{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "data = pd.read_json(r'../..//data/SIR_train_set.json')\n",
    "CVE_ID = data[\"CVE_ID\"]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "      baseScore  impactScore  exploitabilityScore  severity\n0           7.5          3.6                  3.9      HIGH\n1           9.8          5.9                  3.9  CRITICAL\n2           7.5          3.6                  3.9      HIGH\n3           8.1          5.9                  2.2      HIGH\n4           8.8          5.9                  2.8      HIGH\n...         ...          ...                  ...       ...\n5619        7.5          3.6                  3.9      HIGH\n5620        6.1          2.7                  2.8    MEDIUM\n5621        6.5          3.6                  2.8    MEDIUM\n5622        6.5          3.6                  2.8    MEDIUM\n5623        5.4          2.7                  2.3    MEDIUM\n\n[5624 rows x 4 columns]",
      "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>baseScore</th>\n      <th>impactScore</th>\n      <th>exploitabilityScore</th>\n      <th>severity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>7.5</td>\n      <td>3.6</td>\n      <td>3.9</td>\n      <td>HIGH</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>9.8</td>\n      <td>5.9</td>\n      <td>3.9</td>\n      <td>CRITICAL</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7.5</td>\n      <td>3.6</td>\n      <td>3.9</td>\n      <td>HIGH</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8.1</td>\n      <td>5.9</td>\n      <td>2.2</td>\n      <td>HIGH</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8.8</td>\n      <td>5.9</td>\n      <td>2.8</td>\n      <td>HIGH</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5619</th>\n      <td>7.5</td>\n      <td>3.6</td>\n      <td>3.9</td>\n      <td>HIGH</td>\n    </tr>\n    <tr>\n      <th>5620</th>\n      <td>6.1</td>\n      <td>2.7</td>\n      <td>2.8</td>\n      <td>MEDIUM</td>\n    </tr>\n    <tr>\n      <th>5621</th>\n      <td>6.5</td>\n      <td>3.6</td>\n      <td>2.8</td>\n      <td>MEDIUM</td>\n    </tr>\n    <tr>\n      <th>5622</th>\n      <td>6.5</td>\n      <td>3.6</td>\n      <td>2.8</td>\n      <td>MEDIUM</td>\n    </tr>\n    <tr>\n      <th>5623</th>\n      <td>5.4</td>\n      <td>2.7</td>\n      <td>2.3</td>\n      <td>MEDIUM</td>\n    </tr>\n  </tbody>\n</table>\n<p>5624 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_1 = ['baseScore', 'impactScore', 'exploitabilityScore', 'severity']\n",
    "train_data = data[columns_1]\n",
    "train_data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "0       CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N\n1       CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H\n2       CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N\n3       CVSS:3.0/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H\n4       CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H\n                            ...                     \n5619    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H\n5620    CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N\n5621    CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:H/A:N\n5622    CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:H/A:N\n5623    CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:N\nName: vectorString, Length: 5624, dtype: object"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorString = data['vectorString']\n",
    "vectorString"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "{'AV': {'network': 2897, 'adjacent': 5516, 'local': 2334},\n 'AC': {'low': 2659, 'medium': 5396, 'high': 2152},\n 'Au': {'none': 3904, 'single': 2309, 'multiple': 3674},\n 'C': {'none': 3904, 'partial': 7704, 'complete': 3143},\n 'I': {'none': 3904, 'partial': 7704, 'complete': 3143},\n 'A': {'none': 3904, 'partial': 7704, 'complete': 3143},\n 'severity': {'low': 2659, 'medium': 5396, 'high': 2152}}"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 打开并读取JSON文件\n",
    "with open('../..//data/label_word_ids_CVSS2.json', 'r') as file:\n",
    "    # 解析JSON文件\n",
    "    cvss2 = json.load(file)\n",
    "cvss2"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "9"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 打开并读取JSON文件\n",
    "with open('../../data/label_word_ids.json', 'r') as file:\n",
    "    # 解析JSON文件\n",
    "    cvss = json.load(file)\n",
    "cvss['AV']\n",
    "len(cvss)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "{'AV': {'N': 0.20251660258650822,\n  'A': 0.38559944075498076,\n  'L': 0.16315973435861586,\n  'P': 0.24872422229989513},\n 'AC': {'L': 0.5526917480773228, 'H': 0.4473082519226772},\n 'PR': {'N': 0.44796328169822147,\n  'L': 0.3051061388410786,\n  'H': 0.24693057946069993},\n 'UI': {'N': 0.5477760628595482, 'R': 0.4522239371404518},\n 'S': {'U': 0.8439380911435942, 'C': 0.15606190885640583},\n 'C': {'N': 0.44796328169822147,\n  'L': 0.3051061388410786,\n  'H': 0.24693057946069993},\n 'I': {'N': 0.44796328169822147,\n  'L': 0.3051061388410786,\n  'H': 0.24693057946069993},\n 'A': {'N': 0.44796328169822147,\n  'L': 0.3051061388410786,\n  'H': 0.24693057946069993},\n 'severity': {'low': 0.18472974850632207,\n  'medium': 0.37487842156454076,\n  'high': 0.14950673891899402,\n  'critical': 0.2908850910101431}}"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_2 = ['AV', 'AC', 'PR', 'UI', 'S', 'C', 'I', 'A']\n",
    "for column in columns_2:\n",
    "    cvss[column] = {k[0].upper(): v for k, v in cvss[column].items()}\n",
    "# 计算每一行的总值\n",
    "summ = {key: sum(values.values()) for key, values in cvss.items()}\n",
    "\n",
    "# 计算每个值除以总值\n",
    "cvss = {\n",
    "    key: {subkey: value / summ[key] for subkey, value in values.items()}\n",
    "    for key, values in cvss.items()\n",
    "}\n",
    "cvss"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lx\\AppData\\Local\\Temp\\ipykernel_58136\\555386878.py:10: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  train_data_temp = train_data_temp.applymap(transform_value)\n"
     ]
    },
    {
     "data": {
      "text/plain": "     AV AC PR UI  S  C  I  A\n0     N  L  N  N  U  H  N  N\n1     N  L  N  N  U  H  H  H\n2     N  L  N  N  U  H  N  N\n3     N  H  N  N  U  H  H  H\n4     N  L  N  R  U  H  H  H\n...  .. .. .. .. .. .. .. ..\n5619  N  L  N  N  U  N  N  H\n5620  N  L  N  R  C  L  L  N\n5621  N  L  N  R  U  N  H  N\n5622  N  L  N  R  U  N  H  N\n5623  N  L  L  R  C  L  L  N\n\n[5624 rows x 8 columns]",
