report3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e7585b3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import all of the libries that i need \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score \n",
    "import itertools\n",
    "import warnings\n",
    "import copy\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.feature_selection import SequentialFeatureSelector\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import normaltest\n",
    "from statsmodels.stats.diagnostic import lilliefors\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from joblib import parallel_backend\n",
    "\n",
    "from sklearn import preprocessing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fc8e2ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: bottleneck in c:\\users\\spin 3\\anaconda3\\lib\\site-packages (1.3.5)\n",
      "Collecting bottleneck\n",
      "  Obtaining dependency information for bottleneck from https://files.pythonhosted.org/packages/a7/ef/f5ee62f290f61842b34413ae82042825556ee1078e195d0e34c674fdc72e/Bottleneck-1.3.8-cp311-cp311-win_amd64.whl.metadata\n",
      "  Downloading Bottleneck-1.3.8-cp311-cp311-win_amd64.whl.metadata (8.1 kB)\n",
      "Requirement already satisfied: numpy in c:\\users\\spin 3\\anaconda3\\lib\\site-packages (from bottleneck) (1.24.3)\n",
      "Downloading Bottleneck-1.3.8-cp311-cp311-win_amd64.whl (110 kB)\n",
      "   ---------------------------------------- 0.0/110.1 kB ? eta -:--:--\n",
      "   ---------------------- ----------------- 61.4/110.1 kB 1.6 MB/s eta 0:00:01\n",
      "   ---------------------------------------- 110.1/110.1 kB 3.1 MB/s eta 0:00:00\n",
      "Installing collected packages: bottleneck\n",
      "  Attempting uninstall: bottleneck\n",
      "    Found existing installation: Bottleneck 1.3.5\n",
      "    Uninstalling Bottleneck-1.3.5:\n",
      "      Successfully uninstalled Bottleneck-1.3.5\n",
      "Successfully installed bottleneck-1.3.8\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install --upgrade bottleneck\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3da8f239",
   "metadata": {},
   "outputs": [],
   "source": [
    "head= ['Subject_ID','Sex','Age_(years)','Weight_(kg)','Height_(cm)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "34759e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_subjects=pd.DataFrame(data=[[101,'Female',55,73,169],[102,'Male',61,85,180],[103,'Male',23,95,180],[104,'Female',48,55,158],[105,'Male',53,98,175]],columns=['Subject_ID','Sex','Age_(years)','Weight_(kg)','Height_(cm)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b96c4570",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subject_ID</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_(years)</th>\n",
       "      <th>Weight_(kg)</th>\n",
       "      <th>Height_(cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>Female</td>\n",
       "      <td>55</td>\n",
       "      <td>73</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>85</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>95</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104</td>\n",
       "      <td>Female</td>\n",
       "      <td>48</td>\n",
       "      <td>55</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105</td>\n",
       "      <td>Male</td>\n",
       "      <td>53</td>\n",
       "      <td>98</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Subject_ID     Sex  Age_(years)  Weight_(kg)  Height_(cm)\n",
       "0         101  Female           55           73          169\n",
       "1         102    Male           61           85          180\n",
       "2         103    Male           23           95          180\n",
       "3         104  Female           48           55          158\n",
       "4         105    Male           53           98          175"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subjects.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "22bf8599",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creat a data for the candidates\n",
    "# initialising dataframe\n",
    "\n",
    "head2= ['Subject_ID','activity_id','unit']\n",
    "headers = ['time_index','acc_x','acc_y','acc_z','gyr_x','gyr_y','gyr_z','mag_x','mag_y','mag_z']\n",
    "\n",
    "df_raw=pd.DataFrame(columns=headers)\n",
    "for i in range(1,len(head2)+1): \n",
    "\n",
    "    df_raw.insert(loc=0,column=head2[-i],value=\"\")\n",
    " \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a51f544f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Subject_ID</th>\n",
       "      <th>activity_id</th>\n",
       "      <th>unit</th>\n",
       "      <th>time_index</th>\n",
       "      <th>acc_x</th>\n",
       "      <th>acc_y</th>\n",
       "      <th>acc_z</th>\n",
       "      <th>gyr_x</th>\n",
       "      <th>gyr_y</th>\n",
       "      <th>gyr_z</th>\n",
       "      <th>mag_x</th>\n",
       "      <th>mag_y</th>\n",
       "      <th>mag_z</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Subject_ID, activity_id, unit, time_index, acc_x, acc_y, acc_z, gyr_x, gyr_y, gyr_z, mag_x, mag_y, mag_z]\n",
       "Index: []"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7bfc7dac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Define column headers\n",
    "head3 = ['Subject_ID', 'activity_id']\n",
    "headers3 = ['execution_type','start','end']\n",
    "\n",
    "# Create an empty DataFrame with column headers\n",
    "\n",
    "df_raw3 = pd.DataFrame(columns=headers)\n",
    "# Insert empty columns for 'Subject_ID', 'activity_id', 'unit'\n",
    "for i in range(1, len(head2) + 1):\n",
    "    \n",
    "    df_raw3.insert(loc=0, column=head2[-i], value=\"\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "948e7345",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "filename = '111template.txt'\n",
    "df = pd.read_csv(filename, sep=';', header=0)\n",
    "\n",
    "# Display the DataFrame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "93b52472",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time index</th>\n",
       "      <th>acc_x</th>\n",
       "      <th>acc_y</th>\n",
       "      <th>acc_z</th>\n",
       "      <th>gyr_x</th>\n",
       "      <th>gyr_y</th>\n",
       "      <th>gyr_z</th>\n",
       "      <th>mag_x</th>\n",
       "      <th>mag_y</th>\n",
       "      <th>mag_z</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-9.665799</td>\n",
       "      <td>-1.677241</td>\n",
       "      <td>0.615063</td>\n",
       "      <td>-0.014956</td>\n",
       "      <td>0.004388</td>\n",
       "      <td>0.010589</td>\n",
       "      <td>0.587318</td>\n",
       "      <td>0.455106</td>\n",
       "      <td>-0.094949</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-9.665806</td>\n",
       "      <td>-1.684737</td>\n",
       "      <td>0.622513</td>\n",
       "      <td>0.000607</td>\n",
       "      <td>-0.003094</td>\n",
       "      <td>-0.007589</td>\n",
       "      <td>0.587428</td>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.093364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-9.628410</td>\n",
       "      <td>-1.699724</td>\n",
       "      <td>0.585751</td>\n",
       "      <td>0.006007</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>-0.004879</td>\n",
       "      <td>0.588389</td>\n",
       "      <td>0.454722</td>\n",
       "      <td>-0.094907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>-9.628372</td>\n",
       "      <td>-1.684836</td>\n",
       "      <td>0.600733</td>\n",
       "      <td>-0.003085</td>\n",
       "      <td>-0.000281</td>\n",
       "      <td>-0.000340</td>\n",
       "      <td>0.588673</td>\n",
       "      <td>0.455759</td>\n",
       "      <td>-0.092664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-9.643291</td>\n",
       "      <td>-1.639893</td>\n",
       "      <td>0.585661</td>\n",
       "      <td>-0.002932</td>\n",
       "      <td>-0.006807</td>\n",
       "      <td>-0.013043</td>\n",
       "      <td>0.589193</td>\n",
       "      <td>0.453927</td>\n",
       "      <td>-0.093143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time index     acc_x     acc_y     acc_z     gyr_x     gyr_y     gyr_z  \\\n",
       "0           1 -9.665799 -1.677241  0.615063 -0.014956  0.004388  0.010589   \n",
       "1           2 -9.665806 -1.684737  0.622513  0.000607 -0.003094 -0.007589   \n",
       "2           3 -9.628410 -1.699724  0.585751  0.006007  0.000557 -0.004879   \n",
       "3           4 -9.628372 -1.684836  0.600733 -0.003085 -0.000281 -0.000340   \n",
       "4           5 -9.643291 -1.639893  0.585661 -0.002932 -0.006807 -0.013043   \n",
       "\n",
       "      mag_x     mag_y     mag_z  \n",
       "0  0.587318  0.455106 -0.094949  \n",
       "1  0.587428  0.455621 -0.093364  \n",
       "2  0.588389  0.454722 -0.094907  \n",
       "3  0.588673  0.455759 -0.092664  \n",
       "4  0.589193  0.453927 -0.093143  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ed81b581",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subject_ID</th>\n",
       "      <th>activity_id</th>\n",
       "      <th>unit</th>\n",
       "      <th>time_index</th>\n",
       "      <th>acc_x</th>\n",
       "      <th>acc_y</th>\n",
       "      <th>acc_z</th>\n",
       "      <th>gyr_x</th>\n",
       "      <th>gyr_y</th>\n",
       "      <th>gyr_z</th>\n",
       "      <th>mag_x</th>\n",
       "      <th>mag_y</th>\n",
       "      <th>mag_z</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Subject_ID, activity_id, unit, time_index, acc_x, acc_y, acc_z, gyr_x, gyr_y, gyr_z, mag_x, mag_y, mag_z]\n",
       "Index: []"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_raw=pd.DataFrame(columns=headers)\n",
    "df_raw.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e58b0552",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\spin 3\\AppData\\Local\\Temp\\ipykernel_10436\\736965609.py:16: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df_raw = pd.concat([df_raw, df2], ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 6):\n",
    "    for k in range(1, 9):\n",
    "        for j in range(1, 6):\n",
    "            # Read data from template file\n",
    "            df2 = pd.read_csv(f\"{i}{k}{j}template.txt\", sep=';', header=0)\n",
    "            # Read data from test file\n",
    "            #df3 = pd.read_csv(f\"{i}{k}{j}test.txt\", sep=';', header=0)\n",
    "            # Add Subject_ID, activity_id, and unit columns\n",
    "            df2[\"Subject_ID\"] = i\n",
    "            df2[\"activity_id\"] = k\n",
    "            df2[\"unit\"] = j\n",
    "            #df3[\"Subject_ID\"] = i\n",
    "            #df3[\"activity_id\"] = k\n",
    "            #df3[\"unit\"] = j\n",
    "            # Concatenate df2 and df3 to df_raw\n",
    "            df_raw = pd.concat([df_raw, df2], ignore_index=True)\n",
    "            #df_raw = pd.concat([df_raw, df3], ignore_index=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3d30b6b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop  column\n",
