NLP/Models+K-Means/main/K-Means/K-Means_cluster.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "data1 = pd.DataFrame(pd.read_json(r\"E:\\pythonProject_open\\data\\SIR_dataset_processed.json\"))\n",
    "category_replacement = {'HIGH':0.14950673891899402, 'MEDIUM':0.37487842156454076, 'CRITICAL':0.2908850910101431, 'LOW':0.18472974850632207}\n",
    "# data1['severity'] = data1['severity'].replace(category_replacement)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "          CVE_ID                                      Issue_Url_old  \\\n0  CVE-2011-4116  https://github.com/Perl-Toolchain-Gang/File-Te...   \n1  CVE-2013-4891  https://github.com/bcit-ci/CodeIgniter/issues/...   \n2  CVE-2013-7447      https://github.com/mate-desktop/eom/issues/93   \n3  CVE-2013-7458       https://github.com/antirez/redis/issues/3284   \n4  CVE-2013-7458    https://github.com/antirez/linenoise/issues/121   \n5  CVE-2013-7459       https://github.com/dlitz/pycrypto/issues/176   \n6  CVE-2013-7489      https://github.com/bbangert/beaker/issues/191   \n7  CVE-2014-0087   https://github.com/ManageIQ/manageiq/issues/1581   \n8  CVE-2014-5002       https://github.com/panthomakos/lynx/issues/3   \n9  CVE-2014-7919    https://github.com/alexpark07/Bookmark/issues/1   \n\n                                       Issue_Url_new  \\\n0  https://github.com/perl-toolchain-gang/file-te...   \n1  https://github.com/bcit-ci/codeigniter/issues/...   \n2      https://github.com/mate-desktop/eom/issues/93   \n3         https://github.com/redis/redis/issues/3284   \n4    https://github.com/antirez/linenoise/issues/121   \n5    https://github.com/pycrypto/pycrypto/issues/176   \n6      https://github.com/bbangert/beaker/issues/191   \n7   https://github.com/manageiq/manageiq/issues/1581   \n8       https://github.com/panthomakos/lynx/issues/3   \n9    https://github.com/alexpark07/bookmark/issues/1   \n\n                        Repo_new          Issue_Created_At  \\\n0  perl-toolchain-gang/file-temp 2016-04-03 03:41:22+00:00   \n1            bcit-ci/codeigniter 2015-08-04 08:48:37+00:00   \n2               mate-desktop/eom 2015-07-26 22:46:13+00:00   \n3                    redis/redis 2016-05-30 15:35:38+00:00   \n4              antirez/linenoise 2016-06-18 01:24:57+00:00   \n5              pycrypto/pycrypto 2015-12-14 23:33:48+00:00   \n6                bbangert/beaker 2020-05-14 18:02:40+00:00   \n7              manageiq/manageiq 2015-02-05 08:32:09+00:00   \n8               panthomakos/lynx 2018-01-12 00:12:50+00:00   \n9            alexpark07/bookmark 2016-07-12 17:28:56+00:00   \n\n                                         description  \\\n0  APITAG allows unsafe traversal of symlinks API...   \n1  APITAG security and CVE database. Hello APITAG...   \n2  EOM crashes when trying to open a large PNG fi...   \n3  redis cli: permissions when opening history fi...   \n4  Function to save history as private file by de...   \n5  AES.new with invalid parameter crashes python....   \n6  Insecure data serialization method by default ...   \n7  Hardening of the RBAC check. The current code ...   \n8  CVE: APITAG password exposed on the command li...   \n9  can you match up one of your disclosures?. FIL...   \n\n                                   vectorString  severity  baseScore  \\\n0  CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N      HIGH        7.5   \n1  CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N    MEDIUM        6.1   \n2  CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H    MEDIUM        6.5   \n3  CVSS:3.0/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N       LOW        3.3   \n4  CVSS:3.0/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N       LOW        3.3   \n5  CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H  CRITICAL        9.8   \n6  CVSS:3.1/AV:A/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H    MEDIUM        6.8   \n7  CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H      HIGH        8.8   \n8  CVSS:3.0/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H      HIGH        7.8   \n9  CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H      HIGH        7.5   \n\n   impactScore  exploitabilityScore  \n0          3.6                  3.9  \n1          2.7                  2.8  \n2          3.6                  2.8  \n3          1.4                  1.8  \n4          1.4                  1.8  \n5          5.9                  3.9  \n6          5.9                  0.9  \n7          5.9                  2.8  \n8          5.9                  1.8  \n9          3.6                  3.9  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CVE_ID</th>\n      <th>Issue_Url_old</th>\n      <th>Issue_Url_new</th>\n      <th>Repo_new</th>\n      <th>Issue_Created_At</th>\n      <th>description</th>\n      <th>vectorString</th>\n      <th>severity</th>\n      <th>baseScore</th>\n      <th>impactScore</th>\n      <th>exploitabilityScore</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CVE-2011-4116</td>\n      <td>https://github.com/Perl-Toolchain-Gang/File-Te...</td>\n      <td>https://github.com/perl-toolchain-gang/file-te...</td>\n      <td>perl-toolchain-gang/file-temp</td>\n      <td>2016-04-03 03:41:22+00:00</td>\n      <td>APITAG allows unsafe traversal of symlinks API...</td>\n      <td>CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N</td>\n      <td>HIGH</td>\n      <td>7.5</td>\n      <td>3.6</td>\n      <td>3.9</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CVE-2013-4891</td>\n      <td>https://github.com/bcit-ci/CodeIgniter/issues/...</td>\n      <td>https://github.com/bcit-ci/codeigniter/issues/...</td>\n      <td>bcit-ci/codeigniter</td>\n      <td>2015-08-04 08:48:37+00:00</td>\n      <td>APITAG security and CVE database. Hello APITAG...</td>\n      <td>CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N</td>\n      <td>MEDIUM</td>\n      <td>6.1</td>\n      <td>2.7</td>\n      <td>2.8</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CVE-2013-7447</td>\n      <td>https://github.com/mate-desktop/eom/issues/93</td>\n      <td>https://github.com/mate-desktop/eom/issues/93</td>\n      <td>mate-desktop/eom</td>\n      <td>2015-07-26 22:46:13+00:00</td>\n      <td>EOM crashes when trying to open a large PNG fi...</td>\n      <td>CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H</td>\n      <td>MEDIUM</td>\n      <td>6.5</td>\n      <td>3.6</td>\n      <td>2.8</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CVE-2013-7458</td>\n      <td>https://github.com/antirez/redis/issues/3284</td>\n      <td>https://github.com/redis/redis/issues/3284</td>\n      <td>redis/redis</td>\n      <td>2016-05-30 15:35:38+00:00</td>\n      <td>redis cli: permissions when opening history fi...</td>\n      <td>CVSS:3.0/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N</td>\n      <td>LOW</td>\n      <td>3.3</td>\n      <td>1.4</td>\n      <td>1.8</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CVE-2013-7458</td>\n      <td>https://github.com/antirez/linenoise/issues/121</td>\n      <td>https://github.com/antirez/linenoise/issues/121</td>\n      <td>antirez/linenoise</td>\n      <td>2016-06-18 01:24:57+00:00</td>\n      <td>Function to save history as private file by de...</td>\n      <td>CVSS:3.0/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N</td>\n      <td>LOW</td>\n      <td>3.3</td>\n      <td>1.4</td>\n      <td>1.8</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CVE-2013-7459</td>\n      <td>https://github.com/dlitz/pycrypto/issues/176</td>\n      <td>https://github.com/pycrypto/pycrypto/issues/176</td>\n      <td>pycrypto/pycrypto</td>\n      <td>2015-12-14 23:33:48+00:00</td>\n      <td>AES.new with invalid parameter crashes python....</td>\n      <td>CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H</td>\n      <td>CRITICAL</td>\n      <td>9.8</td>\n      <td>5.9</td>\n      <td>3.9</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CVE-2013-7489</td>\n      <td>https://github.com/bbangert/beaker/issues/191</td>\n      <td>https://github.com/bbangert/beaker/issues/191</td>\n      <td>bbangert/beaker</td>\n      <td>2020-05-14 18:02:40+00:00</td>\n      <td>Insecure data serialization method by default ...</td>\n      <td>CVSS:3.1/AV:A/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H</td>\n      <td>MEDIUM</td>\n      <td>6.8</td>\n      <td>5.9</td>\n      <td>0.9</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CVE-2014-0087</td>\n      <td>https://github.com/ManageIQ/manageiq/issues/1581</td>\n      <td>https://github.com/manageiq/manageiq/issues/1581</td>\n      <td>manageiq/manageiq</td>\n      <td>2015-02-05 08:32:09+00:00</td>\n      <td>Hardening of the RBAC check. The current code ...