      "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>AV</th>\n      <th>AC</th>\n      <th>PR</th>\n      <th>UI</th>\n      <th>S</th>\n      <th>C</th>\n      <th>I</th>\n      <th>A</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>N</td>\n      <td>L</td>\n      <td>N</td>\n      <td>N</td>\n      <td>U</td>\n      <td>H</td>\n      <td>N</td>\n      <td>N</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>N</td>\n      <td>L</td>\n      <td>N</td>\n      <td>N</td>\n      <td>U</td>\n      <td>H</td>\n      <td>H</td>\n      <td>H</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>N</td>\n      <td>L</td>\n      <td>N</td>\n      <td>N</td>\n      <td>U</td>\n      <td>H</td>\n      <td>N</td>\n      <td>N</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>N</td>\n      <td>H</td>\n      <td>N</td>\n      <td>N</td>\n      <td>U</td>\n      <td>H</td>\n      <td>H</td>\n      <td>H</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>N</td>\n      <td>L</td>\n      <td>N</td>\n      <td>R</td>\n      <td>U</td>\n      <td>H</td>\n      <td>H</td>\n      <td>H</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5619</th>\n      <td>N</td>\n      <td>L</td>\n      <td>N</td>\n      <td>N</td>\n      <td>U</td>\n      <td>N</td>\n      <td>N</td>\n      <td>H</td>\n    </tr>\n    <tr>\n      <th>5620</th>\n      <td>N</td>\n      <td>L</td>\n      <td>N</td>\n      <td>R</td>\n      <td>C</td>\n      <td>L</td>\n      <td>L</td>\n      <td>N</td>\n    </tr>\n    <tr>\n      <th>5621</th>\n      <td>N</td>\n      <td>L</td>\n      <td>N</td>\n      <td>R</td>\n      <td>U</td>\n      <td>N</td>\n      <td>H</td>\n      <td>N</td>\n    </tr>\n    <tr>\n      <th>5622</th>\n      <td>N</td>\n      <td>L</td>\n      <td>N</td>\n      <td>R</td>\n      <td>U</td>\n      <td>N</td>\n      <td>H</td>\n      <td>N</td>\n    </tr>\n    <tr>\n      <th>5623</th>\n      <td>N</td>\n      <td>L</td>\n      <td>L</td>\n      <td>R</td>\n      <td>C</td>\n      <td>L</td>\n      <td>L</td>\n      <td>N</td>\n    </tr>\n  </tbody>\n</table>\n<p>5624 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#转换数据\n",
    "def transform_value(val):\n",
    "    return val.split(':')[1]\n",
    "temp = []\n",
    "for i in range(vectorString.size):\n",
    "    part =  vectorString[i].split('/')\n",
    "    list_items = part[1::]\n",
    "    temp.append(list_items)\n",
    "train_data_temp = pd.DataFrame(temp, columns=columns_2)\n",
    "train_data_temp = train_data_temp.applymap(transform_value)\n",
    "train_data_temp"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lx\\AppData\\Local\\Temp\\ipykernel_58136\\1422158133.py:2: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  train_data_temp.replace(cvss, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": "            AV        AC        PR        UI         S         C         I  \\\n0     0.202517  0.552692  0.447963  0.547776  0.843938  0.246931  0.447963   \n1     0.202517  0.552692  0.447963  0.547776  0.843938  0.246931  0.246931   \n2     0.202517  0.552692  0.447963  0.547776  0.843938  0.246931  0.447963   \n3     0.202517  0.447308  0.447963  0.547776  0.843938  0.246931  0.246931   \n4     0.202517  0.552692  0.447963  0.452224  0.843938  0.246931  0.246931   \n...        ...       ...       ...       ...       ...       ...       ...   \n5619  0.202517  0.552692  0.447963  0.547776  0.843938  0.447963  0.447963   \n5620  0.202517  0.552692  0.447963  0.452224  0.156062  0.305106  0.305106   \n5621  0.202517  0.552692  0.447963  0.452224  0.843938  0.447963  0.246931   \n5622  0.202517  0.552692  0.447963  0.452224  0.843938  0.447963  0.246931   \n5623  0.202517  0.552692  0.305106  0.452224  0.156062  0.305106  0.305106   \n\n             A  \n0     0.447963  \n1     0.246931  \n2     0.447963  \n3     0.246931  \n4     0.246931  \n...        ...  \n5619  0.246931  \n5620  0.447963  \n5621  0.447963  \n5622  0.447963  \n5623  0.447963  \n\n[5624 rows x 8 columns]",
      "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>AV</th>\n      <th>AC</th>\n      <th>PR</th>\n      <th>UI</th>\n      <th>S</th>\n      <th>C</th>\n      <th>I</th>\n      <th>A</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.447963</td>\n      <td>0.447963</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.447963</td>\n      <td>0.447963</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.202517</td>\n      <td>0.447308</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.452224</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5619</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.447963</td>\n      <td>0.447963</td>\n      <td>0.246931</td>\n    </tr>\n    <tr>\n      <th>5620</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.452224</td>\n      <td>0.156062</td>\n      <td>0.305106</td>\n      <td>0.305106</td>\n      <td>0.447963</td>\n    </tr>\n    <tr>\n      <th>5621</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.452224</td>\n      <td>0.843938</td>\n      <td>0.447963</td>\n      <td>0.246931</td>\n      <td>0.447963</td>\n    </tr>\n    <tr>\n      <th>5622</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.452224</td>\n      <td>0.843938</td>\n      <td>0.447963</td>\n      <td>0.246931</td>\n      <td>0.447963</td>\n    </tr>\n    <tr>\n      <th>5623</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.305106</td>\n      <td>0.452224</td>\n      <td>0.156062</td>\n      <td>0.305106</td>\n      <td>0.305106</td>\n      <td>0.447963</td>\n    </tr>\n  </tbody>\n</table>\n<p>5624 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 替换 DataFrame 中的值\n",
    "train_data_temp.replace(cvss, inplace=True)\n",
    "train_data_temp"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "            AV        AC        PR        UI         S         C         I  \\\n0     0.202517  0.552692  0.447963  0.547776  0.843938  0.246931  0.447963   \n1     0.202517  0.552692  0.447963  0.547776  0.843938  0.246931  0.246931   \n2     0.202517  0.552692  0.447963  0.547776  0.843938  0.246931  0.447963   \n3     0.202517  0.447308  0.447963  0.547776  0.843938  0.246931  0.246931   \n4     0.202517  0.552692  0.447963  0.452224  0.843938  0.246931  0.246931   \n...        ...       ...       ...       ...       ...       ...       ...   \n5619  0.202517  0.552692  0.447963  0.547776  0.843938  0.447963  0.447963   \n5620  0.202517  0.552692  0.447963  0.452224  0.156062  0.305106  0.305106   \n5621  0.202517  0.552692  0.447963  0.452224  0.843938  0.447963  0.246931   \n5622  0.202517  0.552692  0.447963  0.452224  0.843938  0.447963  0.246931   \n5623  0.202517  0.552692  0.305106  0.452224  0.156062  0.305106  0.305106   \n\n             A  baseScore  impactScore  exploitabilityScore  severity  \n0     0.447963        7.5          3.6                  3.9      HIGH  \n1     0.246931        9.8          5.9                  3.9  CRITICAL  \n2     0.447963        7.5          3.6                  3.9      HIGH  \n3     0.246931        8.1          5.9                  2.2      HIGH  \n4     0.246931        8.8          5.9                  2.8      HIGH  \n...        ...        ...          ...                  ...       ...  \n5619  0.246931        7.5          3.6                  3.9      HIGH  \n5620  0.447963        6.1          2.7                  2.8    MEDIUM  \n5621  0.447963        6.5          3.6                  2.8    MEDIUM  \n5622  0.447963        6.5          3.6                  2.8    MEDIUM  \n5623  0.447963        5.4          2.7                  2.3    MEDIUM  \n\n[5624 rows x 12 columns]",
