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ML_Models.ipynb
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## This is Part B of our ML Project"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Part B: Decision Tree Implementation\n"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import torch\n",
"import random\n",
"import os\n",
"import seaborn as sns\n",
"from torch.nn import functional as F\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"import matplotlib.pyplot as plt\n",
"import warnings\n",
"import sklearn\n",
"from sklearn import tree\n",
"from sklearn.model_selection import StratifiedKFold, GridSearchCV,train_test_split,ShuffleSplit\n",
"from matplotlib.gridspec import SubplotSpec\n",
"from sklearn.neural_network import MLPClassifier\n",
"from sklearn.metrics import classification_report,roc_auc_score,roc_curve,precision_recall_curve,average_precision_score,precision_score,recall_score,f1_score,confusion_matrix,ConfusionMatrixDisplay\n",
"from sklearn.metrics import davies_bouldin_score, silhouette_score, calinski_harabasz_score,make_scorer\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.cluster import KMeans,AgglomerativeClustering\n",
"from sklearn.preprocessing import StandardScaler,OneHotEncoder,LabelEncoder,MinMaxScaler\n",
"from sklearn.ensemble import VotingClassifier\n",
"from sklearn.decomposition import PCA\n",
"pd.set_option('display.max_columns', None)\n",
"\n",
"warnings.filterwarnings('ignore')\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"22816\n",
"Sample # 22816 was deleted\n",
"Y 44548\n",
"Name: oral, dtype: int64\n",
"1.0 42088\n",
"2.0 1431\n",
"3.0 743\n",
"4.0 202\n",
"5.0 66\n",
"6.0 10\n",
"Name: Urine protein, dtype: int64\n",
"col_0 Prior Probabity\n",
"Urine protein \n",
"1.0 0.944757\n",
"2.0 0.032122\n",
"3.0 0.016678\n",
"4.0 0.004534\n",
"5.0 0.001482\n",
"6.0 0.000224\n"
]
}
],
"source": [
"# Preprocessing\n",
"df = pd.read_csv(\"/Users/Xy_train.csv\")\n",
"x_train = df.iloc[:,:-1]\n",
"y_train = df.iloc[:,-1]\n",
"\n",
"def isfloat(value):\n",
" try:\n",
" float(value)\n",
" return True\n",
" except ValueError:\n",
" return False\n",
"\n",
"l_1 = list(df[\"waist(cm)\"])\n",
"misplaced_1 = [ind for ind,x in enumerate(l_1) if isfloat(x) == False]\n",
"# The Identified problematic rows\n",
"print(misplaced_1[0])\n",
"\n",
"#Deleting rows with misplaced data ( Run only once)\n",
"try:\n",
" df.drop([misplaced_1[0]],axis=0,inplace = True)\n",
" print(\"Sample # \",misplaced_1[0] ,\"was deleted\")\n",
"except:\n",
" print(\"Already deleted\")\n",
"\n",
"df[\"waist(cm)\"] = df[\"waist(cm)\"].astype(float)\n",
"\n",
"#Identified as useless, because all of the samples are 1 and the only 12 \n",
"# different samples are misformatted\n",
"#Fixing oral values\n",
"df = df[(df[\"oral\"] != \"yes\") & ((df[\"oral\"] != \"12\"))]\n",
"df[\"oral\"] = df[\"oral\"].astype(object)\n",
"print(df[\"oral\"].value_counts())\n",
"\n",
"#Removing the row with the yes value and merging the float and the integers to one value \n",
"df = df[df[\"Urine protein\"] != \"yes\"]\n",
"df[\"Urine protein\"] = df[\"Urine protein\"].astype(float)\n",
"print(df[\"Urine protein\"].value_counts())\n",
"print(pd.crosstab(df[\"Urine protein\"],\"Prior Probabity\")/len(df))\n",
"\n",
"df[\"weight(kg)\"].astype(float)\n",
"df = df[df[\"waist(cm)\"] != \"ok\"]\n",
"df[\"waist(cm)\"] = df[\"waist(cm)\"].astype(float)\n",
"\n",
"#removing the null values\n",
"df.dropna(inplace = True)\n",
"#Removing all unreasonable values - removing the entire tuple if one of the values is unreasonable\n",
"# Fixing systolic values (there were negative values and values above 250)\n",
"df = df[(df[\"systolic\"]> 0) & (df[\"systolic\"]< 250)]\n",
"# Fixing fasting blood sugar values (there were values above 400 )\n",
"df = df[(df[\"fasting blood sugar\"]> 0) & (df[\"fasting blood sugar\"]< 200)]\n",
"# Fixing triglyceride values (there were values above 500 )\n",
"df = df[(df[\"triglyceride\"]> 0) & (df[\"triglyceride\"]< 500)]\n",
"# Fixing HDL cholesterol values (there were values above 300 )\n",
"df = df[(df[\"HDL\"]> 10) & (df[\"HDL\"]< 300)]\n",
"# Fixing LDL cholesterol values (there were values above 1800 )\n",
"df = df[(df[\"LDL\"]> 0) & (df[\"LDL\"]< 300)]\n",
"# Fixing serum creatinine values (there were values above 1.5 )\n",
"df = df[(df[\"serum creatinine\"]> 0) & (df[\"serum creatinine\"]< 1.5)]\n",
"# Fixing AST values (there were values above 50 )\n",
"df = df[(df[\"AST\"]> 0) & (df[\"AST\"]< 50)]\n",
"# Fixing ALT values (there were values above 100 )\n",
"df = df[(df[\"ALT\"]> 0) & (df[\"ALT\"]< 100)]\n",
"# Fixing Gtp values (there were values above 100 )\n",
"df = df[(df[\"Gtp\"]> 0) & (df[\"Gtp\"]< 100)]\n",
"#Deriving a new feature based on existing features\n",
"df[\"BMI\"] = df[\"weight(kg)\"]/((df[\"height(cm)\"]/100)**2)\n"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
"df = df.iloc[:,1:]\n"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>height(cm)</th>\n",
" <th>weight(kg)</th>\n",
" <th>waist(cm)</th>\n",
" <th>eyesight(left)</th>\n",
" <th>eyesight(right)</th>\n",
" <th>hearing(left)</th>\n",
" <th>hearing(right)</th>\n",
" <th>systolic</th>\n",
" <th>relaxation</th>\n",
" <th>fasting blood sugar</th>\n",
" <th>Cholesterol</th>\n",
" <th>triglyceride</th>\n",
" <th>HDL</th>\n",
" <th>LDL</th>\n",
" <th>hemoglobin</th>\n",
" <th>Urine protein</th>\n",
" <th>serum creatinine</th>\n",
" <th>AST</th>\n",
" <th>ALT</th>\n",
" <th>Gtp</th>\n",
" <th>oral</th>\n",
" <th>dental caries</th>\n",
" <th>tartar</th>\n",
" <th>smoking</th>\n",
" <th>BMI</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M</td>\n",
" <td>50.0</td>\n",
" <td>170.0</td>\n",
" <td>75.0</td>\n",
" <td>95.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>138.0</td>\n",
" <td>88.0</td>\n",
" <td>92.0</td>\n",
" <td>257.0</td>\n",
" <td>285.0</td>\n",
" <td>52.0</td>\n",
" <td>148.0</td>\n",
" <td>15.2</td>\n",
" <td>1.0</td>\n",
" <td>0.9</td>\n",
" <td>38.0</td>\n",
" <td>45.0</td>\n",
" <td>67</td>\n",
" <td>Y</td>\n",
" <td>0</td>\n",
" <td>Y</td>\n",
" <td>1</td>\n",
" <td>25.951557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M</td>\n",
" <td>70.0</td>\n",
" <td>160.0</td>\n",
" <td>70.0</td>\n",
" <td>87.8</td>\n",
" <td>0.6</td>\n",
" <td>0.6</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>143.0</td>\n",
" <td>76.0</td>\n",
" <td>128.0</td>\n",
" <td>174.0</td>\n",
" <td>120.0</td>\n",
" <td>51.0</td>\n",
" <td>99.0</td>\n",
" <td>15.8</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>22.0</td>\n",
" <td>24.0</td>\n",
" <td>43</td>\n",
" <td>Y</td>\n",
" <td>0</td>\n",
" <td>Y</td>\n",
" <td>1</td>\n",
" <td>27.343750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M</td>\n",
" <td>35.0</td>\n",
" <td>180.0</td>\n",
" <td>90.0</td>\n",
" <td>99.0</td>\n",
" <td>1.2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>120.0</td>\n",
" <td>78.0</td>\n",
" <td>94.0</td>\n",
" <td>213.0</td>\n",
" <td>264.0</td>\n",
" <td>52.0</td>\n",
" <td>108.0</td>\n",
" <td>16.1</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>16.0</td>\n",
" <td>31.0</td>\n",
" <td>89</td>\n",
" <td>Y</td>\n",
" <td>0</td>\n",
" <td>N</td>\n",
" <td>1</td>\n",
" <td>27.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>F</td>\n",
" <td>50.0</td>\n",
" <td>150.0</td>\n",
" <td>60.0</td>\n",
" <td>78.4</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>128.0</td>\n",
" <td>81.0</td>\n",
" <td>106.0</td>\n",
" <td>180.0</td>\n",
" <td>94.0</td>\n",
" <td>78.0</td>\n",
" <td>91.0</td>\n",
" <td>14.0</td>\n",
" <td>1.0</td>\n",
" <td>0.8</td>\n",
" <td>23.0</td>\n",
" <td>19.0</td>\n",
" <td>16</td>\n",
" <td>Y</td>\n",
" <td>0</td>\n",
" <td>N</td>\n",
" <td>1</td>\n",
" <td>26.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>M</td>\n",
" <td>60.0</td>\n",
" <td>170.0</td>\n",
" <td>65.0</td>\n",
" <td>81.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>116.0</td>\n",
" <td>62.0</td>\n",
" <td>102.0</td>\n",
" <td>134.0</td>\n",
" <td>74.0</td>\n",
" <td>59.0</td>\n",
" <td>60.0</td>\n",
" <td>13.2</td>\n",
" <td>1.0</td>\n",
" <td>0.7</td>\n",
" <td>22.0</td>\n",
" <td>20.0</td>\n",
" <td>13</td>\n",
" <td>Y</td>\n",
" <td>1</td>\n",
" <td>Y</td>\n",
" <td>1</td>\n",
" <td>22.491349</td>\n",
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" <tr>\n",
" <th>44548</th>\n",
" <td>M</td>\n",
" <td>40.0</td>\n",
" <td>160.0</td>\n",
" <td>60.0</td>\n",
" <td>84.5</td>\n",
" <td>0.9</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>151.0</td>\n",
" <td>102.0</td>\n",
" <td>100.0</td>\n",
" <td>206.0</td>\n",
" <td>97.0</td>\n",
" <td>64.0</td>\n",
" <td>122.0</td>\n",
" <td>14.0</td>\n",
" <td>1.0</td>\n",
" <td>0.9</td>\n",
" <td>20.0</td>\n",
" <td>17.0</td>\n",
" <td>15</td>\n",
" <td>Y</td>\n",
" <td>0</td>\n",
" <td>N</td>\n",
" <td>1</td>\n",
" <td>23.437500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44549</th>\n",
" <td>M</td>\n",
" <td>35.0</td>\n",
" <td>180.0</td>\n",
" <td>75.0</td>\n",
" <td>92.0</td>\n",
" <td>1.0</td>\n",
" <td>1.2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>132.0</td>\n",
" <td>86.0</td>\n",
" <td>95.0</td>\n",
" <td>203.0</td>\n",
" <td>209.0</td>\n",
" <td>42.0</td>\n",
" <td>119.0</td>\n",
" <td>15.9</td>\n",
" <td>1.0</td>\n",
" <td>0.9</td>\n",
" <td>19.0</td>\n",
" <td>21.0</td>\n",
" <td>18</td>\n",
" <td>Y</td>\n",
" <td>0</td>\n",
" <td>Y</td>\n",
" <td>0</td>\n",
" <td>23.148148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44550</th>\n",
" <td>M</td>\n",
" <td>20.0</td>\n",
" <td>175.0</td>\n",
" <td>60.0</td>\n",
" <td>76.5</td>\n",
" <td>0.9</td>\n",
" <td>0.3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>99.0</td>\n",
" <td>50.0</td>\n",
" <td>91.0</td>\n",
" <td>162.0</td>\n",
" <td>64.0</td>\n",
" <td>54.0</td>\n",
" <td>95.0</td>\n",
" <td>15.6</td>\n",
" <td>1.0</td>\n",
" <td>1.2</td>\n",
" <td>18.0</td>\n",
" <td>13.0</td>\n",
" <td>18</td>\n",
" <td>Y</td>\n",
" <td>1</td>\n",
" <td>Y</td>\n",
" <td>1</td>\n",
" <td>19.591837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44551</th>\n",
" <td>M</td>\n",
" <td>45.0</td>\n",
" <td>160.0</td>\n",
" <td>65.0</td>\n",
" <td>87.2</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>153.0</td>\n",
" <td>98.0</td>\n",
" <td>99.0</td>\n",
" <td>280.0</td>\n",
" <td>336.0</td>\n",
" <td>53.0</td>\n",
" <td>160.0</td>\n",
" <td>15.9</td>\n",
" <td>1.0</td>\n",
" <td>1.3</td>\n",
" <td>37.0</td>\n",
" <td>48.0</td>\n",
" <td>96</td>\n",
" <td>Y</td>\n",
" <td>1</td>\n",
" <td>Y</td>\n",
" <td>1</td>\n",
" <td>25.390625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44552</th>\n",
" <td>F</td>\n",
" <td>40.0</td>\n",
" <td>150.0</td>\n",
" <td>40.0</td>\n",
" <td>64.7</td>\n",
" <td>1.0</td>\n",
" <td>0.9</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>101.0</td>\n",
" <td>65.0</td>\n",
" <td>94.0</td>\n",
" <td>235.0</td>\n",
" <td>62.0</td>\n",
" <td>62.0</td>\n",
" <td>160.0</td>\n",
" <td>14.0</td>\n",
" <td>1.0</td>\n",
" <td>0.7</td>\n",
" <td>16.0</td>\n",
" <td>15.0</td>\n",
" <td>16</td>\n",
" <td>Y</td>\n",
" <td>0</td>\n",
" <td>Y</td>\n",
" <td>0</td>\n",
" <td>17.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>40096 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" gender age height(cm) weight(kg) waist(cm) eyesight(left) \\\n",
"1 M 50.0 170.0 75.0 95.0 1.0 \n",
"2 M 70.0 160.0 70.0 87.8 0.6 \n",
"3 M 35.0 180.0 90.0 99.0 1.2 \n",
"4 F 50.0 150.0 60.0 78.4 1.0 \n",
"5 M 60.0 170.0 65.0 81.0 1.0 \n",
"... ... ... ... ... ... ... \n",
"44548 M 40.0 160.0 60.0 84.5 0.9 \n",
"44549 M 35.0 180.0 75.0 92.0 1.0 \n",
"44550 M 20.0 175.0 60.0 76.5 0.9 \n",
"44551 M 45.0 160.0 65.0 87.2 0.5 \n",
"44552 F 40.0 150.0 40.0 64.7 1.0 \n",
"\n",
" eyesight(right) hearing(left) hearing(right) systolic relaxation \\\n",
"1 1.0 1.0 1.0 138.0 88.0 \n",
"2 0.6 1.0 1.0 143.0 76.0 \n",
"3 1.0 1.0 1.0 120.0 78.0 \n",
"4 1.0 1.0 1.0 128.0 81.0 \n",
"5 1.0 1.0 1.0 116.0 62.0 \n",
"... ... ... ... ... ... \n",
"44548 1.0 1.0 1.0 151.0 102.0 \n",
"44549 1.2 1.0 1.0 132.0 86.0 \n",
"44550 0.3 1.0 1.0 99.0 50.0 \n",
"44551 1.0 1.0 1.0 153.0 98.0 \n",
"44552 0.9 1.0 1.0 101.0 65.0 \n",
"\n",
" fasting blood sugar Cholesterol triglyceride HDL LDL \\\n",
"1 92.0 257.0 285.0 52.0 148.0 \n",
"2 128.0 174.0 120.0 51.0 99.0 \n",
"3 94.0 213.0 264.0 52.0 108.0 \n",
"4 106.0 180.0 94.0 78.0 91.0 \n",
"5 102.0 134.0 74.0 59.0 60.0 \n",
"... ... ... ... ... ... \n",
"44548 100.0 206.0 97.0 64.0 122.0 \n",
"44549 95.0 203.0 209.0 42.0 119.0 \n",
"44550 91.0 162.0 64.0 54.0 95.0 \n",
"44551 99.0 280.0 336.0 53.0 160.0 \n",
"44552 94.0 235.0 62.0 62.0 160.0 \n",
"\n",
" hemoglobin Urine protein serum creatinine AST ALT Gtp oral \\\n",
"1 15.2 1.0 0.9 38.0 45.0 67 Y \n",
"2 15.8 1.0 1.0 22.0 24.0 43 Y \n",
"3 16.1 1.0 1.0 16.0 31.0 89 Y \n",
"4 14.0 1.0 0.8 23.0 19.0 16 Y \n",
"5 13.2 1.0 0.7 22.0 20.0 13 Y \n",
"... ... ... ... ... ... ... ... \n",
"44548 14.0 1.0 0.9 20.0 17.0 15 Y \n",
"44549 15.9 1.0 0.9 19.0 21.0 18 Y \n",
"44550 15.6 1.0 1.2 18.0 13.0 18 Y \n",
"44551 15.9 1.0 1.3 37.0 48.0 96 Y \n",
"44552 14.0 1.0 0.7 16.0 15.0 16 Y \n",
"\n",
" dental caries tartar smoking BMI \n",
"1 0 Y 1 25.951557 \n",
"2 0 Y 1 27.343750 \n",
"3 0 N 1 27.777778 \n",
"4 0 N 1 26.666667 \n",
"5 1 Y 1 22.491349 \n",
"... ... ... ... ... \n",
"44548 0 N 1 23.437500 \n",
"44549 0 Y 0 23.148148 \n",
"44550 1 Y 1 19.591837 \n",
"44551 1 Y 1 25.390625 \n",
"44552 0 Y 0 17.777778 \n",
"\n",
"[40096 rows x 27 columns]"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [],
"source": [
"\n",
"df = df.drop(['oral'],axis=1)\n",
"# dummies for gender\n",
"df['gender'].replace(['M', 'F'], [1,0], inplace=True)\n",
"# dummies for age\n",
"df['age'].replace(['Young', 'Middle', 'Old'], [2,1,0], inplace=True)\n",
"# dummies for height\n",
"df['height(cm)'].replace(['Middle', 'Tall', 'Short'], [2,1,0], inplace=True)\n",
"# dummies for weight\n",
"df['weight(kg)'].replace(['Underweight', 'Normal', 'Overweight'], [2,1,0], inplace=True)\n",
"# dummies for fasting blood sugar\n",
"df['fasting blood sugar'].replace(['Pre-Diabetes', 'Diabetes', 'Normal'], [2,1,0], inplace=True)\n",
"# dummies for tartar\n",
"df['tartar'].replace(['Y', 'N'], [1,0], inplace=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
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" <td>78.0</td>\n",
" <td>94.0</td>\n",
" <td>213.0</td>\n",
" <td>264.0</td>\n",
" <td>52.0</td>\n",
" <td>108.0</td>\n",
" <td>16.1</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
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],
"text/plain": [
" gender age height(cm) weight(kg) waist(cm) eyesight(left) \\\n",
"1 1 50.0 170.0 75.0 95.0 1.0 \n",
"2 1 70.0 160.0 70.0 87.8 0.6 \n",
"3 1 35.0 180.0 90.0 99.0 1.2 \n",
"4 0 50.0 150.0 60.0 78.4 1.0 \n",
"5 1 60.0 170.0 65.0 81.0 1.0 \n",
"... ... ... ... ... ... ... \n",
"44548 1 40.0 160.0 60.0 84.5 0.9 \n",
"44549 1 35.0 180.0 75.0 92.0 1.0 \n",
"44550 1 20.0 175.0 60.0 76.5 0.9 \n",
"44551 1 45.0 160.0 65.0 87.2 0.5 \n",
"44552 0 40.0 150.0 40.0 64.7 1.0 \n",
"\n",
" eyesight(right) hearing(left) hearing(right) systolic relaxation \\\n",
"1 1.0 1.0 1.0 138.0 88.0 \n",
"2 0.6 1.0 1.0 143.0 76.0 \n",
"3 1.0 1.0 1.0 120.0 78.0 \n",
"4 1.0 1.0 1.0 128.0 81.0 \n",
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"... ... ... ... ... ... \n",
"44548 1.0 1.0 1.0 151.0 102.0 \n",
"44549 1.2 1.0 1.0 132.0 86.0 \n",
"44550 0.3 1.0 1.0 99.0 50.0 \n",
"44551 1.0 1.0 1.0 153.0 98.0 \n",
"44552 0.9 1.0 1.0 101.0 65.0 \n",
"\n",
" fasting blood sugar Cholesterol triglyceride HDL LDL \\\n",
"1 92.0 257.0 285.0 52.0 148.0 \n",
"2 128.0 174.0 120.0 51.0 99.0 \n",
"3 94.0 213.0 264.0 52.0 108.0 \n",
"4 106.0 180.0 94.0 78.0 91.0 \n",
"5 102.0 134.0 74.0 59.0 60.0 \n",
"... ... ... ... ... ... \n",
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"44549 95.0 203.0 209.0 42.0 119.0 \n",
"44550 91.0 162.0 64.0 54.0 95.0 \n",
"44551 99.0 280.0 336.0 53.0 160.0 \n",
"44552 94.0 235.0 62.0 62.0 160.0 \n",
"\n",
" hemoglobin Urine protein serum creatinine AST ALT Gtp \\\n",
"1 15.2 1.0 0.9 38.0 45.0 67 \n",
"2 15.8 1.0 1.0 22.0 24.0 43 \n",
"3 16.1 1.0 1.0 16.0 31.0 89 \n",
"4 14.0 1.0 0.8 23.0 19.0 16 \n",
"5 13.2 1.0 0.7 22.0 20.0 13 \n",
"... ... ... ... ... ... ... \n",
"44548 14.0 1.0 0.9 20.0 17.0 15 \n",
"44549 15.9 1.0 0.9 19.0 21.0 18 \n",
"44550 15.6 1.0 1.2 18.0 13.0 18 \n",
"44551 15.9 1.0 1.3 37.0 48.0 96 \n",
"44552 14.0 1.0 0.7 16.0 15.0 16 \n",
"\n",
" dental caries tartar smoking BMI \n",
"1 0 1 1 25.951557 \n",
"2 0 1 1 27.343750 \n",
"3 0 0 1 27.777778 \n",
"4 0 0 1 26.666667 \n",
"5 1 1 1 22.491349 \n",
"... ... ... ... ... \n",
"44548 0 0 1 23.437500 \n",
"44549 0 1 0 23.148148 \n",
"44550 1 1 1 19.591837 \n",
"44551 1 1 1 25.390625 \n",
"44552 0 1 0 17.777778 \n",
"\n",
"[40096 rows x 26 columns]"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Series([], dtype: float64)"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"# Splitting the data into train and test\n",
"train_df,val_df = train_test_split(df,test_size=0.2,random_state=42)\n",
"\n",
"# Splitting the data into train and validation sets\n",
"y_train = train_df['smoking'].copy()\n",
"y_train.reset_index(drop=True,inplace=True)\n",
"\n",
"X_train = train_df.drop('smoking',axis=1).copy()\n",
"X_train.reset_index(drop=True,inplace=True)\n",
"\n",
"y_val = val_df['smoking'].copy()\n",
"y_val.reset_index(drop=True,inplace=True)\n",
"\n",
"X_val = val_df.drop('smoking',axis=1).copy()\n",
"X_val.reset_index(drop=True,inplace=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
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" <td>1.0</td>\n",
" <td>0.8</td>\n",
" <td>17.0</td>\n",
" <td>15.0</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>24.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>60.0</td>\n",
" <td>160.0</td>\n",
" <td>65.0</td>\n",
" <td>89.0</td>\n",
" <td>1.2</td>\n",
" <td>1.5</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>118.0</td>\n",
" <td>83.0</td>\n",
" <td>92.0</td>\n",
" <td>180.0</td>\n",
" <td>248.0</td>\n",
" <td>43.0</td>\n",
" <td>87.0</td>\n",
" <td>14.6</td>\n",
" <td>1.0</td>\n",
" <td>1.2</td>\n",
" <td>32.0</td>\n",
" <td>32.0</td>\n",
" <td>51</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>25.390625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>25.0</td>\n",
" <td>175.0</td>\n",
" <td>80.0</td>\n",
" <td>91.8</td>\n",
" <td>1.2</td>\n",
" <td>1.5</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>147.0</td>\n",
" <td>87.0</td>\n",
" <td>171.0</td>\n",
" <td>220.0</td>\n",
" <td>252.0</td>\n",
" <td>34.0</td>\n",
" <td>136.0</td>\n",
" <td>15.9</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>27.0</td>\n",
" <td>39.0</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>26.122449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>25.0</td>\n",
" <td>170.0</td>\n",
" <td>60.0</td>\n",
" <td>73.0</td>\n",
" <td>1.2</td>\n",
" <td>0.8</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>116.0</td>\n",
" <td>67.0</td>\n",
" <td>98.0</td>\n",
" <td>173.0</td>\n",
" <td>47.0</td>\n",
" <td>56.0</td>\n",
" <td>108.0</td>\n",
" <td>15.6</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>18.0</td>\n",
" <td>15.0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>20.761246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>25.0</td>\n",
" <td>175.0</td>\n",
" <td>80.0</td>\n",
" <td>83.5</td>\n",
" <td>1.2</td>\n",
" <td>1.2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>129.0</td>\n",
" <td>73.0</td>\n",
" <td>88.0</td>\n",
" <td>213.0</td>\n",
" <td>82.0</td>\n",
" <td>72.0</td>\n",
" <td>124.0</td>\n",
" <td>15.3</td>\n",
" <td>1.0</td>\n",
" <td>0.9</td>\n",
" <td>22.0</td>\n",
" <td>13.0</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26.122449</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gender age height(cm) weight(kg) waist(cm) eyesight(left) \\\n",
"0 0 40.0 150.0 55.0 75.0 1.5 \n",
"1 1 60.0 160.0 65.0 89.0 1.2 \n",
"2 1 25.0 175.0 80.0 91.8 1.2 \n",
"3 1 25.0 170.0 60.0 73.0 1.2 \n",
"4 1 25.0 175.0 80.0 83.5 1.2 \n",
"\n",
" eyesight(right) hearing(left) hearing(right) systolic relaxation \\\n",
"0 1.2 1.0 1.0 120.0 80.0 \n",
"1 1.5 1.0 2.0 118.0 83.0 \n",
"2 1.5 1.0 1.0 147.0 87.0 \n",
"3 0.8 1.0 1.0 116.0 67.0 \n",
"4 1.2 1.0 1.0 129.0 73.0 \n",
"\n",
" fasting blood sugar Cholesterol triglyceride HDL LDL hemoglobin \\\n",
"0 76.0 211.0 142.0 65.0 117.0 13.1 \n",
"1 92.0 180.0 248.0 43.0 87.0 14.6 \n",
"2 171.0 220.0 252.0 34.0 136.0 15.9 \n",
"3 98.0 173.0 47.0 56.0 108.0 15.6 \n",
"4 88.0 213.0 82.0 72.0 124.0 15.3 \n",
"\n",
" Urine protein serum creatinine AST ALT Gtp dental caries tartar \\\n",
"0 1.0 0.8 17.0 15.0 12 0 0 \n",
"1 1.0 1.2 32.0 32.0 51 0 0 \n",
"2 1.0 1.0 27.0 39.0 45 0 1 \n",
"3 1.0 1.0 18.0 15.0 13 0 0 \n",
"4 1.0 0.9 22.0 13.0 16 0 0 \n",
"\n",
" BMI \n",
"0 24.444444 \n",
"1 25.390625 \n",
"2 26.122449 \n",
"3 20.761246 \n",
"4 26.122449 "
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Accuracy score: 0.727930174563591\n",
"Train accuracy score: 0.7282703578999875\n"
]
}
],
"source": [
"#Simple decision tree classifier\n",
"dt = DecisionTreeClassifier(random_state=42,max_depth=3,min_samples_leaf=5,min_samples_split=5,max_features=0.5)\n",
"dt.fit(X_train,y_train)\n",
"y_pred = dt.predict(X_val)\n",
"print(\"Test Accuracy score: \",accuracy_score(y_val,y_pred))\n",
"print(\"Train accuracy score: \",accuracy_score(y_train,dt.predict(X_train)))"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot: >"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Confusion matrix for the decision tree classifier\n",
"cm = confusion_matrix(y_val,y_pred)\n",
"sns.heatmap(cm,annot=True,fmt='d',cmap='Blues',xticklabels=['Non-Smoker','Smoker'],yticklabels=['Non-Smoker','Smoker'])"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0.5, 0.875, 'hemoglobin <= 14.45\\ngini = 0.452\\nsamples = 32076\\nvalue = [20991, 11085]'),\n",
" Text(0.25, 0.625, 'height(cm) <= 167.5\\n0.233\\n14123\\n[12223, 1900]'),\n",
" Text(0.125, 0.375, 'Gtp <= 23.5\\n0.152\\n12242\\n[11230, 1012]'),\n",
" Text(0.0625, 0.125, '0.098\\n8830\\n[8372, 458]'),\n",
" Text(0.1875, 0.125, '0.272\\n3412\\n[2858, 554]'),\n",
" Text(0.375, 0.375, 'gender <= 0.5\\n0.498\\n1881\\n[993, 888]'),\n",
" Text(0.3125, 0.125, '0.095\\n220\\n[209, 11]'),\n",
" Text(0.4375, 0.125, '0.498\\n1661\\n[784, 877]'),\n",
" Text(0.75, 0.625, 'Gtp <= 28.5\\n0.5\\n17953\\n[8768, 9185]'),\n",
" Text(0.625, 0.375, 'height(cm) <= 162.5\\n0.483\\n8558\\n[5076, 3482]'),\n",
" Text(0.5625, 0.125, '0.332\\n1575\\n[1244, 331]'),\n",
" Text(0.6875, 0.125, '0.495\\n6983\\n[3832, 3151]'),\n",
" Text(0.875, 0.375, 'gender <= 0.5\\n0.477\\n9395\\n[3692, 5703]'),\n",
" Text(0.8125, 0.125, '0.194\\n339\\n[302, 37]'),\n",
" Text(0.9375, 0.125, '0.468\\n9056\\n[3390, 5666]')]"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1120x640 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(14,8),dpi = 80)\n",
"tree.plot_tree(dt,label=\"root\",feature_names=X_train.columns,filled=True,rounded=True,fontsize=14)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 480x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"##########\n",
"#Extract feature importance from decision tree classifier and show only features with importance > 0.2\n",
"feature_importance = dt.feature_importances_\n",
"#Plot the feature importance with colors according to the importance\n",
"plt.figure(figsize=(6,10),dpi = 80)\n",
"#Removing the features with importance < 0.2\n",
"relevant_columns = X_train.columns[feature_importance >= 0.01]\n",
"feature_importance = feature_importance[feature_importance >= 0.01]\n",
"plt.barh(relevant_columns,feature_importance,color=['red' if x < 0.2 else 'green' for x in feature_importance])\n",
"#plot the actual feature importance values\n",
"for i, v in enumerate(feature_importance):\n",
" plt.text(v, i, str(round(v,2)), color='blue', fontweight='bold')\n",
" \n",
"plt.xlabel(\"Feature Importance\")\n",
"plt.ylabel(\"Features\")\n",
"plt.title(\"Feature Importance Plot\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 432 candidates, totalling 2160 fits\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.680, test=0.678) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.710, test=0.719) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.629, test=0.622) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.672, test=0.674) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.666, test=0.662) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.676, test=0.676) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.697, test=0.698) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.669, test=0.682) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.697, test=0.692) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.690, test=0.689) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.652, test=0.650) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.709) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.660, test=0.667) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.647, test=0.636) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.689, test=0.687) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.697) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.675, test=0.669) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.682, test=0.688) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.640, test=0.631) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.669, test=0.660) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.682, test=0.680) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.667, test=0.659) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.657, test=0.667) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.688, test=0.677) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.667, test=0.670) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.666, test=0.668) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.689, test=0.702) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.590, test=0.583) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.691, test=0.684) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.651, test=0.651) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.667, test=0.667) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.651, test=0.651) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.665, test=0.675) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.699, test=0.690) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.721) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.693, test=0.694) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.681, test=0.682) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.716) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.707) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.663, test=0.664) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.691, test=0.690) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.685, test=0.686) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.711, test=0.725) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.705, test=0.695) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.708) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.669, test=0.668) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.688, test=0.700) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.703) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.711, test=0.709) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.695, test=0.697) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.673, test=0.668) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.680, test=0.696) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.700, test=0.688) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.689, test=0.688) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.687, test=0.682) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.665, test=0.662) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.679, test=0.671) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.708) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.638, test=0.641) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.711, test=0.725) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.687, test=0.677) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.680, test=0.677) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.705) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.704, test=0.698) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.682, test=0.679) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.706, test=0.700) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.695, test=0.707) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.689, test=0.690) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.689, test=0.691) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.675, test=0.670) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.693, test=0.705) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.714, test=0.704) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.714, test=0.704) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.693, test=0.705) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.714, test=0.704) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.702) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.695, test=0.690) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.697, test=0.702) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.695, test=0.687) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.721) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.676, test=0.682) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.710, test=0.698) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.711, test=0.704) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.704, test=0.705) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.671, test=0.674) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.694, test=0.701) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.690) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.706, test=0.698) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.695, test=0.692) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.692, test=0.687) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.709, test=0.721) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.694) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.709, test=0.706) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.668, test=0.674) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.684, test=0.682) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.679, test=0.691) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.699, test=0.690) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.676, test=0.677) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.672, test=0.681) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.692) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.692) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.719) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.664, test=0.675) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.683, test=0.681) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.691, test=0.697) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.656, test=0.640) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.679, test=0.677) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.665, test=0.675) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.656, test=0.646) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.673, test=0.669) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.651, test=0.651) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.652, test=0.650) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.697, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.681, test=0.678) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.681, test=0.689) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.714, test=0.704) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.699, test=0.695) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.693, test=0.691) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.698) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.733) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.686, test=0.675) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.698) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.707) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.690, test=0.684) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.694, test=0.679) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.712, test=0.707) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.716) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.731) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.710, test=0.703) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.694, test=0.698) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.689, test=0.691) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.723) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.711, test=0.709) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.686, test=0.681) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.687, test=0.692) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.706) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.705, test=0.703) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.672, test=0.670) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.690) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.708, test=0.703) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.709) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.693, test=0.690) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.695, test=0.710) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.694, test=0.691) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.688, test=0.691) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.713, test=0.709) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.713, test=0.720) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.712, test=0.707) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.696, test=0.692) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.712, test=0.716) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.713, test=0.712) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.719, test=0.722) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.714, test=0.716) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.714, test=0.707) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.720, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.713) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.721) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.706, test=0.711) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.731) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.711) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.720, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.731) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.692) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.695, test=0.687) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.717) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.711) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.727, test=0.724) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.709, test=0.721) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.727, test=0.724) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.709, test=0.721) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.699) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.709) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.730, test=0.718) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.705, test=0.700) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.692, test=0.693) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.692, test=0.695) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.724) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.688, test=0.678) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.707, test=0.701) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.684, test=0.685) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.683, test=0.679) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.681, test=0.690) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.713, test=0.701) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.702) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.688, test=0.686) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.684, test=0.675) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.707, test=0.713) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.676, test=0.677) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.685, test=0.694) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.700, test=0.700) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.681, test=0.689) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.691) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.706, test=0.699) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.716) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.697, test=0.689) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.645, test=0.636) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.685, test=0.681) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.666, test=0.668) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.709) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.690, test=0.680) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.697, test=0.693) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.638, test=0.634) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.708, test=0.707) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.671, test=0.677) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.654, test=0.644) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.694) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.667, test=0.664) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.677, test=0.665) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.688, test=0.688) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.719) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.712, test=0.714) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.692) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.714) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.716) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.714, test=0.713) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.707, test=0.714) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.714, test=0.707) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.730, test=0.725) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.723, test=0.721) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.712, test=0.708) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.726) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.727, test=0.717) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.689, test=0.687) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.713) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.725) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.707) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.708) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.708, test=0.705) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.716) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.723) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.710, test=0.701) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.706, test=0.700) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.686, test=0.682) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.699) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.712, test=0.724) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.709) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.716) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.697) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.699, test=0.690) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.700, test=0.698) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.695, test=0.692) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.697, test=0.702) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.695, test=0.692) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.726) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.721) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.727, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.712, test=0.705) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.719, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.709, test=0.707) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.715) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.728, test=0.726) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.719, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.707) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.716) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.711) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.713) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.721) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.694, test=0.702) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.700, test=0.691) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.711) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.700) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.724) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.721, test=0.717) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.713, test=0.711) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.690, test=0.696) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.709, test=0.704) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.709, test=0.706) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.715) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.692, test=0.683) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.712) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.680, test=0.663) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.682, test=0.678) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.693, test=0.695) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.711, test=0.709) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.696, test=0.701) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.679, test=0.671) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.705, test=0.705) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.714) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.716) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.686, test=0.691) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.692, test=0.677) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.696) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.682, test=0.678) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.692, test=0.693) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.684, test=0.671) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.670, test=0.666) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.694, test=0.695) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.710, test=0.717) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.672, test=0.682) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.685, test=0.677) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.673, test=0.667) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.690) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.650, test=0.643) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.708, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.689, test=0.683) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.663, test=0.666) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.658, test=0.643) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.696, test=0.692) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.670, test=0.676) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.634, test=0.631) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.720, test=0.731) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.647, test=0.647) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.713, test=0.710) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.710, test=0.725) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.707) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.723, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.713, test=0.713) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.716) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.687, test=0.695) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.711) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.718) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.711, test=0.709) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.712) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.714, test=0.724) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.705) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.709, test=0.704) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.704, test=0.704) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.692, test=0.684) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.705, test=0.713) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.706) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.709, test=0.705) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.696, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.719) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.731) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.694, test=0.689) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.708) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.678, test=0.674) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.720, test=0.717) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.723) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.684, test=0.682) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.671, test=0.660) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.699, test=0.693) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.683, test=0.682) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.700) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.712, test=0.722) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.719) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.712, test=0.705) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.712) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.713) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.712, test=0.722) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.724) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.721) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.718) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.730, test=0.733) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.706) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.730, test=0.740) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.713, test=0.724) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.708) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.697, test=0.702) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.721) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=gini, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.674, test=0.671) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.676, test=0.684) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.644, test=0.628) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.686, test=0.684) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.642, test=0.642) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.702) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.664, test=0.679) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.706, test=0.696) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.708, test=0.698) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.694, test=0.692) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.675, test=0.674) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.636, test=0.639) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.618, test=0.633) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.697, test=0.686) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.669, test=0.663) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.679, test=0.679) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.692, test=0.697) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.666, test=0.657) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.705) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.615, test=0.614) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.695, test=0.682) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.649, test=0.652) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.705) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.694, test=0.699) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.700) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.665, test=0.659) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.694, test=0.699) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.655, test=0.664) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.649, test=0.636) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.667, test=0.667) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.694, test=0.693) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.625, test=0.618) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.690, test=0.697) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.692) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.693, test=0.695) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.705, test=0.702) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.664, test=0.652) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.696, test=0.690) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.696, test=0.693) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.705, test=0.716) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.694, test=0.696) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.665, test=0.672) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.683, test=0.672) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.697, test=0.693) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.713, test=0.715) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.686, test=0.701) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.690) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.672, test=0.666) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.690, test=0.691) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.713, test=0.708) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.691, test=0.704) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.681, test=0.672) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.693) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.699, test=0.714) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.700, test=0.688) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.660, test=0.662) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.685, test=0.687) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.695, test=0.687) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.699) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.634, test=0.645) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.646, test=0.636) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.703) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.679, test=0.691) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.643, test=0.632) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.706, test=0.706) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.696, test=0.702) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.704, test=0.702) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.708) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.710, test=0.719) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.708) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.694, test=0.692) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.705) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.672, test=0.670) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.671, test=0.684) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.690, test=0.678) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.693, test=0.681) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.681, test=0.682) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.682, test=0.681) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.685, test=0.690) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.648, test=0.640) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.680, test=0.680) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.674, test=0.673) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.709, test=0.721) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.688, test=0.674) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.680, test=0.678) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.710, test=0.713) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.684, test=0.681) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.693, test=0.708) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.693, test=0.691) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.691, test=0.685) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.677, test=0.674) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.686, test=0.692) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.704, test=0.694) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.696) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.692, test=0.693) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.664, test=0.661) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.710, test=0.722) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.684, test=0.683) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.688, test=0.688) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.642, test=0.642) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.664, test=0.675) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.692) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.667, test=0.667) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.689, test=0.681) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.694, test=0.692) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.680, test=0.682) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.697, test=0.708) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.704, test=0.695) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.694, test=0.690) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.613, test=0.609) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.705, test=0.704) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.695, test=0.705) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.705, test=0.696) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.695) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.696, test=0.699) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.713, test=0.713) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.706, test=0.723) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.712, test=0.705) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.700, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.704, test=0.695) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.704, test=0.715) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.700, test=0.692) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.691, test=0.690) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.700, test=0.702) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.699) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.726) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.692, test=0.684) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.686, test=0.686) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.705, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.709, test=0.708) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.708, test=0.704) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.708) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.692, test=0.690) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.642, test=0.637) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.682, test=0.674) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.663, test=0.665) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.674, test=0.685) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.672, test=0.667) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.694, test=0.694) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.714, test=0.707) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.709, test=0.721) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.712, test=0.703) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.713, test=0.714) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.732) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.707) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.715) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.710, test=0.719) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.703) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.714) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.719, test=0.731) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.706) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.721) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.713, test=0.716) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.720, test=0.731) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.713, test=0.716) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.694, test=0.694) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.697) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.711) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.715) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.727, test=0.724) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.709, test=0.721) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.727, test=0.724) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.709, test=0.721) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.714, test=0.712) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.706, test=0.717) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.682, test=0.680) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.710, test=0.712) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.697, test=0.692) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.709, test=0.717) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.692) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.723, test=0.719) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.711, test=0.713) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.699, test=0.694) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.709, test=0.718) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.709) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.695, test=0.696) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.678, test=0.677) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.682, test=0.675) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.709) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.662, test=0.646) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.673, test=0.667) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.716) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.667, test=0.680) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.700, test=0.688) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.716) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.685, test=0.677) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.677, test=0.676) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.684, test=0.674) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.684, test=0.679) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.680, test=0.673) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.654, test=0.668) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.673, test=0.662) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.700) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.682, test=0.684) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.697, test=0.708) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.670, test=0.656) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.686, test=0.680) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.675, test=0.671) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.663, test=0.658) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.670, test=0.658) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.694) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.708, test=0.698) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.724) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.692) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.720) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.711, test=0.713) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.704, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.717, test=0.729) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.707) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.713) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.712, test=0.712) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.696, test=0.690) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.707) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.699, test=0.689) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.694, test=0.688) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.716) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.712) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.705, test=0.715) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.686) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.709) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.708, test=0.709) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.716) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.730) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.706) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.708, test=0.709) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.719, test=0.720) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.720, test=0.719) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.709) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.696, test=0.689) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.705, test=0.704) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.671, test=0.677) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.694, test=0.691) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.705) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.694, test=0.691) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.692) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.707, test=0.705) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.669, test=0.667) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.710, test=0.706) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.707) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.713, test=0.710) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.713, test=0.709) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.720) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.714, test=0.724) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.711) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.714) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.719, test=0.718) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.731, test=0.724) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.714, test=0.721) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.706) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.713) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.708) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.713) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.715) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.719, test=0.731) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.711) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.713) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.713, test=0.716) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.731) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.711) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.717) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.721) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.694, test=0.694) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.696, test=0.690) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.731) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.695) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.726) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.691, test=0.677) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.690, test=0.684) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.706, test=0.706) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.714, test=0.714) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.713, test=0.722) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.713, test=0.702) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.710, test=0.705) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.698) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.708, test=0.707) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.689, test=0.699) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.705, test=0.690) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.709, test=0.704) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.684, test=0.680) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.669, test=0.675) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.692, test=0.681) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.667, test=0.664) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.667, test=0.670) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.682, test=0.693) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.686, test=0.674) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.655, test=0.655) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.664, test=0.661) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.685, test=0.695) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.688) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.680, test=0.667) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.696, test=0.695) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.655, test=0.654) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.695, test=0.710) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.704, test=0.695) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.695, test=0.689) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.692, test=0.690) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.683, test=0.684) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.649, test=0.661) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.672, test=0.660) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.659, test=0.655) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.679, test=0.679) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.718) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.659, test=0.669) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.649, test=0.646) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.667, test=0.664) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.711, test=0.709) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.695, test=0.704) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.722) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.702) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.719, test=0.718) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.717, test=0.725) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.719, test=0.708) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.712, test=0.702) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.725) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.687, test=0.685) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.707, test=0.719) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.704) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.704, test=0.700) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.713, test=0.716) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.691, test=0.687) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.706) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.708) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.688, test=0.687) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.700, test=0.697) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.672, test=0.667) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.705, test=0.701) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.696, test=0.695) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.683, test=0.697) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.710) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.705, test=0.700) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.716) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.701) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.673, test=0.662) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.681, test=0.677) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.705) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.692, test=0.703) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.691, test=0.684) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.711, test=0.703) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.659, test=0.661) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.692, test=0.686) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.694) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.723, test=0.722) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.712, test=0.713) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.706) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.712) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.714, test=0.716) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.719, test=0.714) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.719, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.717, test=0.709) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.728, test=0.726) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.722) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.713, test=0.702) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.706) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.715) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.711) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.715) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.707) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.713, test=0.715) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.715) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.693, test=0.700) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.721) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.694, test=0.702) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.693, test=0.700) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=entropy, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.707, test=0.707) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.690) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.711, test=0.708) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.674, test=0.674) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.713, test=0.714) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.680, test=0.690) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.678, test=0.666) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.665, test=0.659) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.620, test=0.629) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.659, test=0.656) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.651, test=0.636) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.672, test=0.665) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.705) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.650, test=0.643) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.672, test=0.686) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.642, test=0.632) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.613, test=0.610) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.658, test=0.663) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.665, test=0.675) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.647, test=0.636) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.681, test=0.679) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.676, test=0.676) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.644, test=0.646) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.700, test=0.688) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.675, test=0.667) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.665, test=0.665) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.667, test=0.664) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.689, test=0.704) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.699, test=0.694) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.685, test=0.683) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.687, test=0.690) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.678, test=0.679) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.675, test=0.690) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.699, test=0.690) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.686, test=0.681) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.657, test=0.658) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.711, test=0.722) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.692, test=0.680) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.692, test=0.689) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.690, test=0.702) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.697, test=0.707) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.703) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.700, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.668, test=0.667) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.692, test=0.686) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.695, test=0.708) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.665, test=0.666) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.668, test=0.665) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.693, test=0.695) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.692, test=0.693) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.715) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.700, test=0.688) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.672, test=0.670) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.683, test=0.674) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.696, test=0.691) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.680, test=0.674) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.696, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.666, test=0.668) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.691, test=0.684) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.694, test=0.694) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.699, test=0.700) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.697, test=0.689) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.695, test=0.697) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.689, test=0.688) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=3, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.716) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.693, test=0.707) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.694) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.714, test=0.711) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.691, test=0.688) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.692, test=0.691) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.687, test=0.699) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.690) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.691, test=0.684) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.676, test=0.673) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.691, test=0.683) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.704) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.697, test=0.689) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.695, test=0.688) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.699, test=0.694) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.677, test=0.680) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.708) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.697, test=0.689) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.687, test=0.686) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.653, test=0.654) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.712, test=0.710) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.677, test=0.689) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.683, test=0.669) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.661, test=0.661) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.686, test=0.685) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.676, test=0.678) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.693, test=0.685) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.705, test=0.704) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.657, test=0.654) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.695, test=0.698) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.649, test=0.661) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.672, test=0.674) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.670, test=0.681) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.637, test=0.633) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.667, test=0.667) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.679, test=0.679) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.656, test=0.642) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.711, test=0.709) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.705) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.704, test=0.702) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.734) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.686) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.714, test=0.711) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.714, test=0.713) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.713, test=0.713) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.687, test=0.697) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.690) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.707) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.712, test=0.711) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.712, test=0.722) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.709, test=0.702) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.711) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.721) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.710) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.688, test=0.687) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.720) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.716) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.726, test=0.734) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.708) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.714) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.678, test=0.687) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.692, test=0.687) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.685, test=0.683) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.672, test=0.668) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.697, test=0.708) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.711) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.667, test=0.672) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.697, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.712) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.699, test=0.690) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.695, test=0.698) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.726, test=0.722) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.729, test=0.738) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.713, test=0.709) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.711, test=0.709) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.721) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.709, test=0.721) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.713, test=0.706) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.719, test=0.718) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.714) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.707) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.714, test=0.711) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.713, test=0.712) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.706) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.669, test=0.682) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.715) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.724) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.696, test=0.691) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.727, test=0.724) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.709, test=0.721) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.727, test=0.724) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.709, test=0.721) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=5, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.709, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.687, test=0.699) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.689, test=0.681) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.708) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.706, test=0.703) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.713, test=0.714) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.714) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.691) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.697, test=0.693) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.695, test=0.691) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.707, test=0.704) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.707, test=0.716) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.703) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.683, test=0.677) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.711, test=0.707) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.659, test=0.656) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.673, test=0.675) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.685, test=0.672) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.674, test=0.669) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.693, test=0.692) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.692, test=0.700) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.685, test=0.677) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.694) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.706, test=0.706) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.700, test=0.696) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.694) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.696, test=0.695) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.699, test=0.690) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.708, test=0.703) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.698) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.676, test=0.668) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.693, test=0.684) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.699, test=0.693) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.638, test=0.638) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.685, test=0.684) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.644, test=0.659) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.672, test=0.667) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.698) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.648, test=0.653) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.718) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.709) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.708) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.705, test=0.696) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.721) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.717) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.710, test=0.721) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.723, test=0.712) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.728, test=0.722) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.705, test=0.704) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.712, test=0.704) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.710, test=0.719) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.708) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.714, test=0.708) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.716) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.689, test=0.681) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.725) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.669, test=0.655) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.688, test=0.682) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.731) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.679, test=0.676) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.721) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.690) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.709) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.712) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.692, test=0.691) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.687, test=0.688) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.678, test=0.678) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.661, test=0.653) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.676, test=0.667) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.690, test=0.694) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.706, test=0.704) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.652, test=0.649) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.678, test=0.687) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.688, test=0.687) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.717, test=0.707) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.714) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.723, test=0.719) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.727, test=0.726) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.715) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.717) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.708) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.714, test=0.709) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.719, test=0.731) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.730) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.695) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.696, test=0.690) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.688, test=0.705) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.694, test=0.692) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.688, test=0.705) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.699, test=0.695) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.694, test=0.702) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=7, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.710, test=0.708) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.705, test=0.709) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.691, test=0.678) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.677, test=0.668) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.667, test=0.665) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.705, test=0.707) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.683, test=0.693) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.692, test=0.685) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.668, test=0.664) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.671, test=0.667) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.683, test=0.677) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.714, test=0.726) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.709, test=0.704) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.697, test=0.690) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.684, test=0.676) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.690, test=0.685) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.725) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.675, test=0.675) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.715) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.717, test=0.712) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.690, test=0.702) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.672, test=0.664) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.682, test=0.682) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.693, test=0.694) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.681, test=0.692) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.695, test=0.687) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.685, test=0.683) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.689, test=0.690) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.676, test=0.673) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.678, test=0.667) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.633, test=0.631) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.681, test=0.684) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.651, test=0.644) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.697, test=0.708) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.678, test=0.670) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.694, test=0.689) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.694, test=0.707) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.665, test=0.657) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.645, test=0.641) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=5, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.657, test=0.660) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.720) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.708) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.713) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.699, test=0.695) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.697, test=0.698) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.709, test=0.699) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.731) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.702) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.709, test=0.709) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.712, test=0.714) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.730) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.689) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.708, test=0.699) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.712, test=0.713) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.691, test=0.687) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.680, test=0.696) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.707, test=0.696) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.685, test=0.690) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.713, test=0.713) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.701) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.709, test=0.699) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.713, test=0.711) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.709) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.708) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.716) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.683, test=0.674) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.663, test=0.664) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.716) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.668, test=0.658) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.723) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.695, test=0.696) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=10, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.698, test=0.693) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.722) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.711, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.706) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.711) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.719, test=0.719) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.707) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.723, test=0.722) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.732, test=0.734) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.716, test=0.710) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.719, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.692, test=0.684) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.715) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.713) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.698, test=0.711) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.708) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.713) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.721) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.714, test=0.706) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.711) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.721) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.719, test=0.731) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.703, test=0.693) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.702, test=0.697) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.696, test=0.702) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.0s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.0s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.0s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=20, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.0s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=500, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.720, test=0.716) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.716, test=0.729) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.710) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1000, splitter=best;, score=(train=0.721, test=0.722) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.719, test=0.717) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.721, test=0.710) total time= 0.2s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.717, test=0.708) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.722, test=0.719) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.715, test=0.723) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1000, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.720) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1000, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 1/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n",
"[CV 2/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.718, test=0.727) total time= 0.1s\n",
"[CV 3/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.724, test=0.715) total time= 0.1s\n",
"[CV 4/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.725, test=0.716) total time= 0.1s\n",
"[CV 5/5] END class_weight=balanced, criterion=log_loss, max_depth=10, max_features=40, min_samples_leaf=1500, min_samples_split=1500, splitter=best;, score=(train=0.701, test=0.701) total time= 0.1s\n"
]
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"data": {
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"<style>#sk-container-id-18 {color: black;background-color: white;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-18 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-18 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, error_score='raise', estimator=DecisionTreeClassifier(),\n",
" param_grid={'class_weight': ['balanced'],\n",
" 'criterion': ['gini', 'entropy', 'log_loss'],\n",
" 'max_depth': [3, 5, 7, 10],\n",
" 'max_features': [5, 10, 20, 40],\n",
" 'min_samples_leaf': [500, 1000, 1500],\n",
" 'min_samples_split': [500, 1000, 1500],\n",
" 'splitter': ['best']},\n",
" return_train_score=True, scoring='accuracy', verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, error_score='raise', estimator=DecisionTreeClassifier(),\n",
" param_grid={'class_weight': ['balanced'],\n",
" 'criterion': ['gini', 'entropy', 'log_loss'],\n",
" 'max_depth': [3, 5, 7, 10],\n",
" 'max_features': [5, 10, 20, 40],\n",
" 'min_samples_leaf': [500, 1000, 1500],\n",
" 'min_samples_split': [500, 1000, 1500],\n",
" 'splitter': ['best']},\n",
" return_train_score=True, scoring='accuracy', verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
],
"text/plain": [
"GridSearchCV(cv=5, error_score='raise', estimator=DecisionTreeClassifier(),\n",
" param_grid={'class_weight': ['balanced'],\n",
" 'criterion': ['gini', 'entropy', 'log_loss'],\n",
" 'max_depth': [3, 5, 7, 10],\n",
" 'max_features': [5, 10, 20, 40],\n",
" 'min_samples_leaf': [500, 1000, 1500],\n",
" 'min_samples_split': [500, 1000, 1500],\n",
" 'splitter': ['best']},\n",
" return_train_score=True, scoring='accuracy', verbose=3)"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Running Grid Search Cross Validation for hyperparameter tuning\n",
"param_grid = {'max_depth': [3,5,7,10],\n",
" 'min_samples_split': [500,1000,1500],\n",
" 'min_samples_leaf': [500,1000,1500],\n",
" 'max_features': [5,10,20,40],\n",
" 'criterion': ['gini', 'entropy',\"log_loss\"],\n",
" 'splitter': ['best'],\n",
" 'class_weight': ['balanced']}\n",
" \n",
" \n",
"grid_search_2 = GridSearchCV(DecisionTreeClassifier(), param_grid, cv=5, scoring=\"accuracy\", return_train_score=True,verbose=3 , error_score='raise')\n",
"grid_search_2.fit(X_train, y_train)\n"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [],
"source": [
"parameters = pd.DataFrame(grid_search_2.cv_results_[\"params\"])\n",
"train_scores = pd.DataFrame(grid_search_2.cv_results_[\"mean_train_score\"])\n",
"test_scores = pd.DataFrame(grid_search_2.cv_results_[\"mean_test_score\"])\n",
"cv_results = pd.concat([parameters,train_scores,test_scores],axis=1)\n",
"cv_results.columns.values[-2:] = [\"Train Accuracy Score\",\"Test Accuracy Score\"]\n",
"cv_results.sort_values(by=\"Test Accuracy Score\",ascending=False,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_273b9_row0_col2, #T_273b9_row0_col4, #T_273b9_row0_col7, #T_273b9_row0_col8, #T_273b9_row1_col4, #T_273b9_row1_col5, #T_273b9_row2_col4, #T_273b9_row4_col2, #T_273b9_row4_col5, #T_273b9_row5_col2, #T_273b9_row5_col5, #T_273b9_row6_col3, #T_273b9_row6_col4, #T_273b9_row7_col3, #T_273b9_row7_col4, #T_273b9_row7_col5, #T_273b9_row8_col3, #T_273b9_row8_col4, #T_273b9_row9_col3, #T_273b9_row9_col4, #T_273b9_row9_col5 {\n",
" background-color: #08306b;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_273b9_row0_col3, #T_273b9_row0_col5, #T_273b9_row1_col2, #T_273b9_row1_col3, #T_273b9_row2_col3, #T_273b9_row2_col5, #T_273b9_row2_col7, #T_273b9_row3_col3, #T_273b9_row3_col4, #T_273b9_row4_col3, #T_273b9_row4_col4, #T_273b9_row5_col3, #T_273b9_row5_col4, #T_273b9_row6_col5, #T_273b9_row6_col8, #T_273b9_row7_col8, #T_273b9_row8_col5, #T_273b9_row8_col8, #T_273b9_row9_col2, #T_273b9_row9_col8 {\n",
" background-color: #f7fbff;\n",
" color: #000000;\n",
"}\n",
"#T_273b9_row1_col7 {\n",
" background-color: #d3e3f3;\n",
" color: #000000;\n",
"}\n",
"#T_273b9_row1_col8 {\n",
" background-color: #a6cee4;\n",
" color: #000000;\n",
"}\n",
"#T_273b9_row2_col2, #T_273b9_row3_col2, #T_273b9_row6_col2, #T_273b9_row7_col2, #T_273b9_row8_col2 {\n",
" background-color: #94c4df;\n",
" color: #000000;\n",
"}\n",
"#T_273b9_row2_col8 {\n",
" background-color: #b2d2e8;\n",
" color: #000000;\n",
"}\n",
"#T_273b9_row3_col5 {\n",
" background-color: #6aaed6;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_273b9_row3_col7 {\n",
" background-color: #bdd7ec;\n",
" color: #000000;\n",
"}\n",
"#T_273b9_row3_col8 {\n",
" background-color: #cfe1f2;\n",
" color: #000000;\n",
"}\n",
"#T_273b9_row4_col7 {\n",
" background-color: #3383be;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_273b9_row4_col8 {\n",
" background-color: #ddeaf7;\n",
" color: #000000;\n",
"}\n",
"#T_273b9_row5_col7 {\n",
" background-color: #d9e7f5;\n",
" color: #000000;\n",
"}\n",
"#T_273b9_row5_col8 {\n",
" background-color: #f5f9fe;\n",
" color: #000000;\n",
"}\n",
"#T_273b9_row6_col7, #T_273b9_row7_col7, #T_273b9_row8_col7, #T_273b9_row9_col7 {\n",
" background-color: #d8e7f5;\n",
" color: #000000;\n",
"}\n",
"</style>\n",
"<table id=\"T_273b9\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th id=\"T_273b9_level0_col0\" class=\"col_heading level0 col0\" >class_weight</th>\n",
" <th id=\"T_273b9_level0_col1\" class=\"col_heading level0 col1\" >criterion</th>\n",
" <th id=\"T_273b9_level0_col2\" class=\"col_heading level0 col2\" >max_depth</th>\n",
" <th id=\"T_273b9_level0_col3\" class=\"col_heading level0 col3\" >max_features</th>\n",
" <th id=\"T_273b9_level0_col4\" class=\"col_heading level0 col4\" >min_samples_leaf</th>\n",
" <th id=\"T_273b9_level0_col5\" class=\"col_heading level0 col5\" >min_samples_split</th>\n",
" <th id=\"T_273b9_level0_col6\" class=\"col_heading level0 col6\" >splitter</th>\n",
" <th id=\"T_273b9_level0_col7\" class=\"col_heading level0 col7\" >Train Accuracy Score</th>\n",
" <th id=\"T_273b9_level0_col8\" class=\"col_heading level0 col8\" >Test Accuracy Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_273b9_level0_row0\" class=\"row_heading level0 row0\" >129</th>\n",
" <td id=\"T_273b9_row0_col0\" class=\"data row0 col0\" >balanced</td>\n",
" <td id=\"T_273b9_row0_col1\" class=\"data row0 col1\" >gini</td>\n",
" <td id=\"T_273b9_row0_col2\" class=\"data row0 col2\" >10</td>\n",
" <td id=\"T_273b9_row0_col3\" class=\"data row0 col3\" >20</td>\n",
" <td id=\"T_273b9_row0_col4\" class=\"data row0 col4\" >1000</td>\n",
" <td id=\"T_273b9_row0_col5\" class=\"data row0 col5\" >500</td>\n",
" <td id=\"T_273b9_row0_col6\" class=\"data row0 col6\" >best</td>\n",
" <td id=\"T_273b9_row0_col7\" class=\"data row0 col7\" >0.722627</td>\n",
" <td id=\"T_273b9_row0_col8\" class=\"data row0 col8\" >0.721256</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_273b9_level0_row1\" class=\"row_heading level0 row1\" >203</th>\n",
" <td id=\"T_273b9_row1_col0\" class=\"data row1 col0\" >balanced</td>\n",
" <td id=\"T_273b9_row1_col1\" class=\"data row1 col1\" >entropy</td>\n",
" <td id=\"T_273b9_row1_col2\" class=\"data row1 col2\" >5</td>\n",
" <td id=\"T_273b9_row1_col3\" class=\"data row1 col3\" >20</td>\n",
" <td id=\"T_273b9_row1_col4\" class=\"data row1 col4\" >1000</td>\n",
" <td id=\"T_273b9_row1_col5\" class=\"data row1 col5\" >1500</td>\n",
" <td id=\"T_273b9_row1_col6\" class=\"data row1 col6\" >best</td>\n",
" <td id=\"T_273b9_row1_col7\" class=\"data row1 col7\" >0.720866</td>\n",
" <td id=\"T_273b9_row1_col8\" class=\"data row1 col8\" >0.719572</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_273b9_level0_row2\" class=\"row_heading level0 row2\" >381</th>\n",
" <td id=\"T_273b9_row2_col0\" class=\"data row2 col0\" >balanced</td>\n",
" <td id=\"T_273b9_row2_col1\" class=\"data row2 col1\" >log_loss</td>\n",
" <td id=\"T_273b9_row2_col2\" class=\"data row2 col2\" >7</td>\n",
" <td id=\"T_273b9_row2_col3\" class=\"data row2 col3\" >20</td>\n",
" <td id=\"T_273b9_row2_col4\" class=\"data row2 col4\" >1000</td>\n",
" <td id=\"T_273b9_row2_col5\" class=\"data row2 col5\" >500</td>\n",
" <td id=\"T_273b9_row2_col6\" class=\"data row2 col6\" >best</td>\n",
" <td id=\"T_273b9_row2_col7\" class=\"data row2 col7\" >0.720461</td>\n",
" <td id=\"T_273b9_row2_col8\" class=\"data row2 col8\" >0.719479</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_273b9_level0_row3\" class=\"row_heading level0 row3\" >379</th>\n",
" <td id=\"T_273b9_row3_col0\" class=\"data row3 col0\" >balanced</td>\n",
" <td id=\"T_273b9_row3_col1\" class=\"data row3 col1\" >log_loss</td>\n",
" <td id=\"T_273b9_row3_col2\" class=\"data row3 col2\" >7</td>\n",
" <td id=\"T_273b9_row3_col3\" class=\"data row3 col3\" >20</td>\n",
" <td id=\"T_273b9_row3_col4\" class=\"data row3 col4\" >500</td>\n",
" <td id=\"T_273b9_row3_col5\" class=\"data row3 col5\" >1000</td>\n",
" <td id=\"T_273b9_row3_col6\" class=\"data row3 col6\" >best</td>\n",
" <td id=\"T_273b9_row3_col7\" class=\"data row3 col7\" >0.721069</td>\n",
" <td id=\"T_273b9_row3_col8\" class=\"data row3 col8\" >0.719198</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_273b9_level0_row4\" class=\"row_heading level0 row4\" >416</th>\n",
" <td id=\"T_273b9_row4_col0\" class=\"data row4 col0\" >balanced</td>\n",
" <td id=\"T_273b9_row4_col1\" class=\"data row4 col1\" >log_loss</td>\n",
" <td id=\"T_273b9_row4_col2\" class=\"data row4 col2\" >10</td>\n",
" <td id=\"T_273b9_row4_col3\" class=\"data row4 col3\" >20</td>\n",
" <td id=\"T_273b9_row4_col4\" class=\"data row4 col4\" >500</td>\n",
" <td id=\"T_273b9_row4_col5\" class=\"data row4 col5\" >1500</td>\n",
" <td id=\"T_273b9_row4_col6\" class=\"data row4 col6\" >best</td>\n",
" <td id=\"T_273b9_row4_col7\" class=\"data row4 col7\" >0.721934</td>\n",
" <td id=\"T_273b9_row4_col8\" class=\"data row4 col8\" >0.719011</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_273b9_level0_row5\" class=\"row_heading level0 row5\" >128</th>\n",
" <td id=\"T_273b9_row5_col0\" class=\"data row5 col0\" >balanced</td>\n",
" <td id=\"T_273b9_row5_col1\" class=\"data row5 col1\" >gini</td>\n",
" <td id=\"T_273b9_row5_col2\" class=\"data row5 col2\" >10</td>\n",
" <td id=\"T_273b9_row5_col3\" class=\"data row5 col3\" >20</td>\n",
" <td id=\"T_273b9_row5_col4\" class=\"data row5 col4\" >500</td>\n",
" <td id=\"T_273b9_row5_col5\" class=\"data row5 col5\" >1500</td>\n",
" <td id=\"T_273b9_row5_col6\" class=\"data row5 col6\" >best</td>\n",
" <td id=\"T_273b9_row5_col7\" class=\"data row5 col7\" >0.720796</td>\n",
" <td id=\"T_273b9_row5_col8\" class=\"data row5 col8\" >0.718699</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_273b9_level0_row6\" class=\"row_heading level0 row6\" >102</th>\n",
" <td id=\"T_273b9_row6_col0\" class=\"data row6 col0\" >balanced</td>\n",
" <td id=\"T_273b9_row6_col1\" class=\"data row6 col1\" >gini</td>\n",
" <td id=\"T_273b9_row6_col2\" class=\"data row6 col2\" >7</td>\n",
" <td id=\"T_273b9_row6_col3\" class=\"data row6 col3\" >40</td>\n",
" <td id=\"T_273b9_row6_col4\" class=\"data row6 col4\" >1000</td>\n",
" <td id=\"T_273b9_row6_col5\" class=\"data row6 col5\" >500</td>\n",
" <td id=\"T_273b9_row6_col6\" class=\"data row6 col6\" >best</td>\n",
" <td id=\"T_273b9_row6_col7\" class=\"data row6 col7\" >0.720804</td>\n",
" <td id=\"T_273b9_row6_col8\" class=\"data row6 col8\" >0.718668</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_273b9_level0_row7\" class=\"row_heading level0 row7\" >248</th>\n",
" <td id=\"T_273b9_row7_col0\" class=\"data row7 col0\" >balanced</td>\n",
" <td id=\"T_273b9_row7_col1\" class=\"data row7 col1\" >entropy</td>\n",
" <td id=\"T_273b9_row7_col2\" class=\"data row7 col2\" >7</td>\n",
" <td id=\"T_273b9_row7_col3\" class=\"data row7 col3\" >40</td>\n",
" <td id=\"T_273b9_row7_col4\" class=\"data row7 col4\" >1000</td>\n",
" <td id=\"T_273b9_row7_col5\" class=\"data row7 col5\" >1500</td>\n",
" <td id=\"T_273b9_row7_col6\" class=\"data row7 col6\" >best</td>\n",
" <td id=\"T_273b9_row7_col7\" class=\"data row7 col7\" >0.720804</td>\n",
" <td id=\"T_273b9_row7_col8\" class=\"data row7 col8\" >0.718668</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_273b9_level0_row8\" class=\"row_heading level0 row8\" >390</th>\n",
" <td id=\"T_273b9_row8_col0\" class=\"data row8 col0\" >balanced</td>\n",
" <td id=\"T_273b9_row8_col1\" class=\"data row8 col1\" >log_loss</td>\n",
" <td id=\"T_273b9_row8_col2\" class=\"data row8 col2\" >7</td>\n",
" <td id=\"T_273b9_row8_col3\" class=\"data row8 col3\" >40</td>\n",
" <td id=\"T_273b9_row8_col4\" class=\"data row8 col4\" >1000</td>\n",
" <td id=\"T_273b9_row8_col5\" class=\"data row8 col5\" >500</td>\n",
" <td id=\"T_273b9_row8_col6\" class=\"data row8 col6\" >best</td>\n",
" <td id=\"T_273b9_row8_col7\" class=\"data row8 col7\" >0.720804</td>\n",
" <td id=\"T_273b9_row8_col8\" class=\"data row8 col8\" >0.718668</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_273b9_level0_row9\" class=\"row_heading level0 row9\" >356</th>\n",
" <td id=\"T_273b9_row9_col0\" class=\"data row9 col0\" >balanced</td>\n",
" <td id=\"T_273b9_row9_col1\" class=\"data row9 col1\" >log_loss</td>\n",
" <td id=\"T_273b9_row9_col2\" class=\"data row9 col2\" >5</td>\n",
" <td id=\"T_273b9_row9_col3\" class=\"data row9 col3\" >40</td>\n",
" <td id=\"T_273b9_row9_col4\" class=\"data row9 col4\" >1000</td>\n",
" <td id=\"T_273b9_row9_col5\" class=\"data row9 col5\" >1500</td>\n",
" <td id=\"T_273b9_row9_col6\" class=\"data row9 col6\" >best</td>\n",
" <td id=\"T_273b9_row9_col7\" class=\"data row9 col7\" >0.720804</td>\n",
" <td id=\"T_273b9_row9_col8\" class=\"data row9 col8\" >0.718668</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x2aaff7b20>"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_results.head(10).style.background_gradient(cmap='Blues')"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7143658810325477"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0.7138403990024937"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<AxesSubplot: >"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"classifier_2 = grid_search_2.best_estimator_\n",
"display(accuracy_score(y_train,classifier_2.predict(X_train)))\n",
"display(accuracy_score(y_val,classifier_2.predict(X_val)))\n",
"sns.heatmap(confusion_matrix(y_train,classifier_2.predict(X_train)),annot=True,fmt='d',cmap='Blues',xticklabels=['Non-Smoker','Smoker'],yticklabels=['Non-Smoker','Smoker'])"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7138403990024937"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<AxesSubplot: >"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(accuracy_score(y_val,classifier_2.predict(X_val)))\n",
"sns.heatmap(confusion_matrix(y_val,classifier_2.predict(X_val)),annot=True,fmt='d',cmap='Greens',xticklabels=['Non-Smoker','Smoker'],yticklabels=['Non-Smoker','Smoker'])"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'class_weight': 'balanced',\n",
" 'criterion': 'gini',\n",
" 'max_depth': 10,\n",
" 'max_features': 20,\n",
" 'min_samples_leaf': 1000,\n",
" 'min_samples_split': 500,\n",
" 'splitter': 'best'}"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid_search_2.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0.47115384615384615, 0.9, 'gender <= 0.5\\ngini = 0.5\\nsamples = 32076\\nvalue = [16038.0, 16038.0]'),\n",
" Text(0.25, 0.7, 'Gtp <= 23.5\\n0.139\\n12416\\n[9095.153, 740.772]'),\n",
" Text(0.15384615384615385, 0.5, 'Gtp <= 15.5\\n0.104\\n9182\\n[6806.084, 396.429]'),\n",
" Text(0.07692307692307693, 0.3, 'AST <= 18.5\\n0.075\\n5057\\n[3782.771, 153.363]'),\n",
" Text(0.038461538461538464, 0.1, '\\n (...) \\n'),\n",
" Text(0.11538461538461539, 0.1, '\\n (...) \\n'),\n",
" Text(0.23076923076923078, 0.3, 'ALT <= 14.5\\n0.138\\n4125\\n[3023.313, 243.066]'),\n",
" Text(0.19230769230769232, 0.1, '\\n (...) \\n'),\n",
" Text(0.2692307692307692, 0.1, '\\n (...) \\n'),\n",
" Text(0.34615384615384615, 0.5, 'age <= 47.5\\n0.227\\n3234\\n[2289.069, 344.343]'),\n",
" Text(0.3076923076923077, 0.3, '0.297\\n1420\\n[971.097, 215.576]'),\n",
" Text(0.38461538461538464, 0.3, '0.162\\n1814\\n[1317.972, 128.767]'),\n",
" Text(0.6923076923076923, 0.7, 'Gtp <= 28.5\\n0.429\\n19660\\n[6942.847, 15297.228]'),\n",
" Text(0.5384615384615384, 0.5, 'triglyceride <= 88.5\\n0.477\\n9352\\n[3925.646, 6096.9]'),\n",
" Text(0.46153846153846156, 0.3, 'BMI <= 22.674\\n0.494\\n4109\\n[1898.644, 2349.636]'),\n",
" Text(0.4230769230769231, 0.1, '\\n (...) \\n'),\n",
" Text(0.5, 0.1, '\\n (...) \\n'),\n",
" Text(0.6153846153846154, 0.3, 'tartar <= 0.5\\n0.456\\n5243\\n[2027.003, 3747.264]'),\n",
" Text(0.5769230769230769, 0.1, '\\n (...) \\n'),\n",
" Text(0.6538461538461539, 0.1, '\\n (...) \\n'),\n",
" Text(0.8461538461538461, 0.5, 'triglyceride <= 174.5\\n0.372\\n10308\\n[3017.201, 9200.329]'),\n",
" Text(0.7692307692307693, 0.3, 'age <= 57.5\\n0.403\\n6930\\n[2245.519, 5774.259]'),\n",
" Text(0.7307692307692307, 0.1, '\\n (...) \\n'),\n",
" Text(0.8076923076923077, 0.1, '\\n (...) \\n'),\n",
" Text(0.9230769230769231, 0.3, 'Gtp <= 47.5\\n0.3\\n3378\\n[771.682, 3426.07]'),\n",
" Text(0.8846153846153846, 0.1, '\\n (...) \\n'),\n",
" Text(0.9615384615384616, 0.1, '\\n (...) \\n')]"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 4000x1500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(40,15),dpi = 100)\n",
"tree.plot_tree(classifier_2,max_depth = 3 ,label=\"root\",filled=True,rounded=True,fontsize=14,feature_names = X_train.columns)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 480x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Extract feature importance from decision tree classifier and show only features with importance > 0.2\n",
"feature_importance = classifier_2.feature_importances_\n",
"#Plot the feature importance with colors according to the importance\n",
"plt.figure(figsize=(6,10),dpi = 80)\n",
"#Removing the features with importance < 0.2\n",
"relevant_columns = X_train.columns[feature_importance >= 0.01]\n",
"feature_importance = feature_importance[feature_importance >= 0.01]\n",
"plt.barh(relevant_columns,feature_importance,color=['red' if x < 0.2 else 'green' for x in feature_importance])\n",
"#plot the actual feature importance values\n",
"for i, v in enumerate(feature_importance):\n",
" plt.text(v, i, str(round(v,2)), color='blue', fontweight='bold')\n",
" \n",
"plt.xlabel(\"Feature Importance\")\n",
"plt.ylabel(\"Features\")\n",
"plt.title(\"Feature Importance Plot\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 480x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Extract feature importance from decision tree classifier and show only features with importance > 0.2\n",
"feature_importance = classifier_2.feature_importances_\n",
"#Plot the feature importance with colors according to the importance\n",
"plt.figure(figsize=(6,10),dpi = 80)\n",
"#Removing the features with importance < 0.2\n",
"relevant_columns = X_train.columns[(feature_importance <= 0.01) & (feature_importance > 0)]\n",
"feature_importance = feature_importance[(feature_importance <= 0.01) & (feature_importance >0) ]\n",
"plt.barh(relevant_columns,feature_importance,color=['red' if x < 0.2 else 'green' for x in feature_importance])\n",
"#plot the actual feature importance values\n",
"for i, v in enumerate(feature_importance):\n",
" plt.text(v, i, str(round(v,5)), color='blue', fontweight='bold')\n",
" \n",
"plt.xlabel(\"Feature Importance\")\n",
"plt.ylabel(\"Features\")\n",
"plt.title(\"Feature Importance Plot\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Value : [0]\n",
"Ground Truth : 0\n"
]
}
],
"source": [
"#Predicting on sample 15 from the validation set\n",
"print(\"Predicted Value : \" , classifier_2.predict(X_val.iloc[15].values.reshape(1,-1)))\n",
"#Actual value of sample 15 from the validation set\n",
"print(\"Ground Truth : \", y_val.iloc[15])\n",
"\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating The NN"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [],
"source": [
"standard_scaler = StandardScaler() # new_value = (prev_value - mean(prev_value)) / SD(prev_value) --> Z distribution\n",
"X_train_s = standard_scaler.fit_transform(X_train)\n",
"X_valid_s = standard_scaler.fit_transform(X_val)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mission 1 - Create MLP with default value"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MLP-default accuracy on train: 81.762 %\n",
"MLP-default accuracy on test: 76.122 %\n"
]
},
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Loss function')"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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TRaRWderUgbe3t9Ly7sVuALh58yYEQUD58uVhY2OjsFy7dk28qR4AxMXFoX///rC0tISpqSlsbGzQuHFjAG9L3alCjx49kJSUhD179mDdunVo06ZNnkmT1NRUTJw4Ubwfi7W1NWxsbPDy5ctc4ylfvrzCY1NTUxQvXvyD9WLzkvNLrGLFikrrKlWqpPRLTk9PT+HeNHmRyWQA3t4b6FPJ5XLMmzcP5cuXV3heLl68qPC8jB07FqampqhTpw7Kly+PYcOG4d9//1XY18yZM3H58mU4OTmhTp06mDRpUr6TIx+Ts5+qVat+sF9BX+f8uHfvHsqXLy8m1HLklKp7//V7P5GV8znK7wCwdOnSCo9zBpdNmzZV+tzt379f4XMHADo6OgoDLwCoUKECAIjv33v37sHBwUHp85LXOb0fExEREREREZG2ePLkCd68eZPrdZ3KlStDLpeL9+4OCQnBy5cvUaFCBVSrVg2jR4/GxYsXxf5SqRQzZszAnj17YGdnh0aNGmHmzJmIj4//YufzsesWsbGxAD5+DSa/CvL85TdGIk3FexTRV0Eul0MikWDPnj0KMx5ymJqaAnj7TYHmzZvj+fPnGDt2LCpVqgQTExM8fPgQ/fv3h1wuV0k8xYsXh5eXF+bMmYN///0Xf//9d559v/vuO6xcuRIjR46Ep6cnzM3NIZFI0KNHD5XFoyrvzvL5EJlMBgcHB1y+fPmTjzVt2jRMmDAB33zzDaZMmQJLS0vo6Ohg5MiRCs9L5cqVERMTg507d2Lv3r34+++/sXjxYkycOBGTJ08GAHTr1g0NGzbEli1bsH//fsyaNQszZszAP//8g1atWn1yjAVRFF7n3D4bwNv7AuXHu7OiAIhxr1mzBvb29kr99fQK/1fI+zERERERERERkbJGjRohNjYW27Ztw/79+/H7779j3rx5CA8Px8CBAwEAI0eORNu2bbF161bs27cPEyZMQGhoKA4dOoQaNWoU+Jh5zfLJzs7O9RrF5163+BK+hhiJCgNnFNFXoWzZshAEAaVLl851BlLdunUBAJcuXcKNGzcwZ84cjB07Fu3bt4e3tzccHByU9vkpU1bf1atXLxw7dgwymQytW7fOs9/mzZvh5+eHOXPmoEuXLmjevDkaNGiAly9f5tr//SnCr1+/xuPHj/O8qeCHODs7AwBiYmKU1sXExIjrP0WbNm0QGxuLqKioT9p+8+bNaNKkCZYvX44ePXqgRYsW8Pb2zvV5MTExQffu3bFy5UrExcXB19cXU6dORVpamtinePHi+Pbbb7F161bcuXMHVlZWmDp16qeenihnhszHkmL5fZ0L8r5zdnbGzZs3lRJN169fF9cXppybU9ra2ub6ufPy8lLoL5fLlWZy3bhxAwDE96+zszMePXqkNButIOf0uZ9dIqIv5ebNm2jRooX45YGtW7eqdP93796FRCLBqlWrVLrfr5mXl5fS76fPdf/+fRgaGirNaNYUOe+j2bNnf7BfZGQkJBIJIiMjv0xgX7G6detizJgx6g6DiIg0gI2NDYyNjXO9rnP9+nXo6OjAyclJbLO0tIS/vz/+/PNP3L9/H9WrV8ekSZMUtitbtiy+//577N+/H5cvX0ZGRgbmzJnzSfEVK1Ys1+s4n1qmLec6xMeuweT3ukBBnz8ibcZEEX0VOnXqBF1dXUyePFkpgy8IAp49ewbg/7P+7/YRBAHz589X2qeJiQkA5Jmw+ZguXbogODgYixcvVqiv+j5dXV2lmBcsWIDs7Oxc+//222/IzMwUHy9ZsgRZWVmfNDOmVq1asLW1RXh4ONLT08X2PXv24Nq1a/D19S3wPnOMGTMGJiYmGDhwIBISEpTWx8bG5vq858jtedm0aRMePnyo0Jbz2uYwMDCAi4sLBEFAZmYmsrOzlUq72drawsHBQeGcP5WNjQ0aNWqEFStWIC4uTmHdu/Hn93UuyPuudevWiI+Px8aNG8W2rKwsLFiwAKampmJJxcLi4+MDmUyGadOmKbwnczx58kSpbeHCheLPgiBg4cKF0NfXR7NmzQC8Pafs7GyFfgAwb948SCSSfL3PjY2NAXz6Z5eItEtsbCwGDx6MMmXKwNDQEDKZDPXr18f8+fORmppaqMf28/PDpUuXMHXqVKxZswa1atUq1ON9Sf3794dEIoFMJsv1ebx58yYkEkm+EhC5efToESZNmoQLFy6oINrPExISAg8PD9SvX19si4mJwahRo1CvXj0YGhpCIpF8Uplgyj+5XI6ZM2eidOnSMDQ0RPXq1fHnn3+qLZ6rV69i0qRJub7uY8eOxaJFi75oKR8iItJMurq6aNGiBbZt26bwOychIQHr169HgwYNxNsDvH/9xNTUFOXKlROvjbx580bhC7fA28SMmZnZJ18/KVu2LE6ePImMjAyxbefOnUrl3PKrZs2aKF26NMLCwpT+5n/3mkt+r60U5Pkj0nYsPUdfhbJly+KXX37BuHHjcPfuXXTo0AFmZma4c+cOtmzZgkGDBuGHH35ApUqVULZsWfzwww94+PAhZDIZ/v7771zriLq7uwMAhg8fDh8fH+jq6qJHjx75jsnc3FzpWxm5adOmDdasWQNzc3O4uLggKioKBw8ehJWVVa79MzIy0KxZM3Tr1g0xMTFYvHgxGjRogHbt2uU7thz6+vqYMWMG/P390bhxY/Ts2RMJCQmYP38+SpUqhVGjRhV4nznKli2L9evXo3v37qhcuTL69euHqlWrIiMjAydOnMCmTZvQv3//PLdv06YNQkJC4O/vj3r16uHSpUtYt26d0j1uWrRoAXt7e9SvXx92dna4du0aFi5cCF9fX5iZmeHly5dwdHREly5d4OrqClNTUxw8eBBnzpz55G/EvO/XX39FgwYNULNmTQwaNAilS5fG3bt3sWvXLvECVn5fZzc3N+jq6mLGjBlISkqCVCpF06ZNYWtrq3TcQYMGYenSpejfvz+io6NRqlQpbN68Gf/++y/CwsLyvC+WqshkMixZsgR9+/ZFzZo10aNHD9jY2CAuLg67du1C/fr1FRI+hoaG2Lt3L/z8/ODh4YE9e/Zg165d+Omnn2BjYwMAaNu2LZo0aYKff/4Zd+/ehaurK/bv349t27Zh5MiR4reHPsTIyAguLi7YuHEjKlSoAEtLS1StWlVlNYyJSHPs2rULXbt2hVQqVfg9dfz4cYwePRpXrlzBb7/9VijHTk1NRVRUFH7++WcEBgYWyjGcnZ2RmpoKfX39Qtn/x+jp6eHNmzfYsWMHunXrprBu3bp1MDQ0VLoYkV+PHj3C5MmTUapUKbi5ueV7u/3793/S8fLy5MkTrF69GqtXr1Zoj4qKwq+//goXFxdUrly5SCS0ClujRo2Qmpr6wS9IFaaff/4Z06dPR0BAAGrXro1t27ahV69eYqnfL+3q1auYPHkyvLy8lGb+t2/fHjKZDIsXL0ZISMgXj42IiL4+K1aswN69e5XaR4wYgV9++QUHDhxAgwYN8O2330JPTw9Lly5Feno6Zs6cKfZ1cXGBl5cX3N3dYWlpibNnz2Lz5s3iWPTGjRvi9SYXFxfo6elhy5YtSEhI+OTfpQMHDsTmzZvRsmVLdOvWDbGxsVi7dm2+/rbPjY6ODpYsWYK2bdvCzc0N/v7+KF68OK5fv44rV65g3759AAp2TS+/zx+R1hOI1GDlypUCAOHMmTO5rm/cuLFQpUoVpfa///5baNCggWBiYiKYmJgIlSpVEoYNGybExMSIfa5evSp4e3sLpqamgrW1tRAQECD8999/AgBh5cqVYr+srCzhu+++E2xsbASJRCJ87OOQV0zvOnz4sABA2LRpk9j24sULwd/fX7C2thZMTU0FHx8f4fr164Kzs7Pg5+en9JwcOXJEGDRokFCsWDHB1NRU6N27t/Ds2bMPHvfd7XN7Tjdu3CjUqFFDkEqlgqWlpdC7d2/hwYMHCn38/PwEExOTjx7nfTdu3BACAgKEUqVKCQYGBoKZmZlQv359YcGCBUJaWprY7/3zTUtLE77//nuhePHigpGRkVC/fn0hKipKaNy4sdC4cWOx39KlS4VGjRoJVlZWglQqFcqWLSuMHj1aSEpKEgRBENLT04XRo0cLrq6ugpmZmWBiYiK4uroKixcvVjo/Z2dnhTZnZ2fB19dXoe3OnTtK7xVBEITLly8LHTt2FCwsLARDQ0OhYsWKwoQJE8T1+X2dBUEQli1bJpQpU0bQ1dUVAAiHDx8WBEFQOndBEISEhARxvwYGBkK1atWUYsuJedasWcL7AAjBwcFK7e/62Ofx8OHDgo+Pj2Bubi4YGhoKZcuWFfr37y+cPXtW7JPz/omNjRVatGghGBsbC3Z2dkJwcLCQnZ2tsL9Xr14Jo0aNEhwcHAR9fX2hfPnywqxZswS5XK4U+7Bhw3KN6cSJE4K7u7tgYGCQr3MkIu1z+/ZtwdTUVKhUqZLw6NEjpfU3b94UwsLCCu349+7dy/PfZk2Q8+9+ixYthA4dOiitL1++vNC5c+dPfg7OnDmT6+/jvKSkpBT4GPkxd+5cwcjISHj16pVC+7Nnz4Tk5GRBEARh1qxZAgDhzp07hRJDYfvQOKKoePDggaCvr68wLpDL5ULDhg0FR0dHISsr64vHtGnTJoVx3PsCAwMFZ2dnpfENERHRu3L+Hs9ruX//viAIgnDu3DnBx8dHMDU1FYyNjYUmTZoIJ06cUNjXL7/8ItSpU0ewsLAQjIyMhEqVKglTp04VMjIyBEEQhKdPnwrDhg0TKlWqJJiYmAjm5uaCh4eH8Ndff+U7ztyuG8yZM0coUaKEIJVKhfr16wtnz55Vur6R2/UyQcj7Gszx48eF5s2bi9d5qlevLixYsEBc/6FrerldI8jP85fXOebEntfvfCJNwUQRURHxsYv1REXZpyYaiYgKy5AhQwQAwr///puv/pmZmUJISIhQpkwZwcDAQHB2dhbGjRun8KUHQfj/LxkcO3ZMqF27tiCVSoXSpUsLq1evFvsEBwcr/ZGf82WF3L648O4279q/f79Qv359wdzcXDAxMREqVKggjBs3Tlyf1x/WERERQoMGDQRjY2PB3NxcaNeunXD16tVcj3fz5k3Bz89PMDc3F2QymdC/f/98JV1y/t1ftWqVIJVKhRcvXojrTp8+LQAQ/v77b6UExLNnz4Tvv/9eqFq1qmBiYiKYmZkJLVu2FC5cuCD2yflj/P0l5zxzvrxz9uxZoWHDhoKRkZEwYsQIcd27FyX69esnSKVSpfNv0aKFYGFhITx8+PCD59moUSPBy8vrg30+NVH066+/Ci4uLoKRkZFgYWEhuLu7C+vWrRPX57xGMTExQu/evQWZTCZYW1sL48ePF+RyuRAXFye0a9dOMDMzE+zs7ITZs2crHSMhIUH45ptvBFtbW0EqlQrVq1cXVq1apdAnt0SRXC4XAgICBH19feHvv/8WBCH3iyQ5r8WVK1cELy8vwcjISHBwcBBmzJihFMvdu3eFtm3bCsbGxoKNjY0wcuRIYe/evfm68LJo0SIBgHDlyhWF9vXr1wsAhGPHjn1we0H4/y+gbNmyRahSpYpgYGAguLi4CHv27FHqe+7cOaFly5bixammTZsKUVFR4vq8Luq9ex7btm0TAAjnzp37aGxEREREROrGexQRERERkcbZsWMHypQpg3r16uWr/8CBAzFx4kTUrFkT8+bNQ+PGjREaGpprCYtbt26hS5cuaN68OebMmYNixYqhf//+uHLlCoC391acN28eAKBnz55Ys2YNwsLCChT/lStX0KZNG6SnpyMkJARz5sxBu3bt8O+//35wu4MHD8LHxweJiYmYNGkSgoKCcOLECdSvXz/Xe6l069YNr169QmhoKLp164ZVq1Zh8uTJ+Y6zU6dOkEgk+Oeff8S29evXo1KlSqhZs6ZS/9u3b2Pr1q1o06YN5s6di9GjR+PSpUto3LgxHj16BACoXLmyWK5r0KBBWLNmDdasWYNGjRqJ+3n27BlatWoFNzc3hIWFoUmTJrnGN3/+fNjY2MDPz0+8b+DSpUuxf/9+LFiwAA4ODnmeW2ZmJs6cOZPreXyuZcuWYfjw4XBxcUFYWBgmT54MNzc3nDp1Sqlv9+7dIZfLMX36dHh4eOCXX35BWFgYmjdvjhIlSmDGjBkoV64cfvjhBxw9elTcLjU1FV5eXlizZg169+6NWbNmwdzcHP379//gfSSzs7PRv39//PHHH9iyZQs6der0wXN58eIFWrZsCVdXV8yZMweVKlXC2LFjsWfPHrFPSkoKmjZtioMHD2L48OH4+eefceLECYwdOzZfz9f58+dhYmKCypUrK7TXqVNHXJ8fx48fx7fffosePXpg5syZSEtLQ+fOnRXu6XDlyhU0bNgQ//33H8aMGYMJEybgzp078PLyEl+fRo0aYfjw4QCAn376SXyPvhtfTkmcj31miYiIiIiKBHVnqojoLc4ooq8ZZxQRUVGSlJQkABDat2+fr/4XLlwQAAgDBw5UaP/hhx8EAMKhQ4fENmdnZwGAcPToUbEtMTFRkEqlwvfffy+25VXOK78ziubNmycAEJ48eZJn3LnNKHJzcxNsbW0Vytb+999/go6OjtCvXz+l433zzTcK++zYsaNgZWWV5zHfPY+cf/e7dOkiNGvWTBAEQcjOzhbs7e2FyZMn5/ocpKWlKZUkvXPnjiCVSoWQkBCx7UOl5xo3biwAEMLDw3Nd934Z13379gkAhF9++UUsSZhbubz33bp1SwCgUOYkN58yo6h9+/YfLWmc8xoNGjRIbMvKyhIcHR0FiUQiTJ8+XWx/8eKFYGRkpFDuNiwsTAAgrF27VmzLyMgQPD09BVNTU7F03ruvU2ZmptC9e3fByMhI2Ldvn0I8ec0oAiD88ccfYlt6erpgb28vdO7cWWybM2eOAEDYunWr2JaamipUqlQpXzOKfH19hTJlyii1p6SkCACEH3/88YPbC8LbGUUGBgbCrVu3xLac8tTvvsYdOnQQDAwMhNjYWLHt0aNHgpmZmdCoUSOx7WOl5wRBEAwMDIShQ4d+NDYiIiIiInXjjCIiIiIi0ijJyckAADMzs3z13717NwAgKChIof37778HAOzatUuh3cXFBQ0bNhQf29jYoGLFirh9+/Ynx/w+CwsLAMC2bdsgl8vztc3jx49x4cIF9O/fH5aWlmJ79erV0bx5c/E83zVkyBCFxw0bNsSzZ8/E5zA/evXqhcjISMTHx+PQoUOIj49Hr169cu0rlUqho/P2T5Ds7Gw8e/YMpqamqFixIs6dO5fvY0qlUvj7++erb4sWLTB48GCEhISgU6dOMDQ0xNKlSz+6Xc4sk2LFiuU7rvyysLDAgwcPcObMmY/2HThwoPizrq4uatWqBUEQMGDAAIX9vf8e3L17N+zt7dGzZ0+xTV9fH8OHD8fr169x5MgRheNkZGSga9eu2LlzJ3bv3o0WLVrk61xMTU3Rp08f8bGBgQHq1KmjEMvevXtRokQJtGvXTmwzNDREQEBAvo6RmpoKqVSq1G5oaCiuzw9vb2+Fm2tXr14dMplMjDU7Oxv79+9Hhw4dUKZMGbFf8eLF0atXLxw/frxAn41ixYrh6dOn+e5PRERERKQuTBQRFRH9+/eHIAioVauWukMhKrBVq1bh9evX6g6DiAgAIJPJAACvXr3KV/979+5BR0cH5cqVU2i3t7eHhYUF7t27p9BesmRJpX0UK1YML168+MSIlXXv3h3169fHwIEDYWdnhx49euCvv/76YNIoJ86KFSsqratcuTKePn2KlJQUhfb3zyUnKVKQc2ndujXMzMywceNGrFu3DrVr11Z6LnPI5XLMmzcP5cuXh1QqhbW1NWxsbHDx4kUkJSXl+5glSpSAgYFBvvvPnj0blpaWuHDhAn799VfY2trme1tBEPLd931PnjxBfHy8uOT8rhw7dixMTU1Rp04dlC9fHsOGDcuzRNn7r5G5uTkMDQ1hbW2t1P7u63bv3j2UL19eTMzlyCmP9v77OjQ0FFu3bsXmzZvh5eWV73N0dHSERCJRaHv/83Dv3j2ULVtWqV9e75P3GRkZIT09Xak9LS1NXA8ASUlJCs/38+fPFfp/7LP75MkTvHnzJs/PkFwux/379/MVM/D2vfP+ORMRERERFUVMFBERERGRRpHJZHBwcMDly5cLtF1+L+jq6urm2p6fhEJex8i5f04OIyMjHD16FAcPHkTfvn1x8eJFdO/eHc2bN1fq+zk+51xySKVSdOrUCatXr8aWLVvynE0EANOmTUNQUBAaNWqEtWvXYt++fThw4ACqVKmS75lTwP8nBvLr/PnzSExMBABcunQpX9tYWVkBKFjS7H21a9dG8eLFxWX27NkA3iYdYmJisGHDBjRo0AB///03GjRogODgYKV95PYaqeJ1e5+Pjw9MTEzEe/fkV2HE8r7ixYsjPj5eaZ+PHz8GAPFeUyNGjFB4vt+/v9KXiPVdL1++VEroEREREREVRUwUEREREZHGadOmDWJjYxEVFfXRvs7OzpDL5bh586ZCe0JCAl6+fAlnZ2eVxVWsWDG8fPlSqf392R0AoKOjg2bNmmHu3Lm4evUqpk6dikOHDuHw4cO57jsnzpiYGKV1169fh7W1NUxMTD7vBPLQq1cvnD9/Hq9evUKPHj3y7Ld582Y0adIEy5cvR48ePdCiRQt4e3srPSeqnIWRkpICf39/uLi4YNCgQZg5c2a+Sr6VLFkSRkZGuHPnzicfe926dThw4IC49OvXT1xnYmKC7t27Y+XKlYiLi4Ovry+mTp1aoCTNhzg7O+PmzZtKCbjr16+L699Vt25dbN26FSdOnEDXrl2RlZWlkjhyjhUbG6uUkLl161a+tndzc8ObN29w7do1hfZTp06J6wFgzJgxCs/3nDlzChSnjY0NjI2N8/wM6ejowMnJCcDH36MPHz5ERkaGOIOLiIiIiKgo01N3AEREREREqjZmzBisW7cOAwcOxKFDh2BnZ6ewPjY2Fjt37sSIESPQunVr/PTTTwgLC1O4d83cuXMBAL6+viqLq2zZskhKSsLFixdRvXp1AG9nRWzZskWh3/PnzxXuMwT8/8Xw3EpwAW9nXbi5uWH16tUYN26ceJ+jy5cvY//+/Qr3kVG1Jk2aYMqUKbCysoK9vX2e/XR1dZWSBZs2bcLDhw8VypDlJLRyS6oV1NixYxEXF4eTJ0+iYsWKiIiIgJ+fH86fP5/rfW9y6Ovro1atWjh79uwnH7t+/fq5tj979kycsQS8va+Pi4sL9uzZg8zMTPHeO5+jdevW2L9/PzZu3CjepygrKwsLFiyAqakpGjdurLSNt7c3NmzYgK5du6Jv375Yt26dUum6T+Hj44MDBw5g+/btaN++PYC3ZeOWLVuWr+3bt2+PUaNGYfHixVi4cCGAt7OAwsPDUaJECdSrVw/A2/uHubi4fHKcurq6aNGiBbZt24a7d++iVKlSAN4mjdevX48GDRqIpS0/9h6Njo4GADE2bZKWloaMjAyV7MvAwEAlnwfSfHK5HI8ePYKZmRlLPhIREf2PIAh49eoVHBwcPjquZ6JIQxjVCFR3CPQllKik7giosCXzhsfaIPXopELbtyp/H6SeX1ig/osWLcKsWbMQHx8PV1dXLFiwAHXq1Mmzf1hYGJYsWYK4uDhYW1ujS5cuCA0NFS8IhYaG4p9//sH169dhZGSEevXqYcaMGQr3jvDy8lK6IfvgwYMRHh5eoNhJ85QtWxbr169H9+7dUblyZfTr1w9Vq1ZFRkYGTpw4gU2bNqF///4AAFdXV/j5+eG3337Dy5cv0bhxY5w+fRqrV69Ghw4d0KRJE5XF1aNHD4wdOxYdO3bE8OHD8ebNGyxZsgQVKlTAuXPnxH4hISE4evQofH194ezsjMTERCxevBiOjo5o0KBBnvufNWsWWrVqBU9PTwwYMACpqalYsGABzM3NMWnSJJWdx/t0dHQwfvz4j/Zr06YNQkJC4O/vj3r16uHSpUtYt24dypQpo9CvbNmysLCwQHh4OMzMzGBiYgIPDw+ULl26QHEdOnQIixcvRnBwMGrWrAkAWLlyJby8vDBhwgTMnDnzg9u3b98eP//8M5KTk8UEAfD2XjgLFiwAAPHeQgsXLoSFhQUsLCwQGPjhf4tbtGgBe3t71K9fH3Z2drh27RoWLlwIX19fmJmZFegc8zJo0CAsXboU/fv3R3R0NEqVKoXNmzfj33//RVhYWJ7H6dChA1auXIl+/fpBJpMpJE8/1eDBg7Fw4UL07NlTLA+3bt068d/7dy/qRkZGokmTJggODhbfs46Ojhg5ciRmzZqFzMxM1K5dG1u3bsWxY8ewbt26PEvKfYpffvkFBw4cQIMGDfDtt99CT08PS5cuRXp6usL7xc3NDbq6upgxYwaSkpIglUrRtGlT8f5XBw4cQMmSJVGjRg2VxfY1SEtLg5GZFZD1RiX7s7e3x507d5gsoo969OiROOOPiIiIFN2/fx+Ojo4f7MNEERERkSaRqKeq7MaNGxEUFITw8HB4eHggLCwMPj4+iImJyfWm8evXr8ePP/6IFStWoF69erhx4wb69+8PiUQizuI4cuQIhg0bhtq1ayMrKws//fQTWrRogatXryqUzwoICEBISIj42NjYuPBPmL4K7dq1w8WLFzFr1ixs27YNS5YsgVQqRfXq1TFnzhwEBASIfX///XeUKVMGq1atwpYtW2Bvb49x48bles+Yz2FlZYUtW7YgKCgIY8aMQenSpREaGoqbN28qJIratWuHu3fvYsWKFXj69Cmsra3RuHFjTJ48Gebm5nnu39vbG3v37kVwcDAmTpwIfX19NG7cGDNmzChwkqUw/PTTT0hJScH69euxceNG1KxZE7t27cKPP/6o0E9fX1+cGTVkyBBkZWVh5cqVBTqHV69e4ZtvvkGNGjXw888/i+0NGzbEiBEjMGfOHHTq1Al169bNcx99+/bFjz/+iO3btyvMyHrx4gUmTJig0DenzJmzs/NHE0WDBw/GunXrMHfuXLx+/RqOjo4YPnx4vpJt+WVkZITIyEj8+OOPWL16NZKTk1GxYkWsXLlSTJLmpU+fPnj16hW+/fZbyGQyzJo167NiMTU1xaFDh/Ddd99h/vz5MDU1Rb9+/VCvXj107txZIQnw+vVrAG9nyL1r+vTpKFasGJYuXYpVq1ahfPnyWLt27QfvifUpqlSpgmPHjmHcuHEIDQ2FXC6Hh4cH1q5dCw8PD7Gfvb09wsPDERoaigEDBiA7OxuHDx+Gra0t5HI5/v77bwwYMEDrZjZkZGQAWW8greIP6Bp83s6yMxB/ZSUyMjKYKKKPykl+379/XyGxT0REpM2Sk5Ph5OSUry+jSYTCunMnfVGcUaQlOKNI83FGkVYo1BlFNYerbF+p537Nd18PDw/Url1bLAkkl8vh5OSE7777TukCMAAEBgbi2rVriIiIENu+//57nDp1CsePH8/1GE+ePIGtrS2OHDmCRo0aAXg7o8jNzQ1hYWEFODMiovwbMGAAbty4gWPHjqk7FI0TFhaGUaNG4cGDByhRogSAt2Uj//zzT9y6deuDpQGLsq1bt6JXr16IjY1VSnhpuuTkZJibm0NafTAkn5koErIzkH5xKZKSknjhnz4q573H9wsREdH/K8jvR/V87ZiIiIgKh0SisiU9PR3JyckKS273RsnIyEB0dDS8vb3FNh0dHXh7eyMqKirXMOvVq4fo6GicPn0aAHD79m3s3r0brVu3zvPUkpKSAEDpvi3r1q2DtbU1qlatinHjxuHNG9WUuyEiAoDg4GCcOXNGLDFHnyY1NVXhcVpaGpYuXYry5cuLSSIAOHz4MCZMmPDVJokAYMaMGQgMDNS6JJECCVQwFlH3SdCHLFq0CKVKlYKhoSE8PDzEMV1uVq1aBYlEorC8P0tMEARMnDgRxYsXh5GREby9vXHz5s3CPg0iIiL6H5aeIyIi0iQqLD0XGhqKyZMnK7S9e8+IHE+fPkV2djbs7OwU2u3s7HD9+vVc992rVy88ffoUDRo0gCAIyMrKwpAhQ/DTTz/l2l8ul2PkyJGoX78+qlatqrAfZ2dnODg44OLFixg7dixiYmLwzz//fMIZExEpK1myJNLS0tQdxlevU6dOKFmyJNzc3JCUlIS1a9fi+vXrWLdunUK/M2fOqClC1cnrSxJaRaLz+WMSNZXTpY8raMlhAJDJZIiJiREfv1+WcebMmfj111+xevVqlC5dGhMmTICPjw+uXr3K0oNERERfABNFRERElKtx48YhKChIoU1V3/COjIzEtGnTsHjxYnh4eODWrVsYMWIEpkyZonTfDwAYNmwYLl++rFSWbtCgQeLP1apVQ/HixdGsWTPExsaibNmyKomViIg+n4+PD37//XesW7cO2dnZcHFxwYYNG9C9e3d1h0ZEBTR37lwEBATA398fABAeHo5du3ZhxYoVuZYcBt4mhuzt7XNdJwgCwsLCMH78eLRv3x4A8Mcff8DOzg5bt25Fjx49lLZJT09XmOmenJz8uadFRESk1fgVHSIiIk2iwtJzUqkUMplMYcktUWRtbQ1dXV0kJCQotCckJOR5QWDChAno27cvBg4ciGrVqqFjx46YNm2aeOPwdwUGBmLnzp04fPgwHB0dP3j6OTcav3XrVkGeNSIiKmQjR47E5cuX8fr1a6SmpiI6OppJIk2mqvEIFTmfUnIYAF6/fg1nZ2c4OTmhffv2uHLlirjuzp07iI+PV9inubk5PDw88txnaGgozM3NxcXJyUkFZ0dERKS9mCgiIiLSJDmlXlSx5JOBgQHc3d0REREhtsnlckRERMDT0zPXbd68eQMdHcVj6OrqAnj7rdKc/wcGBmLLli04dOgQSpcu/dFYLly4AADafV8IIiIidfvCYxH6cj5Ucjg+Pj7XbSpWrIgVK1Zg27ZtWLt2LeRyOerVq4cHDx4AgLhdQfY5btw4JCUlicv9+/c/99SIiIi0GkvPERER0WcLCgqCn58fatWqhTp16iAsLAwpKSliSZJ+/fqhRIkSCA0NBQC0bdsWc+fORY0aNcTScxMmTEDbtm3FhNGwYcOwfv16bNu2DWZmZuKFAnNzcxgZGSE2Nhbr169H69atYWVlhYsXL2LUqFFo1KgRqlevrp4ngoiIiIgUeHp6Knx5qF69eqhcuTKWLl2KKVOmfNI+pVKpykoiExERERNFREREmkVNZVq6d++OJ0+eYOLEiYiPj4ebmxv27t0rfjM0Li5OYQbR+PHjIZFIMH78eDx8+BA2NjZo27Ytpk6dKvZZsmQJAMDLy0vhWCtXrkT//v1hYGCAgwcPikkpJycndO7cGePHjy/8EyYFcrkcjx49gpmZmdLNqYmIqGgRBAGvXr2Cg4OD0uxelVFF6Tj+PimSPqXk8Pv09fVRo0YNsVRwznYJCQkKs8ITEhLg5uammsCJiIjog5goIiIi0iRqLNMSGBiIwMDAXNdFRkYqPNbT00NwcDCCg4Pz3F9OCbq8ODk54ciRIwWOk1Tv0aNHvDcAEdFX5v79+x+999+nU0XpOJaeK4reLTncoUMHAP9fcjivceD7srOzcenSJbRu3RoAULp0adjb2yMiIkJMDCUnJ+PUqVMYOnRoYZwGERERvYeJIiIiIiL6LGZmZgAAAxc/SHQN1BwNkercODBD3SEQqdyrV8moWr6U+G83UUEVtORwSEgI6tati3LlyuHly5eYNWsW7t27h4EDBwIAJBIJRo4ciV9++QXly5dH6dKlMWHCBDg4OIjJKCIiIipcTBQRERFpEpZpITXIKTcn0TVgoog0ikwmU3cIRIWmUEuFsvScRitoyeEXL14gICAA8fHxKFasGNzd3XHixAm4uLiIfcaMGYOUlBQMGjQIL1++RIMGDbB3714YGhp+8fMjIiLSRkwUERERaRI1lp4jIiIiAvB2PPK5YxKOaYq0gpQcnjdvHubNm/fB/UkkEoSEhCAkJERVIRIREVEBcORFRERERERERERERESkpTijiIiISJOwTAsRERGpG0vPEREREX1VmCgiIiLSJCzTQkREROrG0nP0lYuPj0fLli2hq6uL6OhodYdDRERU6JgoIiIiIiIiIiIi+h+5XI7//vsPenq8bEZERNqBv/GIiIg0Ccu0EBERkbqx9Bx95XISRFlZWRAEARK+H4mISMMxUURERKRJWKaFiIiI1I2l5+grp6+vL/6cnZ3NmUVERKTxOPIiIiIiIiIiIiL6n3cTQ1lZWWqMhIiI6MvgVyKIiIg0Cb99S0REROomkahgRhFLfZH6vJsoyszMhKGhoRqjISIiKnxMFBEREWkSHV5UISIiIjXTkXz+mIRjGlKjd0vPcUYRERFpA37tmIiIiIiIiIiI6H90dXXFn5koIiIibcAZRURERJqEpeeIiIhI3SQ6Kig9xzENqY9EIoGuri6ys7ORmZmp7nCIiIgKHRNFREREmoT1/ImIiEjdJJLPH5NwTENqpq+vj+zsbM4oIiIircCv6BAREREREREREb1DT+/td6uZKCIiIm3AGUVERESahGVaiIiISN1Yeo40QE6iiKXniIhIG3DkRUREpElySr2oYiEiIiL6FGoei0yfPh0SiQQjR44U29LS0jBs2DBYWVnB1NQUnTt3RkJCgsJ2cXFx8PX1hbGxMWxtbTF69Gil2SSRkZGoWbMmpFIpypUrh1WrVn1ynFS06evrA+CMIiIi0g5MFBERERERERGRRjhz5gyWLl2K6tWrK7SPGjUKO3bswKZNm3DkyBE8evQInTp1EtdnZ2fD19cXGRkZOHHiBFavXo1Vq1Zh4sSJYp87d+7A19cXTZo0wYULFzBy5EgMHDgQ+/bt+2LnR18OS88REZE2YaKIiIhIk+SUelHFQkRERPQp1DQWef36NXr37o1ly5ahWLFiYntSUhKWL1+OuXPnomnTpnB3d8fKlStx4sQJnDx5EgCwf/9+XL16FWvXroWbmxtatWqFKVOmYNGiRcjIyAAAhIeHo3Tp0pgzZw4qV66MwMBAdOnSBfPmzVPN80ZFCkvPERGRNuFVICIiIk3C0nNERESkbiociyQnJyss6enpeR522LBh8PX1hbe3t0J7dHQ0MjMzFdorVaqEkiVLIioqCgAQFRWFatWqwc7OTuzj4+OD5ORkXLlyRezz/r59fHzEfZBmYek5IiLSJkwUEREREREREVGR5OTkBHNzc3EJDQ3Ntd+GDRtw7ty5XNfHx8fDwMAAFhYWCu12dnaIj48X+7ybJMpZn7PuQ32Sk5ORmpr6SedHRRdLzxERkTbRU3cA9NaiRYswa9YsxMfHw9XVFQsWLECdOnXUHdYXM7hbI4zyawY7Kxku3XiIoBmbcPbKvTz7B/byQkDXhnCyL4ZnL1Ow5eB5TFiwHekZbwdwAV0bIKBLQzg7WAIArt2Ox7Tf9mD/v1e/yPlQ7gb7VsOoTjVgV8wYl+48RdDSozh7IzHP/oHtXBHQuiqcbMzwLDkVW/6NxYTVUUjPzAYA1K/igFGda6BmWVsUtzJBt192YcfJO1/qdCgPgzvWxqge9WFnaYpLsfEImr8HZ689zLN/YNe6CGhfC0525niW9AZbIq9iwm8R4uf5h94N0KFRZVRwtkZqehZOXb6Pn8MP4Ob9Z1/qlL4uLBlHRERE6qaKMrb/2/7+/fuQyWRis1QqVep6//59jBgxAgcOHIChoeHnHZfof1h6joiItAmvJhUBGzduRFBQEIKDg3Hu3Dm4urrCx8cHiYl5X0DXJF1a1MSM7zti6tI98Ow1AxdvPMT2xcNgU8w01/7dW9bClOHtMW3pHrh1+gVDJq9DFx93hHzXTuzzMOElJizYhnq9Z6J+71mIPH0Dm+YNQuUy9l/qtOg9XRqWw4yBDTD1zzPwHLERF+88w/aQdrAxN8q1f/fGFTClvyem/XkGbkPXYcivh9ClYXmE+HmKfUwM9XDp9lOMDD/ypU6DPqJL0yqYMcwHU1dFwnPgUly8lYDts/vAxsIk1/7dvathyiBvTFt1BG59F2HIjO3o0rQqQgKaiX0aupVC+JYzaDzkd7QJ+gN6ejrYOacvjA31v9RpfV1Yeo6IiIjUTYVjEZlMprDkliiKjo5GYmIiatasCT09Pejp6eHIkSP49ddfoaenBzs7O