    "df_raw.drop(columns=[\"time_index\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a96bebb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#change columns name \n",
    "headers=['time index']\n",
    "columns = {\"time index\" : \"time_index\"\n",
    "          }\n",
    "\n",
    "df_raw.rename(columns=columns,inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ba74bf6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Subject_ID</th>\n",
       "      <th>activity_id</th>\n",
       "      <th>unit</th>\n",
       "      <th>acc_x</th>\n",
       "      <th>acc_y</th>\n",
       "      <th>acc_z</th>\n",
       "      <th>gyr_x</th>\n",
       "      <th>gyr_y</th>\n",
       "      <th>gyr_z</th>\n",
       "      <th>mag_x</th>\n",
       "      <th>mag_y</th>\n",
       "      <th>mag_z</th>\n",
       "      <th>time_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.665799</td>\n",
       "      <td>-1.677241</td>\n",
       "      <td>0.615063</td>\n",
       "      <td>-0.014956</td>\n",
       "      <td>0.004388</td>\n",
       "      <td>0.010589</td>\n",
       "      <td>0.587318</td>\n",
       "      <td>0.455106</td>\n",
       "      <td>-0.094949</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.665806</td>\n",
       "      <td>-1.684737</td>\n",
       "      <td>0.622513</td>\n",
       "      <td>0.000607</td>\n",
       "      <td>-0.003094</td>\n",
       "      <td>-0.007589</td>\n",
       "      <td>0.587428</td>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.093364</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.628410</td>\n",
       "      <td>-1.699724</td>\n",
       "      <td>0.585751</td>\n",
       "      <td>0.006007</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>-0.004879</td>\n",
       "      <td>0.588389</td>\n",
       "      <td>0.454722</td>\n",
       "      <td>-0.094907</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.628372</td>\n",
       "      <td>-1.684836</td>\n",
       "      <td>0.600733</td>\n",
       "      <td>-0.003085</td>\n",
       "      <td>-0.000281</td>\n",
       "      <td>-0.000340</td>\n",
       "      <td>0.588673</td>\n",
       "      <td>0.455759</td>\n",
       "      <td>-0.092664</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.643291</td>\n",
       "      <td>-1.639893</td>\n",
       "      <td>0.585661</td>\n",
       "      <td>-0.002932</td>\n",
       "      <td>-0.006807</td>\n",
       "      <td>-0.013043</td>\n",
       "      <td>0.589193</td>\n",
       "      <td>0.453927</td>\n",
       "      <td>-0.093143</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Subject_ID activity_id unit     acc_x     acc_y     acc_z     gyr_x  \\\n",
       "0          1           1    1 -9.665799 -1.677241  0.615063 -0.014956   \n",
       "1          1           1    1 -9.665806 -1.684737  0.622513  0.000607   \n",
       "2          1           1    1 -9.628410 -1.699724  0.585751  0.006007   \n",
       "3          1           1    1 -9.628372 -1.684836  0.600733 -0.003085   \n",
       "4          1           1    1 -9.643291 -1.639893  0.585661 -0.002932   \n",
       "\n",
       "      gyr_y     gyr_z     mag_x     mag_y     mag_z  time_index  \n",
       "0  0.004388  0.010589  0.587318  0.455106 -0.094949         1.0  \n",
       "1 -0.003094 -0.007589  0.587428  0.455621 -0.093364         2.0  \n",
       "2  0.000557 -0.004879  0.588389  0.454722 -0.094907         3.0  \n",
       "3 -0.000281 -0.000340  0.588673  0.455759 -0.092664         4.0  \n",
       "4 -0.006807 -0.013043  0.589193  0.453927 -0.093143         5.0  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "47bd5e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1, 6):\n",
    "    for k in range(1, 9):\n",
    "        for j in range(1, 6):\n",
    "            # Read data from template file\n",
    "            #df2 = pd.read_csv(f\"{i}{k}{j}template.txt\", sep=';', header=0)\n",
    "            # Read data from test file\n",
    "            df3 = pd.read_csv(f\"{i}{k}{j}test.txt\", sep=';', header=0)\n",
    "            # Add Subject_ID, activity_id, and unit columns\n",
    "            #df2[\"Subject_ID\"] = i\n",
    "            #df2[\"activity_id\"] = k\n",
    "            #df2[\"unit\"] = j\n",
    "            df3[\"Subject_ID\"] = i\n",
    "            df3[\"activity_id\"] = k\n",
    "            df3[\"unit\"] = j\n",
    "            # Concatenate df2 and df3 to df_raw\n",
    "            #df_raw = pd.concat([df_raw, df2], ignore_index=True)\n",
    "            df_raw2 = pd.concat([df_raw, df3], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c61cabb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop  column\n",
    "df_raw2.drop(columns=[\"time_index\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "43679a6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#change columns name \n",
    "headers=['time index']\n",
    "columns = {\"time index\" : \"time_index\"\n",
    "          }\n",
    "\n",
    "df_raw2.rename(columns=columns,inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e7d831f2",
   "metadata": {},
   "outputs": [
    {
     "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>Subject_ID</th>\n",
       "      <th>activity_id</th>\n",
       "      <th>unit</th>\n",
       "      <th>acc_x</th>\n",
       "      <th>acc_y</th>\n",
       "      <th>acc_z</th>\n",
       "      <th>gyr_x</th>\n",
       "      <th>gyr_y</th>\n",
       "      <th>gyr_z</th>\n",
       "      <th>mag_x</th>\n",
       "      <th>mag_y</th>\n",
       "      <th>mag_z</th>\n",
       "      <th>time_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.665799</td>\n",
       "      <td>-1.677241</td>\n",
       "      <td>0.615063</td>\n",
       "      <td>-0.014956</td>\n",
       "      <td>0.004388</td>\n",
       "      <td>0.010589</td>\n",
       "      <td>0.587318</td>\n",
       "      <td>0.455106</td>\n",
       "      <td>-0.094949</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.665806</td>\n",
       "      <td>-1.684737</td>\n",
       "      <td>0.622513</td>\n",
       "      <td>0.000607</td>\n",
       "      <td>-0.003094</td>\n",
       "      <td>-0.007589</td>\n",
       "      <td>0.587428</td>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.093364</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.628410</td>\n",
       "      <td>-1.699724</td>\n",
       "      <td>0.585751</td>\n",
       "      <td>0.006007</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>-0.004879</td>\n",
       "      <td>0.588389</td>\n",
       "      <td>0.454722</td>\n",
       "      <td>-0.094907</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.628372</td>\n",
       "      <td>-1.684836</td>\n",
       "      <td>0.600733</td>\n",
       "      <td>-0.003085</td>\n",
       "      <td>-0.000281</td>\n",
       "      <td>-0.000340</td>\n",
       "      <td>0.588673</td>\n",
       "      <td>0.455759</td>\n",
       "      <td>-0.092664</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.643291</td>\n",
       "      <td>-1.639893</td>\n",
       "      <td>0.585661</td>\n",
       "      <td>-0.002932</td>\n",
       "      <td>-0.006807</td>\n",
       "      <td>-0.013043</td>\n",
       "      <td>0.589193</td>\n",
       "      <td>0.453927</td>\n",
       "      <td>-0.093143</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Subject_ID activity_id unit     acc_x     acc_y     acc_z     gyr_x  \\\n",
       "0          1           1    1 -9.665799 -1.677241  0.615063 -0.014956   \n",
       "1          1           1    1 -9.665806 -1.684737  0.622513  0.000607   \n",
       "2          1           1    1 -9.628410 -1.699724  0.585751  0.006007   \n",
       "3          1           1    1 -9.628372 -1.684836  0.600733 -0.003085   \n",
       "4          1           1    1 -9.643291 -1.639893  0.585661 -0.002932   \n",
       "\n",
       "      gyr_y     gyr_z     mag_x     mag_y     mag_z  time_index  \n",
       "0  0.004388  0.010589  0.587318  0.455106 -0.094949         NaN  \n",
       "1 -0.003094 -0.007589  0.587428  0.455621 -0.093364         NaN  \n",
       "2  0.000557 -0.004879  0.588389  0.454722 -0.094907         NaN  \n",
       "3 -0.000281 -0.000340  0.588673  0.455759 -0.092664         NaN  \n",
       "4 -0.006807 -0.013043  0.589193  0.453927 -0.093143         NaN  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "47c23449",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subject_ID      object\n",
       "activity_id     object\n",
       "unit            object\n",
       "acc_x          float64\n",
       "acc_y          float64\n",
       "acc_z          float64\n",
       "gyr_x          float64\n",
       "gyr_y          float64\n",
       "gyr_z          float64\n",
       "mag_x          float64\n",
       "mag_y          float64\n",
       "mag_z          float64\n",
       "time_index     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "73b06746",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw['unit'] = df_raw['unit'].astype(int)\n",
    "df_raw['Subject_ID'] = df_raw['Subject_ID'].astype(int)\n",
    "df_raw['activity_id'] = df_raw['activity_id'].astype(int)\n",
    "df_raw2['unit'] = df_raw2['unit'].astype(int)\n",
    "df_raw2['Subject_ID'] = df_raw2['Subject_ID'].astype(int)\n",
    "df_raw2['activity_id'] = df_raw2['activity_id'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b45f077d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6 7 8]\n"
     ]
    }
   ],
   "source": [
    "print(df_raw2['activity_id'].unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "a4fd3a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "activity_id\n",
       "1    39845\n",
       "2    37345\n",
       "8    36902\n",
       "3    36380\n",
       "7    33505\n",
       "6    33285\n",
       "4    32660\n",
       "5    31795\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw2['activity_id'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "e4807480",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Subject_ID', 'activity_id', 'unit', 'acc_x', 'acc_y', 'acc_z', 'gyr_x',\n",
      "       'gyr_y', 'gyr_z', 'mag_x', 'mag_y', 'mag_z', 'time_index'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_raw.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "5c34b9a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 276,625 mtime_index rate records due to difference in frequency.\n"
     ]
    }
   ],
   "source": [
    "#count time_index missing\n",
    "time_index_count=df_raw2['time_index'].isna().sum()\n",
    "print(\"There are \" + f'{time_index_count:,.0f}' + \" mtime_index rate records due to difference in frequency.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43dda7e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace heart rate missing\n",
    "df_heart_fill=df_raw2\n",
    "df_heart_fill.loc[:,'heart_rate']=df_raw2.loc[:,['heart_rate']].fillna(method='bfill',axis=0)\n",
    "incomplete_records_count=df_heart_fill.isna().any(axis=0).sum()\n",