      "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>AV</th>\n      <th>AC</th>\n      <th>PR</th>\n      <th>UI</th>\n      <th>S</th>\n      <th>C</th>\n      <th>I</th>\n      <th>A</th>\n      <th>baseScore</th>\n      <th>impactScore</th>\n      <th>exploitabilityScore</th>\n      <th>severity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.447963</td>\n      <td>0.447963</td>\n      <td>7.5</td>\n      <td>3.6</td>\n      <td>3.9</td>\n      <td>HIGH</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>9.8</td>\n      <td>5.9</td>\n      <td>3.9</td>\n      <td>CRITICAL</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.447963</td>\n      <td>0.447963</td>\n      <td>7.5</td>\n      <td>3.6</td>\n      <td>3.9</td>\n      <td>HIGH</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.202517</td>\n      <td>0.447308</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>8.1</td>\n      <td>5.9</td>\n      <td>2.2</td>\n      <td>HIGH</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.452224</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>8.8</td>\n      <td>5.9</td>\n      <td>2.8</td>\n      <td>HIGH</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5619</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.447963</td>\n      <td>0.447963</td>\n      <td>0.246931</td>\n      <td>7.5</td>\n      <td>3.6</td>\n      <td>3.9</td>\n      <td>HIGH</td>\n    </tr>\n    <tr>\n      <th>5620</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.452224</td>\n      <td>0.156062</td>\n      <td>0.305106</td>\n      <td>0.305106</td>\n      <td>0.447963</td>\n      <td>6.1</td>\n      <td>2.7</td>\n      <td>2.8</td>\n      <td>MEDIUM</td>\n    </tr>\n    <tr>\n      <th>5621</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.452224</td>\n      <td>0.843938</td>\n      <td>0.447963</td>\n      <td>0.246931</td>\n      <td>0.447963</td>\n      <td>6.5</td>\n      <td>3.6</td>\n      <td>2.8</td>\n      <td>MEDIUM</td>\n    </tr>\n    <tr>\n      <th>5622</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.452224</td>\n      <td>0.843938</td>\n      <td>0.447963</td>\n      <td>0.246931</td>\n      <td>0.447963</td>\n      <td>6.5</td>\n      <td>3.6</td>\n      <td>2.8</td>\n      <td>MEDIUM</td>\n    </tr>\n    <tr>\n      <th>5623</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.305106</td>\n      <td>0.452224</td>\n      <td>0.156062</td>\n      <td>0.305106</td>\n      <td>0.305106</td>\n      <td>0.447963</td>\n      <td>5.4</td>\n      <td>2.7</td>\n      <td>2.3</td>\n      <td>MEDIUM</td>\n    </tr>\n  </tbody>\n</table>\n<p>5624 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.concat([train_data_temp, train_data], axis=1)\n",
    "train_data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lx\\AppData\\Local\\Temp\\ipykernel_58136\\4130220277.py:2: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  train_data['severity'] = train_data['severity'].replace(category_replacement)\n"
     ]
    },
    {
     "data": {
      "text/plain": "            AV        AC        PR        UI         S         C         I  \\\n0     0.202517  0.552692  0.447963  0.547776  0.843938  0.246931  0.447963   \n1     0.202517  0.552692  0.447963  0.547776  0.843938  0.246931  0.246931   \n2     0.202517  0.552692  0.447963  0.547776  0.843938  0.246931  0.447963   \n3     0.202517  0.447308  0.447963  0.547776  0.843938  0.246931  0.246931   \n4     0.202517  0.552692  0.447963  0.452224  0.843938  0.246931  0.246931   \n...        ...       ...       ...       ...       ...       ...       ...   \n5619  0.202517  0.552692  0.447963  0.547776  0.843938  0.447963  0.447963   \n5620  0.202517  0.552692  0.447963  0.452224  0.156062  0.305106  0.305106   \n5621  0.202517  0.552692  0.447963  0.452224  0.843938  0.447963  0.246931   \n5622  0.202517  0.552692  0.447963  0.452224  0.843938  0.447963  0.246931   \n5623  0.202517  0.552692  0.305106  0.452224  0.156062  0.305106  0.305106   \n\n             A  baseScore  impactScore  exploitabilityScore  severity  \n0     0.447963        7.5          3.6                  3.9  0.149507  \n1     0.246931        9.8          5.9                  3.9  0.290885  \n2     0.447963        7.5          3.6                  3.9  0.149507  \n3     0.246931        8.1          5.9                  2.2  0.149507  \n4     0.246931        8.8          5.9                  2.8  0.149507  \n...        ...        ...          ...                  ...       ...  \n5619  0.246931        7.5          3.6                  3.9  0.149507  \n5620  0.447963        6.1          2.7                  2.8  0.374878  \n5621  0.447963        6.5          3.6                  2.8  0.374878  \n5622  0.447963        6.5          3.6                  2.8  0.374878  \n5623  0.447963        5.4          2.7                  2.3  0.374878  \n\n[5624 rows x 12 columns]",
      "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>AV</th>\n      <th>AC</th>\n      <th>PR</th>\n      <th>UI</th>\n      <th>S</th>\n      <th>C</th>\n      <th>I</th>\n      <th>A</th>\n      <th>baseScore</th>\n      <th>impactScore</th>\n      <th>exploitabilityScore</th>\n      <th>severity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.447963</td>\n      <td>0.447963</td>\n      <td>7.5</td>\n      <td>3.6</td>\n      <td>3.9</td>\n      <td>0.149507</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>9.8</td>\n      <td>5.9</td>\n      <td>3.9</td>\n      <td>0.290885</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.447963</td>\n      <td>0.447963</td>\n      <td>7.5</td>\n      <td>3.6</td>\n      <td>3.9</td>\n      <td>0.149507</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.202517</td>\n      <td>0.447308</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>8.1</td>\n      <td>5.9</td>\n      <td>2.2</td>\n      <td>0.149507</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.452224</td>\n      <td>0.843938</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>0.246931</td>\n      <td>8.8</td>\n      <td>5.9</td>\n      <td>2.8</td>\n      <td>0.149507</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5619</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.547776</td>\n      <td>0.843938</td>\n      <td>0.447963</td>\n      <td>0.447963</td>\n      <td>0.246931</td>\n      <td>7.5</td>\n      <td>3.6</td>\n      <td>3.9</td>\n      <td>0.149507</td>\n    </tr>\n    <tr>\n      <th>5620</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.452224</td>\n      <td>0.156062</td>\n      <td>0.305106</td>\n      <td>0.305106</td>\n      <td>0.447963</td>\n      <td>6.1</td>\n      <td>2.7</td>\n      <td>2.8</td>\n      <td>0.374878</td>\n    </tr>\n    <tr>\n      <th>5621</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.452224</td>\n      <td>0.843938</td>\n      <td>0.447963</td>\n      <td>0.246931</td>\n      <td>0.447963</td>\n      <td>6.5</td>\n      <td>3.6</td>\n      <td>2.8</td>\n      <td>0.374878</td>\n    </tr>\n    <tr>\n      <th>5622</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.447963</td>\n      <td>0.452224</td>\n      <td>0.843938</td>\n      <td>0.447963</td>\n      <td>0.246931</td>\n      <td>0.447963</td>\n      <td>6.5</td>\n      <td>3.6</td>\n      <td>2.8</td>\n      <td>0.374878</td>\n    </tr>\n    <tr>\n      <th>5623</th>\n      <td>0.202517</td>\n      <td>0.552692</td>\n      <td>0.305106</td>\n      <td>0.452224</td>\n      <td>0.156062</td>\n      <td>0.305106</td>\n      <td>0.305106</td>\n      <td>0.447963</td>\n      <td>5.4</td>\n      <td>2.7</td>\n      <td>2.3</td>\n      <td>0.374878</td>\n    </tr>\n  </tbody>\n</table>\n<p>5624 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_replacement = {'HIGH':0.14950673891899402, 'MEDIUM':0.37487842156454076, 'CRITICAL':0.2908850910101431, 'LOW':0.18472974850632207}\n",
    "train_data['severity'] = train_data['severity'].replace(category_replacement)\n",
    "train_data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "# 复制元数据col\n",
    "new_col = train_data.columns"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "(5624, 3)"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PCA降维\n",
    "pca=PCA(3)\n",
    "pca.fit(train_data)\n",
    "new_data=pca.transform(train_data)\n",
    "new_data.shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "          0         1         2\n0  0.013773  1.328624  0.647031\n1  1.539486  0.577848 -0.185040\n2  0.013773  1.328624  0.647031\n3  0.763329 -1.328923  0.107140\n4  1.074067 -0.631252 -0.089019\n5  1.073885 -0.633773 -0.097836\n6  0.616217 -1.754719  0.053234\n7  0.013862  1.327285  0.635674\n8 -0.889005  0.561839 -1.746661\n9  1.073885 -0.633773 -0.097836",
      "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      <th>2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.013773</td>\n      <td>1.328624</td>\n      <td>0.647031</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.539486</td>\n      <td>0.577848</td>\n      <td>-0.185040</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.013773</td>\n      <td>1.328624</td>\n      <td>0.647031</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.763329</td>\n      <td>-1.328923</td>\n      <td>0.107140</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.074067</td>\n      <td>-0.631252</td>\n      <td>-0.089019</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1.073885</td>\n      <td>-0.633773</td>\n      <td>-0.097836</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.616217</td>\n      <td>-1.754719</td>\n      <td>0.053234</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0.013862</td>\n      <td>1.327285</td>\n      <td>0.635674</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>-0.889005</td>\n      <td>0.561839</td>\n      <td>-1.746661</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1.073885</td>\n      <td>-0.633773</td>\n      <td>-0.097836</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 41,
     "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=train_data.columns))\n",
    "data_s.head(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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.savefig(\"cluster1.svg\", dpi=300,format=\"svg\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, '$J(C_K)$')"
     },
     "execution_count": 44,
     "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": [
    "# 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": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ylabel('$J(C_K)$')  # 误差平方和\n",
    "# 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": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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yzkhBSERERGze3VY9GzSwczAdgnwMrqb+KQiJiIgIAOl5xXyyNxOAu+Kb/mwQKAiJiIjIae9tO0KlFXq3b8ElLS+8KnNToCAkIiIiZBeWsfLn44DrzAaBgpCIiIgAi7anU1ZppUfrZlweFmB0OQ1GQUhERMTF5ZdUsOyHo0D1bJDJZDK4ooajICQiIuLiluw6SmFZJZHBvvTrFGh0OQ1KQUhERMSFlZRXsmhHOgB39grH7EKzQaAgJCIi4tI++uk4J4vLaRvgxeCoEKPLaXAKQiIiIi6qvLKK974/AsDYuDDczK41GwQKQiIiIi5r7d5MMvJLCfL14LrurYwuxxAKQiIiIi6ossrKu6c3Vx1zRVs83VwzErjmuxYREXFxG1OyOZRbTDMvN26OaW10OYZREBIREXExVquVd76rng26pWcbfD3cDK7IOApCIiIiLua7Q7kkZhbg5WbmD5e3NbocQykIiYiIuJh5p2eDbo5pTXNvd4OrMZaCkIiIiAv5IT2PHUfycDObGH1FmNHlGE5BSERExIW8c/pOseu6taSlv6fB1RhPQUhERMRF7Msq4Jv9OZhNMLZXuNHlOAUFIRERERcx//RsUELnENq18Da4GuegICQiIuICjpws5rOkLADuitds0BkKQiIiIi5gwbYjVFmhb8cWRIX6GV2O01AQEhERaeKyCkpZtfs4AH/s1c7gapyLgpCIiEgTt3B7OuWVVnq2bUbPsACjy3EqCkIiIiJNWF5xOct+OArAXZoNOouCkIiISBP24a6jFJdX0TnEl74dWxhdjtNREBIREWmiisoqWbwjHYC7eoVjMpkMrsj5KAiJiIg0UR/9dIy8kgrCm3sxqEuI0eU4JQUhERGRJqisoor3vz8CwNi4cCxmzQadi4KQiIhIE7RmTwaZBWWE+nlwbbeWRpfjtJwyCC1ZsoT+/fsTExPDHXfcQVpa9ZLgycnJjBw5kri4OGbOnInVarW9ZuvWrQwbNoz4+HjmzZtX43jr1q1j4MCB9OvXj9WrVzfoexEREWlolVVW3t1W/f/OMbFheLg55f/unYLTdebw4cO8/vrrvPHGG6xdu5bw8HAef/xxysrKGD9+PN27d2fZsmWkpqayfPlyAHJycrj//vu57rrrWLx4MatWrWLLli1AdXiaPHkyEyZMYO7cubz66qvs37/fyLcoIiJSr9YnZ5F2soQALzdG9GhtdDlOzc3oAn5tz549xMTE0L17dwBGjhzJX//6V7766isKCgqYMmUK3t7eTJw4kWeffZaRI0eycuVKQkNDeeCBBzCZTEyYMIGlS5fSu3dvlixZQnx8PKNGjQJgzJgxrFixgocffrhWdTn6Qvszx9MF/PZRv+ynXtlPvbKfemU/o3tltVptm6veenlbfD0txhRih/rslb3HdLogFBkZyZYtW9i7dy9hYWEsXLiQK6+8ksTERGJiYvD2rt4tNyoqitTUVACSkpKIj4+33RYYHR3N7NmzAUhMTOSqq66yHT86OprXX3+91nUFBflf7Ftr0OM2VeqX/dQr+6lX9lOv7GdUr75IzCQ5qxBfDwv3D+5Ccx8PQ+qoDSM/V04ZhIYOHcqIESMACAsLY8mSJbz11luEhYXZnmcymTCbzeTl5VFQUEBERIRtzM/Pj8zMTAAKCwtrvO6XY7Vx4kQ+v7gk6aKZTNXfeEcft6lSv+ynXtlPvbKfemU/o3v1z8+SALg5ujUVRaVkF5U2fBF2qs9enTn2hThdEPrxxx/54osv+PDDD+nUqRNz5szh3nvvpXfv3nh41Ey1np6elJSUYLFYaoydeRw471htWK3Uywe6vo7bVKlf9lOv7Kde2U+9sp8Rvdp5JI9d6adwt5i47Yq2jeZ7ZeTnyukull69ejXXXXcdMTEx+Pv789BDD5GWlkZAQAA5OTk1nltYWIi7u/tZY2ceB847JiIi0pS8s/UwADd0b0WIn6fB1TQOTheEqqqqOHHihO3rwsJCiouLcXNzY9euXbbH09LSKCsrIyAggB49etQY27NnDy1bVq+ZcL4xERGRpiIps4BNB3Ixm+COuLALv0AAJwxCsbGxfPbZZ7zzzjusWrWKCRMmEBISwh133EFBQQHLli0D4M0336Rv375YLBYSEhLYsWMHmzZtory8nDlz5tCvXz8Ahg4dypo1a0hKSqKwsJAFCxbYxkRERJqKd76rvlNsSFQIYc29Da6m8XC6a4SGDh1Kamoq8+fPJysri86dO/Ovf/0Ld3d3pk+fzqRJk5g1axZms5kFCxYAEBgYyJQpUxg3bhw+Pj74+/szY8YMALp27crYsWMZOXIknp6etG/fntGjRxv5FkVERBzqUE4R65OzALizV7jB1TQuJqu1sVxKVS0rK4vdu3cTExNDixYtaoylpaWxf/9+YmNj8fX1rTGWkpJCRkYGcXFxZ110bY/sbMffNRYc7O/w4zZV6pf91Cv7qVf2U6/sZ0Svpn+SzIqfj9OvUyD/uOnShvlLHaA+e3Xm2BfidDNCFxISEsKAAQPOORYeHk54+LmTcGRkJJGRkfVYmYiISMPLyC/l4z0ZAPwxvp3B1TQ+TneNkIiIiNjv/e+PUFFl5fKwAKLbNDO6nEZHQUhERKSROllUzn9/PAbAH+N1bVBdKAiJiIg0Uot3plNSUUXXUD/i27e48AvkLApCIiIijVBhWQWLdx4F4K74cNt+m1I7tQ5CFRUVLF++/LzP2bZtG2VlZXUuSkRERM5v+Q/HyC+toH0LbwZEBhtdTqNV6yBkMpmYMWMG8+bN4+OPP2b37t1UVlbaxrOzs3n44YfZsGGDQwsVERGRaqUVVby/PR2Asb3CsZg1G1RXtb593mKxAHDixAn27t3Le++9x4EDB4iNjeXmm2/mH//4B4MGDeKaa65xeLEiIiICH+8+zonCMlr6ezLsklCjy2nU7A5CiYmJtG/fHm9vb/z9/Zk8ebJtLD8/n7/85S9MmDCBXr168eyzz9ZLsSIiIq6uosrK/G1HALg9Ngx3iy73vRh2d++ZZ54hPj6e22+/naKiIrZs2cKHH37I448/zvXXX09QUBDvvvsuRUVFrFixoj5rFhERcVmfJ2VxNK+E5t7ujOjRyuhyGj27Z4Q++OADcnJy2LJlCxs3bmTChAmUlpZy99138/HHH+Pn5wfAa6+9xi233EJ8fDytWukbJCIi4ihVVivvbD0MwG2Xt8XL3WJwRY2f3TNCS5cu5euvv6ayshKLxcJXX33FNddcQ05ODh9++CEZGdXLe7/55puMGjWKTz/9tN6KFhERcUXf7M8hNbsIXw8Lo3q2MbqcJsHuGaFLLrmE9957D6vVioeHB9OmTSMkJITHH3+cnj17smDBAqKioigtLeWJJ56o08amIiIicm5Wq5V3vqueDRoZ0wZ/r0a3XahTsntGaPv27fTo0QMfHx8OHDiAr68v7du3x2q1EhgYyCeffEJFRQV5eXk1bqcXERGRi7fjSB4/HcvHw2LitivaGl1Ok2F3EEpMTCQxMZH09HS2bdvG4cOHSU1N5bbbbqOsrIyysjIKCwvp3bs3s2fPrs+aRUREXM4736UBMPzSVgT76qyLo9TqrjEfHx+uvPJKoqOjOXbsGLfddhuTJ08mPz+fG2+8kcrKSiZPnsyWLVs4fvx4fdYtIiLiMvZm5LPlUC4WE9wRp81VHcnuIPT2229TXl5Os2bNiIiI4IUXXuChhx6ia9euBAQEsHz5ch5++GHMZjPDhw/no48+qseyRUREXMeZ2aChl4TSJsDL4GqaFruvtLr//vsxm80cP36ciIgIevTowd13343FYmHs2LEEBATQp08fAG644Qb8/f3rrWgRERFXcfBEEV/sywZgrGaDHM7uIGQ2V08etWrVyrY+0E033QTAn/70pxrPbd26taPqExERcWnzt6VhBfpHBBER7Gt0OU1Ondbl/vTTT3nhhRcoLCx0dD0iIiJy2vFTJazdmwnAXfGaDaoPds8IFRUV8cEHH3D33XcTGBjIjz/+yLJly1i5ciU5OTmYTNU731qtVsrKyvjmm2/qrWgRERFX8N73R6isshLbrjmXtm5mdDlNkt0zQkVFRbz22mvk5eURGxvL6NGjOXXqFLm5ucyZMwc3NzfefvttysrKePfdd+uzZhERkSYvp6iMj36qvgP7j700G1Rf7A5CwcHBXHPNNWzbtg0Ad3d321inTp3w8PCgU6dOWCwWOnXq5PhKRUREXMgHO9IpraiiWyt/4to1N7qcJqtW1wglJCTw3Xff1VctIiIiAhSUVrBk11EA7uoVbrv8RByvVkGoZ8+e7Ny5E4CCgoJzPkffLBERkYuzdNdRCkor6RjoQ//IIKPLadLsvlg6Pj4eLy8vsrKy6N+/PydPnmT69OlYrVYGDBhAdnY2AwYMICsriwEDBrBx48Z6LFtERKRpKimvZNGOdADu7BWOWRMM9cruILRw4ULc3NxsMz7e3t6EhIQQHR0NUOOusYqKinooVUREpOlbtTuDnKJyWjfzZGjXEKPLafLsDkIREREAlJSUMHjwYL755huWL1/O8ePHmTBhQr0VKCIi4ioqKqtYsK16O43bY8Nxs9RpuT+pBbuD0IwZM/Dw8MBqtVJSUsLLL79Mbm4un3/+OSUlJbbnubu7c/XVVxMVFVUvBYuIiDRVnyZlcexUKYE+7gy/tKXR5bgEu6Omu7s7Hh4eeHp6Yjab8fDwoGXLloSHh5OcnIyHhwceHh789NNPPPbYY/VZs4jIRaussvL94ZOs2JXO94dPUlllNbokcXFVVqttc9XbLm+Ll7vF4Ipcg90zQpMmTQKgrKyMxYsX8+c//xmAqKioGl9/9dVXPPnkk/VQqoiIY2zYl83sDSlkFpTZHgv182BSQiQJnYMNrExc2VcpJziQU4Svh4Xf92xjdDkuo9YnH93c3HjppZdsX1955ZV07tyZqqoqAHr16sWXX37puApFRBxow75sHlu5p0YIAsgsKOOxlXvYcHqXb5GGZLVambe1ejbolsva4Odp9zyFXKRaByGz2Uzv3r1tX/v4+PDYY4/Zdqf38vKy/beIiDOprLIye0PKeZ/z8hepOk0mDW7b4ZPsOZ6Pp5uZWy9va3Q5LqXOieVf//oXiYmJNR4rKipizJgxF12UiEh92JWed9ZM0K9l5JeyKz2vgSoSqfbO6dmgET1aEejjYXA1ruWigtAdd9zB4sWLbY9VVlayY8cOhxQmIuJo2RcIQbV9nogj7D52im2HT2Ixm7g9NszoclzORZ3DevPNN5k/fz6TJ0+muLjYUTWJiNSLYD/7/qVt7/NEHOHMbNCwS0Jp1czL4GpcT52DkMlkokuXLixZsgSr1crNN99McnLyRRe0fPlyoqKizvqzfPlytm7dyrBhw4iPj2fevHk1Xrdu3ToGDhxIv379WL16dY2x999/n759+zJo0CA2b9580TWKSOPUs20AgT7uF3zeBzvSyS4obYCKxNWlZheyMeUEJuDOuHCjy3FJdb4s3WqtvpjQ19eX2bNns3DhQsaNG3fRBV1//fUMHjzY9nVRUREjRowgIiKCu+++mz/+8Y9cf/31TJw4kUsuuYTevXuTnJzM5MmTmTZtGtHR0fzlL3+hW7dudOrUia+//pqZM2fy8ssvExgYyCOPPMLSpUtp0aLFRdcqIo1LRZUVd/P5920ym2Bjygm2p+XxUP9O3HBpS20mLfXm3dOrSA/oHEyHIB+Dq3FNdQ5Cb7/9Nj4+//umjR49mpiYGL744ouLKujMwoxnLFy4kCFDhrBz505CQ0N54IEHMJlMTJgwgaVLl9K7d2+WLFlCfHw8o0aNAmDMmDGsWLGChx9+mEWLFjFixAhbuBo0aBCff/657bn2cvTvwTPH0+9X+6hf9lOvftsb3xwgo6AMPw8LXu4Wsgv/dy1QS39PJg2MILyFF8+vS2ZPRgHPf5rMJ4mZPHF1Z8KaextYufH0ubKfvb1KP1nMJ3szAfhjfLhL9rY+P1f2HrPOQeh3v/vdWY91796d7t271/WQZyktLeXdd9/lww8/5PXXXyc+Pt72L7Po6Ghmz54NQGJiIldddZXtddHR0bz++uu2scmTJ9cY27ZtW62DUFCQ/8W+nQY9blOlftlPvarp25RsFm6v3tH71dGX0b9LKFsP5JCZX0Kovxe9OgZiOT1btLJLS/7z7QFmf5rM1sMnuW3+DiZd3YU/XtnR9hxXpc+V/S7Uq39+c4hKK/yuczBXXeraCyga+bmqcxAqLi5m4cKFHDhwgMrKStvje/fu5aOPPnJEbaxatYro6GjCwsIoKCiwbfwK4OfnR2ZmdZIuLCwkLCys1mO1ceJEPlYHLi1iMlV/4x193KZK/bKfenW2UyXlTFy8E4CRMa2JDvYhN6eALs096BMRxIkT+eTmFNR4zc3dQolr7c/0T5PZnpbH9I/38t/tR3h6aBciQ3yNeBuG0ufKfvb0KruwjMXbDgMw5rI2ZGfnN2CFzqM+P1dnjn0hdQ5Cjz32GLm5uZSXl+Pm5kZMTAyLFi2q9UzL+XzwwQf85S9/AcBisdQ4Zebp6Wnb7LWuY7VhtVIvP/z1ddymSv2yn3r1PzM/TyEjv4x2Lbz5a/9OZ/Xlt3oV1tybf4+K5qOfjvPPL/ez+3g+ty/YwV29wvljfDs83Fxv8Vh9rux3vl4t/D6dskorPVr7c3lYgMv31MjPVZ1/ir/99lv+/ve/89e//hV3d3ceeeQRnn32WVJSzr9qq70OHTrE4cOH6du3LwABAQHk5OTYxgsLC3F3d7drLDc395xjItL0fbI3k08Ss7CY4LlhUXjXciNLk8nETdGt+fCuWPpHBFFRZWXOlsPc/t4Ofjp6qp6qlqYsv6SCZT8cBeCu+Ha6GN9gdQ5CzZo1Y//+/cTExJCYmEhVVRXx8fHs3LnTIYWtXbuWAQMG2EJLjx492LVrl218z549tGzZ0q6xX9b0yzERadqOnyphxvp9ANzTuz3dWzer87FC/T156cZuvHj9JQT6uHPgRBH3LNrF7C9SKSqrvPABRE5bsusohWWVRAT70K9ToNHluLw6B6H777+fe++9l7KyMi6//HLuvvtuJk+eTJcuXRxS2Ndff02vXr1sXyckJLBjxw42bdpEeXk5c+bMoV+/fgAMHTqUNWvWkJSURGFhIQsWLKgxtnDhQjIyMsjOzmbp0qW2MRFpuqqsVp5dl0RBaSWXtvbnj73bXfQxTSYTg6NCWHxXLNd1C8VK9ZpDt83/nu8O5l7w9SIl5ZUs2lF90f6dvcIxazbIcHW+RuiWW27h8ssvx8fHhxdffJH58+dTVFTEnXfeedFFlZSU8MMPP/Dcc8/ZHgsMDGTKlCmMGzcOHx8f/P39mTFjBgBdu3Zl7NixjBw5Ek9PT9q3b8/o0aOB6gC1bt06rr76agD69Olj+28RaboWbU/n+7Q8vNzMPDusK24OvNurubc7zwzrytBLQvnbp/s4eqqUPy/7ieu7t+Sh/p0I8Nbpdzm3j346zsnictoEeDEkKtTocgQwWa2N6xKttLQ09u/fT2xsLL6+Ne/cSElJISMjg7i4uBoXSAP8+OOPFBcX06tXrzqdj83OdvxdY8HB/g4/blOlftlPvYKUrELGvr+D8korU4Z05ubo1ud8niN6VVRWyRvfHODDnUexAoE+7jw6KJKEzsFN6toPfa7s91u9Kq+s4qa528jIL+XxwZGMjHHtW+ahfj9XZ459IXWeEaqqqmLx4sV88sknZGVlERQUxODBgxk9ejRubnU+7AWFh4cTHn7uZcgjIyOJjIw851h0dHS91SQizqOsooqn1yRSXmmlX6dAburRql7/Ph8PC5MTIhkSFcL0T5M5mFPM46v2MiAyiMcGRRLs51mvf780Hmv3ZpKRX0qQrwfXd6/fz6XYr86J5cUXX2TDhg2MGTOG1q1bk5mZyfvvv8/BgweZOnWqI2sUEbHb/317kJTsQlp4u/PU1V0abFYmpm0A799xBf/57jDvbE1jY8oJvk87yUP9OzH80lZNanZIaq+yysq7pzdXHXNFWzxdcOkFZ1XnILRixQoWLFhAVFSU7bHevXszduxYBSERMcT2tJO89/0RAJ4a2oUg34bdRd7Dzcz4KzswqEswz3+SzN6MAqZ/uo91iVk8OUTbdLiyL1OyOZRbjL+nGzfHnPtUrRijzpE0LCyM48eP13gsOzubtm3bXnRRIiK1VVBawTNrk7ACI3q04qqIIMNq6Rzix39GX8Zf+3fC083M94dPcuv87bz3/REqq3SBjauxWq3M+656NuiWy9rg61F/l49I7dXqu/Gvf/3L9t9RUVE8+OCDDB48mLCwMI4dO8Znn31GQkKCw4sUEbmQWetTOJ5fSlhzLx4eEHHhF9QzN7OJ22PDGBAZxAufJvN9Wh7//HI/nyVl8fTVrrlNh6v67lAuiZkFeLmZufUyTRY4m1rNCKWnp9v+AFx77bV4eHiQmZmJxWLhmmuuOetuLRGR+vZZUhZr92ZiNsGzw7ri41G71aPrU1hzb94YFc2TQzrj52lhz/F8bn9vB29+e5Cyiiqjy5MGcGY26Kbo1jT30dIKzqZWM0IvvvhifdUhIlInmfmlzPi8evXoP8a3I7pN3VePri8mk4kR0a25slMgs9ansDHlBHO2HGZ9cjZPDe3ilDWLY/yQnseOI3m4mU2MiQ278AukwTn17vMiIudzZvXoUyUVdGvlz58csHp0fQrx82TW8G5s2JfNrPUpHMgp4k+LdnHLZW2Y0K+jU81kiWO8c/pOseu6taSlv5ZScEZ1vlj6scceY+PGjaSkpJCWlkZgYCCffPIJ8fHxjqxPROQ3Ld55lK2HT+LpZubZYVG4WZz/lmSTycSgLqe36ejeEivV7+PW+d+z5WDOBV8vjUdKViHf7M/BBNwRp9kgZ1XnGaFvv/2WNWvWsH//ft566y0eeeQRunbtqtkgEWkQqdmF/Our/QA81L8THQJ9DK6odpp7u/PMNVEM7RrCi5/t49ipUv6y7Geu696Sh7VNR6NVWWVlV3oepUdO8d6mAwAM6hJC+0b2+XQldQ5Cv7X7/LRp0xxZn4jIWcorq5i6JpGySitXdgxkZCNel6VPh0A+uDPWtk3Hx7sz2Hwgh0cSIhnUpWlt09HUbdiXzewNKWQWlNV4vHsrP4MqEns47e7zIiK/5c1Nh0jOKiTAy42nhjbc6tH15cw2HXNu60nHQB9yisqZsnovj67cQ1ZBqdHliR027MvmsZV7zgpBAP/86gAb9mUbUJXYo85B6JZbbuGjjz6y7T4fGxtL9+7deeWVVxxYnohITTuP5Nm2Knjy6i4EN/Dq0fUpuk0z3rvjcu7p3Q6L2cTGlBPc8s73fPTjMRrZ/tgupbLKyuwNKed9zstfpGoxTSd1Uctbntng1MPDgz//+c8OKUhE5LcUlFYwbW0iVuCG7i0Z2DnY6JIc7pfbdEz/dB97jufzwmf7+CQxkyev7qJtOpzQrvS8c84E/VJGfim70vO4Irx5wxQldqvzjFBqaiolJSWOrEVE5Lz+/kUqx06V0ibAi0kJxq8eXZ86h/jxn9t68tCZbTrS8mzbdFRoZsFpHDtVwpo9GXY9N/sCYUmMUecgdOedd/LDDz84shYRkd+0ITmLj3dnYDbBc8OiXGK/JsvpRfg+uPMKYts1p7Siin9+uZ+7F+5kX1aB0eW5pNKKKrYczOEfG1MZNW8bw9/eysqf7QtCwX5N5zRuU1Ln3yQ33XQTy5cv17pBIlLvsgpK+dtn1atH39krnJi2AQZX1LDCmnvzxu97sPLn47zy5X72ZhRwx3s7ubNXOPfEt8PDzfnXT2qsrFYraSdL2HQgh80Hc9ielkfpL7ZGsZjg0tb+pGQXUVhW+ZvHaenvSU8X+9w2FnUOQn379uWf//wn48aNY8yYMfj4/G+NhLi4OIcUJyJitVp57pNk8koq6Brqx7192htdkiFMJhM39mhN347/26bjP1sO80VyNk9e3dnlwmF9Kiqr5Pu0k2w+kMPmg7mk59W8DCTUz4M+HQPp26EFce1a4O/lZrtr7LdMHBiBxdy4725sqkzWOt6K8Fu7zJtMJtavX39RRTmj7Ox8HHnThskEwcH+Dj9uU6V+2a+p9erDnem8tCEVTzczC26/nI5BjluYrrH2ymq12rbpyCkqxwT1vk1HY+2VPaxWK6knimzBZ1d6HuWV/3uTbmYTl4UF0KdDC/p0DCQiyOecSzacax2hlv6eTBwYQUITvLDfEerzc3Xm2BdS5xmhDRs21PWlIiJ2OXiiiFe/ql6d98GrOjo0BDVmZ7bpiA1vzitf7mf17gwW7zzKV6knmDKkM306BBpdotPLL6lg2+FcNh3MZfOBnLPu+moT4EXf08EnNry5XQEzoXMw/SOCqleWNpvxrKqiZ9sAzQQ5uaZ/taGINErllVVMXZtIaUUVvdu34Pc92xhdktMJ8HZn2i+26Th6qpQHl/3Mdd1CeWhABM21TYdNldVKcmYBmw/msulADj8dPcUvJn3wdDNzRXgAfToE0rdjIOHNveq0UKfFbCK2XfMmO3vWFGn3eRFxSnM2H2JvRgEBXm5MvaYL5ka+enR96t0hkEV3xvLvbw+yeEc6H+/JZPPBXCYnRDLYhbfpOFlUzpZDuWw+mMOWg7nkFJXXGO8Q6H06+LSgZ9sAvNzr57SiOLc6B6HHHnuM3NxcysvLcXNzIyYmhkWLFjFq1ChH1iciLuiH9DzeOb169JQhnQnx8zS4Iufn42Fh0sAIhkSFMP3TZA6cKOKJ1Xv5JCKIxwZHukQPK6us7D6eb7vWZ8/xfH45IePjbqFX++b06dCC3h0CaRPgZVit4jy0+7yIOJXCsgqmrU2iygrXdQtlUJcQo0tqVKLbNOO92y9n3neHmbc1jS9TT7D9yEkevKoTI3q0anKzQ9kFpWw+mMvmg7l8dyiXUyUVNcY7h/jaZn2i2zTD3aKlBqQm7T4vIk7l5S9SSc8roXUzTyYnRBpdTqPk4Wbmvis7MKhLCM9/msye4/n87bN9fJqYyRNDuhDeovFu01FRWcUPR0/ZrvXZl1VYY9zf04349i3o07EFfTq0cImZMLk4dQ5CZ3af/+abb2y7z1dVVWn3eRGps437sln5cwYm4NlhXfHz1P0cFyMyxJf/3NaTD3ak8+9vD/J9Wh63vbud+/q257YrwnBrJHczHTtVUj3rcyCHbYdP1li40ARc0sqfPh1a0LdjIN1a+Tea9yXOoc6/ZW655RYuv/xy2+7z8+fPp6ioiDvvvNOR9YmIi8guLOOF06tH3xEXzmVhWiDQEc5s09E/MogXPtvH94dP8upXB/gsKYunru5Cl1A/o0s8S2lFFbuO5LHpYA6bD+RyIKeoxngLb3d6d6ie9endvgUtfLR1hdRdnRdUdDVaUNFY6pf9GmOvrFYrD/33ZzYdyKVziC/vjL6sQbaNaIy9uhhWq9W2TUdBaSUWs8nubTrqu1eHc4ttFzl/n3ayxjYWZhP0aN2Mvh0D6dOxBVGhfk59F6Grfa4uRqNeUHHnzp28/PLLHDt2jF9nqaa4srSI1J/lPx5j04FcPCwmnr+2q/bOqie/tU3HhuTq2aGG3KajuLyS7w+fPH2hcw5HTp5jG4sO1cEnrl1zmnlpTSSpH3UOQo888giXXXYZ48ePx91dH1ARqZtDOUW8snE/AA/8riMRwb4GV9T0hfh58tKN3dmQnMXM9SkczCnm3g9+YFTPNkz4XQd8PRx/bZbVamX/iSLbtT47z7GNRc+wgOrVnDsEEhF87m0sRBytzp/2vLw8HnroIdq2bevIekTEhVRUVjF1bRIlFVXEtWvOrZfr90lDSugSwhXhzfnnl/tZtTuDD3f9b5uOvh0vfpuOgtIKth4+Wb1z+7m2sWjmSZ+OgfTpEEhsu4B6CWAiF1LnT91dd93Fv/71L55//nnc3PThFZHam7vlMHuO5+Pv6ca0a6Kc+rqPpirA252p10Rx9S+26fjr8p+5tlsoD5/epqOyylq9f9aRU+fdP6vKamVfZuHpi5xz+PEc21hcHhZg27m9XQtvzfqI4WqVYKZMmVLj6y+//JKEhAR69uyJr+//prNffPFFx1QnIk3WT0dPMe+7wwA8PjiSlv5a78VIZ7bp+L9vD/LBjnTW7Mlk84Fchl0SyufJWTVmc0L9PJiUEElC52BOFpXz3eltLDafYxuL9i28bRc5X6ZtLMQJ1SoI/fo02OjRox1ajIi4hqKySqauTaTSCtdcEsrVXUONLkmo3qZj4ultOp4/vU3Hwh3pZz0vs6CMx1buIby5F0dOlpy1jUVcu+bVt7Z3aEHbgMa7eKO4hloFoT//+c92P/fvf/8748aNo1mzZrUuSkSatn9sTOXIyRJa+nvyqFaPdjo92jRj/ujLGPbmlhqLF/5a2uk7vaq3sai+yDmmrbaxkMal3i7u+fDDDxk9erSCkIjU8GXKCT766Tgm4JlrovD30jWGzmhPRv55Q9AZL15/CYOjtB+cNF71FtsdsU7jSy+9xPjx421fJycnM3LkSOLi4pg5c2aNv2Pr1q0MGzaM+Ph45s2bV+M469atY+DAgfTr14/Vq1dfdF0iUjcnCst44dNkAEZfEUZsu+bGFiS/KftXd3j9lsoqrRgojZvTzl8mJiaycOFCnnzySQDKysoYP3483bt3Z9myZaSmprJ8+XIAcnJyuP/++7nuuutYvHgxq1atYsuWLUB1eJo8eTITJkxg7ty5vPrqq+zfv9+w9yXiqqxWKy98mkxucTmRwb5M6NfB6JLkPIL97Nu2wt7niTgrpwxCVVVVTJ06lbvuuovw8HAAvvrqKwoKCpgyZQrt2rVj4sSJLF26FICVK1cSGhrKAw88QIcOHZgwYYJtbMmSJcTHxzNq1CiioqIYM2YMK1asMOy9ibiqj346ztf7c3DX6tGNQs+2AYReIOS09PekZwOuRi1SH+rtN9HFrA2xaNEikpOTadu2LevXr6esrIzExERiYmLw9q6+AyEqKorU1FQAkpKSiI+Pt/2d0dHR7N69G6ieWerdu7ft2L8cq937cfyf+jpuU/2jfjXeXh05Wcw/Nlb/vD7QryOdQ30Nr8lZe+Usf9wsJiZf4EL2SQMjcLOYDK/VGf/oc+UcvbJHvV2lWNdrhAoLC3nttdcIDw/n6NGjrFixgn//+9/ExsYSFhZme57JZMJsNpOXl0dBQQERERG2MT8/PzIzM23H++XrfjlWG0FBF964rS7q67hNlfplP2fpVUVlFX9a/CPF5VX06RTEg0O7Yj7HYnxGcpZeOZtbgv1p1syLZ1ft4Vje//YCax3gxbQbunHNpa0NrM756XNlPyN7VW9B6P/+7/8IDg6u9es+++wziouLmT9/PoGBgVRUVHDDDTewbNkybr755hrP9fT0pKSkBIvFgoeHx1mPA+cdq40TJxy/+3xQkL/Dj9tUqV/2c7Zevb3pELvSTuLnaeHJwRHk5BQYXZKNs/XKGcW28uOje+LYlZ5HicmMl/V/K0tnZ+cbXZ5T0ufKfvXZqzPHvpB6C0JXXHFFnV53/PhxYmJiCAys3ufGzc2NqKgo9u/fT05OTo3nFhYW4u7uTkBAQI2xM48D5x2rDauVevlA19dxmyr1y37O0Kvdx04xZ/MhAB4b1JmW/l6G13QuztArZ2Y2mbgivDnBwf5kZ+erX3ZSn+xnZK+c7mrFVq1aUVpaWuOxo0eP8sQTT7Br1y7bY2lpaZSVlREQEECPHj1qjO3Zs4eWLVsCnHdMROpPcXklU9cmUWmFIVEhDO2qtWZExPnUKgiNHTuW4uJiAO644w7Gjh17zj8Xo3///qSkpLBo0SKOHz/Ou+++S2JiIldffTUFBQUsW7YMgDfffJO+fftisVhISEhgx44dbNq0ifLycubMmUO/fv0AGDp0KGvWrCEpKYnCwkIWLFhgGxOR+vPPL/dzOLeYUD8PHh8ciTbXFBFnVKtTYzfddJPttNKvr9dxlBYtWvDWW28xa9YsZsyYQUhICK+88gqtW7dm+vTpTJo0iVmzZmE2m1mwYAEAgYGBTJkyhXHjxuHj44O/vz8zZswAoGvXrowdO5aRI0fi6elJ+/bttUeaSD37dn8Oy344BsC0a6Jo5lX709EiIg3BZHXEEtANKCsri927dxMTE0OLFi1qjKWlpbF//35iY2Px9fWtMZaSkkJGRgZxcXE1Lp6215nz4o5iMlHjfLucn/plP6N7lVtUxq3zt5NTVM5tl7dl4sCIC7/IIEb3qjFRr+ynXtmvPnt15tgX0ug2+QkJCWHAgAHnHAsPD7ctwPhrkZGRREZqc0eR+mS1WvnbZ/vIKSqnU5APD/yuo9EliYicl8Mvli4rs29/GhFpelb9nMHGlBO4matXj/bU6tEi4uTq/Fvqo48+qvG11WplyZIlXH311Rdbk4g0QkdOFjP7i+rVo++/sgNdQv0MrkhE5MLqfGrsX//6F5mZmYwbN47169fz8ssv4+HhwdSpUx1Zn4g0AhVVVqatTaKovJLLwgIYExt24ReJiDiBOgehDz74gPHjx7NkyRK8vLx48MEHGTp0qCNrE5FG4t2tafx49BS+HhaeuSYKi5NtoSEi8lvqfGosODiY9957j8jISMLDwxk4cKAj6xKRRmLP8XzeOr169CMJkbQJ8DK4IhER+9VqRuiOO+44a1G0yspKduzYwQ033GBbsfndd991XIUi4rRKyiuZuiaRyiorg7oEc223UKNLEhGplVoFod9aRPH3v/+9Q4oRkcblta8OcCi3mGBfDx4f3FmrR4tIo1PrlaVFRAA2H8zhw11HAZh2TReae2v1aBFpfOp8jdDhw4eZNGkSFRUV7Nmzh+HDh3P99dezfft2R9YnIk7oZHE5z61LBuAPl7Whd4dAgysSEambOgehxx57DB8fHywWCy+++CIDBw4kISGBZ5991pH1iYiTsVqtvPjZPrILy+gQ6M2ftXq0iDRidb59fu/evbzyyiuUlJSQmJjI/Pnzyc7O1oXSIk3cx3sy2LAvG8vp1aO93C1GlyQiUmd1DkJt27ZlzZo1lJeX06NHD8xmM9999x1t2rRxZH0i4kTS84r5+4bq1aPv69ueri0vvKGhiIgzq3MQevLJJ3n00Ufx8vLipZdeYtu2bTz55JP8/e9/d2R9IuIkKqusPLM2icKySmLaNGNs3Lk3OBYRaUzqHIT69u3LN998Y/u6pKSETZs24een/YVEmqIF29LYlX4KH3cLz16r1aNFpGmocxCC6p3mT5w4gdVqtT126tQpnR4TaWKSMgp4c1P16tGTEiJoG+BtcEUiIo5R5yD0/vvvM2PGDMrLy2s8bjKZ2Lt370UXJiLOoaS8kqfXJlJRZWVAZBA3dG9pdEkiIg5T5yD06quv8vjjj3PLLbfg7q6F1ESaqte/OciBE0UE+Xrw5JAuWj1aRJqUOq8j5OvrS+/evRWCRJqw7w7m8sGOdACeHtqF5j76eReRpqXOQeipp57i6aefZt++fY6sR0ScRF5xOc9+kgTAyJjWXNlRq0eLSNNT51Nj06dP5+TJkwwfPpxmzZrVuFts/fr1DilORIxhtVqZ8XkKWQVltGvhzUP9OxldkohIvahzEJoxY4Yj6xARJ7J2byafJ2dhMZt4TqtHi0gTVucg1KtXL0fWISJO4tipEmatTwHgT73b0b2VVo8WkaarztcIiUjT88vVo3u09ueu+HZGlyQiUq8UhETEZuH2I+w4koe3u5nnru2Km1aPFpEmrlanxi655BK2bduGn58fXbt2PWs9EavVqgUVRRqp5MwC3vjmIAATB0QQ1lyrR4tI01erIPT555/b7g7TnWEiTUdpRRVTT68efVVEEDf2aGV0SSIiDaJWQaht27bn/G8Radze+OYAqdlFBPq48+TVnbV6tIi4DF0jJOLith3OZeH26tWjn7q6C4E+HgZXJCLScBSERFzYqZJynllbvXr0TdGt+F1EkMEViYg0LAUhERc2a30KmQVlhDf34qH+EUaXIyLS4BSERFzUJ3sz+SQxC4sJnru2Kz4eWj1aRFxPrS6WPtct87+k2+dFGofjp0qYsb56w+S7e7fj0tbNDK5IRMQYtQpCumVepPGrslp5dl0SBaWVdG/lz91aPVpEXFidb58Xkcbpgx3pfJ+Wh5ebmWeHReFm0RlyEXFd+g0o4kJSsgt5/esDADw0oBPtA30MrkhExFgOD0I5OTkXfYzp06cTFRVl+zNkyBAAkpOTGTlyJHFxccycOROr1Wp7zdatWxk2bBjx8fHMmzevxvHWrVvHwIED6devH6tXr77o+kQao7KKKqauSaSs0kq/ToHcHN3a6JJERAxXq1Njv5SamsqsWbM4cOAAlZWVQPXF0pmZmfz8888XVdTPP//MW2+9xWWXXQaA2WymrKyM8ePH069fP/7xj38wffp0li9fzsiRI8nJyeH+++/nj3/8I9dffz0TJ07kkksuoXfv3iQnJzN58mSmTZtGdHQ0f/nLX+jWrRudOnW6qBpFGpv/+/Yg+7IKae7tzpNXd9Hq0SIiXMSM0JQpU+jUqRO9evWie/fuTJ06FU9PTyZPnnxRBVVUVLBv3z5iY2Np1qwZzZo1w8/Pj6+++oqCggKmTJlCu3btmDhxIkuXLgVg5cqVhIaG8sADD9ChQwcmTJhgG1uyZAnx8fGMGjWKqKgoxowZw4oVKy6qRpHGZnvaSd77/ggAT13dmWBfrR4tIgIXMSOUnJzMv//9b44ePcrzzz9P//798fPz49lnn+Wuu+6qc0HJyclUVVUxYsQIMjIyiIuL4/nnnycxMZGYmBi8vat3xI6KiiI1NRWApKQk4uPjbf/CjY6OZvbs2QAkJiZy1VVX2Y4fHR3N66+/Xuu6HP2P5zPH0z/K7aN+2e/XvSooreCZtUlYgRt7tGJA52DDanM2+lzZT72yn3plv/rslb3HrHMQ6tixI0uWLOHuu+/m8OHD5ObmEhQUxJEjR+p6SABSUlLo2LEjTz/9NC1atODFF1/k6aefpnPnzoSFhdmeZzKZMJvN5OXlUVBQQETE/1bF9fPzIzMzE4DCwsIar/vlWG0EBflfxLtq+OM2VeqX/c706oXFuzieX0q7QB9e+H0Mfp51/rFvsvS5sp96ZT/1yn5G9qrOvxGfeuop/vrXvzJq1ChGjhzJNddcg8lkIiEh4aIKGj58OMOHD7d9PW3aNAYNGkRERAQeHjWn8z09PSkpKcFisdQYO/M4cN6x2jhxIp9fXJt90Uym6m+8o4/bVKlf9vtlrz7dm8V/d6ZjNsEz13ShJL+YknyjK3Qe+lzZT72yn3plv/rs1ZljX0idg9AVV1zB119/jclk4pFHHqF79+74+PjQr1+/uh7ynIKCgqiqqiI4OJh9+/bVGCssLMTd3Z2AgIAad6udeRw471htWK3Uywe6vo7bVKlf51dZZWVXeh6laacoLizlb58lA3BXfDt6tG6m3v0Gfa7sp17ZT72yn5G9qvPF0ikpKdx8882sXbsWgH//+9+89NJLF31qbObMmaxatcr29c6dOzGbzURFRbFr1y7b42lpaZSVlREQEECPHj1qjO3Zs4eWLVsCnHdMpCnZsC+b4W9/x/gPf+SvH+zi8VV7yS+tpG2AF/f21urRIiLnUucgNHXqVOLi4mwzQIsXL2bgwIFMnTr1ogrq2rUrr7zyCps3b+abb75h2rRpjBgxgiuvvJKCggKWLVsGwJtvvknfvn2xWCwkJCSwY8cONm3aRHl5OXPmzLHVNXToUNasWUNSUhKFhYUsWLDA4bNWIkbbsC+bx1buIbOg7Kyx9LwSvtp/8et7iYg0RXU+NbZ3715eeeUV/P2rz7/5+Phwxx13cO21115UQTfeeCMpKSn85S9/wWKxcMMNNzBx4kTc3NyYPn06kyZNYtasWZjNZhYsWABAYGAgU6ZMYdy4cfj4+ODv78+MGTOA6mA1duxYRo4ciaenJ+3bt2f06NEXVaOIM6mssjJ7Q8p5n/PyF6n0jwjCYtZtLCIiv2SyWut2Vu7WW29l4MCB3HfffbbH3nrrLTZs2MAHH3zgsAJ/LSsri927dxMTE0OLFi1qjKWlpbF//35iY2Px9fWtMZaSkmK7Hf/XF13bIzvb8RdLBwf7O/y4TZX69du2p51k/Ic/XvB5/3dLNFeEN6//ghoRfa7sp17ZT72yX3326syxL6TOM0JTp07l3nvvZcWKFYSFhZGenk5eXh5z5syp6yHtEhISwoABA845Fh4eTnh4+DnHIiMjiYyMrMfKRIyRfY7TYRfzPBERV1LnINStWzc++eQTNm7cyLFjxxg+fDgDBgzAz8/PkfWJyAUE+9k3w2nv80REXMlFrazm5+fH9ddf76haRKSWqqzVt8tfSEt/T3q2DWiAikREGhctMSvSSBWUVvDsuiQ2ppy44HMnDozQhdIiIuegICTSCB04UcQjK3ZzKLcYd4uJRxIiCfB2Z/aGlBq30Lf092TiwAgStL+YiMg5KQiJNDIb9mXz7NokisorCfXzYNbwbnRv3QyA/hFB1StLm814VlXRs22AZoJERM5DQUikkaissvLvbw8yf2saAJeHBfDiDZcQ6PO/i6AtZhOx7Zrr1l0RETspCIk0AieLy3nq4718d+gkAKOvaMtfruqEm2Z7REQuioKQiJNLzMjn0ZV7OHaqFC83M08P7cLVXUONLktEpElQEBJxYh/vzuDFz/dRWlFFWHMvZg3vRucQrdUlIuIoCkIiTqi8sop/bNzPkl1HAbiyYyDPXRtFMy93gysTEWlaFIREnEx2QSmPr9rLD0dPAXBvn3b8qU97zCZdDyQi4mgKQiJO5If0PB5btZcThWX4elh47tquXBURZHRZIiJNloKQiBOwWq0s2XWMlzemUlllpVOQDy/d2J12LbyNLk1EpElTEBIxWEl5JTPWp/Dx7gwABncJ4emhXfDxsBhcmYhI06cgJGKgo3klPLpyD0mZBZhN8OffdeT22DBMuh5IRKRBKAiJGOS7g7k8+fFe8koqaO7tzt+u70pcuxZGlyUi4lIUhEQamNVq5d1tR3jjmwNUWeGSln7MGt6NVs28jC5NRMTlKAiJNKDCsgqeW5fMhn3ZANzQvSWPDe6Mp5vZ4MpERFyTgpBIAzmYU8SjK/ZwIKcIN7OJRxIiuCm6ta4HEhExkIKQSAP4MiWbaWuTKCyrJMTPg5k3dKNHm2ZGlyUi4vIUhETqUWWVlbc2H+I/Ww4DcFnbZvzthm4E+3oYXJmIiICCkEi9ySsu5+k1iWw+mAvArZe35a9XdcTNouuBRESchYKQSD1Izizg0ZV7SM8rwdPNzBNDOnNtt5ZGlyUiIr+iICTiYOv2ZjL902RKK6poE+DFrOHdiAr1M7osERE5BwUhEQepqKzin18d4IMd6QD07tCC6dd2JcDb3eDKRETktygIiTjAicIypqzey84jeQDcHR/OuL4dsJh1a7yIiDNTEBK5SD8dPcVjq/aQVVCGr4eFZ66JYkDnYKPLEhEROygIidSR1Wrlvz8e46UNqVRUWekY6MOsG7vRIdDH6NJERMROCkIidVBaUcVL61NY8fNxABI6BzP1mi74euhHSkSkMdFvbZFaOn6qhEdX7mFvRgFmE0zo15GxcWHaKkNEpBFSEBKphe8Pn2TK6r2cLC4nwMuNF667hPgOLYwuS0RE6khBSMQOVquV97en89pX+6myQlSoH7OGd6NNgJfRpYmIyEVQEBK5gKKySp7/JJnPk7MAuK5bKI8P7oyXu8XgykRE5GIpCImcx+HcYh5duZvU7CIsZhOTBkbw+5jWuh5IRKSJUBAS+Q1fp55g6tpECkorCfL1YOYNlxDTNsDoskRExIEUhER+pcpqZc7mQ7y9+TAAMW2aMeOGSwj28zS4MhERcTSz0QVcyD333MPy5csB2Lp1K8OGDSM+Pp558+bVeN66desYOHAg/fr1Y/Xq1TXG3n//ffr27cugQYPYvHlzg9UujU9+SQWTPtptC0G39GzDv2+JVggSEWminDoIrVy5km+++QaAnJwc7r//fq677joWL17MqlWr2LJlCwDJyclMnjyZCRMmMHfuXF599VX2798PwNdff83MmTN57rnneOmll3jqqafIzc017D2J80rJKmTs+zv4Zn8Onm5mpl3ThUcGReJuceofExERuQhOe2rs5MmTzJw5k44dOwLVoSg0NJQHHngAk8nEhAkTWLp0Kb1792bJkiXEx8czatQoAMaMGcOKFSt4+OGHWbRoESNGjGDw4MEADBo0iM8//9z2XHs5+trYM8fTNbf2qe9+fZKYyfPrkimpqKJ1M09eurEbXVv6189fVs/02bKfemU/9cp+6pX96rNX9h7TaYPQzJkzGTx4MKWlpQAkJSURHx9vu1snOjqa2bNnA5CYmMhVV11le210dDSvv/66bWzy5Mk1xrZt21brIBQUVD//U6yv4zZVju5XRWUVM9cl8vbXBwD4XedgXr31Mlr4ejj07zGCPlv2U6/sp17ZT72yn5G9csogtGXLFjZv3szq1auZPn06AAUFBURERNie4+fnR2ZmJgCFhYWEhYXVeqw2TpzIx2qt09s5J5Op+hvv6OM2VfXRr5zCMqas3sv2tDwA7uoVzv39OlBZXEp2calj/hID6LNlP/XKfuqV/dQr+9Vnr84c+0KcLgiVlpYybdo0nnnmGfz8/GyPWywWPDz+9690T09PSkpKLmqsNqxW6uUDXV/Hbaoc1a/dx07x6Mo9ZBaU4eNuYdo1XUjoEmL7O5oCfbbsp17ZT72yn3plPyN75XRB6I033uDSSy9lwIABNR4PCAggJyfH9nVhYSHu7u52jf3y4uhfjolrWvHTMWauT6G80kr7Ft68dGN3Ogb5GF2WiIgYwOmC0KpVq8jNzSU2NhaAkpIS1q5dC8Bll11me96ePXto2bIlAD169GDXrl22635+PbZz50769Olz1pi4lrKKKv7+RQr//fE4AP0jgnhmWBR+nk73YyAiIg3E6e4LXrhwIatWreKjjz7io48+IiEhgQcffJAvvviCHTt2sGnTJsrLy5kzZw79+vUDYOjQoaxZs4akpCQKCwtZsGBBjbGFCxeSkZFBdnY2S5cutY2J68jIL2Xc4h/474/HMQH3X9mBWTd2UwgSEXFxTvd/gVatWtX42sfHhxYtWhAYGMiUKVMYN24cPj4++Pv7M2PGDAC6du3K2LFjGTlyJJ6enrRv357Ro0cDkJCQwLp167j66qsB6NOnj+2/xTVsTzvJE6v3klNUTjMvN56/tit9OwYaXZaIiDgBk9XauC7lSktLY//+/cTGxuLr61tjLCUlhYyMDOLi4mpcIA3w448/UlxcTK9eveq0YWZ2tuPvGgsO9nf4cZuquvTLarWyaEc6r365n0ordA7xZdbwboQ1967fYg2mz5b91Cv7qVf2U6/sV5+9OnPsC3G6GaELCQ8PJzw8/JxjkZGRREZGnnMsOjq6PssSJ1NcXskLnybzSWIWAEO7hvDU1V3wcrcYXJmIiDiTRheERC7kyMliHl25h31ZhVhM8NCACP5wWZs6zQSKiEjTpiAkTcq3B3J4+uNE8ksrCPRx58UbLuHysOZGlyUiIk5KQUiahCqrlf9sOcxbmw5hBXq09mfGDd0I9deu8SIi8tsUhKTRKyitYNraJL5KPQHAyJjWTBwQgYeb060OISIiTkZBSBq11OxCHl25h8O5xXhYTDw2qDPDe7S68AtFRERQEJJG7POkLJ77JIni8ipa+nsyc3g3urfSbs8iImI/BSFxepVVVnal51F65BSeVVVc2roZb206yLvbjgAQ2645f7uuKy18PC5wJBERkZoUhMSpbdiXzewNKWQWlNkec7eYKK+sXnnr9tgwHvhdR9zMujVeRERqT0FInNaGfdk8tnLPWY+fCUGjr2jLX/t3auiyRESkCdFtNeKUKquszN6Qct7nrE/OprJK69eLiEjdKQiJU9qVnlfjdNi5ZOSXsis9r4EqEhGRpkhBSJzSjjT7Ak72BcKSiIjI+egaIXEqu4/n89amg2w6kGvX84P9dKeYiIjUnYKQOIXEjHze3HSIb/bnANVTlR5uZkoqqn7zNS39PenZNqCBKhQRkaZIQUgMlZRZwNubDvHl6e0xzCa4tltL7undjuSswnPeNXbGxIERWHTbvIiIXAQFITFESlYhb20+xBf7soHqADS0ayj39G5H+0AfAMKaezNzeLez1hFq6e/JxIERJHQONqR2ERFpOhSEpEHtP1HI25sO83lyFgAm4OquIfypd3s6BPmc9fyEzsH0jwiqXlnabMazqoqebQM0EyQiIg6hICQN4uCJIuZsOcSniVmcWflncJcQ7u3bjk5Bvud9rcVsIrZdc4KD/cnOzseqpYNERMRBFISkXh3OLWbO5kN8kpjJmbUPEzoHc2+f9kSGnD8AiYiI1DcFIakXR04WM2fLYdbuybAFoAGRQfypT3uiQv2MLU5EROQ0BSFxqPS8Yv6z5TAf787g9JZg9OsUyLi+7bmkpb+xxYmIiPyKgpA4xLFTJfxny2FW7c6w7f/Vt2MLxvVpT/fWzQyuTkRE5NwUhOSiZOSXMu+7w6z46TgVpwNQ7/YtGNe3PT3aKACJiIhzUxCSOskqKOWd79L470/HKD99DiyuXXPu69ueGK32LCIijYSCkNRKdmEZ87emsfyHo5SdDkCXhwVw35XtuTysubHFiYiI1JKCkNjlRGEZ725LY9kPxyg9vf9Xz7bNuK9vB2LbNTe2OBERkTpSEJLzyi0qY8G2IyzZddS2AWqP1s2478r29GrXHJNJKzyLiEjjpSAk53SyuJz3vz/C4p3pFJdXB6DurfwZ17c9fTq0UAASEZEmQUFIajhVUs7729NZvCOdwrJKAC5p6ce4vu25smOgApCIiDQpCkICQH5JBYt2HGHh9v8FoC4hvozr24GrIhSARESkaVIQcnEFpRV8sCOdhdvTyS+tACAy2JdxfdvTPzIIswKQiIg0YQpCLqqwrIIPdx7l/e+PkFdSHYA6Bfkwrm97BnYOVgASERGXoCDkYorKKlmy6ygLtqXZAlCHQG/u7dOewVEhCkAiIuJSFIRcREn5mQB0hNzicgDatfDmT33acXVUKBazApCIiLgepw1Cp06d4sCBA3To0IGAAG3ZUFcl5ZUs//EY87emkVNUHYDCmnvxp97tGXpJKG4KQCIi4sLMRhdwLmvXriUhIYGnnnqK/v37s3btWgCSk5MZOXIkcXFxzJw5E6vVanvN1q1bGTZsGPHx8cybN6/G8datW8fAgQPp168fq1evbtD3YpTSiioW70jnprnb+MfG/eQUldOmmSdPD+3Ckj/GcV33lgpBIiLi8pwuCOXn5/Pss