2RkZODly5cK2yUkJMDe/u3fiPb29khISFBan7PuQ31kMhmMjHL/u4a+Xiw9R0RE2oSJoiJg7ty5CAgIgL+/P1xcXBAeHg5jY2OsWLFC3aF9EcP7NMXKf05gzfaTuH47Ht9N3YDUtAz4dfDMtX9d19KIunAbG/eeRdzj54g4eR1/7T2LWlWcxT67j17GvuNXERv3BLfiEjFp0Q68fpOOOtVLf6nTovcM7+CGlfuuYM3Ba7h+/wW+W3QYqelZ8GteOdf+dSvbI+raY2w8cgNxia8Qcf4+/jp6A7XK24p99kfHYfLaU9gedftLnQZ9xPBunli58xzW7LmA6/ee4Ls5O5Galgk/3xq59q9b1QlRl+Ow8eAlxMW/RMSZWPwVcQm1KpcQ+7QfvRZr917AtbtPcCk2AYOmbUVJewvUqOjwpU6LiIiIiIqwZs2a4dKlS7hw4YK41KpVC7179xZ/1tfXR0REhLhNTEwM4uLi4On59u9OT09PXLp0SeELmwcOHIBMJoOLi4vY59195PTJ2QdpFpaeIyIibcJEkZplZGQgOjpa4YaYOjo68Pb21oobYurr6aJGZSccOhUjtgmCgEOnYvJM6pz87w5quDiJiaFSJazgU78K9h6/kmt/HR0Juvq4w8TIAKcusiyZOujr6aBGOVscunBfbBME4NCFB6hTKfdZXievxaNGWVvUqvA2MVTKTgafWs7YezbvkoSkXvp6uqhRwQGHzv5/4k4QBByKvo06VRxz3ebk5fuoUcFBTAyVKl4MPnXLY+/Jm3keR2b6tpzIi2TWgc9VTqkXVSxEREREn0QV45D8j0XMzMxQtWpVhcXExARWVlaoWrUqzM3NMWDAAAQFBeHw4cOIjo6Gv78/PD09UbduXQBAixYt4OLigr59++K///7Dvn37MH78eAwbNkycxTRkyBDcvn0bY8aMwfXr17F48WL89ddfGDVqVGE8iaRmLD1HRETahPcoUrOnT58iOzs71xtiXr9+Pddt0tPTkZ6ertAmyLMh0dEttDgLi3UxU+jp6SLx+SuF9sRnyahYyi7XbTbuPQurYiaIWDkKEkigr6+L3zYdw6wV+xX6VSnngMjV38PQQA+vU9PR/ftluH47vtDOhfJmLTOCnq4OEl8qXthPfPkGFR0tct1m45EbsJIZImJGZ0gkb5MQv+2+hFmbor9AxPQprM2Noaeng8QXrxXaE5+noGJJ61y32XjwEqzMjRGx8Jv/f523nsGstcdy7S+RSDDru5Y4cTEOV+9oR3nOAmPJOCIiIlI3VZSxVfGYZt68edDR0UHnzp2Rnp4OHx8fLF68WFyvq6uLnTt3YujQofD09ISJiQn8/PwQEhIi9ildujR27dqFUaNGYf78+XB0dMTvv/8OHx8flcZKRQNLzxERkTZhougrFBoaismTJyu06drVhn7xOmqK6Mtq6F4eo7/xwYjQjThz6R7KOllj9ugueBzQEtOX7RX73bibAI8eoTA3NUJH7xpYFtIXLQbOZ7LoK9GwWgmM7uaOEUuO4ExMAso6mGN2QEM87pGC6RvOqjs8UpGGbqUwuk9DjJi7C2euPUDZEpaYPbwVHvd7hel/HFXqHzaqNaqUtkWzQO0ozUlEREREnyYyMlLhsaGhIRYtWoRFixbluY2zszN27979wf16eXnh/PnzqgiRijiWniMiIm3CRJGaWVtbQ1dXN9cbYubcMPN948aNQ1BQkEKbbcOxhRZjYXr64jWysrJha2mm0G5rJUP8s+Rctwn+1hd/7jqNVVvelua7cusRjI2kWDS+J2b8vg+CIAAAMrOycfv+UwDA+Wv34V6lJIb19MJ3UzcU4hlRbp4mpyIrWw5bC8UbvNpaGCP+xZtctwnu44E/D8Vg1f6rAIAr957BWKqHRYFNMGPjWfzvZaYi5GnSG2RlyWFbzFSh3dbSBPHPX+e6TfCAJvhz/39YtescAODK7UQYGxpg0ei2mLHmmPh5BoB5I1ujdb0K8P5uJR4+yf3fBwJLxhEREZH6SSSfPybhLGlSM5aeIyIibcKrSWpmYGAAd3d3hRtiyuVyRERE5HlDTKlUCplMprB8jWXngLfJnPPX7qOJR0WxTSKRoEmdCjidx/2EjAwNIJcrZgnkcvn/ts37WDoSCaQGzI2qQ2aWHOdvJaKJq5PYJpEATVwdcfp67jO8jKR6kAvvv87C/7blH41FUWZWNs7feIQm7v9/fzGJRIImNcvg9JUHuW5jZKify+us/HmeN7I12jWshJYjV+Pe45cqj12j8B5FREREpG4ci5AG4IwiIiLSJrxqXgQEBQXBz88PtWrVQp06dRAWFoaUlBT4+/urO7Qv4te1h7AspC+ir8bh7OW7COzVBMZGUvyx7SQA4PcpffEoMQkTF2wHAOw+ehnD+zTBfzEPcPrSXZR1ssHEoW2w++glMZEQ8l077Pv3Cu4/fgEzE0N0b1ULjWqVR9tvF+cZBxWuX7dewLJR3oi+mYizNxIQ2N4VxoZ6+OPgNQDA70HeePQsBRNXv50ptvv0XQzv4Ib/bj/F6Zh4lC1ugYl9PLD79F3xdTYx1EfZ4ubiMUrZyVC9tDVevE7D/Se5z2ChwvXrX1FYNq4jomMe4ey1hwjsWhfGRvr4Y/fb8hy//9QRj54mY+Jvb5Pju0/cwPBunvjvRjxO/6/03MQBTbH7RIz4OoeN8kV372ro+tOfeP0mA3aWb2csJb1OQ1oG/2gjIiIiIiLV4z2KiIhImzBRVAR0794dT548wcSJExEfHw83Nzfs3bsXdnZ26g7ti9i8/xysi5li4lBf2FmZ4WLMQ7QftgiJz18BAJzsLRVmEE3/fS8EQUDwt23gYGuOpy9eY9fRy5i0cIfYx8bSFMun9IO9tQxJr9Nw+eZDtP12MQ6duv7Fz4/e2nzsFqzNjTCxTx3YFTPBxdtP0H7iDiS+TAUAONmYKb7OG868fZ37eMDByhRPk1Kx6/QdTFpzUuxTs7wt9od2FB/PDGgIAFhz8BoGhf3/LD36cjYfugJrCxNM/KYJ7CxNcfFWPNr/sBaJL1IAAE525goziKb/cfTt6zywKRxszPD05RvsOhGDScsOiX0Gd6wNADiwQDF5HjBtK9buvVD4J/W14Yw7IiIiUjeJ5PPHJBzTkJqx9BwREWkTiSDwTh+awKhGoLpDoC+hRCV1R0CFLfmpuiOgLyD16KRC27dR+6Uq21fqtsEq2xdptuTkZJibm0NaLQASXQN1h0OkMo9PzFd3CEQql5ycDGd7SyQlJUEmk6l83+bm5pC2mgeJvtHHN/gAITMV6XtGFUqcpHly3nuqfL9069YNmzZtwoIFCxAYyGsuRET09SnI70cW/SUiIiIiIiIiInoHS88REZE2Yek5IiIiTcIyLURERKRuLD1HGoCl54iISJswUURERKRJJJwsTERERGom0fn8MQnHNKRmOYkizigiIiJtwJEXERERERERERHRO1h6joiItAlnFBEREWkSlmkhIiIidWPpOdIALD1HRETahIkiIiIiDSLhRRUiIiJSM4lE8vljEo5pSM1Yeo6IiLQJS88RERERERERERG9g6XniIhIm3BGERERkQbhjCIiIiJSN84oIk3A0nNERKRNOKOIiIhIk0hUuBTQokWLUKpUKRgaGsLDwwOnT5/+YP+wsDBUrFgRRkZGcHJywqhRo5CWllagfaalpWHYsGGwsrKCqakpOnfujISEhIIHT0RERKqjprEIkSqx9BwREWkTJoqIiIjos23cuBFBQUEIDg7GuXPn4OrqCh8fHyQmJubaf/369fjxxx8RHByMa9euYfny5di4cSN++umnAu1z1KhR2LFjBzZt2oQjR47g0aNH6NSpU6GfLxERERFpNpaeIyIibcJEERERkQbJKfWiiqUg5s6di4CAAPj7+8PFxQXh4eEwNjbGihUrcu1/4sQJ1K9fH7169UKpUqXQokUL9OzZU2HG0Mf2mZSUhOXLl2Pu3Llo2rQp3N3dsXLlSpw4cQInT5789CeRiIiIPos6xiJEqsbSc0REpE2YKCIiItIgqkwUpaenIzk5WWFJT09XOmZGRgaio6Ph7e0ttuno6MDb2xtRUVG5xlmvXj1ER0eLiaHbt29j9+7daN26db73GR0djczMTIU+lSpVQsmSJfM8LhERERU+JopIE7D0HBERaRMmioiIiChXoaGhMDc3V1hCQ0OV+j19+hTZ2dmws7NTaLezs0N8fHyu++7VqxdCQkLQoEED6Ovro2zZsvDy8hJLz+Vnn/Hx8TAwMICFhUW+j0tERERElB8sPUdERNqEiSIiIiINosoZRePGjUNSUpLCMm7cOJXEGRkZiWnTpmHx4sU4d+4c/vnnH+zatQtTpkxRyf6JiIhIfTijiDQBS88REZE20VN3AERERKQ6qryoIpVKIZVKP9rP2toaurq6SEhIUGhPSEiAvb19rttMmDABffv2xcCBAwEA1apVQ0pKCgYNGoSff/45X/u0t7dHRkYGXr58qTCr6EPHJSIiosKnkkQPE0WkZiw9R0RE2oQzioiIiOizGBgYwN3dHREREWKbXC5HREQEPD09c93mzZs30NFRHIbo6uoCAARByNc+3d3doa+vr9AnJiYGcXFxeR6XiIiIiCg/WHqOiIi0CWcUERERaRI1ffk2KCgIfn5+qFWrFurUqYOwsDCkpKTA398fANCvXz+UKFFCvMdR27ZtMXfuXNSoUQMeHh64desWJkyYgLZt24oJo4/t09zcHAMGDEBQUBAsLS0hk8nw3XffwdPTE3Xr1lXPE0FERERvxyOfOybhhCJSM5aeIyIibcJEERERkQZRVz3/7t2748mTJ5g4cSLi4+Ph5uaGvXv3ws7ODgAQFxenMINo/PjxkEgkGD9+PB4+fAgbGxu0bdsWU6dOzfc+AWDevHnQ0dFB586dkZ6eDh8fHyxevPjLnTgREREpYek50gQsPUdERNqEiSIiIiJSicDAQAQGBua6LjIyUuGxnp4egoODERwc/Mn7BABDQ0MsWrQIixYtKnC8RERERER5Yek5IiLSJkwUERERaRB1zSgiIiIiyiGRqGBMwiENqRlLzxERkTZhooiIiEiDMFFERERE6iaBCkrPMVNEasbSc0REpE10Pt6FiIiIiIiIiIjorUWLFqFUqVIwNDSEh4cHTp8+na/tNmzYAIlEgg4dOii0v379GoGBgXB0dISRkRFcXFwQHh5eCJHnH0vPERGRNmGiiIiISIPk3DxaFQsRERHRp+BYRLNt3LgRQUFBCA4Oxrlz5+Dq6gofHx8kJiZ+cLu7d+/ihx9+QMOGDZXWBQUFYe/evVi7di2uXbuGkSNHIjAwENu3by+s0/golp4jIiJtwkQRERGRJpGocCEiIiL6FByLaLS5c+ciICAA/v7+4swfY2NjrFixIs9tsrOz0bt3b0yePBllypRRWn/ixAn4+fnBy8sLpUqVwqBBg+Dq6prvmUqFgaXniIhImzBRREREREREREREH5WRkYHo6Gh4e3uLbTo6OvD29kZUVFSe24WEhMDW1hYDBgzIdX29evWwfft2PHz4EIIg4PDhw7hx4wZatGiRa//09HQkJycrLKrG0nNERKRN9NQdABEREakOy7QQERGR2qmgdJzAMU2R9PTpU2RnZ8POzk6h3c7ODtevX891m+PHj2P58uW4cOFCnvtdsGABBg0aBEdHR+jp6UFHRwfLli1Do0aNcu0fGhqKyZMnf/J55AdLzxERkTbhjCIiIiINwnsUERERkbpxLEI5Xr16hb59+2LZsmWwtrbOs9+CBQtw8uRJbN++HdHR0ZgzZw6GDRuGgwcP5tp/3LhxSEpKEpf79++rPHaWniMiIm3CGUVERERERERERPRR1tbW0NXVRUJCgkJ7QkIC7O3tlfrHxsbi7t27aNu2rdgml8sBvE3ExMTEwMHBAT/99BO2bNkCX19fAED16tVx4cIFzJ49W6HMXQ6pVAqpVKrKU1PC0nNERKRNOKOIiIhIg3BGEREREakbxyKay8DAAO7u7oiIiBDb5HI5IiIi4OnpqdS/UqVKuHTpEi5cuCAu7dq1Q5MmTXDhwgU4OTkhMzMTmZmZ0NFRvESlq6srJpXUgaXniIhIm3BGERERkSbhNRUiIiJSNwk+f0zCMU2RFRQUBD8/P9SqVQt16tRBWFgYUlJS4O/vDwDo168fSpQogdDQUBgaGqJq1aoK21tYWACA2G5gYIDGjRtj9OjRMDIygrOzM44cOYI//vgDc+fO/aLn9i6WniMiIm3CRBEREREREREREeVL9+7d8eTJE0ycOBHx8fFwc3PD3r17YWdnBwCIi4tTmh30MRs2bMC4cePQu3dvPH/+HM7Ozpg6dSqGDBlSGKeQLyw9R0RE2oSJIiIiIg3CMi1ERESkbqooHccxTdEWGBiIwMDAXNdFRkZ+cNtVq1Yptdnb22PlypUqiEx1WHqOiIi0CRNFREREGoQXVYiIiEjdmCgiTcDSc0REpE2YKNIQL84sVHcI9AXce/pG3SFQIbMw1ld3CERERERERFrv3dJzgiAweUlERBqNiSIiIiINwj9giYiISN04o4g0Qc6MIgDIzs5WeExERKRp+FuOiIhIg/CiChEREakbE0WkCd5NDGVlZTFRREREGk1H3QEQEREREREREREVJTml5wDep4iIiDQfvw5BRESkSfjlWyIiIlI3CT5/TMIxDanZuzOIMjMz1RgJERFR4WOiiIiISIOwTAsRERGpG0vPkSZ4v/QcERGRJmPpOSIiIiIiIiIiondIJBLo6uoCYKKIiIg0H2cUERERaRB++5aIiIjUjTOKSFPo6ekhOzubiSIiItJ4nFFERESkQXIuzKhiISIiIvoU6hiLLFmyBNWrV4dMJoNMJoOnpyf27Nkjrvfy8lLa/5AhQxT2ERcXB19fXxgbG8PW1hajR49WShBERkaiZs2akEqlKFeuHFatWvXJzxMVfTnl53iPIiIi0nScUUREREREREREXzVHR0dMnz4d5cuXhyAIWL16Ndq3b4/z58+jSpUqAICAgACEhISI2xgbG4s/Z2dnw9fXF/b29jhx4gQeP36Mfv36QV9fH9OmTQMA3LlzB76+vhgyZAjWrVuHiIgIDBw4EMWLF4ePj8+XPWH6IvT19QGw9BwREWk+JoqIiIg0CScCERERkbpJ8PljkgJu37ZtW4XHU6dOxZIlS3Dy5EkxUWRsbAx7e/tct9+/fz+uXr2KgwcPws7ODm5ubpgyZQrGjh2LSZMmwcDAAOHh4ShdujTmzJkDAKhcuTKOHz+OefPmMVGkoXJmFDFRREREmo6l54iIiDQIS88RERGRuqlyLJKcnKywpKenf/T42dnZ2LBhA1JSUuDp6Sm2r1u3DtbW1qhatSrGjRuHN2/eiOuioqJQrVo12NnZiW0+Pj5ITk7GlStXxD7e3t4Kx/Lx8UFUVNRnPV9UdLH0HBERaQvOKCIiIiIiIiKiIsnJyUnhcXBwMCZNmpRr30uXLsHT0xNpaWkwNTXFli1b4OLiAgDo1asXnJ2d4eDggIsXL2Ls2LGIiYnBP//8AwCIj49XSBIBEB/Hx8d/sE9ycjJSU1NhZGT02edLRQtLzxERkbZgooiIiEiDcCYQERERqZsqZifnbH///n3IZDKxXSqV5rlNxYoVceHCBSQlJWHz5s3w8/PDkSNH4OLigkGDBon9qlWrhuLFi6NZs2aIjY1F2bJlPytW0lwsPUdERNqCiSIiIiINwkQRERERqZsEKkgU/e8mRTKZTCFR9CEGBgYoV64cAMDd3R1nzpzB/PnzsXTpUqW+Hh4eAIBbt26hbNmysLe3x+nTpxX6JCQkAIB4XyN7e3ux7d0+MpmMs4k0FEvPERGRtuA9ioiIiIiIiIhI48jl8jzvaXThwgUAQPHixQEAnp6euHTpEhITE8U+Bw4cgEwmE8vXeXp6IiIiQmE/Bw4cULgPEmkWlp4jIiJtwRlFREREGoQzioiIiEjdVFl6Lr/GjRuHVq1aoWTJknj16hXWr1+PyMhI7Nu3D7GxsVi/fj1at24NKysrXLx4EaNGjUKjRo1QvXp1AECLFi3g4uKCvn37YubMmYiPj8f48eMxbNgwsdzdkCFDsHDhQowZMwbffPMNDh06hL/++gu7du36rHOlooul54iISFswUURERKRJmCciIiIidZPg88ckBdw+MTER/fr1w+PHj2Fubo7q1atj3759aN68Oe7fv4+DBw8iLCwMKSkpcHJyQufOnTF+/Hhxe11dXezcuRNDhw6Fp6cnTExM4Ofnh5CQELFP6dKlsWvXLowaNQrz58+Ho6Mjfv/9d/j4+HzmyVJRxdJzRESkLZgoIiIiIiIiIqKv2vLly/Nc5+TkhCNHjnx0H87Ozti9e/cH+3h5eeH8+fMFjo++Tiw9R0RE2oKJIiIiIg3C0nNEX5eRfs0RHNgeS/48jJ/m/g0LmTHGDfJFk7qV4GhXDM9evsauyIuYFr4TySlp4nY1XEoiOLA93Co5QRCA6Cv3MGnBVly++RAAIDXQw9xxPeBWqSQqlLLDvuOX0Wf0MnWdJmmJqPO3sGT9IVyMuY+Ep8lYEToArRpXF9cLgoBZv+/Buu1RSH6VitrVS2P66K4o42Qr9glbtR8RJ67g8s2HMNDXQ8z+6UrHOXY2BjN/241rtx/D2NAA3VrVwY+DfaGnp/tFzpM+Th2l54gKA0vPERGRttBRdwBERESkOjkXZlSxkPZZtGgRSpUqBUNDQ3h4eOD06dPqDkmj1XApif4d6+PyjQdiW3Ebc9jbmGPi/C2o12Mavp28Fs08XfDrhN5iHxMjA2yePwwP4l/A2382WgXMxes3adi8YBj0dN8O73V1dJCWlomlGyMReSbmi58baac3aRlwKVcC077vkuv6RWsjsHzTUcwY3Q27fh8FY0MD9BwVjrT0/y/plJmVhTZN3eDXsX6u+7hy8yH6fL8UTepWxoFVoxE+pT/2Hb+MqUt2FMo50afhWIQ0BUvPERGRtmCiiIiIiIiwceNGBAUFITg4GOfOnYOrqyt8fHyQmJio7tA0komRAX4L6Y8R0/7Ey1epYvu12MfwG/s79h67jLsPn+LY2Rv4ZckOtGxYFbr/SwKVL2UPSwsThC7diVv3EnH9djxmLtsDOysZnIpbAnh7wf77GRvxx9YTSHyWrJZzJO3TzNMFPw72RevGrkrrBEHAsr+OYGT/FmjZqBpcypXArxP7IOFpEvYevST2Gz2wNQb3aILKZR1yPca2iHOoXNYBQd+0RGlHG9SrUQ4ThrXDqr+P4/U7s+6IiFSBM4qIiEhbMFFERESkQSQS1S2kXebOnYuAgAD4+/vDxcUF4eHhMDY2xooVK9QdmkaaNaY79v97GUdOf3y2j8zUEK9S0pCdLQcA3LqXgGcvX6NPu3rQ19OFoVQffdp74vrtx4h7/LywQyf6JHGPniHxWTIa1qogtslMjVDDxRlnL9/J934yMrIgleortBlK9ZGWkYmLMfdVFi99Ho5FSFPwHkVERKQtmCgiIiLSICw9R58iIyMD0dHR8Pb2Ftt0dHTg7e2NqKgopf7p6elITk5WWCj/OjV3h2slJ4Qs2v7RvpbmJhg9oBVWbzkhtr1+k462Q+ajW6vaeHx8Hh4cmYNmnpXRbcRiMZlEVNQkPn8FALCxNFNot7E0w5P/rcsPL4/KOHvpDrbsj0Z2thyPn7zE3BX7AAAJT/lvUVHxNtHzuWMRdZ8FEUvPERGR9mCiiIiIiEjLPX36FNnZ2bCzs1Not7OzQ3x8vFL/0NBQmJubi4uTk9OXCvWrV8LOAqHfd8agCauQnvHhbyebmRhiY9hQxNx5jOm/7RLbDaX6+HV8b5z67zaafzMbLQfOxbXYx9gYNhSG7820INI0Xh6VMGFYe4yd9Recvb5H/e5T0cyzMgBAR4eZBSJSLZaeIyIibaGn7gCIiIhIdfjtW/oSxo0bh6CgIPFxcnIyk0X55FqpJGytZIhcM1Zs09PTRb0aZRHQtRHs6o+EXC7A1FiKzb9+i9dv0tBn9DJkvTNTqItPLZQsbokW38yBIAgAgIDxq3Dn0Ey0blQd/xyI/uLnRfQxtv+bSfTk+SvYWZuL7U+ev0KV8iUKtK8hPZtgcA8vJDxNhrnMCPcfP8e08J1wdrBSacz0GVRROo5jGioCWHqOiIi0BRNFREREGoQl4+hTWFtbQ1dXFwkJCQrtCQkJsLe3V+ovlUohlUq/VHga5eiZGNTrMVWhbeHEPrh5NwHz/zgAuVyAmYkhNv86DBmZWegVtFRp5pGRoQHkgiAmiQD87zFnVFDRVdLBCrZWMhw/ewNVKzgCAF6lpOH81Xvw69igwPuTSCSwt3mbcNp64Bwc7CxQrSIT1kWFKsrYckxDRQFLzxERkbZg6TkiIiJSiUWLFqFUqVIwNDSEh4cHTp8+nWdfLy+vXO9H4OvrK/bJ654Fs2bNEvuUKlVKaf306dML9Tw1kYGBAdzd3RERESG2yeVyREREwNPTU42RaZ7Xb9JxLfaxwvImNQPPk1JwLfYxzEwM8feCYTAxMsB3U9bBzNQQtlZmsLUyE5NAkaeuw8LMGLPHdkOFUnaoVMYeiyb2QXZ2No6dvSEeq2Jpe1StUALFZCaQmRqhaoUSqFqhYDM3iAoi5U06Lt94gMs3HgAA4h4/w+UbD/Ag/jkkEgkCujVG2Or92HfsEq7FPsJ3IWthZ22Olo2qift4EP8cl288wMOEF8iWy8X9pbxJF/ssXheBa7GPEHP7Meau3IeFaw7il5GdoavLP2+JSLVYeo6IiLQFZxQRERFpEHV9+Xbjxo0ICgpCeHg4PDw8EBYWBh8fH8TExMDW1lap/z///IOMjAzx8bNnz+Dq6oquXbuKbY8fP1bYZs+ePRgwYAA6d+6s0B4SEoKAgADxsZmZ4o3SKX+CgoLg5+eHWrVqoU6dOggLC0NKSgr8/f3VHZpWqV7RCbWrlQYAnN86SXFdu4m4//g5bt5LQM+gpRgb0Ar7V3wPuVzAxRsP0GX4YiQ8Sxb7/xU2FCXfKcV1bN04AECx2oGFfyKklf67HofOgQvFx5N+3QoA6Na6DuaP741hfZrhTVoGRs/YiOTXqahTvQzWzx2icG+tWb/vwV+7//+LBs37v/1ywN8LA1GvZnkAwKGoa5i/+gAyMrLgUt4BK2cMRDNPly9whpRfEhWUnuOEIioKWHqOiIi0BRNFREREGkRdZafmzp2LgIAAMakQHh6OXbt2YcWKFfjxxx+V+ltaWio83rBhA4yNjRUSRe+XPNu2bRuaNGmCMmXKKLSbmZnlWh6NCqZ79+548uQJJk6ciPj4eLi5uWHv3r2ws7NTd2gar+2Q+eLP/567ma9ETuTp64g8ff2DfVzbB392bEQFUa9meTw+MT/P9RKJBGMCWmNMQOs8+8wf3xvzx/f+4HE2L2Sys6jT0ZF89phEYClNKgJYeo6IiLQF5+YTERFRrtLT05GcnKywpKenK/XLyMhAdHQ0vL29xTYdHR14e3sjKioqX8davnw5evToARMTk1zXJyQkYNeuXRgwYIDSuunTp8PKygo1atTArFmz+I3PzxAYGIh79+4hPT0dp06dgoeHh7pDIiIiIlIblp4jIiJtwUQRERGRBskp9aKKJTQ0FObm5gpLaGio0jGfPn2K7OxspZkndnZ2iI+P/2jMp0+fxuXLlzFw4MA8+6xevRpmZmbo1KmTQvvw4cOxYcMGHD58GIMHD8a0adMwZsyYfD5bREREVBhUNRYhUjeWniMiIm3B0nNERESUq3HjxiEoKEihTSqVqvw4y5cvR7Vq1VCnTp08+6xYsQK9e/eGoaGhQvu78VWvXh0GBgYYPHgwQkNDCyVWIiIiItIeLD1HRETagjOKioCjR4+ibdu2cHBwgEQiwdatW9Ud0he3Yf06tGreFLVrVEPvHl1x6eLFD/ZPTk7GtCmT0axxA9Ryq4q2rX1w7OiRXPsuX/YbXKtUxMzQqYUROhXAri0bMbB7a3Ru7oEfhvTFjWuXP9j/9atXCJ8XCr+OzdHJuw6G9G6PsyePiesHdm+Ndo1rKC3h85RnPNCX8c9ff6Jbuxbwrl8Tg/v3xNUrlz7Y/9WrZMyd8Qs6tPRCs3o10KuzL6L+ParQ50liAqZMGIs23vXh3cAdfj064vrVD793tJlEIlHZIpVKIZPJFJbcki/W1tbQ1dVFQkKCQntCQsJH7x2UkpKCDRs25FpSLsexY8cQExPzwRlHOTw8PJCVlYW7d+9+tC8REREVDlWNRYjUjaXniIhIW3BGURGQkpICV1dXfPPNN0oldbTB3j27MXtmKMYHT0a1aq5Yt2Y1hg4egG0798LKykqpf2ZGBoYM9IellRVmz5sPWzs7PH70CGZmMqW+ly9dxOZNG1ChQsUvcSr0AccO7cPyRXPwbdDPqOBSFds3rUfwD99iydqtsChmqdQ/MzMTE78fAotilhgbMgtW1rZ4kvAIJqZmYp85S9dCni0XH9+7cwsTvx+K+l7Nv8g5kaKI/XuwKGwmvv9xIlyqVsemP9fgh+8GY93mHShmmctnOTMT3w8LgIWlJabMmAtrGzskPH4EU7P/f41fJSdh2MC+qOFeBzPnh8PCohge3L8HM5ny553eUsc1FQMDA7i7uyMiIgIdOnQAAMjlckRERCAw8MM3HN+0aRPS09PRp0+fPPssX74c7u7ucHV1/WgsFy5cgI6ODmxtbQt0DkRERKQ6qigdxzwRFQUsPUdERNqCiaIioFWrVmjVqpW6w1CbNatXolOXbujQsTMAYHzwZBw9Gomt//yNAQGDlPpv2fI3kpKTsHrdBnHQVqKEo1K/NykpGDd2NIIn/4JlS5cU7knQR237ay1atOkE79btAQDffv8zzp48hoO7t6JL72+U+h/cvRWvXyVj5uJV0NN7+zrbFXdQ6GNuoZhg2rx+JexLOKGqm3shnQV9yF/r/0CbDl3Qul1HAMD34yYi6t+j2LV9C/r0V54Jsnv7P0hOTsLiFWvF17i4QwmFPutWr4CtnT3GBf8itjnk8nkn9QsKCoKfnx9q1aqFOnXqICwsDCkpKfD39wcA9OvXDyVKlFC6x9Hy5cvRoUOHXL8YALydQbpp0ybMmTNHaV1UVBROnTqFJk2awMzMDFFRURg1ahT69OmDYsWKqf4kiYiIiEirsPQcERFpCyaKSK0yMzJw7eoVDAgYLLbp6Oigbt16uPjf+Vy3OXL4EKq7uiH0lxAcPhyBYsUs0dq3DfwHBEBXV1fsN+2XEDRq1Bh1PesxUaRmmZmZuHXjmkJCSEdHB67uHrh+Jfcyg6f/PYKKVaojfN50nPo3EuYWxdCoWSt07tVf4XV+9xiRB3ajfdc+LFOhBpmZmbhx/apCQkhHRwfuderiyqX/ct3m+NFIVKnminkzpuL40UOwsLCEd8vW6NVvgPga/3vsMOrUrY+JPwbhwrmzsLGxRYcuPdC2Y5cvcl5fI3W9/7t3744nT55g4sSJiI+Ph5ubG/bu3Qs7OzsAQFxcHHR0FCvexsTE4Pjx49i/f3+e+92wYQMEQUDPnj2V1kmlUmzYsAGTJk1Ceno6SpcujVGjRindV4mIiIi+LFWUjuOYnooClp4jIiJtwUTRVyg9PR3p6ekKbYKu9Ku8afeLly+QnZ2t9E1yKysr3LlzO9dtHjy4j0enTqJ1m7ZYtOQ3xMXFYdqUycjKysKQb9+WONqzexeuXbuK9Rs3F/o50MclJ72APDtbqcScRTErPIy7m+s28Y8fIvH8GTT2boXgGQvw+OF9hM8LRXZ2Fnr2H6zU/9Sxw0h5/QrNWrUtjFOgj0j632f5/RJzlpZWiLt7J9dtHj98gPNnH8K7pS9mhi3Bg/txmDfzF2RlZcE/4Fuxz7a/N6Jbr37o4x+A61cuY/6cUOjp66NVm/aFfl5fI3VeVAkMDMyz1FxkZKRSW8WKFSEIwgf3OWjQIAwapDy7FABq1qyJkydPFjhOIiIiKlxMFJGmYOk5IiLSFjof70JFTWhoKMzNzRWWWTNCP76hhpDLBVhaWmHipClwqVIVLVu1xsBBQ7Bp4wYAQPzjx5g5fSpCZ8z6KpNn9JYgl8PcwhLDfpiAchVd0LCpD7r2GYC923JP/h3YvRXuderDypr3JflayAU5LIpZYvRPk1CxchU0a9EKff0HYdvff/1/H7kc5StWxqBhI1GhYmW069QVbTt0xvZ//vrAnomIiIiIiD4fS88REZG24Iyir9C4ceOUyuoIul9nQqSYRTHo6uri2bNnCu3Pnj2DtbV1rtvY2NhAT09PofxYmbJl8PTpE2RmZODq1St4/uwZenTtJK7Pzs5G9Nkz2PDnOpw5fynX0mVUeGTmxaCjq4uXL54rtL988QwWlrnfl6SYlbXS6+zkXBovnj9FZmam+M0uAEiMf4T/ok/hxymzC+cE6KPM//dZfvFc8bP8/PkzWFrl/lm2slL+LDuXKoPnz/7/NbaytkGpMmUVtnMuVQZHDh1U/UloCH75loiIiNRNIvn8MQnHNFQUsPQcERFpC84o+gpJpVLIZDKF5WudOaNvYIDKLlVw6mSU2CaXy3HqVBSqu9bIdRu3GjVxPy4OcrlcbLt39y5sbGygb2AAj7p1sXnrDmz8e6u4VKlSFa3btMXGv7cySaQG+vr6KFehMv6LPiW2yeVyXDx3GpWqVM91m8pV3fD44X2F1/nhgzhYWlkrJIkA4OCe7TC3sETtug0L5wToo/T19VGhkguizyi+xufOnEKVaq65blPN1Q0PHyh+lu/H3YWVtY34GldzrYH79+4qbHc/7h7s7Iur/iQ0RE6pF1UsRERERJ9CAhWMRcCxCKkfS88REZG2YKKoCHj9+jUuXLiACxcuAADu3LmDCxcuIC4uTr2BfSF9/fzxz+a/sH3rFtyOjcUvIZOQmpqKDh3fzgj6edwYzJ83R+zfrXtPJCW9xIzQqbh79w6OHonE78uWonvP3gAAExNTlC9fQWExMjaGhbkFypevoI5TJADtu/XB/l1bELF3O+7fvY0lc6chLTUVzVq9vc/MvKnjsfq3X8X+rTp0xavkZCz7dSYe3r+HM1HHsGntcrTu2F1hv3K5HBF7tqFpyzbQ1eMkSXXq1qsfdm7djD07t+HunVjMmT4FqampaN22AwBgavA4LF04T+zfvnN3JCcn4dc503H/3l1EHT+CtauWoWPXHmKfrj374sqli1iz8jc8uB+HA3t3YceWzejYteeXPj0iIiIiItIyLD1HRETagldVi4CzZ8+iSZMm4uOcsnJ+fn5YtWqVmqL6clq2ao0Xz59j8cJf8fTpE1SsVBmLl/4Oq/+Vnot//Bg6kv/PadoXL44lvy3HrBmh6NqxHWzt7NC7Tz/4DwhQ1ylQPjRs6oOkly+wfsUSvHj+DGXKVcSkWYtQ7H+l554kxkOi8/+vs42tPSbPWoTfF83B8G+6wcraFm0790LnXv0V9vtf9Ck8SYiHd+sOX/BsKDfNWrTCy5cvsGLpQjx/9hTlKlTC7F/DxdJzCfGPIXnns2xnXxyzf12KhfNmwr9XJ1jb2KJLjz7o1W+A2KdylWqYOisMSxfNx+rfw2HvUALfBY1Fi1Ztvvj5fS04EYiIiIjUjaXnSFOw9BwREWkLiSAIgrqDoM+XxjGLVrj39I26Q6BCZmGs//FO9NWzkxXe6+w+5bDK9hU9ocnHOxEBSE5Ohrm5OaTVAiDRNVB3OEQq8/jEfHWHQKRyycnJcLa3RFJSEmQymcr3bW5uDtefdkDX0OSz9pWdloL/prUtlDhJ8+S891T9flm7di369u2LFi1aYN++fSrbLxER0ZdQkN+PLD1HRERERERERET0HpaeIyIibcHSc0RERBqEZVqIiIhI3Vh6jjQFS88REZG2YKKIiIhIg0h4VYWIiIjUTCKRfPaYhGMaKgr09d+WjGaiiIiINB1LzxEREREREREREb2HpeeIiEhbcEYRERGRBuGXb4mIiEjdWHqONAVLzxERkbZgooiIiEiDsEwLERERqRtLz5GmYOk5IiLSFiw9R0RERERERERE+bZo0SKUKlUKhoaG8PDwwOnTp/O13YYNGyCRSNChQwelddeuXUO7du1gbm4OExMT1K5dG3FxcSqOvGBYeo6IiLQFE0VEREQaJKfUiyoWIiIiok+iinEIxyJF1saNGxEUFITg4GCcO3cOrq6u8PHxQWJi4ge3u3v3Ln744Qc0bNhQaV1sbCwaNGiASpUqITIyEhcvXsSECRNgaGhYWKeRLyw9R0RE2oKJIiIiIg2SU+pFFQsRERHRp+BYRLPNnTsXAQEB8Pf3h4uLC8LDw2FsbIwVK1bkuU12djZ69+6NyZMno0yZMkrrf/75Z7Ru3RozZ85EjRo1ULZsWbRr1w62traFeSofxdJzRESkLZgoIiIiIiIiIiKij8rIyEB0dDS8vb3FNh0dHXh7eyMqKirP7UJCQmBra4sBAwYorZPL5di1axcqVKgAHx8f2NrawsPDA1u3bs1zf+np6UhOTlZYCgNLzxERkbZgooiIiEiDsPQcERERqRvHIprr6dOnyM7Ohp2dnUK7nZ0d4uPjc93m+PHjWL58OZYtW5br+sTERLx+/RrTp09Hy5YtsX//fnTs2BGdOnXCkSNHct0mNDQU5ubm4uLk5PR5J5YHlp4jIiJtoafuAIiIiEh1WKaFiIiI1E0VpeM4ptEMr169Qt++fbFs2TJYW1vn2kculwMA2rdvj1GjRgEA3NzccOLECYSHh6Nx48ZK24wbNw5BQUHi4+Tk5EJJFrH0HBERaQsmioiIiIiIiIiI6KOsra2hq6uLhIQEhfaEhATY29sr9Y+NjcXdu3fRtm1bsS0nMaSnp4eYmBg4OTlBT08PLi4uCttWrlwZx48fzzUOqVQKqVT6uafzUSw9R0RE2oKl54iIiDQIS88RERGRunEsorkMDAzg7u6OiIgIsU0ulyMiIgKenp5K/StVqoRLly7hwoUL4tKuXTs0adIEFy5cgJOTEwwMDFC7dm3ExMQobHvjxg04OzsX+jl9CEvPERGRtuCMIiIiIg3CMi1ERESkbiw9p9mCgoLg5+eHWrVqoU6dOggLC0NKSgr8/f0BAP369UOJEiUQGhoKQ0NDVK1aVWF7CwsLAFBoHz16NLp3745GjRqhSZMm2Lt3L3bs2IHIyMgvdVq5Yuk5IiLSFkwUERERERERERFRvnTv3h1PnjzBxIkTER8fDzc3N+zduxd2dnYAgLi4OOjoFKyATceOHREeHo7Q0FAMHz4cFStWxN9//40GDRoUxink27ul5wRBYAKTiIg0FhNFREREGoR/vBIREZG6cUaR5gsMDERgYGCu6z42C2jVqlW5tn/zzTf45ptvPjMy1cpJFAFvS+zp6uqqMRoiIqLCw0QRERGRBuE1FSIiIlI3VdxjiGMaKgpySs8Bb8vPMVFERESaqmBzgYmIiIiIiIiIiLTAuzOKMjMz1RgJERFR4eKMIiIiIg3CMi1ERESkbiw9R5pCKpVCR0cHcrkcr1+/hqmpqbpDIiIiKhScUURERKRBckq9qGIhIiIi+hQci5Cm0NXVhZ2dHQDg4cOHao6GiIio8DBRRERERERERERftSVLlqB69eqQyWSQyWTw9PTEnj17xPVpaWkYNmwYrKysYGpqis6dOyMhIUFhH3FxcfD19YWxsTFsbW0xevRoZGVlKfSJjIxEzZo1IZVKUa5cOaxatepLnB6pkaOjIwAmioiISLMxUURERKRBckq9qGIhIiIi+hTqGIs4Ojpi+vTpiI6OxtmzZ9G0aVO0b98eV65cAQCMGjUKO3bswKZNm3DkyBE8evQInTp1ErfPzs6Gr68vMjIycOLECaxevRqrVq3CxIkTxT537tyBr68vmjRpggsXLmDkyJEYOHAg9u3bp5onjoqknETRgwcP1BwJERFR4eE9ioiIiDQI8ztERESkbhJ8/pgkZ/Pk5GSFdqlUCqlUqtS/bdu2Co+nTp2KJUuW4OTJk3B0dMTy5cuxfv16NG3aFACwcuVKVK5cGSdPnkTdunWxf/9+XL16FQcPHoSdnR3c3NwwZcoUjB07FpMmTYKBgQHCw8NRunRpzJkzBwBQuXJlHD9+HPPmzYOPj8/nnTAVWUwUERGRNuCMIiIiIiIiIiIqkpycnGBubi4uoaGhH90mOzsbGzZsQEpKCjw9PREdHY3MzEx4e3uLfSpVqoSSJUsiKioKABAVFYVq1aqJ96MBAB8fHyQnJ4uzkqKiohT2kdMnZx+kmZgoIiIibcAZRURERBpEh1OKiIiISM10JJLPHpPkbH///n3IZDKxPbfZRDkuXboET09PpKWlwdTUFFu2bIGLiwsuXLgAAwMDWFhYKPS3s7NDfHw8ACA+Pl4hSZSzPmfdh/okJycjNTUVRkZGn3ayVKQxUURERNqAiSIiIiINwjwRERERqZtEooLSc//bXiaTKSSKPqRixYq4cOECkpKSsHnzZvj5+eHIkSOfFwhpPSaKiIhIGzBRRERERERERERfPQMDA5QrVw4A4O7ujjNnzmD+/Pno3r07MjIy8PLlS4VZRQkJCbC3twcA2Nvb4/Tp0wr7S0hIENfl/D+n7d0+MpmMs4k02LuJIkEQIOE3s4iISAPxHkVEREQaRCKRqGwhIiIi+hRFZSwil8uRnp4Od3d36OvrIyIiQlwXExODuLg4eHp6AgA8PT1x6dIlJCYmin0OHDgAmUwGFxcXsc+7+8jpk7MP0kwODg4AgNTUVLx48ULN0RARERUOzigiIiLSIDrM7xAREZGa6Ug+f0xS0O3HjRuHVq1aoWTJknj16hXWr1+PyMhI7Nu3D+bm5hgwYACCgoJgaWkJmUyG7777Dp6enqhbty4AoEWLFnBxcUHfvn0xc+ZMxMfHY/z48Rg2bJh4X6QhQ4Zg4cKFGDNmDL755hscOnQIf/31F3bt2vV5J0tFmqGhIWxsbPDkyRM8ePAAlpaW6g6JiIhI5ZgoIiIiIiIiIqKvWmJiIvr164fHjx/D3Nwc1atXx759+9C8eXMAwLx586Cjo4POnTsjPT0dPj4+WLx4sbi9rq4udu7ciaFDh8LT0xMmJibw8/NDSEiI2Kd06dLYtWsXRo0ahfnz58PR0RG///47fHx8vvj50pfl6OgoJoqqV6+u7nCIiIhUjokiIiIiDcKScURERKR2EhWMSQq4+fLlyz+43tDQEIsWLcKiRYvy7OPs7Izdu3d/cD9eXl44f/58wYKjr56joyPOnz+PBw8eqDsUIiKiQsFEERERkQZhnoiIiIjUTSL5/DEJxzRUlDg6OgIAE0VERKSxmCjSEM3nH1d3CERElE/Hvm+g7hCIiIiIiCifmCgiIiJNx0QRERGRBpEUtE4LERERkYpJ/vff5+6DqKhgooiIiDSdjroDICIiItXRkahuKahFixahVKlSMDQ0hIeHB06fPp1nXy8vL0gkEqXF19dX7NO/f3+l9S1btlTYz/Pnz9G7d2/IZDJYWFhgwIABeP36dcGDJyIiIpVR11iEqLCUKFECABNFRESkuZgoIiIios+2ceNGBAUFITg4GOfOnYOrqyt8fHyQmJiYa/9//vkHjx8/FpfLly9DV1cXXbt2VejXsmVLhX5//vmnwvrevXvjypUrOHDgAHbu3ImjR49i0KBBhXaeRERERKR9OKOIiIg0HUvPERERaRCJmu78PHfuXAQEBMDf3x8AEB4ejl27dmHFihX48ccflfpbWloqPN6wYQOMjY2VEkVSqRT29va5HvPatWvYu3cvzpw5g1q1agEAFixYgNatW2P27NlwcHBQxakRERFRAeXMBP7cfRAVFTkzil69eoXk5GTIZDI1R0RERKRanFFERESkQSQS1S3p6elITk5WWNLT05WOmZGRgejoaHh7e4ttOjo68Pb2RlRUVL7iXr58OXr06AETExOF9sjISNja2qJixYoYOnQonj17Jq6LioqChYWFmCQCAG9vb+jo6ODUqVMFfeqIiIhIRVQ1FiEqKkxNTcUvOt2+fVvN0RAREakeE0VERESUq9DQUJibmyssoaGhSv2ePn2K7Oxs2NnZKbTb2dkhPj7+o8c5ffo0Ll++jIEDByq0t2zZEn/88QciIiIwY8YMHDlyBK1atUJ2djYAID4+Hra2tgrb6OnpwdLSMl/HJSIiIiLKrypVqgAALl++rOZIiIiIVI+l54iIiDSIjgq/fjtu3DgEBQUptEmlUpXtP8fy5ctRrVo11KlTR6G9R48e4s/VqlVD9erVUbZsWURGRqJZs2Yqj+NrsH379nz3bdeuXSFGQkRElDcdieSzxySqHNMQqUK1atVw7NgxXLp0Sd2hEBERqRwTRURERBpElddUpFJpvhJD1tbW0NXVRUJCgkJ7QkJCnvcXypGSkoINGzYgJCTko8cpU6YMrK2tcevWLTRr1gz29vZITExU6JOVlYXnz59/9Lhfqw4dOuSrn0QiEWdeERERfWmqKB3HPBEVNdWqVQMAXLx4Uc2REBERqR5LzxEREdFnMTAwgLu7OyIiIsQ2uVyOiIgIeHp6fnDbTZs2IT09HX369PnocR48eIBnz56hePHiAABPT0+8fPkS0dHRYp9Dhw5BLpfDw8PjE8+maJPL5flamCQiIiIiUq2cRBFnFBERkSZiooiIiEiDSCQSlS0FERQUhGXLlmH16tW4du0ahg4dipSUFPj7+wMA+vXrh3Hjxiltt3z5cnTo0AFWVlYK7a9fv8bo0aNx8uRJ3L17FxEREWjfvj3KlSsHHx8fAEDlypXRsmVLBAQE4PTp0/j3338RGBiIHj16wMHB4ROfwa9TWlqaukMgIiISqWMsQlTYqlatCgB4+PAhXrx4oeZoiIiIVIuJIiIiIg2SU+pFFUtBdO/eHbNnz8bEiRPh5uaGCxcuYO/evbCzswMAxMXF4fHjxwrbxMTE4Pjx4xgwYIDS/nR1dXHx4kW0a9cOFSpUwIABA+Du7o5jx44plMNbt24dKlWqhGbNmqF169Zo0KABfvvtt4I/cV+h7OxsTJkyBSVKlICpqSlu374NAJgwYQKWL1+u5uiIiEibqWMsQlTYzM3N4ezsDICzioiISPPwHkVERESkEoGBgQgMDMx1XWRkpFJbxYoVIQhCrv2NjIywb9++jx7T0tIS69evL1CcmmLq1KlYvXo1Zs6ciYCAALG9atWqCAsLyzUBR0RERESfrlq1arh37x4uXbqERo0aqTscIiIileGMIiIiIg2iI5GobKGi7Y8//sBvv/2G3r17Q1dXV2x3dXXF9evX1RgZERFpO45FSFPl3Kfo4sWLao6EiIhItTijiIiISIPwkor2ePjwIcqVK6fULpfLkZmZqYaIiIiI3pLg88ckHNNQUZSTKGLpOSIi0jScUURERET0FXJxccGxY8eU2jdv3owaNWqoISIiIiIizVa9enUAwOXLl/MsoUxERPQ14owiIiIiDSJhmRatMXHiRPj5+eHhw4eQy+X4559/EBMTgz/++AM7d+5Ud3hERKTFJBLJZ49JOKahoqhChQrQ19fHq1evcO/ePZQqVUrdIREREakEZxQRERFpEB2J6hYq2tq3b48dO3bg4MGDMDExwcSJE3Ht2jXs2LEDzZs3V3d4RESkxTgWIU2lr68vzio6fvy4mqMhIiJSHc4oIiIiIvpKNWzYEAcOHFB3GERERERaw9vbG9HR0di3bx/69Omj7nCIiIhUgokiIiIiDcIyLdrn7NmzuHbtGoC39y1yd3dXc0RERKTtWHqONJmPjw9mzJiBAwcOQC6XQ0eHxXqIiOjrx0QRERGRBuE1Fe3x4MED9OzZE//++y8sLCwAAC9fvkS9evWwYcMGODo6qjdAIiLSahyTkKaqX78+TExMkJCQgIsXL8LNzU3dIREREX02fu2BiIiI6Cs0cOBAZGZm4tq1a3j+/DmeP3+Oa9euQS6XY+DAgeoOj4iIiEgjGRgYoEmTJgCAffv2qTkaIiIi1WCiiIiISIPklHpRxUJF25EjR7BkyRJUrFhRbKtYsSIWLFiAo0ePqjEyIiLSdhyLkKbz8fEBwEQRERFpDpaeIyIi0iA6vKaiNZycnJCZmanUnp2dDQcHBzVERERE9JaO5PPHJBzTUFGWkyg6fvw4Xr9+DVNTUzVHRERE9Hk4o4iIiIjoKzRr1ix89913OHv2rNh29uxZjBgxArNnz1ZjZERERESarVy5cihdujQyMzNx6NAhdYdDRET02TijiIiISIOwTItmK1asmMJrnJKSAg8PD+jpvR3SZWVlQU9PD9988w06dOigpiiJiEjbqaJ0HMc0VJRJJBK0adMGCxYswNatW9GuXTt1h0RERPRZmCgiIiLSILykotnCwsLUHQIREdFHSfD5YxKOaaio69ixIxYsWIDt27eLX9YhIiL6WvG3GBEREdFXws/PT90hEBER0Vfq/v37kEgkcHR0BACcPn0a69evh4uLCwYNGqTm6L4+DRs2hJWVFZ49e4bjx4/Dy8tL3SERERF9Mt6jiIiISIPoSCQqW+jrkZaWhuTkZIWFiIhIXTgWKZp69eqFw4cPAwDi4+PRvHlznD59Gj///DNCQkLUHN3XR09PD23btgUAbNmyRc3REBERfR4mioiIiDSIRKK6hYq2lJQUBAYGwtbWFiYmJihWrJjCQkREpC4cixRNly9fRp06dQAAf/31F6pWrYoTJ05g3bp1WLVqlXqD+0p17NgRALB161YIgqDmaIiIiD4dE0VEREREX6ExY8bg0KFDWLJkCaRSKX7//XdMnjwZDg4O+OOPP9QdHhERERUxmZmZkEqlAICDBw+iXbt2AIBKlSrh8ePH6gztq9W8eXMYGxsjLi4O0dHR6g6HiIjokzFRREREpEEkEonKFiraduzYgcWLF6Nz587Q09NDw4YNMX78eEybNg3r1q1Td3hERKTFOBYpmqpUqYLw8HAcO3YMBw4cQMuWLQEAjx49gpWVlZqj+zoZGRmJ5ecmTJjAWUVERPTVYqKIiIhIg7D0nPZ4/vw5ypQpAwCQyWR4/vw5AKBBgwY4evSoOkMjIiItx7FI0TRjxgwsXboUXl5e6NmzJ1xdXQEA27dvF0vSUcFNmTIFUqkUe/fuxebNm9UdDhER0SfRU3cARADQ0a04etYqAUsTA8Q+SUHYoVhci3+da99fu1VDDSdzpfao288xZstV8bGzpRGGNCoFN0dz6OpIcPfZG4zffh2Jr9IL7Tzow/g6awe+zkRfRpkyZXDnzh2ULFkSlSpVwl9//YU6depgx44dsLCwUHd4REREVMR4eXnh6dOnSE5OVrif4aBBg2BsbKzGyL5u5cuXx7hx4zBp0iSMGDECLVq0gLm58t84RERERRkTRZ8hMzMT+vr66g7jq9e0ojUCG5fGnIO3cPXxK3R1L4E5naui14povEzNVOr/8/Zr0Nf5/6+XyYz0sbJfDRy+8VRsczA3xKIe1bHrcgJWnIhDSno2SlsbIyNL/kXOiZTxddYOfJ3VT4dfv9Ua/v7++O+//9C4cWP8+OOPaNu2LRYuXIjMzEzMnTtX3eEREZEW05FIPntMwjGN6qWmpkIQBDFJdO/ePWzZsgWVK1eGj4+PmqP7uo0dOxbr1q3DzZs3MWPGDEybNk3dIRERERXIV1V6bu/evWjQoAEsLCxgZWWFNm3aIDY2Vlz/4MED9OzZE5aWljAxMUGtWrVw6tQpcf2OHTtQu3ZtGBoawtraGh07dhTXSSQSbN26VeF4FhYWWLVqFQDg7t27kEgk2LhxIxo3bgxDQ0OsW7cOz549Q8+ePVGiRAkYGxujWrVq+PPPPxX2I5fLMXPmTJQrVw5SqRQlS5bE1KlTAQBNmzZFYGCgQv8nT57AwMAAERERqnjairzu7iWw41L8/7F33/E1X38cx183O0hCRBYRoQS1V6hRJYSq2VaNWkVLhVqlWrU6tKVWrVbNomiLKmrWFivEqBkrVmJEJpJI8vtD3V9vEzvcJN7PPL6Ph3u+53vu55tvbnJ8P99zDiv/vsyZyJuMXhvKraRkGpV2S7d+7K3bRN5IMm6VvXOTkJTMhmP/v7H8bg1vdpy+zpTNZzhxOZ6L0bfYdjIy3RvV8mzoOj8fdJ3NT1PPPT/69OlDr169APD39+fo0aPMnz+fffv28cEHH5g5OhEReZ6pL5I5NW3alDlz5gAQFRWFn58f3377Lc2aNWPKlCmP1NakSZMoVKgQdnZ2+Pn5sWvXroc6bsGCBRgMBpo1a3bPOt26dcNgMDBu3LhHismc7OzsGDVqFAATJ040TgksIiKSVWSpRFF8fDx9+/Zlz549rF+/HgsLC5o3b05KSgpxcXG8/PLLXLhwgWXLlrF//34GDBhASsqdJ85XrFhB8+bNefXVV9m3bx/r169/rDl4P/roIz744AOOHDlCQEAAt27domLFiqxYsYJDhw7x7rvv0q5dO5NO0qBBg/jqq6/49NNPOXz4MPPnz8fN7c5N0y5dujB//nwSEv4/fdLcuXPJnz8/derUecLvWOZnZWGgmFsugsOijGWpwJ6wKF70cHioNhqVcmP9savc+md0gQGoVjgP567f5NvXX2RZ9yp836YsNV9wzvgTkIei6/x80HUWMS9vb29atGhBmTJlzB2KiIiIZEJ79+6lZs2aAPz666+4ublx9uxZ5syZw4QJEx66nYULF9K3b1+GDh3K3r17KVu2LAEBAVy+fPm+x505c4b+/fsbY0jPkiVL2LFjB56eng8dT2bRpEkTypQpQ2xsLOPHjzd3OCIiIo8kS0099/rrr5u8njFjBvny5ePw4cNs376dK1eusHv3bpyd79xAfOGFF4x1v/jiC1q1asXw4cONZXcXbnwUvXv3pkWLFiZl/fv3N/67Z8+erF692rhOwN0OwsSJE+nQoQMARYoUoUaNGgC0aNGCwMBAfv/9d1q2bAnArFmz6NixI4Z7PEKVkJBgklgCSLmdiIWVzSOfj7k52VtjZWEgMt50ZMD1G0l4Oz94juQS7rkoki8nX685YSzLk8OaHDZWtK1SgB+3nmXK5jP4+eTh8yYl+GDRQULOx2T4ecj96To/H3SdM4d7/e2Q7OFRbuLcHW0kIiLyrBkMhifuk6hPk/Fu3LiBg8OdB7jWrFlDixYtsLCwoGrVqpw9e/ah2xkzZgxdu3alU6dOAEydOpUVK1YwY8YMPvroo3SPSU5Opm3btgwfPpwtW7YQFRWVps6FCxeM91QaNWr06CdoZgaDgU8//ZQ333yT8ePH07dvX61VJCIiWUaWShSdOHGCIUOGsHPnTq5evWocLRQWFkZISAjly5c3Jon+KyQkhK5duz5xDJUqVTJ5nZyczJdffsmiRYu4cOECiYmJJCQkGBeCPHLkCAkJCdStWzfd9uzs7GjXrh0zZsygZcuW7N27l0OHDrFs2bJ7xjBy5EiThBeAV71OeAe884Rnl/U0KuXGySvxHAmPM5bd/Q/F1tBrLNp7EYDQK/GU8nSgaVkP3VjOgnSdnw+6zhkjSw0Vlkc2duzYh6pnMBjMkigK2zgaR0fHZ/6+Ik9LUOg1c4cgkuHi42Kf+ntY8OR9EvVpMt4LL7zA0qVLad68OatXr6ZPnz4AXL58+aH/ficmJhIcHMygQYOMZRYWFvj7+xMUFHTP40aMGIGrqyudO3dmy5YtafanpKTQrl07PvzwQ1588cUHxvHfB2hjYjLH/wtatGhByZIlOXz4MF9++SVff/21uUMSERF5KFkqUdS4cWO8vb2ZNm0anp6epKSkUKpUKRITE7G3t7/vsQ/abzAYSE1NNSlLSkq7/kXOnDlNXo8aNYrx48czbtw4SpcuTc6cOenduzeJiYkP9b5wZ/q5cuXKcf78eWbOnEmdOnXw9va+Z/1BgwbRt29fk7KGU/Y88H0yo+ibSdxOScU5p7VJeZ4c1lyLT7zvsXZWFtQtno/p28LStpmcwplrN03Kz167SZn8unllDrrOzwddZ5Gn7/Tp0+YOQURERLKoIUOG0KZNG/r06UOdOnWoVq0acGd0Ufny5R+qjatXr5KcnGycTv8uNzc3jh49mu4xW7duZfr06YSEhNyz3a+//horK6uHftAlvQdoMwMLCwu++OILmjdvzqhRo2jUqBG1atUyd1giIiIPlGUe0rl27RrHjh1j8ODB1K1blxIlSnD9+nXj/jJlyhASEnLPBQPLlCnD+vXr79l+vnz5uHTpkvH1iRMnuHHjxgPj2rZtG02bNuXtt9+mbNmyFC5cmOPHjxv3Fy1aFHt7+/u+d+nSpalUqRLTpk1j/vz5vPPO/UcG2dra4ujoaLJlxWnnAG6npHI8Io6KBXMbywxAxYK5+fvS/Z90e8XXBWtLC9YcMZ0H+XZKKkci4ijobJqk88pjT3jMrYwKXR6BrvPzQdc5c7g71UtGbCIiIiKPQ32RzOmNN94gLCyMPXv2sHr1amN53bp1H3rU8qOKjY2lXbt2TJs2DRcXl3TrBAcHM378eGbNmvXQ133QoEFER0cbt3PnzmVk2E+kWbNmdOzYkdTUVNq1a5fuNHsiIiKZTZZJFOXJk4e8efPyww8/EBoayl9//WUyqqZ169a4u7vTrFkztm3bxqlTp/jtt9+MQ5+HDh3Kzz//zNChQzly5AgHDx40GQJcp04dJk6cyL59+9izZw/dunXD2to6TRz/VbRoUdauXcv27ds5cuQI7733HhEREcb9dnZ2DBw4kAEDBjBnzhxOnjzJjh07mD59ukk7Xbp04auvviI1NZXmzZs/6bcrS1kYfIHXSrvToKQr3s729PMvgr21JSsP3fk+ftKgGO/VSDvCqlEpN7aGXiPm1u00+37efYE6vi40Lu1G/tx2tCjnwUtFnFmyP/ypn4+kT9f5+aDrbH4WhozbRERERB6HIQP6IcoTPR3u7u6UL1+eixcvcv78eQCqVKlC8eLFH+p4FxcXLC0tTe57AERERODu7p6m/smTJzlz5gyNGzfGysoKKysr5syZw7Jly7CysuLkyZNs2bKFy5cvU7BgQWOds2fP0q9fPwoVKpRuHOk9QJuZTJgwgcKFCxMWFpZmRhgREZHMKMskiiwsLFiwYAHBwcGUKlWKPn36MGrUKON+Gxsb1qxZg6urK6+++iqlS5fmq6++wtLSEoDatWvzyy+/sGzZMsqVK0edOnXYtWuX8fhvv/0WLy8vatasSZs2bejfv79xnaH7GTx4MBUqVCAgIIDatWsbk1X/9umnn9KvXz+GDBlCiRIleOutt7h82fSp+datW2NlZUXr1q2xs7N7gu9U1vPXsatM3nSaztULMqNdeYq65qL/b4e4fuPO1H9ujrbkzWk6Ysorjz1lCzix/FBEek2yJfQao9edpHXlAsxuX57XSrvx6bIjHLyQOeYtfh7pOj8fdJ1FRERERDKnlJQURowYgZOTE97e3nh7e5M7d24+++wz4xrQD2JjY0PFihVNZk1JSUlh/fr1xqns/q148eIcPHiQkJAQ49akSRNeeeUVQkJC8PLyol27dhw4cMCkjqenJx9++KHJyKesxMHBgZ9++gmAmTNnsmPHDjNHJCIicn+G1P8uzCNmcebMGYoUKcLu3bupUKHCIx9f89utTyEqERF5Grb0q/HU2u67LP254R/HmCYP92SpSExMDE5OTkRci850T/SKPImg0GvmDkEkw8XHxdK4sg/R0Rn/O/vu34P3f96NbY5cT9RWwo04Jreu/FTifF4NGjSI6dOnM3z4cKpXrw7cWT9o2LBhdO3alS+++OKh2lm4cCEdOnTg+++/p0qVKowbN45FixZx9OhR3NzcaN++Pfnz52fkyJHpHt+xY0eioqJYunTpPd+jUKFC9O7dm969ez9UTHd/9jLbz0vHjh2ZPXs2lSpVYufOnVhYZJnntUVEJBt4lL+P+gtlZklJSYSHhzN48GCqVq36WEkiERGRu7RGkYiIiJibOfoiI0eOpHLlyjg4OODq6kqzZs04duyYSZ3atWuneY9u3bqZ1AkLC6NRo0bkyJEDV1dXPvzwQ27fNp0eeePGjVSoUAFbW1teeOEFZs2a9Vjfp2dt9uzZ/Pjjj3Tv3p0yZcpQpkwZ3n//faZNm/ZI5/DWW28xevRohgwZQrly5QgJCWHVqlW4ubkBd76H/14D+nn21Vdf4eDgwJ49e5gxY4a5wxEREbknJYrMbNu2bXh4eLB7926mTp1q7nBEREQkC9myZQtvv/021apV48KFCwD89NNPbN2qkcYiIvJ82bRpEz169GDHjh2sXbuWpKQk6tevT3x8vEm9rl27cunSJeP2zTffGPclJyfTqFEjEhMT2b59O7Nnz2bWrFkMGTLEWOf06dM0atTIOHVa79696dKlS5aYIi0yMjLdtYiKFy9OZGTkI7UVGBjI2bNnSUhIYOfOnfj5+Rn3bdy48b6Jp1mzZt13NBHcmXXlYUcTZWbu7u4MHz4cuDOi6/r162aOSEREJH1KFJlZ7dq1SU1N5dixY5QuXdrc4YiISBb3pAtH/3uTzO23334jICAAe3t79u3bR0JCAgDR0dF8+eWXZo5ORESeZ+boi6xatYqOHTvy4osvUrZsWWbNmkVYWBjBwcEm9XLkyIG7u7tx+/c0LGvWrOHw4cPMnTuXcuXK0bBhQz777DMmTZpEYmIiAFOnTsXHx4dvv/2WEiVKEBgYyBtvvMHYsWOf+Pv2tJUtW5aJEyemKZ84cSJlypQxQ0TPh8DAQEqWLMnVq1cZOnSoucMRERFJlxJFIiIi2YjBkHGbZG6ff/45U6dOZdq0aVhbWxvLq1evzt69e80YmYiIPO8ysi8SExNjst19MOJBoqOjAXB2djYpnzdvHi4uLpQqVYpBgwZx48YN476goCBKly5tnEINICAggJiYGP7++29jHX9/f5M2AwICCAoKeuTv07P2zTffMGPGDEqWLEnnzp3p3LkzJUuWZNasWYwePdrc4WVb1tbWTJgwAYDJkyczcuRIWrVqpVllREQkU1GiSERERCQLOnbsGLVq1UpT7uTkRFRU1LMPSERE5Cnw8vLCycnJuI0cOfKBx6SkpNC7d2+qV69OqVKljOVt2rRh7ty5bNiwgUGDBvHTTz/x9ttvG/eHh4ebJIkA4+vw8PD71omJieHmzZuPfZ7Pwssvv8zx48dp3rw5UVFRREVF0aJFC/7++29++uknc4eXrdWtW5fXX3+d5ORkPv74YxYuXMj777/Ptm3bzB2aiIgIAFbmDkBEREQyjoWGAj033N3dCQ0NpVChQiblW7dupXDhwuYJSkREhDv9kSftk9w9/ty5cybTw9na2j7w2B49enDo0KE0a/a9++67xn+XLl0aDw8P6taty8mTJylSpMgTxZtVeHp68sUXX5iU7d+/n+nTp/PDDz+YKarnw9ixYzl79iw5cuTAYDCwadMm3nnnHUJCQrC3tzd3eCIi8pzTiCIREZFsxCIDN8ncunbtygcffMDOnTsxGAxcvHiRefPm0b9/f7p3727u8ERE5DmWkX0RR0dHk+1BiaLAwECWL1/Ohg0bKFCgwH3r+vn5ARAaGgrceQgjIiLCpM7d1+7u7vet4+joqJv9cl9eXl7s3r2bTZs2sWTJEjw8PDh+/LjWLRIRkUxB94FEREREsqCPPvqINm3aULduXeLi4qhVqxZdunThvffeo2fPnuYOT0RE5JlKTU0lMDCQJUuW8Ndff+Hj4/PAY0JCQgDw8PAAoFq1ahw8eJDLly8b66xduxZHR0dKlixprLN+/XqTdtauXUu1atUy6EzkeZAnTx7jGkWjR49m8+bNZo5IRESed0oUiYiIZCMZtXi0ZrDL/AwGA5988gmRkZEcOnSIHTt2cOXKFT777DNzhyYiIs85c/RFevTowdy5c5k/fz4ODg6Eh4cTHh5uXDfo5MmTfPbZZwQHB3PmzBmWLVtG+/btqVWrFmXKlAGgfv36lCxZknbt2rF//35Wr17N4MGD6dGjh3EkU7du3Th16hQDBgzg6NGjTJ48mUWLFtGnT58M/R5K9tekSRM6duxIamoq7dq10xqTIiJiVlqjSEREJBvRGkXPHxsbG+NTziIiIpmBBRmwRhGPdvyUKVMAqF27tkn5zJkz6dixIzY2Nqxbt45x48YRHx+Pl5cXr7/+OoMHDzbWtbS0ZPny5XTv3p1q1aqRM2dOOnTowIgRI4x1fHx8WLFiBX369GH8+PEUKFCAH3/8kYCAgMc/2aesRYsW992vBIX5TJgwgS1btnDy5Em6du3KggULsLS0NHdYIiLyHFKiSERERDLEpEmTGDVqFOHh4ZQtW5bvvvuOKlWqpFu3du3abNq0KU35q6++yooVK0hKSmLw4MGsXLmSU6dO4eTkhL+/P1999RWenp7G+oUKFeLs2bMmbYwcOZKPPvooY08uE3rllVcw3Ocm3F9//fUMoxERETGv1NTU++738vJKt+/xX97e3qxcufK+dWrXrs2+ffseKT5zcnJyeuD+9u3bP6No5N8cHByYP38+1atX59dff+XNN99k7ty55MiRw9yhiYjIc0aJIhERkWzEXAOKFi5cSN++fZk6dSp+fn6MGzeOgIAAjh07hqura5r6ixcvJjEx0fj62rVrlC1bljfffBOAGzdusHfvXj799FPKli3L9evX+eCDD2jSpAl79uwxaWvEiBF07drV+NrBweEpnWXmUq5cOZPXSUlJhISEcOjQITp06GCeoERERMiYaWw1SDrjzJw509whyH1UqVKFefPm0a5dO5YsWYK/vz9//fUXdnZ25g5NRESeI0oUiYiIZCMWGXhTJSEhgYSEBJMyW1tb4xz9/zZmzBi6du1Kp06dAJg6dSorVqxgxowZ6Y7ucXZ2Nnm9YMECcuTIYUwUOTk5sXbtWpM6EydOpEqVKoSFhVGwYEFjuYODA+7u7o93klnY2LFj0y0fNmwYcXFxzzgaERGR/7MwPHmfJCP7NCKZXcuWLfHw8KBp06YEBQUxfPhwRo4cae6wRETkOWJh7gBEREQkcxo5ciROTk4mW3r/YU1MTCQ4OBh/f39jmYWFBf7+/gQFBT3Ue02fPp1WrVqRM2fOe9aJjo7GYDCQO3duk/KvvvqKvHnzUr58eUaNGsXt27cf7gSzqbfffpsZM2aYOwwREREReQQ1a9Y0jv765ptv2L17t5kjEhGR54lGFImIiGQjT7pw9L8NHDSIvn37mpSlN5ro6tWrJCcn4+bmZlLu5ubG0aNHH/g+u3bt4tChQ0yfPv2edW7dusXAgQNp3bo1jo6OxvJevXpRoUIFnJ2d2b59O4MGDeLSpUuMGTPmge+bXQUFBWmqEhERMSuD4cn7JJp6Tp5HTZs2pXXr1vz888906tSJoKCg52ZaZRERMS8likRERLKRjLypcq9p5jLa9OnTKV26NFWqVEl3f1JSEi1btiQ1NZUpU6aY7Pt3IqtMmTLY2Njw3nvvMXLkyGcSuzm1aNHC5HVqaiqXLl1iz549fPrpp2aKSkRERGsUiTyJCRMmsH79ev7++29efvllVqxYgYeHh7nDEhGRbE5Tz4mIiMgTcXFxwdLSkoiICJPyiIiIB64dFB8fz4IFC+jcuXO6++8mic6ePcvatWtNRhOlx8/Pj9u3b3PmzJlHOoes6L/TAjo7O1O7dm1WrlzJ0KFDzR2eiIiIiDwGFxcXVqxYgaurK/v27aNq1aps377d3GGJiEg2pxFFIiIi2Yg5Fn62sbGhYsWKrF+/nmbNmgGQkpLC+vXrCQwMvO+xv/zyCwkJCbz99ttp9t1NEp04cYINGzaQN2/eB8YSEhKChYUFrq6uj3UuWUVycjKdOnWidOnS5MmTx9zhiIiImLAwPHmfxBx9GpHMolKlSgQFBdGwYUOOHz9OzZo16devH0OGDCFXrlzmDk9ERLIhjSgSERHJRgwZ+PUo+vbty7Rp05g9ezZHjhyhe/fuxMfH06lTJwDat2/PoEGD0hw3ffp0mjVrliYJlJSUxBtvvMGePXuYN28eycnJhIeHEx4eTmJiInBnLZ5x48axf/9+Tp06xbx58+jTpw9vv/12tk+eWFpaUr9+faKioswdioiISBrm6IuIZDeFCxdm586dtG/fnpSUFEaNGkWRIkWYOHEiycnJ5g5PRESyGY0oEhERkSf21ltvceXKFYYMGUJ4eDjlypVj1apVuLm5ARAWFoaFhenzKceOHWPr1q2sWbMmTXsXLlxg2bJlAJQrV85k34YNG6hduza2trYsWLCAYcOGkZCQgI+PD3369DFZtyg7K1WqFKdOncLHx8fcoYiIiIjIU5A7d25mz55NixYt6Nu3L6dOnaJnz54cPnyYyZMnmzs8ERHJRpQoEhERyUbMOU1LYGDgPaea27hxY5oyX19fUlNT061fqFChe+67q0KFCuzYseOR48wuPv/8c/r3789nn31GxYoVyZkzp8n+B63nJCIi8rRo6jmRjNW0aVMaNmzIlClT6NOnD1OmTKFixYr3XOdTRETkUSlRJCIiko3opkr2N2LECPr168err74KQJMmTTAY/n/hU1NTMRgMmpJERETMRokikYxnY2PDBx98QExMDEOGDOH999+nYMGC1KtXz9yhiYhINqBEkYiIiEgWMnz4cLp168aGDRvMHYqIiIiIPGOffPIJ+/btY8mSJQQEBDBkyBA+/fRTLC0tzR2aiIhkYUoUiYiIZCP/Hlki2dPdKflefvllM0ciIiKSPoPB8MR9EvVpRNJnYWHB3Llz6dWrF9OnT2f48OH8/fff/Pzzz1hZ6TafiIg8HosHVxEREZGs4u5ULxmxSealm2ciIpKZqS8i8nTlyJGDH3/8kblz52JjY8Ovv/5Kp06dSEhIICEhwdzhiYhIFqREkYiIiEgWU6xYMZydne+7iYiIiEj21rZtW3755ResrKyYO3cudnZ22NnZ8cYbbxAfH2/u8EREJAvRmFQREZFsRANNng/Dhw/HycnJ3GGIiIiky2B48j6J+jQiD6dJkybMmzePzp07ExcXB8Bvv/1GWFgYK1asIF++fGaOUEREsgIlikRERLIRC91VeS60atUKV1dXc4chIiKSLguD4Yn7JOrTiDy8li1b0qRJE27evMmhQ4do3rw5u3fvpk6dOmzfvh0HBwdzhygiIpmcpp4TERERyUK0PpGIiIiI/JednR158uShZs2abNu2DQ8PDw4dOkSHDh1ISUkxd3giIpLJKVEkIiKSjWTU4tFaQDrzSk1NNXcIIiIi96W+iIh5+fr6snjxYmxsbFiyZAkfffQRSUlJ5g5LREQyMSWKREREspG7awJkxCaZU0pKiqadExGRzC0j+iHqi4g8kapVqzJlyhQARo0aRcmSJfn999/NHJWIiGRWShSJiIiIiIiIiIhkM++88w4//vgjrq6uhIaG0qxZM3r37q3RRSIikoYSRSIiItmIBYYM20REREQeh/oiIplH586dCQ0N5cMPPwRg/PjxVKpUieHDh7N3714zRyciIpmFlbkDkIxRpnBec4cgz0BCYrK5Q5CnzNJS/yGWJ6Mp40RERMTcMmIaW/VpRDKOg4MD33zzDdWrV6d9+/YcOHCAAwcOMGzYMEaPHk2/fv3MHaKIiJiZRhSJiIiIiIiIiIhkc02bNuXo0aNMnTqV1157DYD+/fvTp08fUlJSzBydiIiYkxJFIiIi2YiFIeM2ERERkcehvohI5uXh4cF7773HsmXLGDVqFADjxo1j4sSJZo5MRETMSYkiERGRbMTCYMiwTURERORxqC8ikvkZDAb69+/P2LFjAfjoo484ceKEmaMSERFzUaJIRERERERERETkOdSrVy/q1KnDzZs36dixIzdv3jR3SCIiYgZKFImIiGQjdxePzohNRERE5HGoLyKSdVhYWDBjxgwcHBzYvn07bm5udO3alWvXrpk7NBEReYaUKBIREclGNPWciIiImJsFGdAXQX0RkWfF29ubn3/+GW9vb2JjY/nxxx9p2bIlycnJ5g5NRESeESWKREREREREREREnmONGjXi1KlTrFmzhpw5c/LXX38xYsQIc4clIiLPiBJFIiIi2YimnhMRERFzU19EJGuysLCgXr16/PDDDwB89tlntGnThokTJ3L9+nUzRyciIk+TEkUiIiLZiEUGbiIiIiKPQ30RkaytTZs2vP/++6SmpvLzzz/Ts2dPypcvz549e8wdmoiIPCXqe4mIiIiIiIiIiIjRxIkTWbduHcOHD6dw4cKcPXuW6tWr8/3335Oammru8EREJIMpUSQiIpKNGAyGDNtEREREHof6IiJZn8FgoG7dugwZMoTg4GCaNm1KYmIi3bp1o0OHDty4ccPcIYqISAZSokhERCQbMWTgJiIiIvI41BcRyV5y587NkiVL+Prrr7GwsOCnn36ievXqXLhwwdyhiYhIBlGiSERERERERERERO7JYDAwYMAA1q9fT758+QgJCaFq1ars3LnT3KGJiEgGsDJ3ACIiIpJxLDRNi4iIiJiZhcHwxH0S9WlEMqfatWuzY8cOGjVqxNGjR6latSp+fn40aNCAvHnzUr16dSpUqGDuMEVE5BEpUSQiIpKN6JaKiIiIZAbqk4hkX4ULF2b79u307NmThQsXsnPnTuPIIltbW44cOYKPj4+ZoxQRkUehqedERERERERERETkoeXJk4e5c+dy/vx5xowZw3vvvYevry8JCQkMGjTI3OGJiMgj0ogiERGRbESztIiIiIi5GQxP3idRn0Yka3Bzc6NPnz4A7N+/n/Lly7Nw4UJ69OjB9evXiYyMpHnz5jg5OZk5UhERuR+NKBIREclGDAZDhm0iIiIij0N9EZHnU9myZXnnnXcAqFWrFk2bNqVTp04UKFCAPn36cOPGDTNHKCIi96JEkYiIiIiIiIiIiDyxzz77jFy5cgHg6upKiRIliIuLY9y4cTRs2JCYmBgzRygiIulRokhERCQbscjATURERORxqC+S/U2aNIlChQphZ2eHn58fu3bteqjjFixYgMFgoFmzZsaypKQkBg4cSOnSpcmZMyeenp60b9+eixcvPqXo5Wny8PAgKCiIFStWcO7cOf7++2/++OMPHB0d2bx5M3Xq1CEqKsrcYYqIyH+o7yUiIpKNaOo5ERERMTf1RbK3hQsX0rdvX4YOHcrevXspW7YsAQEBXL58+b7HnTlzhv79+1OzZk2T8hs3brB3714+/fRT9u7dy+LFizl27BhNmjR5mqchT1GpUqV49dVXsbGxwWAw8Nprr7FhwwZcXFwIDg7m3XffJTU11dxhiojIvyhRJCIiIiIiIiIiD2XMmDF07dqVTp06UbJkSaZOnUqOHDmYMWPGPY9JTk6mbdu2DB8+nMKFC5vsc3JyYu3atbRs2RJfX1+qVq3KxIkTCQ4OJiws7GmfjjwjFSpUYMWKFVhZWfHLL78wa9Ysc4ckIiL/okSRiIhINmLIwE1ERETkcagvkn0lJiYSHByMv7+/sczCwgJ/f3+CgoLuedyIESNwdXWlc+fOD/U+0dHRGAwGcufOne7+hIQEYmJiTDbJ/KpUqcKIESMA6NmzJ0uWLCElJcXMUYmICChRJCIikq1o6jkRERExN3P0RUaOHEnlypVxcHDA1dWVZs2acezYMZM6t27dokePHuTNm5dcuXLx+uuvExERYVInLCyMRo0akSNHDlxdXfnwww+5ffu2SZ2NGzdSoUIFbG1teeGFF56rkRFXr14lOTkZNzc3k3I3NzfCw8PTPWbr1q1Mnz6dadOmPdR73Lp1i4EDB9K6dWscHR3TrTNy5EicnJyMm5eX16OdiJjNgAEDqF27NvHx8bRo0YLSpUuzZs0ac4clIvLcU6JIRERERERERLK0TZs20aNHD3bs2MHatWtJSkqifv36xMfHG+v06dOHP/74g19++YVNmzZx8eJFWrRoYdyfnJxMo0aNSExMZPv27cyePZtZs2YxZMgQY53Tp0/TqFEjXnnlFUJCQujduzddunRh9erVz/R8s4rY2FjatWvHtGnTcHFxeWD9pKQkWrZsSWpqKlOmTLlnvUGDBhEdHW3czp07l5Fhy1NkaWnJ77//zscff4yjoyOHDx8mICCAzp07ExUVZe7wRESeW1bmDkBEREQyjp4AEREREXOz4Mn7JI96/KpVq0xez5o1C1dXV4KDg6lVqxbR0dFMnz6d+fPnU6dOHQBmzpxJiRIl2LFjB1WrVmXNmjUcPnyYdevW4ebmRrly5fjss88YOHAgw4YNw8bGhqlTp+Lj48O3334LQIkSJdi6dStjx44lICDgCc8683NxccHS0jLNSKyIiAjc3d3T1D958iRnzpyhcePGxrK7U41ZWVlx7NgxihQpAvw/SXT27Fn++uuve44mArC1tcXW1jYjTknMwNHRkS+++IIBAwYwZMgQvvvuO2bMmMGqVauYOnWqyc+LiIg8G7qfJCIiko1o6jkRERExt4zsi/x3HZqEhISHiiE6OhoAZ2dnAIKDg0lKSjJZW6d48eIULFjQuLZOUFAQpUuXNplWLSAggJiYGP7++29jnX+3cbfO/dbnyU5sbGyoWLEi69evN5alpKSwfv16qlWrlqZ+8eLFOXjwICEhIcatSZMmxhFZd6eMu5skOnHiBOvWrSNv3rzP7JzEfJycnBg/fjybN2+mWLFiXLx4kSZNmlC6dGkCAwM5evSouUMUEXluKFEkIiIiGWLSpEkUKlQIOzs7/Pz82LVr1z3r1q5dO90bQo0aNTLWSU1NZciQIXh4eGBvb4+/vz8nTpwwaScyMpK2bdvi6OhI7ty56dy5M3FxcU/tHEVEROTZ8vLyMlmLZuTIkQ88JiUlhd69e1O9enVKlSoFQHh4ODY2NuTOnduk7r/X1gkPD0937Z27++5XJyYmhps3bz7WOWY1ffv2Zdq0acyePZsjR47QvXt34uPj6dSpEwDt27dn0KBBANjZ2VGqVCmTLXfu3Dg4OFCqVClsbGxISkrijTfeYM+ePcybN4/k5GTCw8MJDw8nMTHRnKcqz0iNGjUICQlhwIABWFpacujQISZNmkTNmjU5e/asucMTEXkuKFEkIiKSjRgycHsUCxcupG/fvgwdOpS9e/dStmxZAgICuHz5crr1Fy9ezKVLl4zboUOHsLS05M033zTW+eabb5gwYQJTp05l586d5MyZk4CAAG7dumWs07ZtW/7++2/Wrl3L8uXL2bx5M+++++4jRi8iIiIZKSP7IufOnTNZi+ZuAuJ+evTowaFDh1iwYEGGnpfc8dZbbzF69GiGDBlCuXLlCAkJYdWqVcYEWlhYGJcuXXro9i5cuMCyZcs4f/485cqVw8PDw7ht3779aZ2GZDL29vZ8/fXXXLp0iV9//ZUyZcpw9epVmjdvzo0bN8wdnohItqc1ikRERLIRc80YN2bMGLp27Wp8knTq1KmsWLGCGTNm8NFHH6Wpf3camLsWLFhAjhw5jImi1NRUxo0bx+DBg2natCkAc+bMwc3NjaVLl9KqVSuOHDnCqlWr2L17N5UqVQLgu+++49VXX2X06NF4eno+zVMWERGRezAYnrxPcvd4R0fH+65V81+BgYHGh0cKFChgLHd3dycxMZGoqCiTUUX/XlvH3d09zYjou2vx/LtOeuvzODo6Ym9v/9BxZnWBgYEEBgamu2/jxo33PXbWrFkmrwsVKkRqamoGRSZZXb58+Xj99depXLkylSpVYt++fdSoUYO3336bt99+G1dXV3OHKCKSLWlEkYiIiKQrISHhodYFSExMJDg42GS+fgsLC/z9/R96vv7p06fTqlUrcubMCcDp06cJDw83adPJyQk/Pz+TdQRy585tTBIB+Pv7Y2Fhwc6dOx/rnEVERCRrSk1NJTAwkCVLlvDXX3/h4+Njsr9ixYpYW1ubrK1z7NgxwsLCjGvrVKtWjYMHD5qMiF67di2Ojo6ULFnSWOffbdytk976PCLy+AoWLMhvv/2Gvb09+/bto1+/flSsWJGoqChzhyYiki0pUSQiIpKNWGDIsG3kyJEmawLca12Aq1evkpycnO58/Xfn87+fXbt2cejQIbp06WIsu3vc/doMDw9P80ShlZUVzs7OD/W+IiIi8nRkVF/kUfTo0YO5c+cyf/58HBwcjGvc3F03yMnJic6dO9O3b182bNhAcHAwnTp1olq1alStWhWA+vXrU7JkSdq1a8f+/ftZvXo1gwcPpkePHtja2gLQrVs3Tp06xYABAzh69CiTJ09m0aJF9OnTJ2O/iSJCzZo1OXHiBBMmTKBgwYKcP38+3dkKRETkyWnqOckUavnkwb+oM452VlyITmDRgXDOXr+Vbt0PahSkWL6cacoPhccxJegcAK8Wd6FiAUfy2FuTnJJKWNQt/jh8mTP3aFOejVdecCaguAtOdlaci7rFz3svcToy/QVfP3zFB1/XtNf5wMVYJmxJu5jl2xU9qf2CMwv2XWLd8WsZHrs8vNpFnKlXLC9Odlacj77Fgn3hnLme/nXu+3IhfNP5PB+8FMvEbWFpytuU9+DlIs4sCrnE+tDIDI89O8jIqecGDRpE3759Tcru3iTJSNOnT6d06dJUqVIlw9sWERGRZy8jp557WFOmTAGgdu3aJuUzZ86kY8eOAIwdOxYLCwtef/11EhISCAgIYPLkyca6lpaWLF++nO7du1OtWjVy5sxJhw4dGDFihLGOj48PK1asoE+fPowfP54CBQrw448/EhAQ8FjnKSL3lz9/fnr27EmZMmWoXbs233//PcWKFWPLli1YW1szc+ZM46wEIiLy+JQoApKSkrC2tjZ3GM+tCvkdaFHalQUhd24mv1LEmcCXCjJ87UniEpPT1J+28zxWFv//X0NOG0sG1SnMvgsxxrLLcYks2h/O1fgkbCwNvPJCXgKrF2TYmvTblKevspcjLcu5Mzf4Iqeu3cS/WF56v1yIwSuPE5uQ9ppM3haG5b+ucy4bS4YGvMCec9Fp6pbP70DhvPZcv5H0VM9BHqxSAUfeKOPG/H+SgHWLOtOrpjdDV59I9zpP3X7O9PNsa8mn/kUIPh+Tpm45z3+u801d52fF1tb2oRJDLi4uWFpapjtf/935/O8lPj6eBQsWmNyAgf+vAxAREYGHh4dJm+XKlTPW+ffUMAC3b98mMjLyge8rIiIi2cvDrHFjZ2fHpEmTmDRp0j3reHt7s3Llyvu2U7t2bfbt2/fIMYrI43v55Zfp0qULP/74I/369TPZt3DhQgzmWqxVRCSbMOvUc7Vr16Znz5707t2bPHny4ObmxrRp04iPj6dTp044ODjwwgsv8OeffxqPSU5OpnPnzvj4+GBvb4+vry/jx49P0/aMGTN48cUXsbW1xcPDw2SRRYPBwJQpU2jSpAk5c+bkiy++AO48gVSkSBFsbGzw9fXlp59+um/8u3fvpl69eri4uODk5MTLL7/M3r17jfvbtGnDW2+9ZXJMUlISLi4uzJkzB4DY2Fjatm1Lzpw58fDwYOzYsdSuXZvevXs/8vczq6r7Ql62n4liR1g04bGJLAgJJzE5hWqFcqdb/0ZSCjEJycatuGtOEpNT2PuvRNGe8zEcu3KDazeSuBSbyOKDEdhbW5LfKeOfhJeHU8/XhS2nrrPtdBSXYhKYu+ciibdTqOGTJ9368YnJxNy6bdxKuuciMTklTaIot70VrSt48uOO8yRrAVSz8y+Wl62nr7P9bBSXYhOYt/cSickpvFQo/et8IymZmITbxq3kP5/n4PP/uc52VrQq58H0XRdITtF1vh9DBn49LBsbGypWrGgyX39KSgrr169/4Hz9v/zyCwkJCbz99tsm5T4+Pri7u5u0GRMTw86dO03WEYiKiiI4ONhY56+//iIlJQU/P7+Hjl9EREQy1rPui4jI8+Gbb77hhRdeIHfu3HTu3Blra2t++eUXhg0bRkpKirnDExHJ0sy+RtHs2bNxcXFh165d9OzZk+7du/Pmm2/y0ksvsXfvXurXr0+7du24ceMGcOfGU4ECBfjll184fPgwQ4YM4eOPP2bRokXGNqdMmUKPHj149913OXjwIMuWLeOFF14wed9hw4bRvHlzDh48yDvvvMOSJUv44IMP6NevH4cOHeK9996jU6dObNiw4Z6xx8bG0qFDB7Zu3cqOHTsoWrQor776KrGxsQC0bduWP/74g7i4OOMxq1ev5saNGzRv3hyAvn37sm3bNpYtW8batWvZsmWLSbIpu7M0gFduO45eiTeWpQJHr8RT2Nn+odqo5p2b4PMxJCanf/PY0gDVC+XmRmIy56PTLsIuT5+lhQHvPPYcjvj/ZyEVOBIRR2GXHA/VRg2fPOwKiza5zgags18BVh+9ysUYXVtzszQYKJjbniOX//N5joincN6H+zxX98nDnnNpr3OnKvlZc/wql3SdH+juVC8ZsT2Kvn37Mm3aNGbPns2RI0fo3r278cEPgPbt2zNo0KA0x02fPp1mzZqRN2/e/5yHgd69e/P555+zbNkyDh48SPv27fH09KRZs2YAlChRggYNGtC1a1d27drFtm3bCAwMpFWrVnh6ej7W909ERESenDn6IiKS/eXJk4fDhw9z9epVfvzxR+PUkSNGjKBgwYJ8/PHHxntyIiLyaMw+9VzZsmUZPHgwcGcthK+++goXFxe6du0KwJAhQ5gyZQoHDhygatWqWFtbM3z4cOPxPj4+BAUFsWjRIlq2bAnA559/Tr9+/fjggw+M9SpXrmzyvm3atDHevAJo3bo1HTt25P333wfu3PDasWMHo0eP5pVXXkk39jp16pi8/uGHH8idOzebNm3itddeIyAggJw5c7JkyRLatWsHwPz582nSpAkODg7ExsYye/Zs5s+fT926dYE78yc/6OZWQkICCQmmN0uTkxKxtLa573GZUS5bKywtDGmmpIq9lYx7rgeP/vHOY0d+Jzvm7buUZl8p91y8Uzk/1pYGYm7d5rttYcRr2jmzyGVjiaXFnevwbzG3buPu+ODr7ONsT4HcdszefcGkvEEJF1JSYf0JrUmUGeSyvXOdY/97nRNu4+744IRgoTz25HeyY86eiyblAb53rvNfWpMoU3vrrbe4cuUKQ4YMITw8nHLlyrFq1Src3NwACAsLw8LC9PmUY8eOsXXrVtasWZNumwMGDCA+Pp53332XqKgoatSowapVq7CzszPWmTdvHoGBgdStW9e45sCECROe3omKiIiIiIjZ/HvpiC5dunDt2jVGjhzJhQsXGDlyJD///DOzZ8+mVq1aZoxSRCTrMfuIojJlyhj/bWlpSd68eSldurSx7O4Npn+vQTBp0iQqVqxIvnz5yJUrFz/88ANhYWHGehcvXjQmXu6lUqVKJq+PHDlC9erVTcqqV6/OkSNH7tlGREQEXbt2pWjRojg5OeHo6EhcXJwxFisrK1q2bMm8efOAO+sw/P7777Rt2xaAU6dOkZSUZLJ4t5OTE76+vveNfeTIkTg5OZlswb/9cN9jsquXvHNzIfoWZ6/fSrPv+JV4Rv51im83neFwRDydq+Qnl42lGaKUJ1WjcB7OR93idORNY5l3Hjv8i+Zlxs7zZoxMMlJ1n9ycj7rFmev/v84Fc9tRp6gzs/6TJJR7s8CQYdujCgwM5OzZsyQkJLBz506T6d82btzIrFmzTOr7+vqSmppKvXr10m3PYDAwYsQIwsPDuXXrFuvWraNYsWImdZydnZk/fz6xsbFER0czY8YMcuXK9cixi4iISMYxZEA/RFPPicjDGDhwIOHh4SxatAhvb2/OnDlD7dq16dSpExcvXnxwAyIiAmSCRNG/nwSAOzeF/l12dzG6u3ONLliwgP79+9O5c2fWrFlDSEgInTp1IjExEQB7+4eb3ihnzpxPHHuHDh0ICQlh/PjxbN++nZCQEPLmzWuMBe5MP7d+/XouX77M0qVLsbe3p0GDBk/0voMGDSI6Otpkq/j6u096OmYRl3Cb5JRUHGxNEzgOdpbEJNy+x1F32FgaqFjAke1no9Ldn5icypX4JM5cv8W8fZdISYWX7rHukTxdcYnJJKek4mhnOojR0c6K6FsPvs6VvZzYcuq6SXnRfDlxsLPim8a+fP/mi3z/5ou45LShZVl3vnqt2D1ak6cpLuHOdXb473W2ffjrvO3Mf66zSw4cbK0Y+WoxJrcoyeQWJXHJacMbZd35omHRDD+H7MBcU8+JiIiI3KW+iIg8S3Z2drz55pscOHCAzp07k5qayqxZsyhWrBjTpk0jVesZi4g8kNmnnntU27Zt46WXXjJOEQdw8uRJ478dHBwoVKgQ69evv+eUcekpUaIE27Zto0OHDibvVbJkyfvGMnnyZF599VUAzp07x9WrV03qvPTSS3h5ebFw4UL+/PNP3nzzTWMirHDhwlhbW7N7924KFiwIQHR0NMePH7/vEFlbW1tsbU2n68qK084BJKfCuahb+ObLyYFLd9avMQC++XKy6T+Jgf+qkN8RKwsDu8/FPNR7GQArC/1vwxySU1I5e/0mJdxyEXLhznzBBqC4Wy42PGDauEpeTlhbGtjxn4Rg0JkokzWPAPrUKsSOs1FsPX3/nx15OpJTUwmLukkJ15zsv/iv6+yakw0n7z9tXMUCTlhZGNgZFm1SviMs2mTNI4BeNb3ZeTaK7WeiMjJ8ERERERERycIcHR358ccf6dKlC3379iUoKIh3332Xv/76i+HDh6eZnUBERP4vyyWKihYtypw5c1i9ejU+Pj789NNP7N69Gx8fH2OdYcOG0a1bN1xdXWnYsCGxsbFs27aNnj173rPdDz/8kJYtW1K+fHn8/f35448/WLx4MevWrbtvLD/99BOVKlUiJiaGDz/8MN0RTW3atGHq1KkcP36cDRs2GMsdHBzo0KEDH374Ic7Ozri6ujJ06FAsLCyMI6meB+tDr9G+oidh/0w5VaeIM7aWFsbEQPuKHkTdvM2yw1dMjqvmnZv9l2LTrDtkY2mgga8LBy7FEnPrNjltrXi5cB5y21ux78LDJZUk4609dpV3/ApwNvImp6/dxN83L7ZWFmz7J6nzjl9+om7cZvHBCJPjahTOw74LMWmuc3xicpqy5NRUom/dJiI2ETGPdcev0bFyfs5cv8mZyJvULZoXGysLtv8zUqhj5fxE3Uxi6aHLJsdV98lNyMW0n+d0r3NKKjG3bhMRp+ucnufoz4eIiIhkUhkxIkh9GhF5XFWrVmXr1q2MHj2ajz/+mAULFrBgwQJ8fHxISkoiMjKSESNG0K9fP3OHKiKSaZh96rlH9d5779GiRQveeust/Pz8uHbtmsnoIrgzJdy4ceOYPHkyL774Iq+99honTpy4b7vNmjVj/PjxjB49mhdffJHvv/+emTNnUrt27XseM336dK5fv06FChVo164dvXr1wtXVNU29tm3bcvjwYfLnz59mHaQxY8ZQrVo1XnvtNfz9/alevTolSpQwWag7u9t7IZYlhy7zWol8DHrFhwJOdkzaHkZswp2bw3nsrXH6z1RWrrlseMElB0FnotO0l5IKbg42dPUrwJB6RehWtQA5bSwZs/ksl5RAMJvd52JYFBJO01KuDAkoglduO8ZtOkPMP9c5bw4bnOxNr7Obgw3F8uVk6wNGl0nmsed8DL8eiKBJSVcG+9+5zhO2njV+np1zpP08u+WyoahLTmPSUJ6MIQO/RERERB6H+iIiYm4WFhYMGDCALVu2UK9ePaytrTl9+jTnz5/nxo0bDB8+nOjotPeURESeV4ZUTdSZqcTHx5M/f36+/fZbOnfu/NDH9Vhy5ClGJZlFwn9GVkj2Y2mp/xA/D75/48Wn1vbaI1cfXOkh1SvhkmFtSfYWExODk5MTEdeicXR0NHc4IhkmKPT+U+SKZEXxcbE0ruxDdHTG/86++/dgya5T5Mzl8ERtxcfF0rxK4acSp2Q/d3/29PMi9xITE8POnTtxdHTknXfe4fDhw3zzzTd8+OGH5g5NROSpeZS/j1luRFF2s2/fPn7++WdOnjzJ3r17adu2LQBNmzY1c2QiIpIVWRgybhMRERF5HOqLiEhm4+joSL169fDz8zMmh8aNG0dsbCy///77A2ciEhHJ7pQoygRGjx5N2bJl8ff3Jz4+ni1btuDioqe4RUTk0WnqORERETE39UVEJDNr06YNnp6eXLx4EU9PT5o1a0a5cuVYtGiRuUMTETEbJYrMrHz58gQHBxMXF0dkZCRr166ldOnS5g5LREREREREREQk27GxsaFPnz4AxMXFYWdnx40bN3jrrbfo3r07R47cWd7h1q1bJCdrCQAReT5YPbiKiIiIZBUGPXwrkuUkJ6fw1Q8rWbRqN5evxeDu4kSb1/zo37kBhn8+1JevxTDsu9/ZsPMI0bE3ean8C3z94ZsUKehq0tauA6f4fMpygg+dwdLSglLF8vPbhB7Y29mY49TkOXPwyBl+W76N0FOXiIyKZXDfVrxUuYRx/81bCcz8eR1Be44SG3sDN9c8NAnwo1G9ygDExt1g7i8b2HvwJFeuRuPkmJNqlYrTrmUdcuawS/N+MbE36PHRFK5FxrDox4/IldP+mZ2r3J/B8OR9EvVpRORp6tmzJ7du3cLb25s33niDIUOGMHr0aKZOncrUqVPJlSsXcXFxeHl5sWPHDjw9Pc0dsojIU6VEkYiISDaiaVpEsp5xc9Yy47ctTB7WjhKFPdh3JIzAEXNxzGXPe61qk5qaytsf/oCVlSXzRr+HQ047Js3/i2Y9vmPHosHktLcF7iSJ3ug1mT4d6/N1/zexsrTg0IkLWGihD3lGbiUk4VPQnfq1K/D5mAVp9k/7aTX7/z7Nhz1a4JYvN3sPnGTSjBXkzeNA1UrFuXY9lmtRsXRpG0DBAvmIuBLFxOnLuXY9lk/6vJWmvXE//I5PQTeuRcY8i9OTR2Dgyfsk+s0lIk+Tra0tgwcPNr4eNWoUAQEBTJw4keXLlxMXFwfAuXPnaNOmDevWrcPKSrdRRST70tRzIiIiIs+5zZs307hxYzw9PTEYDCxdutTcIT1Xdh04xasvlyGgRikKeualad3yvOJXnOC/zwJwMuwyuw+e4duBrajwojdFC7kx5qO3uJWQxG+rg43tfDJ2Me+9VZs+HetToogHRQu50bxeBWxtrM11avKcqVyuKB3eqmsyiujfjhw/R91aZSlT0ge3fHloWLcShb3dOHbyAgCFvNwY3KcVfhV98XBzplypwnR4qy479x5LM/XPirW7iI+/RYtGLz318xIRkeeDv78/S5cuJTw8nGPHjhEcHEyuXLnYtGkTw4YNM3d4IiJPlRJFIiIi2YiFIeM2eX7Ex8dTtmxZJk2aZO5QnktVyhRm0+5jhJ6NAODg8fPs2H8K/5dKApCQdBsAO9v/P8VqYWGBjbUVO0JOAnAlMpY9h86QzzkX9d/5lmIBg2j07jiC/tkvkhmUKObFzuBjXI2MITU1lf1/n+bCpWtUKFPknsfE37hFDntbLC0tjWVh5y8zf/Em+r3fXCPmMin1RUQkK3NxcaFYsWJUqFCBadOmAfDFF1+wYEHa0bIiItmFxkyKiIhkI5p6Th5Hw4YNadiw4UPXT0hIICEhwfg6JkbTPj2JPh3qERt3iypvfo6lhYHklFQGd3+Nlg3vrNtSrJA7BdzzMGLSMsYOak0Oexsmz9/AxctRRFyLBuDMhasAfDVtJZ/1ak5p3wIsWLGLZu9/x/YFH6dZy0jEHLp3fJUJ05bRvse3WFpaYDAY+KBrE0qXKJRu/eiYeH5esomGdSsay5KSbvP1d7/SuU19XF1yE375+jOKXh6F4Z+vJ21DRMTcWrVqxY4dOxg/fjzt27fHxcUFf39/c4clIpLhlCgSERERkUcycuRIhg8fbu4wso0l6/byy6rdTPu8A8ULe3Dw+AU+HvMrHvmcaP1aVaytLPnpm670/GwePnUHYGlpQe3Kvvi/VJLU1DttpKTc+UfH5jVo26QaAGV8vdi0+xhzlwUxNLCpuU5PxGjZ6p0cDT3P0P5tcHVx4tDRs0yeuQLnPA6UL206qujGjVsM/WYeBfPno+3rrxjLZy5Yh1f+fNSpWfZZhy8iIs+pb7/9losXL/LLL79Qr149HB0dKVKkCDNmzKBcuXLmDk9EJEMoUSQiIpKNGPTwrTwDgwYNom/fvsbXMTExeHl5mTGirG3I+KX07lCP1+tXAuDFF/Jz/lIkY2etpfVrVQEoV6IgW+YPIjruJklJt3HJ44B/x1GUK1EQAHcXRwB8fdxN2vYt5M75cI24EPNLSExi9oL1DO7biioVigHg4+3OybPhLF6+3SRRdONmAp9+NZcc9rZ82rcVVlb/n3buwN+nORMWwWs7D98p+Cdb2urdb2jVrCZvv1nn2Z2U3JPB8OR9EvVpRCSzsLS05KeffuLmzZssX76cmJgY9u3bR0BAANu2beOFF14wd4giIk9MiSIREZFsRPdU5FmwtbXF1tbW3GFkGzcTErGwMF061MLCQEpqSpq6TrnsATgZdpl9R8L4uNtrABT0zItHPidCz142qR8adtm41pGIOSXfTuZ2cjKG/yw8Y2lhIOXu0DjujCQa/NVPWFtZMaR/a2xsrE3qf9LnLRISk4yvj5+8yLjvlzJq6Dt4uOV5uichD83Ak/dJ1KcRkczE1taWP/74g4iICK5du0abNm3Yv38/9evXZ9q0abzyyism/bnU1FQMyniLSBZi8eAqIiIiIiLytDSoUZoxM1ezeushwi5eY/mG/Uyev4FGtf8/tdbSdXvZGnycM+evsnLTAZoHTqTRy2WoU7UEAAaDgZ5v+/P9wo38vn4fp85d4YspyzlxNoJ2TauZ69TkOXPzVgInz1zi5JlLAERcuc7JM5e4fDWKHDnsKF2iEDPmreHA4dOEX77O2k37WL95Py9VLg7cSRJ9MvInbt1Kovd7TblxM4HIqFgio2JJTrmTOPVwc6aQl5txc3fNDYBXfhdyO+Uyy3mLiMjzw83NjZIlS7Jq1SoKFy7M6dOn8ff3x9fXl5UrVwIwbdo08uXLx8iRI80crYjIw9OIIhERkWzEQk+tiWQ5X3/4Jl9OXU7/rxdy9Xoc7i5OdGxRnQFdGhrrRFyN4ZOxi7kSGYubiyOtXvXjwy4NTNrp3uYVbiUm8fGY34iKucGLRfOzeGIgPgXyPetTkufUiVMX+eizWcbX035aDYB/rXL07d6cgb3eYNaCdYya+BuxcTdxzZeb9m/V5VX/ygCEnrnEsdDzAHTuPd6k7ZkTeuOWTyOGsgoLDE/cJ7HQmCIRycTc3d3ZvHkzX3zxBfPmzSM0NJRGjRpRp04d/vrrLwAGDx5M/fr1qVixopmjFRF5MENq6r/G+UuW1WPJEXOHIM9AQmKyuUOQp8zSUv8hfh58/8aLT63tHaFRGdZW1RdyZ1hbkrnFxcURGhoKQPny5RkzZgyvvPIKzs7OFCxY8IHHx8TE4OTkRMS1aBwdHZ92uCLPTFDoNXOHIJLh4uNiaVzZh+jojP+dfffvwbq9Z8np8GRtx8fG4F/B+6nEKdnP3Z89/byIOcTHx/Pxxx8zYcIEY5mvry/Hjh2jfPny7Nq1CysrPasvIs/eo/x91NRzIiIiIs+5PXv2UL58ecqXLw9A3759KV++PEOGDDFzZCIiIiIimVvOnDkZP348S5YsoVatWsyfP59NmzaRJ08e9u3bx8svv0zr1q2ZOnUqt27dMne4IiLpUqJIREQkOzFk4CbPjdq1a5OamppmmzVrlrlDExGRrEh9ERF5DjVr1oxNmzbRunVr3Nzc+PbbbwHYvn07CxYsoHv37hQpUoR58+aZOVIRkbQ07lFERCQbMeiuioiIiJiZ4Z+vJ21DRCQr69ixI97e3oSFhXHhwgW+//57zp07R7t27ciXLx/169c3d4giIkYaUSQiIiIiIiIiIiKSgQwGA3Xq1KFjx4588sknhIaG0qlTJ1JTU2ndujVnzpwxd4giIkZKFImIiGQjBkPGbSIiIiKPJSP6IeqLiEg2Y2Njw+TJk6lUqRKRkZFUq1aN1q1bM2fOHFJSUgC4cuUKhw8fNnOkIvI8UqJIREQkG9ESRSIiImJu6ouIiKTPzs6OX3/9FU9PT8LDw1mwYAEdOnSgTp06DBkyhEKFClG6dGk2b95s7lBF5DmjRJGIiIiIiIiIiIjIM+Dt7c2xY8dYs2YNgwcPJkeOHGzatInPPvuMGzdukJKSwueff27uMEXkOaNEkYiISHaiIUUiIiJibuqLiIjcV65cuahXrx6fffYZBw4coF69ehQrVowJEyZgaWnJ2rVrCQ4ONneYIvIcsTJ3ACIiIpJxDLqrIiIiImZm+OfrSdsQEXkeFClShDVr1hhf79q1i7lz5/LFF1/Qt29fjh8/TrFixShbtiwODg5mjFREsjONKBIRERERERERERHJBAYOHAjAkiVLqFmzJp07d6ZmzZo4OTlRrFgx2rZtS2hoqJmjFJHsRokiERGRbMRgyLhNRERE5HGoLyIi8vhKlSpFq1atAMiXLx916tQhf/78pKamcuLECebPn89rr71GfHy8mSMVkexEiSIRERERERERERGRTGLOnDmcOXOGiIgI1q9fz/nz57l8+TKrVq0if/78HDt2jH79+gFw/fp1kpOTzRyxiGR1ShSJiIhkIxm1drQe4hUREZHHpb6IiMiTsba2xtvbG8O/hlfmy5ePgIAAZs+eDcD333+Ph4cHzs7OFCpUiE8++YQNGzZw6tQprly5QkREBCkpKeY6BRHJYpQoEhERyU6UKRIRERFzU19EROSpqVu3Lv379wcgPDwcgPPnz/Pll19Sp04dihQpgqurK+7u7hQvXpzDhw+bM1wRySKUKBIRERERERERERHJIr766isWL17M1q1biYyMZNGiRTRr1gxfX19sbW2N9U6cOMFLL73E+vXrzRitiGQFShSJiIhkI4YM/BIRERF5HOqLiIg8XZaWljRv3pzq1auTJ08e3nzzTZYsWcLRo0e5efMmycnJXLlyherVqxMdHU2DBg348ccfzR22iGRiShSJiIhkIwZDxm0iIiIij0N9ERER8zEYDFhYWODi4sK6deto06YNt2/fpmvXrnz00Udat0hE0qVEkYiIiIiIiIiIiEg2Y2dnx9y5cxkyZAgAX3/9NW+99RY3b97k5s2bxMfHmzlCEckslCgSERHJRjJq7Wg9xCsiIiKPS30REZHMw2AwMHz4cGbPno21tTW//vor3t7eODg4kCdPHsaPH2/uEEUkE7AydwCSMfYev2LuEOQZsLJSbje7S01NNXcIktXproqIiIiYW0ZketSnERHJUO3bt8fb25vmzZtz5cqd+4jJycn07t2bkJAQ8uTJQ3h4OEOGDKF48eJmjlZEnjXddRYRERERERERERHJ5l5++WWOHTvG6tWrCQsL4+uvvwZg1qxZjB07lp9//pmAgAAuXbrEn3/+Sa1atfj555/NHLWIPAsaUSQiIpKNGPT4rYiIiJiZ4Z+vJ21DREQyXr58+ahfvz4AAwYMwNfXlzlz5lCgQAH+/PNPTpw4QaVKlbh48SIA27dvJ1++fPj7+5szbBF5yjSiSEREJBsxGDJue1STJk2iUKFC2NnZ4efnx65du+5bPyoqih49euDh4YGtrS3FihVj5cqVxv2FChXCYDCk2Xr06GGsU7t27TT7u3Xr9ujBi4iISIYxV19EREQeXdOmTfntt98YP348f/75J/ny5TMmiUqWLElycjJvvPEGS5Ys4eTJk6SkpJg5YhF5GpQoEhERkSe2cOFC+vbty9ChQ9m7dy9ly5YlICCAy5cvp1s/MTGRevXqcebMGX799VeOHTvGtGnTyJ8/v7HO7t27uXTpknFbu3YtAG+++aZJW127djWp98033zy9ExUREZFMafPmzTRu3BhPT08MBgNLly412d+xY8c0D5c0aNDApE5kZCRt27bF0dGR3Llz07lzZ+Li4kzqHDhwgJo1a2JnZ4eXl5f6HSKSrRQpUoRVq1bRokULli5dyt69e6levTrR0dG0aNGCF154ARcXF5o2bcqvv/6qdZZFshElikRERLIRQwZuj2LMmDF07dqVTp06UbJkSaZOnUqOHDmYMWNGuvVnzJhBZGQkS5cupXr16hQqVIiXX36ZsmXLGuvky5cPd3d347Z8+XKKFCnCyy+/bNJWjhw5TOo5Ojo+YvQiIiKSkczRF4mPj6ds2bJMmjTpnnUaNGhg8nDJf9fdaNu2LX///Tdr165l+fLlbN68mXfffde4PyYmhvr16+Pt7U1wcDCjRo1i2LBh/PDDD48Ybdb3qCPJ71qwYAEGg4FmzZqZlKempjJkyBA8PDywt7fH39+fEydOPIXIReRBKlSowG+//UbTpk2xtbVl6dKlvPPOO5QqVQpbW1uuX7/OsmXLePPNN3njjTcIDQ0lISHB3GGLyBNSokhERCQ7ycBMUUJCAjExMSZbev8BSExMJDg42GTOagsLC/z9/QkKCko3zGXLllGtWjV69OiBm5sbpUqV4ssvvyQ5OTnd+omJicydO5d33nkHw3/mopk3bx4uLi6UKlWKQYMGcePGjYf+domIiMhTYIZMUcOGDfn8889p3rz5PevY2tqaPFySJ08e474jR46watUqfvzxR/z8/KhRowbfffcdCxYsME7BNG/ePBITE5kxYwYvvvgirVq1olevXowZM+bRgs3iHnUk+V1nzpyhf//+1KxZM82+b775hgkTJjB16lR27txJzpw5CQgI4NatW0/rNETkIbm4uDB9+nQOHjxIbGwsO3bsYODAgVhZWbF48WKKFi2KnZ0dRYsWZejQoYSGhpo7ZBF5DEoUiYiISLpGjhyJk5OTyTZy5Mg09a5evUpycjJubm4m5W5uboSHh6fb9qlTp/j1119JTk5m5cqVfPrpp3z77bd8/vnn6dZfunQpUVFRdOzY0aS8TZs2zJ07lw0bNjBo0CB++ukn3n777cc7YREREcl0HuahlYe1ceNGXF1d8fX1pXv37ly7ds24LygoiNy5c1OpUiVjmb+/PxYWFuzcudNYp1atWtjY2BjrBAQEcOzYMa5fv/7YcWU1jzqSHCA5OZm2bdsyfPhwChcubLIvNTWVcePGMXjwYJo2bUqZMmWYM2cOFy9eTDOFoIiYl7W1NX5+fnz11Vfs2rULPz8/rKysAAgNDWXEiBEULVqUl156iT59+lCnTh1eeuklzp07Z+bIReRBrMwdgIiIiGQcwyNP1HJvgwYNom/fviZltra2GdJ2SkoKrq6u/PDDD1haWlKxYkUuXLjAqFGjGDp0aJr606dPp2HDhnh6epqU/3s6mNKlS+Ph4UHdunU5efIkRYoUyZBYRURE5NEY/vl60jYAvLy8TMqHDh3KsGHDHrm9Bg0a0KJFC3x8fDh58iQff/wxDRs2JCgoCEtLS8LDw3F1dTU5xsrKCmdnZ+ODL+Hh4fj4+JjUufugTHh4uMkIpezq7kjyQYMGGcseNJIcYMSIEbi6utK5c2e2bNlisu/06dOEh4ebjE53cnLCz8+PoKAgWrVqlaa9hIQEk6RhTEzMk5yWiDyG8uXLs2PHDlJSUoiMjGTNmjX89NNPrFmzhqCgIJPfCZ988glz5swxY7Qi8iBKFImIiGQjhozLE2Fra/tQiSEXFxcsLS2JiIgwKY+IiMDd3T3dYzw8PLC2tsbS0tJYVqJECcLDw0lMTDR5Uvfs2bOsW7eOxYsXPzAWPz8/4M7TbEoUiYiImIfB8OR9krvHnzt3zmT9wcd9aOXfyYbSpUtTpkwZihQpwsaNG6lbt+4Txfo8ud9I8qNHj6Z7zNatW5k+fTohISHp7r+biHuU0ekjR45k+PDhjxi9iDwNFhYWuLi40KZNG9q0aWNcA+7MmTPkz5+fjz76iLlz5/Lhhx9SunRpc4crIvegqedERETkidjY2FCxYkXWr19vLEtJSWH9+vVUq1Yt3WOqV69OaGgoKSkpxrLjx4/j4eFhkiQCmDlzJq6urjRq1OiBsdy9AeHh4fEYZyIiIiKZjaOjo8mWUaObCxcujIuLi3EtDXd39zRr7Ny+fZvIyEjjgy/u7u7pPhhzd5+kFRsbS7t27Zg2bRouLi4Z1u6gQYOIjo42bprWSiTz8PDwoG/fvkyYMIGBAwfy5ptvkpqayscff0xYWBi///47nTt3pmzZsqxcudLc4YrIPzSiSEREJBvJwAFFj6Rv37506NCBSpUqUaVKFcaNG0d8fDydOnUCoH379uTPn9+4xlH37t2ZOHEiH3zwAT179uTEiRN8+eWX9OrVy6TdlJQUZs6cSYcOHYxzX9918uRJ5s+fz6uvvkrevHk5cOAAffr0oVatWpQpU+bZnLiIiIikYeDJ+yRPu09z/vx5rl27Zny4pFq1akRFRREcHEzFihUB+Ouvv0hJSTGOWK5WrRqffPIJSUlJWFtbA7B27Vp8fX2fi2nn4NFHkp88eZIzZ87QuHFjY9ndB4WsrKw4duyY8biIiAiTh30iIiIoV65cunE87Mh3ETG/zz77jMWLF7N8+XKWL19usu/tt9/m4MGDuLi4MGfOHE6cOMG1a9d48803adCggZkiFnk+KVEkIiKSnZgpU/TWW29x5coVhgwZQnh4OOXKlWPVqlXGKUTCwsKwsPj/QGYvLy9Wr15Nnz59KFOmDPnz5+eDDz5g4MCBJu2uW7eOsLAw3nnnnTTvaWNjw7p164xJKS8vL15//XUGDx78dE9WRERE7s8MmaK4uDjj6CC4s+5NSEgIzs7OODs7M3z4cF5//XXc3d05efIkAwYM4IUXXiAgIAC4MwVugwYN6Nq1K1OnTiUpKYnAwEBatWplXCOxTZs2DB8+nM6dOzNw4EAOHTrE+PHjGTt27BOebNbx75HkzZo1A/4/kjwwMDBN/eLFi3Pw4EGTssGDBxMbG8v48ePx8vLC2toad3d31q9fb0wMxcTEsHPnTrp37/60T0lEnjJfX18CAwMZP3481tbWFClShHr16rFlyxZCQkJo27YtcXFxBAcHG4/57bffOHPmDLlz5yYqKgobGxty5MhhxrMQyf4MqampqeYOQp5cta83mzsEeQasrDRbZHanX8nPh639az61to9cis+wtkp45MywtiR7i4mJwcnJiYhr0SbrSIhkdUGh18wdgkiGi4+LpXFlH6KjM/539t2/B7uOXSSXw5O1HRcbQxVfz4eOc+PGjbzyyitpyjt06MCUKVNo1qwZ+/btIyoqCk9PT+rXr89nn31msi5OZGQkgYGB/PHHH1hYWPD6668zYcIEcuXKZaxz4MABevTowe7du3FxcaFnz55pHnTJ7hYuXEiHDh34/vvvjSPJFy1axNGjR3Fzc0szkvy/OnbsSFRUFEuXLjWWff3113z11VfMnj0bHx8fPv30Uw4cOMDhw4exs7N7YEx3f/aexs+1iDy51NRULl++TN68eY0zRRw5coQKFSpw69YtAPLmzUv79u35448/CA0NZejQobzxxhvUqFEDBwcHgoKCKFCggDlPQyTLeZS/jxpRJCIiko0YzDb5nIiIiMgdhn++nrSNR1G7du37PnS1evXqB7bh7OzM/Pnz71unTJkybNmy5ZFiy24edST5wxgwYADx8fG8++67REVFUaNGDVatWvVQSSIRyfwMBoNJYh7ujOQcO3Ys3bt3p2rVqixatAgvLy+qVatGy5YtGTt2LPPnzzeuRfb666+zadMm/V4QeUo0oiib0Iii54NGFGV/+pX8fHiaI4qOhd/IsLZ83TW0Xx6ORhRJdqURRZIdPYsRRXuOX8qQEUWVinlohIg8FI0oEsm6Ll26hJubmzHBnJKSQrly5YzTVhYoUID4+HiuX79Os2bN6NGjB1WrVjUZ7Ski6XuUv4+66ywiIiIiIiIiIiIiz5yHh4fJKEQLCwuGDx8OgLW1Nb/++isLFizAwsKCpUuXUq9ePRwcHMidOzf+/v5cvHgRuPPg7dWrV81yDiLZgRJFIiIi2YghAzcRERGRx6G+iIiIPIlmzZoxbdo0/vzzT/z8/Khfvz5r1qyhXbt25M+fH4Do6GjWr19PgwYNOHz4MDVr1sTV1ZXx48ebOXqRrElrFImIiGQnuqsiIiIi5pYRmR71aUREnlsGg4EuXbqYlNWtW5e6deuSmppKTEwMx44do2nTphw8eJAXX3zRWK93797kypWLzp07P+uwRbI0jSgSERERERERERERkUzPYDDg5ORElSpV+PPPP3FwcACgTJkyvPvuuwB07dqVF198kcaNG9O7d29GjRpF165dqV69OnPmzEnT5qZNmwgJCXmWpyGS6WhEkYiISDZi0OO3IiIiYmaGf76etA0REZH7KVeuHFu3bmXTpk107twZe3t7rK2tmTRpEocPH+bw4cNpjtmzZw/lypWjTJkyACxdupTmzZtjb2/PkSNH8Pb2ftanIZIpKFEkIiKSjRh0T0VERETMzZABfRL1aURE5CGUKVPGmPQBmDhxIv379+f48eOcPHmSU6dOcf78eQoXLsyuXbtYt24dbdu2Zffu3Zw5c4b27dsDcPPmTXr37s2SJUvMdSoiZqVEkYiIiIiIiIiIiIhkC4UKFaJQoUJpyi9fvkzp0qU5dOgQxYsXJy4ujtjYWCpUqMCBAwdYunQpK1eu5NVXX332QYuYmdYoEhERyUYMGbiJiIiIPA71RUREJDNydXVlxowZGAwGzp49y7Vr1yhYsCB//vknvXv3BiAwMJDIyEgANmzYwJgxY7hy5YoZoxZ5NpQoEhERyU6UKRIRERFzU19EREQyqUaNGnHkyBE2bNjAmjVrOHz4MK6urgwdOhRvb29Onz5N8+bN+fHHH/H396dfv354e3vTt29fbt26Ze7wRZ4aTT0nIiIiIiIiIiIiIs8FX19ffH19Tcpy5crFH3/8QY0aNdi8eTObN28GIH/+/Fy4cIGxY8dy48YNpk6dajwmNTWVI0eO4Ovri6Wl5TM9B5GMphFFIiIi2YghA79EREREHof6IiIikhWVLl2a3377DSurO2MrevXqRVhYGAsXLsRgMPD9998zb948ABITE3nzzTd58cUXqVWrFqdPnzZn6CJPTCOKREREshGD7qmIiIiImRkMT94nUZ9GRETMwd/fn82bN3Pp0iWaN2+OwWCgZcuW/P3334wYMYJ3332XvXv3cuTIEf78808Atm/fTrly5ejTpw/t2rXD29ub5ORkbG1tzXw2Ig9PI4pERERERERERERERIBq1arRokULDP96amHIkCHUq1ePGzduMGbMGP7880/s7OyYOXMmL730EjExMQwfPpwXXngBa2tr7Ozs8Pf359SpU2Y8E5GHp0SRiIhINpJRa0frIV4RERF5XOqLiIhIdmNpacny5ctZuHAhnTp1omrVqqxatYqOHTuyadMm5s2bR/369bGw+P/t9vXr11OmTBmaNGlC7dq1GTFiRJp2k5OTCQ4OZvz48Xz++efcuHHjWZ6WiJGmnhMREclOdFdFREREzC0jMj3q04iISCZjY2NDy5YtadmypUm5lZUVbdq0oU2bNsTFxZGYmMjly5fp1q0bmzZt4o8//gBg06ZN1KlThxo1agBw8+ZNGjZsyKZNm4xtrV69muXLl+Pk5PTsTkwEjSgSEREREREREREREXliuXLlwtnZmeLFi/PXX3+xZMkSvv/+e5o3bw7AgAEDSE1NJSUlhU6dOrFp0ybs7e1p2LAhTk5ObN26lYoVK1KpUiWKFy/O9u3bzXxG8rwwa6KoUKFCjBs37qHrnzlzBoPBQEhIyFOL6d9mzZpF7ty5n8l7iYiIZARDBn6JiIiIPA71RURERMDCwoJmzZrx7rvvMmnSJHLkyEFQUBDfffcd3bp1Y+HChVhbW7Ny5UpWrlzJxo0bcXV15eTJkwQHB3Ps2DHatWtHfHw8ALdv3zbzGUl2Ztap53bv3k3OnDkztM1Zs2bRu3dvoqKiMrRdebpeL+9BWz8vnHPaEHo5jjHrTnL4Umy6dSe1LkOFgrnTlG87eY3+v/5tfO2d154eLxemfEEnLA0GTl+7wcdLDhMRm/C0TkMeoHk5D1pXyo9zThtOXoln3F8nORIel27dCS1LU94r7TDboFORDFhy2Pja29mebrUKUa6AE5YWBs5cu8HgZUe5rOtsNi3KedC6coF/rnMcY9ff+zp/91ZpynvlTlO+/VQkAxb/6/PsbE/3Wj6U8/rXdf79iD7P6TDonoqIiIiYmYEn75OoSyMiItmJh4cHffr04YsvvuCDDz4wln///ffUrl0bgHLlyrFv3z5WrlxJ3rx5+eCDDzh16hR9+vTBxsaGH374gV69ejFq1CgM+s+/ZDCzJory5ctnzreXTKJu8Xz0qlOEb9ac4O+LsbxVKT9jW5ai1bQ9XL+RlKb+oCWHsbL8/y9DJ3tr5nSqyF9HrxrL8ue24/u25fjjQDg/bj1DfGIyPi45SExOeSbnJGnV8XUh8GUfvl0XyuFLsbxZMT/fvl6KNjOCibqZ9jp/suwI1hb/v86O9tbMbF+eDcf/f509neyY1KoMKw5FMGN7GPEJ/1zn27rO5lLH14XA2oUZ/c91blnBkzFvlKL1jGCi0vk8f/y76XV2srdmZocKbDh2xVjm6WTH5NZlWX4wnOnbzxqvc4I+zyIiIiIiIiKSRQwYMIC5c+dy4cIF6tevT9euXWnWrJlJHU9PT7p06QJAjhw5aNCgAdOmTTPu//bbb7G3t+ezzz57lqHLc+Chp55bvnw5uXPnJjk5GYCQkBAMBgMfffSRsU6XLl14++23ja+3bt1KzZo1sbe3x8vLi169ehmHykHaqeeOHj1KjRo1sLOzo2TJkqxbtw6DwcDSpUtNYjl16hSvvPIKOXLkoGzZsgQFBQGwceNGOnXqRHR0NAaDAYPBwLBhwwBISEigf//+5M+fn5w5c+Ln58fGjRtN2p01axYFCxYkR44cNG/enGvXrj3w+zJw4ECKFStGjhw5KFy4MJ9++ilJSXduhh4/fhyDwcDRo0dNjhk7dixFihQxvl62bBlFixbFzs6OV155hdmzZ2MwGJ6bUVGtK+dn2f5LrDgYwZlrN/hm9QkSklJ4rbR7uvVjbt0mMj7JuFUplIeEpGT++teN5fdqFWL7yUgmbTzN8cvxXIi6xdbQyHQTT/JsvFUxP38cDGfl35c5E3mT0WtDuZWUTKPSbunWj711m8gbScatsnduEpKS2XDs/4mid2t4s+P0daZsPsOJy/FcjL7FtpOR6Sae5NloVemf63zozud51NpQbiWl8Fqph7vOle5e538lBN+tWYigU5Fpr7M+z+kyZOAmIiIi8jjUFxEREUnL0dGRw4cPExkZyYoVK9Ikif4rICDAmDQqUqQI/fr1A+Dzzz+nX79+3Lhxg82bN/PSSy/RuXNnYmJiSE1NZdu2bfz2228cPHiQffv2MWXKFL7//ntSUvTArdzbQ48oqlmzJrGxsezbt49KlSqxadMmXFxcTJItmzZtYuDAgQCcPHmSBg0a8PnnnzNjxgyuXLlCYGAggYGBzJw5M037ycnJNGvWjIIFC7Jz505iY2ONP/z/9cknnzB69GiKFi3KJ598QuvWrQkNDeWll15i3LhxDBkyhGPHjgF3FhADCAwM5PDhwyxYsABPT0+WLFlCgwYNOHjwIEWLFmXnzp107tyZkSNH0qxZM1atWsXQoUMf+H1xcHBg1qxZeHp6cvDgQbp27YqDgwMDBgygWLFiVKpUiXnz5plkeefNm0ebNm0AOH36NG+88QYffPABXbp0Yd++ffTv3//hLko2YGVhwNfdgTk7zhnLUoHdZ6Iold/hodpoXMadtUeucCvpzi87A/BSYWfm7TrP2JalKOaai0vRt5iz4xybTzw4+ScZz8rCQDG3XMzdZXqd94RF8aLHw13nRqXcWH/sKrdu//86Vyuch/m7L/Dt6y9S1DUnl6ITmLvrHFtCI5/CWciD3LnODvy087yxzHidPR0fqo3XSruz/uh/P895mLfrPN++Xopibjm5FH2Ln3aeZ0uoPs/p0ehzERERMTeDIQOmnlOfRkREsqEcOXI8Uv2pU6fSvn17KlWqhL29PS4uLgwaNIgxY8Ywd+5cLl++DEBQUBAbN27E3d2d7du3p9tWSEgIkydP5tatW1y+fBlvb+8nPh/JPh56RJGTkxPlypUzJoY2btxInz592LdvH3FxcVy4cIHQ0FBefvllAEaOHEnbtm3p3bs3RYsW5aWXXmLChAnMmTOHW7dupWl/7dq1nDx5kjlz5lC2bFlq1KjBF198kW4s/fv3p1GjRhQrVozhw4dz9uxZQkNDsbGxwcnJCYPBgLu7O+7u7uTKlYuwsDBmzpzJL7/8Qs2aNSlSpAj9+/enRo0axqTV+PHjadCggTHB06tXLwICAh74fRk8eDAvvfQShQoVonHjxvTv359FixYZ97dt25aff/7Z+Pr48eMEBwfTtm1b4M48lL6+vowaNQpfX19atWpFx44d7/ueCQkJxMTEmGwptxMfGGtmlDuHNVYWBiLjTeOPvJFI3pw2Dzy+pIcDRfLl5I8D4cayPDmtyWlrRTs/L3aeuk7vRQfZdPwaI5uXTHfNG3n6nOzvXmfTESDXbyQ91HUu4Z6LIvlysvzgv65zDmty2FjRtkoBdp6+Tt9f/2Zz6DU+b1KCcgUeLikhGev/1/k/n+f4RPLmtH7g8Xev8x/pXOe3/bzYeSaSPr8cYvOJa3zRtATlCujzLCIiIiIiIiLZl6WlpXHGLoCPPvqIpUuXUqBAAWOSqF27dnh7e3Pq1Cm2b9+OnZ0dFStWxMnJCScnJ2rXro3BYGDq1KnUr1+f/PnzU6hQIebOnWvOU5NM5qETRQAvv/wyGzduJDU1lS1bttCiRQtKlCjB1q1b2bRpE56enhQtWhSA/fv3M2vWLHLlymXcAgICSElJ4fTp02naPnbsGF5eXri7/3+6sSpVqqQbR5kyZYz/9vDwADB+MNJz8OBBkpOTKVasmEk8mzZt4uTJkwAcOXIEPz8/k+OqVav2wO/JwoULqV69ujEpNXjwYMLCwoz7W7VqxZkzZ9ixYwdwZzRRhQoVKF68uPG8K1eubNLmvc77rpEjRxo/6He3CxvmPTDW7KhxGXdCL8dx+FKssczin0fPtoReY8GeC5y4HM9PO8+xLTSSZuU8zBWqPIFGpdw4eSWeI+FxxrK7i/ZtDb3Gor0XCb0Sz7xd59l+KpKmZXWds6LXSrsTer/rHHznOs/ddZ7tJyNpVjb96SlFk8+JiIiIuakvIiIi8rQ0bdqUw4cPM378eLZu3cqcOXPYt28fPXr0YMCAAZw6dYo9e/YQFRVFVFQUGzZsMK5ztG7dOq5fvw7A+++/n+59enk+PfTUcwC1a9dmxowZ7N+/H2tra4oXL07t2rXZuHEj169fN44mAoiLi+O9996jV69eadopWLDgEwVtbf3/J9Pv3kS83xyLcXFxWFpaEhwcjKWlpcm+u1PTPY6goCDatm3L8OHDCQgIwMnJiQULFvDtt98a67i7u1OnTh3mz59P1apVmT9/Pt27d3/s9wQYNGgQffv2NSmr992uJ2rTXKJuJHE7JRXn/4wqcc5hw7X4+4+SsrO2wL9EPqZtOZO2zeQUTl+9YVJ+5toNymqkiVlE37x7nU1HleTJYf3g62xlQd3i+Zi+LcykPPrmnet85tpNk/Kz125SJr+uszn8/zr/5/Oc04Zr8fdfT8jO+u51Ppu2zeQUzlwz/TyfjbxBaV3ndGmaFhERETE3TT0nIiLydDk4OJjcd8+TJw8TJ068Z/3OnTtjY2PDn3/+SevWrfn666/Ztm0b7dq1Y/Lkybi5ueHmlv760vJ8eKQRRXfXKRo7dqwxKXQ3UbRx40Zq165trFuhQgUOHz7MCy+8kGazsUk71ZSvry/nzp0jIiLCWLZ79+5HPiEbGxuSk5NNysqXL09ycjKXL19OE8vdEUwlSpRg586dJsfdHQV0L9u3b8fb25tPPvmESpUqUbRoUc6ePZumXtu2bVm4cCFBQUGcOnWKVq1amZz3nj17TOo/6LxtbW1xdHQ02SysHjx9V2Z0OyWVY+GxVPLObSwzAJUK5ebQhdh7HgdQxzcf1pYWrPrbdDTZ7ZRUjoTHUtDZ3qS8oLM94TEJGRW6PILbKakcj4ijYsHcxjIDULFgbv6+dP/r/IqvC9aWFqw5ks51johLc5298tgTHpN2ekt5+u5c59j0r/PFmPse+0qxO9d59eH0Ps9xeOVJe50j9HkWEREREREREXko7dq1Y/78+TRu3JiffvoJBwcHtm3bRtmyZfHw8GDo0KHGurdv32b+/Pk0aNCA6dOnG8v379/PH3/8wYYNGzh//nx6byNZ1CMlivLkyUOZMmWYN2+eMSlUq1Yt9u7dy/Hjx01GFA0cOJDt27cTGBhISEgIJ06c4PfffycwMDDdtuvVq0eRIkXo0KEDBw4cYNu2bQwePBj4/6ihh1GoUCHi4uJYv349V69e5caNGxQrVoy2bdvSvn17Fi9ezOnTp9m1axcjR45kxYoVAPTq1YtVq1YxevRoTpw4wcSJE1m1atV936to0aKEhYWxYMECTp48yYQJE1iyZEmaei1atCA2Npbu3bvzyiuv4Onpadz33nvvcfToUQYOHMjx48dZtGgRs2bNeuTzzsp+3n2BJmU9eLWUG9557RkQUBQ7awvjejRDGvnSvVahNMc1LuPO5hNXibl1O82+eTvP418iH03KulMgtx1vVPCk+gt5+W3vxad9OnIPC4Mv8FppdxqUdMXb2Z5+/kWwt7Zk5aE7yeFPGhTjvRppF9FrVMqNraHX0r3OP+++QB1fFxqXdiN/bjtalPPgpSLOLNkfnqauPBsL9lygcRl3Grx45zr3r/cC9tYWrPjnOg9uWIz3ahZKc9xrpd3Zcs/rfJ66xfPRuLT7netc3oOXiuRlScilp306WZImnhMRERFzU19EREQkc/Px8WHhwoWULl0aV1dXUlNTGTFiBF9++SUTJ06kaNGitG3bltWrV9OtWzf279/P+vXrqVChAk2aNKFOnTp4eXlRvXp1FixYYO7TkQzwSIkiuLNOUXJysjFR5OzsTMmSJXF3d8fX19dYr0yZMmzatInjx49Ts2ZNypcvz5AhQ0ySJP9maWnJ0qVLiYuLo3LlynTp0oVPPvkEADs7u4eO76WXXqJbt2689dZb5MuXj2+++QaAmTNn0r59e/r164evry/NmjVj9+7dxmnwqlatyrRp0xg/fjxly5ZlzZo1xkTVvTRp0oQ+ffoQGBhIuXLl2L59O59++mmaeg4ODjRu3Jj9+/fTtm1bk30+Pj78+uuvLF68mDJlyjBlyhTjedva2j70eWdl649eYeKGU3Sp4c2cjhUp6pqTPosOcf3Gnamq3BxtyZvLdMRUQWd7ynk58ceB9BMCm05c45vVJ3i7ihdz36lI4zLufLzkMAcu3H9Ugzw9fx27yuRNp+lcvSAz2pWnqGsu+v/2n+v8nynLvPLYU7aAE8sPRaTXJFtCrzF63UlaVy7A7Pblea20G58uO8JBXWez+evYVSZtOkWX6t7MbF+Boq456ffr3w93nQ+m/3neHHqN0WtDaVOlAHM6VKBxaXcG/67P873cneolIzYRERGRx6G+iIiISObXsGFDDhw4QEREBCNHjgTgk08+oWfPnpw5c4Z8+fJRvnx5bt++Tbt27WjdujUpKSkUKVKE4sWLYzAY2L59O61bt2bDhg3pvkdqairXrl17lqclj8mQmpqaau4g7mXbtm3UqFGD0NBQihQpYu5wnpkvvviCqVOncu7cuYc+ptrXm59iRJJZWFk9cm5XsphM/CtZMtDW/jWfWtuXou+/7tej8HDKmtOayrMXExODk5MTEdeicXTU+mGSfQSF6j+1kv3Ex8XSuLIP0dEZ/zv77t+DY2FXcHjCtmNjYvAtmO+pxCnZz92fPf28iIg8vkGDBvHVV19RuHBh+vfvT8eOHYmOjqZkyZJcv34dwDhYwt7enosXL9KvXz8WLFhAuXLl2LNnD5aWlsb2IiMjad26NWvWrOHDDz/kq6++wsJC9zafpUf5+2j1jGJ6KEuWLCFXrlwULVqU0NBQPvjgA6pXr57tk0STJ0+mcuXK5M2bl23btjFq1Kh7TtEnIiJyPwZN1CIiIiJmZvjn60nbEBERkWdn5MiRdO/eHU9PT6ys7qQN7O3tGT9+PO3bt8fJyYlff/0Ve/s760h7enry3Xff8eeffxISEsKMGTOoUqUKhw4d4sqVK0yYMIHTp08DMGrUKC5dusQPP/yAvb098fHxrFy5EisrK9zd3SlfvvwjzSomGS9TJYpiY2MZOHAgYWFhuLi44O/vz7fffmvusJ66EydO8PnnnxMZGUnBggXp168fgwYNMndYIiKSFemeioiIiJhbRiwypD6NiIjIM3d3mZZ/e/vtt3F0dKRIkSJpBnS4uLjw6aef0r9/f9599900x/r4+NClSxeGDBnC3Llz+fPPP2ncuDF//PGHyZR0rq6u9OvXj+7du+Pg4JDxJyYPlKmnnpOHp6nnng+aei7706/k58PTnHouPCYpw9pyd7TOsLYke9PUc5Jdaeo5yY6exdRzx89dzZCp54p5uWgqMXkomnpORMR8EhMTKVeuHEeOHMHBwYEKFSrg7u5unMLO2dmZ1atX895773H27FnjcT4+Pri7u3Py5EkuX74MQJ48eejVqxe9evXC2dk5zXtduHCBbt26sW/fPn744QdeffVVkziCgoKoUaOGyRR4z7NH+fuou84iIiLZiCEDNxEREZHHob6IiIjI88PGxoagoCAOHTpEZGQkGzduZMGCBXz55ZfGZE9AQAChoaEsXryY7t27M2/ePI4fP8727ds5f/48s2bNwtfXl+vXrzN8+HC8vb0ZOHAgp06dIjU1lStXrvDdd99RqlQpli9fzoULF3jttdcYPny48aHrAQMGULt2bTp16qQHsR+DRhRlExpR9HzQiKLsT7+Snw9Pc0TR5diMG1Hk6qARRfJwNKJIsiuNKJLs6FmMKAo9nzEjil4ooBFF8nA0okhEJOtLTk7mt99+44svvuDAgQPG8ly5cnHz5k2Sk5MBqFy5MqVLl2bGjBkATJgwgddff53ChQuTkJAAwHfffUenTp3Yu3cvlSpVMq6r9LzRiCIREREREREREREREckSLC0tadmyJSEhIfzxxx/UqFEDa2tr4uLiSE5OpmLFikyYMIFt27Yxffp0vv76awA+/vhj+vbtS0JCAnny5AGgd+/e5M2bl1q1alG/fn0SExMfKZbFixfTs2dPYmNjM/w8MysrcwcgIiIiGcegiVpERETEzAz/fD1pGyIiIvL8MRgMvPbaa7z22mskJSVx8uRJ7OzsKFSokEm9/v378/vvv7N9+3YWLlwIwIIFC5g5cyYLFiwgOTkZg8HA1q1bef/995k2bRoGw4P7Fzdv3uSdd94hOjqaiIgIFi5c+FDHZXUaUSQiIpKdaJEiERERMTf1RURERCQDWFtbU7x48TRJIgALCwt++OEHrK3vTJvv5+dHvXr1mD17Nr/88gt///03K1aswGAwMH36dAIDAzlx4gQbNmygW7dujBs3Lt0lIH799Veio6MB+OWXX5gwYcJTPcfMQokiERERERERERERERHJUl588UW+/vpr8uTJw6hRozAYDNjY2PDGG29QsmRJGjZsyDfffAPA5MmTKVasGHXq1OH777+nT58+dOvWjaioKBYvXsyaNWsA+PHHHwEoX748cGfk0rfffsvt27fNc5LPiBJFIiIi2YgGFImIiIi5qS8iIiIiz0qfPn2IjIykZs2a6e7v168fv//+OwEBARgMBpycnGjRooVxRJKzszOvv/46AQEB9OrVi82bN2NhYcEff/xBu3btuH37Nv3796dSpUqEhIQAsH//ft5//31WrVr1DM/06VKiSEREJBsxGDJuExEREXkc6ouIiIhIZmEwGGjSpAmrVq3i+vXrRERE8Ntvv/HLL79gY2NDamoq+fPnB+C7774DoFGjRuTPn59Zs2bx448/4uzszP79+/Hz86N9+/ZUrlyZKVOm0LBhQwICAjhx4kSa9z179ixXr159puf6JJQoEhERkQwxadIkChUqhJ2dHX5+fuzateu+9aOioujRowceHh7Y2tpSrFgxVq5cadw/bNgwDAaDyVa8eHGTNm7dukWPHj3ImzcvuXLl4vXXXyciIuKpnJ+IiIiIiIiIZF1OTk7Y2toC0KJFC44ePcqRI0c4d+4cI0aMMNbr0qULcGcdpM6dO3PkyBGaNGlCYmIiP/30E0lJSfj5+WFtbc2aNWuoXLmyceq61NRUxowZQ5EiRfDy8qJnz56cO3funjGdPn2a33//nZSUlKd45g+mRJGIiEg2YsjAr0excOFC+vbty9ChQ9m7dy9ly5YlICCAy5cvp1s/MTGRevXqcebMGX799VeOHTvGtGnTjE/x3PXiiy9y6dIl47Z161aT/X369OGPP/7gl19+YdOmTVy8eJEWLVo82jdNREREMlhG9EM0pEhERESeLh8fH4oXL47BYODTTz9l5syZDBkyhNdee82knqurK0uXLmXKlClUqVKFmTNnEhQUxNGjR6levTrR0dE0bNiQBg0aULt2bfr160dycjK3bt1i4sSJlChRghkzZpCammpsMzU1le+//55SpUrRrFkzPv7442d9+iYMqf+OTrKsal9vNncI8gxYWSm3m93pV/LzYWv/9OfNzQjXbyRnWFt5clg+dF0/Pz8qV67MxIkTAUhJSTE+OfPRRx+lqT916lRGjRrF0aNHsba2TrfNYcOGsXTpUuMcwP8VHR1Nvnz5mD9/Pm+88QYAR48epUSJEgQFBVG1atWHjl+eTExMDE5OTkRci8bR0dHc4YhkmKDQa+YOQSTDxcfF0riyD9HRGf87++7fgzOXIp+47ZiYGAp5OD+VOCX7ufuzp58XERF51hISEnjvvfeYPXu2sczKyoqxY8dSokQJPv30U4KCggCMs6TExcURGxtLdHS0SVsLFy6kZcuWGRbbo/x91F1nERERSVdCQgIxMTEmW0JCQpp6iYmJBAcH4+/vbyyzsLDA39/f2Bn6r2XLllGtWjV69OiBm5sbpUqV4ssvvyQ52TTRdeLECTw9PSlcuDBt27YlLCzMuC84OJikpCST9y1evDgFCxa85/uKiIiIiIiIiGQUW1tbZs6cyaZNm5gxYwZjx44lODiYwMBA6taty5YtW/j666+xtrbm6NGjHD16lPPnzxMdHY2trS2jR4+mf//+AHTq1ImDBw+a5TyszPKuIiIikumNHDmS4cOHm5QNHTqUYcOGmZRdvXqV5ORk3NzcTMrd3Nw4evRoum2fOnWKv/76i7Zt27Jy5UpCQ0N5//33SUpKYujQocCdUUqzZs3C19eXS5cuMXz4cGrWrMmhQ4dwcHAgPDwcGxsbcufOneZ9w8PDn+zkRUREREREREQegsFgoFatWtSqVSvNPktLSwYMGEDLli35+++/yZkzJ7ly5SJXrlx4eHjg5OREcnIy+/fvZ+3atcyePZvRo0c/83NQokhERCQbMWTgdP6DBg2ib9++JmV3F318UikpKbi6uvLDDz9gaWlJxYoVuXDhAqNGjTImiho2bGisX6ZMGfz8/PD29mbRokV07tw5Q+IQERGRjGcwPHmfJCP7NCIiIiLmVqhQIQoVKpTuPktLS37++WcWLVpEt27dnm1g/9DUcyIiIpIuW1tbHB0dTbb0EkUuLi5YWloSERFhUh4REYG7u3u6bXt4eFCsWDEsLf+/DlKJEiUIDw8nMTEx3WNy585NsWLFCA0NBcDd3Z3ExESioqIe+n1FREQke9q8eTONGzfG09MTg8HA0qVLTfanpqYyZMgQPDw8sLe3x9/fnxMnTpjUiYyMpG3btjg6OpI7d246d+5MXFycSZ0DBw5Qs2ZN7Ozs8PLy4ptvvnnapyYiIiLPgbx589K9e3cMZnpaRokiERGRbMSQgV8Py8bGhooVK7J+/XpjWUpKCuvXr6datWrpHlO9enVCQ0NJSUkxlh0/fhwPDw9sbGzSPSYuLo6TJ0/i4eEBQMWKFbG2tjZ532PHjhEWFnbP9xUREZGn71n3RQDi4+MpW7YskyZNSnf/N998w4QJE5g6dSo7d+4kZ86cBAQEcOvWLWOdtm3b8vfff7N27VqWL1/O5s2beffdd437Y2JiqF+/Pt7e3gQHBzNq1CiGDRvGDz/88HjfKBEREZFMQlPPiYiIZCPmmqalb9++dOjQgUqVKlGlShXGjRtHfHw8nTp1AqB9+/bkz5+fkSNHAtC9e3cmTpzIBx98QM+ePTlx4gRffvklvXr1MrbZv39/GjdujLe3NxcvXmTo0KFYWlrSunVrAJycnOjcuTN9+/bF2dkZR0dHevbsSbVq1ahateqz/yaIiIgIYJ6p5xo2bGgybe2/paamMm7cOAYPHkzTpk0BmDNnDm5ubixdupRWrVpx5MgRVq1a97cVrwAALGxJREFUxe7du6lUqRIA3333Ha+++iqjR4/G09OTefPmkZiYyIwZM7CxseHFF18kJCSEMWPGmCSURERERLIaJYpERETkib311ltcuXKFIUOGEB4eTrly5Vi1ahVubm4AhIWFYWHx/4HMXl5erF69mj59+lCmTBny58/PBx98wMCBA411zp8/T+vWrbl27Rr58uWjRo0a7Nixg3z58hnrjB07FgsLC15//XUSEhIICAhg8uTJz+7ERURE5KmKiYkxeW1ra/vIayaePn2a8PBw/P39jWVOTk74+fkRFBREq1atCAoKInfu3MYkEYC/vz8WFhbs3LmT5s2bExQURK1atUxGPwcEBPD1119z/fp18uTJ85hnKSIiImJeShSJiIhkI+Zc9zkwMJDAwMB0923cuDFNWbVq1dixY8c921uwYMED39POzo5Jkybdc5oZERERefYMPHmf5O7xXl5eJuVDhw5l2LBhj9RWeHg4gPEBlrvc3NyM+8LDw3F1dTXZb2VlhbOzs0kdHx+fNG3c3adEkYiIiGRVShSJiIhkJ+bMFMlzKzU1FYDY/zz1LZLVxcfFmjsEkQx345+f67u/u5+KDMwUnTt3DkdHR2Pxo44mEhEREZEHU6JIRERERJ5IbOydm44v+Hg9oKaIiGQWsbGxODk5mTuMB3J0dDRJFD0Od3d3ACIiIvDw8DCWR0REUK5cOWOdy5cvmxx3+/ZtIiMjjce7u7sTERFhUufu67t1RERERLIiJYpERESyEYOGFIkZeHp6cu7cORwcHDA86erlcl8xMTF4eXmlecJeJKvTz/azk5qaSmxsLJ6enk/tPQz/fD1pGxnFx8cHd3d31q9fb0wMxcTEsHPnTrp37w7cmRI3KiqK4OBgKlasCMBff/1FSkoKfn5+xjqffPIJSUlJWFtbA7B27Vp8fX2fu2nnJk2axKhRowgPD6ds2bJ89913VKlSJd26ixcv5ssvvyQ0NJSkpCSKFi1Kv379aNeunbFOXFwcH330EUuXLuXatWv4+PjQq1cvunXr9qxOSURE5LmmRJGIiEg2onv0Yg4WFhYUKFDA3GE8VzLiCXuRzEg/28/G0x5JZDA8eZ/kUY+Pi4sjNDTU+Pr06dOEhITg7OxMwYIF6d27N59//jlFixbFx8eHTz/9FE9PT5o1awZAiRIlaNCgAV27dmXq1KkkJSURGBhIq1atjEm1Nm3aMHz4cDp37szAgQM5dOgQ48ePZ+zYsU92slnMwoUL6du3L1OnTsXPz4//tXfnYVEd6RrA3wZt9kVUaFBsFBdggogbweQKiURcxrjNaBxGEbdRwKi4MCYiaFQ0RkFMFMcNjMYlC8QhGY0i4IYbiiFKUFHEjECiBrE1AtJ1//BybjogoCDN8v54+nnoqjp1vuo6faBPddWJjIyEt7c3srKyKtznCQAsLCzw/vvvw8HBAXK5HAkJCfDz84OlpSW8vb0BAEFBQThy5Ah27twJOzs7fPfdd/D394eNjQ3efvvt+m4iERFRs6Oj7QCIiIiIiIiIiGrj3LlzcHV1haurK4CnAw+urq5YvHgxAGDBggWYOXMmpk2bhj59+kClUuHAgQPQ19eX6ti1axccHBwwYMAADBkyBK+//jr+9a9/SflmZmb47rvvcOPGDfTq1Qtz587F4sWLMW3atPptrJatXbsWU6dOhZ+fH5ycnBAdHQ1DQ0Ns27at0vKenp4YOXIkHB0dYW9vj1mzZqF79+44fvy4VObkyZPw9fWFp6cn7OzsMG3aNLi4uODMmTP11SwiIqJmjTOKiIiImhBOKCIiIiJtk6H2/5M87/aenp4QQjy7PpkMS5cuxdKlS59ZxsLCAp999lmV++nevTuOHTv2nNE1HSUlJUhLS8PChQulNB0dHXh5eSE1NbXa7YUQOHLkCLKysrBq1SopvV+/fti/fz8mTZoEGxsbJCcn48qVK8+crVVcXIzi4mLpeVFRUS1aRURERBwoIiIiako4UkTUpOnp6SE0NBR6enraDoWoTvHYbmK0MVJE9eLOnTsoKyuDlZWVRrqVlRV+/PHHZ253//59tGvXDsXFxdDV1cWGDRvw1ltvSfnr16/HtGnT0L59e7Ro0QI6OjrYvHkz+vfvX2l94eHhWLJkSd00ioiIiDhQRERERETUWOjp6SEsLEzbYRDVOR7bRE2biYkJ0tPToVKpkJiYiKCgIHTq1Amenp4Ang4UnTp1Cvv374dSqcTRo0cREBAAGxsbeHl5Vahv4cKFCAoKkp4XFRXB1ta2vppDRETU5HCgiIiIqAmR8eu3REREpGWy//upbR3U8LRp0wa6urooKCjQSC8oKIBCoXjmdjo6OujcuTMAoEePHsjMzER4eDg8PT3x22+/4b333kNcXByGDh0K4OkSf+np6fjoo48qHSjS09PjDEQiIqI6pKPtAIiIiKjuyGR19yAiIiJ6EfxfpOmSy+Xo1asXEhMTpTS1Wo3ExES4u7vXuB61Wi3dY6i0tBSlpaXQ0dG8RKWrqwu1Wl03gRMREVGVOKOIiIiIiIiIiOpMUVFRg6iDXo6goCD4+vqid+/e6Nu3LyIjI/Hw4UP4+fkBACZMmIB27dohPDwcwNP7CfXu3Rv29vYoLi7Gt99+i08//RQbN24EAJiamsLDwwPz58+HgYEBlEolUlJSsGPHDqxdu7ZGMQkhAPC4ISIi+r3yv4vlfyerwoGiJiI1uPIbPDZVxcXFCA8Px8KFCzndvIliHzcP7Oe6p8+/7ERERKQlcrkcCoUCXTrWzb1iFAoF5HJ5ndRFdWfs2LH45ZdfsHjxYuTn56NHjx44cOAArKysAAC5ubkas4MePnwIf39//PTTTzAwMICDgwN27tyJsWPHSmX27NmDhQsXwsfHB/fu3YNSqcTy5csxffr0GsX04MEDAOB9ioiIiCrx4MEDmJmZVVlGJmoynETUwBQVFcHMzAz379+HqamptsOhl4B93Dywn4mIns8nn3yC1atXIz8/Hy4uLli/fj369u2r7bCIXtjRo0exevVqpKWlIS8vD3FxcRgxYoS2w6JaePz4MUpKSuqkLrlcDn19/Tqpi5o2tVqN27dvw8TEBLI6WrewqKgItra2uHXrVpP4rML2NGxsT8PG9jRsbM+zCSHw4MED2NjYVFji9Y/4vWMiIiIiokZg7969CAoKQnR0NNzc3BAZGQlvb29kZWXB0tJS2+ERvZCHDx/CxcUFkyZNwqhRo7QdDtUBfX19Du5QvdPR0UH79u1fSt2mpqZN4sJjObanYWN7Gja2p2FjeypX3UyiclUPIxERERERUYOwdu1aTJ06FX5+fnByckJ0dDQMDQ2xbds2bYdG9MIGDx6MZcuWYeTIkdoOhYiIiIio2eJAERERERFRA1dSUoK0tDR4eXlJaTo6OvDy8kJqaqoWIyMiIiIiIqLGjgNF1Cjp6ekhNDQUenp62g6FXhL2cfPAfiYiqpk7d+6grKxMulF4OSsrK+Tn52spKiIioqarqX1WYXsaNranYWN7Gja2p27IhBCiXvdIRERERETP5fbt22jXrh1OnjwJd3d3KX3BggVISUnB6dOntRgdUd2QyWSIi4vDiBEjtB0KEREREVGzwhlFREREREQNXJs2baCrq4uCggKN9IKCAigUCi1FRURERERERE0BB4qIiIiIiBo4uVyOXr16ITExUUpTq9VITEzUmGFERERERERE9LxaaDsAIiIiIiKqXlBQEHx9fdG7d2/07dsXkZGRePjwIfz8/LQdGtELU6lUuHbtmvT8xo0bSE9Ph4WFBTp06KDFyIiIiIiImg/OKKJGKTk5GTKZDIWFhXValhq3sLAw9OjRQ3o+ceJErnH/goQQmDZtGiwsLCCTyZCenq7tkIiImr2xY8fio48+wuLFi9GjRw+kp6fjwIEDsLKy0nZoRC/s3LlzcHV1haurK4CnA6Kurq5YvHixliMjIiIiImo+OFBEjVK/fv2Ql5cHMzOzOi1LRE8dOHAAMTExSEhIQF5eHoqKijBs2DDY2NhAJpMhPj5e2yESETVLgYGBuHnzJoqLi3H69Gm4ublpOySiWvH09IQQosIjJiZG26ERUTP2ySefwM7ODvr6+nBzc8OZM2e0HVKNhIeHo0+fPjAxMYGlpSVGjBiBrKwsjTKenp6QyWQaj+nTp2sp4qqFhYVViNXBwUHKf/z4MQICAtC6dWsYGxtj9OjRFe7n2NDY2dlVaJNMJkNAQACAht8/R48erfLagBACixcvhrW1NQwMDODl5YWrV69qlLl37x58fHxgamoKc3NzTJ48GSqVqh5b8f+qak9paSmCg4Ph7OwMIyMj2NjYYMKECbh9+7ZGHZX16cqVK+u5JU9V1z8TJ06sEOugQYM0yjSW/gFQ6XtJJpNh9erVUpmG0j81OT/X5JyWm5uLoUOHwtDQEJaWlpg/fz6ePHlSJzFyoIjqXUlJSa3rkMvlUCgUkMlkdVqWXp666HeqP9nZ2bC2tka/fv2gUCjw8OFDuLi44JNPPtF2aM/EY4yIiIiIiGpr7969CAoKQmhoKM6fPw8XFxd4e3vj559/1nZo1UpJSUFAQABOnTqFQ4cOobS0FAMHDsTDhw81yk2dOhV5eXnS48MPP9RSxNX705/+pBHr8ePHpbw5c+bg3//+Nz7//HOkpKTg9u3bGDVqlBajrd7Zs2c12nPo0CEAwF//+lepTEPun+quDXz44YeIiopCdHQ0Tp8+DSMjI3h7e+Px48dSGR8fH1y6dAmHDh1CQkICjh49imnTptVXEzRU1Z5Hjx7h/PnzCAkJwfnz5/HVV18hKysLb7/9doWyS5cu1eizmTNn1kf4FdTk2s2gQYM0Yt29e7dGfmPpHwAa7cjLy8O2bdsgk8kwevRojXINoX9qcn6u7pxWVlaGoUOHoqSkBCdPnkRsbCxiYmLqbia+IKolDw8PERAQIAICAoSpqalo3bq1WLRokVCr1UIIIZRKpVi6dKkYP368MDExEb6+vkIIIY4dOyZef/11oa+vL9q3by9mzpwpVCqVVO/jx4/FggULRPv27YVcLhf29vZiy5YtQgghkpKSBADx66+/CiGEyMnJEX/+85+Fubm5MDQ0FE5OTuKbb76ptKwQQnzxxRfCyclJyOVyoVQqxUcffaTRJqVSKZYvXy78/PyEsbGxsLW1FZs2bXpJr2DTVH5czJo1S7Ru3Vp4enqKjIwMMWjQIGFkZCQsLS3F3//+d/HLL79I25SVlYlVq1YJe3t7IZfLha2trVi2bJmUv2DBAtGlSxdhYGAgOnbsKBYtWiRKSkqk/NDQUOHi4iI99/X1FcOHD6+P5jYpvr6+AoD0UCqVGvkARFxcXLX1qNVqERoaKmxtbYVcLhfW1tZi5syZUn5V73EhhEhOThZ9+vQRcrlcKBQKERwcLEpLS6X8yo4xIUS1xxkREREREdGz9O3bVwQEBEjPy8rKhI2NjQgPD9diVC/m559/FgBESkqKlObh4SFmzZqlvaCewx8/4/9eYWGhaNmypfj888+ltMzMTAFApKam1lOEtTdr1ixhb28vXUNrTP3zx2sDarVaKBQKsXr1aimtsLBQ6Onpid27dwshhLh8+bIAIM6ePSuV+c9//iNkMpn473//W2+xV6Ym1zrOnDkjAIibN29KaUqlUkRERLzc4F5AZe2p7jpZY++f4cOHizfffFMjraH2zx/PzzU5p3377bdCR0dH5OfnS2U2btwoTE1NRXFxca1j4owiqhOxsbFo0aIFzpw5g3Xr1mHt2rXYsmWLlP/RRx/BxcUFFy5cQEhICLKzszFo0CCMHj0a33//Pfbu3Yvjx48jMDBQ2mbChAnYvXs3oqKikJmZiU2bNsHY2LjS/QcEBKC4uBhHjx5FRkYGVq1a9cyyaWlpGDNmDN555x1kZGQgLCwMISEhFZa3WLNmDXr37o0LFy7A398fM2bMqDAlkKoWGxsLuVyOEydOYOXKlXjzzTfh6uqKc+fO4cCBAygoKMCYMWOk8gsXLsTKlSsREhKCy5cv47PPPtO474KJiQliYmJw+fJlrFu3Dps3b0ZERIQ2mtakrVu3DkuXLkX79u2Rl5eHs2fPvlA9X375JSIiIrBp0yZcvXoV8fHxcHZ2lvKreo//97//xZAhQ9CnTx9cvHgRGzduxNatW7Fs2TKNffz+GIuOjkZhYWG1xxkREREREVFlSkpKkJaWBi8vLylNR0cHXl5eSE1N1WJkL+b+/fsAAAsLC430Xbt2oU2bNnjllVewcOFCPHr0SBvh1cjVq1dhY2ODTp06wcfHB7m5uQCeXtspLS3V6CsHBwd06NCh0fRVSUkJdu7ciUmTJmmsgtOY+uf3bty4gfz8fI0+MTMzg5ubm9QnqampMDc3R+/evaUyXl5e0NHRwenTp+s95ud1//59yGQymJuba6SvXLkSrVu3hqurK1avXl1nS4G9DMnJybC0tES3bt0wY8YM3L17V8przP1TUFCAb775BpMnT66Q1xD754/n55qc01JTU+Hs7KxxrdTb2xtFRUW4dOlSrWNqUesaiADY2toiIiICMpkM3bp1Q0ZGBiIiIjB16lQAwJtvvom5c+dK5adMmQIfHx/Mnj0bANClSxdERUXBw8MDGzduRG5uLvbt24dDhw5Jb5BOnTo9c/+5ubkYPXq0dBG6qrJr167FgAEDEBISAgDo2rUrLl++jNWrV2PixIlSuSFDhsDf3x8AEBwcjIiICCQlJaFbt27P/wI1U126dJGmSC9btgyurq5YsWKFlL9t2zbY2triypUrsLa2xrp16/Dxxx/D19cXAGBvb4/XX39dKr9o0SLpdzs7O8ybNw979uzBggUL6qlFzYOZmRlMTEygq6sLhULxwvXk5uZCoVDAy8sLLVu2RIcOHdC3b18AwJUrV6p8j2/YsAG2trb4+OOPpXWob9++jeDgYCxevBg6Ok+/5/D7Ywyo/jjr2rXrC7eHiIiIiIiatjt37qCsrEzjIhwAWFlZ4ccff9RSVC9GrVZj9uzZeO211/DKK69I6X/729+gVCphY2OD77//HsHBwcjKysJXX32lxWgr5+bmhpiYGHTr1g15eXlYsmQJ/ud//gc//PAD8vPzIZfLK1ywt7KyQn5+vnYCfk7x8fEoLCzUuBbVmPrnj8pf98reP+V5+fn5sLS01Mhv0aIFLCwsGny/PX78GMHBwRg3bhxMTU2l9HfffRc9e/aEhYUFTp48iYULFyIvLw9r167VYrSVGzRoEEaNGoWOHTsiOzsb7733HgYPHozU1FTo6uo26v6JjY2FiYlJheUnG2L/VHZ+rsk5LT8/v9L3V3lebXGgiOrEq6++qvHtB3d3d6xZswZlZWUAoDESDQAXL17E999/j127dklpQgio1WrcuHEDGRkZ0NXVhYeHR432/+6772LGjBn47rvv4OXlhdGjR6N79+6Vls3MzMTw4cM10l577TVERkairKwMurq6AKCxvUwmg0KhaBRrEjckvXr1kn6/ePEikpKSKp3plZ2djcLCQhQXF2PAgAHPrG/v3r2IiopCdnY2VCoVnjx5ovHHmbRnxYoVGoMzly9fxl//+ldERkaiU6dOGDRoEIYMGYJhw4ahRYsWSE9Pr/I9npmZCXd3d43zymuvvQaVSoWffvoJHTp0AKB5jAHVH2ccKCIiIiIiouYgICAAP/zwg8Y9fQBo3GvE2dkZ1tbWGDBgALKzs2Fvb1/fYVZp8ODB0u/du3eHm5sblEol9u3bBwMDAy1GVje2bt2KwYMHw8bGRkprTP3TnJSWlmLMmDEQQmDjxo0aeUFBQdLv3bt3h1wuxz/+8Q+Eh4dDT0+vvkOt0jvvvCP97uzsjO7du8Pe3h7JyclVXo9rDLZt2wYfHx/o6+trpDfE/nnW+VnbuPQc1QsjIyON5yqVCv/4xz+Qnp4uPS5evIirV6/C3t7+uf/gT5kyBdevX8f48eORkZGB3r17Y/369bWKuWXLlhrPZTIZ1Gp1repsbn7f7yqVCsOGDdPo8/T0dFy9ehX9+/evts9TU1Ph4+ODIUOGICEhARcuXMD777+PkpKSl90MqoHp06dr9KuNjQ1sbW2RlZWFDRs2wMDAAP7+/ujfvz9KS0vr7J/6ys4tVR1nREREREREz9KmTRvo6uqioKBAI72goKBWqy3Ut8DAQCQkJCApKQnt27evsqybmxsA4Nq1a/URWq2Ym5uja9euuHbtGhQKBUpKSlBYWKhRprH01c2bN3H48GFMmTKlynKNqX/KX/eq3j+VfQn7yZMnuHfvXoPtt/JBops3b+LQoUPVfmHZzc0NT548QU5OTv0EWAudOnVCmzZtpOOrMfYPABw7dgxZWVnVvp8A7ffPs87PNTmnKRSKSt9f5Xm1xYEiqhN/XKfy1KlT6NKlizQ754969uyJy5cvo3PnzhUecrkczs7OUKvVSElJqXEMtra2mD59Or766ivMnTsXmzdvrrSco6MjTpw4oZF24sQJdO3a9ZnxUu317NkTly5dgp2dXYU+NzIyQpcuXWBgYIDExMRKtz958iSUSiXef/999O7dG126dMHNmzfruRX0LBYWFhp92qLF0wmrBgYGGDZsGKKiopCcnIzU1FRkZGRU+x53dHREamoqhBBS2okTJ2BiYlLlB53qjjMiIiIiIqJnkcvl6NWrl8bnUrVajcTERLi7u2sxspoRQiAwMBBxcXE4cuQIOnbsWO026enpAABra+uXHF3tqVQqZGdnw9raGr169ULLli01+iorKwu5ubmNoq+2b98OS0tLDB06tMpyjal/OnbsCIVCodEnRUVFOH36tNQn7u7uKCwsRFpamlTmyJEjUKvV0qBYQ1I+SHT16lUcPnwYrVu3rnab9PR06OjoVFjCrSH66aefcPfuXen4amz9U27r1q3o1asXXFxcqi2rrf6p7vxck3Oau7s7MjIyNAbzygcvnZycah0jB4qoTuTm5iIoKAhZWVnYvXs31q9fj1mzZj2zfHBwME6ePInAwEDp2/5ff/01AgMDATy9/4yvry8mTZqE+Ph43LhxA8nJydi3b1+l9c2ePRsHDx7EjRs3cP78eSQlJcHR0bHSsnPnzkViYiI++OADXLlyBbGxsfj4448xb9682r8Q9EwBAQG4d+8exo0bh7NnzyI7OxsHDx6En58fysrKoK+vj+DgYCxYsAA7duxAdnY2Tp06ha1btwJ4ei+a3Nxc7NmzB9nZ2YiKikJcXJyWW9V8qFQqaXYO8PQmlenp6dKNRCsTExODrVu34ocffsD169exc+dOGBgYQKlUVvse9/f3x61btzBz5kz8+OOP+PrrrxEaGoqgoCDp/kSVqe44IyKipmfixIkYMWKE9NzT01O6D2Z9Sk5Ohkwmq/AtwN+TyWSIj4+vcZ1hYWHo0aNHreLKycmBTCaT/oYTEVHVgoKCsHnzZsTGxiIzMxMzZszAw4cP4efnp+3QqhUQEICdO3fis88+g4mJCfLz85Gfn4/ffvsNwNPluD/44AOkpaUhJycH+/fvx4QJE9C/f/9nLt+vTfPmzUNKSgpycnJw8uRJjBw5Erq6uhg3bhzMzMwwefJkBAUFISkpCWlpafDz84O7uzteffVVbYdeJbVaje3bt8PX11f6kiXQOPqnqmsDMpkMs2fPxrJly7B//35kZGRgwoQJsLGxkf5Xc3R0xKBBgzB16lScOXMGJ06cQGBgIN555x2NJfgaQntKS0vxl7/8BefOncOuXbtQVlYmvafKV7dJTU1FZGQkLl68iOvXr2PXrl2YM2cO/v73v6NVq1YNqj0qlQrz58/HqVOnkJOTg8TERAwfPhydO3eGt7c3gMbVP+WKiorw+eefVzqbqCH1T3Xn55qc0wYOHAgnJyeMHz8eFy9exMGDB7Fo0SIEBATUzTJ6gqiWPDw8hL+/v5g+fbowNTUVrVq1Eu+9955Qq9VCCCGUSqWIiIiosN2ZM2fEW2+9JYyNjYWRkZHo3r27WL58uZT/22+/iTlz5ghra2shl8tF586dxbZt24QQQiQlJQkA4tdffxVCCBEYGCjs7e2Fnp6eaNu2rRg/fry4c+dOpWWFEOKLL74QTk5OomXLlqJDhw5i9erVGrFVFrOLi4sIDQ2t3YvVjHh4eIhZs2ZppF25ckWMHDlSmJubCwMDA+Hg4CBmz54tHStlZWVi2bJlQqlUSn2zYsUKafv58+eL1q1bC2NjYzF27FgREREhzMzMpPzQ0FDh4uIiPff19RXDhw9/ia1suiIiIoRSqZSel7+P/vjw9fV9Zh1xcXHCzc1NmJqaCiMjI/Hqq6+Kw4cPS/lVvceFECI5OVn06dNHyOVyoVAoRHBwsCgtLZXyKzvGhKj+OCMiopfP19dX+lvRsmVLYW9vL5YsWaJxHq/Lff3+7/3du3dFUVFRjbat7P/EF1WTugCIuLi4Gtf5x/9tXsSNGzcEAHHhwoVa1UNE1JysX79edOjQQcjlctG3b19x6tQpbYdUI5V9ZgMgtm/fLoQQIjc3V/Tv319YWFgIPT090blzZzF//nxx//597Qb+DGPHjpU+L7Zr106MHTtWXLt2Tcr/7bffhL+/v2jVqpUwNDQUI0eOFHl5eVqMuGYOHjwoAIisrCyN9MbQP9VdG1Cr1SIkJERYWVkJPT09MWDAgArtvHv3rhg3bpwwNjYWpqamws/PTzx48EALram6PeX/Q1X2SEpKEkIIkZaWJtzc3ISZmZnQ19cXjo6OYsWKFeLx48cNrj2PHj0SAwcOFG3bthUtW7YUSqVSTJ06VeTn52vU0Vj6p9ymTZuEgYGBKCwsrLB9Q+qf6s7PQtTsnJaTkyMGDx4sDAwMRJs2bcTcuXPr7DOW7P8CJXphnp6e6NGjByIjI7UdChERERE1ABMnTkRBQQG2b9+O4uJifPvttwgICMDy5cuxcOHCCuVLSkogl8tfeF+FhYXPNVOnXHJyMt544w38+uuvMDc3f6H9P09dMpkMcXFxGjOgqhIWFob4+PhazQbKyclBx44dceHChVrPTiIiIiIioqaJS88REREREVGd09PTg0KhgFKpxIwZM+Dl5YX9+/cD+P/l4pYvXw4bGxt069YNAHDr1i2MGTMG5ubmsLCwwPDhwzVuNFtWVoagoCCYm5ujdevWWLBgAf74vbc/Lj1XXFyM4OBg2NraQk9PD507d8bWrVuRk5ODN954AwDQqlUryGQyTJw4EcDTJWHCw8PRsWNHGBgYwMXFBV988YXGfr799lt07doVBgYGeOONN17ohrjBwcHo2rUrDA0N0alTJ4SEhKC0tLRCuU2bNsHW1haGhoYYM2YM7t+/r5G/ZcsWODo6Ql9fHw4ODtiwYcNzx0JERERERM0XB4qIiIiIiOilMzAwkNZzB4DExERkZWXh0KFDSEhIQGlpKby9vWFiYoJjx47hxIkTMDY2xqBBg6Tt1qxZg5iYGGzbtg3Hjx/HvXv3qr1n4YQJE7B7925ERUUhMzMTmzZtgrGxMWxtbfHll18CeHqj2Ly8PKxbtw4AEB4ejh07diA6OhqXLl2S1jJPSUkB8HRAa9SoURg2bBjS09MxZcoU/POf/3zu18TExAQxMTG4fPky1q1bh82bNyMiIkKjzLVr17Bv3z78+9//xoEDB3DhwgX4+/tL+bt27cLixYuxfPlyZGZmYsWKFQgJCUFsbOxzx0NERERERM1Ti+qLEFUtOTlZ2yEQERERUQMlhEBiYiIOHjyImTNnSulGRkbYsmWLtOTczp07oVarsWXLFshkMgDA9u3bYW5ujuTkZAwcOBCRkZFYuHAhRo0aBQCIjo7GwYMHn7nvK1euYN++fTh06BC8vLwAAJ06dZLyLSwsAACWlpbScnHFxcVYsWIFDh8+DHd3d2mb48ePY9OmTfDw8MDGjRthb2+PNWvWAAC6deuGjIwMrFq16rlem0WLFkm/29nZYd68edizZw8WLFggpT9+/Bg7duxAu3btAADr16/H0KFDsWbNGigUCoSGhmLNmjXSa9KxY0dcvnwZmzZtgq+v73PFQ0REREREzRMHioiIiIiIqM4lJCTA2NgYpaWlUKvV+Nvf/oawsDAp39nZWeO+RBcvXsS1a9dgYmKiUc/jx4+RnZ2N+/fvIy8vD25ublJeixYt0Lt37wrLz5VLT0+Hrq4uPDw8ahz3tWvX8OjRI7z11lsa6SUlJXB1dQUAZGZmasQBQBpUeh579+5FVFQUsrOzoVKp8OTJE5iammqU6dChgzRIVL4ftVqNrKwsmJiYIDs7G5MnT8bUqVOlMk+ePIGZmdlzx0NERERERM0TB4qIiIiIiKjOvfHGG9i4cSPkcjlsbGzQooXmRw8jIyON5yqVCr169cKuXbsq1NW2bdsXisHAwOC5t1GpVACAb775RmOABnh636W6kpqaCh8fHyxZsgTe3t4wMzPDnj17pFlKzxPr5s2bKwxc6erq1lmsRERERETUtHGgiIiIiIiI6pyRkRE6d+5c4/I9e/bE3r17YWlpWWFWTTlra2ucPn0a/fv3B/B05kxaWhp69uxZaXlnZ2eo1WqkpKRIS8/9XvmMprKyMinNyckJenp6yM3NfeZMJEdHR+zfv18j7dSpU9U38ndOnjwJpVKJ999/X0q7efNmhXK5ubm4ffs2bGxspP3o6OigW7dusLKygo2NDa5fvw4fH5/n2j8REREREVE5HW0HQERERERE5OPjgzZt2mD48OE4duwYbty4geTkZLz77rv46aefAACzZs3CypUrER8fjx9//BH+/v4oLCx8Zp12dnbw9fXFpEmTEB8fL9W5b98+AIBSqYRMJkNCQgJ++eUXqFQqmJiYYN68eZgzZw5iY2ORnZ2N8+fPY/369YiNjQUATJ8+HVevXsX8+fORlZWFzz77DDExMc/V3i5duiA3Nxd79uxBdnY2oqKiEBcXV6Gcvr4+fH19cfHiRRw7dgzvvvsuxowZA4VCAQBYsmQJwsPDERUVhStXriAjIwPbt2/H2rVrnyseIiIiIiJqvjhQREREREREWmdoaIijR4+iQ4cOGDVqFBwdHTF58mQ8fvxYmmE0d+5cjB8/Hr6+vnB3d4eJiQlGjhxZZb0bN27EX/7yF/j7+8PBwQFTp07Fw4cPAQDt2rXDkiVL8M9//hNWVlYIDAwEAHzwwQcICQlBeHg4HB0dMWjQIHzzzTfo2LEjgKf3Dfryyy8RHx8PFxcXREdHY8WKFc/V3rfffhtz5sxBYGAgevTogZMnTyIkJKRCuc6dO2PUqFEYMmQIBg4ciO7du2PDhg1S/pQpU7BlyxZs374dzs7O8PDwQExMjBQrERERERFRdWTiWXd+JSIiIiIiIiIiIiKqY3Z2dpg9ezZmz56t7VCICJxRRERERERERERERNRkTZw4ESNGjAAAeHp61uvgTExMDMzNzSuknz17FtOmTau3OIioai20HQARERERERERERERNR4lJSWQy+UvvH3btm3rMBoiqi3OKCIiIiIiIiIiIiJq4iZOnIiUlBSsW7cOMpkMMpkMOTk5AIAffvgBgwcPhrGxMaysrDB+/HjcuXNH2tbT0xOBgYGYPXs22rRpA29vbwDA2rVr4ezsDCMjI9ja2sLf3x8qlQoAkJycDD8/P9y/f1/aX1hYGICnS89FRkZK9efm5mL48OEwNjaGqakpxowZg4KCAik/LCwMPXr0wKeffgo7OzuYmZnhnXfewYMHD17ui0bUTHCgiIiIiIiIiIiIiKiJW7duHdzd3TF16lTk5eUhLy8Ptra2KCwsxJtvvglXV1ecO3cOBw4cQEFBAcaMGaOxfWxsLORyOU6cOIHo6GgAgI6ODqKionDp0iXExsbiyJEjWLBgAQCgX79+iIyMhKmpqbS/efPmVYhLrVZj+PDhuHfvHlJSUnDo0CFcv34dY8eO1SiXnZ2N+Ph4JCQkICEhASkpKVi5cuVLerWImhcuPUdERERERERERETUxJmZmUEul8PQ0BAKhUJK//jjj+Hq6ooVK1ZIadu2bYOtrS2uXLmCrl27AgC6dOmCDz/8UKPO39/vyM7ODsuWLcP06dOxYcMGyOVymJmZQSaTaezvjxITE5GRkYEbN27A1tYWALBjxw786U9/wtmzZ9GnTx8ATweUYmJiYGJiAgAYP348EhMTsXz58tq9METEGUVEREREREREREREzdXFixeRlJQEY2Nj6eHg4ADg6Syecr169aqw7eHDhzFgwAC0a9cOJiYmGD9+PO7evYtHjx7VeP+ZmZmwtbWVBokAwMnJCebm5sjMzJTS7OzspEEiALC2tsbPP//8XG0lospxRhERERERERERERFRM6VSqTBs2DCsWrWqQp61tbX0u5GRkUZeTk4O/vznP2PGjBlYvnw5LCwscPz4cUyePBklJSUwNDSs0zhbtmyp8Vwmk0GtVtfpPoiaKw4UERERERERERERETUDcrkcZWVlGmk9e/bEl19+CTs7O7RoUfPLxWlpaVCr1VizZg10dJ4uXLVv375q9/dHjo6OuHXrFm7duiXNKrp8+TIKCwvh5ORU43iI6MVx6TkiIiIiIiIiIiKiZsDOzg6nT59GTk4O7ty5A7VajYCAANy7dw/jxo3D2bNnkZ2djYMHD8LPz6/KQZ7OnTujtLQU69evx/Xr1/Hpp58iOjq6wv5UKhUSExNx586dSpek8/LygrOzM3x8fHD+/HmcOXMGEyZMgIeHB3r37l3nrwERVcSBIiIiIiIiIiIiIqJmYN68edDV1YWTkxPatm2L3Nxc2NjY4MSJEygrK8PAgQPh7OyM2bNnw9zcXJopVBkXFxesXbsWq1atwiuvvIJdu3YhPDxco0y/fv0wffp0jB07Fm3btsWHH35YoR6ZTIavv/4arVq1Qv/+/eHl5YVOnTph7969dd5+IqqcTAghtB0EERERERERERERERER1T/OKCIiIiIiIiIiIiIiImqmOFBERERERERERERERETUTHGgiIiIiIiIiIiIiIiIqJniQBEREREREREREREREVEzxYEiIiIiIiIiIiIiIiKiZooDRURERERERERERERERM0UB4qIiIiIiIiIiIiIiIiaKQ4UERERERERERERERERNVMcKCIiIiIiIiIiIiIiImqmOFBERERERERERERERETUTHGgiIiIiIiIiIiIiIiIqJn6X0HFgaYu3EcIAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 2000x500 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = MLPClassifier()\n",
"model.fit(X_train_s, y_train)\n",
"print(f'MLP-default accuracy on train: {round(model.score(X_train_s, y_train)*100,3)} %')\n",
"pred_y = model.predict(X_valid_s)\n",
"NN_report = classification_report(y_val,pred_y, output_dict=True)\n",
"# Model Accuracy, how often is the classifier correct\n",
"print(f'MLP-default accuracy on test: {round(accuracy_score(y_val,pred_y)*100,3)} %')\n",
"\n",
"fig, ax = plt.subplots((1),(3),figsize = (20,5))\n",
"#plot 1\n",
"sns.heatmap(pd.DataFrame(NN_report).iloc[:-1, :].T, annot=True, cmap = \"Blues\", ax = ax[0])\n",
"ax[0].set_title('Heat Map for Classification report')\n",
"#plot 2\n",
"cm = confusion_matrix(y_val,pred_y)\n",
"disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)\n",
"disp.plot(cmap=\"Blues\", ax=ax[1])\n",
"ax[1].set_title('Confusion Matrix (1-smoking,0-not)')\n",
"#plot 3\n",
"ax[2].plot(model.loss_curve_, color = \"Black\")\n",
"ax[2].set_xlabel(\"Iteration\")\n",
"ax[2].set_ylabel(\"Loss\")\n",
"ax[2].set_title('Loss function')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"**The conclusions that can be drawn from the graphs shown:**\n",
"\n",
" 1) From the loss function you can see that the loss continues to decrease even after 200 iterations,\n",
" therefore we will want a relatively high number in limiting the number of iterations later.\n",
" 2) The model convergence in the default mode happens relatively slowly.\n",
" 3) The model classifies in a significantly better way the examples of non-smokers.\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mission 2 - Tuning the Model"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [],
"source": [
"#First try tuning with 3 layer (size,size,2)\n",
"train_accs = []\n",
"test_accs = []\n",
"for size_ in range(1, 30, 2):\n",
" model = MLPClassifier(random_state=1,\n",
" hidden_layer_sizes=(size_, size_, 2),\n",
" max_iter=200, # from the graphs shown \n",
" activation='logistic', # for binary classification\n",
" verbose=False,\n",
" learning_rate_init=0.001,\n",
" alpha=0.00)\n",
" model.fit(X_train_s, y_train)\n",
" train_acc = model.score(X_train_s, y_train)\n",
" train_accs.append(train_acc)\n",
" test_acc = model.score(X_valid_s, y_val)\n",
" test_accs.append(test_acc)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 700x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Max accuracy on train: 0.7874111485222597 \n",
"Max accuracy on valid: 0.767206982543641\n"
]
}
],
"source": [
"plt.figure(figsize=(7, 4))\n",
"plt.plot(range(1, 30, 2), train_accs, label='Train')\n",
"plt.plot(range(1, 30, 2), test_accs, label='Valid')\n",
"plt.legend()\n",
"plt.xlabel('# neurons')\n",
"plt.ylabel('Accuracy')\n",
"plt.title('Tuning the number of neurons in 3 layers networks ')\n",
"plt.show()\n",
"print(f\"Max accuracy on train: {max(train_accs)} \\nMax accuracy on valid: {max(test_accs)}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"2nd try tuning with more attributes in Grid Search:\n",
"\n",
" 1) 3 types of 4 layers\n",
" 2) 3 activation function\n",
" 3) 2 solver\n",
" 4) 3 values of learning rate init\n",
" 5) 3 values of alpha"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 162 candidates, totalling 810 fits\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.4s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.1s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.3s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.0s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.7s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.0s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.4s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 1.8s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.0s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.0s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.1s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.8s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.3s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.4s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 3.2s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.7s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.4s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.0s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 2.9s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.3s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.8s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.8s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.2s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.9s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.0s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.4s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.1s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.7s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.8s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.3s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.7s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.1s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.1s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.9s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 4.4s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 4.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 1.7s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.8s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 3.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.2s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.5s\n",
"[CV] END activation=logistic, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.3s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.4s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.8s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.0s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.9s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.4s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.9s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 1.7s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.2s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.8s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.4s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.2s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.9s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.0s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.7s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.7s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 2.7s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.1s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.1s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.4s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.1s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 1.8s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.9s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.8s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.2s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.9s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.3s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.9s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.4s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.4s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 2.3s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.7s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 1.9s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.9s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.0s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.2s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.4s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.7s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.2s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.1s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.1s\n",