</td>\n      <td>CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H</td>\n      <td>HIGH</td>\n      <td>8.8</td>\n      <td>5.9</td>\n      <td>2.8</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CVE-2014-5002</td>\n      <td>https://github.com/panthomakos/lynx/issues/3</td>\n      <td>https://github.com/panthomakos/lynx/issues/3</td>\n      <td>panthomakos/lynx</td>\n      <td>2018-01-12 00:12:50+00:00</td>\n      <td>CVE: APITAG password exposed on the command li...</td>\n      <td>CVSS:3.0/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H</td>\n      <td>HIGH</td>\n      <td>7.8</td>\n      <td>5.9</td>\n      <td>1.8</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CVE-2014-7919</td>\n      <td>https://github.com/alexpark07/Bookmark/issues/1</td>\n      <td>https://github.com/alexpark07/bookmark/issues/1</td>\n      <td>alexpark07/bookmark</td>\n      <td>2016-07-12 17:28:56+00:00</td>\n      <td>can you match up one of your disclosures?. FIL...</td>\n      <td>CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H</td>\n      <td>HIGH</td>\n      <td>7.5</td>\n      <td>3.6</td>\n      <td>3.9</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CVE_ID = data1[\"CVE_ID\"]\n",
    "data1.head(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "# 去除无用数据列\n",
    "data1 = data1.drop([\"CVE_ID\"],axis=1)\n",
    "data1 = data1.drop([\"Issue_Url_old\"],axis=1)\n",
    "data1 = data1.drop([\"Issue_Url_new\"],axis=1)\n",
    "data1 = data1.drop([\"Repo_new\"],axis=1)\n",
    "data1 = data1.drop([\"Issue_Created_At\"],axis=1)\n",
    "data1 = data1.drop([\"description\"],axis=1)\n",
    "data1 = data1.drop([\"vectorString\"],axis=1)\n",
    "data1 = data1.drop([\"severity\"],axis=1)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "# 复制元数据col\n",
    "new_col = data1.columns"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "(7037, 2)"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PCA降维\n",
    "pca=PCA(2)\n",
    "pca.fit(data1)\n",
    "new_data=pca.transform(data1)\n",
    "new_data.shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "          0         1\n0  0.022365  1.330027\n1 -0.863975  0.597988\n2 -0.454437  0.162479\n3 -2.372864 -0.154303\n4 -2.372864 -0.154303\n5  1.539811  0.528399\n6  0.138943 -2.722275\n7  1.063009 -0.639149\n8  0.596053 -1.722707\n9  0.022365  1.330027",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.022365</td>\n      <td>1.330027</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.863975</td>\n      <td>0.597988</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-0.454437</td>\n      <td>0.162479</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-2.372864</td>\n      <td>-0.154303</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-2.372864</td>\n      <td>-0.154303</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1.539811</td>\n      <td>0.528399</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.138943</td>\n      <td>-2.722275</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1.063009</td>\n      <td>-0.639149</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.596053</td>\n      <td>-1.722707</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0.022365</td>\n      <td>1.330027</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对数据进行预处理标准化\n",
    "scaler=preprocessing.StandardScaler().fit(new_data)\n",
    "data_s=pd.DataFrame(scaler.fit_transform(new_data,y=data1.columns))\n",
    "data_s.head(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 元数据散点图\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.scatter(data_s[0],data_s[1],c='r',label='散点')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.cluster import KMeans"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, '$J(C_K)$')"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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uOMT+vGKTKxIREXEMBR0B4JJW4fRsFU5FlZ3//LjH7HJEREQcwm2CzqxZs+jbty+JiYnceeedZGZmApCenk5KSgrJyclMnjxZd/k9Dw9f3gYD+H77UbZmFZpdjoiIyHlzi6Czb98+Xn/9daZOncq8efOIjY3lH//4B2VlZYwaNYqEhARSU1PJyMhg9uzZZpfrtuKjGnNNpygAXluqrSFERMT9+ZhdQF1s3bqVxMREEhISAEhJSeHPf/4zS5YswWazMXbsWAIDAxk9ejQTJ04kJSWlXuMbhuNrrhnTGWM70wN9WrMg/Shr9uWxcm8uvdo0Oadx3LV/R/H2/kHHwNv7Bx0D9X/qV2eMXRduEXTi4uJYuXIl27Zto0WLFsyYMYPevXuTlpZGYmIigYGBAMTHx5ORkVHv8Zs2DXZ0yS4Z2xkiIoK569LWvP3jbqYu28ugi1titZz7p9Td+nc0b+8fdAy8vX/QMVD/5vbvNkHn6quvZtiwYQC0aNGCWbNm8eabb9KiRYva9xmGgcViIT8/n9DQ0DqPf+xYIY6epTGM6r9cZ4ztbLcmxvDJ6n2kZRUyfWkGgxOi6z2GO/fvCN7eP+gYeHv/oGOg/p3Xf83YdeEWQWfjxo0sWrSImTNn0rZtW95++23uvfdeevbsiZ+f3ynv9ff3p6SkpF5Bx27HaR9CZ47tLKEBvtx9SUv+vXQ3b/y4hys7ROLvc26Xc7lj/47k7f2DjoG39w86Burf3P7d4mLkr776ikGDBpGYmEhwcDB/+ctfyMzMJDQ0lJycnFPeW1RUhK+vr0mVeo5bul9AVGM/sgpLmfnzAbPLEREROSduEXSqqqo4duxY7eOioiKKi4vx8fFh/fr1tc9nZmZSVlZWr7M5cmYBvlbuP7E1xHurMikoKTe3IBERkXPgFkEnKSmJ+fPn89577zF37lwefPBBIiMjufPOO7HZbKSmpgIwbdo0evXqhdVqNblizzCoczTtIhpRUFLBez9lml2OiIhIvbnFNTpXX301GRkZvP/++xw9epT27dvz73//G19fXyZNmsSYMWOYMmUKFouF6dOnm12ux7BaqreG+OvnW/j05wPc3P0CYkICzC5LRESkzgy7B9wV7ujRo2zZsoXExETCw8Pr/f3Z2c65IjwiItgpY7uS3W5n1MyNrNufz+CEaMZfE1+n7/OU/s+Vt/cPOgbe3j/oGKh/5/VfM3ZduMXU1e+JjIykX79+5xRy5OwMw+CRy6s3/Px6y2F2Hi0yuSIREZG684igI87VpVkIAzpEYAf+vXS32eWIiIjUmYKO1MmDfdpgtRgs253D2sw8s8sRERGpEwUdqZOW4YFc3zUGgFeXaMNPERFxDwo6Umd/urQVjXytbM0qZEF6ttnliIiI/C4FHamzpkF+3JFUvbfY1B93U15ZZXJFIiIiZ6egI/Vye1ILmjTyZX9eCZ9vPGR2OSIiImeloCP10sjPyr2XtgLg7RX7sJVWmFyRiIjIb1PQkXob1jWGluGB5BaX8+Ga/WaXIyIi8psUdKTefKwWHurTGoCP1uwn21ZqbkEiIiK/QUFHzskV7SPo2iyYkooq3lqxz+xyREREzkhBR85J9dYQbQH4ctMh9uQcN7kiERGR0ynoyDnr3iKUy9o2odIOU3/cY3Y5IiIip1HQkfPy0GVtsBiwaEc2Gw8WmF2OiIjIKRR05Ly0iwhicEI0AK8t2aWtIUREpEFR0JHzdl+v1vj7WFh/oIAlGTlmlyMiIlJLQUfOW3SwP7de1ByA15fupqJKZ3VERKRhUNARh7grOZbQAB925xznq81ZZpcjIiICgI/ZBYhnCA7w4cYLL+Cdlft4/cfdtI1oRHRpFXl5RdRcthMW6EtMSIC5hYqIiFdR0BGHyCooYfrqTADyiisY+fGG097jZzVIHZGssCMiIi6jqStxiLzicsoqz35tTlmlnbzichdVJCIioqAjIiIiHkxBR0RERDyWgo6IiIh4LAUdERER8VgKOiIiIuKxFHRERETEYynoiEOEBfriZzXO+h4DCPbXrZtERMR19FtHHCImJIDUEcm198kxDAgLCyIvr4iDeSU8+U0aZZV2FqRnc9clsSZXKyIi3kJBRxwmJiSg9q7HhgEREcFk+1uIjwrmsdIKJn2/g/8s28MlrcLoFB1scrUiIuINNHUlLjG0Swz920dQWWXnya/TKC6vNLskERHxAgo64hKGYfDEwPZENfZjX24xLy7KMLskERHxAgo64jKhgb788w8dMYAvN2XxQ/pRs0sSEREPp6AjLnVxbBh/PHEx8jPzd3C4sNTkikRExJO5RdCZPXs28fHxp/2ZPXs2q1at4tprr6VHjx68++67ZpcqdXB/r1Z0im5MQUkFE+alUVl19l3PRUREzpVbBJ3BgwezevXq2j//+9//CA8Pp127djzwwAMMGjSITz/9lLlz57Jy5Uqzy5Xf4Wu1MGlQJwJ9LazJzOfDNfvNLklERDyUWywv9/Pzw8/Pr/bxjBkzGDhwID///DNRUVE89NBDGIbBgw8+yGeffUbPnj3rNb5x9vvcnZOaMZ0xtjv4vf5bNQnkb/3jePq79Nol551jPGfJubf//YOOgbf3DzoG6v/Ur84Yu07vtdvtbjVvUFpayhVXXMHMmTN5/fXX8ff3Z8KECQAcOXKEu+66i3nz5plbpNSJ3W7nwY/WMW9zFm0igvjqkT4E6c7JIiLiQG73W2Xu3Ll069aNFi1aYLPZaNeuXe1rjRs35siRI/Ue89ixQhwd9wwDmjYNdsrY7qCu/f+tbxvW7c1hd3YRT3y2gXFXd3BdkU7k7X//oGPg7f2DjoH6d17/NWPXhdsFnU8++YRHHnkEAKvVesqUlr+/PyUlJfUe027HaR9CZ47tDn6v/5AAXyZe25EHZm7ky01Z9GodTv8Oka4r0Mm8/e8fdAy8vX/QMVD/5vbvFhcj19i7dy/79u2jV69eAISGhpKTk1P7elFREb6+vmaVJ+fo10vOswrqH1ZFRETOxK2Czrx58+jXr19tmOnatSvr16+vfX3r1q1ER0ebVJ2cj1OWnH+7XUvORUTEIdwq6CxdupRLLrmk9nH//v1Zt24dy5cvp7y8nLfffps+ffqYWKGcq5OXnK/VknMREXEQtwk6JSUlbNiwge7du9c+16RJE8aOHct9991H79692b17Nw888ICJVcr5aBkeyN+uiAPgP8v2sDWr0OSKRETE3bnNxcgBAQFs3rz5tOeHDx9Onz592LVrF0lJSQQFBZlQnTjKkC7RLN+Tw8L0bMZ9k8b0Oy6ikZ/V7LJERMRNuc0ZnbOJjY2lb9++Cjke4Ne7nL+kXc5FROQ8eETQEc8SEnDSLuebtcu5iIicOwUdaZAujg3jLi05FxGR86SgIw2WlpyLiMj5UtCRBsvnV0vOp6/ONLskERFxMwo60qCdvOT8jeV72aIl5yIiUg8KOtLgDekSzYAOEVRW2XnqmzSOl1WaXZKIiLgJBR1p8LTkXEREzpWCjrgFLTkXEZFzoaAjbkNLzkVEpL4UdMSt3N+rFZ1jgrXkXERE6kRBR9yKj9XCpD901JJzERGpk/MKOuXl5Rw6dIhdu3aRl5fnoJJEzi42PJC/9deScxER+X313r3cZrMxZ84cvvnmGzZu3Eh5eTl2ux3DMIiJiaF3797cfPPNdOvWzRn1igAwJCGaFbtzWJCezVPa5VxERH5DvYLOu+++yxtvvEFsbCxXXHEF999/P1FRUQQEBJCfn096ejpr165l5MiRdOvWjXHjxtG6dWsnlS7ezDAMxg5sz6ZDhezLLebFRTsZd3W82WWJiEgDU6+gs2nTJj788EPat29/xte7devGjTfeyMSJE0lNTWXNmjUKOuI0IQG+TLw2ngdmbmTO5sP0atOEAR0izS5LREQakHoFnZdeeqlO7/Pz82P48OHnVJBIfdQsOX9vVSbPfL+DhJhgYkICzC5LREQaiPNedTV16lRH1CFyzmqWnBeWVjB+npaci4jIL8476KxZs4ZZs2ad9nxVVRUvvvji+Q4v8rtOXnK+br+WnIuIyC/OO+i8+uqrzJw5kx9++KH2uaysLG677Tays7PPd3iROtGScxEROZPzDjqNGzfmP//5Dy+//DLr1q1j4cKF3Hbbbdx2220899xzjqhRpE6GJERzpXY5FxGRk9T7Pjq/9vLLL9OhQwf+/Oc/89e//pWYmBjef/99YmNjHVGfSJ1pybmIiPzaeZ/RCQ8PZ9myZUybNo2CggLy8vJ46aWXmDp1KgsWLHBEjSJ1VrPk3ADmbD7MQu1yLiLi1c77jM7dd999yuPMzEx27NjBjh07+Pbbb7nyyivP90eI1MvFsWHc3SOWd3/SknMREW9X76Czc+dOvvrqK0aMGEFISMhpr8fGxhIbG0v//v0dUqDIubjv0las2pvHlqxCxs/bztSbumG1GGaXJSIiLlbvqatp06axY8eOM4ac0tJSMjIyHFKYyPnwsVp4+g8daeRrZd3+fD7QknMREa9U76CzYcMG7rzzzjO+5u/vz7hx45g2bdp5FyZyvqqXnLcDYJqWnIuIeKV6B52srCxatWr1m6/feuutp9xTR8RMgxOiubJDJJVVdsZ9vU1LzkVEvEy9g05oaChHj/72SpZu3bqxd+/e8ypKxFGql5zHER3sT2ZeCf