbckSzDNLRXIxQRhCQn87JEnSAI8D5ahx0P2yLB/v\n9/mtwD8BQeApWZZ/PdwxF6M9Tftx+tTRCL1WzyUJXmfoa20JJeBwHT6E4vNRNkhbTXIySu/QIayl\nfB5JGWJKsCCMNV9bm5pdchgag/G8gBVgb3MFJ7vDgc5t028cNglWS6ebZ/4uU308MnlPXoaFu6+R\nKCkMr3XUaNRANVFTC62mJKwmO9Py7DDnTrrLptD41JMQDGJQ/Hym8UM6r15HlUliV9sp9Jn1A56n\n/1/L2uJruWbK5WOWYEjx++nasonOjR/iratFo9djnlVK6pprsJTOPq99wOHAeaBavYcfqCLoGLgO\nM4BGr8dUImGZW4alrBxDdvTrcjV9pcnMs6KrpSsMraPHw993n2ZHdSPdLh+WZD1LS7O5eslkslIT\nU588GFTodnnpcnjpdHjocnrpcnjYd3TwJD79HavtYkGc17EGlSCba3fw2vG30DncfGpjF9kd4Smy\nwUmpdCu9pLaHnzs6MkzY1t7IvEtuACB5ahHJU4tIu/palGAQb0O9GsTKMu6jMoGurvOu629vp2fn\nDnp2qmv3dbYUTCUlahBbImHIL4iYgtufr7WFnr+/Q/quj0h1ONDZbHQvW4FuzTURMxQUvx/nwQM4\ndu/CsX9vxHKN/jTGZKzz5mFbvBTz7DmDvjwSBCFSol/p3AIYZVleIUnSUuDRvn1nPQrMA1zAQUmS\nngOuGOaYi4qiKBEJmBZlzYt7jUrF78d97CjOqgqclZV4GwZ+cDvLkJevThkrK8dUPI3uj7bT9PRT\nA2azM+TmkX33F+LaX0EQouMf4OFsIEqvW53q3+8tvi/oZ0PNW6Ht0kkSM9NnDHqOQDDIu7treXXr\ncby+8MiuXqdh7YqpXLdsypB1VsdCyvIVaC1mGp74FYrPB8EAqe88zy2f/Rz79B3DFsSwBrK5duoV\nY9JXgKDXS90vfob78KHQPsXv70uYVEnGbbeTdt0NeGvP4Oh70dhbc2zIEhj6tDQ1SJ1bhnnWbLQi\n0+64q21x8L/P7YuYleDs9fPB3jp2HGjiO+vKmZYf/bpInz+gBqJ9QWinw0uXU/3a7ewLUB1eul3e\nWKqlhHQ5h5+GOxJ1jgb+cvhlTnWfIb3Tz80bO0lxhe8llrJyteZycjL1Jw/ibG/Gkp5FydTBX5xo\ntFqM+QUY8wtIvfxKtV5rcxPuIzLuI0dwHTmMv63tvOMCPWpZGceejwHQms2YZqhBrGmGRPLkyWj0\nenpPHKf2Z/9L0OXqd2wPne++Q8+O7eR/+2ECDoc6orp3D0HX+aO+ABqDAUtZObbFS7DMLUdrMMT6\n1ygIn1iJDlpXAm8DyLK8U5KkRed87gXSIPQsoURxzEWlpusktf3K3FxWuCIu5/V3deKsrsJZWYHr\n4IFB3/gB+DQ6TplyqbHkc93nrmXq7KKIz+0rV2HIzqH972/jrKyAQAB9WhopK1eRtuYadGZzXPos\nCMLI6FOim7qnNZvhnPVRm2q30dbbAYAGDbdOu2HQ4080qNlETzdFjupJhancfa1E7qSRTflNJGvZ\nPPK//V3qH/u5et9TFNr+8gyLyi3sLjVHDquew2+I7iVAvLS98teIgPVcretfpuPv7xBwDF4iCo2G\n5OJpWMrKsZaVYygoHPMyNMLggkGFX71Sfd40+rPcHj+/XF/Ff39pGYEgoeCzy6kGnmpw6gmPljq8\nuDyxZ34eiRRLfAIrb8DL3068x/tnNhNUghQ0eblxcxdGXziitq++gqw77wqt48ybWgpDBKuD0Wg0\nGLJzMGTnYL9UTSjna2vFfeQI7qPqlGJf4/lJyYIuF86K/Tgr9qvnMRpJLp6G58RxgoPMNgs4ejj9\nn/9v0JdIGr0e89wytZpC2TzxAkkQRinRQWsK0P8pwC9JklaW5bOv1h4B9gAOYL0sy92SJA13zEWl\n/yjrtH5lbvw93biqqwj29pKUlY15VumgU1egb23TyZPqaGpVJZ6TJ4a8blJGJpayMrZ7JvG3Bj1+\nrZ7J2VamlU4dsL1pRgn5M0pQgkEUvx9NUpJ4MBKEcaQEAnRv3zZ8Q8C2dFnE76vD5+Ttk++Htlfk\nLRmwpEuv188rm0/w3p4zEc9llmQ96y6fzsqy3Al5HzCXSBT8ww+o+/kjBLrVbMArKpwke4JsmWch\npz1AZoefoBbOZBvotqoPyt7g0Ovq4ingdtO5eePw7QYIWLUWC5Y5c9UZMbPnXlSJkS42lcfb1FJN\nQ+hyevnaz7cQCCYmz5Nep8FuMZJqNWC3GtHrNOw61DzscctKR1/m6VD7EZ4/vJ7WXnU5gXSilzU7\nu9H1e6LL+NQ60q69LmH3kqRJGSQtzyBluToo4O/qwn1U7lsXewRvXe15gafi8eA+FEX1hHMDVp0O\ny+w56ojqvAXoTImZ+i0In0SJDlq7gf5DAaHgU5KkQuDrwBTACfxFkqTbUQPWAY8ZTFqaGb3+wsuy\n1upqjyhzc9Psq5hkN3LiD3+k6d33I1KhG7MyKX7gPtKXhLN6+h1OOvfvp/3jvXTu3Yuvq5vBaHQ6\nUkpnkbZoIWmLFmDKz8fV6+fff/gOfq2aIfimVdPIyhIlR4SL34V6zzjL09LKkf/7Od0HBx+lO0tn\nMTP9M58iOTN8W31j71u4/eroQbLeyD2LbiXVFDlqu/tgI0+sr6SlI3KWxmXzC7j/5jmk2qLL/jtu\nMueQ8ZMfceDffoinWX1AX3DYTWlNL8n9RngUoKbAyPtLbKRnZpOZObrEM0owiL+nB29HB972Drwd\nHfg6OkPfn932tLWpU5ijZCkqIm3RAtIWLsBWMkNkFh0Hsdw3TkdZtziWgNVk1JOeYiQtJZl0W7L6\nNcVIqi05vD8lGavp/JfMj/xlDxv31g567uVzc1k0N2/EfTqru7eHp/e/zJZTu9QdisLigy5WVISn\nz2r0emZ88+tkrloZ83VikmmD6QVw3ZUA+Hp66Dl0mK4DB+k+cBBHzfGosrGHaDSklpeRceklTFq2\nFL14iSQICZHooHUbcCPwsiRJy4Cqfp8lA37AI8uyIklSM5Dad8xNgxwzoI6O6GoDTjQbat4jqKg3\nxlSjnan6Iir/839C01P68zS3cOi//pvMO+9C8frUAvHHjg55Y9WlpGCZW46lrAxz6ZzQGz8n4Gx1\n8N7HZ/B41YDVZNQxuzCVlpYhpqEJwgQXbcBxod4zABz79tD41B8i1k5pLVaCXi/4IkcKdamp5H3l\n6/ToLPT0/W43u1p451i47uBVhavxObS09I3odTo8PPveUT4+HDkSk2FP5u5rJOYUT8LX66Wld+xG\nJWOmt5L/vX+k9mePqKMpEBGwAmiA6bUe7I4A7i9dM+g9UPH78Xd34e/sItDdhb+rE39nJ4Guvu+7\nugh0deLv7o66VFh0P4Oeoh//lKQ0NZmVB/BEWQ9XGN5IXlLEct9wOEa+LtRqSgqNiqZa1K92q4FU\nqxG7xaB+bzFiNAwfQPc6PfQOsDb1zium4e71sfNg03mfLSjJ5O41JTE9DyiKws7GPaw/+gZOv/r3\npQkqXLHbwZya8AswrdlC3te+ASXSxHjuKJqJtWgm1htvI9jrxl1TQ8tLz+OtHTywP8u2/BKyvng/\nAB1uBdwT4OcREmq0LzeF2CQ6aH0FWCNJ0tk5bF+QJOlOwCLL8pOSJP0J2C5JkhuoAf4IBICr+x+T\n4D6OC2/Ax7b6naHtS/OX03vwwIABa38tz/1l8A81GpKLitRAdW45xsmTB51SrCgKH+6rC22vmJMb\n1T+AgiCMj6DXS8tLz9P14QcR+60LFqrJ0LQaej7aQe/Jk2j0OkzSTKwLFp2XmXJDzVuhl2V2QwpX\nTr5UPb+isLminpc+rMHdb82cVqPh6iWF3HxJ0QV5j9CnplH4Dz+g5gffhSEyoWd2+jF/eIyOU2pO\ngEBfIHo2MB1yXWkCmYqKQwGrcOGZnB3dw+1VCwu4dulkUiyGMUlolqTX8aWbZnP9sil8dKCRLqeX\nFLOBpaXZMZe5aXa18Jz8Ckc6joWv4wty20cecs6EA1Z9RgYF3/wOhtzYR3ITSZtswjJ7Dt6GVbQ8\n/+yw7c3TB09gJwhC/CQ0aJVlWQEeOmf3kX6f/wz42QCHnnvMRWegMjfdbz414vNozWYss+dgKSvH\nPHsu+pTopvfKpztpaAu/NV49P3/E1xYEYWx46uto+M0TodFCAE1SEpmf+Sz2VatDU/9Sr7hqyPMc\n6zzB/n5LEtZOuxaDzkBdq5On3z7MsdrIRERTc2zce93MqB+8J6qg1zNkwHqWa9dOXLt2DtsuKjod\n+pQUdPZU9HY7ensqOrsdfWoq+hQ7Onsq7W9sUJPbDcG+euyyGQvxt3hWFi98cBRn7+DJk5L0WtZe\nMhWbeewzyhZmWSnMmj6qc/iDft47vYm3Tr6PPxj+Oe29Wu7c7sfYGH7hY5xaRP7Xv4XeHn225PGS\nsvwSWte/jOIdfFaJ1mTCtmTpGPZKED65RBXjcXBemZtstcxNW/P503QGorOnkrJ8hVqSZtr0mNY2\n9R9lnTk5lfyMiZP9UxAElaIodG3eRMsLz0Y8OBny8sn90kMY8wuiPldQCbL+6Buh7QJrHgsyynl1\ny3He3HEqYk2dMUnHbauKuXJhAVrtxEu0NFK+lpa4nUtjMKC32yOCUX1qX0BqTw0FpzqrdcjkeQBZ\nn7+XMz/+T/zt55fkALAuXIRtcWLrdguJZUzS8cXrZ/GrV6oJKgooCjqCBNCGMll//mppXALWeDje\ndYpnD79MgzPy+WWRks+lH54g2NEZ2mcpKyf3S19Ba5zg6+H76CwWsu++l8bf/27gDMFaLdn3flFk\nBRaEMSKC1nFwbpmb1QWXAER948u8fV0oC14sOh0e9h4JP8SJUVZBmHgCLidNf/ojjo93R+y3X3Y5\nmes+M+IHv71NFZzqORPaXpxyGf/x1J7zMpuWT5vE566WmGS/eB7Eon6o1GgwSTP7AtGzo6N9wWmq\n+r02OTluWU6T0tKY/E//QutfX6Zn985Q8j2d3U7qFVeRfu31wwa+wsQ3vyST7143heN/3UB+wyFM\nQS+92iRqs0oouHktZWW5493FEXP73WyoeZutdR+h9KuAbE2ycIduPinPvRVRau/ckjYXipRlptaq\nBgAAIABJREFUK9BZrbS99iq9x4+H9idPn0HGzbdinjXysjyCIMRGBK3jYOOZraHvp9mnUmhTg0br\n/AVq4fghaPR6LHPmjur6WyrqQ6MqdouBBSWZozqfIAjx5T52lIbf/Rp/W3gETms2k33PF7AtXDzE\nkQPzBXxsOP52aNseKODZVzsi2tgtBu5aU8JCKXNClrEZDWPhZPTpkwYd0TwrZcVKcr5w3xj1SqVP\nTSPnvgfI/Mxn8TY1otHrMeblo9GLf54vFp4zZ0j6w8+Y3m9ddHLQx/TGA2ifrsGd+jCmGRfGukhF\nUahoqebFI6/S5Y1c570sdxHXtGXS+fSfCfZLRpZx+zrSrklcSZtEs8wpwzKnDG9zM4GebvQpdpIy\nxXOTIIw18a/iGOvo7aSi9UBoe3VhONW7feUqOt5+a8iEHymXXobOFvv6skAwyKaK8CjvpeV5Y5L0\nQRCE4SnBIO1/e4O2116NyAyePH0GuQ98maRJk2I678babbT39gWpCjQdmBrx+er5+dx+WTHm5KTz\nD74IaLRa0q+9juZn/zx4I52OtKuuHrtOnXt5iwVT8bRxu76QGEowSP0Tvxz03/Vgby/1jz9G0f/8\nL1rDxJ4i3NHbyYtHNlDZ7xkGIMuUwZ3SrUzafoi2V58O7dfo9eR88YGLZs2nISsLsrLGuxuC8Ikl\ngtYxtrluR0SZm/KM2aHPdFYr+d/8Nmd++t8DLvy3zJtP5rrPjOr6lcfaaO9W099rNLB63sTM3icI\nnzS+jg4af/9b3If71V7VaEi/8SYm3XjTsNPqFEWhpq6bU009aLUapMJU8jIsOLxO3joZzjjsbylE\n6VXrCOZlWLj7GomSwtSE/EwTif3yK/G1tNDx7jvnfabR68l54MsYCwvHoWfCxcxZWYFvmHwVgZ5u\nenbtxL7y0jHq1cgElSCba3fw2vG38ATCzyY6jY6rp6zm6oJVtD/3HG1bNoc+O1vSxlwijUeXBUG4\nCImgdQydW+ZmVf5ydNrIB9HkomIMObl4TqsFybW2FMwlJdhXrsI8Z+6op9f0T8A0b3oG6SkXz7o1\nQbhQOSr20/jUkwQdjtA+fVoaOfd/CbM0c9jjTzX28Ie/HeJMsyNif+nUNHzZVXgUNXOuEtDhq52O\nXqdh7YqpXLdsyidmpoVGoyHzjjuxLl5C18YP8Zw5DTodltLZ2C+7POZRbEEYils+HHW7iRi01jka\n+MvhlznVfSZif7F9CndKnyJHb6f+8cdxVVeFPkvKyCT/m9+esCVtBEG4MImgdQx9fF6Zm/OnzPi7\nukIBK0DBN75FclFxXK7f1OGi+kR7aPtykYBJEMZV0Oej9eUX6Xz/3Yj9lnnzybn3PnRW67DnqGt1\n8pPn9uL2BM777FBjLcaMA2j64lJ/fTFSbjZ3XyuRO+mTmTHcVDxNTMMVxozSb5r/UFyHD+GsrsRc\nOmdMk2+d6alnd9Neuj0ObAYLi7PnMzmlAG/Ay99OvMf7ZzaHZocBJOuSuWX6dVySt5RgVxdnHvmx\n+gKoz4VU0kYQhAuLCFrHiFrmJpyAaVH2PKyG8x8anVWVoe91djvGKVPj1odN+8JrWbNSTZQWpcft\n3IIgjIy3sYGG3zwR8cCn0evJWPcZUi+/MupZFes31QwYsAIkFchotH2ZPb3J3Fl+NavLJ1+wCVEE\n4UJjnDwlqnb+jnbqfv4o+vR0Ui65FPslK0nKSFyyH1/AxzOHXmRPc2Sd4A/ObKE4ZQqd3u7wOvg+\n8zPncnvJTaQa7Xjqaqn7v0fxt4dfhFvK55H74EMXTEkbQRAuLCJoHSM1XSepczSEts+WuTmXs2J/\n6HvL3PK4vXH1+gJsqexXZmd+Plrx4CoIY05RFLq3baX52Wcia6/m5Kq1VwsnR32ubqeX/cdaB/xM\na2tHl94c2v70zBtYPTW6B2hBEOLDtmgxLS89HzH1fyj+9nbaX99A+xuvYZ5Vin3lKizzF6BNim+S\ntD8ffum8gPWs492nIrZTjXbuKLmFskw1B4fr0EHqH38ssqTN5VeQdefnRIkmQRASRgStYySyzE1R\nqMxNf0GfD+fB6tC2tbw8btfffbgZZ69aA1Cv07LyAqwLJwgXuoDLRfOf/0TPro8i9qdcuoqsz9w1\n4hGKtu7e82vea9RR16TC8Fq6oDOFFQULY+qzIAix0xqN5HzhfuoffwwCA8+ISL1yDb7mJpzVVYR+\noRUF18EDuA4eQGu1krJsOfaVqzAWjD5ZWIOziY+b9g/fELi8YCU3Fl9Nsl7Nf9G9YzuNf/x9xM+S\n8ek7SLv6WjGDQxCEhBJB6xho7+04p8zNwKOs7iMyiqcvs69ej3nW7AHbxWJjvwRMS2ZlYTVdnKUt\nBGGich+vofG3v8bX2hLapzWZyP78vTGXhEg29CVy0wTRZZ5Bn30arcl5XjulbiZJw2QfFgQhMazl\n8yj87vdp3fBKRHbw5GnTmbT25lDtdV97O93bt9K1dTP+1vAMiqDDQed779L53rskFxWTcukqbIuX\nojOZYupPtAHroqx53F5yE6DOEGl/83XaXl0f+lyj15Nz34PYFi+JqR+CIAgjIYLWMbCl7qNBy9z0\n56zYF/reNHMW2uT4ZPY91dhDTX13aFskYBKEsaMEg3S88xatr66PGJ1ILp6m1l4dRZH6nHQzOZOM\ntE/ahi514GnCwV4T5bmSGAURhHFkmlFC4Xe/j6+9nUBXJzqb7bw1q0np6Uy68SbSr78Rt3yYri2b\ncOzdg+L3h9r0njhO74njtDz/LLbFS7GvXEXy9OlR/34HlSBneuqGbwho+jK4KX4/TX/5E92ipI0g\nCONIBK0JFk2ZG1DfYjoqw+tLrGXxmxrcv8zN5CwrxXkpcTu3IAiD83d20vj73+E6FJ5pgUZD+nU3\nMOmmW9DoR3cL1mg0WKeeoks3cMAKoE12M226e9DPBUEYO0np6SSlD50EUaPVYp5VinlWKQGHg+6P\ndtC1ZRPeutpQG8XrpXvbFrq3bcGQk0vKpatIWX4J+pSB/33v6O3ko4Y97GjYTVtv+4BtzpVitBLs\ndVP/xK9wHQgvXUrKyCT/W9/BkCOWGQmCMHZE0Jpg0ZS5AfDW10VMB7KUz4vL9V29fj462BjavnxB\nvhhxEYRRUhSFmq6TnOmpQ6PRMCO1mHxr5AOcs6qSxj/8jkBPT2ifzp5K7v0PYp5VGpd+nGzspDZ4\nAM0wM38POvZyNWIKnyBcaHRWK2lXrSH1yqvwnDxB19bN9Oz8iGBvb6iNt7GB1pdeoHX9y1jL52G/\n9DLMs+cQRKGq9SDbG3ZzsE1G4dwF8ENblDydM/8jStoIgjAxiKA1gc4tc7M4e/6AZW4gMmuwIb+A\npEkZcenD9uoGvD51arLJqGNZaU5czisIn1Rneup45tCLEdnAAWakFnN36R2k6W20/vUlOt59J+Jz\ny9wysr94P3rb8DMdAsEA3d4eurzddHt66PL20O3pVr96u+ny9NDlUb9qkoZ/ED3WeYKgEkSrEZk9\nBeFCpNFoSC4qJrmomMx1d9Lz8W66t27GffRIuFEggGPvHhx79