INTRUSION_DETECTION-FINAL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4f130d65",
   "metadata": {
    "papermill": {
     "duration": 0.002307,
     "end_time": "2023-07-05T16:38:52.937385",
     "exception": false,
     "start_time": "2023-07-05T16:38:52.935078",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Basic Data Preprocessing, along with Encoding and Feature Detection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a569dbf2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-05T16:38:52.944416Z",
     "iopub.status.busy": "2023-07-05T16:38:52.943807Z",
     "iopub.status.idle": "2023-07-05T16:38:54.446362Z",
     "shell.execute_reply": "2023-07-05T16:38:54.444913Z"
    },
    "papermill": {
     "duration": 1.508745,
     "end_time": "2023-07-05T16:38:54.448618",
     "exception": false,
     "start_time": "2023-07-05T16:38:52.939873",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import re\n",
    "import sklearn\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as matplot\n",
    "%matplotlib inline\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa727a52",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-05T16:38:54.455292Z",
     "iopub.status.busy": "2023-07-05T16:38:54.455018Z",
     "iopub.status.idle": "2023-07-05T16:38:55.137576Z",
     "shell.execute_reply": "2023-07-05T16:38:55.136806Z"
    },
    "papermill": {
     "duration": 0.687825,
     "end_time": "2023-07-05T16:38:55.139306",
     "exception": false,
     "start_time": "2023-07-05T16:38:54.451481",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125972, 43)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(22543, 43)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(148515, 43)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "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",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>S0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>neptune</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>232</td>\n",
       "      <td>8153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>normal</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>199</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>REJ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>neptune</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0    1        2    3    4     5   6   7   8   9   ...    33    34    35  \\\n",
       "0   0  udp    other   SF  146     0   0   0   0   0  ...  0.00  0.60  0.88   \n",
       "1   0  tcp  private   S0    0     0   0   0   0   0  ...  0.10  0.05  0.00   \n",
       "2   0  tcp     http   SF  232  8153   0   0   0   0  ...  1.00  0.00  0.03   \n",
       "3   0  tcp     http   SF  199   420   0   0   0   0  ...  1.00  0.00  0.00   \n",
       "4   0  tcp  private  REJ    0     0   0   0   0   0  ...  0.07  0.07  0.00   \n",
       "\n",
       "     36    37    38   39    40       41  42  \n",
       "0  0.00  0.00  0.00  0.0  0.00   normal  15  \n",
       "1  0.00  1.00  1.00  0.0  0.00  neptune  19  \n",
       "2  0.04  0.03  0.01  0.0  0.01   normal  21  \n",
       "3  0.00  0.00  0.00  0.0  0.00   normal  21  \n",
       "4  0.00  0.00  0.00  1.0  1.00  neptune  21  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "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>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>...</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>attack_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>S0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>neptune</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>232</td>\n",
       "      <td>8153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration protocol_type  service flag  src_bytes  dst_bytes  land  \\\n",
       "0         0           udp    other   SF        146          0     0   \n",
       "1         0           tcp  private   S0          0          0     0   \n",
       "2         0           tcp     http   SF        232       8153     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  ...  dst_host_srv_count  \\\n",
       "0               0       0    0  ...                   1   \n",
       "1               0       0    0  ...                  26   \n",
       "2               0       0    0  ...                 255   \n",
       "\n",
       "   dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "0                     0.0                    0.60   \n",
       "1                     0.1                    0.05   \n",
       "2                     1.0                    0.00   \n",
       "\n",
       "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "0                         0.88                         0.00   \n",
       "1                         0.00                         0.00   \n",
       "2                         0.03                         0.04   \n",
       "\n",
       "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "0                  0.00                      0.00                   0.0   \n",
       "1                  1.00                      1.00                   0.0   \n",
       "2                  0.03                      0.01                   0.0   \n",
       "\n",
       "   dst_host_srv_rerror_rate  attack_type  \n",
       "0                      0.00       normal  \n",
       "1                      0.00      neptune  \n",
       "2                      0.01       normal  \n",
       "\n",
       "[3 rows x 42 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(r\"C:\\Users\\rupin\\Downloads\\NSLKDD improved\\KDDTrain+.txt\")\n",
    "test = pd.read_csv(r\"C:\\Users\\rupin\\Downloads\\NSLKDD improved\\KDDTest+.txt\")\n",
    "train.shape\n",
    "test.shape \n",
    "train.columns = range(train.shape[1])\n",
    "test.columns = range(test.shape[1])\n",
    "labels = ['duration', 'protocol_type', 'service', 'flag', 'src_bytes',\n",
    "'dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot',\n",
    "'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell',\n",
    "'su_attempted', 'num_root', 'num_file_creations', 'num_shells',\n",
    "'num_access_files', 'num_outbound_cmds', 'is_host_login',\n",
    "'is_guest_login', 'count', 'srv_count', 'serror_rate',\n",
    "'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate',\n",
    "'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count',\n",
    "'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate',\n",
    "'dst_host_srv_diff_host_rate', 'dst_host_serror_rate',\n",
    "'dst_host_srv_serror_rate', 'dst_host_rerror_rate',\n",
    "'dst_host_srv_rerror_rate', 'attack_type', 'difficulty_level']# subclass - > attack_type\n",
    "combined_data = pd.concat([train, test])\n",
    "combined_data.shape\n",
    "combined_data.head(5)\n",
    "\n",
    "combined_data.columns = labels\n",
    "combined_data = combined_data.drop('difficulty_level', axis= 1)\n",
    "combined_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee125bae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-05T16:38:55.148844Z",
     "iopub.status.busy": "2023-07-05T16:38:55.148327Z",
     "iopub.status.idle": "2023-07-05T16:38:55.299961Z",
     "shell.execute_reply": "2023-07-05T16:38:55.299096Z"
    },
    "papermill": {
     "duration": 0.158586,
     "end_time": "2023-07-05T16:38:55.301761",
     "exception": false,
     "start_time": "2023-07-05T16:38:55.143175",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'worm', 'land', 'httptunnel', 'snmpgetattack', 'sqlattack', 'mscan', 'phf', 'guess_passwd', 'pod', 'ftp_write', 'warezmaster', 'processtable', 'ps', 'ipsweep', 'buffer_overflow', 'neptune', 'portsweep', 'normal', 'apache2', 'xsnoop', 'teardrop', 'warezclient', 'loadmodule', 'snmpguess', 'named', 'mailbomb', 'perl', 'xterm', 'xlock', 'multihop', 'imap', 'udpstorm', 'saint', 'rootkit', 'smurf', 'spy', 'satan', 'sendmail', 'back', 'nmap'}\n",
      "\n",
      "Describing attack_type: \n",
      "min 0\n",
      "max 39\n",
      "mean 15.782244217755782\n",
      "mode 0    16\n",
      "Name: attack_type, dtype: int32\n",
      "looks like 16 is 'normal' \n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "\n",
    "print(set(list(combined_data['attack_type']))) # use print to make it print on single line \n",
    "combined_data['attack_type'] = le.fit_transform(combined_data['attack_type'])\n",
    "combined_data['protocol_type'] = le.fit_transform(combined_data['protocol_type'])\n",
    "combined_data['service'] = le.fit_transform(combined_data['service'])\n",
    "combined_data['flag'] = le.fit_transform(combined_data['flag'])\n",
    "\n",
    "print('\\nDescribing attack_type: ')\n",
    "print(\"min\", combined_data['attack_type'].min())\n",
    "print(\"max\", combined_data['attack_type'].max())\n",
    "print(\"mean\", combined_data['attack_type'].mean())\n",
    "print(\"mode\", combined_data['attack_type'].mode())\n",
    "print(\"looks like 16 is 'normal' \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0743bc5c",
   "metadata": {
    "papermill": {
     "duration": 0.003142,
     "end_time": "2023-07-05T16:38:55.308344",
     "exception": false,
     "start_time": "2023-07-05T16:38:55.305202",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Feature Elimintation along with dataset split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48b878e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-05T16:38:55.316587Z",