/6YafZJYmIiAvVO+gkJycze/bs3x7QYqG0tPS8ihJxpJOXnM/dcpgF27XkXETEW9Q76IwYMYJZs2bx6aefnvH1n3/+WTcLlAanZsk5wLPzd5BVUGJyRSIi4gr1DjpdunRh/PjxTJw4kXvuuYcFCxZw8OBB8vLyWLBgAf/6178YPHiwM2oVOS/3XdqKBO1yLiLiVc7phoE333wzbdu25fnnn+fhhx/GMKrvT2K32+ndu/dpNxEUaQhqlpzfMX1d7ZLze3q0NLssERFxonO+M3JSUhKfffYZGRkZbN26lZKSEtq3b8+FF17owPJEHKtmyfk/v0tn2vK9XNIqnISYYLPLEhERJ6nX1NXBgwdPe65du3YMGTKEm2666bSQc/jw4fMq7kxeeOEFRo0aVfs4PT2dlJQUkpOTmTx5Mna7piPk7LTkXETEe9Qr6Nx444089dRTbNy48TffU1hYyMyZMxk8eDDffffdeRd4srS0NGbMmMH/+3//D4CysjJGjRpFQkICqampZGRknHVFmAhoybmIiDep19TV119/zRtvvMGIESPw9/cnISGBqKgo/P39yc/PJyMjgx07dpCQkMDf//53+vbt67BCq6qqeOqpp7j77rtrV3UtWbIEm83G2LFjCQwMZPTo0UycOJGUlJR6jW04YQukmjGdMbY7aOj9hwb68vQf4hk1cyNztxymd9smXBkf6bDxG3r/ruDtx8Db+wcdA/V/6ldnjF2n99rPYa6npKSExYsXs3btWg4ePEhJSQnh4eF07tyZPn360KFDh/oO+bs++ugjXnjhBZ588knCw8O57LLLePPNN9mwYQNvvfUWUH0xdI8ePVi1apXDf754pn99t51/L9pJSIAP8/5yOc3DAs0uSUREHOicgo6rFRUVMWDAACIjIxk4cCCrV6+muLiYpKQkSktLGT9+fO17e/bsyXfffUdoaGidxz92rBBHHwXDgKZNg50ytjtwl/4rKqsY+fEGtmQVclGLUP5zs2N2OXeX/p3J24+Bt/cPOgbq33n914xdF/VedTVx4kQ6d+5M586d6dChA76+vvUusL7mz59PcXEx77//Pk2aNKGiooIhQ4aQmprKDTfccMp7/f39KSkpqVfQsdtx2ofQmWO7g4bev9Vy6pLz91c5dsl5Q+/fFbz9GHh7/6BjoP7N7b/eQWfz5s18/vnnlJSU4OPjQ1xcHJ06daJz58506tSJTp06ERQU5NAis7KySExMpEmTJtVF+/gQHx/Prl27yMnJOeW9RUVFLglf4jlOW3LeMoyEZiFmlyUiIg5Q76Aza9Ysqqqq2LVrF1u3bmXbtm1s3bqVH374gfz8fCwWCy1btqRXr17ccccdtG3b9ryLjImJOW3/rIMHD/LEE0/w4Ycf1j6XmZlJWVlZvc7miED1kvPlu3NZkH6UJ79J46M7L6aRn9XsskRE5DzVewsIqN64My4ujqFDh/L444/z/vvv89NPP7Fw4UJeffVVrrnmGjZs2MD111/PmjVrzrvIvn37snPnTj7++GOysrL44IMPSEtL46qrrsJms5GamgrAtGnT6NWrF1arfkFJ/Zy85Hy/lpyLiHgMp16M/Prrr7N06VI++eST8x5r7dq1TJkyhbS0NCIjI3niiSfo378/CxcuZMyYMfj7+2OxWJg+fTpxcXH1Gjs72zkXSkVEBDtlbHfgrv2v25/HAzM3UmWH5wZ3Oucl5+7avyN5+zHw9v5Bx0D9O6//mrHr4py3gKiLYcOG8fbbbztkrIsvvviMO6YPGDCA+fPns2XLFhITEwkPD3fIzxPvdFGLMO6+JJb//pTJs/N30KVZMDEhAWaXJSIi5+icpq7qqnnz5mcMJ44WGRlJv379FHLEIe69tBVdmlXvcv6UdjkXEXFrTg06gFNuHijiTDW7nDfytfLziV3ORUTEPTk96Ii4oxZhgfx9QDsApi3fy5ZDBSZXJCIi50JBR+Q3DOoczcD46l3On/wmjaKyCrNLEhGRelLQEfkNhmEw9sr2xNQuOc8wuyQREaknBR2RswgO8OGff+iIxYCvthxm/vajZpckIiL1oKAj8ju6twjl7ktiAXh2fjpZBSUmVyQiInWloCNSBzVLzm2llVpyLiLiRhR0ROpAS85FRNyTgo5IHWnJuYiI+1HQEakHLTkXEXEvCjoi9aAl5yIi7kVBR6SetORcRMR9KOiInIPuLUK5u0dLQEvORUQaMgUdkXN0b8+Wvyw5/yZNS85FRBogBR2Rc3TKkvMDBby/SkvORUQaGgUdkfPQIiyQxwbEAfDm8j1s1pJzEZEGRUFH5Dz9oXMUV8VHUmmHJ7/WknMRkYbEx+wCRNydYRj848r2/Lw/nwP5JYz7Oo37e7cirLSKvLwi7Ccu3QkL9CUmJMDcYkVEvIyCjogDFJVVkFtcDsDSXTks3ZVz2nv8rAapI5IVdkREXEhTVyIOkFdcTsXvrLoqq7STdyIMiYiIayjoiIiIiMdS0BERERGPpaAjIiIiHktBR8SF7Lp5soiISynoiLjQy//bRb4uSBYRcRkFHREX+nl/Pnd+uE53UBYRcREFHREHCAv0xc9qnPU9vhaDZiH+HCoo5U+fbOCjNfuxay5LRMSpdMNAEQeICQkgdURy7X1yDAPCwoJOuzNyY38fnvl+BwvSj/LK/3axbn8+T13dgdBAXxOrFxHxXAo6Ig4SExJQe9djw4CIiGCy/S2nXYD87OCOJG0M5aVFGSzJOMadH67j2cGd6NIsxISqRUQ8m6auRFzMMAxSEi/gv8O7ExsWUDuVNWOtprJERBxNQUfEJPHRjfngjou4skMklVV2Xl68i799uVWrskREHEhBR8REjf19eHZwRx4fEIev1aidytKqLBERx1DQETGZYRjceOEFvDu8Oy00lSUi4lBuE3QmTZpEfHx87Z+BAwcCkJ6eTkpKCsnJyUyePFm/GMRtxUc3ZvoZprIKSjSVJSJyrtwm6GzevJk333yT1atXs3r1aj7//HPKysoYNWoUCQkJpKamkpGRwezZs80uVeScnWkq647pmsoSETlXbrG8vKKigh07dpCUlERQUFDt8wsWLMBmszF27FgCAwMZPXo0EydOJCUlpV7jG2e/z9s5qRnTGWO7A/V/6tf6fa/BTd0voOsFIfxj7lb255Xwp0828Ojlbbjt4uYYbnJQ9Rk49as38vZjoP5P/eqMsev0XrsbzPVs3bqV22+/nYiICA4fPkxycjJPP/00s2fPZsOGDbz11lsA2O12evTowapVq0yuWMQxCkvK+UfqJr7edAiAKztF8+JNiYQ20g0GRUTqwi3O6OzcuZM2bdowbtw4wsPDee655xg3bhzt27enRYsWte8zDAOLxUJ+fj6hoaF1Hv/YsUKH7yptGNC0abBTxnYH6t9x/U+4Ko6u0UG8uCiDBdsOc80r/+O5IQ3/BoP6DHh3/6BjoP6d13/N2HXhFkFn6NChDB06tPbx+PHjGTBgAO3atcPPz++U9/r7+1NSUlKvoGO347QPoTPHdgfq3xH9V99gsEtMCP/4qnoqa+TH1VNZwy9q+FNZ+gx4d/+gY6D+ze3fbS5GPlnTpk2pqqoiIiKCnJycU14rKirC11en9cXz/LIqK0KrskRE6sgtgs7kyZOZO3du7eOff/4Zi8VCfHw869evr30+MzOTsrKyep3NEXEn1auyOmlVlohIHblF0OnYsSOvvPIKK1as4Mcff2T8+PEMGzaM3r17Y7PZSE1NBWDatGn06tULq9VqcsUizlNzg8H/Dr+w9gaD9+oGgyIiZ+QW1+hcd9117Ny5k0ceeQSr1cqQIUMYPXo0Pj4+TJo0iTFjxjBlyhQsFgvTp083u1wRl+gYHcz0Oy7ime/TWZCezcuLd7EuM5+nrulASICmb0VEwE2Wl/+eo0ePsmXLFhITEwkPD6/392dnO+eK8IiIYKeM7Q7Uv+v6t9vtpG44xEuLMyivtNMsxJ9nB5u/KkufAe/uH3QM1L/z+q8Zuy7cYurq90RGRtKvX79zCjki7k5TWSIiv80jgo6I/DKVdWWHCCpOrMr6u1ZliYiXU9AR8SC/XpX1vxOrsrZoVZaIeCkFHREPc6aprD9pKktEvJSCjoiH0lSWiIiCjohHq5nKekxTWSLipRR0RDycYRjcpKksEfFSCjoiXuJMU1mPzdFUloh4NgUdES/y66msxTs1lSUink1BR8TLnDyV1TxUU1ki4tkUdES8VMfoYD68U1NZIuLZFHREvNiZprLu1FSWiHgQBR0RL/frqayDmsoSEQ+ioCMiwC9TWQM0lSUiHkRBR0RqNfb34bnBnfh7f01liYhnUNARkVMYhsHN3U+fyvp43QFNZYmI21HQEZEz+vVU1kuLMjSVJSJuR0FHRH6TprJExN0p6IjIWdVMZb2jqSwRcUMKOiJSJ500lSUibkhBR0TqTFNZIuJufMwuQETcS81UVtcLghk7dxsH8kv40ycbeLRvW/q1a0J+ScWJ90FYaRV5eUXUzHCFBfoSExJgYvUi4m0UdETknNRMZU36Pp2F6dm8tCiDVxZnUHWWy3b8rAapI5IVdkTEZTR1JSLn7OSpLB8LZw05AGWVdvKKdU2PiLiOgo6InJeaqaynro43uxQRkdMo6IiIQ7Rp2sjsEkRETqOgIyIiIh5LQUdEXEr3GBQRV1LQERGXem7hDnYdKzK7DBHxEgo6IuJS27Js3PbBOl5enIGttMLsckTEwynoiIhDhAX64mc1zvoeX6tBz1ZhVFbZmbH2ACn/Xc3XWw5TpfksEXES3TBQRBwiJiSA1BHJtffJMQwICws6452RV+zJ4V8/ZLAvt5gJ324ndcMhHhvQjo7RwSZ2ICKeSEFHRBwmJiSg9q7HhgEREcFk+1tOuwD50tZN+OSuMD5ee4C3V+5l06EC/vjhz1zfrRkP9GlNWKCvCdWLiCfS1JWImMLXauGPl8Ty2T3JXN0xEjswe+MhbvzvalI3HKTy926zLCJSB24ZdEaOHMns2bMBWLVqFddeey09evTg3XffNbkyEamvqGB/Jg3qxLRbutE+Moj8kgqeX7CTuz76mQ0H8s0uT0TcnNsFnTlz5vDjjz8CkJOTwwMPPMCgQYP49NNPmTt3LitXrjS5QhE5Fxe1COODOy7i7/3bEezvw/YjNv70yQYmzEsju6jM7PJExE251TU6eXl5TJ48mTZt2gDVoScqKoqHHnoIwzB48MEH+eyzz+jZs2e9xjXOvlDknNSM6Yyx3YH6P/WrNzqXY+BrNbjlouZc1TGS15fu4ctNWXy99QiLdx7j3l6tuLX7BfhY3eO/z/QZ0DFQ/6d+dcbYdXqv3e4+6zrHjh2Ln58fpaWlXHLJJaxevRp/f38mTJgAwJEjR7jrrruYN2+euYWKiENsyMzjqTlb2JCZB0BcVGMmDEmgT/sIcwsTEbfhNmd0Vq5cyYoVK/jqq6+YNGkSADabjXbt2tW+p3Hjxhw5cqTeYx87Vujw29IbBjRtGuyUsd2B+vfu/sExx6B5oJW3bu7K3M2H+feS3ew8YuOOd35iQIcI/tqvbe0Kr4ZInwEdA/XvvP5rxq4Ltwg6paWljB8/ngkTJtC4cePa561WK35+frWP/f39KSkpqff4drvz9t9x5tjuQP17d/9w/sfAwGBolxiuiItg2vI9zFp/kIXp2fy4K4d7esRyR1Is/j4NdzpLnwEdA/Vvbv8N91+Hk0ydOpUuXbrQr1+/U54PDQ0lJyen9nFRURG+vrr/hognCg7w4W/94/jwzovo3iKU0ooq3li2l1veW8PSjGNmlyciDZRbnNGZO3cuubm5JCUlAVBSUlJ7HU737t1r37d161aio6NNqVFEXKN9ZGOm3dyN79OO8n9LdnEgv4TRX2yhd5smjL6iHS3DA80uUUQaELcIOjNmzKCi4pfN/6ZMmUJiYiLXX389/fr1Y/ny5SQnJ/P222/Tp08fEysVEVcwDIOrO0VxWbumvLNyHzPW7mfZ7hxW7cvl9otbMKJnSwJ9rWaXKSINgFsEnZiYmFMeN2rUiPDwcJo0acLYsWO57777aNSoEcHBwTz//PMmVSkirtbIz8ojl7dhaJdoXlyUwYo9uby3KpNvth7mz33bMjA+EsNb1/aKCOBmy8t/S2ZmJrt27SIpKYmgoKB6f392tnOuCI+ICHbK2O5A/Xt3/+D6Y2C321mSkcNLizM4mF+9KOHi2FD+1j+OuIj6/7twvvQZ0DFQ/87rv2bsunCLi5F/T2xsLH379j2nkCMinsEwDPrGNeXTuy7mvl6t8PexsDYznzs+WMuLizIoLKn4/UFExON4RNAREakR4Gvl3ktbMfPuJK5oH0GlHT5Zd4Ab313NnM1ZVHnjf1qLeDEFHRHxSBeEBjBlaGdeS+lCq/BAco6X8/R36Yz8eD1bswrNLk9EXERBR0Q8Ws/WTfj4rot59PI2NPK1svlQIXd/9DPPfJ9O3vFys8sTESdT0BERj+drtXBnciyfjUji2k5R2IEvNmWR8u5qZq0/SGWVprNEPJWCjoh4jcjG/vzzDx1585ZE2kcGUVBSwZSFO7nzw3Ws359vdnki4gQKOiLidbq3COWDOy7isQFxBPv7sONoEfd+uoFx36Rx1FZqdnki4kAKOiLilXwsBjddeAGpI5IY1jUGA/h22xFu/O8apq/OpLyyyuwSRcQBFHRExKuFN/Lj/13Vgfdu706XZsEcL6/k1SW7ue2Dtfy0J9fs8kTkPCnoiIgAnWOCeWf4hYy7ugNNGvmyJ6eYh1M38dicrbV3WhYR96OgIyJygsUwGNolhs/uSebWi5pjNWDRjmxufm8Nb63YS0l5pdklikg9KeiIiPxKcIAPY65ox4d/vJiLY0MprajizeV7ueW9NfxvZzYesEWgiNdQ0BER+Q1xEUH856ZuPDOoI1GN/ThYUMrfvtzKn2dvZm/OcbPLE5E6UNARETkLwzC4qmMUs+5J5u5LYvGxGKzYk8ut76/ltSW7OV6m6SyRhszH7AJERNxBIz8rD13WhsEJ0by0OIPlu3P5YHUm3247zKOXt+WqjpEcLiwlr7h6WwnDgLDSKvLyiqiZ6QoL9CUmJMDELkS8j4KOiEg9tGrSiFeu78LSXTm8tCiDA/klPPlNGh+vO0D6ERvlZ9lOws9qkDoiWWFHxIU0dSUiUk+GYXB5u6Z8encSo3q3wt/HwpaswrOGHICySnvtGR8RcQ0FHRGRc+TvY2Fkz1bMuieJ5Ngws8sRkTNQ0BEROU/NQgJ4tG8bs8sQkTNQ0BERERGPpaAjIuJC475OI3XDQWylFWaXIuIVFHRERFxoT24xzy/YyTVvrGTCt9tZvz9fd1oWcSItLxcRcaHbLm7Oij257D52nK+3HObrLYdpFR7IdV1jGJQQTZNGfmaXKOJRFHRERBwgLNAXP6tBWeXZ76Mz/KLm/KVvWzYdKuTLTYf4Pu0oe3OLeXXJbl7/cQ992zXluq4x9GgVjtViuLADEc+koCMi4gAxIQGkjkg+9c7IYUG/eWfkbheE0O2CEP7arx3ztx/ly01ZbMkq5Icd2fywI5uYYH+GdolhSJdo3WBQ5Dwo6IiIOEhMSEBtKDEMiIgIJtvfwtkuwWns78P13Zpxfbdm7Dhq48tNWXyz9QhZhaW8uWIvb63YS8/W4QzrGsNl7Zria9WllSL1oaAjItJAtI9szN/6x/HwZW1YvPMYX246xJrMfFbsyWXFnlzCA30ZlBDNdV1iaN20kdnlirgFBR0RkQYmwNfKNZ2iuKZTFJm5xczZnMXcLYc5VlTGh2v28+Ga/VzYPITrusZwZYdIAnytZpcs0mAp6IiINGCx4YE8dFkb7u/dmmW7cvhy0yGW7c5h/YEC1h8o4F8/ZHBNpyiGdY2hY3Sw2eWKNDgKOiIibsDHYtA3ril945pypLCUr7Yc5svNWRzMLyF1wyFSNxwiPqox13WN4ZqOUQQH6J93EVDQERFxO1HB/ozo2ZK7e8SyZl8eX27KYtHObLYfsTFl4U7+73+7GNAhguu6xtC9eSiGoWXq4r0UdERE3JTFMLikVTiXtAonr7iceduO8MXGQ+w6dpxvth7hm61HaBkeyHVdqm9G2DRINyMU7+NWQaegoIDdu3fTunVrQkNDzS5HRKTBCAv0ZfhFzbm1+wVsySrki01ZfJ92hH25xby2dDdTl+3h8hM3I+ypmxGKF3GbGzLMmzeP/v378+STT9K3b1/mzZsHQHp6OikpKSQnJzN58mTtGSMiXs0wDLo0C+HJqzowb1RPnryqPV2aBVNZZWfRjmz+MnszQ9/6iWnL9nAwv8TsckWczi2CTmFhIRMnTuTDDz9k7ty5PPXUU7zwwguUlZUxatQoEhISSE1NJSMjg9mzZ5tdrohIgxDk58N1XZvx7m3d+fiPF3PrRc0JDfDhiK2Mt1fuY9jbq3jks00s2H6Usooqs8sVcQq3CDo2m40nnniCjh07AtC5c2dyc3NZsmQJNpuNsWPH0rJlS0aPHs1nn31mcrUiIg1PXGQQY65ox9f39+SZQR1JbhmGHVi5N5exX21j0Js/8fLiDHYfO252qSIO5RbX6DRr1oyhQ4cCUF5ezvvvv8/AgQNJS0sjMTGRwMBAAOLj48nIyKj3+M5YkFAzprcudlD/p371Rt5+DBpq/wG+Fq7uFMXVnaLYn3fiZoSbD3PUVsaMtQeYsfYAic1DGHbiZoSBfud+M8KGegxcRf2f+tUZY9fpvXY3uqglLS2Nu+66C19fX7755humTp1KaWkp48ePr31Pz549+e6773SxsohIHVVUVrF4+1E+WZ3Jou1HqKyq/rXQ2N+HoRdewK3JsXTVMnVxU24VdOx2O1u2bOG5556jadOmxMbGUlFRwdixY2vf07dvX2bOnEl0dHSdxz12rPCsm+6dC8OApk2DnTK2O1D/3t0/6Bi4a/9HbSduRrgpi/15v1ys3CEyiOu6xnBt5yhCAnzrNJa7HgNHUf/O679m7Lpwi6mrGoZh0KVLF55//nkGDhzI6NGj2bFjxynvKSoqwte3bv8nrGG347QPoTPHdgfq37v7Bx0Dd+s/Isifuy9pyR+TY1mXmc8Xmw6xaEc26UeLeOGHDF5dspsr2kcwrGsMF7U4/SxPVkEJecXlQPUvo7CSKvLyimqPQVigb+0O797C3T4DjmZ2/24RdFatWsWiRYt4/PHHAfDz88MwDNq1a8esWbNq35eZmUlZWZmmrUREzpPFMEhqGUZSyzDyi8v5dtsRvtiUxc7sIr7ddoRvtx0hNiyA67o2Y1BCNBFBfmQVlJDy39WUVf72bzU/q0HqiGSvCztiHrdYddW6dWtmzpzJp59+yqFDh3jppZfo3bs3ffv2xWazkZqaCsC0adPo1asXVqt28hURcZTQQF9uuag5M/54Ee/d3p1hXWNo5GslM6+Efy/dzeBpK/n7l1tYtDP7rCEHoKzSXnvGR8QV3OYanWXLlvHss89y6NAh+vTpw4QJE2jSpAkLFy5kzJgx+Pv7Y7FYmD59OnFxcfUaOzvbOfOHERHBThnbHah/7+4fdAw8vf/jZZUs2H6ULzZlselQQb2+d/od3b1ip3VP/wz8Hmf2XzN2XbjF1BVA7969+frrr097fsCAAcyfP58tW7aQmJhIeHi4CdWJiHiXRn5WhnaNYWjXGDKyi5izOYs5m7KwlVWaXZrIKdxi6ur3REZG0q9fP4UcERETtIsI4q/92vFqStc6vX9pxjF2HztOlTee5hCXc5szOiIi0rD5Wut2n503V+zjzRX7CPKz0jkmmC7NgkmICSahWQgR2mFdHExBR0REXKpDZBB7c4spKqtk9b48Vu/Lq30tOtj/pOATTKfoYAJ9tcBEzp2CjoiIuNS4qzsQF9mYjOwitmQVsuVQAVuyCtmVfZzDhaUcLixlYXo2ABajemqsc0wwXWKC6dIshDZNG2G16C7NUjcKOiIi4hBhgb74WY3fvY9OWKAvPhaD+KjGxEc15oZuzQAoKqsg7bCNzYcKawPQEVsZO44WseNoEV9uygIg0NdCp+jqsz5dmgXTOSaY6GB/bVEhZ6SgIyIiDhETEkDqiORT74wcFlTnOyMH+flwcWwYF8eG1T53pLC0OvScCD5bs2wcL69k3f581u3Pr31fRJBf7XRXQkx1+Gnsr19xoqAjIiIOFBMSUBtkau+j4m855/uoRAX7ExXszxXtIwCorLKzJ+c4W06c9dl8qICM7CKyi8r4X8Yx/pdxrPpnA62bNKoNPl2aBRMXEYSP1SMWG0s9KOiIiIjbsFoM2kUE0S4iiKFdYwAoKa8k7bDtlDM/BwtK2Z1znN05x/lqy2EA/H0sxEc1PmXKq3logKa8PJyCjoiIuLUAXysXtgjlwha/7HOYc7yMLYcK2ZxVyNYTZ38KSyvYeLCAjQd/uZNzWKDvKVNeCTHBhAbWb2NoadgUdERExOM0aeTHZe2aclm7pgBU2e1k5hafOONTHYDSj9jIKy5n2e4clu3Oqf3e2LAAEpqF1J756RDZGD+fuk15nbZ7e6l2bzebgo6IiHg8i2HQqkkjWjVpxB86RwNQVlFF+lHbL2d+sgrZl1tMZl4JmXklfLvtCAA+FoMOJ015JcQEExseiOVXU17avb1hUtARERGv5OdjoUuzELo0C+GWE8/lF5ez9XAhmw9VB5/NhwrJKy5n64kgNGt99fuC/X2qV3c1q76/T0KzYPKKy+u8e7unBp2GeEZLQUdEROSE0EBfLm3dhEtbNwHAbrdzsKDkpFVehWw/YqOwtIKVe3NZuTe39nsjgrz72p6GekZLQUdEROQ3GIZB89BAmocGclXHKAAqKqvYmV100o0NC9mTc5zsovI6jfn8gh2EN/LD12rBz2qc+GrB12pUf/Wpfr76uV+9x+fUx6e+dvL3VL9mtRguW1XWUM9oKeiIiIjUg4/VQsfoYDpGB3PjiedspRV8u+0Ikxfu/N3v35Jlc26BJzGonqKrCVE+FuPE4zMEJevJr538/IngdcbXfglnWQUlLuurPhR0REREzlNjfx+6NAuu03vv79WKyMZ+lFXaKa+soqyiivJKO2WVVZRV/vK/q1878Z6ax5UnP7ZXf2/VyeNUcfJJFTtQWlFFaQVApTNab/AUdERERFyoT9smdIyuWyg6F5VVv4ShM4WpU16rOEO4Oul7Tg1Wvz1eeaWdwtIKDuQ3vLM6CjoiIiIexGoxsFqsBPhaXfpz0w4XcueHP7v0Z9aFNv0QERFxgJrd28+mZvd2cR2d0REREXGA8929XZxDQUdERMRBHL17uzupOaP1e/fRcfUZLQUdEREROW8N9YyWgo6IiIg4REM8o6WLkUVERMRjKeiIiIiIx1LQEREREY+loCMiIiIeS0FHREREPJaCjoiIiHgsBR0RERHxWAo6IiIi4rEUdERERMRj6c7IVN+90VljOmNsd6D+T/3qjbz9GHh7/6BjoP5P/eqMsev0XrvdG7YaExEREW+kqSsRERHxWAo6IiIi4rEUdERERMRjKeiIiIiIx1LQEREREY+loCMiIiIeS0FHREREPJaCjoiIiHgsBR1xioKCAjZs2EB+fr7ZpYiIiBdT0HGwBQsWMGDAADp37sx1111HRkaG2SW53Lx58+jfvz9PPvkkffv2Zd68eWaXZJqRI0cye/Zss8twuUmTJhEfH1/7Z+DAgWaXZIoXXniBUaNGmV2Gy82ePfuUv/+aP970/4VZs2bRt29fEhMTufPOO8nMzDS7JJdKTU1l8ODBJCUlMXr0aHJycswrxi4Os3fvXntycrL966+/th89etT+6KOP2m+55Razy3KpgoICe48ePezbtm2z2+12e2pqqv2KK64wuSpzfPnll/YOHTrYU1NTzS7F5W655Rb74sWL7fn5+fb8/Hx7YWGh2SW53LZt2+wXXnihfd++fWaX4nKlpaW1f/f5+fn2Q4cO2Xv06GHfu3ev2aW5xN69e+19+/a1b9682X7gwAH72LFj7bfddpvZZbnMsmXL7BdeeKH9xx9/tB84cMB+77332ocPH25aPTqj40AZGRmMGTOGP/zhD0RERDB8+HC2bdtmdlkuZbPZeOKJJ+jYsSMAnTt3Jjc31+SqXC8vL4/JkyfTpk0bs0txuYqKCnbs2EFSUhIhISGEhITQuHFjs8tyqaqqKp566inuvvtuYmNjzS7H5fz8/Gr/7kNCQvjiiy8YOHAgLVu2NLs0l9i6dSuJiYkkJCRwwQUXkJKSwt69e80uy2W++OILbrjhBnr37s0FF1zAY489xtq1a8nLyzOlHgUdB7riiiu45ZZbah/v3r2bVq1amViR6zVr1oyhQ4cCUF5ezvvvv++V0xaTJ0/myiuv5MILLzS7FJdLT0+nqqqKYcOG0a1bN0aOHMnBgwfNLsulPv74Y9LT02nevDkLFy6krKzM7JJMU1paygcffMD9999vdikuExcXx8qVK9m2bRuFhYXMmDGD3r17m12Wy+Tm5tKsWbPaxxZLddSwWq2m1KOg4yRlZWW8++673HrrrWaXYoq0tDT69OnD0qVLefLJJ80ux6VWrlzJihUr+Pvf/252KabYuXMnbdq0YcqUKcyZMwcfHx/GjRtndlkuU1RUxGuvvUZsbCwHDx7kvffe47bbbqOkpMTs0kwxd+5cunXrRosWLcwuxWXi4uK4+uqrGTZsGElJSaxfv57HH3/c7LJcpnPnzixevJiqqioAPv/8c7p27UpwcLAp9SjoOMlrr71GYGAgN910k9mlmCI+Pp533nmHVq1aeVXQKS0tZfz48UyYMMHrpmtqDB06lNmzZ9O9e3dat27N+PHjWb58OTabzezSXGL+/PkUFxfz/vvv8+ijj/Luu+9SVFTEl19+aXZppvjkk08YPny42WW41MaNG1m0aBEzZ85kzZo1DB48mHvvvRe73W52aS4xcuRIqqqquP7667nlllt48803ueOOO0yrR0HHCVasWMFHH33Eiy++iK+vr9nlmMIwDLp06cLzzz/P999/T0FBgdklucTUqVPp0qUL/fr1M7uUBqNp06ZUVVVx5MgRs0txiaysLBITE2nSpAkAPj4+xMfHe9U1GjX27t3Lvn376NWrl9mluNRXX33FoEGDSExMJDg4mL/85S9kZmaSlpZmdmkuERISwowZM3j11VeJj4+nbdu2DBkyxLR6fEz7yR4qMzOTMWPG8NRTTxEXF2d2OS63atUqFi1aVHua1s/PD8MwaudoPd3cuXPJzc0lKSkJgJKSEubNm8fGjRuZMGGCucW5yOTJk+ncuXPtP2w///wzFovllDl7TxYTE0Npaekpzx08eJDu3bubVJF55s2bR79+/bzuP/iqqqpOWYRRVFREcXExlZWVJlblelFRUcyfP5+nn37atOtzQEHHoUpKShg1ahQDBgxg4MCBFBUVAdCoUSMMwzC5Otdo3bo1M2fOpHXr1lx++eW88sor9O7d22umcWbMmEFFRUXt4ylTppCYmMj1119vYlWu1bFjR1555RUiIiKorKzk6aefZtiwYQQGBppdmkv07duXp59+mo8//pgrrriC77//nrS0NP7v//7P7NJcbunSpV712a+RlJTEP/7xD9577z2aNm3KrFmziIyMJD4+3uzSXGr69Om0bduWK6+80tQ6DLu3TBq6wIIFC3jooYdOe37hwoVedSHesmXLePbZZzl06BB9+vRhwoQJtafxvc0//vEPLrnkEm644QazS3GpF198kY8//hir1cqQIUMYPXo0jRo1Mrssl1m7di1TpkwhLS2NyMhInnjiCfr37292WS5VUlJCUlISX375Je3atTO7HJey2+1MnTqVzz77jKNHj9K+fXueeeYZOnfubHZpLpOfn89VV13FW2+9Rbdu3UytRUFHREREPJZ3XDghIiIiXklBR0RERDyWgo6IiIh4LAUdERER8VgKOiIiIuKxFHRERETEYynoiIiIiMdS0BERERGPpaAjIiIiHktBR0Q8zuTJk3nwwQfNLkNEGgAFHRHxONu2baNTp05mlyEiDYCCjoh4nLS0NAUdEQEUdETEw2RlZZGbm0vHjh0BKCgoYNSoUQwfPpyjR4+aXJ2IuJqCjoh4lG3bthESEkKLFi3Yvn07N954I1FRUXzwwQdERkaaXZ6IuJiCjoh4lG3bttGxY0fmzp3LnXfeyZ/+9Cf++c9/4uvra3ZpImICw263280uQkTEUR599FFWrlwJwLRp0+jevbvJFYmImXRGR0Q8yrZt2xg4cCClpaUUFBSYXY6ImExBR0Q8hs1mIzMzk9tvv51x48YxevRoduzYYXZZImIiH7MLEBFxlLS0NKxWK3FxcXTu3JkdO3YwatQoZs2aRZMmTcwuT0RMoDM6IuIx0tLSaNu2LX5+fgA89thjtGnThkceeYSysjKTqxMRM+hiZBEREfFYOqMjIiIiHktBR0RERDyWgo6IiIh4LAUdERER8VgKOiIiIuKxFHRERETEYynoiIiIiMdS0BERERGPpaAjIiIiHktBR0RERDzW/wclMolWm1MdAgAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 确定Kmeans K值\n",
    "inertia=[]\n",
    "for k in range(2,10):\n",
    "    kmeans=KMeans(n_clusters=k,random_state=1).fit(data_s)\n",
    "    inertia.append(np.sqrt(kmeans.inertia_))\n",
    "plt.plot(range(2,10),inertia,marker='s')\n",
    "plt.xlabel('$k$') # K\n",
    "plt.ylabel('$J(C_K)$') # 误差平方和"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 存放轮廓系数\n",
    "Scores=[]\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "for k in range(2,9):\n",
    "    estimator=KMeans(n_clusters=k) #构造聚类器\n",
    "    estimator.fit(data_s)\n",
    "    Scores.append(silhouette_score(data_s,estimator.labels_,metric='euclidean'))\n",
    "X=range(2,9)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('轮廓系数')\n",
    "plt.plot(X,Scores,'o-')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calinski_haarbaz指数\n",
    "from sklearn.metrics import calinski_harabasz_score\n",
    "haraba=[]\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "for k in range(2,7):\n",
    "    estimator=KMeans(n_clusters=k) #构造聚类器\n",
    "    estimator.fit(data_s)\n",
    "    haraba.append(calinski_harabasz_score(data_s,estimator.labels_))\n",
    "X=range(2,7)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('calinski_harabaz指数')\n",
    "plt.plot(X,haraba,'o-')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "#建立模型\n",
    "cluster=KMeans(n_clusters=4,random_state=5).fit(data_s)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 1.50178258,  0.54620602],\n       [-0.89033613, -0.13211205],\n       [ 0.76298415, -1.22748447],\n       [-0.14630959,  1.42249832]])"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#聚类中心\n",
    "centers=cluster.cluster_centers_ #聚类中心\n",
    "centers"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "2901.1907934178525"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看总距离平方和\n",
    "inertia = cluster.inertia_\n",
    "inertia"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "0.6103293846770578"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 轮廓系数均值\n",
    "c_preds1 = cluster.labels_\n",
    "silhouette_score(data_s,c_preds1)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x1a85a7129d0>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.scatter(data_s.values[:, 0], data_s.values[:, 1], c=c_preds1)#原始数据散点图，按照分类查看\n",
    "plt.scatter(centers[:, 0], centers[:, 1],\n",
    "            marker='x', s=169, linewidths=3,\n",
    "            color='r', zorder=10) # 重心红色X进行突出"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "clust_prod = data1.copy()\n",
    "clust_prod['标签'] = c_preds1\n",
    "clust_prod.insert(loc=0,column=\"CVE_ID\",value=CVE_ID)\n",
    "clust_prod.head(10)\n",