"[CV] END activation=logistic, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 3.7s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.3s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.7s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 2.8s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.7s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.2s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 1.9s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.3s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 1.4s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.1s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.3s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 3.8s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.1s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.3s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.7s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.8s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 2.9s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.4s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.9s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.4s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 1.8s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.0s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.4s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.9s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 3.2s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.4s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.9s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 3.1s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 5.4s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.3s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.1s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.2s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.3s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.1s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.3s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 1.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.2s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.6s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 1.0s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 0.7s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.7s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.9s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.9s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.5s\n",
"[CV] END activation=logistic, alpha=0.0005, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.2s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 2.3s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 4.6s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 2.7s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.6s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.0s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 2.4s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.6s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.7s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 2.9s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 2.8s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 2.0s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 2.7s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 3.5s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 2.6s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 3.3s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.2s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.6s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.6s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.2s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.9s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.5s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.9s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 3.0s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 1.8s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.8s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.2s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.5s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.9s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.3s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.4s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 4.9s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.3s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.9s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.9s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.7s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 6.5s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.9s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 6.6s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 5.9s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 5.9s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 3.1s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 2.5s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 4.2s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 2.6s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 3.2s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.3s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.1s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.8s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.2s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.6s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.6s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.7s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 3.0s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.4s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 3.8s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.6s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 3.3s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.8s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.5s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 3.2s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.0s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.5s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.7s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 2.8s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.5s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.1s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.8s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 7.3s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 5.0s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.7s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 1.8s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 2.4s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 1.8s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 3.1s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 3.3s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.6s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.2s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.0s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.5s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 4.3s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.7s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 3.0s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.6s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.9s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 3.9s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.5s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.4s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.9s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 3.1s\n",
"[CV] END activation=relu, alpha=0.0001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.7s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 2.2s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.1s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 2.9s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.7s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.1s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 2.7s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 2.5s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.4s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 5.8s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 3.4s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 2.5s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 3.0s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 4.1s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 1.5s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 4.3s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 1.6s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.6s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.7s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 1.6s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 1.5s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.4s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.5s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.3s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.2s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.0s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.1s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.9s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.9s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.4s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.2s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 2.8s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.1s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 5.0s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 4.0s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.6s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 6.7s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.3s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 6.1s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 2.7s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 5.1s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 4.2s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 2.7s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 2.9s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 3.2s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 4.6s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.6s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 1.8s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 1.8s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.6s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.7s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 3.1s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 4.1s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 2.4s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 4.3s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 4.5s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 3.3s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.3s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.6s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.6s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 15, 20), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 1.1s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.4s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 4.0s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.6s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.9s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.6s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.6s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 6.1s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 6.7s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 7.4s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.001, solver=adam; total time= 4.8s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 2.7s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 3.2s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 2.3s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 4.4s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=sgd; total time= 3.9s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.4s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 2.6s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 4.8s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 5.4s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.005, solver=adam; total time= 3.0s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 6.0s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 5.1s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 3.8s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 4.3s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=sgd; total time= 4.3s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 3.4s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 3.4s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.3s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.6s\n",
"[CV] END activation=relu, alpha=0.001, hidden_layer_sizes=(5, 10, 20, 30), learning_rate=constant, learning_rate_init=0.01, solver=adam; total time= 2.5s\n",
"[CV] END activation=relu, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.1s\n",
"[CV] END activation=relu, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.7s\n",
"[CV] END activation=relu, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 0.3s\n",
"[CV] END activation=relu, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 4.1s\n",
"[CV] END activation=relu, alpha=0.0005, hidden_layer_sizes=(5, 10, 10, 10), learning_rate=constant, learning_rate_init=0.001, solver=sgd; total time= 3.2s\n"
]
}
],
"source": [
"param_grid = {\n",
" 'hidden_layer_sizes': [(5,10,10,10),(5,10,15,20),(5,10,20,30)], \n",
" 'activation': ['logistic', 'relu', 'tanh'],\n",
" 'solver': ['sgd', 'adam'],\n",
" 'learning_rate_init': [0.001,0.005,0.01],\n",
" 'learning_rate': ['constant'],\n",
" 'alpha': [0.0001,0.001,0.0005]\n",
"}\n",
"\n",
"grid_search = GridSearchCV(MLPClassifier(), param_grid, \n",
" scoring=['f1', 'accuracy'], refit='accuracy', verbose=2)\n",
"grid_search.fit(X_train_s, y_train)\n",
"print(f'Score: {grid_search.best_score_}')\n",
"print(f'Parameters: {grid_search.best_params_}')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see after tunings, we will choose activation = 'relu', alpha = 0.0005, learning rate init = 0.005, solver = 'sgd'\n",
"now we will try to use only columns with high correltion with target"
]
},
{
"cell_type": "code",
"execution_count": 131,
"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>gender</th>\n",
" <th>age</th>\n",
" <th>height(cm)</th>\n",
" <th>weight(kg)</th>\n",
" <th>waist(cm)</th>\n",
" <th>triglyceride</th>\n",
" <th>HDL</th>\n",
" <th>hemoglobin</th>\n",
" <th>serum creatinine</th>\n",
" <th>ALT</th>\n",
" <th>Gtp</th>\n",
" <th>dental caries</th>\n",
" <th>tartar</th>\n",
" <th>BMI</th>\n",
" <th>smoking</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>40.0</td>\n",
" <td>150.0</td>\n",
" <td>55.0</td>\n",
" <td>75.0</td>\n",
" <td>142.0</td>\n",
" <td>65.0</td>\n",
" <td>13.1</td>\n",
" <td>0.8</td>\n",
" <td>15.0</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>24.444444</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>60.0</td>\n",
" <td>160.0</td>\n",
" <td>65.0</td>\n",
" <td>89.0</td>\n",
" <td>248.0</td>\n",
" <td>43.0</td>\n",
" <td>14.6</td>\n",
" <td>1.2</td>\n",
" <td>32.0</td>\n",
" <td>51</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>25.390625</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>25.0</td>\n",
" <td>175.0</td>\n",
" <td>80.0</td>\n",
" <td>91.8</td>\n",
" <td>252.0</td>\n",
" <td>34.0</td>\n",
" <td>15.9</td>\n",
" <td>1.0</td>\n",
" <td>39.0</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>26.122449</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>25.0</td>\n",
" <td>170.0</td>\n",
" <td>60.0</td>\n",
" <td>73.0</td>\n",
" <td>47.0</td>\n",
" <td>56.0</td>\n",
" <td>15.6</td>\n",
" <td>1.0</td>\n",
" <td>15.0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>20.761246</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>25.0</td>\n",
" <td>175.0</td>\n",
" <td>80.0</td>\n",
" <td>83.5</td>\n",
" <td>82.0</td>\n",
" <td>72.0</td>\n",
" <td>15.3</td>\n",
" <td>0.9</td>\n",
" <td>13.0</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26.122449</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gender age height(cm) weight(kg) waist(cm) triglyceride HDL \\\n",
"0 0 40.0 150.0 55.0 75.0 142.0 65.0 \n",
"1 1 60.0 160.0 65.0 89.0 248.0 43.0 \n",
"2 1 25.0 175.0 80.0 91.8 252.0 34.0 \n",
"3 1 25.0 170.0 60.0 73.0 47.0 56.0 \n",
"4 1 25.0 175.0 80.0 83.5 82.0 72.0 \n",
"\n",
" hemoglobin serum creatinine ALT Gtp dental caries tartar BMI \\\n",
"0 13.1 0.8 15.0 12 0 0 24.444444 \n",
"1 14.6 1.2 32.0 51 0 0 25.390625 \n",
"2 15.9 1.0 39.0 45 0 1 26.122449 \n",
"3 15.6 1.0 15.0 13 0 0 20.761246 \n",
"4 15.3 0.9 13.0 16 0 0 26.122449 \n",
"\n",
" smoking \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 "
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_df = pd.concat((X_train, y_train),axis = 1)\n",
"df_corr = temp_df.corr().loc[:,[\"smoking\"]]\n",
"new_corr = pd.DataFrame(df_corr['smoking'].between(left=0.09, right=0.9))\n",
"new_corr['smoking1'] = pd.DataFrame(df_corr['smoking'].between(left=-0.9, right=-0.09))\n",
"# create list for new df\n",
"list_for_remove = []\n",
"for d,r in new_corr.iterrows():\n",
" if r[0] == False and r[1] == False:\n",
" list_for_remove.append(d)\n",
"list_for_remove.remove('smoking')\n",
"# remove columns with minimum correlation and create new data set\n",
"df_new = temp_df.copy() \n",
"df_new = df_new.drop(columns=list_for_remove)\n",
"df_new.head()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 225,
"metadata": {},
"outputs": [],
"source": [
"wanted = df_new.columns"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [],
"source": [
"# Pruned Data\n",
"X_train_new = df_new.drop(columns=['smoking'])\n",
"y_train_new = df_new['smoking']\n",
"# Split the data into train and validation\n",
"X_train_new, X_val_new, y_train_new, y_val_new = train_test_split(X_train_new, y_train_new, test_size=0.2, random_state=1)\n",
"X_train_new_s = standard_scaler.fit_transform(X_train_new)\n",
"X_valid_new_s = standard_scaler.fit_transform(X_val_new)"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"size: 1\n",
"size: 5\n",
"size: 9\n",
"size: 13\n",
"size: 17\n",
"size: 21\n",
"size: 25\n",
"size: 29\n"
]
}
],
"source": [
"train_accs = []\n",
"test_accs = []\n",
"for size_ in range(1, 30, 4):\n",
" print(f\"size: {size_}\")\n",
" model = MLPClassifier(random_state=1,\n",
" hidden_layer_sizes=(size_, size_, size_,2),\n",
" max_iter=400, # from the graphs shown \n",
" activation='relu', \n",
" verbose=False,\n",
" solver='sgd',\n",
" alpha=0.0005,\n",
" learning_rate_init=0.005)\n",
" model.fit(X_train_new_s, y_train_new)\n",
" train_acc = model.score(X_train_new_s, y_train_new)\n",
" train_accs.append(train_acc)\n",
" test_acc = model.score(X_valid_new_s, y_val_new)\n",
" test_accs.append(test_acc)"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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18ICK7I/dunVDYWEhjhw5Al9fX6SlpaFbt244f/68XoBr2bIlnJ2dATzY95o3b663LLlcjkaNGpXYNw3t38UOHDiAmJgYfPDBB3rHvRV76623sGHDBvTt2xfe3t545pln8Morr6BPnz7lrhtpMcBRmUq7yKdarTbYs1Jab4tQykHzVTVvVSmvhuLLn6xevdrgH8eKntH3OOv68MHbFVmmofbKbNPidX/33XcRHh5ucJpHw/XDvSdiqej3dPjw4Rg1apTBadu0aQOg7J8HQ6pi/Suyr0ybNg39+/fHb7/9hp07d2LWrFmIjIzE3r170bZt22r5TEPee+89vPzyy5DL5boTKIoPfk9KSoJKpYKXl1eF68jMzESPHj1gb2+PTz75BI0bN4alpSVOnTqFDz74QO9yRE5OTnjuued0AW7Tpk1QKpV6vV3G/vxaWFiUCNoA8PXXX2P06NH4/fffsWvXLkyZMgWRkZE4evRouSfrVGTbajQaBAYGYuHChQanffSfMkM6dOgAS0tL/PXXX/Dz84ObmxuaNWuGbt26YcmSJVAqlTh48GCJ41aNUdb+3apVK2RmZmL16tV44403Svwz5+bmhvj4eOzcuRPbt2/H9u3bsWLFCowcObLECT1kGAMclcnJyanE2UeA9j+1Ro0a1Wgt/v7+AFDirNbS2gx53KvOFw8rubm5ISws7LGWVR5D216lUiE5OblaPq+4d+Jhly5d0h3UXfz9Njc3r/J19/f3x5kzZ6DRaPT+YBYPkRV/76uDq6sr7OzsoFary12v4t6vR78vxvQQVte6Nm7cGO+88w7eeecdXL58GcHBwfj666/x888/V2p5lZGUlIS1a9di7dq1Jd5r164dgoKCEB8fX+Hl7d+/H3fv3sXmzZvRvXt3Xfv169cNTj9y5Ei88MILOH78ONasWYO2bduiVatWuver8uc3MDAQgYGBmDlzJg4fPowuXbogOjpad5D+42jcuDFOnz6NXr16lfs7q7T3i4dmDx48CD8/P93Qe7du3aBUKrFmzRqkpqbqbdfifS8hIUHv97tKpcL169eN2mYuLi7YtGkTunbtil69euHQoUMlwrtcLkf//v3Rv39/aDQavPXWW/jf//6HWbNmGRxpIX08Bo7K1LhxYxw9ehQqlUrXtnXrViQlJdV4LV5eXmjdujV++ukn3L9/X9d+4MABnD17tkLLsLGxMRhIKyo8PBz29vaYN28eCgoKSrxv6DIGldW4cWP89ddfem1Lly4ttbfncf322296x7DFxcXh2LFjujPj3Nzc0LNnT/zvf/8zGCIfZ92fffZZpKSk6J2FW1hYiO+//x62tra6Y5iqg0wmw6BBg/Drr78aHPp7eL2KA8DD3xe1Wl3q0KshVb2uubm5epfIKK7Tzs5O75ITNWHLli0lHoMHDwagPcv5m2++MWp5xb1VD/dOqVQqLFmyxOD0ffv2hYuLC+bPn48DBw6UuKZlVfz8KhQKFBYW6rUFBgZCKpVW2fZ+5ZVXcOvWLfz4448l3svLy9M7o7us32ndunXDsWPHsG/fPl2Ac3FxQYsWLXRnfha3A9pjH+VyOb777ju9bb5s2TJkZWWhX79+Rq2Hj48P9uzZg7y8PPTu3Rt3797VvffwcwCQSqW6nu6a3m9NFXvgqEyvv/46Nm3ahD59+uCVV17B1atX8fPPP+sd4FyT5s2bhxdeeAFdunTBmDFjcO/ePSxatAitW7fWC3Wlad++PX744Qd89tlnaNKkCdzc3Eo9HsYQe3t7/PDDDxgxYgTatWuHIUOGwNXVFYmJiYiJiUGXLl2waNGix1lFnddffx1vvvkmBg0ahN69e+P06dPYuXMnXFxcqmT5j2rSpAm6du2KCRMmQKlUIioqCg0aNMD777+vm2bx4sXo2rUrAgMDMW7cODRq1Aipqak4cuQIbt68idOnT1fqs8ePH4///e9/GD16NE6ePImAgABs2rQJf//9N6KiomBnZ1dVq2nQF198gX379iE0NBTjxo1Dy5YtkZGRgVOnTmHPnj3IyMgAoB0WevLJJzFjxgxkZGTA2dkZ69atK/EHvSxVva6XLl1Cr1698Morr6Bly5YwMzPDli1bkJqaiiFDhhi1rMdl6G4AxT1uxeHKGJ07d4aTkxNGjRqFKVOmQCKRYPXq1aUO5Zqbm2PIkCFYtGgRZDKZ3okiQNX8/O7duxeTJk3Cyy+/jGbNmqGwsBCrV6/W/SNQFUaMGIENGzbgzTffxL59+9ClSxeo1WpcvHgRGzZswM6dO9GhQwcA2t9pe/bswcKFC+Hl5YWGDRvqruvWrVs3fP7550hKStILat27d8f//vc/BAQE6A35urq6YsaMGZg7dy769OmD559/HgkJCViyZAk6duxYqYu8N2nSBLt27ULPnj0RHh6OvXv3wt7eHq+//joyMjLw9NNPw8fHBzdu3MD333+P4ODgKr/7T13FAEdlCg8Px9dff42FCxdi2rRp6NChA7Zu3aq7BldN69+/P3755Rd8/PHHmD59Opo2bYqVK1di1apVFbqQ7uzZs3Hjxg0sWLAA2dnZ6NGjh1EBDgBeffVVeHl54YsvvsCXX34JpVIJb29vdOvWrcwzsow1btw4XL9+HcuWLcOOHTvQrVs37N69G7169aqyz3jYyJEjIZVKERUVhbS0NISEhGDRokXw9PTUTdOyZUucOHECc+fOxcqVK3H37l24ubmhbdu2mD17dqU/28rKCvv378f06dOxatUqKBQKNG/eHCtWrKiR2565u7sjLi4On3zyCTZv3owlS5agQYMGaNWqla6notiaNWvwxhtv4IsvvoCjoyPGjh2Lp556Cr17967QZ1X1uvr6+mLo0KGIjY3F6tWrYWZmhieeeAIbNmyoskAhlgYNGuh+38ycORNOTk4YPnw4evXqVepxmCNHjsSiRYvQq1cvvX232OP+/AYFBSE8PBx//vknbt26BWtrawQFBWH79u148sknH3udAW1v1G+//YZvvvkGP/30E7Zs2QJra2s0atQIU6dORbNmzXTTLly4EOPHj8fMmTORl5eHUaNG6QJc586dIZPJdDUW69atG/73v//phbpiH3/8MVxdXbFo0SK8/fbbcHZ2xvjx4zFv3rxK30UnMDAQ27dvR1hYGPr3748dO3Zg+PDhWLp0KZYsWYLMzEx4eHhg8ODB+Pjjjw0ed0glSYSaPEKcqJoEBwfD1dWVp6QT1XOnT59GcHAwfvrpJ4wYMULscoiqDWMumZSCgoISw1X79+/H6dOnS9wWi4jqnx9//BG2trYYOHCg2KUQVSsOoZJJuXXrFsLCwjB8+HB4eXnh4sWLiI6OhoeHB958802xyyMikfz555/4999/sXTpUkyaNIk3nac6j0OoZFKysrIwfvx4/P3330hPT4eNjQ169eqFL774QrQTK4hIfAEBAUhNTUV4eDhWr15d7Se+EImNAY6IiIjIxPAYOCIiIiITwwBHREREZGJ4EoMBGo0Gt2/fhp2d3WPfeomIiIioPIIgIDs7G15eXhW6Fh4DnAG3b9+u0M2CiYiIiKpSUlKS3h0ySsMAZ0Dx2UtJSUmwt7cXuRoiIiKq6xQKBXx9fSt8BjUDnAHFw6b29vYMcERERFRjKnroFk9iICIiIjIxDHBEREREJoYBjoiIiMjE8Bi4ShIEAYWFhVCr1WKXYrJkMhnMzMx4qRYiIiIjMcBVgkqlQnJyMnJzc8UuxeRZW1vD09MTcrlc7FKIiIhMBgOckTQaDa5fvw6ZTAYvLy/I5XL2IFWCIAhQqVRIT0/H9evX0bRp0wpduJCIiIgY4IymUqmg0Wjg6+sLa2trscsxaVZWVjA3N8eNGzegUqlgaWkpdklEREQmgV0elcTeoqrB7UhERGQ8/vUkIiIiMjEcQiUiIiIyoECtQXJmPhIzcpGWnY+B7cq/R2lNYYCjxxIQEIBp06Zh2rRpYpdCRERkFEEQkJlbgMSMXN0j6aHnyVn5UGsEAIBEAvRr4wkLM5nIVWsxwNUT5Z0pO2fOHHz88cdGL/f48eOwsbGpZFVERETVS1Wowa3MPP2AdvfB82xlYZnzW5hJ4etsDT9na+Qo1QxwVLOSk5N1z9evX4/Zs2cjISFB12Zra6t7LggC1Go1zMzK3z1cXV2rtlAiIiIjCIKAjByVwR60pIw83M7KgyCUvQw3Owv4FYW04rDm10D71dXWAlJp7btcGANcFRAEAXkFNX9HBitzWYWvQefh4aF77uDgAIlEomvbv38/nnrqKWzbtg0zZ87E2bNnsWvXLvj6+iIiIgJHjx5FTk4OWrRogcjISISFhemW9egQqkQiwY8//oiYmBjs3LkT3t7e+Prrr/H8889X3YoTEVG9kl+gxs17eXrh7OGwlqsq+2+wlblMP5w5W+kCmo+TNSzNa0evmjEY4KpAXoEaLWfvrPHP/feTcFjLq+5bOH36dHz11Vdo1KgRnJyckJSUhGeffRaff/45LCws8NNPP6F///5ISEiAn59fqcuZO3cuFixYgC+//BLff/89hg0bhhs3bsDZ2bnKaiUiorpDEASk31c+CGh38/QCWooiv8z5JRLAw97yoYCm35vmYlv3LrrPAEc6n3zyCXr37q177ezsjKCgIN3rTz/9FFu2bMEff/yBSZMmlbqc0aNHY+jQoQCAefPm4bvvvkNcXBz69OlTfcUTEVGtlqdSI+me9vizpHslhzvzCzRlzm8jl8GvgQ38nK3g66Qd4iwOaN6OVibZi/Y4akWAW7x4Mb788kukpKQgKCgI33//PUJCQkqdPioqCj/88AMSExPh4uKCl156CZGRkXpX8jd2mY/DylyGfz8Jr5Zll/e5ValDhw56r+/fv4+PP/4YMTExSE5ORmFhIfLy8pCYmFjmctq0aaN7bmNjA3t7e6SlpVVprUREVLtoNALSspWlntGZnq0sc36pBPB0sHrQg9bAWq9HzcnavM71oj0O0QPc+vXrERERgejoaISGhiIqKgrh4eFISEiAm5tbienXrl2L6dOnY/ny5ejcuTMuXbqE0aNHQyKRYOHChZVa5uOSSCRVOpQplkfPJn333Xexe/dufPXVV2jSpAmsrKzw0ksvQaVSlbkcc3NzvdcSiQQaTdn/WRERUe2XoyzU9aKVOGHgXh5UhWX/rrezMNMde6Z/TJo1vBytIDfj/QUqSvTUsXDhQowbNw5jxowBAERHRyMmJgbLly/H9OnTS0x/+PBhdOnSBa+++ioA7UH0Q4cOxbFjxyq9TDLs77//xujRo/Hiiy8C0PbI/ffff+IWRURE1UatEZCiyNcOc+qFM+3rO/fL/gdeJpXA29GqRDjTvraCgxV70aqKqAFOpVLh5MmTmDFjhq5NKpUiLCwMR44cMThP586d8fPPPyMuLg4hISG4du0atm3bhhEjRlR6mUqlEkrlg65dhUJRFatn8po2bYrNmzejf//+kEgkmDVrFnvSiIhMmEYj4M59JZLu5eLmvTzdmZ3a57m4lZmHAnXZ19xwtDY3GND8nK3h6WAJMxl70WqCqAHuzp07UKvVcHd312t3d3fHxYsXDc7z6quv4s6dO+jatSsEQUBhYSHefPNNfPjhh5VeZmRkJObOnVsFa1S3LFy4EK+99ho6d+4MFxcXfPDBBwy3RES1mCAIuJujeiiU5T0U1rRfyxvmNJdJ4OOkDWi+TlZ6w52+ztZwsDIvc36qGaIPoRpr//79mDdvHpYsWYLQ0FBcuXIFU6dOxaeffopZs2ZVapkzZsxARESE7rVCoYCvr29VlVzrjB49GqNHj9a97tmzJwQDVzkMCAjA3r179domTpyo9/rRIVVDy8nMzKx0rURE9EDxrZ8eDmVJGUVfi16XdzZn8ckCPk5WRUFN+1X72gqeDlaQ1cIL15I+UQOci4sLZDIZUlNT9dpTU1P1Ljz7sFmzZmHEiBF4/fXXAQCBgYHIycnB+PHj8dFHH1VqmRYWFrCwsKiCNSIiIno8WY8EtEeDWk45F60tviaaLqAVffUpuvyGh4MlzDnMafJEDXByuRzt27dHbGwsBgwYAADQaDSIjY0t9Tpjubm5kEr1dzyZTHs5DUEQKrVMIiKimpKdX1Di+LOHA1t2ftn35gS0t37ycbKCr7P1Q0FN+9zT0bLW3K+Tqo/oQ6gREREYNWoUOnTogJCQEERFRSEnJ0d3BunIkSPh7e2NyMhIAED//v2xcOFCtG3bVjeEOmvWLPTv318X5MpbJhERUXXJVRWWODkgKSMPNzO1rzNzC8pdhoutHN4P9549FNbq40VrqSTRA9zgwYORnp6O2bNnIyUlBcHBwdixY4fuJITExES9HreZM2dCIpFg5syZuHXrFlxdXdG/f398/vnnFV4mERFRZenuy/nwMGfGg+HOuzllX2oDAJyszUscf1bcg+btZFUnri1K1UsiGDrqvJ5TKBRwcHBAVlYW7O3t9d7Lz8/H9evX0bBhQ707P1DlcHsSUW2jLFTjdma+fg/aQz1pd+6XfUcBALC3NDMc0Ipe21owoJG+srKHIdyDiIioXlFrBNzS9aA9ODmguFctLVuJ8ro2bOSyoiFN60eORdMGNF5qg6obAxwREdULSRm5WHc8ERtO3Cz3vpxW5jKDw5vFvWq8owCJjQGOiIjqrAK1BrEXUrE2LgkHL6fretYszKS6QFbybE4rONvIGdCoVmOAIyKiOicpIxfrjydhw4kkpD3U29atqQuGhvghrIU7b5xOJo0BjiqsZ8+eCA4ORlRUFADtnRqmTZuGadOmlTqPRCLBli1bdNfkIyKqLtretjT8EpeIvx7qbXOxlePlDr4Y0tEX/g1sxC2SqIowwNUT/fv3R0FBAXbs2FHivYMHD6J79+44ffo02rRpU+FlHj9+HDY2/GVIROK6eU/b27b+uH5vW9cm2t623i3Z20Z1DwNcPTF27FgMGjQIN2/ehI+Pj957K1asQIcOHYwKbwDg6upalSUSEVVYoVqD2Iva3rYDlx70tjWwedDbFuDCfzCp7uK/JFVBEABVTs0/jLiE33PPPQdXV1esXLlSr/3+/fvYuHEjBgwYgKFDh8Lb2xvW1tYIDAzEL7/8UuYyAwICdMOpAHD58mV0794dlpaWaNmyJXbv3m3MViQiKtfNe7lYuCsBXebvxRurT2J/gja8dWnSAItebYsjM3phet8nGN6ozmMPXFUoyAXmedX85354G5BX7JeUmZkZRo4ciZUrV+Kjjz7SnV21ceNGqNVqDB8+HBs3bsQHH3wAe3t7xMTEYMSIEWjcuDFCQkLKXb5Go8HAgQPh7u6OY8eOISsrq8xj44iIKqpQrcHeot62/Y/0tr3UwQdDOvqhIQMb1TMMcPXIa6+9hi+//BIHDhxAz549AWiHTwcNGgR/f3+8++67umknT56MnTt3YsOGDRUKcHv27MHFixexc+dOeHlpw+y8efPQt2/falkXIqr7bmXmac8kPZ6EFEW+rr1z4wYYGuKHZ1q586btVG8xwFUFc2ttb5gYn2uEJ554Ap07d8by5cvRs2dPXLlyBQcPHsQnn3wCtVqNefPmYcOGDbh16xZUKhWUSiWsrSv2GRcuXICvr68uvAFAp06djKqPiKhQrcH+hHSsjUvE/oQ0aIp625xt5Hi5vQ8Gd/RFI1dbcYskqgUY4KqCRFLhoUyxjR07FpMnT8bixYuxYsUKNG7cGD169MD8+fPx7bffIioqCoGBgbCxscG0adOgUpV/U2Yiosd1u7i37UQSkrMe9LZ1atQAQ0P9EM7eNiI9DHD1zCuvvIKpU6di7dq1+OmnnzBhwgRIJBL8/fffeOGFFzB8+HAA2mPaLl26hJYtW1ZouS1atEBSUhKSk5Ph6ekJADh69Gi1rQcRmT61RsD+hDSsPZaIfY/0tr3U3gdD2NtGVCoGuHrG1tYWgwcPxowZM6BQKDB69GgAQNOmTbFp0yYcPnwYTk5OWLhwIVJTUysc4MLCwtCsWTOMGjUKX375JRQKBT766KNqXBMiMlXJWXm667Y93Nv2ZCNnDA3xQ5/WHuxtIyoHA1w9NHbsWCxbtgzPPvus7pi1mTNn4tq1awgPD4e1tTXGjx+PAQMGICsrq0LLlEql2LJlC8aOHYuQkBAEBATgu+++Q58+fapzVYjIRKg1Ag5c0va27b34oLfNydpc29sW4ofG7G0jqjCJIBhxMbF6QqFQwMHBAVlZWbC3t9d7Lz8/H9evX0fDhg1haWkpUoV1B7cnUd2WnJWHDcdvYv3xRNx+qLcttKEzXg31Q3grD1ias7eNqKzsYQh74IiIqEqpNQL+upSONccSsfdiqq63zdHaHC+10/a2NXFjbxvR42CAIyKiKpGqyNcd23YrM0/XHtLQGa8WHdvG3jaiqsEAR0RElabWCPjrcrru2DZ1UXebo7U5BrXzwdAQXzRxsxO5SqK6hwGOiIiMlqrIx4bjSVj3aG9bgDOGhvqib2tP9rYRVSMGuEriuR9Vg9uRyHSoNQIOFvW2xT7U2+Zg9aC3rak7e9uIagIDnJHMzc0BALm5ubCyshK5GtOXm5sL4MF2JaLaJ02Rjw0nkvBLnH5vW8cAJwwN8cOzgextI6ppDHBGkslkcHR0RFpaGgDA2toaEolE5KpMjyAIyM3NRVpaGhwdHSGT8Zc/UW2i0Qg4eOUO1h67gT0XHvS22VuaYVB7HwwN8UMz9rYRiYYBrhI8PDwAQBfiqPIcHR1125OIxJeWnY+NJ27il7hE3Lz3oLetg7+2t61fG/a2EdUGDHCVIJFI4OnpCTc3NxQUFIhdjskyNzdnzxtRLaDRCDh05Q7WHkvEngupKHyot21gO21vW3MP9rYR1SYMcI9BJpMxgBCRySrubVt3PBFJGQ9629oX97YFesJKzt9xRLWRVOwCAGDx4sUICAiApaUlQkNDERcXV+q0PXv2hEQiKfHo16+fbpr79+9j0qRJ8PHxgZWVFVq2bIno6OiaWBUiolpNU3Qm6YSfT6Jz5F58uTMBSRl5sLM0w+jOAdgxrRt+ndAZL7X3YXgjqsVE74Fbv349IiIiEB0djdDQUERFRSE8PBwJCQlwc3MrMf3mzZuhUql0r+/evYugoCC8/PLLuraIiAjs3bsXP//8MwICArBr1y689dZb8PLywvPPP18j60VEVJukZyux8WQS1sUlITEjV9fezs8RQ0P88FwbLwY2IhMi+s3sQ0ND0bFjRyxatAgAoNFo4Ovri8mTJ2P69Onlzh8VFYXZs2cjOTkZNjY2AIDWrVtj8ODBmDVrlm669u3bo2/fvvjss8/KXaaxN5QlIqqNNBoBh6/exdq4G9h1/sGxbXaWZhjY1htDQ/3whAd/xxHVBiZ1M3uVSoWTJ09ixowZujapVIqwsDAcOXKkQstYtmwZhgwZogtvANC5c2f88ccfeO211+Dl5YX9+/fj0qVL+OabbwwuQ6lUQqlU6l4rFIpKrhERkXg0GgFX0+/jVOI9nLqRicPX7ugd29a2qLetP3vbiEyeqAHuzp07UKvVcHd312t3d3fHxYsXy50/Li4O586dw7Jly/Tav//+e4wfPx4+Pj4wMzODVCrFjz/+iO7duxtcTmRkJObOnVv5FSEiEkFWXgHikzJx6sY9nEq8h/ikTGTnF+pNY2dhhhfbeWNIRz+09GJvG1FdIfoxcI9j2bJlCAwMREhIiF77999/j6NHj+KPP/6Av78//vrrL0ycOBFeXl4ICwsrsZwZM2YgIiJC91qhUMDX17fa6yciqqhHe9dOJd7DlfT7ePQgGCtzGdr4OKCdvxPa+TmhS5MGsJab9K96IjJA1J9qFxcXyGQypKam6rWnpqaWe3HXnJwcrFu3Dp988olee15eHj788ENs2bJFd2ZqmzZtEB8fj6+++spggLOwsICFhcVjrg0RUdV5uHftn6RMxCfeg+KR3jUA8HO2Rjs/R11ge8LDDmayWnGBASKqRqIGOLlcjvbt2yM2NhYDBgwAoD2JITY2FpMmTSpz3o0bN0KpVGL48OF67QUFBSgoKIBUqv8LTCaTQaPRVGn9RERVobK9a239HOFiy38+ieoj0fvVIyIiMGrUKHTo0AEhISGIiopCTk4OxowZAwAYOXIkvL29ERkZqTffsmXLMGDAADRo0ECv3d7eHj169MB7770HKysr+Pv748CBA/jpp5+wcOHCGlsvIqLSKPILEJ+oDWqnEtm7RkTGEz3ADR48GOnp6Zg9ezZSUlIQHByMHTt26E5sSExMLNGblpCQgEOHDmHXrl0Gl7lu3TrMmDEDw4YNQ0ZGBvz9/fH555/jzTffrPb1ISJ62KO9a/8k3cPltNJ719r6OaGdnyPa+jnB1Y69a0RkmOjXgauNeB04IqqsyvauNfewgzl714jqLZO6DhwRkSnTaARcu3Nfd9zaqUTDvWuW5lK08XFEO/auEVEVYYAjIqog9q4RUW3BAEdEZAB714ioNmOAIyKCtnftdFKmLrD9U0rvmq+zVVFYKzoz1JO9a0RU8xjgiKjeYe8aEZk6BjgiqvOy84vvapCpu2doVl5BienYu0ZEpoIBjojqFF3vWmIm/im69tqltOxSe9fa+jnqAht714jIVDDAEZHJKlBrcOe+ElfS7rN3jYjqFQY4Iqp1lIVqpGcrkZatRJpCibTsfN3XVIW2PT07H3dzVCV61oCi3jVvR7T1d9TdM9TNzrLmV4SIqJowwBFRjckvUD8IZNlKpCryDYa0e7kle9BKYyaVwMvRCm39HNHWV3vttRae9uxdI6I6jQGOiB5brqpQ2zOmeBDMdD1oxb1minyDl+UojVwmhaudBdzsLeBmZwF3e0u42VnAzc6yqE371dlaDqlUUo1rR0RU+zDAEZFBgiDgvrIomGUXBTLFQ71muh4zJe4rKx7MLMykcLO3gPsjQczNzlIvqDlam0MiYTAjIjKEAY6onhEEAYq8woeOJ9MfzkxXKJFaFM7yCtQVXq6VuQzujwYye4sHbXYWcLO3hL2lGYMZEdFjYoAjqiMEQcC93AK9IUvtwf4le82UhZoKL9fOwgyuRcOYbnaWBkOam50FbC0YzIiIagoDHFEtpNYIyC9QI79AjbziryoN7uQUBbOiocvUh0JaWnY+CtQGTskshb2lmXa4soxhTDd7C1jL+WuCiKi24W9mogoSBAEFakEXqIrDVZ5K+1VZoNG9zi8s+lqgRn5x+8PzqR60P7qs/EINVEb0kD3Kydq85DBm0fBlcThztbOApbmsCrcOERHVJAY4MnkajfAgMBVqHgpO+qEoX/VQb1bBQ6HrkXa90KVSQ1n4IKRpKt7BVWXkZlJYmctgaS6Fs03xGZmGhzFd7SxgYcZgRkRU1zHAUa3y720Ffou/hRxlYYV7tR6nt6qypBLtQftWchkszLRfi0OWpXnxc9mDacy1Iezhdku5DJZm0ofmLXrvoXYLMxlkvEQGERE9ggGOag1BEDBt/T+4lHq/0st4uLdKLxQVB6PSAtYjQerBvAYClrkM5jIJD9gnIiLRMMBRrXEp9T4upd6HXCbFhJ6NYS1nbxUREZEhDHBUa2w9cxsA0L2ZK97u3UzkaoiIiGov3iyQagVBEBBzJhkA0D/IU+RqiIiIajcGOKoV/k1W4NqdHFiYSdGrhbvY5RAREdVqDHBUK2wt6n17qrkbbC04sk9ERFQWBjgS3cPDp89x+JSIiKhcDHAkurO3spCYkQsrcxmefsJN7HKIiIhqvVoR4BYvXoyAgABYWloiNDQUcXFxpU7bs2dPSCSSEo9+/frpTXfhwgU8//zzcHBwgI2NDTp27IjExMTqXhWqhOLh06dbuGnvu6m8D+QrAGW29rkqByjIAwrygUIlUKgC1AWAuhDQqAGNBhBEuEUCERGRSEQ/2Gj9+vWIiIhAdHQ0QkNDERUVhfDwcCQkJMDNrWRvzObNm6FSqXSv7969i6CgILz88su6tqtXr6Jr164YO3Ys5s6dC3t7e5w/fx6WlpY1sk5UcXpnn7bxBLZ/AByLfsylSgCJpJyvUgNtFZ3XwNfKzFPmZxuqTwJIzbQPmRyQmRc9Nwek5tqvpT0vnk4mL3seQ8uuyDy8qDERUY2SCIK4XRehoaHo2LEjFi1aBADQaDTw9fXF5MmTMX369HLnj4qKwuzZs5GcnAwbGxsAwJAhQ2Bubo7Vq1dXqiaFQgEHBwdkZWXB3t6+UsugijmVeA8DlxyGjVyGk+92gOW3LQC1qvwZqXaRPhzqzIq+yh96Xs2B0soR8GwL2DQQe0sQEVWKsdlD1B44lUqFkydPYsaMGbo2qVSKsLAwHDlypELLWLZsGYYMGaILbxqNBjExMXj//fcRHh6Of/75Bw0bNsSMGTMwYMAAg8tQKpVQKpW61wqFovIrRUYp7n0La+kOy4tbtOHNIxAYu7toWFQo5ysqOJ0ACJoqWIaBr48zr95XPLRMjeFpNIVFQ8cF2m2lLihqK3pd/FxTNMSsVj14bmgeTUHRvAUGpis08H4BIKhLfiM1Re+LzSkA8G4PeHfQfvVsA5hbiV0VEVGVEzXA3blzB2q1Gu7u+tf9cnd3x8WLF8udPy4uDufOncOyZct0bWlpabh//z6++OILfPbZZ5g/fz527NiBgQMHYt++fejRo0eJ5URGRmLu3LmPv0JkFI3mobNP23gBB3/WvhE8nH90azONpigAPhoOHwl6FQ6HFQme5SxbcRu4ewW495/2ce5Xba1SM8CtJeBTFOi82wMuzQCpTMwtSET02EQ/Bu5xLFu2DIGBgQgJCdG1aTQaAMALL7yAt99+GwAQHByMw4cPIzo62mCAmzFjBiIiInSvFQoFfH19q7l6Opl4DymKfNhZmKGHYxqQHK8dEgt8udx5SURSKSCVA2ZysSvRl5cJ3D4F3DoJ3DoF3DwB5KQBKWe0jxPLtdPJ7QCvYG2YKw529l5iVk5EZDRRA5yLiwtkMhlSU1P12lNTU+Hh4VHmvDk5OVi3bh0++eSTEss0MzNDy5Yt9dpbtGiBQ4cOGVyWhYUFLCwsKrEG9DiKe996t3KH/Ow6bWPzPjyOiSrHyhFo/LT2AWiHnbNuFgW6osftfwBVNvDfQe2jmJ3ngx467/aAV1vAkse/ElHtJWqAk8vlaN++PWJjY3XHp2k0GsTGxmLSpEllzrtx40YolUoMHz68xDI7duyIhIQEvfZLly7B39+/SuunylNrBMSc1Qa451u7AjHrtW8EDy9jLiIjSCSAo6/20WqAtk1dCNxJ0Ia5mye0PXVp54HsZODiVu1DOzPg2rwo0LXTHlPn3kp7wgQRUS0g+hBqREQERo0ahQ4dOiAkJARRUVHIycnBmDFjAAAjR46Et7c3IiMj9eZbtmwZBgwYgAYNSvbWvPfeexg8eDC6d++Op556Cjt27MCff/6J/fv318QqUQXEXc9AerYSDlbm6IJ/gJx0wMYNaBImdmlUl8nMtEHMvRXQbqS2TZUDJJ/W76nLTATSL2of8Wu005lZAh5tHhp6bQc4NeQlVIhIFKIHuMGDByM9PR2zZ89GSkoKgoODsWPHDt2JDYmJiZBK9a83nJCQgEOHDmHXrl0Gl/niiy8iOjoakZGRmDJlCpo3b45ff/0VXbt2rfb1oYqJOXsbABDeyh3mZxZoG4MGa//AEtUkuQ3g31n7KHY/Tds7d+skcOuE9mt+FnAzTvs4VjSdlbP+0Kt3ex4CQEQ1QvTrwNVGvA5c9SpUaxA6LxZ3c1RY+2pjdP6ti/YsxLeOAm4txC6PqCRBADKuPTT0elJ7YoShaxbyUiZEVAkmdR04qp+OXc/A3RwVnKzNEXo/VhvevNoxvFHtJZEADRprH21e0bYVqoDUsw966m6eAO5e5qVMiKhGMMBRjdt6Rjt82qe1J2Sni84ibjtMxIqIKsFM/iCQYZy2LS9Te6Zr8bF0vJQJEVUTBjiqUQVqDbafSwEADPbJAM6cBWQWQOtBIldGVAWsHIHGT2kfgHboVXHrwbDrrVO8lAkRVQkGOKpRh6/eRWZuAVxs5WhzJ0bb+EQ/wMpJ3MKIqoNEAjj4aB/FlzLRqLVnt+p66U4Caf/yUiZEZBQGOKpRW09rh0+fa+kC6dmN2sZgDp9SPSKVlXIpkzMPznjlpUwqTl0IqJVAYdFDrdQen1iYrz3JpFCp/1xd9J7UTPuPo5UzYO2s/WrlyGMTyWQwwFGNURVqsPO8dvj0VafzQF6GdtioeLiJqL6S2wD+nbSPYvfT9a9Nd+skkJ9ZOy5lYjA0KR8KSI+EphLTGAhZJZaRr52mvM8R1FW7bpYOj4Q6pwfPrYteP9omt63fIbou0mgA1X1AqQDyFYAyW3voQy26VikDHNWYQ1fSocgvhJudBZre/kPbGDSE//ESGWLrqr21XPM+2telXcokLwO4slv7KFZ8KROvttpeu1J7pioZrASNKJukXBKp9phas6KH3nO5frumULvtcjOAvHvaP9SA9np/+VnAvesV/1ypecWC3qOBsLbdT7iuKFQWhS6FfgBTFn3NVwDKrIeeK0o+V2YDMHCVtVl3as1hDAxwVGO2ntbeOuuVJ+SQnNujbeTwKVHFlHopk3P6vXR3LpW8lEm11iXVhsRHA5Lec7n+NCWClaV2mvLmK+1zipfxOBcCVxdozyLWhbqiYFf8vDjoPdqmVgKaAu3Zxjlpxn2m3PbB0G1ZQe/hQGjpCDxycfs6w1Cvl14IMxC08rMeac/Wfk+qitQMsLDXnlBkYQ8U5DHAUf2SX6DG7n9TAQCDLQ5rhz18QgCXpiJXRmTCzORFJzi0g8FLmaSc1faWVVVAMrSMunL3FJm5ttfT1rXi8wiC9g/6w6FPF/QygNx7BtoytEPhQlFYUd0HshIr/pkSqTbEGQx6TqWHP3Pr6h3mfaxer4emM9TrVVlyW23osrB7EMD0nhcHM7tHnjs8mM7MstYOj9eRnzyq7f66lI5sZSE87S3gc2OLtpHXfiOqeo9eyoSqj0QCyK21Dwefis+n0WhD3KM9eiV6/IrbisKf6r42+OUVvWcMmcUjoc5Rv8evOOhZOmhDabm9Xtn6Qa1Ke73MHwlWDg+elwhjDo+0PzRdHT88hwGOasTWM9rh07GNMyG5cBEwswJavShyVUREIpBKtYHJ2tm4+QqVhodx9cKfgUCoKdAGrOxk7aM6ye1K6fEqL4w9NExpZlFre71qEwY4qnb5BWrsuaAdPn1B2KdtbNFf+4NMREQVY2YB2HloHxUlCNqeu1KD3j39IJiv0N67t7SerdKGHy3s6kWvV23CAEfVbt/FNOSq1GjoIIPLf39qGzl8SkRU/SSSB+HKyV/saqgK1dFTWag22XpW22U/1ecSJPlZgIMvENBd5KqIiIhMFwMcVatcVSH2XtCeWt9LWXTpkKChdfc0eCIiohrAv6JUrWIvpCGvQI12jnmwvVV04+7goeIWRUREZOIY4KhaxRSdfTrV9QQkggbw7wI4NxK5KiIiItPGAEfV5r6yEPsS0gAIeFKxU9vIOy8QERE9NgY4qjZ7/k2FslCD/k5JsMi6BpjbAC1fELssIiIik8cAR9Wm+OK94+2OaBtaDQAsbMUriIiIqI5ggKNqkZVXgL8upcMSSrS8F6ttDH5V3KKIiIjqCKMDXEBAAD755BMkJhpx812qd/b8mwqVWoPRjmcgK7gPOAUAfp3FLouIiKhOMDrATZs2DZs3b0ajRo3Qu3dvrFu3DkplFd7EluqErWduAwBetfhb2xD0Kq/9RkREVEUqFeDi4+MRFxeHFi1aYPLkyfD09MSkSZNw6tSp6qiRTExmrgoHL9+BN9Lhm3Vc28hrvxEREVWZSneJtGvXDt999x1u376NOXPm4P/+7//QsWNHBAcHY/ny5RAEoSrrJBOy63wqCjUC3nCMgwQC0LA74OgndllERER1RqVvZl9QUIAtW7ZgxYoV2L17N5588kmMHTsWN2/exIcffog9e/Zg7dq1VVkrmYg/z9yGBBq8gP3ahuDhotZDRERU1xjdA3fq1Cm9YdNWrVrh3LlzOHToEMaMGYNZs2Zhz5492LJlS4WXuXjxYgQEBMDS0hKhoaGIi4srddqePXtCIpGUePTr18/g9G+++SYkEgmioqKMXVWqhLv3lTh89S5CJAlwyL8FyO2AFv3FLouIiKhOMboHrmPHjujduzd++OEHDBgwAObm5iWmadiwIYYMGVKh5a1fvx4RERGIjo5GaGgooqKiEB4ejoSEBLi5uZWYfvPmzVCpVLrXd+/eRVBQEF5++eUS027ZsgVHjx6Fl5eXEWtIj2Pn+VSoNQJedzgCKAG0fhGQW4tdFhERUZ1idA/ctWvXsGPHDrz88ssGwxsA2NjYYMWKFRVa3sKFCzFu3DiMGTMGLVu2RHR0NKytrbF8+XKD0zs7O8PDw0P32L17N6ytrUsEuFu3bmHy5MlYs2ZNqXVS1dt65jaskY+eBUVnn3L4lIiIqMoZHeDS0tJw7NixEu3Hjh3DiRMnjFqWSqXCyZMnERYW9qAgqRRhYWE4cuRIhZaxbNkyDBkyBDY2Nro2jUaDESNG4L333kOrVq3KXYZSqYRCodB7kPHSs5U4eu0unpUdg7kmD2jQBPANEbssIiKiOsfoADdx4kQkJSWVaL916xYmTpxo1LLu3LkDtVoNd3d3vXZ3d3ekpKSUO39cXBzOnTuH119/Xa99/vz5MDMzw5QpUypUR2RkJBwcHHQPX1/fiq8E6ew4lwyNAIy2Lu59exWQSMQtioiIqA4yOsD9+++/aNeuXYn2tm3b4t9//62Soipq2bJlCAwMREjIg16ekydP4ttvv8XKlSshqWB4mDFjBrKysnQPQwGVyvfnmWT4SVLRuuAcIJECbSp2HCQREREZx+gAZ2FhgdTU1BLtycnJMDMz7pwIFxcXyGSyEstLTU2Fh4dHmfPm5ORg3bp1GDt2rF77wYMHkZaWBj8/P5iZmcHMzAw3btzAO++8g4CAgFLXyd7eXu9BxklV5OP4fxkYJPtL29DoKcDBW9yiiIiI6iijA9wzzzyj67EqlpmZiQ8//BC9e/c2allyuRzt27dHbGysrk2j0SA2NhadOnUqc96NGzdCqVRi+HD9g+RHjBiBM2fOID4+Xvfw8vLCe++9h507dxpVH1XctrPJgKDBq/Ki4dO2w8QtiIiIqA4z+jIiX331Fbp37w5/f3+0bdsWABAfHw93d3esXr3a6AIiIiIwatQodOjQASEhIYiKikJOTg7GjBkDABg5ciS8vb0RGRmpN9+yZcswYMAANGjQQK+9QYMGJdrMzc3h4eGB5s2bG10fVczWM8noJP0Xrpo0wNIBaG74unxERET0+IwOcN7e3jhz5gzWrFmD06dPw8rKCmPGjMHQoUMrdbmOwYMHIz09HbNnz0ZKSgqCg4OxY8cO3YkNiYmJkD5yE/SEhAQcOnQIu3btMvrzqOrdzszDyRv3EGV+QNvQ+iXA3FLcooiIiOowicCblpagUCjg4OCArKwsHg9XAf938Bq+jTmJE5YTYQEl8PpewKe92GURERGZDGOzR6Xvhfrvv/8iMTFR764IAPD8889XdpFkoraeSUY/2VFteHN9AvAueZYyERERVR2jA9y1a9fw4osv4uzZs5BIJCjuwCu+ZIdara7aCqlWS8rIRXxSJmbKi84+5bXfiIiIqp3RZ6FOnToVDRs2RFpaGqytrXH+/Hn89ddf6NChA/bv318NJVJtFnM2GY0kt9FBegmQyIA2g8UuiYiIqM4zugfuyJEj2Lt3L1xcXCCVSiGVStG1a1dERkZiypQp+Oeff6qjTqqlYs4kP7j2W5MwwK7s6/cRERHR4zO6B06tVsPOzg6A9kK8t2/fBgD4+/sjISGhaqujWu2/Ozk4f+seBsoOaRt47TciIqIaYXQPXOvWrXH69Gk0bNgQoaGhWLBgAeRyOZYuXYpGjRpVR41US8WcTUY36Vl4SjIAK2egWV+xSyIiIqoXjA5wM2fORE5ODgDgk08+wXPPPYdu3bqhQYMGWL9+fZUXSLXX1jPJeEtWdO23wJcBM7m4BREREdUTRge48PBw3fMmTZrg4sWLyMjIgJOTU4VvHk+m72r6fdxKvo1nLE5qGzh8SkREVGOMOgauoKAAZmZmOHfunF67s7Mzw1s9E3MmGc/LjsBCUgC4twY82ohdEhERUb1hVIAzNzeHn58fr/VG2HrmNl4qHj4NHsZrvxEREdUgo89C/eijj/Dhhx8iIyOjOuohE3ApNRtC2kUES69BkJoBbV4RuyQiIqJ6xehj4BYtWoQrV67Ay8sL/v7+sLGx0Xv/1KlTVVYc1U5bzyTret8kzfoANi4iV0RERFS/GB3gBgwYUA1lkKkQBAHbTydibfG134J58gIREVFNMzrAzZkzpzrqIBNxITkbvhlH4CrPgsbaFdKmvcUuiYiIqN4x+hg4qt9izt7Gy0XDp9KgwYDMXOSKiIiI6h+je+CkUmmZlwzhGap1lyAIOHg6AVOlRcc5Br8qbkFERET1lNEBbsuWLXqvCwoK8M8//2DVqlWYO3dulRVGtc/52wq0y9oDubkaao8gyNxbiV0SERFRvWR0gHvhhRdKtL300kto1aoV1q9fj7Fjx1ZJYVT7/HnmNl6S/QUAkLUdLnI1RERE9VeVHQP35JNPIjY2tqoWR7WMIAhIiD+M1tL/oJaaA4EviV0SERFRvVUlAS4vLw/fffcdvL29q2JxVAudvpmF7jm7tC+a9QWsncUtiIiIqB4zegj10ZvWC4KA7OxsWFtb4+eff67S4qj22PbPDbwh+xsAIGs3QuRqiIiI6jejA9w333yjF+CkUilcXV0RGhoKJyenKi2OageNRoDizFY0kGQj39IVlo2fFrskIiKies3oADd69OhqKINqs3+S7qGXMhaQAWZthwIyo3cbIiIiqkJGHwO3YsUKbNy4sUT7xo0bsWrVqiopimqXvSfO4ynpPwAAs3Y8+5SIiEhsRge4yMhIuLiUvHm5m5sb5s2bVyVFUe2h0QgwP78RZhINspyDANfmYpdERERU7xkd4BITE9GwYcMS7f7+/khMTKySoqj2OH79LvoU7gUA2ISOErkaIiIiAioR4Nzc3HDmzJkS7adPn0aDBg0qVcTixYsREBAAS0tLhIaGIi4urtRpe/bsCYlEUuLRr18/ANo7Q3zwwQcIDAyEjY0NvLy8MHLkSNy+fbtStdV3p47twxPSJBRI5DBrM0jscoiIiAiVCHBDhw7FlClTsG/fPqjVaqjVauzduxdTp07FkCFDjC5g/fr1iIiIwJw5c3Dq1CkEBQUhPDwcaWlpBqffvHkzkpOTdY9z585BJpPh5ZdfBgDk5ubi1KlTmDVrFk6dOoXNmzcjISEBzz//vNG11XdqjYAGlzcBADL8ngGsHMUtiIiIiAAAEkEQBGNmUKlUGDFiBDZu3AgzM+3ZiBqNBiNHjkR0dDTkcrlRBYSGhqJjx45YtGiRblm+vr6YPHkypk+fXu78UVFRmD17NpKTk2FjY2NwmuPHjyMkJAQ3btyAn59fifeVSiWUSqXutUKhgK+vL7KysmBvb2/U+tQlRxJuocXajnCU5KBw2GaYNe0ldklERER1kkKhgIODQ4Wzh9E9cHK5HOvXr0dCQgLWrFmDzZs34+rVq1i+fLnR4U2lUuHkyZMICwt7UJBUirCwMBw5cqRCy1i2bBmGDBlSangDgKysLEgkEjg6Ohp8PzIyEg4ODrqHr6+vUetRV10/vAmOkhxkmrvBrHFPscshIiKiIpW+oFfTpk3RtGnTx/rwO3fuQK1Ww93dXa/d3d0dFy9eLHf+uLg4nDt3DsuWLSt1mvz8fHzwwQcYOnRoqYl2xowZiIiI0L0u7oGrzwrVGvjd2AIAUDR7CY5SmcgVERERUTGje+AGDRqE+fPnl2hfsGCB7ji0mrJs2TIEBgYiJCTE4PsFBQV45ZVXIAgCfvjhh1KXY2FhAXt7e71HfXfi7L/oJMQDALx6vCZuMURERKTH6AD3119/4dlnny3R3rdvX/z1119GLcvFxQUymQypqal67ampqfDw8Chz3pycHKxbtw5jx441+H5xeLtx4wZ2797NUGakjCOrIZMI+M+mDczcHq+nlYiIiKqW0QHu/v37Bo91Mzc3h0KhMGpZcrkc7du3R2xsrK5No9EgNjYWnTp1KnPejRs3QqlUYvjwkncGKA5vly9fxp49eyp9eZP6SlWgRovUPwAA6sChIldDREREjzI6wAUGBmL9+vUl2tetW4eWLVsaXUBERAR+/PFHrFq1ChcuXMCECROQk5ODMWPGAABGjhyJGTNmlJhv2bJlGDBgQIlwVlBQgJdeegknTpzAmjVroFarkZKSgpSUFKhUKqPrq4/OHtuDhriNPFggoAdvnUVERFTbGH0Sw6xZszBw4EBcvXoVTz/9NAAgNjYWa9euxaZNm4wuYPDgwUhPT8fs2bORkpKC4OBg7NixQ3diQ2JiIqRS/ZyZkJCAQ4cOYdeuXSWWd+vWLfzxh7b3KDg4WO+9ffv2oWfPnkbXWN+oTqwGACQ4PYVgKw49ExER1TZGXwcOAGJiYjBv3jzEx8fDysoKQUFBmDNnDpydndG6devqqLNGGXstlrpEmXcfqvlNYIc8XHhmLVp07id2SURERHVetV8HDgD69euHv//+Gzk5Obh27RpeeeUVvPvuuwgKCqrM4qgWubR/HeyQh9twQ/PQPmKXQ0RERAZUKsAB2rNRR40aBS8vL3z99dd4+umncfTo0aqsjURgfvYXAMBlz/6QynjtNyIiotrIqGPgUlJSsHLlSixbtgwKhQKvvPIKlEolfvvtt0qdwEC1S/6dG2iWcxKQAM5dRotdDhEREZWiwj1w/fv3R/PmzXHmzBlERUXh9u3b+P7776uzNqphifuWQyoRcEraGq1bBYpdDhEREZWiwj1w27dvx5QpUzBhwoTHvoUW1UKCAMdLGwEANwMGop1EInJBREREVJoK98AdOnQI2dnZaN++PUJDQ7Fo0SLcuXOnOmujGpR/9RDcCm7hvmCJhl158V4iIqLarMIB7sknn8SPP/6I5ORkvPHGG1i3bh28vLyg0Wiwe/duZGdnV2edVM3SD64AAOw364LWDT1FroaIiIjKYvRZqDY2Nnjttddw6NAhnD17Fu+88w6++OILuLm54fnnn6+OGqm6qXLgmrgNAJDZ7GVIOHxKRERUq1X6MiIA0Lx5cyxYsAA3b97EL7/8UlU1UQ1TntkCSyEP1zXuCO7SV+xyiIiIqByPFeCKyWQyDBgwQHcLKzItiqOrAACxlmFo5e0gcjVERERUnioJcGTC7v0H1ztx0AgSqFsP4fApERGRCWCAq+eUJ34GABzStEaPkGBxiyEiIqIKYYCrzzQaFJ5aAwA4aNMbzd3tRC6IiIiIKoIBrj67cQg2ebehEKxgF/wih0+JiIhMBANcPaY6sRoAsFXdCX3bNhS5GiIiIqooBrj6Kl8B6QXtWcPHHPqiKYdPiYiITAYDXH31728w0+TjisYLTdr2FLsaIiIiMgIDXD1VcFJ79ukmdXf0C/ISuRoiIiIyBgNcfXT3KsxvHYNakOCcS180crUVuyIiIiIyAgNcfRSvvXTIX5o26Ny2tcjFEBERkbEY4OobjRrqeO19azeqe+C5QA6fEhERmRoGuPrm2n7Ism8jU7BBqudT8GtgLXZFREREZCQGuPomfi0A4Hd1Z4QH+YtcDBEREVUGA1x9kpcJ4cJWAMAmdQ88G+gpckFERERUGQxw9cn5zZCo83FR4wszn7bwceLwKRERkSmqFQFu8eLFCAgIgKWlJUJDQxEXF1fqtD179oREIinx6Nevn24aQRAwe/ZseHp6wsrKCmFhYbh8+XJNrErt9o/27NON6u54Lshb5GKIiIioskQPcOvXr0dERATmzJmDU6dOISgoCOHh4UhLSzM4/ebNm5GcnKx7nDt3DjKZDC+//LJumgULFuC7775DdHQ0jh07BhsbG4SHhyM/P7+mVqv2SU8Abp1AoSDF7+queDbQQ+yKiIiIqJJED3ALFy7EuHHjMGbMGLRs2RLR0dGwtrbG8uXLDU7v7OwMDw8P3WP37t2wtrbWBThBEBAVFYWZM2fihRdeQJs2bfDTTz/h9u3b+O2332pwzWqZomu/7dO0RcOAAHg6WIlcEBEREVWWqAFOpVLh5MmTCAsL07VJpVKEhYXhyJEjFVrGsmXLMGTIENjY2AAArl+/jpSUFL1lOjg4IDQ0tNRlKpVKKBQKvUedoi4ETq8DUHTrLJ68QEREZNJEDXB37tyBWq2Gu7u7Xru7uztSUlLKnT8uLg7nzp3D66+/rmsrns+YZUZGRsLBwUH38PX1NXZVarere4H7qbgr2GGf0JZnnxIREZk40YdQH8eyZcsQGBiIkJCQx1rOjBkzkJWVpXskJSVVUYW1RLz2xvW/q7ugXUM3uNlbilwQERERPQ5RA5yLiwtkMhlSU1P12lNTU+HhUfZB9jk5OVi3bh3Gjh2r1148nzHLtLCwgL29vd6jzsjNABK2A9DeOqtfG946i4iIyNSJGuDkcjnat2+P2NhYXZtGo0FsbCw6depU5rwbN26EUqnE8OHD9dobNmwIDw8PvWUqFAocO3as3GXWSWc3AWoVzmkCkAB/9G3Ns0+JiIhMnZnYBURERGDUqFHo0KEDQkJCEBUVhZycHIwZMwYAMHLkSHh7eyMyMlJvvmXLlmHAgAFo0KCBXrtEIsG0adPw2WefoWnTpmjYsCFmzZoFLy8vDBgwoKZWq/YoGj7dpO6Ozo1d4GJrIXJBRERE9LhED3CDBw9Geno6Zs+ejZSUFAQHB2PHjh26kxASExMhlep3FCYkJODQoUPYtWuXwWW+//77yMnJwfjx45GZmYmuXbtix44dsLSsZ8d+pZwDkk+jAGb4Xd0Z77fhyQtERER1gUQQBEHsImobhUIBBwcHZGVlmfbxcDs+BI4uxnZ1R0xSR+DER2FwspGLXRURERE9wtjsYdJnoVIZ1AXAmfUAtCcvdGniwvBGRERURzDA1VWXdwG5d5AhccQBTRCe4/ApERFRncEAV1fFrwUAbCzoAqnMDOEtefYpERFRXcEAVxfdTwcu7QAAbFL3QLemrnCwNhe5KCIiIqoqDHB10dkNgKYQF6VNcVnw4fApERFRHcMAV9cIAvDPGgDAz8qukJtJ0bulezkzERERkSlhgKtrkk8DaedRKJHjD3Un9GjmCjtLDp8SERHVJQxwdU3RyQt/yUKggC2HT4mIiOogBri6pFCpPf4NwKrcLrAwk6JXCw6fEhER1TUMcHXJpR1A3j1km7vioCYQTz/hBlsL0e+WRkRERFWMAa4uKTp54XehGzSQ4rk2XiIXRERERNWBAa6uyE4BruwBACzL6QIrcxmeesJV5KKIiIioOjDA1RVn1gOCGkm2gbgueKJXCzdYyzl8SkREVBcxwNUFD137bU1+VwDg8CkREVEdxgBXF9w6BdxJgEZmiTX328NGLkPP5hw+JSIiqqsY4OqC+J8BAOcceiAb1ujd0h2W5jKRiyIiIqLqwgBn6grygbO/AgCWKjoBAPpx+JSIiKhOY4AzdRe3AsosKG28EHO/CewszdC9mYvYVREREVE1YoAzdUW3zjpi9wwESPFMSw9YmHH4lIiIqC5jgDNlWbeAq3sBAFF3OgIA731KRERUDzDAmbLTvwAQoHALQfx9JzhYmaNLEw6fEhER1XUMcKZKEHTDp7sswgAA4a3cITfjt5SIiKiu4197U5V0DMi4CsHcBlG3WgDgxXuJiIjqCwY4UxWvvfNCmm8f3MyVwcnaHJ0bNxC5KCIiIqoJDHCmSJUDnNsCAPhN6AEA6NPaE2YyfjuJiIjqA/7FN0UXtgKqbAhOAYj+zx0A0J9nnxIREdUboge4xYsXIyAgAJaWlggNDUVcXFyZ02dmZmLixInw9PSEhYUFmjVrhm3btuneV6vVmDVrFho2bAgrKys0btwYn376KQRBqO5VqTlFt876z+d53MtTw8VWjtBGHD4lIiKqL8zE/PD169cjIiIC0dHRCA0NRVRUFMLDw5GQkAA3N7cS06tUKvTu3Rtubm7YtGkTvL29cePGDTg6OuqmmT9/Pn744QesWrUKrVq1wokTJzBmzBg4ODhgypQpNbh21eTeDeD6XwAkWJvfFUAh+rb2hEwqEbsyIiIiqiGiBriFCxdi3LhxGDNmDAAgOjoaMTExWL58OaZPn15i+uXLlyMjIwOHDx+Gubk5ACAgIEBvmsOHD+OFF15Av379dO//8ssv5fbsmYzT6wAAmoDuWHdZ28SL9xIREdUvog2hqlQqnDx5EmFhYQ+KkUoRFhaGI0eOGJznjz/+QKdOnTBx4kS4u7ujdevWmDdvHtRqtW6azp07IzY2FpcuXQIAnD59GocOHULfvn1LrUWpVEKhUOg9aiWNRnf26QX355CdXwg3Owt0CHAWuTAiIiKqSaL1wN25cwdqtRru7u567e7u7rh48aLBea5du4a9e/di2LBh2LZtG65cuYK33noLBQUFmDNnDgBg+vTpUCgUeOKJJyCTyaBWq/H5559j2LBhpdYSGRmJuXPnVt3KVZfEw0DmDUBuh1WZbQDcxbOBHD4lIiKqb0Q/icEYGo0Gbm5uWLp0Kdq3b4/Bgwfjo48+QnR0tG6aDRs2YM2aNVi7di1OnTqFVatW4auvvsKqVatKXe6MGTOQlZWleyQlJdXE6hjvH23vW