    "data_set_size=df_heart_fill.shape[0]\n",
    "print(\"After filling the missing heart data, there are \" + f'{incomplete_records_count:,.0f}'.format(incomplete_records_count) + \n",
    "      \" records missing one or more values, out of a data set of size \" + f'{data_set_size:,.0f}'+\".\")\n",
    "df_master=df_heart_fill.dropna(axis=0,how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "8bd2a231",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After filling the missing time_index, there are 0 records missing one or more values, out of a data set of size 281,717.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\spin 3\\AppData\\Local\\Temp\\ipykernel_10436\\3366435413.py:3: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  time_index_fill.loc[:,'time_index']=df_raw2.loc[:,['time_index']].fillna(method='bfill',axis=0)\n"
     ]
    }
   ],
   "source": [
    "#replace heart rate missing\n",
    "time_index_fill=df_raw2\n",
    "time_index_fill.loc[:,'time_index']=df_raw2.loc[:,['time_index']].fillna(method='bfill',axis=0)\n",
    "incomplete_records_count=time_index_fill.isna().any(axis=0).sum()\n",
    "data_set_size=time_index_fill.shape[0]\n",
    "print(\"After filling the missing time_index, there are \" + f'{incomplete_records_count:,.0f}'.format(incomplete_records_count) + \n",
    "      \" records missing one or more values, out of a data set of size \" + f'{data_set_size:,.0f}'+\".\")\n",
    "df_master=time_index_fill.dropna(axis=0,how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "a7763e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df_raw[['Subject_ID', 'unit', 'acc_x', 'acc_y', 'acc_z', 'gyr_x',\n",
    "       'gyr_y', 'gyr_z', 'mag_x', 'mag_y', 'mag_z', 'time_index']] .values\n",
    "X_test = df_raw2[['Subject_ID', 'unit', 'acc_x', 'acc_y', 'acc_z', 'gyr_x',\n",
    "       'gyr_y', 'gyr_z', 'mag_x', 'mag_y', 'mag_z', 'time_index']] .values\n",
    "y_train = df_raw['activity_id'].values\n",
    "y_test = df_raw2['activity_id'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "1af5bdc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(y_train))\n",
    "print(type(X_train))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "91657e67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.000000e+00,  1.000000e+00, -9.665799e+00, -1.677241e+00,\n",
       "         6.150630e-01, -1.495600e-02,  4.388000e-03,  1.058900e-02,\n",
       "         5.873180e-01,  4.551060e-01, -9.494900e-02,  1.000000e+00],\n",
       "       [ 1.000000e+00,  1.000000e+00, -9.665806e+00, -1.684737e+00,\n",
       "         6.225130e-01,  6.070000e-04, -3.094000e-03, -7.589000e-03,\n",
       "         5.874280e-01,  4.556210e-01, -9.336400e-02,  2.000000e+00],\n",
       "       [ 1.000000e+00,  1.000000e+00, -9.628410e+00, -1.699724e+00,\n",
       "         5.857510e-01,  6.007000e-03,  5.570000e-04, -4.879000e-03,\n",
       "         5.883890e-01,  4.547220e-01, -9.490700e-02,  3.000000e+00],\n",
       "       [ 1.000000e+00,  1.000000e+00, -9.628372e+00, -1.684836e+00,\n",
       "         6.007330e-01, -3.085000e-03, -2.810000e-04, -3.400000e-04,\n",
       "         5.886730e-01,  4.557590e-01, -9.266400e-02,  4.000000e+00],\n",
       "       [ 1.000000e+00,  1.000000e+00, -9.643291e+00, -1.639893e+00,\n",
       "         5.856610e-01, -2.932000e-03, -6.807000e-03, -1.304300e-02,\n",
       "         5.891930e-01,  4.539270e-01, -9.314300e-02,  5.000000e+00]])"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "4b1e92a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.2329518 , -1.41421356, -1.75633635, -0.41221348,  0.04125777,\n",
       "        -0.09617866, -0.00213778,  0.05693358,  1.64821913,  0.94716039,\n",
       "        -0.03203682, -1.573816  ],\n",
       "       [-1.2329518 , -1.41421356, -1.7563376 , -0.41343728,  0.0426764 ,\n",
       "        -0.01592446, -0.02586684, -0.00453226,  1.6484745 ,  0.94818087,\n",
       "        -0.02836432, -1.57165335],\n",
       "       [-1.2329518 , -1.41421356, -1.74964295, -0.41588405,  0.03567619,\n",
       "         0.01192189, -0.01428774,  0.00463115,  1.65070556,  0.94639948,\n",
       "        -0.0319395 , -1.56949069],\n",
       "       [-1.2329518 , -1.41421356, -1.74963615, -0.41345344,  0.03852906,\n",
       "        -0.03496311, -0.01694545,  0.01997901,  1.6513649 ,  0.94845432,\n",
       "        -0.0267424 , -1.56732804],\n",
       "       [-1.2329518 , -1.41421356, -1.75230696, -0.40611607,  0.03565905,\n",
       "        -0.03417413, -0.03764257, -0.02297404,  1.65257213,  0.94482417,\n",
       "        -0.02785226, -1.56516539]])"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = preprocessing.StandardScaler().fit(X_train)\n",
    "X_train = scaler.transform(X_train.astype(float))\n",
    "\n",
    "scaler = preprocessing.StandardScaler().fit(X_test)\n",
    "X_test = scaler.transform(X_test.astype(float))\n",
    "\n",
    "\n",
    "X_train[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "d2ed186f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subject_ID       int32\n",
       "activity_id      int32\n",
       "unit             int32\n",
       "acc_x          float64\n",
       "acc_y          float64\n",
       "acc_z          float64\n",
       "gyr_x          float64\n",
       "gyr_y          float64\n",
       "gyr_z          float64\n",
       "mag_x          float64\n",
       "mag_y          float64\n",
       "mag_z          float64\n",
       "time_index     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw2.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "8db2cefd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "activity_id    int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "525ae984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_columns = df_raw.select_dtypes(include=['float64', 'int64','int32',]).columns\n",
    "correlation_matrix = df_raw[numerical_columns].corr(method='pearson')\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(data=correlation_matrix, cmap='coolwarm', annot=False, fmt=\".2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "2f5fd2fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\spin 3\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\spin 3\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\spin 3\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\spin 3\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\spin 3\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\spin 3\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\spin 3\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\spin 3\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18a23834990>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df_raw, col='activity_id', col_wrap=3)\n",
    "g.map(sns.histplot, \"acc_x\")\n",
    "g.set_axis_labels(\"acc_x\", \"Frequency\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "5100adc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bar chart\n",
    "pivot1= pd.pivot_table(df_raw,values='time_index',index='Subject_ID',columns='activity_id',margins=False, aggfunc='count')\n",
    "#pivot1.style.format('{:,.0f}') #apply(lambda x: '{,:.0}'.format(x))\n",
    "\n",
    "ax=pivot1.plot(kind=\"bar\",title=\"Figure 1: Subjects' Activty by Number of Time Stamps\",figsize=(10,8),stacked=True);\n",
    "\n",
    "#ax.yaxis.set_major_formatter('{x:,.0f}')\n",
    "ax.set_ylabel(\"No. of time stamps\")\n",
    "#box=ax((1,1))\n",
    "plt.legend(bbox_to_anchor=(1,1));\n",
    "#ax.set_position(1,1.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "096a5e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_raw['activity_id'].plot(kind='hist', bins=10, alpha=0.7)\n",
    "plt.xlabel('Value')  # Optional: customize X-axis label\n",
    "plt.ylabel('Frequency')  # Optional: customize Y-axis label\n",
    "plt.title('Histogram of activity_ID')  # Optional: add title\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "5ed1e6bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate value counts for 'activity_id' column\n",
    "value_counts = df_raw['activity_id'].value_counts()\n",
    "\n",
    "# Plot pie chart\n",
    "plt.figure(figsize=(8, 8))  # Optional: customize figure size\n",
    "plt.pie(value_counts, labels=value_counts.index, autopct='%1.1f%%', startangle=140)\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.title('Pie Chart of activity_ID')  # Optional: add title\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4bf275ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "activity_id\n",
       "1    39845\n",
       "2    37345\n",
       "3    36380\n",
       "7    33505\n",
       "6    33285\n",
       "4    32660\n",
       "8    31810\n",
       "5    31795\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw['activity_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "4c693653",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 8, 8, 8])"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "ad0ffb36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (276625, 12) (276625,)\n",
      "Test set: (281717, 12) (281717,)\n"
     ]
    }
   ],
   "source": [
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "6de83aaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "82115fa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Assuming X_test is a NumPy array\n",
    "#nan_mask_test = ~np.isnan(X_test).any(axis=1)\n",
    "#X_test = X_test[nan_mask_test]\n",
    "\n",
    "# Assuming X_train is a NumPy array\n",
    "#nan_mask_train = ~np.isnan(X_train).any(axis=1)\n",
    "#X_train = X_train[nan_mask_train]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "ea607a0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (276625, 12) (276625,)\n",
      "Test set: (5092, 12) (281717,)\n"
     ]
    }
   ],
   "source": [
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "438ea2ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example data X\n",
    "\n",
    "# Elbow Method\n",
    "inertia = []\n",
    "for k in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(X_train)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "plt.plot(range(1, 11), inertia)\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('Elbow Method')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "52147446",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "# Example data X\n",
    "\n",
    "# Elbow Method\n",
    "inertia = []\n",
    "for k in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(X_train)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "plt.plot(range(1, 11), inertia)\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('Elbow Method')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "b5a96c39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No NaN values in y_train\n"
     ]
    }
   ],
   "source": [
    "# Check if there are any NaN values in y_trainv\n",
    "if np.isnan(y_train.astype(float)).any():\n",
    "    print(\"NaN values found in y_train\")\n",
    "else:\n",