    "clust_prod.to_csv(\"PCA+KMeans聚类.csv\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 4 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制子图\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "for i in range(4):\n",
    "    plt.subplot(1,4,i+1)\n",
    "    plt.hist(x = clust_prod[clust_prod.标签==i].baseScore, # 指定绘图数据\n",
    "             bins = 5, # 指定直方图中条块的个数\n",
    "             color = 'steelblue', # 指定直方图的填充色\n",
    "             edgecolor = 'black' # 指定直方图的边框色\n",
    "            )\n",
    "    plt.title('第'+str(i)+'类')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [],
   "source": [
    "# 层次聚类\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.metrics import confusion_matrix\n",
    "clustering = AgglomerativeClustering(linkage='ward', n_clusters=3)\n",
    "res = clustering.fit(data_s)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 存放轮廓系数\n",
    "Scores = []\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "for k in range(2, 9):\n",
    "    estimator = AgglomerativeClustering(n_clusters=k)  # 构造聚类器\n",
    "    estimator.fit(data_s)\n",
    "    Scores.append(silhouette_score(data_s, estimator.labels_, metric='euclidean'))\n",
    "X = range(2, 9)\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('轮廓系数')\n",
    "plt.plot(X, Scores, 'o-')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: >"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x1000 with 2 Axes>",
      "image/png": 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K0I4dO6pjx46ujAX5KLhc2Vztn5J6UVUqVXCu+/sX1enfz0iSUi9eVPmgP88XULSoTv1vG3ArSb2crvIlA5zrNptN3l42nb90WcWLFMr2mC4R1bVg4361fnW+JKl5WLA616sqSdrx2ymt+vk3NQ0tr6NJFzT7251qUq2coh68J/9fDHATeQX4K/1oYtbBzEx5FQ+Q43xKtscUDg9TsXbNlLp2o3xDyqnEwEd18fut+v2Vt+QVUFTpx/48nyP1onxK356fLwG45eW4EpCRkaGPP/5Ys2bNco7FxcWpbdu2atSokUaOHKlz587lR4xwA29vb/n5+TrXC/n5Ku1/f9Hx9vaWn++f2/z+sg24lXh7ecnP2zvLmJ+Pty7ZM65xhPTpD7tVrLCvVo6K1Fejuioj09AbK7dIkmI3H1CtkFKa2bu1nrq/rmb3e0DzN+7Tr6eT8/V1ADddRqZkT88y5LicLlvh7JNnSSre7R9K27FHJwaNVdKsuTreZ5QCe3a80vufmSnjL+dzXLbLdo1EHEDO5DgJePnllzVv3jyVL19ekrRjxw4988wz6ty5s9566y1duHBBkyZNyrdAcXMFFg/Q2bN/fjBJvXhJvr4+/9tWTEnn/tx28eIl+fqYLioBBVZgUT+dTU3LMpZ6OV2+f0sM/mrF9sPq3exuBd0WoLK3+WtYu3r6cssBSdLJ5FQ1DQ12ttmVvc1fJfwLK+Ev9wwAt4LM5AvyKhmYZczLv4iM9Gsn0D5lSil13WbnekbiaWUmJcu3QpAyky/Iu8Sf57vRuWAh7m758eB2oBwnAf/973/19ttvq0uXLpKkqVOnql27dhoyZIgaNGigF198Ud99912+BYqb6+6w6tqxa49zfe/+eN1RutSVbTWqaccvf27bcyBed1CWxS2oZnAp7Txy2rl+LOmC0jMcCix67ZnQDMNQUsqficOZC5fkcFyZVrRMoL8u/+WDy8XL6Tp/8bLuCCyaD9ED7nP5l31Zevp9ypeRzc9XjuRrJ7wZJ3+XV+E/31u2ooXlHVhMGSfP6PLP+1W4zp+zaRUKq6rMk7/nT/CAReQ4CShatKhSU1MlSWvXrtWOHTs0bNgw5/akpCT5/qVFBJ4hJTVV6RlX/zWlZdN7tG3nbq3fvE3pGRl6/9OFurdhPUlSmxZNtTJurfbHH9bFi5f0yYLFurdR/ZsdOpDv6lUqo5TL6Vr0v7/kz1nzsxpVDZK3l5fOX7IrM5u/9NStdIfeX/uzFm89qIWb9uvVxRvUPCxEktQuvLJiNx/QxoMndPxsil5dvEGVSgeqetkSN/V1Afnt0paf5eVf1DktaIn+PXVp/TbJ4ZBXMX/J6+qPHxeWr1Hxrv9QkXvqyKfcHSr90lDZDyfIvu+QUlZ9r2LtW8ivWiXZihbWbb0e1MUftt7slwXcUnLcwzF48GA9+eSTuvvuu/XTTz+pb9++CgkJUVJSkj799FPFxMTo4Ycfzs9YkQ8efvwpjXp6gFo3a5JlvMRtgXp+WH8NGjlWRYsUVvFiAZo0ZrgkqUa1KurV7UF17ztMhfz8VCG4vHo8zE3iuPX4eHtp3MONNfrz7/TvlVtls9k058kr0xw2e/kzfT60k2qUK5nlmMFt6yn1crr+vXKrLl5OV+Pq5fR8p4aSpMbVyunpdvX1yqINOpmcqtCgknrtny2c7UHALSPToVNj31DZ6VEqNbKfDIehY088J0mqsjFWRx4eJPveQ1kOubT+J/3++nsqPXaofMqWln1vvBKfudJmbN93SOfmLVLIgplyXE5X+m/HlPzZ0pv+slAA3eABjrg2m3Gjx1/+xc6dO7V161ZVqlRJLVtemXP+8OHDmj59upo2baoePXrIK5vs/noqjV4uSTowkofmFERHjyfq8G8Jqh9+t4oWLZJlW/zh33Ty9BlF1K1FFegmqvbaX1qxGu5wYyTW8fuFS9p97Ixqh5TSbf6F3R0OTArb9OfUxt8sedONkViHd6kSKnRXNaXt2HPdVqCc8r2zgnzuKKVLW3ZK3BNw07TpPEy/Tung7jCydemLCe4O4ZqKdB/n7hCuK1d3c9auXVu1a9fOMla5cmW9/fbbLg0KBUdwubLXnF70zsoVdWflG8/7DHi6UsWKqFkNnk4K5Fbm72d1cd0ml50vPf6I0uOPuOx8gJWZelhYenq6/u///k/dunXTfffdpwMHDqhr1646coQ3JgAAAG4Sd88AZIXZgf5qwoQJWrlypSIjI5WamqrChQurbt26Gjt2rKvjAwAAAOBippKAr7/+WjNnzlSPHj3k7e0tb29vPfnkk9qxg/5kAAAAoKAz9YSnsmXLavPmzapQoYIkyWaz6eDBgwoOpmcWAAAAN4kHtN0UVKaSgOeee06DBw/W/PnzdenSJU2ePFlbt27V1KlTXR0fAAAAABczlQQ0a9ZMy5Yt08qVKxUWFqayZcvqueeeU0hIiKvjAwAAAOBippIASapYsaIGDhzoylgAAACAnDNoBzLL1I3BR44c0YgRI5SRkaHdu3frwQcfVMeOHbV1K4/wBgAAAAo6U0nAqFGjVLRoUXl7e2vy5Mlq0aKFWrVqpQkTCu5T2wAAAABcYaodaM+ePfr3v/+ttLQ07d27Vx999JF+//13zZ0719XxAQAAANljdiDTTCUB5cuX14oVK5Senq5atWrJy8tLGzduVLly5VwdHwAAAAAXM5UEjBkzRs8//7wKFy6s6dOna/PmzRozZoxee+01V8cHAAAAwMVMJQFNmjTR999/71xPS0vTjz/+qICAAJcFBgAAAFyXYbg7Ao9l6sbgvytcuLACAgKUlJTkitMBAAAAyEemKgHx8fGaOnWqfv31V2VmZkqSDMPQqVOn9Msvv7g0QAAAAACuZSoJiIqKUv369XXHHXfo/PnzioyM1JQpUzRy5EhXxwcAAABkj9mBTDPVDrR//37169dP3bt3V2Jiopo3b65JkyYpNjbW1fEBAAAAcDFTSUDlypW1YMEChYaG6siRIzp79qxuv/12HT161NXxAQAAAHAxU+1AL774op5++ml169ZNkZGRateunWw2m1q1auXq+AAAAIDs0Q5kmqkkoH79+vruu+8kSX379lXTpk2Vlpam++67z6XBAQAAAHA9U0lAUlKSJk6cqFWrVikzM1Pe3t5q3ry5atasqTvuuMPVMQIAAABwIVP3BERFRenSpUv6/PPP9cMPP2jBggWy2Wx64YUXXB0fAAAAkD3DUXCXAs5UJWDTpk1aunSpgoODJUklS5ZUVFSUOnfu7NLgAAAAALieqUpAs2bNtGzZsixjX331lRo1auSSoAAAAADknxxXAh577DHZbDZJkt1u19dff61PP/1U5cqV06lTp3T8+HHVrl073wIFAAAA/spwGO4OwWPlOAl4+OGHs6x3797d5cEAAAAAyH85TgK6dOmSn3EAAAAAuElM3RgMAAAAuB0PCzPN1I3BAAAAADwXSQAAAABgMbQDAQAAwDN5wEO5CioqAQAAAIDFkAQAAAAAFkM7EAAAADwTDwszjUoAAAAAYDEkAQAAAIDFkAQAAADAMzkcBXfJhf379ysyMlIRERGaOnWqDOP6bU6GYWjcuHFq2LChGjRooNGjRystLS1X1yQJAAAAANzEbrdr4MCBqlmzpmJiYhQfH6/Y2NjrHrN48WIdPnxYX375pT755BMdOHBA0dHRubouSQAAAADgYna7XSkpKVkWu91+1X7r1q1TSkqKoqKiVKFCBQ0fPlwLFy687rl37typBx54QOXLl1doaKjatGmjI0eO5Co+kgAAAAB4Jne3/FxniY6OVv369bMs2f21fu/evQoPD1eRIkUkSaGhoYqPj7/uy65ataqWLl2q33//XceOHdOKFSvUpEmTXH3rmCIUAAAAcLEBAwaoT58+Wcb8/Pyu2i8lJUXBwcHOdZvNJi8vLyUnJyswMDDbc3fr1k2fffaZ7r33XklSy5Yt1aVLl1zFRyUAAAAAcDE/Pz8FBARkWbJLAry9va8aL1So0HVv9J07d66KFy+ub7/9VmvWrFFmZqamTZuWq/hIAgAAAOCZDKPgLjkUGBiopKSkLGOpqany9fW95jFLly5V3759Va5cOQUFBWnEiBE3vI/g70gCAAAAADepVauWtm/f7lxPSEiQ3W6/ZiuQJDkcDp05c8a5fvr0aWVmZubqutwTAAAAALhJRESEUlJSFBMTo8jISEVHR6tJkyby9vbW+fPn5e/vL29v7yzHNGjQQLNnz5a3t7fS09M1e/ZstWrVKlfXJQkAAACAZ8rlQ7kKIh8fH02aNEkjRozQtGnT5OXlpXnz5km6kiAsWrRIYWFhWY555plnlJKSounTpys1NVVNmzbVmDFjcnddl70CAAAAALnWunVrrVq1Srt27VJ4eLhKlCghSdq3b1+2+xcvXjzXNwL/HUkAAAAA4GalS5dWixYtbtr1SAIAAADgmRw5n4UHWTE7EAAAAGAxJAEAAACAxdAOBAAAAM9keP7sQO5CJQAAAACwGJIAAAAAwGJoBwIAAIBnYnYg06gEAAAAABZDEgAAAABYDO1AAAAA8EiGg9mBzKISAAAAAFgMSQAAAABgMbQDAQAAwDMxO5BpVAIAAAAAiyEJAAAAACyGdiAAAAB4JoPZgcyiEgAAAABYDEkAAAAAYDG0AwEAAMAzMTuQaVQCAAAAAIshCQAAAAAshnYgAAAAeCYHswOZRSUAAAAAsBiSAAAAAMBiaAcCAACAZ2J2INOoBAAAAAAWQxIAAAAAWAztQAAAAPBMBrMDmUUlAAAAALAYkgAAAADAYmgHAgAAgGdidiDTqAQAAAAAFkMSAAAAAFgM7UAAAADwSIaD2YHMohIAAAAAWAxJAAAAAGAxtAMBAADAMzE7kGlUAgAAAACLIQkAAAAALIZ2IAAAAHgm2oFMoxIAAAAAWAxJAAAAAGAxtAMBAADAMxk8LMwsKgEAAACAxZAEAAAAABZDOxAAAAA8E7MDmUYlAAAAALAYkgAAAADAYmgHAgAAgEcyaAcyjUoAAAAAYDEkAQAAAIDF0A4EAAAAz0Q7kGlUAgAAAACLIQkAAAAALIZ2IAAAAHgmh8PdEXgsKgEAAACAxZAEAAAAABZDOxAAAAA8E7MDmUYlAAAAALAYkgAAAADAYmgHAgAAgGeiHcg0KgEAAACAxZAEAAAAABZDOxAAAAA8kmHQDmQWlQAAAADAYkgCAAAAAIuhHQgAAACeidmBTKMSAAAAAFgMSQAAAABgMbQDAQAAwDPRDmQalQAAAADAYkgCAAAAAIspMO1A1V7b4+4QAI8Ttinc3SEAHqlN52HuDgGACxi0A5lGJQAAAACwGJIAAAAAwGIKTDvQnoY73B0C4BH+2gJ0YGSYGyMBPMtf206PzujvxkgAzxI8/F13h3Btt0g70P79+xUVFaUjR46oa9euev7552Wz2a65/+jRo/Xll19eNR4XF6fg4OAcXZNKAAAAAOAmdrtdAwcOVM2aNRUTE6P4+HjFxsZe95hx48Zp8+bNzuXdd99VpUqVFBQUlOPrkgQAAAAAbrJu3TqlpKQoKipKFSpU0PDhw7Vw4cLrHlOkSBEVL17cuXz00UcaMmSIvL29c3zdAtMOBAAAAOSKw90BXJvdbpfdbs8y5ufnJz8/vyxje/fuVXh4uIoUKSJJCg0NVXx8fI6vs3PnTh09elQdOnTIVXxUAgAAAAAXi46OVv369bMs0dHRV+2XkpKSpY/fZrPJy8tLycnJObrOxx9/rJ49e8rLK3cf66kEAAAAAC42YMAA9enTJ8vY36sAkuTt7X3VeKFChZSWlqbAwMDrXuPcuXOKi4vTmDFjch0fSQAAAAA8UkF+WFh2rT/ZCQwM1IEDB7KMpaamytfX94bHrlq1Sg0aNLhhspAd2oEAAAAAN6lVq5a2b9/uXE9ISJDdbs/RB/uVK1fq/vvvN3VdkgAAAADATSIiIpSSkqKYmBhJV+4laNKkiby9vXX+/HllZmZme1xaWpo2bdqkRo0ambouSQAAAAA8k8MouEsO+fj4aNKkSZo4caIaNWqkuLg4jRw5UtKVBGH//v3ZHrdt2zYFBgYqJCTE1LeOewIAAAAAN2rdurVWrVqlXbt2KTw8XCVKlJAk7du375rHNG7cWD/88IPpa5IEAAAAAG5WunRptWjR4qZdjyQAAAAAnqkAPyysoOOeAAAAAMBiSAIAAAAAi6EdCAAAAB6pID8srKCjEgAAAABYDEkAAAAAYDG0AwEAAMAzMTuQaVQCAAAAAIshCQAAAAAshnYgAAAAeCRmBzKPSgAAAABgMSQBAAAAgMXQDgQAAADPxOxAplEJAAAAACyGJAAAAACwGNqBAAAA4JEM2oFMoxIAAAAAWAxJAAAAAGAxtAMBAADAM9EOZBqVAAAAAMBiSAIAAAAAi6EdCAAAAB6J2YHMoxIAAAAAWAxJAAAAAGAxtAMBAADAM9EOZBqVAAAAAMBiSAIAAAAAi6EdCAAAAB6J2YHMoxIAAAAAWAxJAAAAAGAxtAMBAADAI9EOZB6VAAAAAMBiSAIAAAAAi6EdCAAAAB6JdiDzqAQAAAAAFkMSAAAAAFgM7UAAAADwTIbN3RF4LCoBAAAAgMWQBAAAAAAWQzsQAAAAPBKzA5lHJQAAAACwGJIAAAAAwGJoBwIAAIBHMhzMDmQWlQAAAADAYkgCAAAAAIuhHQgAAAAeidmBzKMSAAAAAFgMSQAAAABgMbQDAQAAwCMZBrMDmUUlAAAAALAYkgAAAADAYmgHAgAAgEdidiDzqAQAAAAAFkMSAAAAAFgM7UAAAADwSIaD2YHMohIAAAAAWAxJAAAAAGAxtAMBAADAIxmGuyPwXFQCAAAAAIshCQAAAAAshnYgAAAAeCRmBzKPSgAAAABgMSQBAAAAgMXQDgQAAACPRDuQeVQCAAAAAIshCQAAAAAshnYgAAAAeCQeFmYelQAAAADAYkgCAAAAAIuhHQgAAAAeidmBzKMSAAAAAFgMSQAAAABgMbQDAQAAwCMZBu1AZlEJAAAAACyGJAAAAACwGNqBAAAA4JEMh7sj8FxUAgAAAAA32r9/vyIjIxUREaGpU6fKyOGjkB0Oh3r06KH3338/19ckCQAAAADcxG63a+DAgapZs6ZiYmIUHx+v2NjYHB372Wef6cKFC3rsscdyfV2SAAAAAHgkh2ErsEtOrVu3TikpKYqKilKFChU0