     "iopub.status.busy": "2023-07-05T16:38:55.315716Z",
     "iopub.status.idle": "2023-07-05T16:38:55.913107Z",
     "shell.execute_reply": "2023-07-05T16:38:55.912178Z"
    },
    "papermill": {
     "duration": 0.603747,
     "end_time": "2023-07-05T16:38:55.915379",
     "exception": false,
     "start_time": "2023-07-05T16:38:55.311632",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Perform feature elimination\n",
    "corr_matrix = combined_data.corr().abs().sort_values('attack_type')\n",
    "leastCorrelated = corr_matrix['attack_type'].nsmallest(10)\n",
    "leastCorrelated = list(leastCorrelated.index)\n",
    "leastSTD = combined_data.std().to_frame().nsmallest(5, columns=0)\n",
    "leastSTD = list(leastSTD.transpose().columns)\n",
    "featureElimination = set(leastCorrelated + leastSTD)\n",
    "combined_data_reduced = combined_data.drop(featureElimination, axis=1)\n",
    "\n",
    "# Define the features and target variable after feature elimination\n",
    "data_x = combined_data_reduced.drop('attack_type', axis=1)\n",
    "data_y = combined_data_reduced['attack_type']\n",
    "\n",
    "# Define the number of components for PCA\n",
    "n_components = 10  # Adjust this number as needed\n",
    "\n",
    "# Instantiate PCA\n",
    "pca = PCA(n_components=n_components)\n",
    "\n",
    "# Fit and transform PCA on the reduced data\n",
    "X_pca = pca.fit_transform(data_x)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_pca, data_y, test_size=0.5, random_state=42)\n",
    "\n",
    "# Now X_train and X_test contain the transformed features after PCA,\n",
    "# and y_train and y_test contain the corresponding labels\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d11e543b",
   "metadata": {
    "papermill": {
     "duration": 0.003406,
     "end_time": "2023-07-05T16:38:55.924025",
     "exception": false,
     "start_time": "2023-07-05T16:38:55.920619",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Training the Random Forest Classifier Model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c980e1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-05T16:38:55.933027Z",
     "iopub.status.busy": "2023-07-05T16:38:55.932647Z",
     "iopub.status.idle": "2023-07-05T16:38:57.921872Z",
     "shell.execute_reply": "2023-07-05T16:38:57.921067Z"
    },
    "papermill": {
     "duration": 1.995921,
     "end_time": "2023-07-05T16:38:57.923548",
     "exception": false,
     "start_time": "2023-07-05T16:38:55.927627",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', n_estimators=10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier processing...\n",
      "RandomForestClassifier Score: 0.93\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17ed3d0e950>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17ed3d0f190>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17ed3d0fd50>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.05)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'False Positive Rate')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'True Positive Rate')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Receiver Operating Characteristic (ROC)')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17ed3cfb250>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAHFCAYAAAAe+pb9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAACcg0lEQVR4nOzdeVzM2/8H8Ne0TKtGRYtQkShttuxrrhS+1p+SpbJc2ZN9y75cO7nZZYuL697Lvdaua1f2iBAVFUUllbTNzPn9kYbRREP1qeb9fDyG5ny292eqmXfvzznnw2OMMRBCCCGEKCAlrgMghBBCCOEKJUKEEEIIUViUCBFCCCFEYVEiRAghhBCFRYkQIYQQQhQWJUKEEEIIUViUCBFCCCFEYVEiRAghhBCFRYkQIYQQQhQWJULku+zevRs8Hk/yUFFRgbGxMdzd3fH06VOuwwMAmJmZwcvLi+swisjKysKKFSvQpEkTaGtrQ0tLCw4ODli2bBmysrK4Dq/Eli1bhr/++qtI+4ULF8Dj8XDhwoVyj6lQTEwMxo8fD0tLS2hoaEBTUxONGzfG3Llz8fLlS8l6nTp1go2NDWdx/ogDBw5g/fr1Zbb/7/n9uXbtGhYsWIB3794VWdapUyd06tSpVGIr5OTkBB8fH8nzwp+9woeysjJq1qyJXr164datWzL3wRjDgQMH0KVLF+jq6kJNTQ316tXDuHHjEB8fX+yx//77b/Tq1QuGhobg8/nQ09ODk5MTgoODkZ+fDwBIS0tD9erVZf6ekAqEEfIdgoKCGAAWFBTEQkND2fnz59mSJUuYhoYGMzAwYG/fvuU6RHbnzh327NkzrsOQkpSUxGxsbJiGhgabMWMGO3v2LDt79iybOXMm09DQYDY2NiwpKYnrMEtES0uLeXp6FmlPT09noaGhLD09vfyDYoz9/fffTEtLi5mamrJVq1axf//9l507d46tX7+e2dnZMQcHB8m6HTt2ZI0bN+Ykzh/Vo0cPZmpqWmb7/57fn1WrVjEALDY2tsiyhw8fsocPH5ZSdIz99ddfTE1NjSUkJEjazp8/zwCwZcuWsdDQUHbp0iW2YcMGpqenxzQ1NVlUVJTUPkQiEXNzc2MA2KBBg9hff/3Fzp8/zzZs2MBq167Nqlevzq5cuSK1jVgsZl5eXgwAc3V1Zfv372cXL15kx48fZ5MnT2Y6Ojps/fr1kvUXLFjALCwsWG5ubqmdOyldlAiR71KYCN28eVOqfeHChQwA27VrF0eRcUsoFLKcnJxil3fr1o2pqKiwy5cvF1l2+fJlpqKiwpydncsyRJm+FbcsxSVCXIqJiWFaWlqsSZMm7N27d0WWi8VidvToUcnz8kiExGIx+/DhQ6nvt6wSoR+J9WuJUGlzdHRk7u7uUm2FidCRI0ek2vfs2cMAMH9/f6n2ZcuWMQBsxYoVRfaflJTETE1NmaGhIUtLS5O0//LLLwwAW7hwocy4EhMTpX6/k5KSmIqKCgsODpb3FEk5oUSIfJfiEqETJ04wAGz58uVS7Tdv3mS9evViurq6TE1NjTk4OLBDhw4V2W9CQgIbNWoUq127NlNVVWXGxsasf//+UlWS9PR0NmXKFGZmZsZUVVVZrVq12KRJk9j79++l9mVqair5oH7z5g1TVVVlc+fOLXLMR48eMQBsw4YNkrbExET2888/MxMTE6aqqsrMzMzYggULWH5+vmSd2NhYBoD98ssvbPHixczMzIwpKyuzU6dOyXzNbt68yQCw0aNHF/OqMvbzzz8zAOzWrVuSNgBs3LhxbMuWLaxBgwaMz+czKysrdvDgwSLb/2jc2dnZzM/Pj9nb2zMdHR2mq6vLWrVqxf766y+p4wAo8ujYsSNj7NOH0fnz5yXre3p6Mi0tLfb06VPm4uLCtLS0WO3atZmfn1+RBCw+Pp7179+faWtrM4FAwDw8PNiNGzckFcivGT9+PAPAQkNDv7peocJE6MaNG6xdu3ZMQ0ODmZubs+XLlzORSCRZr6SvS+FrM27cOLZ582bWqFEjpqqqyjZv3swYK6gOODo6Ml1dXVatWjXWpEkTtmPHDiYWi4vsJzg4mLVq1YppaWkxLS0tZm9vz3bs2CGJW9b3oFBubi5bvHgxa9iwIePz+axGjRrMy8uLvXnzRuoYpqamrEePHuzo0aPMwcGBqampsRkzZkiWfZ7oikQitnjxYmZpacnU1dWZQCBgtra2kurH/PnzZcZU+HPQsWNHyc9IoZycHLZw4ULWqFEjpqamxvT09FinTp3Y1atXv/p9u3PnDgPATpw4IdVeXCL08OHDIr97ubm5TFdXl1lZWcl8/Rlj7MCBAwwAW716NWOMsby8PKanp8caNWpU7DayuLi4sPbt25d4fVK+VMroihtRULGxsQAAS0tLSdv58+fRvXt3tGzZElu2bIFAIMBvv/0GNzc3fPjwQdIP4eXLl2jRogXy8/Mxe/Zs2NnZITU1FWfOnEFaWhoMDQ3x4cMHdOzYEQkJCZJ1Hj58CH9/f0RERODff/8Fj8crElfNmjXRs2dP7NmzBwsXLoSS0qfucUFBQeDz+Rg8eDAAICkpCY6OjlBSUoK/vz/q16+P0NBQLFmyBM+fP0dQUJDUvjdu3AhLS0usXr0aOjo6aNCggczXJiQkBADQp0+fYl+/Pn36YNu2bQgJCUGzZs0k7cePH8f58+exaNEiaGlpITAwEIMGDYKKigoGDBhQanHn5ubi7du3mDp1KkxMTJCXl4d///0X/fr1Q1BQEIYNGwYACA0NRZcuXdC5c2fMmzcPAKCjo1PseQFAfn4+/ve//2HEiBGYMmUKLl26hMWLF0MgEMDf3x9AQf+pzp074+3bt/jll19gYWGB06dPw83N7av7LnT27FkYGhqiVatWJVq/8HUbPHgwpkyZgvnz5+PPP//ErFmzUKtWLcn5lvR1KfTXX3/h8uXL8Pf3h5GREQwMDAAAz58/x+jRo1G3bl0AQFhYGCZMmICXL19KXgMA8Pf3x+LFi9GvXz9MmTIFAoEADx48wIsXLwAAgYGB+PnnnxEdHY0///xT6thisRi9e/fG5cuXMX36dLRp0wYvXrzA/Pnz0alTJ9y6dQsaGhqS9e/cuYNHjx5h7ty5MDc3h5aWlszXaeXKlViwYAHmzp2LDh06ID8/H48fP5b0Bxo5ciTevn2LgIAA/PHHHzA2NgYAWFtby9yfUCiEi4sLLl++DF9fX3Tp0gVCoRBhYWGIi4tDmzZtiv2e/fPPP1BWVkaHDh2KXedzst6Xbt++jbS0NPz8888y3zMAoFevXlBSUkJISAimTJmCW7du4e3btxg1alSx28jSqVMnzJo1C+/evUP16tVLvB0pJ1xnYqRyKqwIhYWFsfz8fJaZmclOnz7NjIyMWIcOHaQqEI0aNWJNmjSRamOMsZ49ezJjY2PJX97Dhw9nqqqqLDIystjjLl++nCkpKRWpRP3+++8MADt58qSk7cu/aI8fP84AsLNnz0rahEIhq1WrFuvfv7+kbfTo0UxbW5u9ePFC6hirV69mACT9HAorK/Xr12d5eXnfesmYj48PA8AeP35c7DqF1akxY8ZI2gAwDQ0NqaqYUChkjRo1YhYWFmUat1AoZPn5+WzEiBGsSZMmUsuKuzRWXEUIADt8+LDUuq6urqxhw4aS57/++isDUKSqNnr06BJVhNTV1VmrVq2+us7nCisr169fl2q3trb+6iXKr70uAJhAIPhmPzmRSMTy8/PZokWLmL6+vqTCEBMTw5SVldngwYO/un1xl8YOHjzIAEhdAmTsU0UyMDBQ0mZqasqUlZXZkydPiuzny9+fnj17SvWvkuVrl8a+rAjt3buXAWDbt2//6j5lcXFxYY0aNSrSXvizd+jQIZafn88+fPjArl69yho2bMisra2lLnH99ttvDADbsmXLV49laGjIrKys5NrmSyEhITJ/rknFQKPGyA9p1aoVVFVVUa1aNXTv3h26uro4duwYVFQKio3Pnj3D48ePJdUWoVAoebi6uiIxMRFPnjwBAJw6dQqdO3eGlZVVscf7559/YGNjAwcHB6l9OTs7f3OkkouLC4yMjKQqI2fOnMGrV68wfPhwqWN07twZtWrVkjqGi4sLAODixYtS+/3f//4HVVVV+V64YjDGAKDIX5tOTk4wNDSUPFdWVoabmxuePXuGhISEUo37yJEjaNu2LbS1taGiogJVVVXs3LkTjx49+qFz4/F46NWrl1SbnZ2dpMpRGGPhz9LnBg0a9EPH/hojIyM4Ojp+NS5AvtelcATSl/777z907doVAoEAysrKUFVVhb+/P1JTU/HmzRsABZVDkUiEcePGfdf5/PPPP6hevTp69eol9XPg4OAAIyOjIr8jdnZ2UpWS4jg6OuLevXsYO3Yszpw5g4yMjO+Kr9CpU6egrq4u9btXUq9evZJU2WRxc3ODqqoqNDU10bZtW2RkZODEiRPfVY1hjMlV/ZGlMNbPRyySioMSIfJD9u7di5s3b+K///7D6NGj8ejRI6kPrdevXwMApk6dClVVVanH2LFjAQApKSkAgOTkZNSuXfurx3v9+jXu379fZF/VqlUDY0yyL1lUVFQwdOhQ/Pnnn5Jy/u7du2FsbAxnZ2epY/z9999FjtG4cWOpeAsVXgL4lsLLIYVlelmeP38OAKhTp45Uu5GRUZF1C9tSU1NLLe4//vgDAwcOhImJCfbv34/Q0FDcvHkTw4cPR05OTonOsziamppQV1eXalNTU5Pab2pqqlTCV0hWmyx169b96usri76+fpE2NTU1ZGdnS57L+7rIem1v3LiBbt26AQC2b9+Oq1ev4ubNm5gzZw4ASI6XnJwMAN/8XSjO69ev8e7dO/D5/CI/C0lJSd/98ztr1iysXr0aYWFhcHFxgb6+PpycnIodlv4tycnJqFWrltRl6pLKzs4u8rP0uV9++QU3b97ExYsXMWfOHLx+/Rp9+vRBbm6uZJ2S/D5mZWUhJSVF8vtYkm1kKYz1858pUnFQHyHyQ6ysrNC8eXMAQOfOnSESibBjxw78/vvvGDBgAGrUqAGg4E20X79+MvfRsGFDAAX9eAqrG8WpUaMGNDQ0sGvXrmKXf423tzdWrVol6aN0/Phx+Pr6QllZWWofdnZ2WLp0qcx91KpVS+p5Sf9a/OmnnzB79mz89ddfRSoehQrnG/npp5+k2pOSkoqsW9hW+EFeGnHv378f5ubmOHTokNTyzz9AypK+vj5u3LhRpF3W+cvi7OyMgIAAhIWFydVP6FvkfV1kvba//fYbVFVV8c8//0h9iH85x0zNmjUBAAkJCUUS4pKoUaMG9PX1cfr0aZnLq1Wr9s1YZVFRUYGfnx/8/Pzw7t07/Pvvv5g9ezacnZ0RHx8PTU1NueKsWbMmrly5ArFYLHcyVKNGDbx9+7bY5fXq1ZO8L3Xo0AEaGhqYO3cuAgICMHXqVABAs2bNoKuri+PHj2P58uUyX4fjx49DLBZLfh+bN28OPT09HDt2rNhtZCmM9VvvT4QbVBEipWrlypXQ1dWFv78/xGIxGjZsiAYNGuDevXto3ry5zEfhG7OLiwvOnz8vuVQmS8+ePREdHQ19fX2Z+zIzM/tqfFZWVmjZsiWCgoJw4MAB5Obmwtvbu8gxHjx4gPr168s8xpcJRUk1b94c3bp1w86dO3H16tUiy69cuYJdu3ahe/fuUh2lAeDcuXOS6hoAiEQiHDp0CPXr15dUDkojbh6PBz6fL/UGn5SUhGPHjhVZ98uqSWno2LEjMjMzcerUKan23377rUTbT548GVpaWhg7dizS09OLLGeMFelcXBLyvC5f24eKiopU0p2dnY19+/ZJrdetWzcoKytj8+bNX91fca9/z549kZqaCpFIJPPnoPAPjx9RvXp1DBgwAOPGjcPbt28llUw1NTXJeX2Li4sLcnJysHv3brmP36hRI8TExJR4/enTp8PCwgIrVqxAZmYmAIDP52PatGl49