Prediction system.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Libraries\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "from tkinter import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import sqlite3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\LalitPatel'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#List of the symptoms is listed here in list l1.\n",
    "\n",
    "l1=['back_pain','constipation','abdominal_pain','diarrhoea','mild_fever','yellow_urine',\n",
    "    'yellowing_of_eyes','acute_liver_failure','fluid_overload','swelling_of_stomach',\n",
    "    'swelled_lymph_nodes','malaise','blurred_and_distorted_vision','phlegm','throat_irritation',\n",
    "    'redness_of_eyes','sinus_pressure','runny_nose','congestion','chest_pain','weakness_in_limbs',\n",
    "    'fast_heart_rate','pain_during_bowel_movements','pain_in_anal_region','bloody_stool',\n",
    "    'irritation_in_anus','neck_pain','dizziness','cramps','bruising','obesity','swollen_legs',\n",
    "    'swollen_blood_vessels','puffy_face_and_eyes','enlarged_thyroid','brittle_nails',\n",
    "    'swollen_extremeties','excessive_hunger','extra_marital_contacts','drying_and_tingling_lips',\n",
    "    'slurred_speech','knee_pain','hip_joint_pain','muscle_weakness','stiff_neck','swelling_joints',\n",
    "    'movement_stiffness','spinning_movements','loss_of_balance','unsteadiness',\n",
    "    'weakness_of_one_body_side','loss_of_smell','bladder_discomfort','foul_smell_of urine',\n",
    "    'continuous_feel_of_urine','passage_of_gases','internal_itching','toxic_look_(typhos)',\n",
    "    'depression','irritability','muscle_pain','altered_sensorium','red_spots_over_body','belly_pain',\n",
    "    'abnormal_menstruation','dischromic _patches','watering_from_eyes','increased_appetite','polyuria','family_history','mucoid_sputum',\n",
    "    'rusty_sputum','lack_of_concentration','visual_disturbances','receiving_blood_transfusion',\n",
    "    'receiving_unsterile_injections','coma','stomach_bleeding','distention_of_abdomen',\n",
    "    'history_of_alcohol_consumption','fluid_overload','blood_in_sputum','prominent_veins_on_calf',\n",
    "    'palpitations','painful_walking','pus_filled_pimples','blackheads','scurring','skin_peeling',\n",
    "    'silver_like_dusting','small_dents_in_nails','inflammatory_nails','blister','red_sore_around_nose',\n",
    "    'yellow_crust_ooze']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "l2=[]\n",
    "for i in range(0,len(l1)):\n",
    "    l2.append(0)\n",
    "print(l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#List of Diseases is listed in list disease.\n",
    "\n",
    "disease=['Fungal infection', 'Allergy', 'GERD', 'Chronic cholestasis',\n",
    "       'Drug Reaction', 'Peptic ulcer diseae', 'AIDS', 'Diabetes ',\n",
    "       'Gastroenteritis', 'Bronchial Asthma', 'Hypertension ', 'Migraine',\n",
    "       'Cervical spondylosis', 'Paralysis (brain hemorrhage)', 'Jaundice',\n",
    "       'Malaria', 'Chicken pox', 'Dengue', 'Typhoid', 'hepatitis A',\n",
    "       'Hepatitis B', 'Hepatitis C', 'Hepatitis D', 'Hepatitis E',\n",
    "       'Alcoholic hepatitis', 'Tuberculosis', 'Common Cold', 'Pneumonia',\n",
    "       'Dimorphic hemmorhoids(piles)', 'Heart attack', 'Varicose veins',\n",
    "       'Hypothyroidism', 'Hyperthyroidism', 'Hypoglycemia',\n",
    "       'Osteoarthristis', 'Arthritis',\n",
    "       '(vertigo) Paroymsal  Positional Vertigo', 'Acne',\n",
    "       'Urinary tract infection', 'Psoriasis', 'Impetigo']\n",
    "\n",
    "#disease = [df['prognosis'].unique()]\n",
    "#print(disease)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>itching</th>\n",
       "      <th>skin_rash</th>\n",
       "      <th>nodal_skin_eruptions</th>\n",
       "      <th>continuous_sneezing</th>\n",
       "      <th>shivering</th>\n",
       "      <th>chills</th>\n",
       "      <th>joint_pain</th>\n",
       "      <th>stomach_pain</th>\n",
       "      <th>acidity</th>\n",
       "      <th>ulcers_on_tongue</th>\n",
       "      <th>...</th>\n",
       "      <th>pus_filled_pimples</th>\n",
       "      <th>blackheads</th>\n",
       "      <th>scurring</th>\n",
       "      <th>skin_peeling</th>\n",
       "      <th>silver_like_dusting</th>\n",
       "      <th>small_dents_in_nails</th>\n",
       "      <th>inflammatory_nails</th>\n",
       "      <th>blister</th>\n",
       "      <th>red_sore_around_nose</th>\n",
       "      <th>yellow_crust_ooze</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prognosis</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fungal infection</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fungal infection</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fungal infection</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fungal infection</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fungal infection</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 132 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  itching  skin_rash  nodal_skin_eruptions  \\\n",
       "prognosis                                                    \n",
       "Fungal infection        1          1                     1   \n",
       "Fungal infection        0          1                     1   \n",
       "Fungal infection        1          0                     1   \n",
       "Fungal infection        1          1                     0   \n",
       "Fungal infection        1          1                     1   \n",
       "\n",
       "                  continuous_sneezing  shivering  chills  joint_pain  \\\n",
       "prognosis                                                              \n",
       "Fungal infection                    0          0       0           0   \n",
       "Fungal infection                    0          0       0           0   \n",
       "Fungal infection                    0          0       0           0   \n",
       "Fungal infection                    0          0       0           0   \n",
       "Fungal infection                    0          0       0           0   \n",
       "\n",
       "                  stomach_pain  acidity  ulcers_on_tongue  ...  \\\n",
       "prognosis                                                  ...   \n",
       "Fungal infection             0        0                 0  ...   \n",
       "Fungal infection             0        0                 0  ...   \n",
       "Fungal infection             0        0                 0  ...   \n",
       "Fungal infection             0        0                 0  ...   \n",
       "Fungal infection             0        0                 0  ...   \n",
       "\n",
       "                  pus_filled_pimples  blackheads  scurring  skin_peeling  \\\n",
       "prognosis                                                                  \n",
       "Fungal infection                   0           0         0             0   \n",
       "Fungal infection                   0           0         0             0   \n",
       "Fungal infection                   0           0         0             0   \n",
       "Fungal infection                   0           0         0             0   \n",
       "Fungal infection                   0           0         0             0   \n",
       "\n",
       "                  silver_like_dusting  small_dents_in_nails  \\\n",
       "prognosis                                                     \n",
       "Fungal infection                    0                     0   \n",
       "Fungal infection                    0                     0   \n",
       "Fungal infection                    0                     0   \n",
       "Fungal infection                    0                     0   \n",
       "Fungal infection                    0                     0   \n",
       "\n",
       "                  inflammatory_nails  blister  red_sore_around_nose  \\\n",
       "prognosis                                                             \n",
       "Fungal infection                   0        0                     0   \n",
       "Fungal infection                   0        0                     0   \n",
       "Fungal infection                   0        0                     0   \n",
       "Fungal infection                   0        0                     0   \n",
       "Fungal infection                   0        0                     0   \n",
       "\n",
       "                  yellow_crust_ooze  \n",
       "prognosis                            \n",
       "Fungal infection                  0  \n",
       "Fungal infection                  0  \n",
       "Fungal infection                  0  \n",
       "Fungal infection                  0  \n",
       "Fungal infection                  0  \n",
       "\n",
       "[5 rows x 132 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading the training .csv file\n",
    "df=pd.read_csv(r\"C:\\Users\\LalitPatel\\Downloads\\Internship work\\Dataset\\training.csv\")\n",
    "DF= pd.read_csv(r\"C:\\Users\\LalitPatel\\Downloads\\Internship work\\Dataset\\training.csv\", index_col='prognosis')\n",
    "#Replace the values in the imported file by pandas by the inbuilt function replace in pandas.\n",
    "\n",
    "df.replace({'prognosis':{'Fungal infection':0,'Allergy':1,'GERD':2,'Chronic cholestasis':3,'Drug Reaction':4,\n",
    "    'Peptic ulcer diseae':5,'AIDS':6,'Diabetes ':7,'Gastroenteritis':8,'Bronchial Asthma':9,'Hypertension ':10,\n",
    "    'Migraine':11,'Cervical spondylosis':12,\n",
    "    'Paralysis (brain hemorrhage)':13,'Jaundice':14,'Malaria':15,'Chicken pox':16,'Dengue':17,'Typhoid':18,'hepatitis A':19,\n",
    "    'Hepatitis B':20,'Hepatitis C':21,'Hepatitis D':22,'Hepatitis E':23,'Alcoholic hepatitis':24,'Tuberculosis':25,\n",
    "    'Common Cold':26,'Pneumonia':27,'Dimorphic hemmorhoids(piles)':28,'Heart attack':29,'Varicose veins':30,'Hypothyroidism':31,\n",
    "    'Hyperthyroidism':32,'Hypoglycemia':33,'Osteoarthristis':34,'Arthritis':35,\n",
    "    '(vertigo) Paroymsal  Positional Vertigo':36,'Acne':37,'Urinary tract infection':38,'Psoriasis':39,\n",
    "    'Impetigo':40}},inplace=True)\n",
    "#df.head()\n",
    "DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Distribution graphs (histogram/bar graph) of column data\n",
    "def plotPerColumnDistribution(df1, nGraphShown, nGraphPerRow):\n",
    "    nunique = df1.nunique()\n",
    "    df1 = df1[[col for col in df if nunique[col] > 1 and nunique[col] < 50]] # For displaying purposes, pick columns that have between 1 and 50 unique values\n",
    "    nRow, nCol = df1.shape\n",
    "    columnNames = list(df1)\n",
    "    nGraphRow = (nCol + nGraphPerRow - 1) / nGraphPerRow\n",
    "    plt.figure(num = None, figsize = (6 * nGraphPerRow, 8 * nGraphRow), dpi = 80, facecolor = 'w', edgecolor = 'k')\n",
    "    for i in range(min(nCol, nGraphShown)):\n",
    "        plt.subplot(nGraphRow, nGraphPerRow, i + 1)\n",
    "        columnDf = df.iloc[:, i]\n",
    "        if (not np.issubdtype(type(columnDf.iloc[0]), np.number)):\n",
    "            valueCounts = columnDf.value_counts()\n",
    "            valueCounts.plot.bar()\n",
    "        else:\n",
    "            columnDf.hist()\n",
    "        plt.ylabel('counts')\n",
    "        plt.xticks(rotation = 90)\n",
    "        plt.title(f'{columnNames[i]} (column {i})')\n",
    "    plt.tight_layout(pad = 1.0, w_pad = 1.0, h_pad = 1.0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scatter and density plots\n",
    "def plotScatterMatrix(df1, plotSize, textSize):\n",
    "    df1 = df1.select_dtypes(include =[np.number]) # keep only numerical columns\n",
    "    # Remove rows and columns that would lead to df being singular\n",
    "    df1 = df1.dropna('columns')\n",
    "    df1 = df1[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values\n",
    "    columnNames = list(df)\n",
    "    if len(columnNames) > 10: # reduce the number of columns for matrix inversion of kernel density plots\n",
    "        columnNames = columnNames[:10]\n",
    "    df1 = df1[columnNames]\n",
    "    ax = pd.plotting.scatter_matrix(df1, alpha=0.75, figsize=[plotSize, plotSize], diagonal='kde')\n",
    "    corrs = df1.corr().values\n",
    "    for i, j in zip(*plt.np.triu_indices_from(ax, k = 1)):\n",
    "        ax[i, j].annotate('Corr. coef = %.3f' % corrs[i, j], (0.8, 0.2), xycoords='axes fraction', ha='center', va='center', size=textSize)\n",
    "    plt.suptitle('Scatter and Density Plot')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACVkAAAUPCAYAAACmqXJnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAMTQAADE0B0s6tTgAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeXhN1/7H8U8GIoYg5KYqMpBEQhRpTUXMQ1FXzRopoq65aqy2qtKrA1U6XOlPqVlJuKa6iPYqSqkoWqTmRFCKKK0aKrJ/f3jsmyPJyTlkwvv1PJ6ne6+191nrS886a5/vWcvBMAxDAAAAAAAAAAAAAAAAAIBMOeZ3AwAAAAAAAAAAAAAAAACgICPJCgAAAAAAAAAAAAAAAACsIMkKAAAAAAAAAAAAAAAAAKwgyQoAAAAAAAAAAAAAAAAArCDJCgAAAAAAAAAAAAAAAACsIMkKAAAAAAAAAAAAAAAAAKwgyQoAAAAAAAAAAAAAAAAArCDJCrhHjRs31rhx4+75+rlz58rLy8tqHV9fX82aNeueX8NWy5YtU/PmzXPkXps2bZKDg4NSU1Nz5H55qV+/fpo0aVJ+NwMAkE52Y6GDg4O+/vrrPGyR7SZMmKAGDRrYfd2uXbtUuXJl3bp1677bkJSUJAcHBx09evS+75XX3n77bQ0cODC/mwEABcasWbPk6+trc/17HYfSy25+Z8u89kFVEPrGXP025uoACqKcGGeRvd69e6tnz565/jqnTp3S448/rt9//z1H7leQn1VYs2jRIj3zzDP53QwAyBXZPWfOqzkg3/3mLeaTyA0kWQH3aPny5Ro7dqwkKTU1VQ4ODtq0aVOOvkZ8fLzCw8Nz9J53MwxDb7zxhl599dVcfZ2C4OzZs+rQoYOKFi2qcuXK6Z133rEof+WVVzR58mT98ccf+dRCAIC9zpw5o7CwsPxuRo4aN26cxowZIycnp/xuSq6aOHGiateuLRcXl0y/nBgyZIhiYmKUmJiYD60DANiiW7du2rNnT34347717NlTvXv3tjiX3317VObqSUlJ6tOnj3x8fOTq6qrg4GB9+umnFnWYqwPIbw0aNNCECRMszo0aNUqrV6/OnwY9Qj766CNNnz49119n4sSJ6tu3r9zc3HL9tfLTyy+/rEqVKsnV1VWenp7q2bOnzp49a5Z3795dBw8e1NatW/OxlQCQP/JqDsh3v7kjOTlZJUuWzJAox3wSuYEkK+Aeubu7q3jx4rn6Gh4eHnJ1dc3V1/jmm2905coVNW3aNFdfpyDo1q2bLl68qO+++07R0dF69913NXv2bLPc399flStX1pIlS/KxlQAAezz22GMqXLhwjt83LS0tX36Zc+zYMW3ZskVdunTJ89fOa6mpqerZs6e6deuWaXnJkiXVunXrPPllFwDg3ri6usrDwyNfXvvmzZsyDCPX7p+ffZMenbn6wYMH5eTkpNmzZ+vAgQMaN26cRo4cqfnz55t1mKsDKIiKFy8ud3f3/G7GQ69kyZIqWbJkrr7GH3/8oYULFyoiIiJXX6cgqF69uubMmaOff/5Zq1ev1okTJyxWCnNyclKPHj0yJDwDwKMgt+eAN27ckMR3v7nBMAz16tVL9erVy1DGfBK5gSQr4B6l3y7Q399fktSkSRM5ODiYv4BNTU3V+PHj5e3trSJFiig4OFhffvmlxX2WLVsmPz8/lSpVSpGRkeYgK1kuGXlnu5+VK1eqdu3aKlasmBo3bqzk5GSz/vXr19WnTx8VK1ZMFSpU0IIFC+Tl5aW5c+dm2Y+lS5eqdevWcnBwMM9l1+758+fL399fLi4uqlatmtatW5fl/TNb0vnurRYdHBw0e/ZsNW3aVK6urqpdu7aOHz+uTZs2KSQkRG5uboqIiND169ctYvPBBx+oS5cuKlasmIKDg7Vx48Ys2/HTTz9py5YtmjVrlmrUqKHnnntOw4cP18cff2xRr23btlq6dGmW9wEA5LzFixcrKChIRYoU0WOPPaZ//OMfWdYdNWqU/P39zfEv/RL8d5Yt/u9//6sqVaqoRIkS6tChg3777bds23BnnF22bJlq166tIkWKaO/evfruu+/UpEkTlSpVSh4eHurRo4cuXLhgXnf8+HG1bt1abm5ucnNzU506dTJszfevf/1L5cqVU9myZTVmzBirXwgvW7ZM9evXz/Dr2Y8//tgceytWrGiReLRu3TpVq1ZNLi4u8vf3t/hS8m6ZbWlx91jt6+ur999/Xx07dlTRokVVpUoVxcfHa9++fapTp46KFy+utm3b6uLFi+Y1jRs31pgxY9S/f3+VKFFCvr6+2U5cJ0yYoJdeekkVK1bMsg7jMoAHlS3vi9m9f2/atEnBwcFydXVVu3btlJKSYlG+evVq1a1bVyVKlNDjjz+uQYMG6c8//7S7rdevX1e/fv30t7/9Ta6urgoKCtLKlSszrXv27FmFhISof//+Mgwjw3YKd8aUcePGyd3dXY8//rimTp1qc1uWLFmiKlWqyNXVVSEhIVq2bJlZdmecX79+vapWrSpXV1elpKRkmF9Kmc+lY2NjVaNGDRUpUkRNmzbVyZMnJd0ejxYtWqR58+bJwcHBnBvf3bfU1FSNGTPGjFOLFi105MgRs/zOGJvVuG8Yhl599VWVL19eRYoUUcWKFTVjxowsY/GozNXvJFQ3a9ZMFStWVHh4uCIiIjL8G+QzAQBr7uf9Mbt5ZO/evbVt2zZFRUXJwcHB3Lr37rlVdmN/ZlsQ3X2PP//8Uy+++KJKly6t4sWLq1OnTvr1118tXsPamGfPmJ7eV199pZo1a8rV1VVly5ZV27Ztbe6XJO3evVuNGzeWq6urfH199eabb1r8aOn8+fMKDw9XqVKlVLZsWYWHh5ufa+7E/+4/d1YOy2y+mt0488EHH8jT01MlS5bUyJEjFR4enmHFyvTWrl2rcuXKKTAw0OL8kiVLzM9qXl5emjhxolm2c+dO1atXTy4uLqpQoYImT56c5f1t+btv3LixRo8erX79+plxXrt2rU6dOqXmzZurWLFiql+/vk6cOGFecy+fu/r06aOwsDD5+vqqTp06GjNmjLZt22ZRp23btlq5cqVu3rxp9V4AUBBl95z58uXLWY4h6d+vV6xYoVKlSumvv/6yuD4kJEQffPCBJOnWrVt644035OXlpRIlSqhx48b66aefzLp33uunTZum8uXL66mnnpLEd785OZ+8Y9q0aXJ3d1f37t0zLWc+iZxGkhWQA3bs2CFJ+ve//60zZ87oo48+kiS9+eabmjlzpj788EMdOHBAU6dOVaFChczrUlJSNG/ePK1evVorVqzQqlWr9Nlnn1l9rQkTJmjSpEnauXOnrl69quHDh5tlEydO1FdffaVVq1ZpzZo1mjNnToYH8Xfbtm2bQkNDLc5Za/d3332nyMhIvfTSS/rpp5/03HPPqUOHDkpKSrI5XpmZOHGihg8frt27d8vZ2VnPP/+8Jk6cqLlz52rdunVat26dZs6caXHN5MmT9eyzz2rv3r1q2LChevbsmeEDzx07d+6Ul5eXxWS5WbNm2rdvn65du2aee+qpp7R9+3alpaXdV38AALY5c+aM+vTpo6ioKB06dEhr1qzRk08+maGeYRgaMmSI1qxZoy1btsjb2zvLe94ZP7755hvt27fP4kFodt544w1NnDhRCQkJCggI0JUrVzRw4EDt2rVL69at08mTJzVo0CCz/pAhQ+Tp6an4+Hjt2rVLL730khwd//cR+6efflJ8fLw2btyoWbNm6cMPP9SaNWuyfP3MxuWZM2dq3Lhxev3115WQkKDPP//cTMJKSkpShw4d1KFDB/300096+eWXFRkZmeEhqb2mTJmijh07au/evapcubIiIiI0bNgwTZ48WVu3btXhw4f19ttvW1wzY8YMBQUFac+ePerdu7f69Omjc+fO3Vc7nnrqKR05cuS+7wMA+cHa+2J279+XL1/Wc889pyZNmmjPnj1q166d3nvvPYv7X79+Xa+//rp+/PFHLVmyRN98842ioqLsbufHH3+sH374QevWrVNCQoKmTZuW6VY5p06dUqNGjdSsWTP93//9n8XD2vRWr16tmzdvaseOHZowYYJGjhxp8aA5Kxs3btTQoUMVFRWlAwcO6LXXXtMLL7xgzrfviIqK0syZM7Vv3z67tvR5/fXXNXnyZH3//fdKTU01V6sYNWqUOnXqpK5du+rMmTM6c+ZMptdPnjxZ8+bN05w5cxQfHy9XV1e1b99et27dMutYG/eXLl2qL774QrGxsTp06JA+//xzeXp6ZtneR2WunpkLFy5kWB2GuToAa3Li/TGreeRHH32k2rVra+TIkTpz5ozi4+OzbMf9zomGDx+uzZs3a9WqVdqyZYtOnz5t1+pKto7p6aWmpqpz587q3bu3Dh48qI0bN6pFixY29yslJUUtWrRQmzZttG/fPs2dO1dffPGF+QW0JHXu3FmS9O2332rTpk26dOmS+cXs008/bY6/Z86c0apVq1SoUCE9/fTTWbbZ2jjz9ddf69VXX9Xbb7+tnTt36q+//srwo+e7ZTbmbtiwQS+88IL69Omj/fv3a+nSpSpXrpyk2ytftWnTRlWrVtXevXs1efJkRUVF6YsvvrD6Otn57LPPFBISot27d6tt27aKiIjQiy++qJdfflk//PCDDMPQiBEjLK65189dknTp0iV98cUXGX6IFRoaquvXr2vv3r331R8AyGu2PGe2da7Spk0bGYahuLg489zPP/+shIQEde3aVdLtuenatWu1ePFi7dmzR/Xr11eLFi30+++/m9fs3btX33//vTZs2KDY2Ngs2853v/9j73wyISFBH374odVVGJlPIscZAO5Jo0aNjNdff90wDMO4efOmIcn45ptvzPKrV68aLi4uxtKlSzO9fs6cOYaDg4Nx9uxZ89w//vEPo1OnTuaxj4+PMXPmTMMwDCMxMdGQZMTExJjlX3zxhVGmTBnzuGzZsmZ9wzCMQ4cOGZKMOXPmZNkPNzc3Y8WKFTa3u1u3bkaXLl0sztWpU8cYNWqUYRiG8c033xiSjJs3bxqGYRi9evUywsPDLeqnj51hGIYkY9KkSebx4sWLDUlGfHy8ea5///5Gx44dzWMfHx9j4MCB5vEvv/xiSDL27duXabvffvttIzQ01OLcgQMHDElGcnKyee7HH380JBnnz5/P9D4AgJy1a9cuw83Nzfjjjz8yLffx8TFmzJhh9O3b1wgJCbEYNw3j9hjy1VdfGYbxvzHo+++/N8vfeecd48knn8y2HXfG2blz51qtt337dsPZ2dlITU01DMMwQkJCjPnz52da98033zRKly5tXLt2zTzXsmVLY+TIkVne/4knnjCmTZtmcc7b29t4//33M63/yiuvGLVq1bI4161bN6Nz584W/Tpy5IjZpvr161vUv3usvnuM3b59uyHJ4rPBu+++azGuNmrUyHjmmWfM45s3bxpFixY1vvzyyyz7ekdmbbrj8uXLGT4TAMCDILv3xezev6Ojo43y5cub86o75T4+Plm+5uLFiw0/Pz/z2Nr7a3pDhgwxIiMjMy27M7YeOXLE8PPzM0aPHm1RPmfOHKN8+fLmca9evYwqVapY1AkMDDQ++eSTbNvRpEmTDPX69etn9O3b16ItmzZtsqhz9/zSMDKfS3/66adm+ZEjRyzmj+Hh4UavXr2s9s3T09OYPn26eZySkmK4uroaa9asMQwj+3F/ypQpRrNmzYy0tLRsY2EYj85c/W47duwwChcubGzbts3iPHN1AFnJqfdHa/PI+vXrG2+++abFPe4eZ7Mb++8eV+6+x++//244Ozsb//nPf8zyn3/+2ZBk7N+/33wNa2OetTE9KxcuXMjwfDS97PoVFRVl8TzbMAxj0aJFRqVKlQzDMIzNmzcbnp6eFp9pTp8+bUgyTp48aXHdr7/+anh5eRkTJ040z2U3X717nOncubNF/dTUVKNChQoZxvn02rdvbwwbNsziXFhYmDF48OBM63/66afG448/btGnV155xXjqqafM4/TPKrL7uzeMjHE+c+aMIcniWcDixYsNd3d38/heP3dNnz7dKFasmCHJqFu3rpGSkpKhTunSpbP8fwoACipbnjNbG0Pufr+OiIiwGFPSv3dfu3bNcHV1zTDPCQgIMBYsWGDWL168eIb28N1vzs0n//rrLyM0NNSIjY01DCPzMdcwmE8i57GSFZBLjh49qhs3bqhx48ZZ1vHw8LD45epjjz2W7a+bqlWrZlE/JSVFt27d0qVLl3ThwgWLrOzAwECVKFHC6v2uX78uFxcXm9t96NAh1a1b1+JcvXr1dOjQIauvk530/boTk6pVq1qcO3/+fJbXPPbYY5KUZfwMK1szpXdnH+T0q1sBAHJP9erV9cQTT6hixYrq3bu3YmNjM/wy5a233tLGjRu1adMmqys+3HH3+GDPL4dr1qxpcXzq1ClFRESoYsWKKlGihJo1a6bU1FSdPXtWkjRo0CC9+OKLatWqlaZMmWJuPXRHQECAihQpYnN77h6X//jjDyUnJz9w47Kzs7PKli173ytQMS4DeJBZe1/M7v370KFDCg0NlbOzs1leu3Zti/oJCQl67rnn5O3trRIlSqhPnz4ZxiFbREREaNmyZXryySf12muv6YcffshQp3HjxmrXrp3VrXDuCAkJsTi2dSzet2+fRo8ereLFi5t/5s6dq+PHj1vUu3ustlX6+Pn7+6t06dI2j5eXL1/Wr7/+avF35u7ursqVK1vcw9q436lTJyUkJCg4ONhcqcSaR2Wunt7hw4f197//XVFRURlWMOEzAYCs5NT74/3MIzO7h71zouPHjys1NdWirUFBQSpVqpTN7+W2jOl3K1OmjLp3766QkBB1795dc+bM0ZUrV2zu1759+7R69WqL8btv375KSkpSWlqa9u3bp/Pnz6tUqVJm+Z2dBtKP8ampqerSpYtq1aql1157zWqbrY0zR44csXg+7uTkpBo1ali9391jriTt37/f6r+pJ5980uJzWl6NuRcvXrRYRfNePneFh4drz549+vrrr1WoUCG9+OKLGeq4uroy5gJ44NjynNmeuUr37t21evVqczu7pUuXqlu3bpKkY8eO6dq1a6pbt67FGHjs2DGL8S0gIEDFixfPtu1895v5Ndn9HU2cOFEBAQHq0qWL1XYwn0ROI8kKyCW2JPWk3zpQur0/bXZLFaa/5s72DIZhmK+X1ZYNWSlTpowuXbpkHmfXbluTle5wdHTMcE1m+7ln1q+7z90dm8yuySp+np6eGQbh8+fPy9HRUWXLljXPXbx4UdLtuAAAcp+zs7M2bdqkmJgYeXp6asyYMXr66actJsCNGzfWr7/+qq+++sqme2Y3flhTtGhRi+PevXvrxIkT+uyzzxQfH69ly5ZJ+t9YNnDgQP38889q06aN1q9fr6CgIH377beZtsWW9jws43JWdex1Z1xOP1YDwIPC2vuiLe/v2c3t2rdvLwcHBy1atEi7du3Sxx9/rNTUVLvbWbt2bSUmJurll1/WiRMnVL9+fU2ZMsWiTuvWrbV27dost9JL717HgytXrmjKlCnau3ev+SchIUELFiywqHf3WG3r2GbvXPleWOu7r6+vjhw5ookTJ+rKlSt69tlnNXTo0Czv9bB8Jshurn7H8ePH1axZM0VGRmrs2LEZypmrA8hKTr0/3s88MrN73H2f7N53bWlndvewZUzPzOLFi7VhwwZVrlxZU6ZMUUhIiMU2RNb6deXKFXXv3t1i/N63b58OHjwoR0dHXblyRf7+/hble/fu1ZEjR1SrVi3znsOHD9eFCxc0f/78bMdsa+OMLZ+h7nb3mJud/Bxz7379e/ncVbJkSQUEBKhZs2ZavHixVqxYkWGLwd9++415OIAHji3Pme2Zq7Ro0UKFChXS2rVrzbHtTjLPnYTkTZs2WYxvhw4d0pAhQ8x73D1/zQrf/Vq/Jqu/o82bN2vp0qVydnaWs7Oz+vbtq9OnT8vZ2VkbNmww6zGfRE4jyQrIAU5OTnJ0dLT4FUlAQIBcXFy0adOmPGlD6dKlVbZsWYtfKB05ckR//PGH1euqV6+ugwcPmsfZtTsoKEg7duywOLd9+3YFBQVlWt/Dw8Nc7UOS/vrrr/vOfL4XtWvX1qlTp3TkyBHz3MaNG1WtWjUzg1m6/WvwgIAAmz/4AADun5OTk5o0aWLuO//DDz9o7969Znnjxo0VExOjvn376ssvv8zTtu3YsUMjRoxQ8+bNFRQUpAsXLmSoU7FiRQ0bNkxff/21GjVqpMWLF9/z6909Lru5ucnb2zvXxmXp9i+PC6qEhAQVLVpUAQEB+d0UAMhR2b1/V65cWbt377aYY8bHx5v/feHCBR07dkzjx49Xw4YNVbly5Qzv7/Zwd3dXRESEFi1apLfeekuzZ8+2KI+OjlbdunXVokULiy9dc1L16tV1/Phx+fv7W/wpX7681evuHtvOnz+faSx27txp/vexY8f022+/qXLlypJuP8RNH+u7lSxZUp6enhZ/ZxcvXtShQ4eyHHMzU6xYMXXu3FkzZ87UrFmz9Pnnn2dZ91GZq0tScnKymjZtqg4dOuidd97JtA5zdQBZyen3x8xkN07YwsPDQykpKRZfQKafi1WqVEnOzs4WbT148KAuXbpkttWWMS+7MT0rderUUVRUlPbs2aNLly7pv//9r03XVa9eXQkJCRnGb39/f7M8OTlZbm5uGcrvPJOdP3++Fi5cqFWrVtm02oc1gYGBFs/Hb926ZfF8Ias+pB9zpdsrRFn7N/XDDz9YJLdnN+Za+7vPT3e+tE6/KldiYqKuXbum6tWr51ezAOCeZfec2R6FChVSx44dFRMTo9jYWDVq1MhcWSk4OFiFCxfWmTNnMoxv7u7uOdYfvvu1bs6cOfrxxx/NJLe33npLnp6e2rt3r8XqyMwnkdOcs68CIDsODg6qUKGCmbRTtGhRFS9eXCNHjtRLL70kR0dH1axZU0eOHFFaWppat26dK+3o37+/JkyYID8/P5UtW1YjR45UkSJFrGY4t2jRQmvXrjWPXV1drbb7pZdeUlhYmP71r3+pZcuWWrhwofbs2aMlS5Zkev+wsDBNnTpVy5cvV5UqVTR58uQMy3PmhSeeeEJhYWHq16+fPvroIyUlJWnatGn68MMPLept27ZNzZs3z/P2AcCj6vvvv9emTZvUokULlSlTRkuXLpWLi4t8fHws6rVr106zZ882l2lu1qxZnrSvUqVKWrBggapWrapjx45l+OJv+PDhatu2rfz9/XXy5En99NNPatWq1T2/XosWLTRmzBiLc+PGjdPIkSNVtmxZhYWF6fTp0zp79qy6dOmigQMHatq0aRo/frx69uypDRs2aNmyZdqyZUum92/YsKEGDRqkTz/9VM2bN9ecOXOUlJSkJ5544p7bfK+Sk5N18eJFnT17Vn/++af5wCP9Vg7btm1To0aNLB74AsDDILv37+eff16vvvqqhg0bpqFDh2rz5s2Ki4uTm5ubpNsPWkuXLq2ZM2dq5MiRio+P14wZM+6pLdOmTZOXl5dq1Kih69evmytZpOfo6Ki5c+eqS5cuatWqlTZu3Gi2Jae89tpr6tq1q7y8vNS2bVtdu3ZN3377rTw8PMwtGTITFhamcePGKTw8XB4eHho3blyGLX8k6YMPPlClSpXk4eGhYcOGKSwszNxix8fHR8uWLVNSUpKKFy+e6coNw4YNU1RUlHx9feXj46NXX31VPj4+No/78+bNk2EYqlOnjpycnLRy5coMcU7vUZmrnz59Wk2aNFH16tX12muvmQ/qCxcubPHlBHN1AFnJ6ffHzPj4+GjHjh06ffq0ihYtqtKlS9vdzlq1asnR0VFvvfWWevXqpbVr12rLli3mWFSiRAlFRkbq5ZdfVokSJVSsWDENGjRILVq0UJUqVSRlP+bZMqbfLTExUbNmzVL79u312GOPaevWrbpy5YrNP3QZPHiwZsyYoX79+mnIkCEqUqSIfvzxRx0+fFjjxo1Ty5YtVa1aNXXs2FHvvvuuypcvr2PHjmnp0qX67LPPtG/fPg0YMEDTp09X8eLFzXHgzrZL9urfv7/atm2rJk2aqH79+oqOjtalS5eyfT7+z3/+Uzdu3DDj+frrr6tdu3aqVKmS2rVrp99++00HDhxQnz59FB4ernHjxmngwIEaOXKk9uzZo08++UQzZ87M9P7Z/d3nlQsXLig6Olpt2rSRh4eHTpw4oTfeeEM1a9a0+Heybds2BQcH6/HHH8/T9gHA/bL1ObM9unXrpr///e967LHH9Morr5jn3dzcNGTIEA0cOFB//fWXQkNDdfbsWX355ZcKDw+32BLvfvHdb9b8/Pwsjnft2iVnZ+cMYyzzSeQ0VrICcsjkyZO1aNEilStXzlwKMioqSn369NGQIUMUHBys4cOH3/evnqwZN26cmjVrpmeffVZt2rRRr169VLRo0UwfMN8RHh6u7777zmLPW2vtfvrppzV79mx9+OGHCgkJ0YoVK7Ry5Ur5+vpmev82bdpoxIgR6t+/vxo1aqQnnnhCoaGhOdpvW8XExKhkyZKqV6+e+vfvr1deeUWRkZFmeWpqqlauXKk+ffrkS/sA4FHk5uam//73v2rZsqWCg4O1ePFiLV++3NyjPb1u3brpk08+UYcOHfTdd9/lSftmzZqlo0ePqlq1anrjjTc0ceJEi/KbN2/qH//4h4KCgtSjRw89//zzFktC26tdu3a6fPmy9uzZY57r16+foqKiFBUVpeDgYPXp08f8tZKPj49WrlypFStWKCQkRB9++KE+//xzi1/qpBcSEqJp06bpn//8p2rVqqW0tDQ999xz99ze+zF+/HjVrFlTM2bM0N69e1WzZk3VrFnTos7SpUsZlwE8lLJ7/y5VqpRWrFihr776StWrV9eKFSssknCdnJy0aNEibdiwQVWrVtWMGTP01ltv3VNbihUrpn/+85+qXr26GjduLHd3d3366acZ6jk7O2vJkiVyd3dX27ZtdfXq1XvrfBbat2+vxYsXa8GCBapWrZqaN2+uNWvWZPtA/MUXX1THjh3VsWNHtW3bVj169NDf/va3DPXeeustjRgxQiQ+FukAACAASURBVLVr15Z0e9WMO/r16yd3d3dVqVJFHh4emb7O6NGj1atXL/Xu3VtPPfWUrl69qtWrV8vJycmm/pUsWVLR0dGqXbu2ateurYsXL1r9gv9Rmat/9dVXOn78uFavXq3HH39c5cqVU7ly5dSxY0ezDnN1ANnJyffHzIwaNUopKSmqWLFihjmLrcqWLas5c+Zo4cKFqlGjhn788UcNHDjQos4HH3yghg0b6tlnn1VYWJjKly9vsW1udmOerWN6ekWLFtX+/fv197//XZUrV9bbb7+t2bNn29zPChUqaMuWLTp58qTq16+vWrVqacqUKfL29pZ0O1F7/fr1qly5sjp27KiqVatq6NChKlWqlCTphx9+0LVr1xQZGWmOAeXKlbNpm8PMNG/eXO+8847Gjh2r2rVry9nZWc2bN7f6fDw0NFSVKlWy+DK6ZcuWmjNnjj777DNVrVpVnTp1MhPASpQoYW4dVb16dY0ePVpvvvmmnn/++Uzvb8vffV4oVKiQdu3apbZt2yowMFAvvPCCKleurP/85z8Wn2eYhwN4UNnznNlWTZo0UYkSJZScnGwxR5Gk999/X4MGDdKoUaNUuXJlde3aVSdPnszxLen47vf+MJ9EbnAw7N1kE8AD4+TJk/L29tbOnTst9ri/W//+/VW+fHmNHz8+D1tX8CxYsEDz5s3T119/nd9NAQA8wt59910dOXLE5m0dHlbffvut+vXrp/3797OSFQDgniUlJcnPz09Hjhwxty56UDBXv425OgDgXhmGocqVK6tfv34aPXp0lvUWL16smTNnauPGjXnYuoInMTFR9erV06FDh1SyZMn8bg4AIBN892sf5pPIDaxkBTxEjh07pnnz5uno0aPauXOnIiIiFBQUpKeeesrqdRMmTDB/PfQoc3Bw0EcffZTfzQAAPOKGDRsmf3//XF398kFw+fJlff755yRYAQAeWczVb2OuDgCwx5QpU3TgwAElJCRo6NChSk5OVpcuXaxe0717d7Vq1Uq///57HrWyYDp9+rQ+//xzEqwAoADhu9/7w3wSuYGVrICHyPHjx9WjRw8dOHBARYoUUYMGDfTJJ5+oQoUK+d00AADyVXJysqpUqZJpWcOGDbVu3bo8bhEAAHmjoIyB77zzjt55551My2bMmKHw8PBced0HeSUrAABywoABA7Rw4cJMy9atW6eGDRvmcYtyV/v27bVt2zbduHFDISEhev/99x+6PgIAHh189wsUPCRZAQAA4KGXmpqqpKSkTMtcXV1Vvnz5vG0QAAB5pKCMgRcvXtTFixczLfP09FSJEiXypB0AADxqzp07l+UKTeXLl5erq2setwgAAAB4cJFkBQAAAAAAAAAAAAAAAABWOOZ3AwAAAAAAAAAAAAAAAACgIHPO7wbkJRcXF3l4eOTIvW7cuCEXF5ccudfDjljZh3jZh3jZjljZJyfjdf78ed24cSNH7vUgyalxl3+79iFe9iFetiNW9iFe9smpeDHm3j/+7dqHeNmOWNmHeNmHeNmOue79Y9zNP8TLdsTKPsTLdsTKPoy7949xN38QK/sQL/sQL9sRK/vk1bj7SCVZeXh46NSpUzlyr7i4OLVq1SpH7vWwI1b2IV72IV62I1b2ycl4eXl55ch9HjQ5Ne7yb9c+xMs+xMt2xMo+xMs+ORUvxtz7x79d+xAv2xEr+xAv+xAv2zHXvX+Mu/mHeNmOWNmHeNmOWNmHcff+Me7mD2JlH+JlH+JlO2Jln7wad9kuEAAAAAAAAAAAAAAAAACsIMkKAAAAAIB74Ovrq6CgINWoUUM1atRQTEyMJOncuXNq3bq1AgICFBISoq1bt5rXXL16VT169JC/v78CAwO1fPlysywtLU1Dhw5VpUqV5O/vr+jo6DzvEwAAAADg0cZcFwCArD1S2wUCAAAAAJCTli1bppCQEItzY8eOVd26dbV+/XrFx8erc+fOOnbsmJydnTVlyhS5uLjo6NGjSkxMVL169dSkSROVLl1aCxcuVEJCgg4fPqzLly8rNDRUTZs2VVBQUD71DgAAAADwKGKuCwBA5ljJCgAAAACAHBQbG6vBgwdLkmrVqiVPT0/zF74xMTFmmZ+fn8LCwrRq1SqzbMCAAXJycpK7u7u6du2qJUuW5E8nAAAAAABIh7kuAACsZAUAAAAAwD0LDw9XWlqa6tSpo3fffVeOjo5KS0uTh4eHWcfX11fJycmSpOTkZPn4+NhctmvXrgyvOXXqVE2dOtU8vnTpkuLi4nKkP9evX8+xez0KiJftiJV9iJd9iJftiBUAALBFfsx1Jea7BQWxsg/xsg/xsh2xsk9exYskKwAAAAAA7sGWLVvk7e2tmzdvaty4cerVq5cWLFggBwcHi3qGYVgcpy+3p+yOESNGaMSIEeaxl5eXWrVqdc/9SC8uLi7H7vUoIF62I1b2IV72IV62I1YAACA7+TXXlZjvFhTEyj7Eyz7Ey3bEyj55FS+2CwQAAAAA4B54e3tLkgoVKqSXX35Z3377rcqUKSNJOn/+vFnvxIkTZl1vb28lJSXZXQYAAAAAQF5grgsAQNZIsgIAAAAAwE5//vmnLl26ZB4vXrxYNWvWlCR16dJF06dPlyTFx8fr7NmzatCgQYayxMREbd68We3btzfLZsyYoVu3bunixYuKiYlRt27d8rJbAAAAAIBHGHNdAACsY7tAAAAAAADs9Ouvv6pTp066deuWDMNQxYoVNX/+fEnSpEmTFBERoYCAABUuXFgLFiyQs/Pt6ffo0aMVGRkpf39/OTo6avr06XJ3d5ckRUREKD4+XoGBgWbd4ODg/OkgAAAAAOCRw1wXAADrSLICAAAAAMBOFStW1J49ezIt8/T01IYNGzItK1asmGJiYjItc3JyMn/5CwAAAABAXmOuCwCAdWwXCAAAAAAAAAAAAAAAAABWkGQFAAAAAAAAAAAAAAAAAFaQZAUAAAAAAAAAAAAAAAAAVpBkBQAAAAAAAAAAAAAAAABWkGQFAAAAAAAAAAAAAAAAAFaQZAUAAAAAAAAAAAAAAAAAVpBkBQAAAAAAAAAAAAAAAABWkGQFAAAAAAAAAAAAAAAAAFaQZAUAAAAAAAAAAAAAAAAAVpBkBQAAAAAAAAAAAAAAAABWkGQFAAAAAAAAAAAAAAAAAFaQZAUAAAAAAAAAAAAAAAAAVpBkBQAAAAAAAAAAAAAAAABWkGQFAAAAAAAAAAAAAAAAAFaQZAUAAAAAAAAAAAAAAAAAVpBkBQAAAAAAAAAAAAAAAABWOOd3Ax5UZy9fl+/Y/+R3Mywkvdc2v5sAAECOK4hjrsS4CwB4ODHuAgCQdxh3AQDIO4y7AICcwEpWAAAAAAAAAAAAAAAAAGAFSVYAAAAAAAAAAAAAAAAAYAVJVgAAAAAAAAAAAAAAAABgBUlWAAAAAAAAAAAAAAAAAGAFSVYAAAAAAAAAAAAAAAAAYAVJVgAAAAAAAAAAAAAAAABgBUlWAAAAAAAAAAAAAAAAAGAFSVYAAAAAAAAAAAAAAAAAYAVJVgAAAAAAAAAAAAAAAABgBUlWAAAAAAAAAAAAAAAAAGAFSVYAAAAAAAAAAAAAAAAAYAVJVgAAAAAAAAAAAAAAAABgBUlWAAAAAAAAAAAAAAAAAGAFSVYAAAAAAAAAAAAAAAAAYAVJVgAAPISioqLk4OCg/fv3S5LOnTun1q1bKyAgQCEhIdq6datZ9+rVq+rRo4f8/f0VGBio5cuXm2VpaWkaOnSoKlWqJH9/f0VHR+d5XwAAAAAAAAAAAAAgvznndwMAAEDO2r17t3bs2CFvb2/z3NixY1W3bl2tX79e8fHx6ty5s44dOyZnZ2dNmTJFLi4uOnr0qBITE1WvXj01adJEpUuX1sKFC5WQkKDDhw/r8uXLCg0NVdOmTRUUFJSPPQQAAAAAAAAAAACAvMVKVgAAPERu3LihwYMHKzo6Wg4ODub52NhYDR48WJJUq1YteXp6mqtZxcTEmGV+fn4KCwvTqlWrzLIBAwbIyclJ7u7u6tq1q5YsWZLHvQIAAAAAAAAAAACA/EWSFQAAD5Hx48erZ8+e8vPzM8+lpKQoLS1NHh4e5jlfX18lJydLkpKTk+Xj42N3GQAAAAAAAAAAAAA8KtguEACAh8T27dsVHx+v9957L0NZ+lWtJMkwjCzL7SlLb+rUqZo6dap5fOnSJcXFxdnWeCvcCktRoan3fZ+clhN9yw3Xr18vsG0riIiX7YiVfYiXfYgXAAAAAAAAAKCgI8kKAICHxObNm3Xw4EFzFatTp06pVatWmjVrliTp/Pnz5mpWJ06ckLe3tyTJ29tbSUlJFmVt2rSxKKtVq1aG6+42YsQIjRgxwjz28vJSq1at7rtf82JX6c3dBe8jS9J799+33BAXF5cjcX9UEC/bESv7EC/7EC8AAAAAAAAAQEHHdoEAADwkxo4dq19++UVJSUlKSkqSl5eX4uLi9Mwzz6hLly6aPn26JCk+Pl5nz55VgwYNJMmiLDExUZs3b1b79u3NshkzZujWrVu6ePGiYmJi1K1bt/zpIAAAAAAAAAAAAADkk4K3LAQAAMhxkyZNUkREhAICAlS4cGEtWLBAzs63PwaMHj1akZGR8vf3l6Ojo6ZPny53d3dJUkREhOLj4xUYGGjWDQ4Ozrd+AAAAAAAAAAAAAEB+IMkKAICHVFJSkvnfnp6e2rBhQ6b1ihUrppiYmEzLnJyczFWuAAAAAAAAAAAAAOBRxXaBAAAAAAAAAAAAAAAAAGAFSVYAAAAAAAAAAAAAAAAAYAVJVgAAAAAAAAAAAAAAAABgBUlWAAAAAAAAAAAAAAAAAGAFSVYAAAAAAAAAAAAAAAAAYEWeJVlFRUXJwcFB+/fvlySdO3dOrVu3VkBAgEJCQrR161az7tWrV9WjRw/5+/srMDBQy5cvN8vS0tI0dOhQVapUSf7+/oqOjs6rLgAAAAAAAAAAAAAAAAB4BDnnxYvs3r1bO3bskLe3t3lu7Nixqlu3rtavX6/4+Hh17txZx44dk7Ozs6ZMmSIXFxcdPXpUiYmJqlevnpo0aaLSpUtr4cKFSkhI0OHDh3X58mWFhoaqadOmCgoKyouuAAAAAAAAAAAAAAAAAHjE5PpKVjdu3NDgwYMVHR0tBwcH83xsbKwGDx4sSapVq5Y8PT3N1axiYmLMMj8/P4WFhWnVqlVm2YABA+Tk5CR3d3d17dpVS5Ysye1uAAAAAAAAAAAAAAAAAHhE5fpKVuPHj1fPnj3l5+dnnktJSVFaWpo8PDzMc76+vkpOTpYkJScny8fHx+ayXbt2ZfraU6dO1dSpU83jS5cuKS4uLkf65VZYigpNzZF75ZSc6ltOu379eoFtW0FEvOxDvGxHrOxDvAAAAAAAAAAAAAAAd+RqktX27dsVHx+v9957L0NZ+lWtJMkwjCzL7SlLb8SIERoxYoR57OXlpVatWtnW+GzMi12lN3fnyW6LNkt6L2f6ltPi4uJyLO6PAuJlH+JlO2JlH+IFAAAAAAAAAAAAALgjV7cL3Lx5sw4ePCg/Pz/5+vrq1KlTatWqlXbu3ClJOn/+vFn3xIkT8vb2liR5e3srKSnJ7jIAAAAAAAAAAAAAAAAAyGm5mmQ1duxY/fLLL0pKSlJSUpK8vLwUFxenZ555Rl26dNH06dMlSfHx8Tp79qwaNGggSRZliYmJ2rx5s9q3b2+WzZgxQ7du3dLFixcVExOjbt265WY3AAAAAAAAAAAAAAAAADzC8m2/u0mTJikiIkIBAQEqXLiwFixYIGfn280ZPXq0IiMj5e/vL0dHR02fPl3u7u6SpIiICMXHxyswMNCsGxwcnF/dAAAAAAAAAAAAAAAAAPCQy9Mkq/Tb/Hl6emrDhg2Z1itWrJhiYmIyLXNycjJXuQIAAAAAAAAAAAAAAACA3Jar2wUCAAAAAAAAAAAAAAAAwIOOJCsAAAAAAAAAAAAAAAAAsIIkKwAAAAAAAAAAAAAAAACwgiQrAAAAAAAAAAAAAAAAALCCJCsAAAAAAAAAAAAAAAAAsIIkKwAAAAAAAAAAAAAAAACwgiQrAAAAAAAAAAAAAAAAALCCJCsAAAAAAAAAAAAAAAAAsIIkKwAAAAAAAAAAAAAAAACwgiQrAAAAAAAAAAAAAAAAALCCJCsAAAAAAAAAAAAAAAAAsIIkKwAAAAAAAAAAAAAAAACwgiQrAAAAAAAAAAAAAAAAALCCJCsAAAAAAAAAAAAAAAAAsIIkKwAAAAAAAAAAAAAAAACwgiQrAAAAAAAAAAAAAAAAALCCJCsAAAAAAAAAAAAAAAAAsIIkKwAAAAAAAAAAAAAAAACwgiQrAAAAAAAAAAAAAAAAALCCJCsAAAAAAAAAAAAAAAAAsIIkKwAAAAAAAAAAAAAAAACwgiQrAAAAAAAAAAAAAAAAALCCJCsAAAAAAAAAAAAAAAAAsIIkKwAAAAAAAAAAAAAAAACwgiQrAAAAAAAAAAAAAAAAALCCJCsAAAAAAAAAAAAAAAAAsIIkKwAAAAAAAAAAAAAAAACwgiQrAAAAAAAAAAAAAAAAALCCJCsAAAAAAAAAAAAAAAAAsIIkKwAAAAAAAAAAAAAAAACwgiQrAAAAAAAAAAAAAAAAALCCJCsAAAAAAAAAAAAAAAAAsIIkKwAAAAAAAAAAAAAAAACwgiQrAAAAAAAAAAAAAAAAALCCJCsAAAAAAAAAAAAAAAAAsIIkKwAAAAAAAAAAAAAAAACwgiQrAAAAAAAAAAAAAAAAALCCJCsAAAAAAAAAAAAAAAAAsIIkKwAAAAAAAAAAAAAAAACwgiQrAAAAAAAAAAAAAAAAALCCJCsAAAAAAAAAAAAAAAAAsIIkKwAAAAAAAAAAAAAAAACwgiQrAAAAAAAAAECBFxUVJQcHB+3fv1+SdO7cObVu3VoBAQEKCQnR1q1bzbpXr15Vjx495O/vr8DAQC1fvtwsS0tL09ChQ1WpUiX5+/srOjo6z/sCAAAAAHjwOOd3AwAAAAAAAAAAsGb37t3asWOHvL29zXNjx45V3bp1tX79esXHx6tz5846duyYnJ2dNWXKFLm4uOjo0aNKTExUvXr11KRJE5UuXVoLFy5UQkKCDh8+rMuXLys0NFRNmzZVUFBQPvYQAAAAAFDQsZIVAAAAAAAAAKDAunHjhgYPHqzo6Gg5ODiY52NjYzV48GBJUq1ateTp6WmuZhUTE2OW+fn5KSwsTKtWrTLLBgwYICcnJ7m7u6tr165asmRJHvcKAAAAAPCgIckKAICHSMuWLfXEE0+oRo0aatiwofbu3StJaty4sSpWrKgaNWqoRo0amjZtmnkNWygAAAAAAAqy8ePHq2fPnvLz8zPPpaSkKC0tTR4eHuY5X19fJScnS5KSk5Pl4+NjdxkAAAAAAFlhu0AAAB4isbGxKlWqlCRp5cqVioyM1O7duyVJH3/8sdq1a5fhGrZQAAAAAAAUVNu3b1d8fLzee++9DGXpV7WSJMMwsiy3pyy9qVOnaurUqebxpUuXFBcXZ1vjs+FWWIoKTc2Re+WknOpfTrt+/XqBbVtBQ6zsQ7xsR6zsQ7wAAHj4kGQFAMBD5E6ClSRdvnxZjo7ZL1oZExOjuXPnSrLcQqF3795ZbqEwYcKEXOoBAAAAAAD/s3nzZh08eNBcxerUqVNq1aqVZs2aJUk6f/68uZrViRMn5O3tLUny9vZWUlKSRVmbNm0symrVqpXhuruNGDFCI0aMMI+9vLzUqlWrHOnbvNhVenN3wXtEn/RezvQvp8XFxeVY7B92xMo+xMt2xMo+xAsAgIcP2wUCAPCQeeGFF1ShQgWNGzdO8+bNM8+PHj1a1apVU7du3XT8+HHzPFsoAAAAAAAKqrFjx+qXX35RUlKSkpKS5OXlpbi4OD3zzDPq0qWLpk+fLkmKj4/X2bNn1aBBA0myKEtMTNTmzZvVvn17s2zGjBm6deuWLl68qJiYGHXr1i1/OggAAAAAeGAUvJ/JAACA+zJ//nxJ0rx58zR69GitXbtWCxYsUIUKFWQYhqZPn6527dopISHBvKYgb6HA9gn2YRly+xAv2xEr+xAv+xAvAABwLyZNmqSIiAgFBASocOHCWrBggZydbz/yHj16tCIjI+Xv7y9HR0dNnz5d7u7ukqSIiAjFx8crMDDQrBscHJxv/QAAAAAAPBhIsgIA4CHVq1cvDRgwQCkpKapQoYKk2wlTQ4YM0ahRo5SSkqIyZcoU+C0U2D7BPixDbh/iZTtiZR/iZR/iBQAAbJWUlGT+t6enpzZs2JBpvWLFiikmJibTMicnJ3OVKwAAAAAAbMV2gQAAPCR+//13/fLLL+bxihUrVKZMGbm5uenXX381z//73/+Wp6enypQpI4ktFAAAAAAAAAAAAAAgOwVvWQgAAHBPLl++rE6dOunatWtydHSUh4eH1qxZo7/++ktt27bVjRs35OjoqLJly2r16tXmdWyhAAAAAAAAAAAAAADWkWQFAMBDokKFCtq5c2emZbt27cryOrZQAAAAAAAAAAAAAADr2C4QAAAAAIB7FBUVJQcHB+3fv1+SdO7cObVu3VoBAQEKCQnR1q1bzbpXr15Vjx495O/vr8DAQC1fvtwsS0tL09ChQ1WpUiX5+/srOjo6z/sCAAAAAMAdzHcBAMiIlawAAAAAALgHu3fv1o4dO+Tt7W2eGzt2rOrWrav169crPj5enTt31rFjx+Ts7KwpU6bIxcVFR48eVWJiourVq6cmTZqodOnSWrhwoRISEnT48GFdvnxZoaGhatq0qYKCgvKxhwAAAACARxHzXQAAMsdKVgAAAAAA2OnGjRsaPHiwoqOj5eDgYJ6PjY3V4MGDJUm1atWSp6en+evemJgYs8zPz09hYWFatWqVWTZgwAA5OTnJ3d1dXbt21ZIlS/K4VwAAAACARx3zXQAAspbrK1m1bNlSZ8+elaOjo0qUKKFPPvlENWrUUOPGjZWcnCw3NzdJUq9evTR8+HBJt5eU7Nu3r+Lj4+Xo6Kj33ntPHTt2lHR7Sclhw4Zp7dq1cnBw0IgRIzRo0KDc7gYAAAAAAKbx48erZ8+e8vPzM8+lpKQoLS1NHh4e5jlfX18lJydLkpKTk+Xj42Nz2a5duzJ97alTp2rq1Knm8aVLlxQXF5cj/XIrLEWFpubIvXJSTvUvp12/fr3Atq2gIVb2IV72IV62I1YAACA7zHfzVkH8bMZnRvsQL/sQL9sRK/vkVbxyPckqNjZWpUqVkiStXLlSkZGR2r17tyTp448/Vrt27TJcw5KSAAAAAICC6v/Zu9vYLM/zfsA/vwwi0DIw8dwPxkAwLmw0Qkho0BFUkBYITVDU8DI0OamYxFgpq+bUE9MWtd2mLEzIrViMwqeNwBTMFhK6qMMkaoaE+oJRlEkTbQnExssQgcFwpoLTgf3/kPH8Q0Of2snjBwcfh4TE7fO+H1/n+eX0bZ2+ru9///vp6urK008//aHYB//KN0kGBwd/YXw4sQ9qaWlJS0tL4bq+vj7Lly8f2uJ/id37D+Zrr4/4rwqGrefp0uRXap2dnSWr/Z1OrYZHvYZHvYZOrQCAYrzvlt9ofN/1M+PwqNfwqNfQqdXwlKteI35c4I0BqyTp6+tLZeUv/5a2lAQAAGC0OnLkSH784x9nxowZmT59et5+++0sX748x44dS5JcuHChcO+ZM2fS0NCQJGloaEhPT8+wYwAAAFAO3ncBoLgRH7JKksceeyxTp07Nn//5n2f37t2Fr7e2tuYzn/lM1q1bl7feeqvw9eFuKXkjBgAAACNt69atOXv2bHp6etLT05P6+vp0dnbmwQcfzJo1a9Le3p4k6erqyrlz57J48eIkuSnW3d2dI0eOZNWqVYXYrl27cv369Vy6dCkdHR1Zt27d7UkQAACAMcn7LgAUV5Y9EZ977rkkye7du9Pa2prvfOc72bNnT6ZOnZrBwcG0t7fnoYceyokTJwrPlGJLybF2bu9oPY/TWaHDo17Do15Dp1bDo14AwEexbdu2NDc3Z9asWRk3blz27NmT6ur3X71bW1uzYcOGNDY2prKyMu3t7ampqUmSNDc3p6urK01NTYV758yZc9vyAAAAgA/yvgsAZRqyuuHxxx/Ppk2bcvHixUydOjXJ+wNTX/7yl/PVr341Fy9ezJQpUwrbRtbW1iZ5f9vIlStXJvn/W0ouWLCgEPtFW0qOtXN7R+OZvYmzQodLvYZHvYZOrYZHvQCAofrgsQd1dXU5fPjwLe+bOHFiOjo6bhmrqqoq/NUvAAAAjAbedwHgZiN6XOC7776bs2fPFq5ffPHFTJkyJXfffXfeeeedwtdfeOGF1NXVZcqUKUlsKQkAAAAAAAAAAIweI7oVU19fXx599NFcvXo1lZWVqa2tzcsvv5yf/exn+fznP5/33nsvlZWVueeee/Ltb3+78JwtJQEAAAAAAAAAgNFiRIespk6dmmPHjt0ydvz48V/4nC0lAQAAAAAAAACA0WJEjwsEAAAAAAAAAAD4pDNkBQAAAAAAAAAAUIQhKwAAAAAAAAAAgCIMWQEAAAAAAAAAABRhyAoAAAAAAAAAAKAIQ1YAAAAAAAAAAABFGLICAAAAAAAAAAAowpAVAAAAAAAAAABAEYasAAAAAAAAAAAAijBkBQAAAAAAAAAAUIQhKwAAAAAAAAAAgCIMWQEAAAAAAAAAABRhyAoAAAAAAAAAAKAIQ1YAAAAAAAAAAABFGLICAAAAAAAAAAAowpAVAAAAAAAAAABAEYasAAAAAAAAAAAAijBkBQAAAAAAAAAAUIQhKwAAAAAAAAAAgCIMWQEAAAAAAAAAABRhyAoAAAAAAAAAAKAIQ1YAAAAAAAAAAABFGLICAAAAAAAAAAAowpAVAAAAAAAAAABAEYasAAAAAAAAAAAAijBkBQAAAAAAAAAAUIQhKwAAAAAAAAAAgCIMWQEAAAAAAAAAuF7CRgAAIABJREFUABRhyAoAAAAAAAAAAKAIQ1YAAAAAAAAAAABFGLICAAAAAAAAAAAowpAVAAAAAAAAAABAEYasAAAAAAAAAAAAijBkBQAAAAAAAAAAUIQhKwAAAAAAAAAAgCIMWQEAAAAAAAAAABRhyAoAAAAAAAAAAKAIQ1YAAAAAAAAAAABFGLICAAAAAAAAAAAowpAVAAAAAAAAAABAEYasAAAAAAAAAAAAijBkBQAAAAAAAAAAUIQhKwAAAAAAAAAAgCIMWQEAAAAAAAAAABRhyAoAAAAAAAAAAKAIQ1YAAAAAAAAAAABFGLICAAAAAAAAAAAowpAVANxBHnjggdx3332ZN29e7r///rzxxhtJkvPnz2fFihWZNWtW5s6dm6NHjxaeuXLlStavX5/GxsY0NTXlwIEDhdjAwEC2bNmSmTNnprGxMTt37ix7TgAAAAAAAAC3W/XtXgAAUDr79+/PpEmTkiQvvfRSNmzYkNdffz1bt27NwoULc+jQoXR1dWX16tU5ffp0qqurs3379owfPz6nTp1Kd3d3Fi1alKVLl2by5MnZu3dvTpw4kZMnT6avry/z58/PsmXLMnv27NucKQAAAAAAAED52MkKAO4gNwaskqSvry+Vle+3+v3792fz5s1JkgULFqSurq6wm1VHR0chNmPGjCxZsiQHDx4sxDZt2pSqqqrU1NRk7dq12bdvXzlTAgAAAAAAALjt7GQFAHeYxx57LK+99lqS5NChQ7l48WIGBgZSW1tbuGf69Onp7e1NkvT29mbatGlDjh0/frwcaQAAAAAAAACMGoasAOAO89xzzyVJdu/endbW1uzZsycVFRU33TM4OHjT9Qfjw4l9UFtbW9ra2grXly9fTmdn5/AT+Dl3j0u+Mf/ax/6cUitFbiOhv79/1K5tNFKvoVOr4VGv4VEvAAAAAABGO0NWAHCHevzxx7Np06bC9YULFwq7WZ05cyYNDQ1JkoaGhvT09NwUW7ly5U2xBQsWfOi5n9fS0pKWlpbCdX19fZYvX/6x89i9/2C+9vro+5Gl5+mPn9tI6OzsLEndxwr1Gjq1Gh71Gh71AgAAAABgtKu83QsAAErj3XffzdmzZwvXL774YqZMmZKampqsWbMm7e3tSZKurq6cO3cuixcvTpKbYt3d3Tly5EhWrVpViO3atSvXr1/PpUuX0tHRkXXr1pU5MwAAAAAAAIDba/RtCwEAfCR9fX159NFHc/Xq1VRWVqa2tjYvv/xyKioqsm3btjQ3N2fWrFkZN25c9uzZk+rq938MaG1tzYYNG9LY2JjKysq0t7enpqYmSdLc3Jyurq40NTUV7p0zZ85tyxEAAAAAAADgdjBkBQB3iKlTp+bYsWO3jNXV1eXw4cO3jE2cODEdHR23jFVVVRV2uQIAAAAAAAAYqxwXCAAAAAAAAAAAUIQhKwAAAAAAAAAAgCJGfMjqgQceyH333Zd58+bl/vvvzxtvvJEkOX/+fFasWJFZs2Zl7ty5OXr0aOGZK1euZP369WlsbExTU1MOHDhQiA0MDGTLli2ZOXNmGhsbs3PnzpFOAQAAAAAAAAAAGMOqR/ob7N+/P5MmTUqSvPTSS9mwYUNef/31bN26NQsXLsyhQ4fS1dWV1atX5/Tp06murs727dszfvz4nDp1Kt3d3Vm0aFGWLl2ayZMnZ+/evTlx4kROnjyZvr6+zJ8/P8uWLcvs2bNHOhUAAAAAAAAAAGAMGvGdrG4MWCVJX19fKivf/5b79+/P5s2bkyQLFixIXV1dYTerjo6OQmzGjBlZsmRJDh48WIht2rQpVVVVqampydq1a7Nv376RTgMAAAAAAAAAABijRnwnqyR57LHH8tprryVJDh06lIsXL2ZgYCC1tbWFe6ZPn57e3t4kSW9vb6ZNmzbk2PHjx2/5fdva2tLW1la4vnz5cjo7O0uS093jkm/Mv1aSzyqVUuVWav39/aN2baOReg2Peg2dWg2PegEAAAAAAABwQ1mGrJ577rkkye7du9Pa2po9e/akoqLipnsGBwdvuv5gfDixD2ppaUlLS0vhur6+PsuXLx9+Arewe//BfO31spRvyHqeLk1updbZ2Vmyuo8F6jU86jV0ajU86gUAAAAAAADADSN+XOAHPf7444UdrZLkwoULhf+fOXMmDQ0NSZKGhob09PQMOwYAAAAAAAAAAFBqIzpk9e677+bs2bOF6xdffDFTpkxJTU1N1qxZk/b29iRJV1dXzp07l8WLFyfJTbHu7u4cOXIkq1atKsR27dqV69ev59KlS+no6Mi6detGMg0AAAAAAAAAAGAMG9Hz7vr6+vLoo4/m6tWrqaysTG1tbV5++eVUVFRk27ZtaW5uzqxZszJu3Ljs2bMn1dXvL6e1tTUbNmxIY2NjKisr097enpqamiRJc3Nzurq60tTUVLh3zpw5I5kGAAAAAAAAAAAwho3okNXUqVNz7NixW8bq6upy+PDhW8YmTpyYjo6OW8aqqqoKu1wBAAAAAAAAAACMtBE9LhAAAAAAAAAAAOCTzpAVAAAAAAAAAABAEYasAAAAAAAAAAAAijBkBQAAAAAAAAAAUIQhKwAAAAAAAAAAgCIMWQEAAAAAAAAAABRhyAoAAAAAAAAAAKAIQ1YAAAAAAAAAAABFGLICAAAAAAAAAAAowpAVAAAAAAAAAABAEYasAAAAAAAAAAAAijBkBQAAAAAAAAAAUIQhKwAAAAAAAAAAgCIMWQEAAAAAAAAAABRhyAoAAAAAAAAAAKAIQ1YAAAAAAAAAAABFGLICAAAAAAAAAAAowpAVAAAAAAAAAABAEYasAAAAAAAAAAAAijBkBQAAAAAAAAAAUIQhKwAAAAAAAAAAgCIMWQEAAAAAAAAAABRhyAoAAAAAAAAAAKAIQ1YAAAAAAAAAAABFGLICAAAAAAAAAAAowpAVAAAAAAAAAABAEYasAAAAAAAAAAAAijBkBQAAAAAAAAAAUIQhKwAAAAAAAAAAgCIMWQEAAAAAMKo98MADue+++zJv3rzcf//9eeONN5Ik58+fz4oVKzJr1qzMnTs3R48eLTxz5cqVrF+/Po2NjWlqasqBAwcKsYGBgWzZsiUzZ85MY2Njdu7cWfacAAAA+GSpvt0LAAAAAACAYvbv359JkyYlSV566aVs2LAhr7/+erZu3ZqFCxfm0KFD6erqyurVq3P69OlUV1dn+/btGT9+fE6dOpXu7u4sWrQoS5cuzeTJk7N3796cOHEiJ0+eTF9fX+bPn59ly5Zl9uzZtzlTAAAARis7WQEAAAAAMKrdGLBKkr6+vlRWvv+r7f3792fz5s1JkgULFqSurq6wm1VHR0chNmPGjCxZsiQHDx4sxDZt2pSqqqrU1NRk7dq12bdvXzlTAgAA4BPGTlYAAAAAAIx6jz32WF577bUkyaFDh3Lx4sUMDAyktra2cM/06dPT29ubJOnt7c20adOGHDt+/Hg50gAAAOATypAVAAAAAACj3nPPPZck2b17d1pbW7Nnz55UVFTcdM/g4OBN1x+MDyd2Q1tbW9ra2grXly9fTmdn50dL4OfcPS75xvxrJfmsUipVfqXW398/atc22qjV8KjX0KnV8KgXANx5DFkBAAAAAPCJ8fjjj2fTpk2F6wsXLhR2szpz5kwaGhqSJA0NDenp6bkptnLlyptiCxYs+NBzH9TS0pKWlpbCdX19fZYvX16SPHbvP5ivvT76fkXf83Rp8iu1zs7OktX+TqdWw6NeQ6dWw6NeAHDnqbzdCwAAAAAAgF/k3XffzdmzZwvXL774YqZMmZKampqsWbMm7e3tSZKurq6cO3cuixcvTpKbYt3d3Tly5EhWrVpViO3atSvXr1/PpUuX0tHRkXXr1pU5MwAAAD5JRt+fyQAAAAAAwP/p6+vLo48+mqtXr6aysjK1tbV5+eWXU1FRkW3btqW5uTmzZs3KuHHjsmfPnlRXv/9r79bW1mzYsCGNjY2prKxMe3t7ampqkiTNzc3p6upKU1NT4d45c+bcthwBAAAY/QxZAQAAAAAwak2dOjXHjh27Zayuri6HDx++ZWzixInp6Oi4ZayqqqqwyxUAAAAMheMCAQAA4CN44IEHct9992XevHm5//7788YbbyRJzp8/nxUrVmTWrFmZO3dujh49WnjmypUrWb9+fRobG9PU1JQDBw4UYgMDA9myZUtmzpyZxsbG7Ny5s+w5AQAAMLZ51wWAX8xOVgAAAPAR7N+/P5MmTUqSvPTSS9mwYUNef/31bN26NQsXLsyhQ4fS1dWV1atX5/Tp06murs727dszfvz4nDp1Kt3d3Vm0aFGWLl2ayZMnZ+/evTlx4kROnjyZvr6+zJ8/P8uWLcvs2bNvc6YAAACMFd51AeAXs5MVAAAAfAQ3fumcJH19famsfP8Ve//+/dm8eXOSZMGCBamrqyv8hW9HR0chNmPGjCxZsiQHDx4sxDZt2pSqqqrU1NRk7dq12bdvXzlTAgAAYIzzrgsAv5idrAAAAOAjeuyxx/Laa68lSQ4dOpSLFy9mYGAgtbW1hXumT5+e3t7eJElvb2+mTZs25Njx48c/9D3b2trS1tZWuL58+XI6OztLks/d45JvzL9Wks8qpVLlV2r9/f2jdm2jjVoNj3oNj3oNnVoBAENxO951E++7o4WfGYdHvYZHvYZOrYanXPUyZAUAAAAf0XPPPZck2b17d1pbW7Nnz55UVFTcdM/g4OBN1x+MDyd2Q0tLS1paWgrX9fX1Wb58+UdL4Ofs3n8wX3t99P2qoOfp0uRXap2dnSWr/Z1OrYZHvYZHvYZOrQCAobgd77qJ993Rws+Mw6New6NeQ6dWw1OuejkuEAAAAD6mxx9/vPBXvkly4cKFwv/PnDmThoaGJElDQ0N6enqGHQMAAIBy864LADczZAUAAADD9O677+bs2bOF6xdffDFTpkxJTU1N1qxZk/b29iRJV1dXzp07l8WLFyfJTbHu7u4cOXIkq1atKsR27dqV69ev59KlS+no6Mi6devKnBkAAABjlXddAChu9O2JCAAAAKNcX19fHn300Vy9ejWVlZWpra3Nyy+/nIqKimzbti3Nzc2ZNWtWxo0blz179qS6+v3X79bW1mzYsCGNjY2prKxMe3t7ampqkiTNzc3p6upKU1NT4d45c+bcthwBAAAYW7zrAkBxhqwAAABgmKZOnZpjx47dMlZXV5fDhw/fMjZx4sR0dHTcMlZVVVX4y18AAAAoN++6AFCc4wIBAAAAAAAAAACKMGQFAHeI/v7+PPLII2lqasq8efOyYsWK9PT0JEk+97nP5d577828efMyb968fPOb3yw8d+XKlaxfvz6NjY1pamrKgQMHCrGBgYFs2bIlM2fOTGNjY3bu3FnutAAAAAAAAABuO8cFAsAdZOPGjXnwwQdTUVGRZ555Jhs3bixs4bxjx4489NBDH3pm+/btGT9+fE6dOpXu7u4sWrQoS5cuzeTJk7N3796cOHEiJ0+eTF9fX+bPn59ly5Zl9uzZ5U4NAAAAAAAA4LaxkxUA3CHuuuuurFy5MhUVFUmShQsX5q233vqlz3V0dGTz5s1JkhkzZmTJkiU5ePBgIbZp06ZUVVWlpqYma9euzb59+0YuCQAAAAAAAIBRyJAVANyhduzYkYcffrhw3drams985jNZt27dTcNXvb29mTZtWuF6+vTp6e3t/aUxAAAAAAAAgLHCcYEAcAd66qmn8uabb+bZZ59NkuzZsydTp07N4OBg2tvb89BDD+XEiROF+2/sfpUkg4ODN31WsdgHtbW1pa2trXB9+fLldHZ2fuxc7h6XfGP+tY/9OaVWitxGQn9//6hd22ikXkOnVsOjXsOjXgAAAAAAjHaGrADgDrN9+/YcOHAgr776aiZMmJAkmTp1apL3B6a+/OUv56tf/WouXryYKVOmpKGhIT09PamtrU2SnDlzJitXrkySQmzBggWFWENDwy2/b0tLS1paWgrX9fX1Wb58+cfOZ/f+g/na66PvR5aepz9+biOhs7OzJHUfK9Rr6NRqeNRreNQLAAAAAIDRznGBAHAHaWtry/PPP59XXnklkyZNSpJcu3Yt77zzTuGeF154IXV1dZkyZUqSZM2aNWlvb0+SdHd358iRI1m1alUhtmvXrly/fj2XLl1KR0dH1q1bV+asAAAAAAAAAG6v0bctBADwkbz99tt54okncu+992bp0qVJkvHjx+e73/1uPv/5z+e9995LZWVl7rnnnnz7298uPNfa2poNGzaksbExlZWVaW9vT01NTZKkubk5XV1daWpqKtw7Z86c8icHAAAAAAAAcBsZsgKAO0R9fX0GBwdvGTt+/PgvfG7ixInp6Oi4ZayqqqqwyxUAAAAAAADAWDWixwX29/fnkUceSVNTU+bNm5cVK1akp6cnSfK5z30u9957b+bNm5d58+blm9/8ZuG5K1euZP369WlsbExTU1MOHDhQiA0MDGTLli2ZOXNmGhsbs3PnzpFMAQAAAAAAAAAAGONGfCerjRs35sEHH0xFRUWeeeaZbNy4MYcPH06S7NixIw899NCHntm+fXvGjx+fU6dOpbu7O4sWLcrSpUszefLk7N27NydOnMjJkyfT19eX+fPnZ9myZZk9e/ZIpwIAAAAAAAAAAIxBI7qT1V133ZWVK1emoqIiSbJw4cK89dZbv/S5jo6ObN68OUkyY8aMLFmyJAcPHizENm3alKqqqtTU1GTt2rXZt2/fyCUBAAAAAAAAAACMaSO+k9UH7dixIw8//HDhurW1NX/6p3+a3/iN38hf//Vf5957702S9Pb2Ztq0aYX7pk+fnt7e3l8YO378+C2/X1tbW9ra2grXly9fTmdnZ0lyuXtc8o3510ryWaVSqtxKrb+/f9SubTRSr+FRr6FTq+FRLwAAAAAAAABuKNuQ1VNPPZU333wzzz77bJJkz549mTp1agYHB9Pe3p6HHnooJ06cKNx/Y/erJBkcHLzps4rFPqilpSUtLS2F6/r6+ixfvvxj55Iku/cfzNdeL+uM2i/V83Rpciu1zs7OktV9LFCv4VGvoVOr4VEvAAAAAAAAAG4Y0eMCb9i+fXsOHDiQf/mXf8mECROSJFOnTk3y/sDUl7/85bz11lu5ePFikqShoSE9PT2F58+cOZOGhoZfGgMAAAAAAAAAACi1ER+yamtry/PPP59XXnklkyZNSpJcu3Yt77zzTuGeF154IXV1dZkyZUqSZM2aNWlvb0+SdHd358iRI1m1alUhtmvXrly/fj2XLl1KR0dH1q1bN9JpAAAAAAAAAAAAY9SInnf39ttv54knnsi9996bpUuXJknGjx+f7373u/n85z+f9957L5WVlbnnnnvy7W9/u/Bca2trNmzYkMbGxlRWVqa9vT01NTVJkubm5nR1daWpqalw75w5c0YyDQAAAAAAAAAAYAwb0SGr+vr6DA4O3jJ2/PjxX/jcxIkT09HRcctYVVVVYZcrAAAAAAAAAACAkTbixwUCAAAAAAAAAAB8khmyAgAAAAAAAAAAKMKQFQAAAAAAAAAAQBGGrAAAAAAAAAAAAIowZAUAAAAAAAAAAFCEISsAAAAAAAAAAIAiDFkBAAAAAAAAAAAUYcgKAAAAAAAAAACgCENWAAAAAAAAAAAARRiyAgAAAAAAAAAAKMKQFQAAAAAAAAAAQBGGrAAAAAAAAAAAAIowZAUAAAAAAAAAAFCEISsAAAAAAAAAAIAiDFkBAAAAAAAAAAAUMeQhq3/+53/Ou+++myTZvn17Vq9enX//938fsYUBwFil5wJA+ei7AFA++i4AlI++CwClN+Qhqz/7sz/L3XffnX/7t3/L3r178zu/8zv5wz/8w5FcGwCMSXouAJSPvgsA5aPvAkD56LsAUHpDHrKqrq5Okhw+fDgbN27MH/zBH+SnP/3piC0MAMYqPRcAykffBYDy0XcBoHz0XQAovSEPWV2/fj0/+MEP8sILL2Tp0qVJkv/93/8dsYUBwFil5wJA+ei7AFA++i4AlI++CwClN+Qhq7/6q7/Kpk2bsnjx4syZMyc/+clPMmvWrJFcGwCMSXouAJSPvgsA5aPvAkD56LsAUHrVQ71x2rRpeeONNwrXn/70p/P1r399JNYEAGOangsA5aPvAkD56LsAUD76LgCU3pB3svriF784pK8BAB+PngsA5aPvAkD56LsAUD76LgCU3i/dyeq//uu/cv78+fT39+dHP/pRBgcHkyR9fX356U9/OuILBICxQs8FgPLRdwGgfPRdACgffRcARs4vHbL6h3/4h3zrW9/K2bNns3LlysLXf+3Xfi1/8id/MqKLA4CxRM8FgPLRdwGgfPRdACgffRcARs4vHbL6yle+kq985Sv5y7/8yzz55JPlWBMAjEl6LgCUj74LAOWj7wJA+ei7ADByfumQ1Q1PPvlkBgYGcu7cuVy7dq3w9YaGhhFZGACMVXouAJSPvgsA5aPvAkD56LsAUHpDHrLavXt3tmzZkl/5lV9JZWVlkqSioiLnz58fscUBwFik5wJA+ei7AFA++i4AlI++CwClN+Qhq7/4i7/IsWPHMnv27JFcDwCMeXouAJSPvgsA5aPvAkD56LsAUHqVQ72xtrZWEwaAMtBzAaB89F0AKB99FwDKR98FgNIb8pDVF77whTzzzDO5dOlSrly5UvgHAJSWngsA5aPvAkD56LsAUD76LgCU3pCPC9y6dWuS5I/+6I9SUVGRwcHBVFRU5Pr16yO2OAAYi/RcACgffRcAykffBYDy0XcBoPSGPGQ1MDAwkusAAP6PngsA5aPvAkD56LsAUD76LgCU3pCPCwQAAAAAAAAAABiLhryTVWVlZSoqKj70dVtKAkBp6bkAUD76LgCUj74LAOWj7wJA6Q15yOp//ud/Cv+/evVqnnvuufzsZz8bkUUBwFim5wJA+ei7AFA++i4AlI++CwClN+TjAidOnFj4d88996SlpSWHDh0aybUBwJik5wJA+ei7AFA++i4AlI++CwClN+Qhq5/35ptv5j/+4z9KuRYA4Bb0XAAoH30XAMpH3wWA8tF3AeDjG/JxgbW1tYVze69du5br169nx44dI7YwABir9FwAKB99FwDKR98FgPLRdwGg9IY8ZHX8+PH//1B1dT71qU+lqqpqRBYFAGOZngsA5aPvAkD56LsAUD76LgCU3pCPC5w2bVp+/dd/PefOnct//ud/5mc/+9lIrgsAxiw9FwDKR98FgPLRdwGgfPRdACi9Ie9k9b3vfS+rV69OXV1dBgcHc+HChfzTP/1TFi1aNJLrA4AxR88FgPLRdwGgfPRdACgffRcASm/IQ1YtLS35x3/8x/z2b/92kvcb8x//8R/nBz/4wYgtDgDGIj0XAMpH3wWA8tF3AaB89F0AKL0hHxfY399faMJJ8tnPfjb9/f0jsigAGMv0XAAoH30XAMpH3wWA8tF3AaD0hjxkNWHChLz66quF63/913/NhAkTRmRRADCW6bkAUD76LgCUj74LAOWj7wJA6Q35uMC//du/zRe+8IWMHz8+FRUVee+99/LCCy+M5NoAYEzScwGgfPRdACgffRcAykffBYDSG/KQ1dmzZ3P8+PG88847GRwczKc+9an88Ic/HMm1AcCYpOcCQPnouwBQPvouAJSPvgsApTfk4wKffPLJ1NbWZu7cufnMZz6Te+65J08++eRIrg0AxiQ9FwDKR98FgPLRdwGgfPRdACi9IQ9Z/byKiooMDAyUci0AwC3ouQBQPvouAJSPvgsA5aPvAsDHN+Qhq7vvvvumLSR/8IMf5Fd/9VdHZFEAMJbpuQBQPvouAJSPvgsA5aPvAkDpVQ/1xm3btuWRRx7Jb/7mbyZJfvSjH+XFF18csYUBwFil5wJA+ei7AFA++i4AlI++CwClN+Qhq0WLFuXEiRP5/ve/nyT57Gc/m0mTJo3YwgBgrPqoPbe/vz+/+7u/mxMnTmTChAn51Kc+lWeffTbTp0/P+fPn89hjj+X06dMZP358nn322SxevDhJcuXKlfz+7/9+urq6UllZmaeffjpf+MIXkiQDAwP5yle+ku985zupqKhIS0tLvvSlL41c8gBQZt51AaB89F0AKB99FwBKb8hDVkkyefLkrFy5cqTWAgD8n4/aczdu3JgHH3wwFRUVeeaZZ7Jx48YcPnw4W7duzcKFC3Po0KF0dXVl9erVOX36dKqrq7N9+/aMHz8+p06dSnd3dxYtWpSlS5dm8uTJ2bt3b06cOJGTJ0+mr68v8+fPz7JlyzJ79uwRyBoAbg/vugBQPvouAJSPvgsApVV5uxcAAJTGXXfdlZUrV6aioiJJsnDhwrz11ltJkv3792fz5s1JkgULFqSuri5Hjx5NknR0dBRiM2bMyJIlS3Lw4MFCbNOmTamqqkpNTU3Wrl2bffv2lTs1AAAAAAAAgNvKkBUA3KF27NiRhx9+OBcvXszAwEBqa2sLsenTp6e3tzdJ0tvbm2nTpg07BgAAAAAAADBWDOu4QADgk+Gpp57Km2++mWeffTZXr14t7G51w+Dg4E3XH4wPJ/ZBbW1taWtrK1xfvnw5nZ2dH2n9H3T3uOQb86997M8ptVLkNhL6+/tH7dpGI/UaOrUaHvUaHvUCAAAAAGC0M2QFAHeY7du358CBA3n11VczYcKETJgwIUly4cKFwm5WZ86cSUNDQ5KkoaEhPT09N8VWrlx5U2zBggUfeu7ntbS0pKWlpXBdX1+f5cuXf+x8du8/mK+9Pvp+ZOl5+uPnNhI6OztLUvexQr2GTq2GR72GR70AAAAAABjtHBcIAHeQtra2PP/883nllVcyadKkwtfXrFmT9vb2JElXV1fOnTuXxYsXfyjW3d2dI0eOZNWqVYXYrl27cv369Vy6dCkdHR1Zt25dmbMCAAAAAAAAuL1GdMiqv78/jzzySJqamjJv3rysWLEiPT09SZLz589FR3wsAAAgAElEQVRnxYoVmTVrVubOnZujR48Wnrty5UrWr1+fxsbGNDU15cCBA4XYwMBAtmzZkpkzZ6axsTE7d+4cyRQA4BPj7bffzhNPPJHLly9n6dKlmTdvXn7rt34rSbJt27Z873vfy6xZs/LFL34xe/bsSXX1+7tDtba25urVq2lsbMzy5cvT3t6empqaJElzc3M+/elPp6mpKQsWLEhra2vmzJlz23IEAAAAAAAAuB1G/OydjRs35sEHH0xFRUWeeeaZbNy4MYcPH87WrVuzcOHCHDp0KF1dXVm9enVOnz6d6urqbN++PePHj8+pU6fS3d2dRYsWZenSpZk8eXL27t2bEydO5OTJk+nr68v8+fOzbNmyzJ49e6RTAYBRrb6+PoODg7eM1dXV5fDhw7eMTZw4MR0dHbeMVVVVFXa5AgAAAAAAABirRnQnq7vuuisrV65MRUVFkmThwoV56623kiT79+/P5s2bkyQLFixIXV1dYTerjo6OQmzGjBlZsmRJDh48WIht2rQpVVVVqampydq1a7Nv376RTAMAAAAAAAAAABjDRnTI6uft2LEjDz/8cC5evJiBgYHU1tYWYtOnT09vb2+SpLe3N9OmTRt2DAAAAAAAAAAAoNRG/LjAG5566qm8+eabefbZZ3P16tXC7lY3/PzxRh+MDyf2QW1tbWlraytcX758OZ2dnR9p/T/v7nHJN+ZfK8lnlUqpciu1/v7+Ubu20Ui9hke9hk6thke9AAAAAAAAALihLENW27dvz4EDB/Lqq69mwoQJmTBhQpLkwoULhd2szpw5k4aGhiRJQ0NDenp6boqtXLnyptiCBQs+9NzPa2lpSUtLS+G6vr4+y5cvL0lOu/cfzNdeL9uM2pD0PF2a3Eqts7OzZHUfC9RreNRr6NRqeNQLAAAAAAAAgBtG/LjAtra2PP/883nllVcyadKkwtfXrFmT9vb2JElXV1fOnTuXxYsXfyjW3d2dI0eOZNWqVYXYrl27cv369Vy6dCkdHR1Zt27dSKcBAAAAAAAAAACMUSO6FdPbb7+dJ554Ivfee2+WLl2aJBk/fnx++MMfZtu2bWlubs6sWbMybty47NmzJ9XV7y+ntbU1GzZsSGNjYyorK9Pe3p6ampokSXNzc7q6utLU1FS4d86cOSOZBgAAAAAAAAAAMIaN6JBVfX19BgcHbxmrq6vL4cOHbxmbOHFiOjo6bhmrqqoq7HIFAAAAAAAAAAAw0kb8uEAAAAAAAAAAAIBPMkNWAAAAAAAAAAAARRiyAgAAAAAAAAAAKMKQFQAAAAAAAAAAQBGGrAAAAAAAAAAAAIowZAUAAAAAAAAAAFCEISsAAAAAAAAAAIAiDFkBAAAAAAAAAAAUYcgKAAAAAAAAAACgCENWAAAAAAAAAAAARRiyAgAAAAAAAAAAKMKQFQAAAAAAAAAAQBGGrAAAAAAAAAAAAIowZAUAAAAAAAAAAFCEISsAAAAAAAAAAIAiDFkBAAAAAAAAAAAUYcgKAAAAAAAAAACgCENWAAAAAACMWv39/XnkkUfS1NSUefPmZcWKFenp6UmSnD9/PitWrMisWbMyd+7cHD16tPDclStXsn79+jQ2NqapqSkHDhwoxAYGBrJly5bMnDkzjY2N2blzZ7nTAgAA4BPGkBUAAAAAAKPaxo0b85Of/CRvvPFGHnrooWzcuDFJsnXr1ixcuDBvvvlm/u7v/i6/93u/l2vXriVJtm/fnvHjx+fUqVPp7OzMl770pfz3f/93kmTv3r05ceJETp48mWPHjuVv/uZv8uMf//i25QcAAMDoZ8gKAAAAAIBR66677srKlStTUVGRJFm4cGHeeuutJMn+/fuzefPmJMmCBQtSV1dX2M2qo6OjEJsxY0aWLFmSgwcPFmKbNm1KVVVVampqsnbt2uzbt6/cqQEAAPAJYsgKAAAAAIBPjB07duThhx/OxYsXMzAwkNra2kJs+vTp6e3tTZL09vZm2rRpw44BAADArVTf7gUAAAAAAMBQPPXUU3nzzTfz7LPP5urVq4XdrW4YHBy86fqD8eHEbmhra0tbW1vh+vLly+ns7PzI6/+gu8cl35h/rSSfVUqlyq/U+vv7R+3aRhu1Gh71Gjq1Gh71AoA7jyErAAAAAABGve3bt+fAgQN59dVXM2HChEyYMCFJcuHChcJuVmfOnElDQ0OSpKGhIT09PTfFVq5ceVNswYIFH3rug1paWtLS0lK4rq+vz/Lly0uSz+79B/O110ffr+h7ni5NfqXW2dlZstrf6dRqeNRr6NRqeNQLAO48jgsEAACAYerv788jjzySpqamzJs3LytWrEhPT0+S5Pz581mxYkVmzZqVuXPn5ujRo4Xnrly5kvXr16exsTFNTU05cOBAITYwMJAtW7Zk5syZaWxszM6dO8udFgCMWm1tbXn++efzyiuvZNKkSYWvr1mzJu3t7UmSrq6unDt3LosXL/5QrLu7O0eOHMmqVasKsV27duX69eu5dOlSOjo6sm7dujJnBQCji3ddAChu9P2ZDAAAAHwCbNy4MQ8++GAqKiryzDPPZOPGjTl8+HC2bt2ahQsX5tChQ+nq6srq1atz+vTpVFdXZ/v27Rk/fnxOnTqV7u7uLFq0KEuXLs3kyZOzd+/enDhxIidPnkxfX1/mz5+fZcuWZfbs2bc7VQC4rd5+++088cQTuffee7N06dIkyfjx4/PDH/4w27ZtS3Nzc2bNmpVx48Zlz549qa5+/9fera2t2bBhQxobG1NZWZn29vbU1NQkSZqbm9PV1ZWmpqbCvXPmzLk9CQLAKOJdFwB+MUNWAAAAMEx33XVX4bihJFm4cGG+9a1vJUn279+f7u7uJMmCBQtSV1eXo0eP5nOf+1w6Ojry93//90mSGTNmZMmSJTl48GC++MUvpqOjI5s2bUpVVVVqamqydu3a7Nu3L1//+tfLnR4AjCr19fUZHBy8Zayuri6HDx++ZWzixInp6Oi4ZayqqqqwyxUA8D7vuvD/2Lv/WKvr+37gz3thkA3XVNBiusvlghcqjraUhFZaxoSRYK2S1YLUWFqHaUfEH8tdcbTrVk2WVhtym5ldI+s6dbiV6ywppj+8ru3Gxrq019i6WtIK9sLVdShisZ2IEe79/uHXUxH49PORe+85Fx6PZEnPeZ97eH+e+7SvezhPPh+AYkpWAAAAcJJuu+22XHrppdm/f38GBgZy9tln19ba2trS39+fJOnv78/UqVNLrz300EPH/FmdnZ3p7OysPT5w4EB6enqG5DjeMC65ee7hIXmvoTRUxzfUDh061LB7azSyqkZe1cirPFkBAFWM5GfdxOfdRuF3xmrkVY28ypNVNSOVl5IVAAAAnITPfOYz2blzZ+6444688MILaWpqOmr9tVfeePV6lbVXdHR0pKOjo/a4paUlS5cufd37f7W7792aTz/ceH9VsPuWoTm+odbT0zNk2Z/qZFWNvKqRV3myAgDKGunPuonPu43C74zVyKsaeZUnq2pGKq/mYf8TAAAA4BS1YcOGbNmyJd/4xjfyW7/1W5k0aVKSZN++fbXX7NmzJ62trUmS1tbW7N69u/IaAAAAjBSfdQHg+JSsAAAA4HXo7OzMl770pfzLv/xL3vjGN9aeX7FiRbq6upIkvb292bt3bxYsWHDMWl9fX7Zt25Zly5bV1jZu3JgjR47k2WefTXd3d1auXDnCRwUAAMDpzGddADixxrsmIgAAADS4J598Mn/6p3+a6dOnZ9GiRUmS8ePH57vf/W5uvfXWrFq1KjNmzMi4ceOyadOmjB378sfvdevWZfXq1Wlvb09zc3O6uroyceLEJMmqVavS29ubmTNn1l47a9as+hwgAAAApx2fdQGgmJIVAAAAVNTS0pLBwcHjrk2ePDkPPvjgcdcmTJiQ7u7u466NGTOm9i9/AQAAYKT5rAsAxdwuEAAAAAAAAAAAoICSFQAAAAAAAAAAQAElKwAAAAAAAAAAgAJKVgAAAAAAAAAAAAWUrAAAAAAAAAAAAAooWQEAAAAAAAAAABRQsgIAAAAAAAAAACigZAUAAAAAAAAAAFBAyQoAAAAAAAAAAKCAkhUAAAAAAAAAAEABJSsAAAAAAAAAAIACSlYAAAAAAAAAAAAFlKwAAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAOAUcf3116etrS1NTU159NFHa89feOGFmT59eubMmZM5c+bk85//fG3t4MGDueKKK9Le3p6ZM2dmy5YttbWBgYFcd911Offcc9Pe3p7bb799RI8HAAAAAAAAoFGMrfcGAIChsXz58tx4441ZsGDBMWu33XZbLrnkkmOe37BhQ8aPH59du3alr68v8+fPz6JFi3LmmWfmnnvuyY4dO/LYY4/lueeey9y5c7N48eKcd955I3E4AAAAAAAAAA3DlawA4BSxcOHCtLS0VPqZ7u7urF27Nkkybdq0LFy4MFu3bq2trVmzJmPGjMnEiRNz+eWXZ/PmzUO+bwAAAAAAAIBGp2QFAKeBdevW5a1vfWtWrlyZn/70p7Xn+/v7M3Xq1Nrjtra29Pf3/9o1AAAAAAAAgNOJ2wUCwClu06ZNmTJlSgYHB9PV1ZVLLrkkO3bsqK03NTXV/vPg4OBRP1u09lqdnZ3p7OysPT5w4EB6enpOdvt5w7jk5rmHT/p9htpQHNtwOHToUMPurRHJqzxZVSOvauQFAAAAAECjU7ICgFPclClTkrxcmLr22mvz8Y9/PPv378+kSZPS2tqa3bt35+yzz06S7NmzJxdffHGS1NbmzZtXW2ttbT3hn9PR0ZGOjo7a45aWlixduvSk93/3vVvz6Ycb71eW3bec/LENh56eniHJ/XQhr/JkVY28qpEXAAAAAACNbthvF3j99denra0tTU1NefTRR2vPX3jhhZk+fXrmzJmTOXPm5POf/3xt7eDBg7niiivS3t6emTNnZsuWLbW1gYGBXHfddTn33HPT3t6e22+/fbgPAQBGrcOHD+epp56qPf7yl7+cyZMnZ9KkSUmSFStWpKurK0nS19eXbdu2ZdmyZbW1jRs35siRI3n22WfT3d2dlStXjvxBAAAAAAAAANTZsF8WYvny5bnxxhuzYMGCY9Zuu+22XHLJJcc8v2HDhowfPz67du1KX19f5s+fn0WLFuXMM8/MPffckx07duSxxx7Lc889l7lz52bx4sU577zzhvtQAKChrV27Nlu3bs3evXuzZMmSnHHGGXnkkUfyvve9Ly+++GKam5tz1lln5f7776/9zLp167J69eq0t7enubk5XV1dmThxYpJk1apV6e3tzcyZM2uvnTVrVl2ODQAAAAAAAKCehr1ktXDhwso/093dnbvuuitJMm3atCxcuDBbt27NVVddle7u7qxZsyZjxozJxIkTc/nll2fz5s256aabhnbjADDKdHV11a5K9WoPPfTQCX9mwoQJ6e7uPu7amDFjjvt+AAAAAAAAAKebYS9ZFVm3bl0+8YlP5Pzzz89nP/vZTJ8+PUnS39+fqVOn1l7X1taW/v7+E66d6Mvjzs7OdHZ21h4fOHAgPT09Q7L3N4xLbp57eEjea6gM1bENtUOHDjXs3hqRvKqRV3myqkZeAAAAAAAAALyibiWrTZs2ZcqUKRkcHExXV1cuueSS7Nixo7be1NRU+8+Dg4NH/WzR2qt1dHSko6Oj9rilpSVLly4diu3n7nu35tMP17WjdozdtwzNsQ21np6eIcv9dCCvauRVnqyqkRcAAAAAAAAAr2iu1x88ZcqUJC8Xpq699tr89Kc/zf79+5Mkra2t2b17d+21e/bsSWtr669dAwAAAAAAAAAAGGp1KVkdPnw4Tz31VO3xl7/85UyePDmTJk1KkqxYsSJdXV1Jkr6+vmzbti3Lli2rrW3cuDFHjhzJs88+m+7u7qxcuXLkDwIAAAAAAAAAADgtDPv97tauXZutW7dm7969WbJkSc4444w88sgjed/73pcXX3wxzc3NOeuss3L//ffXfmbdunVZvXp12tvb09zcnK6urkycODFJsmrVqvT29mbmzJm1186aNWu4DwMAAAAAAAAAADhNDXvJqqurq3ZVqld76KGHTvgzEyZMSHd393HXxowZc9z3AwAAAAAAAAAAGA51uV0gAAAAAAAAAADAaKFkBQAAAAAAAAAAUEDJCgAAAAAAAAAAoICSFQAAAAAAAAAAQAElKwAAAAAAAAAAgAJKVgAAAAAAAAAAAAWUrAAAAAAAAAAAAAooWQEAAAAAAAAAABRQsgIAAAAAAAAAACigZAUAAAAAAAAAAFBAyQoAAAAAAAAAAKCAkhUAAAAAAAAAAEABJSsAAAAAAAAAAIACSlYAAAAAAAAAAAAFlKwAAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFFCyAgAAAAAAAAAAKKBkBQAAAAAAAAAAUEDJCgAAAAAAAAAAoICSFQAAAAAAAAAAQAElKwAAAAAAAAAAgAJKVgAAAAAAAAAAAAWUrAAAAAAAAAAAAAooWQEAAAAAAAAAABRQsgIAAAAAAAAAACigZAUAAAAAAAAAAFBAyQoAAAAAAAAAAKCAkhUAAAAAAAAAAEABJSsAAAAAAAAAAIACSlYAAAAAAAAAAAAFlKwAAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFFCyAgAAAAAAAAAAKKBkBQAAAAAAAAAAUEDJCgAAAAAAAAAAoICSFQAAAAAAAAAAQAElKwAAAAAAAAAAgAJKVgBwirj++uvT1taWpqamPProo7Xnn3766Vx00UWZMWNGZs+ene3bt9fWDh48mCuuuCLt7e2ZOXNmtmzZUlsbGBjIddddl3PPPTft7e25/fbbR/R4AAAAAAAAABqFkhUAnCKWL1+e7du3Z+rUqUc9v379+lxwwQXZuXNn7rzzzlx55ZU5fPhwkmTDhg0ZP358du3alZ6enlxzzTX5+c9/niS55557smPHjjz22GP53ve+l8997nP58Y9/POLHBQAAAAAAAFBvSlYAcIpYuHBhWlpajnn+3nvvzdq1a5Mk8+bNy+TJk2tXs+ru7q6tTZs2LQsXLszWrVtra2vWrMmYMWMyceLEXH755dm8efMIHQ0AAAAAAABA41CyAoBT2P79+zMwMJCzzz679lxbW1v6+/uTJP39/Udd+arsGgAAAAAAAMDpZOxw/wHXX3997r///uzZsyc//OEPM3v27CTJ008/nQ9/+MN5/PHHM378+Nxxxx1ZsGBBkuTgwYO5+uqr09vbm+bm5txyyy257LLLkiQDAwO54YYb8vWvfz1NTU3p6OjINddcM9yHAQCjVlNT01GPBwcHT7heZe21Ojs709nZWXt84MCB9PT0VN7va71hXHLz3MMn/T5DbSiObTgcOnSoYffWiORVnqyqkVc18gIAAAAAoNENe8lq+fLlufHGG2sFqlesX78+F1xwQR544IH09vZm+fLlefzxxzN27Nhs2LAh48ePz65du9LX15f58+dn0aJFOfPMM3PPPfdkx44deeyxx/Lcc89l7ty5Wbx4cc4777zhPhQAGHUmTZqUJNm3b1/talZ79uxJa2trkqS1tTW7d+8+au3iiy8+am3evHnH/NzxdHR0pKOjo/a4paUlS5cuPeljuPverfn0w8P+K0tlu285+WMbDj09PUOS++lCXuXJqhp5VSMvAAAAAAAa3bDfLnDhwoVpaWk55vl77703a9euTZLMmzcvkydPzvbt25Mk3d3dtbVp06Zl4cKF2bp1a21tzZo1GTNmTCZOnJjLL788mzdvHu7DAIBRa8WKFenq6kqS9Pb2Zu/evbXy86vX+vr6sm3btixbtqy2tnHjxhw5ciTPPvtsuru7s3LlyvocBAAAAAAAAEAdDXvJ6nj279+fgYGB2lUzkqStrS39/f1Jkv7+/kydOrXyGgCcztauXZuWlpY8+eSTWbJkSdrb25Mkt956a77zne9kxowZueqqq7Jp06aMHfvylaHWrVuXF154Ie3t7Vm6dGm6uroyceLEJMmqVavylre8JTNnzsy8efOybt26zJo1q27HBwAAAAAAAFAvdbv3TlNT01GPBwcHT7heZe3VOjs709nZWXt84MCB9PT0vK79vtYbxiU3zz08JO81VIbq2IbaoUOHGnZvjUhe1cirPFlVMxrz6urqql2V6tUmT56cBx988Lg/M2HChHR3dx93bcyYMcd9PwAAAAAAAIDTTV1KVpMmTUqS7Nu3r3Y1qz179qS1tTVJ0tramt27dx+1dvHFFx+1Nm/evGN+7rU6OjrS0dFRe9zS0pKlS5cOyTHcfe/WfPrhunXUjmv3LUNzbEOtp6dnyHI/HcirGnmVJ6tq5AUAAAAAAADAK+pyu8AkWbFiRe3qGL29vdm7d28WLFhwzFpfX1+2bduWZcuW1dY2btyYI0eO5Nlnn013d3dWrlxZn4MAAAAAAAAAAABOecN+Kaa1a9dm69at2bt3b5YsWZIzzjgju3btyq233ppVq1ZlxowZGTduXDZt2pSxY1/ezrp167J69eq0t7enubk5XV1dmThxYpJk1apV6e3tzcyZM2uvnTVr1nAfBgAAAAAAAAAAcJoa9pJVV1dX7apUrzZ58uQ8+OCDx/2ZCRMmpLu7+7hrY8aMOe77AQAAAAAAAAAADIe63S4QAAAAAAAAAABgNFCyAgAAAAAAAAAAKKBkBQAAAAAAAAAAUEDJCgAAAAAAAAAAoICSFQAAAAAADev6669PW1tbmpqa8uijj9aef/rpp3PRRRdlxowZmT17drZv315bO3jwYK644oq0t7dn5syZ2bJlS21tYGAg1113Xc4999y0t7fn9ttvH9HjAQAAYHRSsgIAAAAAoGEtX74827dvz9SpU496fv369bnggguyc+fO3Hnnnbnyyitz+PDhJMmGDRsyfvz47Nq1Kz09Pbnmmmvy85//PElyzz33ZMeOHXnsscfyve99L5/73Ofy4x//eMSPCwAAgNFFyQoAAAAAgIa1cOHCtLS0HPP8vffem7Vr1yZJ5s2bl8mTJ9euZtXd3V1bmzZtWhYuXJitW7fW1tasWZMxY8Zk4sSJufzyy7N58+YROhoAAABGq7H13gAAAACMNtdff33uv//+7NmzJz/84Q8ze/bsJC/ftujDH/5wHn/88YwfPz533HFHFixYkOTl2xZdffXV6e3tTXNzc2655ZZcdtllSV6+bdENN9yQr3/962lqakpHR0euueaauh0fADS6/fv3Z2BgIGeffXbtuba2tvT39ydJ+vv7j7ry1a9be+ihh47753R2dqazs7P2+MCBA+np6RmSY3jDuOTmuYeH5L2G0lAd31A7dOhQw+6t0ciqGnmVJ6tqRmtePu8CwIkpWQEAAEBFy5cvz4033lj7C+VXvHLbogceeCC9vb1Zvnx5Hn/88YwdO/ao2xb19fVl/vz5WbRoUc4888yjblv03HPPZe7cuVm8eHHOO++8Oh0hADS+pqamox4PDg6ecL3K2qt1dHSko6Oj9rilpSVLly59Xft9rbvv3ZpPP9x4f0W/+5ahOb6h1tPTM2TZn+pkVY28ypNVNaM1L593AeDE3C4QAAAAKnLbIgCor0mTJiVJ9u3bV3tuz549aW1tTZK0trZm9+7dldcA4HTn8y4AnFjj/TMZAAAAGIXctmj4NOotNkbr7T/qQVbVyKsaeZV3qmW1YsWKdHV15aabbkpvb2/27t1bu+rGK2t33XVX+vr6sm3bttxxxx21tY0bN+ayyy7Lc889l+7u7jzwwAP1PBQAaGg+7w6fRvzd7FT7nXG4yasaeZUnq2pGKi8lKwAAABgibls0PNy2aPSTVTXyqkZe5Y3WrNauXZutW7dm7969WbJkSc4444zs2rUrt956a1atWpUZM2Zk3Lhx2bRpU8aOfXmOrVu3LqtXr057e3uam5vT1dWViRMnJklWrVqV3t7ezJw5s/baWbNm1e34AGA08Hl3eDTi593R+jtjvcirGnmVJ6tqRiqvxpskAAAAMAq9+rZFr/zr3uPdmujVaxdffPFRa/PmzTvm5wDgdNfV1ZWurq5jnp88eXIefPDB4/7MhAkT0t3dfdy1MWPGHPf9AIDj83kXAF7WXO8NAAAAwKnilVsTJTnhbYuS1G5btGzZstraxo0bc+TIkTz77LPp7u7OypUr63MQAAAA8Bo+7wKAK1kBAABAZW5bBAAAwKnI510AODElKwAAAKjIbYsAAAA4Ffm8CwAn5naBAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFFCyAgAAAAAAAAAAKDC23hsAAABgZLSt/1q9t3BcGxf5aAoAAAAAQGNzJSsAAAAAAAAAAIACSlYAAAAAAAAAAAAFlKwAAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFFCyAgAAAAAAAAAAKKBkBQAAAAAAAAAAUEDJCgAAAAAAAAAAoICSFQAAAAAAAPpEyhEAACAASURBVAAAQIGx9d4AAKe3tvVfq/cWjmvjIiMSAAAAAAAAgJe5khUAAAAAAAAAAEABJSsAAAAAAAAAAIACSlYAAAAAAAAAAAAFlKwAAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFFCyAgAAAAAAAAAAKKBkBQAAAAAAAAAAUEDJCgAAAAAAAAAAoICSFQAAAAAAAAAAQAElKwAAAAAAAAAAgAJKVgBwmmhra8t5552XOXPmZM6cOenu7k6SPP3007nooosyY8aMzJ49O9u3b6/9zMGDB3PFFVekvb09M2fOzJYtW+q1fQAAAAAAAIC6qWvJype9ADCy7rvvvvzgBz/ID37wg6xcuTJJsn79+lxwwQXZuXNn7rzzzlx55ZU5fPhwkmTDhg0ZP358du3alZ6enlxzzTX5+c9/Xs9DAAAAAAAAABhxY+u9gfvuuy+zZ88+6rlXvux94IEH0tvbm+XLl+fxxx/P2LFjj/qyt6+vL/Pnz8+iRYty5pln1ukIAGB0u/fee9PX15ckmTdvXiZPnpzt27fnwgsvTHd3d+66664kybRp07Jw4cJs3bo1V111Vf02DAAAAAAAADDC6l6yOh5f9gLA8LjyyiszMDCQd73rXfnsZz+b5ubmDAwM5Oyzz669pq2tLf39/UmS/v7+TJ069bhrr9XZ2ZnOzs7a4wMHDqSnp+ek9/yGccnNcw+f9PsMtaE4tuFw6NChht1bI5JXebKqplHzasT/PU2SQ4cON2ReAAAAAADwirqXrIbzy14A4Ff+/d//Pa2trXnppZfyqU99Kh/5yEeyadOmNDU1HfW6wcHBox6/ev21a6/W0dGRjo6O2uOWlpYsXbr0pPd9971b8+mH6/4ryzF233LyxzYcenp6hiT304W8ypNVNY2aV9v6r9V7C8e1cdHYhswLAAAAAABeUddvLIf7y97huqJG0phX1WjUf/ndqP+Kv1HJqxp5ldeoWTXa/5a+4lS8okZra2uS5Dd+4zfyJ3/yJ5k5c2YmTZqUJNm3b1+t4Lxnz57aa1tbW7N79+6j1i6++OI67B4AAAAAAACgfupashruL3uH64oaSWNeVcMVNU4N8qpGXuU1alauqDEynn/++bz00kt54xvfmCT50pe+lHe84x1JkhUrVqSrqys33XRTent7s3fv3ixYsOCotbvuuit9fX3Ztm1b7rjjjrodBwAAAAAAAEA9NNfrD37++edz4MCB2uPjfdmb5IRf9iapfdm7bNmyEd49AIwuTz31VBYtWpS3ve1teetb35pt27blH/7hH5Ikt956a77zne9kxowZueqqq7Jp06aMHftykXjdunV54YUX0t7enqVLl6arqysTJ06s56EAAAAAAAAAjLi6XYrpqaeeygc+8IEcOXIkg4ODmT59+lFf9q5atSozZszIuHHjjvmyd/Xq1Wlvb09zc7MvewGghOnTp+f73//+cdcmT56cBx988LhrEyZMSHd393BuDQAAAAAAAKDh1a1k5cteAAAAAAAAAABgNKjb7QIBAAAAAAAAAABGAyUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFFCyAgAAAAAAAAAAKKBkBQAAAAAAAAAAUEDJCgAAAAAAAAAAoICSFQAAAAAAAAAAQAElKwAAAAAAAAAAgAJKVgAAAAAAAAAAAAWUrAAAAAAAAAAAAAooWQEAAAAAAAAAABRQsgIAAAAAAAAAACigZAUAAAAAAAAAAFBAyQoAAAAAAAAAAKCAkhUAAAAAAAAAAEABJSsAAAAAAAAAAIACSlYAAAAAAAAAAAAFlKwAAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFFCyAgAAAAAAAAAAKKBkBQAAAAAAAAAAUEDJCgAAAAAAAAAAoICSFQAAAAAAAAAAQAElKwAAAAAAAAAAgAJKVgAAAAAAAAAAAAXG1nsDAAAAAAAAAK9H2/qv1XsLx7Vxka9hAeBU40pWAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFFCyAgAAAAAAAAAAKKBkBQAAAAAAAAAAUEDJCgAAAAAAAAAAoICSFQAAAAAAAAAAQAElKwAAAAAAAAAAgAJKVgAAAAAAAAAAAAWUrAAAAAAAAAAAAAooWQEAAAAAAAAAABRQsgIAAAAAAAAAACigZAUAAAAAAAAAAFBgbL03AAAAAABwqmlb/7V6b+EYGxf562AAAABev0b8rJuM3OddV7ICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFBi1JaudO3fm3e9+d2bOnJl3vvOd2bFjR723BACnJDMXAEaOuQsAI8fcBYCRY+4CcCoYtSWrP/7jP87HPvaxPPbYY7nxxhtz9dVX13tLAHBKMnMBYOSYuwAwcsxdABg55i4Ap4JRWbJ6+umn8/DDD+dDH/pQkuQDH/hA+vr6snv37vpuDABOMWYuAIwccxcARo65CwAjx9wF4FQxtt4beD2eeOKJvPnNb87YsS9vv6mpKa2trenv709bW1vtdZ2dnens7Kw93rt3b1paWoZkD//3f/+XM844Y0jea6i03FPvHRxfI2bVyORVjbzKk1U1K+8Yurz27ds3JO9TD2VnbjJ8c7dRz11z99Qgr/JkVY28qhmquTuaZ27is24Rc3f0k1U18qpGXuX5rPsr5u6Jmbujn6yqkVd5sqrG3P0Vc/fEGnHuNmpWjUpe1cirPFlVM1Jzd1SWrJKXh++rDQ4OHvOajo6OdHR0DMuf39LSkieffHJY3vtUI6tq5FWNvMqTVTXy+pUyMzcZvrnr/xfVyKsaeZUnq2rkVY28fsVn3dFFXuXJqhp5VSOv8mR1NHN3dJFXebKqRl7lyaoaeR3N3B09ZFWNvKqRV3myqmak8hqVtwucMmVKnnzyyRw+fDjJy0P4iSeeSGtra513BgCnFjMXAEaOuQsAI8fcBYCRY+4CcKoYlSWrN73pTXnHO96Re+55+fqJX/7yl9PW1nbMbYsAgJNj5gLAyDF3AWDkmLsAMHLMXQBOFWNuuummm+q9iddj/vz5+Yu/+It87nOfS29vb+6888686U1vGvE9UI6sqpFXNfIqT1bVyOtlZu7oI69q5FWerKqRVzXyepm5O/rIqzxZVSOvauRVnqx+xdwdfeRVnqyqkVd5sqpGXr9i7o4usqpGXtXIqzxZVTMSeTUNHu+GtwAAAAAAAAAAACQZpbcLBAAAAAAAAAAAGClKVgAAAAAAAAAAAAWUrAAAAAAAAAAAAAooWQEAAAAAAAAAABRQsgIAAAAAAAAAACigZPVrDAwM5Atf+EIWL16c9vb2tLe3Z/Hixfnbv/3bHDlypN7ba0ibN2/OZz7zmfzgBz846vnPfvazddrR6PLWt7613ltoSF/5yleyf//+JMkzzzyTlStXZvr06Xn/+9+fn/3sZ3XeXeP56U9/mg9+8IP5+Mc/noMHD+aP/uiPMm3atFx66aXp7++v9/bghMzd6szd18/MPTFztxpzl9HK3K3O3H39zN0TM3fLM3MZzczdaszck2Punpi5W565y2hl5lZn7p4cc/f4zNxqzN3Ro2lwcHCw3ptoZGvWrMn//u//Zs2aNWlra8vg4GD27NmTO+64I+ecc042btxY7y02lE996lPZtm1b5syZk/vuuy/r16/PDTfckCSZO3duHn744TrvsLG8853vPOa5//7v/87b3va2JMn3vve9kd5Sw5o1a1Z+9KMfpbm5OVdddVVaWlpy5ZVX5hvf+Ea+/e1v56tf/Wq9t9hQ/uAP/iCLFy/OL37xi3z729/O4sWL85GPfCT3339/tm3blm984xv13mJDGRgYyBe/+MV86Utfqv2i0tramg9+8IO5+uqrM2bMmDrv8PRh7lZj7pZn5lZj7lZj7lZj7jYOc7cac7c8c7cac7c8M7caM7exmLvlmbnVmLvVmLvlmbvVmLuNw8ytxtytxtwtz8ytxtytpp5zV8nq15gxY0Z27tx5zPODg4OZMWNGdu3aVYddNa63ve1teeihhzJu3Ljs3bs3y5Ytyx/+4R/mk5/8ZN7xjnfk+9//fr232FDOP//8vOc978mqVasyODiYwcHBXHHFFdm8eXOS5Pd///frvMPGcf7552fHjh1Jcsy55Nw61tvf/vY88sgjGRwczO/8zu8c1QifM2fOMf8a4XTnQ1fjMHerMXfLM3OrMXerMXerMXcbh7lbjblbnrlbjblbnplbjZnbWMzd8szcaszdaszd8szdaszdxmHmVmPuVmPulmfmVmPuVlPPuTt22N75FNHU1JRnnnkmZ5111lHPP/PMM2lqaqrTrhrXwMBAxo0blyQ555xz8s1vfjPvfe97c+TIEXkdx/e///38+Z//eTo7O2v/hf/N3/xNA/g4Jk+enP/6r//K/Pnz09LSkr179+acc87JL3/5y7z00kv13l7DOXLkSAYGBnLw4MH84he/yC9/+cv89m//dg4fPpwXX3yx3ttrON/61reO+dB1/vnn56KLLsqMGTPqtKvTk7lbjblbnplbjblbjblbjbnbOMzdaszd8szdaszd8szcaszcxmLulmfmVmPuVmPulmfuVmPuNg4ztxpztxpztzwztxpzt5p6zl0lq1/jz/7sz/L2t789l112WaZOnZqmpqb09fXlK1/5Sm6++eZ6b6/hnHHGGdm9e3fa2tqSJG94wxvS09OTpUuX5kc/+lF9N9eAxo8fnw0bNuQ//uM/cumll2bt2rX13lLDuu222/L+978/7373u3POOefkXe96VxYtWpTe3t584hOfqPf2Gs7FF1+c97znPXnxxRfzsY99LB/84AezaNGifOtb38rv/d7v1Xt7DceHrsZh7lZj7pZn5lZj7lZj7lZj7jYOc7cac7c8c7cac7c8M7caM7exmLvlmbnVmLvVmLvlmbvVmLuNw8ytxtytxtwtz8ytxtytpp5z1+0CS9i9e3fuu+++o+7l+IEPfCDTpk2r884az7/+679m4sSJefvb337U888//3z++q//Op/85CfrtLPGd/Dgwaxbty7bt2/PI488Uu/tNKQXXngh//RP/5QdO3bkpZdeytSpU3P55ZdnypQp9d5aQ7r//vuTJJdeemkefvjh3HXXXZk+fXrWrl1b+1cJvOyLX/xi/vIv//KEH7quvvrqem/xtGLulmfuvj5mbjnmbjXmbnnmbmMxd8szd18fc7ccc7c8M7c8M7fxmLvlmLmvn7lbjrlbnrlbnrnbWMzc8szd18/c/fXM3GrM3fLqOXeVrAAgPnQBwEgydwFgZJi5ADByzF0AGDn1mrtKVifhq1/9ai655JJ6b2PUkFc18ipPVtXIi9HKuVuNvMqTVTXyqkZejFbO3WrkVZ6sqpFXebJiNHP+lierauRVjbzKkxWjlXO3GnlVI6/yZFWNvBpLc703MJpt3bq13lsYVeRVjbzKk1U18qrmq1/9ar23wP/n3K1GXuXJqhp5VSOvaszdxuHcrUZe5cmqGnmVJ6tqzNzG4vwtT1bVyKsaeZUnq2rM3cbh3K1GXtXIqzxZVSOvaoZ77rqSVUn9/f1HXWastbW1zjtqbPKqRl7lyaoaeZ28j370o/nCF75Q722cdpy71cirPFlVI69q5HXyzN36cO5WI6/yZFWNvMqT1ckzc+vH+VuerKqRVzXyKk9WJ8/crQ/nbjXyqkZe5cmqGnmdvOGeu0pWv8aPf/zjrF69On19fWltbc3g4GCeeOKJTJs2LX//93+f8847r95bbCjyqkZe5cmqGnkxWjl3q5FXebKqRl7VyIvRyrlbjbzKk1U18ipPVoxmzt/yZFWNvKqRV3myYrRy7lYjr2rkVZ6sqpHXKDJIoXe9612D99133zHP//M///PgvHnz6rCjxiavauRVnqyqkdfQmTFjRr23cFpx7lYjr/JkVY28qpHX0DF3R5Zztxp5lSerauRVnqyGjpk78py/5cmqGnlVI6/yZDV0zN2R5dytRl7VyKs8WVUjr6Ez3HPXlax+jbe85S35yU9+UnntdCWvauRVnqyqkVc1O3bsOOHakiVL8rOf/WwEd3N6c+5WI6/yZFWNvKqRVzXmbuNw7lYjr/JkVY28ypNVNWZuY3H+lierauRVjbzKk1U15m7jcO5WI69q5FWerKqRVzX1nLvNw/bOp4izzjormzZtysDAQO25gYGB3H333Zk0aVIdd9aY5FWNvMqTVTXyqmb27Nm55JJL8r73ve+Y/3vmmWfqvb3TinO3GnmVJ6tq5FWNvKoxdxuHc7caeZUnq2rkVZ6sqjFzG4vztzxZVSOvauRVnqyqMXcbh3O3GnlVI6/yZFWNvKqp69wd1utknQJ27tw5uHjx4sEzzzxz8Hd/93cHZ8+ePfjGN75xcNGiRYM/+clP6r29hiOvauRVnqyqkVc1bW1tg//zP/9z3LWWlpYR3s3pzblbjbzKk1U18qpGXtWYu43DuVuNvMqTVTXyKk9W1Zi5jcX5W56sqpFXNfIqT1bVmLuNw7lbjbyqkVd5sqpGXtXUc+66XWBJ+/btyxNPPJEkmTJlSs4+++w676ixyasaeZUnq2rkVc4NN9yQFStWZMGCBcesXXvttfmbv/mbOuzq9ObcrUZe5cmqGnlVI69yzN3G49ytRl7lyaoaeZUnq3LM3Mbk/C1PVtXIqxp5lSercszdxuPcrUZe1cirPFlVI69y6jl3lawAAAAAAAAAAAAKNNd7AwAAAAAAAAAAAI1MyQoAAAAAAAAAAKCAkhUAAAAAAAAAAEABJSsAAAAAAAAAAIACSlYAAAAAAAAAAAAFlKwAAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAIC6ufDCC/OpT32q1Gvb2tryd3/3d8O8o9dv9+7daWpqyq5du07qfZ588sm8+c1vzi9+8Ysh2VdTU1O++c1vDsl7jaR//Md/zHvf+956bwOAYVDPmW5eDy3zGqB+/u3f/i1NTU05fPjwCV+zYMGC3HTTTbXHwzVv7rvvvixZsmRI3qvMcTWqj370o7n11lvrvQ2AUWM0/28+R7v22mvzV3/1V0PyXjfddFMWLFgwJO81kgYGBjJr1qx897vfrfdWGGZKVjBC7rrrrrS0tBS+5tV/0fzav/gt8/NFnn/++bS0tKSvr+91v8eJ9jqa/Od//mdmz56dgYGBem8FgCRbtmzJ+vXrS722t7c3V155Zen3/tCHPpSrrrrqde6suv/H3p2H93Dv//9/ZCH2RohQREISoqGoKNWmOIcgpa21SiTU1qou1hRF+tWFo+poKd2spxpaWx2EFtEQTYilaU6siRy1NhqHIpbM7w+/zCdvSd7yJlu5364r12XmNTPv1+spV57vmXnOa2rXrq1Tp07J09Pzno4zdepUvfTSS6pUqVIB9azk+uGHH/T444+rTJkycnV11Ztvvmm2vfDCC0pKSlJ0dHQx9hAA/lpq1aqlhQsXFnc3SjTytW3s7Oxy/YmNjZVEvgaA4vTEE0/o1KlTcnR0zPc+p06dUkBAgKRb52N2dnb33A/DMPT222/rrbfeuudjlXQ7duxQ69atVaFCBbm5uSk0NFR//PGH2T5u3DhNnz5dFy9eLMZeAgBKuokTJ6pNmzbF3Y0Ck5qaqq+//lojRowo7q4UuiVLlsjPz09ly5aVr6+vVq5cabbZ29trzJgxmjBhQjH2EEWBIiugBLH15rEtPv74Y7Vt2/aeLySXdFOmTMlx8fe5554z21u3bi0XFxd9/fXXxdhLAEAWFxcXVahQIV/burq6qmzZsoXco7vn4OCg6tWry8HB4a6PcfHiRS1dulTBwcEF2LOSaevWrerWrZtefPFFHThwQFu2bLF48tnBwUF9+vTRp59+Woy9BADcb8jXtjl16pTFz+jRo1W7dm01b95cEvkaAIpT6dKlVb16dZv2qV69ukqXLl2g/di6dasuXbqkdu3aFehxS5qLFy/qmWeeUfPmzbV//36tXbtWe/futbih7OXlpfr16+ubb74pxp4CALJcu3atuLvwQPjiiy/UqVMnPfTQQ8XdlUK1bt06DR48WGFhYfr11181evRovfjiixYzV/Xo0UM7d+7UoUOHirGnKGwUWQElSGHdPDYMQ/PmzVP//v0L/NglUYsWLSwuAt/+JHe/fv24AAwAJUT21wUePnxYHTp0UNmyZVWtWjWNGTPGYqro3GZ8XL16tVq0aKHy5curTZs2Sk1NlXSr6PZf//qXFi1aZBbd5qcvo0ePVkhIiMqXL686depo+fLlZvuZM2fUo0cPVa9eXRUrVlRAQID27dtntuc1C+W3334rT09POTs7a+DAgcrIyMizD+vXr1eNGjXk4+Njsf6bb75Ro0aN5OTkpFq1allMvRwbG6tWrVrJyclJtWvX1vTp0/M8fm4zY94+/XKbNm00ZswYDR48WBUrVpSHh4fWr1+vEydO6O9//7vKly+v1q1b6/jx4+Y+oaGh6tevnyZOnCgXFxc9/PDDmjlzZp79kKTRo0dr1KhRev311+Xj46NGjRopKCjIYpugoCCtXr1a169ft3osAHiQLFu2TA0aNFCZMmVUvXp1DRkyRNKtv9+//fabBgwYIDs7O/Op2Bs3bmjs2LGqVq2aypYtq/bt2+vw4cPm8bLywD//+U/VqFFDzs7Oeu+995SRkaFhw4apUqVK8vLy0ubNm819kpKS1LlzZ1WtWlXOzs7q3LlzjlmT9+zZo3bt2qlcuXJycXGxePhFki5cuKCePXuqfPny8vX11ZYtW/I1fvL1/8WhKPJ19erVLX7Wrl2r4OBg2dv/3yVF8jUA3L0vv/xSTZo0MXPa22+/neOVSbNnz5aXl5ecnJxUt25d87z49lcsGYahCRMmqHLlynJ1ddU//vGPHJ+X9brAlJQUtW/f3lxnZ2enhQsXqmPHjho7dqzFPj/++KMqVKigS5cu5TqGFStWqGPHjhbn3Tdu3NCkSZPk7u6uMmXKyNfXV99//73ZvnjxYnNMjRo10oYNG/KMUVb+yi77tYSsMXz11Vdq166dypYtqxYtWujYsWPatm2b/Pz8VKlSJQUHB+vq1avmPh4eHvrwww/z/X0kKSlJ6enpCg8PV7169fT4449r0KBB2r17t8V2QUFBWrFiRZ7HAYAHSW5vxbnTq2utnVcdO3ZMXbp0UYUKFfTwww/r1Vdf1eXLly0+b9q0aerevbvKlSun2bNnKy0tTT179pSLi4vKly+vRx99VDExMXfs+59//qlBgwapcuXKqlChgrp3764zZ86Y7XdzfiXdOt979913FRUVZebglJQUSdKGDRvMsXt5eWnx4sXmfne6Hi5JV69e1YABA1S+fHnVrl1bS5YssZjxOrfXM+Z2/jl79mzVrVtX5cqVk7+/v7Zt22Z1TCtWrFDnzp0t1p09e1b9+vUzH7Ju3bq1jh49KunO1ylud6ffo6xxbdq0SQ0bNlS5cuXUq1cvXb16VZ988okefvhhVatWzeI8PD/xvN3XX3+tPn36qF+/fqpbt65eeuklPfPMM5o1a5a5TaVKldS6dWt9++23VmOGvzaKrIACdKeTR0lWL9za8gq+zZs3q2nTpipbtqyqVq2a46ZkdrGxsTp79qzatm2b4xhZr+hxc3PTK6+8Yrbd6UZ3dvlJyqGhoerbt6/eeustubi4yM3NTQsWLFB6erp69uypChUqqHHjxhYXv7MuIn/yySeqUaOGqlatqrFjx8owDKuxKVWqlMVFYGdnZ4v2oKAg7dy5U7/99pvV4wAAis7Nmzf17LPPysnJSbGxsVq0aJEWL15s9QakdCtXTJs2TbGxsbp8+bL5urnRo0ere/fu6tWrl1l0mx/z58+Xl5eX4uPjNWTIEPXt29e8CXvlyhUFBARo8+bN2rNnjxo2bKiuXbtaXKS9XVpamhYtWqS1a9dq1apVWrNmjT777LM8t9+xY4eaNWtmsW7Tpk3q37+/BgwYoISEBK1YsUI1atSQdOtJ2s6dO+uRRx7Rvn37NH36dIWHh9/zjI2fffaZ/Pz8FB8fr6CgIAUHB2vQoEF64403tGfPHhmGoZEjR1rss3btWl2/fl27du3SlClTNGrUKB04cCDX458+fVrx8fFydnaWv7+/atSooeeff97iRrAkNWvWTFevXrX4fgAAD7JTp05pwIABCg8P18GDB7Vu3To99thjkm69grdGjRqaNWuWTp06ZU5ZP336dC1atEgLFixQXFycypYtq65du+rmzZvmcQ8cOKB9+/Zp69atmjVrliZMmKCuXbvqkUce0Z49exQYGKj+/fubTwFfunRJPXr0UHR0tKKjo1W6dGm98MIL5vHOnTunv/3tb6pbt65+/vlnRUVFqVWrVhZjmT59urp06aJ9+/bpqaeeUr9+/fL9lDH5+pbCzte5jfvQoUMKCQmxWE++BoC7l5mZqRkzZighIUHz5s3TF198YZGDPv/8c02cOFETJkxQYmKivvzyyzxfVbt48WLNnj1bn332mbZt26aYmBjt378/121r165tFilnnTP37t1boaGh+te//qXMj3h2qQAAIABJREFUzExz2yVLlqhbt255zkKdW16cPHmyPv/8c82aNUu//vqrZs6cqVKlSkmSdu7cqYEDB+q1117TgQMH9Pzzz+u5554zbzDfralTp+rNN99UfHy8HB0d9eKLL2rq1KlauHChNmzYoA0bNujzzz+32MeW7yP169dX5cqVtXDhQt28eVO///67Vq1apQ4dOlhs17x5c8XExFjEEACQP9bOq65du6bAwEB5e3trz549WrNmjeLi4jRq1CiLY0yfPl0dO3ZUQkKCXnjhBb399tu6ePGitm/frgMHDmjy5Mn5mtXxzTffVFRUlNasWaPt27frt99+yzGb8d2cX/Xu3VtvvPGGWrVqZebg2rVrKyUlRc8995yee+45HThwQG+88YYGDhyoHTt2WOyf1/Vw6VYu3Lx5s9asWaN169ZpwYIFSktLu+NYs/vqq6/0z3/+U3PnzlVCQoL69++vzp0755mn09LSlJSUlOO7QLdu3XT06FF9//332rt3r4YOHWreR87PdYq78f7772vx4sXatGmTtmzZoq5du2rv3r3asmWLpk+frnHjxuX4/7EWz9tlZGTkmCylXLly2rlzp8W65s2b5/h/w33GAFBgxo8fb1SvXt347rvvjCNHjhjr1683NmzYYBiGYSxYsMAoU6aM8cwzzxgHDhwwtmzZYri4uBizZ882969Tp47x+eefG4ZhGMnJyYYk4/Dhw+b+NWvWNAzDMK5fv25UqlTJmDVrlpGSkmLs37/f+Oijj/Ls14cffmi0aNHCYt2vv/5qlCpVyhg/fryRmJho7Nmzx+zLjRs3DF9fX7Ov69evN6pVq2a8++67ufZ169athiTj+vXrZnv2/hqGYYSEhBgVK1Y0wsLCjIMHDxpTp041SpUqZXTq1MlYunSpcejQIeP55583mjVrZu4zefJko2LFikb//v2NxMREY9WqVUapUqWMtWvX5jnWyZMnGxUqVDDc3NwMb29v45VXXjHOnz+fY7vq1asby5cvz/M4AICi8fTTTxsTJkwwNmzYYJQpU8ZIS0sz2z799FOjatWq5nJueTIiIsJs//rrr40qVaqYy3379jVCQkJs6svjjz9usa5169bGqFGjct3+xo0bRvny5Y2oqCiLPmXP3XZ2dsbp06fNfYYMGWJ07949zz507drVeP311y3WBQQEGMOHD891+08//dR4+OGHLXLwuHHjjObNm5vLkozNmzebfcqenw3jVu5s3bq1ufz0008bnTp1MpdPnTplSDL+8Y9/mOuWLVtmuLi4mMshISFGw4YNLY7r4+NjfPzxx7n2OyYmxpBkVKtWzVi2bJkRGxtrBAUFGb6+vhZjMQzDqFy5srFixYpcjwMAD5rdu3cblSpVMi5evJhre82aNY0FCxZYrHNzczPmzJljLqelpRlly5Y11q1bZxjGrTxQuXJl4+rVq+Y29evXN4KCgszlrFxw4MCBXD83q/348eOGYRjGpEmTDD8/PyMzMzPX7evUqWO8/PLL5vLJkycNScYvv/xiZfS3kK9vKYp8fbvBgwcbTzzxRK5t5GsAKBjvv/++0bZtW3PZ3d3d4m97drdfk23RooUxbtw4s/38+fNG2bJljcmTJ5vrsuebzZs3G7ffIrpy5Yrh7OxsbNq0yTAMw/jzzz+NChUqmPvkplKlSsaqVavM5cuXLxtOTk555oXevXsbPXv2tFj3+OOPG6NHj851XCEhIUbfvn0tts+6lpB9XNOmTTOXly1bZkgy4uLizHVDhw41unXrZi7fzfeRPXv2GLVr1zYcHBwMSUZQUJBx7do1i232799vSDLOnTuX53EA4EGR/Xpuluy56Pa/+dbOqxYtWmQ89thjFut27NhhlC5d2rhx44b5eaGhoRbbPPPMM8Y777xjU7//97//GY6Ojsa///1vc91//vMfQ5KRkJBgGMa9nV9NmDDBePrppy3WjRs3zvD397dY17t3b6NHjx6GYeTvenjVqlUt4n3w4EFDknmdID/3cz09PY3vv//eoh/t27c3/t//+3+5jiU+Pt6QZPzxxx/mui1bthilS5c2Tpw4kes++blOkf38N7+/Rz///LPZPnToUMPFxSXHtY6se+H5ieft5s6dazg7OxsxMTFGZmamsW3bNqN8+fJG6dKlLbb75z//aTzyyCN5Hgd/fcxkBRSQK1eu6MMPP9THH3+sbt26qV69eurUqZM6duxobpORkaEvvvhCjRo1Utu2bdWjRw9FRUXZ/FkXLlzQ//73P3Xr1k116tRR48aN9cYbb+S5/fHjx81K7yzTpk1Thw4d9O6778rX11fNmjUz3x+/efNmJScna9GiRWrUqJE6deqk8PBwffTRRzb3NbvatWvr/fffl4+Pj8LCwlS6dGnVrVtXffv2lbe3t8aOHav4+HhdvHjR3MfR0VHz58+Xr6+vnnvuObVt29ZqzFq2bKnFixdr8+bN+vDDDxUVFaVnn302x+xXNWrUyDFbBgCg+Bw8eFDe3t5ycXEx17Vq1Uq///67zp8/n+d+jRo1Mv9dvXp1paWl3dMTLy1atMixfPDgQUnS9evXNX78ePn6+srZ2VkPPfSQLl++rP/+9795Hs/V1VVubm4WfTx79mye21+9elVOTk4W6xISEsxXPt3u4MGDeuyxx+To6Giua9Wqldnnu5U9rln9f+SRRyzWnT9/3iLWfn5+FsewNtasJ3pfeeUVvfDCC/L399eiRYt06NChHNN1ly1bVleuXLmn8QDA/eLRRx9V48aNVbduXYWGhmr58uVWZ3+6cOGCzpw5o5YtW5rrXFxcVL9+fYtc4e3tbZF/3Nzccvzdl27NUJV13OHDh8vb29t8naAkMycmJCTo6aeftvq63ttzuCSrOTI78vUthZ2vs7ty5YqWL1+eYxarLORrALg7O3fuVIcOHVSzZk1VqFBBU6ZMMXPWxYsXlZqammd+ud3BgwctcmTlypXNHJ1fZcqUUe/evbVkyRJJ0qpVq+Ts7Kx27drluc/tefHIkSPKyMiwmhezfzeRii4vZn2XyW2fO30f+fPPPzV48GA999xziouL0w8//KCTJ0/q9ddft9gua4YL8iIA2M7aedUvv/yi/fv3q0KFCuZP+/btde3aNYs31zRt2tRiv8GDB+u9997TU089pXfeeSdf+ebYsWO6ceOGRb5q0KCBnJ2dLfa/2/Or3OQ3P+Z1PTw9PV2///67Odu1JPn4+KhixYr57sOlS5eUnJys3r17W8R569atOnbsWK77ZM0anf27QEJCgry9vVWzZs0c2+f3OsXduP27QNaribOvu9N3AWv3F4YOHarg4GAFBASoVKlSGjhwoIKDg2Vvb1lyw/nx/Y8iK6CA3OnkUbL9wm1eqlSpohdeeEF+fn564YUXtGDBAl26dCnP7e/mAvDd3Oi+k+xfNhwcHFSlShWrF+6lWxf7y5QpYy7fKWYdO3bU888/r0aNGqlLly5as2aNfvrpJ+3Zs8diOxIcAJQstxfD5lfW6wYkmTdy7/ZY2Y+Rm2nTpmnRokWaOnWqoqOjtW/fPjk7O+v69ev56l/W8a29MqBKlSpKT0/Pd39tHau9vX2OfXLrf25xvVOsbRlrVs6vX7++ua5KlSqqWrVqjpvgf/zxh6pWrZr3oADgAeLo6Kht27YpIiJCbm5uGjt2rJ544ol8v2YvL7n9Dc/t737W3/VRo0YpKipKs2bN0q5du8yp8bNySn7yk7Xj3wn5+pbCztfZrVq1SteuXVPv3r1zbSdfA4DtLl68qKCgIHl6euq7775TfHy8wsLCbMqnt7OWI/MrNDRUK1eu1J9//qnFixerX79+OW4eZnd7XrxTv4szL96e82z5PrJs2TKlp