fPhwLVy48IbHnTx5Um+88YZefPFF+fr65vp7RxIAAAAAuJjdbldKSkqWxW63X7Xf3r17FR4eriJFikiSQkNDFR8ff8Pzv/rqqypXrpwSExP1008/5To+kgAAAADAxaKjo1W/fv0sS3R09FX7paSkKDg42Llus9nk5eWl5OTka55727Zt+uqrr1S2bFkdOXJEo0eP1ssvv5yr+PI0O1B6erree+89xcXFKTExUe+//76ioqI0Y8YMVahQIS+nBgAAAK6rID8sbMCAAerTp0+WMT8/v6v28/b2vmq8UKFCSktLU2BgYLbnnj9/vsLDwxUdHS2bzaZHHnlELVu2VK9evVSlSpUcxZenSsCECRO0cuVKRUZGKjU1VYULF1bdunU1duzYvJwWAAAA8Gh+fn4KCAjIsmSXBAQGBiopKSnLWGpq6nX7/E+ePKlmzZrJZruSBAUFBalkyZJKSEjIcXx5SgK+/vprzZw5Uz169JC3t7e8vb315JNPaseOHXk5LQAAAGAJtWrV0vbt253rCQkJstvt16wCSFKZMmV0+fJl53pqaqqSk5NVpkyZHF83T0lA2bJltXnzZue6zWbTwYMHs/Q1AQAAAPnBcNgK7JJTERERSklJUUxMjKQr9xI0adJE3t7eOn/+vDIzM686pmPHjpo/f77Wr1+vY8eOacKECapSpYpCQ0NzfN083RPw3HPPafDgwZo/f74uXbqkyZMna+vWrZo6dWpeTgsAAABYgo+PjyZNmqQRI0Zo2rRp8vLy0rx58yRdSRAWLVqksLCwLMfce++9GjlypMaPH68TJ04oLCxM//nPf5ztQTm6bl6CbtasmZYtW6aVK1cqLCxMZcuW1XPPPaeQkJC8nBYAAACwjNatW2vVqlXatWuXwsPDVaJECUnSvn37rnlMt27d1K1bN9PXzFMSIEkVK1bUwIED83oaAAAAIFcMw90RuE7p0qXVokWLm3a9PN0TsG7duuvOYQoAAACg4MlTEjB+/Hj98ssvrooFAAAAwE2QpyTgscce00cffaSMjAxXxQMAAADkiLtnAHLF7EDukqd7Am677TadP39eDz/8sHr06KGiRYs6tz300EN5jQ0AAABAPshTEhAbGytfX18FBgZq5cqVznGbzUYSAAAAABRQeUoC/pjDFAAAALjZHEbBb7spqPJ0T8Afzpw5o59//llJSUmuOB0AAACAfJSnSsCFCxc0evRoxcXFyc/PT+np6br//vv16quvKiAgwFUxAgAAAHChPFUCJkyYIIfDobVr12rnzp1as2aNMjIyNH78eBeFBwAAAGTPMGwFdino8lQJ+O677xQTE6MyZcpIksqUKaOoqChFRka6JDgAAAAArpenSkBQUJA2bNiQZWzDhg0qV65cnoICAAAAkH/yVAkYM2aM+vfvr5UrVyokJEQJCQnatm2b3n33XVfFBwAAAGTLMNwdgefKUyUgIiJCK1asUKNGjWSz2dSoUSOtWLFCDRo0cFV8AAAAAFwsT5UA6UpLUP/+/V0RCwAAAICbIE+VgJMnT2rIkCFau3atJKlLly568skndfr0aZcEBwAAAFyLw7AV2KWgy1MS8NJLL8nX11c1atSQJM2YMUMlS5bUuHHjXBIcAAAAANfLUzvQli1btHLlSucUoZUrV9azzz6rjh07uiQ4AAAAAK6Xp0pAcHCwfvzxxyxjTBEKAACAm8HdDwSz7MPCoqKiNGjQIC1fvlwhISE6evSotmzZorfffttV8QEAAABwsTxVAho3bqxly5YpIiJChmGoQYMGWrp0qRo3buyq+AAAAAC4WJ6nCA0ODtaAAQOUmJio8+fPq2zZsq6ICwAAALguHhZmXq4rAadOndKwYcM0ePBg59j06dPVqlUrPfzww2rbtq327t3r0iABAAAAuE6uk4DRo0fL4XBoyJAhkqTFixfrk08+0Ycffqiff/5ZDz74oCZNmuTyQJF3BxPP6tFZy3TfhM80Y8UWGTdInw3D0CuL1qvZy5+p6YRP9dKC75WWnpFlH4fD0OPvrNDc73blZ+iAW509l6wHuj6hYydO5mj/zdt2qtOj/dW0fXd99Hlslm3//fY73f9wb7Xs/E+tWLUmH6IFCoaaNUO1/sflOn1yl6ZOfjHHxzW+p4F2/bLuqvGXXnxWpxJ/UeqFQ1q4YI4CAvxdGS5gOblOArZv366XXnpJYWFhyszM1Jtvvql+/fqpYcOGstls6tGjh3bv3p0fsSIP7BmZGjZ3te4qf7s+HdJBh06d0+KtB697zLJth/Tr6fP6fGgnfTDgH4o/eU7vr/k5yz4LNu5TSppdPZuE5Wf4gNucPZeswc+Ny3ECkHT2nIaOmqD2bZrrk+gZWv7fb7Vp6w5J0oFDv2rUhGka8ERPRb8xSbPmzNPh347mZ/iAW/j5+WlR7If66aef1ahxe4WFVVfvxx+54XH16tbSwgVzVMjPL8t4z55d1LPnw+rQsZdq12mlGjWqadTzQ/IrfHgQdz8QzFIPC6tUqZLWr18vSXr33Xd1/vx5PfHEE87tW7duVenSpV0WIFzj+33HlJJm14gOEQq5vbiGPlBPi7ZcPwn4JeG02txdUeVKBKha2RJqWbOCjpy54Nx+6vxFzfzvTxrduZF8vfN0jzlQYD03borat22Z4/2X//dblS51uwb2eVQVQ8prYJ9HFbPsa0lSzJKv1LBeuLp2bqfqd1ZWz8hOWvp1XH6FDrhNu3YtFRhYTCOfG69Dh37Ti2On6F99el73mKJFi2jB/Dl6+50Pr9oWElxO//rXM9q8Zbvi43/VggVLVCe8Zj5FD1hDrj+5jR07VtOnT1ft2rUVHR2tV155RQEBAUpISNDjjz+u0aNHa9iwYfkRK/Jg/4kk1QoprSJ+V+4Fr162hA6dOnfdY+4sc5uWbz+kMxcu6fjZFH2947DuqRrk3D592SaVuy1AiedStf23U/kZPuA240c9rV7dHszx/vsOHlbDerVls135K1CtsFDt3nfQua1R/XDnvrXu+nMbcCsJr32XNm78SZcupUmSdu7crbCwatc9Jj09Q/c1f1Dff7/xqm3Tpr+lDRu3OterV79TBw4edm3QgMXkenagOnXqKC4uTvHx8QoKClLJkiUlSYUKFVKrVq304osvqnr16i4PFHmTejld5UsGONdtNpu8vWw6f+myihcplO0xXSKqa8HG/Wr96nxJUvOwYHWuV1WStOO3U1r1829qGlpeR5MuaPa3O9WkWjlFPXhP/r8Y4CYKLpe7Gc9SUi+qSqUKznV//6I6/fsZSVLqxYsqH/Tn+QKKFtWp/20DbiXFixXT4V8TsoxlZjp0222BOncuOdtj0tPTdfx4oqpVrXzdc1erVkUPPdhOEY3auSxeeC5PeChXQWWqh6Nw4cKqWbOmMwGQpDvuuENPPPGEqlWrpszMTJcFCNfw9vKSn7d3ljE/H29dsmdc4wjp0x92q1hhX60cFamvRnVVRqahN1ZukSTFbj6gWiGlNLN3az11f13N7veA5m/cp19PZ/+PO2AV3t7e8vPzda4X8vNVWtrlP7f5/rnN7y/bgFtJRmaG7JftWcbS0i6raNEieTqvzWbTnHdf13vvf6bdu/fn6VyA1eWpkXv8+PGy27O+yTds2KD27dvnKSi4XmBRP51NTcsylno5Xb5/Swz+asX2w+rd7G4F3Ragsrf5a1i7evpyywFJ0snkVDUNDXa2PJS9zV8l/Asr4S/3DABWFFg8QGfP/pkMp168JF9fn/9tK6akv/wV9OLFS/L1yfPjWoACJynpnEqVvj3LWLFi/ld9ZsitF8c8oxIlb9Oo0RPzdB4AeUwCvvjii6ve0FWrVtWxY8fyFBRcr2ZwKe08ctq5fizpgtIzHAos6nfNYwzDUFLKn4nDmQuX5HBcmVa0TKC/Lv9lutCLl9N1/uJl3RFYNB+iBzzH3WHVtWPXHuf63v3xuqN0qSvbalTTjl/+3LbnQLzu+NsHJeBWsGXLDt3TqJ5zvVKlEBUq5KekpHOmz9mxw/165un+eqT7k857DQB3zwDkybMDmfoT1KJFiyRd+ZC4dOlSFSlSxLm+YcMG3X333S4LEK5Rr1IZpVxO16ItB/RQg2qas+ZnNaoaJG8vL52/ZJd/IR95e2XNCetWukPvr/1ZXl42pWc69OHan9U8LESS1C68sqI+X6dGVcsp5PZienvVNlUqHajqZUu44+UBN11KaqoKFSp01V/yWza9R6+8/rbWb96mBnVr6f1PF+rehlc+DLVp0VSPDRqhXo88qOCgsvpkwWJ1fKCVO8IH8tW67zaoePFi6v34I/po7nyNHjVUcau/l8PhUGBgcV24kCKHw5Hj89WoUVUfz3tLQ4a9oISE4/L3LyqHw0EyAOSBqSQgJiZG0pXevKVLl8r7fy0lXl5eqlChgmbMmOG6COESPt5eGvdwY43+/Dv9e+XWK32VTz4gSWr28mf6fGgn1ShXMssxg9vWU+rldP175VZdvJyuxtXL6flODSVJjauV09Pt6uuVRRt0MjlVoUEl9do/Wzjbg4Bb3cOPP6VRTw9Q62ZNsoyXuC1Qzw/rr0Ejx6pokcIqXixAk8YMlyTVqFZFvbo9qO59h6mQn58qBJdXj4c7uiN8IF9lZmaq/8CR+mTe25o65SU5HA61vr+rJOnM6T2qH9FWO3bk/CGTT/btpYAAf334/n/04fv/kST9+muCqlZnMgrALJtxo8fGXkeNGjW0ZcsWBQQE3Hjna6g0erkkaU/DHabPgZz7/cIl7T52RrVDSuk2/8LuDgcmhG36c4rJAyN5SFtBdfR4og7/lqD64XdfdTNk/OHfdPL0GUXUrSXfv9wojPxV7bU/W7GOzujvxkiso0yZ0qpfr7Y2bPxJSUln3R0OTAoe/q5+ndLB3WFka0O5h90dwjXdczz2xju5UZ7uSOvevbv8/K7dU46Cp1SxImpWI9jdYQC3vOByZa85veidlSvqzsoVb3JEwM138uRprVjJA/GAgihPNwZPmDAhSxJw5gzzXQMAAAAFXZ6SgIMHD6pLly5auXKlJOmJJ55Qhw4ddPgwT/EDAABA/nL3DECePDtQnpKAsWPHKiIiQk2bNpV0ZcrQli1baty4cS4JDgAAAIDr5SkJ2LNnj/r166dixYpJkooWLarHHntMu3bl/I5/AAAAADdXnpKA0NBQffnll1nGFi9erGrVquUpKAAAAOBGDMNWYJeCLk+zA40dO1ZPPvmkFi9erODgYB07dkzJycmaM2eOq+IDAAAA4GJ5SgLuuusuff311/r222+VmJiozp07q0WLFnl6bgAAAACA/JWnJECSAgIC1KlTpyxjSUlJKlmy5DWOAAAAAPLO4e4APFiekoD4+HhNnTpVv/76qzIzMyVJhmHo1KlT+uWXX1wSIAAAAADXytONwVFRUbrzzjvVsGFD1axZU2PHjlWhQoU0cuRIV8UHAAAAwMXylATs379f/fr1U/fu3ZWYmKjmzZtr0qRJio2NdVV8AAAAQLYM2QrsUtDlKQmoXLmyFixYoNDQUB05ckRnz57V7bffrqNHj7oqPgAAAAAulqck4MUXX9THH3+sCxcuKDIyUu3atVOPHj3UqlUrV8UHAAAAwMXydGNw/fr19d1330mSnnvuOTVr1kwXL17Ufffd55LgAAAAgGtxGO6OwHPleYpQm80mh8Ohs2fPqmHDhrLZCn4PFAAAAGBleWoH+v333/XUU0+pdu3auvfee1WrVi0988wzOnPmjKviAwAAAOBieZ4iVJIWLFigH374QZ9//rkuX77sHAcAAADyi0O2ArsUdHlqB9q2bZsWL16s8uXLS5JKliypMWPGXPUEYQAAAAAFR54qAQ0bNtTKlSuzjK1YsUL33ntvnoICAAAAkH/yVAk4ffq0XnvtNX3yyScqU6aMTpw4oZMnT6pOnTp6/PHHJUlz5851SaAAAADAX3nCQ7kKqjwlAY8++qir4gAAAABwk+QpCbjtttv07bffKj09/aptkydPzsupAQAAAOSTPCUBI0aMULt27Zw3BgMAAAA3i8PdAXiwPCUBDzzwgGrWrKlu3brJz8/PVTEBAAAAyEd5SgLsdrsmTpyoSZMmOccMw5DNZtOePXvyHBwAAAAA18tTEvDjjz8qOjpaVatWdVU8AAAAQI4wO5B5eUoCWrRooffee08dOnRQoUKFsmzjPgEAAACgYMpTEnD06FFJ0rJly7KM22w2PfTQQ3k5NQAAAIB8kqckYN68ea6KAwAAAMgVZgcyz8vdAQAAAAC4uXJdCQgLC9PmzZsVEBCgGjVqyGbLekMGswMBAAAABVuuk4BvvvlGAQEBkqS4uDiXBwQAAADkBO1A5uU6CfjrrD/MAAQAAAB4Hu4JAAAAACwmT7MDAQAAAO7Cw8LMoxIAAAAAWAxJAAAAAGAxtAMBAADAIznoBjKNSgAAAABgMSQBAAAAgMXQDgQAAACP5GB2INOoBAAAAAAWQxIAAAAAuNH+/fsVGRmpiIgITZ06VYZh3PCYTp06KTQ01LmMGTMmV9ekHQgAAAAe6cYflQs+u92ugQMHqmnTpnrjjTc0adIkxcbGKjIy8prHXLp0SQkJCVq/fr18fK58nPfz88vVdakEAAAAAC5mt9uVkpKSZbHb7Vftt27dOqWkpCgqKkoVKlTQ8OHDtXDhwuuee/fu3QoNDVXJkiVVvHhxFS9eXIULF85VfCQBAAAAgItFR0erfv36WZbo6Oir9tu7d6/Cw8NVpEgRSVJoaKji4+Ove+6ff/5ZiYmJuueee9SgQQONGzcu2wTjemgHAgAAgEdyuDuA6xgwYID69OmTZSy7lp2UlBQFBwc71202m7y8vJScnKzAwMBsz33o0CHVr19fQ4YM0YULFzRy5Eh9+OGH6t+/f47jIwkAAAAAXMzPzy9Hffre3t5X7VeoUCGlpaVdMwl4+eWXs6wPHjxYc+fOzVUSQDsQAAAA4CaBgYFKSkrKMpaamipfX98cn6NkyZI6depUrq5LEgAAAACP5LDZCuySU7Vq1dL27dud6wkJCbLb7desAkhS9+7ddeLECef69u3bVa5cuVx970gCAAAAADeJiIhQSkqKYmJiJF25obhJkyby9vbW+fPnlZmZedUxVatW1dixY7Vjxw59+eWX+uCDD9SzZ89cXZckAAAAAHATHx8fTZo0SRMnTlSjRo0UFxenkSNHSrqSIOzfv/+qY0aNGiU/Pz89/vjjmjlzpp577jl16dIld9d1SfQAAADATXYrPCxMklq3bq1Vq1Zp165dCg8PV4kSJSRJ+/bty3b/4sWL66233srTNUkCAAAAADcrXbq0WrRocdOuRzsQAAAAYDFUAgAAAOCRCvLDwgo6KgEAAACAxZAEAAAAABZDOxAAAAA8kiPnz+TC31AJAAAAACyGJAAAAACwGNqBAAAA4JEcoh/ILCoBAAAAgMWQBAAAAAAWQzsQAAAAPJLh7gA8GJUAAAAAwGJIAgAAAACLoR0IAAAAHomHhZlHJQAAAACwGJIAAAAAwGJoBwIAAIBHcrg7AA9GJQAAAACwGJIAAAAAwGJoBwIAAIBH4mFh5lEJAAAAACyGJAAAAACwGNqBAAAA4JF4WJh5VAIAAAAAiyEJAAAAACyGJAAAAACwGO4JAAAAgEfiicHmUQkAAAAALIYkAAAAALAY2oEAAADgkWgHMo9KAAAAAGAxJAEAAACAxdAOBAAAAI9k8MRg06gEAAAAABZDEgAAAABYDO1AAAAA8EjMDmQelQAAAADAYkgCAAAAAIuhHQgAAAAeiXYg86gEAAAAABZDEgAAAABYDO1AAAAA8EiGuwPwYFQCAAAAAIshCQAAAAAshnYgAAAAeCSHzd0ReC4qAQAAAIDFkAQAAAAAFkM7EAAAADwSDwszj0oAAAAAYDEkAQAAAIDF0A4EAAAAj0Q7kHlUAgAAAACLIQkAAAAALIZ2IAAAAHgkw90BeDAqAQAAAIDFkAQAAAAAFkM7EAAAADySw+buCDwXlQAAAADAYkgCAAAAAIuhHQgAAAAeiYeFmUclAAAAALAYkgAAAADAYmgHAgAAgEfiYWHmUQkAAAAALIYkAAAAALAY2oEAAADgkRw0BJlWYJKAsE3h7g4B8DjVXtvj7hAAjxQ8/F13hwAAbkU7EAAAAGAxBaYSAAAAAOQGDwszr8AkAd8sedPdIQAeoU3nYc6vj87o78ZIAM/y1xagAyPD3BgJ4FloPb010Q4EAAAAWEyBqQQAAAAAucHcQOZRCQAAAAAshiQAAAAAsBjagQAAAOCRmB3IPCoBAAAAgBvt379fkZGRioiI0NSpU2UYOb/b4fz582ratKmOHj2aq2uSBAAAAABuYrfbNXDgQNWsWVMxMTGKj49XbGxsjo+fNm2aTp8+nevrkgQAAADAIzlsBXfJqXXr1iklJUVRUVGqUKGChg8froULF+bo2M2bN2v16tW67bbbcv29IwkAAAAAXMxutyslJSXLYrfbr9pv7969Cg8PV5EiRSRJoaGhio+Pz9H5x44dqxdffFH+/v65jo8kAAAAAHCx6Oho1a9fP8sSHR191X4pKSkKDg52rttsNnl5eSk5Ofm65/+///s/VapUSe3btzcVH7MDAQAAwCM5CvDjwgYMGKA+ffpkGfPz87tqP29v76vGCxUqpLS0NAUGBmZ77vj4eH3++ef68ssvTcdHEgAAAAC4mJ+fX7Yf+v8uMDBQBw4cyDKWmpoqX1/fbPc3DEMvvfSSnnnmGZUpU8Z0fLQDAQAAAG5Sq1Ytbd++3bmekJAgu91+zSrA8ePHtXXrVk2bNk0NGjRQgwYNdPz4cXXu3FlLly7N8XWpBAAAAMAjFdxmoJyLiIhQSkqKYmJiFBkZqejoaDVp0kTe3t46f/68/P395e3t7dy/TJkyiouLy3KORx99VDNmzFBYWFiOr0sSAAAAALiJj4+PJk2apBEjRmjatGny8vLSvHnzJF1JEBYtWpTlw72Pj0+WG4n/GCtbtmyuZgkiCQAAAADcqHXr1lq1apV27dql8PBwlShRQpK0b9++HB2/evXqXF+TJAAAAAAeyeHuAFyodOnSatGixU27HjcGAwAAABZDEgAAAABYDO1AAAAA8EgF+WFhBR2VAAAAAMBiSAIAAAAAi6EdCAAAAB6JZiDzqAQAAAAAFkMSAAAAAFgM7UAAAADwSLfSw8JuNioBAAAAgMWQBAAAAAAWQzsQAAAAPBIPCzOPSgAAAABgMSQBAAAAgMXQDgQAAACPRDOQeVQCAAAAAIshCQAAAAAshnYgAAAAeCQeFmYelQAAAADAYkgCAAAAAIuhHQgAAAAeyWB+INOoBAAAAAAWQxIAAAAAWAztQAAAAPBIzA5kHpUAAAAAwGJIAgAAAACLoR0IAAAAHsnB7ECmUQkAAAAALIYkAAAAALAY2oEAAADgkWgGMo9KAAAAAGAxJAEAAACAxdAOBAAAAI/E7EDmUQkAAAAALIYkAAAAALAY2oEAAADgkRzuDsCDUQkAAAAALIYkAAAAALAY2oEAAADgkQxmBzKNSgAAAABgMSQBAAAAgMXQDgQAAACPxOxA5lEJAAAAACyGJAAAAACwGNqBAAAA4JGYHcg805WApUuX6tlnn1WPHj3066+/6umnn1ZSUpIrYwMAAACQD0wlAW+88YZee+01BQcHa9++ffLyunKacePGuTQ4AAAAAK5nqh1o/vz5+vDDDxUaGqrPP/9cPj4+ioqKUocOHVwdHwAAAJAtZgcyz1QloHjx4kpMTMwydvLkSZUqVcolQQEAAADIP6YqAYMGDdLgwYPVtm1b2e12ffjhh4qLi9PQoUNdHR8AAAAAFzOVBDz00EMqX768lixZooYNG+rSpUuaOnWqGjRo4Or4AAAAgGw5DGYHMsv0FKERERGKiIhwZSwAAAAAbgJT9wTEx8crLS3N1bEAAAAAuAlMJQG9e/fWjh07XB0LAAAAkGNGAV4KOlNJQJcuXRQbG+vqWAAAAADcBKaSgCZNmui3335T//79tXbtWm3evNm5AAAAACjYTN0YPGbMGEnSqVOnNGHCBOe4zWZTXFycayIDAAAArsPhEY03BZOpJGD16tWujgMAAADATWKqHeivkpKSXBEHAAAAgJvEVCXAbrfrtdde0/z583X58mUVLlxYPXr00LPPPis/Pz9XxwgAAABcxaAdyDRTlYCpU6dqx44dev/99/Xdd9/pvffe008//aRp06a5Oj4AAAAALmYqCVixYoWmTJmievXqqVSpUqpXr54mT56s5cuXuzo+AAAAAC5mKgkIDAzU4cOHs4wdPnxYgYGBLgkKAAAAuBFHAV4KOlP3BDzzzDMaMWKEOnXqpJCQECUkJGjp0qWaOnWqq+MDAAAA4GKmKgHt2rXTRx99JEnavHmzbDab5s2bp7Zt27o0OAAAAACuZ6oSIEm1a9dW7dq1XRkLAAAAkGM8LMw8U5WAlJQUTZ48WZs2bZIkPfnkkxo/frxSU1NdGhwAAAAA1zOVBIwdO1a7d+/WHXfcIUnq27evEhISNGnSJJcGBwAAAMD1TLUDfffdd1q0aJHKly8vSbrnnntUvnx5de3a1aXBAQAAANfCw8LMM1UJKFmypA4cOJBl7NChQ7rttttcERMAAACAfGSqEjB06FA9++yz6ty5s0JCQnTs2DEtXryYdiAAAADAA5hKAjp27Khy5cpp0aJF2rhxo4KCgvT++++rTp06Lg4PAAAAyJ4nPJSroDI9RWi9evVUr149ZWRkKC0tTQEBAa6MCwAAAEA+yfE9ASkpKZo6darGjRvnHPv000/VoEEDRUREqGfPnjp+/Hi+BAkAAADAdXKcBIwZM0bbtm1TmzZtJEnr1q3Tq6++qnHjxmn16tWqXLmyJkyYkG+BAgAAAH9lGEaBXQq6HLcD/fDDD1q0aJGCg4MlSdOnT1fPnj3VpUsXSVduFu7UqVP+RAkAAAAgi+PHj+vMmTMKDQ2Vn59fro7NcSWgdOnSOnz4sCQpNjZWR44c0cCBA53bDx06pOLFi+fq4gAAAIDV7d+/X5GRkYqIiNDUqVNzVEmYPHmyunTpopEjR6p169aKj4/P1TVznASMGjVKzz77rNq1a6eXXnpJzz//vG6//XYlJiYqKipKTz/9tB5//PFcXRwAAAAwyyGjwC45ZbfbNXDgQNWsWVMxMTGKj49XbGzsdY/ZuHGj1qxZo7i4OH399ddq2rSpZs+enavvXY7bgVq0aKHly5drx44dqlixokJDQyVJmZmZCggI0Ouvv67mzZvn6uIAAADArchut8tut2cZ8/Pzu6ptZ926dUpJSVFUVJSKFCmi4cOHa8KECYqMjLzmuf38/DRx4kTn7JxhYWH68ccfcxVfrqYILVOmjNq2bauUlBTnWPny5TVmzJhcXRQAAAC4lUVHR2vWrFlZxoYMGaKhQ4dmGdu7d6/Cw8NVpEgRSVJoaOgNW3vq1q3r/DopKUkxMTF67LHHchWfqecENGnSRPfdd5/at2+vVq1aOYMGAAAAbpaC/LCwAQMGqE+fPlnGsrt5NyUlxTnxjiTZbDZ5eXkpOTlZgYGB173G/Pnz9corr6hBgwbq2rVrruLL8T0Bf7V06VLVq1dPH3/8se699149/fTT+vrrr3X58mUzpwMAAABuKX5+fgoICMiyZJcEeHt7XzVeqFAhpaWl3fAaDz30kP7973/r4MGD+vjjj3MVn6kkoGLFiurbt68+++wzrVq1SvXq1dNLL72kxo0b67nnnsv13ckAAACAFQUGBiopKSnLWGpqqnx9fW94rJ+fn1q2bKlhw4Zp4cKFubquqXagP4Jbs2aN/vvf/2rdunWqXLmy2rdv77zDedWqVWZPDQAAANyQkYtZeAqqWrVqacGCBc71hIQE2e3267YCffTRRypZsqTzGV2+vr7y8srd3/ZNJQEDBgzQhg0bVKVKFbVr104jR45USEiIJOm3337L9RRFAAAAgBVFREQoJSVFMTExioyMVHR0tJo0aSJvb2+dP39e/v7+8vb2znJMSEiIxowZo9KlS+uOO+7Qe++9pw4dOuTquqaSgPDwcI0ePVqVK1e+alvFihW1bds2M6cFAAAALMXHx0eTJk3SiBEjNG3aNHl5eWnevHmSriQIixYtUlhYWJZjWrVqpf79+2vkyJHKyMhQt27d1K9fv9xd10ywTz31lJnDAAAAAJfJzUO5CrLWrVtr1apV2rVrl8LDw1WiRAlJ0r59+655TJ8+fa6afSg3TN0YPHv2bKWnp2cZW79+vXr16mU6EAAAAMCqSpcurRYtWjgTgPxmKgmYMWPGVdOBVq1aVdu3b3dFTAAAAADyUa7agTZv3ixJMgxDW7duVdGiRZ3rP/zwg6pUqeL6CAEAAIBsGMat0Q7kDrlKAkaNGiXpypPMxo8fL5vNJkny8vJSxYoVNW3aNNdHCAAAAMClcpUErF69WpJUo0YNLV26VAEBAfkSFAAAAID8Y2p2oKZNm8rHx/RzxgAAAIA8c7g7AA9m6pP8nDlzXB0HAAAAgJvE1OxAAAAAADwXPT0AAADwSMYt8rAwd8hxEtC6dWstWbJE/v7+atWqlXNmoL+Li4tzWXAAAAAAXC/HScDkyZNVpEgRSdKUKVPyLSAAAAAA+SvHSUDDhg2z/RoAAABwBwftQKZxYzAAAABgMSQBAAAAgMUwOxAAAAA8kmHQDmQWlQAAAADAYnKcBLRq1UqtW7e+4YKCy69qRQV/8aYqr1+o20f2y/mBNpvKf/KGbnsiMsuwf9umqvjNXFVa86kC2rdwbbBAAVGzZqjW/7hcp0/u0tTJL+b4uMb3NNCuX9ZdNf7Si8/qVOIvSr1wSAsXzFFAgL8rwwUKlLPnkvVA1yd07MTJHO2/edtOdXq0v5q2766PPo/Nsu2/336n+x/urZad/6kVq9bkQ7SAteS4HYhpQT2cr6+C3n5ZF3/YqsSRk1X6hUEq1qWtLnz53xseWrx7B3kV89e5jxc5x/yqVlTZaaN0euJbStu5V2XfHKvLuw8q/dej+fgigJvLz89Pi2I/1H9XrdU/ez2lf8+YqN6PP6KP5s6/7nH16tbSwgVzdOlSWpbxnj27qGfPh9WhYy8lnT2nxYs+0qjnh+ilsVPz82UAbnH2XLIGPzcuxwlA0tlzGjpqgnr3eFjt72+h58ZNUVi1O9WwfrgOHPpVoyZM05jhg1W7ZqieeWGSwqpXVeWKwfn8KlDQMTuQeaamCIXn8W/WQF7F/PX71GgZaZd15t8fqvSLg2+YBHiXLqnbn+mjxKdfljIynePFu/5DFzfu1PmYryRJyZ8sUbHOrZX05kf5+jqAm6ldu5YKDCymkc+N16VLaXpx7BTN/M8r100CihYtogXz5+jtdz5Unyd6ZNkWElxO//rXM9q8ZbskacGCJWpQPzw/XwLgNs+Nm6L2bVtq5+59Odp/+X+/VelSt2tgn0dls9k0sM+jiln2tRrWD1fMkq/UsF64unZuJ0nqGdlJS7+O07D+vfPzJQC3NO4JsAi/0CpK27FHRtplSZJ93yH5Va14w+NKRQ1SxvFT8ilbWoXr3JXlfJc2bneuX/55nwrVrObyuAF3Cq99lzZu/Mn5F/2dO3crLOz6P+fp6Rm6r/mD+v77jVdtmzb9LW3YuNW5Xr36nTpw8LBrgwYKiPGjnlavbg/meP99Bw+rYb3astlskqRaYaHave+gc1ujvyTMte76cxsAc0gCLMIrwF/pRxOzDmZmyqt4wDWPKRwepmLtminj5Gn5hpTTHZNHqtSYwf87X1GlH/vzfI7Ui/IpfXu+xA64S/FixXT414QsY5mZDt12W+A1j0lPT9fx44nX3P6HatWq6KEH22nOe5/kOU6gIAouVzZX+6ekXlT5oD+P8fcvqtO/n5EkpV7Mui2gaFGd+t82WJtRgP8r6Jgi1Cr+0srzB8fldNkKF5LOp2R7SPFu/1Dajj06MWisJOn8wpWq+M1cJX+yWMrMlGFP/8u57LIVKZQ/sQNukpGZIdtlW5axtLTLKlq0iM6dSzZ9XpvNpjnvvq733v9Mu3fvz2uYwC3B29tbfn6+zvVCfr5K+1/12tvbW36+f27z+8s2AOZQCbCIzOQL8iqZ9a+XXv5FZKRnXPMYnzKllLpus3M9I/G0MpOS5VshSJnJF+Rd4s/z3ehcgCdKSjqnUn+rcBUr5i+73Z6n87445hmVKHmbRo2emKfzALeSwOIBOnv2z+Q69eIl+fr6/G9bMSX9JfG+ePGSfH34OyaQFzl+B7Vu3VpLliyRv7+/WrVq5ezZ+7u4uDiXBQfXufzLPhXv9g/nuk/5MrL5+cqRfOGax2Sc/F1ehf2c67aiheUdWEwZJ8/o8s/7VbhOmC7Efi1JKhRWVZknf8+/FwC4wZYtO9T3X4861ytVClGhQn5KSjpn+pwdO9yvZ57ur3vv63TV7EGAld0dVj3L1J9798frjtKlrmyrUU07ftmjyE4PSJL2HIjXHbSgQpKDh4WZluMkYPLkySpSpIgkpgv1RJe2/Cwv/6LOaUFL9O+pS+u3SQ6HvIr5y5F6SXI4shxzYfkalX1ttC6u36b0I8dVcmhv2Q8nyL7vkFIkBX8yQ8nzFin9WKJu6/WgLixd7Z4XB+STdd9tUPHixZzTgo4eNVRxq7+Xw+FQYGBxXbiQIsff3jfXU6NGVX087y0NGfaCEhKOy9+/qBwOB8kALCUlNVWFChW66i/5LZveo1def1vrN29Tg7q19P6nC3Vvw3qSpDYtmuqxQSPU65EHFRxUVp8sWKyOD7RyR/jALcPUFKFMF+qBMh06NfYNlZ0epVIj+8lwGDr2xHOSpCobY3Xk4UGy7z2U5ZBL63/S76+/p9Jjh8qnbGnZ98Yr8ZlJkq7MLnRu3iKFLJgpx+V0pf92TMmfLb3pLwvIT5mZmeo/cKQ+mfe2pk55SQ6HQ63v7ypJOnN6j+pHtNWOHbtyfL4n+/ZSQIC/Pnz/P/rw/f9Ikn79NUFVq9+TL/EDBdHDjz+lUU8PUOtmTbKMl7gtUM8P669BI8eqaJHCKl4sQJPGDJck1ahWRb26PajufYepkJ+fKgSXV4+HO7ojfOCWYTMM99ZRKo1eLkn6Zsmb7gzDMrxLlVChu6opbcee67YC5ZTvnRXkc0cpXdqyU+KegJuiTedhzq+Pzujvxkiso0yZ0qpfr7Y2bPxJSUln3R0OTAoe/q7z6wMjw9wYCa7n6PFEHf4tQfXD71bRokWybIs//JtOnj6jiLq15PuXG4WRv6q9tke/Tung7jCydV/51u4O4Zq+O1awW+S5q8ZiMn8/q4vrNrnsfOnxR5Qef8Rl5wMKopMnT2vFyoL9jzlwqwguV/aa04veWbmi7qx842fcALgxU0lAWlqaZs2apa+//lqnT5/W7bffrjZt2mjo0KEKCLj2vPMAAAAA3M9UEvDSSy/p0KFDGjlypIKCgnTq1CnNmTNHY8eO1YwZM1wdIwAAAHAVhwc8lKugMpUErF69WrGxsapY8c+SXLVq1dS1a1eXBQYAAAAgf5h6WNjdd9+tbdu2ZRnbuXOnatSo4ZKgAAAAAOSfHFcCoqKinF/7+/srKipKn3/+ucqXL6/ExERt27ZNTZs2zZcgAQAAgL+jHci8HCcB5cuXz/L1XXfd5VyvXLmyGjdu7NrIAAAAAOSLHCcBQ4YMueE+drs9T8EAAAAAyH+mbgw+ffq03nnnHR0+fFiZmZmSJMMwdOjQIf3www8uDRAAAADIjpufeevRTN0Y/Pzzzys5OVmFCxdW4cKF1a5dOx0+fFg9e/Z0dXwAAAAAXMxUErB9+3ZFRUWpb9++Sk1N1aOPPqpXXnlF33//vavjAwAAAOBiptqBypQpo++//17t27fXwYMHlZaWpurVq2vfvn2ujg8AAADIFrMDmWcqCRg5cqRGjBihZs2aqVWrVoqMjJRhGKpfv76r4wMAAADgYqaSgDZt2ui7776Tv7+/Xn75ZS1btkypqal66KGHXBweAAAAAFczlQRIUvHixSVJ3t7e6tKli8sCAgAAAHLCoB3INFM3Bs+ePVvp6elZxtavX69evXq5JCgAAAAA+cdUEjBjxgxdvnw5y1jVqlW1fft2V8QEAAAAIB/lqh1o8+bNkq48mGHr1q0qWrSoc/2HH35QlSpVXB8hAAAAkA0eFmZerpKAUaNGSZJsNpvGjx8vm80mSfLy8lLFihU1bdo010cIAAAAwKVylQSsXr1aklSjRg0tXbpUAQEB+RIUAAAAgPxjanagpk2bysfH9MRCAAAAQJ7xsDDzTH2SnzNnjqvjAAAAAHCTmJodCAAAAIDnoqcHAAAAHonZgczLcRLQunVrLVmyRP7+/mrVqpVzZqC/i4uLc1lwAAAAAFwvx0nA5MmTVaRIEUnSlClT8i0gAAAAAPkrx0lAw4YNs/0aAAAAcAdmBzKPG4MBAAAAi8lTEnDmzBn9/PPPSkpKclU8AAAAAPKZqdmBLly4oNGjRysuLk5+fn5KT0/X/fffr1dffZWnCAMAAOCmMGgHMs1UJWDChAlyOBxau3atdu7cqTVr1igjI0Pjx493cXgAAAAAXM1UJeC7775TTEyMypQpI0kqU6aMoqKiFBkZ6dLgAAAAALieqUpAUFCQNmzYkGVsw4YNKleunEuCAgAAAG7EYRgFdinoTFUCxowZo/79+2vlypUKCQlRQkKCtm3bpnfffdfV8QEAAABwMVOVgIiICK1cuVKNGjWSzWZTo0aNtGLFCjVo0MDV8QEAAABwMVOVAEkqW7as+vfv78pYAAAAgBxjdiDzeFgYAAAAYDEkAQAAAIDF5LgdqFWrVrLZbDfcLy4uLk8BAQAAADnhCbPwFFQ5TgKmTJmSn3EAAAAAuElynAQ0bNgwP+MAAAAAcJOYnh0IAAAAcCdmBzKPG4MBAAAAizFVCUhNTdV//vMfxcXF6cyZMypVqpQeeOABDR48WEWLFnV1jAAAAABcyFQSMHbsWB09elRRUVG64447dOLECc2ZM0cnT57Ua6+95uoYAQAAgKswO5B5ppKAtWvXKjY2VhUqVJAk1a5dW6GhoYqMjHRpcAAAAABcz9Q9AWFhYdq+fXuWse3btyssLMwVMQEAAADIR6YqAYULF9aoUaP0xRdfqGzZsjp27Jh27Nihli1bKioqSpI0efJklwYKAAAA/BWzA5lnKgkIDw9XeHi4c71y5cpq2rSpy4ICAAAAkH9MJQFDhgxxdRwAAACAJe3fv19RUVE6cuSIunbtqueff142m+26x8yaNUtz587VxYsX1bx5c02dOlUBAQE5vqapewJGjBih33//PcvY3r171b17dzOnAwAAAHLNYRgFdskpu92ugQMHqmbNmoqJiVF8fLxiY2Ove8ySJUu0dOlSzZkzR8uXL1d8fLxmz56dq++dqSTAx8dH7du314cffqiUlBRNnTpVvXr1UsuWLc2cDgAAALCkdevWKSUlRVFRUapQoYKGDx+uhQsXXveYxMRETZkyRbVr11bFihXVvn177d69O1fXNdUONHXqVB04cECjR4/W66+/rnr16mn58uUqU6aMmdMBAAAAtxS73S673Z5lzM/PT35+flnG9u7dq/DwcBUpUkSSFBoaqvj4+Oueu3///lnWDx8+rIoVK+YqPlOVgBMnTui9997TqVOn1LNnT+3du1cff/yxLl68aOZ0AAAAQK4ZBfi/6Oho1a9fP8sSHR191WtISUlRcHCwc91ms8nLy0vJyck5+h4cPnxYq1atynVbvqlKQPv27dW1a1etXLlSAQEBeuKJJ/Tyyy+rbdu2+v77782cEgAAALhlDBgwQH369Mky9vcqgCR5e3tfNV6oUCGlpaUpMDDwutdwOBx64YUX1K1bN1WrVi1X8ZlKAj777DPVqFFD0pVSR1BQkN566y198803Zk4HAAAA3FKya/3JTmBgoA4cOJBlLDU1Vb6+vjc89u2331ZycrKef/75XMdnqh0oODhYL730kpo0aaI6depo3759at68ucqXL2/mdAAAAECuGYajwC45VatWLW3fvt25npCQILvdfsMqwOrVq/XBBx9o5syZzvsJcsNUEvDCCy/o2LFjmjJliooUKaJixYo5W4IAAAAA5ExERIRSUlIUExMjSYqOjlaTJk3k7e2t8+fPKzMz86pj4uPjNWLECL300ksqW7asUlNTdenSpVxd11QS8OOPP+qVV15Rs2bN5OXlJZvNpi5dulxVygAAAABwbT4+Ppo0aZImTpyoRo0aKS4uTiNHjpR0JUHYv3//Vcd88cUXunjxokaNGqV69eqpXr166tChQ+6uaybYKlWqKDY2VoMHD5bNZpPNZtPmzZtzfUMCAAAAYJZDOX8oV0HWunVrrVq1Srt27VJ4eLhKlCghSdq3b1+2+7/wwgt64YUX8nRNU0nAiy++qP79++vzzz9XamqqnnnmGR0/flzvvPNOnoIBAAAArKh06dJq0aLFTbueqSSgdu3aWrVqldasWaPExESVLVtWLVq0ULFixVwdHwAAAAAXM5UESFKxYsXUqVMnV8YCAAAA5Jhh3BrtQO5g6sZgAAAAAJ6LJAAAAACwGNPtQAAAAIA73SqzA7kDlQAAAADAYkgCAAAAAIuhHQgAAAAeidmBzKMSAAAAAFgMSQAAAABgMbQDAQAAwCM5aAcyjUoAAAAAYDEkAQAAAIDF0A4EAAAAj2TwsDDTqAQAAAAAFkMSAAAAAFgM7UAAAADwSDwszDwqAQAAAIDFkAQAAAAAFkM7EAAAADySg9mBTKMSAAAAAFgMSQAAAABgMbQDAQAAwCMxO5B5VAIAAAAAiyEJAAAAACyGdiAAAAB4JAftQKZRCQAAAAAshiQAAAAAsBjagQAAAOCRmB3IPCoBAAAAgMWQBAAAAAAWQzsQAAAAPJJDtAOZRSUAAAAAsBiSAAAAAMBiaAcCAACAR2J2IPOoBAAAAAAWQxIAAAAAWAztQAAAAPBIDtqBTKMSAAAAAFgMSQAAAABgMbQDAQAAwCMZPCzMNCoBAAAAgMWQBAAAAAAWQzsQAAAAPBKzA5lHJQAAAACwGJIAAAAAwGJoBwIAAIBHMmgHMo1KAAAAAGAxJAEAAACAxdAOBAAAAI/Ew8LMoxIAAAAAWAxJAAAAAGAxtAMBAADAIzE7kHlUAgAAAACLIQkAAAAALIZ2IAAAAHgk2oHMoxIAAAAAWAxJAAAAAGAxtAMBAADAI9EMZB6VAAAAAMBibIab76ioNHq5Oy8PAACAG/h1Sgd3h5AtH7/y7g7hmjLsx9wdwnW5PQkAAAAAcHPRDgQAAABYDEkAAAAAYDEkAQAAAIDFkAQAAAAAFkMSAAAAAFgMSQAAAABgMSQBAAAAgMWQBAAAAAAWQxIAAAAAWAxJAAAAAGAxJAEFXGxsrB577LGbdr2YmBi1bNlSjRo10ksvvSS73X7Trg2YdfToUYWGhro7jGvavn27Hn74YdWtW1dPPPGETp486e6QYBEbN25Uq1atbtpx2Zk5c6ZGjx5t+npmt5mxZs0atWvXTvXr19ewYcN04cIFl50bKGhIAuC0c+dOTZs2TTNmzNDHH3+sjRs36vPPP3d3WMANlStXTps3b3Z3GHrssccUGxubZezSpUt66qmn9M9//lMrVqyQv7+/Jk6c6KYIgZypX7++lixZku22Vq1aaePGjTftemaPy22ciYmJGjFihKKiorRo0SKdPHlSs2bNynVMgKcgCYDTli1bVLNmTdWtW1fVqlXTgAEDdO7cOXeHBdyQl5eXihcv7u4wshUfH6/z588rMjJSQUFBGjx4sDIzM90dFnBdPj4+CggIKPDXc2WcO3bsUKlSpdS8eXOFhIToqaee0qVLl1xybqAgIgnwAOnp6Ro2bJjq1q2rJ598UmfOnJEkbd26VQ899JDCw8PVtWtXHTx40HnMjz/+qPbt2ys8PFw9evTQb7/95ty2bt06derUSQ0aNNCYMWOcLT+VKlXS1q1btWbNGklSZGSkhg0b5jzuww8/VIsWLdSoUSONGzdO6enpzm2ffPKJWrVqpaZNm2rmzJlyOBzObaGhoTpw4IDGjh2rhg0b6uLFi5IkwzA0Z84ctWzZUk2bNtVHH33k+m8eLOHv7UCPPfaYoqKidM899zj/sle3bl3FxcUpNjZWXbt21aBBg1S/fn317dtXp06dch57vffV7t271b17d9WtW1c9evTQgQMHJEljx45VaGioNm3apKioKIWGhmrs2LGSpKCgIHl5eentt99WRkaG7rrrLr3zzjvOc65fv16dOnVS3bp11a9fPyUmJjq3bd68WQ8++KAiIiI0YsQInT9/PstrjI2N1QcffKCWLVtq7dq1zm3Xeo+jYNu5c6e6deum+vXra8iQIbpw4YL27t2rOnXq6NixY5KkUaNGafjw4ZKutNn069dPvXr1Uv369fXss88qJSUlR9e63s+WlH2bTd++fRUaGqpjx47p8ccfV2hoqN59913n9m+++UYPPPCA6tSpo969e2dpe0tOTtbjjz+uevXqacSIEc7fA9e7Xk7kNs633npL/fv3d+7722+/qVatWrpw4YIqVqyoI0eOKCYmRoZhqHnz5nr55Zed+y5btkxt27Z1fq//2ir01Vdf6YEHHlCjRo308ssv6/Lly85trVq10o8//qjXX39d9957r/PfDUlatGiR2rZtq0aNGmnGjBkyDCPX3wPANAMFWkxMjFG9enVj1qxZxpEjR4z+/fsbw4YNMzIzM417773XmDVrlnHq1CljypQpRr9+/ZzHNWnSxJg9e7Zx/Phx44UXXjCeffZZwzAM49dffzVq1qxpzJ8/3zh8+LDRsWNH46233nIe99prrxlhYWFG9+7djW3btjnHly5dajRu3NjYtGmTcfDgQaNdu3bGBx98YBiGYXz11VfGPffcY2zYsMH45ZdfjPvvv9+5zTAMo3r16kb37t2NV1991Vi/fr2RkZFhGIZhfPnll0aDBg2MrVu3Gps3bzbCw8ONzZs35983E7eshIQEo3r16s71Xr16Gb169TK+/fZbo3r16sa8efOM0aNHG2PGjHG+p6Kjo40jR44YAwcONAYOHGgYhnHd99X58+eNxo0bG2+99ZaRmJhoTJo0yejcubNhGIZx6dIlIzk52ejRo4fx8ccfG8nJycalS5ec8axcudKoX7++0bJlS+PLL790jh85csSoXbu2sWDBAuP48ePG008/bQwaNMgwDMM4fvy4UadOHeOLL74wjhw5YgwYMMC57Y/X+Mgjjxh9+/Y11q5dayQlJRmGceP3OAqm5ORko2HDhsabb75pHD161Ojdu7fxwgsvGIZhGBMnTjSef/55Y9++fUb9+vWNxMREwzAM48033zSqV69ufPnll8bhw4eNbt26GRMnTsxy3g0bNhgtW7bMMnajn61rHZeammokJycbzZs3N1avXm0kJycbly9fNgzDMM6dO2fcfffdRkxMjHHy5Enj2WefNcaNG5clzoULFxqHDh0yunTpYkydOvWG1zO77XpxxsfHG3fffbdx4cIFwzAMY/bs2c73v2EYxty5c41atWoZHTp0MNasWeMc37p1qxEeHm588803xpEjR4x//vOfxqRJkwzDMIwdO3YY4eHhxqpVq4yDBw8a3bp1c24zDMNo2bKl8cgjjxgjR440fvzxRyM1NdUwDMPYtGmTUbNmTSMuLs7Ys2eP0bRpU2PRokXZvk4gP1AJ8AClS5fWoEGDFBISoiFDhuibb76RYRj68ssv9eSTT+rEiRM6f/68Dh8+7DymUKFCysjIUGBgoCZOnKgpU6ZIklasWKGwsDB169ZNlSpV0qOPPqrVq1c7jxsxYoS++uorBQcHq1evXvrmm28kXblBuXfv3oqIiNCdd96p1157TfXq1ZMkzZ8/X71791ajRo1Us2ZNDRky5Kp7CapXr+78y6y3t7ckafHixXrkkUdUr149NWjQQC1btswSC5AX7du3V9WqVSVJ3bp1U7ly5ZSRkSFJKlu2rJ588kmFhIRo6NChWrt2rXPbtd5Xa9asUfHixfXUU0+pTJkyGjJkiAYMGCBJKly4sIoXLy4fHx8VKVJExYsXV+HChZ2xtGvXTt9++626dOmisWPHaurUqZKk5cuXq0GDBuratauCgoI0evRodevWTZK0ZMkS1a1bV4888ohCQkI0YcIExcXF6fTp087zXrx4UW+//baaNWumEiVKSLrxexwF05o1a+Tr66shQ4aofPny+te//uX8//b000/rhx9+0OjRo50/f3+oV6+eHnroIVWqVElPPvlkjv5f5+RnKztFixZV8eLF5eXl5fzaz8/Pue3bb79Vhw4ddOTIEV28eDHL76Tw8HBFRkaqcuXK6t+/v77++msz36YcuV6cVapUUZUqVfTtt99KulK9aN++vfPYxx57THFxcbrnnns0aNAgffLJJ5Ku/LvQuXNntW7dWiEhIXr55ZfVunVrSdLChQvVqVMntWnTRnfeeadGjRql+fPnZ/mrfkBAgKZPn67GjRuraNGikq78DmzTpo1atWqlGjVq6MEHH+S9ipvKx90B4MbKly8vL68r+dofH2TOnj2rDz/8UAsXLlRISIiCgoKytOBMnz5ds2bN0pw5c1SjRg1FRUWpZs2aSkxM1J49e9SgQQNJUmZmpvMfpEOHDikwMFAVKlTQa6+9pooVK2rKlClq06aNTpw4oZCQEOf5a9as6fz679sqVKigEydOZHkN2c1wlJiYqJ9++klffPGFJOny5ctq06ZNXr9dgKQriXB2X0tXkgCbzSZJKlOmjDIzM3Xu3DmVKlXqmu+rEydOKDg42HmOwMDALB8eruXkyZO6fPmyKlSooKFDh6phw4bq3bu3HnvssavOWbZsWZUtWzbb65UpU0Z+fn46ceKESpcuLUnq0aOH88PNH673HkfBlZiYqKSkJEVEREiSHA6HUlNTdfnyZRUrVkz/+Mc/9Nlnn6lr165ZjgsKCnJ+XaZMGf3+++83vFZOfrZyyzAMvf7661q9erWqVKkif3//LL+T/nq9cuXK3TDhyE//+Mc/tGrVKjVu3Fj79u1zthMdPXpU3t7eCgoK0osvvqi77rpL48ePV7du3XTixAk1bNjQeY4/kgnpyvfzj/9v0pXfgWlpaUpKStLtt98u6dq/Azdu3Oh8r6anpxfoWc5w6yEJ8AAnT56UYRiy2Ww6deqUvL29tX//fi1cuFArVqzQ7bffrrVr12rXrl2SrsxGkpmZqQ8++EAZGRnO6dmWLl2qsmXLqmXLlho1apSkK79o/rjxafr06c6/5EtS48aNNW/ePElXftEcPXrUGdOSJUu0ceNGvfLKKwoKClJCQoJzW0JCgsqVK5flNRQpUuSq11W2bFlFRkaqXbt2kiS73S5fX19XfduAazp+/LjzPXXixAn5+PioRIkS2rhx4zXfV0FBQc6+bElKTU1V9+7d9cEHHzg/ONlstqt6eleuXKm4uDjneykiIkI+Pj46f/68goKCtGXLFue+hw8f1rPPPqvY2FiVK1dOGzZscG47efKk7HZ7lvfWtd5X13qPo+AqW7asatasqTfeeEPSlQ/VKSkp8vHx0fHjx7V48WLVrVtXb7/9dpbpNv/6M3nixAmVKlXqhtfKyc/W9WT3c75s2TJt375dq1evlr+/vz755BN99dVXWWL7w6lTp3IUZ15lF6d0pUoYHR2tZcuW6b777pO/v7+kK/e9paamavLkyZKu/A68fPmyLl++fNX7f8OGDZozZ47mzJmT7e/AwoULq2TJks6xa71Xu3fvrieeeEKSlJGRkSVxAvIb7UAe4MSJE5ozZ46OHj2qWbNmqXnz5kpLS5MknT9/Xlu3btXkyZOd/9hlZmaqb9++WrJkic6cOSPDMJyzkXTo0EFbtmzRb7/9Jj8/P82bN09RUVGSpKZNm2rBggXavn27EhIS9O6776pp06aSrtwkPHfuXG3ZskXx8fF67733VKFCBUnSI488oo8++kibNm3S7t27NXPmTPXo0eOGr+vBBx/UihUrlJqaKsMwNHbsWH366acu//4Bf3fq1ClFR0crISFBs2bNUuvWreXt7a3U1FRJ2b+vWrRooeTkZEVHRysxMVHvvPOOMjMzs3yYqVChgjZs2KBTp07pxx9/VGZmpho3bqxt27Zp2bJlOnnypGbOnKnSpUurSpUqzvdjbGysTpw4oXfeeUe33367vLy81KlTJ23btk3z589XQkKCxo8frzZt2tzww9P13uMouFq0aKETJ05o586dKly4sL7++mv169dPhmHo5ZdfVteuXTV9+nQtWLBAe/bscR63fft2ffnll/r11181e/ZsPfDAAze8ltmfrT9UqFBBP/zwg06dOqX169dLkvPf8eTkZK1du1bvvPNOlg/g27Zt0+LFi/Xbb79p9uzZLp3bPzdx/jFeuXJlzZo1S//4xz+c4/fdd5++/vprff/99zpx4oRmzpypOnXqqFixYnr44Ye1ZMkSffvtt87fj38kTd26ddPSpUv1zTff6NChQ5oyZYq6d+/urDZey0MPPaTVq1fr999/l4+Pj9544w39+9//zpfvBZCtm38bAnIjJibGiIyMNAYMGGDUrVvX6NOnj5GYmGjY7XZj+PDhRp06dYyOHTsa7733nnHXXXcZp0+fNgzjyo2I7dq1M2rVqmV07NjR2LRpk/Oca9euNTp27GiEh4cbjz32mHH48GHDMAwjIyPDmD59unHvvfcaDRo0MJ599lnjzJkzzuM++OADo1mzZkajRo2MSZMmGXa73blt3rx5RosWLYx7773XePPNN43MzEznturVqxsJCQlXvTaHw2G8++67RvPmzY369esbo0aNMi5evOjqbyEsILsbg2NiYrKMv/nmm8aoUaOMmJgY45FHHjEGDRpkhIeHG//617+MU6dOGYZh3PB9tWvXLuORRx4x6tSpY/Tq1cs4ePBgljiOHj1qdO/e3ahZs6bRpk0b53tkyZIlRtu2bY06deoYPXv2NHbv3u085ocffjA6duxo1K1b1xgwYIDzpk/DMIyNGzcanTt3NurXr288++yzRnJy8lWvMTvXeo+jYNuxY4fRtWtXIzw83IiMjDR27NhhrFy50mjSpInzRta3337b6Nq1q5GZmWm8+eabxoABA4yePXsaderUMZ599lnnfn+41k211/vZut5xhmEYu3fvNjp27GjcddddRs+ePQ3DMIwLFy4Y//rXv4zw8HCjW7duxptvvmncc889RlpamvHmm28affv2Nf75z38a9erVM4YNG2acP38+x9czuy27OP/w7rvvGnXq1Lnqd877779vtGjRwqhbt67Rr1+/LL+7li5datx///1GgwYNjOeeey7L93rlypVG27ZtjYYNGxrjx4830tLSnNtatmxpbNiwIdsYY2Njjfvvv9+oU6eOMWjQIOP333/Pdj8gP9gMg/moAFhHbGysvvzyS2d7DuCpZs6cqWPHjjknfkDO/Pbbb1q2bJkOHTqk119/3d3hAG7DPQEAAMAyHnroIZUsWVLR0dHuDgVwKyoBAAAAgMVwYzAAAABgMSQBAAAAgMWQBAAAAAAWQxIAAAAAWAxJAAAAAGAxJAEAAACAxZAEAAAAABZDEgAAAABYzP8Dn+49m+jIKsQAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 热力图-相关性分析\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(10,10))\n",
    "colnm=data1.columns.tolist()[:6]\n",
    "color = sns.color_palette()\n",
    "sns.heatmap(data1[colnm].corr(),linecolor=color[0],linewidths=1.0,annot=True,fmt='0.2f')"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}