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Last active January 25, 2017 21:32
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Machine Learning & sklearn intro
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Machine Learning Intro\n",
"\n",
"This is a notebook that helps with the examples covered in [A Weekend With sklearn](). The examples are as follows:\n",
"\n",
"1. You are given a bag with 100 items, you can select 20 items to inspect and remove from the bag. Afterwards, you will inspect an item in the bag and predict its color.\n",
"1. You are given a bag with 100 items, you can select 20 items to inspect and remove from the bag. Afterwards, I will give you the height of an item from the bag and you will predict its width.\n",
"1. You are given a bag with 100 items, you can select 20 items to inspect and remove from the bag. There are two undefined groups in this bag. You can inspect another item from the bag and let me know which group it belongs to.\n",
"\n",
"Let's begin by setting up the data we will use!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import random\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"## Generating the data\n",
"total_items = 100\n",
"item_shapes = pd.Series([\"cube\"]* int(0.4*total_items) + [\"sphere\"]* int(0.6*total_items))\n",
"item_colors = pd.Series([\"red\"]* int(0.4*total_items) + [\"blue\"]* int(0.6*total_items))\n",
"item_weight = pd.Series([random.randrange(10,100) for i in range(total_items)])\n",
"item_size = pd.Series([random.choice([\"S\", \"M\", \"L\"]) for i in range(total_items)])\n",
"item_height = pd.Series([random.randrange(10,100) for i in range(total_items)])\n",
"item_width = 2*item_height\n",
"\n",
"features = pd.DataFrame({\n",
" \"shape\": item_shapes,\n",
" \"weight\": item_weight,\n",
" \"size\": item_size,\n",
" \"color\": item_colors,\n",
" \"height\": item_height,\n",
" \"width\": item_width\n",
" })"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example 1 - Classification\n",
"\n",
">You are given a bag with 100 items, you can select 20 items to inspect and remove from the bag. Afterwards, you will inspect an item in the bag and predict its color.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" <th>width</th>\n",
" <th>shape_cube</th>\n",
" <th>shape_sphere</th>\n",
" <th>size_L</th>\n",
" <th>size_M</th>\n",
" <th>size_S</th>\n",
" </tr>\n",
" </thead>\n",
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" <td>76</td>\n",
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" <td>0</td>\n",
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"text/plain": [
" height weight width shape_cube shape_sphere size_L size_M size_S\n",
"0 76 96 152 1 0 0 0 1\n",
"1 98 30 196 1 0 1 0 0\n",
"2 77 79 154 1 0 0 0 1\n",
"3 24 98 48 1 0 0 0 1\n",
"4 76 51 152 1 0 1 0 0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## We'll define our properties as everything except color and one hot encode that\n",
"## One hot encoding means that if we have a value of \"red\" and a value of \"blue\" it will generate two coluns\n",
"## that are boolean values\n",
"## Check out the table below\n",
"properties = pd.get_dummies(features.drop(\"color\", axis=1))\n",
"properties.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>blue</th>\n",
" <th>red</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" <tr>\n",
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" <td>0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" blue red\n",
"0 0 1\n",
"1 0 1\n",
"2 0 1\n",
"3 0 1\n",
"4 0 1"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels = pd.get_dummies(item_colors)\n",
"labels.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Start with any necessary imports\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"## Split the data\n",
"properties_training, properties_testing, labels_training, labels_testing = train_test_split(properties, labels, test_size=0.8)\n",
"\n",
"# 1. Define the model you want to use\n",
"magic = DecisionTreeClassifier()\n",
"\n",
"# 2. Train the model\n",
"magic.fit(properties_training, labels_training)\n",
"\n",
"# 3. Predict new values\n",
"predictions = magic.predict(properties_testing)\n",
"\n",
"## Score the data\n",
"accuracy = accuracy_score(labels_testing, predictions)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"accuracy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For scoring, I chose accuracy but you can try out many different score from the [classification metrics list on sklearn](http://scikit-learn.org/stable/modules/classes.html#classification-metrics)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example 2 - Regression\n",
">You are given a bag with 100 items, you can select 20 items to inspect and remove from the bag. Afterwards, I will give you the height of an item from the bag and you will predict its width."
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>height</th>\n",
" <th>weight</th>\n",
" <th>width</th>\n",
" <th>shape_cube</th>\n",
" <th>shape_sphere</th>\n",
" <th>size_L</th>\n",
" <th>size_M</th>\n",
" <th>size_S</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>76</td>\n",
" <td>96</td>\n",
" <td>152</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>98</td>\n",
" <td>30</td>\n",
" <td>196</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>2</th>\n",
" <td>77</td>\n",
" <td>79</td>\n",
" <td>154</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>24</td>\n",
" <td>98</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>76</td>\n",
" <td>51</td>\n",
" <td>152</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" height weight width shape_cube shape_sphere size_L size_M size_S\n",
"0 76 96 152 1 0 0 0 1\n",
"1 98 30 196 1 0 1 0 0\n",
"2 77 79 154 1 0 0 0 1\n",
"3 24 98 48 1 0 0 0 1\n",
"4 76 51 152 1 0 1 0 0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## We'll define our properties as just the height\n",
"properties.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 152\n",
"1 196\n",
"2 154\n",
"3 48\n",
"4 152\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels = item_width\n",
"labels.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Start with any necessary imports\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import r2_score\n",
"\n",
"## Split the data\n",
"properties_training, properties_testing, labels_training, labels_testing = train_test_split(properties, labels, test_size=0.8)\n",
"\n",
"# 1. Define the model you want to use\n",
"magic = LinearRegression()\n",
"\n",
"# 2. Train the model\n",
"magic.fit(properties_training, labels_training)\n",
"\n",
"# 3. Predict new values\n",
"predictions = magic.predict(properties_testing)\n",
"\n",
"## Score the data\n",
"r2 = r2_score(labels_testing, predictions)\n",
"r2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For scoring, I chose `R^2` but you can try out many different score from the [regression metrics list on sklearn](http://scikit-learn.org/stable/modules/classes.html#regression-metrics)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example 3 - Clustering\n",
">You are given a bag with 100 items, you can select 20 items to inspect and remove from the bag. There are two undefined groups in this bag. You can inspect another item from the bag and let me know which group it belongs to."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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hzvRDRixLHpoqhRkMhoilrB3fYDA0swbjuTnVQjRwYO9Hdla0Qh2vTaF6D6hV\nANXPtfdDm97uvXhR1sXFxe01pZzRaQVZRS+CnO391VxYyIqiEAqFInmg2e5JnClUS97r9bZ4gPB4\nPIRCoYQXyXgWVCw3Z6asaT1G0OuFTItNMBhkxYoV1NTU0KdPH4466qi4Y6jfS2NjIw6HA5PJRDgc\nxm63J/XQpu2UlWn0eN/Estrr6+uprKxsnwnlkE4ryNovvD2q+mjdutFWmc1m071bNxptT2JVSNT6\n4HpGO+9sPkBoA8FUkrGmO2KJyPYm02KzZcsW7rrrftav308waMVm83PccZXcd98MSkpKWoz9/vvv\n8/LLb7J9+37y8sycccZJXHzx73A4HGk/tGUqd1qPLmto+SDVGVovQicWZC3tZWGoAWW5EOJsnWMo\nFMLr9RIMBiM9iYPBIMFgMOs/9HTOKda8k+2lnO75pRvdq4q0+pmOTF1dHWvXrsVkMnHMMcfoIkBN\nSzgc5p57ZrJuXQk9eszEau2F272OTz55kMcfn83//d/dzd7/3nvvcd99cwgGR1JYeAK1tduYM+ef\n7NxZzYMP3hvz3on30KaW941XiSyeSOtRaBMhXtpTUVFRO8wmtwhBJreCrFrEwWAw4tbNRaBTps9R\nK2hq9LYqaJlsnZdptJ2jDAYDTqezzT7Kuc5RTzVX1uPxxOyMpHfmz5/P7Nn/YP/+MJIEPXrYueuu\nqZx66qntPbUIa9eu5fvv91BaOgObrTcAeXlH4PdfwQcf/Jnrr99Hly5dgKbfxrx5CwkGT6dPn+sB\nKCo6kdra3nzyyf2sX7+eww47LKFxE70f4jXgaO1+0GMaFsR3WQsL+QBG+4Xnan9V65pWLZtsdi7R\nkqlzTEXQskUy59RW56jWxoj1/1VyGbkez5pWS44aDIaMti9MZ66J8tlnnzFz5guEwxfTrdtFKIqf\nXbue5Y47ZvLyy7055JBDgKZUuo8++og9e/ZQWVnJ8OHDE0qtytR51tbWEgiAzdan2et2ex9cLona\n2tqIIO/fv59du+ooLv5Vs/cWFBzHvn0WNm3alLAgxyNR70o4HG62BRLt9tYr0Q8ODQ0NIqirs5DN\nCORoIVYDhkKhEB6PJytjtjWfVBapaCFuTdD0FGSUyc5RejknFa31pNYAh8Ssp/aq4xzNP//5Fj7f\nYHr2vC7yWo8ed7Nz51reffddbrjhBtatW8e0afewfbsf6I7B8E8GDXqRxx57kB49esQ9dia/r4MP\nPpi8PKjtcdqfAAAgAElEQVSvX0px8YjI6/X1S+nSxUpFRUXkNafTidVqwOfbRUHBMZHXA4E9GI2B\nrD2EJ+Nd0T64+f3+ZqlY7S3YwkLuhGTbQo4nxKprWl0gcxVUkeoYqViW2sj1bFti8b431XJMtmHF\ngUAi1pMq0u29F7l9ezVmc3PXtCQZUZTD2L27imAwyPTpf+Knn/pTXn43ZnMJPt9PrF17Jw8/PIvH\nHnska3PTUllZyemnD2HhwscJBHbjcPTH5VqJorzFJZeMbbbn7XQ6Of3043n55VdxOPridA4kGNzH\nzp2zOfhgR9rlFZMlXuS/+pCtFrzJRRBZIkSvG2qBHrGH3EnIpCC3JcTaMXNJsiIZLcQdpSdxpupk\nq+jJ2k+HdPYiE7WmU7lOhx7ah2++WYGiyEhS09xk2YskfUNl5UhWrVrF5s11lJbegNncFMlss/Wh\nsHAyn39+b4uuPLHOO1Pcdts0unZ9gUWLXqGxMUhFhYOLLjqfCy+8sMV7r7zy9+zceT9ffnkbe/YU\nYDA00revmbvuuklXVbGgqa6/KtbaBzdtgZNcb4Noj6V65oSFfICjLraZWHQTFWLt2Orncmkht3We\nmXDx5tJCVscBslKes63xO7pYt2VNx1uUM2U5XXDBeP7zn1vYtetuiosvRJZ91NbOpUcPD+eeey7f\nfvstoRBYLM1d0xZLKY2NTdG38QQ50/efzWZjypQpTJo0KVKoIt4+dkFBAbNmPcRXX33Fli1bKCoq\n4qijjspZzEgyRHsL1Qc37dqVaBBZuil6sTIGXC4XDodDF+VYs02nFmQVdWFN5QecrBBrx1Q/nwva\nGq8ju3jjFfXINHr2DmTyPkpkUY5VHlK9PupriSzKgwcP5uGH72DWrGfYvv0DAI4+uoI77vgT5eXl\nPwc+Ql3dfykpOSfyubq6/1FRkUfv3r0zdt6JYrfbE0rLkiSJY445hmOOadpHdrvd2Z5aUiRzz6Sb\nopfsw5v272oOsp5/f5miUwuyKsSq6CQjyKkKsXZs9TjtSTaEOFfnppaybGxszFpRj0QeZDoCwWCQ\nDRs2YDAY6N+/f0rfr3ZRjlUeUo3oVZu2qLRVderUU0/l5JNPZtOmTRiNRg466KDI/CoqKhg79lRe\nfPFx/P6t2GyH4nYvx2z+H5MmTcJqtaZ5ZXKLnkQl3bSnVIPIIL6HJdac6urqstZARm90akFWSUZA\n0hXiVMbMBNHjpbPXWlNTA0C3bt0SGivTaHOggZynXu3fvz/izu8ILFmyhIcfns2OHbUAHHJIKXfe\neRPHH3982sfWLspGo5FgMIjVao003IguaKH9XLRI9+/fn++++457772fb7/dQllZCeeccwY33TSV\nsrLuvP76u+zfv4BBg0qZMOE6fvvb30aOt2/fPv7973/z9dffk5/vYOTIXzN48GBdCmB1dTXr1q3D\nZDJx9NFHH3BiEy+ILHobJFYDDnX9UQPODAZD1ixkWZa55557ePnll6mqqqK8vJwrrriC6dOnN3vf\n3XffzfPPP09dXR3Dhg3j6aefjqTjZZpO234RfmnBGAqFcLlcFBQUxBXWWEJst9tTdo3KskxdXR15\neXk52RtRx3M6nSiKEhHiZPZaV69ezS233MGqVasAGDJkCA8//ABDhgxp9r5ErmcqRKdemc1m/H4/\nRUVFWXOt+/1+3G43xcXFfPXVV9x330xWr14HwLHHHsG0aVMZMmQIe/fuZd26deTn5zN48OCc9nhW\n65/HauX59ddfc8UVN+ByjaCk5FIUJUht7Vy6dl3Nq68+R2UG6wMrioLb7cZqtcb0VMSqOqVtXbhi\nxQpmzJhFXd0h2GzHEwhsxWz+gquuOpsrr/xDpBhKdJT/rl27uPHG6axfH8ZgGIos78FmW8dFF53M\n1VdfpZsgqoaGBhYsWMgbb3zInj1+DAaJnj0d/PGPlzJixIiczycQCLR7O8hot3coFIqkoM6fP59p\n06ZRWVmJLMtMmjSJI488kiOPPJLKysq0f/MPPPAAf/nLX5g3bx4DBw5k5cqVXHHFFTzwwANcd11T\nGt5DDz3EQw89xLx586isrGT69Ol88803fP/996ms220+UQhBbqMnsirEPp8vskeZjhBrj1tbW/tz\nzmJLt9uWLVv4+OOPcTgcnHnmmWkHg6iCrLqFkg162rRpE7/61am43f2RpGsBCUV5CqfzBz777EMO\nPvjgyHsz3WM6VsS31WolFArR0NBAYWFh1gRQFeTa2lrOPvsC9u49BLv9CkDB632Brl23cM45p7Fw\n4fu43WGMRjjkkHJmzXqQww8/PCtziqY1Qb7rrhm8+upOKipe0UQxB9i9ewzXXfcrpk6dmrF5qIKc\nbAlYRWlqSjJp0rV89VVvunS5Db/fj9lsxud7H4vleV566a+Ul5fHbLwxc+bDzJ+/jT59ZmEyNUXi\n7t27mFDocZ577n4GDRqUsXNMFUVpqm09ffps9u/PR5adSJKMweChb1+F559/KKMPR4mgVgzMZL/o\ndFF7IdvtdrZt28ZHH33EJ598wmeffUYoFGLfvn0A/OEPf+DZZ59Na6xzzjmHsrIynnvuuchr48eP\nx+FwMG/ePADKy8u55ZZbuPHGG4Ffggj/8Y9/cMEFFyQ7ZJuCrP+InRwQy8WqunRdLhdutxuDwUBB\nQQH5+fkZsfriuXVlWWbq1BsZOHAgV199NRMmTKBPn4N48803UxpHex5A5Dzy8vKSErG//e1veDx5\nmM2LMZsvxGy+ALP533g8+TzzzDNxx04HWZbxeDzU1dURCASw2+0UFRXlNP1KHecf//gH+/blU1Ly\nEk7nuTidoykufoXdu90899xC/P7rKSz8GLt9Pj/80J3f//6PkWvenmzYsA2z+biIGAMYDBYkaTCb\nNv3UjjP7BUmS2Lt3Lz/+uBuPZwjr1//E5s3V/PjjNvbuHcC+fQpr166NLNYejwe3243H48Hr9bJk\nyXLy8n4bEWOALl3OwO0uYfny5e14Zs15/fU32bkzRCh0Jmbz/2E03obffyQ//LCb999/P+fz0XP8\ng8FgoG/fvkycOJEjjjiCs846i5qaGnbu3Ml7773HlVdemfYYJ510EkuWLGHDhg1AU3nUzz//PLIV\nsmXLFqqqqhg5cmTkMwUFBRx//PF88cUXaY8fi069h6wutlpxVJSWrficTmfWonajfxTPPvvszwL3\nKHAVsB+v92Yuu2wCa9euaWaJtkas85BlObK/lywrVqwhHB6J2fyLFSZJDsLh37BixZoW55UOiQaa\n5XIffvXqbzEYRmAw/LJvLEkOAgEwGkdTWHgVACZTKSbTE+zaNYLFixfHzFHNJX36lLF69TfNAhYV\nRUZRvqOi4sh2nZsWs9nMvn011Nc3YLMdjMWSjyx7aWz8Fq93Pw6HA6fT2SJYSHV/N7nCQ4CEJEHT\nLZF69oQWr9fLV199hc/nY8CAAa1WB2uNdet+RFGG43ReHZmPydSP/ftXs2bNmjY+nR30tMcO8RtL\nqHvI5eXllJeXZ2Ss22+/HZfLxWGHHYbRaESWZf70pz9x0UUXAVBVVYUkSS3S6kpLS6mqqsrIHKLp\n1IKsot6UwWCwmWs6W0KsHTf6BnzqqeeQpAtQlJt+fsWJovwDRenJ3//+d+67775Wj6m6/zweD+Fw\nuFn0cW1tbcri1aNHdwyG9S1eNxjW06NH9xbnpc4lGTJd1COTlJV1QVE2Rb0aQlEaMJmOavaq0dgN\nSerFjh07cjfBOIwfP5b33ruVPXsepaTkMhQlyL59z1FQsJsxY+5u+wBJkM6DkcFgIBz2Ah9iNI5D\nkposefgfslwf2QeOVdJx5MihvPrqe3TpchpGYz6gsH//f3E49nH00UdHPFyp5MiuWLGCWbOeZ/t2\nP+GwgaIimXHjhjNp0sSk7suma2MGeqEdVpJsQAVGYzjOJ7OHHi3kWFHWDQ0N9OzZM+Njvfbaa7zy\nyiu8+uqrDBw4kDVr1nDDDTdQXl7OZZddlvHxEqFTC7IqiGq0biAQyGpP3Hjja9m5cweKEn0z2IHD\n2LlzZ6vHa6u3bzqFLC6//DLeeecSAoGZmM3X/zzeE8BKLr/8pZSOqRIdMJfo/nYuLGR1jPHjx7J4\n8U24XM+QlzcRUHC5nsVoDGE0ftPsM6HQLhRlG5WVF2dtXoly/PHHc889f2TWrOfYt+8NoKmb0p13\n3p52g4NMUldXR3FxD4LBGny+S1GUw5GknzCba3A4Slp0ttIyYcKlrF49nY0br8ZoHEo4vAeLZS0X\nXji8mfXTVhvL6L3pmpoaHnjgafbsGUrv3hMwmfKpqfmQefPmUlFRzplnnpnUOR5xxCHs2LGZhobv\nsVrLUJQwfv9WrNYdVFQcy9/+9iwul5tDDqlkxIgROWmmoEcLOXpOdXV1WYkDuPXWW7njjjs4//zz\nARg0aBBbt27lwQcf5LLLLqOsrAxFUVpUg6uuruboo4/O+HygkwtyMBjE5XJFcibNZnNOK+nEEsgj\njjic5cvfRZZv5ZcYgCoUZRVHHHFOi2MAEYs4FAq12ds3VfE6/fTTueOOm3nooQcJBB79eTELcOut\nN3HGGWe0OK9ExorlVs9FK8pU+M1vfsNNN03m8cdnUVf3JIqiYLcHGD36ND76aDG1tb3IyxtNKLSb\nxsZHqawsYtSoUe09baApUOWMM87gq6++wmg06rLfcI8ePSgtLUaSTsdkKsDr3YzZfDxmc3cMhrn0\n7ds37md79uzJ008/wttvv82aNd9TVJTPyJHXc8wxx2A0GptFw8bKkY0We1WkP/zwQ6qq7Bx00DWY\nTE2Bl6Wlp7N58yYWL/4oKUFWFIXzzz+X7777B3v3vkc4fBSK4sVu/5iSEjcff/wjPt8AjMZSZPlz\n3n9/Kffcc1PK7vFE56RHotethoaGrNSxVtuVajEYDJEo7759+1JWVsaSJUs48sim7R2Xy8WyZcuY\nMmVKxucDnVyQ1adih8OB1+vN+dNiLEG+7bZpnHfeecBFwNXAXozGeykoKGjhRtHm47YlxOp46cz1\njjvu4He/+x3/+c9/UBSFM844I+XI0Laseb0RCoWYPHkyp512GkuXLsVgMDB8+HC6d+/Oyy+/zDPP\nzKO+/lmMRhgy5GAeeeQpXUWv5ufnc/LJJ7f3NOJit9u5+OKzeOyxN5Hl8XTrdh4ez0Y8npc555yj\n2oyd6NatG5MmTWr2WmNjY4t7PtGKU6FQiJqaGmS5O2AkFApFrGebrRe7dy9Nem962LBh3HBDHa+9\ntoTq6i8wmSR69DCyb18hZvOl9O17FgDBYCPff/8gr732T6ZO/WPCx08WbVEkvRDrISFbnZ7OOecc\n7r//fnr27MmgQYNYvXo1jz32GL///e8j75k6dSr3338/hxxyCJWVlcyYMYOePXsyevTojM8HOrkg\nq0IARAKIcokktWz7eOaZZzJnzhxuv30Ge/a8DsDgwcfz+ONvYjKZIgtHKj2JM1F7ubKyMqEIx3hj\nJfsQ0dYYkN0nfdV7oj5NV1ZW0qdPn2b5s5deeinnnXceGzdupLCwMOImFSTHJZdcgsFgYP78d9i3\n7w0cDiMXXjicK6/8Q9LHSrYsZKyKU/369cNkWk0oVIvZ3OXn314Yl2sVQ4aU4Xa7k2pjKUkSF1xw\nAaeffjo//vgjZrOZXbt2MWvWR5SX/+JlMpvzKCk5jaVL53HNNf4OV40sHaIfchQle52eZs+ezYwZ\nM5gyZQp79uyhvLyca665hhkzZkTec+utt+LxeLjqqquoq6tj+PDhLF68OGu1Izp1HrK6dwlNT9Oy\nLOe0ao7b7SYUCsV8+guFQvz444+4XC5mz36Sd95ZjCyHGThwELffPo0RI0Zgt9uTqlDV0NAAkBO3\nfF1dHRaLJZIbG13UI9m5xyKbxVW084UmC85sNkceJOCXPrIWi6WZdaWSi6440Hoeci5R09SSzUOO\nxu/3U1NTQ1FRUcpFK9oqUpIIbrebqVPv5OuvnXTtOhazuZCamg/Iz1/Kffddx1FHHdUsylslVlnI\ncDiM3+/H4XA0E/7Fixfz8MNLOPTQvzZLTauu/hSTaS4vvvhY1gTZ7XZjMpl0Jfiqp1Lb23vQoEEs\nWrQoa/u2OaTNH36ntpCju5y0x55KvDFNJhO9evXihBOGs317GEWZCZSwbt3fueKK3/POO2/xq1/9\nKmPjZYtU+iknSjYs5Oj52mw2fD4fZrM5btpVa3uU0V1xYpWMzIZItyfpnovVas1KVG2yOJ1O7r33\ndp57bi5ffjkbjwcOP7yYCROu5Ljjjmv23kS6IUFT4Kj2Ie3www+nqOhtqqs/oqysqS90OOxn374l\nnHNOf12JZS6ItQ2gdtbqDHRqQdbSHoLc1sL16quv8tNPOzAY1mA0HvRzfuV4wuFTePTRx5ISZLfb\nzUsvvcSnn35Kfn4+Y8aM4YwzzsjqHpKaRqZW19JzP+Xo3Gd1vuFwGJ/PF3lfItsC6bQzTKd13YGA\ny+Xis88+o6amhvLycoYNG5aW5Z/uNezRowd3330n+/fvx+/3061bNwKBQCT2QTtOa9+7tumGVqi7\ndOnCmDHHM3/+i/zww1eYzd0JBNZw0EFuzj//xrTm3hbp5mdng+g5hcPhrAV16ZFOL8iqELeXIMca\nU+1J/MUXXyBJR2M0HqT5jAlFGcOyZbMSGiMcDrNs2TKuv/4mNmzYiaKchMGwjddfn8zFF49j9uy/\nZvRHqQqb6rrNRRvHdL63tnKfM2GFa/coo9sZRtd1jramc+HyVgmHwzQ2NpKfn98uwT7ff/89d9/9\nKNu2yUAZkvQ/Dj10Iffffye9evVK6liZ/i2XlJSwcuVK/vrXZ/nxxyocDjOnnXYc48aNjRu8p/3e\n1e9afbjQWtLjx4+nT58+fP75Murqqhk48BBOPfVUysrKCAaDWXlAS7fTUzaJzkGWJKld623nkk4v\nyCqqOObyqTF6TFWIVYuse/fuSNLnKEoQSdLug22ia9eubR5/yZIlTJt2Jxs2/EgoVIAkvY3TORiL\nxYLf/zqvvHItY8acy2mnnZb2uUQLmyRJmEymrO5rJvs9NTY2snbtWsxmM4MHDwaaanQvWLCAH37Y\nRHl5d8aOPY8TTjihxWd3796Ny+WivLw8Y4uDeo20pOLyTld8QqEQr732Gq+//m/273dTWlrIhRee\nzfjx45O6xunMIxwO8/DDs9m69RAqK2/GbC7A76/h228f5PHHn+aRR/7UruKxatUq/vSnF6ivH0xJ\nyRhqa2uYM+d9tm7dwfTptyX8AKOeQ3Qby1NOOYWTTz45IS9KvDaWBwLR95DapEZv0eDZotMLstZC\nbo+xIX6pyAkTJvDUU88SDN6C2Xwf4ESWFyBJrzJx4u2tHvu7777j0ksn4/WejCz7gLNQlIE/Vy0y\nYrGcj8/3BO+++25agqzmEns8nmZFPTweT9LHqq+vZ+HChaxatYqCggLOPvtsTjzxxFa/m0Q9G6+8\n8gqPPPIU+/Z5kSSoqChkwoQLePbZl6mutmEwnIiibGbhwj9yxx1XM3nyZKCpCMD06f+PpUu/JhSS\nKC62MXHi+UyaNCkr90yqLm9oCu5KxeX9zDN/44UXPsRoHI3TOZAtW9Ywc+Z8GhvdTJx4RSZPLy7r\n1q1j48Y6KipmYDY3BVZard3o3v1ivvrqQXbu3Nlu+8qKovDPf75DXd2R9O9/feS6ulz9+eKLmaxb\nty6Sp9raMVqjNS9Ksm0sk/Gi6EnQY1ntai9kPc0zm3R6QVZRv3BZlnOeslJfXx/TXTpw4ED+8pdH\nuemmWwmHX0SSbChKHeecc3akPVg8nn/+BXy+MhyOeQQCQ4B8JCkPRanH7/f93IbR3moFpNbIdFGP\nqqoqLrxwAuvX70NRhiJJ3/Pii4uYOnUiQ4cO5fnn/863326kZ89SLr30AsaNG5fwj/Sjjz5ixow/\nEwj8jsLCiwiH3Wzd+jTTp9+PyTSCioo5GAxNbSn37XuSP//5WX7729/SvXt3rrvuZpYv91JQcB95\neX3Zt+9/PPLIPOx2eyrdXlKiLZd3IBBIyuUdDoepqqrC4XAgyzJvvPE/7Par6dZtDABFRb+iqqqI\n+fNfY/z4cTmJym9KhQOzuaTZ62ZzCY2NTX9PhUws5H6/nw0bqigpObfZ8QoK+rN7dxFbtmxpU5BT\npbUHtGiRjmdNaztkaY+hN2IJslrHurPQ6QVZ/fJVEczFjap17wIR126sB4FJkyZx+umns2jRItxu\nN6eccgrHHXdcmwvNd9/9iCyfhCRZMJtPxe9/HUWZAjgIh0OEQsuA1Zx66hVJz19bGSxeUY9YOdat\n8fjjf+WHH0IUFy/GZKr4Of/wBR5++P9hsTgIhYZisUxg167vWbnyfrZs2cott0yLeSy/38+yZctw\nu90cddRRvPjiK3i9x9Ct2y1IkoTFYsBkuotNmxZQWDgGg8EZmXNJySSqq//BJ598Qo8ePVizZivF\nxXNwOo8AJKzWw9izx83cua8xduxYoP2CY1SXtyrCDoejTZf3Bx98wLx5C9i1y4XJBAcf3JX9+/30\n6tW8aEhR0Sns3/8SW7du5Ygjjsj6ufTr14/iYgP79n1IWdkvRRf27fuI8nJ7u+4hN9W1N7N3755m\nr4dCbiSpMaEtjEzeI/HyphOJSegIAYPaualFQfQ830zS6QVZJRdFJqJrNqt5rW3Vbe7ZsyfXXntt\nUmNVVvZk2bI1P7uRxxAI/AtFOQk4G1luIBD4N8OGHce5556b0PE2bdrEqlWrsNvtHHfccdjt9jaL\neiR6LRVFYdGi/2CxXI3JVAE0fR/5+Zfx0093YrGMplev2ZFx6uqe59ln/8Kll16C3W5vNs6XX37J\ntGnT2b69jnAYHI6mhdNonILBYMRgUB/ALICFUCh6jgbULkFbtmwhHC7AZmteR9fpPIE9e96gvr5e\nd8EmrVlUH3/8MTNnzsXrHUlR0UiCwRqWLp1HY+M2unTZTH5+0c+LPfj9OzGbyVlefpcuXbjwwt/w\n3HMvsnXrTpzOfjQ0fI3VupTLLrsokpcaTSAQoK6ujvz8/KyVAzUajZx++vE899x/qK/vT0HBYYTD\nHrZseZGKCoVjjz02K+MmSyIxCdHWtM/na9OazhWtdXrqLAhB/plsCnIsIc7Ly0OSpIi7OtNMnHgF\nr78+mrq6fiiKh6a6LrXAk/TrdyiTJk3lyiuvbLOgRiAQ4JZbbuONN97F7wejESoquvDss39tkYup\nJdlgoGAwSFPnm18IBrciy0YslrFRrsLfsXfvY6xYsaJZOciamhquuWYaNTXHUFQ0DaOxKy7Xm9TW\n3ofRuABJmqqZnxOjMUw4/CayPA6DoSnfs67uJfLyggwbNozvvvsOSXIRCGzHbu8T+azP9z0lJXby\n8/N16fqLRrWoXn/9X3i9J9Gnz7TI9czPH8jatWPYvv1eDj74SSyWcny+n6ipeZYRI/rQvXv3SDGX\nbEd5T5hwGV27duGtt/7Drl3/Y8iQHowf/3tGjBjR4r2yLLNo0SLefPMD9u71kpdn4swzT+Ciiy6M\nK97pMHbsWLZu3cnnnz9CVVUBkuShokLhxhsnJiwY7eVFiX5Ag6aURL/fj9lsjoh2PGtaK9LZPIdY\nLuv6+vqcFmtqbzq9IKtffjYEua19VtWlm41FfdCgQRQWFuHxdAduBMqAt7Fa/84tt0zl0ksvbfMY\nsizz6KOP8sor/8Fsfoji4tGEw9vZsWM6Eydew9KlH8T9sSSTRmYwGDj11GG8+eYbyPKFGAxNkdl+\n/0oghNXqj5qXC0kiUmBEHeedd95hzx7o0uVBjMb8n13QV+D1fo/HM5eamnspLLwERfFQV/ck5eVF\nmEybqK4ejcHwKxRlM2bzaq677nJ69epFt27dOOigYn78cTpdutyB1doXl+t/BAIvcsEF5/wcre6P\nPh1dIssy69dvIy/vvGYLns3Wi5KSo8jPX0dNzR9QlBIMhv0MHtyFW265pVk1O2gZRLRx40befXcx\nGzbspLS0iFNPHc7w4cNTmqPBYODss8/m7LPPbtPF++abb/Lkk//BZDqTwsIjaWjYzJw5i6itdXHj\njb/Uf86UgNjtdu688xa+++47Nm/ejNPp5Nhjj01YLPT24KZeF23Bm3jWtNoND3KTLx+9h9xZcpBB\nCHKETApytBC3ts+aqTGj+de//kV9fZCuXV8jHG7qV2yx/IqGhkaeeur5VgVZTb9qKiayAIPhCvLy\nLgTAYOhHQcETVFefxLvvvsvvfve7jMz3xhtvYOnSCezZczYGw+koSjXwPr16leByPU84fAJGYwmy\n7Ke29lFKS50MHz48slgEg0G2bt0K9MZkKkCSDKi/67y8o7FaF9K9+/vs2bMAgwH69+/OAw88HWkO\nsXbt15SWljBu3ExOPbWpYpLNZuOpp2Zx3XXT2LLlcmprwW5XuPDCEVx99dXtYvGkisFgoFu3IjZt\n2hx5TVHC1NZ+it+/jvPPP5UTTjiB/fv307NnT0444YTI/RovynvFihU88MDf2LevErt9KKtXb+aj\nj57lj3+sSTplKprWPuv1elmw4APM5nPp1WscAIWFA9m7tytLljzJ+PHbs9IlSZIkBg0alJVWgLkm\nljXaVoR/W3vT8dpYJjsnLcJl3cmIviHTFcdUuhhlQ5A3bdqE0XgQZnNv1OEVRcFkGs6WLf9qUWlI\n/bs2/aqqqoqamr1AP2Q5jMHQ9EM1GrtjMHRn9+7dccdP9loeeuihvPXWfF54YQ6fffZfioryGTv2\nJo499lgmTryWHTtOo6lH7kby8108/PBDOBwO6uvrCQQC+P1++vbtiyS9Rzi8B7O5LHJsn28pQ4ce\nxcsvz+Gbb77BYrFwxBFHRBaeu+++u9V5zZ8/l/Xr1+NyuTjooIM46KCmQi2xSiNmmurqahYsWMAX\nX6zFZrPwm9/8itGjR6fklj3vvNN55JE32bfvEPLyBrNhw13U1q7DaMzn7be/44cftnPvvbfRr1+/\nZttFI6QAACAASURBVJ+LFeUtyzIvvbSQuroT6NfvZqCp+MXOnfOZO/dNTjrpJIqKirJS2KS6upr9\n+4MUFx/T7PXi4mPYvFli+/bsCHI66LEqViK0FkCWaBvLRBpvqMdUx1Spr6+nvLw8g2ekbzq9IGtJ\nR5C1QpxoFyP1Zs+GIPfs2RNZ/glZ3ovB8EsRkVBoFRUVZS3SZ7RFPSwWC3PmzGHWrL/hdnuR5f/i\n8QyjsLCAvLx8gsH1KMpuDj300FbnkOx5HXTQQfzpT/e3eH3x4oW8+eabbNiwgbKyQZx33nn07t2b\nxsbGSDcmp9PJuHHjmDt3Phs2XEtBwRRMpu64XG9isXzMH/5wH3a7naFDhyY1J2gK6jn22GOxWCwx\n08SytdhWVVUxZcqtbNhgxmI5hXDYzYoVC1mx4itmzrw/6RSz8ePHs2PHLv71r8dYt+4n3O4+5OXd\nxoABI7DZAvz445+5995HmTv3yTaPvWPHDjZv3k9p6fUYjU3vVRSF0tJz2LnzLTZs2MCJJ56YlVre\nBQUF2GwKXu8O8vJ+6ZPs9W7HaqVTuThTJd1KXYla09HpWPFS8eLVgxAu605Msqk6kH47wWwJ8nnn\nncdDD/2FmporcTj+D6OxHK/3NSTpNX7/+5uB+EU9PvzwQx566BkU5Y8UF+exf/99hMMl1NaOIBxu\nRJb/ysCBvVstKJIpgQqFQhQVFUV6lKru9Lq6umaLu9VqxWq1MmfOU0yf/n+sXHkTgUBTAZAbbpiW\nVDP5bJxHKrz++hts2GClV68nMZma3HZu9xl88MHNfP7555xyyilJHc9kMjFt2k2cccZpTJx4CwbD\nVfTpc07E81FRMYVNm65nzZo1bUYOm0wmJAlkOdjsdVn2YzA0ufvVxgiZruVdUlLCyScfwcKFr2Ox\ndKGgYABe70527JjLccd14bDDDov8jvVkleppLpD5+SRqTcdqvKFWnFNTt9TXstULWa90ekHW3pRq\nzdlE0ApxMj2JY42fDUEuLi7m5ZfncOWVf2TbtlHIMthsBi6/fDwTJ06MCHGsYLP5818nFDqGkpIb\nfp5biPr6pwmFXsDvlzjrrJE8/PCDrUZoa/fHU/nhr1+/nieeeIqPPlqGJEmMGnUyV199JSUlJZFq\nZna7ncbGxmafO/jgg5k/fx7btm2jsbGRgw8+uMN2zPn009VYradHxBjA6RzEnj2HsmrVqoggJ3t9\nCwoKcDiKyM8/LCLGAFZrOX5/k1XSFj169ODww8tZunQB+fkDMBptKIrM7t2vUVFhblYoo63CJsnU\n8lYX+8mTL6eu7q8sXz6T6mozFkuQo48u5uab/xhpd6gn9OayzmWQWSLWtHofQFOMwFlnnYXb7SYv\nL4///ve/FBUVcdRRR9GjR4+MXMddu3Zx2223sXjxYjweD/369WPu3Lkcc8wv2yB33303zz//PHV1\ndQwbNoynn36aQw45JO2xW6PTC7KWRCzk6L6+qQqxlmz9OI499liWLfuEL7/8EpfLxdFHH43NZiMQ\nCODz+eLuce/atQdJGgI0XZOCgqvIy7ucvXv/wPDh9bz88ryszFdl69atTJhwDTt3luFw3IIsB3nl\nlfksW3YNL7/8AhUVFW3Wtu3du3dW55gLLBbzz2VPf6HpXvGm3OMXmsS0tNTBzp2fkZd3WOT12trP\nKShQElp0JEliypTfs2vXQ2zefA0wAFneQklJFdddd2VCNcwTyZuN5/K22WxMn34bGzduZOfOnXTt\n2pUjjzwy51X2kkFPggztO59Y1rS2wcukSZNYvXo1S5Ys4bXXXmPu3LkAdO3alVWrVqX1+1YFduTI\nkbz//vt07dqVDRs2NGvx+NBDDzF79mzmzZtHZWUl06dP54wzzuD777/PeO91LZ1ekBMN6lLb8GW6\nr2+2LGQVs9nM8OHDm1n0iqK0cK2rC6HRaOSoowawatWnKEoASVJvPhmTaT3HHZeY6zcdC/mll15i\n1y4nZWV/B2woCuTljWLLljG89957/OEPf2g2TrLbDMkS7zvKxIKmKApbt25l//79VFZW0qVLl8jf\nTjvtJL755j94vWditzftldbW/g+7fRsnnTQh5TEtFguXXHIujzzyKlu3+igsHILHswWf7y3OP//Y\nhBe7/v378+STD/Lf//6X7dt3UFIygJNOmszAgQNTnltb1lS0y7t379707t0bg8FAMBhEluWkPF25\nQswnMdRti8svv5wJEybQv39/vvzyS/Ly8li7di1ff/112kFeM2fOpHfv3jz//POR1/r06dPsPY8/\n/jgzZszg7LPPBmDevHmUlpby1ltvZbVkbqcXZGi9BWN0w/pMCbF27GwSbdFDU/6u+pQXDAb529/+\nxksvLaCmZi8DBvRj9OgzKSz8N/v3T8DhmIyihPB6n6NrV29C+cttoSgKixcv5o033mLXrhqOOKI/\nEyZcEnFzLlv2NQbDKSiKDUkCo9GAydQNRTmeNWu+Tnv8dIgXfJIKe/bs4cEH/8yKFRvx+yXy82HM\nmBFcc81VmM1mxo8fz9KlK/jww/F4vQ4kKUhhocykSWOaudZSYfTo0ZjNZl59dRG7dv2Xbt2sjB79\nay6++OKkjtO9e3cuueQSoGkbR+1/nUlSdXlDUxnVWC7vzo7eXOgQe04ul4suXbpQXl5O3759GTNm\nTNrjvP3224waNYoLLriAjz/+mIqKCq699tpInMqWLVuoqqpi5MiRkc8UFBRw/PHH88UXXwhBzhVa\nQda2QpQkKdKwPhuLTTYsvHgPEmowlMrtt9/J/PkfIEkXYjb3Y+XKD/nmm79y7bWX8tFHS1m37hok\nCU44YQD33vtCiyfJ1s4LYj+Jz549m7/85WWCwZMwm4/h++8/4733ruapp2ZyzDHHkJdnIxyuwmg0\nIMsNhMMhDIYiYDeFhb1ajJPLp/1Yi0Yq48uyzPTp97NsmUzXrv+PoqJK6uuX8fe/v4DTaWfy5Mk/\nj2XCZOqGyTQISfJhMPyI1+uP2wRFURR27dpFY2MjvXr1ius6liSJs846izPPPJOGhgYcDkdabvD2\noDWXdyAQaGZNaz+TbpR3OvMVxCf6t6VmfmQ6ynrz5s08/fTT3Hzzzdx1110sX76c66+/HqvVymWX\nXUZVVRWSJFFaWtrsc6WlpVRVVWV0LtEIQdagLu4ejyfSk1hthZitJ+tMC4qiKHi93rgPEtrx1q9f\nzz//+R5W64Pk5Z0HQF7e+ezdeyuLFv2Hjz9+n6qqKgwGQ9LBFPEEeefOnTzzzCsYDNdRXj4RAFm+\nll27rufBB2fx0ksvMGbMWXz66Uw2bz6XYLAGRQGj0Up+fjVnnXVV2tcoWdRrplpk6kKeLNu2bWPj\nxo0UFBQgyzJff72LsrJZOJ1N6WPdup1DKNTAwoVvcPHFF/Ovf/2LlSsb6NfvBWy2phrf9fUreeed\n+/j1r5dx0kknNTv+7t27efzxp1m5cgvBIHTtauHii3/L2LFj4353BoPhgIpi1bq8w+FwpOFGJqO8\nU0FvLmJFUXTpLYiu0qWuv5lElmWGDh3KfffdB8BRRx3FunXreOaZZ7jssssyOlayCEHmFytVrfrk\n8/myLsTasTNVHSxWT+Xo+WvHW716NT6fiW7dzmn297y8MezY8TY7d+5M2CJOlBUrVtDQIFFW1lTh\nq8nVCPn5F7Bp04243W5OPfVUrNb72b/fB0wEzIRC/6awcA8lJc3b8+XCQlYUhVAohN/vb5a/qS4e\n4XA48t+xFvFAIMCf//wXFi9eTkODhMUC+fl+GhoUysub53Ln5Q2ivv4lamtr+eij5ZjNp0TEGKCw\n8FhqavqxfPnyZoLs9/u5++6ZrFljp3v32ygo6MbevZ/x+ONv4XQ6GTVqVDYuTTP0Jjrah9BMR3mn\nMx+9oLf5RN8/ah3rTM+zR48eDBgwoNlrAwYMYOHChQCUlZWhKArV1dXNrOTq6mqOPvrojM4lGv09\nIrUDXq+Xurq6iCA3pYU4cvIEma6gqEU96uvr8Xq9WCwWioqK4s5fO15BQQEGQ5hwuKbZe0KhKoxG\n0upkFM9CbloQZcJhL6FQGFlWMBgkJCmAwSBhtVpZtGgRstyD/v3/Qc+e59Gr19kMHDgXOIKXX34l\n5TmlghoEp1Y2U/OdtQt7IBDA4/HgdrvxeDz4/X6CwSDhcBhFUZg3bx5vvPE1RuM0KivfpKTkr+zc\neSi1tVtpaPim2XiNjd9SWGiluLj452sX6x40triuK1eu5LvvaunT5zaKi4/F4ehDr16XEA7/mgUL\nFutOLLNNIuerurwtFgs2mw2Hw4HT6cRut0e+Y9X97fP5It+xuhWkBpG1NZYer73e5hSvSlc2vDfD\nhg1j/fr1zV5bv359xPjo27cvZWVlLFmyJPJ3l8vFsmUtvVKZRggyRIpiOJ3OnI+tdYcmg1r0v76+\nHrfbjclkorCwEKfTmfCDxIgRIygtdVJbex/hcFPuaTC4Ga/3KUaOHNYs4jdVtOelKApDhw6luNhI\nTc1sQPm5IEAjDQ1/57jjBlJaWsoPP6xHlo8hP7+c0tLudO/eHbs9H4tlOF99tb7VMTJFKBSioaGB\nhoYGgEiuttFojAiz6kpTvREWiwWDwRCxpr1eL7W1tSxc+D8sljEUF5+CJJn+P3tvHt5Weaf9f7Qv\ntrzvS7wldhYnTuLsCyQhIUCAsEOAMpROSwuUhjJDlym89C0dhnam/fFCp8AwU0oLgdKWECAhGyRk\n35zddhInjhM7tuzYli1Zttbz+8M5ypEs2ZItyWbwfV1cgCyd5znbcz/f7f6i1xdQVPRT5PIE6up+\nREfHPuz2Zpqb12Gz/YU77rgOnU7HwoXlOBxfYrdf3TCZzSdQqaopLy/3mm9jYyMuVyparbdkZFzc\nZOrr2/wqjI2iL0SrWKVSodFoPCSt1+vRarWejZh4j6UbsZ6eHq+N2EjHSLKQAxFyJCzkp556ir17\n9/Liiy9y9uxZ3n33Xd58802eeOIJz3dWr17NCy+8wMcff8zx48d56KGHyMnJYeXKlf0ceegYdVnT\nK7soxpcg+kXzoUC01gKJegQznnh+MTExvPzyv/G97z1Ne/u1yGSZCEItkyZl8/OfB9Z3DnYcKcQ5\nK5VKfvSjx/nFL/4fRuM+BGEscnkFublyfvazVwFISkpEJjuGILiRya5uLhyOOtLTE/sdZ6iQJsPJ\n5XJiY2OxWq2eTY6/8cRFXAoxZtnV1YXF4kCnK/JK3lMq00lMzCM/v4u2tp/T2QkGg4x7713EQw/1\nljStXLmSHTsOcOTI91Eo5uB2dyOX7+eGGyb22amnpKQgl1/Gbm9Frb66kbJYzlBQYIho7WQk0NbW\nxtatWzl4sBq1WsHcuVNZsmRJRForDoShurxHEvGJGKkbBn+EHG7MmDGDDz/8kB//+Mf84he/oKCg\ngJdffpn77rvP851nnnkGq9XKo48+islkYuHChWzYsCHi75EshBszMu9gGOB0Oj0vlslkIjY2NmoL\nmN1ux2KxkJCQMKBlK5Ka6D7V6XQhZ8ZaLBbcbrfXg97S0sLHH39Mc3Mz48eP54YbbhjywicIAu3t\n7eh0Os+8RS+ESqWiurqatWvX0tJymZKSYm677TZPvOb48eOsWvUYNtv9pKb+IzKZCpPpQ+z23/Db\n3/7IUxsIeBLYpEX9g51vT08P3d3dnmQ4sbyto6MDpVKJXq/3WJpi3oHVavWynHzhcrm4//7vcO7c\nfMaMeYze103AYjmFyfQ0v/71k6SkpGAymTwtH6Vxy87OTjZs2MC+fUdQq1Vce+1cli1b5vV82mw2\nLBYLq1f/C6dOZZGV9U00mnRaW3fQ3f02//RPt3H77bcP6foEA7HHbkxMzJBIqK2tjX/915c5flyB\nTleOy2XD5TrANdek8vTT3w/qmbfZbJ6krmjCn7CJdCM2nFne0jl2dXWh0WhGTGa9WJ6p0+k8m9u3\n3nqLL774whPb/V+AAW/yqIVMZHsiBzt2f2O6XC6sVuug9bJ9x/MdKzU1lUceeSTkY/UHcRHav38/\nf/jDnzl8uBqtVs0ttyzhsce+x4QJE/okVoiYPHkyP/vZ47z00u9pbPwAQZATG2vjO99ZyU033TTg\n+QQDm83GgQMHsFgsjBs3zkuSM5zJfAqFglWrbuHFF9+hvl5DYuICenrqMZn+xLx5Y1iwYAEymczL\n0pK6l1UqFbfddht33HGH1wLuC61Wy/PP/zO/+tUrVFf/FIcDEhJkrFq1iFtvvTUs5xItbN26lRMn\nFIwd+2PUagMAFssCdu78FYsWHWLOnDkDHmO4LEB/wiYi2ajVag9hRzvLe6QjmjHkkYxRQvaDkULI\nkZDpjDSklmZVVRWrVz9HW9tE4uJ+TGeniTff/AtHj1bxpz+92a8Vft9997F48WJ27NiBw+Fgzpw5\nFBQUBPx+IGzbto2//GUt585dorAwi7vvXklcXBz/5//8mro6Cw6HgMHg5vbbr+Hpp3/oV/d6qIl3\nK1euxG638+67H9PS8iFqNdx2Wxnf//5jnoVbugEItkxHXLzF7xcVFfH73/+GqqoqLBYLY8eODUse\nQLRx6NAptNpyDxkDxMbm4nAUUVVVFRQhj0T4UyCLZpa3dFzx+CMNvmVPo4T8NYRvecRwE3Ik1cEi\ndX5itreoR6vVavnzn9fQ1lbEmDGvIZP1Pmo227VUVDzAli1bvFzP/pCens5dd93V73f6k+j84IMP\nePHFt+jpmYlOt5ALF46za9eLyGTd9PRcR0bGo6jVKXR0fMGaNf9JQcHfWbVq1RCuQuA53nPPPdx6\n6600NjYSFxdHYmIiZrMZh8PRx20YbMzSl6TFWPe4ceM8C/lwYKjPqUajxOXq8fMXOyqVwc/nX030\nJ2wykJb3cLq8IwF/a1JHRwcZGRl+vv2/F6OE7IPhJOSBRD3CNV64hUj8tXBUKBRUVFSj0z3iIWMA\njaYAQRjP8ePHByTkoaCrq4vXXnsXp/N28vO/d+XTe6ms/A7t7UcoL38KrTYJuVxGauoKLlw4x9/+\ntiEkQg71vmi1WvLz8/nss8/461830NDQQVycmltuuYZ77rlnwK5UgRbwnp4ej2DJQFbWSE0ykmLO\nnDIOHPgSs3k+BkNvKUpLy0FiYi5QVrYk6OOMtPMMZj7+XN7hFjYZiRayvzl1dnYyfvz4QD/5X4lR\nQvbBcBGy3W6nq6srInFM3/HCdX4Oh4Pu7m6cTqffbO+EhFguXzZ6/UYQXAhCMwbD5LDMIZCFfOrU\nKVpa7KSk3AKIi5kbrTYPt/sSTqfK6/rq9UUYjZ8E1PgN1zX79NNP+fd//wCX6zoSEqZx+XItv//9\nJzQ3t/L006tDPp5oMbndbk8IIFgry3fxHuoCHa5rtHjxYo4dq2bnzn+nvr4IQbARE3OR22+fTmlp\naVTnEg4MdS6REjYZSYQMfeczGkP+mkL6IESTkEU3L/QSskajQafTRcXVOBRxed8kM38tHAFuvvk6\nfvObtZjNC4iNnYsgOGhufh2DoTXiylEajQaFAhyODpTKdASh14rQaNKAizidzcDVl91s3s/cuXkB\ny5r6Q7DPi91uZ82aTxCEmygo+AcAEhNn0tqazcaN/4+77qoLizJaf1ZWoOQxwK8lPRyLtlar5Yc/\nfIJFiyqoqqpCpYqjrGwpkyZNGnEkEgrCPffBurzFeYjVGiPB5e3vHTKbzWHXsR7pGCVkH4jlLJGE\nr5sXegkkGsIkQ3nxfGt0B0oyu/fee6mqOs3nnz9NR0cagtBFfLyVn/zk8Yg3+h47diwFBQmcOPHf\njBnzPCqVAbe7m56eSlJS7LS1/SvwDVSqFNratqDX7+WBB3444HGHsmEzGo0Yjd0kJc32+jwpaQ5n\nzrxCbW3tgITc1NTE6dOn0el0TJ48OejyNKmVJULqCpUu3oGSx6KZ/atSqZg9ezazZ88e+MsjHNH2\nuA3k8hb1Fux2u2dTNtxZ3r4GgiAIo0ldX1dIHwRRaSlSEIlYKuphNpuj9vD3lwQVCL6dr4JNMtNo\nNPzbv/2S6upqKioq0Ol0LFmyhNzc3H5/Fwp8k+Kk1vs//dPjPPfcr2loeAgoBM4xZoyLn/zkZdav\n38yuXS9htcooKornO9/5HosXLw7bvPyht74denqaMBiuxsZ6ehrRaAQMhsAJSy6Xi7feept16/Zi\nMslRKgXy87X84AePUFJSEvB3/cEfScPAyWO+SUUjtURnJM5pOODr8hbrxfV6vZc1Ha0sb3/wtx6N\nuqy/xhAtn0i5rH1FPaRu3mi6yUOptfaXOR1KbFtcBGbNmsWsWbOGNO+BIG4apApbc+fOZc2aN9i0\naROXLl0iM3Myy5cvJzU1lSVLltDS0oLVaiU7O7tfpbNweU0SExNZtGgKf/vb++h0OcTGjsVma+Hi\nxTcoLY339IP2h40bN7JmzUFiYr7JuHELcDg6OHfuHX71qzf4j/94dki6477ozxXqS9TS30hdocOd\nPDaUkEw4YDKZuHjxIiqVipycHGDkbRBGWpa37zE6OzuHLPjzVcMoIfsg3OQYjKhHNF/UYAhZ1Mnu\n7u7G7XYPOrYdzY2G6GXwzUxPTU3lgQce8Pub1NTUqMxNim9/+xGMxl9z6NC/0NhoQKGwUFKi50c/\neqpf1aQNG74EriE9fREAGk0yBQXf5ty51ezdu5elS5dGdN6BXKH+rCuxdal08ZZa0iONmMIJQRD4\n4osv2LjxCK2tMhQKgexsObfccm3EOwUFi/6yrKOR5R1oTv5c1qMx5K85RBIZ6g47FFGPaGd29wdf\nl7rBYBhU799IQ9w0WK1WANRqddQ6dIkYzPORmJjIr371C44ePcrFixdJSkpi5syZA8aCjUYTer23\nq1+p1AFpmEymkOcRDvgu3jKZDLvdjl6v72NJi53UoG/ymM1m48yZMzgcDgoLC7/SVtHRo0f561+P\noNEsobh4Gk5nD2fPbuG99zaTl5fXp33ocCDUtS1SWd79zcmfxO/XAaOEfAVSl/VQMBhRj5HgsnY6\nnXR3dw+YOR3qWJFIkJNuGpRKJU6nE41GE1EyHsijEAoUCgXTp09n+vTpQf+muDiLnTuPkJ6+2HMP\ne3qakcvryckZOcpVUqtYRH/JYydOnGDNmvXU17sQBDkpKbBy5RxuvPHGId/P4bDE9+8/hsMxgaKi\n3hCNQqFi7Ngbqaqq5cSJE1xzzTVRn1OkEE6Xt/g3ER0dHZ7ual8njLZf9EEoMVYpBEHAarViMpmw\n2+3odDoSEhKCEvYYTkJ2uVxYLBY6OztxuVzExsYSFxcXNtH5cLv/pS0RDQbDiM9MDxdWrrwRg+Eo\nZ8/+Fx0dlVy+vIfa2v+gtDSeGTNmDPf0+oXUuhKFY2JiYujq6uKPf1zPpUtTycv7CUVFz2G1LudP\nf9rNrl27PC0N7XZ7yC0Nh8vjdPmyhZiYNK/P5HIlMlkKFotlWObki0jG10WrONT2lYLQ28Wuvb2d\nffv20dzcTHx8fETfvX/7t39DLpfzwx96V1c899xzZGVlodfrWbZsGTU1NRGbgy9GLeQr8N2lBftC\nDzXxSRwz2oQsdioKNXN6MGMNFf2VW4kWeDQX4OFIGJo5cybPPNPDe+99TF3dLlQquPnmYr75zW+g\n1WojWhkQLEK9BxUVFTQ1GZg48R7k8l5LKD9/CdXVDRw4cJSZM2cOa+bvYJCXl0xNzTlgLtDbuer8\n+bPU1e3h5Mk8SktLGTNmzPBOMsoIxuVtt9sRBIE9e/Zw3333IZfLMRgMrFq1iqlTpzJ16lSmTZvm\n6Qg3VBw4cIA33niDsrIyr89feuklXn31Vd5++23y8/P52c9+xvLly6mqqopKB8BRQvaBlLD6c5eE\nK/FJHDOaYiSAJ/YaCXlOEUM9L1EWUkwSiuRchwOhEvvChQuZN28ezc3NaLVaT6xVFJf5qqGjowO5\nPN1DxsCVzWEWly+fD1l5zLfhxnBg7tyZHDmylurqj5DJsqisPEd7ezXJyTqqq9N5/fV1PPjgdQE7\nnUUDw52BLkJ0eYuErNFoWLJkCdu2bWP9+vW8//77XLp0ifXr12M2m7nvvvtYs2bNkMe1WCw8+OCD\nvPnmm/ziF7/w+tvLL7/Ms88+65H1ffvtt0lPT2ft2rXcc889Qx57IIwSsg9EQg30Qven3TxYRIOQ\npRsIAKVSSWxs7IiyLkSEc7MTDvR3fwazsJ04cYK1a9dTWXmBhIQYli2bw4oVK4LagSsUCjIzM0Me\ncySit3HAThyOLlSq3tCDIAhYLKcpKrpqCYWS+StNHhNJO5rKY4WFhTzyyI188skXfPzxX3E4kpg6\ndRolJctITMzi1KnP+OyzXRQXFw9rfHQkELI/aLVapk+fzpkzZ5gwYQIbNmzA7XZz/vz5sHmBHn/8\ncW655RaWLFniRci1tbU0NTVx3XXXeT6Li4tj9uzZ7NmzZ5SQo4lgXNb+RD36q18NZexIEbK4geju\n7sblcqFWq7Hb7ahUqogT3GDOK9Qs78HG/IcLR48e5YUX/ovW1hISEh7g/Plmfve77Zw/X8/q1U+M\n2IUyEigvL6e4eA9VVW+SmXkdSqWWxsY9pKZe4Nprv9Hvb/25QaUkLXpVhqPv8IQJE1CpVJw5YyY3\n92FiYq5uLrKzp1FfX4XRaCQrKyus4waLkWIhi/BXhiUVBZHL5RQWFoZlrPfee48jR45w8ODBPn9r\nampCJpP1cYunp6fT1NQUlvEHwighB4D0JZaKeoQrA1mKwahnBQNfMZK4uDiUSiUmkynqLvKBEKw+\n9nBBmisAeLlGg80kFwSBDz5YR2vrJEpKVnvudVtbCV988QorVpyhuLg4YucwHOjs7OTgwYM0NDRi\nMMQwbdo0jzyowWDgyScf5oMPPuLYsT/R0wNTpsRx2213Dkpa1Vd5TKVSoVarByzPiYTymFKpRK+P\nQa3We33ucjmQyxn27OGRTsiRkM2sr69n9erVbNmyZUStLVKMEvIVSC1k0bILRtQjnGOHi5ClM59O\n6AAAIABJREFUNdCB5h0NQg7mXELVxw40RiTPR8zwtVqtHmtMuqg7nU66uroGbHNotVo5daqJlJSV\nXn9LTCyjudlATU3N/ypCNhqNvPLKHzl9GmSyAlyuS2zYcIyHH17G3Lm9SU+ZmZk8+eR3aWtrw+l0\nkpKSEnbPzUDlOVKXt/Q3Q0key8nJYcwYDWfP7iY/fykKhRKXy0l9/T7KymJJS0sb+CARwkj1Jknf\nCZPJFHZCPnToEC0tLUyfPt2ryuTLL7/k1Vdfpbq6GkEQMBqNXlay0WiMmqjLKCEHgBjDHAxJhIpw\nkYpvDXSgeUdrd9zfRuOrkLAl3ZBBbzxJFOdXKpUIguBFxP6SjaSLem8piAKLpd1nnG5ksp6gG0WM\nZEjv38cfb6C6OoHx47+FSqVDEARqazfy3ntbmDhxoteCG07BjP6UqKTzFOPSorU0mOSxQHFppVLJ\nrbcu5p13NlJdXX8lec3ImDF2VqxYMazP+Uh1WUthNpvDllEtYunSpRw/ftzrs4cffpgJEybw4x//\nmMLCQjIyMti6datHxrazs5N9+/bx+OOPh3UugTBKyFcgltCI5UsulysipUCBxobBE7JIbt3d3chk\nfeUj/Y03XLvkSCRsheN8xFi7SqXy2iyIROpwODzPiLR1obggi3WXYhxTtLp8F/X58yfy3nsbMBhK\niIkZg9vt4Pz598jKclNeXj6kcxhJ6Orq4vDhC6Sl3YVKpQN6r9WYMYs5c2YPp06dipi+uRiqCdUt\nOdjkMX+WtEwmY+zYsTzxRCIHDx7EbLaQnl5MaWnpV1qJLBIYKIYcLsTExDBx4sQ+nyUnJ3uy3lev\nXs0LL7zA2LFjyc/P59lnnyUnJ4eVK1eGdS6BMErIV+B0OjGZTJ5dsEKhiJrFMhQxEmkNdLDkFi1C\n9rWQR6Isp9vtZv369Xz00VaMxg6ysxNZvnwhCxcu9GzIenp6PHMXF1ypnq8oaiBCakX5kvTdd99F\nXd2rHDz4c5zOTMBEWlo33/72KnQ6HU6nM6qaz2azmfb2dhITE/vtNhUqejcuoFB4LzFyuQJBiJyC\n25dffsmuXZW0tXWTnW1gyZJZQ9roBKqh9WdJ+0sei4uLY/78+SiVyhHhAQnGezAc8J1PtFov+o77\nzDPPYLVaefTRRzGZTCxcuJANGzZEpQYZRgnZA4VCgU6nQ6PR0NXVNSxzCEWMZKilV9G0kEVVnkgl\nbA1lg/HOO+/w5ptfAtei1xdw7FgllZVrEQSB2267DZfLhUKh8Eh0+o4j/s03Pu9LOOKinpSUxP/9\nvz/lyJEj1NbWotfrKS8vJzEx0WN1S78fqcYMNpuNtWvXsW3bCSwWAYNBzqJFpdx2262DXnyk1yY2\nNpYJE9LYsWMvyckTPLXGjY37SUpyMm7cuLCchxTr1n3Khg0NxMTMQa1OpKbmPBcufInL5Qq7Ne4v\npuxrSfvmGUg3dIHyDL6O8PfuRquxxOeff97ns+eff57nn38+4mP7wyghX4FcLkenu+paE5t4RwOh\nvJRiCZOYOT2Y0qtou6wtFktUYvGhoq2tjQ8+2IZafQ/p6UuRyeRkZCzkwoVk3n9/I0uWLPFsdPR6\nPXa73RPzliZ3SYU5RPL03RyJFrIIUXlImtEr3pdg3aNDWdD/+te/87e/1ZKUdBupqfl0dNTy/vsb\ncLk+ZNWqewd1TClkMhk333w9tbXvcvLkK8TEjKenpxmt9jT33juT5OTkIY8hhdFoZNeuWlJTbyMl\npRi73U56egm1tWo+//wg06dPD0uJYn8I5PIW8wzEkIe/uLSv2ztSGIkWsm9MWxAEOjo6vnadnmCU\nkL0gLohyuTyqUoTBuKzDWRYUaUKWxrQBj6ZtJBeBUM/H7XZz8uRJLl92kps7G6VShVwuQxAgOXk2\nzc2f0NDQQElJCQ6Hg56eHtxuNyqVqo80qki20n8CkajvwuO78ZPGpAGPe7w/96h4P8VnYyBLuq2t\njW3bqkhJuYf09F53bkxMBnK5gm3b/s4NN7SHJc5ZWFjIM898i507d3H27EmSkmKZNevWPnKF4YDR\naKSjQ8aECd6Wd0pKCUbjITo6OsK+CQgG4n1QKBQez4Nv8pg0J0H8TaTbVo5kQobouaxHGkYJ2Q+i\nbUH2R8hDLQsKNF4kzs83YUtMhop0Ylwox5bG3XsTseQIQgdyefKVWC/YbJdRq0Gv19PV1eWpPw/U\nfUYsq/Gn0xuIpAPVvEoXa+nxpWOI9c+iFS0eV2qp+7O6xHFaWlqwWGTk5nrX+iYkjKO+Hi5fvhy2\nxKPMzEzuvvuusByrP/SGmwR6etrR6a5mbFutbWi1DHv8VnqPB5s81t89DRZfhZIniJ7LeqRhlJD9\nYDiykH3HFASB7u7uiDR/iMT5+UvYgt5syZGyCIj9k8Xs7vLyciZNSuHgwXcoKnoctToRm60Zo/Gv\nLFyYicFg8GTb+8aJB8JQSVqqxzwQSYtJZf76EPtLNIqNjUWrdWM2X0SjuWqFmM0XiYkRvpILYWFh\nIUVFOiorN1JYeBNyuY7Ozku0tu7i5psLotIVzB+CffaDSR7r756Gqjw2ki1kh8NBV1fX1zIbfZSQ\nJRCJKtxCHaGM7Zs5PZjuUdFEf6500R0baUIeaIMhnaMYd1coFLhcLp588tv88pcvU1PzNJCCTNbM\nxIl6vvnNx9BqtWG17oMhaanFC31JWvo7Xxe20+n0WF9SS9o30Sg+Pp6pUzPZsuVD3G6Ijy/AbD5P\nU9PHrFiRT2pqaljON5pQKBSsWnUr77zzETU1r9PToyI21s68eaksX75suKc3aISaPCb9jb9cg5Gy\nOZbCd53t7OxEpVJ5cnq+TpCFcING3p0MMxwOhyeeY7FYSEhIiBoRmkwmr3ihWq1Gr9dHZPyenh6s\nVuuQxBh8Xek6na6PK93tdmMymYiNjfWbuSsIAqdOnaK5uZnMzEzGjh07KPLr7Oz0WH79zVG0dKXu\nQblcTnd3N7t376axsZG0tDRmzZpFfHx8xO69yWRi586dnDhRg16vYdasacycOdPjwpQuuNJ/REgX\naXEhlmqT+8aXpf+IJN3R0cEf/7iGQ4fqsVhkxMS4mT49i/vvvxuDwTAoKcmenh4EQRjWhdThcFBd\nXU1rays5OTkUFRUNqzUotjmV9gKOBPwpj/l6VMT76HQ6PeWRI8FS7urq8sicApw7d47rrruO5ubm\nEWuIDBIDXuxRC1kCabYrRG83KW4ExLhruJpWBMJQPABS0Qxx8Q0kQtLfdWxtbeVXv/r/OHjwIl1d\nEBsLs2cX8M///IOQXab+3P1S3WmxnA2uugDFBcrtdiMIgocQI71wtra28utf/yfHjwuo1aU4nV1s\n2/YRt956in/4h4c8xOkvxii1iKVWNFz1RkgXXvF8fcuwZDIZsbGxPP74t7l06RImk4mkpCQyMjK8\nYtOR1nuOBFQqFRMnTqS7uxudTjci5xgJSJ8ZqfKY9H5K76koIhTp5LFg4Ls+iKIgX5d7J8UoIftB\ntAjZ6XTS3d3tUYGSy+VhFWcIhMEQsm/t81AUtgRB4Le//R1ffNFNdva/kJVVTGdnJVu2vIFe/zr/\n8i8/CvmYgeYouvtFPWq42mKzp6cHu92OTNarbhYJnXJfbN68mWPHVBQXP4Va3WvRt7YeZ8OGPzBn\nzinGjx/v93fiQinGneVyORqNxuN692dJi4u0b2xRJGlBEMjIyCAjI8PzN+niHIyUpPT44kZnpGCk\nzsVsNlNbW0t3dzdJSUnk5+dHpNmB1CsiQtQD0Gq1EU0eCxaBVLri4uJG1P2LFkYJWQJfCzkSakLg\n3fxBdLXabLYRGd8B79rnUBS2Am1s6urq2LfvHBkZTxMX1ytZl5AwGafzAXbseJXGxsaQev6KRGU2\nm73qs0WyEmOs4sJkt9s911uj0URFHlXEnj1VGAzXesgYICmplKamNCorK/0SsrjREL0SWq3WKzwQ\nyJIW//EtqemPpP0ljvmqjvVH0tBrfUnrsYfb4hpO+Hv2P/lkN01NamQyAwpFLcXFJ7n55uuiuhn3\nzWWIVPJYqPOCr2+GNYwSsl/4i8WFA2KfVn+Z0w6HI2q1z8F6AHy7Rg229tl3nLa2Nrq7ISWlwOvz\n2NgCmpp6/x4sIYuLhxgPFt39UleduICIHolA9cTRgEIhQxD6bvRkMv/eilDnPJC7OxiSliJYkhar\nAkT4q6uNlvhFNCB6A0IhJZvNxsaNe7l8uYDi4rnI5Qp6eiycOLGF1NQDLF26JIIz7kUgr9hgksd8\nn5vBbL78rUEmk4m4uLiQjvO/BaOE3A/CRci+cVetVtsnvhXNUquBCNl34zCU2md/v8nOzsZgAJPp\nKGlp13o+b28/QlycLKjG7b7XVC6Xe15if3Hinp4eTz1xTExMv3Fil8uFxWIZ8HuDwZw5k3jrrX3Y\nbHPRaHqtgJaWCmJjW5g06VbP98Q5i9nrQ5lLf3WvviVYUhdisCQNeMUfdTqd3/hltMUvIoHm5mYO\nHKjgzJkW1GoFU6bkUV4+PWAim/R6Xrx4kcZGgby8GYhSolptLCkppZw8uZuFC22eXIdIIthr3N/m\nbqCOWL6WdKAx/bmsv66iIDBKyF6QuqzDQZC+Qhn9xV1HAiH7llyFoyWiv/NKT0/nhhtmsGbNn3C5\nujEYemPIFsvfuPPOef3WH/qLE0sXiKHEid1uN5s2beKzz3bT0mIlMVHLjTfO5YYbbghbE4zrr1/G\n8eM1HDnyEjABt9uMXn+OO+6Yxrhx4zzPjLgZCja2bbPZsFgsxMbGBrWoSy1df0lAoZC0WHIleiNE\n4vUl3GDEL6Su7qGSdLjJvaWlhb/8ZTP19ckkJ8+ju9vGJ59UcenSJu64Y8WAG6beaypHofCuOFCp\ntNjtMo+ITiQx1DVmoM3dQJsvqSUtXRt8Y8ijhDwKLwyFIEXS6O7uDrqzUTQJWYRUeCJcCVvB4tFH\n/xGt9k9s2LAGk8lFfLyS+++fz4MPPhjwN6JAvzSWLZfL6erq8li1SqUShULhpTEdbJz4o4/W8Yc/\n7EGpvIb4+LE0Ntby2mvbsFi6uPfee8Jy3gkJCfz4x0+ye/duTp06i16vZdq0e5k6dSpOp9Mj0alW\nq4PaDDmdTrZs2cK2bUcxmZwkJChZtKiMpUuXDkrjPFSSlkL8nXTDF8iSFsu0pCQdKIvcn1u0v+sS\nqffo6NHj1NcnMGHCTZ53Izk5jxMn1jJtWm2/DTNkMhlpaWkkJLi4fLmWtLQiz9+am2soLY2JmnhJ\nuDcq/pLHAm2+fOPS0hwG8TidnZ1h7Y/9VcIoIUsQDheylDSUSmXQcdehlCKFCunxfUku3CVXga6j\nVqvl0Ue/zf3330drayspKSl96ohF+NYTx8bGolKpPC+7qKIlimpId+cKhcKz4eivXMdisfDJJ3vQ\n6W4iN3cpAMnJpVy6FM/69X/n+uuXeSx3t9tNQ0MDgiCQnZ0dsvUcExPDsmXLWLasV7BCFC4ZSKLT\nHz79dAN/+Uslev1C4uPzaG2t409/2oHN5mDlyltCmpc/+CNpcc5iGED8nnQT1F+TDV+SFo8vlQYd\nSL87mpnAIs6dayE+frIX8eh08TidKTQ3N/slZOmcExMTmT07j61b92A2t6DXx9PRcZGkpCZmz54f\nFXd9NL1w0vsqHV9qSYtVAT09PTz77LPs37+f1NRU4uLi2LVrF2VlZQHXhVDw4osv8uGHH1JdXY1O\np2PevHm89NJLFBcXe33vueee480338RkMjF//nx+//vfM3bs2ABHDT9GCTkAQiVkf5nToZTRDAch\nixZ8JFoiStHfdTQYDAGzS8U4sVgzqdfrPeIB4kstk8k8VrHoapfL5ajVas/L74+kpf/I5XIaGxtp\nb3eTmend+CA5uYwLF/5OQ0MDiYmJVFdX8847azl3rhNBgPz8WO69d8WgGiaIIQKbzRaSe1pEZ2cn\nW7ceIy7uJrKze9sLJiTk09Cg4/PP17N48bVhT47pL+PbN3GsP3e09P0S75MU/kjaX02tP5KWHjec\n71JsrJpLl7xbs/aGSayo1cFZdHPnziEhIZ7jx2vo6DjLxImJlJVdS3Z2dtjmORCGM04vvq8i7HY7\ndrsdnU7HggUL6O7uZu/evdTV1bF27VpkMhnFxcW8//77Q2pKsmPHDr7//e8zY8YMnE4nP/nJT7j+\n+uupqqryxP9feuklXn31Vd5++23y8/P52c9+xvLly6mqqhrthzzcCJaQpdabb+b0YBDpHaw4X/G/\nI90ScTDHDVRPLC76vnHiYEjNlyykJC3u5pVKFxZLE1ptCiBDJoPubiMajYDBYKCxsZGXX36HxsYS\nsrMXI5PJqaraziuv/IVnn00gLy8vpPMTSW2wpVfNzc10dkJOTonX58nJJdTXr6e5uTmshCxuOkWv\nhG9YQ7RUpRs7qSXUXyesYElauqD7I2mz2UxjYyOCIJCZmUlMTEzYynUmTSqkqqqS9vZcEhNzcLtd\nXLx4mORkC4WFhX5/4xsjlcvlTJo0iUmTJg1qDkNFNDb8oUKMS995553ceeed3HLLLbzwwguMHz+e\nw4cPU1FREVIZpD+sX7/e6//feust0tLSOHToEAsWLADg5Zdf5tlnn+Xmm28G4O233yY9PZ21a9dy\nzz3hCVkNhFFClsDXZd1fHbI0yxcYcgJUsKVIg4VvwhZcja1GEqF6GgLFicVFV1pPHAqpBSILkSQy\nMjKYMiWDbdv+jkJhIDY2C6u1kVOnXmfiRBsul4vdu3dTXx/HxIkPebJkS0oeoLLyN+zcuSsoQh6I\n1EJBb5MI6OoyotVeTYLp6mpCqyUsrj64+qyL3h+9Xh+0N0W87qE02RA9F76xYmmsWYSUpCsrK9m8\n+RB1dRbcbhd5eQYWLy5j/PjxOJ1OXC4XVVVVnDp1AavVydixaZSVTSE1NTWo97a0tBSjsYUDB7bQ\n1BQL2ElJsbF8+fRhae04GIw0Qg5U9pScnMy0adOYNm0ajzzySNjHNZlMyGQyT6y6traWpqYmrrvu\nOs934uLimD17Nnv27Bkl5OGCSCCBiMSX2MKVABWplyRQwlZHR8eIejFFzV9pzbNSqfSQpnhPwllP\n7EvS3/3uI9hsb3D8+G+oq5NhNB4H9NTWFvCTn7yG09mAy7XMQwy9ZCBHoyni4sWzA56fzWYbFKkF\nQnp6OlOnZvL55xtQqfQYDNmYzfU0NHzGkiWZtLW1sW/fPlwuNwUF+ZSWloY0ppg9LX3WwyGiIhJo\nfyQtZm2LkJK0VCdAvBcNDQ28/fZGamrcCEIKIHDpkomWli958sleNbLNm7fy5ZctyOUFqFQ6amrO\nc+LEem6/fRFpaWl+y7CkUCgULFt2HaWljTQ1NaFUKsnLyxswI3gkvWcjDb4bBEEQMJvNERUGEQSB\n1atXs2DBAiZOnAhAU1MTMpmM9PR0r++mp6fT1NQUsbn4YpSQA8DXQvYlNrVajU6nC1s5TCQs5P4S\ntqKV1R2sp2GgOLE4X1FqdKi1uf6QkpLC88//iBMnTvCf//nfuFwLmTjxYeLi8mhvP01FxSs4HJsY\nP/4bSHXiu7rOkZIS65mX1LLrL+Y6VMhkMu677w56et7j+PE3uXhRgV7vYu7cJGJjtbzyygas1mxA\nhUr1BXPnHufBB+8NyisiTdoaqiUf7LmEStJS8jx4sILDh9vQ6a7HYJh4ZWE/yaFDm9mzZx8zZkxn\n//4m0tKWkpiYg1wuw+WaRlXVek6erCYjIyNo4YusrKygauXFcxhJGIkWsu98Ojo6IkrIjz32GJWV\nlezatStiYwwWo4TsA3HhF+NT4C0dKcoyhlswIpyE7Kuw5a/bUjTLrNxuN0eOHGHfvgN0dXUzfvxY\nFixYgEaj8Vi60laHg40ThwMKhYL4+HgsFgOTJv0D8fG9amLJyeMZO/Zejh59gTNn/kxh4a0Igoz6\n+m0kJdUzY8b9WK1W4OpCLpPJPIlHkSK1hIQEnnzyO9TW1tLW1kZSUhI2m41XXtlAXNztFBb2ZpFa\nLEZ27XqH8eMPMn/+/IDH8000C4clP1gEQ9Kiu/vYsUqs1mxycxchl/de+5iYLKqrqzh9+iw5OVl0\ndcWQm5t1xe3de7zExLGcObOH5cuVHoW3UPS7h0MadKgYafP1tZAjKZ35xBNPsH79enbs2OEVlxYb\nqxiNRi8r2Wg0Mm3atIjMxR9GCTkAxIfEbDZ7LJ9QM6cHM95QSDIUha1oEvLf/vY31q49gsVSgFye\nwCefbGH9+m089dSjpKWlhS1ODNDe3s6FCxfQ6XQUFRUNyoNhMpno6eld0KVISxtHfn4BGRlHaWw8\njSBAXp6Ke++9g7KyMg9ZiNnF0usrbpJ8s7vDAblcTlFREUVFvbWt69atw2rN8pAxQGxsOgrFBI4e\nPe2XkCPlng43fEla9Jr0knAsMlnvNb1KqjoEoevKte6NU4vXvbdMroeYGFkfb4w/aVDxO8GoU0nd\n6iPlGo40ax36Xh/RAxYJYZAnnniCjz76iO3btzNmzBivvxUUFJCRkcHWrVuZMmUK0FvFsG/fPh5/\n/PGwzyUQRgnZD6T1lE6nM+KZyDA0QvaNa/uT5hwu1NXV8eGHB9Bq/4ExYxZcEb9o5ejR37Bt2zYe\neeSRgHHinp4ej7DKQHFit9vNRx+t45NP9tPWBmo1FBcb+Na3VpGfnw/0kuLevXvZu/cwnZ3dTJgw\nhkWLriUjI8PrWJmZmcTFCbS1VZGWdnV33NZWTX5+Er/85VMYjUbcbjeFhYVotVqveYjKYGIrR2ny\nmN1u98q89VeCNVT0unX7vtpyuRKHw+X3+1IPkFarDVsoJlLwDQVMmTKevXtraWioQK2OAWQ4HNYr\nkqTjyc3NJSHhOLW1B8nJKUMul2OzmTGbq7n22nwPwYfSZCMYdSrxOIPRvo4URsIcRPgSckdHBzEx\nMWH3yjz22GOsWbOGdevWERMTg9FoBCA+Pt7z/q5evZoXXniBsWPHkp+fz7PPPktOTg4rV64M61z6\nwygh+6C7u5uurqu1hmJyUTQQqtXqL66t1+upr69n7969WK1WiouLmTVrVp9ziJaFfPz4cTo7k8jO\nnoPdbgNk6PUpJCYuYteuDTz0kNPzUvrGieVyedBx4u3bt/PnPx9Er7+VwsLp2GwmjhxZxyuvvMUv\nfvEMOp2ONWveZ926U7jdpajVCRw+fIK9e1/jn/7pH8nJyfEcKz09nYULi1m3bi0ORxcGQy7t7Wfo\n6fmcu+6aQWJiYh95T5Ec/KlsBepRGymSLigoQK3egtnciMHQ65az2Sz09FRRWnq1RMqfe1oUWRnJ\n8BffnjNnNmvX7ufEic9xOIoANxpNDbNm9TB//jzS09O59dY5bNhQwblzFxAELQpFC6WlWsaPH3/F\nyr7qgvZVkRqIpANJg4r31Wq1er4/XPrdI9FCBv+ymeG+Jq+99hoymYxFixZ5ff6HP/yBhx56CIBn\nnnkGq9XKo48+islkYuHChWzYsCFqNcgwSsh9IFo2KpUKs9kc9bGDfWl8FcHEuPZnn33Gq69+QFtb\nPDJZPErlDubN28jPfvaMlzTfQMlW4YDoBhUEGS6XE6VSJbG85DidLi/39GDjxIIgsHnzHmSyWWRn\nLwRApYph3LgHOXv2lxw5coTMzEw2bqwkIeF+UlN7BQZcrmVUVv4nGzZs4tvf9i6teOCB+4iNXcfn\nn39CR4ebpCQly5fP4sYbb/D6npQcglHZki7kkSLpSZMmMW/ecXbufBeZrASFQk1PTzVlZXJmzZqF\ny+XCZDIBvW34Rqp72hf9xbc7OjpITEynuFhNT48JQXCjVMpJTIz3XKvJkyeTmZnJuXPnsNvtpKUV\nU1BQ4AmXhNJTWkq+0u+K/4jSoGJegVqtHlC/O1hp0KFiJN1n3/VO7IUcbgS71j3//PM8//zzYR8/\nWIwSsg/0er2nZhGiu6sMhpD9JWyJxNXQ0MDvfvcB3d0rKC6+E7lcgcVynu3bf8XEiX/nG9/4Rkhj\nDQVOp5Ouri7GjRtHbOxOzOaTpKaW43YLOJ1WOjp2cNNNk5DL5UMWyXC73bS0mImN9Y4LqdUG3O4k\n2tvbMZvNmM3x5OZO8fxdoVCRnDybQ4c+5JFHXF5EqtFouOeeu7n55hVXFvtEL9f0UFW2pAiWpMUw\nivibQCStVCp54IF7GD/+EEePnsLpdDNx4nhmzZrFqVOn+PzzAxiN3ej1csrLC5gxo5yUlJSodBoa\nLKSlbv50vquqaklOnkd5+Qw6Oi4BMhISsjhzZj21tbWkpqYCvZn0KSkpfY4fjp7SviQt/r8vSYN3\n/+GBpEHDRdIjzUL2FU2Bq52eRtKmIZoYJeQAiEQZUjBjBhpPdOX666UsYt++fbS2xlJcfIdHuCI2\nNh+9/jo2bdrkRciRgu+GYdKkSdxwwyTWr/9vLl/eh1IZj812lIkTXdx0042exhBDyUJWKBTk5CRx\n5MhpMjJmej7v7m5FobhMWtoCOjs7AQeC4EYmu7r4ulw2VKrAmbJ6vR69Xu/5/3CpbA2E/kha3DAO\nRNKzZ89m7ty5nt/u3buXd9/dh9s9heTkPE6f3sLmzevJytrOrFklzJ8/kXnz5kW0vClU+LahDOSB\n6Oqyo9XGolJpvPpsy2QxXmQaDKTXUYQvSYu66b5eDJGkxedEEAQUCkUf1TGRyJVKpccl6i9xLBBJ\nD0Z1zB8BjgRI5/N17oUMo4QcENL4UTTH9CXkUBK2eq21GORy79uqUhmwWm1eCRThtpB9NwxiYobN\nZuOBB1YxYUIF+/cfxmo1Ulo6mfnz5xMbG4sgCGGpJ16+fCFVVR9x7pyBtLRyurouU1PzAenp9dTX\n11NQUEBKipX6+m3k5i5BJpPR02PCZNrNjTdOCIqEwqmyNRiIJC2Nafmz5kSSdrlcGI1GBEEgOTmZ\n7dsrgGlMmLCE48fXYTTq0OsfwWS6TENDHB98UAngkRIcToRav11QkMqpU3W43aWezWgguol/AAAg\nAElEQVRPjxm5vJmUlKlDnk8gkvb1Yvhm14fSU1rcgIWq3x0uadBow9/6E8mSp68CRgnZB1LCimZp\nkDim1N3lL2GrPwIoKSlBo9lCR0c18fHjAXC7nZhMu7j22hKvFzVc5yYIvf17rVarZ+EUXbtif1yN\nRsOsWbOYMWOG12/FZCdxLtL5ORwOmpqa0Gg0QUkbzp49m0cf7Wbduu1cuLCVurpzuN0CNttS/vzn\nOlJSjjJlShoVFZs4efIwkIhCcY7y8pg+cWFf9Key5XQ6qa+vRxAEcnJyol6zG4gozp49y8cff8H5\n81ZcLkhIsHPxYgt5eTfR1lbP+fMXiIlZQWxsIUbjLuLj83E6Y9m5cz8zZ86kp6cHuVwesPFHJCHd\n+ASrxFZaOonKyk1UV28kKWksLpeDjo5qysr0nnKwcMPXiyHmdQBeXav6s6Sl76FIvr5jSKVHRZKW\nJo75CpoEIumRZiEHclmPWsij8IvhIGTRJekvYWsglJWVsXjxWD777De0tV2LWp1IR8cecnMvcddd\n/+x3rKFA3DC4XC6PcpmYICNaDwqFAp1O5xFXgatWg5gQJUIklv379/Pxx9tpbLShVEJZWSarVt3Z\nr8C8TCZj8eLFzJs3j9dee4NNmxIoLX0KrTYBQXBTW7uRU6d28oMf3Mm5c+fo7u4mL+86ysvLvVzS\nUgxkpVVXV/P3v2+mrq4HQYCcHDUrVy4aUleacKCtrY01azZSX59LTs5stFot9fVHuHDhHfT6KhIT\nC7DZwGDIoafHQq8rH2JjM6mra+ONN/5IW5scuRxKSpK59tp5fSQFIwFpXD5UedGkpCTuvPM6KiqO\ncubMPhQKGfPmjWH69GkR2yRdvnzZU/6WkJBAXFyc53n3dav7a7Dhz5IOtRMWBE/SIsQOb8NNzP4I\nWcyy/rpilJB9EAkrMhS4XC46OzuRy0Nv4ahQKPjnf36K8eM/ZsuWvXR2Wlm2rJjbb38woJUwGOEC\n3zjxQLrTYj2xvxpXX5dfRUUFv//9p9hsc0lLK8fptLB16xaMxv/i2Wd/SExMTL/zlcvl1NS0kpl5\nM1ptr+tLJpOTl7eUU6f2YbVaufPOOwc8x4H0spuamvif//mYlpYJjBkzD5lMQV3dHt56ayNPPZXY\nR3ggWnA6nRw8eJALF9RMnHgTanVvfLuk5DrOnz/O+fObMBi+gVaroKfnEhZLB+npalJSUqitPUhN\nTQ0u1/Xk5s5AENx8+eVx6us/4ZvfvIOEhISILeLS6y3G5EUN4fT09KBKT1JTU1m+fCnLlgVX8ysm\nHooqccFCEASOHj3KwYOX6OjQ4nQ60elOM2NGNnPmzPJrzQ/U3CRQu0ppedRAJC3GpMXxRJKWjgN4\nNsFSa72/XuGRhnRMs9k8bO/OSMAoIfeDaBGyGH8VY39DaeGo1Wq5++67ufvuu/v9XqBjd3V1UVtb\ni1qt7qN0FShODP3rTvdXTywuIuLf9uypwGabQknJXZ4FJSYmm6qql9i9ezdz5szpk10sXXx7LQMB\npVLrNY5crkAQVF4Lnj/4JhEFmveRI0e4dCmeyZNvRVSHKim5mePHmzlw4FDUFxXpvM1mMypVDhqN\n9zUoKirHbK5FEPbidJ6kufkSeXlzmTp1HhZLA3V1W4mJyWL69Hs8x0xIyObUqTVUVFQwY8YMz+Lt\ne/3DMW8xaau2tpbt249gNPaGb9LT5VxzTRnjxo0L6pgDubcFQaC6upqjR2sxmZxotTImTMhg2rSy\noIi/sbGRvXsvoVROYsyYdBQKBRbLZY4cOUxubgO5ublBz3MgkvZnSQci6UCdsMTfOJ1OT+/hkSAN\n6m9tNZlMTJ48OSLjfRUwSsj9INKE7JuwpVQqcTqdXuU1kYI0i1w8z02bNvH++5tpbnahUEBWlpyl\nS+dRUlJCYWEhNpvNb5xYvEb+6olDbaZw/nwL8fFzPMfqjTOnIJfn0NnZ6ann7K9Od9KkbL744gCp\nqVM8CT4tLceIj7cyduxYv+OKsXBxszFQGVNzcxtqdbaHjEXodDk0NZ0P6lzDAX/zzsjIQCarweVy\nolBcfcVtNiMrVixi5szpnDs3hb17D9PScpbGxloMBhgzxoxWu8JrA9JbYpRPV5cVnU7nt13iYEg6\n0PVubm5m/foKrNZx5OT0duK5dKmSDRsOExcXFxbXeXV1NZs21aBUFpOYmIHV2sH27VVYrftYvHjh\ngPM+d+48HR2xFBVloFKprnh+cjCZGrh48VLQhOwP/kja14sUqBOWr4UrJWmpVS3W/ktVxwbS7w4k\nDToU+HNZR7rT00jHKCH7wNdlHYksa38JW2KcVcyijLTryLes68CBA7z22mcIwvVkZc3h9Ol3OHRo\nCxs2nKa4OJeSkjgeffRBJkyY0CdO7E932l+tqDjewYMH2bZtL5cutZOfn8rixfM9+rEZGfEcPnwB\nmAeA3W6hoeFL6uu3cuzYZEpLSykpKfHcG3HxuHjxIlarlfT0dK65Zg5Hj77Ltm0/wO3W43b3kJjY\nw4MPLiQ7O7vPtehPZSsQ0tKSsNvrrpRRXdUt7u6+SFZW0uBvTD9wu92cO3eOuro6AHJzcz2i+Gq1\nGo1Gg1wup7S0lDFjTnLq1Kfk5MxCoVDR0HCEpKRGpk9fQXZ2NtnZ2SxYsIBLly5hNptJSkpi796D\nbNvmLYbTm9PQRnx86oDZ3cGSdH9JWzU1Z2lrS2TChKsJgAUFM6iqukxNzdkhE7LL5eLYsVqUynHk\n5vYSfmxsEmq1nurq3UyZ0hqwt7GY82CxdKFSJfQJYygUWmw205Dm5w++XiQIrV2lTCbDbrfjdrtR\nKBR+s7ulpBuKNKhv4lio65bv90djyKPoA2nWb7gt5P4StqS70mhBPL/Nm7/Eai1lwoRbOH/+Yy5e\nvIBe/ww2mxOZTEN19QFef/1dXnzxp2i1Wq84sW85UH9ayJs2beLtt3dit08hJmYWFy7UUFHxAd/9\nbhdz585l8eI5HD/+GRcvZpCQMI6Kitepr28iJmYax46lcPHi+9xzz2xuvPEG5HI5RqORNWs+pKqq\nHYcDkpNlLFw4kaQkHVZrAyaTBqXSjVYr82yClEqlZ342my1olS0ppk2bxhdfnKCq6kNyc+chk8mp\nr99HaqqRGTOWhOfmSOByufj008/YsaMeqzUVl8uJVnuShQtzueWWFV5kmZCQwP3338T69V9w7txf\ncbshL0/N9ddf4+VKl8lkXhuU0tLxHDz4BRcuHCI7ewput4sLFw6SlmaipMS7FCqYWl1/JA1XdZ39\nJW11dHShVvfd0KjVSXR0tA3y6l2F1WrFZHISH+9N7AkJ6TQ3y+ns7OxDyL7W/Jgx2Zw61YDTaUet\n7vUUORw2HI4mMjL6bvgigcGQtPi+iv8Wf+MraCJ+15d0pd8TidofSQcrDeqvxHO07GkUARFOQna7\n3VitVk/pjL+ELV83ciThe/z6+lZiY8txu13U1e0EFhATswCH4xgKhYaiooepqXmeiooK5s6d6zdO\nPFBWrNls5qOPdiOXL6Ok5Lorny7gzJkPWbv2c2bMmMG8efNobzfx8cefceDAbzEaMxgz5m6mTy8n\nMTGJ+vrdfPjheqZOLSMxMZE33niXysokxoy5DY0mHqPxKL/73es0N3ejUk0hPj4TsNHRcYH33/+C\nqVOnkH+l2YQIsfxKXHCCufbp6el861u3snbtZmpr30IQoKhIw8qVNw3JZRkIVVVVbNtWT1LSCrKz\ne5thdHY2sGfPBiZNOsf48eO9vp+bm8u3v/2gJws4PT19wEz9wsJCVq5sZ/Pmg5w+fRCZDDIzZdxw\nw9w+DTj8oT+SttvtXou3IAhYrdY+lnRychx2u9FTMgdiww4jycl9FbZCRW8dvwyrtYO4uKvHs1o7\n0GjcfcJFLpfLy5Ol1WopKiqitraR06d3Ehs7BplMhtlcx7hxUFBQ4Dtk1CAlabFhiGgVS5Mu/Xky\nxH+L6E+/Wya7qjrmj6SDlQb1966NWsij6APpwzJUl7VvIlR/CVu+buRIwnesvLwUampOkZw8i+5u\nK2r1GGQyNzKZBZ0u4YoMZRwmkwmZTDaoOHFdXR2XLwvk58/y+jwzczYNDftobGxkzJgx3HzzCubP\nn8eTTz5Pbu4KSkoW09PTg9VqJStrNlVV26msrCQ+Pp7Tp+0UF9+HRhN35TyuZefO39DRkUpx8SNo\ntQUIgoDZvJva2lfZv3+/h5DFRgpiYpEIsaQkUGxORHFxMU8/XcSlS5cQBIGsrKyINSKprDyNzZZD\nTEy6J8aYllbE5ctjOHPmbB9CFs+jv1IxfygvL6ekpIT6+nrkcjm5ubnodLpBz1sQBK9GEKKbN5Al\nnZGRQVLSGSort5KVNRGZTIbReJqsLCvFxcEldfUHlUrFpEnZfP55NWq1jsTETKzWDurrDzNhgs7j\nEhfnLW6gpcl9Go2GJUvmkZNzmrNnzyIIAjNmpFNcXByV/I/+IIbDuru7A3ohfC3pcJO0+N3+pEGl\nrnPx3zKZLKoW8u9+9zv+/d//naamJsrKynjllVeYOXPmwD+MIEYJuR9I3TqhWqy+CVvBtEQcLkJ2\nOBzMnz+L3bvf48KFT9HpNLS3V9DToyQ+HtLS0ujqakKlaictLQ2z2dxvnDgQ1Go1SiU4HF2o1VcF\nJxwOC0olXgtHfHw8SUlJ1NfD3r2H6Ox0IpdDcrIOubw387StrQ1BSPKQ8dXjOXG5JqLV5nvO1WCY\nR1PTXzl37pxflS1/7j6pRedPN1q0CCNhEYsQRUnM5i5kslg0GrUnUa333NTY7V39HCF0xMbG+iX4\nUDBQklwgS1qr1XLzzfPYvbuCixc3AVBYqGPOnKloNBpsNtuQs7unTJlMd/dBTp7cR02NDI1GYMIE\nHddcM9tTqictwfK3gdbpdJSVlTHMJedeEL1wYmw+0HoTyN0dTE5AqCQtlQb1JWlxnTObzUybNo3C\nwkJSU1P57LPPWLBgAePHj4/YBvf999/n6aef5o033mDWrFn89re/Zfny5Zw+fdqv1nm0MErI/WAw\nLmTpDlUqmBFMbHI4CFmcZ1lZGT/4gZ0PPthMV1ctly8fJiamjZKSFZjNlTQ2fsL06QYKCwtRKBSD\n6plbVFREYaGeysoNFBevQqnU4nB00dCwmQUL0rzconK5nJycWDZs+Ay9/nvExeXhdjuoqdmIwXCE\nnJzb6erqQiY7hM3W6UXKcrkCudyBy2VHoVDjdrvp6rqMUqkgOzvTq+uV9HoEE5MLhqTDAV9RkpKS\nAo4ePYPD0Y1GEwtAT08ncIGCgqFLQ4YT0pyCYDdtUnd3YWEhBQUFtLe343K5MBgMAd2twXgyfKFS\nqViwYC6TJrXT2dmJVqslLS3N40YfSDd7pMF38xOKoIqIweYE+JPs9BeTFr8vJWmxJaUgCDz++OMc\nPnyYkydP8r3vfQ/o3fTcfPPN/OUvfxnspQmI3/72tzz66KOe1ouvvfYan376Kf/zP//DM888E/bx\ngsUoIfuB+GCFSpC+CVtxcXEh7fCiRchifEn8b7GeePHixcyZM4fz589z+PBhdu8+SVPTG7jdLpYu\nzeXee+8nLi5u0MpHCoWChx++i9/97h1On34RSAcuMX68klWrHvZkEVutVjIzM1EoVGi1rbjdn9LV\nNQGXqwO9vpKYGB0tLS3MnDmTkpLtnDy5htzc6z0x5KwsJUZjLZcv70ImS0MQnAhCLdnZJubNG1gU\nREQwJO1wODwk7Vt+5VsjHSz8ZSGXl5dTXX2eI0feR68fDwh0d1czfbqWiRMnhnT8SKE/N2+okMlk\nJCX1Te4K5yZJ7Gvtu/kZSueuaGMwm59gMRSSliaQib+TkrT43wqFAr1ez1NPPcWFCxfYsWMH9fX1\nHDt2jEOHDkVkQ+RwODh06BA//elPvc516dKl7NmzJ+zjhQJZCIv/yOrdFUGI3XScTqdHW7W/hcU3\nYUvcoQ7Gzd3e3k5MTExEWuFJ3ejd3d0cP34cs9lMRkYGZWVlnviXOG+LxcLFixeJiYkhIyMjpHri\n/tDR0cGhQ4cwmUykpqYyffp0Ojo6ePvtv3L6tAWbDRISoLn5PDrdHYBAa+sF1GodWVlTaW09wn33\nxXHbbbfR2NjIe++tpaqqDbsdUlMVzJ49ll27TnLkiBy3Ow+53IVef4Ebbsjgu999JOxSir4LlNQd\n54+kA9Vw+hKaTqfzeu66uro4fPgwJ07UIpfLmDSpgOnTpw8pxhsO9JZGOb3CM+F6VoId3/f6+ysB\n8kfS4uZU3ERHu2HIYCG1iv09K9Gei793QIT4DohkLSb5CYLgtU5u3LiRJ554gtbW1og+O42NjWRn\nZ7Nnzx5mz57t+fxHP/oRX375ZSRJecCTGrWQ/SBYCzmUhK1QEAkLWao73drayn/91xpOnjQjCGko\nlRUUFW3i8ccf4tKlS+zZU0F7u4Xi4mwWLbqW3NzcsC5S8fHxLFlytTTIbrfz5pvvUVmZQkHBg+j1\nybS0nOTChV9gMNSyaNGTnu+6XA5aW7cQF5cDQGZmJqtXf5fGxka6urpITExEpVIxffp09u07wPHj\nF9BqVcyaNZtrrrkmIrrGwSguBRIyEZsQiBKj/RFaTEwMCxYsGBHdmESMBEIbbLgB8BCHTqcLSqVr\nJCCSVvFgEKwl7dsGs6Kigh07djBt2jSqq6v5j//4D+bOnRuVKpORilFC7geBCNlfwlYwHWmCGS/c\ntc9i2YbD4UCpVGIwGHj99bc4cSKBvLzHUavjsNs7qax8hx/+8KfIZIW43aVoNOM4caKSY8f+yNNP\n/yNZWVlhm5MvqqqqqKmxMW7cnR796fT0MrKzl1FXt5Xz56eQnT0Lh6OburrN5ObamDrVO26anJzs\n0bnWarUUFBRQWFjIqlURm3a/8CVpqdCC6IERM9VFWCwWzp49i9HYjsGgZ8KEYvLy8obnBAZAOOKW\nkUR/JO1wOPpIUkq12SORExAOiOuO2HwjHG1LIwVfkpaWj4mb0JqaGl5//XVMpl4xlZSUFFQqFT//\n+c+59957IxaKSUlJQaFQYDQavT43Go1BlfdFEiPzbo4Q+CNku90+qIStUMYMByGLlov48ooCJA0N\nDRw7ZiQr6x+JjU1BEAQUikTi4iZz8OBGpk9fQV7eHGQycLuXcOrUa6xf/xnf+tY3I7Y4dXZ24nLp\nPWQsYsyYhQjCbnS6rZw9uw2lEsaO1fDAA3d64ouDUdkaDkiFFsSaZ7GGWyaTYTKZ+PvfN3PunBqV\nKgens4MdOz7jxhsnMXPmzLDoRocL0izkkXzN/UG0lMXQkkKhGLbEvVAw0qziYOHrWhc3EW632xNq\n+cEPfsDs2bM5ceLEleYyv2fq1KkRI2SVSkV5eTlbt27l1ltv9cxz69atPPnkkwP8OrIYJWQ/8HVZ\ni8XuQ0nYCmXsoRCyr/Wu0+k88ejebOMu7HZITk701OD2vuzNOBzpJCZOQKVSedSU4uPL2bdvHXfe\nafLKaA0nQaSlpaFWd2E2X8JguGqJt7efZcaMUp544ptcvHgRlUpFYWEhKpXKI2M4GJWt4YRvApFY\nVrN9+y4uXkxj8uQVyOW9C9bFi0f4/PN95OXleXrEinE48V5EkyCkMe6v0jWHvt2kpKGlkZRd74uv\nklXsi0CbCKPRyJNPPklVVRUfffQRCxcu9Lp+ojcpkvjhD3/Iww8/THl5uafsyWq18vDDD0d03IHw\n1bizwwiZTOaxigfTEnEw4w32YbTb7R63kEaj8dQhSnWns7OzSU9X0tR0kJycpVcsZAVdXRdQq90Y\nDAYv8XiFQkCr1aDX68NaeiLFuHHjmDYtmV273ic9fQk6XTKXL1eiUFRw3XVLiI+P96j3iKQgipJ8\nVTNipfFWm81GVVUTqanXolT2un3lcgV5eVM5deoEJpOJzMzMgJmtkSYI301EtJO2hoLBbCJGSgmc\n1M0bqB56JCKQVSwIAh9++CFPPfUUd955J++++y4Gg6HP7wdTmRAq7rnnHi5fvsxzzz2H0Whk6tSp\nbNy4kdTU1IiOOxBGCdkPRFIUFyCXyxW2hK1gxw4FLpfLk4G7f/9hOjp6KCzM5Jpr5ntikKK7VKPR\ncP31M3nrrc3U1LSRmFiMxXIevf4CRUUCLS37yMm5FplMRk+PCZNpHzfdVEp3dzfV1dU4nU4KCgpI\nT08PanES1bD6u25yuZyHH15FYuIn7N//Ie3tMrKzNVx//TWeLMhAluVXZYGSWjmDibcOF0FIPRH+\n+kKPZIh6AOHYRESTpH2fl6+SJ0IqTiK1itva2nj66afZvXs3b7/9NsuXLx/2d/exxx7jscceG9Y5\n+GK07MkPHA4Hra2tnmw/MRkqGrBYLLjdbo+Lsj9I48Tbt2/n/ff3YbWOR6/PwmI5Rdr/396Zx1VV\n5///eS7KpqK4gZoiYu4bsWhq5frFnNRJzaxp0amvDGq4lFvf1CwzcZqoXLMUtBm3cswWrSZ/pmYs\nagKumObUGIliAgLKen5/0Dlz7uVe7gXudvTzfDx6PPJwgM/hnnPen/f2erfMIjb2UXr06KE+5CUl\nJUiSRHp6Ov/v/yXzyy/XadWqCcOG3UtRURFbt35Lbm4A0AQPjwv06dOAe+/tw2efpXDlSn1k2YPG\njW8RFdWdMWNGG6mZaV9MtW39yc/Pp7CwkGbNmqlVr5Y8S3fHtB2ouk3EJ5/s4ZtvyunW7Q94eFQa\n60uX0qlf/wgxMWNt0vc1ZyCq+wyq2yhpjYI2EqEHXFn5Xd1nANaNtC0qYe6IqWSncr/IssyXX37J\nc889x5AhQ3jnnXfw9/d39XJdhdUPUhhkM1RUVJCXl4eXlxdFRUUATjXI5eXl1b6AtXliqJxYtGjR\nSgoKhtKu3VBkufKczMy/07v3JebOna7u3E09Ba2IP8CFCxdIS0ujqKiI9u3b07p1a+Ljt1JUNICg\noEEYDPXIzk7nxo1PmDZtGJGRkVUX+DvW+nNNc9KmLx5FNlIpwvH29taNUdB6lrYYhatXr7Jjxxf8\n+KMnXl53UVqaT8OGl/nDH3oSHh5u8fusUZse6dvFKCj3i6vXXt1nAP810srXtAVnekC7AdJKdubn\n5/N///d/fPbZZ6xdu5aHH37Y5Z+FixF9yLVBeSDAcTORLVHdDau8cEzzxMePH+fKlQpCQgYgyyBJ\nlT8nIOBeMjPX8csvv9CmTRt1Xq4W03+HhIQQEhKi/nvv3r1cvepPjx7D1bUFBoaSl3eB1NT0ag2y\npdYfxYO2pPLj4eGhbjqg6ibCnTH1LG0NT7do0YInnxzDqVOnyMrKoWFDb7p0GVLntqea9kgrKAZN\nL393c+pm7hJFsfYZmIa6FQOn3bA6