6dr9uzZatq0qf72t7/pk08+0bx583ThwgVzu6zr51WqVMnX+ADgfnb733Br52V3cunSJQUEBGjfvn3mz/79+3X48GGLiSbKlStnsV/Xrl117NgxBQcHKz4+Xo0bN1ZERITVz8pvrrrb86uC+szs53xZ+1v7LpCVz/P6P/nzzz8lSV9//bVFnP/zn//o/fffz/WYWfnOlu8Ctsrv79HtscnP/48t9xfs7e01e/Zs/fnnnzp+/LgOHz6shx56SJ6enhbbnT9/nvPj+xxFVkABsfUisnRvyXbZsmXatGmT6tevrxkzZsjPzy/P9+rmdgHYWn/v5kT39v3udKIr3fnCfV772BKzevXqydnZWcnJyRbrSXAAULI0aNBAhw8ftijmjYmJkaurq0XRry1KlSpl86xWsbGxFstxcXFmIdCuXbvUs2dPde/eXX5+fnJyctIff/xxV33Ly6OPPqqkpCSLdX5+ftq2bVuu2zdo0EB79uwx32cv3YpbgwYNct3e1dVVaWlpFnn6l19+ufeO28jT01PVqlXTkSNHzHVZT1u5u7ub65KTk3XlyhU9+uijRd5HACipHBwc1LZtW02bNk2xsbHas2eP9u3bJyln7nvooYfk5uamXbt2mevOnz+vgwcP5pkr8mPXrl0aNGiQgoKC1LBhQ4sbi9KtJ0G3b99e4BdWs5Cvi96iRYv03HPP6aGHHsrRRr4GgLtz8OBBpaena9q0aWrZsqV8fHwsHjqpVKmS3N3d88wvt/Px8bHIkenp6RbnXLfLuu56+3lzy5Yt5e7urjlz5ujHH39U//79rX7u7Xkxa4ZMa3kx+3cT6c558fTp0+bytWvX7nmmi7tx+fLlHMVmWTd9s3/nSUxMlLe3d46b/ADwILr9b/idzmusnVdl5ZtatWrJy8vL4uf2e4m3q1GjhoYMGaLVq1frpZde0qJFi6xuX69ePTk6Olrkq6SkJKWnp9/TuXSW3K5b25ofb1e5cmVVrVrVYtKJw4cPW7w9yNXVVZLy/D+pVq2aqlevrtTU1Bwxzj6JSHb16tVThQoVLL4LNGrUSIcPH9bJkydzbH831yls/T0qbKVKlVLNmjVlGIZWrVqlLl26WLQnJiZyfnyfo8gKKCB3OnksDI8//rjCw8O1d+9epaen68cff8x1u9wuADdq1Mjqia4tN7rvlJSLU2pqqtLT0+Xh4WGuy8jI0LFjx0hwAFCCdOjQQZ6engoNDVVCQoI2bNigyZMnW30d7p3UqVNHe/fuVUpKin7//fd87ZOQkKB3331Xhw4d0vvvv6+YmBgNHTpU0q0Txo0bNyo+Pl7x8fEKCQmxmG2xILRv31579uxRRkaGuW7ChAn67LPP9NFHH+nw4cOKjY3VggULJEl9+/ZVRkaGXn75ZSUlJWnZsmX6+OOP84ybv7+/7O3t9c477+jIkSOaPXu2tm/fXqBjyA97e3u9/vrrmjVrlv79738rKSlJgwYNUsOGDdWqVStzux07dsjX11cPP/xwkfcRAEqin3/+WdOmTVN8fLyOHz+uxYsXy8nJSXXq1JF0K/dt375dp0+fNgufXn/9dYWHh2v9+vX69ddfFRoaqjp16igwMPCu+1GvXj19++23SkxMVHR0tMaMGWPR/uqrryo1NVWDBw/WL7/8osTERM2YMePuB34b8nXR+u233/TDDz/k+apA8jUA3B13d3eVKlVKc+fO1bFjxzRv3jytXr3aYpuJEyfqnXfe0cKFC3Xs2DH99NNPWrFiRa7He/nllzVnzhwzRw8aNEgODg55fn7W94f169fr999/t8hrISEhmjhxopo2bSpfX1+r42jfvr127NhhLpctW1ajRo3Sa6+9ppUrVyo5OVmbNm3Sxo0bJclc/8knn+jQoUOaNGmS9u7dq1deeSXX4wcEBGj79u1auXKlkpKSNGzYsHuexfNu/P3vf9fx48c1fvx4HTlyRLGxsXrzzTf11FNPydnZ2dxux44d+vvf/17k/QOAkiggIEBfffWV4uLitHv3bo0dO9bq9nc6rypdurR69+6tuLg4HTlyRN9//71Gjx5t9ZiTJ0/WunXrdOzYMe3evVs7duywmF0/NxUrVtTAgQP1xhtv6KefflJ8fLxCQ0PVvn17NWzY0LYg5KJOnTo6ePCgkpKS9PvvvyszM1Mvv/yy9u/fr0mTJunQoUP65JNP9O2339p0fXzo0KGaMmWKtmzZogMHDuiVV15RmTJlzEkuvLy89PDDD2vKlCk6cuSIli5dquXLl5v729nZafz48Xr77be1YMECHT16VLt379YHH3ygLVu25PqZWQ+CZf8u0LZtW/n7+6t79+7asWOHjh49qn/9619mkbSt1yls/T0qLKdPn9aXX36pQ4cOadeuXXr22Wd15cqVHP3hu8D9jyIroIDc6eSxICUnJ2vChAn6+eefdfz4ca1YsUKXLl2St7d3rtu3bdtWJ0+e1IkTJ8x148aN06ZNmzRhwgQlJSVp//79+uSTTyTZfqP7Tkm5KI0dO1bR0dFKSUnR1q1b1a1bN7Vq1criHcRxcXEqV66cmjdvXix9BADkZG9vrzVr1ujKlSvy9/dXSEiI+vfvf08nTIMHD5aLi4saNmxoFgTfyZAhQ5SYmKimTZtq7ty5Wrp0qZlfJ06cKE9PTz355JPq3r27hgwZUuDT/zdr1kz16tXT+vXrzXUdOnTQggUL9Nlnn+mRRx5R9+7dzcLmihUrav369frll1/06KOPasyYMZo8ebJefPHFXI9ftWpVLViwQEuXLlWTJk20f/9+vfzyywU6hvwKCwvT0KFDNXDgQLVs2VLXrl3TunXrLG4CrFixQgMGDCiW/gFASVSpUiX9+OOP6tChg3x9fbVs2TKtXLnSfKJ0ypQp+vnnn1W7dm09++yzkqQxY8YoJCREoaGhat68uS5fvqy1a9davel6Jx9++KEMw9Bjjz2mIUOG6J133rFod3V11Q8//KBDhw7J399fTz31lPlKwYJAvi5aS5YsUfXq1dW+fftc28nXAHB3qlWrps8++0xz585Vo0aNtGnTJoWFhVlsM3jwYIWHhys8PFy+vr4aMGCAxYwU2YWGhmr48OEaNGiQAgIC1Lx5c6sPmXp4eGjcuHEaMGCAXF1dtWzZMrMtODhYN27cuOMsVtKtm947d+7UuXPnzHXh4eEaMGCAXn31Vfn6+urNN980Z+x44okn9NVXX2nWrFny8/PTqlWrtHr1aouHZLPr3LmzRo4cqaFDh+rpp59W48aN1axZszv2q6A1bNhQq1at0ubNm9WkSRN16dJFHh4eFnG7ceOGVq9eTV4EgP/f+PHj1aRJE7Vr1059+/bV+PHjrW5/p/Oqbdu2qXTp0mrfvr0effRRTZw40eJVgblxdHTU6NGj1bBhQwUFBalFixaaOnXqHfv+4Ycf6qmnnlKXLl0UEBCgmjVrasmSJfkfvBU9evRQixYt5O/vL1dXV6WmpqpOnTpavXq1Vq1aJT8/P82aNUtffvmlnnjiiXwfd+LEifrb3/6mLl26qHPnzgoJCVG5cuXk5OQk6dYMTEuXLlVMTIwaN26s7777TuPGjbM4xogRIzR9+nRNnz5dvr6+6tKli2JjY1WzZs08Pzc0NDRHEfjKlSvl4eGhzp07q0mTJpo3b54545it1yls/T0qLIZhaN68eWrSpIkCAwNVtmxZ/fTTTxbXHPbu3as//vjDvCaD+5OdUVhztwMPoBs3bmjy5MlasGCBzp8/r3r16mn69OkKCgrSwoULNXHiRItCpylTpuiHH35QdHS0pFsnthMnTtSgQYOUkpIiT09PHT58WF5eXhb7nzlzRkOGDNHPP/+s9PR01a1bV2FhYVZPert27aq2bdvqzTffNNdFRkZq/PjxSkhIUOXKldWzZ099/PHHkm5NIfnKK68oOjpaFStWVEhIiN5//305Ojrm6Kskbd26VcOHD1dKSooCAwMVEBCgDz/80BxvaGiobty4oaVLl5qff/sxbh/z7fHJ6zjZ9e7dW9u3b1daWpoefvhhBQYGaurUqRY3119//XXduHFDc+bMycf/KgCgMLVq1Updu3bVW2+9VdxdUZs2bfTkk0/m6yS7MC1btkyff/55nk8HPSiSk5PVqlUrHTx4MNdXEwEAHkzk65KFfA0A96f4+Hi1atVKv/32m6pWrXrH7YcOHaqaNWtq0qRJRdC7kmvJkiVatGiRfvjhh+LuCgAA+u9//yt3d3fFxsbK39+/0D7n5s2baty4sT799FMFBAQU2uf8Fbz00kvy9PTUxIkTi7srKEQUWQEPiJiYGIWEhOg///nPPT2x/Fd38eJFeXl5KSYmRnXr1i3u7gDAA+vatWv69ddf1aZNGy1evLhEPNlRUm7aGoah6dOn6+WXX1alSpWKtS/FKTo6WhcuXFBQUFBxdwUAUIKQr0sW8jUA3F+uX7+u3377TSNGjFDFihX19ddf52u/U6dOacWKFXrttdcKuYcl29KlS9W0aVM98sgjxd0VAMAD6OjRo4qOjlbr1q11/vx5jR07VmfOnFFiYqL5ysDCEhMTo3Pnzqlr166F+jklWWZmpqZNm6bXXntN5cuXL+7uoBBRZAU8QD799FMFBQXJ3d29uLtSbBISErR//3717du3uLsCAA+0bdu2qUuXLnr++ee1YMGCIikA7tSpk3766adc2xITE9W/f/8ScdMWAIAHGfkaAIDis23bNrVr105+fn5at27dA30dGQBwf7vTuee95MD33ntP7733Xq5t8+fPL7R7lMeOHVOfPn3066+/qkyZMnryySf18ccfq3bt2oXyecCDiiIrAAAAPBB+++03XblyJdc2Dw8P85W4AACg+JCvAQAAAACFrTDPPc+fP6/z58/n2ubm5qaKFSve9bEBFD+KrAAAAAAAAAAAAAAAAADACvvi7gAAAAAAAAAAAAAAAAAAlGQUWQEAAAAAAAAAAAAAAACAFXf/MtG/ICcnJ7m6uhbIsTIyMuTk5FQgx7rfESvbEC/bEK/8I1a2Kch4nTt3ThkZGQVyrL+Sgsq7/O7ahnjZhnjlH7GyDfGyTUHFi5x77/jdtQ3xyj9iZRviZRvilX+c69478m7xIV75R6xsQ7zyj1jZhrx778i7xYNY2YZ42YZ45R+xsk1R5d0HqsjK1dVVJ06cKJBjRUZGKjAwsECOdb8jVrYhXrYhXvlHrGxTkPGqVatWgRznr6ag8i6/u7YhXrYhXvlHrGxDvGxTUPEi5947fndtQ7zyj1jZhnjZhnjlH+e69468W3yIV/4RK9sQr/wjVrYh79478m7xIFa2IV62IV75R6xsU1R5l9cFAgAAAAAAAAAAAADk4eGhBg0aqEmTJmrSpIkiIiIkSWfPnlXHjh3l7e0tPz8/RUdHm/tcvnxZffr0kZeXl3x8fLRy5UqzLTMzUyNGjFC9evXk5eWluXPnFvmYAAAoKA/UTFYAAAAAAAAAAAAAgLx9++238vPzs1gXFhamli1bauPGjYqLi1OPHj109OhROTo6asaMGXJyctKRI0eUnJysVq1aqW3btqpcubKWLl2qxMREHTp0SBcuXFCzZs3Url07NWjQoJhGBwDA3WMmKwAAAAAAAAAAAABAnpYvX67hw4dLkvz9/eXm5mbOZhUREWG2eXp6KiAgQGvWrDHbhg0bJgcHB7m4uKhXr1765ptvimcQAADcI4qsAAAAAAAAAAAAAACSpL59+6pRo0YaNGiQzp07p7S0NGVmZsrV1dXcxsPDQ6mpqZKk1NRU1alTx+Y2AAD+anhdIAAAAAAAAAAAAABA27dvl7u7u65fv66JEycqJCRES5YskZ2dncV2hmFYLGdvt6Utu5kzZ2rmzJnmcnp6uiIjI+9qHLe7evVqgR3rfkesbEO8bEO88o9Y2aao4kWRFQAAAAAAAAAAAABA7u7ukqRSpUrpjTfekI+Pj6pUqSJJOnfunDmb1fHjx81t3d3dlZKSYtHWuXNnizZ/f/8c+91u5MiRGjlypLlcq1YtBQYGFsi4IiMjC+xY9ztiZRviZRvilX/EyjZFFS9eFwgAAAAAAAAAAAAAD7g///xT6enp5vKyZcvUtGlTSVLPnj01Z84cSVJcXJxOnz6tJ598MkdbcnKyoqKi1LVrV7Nt/vz5unnzps6fP6+IiAj17t27KIcFAECBYSYrAAAAAAAAAAAAAHjAnTlzRt27d9fNmzdlGIbq1q2rxYsXS5KmTZum4OBgeXt7q3Tp0lqyZIkcHW/dah4zZowGDhwoLy8v2dvba86cOXJxcZEkBQcHKy4uTj4+Pua2vr6+xTNAAADuEUVWAAAAAAAAAAAAAPCAq1u3rvbu3Ztrm5ubmzZt2pRrW/ny5RUREZFrm4ODgznLFQAAf3W8LhAAAAAAAAAAAAAAAAAArKDICgAAAAAAAAAAAAAAAACsoMgKAAAAAAAAAAAAAAAAAKygyAoAAAAAAAAAAAAAAAAArKDICgAAAAAAAAAAAAAAAACsoMgKAAAAAAAAAAAAAAAAAKygyAoAAAAAAAAAAAAAAAAArKDICgAAAAAAAAAAAAAAAACsoMgKAAAAAAAAAAAAAAAAAKygyAoAAAAAAAAAAAAAAAAArKDICgAAAAAAAAAAAAAAAACsoMgKAAAAAAAAAAAAAAAAAKygyAoAAAAAAAAAAAAAAAAArKDICgAAAAAAAAAAAAAAAACsoMgKAAAAAAAAAAAAAAAAAKygyAoAAAAAAAAAAAAAAAAArKDICgAAAAAAAAAAAAAAAACscCzuDvxVnb5wVR5h/y7ublhI+SCouLsAAECBK4k5VyLvAgDuT+RdAACKDnkXAICiQ94FABQEZrICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAABU311xAAAgAElEQVQAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAAriqzIKjw8XHZ2dkpISJAknT17Vh07dpS3t7f8/PwUHR1tbnv58mX16dNHXl5e8vHx0cqVK822zMxMjRgxQvXq1ZOXl5fmzp1bVEMAAAAAAAAAAAAAAAAA8AByLIoPiY+P165du+Tu7m6uCwsLU8uWLbVx40bFxcWpR48eOnr0qBwdHTVjxgw5OTnpyJEjSk5OVqtWrdS2bVtVrlxZS5cuVWJiog4dOqQLFy6oWbNmateunRo0aFAUQwEAAAAAAAAAAAAAAADwgCn0mawyMjI0fPhwzZ07V3Z2dub65cuXa/jw4ZIkf39/ubm5mbNZRUREmG2enp4KCAjQmjVrzLZhw4bJwcFBLi4u6tWrl7755pvCHgYAAAAAAAAAAAAAAACAB1Shz2Q1adIk9evXT56enua6tLQ0ZWZmytXV1Vzn4eGh1NRUSVJqaqrq1KmT77bdu3fn+tkzZ87UzJkzzeX09HRFRkYWyLgqlZbCm90okGMVlIIaW0G7evVqie1bSUS8bEO88o9Y2YZ4AQAAAAAAAAAAAACyFGqRVUxMjOLi4vTBBx/kaMs+q5UkGYaRZ7stbdmNHDlSI0eONJdr1aqlwMDA/HX+DhYtX6PJ8UXytsV8S/mgYMZW0CIjIwss7g8C4mUb4pV/xMo2xAsAAAAAAAAAAAAAkKVQXxcYFRWlpKQkeXp6ysPDQydOnFBgYKBiY2MlSefOnTO3PX78uNzd3SVJ7u7uSklJsbkNAAAAAAAAAAAAAAAAAApaoRZZhYWF6eTJk0pJSVFKSopq1aqlyMhIderUST179tScOXMkSXFxcTp9+rSefPJJSbJoS05OVlRUlLp27Wq2zZ8/Xzdv3tT58+cVERGh3r17F+YwAAAAAAAAAAAAAAAAADzAiu19d9OmTVNwcLC8vb1VunRpLVmyRI6Ot7ozZswYDRw4UF5eXrK3t9ecOXPk4uIiSQoODlZcXJx8fHzMbX19fYtrGAAAAAAAAAAAAAAAAADuc0VaZJX9NX9ubm7atGlTrtuVL19eERERubY5ODiYs1wBAAAAAAAAAAAAAAAAQGEr1NcFAgAAAAAAAAAAAAAAAMBfHUVWAAAAAAAAAAAAAAAAAGAFRVYAAAAAAAAAAAAAAAAAYAVFVgAAAAAAAAAAAAAAAABgBUVWAAAAAAAAAAAAAAAAAGAFRVYAANyHwsPDZWdnp4SEBEnS2bNn1bFjR3l7e8vPz0/R0dHmtpcvX1afPn3k5eUlHx8frVy50mzLzMzUiBEjVK9ePXl5eWnu3LlFPhYAAAAAAAAAAAAAKG6Oxd0BAABQsOLj47Vr1y65u7ub68LCwtSyZUtt3LhRcXFx6tGjh44ePSpHR0fNmDFDTk5OOnLkiJKTk9WqVSu1bdtWlStX1tKlS5WYmKhDhw7pwoULatasmdq1a6cGDRoU4wgBAAAAAAAAAAAAoGgxkxUAAPeRjIwMDR8+XHPnzpWdnZ25fvny5Ro+fLgkyd/fX25ubuZsVhEREWabp6enAgICtGbNGrNt2LBhcnBwkIuLi3r16qVvvvmmiEcFAAAAAAAAAAAAAMWLIisAAO4jkyZNUr9+/eTp6WmuS0tLU2ZmplxdXc11Hh4eSk1NlSSlpqaqTp06NrcBAAAAAAAAAAAAwIOC1wUCAHCfiImJUVxcnD744IMcbdlntZIkwzDybLelLbuZM2dq5syZ5nJ6eroiIyPz13krKpWWwpvduOfjFLSCGFthuHr1aontW0lEvPKPWNmGeNmGeAEAAAAAAAAASjqKrAAAuE9ERUUpKSnJnMXqxIkTCgwM1BdffCFJOnfunDmb1fHjx+Xu7i5Jcnd3V0pKikVb586dLdr8/f1z7He7kSNHauTIkeZyrVq1FBgYeM/jWrR8jSbHl7yvLCkf3PvYCkNkZGSBxP1BQbzyj1jZhnjZhngBAAAAAAAAAEo6XhcIAMB9IiwsTCdPnlRKSopSUlJUq1YtRUZGqlOnTurZs6fmzJkjSYqLi9Pp06f15JNPSpJFW3JysqKiotS1a1ezbf78+bp586bOnz+viIgI9e7du3gGCAAAAAAAAAAAAADFpORNCwEAAArctGnTFBwcLG9vb5UuXVpLliyRo+OtrwFjxozRwIED5eXlJXt7e82ZM0cuLi6SpODgYMXFxcnHx8fc1tfXt9jGAQAAAAAAAAAAAADFgSIrAADuUykpKea/3dzctGnTply3K1++vCIiInJtc3BwMGe5AgAAAAAAAAAAAIAHFa8LBAAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAAAAAAAAAAAAAAKyiyAgAAAAAAAAAAAAAAAAArKLICAAAAAAAAAAAAAAAAACsosgIAAAAA4C6Fh4fLzs5OCQkJkqSzZ8+qY8eO8vb2lp+fn6Kjo81tL1++rD59+sjLy0s+Pj5auXKl2ZaZmakRI0aoXr168vLy0ty5c4t8LAAAlHTkXQAAAABAcSr0IqsOHTqocePGatKkiZ566int27dPktSmTRvVrVtXTZo0UZMmTfTRRx+Z+3ACDAAAAAAo6eLj47Vr1y65u7ub68LCwtSyZUsdPnxYCxYsUN++fXXjxg1J0owZM+Tk5KQjR44oMjJSr7zyiv744w9J0tKlS5WYmKhDhw4pNjZW06dPV1JSUrGMCwCAkoi8CwAAAAAoboVeZLV8+XIdOHBA+/bt06hRozRw4ECzbfbs2dq3b5/27dunN99801zPCTAAAAAAoCTLyMjQ8OHDNXfuXNnZ2Znrly9fruHDh0uS/P395ebmZs6qERERYbZ5enoqICBAa9asMduGDRsmBwcHubi4qFevXvrmm2+KeFQAAJRM5F0AAAAAQEngWNgf4OzsbP77woULsre/c11XRESEFi5cKMnyBDg0NDTPE+ApU6YU0ggAAAAAALA0adIk9evXT56enua6tLQ0ZWZmytXV1Vzn4eGh1NRUSVJqaqrq1KmT77bdu3fn+tkzZ87UzJkzzeX09HRFRkYWyLgqlZbCm90okGMVpIIaX0G7evVqie1bSUOsbEO8bEO88u+vGivybtEqqb8jf9Xf3+JArGxDvPKPWNmGeAEAcP8p9CIrSerfv7+2bt0qSdq4caO5fsyYMXrrrbfUsGFDvf/++6pbt66kgjsBBgAAAACgoMXExCguLk4ffPBBjrbss2tIkmEYebbb0pbdyJEjNXLkSHO5Vq1aCgwMzF/n72DR8jWaHF8klwpskvJBwYyvoEVGRhZY7O93xMo2xMs2xCv//oqxIu8WPfLuXx+xsg3xyj9iZRviBQDA/adIzuAWL14sSVq0aJHGjBmj9evXa8mSJapdu7YMw9CcOXP0zDPPKDEx0dynIE6AH7SnjEpqNTyV+rYhXrYhXvlHrGxDvAAAQF6ioqKUlJRkzqZx4sQJBQYG6osvvpAknTt3zpxV4/jx43J3d5ckubu7KyUlxaKtc+fOFm3+/v459gMA4EFG3gUAAAAAlBRF+phMSEiIhg0bprS0NNWuXVvSrYKpV199VaNHj1ZaWpqqVKlSYCfAD9pTRjxhdH8gXrYhXvlHrGxDvAAAQF7CwsIUFhZmLnt4eGjdunXy8/NTz549NWfOHE2ZMkVxcXE6ffq0nnzySUky2xYuXKjk5GRFRUVp3rx5Ztv8+fPVrVs3XbhwQRERERYzQQMA8KAi7wIAAAAASgr7wjz4//73P508edJcXrVqlapUqaJKlSrpzJkz5vrvvvtObm5uqlKliqT/OwGWZJ4Ad+3a1WybP3++bt68qfPnzysiIkK9e/cuzGEAAAAAAJAv06ZN086dO+Xt7a3Q0FAtWbJEjo63HtAZM2aMrly5Ii8vLwUGBmrOnDlycXGRJAUHB6t+/fry8fGRv7+/xowZI19f3+IcCgAAJR55FwAAAABQlAp1KqYLFy6oe/fuunLliuzt7eXq6qp169bp2rVrCgoKUkZGhuzt7VW1alWtXbvW3G/MmDEaOHCgvLy8ZG9vn+MEOC4uTj4+Pua2nAADAAAAAIpLSkqK+W83Nzdt2rQp1+3Kly+viIiIXNscHBzMh40AAEDeyLsAAAAAgOJSqEVWtWvXVmxsbK5tu3fvznM/ToABAAAAAAAAAAAAAAAAlBSF+rpAAAAAAAAAAAAAAAAAAPiro8gKAAAAAAAAAAAAAGAKDw+XnZ2dEhISJElnz55Vx44d5e3tLT8/P0VHR5vbXr58WX369JGXl5d8fHy0cuVKsy0zM1MjRoxQvXr15OXlpblz5xb5WAAAKCiF+rpAAAAAAAAAAAAAAMBfR3x8vHbt2iV3d3dzXVhYmFq2bKmNGzcqLi5OPXr00NGjR+Xo6KgZM2bIyclJR44cUXJyslq1aqW2bduqcuXKWrp0qRITE3Xo0CFduHBBzZo1U7t27dSgQYNiHCEAAHeHmawAAAAAAAAAAAAAAMrIyNDw4cM1d+5c2dnZmeuXL1+u4cOHS5L8/f3l5uZmzmYVERFhtnl6eiogIEBr1qwx24YNGyYHBwe5uLioV69e+uabb4p4VAAAFAyKrAAAuI906NBBjRs3VpMmTfTUU09p3759kqQ2bdqobt26atKkiZo0aaKPPvrI3IepnAEAAAAAAAAAkjRp0iT169dPnp6e5rq0tDRlZmbK1dXVXOfh4aHU1FRJUmpqqurUqWNzGwAAfzW8LhAAgPvI8uXL5ezsLElavXq1Bg4cqPj4eEnS7Nmz9cwzz+TYh6mcAQAAAAAAAAAxMTGKi4vTBx98kKMt+6xWkmQYRp7ttrRlN3PmTM2cOdNcTk9PV2RkZP46fweVSkvhzW4UyLEKUkGNryBdvXq1RParpCJetiFe+UesbFNU8aLICgCA+0hWgZUkXbhwQfb2d560MiIiQgsXLpRkOZVzaGhonlM5T5kypZBGAAAAAAAAAAAoDlFRUUpKSjJnsTpx4oQCAwP1xRdfSJLOnTtnzmZ1/Phxubu7S5Lc3d2VkpJi0da5c2eLNn9//xz73W7kyJEaOXKkuVyrVi0FBgYWyNgWLV+jyfEl79Z4ygcFM76CFBkZWWBxfxAQL9sQr/wjVrYpqniVvEwCAP8fe3cbm+V53o//64fhCNQMTDz3BZgn40LHKoSEBh1FBWmB0ARFCQ9Dk5OKSoyVsmjOPFFNUdttSsOE3IrGCF79SmAKZgsJXdRiUjVDQl0boyiTJrryEBsviwgMhjMVnA7s/4v8czc07K6d3L5x8OcjRcrl47pun+chpIOHr88L+EgeeeSRvPzyy0mSI0eOFL7e2tqar371q/n0pz+db37zm5k5c2aS4R/lfOLEiVt+35H6KSM/YTQ8frJhePRr6PRqePRrePQLAAAAbr9t27Zl27Zthevp06fnxRdfzLx587J27dq0t7fn61//erq6unL+/PksWbIkSQq17373u+nu7s6xY8eye/fuQm3Pnj156KGH0tfXl46Ojpv+3hoAPk6ErADgDvPMM88kSfbu3ZvW1tZ8//vfz759+zJ16tQMDg6mvb09999/f06ePFl4phRHOY/UTxn5CaPh8ZMNw6NfQ6dXw6Nfw6NfAAAAMLpt3749zc3NmT17dsaNG5d9+/aluvrdv7dtbW3Nxo0b09jYmMrKyrS3t6e2tjZJ0tzcnK6urjQ1NRXunTt37m3bBwB8FKPvXywBgJJ49NFHs3nz5ly6dClTp05N8m5g6itf+Ur+4i/+IpcuXcrkyZNLdpQzAAAAAAB3jp6ensL/19fX5+jRo7e8b8KECeno6LhlraqqKu3t7SOxPAAou8rbvQAAoDTefvvtvPnmm4Xr559/PpMnT87dd9+dt956q/D15557LvX19Zk8eXKSXx3lnKRwlPPq1asLtT179uTGjRu5fPlyOjo6sn79+jLuCgAAAAAAAOD2c5IVANwh+vr68vDDD+fatWuprKxMXV1dXnzxxfzyl7/MF77whbzzzjuprKzMPffck+9973uF5xzlDAAAAAAAAFCckBUA3CGmTp2aV1555Za1EydO/J/POcoZAAAAAAAAoDivCwQAAAAAAAAAAChCyAoAAAAAAAAAAKAIISsAAAAAAAAAAIAihKwAAAAAAAAAAACKELICAAAAAAAAAAAoQsgKAAAAAAAAAACgCCErAAAAAAAAAACAIoSsAAAAAAAAAAAAihCyAgAAAAAAAAAAKELICgAAAAAAAAAAoAghKwAAAAAAAAAAgCKErAAAAAAAAAAAAIoQsgIAAAAAAAAAAChCyAoAAAAAAAAAAKAIISsAAAAAAAAAAIAihKwAAAAAAAAAAACKELICAAAAAAAAAAAoQsgKAAAAAAAAAACgCCErAAAAAAAAAACAIoSsAAAAAAAAAAAAihCyAgAAAAAAAAAAKELICgAAAAAAAAAAoAghKwAAAAAAAAAAgCKErAAAAAAAAAAAAIoQsgIAAAAAAAAAAChCyAoAAAAAAAAAAKAIISsAAAAAAAAAAIAihKwAAAAAAAAAAACKELICAAAAAAAAAAAoQsgKAAAAAAAAAACgCCErAAAAAAAAAACAIoSsAAAAAAAAAAAAihCyAgAAAAAAAAAAKELICgAAAAAAAAAAoAghKwAAAAAAAAAAgCKErAAAAAAAAAAAAIoQsgIAAAAAAAAAAChCyAoAAAAAAAAAAKAIISsAAAAAAAAAAIAihKwAAAAAAAAAAACKELICAAAAAAAAAAAoQsgKAAAAAAAAAACgCCErAAAAAAAAAACAIoSsAAAAAAAAAAAAihCyAgAAAAAAAAAAKELICgAAAAAAAAAAoIgRD1nde++9+cxnPpP58+fnc5/7XF577bUkyYULF7Jy5crMnj078+bNy/HjxwvPXL16NRs2bEhjY2Oamppy6NChQm1gYCBbt27NrFmz0tjYmF27do30FgAAAAAAAAAAgDGseqS/wcGDBzNx4sQkyQsvvJCNGzfm1VdfzbZt27Jo0aIcOXIkXV1dWbNmTc6ePZvq6urs2LEjNTU1OXPmTLq7u7N48eIsW7YskyZNyv79+3Py5MmcOnUqfX19WbBgQZYvX545c+aM9FYAAAAAAAAAAIAxaMRPsnovYJUkfX19qax891sePHgwW7ZsSZIsXLgw9fX1hdOsOjo6CrUZM2Zk6dKlOXz4cKG2efPmVFVVpba2NuvWrcuBAwdGehsAAAAAAAAAAMAYNeInWSXJI488kpdffjlJcuTIkVy6dCkDAwOpq6sr3DN9+vT09vYmSXp7ezNt2rQh106cOFGObQAAAAAAAAAAAGNQWUJWzzzzTJJk7969aW1tzb59+1JRUXHTPYODgzddv78+nNr7tbW1pa2trXB95cqVdHZ2Dn8Dt3D3uOQbC66X5LNKpVR7K7X+/v5Ru7bRSL+GR7+GTq+GR78AAAAAAAAAeE9ZQlbvefTRR7N58+bC9cWLFwunWZ07dy4NDQ1JkoaGhvT09NxUW7Vq1U21hQsXfuC5X9fS0pKWlpbC9ZQpU7JixYqS7GXvwcP52qtlbd9v1PNUafZWap2dnSXr+1igX8OjX0OnV8OjXwAAAAAAAAC8p3IkP/ztt9/Om2++Wbh+/vnnM3ny5NTW1mbt2rVpb29PknR1deX8+fNZsmRJktxU6+7uzrFjx7J69epCbc+ePblx40YuX76cjo6OrF+/fiS3AQAAAAAAAAAAjGEjehRTX19fHn744Vy7di2VlZWpq6vLiy++mIqKimzfvj3Nzc2ZPXt2xo0bl3379qW6+t3ltLa2ZuPGjWlsbExlZWXa29tTW1ubJGlubk5XV1eampoK986dO3cktwEAAAAAAAAAAIxhIxqymjp1al555ZVb1urr63P06NFb1iZMmJCOjo5b1qqqqgqnXAEAAAAAAAAAAIy0EX1dIAAAAAAAAAAAwMedkBUAAAAAAAAAAEARQlYAcAe5995785nPfCbz58/P5z73ubz22mtJkgsXLmTlypWZPXt25s2bl+PHjxeeuXr1ajZs2JDGxsY0NTXl0KFDhdrAwEC2bt2aWbNmpbGxMbt27Sr7ngAAAAAAAABut+rbvQAAoHQOHjyYiRMnJkleeOGFbNy4Ma+++mq2bduWRYsW5ciRI+nq6sqaNWty9uzZVFdXZ8eOHampqcmZM2fS3d2dxYsXZ9myZZk0aVL279+fkydP5tSpU+nr68uCBQuyfPnyzJkz5zbvFAAAAAAAAKB8nGQFAHeQ9wJWSdLX15fKyndH/cGDB7Nly5YkycKFC1NfX184zaqjo6NQmzFjRpYuXZrDhw8Xaps3b05VVVVqa2uzbt26HDhwoJxbAgAAAAAAALjtnGQFAHeYRx55JC+//HKS5MiRI7l06VIGBgZSV1dXuGf69Onp7e1NkvT29mbatGlDrp04caIc2wAAAAAAAAAYNYSsAOAO88wzzyRJ9u7dm9bW1uzbty8VFRU33TM4OHjT9fvrw6m9X1tbW9ra2grXV65cSWdn5/A38GvuHpd8Y8H1j/w5pVaKvY2E/v7+Ubu20Ui/hk6vhke/hke/AAAAAAAY7YSsAOAO9eijj2bz5s2F64sXLxZOszp37lwaGhqSJA0NDenp6bmptmrVqptqCxcu/MBzv66lpSUtLS2F6ylTpmTFihUfeR97Dx7O114dfb9l6Xnqo+9tJHR2dpak72OFfg2dXg2Pfg2PfgEAAAAAMNpV3u4FAACl8fbbb+fNN98sXD///POZPHlyamtrs3bt2rS3tydJurq6cv78+SxZsiRJbqp1d3fn2LFjWb16daG2Z8+e3LhxI5cvX05HR0fWr19f5p0BAAAAAAAA3F6j71gIAOBD6evry8MPP5xr166lsrIydXV1efHFF1NRUZHt27enubk5s2fPzrhx47Jv375UV7/724DW1tZs3LgxjY2NqaysTHt7e2pra5Mkzc3N6erqSlNTU+HeuXPn3rY9AgAAAAAAANwOQlYAcIeYOnVqXnnllVvW6uvrc/To0VvWJkyYkI6OjlvWqqqqCqdcAQAAAAAAAIxVXhcIAAAAAAAAAABQhJAVAAAAAAAAAABAEUJWAAAAAAAAAAAARQhZAQAAAAAAAAAAFCFkBQAAAAAAAAAAUISQFQAAAAAAAAAAQBFCVgAAAAAAAAAAAEUIWQEAAAAAAAAAABQhZAUAAAAAAAAAAFCEkBUAAAAAAAAAAEARQlYAAAAAAAAAAABFCFkBAAAAAAAAAAAUIWQFAAAAAAAAAABQhJAVAAAAAAAAAABAEUJWAAAAAAAAAAAARQhZAQAAAAAAAAAAFCFkBQAAAAAAAAAAUISQFQAAAAAAAAAAQBFCVgAAAAAAAAAAAEUIWQEAAAAAAAAAABQhZAUAAAAAAAAAAFCEkBUAAAAAAAAAAEARQlYAAAAAAAAAAABFCFkBAAAAAAAAAAAUIWQFAAAAAAAAAABQhJAVAAAAAAAAAABAEUJWAAAAAAAAAAAARQhZAQAAAAAAAAAAFCFkBQAAAAAAAAAAUISQFQAAAAAAAAAAQBFCVgAAAAAAAAAAAEUIWQEAAMCHcO+99+Yzn/lM5s+fn8997nN57bXXkiQXLlzIypUrM3v27MybNy/Hjx8vPHP16tVs2LAhjY2NaWpqyqFDhwq1gYGBbN26NbNmzUpjY2N27dpV9j0BwGhl7gIAAHC7Vd/uBQAAAMDH0cGDBzNx4sQkyQsvvJCNGzfm1VdfzbZt27Jo0aIcOXIkXV1dWbNmTc6ePZvq6urs2LEjNTU1OXPmTLq7u7N48eIsW7YskyZNyv79+3Py5MmcOnUqfX19WbBgQZYvX545c+bc5p0CwO1n7gIAAHC7OckKAAAAPoT3/qE3Sfr6+lJZ+e4fsQ8ePJgtW7YkSRYuXJj6+vrCqRodHR2F2owZM7J06dIcPny4UNu8eXOqqqpSW1ubdevW5cCBA+XcEgCMWuYuAAAAt5uTrAAAAOBDeuSRR/Lyyy8nSY4cOZJLly5lYGAgdXV1hXumT5+e3t7eJElvb2+mTZs25NqJEyc+8D3b2trS1tZWuKPwQbwAACAASURBVL5y5Uo6OztLsp+7xyXfWHC9JJ9VSqXaX6n19/eP2rWNNno1PPo1PPo1dB/3Xpm75TFaf4183H/9lpNeDY9+DZ1eDY9+AcCdR8gKAAAAPqRnnnkmSbJ37960trZm3759qaiouOmewcHBm67fXx9O7T0tLS1paWkpXE+ZMiUrVqz4cBv4NXsPHs7XXh19f1XQ81Rp9ldqnZ2dJev9nU6vhke/hke/hu7j3itztzzM3Y8/vRoe/Ro6vRoe/QKAO4/XBQIAAMBH9OijjxZO1kiSixcvFv7/3LlzaWhoSJI0NDSkp6dn2DUA4FfMXQAAAG4HISsAAAAYprfffjtvvvlm4fr555/P5MmTU1tbm7Vr16a9vT1J0tXVlfPnz2fJkiVJclOtu7s7x44dy+rVqwu1PXv25MaNG7l8+XI6Ojqyfv36Mu8MAEYfcxcAAIDRYPSdRQwAAACjXF9fXx5++OFcu3YtlZWVqaury4svvpiKiops3749zc3NmT17dsaNG5d9+/aluvrdP363trZm48aNaWxsTGVlZdrb21NbW5skaW5uTldXV5qamgr3zp0797btEQBGC3MXAACA0UDICgAAAIZp6tSpeeWVV25Zq6+vz9GjR29ZmzBhQjo6Om5Zq6qqKpy2AQD8irkLAOVz77335vz586msrMwnPvGJfOc738n8+fNz4cKFPPLIIzl79mxqamqye/fuwumRV69ezZe+9KV0dXWlsrIyTz31VB566KEkycDAQB577LF8//vfT0VFRVpaWvLlL3/5dm4RAD40ISsAAAAAAAAAcvDgwUycODFJ8sILL2Tjxo159dVXs23btixatChHjhxJV1dX1qxZk7Nnz6a6ujo7duxITU1Nzpw5k+7u7ixevDjLli3LpEmTsn///pw8eTKnTp1KX19fFixYkOXLl2fOnDm3eacAMHyVt3sBAAAAAAAAANx+7wWskndf2VtZ+e4/Jx88eDBbtmxJkixcuDD19fU5fvx4kqSjo6NQmzFjRpYuXZrDhw8Xaps3b05VVVVqa2uzbt26HDhwoJxbAoCScZIVAAAAAAAAAEmSRx55JC+//HKS5MiRI7l06VIGBgZSV1dXuGf69Onp7e1NkvT29mbatGlDrp04caIc2wCAkhOyAgAAAAAAACBJ8swzzyRJ9u7dm9bW1uzbty8VFRU33TM4OHjT9fvrw6m9X1tbW9ra2grXV65cSWdn5/A3cAt3j0u+seB6ST6rlEq1v1Lq7+8flesarfRrePRr6PRqeMrVLyErAAAAAAAAAG7y6KOPZvPmzYXrixcvFk6zOnfuXBoaGpIkDQ0N6enpuam2atWqm2oLFy78wHO/rqWlJS0tLYXrKVOmZMWKFSXZy96Dh/O1V0ffP433PFWa/ZVSZ2dnyfo+FujX8OjX0OnV8JSrX5Uj/h0AAAAAAAAAGNXefvvtvPnmm4Xr559/PpMnT05tbW3Wrl2b9vb2JElXV1fOnz+fJUuWJMlNte7u7hw7diyrV68u1Pbs2ZMbN27k8uXL6ejoyPr168u8MwAojdEX1wUAAAAAAACgrPr6+vLwww/n2rVrqaysTF1dXV588cVUVFRk+/btaW5uzuzZszNu3Ljs27cv1dXv/lNza2trNm7cmMbGxlRWVqa9vT21tbVJkubm5nR1daWpqalw79y5c2/bHgHgoxCyAgAAAAAAABjjpk6dmldeeeWWtfr6+hw9evSWtQkTJqSjo+OWtaqqqsIpVwDwced1gQAAAAAAAAAAAEWMaMiqv78/Dz74YJqamjJ//vysXLkyPT09SZLPf/7zmTlzZubPn5/58+fnW9/6VuG5q1evZsOGDWlsbExTU1MOHTpUqA0MDGTr1q2ZNWtWGhsbs2vXrpHcAgAAAAAAAAAAMMaN+OsCN23alPvuuy8VFRV5+umns2nTpsJRkjt37sz999//gWd27NiRmpqanDlzJt3d3Vm8eHGWLVuWSZMmZf/+/Tl58mROnTqVvr6+LFiwIMuXL8+cOXNGeisAAAAAAAAAAMAYNKInWd11111ZtWpVKioqkiSLFi3K66+//huf6+joyJYtW5IkM2bMyNKlS3P48OFCbfPmzamqqkptbW3WrVuXAwcOjNwmAAAAAAAAAACAMW3ET7J6v507d+aBBx4oXLe2tuarX/1qPv3pT+eb3/xmZs6cmSTp7e3NtGnTCvdNnz49vb29/2ftxIkTt/x+bW1taWtrK1xfuXIlnZ2dJdnL3eOSbyy4XpLPKpVS7a3U+vv7R+3aRiP9Gh79Gjq9Gh79AgAAAAAAAOA9ZQtZPfnkkzl9+nR2796dJNm3b1+mTp2awcHBtLe35/7778/JkycL9793+lWSDA4O3vRZxWrv19LSkpaWlsL1lClTsmLFio+8lyTZe/BwvvZqWTNqv1HPU6XZW6l1dnaWrO9jgX4Nj34NnV4Nj34BAAAAAAAA8J4RfV3ge3bs2JFDhw7lBz/4QcaPH58kmTp1apJ3A1Nf+cpX8vrrr+fSpUtJkoaGhvT09BSeP3fuXBoaGn5jDQAAAAAAAAAAoNRGPGTV1taWZ599Ni+99FImTpyYJLl+/Xreeuutwj3PPfdc6uvrM3ny5CTJ2rVr097eniTp7u7OsWPHsnr16kJtz549uXHjRi5fvpyOjo6sX79+pLcBAAAAAAAAAACMUSP6vrs33ngjjz/+eGbOnJlly5YlSWpqavKjH/0oX/jCF/LOO++ksrIy99xzT773ve8Vnmttbc3GjRvT2NiYysrKtLe3p7a2NknS3Nycrq6uNDU1Fe6dO3fuSG4DAAAAAAAAAAAYw0Y0ZDVlypQMDg7esnbixIn/87kJEyako6PjlrWqqqrCKVcAAAAAAAAAAAAjbcRfFwgAlEd/f38efPDBNDU1Zf78+Vm5cmV6enqSJJ///Oczc+bMzJ8/P/Pnz8+3vvWtwnNXr17Nhg0b0tjYmKamphw6dKhQGxgYyNatWzNr1qw0NjZm165d5d4WAAAAAAAAwG03oidZAQDltWnTptx3332pqKjI008/nU2bNuXo0aNJkp07d+b+++//wDM7duxITU1Nzpw5k+7u7ixevDjLli3LpEmTsn///pw8eTKnTp1KX19fFixYkOXLl2fOnDnl3hoAAAAAAADAbeMkKwC4Q9x1111ZtWpVKioqkiSLFi3K66+//huf6+joyJYtW5IkM2bMyNKlS3P48OFCbfPmzamqqkptbW3WrVuXAwcOjNwmAAAAAAAAAEYhJ1kBwB1q586deeCBBwrXra2t+epXv5pPf/rT+eY3v5mZM2cmSXp7ezNt2rTCfdOnT09vb+//WTtx4sQtv19bW1va2toK11euXElnZ+dH3sfd45JvLLj+kT+n1Eqxt5HQ398/atc2GunX0OnV8OjX8OgXAAAAAACjnZAVANyBnnzyyZw+fTq7d+9Okuzbty9Tp07N4OBg2tvbc//99+fkyZOF+987/SpJBgcHb/qsYrX3a2lpSUtLS+F6ypQpWbFixUfey96Dh/O1V0ffb1l6nvroexsJnZ2dJen7WKFfQ6dXw6Nfw6NfAAAAAACMdl4XCAB3mB07duTQoUP5wQ9+kPHjxydJpk6dmuTdwNRXvvKVvP7667l06VKSpKGhIT09PYXnz507l4aGht9YAwAAAAAAABgrhKwA4A7S1taWZ599Ni+99FImTpyYJLl+/Xreeuutwj3PPfdc6uvrM3ny5CTJ2rVr097eniTp7u7OsWPHsnr16kJtz549uXHjRi5fvpyOjo6sX7++zLsCAAAAAAAAuL1G37t3AIAP5Y033sjjjz+emTNnZtmyZUmSmpqa/OhHP8oXvvCFvPPOO6msrMw999yT733ve4XnWltbs3HjxjQ2NqaysjLt7e2pra1NkjQ3N6erqytNTU2Fe+fOnVv+zQEAAAAAAADcRkJWAHCHmDJlSgYHB29ZO3HixP/53IQJE9LR0XHLWlVVVeGUKwAAAAAAAICxyusCAQAAAAAAAAAAihCyAgAAAAAAAAAAKELICgAAAAAAAAAAoAghKwAAAAAAAAAAgCKErAAAAAAAAAAAAIoQsgIAAAAAAAAAAChCyAoAAAAAAAAAAKAIISsAAAAAAAAAAIAihKwAAAAAAAAAAACKELICAAAAAAAAAAAoQsgKAAAAAAAAAACgCCErAAAAAAAAAACAIoSsAAAAAAAAAAAAihCyAgAAAAAAAAAAKELICgAAAAAAAAAAoIghh6z+6Z/+KW+//XaSZMeOHVmzZk3+7d/+bcQWBgBjlZkLAOVj7gJA+Zi7AFA+5i4AlN6QQ1Z/9Vd/lbvvvjv/+q//mv379+cP//AP86d/+qcjuTYAGJPMXAAoH3MXAMrH3AWA8jF3AaD0hhyyqq6uTpIcPXo0mzZtyp/8yZ/kF7/4xYgtDADGKjMXAMrH3AWA8jF3AaB8zF0AKL0hh6xu3LiRn/zkJ3nuueeybNmyJMn//u//jtjCAGCsMnMBoHzMXQAoH3MXAMrH3AWA0htyyOpv//Zvs3nz5ixZsiRz587Nz3/+88yePXsk1wYAY5KZCwDlY+4CQPmYuwBQPuYuAJRe9VBvnDZtWl577bXC9ac+9al8/etfH4k1AcCYZuYCQPmYuwBQPuYuAJSPuQsApTfkk6y++MUvDulrAMBHY+YCQPmYuwBQPuYuAJSPuQsApfcbT7L6r//6r1y4cCH9/f352c9+lsHBwSRJX19ffvGLX4z4AgFgrDBzAaB8zF0AKB9zFwDKx9wFgJHzG0NWf//3f59vf/vbefPNN7Nq1arC13/7t387f/mXfzmiiwOAscTMBYDyMXcBoHzMXQAoH3MXAEbObwxZPfbYY3nsscfyN3/zN3niiSfKsSYAGJPMXAAoH3MXAMrH3AWA8jF3AWDk/MaQ1XueeOKJDAwM5Pz587l+/Xrh6w0NDSOyMAAYq8xcACgfcxcAysfcBYDyMXcBoPSGHLLau3dvtm7dmt/6rd9KZWVlkqSioiIXLlwYscUBwFhk5gJA+Zi7AFA+5i4AlI+5CwClN+SQ1V//9V/nlVdeyZw5c0ZyPQAw5pm5AFA+5i4AlI+5CwDlY+4CQOlVDvXGuro6QxgAysDMBYDyMXcBoHzMXQAoH3MXAEpvyCGrhx56KE8//XQuX76cq1evFv4DAErLzAWA8jF3AaB8zF0AKB9zFwBKb8ivC9y2bVuS5M/+7M9SUVGRwcHBVFRU5MaNGyO2OAAYi8xcACgfcxcAysfcBYDyMXcBoPSGHLIaGBgYyXUAAP8/MxcAysfcBYDyMXcBoHzMXQAovSG/LhAAAAAAAAAAAGAsGvJJVpWVlamoqPjA1x0pCQClZeYCQPmYuwBQPuYuAJSPuQsApTfkkNX//M//FP7/2rVreeaZZ/LLX/5yRBYFAGOZmQsA5WPuAkD5mLsAUD7mLgCU3pBfFzhhwoTCf/fcc09aWlpy5MiRkVwbAIxJZi4AlI+5CwDlY+4CQPmYuwBQekMOWf2606dP5z/+4z9KuRYA4BbMXAAoH3MXAMrH3AWA8jF3AeCjG/LrAuvq6grv7b1+/Xpu3LiRnTt3jtjCAGCsMnMBoHzMXQAoH3MXAMrH3AWA0htyyOrEiRO/eqi6Op/85CdTVVU1IosCgLHMzAWA8jF3AaB8zF0AKB9zFwBKb8ivC5w2bVp+53d+J+fPn89//ud/5pe//OVIrgsAxiwzFwDKx9wFgPIxdwGgfMxdACi9IZ9k9eMf/zhr1qxJfX19BgcHc/HixfzjP/5jFi9ePJLrA4Axx8wFgPIxdwGgfMxdACgfcxcASm/IIauWlpb8wz/8Q/7gD/4gybuD+c///M/zk5/8ZMQWBwBjkZkLAOVj7gJA+Zi7AFA+5i4AlN6QXxfY399fGMJJ8tnPfjb9/f0jsigAGMvMXAAoH3MXAMrH3AWA8jF3AaD0hhyyGj9+fH74wx8Wrv/5n/8548ePH5FFAcBYZuYCQPmYuwBQPuYuAJSPuQsApTfk1wV+5zvfyUMPPZSamppUVFTknXfeyXPPPTeSawOAMcnMBYDyMXcBoHzMXQAoH3MXAEpvyCGrN998MydOnMhbb72VwcHBfPKTn8xPf/rTkVwbAIxJZi4AlI+5CwDlY+4CQPmYuwBQekN+XeATTzyRurq6zJs3L7/3e7+Xe+65J0888cRIrg0AxiQzFwDKx9wFgPIxdwGgfMxdACi9IYesfl1FRUUGBgZKuRYA4BbMXAAoH3MXAMrH3AWA8jF3AeCjG3LI6u67777pCMmf/OQn+cQnPjEiiwKAsczMBYDyMXcBoHzMXQAoH3MXAEqveqg3bt++PQ8++GB+93d/N0nys5/9LM8///yILQwAxiozFwDKx9wFgPIxdwGgfMxdACi9IYesFi9enJMnT+Zf/uVfkiSf/exnM3HixKLP9Pf354/+6I9y8uTJjB8/Pp/85Ceze/fuTJ8+PRcuXMgjjzySs2fPpqamJrt3786SJUuSJFevXs2XvvSldHV1pbKyMk899VQeeuihJMnAwEAee+yxfP/7309FRUVaWlry5S9/+cPuHwBGnQ8zcwGAD8fcBYDyMXcBoHzMXQAovSGHrJJk0qRJWbVq1bC+waZNm3LfffeloqIiTz/9dDZt2pSjR49m27ZtWbRoUY4cOZKurq6sWbMmZ8+eTXV1dXbs2JGampqcOXMm3d3dWbx4cZYtW5ZJkyZl//79OXnyZE6dOpW+vr4sWLAgy5cvz5w5c4a1LgAYzT7MzAUAPhxzFwDKx9wFgPIxdwGgtCpH8sPvuuuurFq1KhUVFUmSRYsW5fXXX0+SHDx4MFu2bEmSLFy4MPX19Tl+/HiSpKOjo1CbMWNGli5dmsOHDxdqmzdvTlVVVWpra7Nu3bocOHBgJLcBAAAAAAAAAACMYcM6yeqj2rlzZx544IFcunQpAwMDqaurK9SmT5+e3t7eJElvb2+mTZs25NqJEydu+f3a2trS1tZWuL5y5Uo6OztLspe7xyXfWHC9JJ9VKqXaW6n19/eP2rWNRvo1PPo1dHo1PPoFAAAAAAAAwHvKFrJ68sknc/r06ezevTvXrl0rnG71nsHBwZuu318fTu39Wlpa0tLSUrieMmVKVqxY8aHW/+v2Hjycr71a1ozab9TzVGn2VmqdnZ0l6/tYoF/Do19Dp1fDo18AAAAAAAAAvGdEXxf4nh07duTQoUP5wQ9+kPHjx2fy5MlJkosXLxbuOXfuXBoaGpIkDQ0N6enpGXYNAAAAAAAAAACg1EY8ZNXW1pZnn302L730UiZOnFj4+tq1a9Pe3p4k6erqyvnz57NkyZIP1Lq7u3Ps2LGsXr26UNuzZ09u3LiRy5cvp6OjI+vXrx/pbQAAAAAAAAAAAGPUiIas3njjjTz++OO5cuVKli1blvnz5+f3f//3kyTbt2/Pj3/848yePTtf/OIXs2/fvlRXv/v6vdbW1ly7di2NjY1ZsWJF2tvbU1tbmyRpbm7Opz71qTQ1NWXhwoVpbW3N3LlzR3IbAPCx0N/fnwcffDBNTU2ZP39+Vq5cWTj98cKFC1m5cmVmz56defPm5fjx44Xnrl69mg0bNqSxsTFNTU05dOhQoTYwMJCtW7dm1qxZaWxszK5du8q9LQAAAAAAAIDbrnokP3zKlCkZHBy8Za2+vj5Hjx69ZW3ChAnp6Oi4Za2qqqpwyhUAcLNNmzblvvvuS0VFRZ5++uls2rQpR48ezbZt27Jo0aIcOXIkXV1dWbNmTc6ePZvq6urs2LEjNTU1OXPmTLq7u7N48eIsW7YskyZNyv79+3Py5MmcOnUqfX19WbBgQZYvX545c+bc7q0CAAAAAAAAlM2Ivy4QACiPu+66K6tWrUpFRUWSZNGiRXn99deTJAcPHsyWLVuSJAsXLkx9fX3hNKuOjo5CbcaMGVm6dGkOHz5cqG3evDlVVVWpra3NunXrcuDAgXJvDQAAAAAAAOC2ErICgDvUzp0788ADD+TSpUsZGBhIXV1doTZ9+vT09vYmSXp7ezNt2rRh1wAAAAAAAADGihF9XSAAcHs8+eSTOX36dHbv3p1r164VTrd6z6+/zvf99eHU3q+trS1tbW2F6ytXrqSzs/NDrf/97h6XfGPB9Y/8OaVWir2NhP7+/lG7ttFIv4ZOr4ZHv4ZHvwAAAAAAGO2ErADgDrNjx44cOnQoP/zhDzN+/PiMHz8+SXLx4sXCaVbnzp1LQ0NDkqShoSE9PT031VatWnVTbeHChR947te1tLSkpaWlcD1lypSsWLHiI+9n78HD+dqro++3LD1PffS9jYTOzs6S9H2s0K+h06vh0a/h0S8AAAAAAEY7rwsEgDtIW1tbnn322bz00kuZOHFi4etr165Ne3t7kqSrqyvnz5/PkiVLPlDr7u7OsWPHsnr16kJtz549uXHjRi5fvpyOjo6sX7++zLsCAAAAAAAAuL1G37EQAMCH8sYbb+Txxx/PzJkzs2zZsiRJTU1NfvrTn2b79u1pbm7O7NmzM27cuOzbty/V1e/+NqC1tTUbN25MY2NjKisr097entra2iRJc3Nzurq60tTUVLh37ty5t2eDAAAAAAAAALeJkBUA3CGmTJmSwcHBW9bq6+tz9OjRW9YmTJiQjo6OW9aqqqoKp1wBAAAAAAAAjFVeFwgAAAAAAAAAAFCEkBUAAAAAAAAAAEARQlYAAAAAAAAAAABFCFkBAAAAAAAAAAAUIWQFAAAAAAAAAABQhJAVAAAAAAAAAABAEUJWAAAAAAAAAAAARQhZAQAAAAAAAAAAFCFkBQAAAAAAAAAAUISQFQAAAAAAAAAAQBFCVgAAAAAAAAAAAEUIWQEAAAAAAAAAABQhZAUAAAAAAAAAAFCEkBUAAAAAAAAAAEARQlYAAAAAAAAAAABFCFkBAAAAAAAAAAAUIWQFAAAAAAAAAABQhJAVAAAADFN/f38efPDBNDU1Zf78+Vm5cmV6enqSJBcuXMjKlSsze/bszJs3L8ePHy88d/Xq1WzYsCGNjY1pamrKoUOHCrWBgYFs3bo1s2bNSmNjY3bt2lXubQHAqGTuAgAAMBoIWQEAAMCHsGnTpvz85z/Pa6+9lvvvvz+bNm1Kkmzbti2LFi3K6dOn8//+3//LH//xH+f69etJkh07dqSmpiZnzpxJZ2dnvvzlL+e///u/kyT79+/PyZMnc+rUqbzyyiv5u7/7u/z7v//7bdsfAIwm5i4AAAC3m5AVAAAADNNdd92VVatWpaKiIkmyaNGivP7660mSgwcPZsuWLUmShQsXpr6+vnCqRkdHR6E2Y8aMLF26NIcPHy7UNm/enKqqqtTW1mbdunU5cOBAubcGAKOOuQsAAMBoIGQFAAAAH9HOnTvzwAMP5NKlSxkYGEhdXV2hNn369PT29iZJent7M23atGHXAIBfMXcBAAC4Hapv9wIAAADg4+zJJ5/M6dOns3v37ly7dq1wysZ7BgcHb7p+f304tfe0tbWlra2tcH3lypV0dnZ+6PW/393jkm8suF6SzyqlUu2v1Pr7+0ft2kYbvRoe/Roe/Rq6O6FX5u7IG62/Ru6EX7/lolfDo19Dp1fDo18AcOcRsgIAAIAPaceOHTl06FB++MMfZvz48Rk/fnyS5OLFi4VTNc6dO5eGhoYkSUNDQ3p6em6qrVq16qbawoULP/Dc+7W0tKSlpaVwPWXKlKxYsaIk+9l78HC+9uro+6uCnqdKs79S6+zsLFnv73R6NTz6NTz6NXQf916Zu+Vh7n786dXw6NfQ6dXw6BcA3Hm8LhAAAAA+hLa2tjz77LN56aWXMnHixMLX165dm/b29iRJV1dXzp8/nyVLlnyg1t3dnWPHjmX16tWF2p49e3Ljxo1cvnw5HR0dWb9+fZl3BQCjk7kLACOvv78/Dz74YJqamjJ//vysXLkyPT09SZILFy5k5cqVmT17dubNm5fjx48Xnrt69Wo2bNiQxsbGNDU15dChQ4XawMBAtm7dmlmzZqWxsTG7du0q97YAoGRG34/JAAAAwCj3xhtv5PHHH8/MmTOzbNmyJElNTU1++tOfZvv27Wlubs7s2bMzbty47Nu3L9XV7/7xu7W1NRs3bkxjY2MqKyvT3t6e2traJElzc3O6urrS1NRUuHfu3Lm3Z4MAMIqYuwBQPps2bcp9992XioqKPP3009m0aVOOHj2abdu2ZdGiRTly5Ei6urqyZs2anD17NtXV1dmxY0dqampy5syZdHd3Z/HixVm2bFkmTZqU/fv35+TJkzl16lT6+vqyYMGCLF++PHPmzLndWwWAYROyAgAAgGGaMmVKBgcHb1mrr6/P0aNHb1mbMGFCOjo6blmrqqoqnLYBAPyKuQsA5XHXXXcVXq2bJIsWLcq3v/3tJMnBgwfT3d2dJFm4cGHq6+tz/PjxfP7zn09HR0e++93vJklmzJiRpUuX5vDhw/niF7+Yjo6ObN68OVVVVamtrc26dety4MCBfP3rXy/39gDgI/O6QAAAAAAAAABusnPnzjzwwAO5dOlSBgYGUldXV6hNnz49vb29SZLe3t5MmzZt2DUA+LhxkhUAAAAAAAAABU8++WROnz6d3bt359q1a6moqLip/uunTL6/Ppza+7W1taWtra1wfeXKlXR2dn6o9f9/7d1xjN91fT/w5127NrOMyBUs2a7Xa7k7Katam1Spdh3tSMoQm6ktlbBOVjLXUMHlZll1bsI/E0xzZmZH6IwDVjJ7HRKPuOkxdenWuMwzKBs2CsVrD+YKBSw6awnt3e8Pfn7l6PXj5wN39/1e+3gkJn4/7+/32/fn6UdePfrs5/NK585Kbl12YkK+ayJN1PlNpOPHjzfkvhqVvKqRV3myqmaq8lKyAgAAAAAAACBJsmPHjtx///356le/mte97nV53etelyQ5cuRI7W5Whw4dSltbW5Kkra0tBw8eDxWVVQAAIABJREFUHLP288cO/nxt+fLlp3zulbq7u9Pd3V173dramrVr107IOd2zpz+feKjx/mj84G0Tc34TaWBgYMJyPxvIqxp5lSeraqYqL48LBAAAAAAAACA9PT35/Oc/n3/5l3/J61//+trxDRs2pLe3N0kyODiYw4cPZ+XKlaesDQ0NZe/evVm3bl1tbefOnTl58mSee+659PX1ZePGjVN8VgAwMRqvrgsAAAAAAADAlHryySfzp3/6p1m0aFFWr16dJJk9e3b+8z//M7fffns2bdqUzs7OzJo1K7t27crMmS/9UfO2bduyefPmdHR0pLm5Ob29vWlpaUmSbNq0KYODg+nq6qq9d/HixfU5QQB4jZSsAAAAAAAAAM5yra2tGR0dHXdt3rx5efDBB8ddmzNnTvr6+sZdmzFjRu0uVwAw3XlcIAAAAAAAAAAAQAElKwAAAAAAAAAAgAJKVgAAAAAAAAAAAAWUrAAAAAAAAAAAAAooWQEAAAAAAAAAABRQsgIAAAAAAAAAACigZAUAAAAAAAAAAFBAyQoAAAAAAAAAAKCAkhUAAAAAAAAAAEABJSsAAAAAAAAAAIACSlYAAAAAAAAAAAAFlKwAAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAAAAAAAAAAAACkx6yeqmm25Ke3t7mpqa8sgjj9SOX3bZZVm0aFGWLl2apUuX5tOf/nRt7dixY7nmmmvS0dGRrq6u3H///bW1kZGR3HjjjbnooovS0dGRO+64Y7JPAQAAAAAAAAAAOIvNnOxfYP369bn55puzcuXKU9Y+85nP5Kqrrjrl+I4dOzJ79uwcOHAgQ0NDWbFiRVavXp3zzjsv9957b/bv359HH300zz//fJYtW5Y1a9bk4osvnuxTAQAAAAAAAAAAzkKTfierVatWpbW1tdJn+vr6snXr1iTJwoULs2rVqvT399fWtmzZkhkzZqSlpSVXX311du/ePeH7BgAAAAAAAAAASKagZFVk27ZtedOb3pSNGzfmBz/4Qe348PBwFixYUHvd3t6e4eHhX7oGAAAAAAAAAAAw0Sb9cYGns2vXrsyfPz+jo6Pp7e3NVVddlf3799fWm5qaav99dHR0zGeL1l6up6cnPT09tddHjx7NwMDARGw/585Kbl12YkK+a6JM1LlNtOPHjzfs3hqRvKqRV3myqkZeAAAAAAAAAPxc3UpW8+fPT/JSYepDH/pQPvKRj+TZZ5/N3Llz09bWloMHD+aCCy5Ikhw6dChXXnllktTWli9fXltra2sb99fo7u5Od3d37XVra2vWrl07Ifu/Z09/PvFQ3eIb18HbJubcJtrAwMCE5X42kFc18ipPVtXICwAAAAAAAICfq8vjAk+cOJGnnnqq9voLX/hC5s2bl7lz5yZJNmzYkN7e3iTJ0NBQ9u7dm3Xr1tXWdu7cmZMnT+a5555LX19fNm7cOPUnAQAN5qabbkp7e3uampryyCOP1I5fdtllWbRoUZYuXZqlS5fm05/+dG3t2LFjueaaa9LR0ZGurq7cf//9tbWRkZHceOONueiii9LR0ZE77rhjSs8HAAAAAAAAoFFM+q2Ytm7dmv7+/hw+fDiXX355zjnnnDz88MN517velRdeeCHNzc05//zz88ADD9Q+s23btmzevDkdHR1pbm5Ob29vWlpakiSbNm3K4OBgurq6au9dvHjxZJ8GADS89evX5+abb87KlStPWfvMZz6Tq6666pTjO3bsyOzZs3PgwIEMDQ1lxYoVWb16dc4777zce++92b9/fx599NE8//zzWbZsWdasWZOLL754Kk4HAAAAAAAAoGFMesmqt7e3dleql/vWt7512s/MmTMnfX19467NmDFj3O8DgLPdqlWrKn+mr68vd999d5Jk4cKFWbVqVfr7+3Pdddelr68vW7ZsyYwZM9LS0pKrr746u3fvzi233DKxGwcAAAAAAABocJNesgIA6m/btm356Ec/mksuuSSf/OQns2jRoiTJ8PBwFixYUHtfe3t7hoeHT7tWVJLu6elJT09P7fXRo0czMDDwmvd+7qzk1mUnXvP3TLSJOLfJcPz48YbdWyOSV3myqkZe1cgLAAAAAIBGp2QFAGe4Xbt2Zf78+RkdHU1vb2+uuuqq7N+/v7be1NRU+++jo6NjPlu09krd3d3p7u6uvW5tbc3atWtf6/Zzz57+fOKhxvsty8HbXvu5TYaBgYEJyf1sIa/yZFWNvKqRFwAAAAAAja653hsAACbX/Pnzk7xUmPrQhz6UH/zgB3n22WeTJG1tbTl48GDtvYcOHUpbW9svXQMAAAAAAAA4myhZAcAZ7MSJE3nqqadqr7/whS9k3rx5mTt3bpJkw4YN6e3tTZIMDQ1l7969WbduXW1t586dOXnyZJ577rn09fVl48aNU38SAAAAAAAAAHXWeM/eAQBela1bt6a/vz+HDx/O5ZdfnnPOOScPP/xw3vWud+WFF15Ic3Nzzj///DzwwAO1z2zbti2bN29OR0dHmpub09vbm5aWliTJpk2bMjg4mK6urtp7Fy9eXJdzAwAAAAAAAKgnJSsAOEP09vbW7kr1ct/61rdO+5k5c+akr69v3LUZM2aM+30AAAAAAAAAZxuPCwQAAAAAAAAAACigZAUAAAAAAAAAAFBAyQoAAAAAAAAAAKCAkhUAAAAAAAAAAEABJSsAAAAAAAAAAIACSlYAAAAAAAAAAAAFlKwAAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFFCyAgAAAAAAAAAAKKBkBQAAAAAAAAAAUEDJCgAAAAAAAAAAoICSFQAAAAAAAAAAQAElKwAAAAAAAAAAgAJKVgAAAAAAAAAAAAWUrAAAAAAAAAAAAAooWQEAAAAAAAAAABRQsgIAAAAAAAAAACigZAUAAAAAAAAAAFBAyQoAAAAAAAAAAKCAkhUAAAAAAAAAAEABJSsAAAAAAAAAAIACSlYAAAAAAAAAAAAFlKwAAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFFCyAgAAAAAAAAAAKKBkBQAAAAAAAAAAUEDJCgAAAAAAAAAAoICSFQAAAAAAAAAAQAElKwAAAAAAAAAAgAJKVgAAAAAAAAAAAAWUrAAAAAAAAAAAAAooWQEAAAAAAAAAABRQsgIAAAAAAAAAACigZAUAAAAAAAAAAFBAyQoAAAAAAAAAAKCAkhUAAAAAAAAAAEABJSsAAAAAAAAAAIACk16yuummm9Le3p6mpqY88sgjteNPP/10rrjiinR2dmbJkiXZt29fbe3YsWO55ppr0tHRka6urtx///21tZGRkdx444256KKL0tHRkTvuuGOyTwEAAAAAAAAAADiLTXrJav369dm3b18WLFgw5vj27dtz6aWX5rHHHstdd92Va6+9NidOnEiS7NixI7Nnz86BAwcyMDCQG264IT/60Y+SJPfee2/279+fRx99NN/85jfzqU99Kt/73vcm+zQAAAAAAAAAAICz1KSXrFatWpXW1tZTju/Zsydbt25Nkixfvjzz5s2r3c2qr6+vtrZw4cKsWrUq/f39tbUtW7ZkxowZaWlpydVXX53du3dP9mkAAAAAAAAAAABnqUkvWY3n2WefzcjISC644ILasfb29gwPDydJhoeHx9z5quwaAAAAAAAAAADARJtZr1+4qalpzOvR0dHTrldZe7menp709PTUXh89ejQDAwOvar+vdO6s5NZlJybkuybKRJ3bRDt+/HjD7q0RyasaeZUnq2qmY1433XRTHnjggRw6dCj//d//nSVLliRJnn766fzBH/xBHn/88cyePTt33nlnVq5cmSQ5duxYrr/++gwODqa5uTm33XZb3vve9yZJRkZG8uEPfzj//M//nKampnR3d+eGG26o2/kBAAAAAAAA1EtdSlZz585Nkhw5cqR2N6tDhw6lra0tSdLW1paDBw+OWbvyyivHrC1fvvyUz71Sd3d3uru7a69bW1uzdu3aCTmHe/b05xMP1a2jNq6Dt03MuU20gYGBCcv9bCCvauRVnqyqmY55rV+/PjfffHOtQPVz27dvz6WXXpqvfOUrGRwczPr16/P4449n5syZ2bFjR2bPnp0DBw5kaGgoK1asyOrVq3Peeefl3nvvzf79+/Poo4/m+eefz7Jly7JmzZpcfPHFdTpDAAAAAAAAgPqoy+MCk2TDhg3p7e1NkgwODubw4cO1PxR++drQ0FD27t2bdevW1dZ27tyZkydP5rnnnktfX182btxYn5MAgAayatWqtLa2nnJ8z5492bp1a5Jk+fLlmTdvXvbt25ck6evrq60tXLgwq1atSn9/f21ty5YtmTFjRlpaWnL11Vdn9+7dU3Q2AAAAAAAAAI1j0ktWW7duTWtra5588slcfvnl6ejoSJLcfvvt+cY3vpHOzs5cd9112bVrV2bOfOnOUNu2bcvPfvazdHR0ZO3atent7U1LS0uSZNOmTXnjG9+Yrq6uLF++PNu2bcvixYsn+zQAYFp69tlnMzIyUrs7ZJK0t7dneHg4STI8PJwFCxZUXgMAAAAAAAA4m0z68+56e3trd6V6uXnz5uXBBx8c9zNz5sxJX1/fuGszZswY9/sAgPE1NTWNeT06Onra9Sprr9TT05Oenp7a66NHj2ZgYKDyfl/p3FnJrctOvObvmWgTcW6T4fjx4w27t0Ykr/JkVY28qpEXAAAAAACNbtJLVgBA/cydOzdJcuTIkdrdrA4dOpS2trYkSVtbWw4ePDhm7corrxyztnz58lM+N57u7u50d3fXXre2tmbt2rWv+Rzu2dOfTzzUeL9lOXjbaz+3yTAwMDAhuZ8t5FWerKqRVzXyAgAAAACg0U364wIBgPrasGFD7S6Qg4ODOXz4cFauXHnK2tDQUPbu3Zt169bV1nbu3JmTJ0/mueeeS19fXzZu3FifkwAAAAAAAACoo8a7LQQA8Kps3bo1/f39OXz4cC6//PKcc845OXDgQG6//fZs2rQpnZ2dmTVrVnbt2pWZM1/6LcC2bduyefPmdHR0pLm5Ob29vWlpaUmSbNq0KYODg+nq6qq9d/HixXU7PwAAAAAAAIB6UbICgDNEb29v7a5ULzdv3rw8+OCD435mzpw56evrG3dtxowZ434fAAAAAAAAwNnG4wIBAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAKrrpppvS3t6epqamPPLII7XjTz/9dK644op0dnZmyZIl2bdvX23t2LFjueaaa9LR0ZGurq7cf//9tbWRkZHceOONueiii9LR0ZE77rhjSs8HABqZuQsAAEAjULICAACAitavX599+/ZlwYIFY45v3749l156aR577LHcddddufbaa3PixIkkyY4dOzJ79uwcOHAgAwMDueGGG/KjH/0oSXLvvfdm//79efTRR/PNb34zn/rUp/K9731vys8LABqRuQsAAEAjULICAACAilatWpXW1tZTju/Zsydbt25Nkixfvjzz5s2r3VWjr6+vtrZw4cKsWrUq/f39tbUtW7ZkxowZaWlpydVXX53du3dP0dkAQGMzdwEAAGgESlYAAAAwAZ599tmMjIzkggsuqB1rb2/P8PBwkmR4eHjMHTjKrgEApzJ3AWByeEwvAJzezHpvAAAAAM4UTU1NY16Pjo6edr3K2sv19PSkp6en9vro0aMZGBh4Vft9pXNnJbcuOzEh3zWRJur8Jtrx48cbdm+NRlbVyKsaeZV3pmVl7k6ORr1GzrTrdzLJqhp5lSeraqZrXuvXr8/NN9+clStXjjn+88f0fuUrX8ng4GDWr1+fxx9/PDNnzhzzmN6hoaGsWLEiq1evznnnnTfmMb3PP/98li1bljVr1uTiiy+u0xkCwKunZAUAAAATYO7cuUmSI0eO1O6qcejQobS1tSVJ2tracvDgwTFrV1555Zi15cuXn/K5V+ru7k53d3ftdWtra9auXTsh53DPnv584qHG+1cFB2+bmPObaAMDAxOW/ZlOVtXIqxp5lXcmZWXuTh5zd/qTVTXyKk9W1UzXvFatWjXu8T179mRoaCjJ2Mf0XnbZZenr68vdd9+dZOxjeq+77rrTPqb3lltumaIzAoCJ43GBAAAAMEE2bNiQ3t7eJMng4GAOHz5c+9u/L18bGhrK3r17s27dutrazp07c/LkyTz33HPp6+vLxo0b63MSADBNmLsAMDU8phcAXtJ4f00GAAAAGtzWrVvT39+fw4cP5/LLL88555yTAwcO5Pbbb8+mTZvS2dmZWbNmZdeuXZk586Ufvbdt25bNmzeno6Mjzc3N6e3tTUtLS5Jk06ZNGRwcTFdXV+29ixcvrtv5AUAjMXcBoP48pndyNOIjJafroy7rRV7VyKs8WVUzVXkpWQEAAEBFvb29tbtjvNy8efPy4IMPjvuZOXPmpK+vb9y1GTNmjPt9AIC5CwD15jG9k6cRH9M7XR91WS/yqkZe5cmqmqnKy+MCAQAAAAAAADgtj+kFAHeyAgAAAAAAACAe0wsARZSsAAAAAAAAAPCYXgAo4HGBAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFJhZ7w0AcHZr3/5P9d7CuHauNiIBAAAAAAAAeIk7WQEAAAAAAAAAABRQsgIAAAAAAAAAACigZAUAAAAAAAAAAFBAyQoAAAAAAAAAAKCAkhUAAAAAAAAAAEABJSsAAAAAAAAAAIACSlYAAAAAAAAAAAAFlKwAAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFJhZ7w0AAAAwNdq3/1O9tzCunav9aAoAAAAAQGNzJysAAAAAAAAAAIACSlYAAAAAAAAAAAAFlKwAAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFFCyAgAAAAAAAAAAKKBkBQAAAAAAAAAAUEDJCgAAAAAAAAAAoICSFQAAAAAAAAAAQAElKwAAAAAAAAAAgAJKVgAAAAAAAAAAAAXqWrJqb2/PxRdfnKVLl2bp0qXp6+tLkjz99NO54oor0tnZmSVLlmTfvn21zxw7dizXXHNNOjo60tXVlfvvv79e2wcAAAAAAAAAAM4Cdb+T1X333ZfvfOc7+c53vpONGzcmSbZv355LL700jz32WO66665ce+21OXHiRJJkx44dmT17dg4cOJCBgYHccMMN+dGPflTPUwCAaUG5GQAAAAAAAODVmVnvDYxnz549GRoaSpIsX7488+bNy759+3LZZZelr68vd999d5Jk4cKFWbVqVfr7+3PdddfVb8MAME3cd999WbJkyZhjPy83f+UrX8ng4GDWr1+fxx9/PDNnzhxTbh4aGsqKFSuyevXqnHfeeXU6AwAAAAAAAICpV/eS1bXXXpuRkZG8/e1vzyc/+ck0NzdnZGQkF1xwQe097e3tGR4eTpIMDw9nwYIF4669Uk9PT3p6emqvjx49moGBgQnZ97mzkluXnZiQ75ooE3VuE+348eMNu7dGJK9q5FVeo2bVaP8s/bnjx080ZF6TQbkZAAAAAAAAoFhdS1b/9m//lra2trz44ov5+Mc/ng984APZtWtXmpqaxrxvdHR0zOuXr79y7eW6u7vT3d1de93a2pq1a9dOyN7v2dOfTzxU947aGAdvm5hzm2gDAwMTlvvZQF7VyKu8Rs2qffs/1XsL49q5emZD5vVaTWa5GQAAAAAAAOBMVdeWUFtbW5LkV37lV/Inf/In6erqyty5c5MkR44cqf2B76FDh2rvbWtry8GDB8esXXnllXXYPQBML5Ndbp6sO0g24t0jE3eQPFPIqzxZVdOoeTXiP0+Ts+sOkgAAAAAATE91K1n99Kc/zYsvvpjXv/71SZLPf/7zeetb35ok2bBhQ3p7e3PLLbdkcHAwhw8fzsqVK8es3X333RkaGsrevXtz55131us0AGDamOxy82TdQbIR7x6ZuIPkmUJe5cmqmkbNyx0kAQAAAADg1Wmu1y/81FNPZfXq1Xnzm9+cN73pTdm7d2/+/u//Pkly++235xvf+EY6Oztz3XXXZdeuXZk586U/XN22bVt+9rOfpaOjI2vXrk1vb29aWlrqdRoAMC389Kc/zdGjR2uvxys3JzltuTlJrdy8bt26Kd49AAAAAAAAQH3V7bYQixYtyre//e1x1+bNm5cHH3xw3LU5c+akr69vMrcGAGecp556Ku973/ty8uTJjI6OZtGiRWPKzZs2bUpnZ2dmzZp1Srl58+bN6ejoSHNzs3IzAAAAAAAAcFZqvGfvAAATTrkZAAAAAAAA4NWr2+MCAQAAAAAAAAAApgMlKwAAAAAAAAAAgAJKVgAAAAAAAAAAAAWUrAAAAAAAAAAAAAooWQEAAAAAAAAAABRQsgIAAAAAAAAAACigZAUAAAAAAAAAAFBAyQoAAAAAAAAAAKCAkhUAAAAAAAAAAEABJSsAAAAAAAAAAIACSlYAAAAAAAAAAAAFlKwAAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFFCyAgAAAAAAAAAAKKBkBQAAAAAAAAAAUEDJCgAAAAAAAAAAoICSFQAAAAAAAAAAQAElKwAAAAAAAAAAgAJKVgAAAAAAAAAAAAWUrAAAAAAAAAAAAAooWQEAAAAAAAAAABRQsgIAAAAAAAAAACigZAUAAAAAAAAAAFBAyQoAAAAAAAAAAKCAkhUAAAAAAAAAAEABJSsAAAAAAAAAAIACSlYAAAAAAAAAAAAFZtZ7AwAAAAAAZ5r27f9U7y2cYudq/zoYgDNPI87cxNwFgDORO1kBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABZSsAAAAAAAAAAAACihZAQAAAAAAAAAAFFCyAgAAAAAAAAAAKKBkBQAAAAAAAAAAUEDJCgAAAAAAAAAAoICSFQAAAAAAAAAAQIGZ9d4AAAAAAAAAAADQ2Nq3/1O9tzCunaunpv7kTlYAAAAAAAAAAAAFlKwAAAAAAAAAAAAKKFkBAAAAAAAAAAAUULICAAAAAAAAAAAooGQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAtO2ZPXYY4/lHe94R7q6uvK2t70t+/fvr/eWAOCMZOYCwNQxdwFg6pi7ADB1zF0AzgTTtmT1x3/8x/ngBz+YRx99NDfffHOuv/76em8JAM5IZi4ATB1zFwCmjrkLAFPH3AXgTDAtS1ZPP/10Hnroofz+7/9+kuR973tfhoaGcvDgwfpuDADOMGYuAEwdcxcApo65CwBTx9wF4Ewxs94beDWeeOKJ/Pqv/3pmznxp+01NTWlra8vw8HDa29tr7+vp6UlPT0/t9eHDh9Pa2johe/i///u/nHPOORPyXROl9d5672B8jZhVI5NXNfIqT1bVbLxz4vI6cuTIhHxPPZSducnkzd1GvXbN3TODvMqTVTXyqmai5u50nrmJn3WLmLvTn6yqkVc18irPz7q/YO6enrk7/cmqGnmVJ6tqzN1fMHdPrxHnbqNm1ajkVY28ypNVNVM1d6dlySp5afi+3Ojo6Cnv6e7uTnd396T8+q2trXnyyScn5bvPNLKqRl7VyKs8WVUjr18oM3OTyZu7/reoRl7VyKs8WVUjr2rk9Qt+1p1e5FWerKqRVzXyKk9WY5m704u8ypNVNfIqT1bVyGssc3f6kFU18qpGXuXJqpqpymtaPi5w/vz5efLJJ3PixIkkLw3hJ554Im1tbXXeGQCcWcxcAJg65i4ATB1zFwCmjrkLwJliWpas3vCGN+Stb31r7r33pfsnfuELX0h7e/spjy0CAF4bMxcApo65CwBTx9wFgKlj7gJwpphxyy233FLvTbwaK1asyF/8xV/kU5/6VAYHB3PXXXflDW94w5TvgXJkVY28qpFXebKqRl4vMXOnH3lVI6/yZFWNvKqR10vM3elHXuXJqhp5VSOv8mT1C+bu9COv8mRVjbzKk1U18voFc3d6kVU18qpGXuXJqpqpyKtpdLwH3gIAAAAAAAAAAJBkmj4uEAAAAAAAAAAAYKooWQEAAAAAAAAAABRQsgIAAAAAAAAAACigZAUAAAAAAAAAAFBAyQoAAAAAAAAAAKCAktUvMTIyks9+9rNZs2ZNOjo60tHRkTVr1uSqb75uAAAKQElEQVRv//Zvc/LkyXpvryHt3r07f/VXf5XvfOc7Y45/8pOfrNOOppc3velN9d5CQ/riF7+YZ599NknyzDPPZOPGjVm0aFHe85735Ic//GGdd9d4fvCDH+T9739/PvKRj+TYsWP5wz/8wyxcuDDvfve7Mzw8XO/twWmZu9WZu6+emXt65m415i7Tlblbnbn76pm7p2fulmfmMp2Zu9WYua+NuXt65m555i7TlZlbnbn72pi74zNzqzF3p4+m0dHR0XpvopFt2bIl//u//5stW7akvb09o6OjOXToUO68885ceOGF2blzZ7232FA+/vGPZ+/evVm6dGnuu+++bN++PR/+8IeTJMuWLctDDz1U5x02lre97W2nHPuv//qvvPnNb06SfPOb35zqLTWsxYsX57vf/W6am5tz3XXXpbW1Nddee22+/OUv5+tf/3q+9KUv1XuLDeV3fud3smbNmvz4xz/O17/+9axZsyYf+MAH8sADD2Tv3r358pe/XO8tNpSRkZF87nOfy+c///nab1Ta2try/ve/P9dff31mzJhR5x2ePczdaszd8szcaszdaszdaszdxmHuVmPulmfuVmPulmfmVmPmNhZztzwztxpztxpztzxztxpzt3GYudWYu9WYu+WZudWYu9XUc+4qWf0SnZ2deeyxx045Pjo6ms7Ozhw4cKAOu2pcb37zm/Otb30rs2bNyuHDh7Nu3br83u/9Xj72sY/lrW99a7797W/Xe4sN5ZJLLsk73/nObNq0KaOjoxkdHc0111yT3bt3J0l++7d/u847bByXXHJJ9u/fnySnXEuurVO95S1vycMPP5zR0dH8xm/8xphG+NKlS0/52whnOz90NQ5ztxpztzwztxpztxpztxpzt3GYu9WYu+WZu9WYu+WZudWYuY3F3C3PzK3G3K3G3C3P3K3G3G0cZm415m415m55Zm415m419Zy7Myftm88QTU1NeeaZZ3L++eePOf7MM8+kqampTrtqXCMjI5k1a1aS5MILL8xXv/rV/O7v/m5Onjwpr3F8+9vfzp//+Z+np6en9n/4X/3VXzWAxzFv3rz8x3/8R1asWJHW1tYcPnw4F154YX7yk5/kxRdfrPf2Gs7JkyczMjKSY8eO5cc//nF+8pOf5Nd+7ddy4sSJvPDCC/XeXsP52te+dsoPXZdcckmuuOKKdHZ21mlXZydztxpztzwztxpztxpztxpzt3GYu9WYu+WZu9WYu+WZudWYuY3F3C3PzK3G3K3G3C3P3K3G3G0cZm415m415m55Zm415m419Zy7Sla/xJ/92Z/lLW95S9773vdmwYIFaWpqytDQUL74xS/m1ltvrff2Gs4555yTgwcPpr29PUly7rnnZmBgIGvXrs13v/vd+m6uAc2ePTs7duzIv//7v+fd7353tm7dWu8tNazPfOYzec973pN3vOMdufDCC/P2t789q1evzuDgYD760Y/We3sN58orr8w73/nOvPDCC/ngBz+Y97///Vm9enW+9rWv5bd+67fqvb2G44euxmHuVmPulmfmVmPuVmPuVmPuNg5ztxpztzxztxpztzwztxozt7GYu+WZudWYu9WYu+WZu9WYu43DzK3G3K3G3C3PzK3G3K2mnnPX4wJLOHjwYO67774xz3J83/vel4ULF9Z5Z43nX//1X9PS0pK3vOUtY47/9Kc/zV//9V/nYx/7WJ121viOHTuWbdu2Zd++fXn44YfrvZ2G9LOf/Sz/8A//kP379+fFF1/MggULcvXVV2f+/Pn13lpDeuCBB5Ik7373u/PQQw/l7rvvzqJFi7J169ba30rgJZ/73Ofyl3/5l6f9oev666+v9xbPKuZueebuq2PmlmPuVmPulmfuNhZztzxz99Uxd8sxd8szc8szcxuPuVuOmfvqmbvlmLvlmbvlmbuNxcwtz9x99czdX87MrcbcLa+ec1fJCgDihy4AmErmLgBMDTMXAKaOuQsAU6dec1fJ6jX40pe+lKuuuqre25g25FWNvMqTVTXyYrpy7VYjr/JkVY28qpEX05Vrtxp5lSerauRVnqyYzly/5cmqGnlVI6/yZMV05dqtRl7VyKs8WVUjr8bSXO8NTGf9/f313sK0Iq9q5FWerKqRVzVf+tKX6r0F/j/XbjXyKk9W1cirGnlVY+42DtduNfIqT1bVyKs8WVVj5jYW1295sqpGXtXIqzxZVWPuNg7XbjXyqkZe5cmqGnlVM9lz152sShoeHh5zm7G2trY676ixyasaeZUnq2rk9dr90R/9UT772c/WextnHdduNfIqT1bVyKsaeb125m59uHarkVd5sqpGXuXJ6rUzc+vH9VuerKqRVzXyKk9Wr525Wx+u3WrkVY28ypNVNfJ67SZ77ipZ/RLf+973snnz5gwNDaWtrS2jo6N54oknsnDhwvzd3/1dLr744npvsaHIqxp5lSerauTFdOXarUZe5cmqGnlVIy+mK9duNfIqT1bVyKs8WTGduX7Lk1U18qpGXuXJiunKtVuNvKqRV3myqkZe08gohd7+9reP3nfffacc/8d//MfR5cuX12FHjU1e1cirPFlVI6+J09nZWe8tnFVcu9XIqzxZVSOvauQ1cczdqeXarUZe5cmqGnmVJ6uJY+ZOPddvebKqRl7VyKs8WU0cc3dquXarkVc18ipPVtXIa+JM9tx1J6tf4o1vfGO+//3vV147W8mrGnmVJ6tq5FXN/v37T7t2+eWX54c//OEU7ubs5tqtRl7lyaoaeVUjr2rM3cbh2q1GXuXJqhp5lSeraszcxuL6LU9W1cirGnmVJ6tqzN3G4dqtRl7VyKs8WVUjr2rqOXebJ+2bzxDnn39+du3alZGRkdqxkZGR3HPPPZk7d24dd9aY5FWNvMqTVTXyqmbJkiW56qqr8q53veuU/zzzzDP13t5ZxbVbjbzKk1U18qpGXtWYu43DtVuNvMqTVTXyKk9W1Zi5jcX1W56sqpFXNfIqT1bVmLuNw7VbjbyqkVd5sqpGXtXUde5O6n2yzgCPPfbY6Jo1a0bPO++80d/8zd8cXbJkyejrX//60dWrV49+//vfr/f2Go68qpFXebKqRl7VtLe3j/7P//zPuGutra1TvJuzm2u3GnmVJ6tq5FWNvKoxdxuHa7caeZUnq2rkVZ6sqjFzG4vrtzxZVSOvauRVnqyqMXcbh2u3GnlVI6/yZFWNvKqp59z1uMCSjhw5kieeeCJJMn/+/FxwwQV13lFjk1c18ipPVtXIq5wPf/jD2bBhQ1auXHnK2oc+9KH8zd/8TR12dXZz7VYjr/JkVY28qpFXOeZu43HtViOv8mRVjbzKk1U5Zm5jcv2WJ6tq5FWNvMqTVTnmbuNx7VYjr2rkVZ6sqpFXOfWcu0pWAAAAAAAAAAAABZrrvQEAAAAAAAAAAIBGpmQFAAAAAAAAAABQQMkKAAAAAAAAAACggJIVAAAAAAAAAABAASUrAAAAAAAAAACAAkpWAAAAAAAAAAAABf4f4GFcLMUOyUMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2400x17408 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPerColumnDistribution(df, 10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotScatterMatrix(df, 20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      back_pain  constipation  abdominal_pain  diarrhoea  mild_fever  \\\n",
      "0             0             0               0          0           0   \n",
      "1             0             0               0          0           0   \n",
      "2             0             0               0          0           0   \n",
      "3             0             0               0          0           0   \n",
      "4             0             0               0          0           0   \n",
      "...         ...           ...             ...        ...         ...   \n",
      "4915          0             0               0          0           0   \n",
      "4916          0             0               0          0           0   \n",
      "4917          0             0               0          0           0   \n",
      "4918          0             0               0          0           0   \n",
      "4919          0             0               0          0           0   \n",
      "\n",
      "      yellow_urine  yellowing_of_eyes  acute_liver_failure  fluid_overload  \\\n",
      "0                0                  0                    0               0   \n",
      "1                0                  0                    0               0   \n",
      "2                0                  0                    0               0   \n",
      "3                0                  0                    0               0   \n",
      "4                0                  0                    0               0   \n",
      "...            ...                ...                  ...             ...   \n",
      "4915             0                  0                    0               0   \n",
      "4916             0                  0                    0               0   \n",
      "4917             0                  0                    0               0   \n",
      "4918             0                  0                    0               0   \n",
      "4919             0                  0                    0               0   \n",
      "\n",
      "      swelling_of_stomach  ...  pus_filled_pimples  blackheads  scurring  \\\n",
      "0                       0  ...                   0           0         0   \n",
      "1                       0  ...                   0           0         0   \n",
      "2                       0  ...                   0           0         0   \n",
      "3                       0  ...                   0           0         0   \n",
      "4                       0  ...                   0           0         0   \n",
      "...                   ...  ...                 ...         ...       ...   \n",
      "4915                    0  ...                   0           0         0   \n",
      "4916                    0  ...                   1           1         1   \n",
      "4917                    0  ...                   0           0         0   \n",
      "4918                    0  ...                   0           0         0   \n",
      "4919                    0  ...                   0           0         0   \n",
      "\n",
      "      skin_peeling  silver_like_dusting  small_dents_in_nails  \\\n",
      "0                0                    0                     0   \n",
      "1                0                    0                     0   \n",
      "2                0                    0                     0   \n",
      "3                0                    0                     0   \n",
      "4                0                    0                     0   \n",
      "...            ...                  ...                   ...   \n",
      "4915             0                    0                     0   \n",
      "4916             0                    0                     0   \n",
      "4917             0                    0                     0   \n",
      "4918             1                    1                     1   \n",
      "4919             0                    0                     0   \n",
      "\n",
      "      inflammatory_nails  blister  red_sore_around_nose  yellow_crust_ooze  \n",
      "0                      0        0                     0                  0  \n",
      "1                      0        0                     0                  0  \n",
      "2                      0        0                     0                  0  \n",
      "3                      0        0                     0                  0  \n",
      "4                      0        0                     0                  0  \n",
      "...                  ...      ...                   ...                ...  \n",
      "4915                   0        0                     0                  0  \n",
      "4916                   0        0                     0                  0  \n",
      "4917                   0        0                     0                  0  \n",
      "4918                   1        0                     0                  0  \n",
      "4919                   0        1                     1                  1  \n",