OgRVq1aVWSbN2/eYNasWTA0NMTIkSMBAKqqqpgxYwYeP36MxYsXyzzWmzdvivx+F8ZaXMdxwi2qCJFSpauri1mzZmH69Ok4cOAAhgwZgq1bt8LFxQXOzs7w8vKCiYkJ3r59i0ePHuHOnTs4cuQIAGDRokU4deoUOnTogNmzZ8PW1hbv3r3D6dOn4efnh0aNGsHX1xdHjx5Fhw4dMHnyZNjZ2UEsFiMuLg5nz57FlClT0LJly6/GOHz4cIwePRqvXr1CmzZtinwwLFq0CCEhIWjTpg0mTpyIhg0bIicnB8+fP8fJkyexZcuW775ssXfvXnTt2hXdunXDxIkT4eTkBKCg78iGDRvQqFEjmR8MNWrUQJcuXTBv3jzJqLHHjx9LJQilEXfPnj3xxx9/YOzYsRgwYADi4+OxePFiGBsbF5kx3NbWFhcuXMDff/8NY2NjVKtW7Yc/ZD09PbFu3ToMGTIES5YsgYWFBU6dOoUzZ84AwDcrB+bm5pJqn4ODA8aPH48mTZoAACIjI7Fr1y4wxtC3b1+54pLndSlOjx49sHbtWnh4eODnn39GamoqVq9eLUkeCpmZmWH27NlYvHgxsrOzMWjQIAgEAkRGRiIlJQULFy4EUPD6//HHH9i8eTOaNWsGJSUlNG/eHO7u7ggODoarqysmTZoER0dHqKqqIiEhAefPn0fv3r3lPn+gYASVjY0Nmjdvjpo1a+LFixdYv349TE1NJSMlbW1tAQAbNmyAp6cnVFVV0bBhwyJVKKCg31dQUBB8fHzw5MkTdO7cGWKxGNevX4eVlRXc3d2LjaVTp07YtWsXoqKiStS/SVVVFcuWLcPAgQOxYcMGzJ07FwAwY8YM3Lt3T/K/m5sbBAIB7t+/j1WrViEzMxP//PMPBAKBZF+FydP8+fNx48YNeHh4oE6dOkhPT8elS5ewbds2LFy4EG3btpVsExYWBn19fcnrQyoYTrtqk0qruHmEGCuYc6Vu3bqsQYMGTCgUMsYYu3fvHhs4cCAzMDBgqqqqzMjIiHXp0qXI6Iv4+Hg2fPhwZmRkJJkjaODAgez169eSdd6/f8/mzp0rmSOlcD6TyZMnS42s+nLUS6H09HSmoaHx1RErycnJbOLEiczc3JypqqoyPT091qxZMzZnzhzJfEWFo69WrVol12v3/v17tmzZMubg4MA0NTWZpqYms7OzY0uWLCkyFxJjn+alCQwMZPXr12eqqqqsUaNGMidoK424V6xYwczMzJiamhqzsrJi27dvl8wR87nw8HDWtm1bpqmpWeJ5hL4ka79xcXGsX79+TFtbm1WrVo3179+fnTx5kgFgx44d++prWyg6OpqNHTuWWVhYMDU1NaahocGsra2Zn5+f1Iim4iZU9PT0LDIiq6SvS+H3S5Zdu3axhg0bMjU1NVavXj22fPlytnPnTpkjrfbu3ctatGjB1NXVmba2NmvSpInUqLm3b9+yAQMGsOrVqzMejycVR35+Plu9ejWzt7eXbN+oUSM2evRo9vTpU8l6hfMIyfLl78+aNWtYmzZtWI0aNRifz2d169ZlI0aMYM+fP5fabtasWaxWrVpMSUnpm/MIZWdnM39/f8n8WPr6+qxLly7s2rVrMmMqlJ6ezrS1tdnKlSul2oubR6hQy5Ytma6urtRkm2KxmAUHB7NOnTqx6tWrMz6fz8zNzdmYMWOKjMD83LFjx1iPHj1YzZo1mYqKCtPV1WWdO3dmW7ZskZpFWiwWM1NTUzZhwoSvnhPhDo+xj8NUCCEVEo/Hw7hx47Bp0yauQ+HMsmXLMHfuXMTFxX13NY5ULRMmTMC5c+fw8OHDHx7VVZbOnTuHbt264eHDh2jUqBHX4RAZ6NIYIaRCKUz4GjVqhPz8fPz333/YuHEjhgwZQkkQkZg7dy727t2Lo0ePSiYVrYiWLFmC4cOHUxJUgVEiRAipUDQ1NbFu3To8f/4cubm5qFu3LmbMmCHp10EIUDClQnBwMNLS0rgOpVhpaWno2LGjZKoQUjHRpTFCCCGEKCwaPk8IIYQQhUWJECGEEEIUFiVChBBCCFFYCtdZWiwW49WrV6hWrVqFHnJJCCGEkE8YY8jMzPzue9QVR+ESoVevXn3X/XsIIYQQwr34+PhSnUpD4RKhwqne4+PjoaOjw3E0hBBCCCmJjIwM1KlTR+YtW36EwiVChZfDdHR0KBEihBBCKpnS7tZCnaUJIYQQorAoESKEEEKIwqJEiBBCCCEKixIhQgghhCgsSoQIIYQQorAoESKEEEKIwqJEiBBCCCEKixIhQgghhCgsSoQIIYQQorAoESKEEEKIwuI0Ebp06RJ69eqFWrVqgcfj4a+//vrmNhcvXkSzZs2grq6OevXqYcuWLWUfKCGEEEKqJE4ToaysLNjb22PTpk0lWj82Nhaurq5o37497t69i9mzZ2PixIk4evRoGUdKCCGEkKqI05uuuri4wMXFpcTrb9myBXXr1sX69esBAFZWVrh16xZWr16N/v37l1GUhBBCCKmqKtXd50NDQ9GtWzepNmdnZ+zcuRP5+flQVVXlKDJCqhixGBDmAPnZgDAbyM8B8j98bPtQ8FyUx3WUhHCGgYGxwq8L/mGFX3+2DMDHr9mn5YXrfrZO4Tbs4wZM0vrpC/bFvgqXM8mGwGdfSi+Xsa3U+pL4pXdQsJx9ti/Z5yVZl31a98vXRhKj1Dbss2VFj/P5+aRlZKIsVKpEKCkpCYaGhlJthoaGEAqFSElJgbGxcZFtcnNzkZubK3mekZFR5nGS0vP5L3DhLzTDp1/OL38pC9s+X7dwGWQsByC1v8/fbCS//J/v77NfzoJtix6v8M1BetvP1v1i37Jih4z9MZEQPFEOePk5UBLlgJefDSVRNnjCHPCEOVASZkNJlAMlYcGDJ/qiTfJ/QZuyKPfj8tyC5x+/VhZlQ1lMSQ4hX8P7+CDlx3lfVpnst1IlQgDA40n/6BV+mH3ZXmj58uVYuHBhmcel6MRihoycfKRm5eFtVh5S3xf8/zYrF2+z8vE2K1ey7G1WHt59yIdIzIr8VSX94V8VMGghB9XwAdV42aiGD9DhffjieRaqIRvVPmvXwQdoIRsavDyoIQ/qyAOfJ+LkDPKYMnKghhzwkcNUkf3x63yooMp8mwip4Hif/Sv5qphMrEgzT1b7l3ssbr0vdiVjA1n7kLUfWcf/1jE/P97gzuk4G/PoK9F9n0qVCBkZGSEpKUmq7c2bN1BRUYG+vr7MbWbNmgU/Pz/J84yMDNSpU6dM46yqGGN48joTpx8k4emb93j7MdlJzcpD2oc8iMSV+2ORx/v4Vx6P9/F/Bg3kQgfZH5OTLFTjfYDOxwSmIGn5AG0ULNP+mMho49P/WsiGCsSlHmsu+MiBGvJ4fOTy+Mj9+HUe1JDLU/usrXAdNeTz+MjjFSzP+/g8l6desB1PDfk8NeQpFW5TsDyfpw6xkvLH1wPgFb7x8T49L3yjKvxj5NO6n7+WhUs+X/Zpe8k+ULDwy20L3wsL2/D5tpDe/tP+eF9s++U2n+KR2lZyfjzJtrKOJ3XuH3dU5Lw+P/Znx/tyW1mvpXTcH9t4n44la9svv08yX8svvo/4crmM7xMg43X7cn8yvk9FXguZr3XR7+OXx5P1PS7yPS3m+wh8+VoXjU1yVN5XYpf5c/Dl9+nj6/CV5V/GXuR7/Fkcii4mJgY3btyAu7s7AMAkIwPYKSj141SqRKh169b4+++/pdrOnj2L5s2bF9s/SE1NDWpqauURXpXEGMPDVxk49SARpyKSEJPy9dJkNTUV6GnzoafFh75Wwf96WmrQ1+JD97M2XU0+VJR5Mt8MZL3ZfGyW+WYDyHjzAgOE2eDlZnx8ZH72dQaQkwFebjp4uZnAx+cF/7/77OsMgJVSFYanDKjrAOoCQO3z/3U+/f9lm1o1QFUDUNEo+F9VA1BRB1TUoaakBPqpJoRURYwx7N27F+PHj0dubi4aNWoEBweHMjsep4nQ+/fv8ezZM8nz2NhYhIeHQ09PD3Xr1sWsWbPw8uVL7N27FwDg4+ODTZs2wc/PD6NGjUJoaCh27tyJgwcPcnUKVRJjDPcS0nEqIhEnHyQi/m22ZBlfRQkdLWuipbkealZT+5jo8KGvpQZdLVWoqSiXf8AiIZB0H3hxreDx8hbwIRUQC0tn/zylzxIUQUHC8nkCI/W/4LP1PlumqgnQX3iEEPJVaWlpGD16NI4cOQIAaN++PXR1dcv0mJwmQrdu3ULnzp0lzwsvYXl6emL37t1ITExEXFycZLm5uTlOnjyJyZMn49dff0WtWrWwceNGGjpfCsRihjtxaTgZkYTTDxLxKj1HskxdVQldGhmgu40xujQygLYax4VEYS7w8g7w4mpB4hN/Hch7L3tdnlJBZeXLxERWRUaSyHyxjK9FSQwhhJSxCxcuYOjQoUhISICKigoWLlyIGTNmQFm5bP/A5jFWdbqllkRGRgYEAgHS09Oho6PDdTicEokZbj5/i1MRiTj1IAlvMj+NrtPiK6OLlSFcbYzQsWFNaPI5TH7ysoCEm58qPgk3C4Zxf05NAJi2BkzbAHVbAzomBYkMX5uSGEIIqeD8/f2xZMkSMMbQoEEDBAcHo0WLFlLrlNXnd6XqI0R+nFAkRljMW5x8kIizD5OQ8v7TMOlqairoam0IFxsjdLCsCXVVDi5zAUBOOhB3/WPF5yrw6m7Ry1xaNQuSHtO2Bf8bWANKHMVLCCHkh1SrVg2MMYwaNQpr166FtrZ2uR2bEiEFkCcU41p0Ck5FJOFsZBLSPuRLlgk0VPGTtSF62BqjjYU+N318slKAuFDg+cfE5/UDgH0x0krH5FPSY9oWqNGAKj2EEFJJMcaQkpKCmjVrAgCmTJmCFi1aoFOnTuUeCyVCVVROvghXnqbg5INE/Bv5Ghk5nyoqelp8ODc2hIuNMVrX14eqcjnfci7j1cfLXB/7+CQ/LrqOXr2PSU+7gv+r16XEhxBCqoDk5GSMGDECUVFRuHPnDjQ1NaGkpMRJEgRQIlSl5OSLcOFJMk49SMS5R2/wPvdT8lOzmhq6NzaCi60RHM30oFJeyQ9jQNrzT/17XlwpeP4lA+uPiU8boG4bQKfoLOGEEEIqt9OnT8Pb2xtJSUng8/m4du0aunbtymlMlAhVclm5Qlx4koyTDxJx/vEbfMj7NO+NkY46utsYwdXWGM1MdaGsVA4VFcaAlCjg+ZVPyU/mK+l1eEqAkd2nS111WwNasifEJIQQUvnl5ORgxowZ2LhxIwDA2toaBw4cgL29PceRUSJUKWXm5OO/x29wMiIRF54kI1f4qT+NSXUNuNoaobuNMZrUqQ6lsk5+xKKCPj2fX+r6kCq9jpIqYNL0Y+LTFqjjWDCiixBCSJUXEREBDw8PPHjwAAAwfvx4rFy5EhoaGhxHVoASoUokMycf8/56gJMRScgTfUp+TPU14WJjDFdbI9iaCMp2anZhbsEorsJqT/z1glmYP6eiAdRp8aniY9Ic4GuWXUyEEEIqrNmzZ+PBgwcwMDBAUFAQXF1duQ5JCiVClURieja8g27icVImAKBeTS242hjDxdYI1sY6ZZf85L4HEm4AL0I/zdr85Rw+/GpA3VYFSY9ZO8DYAVDhl008hBBCKpUtW7ZgxowZWLt2LQwMDLgOpwiaULESiHyVgeG7byIpIwc1q6lh8+CmaGaqWzbJz4e3BUPZCys+ifeK3m9Ls8anjs2mbQBDG5rDhxBCCADg2LFjCA0NxYoVK0p1vzShooK6FJWMscF38D5XiAYG2gjyboHauqV4mUkylP3jI/lR0XUEdT8mPa0LLnfpW9BQdkIIIVKysrLg5+eHbdu2AQC6dOmCbt26cRzVt1EiVIEdvhWP2X9EQChmaFVPD1uHNodAQ/X7d8gY8DbmY6fm0IL/370oul6Nhp+Snrqtgep1vv+YhBBCqrzbt2/Dw8MDUVFR4PF4mDp1Kjp27Mh1WCVCiVAFxBjDun+fYuO5pwCAPg618MsAO/lnfRaLgDeRn6o9caHA+9fS60iGsrf5bCh7jVI6E0IIIVWZSCTCqlWrMG/ePAiFQpiYmGDv3r3o0qUL16GVGCVCFUyeUIyZf9zHH3deAgDGd7bAlG6WJesPJMwDEsM/VXziwoDcdOl1lPkFo7gKb1Bam4ayE0II+T5ubm44evQoAKB///7Ytm0b9PT0OI5KPpQIVSAZOfnw2Xcb16JToazEw9I+NnB3rFv8BjnpQPxNID6sIOlJuAUIs6XX4WsDdVp+utRVqymgql62J0IIIUQhDBkyBGfOnMHGjRvh5eVVttO3lBFKhCqIV+8Khsc/eZ0JLb4yfh3cFJ0afjbMkLGC/jxx1z8mPtcLLnvhi0F/GnpfjOiyBZTp20wIIeTHZWRk4PHjx3B0dAQA9OnTBzExMZKbp1ZG9AlZATx8lY7hu2/idUYuDKqpIci7BRobagIvb0snPu+Tim6sa14wh0+dlgX9e2o2pBFdhBBCSl1oaCgGDx6M9PR0REREoFatWgBQqZMggBIhzl148gbjgu9AOS8Dg/USMMMmHTpn1xckQfkfpFdWUgWM7T8lPnVaAtUMOYmbEEKIYhAKhViyZAmWLFkCkUgEMzMzvH79WpIIVXaUCHFFJETkf/vx6tJxHOVFwVI9AUofGHDjs3XUq3+s9LQE6rQquF+XasW4NwshhJCqLyYmBkOGDEFoaCiAgj5BmzZtgkAg4Diy0kOJEBdiLoKdngnrN5Gw/nxEvK55weWtwsSnhiWgpMRZmIQQQhTXnj17MH78eLx//x4CgQCbN2/GoEGDuA6r1FEiVJ7exgJn5wKP/wEPQBrTxjHWAf37/h+qNWgHaFe8e7AQQghRTGFhYXj//j3at2+Pffv2wdTUlOuQygQlQuUhNxO4vAYI/RUQ5QE8ZZzV6oVpKa7o3boxqjWx4TpCQgghBEKhECoqBanBmjVrYGNjAx8fHygrV937SdJ1l7IkFgN3g4GAZsCVdQVJUL3OiB5wBj+nDEQmTxsj29XjOkpCCCEKLi8vDzNnzoSLiwvEYjEAQFNTE+PGjavSSRBAFaGyE38DODUdeHW34LlePcB5GWDZHb8evgcAcLExRl39UryBKiGEECKnx48fY/Dgwbhz5w4A4OzZs+jevTvHUZUfSoRKW/pL4N8FQMThguf8akDH6UDL0YCKGl69y8bxe68AAD93oGoQIYQQbjDGsHXrVvj5+SE7Oxt6enrYsWOHQiVBACVCpSc/G7gWUHAJLP8DAB7QdCjQZZ5UJ+hdV2Ild5O3r1Ods3AJIYQoruTkZIwYMQJ///03AKBr167Ys2dPlZkbSB6UCP0oxoCHfwIh/kB6fEFb3dZA9xVALQepVdOz83HwRhwAYHSH+uUcKCGEEFLA3d0d//33H/h8PpYvXw5fX18oKeh0LZQI/Yi0F8CfPkDctYLnOrWBbouBxn1l3ubiwPU4ZOWJYGmojU4NK/eU5IQQQiqvNWvWYPjw4QgKCoK9vT3X4XCKEqHvJRYBv3sX3ApDRQNoNxloMwHgy+78nCsUIehqLABgVPt6lfIOvYQQQiqniIgI3L17F8OGDQMAODg44Pbt2/RZBEqEvl/Y5oIkSE0HGH2xYFTYVxy7+wpvMnNhqKOG3g4m5RQkIYQQRSYWixEQEIAZM2ZALBbDxsYGTZs2BQBKgj6iROh7pEYD/y0p+Lrbkm8mQWIxw7bLMQCA4W3NwVdRzOuwhBBCyk9iYiK8vLxw9uxZAECPHj1Qu3ZtjqOqeOgTWV6MAX9PAoTZgHkHoOmwb25y/skbPHvzHtpqKhjUsm45BEkIIUSRHTt2DLa2tjh79izU1dURGBiIv//+GwYGdCunL1FFSF63dwPPLxf0C+q1UWan6C9tuRgNAPBoWRc66qplHCAhhBBFNmnSJGzcuBFAQV+gAwcOwMrKiuOoKi6qCMkj/WXBMHkAcJoH6Jl/c5MbsW9x83ka+MpKGN722+sTQgghP8LMzAwAMHXqVISFhVES9A1UESopxoATfkBuBmDSHGjpU6LNfj3/DADQv1ltGAnUyzJCQgghCkgkEiEpKQkmJgUDcSZNmoR27dqhRYsWHEdWOVBFqKQeHAWiTgNKqkDvTYDSt29C9+BlOi5GJUOJB/h0pNtpEEIIKV3x8fFwcnJCly5dkJWVBQBQUlKiJEgOlAiVRFZKwQ1UgYL7hhmUrMwYeKGgGtTLvhZM9bXKKjpCCCEK6NChQ7Czs8PFixfx8uVL3L17l+uQKiVKhEri1AzgQypg0Bho61uiTZ69eY9TD5IAAGM7WZRhcIQQQhRJRkYGPD094e7ujnfv3sHR0RHh4eFo164d16FVSpQIfcuTU8CD3wGeUsElMRV+iTbbcjEajAE/WRuioVG1Mg6SEEKIIggNDYWDgwP27t0LJSUlzJs3D1euXIGFBf3B/b2os/TX5KQD/0wu+Lr1eMCkaYk2S0j7gL/uvgQAjO1EN1clhBBSOpYsWYLY2FiYmZlh3759VAUqBVQR+poQfyAzsWDm6M6zS7zZtksxEIoZ2lroo0ld3TIMkBBCiCLZsWMHxowZQ5fCShElQsWJvVQweSIA/C8AUNUo0WZvMnPw2814AMA46htECCHkOzHGsHfvXkyePFnSZmxsjMDAQAgEAg4jq1ro0pgseR+A4xMKvm4+HDAreda968pz5AnFcKhTHa3r65dRgIQQQqqytLQ0+Pj44PDhwwCAnj17wsnJieOoqiZKhGQ5vxRIew7omABdF5Z4s/QP+dgf9gIAMK6zBd3ZlxBCiNwuXLiAoUOHIiEhASoqKli4cCE6derEdVhVFiVCX0q4BYQFFnzdcz2grlPiTfeGPsf7XCEaGlaDUyO6sR0hhJCSy8vLg7+/P1auXAnGGBo0aIDg4GCaHLGMUSL0OWEucGw8wMSA7UDAsluJN/2QJ8Suq7EAgLGd60NJiapBhBBCSq5Pnz44deoUAGDkyJFYt24dtLW1OY6q6qPO0p+7vBZIfgRo1gC6r5Br04M34pH2IR+m+proYWtcRgESQgipqsaMGQN9fX388ccf2L59OyVB5YQqQoVePwQury742nUVoFXyjs65QhG2X4oBAPh0rA8VZcovCSGEfF1ycjIeP36M9u3bAwB69eqFmJgY6OiUvEsG+XH0iQ0AIiFwbBwgFgINewCN+8q1+Z93XiIpIweGOmro19SkjIIkhBBSVZw5cwZ2dnbo3bs3EhISJO2UBJU/SoSAgs7Rr+4CagKgxxpAjtFeQpEYmy9GAwBGta8HNZVv35WeEEKIYsrJyYGvry+6d++OpKQkGBsbIzMzk+