+CzmThQZGTfVB3d1vPfatkNNrq8\nPeftP+tS7TT8v/htZEmbefPJfeDLoqSNIAjjQgStCXSs80TEg+1lg5S5ASKnBsdplFVRlIipwSvm\n5GI0iGQsghCrRmcT/7fvNwSdLuad6CWr3Yei0VCXlcSRKTX8/sNfcPvuAL7T4ZEJdDoyb19H6lVX\n4w36aHe1qgGpp5tub0/E92e/OnznJ1MaUJSTJhQUFEWJur0gCBOX1mjEfslK7JesxNtQT9fWLXRt\n30qw36yOpB438yrdzKuE0zlJHCg20Totk6WFS1iWu5hUYwrPy69Qc2A7M0/0Yu4N4jJqkacmM8dW\nzNw39+Pv7V/S5kqy7rxLlLQRBGHciKA1gTbWbgt9r5a5yRuwXaCnh96aY6HteE0NPnKmk4Y2V2h7\ntUjAJAij8vrxv1N4rJOrdnaT1K96RemJXlbt6UGrtODrt9+dambPmmmcsVXQvXkrvQHP2HcaKLDm\nDbiWXhCEC5eiKNRb/Gyfq2dfhp3c01pm17iZ0uBF228CxuRGH5MbfWgrAqQsa8W20ok228oVG5tY\nsqcj4pzzj7hBs1d9ydVHlLQRBGEiEEFrgrT3dlAZRZkbAGd1Zag2m85qI7moOC59+GBveJRVKkwl\nP2PgqcmCIAzP4XPSXr2Xm3d0RzwQnmX0R24fLEpm4yIzvqQmcJ3fPhoGbRIpxhTsBhspBhspxhRc\nPTq27WtH8RlRvMlkTK/HYTox5HlW5S+PrQOCIEw4Dp+T3Y372F6/i3pnOGdFTaGRmkIjVleAxWd0\nlNa40Hc6Qp8HnU4633+XzvffRWuxEHQOMqOj73lEo9eTc/+D2BaJ9fCCIIw/EbQmSLRlbgAcFeGp\nwZa5ZXGZftPl8LD3SLge5OULxCirIIxGR28XSyodAwas/QU08O6yFOSiwUtWmfWmfsFoCnajGpTa\n+wLTs1+TdcaI0Y2mdhc/fHM3AY8ZgAx7Mg+vupzfHXwy4uG1v7kZpSzLXTTyH1gQhAkjqAQ50lHD\n9vpdVLRU41cC57UxaJNYkF3OitwlFNungKL0lc7ZjGPvxxGlcwYNWPvJ/OznRMAqCMKEIYLWBPAG\nfGyrG77MDaj1z1wHqkLb8ZoavLminkBQfbpOsRhYUBJ7LUhBEMDY4ya/xTdsu6AGdAvKWJFsjwhA\nQ0GpwUaSLmnE1+/1+nlsfRVuj/qwatBr+dptc8my2fj2gi/zxol32dnwcWgKst2QwqqC5ayZvFpM\nDRaEC5RaqubjvlI1HQO2mZJSyIrcxSzMnodJ3+9lmUYTWTpn5w66tmzGW3smqmt7GxqGbyQIgjBG\nRNCaAB837cPpH77MDYD76BGC7r5kBzod5tlzRn39YFBhU0V9aHtVeR56nUieIAijYfPp6IyiXVIQ\nHir9fFwzbCqKwh/ePER9a3h05J7rZjI52waAOcnMupKbuWXadbS429BptGSaMkSwKggTiKIoNLqa\n6fJ0YzNYybPkDLhO1B/0U916iG0NuzjUdmTAUjUWvZklOQtYnrf4vHJbA9FZraRduYbUK66i/vHH\ncO7bO+wxgZ7u6H4wQRCEMSCC1jhTy9yEEzANVeYGIrMGm0skdCbTqPtQUdNKe7c62qLRwOp5AyeA\nEgQhekn21OgaJhvRJI18JHUob+86zcdyeLr/VYsKWD77/PJVBp0hqgdYQRDG1sE2mdeOv82ZnnCu\niRxLNjcWXc38rLkANDqb2d6wi50NewbNID4zbQYr8hZTljmHJO3IH+E0Gg3GgsKoglZ9atqIzy8I\ngpAoImiNs5GUuVEUJaI+a7ymBn/YLwFT+bQM0lMGX1snCEJ09KmpmEtn4zp4YMh29uUr41oW4sDJ\ndl7eWBPaLilMZd3l0+N2fkEQEmtPUwVPHXj2vBHTRmcTT1Y/w5KcBbS62znedXLA41ONdpbnLmJZ\n7mIyTOmj7k/KsuW0v74hqnaCIAgThQha46z/KOv01MHL3AD4mhrxNTeFti1low9amztcVJ8IFwO/\nQiRgEoS4mXTzrbjkwxA4PwkKgNZiIf2aa+N2vdZON7/ZcOBsMk/SbEYeumWOmO4vCBeIXr+H5+S/\nDjjF96xdjeePemo1WsoyZrMibwmz0meg1cTvd96QnUPKpavo3rJ50Da2pcsxFhTG7ZqCIAijldCg\nVZIkDfA4UA70