      "\n",
      "[4920 rows x 95 columns]\n"
     ]
    }
   ],
   "source": [
    "X= df[l1]\n",
    "y = df[[\"prognosis\"]]\n",
    "np.ravel(y)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>itching</th>\n",
       "      <th>skin_rash</th>\n",
       "      <th>nodal_skin_eruptions</th>\n",
       "      <th>continuous_sneezing</th>\n",
       "      <th>shivering</th>\n",
       "      <th>chills</th>\n",
       "      <th>joint_pain</th>\n",
       "      <th>stomach_pain</th>\n",
       "      <th>acidity</th>\n",
       "      <th>ulcers_on_tongue</th>\n",
       "      <th>...</th>\n",
       "      <th>blackheads</th>\n",
       "      <th>scurring</th>\n",
       "      <th>skin_peeling</th>\n",
       "      <th>silver_like_dusting</th>\n",
       "      <th>small_dents_in_nails</th>\n",
       "      <th>inflammatory_nails</th>\n",
       "      <th>blister</th>\n",
       "      <th>red_sore_around_nose</th>\n",
       "      <th>yellow_crust_ooze</th>\n",
       "      <th>prognosis</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 133 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   itching  skin_rash  nodal_skin_eruptions  continuous_sneezing  shivering  \\\n",
       "0        1          1                     1                    0          0   \n",
       "1        0          0                     0                    1          1   \n",
       "2        0          0                     0                    0          0   \n",
       "3        1          0                     0                    0          0   \n",
       "4        1          1                     0                    0          0   \n",
       "\n",
       "   chills  joint_pain  stomach_pain  acidity  ulcers_on_tongue  ...  \\\n",
       "0       0           0             0        0                 0  ...   \n",
       "1       1           0             0        0                 0  ...   \n",
       "2       0           0             1        1                 1  ...   \n",
       "3       0           0             0        0                 0  ...   \n",
       "4       0           0             1        0                 0  ...   \n",
       "\n",
       "   blackheads  scurring  skin_peeling  silver_like_dusting  \\\n",
       "0           0         0             0                    0   \n",
       "1           0         0             0                    0   \n",
       "2           0         0             0                    0   \n",
       "3           0         0             0                    0   \n",
       "4           0         0             0                    0   \n",
       "\n",
       "   small_dents_in_nails  inflammatory_nails  blister  red_sore_around_nose  \\\n",
       "0                     0                   0        0                     0   \n",
       "1                     0                   0        0                     0   \n",
       "2                     0                   0        0                     0   \n",
       "3                     0                   0        0                     0   \n",
       "4                     0                   0        0                     0   \n",
       "\n",
       "   yellow_crust_ooze  prognosis  \n",
       "0                  0          0  \n",
       "1                  0          1  \n",
       "2                  0          2  \n",
       "3                  0          3  \n",
       "4                  0          4  \n",
       "\n",
       "[5 rows x 133 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading the  testing.csv file\n",
    "tr=pd.read_csv(r\"C:\\Users\\LalitPatel\\Downloads\\Internship work\\Dataset\\testing.csv\")\n",
    "\n",
    "#Using inbuilt function replace in pandas for replacing the values\n",
    "\n",
    "tr.replace({'prognosis':{'Fungal infection':0,'Allergy':1,'GERD':2,'Chronic cholestasis':3,'Drug Reaction':4,\n",
    "    'Peptic ulcer diseae':5,'AIDS':6,'Diabetes ':7,'Gastroenteritis':8,'Bronchial Asthma':9,'Hypertension ':10,\n",
    "    'Migraine':11,'Cervical spondylosis':12,\n",
    "    'Paralysis (brain hemorrhage)':13,'Jaundice':14,'Malaria':15,'Chicken pox':16,'Dengue':17,'Typhoid':18,'hepatitis A':19,\n",
    "    'Hepatitis B':20,'Hepatitis C':21,'Hepatitis D':22,'Hepatitis E':23,'Alcoholic hepatitis':24,'Tuberculosis':25,\n",
    "    'Common Cold':26,'Pneumonia':27,'Dimorphic hemmorhoids(piles)':28,'Heart attack':29,'Varicose veins':30,'Hypothyroidism':31,\n",
    "    'Hyperthyroidism':32,'Hypoglycemia':33,'Osteoarthristis':34,'Arthritis':35,\n",
    "    '(vertigo) Paroymsal  Positional Vertigo':36,'Acne':37,'Urinary tract infection':38,'Psoriasis':39,\n",
    "    'Impetigo':40}},inplace=True)\n",
    "tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2400x17408 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPerColumnDistribution(tr, 10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotScatterMatrix(tr, 20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    back_pain  constipation  abdominal_pain  diarrhoea  mild_fever  \\\n",
      "0           0             0               0          0           0   \n",
      "1           0             0               0          0           0   \n",
      "2           0             0               0          0           0   \n",
      "3           0             0               1          0           0   \n",
      "4           0             0               0          0           0   \n",
      "5           0             0               1          0           0   \n",
      "6           0             0               0          0           0   \n",
      "7           0             0               0          0           0   \n",
      "8           0             0               0          1           0   \n",
      "9           0             0               0          0           0   \n",
      "10          0             0               0          0           0   \n",
      "11          0             0               0          0           0   \n",
      "12          1             0               0          0           0   \n",
      "13          0             0               0          0           0   \n",
      "14          0             0               1          0           0   \n",
      "15          0             0               0          1           0   \n",
      "16          0             0               0          0           1   \n",
      "17          1             0               0          0           0   \n",
      "18          0             1               1          1           0   \n",
      "19          0             0               1          1           1   \n",
      "20          0             0               1          0           0   \n",
      "21          0             0               0          0           0   \n",
      "22          0             0               1          0           0   \n",
      "23          0             0               1          0           0   \n",
      "24          0             0               1          0           0   \n",
      "25          0             0               0          0           1   \n",
      "26          0             0               0          0           0   \n",
      "27          0             0               0          0           0   \n",
      "28          0             1               0          0           0   \n",
      "29          0             0               0          0           0   \n",
      "30          0             0               0          0           0   \n",
      "31          0             0               0          0           0   \n",
      "32          0             0               0          1           0   \n",
      "33          0             0               0          0           0   \n",
      "34          0             0               0          0           0   \n",
      "35          0             0               0          0           0   \n",
      "36          0             0               0          0           0   \n",
      "37          0             0               0          0           0   \n",
      "38          0             0               0          0           0   \n",
      "39          0             0               0          0           0   \n",
      "40          0             0               0          0           0   \n",
      "\n",
      "    yellow_urine  yellowing_of_eyes  acute_liver_failure  fluid_overload  \\\n",
      "0              0                  0                    0               0   \n",
      "1              0                  0                    0               0   \n",
      "2              0                  0                    0               0   \n",
      "3              0                  1                    0               0   \n",
      "4              0                  0                    0               0   \n",
      "5              0                  0                    0               0   \n",
      "6              0                  0                    0               0   \n",
      "7              0                  0                    0               0   \n",
      "8              0                  0                    0               0   \n",
      "9              0                  0                    0               0   \n",
      "10             0                  0                    0               0   \n",
      "11             0                  0                    0               0   \n",
      "12             0                  0                    0               0   \n",
      "13             0                  0                    0               0   \n",
      "14             0                  0                    0               0   \n",
      "15             0                  0                    0               0   \n",
      "16             0                  0                    0               0   \n",
      "17             0                  0                    0               0   \n",
      "18             0                  0                    0               0   \n",
      "19             0                  1                    0               0   \n",
      "20             1                  1                    0               0   \n",
      "21             0                  1                    0               0   \n",
      "22             0                  1                    0               0   \n",
      "23             0                  1                    1               0   \n",
      "24             0                  0                    0               0   \n",
      "25             0                  1                    0               0   \n",
      "26             0                  0                    0               0   \n",
      "27             0                  0                    0               0   \n",
      "28             0                  0                    0               0   \n",
      "29             0                  0                    0               0   \n",
      "30             0                  0                    0               0   \n",
      "31             0                  0                    0               0   \n",
      "32             0                  0                    0               0   \n",
      "33             0                  0                    0               0   \n",
      "34             0                  0                    0               0   \n",
      "35             0                  0                    0               0   \n",
      "36             0                  0                    0               0   \n",
      "37             0                  0                    0               0   \n",
      "38             0                  0                    0               0   \n",
      "39             0                  0                    0               0   \n",
      "40             0                  0                    0               0   \n",
      "\n",
      "    swelling_of_stomach  ...  pus_filled_pimples  blackheads  scurring  \\\n",
      "0                     0  ...                   0           0         0   \n",
      "1                     0  ...                   0           0         0   \n",
      "2                     0  ...                   0           0         0   \n",
      "3                     0  ...                   0           0         0   \n",
      "4                     0  ...                   0           0         0   \n",
      "5                     0  ...                   0           0         0   \n",
      "6                     0  ...                   0           0         0   \n",
      "7                     0  ...                   0           0         0   \n",
      "8                     0  ...                   0           0         0   \n",
      "9                     0  ...                   0           0         0   \n",
      "10                    0  ...                   0           0         0   \n",
      "11                    0  ...                   0           0         0   \n",
      "12                    0  ...                   0           0         0   \n",
      "13                    0  ...                   0           0         0   \n",
      "14                    0  ...                   0           0         0   \n",
      "15                    0  ...                   0           0         0   \n",
      "16                    0  ...                   0           0         0   \n",
      "17                    0  ...                   0           0         0   \n",
      "18                    0  ...                   0           0         0   \n",
      "19                    0  ...                   0           0         0   \n",
      "20                    0  ...                   0           0         0   \n",
      "21                    0  ...                   0           0         0   \n",
      "22                    0  ...                   0           0         0   \n",
      "23                    0  ...                   0           0         0   \n",
      "24                    1  ...                   0           0         0   \n",
      "25                    0  ...                   0           0         0   \n",
      "26                    0  ...                   0           0         0   \n",
      "27                    0  ...                   0           0         0   \n",
      "28                    0  ...                   0           0         0   \n",
      "29                    0  ...                   0           0         0   \n",
      "30                    0  ...                   0           0         0   \n",
      "31                    0  ...                   0           0         0   \n",
      "32                    0  ...                   0           0         0   \n",
      "33                    0  ...                   0           0         0   \n",
      "34                    0  ...                   0           0         0   \n",
      "35                    0  ...                   0           0         0   \n",
      "36                    0  ...                   0           0         0   \n",
      "37                    0  ...                   1           1         1   \n",
      "38                    0  ...                   0           0         0   \n",
      "39                    0  ...                   0           0         0   \n",
      "40                    0  ...                   0           0         0   \n",
      "\n",
      "    skin_peeling  silver_like_dusting  small_dents_in_nails  \\\n",
      "0              0                    0                     0   \n",
      "1              0                    0                     0   \n",
      "2              0                    0                     0   \n",
      "3              0                    0                     0   \n",
      "4              0                    0                     0   \n",
      "5              0                    0                     0   \n",
      "6              0                    0                     0   \n",
      "7              0                    0                     0   \n",
      "8              0                    0                     0   \n",
      "9              0                    0                     0   \n",
      "10             0                    0                     0   \n",
      "11             0                    0                     0   \n",
      "12             0                    0                     0   \n",
      "13             0                    0                     0   \n",
      "14             0                    0                     0   \n",
      "15             0                    0                     0   \n",
      "16             0                    0                     0   \n",
      "17             0                    0                     0   \n",
      "18             0                    0                     0   \n",
      "19             0                    0                     0   \n",
      "20             0                    0                     0   \n",
      "21             0                    0                     0   \n",
      "22             0                    0                     0   \n",
      "23             0                    0                     0   \n",
      "24             0                    0                     0   \n",
      "25             0                    0                     0   \n",
      "26             0                    0                     0   \n",
      "27             0                    0                     0   \n",
      "28             0                    0                     0   \n",
      "29             0                    0                     0   \n",
      "30             0                    0                     0   \n",
      "31             0                    0                     0   \n",
      "32             0                    0                     0   \n",
      "33             0                    0                     0   \n",
      "34             0                    0                     0   \n",
      "35             0                    0                     0   \n",
      "36             0                    0                     0   \n",
      "37             0                    0                     0   \n",
      "38             0                    0                     0   \n",
      "39             1                    1                     1   \n",
      "40             0                    0                     0   \n",
      "\n",
      "    inflammatory_nails  blister  red_sore_around_nose  yellow_crust_ooze  \n",
      "0                    0        0                     0                  0  \n",
      "1                    0        0                     0                  0  \n",
      "2                    0        0                     0                  0  \n",
      "3                    0        0                     0                  0  \n",
      "4                    0        0                     0                  0  \n",
      "5                    0        0                     0                  0  \n",
      "6                    0        0                     0                  0  \n",
      "7                    0        0                     0                  0  \n",
      "8                    0        0                     0                  0  \n",
      "9                    0        0                     0                  0  \n",
      "10                   0        0                     0                  0  \n",
      "11                   0        0                     0                  0  \n",
      "12                   0        0                     0                  0  \n",
      "13                   0        0                     0                  0  \n",
      "14                   0        0                     0                  0  \n",
      "15                   0        0                     0                  0  \n",
      "16                   0        0                     0                  0  \n",
      "17                   0        0                     0                  0  \n",
      "18                   0        0                     0                  0  \n",
      "19                   0        0                     0                  0  \n",
      "20                   0        0                     0                  0  \n",
      "21                   0        0                     0                  0  \n",
      "22                   0        0                     0                  0  \n",
      "23                   0        0                     0                  0  \n",
      "24                   0        0                     0                  0  \n",
      "25                   0        0                     0                  0  \n",
      "26                   0        0                     0                  0  \n",
      "27                   0        0                     0                  0  \n",
      "28                   0        0                     0                  0  \n",
      "29                   0        0                     0                  0  \n",
      "30                   0        0                     0                  0  \n",
      "31                   0        0                     0                  0  \n",
      "32                   0        0                     0                  0  \n",
      "33                   0        0                     0                  0  \n",
      "34                   0        0                     0                  0  \n",
      "35                   0        0                     0                  0  \n",
      "36                   0        0                     0                  0  \n",
      "37                   0        0                     0                  0  \n",
      "38                   0        0                     0                  0  \n",
      "39                   1        0                     0                  0  \n",
      "40                   0        1                     1                  1  \n",
      "\n",
      "[41 rows x 95 columns]\n"
     ]
    }
   ],
   "source": [
    "X_test= tr[l1]\n",
    "y_test = tr[[\"prognosis\"]]\n",
    "np.ravel(y_test)\n",
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    prognosis\n",
      "0           0\n",
      "1           1\n",
      "2           2\n",
      "3           3\n",
      "4           4\n",
      "5           5\n",
      "6           6\n",
      "7           7\n",
      "8           8\n",
      "9           9\n",
      "10         10\n",
      "11         11\n",
      "12         12\n",
      "13         13\n",
      "14         14\n",
      "15         15\n",
      "16         16\n",
      "17         17\n",
      "18         18\n",
      "19         19\n",
      "20         20\n",
      "21         21\n",
      "22         22\n",
      "23         23\n",
      "24         24\n",
      "25         25\n",
      "26         26\n",
      "27         27\n",
      "28         28\n",
      "29         29\n",
      "30         30\n",
      "31         31\n",
      "32         32\n",
      "33         33\n",
      "34         34\n",
      "35         35\n",
      "36         36\n",