uwFBolQqnRBcPlAcB5KaAjX/+eExGJeJH6AbqaqhjkWLcMAiSEEFIVREREoEWLFtiwYQMAYPz48bh16xasrKw4jkyxUSJ0di4gzAHqdQKaDJFrU7GYIfB8QTXIu605tNSoyxUhhJCiNmzYgBYtWuDBgwcwMDDAiRMnEBAQAA2Nkt21gJQdxU6EGANeXC342mm+XJfEAOC/x2/w5HUmtNVU4NnarPTjI4QQUiVERUUhNzcXPXr0QEREBFxdXbkOiXyk2CWM928K7jDPUwIMrOXalDGGXy88AwAMaWUKgaZqWURICCGkksrNzYWamhoAYNWqVWjZsiWGDh1Kdx2oYBS7IpT8uOB/XXNAVV2uTUOjU3E37h34KkoY3s6s9GMjhBBSKWVlZcHHxwfdu3eHSCQCAGhqamLYsGGUBFVAil0RSn5S8H/NhnJvGvBfQTVoUIs6MKgmXxJFCCGkarp9+zYGDx6MJ08KPl8uXbqEzp07cxwV+RrFrgilfF8idPtFGkJjUqGixMPPHeuXQWCEEEIqE5FIhF9++QWtWrXCkydPYGJign///ZeSoEqAKkIAULORXJv9er6gGtS/aW2YVKce/4QQosji4+MxdOhQXLx4EQDQv39/bN26Ffr6Jb9DAeGOYleECvsI1bAs8SYPXqbjv8dvoMQDxnSiahAhhCg6Dw8PXLx4EVpaWti5cyeOHDlCSVAloriJ0Ie3QFZywddyJEKBH0eK9bSrBbMaWmURGSGEkEpk06ZNaN++PcLDwzF8+HDqEF3JKG4ilFKQ0EBQB1Ar2R1+n73JxKkHSQCAcZ0tyioyQgghFVhoaCi2b98ueW5vb4+LFy/CwoI+FyojzhOhwMBAmJubQ11dHc2aNcPly5e/un5wcDDs7e2hqakJY2NjeHt7IzU1Vf4Dp0QV/C9HR+nA89FgDOhmbYiGRtXkPyYhhJBKSygUYsGCBWjfvj3Gjh2L27dvS5ZRFajy4jQROnToEHx9fTFnzhzcvXsX7du3h4uLC+Li4mSuf+XKFQwbNgwjRozAw4cPceTIEdy8eRMjR46U/+CpHytCJewoHZf6AcfuvQIAjO9CWT8hhCiSmJgYdOjQAQsXLoRIJIKbmxtVgKoIThOhtWvXYsSIERg5ciSsrKywfv161KlTB5s3b5a5flhYGMzMzDBx4kSYm5ujXbt2GD16NG7duiX/wd/GFPyvX7If5M0XoyESM3SwrAm72tXlPx4hhJBKhzGGvXv3wt7eHqGhodDR0UFwcDD2798PgUDAdXikFHCWCOXl5eH27dvo1q2bVHu3bt1w7do1mdu0adMGCQkJOHnyJBhjeP36NX7//Xf06NGj2OPk5uYiIyND6gEAEOUU/K/27UtcienZOHo7AQAwnvoGEUKIwvD29oanpyfev3+Pdu3a4d69e/Dw8OA6LFKKOEuEUlJSIBKJYGhoKNVuaGiIpKQkmdu0adMGwcHBcHNzA5/Ph5GREapXr46AgIBij7N8+XIIBALJo06dOgULGCv4vwTXdbddikGeSAxHcz04muuV7AQJIYRUek2aNIGKigqWLl2KCxcuwMzMjOuQSCnjvLP0lx3MGGPFdjqLjIzExIkT4e/vj9u3b+P06dOIjY2Fj49PsfufNWsW0tPTJY/4+PiPBxJ/DODrL0HK+1wcvFHQZ4mqQYQQUrXl5eXh+fPnkucTJkzAvXv3MHv2bCgrK3MXGCkznM0sXaNGDSgrKxep/rx586ZIlajQ8uXL0bZtW0ybNg0AYGdnBy0tLbRv3x5LliyBsbFxkW3U1NQkd/+V8rEg9K1EaNeVWOTki2FfW4D2DWp8+8QIIYRUSk+ePIGHhwcyMjJw9+5daGtrQ0lJCdbW1lyHRsoQZxUhPp+PZs2aISQkRKo9JCQEbdq0kbnNhw8foKQkHXJhhs4KL3WVVAkqQukf8rE39AWAgnmDaHgkIYRUPYwxbN26FU2aNMGdO3fw9u1bPHr0iOuwSDnh9NKYn58fduzYgV27duHRo0eYPHky4uLiJJe6Zs2ahWHDhknW79WrF/744w9s3rwZMTExuHr1KiZOnAhHR0fUqlVLvoMXJkIoPrnZE/oc73OFaGhYDV2tZFepCCGEVF7Jycno06cPfHx8kJ2dja5du+L+/fto0aIF16GRcsLpTVfd3NyQmpqKRYsWITExETY2Njh58iRMTU0BAImJiVJzCnl5eSEzMxObNm3ClClTUL16dXTp0gW//PLLdxy9sLO07FwwK1eIXVdjAQDjulhASYmqQYQQUpWcOXMGXl5eSEpKAp/Px/Lly+Hr61vkygOp2nhM7mtKlVtGRgYEAgHSN7SHztt7wKBDQMPuRdbbdikay04+hnkNLfzr1xHKlAgRQkiVwRhDz549cfLkSVhZWeHAgQNwcHDgOizyFZLP7/R06OjolNp+FTftlfQRKprg5OSLsP1yQTVoTKf6lAQRQkgVw+PxsHPnTkyfPh23b9+mJEiBUSIk49LY4VvxSM7MhUl1DfRtYlLOgRFCCCltYrEYGzZswNixYyVtRkZG+OWXX6ChocFhZIRrnPYR4lQxEyrmCcXYerHg9hujO9aDqrLi5oqEEFIVJCYmwtvbG2fOnAFQ0D+1Y8eOHEdFKgrF/ZRnsjtL/3X3JV6+y0bNamoY2LwOB4ERQggpLceOHYOdnR3OnDkDdXV1BAYGokOHDlyHRSoQBa4IFR0+LxIzbL4YDQAY1d4c6qo0iyghhFRGWVlZmDJlCrZu3QoAcHBwwIEDB2BlZcVxZKSioUTos4rQiYhExKZkobqmKga3NOUoMEIIIT+CMQZXV1dcunQJADBt2jQsXrxY9l0GiMJT3ESo0MdESCxm+PW/ZwCA4W3NoaVGLw0hhFRGPB4PM2bMQHR0NPbs2QMnJyeuQyIVmOJ+2n9REboYlYwnrzOhraYCz9Zm3MVFCCFEbvHx8YiKipIkPa6urnj69CmNCCPfpMCdpaXnEboblwYAcLU1gkBTlauoCCGEyOnQoUOws7PDgAEDpO5GQEkQKQnFTYS+uMXGy3c5AABTfS2uAiKEECKHjIwMeHp6wt3dHe/evYOlpSVEIhHXYZFKRnEToS8ujb16lw0AqFVdnauICCGElFBoaCgcHBywd+9eKCkpYd68ebhy5QrMzc25Do1UMtRH6OPw+VfpHxMhAZVSCSGkomKMYfHixVi0aBFEIhHMzMywb98+tGvXjuvQSCWlwBWhT5fGxGKGxI+XxmpVp0SIEEIqKh6Ph9TUVIhEIgwZMgTh4eGUBJEforgVIXy6xUZKVi7yRGIo8QAjAV0aI4SQioQxhqysLGhrawMAVqxYgS5duqB3794cR0aqAgWuCH3qI/TqYzXIUEed7i1GCCEVSFpaGtzc3ODq6irpCK2hoUFJECk1ilsR+mz4/KeO0nRZjBBCKorz589j2LBhSEhIgIqKCq5fv442bdpwHRapYhS3/CFVEaJEiBBCKoq8vDzMmDEDTk5OSEhIQIMGDXDt2jVKgkiZUOCK0Mf/eUp4SUPnCSGkQnj8+DEGDx6MO3fuAABGjRqFtWvXSvoHEVLaFDgR+rwilAUAMKGKECGEcIYxBi8vL9y5cwd6enrYsWMH+vbty3VYpIqjS2PgSTpL0xxChBDCHR6Phx07dqBnz56IiIigJIiUC8VNhD67xQb1ESKEEG6cOXMGAQEBkuc2Njb4+++/UatWLQ6jIopE4S+N5YoYUrPyANClMUIIKS85OTmYMWMGNm7cCGVlZbRu3RrNmzfnOiyigBQ4EWIAD3idmQsA0OIrQ0dDcV8OQggpLxEREfDw8MCDBw8AAGPGjEHjxo05joooKsW9NPaxIvQmMx9AwWUxHo/HZUSEEFKlicVibNiwAS1atMCDBw9gYGCAEydOICAgABoaVJEn3FDcEsjHe429ziy4LEb9gwghpOwwxtCvXz8cO3YMANCzZ0/s3LkTBgYGHEdGFJ3CV4QSMygRIoSQssbj8dC1a1eoq6sjMDAQx48fpySIVAgKXBEqSIReZxT0ETKhyRQJIaRUZWVl4eXLl7C0tAQAjBs3Dj169IC5uTnHkRHyicJXhJI+dpamihAhhJSe27dvo2nTpnBxcUFmZiaAgqoQJUGkolHcROgjujRGCCGlRyQSYcWKFWjVqhWioqKQm5uL2NhYrsMipFjflQgJhUL8+++/2Lp1qyTTf/XqFd6/f1+qwZWpjxWhVxk0hxAhhJSGuLg4ODk5YdasWRAKhejfvz/u378POzs7rkMjpFhy9xF68eIFunfvjri4OOTm5uKnn35CtWrVsHLlSuTk5GDLli1lEWcZEAPgIU/IwOMBhjrUR4gQQr7XoUOHMHr0aKSnp0NLSwsBAQHw8vKiaUlIhSd3RWjSpElo3rw50tLSpOZ96Nu3L86dO1eqwZUHMXgwqKYGvorCXyUkhJDvwhjD/v37kZ6eDkdHR4SHh8Pb25uSIFIpyF0RunLlCq5evQo+ny/VbmpqipcvX5ZaYOVFDB5dFiOEkO/AGAOPxwOPx8POnTuxY8cOTJs2DaqqqlyHRkiJyV0GEYvFEIlERdoTEhJQrVq1UgmqPDHwqKM0IYTIQSgUYsGCBRgxYoSkzcDAALNnz6YkiFQ6cidCP/30E9avXy95zuPx8P79e8yfPx+urq6lGVu5YFQRIoSQEouJiUGHDh2wcOFCBAUF4dq1a1yHRMgPkfvS2Lp169C5c2dYW1sjJycHHh4eePr0KWrUqIGDBw+WRYxlSkwVIUII+SbGGPbu3Yvx48fj/fv30NHRwebNm9GmTRuuQyPkh8idCNWqVQvh4eH47bffcPv2bYjFYowYMQKDBw+ulDfNE0OJEiFCCPmKtLQ0jB49GkeOHAEAtG/fHvv27YOpqSnHkRHy4+ROhC5duoQ2bdrA29sb3t7eknahUIhLly6hQ4cOpRpgWWMAatHtNQghRCbGGJydnXHz5k2oqKhg4cKFmDFjBpSVlbkOjZBSIXcfoc6dO+Pt27dF2tPT09G5c+dSCao8iaFEfYQIIaQYPB4PixYtgqWlJa5du4bZs2dTEkSqFLkrQoXDJb+UmpoKLS2tUgmqPKnzVSDQoFEOhBBS6PHjx4iNjYWLiwsAoHv37njw4AGNCCNVUokToX79+gEo+OvAy8sLampqkmUikQj379+vlJ3mjASaNOkXIYSg4A/dbdu2YfLkyVBVVcW9e/dgZmYGAJQEkSqrxImQQCAAUPCLUq1aNamO0Xw+H61atcKoUaNKP8IyZlxdk+sQCCGEc8nJyRg5ciSOHz8OAGjbtm2RiXMJqYpKnAgFBQUBAMzMzDB16tRKeRlMFhoxRghRdKdPn4a3tzeSkpLA5/OxfPly+Pr6QkmJbj1Eqj65+wjNnz+/LOLgjIEOJUKEEMXEGIOfn59kklxra2scOHAA9vb23AZGSDmSOxECgN9//x2HDx9GXFwc8vLypJbduXOnVAIrL7paat9eiRBCqqDP+0eOHz8eK1eurJTzwRHyI+Sue27cuBHe3t4wMDDA3bt34ejoCH19fcTExEhGGFQWIsZDdU3qAEgIURxisRjv3r2TPF++fDnOnTuHgIAASoKIQpI7EQoMDMS2bduwadMm8Pl8TJ8+HSEhIZg4cSLS09PLIsYyI4YSdDWpMyAhRDEkJibC1dUVPXv2hFAoBACoq6ujS5cuHEdGCHfkToTi4uIkw+Q1NDSQmZkJABg6dGilu9eYGFQRIoQohmPHjsHOzg5nzpzB7du3cffuXa5DIqRCkDsRMjIyQmpqKgDA1NQUYWFhAIDY2Fgwxko3ujLHo4oQIaRKy8rKgo+PD/r06YOUlBQ4ODjg9u3baNGiBdehEVIhyJ0IdenSBX///TcAYMSIEZg8eTJ++uknuLm5oW/fvqUeYFkSgwcBVYQIIVXU7du30bRpU2zduhUAMHXqVISFhcHa2prjyAipOHhMzjKOWCyGWCyGikrBgLPDhw/jypUrsLCwgI+PT4WfgCsjIwMCgQDpM6tBma8OzQWvaWZpQkiVwxhDy5YtcfPmTZiYmGDPnj1wcnLiOixCvpvk8zs9HTo6OqW2X7kToa95+fIlTExMSmt3ZeLzREhJTRPaC5K4DokQQspEZGQkli5dioCAAOjp6XEdDiE/pKwSoVKZNjQpKQkTJkyAhYVFaeyuHFEliBBSdRw6dAhr1qyRPLe2tkZwcDAlQYR8RYkToXfv3mHw4MGoWbMmatWqhY0bN0IsFsPf3x/16tVDWFgYdu3aVZaxlj4eTR9PCKn8MjIy4OnpCXd3d8yYMaPSTWxLCJdKPLP07NmzcenSJXh6euL06dOYPHkyTp8+jZycHJw6dQodO3YsyzjLBvUNIoRUcqGhoRg8eDBiY2OhpKSE2bNnw9bWluuwCKk0SpwInThxAkFBQejatSvGjh0LCwsLWFpaSu5RUxnxqCJECKmkhEIhlixZgiVLlkAkEsHMzAz79u1Du3btuA6NkEqlxInQq1evJEMu69WrB3V1dYwcObLMAisXlAgRQiohxhi6d++Oc+fOAQCGDBmCTZs2QSAQcBwZIZVPiTMBsVgMVdVPc+4oKytDS0urTIIqL1QRIoRURjweD/3794eOjg6Cg4Oxb98+SoII+U4lrggxxuDl5QU1tYK7tefk5MDHx6dIMvTHH3+UboRlSYkSIUJI5ZCWloaXL1/CxsYGAODj44O+ffvCyMiI48gIqdxKnAl4enrCwMAAAoEAAoEAQ4YMQa1atSTPCx/yCgwMhLm5OdTV1dGsWTNcvnz5q+vn5uZizpw5MDU1hZqaGurXr//do9WUqLM0IaQSuHDhAuzs7NCrVy9kZGQAKKgKURJEyI8rcUUoKCio1A9+6NAh+Pr6IjAwEG3btsXWrVvh4uKCyMhI1K1bV+Y2AwcOxOvXr7Fz505YWFjgzZs3krsoy4tHFSFCSAWWl5cHf39/rFy5EowxWFhYIDExsVQnkyNE0ZXqzNLyatmyJZo2bYrNmzdL2qysrNCnTx8sX768yPqnT5+Gu7s7YmJivnuCsM9nllbXrw3+1Mjvjp8QQsrKkydP4OHhIZkTaOTIkVi3bh20tbU5jowQblTomaW/R15eHm7fvo1u3bpJtXfr1g3Xrl2Tuc3x48fRvHlzrFy5EiYmJrC0tMTUqVORnZ39XTEoUUWIEFLBMMawdetWNGnSBHfu3IGenh6OHj2K7du3UxJESBko8aWx0paSkgKRSARDQ0OpdkNDQyQlyb7/V0xMDK5cuQJ1dXX8+eefSElJwdixY/H27dti+wnl5uYiNzdX8rzw+jpAiRAhpGI6efIksrOz0bVrV+zevbvC38ORkMqM80zgyzu/M8aKvRu8WCwGj8dDcHAwHB0d4erqirVr12L37t3FVoWWL18u1Zm7Tp06kmVKNHyeEFJBiMViAAXviTt27EBAQADOnDlDSRAhZYyzTKBGjRpQVlYuUv158+ZNkSpRIWNjY5iYmEiNTrOysgJjDAkJCTK3mTVrFtLT0yWP+Pj4TwspESKEcCwnJwe+vr7w9PSUtNWsWRPjx4+nqjUh5eC7fsv27duHtm3bolatWnjx4gUAYP369Th27FiJ98Hn89GsWTOEhIRItYeEhKBNmzYyt2nbti1evXqF9+/fS9qioqKgpKSE2rVry9xGTU0NOjo6Ug8JGj5PCOFQREQEWrRogQ0bNmD//v24desW1yERonDkToQ2b94MPz8/uLq64t27dxCJRACA6tWry33fMT8/P+zYsQO7du3Co0ePMHnyZMTFxcHHxwdAQTVn2LBhkvU9PDygr68Pb29vREZG4tKlS5g2bRqGDx8ODQ0NeU+FKkKEEE6IxWJs2LABLVq0wIMHD2BgYIATJ06gefPmXIdGiMKROxMICAjA9u3bMWfOHCgrK0vamzdvjoiICLn25ebmhvXr12PRokVwcHDApUuXcPLkSZiamgIAEhMTERcXJ1lfW1sbISEhePfuHZo3b47BgwejV69e2Lhxo7ynUYASIUJIOUtMTISrqyt8fX2Rm5uLHj16ICIiAq6urlyHRohCknseIQ0NDTx+/BimpqaoVq0a7t27h3r16uHp06ews7P77qHs5eXzeYR06jQGxoZyHRIhREEwxuDg4ID79+9DXV0da9euhY+PT7EDRAghn1SYeYTMzc0RHh5epP3UqVOSu9NXHvTmQwgpPzweD6tXr0aTJk1w+/ZtjBkzhpIgQjgm9zxC06ZNw7hx45CTkwPGGG7cuIGDBw9i+fLl2LFjR1nEWHbo0hghpIzdvn0bCQkJ6N27NwDgp59+gpOTE40II6SCkDsR8vb2hlAoxPTp0/Hhwwd4eHjAxMQEGzZsgLu7e1nEWHboLzFCSBkRiURYvXo15s6dCw0NDdy7dw/m5uYAaDJXQiqS75pZetSoURg1ahRSUlIgFothYGBQ2nGVD6oIEULKQHx8PIYOHYqLFy8CKLh1EN0olZCKSe5MYOHChYiOjgZQMClipU2CAKoIEUJK3aFDh2BnZ4eLFy9CS0sLO3fuxJEjR6Cvr891aIQQGeROhI4ePQpLS0u0atUKmzZtQnJyclnEVT6oIkQIKSWMMXh7e8Pd3R3v3r2Do6MjwsPDMXz4cOoQTUgFJncmcP/+fdy/fx9dunTB2rVrYWJiAldXVxw4cAAfPnwoixjLDiVChJBSwuPxUKNGDSgpKWHu3Lm4cuUKLCwsuA6LEPINcs8j9KWrV6/iwIEDOHLkCHJycqTu7l4RSc0jVL8lMDLk2xsRQogMQqEQaWlpqFmzJgAgNzcX4eHhaNmyJceREVL1VJh5hL6kpaUFDQ0N8Pl85Ofnl0ZM5YcqQoSQ7xQTE4MOHTqgd+/eEAqFAArubUhJECGVy3dlArGxsVi6dCmsra3RvHlz3LlzBwsWLChyJ/kKjxIhQoicGGPYu3cvHBwcEBoaiocPHyIyMpLrsAgh30nu4fOtW7fGjRs3YGtrC29vb8k8QpUSJUKEEDmkpaXBx8cHhw8fBgC0a9cO+/btg5mZGbeBEUK+m9yJUOfOnbFjxw40bty4LOIpXzSSgxBSQhcuXMDQoUORkJAAFRUVLFy4EDNmzJC6+TQhpPKROxFatmxZWcTBDUqECCElIBaLMW3aNCQkJKBBgwYIDg5GixYtuA6LEFIKSpQI+fn5YfHixdDS0oKfn99X1127dm2pBFYu6NIYIaQElJSUsHfvXgQEBGDlypXQ1tbmOiRCSCkpUSJ09+5dyYiwu3fvlmlA5YsqQoSQohhj2L59O1JSUjB79mwAgJWVFQIDAzmOjBBS2kqUCJ0/f17m15UeVYQIIV9ITk7GqFGjcOzYMSgpKcHV1RUODg5ch0UIKSNyZwLDhw9HZmZmkfasrCwMHz68VIIqN5QIEUI+c+bMGdjZ2eHYsWPg8/lYvXo17OzsuA6LEFKG5M4E9uzZg+zs7CLt2dnZ2Lt3b6kEVW4oESKEAMjJycHkyZPRvXt3JCUlwdraGjdu3MDkyZOhpETvE4RUZSUeNZaRkQHGGBhjyMzMhLq6umSZSCTCyZMnK9+d6GnUGCEKTywWo3PnzggLCwMAjB8/HitXroSGhgbHkRFCykOJE6Hq1auDx+OBx+PB0tKyyHIej4eFCxeWanBljipChCg8JSUleHl5ISYmBkFBQXB1deU6JEJIOSpxInT+/HkwxtClSxccPXoUenp6kmV8Ph+mpqaoVatWmQRZZigRIkQhJSYmIikpCU2aNAEA/Pzzz/i///s/qfc1QohiKHEi1LFjRwAF9xmrW7cueFXhslJVOAdCiFyOHTuGESNGQEtLC/fu3ZNUuykJIkQxlSgRun//PmxsbKCkpIT09HREREQUu27lGmFBiRAhiiIrKwtTpkzB1q1bAQB16tTBu3fvUL16dW4DI4RwqkSJkIODA5KSkmBgYAAHBwfweDwwxoqsx+PxIBKJSj3IMkOXxghRCLdv34aHhweioqLA4/EwdepULF68GGpqalyHRgjhWIkSodjYWNSsWVPydZVBiRAhVZpYLMaqVaswd+5cCIVCmJiYYO/evejSpQvXoRFCKogSJUKmpqYyv670qI8QIVUaj8fD9evXIRQK0b9/f2zbto36AhFCpHzXhIonTpyQPJ8+fTqqV6+ONm3a4MWLF6UaXJmjihAhVVLhvRF5PB62b9+OvXv34siRI5QEEUKKkDsTWLZsmWSisdDQUGzatAkrV65EjRo1MHny5FIPsExRIkRIlZKRkQFPT08MHTpU0o9RX18fQ4cOrRojXQkhpa7Ew+cLxcfHw8LCAgDw119/YcCAAfj555/Rtm1bdOrUqbTjK1uUCBFSZVy7dg1DhgxBbGwslJSUMGvWLNjb23MdFiGkgpM7E9DW1kZqaioA4OzZs+jatSsAQF1dXeY9yCo2+guRkMpOKBRiwYIFaN++PWJjY2FmZoaLFy9SEkQIKRG5K0I//fQTRo4ciSZNmiAqKgo9evQAADx8+BBmZmalHV/ZoooQIZVadHQ0hgwZIrlP2JAhQ7Bp0yYIBAKOIyOEVBZyZwK//vorWrdujeTkZBw9ehT6+voACubpGDRoUKkHWKaozwAhlZZYLEbPnj0RFhYGgUCAAwcOYN++fZQEEULkwmOyZkaswjIyMiAQCJA+sxp0WnsB/9vIdUiEkO/033//YdGiRdizZ0/VmtqDEFKE5PM7PR06Ojqltl+5L40BwLt377Bz5048evQIPB4PVlZWGDFiROX7S4wqQoRUKufPn0dKSgr+7//+DwDQpUsXdO7cmUaEEUK+m9yXxm7duoX69etj3bp1ePv2LVJSUrBu3TrUr18fd+7cKYsYyw71ESKkUsjLy8PMmTPh5OSE4cOHIzo6WrKMkiBCyI+QuyI0efJk/O9//8P27duholKwuVAoxMiRI+Hr64tLly6VepBlhhIhQiq8x48fY/DgwZI/tNzd3WFoaMhxVISQqkLuROjWrVtSSRAAqKioYPr06WjevHmpBlf26C9JQioqxhi2bt0KPz8/ZGdnQ09PDzt27EDfvn25Do0QUoXIXRLR0dFBXFxckfb4+HhUq1atVIIqN1QRIqRCEovF6NevH8aMGYPs7Gx07doVERERlAQRQkqd3JmAm5sbRowYgUOHDiE+Ph4JCQn47bffMHLkyEo4fJ4SIUIqIiUlJTRs2BB8Ph9r167FmTNnUKtWLa7DIoRUQXJfGlu9ejV4PB6GDRsGoVAIAFBVVcWYMWOwYsWKUg+wTFEiREiFkZOTg7dv30oSnkWLFmHo0KFo3Lgxx5ERQqqy755H6MOHD4iOjgZjDBYWFtDU1Czt2MqE1DxCnSYAzku5DokQhRcREQEPDw9oamriypUrUFVV5TokQkgFU1bzCJW4JPLhwweMGzcOJiYmMDAwwMiRI2FsbAw7O7tKkwQVQcNuCeGUWCzGhg0b0KJFCzx48ADPnz/Hs2fPuA6LEKJASpwIzZ8/H7t370aPHj3g7u6OkJAQjBkzpixjK3t0aYwQziQmJsLFxQW+vr7Izc1Fjx49EBERASsrK65DI4QokBL3Efrjjz+wc+dOuLu7Ayi4uWHbtm0hEomgrKxcZgGWKUqECOHEsWPHMGLECKSmpkJdXR1r166Fj48PTY5ICCl3Jc4E4uPj0b59e8lzR0dHqKio4NWrV2USWPmgN11CyptIJMKSJUuQmpoKBwcH3LlzB2PGjKEkiBDCiRInQiKRCHw+X6pNRUVFMnKsUqKKECHlTllZGcHBwZgxYwbCwsLoUhghhFMlvjTGGIOXlxfU1NQkbTk5OfDx8YGWlpak7Y8//ijdCMsSJUKElDmRSIRVq1YhJycHCxYsAABYWlpWvuk2CCFVUokTIU9PzyJtQ4YMKdVgyh2V4gkpU/Hx8Rg6dCguXrwIHo+HAQMGwMbGhuuwCCFEosSJUFBQUFnGwQ2qCBFSZg4dOgQfHx+8e/cOWlpaCAgIoMkRCSEVjtwzS1cplAgRUuoyMjIwYcIE7N27F0DBwIrg4GBYWFhwHBkhhBSl4IkQXRojpDSJRCK0a9cOERERUFJSwpw5czBv3jyaKZoQUmEpeEmEEiFCSpOysjImTZoEMzMzXLx4EYsWLaIkiBBSoSl2IkSXxgj5YTExMbhx44bk+fDhw/HgwQO0a9eOw6gIIaRkFDsToESIkO/GGMPevXthb2+P/v37Iy0tDQDA4/GkptQghJCK7LsygX379qFt27aoVasWXrx4AQBYv349jh07VqrBlTlKhAj5LmlpaXB3d4enpyfev38PMzMzfPjwgeuwCCFEbnJnAps3b4afnx9cXV3x7t07iEQiAED16tWxfv360o6vbFFnaULkduHCBdjZ2eHw4cNQUVHB0qVLceHCBZiYmHAdGiGEyE3uRCggIADbt2/HnDlzpG622rx5c0RERJRqcGWOKkKElJhIJMLMmTPRpUsXJCQkoEGDBrh27Rpmz55deW+8TAhReHJnArGxsWjSpEmRdjU1NWRlZZVKUOWGEiFCSkxJSQnR0dFgjGHkyJG4c+cOWrRowXVYhBDyQ+TOBMzNzREeHl6k/dSpU7C2ti6NmMoRXRoj5GsYY8jOzgZQ0Al669atOHbsGLZv3w5tbW2OoyOEkB8n94SK06ZNw7hx45CTkwPGGG7cuIGDBw9i+fLl2LFjR1nEWHaoIkRIsZKTkzFy5Ejw+XwcPnwYPB4Penp6+N///sd1aIQQUmrkzgS8vb0xf/58TJ8+HR8+fICHhwe2bNmCDRs2wN3dXe4AAgMDYW5uDnV1dTRr1gyXL18u0XZXr16FiooKHBwc5D6mBHWWJkSm06dPw87ODsePH8fx48fx6NEjrkMihJAy8V0lkVGjRuHFixd48+YNkpKSEB8fjxEjRsi9n0OHDsHX1xdz5szB3bt30b59e7i4uCAuLu6r26Wnp2PYsGFwcnL6nvA/oYoQIVJycnIwadIkuLi4ICkpCdbW1rhx40YlvOxNCCEl80OZQI0aNWBgYPDd269duxYjRozAyJEjYWVlhfXr16NOnTrYvHnzV7cbPXo0PDw80Lp16+8+NgCqCBHymYiICLRo0QIbN24EAIwfPx63bt2Cvb09x5ERQkjZkbuPkLm5OXhfSSBiYmJKtJ+8vDzcvn0bM2fOlGrv1q0brl27Vux2QUFBiI6Oxv79+7FkyZJvHic3Nxe5ubmS5xkZGZ8WUkWIEAAFQ+P79euHZ8+ewcDAAEFBQXB1deU6LEIIKXNyJ0K+vr5Sz/Pz83H37l2cPn0a06ZNK/F+UlJSIBKJYGhoKNVuaGiIpKQkmds8ffoUM2fOxOXLl6GiUrLQly9fjoULF8peSIkQIQAKbpa6fft2rF27Fjt27PihSi8hhFQmcidCkyZNktn+66+/4tatW3IH8GV1iTEms+IkEong4eGBhQsXwtLSssT7nzVrFvz8/CTPMzIyUKdOnY8Hp0SIKK7jx4/j/fv38PDwAAB06tQJnTp14jYoQggpZ6WWCbi4uODo0aMlXr9GjRpQVlYuUv158+ZNkSoRAGRmZuLWrVsYP348VFRUoKKigkWLFuHevXtQUVHBf//9J/M4ampq0NHRkXp8Qn2EiOLJysqCj48PevfujZ9//hnR0dFch0QIIZyRuyJUnN9//x16enolXp/P56NZs2YICQlB3759Je0hISHo3bt3kfV1dHSK3MIjMDAQ//33H37//XeYm5vLHzRVhIiCuX37Njw8PBAVFQUAGDNmDGrXrs1xVIQQwh25E6EmTZpIXbpijCEpKQnJyckIDAyUa19+fn4YOnQomjdvjtatW2Pbtm2Ii4uDj48PgILLWi9fvsTevXuhpKQEGxsbqe0NDAygrq5epL3EKBEiCkIkEmHVqlWYN28ehEIhTExMsGfPnh+fgoIQQio5uROhPn36SD1XUlJCzZo10alTJzRq1Eiufbm5uSE1NRWLFi1CYmIibGxscPLkSZiamgIAEhMTvzmn0A+h4fNEAQiFQjg7O0suH/fv3x/btm2Tq4JLCCFVFY8xxkq6slAoRHBwMJydnWFkZFSWcZWZjIwMCAQCpM+sBh2PXYDtAK5DIqTMzZ49Gxs3bkRAQAC8vLy+OgUGIYRURJLP7/T0L/r7/hi5rg2pqKhgzJgxUvPyVGr0YUCqqIyMDKlq6sKFC3H//n14e3tTEkQIIZ+Ru5NMy5Ytcffu3bKIpfxRHyFSBYWGhsLBwQH9+/dHfn4+AEBVVRX16tXjODJCCKl45O4jNHbsWEyZMgUJCQlo1qwZtLS0pJbb2dmVWnBlj/4yJlWHUCjEkiVLsGTJEohEIojFYsTFxaF+/fpch0YIIRVWiROh4cOHY/369XBzcwMATJw4UbKMx+NJJkIUiUSlH2VZoYoQqSJiYmIwZMgQhIaGAgAGDx6MX3/9FQKBgOPICCGkYitxIrRnzx6sWLECsbGxZRlP+aJEiFRyjDHs3bsX48ePx/v376Gjo4PNmzdLZosmhBDydSVOhAoHlxUOba8SKBEilZxIJEJgYCDev3+P9u3bY9++fVXrd5QQQsqYXH2Eqtxok6p2PkRhFF6KVlFRwf79+3H06FFMmzYNysrKXIdGCCGVilyJkKWl5TeTobdv3/5QQOWKKkKkksnLy4O/vz+UlZWxdOlSAECDBg0wc+ZMjiMjhJDKSa5EaOHChVWr8yUlQqQSefLkCTw8PHDnzh3weDwMGzYMDRs25DosQgip1ORKhNzd3WFgYFBWsXCALo2Rio8xhm3btmHy5MnIzs6Gnp4etm/fTkkQIYSUghInQlWufxBAfYRIhZecnIyRI0fi+PHjAICuXbti9+7dMDEx4TgyQgipGuQeNVal0KUxUoEJhUK0bdsWT58+BZ/Px/Lly+Hr6wslJfq5JYSQ0lLid1SxWFzFLouBEiFSoamoqGD27NmwsrLC9evX4efnR0kQIYSUMsV+V6VLY6SCiYiIwLVr1yTPPT09cffuXTg4OHAXFCGEVGEKnggp9umTikMsFmPDhg1o0aIFBg4cKJmGgsfjQU1NjePoCCGk6pL7pqtVCiVCpAJITEyEt7c3zpw5AwCwt7evXPfsI4SQSkyxMwFKhAjHjh07Bjs7O5w5cwbq6ur49ddf8c8//6BmzZpch0YIIQpBsStCNI8Q4YhQKMT48eOxdetWAICDgwOCg4NhbW3NcWSEEKJYFLskQhUhwhFlZWWkpaUBAKZOnYqwsDBKggghhAOKXRGiRIiUI5FIhOzsbGhra4PH42HLli3w8fFB586duQ6NEEIUlmJnAnRljJST+Ph4dO3aFcOGDZNMTqqrq0tJECGEcIwqQoSUscOHD2P06NF49+4dtLS08OzZMzRo0IDrsAghhEDhK0KKffqkbGVkZMDLywtubm549+4dHB0dcffuXUqCCCGkAlHsTIASIVJGQkND4eDggD179kBJSQlz587FlStXKAkihJAKRrEvjVEnIVIGhEIhhgwZgtjYWJiammL//v1o164d12ERQgiRQbFLIlQRImVARUUFQUFBGDJkCO7du0dJECGEVGCKXRGiRIiUAsYY9u3bB8YYPD09AQAdOnRAhw4dOI6MEELIt1AiRMgPSEtLg4+PDw4fPgxNTU20b98e9erV4zosQgghJaTgiRD1ESLf78KFCxg6dCgSEhKgoqKCuXPnwtTUlOuwCCGEyEHBEyGqCBH55eXlwd/fHytXrgRjDA0aNEBwcDBatGjBdWiEEELkpOCJEFWEiHzy8/PRrl073Lx5EwAwcuRIrFu3Dtra2hxHRggh5HsoeEmEEiEiH1VVVTg7O0NPTw9Hjx7F9u3bKQkihJBKTLETIbo0RkogOTkZMTExkuf+/v548OAB+vXrx2FUhBBCSoNiZwKUCJFvOH36NOzs7PB///d/yMvLA1BQFTI2NuY4MkIIIaVBsTMBSoRIMXJycuDr6wsXFxckJSUhJycHSUlJXIdFCCGklCl2JkCdpYkMERERaNGiBTZs2AAAmDBhAm7duoW6detyHBkhhJDSpuCJkGKfPpEmFouxYcMGtGjRAg8ePICBgQFOnDiBjRs3QkNDg+vwCCGElAHFzgQoESKfEYvF+O2335Cbm4uePXsiIiICrq6uXIdFCCGkDCn2PEI0fJ6g4F5hPB4PKioq2L9/P0JCQjB69Gjw6NIpIYRUeYqdCFFFSKFlZWXBz88PAoEAK1euBADUr18f9evX5zgyQggh5UXBEyH6i19R3bp1C4MHD0ZUVBSUlJQwevRoSoAIIUQBKXZJhCpCCkckEmHFihVo3bo1oqKiYGJigpCQEEqCCCFEQVFFiCiMuLg4DBs2DBcvXgQA9O/fH9u2bYOenh7HkRFCCOGKgidCVBFSFHl5eWjfvj3i4uKgpaWFgIAAeHl5UYdoQghRcIqdCVAipDD4fD6WLFkCR0dHhIeHw9vbm5IgQgghlAiRquvatWuSy2AAMGTIEFy9ehUWFhYcRkUIIaQiUfBMgCoCVZFQKMSCBQvQvn17DBo0CKmpqQAgmSuIEEIIKaTYnwpUEapyYmJiMHjwYISFhQEAnJycKPkhhBBSLMXOBCgRqjIYY9izZw/s7e0RFhYGgUCAAwcOYN++fRAIBFyHRwghpIJS7D+VqbNslZCXl4ehQ4fi8OHDAID27dtj3759MDU15TgyQgghFZ1il0SoIlQl8Pl8qKioQEVFBUuXLsX58+cpCSKEEFIiVBEilVJeXh6ys7Mll70CAwMxefJkNG/enOPICCGEVCYKWxIRK+6pV3qPHz9G69at4enpCcYYAEAgEFASRAghRG4KnA1QNaiyYYxh69ataNq0Ke7cuYPLly/j+fPnXIdFCCGkElPYRIhRIlSpJCcno0+fPvDx8UF2dja6du2KiIgImJubcx0aIYSQSkxxEyHqH1RpnD59GnZ2djh+/Dj4fD7Wrl2LM2fOoFatWlyHRgghpJJT4M7SCpsDVip5eXkYO3YskpKSYG1tjQMHDsDe3p7rsAghhFQRCpsNMK4DICXC5/Oxb98+TJgwAbdu3aIkiBBCSKlS2IoQozmEKiSxWIyAgABoaWlh5MiRAIC2bduibdu2HEdGCCGkKuI8GwgMDIS5uTnU1dXRrFkzXL58udh1//jjD/z000+oWbMmdHR00Lp1a5w5c+Y7j8z5qZMvJCYmwsXFBb6+vpg4cSKNCCOEEFLmOM0GDh06BF9fX8yZMwd3795F+/bt4eLigri4OJnrX7p0CT/99BNOnjyJ27dvo3PnzujVqxfu3r0r97EZ9ZWuUI4dOwZbW1ucPXsW6urqWLNmDc0OTQghpMzxWOGMdBxo2bIlmjZtis2bN0varKys0KdPHyxfvrxE+2jcuDHc3Nzg7+9fovUzMjIgEAjwek4tGCx5+V1xk9KTlZWFKVOmYOvWrQAABwcHHDhwAFZWVhxHRgghpCIp/PxOT0+Hjo5Oqe2Xsz5CeXl5uH37NmbOnCnV3q1bN1y7dq1E+xCLxcjMzISenl6x6+Tm5iI3N1fyPCMjo+AL6iPEudzcXDg6OiIyMhIAMG3aNCxevBhqamocR0YIIURRcJYNpKSkQCQSwdDQUKrd0NAQSUlJJdrHmjVrkJWVhYEDBxa7zvLlyyEQCCSPOnXqAKAJFSsCNTU1/N///R9MTEzw77//YuXKlZQEEUIIKVecl0V4X0xsyBgr0ibLwYMHsWDBAhw6dAgGBgbFrjdr1iykp6dLHvHx8YVH/pGwyXeKj49HVFSU5PncuXNx//59ODk5cRgVIYQQRcVZIlSjRg0oKysXqf68efOmSJXoS4cOHcKIESNw+PBhdO3a9avrqqmpQUdHR+oB0PB5Lhw6dAh2dnYYOHCg5HKliorKVy9tEkIIIWWJs2yAz+ejWbNmCAkJkWoPCQlBmzZtit3u4MGD8PLywoEDB9CjR4/vPj7dYqP8ZGRkwNPTE+7u7nj37h3U1dWRlpbGdViEEEIItxMq+vn5YejQoWjevDlat26Nbdu2IS4uDj4+PgAKLmu9fPkSe/fuBVCQBA0bNgwbNmxAq1atJNUkDQ0NCAQCuY5NfYTKx7Vr1zBkyBDExsZCSUkJc+bMwbx586Cqqsp1aIQQQgi3iZCbmxtSU1OxaNEiJCYmwsbGBidPnpTMH5OYmCg1p9DWrVshFAoxbtw4jBs3TtLu6emJ3bt3y3l0SoTKklAoxJIlS7B48WKIxWKYmZlh3759aNeuHdehEUIIIRKcziPEhcJ5CBLmW8BkwVOuw6myhEIhOnbsKKkIbdq0Se6qHSGEEFKoys0jxDXG/YC5KocxBrFYDGVlZaioqGD//v0ICwvDoEGDuA6NEEIIkUlhEyFQZ+lSlZaWBh8fH5iYmGDt2rUAAHNzc5ibm3McGSGEEFI8hU2EqLN06blw4QKGDh2KhIQEqKqqwtfXF3Xr1uU6LEIIIeSbFPb6EM0j9OPy8vIwc+ZMdOnSBQkJCWjQoAGuXr1KSRAhhJBKQ2ErQgqcA5aKx48fY/Dgwbhz5w4AYOTIkVi3bh20tbU5jowQQggpOYVNhBRqqFwpy83NRZcuXZCYmAg9PT3s2LEDffv25TosQgghRG4KWxahS2PfT01NDatXr0bXrl0RERFBSRAhhJBKS2ErQqBESC6nT5+Gqqqq5OaoHh4eGDRoUIlukEsIIYRUVAqcDdAHeEnk5ORg0qRJcHFxwZAhQ5CcnCxZRkkQIYSQyk5hK0I0fP7bIiIi4OHhgQcPHgAABgwYQJ2hCSGEVCkKmwjRhIrFE4vFCAgIwIwZM5CbmwsDAwMEBQXB1dWV69BIORCJRMjPz+c6DEKIAuLz+VBSKt+LVQqbCFFFSLacnBz06dMHZ86cAQD06NEDu3btgoGBAceRkbLGGENSUhLevXvHdSiEEAWlpKQEc3Nz8Pn8cjum4iZCVBGSSV1dHQYGBlBXV8fatWvh4+NDfYEURGESZGBgAE1NTfq+E0LKlVgsxqtXr5CYmIi6deuW23uQwiZCCt1P/AtZWVnIzc2Fnp4eAGDTpk2YNWsWrKysOI6MlBeRSCRJgvT19bkOhxCioGrWrIlXr15BKBRCVVW1XI6psNkAVYQK3L59G02bNoWnpycYK5hmUkdHh5IgBVPYJ0hTU5PjSAghiqzwkphIJCq3YypuIqS4pw6g4Ifsl19+QatWrRAVFYW7d+/i5cuXXIdFOEaXwwghXOLiPUiBswHFfcOPj4+Hk5MTZs6cCaFQiP79++PevXuoXbs216ERQggh5UphEyFFvTR26NAh2NnZ4eLFi9DS0sLOnTtx5MgR6hdCCJFy9epV2NraQlVVFX369JF7+wsXLoDH41W6UYhPnjyBkZERMjMzuQ6lypk6dSomTpzIdRhFKG4ipICnnpOTg9mzZ+Pdu3dwdHREeHg4hg8fTpdDSKXl5eUFHo8HHo8HFRUV1K1bF2PGjEFaWlqRda9duwZXV1fo6upCXV0dtra2WLNmjcy+COfPn4erqyv09fWhqakJa2trTJkyRaEuH/v5+cHBwQGxsbHYvXs31+HILS0tDUOHDoVAIIBAIMDQoUNLlJTNmTMH48aNQ7Vq1co+SI4cPXoU1tbWUFNTg7W1Nf78889vbnP48GE4ODhAU1MTpqamWLVqVZF1goODYW9vD01NTRgbG8Pb2xupqamS5dOnT0dQUBBiY2NL9Xx+lOJlAx8p4k1X1dXVERwcjLlz5+LKlSuwsLDgOiRCflj37t2RmJiI58+fY8eOHfj7778xduxYqXX+/PNPdOzYEbVr18b58+fx+PFjTJo0CUuXLoW7u7tkoAAAbN26FV27doWRkRGOHj2KyMhIbNmyBenp6VizZk25nVdeXl65HUuW6OhodOnSBbVr10b16tU5jeV7eHh4IDw8HKdPn8bp06cRHh6OoUOHfnWbhIQEHD9+HN7e3j90bK6/d18TGhoKNzc3DB06FPfu3cPQoUMxcOBAXL9+vdhtTp06hcGDB8PHxwcPHjxAYGAg1q5di02bNknWuXLlCoYNG4YRI0bg4cOHOHLkCG7evImRI0dK1jEwMEC3bt2wZcuWMj1HuTEFk56ezgCwRys6cR1KmcvPz2fz589ngYGBXIdCKrjs7GwWGRnJsrOzJW1isZhl5eaX+0MsFpc4bk9PT9a7d2+pNj8/P6anpyd5/v79e6avr8/69etXZPvjx48zAOy3335jjDEWHx/P+Hw+8/X1lXm8tLS0YmNJS0tjo0aNYgYGBkxNTY01btyY/f3334wxxubPn8/s7e2l1l+3bh0zNTUtci7Lli1jxsbGzNTUlM2cOZO1bNmyyLFsbW2Zv7+/5PmuXbtYo0aNmJqaGmvYsCH79ddfi42TMcZycnLYhAkTWM2aNZmamhpr27Ytu3HjBmOMsdjYWAZA6hEUFFTsfqZNm8Zq167N+Hw+s7CwYDt27GCMMXb+/HkGQPKapaSkMHd3d2ZiYsI0NDSYjY0NO3DggNT+jhw5wmxsbJi6ujrT09NjTk5O7P3795L9tWjRgmlqajKBQMDatGnDnj9/LjOuyMhIBoCFhYVJ2kJDQxkA9vjx42JflzVr1rDmzZtLtZUk7o4dO7Jx48axyZMnM319fdahQwfGGGMPHz5kLi4uTEtLixkYGLAhQ4aw5ORkyXanTp1ibdu2ZQKBgOnp6bEePXqwZ8+eFRtfaRg4cCDr3r27VJuzszNzd3cvdptBgwaxAQMGSLWtW7eO1a5dW/L7umrVKlavXj2pdTZu3Mhq164t1bZ7925Wp06dYo8l672oUOHnd3p6erHbfw+FnUeoqleEYmJiMGTIEISGhkJdXR29evWiztBELtn5Ilj7nyn340YucoYm//vemmJiYnD69Gmp+UfOnj2L1NRUTJ06tcj6vXr1gqWlJQ4ePAg3NzccOXIEeXl5mD59usz9F1cZEYvFcHFxQWZmJvbv34/69esjMjISysrKcsV/7tw56OjoICQkRFKlWrFiBaKjo1G/fn0AwMOHDxEREYHff/8dALB9+3bMnz8fmzZtQpMmTXD37l2MGjUKWlpa8PT0lHmc6dOn4+jRo9izZw9MTU2xcuVKODs749mzZ6hTpw4SExPRsGFDLFq0CG5ubhAIBDL3M2zYMISGhmLjxo2wt7dHbGwsUlJSZK6bk5ODZs2aYcaMGdDR0cGJEycwdOhQ1KtXDy1btkRiYiIGDRqElStXom/fvsjMzMTly5fBGINQKESfPn0watQoHDx4EHl5ebhx40axl/VDQ0MhEAjQsmVLSVurVq0gEAhw7do1NGzYUOZ2ly5dQvPmzeWKu9CePXswZswYXL16FYwxJCYmomPHjhg1ahTWrl2L7OxszJgxAwMHDsR///0HoGAONz8/P9ja2iIrKwv+/v7o27cvwsPDi73NxLJly7Bs2TKZywqdOnUK7du3L/a1mTx5slSbs7Mz1q9fX+z+cnNzi0ytoaGhgYSEBLx48QJmZmZo06YN5syZg5MnT8LFxQVv3rzB77//jh49ekht5+joiPj4eLx48QKmpqZfPY/yoriJUBW9KsgYw759+zBu3Di8f/8eOjo62Lx5MyVBpMr6559/oK2tDZFIhJycHADA2rVrJcujoqIAoNi5sRo1aiRZ5+nTp9DR0YGxsbFcMfz777+4ceMGHj16BEtLSwBAvXr15D4XLS0t7NixQ+r2AnZ2djhw4ADmzZsHoKAfRosWLSTHWbx4MdasWYN+/foBAMzNzREZGYmtW7fKTISysrKwefNm7N69Gy4uLgAKkqmQkBDs3LkT06ZNg5GREXg8HgQCAYyMjGTGGhUVhcOHDyMkJARdu3b95jmbmJhIJaMTJkzA6dOnceTIEUkiJBQK0a9fP8kHpK2tLQDg7du3SE9PR8+ePSUJ4dfmOktKSpJ5WyADAwMkJSUVu93z58/RrFkzueIuZGFhgZUrV0qe+/v7o2nTplJJy65du1CnTh1ERUXB0tIS/fv3lzrWzp07YWBggMjISNjY2MiM0cfHBwMHDiz2HApjLk5SUhIMDQ2l2gwNDb/6ujg7O2Py5Mnw8vJC586d8ezZM0nilJiYKEmEgoOD4ebmhpycHAiFQvzvf/9DQECAzNieP39OiRApfWlpafDx8cHhw4cBAO3bt8e+ffsqzA8bqVw0VJURuciZk+PKo3Pnzti8eTM+fPiAHTt2ICoqChMmTCiyHvusH9CX7YWVhc+/lkd4eDhq164tSU6+l62tbZF7LA0ePBi7du3CvHnzwBjDwYMH4evrCwBITk5GfHw8RowYgVGjRkm2EQqFxVZxoqOjkZ+fj7Zt20raVFVV4ejoiEePHpU41vDwcCgrK6Njx44lWl8kEmHFihU4dOgQXr58idzcXOTm5kJLSwsAYG9vDycnJ9ja2sLZ2RndunXDgAEDoKurCz09PXh5ecHZ2Rk//fQTunbtioEDB341YZX1ffzW9zc7Oxvq6upyxV3oy0rS7du3cf78eWhraxc5TnR0NCwtLREdHY158+YhLCwMKSkpEIvFAIC4uLhiEyE9PT3JXQC+15evwbdel1GjRiE6Oho9e/ZEfn4+dHR0MGnSJCxYsEBS9YyMjMTEiRPh7+8PZ2dnJCYmYtq0afDx8cHOnTsl+9LQ0AAAfPjw4YfOoTRVzbJICVS1m65mZ2ejWbNmOHz4MFRUVLB06VKcP3+ekiDy3Xg8HjT5KuX+kDcR0dLSgoWFBezs7LBx40bk5uZi4cKFkuWFyUlxH/KPHz9GgwYNJOump6cjMTFRrhgK39yLo6SkVCQRK5zN+8tz+ZKHhweioqJw584dXLt2DfHx8XB3dwcAyQfn9u3bER4eLnk8ePAAYWFhMmMpjEPeD8Mvfeucv7RmzRqsW7cO06dPx3///Yfw8HA4OztLOhYrKysjJCQEp06dgrW1NQICAtCwYUPJCKOgoCCEhoaiTZs2OHToECwtLYs9RyMjI7x+/bpIe3JycpFqyOdq1KhRZMTht+Iu9OX3TiwWo1evXlLfl/DwcDx9+hQdOnQAUHBpNjU1Fdu3b8f169clHZa/1tl62bJl0NbW/urj8uXLxW5vZGRUpPrz5s2br74uPB4Pv/zyC96/f48XL14gKSkJjo6OAAAzMzMAwPLly9G2bVtMmzYNdnZ2cHZ2RmBgIHbt2iX1+/T27VsABbfSqCgUNhFCFesjpKGhAW9vbzRo0ADXrl3D7Nmz5e6fQEhVMH/+fKxevRqvXr0CAHTr1g16enoyR3wdP34cT58+xaBBgwAAAwYMAJ/Pl7rE8bnihl/b2dkhISFBcontSzVr1kRSUpJUMhQeHl6i86lduzY6dOiA4OBgBAcHo2vXrpIPLUNDQ5iYmCAmJgYWFhZSD3Nzc5n7s7CwAJ/Px5UrVyRt+fn5uHXrlly31rG1tYVYLMbFixdLtP7ly5fRu3dvDBkyBPb29qhXrx6ePn0qtQ6Px0Pbtm2xcOFC3L17F3w+X2pod5MmTTBr1ixcu3YNNjY2OHDggMxjtW7dGunp6bhx44ak7fr160hPT0ebNm2KjbFJkyaIjIyUO25ZmjZtiocPH8LMzKzI90ZLSwupqal49OgR5s6dCycnJ1hZWcmc9uFLPj4+RZKrLx9fVqe+fG1CQkKk2s6ePfvV16WQsrIyTExMwOfzcfDgQbRu3VpyCfLDhw9F+jUVfgZ9/nP/4MEDqKqqonHjxt88Xrkp1a7XlUBhr/OIld2/vXIF9/jxYxYZGSl5np+fzzIzMzmMiFRWXxupUZHJGjXGGGPNmjVj48aNkzw/cuQIU1ZWZqNGjWL37t1jsbGxbMeOHUxXV5cNGDBAaqTar7/+yng8Hhs+fDi7cOECe/78Obty5Qr7+eefmZ+fX7GxdOrUidnY2LCzZ8+ymJgYdvLkSXbq1CnGWMEoJh6Px1asWMGePXvGNm3axHR1dWWOGpNl27ZtrFatWqxGjRps3759Usu2b9/ONDQ02Pr169mTJ0/Y/fv32a5du9iaNWuKjXXSpEmsVq1a7NSpU+zhw4fM09OT6erqsrdv30rWEQgExY4WK+Tl5cXq1KnD/vzzTxYTE8POnz/PDh06xBgrOmrM19eX1alTh129epVFRkaykSNHMh0dHck5h4WFsaVLl7KbN2+yFy9esMOHDzM+n89OnjzJYmJi2MyZM9m1a9fY8+fP2ZkzZ5ient5XR8R2796d2dnZsdDQUBYaGspsbW1Zz549v3o+x48fZwYGBkwoFEravhU3YwWjxiZNmiS1r5cvX7KaNWuyAQMGsOvXr7Po6Gh25swZ5u3tzYRCIROJRExfX58NGTKEPX36lJ07d461aNGCAWB//vnnV+P8EVevXmXKyspsxYoV7NGjR2zFihVMRUVFaoRdQEAA69Kli+R5cnIy27x5M3v06BG7e/cumzhxIlNXV2fXr1+XrBMUFMRUVFRYYGAgi46OZleuXGHNmzdnjo6OUsefP3++1L6/xMWoMYVNhO6vdOE6lO8mFovZli1bmIaGBrO1ta10H16k4qlqiVBwcDDj8/ksLi5O0nbp0iXWvXt3JhAIGJ/PZ9bW1mz16tVSH3qFQkJCmLOzM9PV1WXq6uqsUaNGbOrUqezVq1fFxpKamsq8vb2Zvr4+U1dXZzY2Nuyff/6RLN+8eTOrU6cO09LSYsOGDWNLly4tcSKUlpbG1NTUmKampsw/doKDg5mDgwPj8/lMV1eXdejQgf3xxx/Fxpqdnc0mTJjAatSoUWT4fKGSJELZ2dls8uTJzNjYWDJ8fteuXYyxoolQamoq6927N9PW1mYGBgZs7ty5bNiwYZJzjoyMZM7OzpIh/ZaWliwgIIAxxlhSUhLr06eP5DimpqbM39+fiUSiYmNLTU1lgwcPZtWqVWPVqlVjgwcP/ur0B4wxJhQKmYmJCTt9+rTUfr4WN2OyEyHGGIuKimJ9+/Zl1atXZxoaGqxRo0bM19dXkniHhIQwKysrpqamxuzs7NiFCxfKPBFirOAPg4YNGzJVVVXWqFEjdvToUanl8+fPl/rZTE5OZq1atWJaWlpMU1OTOTk5SSVOhTZu3Misra2ZhoYGMzY2ZoMHD2YJCQlS61haWrKDBw8WGxsXiRCPsWJ6EFZRGRkZEAgEuL+qB2yn/sN1OHJLTk7GyJEjcfz4cQCAk5MTfvvtN9SoUYPjyEhllpOTg9jYWJibmxfpLEqIIgkMDMSxY8dw5kz5Tx1R1Z04cQLTpk3D/fv3oaIie6zW196LCj+/09PToaOjU2pxKeyoscrYWfrMmTPw8vJCUlIS+Hw+li9fDl9f32LnmyCEECKfn3/+GWlpacjMzKzSt9ngQlZWFoKCgopNgrhSsaIpR5VpQsW8vDzMmDFDMm+DtbU1Dhw4AHt7e24DI4SQKkZFRQVz5szhOowq6VvzH3Gl8mQDpa7yVISUlZVx+/ZtAMD48eNx69YtSoIIIYSQUqDAFaGKnQiJxWKIRCKoqqpCWVkZ+/btw8OHD+Hq6sp1aIQQQkiVobAVoYp8i43ExES4urpi2rRpkjZTU1NKggghhJBSVnGzgbJWQStCx44dg52dHc6cOYPt27dLJoUjhBBCSOlT3ESogvURysrKgo+PD/r06YOUlBQ4ODjg1q1bqFWrFtehEUIIIVWWwiZCFWnU2O3bt9G0aVNs3boVADB16lSEhYXJNd09IYQQQuSnuJ2lK0hF6MOHD3BxcUFycjJMTEywZ88eODk5cR0WIYQQohAqTllEQWlqamLjxo3o378/7t27R0kQIaRCuHr1KmxtbaGqqoo+ffrIvf2FCxfA4/GKvVFtRfXkyRMYGRkhMzOT61CqnKlTp2LixIlch1GEwiZCXF4aO3z4sNT07e7u7jhy5Aj09fU5i4mQysjLyws8Hg88Hg8qKiqoW7cuxowZI/Mu3teuXYOrqyt0dXWhrq4OW1tbrFmzBiKRqMi658+fh6urK/T19aGpqQlra2tMmTIFL1++LI/TqhD8/Pzg4OCA2NhY7N69m+tw5LZ06VK0adMGmpqaqF69eom3mzNnDsaNG1elZ5U+evQorK2toaamBmtra/z555/f3Obw4cNwcHCApqYmTE1NsWrVqiLrBAcHw97eHpqamjA2Noa3tzdSU1Mly6dPn46goCDExsaW6vn8KMVNhDg49YyMDHh5ecHNzQ3Dhg1DcnKyZBmvgo5iI6Si6969OxITE/H8+XPs2LEDf//9N8aOHSu1zp9//omOHTuidu3aOH/+PB4/foxJkyZh6dKlcHd3x+e3XNy6dSu6du0KIyMjHD16FJGRkdiyZQvS09OxZs2acjuvvLy8cjuWLNHR0ejSpQtq164tVyJRUeTl5eH//u//MGbMmBJvk5CQgOPHj8Pb2/uHj11RhYaGws3NDUOHDsW9e/cwdOhQDBw4ENevXy92m1OnTmHw4MHw8fHBgwcPEBgYiLVr12LTpk2Sda5cuYJhw4ZhxIgRePjwIY4cOYKbN29i5MiRknUMDAzQrVs3bNmypUzPUW6legvXSqDw7rU3Ngwp1+Neu3aNmZubMwBMSUmJzZs3j+Xl5ZVrDIQUR+Ydn8VixnLfl//j4525S0LWHdv9/PyYnp6e5Pn79++Zvr4+69evX5Htjx8/zgCw3377jTHGWHx8POPz+czX11fm8b529/K0tDQ2atQoZmBgwNTU1Fjjxo3Z33//zRgruJu3vb291Prr1q2Teff5ZcuWMWNjY2ZqaspmzpzJWrZsWeRYtra2zN/fX/J8165drFGjRkxNTY01bNiQ/frrr8XGyRhjOTk5bMKECZI7vX9+9/nY2FgGQOpR3F3oc3Jy2LRp01jt2rUld5/fsWMHY6zo3edTUlKYu7s7MzExYRoaGszGxoYdOHBAan9HjhxhNjY2TF1dnenp6TEnJyf2/v17yf5atGjBNDU1mUAgYG3atGHPnz//6nkyxlhQUBATCATfXI8xxtasWcOaN28u1VaSuDt27MjGjRvHJk+ezPT19VmHDh0YY4w9fPiQubi4MC0tLWZgYMCGDBnCkpOTJdudOnWKtW3blgkEAqanp8d69OjBnj17VqJYv9fAgQNZ9+7dpdqcnZ2Zu7t7sdsMGjSIDRgwQKpt3bp1rHbt2kz88fd11apVrF69elLrbNy4kdWuXVuqbffu3axOnTrFHouLu89TZ+kyJhQKsWTJEixZsgQikQimpqbYv38/2rVrVy7HJ+S75X8AlnEwfcPsVwBf67s2jYmJwenTp6GqqippO3v2LFJTUzF16tQi6/fq1QuWlpY4ePAg3NzccOTIEeTl5WH69Oky919cZUQsFsPFxQWZmZnYv38/6tevj8jISCgrK8sV/7lz56Cjo4OQkBBJlWrFihWIjo5G/fr1AQAPHz5EREQEfv/9dwDA9u3bMX/+fGzatAlNmjTB3bt3MWrUKGhpacHT01PmcaZPn46jR49iz549MDU1xcqVK+Hs7Ixnz56hTp06SExMRMOGDbFo0SK4ublBIBDI3M+wYcMQGhqKjRs3wt7eHrGxsUhJSZG5bk5ODpo1a4YZM2ZAR0cHJ06cwNChQ1GvXj20bNkSiYmJGDRoEFauXIm+ffsiMzMTly9fBmMMQqEQffr0wahRo3Dw4EHk5eXhxo0bpV5Jv3TpEpo3by5X3IX27NmDMWPG4OrVq2CMITExER07dsSoUaOwdu1aZGdnY8aMGRg4cCD+++8/AAXTpvj5+cHW1hZZWVnw9/dH3759ER4eXuzNtJctW4Zly5Z99TxOnTqF9u3by1wWGhqKyZMnS7U5OztL7mUpS25uLjQ1NaXaNDQ0kJCQgBcvXsDMzAxt2rTBnDlzcPLkSbi4uODNmzf4/fff0aNHD6ntHB0dER8fjxcvXsDU1PSr51FeFDcRKoc+Qh8+fEDXrl0RGhoKABg8eDB+/fXXYt9UCCHy++eff6CtrQ2RSIScnBwAwNq1ayXLo6KiAKDY6SgaNWokWefp06fQ0dGBsbGxXDH8+++/uHHjBh49egRLS0sAQL169eQ+Fy0tLezYsQN8Pl/SZmdnhwMHDmDevHkACvphtGjRQnKcxYsXY82aNejXrx8AwNzcHJGRkdi6davMRCgrKwubN2/G7t274eLiAqAgmQoJCcHOnTsxbdo0GBkZgcfjQSAQwMjISGasUVFROHz4MEJCQtC1a9dvnrOJiYlUMjphwgScPn0aR44ckSRCQqEQ/fr1k3xA2traAgDevn2L9PR09OzZU5IQlsX0Is+fP0ezZs3kiruQhYUFVq5cKXnu7++Ppk2bSiUtu3btQp06dRAVFQVLS0v0799f6lg7d+6EgYEBIiMjYWNjIzNGHx+fb9681MTEpNhlSUlJMDQ0lGozNDREUlJSsds4Oztj8uTJ8PLyQufOnfHs2TNJ4pSYmChJhIKDg+Hm5oacnBwIhUL873//Q0BAgMzYnj9/TokQ98q+IqSpqQlLS0s8fPgQmzdvhoeHR5kfk5BSo6pZUJ3h4rhy6Ny5MzZv3owPHz5gx44diIqKwoQJE4qsxz7rB/Rle2Fl4fOv5REeHo7atWtLkpPvZWtrK5UEAQV/QO3atQvz5s0DYwwHDx6Er68vACA5ORnx8fEYMWIERo0aJdlGKBQW+wdXdHQ08vPz0bZtW0mbqqoqHB0d8ejRoxLHGh4eDmVlZXTs2LFE64tEIqxYsQKHDh3Cy5cvkZubi9zcXGhpFVT/7O3t4eTkBFtbWzg7O6Nbt24YMGAAdHV1oaenBy8vLzg7O+Onn35C165dMXDgQLkT1m/Jzs6Gurq6XHEX+rKSdPv2bZw/fx7a2tpFjhMdHQ1LS0tER0dj3rx5CAsLQ0pKCsRiMQAgLi6u2ERIT08Penp6P3KaRX7Gv/VzP2rUKERHR6Nnz57Iz8+Hjo4OJk2ahAULFkiqnpGRkZg4cSL8/f3h7OyMxMRETJs2DT4+Pti5c6dkXxoaGgAKCgUVhQJ3li6bRCgtLU2qE3RAQADu3btHSRCpfHi8gktU5f2QMxHR0tKChYUF7OzssHHjRuTm5mLhwoWS5YXJSXEf8o8fP0aDBg0k66anpyMxMVGuGArf3IujpKRUJBHLz8+XeS5f8vDwQFRUFO7cuYNr164hPj4e7u7uACD54Ny+fTvCw8MljwcPHiAsLExmLIVxyPth+KVvnfOX1qxZg3Xr1mH69On477//EB4eDmdnZ0nHYmVlZYSEhODUqVOwtrZGQEAAGjZsKBlhFBQUhNDQULRp0waHDh2CpaVlsef4vWrUqFFkxOG34i705fdOLBajV69eUt+X8PBwPH36FB06dABQcGk2NTUV27dvx/Xr1yUdlr/W2XrZsmXQ1tb+6uPy5cvFbm9kZFSk+vPmzZsiVaLP8Xg8/PLLL3j//j1evHiBpKQkODo6AgDMzMwAAMuXL0fbtm0xbdo02NnZwdnZGYGBgdi1a5fU79Pbt28BADVr1iz2eOVNcROhMrg0duHCBdjZ2cHLy0vyZlOtWjXJDwohpOzNnz8fq1evltynr1u3btDT05M54uv48eN4+vQpBg0aBAAYMGAA+Hy+1CWOzxU3J46dnR0SEhIkl9i+VLNmTSQlJUklQ+Hh4SU6n9q1a6NDhw4IDg5GcHAwunbtKvnQMjQ0hImJCWJiYmBhYSH1MDc3l7k/CwsL8Pl8XLlyRdKWn5+PW7duyXW5ydbWFmKxGBcvXizR+pcvX0bv3r0xZMgQ2Nvbo169enj69KnUOjweD23btsXChQtx9+5d8Pl8qaHdTZo0waxZs3Dt2jXY2NjgwIEDJY63JJo0aYLIyEi545aladOmePjwIczMzIp8b7S0tJCamopHjx5h7ty5cHJygpWVlcxpH77k4+NTJLn68vFldepzrVu3RkhIiFTb2bNn0aZNm28eW1lZGSYmJuDz+Th48CBat24NAwMDAAUVni/7NRVWiz7/uX/w4AFUVVXRuHHjbx6vvCjupbFS7GSXl5cHf39/rFy5EowxaGho4PXr18VeWyeElJ1OnTqhcePGWLZsGTZt2gQtLS1s3boV7u7u+PnnnzF+/Hjo6Ojg3LlzmDZtGgYMGCDpc1GnTh2sW7cO48ePR0ZGBoYNGwYzMzMkJCRg79690NbWlplQdezYER06dED//v2xdu1aWFhY4PHjx+DxeOjevTs6deqE5ORkrFy5EgMGDMDp06dx6tQp6OjolOicBg8ejAULFiAvLw/r1q2TWrZgwQJMnDgROjo6cHFxQW5uLm7duoW0tDT4+fkV2ZeWlhbGjBmDadOmQU9PD3Xr1sXKlSvx4cMHjBgxosSvs5mZGTw9PTF8+HBJZ+kXL17gzZs3MvuwWFhY4OjRo7h27Rp0dXWxdu1aJCUlSZKv69ev49y5c+jWrRsMDAxw/fp1JCcnw8rKCrGxsdi2bRv+97//oVatWnjy5AmioqIwbNiwYuOLi4vD27dvERcXB5FIJEk8LSwsZF6uAgr6wowcORIikUjyIf6tuIszbtw4bN++HYMGDcK0adNQo0YNPHv2DL/99hu2b98OXV1d6OvrY9u2bTA2NkZcXBxmzpz5zdf9Ry+NTZo0CR06dMAvv/yC3r1749ixY/j333+lEuNNmzbhzz//xLlz5wAAKSkp+P3339GpUyfk5OQgKCgIR44ckUqCe/XqhVGjRmHz5s2SS2O+vr5wdHSUumfm5cuX0b59e7krimWqVMegVQKFw++ubvq5VPb3+PFj1rRpU8kw05EjR7LMzMxS2Tch5eVrQ1YrMlnD5xljLDg4mPH5fBYXFydpu3TpEuvevTsTCASMz+cza2trtnr1aiYUCotsHxISwpydnZmuri5TV1dnjRo1YlOnTmWvXr0qNpbU1FTm7e3N9PX1mbq6OrOxsWH//POPZPnmzZtZnTp1mJaWFhs2bBhbunSpzOHzsqSlpTE1NTWmqakp8/0lODiYOTg4MD6fz3R1dVmHDh3YH3/8UWys2dnZbMKECaxGjRpFhs8XEggExQ6b/3w/kydPZsbGxpLh87t27WKMFR0+n5qaynr37s20tbWZgYEBmzt3Lhs2bJjknCMjI5mzs7NkSL+lpSULCAhgjDGWlJTE+vTpIzmOqakp8/f3ZyKRqNjYPD09i0wDAICdP3++2G2EQiEzMTFhp0+flrR9K27GCobPT5o0qcj+oqKiWN++fVn16tWZhoYGa9SoEfP19ZUMOQ8JCWFWVlZMTU2N2dnZsQsXLjAA7M8///zq6/6jjhw5who2bMhUVVVZo0aN2NGjR6WWz58/X+pnMzk5mbVq1YppaWkxTU1N5uTkxMLCworsd+PGjcza2pppaGgwY2NjNnjwYJaQkCC1jqWlJTt48GCxsXExfJ7HWDE9CKuojIwMCAQCXN00Gm3Gff+kTowxbNu2DZMnT0Z2djb09PSwfft2ycgNQiqTnJwcxMbGwtzcvEhnUUIUSWBgII4dOyY1+z8pHSdOnMC0adNw//59qKjIviD1tfeiws/v9PT0EldTS0JhL42xH7wylp2djVWrViE7Oxv/3969B0V1nn8A/+6yu1xWwcZELoIgJIhJExOgoDjUMSFosZLQemllojIxSonBQA1KbQO2tU5kJETHS8cqNA5UjIpjJ6RKjCKo03CNIkyCkaJWqMWIElAQePpHfuzPlRVdXHbJ7vczszOe97zn7HN8hPN4znvOGxERgZycnAEfWSQiouFv6dKluH79Otra2qx6mg1LaG9vR3Z29n2LIEsZXtGY0aNOseHk5ITc3FyUlpYiKSnpvi+/IiKi7w+VSoU1a9ZYOgyr9KD3H1mK7RZCRj41dvv2baxatQp+fn662XNDQ0P1XqhFRERE3y82WwgZ4+zZs1iwYAFqamrg4OCAefPm8YkwIiIiK2Cz93Me5opQb28vsrKyEBwcjJqaGowZMwb79+9nEURWy8aenSCiYcYSv4Ns9orQg8YINTU1YfHixThy5AgA4Kc//aluHhgia9M3SWlHR8fwer8HEdmUu980bi42WwgNpL29HUFBQWhqaoKDgwMyMzMRHx9v8pmOiYYLOzs7jBo1ClevXgXw3cMA/PdORObU29uL//73v3BycjLrk2U2WwgNdGtMq9Xirbfewt69e5GXlzcksxwTDTd9t3z7iiEiInNTKpUYN26cWf8jxkLo/1RUVMDe3l43429KSgqSk5Nhb29vifCIzE6hUMDd3R1jxowxOCEoEdFQ02g0Zn8djcULoa1btyIjIwNNTU145plnkJWVhfDw8Pv2Ly4uRnJyMs6dOwcPDw+kpKQgPj7e6O/tm32+p6cHGRkZ+N3vfocJEyagrKwMjo6OsLOzM+s9SqLhgv/2iciWWPSpsfz8fLz99ttYs2YNqqqqEB4ejp/85Ce4ePGiwf4NDQ2IiopCeHg4qqqq8Jvf/AaJiYnYv3//IL5dgUuXLuGll15Camoquru7ERAQoBuoRURERNbPonONhYaGIjAwENu2bdO1TZw4Ea+++irWr1/fr/+qVatw6NAh1NXV6dri4+PxxRdf4PTp0w/1nX1zlax5IxpbPjqB1tZWaLVabN68GYsXL+YAUSIiomFoqOYas9gVoa6uLlRUVCAyMlKvPTIyEqdOnTK4zenTp/v1nzFjBsrLy40e07BuxyG0trYiJCQE1dXViIuLYxFERERkYyw2RqilpQU9PT1wdXXVa3d1dUVzc7PBbZqbmw327+7uRktLC9zd3ftt09nZic7OTt3yjRs3dH9OSUlBSkoK1Go1bt68+SiHQ0REREOo7zxt6htZFh8sfe9VGBEZ8MqMof6G2vusX78ea9euNbhuw4YN2LBhgzHhEhERkQVdu3YNLi4uJtufxQqhxx9/HHZ2dv2u/ly9erXfVZ8+bm5uBvurVCqMHj3a4DapqalITk7WLbe2tsLb2xsXL1406V8kDc7Nmzfh5eWFS5cumfSeLxmPuRg+mIvhg7kYPm7cuIFx48bhscceM+l+LVYIaTQaBAUFoaioCDExMbr2oqIivPLKKwa3mTJlCv7+97/rtR05cgTBwcG6KQLuZW9vb/BdQC4uLvxHPYw4OzszH8MEczF8MBfDB3MxfJj6PUMWfXw+OTkZf/nLX7Br1y7U1dUhKSkJFy9e1L0XKDU1FQsXLtT1j4+PR2NjI5KTk1FXV4ddu3Zh586dWLlypaUOgYiIiL7HLDpGaP78+bh27Rp+//vfo6mpCT/84Q9RWFgIb29vAN9NfHr3O4XGjx+PwsJCJCUlYcuWLfDw8MCmTZvw85//3FKHQERERN9jFh8snZCQgISEBIPrcnJy+rVNmzYNlZWVg/4+e3t7pKWlceqMYYL5GD6Yi+GDuRg+mIvhY6hyYdEXKhIRERFZkkXHCBERERFZEgshIiIislkshIiIiMhmsRAiIiIim2WVhdDWrVsxfvx4ODg4ICgoCCUlJQP2Ly4uRlBQEBwcHODr64vt27ebKVLrZ0wuDhw4gJdffhlPPPEEnJ2dMWXKFBw+fNiM0Vo/Y382+pw8eRIqlQrPP//80AZoQ4zNRWdnJ9asWQNvb2/Y29vDz88Pu3btMlO01s3YXOTm5mLSpElwcnKCu7s74uLicO3aNTNFa71OnDiB2bNnw8PDAwqFAgcPHnzgNiY5f4uV2bNnj6jVatmxY4fU1tbKihUrRKvVSmNjo8H+Fy5cECcnJ1mxYoXU1tbKjh07RK1Wy759+8wcufUxNhcrVqyQ9957Tz7//HP56quvJDU1VdRqtVRWVpo5cutkbD76tLa2iq+vr0RGRsqkSZPME6yVG0wuoqOjJTQ0VIqKiqShoUH++c9/ysmTJ80YtXUyNhclJSWiVCrlgw8+kAsXLkhJSYk888wz8uqrr5o5cutTWFgoa9askf379wsAKSgoGLC/qc7fVlcIhYSESHx8vF5bQECArF692mD/lJQUCQgI0GtbtmyZTJ48echitBXG5sKQp59+WtauXWvq0GzSYPMxf/58+e1vfytpaWkshEzE2Fx88skn4uLiIteuXTNHeDbF2FxkZGSIr6+vXtumTZvE09NzyGK0RQ9TCJnq/G1Vt8a6urpQUVGByMhIvfbIyEicOnXK4DanT5/u13/GjBkoLy/HnTt3hixWazeYXNyrt7cXbW1tJp9gzxYNNh/Z2dn4+uuvkZaWNtQh2ozB5OLQoUMIDg7Ghg0bMHbsWPj7+2PlypW4deuWOUK2WoPJRVhYGC5fvozCwkKICP7zn/9g3759mDVrljlCpruY6vxt8TdLm1JLSwt6enr6zV7v6urab9b6Ps3NzQb7d3d3o6WlBe7u7kMWrzUbTC7utXHjRrS3t2PevHlDEaJNGUw+6uvrsXr1apSUlEClsqpfFRY1mFxcuHABpaWlcHBwQEFBAVpaWpCQkIBvvvmG44QewWByERYWhtzcXMyfPx+3b99Gd3c3oqOjsXnzZnOETHcx1fnbqq4I9VEoFHrLItKv7UH9DbWT8YzNRZ+//e1vSE9PR35+PsaMGTNU4dmch81HT08PFixYgLVr18Lf399c4dkUY342ent7oVAokJubi5CQEERFRSEzMxM5OTm8KmQCxuSitrYWiYmJePfdd1FRUYF//OMfaGho0E0WTuZlivO3Vf037/HHH4ednV2/Sv7q1av9qsY+bm5uBvurVCqMHj16yGK1doPJRZ/8/Hy8/vrr+OijjxARETGUYdoMY/PR1taG8vJyVFVVYfny5QC+OxmLCFQqFY4cOYIXX3zRLLFbm8H8bLi7u2Ps2LFwcXHRtU2cOBEigsuXL+Opp54a0pit1WBysX79ekydOhXvvPMOAOC5556DVqtFeHg4/vjHP/IughmZ6vxtVVeENBoNgoKCUFRUpNdeVFSEsLAwg9tMmTKlX/8jR44gODgYarV6yGK1doPJBfDdlaDFixcjLy+P99xNyNh8ODs74+zZs6iurtZ94uPjMWHCBFRXVyM0NNRcoVudwfxsTJ06FVeuXMG3336ra/vqq6+gVCrh6ek5pPFas8HkoqOjA0ql/qnTzs4OwP9fjSDzMNn526ih1d8DfY9C7ty5U2pra+Xtt98WrVYr//rXv0REZPXq1fLaa6/p+vc9fpeUlCS1tbWyc+dOPj5vIsbmIi8vT1QqlWzZskWampp0n9bWVksdglUxNh/34lNjpmNsLtra2sTT01PmzJkj586dk+LiYnnqqadkyZIlljoEq2FsLrKzs0WlUsnWrVvl66+/ltLSUgkODpaQkBBLHYLVaGtrk6qqKqmqqhIAkpmZKVVVVbpXGQzV+dvqCiERkS1btoi3t7doNBoJDAyU4uJi3bpFixbJtGnT9PofP35cXnjhBdFoNOLj4yPbtm0zc8TWy5hcTJs2TQD0+yxatMj8gVspY3827sZCyLSMzUVdXZ1ERESIo6OjeHp6SnJysnR0dJg5autkbC42bdokTz/9tDg6Ooq7u7vExsbK5cuXzRy19Tl27NiA54ChOn8rRHgtj4iIiGyTVY0RIiIiIjIGCyEiIiKyWSyEiIiIyGaxECIiIiKbxUKIiIiIbBYLISIiIrJZLISIiIjIZrEQIiI9OTk5GDVqlKXDGDQfHx9kZWUN2Cc9PR3PP/+8WeIhouGNhRCRFVq8eDEUCkW/z/nz5y0dGnJycvRicnd3x7x589DQ0GCS/ZeVlWHp0qW6ZYVCgYMHD+r1WblyJY4ePWqS77ufe4/T1dUVs2fPxrlz54zez/e5MCUa7lgIEVmpmTNnoqmpSe8zfvx4S4cF4LtJXZuamnDlyhXk5eWhuroa0dHR6OnpeeR9P/HEE3Bychqwz4gRI4yanXqw7j7Ojz/+GO3t7Zg1axa6urqG/LuJ6OGwECKyUvb29nBzc9P72NnZITMzE88++yy0Wi28vLyQkJCgN6v5vb744gtMnz4dI0eOhLOzM4KCglBeXq5bf+rUKfz4xz+Go6MjvLy8kJiYiPb29gFjUygUcHNzg7u7O6ZPn460tDTU1NTorlht27YNfn5+0Gg0mDBhAnbv3q23fXp6OsaNGwd7e3t4eHggMTFRt+7uW2M+Pj4AgJiYGCgUCt3y3bfGDh8+DAcHB7S2tup9R2JiIqZNm2ay4wwODkZSUhIaGxvx5Zdf6voMlI/jx48jLi4ON27c0F1ZSk9PBwB0dXUhJSUFY8eOhVarRWhoKI4fPz5gPETUHwshIhujVCqxadMm1NTU4K9//Ss+++wzpKSk3Ld/bGwsPD09UVZWhoqKCqxevRpqtRoAcPbsWcyYMQM/+9nPcObMGeTn56O0tBTLly83KiZHR0cAwJ07d1BQUIAVK1bg17/+NWpqarBs2TLExcXh2LFjAIB9+/bh/fffx5///GfU19fj4MGDePbZZw3ut6ysDACQnZ2NpqYm3fLdIiIiMGrUKOzfv1/X1tPTg7179yI2NtZkx9na2oq8vDwA0P39AQPnIywsDFlZWborS01NTVi5ciUAIC4uDidPnsSePXtw5swZzJ07FzNnzkR9ff1Dx0REgFXOPk9k6xYtWiR2dnai1Wp1nzlz5hjsu3fvXhk9erRuOTs7W1xcXHTLI0eOlJycHIPbvvbaa7J06VK9tpKSElEqlXLr1i2D29y7/0uXLsnkyZPF09NTOjs7JSwsTN544w29bebOnStRUVEiIrJx40bx9/eXrq4ug/v39vaW999/X7cMQAoKCvT6pKWlyaRJk3TLiYmJ8uKLL+qWDx8+LBqNRr755ptHOk4AotVqxcnJSTeTdnR0tMH+fR6UDxGR8+fPi0KhkH//+9967S+99JKkpqYOuH8i0qeybBlGRENl+vTp2LZtm25Zq9UCAI4dO4Y//elPqK2txc2bN9Hd3Y3bt2+jvb1d1+duycnJWLJkCXbv3o2IiAjMnTsXfn5+AICKigqcP38eubm5uv4igt7eXjQ0NGDixIkGY7tx4wZGjBgBEUFHRwcCAwNx4MABaDQa1NXV6Q12BoCpU6figw8+AADMnTsXWVlZ8PX1xcyZMxEVFYXZs2dDpRr8r7PY2FhMmTIFV65cgYeHB3JzcxEVFYUf/OAHj3ScI0eORGVlJbq7u1FcXIyMjAxs375dr4+x+QCAyspKiAj8/f312js7O80y9onImrAQIrJSWq0WTz75pF5bY2MjoqKiEB8fjz/84Q947LHHUFpaitdffx137twxuJ/09HQsWLAAH3/8MT755BOkpaVhz549iImJQW9vL5YtW6Y3RqfPuHHj7htbX4GgVCrh6ura74SvUCj0lkVE1+bl5YUvv/wSRUVF+PTTT5GQkICMjAwUFxfr3XIyRkhICPz8/LBnzx786le/QkFBAbKzs3XrB3ucSqVSl4OAgAA0Nzdj/vz5OHHiBIDB5aMvHjs7O1RUVMDOzk5v3YgRI4w6diJbx0KIyIaUl5eju7sbGzduhFL53RDBvXv3PnA7f39/+Pv7IykpCb/85S+RnZ2NmJgYBAYG4ty5c/0Krge5u0C418SJE1FaWoqFCxfq2k6dOqV31cXR0RHR0dGIjo7Gm2++iYCAAJw9exaBgYH99qdWqx/qabQFCxYgNzcXnp6eUCqVmDVrlm7dYI/zXklJScjMzERBQQFiYmIeKh8ajaZf/C+88AJ6enpw9epVhIeHP1JMRLaOg6WJbIifnx+6u7uxefNmXLhwAbt37+53q+Zut27dwvLly3H8+HE0Njbi5MmTKCsr0xUlq1atwunTp/Hmm2+iuroa9fX1OHToEN56661Bx/jOO+8gJycH27dvR319PTIzM3HgwAHdIOGcnBzs3LkTNTU1umNwdHSEt7e3wf35+Pjg6NGjaG5uxvXr1+/7vbGxsaisrMS6deswZ84cODg46NaZ6jidnZ2xZMkSpKWlQUQeKh8+Pj749ttvcfToUbS0tKCjowP+/v6IjY3FwoULceDAATQ0NKCsrAzvvfceCgsLjYqJyOZZcoASEQ2NRYsWySuvvGJwXWZmpri7u4ujo6PMmDFDPvzwQwEg169fFxH9wbmdnZ3yi1/8Qry8vESj0YiHh4csX75cb4Dw559/Li+//LKMGDFCtFqtPPfcc7Ju3br7xmZo8O+9tm7dKr6+vqJWq8Xf318+/PBD3bqCggIJDQ0VZ2dn0Wq1MnnyZPn000916+8dLH3o0CF58sknRaVSibe3t4j0Hyzd50c/+pEAkM8++6zfOlMdZ2Njo6hUKsnPzxeRB+dDRCQ+Pl5Gjx4tACQtLU1ERLq6uuTdd98VHx8fUavV4ubmJjExMXLmzJn7xkRE/SlERCxbihERERFZBm+NERERkc1iIUREREQ2i4UQERER2SwWQkRERGSzWAgRERGRzWIhRERERDaLhRARERHZLBZCREREZLNYCBEREZHNYiFERERENouFEBEREdksFkJERERks/4HZWza/TPvMTsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "# Generate sample data\n",