AvfLsny877Ns4HlAQS15Pw/4PvA74ElAAgLAA7IsH0lkP+OlvbeDipbq0PZQo6wA\njn6jrIbcPAxZWaPuw8Z94bWsWakmSotG/1ZWEASVadp08r/2TRp//zsCjp6Iz5IyMsn9ytdIyohP\n0jOvL8CvXqnG4VaTP+l1Gr5y6xzsFkNczi8IQuLtad6P298bdftscxYr8hazNGchNoM1Yf3Kvutu\nNBotXVs2hUrcnJWy/BKy7r43YdcWBEGIRaJHWm8BjLIsr5AkaSnwaN8+ZFluAi4HkCRpGfCfqAHr\n1YBFluWVkiRdBfwIuD3B/YyLzbU7Qm9ThytzA0RODS4rH/X1ff4AW6vCU5NXz89HK4qBC0JcWeaW\nUfSTR3Ds2U3vyZOg1WIukbCUlaPRxSfxkaIoPPOOzKmmcGB815oSpuXZ43J+QRDGRp1j4FJU55qU\nnM69sz9DUcqUAZMzxZtGryf77ntJu/Z6enZ9hL+rC73Nhm3JMgw556+XFwRBGG+JDlpXAm8DyLK8\nU5KkwYoFPgbcKcuyIklSL2DvG6W1A94E9zEuvAEf2+t3hbYvy18xZObOgNOJ+9jR0HY81rPuPtzc\nb1RGy8oykZBFEBJBazCQsvwSUpYPPZsiVh/srWNbdfhhd1V5LpfNE1P9BeFCo9dE9yJrRmoxxfap\nie3MAAxZWUy68aYxv64gCMJIJXphVArQ1W/bL0lSxDUlSVoLVMuyfKxv11bABBwGfgP8IsF9jIv+\nZW6StHpW5A1dkNtZXQXBIABaswXTtNEnVumfgGnxzCyspvhmMBUEIfGO1nby/PvhF1pFuSnctUYa\nxx4JghArKX1GlO1EcjVBEIShJHqktRuw9dvWyrIcPKfN54Cf99v+HrBNluV/liQpH/hQkqQ5siwP\nOuKalmZGrx+/eoSKorB1z47Q9sopSyjKH3p6TbsczkCavmgBWTlRltMYRE1tJzX14Zpqt105g8xM\n2xBHCMIn13jfMwbT1uXm1xsOEAiqywzsVgP/dv8yMlJHXwpLEITRieW+sSpjAa+d+Bu13Q2Dtplk\nSuPq0hUk6cSLZkH4pJAk6R7glCzLG8e7LwCSJB2SZXnWKM/xFPCELMu7hm0cPuabwOeBb8qyvG2o\ntqMOWiVJekOW5RsH+XgbcCPwct+61aoB2iySZXlHv20r4dHZzr4+DvmvREeHa2SdjrOjHTWc6gqP\nci7NWEJLS8+g7ZVAgPaPw9kC9dLsIdtHY/0H4ZGZyVlW0k36UZ9TEC400b6oGe97xkD8gSA/eXYf\nHT1qjWWtRsOXb5qN4vOL32VBSJCRvNyN9b7xxVl38Yv9v6PT03XeZ5YkM/fP+Tyd7b2o+SoFQZjo\n4jEoJMvy03HoSjwNnuI8sW4CbpJluX64hvEYaR1qodUrwBpJks5Gzl+QJOlO1ERLT0qSlEHk9GGA\nnwJPSZK0pa9//yjLsjsO/UyYjbXbQ98PV+YGwF1zjKCrr3C4VotlztxRXd/V6+ejg+H1b6sX5I9J\nIgdBEOLnufePcqwufDtcd8V0pMlp49gjQRDiIduSxQ8Wf5NNtdvY1biXLm8P1iQLi7LnsbrgEtKS\nRzfTShCEialvNPUmwIK69PFlYC2QBGwHKoFk4HrUJZW5wD2yLFcMcr6/9LXRA/cDx4E/AFl9f/5J\nluW3JUmqRh0onA78EVgNzAG+CNQDf0J9S5YF/FCW5Vf6XWMhauJcBdgvy/K3JEm6A/gmarWXZ2RZ\nfnyYn3tSX7+sQA9wL+A4p6//DKQCC4AXJUm6UpZlz1DnjUfQOmhkLsuyAjx0zu4j/T5vRe1s/2M6\ngVvj0K8xcW6Zm9UFK4c9pn/WYNP0GegsllH1YceBRrw+dda1yahjWWn2qM4nCMLY2lrZELEmfVlp\nNmsWFYxjjwRBiCebwcqNxddwY/E1490VQRDGlluW5U9JkvQoYJdl+WpJktYDs4CzwalPluXr+gb2\n7gG+c+5JJElKAUpQA9BpqEFuIfC6LMsv9VVp+Q5qAtzJwCXAFOAlYCZqsPxp4P9QBxxLATOwQ5Kk\nV/td6jHg07Is10mS9EtJkq5BreLyXWAn6rLO4fwj8Oe+ft2OWtL0N+f2VZblOyRJegC4Y7iAFRKf\niOmi17/MTZoxlbKM0mGPcVaGX6CMNmuwoih8sLc2tL1idi7JhkQvVRYEIV5ONHTzp3fk0HZBppV7\nrpspZksIgiAIwoXv7NLILsIDd12oI6zntqk/Z3+ILMvdwH8Bz/Z9VYB24BpJkv4IfAV1BBegTpbl\nrr7r1PQNIva/5i5Zlr19A4UdqNVazpKAZyRJ+hBYghr4/gPqaOnfgfQofuZZwLckSfoA+AaQ3Xed\ngfqq6fszLBG0jsK5ZW5W5S8fsswNgLe5GW9DeNq2dZRB65EznTS0hdfZrF4gymIIwoWi2+XlV69U\n4Q+oMyUsyXq+9qm5GJMmXpIoQRAEQRBGLJq1osO2kSQpF5gry/LNwL8BP0AdlT0gy/K9wLtEGfwB\ncyVJ0kqSlAqk9AWvZ489CNwuy/LlwM9QR1e/iLpc80rgHkmSzMOcX0addnwF6gjtm6hBbyx9DYnH\nkNwndjhgpGVuAJyV4anBSVnZJGWProj3B/2mFEqFqeRnjG6qsSAIYyMQDPKbDQdo71ZnxGiAB2+a\nTZbIFCwIgiAInxRRJUCSZblBkqTivpw/AdTAtQN4TpKk61HXyGZEeU4d8DfUUdOzU5HPHvNd4BVJ\nkpKARtTpwPuBtyRJ6gbekWV5sKx0Z8/xY+APkiT9E2qseR/qKHMsfQ3RKMrokkVJkvQtWZZ/PnzL\nxGlp6RnzjFeKovDj3T+nzqGmsV+Ru5i7Zn162ONqH/kJrkMHAUhdcw1Zd9wZcx+6HB6++/j2UHmM\nL988myWzxHpW4ZMrM9MW1Uu08bhnnOvFD47x9q7Toe1bVxWzdsXU8euQIHwCRXvPgIlx3xAEYfyN\n5L4x0UiSNAV4TJblm8a7LyMV1Uhr3yLc/wLSCM89VmRZLh7vgHW8HOs8HgpYAS4ruGTYYwJuN64j\n4bVr1rLyUfVhc2VDKGBNsRhYUJI5qvMJgjA2dh1qighY58/I4IblU8axR4IgCIIgTASSJL1IeCRS\ngzoa+fJwWXvHSt8I6hoiR0mPybL8YCKvG+304MdQh4+rGb86PhPKxtpw/dvpqUUUDFPmBsB1oBoC\nAQC0JhOmGSUxXz8YVNi0Pzw1eFV5LnqdWKIsCBNdbYuDp/52OLSdk27m/htL0YrES4IgCILwiSfL\n8roEnvsUahme0ZzjR8CP4tOj6EUbtLbKsvxGQntyAWlzd1DRciC0HU2ZG4gsdWOePReNPvYlxRU1\nreG1cBq4rFwkYBKEic7V6+OX66vw+NSXV0aDjq/dNheTUWT8FgRBEARBGEy0T0pb+uoLvY1ajBYA\nWZY3J6RXE9yWupGXuVGCQZxVlaFta/nopgb3r+lYPi2DSfYBM2QLgjBBBBWF375+kOYOd2jf/TeU\nkieSpwmCIAiCIAwp2qD1bFrc+f32KcAV8e3OxOcNeCPL3BQMX+YGoPfEcQKOHnVDo8EypyzmPjR3\nuKg+0R7avlyUuRGECe+1rSeorGkLbd+wfAoLJbEOXRAEQRCE0Vv78IZk4FNAGeAD3gM2vf7IzRfF\n0s6ogta+Wj0CsDuGMjcQOTU4edp0dDZbzH3YuD9c5zUzNZnZRdHU+RUEYbzsP9bKa9tOhrbnFKVz\n66XF49chQRAEQRAuGmsf3nAT8HvCCZwA/hnYt/bhDbe//sjNx8enZ/ETbfbglcA/AFbULFY6YIos\ny1MT17WJR1EUNp4JJ2BanD0fa1J0U/sc/YLW0WQN9vkDbK0MZy1ePT9fJHARhAmsqd3F714/GNrO\nsCfz4E2z0WrF760gCIIgCKOz9uENVwDrUeOzc80HNq59eMOC1x+5uXWk55YkSQ/8AZgKGID/kmX5\n9VF0N2bRppt9EngVNcj9FXAUeDRRnZqojnUep97ZGNqOpswNgK+tFW9dbWjbUj4v5j7sPtyMw+0D\nQK/TsnJubsznEgQhsXq9fh5bX4Xb4wfAoNfytdvmYjUljXPPBEEQBEG4SPyYgQPWswqBr8Z47s+h\nJuRdBVwH/DLG84xatEGrW5blp4CNQAfwAHB7ojo1UfUvczMjtTiqMjcQOTVYn5GBIS/2Naj9EzAt\nnpmFzWyI+VyCICSOoij84W+HqW91hvbdc91MJmfHvjRAEARBEAThrLUPb5AI5x4ayj0xXuJF4F/7\nvteirpUdF9EGrb2SJKUDMrBMlmUFyEpctyae88vcRDfKCuCorAh9by0rRxPjdN7TTT3U1HeHtkUC\nJkGYuN7edZqPDzeHtq9aVMDy2Tnj2CNBEARBEC4yBVG2iylokGXZJcuyU5IkG/AS6jrZcRFt0Poo\n8ALwOnC3JEkHgD0J69UEdG6Zm7lRlLkBCPb24j58KLRtKZ8/ROuhfbgvPMo6OcvKtLyUmM8lCELi\nHDjZzssba0LbJYWprLt8+jj2SBAEQRCEi1C061Tbhm8yMEmSCoEPgKdlWX4h1vOMVlRBqyzLLwFX\ny7LcAyxEnd/8+UR2bCLxBrxsq98Z2o62zA2A69ABFL+6nk1jNGIqkWLqg6vXz44D4fW0qxfkxzxi\nKwhC4rR2ufnNhgMofQnm02xGHrplDnpdtO8IBUEQBEEQolIJHI6i3XOxnFySpGzgHeB7siw/Hcs5\n4iWqpyhJktKA30qS9AFgAr4O2BPZsYlkd9M+XH43MLIyNwCOivDUYEvpHLRJsSVg2XGgEa8vCECy\nQcey0uyYziMIQuJ4fQF+tb66X7I0DV+5dQ52i1h7LgiCIAhCfPXVYP2PYZp1Ab+I8RL/CKQC/ypJ\n0oeSJH0gSZIxxnONSlQlb4DfAX9HXejbA9QDfwZuSFC/Jozzy9wsiLrMjRIM4qwMJ2GKNWuwoigR\nU4MvmZNLsiHa/3SCIIwFRVF45h2ZU009oX2fXVPCtLxPzPs9QRAEQRDG2OuP3PzC2oc3ZAOPcH5s\n1wTc8vojN5+K5dyyLH8L+NYouxgX0c5XK5Jl+bdAUJZljyzL/0L0C38vaEfPKXOzujD6BEyeUycJ\ndIcTJ1nmlsXUhyNnOiMykK6eH13WYkEQxs6H++rYVh2+V6wqz2X1PJEsTRAEQRCExHr9kZt/ARSh\njrquB55HrfZS/PojN380jl2Lm2iH6/ySJNlBzUQkSdIMIJiwXk0g55a5ybdGXxe1f9Z2s2n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L5Tfm51bS99FkXEU4cbEbHk8HJK6XHgPx6vYAXYsaM2j75u3dHKl+5/lvaOTsZ0HGR229aubQ1n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\nkiRJyq3/Dx6RBoJWGQ0fAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1198f1ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"r.plot_grid_scores('max_depth');"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x119969c18>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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zk927d2mvVREJmkbyUSQnpy///vdb5OfnO12KiEQJhXyUUcBLvHr88T/z5JN/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\nXkQkhv1/gSFF10evt7IAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f7e14e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"r.plot_roc_curve()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x119991828>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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knldfjuv9OzWahqZr9jr9xo1QWdn5RCQQaBQ+uwG9orwhfDaNxGJ7lHgo6pFO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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119a29e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"r.plot_learning_curve()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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