      "37         37\n",
      "38         38\n",
      "39         39\n",
      "40         40\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list1 = DF['prognosis'].unique()\n",
    "def scatterplt(disea):\n",
    "    x = ((DF.loc[disea]).sum())#total sum of symptom reported for given disease\n",
    "    x.drop(x[x==0].index,inplace=True)#droping symptoms with values 0\n",
    "    print(x.values)\n",
    "    y = x.keys()#storing nameof symptoms in y\n",
    "    print(len(x))\n",
    "    print(len(y))\n",
    "    plt.title(disea)\n",
    "    plt.scatter(y,x.values)\n",
    "    plt.show()\n",
    "\n",
    "def scatterinp(sym1,sym2,sym3,sym4,sym5):\n",
    "    x = [sym1,sym2,sym3,sym4,sym5]#storing input symptoms in y\n",
    "    y = [0,0,0,0,0]#creating and giving values to the input symptoms\n",
    "    if(sym1!='Select Here'):\n",
    "        y[0]=1\n",
    "    if(sym2!='Select Here'):\n",
    "        y[1]=1\n",
    "    if(sym3!='Select Here'):\n",
    "        y[2]=1\n",
    "    if(sym4!='Select Here'):\n",
    "        y[3]=1\n",
    "    if(sym5!='Select Here'):\n",
    "        y[4]=1\n",
    "    print(x)\n",
    "    print(y)\n",
    "    plt.scatter(x,y)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "root = Tk()\n",
    "pred1=StringVar()\n",
    "def DecisionTree():\n",
    "    if len(NameEn.get()) == 0:\n",
    "        pred1.set(\" \")\n",
    "        comp=messagebox.askokcancel(\"System\",\"Kindly Fill the Name\")\n",
    "        if comp:\n",
    "            root.mainloop()\n",
    "    elif((Symptom1.get()==\"Select Here\") or (Symptom2.get()==\"Select Here\")):\n",
    "        pred1.set(\" \")\n",
    "        sym=messagebox.askokcancel(\"System\",\"Kindly Fill atleast first two Symptoms\")\n",
    "        if sym:\n",
    "            root.mainloop()\n",
    "    else:\n",
    "        from sklearn import tree\n",
    "\n",
    "        clf3 = tree.DecisionTreeClassifier() \n",
    "        clf3 = clf3.fit(X,y)\n",
    "\n",
    "        from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "        y_pred=clf3.predict(X_test)\n",
    "        print(\"Decision Tree\")\n",
    "        print(\"Accuracy\")\n",
    "        print(accuracy_score(y_test, y_pred))\n",
    "        print(accuracy_score(y_test, y_pred,normalize=False))\n",
    "        print(\"Confusion matrix\")\n",
    "        conf_matrix=confusion_matrix(y_test,y_pred)\n",
    "        print(conf_matrix)\n",
    "\n",
    "        psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()]\n",
    "\n",
    "        for k in range(0,len(l1)):\n",
    "            for z in psymptoms:\n",
    "                if(z==l1[k]):\n",
    "                    l2[k]=1\n",
    "        inputtest = [l2]\n",
    "        predict = clf3.predict(inputtest)\n",
    "        predicted=predict[0]\n",
    "\n",
    "        h='no'\n",
    "        for a in range(0,len(disease)):\n",
    "            if(predicted == a):\n",
    "                h='yes'\n",
    "                break\n",
    "\n",
    "    \n",
    "        if (h=='yes'):\n",
    "            pred1.set(\" \")\n",
    "            pred1.set(disease[a])\n",
    "        else:\n",
    "            pred1.set(\" \")\n",
    "            pred1.set(\"Not Found\")\n",
    "        #Creating the database if not exists named as database.db and creating table if not exists named as DecisionTree using sqlite3 \n",
    "        import sqlite3 \n",
    "        conn = sqlite3.connect('database.db') \n",
    "        c = conn.cursor() \n",
    "        c.execute(\"CREATE TABLE IF NOT EXISTS DecisionTree(Name StringVar,Symtom1 StringVar,Symtom2 StringVar,Symtom3 StringVar,Symtom4 TEXT,Symtom5 TEXT,Disease StringVar)\")\n",
    "        c.execute(\"INSERT INTO DecisionTree(Name,Symtom1,Symtom2,Symtom3,Symtom4,Symtom5,Disease) VALUES(?,?,?,?,?,?,?)\",(NameEn.get(),Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get(),pred1.get()))\n",
    "        conn.commit()  \n",
    "        c.close() \n",
    "        conn.close()\n",
    "        \n",
    "        #printing scatter plot of input symptoms\n",
    "        #printing scatter plot of disease predicted vs its symptoms\n",
    "        scatterinp(Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get())\n",
    "        scatterplt(pred1.get())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred2=StringVar()\n",
    "def randomforest():\n",
    "    if len(NameEn.get()) == 0:\n",
    "        pred1.set(\" \")\n",
    "        comp=messagebox.askokcancel(\"System\",\"Kindly Fill the Name\")\n",
    "        if comp:\n",
    "            root.mainloop()\n",
    "    elif((Symptom1.get()==\"Select Here\") or (Symptom2.get()==\"Select Here\")):\n",
    "        pred1.set(\" \")\n",
    "        sym=messagebox.askokcancel(\"System\",\"Kindly Fill atleast first two Symptoms\")\n",
    "        if sym:\n",
    "            root.mainloop()\n",
    "    else:\n",
    "        from sklearn.ensemble import RandomForestClassifier\n",
    "        clf4 = RandomForestClassifier(n_estimators=100)\n",
    "        clf4 = clf4.fit(X,np.ravel(y))\n",
    "\n",
    "        # calculating accuracy \n",
    "        from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "        y_pred=clf4.predict(X_test)\n",
    "        print(\"Random Forest\")\n",
    "        print(\"Accuracy\")\n",
    "        print(accuracy_score(y_test, y_pred))\n",
    "        print(accuracy_score(y_test, y_pred,normalize=False))\n",
    "        print(\"Confusion matrix\")\n",
    "        conf_matrix=confusion_matrix(y_test,y_pred)\n",
    "        print(conf_matrix)\n",
    "    \n",
    "        psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()]\n",
    "        for k in range(0,len(l1)):\n",
    "            for z in psymptoms:\n",
    "                if(z==l1[k]):\n",
    "                    l2[k]=1\n",
    "\n",
    "        inputtest = [l2]\n",
    "        predict = clf4.predict(inputtest)\n",
    "        predicted=predict[0]\n",
    "\n",
    "        h='no'\n",
    "        for a in range(0,len(disease)):\n",
    "            if(predicted == a):\n",
    "                h='yes'\n",
    "                break\n",
    "        if (h=='yes'):\n",
    "            pred2.set(\" \")\n",
    "            pred2.set(disease[a])\n",
    "        else:\n",
    "            pred2.set(\" \")\n",
    "            pred2.set(\"Not Found\")\n",
    "         #Creating the database if not exists named as database.db and creating table if not exists named as RandomForest using sqlite3\n",
    "        import sqlite3 \n",
    "        conn = sqlite3.connect('database.db') \n",
    "        c = conn.cursor() \n",
    "        c.execute(\"CREATE TABLE IF NOT EXISTS RandomForest(Name StringVar,Symtom1 StringVar,Symtom2 StringVar,Symtom3 StringVar,Symtom4 TEXT,Symtom5 TEXT,Disease StringVar)\")\n",
    "        c.execute(\"INSERT INTO RandomForest(Name,Symtom1,Symtom2,Symtom3,Symtom4,Symtom5,Disease) VALUES(?,?,?,?,?,?,?)\",(NameEn.get(),Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get(),pred2.get()))\n",
    "        conn.commit()  \n",
    "        c.close() \n",
    "        conn.close()\n",
    "        #printing scatter plot of disease predicted vs its symptoms\n",
    "        scatterplt(pred2.get())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred4=StringVar()\n",
    "def KNN():\n",
    "    if len(NameEn.get()) == 0:\n",
    "        pred1.set(\" \")\n",
    "        comp=messagebox.askokcancel(\"System\",\"Kindly Fill the Name\")\n",
    "        if comp:\n",
    "            root.mainloop()\n",
    "    elif((Symptom1.get()==\"Select Here\") or (Symptom2.get()==\"Select Here\")):\n",
    "        pred1.set(\" \")\n",
    "        sym=messagebox.askokcancel(\"System\",\"Kindly Fill atleast first two Symptoms\")\n",
    "        if sym:\n",
    "            root.mainloop()\n",
    "    else:\n",
    "        from sklearn.neighbors import KNeighborsClassifier\n",
    "        knn=KNeighborsClassifier(n_neighbors=5,metric='minkowski',p=2)\n",
    "        knn=knn.fit(X,np.ravel(y))\n",
    "    \n",
    "        from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "        y_pred=knn.predict(X_test)\n",
    "        print(\"kNearest Neighbour\")\n",
    "        print(\"Accuracy\")\n",
    "        print(accuracy_score(y_test, y_pred))\n",
    "        print(accuracy_score(y_test, y_pred,normalize=False))\n",
    "        print(\"Confusion matrix\")\n",
    "        conf_matrix=confusion_matrix(y_test,y_pred)\n",
    "        print(conf_matrix)\n",
    "\n",
    "        psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()]\n",
    "\n",
    "        for k in range(0,len(l1)):\n",
    "            for z in psymptoms:\n",
    "                if(z==l1[k]):\n",
    "                    l2[k]=1\n",
    "        inputtest = [l2]\n",
    "        predict = knn.predict(inputtest)\n",
    "        predicted=predict[0]\n",
    "\n",
    "        h='no'\n",
    "        for a in range(0,len(disease)):\n",
    "            if(predicted == a):\n",
    "                h='yes'\n",
    "                break\n",
    "\n",
    "\n",
    "        if (h=='yes'):\n",
    "            pred4.set(\" \")\n",
    "            pred4.set(disease[a])\n",
    "        else:\n",
    "            pred4.set(\" \")\n",
    "            pred4.set(\"Not Found\")\n",
    "         #Creating the database if not exists named as database.db and creating table if not exists named as KNearestNeighbour using sqlite3   \n",
    "        import sqlite3 \n",
    "        conn = sqlite3.connect('database.db') \n",
    "        c = conn.cursor() \n",
    "        c.execute(\"CREATE TABLE IF NOT EXISTS KNearestNeighbour(Name StringVar,Symtom1 StringVar,Symtom2 StringVar,Symtom3 StringVar,Symtom4 TEXT,Symtom5 TEXT,Disease StringVar)\")\n",
    "        c.execute(\"INSERT INTO KNearestNeighbour(Name,Symtom1,Symtom2,Symtom3,Symtom4,Symtom5,Disease) VALUES(?,?,?,?,?,?,?)\",(NameEn.get(),Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get(),pred4.get()))\n",
    "        conn.commit()  \n",
    "        c.close() \n",
    "        conn.close()\n",
    "        #printing scatter plot of disease predicted vs its symptoms\n",
    "        scatterplt(pred4.get())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred3=StringVar()\n",
    "def NaiveBayes():\n",
    "    if len(NameEn.get()) == 0:\n",
    "        pred1.set(\" \")\n",
    "        comp=messagebox.askokcancel(\"System\",\"Kindly Fill the Name\")\n",
    "        if comp:\n",
    "            root.mainloop()\n",
    "    elif((Symptom1.get()==\"Select Here\") or (Symptom2.get()==\"Select Here\")):\n",
    "        pred1.set(\" \")\n",
    "        sym=messagebox.askokcancel(\"System\",\"Kindly Fill atleast first two Symptoms\")\n",
    "        if sym:\n",
    "            root.mainloop()\n",
    "    else:\n",
    "        from sklearn.naive_bayes import GaussianNB\n",
    "        gnb = GaussianNB()\n",
    "        gnb=gnb.fit(X,np.ravel(y))\n",
    "\n",
    "        from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "        y_pred=gnb.predict(X_test)\n",
    "        print(\"Naive Bayes\")\n",
    "        print(\"Accuracy\")\n",
    "        print(accuracy_score(y_test, y_pred))\n",
    "        print(accuracy_score(y_test, y_pred,normalize=False))\n",
    "        print(\"Confusion matrix\")\n",
    "        conf_matrix=confusion_matrix(y_test,y_pred)\n",
    "        print(conf_matrix)\n",
    "        psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()]\n",
    "        for k in range(0,len(l1)):\n",
    "            for z in psymptoms:\n",
    "                if(z==l1[k]):\n",
    "                    l2[k]=1\n",
    "\n",
    "        inputtest = [l2]\n",
    "        predict = gnb.predict(inputtest)\n",
    "        predicted=predict[0]\n",
    "\n",
    "        h='no'\n",
    "        for a in range(0,len(disease)):\n",
    "            if(predicted == a):\n",
    "                h='yes'\n",
    "                break\n",
    "        if (h=='yes'):\n",
    "            pred3.set(\" \")\n",
    "            pred3.set(disease[a])\n",
    "        else:\n",
    "            pred3.set(\" \")\n",
    "            pred3.set(\"Not Found\")\n",
    "         #Creating the database if not exists named as database.db and creating table if not exists named as NaiveBayes using sqlite3\n",
    "        import sqlite3 \n",
    "        conn = sqlite3.connect('database.db') \n",
    "        c = conn.cursor() \n",
    "        c.execute(\"CREATE TABLE IF NOT EXISTS NaiveBayes(Name StringVar,Symtom1 StringVar,Symtom2 StringVar,Symtom3 StringVar,Symtom4 TEXT,Symtom5 TEXT,Disease StringVar)\")\n",
    "        c.execute(\"INSERT INTO NaiveBayes(Name,Symtom1,Symtom2,Symtom3,Symtom4,Symtom5,Disease) VALUES(?,?,?,?,?,?,?)\",(NameEn.get(),Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get(),pred3.get()))\n",
    "        conn.commit()  \n",
    "        c.close() \n",
    "        conn.close()\n",
    "        #printing scatter plot of disease predicted vs its symptoms\n",
    "        scatterplt(pred3.get())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Tk class is used to create a root window\n",
    "root.configure(background='Ivory')\n",
    "root.title('Patient Diagnoses and History Management System')\n",
    "root.resizable(0,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Symptom1 = StringVar()\n",
    "Symptom1.set(\"Select Here\")\n",
    "\n",
    "Symptom2 = StringVar()\n",
    "Symptom2.set(\"Select Here\")\n",
    "\n",
    "Symptom3 = StringVar()\n",
    "Symptom3.set(\"Select Here\")\n",
    "\n",
    "Symptom4 = StringVar()\n",
    "Symptom4.set(\"Select Here\")\n",
    "\n",
    "Symptom5 = StringVar()\n",
    "Symptom5.set(\"Select Here\")\n",
    "Name = StringVar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "prev_win=None\n",
    "def Reset():\n",
    "    global prev_win\n",
    "\n",
    "    Symptom1.set(\"Select Here\")\n",
    "    Symptom2.set(\"Select Here\")\n",
    "    Symptom3.set(\"Select Here\")\n",
    "    Symptom4.set(\"Select Here\")\n",
    "    Symptom5.set(\"Select Here\")\n",
    "    NameEn.delete(first=0,last=100)\n",
    "    pred1.set(\" \")\n",
    "    pred2.set(\" \")\n",
    "    pred3.set(\" \")\n",
    "    pred4.set(\" \")\n",
    "    try:\n",
    "        prev_win.destroy()\n",
    "        prev_win=None\n",
    "    except AttributeError:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter import messagebox\n",
    "def Exit():\n",
    "    qExit=messagebox.askyesno(\"System\",\"Do you want to exit the system\")\n",
    "    \n",
    "    if qExit:\n",
    "        root.destroy()\n",
    "        exit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Headings for the GUI written at the top of GUI\n",
    "w2 = Label(root, justify=LEFT, text=\"Diagnoses System\", fg=\"Red\", bg=\"Ivory\")\n",
    "w2.config(font=(\"Times\",30,\"bold italic\"))\n",
    "w2.grid(row=1, column=0, columnspan=2, padx=100)\n",
    "w2 = Label(root, justify=LEFT, text=\" \", fg=\"Pink\", bg=\"Ivory\")\n",
    "w2.config(font=(\"Times\",30,\"bold italic\"))\n",
    "w2.grid(row=2, column=0, columnspan=2, padx=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Label for the name\n",
    "NameLb = Label(root, text=\"Name of the Patient *\", fg=\"Red\", bg=\"Ivory\")\n",
    "NameLb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "NameLb.grid(row=6, column=0, pady=15, sticky=W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating Labels for the symtoms\n",
    "S1Lb = Label(root, text=\"Symptom 1 *\", fg=\"Black\", bg=\"Ivory\")\n",
    "S1Lb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "S1Lb.grid(row=7, column=0, pady=10, sticky=W)\n",
    "\n",
    "S2Lb = Label(root, text=\"Symptom 2 *\", fg=\"Black\", bg=\"Ivory\")\n",
    "S2Lb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "S2Lb.grid(row=8, column=0, pady=10, sticky=W)\n",
    "\n",
    "S3Lb = Label(root, text=\"Symptom 3\", fg=\"Black\",bg=\"Ivory\")\n",
    "S3Lb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "S3Lb.grid(row=9, column=0, pady=10, sticky=W)\n",
    "\n",
    "S4Lb = Label(root, text=\"Symptom 4\", fg=\"Black\", bg=\"Ivory\")\n",
    "S4Lb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "S4Lb.grid(row=10, column=0, pady=10, sticky=W)\n",
    "\n",
    "S5Lb = Label(root, text=\"Symptom 5\", fg=\"Black\", bg=\"Ivory\")\n",
    "S5Lb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "S5Lb.grid(row=11, column=0, pady=10, sticky=W)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Labels for the different algorithms\n",
    "lrLb = Label(root, text=\"DecisionTree\", fg=\"white\", bg=\"red\", width = 20)\n",
    "lrLb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "lrLb.grid(row=15, column=0, pady=10,sticky=W)\n",
    "\n",
    "destreeLb = Label(root, text=\"RandomForest\", fg=\"Red\", bg=\"Orange\", width = 20)\n",
    "destreeLb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "destreeLb.grid(row=17, column=0, pady=10, sticky=W)\n",
    "\n",
    "ranfLb = Label(root, text=\"NaiveBayes\", fg=\"White\", bg=\"green\", width = 20)\n",
    "ranfLb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "ranfLb.grid(row=19, column=0, pady=10, sticky=W)\n",
    "\n",
    "knnLb = Label(root, text=\"kNearestNeighbour\", fg=\"Red\", bg=\"Sky Blue\", width = 20)\n",
    "knnLb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "knnLb.grid(row=21, column=0, pady=10, sticky=W)\n",
    "OPTIONS = sorted(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking name as input from user\n",
    "NameEn = Entry(root, textvariable=Name)\n",
    "NameEn.grid(row=6, column=1)\n",
    "\n",
    "#Taking Symptoms as input from the dropdown from the user\n",
    "S1 = OptionMenu(root, Symptom1,*OPTIONS)\n",
    "S1.grid(row=7, column=1)\n",
    "\n",
    "S2 = OptionMenu(root, Symptom2,*OPTIONS)\n",
    "S2.grid(row=8, column=1)\n",
    "\n",
    "S3 = OptionMenu(root, Symptom3,*OPTIONS)\n",
    "S3.grid(row=9, column=1)\n",
    "\n",
    "S4 = OptionMenu(root, Symptom4,*OPTIONS)\n",
    "S4.grid(row=10, column=1)\n",
    "\n",
    "S5 = OptionMenu(root, Symptom5,*OPTIONS)\n",
    "S5.grid(row=11, column=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Buttons for predicting the disease using different algorithms\n",
    "dst = Button(root, text=\"Prediction 1\", command=DecisionTree,bg=\"Red\",fg=\"yellow\")\n",
    "dst.config(font=(\"Times\",15,\"bold italic\"))\n",
    "dst.grid(row=6, column=3,padx=10)\n",
    "\n",
    "rnf = Button(root, text=\"Prediction 2\", command=randomforest,bg=\"Light green\",fg=\"red\")\n",
    "rnf.config(font=(\"Times\",15,\"bold italic\"))\n",
    "rnf.grid(row=7, column=3,padx=10)\n",
    "\n",
    "lr = Button(root, text=\"Prediction 3\", command=NaiveBayes,bg=\"Blue\",fg=\"white\")\n",
    "lr.config(font=(\"Times\",15,\"bold italic\"))\n",
    "lr.grid(row=8, column=3,padx=10)\n",
    "\n",
    "kn = Button(root, text=\"Prediction 4\", command=KNN,bg=\"sky blue\",fg=\"red\")\n",
    "kn.config(font=(\"Times\",15,\"bold italic\"))\n",
    "kn.grid(row=9, column=3,padx=10)\n",
    "\n",
    "rs = Button(root,text=\"Reset Inputs\", command=Reset,bg=\"yellow\",fg=\"purple\",width=15)\n",
    "rs.config(font=(\"Times\",15,\"bold italic\"))\n",
    "rs.grid(row=10,column=3,padx=10)\n",
    "\n",
    "ex = Button(root,text=\"Exit System\", command=Exit,bg=\"yellow\",fg=\"purple\",width=15)\n",
    "ex.config(font=(\"Times\",15,\"bold italic\"))\n",
    "ex.grid(row=11,column=3,padx=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Showing the output of different aldorithms\n",
    "t1=Label(root,font=(\"Times\",15,\"bold italic\"),text=\"Decision Tree\",height=1,bg=\"Light green\"\n",
    "         ,width=40,fg=\"red\",textvariable=pred1,relief=\"sunken\").grid(row=15, column=1, padx=10)\n",
    "\n",
    "t2=Label(root,font=(\"Times\",15,\"bold italic\"),text=\"Random Forest\",height=1,bg=\"Purple\"\n",
    "         ,width=40,fg=\"white\",textvariable=pred2,relief=\"sunken\").grid(row=17, column=1, padx=10)\n",
    "\n",
    "t3=Label(root,font=(\"Times\",15,\"bold italic\"),text=\"Naive Bayes\",height=1,bg=\"red\"\n",
    "         ,width=40,fg=\"orange\",textvariable=pred3,relief=\"sunken\").grid(row=19, column=1, padx=10)\n",
    "\n",
    "t4=Label(root,font=(\"Times\",15,\"bold italic\"),text=\"kNearest Neighbour\",height=1,bg=\"Blue\"\n",
    "         ,width=40,fg=\"yellow\",textvariable=pred4,relief=\"sunken\").grid(row=21, column=1, padx=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree\n",
      "Accuracy\n",
      "0.9512195121951219\n",
      "39\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "['chest_pain', 'back_pain', 'mild_fever', 'knee_pain', 'Select Here']\n",
      "[1, 1, 1, 1, 0]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[114 114 114 114 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "Accuracy\n",
      "0.9512195121951219\n",
      "39\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "[114 114 114 114 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n",
      "Accuracy\n",
      "0.9512195121951219\n",
      "39\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "[114 108 108 108 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kNearest Neighbour\n",
      "Accuracy\n",
      "0.926829268292683\n",
      "38\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "[114 108 108 108 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree\n",
      "Accuracy\n",
      "0.9512195121951219\n",
      "39\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "['chest_pain', 'back_pain', 'mild_fever', 'knee_pain', 'foul_smell_of urine']\n",