    "X, y = make_classification(n_samples=1000, n_classes=2, n_features=10, n_informative=2, n_clusters_per_class=1, random_state=42)\n",
    "\n",
    "# Split data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)\n",
    "\n",
    "# Create RandomForestClassifier\n",
    "RF = RandomForestClassifier(n_estimators=10, criterion='entropy', max_features='sqrt', bootstrap=True)\n",
    "\n",
    "# Train RandomForestClassifier\n",
    "RF.fit(X_train, y_train)\n",
    "\n",
    "# Score RandomForestClassifier\n",
    "rf_score = RF.score(X_test, y_test)\n",
    "print('RandomForestClassifier processing...')\n",
    "print('RandomForestClassifier Score:', rf_score)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred = RF.predict_proba(X_test)\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "n_classes = y_pred.shape[1]  # Number of classes based on y_pred\n",
    "for i in range(n_classes):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_test, y_pred[:, i], pos_label=i)\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "\n",
    "# Plot ROC curve for each class\n",
    "plt.figure()\n",
    "for i in range(n_classes):\n",
    "    plt.plot(fpr[i], tpr[i], label='ROC curve of class {0} (area = {1:0.2f})'.format(i, roc_auc[i]))\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC)')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a05031f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(criterion=&#x27;entropy&#x27;,\n",
       "                                              n_estimators=10),\n",
       "             param_grid={&#x27;max_depth&#x27;: [None, 10, 20],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;n_estimators&#x27;: [10, 50, 100]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(criterion=&#x27;entropy&#x27;,\n",
       "                                              n_estimators=10),\n",
       "             param_grid={&#x27;max_depth&#x27;: [None, 10, 20],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;n_estimators&#x27;: [10, 50, 100]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=10)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=10)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(criterion='entropy',\n",
       "                                              n_estimators=10),\n",
       "             param_grid={'max_depth': [None, 10, 20],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'n_estimators': [10, 50, 100]})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'max_depth': 10, 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 100}\n",
      "Accuracy: 0.928\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define the parameter grid for grid search\n",
    "param_grid = {\n",
    "    'n_estimators': [10, 50, 100],\n",
    "    'max_depth': [None, 10, 20],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "\n",
    "# Instantiate GridSearchCV\n",
    "grid_search = GridSearchCV(estimator=RF, param_grid=param_grid, cv=5)\n",
    "\n",
    "# Perform grid search\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters found by grid search\n",
    "print(\"Best Parameters:\", grid_search.best_params_)\n",
    "\n",
    "# Get the best model\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "# Evaluate the best model on the test set\n",
    "accuracy = best_model.score(X_test, y_test)\n",
    "print(\"Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7d098207",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Feature_0  Feature_1  Feature_2  Feature_3  Feature_4  Feature_5  \\\n",
      "0     2.218210  -0.709021  -1.793892  -1.153332  -0.391227   1.637681   \n",
      "1     0.841023  -1.089633  -0.847634   1.809306  -0.886888   1.245940   \n",
      "2     0.719914  -1.244486   1.010840  -0.633273   0.980464  -0.405691   \n",
      "3     0.549020   0.686568   1.366389   0.589639   1.453814  -0.906938   \n",
      "4    -0.419654   1.221295   1.548639  -1.336569   2.273292  -2.171021   \n",
      "..         ...        ...        ...        ...        ...        ...   \n",
      "495  -1.561003   0.520689  -0.237807   0.473145   3.113533  -3.554387   \n",
      "496   1.574242   0.610216  -0.962733  -0.342549  -0.714525   1.531963   \n",
      "497   1.410559  -1.615887   0.204759  -0.022300  -0.499035   1.253184   \n",
      "498   0.156409   0.551964   0.822350  -0.206985   1.225221  -0.944746   \n",
      "499  -0.246759  -0.900448  -0.822166  -0.451303   2.199165  -2.006449   \n",
      "\n",
      "     Feature_6  Feature_7  Feature_8  Feature_9  \n",
      "0     0.918234  -1.687344   1.953259  -1.355448  \n",
      "1     0.347222  -0.218046  -0.784762  -0.652089  \n",
      "2     0.299393  -0.096760   0.449827   3.529055  \n",
      "3     0.229204  -2.651828   0.741074  -0.737737  \n",
      "4    -0.170971   1.336657   0.133958   0.320217  \n",
      "..         ...        ...        ...        ...  \n",
      "495  -0.642638  -0.702911  -1.079533   0.466431  \n",
      "496   0.651116   0.170464   0.156973  -0.206611  \n",
      "497   0.583592  -0.688886   0.299648   0.765878  \n",
      "498   0.066310  -1.031025  -0.344098  -0.836980  \n",
      "499  -0.099455  -1.017911   1.419456  -1.287605  \n",
      "\n",
      "[500 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "X_test_df = pd.DataFrame(X_test, columns=[f\"Feature_{i}\" for i in range(X_test.shape[1])])\n",
    "\n",
    "# Print the DataFrame\n",
    "print(X_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d429b908",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Attack\n",
      "Normal\n",
      "Attack\n",
      "Normal\n",
      "Normal\n",
      "Attack\n",
      "Attack\n"
     ]
    }
   ],
   "source": [
    "predictions = RF.predict(X_test)\n",
    "\n",
    "for prediction in predictions:\n",
    "    if prediction == 0:\n",
    "        print(\"Normal\")\n",
    "    else:\n",
    "        print(\"Attack\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "29bccbd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7861\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7861/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import numpy as np\n",
    "\n",
    "# Define the prediction function\n",
    "def predict_attack(features):\n",
    "    try:\n",
    "        # Split the input string into a list of features\n",
    "        features_list = features.split()\n",
    "        # Convert the features to a numpy array\n",
    "        features_array = np.array(features_list, dtype=float)\n",
    "        # Reshape the array into a 2D array with a single row\n",
    "        features_array = features_array.reshape(1, -1)\n",
    "        # Make predictions using the RandomForestClassifier model\n",
    "        prediction = RF.predict(features_array)[0]\n",
    "        # Map prediction to corresponding label\n",
    "        if prediction == 0:\n",
    "            return \"Normal\"\n",
    "        else:\n",
    "            return \"Attack\"\n",
    "    except:\n",
    "        return \"Invalid input. Please enter numeric features separated by spaces.\"\n",
    "\n",
    "# Create a text box for input\n",
    "textbox = gr.Textbox(placeholder=\"Enter features separated by spaces\", label=\"Features:\")\n",
    "\n",
    "# Create the Gradio interface with a larger text box and a clear title\n",
    "iface = gr.Interface(predict_attack, \n",
    "                     textbox, \n",
    "                     \"label\", \n",
    "                     title=\"Attack Prediction\",\n",
    "                     description=\"Enter the features separated by spaces. You will receive a prediction of 'Normal' or 'Attack'.\",\n",
    "                     theme=\"huggingface\")\n",
    "\n",
    "# Launch the interface\n",
    "iface.launch()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb31346e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a5a7186",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 14.101261,
   "end_time": "2023-07-05T16:38:58.860172",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-07-05T16:38:44.758911",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}