8/y3nvvsWrVKqZOncpLL71EWVkZ48ePp3v37ixbtozU1FSWL18OQE5ODvfffz/XXXcdixcvZtWqVWzZsgWoDk+TJ09mwoQJzJ07l1dffZX9+/cb+RbrVVlFFUt2HeXmuVv5+xepZBeW0crfkyeHdGbZ3XEMv7SVApCIiMhpTheECgoKeOKJJ+jatSsA3bp1Izc3l6+++oqCggKmTJlCu3btmDhxIkuXLgVg5cqVhIaG8sADD9ChQwcmTJhgG1uyZAnx8fGMGjWKqKgoxowZw4oVKwx7f/WlvLKK5T8c5eb/bGPW+hQyC8oI9fNgyuBIlt8Tx4jo1rhZnO7bLSIiYiinu0aodevWDB8+HIDy8nLmz5/PkCFDSExMJCYmBm9vbwCioqJITU0FICkpifj4eNuif9HR0cyePRuAxMRErrrqKtvxo6Ojef3112tdl6NvpjpzvIs9bkVlFav3ZDB382GOnSoFIMTPgz/Gt2NEj1Z4uDWN8OOofrkC9cp+6pX91Cv7qVf2q89e2XtMpwtCZyQmJnLnnXfi7u7OmjVreOONNwgLC7ONm0wmzGYzeXl5FBQUEBERYRvz8/MjMzMTgMLCwhqv++VYbQQF+V/Eu3H8cSsqq/jvznRe25DC4ZwiAEL8PZkwIILberXDy93iyDKdRn19H5oi9cp+6pX91Cv7qVf2M7JXThuEoqKimDt3Li+++CJPPfUU4eHheHh41HiOp6cnJSUlWCyWGmNnHgfOO1YbJ07k84trsy+ayVT9ja/tcSurrKzbm8mczYdIO1n9PgJ93LmzVzgjY1rj5W6hIK+IAseV6hTq2i9XpF7ZT72yn3plP/XKfvXZqzPHvhCnDUImk4lLL72UGTNmMGTIECZOnMi+fftqPKewsBB3d3cCAgLIyck563HgvGO1YbVSLx9oe49bWWXls6Qs5mw+xKHcYgCae7szNi6M3/dsg/fpGaCm/kNXX9+Hpki9sp96ZT/1yn7qlf2M7JXTXUCydetWZs6cafvaw8MDk8lEREQEu3btsj2elpZGWVkZAQEB9OjRo8bYnj17aNmyJcB5xxqDKquVTxMzuW3+dp5ek8ih3GICvNx4oF8HVvypF3fEhdtCkIiIiNSO0wWhDh068OGHH7J48WKOHTvGyy+/zJVXXkn//v0pKChg2bJlALz55pv07dsXi8VCQkICO3bsYNOmTZSXlzNnzhz69esHwNChQ1mzZg1JSUkUFhayYMEC25gzq7JaWZ+cxeh3t/Pkx4kcyCmimZcb91/ZgY/+1Iu74tvh46EAJCIicjFMVqvzTdx9++23/O1vf+PYsWP069ePZ555hsDAQNavX8+kSZPw9PTEbDazYMECIiMjAVi0aBEvvPACPj4++Pv7s3jxYoKDgwH4xz/+wdy5c/H09KR9+/YsXLgQLy+vWtWUne2485eVVVZ2pedRajbjWVVFz7YBti0urFYrX6ac4K3Nh9iXVQiAn6eF0VeEcdvlbfHzdNqzmfXKZILgYH+Hfh+aKvXKfuqV/dQr+6lX9qvPXp059gWf54xB6HyysrLYvXs3MTExtGjRosZYWloa+/fvJzY2Fl9f3xpjKSkpZGRkEBcXd9ZF1/Zw1Ddpw75sZm+oXufnjFA/DyYNjMDNYuatTYdIyqy+1NnXw8Jtl7dl9BVh+Hu5ZgA6Q79Y7Kde2U+9sp96ZT/1yn4KQo2II75JG/Zl89jKPRd8no+7hVsvb8PoK8II8K79hd1NkX6x2E+9sp96ZT/1yn7qlf2cIQi59jRDA6qssjJ7Q8oFn3dHXBhjY8Np7qMAJCIiUt+c7mLppmpXel6N02G/5cqOgQpBIiIiDURBqIFk2xGCavM8ERERuXgKQg0k2M++C7TtfZ6IiIhcPAWhBtKzbQChFwg5Lf096dk2oIEqEhEREQWhBmIxm5iUEHne50wcGGFbT0hERETqn4JQA0roHMzM4d3Omhlq6e/JzOHdSOgcbFBlIiIirkm3zzewhM7B9I8I+s2VpUVERKThKAgZwGI2EduuuRbcEhERMZhOjYmIiIjLUhASERERl6UgJCIiIi5LQUhERERcloKQiIiIuCwFIREREXFZCkIiIiLishSERERExGUpCImIiIjL0srSdjI5eAeMM8dz9HGbKvXLfuqV/dQr+6lX9lOv7FefvbL3mCarVRs8iIiIiGvSqTERERFxWQpCIiIi4rIUhERERMRlKQiJiIiIy1IQEhEREZelICQiIiIuS0FIREREXJaCkIiIiLgsBSFpFE6dOsUPP/xAXl6e0aWIiEgToiBkgM8//5xBgwbRrVs3brzxRlJTU40uyamtXbuWhIQEnnrqKfr378/atWuNLsnp3XPPPSxfvtzoMpza9OnTiYqKsv0ZMmSI0SU5vZdeeonx48cbXYbTWr58eY3P1Jk/+lk8tyVLltC/f39iYmK44447SEtLM6YQqzSoQ4cOWePi4qwff/yxNSsry/rggw9a//CHPxhdltM6deqUNT4+3rp3716r1Wq1Llu2zDpw4ECDq3JuK1assHbp0sW6bNkyo0txan/4wx+sGzdutObl5Vnz8vKs+fn5Rpfk1Pbu3Wvt2bOn9fDhw0aX4rRKS0ttn6e8vDzrsWPHrPHx8dZDhw4ZXZrTOXTokLV///7Wn3/+2Zqenm6dMmWKdfTo0YbUohmhBpaamsqkSZO49tprCQ4O5rbbbmPv3r1Gl+W0CgoKeOKJJ+jatSsA3bp1Izc31+CqnNfJkyeZOXMmHTt2NLoUp1ZRUcG+ffuIjY2lWbNmNGvWDD8/P6PLclpVVVVMnTqVu+66i/DwcKPLcVoeHh62z1OzZs346KOPGDJkCO3atTO6NKezZ88eYmJi6N69O23atGHkyJEcOnTIkFoUhBrYwIED+cMf/mD7+sCBA7Rv397Aipxb69atGT58OADl5eXMnz9fpzDOY+bMmQwePJiePXsaXYpTS05OpqqqihEjRhAdHc0999zD0aNHjS7LaS1atIjk5GTatm3L+vXrKSsrM7okp1daWsq7777LfffdZ3QpTikyMpItW7awd+9e8vPzWbhwIVdeeaUhtSgIGaisrIx58+Zx6623Gl2K00tMTKRfv358/fXXPPXUU0aX45S2bNnC5s2beeSRR4wuxemlpKTQsWNHZs2axcqVK3Fzc+Ppp582uiynVFhYyGuvvUZ4eDhHjx7lnXfeYfTo0ZSUlBhdmlNbtWoV0dHRhIWFGV2KU4qMjGTo0KGMGDGC2NhYdu3axWOPPWZILQpCBnrttdfw9vZm1KhRRpfi9KKiopg7dy7t27dXEDqH0tJSpk2bxjPPPKNTPHYYPnw4y5cv57LLLqNDhw5MmzaNTZs2UVBQYHRpTuezzz6juLiY+fPn8+CDDzJv3jwKCwtZsWKF0aU5tQ8++IDbbrvN6DKc1o8//sgXX3zBhx9+yPfff8/111/Pvffei9VqbfBaFIQMsnnzZt5//31mz56Nu7u70eU4PZPJxKWXXsqMGTP49NNPOXXqlNElOZU33niDSy+9lAEDBhhdSqMUFBREVVUVmZmZRpfidI4fP05MTAyBgYEAuLm5ERUVZdj1HI3BoUOHOHz4MH379jW6FKe1evVqrrvuOmJiYvD39+ehhx4iLS2NxMTEBq/FrcH/RiEtLY1JkyYxdepUIiMjjS7HqW3dupUvvvjCNmXq4eGByWTCbFaG/6VVq1aRm5tLbGwsACUlJaxdu5Yff/yRZ555xtjinNDMmTPp1q0bN9xwAwA7d+7EbDbTunVrgytzPq1ataK0tLTGY0ePHuWyyy4zqCLnt3btWgYMGKB/5J5HVVVVjRtfCgsLKS4uprKyssFrURBqYCUlJYwfP55BgwYxZMgQCgsLAfDx8cFkMhlcnfPp0KEDH374IR06dOCqq67ilVde4corr9Tpn19ZuHAhFRUVtq9nzZpFTEwMN910k4FVOa+uXbvyyiuvEBwcTGVlJc8//zwjRozA29vb6NKcTv/+/Xn++edZtGgRAwcO5NNPPyUxMZF//vOfRpfmtL7++mv97F1AbGwsjz/+OO+88w5BQUEsWbKEkJAQoqKiGrwWk9WIE3Iu7PPPP+eBBx446/H169frorrf8O233/K3v/2NY8eO0a9fP5555hnbNL2c2+OPP06vXr24+eabjS7Fac2ePZtFixZhsVi44YYbmDhxIj4+PkaX5ZS2b9/OrFmzSExMJCQkhCeeeIKEhASjy3JKJSUlxMbGsmLFCiIiIowux2lZrVbeeOMNli5dSlZWFp07d+aFF16gW7duDV6LgpCIiIi4LF1oISIiIi5LQUhERERcloKQiIiIuCwFIREREXFZCkIiIiLishSERERExGUpCImIiIjLUhASEZezfPlybrzxRtvX//nPf+jbty9ZWVkGViUiRtAWGyLi0vbu3csrr7zC66+/TkhIiNHliEgD04yQiLis4uJiJk6cyO23387vfvc7o8sREQMoCImIy3rxxRfx9fXl4YcfNroUETGITo2JiEs6ePAgiYmJ9OjRA3d3d6PLERGDaEZIRFxSSUkJjz76KElJSWzYsMHockTEIApCIuKSIiMjueeeexg1ahSvvfYaVqvV6JJExAAKQiLiktzcqq8MuO+++0hJSeGzzz4zuCIRMYKCkIi4tJYtW2pWSMSFKQiJiMu77777OHjwIGvXrjW6FBFpYCar/gkkIiIiLkozQiIiIuKyFIRERETEZSkIiYiIiMtSEBIRERGXpSAkIiIiLktBSERERFyWgpCIiIi4LAUhERERcVkKQiIiIuKyFIRERETEZf0/XiutYxzYAuEAAAAASUVORK5CYII="
     },
     "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(2,9):\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(2,9)\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": 47,
   "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": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 0.36355751, -1.81817227,  0.18306699],\n       [-0.95490222,  0.17679215, -1.69155755],\n       [ 1.31656935,  0.10349958, -0.16019117],\n       [-0.51047021,  0.36281987,  0.90438576]])"
     },
     "execution_count": 48,
     "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": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "4749.265326507992"
     },
     "execution_count": 49,
     "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": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "0.5513427055662409"
     },
     "execution_count": 50,
     "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": 51,
   "outputs": [
    {
     "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\n",
    "plt.savefig(\"cluster.svg\", dpi=300,format=\"svg\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 53,
   "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": 54,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x1000 with 2 Axes>",
      "image/png": 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"
     },
     "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=train_data.columns.tolist()[:12]\n",
    "custom_x_labels = [\"AV\",\"AC\",\"PR\",\"UI\",\"S\",\"C\",\"I\",\"A\",\"BS\",\"IS\",\"ES\",\"Severity\"]\n",
    "custom_y_labels = [\"AV\",\"AC\",\"PR\",\"UI\",\"S\",\"C\",\"I\",\"A\",\"BS\",\"IS\",\"ES\",\"Severity\"]\n",
    "color = sns.color_palette()\n",
    "sns.heatmap(train_data[colnm].corr(),linecolor=color[0],linewidths=1.0,annot=True,fmt='0.2f',xticklabels=custom_x_labels,yticklabels=custom_y_labels)\n",
    "plt.savefig(\"heatmap.svg\", dpi=300,format=\"svg\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [],
   "source": [],
   "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
}