      "[1, 1, 1, 1, 1]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAD5CAYAAAAeEfMjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAWMElEQVR4nO3ce5RlZX3m8e9jd6uACiilIw3SGgkBidcaNGqc1jgKxojmpnghYBLGjPeJZNRxIpOrBpMljgYkSAjoggREQ5QRFEWMF7AaGmgEtBfg0EBCOQ4QhJHbb/7Yu+lDUbdTnLerqvv7WatW7cu79/vud+9znn2rSlUhSVIrD1vsBkiStm0GjSSpKYNGktSUQSNJasqgkSQ1tXKxGzCd3XbbrdasWbPYzZCkZWPdunU/qqqxxW7HdJZk0KxZs4aJiYnFboYkLRtJfrjYbZiJt84kSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU3NGTRJTkxyc5INM8xPko8l2ZjksiTPnjJ/RZJLknxhVI2WJC0f87miOQk4cJb5BwF79z9HAMdOmf9O4MqFNE6StPzNGTRVdQHw41mKHAycXJ3vALskeSJAkj2AXwZOGEVjJUnLzyie0awGrh8Y39RPA/go8AfAfXOtJMkRSSaSTExOTo6gWZKkpWAUQZNpplWSVwI3V9W6+aykqo6vqvGqGh8bGxtBsyRJS8EogmYTsOfA+B7AjcALgFcluQ44DXhJkk+PoD5J0jIyiqA5Czi0f/vsecCtVXVTVb2vqvaoqjXA64CvVtUbR1CfJGkZWTlXgSSnAmuB3ZJsAj4IrAKoquOAs4FXABuBO4DDWzVWkrT8zBk0VXXIHPMLeOscZc4Hzh+mYZKkbYP/GUCS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKbmDJokJya5OcmGGeYnyceSbExyWZJn99P3TPK1JFcmuSLJO0fdeEnS0jefK5qTgANnmX8QsHf/cwRwbD/9HuD3q2pf4HnAW5Pst/CmSpKWozmDpqouAH48S5GDgZOr8x1glyRPrKqbqurifh3/BlwJrB5FoyVJy8contGsBq4fGN/ElEBJsgZ4FnDhCOqTJC0jowiaTDOt7p+ZPAr4LPCuqrptxpUkRySZSDIxOTk5gmZJkpaCUQTNJmDPgfE9gBsBkqyiC5nPVNWZs62kqo6vqvGqGh8bGxtBsyRJS8EoguYs4ND+7bPnAbdW1U1JAnwKuLKq/moE9UiSlqGVcxVIciqwFtgtySbgg8AqgKo6DjgbeAWwEbgDOLxf9AXAm4DLk6zvp72/qs4e5QZIkpa2OYOmqg6ZY34Bb51m+j8z/fMbSdJ2xP8MIElqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKmplXMVSHIi8Erg5qraf5r5AY4BXgHcARxWVRf38w7s560ATqiqD42w7Q/w+Utu4OhzrubGW+5k91124MiX78Orn7W6VXXLnv01HPtrOPbXcLb1/pozaICTgI8DJ88w/yBg7/7nucCxwHOTrAA+AfxHYBPw3SRnVdX3Hmqjp/r8JTfwvjMv58677wXghlvu5H1nXg6wTe2sUbG/hmN/Dcf+Gs720F9z3jqrqguAH89S5GDg5Op8B9glyROBA4CNVXVNVd0FnNaXHbmjz7n6/p202Z1338vR51zdorplz/4ajv01HPtrONtDf43iGc1q4PqB8U39tJmmTyvJEUkmkkxMTk4O1YAbb7lzqOnbO/trOPbXcOyv4WwP/TWKoMk002qW6dOqquOraryqxsfGxoZqwO677DDU9O2d/TUc+2s49tdwtof+GkXQbAL2HBjfA7hxlukjd+TL92GHVSseMG2HVSs48uX7tKhu2bO/hmN/Dcf+Gs720F/zeRlgLmcBb0tyGt3LALdW1U1JJoG9kzwZuAF4HfD6EdT3IJsfmG3Lb22Mkv01HPtrOPbXcLaH/krVjHezugLJqcBaYDfgX4EPAqsAquq4/vXmjwMH0r3efHhVTfTLvgL4KN3rzSdW1Z/Op1Hj4+M1MTGxkO2RpO1SknVVNb7Y7ZjOnFc0VXXIHPMLeOsM884Gzl5Y0yRJ2wL/M4AkqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU3NK2iSHJjk6iQbk7x3mvm7JvlcksuSXJRk/4F5705yRZINSU5N8shRboAkaWmbM2iSrAA+ARwE7AcckmS/KcXeD6yvqqcDhwLH9MuuBt4BjFfV/sAK4HWja74kaambzxXNAcDGqrqmqu4CTgMOnlJmP+A8gKq6CliT5An9vJXADklWAjsCN46k5ZKkZWE+QbMauH5gfFM/bdClwK8CJDkA2AvYo6puAD4C/G/gJuDWqjr3oTZakrR8zCdoMs20mjL+IWDXJOuBtwOXAPck2ZXu6ufJwO7ATkneOG0lyRFJJpJMTE5OznsDJElL23yCZhOw58D4Hky5/VVVt1XV4VX1TLpnNGPAtcBLgWurarKq7gbOBJ4/XSVVdXxVjVfV+NjY2AI2RZK0FM0naL4L7J3kyUkeTvcw/6zBAkl26ecB/A5wQVXdRnfL7HlJdkwS4JeAK0fXfEnSUrdyrgJVdU+StwHn0L01dmJVXZHkLf3844B9gZOT3At8D/jtft6FSc4ALgbuobuldnyTLZEkLUmpmvq4ZfGNj4/XxMTEYjdDkpaNJOuqanyx2zEd/zOAJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKbmFTRJDkxydZKNSd47zfxdk3wuyWVJLkqy/8C8XZKckeSqJFcm+YVRboAkaWmbM2iSrAA+ARwE7AcckmS/KcXeD6yvqqcDhwLHDMw7BvhSVf0c8AzgylE0XJK0PMzniuYAYGNVXVNVdwGnAQdPKbMfcB5AVV0FrEnyhCSPAV4EfKqfd1dV3TKy1kuSlrz5BM1q4PqB8U39tEGXAr8KkOQAYC9gD+ApwCTwt0kuSXJCkp2mqyTJEUkmkkxMTk4OuRmSpKVqPkGTaabVlPEPAbsmWQ+8HbgEuAdYCTwbOLaqngX8BHjQMx6Aqjq+qsaranxsbGy+7ZckLXEr51FmE7DnwPgewI2DBarqNuBwgCQBru1/dgQ2VdWFfdEzmCFoJEnbpvlc0XwX2DvJk5M8HHgdcNZggf7Nsof3o78DXFBVt1XVvwDXJ9mnn/dLwPdG1HZJ0jIw5xVNVd2T5G3AOcAK4MSquiLJW/r5xwH7AicnuZcuSH57YBVvBz7TB9E19Fc+kqTtQ6qmPm5ZfOPj4zUxMbHYzZCkZSPJuqoaX+x2TMf/DCBJasqgkSQ1ZdBIkpoyaCRJTRk0kqSmDBpJUlMGjSSpKYNGktSUQSNJasqgkSQ1ZdBIkpoyaCRJTRk0kqSmDBpJUlMGjSSpKYNGktSUQSNJasqgkSQ1ZdBIkpoyaCRJTRk0kqSmDBpJUlMGjSSpKYNGktRUqmqx2/AgSSaBHy5w8d2AH42wOds6+2s49tdw7K/hPJT+2quqxkbZmFFZkkHzUCSZqKrxxW7HcmF/Dcf+Go79NZxttb+8dSZJasqgkSQ1tS0GzfGL3YBlxv4ajv01HPtrONtkf21zz2gkSUvLtnhFI0laQgwaSVJTBo0kqalFC5okJyX59RGs511JdhxFmwbW+ZYkh45ynQuVZE2SDQ9xHWuTfGFUbZpm/d9qte5RS/KqJO/th49K8p5pyszZ50lOTXJZkne3amtrozi2WksynuRjC1juHUmuTPKZBdZ7+0KWW6jBfbHQz2uSRyT5SpL1SV67wHbsnuSMhSw7m5WjXuEieBfwaeCOUa2wqo4b1bq2B1X1/MVuw3xV1VnAWQ9lHUn+HfD8qtprNK2ato6VVXVPq/UvF1U1AUwsYNH/DBxUVdeOuElL2bOAVVX1zIUs3B9zNwIP+QJgqq12RZPk0P4M8NIkp/STX5TkW0muGby6SXJkku/25f9HP22nJF/sl9+Q5LVJ3gHsDnwtyddmqfv2JH+Z5OIk5yUZ66f/bl/PpUk+u/nKaPBMN8n5ST6c5KIk30/yi426aDYrk/xd3x9nJNkxyR/2bd+Q5Pgk6dv71P6s5tJ+e39mcEVJ/n2SS5I8ZbqK+m0/JclXk/wgye/20x/V993FSS5PcvDAMrf3v9f2/XVGkquSfGZzu7aG/qzwqiQn9P3ymSQvTfLNflsOSHJYko9Ps+xz+j77NvDWOao6F3h8f+b4i0l+JsmXkqxL8o0kP5dk5yTXJXlYv/4dk1yfZNV05fsyJyX5q/5Y/vCo+2c2SZ7SHxdHJjmzb98PkvzFQJmXJfl2fwycnuRR/fTnJPl6vz3nJHniLPWcn+Sj/ed+Q5ID+ukH9NMu6X/v00+//+y+PzZP7NdxTf/5n66O44CnAGcleXeSxyb5fP/5+U6Spw+s7z0Dy21IsmYeffXEJBf0+3/D5u+EdN8zH+774Sv9Nm1u66v6MiuSHJ0t32//aa76pqn/QduT5PF0J9zP7Ns19XN/fpLxfni3JNf1w4f1+/KfgHPzwCurw4Y9FmZUVc1/gKcBVwO79eOPBU4CTqcLu/2Ajf28l9G9S55+3heAFwG/BvzNwDp37n9ft3m9s9RfwBv64T8EPt4PP26gzJ8Ab++HjwLe0w+fD/xlP/wK4Ctbo88G2rWmb/8L+vETgfcAjx0ocwrwK/3whcBr+uFHAjsCa/t+fD6wDnjSLPUdBVwK7ED3f5eupwvzlcBj+jK7ARvZ8nr87f3vtcCtwB79vvs28MKt3Ff3AD/f17+u768ABwOfBw4b2P+D+/ky4D/0w0cDG+aoZ8PA+HnA3v3wc4Gv9sP/CLy4H34tcMIc5U/q99OKrdhfG4B9gEuAZ/b9cw2wc3/8/BDYs9/nFwA79cv+V7rP0irgW8DYwHaeOEud59N/juk+1xv64ccAK/vhlwKfHTimvjCwv74FPKJvz/+hO4Ofrp7r2PJ98z+BD/bDLwHWT93//fgGYM3gMT3Dun8f+G/98Arg0f1w0V1FAXyO7oRkFfCMgTqPAD7QDz+C7mrtyYPH1OA2z1D/TNsz43J9v48PfH6v64cPAzbRf59MacdQx8Jsx9rWunX2EuCMqvoRQFX9uD/R/XxV3Qd8L8kT+rIv638u6ccfBewNfAP4SJIP03XmN4ao/z7g7/vhTwNn9sP7J/kTYJe+nnNmWH5z+XV0O2Jru76qvtkPfxp4B3Btkj+gC5LHAlckOR9YXVWfA6iq/wfQ9/W+dAH+suouj2fzj1V1J3Bnf3Z9APBF4M+SvIiuP1cDTwD+ZcqyF1XVpr7e9XT99c8L3O6FuLaqLu/rvwI4r6oqyeXMsO+S7AzsUlVf7yedAhw0n8r6M7nnA6dny8XbI/rff0/3xfs14HXAX89RHuD0qrp3PnWPyBhdIP5aVV2R5Jl0fXYrQJLvAXvRfUb2A77Zt/vhdCcS+wD7A1/up68AbpqjzlMBquqCJI9JsgvwaODvkuxN94W9aoZlv1hVPwV+muRmumNw0xz1vZDuRJWq+mqSx/X7fKG+C5yYZBXdd9j6fvpdwJf64cuBn1bV3VOOvZcBT8+WOzg7032/fX+I+ke9PV+uqh/PMG+YY2FGWytoQnfwTPXTKWU2//7zqvrkg1aSPIfuquLPk5xbVX+0wPZsbstJwKur6tIkh9GdEUxnczvvZXGea03tuwL+mu4M5fokR9Gdccx2m+qmvsyzgLmCZrr63kD3pfSc/sNzXb++qQb36WL012D99w2M3zdLW2Y6PufjYcAtNf198bPojtXHAs8BvgrsNEt5gJ8ssB0LdSvdVesLgCv6adPtw9B9IR0yuHCSnweuqKpfGKLO6Y6vPwa+VlWv6W9fnT/Dsgs5vqb7XBTd1e/g44PpjucHL9gF5IuAXwZOSXJ0VZ0M3F39KT4Dx15V3ZdkcztDd+fkASe187llN1h8umbNsczgtk7dztmOuXkfC7PZWs9ozgN+M8njoLvHOEvZc4A3Z8v939VJHp9kd+COqvo08BHg2X35f6M7G5rNw9jygOv1bDnDfjRwU39m8oYht2lrelKSzR/kQ9jS/h/1/fTrAFV1G7Apyavh/rdQNr+RdwvdB+PPkqydo76Dkzyy319r6c7gdgZu7kPmxXRnNtuEqroFuDXJC/tJ8z4W+j6/NslvAKTzjH7e7cBFwDF0V+H3zlZ+kdwFvBo4NMnrZyn3HeAFSZ4K9z9z+lm6W+Jjm4/PdM+gnjZHna/ty74QuLU/Y94ZuKGff9hCN2YGF9Dv0/7Y/1G/H66j/x5J8my6W1hzSrIX3Wfhb4BPseW7aD7OAX6v/84hyc8m2WmI5WHm7ZnNdXQnO/DQH/bPdCzMaKucbfaX5H8KfD3JvWy5LTZd2XOT7At8u78sux14I/BU4Ogk9wF3A7/XL3I88L+S3FRVL55htT8BnpZkHd0Z3OZX//473TONH9Jd6s4VWIvlSuC3knwS+AFwLLArXZuvowuCzd4EfDLJH9H1029snlFV/5rkV+j6681VdeEM9V1Ed6vsScAfV9WN6V4T/ackE8B64KpRbuAScDjd7ZA7mPkW6kzeAByb5AN0t3xOo3vOBd3ts9N54NXybOW3uqr6SZJXAl+muzU7XZnJ/qr/1CSbb/V9oKq+398G+lh/+2Yl8FG2XB1N5/+meyX+McCb+2l/QXfr7L/QXfmN0lHA3ya5jO7t1N/qp3+WLmDX032G5nv7ai1wZJK76b6fhvlTiBPobqNdnO4LbpIu6IdxFNNvz2w+AvxDkjfxEPt3pmOBWfpvu/hfZ0lur6rZ34oQ0L2JQ/cg9COL3RZte/rniO+p7rVlbSf8zwCSpKa2hT/YvF+SC3ngGzwAb/Jq5sGSHA68c8rkb1bVXH9Dst1I8nIe/Pcs11bVaxajPctJkk/QvWAw6JiqWrsIzVmQ/kWHU6ZM/mlVPXcr1b/NfEa3i1tnkqTF460zSVJTBo0kqSmDRpLUlEEjSWrq/wNQzwinYtUXEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[114 108 108 108 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "Accuracy\n",
      "0.9512195121951219\n",
      "39\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "[114 114 114 114 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n",
      "Accuracy\n",
      "0.9512195121951219\n",
      "39\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "[114 108 108 108 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEJCAYAAACZjSCSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAZT0lEQVR4nO3df5RcdX3/8ecrJEAgSgq7oEmVTRVjEY5YF1ErNsVCqAcl8qMQ+fJD20bEX7SCwvfUEq0UJPrVb+FwNGpcRIsiQuQL1dRGMCo/NyYxCRClkmoCJRsx1mBEkry/f9zPsDfjzM7uzszOsp/X45w5e+dzP/fez73zufd1596ZHUUEZmaWr0mdboCZmXWWg8DMLHMOAjOzzDkIzMwy5yAwM8ucg8DMLHMOAjOzzDkILAuSzpR0r6QnJW1Jwxeo0Cfpd5K2lx5r0nQ9kqJUvlHSJVXz3ihph6RfS9om6S5J50vy/mXPCu6oNuFJej/wf4FFwPOAQ4DzgT8F9k7VroqIaaXHy6tmMz0ipgGnAR+SdHzV+DdFxHOAQ4ErgQ8Cn2/PGpm1loPAJjRJBwAfAS6IiJsi4tdRWBURZ0XEUyOZX0T0A+uBo+qM/1VE3AqcAZwr6Yhm18Gs3RwENtG9BtgH+EYrZibp1cARwMND1YuI+4BNwLGtWK5ZOzkIbKLrArZGxM5KQbqGvy1d1399Kr4olVUe11XNZ6ukHcDdwLXA0mEs+1HgwFashFk7Te50A8za7BdAl6TJlTCIiNcCSNrE4MnQxyPiH4aYTxcQwIXAfGAK8LsGy54JPNFE283GhN8R2ER3N/AUcHKzM4qIXRHxCeC3wAVD1ZV0NEUQfL/Z5Zq1m4PAJrSI2AZ8GLhW0mmSpkmaJOkoYP9RzvZK4AOS9q0eIem5kk4CvgJ8KSLWjrrxZmNE/j0Cy4Gks4D3UdzofRL4KcXHO/uAxcBb2fNSz28joktSD/AIMKVyaUmSgHXApyPiakkbKT6SuhPYDTwAfCmN39XudTNrloPAzCxzvjRkZpY5B4GZWeYcBGZmmXMQmJllblx8oayrqyt6eno63Qwzs2eVlStXbo2I7mbnMy6CoKenh/7+/k43w8zsWUXSf7ViPr40ZGaWOQeBmVnmHARmZplzEJiZZc5BYGaWuYafGpK0BDgJ2BIRR6Sy04GFwB8Dr0o/31ee5oUU/3hrYUR8vNWNrli6ajOLlm3g0W07mDF9KhfPnc28V8xs1+LMrE1y3JfH0zoP5x1BH3BiVdk64BRgRZ1pPgl8c/TNamzpqs1cevNaNm/bQQCbt+3g0pvXsnTV5nYu1sxaLMd9ebytc8MgiIgVVP3KUkQ8GBEbatWXNI/iX/yub0kL61i0bAM7nt7zP/zueHoXi5bVbJaZjVM57svjbZ1beo9A0v7AByl+CKRR3QWS+iX1DwwMjHhZj27bMaJyMxufctyXx9s6t/pm8YeBT0bE9kYVI2JxRPRGRG9398i/IT1j+tQRlZvZ+JTjvjze1rnVQXAMcFX6xaYLgf8t6d0tXgYAF8+dzdQpe+1RNnXKXlw8d3Y7FmdmbZLjvjze1rml/2soIo6tDEtaCGyPiGtauYyKyt318XLX3cxGJ8d9ebytc8OfqpR0AzAH6AIeBy6juHl8NdANbANWR8TcqukWUgRBw4+P9vb2hv/pnJnZyEhaGRG9zc6n4TuCiJhfZ9QtDaZbOJoGmZnZ2PI3i83MMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIzs8w1DAJJSyRtkbSuVHa6pPWSdkvqLZW/StLq9Fgj6S3tariZmbXGcN4R9AEnVpWtA04BVtQo742Io9I0n5E0udlGmplZ+zQ8SEfECkk9VWUPAkiqrvub0tN9gWi6hWZm1lYtv0cg6RhJ64G1wPkRsbNOvQWS+iX1DwwMtLoZZmY2TC0Pgoi4NyJeBhwNXCpp3zr1FkdEb0T0dnd3t7oZZmY2TG371FC6fPQkcES7lmFmZs1raRBImlW5OSzpUGA2sLGVyzAzs9ZqeLNY0g3AHKBL0ibgMuAJ4GqgG7hd0uqImAu8DrhE0tPAbuCCiNjarsabmVnzhvOpofl1Rt1So+71wPXNNsrMzMaOv1lsZpY5B4GZWeYcBGZmmXMQmJllzkFgZpY5B4GZWeYcBGZmmXMQmJllzkFgZpY5B4GZWeYcBGZmmXMQmJllzkFgZpY5B4GZWeYcBGZmmXMQmJllzkFgZpY5B4GZWeYcBGZmmXMQmJllzkFgZpY5B4GZWeYcBGZmmXMQmJllrmEQSFoiaYukdaWy0yWtl7RbUm+p/HhJKyWtTX+Pa1fDzcysNYbzjqAPOLGqbB1wCrCiqnwr8KaIOBI4F7i+2QaamVl7TW5UISJWSOqpKnsQQFJ13VWlp+uBfSXtExFPNd1SMzNri3beIzgVWFUvBCQtkNQvqX9gYKCNzTAzs6G0JQgkvQz4GPCOenUiYnFE9EZEb3d3dzuaYWZmw9DyIJD0h8AtwDkR8Z+tnr+ZmbVWS4NA0nTgduDSiPhBK+dtZmbtMZyPj94A3A3MlrRJ0l9LeoukTcBrgNslLUvV3w28GPiQpNXpcXDbWm9mZk0bzqeG5tcZdUuNuh8FPtpso8zMbOz4m8VmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmGgaBpCWStkhaVyo7XdJ6Sbsl9ZbKD5J0h6Ttkq5pV6PNzKx1hvOOoA84sapsHXAKsKKq/LfAh4CLmm6ZmZmNiYZBEBErgCeqyh6MiA016j4ZEd+nCAQzM3sW6Ng9AkkLJPVL6h8YGOhUM8zMstexIIiIxRHRGxG93d3dnWqGmVn2/KkhM7PMOQjMzDI3uVEFSTcAc4AuSZuAyyhuHl8NdAO3S1odEXNT/Y3Ac4G9Jc0DToiIB9rTfDMza1bDIIiI+XVG3VKnfk8zDTIzs7HlS0NmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmGgaBpCWStkhaVyo7XdJ6Sbsl9VbVv1TSw5I2SJrbjkabmVnrDOcdQR9wYlXZOuAUYEW5UNLhwJnAy9I010raq/lmmplZuzQMgohYATxRVfZgRGyoUf1k4CsR8VREPAI8DLyqJS01M7O2aPU9gpnAz0vPN6Wy3yNpgaR+Sf0DAwMtboaZmQ1Xq4NANcqiVsWIWBwRvRHR293d3eJmmJnZcLU6CDYBLyg9/0Pg0RYvw8zMWqjVQXArcKakfSTNAg4D7mvxMszMrIUmN6og6QZgDtAlaRNwGcXN46uBbuB2SasjYm5ErJd0I/AAsBN4V0TsalvrzcysaQ2DICLm1xl1S536lwOXN9MoMzMbO/5msZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmlrmGQSBpiaQtktaVyg6U9G1JP0l//yCV7y3pC5LWSlojaU4b225mZi0wnHcEfcCJVWWXAMsj4jBgeXoO8LcAEXEkcDzwCUl+12FmNo41PEhHxArgiarik4Hr0vB1wLw0fDhFMBARW4BtQG9LWmpmZm0x2rP1QyLiMYD09+BUvgY4WdJkSbOAVwIvqDUDSQsk9UvqHxgYGGUzzMysWa2+bLME2AT0A58C7gJ21qoYEYsjojcieru7u1vcDDMzG67Jo5zucUnPj4jHJD0f2AIQETuBv6tUknQX8JPmm2lmZu0y2ncEtwLnpuFzgW8ASNpP0v5p+HhgZ0Q80HQrzcysbRq+I5B0AzAH6JK0CbgMuBK4UdJfAz8DTk/VDwaWSdoNbAbObkejzcysdRoGQUTMrzPqDTXqbgRmN9kmMzMbQ/6Mv5lZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmljkHgZlZ5hwEZmaZcxCYmWXOQWBmlrnJjSpIWgKcBGyJiCNS2YHAV4EeYCPwVxHxS0lTgM8Bf5Lm/cWIuKI9Tc/P0lWbWbRsA49u28GM6VO5eO5s5r1iZqeb1VZe5zzW2TprOO8I+oATq8ouAZZHxGHA8vQc4HRgn4g4Engl8A5JPS1paeaWrtrMpTevZfO2HQSwedsOLr15LUtXbe5009rG65zHOlvnNQyCiFgBPFFVfDJwXRq+DphXqQ7sL2kyMBX4HfA/rWlq3hYt28COp3ftUbbj6V0sWrahQy1qP69zYaKvs3XeaO8RHBIRjwGkvwen8puAJ4HHgJ8BH4+I6hABQNICSf2S+gcGBkbZjHw8um3HiMonAq9z43KzVmj1zeJXAbuAGcAs4P2S/qhWxYhYHBG9EdHb3d3d4mZMPDOmTx1R+UTgdW5cbtYKow2CxyU9HyD93ZLK3wp8KyKejogtwA+A3uabaRfPnc3UKXvtUTZ1yl5cPHd2h1rUfl7nwkRfZ+u80QbBrcC5afhc4Btp+GfAcSrsD7waeKi5JhrAvFfM5IpTjmTm9KkImDl9KleccuSE/jSJ1zmPdbbOU0QMXUG6AZgDdAGPA5cBS4EbgRdSHPxPj4gnJE0DvgAcDgj4QkQsatSI3t7e6O/vb2I1zMzyI2llRDR91aXh9wgiYn6dUW+oUXc7xUdIzczsWcLfLDYzy5yDwMwscw4CM7PMOQjMzDLX8FNDY9IIaQD4ryZm0QVsbVFzng1yW1/wOufC6zwyh0ZE09/IHRdB0CxJ/a34CNWzRW7rC17nXHidO8OXhszMMucgMDPL3EQJgsWdbsAYy219weucC69zB0yIewRmZjZ6E+UdgZmZjZKDwMwscw6CcU7SmyVdUmfc9vR3hqSb0vBRkt44lm209pB0vqRz0vB5kmaUxn1O0uGda13nSNooqavT7ZhIWhoEki6UtF8r5zmCZd8pqW2fxZX0b5Kmt2v+9UTErRFxZYM6j0bEaenpUUBTQSCpT9JpjWuOL5LmSHptp9vRKhHx6Yj4Ynp6HsUv/1XG/U1EPNCRhmWsVftGO46V5ROHkWr1O4ILgY4EQbtFxBsjYlur5idpqaSVktZLWpDKTpT0Q0lrJC1PZedJuiYNz5J0t6T7Jf1TaV49ktZJ2hv4CHCGpNWSzpD0E0ndqd4kSQ+PxdmUpL0a12q5OUBHgkDSxyRdUHq+UNL7JS1Kr81aSWekcXMkfVfSjZJ+LOlKSWdJui/Ve1FpHhelA08v8OX0uk4tn/hI2i7p8tRv7pF0SCp/UXp+v6SPVN5BjtH2OEfSj1Kbrpd0qKTlqWy5pBemenscWEvvcidJujbtH7elE7HyAfg9aV9ZK+mlY7VeLdTyY2XVicOIJx7VA9gfuB1YA6yj+MGa3wFrgTtSnfnp+TrgY6VptwMfA1YC/0HxW8d3Aj8F3pzq9ADfA36YHq8tTf+BNN81wJWp7M40z/uAHwPHDtH28yh+Ve1bwAbgstK4pald64EFpfKNFF8F7wEeBD6b6vw7MHUU2+/A9Hdq2j6HAD8HZlWNPw+4Jg3fCpyTht8FbC9tq3XV9dPzy4AL0/AJwNdL456ZLj2/CFgI9AGnpbKjgbvStr4PeA6wF7AIuB/4EfCOVHcOcAfwr8ADNfrIGUNsjzcAq9LrugTYp7TdP5z6wFrgpXWm7wH+G9gMrAaOBQ4Flqc2LgdemOr2Af+S1uunpXWdBFybXtfbgH8rjdsIdKXhXuDO0n6wJE3za+DkVP4Axa/3fTttr0MofsTp+Wk7bUvD+6Q2fzhN9z7gU2l4IXBRqX/3ltb3medAAG9Kw1cB/5CGbwPmp+HzSf2l3Q/gZRT7VWV7HQj8P+Dc9PztwNLSa3Fa+diQ/p6Wtv8k4HnAL6tei/ek4QuAz7VxXc5J/WcNcH29vpPqXszgPlF5PX9vHwDeS9Wxss6ytwOfoOj7y4HuVP63aTlrgK8D+9XpL8M6HkZEU0FwKvDZ0vMD2HNnmZE6fjfFD+B8B5hX6rh/mYZvoTiYTgFeDqxO5fsB+6bhw4D+NPyX6UWorPyBpRX/RBp+I/AfQ7T9POAx4CAGD8S9VfOrlB9UPhBQHHB2Akel8huB/zWK7bcwvZBrgF8BHwK+XKetlSD4BTAlDT+X4QXBC4AfpuGvACeVxj0zXXq+RxAAe1N09qNLy5wMLGDwYLMP0A/MojjAPclgmP1eH6mzLfalCMGXpOdfZDC8NjLMnZ7SjpCeD3Xw+RrFQeZw4OFU3ujgUysI/rny+lMc/P4TOIbi97o/Cby91J7rgTen7fTtUvkK4E/T8HGldj6zPgwdBE8x+FHwMyrbiKK/TK7uL+1+AO8BLq8q28pg350CbC29FrWC4FPA20rlN1e9FjPT8DEMsa83uR61Aq1e3zmB4vsASuNuA15PnX2g3J+GWH4AZ6Xhf2TwOHBQqc5HGdw/qvvLsI6HEdHUpaG1wF+kt8THRsSvqsYfTbGzDETETuDLacNAkYbfKs3nuxHxdBruSeVTgM9KWkux4Ss3xv6C4icwfwMQEU+Ulnlz+ruyNJ96vh0Rv4iIHWm616Xy90paA9xDcRA9rMa0j0TE6hEsaw+S5qT1eE1EvJziTHgNxQvfyHDqDFaO+DnwuKTjKHaab45g8tnAYxFxf5rX/6TX8gTgHEmrgXspArWyne6LiEfScKM+Ul7OIxHx4/T8Ogb7CozsdS17DcW7EygOwq8rjVsaEbujuM5+SCp7HfC1VP7fFO9uGjkBuCRtiwOAP6AIna9QHBTqeao0vLv0fDfD+OXAKk9H2uOBXaOYvtVE435aGb+TdIlakihOPirzGEple7VzfY8DboqIrbDHsaZW3zkhPVZRnMG/lGKfGO4+UMtu4Ktp+EsM9t8jJH0vHRvPogisWoa934w6CNJO+0qKFb1C0j9WVRnqhSx33Gd2gogo7wR/R/EbyS+nOAMrd5B6nWwknaN6HlHnAL3vEMsZ7rKqHQD8MiJ+k65vvprizPrPJM0CkHRgjel+AJyZhs+qM+9fU1y+KfscRUe6MSJ2lcqf2QmT6nWtt61FcRZyVHrMioh/T+OerFQaRh8pz28ordrpy+tSfg1V9beW8rYqbycBp0bEURSXtx4C/hy4ieJM/wxJe6X7NK+neKs+GrVe10buoTgjhcF+MxaWA38l6SB4pi/fxZ599/tpeCNFHwE4meIEkDT+1HSv4BCKd1FjrV7/r9d3rijtEy+OiM+PYB8Yjkpb+oB3R8SRFJdNax2jyu1suN+MOghUfJTtNxHxJeDjwJ+wZ2e9l+LA1pVuHM4HvjuCRRxAcTa6Gzib4jorFJeR3l65417ngDkcx0s6UNJUYB7FQbbWAbodvgVMlvQj4J8odtgBiksuN6d3JF+tMd37gHdJuj+1tZY7gMMrN4tT2a3ANOALVXUfBw6WdJCkfYCTqsY/BMyQdDSApOdImgwsA94paUoqf4mk/asbUqeP1PIQ0CPpxen52Yysr1RUHyzrHXzqGergs5HBA9appfJlFDcuFRHrKS6Fbo6Ixygue1auL38H+EB6pzEafcCnKzeLhznNhcDfS7qP4n7ESM5GRy1th8uB76a+/H8orou/LfX5syn6MhT32v4stfEYBk8kvg5sorg8+xmK48mYtL+kVqDVs4ziuDQt1Z0p6eAh9oHhBPskisuVAG9lsP8+B3gs7X/1TghHponrZ3MpOvlqihsXvRTXBh9i8GbxWxm8WXxVadrtpeGF7Hldt3KN8LA0/3uAK6qmuYTiZtxq4J9L18Qq10y7gI1DtP08imv7t1O6WUxxVv7NtNyvpXnOKV/To8519dFux7F4pNfme3XGvRd4mOKmZh+1bxbfQ3Ewu4ciUCZRXBuvvLZ3UATTHOC2ofrIEG0c6mbx712brzOPl5SWd2x6rb5D7ZvFta5LTwI+nfrW0tQXjk/jjqW46fY9ih36zlQ+leJAVdkWt9VrXwde9/0YvHdwJvCNTrdphO2flv4eRHHv5XkdaMO56XVdk/pNzb6Tht+X+sFa4G7gRfX2AaqOlXWWvZ3iRHFl6seVm8XvBB6hOD5dDfSl8oXUuKdEg+NhROT5v4YknUexkd7d6ba0m4ovo72T4qZTozPi7EmaFhHb01ngfRQ3cUd7Ft9Rko4FrqG4bLGN4sb1w51t1fBJuhOYTnFZ+KqI6Otog8aYpO0RMW1MluUgMBuU+8HHxg8HQYtImkvxWdqyRyLiLZ1oj4GkWyg+alr2wYhYNoJ5vI3Ba8wVP4iIdzXbPrOxJuleisvSZWdHxNoxa8NEDgIzM2vM/3TOzCxzDgIzs8w5CMzMMucgMDPL3P8HeSq3ksR3gEcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kNearest Neighbour\n",
      "Accuracy\n",
      "0.926829268292683\n",
      "38\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "[114 108 108 108 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree\n",
      "Accuracy\n",
      "0.9512195121951219\n",
      "39\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "['chest_pain', 'back_pain', 'mild_fever', 'knee_pain', 'foul_smell_of urine']\n",
      "[1, 1, 1, 1, 1]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAD5CAYAAAAeEfMjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAWMElEQVR4nO3ce5RlZX3m8e9jd6uACiilIw3SGgkBidcaNGqc1jgKxojmpnghYBLGjPeJZNRxIpOrBpMljgYkSAjoggREQ5QRFEWMF7AaGmgEtBfg0EBCOQ4QhJHbb/7Yu+lDUbdTnLerqvv7WatW7cu79/vud+9znn2rSlUhSVIrD1vsBkiStm0GjSSpKYNGktSUQSNJasqgkSQ1tXKxGzCd3XbbrdasWbPYzZCkZWPdunU/qqqxxW7HdJZk0KxZs4aJiYnFboYkLRtJfrjYbZiJt84kSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU3NGTRJTkxyc5INM8xPko8l2ZjksiTPnjJ/RZJLknxhVI2WJC0f87miOQk4cJb5BwF79z9HAMdOmf9O4MqFNE6StPzNGTRVdQHw41mKHAycXJ3vALskeSJAkj2AXwZOGEVjJUnLzyie0awGrh8Y39RPA/go8AfAfXOtJMkRSSaSTExOTo6gWZKkpWAUQZNpplWSVwI3V9W6+aykqo6vqvGqGh8bGxtBsyRJS8EogmYTsOfA+B7AjcALgFcluQ44DXhJkk+PoD5J0jIyiqA5Czi0f/vsecCtVXVTVb2vqvaoqjXA64CvVtUbR1CfJGkZWTlXgSSnAmuB3ZJsAj4IrAKoquOAs4FXABuBO4DDWzVWkrT8zBk0VXXIHPMLeOscZc4Hzh+mYZKkbYP/GUCS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKbmDJokJya5OcmGGeYnyceSbExyWZJn99P3TPK1JFcmuSLJO0fdeEnS0jefK5qTgANnmX8QsHf/cwRwbD/9HuD3q2pf4HnAW5Pst/CmSpKWozmDpqouAH48S5GDgZOr8x1glyRPrKqbqurifh3/BlwJrB5FoyVJy8contGsBq4fGN/ElEBJsgZ4FnDhCOqTJC0jowiaTDOt7p+ZPAr4LPCuqrptxpUkRySZSDIxOTk5gmZJkpaCUQTNJmDPgfE9gBsBkqyiC5nPVNWZs62kqo6vqvGqGh8bGxtBsyRJS8EoguYs4ND+7bPnAbdW1U1JAnwKuLKq/moE9UiSlqGVcxVIciqwFtgtySbgg8AqgKo6DjgbeAWwEbgDOLxf9AXAm4DLk6zvp72/qs4e5QZIkpa2OYOmqg6ZY34Bb51m+j8z/fMbSdJ2xP8MIElqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKmplXMVSHIi8Erg5qraf5r5AY4BXgHcARxWVRf38w7s560ATqiqD42w7Q/w+Utu4OhzrubGW+5k91124MiX78Orn7W6VXXLnv01HPtrOPbXcLb1/pozaICTgI8DJ88w/yBg7/7nucCxwHOTrAA+AfxHYBPw3SRnVdX3Hmqjp/r8JTfwvjMv58677wXghlvu5H1nXg6wTe2sUbG/hmN/Dcf+Gs720F9z3jqrqguAH89S5GDg5Op8B9glyROBA4CNVXVNVd0FnNaXHbmjz7n6/p202Z1338vR51zdorplz/4ajv01HPtrONtDf43iGc1q4PqB8U39tJmmTyvJEUkmkkxMTk4O1YAbb7lzqOnbO/trOPbXcOyv4WwP/TWKoMk002qW6dOqquOraryqxsfGxoZqwO677DDU9O2d/TUc+2s49tdwtof+GkXQbAL2HBjfA7hxlukjd+TL92GHVSseMG2HVSs48uX7tKhu2bO/hmN/Dcf+Gs720F/zeRlgLmcBb0tyGt3LALdW1U1JJoG9kzwZuAF4HfD6EdT3IJsfmG3Lb22Mkv01HPtrOPbXcLaH/krVjHezugLJqcBaYDfgX4EPAqsAquq4/vXmjwMH0r3efHhVTfTLvgL4KN3rzSdW1Z/Op1Hj4+M1MTGxkO2RpO1SknVVNb7Y7ZjOnFc0VXXIHPMLeOsM884Gzl5Y0yRJ2wL/M4AkqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU0ZNJKkpgwaSVJTBo0kqSmDRpLUlEEjSWrKoJEkNWXQSJKaMmgkSU3NK2iSHJjk6iQbk7x3mvm7JvlcksuSXJRk/4F5705yRZINSU5N8shRboAkaWmbM2iSrAA+ARwE7AcckmS/KcXeD6yvqqcDhwLH9MuuBt4BjFfV/sAK4HWja74kaambzxXNAcDGqrqmqu4CTgMOnlJmP+A8gKq6CliT5An9vJXADklWAjsCN46k5ZKkZWE+QbMauH5gfFM/bdClwK8CJDkA2AvYo6puAD4C/G/gJuDWqjr3oTZakrR8zCdoMs20mjL+IWDXJOuBtwOXAPck2ZXu6ufJwO7ATkneOG0lyRFJJpJMTE5OznsDJElL23yCZhOw58D4Hky5/VVVt1XV4VX1TLpnNGPAtcBLgWurarKq7gbOBJ4/XSVVdXxVjVfV+NjY2AI2RZK0FM0naL4L7J3kyUkeTvcw/6zBAkl26ecB/A5wQVXdRnfL7HlJdkwS4JeAK0fXfEnSUrdyrgJVdU+StwHn0L01dmJVXZHkLf3844B9gZOT3At8D/jtft6FSc4ALgbuobuldnyTLZEkLUmpmvq4ZfGNj4/XxMTEYjdDkpaNJOuqanyx2zEd/zOAJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKYMGklSUwaNJKkpg0aS1JRBI0lqyqCRJDVl0EiSmjJoJElNGTSSpKbmFTRJDkxydZKNSd47zfxdk3wuyWVJLkqy/8C8XZKckeSqJFcm+YVRboAkaWmbM2iSrAA+ARwE7AcckmS/KcXeD6yvqqcDhwLHDMw7BvhSVf0c8AzgylE0XJK0PMzniuYAYGNVXVNVdwGnAQdPKbMfcB5AVV0FrEnyhCSPAV4EfKqfd1dV3TKy1kuSlrz5BM1q4PqB8U39tEGXAr8KkOQAYC9gD+ApwCTwt0kuSXJCkp2mqyTJEUkmkkxMTk4OuRmSpKVqPkGTaabVlPEPAbsmWQ+8HbgEuAdYCTwbOLaqngX8BHjQMx6Aqjq+qsaranxsbGy+7ZckLXEr51FmE7DnwPgewI2DBarqNuBwgCQBru1/dgQ2VdWFfdEzmCFoJEnbpvlc0XwX2DvJk5M8HHgdcNZggf7Nsof3o78DXFBVt1XVvwDXJ9mnn/dLwPdG1HZJ0jIw5xVNVd2T5G3AOcAK4MSquiLJW/r5xwH7AicnuZcuSH57YBVvBz7TB9E19Fc+kqTtQ6qmPm5ZfOPj4zUxMbHYzZCkZSPJuqoaX+x2TMf/DCBJasqgkSQ1ZdBIkpoyaCRJTRk0kqSmDBpJUlMGjSSpKYNGktSUQSNJasqgkSQ1ZdBIkpoyaCRJTRk0kqSmDBpJUlMGjSSpKYNGktSUQSNJasqgkSQ1ZdBIkpoyaCRJTRk0kqSmDBpJUlMGjSSpKYNGktRUqmqx2/AgSSaBHy5w8d2AH42wOds6+2s49tdw7K/hPJT+2quqxkbZmFFZkkHzUCSZqKrxxW7HcmF/Dcf+Go79NZxttb+8dSZJasqgkSQ1tS0GzfGL3YBlxv4ajv01HPtrONtkf21zz2gkSUvLtnhFI0laQgwaSVJTBo0kqalFC5okJyX59RGs511JdhxFmwbW+ZYkh45ynQuVZE2SDQ9xHWuTfGFUbZpm/d9qte5RS/KqJO/th49K8p5pyszZ50lOTXJZkne3amtrozi2WksynuRjC1juHUmuTPKZBdZ7+0KWW6jBfbHQz2uSRyT5SpL1SV67wHbsnuSMhSw7m5WjXuEieBfwaeCOUa2wqo4b1bq2B1X1/MVuw3xV1VnAWQ9lHUn+HfD8qtprNK2ato6VVXVPq/UvF1U1AUwsYNH/DBxUVdeOuElL2bOAVVX1zIUs3B9zNwIP+QJgqq12RZPk0P4M8NIkp/STX5TkW0muGby6SXJkku/25f9HP22nJF/sl9+Q5LVJ3gHsDnwtyddmqfv2JH+Z5OIk5yUZ66f/bl/PpUk+u/nKaPBMN8n5ST6c5KIk30/yi426aDYrk/xd3x9nJNkxyR/2bd+Q5Pgk6dv71P6s5tJ+e39mcEVJ/n2SS5I8ZbqK+m0/JclXk/wgye/20x/V993FSS5PcvDAMrf3v9f2/XVGkquSfGZzu7aG/qzwqiQn9P3ymSQvTfLNflsOSHJYko9Ps+xz+j77NvDWOao6F3h8f+b4i0l+JsmXkqxL8o0kP5dk5yTXJXlYv/4dk1yfZNV05fsyJyX5q/5Y/vCo+2c2SZ7SHxdHJjmzb98PkvzFQJmXJfl2fwycnuRR/fTnJPl6vz3nJHniLPWcn+Sj/ed+Q5ID+ukH9NMu6X/v00+//+y+PzZP7NdxTf/5n66O44CnAGcleXeSxyb5fP/5+U6Spw+s7z0Dy21IsmYeffXEJBf0+3/D5u+EdN8zH+774Sv9Nm1u66v6MiuSHJ0t32//aa76pqn/QduT5PF0J9zP7Ns19XN/fpLxfni3JNf1w4f1+/KfgHPzwCurw4Y9FmZUVc1/gKcBVwO79eOPBU4CTqcLu/2Ajf28l9G9S55+3heAFwG/BvzNwDp37n9ft3m9s9RfwBv64T8EPt4PP26gzJ8Ab++HjwLe0w+fD/xlP/wK4Ctbo88G2rWmb/8L+vETgfcAjx0ocwrwK/3whcBr+uFHAjsCa/t+fD6wDnjSLPUdBVwK7ED3f5eupwvzlcBj+jK7ARvZ8nr87f3vtcCtwB79vvs28MKt3Ff3AD/f17+u768ABwOfBw4b2P+D+/ky4D/0w0cDG+aoZ8PA+HnA3v3wc4Gv9sP/CLy4H34tcMIc5U/q99OKrdhfG4B9gEuAZ/b9cw2wc3/8/BDYs9/nFwA79cv+V7rP0irgW8DYwHaeOEud59N/juk+1xv64ccAK/vhlwKfHTimvjCwv74FPKJvz/+hO4Ofrp7r2PJ98z+BD/bDLwHWT93//fgGYM3gMT3Dun8f+G/98Arg0f1w0V1FAXyO7oRkFfCMgTqPAD7QDz+C7mrtyYPH1OA2z1D/TNsz43J9v48PfH6v64cPAzbRf59MacdQx8Jsx9rWunX2EuCMqvoRQFX9uD/R/XxV3Qd8L8kT+rIv638u6ccfBewNfAP4SJIP03XmN4ao/z7g7/vhTwNn9sP7J/kTYJe+nnNmWH5z+XV0O2Jru76qvtkPfxp4B3Btkj+gC5LHAlckOR9YXVWfA6iq/wfQ9/W+dAH+suouj2fzj1V1J3Bnf3Z9APBF4M+SvIiuP1cDTwD+ZcqyF1XVpr7e9XT99c8L3O6FuLaqLu/rvwI4r6oqyeXMsO+S7AzsUlVf7yedAhw0n8r6M7nnA6dny8XbI/rff0/3xfs14HXAX89RHuD0qrp3PnWPyBhdIP5aVV2R5Jl0fXYrQJLvAXvRfUb2A77Zt/vhdCcS+wD7A1/up68AbpqjzlMBquqCJI9JsgvwaODvkuxN94W9aoZlv1hVPwV+muRmumNw0xz1vZDuRJWq+mqSx/X7fKG+C5yYZBXdd9j6fvpdwJf64cuBn1bV3VOOvZcBT8+WOzg7032/fX+I+ke9PV+uqh/PMG+YY2FGWytoQnfwTPXTKWU2//7zqvrkg1aSPIfuquLPk5xbVX+0wPZsbstJwKur6tIkh9GdEUxnczvvZXGea03tuwL+mu4M5fokR9Gdccx2m+qmvsyzgLmCZrr63kD3pfSc/sNzXb++qQb36WL012D99w2M3zdLW2Y6PufjYcAtNf198bPojtXHAs8BvgrsNEt5gJ8ssB0LdSvdVesLgCv6adPtw9B9IR0yuHCSnweuqKpfGKLO6Y6vPwa+VlWv6W9fnT/Dsgs5vqb7XBTd1e/g44PpjucHL9gF5IuAXwZOSXJ0VZ0M3F39KT4Dx15V3ZdkcztDd+fkASe187llN1h8umbNsczgtk7dztmOuXkfC7PZWs9ozgN+M8njoLvHOEvZc4A3Z8v939VJHp9kd+COqvo08BHg2X35f6M7G5rNw9jygOv1bDnDfjRwU39m8oYht2lrelKSzR/kQ9jS/h/1/fTrAFV1G7Apyavh/rdQNr+RdwvdB+PPkqydo76Dkzyy319r6c7gdgZu7kPmxXRnNtuEqroFuDXJC/tJ8z4W+j6/NslvAKTzjH7e7cBFwDF0V+H3zlZ+kdwFvBo4NMnrZyn3HeAFSZ4K9z9z+lm6W+Jjm4/PdM+gnjZHna/ty74QuLU/Y94ZuKGff9hCN2YGF9Dv0/7Y/1G/H66j/x5J8my6W1hzSrIX3Wfhb4BPseW7aD7OAX6v/84hyc8m2WmI5WHm7ZnNdXQnO/DQH/bPdCzMaKucbfaX5H8KfD3JvWy5LTZd2XOT7At8u78sux14I/BU4Ogk9wF3A7/XL3I88L+S3FRVL55htT8BnpZkHd0Z3OZX//473TONH9Jd6s4VWIvlSuC3knwS+AFwLLArXZuvowuCzd4EfDLJH9H1029snlFV/5rkV+j6681VdeEM9V1Ed6vsScAfV9WN6V4T/ackE8B64KpRbuAScDjd7ZA7mPkW6kzeAByb5AN0t3xOo3vOBd3ts9N54NXybOW3uqr6SZJXAl+muzU7XZnJ/qr/1CSbb/V9oKq+398G+lh/+2Yl8FG2XB1N5/+meyX+McCb+2l/QXfr7L/QXfmN0lHA3ya5jO7t1N/qp3+WLmDX032G5nv7ai1wZJK76b6fhvlTiBPobqNdnO4LbpIu6IdxFNNvz2w+AvxDkjfxEPt3pmOBWfpvu/hfZ0lur6rZ34oQ0L2JQ/cg9COL3RZte/rniO+p7rVlbSf8zwCSpKa2hT/YvF+SC3ngGzwAb/Jq5sGSHA68c8rkb1bVXH9Dst1I8nIe/Pcs11bVaxajPctJkk/QvWAw6JiqWrsIzVmQ/kWHU6ZM/mlVPXcr1b/NfEa3i1tnkqTF460zSVJTBo0kqSmDRpLUlEEjSWrq/wNQzwinYtUXEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[114 114 114 114 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "Accuracy\n",
      "0.9512195121951219\n",
      "39\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "[114 114 114 114 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n",
      "Accuracy\n",
      "0.9512195121951219\n",
      "39\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "[114 108 108 108 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kNearest Neighbour\n",
      "Accuracy\n",
      "0.926829268292683\n",
      "38\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "[114 108 108 108 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree\n",
      "Accuracy\n",
      "0.9512195121951219\n",
      "39\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "['chest_pain', 'back_pain', 'mild_fever', 'knee_pain', 'foul_smell_of urine']\n",
      "[1, 1, 1, 1, 1]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[114 114 114 114 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "Accuracy\n",
      "0.9512195121951219\n",
      "39\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "[114 108 108 108 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n",
      "Accuracy\n",
      "0.9512195121951219\n",
      "39\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "[114 108 108 108 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kNearest Neighbour\n",
      "Accuracy\n",
      "0.926829268292683\n",
      "38\n",
      "Confusion matrix\n",
      "[[1 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 1 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 0 0 1]]\n",
      "[114 108 108 108 114 114]\n",
      "6\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calling this function because the application is ready to run\n",
    "root.mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}