    "    print(\"No NaN values in y_train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "9ade04cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No NaN values in x_trainv\n"
     ]
    }
   ],
   "source": [
    "# Check if there are any NaN values in y_trainv\n",
    "if np.isnan(X_train.astype(float)).any():\n",
    "    print(\"NaN values found in X_trainv\")\n",
    "else:\n",
    "    print(\"No NaN values in x_trainv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "bcafd7d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=4)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=4)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 4\n",
    "#Train Model and Predict  \n",
    "neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
    "neigh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "60fcc5b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44e34a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "12ac1864",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "yhat = neigh.predict(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "ec2ac6aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1]\n",
      "[1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(y_test[0:5])\n",
    "print(yhat[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "0ae92e1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.9985286940804338\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "d0821342",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set Accuracy:  0.9769804449145774\n"
     ]
    }
   ],
   "source": [
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "75c1e969",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "f7455fd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 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-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 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-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 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-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 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-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=11)</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-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=11)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=11)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 11)\n",
    "drugTree # it shows the default parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "712e8f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=11)</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-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=11)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=11)"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugTree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "fc413577",
   "metadata": {},
   "outputs": [],
   "source": [
    "predTree = drugTree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "15623eea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1]\n",
      "[1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print (predTree [0:5])\n",
    "print (y_test [0:5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "2ac112da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy:  0.9488884234888203\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_test, predTree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "1910e5ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 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-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 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-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 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-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 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-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</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-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn import svm\n",
    "clf = svm.SVC(kernel='rbf')\n",
    "clf.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "4a7b5abd",
   "metadata": {},
   "outputs": [],
   "source": [
    "yhatsvm = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "e9ded56d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1]\n",
      "[1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(yhatsvm [0:5])\n",
    "print(y_test[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "0d9e97d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9911951688362133"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(y_test, yhatsvm, average='weighted') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "217411c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "8330e1ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "d95b923c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.97      0.99      0.98     39845\n",
      "           2       0.99      0.97      0.98     37345\n",
      "           3       1.00      1.00      1.00     36380\n",
      "           4       0.99      1.00      1.00     32660\n",
      "           5       1.00      0.99      0.99     31795\n",
      "           6       0.98      1.00      0.99     33285\n",
      "           7       1.00      0.98      0.99     33505\n",
      "           8       1.00      1.00      1.00     36902\n",
      "\n",
      "    accuracy                           0.99    281717\n",
      "   macro avg       0.99      0.99      0.99    281717\n",
      "weighted avg       0.99      0.99      0.99    281717\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[39573   226    39     0     0     0     1     6]\n",
      " [ 1275 36066     4     0     0     0     0     0]\n",
      " [   23     0 36341     1     2    12     0     1]\n",
      " [    0     0    16 32629     6     8     0     1]\n",
      " [    1     0     4   265 31471    53     1     0]\n",
      " [    0     0     5     2     0 33265    13     0]\n",
      " [    0     3     0     0     0   511 32991     0]\n",
      " [    0     0     0     0     1     0     0 36901]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Mr/h80hSqVPUnMTGRixcvKBLjq3qjSBE++3wyXl5vYjAY+OH7pbRr04r9h45SrHhxmjdtTHnfCmz6fTsA48Z+StvWLdj9x37MzLJ3nr5yxXKGDw3h6znzqVLVn9mzZtCyWSDHTkXh4uKidngZklvaklvakZiYSHnfCnTr3pOO7dqoHc4ruXPnDvXr1KBOnXqsWbeJQoUKcfbsGQoUKKB2aCKb0xie/hbPYXQ6HVqtFuvyfV54+/WrO7/g4xlruBJzh7WzP8C9zjDuJT4EwCG/Ddd3TaH5B3PYceBx78bpDeOY/eMOZi/bma44ypd6g4PLR9Kw53T+OHruueX2LRvO5r0nGT93Q5pjtw9+na5zvciNGzco9oYrv2/bSc1atZ9Z5tdVK+nZvSs37yZgYWHBnTt38CpehFWrf6Ne/QavHYMmE/9CKOzixOeTp1LEw4NWzZtw/cYdHBwcAIiPj8e9UAHWb/qd+g0aZloMSqhV3R+/ylWYMWs2AHq9Hi9PD/oFD2DosBEqR5cxuaUtuaUdT7O11LB81WpatmqtdigZMurjEYTv+4NtO/e8vLACdDodrs5a4uPjjZ8nWXVerVaLtf9QNBbWitRpeJRE0oGpWd6W7CJ7/yn7mszMNLQL9MPO1ooDx6OxtrLAYDCQlPzIWOZh0iP0egPVK5Y0ee5HPRpzZccXhP80nMHdGmBu/vwfVY93qvP3hdgXJjN1q5aiVHEX9kY8v8zr0v0zlFSggNNzy8TrHr/QLSwed85t3xqGXq/n2tWrvFXeBy9PD7p06sCVy5czLc6MSk1NZcXyn0lMTMS/WgBJSUloNBqsrf/9ELCxscHMzIx9f+xVMdKXS05O5uiRCJOky8zMjPr1G3Jwf7iKkWVcbmlLbmlHbrFh/W9U8qvMex3bUbSwC9Uqv8XiRQvVDkvkALluyAmgrFdhdi79CBsrCxIeJNHho4WcPh/DzTsJJD5IZuKHrRg9+zc0aJjwYSssLMxxK/hvNjv3p10c/esyd3SJVKtQgvEDWuJWSMvwab+mOZe1lQUdmlRm2pKwNMcc8ttwbstErC0tSNXr+XDScrYfOJ0pbdbr9QwdMpiA6jUoW67cM8vcvHmTyZ9PoEevPsZ90dHn0ev1TP1iElO/moFWq2XcmE9p3qQxB48cw8rq+T1fme3kiRPUrRXAw4cPyZ8/P8tXraaMjw8FCxXCzs6OT0YOZ/yEzzEYDIz6eASpqanEXL+uWrzpcfPmTVJTU3FxcTXZ7+LqSlRU5rw2MktuaUtuaUduEX3+PAu/mcfAQSEMG/4xEYcP8dHggVhZWdGlW5Da4SlPlm0rJlcmNH9fiMW/4yS0+W15p+FbLBzflca9Z3L6fAydh33LrI878EGnOuj1BlZsjuDIn5fQPzXyNuuH7cZ/nzxzjeSUR8z+pBOfzvqN5JRHJudqVb8C9vls+GHdgTRx3EtMwr/jJPLbWlPP35svPmpD9JVb7Ik4o3ibBw0M5s9TJ9m649ndtDqdjjatmlO6jA+jRo817tfr9aSkpPDl9Jk0bNQYgNDvl+Hp4c6unTto1DhQ8VjTq5S3NwcORxIfH8/qX1fRp2cQv2/bRRkfH378eSUD+/dj7uxZmJmZ0b5DJ956q1K2nz8jhHgxvV5PJb/KjJ/wOQAV33qLU6dOsnDB/Fya0MgqJ6WomtDs3r2bqVOnEhERwfXr11m9ejWtW7d+7XpTHqVy/vJNAI7+dRm/skUJ7lSXARN/Ztv+05RtOQ5nRzsePdITn/CA6LDPubAl4rn1HTpxAUtLc4oVduLMxTiTY91bV2fTnpPE3b6X5nkGg8EYx/G/r+Lt6cbQno0VT2gGf9ifTRs3ELZtF0WKFElz/N69e7Rq3gR7e3uWr/wVS0tL4zE3d3cASpf5dyVYoUKFKFiwIJcvX1I0zoyysrKipJcXAJX8/Ig4fIg5X89k9rxvaNioMX9GnePmzZtYWFjg6OhI8SJuFC9RQtWYX6ZgwYKYm5sTF2e6IisuNhY3NzeVono1uaUtuaUduYWbuztlnvo8AihdugxrVv+iUkQip1A1nUtMTKRChQrMmTMnU89jptFgbWWau926m0h8wgPqVCmFi1N+1u868dznV/AuQmqqnhv/SVqKFXamTpU3CV2TvnH2Z8XxOgwGA4M/7M9va9ewacs2int6pimj0+lo0TQQKysrVv66FhsbG5PjAQE1ADjzt+ly75s3b1K0aDHFYlWCXq8nKSnJZF/BggVxdHRk547txMXF0bx5S5WiSx8rKyvequTHju3bjPv0ej07dmyjarUAFSPLuNzSltzSjtwioHoN/n7q8wjgzJm/s93nkWI0mn97aV57kyEn1TRp0oQmTZooWuf4AS3Z8scpLl+/g72dDR2aVKZ25Tdp8cFcALq2rEZUdAw37iTg7+vJl0Pf5esfdxh7Xvx9PalSrhi7Dp/hXuJDqvl68sWQtvy08RB37z0wOVdQ62rE3NSx5Y9TaeIY0rMxR05d4vyVG1hbWfB2zbK816wqAyf9rFhbBw0MZsXPP7HilzXkt7cnJiYGAK1Wi62trTGZeXD/PotDv0en06HT6YDHvTDm5ua8WaoUzVu0YmjIIGbP+wZ7ewdGj/oYb+/S1KlbT7FYM+rTT0YS+HYTPDyKcu/ePZb/vIzdu3aybuMWAL4LXYJ36TIUKlSIA/vDGRLyIQM+HEwpb2/VYk6vgYNC6NMzCD+/ylSuUpXZs2ZwPzGRbkE91A4tw3JLW3JLOxISEjh39qzx8YXoaI5FRlLAyYmiRYuqGFn6DRg4mHq1qzNl8ue0fbc9hw4dZPGiBcyet0Dt0DKHmebxplRdeViOmkOTlJRk8hf6ky/npxVyys+3n3XDraAD8QkPOXnmKi0+mGucjFuquAvjB7TESZuPi9duM+XbLSZzZpKSU2gX6McnfZtibWnBhWu3+PrHHcz6frvJeTQaDV1bVOP73w6g16dd+W5nY8XMj9vzhosjD5JS+PtCLD1HLWXV70eU+nGw8Jv5AAQ2NE08vlm0mK7duhN59AiHDj6e21OuzJsmZf76+zzFihcHYNGSpQwbMpg2rZpjZmZGzVp1WLt+k8nQVFa7ERdHrx7diLl+Ha1WS7nyvqzbuIUGDR9f4PDvv6MYPWokt2/fpljx4gwb8QkDBw1WLd6MaNe+Azdv3GD8uNHExsTgW6Eia9dvxtXV9eVPzmZyS1tySzuORBw2+TwYPjQEgC5dg1i4OFSlqDKmcpUqLF+1mtGfjOTzCeMp7unJ1Gkz6PReZ7VDE9lctrkOjUajeekcmrFjxzJu3Lg0+192HZqcQInr0GQHmXkdGiGEUJrq16GpNQqNhc3Ln5AOhkcPSdozQa5DkxOMHDmS+Ph443Y5G10rRQghhMgwufWBYnLUkJO1tbXJxdSEEEIIISCHJTRCCCFEriLXoVGMqglNQkICZ5+akR8dHU1kZCROOWhGvhBCCCHUp2pCc/jwYerV+3dGfkjI4xn5QUFBhIaGqhSVEEIIkUXk1geKUTWhqVu3LtlkkZUQQgiR9WTISTF5u/VCCCGEyBVkUrAQQgihFhlyUowkNEIIIYRaZMhJMXm79UIIIYTIFaSHRgghhFCLDDkpRhIaIYQQQjUKDjnl8UGXvN16IYQQQuQK0kMjhBBCqEWGnBQjPTRCCCGEyPGkh0YIIYRQi0aj4LJt6aERQgghhBqeXIdGqS2d5s2bh6+vLw4ODjg4OBAQEMCmTZuMx+vWrYtGozHZ+vbta1LHpUuXaNasGfny5cPFxYWhQ4fy6NEjkzI7d+6kUqVKWFtb4+Xl9cz7NM6ZM4fixYtjY2ODv78/Bw8ezNjP8B+S0AghhBB5TJEiRZg8eTIREREcPnyY+vXr06pVK06dOmUs06dPH65fv27cpkyZYjyWmppKs2bNSE5OZt++fSxdupTQ0FBGjx5tLBMdHU2zZs2oV68ekZGRDBo0iN69e7NlyxZjmeXLlxMSEsKYMWM4cuQIFSpUIDAwkLi4uAy3SWPIwXeH1Ol0aLVarMv3QWNupXY4r+X2wa/VDkERmjze5SmEyFl0Oh2uzlri4+NxcHDI0vNqtVqs356GxtJWkToNKQ9I2vzRK7fFycmJqVOn0qtXL+rWrUvFihWZMWPGM8tu2rSJ5s2bc+3aNVxdXQGYP38+w4cP58aNG1hZWTF8+HA2bNjAyZMnjc/r2LEjd+/eZfPmzQD4+/tTpUoVZs+eDYBer8fDw4MBAwYwYsSIDMUvPTRCCCGEWjJhyEmn05lsSUlJLwwhNTWVn3/+mcTERAICAoz7f/zxRwoWLEi5cuUYOXIk9+/fNx4LDw+nfPnyxmQGIDAwEJ1OZ+zlCQ8Pp2HDhibnCgwMJDw8HIDk5GQiIiJMypiZmdGwYUNjmYyQScFCCCFELuLh4WHyeMyYMYwdOzZNuRMnThAQEMDDhw/Jnz8/q1evxsfHB4D33nuPYsWKUbhwYY4fP87w4cOJiori119/BSAmJsYkmQGMj2NiYl5YRqfT8eDBA+7cuUNqauozy5w+fTrD7ZaERgghhFBLJlyH5vLlyyZDTtbW1s8s7u3tTWRkJPHx8axatYqgoCB27dqFj48P77//vrFc+fLlcXd3p0GDBpw7d46SJUsqE6/CJKERQggh1JIJd9t+snLpZaysrPDy8gLAz8+PQ4cOMXPmTL755ps0Zf39/QE4e/YsJUuWxM3NLc1qpNjYWADc3NyM/3+y7+kyDg4O2NraYm5ujrm5+TPLPKkjI2QOjRBCCCHQ6/XPnW8TGRkJgLu7OwABAQGcOHHCZDVSWFgYDg4OxmGrgIAAtm3bZlJPWFiYcZ6OlZUVfn5+JmX0ej3btm0zmcuTXrmih+ZM2BdZOjs9Mzg1/0rtEBRxZ8NHaocghBA5h0q3Phg5ciRNmjShaNGi3Lt3j2XLlrFz5062bNnCuXPnWLZsGU2bNsXZ2Znjx48zePBgateuja+vLwCNGzfGx8eHrl27MmXKFGJiYhg1ahTBwcHGIa6+ffsye/Zshg0bRs+ePdm+fTsrVqxgw4YNxjhCQkIICgqicuXKVK1alRkzZpCYmEiPHj0y3PxckdAIIYQQIv3i4uLo1q0b169fR6vV4uvry5YtW2jUqBGXL19m69atxuTCw8ODtm3bMmrUKOPzzc3NWb9+Pf369SMgIAA7OzuCgoIYP368sYynpycbNmxg8ODBzJw5kyJFirBo0SICAwONZTp06MCNGzcYPXo0MTExVKxYkc2bN6eZKJweueI6NJdibuf4Hhq31jPUDkER0kMjhMhJ1L4OjU2L2Ypeh+bhuv5Z3pbsQnpohBBCCJU8ua2AQpUpU08OJZOChRBCCJHjSQ+NEEIIoRbNP5tSdeVhktAIIYQQKpEhJ+XIkJMQQgghcjzpoRFCCCFUIj00ypGERgghhFCJJDTKkSEnIYQQQuR40kMjhBBCqER6aJQjPTRCCCGEyPGkh0YIIYRQi1yHRjGS0AghhBAqkSEn5ciQkxBCCCFyvDyR0Pyxdzcd2raidAkPHPNZsP63tcZjKSkpjBk1gupVKlK4oAOlS3jwv97duX7tmrHMnt07ccxn8cztyOFDAFy8eOGZxw8d3P9KMfdpXoGD87oR+2t/Yn/tz87pnWhcubhJGf8y7mz6oh031w4k9tf+hH3ZARurfzvdCtjbsGR4U2J/7c/1X4KZN7gxdjaWac416N3KHP+2B3fXfci5H99nWCd/k+NWluaM7V6DqO/6cHfdh5xe2ptujcu9UruUMnXKZGwtNQwJGaRqHK9q/tw5eHsVxzG/DbWq+3Po4EG1Q3pluaUt0o7sJbe042U0mn97aV5/U7s16soTQ073ExMpX96XLt160LXTu6bH7t/nWORRho74hHLlfbl79w4jhgymU7t32PnHAQD8q1Un6vwVk+dNHD+GXTu385ZfZZP9azdsoXSZssbHTs7OrxTz1Rv3+HTxHs5evYNGo6FLIx9Wjm1NteDv+eviLfzLuLN2Ylu+/PkgIXO38yhVj2+JQugNBmMdS4Y3xc3JjuYjV2FpYc43HwUyZ1Ajuk/eaCwzrV89GvgVZ+TC3ZyMvoGTvQ0FHExvZf/DJ81xdcxH3+lbOHftLu5Odpip+M45fOgQ3y78hvLlfVWL4XWsXLGc4UND+HrOfKpU9Wf2rBm0bBbIsVNRuLi4qB1ehuSWtkg7spfc0o700KDgkFMen0SjMRie+gbMYXQ6HVqtlksxt3FwcEjXcxzzWfDDz7/QvGWr55Y5cvgQ9WsHcCLqPB4eRdMcT0lJoYxXUd7vG8ywkaOAxz00Fcp4sTv8ML4VKma4LW6tZ7y0zNVVH/Dxwt0s3XKSXTM6se3IRcZ/t++ZZb09nIhc1IMa/X/gyJlYABpVLs6az9rg1fkbrt9OxNvDiUPzu+H3v6WcuXLnmfU0qlyc70Y2w6f7t9y59/ClMd7Z8NFLy7yOhIQEAqpWYubXc5n8+QR8K1Tky69mZOo5lVaruj9+laswY9ZsAPR6PV6eHvQLHsDQYSNUji5jcktbpB3ZS1a2Q6fT4eqsJT4+Pt3fI0qdV6vV4th+IRqrfIrUaUi+z90VfbK8LdlFnhhyyiidLh6NRoNW6/jM4xs3rOP2rVt07to9zbH32r2DVzF33m5Qm43r1ykSj5mZhnZ1vLGztuTAX9copLWlapnC3Lj7gB3TO3Hh5778PrU91cu+YXyOf5nC3Ln30JjMAGw/chG9wUCV0u4ANKtWkujr8TT1L8FfS3tzemlv5g5qTAF7G+NzmlUryZEzsYS0q8K5H9/n+Lc9mNSnjsnQVlYaNCCYt5s0o36Dhqqc/3UlJydz9EiESfxmZmbUr9+Qg/vDVYws43JLW6Qd2UtuaUd6KTfcpGRPT86UJ4acMuLhw4eMGfUx77bv+NwM94fQxTRo2Jg3ihQx7stvl58Jk6dSrVp1zMzM+G3tr3Tu0IYfl/9K0+YtXimWssULsnNGJ2ysLEh4kEyH8b9x+tJtqv6TkHzSNYCRC3dx/NwNOjf0YePkd/H731LOXbuLq1M+bty9b1Jfqt7A7XsPcXWyA6C4u5airg60qVWK3lM3YWamYcr/6rFsVAuaDF8JgKe7lupl3+Bh8iM6jP8NZwdbZvZvgJODDf+btuWV2vWqViz/mcijR9i7/1CWnldJN2/eJDU1FRcXV5P9Lq6uREWdVimqV5Nb2iLtyF5ySzvSTZZtK0bVhGbSpEn8+uuvnD59GltbW6pXr84XX3yBt7e3KvGkpKTQvUtHDAYD02bOeWaZq1eusG3r7yz54WeT/c4FC9J/4GDj40qVq3D9+nW+nvHlKyc0f1+5jf8H36PNZ8U7tUqxcMjbNB66HDOzx6/abzce5/vfTwFw7FwcdSsWJSiwHKOX7E1X/WYaDTZWFvSaupmzVx8POfWbvoXwOV15s0gBzly5g5lGg8FgoMfkjejuJwMwfMFOlo1qyYdfb+Nh8qNXaltGXb58maEhH7J+Uxg2NjYvf4IQQog8RdUhp127dhEcHMz+/fsJCwsjJSWFxo0bk5iYmOWxPElmLl++xJr1m5/bO/Pj96E4OTvTtNnLk5TKlaty/ty5V4/pkZ7z1+5y9Gwco5fs5UT0DYJbV+L6rQQA/rp4y6R81OXbeLg8jjv29n0KOZqOy5qbaXCytyH29uOfb8ztRFIepRqTGYDTl24DGOuJuZ3ItVsJxmTmSRkzMw1vFMz/ym3LqKNHIoiLiyOgaiXy21iQ38aCPbt3MXf2LPLbWJCampplsbyOggULYm5uTlxcrMn+uNhY3NzcVIrq1eSWtkg7spfc0o50U3K4KY8POama0GzevJnu3btTtmxZKlSoQGhoKJcuXSIiIiJL43iSzJw/d5a167c8d2WSwWDgx++X0vG9Llhapl3+/F8njh/D1c1dsTjNNBqsLc25GKvj2s17lCpSwOS41xsFuBSnA+DAX9coYG/DW17/rgioW7EoZhoNh05fByD81FUsLczxdNcay7z5T52XYnXGMu5O+U2We79ZpACpqXqu3kxQrG0vU69+Aw4fPcGBw5HGrZJfZTp26syBw5GYm5tnWSyvw8rKircq+bFj+zbjPr1ez44d26haLUDFyDIut7RF2pG95JZ2iKyXrebQxMfHA+Dk5PTM40lJSSQlJRkf63S6dNWbkJDA+XNnjY8vXozm+LFICjg54ebmTrf32nM88ig//7KW1NRUYmNiACjg5ISVlZXxebt3bufihWi6de+V5hzLfvgOKysr4wqndWtX88N3S5g1d0G6Yvyv8T1qsuVQNJdv3MPe1ooO9UpT29eDFp/8AsD0VYcZ1bU6J87f4Nj5G3Rp6IO3RwHem/Ab8Li3ZsuhaOYMaszAr7diaW7G9OD6rNx1muv/9NBsP3qRI2di+SYkkKHzd2KmgRn9G7A14oKx12b5jtOM7BzAgo8C+ez7fTg72PJ579os/f1klg03Adjb21O2nOm1b+zs7HBydk6zP7sbOCiEPj2D8POrTOUqVZk9awb3ExPpFtRD7dAyLLe0RdqRveSWdqSHkpN5ZVJwNqHX6xk0aBA1atSg3HO+oCZNmsS4ceMyXPfRI4dp8fa/M+Y/GT4EgE5dujHik9Fs2vB4NVKtan4mz1u3eSu1atc1Pv5+6RL8qwVQyrv0M88zdfJELl+6iIWFBW+W8mbJ9z/R6p22GY4XoJBjPr4d2gQ3Jzvi7ydzMvoGLT75he1HLgIwe/URbCwtmNK3HgXsbThx/gbNR/5C9PV4Yx09vtjI9OD6bJzcDr3BwJq9Z/ho7nbjcYMB3h29mq+C6xP2ZQcSH6bw++FoRizYZSyT+DCFZiNX8dUH9fnj6y7cvveQX3ZHMTb0j1dql4B27Ttw88YNxo8bTWxMDL4VKrJ2/WZcXV1f/uRsJre0RdqRveSWdqSHJDTKyTbXoenXrx+bNm1i7969FHlq9dDTntVD4+HhkaHr0GRX6bkOTU6Q2dehEUIIJal9HRrnzkswU+g6NPrk+9z6sUeevQ5Ntuih6d+/P+vXr2f37t3PTWYArK2tsba2zsLIhBBCiEwky7YVo2pCYzAYGDBgAKtXr2bnzp14enqqGY4QQgiRpWTISTmqJjTBwcEsW7aMtWvXYm9vT8w/k3G1Wi22trYvebYQQgghxGOqJjTz5s0DoG7duib7lyxZQvfu3bM+ICGEECILSQ+NclQfchJCCCHyKklolCM3pxRCCCFEjpctVjkJIYQQeZH00ChHemiEEEIIkeNJD40QQgihFrkOjWIkoRFCCCFUIkNOypEhJyGEEELkeNJDI4QQQqhEemiUIwmNEEIIoRJJaJQjQ05CCCGEyPGkh0YIIYRQi6xyUowkNEIIIYRKZMhJOTLkJIQQQuQx8+bNw9fXFwcHBxwcHAgICGDTpk3G4w8fPiQ4OBhnZ2fy589P27ZtiY2NNanj0qVLNGvWjHz58uHi4sLQoUN59OiRSZmdO3dSqVIlrK2t8fLyIjQ0NE0sc+bMoXjx4tjY2ODv78/BgwdfqU2S0AghhBAqedJDo9SWXkWKFGHy5MlERERw+PBh6tevT6tWrTh16hQAgwcPZt26daxcuZJdu3Zx7do12rRpY3x+amoqzZo1Izk5mX379rF06VJCQ0MZPXq0sUx0dDTNmjWjXr16REZGMmjQIHr37s2WLVuMZZYvX05ISAhjxozhyJEjVKhQgcDAQOLi4jL+szTk4Fte63Q6tFotl2Ju4+DgoHY4r8Wt9Qy1Q1DEnQ0fqR2CEEKkm06nw9VZS3x8fJZ+jzz5/iryv58xs8qnSJ365Ptc+abjK7fFycmJqVOn8u6771KoUCGWLVvGu+++C8Dp06cpU6YM4eHhVKtWjU2bNtG8eXOuXbuGq6srAPPnz2f48OHcuHEDKysrhg8fzoYNGzh58qTxHB07duTu3bts3rwZAH9/f6pUqcLs2bMft0Gvx8PDgwEDBjBixIgMxS89NEIIIYRKNCjYQ/PPrGCdTmeyJSUlvTCG1NRUfv75ZxITEwkICCAiIoKUlBQaNmxoLFO6dGmKFi1KeHg4AOHh4ZQvX96YzAAEBgai0+mMvTzh4eEmdTwp86SO5ORkIiIiTMqYmZnRsGFDY5mMkIRGCCGEUElmDDl5eHig1WqN26RJk5557hMnTpA/f36sra3p27cvq1evxsfHh5iYGKysrHB0dDQp7+rqSkxMDAAxMTEmycyT40+OvaiMTqfjwYMH3Lx5k9TU1GeWeVJHRsgqJyGEECIXuXz5ssmQk7W19TPLeXt7ExkZSXx8PKtWrSIoKIhdu3ZlVZiKk4RGCCGEUEsmXIfmycqll7GyssLLywsAPz8/Dh06xMyZM+nQoQPJycncvXvXpJcmNjYWNzc3ANzc3NKsRnqyCurpMv9dGRUbG4uDgwO2traYm5tjbm7+zDJP6siIXJHQWFuaY21prnYYryW3TKYt0GSK2iEo5s6mYWqHIITI5bLTdWj0ej1JSUn4+flhaWnJtm3baNu2LQBRUVFcunSJgIAAAAICApg4cSJxcXG4uLgAEBYWhoODAz4+PsYyGzduNDlHWFiYsQ4rKyv8/PzYtm0brVu3Nsawbds2+vfvn+H4c0VCI4QQQoj0GzlyJE2aNKFo0aLcu3ePZcuWsXPnTrZs2YJWq6VXr16EhITg5OSEg4MDAwYMICAggGrVqgHQuHFjfHx86Nq1K1OmTCEmJoZRo0YRHBxsHOLq27cvs2fPZtiwYfTs2ZPt27ezYsUKNmzYYIwjJCSEoKAgKleuTNWqVZkxYwaJiYn06NEjw22ShEYIIYRQiVo9NHFxcXTr1o3r16+j1Wrx9fVly5YtNGrUCIDp06djZmZG27ZtSUpKIjAwkLlz5xqfb25uzvr16+nXrx8BAQHY2dkRFBTE+PHjjWU8PT3ZsGEDgwcPZubMmRQpUoRFixYRGBhoLNOhQwdu3LjB6NGjiYmJoWLFimzevDnNROF0tT83XIcm9lbWXj9APJ8MOQkhchK1r0Pj2X8VZtYKXYcm6T7Rs9/N8rZkF7JsWwghhBA5ngw5CSGEECrRaJS7qWQevzelJDRCCCGEajQKJiJ5PKGRISchhBBC5HjSQyOEEEKoJDtdhyankx4aIYQQQuR40kMjhBBCqESj4ByaPN5BIwmNEEIIoRYzMw1mZspkIgaF6smpZMhJCCGEEDme9NAIIYQQKpEhJ+VIQiOEEEKoRFY5KUeGnIQQQgiR40lC84+pX0yiRrUqFCpgT9HCLrRr25q/o6JMyvTv9z98vEtSwN4WD/dCtGvTiqjTp1WKOGPmz52Dt1dxHPPbUKu6P4cOHsyyc/dpXpGD33Qnds2HxK75kJ0zO9O4iqdJGf8yhdk0pQM3fxtE7JoPCZvWCRurfzsQV45vw98/9uXOhhDO//wB3w5vhrtz/meer0RhR+LWDuL66oEm+8sUc+an0a04/f3/eBA2jP7v+Cnf2HTYu2c3bVu3wLNoYWwtNfy2do0qcbyu9LxnsqMX/fxTUlL4ZORwKlcsj7PWDs+ihenVvRvXrl1TL+AMUvO9rpTc8h5JjydDTkpteZkkNP/Ys3sXffsFs2vvftZvCuNRSgrNmzYmMTHRWOatSn4sWLSEyBN/8duGLRgMBpo3bUxqaqqKkb/cyhXLGT40hE9GjSH84BF8fSvQslkgcXFxWXL+qzfv8em3u6ke/B01gr9jZ+QlVo5rQ5lizsDjZGbtpHZsi7hArQHfU7P/98xfewT9UzeC3x15iS4T1lKhxyLeG7+GEu6OLPu0VZpzWZib8d3HLfjj5OU0x/JZWxJ9PZ5Pv93F9VsJmdfgl0hMTKS8bwVmzJqjWgxKSM97Jjt60c///v37RB49wohPPiX84BF+XvErf/8dRbt3WqoQacap/V5XSm55j6THkyEnpba8TGMwPPWtkcM8uf167C3lb5V+48YNihZ2IWz7LmrWqv3MMieOH6eqXwVOnT5LiZIlFT2/kmpV98evchVmzJoNgF6vx8vTg37BAxg6bISi5yrQZEq6yl39ZQAfL9zJ0s0n2DWrC9siLjB+6d50n6dZgBcrxr6Dtuk0HqXqjfsn9K6Du3N+dhy9yNR+9XF/Z9Yzn3/6+/8x+9fDzF4d8dxz3Nk0LN3xvCpbSw3LV62mZavWmX6uzJae90x2k56f/+FDh6hVvSpR5y5StGjRrAvuFWTlez2rZPZ7RKfT4eqsJT5e+e+Rl51Xq9XiM2wN5tZ2itSZmpTIn1NaZ3lbsgvpoXkOXXw8AAUKOD3zeGJiIt8tXUJxT0+KeHhkZWgZkpyczNEjEdRv0NC4z8zMjPr1G3Jwf3iWx2NmpqFd3dLY2Vhy4M9rFHLMR9Uyhblx9z47ZnTmwopgfp/Wiepl33huHQXsbehY34f9f141SWbqVCxKm9reDPo6LCuaIv7jZe+ZnEqni0ej0eDo6Kh2KC+U3d7rIn2kh0Y5ktA8g16vZ+hHgwioXoOy5cqZHPtm3lwKOuanoGN+ft+yiQ2bwrCyslIp0pe7efMmqampuLi4mux3cXUlJiYmy+IoW7wgN34bRPzGj5j1YWM6jFvD6Uu38HTXAvBJtxos3nSMViNXEnkmlo1TOlDyjQImdUzoXYebvw3i2q8D8XCxp93oX43HnOxtWDi0KX2mbuTe/eQsa5d47EXvmZzs4cOHjBo5nPYdOmX7v3izy3tdCLWomtDMmzcPX19fHBwccHBwICAggE2bNqkZEgCDBgRz6tRJvvvx5zTHOr7Xmf2HjhK2fRdvvlmKLp3a8/DhQxWizFn+vnIb/76h1B7wPQvXRbJwaFNKF3XG7J+/KL7dEMn3W05y7Fwcw+Zv5+8rtwkKLG9Sx/QVB6nWbynNhi8nVW9g0fBmxmNzQ95m+fY/+ePElSxtl3jsRe+ZnColJYUundpjMBiYNWee2uGIXEomBStH1evQFClShMmTJ/Pmm29iMBhYunQprVq14ujRo5QtW1aVmAYN7M/GjevZun03RYoUSXNcq9Wi1WrxevNNqvpXw71QAdauWU2Hjp1UiPblChYsiLm5OXFxsSb742JjcXNzy7I4Uh7pOX/tLgBHz8Ti5+1G8Dt+fLn8AAB/XbxlUj7q0m08XEz/Ir6le8At3QPOXr1D1KVbnP3pA/zLFObAX9eoU7EozQK8GNSuKgAawNzcjHubhxA8fQvfbTmR6W3Mq172nsmJUlJS6NypPZcuXmRT2PZs3zsD2ee9LjJGg4LXoSFvZzSq9tC0aNGCpk2b8uabb1KqVCkmTpxI/vz52b9/f5bHYjAYGDSwP7+tXc3m37dT3NMzXc8xGAwkJyVlQYSvxsrKircq+bFj+zbjPr1ez44d26haLUC1uMw0GqytzLkYE8+1m/coVcR03oVXkQJciot/4fMBrCzNAaj74Q/49w01buO/24suMQn/vqH89sffmdeQPOxV3jM5wZNk5tzZM2zYshVnZ2e1Q0qX7PpeFyKrZJsrBaemprJy5UoSExMJCHj2my8pKYmkp5IHnU6n2PkHDQhm+c/LWPnrWvLb2xvHnLVaLba2tkSfP8+qlctp0LAxBQsV4uqVK0ybOhlbW1sCmzRVLI7MMHBQCH16BuHnV5nKVaoye9YM7icm0i2oR5acf3zP2mw5dJ7LcTrsba3oUN+H2hWK0mLkCuDxUNKooJqcOB/HsXNxdGlUDm8PJ94bvxaAKqXd8fN2Y9/Jq9y99xDPwo6M6V6Tc1fvcOCvx9cHibp02+SclUq5oTcY+PPCTeM+SwszyhQrCDxOhAoXtMe3pAsJD5KNvUdZISEhgXNnzxofX4iO5lhkJAWcnLL9Kpqnvew9k1296Ofv7u7Oex3e5ejRI/y6Zj2pqanGdjk5OWXr+XKg/ntdKbnlPZIecusD5aie0Jw4cYKAgAAePnxI/vz5Wb16NT4+Ps8sO2nSJMaNG5cpcSz45vEYeeMGdU33L1pC16DuWNvY8MfePcyeNYM7d+7g4upKzZq12bF7Hy4uLpkSk1Late/AzRs3GD9uNLExMfhWqMja9ZtxdXV9+ZMVUMgxH98Oa4abkx3xiUmcjL5Bi5Er2H7kIgCzV0dgY2XBlL71KWBvw4nzN2g+fAXR1+8CcP9hCq1qlGJUt5rY2VgScyuB3w9H88WPv5Gckv5rALk75+fA/O7Gx4PbV2Vw+6rsPnaJwCFZN/fjSMRhAhvWMz4ePjQEgC5dg1i4ODTL4nhdL3vPZFcv+vmPGj2W9et+A8C/ckWT523ZuoPadepmVZivRO33ulJyy3skPeTWB8pR/To0ycnJXLp0ifj4eFatWsWiRYvYtWvXM5OaZ/XQeHh4ZMp1aMSrSe91aHKCrLgOjRBCXWpfh6bCx+swt1HoOjQPEzn2eYs8ex0a1XtorKys8PLyAsDPz49Dhw4xc+ZMvvnmmzRlra2tsba2zuoQhRBCiEwhQ07KUT2h+S+9Xm/SCyOEEELkVjLkpBxVE5qRI0fSpEkTihYtyr1791i2bBk7d+5ky5YtaoYlhBBCiBxG1YQmLi6Obt26cf36dbRaLb6+vmzZsoVGjRqpGZYQQgiRJWTISTmqJjTffvutmqcXQgghRC6R7ebQCCGEEHmFzKFRjiQ0QgghhFqUvAdT3s5n5G7bQgghhMj5pIdGCCGEUIkMOSlHEhohhBBCJbLKSTky5CSEEEKIHE96aIQQQgiVyJCTciShEUIIIVQiQ07KkSEnIYQQQuR40kMjhBBCqESGnJQjPTRCCCGEyPGkh0YIIYRQifTQKEd6aIQQQgiVPJkUrNSWXpMmTaJKlSrY29vj4uJC69atiYqKMilTt25dY8L1ZOvbt69JmUuXLtGsWTPy5cuHi4sLQ4cO5dGjRyZldu7cSaVKlbC2tsbLy4vQ0NA08cyZM4fixYtjY2ODv78/Bw8eTH9j/iEJjRBCCJHH7Nq1i+DgYPbv309YWBgpKSk0btyYxMREk3J9+vTh+vXrxm3KlCnGY6mpqTRr1ozk5GT27dvH0qVLCQ0NZfTo0cYy0dHRNGvWjHr16hEZGcmgQYPo3bs3W7ZsMZZZvnw5ISEhjBkzhiNHjlChQgUCAwOJi4vLUJtkyEkIIYRQiVpDTps3bzZ5HBoaiouLCxEREdSuXdu4P1++fLi5uT2zjt9//50///yTrVu34urqSsWKFfnss88YPnw4Y8eOxcrKivnz5+Pp6cm0adMAKFOmDHv37mX69OkEBgYC8NVXX9GnTx969OgBwPz589mwYQOLFy9mxIgR6W6T9NAIIYQQKsmMISedTmeyJSUlvTSO+Ph4AJycnEz2//jjjxQsWJBy5coxcuRI7t+/bzwWHh5O+fLlcXV1Ne4LDAxEp9Nx6tQpY5mGDRua1BkYGEh4eDgAycnJREREmJQxMzOjYcOGxjLpJT00QgghRC7i4eFh8njMmDGMHTv2ueX1ej2DBg2iRo0alCtXzrj/vffeo1ixYhQuXJjjx48zfPhwoqKi+PXXXwGIiYkxSWYA4+OYmJgXltHpdDx48IA7d+6Qmpr6zDKnT5/OULsloRFCCCFUkhlDTpcvX8bBwcG439ra+oXPCw4O5uTJk+zdu9dk//vvv2/8d/ny5XF3d6dBgwacO3eOkiVLKhKzkiShEUIIIVSiQcFbH/zzfwcHB5OE5kX69+/P+vXr2b17N0WKFHlhWX9/fwDOnj1LyZIlcXNzS7MaKTY2FsA478bNzc247+kyDg4O2NraYm5ujrm5+TPLPG/uzvNIQiMUdWfTMLVDUEzhnsvUDkER1xa/p3YIQohsxmAwMGDAAFavXs3OnTvx9PR86XMiIyMBcHd3ByAgIICJEycSFxeHi4sLAGFhYTg4OODj42Mss3HjRpN6wsLCCAgIAMDKygo/Pz+2bdtG69atgcdDYNu2baN///4ZapMkNEIIIYRKzDQazBTqoslIPcHBwSxbtoy1a9dib29vnPOi1WqxtbXl3LlzLFu2jKZNm+Ls7Mzx48cZPHgwtWvXxtfXF4DGjRvj4+ND165dmTJlCjExMYwaNYrg4GDjMFffvn2ZPXs2w4YNo2fPnmzfvp0VK1awYcMGYywhISEEBQVRuXJlqlatyowZM0hMTDSuekovSWiEEEKIPGbevHnA44vnPW3JkiV