I69aU7RpJEC912VZ5uDBg8TExBAaGkpGRkad51jfKQiDbAEl\nl2vvvmBbf68p5uYoK6o3ld8HFRUlSJKPavSKi4vw8AA/P79aqzlV6kU3rfIy8PJqyvXrF2t8bZIk\nVTvo3nR0mzIZSNEXdrfeUC3anGVtPEs/Pz/uvfdeB66wElMDoSgnKRsg5W9+69YttTDHEdX19kCv\nVcjKfaxsTj08PNQ549bEZNzBSCv3R2lpqdEGqKioiJdffpmtW7fy9ttv8/jjj7vNxkgPuP+d62Jc\nbZAt9RMDqkRgp06daNu2Hv/+95d06PBHKipkysuLyck5yAMP3FWnysE2bdpgMBw0GuBQUVHOjRtn\n6NSp7sPYlReN8nJSQuja/LG5Yhl38h5Mc5a1GbzhKix5lrZqFrvSSOu9H7qoqMhswVltFN+c+Swo\nG0/4r8KZLMscOXKEKVOm0L59e9LS0hw6Be12RRhkC7jaQ1byp9pwkHaMmbImRcd24sQHeffdXZw6\ndRYPj7swGH6ic2eZCRMm1ekB7d27Nz17pnL8eALNm/fHw8OTK1eO0rbtNe6776E6X6+p4pOlNibT\nUHd1LyalstvRmAtPO+t31xVrnqWtcojmjLSje6RNNb/1VIVs2oZlTVioLrKs9jbSWjEb07a95cuX\ns379epYtW0Z0dLTwimuJKOqyQGlpqRqWKSoqwt/f3ykvWuX3KWFDS/3EystOq1R17do1MjIyuHHj\nBgEBAURGRtpl2Pdvv/3Gl1/+i9TU85SXy3Tr1poRI4bQvn37Ov3cuqpsmb6YysrK1K852oPTi0KY\nObRrr2vRVl0GO9R27a4aYlFXHLn2mhbv1dRIa9eurbg/efIkU6ZMoUmTJmzcuNGo/kRQBVFlXVsU\ng1xSUkJBQQFNmjRx+K6vrKyMwsJCtb2nQYMGqmH+b65YUo2zVqnK3hKe5igpKaG8vLzO04UctXZr\nU2fsIWKiDU876+9uL7R5P0eu3RFG2llrdwSWPEtHY48pZFqPXrv2srIy4uPjiY+PZ+HChcycOVM3\nn4cLEVXWtcVUPtORYWvTPDFUhqgVw6vIWCprUYTwnR0mres0HEu9rfZae3VhVu1IzdqImNgaWndH\nnL12W0Q0tHOkAbMtcEr6RlvJq6e/O5j3LJ219uoGnNgS7taKI2k9+nPnzhEdHY0syxw+fJju3bs7\n5XruBIRBtoIjDbJywyt9k76+vhgMBgoKClQPDP7bmqRUwupRqcpVKluWjLQl42DOc1A2THoMT7tL\nO5A5I20tF6pEh2RZ1pUgDBh79O6ydmWTUxMjDZCcnExqaiqhoaGcOXOG+Ph4Zs+ezYIFC3SjC6AX\nhEG2gCM9ZEv9xEpOGKCoqEh9iQGq4o+79Vhawx1VtmpqHBQ8PT118wIyLXxyx3YgSwVLShRDm24o\nKyujoKDAJcV7NcW0CtmdPXpTI61UfwPqujMzM1m7di15eXkABAQEcPz4ceLi4hg/fjxdunRx5SXc\nVrjXE+qG2Nsga/uJ69evrw5zUAwCQMOGDc325EKlgSsuLrZbkYyjMFXZcrd5uaZojYM2tA6owvgl\nJSWqJ+0ObT+WqGs/tCtR/s6KFrIyqUwryaqdIe1On4MrJTvrivae1+boKyoqVIJrlPQAACAASURB\nVNXAWbNmERYWxokTJzh27Bh/+9vf6NSpkzDIdkQYZAvY20NWRNeVdodGjRqpL3ptnlgbnlZm5Soe\ncXUi9qbFSq5Cz5OBoPreVmttP/auKK4pt4tBMOfRm4ZZ3alHWu957vJy81Olfv31V5577jnOnz/P\n559/Tv/+/Y2uSXl3OZLXX3+dXbt2cfbsWXx8fOjfvz9xcXF06tRJPWfy5Mls2rTJ6PtGjBjBnj17\n1H8XFxcze/Zstm/fTnFxMVFRUaxZs4aWLVs6dP01RVRZW0BRh4LKth9fX1+8vb1r/HNM88RKI73y\nO7Q5M7A9T6w8DNreXNPiDFPxDEejNWZ6C63XtorXlopiR4uYmCvactdwrjnsoZ1tS4W9I3qkTXWc\n9XTPm26CFP1sWZb56KOPeP7555k4cSLLly+nYcOGLlnjyJEjeeyxxwgPD6esrIwFCxZw8uRJzpw5\no3Z7TJ48mStXrpCYmKi+A728vIzmAcTExLB37142bdqEn58f06ZNw8PDg0OHDjnzckTbU23RGuTr\n16/j7e1do3Yf5SWpvGi8vb1Vb8tcP7E9jJnyQtIaaQVHijaYGjNvb2+3y1dawtSY2WMggS2bJWvt\nJraiZ7UqbTuQI1qZHNkjbWm6kV7Q1nZoN0E5OTnMnj2bI0eO8P777zNs2DC3up9ycnJo2bIlBw8e\nZODAgUClQc7Ly+Of//yn2e/Jz8+nRYsWbNu2jYcffhiAzMxMunbtSnJycrV6/HZGtD3VFu1NKEk1\nGzBhLU+sGGIlDK3tya1L8Y25NgdTI20uxKoY6Zp6b3puBYKqxszc8I3aYEvRWF3zoHoo2rKEorrm\naIEPW9qvajPDWO9esfLMau8bWZb5/PPPiY2NJSoqivT0dLuICtmb3NxcJEmiadOmRse/+eYbAgIC\n8Pf3Z8iQISxdulQ959ixY5SVlTF06FD1/M6dO9OuXTuSkpKcaZCtoo8n2MUo2r7WqGmeWNtP7Ahj\npvXGlDC59oWkFI2Za/nRGmlz6FmpytSjd4Yxq26zVBMZSr3n6F2d565Lj7Syqa7pJC93QesVa5/Z\nvLw85s2bx7/+9S/WrVvH6NGj3fJ+kmWZmTNnMnDgQLp166Yef/DBBxk3bhzBwcFcuHCBBQsWMHLk\nSJKSkpAkicuXL+Pp6VllxnxAQACXL1929mVUizDIFjD1kKszyOb6ibXDEbS604DLenLB+IWkVLCa\nTlvSVhhrvTdlc1FcXKx69HrTEdbm6F1pzKoTMbEU0VA2hrIs61KtShtNcSfd79q0wXl4eKgpIVcX\nUlqjOq94//79TJ06lX79+pGRkVGnQTSOZurUqZw+fZrDhw8bHZ8wYYL6/927d6dnz56EhITwzTff\nMHjwYGcvs04Ig2wDlkLWrsgTOwJr3pu59ivF61Y2K+7wYq2OsrIybt265XKBjOqwFNFQNknamoDy\n8nIKCwvtmo92FKbiJErPvTujPBP16tWjpKRE1UlX7nlrKlfustlQonamXnFhYSGLFi3iww8/ZOXK\nlUycONEt1muJ6dOns2fPHg4dOkSrVq2qPTc4OJjmzZtz/vx5Bg8eTGBgICUlJeTn5xt5ydnZ2QQG\nBjp66TVCGORq0BobUw9ZqzttLU+sPBT2yBM7A+3LRVu8AqhhbOUla3q+O72MoGo/tLv/7U1Ragy0\nqQFb5Q+tpR0cjbVWJnfHUohXQaty5W490qZFZ0p4XZZlkpOTiY6OplOnTqSnp9OmTd3HqDqS6dOn\ns3v3bg4cOEC7du2snn/p0iWuXbumGu6wsDDq1avHvn37jIq6fv75Z6fMH68Josq6GpQXnZIXbtKk\nifqQKnliJfSmeJWmbUxa7WZ7VPA6E2sqW66ctmSN2yHXamsbljnDYK7C3pmfhT1amVyFaYhXaSOz\n9Xttab9y5GdhWnSmRCRu3brFa6+9xsaNG1mxYgXPPPOMW0ZUtEydOpWtW7fyySefGPUeN27cGG9v\nbwoLC1myZAnjxo0jMDCQ8+fPM2/ePAoLC8nIyFCjflOnTmXv3r0kJCTQqFEjYmNjMRgMou1JTygT\nn27evMnNmzfx9vY2yoEpH7Y5Q+zKPHFdMfUqlY2ENay9jJwlnOEu+s21wV4bCVd9Ftrqb73lucG6\nV1wbnNUjbUmgRJZlMjIymDJlCs2aNSMhIYHg4OA6XZOzsPS3SEhI4KmnnuLWrVv88Y9/JC0tjdzc\nXFq3bk1UVBSvvPKKUT68uLiYF154ga1bt1JcXMyIESNYvXq1s4VBhEGuC6WlpWquTqm61PYj6ylP\nbAuO8Cqr6wW1d0+uaStQTTwbd8DRGwlb+3Jr2wan51nFpuF1R9879u6R1kZUtPdOaWkpb7zxBqtW\nreLll19m+vTputog3WYIg1wXbt68yY0bN9TdrJ+fHx4eHup8Yq0h1rM4BjhXZau6Oa217cnVc3ja\n1BjYGpGw1+/WphxqI2LijpONakJ5uXnpSGdTWyOtrfFQ7n2AM2fOEB0dTb169UhMTBSa065HGOS6\ncP36dUpLS/H09OTmzZtqhd7tlCd2h41EXcKreg5Pg3vmWq0NtjfXlwvo7t53tldcG8wJmWjf2UrB\nqSzLXL9+naCgIGRZZvXq1SxfvpwXXniB+fPnu9113aEIg1wXSktLKSsrU0e/KS+ievXqYTAY1FYa\nPeaJHSEZaU9s8RYUQ65H6cLaame7AmsbJqgc1ad9NtzlPrKEu3jFtaGiokLtp1c4e/YsQ4YMoXnz\n5jRq1IjCwkJefPFFHn30Ubdr7bmDEQa5LuTl5akvfqUP1LQf2WAwqHNy9eKZ6VVlS+nJ1faFKmg9\nN1dUdduKaTuKu22ErKHdyEFlX65WiU7BWQV8NcXSQAW9oK2T0KYHrl+/zjvvvMN3333H1atXycnJ\n4dq1awA89NBDfPrppy5euQChZV03QkNDqVevHuHh4URERBAcHMyWLVvw9fVl+fLlasju1q1bqqep\nLYpxN6Og9LTqUWUL/usZKLKLykZC2+pjqmxlOpbSlZ+H3sPrWq/SUl+uaXjVnASlq0RM3DE9UBMU\njXzTOolffvmF6dOn8+9//5vExET69esHwE8//cTRo0ed8ozbMiYRYNGiRbz//vvk5uYyYMAA1q5d\nS8eOHdWv62VMoqMQHnI1FBYWcvz4cQ4ePMjf//53zp49i7+/P/3796dDhw707duXiIgIAgMDq0ge\nKriDp6D1CvTqldla9KSEV7VGurpxiM4wCnrIVVZHXarXreWjnSFiovdWLNP1+/r6qs7Ajh07mDNn\nDk888QTLli2jQYMGLlmjLWMS4+LiiIuLY/PmzbRv356XXnqJEydOcObMGbUQzU3GJDoKEbKuK1ev\nXqVPnz5cu3aNWbNmMWnSJE6ePElycjIpKSl8//33+Pv7Ex4eTmRkJBEREfTp0wdPT0+bWn0cqWpl\nWn2sN6/AdCpQXWblmopmmBoFR0U1tK1Aevv7g/29Sq2IibkNrL2FM7Tr11v1PVhe/5UrV5g5cybp\n6em8//77DBkyxK2uy9yYxNatWzNnzhxmzZoFVI5FDAgIYNOmTUyYMMGdxiQ6ChGyristWrRgxowZ\nPPLII2ozfefOnRk3bpxq8E6cOEFycjLJycls2rSJixcv0r17dyIiIoiIiCAyMpLg4GAjb0Erd2g6\nwMEeXrQ1lS13RxteV8LTtfVqqhseoBgEJScK9hFqMJ1qVJf1uwLTojN7pTeU3uaaTr6qaerB1KvU\nW3pGG1XRrl+WZT799FNiY2MZNWoUaWlpNG7c2NXLrYLpmMSLFy9y+fJloxGIfn5+9O3bl6SkJCZM\nmMDRo0d1MybRUQiDbANz5841e1ySJDw9PQkLCyMsLIxp06YhyzK//fYbKSkpJCcn8+GHHzJ37lwM\nBoNqoMPDwwkPD6dhw4ZGLyLTWcW1Ca2aqmzpbUScaXjdUVOBHDU72rR6XW8zok2jKs5Yv6XJV9Xl\no81FmZQ1Wsq16gVLFeDXr19n7ty5fPPNN2zYsIE//OEPbnldslx1TOLly5eRJImAgACjc7UjELOz\ns3UzJtFRCINsZyRJolmzZowcOZKRI0cClQ9YZmYmKSkppKSksHjxYk6fPk1ISIiRF925c2cANQdq\nOjSguipivYtjgHH1tyvGUip/19rOjla8YnvKLjoTV88q1mIuqmHL56GI9ijDLPTqFRsMBiOv+Ouv\nv2bq1Kncf//9ZGRk0KxZM1cv1yKWxiQKrCMMshPw8PCgW7dudOvWjcmTJyPLMoWFhRw9epSkpCS+\n/vprli1bxo0bN7jnnntUAx0REUHz5s2NipTMeW2SJKnGW4/Vu6aGwF1aUbRGwZbZ0Qqenp6qUdcD\npl69u0ZVqvs8TFvhKioqKCws1EUrHBinmLSb0YKCAl566SV27drF6tWreeSRR9z2GsDymMTAwEBk\nWSY7O9vIS87OziY0NFQ9Ry9jEh2FMMguQJIkGjZsyKBBgxg0aBBQ+VL8+eefSUpKIjk5mbfeeovj\nx48TGBioetERERH06tWLevXqUV5ersp6ent7qz9bebk6umDMHjgrPG1PtKFuc6Mplb+/4rlVF1p1\nBxwxTMGZKNKRNWmFs3e9Rl0wnSyljKiUZZnDhw8THR1Njx49yMjIsDoH2NVUNyYxODiYwMBA9u3b\nR69evYDKoq6UlBSmTZsG6GtMoqMQVdZuimKs0tLS1FB3amoqly5domfPnjRp0oSkpCTatm3LoUOH\nqF+/vpHnpmCvSTL2Rq/iJArVhXerkzt0ZpV9dei9FQuMK9iry3WbFo2VlZW5hYiJpc3QzZs3efXV\nV9m8eTN/+9vfePrpp90+4mVtTCLAihUriIuLIzExkfbt27Nw4UJOnTrFqVOn1IiSm4xJdBSi7el2\nQpZltm/fzgsvvEBWVhYDBw7kwoULlJeXG+WiQ0ND8fHxMSpScnUvroLWkOm1J9S0aMsWo+pOs6P1\nLpBhj2EWtkizOuoZsTRvWZZljh8/zpQpU2jdujUbNmwgKCjIbr/XkVgbk6jw8ssvs379enJzc7nv\nvvtYvXp1FWEQNxiT6CiEQb6duHbtGkFBQURERPDOO+/Qo0cPysvLOX36tNp2lZqayrlz5+jatata\n0R0REcHdd99dRTTD1oIxe+Du2tm2oDVkdfXqXTGvWO8CGUCVyUb2vIeUvndrIiZ1iWxUVFRQVFRU\nxSsuKSnhr3/9K2vWrOHVV19l6tSpbu8VC2qMMMi3G5mZmXTq1Mniy0CWZfLy8jhy5IgqXpKamkpp\naSlhYWFGrVf+/v5VVK0U7Jn7tKchcwXOMmSOnB2t51nF4LoKcHtFNkw1zLXDUE6fPs2UKVPw8fEh\nISGhityk4LZBGGRB5Uvlxx9/VAvGjhw5QkZGBm3btjUKdXfv3h2DwWDVINgqc6iniUbmcIdWsrrO\njnanVqbaoP0MAJtTBI5cT3WRDXMiJrIsq59B/fr18fHxUQvPVq1axYoVK1iwYAHPP/+87vL4ghoh\nDLKgKsoL4vvvvzfyonNycggNDSUsLIzIyEgiIyONdLptLRi7HcLTpkph7mLIbA11K4ZAr/rlYLyZ\ncOd2Pmv66QqXL1/Gw8ODDh068OOPPxITE8PNmzdJSEigd+/eLli5wMkIgyywDVmWycrKUnPR5nS6\nw8PD6dOnD15eXhYLxhTRe8DIG9ALpq1YepizbG2IvSuL+GpDdeFdvaCtoFbu/xdffJGEhASaNGnC\nzZs3iYyMZObMmQwYMKCKgpXgtkQYZEHtMKfTfeTIEYs63deuXWP//v0MGTLESBRDL3OK4fbIdWs3\nE15eXkbG2plFfLXF1CvW24YOqhaeKc9DRkYGy5cv5/r165SXl3Pu3DmuXr0KwKpVq9R+XMFtizDI\nAvthqtOtLRiDSo942bJljB49Gj8/P5sqiN1BLEPvuW6wvplQpiyZVhEruHp2tCUNcD2hpIJM27Eq\nKirYsmUL8+fPZ9KkSSxduhRfX19VDCg1NZXQ0FCj9h9HcejQIf76179y7Ngxfv31Vz7++GNGjx6t\nfn3y5Mls2rTJ6HtGjBjBnj171H/f6TOL64AwyALHkZqaSkxMDN9//z2DBw+mU6dOpKSkVNHpjoiI\noEuXLkiSZLeCMXvgikEK9sZ0VnFNZEfdZXa0thVI716x6X2UnZ1NbGwsp0+fZuPGjdx///0uvbYv\nvviC7777jrCwMMaOHcuuXbuqGOQrV66QmJioRlS8vLyMJkrd5jOLHYkYvyhwHMePH0eWZZKSkujX\nrx9AFZ3uffv2WdXpVoyBVhfa0Qpj2hyfOxcMVUddW5mqm7KkGGnTASemRrqus5G1XrEiG6kntBsi\nrVcsyzK7du1i1qxZPPzww2zZsoVGjRq5ermMGDGCESNGAGDJGfPy8qJFixZmv5afn8/GjRvZtm0b\nDzzwAFAp/tG1a1dSU1PviBGJjkR4yIJaU1FRgSzLVj0yU53u1NRUVadbES6JjIxUdbq1XpvWYzPN\ne9bGgN4OkpHODrGbjqU0DXXXZuOkdw1tsDzm8bfffuOFF17g22+/5b333mPEiBFueW0Gg8FsyHr3\n7t3Ur18ff39/hgwZwtKlS9W5xvv372fYsGFcv37daABE+/btmTVrFjNmzHD6degI4SELHIetBlGS\nJIKCgggKCmLixImqZ5SWlqbmotevX8+lS5fo3bu3KlwSGRlJ27ZtjXKfWo+tppKTrhzvaA/M9eQ6\nI8Ruz9nRloYp6AlToRhfX1/VK/7iiy+YPn06Q4cOJSMjQzVkeuHBBx9k3LhxBAcHc+HCBRYsWMDI\nkSNJSkpCkiQuX758x88sdiT6ehLszOrVq3njjTe4fPkyvXv3ZuXKlURERLh6Wbc9SgVw37596du3\nL1D5krty5Ypa0f3BBx8wY8YMfHx8quh0N2jQwCgXbc4YaAvGlBeo4lEqc2b1hDv15NZ2drTBYFDb\n5PTsFSvFc1qv+MaNG7z44ot89tlnrFmzhrFjx+ru2gAmTJig/n/37t3p2bMnISEhfPPNNwwePNiF\nK7szuGMN8vbt23n++edZv349kZGRxMfHExUVxblz52jevLmrl3fHIUkSAQEBjBkzhjFjxqgveK1O\n944dOzh37hxdunQxKhjT6nQrBloxBlo8PT3x8vLSVa74dphVrBhoraiMYtjcZQyiNbSpDu2mTpZl\nDh48SExMDH369CE9Pf22mt0bHBxM8+bNOX/+PIMHDxYzix3MHZtD7tevH3379uXtt98GKh+4tm3b\nEhsby9y5c128OoE5bNHpVnLSWVlZbN++nb/85S80btzYqIDF1S0+tnI75FnLy8spKipSveL69etX\nO4XMndrhFLTXoE11FBUVsWTJErZs2UJ8fDxPPPGErjZ75nLIply6dImgoCB2797NQw89RH5+Pi1a\ntGDbtm1GM4u7du1KcnKyKOqqHpFDNkdpaSnHjh3jxRdfVI9JksSwYcNISkpy4coE1SFJEk2aNGH4\n8OEMHz4cMNbpTklJYdmyZaSlpQGVhSbNmzdn0KBBqk63UjBWVlZm5EXbo2DMXpgWnuk1z2rLNZiq\njJkLdbtqdrTpNWi94iNHjhAdHU1QUBBpaWm0bdvWaeuqC4WFhZw/f17doP7444+kp6fTtGlTmjZt\nypIlSxg3bhyBgYGcP3+eefPm0alTJ6KiogDw8/PjmWeeYfbs2fj7+6sziwcMGCCMsR3Q11NuJ3Jy\ncigvL68iVxcQEEBmZqaLViWoDQaDgY4dO9KxY0datWrFp59+iqenJ08//TQdOnTgyJEjrF+/nqtX\nrxIaGqoWi0VGRtKqVSujim5LBWPODKnqfVYx1OwatKFuBVOtbqUVDpw3O1obndBeQ3FxMcuXL2f9\n+vUsW7aM6OhoXXnFR48eZfDgwWr04fnnnwfg6aefZs2aNWRkZLB582Zyc3Np3bo1UVFRvPLKK0Zp\nkvj4eDw8PBg/frzRzGJB3bkjDbLg9qSkpIRu3bqxf/9+OnTooB431el+9913iY6OVnW6lVx0nz59\n8Pb2tlowpm3xsRdalSe9Fp5Z8ihrSnVV3dYK+eqagrBUBS7LMidPnuR///d/ady4MUeOHHGKspa9\neeCBB8wOvlD44osvrP4MLy8vVq5cycqVK+25NAF3aA65tLQUX19fdu7caZQ/mTRpEnl5eezatcuF\nqxPUBVmWrb6MTXW6lVy0JZ1u0wpiewplKC1dep5VDM737B0xO9pSzr6srIy33nqLN998k5deeolZ\ns2bpbrMkcAuEdKYlzBV1tWvXjtjYWObMmePi1QmcjTmd7iNHjmAwGIy86LCwMPz8/Kr04SrUpGBM\n77OKoWpPrit1wGs7O9rUK9aKxZw7d47o6GgqKipISEigR48eLrk2wW2BMMiW2LFjB5MmTWLdunVq\n29NHH33E2bNnLcrG2YPXX3+dXbt2cfbsWXx8fOjfvz9xcXF06tTJ6LxFixbx/vvvk5uby4ABA1i7\ndq0uQ2R6pry8nMzMTFJSUtT/Tp06RceOHY0UxhSdbnMKY+YKk4AqgxScXbBkDyz15LoLtsyOVnqj\nZVk20tEuLy/n3XffZenSpcyaNYsXX3zRLdvNBLpCGOTqWLNmDStWrCA7O5s+ffqwcuVKwsPDHfo7\nR44cyWOPPUZ4eDhlZWUsWLCAkydPcubMGXx8fACIi4sjLi6OzZs30759e1566SVOnDjBmTNnjEYb\nCpyLqU53amoqKSkpRjrdipFu0aKFRW9NQfHG3Gn8oS24k1dcU7Sh7tLSUiMDvXTpUk6ePEnPnj05\ncOAApaWl/P3vfycsLExXn4/AbREG2d3JycmhZcuWHDx4kIEDBwLQunVr5syZw6xZs4BKQfeAgAA2\nbdpkpKQjcD226nR37tyZxMREvLy8eOyxx9QWLAVHFozZE0v6zXrCnOpZeXk5mzdv5qOPPiIjI4Pc\n3FwAgoKCiIyMZMaMGQwYMMDFKxfoHKsPins+9XcQubm5SJKkat5evHiRy5cvM3ToUPUcPz8/+vbt\nK3qk3RBFp3vixIm89dZbHD58mNzcXLZt28bAgQNJS0vjiSeeoH379ixatIjDhw/z1Vdfce3aNRo2\nbEiDBg1UGczS0lKKioq4ceMG+fn5FBYWUlxcbFRI5ipkWaaoqIjCwkIMBgONGjXSXUuWkisuKCig\nvLwcX19fVYc6JyeHvXv3kpWVxe7du7l48SLbt29n/Pjx/PrrrxQUFDhtnYcOHWL06NG0adMGg8HA\nJ598UuWcRYsW0bp1a3x9fRk+fDjnz583+npxcTHTpk2jefPmNGrUiPHjx3PlyhVnXYKgloi2Jxci\nyzIzZ85k4MCBdOvWDYDLly+rMpJahHi7PtDqdN9zzz2cP3+erKwsevfuzZ///GeysrLYvHkzsbGx\nRjrdERER3HPPPTRo0MAoF60MkgD7tvfUBEuzfvWEdkKWVgtclmV27tzJ7NmzefTRR9mxYwcNGzYE\nKoVlXBGRKiwspE+fPjzzzDOMHTu2ytfj4uJYtWqVUUorKirKKKU1c+ZM9u7dy86dO9WZxePGjRMz\ni90cYZBdyNSpUzl9+jSHDx929VIEDqBevXpcuXKFN998k+eee07Ns9ZUpxswartylpKV1ojptQoc\n/ruhgMoJWYrRunbtGrNmzSI1NZUtW7YwfPhwt9hoWJtZ/Pbbb7Nw4UIeeughADZv3kxAQAAff/wx\nEyZMEDOLdYwwyC5i+vTp7Nmzh0OHDtGqVSv1eGBgILIsk52dbeQlZ2dnExoa6oqlCmqJJEls27at\nykteUafq1asXvXr1YsqUKVV0uj/77DMWLVpEaWmpUcFYREQETZs2NSoWc4TC2O3gFWvFVrQbClmW\n2bNnD7GxsURFRZGRkUGTJk1cvVybsJbSmjBhAkePHqWsrMzonM6dO9OuXTuSkpKEQXZjhEF2AdOn\nT2f37t0cOHCAdu3aGX0tODiYwMBA9u3bR69evYDKoq6UlBSmTZvmiuUK6oCtRswWne64uDjS09Np\n166d0czo7t274+HhUWW6koKpeEl1Xu7t5hWbbijy8vKYN28eX331FevWrWPMmDG62mjYktLKzs4W\nM4t1iv6eNJ0zdepU/vGPf7BlyxYaNGhAdnY22dnZRrnCmTNnsnTpUj799FNOnDjBU089xV133cWY\nMWMcurbly5djMBiYPXu20XFrBSQCx6DodD/55JOsWrWKlJQUrl+/TkJCAvfccw9JSUn86U9/ok2b\nNjz44IMsXryYL774gry8PBo1aqQWjEmSRElJiVHBWFFRkVHBmLbgqaysDB8fH7XgSU8oXnFRUREe\nHh40atRIDVHv37+ffv36cfPmTU6cOMEf//hHXRljwe2P8JCdzLp165AkiUGDBhkdT0hI4KmnngJg\n7ty5FBUVER0dTW5uLvfddx979+51aA+yMoShd+/eRsdtKSAROAdlHvLAgQPVFjlLOt1NmjRRPeiI\niAhCQ0Px8vIyKhjTetEKeu2NBsstWYWFhSxevJgdO3bw9ttv8/jjj+vu2hRsSWmJmcX6RfQhCygo\nKCAsLIy1a9fy6quvEhoayptvvgmInmi9YYtOt2Ko27dvz549e7hx4wajRo1CkqQqOt3acLe7GjFT\noRLFs5dlmZSUFKKjo7n77rt57733aNOmjauXWyPMzSy29Exu3ryZRx55RMwsdl/EPGSBdaZNm8ao\nUaMYMmQIr776qnrclgISgXshSRKenp6EhYURFhbGtGnTquh0f/TRR8yZM0ftex42bBiBgYFVdLqV\n0YfK+EOlYEwrXuJqI21JvvPWrVssW7aMDRs2EBcXx7PPPqub8Ht1M4vbtm2rprQ6duxI+/btWbhw\noVFKS8ws1i/CIN/hbNu2jbS0NI4ePVrla6In+vZAkiSaNWvGyJEjefDBB9mwYQPJyck0aNCAmJgY\nCgoKWLx4MadOnSIkJMRIAlSr013XgjF7oh31qB1XKcsy6enpTJkyhaZNx831fQAACyBJREFUm3Ls\n2DGjUZx6oLqZxRs3brQppSVmFusTEbK+g7l06RLh4eF8/fXX6hSbwYMHqyHrpKQkBg4cSFZWlpFR\nfvTRRzEYDGzdutVVSxfUgQkTJtCwYUPefPNNtd3Hmk63Nh/dsmXLKtOutKFurYF2RKi7vLycoqKi\nKqMeS0tLeeONN1i5ciWLFy8mNjZWNxrbgjsCoWUtsMzu3bsZO3as6llA5ctOyR+ePXuWjh07kpaW\nprZgAQwaNIjQ0FDi4+NdtXRBHVDGPVrDVp3unj174unpaVQwZjpVyVS8pDZGWusVGwwGfH19VYN7\n9uxZpkyZgoeHBwkJCarynUDgRgiDLLBMYWEhP/30k9GxSZMm0bVrV+bPn0/Xrl2tFpAI7hyU1qi0\ntDSjgrFLly7Rq1cvo1B327ZtjSYrWRpJaWvBWHl5OTdv3qS8vNzIKy4vL2fNmjW8/vrrvPDCC8yf\nP9+mzYZA4AKEQRbUDG3IGmDFihXExcWRmJioFpCcOnWKU6dOOaTtKSsri3nz5rF3716Kioq4++67\n1b5bBTEr2n2QZZkrV66obVepqakcPXoUHx8fIy/6nnvuwdfX16hgrKysTP05lgrGlE3ArVu31JYs\nxeBevHiRmJgY8vLySExMpE+fPi4vMhMIqkFUWQtqhukLzZk90YqBHTp0KF9++SXNmzfnhx9+wN/f\nXz1H9EW7F0rR35gxYxgzZoxZne4PP/ywik53eHg4nTp1Aqi2YKyiogJZlvHw8KBBgwZqgVliYiIL\nFy7kL3/5C4sXL8bb29tVfwKBwG4ID1ngNsyfP5+kpCQOHDhg8RzRF60/THW6lVB3SUkJYWFhRka6\nWbNmlJaWkpSUxN13361OXlq3bh2bNm2id+/e/Pzzz1y7do0PPviA+++/X3jFAr0gQtYC/dC9e3dG\njBjBf/7zHw4cOECbNm2YOnUqzz77LFAZogwJCRFFZrcBpjrdqamppKen06pVK7y9vcnMzGTevHnM\nnTuX+vXr8+2335KYmEhGRgY//PCDmksODQ1l8uTJTJkyxdWXJBBYw6pB1kenvOCO4Mcff2Tt2rV0\n7tyZr776ipiYGGJjY/nggw8A0Rd9O2Gq052cnMxbb71FTk4O2dnZTJw4ka1bt3LXXXcxbNgwYmNj\nSU5OZtWqVRQUFJCcnMyKFSsIDg5Wx1G6iiVLlmAwGIz+M63yFnrwAlsQOWSB21BRUUFkZKSqFta7\nd29OnjzJunXrePLJJ128OoEjuXjxIjNmzOBPf/oT8fHxNGnSRNXp/vbbb1m3bh0ff/wxjRs3BqBv\n37707duX2NhYF6+8kh49erBv3z61fVBb6S3qHgS2IjxkgdvQqlUrunbtanSsa9eu/Pzzz4CxsL4W\nIZqvf0JCQjhz5gwJCQmqWIkkSbRp04ZHH32U/fv3q8bYHalXrx4tWrSgZcuWtGzZkqZNm6pfe/vt\nt1m4cCEPPfQQPXr0YPPmzWRlZfHxxx+7cMUCd0QYZIHbMGDAADIzM42OZWZmEhQUBBjPilZQZkX3\n79/fqWsV2J+QkBBXL6HW/PDDD7Rp04aQkBCeeOIJ/vOf/wDW9eAFAi3CIAvchlmzZpGcnMzrr7/O\nhQsX2LJlC++//z7Tp09Xz3H2rOiKigoWLlxIhw4d8PX1pWPHjixdurTKeSJHeOfSr18/EhMT+fLL\nL1m3bh0XL17k/vvvp7CwUNQ9CGqGMpzchv8EAofz+eefyz179pR9fHzkbt26yRs2bKhyzuLFi+VW\nrVrJPj4+8v/8z//IP/zwg8PW89prr8ktWrSQ9+7dK//000/yzp075UaNGskrV65Uz1m+fLns7+8v\nf/rpp/KJEyfkMWPGyB06dJCLi4sdti6B+5Kbmys3btxY3rhxo/zdd9/JBoNBvnz5stE5EyZMkCdO\nnOiiFQpchFU7K9qeBIJqGDVqFIGBgbz33nvqsfHjx+Pr68vmzZsB0RstqEpkZCTDhw/n2WefFa16\nAgXR9iQQ1IX+/fuzb98+fvjhBwDS09M5fPgwI0eOBESOUFCVgoICzp8/T+vWrUXdg6BGiLYngaAa\n5s+fT35+Pl26dFGlHF977TUmTpwIiN5oAcyZM4dRo0YRFBTEL7/8wuLFi6lfv756jyh1Dx07dlT1\n4B1Z9yDQL8IgCwTVsH37drZs2cK2bdvo1q0baWlpzJgxg9atW4veaAFQOVf88ccf59q1a7Ro0YKB\nAweSnJxMs2bNAOfqwQv0jcghCwTV0K5dOxYsWEBMTIx67LXXXuMf//gHp0+fFnKeAoHAVkQOWSCo\nC0VFRXh4eBgdMxgM6mxfkSMUCAT2QhhkgaAaRo0axdKlS9mzZw8//fQTu3btIj4+nrFjx6rnOLo3\n+tChQ4wePZo2bdpgMBj45JNPqpxjrQ+6uLiYadOm0bx5cxo1asT48eO5cuWKXdYnEAjsgzDIAkE1\nrFq1ivHjxzNt2jS6devG3LlziYmJ4ZVXX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"text/plain": [
"<matplotlib.figure.Figure at 0x10e266ac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## For this example let's look at the height, width and weight\n",
"properties = features[[\"height\", \"width\", \"weight\"]]\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"\n",
"ax.scatter(properties[\"height\"],properties[\"width\"],properties[\"weight\"])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.47806875867054166"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Start with any necessary imports\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.metrics import silhouette_score\n",
"\n",
"# 1. Define the model you want to use\n",
"magic = KMeans(n_clusters=2)\n",
"\n",
"# 2. Train the model\n",
"magic.fit(properties)\n",
"\n",
"## Score the data\n",
"silhouette = silhouette_score(properties, magic.labels_)\n",
"silhouette"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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kWcr69k0mUytrMJmbU01EA/v3emRXRS/UycoUqs+AmgVQ/V1HT9qM9uwli7Iu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\n64HXJUm6ym+CJQcdWGEtELgt/w93i7bKukDrVAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112089748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"\n",
"ax.scatter(properties[\"height\"],properties[\"width\"],properties[\"weight\"], c=magic.labels_.astype(np.float))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For scoring, I chose silhouette but you can try out many different score from the [clustering metrics list on sklearn](http://scikit-learn.org/stable/modules/classes.html#clustering-metrics)."
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:py35]",
"language": "python",
"name": "conda-env-py35-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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