0794dKysrtm7dakwuPDw8aNu2LaNGjTKWNTc3Z/369fTr14+AgADs7OwICgpi/PjxxjKenp5s2LCBwYMHM3PmTIoUKcKiRYuMS7YBOnTowI0bNxg9ejQxMTFUrFiRzZs3p5ko/DIag8FgeIWfRbag0+nQarXE3opP93ihEOklQ05C5H46nQ5XZy3x8Vn7PfLk+6ve1K1Y2NopUuejB4nsGNowy9uSXUgPjRBCCKESuZeTcuTCekIIIYTI8aSHRgghhFCJmebxplRdeZkkNEIIIYRaNAoOFeXxhEaGnIQQQgiR40kPjRBCCKGSp28qqURdeZkkNEIIIYRKNP/8p1RdeZkMOQkhhBAix5MeGiGEEEIlsspJOdJDI4QQQogcT3pohBBCCJXIlYKVIz00LzF/7hy8vYrjmN+GWtX9OXTwoNohvZKc1o69e3bTtnULPIsWxtZSw29r16Qpc/qvv3j3nZa4Omtx1tpRo1oVLl26lGkx9ajvxZ4JTbj4TTsuftOOLaMb09DXHQBHOysmd/XjwBfNubqoPcent2JSFz/sbS3T1NOppid7JjTh2qIORM1uw5RulY3HapR24YdBtflz1jtcXtieXZ814d2A4ibPtzDXMLRVOSKmtuDaog7sntCEBuXdM63dL3L16lV6dOvCG67OFLC3pXLF8kQcPqxKLK8qNTWVcWM+pfSbnhSwt8XHuySTJn5GTr3NXU57rz9Let7/ucWTVU5KbXmZ9NC8wMoVyxk+NISv58ynSlV/Zs+aQctmgRw7FYWLi4va4aVbTmxHYmIi5X0r0K17Tzq2a5Pm+Plz52hQtyZBPXoxavQ4HBwc+PPPU9jY2GRaTNduP2DcimOcj72HRgMda3ryw6Da1P10MxrA3dGW0T8dJepaPB7OdkzrUQV3R1u6z95rrOODt0vzwdulGbP8KBHnbpHP2oKiBf+9MV3VNwvy5+W7zFr/J3G6hwRWfIN5/6uG7kEyv0deA+CTthVoV704gxYf5Mz1eOqXd+e7D2vx9mdhnLh4J9Pa/1937tyhfp0a1KlTjzXrNlGoUCHOnj1DgQIFsiwGJUyb+gULv5nHwsVL8fEpS0TEYf7XuwcODlqCBwxUO7wMyYnv9Wd52ftfiGeRu22/QK3q/vhVrsKMWbMB0Ov1eHl60C94AEOHjVD8fJklp7fD1lLD8lWradmqtXFf184dsbSwZPHS7zPtvOm52/a5uW0Z8/NRfth9Ps2xVlU8mN+3OkX6rCBVb0Cbz5JTM9/hvem72P1nbLrj+DmkDjd0Dxmw6AAAp2a25qvfTvHttjPGMksH1ORBcip9vwlP8/zMutv2qI9HEL7vD7bt3JMp9WeVNq2a4+LiyvyF3xr3dWzfFlsbW5Z894OKkWVcTn+vP8uz3v9KUvtu282/3omlbX5F6kx5kMD6AXXlbtsv8ttvv6W7wpYtW75yMNlJcnIyR49EMHT4SOM+MzMz6tdvyMH9ab80sqvc0o6n6fV6Nm/cQMiQYbRoGsixyKMUK+7J0OEjM+1D77/MNBpaVy1KPmsLDp29+cwyDvmsuPcghVT9478Z6pVzx0yjwb1APvZPbkZ+G0sOnrnBpz8d5ert+889l0M+S/6+pjM+trY0Jykl1aTMg+RUqpUqpEDL0m/D+t9o2CiQ9zq2Y+/uXRQu/Abv9/2Anr37ZGkcr6taQHW+XbSAM3//zZulSnH82DHC/9jL5KlfqR1ahuTG93peIBfWU066EprWrVunqzKNRkNqaurLC+YAN2/eJDU1FRcXV5P9Lq6uREWdVimqjMst7XhaXFwcCQkJfDllMmPGTWDC51/w+++b6diuDVu27qBW7TqZdu4yRbRsGd0YG0tzEh8+ouvMPUQ9lWw84ZTfmiGtyrF051njvmIu+TEzg8EtfPj4xyPo7ifz8bsV+GVYPWp9somUVH2aelpXLcpbns6ELDlk3Lf9xHU+eLs0+6JuEB13jzo+bjSv7IF5Fq/ZjD5/noXfzGPgoBCGDf+YiMOH+GjwQKysrOjSLShLY3kdQ4aNQKfTUaFcaczNzR/PqflsIp3e66x2aBmSG9/rQmREuhIavT7tB60QannyemzeshUDBw0GoELFihwI38fCBfMzNaE5e/0edUZtwiGfJS2rFGXu+9Vo8flWk6TG3saC5R/VIepqPF+sPmHcb6YBKwtzRv4QwY6TMQD0mfsHp79+h1o+rmw/cd3kXDXLuPB1n2oMWnyQ01fjjftH/hDBjJ5VOfBFMwwGiI5LYNme83SuXSLT2v0ser2eSn6VGT/hcwAqvvUWp06dZOGC+TkqoVm1cgU///Qjod8vw8enLMePRTL0o0G4uxfOUe0QOZOsclLOa61yevjwoVJxMHnyZDQaDYMGDVKsztdRsGBBzM3NiYsznesQFxuLm5ubSlFlXG5px9MKFiyIhYUFZcr4mOz3Ll2Gy5m4ygkgJVVPdFwCxy7c4bOVxzh5+S7/a+xtPJ7fxoKVQ+tx7+Ejus7azaPUf6eoxd59/H6Jeio5uXUviVv3kijinM/kPNW9XVg2uA6jfoxg+R/RJsdu3Uui68w9FOmzggoha/Efvp7Eh4+4GJeQGU1+Ljd39zS/g9Kly3D5cub+DpT28YihDBk6gvYdOlKufHne69KVAR8OZuqUSWqHliG58b2eF8gqJ+VkOKFJTU3ls88+44033iB//vycP/94MuSnn37Kt99++5JnP9uhQ4f45ptv8PX1faXnZwYrKyvequTHju3bjPv0ej07dmyjarUAFSPLmNzSjqdZWVnhV7kKf0dFmew/c+ZvihYrlqWxmGnAytIceNwz88uw+iQ/0tN5+i6SUkx7Ng+cuQGAl/u/k/Uc7axwtrfm8s1E474apV34+aM6jFseydKd55577qQUPdfvPMDCXEOLKh5sPHJVyaa9VED1Gvz99zN+B0Wz9nfwuh7cv4+ZmelHobm5eY7rmc6N73UhMiLDy7YnTpzI0qVLmTJlCn36/Dv5r1y5csyYMYNevXplqL6EhAQ6d+7MwoULmTBhQkbDyVQDB4XQp2cQfn6VqVylKrNnzeB+YiLdgnqoHVqG5MR2JCQkcO7sv/NPLkRHcywykgJOThQtWpTBHw2l63sdqFmrNnXq1uP3LZvZuH4dW7buzLSYPm1Xga3Hr3Hl1n3y21jwbkBxapZ25d2pO4zJjK2VOf+bvw97W0vjNWhu6pLQGwyci7nHhojLTOrix+DFB7n3IIVP21fkzDUde/56/Fd1zTIu/BRSl2+2RLHu8GVctI+XoSc/0nM3MRkAvxLOuDvZcuLiHdwL5GP4O+Ux02iYtfHPTGv7swwYOJh6taszZfLntH23PYcOHWTxogXMnrcgS+N4XU2bteCLyRPxKFoUH5+yREYeZdaMr+jWvafaoWVYTnyvP8vL3v+5iZlGg5lCXStK1ZNTZXjZtpeXF9988w0NGjTA3t6eY8eOUaJECU6fPk1AQAB37mTsOhhBQUE4OTkxffp06tatS8WKFZkxY8YzyyYlJZGUlGR8rNPp8PDwyLRl2wDz5sxm+ldTiY2JwbdCRaZNn0VVf/9MOVdmymnt2L1rJ4EN66XZ36VrEAsXhwKwdMlipk6ZxNUrVyhVyptRY8bRomUrxWL477LtWb38qe3jiqujLboHKZz653oxO0/FUKO0C+s+bvjMeiqErDX2wNjbWDCxsx/NK3ugNxj443QcH/8QYVzlNLtPNd6rlXYuzN6/Ymk56fFf3tW9XZjWvQrFCuUnMSmFsGPXGb8ikpi7D555/sxatg2wccN6Rn8ykrNnz1Dc05OBH4bkuFVO9+7dY9yYT/lt7WpuxMXhXrgw7Tt04uNRo7GyslI7vAzLae/1Z0nP+18pai/bfmfebkWXba/uVzvPLtvOcEJja2vL6dOnKVasmElC8+eff1K1alUSEtI/jv/zzz8zceJEDh06hI2NzUsTmrFjxzJu3Lg0+zMzoRF5V3quQ5MTZGZCI0ROp3ZC00bhhObXPJzQZHgOjY+PD3v2pL2Q1qpVq3jrrbfSXc/ly5f58MMP+fHHH9N9ddeRI0cSHx9v3C5fvpzu8wkhhBDZzZNVTkpteVmG59CMHj2aoKAgrl69il6v59dffyUqKorvvvuO9evXp7ueiIgI4uLiqFSpknFfamoqu3fvZvbs2SQlJWFubm7yHGtra6ytrTMashBCCCFyuQwnNK1atWLdunWMHz8eOzs7Ro8eTaVKlVi3bh2NGjVKdz0NGjTgxIkTJvt69OhB6dKlGT58eJpkRgghhMhtzDSPN6Xqyste6eaUtWrVIiws7LVObG9vT7ly5Uz22dnZ4ezsnGa/EEIIkRvJhfWU88p32z58+DB//fUX8HhejZ+fn2JBCSGEEEJkRIYTmitXrtCpUyf++OMPHB0dAbh79y7Vq1fn559/pkiRIq8czM6dO1/5uUIIIUROlMc7VhST4VVOvXv3JiUlhb/++ovbt29z+/Zt/vrrL/R6Pb17986MGIUQQohcSVY5KSfDPTS7du1i3759eHv/e/8ab29vvv76a2rVqqVocEIIIYQQ6ZHhhMbDw4OUlJQ0+1NTUylcuLAiQQkhhBB5gaxyUk6Gh5ymTp3KgAEDOHz4sHHf4cOH+fDDD/nyyy8VDU4IIYQQIj3S1UNToEABk7G5xMRE/P39sbB4/PRHjx5hYWFBz549ad26daYEKoQQQuQ2smxbOelKaJ53byUhhBBCvDrNP5tSdeVl6UpogoKCMjsOIYQQQohX9soX1gN4+PAhycnJJvvy4h0+hRBCiFdhptFgptBQkVL15FQZnhScmJhI//79cXFxwc7OjgIFCphsQgghhEgfjUbZLS/LcEIzbNgwtm/fzrx587C2tmbRokWMGzeOwoUL891332VGjEIIIYQQL5ThIad169bx3XffUbduXXr06EGtWrXw8vKiWLFi/Pjjj3Tu3Dkz4hRCCCFyHVnlpJwM99Dcvn2bEiVKAI/ny9y+fRuAmjVrsnv3bmWjE0IIIXIxGXJSToYTmhIlShAdHQ1A6dKlWbFiBfC45+bJzSqFEEIIkX1NmjSJKlWqYG9vj4uLC61btyYqKsqkzMOHDwkODsbZ2Zn8+fPTtm1bYmNjTcpcunSJZs2akS9fPlxcXBg6dCiPHj0yKbNz504qVaqEtbU1Xl5ehIaGpolnzpw5FC9eHBsbG/z9/Tl48GCG25ThhKZHjx4cO3YMgBEjRjBnzhxsbGwYPHgwQ4cOzXAAQgghRF71ZJWTUlt67dq1i+DgYPbv309YWBgpKSk0btyYxMREY5nBgwezbt06Vq5cya5du7h27Rpt2rQxHk9NTaVZs2YkJyezb98+li5dSmhoKKNHjzaWiY6OplmzZtSrV4/IyEgGDRpE79692bJli7HM8uXLCQkJYcyYMRw5coQKFSoQGBhIXFxchn6WGoPBYMjQM/7j4sWLRERE4OXlha+v7+tUlWE6nQ6tVkvsrXhZLi4UV7jnMrVDUMS1xe+pHYIQ2ZZOp8PVWUt8fNZ+jzz5/ur53QGs8uVXpM7k+wks7ubP5cuXTdpibW2NtbX1C59748YNXFxc2LVrF7Vr1yY+Pp5ChQqxbNky3n33XQBOnz5NmTJlCA8Pp1q1amzatInmzZtz7do1XF1dAZg/fz7Dhw/nxo0bWFlZMXz4cDZs2MDJkyeN5+rYsSN3795l8+bNAPj7+1OlShVmz54NgF6vx8PDgwEDBjBixIh0tz/DPTT/VaxYMdq0aZPlyYwQQgiR02XGHBoPDw+0Wq1xmzRp0kvjiI+PB8DJyQmAiIgIUlJSaNiwobFM6dKlKVq0KOHh4QCEh4dTvnx5YzIDEBgYiE6n49SpU8YyT9fxpMyTOpKTk4mIiDApY2ZmRsOGDY1l0itdq5xmzZqV7goHDhyYoQCEEEKIvCozVjk9q4fmRfR6PYMGDaJGjRqUK1cOgJiYGKysrNLMjXV1dSUmJsZY5ulk5snxJ8deVEan0/HgwQPu3LlDamrqM8ucPn06Pc02SldCM3369HRVptFoJKERQgghVOTg4JCh4bPg4GBOnjzJ3r17MzGqzJeuhObJqiYh8pKr33ZSOwRFFP9gldohKOLC3HfVDkEIxZmhwNyPp+rKqP79+7N+/Xp2795NkSJFjPvd3NxITk7m7t27Jr00sbGxuLm5Gcv8dzXSk1VQT5f578qo2NhYHBwcsLW1xdzcHHNz82eWeVJHein1cxRCCCFEBj0ZclJqSy+DwUD//v1ZvXo127dvx9PT0+S4n58flpaWbNu2zbgvKiqKS5cuERAQAEBAQAAnTpwwWY0UFhaGg4MDPj4+xjJP1/GkzJM6rKys8PPzMymj1+vZtm2bsUx6vdbNKYUQQgiR8wQHB7Ns2TLWrl2Lvb29cc6LVqvF1tYWrVZLr169CAkJwcnJCQcHBwYMGEBAQADVqlUDoHHjxvj4+NC1a1emTJlCTEwMo0aNIjg42Dhvp2/fvsyePZthw4bRs2dPtm/fzooVK9iwYYMxlpCQEIKCgqhcuTJVq1ZlxowZJCYm0qNHjwy1SRIaIYQQQiUaDZgpdIXfjMwtnjdvHgB169Y12b9kyRK6d+8OPJ4/a2ZmRtu2bUlKSiIwMJC5c+cay5qbm7N+/Xr69etHQEAAdnZ2BAUFMX78eGMZT09PNmzYwODBg5k5cyZFihRh0aJFBAYGGst06NCBGzduMHr0aGJiYqhYsSKbN29OM1H4pe1/3evQqEmuQyMyUw5+a5jwDP5F7RAUIXNoRGZQ+zo0H/x0CGuFrkOTdD+BuZ2qZHlbsguZQyOEEEKIHO+VEpo9e/bQpUsXAgICuHr1KgDff/99jl/yJYQQQmQltSYF50YZTmh++eUXAgMDsbW15ejRoyQlJQGPrzL4+eefKx6gEEIIIcTLZDihmTBhAvPnz2fhwoVYWloa99eoUYMjR44oGpwQQgiRm5lplN3ysgyvcoqKiqJ27dpp9mu1Wu7evatETEIIIUSe8PQ9mJSoKy/LcA+Nm5sbZ8+eTbN/7969lChRQpGghBBCCCEyIsMJTZ8+ffjwww85cOAAGo2Ga9eu8eOPPzJkyBD69euXGTEKIYQQuZKZRqPolpdleMhpxIgR6PV6GjRowP3796lduzbW1tYMGTKEAQMGZEaMQgghRK6k9r2ccpMMJzQajYZPPvmEoUOHcvbsWRISEvDx8SF/fmUuDCSEEEIIkVGvfOsDKysr482nhBBCCJFxMilYORnuoapXrx7169d/7pZb7N2zm7atW+BZtDC2lhp+W7tG7ZBey/y5c/D2Ko5jfhtqVffn0H9u+Z7TTJ0yGVtLDUNCBqkdiompX0yiZkBVXJwcKPaGK+3bvsPfUVFpyh3YH06Txg0o6JgfV2ctjerX4cGDB8bjpd/0JJ+Vmcn25ZTJisQYVKcE20c35MzMVpyZ2Yr1w+tRv5yb8XiXWp78+lEdzsxsRcyCd3GwtXxuXVYWZmz9tCExC96lbBGtcf+QFj7ELHg3zXb+69bGMt7uDizqW41DnzchZsG79GngpUj7XmbC+LHYWmpMtgrlShuP9+/3P3y8S1LA3hYP90K0a9OKqNOnsyS215WbPrdy22fW85ih4Bwa8nZGk+GEpmLFilSoUMG4+fj4kJyczJEjRyhfvnxmxKiKxMREyvtWYMasOWqH8tpWrljO8KEhfDJqDOEHj+DrW4GWzQJNbvmekxw+dIhvF35D+fK+aoeSxp49u/lfvw/YuSecdRt/J+VRCi2aBZKYmGgsc2B/OK2aN6FBw0bs/uMAe/YdpG+/YMzMTN+On44Zx/lL14xbv2Bl5qhdu/OAib+epPHEbQRO3MbeqDhCP6iOt/vje7/YWpmz/VQMMze9/Ev807blib37IM3+ub9HUX7IOpMt6lo86yKuGMvYWplz6UYiE1afIDY+bR2ZyadsWaIvXzdu23b+e5Xztyr5sWDREiJP/MVvG7ZgMBho3rQxqampWRrjq8gtn1u57TNLZI0MDzlNnz79mfvHjh1LQkLCaweUXQS+3YTAt5uoHYYiZs34ih69+tCt++NbsX89dz6bNm1gaehihg4boXJ0GZOQkECPoM7Mnb+QyZ9PUDucNH5bv8nk8YJFSyj2hitHj0RQs9bj6zcNGxJCv+ABDHnqZ1/K2ztNXfb29ri5uaXZ/7rCjl83eTx5zSmC6pSkUgknoq7rWLjt8WUZqpcq9MJ66pdzo46PK73nh9OgvLvJsftJqdxP+jcB8CmixbuwlmE/HDXui7x4h8iLdwAY9U7W/jFkYW7x3J9trz7vG/9drHhxxoybQFW/Cly8cIESJUtmVYivJLd8buWmz6yXkSEn5Sg2KbpLly4sXrxYqeqEQpKTkzl6JIL6DRoa95mZmVG/fkMO7g9XMbJXM2hAMG83aWbSnuxMFx8PQIECTgDExcVx6OABXFxcqFe7BsWLuNG4QV32/ZH2PmhfTv2CIm4FqValEtOnTeXRo0eKx2emgVZVipDPypyI87fS/byC9tZ82bUSAxYf4kHyy3suOtf05GzMPQ6cvfk64Srm7NkzeBYtTJlSJejetTOXLl16ZrnExES+W7qE4p6eFPHwyOIo86bc9pn1MnKlYOW88qTg/woPD8fGxkap6oRCbt68SWpqKi4urib7XVxdiYrKGfMCnlix/Gcijx5h7/5DaoeSLnq9nqFDBhNQvQZly5UD4EL0eQAmfjaOz7+Yiq9vRZb9+B1NAxty+OgJvN58E4APggdQ8a1KFCjgxP79+xgz6mNiYq7zxdSvFImt9BsObBheH2tLMxKTHtFzXjh/X7+X7ufP6lGF73ad59jFO3g453thWWsLM9r4F2X25uzxeqtS1Z8F34ZSqpQ3MTHXmfjZOBrWq0VE5Ens7e0B+GbeXD4ZOYzExERKeXuzYVMYVlZWKkeeN+SmzyyRtTKc0LRp08bkscFg4Pr16xw+fJhPP/1UscCEeNrly5cZGvIh6zeF5ZjEedDAYP48dZKtO/YY9+n1egB69n6fbkGPu9MrvvUWO7dv57vQxYyfOAmAgYNCjM8p7+uLlZUVAz7oy/gJk7C2tn7t2M7F3KPBZ2E42FrS3K8Is3pU4Z0vd6YrqelV3ws7GwtmpWOODUCTt94gv40Fy/ddfN2wFfH0kEx5X1+qVPXHu2Qxflm5gu49ewHQ8b3ONGjYiJiY68z46ku6dGrP9t1/5JjXnsg5NBoUuyBeXh9yynBCo9VqTR6bmZnh7e3N+PHjady4cYbqGjt2LOPGjTPZ5+3tzekcsqIgJyhYsCDm5ubExcWa7I+Ljc2U+RmZ5eiRCOLi4gioWsm4LzU1lb17djN/7mziE5MwNzdXMUJTgz/sz6aNGwjbtosiRYoY97u5PZ5rUqaM6SUPvEuX4fLly8+tr0oVfx49esTFCxeeOd8mo1JSDVy48Xii8vFLd6lYvAC9G7zJsB9efoPZmqULUbmEM5fmmv5xs+WTBvx64BIDQw+b7O9c05Ow49e5eS/ptePODI6Ojni9WYpz5/69pYtWq0Wr1eL15ptU9a+Ge6ECrF2zmg4dO6kYad6QWz6zRNbLUEKTmppKjx49KF++PAUKFFAkgLJly7J169Z/A7JQbBRM8Ph6QW9V8mPH9m20bNUaeNxLsGPHNvp+0F/d4DKgXv0GHD56wmTf+7174O1dmo+GDs82yYzBYCBk0AB+W7uGLWE7KO7paXK8WPHiuBcuzN9/my7lPnPmbxoHvv3ceo8fi8TMzIxCLi6ZEreZRoO1Rfqm1I36OZIv1pwyPnZ1tGX5oFr8b+EBjpy/bVK2qHM+angXImjOPkXjVVJCQgLR58/h1rnrM48bDAYMBgPJSdkzIcttcstnVnrJpGDlZCh7MDc3p3Hjxvz111+KJTQWFs9fbaCmhIQEzj11E84L0dEci4ykgJMTRYsWVTGyjBs4KIQ+PYPw86tM5SpVmT1rBvcTE41DHjmBvb29cR7KE3Z2djg5O6fZr6ZBA4NZ8fNPrPhlDfnt7YmJiQEe/8Vva2uLRqNhcMgQJowfi69vBXwrVOSH75fyd9Rplv28Eni8rPvQwQPUrlMPe3t7DuwPZ/jQEDq910WR993H75Rj+8kYrt6+j52NBW2qFqV6qUJ0nPl4aKyQgzUuDjYUd7EDoMwbWhIepnD19n3u3k/h6u0HwL/LrBOTHk9WvhCXwPX/LOHuWMOT2PiHbDtpurIKwNJcQ6l/lopbWpjh7mhL2SJaEpMeGXuPMsOIYUNo1rwFRYsW49q1a0wYPwZzc3Pad+xE9PnzrFq5nAYNG1OwUCGuXrnCtKmTsbW1JbBJ00yLSSm55XMrN3xmpZeSk3llUnAGlStXjvPnz+P5n788X9WZM2coXLgwNjY2BAQEMGnSpOe+8ZKSkkh66q8knU6nSAzPciTiMIEN6xkfDx/6eE5Dl65BLFwcmmnnzQzt2nfg5o0bjB83mtiYGHwrVGTt+s24urq+/MkiQxZ+Mx/A5LUD8M2ixXTt1h2A/gMH8fDhQ4YNDeHO7duU963A+k2/G5cEW1lbs3LFciZ+No6kpCSKF/ek/8BBJvNqXkdBe2u+7lEFF60N9x6k8OfVeDrO3MPuvx5f4yOoTkmGtPh3SGztsLoAfLjkEMvD0z8PRqOBDtWLsTz8AnpD2uNujrZsG93I+PiDQG8+CPRmX9QN2kzb9WqNS4erV6/QrUsnbt+6RcFChaheoya79u6nUKFCpKSk8MfePcyeNYM7d+7g4upKzZq12bF7Hy6Z1DumpNzyuSWfWeJVaAwGwzM+ap5v8+bNjBw5ks8++ww/Pz/s7OxMjjs4OKS7rk2bNpGQkIC3tzfXr19n3LhxXL16lZMn/11t8LRnzbkBiL0Vn6HzCpEeGXxrZFuewb+oHYIiLsx9V+0QRC6k0+lwddYSH5+13yM6nQ6tVsuna49iY5f2++5VPEy8x2et3srytmQX6e6hGT9+PB999BFNmz7udm3ZsiWapwbsDAYDGo0mQ1fTbNLk39UGvr6++Pv7U6xYMVasWEGvXr3SlB85ciQhIf/+larT6fCQa0MIIYTIoWTISTnpTmjGjRtH37592bFjR6YF4+joSKlSpTj71Bjw06ytrRVZsiqEEEKI3CXdCc2T7vc6depkWjAJCQmcO3eOrl2fvdpACCGEyE2kh0Y5Gbr1gUbhNWFDhgxh165dXLhwgX379vHOO+9gbm5Op05yrQchhBC5n0ajUXTLyzK0yqlUqVIv/YHdvn37hcefduXKFTp16sStW7coVKgQNWvWZP/+x6sNhBBCCCHSK0MJzbhx49JcKfh1/Pzzz4rVJYQQQuQ0MuSknAwlNB07dswR12IQQgghRN6S7oQmr4/NCSGEEEqTWx8oJ8OrnIQQQgihDDONRrG7bStVT06V7oRGr9dnZhxCCCGEEK9Mbm0thBBCqEQmBStHEhohhBBCLQrOoSGPJzQZurCeEEIIIUR2JD00QgghhErM0GCmUNeKUvXkVJLQCCGEECqRZdvKkSEnIYQQQuR40kMjhBBCqERWOSlHemiEEEIIkeNJQiOEEEKo5MmVgpXaMmL37t20aNGCwoULo9FoWLNmjcnx7t27o9FoTLa3337bpMzt27fp3LkzDg4OODo60qtXLxISEkzKHD9+nFq1amFjY4OHhwdTpkxJE8vKlSspXbo0NjY2lC9fno0bN2aoLSAJjRBCCKGaJ5OCldoyIjExkQoVKjBnzpznlnn77be5fv26cfvpp59Mjnfu3JlTp04RFhbG+vXr2b17N++//77xuE6no3HjxhQrVoyIiAimTp3K2LFjWbBggbHMvn376NSpE7169eLo0aO0bt2a1q1bc/LkyQy1R+bQCCGEEHlQkyZNaNKkyQvLWFtb4+bm9sxjf/31F5s3b+bQoUNUrlwZgK+//pqmTZvy5ZdfUrhwYX788UeSk5NZvHgxVlZWlC1blsjISL766itj4jNz5kzefvtthg4dCsBnn31GWFgYs2fPZv78+eluj/TQCCGEECoxQ8Ehp3+uQ6PT6Uy2pKSkV45v586duLi44O3tTb9+/bh165bxWHh4OI6OjsZkBqBhw4aYmZlx4MABY5natWtjZWVlLBMYGEhUVBR37twxlmnYsKHJeQMDAwkPD89QrJLQCCGEECrJjCEnDw8PtFqtcZs0adIrxfb222/z3XffsW3bNr744gt27dpFkyZNSE1NBSAmJgYXFxeT51hYWODk5ERMTIyxjKurq0mZJ49fVubJ8fSSISchhBAiF7l8+TIODg7Gx9bW1q9UT8eOHY3/Ll++PL6+vpQsWZKdO3fSoEGD145TaZLQCPEcmlxy2c0Lc99VOwRFuHb7Xu0QFBH7XVe1QxDZiBnKDZU8qcfBwcEkoVFKiRIlKFiwIGfPnqVBgwa4ubkRFxdnUubRo0fcvn3bOO/Gzc2N2NhYkzJPHr+szPPm7jyPDDkJIYQQKvnvsujX3TLTlStXuHXrFu7u7gAEBARw9+5dIiIijGW2b9+OXq/H39/fWGb37t2kpKQYy4SFheHt7U2BAgWMZbZt22ZyrrCwMAICAjIUnyQ0QgghRB6UkJBAZGQkkZGRAERHRxMZGcmlS5dISEhg6NCh7N+/nwsXLrBt2zZatWqFl5cXgYGBAJQpU4a3336bPn36cPDgQf744w/69+9Px44dKVy4MADvvfceVlZW9OrVi1OnTrF8+XJmzpxJSEiIMY4PP/yQzZs3M23aNE6fPs3YsWM5fPgw/fv3z1B7JKERQgghVKJReMuIw4cP89Zbb/HWW28BEBISwltvvcXo0aMxNzfn+PHjtGzZklKlStGrVy/8/PzYs2ePyZycH3/8kdKlS9OgQQOaNm1KzZo1Ta4xo9Vq+f3334mOjsbPz4+PPvqI0aNHm1yrpnr16ixbtowFCxZQoUIFVq1axZo1ayhXrlyG2qMxGAyGDP4Msg2dTodWqyX2VnymjBcKIbIPmUMjMoNOp8PVWUt8fNZ+jzz5/pq/4xS2+e0VqfNBwj361iub5W3JLmRSsBBCCKGSV7llwYvqysskoRFCCCFUlLfTEOXIHBohhBBC5HjSQyOEEEKo5FVuKvmiuvIySWiEEEIIlSh5/ZjccjHQVyVDTkIIIYTI8aSHRgghhFBJZtz6IK+ShEYIIYRQiQw5KSevJ3RCCCGEyAWkh0YIIYRQyavcsuBFdeVl0kMjhBBCiBxPEpqXmD93Dt5exXHMb0Ot6v4cOnhQ7ZBeSU5vx4TxY7G11JhsFcqVVjusVzL1i0nUqFaFQgXsKVrYhXZtW/N3VJTaYb0ytV5bvRqW4o/Jzbm8qAOXF3UgbNzbNKxQ2Hh8Ri9/Iqe3Jia0E+fmt2NZSF3eLPzv/W3KFS3At/1rcurrNsSEduLg1Jb0fTvta8rKwoxP21fkxMx3iFv6HsdnvkOXOiWNx9+rXYL4ZV1NttjQ9zK38cDePbtp27oFnkULY2up4be1a0yOTxg/lgrlSuOstcO9UAGaBjbk4IEDmR6XUnL6Z1Z6PZlDo9SWl8mQ0wusXLGc4UND+HrOfKpU9Wf2rBm0bBbIsVNRuLi4qB1euuWWdviULcuGzVuNjy0scubLd8/uXfTtF4xf5So8evSIMZ9+TPOmjTl6/E/s7OzUDi9D1HxtXb19n7E/H+FczD00wHu1S/LTR3WpNXIDp6/GExl9mxV/RHPlZiIF8lszoq0vq0c0xPfD1egNBip6OnFD95D35+zl6u37VH2zEDN7VyNVb2Dh7/8mmKEDa+OitWHAwnDOx9zDtYBtmnvmxN9PpvJHa42Ps+KWv4mJiZT3rUC37j3p2K5NmuNeb5Zi+szZeHqW4MGDB3w9czotmjbm5OmzFCpUKPMDfA255TMrPWSVk3LkbtsvUKu6P36VqzBj1mwA9Ho9Xp4e9AsewNBhIxQ/X2bJDe2YMH4s69au4UBEpNqhKO7GjRsULexC2PZd1KxVW+1wMiQrX1vpudv2hQXt+XTZEb7feTbNsbIejuz7ogUVB60mOi7hmc//sntVvN/Q0mJiGAANfAuzeEAtKg5azZ3E5Gc+573aJZjUtQrF+ixPVzsy427btpYalq9aTctWrZ9b5sldpTdu2Uq9+g0Uj0FJWfm6Uvtu29/vjSKfQnfbvp9wj641vfPs3bbzekL3XMnJyRw9EkH9Bg2N+8zMzKhfvyEH94erGFnG5JZ2AJw9ewbPooUpU6oE3bt25tKlS2qHpAhdfDwABQo4qRxJxmSn15aZRkPbgOLks7bg4JkbaY7ns7agcx0vLsTd48qt+8+txyGfJXcSkoyPm/oVITL6Fh+2KMtfs9sSMa0VE96rhI2lucnz8ttYcGLmO5z6ug3LQupS+g2tco1TQHJyMt8uWoBWq6W8bwW1w3mh7PS6ygoy5KScnNlnnwVu3rxJamoqLi6uJvtdXF2JijqtUlQZl1vaUaWqPwu+DaVUKW9iYq4z8bNxNKxXi4jIk9jbK/PXjRr0ej1DPxpEQPUalC1XTu1wMiQ7vLZ8PBwJG/c2NpbmJDx8ROfpO4m6Gm883rthKca9V4n8Npb8fS2e1p9vJSVV/8y6qr5ZiDbVitN+6nbjvuIu+alWyoWHyal0nr4TZ3trpvXwp4C9NcHfPP5yPXNdR/CCcE5duoODrSUDmpfl93FvU23YOq7dfn7ylBU2blhPt84duX//Pm7u7qzfFEbBggVVjellssPrKivJKiflqN5Dc/XqVbp06YKzszO2traUL1+ew4cPqx2WyGYC325C23fbUd7Xl0aNA1mzbiPxd+/yy8oVaof2WgYNCObUqZN89+PPaoeSI525pqPWyA00GL2JxVv/Zn7fGng/1Tuy4o9oan28gSbjt3D2uo7QD2tjbZn2Y69MEUd++qguk389zvYT1437zTQaDBjoM2cvR87dIizyGp/8cJj3apU09tIcOnOTn/ec58TFO/xxOo4u03dyS/eQHg3ezPwfwEvUqVuPA4cj2bF7H40bv02X99oTFxendlhCZApVE5o7d+5Qo0YNLC0t2bRpE3/++SfTpk2jQIECaoYFQMGCBTE3NycuLtZkf1xsLG5ubipFlXG5pR3/5ejoiNebpTh3Lu1ciZxi0MD+bNy4ni1hOyhSpIja4WRYdnhtpaTqOR97j8jo24xbfpSTl+7Q76mVSroHKZyPuce+03F0m7GbN921NK9c1KQO7ze0/PZJQ0K3n+HLNSdMjsXcfcD12/fRPUgx7ou6Go+ZmYbCzvmeGdOjVAPHL96hhKv6PYd2dnaU9PLCv1o15i/8FgsLC5Yu+VbtsF4oO7yustKTu20rteVlqiY0X3zxBR4eHixZsoSqVavi6elJ48aNKVmy5MufnMmsrKx4q5IfO7ZvM+7T6/Xs2LGNqtUCVIwsY3JLO/4rISGB6PPncHNzVzuUDDMYDAwa2J/f1q5m8+/bKe7pqXZIryQ7vrbMNBqsLMyfeezJB/7TPTSl39CyflQjftp9ns9WRKZ5zoG/b+BWIB921v+Oznu5O5Cq13PtOXNxzDQafDwcibn74PUakwn0ej1JSUkvL6ii7Pi6ykxmaBTd8jJV59D89ttvBAYG0q5dO3bt2sUbb7zBBx98QJ8+fZ5ZPikpyeTNqNPpMjW+gYNC6NMzCD+/ylSuUpXZs2ZwPzGRbkE9MvW8SssN7RgxbAjNmregaNFiXLt2jQnjx2Bubk77jp3UDi3DBg0IZvnPy1j561ry29sTExMDgFarxdbWVuXoMkbN19aYDm8RduwqV24mkt/WknbVPalZxpU2k7dR3CU/baoVZ/uJa9zUPaSwkx2DW5blYXIqv0deAx4PM637pBHbjl9j9sY/cdHaAJCqN3Dr3uPPmZV/RDP0nfLM7Vudz1cdw9nems/e8+OHned4mJIKwLB3ynP47E3Ox95Dm8+Kgc198Chox3c7Mrf3MCEhgXNn/z3HhehojkVGUsDJCWdnZ76YNJFmzVvi5u7OrZs3+WbeHK5dvUqbtu0yNS4l5IbPLJH1VE1ozp8/z7x58wgJCeHjjz/m0KFDDBw4ECsrK4KCgtKUnzRpEuPGjcuy+Nq178DNGzcYP240sTEx+FaoyNr1m3F1dX35k7OR3NCOq1ev0K1LJ27fukXBQoWoXqMmu/buz/bX03iWBd/MA6Bxg7qm+xctoWtQ96wP6DWo+doq5GDD/H41cHO0RXc/hVOX79Bm8jZ2nLyOm6MtAaVd6NekNI52VsTFP2Tf6Tgajd3MTd1DAFr5F6WQ1oaOtUrQsVYJY70XbyTg++FqABKTHtF60lamBlVl54Sm3E5IYvX+i0x4qjfH0c6amb2r4epoy93EZCKjb9F4zGaTycmZ4UjEYQIb1jM+Hj40BIAuXYP4eu58oqJO88P3S7l18yZOzs5UrlyFrTv24FO2bKbGpYTc8JmVXkoOFeX1ISdVr0NjZWVF5cqV2bdvn3HfwIEDOXToEOHhaZfnPauHxsPDI9OuQyOEyD7Scx2anCAzrkMjXp3a16FZHn5G0evQdAh4M89eh0bVHhp3d3d8fHxM9pUpU4ZffvnlmeWtra2xtrbOitCEEEKITKf55z+l6srLVE1oatSoQdR/7mHz999/U6xYMZUiEkIIIbKODDkpR9VVToMHD2b//v18/vnnnD17lmXLlrFgwQKCg4PVDEsIIYQQOYyqCU2VKlVYvXo1P/30E+XKleOzzz5jxowZdO7cWc2whBBCiCyhUXDJtgw5qax58+Y0b95c7TCEEEKILCdDTspR/dYHQgghhBCvS/UeGiGEECKvkh4a5UhCI4QQQqhElm0rR4achBBCCJHjSQ+NEEIIoRIzzeNNqbryMumhEUIIIUSOJz00QgghhEpkDo1yJKERQgghVCKrnJQjQ05CCCGEyPGkh0YIIYRQiQblhoryeAeNJDRCCCGEWmSVk3JkyEkIIYQQOZ700AghhBAqkVVOypEeGiGEEEIlT1Y5KbVlxO7du2nRogWFCxdGo9GwZs0ak+MGg4HRo0fj7u6Ora0tDRs25MyZMyZlbt++TefOnXFwcMDR0ZFevXqRkJBgUub48ePUqlULGxsbPDw8mDJlSppYVq5cSenSpbGxsaF8+fJs3LgxY41BEhohhBAiT0pMTKRChQrMmTPnmcenTJnCrFmzmD9/PgcOHMDOzo7AwEAePnxoLNO5c2dOnTpFWFgY69evZ/fu3bz//vvG4zqdjsaNG1OsWDEiIiKYOnUqY8eOZcGCBcYy+/bto1OnTvTq1YujR4/SunVrWrduzcmTJzPUHo3BYDBk8GeQbeh0OrRaLbG34nFwcFA7HCFEJnLt9r3aISgi9ruuaocgnqLT6XB11hIfn7XfI0++v7YcuYBdfmXOm5igI7BS8Vdqi0ajYfXq1bRu3Rp43DtTuHBhPvroI4YMGQJAfHw8rq6uhIaG0rFjR/766y98fHw4dOgQlStXBmDz5s00bdqUK1euULhwYebNm8cnn3xCTEwMVlZWAIwYMYI1a9Zw+vRpADp06EBiYiLr1683xlOtWjUqVqzI/Pnz090G6aERQgghchGdTmeyJSUlZbiO6OhoYmJiaNiwoXGfVqvF39+f8PBwAMLDw3F0dDQmMwANGzbEzMyMAwcOGMvUrl3bmMwABAYGEhUVxZ07d4xlnj7PkzJPzpNektAIIYQQKjFDg5lGoe2fScEeHh5otVrjNmnSpAzHFRMTA4Crq6vJfldXV+OxmJgYXFxcTI5bWFjg5ORkUuZZdTx9jueVeXI8vWSVkxAiR7gW2kXtEBTh1v0HtUNQTEwu+Z2oSYNyF8R7Us/ly5dNhpysra0VOkP2Jj00QgghRC7i4OBgsr1KQuPm5gZAbGysyf7Y2FjjMTc3N+Li4kyOP3r0iNu3b5uUeVYdT5/jeWWeHE8vSWiEEEIItWgU3hTi6emJm5sb27ZtM+7T6XQcOHCAgIAAAAICArh79y4RERHGMtu3b0ev1+Pv728ss3v3blJSUoxlwsLC8Pb2pkCBAsYyT5/nSZkn50kvSWiEEEIIlWgU/i8jEhISiIyMJDIyEng8ETgyMpJLly6h0WgYNGgQEyZM4LfffuPEiRN069aNwoULG1dClSlThrfffps+ffpw8OBB/vjjD/r370/Hjh0pXLgwAO+99x5WVlb06tWLU6dOsXz5cmbOnElISIgxjg8//JDNmzczbdo0Tp8+zdixYzl8+DD9+/fPUHtkDo0QQgiRBx0+fJh69eoZHz9JMoKCgggNDWXYsGEkJiby/vvvc/fuXWrWrMnmzZuxsbExPufHH3+kf//+NGjQADMzM9q2bcusWbOMx7VaLb///jvBwcH4+flRsGBBRo8ebXKtmurVq7Ns2TJGjRrFxx9/zJtvvsmaNWsoV65chtoj16ERQuQIqfoc+1Fl4o2eP6odgmJyw6Rgta9Dsy3yEvntlTlvwj0dDSoWzfK2ZBfSQyOEEEKoJDNWOeVVModGCCGEEDme9NAIIYQQapEuGsVID40QQgghcjzpoRFCCCFU8irLrV9UV14mCY0QQgihEo3m8aZUXXmZDDkJIYQQIseTHhohhBBCJTInWDmS0AghhBBqkYxGMTLkJIQQQogcTxKal5g/dw7eXsVxzG9Drer+HDp4UO2QMmTB/HlUecsXFycHXJwcqFMzgC2bN6kd1ivL6b+PJ3JLOyB7t2XiZ2PJb21msr1Vvozx+OJFC3i7UT3cC2rJb23G3bt309QxZfJEGtSpQSFHO95wKZApcfZs8CZ/fN6MSwvbc2lhe34fE0hD38c393O0s2JKt8ocmtqS64s7cmLGO3zRtTIOtpYmddQu68aW0YFcXtiBqNltGdvhLczNTP9kb+1flD0Tm3Lt246cmNGaAc18TI67Otqy8IMaHJ7aktvfdWZSF79MaW96ZOfXlZLUvDllbiMJzQusXLGc4UND+GTUGMIPHsHXtwItmwUSFxendmjp9kaRInz2+WT2HYjgj/2HqVuvPu3atOLPU6fUDi3DcsPvA3JPOyBntKWMT1nOXbxm3MJ27DEee3D/Po0aBzJk+MjnPj85OZl32rxL7/f7ZlqM127fZ+zyo9QdtYl6n25i958xLAupQ+k3tLgXsMXNMR+fLosgYMR6ghfso4FvYb7uU834/HJFHVk5pB7bjl+j9qgN9Jy9hyaVijC2w1vGMg19C7OwX00WbztDwIj1fBR6iA/eLk2fRqWMZawtzLh1L4kv157g5KU7mdbel8kJryulPFnlpNSWl8nNKV+gVnV//CpXYcas2QDo9Xq8PD3oFzyAocNGKH6+rFLYxYnPJ0+le89eaoeSIbnl95Fb2gFZ25ZXuTnlxM/Gsv63tYQfOvrCcrt37aRp4/pcib2No6PjM8v88F0ow4cM5mrc633Rp/fmlNHz2zH6pyN8v+tcmmOtqhZlQb8aFO71M6l6A5+2r0i9cu7UH/1v7+vbb73BkgG1ePODVSQ8fMTCD2pgaW5G96//Tejeb+TNwOY+lPtwdZpzrP+kEScu3mbkDxHPjTGzbk6Zla8rtW9OuefkFUVvTlmrXJE8e3NK6aF5juTkZI4eiaB+g4bGfWZmZtSv35CD+8NVjOzVpaamsmL5zyQmJuJfLUDtcDIkt/w+cks7IOe05dzZM3gVf4Ny3iXpGdSFy5cuqR3SC5lpNLSpVox81hYcPHPzmWUc8llx70GKMcmztjDjYUqqSZkHyanYWllQ0dP5nzLmJP23TEoqRZztKFrQLhNa8mpyyutKKRqFt7xMEprnuHnzJqmpqbi4uJrsd3F1JSYmRqWoXs3JEyco6JgfrZ01A4P7snzVasr4+Lz8idlIbvl95JZ2QM5oS5Uq/sxftIQ16zYx4+u5XLwQTeMGtbl3757aoaXhU8SRK4s6EBfaiek9/OkyYxdR1+LTlHPKb82w1uUI3XHGuG/b8ev4v1mQtgHFMdNocC9gy/B3ygOP58UAbDtxjeaVi1K7rBsaDZR0s6d/kzImZbKDnPC6EtmTqsu2ixcvzsWLF9Ps/+CDD5gzZ44KEeVOpby9OXA4kvj4eFb/uoo+PYP4fduuHJfUCJFRjd9uYvx3ufK+VK7qj8+bxfl11QqCemSvIdcz13XU+mQDDrZWtKpalHn/q06zCWEmSY29rSUrhtTj9NV4Jv963Lh/x8nrjP7pKF/1qMo3fauTlKJn6toTVC/tiv6fXpylO87i6WLP8o/qYmluxr0HKczfcpqRbSugz7kzD3I+WbatGFUTmkOHDpGa+m8X6MmTJ2nUqBHt2rVTMarHChYsiLm5OXFxsSb742JjcXNzUymqV2NlZUVJLy8AKvn5EXH4EHO+nsnsed+oHFn65ZbfR25pB+TMtjg6OuL1ZinOnzurdihppKTqiY5NAODYhdtUKuFM37dLM3jxAQDy21iwamh9Eh6m0GXGLh6lmiYhczb9xZxNf+HmaMvdxGSKFrJjbIe3uHAjwVhm7PKjjF8RiaujDTd1SdQp+/j3dCEugewiJ76uXofcy0k5qg45FSpUCDc3N+O2fv16SpYsSZ06ddQMC3icBLxVyY8d27cZ9+n1enbs2EbVHDb/5L/0ej1JSUlqh5EhueX3kVvaATmzLQkJCUSfP4erm7vaobyUmUaDtcXjj2h7W0t+Hd6AlFQ9nb7aSVKK/rnPi7n7gIcpqbwbUJzLNxM5Fn3b5LjeYOD6nQekpOp5N6A4B/6+wa172efzICe+rkT2kG2uFJycnMwPP/xASEgImuesPUtKSjL5ItbpdJka08BBIfTpGYSfX2UqV6nK7FkzuJ+YSLegHpl6XiV9+slIAt9ugodHUe7du8fyn5exe9dO1m3conZoGZYbfh+Qe9oB2b8tHw8fQpNmLShatBjXr19j4vixmJmb065DJwBiY2KIjY0x9ticOnkCe3t7ingUxcnJCYDLly5x585tLl++RGpqKsePRQJQoqQX+fPnVyTO0e0rsvXYNa7cSiS/jSXvVi9OzTKutJmy7Z9kpj75rCx4f94u7G0tsf/nGjQ3dUnG4aIBzXzYduwaeoOBFlWKMqhFWXp8vcd43Cm/Na2qFmXvX7HYWJrTuU5JWvkXpdmEMJNYyhd9fK0dO2sLnO1tKF+0AMmP9M+cz5NZsvvrSklyc0rlZJuEZs2aNdy9e5fu3bs/t8ykSZMYN25clsXUrn0Hbt64wfhxo4mNicG3QkXWrt+Mq6vry5+cTdyIi6NXj27EXL+OVqulXHlf1m3cQoOGjdQOLcNyw+8Dck87IPu35erVq/To9h63b92iYKFCBFSvyY7d4RQqVAiARQvnM2nCeGP5wAaPe4fnL1xMl27dAZgwfgw/fr/UWKZ61UoAbPx9O7Xr1FUkzkIONszvWx1XR1t091M4dfkObaZsY+fJGGqWcaWK1+N4I79qbfI830GruXQzEYBGvoUZ0rIcVpZmnLx0h/e+2sXW49dMyneqVYLP3quEBg2Hzt6g+cQwjpy/ZVJmz+fNjP9+q4Qz7Wt4culGAr6D1yjS1vTI7q8rJckUGuVkm+vQBAYGYmVlxbp1655b5lk9NB4eHpl2HRohRPbxKtehyY7Sex2anCCzrkOTldS+Dk34n1cVvQ5NgM8befY6NNmih+bixYts3bqVX3/99YXlrK2tsba2zqKohBBCiEwmXTSKyRYJzZIlS3BxcaFZs2YvLyyEEELkErLKSTmqX1hPr9ezZMkSgoKCsLDIFvmVEEIIIXIY1TOIrVu3cunSJXr27Kl2KEIIIUSWklVOylE9oWncuDHZZF6yEEIIkaVkCo1yVB9yEkIIIYR4Xar30AghhBB5lnTRKEZ6aIQQQgiR40kPjRBCCKESWbatHElohBBCCJXIKiflyJCTEEIIIXI86aERQgghVCJzgpUjCY0QQgihFsloFCNDTkIIIYTI8aSHRgghhFCJrHJSjiQ0QgghhFoUXOWUx/MZGXISQgghRM4nPTRCCCGESmROsHKkh0YIIYQQOZ4kNEIIIYRaNApv6TR27Fg0Go3JVrp0aePxhw8fEhwcjLOzM/nz56dt27bExsaa1HHp0iWaNWtGvnz5cHFxYejQoTx69MikzM6dO6lUqRLW1tZ4eXkRGhqa/iAzSBIaIYQQQiUahf/LiLJly3L9+nXjtnfvXuOxwYMHs27dOlauXMmuXbu4du0abdq0MR5PTU2lWbNmJCcns2/fPpYuXUpoaCijR482lomOjqZZs2bUq1ePyMhIBg0aRO/evdmyZcvr/+CeQebQCCGEEHmQhYUFbm5uafbHx8fz7bffsmzZMurXrw/AkiVLKFOmDPv376datWr8/vvv/Pnnn2zduhVXV1cqVqzIZ599xvDhwxk7dixWVlbMnz8fT09Ppk2bBkCZMmXYu3cv06dPJzAwUPH2SA+NEEIIoZInN6dUagPQ6XQmW1JS0jPPfebMGQoXLkyJEiXo3Lkzly5dAiAiIoKUlBQaNmxoLFu6dGmKFi1KeHg4AOHh4ZQvXx5XV1djmcDAQHQ6HadOnTKWebqOJ2We1KE0SWiEEEIIlWTGFBoPDw+0Wq1xmzRpUprz+vv7ExoayubNm5k3bx7R0dHUqlWLe/fuERMTg5WVFY6OjibPcXV1JSYmBoCYmBiTZObJ8SfHXlRGp9Px4MGDjP6oXkqGnIQQOYK5We5YlBoT2kXtEBRToPHnaofw2gyPHqodguIuX76Mg4OD8bG1tXWaMk2aNDH+29fXF39/f4oVK8aKFSuwtbXNkjiVJj00QgghhFoyoYvGwcHBZHtWQvNfjo6OlCpVirNnz+Lm5kZycjJ37941KRMbG2ucc+Pm5pZm1dOTxy8r4+DgkClJkyQ0QgghhErUXOX0tISEBM6dO4e7uzt+fn5YWlqybds24/GoqCguXbpEQEAAAAEBAZw4cYK4uDhjmbCwMBwcHPDx8TGWebqOJ2We1KE0SWiEEEKIPGbIkCHs2rWLCxcusG/fPt555x3Mzc3p1KkTWq2WXr16ERISwo4dO4iIiKBHjx4EBARQrVo1ABo3boyPjw9du3bl2LFjbNmyhVGjRhEcHGzsEerbty/nz59n2LBhnD59mrlz57JixQoGDx6cKW2SOTRCCCGESjQod3PKjFRz5coVOnXqxK1btyhUqBA1a9Zk//79FCpUCIDp06djZmZG27ZtSUpKIjAwkLlz5xqfb25uzvr16+nXrx8BAQHY2dkRFBTE+PHjjWU8PT3ZsGEDgwcPZubMmRQpUoRFixZlypJtAI3BYDBkSs1ZQKfTodVqib0VbzIBSgghRObLLZOCk/Z8Rnx81n6PPPn+Ohkdh71C572n01HO0yXL25JdSA+NEEIIoRK5OaVyJKERQgghVPL0BfGUqCsvk0nBQgghhMjxpIdGCCGEUI0MOilFEhohhBBCJTLkpBwZchJCCCFEjic9NEIIIYRKZMBJOZLQCCGEECqRISflyJCTEEIIIXI8SWheYv7cOXh7Fccxvw21qvtz6OBBtUN6JdKO7CU3tGPvnt20bd0Cz6KFsbXU8NvaNWqH9Fpyw+8E1G1Hn5aVOLiwN7HrPiJ23Ufs/LobjauWMCnj7/MGm6a9x80NQ4hd9xFhM7pgY/XvYEHFN11ZP6UT138L4crqQcwOaYKdjaVJHR4uDvz6eXtubRzKxV8+5PP/1cfc7N/uCTcnO0I/acXxpf8jcetIpgY3zNyGv4bscnPK3EASmhdYuWI5w4eG8MmoMYQfPIKvbwVaNgs0ubtoTiDtyF5ySzsSExMp71uBGbPmqB3Ka8stvxO123H1ho5PF+2get/F1Oi3hJ1HL7Lys3aUKV4QeJzMrJ3cgW2Ho6kVHErND5Ywf3UE+n/uwOPunJ8NU9/j3LU71A4OpdWI5fgUL8jC4S2M5zAz0/Dr5+2xsjCn3oCl9PliHV0CfRndo7axjJWlBTfv3mfyD39w/FxslrRdqE/u5fQCtar741e5CjNmzQZAr9fj5elBv+ABDB02QvHzZRZpR/aSW9rxNFtLDctXraZlq9Zqh/JKcsvvJKvbkZ57OV1dM5iPv9nO0k3H2DU7iG0R0YxfsvuZZXs2q8joHnXwbDeTJ99MZT0LcfjbPpTtMo/z1+7QuGoJfp3YnhLtvybuTiIAvVu8xYQ+9fBoM4OUR3qTOrd81Znj52IZOmfrM8+p9r2c/r58U9F7OZXyKJhn7+UkPTTPkZyczNEjEdRv8G9XpZmZGfXrN+Tg/nAVI8sYaUf2klvakZvklt9JdmuHmZmGdvV8sLOx5MCfVynkmI+qPm9w4+59dnzdjQurPuT36V2oXq6I8TnWVhakPErl6T+zHyQ9AqB6+cfl/H2KcDL6hjGZAQg7dB5tfht8ihfKmsYpSKPwlpdJQvMcN2/eJDU1FRcXV5P9Lq6uxMTEqBRVxkk7spfc0o7cJLf8TrJLO8p6FuLGhiHEbxnOrMFv02HML5y+eBNPd0cAPulWk8UbImk14mciz8Sw8cv3KPlGAQB2Hr2Aq5Mdgzv4Y2lhhmN+Gyb0qQeAm1N+AFyd7EySGcD42NXJLotaKbIjVROa1NRUPv30Uzw9PbG1taVkyZJ89tln5OBRMCGEyNP+vnwL/z7fUvuDUBb+doSFw1tQulhBzP6ZtPvt+qN8v/k4x87GMmzuVv6+fJugJhUA+OvCTfpMXsfAdv7c3jSMC6sGciHmLjG3E3Lt98KTZdtKbXmZqteh+eKLL5g3bx5Lly6lbNmyHD58mB49eqDVahk4cKCaoVGwYEHMzc2JizOdUBYXG4ubm5tKUWWctCN7yS3tyE1yy+8ku7Qj5ZGe89fuAHD0TAx+3u4Et6nClz/tA+CvizdNykdduomHy7/zPZZv/5Pl2//EpYAdiQ+SMQAD361K9PW7AMTeTqRy6cImdbgUsDMey2mUXJ0kq5xUtG/fPlq1akWzZs0oXrw4/2/vzuOiqvc+gH/OoAzbiDvIKoUoEGogGlwTLBRuN9OMB80N1Lgl14tLoqhsblFZanhLUUu8iqKVmtfUHlJEvaa5MCSLqIgsiSx6ZX0Ylvk+f/AwTyOaYMjh4Pfdq5ecZc75/Bhm5ju/8zvn+Pr6YuzYsfi5A5wuqaurixedXZB04rhmnlqtRlLScQx/yU3EZK3D7ehYOks7OpPO8px01HbIZALkXXWQe6cMt0srYGfZS2u5rUVP5BWVNXtc8X+qUFVTB19Pe9TU1uP4xRwAwPmMArxg0wd9uhto1n3VxQZllTXNiiX2bBG1h8bd3R1btmzBtWvXYGdnh9TUVJw5cwbr1q176PoqlQoqlUozXV5e/lTzBc9fiMBZ/nBxGYZhrsPxj5gNqK6qwgz/mU91v22N29GxdJZ2VFZWIvvGDc30rZwcpCqV6NGzJ6ysrERM1nqd5TkRux0r3/HEDz9nI7+oHAoDXUx61RGjhlhj3JI9AID1e88hzP9lXMkuRuqNIkzzdsJAq16YsmK/ZhvvTXDBufQCVP5PHV51scEH776C8K1JKKtqfO//8WIOMnNL8eXSN7A89gRMehoicpYHYr+7hNq6Bs12Bj/fFwBgqK+L3sYGGPx8X9TWq3G1oxU9fO+DNiNqQRMaGory8nIMGjQIOjo6aGhowJo1azB16tSHrh8dHY0VK1a0W77/8puE0pISrFwRgaI7dzB4yFB8d/gYTExMHv/gDoTb0bF0lnZcvnQR3l6jNdNLQhYCAKZN98fWr+JESvVkOstzInY7+nQ3wJeh42Da0whlVSqk3SzGuCV7cOLSLQDAP769AD3dLvg4yAs9FHq4crMYr4fsQc7t+5ptDBtkhjD/l2Gkr4us/LuYu/4o9iSmaZar1YS3lu/DZ/N9cPIf/qiqqUP8f//S7FTw81vf0fzsMrAfJnu9gNw79zFoyhdP9XfQWlzPtB1Rr0OTkJCAkJAQrF27Fo6OjlAqlZg/fz7WrVsHf3//Zus/rIfG0tLyqV2HhjHG2KO15Do0HZ3Y16G5+evdNr0OzXPmvZ7Z69CI2kMTEhKC0NBQTJ48GQDg5OSE3NxcREdHP7SgkcvlkMvl7R2TMcYYeyr45pRtR9RBwdXV1ZDJtCPo6OhArVY/4hGMMcYYY82J2kMzbtw4rFmzBlZWVnB0dERKSgrWrVuHWbNmiRmLMcYYaydteVPJZ7uLRtSCZuPGjQgPD0dQUBCKi4thZmaGd999FxEREWLGYowxxtoFH3JqO6IWNAqFAhs2bMCGDRvEjMEYY4wxieN7OTHGGGNM8kTtoWGMMcaeZXzIqe1wDw1jjDHGJI97aBhjjDGR8M0p2w4XNIwxxphI+JBT2+FDTowxxhiTPO6hYYwxxkTCN6dsO9xDwxhjjDHJ4x4axhhjTCzcRdNmuKBhjDHGRMJnObUdPuTEGGOMMcnjHhrGGGNMJHzadtvhgoYxxhgTCQ+haTt8yIkxxhhjksc9NIwxxphYuIumzXAPDWOMMSYSoY3/a63PP/8c/fv3h56eHkaMGIGff/75KbSyfXBBwxhjjD2D9u7di4ULFyIyMhKXL1/GkCFD4O3tjeLiYrGjPREuaBhjjDGRNJ3l1Fb/t8a6desQGBiImTNnwsHBAZs3b4aBgQG++uqrp9PYp0zSY2iICABQUV4uchLGGHv2UH2N2BH+MKpXNf77f58n7a28DT+/mrb14DblcjnkcrnWvNraWly6dAlLly7VzJPJZPDy8sJPP/3UZpnak6QLmoqKCgCArY2lyEkYY4xJWUVFBYyNjdttf7q6ujA1NcWANv78MjIygqWl9jYjIyMRFRWlNa+0tBQNDQ0wMTHRmm9iYoKrV6+2aab2IumCxszMDPn5+VAoFBCe0hWFysvLYWlpifz8fHTr1u2p7KM9dJZ2AJ2nLdyOjqWztAPoPG1pj3YQESoqKmBmZvZUtv8oenp6yMnJQW1tbZtul4iafR4+2DvTWUm6oJHJZLCwsGiXfXXr1k3SbwxNOks7gM7TFm5Hx9JZ2gF0nrY87Xa0Z8/Mb+np6UFPT0+Ufffu3Rs6OjooKirSml9UVARTU1NRMv1RPCiYMcYYe8bo6urCxcUFx48f18xTq9U4fvw43NzcREz25CTdQ8MYY4yxJ7Nw4UL4+/tj2LBhGD58ODZs2ICqqirMnDlT7GhPhAuax5DL5YiMjJT8McjO0g6g87SF29GxdJZ2AJ2nLZ2lHR3VpEmTUFJSgoiICNy5cwdDhw7FsWPHmg0UlgqBxDpXjTHGGGOsjfAYGsYYY4xJHhc0jDHGGJM8LmgYY4wxJnlc0DDGGGNM8rigYYwxxpjkcUHzjOCT2TqGwsJCZGRkiB2jTTQ0NACQ/t9WdXV1m19+XgwFBQVISUkROwZjouGC5hGa3qylrKqqChUVFSgvL39q97pqL/fu3cPVq1dx/fp1yX74/Prrr3ByckJYWBguXrwodpw/RKlUYsKECaiurpb031ZaWhr8/Pxw7tw5qFQqseM8sfT0dLi7u2PXrl0AGq/4KlUFBQXYt28f9u/fjytXrogdh0kIFzQPce3aNWzYsAGFhYViR3liGRkZmDhxIjw8PGBvb4/4+HgA0vw2nZaWBi8vL/j5+cHJyQkff/yxJAvO69evo6ysDGVlZdi4cSMuX76sWSal5yU1NRXu7u5wdHSEgYGBZr6U2gA0FgEvv/wyLCwsYGNjI9mLt6WmpmL48OHo0qULdu/ejeLiYshk0nxrv3LlCkaOHIm1a9ciKCgIy5cvR3Z2ttixmFQQ03L9+nXq2bMnCYJAS5cupZKSErEjtVp6ejr16tWLFixYQPHx8bRw4ULq2rUrpaSkiB2t1ZrasmjRIkpPT6dPPvmEBEGgvLw8saO12t27d+mNN96g2NhYcnZ2pqlTp1JaWhoRETU0NIicrmVSU1PJ0NCQQkJCtOarVCqREj2ZyspKGjt2LM2ZM0czLzMzk1JSUig3N1fEZK2jVCpJX1+fli1bRiUlJeTo6EirV68mtVpNarVa7HitcuvWLTI3N6fQ0FCqrKykI0eOkKmpKZ0/f17saEwiuKD5jcrKSpo1axYFBATQ559/ToIgUEhIiKSKmrt379LYsWMpODhYa76npyf9/e9/JyKSzBtdSUkJjRo1iubNm6eZp1arycfHh86ePUspKSmSKWzq6+upuLiY7OzsqKCggPbv30+urq4UGBhI7u7u9NZbb4kd8bEKCwvJ1NSUvL29iaixTfPnz6e//OUvNGjQIFq/fj1lZmaKnLJlampqaOTIkXT58mWqr68nb29vcnV1JYVCQS+99BJt27ZN7IiPlZqaSnK5nJYtW0ZEjUWxr68vubq6ataRymudiCg2NpY8PT21Mr/22msUGxtLO3bsoBMnToiYjkkB38vpN2QyGVxcXNCrVy9MmjQJvXv3xuTJkwEAixcvRu/evUVO+Hh1dXW4f/8+fH19ATQeS5fJZLCxscG9e/cAQDJjHgRBgI+Pj6YtALB69Wr88MMPuHPnDkpLS+Ho6IiwsDCMHDlSxKSPJ5PJ0KdPH7i6uiItLQ1vvvkm5HI5/P39oVKpEBgYKHbEFnFzc0N+fj6+++47bN68GXV1dRg6dCj69++PmJgYpKWlISIiAlZWVmJH/V33799HVlYWSktLERISAgDYtm0bbt++jRMnTiAsLAzGxsZaf3sdjUqlwuLFi7Fy5UrN63z16tUYMWIENm3ahDlz5kjmtQ40HrLMy8uDUqnEiy++iDVr1uDo0aOora1FWVkZcnNz8dFHHyEgIEDsqKyjErui6mgqKyu1phMSEkgQBFq0aBGVlpYSUeM3oZs3b4oRr0WuXbum+bm2tpaIiMLCwmj69Ola61VUVLRrridRXl6u+XnPnj0kCALt3buX7t69S8nJyeTq6kpRUVEiJmydGTNmUGhoKBERzZ49m3r06EEODg40a9YsSXSt3759m2bMmEH6+vo0ZswYzWuCiCg+Pp66d+9OR44cETFhy6jVapo8eTLNnTuXXn/9dTp27JhmWX5+Pk2bNo3ee+89qq+vl0wvh1qtpvv379OECRPIz89PUtmJiG7evEnu7u5ka2tLb731FgmCQAcPHiS1Wk1FRUUUHBxMnp6eVFpaKql2sfbDPTQPMDQ0BNB4lpNMJsOkSZNARJgyZQoEQcD8+fPxySefIDc3Fzt37tQaFNlRDBgwAEBj70zXrl0BNH77KS4u1qwTHR0NuVyO4OBgdOnScf8MFAqF5mc3NzdcvHgRzs7OAIBRo0ahb9++uHTpkljxWoyIIAgCXnnlFeTk5CAoKAhHjhzBpUuXoFQqERISAl1dXQwePBh6enpix32kfv36ITo6Gubm5vDy8kKvXr00bZsyZQoiIyORlJSEP//5z2JH/V2CIOD999+Hp6cnqqur8de//lWzzMLCAiYmJrhw4QJkMplkejkEQYCxsTGmT58OX19fBAcH409/+pPYsVrMxsYGu3btwoULF5CRkQFBEDB+/HgAQN++fWFmZobk5GQYGhpK5jlh7avjfpKJTEdHB0QEtVqNyZMnQxAETJ8+HYcOHUJ2djYuXLjQIYuZ35LJZJoPm6ZpAIiIiMDq1auRkpLSoYuZB1lbW8Pa2hpAY7FWW1sLIyMjDB48WORkj9f0HNjY2GDmzJkwMTHB4cOHYWNjAxsbGwiCgCFDhnToYqaJmZkZQkNDNVkFQQAR4d69e+jTpw+GDh0qbsAWGjZsGI4ePQoPDw9s2bIFzz33HBwdHQE0Hrq1s7NDfX295kuBVLz++usYM2YMNm3aBGdnZ+jr64sdqcWaXg/btm3DxYsXUVtbC11dXQBAUVER+vfvL8kzHFn7EIgkdq5lO2v69QiCgFdffRVKpRInT56Ek5OTyMlapunYelRUFAoLCzFgwACEhYXh7Nmzmp4OqYqIiMCOHTvw448/anqlOrq6ujrs3LkTw4YNw+DBg7UKTqmLjIzEnj17kJiYqCk8peDUqVN4++23YWFhAScnJ9TW1uLQoUM4c+YMXnjhBbHjPZEPP/wQ0dHRyMrKgqmpqdhxWi0jIwPu7u5Yvnw5TE1NkZaWhi1btuDUqVOSee9l7U86X89FIggCGhoaEBISgqSkJCiVSkm9oJp6Zbp27YqtW7eiW7duOHPmjKSLma+//hrJyclISEhAYmKiZIoZoPF5CAgI0DwvnaGYSUhIQFJSEr7++mscP35cUsUM0Hjo8sSJE9i1axfOnTuHAQMGSLaYaSqQ3333XXzzzTeoqakRO9ITcXBwwIEDBxAYGAiZTAZzc3MkJydL6r2XtT/uoWmBhoYGxMXFwcXFRTLd6Q+6ePEihg8fjrS0NDg4OIgd5w9JT0/HypUrERUVBXt7e7HjPPN++eUXLFu2DB999JHmkI1UNV1hV6oXpmtCRKiurtaMCZSqe/fuoa6uDnK5HN27dxc7DuvguKBpoc5waKCqqkryb3BN6urqJDe2oTP77VgHxhgTAxc0jDHGGJM8aferMsYYY4yBCxrGGGOMdQJc0DDGGGNM8rigYYwxxpjkcUHDGGOMMcnjgoYxxhhjkscFDWMiCwgIwIQJEzTTnp6emD9/frvnOHnyJARBwP379x+5jiAIOHjwYIu3GRUV9YcvRnnr1i0IggClUvmHtsMY69y4oGHsIQICAiAIAgRBgK6uLmxtbbFy5UrU19c/9X3v378fq1atatG6LSlCGGPsWcD3cmLsEXx8fLB9+3aoVCocOXIEf/vb39C1a1csXbq02bpteaXcnj17tsl2GGPsWcI9NIw9glwuh6mpKaytrTFnzhx4eXnh0KFDAP7/MNGaNWtgZmaGgQMHAgDy8/Ph5+eH7t27o2fPnhg/fjxu3bql2WZDQwMWLlyI7t27o1evXli8eDEevFj3g4ecVCoVlixZAktLS8jlctja2uLLL7/ErVu3MHr0aABAjx49IAgCAgICADTekyg6Oho2NjbQ19fHkCFD8M0332jt58iRI7Czs4O+vj5Gjx6tlbOllixZAjs7OxgYGOC5555DeHg46urqmq0XGxsLS0tLGBgYwM/PD2VlZVrLt23bBnt7e+jp6WHQoEH44osvWp2FMfZs44KGsRbS19dHbW2tZvr48ePIyspCYmIiDh8+jLq6Onh7e0OhUOD06dP497//DSMjI/j4+Gge9+mnnyIuLg5fffUVzpw5g3v37uHAgQO/u98ZM2Zgz549iImJQWZmJmJjY2FkZARLS0t8++23AICsrCwUFhbis88+AwBER0fjn//8JzZv3oz09HQsWLAA06ZNQ3JyMoDGwmvixIkYN24clEol3nnnHYSGhrb6d6JQKBAXF4eMjAx89tln2Lp1K9avX6+1zo0bN7Bv3z7861//wrFjx5CSkoKgoCDN8vj4eERERGDNmjXIzMzEBx98gPDwcOzYsaPVeRhjzzBijDXj7+9P48ePJyIitVpNiYmJJJfLadGiRZrlJiYmpFKpNI/ZuXMnDRw4kNRqtWaeSqUifX19+uGHH4iIqF+/fvTxxx9rltfV1ZGFhYVmX0REHh4eNG/ePCIiysrKIgCUmJj40JxJSUkEgP7zn/9o5tXU1JCBgQGdPXtWa93Zs2fT22+/TURES5cuJQcHB63lS5YsabatBwGgAwcOPHL52rVrycXFRTMdGRlJOjo6VFBQoJl39OhRkslkVFhYSEREzz//PO3evVtrO6tWrSI3NzciIsrJySEAlJKS8sj9MsYYj6Fh7BEOHz4MIyMj1NXVQa1WY8qUKYiKitIsd3Jy0ho3k5qaihs3bkChUGhtp6amBtnZ2SgrK0NhYSFGjBihWdalSxcMGzas2WGnJkqlEjo6OvDw8Ghx7hs3bqC6uhpjxozRml9bW4sXX3wRAJCZmamVAwDc3NxavI8me/fuRUxMDLKzs1FZWYn6+np069ZNax0rKyuYm5tr7UetViMrKwsKhQLZ2dmYPXs2AgMDNevU19fD2Ni41XkYY88uLmgYe4TRo0dj06ZN0NXVhZmZGbp00X65GBoaak1XVlbCxcUF8fHxzbbVp0+fJ8qgr6/f6sdUVlYCAL7//nutQgJoHBfUVn766SdMnToVK1asgLe3N4yNjZGQkIBPP/201Vm3bt3arMDS0dFps6yMsc6PCxrGHsHQ0BC2trYtXt/Z2Rl79+5F3759m/VSNOnXrx/Onz+PUaNGAWjsibh06RKcnZ0fur6TkxPUajWSk5Ph5eXVbHlTD1FDQ4NmnoODA+RyOfLy8h7Zs2Nvb68Z4Nzk3Llzj2/kb5w9exbW1tZYvny5Zl5ubm6z9fLy8nD79m2YmZlp9iOTyTBw4ECYmJjAzMwMN2/exNSpU1u1f8YY+y0eFMxYG5k6dSp69+6N8ePH4/Tp08jJycHJkycRHByMgoICAMC8efPw4Ycf4uDBg7h69SqCgoJ+9xoy/fv3h7+/P2bNmoWDBw9qtrlv3z4AgLW1NQRBwOHDh1FSUoLKykooFAosWrQICxYswI4dO5CdnY3Lly9j48aNmoG27733Hq5fv46QkBBkZWVh9+7diIuLa1V7BwwYgLy8PCQkJCA7OxsxMTEPHeCsp6cHf39/pKam4vTp0wgODoafnx9MTU0BACtWrEB0dDRiYmJw7do1XLlyBdu3b8e6detalYcx9mzjgoaxNmJgYIBTp07BysoKEydOhL29PWbPno2amhpNj83777+P6dOnw9/fH25ublAoFHjzzTd/d7ubNm2Cr68vgoKCMGjQIAQGBqKqqgoAYG5ujhUrViA0NBQmJiaYO3cuAGDVqlUIDw9HdHQ07O3t4ePjg++//x42NjYAGse1fPvttzh48CCGDBmCzZs344MPPmhVe9944w0sWLAAc+fOxdChQ3H27FmEh4c3W8/W1hYTJ07Ea6+9hrFjx2Lw4MFap2W/88472LZtG7Zv3w4nJyd4eHggLi5Ok5UxxlpCoEeNRmSMMcYYkwjuoWGMMcaY5HFBwxhjjDHJ44KGMcYYY5LHBQ1jjDHGJI8LGsYYY4xJHhc0jDHGGJM8LmgYY4wxJnlc0DDGGGNM8rigYYwxxpjkcUHDGGOMMcnjgoYxxhhjkve/whSrE1QAmV4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix = confusion_matrix(y_test, yhatsvm, labels=[1,2,3,4,5,6,7,8])\n",
    "np.set_printoptions(precision=8)\n",
    "\n",
    "print (classification_report(y_test, yhatsvm))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['1', '2','3','4','5','6','7','8'],normalize= False,  title='Confusion matrix')"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}