2HLAdh2MQsA0D+Iw6dERET1jWg9cC4uLpDJZEhNTdVrT01NhYeHh8F5PD09YW5uDplMpmtr0aIFUlJSoFKpIJfL8d5772H69OkYMmQIACAwMBA3btxAZGQkRo0aZXC5FhYWsLCwqKI1qybK+8C/vwMATjo9i/vKQng6WKKtr5PIhREREVFNE60HTi6Xo3379oiNjdW1aTQaxMbGolOnTgbn6dKlC65cuQKNRqNru3TpEjw9PSGXywEAubm5kEr1V0smk+nNY5L+/Q0oyAEaNMGaW9qA2y/QE1IOnxIREdU7og6hRkRE4Mcff8SqVatw4cIFTJgwATk5ObqzUkeOHIkZM2bopp8wYQIyMjIwdepUXLp0CTExMZg3bx4mTpyom6Z///74/PPPERMTg//++w9btmzBwoUL8eKLL9b4+lWpouHTgsCh2HMxDQDwXBDvfUpERFQfiXoZkcGDByM9PR2zZ89GSkoKgoODsWPHDt2JDYmJiXq9ab6+vti5cyfefvtttGnTBt7e3pg6dSo++OAD3TTff/89Zs2ahbfeegtpaWnw8vLCG2+8gdmzZ9f4+lWZu1e1JzBIpDho1Qu5qtvwcbJCkI+D2JURERGRCCRCnbpFQdVQKBRwcHBAVlYW7O3txS4H2PsZ8NeXQONemCidiZizyXijRyPM6NtC7MqIiIioChibPUzqLNR6SaMB4n8BAOS3HorYi9qTPp4L5PApERFRfcUAV9tdPwAobgKWDtgjdEB+gQb+DazR2rsW9AwSERGRKBjgarv4tdqvrV/Cn+fvAtDeOksi4dmnRERE9RUDXG2WnwVc+AMAkNNyMPYlpAMA+nH4lIiIqF5jgKvNzm0GCvMB1yewO9MbqkINGrnaoIWnndiVERERkYgY4Gqz4uHT4Fex9WwyAOC5Nl4cPiUiIqrnGOBqq/RLwM04QCKDotkgHLikHT59rg3vfUpERFTfMcDVVqeLet+a9sauRKBALaCZuy2auXP4lIiIqL5jgKuNNGrg9Drt8+BXsfXMbQA8eYGIiIi0GOBqo6v7gOxkwMoZmb69cOjyHQDAc0EcPiUiIiIGuNop/mft18CXsfNiBgo1Alp42qOxq624dREREVGtwABX2+TdAy7GaJ+3HYatZ4rPPmXvGxEREWkxwNU2ZzcBahXg3hp3bZvj8NUHd18gIiIiAhjgap/4NdqvwcOw499UqDUCAr0d4N/ARty6iIiIqNZggKtNUv8Fbv8DSM2ANq9g62nt8Gk/9r4RERHRQxjgapPi3rdmfZCmscWx69rh036BDHBERET0AANcbaEuAM5s0D4PHoYd51KgEYAgX0f4OluLWxsRERHVKgxwtcWVPUBOGmDjCjTtrTv7tD+HT4mIiOgRDHC1RfHwaZvBSM1R4/h/GQCAZzl8SkRERI9ggKsNcu4CCTu0z4NfRcyZZAgC0N7fCV6OVuLWRkRERLUOA1xtcHYDoCkAPIMB91aIOcuL9xIREVHpGOBqg4eu/XY7Mw8nb9yDRMLhUyIiIjKMAU5syWeAlLOATA4EvoSYopMXOgY4w93eUuTiiIiIqDZigBNb/Frt1+bPAtbO2HqWZ58SERFR2RjgxFSo0h7/BgDBw5CUkYvTSZmQSoA+rRngiIiIyDAGODFd3gnk3gVsPYDGT+tOXniyUQO42lmIXBwRERHVVqIHuMWLFyMgIACWlpYIDQ1FXFxcmdNnZmZi4sSJ8PT0hIWFBZo1a4Zt27bpTXPr1i0MHz4cDRo0gJWVFQIDA3HixInqXI3K+afo5IWgwYDMDFvP3AYAPNfGS8SiiIiIqLYzE/PD169fj4iICERHRyM0NBRRUVEIDw9HQkIC3NzcSkyvUqnQu3dvuLm5YdOmTfD29saNGzfg6Oiom+bevXvo0qULnnrqKWzfvh2urq64fPkynJycanDNKiA7Fbi8S/s8eBj+u5ODc7cUkEkl6NPaQ9zaiIiIqFYTNcAtXLgQ48aNw5gxYwAA0dHRiImJwfLlyzF9+vQS0y9fvhwZGRk4fPgwzM3NAQABAQF608yfPx++vr5YsWKFrq1hw4bVtxKVdXYDIKgB7w6Aa3PE7LsCAOjcuAGcbeQiF0dERES1mWhDqCqVCidPnkRYWNiDYqRShIWF4ciRIwbn+eOPP9CpUydMnDgR7u7uaN26NebNmwe1Wq03TYcOHfDyyy/Dzc0Nbdu2xY8//lhmLUqlEgqFQu9RrQThwfBp22EAgD9PFw+f8uQFIiIiKptoAe7OnTtQq9Vwd3fXa3d3d0dKSorBea5du4ZNmzZBrVZj27ZtmDVrFr7++mt89tlnetP88MMPaNq0KXbu3IkJEyZgypQpWLVqVam1REZGwsHBQffw9fWtmpUsze1/gPQLgJkl0GogrqTdx8WUbJhJJQhvxeFTIiIiKpuoQ6jG0mg0cHNzw9KlSyGTydC+fXvcunULX375JebMmaObpkOHDpg3bx4AoG3btjh37hyio6MxatQog8udMWMGIiIidK8VCkX1hjh1AeD7JODoC1g5IubvywCAbk1d4GjN4VMiIiIqm2gBzsXFBTKZDKmpqXrtqamp8PAw3Avl6ekJc3NzyGQyXVuLFi2QkpIClUoFuVwOT09PtGzZUm++Fi1a4Ndffy21FgsLC1hY1OBlO/xCgbE7tUEO0J192o9nnxIREVEFiDaEKpfL0b59e8TGxuraNBoNYmNj0alTJ4PzdOnSBVeuXIFGo9G1Xbp0CZ6enpDL5bppEhIS9Oa7dOkS/P39q2EtHpPMHJdSs3E57T7kMimeaeVe/jxERERU74l6HbiIiAj8+OOPWLVqFS5cuIAJEyYgJydHd1bqyJEjMWPGDN30EyZMQEZGBqZOnYpLly4hJiYG8+bNw8SJE3XTvP322zh69CjmzZuHK1euYO3atVi6dKneNLXJ1qKTF7o3c4W9pbnI1RAREZEpEPUYuMGDByM9PR2zZ89GSkoKgoODsWPHDt2JDYmJiZBKH2RMX19f7Ny5E2+//TbatGkDb29vTJ06FR988IFumo4dO2LLli2YMWMGPvnkEzRs2BBRUVEYNmxYja9feQRBwNaim9fz7FMiIiKqKIkgCILYRdQ2CoUCDg4OyMrKgr29fbV9zr+3FXj2u4OQm0lxalZv2FqY1DklREREVEWMzR6i30qrPis+eeGp5q4Mb0RERFRhDHAi0R8+5dmnREREVHEMcCI5d0uBxIxcWJpL0atFyfu+EhEREZWGAU4kxcOnvZ5wh7Wcw6dERERUcQxwIuDZp0RERPQ4GOBEEJ+UiVuZebCWy/DUExw+JSIiIuNw7E4EEokEPZq5ooGtHJbmsvJnICIiInoIA5wIgn0dseq1EPASfERERFQZHEIVkUQiEbsEIiIiMkEMcEREREQmhgGOiIiIyMQwwBERERGZGAY4IiIiIhPDAEdERERkYhjgiIiIiEwMAxwRERGRiWGAIyIiIjIxDHBEREREJoYBjoiIiMjE8F6oBhTfo1ShUIhcCREREdUHxZmjovdJZ4AzIDs7GwDg6+srciVERERUn2RnZ8PBwaHc6SRCRaNePaLRaHD79m3Y2dmVuOG8QqGAr68vkpKSYG9vL1KFdRe3b/Xi9q1e3L7Vi9u3+nEbV6+ytq8gCMjOzoaXlxek0vKPcGMPnAFSqRQ+Pj5lTmNvb8+duxpx+1Yvbt/qxe1bvbh9qx+3cfUqbftWpOetGE9iICIiIjIxDHBEREREJoYBzkgWFhaYM2cOLCwsxC6lTuL2rV7cvtWL27d6cftWP27j6lWV25cnMRARERGZGPbAEREREZkYBjgiIiIiE8MAR0RERGRiGOCIiIiITAwDnJEWL16MgIAAWFpaIjQ0FHFxcWKXVCd8/PHHkEgkeo8nnnhC7LJM1l9//YX+/fvDy8sLEokEv/32m977giBg9uzZ8PT0hJWVFcLCwnD58mVxijVB5W3f0aNHl9if+/TpI06xJigyMhIdO3aEnZ0d3NzcMGDAACQkJOhNk5+fj4kTJ6JBgwawtbXFoEGDkJqaKlLFpqUi27dnz54l9uE333xTpIpNyw8//IA2bdroLtbbqVMnbN++Xfd+Ve27DHBGWL9+PSIiIjBnzhycOnUKQUFBCA8PR1pamtil1QmtWrVCcnKy7nHo0CGxSzJZOTk5CAoKwuLFiw2+v2DBAnz33XeIjo7GsWPHYGNjg/DwcOTn59dwpaapvO0LAH369NHbn3/55ZcarNC0HThwABMnTsTRo0exe/duFBQU4JlnnkFOTo5umrfffht//vknNm7ciAMHDuD27dsYOHCgiFWbjopsXwAYN26c3j68YMECkSo2LT4+Pvjiiy9w8uRJnDhxAk8//TReeOEFnD9/HkAV7rsCVVhISIgwceJE3Wu1Wi14eXkJkZGRIlZVN8yZM0cICgoSu4w6CYCwZcsW3WuNRiN4eHgIX375pa4tMzNTsLCwEH755RcRKjRtj25fQRCEUaNGCS+88IIo9dRFaWlpAgDhwIEDgiBo91dzc3Nh48aNumkuXLggABCOHDkiVpkm69HtKwiC0KNHD2Hq1KniFVXHODk5Cf/3f/9Xpfsue+AqSKVS4eTJkwgLC9O1SaVShIWF4ciRIyJWVndcvnwZXl5eaNSoEYYNG4bExESxS6qTrl+/jpSUFL192cHBAaGhodyXq9D+/fvh5uaG5s2bY8KECbh7967YJZmsrKwsAICzszMA4OTJkygoKNDbh5944gn4+flxH66ER7dvsTVr1sDFxQWtW7fGjBkzkJubK0Z5Jk2tVmPdunXIyclBp06dqnTf5c3sK+jOnTtQq9Vwd3fXa3d3d8fFixdFqqruCA0NxcqVK9G8eXMkJydj7ty56NatG86dOwc7Ozuxy6tTUlJSAMDgvlz8Hj2ePn36YODAgWjYsCGuXr2KDz/8EH379sWRI0cgk8nELs+kaDQaTJs2DV26dEHr1q0BaPdhuVwOR0dHvWm5DxvP0PYFgFdffRX+/v7w8vLCmTNn8MEHHyAhIQGbN28WsVrTcfbsWXTq1An5+fmwtbXFli1b0LJlS8THx1fZvssAR7VC3759dc/btGmD0NBQ+Pv7Y8OGDRg7dqyIlREZb8iQIbrngYGBaNOmDRo3boz9+/ejV69eIlZmeiZOnIhz587xmNhqUtr2HT9+vO55YGAgPD090atXL1y9ehWNGzeu6TJNTvPmzREfH4+srCxs2rQJo0aNwoEDB6r0MziEWkEuLi6QyWQlzhRJTU2Fh4eHSFXVXY6OjmjWrBmuXLkidil1TvH+yn255jRq1AguLi7cn400adIkbN26Ffv27YOPj4+u3cPDAyqVCpmZmXrTcx82Tmnb15DQ0FAA4D5cQXK5HE2aNEH79u0RGRmJoKAgfPvtt1W67zLAVZBcLkf79u0RGxura9NoNIiNjUWnTp1ErKxuun//Pq5evQpPT0+xS6lzGjZsCA8PD719WaFQ4NixY9yXq8nNmzdx9+5d7s8VJAgCJk2ahC1btmDv3r1o2LCh3vvt27eHubm53j6ckJCAxMRE7sMVUN72NSQ+Ph4AuA9XkkajgVKprNJ9l0OoRoiIiMCoUaPQoUMHhISEICoqCjk5ORgzZozYpZm8d999F/3794e/vz9u376NOXPmQCaTYejQoWKXZpLu37+v95/y9evXER8fD2dnZ/j5+WHatGn47LPP0LRpUzRs2BCzZs2Cl5cXBgwYIF7RJqSs7evs7Iy5c+di0KBB8PDwwNWrV/H++++jSZMmCA8PF7Fq0zFx4kSsXbsWv//+O+zs7HTHBjk4OMDKygoODg4YO3YsIiIi4OzsDHt7e0yePBmdOnXCk08+KXL1tV952/fq1atYu3Ytnn32WTRo0ABnzpzB22+/je7du6NNmzYiV1/7zZgxA3379oWfnx+ys7Oxdu1a7N+/Hzt37qzafbdqT5St+77//nvBz89PkMvlQkhIiHD06FGxS6oTBg8eLHh6egpyuVzw9vYWBg8eLFy5ckXsskzWvn37BAAlHqNGjRIEQXspkVmzZgnu7u6ChYWF0KtXLyEhIUHcok1IWds3NzdXeOaZZwRXV1fB3Nxc8Pf3F8aNGyekpKSIXbbJMLRtAQgrVqzQTZOXlye89dZbgpOTk2BtbS28+OKLQnJysnhFm5Dytm9iYqLQvXt3wdnZWbCwsBCaNGkivPfee0JWVpa4hZuI1157TfD39xfkcrng6uoq9OrVS9i1a5fu/aradyWCIAiPmzaJiIiIqObwGDgiIiIiE8MAR0RERGRiGOCIiIiITAwDHBEREZGJYYAjIiIiMjEMcEREREQmhgGOiIiIyMQwwBERERGZGAY4IiIiIhPDAEdEdUp6ejrkcjlycnJQUFAAGxsbJCYmil0WEVGVYoAjojrlyJEjCAoKgo2NDU6dOgVnZ2f4+fmJXZZBKpVK7BKIyEQxwBFRnXL48GF06dIFAHDo0CHd87KMHj0aAwYMwFdffQVPT080aNAAEydOREFBgW4apVKJd999F97e3rCxsUFoaCj279+ve//jjz9GcHCw3nKjoqIQEBBQ4nM+//xzeHl5oXnz5gCAs2fP4umnn4aVlRUaNGiA8ePH4/79+0bVt2TJEjRt2hSWlpZwd3fHSy+9ZMxmIyITYyZ2AUREjysxMRFt2rQBAOTm5kImk2HlypXIy8uDRCKBo6MjXn31VSxZsqTUZezbtw+enp7Yt28frly5gsGDByM4OBjjxo0DAEyaNAn//vsv1q1bBy8vL2zZsgV9+vTB2bNn0bRp0wrXGhsbC3t7e+zevRsAkJOTg/DwcHTq1AnHjx9HWloaXn/9dUyaNAkrV66sUH0nTpzAlClTsHr1anTu3BkZGRk4ePBgJbYkEZkKBjgiMnleXl6Ij4+HQqFAhw4dcOzYMdjY2CA4OBgxMTHw8/ODra1tmctwcnLCokWLIJPJ8MQTT6Bfv36IjY3FuHHjkJiYiBUrViAxMRFeXl4AgHfffRc7duzAihUrMG/evArXamNjg//7v/+DXC4HAPz444/Iz8/HTz/9BBsbGwDAokWL0L9/f8yfPx/u7u4Vqs/GxgbPPfcc7Ozs4O/vj7Zt21ZmUxKRieAQKhGZPDMzMwQEBODixYvo2LEj2rRpg5SUFLi7u6N79+4ICAiAi4tLmcto1aoVZDKZ7rWnpyfS0tIAaIc41Wo1mjVrBltbW93jwIEDuHr1qlG1BgYG6sIbAFy4cEF3zF6xLl26QKPRICEhoUL19e7dG/7+/mjUqBFGjBiBNWvWIDc316i6iMi0sAeOiExeq1atcOPGDRQUFECj0cDW1haFhYUoLCyEra0t/P39cf78+TKXYW5urvdaIpFAo9EAAO7fvw+ZTIaTJ0/qhSgAup49qVQKQRD03nv4GLViDwc1Y5RVn52dHU6dOoX9+/dj165dmD17Nj7++GMcP34cjo6Olfo8Iqrd2ANHRCZv27ZtiI+Ph4eHB37++WfEx8ejdevWiIqKQnx8PLZt2/ZYy2/bti3UajXS0tLQpEkTvYeHhwcAwNXVFSkpKXohLj4+vtxlt2jRAqdPn0ZOTo6u7e+//4ZUKtWd5FARZmZmCAsLw4IFC3DmzBn8999/2Lt3b8VXkohMCgMcEZk8f39/2NraIjU1FS+88AJ8fX1x/vx5DBo0CE2aNIG/v/9jLb9Zs2YYNmwYRo4cic2bN+P69euIi4tDZGQkYmJiAAA9e/ZEeno6FixYgKtXr2Lx4sXYvn17ucseNmwYLC0tMWrUKJw7dw779u3D5MmTMWLECN3xb+XZunUrvvvuO8THx+PGjRv46aefoNFojAqARGRaGOCIqE7Yv38/OnbsCEtLS8TFxcHHxweenp5VtvwVK1Zg5MiReOedd9C8eXMMGDAAx48f111jrkWLFliyZAkWL16MoKAgxMXF4d133y13udbW1ti5cycyMjLQsWNHvPTSS+jVqxcWLVpU4docHR2xefNmPP3002jRogWio6Pxyy+/oFWrVpVeXyKq3STCowdtEBEREVGtxh44IiIiIhPDAEdERERkYhjgiIiIiEwMAxwRERGRiWGAIyIiIjIxDHBEREREJoYBjoiIiMjEMMARERERmRgGOCIiIiITwwBHREREZGIY4IiIiIhMzP8DM44WGXjsM04AAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 700x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Max accuracy on train: 0.8049883086515979 \n",
"Max accuracy on valid: 0.7609102244389028\n"
]
}
],
"source": [
"plt.figure(figsize=(7, 4))\n",
"plt.plot(range(1, 30, 4), train_accs, label='Train')\n",
"plt.plot(range(1, 30, 4), test_accs, label='Valid')\n",
"plt.legend()\n",
"plt.xlabel('# neurons')\n",
"plt.ylabel('Accuracy')\n",
"plt.title('Tuning the number of neurons in 4 layers networks ')\n",
"plt.show()\n",
"print(f\"Max accuracy on train: {max(train_accs)} \\nMax accuracy on valid: {max(test_accs)}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_val"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [],
"source": [
"# Transforming to Tensors \n",
"x_train_tensor = torch.tensor(X_train_new_s, dtype=torch.float)\n",
"y_train_tensor = torch.tensor(y_train_new.values, dtype=torch.float)\n",
"x_test_tensor = torch.tensor(X_valid_new_s, dtype=torch.float)\n",
"y_test_tensor = torch.tensor(y_val_new.values, dtype=torch.float)\n"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [],
"source": [
"\n",
"non_smoking_weigth = torch.tensor((df['smoking'].value_counts()[0] / len(df)),dtype=torch.float)\n",
"smoking_weigth = torch.tensor((df['smoking'].value_counts()[1] / len(df)),dtype=torch.float)\n",
"# Define the weights for each sample\n",
"weights = torch.tensor([non_smoking_weigth, smoking_weigth]) # Class 0 has weight ~65%, class 1 has weight ~35%\n"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([0.6557, 0.3443])"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weights"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([25660, 14])"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train_tensor.shape"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [],
"source": [
"# Define the model\n",
"class MLP(nn.Module):\n",
" def __init__(self, input_size, hidden_size, middle_size, num_classes):\n",
" super(MLP, self).__init__()\n",
" self.fc1 = nn.Linear(input_size, middle_size)\n",
" self.relu = nn.ReLU()\n",
" self.drop1 = nn.Dropout(0.25)\n",
" self.fc2 = nn.Linear(middle_size, hidden_size)\n",
" self.drop2 = nn.Dropout(0.25)\n",
" self.fc3 = nn.Linear(hidden_size, hidden_size)\n",
" self.fc4 = nn.Linear(hidden_size, num_classes)\n",
" \n",
" def forward(self, x):\n",
" out = self.fc1(x)\n",
" out = self.relu(out)\n",
" out = self.drop1(out)\n",
" out = self.fc2(out)\n",
" out = self.drop1(out)\n",
" out = self.fc3(out)\n",
" out = self.fc4(out)\n",
" return out\n",
"\n",
"model = MLP(14, 128, 64, 2)"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch [50/500], Loss: 0.3846\n",
"Epoch [100/500], Loss: 0.3801\n",
"Epoch [150/500], Loss: 0.3785\n",
"Epoch [200/500], Loss: 0.3770\n",
"Epoch [250/500], Loss: 0.3760\n",
"Epoch [300/500], Loss: 0.3755\n",
"Epoch [350/500], Loss: 0.3749\n",
"Epoch [400/500], Loss: 0.3748\n",
"Epoch [450/500], Loss: 0.3761\n",
"Epoch [500/500], Loss: 0.3743\n"
]
}
],
"source": [
"# Define the loss function and optimizer\n",
"criterion = nn.CrossEntropyLoss(weight=weights)\n",
"optimizer = optim.Adam(model.parameters(), lr=0.005)\n",
"\n",
"# Train the model\n",
"for epoch in range(500):\n",
" # Forward pass\n",
" outputs = model(x_train_tensor)\n",
" loss = criterion(outputs, y_train_tensor.long())\n",
" \n",
" # Backward and optimize\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
" \n",
" if (epoch+1) % 50 == 0:\n",
" print(f'Epoch [{epoch+1}/500], Loss: {loss.item():.4f}')"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([6416])"
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test_tensor.shape"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([25660, 14])"
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_test_tensor.shape"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of the model on the train data: 73.24%\n",
"Accuracy of the model on the test data: 73.78%\n"
]
}
],
"source": [
"# Test the model\n",
"with torch.no_grad():\n",
" correct = 0\n",
" total = 0\n",
" outputs = model(x_test_tensor)\n",
" _, predicted = torch.max(outputs.data, 1)\n",
" total += y_test_tensor.size(0)\n",
" correct += (predicted == y_test_tensor).sum().item()\n",
"\n",
" correct_train = 0\n",
" total_train = 0\n",
" outputs2 = model(x_train_tensor)\n",
" _, predicted = torch.max(outputs2.data, 1)\n",
" total_train += y_train_tensor.size(0)\n",
" correct_train += (predicted == y_train_tensor).sum().item()\n",
"\n",
"\n",
" \n",
" print(f'Accuracy of the model on the train data: {100 * correct / total:.2f}%')\n",
" print(f'Accuracy of the model on the test data: {100 * correct_train / total_train:.2f}%')"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iteration 1, loss = 0.52514388\n",
"Iteration 2, loss = 0.47096547\n",
"Iteration 3, loss = 0.46393652\n",
"Iteration 4, loss = 0.45960528\n",
"Iteration 5, loss = 0.45760861\n",
"Iteration 6, loss = 0.45614400\n",
"Iteration 7, loss = 0.45314098\n",
"Iteration 8, loss = 0.45240514\n",
"Iteration 9, loss = 0.45141986\n",
"Iteration 10, loss = 0.45146588\n",
"Iteration 11, loss = 0.45081923\n",
"Iteration 12, loss = 0.45047783\n",
"Iteration 13, loss = 0.44937677\n",
"Iteration 14, loss = 0.44906495\n",
"Iteration 15, loss = 0.44912386\n",
"Iteration 16, loss = 0.44894601\n",
"Iteration 17, loss = 0.44791580\n",
"Iteration 18, loss = 0.44793784\n",
"Iteration 19, loss = 0.44767597\n",
"Iteration 20, loss = 0.44707069\n",
"Iteration 21, loss = 0.44711857\n",
"Iteration 22, loss = 0.44673453\n",
"Iteration 23, loss = 0.44644090\n",
"Iteration 24, loss = 0.44609463\n",
"Iteration 25, loss = 0.44641455\n",
"Iteration 26, loss = 0.44584736\n",
"Iteration 27, loss = 0.44543556\n",
"Iteration 28, loss = 0.44554421\n",
"Iteration 29, loss = 0.44517749\n",
"Iteration 30, loss = 0.44486071\n",
"Iteration 31, loss = 0.44473113\n",
"Iteration 32, loss = 0.44479831\n",
"Iteration 33, loss = 0.44430385\n",
"Iteration 34, loss = 0.44498018\n",
"Iteration 35, loss = 0.44401062\n",
"Iteration 36, loss = 0.44388465\n",
"Iteration 37, loss = 0.44402827\n",
"Iteration 38, loss = 0.44370086\n",
"Iteration 39, loss = 0.44313133\n",
"Iteration 40, loss = 0.44314159\n",
"Iteration 41, loss = 0.44332672\n",
"Iteration 42, loss = 0.44242348\n",
"Iteration 43, loss = 0.44257096\n",
"Iteration 44, loss = 0.44282767\n",
"Iteration 45, loss = 0.44247830\n",
"Iteration 46, loss = 0.44272024\n",
"Iteration 47, loss = 0.44208470\n",
"Iteration 48, loss = 0.44189321\n",
"Iteration 49, loss = 0.44171715\n",
"Iteration 50, loss = 0.44190339\n",
"Iteration 51, loss = 0.44168238\n",
"Iteration 52, loss = 0.44220799\n",
"Iteration 53, loss = 0.44213526\n",
"Iteration 54, loss = 0.44141588\n",
"Iteration 55, loss = 0.44127069\n",
"Iteration 56, loss = 0.44183847\n",
"Iteration 57, loss = 0.44191195\n",
"Iteration 58, loss = 0.44089000\n",
"Iteration 59, loss = 0.44136825\n",
"Iteration 60, loss = 0.44095969\n",
"Iteration 61, loss = 0.44064888\n",
"Iteration 62, loss = 0.44067757\n",
"Iteration 63, loss = 0.44120063\n",
"Iteration 64, loss = 0.44040943\n",
"Iteration 65, loss = 0.44064265\n",
"Iteration 66, loss = 0.44110770\n",
"Iteration 67, loss = 0.44055881\n",
"Iteration 68, loss = 0.44008105\n",
"Iteration 69, loss = 0.44002018\n",
"Iteration 70, loss = 0.43985181\n",
"Iteration 71, loss = 0.44073855\n",
"Iteration 72, loss = 0.43921048\n",
"Iteration 73, loss = 0.43962823\n",
"Iteration 74, loss = 0.43952614\n",
"Iteration 75, loss = 0.43889801\n",
"Iteration 76, loss = 0.43924844\n",
"Iteration 77, loss = 0.43924773\n",
"Iteration 78, loss = 0.43916021\n",
"Iteration 79, loss = 0.44021910\n",
"Iteration 80, loss = 0.43913137\n",
"Iteration 81, loss = 0.43961309\n",
"Iteration 82, loss = 0.43953152\n",
"Iteration 83, loss = 0.43867929\n",
"Iteration 84, loss = 0.43913739\n",
"Iteration 85, loss = 0.43889180\n",
"Iteration 86, loss = 0.43895159\n",
"Iteration 87, loss = 0.43868811\n",
"Iteration 88, loss = 0.43879689\n",
"Iteration 89, loss = 0.43891879\n",
"Iteration 90, loss = 0.43933586\n",
"Iteration 91, loss = 0.43830113\n",
"Iteration 92, loss = 0.43872424\n",
"Iteration 93, loss = 0.43881473\n",
"Iteration 94, loss = 0.43898292\n",
"Iteration 95, loss = 0.43803508\n",
"Iteration 96, loss = 0.43884607\n",
"Iteration 97, loss = 0.43822438\n",
"Iteration 98, loss = 0.43854456\n",
"Iteration 99, loss = 0.43830731\n",
"Iteration 100, loss = 0.43868744\n",
"Iteration 101, loss = 0.43833974\n",
"Iteration 102, loss = 0.43827961\n",
"Iteration 103, loss = 0.43866493\n",
"Iteration 104, loss = 0.43782162\n",
"Iteration 105, loss = 0.43771831\n",
"Iteration 106, loss = 0.43835716\n",
"Iteration 107, loss = 0.43826734\n",
"Iteration 108, loss = 0.43836891\n",
"Iteration 109, loss = 0.43867720\n",
"Iteration 110, loss = 0.43755370\n",
"Iteration 111, loss = 0.43796016\n",
"Iteration 112, loss = 0.43737947\n",
"Iteration 113, loss = 0.43788683\n",
"Iteration 114, loss = 0.43813866\n",
"Iteration 115, loss = 0.43775756\n",
"Iteration 116, loss = 0.43719193\n",
"Iteration 117, loss = 0.43725636\n",
"Iteration 118, loss = 0.43821031\n",
"Iteration 119, loss = 0.43763923\n",
"Iteration 120, loss = 0.43756076\n",
"Iteration 121, loss = 0.43780216\n",
"Iteration 122, loss = 0.43781062\n",
"Iteration 123, loss = 0.43730164\n",
"Iteration 124, loss = 0.43738280\n",
"Iteration 125, loss = 0.43743466\n",
"Iteration 126, loss = 0.43732925\n",
"Iteration 127, loss = 0.43725225\n",
"Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n"
]
},
{
"data": {
"text/html": [
"<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-20 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-20 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(alpha=5e-06, hidden_layer_sizes=(10, 10, 10, 2),\n",
" learning_rate_init=0.005, random_state=1, verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" checked><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(alpha=5e-06, hidden_layer_sizes=(10, 10, 10, 2),\n",
" learning_rate_init=0.005, random_state=1, verbose=True)</pre></div></div></div></div></div>"
],
"text/plain": [
"MLPClassifier(alpha=5e-06, hidden_layer_sizes=(10, 10, 10, 2),\n",
" learning_rate_init=0.005, random_state=1, verbose=True)"
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = MLPClassifier(random_state=1,\n",
" hidden_layer_sizes=(10, 10,10,2),\n",
" max_iter=200,\n",
" activation='relu', \n",
" verbose=True,\n",
" solver='adam',\n",
" alpha=0.000005,\n",
" learning_rate_init=0.005)\n",
"model.fit(X_train_new_s, y_train_new)"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MLP-Final accuracy on train: 77.611 %\n",
"MLP-Final accuracy on test: 75.92 %\n"
]
},
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Loss function')"
]
},
"execution_count": 185,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 2000x500 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(f'MLP-Final accuracy on train: {round(model.score(X_train_new_s, y_train_new)*100,3)} %')\n",
"\n",
"pred_y = model.predict(X_valid_new_s)\n",
"NN_report = classification_report(y_val_new,pred_y, output_dict=True)\n",
"# Model Accuracy, how often is the classifier correct\n",
"print(f'MLP-Final accuracy on test: {round(accuracy_score(y_val_new,pred_y)*100,3)} %')\n",
"\n",
"fig, ax = plt.subplots((1),(3),figsize = (20,5))\n",
"#plot 1\n",
"sns.heatmap(pd.DataFrame(NN_report).iloc[:-1, :].T, annot=True, cmap = \"Blues\", ax = ax[0])\n",
"ax[0].set_title('Heat Map for Classification report')\n",
"#plot 2\n",
"cm = confusion_matrix(y_val_new,pred_y)\n",
"disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)\n",
"disp.plot(cmap=\"Blues\", ax=ax[1])\n",
"ax[1].set_title('Confusion Matrix (1-smoking,0-not)')\n",
"#plot 3\n",
"ax[2].plot(model.loss_curve_, color = \"Black\")\n",
"ax[2].set_xlabel(\"Iteration\")\n",
"ax[2].set_ylabel(\"Loss\")\n",
"ax[2].set_title('Loss function')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# K-Means Clustering"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {},
"outputs": [],
"source": [
"def visualizing_results(pca_result, label, centroids_pca):\n",
" \"\"\" Visualizing the clusters\n",
" :param pca_result: PCA applied data\n",
" :param label: K Means labels\n",
" :param centroids_pca: PCA format K Means centroids\n",
" \"\"\"\n",
" # ------------------ Using Matplotlib for plotting-----------------------\n",
" x = pca_result[:, 0]\n",
" y = pca_result[:, 1]\n",
"\n",
" plt.scatter(x, y, c=label, alpha=0.9, s=12.5) # plot different colors per cluster\n",
" plt.title('K Means clusters')\n",
" plt.xlabel('PCA 1')\n",
" plt.ylabel('PCA 2')\n",
"\n",
" plt.scatter(centroids_pca[:, 0], centroids_pca[:, 1], marker='X', s=200, linewidths=1.5,\n",
" color='red', edgecolors=\"black\", lw=1.5)\n",
"\n",
" plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Explained variation per principal component: [0.56345013 0.24504734]\n",
"Cumulative variance explained by 2 principal components: 80.85%\n",
"\n",
"\n",
"4. Visualizing the data\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"# Reducing the Dimensionality of the data to 2D\n",
"pca_2 = PCA(n_components=2)\n",
"pca_2_result = pca_2.fit_transform(temp_df)\n",
"print('Explained variation per principal component: {}'.format(pca_2.explained_variance_ratio_))\n",
"print('Cumulative variance explained by 2 principal components: {:.2%}'.format(np.sum(pca_2.explained_variance_ratio_)))\n",
"# fitting KMeans\n",
"kmeans = KMeans(n_clusters=2)\n",
"kmeans.fit(temp_df)\n",
"centroids = kmeans.cluster_centers_\n",
"centroids_pca = pca_2.transform(centroids)\n",
"\n",
"print(\"\\n\\n4. Visualizing the data\")\n",
"visualizing_results(pca_2_result[1:], kmeans.labels_[1:], centroids_pca)\n"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [],
"source": [
"temp_df_val = pd.concat([X_val, y_val], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 172,
"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>gender</th>\n",
" <th>age</th>\n",
" <th>height(cm)</th>\n",
" <th>weight(kg)</th>\n",
" <th>waist(cm)</th>\n",
" <th>eyesight(left)</th>\n",
" <th>eyesight(right)</th>\n",
" <th>hearing(left)</th>\n",
" <th>hearing(right)</th>\n",
" <th>systolic</th>\n",
" <th>relaxation</th>\n",
" <th>fasting blood sugar</th>\n",
" <th>Cholesterol</th>\n",
" <th>triglyceride</th>\n",
" <th>HDL</th>\n",
" <th>LDL</th>\n",
" <th>hemoglobin</th>\n",
" <th>Urine protein</th>\n",
" <th>serum creatinine</th>\n",
" <th>AST</th>\n",
" <th>ALT</th>\n",
" <th>Gtp</th>\n",
" <th>dental caries</th>\n",
" <th>tartar</th>\n",
" <th>BMI</th>\n",
" <th>smoking</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>30.0</td>\n",
" <td>175.0</td>\n",
" <td>70.0</td>\n",
" <td>82.0</td>\n",
" <td>1.5</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>113.0</td>\n",
" <td>64.0</td>\n",
" <td>93.0</td>\n",
" <td>208.0</td>\n",
" <td>96.0</td>\n",
" <td>70.0</td>\n",
" <td>119.0</td>\n",
" <td>14.2</td>\n",
" <td>1.0</td>\n",
" <td>1.2</td>\n",
" <td>23.0</td>\n",
" <td>19.0</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>22.857143</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>40.0</td>\n",
" <td>155.0</td>\n",
" <td>65.0</td>\n",
" <td>82.5</td>\n",
" <td>1.0</td>\n",
" <td>0.7</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>97.0</td>\n",
" <td>70.0</td>\n",
" <td>104.0</td>\n",
" <td>182.0</td>\n",
" <td>47.0</td>\n",
" <td>69.0</td>\n",
" <td>103.0</td>\n",
" <td>16.7</td>\n",
" <td>1.0</td>\n",
" <td>0.8</td>\n",
" <td>14.0</td>\n",
" <td>15.0</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>27.055151</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>50.0</td>\n",
" <td>150.0</td>\n",
" <td>50.0</td>\n",
" <td>72.4</td>\n",
" <td>1.0</td>\n",
" <td>0.8</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>125.0</td>\n",
" <td>79.0</td>\n",
" <td>106.0</td>\n",
" <td>246.0</td>\n",
" <td>89.0</td>\n",
" <td>65.0</td>\n",
" <td>163.0</td>\n",
" <td>13.3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>16.0</td>\n",
" <td>11.0</td>\n",
" <td>18</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>22.222222</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>40.0</td>\n",
" <td>180.0</td>\n",
" <td>70.0</td>\n",
" <td>84.0</td>\n",
" <td>0.7</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>118.0</td>\n",
" <td>68.0</td>\n",
" <td>107.0</td>\n",
" <td>183.0</td>\n",
" <td>54.0</td>\n",
" <td>80.0</td>\n",
" <td>92.0</td>\n",
" <td>13.2</td>\n",
" <td>1.0</td>\n",
" <td>1.2</td>\n",
" <td>25.0</td>\n",
" <td>22.0</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>21.604938</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>60.0</td>\n",
" <td>155.0</td>\n",
" <td>55.0</td>\n",
" <td>78.0</td>\n",
" <td>1.5</td>\n",
" <td>1.2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>107.0</td>\n",
" <td>61.0</td>\n",
" <td>121.0</td>\n",
" <td>252.0</td>\n",
" <td>192.0</td>\n",
" <td>48.0</td>\n",
" <td>165.0</td>\n",
" <td>13.6</td>\n",
" <td>1.0</td>\n",
" <td>0.8</td>\n",
" <td>17.0</td>\n",
" <td>21.0</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>22.892820</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8015</th>\n",
" <td>1</td>\n",
" <td>30.0</td>\n",
" <td>165.0</td>\n",
" <td>85.0</td>\n",
" <td>95.0</td>\n",
" <td>1.2</td>\n",
" <td>1.5</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>122.0</td>\n",
" <td>88.0</td>\n",
" <td>102.0</td>\n",
" <td>219.0</td>\n",
" <td>177.0</td>\n",
" <td>55.0</td>\n",
" <td>129.0</td>\n",
" <td>15.4</td>\n",
" <td>1.0</td>\n",
" <td>0.8</td>\n",
" <td>30.0</td>\n",
" <td>37.0</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>31.221304</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8016</th>\n",
" <td>1</td>\n",
" <td>40.0</td>\n",
" <td>170.0</td>\n",
" <td>70.0</td>\n",
" <td>82.2</td>\n",
" <td>1.5</td>\n",
" <td>0.9</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>128.0</td>\n",
" <td>84.0</td>\n",
" <td>98.0</td>\n",
" <td>151.0</td>\n",
" <td>190.0</td>\n",
" <td>49.0</td>\n",
" <td>64.0</td>\n",
" <td>16.2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>32.0</td>\n",
" <td>30.0</td>\n",
" <td>89</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>24.221453</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8017</th>\n",
" <td>1</td>\n",
" <td>20.0</td>\n",
" <td>170.0</td>\n",
" <td>65.0</td>\n",
" <td>83.8</td>\n",
" <td>0.2</td>\n",
" <td>0.3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>120.0</td>\n",
" <td>80.0</td>\n",
" <td>78.0</td>\n",
" <td>147.0</td>\n",
" <td>78.0</td>\n",
" <td>63.0</td>\n",
" <td>68.0</td>\n",
" <td>17.4</td>\n",
" <td>1.0</td>\n",
" <td>0.9</td>\n",
" <td>21.0</td>\n",
" <td>16.0</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>22.491349</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8018</th>\n",
" <td>1</td>\n",
" <td>65.0</td>\n",
" <td>160.0</td>\n",
" <td>55.0</td>\n",
" <td>74.0</td>\n",
" <td>0.7</td>\n",
" <td>0.5</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>145.0</td>\n",
" <td>82.0</td>\n",
" <td>99.0</td>\n",
" <td>178.0</td>\n",
" <td>59.0</td>\n",
" <td>72.0</td>\n",
" <td>93.0</td>\n",
" <td>13.8</td>\n",
" <td>1.0</td>\n",
" <td>0.9</td>\n",
" <td>33.0</td>\n",
" <td>16.0</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>21.484375</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8019</th>\n",
" <td>1</td>\n",
" <td>55.0</td>\n",
" <td>160.0</td>\n",
" <td>50.0</td>\n",
" <td>75.8</td>\n",
" <td>0.9</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>124.0</td>\n",
" <td>82.0</td>\n",
" <td>115.0</td>\n",
" <td>209.0</td>\n",
" <td>133.0</td>\n",
" <td>60.0</td>\n",
" <td>122.0</td>\n",
" <td>16.4</td>\n",
" <td>1.0</td>\n",
" <td>0.9</td>\n",
" <td>23.0</td>\n",
" <td>22.0</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>19.531250</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8020 rows × 26 columns</p>\n",
"</div>"
],
"text/plain": [
" gender age height(cm) weight(kg) waist(cm) eyesight(left) \\\n",
"0 1 30.0 175.0 70.0 82.0 1.5 \n",
"1 1 40.0 155.0 65.0 82.5 1.0 \n",
"2 0 50.0 150.0 50.0 72.4 1.0 \n",
"3 1 40.0 180.0 70.0 84.0 0.7 \n",
"4 0 60.0 155.0 55.0 78.0 1.5 \n",
"... ... ... ... ... ... ... \n",
"8015 1 30.0 165.0 85.0 95.0 1.2 \n",
"8016 1 40.0 170.0 70.0 82.2 1.5 \n",
"8017 1 20.0 170.0 65.0 83.8 0.2 \n",
"8018 1 65.0 160.0 55.0 74.0 0.7 \n",
"8019 1 55.0 160.0 50.0 75.8 0.9 \n",
"\n",
" eyesight(right) hearing(left) hearing(right) systolic relaxation \\\n",
"0 2.0 1.0 1.0 113.0 64.0 \n",
"1 0.7 1.0 1.0 97.0 70.0 \n",
"2 0.8 1.0 1.0 125.0 79.0 \n",
"3 1.0 1.0 1.0 118.0 68.0 \n",
"4 1.2 1.0 1.0 107.0 61.0 \n",
"... ... ... ... ... ... \n",
"8015 1.5 1.0 1.0 122.0 88.0 \n",
"8016 0.9 1.0 1.0 128.0 84.0 \n",
"8017 0.3 1.0 1.0 120.0 80.0 \n",
"8018 0.5 1.0 1.0 145.0 82.0 \n",
"8019 1.0 1.0 1.0 124.0 82.0 \n",
"\n",
" fasting blood sugar Cholesterol triglyceride HDL LDL hemoglobin \\\n",
"0 93.0 208.0 96.0 70.0 119.0 14.2 \n",
"1 104.0 182.0 47.0 69.0 103.0 16.7 \n",
"2 106.0 246.0 89.0 65.0 163.0 13.3 \n",
"3 107.0 183.0 54.0 80.0 92.0 13.2 \n",
"4 121.0 252.0 192.0 48.0 165.0 13.6 \n",
"... ... ... ... ... ... ... \n",
"8015 102.0 219.0 177.0 55.0 129.0 15.4 \n",
"8016 98.0 151.0 190.0 49.0 64.0 16.2 \n",
"8017 78.0 147.0 78.0 63.0 68.0 17.4 \n",
"8018 99.0 178.0 59.0 72.0 93.0 13.8 \n",
"8019 115.0 209.0 133.0 60.0 122.0 16.4 \n",
"\n",
" Urine protein serum creatinine AST ALT Gtp dental caries tartar \\\n",
"0 1.0 1.2 23.0 19.0 16 0 1 \n",
"1 1.0 0.8 14.0 15.0 24 1 1 \n",
"2 1.0 1.0 16.0 11.0 18 0 0 \n",
"3 1.0 1.2 25.0 22.0 29 1 1 \n",
"4 1.0 0.8 17.0 21.0 27 0 1 \n",
"... ... ... ... ... ... ... ... \n",
"8015 1.0 0.8 30.0 37.0 60 0 1 \n",
"8016 1.0 1.0 32.0 30.0 89 0 1 \n",
"8017 1.0 0.9 21.0 16.0 21 0 0 \n",
"8018 1.0 0.9 33.0 16.0 14 0 1 \n",
"8019 1.0 0.9 23.0 22.0 20 0 1 \n",
"\n",
" BMI smoking \n",
"0 22.857143 0 \n",
"1 27.055151 1 \n",
"2 22.222222 0 \n",
"3 21.604938 0 \n",
"4 22.892820 0 \n",
"... ... ... \n",
"8015 31.221304 0 \n",
"8016 24.221453 1 \n",
"8017 22.491349 1 \n",
"8018 21.484375 0 \n",
"8019 19.531250 1 \n",
"\n",
"[8020 rows x 26 columns]"
]
},
"execution_count": 172,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_df_val"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Explained variation per principal component: [0.56664447 0.2444666 ]\n",
"Cumulative variance explained by 2 principal components: 81.11%\n",
"\n",
"\n",
"4. Visualizing the data\n"
]
},
{
"data": {
"image/png": 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IIRp8bY1Go9FsW9Tn+a1L+TXbJN+P/okXrxtFNBzDtExOvWEwp904uEGFjD/D12BraTQajWbHQYfVNNscs3+ZxzNXvEq4PILltUjEbd6+/0N+fG9iY5um0Wg0mn8BWhxptjmmf/8ndtxOldsHsvzYutxeo9FoNFsJLY402xyGaZCeCJdMizNMXW6v0Wg0mi2PzjnSbHP0OaYXYx7/jHCZ6mKdiCWwPCZ9T9yvsU3bIiyZvZyx/x1PuCzMznt34fCzD9ajTDQajaYR0eJIs83RoXs7bnnrKp4Z9grFa0vILshiyH1nsveAPRrbtAZn7pQF3Hncg4TKwiAE34z6gblTFjDs6SG6ik6j0WgaCV3KX090Kf/WQ0pJqCxMIMu/w3pSbh50L39NmEMgO4AQglg4hnQkD393Jzv37tLY5mk0Gs0OQ32e3zvmE0ezQyCEIDMnY4cVRgBrFq9DGEbKS+TxeUgkbNYvL2xkyzQajebfy4771NFotgNad2mBdBykoxy4sUgMy2PSvEPTRrZMo9Fo/r1ocaTRNCIXPHAW2QVq1lu4PIxjOxx98RHstGenxjZNo9Fo/rXohGyNphHpvHsHHhs3gu/f/plIeYSd9+6yw1blaTQazfaCTsiuJzohW6PRaDSa7Q+dkK3RaDQajUazmWhxpNFoNBqNRpOGFkcajUaj0Wg0aWhxpNFoNBqNRpOGFkcajUaj0Wg0aWhxpNFoNBqNRpOGFkcajUaj0Wg0aWhxpNFoNBqNRpOGFkcajUaj0Wg0aWhxpNFoNBqNRpOGFkcajUaj0Wg0aWhxpNFoNBqNRpOGFkcajUaj0Wg0aViNbYBGsy0TKgvz7Rs/sn5FIa06Nefwc/rh9Xka2yyNRqPRbEG0ONJoaiBYEuSWQfex6K9lCAFI+OnDXxjx8Y1aIGk0Gs0OjA6raTQ18PGzX7Hor2X4Al4CWQE8fg9/TZjDd2+Ob2zTNBqNRrMF0eJIo6mBNYvXIR2JaZkAWB4LIQSrFq5pZMs0Go1GsyXR4kijqYGmbQoQAhzbAcBO2DhS0rRNk0a2TKPRaDRbEi2ONJoaOHboQFp2ak4kFCVYEiIWjtFl9w4ccc7BjW2aRqPRaLYgOiFbo6mB/Oa5PDz2Lj5/4Rs2rCykZafmHDt0IIGsQGObptFoNJotiJBSysY2YnuitLSU3NxcSkpKyMnJaWxzNBqNRqPR1IH6PL91WE2j0Wg0Go0mje1GHD3wwAPss88+ZGdn07x5c44//njmzp1baZ9IJMLll19OkyZNyMrK4qSTTmLNmsqVRUuXLuXoo48mIyOD5s2bc8MNN5BIJLbmpWg0Go1Go9mG2W7E0Y8//sjll1/O5MmT+fbbb4nH4wwYMIBgMJja55prruGzzz7j/fff58cff2TlypWceOKJqddt2+boo48mFosxceJERo0axeuvv86dd97ZGJek0Wg0Go1mG2S7zTlat24dzZs358cff+Tggw+mpKSEZs2aMXr0aE4++WQA5syZw6677sqkSZPo06cPX375JccccwwrV66kRYsWALz44ovcdNNNrFu3Dq/Xu8nz6pwjjUaj0Wi2P/4VOUclJSUAFBQUADB16lTi8TiHH354ap9u3brRvn17Jk2aBMCkSZPo2bNnShgBDBw4kNLSUv76669qzxONRiktLa30pfl3MX/6Ip676jUeHfIcnz7/NYm4DsNqNBrNjsx2WcrvOA5XX301Bx54ID169ABg9erVeL1e8vLyKu3bokULVq9endonXRglX0++Vh0PPPAAI0aMaOAr0GwvzJo0l7tOeIRIMIKUkh/HTGbulPlc/+pQhBCNbZ5Go9FotgDbpefo8ssv588//+Sdd97Z4ue65ZZbKCkpSX0tW7Zsi59Ts+3wxoj3iZRHCGQHyMzNxPJY/PzhL8ye/Hdjm7ZNIe01OEXDcNYeiLNuIDL4BttpxF6j0Wi2P8/RsGHD+Pzzzxk/fjxt27ZNbW/ZsiWxWIzi4uJK3qM1a9bQsmXL1D6//vprpfWS1WzJfTbG5/Ph8/ka+Co02wvrlq1HmEbKS+TxWoTKYhSuLm5cw7YhpIwgi4aB/TdggRNElj+BECZknNXY5mk0Gk292W48R1JKhg0bxkcffcT3339Pp06dKr3eu3dvPB4P3333XWrb3LlzWbp0Kfvvvz8A+++/PzNnzmTt2rWpfb799ltycnLo3r371rkQzXZFu25tkI6D46j5atFIDMtj0apzi00c+S8iNh3seUAARACMLJAOMvR+Y1um0Wg0m8V24zm6/PLLGT16NJ988gnZ2dmpHKHc3FwCgQC5ublccMEFXHvttRQUFJCTk8MVV1zB/vvvT58+fQAYMGAA3bt35+yzz+bhhx9m9erV3H777Vx++eXaO6SplgvuP5OFM5ZQtLYEIQQCOOGao+myR8fGNm0bIgJIKv2tJQyQkcYySKPRaP4R200pf03JryNHjuS8884DVBPI6667jrfffptoNMrAgQN5/vnnK4XMlixZwmWXXcYPP/xAZmYm5557Lg8++CCWVTedqEv5/32sX7GB8e9PJlQWpmvvzux75F46GTsNaa9FbjgJnFIQGYANxMA/GCNXFzNoNJptg/o8v7cbcbStoMWRRlMVGZ2ALLkFZClggKc3Iu9xhJHd2KZpNBoNUL/n93YTVtNoNNsuwncgNP0EEvNBZIK1M0LojxeNRrN9oj+9NBpNgyCMfPDu09hmaDQazT9mu6lW02g0Go1Go9kaaHGk0Wg0Go1Gk4YWRxqNRqPRaDRpaHGk0Wg0Go1Gk4ZOyNZotnHmTlnAyNvfZs2SdbTu3IILHjiLzrt3aGyzdhikjENsGshisLoirM6NbZJGo2lktDjSaLZhlsxezp3HPUioLIwwDNYv38Adxz3Io+OG06qTHmHyT5FOObL4KohPA+mA8EL2dYiM0xvbNI1G04josJpGsw0z9r/jCZWFCWQHCGT5CWQFKC0s5/vRPze2aUh7BU7ZozgltyKDryK3w3EhsvwFiE8FvKo/Ewlk2aPI+KzGNk2j0TQi2nOk0fxDwuVhHEeSmZPR8GuXhUGI1LgSYQiEgEh54woRmViGLDoX7A2ARAoDohMh/wWE8DaqbfUi/rs7Fs6jvpcZIMsgPhs8ehi1RvNvRXuONJrNpLw4yP1nPclZHS7jP52GcufxD1G0tqRBz9G1t8p/iYVjSEcSDUVBws57d2nQ89QXGXpVCSORCUYO4FMemMjYRrWr3hi5gAOpKUoOCAFGVmNapdFoGhktjjSazeSpoS8z6dMpOFI9W3//biaPnPcstY0rDJeHef6akQzpfjWX7HU97z/2GbZt17j/Eef0Y8A5/ZCOJFQWBuDYywbS98T9Gvx66kVilfpXuB8hwvW8OGsax57NRGScqfKMZBnIIMgQmF3Ae2Bjm6bRaBoRHVbTbHUS8QSWZ/t+64XKwkz5ejqmx8IXUGGkmBDMmvw3G1YW0rRNkyrHSCl57MIX+OWLaQhDIB3Jf0e8Rzwa58xbT6z2PIZhMOyZCxh4fn/WLdtAiw7N2GmvTlv02uqE1RFik0HaIEyQMbXdbN+oZtUX4TsI8h5FBl8FpxCs3RDZ1yO050ij+VezfT+hNNsVkz6bwis3v0nRmhKat2vKZU+exx79dmtsszaLpHdIpG0TQhU8OU71nqNVC9cw5esZWF4Lr18JqnB5mE+f/5ozbjkhlVe0MUIIdu7dhZ17N24oLR2ReREyNhHsZSAFIMF3OPj6N7Zp9Ub4DkH4DmlsMzQazTaEDqtptgp/TpjDI+c/x9ql63EcyYr5q7j3tCdYMmtZY5u2WWTmZNDjwG4k4glikTjxqPrqvEcHmrYpqPaYUFkY6UgMs+LXzjBN4tEYdqLm0No/obw4yLK5KwiXhxt0XWE2RRT8F5F1LWSci8i5G5H7EELojxSNRrP9oz1Hmq3CT2MmE4/GCWQHEEIg/R7C5RF+/uhXOnRv19jmbRbXvnwpD5/3HLMn/41EJU/f/N8rMYzKAmHa2D8Y+9Z4wqVhLK9FNBTFyDKQUmLHE3Tbt1uDhxmllLz/6Ke8+/DHJOIO/kwfQ584j36nHtBg5xBGHmSeQ/X+Lo1Go9l+0eJIs1WIR+MAlUJHQkAilmgsk/4x+S3yuO+LW9iwsgjpODRpU4BpmpX2+enDX3j8whdS1y8lmB6DSDCCEIK2O7fm6hcvbnDbxr8/ibfu/QAJeLwWodIQTw19mVZdWmxT4TmNRqPZFtHiSLNV2OOQ3Rj71ngiwQger4dYJIZhGuzeb/vuJWMYBs3aVk2+TvLG8HdJxBJk5GYghCAajiFth8uePI8WHZuz6347EcgKNLhdU76ZgW3bZOZmAmB6TCLlEaaNnanFkUaj0WwCnSCg2SocfMr+nHbD8RimQSwax+PzMOS+M9nr0J6NbdoWQ0pJ8doSTI+Z8phZHhMpJe12aU2vw3puEWEEYLp5TeltBWTado1Go9HUjPYcabYKQgjOuv0kBp5/CBtWFtG8fVPyW+Q1tlkNgp2wGfvmeBbNXEp2QRYDzj2EZm2bpMJm839fhHQkCIiGY/j8Hlp1ablFbdp/8D788N5EwmUqzykRS+DP9LHvUXtt0fNqNBrNjoCQtXWs01ShtLSU3NxcSkpKyMnJaWxzNI2M4zg8dsEL/PTBZBAgHUl+izwe+uYOWnVuwfzpixh+wiOUrC9FCIFhGVzyyDkcecFhW9y2r0aO4/U73iEaipLbNJsrnruQ3kfsscXPq9FoNNsi9Xl+a3FUT7Q40qQz9dsZjDj5UUzLwuv34DgO4bII/U7dnxteuxyAtcvWM/mzqcQiMXY7sBu77td1q9ln2zah0jBZeZk19lHSaDSafwP1eX7rsJpG8w9Yu3Q9ju3gz1S/SoYQJOIJxr39M39NmEv/0w/krNtP4rihAxvFPtM0yc7X3Z63BlImIDgSGf0GEAj/QNUDSuiPWY1me0P/1mo0/4Bm7ZpimAaJWAKPz0NZcRA7boNlUriqiA+e+JxoOMbFD5/d2KZqtjCy7HEIj1aZ70hk4m+wixE51zW2aRqNpp7o0hWN5h/Q6/CeHHDcPsRjCUKlIeKROMIQZOVnqoaXhuDr18eRiG+//Zw0m0Y65RB+HzDByAIjGzAg/A7SCTa2eRqNpp5ocaTR/AMMw+D614Zy2WPn0vfEPnh8HjJyMpCOJBqK4tgOdsImFok3tqmaLYksBxwgvQmopbZJLY40mu0NHVbTbFVWzF/FwhlLyMgJ0POgXVMDWLdnLI/FURcdzhHn9mPe1AUs/3sVtm274RXIyA7w0weTkRJ22bsznXp2aFyDt1OkEwRZAkYzhPA0tjmVMZqC0RzslaQ+VmUUzLZg1NwkVKPRbJvoarV6oqvVNp+xb47nhWteJxqOYhgGO+3VieEf3kBOk+zNXjMajhINxcguyNomqrG+GfUDj17wvOprBBimQDogTIHH68Hrs7j40XMZdL6aXr9gxmL+mjAXr9/DPkfuRZNW+Y1pfoMjpQRZBsKDEJvX8FJKCcEXkcGRgA1GHiLnHoSv4ebENQQy/gey+GqwN4AAjKaIvKcQnh6NbZpGo0FXq2m2QVbMX8UL175OLBIjIzsD27aZN20hr97yFtf836X1Xi8RT/DyTW/y7Rs/Ih1J211ac8PIy2nfrc0WsL7uxKNxvH4PXr8HIQzCwQjxSBxDGGTkBAiXR3j5xjfY45DuzJ70N89e+RrxaBwpIb9FLiM+upHOu+8YniWZWI4suRkScwCB9A9C5NyGEP76LRQegwy+hMoCsMBejyy5AQreRljtt4Dlm4fw7A4F70F8GiDA0wthaq+RRrM9onOONFuFhX8sJRqKEshSScqWx0IIwexf5m3Wem/d9wH/e/k77IQDQrD4z6Xcc8pjhMrCDWx5/cguyEIgMEwz1ZkawDANhBD4M33EInFmTfqbF64dRTwaJ5AdIJAdoHhtCc8Me6VR7W8opIwpL0p8JsqN4kDkM2TZo/VfK/o9yv2WAcILIkvl+MQmNbTZ/xhhNkX4ByD8R2hhpNFsx2hxpNkqZOYEMAxD5eKgQiWO45CVl7lZ641/fxJCgD/Th9fvwZfhY82SdcyburAhza43+x7Vi857dCAaihIqDaW2+zN8ANhxG8MwiAajRMNRfBk+1TnbEJiWydI5K3Acp7HMbzgS88BeAMIPwgciABgQ+Yr6R/KrCZfKGrZrNBpNA6DFkWar0OOgXdmpVydi4RjhsjCh0hCWx+KEK4/arPWSs8oqbYNGFxb+DB/3fHYzR15wGF17dabbvjvhz/IRC8dUqX80zp6H9qDHQd0wDINEvEIs2gmbnCbZGMaO8GtZkwDajBRHzx6ADU4hOKXKa2RkgbfPPzFQo9FoakTnHGm2Cl6fh+Ef3MCrt45mzi/zyMzL4PhhR3LQSZv3gNv/uH349PmvCJeFScQT2AmH7IIsOvZo18CWK6SUdU74zinIZuiT56e+n/z5VL54+VtCJWF2O3AXzrj1RPwZPg49sy/fvvEjwZjKOfJ4Lf5z+8lbxP6tjrWTqtSyl4KUqDJ3B3z965U4LxPzIfgWSlQl3K8MyH10m8o30mg0Oxa6Wq2e6Gq1bYNoOMrdpzzOr19OAwlCCDw+iwOO35db3ryywbwvKxes5plhrzD/90VkZGdw8rXHcMylAxqkMs5O2Hzy3FdMH/cnvoCXQ888iP2P3bsBrN42kIkFyOLrlUBCgO8gVWVm1H2ciVN8PUS+VXlGoMrjiSPyn0L4DtkSZms0mh0UXa2m2eHxBXypyjB/ph/TMknEEvz6xTRmTfqbHgd2+8fnCJYEueuEh1m9aC2mZVIYLOaVm9/Cl+FjwLmH/OP1TcvkxKuO5sSrjv7Ha21JpL0BomNVM0NPT4R3nzodJ6wu0OR9sFervCOjaf1Fpb0CEJA6zgcyBvaa+q2j0Wg09UCLI812y4aVhZiWheVRb2PLYxGLxCleW9Ig68/4cRarF63Fl+nHNJUnKlgS5OvXxzWIONoWkdKB8HvI6M8gPODpDaH/grMat3kPZF2OyLygTusJYYHVdvMNsjpDYpZbrWYAMRAmmFsmfKrRaDSgxZFmO6ZD97asXboex3EQQhANR7G8Jm26tmqQ9eOROFJKjDRvhxAGsXCsQdbfFpHlT0LoDSVGACJfASaIHJQ4CiPLXwBfP4S10xa3R2RdjoxOAWcFSBuQ4NkL6dlP16oBMj4LGf4QnDKEZzfIOGPb6x6u0WyH7AhlMZp/KRc9dDZN2xQQKY8QCUYAOP3G4+nUo2ESdXfZdycysgOEy8Jp89Ekex3Ws0HW39aQ9noIjUYNT81RXzhAXIW1hAD86vvEkq1ikzBbQ2Bw8jtlW2I2BJ/eKufflpGx6ciiCyD0PkS+RJY/jiy5RXn/NBrNP0J7jjTbLa06t+Dx8fcw4aNfCZeF6dq7M3v2b7hRDS07Nufaly/jyUv/j1BZCMMw6HPs3px1+0kNdo5tCqcQJYbSPQ8GYKuKMyGAOEo8FWwVk6SMQ/hNFeITbk8sGYLQaGTmeQhjxxq3Uh9k+TPghFSyuhAqWT36verQ7d1xEvs1msZAiyPNdk1+81yOueSILbZ+n2N6838zHmX53yvJzMukfbc228QMty2C2Vo1a3TKqfhoMAGpegtJ1EPYd5jbe2grIEMqAbvSR5UHiINTDP9icYSzSuVhpd6PXjXHzlnfqGZpNDsCWhxpNJsgp0k23fffpbHNqJHvRv/E5M+m4M/0c9I1x9Bxt81LVhZGFuTciSy5EwgCUnmIMi+H+AyQZQjvnpBxDkJspYi8yHGn3a8AvGqbjChRZDZMbtl2i9kZEiuAZLJ6RHnYTN3/SaP5p2hxpNE0ILMmzeXF60axatFamrVpwoUPnkWvw3ffYud79ILn+fr1canG02P/+yMjPrmRfQf12qz1hH8gmB0h9puqCvMehLDaAqc1mM31skcIyL1XzWlzStyCuQxE7oj6D7DdwRDZ1yMTc8FZB9L1HmWch/B0b1zDNJodAN0Esp7oJpCamlg6ZwU3HDqcUGkY02OSiNv4Mrzc/7/b2GXvLg1+vjm/zefKPreqWWXJyIpUnq4P1r3W4OdrTKS9AqITARu8+yKszo1t0jaBtNeqikJZDp7dwHvwjhv2rQEp46rvlZGFMPIa2xzNNoxuAqnZ4UjEE8yePI/y4iAde7SjVacWjW1SJWLROJ+98DXB0jCBbD+GYeCVknBZmB/embBFxNHM8bNSwij5QJRSUl5U3uDnamyE2QYyTmmQtaSMgL0KjLztPqFbmM0h85zGNqPRkPE/kMU3gbMWEMjAYET2zbqdgeYfo8WRZpsnWBri3tOe4K+Jc5C2xJvh5bLHz+Pw/xzc2KYBMP/3RTzwn6dZtXA18WgCx3HIys1EGEqwxCL164u06M+lLJi+GH+mlz0P7UlWbma1+7Xo0Ez9RwKC1LR7y6t/rWtCRn9GltwOshQwkRlnILKuQgizfuvIOCQWAQKsjvphvBmoezhPJdxbO9VrrAyozu2y+GqwN6gO7NgQ/gCMJpA1dIvYrPn3oD9FNdssRWtL+PKV7xg/ZhJLZi0nkOXHzDCJlEd54ZrX2XnvLrTv1qZRbQyWhrj/zCdZu2wDHp+HeDRBPBKnnCAenwchBD367lqxf0mQ0g3lNGlTgNdX9YH6xf99y8s3v0m4LIJjOxiWQb9T9ueqFy4mMyej0r59T9yPVl1asGrBGqTjRscFDL580Ba95u0VmViCLLkRnCAIP5BQDS/NFpBxVj3WWYYsvgbshWqDtRPkPaG8W5o6Ie316h4m/kIl/jeFvMcQnnrk58Wng7NBtXhIFgg45cjItwgtjjT/EC2ONP8Ix3FYtXAN8WiC1l1a4PV7673GygWrefuBj1i1cA2td2rJmbeeiNfv4YbD72bt0vVEQ1GkI4mUR8gqyCKQ7SdUEmLB9MWUF5Uz6dMpOLZDryN2p/cRW6nE3GXRH0tYt3wDhmkgJXgDHmLhOIloHJ/fy3FXHcUhpx2AlJLR933IB099jpNwyMrL5KoXLmKfQXul1loyezmv3vJWShgBOAmHH9+fhGmZ3DTqikrnNgyD/5vxKHef8jh/T1mA1+/h6IsO56zbT66z/dIphtCbKqfHaInIPBuxlXoYbXViv7rCyO0LhAecUmTkB0QdxZGUDrLkeuXxSCaEx+cii2+Agrf+dfk+m4ssvRvif7geHwPstcji66DJR/XwIKUl2lW7XaPZfLQ40mw2pYVlPHzus/z58xyklDRv15Rb3rqKzrt3qPMaa5et56YB91C0pgQhYO5v85nxw1/0Pnx31i5Zhz/Tjx1LEI8liMcTxCIxLI+FMASL/lzKM5e/QjymOjh/8fJYLnroPxx9ce19j2LRONO+/YPSDWV02K3dZucDLf5rGS9cM4pIMFqxUYBhCnKb5fLkT/fQuktLVi9eyys3v8WEj3/FtAy8AR8l60t5+LznePzHEbTbRXkcFs9cSjQSx7HVOJRkqEw6kl8+n0q4PEwgK1DJBn+Gn/u/uBVQQvW9hz/htmPux5/pZ/9j96Zlx2bs1Ksz/gxfFfulU4osHKI8IO7sMhkdCwWjqhVIjuNA6W2q0SCOajSY+wSGsXWqxqSUylMgI2C2VHPb6kWy/YAbh0xSn5Casw4S80F4Vdm82giJv0EWgdhBhWUDIqUD8d9AWOo+AhBQ/ZnsRWDUsQO9dy+3zcNaVJsHW+Xf+Y/cQpZr/k1ocaTZbJ6/eiQzfvgrFT5avXgt953xJM/99mC1D+Pq+Pq1cRSvLSGQHcAwBI7jULi6mJk/z0E6EsM08GX6SMRtpJREQzFsj037Xdvw/Vs/EY/FCWQrwRAujzDyjnc45LQDyKwhTydYGmL4iY8w55d5SCmxPBan33Q8p998Qr2uff3KQu4c/CDrlm+o/IIEx5Z03r0Drbu0ZP7vi7jrhIdZv6IQx3aw42CYJgF3LMn0cX+lxFFWfmYlz4OUUkUcDIGElDepJq7vP5yZP81OfT/+/Un4Mry037Utd7x3bdUk9vAYNzQUAMNUs8vsZRB6F7Iuq3qCkssgOq7i++h3UHgGNP2oLrfsHyGdILJ0uHt+qQbP5j1av/luvgPAyFPNI/ECCRCmal9QV4QHJazSvRXJ/+u8o7rheu2IpG1z3O11+9wAVDJ93tPIklvAXg5YEDgNMs9vUGs1/070bDXNZuE4DlO//QPDMvH4PFheC2/Ax/rlG1g6e3md1yndUAZCKAEgJYmYTSKWIFgSVOexHbx+L/4sH8IQ5DTJ4sAT9uXm/15JsDSE5bUQQiCEwOvzEI/GWb+yCFAVbp+/9C0vXPM6o+//kKI1xbzzwEfM+WUe3oCXjJwMpJS889DH/DlhNotmLmHmT7MpWlO8SbsnffIbRWtKMK2qXgfLY9K6ixIiL13/BqUbyjAt9asmgUh5BDthA1QSQz0P7k63fXdCuPci6eAwLINu++5Uo+AD+GbUuErCKEksEmfZnBU8duELVV6TtqrwSXlOhKmaYTtrq+zrOBGI/uB+Z5D66Ej8hROdoQagJpaxpTqDyLLHIPqt+50JiUXIoiuRTrDOawizFSLvKTDbABJEAJF1BfiPr7shIh98fYEEyLD6IgG+/ggju+7r/IsRQkDgZNSbLejewxh49oR6tmgQnl0RTT5CNPsG0Xw8Rs51m+FR1Giqot9Fms1CCIFlmUSdikqsZFl5dYKhJjq43Zzj0TjxaDwVoipaUwJAqDyMaah8nn2P3IvhH96A5bFSFWHF60rwuInNsWgcX8BL09b5JOIJ7jntcX4fO1PZBox9czy5TbORgOVRb31fho9QaYjnrxrJivlrkI6DP8vPVc9fxJ79d+OlG/7L79/NxOv3MOj8QznxmqMxTZNoOJYSZempDx6fhemxKGipSsSXz1uFYRpYHotELIHSPJJweYTs/Cx6H7E7sWic+dMWEovEue6Vy3jz3jGMf38S8Ugcy+dhl326cN2rtSeYzvllfrXbpaO8YwunLyYcjBDIrAiBCasdEgkyrjwiMuFqpWo6bDuFVM3tcCm+BEkMEOA7HHLvRoi6ewDqRPR7wKjI88FQ4zMSc8GrGl7KxCJILFZdvT09q+3iLbx7QtMvQJaAyKxSZSZlFBAIUX3unBACcu5Flt0HkXGpcSoi+2akTIC9Utlmtt56XcS3Q0TWZSAEMvQhEANvH0TObZslbNTvoA5nahoWLY40m4UQgkPPPIhPn/+KSDCCMAR23KZrr8506N62zusMGtKf6eP+ZPJnU4iGldBSXh0VdmrdpSV7HdqTNl1bMeiCQ1OixjAMLn38XB678AXCZWGEEJimwfn3nE5mbibjx0zi97EzsXwePF4Lx3ZYt2w90nHAkUgpEULg2A7xWILFfy3Hl+HD9FmESsM8eclLdNitHXN/m48wDKSU/Pfu90nEbc645QR27bMzhmlgJ2wMw0iFvKSEZm0KOOqiwwBo2iafxYXl+HJ8BJwAobIwSChokceNo4bhy/ByRZ9bWDZnBQC5zXK49a2ruealS1i1cA3CMGjVuTmmWbvgbNGxWfU/J0NdozAEP38wmZ16daZTD3e8ROBkiHwLsSlpkaE9IHB61YWMlqiQR5QqIkmGQGQACYh+jSxvhci+ulZ7NwvpVJNr63rkgqPUINbkYFxff8h9sNoSe/Uwzdto6UJkyV0QmwwIpP8wRPZt1SYHCyMLkfsAMifp/TORieUVidqg7mPuwwiz6T+65B0VITyQdQUi64rU76JGsy2hO2TXE90hu4JYNM7I295m7JvjsW2b3Q7oxtUvXkyTVvVrrGfbNiNve5sPnvgcf5Y/VfEWLgvTsnNz/m/6YzUeO2vS3Gqr1cY8/hlvjHivUgJzsCTETnt1ZPnclURCUfcv+4q3f4ZbKi+lpLw4qJwCAV/KMxUsCeL1eTjj1hPZ45DdmPvrfEbe8Q6xSBw7YdOkVT4HDN6bU28YTNM2TQCY9t1M7jvjCaIhJfyEgP5n9OWqFy7CNE0u3/dm/p6yoOKCBBS0zOe1WU/UGkbbmFgkxtldLqdwVXGl7R6fhR23EYbA8lh4/B6G3Hcmx146ACkjyMLLVHJsMobn7YvIf6ZaUeGEP4GSmwG7wlhMJTTSSqmxumA0/aDOtteGlA4EX0KWvwiEUWIoQ9lgdYa8pyExB4pvAiGBAEogxRBZVyIyL6jTOWTRJe59sFD3wgbfQIy8h+pwvI0sPAvis93qK6l69/j6YOS/tNnXrtHUF+kUIstfVkUDZktE5nkIq+Eb0G6v6A7Zmi2O4zh88dK3/Pb1dHwZXnY/uDuXPnYuOU3qn3dhmib7Hd2bz174plKnZyklzVyRkdw25ZsZrFqwhoJWefQ5pjfd99+l2qGwzds3BQnxWCLlORICdu7dhQsfOIv3H/+MotUl7LRXRxbNXMq8aQvTrk0qB4UUGIZ66MdjCWLhGNFQjDdGvI/ngY+48rkLeX7KQ6xcsIb8Frl03K1dlb+Aex3Wk/s+v4Xv3vyJSChK9/13ZuD5/TEMg6K1xcybqs6bbBgpHUnxmmKWzF5B9z4713rf0v/i9vq9vPrXE9x35lOq6i0UxfRYxGNxok6UQE4GlsckUh7htdtG0/OgXenQ8TtI/O72ifGoB3p8MoQ/rrYbtREYjGPuBOH3gQQYbSD0IlWSkxuyIWLoLWTwJSW+pBeIASGwdgUjBzacADIKxEHmgWEAPnCiyNjviLroS2cNxKcBHlfcoPJgot8jZWTTM9zsVW5pvy+t+kpCbCrSKa93c0ONZnOQTjmy8CKw56d+JWV0PBS8jrA6Na5x2yFaHGk2i4+f+ZLX73xHVZQZBj99MJkNKwu574tbU6Gv+rDbgbuw/3F7M+GT39yO0oJAlp+z71IPacdxeOrS/2PcuxNVaAzoedCu3PXB9fgCVfNbDhi8D3sd3pPfx84kEY0jgWZtm3DqDcfRtE2TSo0Zv3vrJ56+/GVCpSEMy8RJ2LTs3ILSDWWUFZZjWibxaBwpVffpQJafcFmY5695nZFznqJXx+a1Xlu3fbvSbd+uVbavXrQOJSYqb3ekxOOreg8d97rn/raAF699nZULVtOkdQFD7juTfY/ci6y8LB74322Vjhm6z00sm7sSj9s125+lekQt/GMJ7dsuUh+ihitmhBdkHGkvrrFTjOHdDby7AW4rgMin4KwE6UGVUhuIwOBa70d9kJHPVazSyHKLxBIqjIdXNQHEi/ImOUApyDSvpaijKJGJ5AFpG93/S3vTbXNEeosA0v5v1K9NgEbzT4h8pYQRGW71qQSnGBl6C5Fze2Nbt92hxZFms/j8xW+QjgpFObZDeXGQ37/7k0v2vIGzbj+JQ8/oW6/1DMPg+teGsssL3zD/90Vk5mYwaMihqZ5JP3/4C9+N/jmtygtmjJ/FJ89+xak3VH4Yh8rCTP5sCnv0606rTi2QjiS3WQ5HXnAo+S3yqpz70DP7EovEeP+xzygvDtK1Vyf2ObIXr936FnbCxo675zQE/gwfZYXl2AmbeDTBrIlzKzVyrA95zXPw+DzEIvFKw2N9AW+lXlErF67h3tMeZ8ms5ap3oVQ3wPKYrJi3iofOeZp7Pr25igftzwlziASjJGIJHNvGME3shMo/ysrLRJhN1TB3t8cR0gZkpR5HUtoQm6C8I2Yb8B6gkrPjUwGVnEz546rPj8hGZJ6ryqkbCpmgsjpJVsnNVR4q4VP/OlFUBVlQ3SDhQ2ScVLdzmK3A6gLxuWnnjaskYaMOriejpaq0ik1V506F5Q5FiEDtx2o0DYVTSCVBLoQSSPa6RjVre0WLI81mEQ5GMUxVcl5WVJ4SEKsWreHpoS9jmgb9Tj2gXmtaHosTrjyq2tfm/DqfaDhaJRd49i/zKn1ftKaY2499ULUTcFsEnHztsZx564k1nlcIwZEXHMaRF6gk6ngszjldhqVEVSKWIFgcQjoqFynZ5sZ2bJ694lWemngfec1y63WtAC07NueoCw/n8//7lkRceS9Mj8llT56fSsAuWlPM0N43EiwJVTrW4/fgy1CtCMKlYca9M6GSOBrz+Ge8ec8YYtE4iViCknVleANehICuvTqzZ//dwNMGwp+DvVp9iCLAagcBda+kjKtxG9EfUJ4ZAzx7Q3yeangIYDZR5fHWLoCn4RNrfYdAYoFq/Iip/jVywQlR8WYwVGhQhlUfI7MVIutyhHefOp1CCAtyH0OWXKvOhQDvXojc++p4vAF5jyBL7lBduHEr2HLurPflajSbjdUZle8WVX80yITy5Hqqeq01m0aLI81m0aNvNyZ9+lslYSQEZOVmECoL89mL39RbHNXGyvmrq60kX/znUt59+BPWLVtPiw7NWDJnBUtnu5Vnlkm4PMLo+z9kylfT2XmfLpx2Y0WydE0Ury0lXB7B4/NgmiZmwMSO2xWdsF0d4c/wUbi6mMmfTWXQkEM3eQ1Fa4qZ8PFvhMsj7LrfTvTouyuXPnEu7bq1Yfq4P/H6PfQ/oy/7HdUrdcx7j3xaRRgBxF1vkxCqQWQ8Ek+9tmTWMt669wMcR5KVl0ksHCNcFsbrs+h/Rl/Ovfs0N+m9ORSMQgZfUyXoZjtE5gUVk+rDH7hNF71uyC0KsfGABcIVg/YGZPFNiKafbZGKI5F1CdJZo0IGxJUYy30IGXoXomNd0STUg8C7NyJ/ZL2HyIJqa0DBaLBXoErx29RrHWEUIPKfQzoh9UDaVJ6SRtPQ+A4F/7EQ+Vx5PgGsnpBxXqOatb2ixZFms7j4of8w5avpRIvTmvAJgePmIFX3QE8SDUcZ8/jn/D1lAVn5mRxzyQB23a/2v258GdX3nVk+bxVv3jvG/c4VC1L1WnJsh0gwgnQkf09byMKZS5g2diaPjhtOfvOaPT25zXLwBbyEyiJpE+4FpmWkump7fB48fg/RYFSV57sES4IgRJUhscvmruD2Yx+kcFURQggM0+Cc4ady4lVHc9zQgRw3tPouzeuWra/RzmTIzzAEux3YLe1cK4nH4mRkZyCEwJfhw3Ek+S3zGPZwABk8BWdtCDx7InJux8i5tdr1ZeJvN3XGvffCcj1MhhuGk4BXJTQ7G8CsPfeq+nMshtg0FRrz7l+l9F0ILyL3XmTWMJDlrmgJgNUNWepAdDwglTDKfaCSoJH2Wgh/gpTFCGsX8B9dq+ARwgNWx3pfQ6U1jIxN76TRbAGEMCDnbvAfBoklYDQB/xEN33PsX4IWR5rNYtakv0nEEwSy/UTKo6kZYKHSEKZl0vOgbtUeZyds7j/zKaaN/SPVBfqXz6cy/MMbKiVJb0xWfg3JtVKFmDweCzthU1ZYnuownRxYiwB/pg/LY7F22XrGvvEjp1x/XI3n8vo8nH/fmbx47euEXeFjWib7HLkfv305TXmUPCbRUAzDNNh57y4UrS3h8Qtf4M8Jas5cz77duO61y8lrqspFX7r+DQpXFeHP8iOEIBKM8t+732ffo3rRtmurGm3JzKs55yUeiWNaJoOHDeLwsw9Obc9rloNpmiTiCTw+D1JKHNuhoHkMWfaoEjXCgNiPyOLVUPBG9Z4OkeuGEJ00MeTedBlVYgUbpIGM/YEIHF6jrdUhI+OQpbdWhMjM5pD3LJiqT1Z6lZcwW1Y2zchG5D2OdFwbRE7l0SuJ5ciiIeCoLuASqXoY5dyne+podliEECoUrfXQP0a3cNVsFhtcD0ggK0BmbkaqFN1OOPTo241z766mkSDwx/hZ/P79TDx+L5m5mWTkZhAJRRl9/4e1nq9Tz2q6NruYbrm9aZmqQ7VlEioJEY2o3kJJT49hGgigeG3JJq9v0Pn9ueO9aznqwsM56sLDueO9a7nlzSvoc+ze2AmbSHkE01Len90O2IWHz32W37+fSSwcJ1gcYvLn0zij7SV899Z4AJbMWo5hGhiG4XpzvCRiCRUu3Ag7Yacq03bv173ah3mztk2474tbee63B7ng/rMq7bPr/jvTe8DuxKNxgiVBQiUhfAEPpw1brYSOkeU2bQyoROrY9Grvgcg4WY3LkEFwylwxFAAc1WE61e/IgNLbkfFZm7yvSaRTjiy9UwkjkaW+7LXIwv8g1x2CXHcITvHVSKe01nWEkYUwcqvcI1n+HNhrgAyVj4QHIl9CbGKdbdRoNP9etOdIs1m07qL+ko9F4ngDXgzTIByMsP+xe3PLm1fWWM5fsq4UJ+FgZarwhhCql1Dh6uIazyWl5LPnv0klQqejwmhqo2p2aHDsJUewdsk65v2+iLVL1xNwvTXxqIrDt9u1bh28ex+xR6qpZJJb3rySub8toHhtCW13bkW7XdqwfmUhs3/5G4QgFomm9k3EEjx07rNYXosmrfMpWlOSyhNKxBIYpkFe84pGZCXrS3lm2KtM/34mwjQ49Iy+9D/9QALZfhKxhBpX4nrFOu/RgZ4HVe9pM02Tm/97JWMe/5zZk/9mw8pCPH4vX79VhExk0rt/ck+3CaYMV7uOMNtAwevIsqdVrpEMUvFDEOp4EQD8QAgZGYvwdK/TvcVeqsSWCLhVNaB6FUVAuPckMg7JnYi8J+u2ZqX1FysbU2X2bs6UvaL+a2k0mn8d2nOk2Sz2PWovDjq5D3Y8Qbg8TCwSp90urRn29JBa+xy13bk1ltciXB4mVBYmWBwkHkvQvnvNnqFgSYi1y9aTmZOBYaR5CAS03bkV0XCMcHmYaCTGTnt2JBaJMW3sTApXFeHYDmVF5RSvLSEWjrHvUb04/D8HbfZ1CyFov2sb9j1qL9rt0kZtdMODSfGVjnQkL143iv/ccQpev4dQaYhgcZBELMGBJ+xL115q0KZt2zx49tP88sVUEnGbWDjG/14ey/ejf2bw5YMwPSaGZYIQ5DbN4YIHzqrVTq/fyxm3nEB2kyyWzV3JgumLmPiVl/svacmEL0xVti+DymNTi6ARVgdV6k4MRLb6SgokkeOKo+TPJFHjOlUwcgEz7RgH5YkSbnm+X+UhRSfUa7hsCrOtslE67oaYEkobhec0Go2mOrTnSFMnNqwq4s17xrB01nKatW/CaTcM5tqXL2WfgXuydPYK8prn0v+MA8muKTfIZae9OjFoyKF8+OQXKY+PMATxSAzHcVIdqdPxZ/rweC3Ki4MVaS+o3kjXvTaUOZPnqWq1js2JRWK8da8aXRGLxFXOkXsOr9/Lf+48ebOaVAL8PXUBj1/8ImsWr8Pr83DqDYM58eqjKWiVT9denZjxY/VhpbLCILv3687dH9/I16//QLgszK59dmbwsEGpcNCqBWuYPWmeSvR2x5WEy8N8//ZPXP3iJRw55DDCwQitu7Tk4FP6MP27P3nh2pEsnrkMO+HQuksLzhl+aqWeSwv/WMLEj37D9JiYloUQXiLBckY/mcOBRy0DIweRex/CbFH7hcengRRus0gJ0hU1sgikgapm8yC8+9X5XgqzDdJ/DEQ+ViE7kiLGR+W+Rsm+QfVDZF2GjP2mer/ImLu+Fxmdqno12auRZfdD/C8w8hCZFyECx9T7PBqNZsdEiyPNJikrKueWI+9l1YI1AMydMp/p3//JQ9/eySGnHVjv9Uo3lGH5LCyP6ebgwNRvZ/D7dzOrhLFA5Qwd9p+DGfPYZ5W2CwH/+7+x3DDy8tS2W468162YE1UaK8ZjcX54ewLn3VM1HyoSijL31/nEYwl22qtjlb5FG1YVcfcpj1GyrtT1fEUYNfw9sguyGHDuIdz0xhVcd8hdrHTvUTo5BVmsWrgGx5GcO+LUalsJxGMJpFtt5zgOodIwsUiMcFmEe059HCFAGAZ7HLIb65dv4OuR44ik9X0KlYW5/8yn2KP/bhSvLaGgZR6799uNWEz1OZJSIhCYHpOiDc0QebeDtRPCrL2tAQBGNirPSKIGz0bTXnTU9xmXIHz1ey+InNvAaouM/qQ6bDvLVbNJmWyZEAff/ps1fkNYnZEFr8CGc0EWokJ/JoRHIYWA6HeQWKoa5iWKkaV3gfAi/AM2ubaTWAfxGWC2xvDWMYyo0Wi2K7Q40mySH96dyKoFa/Bn+jFMwx3MGuLzF7/h8qeG1Hu9lQtWY5pmaiislJJ4aYi1SyuXrZesL2X+74swLZNOPdtjeU0VwXIkpqU8TPN+X1jpGMurys2THqZkTpIwBMIRlcruk6xevJa7T3mM5XNXIqUkuyCLfY/cCxA0a9eE44YOZPr3f1K8toRAthva80N5cZAf35tIXvNc/vx5NvsP3ofPX/wmNWQWVCVd+13bcNUBt+HYDh6fhwsfPCvVcDJJm66taNG+GSsWrMaxHRKxihCVYye9Kg5Tv5nB1G9mVLQYcCNciViC8liCSZ9Owev3Ml8uZtrYmRX9jwRIJImYTVZ+LsJXvZdHShXaEqLCgycyzkTGproJ2clrs4AclDiK1E1kbYQQHsi8EJF5oTq3vRpZfD0kZqsdvPsgcurWiLHa9Z0iJEG36s5SYksGITTazW3KdLsJS5BlyND7mxRHTvmzUP4sSU+XY+4KTd7FMHRfI41mR0KLI80mKSssxzAMDFM9MIXbln7DyiJ+/ugX7LjNrn260rx9szqt17pLSxZMX4x0lGhJJic3b1/R42bG+L+4/4wnKS8OYRiCglb5mJZqxmg7Dk7MAQnLZq/gygNvZa9De3LmrSdy8Mn7M+2bP4hF4+qZ5wqj5CDbrr07V7Hnqcv+j2VzVqRmtK1fUcj/Xv4OX8ALhmDCR78w8PxDUaKh4jghBCsWrOa+M55AOpJIsnVAGo7t8NfEuZgeC2/ASzQc46Ub3qBjj/aVejt5fR5ufftq7j75UZbNXemegBojSinBJKtuN0yBL8NP2YYyde3pNgloUc3PSTrlyLIHlUcFkL7DENk3q2owXz/IfQgZfAXic4CwKzjc+U3EqKhc+ycI1cjOs5vquO0/HlFNmLXOpM9Mk6GK0SIyhkq3dNskCKHCg7L23CYnOhHKn6HSTbdnQ/GlUPD65tup0Wi2ObQ40mySjj3aIZHEwjE8fk9qvtkfP/7Fr1/+DqhOzDe+Poxeh+++yfXOuv0k/hg/i5J1pamcmwMG78Neh/UEYNp3f3DzgHtTOUkIWLd8vdu7p/JDOBG3mTd1EQtnLGHJrOU0bVOA4/ZcSmKaBkIKBp7fnz3678bkz6fi8Vl0P2AXPF6Lv6csxLRMTI+ZahoJbuNJw2D536tYs3gtGdlq4Kw34HW7gkvWL9sAQpCIJ6oII1AVdNKR5LlNIQNZfiLlEWZNnFul8WXH3dox4uMbuXyfmzFMg1gkRjxafZJzhTepKuHyCL4MHwgl5vzZfuIRFbYDyMqv3DtJyiiyaKg7md5SicuRz5Eyish7RP0I/Ich/IchQ+8oEUVUhcJkBIwA1JJvJO0NyNCrkFgMZmtExnkIq3LFoEwsRhZdCM56UrFQexFkX1fjupvEs6tqhGevRQm45M/Hg6qMKwFyUX2SJGxq3Ej4E6pVq7GJOMHRGJlnbr6tGo1mm0KLI80m2f/YvRl0fn++fv0HQqVhhCHw+DxEQlECWQGEEARLQjx+0Yu8PPOxVLisJtrs1IrHfxjBN6N+pKywnI492jHg3EMwDINfv/qdO497sEIYgVsJliAhkmNK3KZ+7i7SkcTCcSZ89CuGaeDL9JHTJBvHtgmVhslvmccd711HWWEZV/a5VXXvFoI2XVtywpVH4TgOiUQCH76U6BBCpDpZSyAajnHjqCt44uIXKS8OYpgGfY7pzaTPphAPxyvbuxGO7RALR/EGfClvltfvqXbf1l1a0q5bG5bOXo7H56lRHNWGdCSJWBzTUj2VwmWRSq9XdP0G6RQhiy5xJ9wDxIEswILoOJzESoThB5GvhGzgVJUXFBqt9jVyETl3IaydKtaMzUCG/qu6ZltdIToR7GUo75KBjI6DgjcRZkXzS1n2uBqQKTLdhpNhCI1G+gciPD3qfQ8AhJEHuY8jiy4FucHd6lM5VE4J6g0UBgzwHozIurT2BRPza36t7B4c36EYlq6GqwmZWAqRL5EyhPDsAb7+O2RDThmf4w5GzlE5c3qUzHbJdlXKP378eI499lhat26NEIKPP/640utSSu68805atWpFIBDg8MMPZ968yoNJCwsLOeuss8jJySEvL48LLriA8vLyrXgV2x9CCC5+5BzOuPkEDjppP06+5mg1RsPrNlY0BN6Al7KiclYtXFunNZu3b8Z/7jiZy544jyMvOAzTMvlm1A/cc8pj2IlqvCJurhGQ6qydxE7YKXHi2A7R8ghSSgzTxHJFXKg0xKPnP095cQh/lh+Pz2Lh9MU8NuQFQqVhosEYZYXlKc+UMAXCMFwvmaRlpxbsPWAPXp31JE9Pup/XZj3JRQ+djePIWoVRkvJiVcIfKguR2yyHPsfuXe1+pmVy2RPnYXpMJWoEqQab6UVchmmkvheGILtJdqURK/Fogl3325lApi/1ADIMgdfvYfJnU9QAXVxREp+z0Y0ud0v9w7D+GOS6Aao5Y2I5QhgY2dcgmn2NaPIe5L8Jwo+MTVODamPTldiKfKPGgoTeAnseEEEJrwjYK5HBUZUvPLFAXU/yYSkFyCCy5GZk+fNIGWNzEN49IOcWwAciD4wctTYmeA9A5D2HKHgDkfekGktSA1LKTfRIst25c5rqkPFZ6j0UfAFCbyBLrlONOncwZPBNdZ0ldyBLrkEWDkE6RY1tlmYz2K48R8FgkD322IMhQ4Zw4olVp6w//PDDPP3004waNYpOnTpxxx13MHDgQGbNmoXfr9T7WWedxapVq/j222+Jx+Ocf/75XHzxxYwePXprX852QzwW54GznmbK19NVJZhp4Ni260lQD2Q74SAMQVbe5s2WikVivHrLW6mQXXVYPhPHljjViac0HEcSLAmRmZNBPBwjGopyx3EPEg3F8GV4MQyDkuJSN2lbIm0lbhLxRIXISjgUrSnGG/DStVdnjr5YjcbwZ/jokNZEsk2XliyZtbxO1xh1w5JD7juTZm2rJjAn4gkW/7WMZ4e9Qjwax+O3iLleKcMyKl23YzuVxFEiGscwDDKy/fQ79UD2OqwnobIQf02ci9fvwZ/lx7RMErEEibhN8doSsvIyVdWVMFSIjGSfpqRHBVzFAvE/kSXXQMFohPAgjAJkbIbqjJ1sDunZXfU9kiG3J5JI89CkrYUN0R+Bm9XZwp+4TRvdJpD4UGIKSCxGlr+kxFPuI5USxeuK8B2GtN6AxLyKsn7hQ2RdjPDuW8dVHPc6/BW2VTlRdr1t29aRTrl6jxAHq0eV2Xd1XqfsEfVeEFlujlcYQiORgUGVvI7bMzI+B1n+JCpMm63+TfyFLHsKkTu8cY3T1JvtShwdeeSRHHnkkdW+JqXkySef5Pbbb2fw4MEAvPHGG7Ro0YKPP/6Y008/ndmzZ/PVV1/x22+/sffe6i/3Z555hqOOOopHH32U1q1bb7Vr2Z744v/G8ttXv+Pxe7E8JvFonEQ0Dm44LfnHfv8z+tKs3eZ9eBauKaasqLzWMJIQBtn5AaKhKNFwDCPl2Um+XtEtOxaOgZQk4jZevweP10M0FEtVkiUFUTrJOWyVN0pue/sqMrKr9yr0P6Mvo+58t2bvUVpSdU7TbGKhGKPv/4B+p+6PaVYMQV0yezl3HPsAa5dtUMNk3ZBYkmoFobuux+fhtBuPJ5GwWTZ7ORM+/oWxb47HjidwXG9bLBonuyCLWCRGRk4GTVrnu/blqEaJIhtkKRVNGV0hYCSv21ACJbEQaXVVydnlT7v7Z6hqsPh0dyyJUeEBquScTrsZjgpzydhvyNJ73P2SryfFh8/tlh2D6Dg16sRT/cy+2hBGJuS/qB5c8VlgFCAyh9RDGIEQJtK7F8QmgbSo0vBS5IGvf3WHblNIKVXIx9kAZjuE1b7mfRNLkcXDVDdzJIh8yHsU4a3e61kr9mKVwJ96X/iAoPLG7SDiiMTfSnwn/zDAAkdAfGZjW6bZDLarsFptLFq0iNWrV3P44RXDL3Nzc9lvv/2YNGkSAJMmTSIvLy8ljAAOP/xwDMPgl19+qXbdaDRKaWlppa9/G4tnLkVK8HgthFDNFC2fhz3778bOvTvTqWd7Tr1xMFc8e0G9cgjWLd/A8BMfYdh+NzP8xEc2mV8Tj8Qp3VBGPKryaQZfMYgeaSM0kgnHyVBUbtNsLJ9FRm6GGk7rNleMhWsJ0ciK40F1vf5pTPXvDYDDzuxbe1gtraUA7pSMlfPX8N1bP1UIuWiM6/rfxaqFa91EbyWG7LhdVaxVgx1PcOzQAUTKI/zyv2nEInHi0XjKywdK+JUXBfH4PAx94rxUXpjIPFd1opahtBXdsSJEVXhtowuS5c+4VVvJ5opBlFgwwYm5GseGKvcl2dDRUA9acHscJdxu2/mo2W2uDSKn4iGD43qhakcmFuKUjsApugKn7Emc0Bicssch8iki62qMph9iFLwCnt3VX/p23cLAACJnuKqiEz4qPjoFiHbQ5P1tvpxfygSy9E5k4VnI4suRG06sGt5M37/0djXdHT+QCU4RsuQmpBOq8ZgaMVpt9J6IARYYm2hAuj0hspUXNlm5KSXggJHfmFZpNpPtynNUG6tXqwGeLVpU/mVr0aJF6rXVq1fTvHnzSq9blkVBQUFqn4154IEHGDFixBawePshuyALISASjLj5QOoDbq/DdueU647drDXXLd/AkO5XESmPbnrnNKQjsV1vyA/vTqhyfNJ7JKWkcE0JdtwmGoriz/STmZtB6YYyDENUqXrbeA31H0AIitYUV2+LlHz52ri62S0lZevLUs+GZ4e9yqKZS7n44bOZ8PGvlKytQXTXoTm0nXD46rXv+eHdCQjDwDQNEOq+OI5DVl4mkVCU7IIs7v3sZnbu3QUnOhmKrwZZtNFJXOEiy1HiqBTIBBkFqwvSaAKhN6nw9CTtDAE+MNup0Jr9t3pdmEAztzKswpskMk5SYbnwR0BIhdNEtgq7yCgVc99QIRjhB6tjrffBiU6B4kvU/giIjiXpAZMCCL0HBSMh/gey9G53PwMZOA2RfS1CmLWuL8yWUPBf1VVbxpGJJRB6GZxiKL4amXMXwrtnrWs0KuF3IfIZ6mM/AESQ5U+Dpztio0o9KWMQn62Es0g+JvzgFClPklE/D57IvhZZfAU45e4fIAICJyuxuaPgOwCs7pD4S3mMcEB4EZnnN7Zlms1gh/EcbSluueUWSkpKUl/Lli1rbJO2OoMuOBQhhEpcDkWJhlSJecfdVO5NLBLjlVveYkj3q7lgt6t5Y8R7JOK1e4Eeu/AFJWwE9fI2pVO4sjjlaQFSlWAAGbkZZOZmgCBldyQYIZDt5+w7T0k1kawOmUyylqo7d+udqlYg2bbNExe/xDsPflSzgQJVlZbMMXY1iC/Dh2EZ/O/lsfz58xw2rNj8hE3TMvH4PKl8MCFUsraACq+VKbAsk1327qKEUWIFFF2I6hxdnXcn5nbFNknNPPN0U0nLMqi+x4d6yCa9QY5qHZB5NqLgdUTO/YjsGxD5LyGavg/ePko0GbmIrIuRviNUyCaVrOooASVLgQwwmoMMKpEmvIic22odcyITy6DoYpBlKC9W3LXTcR/uGeCsQpbejyy5Uz2kccehhN9SwqEOCOFDeHupc5Q/5LYJkCrcWHyFqsjaRpGxaW4I1e+K1gzAgdj0ava2QHip3L/KQf3CZlazf+0I796I/Fch4wwIHI/IvhORfesOVa0mhA+R/4ISfZ6u4O2DyHuq3p3jNdsGO4znqGVL9QBbs2YNrVpVlAivWbOGPffcM7XP2rWV3eiJRILCwsLU8Rvj8/nw+XxbxujthGBJCCnB8qi/rA3LQCD44Mkv2GfQXjx31Ui+H/1T6oPu/cc+I1gS4rLHz6txzXXL3G7YMi0cthlEqwmRGaaqyjIMg4ysAOGgqvoqaJnH+feewSGnHUjx+lI+eup/Na5rGAamx2TvAXtyxDn9qrz+9Wvj+G70T7X2G0Iq8eI3DSLBCg+XEphgWgbL/15J5z07VG3WmIbX7S2VXsUnDIHlsfD4LOy4jeWx2PfIvfjh3Qk4poPlsYjHEggEsXCMrLxMzhl+qjo49CYVna6rMTqVFO0F/2GI7JvAaIYQBlJG3DylIlSH7GTXbA9k3QyBU91E/aMrrSoKXkHKOKBCswRfRTplIHJRHqpylJAxEDkjkN4DIDxGhdJ8/Wrs6J2yuvwpIBnuMaiY1UZqXRwgPledL5kYjAecUmR0IiKj7n2KZOR/IONp+SU+Jcyi34N1Xp3X2aokRY2Urs2usK2m1FwIAzL+gwz+nxKSyf19h7pDfTfj9J7uiFqGHO8ICCMXkXNHY5uhaQB2GHHUqVMnWrZsyXfffZcSQ6Wlpfzyyy9cdtllAOy///4UFxczdepUevfuDcD333+P4zjst1/dh2b+21i/vNAdq5GdysUJl0dYs2QdxetK+H70T9gJ9YD2BrzEowm+feNHzr/3DPwZSljats365YV4fBb5LfJo0qqApbNrK43efBxbUrKulOz8LBzHoaBlPq/NeZJApj8l4PY/dm8+f+nb1HiNdHGyU69O9D1hP7LyMikrLOeVm9+iY4/2HHH2wZiWEoh/T11Yo3dMGCKVLB4uj1RZP4mdcPjtq9+5/d1r6XVYT6Z++0e163kDXrx+L5FgRCXDu40l49G4m39l0v+MvhwweB+CJUGmfTcTj99DXvNcdjtgF1p2as4R5x5C+25t3Bu0qbw5d8yGkY/IuqKSx0YIP+SOQJbcDE4IFYZrBoFBYC9Elj2MFKr3FZ59U6JGOkEQVoWnQMYqknOFn5S48B4J/iOg+CaI/QQ4EPlIhaz8g5T5TgLK7ob4n8rDlXUlJBZRIYrcxLFUIyzpeslskOtV7otI7pO8sHp+FCZbC1TxfNS/L9XWQgQGIyNfukLUVPfBKFD3uzoyL0YIHzL8KRBTvaCyr9qhvD0aTU1sV+KovLyc+fMrGrEtWrSI6dOnU1BQQPv27bn66qu599576dq1a6qUv3Xr1hx//PEA7LrrrgwaNIiLLrqIF198kXg8zrBhwzj99NN1pVottOjYDMtjEovE8GX4VNjJcWjdpSXvPPRxyisSjyWIuPk90pEq1yfDx/K/V/LAf55mxbxVCCHoPWB3LnzkP1yx7y3Vel6EITAtg0Rs80dSSEdSuqGMQLafSx87hww3AXnqtzMYNfw91i1dr/5oNoV7PepB6s/0c/N/r8QX8HLTgHtYt1xVjyEl7z70MU/8fA95TXNYOnt5jS0FDMNAOuo1wzIwDAMnYVfrH/v1y98xDIM7x1zH4LxzKxLCRYWYioZjxCNxhCFo0rqADSsLVUsD994ZpkFBq3wyczK4473rWL+ikFgkRosOzbA81fyKe3pC5P3ab6DVRYXRqqlmEr5+UPAWxH5FygSEP1b5PKlQFkgs4BVk1lCITYX472q7/whE9m3g7Q1BA5Vv5Adiymvk2xdZ/gLEfgC8gA+cELLkLrB2QRodYMOxYC+oMKjwV9VGIPVxli5QBCph3CU1QiQOZLhhJgvhr74KtiaEtw8y+q2bt+R2ChcWeDbRZbsREd7ekPe48rLZ68DTGZF9i8qlqm5/YULmEERm/ecnajTbO9uVOJoyZQr9+1eUy1577bUAnHvuubz++uvceOONBINBLr74YoqLi+nbty9fffVVqscRwFtvvcWwYcM47LDDMAyDk046iaeffnqrX8v2RJc9OnLMpQP49PmvCJeHkQ7kNMlm8OUDuf/Mpyp5RaQjiQQjdO3VmZwm2cQiMe494wlW/L0Kj8+DdCS/fDGNQJafI87tx9fVJDQbpoHH6/lH4ihJVl4m+x+nqhNnTf6b+854klgkhpSqeSROMlwoaN6+KXeNuZ4Ou7bl8YteYP3yDSSiFb2Plv+9kiv73Eqvw3vy18S51Z5PdQ+3iIZjBLIDBLLUe69kXSk4Va8nGorx99QF7Ny7C/1OOYAf35tYKXdqp16d6LpXJ8LlEbrvvwv/e2Ustu0gEJhek0Cmn2goyi+fT2XB74vIbZbDwSf3IZCl+ij9MX4Wf/48B4/PQ98T9qVV5xYI3wHIsvS+RtWQWFBrmbewOoPVGUJvIRN/A1Eq5y8l1Ff5E4DXzV+REPkf0gki/APBfyyEvwARBiwI/AcCx0PhmSAFGMlwdpbyKsVngjMhTRglq+psSCwFI9etaHM9G1ZPtW5iqvq30ggRRyWRG80QWVcqe+pD4ASwl7idwqMg/IicG1XTyW0Y4TsI4Tuosc3QaLZ5hKxLe19NitLSUnJzcykpKSEnJ6exzdlqSCn5+aNf+XvKAjKyA/Q77QAWTF/Ew+c+i+X1ECoN4ThqGKxhGvzfH4/RYde2zJ++iOv63YXls1JejEgwitdv0aJjc+b+WnUkQ3I8Sa0l96i+QaHScKUJ9tWt1WWPjhx4wr58/tI31SY/B7J89D5iDwI5ATp2b0fbXVrzweOf8+eE2Ti2rOLJqYlAlp9rX7mUVYvW8tY9H2B5Kiq64rFEjddz3NCBXPGsmkz/4vWj+OHdCQSLQ2TmZdDnmN6cecuJNG/fzK3wu5qIG6pLRo4My8C0zFSQqHPPDtz/1W2Me3sCr902Gsd2kNImK9dg+Nt70bXPcapHUfRHKsrxN8bCaDmr1usFcMoegeBIag8nZYCR5R5QghJSJmCA2RmyrwajhYpQGU2RJbdCbIrqZg1uO4EQIvcRZHQihEe7F5+WNwOq8snsBEY2wuoGGaerqrTwp64dbhUboMrTgewbMDL/o0r67aWq7NrsXOfQkbRXq3lwZmuEUVCnY7YHZPQHZPANlTDv6YnIvgaxiZJ0mViuwpdm2x3qXmh2HOrz/N6uPEeaxkMIwUEn7sdBJ+7H6sVreeicZ1g4YwmRUBQRieELeFVvHtuh8+4dUl2kDcOolP6hUGGsmnKOpFt1VRM5TbOJhqLssncX/vx5Tq3iSDqSpXNWsOCu92rsRxQuj/Lzx7+qMJsh8AW85DbNSVWXCSHqVFLv8Xl48OxnUhV0lZoMCJUo7lTTfHLBjCU4joNhGJxy3XFM/mwqweIQ5UUhvnvzJ6Z//xeP/TBC9UZyVCfy9BEqTsKpFOKbO3UBL13/BhM/+Q3HdvBnAjJMebHB89eP44nPxkDOvSrfJPwZKql6I4y6NfMURqs6JNQnc3/stLtiqy97LpQ9B3IV0gkDjmoHgACnTIWqZAKsduDdH+zVlddM776dWAr2GkT+y2B1RZbeB5HPqSyKkrt7gQRCBpHhj5ClD6GaT5rgPxJy7kKI6uffVVrGbAk1hKW2V2T0B2Tx9W5elaFyyRLzoWAkQlQtTpEygSx7wB3Ma6scsuzbEIFjtrrtGk1DoUv5NfUiHotz72mPM//3RYhkg0FbEimPEovEsRM2O+/dJbV/u26t6bBrW8LlEUo3lFG6oYxELEGvAXvUKoCikZq9RqUbyoiGYkwf9yexSC2hIVTILBFL1N6oESqe347EcSTF60pSni5Zx/lppYVllVsLJBFgCEFGTvVdtv+aOIcHznqKRDzBV69+z4ZVRfizVF8mf5afDSuL+HrkOILuwNvcpjlkZAXwZ/mrPPOT9k78dAqJWAJvwIMgiBBgWrBikRdph6DsIci+Hcw2VLuI/4xNXi8AgRNV2X2t2OpBK8tqePlPcIIoERNxZ7GFVem/CICvDyLvRYSRrUJvZsfklVasIXJUBRpRZOQTZNn9EPnY3cVDZTEVUDYhkCILWXq/W6EXAIQSVKH/1u36d0Bk8I2KSjwjCwhAYhbEJld/QOhtCH/gfuPmiJXejYzP3lomazQNjhZHmko4jsOfP8/mx/cm8vfUBVVeXzp7BcvmrsTr96pqrY00gyEEP747kaVzlFfI4/Ww96C9sBO2musVS+DYDrsftCt5zXNrMUS1YkkNXU3HrUQOZAdS1WM1IaH2cvtqiEfjRMMxbLtuHaornawaTMt0572FKw2HTT9u/JjJfPLsV5RuKEPgetzcf4VQOUud9+iIlBALR1UozTRqPGe4LIxhGSRicUC6OVZQ0NxBGF6wN0DxFW6zxjTvS2qB190qpdoRRgYUvA9Ge6r/OBFueMym9tBbej5Q8r4UQ/4rGPkvIaw0T2STz8F/kuvdMoBcED71ppBSlZ5HvgZMMDJB5AGZ7r4BklnvIvMiBBKIqzJ3YSqvh3SQsd82ee3/BJlYrob02uu36Hk2C6cYN5bsbjBV4rpTvbiVsclp/ZMsV6TGID5tKxms0TQ8OqymSZGIJ3jk/OeY9OkUHMfBtEx2O7Abzdo2wZ/h49CzDlLNE92E4Y1zaIQQOI4kFo2zasFq2ndrQ7A0xKfPf5UafopUnbbfvGcMex7ag1UL19SYy6MmV1S85vF7kFKSiCbwBrxYHousfJPCVdV7mSyPWWsn7JpI2rOpAbfpeAMeYuHqvVhJb5Kq4EuO11CvCSGQqMqzP8b/xT6DegEqR8njVb2KJNB+1zbsP3hvCu7MY+V8N7QkwPJa1YYVs/IyOeLsg/n8pW8IlirR4vXBeTeXupVaBsTGUVmQpIkkGUaWjgAjD+E7uGIP6SYyi8xUXo5hNUc2+wJZ+ohqqJjMJxJe1wPRElHwlAq9RMfWcAeru9eOmqm20Tw1w/BC3gPI6ETVSBLbfbPEVT8jTw/Vbyg1mdftZyQF5N6veviYHRCeXZCh90mNeSDihpLstOaUDYuUDrLsYdXDCRvwQvaNiIyTtsj5NgvP7irpXbojYQgp8VlTN2vhCv70/klSojx2Gs32iRZHGgCK1pZw0xF3s2im6vArTIERs/nty9/x+CxMy+S7t8Zz03+vpM3OrVg6e3nVUFMyQdgQ5LfMU+uuKSERTWB5LaKhGHY8oUr+g1F+fG/iJpOc00n29EkhVbZLdT2EDNMgkB2grLCafJoGRhiiRmFUeUcq5Q8Dle5hyfpyBg3pz7SxfzDl6+nEo3EEsM/APRl4fn/efegTNqwsJJAdQADxeALHcVIOk3SOvOBQzrvndNrv2paZ4z7Ba82m//El9NjPLTmXNlX6AaXszAG8IMuQka9T4kiGP0KWPaX65BjNIXcEePaG8LvI8EfKGwVAFhjuR4uMgZmJsNohA6dB9AeqDm3NcnvvVHfPsmq+nb4DIOtyVfpPGDAhcBIi4yxk9HvVPkAmk+LV+BM8PSD2Czi/I4UJvkPAeAGcVVSaI5eYjQx/hAicUOP5a0JKqQa7CjWKpVJyd/h9CL+jbMUHMqJEo7XTNlPpJrKvUhWIiVmuR8iHyL4O4ela/f7+Y5DR8e7P0KMEsVEAaaJao9ne0OJIg+M4PHTOMyz6s2L0gbQlttuzxvJY+LP8hEvDvDH8PXofvjsLZyyuGgVxpErcPmk/uvbqDECT1vl4/BbF60qr7F/vUn1Z4YWJhmJEwzFMy0Q6EtNjYnksDENgmAaJuM1FD/+HL176lrm/VQ0PNiRJ709d7K+NBTMWM+Hj37jt7auZ/PlU1i5dT4sOzdjv6F6YlsnMn2cDAn+mSor14SNUGqJ9j/bM+31Ran3TMvlj/CzC5REGDTmUgef3h8iXyNgEwFQCofSeGozyosREGOVBUV2nZfQHZOm9ypsgvOCsQBZfrUraQ2+phyigPDDF4ARIju4QgdMAMPz9cLKuVZVyxJUtgRPVV9GFqNEh6Tc2EwKDa71nIvMC8B0B9jIl2KydlBjJfQBZfK2aQC+jgAUyDBtOdLt8C8CPyL0bsm+DkmHuiqY6r4wjy/+v3uJI2muRJTdVTGL37gu596UqvWTsV3WvktV7ZAJBiE+BeoojGZ+FDI5U1XLWLoisSxFGXr3WqA5h5KsZdLFfVMNQTzeEtVPN+/sPA3knsvxZFZLzdEPk3FVj/ySNZntAiyMNa5eu58+fZ9f48BaGQAiBYZks/3slS2Ytw+P34jUEkfKIEieWiT/Ty7GXDeS8u09P/bUsHUlGTgbFNQ1WrSNJ75BpmVheU4Wn0sSSHbex4zb+TB/RcAzpqKaNHXZrj5i6sF4eqvpS35ym6sjICWAnHD588nP6nbI/Bx6/b5V9fAEv8VicojVRkGB6TAzToGuvziybuxLTY+Lze7FthwUzlvDJs19x5q0nsnTOChb/mU9W3qns3q87llmM5CGqzfMhRqr7M0B8KtJegwx/Q6VxGdJSZfnBN9UaRrbaX7oiRGSAWYDIPB/pG1AxcjbrQhz/IAi+rAREYj4i/iey6WQoudoN9dlqWnv+yxhG7eW2gOrHtFFPJmG2QuaPgpLrXG8VYC90rzcDNUy3XJX65zygwkYEVN6RuhCV81QPpHSQJTe6DS/dUFNsArLkDkT+s+5eaSEoJKopZQwZ+QH8gxBmm7qdKz4LWXShOyNOQHwaMj4N8l9XeWCbiXQKwV4ORtNK4dRNIQKDVQdumUDUt9u4RrMNot/FGqQja83N8fq9qorLtrE8HmIJNekdwOf3EiwNcfpNx3PajYPxBSpKfaPhKA+c9RSrF65pEBtBCYRweaTaaBCQ6tZteS0KVxezbtkGkBJfpo9oKLpJ701jES6LIASsWbKOojXF5LfIS70WLA3x4ZOfM+Wb6ZXyoOy4nRo0axhG6t4rb5rDqoVr+N/LY3nl5jeJReMYhsEu+3Thrg9uICNwVDXDVtNnkinPCk6xO7fMDYUJgcrxKVb/ElP7Sp+be+IB4SAKXkLaxcjSm6DkVhxMdxDn4xAaBZFPUrFAmZiFkKWIgmdpSIRcj4xNdJO1feC4/aGIAJmqEk6G3CTigOsly0K9SRJg7V6/EzprXI+R1xVbqGuMTUY65QgjCxE4xu2sXUalxpnx6cjCc6HgzTp5XFQPovI0sZpQc+Oi30M1JfRSxlFdvLNq7OEkwx8jSx8k2YdKBk5FZF+vcrTqiBZGmh0FXa2moVm7JlWTVlxMyyQajhIORsjOz6JD93aVCriSc8SatW2SejjP+OEvLt7jOk5tdRG//G8aVFdxVgcMUw1/FW6oDFDdrTelcITq4B3ICrhJ3GpzfotcDGvTtmTkBGjXrTV5LWqppqsrdbx0KVULgQ0ri7h4j+uZ8PGvgAq1Ddn1at4Y/n61eU1CCNVgMhqneF0JJetKKFlfSjwaZ/7vi/i/m/5LIm6TkZOBx+9hzq/zeWP4u2o4pshCfQQkK4ySneSzQTRxvUEGJP5W+T3CVGLCKSFZCq9yZyTIEvWAlmEQOUgnokJVzjrcia8Q+wlZeD6EP1LHGdmpc8jgG+5g2s1HRsbirD8BZ+3BOIVDkPFZyjaS3qDkDyP5/lGhPWF1QmTf4Vaqlbs5Va0QOXdWXj+xFBl6GxkajUwsrMaCTf+whe9ARO69KO9V+vs4DvYaZGh03S7W2UClijJhqf87hZVtlg6y/AXkuoOR6/ojN5xYbYm9jM9UfaFSQ4elarYZ3sSYmS2A7kus2RbQMl9DIm7jC3gJlUUqvyDg/HtOJxaJ48vwctDJfZj6zQzm/DafkvWlKtwGZOVnsXs/NW178V/LuPf0J1QXZ0GdewRtjDCECle5Dq2kIKpv9ZnH68G0DJyEQ7AkhJOo3ZYue3bk/v/dSrAkxOcvfstnL35NPPoPholuxud8WVEZj134PC06Nuehc56mcHUNlVPuc3HDqiLshF0ldLj4r2XYcZvcZtkIIbA8FrFInNm/zEcIL9J/rNsLyHLtjJMSPMKoCP0YzcE/GGGvUDkuJNR+IhuVy+OKJRkEIxeRez8y9pMSSkDFmA8JiTmuhym9maCp1pQhENULUmmvd/vsJMDTq8poExmdoIbhyrgScfGpULYEFT4rVf8SIDVnTZapawycgjBbQaAVeHaG2Azl9fEdUKnLs4z+giy51g1jAUYG5D5ceRSH0VzNrYv9njxK3VPvQQijIrFc+AchrZGQKKz4IeIAUUgsq/b6q+DpBrFf3RwwyxU1BmycGxQajQy+5J7HgsRCZPGVUPAOwmxSsV/sNypaGrj7OqXI6ATw7AlGE4RZt8agm4tMLFBhzvjfSCMPkXXxZiXEazQNgRZHGvwZPpq2a8rqhWswLAMnIbETNpl5AY66+HCy8ys+2KPhGDgylesjhKDfyfvTZqdWAEz4+FcioSiBnICas7aZoazacoS8fo8qXxei+nwfqUSUYQqCxUEcR2Jabml+DeE4AF+Gj/0H782V+99GWZEaUNoYE8jtuENZYZCrD7xN9Vqq6VZI8GX6mDN5Hv4sP4logni0wvuS7ENVVhQkt2m2Gk7vOGTnq5CoyL4BmVgA8d/UYhggWoEsrAhBGZmIzEvUfci6HBk4HdYf6woZ19MkM4E4ZF+P8B+BMFu6yd9Q8fBP71+Q5fbMcT06MgZWJ7dKrprLjM9WJfv2OrWOkQm5DyJ8ByOdchW2Cr7sho1y3TCTA/Z6yDgdIl+oxGIhQDQHcydVgeU7EDLOSp1HWDtVFReAlDay9HY3jJUcg1KOLLkTmn2NcEvZhTAg92El0uJ/qP28ByJy76lYK7EMWTocEtMrfogp8YgSOnVAZF6MjE2FxF8ghVojcIbqIp5ue+R/SuSmxJkH7LUqAdxMnye3Uf8tKQEbouOQsZ8BE5nxHzWHbgv8TkinEFl0mQpNYoG9UhUAiIz6z73TaBoALY40AFzxzAXcd8aTREJRTEvg8Vtc/PA5lYRRuDzM2w98hOWzyCrIQjoOkVCUH9+fyEWPnI3X51F5MO7+aoCs9c88L9UgpaTPsb1Z+McSVi9aV+V1f5afWDhKPBrHdnN0Ysk2ALUINek4aiaa18Kf4SMWiZNI2Hj9nk124t4SRDcxW04IgcdrUbKuFI/Pi+UxicfiFdfo/mvHbUrWl2F5TDxeD4OHDXTDQ+9DYjbq4ZwMj20AX1/1vZGPyDgN4emROqdhNkFmDUGWP+eO9zBAOODth8j4T8WD09odJYgcKt10kQU5d0Pp8Ipmg2YzRO79NefClNyuhA6ZQBk465FFFyJNt7TcWY6qSHO9L/hJhpyE2QaavAfxGeqavPtsckZYFZwi9SW8FWEsfMojZa8Ft0ElgDCbQ/6rIItcG/LSihPKkYVngbO28j1J5XkZ4D90k+ZIpxxZ/owSg2Z78OyJ8B8N3v2quYfVeVolldoWgGppEPw/N2Tqce9nAuUZjAMhCD6PjM+B/KdSgrDBiP4M9hrXc+WKRVmGDH+sxZGmUdDiSAPAnv178Oi44Uz6dArF60qYNXEur94ymncf+phTrj+OQUMOpWS9Gv1heS2EEAhTPWxj0ThlheWsWrCaBdMXEQ3HCAcjGEbFLDHTYyJtB6cBqsbi0QSTPptaJU9KGALLMhECWnVpyaI/3NYErtOi2tEeaSTiCVURZxpEwzFiEVURF0uoeWaWx8S0zEqiq9EQSiQWrS4BIBqKYnqqF3+q0s+ha6+unHbj8ezb73dk2bPuQy95T5I5RFI9pKydITpedT8OnASZF1Yk22YMQeBFhj8EYuDti8i6KvVgduwgBF+hcoI3QACR9wTCdyCOtTNEPlNeI/8ghKf6BoNShtVAWOFxk5jTBKM9z73AfPdcQTeEZrr7WeDdE2G2BrN1PW9wGkaOe/5kPg6khEM1pfNCCBBVB6/K4uvAWV1lu7rvHjDbI7x9azVFyjiy+Crl+ZGu+LRXgq9vteJS+PojE3PdEKcbfjPywNur8n5WW8h/Flkywr3fGe7vl4FKYHffG7EfkGWPIHJuq9XOeiOTlZNpjTulcO+5RrP10eJIk6LDrm1p2jqfaw66k1WL1mBYJuHlYV68bhQen4eDT9mfjBzVWDE5dywejZPXLJdZk//mof88XcnbkT5kdVPCpL5UF3aTUhKPqSaTi/9cWpHrVEc9lrQ3HktgJyqHs6RUw3CT1XANhRCiIr+qxp2oeg3VXJMdt6s2xBQqXGiaBjeMvJxWnZsj112pXhA+VyBttGjiL0jMcwWBRAZfROBA1lDXZgMyz0FknlPFBmmvhsKzwV5SzYUkkGUPIbkBgiPdkncJoXdxsq9BePdV0+2FRyVnhz9WA09JqDAZNXnS4u7DPAFEXYHkgMhGht6F7E5qLttmIoQXMi9S3hqnBHXvTETm+ZVyiWrDSaxy2xRUewbw9kLk3L1pO+Mz3LEcfjA84MRBliCLb0D6PkJkXYvw7Fyxf+YFKik+/AkQB7MpIvfBaivihGd3RNMP1L0PvY0se4wKYZR8Ewr1c8m6qs7XXie8e7rVg+WoZPWEus3e/RruHBpNPdDVappKTB/3FysXrsHjtbA8JhnZAeyEzdcjx+HxWhx72UBA5fKEy8L4M3xc8dyFPH/la5sMA21x0jRBujCrL3Z8ozyf5B/NDRweRLheHSmrnyGXJM0Wj7/2kQw5TbIxPWm/1hIi5RECWQFVlZjqY2RQqZ9RlRO6nheRCQhk6L1NJtZLGUcWDqlBGAHEIbEEioapBGDpAelTOU6ltyM3HI/ccBJOZDJyw0nI0rvcBpNRVFPKmk5sk2zqqP7eM9z/OxD+HFlyE45digx/gQy9r3oEyTiy/EWcDWfhFJ6PDH9Y+/VlnI/IGQG+/uDrh8i+HTIvrfV+VKK2Qba+EzEK3kBYHTe9juMKP0xXDJaQCifGJiGLLkKmJXUL4cHIuQPR9GtEkw8QTf+H8O5T6ymE8ICvnxIrqZbuSa+OV51P1vLz2AyEtRMi5y6V6E5YncN3JGQOadDzaDR1RXuONJVYt3wDsXBM9QSCVBl9WVE5r932Np8+/xWObeM4Dk3aFHDzf69k596dKVn/z5o8btNslMPTkOvWeyjuJnKfgiVBHFumRFfS5uJ1Jbx6y1ucf+8ZeMyOkJhJ9fPM0oyTJSDdhyEhKoU9qjsi9hvY8zdxBRaQHGDq9khK2iGBxCIoPp/KuTJe9zgnzeZ0d1rCTZaWbqjJq5K2AYhCdBIUnoq0V7rbPKoyLTGbZEWejE9HOOVQjTfMcWJQehck/lRiMevyylVqdaKWn1v2jXVfxuoK+FVCvLLO/deLqswrUeFK18uXRFWmNaGuCKsDMu9pKBpChccuQ53PbAdG3deq8zkDR6mO4vYSMHLB7NIoBREaDWhxpElDSsnoBz6o9Be0dCS2Y7Ns7gpWzFuFaZlk5mViJxyKVhcz+bMpdO+zM/5MP/Holp9jtj2R3zKPotXFW+18yVww6djkNc8hWBwi7g6ltRM2X/zfd4TLIlz1+CAon1GHFSUqwRn1MI7PrH3ERWJTwggqhFGSNIEmPK43JCmMklVcccADVg81DoQwqSovq6fqlSTLwdMLQmM20m9uzoy90vWCGWrf+DQVikuGhpxyZPBVyDi70gPZcRzYMFgNYk1SdCFO4GxE5pkIq3MdrhmE1QlZJQcLMHfBsPIqbZLShvAYZGwKiAAicGzK2yOsdpBzC7L0fjcHC1Tuk9uzSgqkLK9re61aMXz7IpuMUaX/ySRyoxUi92GQIZzy/1NhPpGLyPyPCov+Q4TZFNyWAdIpRwbfQtorVBgw48wGGY+i0dQFLY40KSZ/PpWiVSXVvhaPxREY5DVXowksj0k0BAtmqBDKPoP25PvRP281W7cHitYUY5hGg4wXqQvJ0n1QuVFJYYRQ7Q9My2H8mPFceHtbMutVbKRCVbL8aUTBqzXvZral1l4JVUjfz0xLuk5/Pa3izbMrFLyoRpY4JeDtjfAPqtTB2UksgPivIE11bLL/jzBJjQaRPiCsvEypTgOW642JU6msPTImTRglxY2E8BvIyKeQc0vdevH4j4Pge2DPJZX8LrIRBS9WviNSIsseVM0Xk16tyJeqp5K/vzI1cDx4eiCDoyD8saqik8nEaYmwdtu0PXVEeLpBkzEVs+I8PUEEkEWXql5SSRtjkyDvKdUstAGQTjmyaAgk/lZ5b0JA5GsoGKUFkmaroMWRJsXqRWtrfM0wTOyEjZ2w1XgK90Mxr7nqTeM4smoy8L+dzQib/dPzJUlPHDctk0BmhETcQToQK/mCzGb1WdivQlbOKmRsCrLsEeWJMVuD2RmclSCywT8YjM7gbGrQrztqRIZJjSXB6+bTpIfTkrkuQLIpoFEA2VfWuLLIvUd5OhJuJZvZQvVjSixRa4m0xGLiqYc7Mg6eXauWqKe8YWnhv5R5MTVuw9MLYXWo9YqFkQVNRirvVGIxmC0RGedVnaVmL4LwhyhvkF/ZJ8uR5U+mxBG4PZlyRihvVOQzUo04RT4yNk1VrxnVN9SsL8LIAd+BFZcdnaCEET6VFC7dsvvgq/9YHEkZQZY9o5qTOutR770sVFXeEgi9DVmX/aNzaDR1QYsjTYqClnk1vyhUs8hkLpKUkkB2gKMvOpyJn/zGinmrMEwD22nYqjRN7RimcJs71ixKPb4Yju2QiBl07BYht0l9ezaVKz1hlyGLhilRI0y3cup3VHm7UB2sMy+D8peA4nQrSY2kEH7lzZE2ykNjqnBXKuk66QVKyy0STSH/rSpCQtqrVXgpMdMN7QxBBI6DgjeR8T9BxhGe3SD2qxoIK8tItX+wdgNnlduMEiVWcu6hClay8mvj+ytUWI6g8m5sQhwBCCMfkX197Ts561ECMeAelOxWXfUPFyEMyBmOtDpD2eOumUEVkkvMhYLXGr4fEaj+VNIh1bBSuF3VnQ3/aFkppWqsGf3aDa9KUu8JkanywO3VDRIy1Gg2hRZHmhR9ju1Ns3ZN1LDWjcjI8nP1i5fw54Q5LJyxhCat8znywsN45Za3mDd1IYm43eDl+ppaEOn/qd1b13O/ck64cD0FLeI0axXHqLVG1aT6xoGAXO+esgDl8XEqjhHumI7gM25idJa7ThwljOIqbGY0B3sxKgTkHus/WvVWio1XPYWcGKoKy/XwyDIouRrHfzQYLVTSt7MeohPc5owWyEJk6XCVrxOfCpFvAQfp7QOZFyqvVuRTN2xnquaNWdcgDLfCzden0riQivvcAiXiNq7syyY9PNZgmO0Aj7o/0p2/JhNpIm0j84RARser/VLdwRMqeTw2SVWdNTTWTsrzRxhkAKVaHBX2/Cc4qyD6HWp4sVkhkGTQ/b8D1f2MNJotgJD1GHwVDoeZOnUqBQUFdO/evdJrkUiE9957j3POqVrtsSNRWlpKbm4uJSUl5ORUP+6gsXAchxk//EXh6mJ2P7g7zdrWv6Lk16+mcedxD6s+Py65zXK4/b1r2bNf5VyGl64fxecvfYsv4MO2bUKl4a0bRtJU7bO4EXv2LeOu1xbjCzgYBtRe/KM6Oqe6O2+cE4QBxMFoqsJQMpmf5nfFURCVwO1FdTl2RYWMokJp7uBaHJRnRFJJJGGrtdKn1Ve6SA9KlCU7esfcbR5SidsiD1VZl8w5SnbOdqhodJntXkcmouA91QCxGmR8lmpNIIOkhucC6m9Kr2unADLB1xtyn8Qw/NWuVR9k6C1k2eOkRKrIReQ/r7xg1eCsO6GiISagxGgMkfsAInDMP7anWhuDr6u+T7h5blZnRP7/Icx6xWsrr5mYj9xwKkocWUr4psKuAAaYHREFb27xOW+aHZP6PL/r7Dn6+++/GTBgAEuXLkUIQd++fXnnnXdo1UrN1CopKeH888/f4cXRtkp5cTlD976ZVQvXAGp0x/n3nM7pN286WTRYGiJcFia/ZR5fj/wBy2viz/QSCcZwbJuywnKWzV7Bnv12I1gaonRDGU1aFzBvqppMHioLV5rppXH/ot/c6eJJAVOHww0ETi07nnDROkxTprxFUtYmkCTIYhA5rqhIb3iZ5k1yNm7bEKFyJ+PkPD03GRpH5Y0IVKUYULVvkZskTYSqOGn/SvdfP0ocxalUJi/XA1lu+C6edg1mxTUSApGvQmrxPyqN/wCQ9jr14I/+6ArAbDC8bm5NiSsOy6kopy+B6Pew4RRo9lk19tcPkXGW8hTFpqGG4B5eo4BTBwgqe7ZigK9Gb1NDIDLPU12247OU58zX7583hTTbKs+Qsw41r89HxZDjTFRe2mpkaCQi+4Z/fA0aTW3UWRzddNNN9OjRgylTplBcXMzVV1/NgQceyA8//ED79u03vYBmi3LHcQ+lhBFCJQK/dvvb7NF/N3bdr/oPyUQ8wcs3vcm3o37EcRyatWuCx+vBsSWhcASJ6pMjbYe37h1D4aoiPn72S+yEGl7arF3TbWOUxjZGm51bsXrR2s0PM9ZDU+U1z6WwlnYBOfk2tg2xqMDrq8vCEnwDVAiqCjbKS1PXZp/SPcadv4ZBzWHAKJBF1VL/jc/vrltlmfQNcVLdsivZkiQBshBw1HiUtIo36ZQhiy5S/ZZIjlcpAZmnwoJY4DsCwsmmjmk5UvZcnNgfGN7da7mGuiG8+8AmmjUm7cVeShUXorCgLk0l64GUcZVXZOQiRADh2R08//xakwjhh9yHkMXXuz8fVxgZ+aQeVY47LkWj2cLUWRxNnDiRsWPH0rRpU5o2bcpnn33G0KFDOeiggxg3bhyZmZmbXkSzxZg3bRFAqtOyRCIdyU9jJtcojt575FP+98p3GKaBaRqsXrQWwzKw4wnl9UhODQCK1pTw5j1jUsdGghGCJaGahZEA0zT+dcLJG/CyasGarRZeLNlQe/PNv2dk0Kl7hEjIQIgEnk3m5wp3SGtN3cA3x0PouMm6AWoWVw4VLrOkV6iWeyjdB2cl0eOlIs9p42M3/t4NsYU/Qgo/ZN+i+htFvlEVY8IdYSHd3CdZUnGqxJ8122avABpOMGwSpxglFnNIedWSM9Gc0lTPoLoiZVwNgXUKweqEcGewyegEZOkdFYNpsy5WXcMbuEmj8PZWrQMSM5HR3yD8luvuRCXxCwM2rvDbxpGR75DRcYBE+Poh/AMa2yRNHaizOAqHw1hWxe5CCF544QWGDRtGv379GD169BYxUFM3DLP6LFvLV/OP+OcPfwGpqtBAlXyHy8NYPg+xcMytfBZ4Ax6iocoPNOlIQqW1jXTgXyeMAHXftiKbusf/fbQFXXqE2XmPEIZRlx5EUomDWrtnby5hVKjEoqKcPl2ExYBMUuMjaiXdK2S5SdGOG7ZLPzb9mtP/b5IauxEahXRCkDtchRWTfZEqzZ1LWzM+tfrtUO/O0VI6EPtFDaQ124Jn7/oJDrOFSsR2Nrgl74AoB6NZvZOXpRNCFl/h9i9S1Wgy42zwHwsl14MTUmE+osjyZxBGc9gCOU3CbALmIeA9QFXdxaeQGkxrtkVknNfg59xcpAyr3CijabWVgTI0Gln2qCvmUT2r7DWIzLO3tqmaelLn2WrdunVjypQpVbY/++yzDB48mOOOO65BDdPUjwOO2xtQokU6KhxmeS2OvujwKvtKKQmWBKn+QSnYtU9XLI+JN+DFU40wSl9H08i4ArYmoiGD4ed14PFr21FaaLj5Rm6yco1sCWHkJmj7DkFkXaVygvCQEktGG/AdpsZGbLJY20B5obJR15JQ+T+yDPWeNit2FZkowWWA0dY9n9s3J4WEyIfIdYcjg/9V+VNOGZUbUtYFC0qud4flbhop48iSG5HFQ5EldyGLLkGWjlCCqY4I4UXk3uOOSylX4lBkI3LvrdQcs06EXqvoXySyAaFmwoVGo9o3ZKkqNZEJ0kZGv6/f+vVECK9KRM++AQKnIDIvRxS8sc0kY8vgf5HrDkWuPxa57ghk5LvKr8sYsvxZJTSNHPWFRAafV6JKs01TZ8/RCSecwNtvv83ZZ1dVvM8++yyO4/Diiy9Wc6Rma3DjqGGESsP88r9pOI5DZk4Gt79zDS06NK+03+K/lvHokOdYMX818ViCRCxB6YYESIkjJaZpMveX+di2Q0KX5m8X1CxSJVl56mc46etc9j2slMNOKqb6Uv1k1df/s3feYZJUVRv/3arOPXl2gSVLzihBkCQggopiBFQUJQcTIoqIIKCAqIAiUQGRJEH9BFRAoqBkyTmzLLB5YueqOt8f51aHme6Znt3Z3O/zzLM73VW3bt3u6fv2Oe95T1hNNpkIIzYOuCtB+nDt3VV4CEwEk9gLE9sWyVyNFO9RsbQM0DiFZ7RlCEAQQz2Vwuoxx96HFW9LBrUaaAN3YwhmUBt1CuGpmSXYMSa6ecVsBGcAGb4M03Xm+Kfk/gyFO+25KaCo5oexbSG5d82hEvRpukvyEN0CE92w/JyJ7wg912oECgOxHcYWcDeAlJ7T1FXZ6yGp6xfManTGhK8xURgTg9QBS523keT/hQyfi65BFIJ+ZOBH4P4BE7WV3EGfFjbURJSiGgUL+sBNLoGZt9AsmiZHJ5xwAieccELD5y+88EIuvPDCSZlUCxOH4zicdtPxBEFAMV8kkRpdUjzUN8ypX/gVc96eR9Sm23yvtgN94AU46XjL6XppwEQ6cdQ73YgV51M2gm6M0sJdbExYYmTimMQnNNKV+Bgm8bHao4JB3ZzJMLa2SWzax6GiTcrY5wIqVXLhTfsaUSn9q8n5VuufQtI1Hoo20hSAP1NnKQIUVGiMTaEV7lWfJ2clS0ZEK+EAiIN4iPcihgo5Eu8tbdcRCpFNHDpOxCQ/XT7GRNZGnG4oPgbec4iJaD+yOhApQfZPmrIyHZjUvkrOik/rvQahHUMaEG0ZUnpcm/uWPatcTHz30WOXXkay10AwXwlc6iCMs/zpUSV/r6bKHJvODS0oCv+FkBw5PaoFC+ajhB0lS06XVjy2sFSjZQK5nMFxnLrECOC5/77E3BnzSLYlcFwHCYSCgHENEdct9+IKXbCXJkTjEXqmdTPrzTlLeiqLDwvJVUQMA3NdgsAQiwfEU1q55rrVR1WTiEYi7BALwtasqt9dE9N+ggpuQR2svbc0khTdWtMMuetojoh0AVmQsIS9TmuPkWmzCc87PH4i0VNrQxDMJ8j8CTIXg2QRd3XoOFXvL//PynxNt72U9VcQjXYZU+u/IoM/q2qca0AyyOCZENuuTIDEe1Xdy4P3LBfthK5fjWoGK+Ij/d9TmwLbTkXyf1dzTRmk8hoXrc4nCpnL7LWLSghMHNN2GCRq9UZSegHpO9SSKEGK90Hxf9B9yaJx6l6SCFPZwRAVXy6DBLMq7fpMFDpORgZ+SJm8mwSm4+Tlbz2WQ7TI0QqEIAg0ghD+XdvokARCKajaGJfCoFEQCPPf61/S01jqEI1HKBUak5ogMBhHX/TzT1iDNdZ7jbU2GFni7tAcCViAN4YzBeIfx3R8D2OS2iJi+FzIXqOCZ+NCfHd1yA5mNjGggfiWEPkAMATedCjcQyXSNNlvXgfc9W3z2ZEErB5ZdNSQcegUyvon71WYfxiYPBqRiam4WWbr8zJsiZ6A24t405F5XwDiEN/Negm5NlIGqrfKqd1ASI4GfqzEiJRu3MGAbspT/oExlfSNFO6xqTxb7SdRFaEH8ywBStlUZKgztGafMqBzje0MneeCMzolJJnLlCyYdkv2StpepvBvSHx0QRZ/qYWJ74bk/kZthFMgfwfS9m3tRwfaD8/9IxT/oyQ4vmMl7dbCUo0WOVrO8ejtT3LTBbcx3DfM+zZbi47eNgbnDRFPxgnCPmhVhGlphV/y8Sf0LX7FwEprTuHd12chfmNSIGIoFkEGXP59UxcHfr9aQxJ6EYUYKzo0su9ZEwj6IH8jInN0Uy38W0W+OFb0W4L8nVQ24/GiUwLFhzDpwzGxrZHCvUjh3nHOWRiUMJ0/gWAQyV4HxQfQj82RVgMRMEmbvwyNLg26tj5qjGnUfygI9VS29N50gTsNIu8Dfw4UblFiQQlKj9pxXCBpIxbWJdx2pxfJgfca5bYbACS0zN+bAdH1K9Mc/j21EcISNX/8JqrnSr0iDB+K/4a+ryPBDD0ufQCkDlbxtz9LxyoXCESsuH2eRgqL/1NiGN8V405rZvER701N6RGD2Ie0km0pgEnsgZh21I8p9DxJK4ksPQfxD1WOjW4E0Y2W1FRbWEC0yNFyjIf/8T9+/tXfUirqB+CLD79KPBXDjbiUCiUikQjd7+ti5huzl8poUQvjIzuUH5MYYSp0o1Q0zJoRHWfEsd4IAZrS6p/ADH2QFBTu1wiK94KdVxh5iGklVPm6Y13fGh1Kyeo4gNiHIbEP5P88zrkLCkG8GTipz0F8N2TgeI28iNXehKtrOjWyI31jjjW6As72UUt8HJxVYPDHjLY4sClPmQ9i0zGJPSASirJjSmpqKqCs+WYoXCckUS/b38IN3QrXTS/q+C0NiFHVXLynrW1AHhk+H0ME0l9XR+7S06obMy5QAOMi/gwYOpsyIRz+HXRfMGYERbzXkczvIfdPyhowd2XouhATXR8JBvReTBwiG2sKa3HD6QI/b9/LYfTVryKoLSzLmGCtZ2MEQcDf//73yRquhUnAjWffQqlYItmWxCt5+J72PyvmSyTScQ75+QGsvfkaRONL4IOlhUlB3xju2CFCypBq8/nCkfU0WxMIGxqD9lKbSF/BkNQM2k3VCqqlZFuRhIaO4yGM1DgaZQGk/9sTIEYRFuj7YO4GAIxxMZ1nYTp+hmk7FNNxIqS+qvOSedoMt9y/jSbmZLvNSwDD58PgcahuKdfg3AAiW2DajtC+aTbNZoyLSX3NHjKkmh8pQWKvWlG2WMIUioPL62mg46cQ274yLyI0fk1ECYFJ60uZ+4uO0nYURNbR+UtG1yLxScjcaCNh1lYhmI8MntZwVaT4GDLvAMj9xa5FQefjz0YGT9Ln534G6TsMmf81ZP7BiD+6Wfaihkl+0i5RAdVj5cBdEyL1e+C1sGxhoSNHr776KpdffjlXXHEFc+bMoVRq9dhaWjA4bwjHcWyLD7/8WRdPxRjuz3LhMX/AL/kLXJnWtVInex++B9ef9bdW2f/SCpsyNcAXvz2LNdarJ3hu9vV3gAJEN4WOs6DvYAimNzGBvKbQ3PeBu5am1fyZ1IqvJ/A9Lb4jmC6C4YugcHvzczdxrRaaEGLW3VthjFs2PpT8HZA7W48pN7YNowbjmYHGKwLrUY12G0HA+EjuZhV2Jz4N6YMwJoLE9wL/bU1dkYDE7pi2I2rONk4bEtsaig+hBMnXeUc2xCR2wyT30IpBjFazDV9AraVBGIOs2jaMIeyrZ9wp0HMV5G9XHVNkPYQOyN+iczJhtCoC3huIBHW9mGTwNNVh1ZCzjBJr71Wk7ziN0JmUzr/0NDJ0OqbrnCbWcBKRPgKCnFoy4EN0E0znz5fL6rwVEQsUOcrlclx55ZXssssubLjhhjzwwAOcfPLJzJgxY7Ln18JCYP2t10FElBiFEHVxLhVKeAVvoUr2jWOYunpvQ3fuFpY0dDNyXMGNCJtsk8Vd4K9DCUjsC5HNgRRkroSOHwPjRZAMOGlMx6kYtxfjTsV0X4aaQBpUJ9NN89ErBwqPIHM/DtnrmzynE5L72WjGWBV5I0vfXf2J1NeLSP5ftnqrXVMspqdyTkO44KykqRfJWhIwgb/B0hPqO+TNQDIXIMMXIrm/w/z9IXezirKdBCb15boVUabzDIhtbQXeUYhuiuk6T0kfYJwOjNOOaTtciU50W5QMhT5Yjv6IZ9NvAcSq9DVOGyb1eUz6EG2V4fbac+y6i+i5Tnd9YiQl8N+jEt2qfrKkmiXps5Erew/GheIjza/hJMGYKE7H9zEr3Y+Zei9O758wkbUW+zxaWDQwMgGb40cffZRLL72U6667jnXXXZcDDjiA448/nqeffppNNlkxFPiDg4N0dnYyMDBAR8dEUguLH32z+jlpn5/z+tNvjVnRtKAwxtDek2aob5gJmPq2sAAYryptLLiuMGXVEr+99XU6exbUpsFldEXbFHBTtvFpI0Qh/Q2M2wnRD5R1JsHsHTXiEGqPggGaK+N3UGdsDzWsbPTxFacsiG77BmR+b8lRI8Rgyp0weKKNrNgIibs6pvt3iLMSBH0aFTBpyP+fltPLoF7LtNnrZeyUPOpGj6I7QdfPIXu1RnqIqvFj00hYp2WsRsiKw6WgYxEDSpD4OE7Xz+uOIBLYHnAC7qoY05gxiwTaf670lL5WkXVg+GKNDGEg9kFM568wTluD8wUZOl2jK+GHhIliOk7DjDC6DBHM2bPsFVW2RwCNFKW+oo7dRCibKwbD4E7BmXpnw/tooQWY2P7dNDnaYostGBwc5Mtf/jIHHHAAm26qedVoNMpTTz3VIkdLKXLDOf74kxv4v9/+k6CJXmdu1MEvtZjO0oBILELg+6Q70+SGcguUukx3eFx810t0dHvEEqoZntzCxBHd4OsibsvQY5iOH2GSnyWY92Wt6gn7gUk/E2tqO15VW8RGRj6gYufB06nZaOshtiOm6wIt2y89CyaFRDaHwdOg9DB6nzGIbq2iZClUzdlFo2BJ9W7y3rKPVRG41Fcx7T+uafciUkRm7QCM3UC44kfVWTGNlJwlSCOrDePgroYz9dZxxmwMCQaRoXO0nYhpw6S+DIlPYoxBgiHwXrdkad1y1KnhWOJD7jrriJ7AJD+Fie/S+Pj87cjAiXZ9berPXR3T9m0k/jHoOwhKz6Drq15NJn2URrsWI0RKSuqdziUjCG9hwpjI/t10kP2ll15i//33Z7fddlthiNDygGRbksf+9STRWJRiUBw3jTbZXbZbqMAYW+ndJFzXIZ6Mkc8WCBawj11nj0dHt0+pZIgnZBHYNjRDpOP6Ixlk8CwlIR0/QvqOstVbVoeCy7gEpoxwPUaSJNvUNrIBxHfGpI+A4n8RY2yl1xhaoOJDOqe2b2KimyLuOtB3hDY+LV+jACVbzu9029SYFR+bdkznaeBM0QauQRZ1ThZIfw2n/XujLmlMDGn/Dgz9tM6EXIhsApGNIbIaZP+som+xpECq7QSqq88K5So1EYH835D87SA+JrErJL84JqERKSH9x9jSewfERwZ/gsGH5GcwTjvEtmy8jqPu0dUWIKkDmjs+sReYTiR/G0gRE98OiX9K7QMGvg/ei2hkzgNnJUz6K5A6qOn5TAYkf5uNHGaUJLYfrwLtFpYbNE2OXn/9da644gqOOuoocrkcX/rSlzjggANam+kygIG5Q/jeaOG1cc2oMnCv6BNNRCnlW8L6ycZE6U33Kl3MfXc+XtFb4Cr1Vd+nZMBxDIJMgBc1ExGCihh5rJSfLV831rzQfwsT2xZ6rkHyt0D+NvBn6DgSXjsGpFDPoLHEzTEtgQ/7f5mo+u60HVkmABLbGZw1wH95jHEAfCg9CH0PIyTsuDOov/i2Ks2kCYkfHafqxg5I+girifJUk5M+puFVTeoL1kDx3RHPRCC+E077d+19bI/0H1uxC3CmQjCbeutfrl7LXoEMn2cr1UBKj2pX+PZjGy9D6WnVNpmE1fQAwRCS+SMm+ZnG500iTHx7TFyr5yQYhIGjoXAfZY8nk7Z6r9S4ZG+yIcUnkIGTlJyauHpgDZ4C7kqjHMkXaHwRKNyNlJ7VdGVi74atYFpYdGhaSbvaaqtx4okn8uqrr3LVVVcxc+ZMdtxxRzzP44orruDll8f74GlhSWHq6r26wY5AI38cN+Kw0tqt3j+TjgkSnJlvzMYrLDgxApg6rYCIwXWEiX2PcWg+xJRmbBGy9feRrN6LY9tmuGuoV403nUr5ePVHUoaxv7/FgaKSCuOocDq6LWSvQmbvSNB/AkHxJb3uhHpZBfoTvM6YxExK1pIgB04bxkZTJHs9DP9GCZ8/E/I3wXDjSipjktD9OxV246BrGVfRcvJzleNiH8BM+Qum63xM14XQ/RvdnImjESorlnY3xiT31ghQ5vd6crkrvKuVaMEYfkwybNeg6jU11sl7CUD6jobC3VQIoF1zk9DX3ntx8c6ncI+N2oldkyJIHsnfPTnjD52NDBxnie1vkfkHIN4bkzJ2C81jgcqMdt99d66++mree+89zj//fO6++2422mgjtthii8meXwsLiaf+/Rzx5MT6+JTyHkNzlswHYQuTi3/d0Mv0l+PEU8qwmidIHs2xsiIwwPjtRwQtCy8h+XvsQ0PaqNNEUTflLGViQgkVaI8l0g5L4ONAFLxnoHifnit5yP8V5n8WmfsxKD3ZxL1AhRCOl96zvcbIgJPCdJwK/kyCufsigz+x6ZZEhZDkrkf8xn0BnegGmN4/Q+JjSvLiu2K6Lx1V/WScHkx8J0x8B5zo5pj0ofqiGleJkjMN0/0rPViydhOvJpgRwFf37EaIbKARGclY8udp5CnaXCpNSq8guX8ghf8SBDmk8G8kex1S+I8KvCeAoPSSdQkfCc8KvJdE5sKmLslTcUAvTgpJk9KzkPsT5egYKfDnIkNnL/TYLUwMC+Vz1NnZydFHH83RRx/Nk08+yeWXXz5Z82phEvDPS+/id9+/ksxgtv4BDTStvueT8yYu/m1h6UM0JqyyVoHAB2dCmYcEzet/JjImMPxbAtOJSe5hHxdb9VX9nvNH/FsNg6bdbPd4SpYEhKmumBV429Yokmd836EQzRBCB2J7QOpjSiCi2wAZZP7XbNNVSwBkAHUUj+gcg/ngTm04qom8D9P16ybnac9pOwqim2sqzKQg8bFKCsZ0aENZfwZayWY9iZxubVfSaEx3GnT8BBk8FYKMkq/ohpj2H447H8lcjQz/2r4emnaUsijdgfhHofPM5tNgxVDvVe/DKg/u2qrJWpxw16Buytl7c+HH9t+2lgXW2dzYaKDfihwtbjRNjnK5HHfccQe77bYb7e3tNc8NDg4yffp0fvnLX076BFtYMAzOH+LyH11LqVBq+Hkfibgt88blGkIyHRCLw1C/Q2fvRL61T+TYZolUlYB46CRk+EJNd/nvMHbkKdwYHY1exD4Kbi8M/ojRpEe0n1t5/lLnmJFoVl8FGjEKIHgBBmwaJboZEtnERovadHMLI2+St2M7yNC5ENvCGjeObtw6EhIMI5nfQelFcKZg0l+p23LDxHeC+E6jHzcGOs9QF/EwUuSkMJ0/w5jEmNc2iY9BZDNt92JSENt61DkiRcj9FfHewrhTkMimMHyu3q9pt5GnuWiKsFOrzwr/gtz2kPqcvcesNg6WfohsoFq0mnuIIXVtJIwe3/XLxW66aNw1kZCUh3MhxkLlv0M4K4EJe/fFtYJDAnBWXfixW5gQmiZHv/vd77j55pvZZ599Rj3X0dHBeeedx9tvv803vvGNSZ1gCwuGOW/Po1QsEYm6FAv2j3jE326LGC3/WH/LLMW8IQjCSqZm0ex7w4qsm0L19QXkXfCjGuEY07k6fOMGWmLvvQTR3Wi8GU3UikLQ+yihKbpGfkiWRJmoLWW3Op/SE1q6L4BjgHYbNQrTL9YzqfgAUnoIik9A94Vjln+LFJC+I8F71pY4ClK8B7ovm1BXdxPbEnqvg8IDOp/YtpjI2ojktRpNhrU3WWTN0edGVofI6g3mV0L6vmUtDlTsj+msRD1qSjN9wLMRPQ/xXlG6G/QhfUfo6wmAi6QOwmn/VuVC8R3BTLEky/aBw4H0sZi2w5ZMQVBkfSV7wRD6vgFMHqKTICuJfkCja4XbNfomqIlq+7cXfuwWJoSmNUfXXHMNxxxzTMPnjznmGP74xz9OxpxamAT0TOvGjbgEYr9BTsKXmhaWLcTiAYef/C6pdp9ofKKEoVlytKCmkiFKIPPQyrTxUi1GiRQRKN7TxPHNwrY4wUBiT93kSVLj0uysBYlP2MMH7bwzdg4xtDs7Vjfl2Mq8GDjT9F/TZfVHcdXQFB8Ye0qFe1VDRULL8k07BBkk07x0QYIhJBjGuNPUtTq1rxIjfx4y/+tI/zeQ/u8j8/ZDcv9oelwA8rdaYhS2QUnaNiseo8mpp9G8YB6Ij3E6dX7DF1pilLRpJAPZPyDF/5XPNO6qmO7zlZDQDmYKpuPkJUeMAONOwXScBk6bkiJTAHd97bW3sGMbB9N5Oqb9+9qXLrU/pvsKTHTzSZh5CxNB05GjV155hS23bCzI22KLLXjllVcmZVItLDySbQk222kjHr31CYxjFqpNSAvLHtZYL8dPr3qDnpU8HAfaOoKKdKNpNJNumizD0Aa6uBqEou0ok8/2rT6pcCfEd1OXbMnppp06EhJ7wLzPUvETAq2aGtK0ExFIfwWy16CRtAikD9SITdBXpYSP6LjjNUoN5tuogf2INgbEQNBY1F2+k2C+migWVcgsse0xnT/F2CpBGfollF6g3IJDhpHBn0J0S40WjRxPinoPTk8l2uXP0HUIfzeuzs+kKwSxhjiL/fGQ+J66gqUX9JzwHkkAw+C9qi1OLExsS8yUm5FgGEyqbtuRxQ2T+AhEN4LS87qO0a0wTmpyxjYR9YWalNFaWFA0TY48z2POnDmsuebo8CvAnDlz8LzJb1HRwsRRyBU4aZ+f8+LDr+C4DuIFmqKPuiTTCYb6xmqj0MLygGPPnkHPSppO9X0IAnAjE63tWRJO6QZwIbadVk0RgeyV6EZbsj851AV6bfAn8wuZNZAs3APdl2sjVadXIzYDJykRIkJZYA1o1VQRYlth2r4DyX1tf7OVMZE1CYJ5alcgvhII8mAiiLsyMnCaRpBMDJP8vDpohxu/u47OJZhbWRMMRDYcNWsRqVTH4SL9J9iojv14L96v8+/6rUZbSk+q0LfcNiQNZHSeI8iRZP+KDJ+t2imTho4T1cvJXUXnJyUlSGJL/xOf0nssPQ/ea5TTYKFYHgcTzAHWUXF6KdD0m7GtXkS0bD13M0TfX5Pua9SiZEnBuKuBu9qSnkYLiwhNk6NNN92UO++8k6233rru8//617/KLUVaWLK46+r7efHhV4kl40SiLqVCiaH5wziuixNxcVyHwG+1CFmesfp6eWJhezEbZFk2YoeCEoEITscJBP3HUz91V9DoSvkjzEZ+FgoGSIIMYfy3MLEtVRTdd6jd6AMq4u5QJGwgto12YzeOJRcVgmHavo2UnrbtRBwlArHtYeg3mlIy+pgMn4uhBOlD9W7896gVuftAFPwhgnlfBXcaJvlpcDo1SuS/o5qe5Be1wstErQeSXZriQ6qDCtN7/jsawQojYcbRNFEVpPBf7Ysmvo4dDCjJcqdBYm8lMKUnIShY/jYN03YUxlbkBXP21EiXsYJp8fSeLAE0qa8jhQeVdEqVJi57rfUCbYPOMzDxXRf4FV1QSPExJPNHTQVGN8W0fbOcDmxhxUDT5Ojggw/m2GOPZdNNN+WTn6y1Sb/llls4/fTTOeecxkZnLSw+zJ4+F8cxRKLWIdim1Iq5IsVcsyXNLSzLOOsba7HOpnm+cORs2rt83f+WmTh9CWQI8WdrdVMjyHxqKV/YhmQB3d2NiwqHnYpRZf52LaM2KbuBh+JzS8ScaeqM3aBE37grQc/VkP8XUrgPivdD8T8oyXKBbnAckAySuQZSh2h0Z+isEfclel+Fv+hDJaMtQUzSpv9i+m/mMjTdF9dzjKk6366Vu571faoine4HVKM070tKOiMbgpO269Fmx4kCGSj8GxPdArovgux1iP8WxpkKyf21ak2KyPDvdT6Ss6QorvOPrA0R/RJtYltC9yVI9iolId5r2qvMpHXOwTAycDJM+Ye2LBkDUnoFGTpDhfLOVEz7dzDxncc8p+FYxcfUeFLyqOD+OaT0HPT8ARMSzhaWezRNjg4//HDuu+8+9tlnHzbaaCM23FDDuy+++CIvv/wy++23H4cfvngb/7VQHyutOYUgEK1Gk4Dh/lYabUXD0w+lefaRNM88lOaMa18nkQ4q2YulHgGUXkb6f8DY8a7wuRi6cXvqQeO/xtjnNRrOlt1HNkBMOxIUNe0VuiETQ6M5oXirTYnE4E+g57KGwxqnA4m9XwmPCCo+t75MMoRWtwEMI1LQknkZCs+uM2KoBcvZdFeP1fxEgD47v4L+LimgBLFtwXQReG9D/v9GD+nPgIHjbZrMhcJMjTyJjJqCiLahMSaptgQjl3HgVMjfQphGK1fsRTbHdJ1Vo80xsS01QiclZPYONuIVaoo0iof/FjibNVxf8WcifYfra2Fc8Pq1zUr3RZjYNg3Pazhe5gq7rmHVnQfec1D4DyQ+MuHxWlg2MSFl29VXX811113H+uuvz8svv8xLL73EhhtuyJ/+9Cf+9Kc/Lao5tjBBfOQrO7PRdutRyhfJDOo3XTey+HoPtbDkEYsLsUTA688nuPfmTnzfYSnQsU4AgVZ1STM2AUUrDC6C/yoTJ0ZhxMnqY/xXoe9gmPtRjRzhoRGjgcrYJgVOUq9bekY7tI+F0ssgBY2KOBEqjKNgy9QzWlY/Zz+CmZtSSd9VRXxGzTl83pbNyyChp5JGpTwgqx5FnT/XiFThHirpR2swCCBzlASaNr03k7LCalTPJEWdH1FMfJe6txiUXiGYvSPkb0RJZAnotGvrQTAdyVyswuoREG+GEjPJqJGmhP3iHFs9OAbyd4D06dqalN6DFJDcTWOf1wjBXHvdcI1dXeaxXMVbWO4wYYfs/fbbj/32229RzKWFSUI8GeenN/+QWy+9i7//7g7eeeU92nvaGJw71NIarSAYHnSJRATfM9z9lx723L9vGap+CSMOE0iPSRVxmTCsDxEeSko69P/Be4BbSV3VnFICEwC+FUKP/VEqJmUjIr6eW3euCZBmWlBU/w27SiiIUl4vk6yas4Gu8yvRGjNWRVV17tUSrMQeUHxQiZ3TBunDoY7PUhDkYf6XLEEL4QFVqU/JQf4fSDAEXeeVS/HFexP6vm7n7wNZO/eYCrzd+l5LZVRrp8BGe6iQu4kias0vxdM1IKfi9ej6lUsWn0Byf4ZgUNODqQMxZnSbJvFeRYYvUo2Xu7Zqlxp4R7WwdKHp75JBEHDWWWex4447su222/LDH/6QXK5Z87cWFjcSqTif/fYn2P/7nyYWj1LIFlrEaEWCGLySQQReeDzJrLdjFU++phGj+fq2yYxMRoGJ9vZbWLl5KH4Oe8qFH40BWs01cuMraoRBPIjtBNKHSA7J345kb0CKT+msio8QzP0U9B+n0RAZtmQGRq/tRD9Po5DYxwqvw0hTTIlRmWA6GGMQfyZB8VkwvZTbuJT72NmxKKq4WgIgCyaCSX8NpvxLSUpQgOHzkLkfRwoP1U6lcM8IYhQifF0ilpjFNFUZzKockb1KozKm0849PM+r+ncMxD5g7zWr4nFR0beJfmDs8xoh8RmUqPaBzAMJMOkjVWcFSOFhNa/M3QKFfyNDv0UGjh/VN0686cj8QyB/p7qc529F+g4es8deC0sPmo4cnX766ZxyyinsscceJJNJfvOb3zB79uxWP7WlEL7nc+fV9zH9hXfo6G3jYwfvxs0XjSFsbWH5hhieeSjNtLWKE9QcTUS8Pxlu66GWZqJFA1XtRSbDfkAGKxVWiI1MNJpTSTe9wn+1CiyYYTNdEST5edXeSNaWu9vjy4RhJKFrZu6m6seD/D/t4y44q6lIXcK5liC2CzJ4lm7kDNe5Zvmm7c8wiFa6mfZjlBBkrkbyf7MRmRL4M5C+w5Cu83ASu9nzq4ndyD5oxhpghs9RNUfAn63HG4GgukIvAoVbIbMmtB3VeEVi20Lbd5Dh8+w8XCWNqS82PKcRxJ8FA8dY/Zn10zIJjaCFx2QutCnSUJNUhMK/VeQe26pyXO7PlvSlbVViAMFsyN8M6UMmPLcWFi+aJkdXXnklF154IUcccQQAd955J3vvvTeXXnopjrNMiRmWa/i+zy++fj4P3vyYShECoXuVLhzHwZ+UDayFZQVhdsH3DFf+cmV2/PgA7V1Lc/QwjHZMdI6OPbfReeG4Y73/Qx2QTe3UREHGKmiwLS1krm7ypktTMOQge62OaToqxKJsVbCgf4tpcFJa1UUBiNpoS1Yfi6xvm5QaiO2ov2cvp9LGpB6sxxCuVr21/QgT/yAmsjYAUrjHRpPCtBc63sD3EPdPmOiGEN2RSmqy+jrWF0pstZ0UIPK+2sa3kXWheJ8lRlVOpSYBUkQK/9EGu2PApA9U805/uvbri2ywYA7a+b9rRLBMaATIILk/q2s1gD9Ln6s29qRgHcKrEGqUQrGfcUAMEgwsQynuFRdNs5rp06fziU98ovz7HnvsgTGGd999d5FMrIUFwyP/fIIHb36MSCxCLBHDK3q899osbUDbwgoEqflfbjjCu2827uW1YAgbbk4GHDvWRL9ohRu7gOmmQpTiVNp/GDQ1Ntb9hwLgakSA9nHOA03JhX2/wiqyMNpU1IqrYIBK6s6tikxNFMPo/YZ/zz5KfBI6/9TXMVNuw0y5HdN1ARQfsRv8WIQz3Kr1/k3s/WViVHk+bAviVI6XApK7HgAnsjJ0nknN923TCd2XQXRDO98iRNbAdP2qpq+cSR8CkY2r7knQ1882c22yfN5E1sDEd8RENxyTGIkUCIZ+TTD38wTzvoRk/1ROiUkwoPdXJjRGiaE/V802QQmnBPYnbD0TVdJXPZ/ohpXIkogSQwQT2aCp+2lhyWJCDtmJRG1X5mg0SqnU2nSXJsx6S/PZbsRlcN4ggb9sWP+1MPkof5ZHhf2+OZsNtlzYPmghDLhrAhHd1Ap/H+PQDUFeavx8KHhOfR1yf9O01IRQ1dxUwm/uYVonFNT6QD09TDXqkQcPKCmRkf7m5iGDUBM5CTfPahRRsuaPeK66Am2sqQ5VHRMSMY36GLe7xnNJTDOtVqoq4kw7RNaoedYk9kSKD1aNE6YwnZoKLif5aYLoDlD6D5CC+IdxnAQSu0ZbghBAZF21AKge32mH7suR3F9h+LcVgblkwLiY5KcqM5WCRuic7gVyzBYRNbIs3I76VgnivYiRHKQPxkQ20ia6ktf0IraPXv5WJJgFnT/HtH8f8V6GYCZhVNK0HYmJrFd7seR+Sk4L99k0nKP90hKfGDWvFpY+NE2ORISvf/3rxOMVFp/P5znyyCNJpyvfgv76179O7gxbmBCmrt6LAJnBbIsYtYAbEY48bQafPLBvEkazEZn2n0Ly4zB4EuTHalgaAXl7jOdj0PsPnOhaSOEB1XJMCgQlIGGqbGHSyXmbUprItZs5bBglbmlUJxNGnsJI3MiKtjh6P0WIbmlNHKu/mPqavottW3MZk/gkUnoSZKy0YngdFzpOH0VeSO4Lhceg8DcqacEUGB8T2bjmUCcyFSKfrYwsRU1DuWuOSWaMk8Kkv4LEt1fy4r0Kph3TdpgVSKNi98GfUe6v13aUCsYnAv9tKNyl5zv2y75k1NsodRAkPqYO4/m/oZ5RASomj0HpCaT/e5ieP2J6rtU+fDIM0c0xsQ+OvicThc5zVIDuz9JWI7Htx033ieQgGASnV/ustbBE0PTKf+1ro9+EX/nKVyZ1Mi0sPLbbeyvev9umPHTL/8Y/uIXlHq4L019OoR/0C4sYUICh7+vPuLBVTw1RtC7MIEO/YvzUz0TJ/sL0eqy+3mR9yTDWPyijY8beD4kvwOApqK4pJHUdEN8DSk/Z6ER4rkDi8xDdCkrPoGaSOcqRH9M7upw8+TmMDFnH6nmMXuOoHTsJHafiJOp4GAXzVFNVtguwab3YjtpctwGk8G9k4BQbTXMhfRCkjxyTHJjIepjeP1nfqEil3L/0rO1vV7BptgIy/BtwV9Feb81ChuwaVKfqXBt985TQdJyEJPaE/iM1/Oq06RqJoyX+/tuYyPsgtf+4lzPGhSadukUEMhcrUaMEJoEkPo9J7VvTY66FxYOmydEf/vCHRTmPFiYJkWiEg8/4Mo/d9qQ6ZLewgsKw7qYZjj13BquupZVBC++OPZG0XITxIzYORmxaKXhvnGPjjE5PLUq0o/qe6nL3hYWL3ocPqQNwOo4nmLe//m569TqSAYagcDeE5pTOqhDZEBPfFlJfguJ/NPVD6O8TuoOvNOqKxhhIfx1SBwIFJHuj9lkznerNI7NVKB7dDOOuUj5PJIDcDUjh35akDQBJlCAX1Ym889yG7TTEew0Z+CEEWUtmPCTzO4yzMqQ+Z4+ZDv572pNtxOZfrUkCoPCAXrfcyiQGwSBSuH9i5MhdS+896EOjdlYoHtm8fE1jDMS2QoiBCRvm1txd89ebCHI3IplLKjomGYbsJUjuBug6BxPfse5pUnwcvDfB7YXYDqPXroUFQitmtxyie6VO4uk4Xv8CmqAtIwgkoJ+5dNKDO0b42RePAebTxRScZcsmeoFhHOHdNxM8/1iKtTfKL8a2IaGWpgl/GqLamR3AWWN0tU8ZDouXGEFFo7SwablqOEBOiUjb0dpc1nu9ch0TsSk8K0h24lb7MgfT/suyz05Aws4pXN8CEFHdVgMY4wBJrepKH4iInm/MxnWPl+HzIHsFSCj4RkmOSasWJ5iJkXlAg670xYc0/TWKzNwLyc/aCMll9h4iSOpATNu3xogqjephUv/xcWCcNug8HRk43uqlfCAB0Y0RyZVTisbEkPjOmoKTgl5HCioud9cY4woLDsnfWVUVGFbtCcgwMvBjmHqrtpYJjxdBhs+B7DUgtidg9IPQfV7NcS0sGFaMnWIFQ9fUTj520O5E4ssv9w0k4Bke4nHu41HuoSj1oxpFKfAo9/A49/EMDxFMSD+y7CLV5hMEcMsVU4gs1rdBDt1Mm4gyJT9TiTzEx+pZtahes2Y+/hY2SuCgwvUtoP1ETOcvMD2XI/lbkbmfsimnHGo4WEV4nNBUMwb44M+sDJn5vY5JEo0aRYEIxhk/YiDiEQz9BpmzEzJ7J4L5B2oEp/qYoF83XBxw2qlUp2UsKanSKDWEabB0Bgp3I5nfoa9rUsfL/gEKtzUeLr4zasw4rCRFMmAimPju497zqBnEd4SOn9mIlm2zkrsR6ftWTRsY03GyGnyqIQZEN8F0njPpkRkJ5iP5e2zbkrCysJr0RUEGNMpWjeJ/IHu1HmvagTiUHoFMK8szGVh+d88VCJnBLK8/9RYvPPQyQ33DdPR28JlvfZyead1cdsI1y50zdkiM5qA2EsMM8Dj3sZXsQqwqzF+UAo9zH8MMADCHd3mGh9hctl/OI0iCMao3yg0v7p56E3iv5f5CEMyDzvMwTgpZIF0R6MdYnLH9iOrAXcNuOGH7kEl2/DdTbSl3AfzXMfFtMZH1CAZ/A9kLRhzs2Yo4Wyovjt0fbVSouoVGMBNMlR2AiGppGkbeqpC5TIlIWG1WfArp/zb0XFtpMVKOqESpWCxYnyTJ63OxrcFZue4lxHsD8d6y9x6m4zytPEt8FCk9rmOVW5kkNTpSfAKT+Hj9pYxuBF1naVNbGQQnjWn7DqZsQjlB5P5qoy2dFQ+q0mNQ/C/Ed9VrOp3Q9Vu7rh44K9kI3ORBSs8g/cdU3NZH+USF1gkOOCN6zHm2CrQsoI9CkENKL7R8lCYBLXK0jOPl/73GmQf8hplvzMYr+RjHEI1H+b/z/sG+x+1DENjNakH3naUM1cQoDpwDnAbMGkGQqonRysDJwLGsOASpkDc4Dqy/xdKcWvW04mfgO0hyXyb+JrVRGVw1/pM4yPzmT49sqY7FpO1lF5YcxXQDC+bonKobl+JBMEgw/AfINqrK88FdXzd/GbKl5kDyAEx0E6T0ApK9Tkv5pQCSsBGmHJio6mnGgeRvspX4ITGJgP+mCrzj29nprqKVb0GfPm/a7MZtUz3xHTAdP62bApPiU0j/N2yEx6aIxIDThkkfAYm9YfBxmy7Moq9hUl/6cVJBJv5hmHqHzsvpXOAIjnhvgfcilfSv1XZJDvxagmmMAXfKAl1n3HlICen/AfhzLVEUNJ07Yl5GILkfxumpHcB02DX2lSyHqUane5HMd0VDixwtwyjmi/z8q79l9vS5ZfG1BEIxV2TO2/O45Lgry8caYwgds5dVjCRGNwF7AbvZn5AgbS7b8wwPlYnRPcDGwLrAp1kxCFKp4DB1tSJHnLIMmLQW7oXYhxfgxFCfEVhBd1ju7lP5xh1uNHXgPQvigsnrt29ZyG8QJmU3q7DTvZ2H5MBpR0wMMueNcQ2jPk/O6uqRQwkT3QziH9UIQ9/hEGTsffvAfAgSujGmDsFEa/VDIgUgVktixB+hzK81gARUr9J5mtXl2Oou0wEdP8HEP4xxGhtYytDP9RzTBo6x8zXQfTUmui4SDGqrjXKEMUAjfumGUaOaFTLuQpEVyd9mK+gGUG+sPqATrQ6LjDJyXKTwZyk5N3EbCQQkra9R+iAoPglGMPFdIXXA6PMTe0L2SrUnCOx710ljFqBtSgujsXzuDCsI3n1tFvPenY8btX9YI77I+SW//DksgZSJ0RobrcpqG0xjWUIjYgRKfO4BVkZTbA9y+yhihD3+JnQLDQnS8qxBmvNutCJdWarhQekBNI2TZmIi21D4XQSG8LwSd/8nQyZbYnSKooJM1nD3fa/gmdCt2Hrn1GAkiRjHDVwGNDXmrgnOmjqmDIGTwnSchvFes2mpBjBpIAnBO5jYljjtx2ISe2rj2OFLbOVXkgrhs/eW/hZO+9GVaRQeIpizNzJ7J2TuR5H87ZVrxHfR86SgqSTJgNML0U1qpxLfBbouqFTASQ6GTofSo2OvgT9dSUZIwEwCTIAJG80WH0NtATqo+FAZcFYdRe4mG+LPQgZP1XsxnZRbysh8jXQ5q8HidK92wvd6teDfVy1V22E4vZfh9FyOSR+opHAEjNOF6b5MjSWjG0B8F0z37xf5Oq4oaEWOlmHEk/qtsFy40cSXXsd1OPj0L3H2oRcv0rlNNvqZW9YYnUOFGIUICZJGkBhFjELsZc//BkqQ+plLD6NLoJcHbLJNlqmrlhCZjDL+RQjTQcVvJ6X/yvCEhykWhf0Pf4+bb8+w5aYx7rhhdXp7Rm8q8+b7fHS/d3nquTz7fKyf6y/fjVi8V317oltB6V4lD/E9YeD74L+jG1ngAHPHuhE0ndYFPRdhSk/YCqctMJE1CHI30bjyLUlZnCyiG3g1QjdmCduHhPAgcyGB94pqUIJ3bTrMpqv8OcjAieD0YGLbYtq+iwRz1bUZX32COn+JqZeKyV4F/hyNAuFAYKumev+GaRS9caaB94rehzEoaXU1VQdomg0wsUoaLRgGE1fzw8IDmlaMbIgZQdiqIeW2HYnme6h5r1pilLJpqCiVwoEE+G8hgz+2FgWL/g/GON1I4lOQ/z9dA9C3UHLfpqvNjLsypvP0RTfJFRityNEyjFXetxLv330zJAjGbf5rHIPjGhzX4dF/PU12YGnWooxGJz20oYLE04AX6hyzMXA/8EP7b73vTy/Y8wHa6KSTnjpHLevQD/a570Z55w2NdshCZIsWOdLfxSQ+ZkvZMzYdNTFUEyOAp54r8tH9ZjBvfu1YSoxm8NRzGsG5+bb57H/QHRQzD0IwAye+GU7bN5UoFe6CyIYoMchr+q1hn7UwJZcC/2WM9yomsQcmuTcmbMcRWYfGUbEoZZ8jJw3RzWqfjqyv2hNKVMLENuogA5D/C3jPQDCfctrNBFYvVELyWglmnJRWXPXehOm9ATPlZkxsy/JlJJiP+O9pqX/xCZvuiWiZuEkpOfNebvg6mPbjbIpy2KbkfEjsD7hI8THEWU0r4CSjOibJ26Dctsj8g5CBY5GBnyDzD0QyV9W9huRvR+bugczeBZn7CaTw38pz/jyCwTMJ5h9OMHCy6otCOB1UGhAHKHED9ZJqV8JW+I9dw8UD03ECpA6ByFoQWRuTPhLTfsxiu34LjbHCkqMLLriAtddem0QiwXbbbccjjzyypKc0YRhj+P4fvsHuX96Zjt72MY91XEPgC17R45+X3EGwjGmPXBNhK3ahjU5moRGiegRpfeBM++9IvEAlstRGJ1uxy5j+SMs69vjCfKZMK45/4BKBTVPFP4dJfxGT+Aim/Ue2Imdi39qriVEcuACNHI4kSBViVGRle1wcuPn2QfY/7C2K/VcTzD+YYM7e0HcgZC6Ewq2UxbFSUtFzxyWMDrqHaa4sSAEpPYmIh3hvI/47iATae6uczrEVSOVSfB9QbZLpOB3j1voHmbbvVPnrVJfSByMeq/q7ro4+VZeoG4OJrK5u1DZCIUGWoP8HyJw9kbmfRObtqyJv8VU4LUNKaMA6RteHiW+P6blUzSqTn4X2U4AiMu9zSN+h0HeIpoGcbpSciEboJGtF0klblu4jw79BSi/WrnLxUY2E+bbs3X8XGTgOKb2IBINI38GQvQ6KD0PuJmT+1xHPtrCJbAKx7YDCiP50YZTGsY8tvn6hxsRw2r+FM+UmnCl/0x5tLRPHpQIrJDm6/vrrOfbYY/nJT37C448/zpZbbslee+3F7Nmzl/TUJoxkW4K1Nll93OP80rKvrYmZeFMEqR7qEaNYk92+l1UYF2LjyGSWDAwkv4zp+g2m64xyebRJfQEz9R5NbTWJkcToJuBoKhq0kCC9/FqxhhjdY48LNWg33z7I/odPpzj8IPivjLhKHtUkiaaths+kcXqsqM8NX2xJxmeQufsgfYdBkMW0H4u2/VBvIoir0Ln3WtWL9N5UtzzduKtgeq7WtiJlQhVu7uFmWk0qLVmTLBgHE9t+zHWU4bOh8C/7mwv+a6pxomArz3K6DgLijK1XNNHNcDp+iNN5CoYs5P+sc5OEEqzsNUqQui/F9P4N03kWlJ63HDTUK6VAiqOiVJK/AxVPt1k9U5vOrXA35G8B/y2NcDntquGS+UjuTzov42I6fwXRbak1KM1BUNAUaGRdcJbPNHsLE8MKSY7OOeccDjvsMA466CA22WQTLr74YlKpFJdffvmSntqE8feL7+DSH15D/+yBJT2VxYJ6BGnkVjYSr7CiESNhyqpF9vn6vIkGYRYbTHRzjRaNqBY0xrHl9ePD80YTo3oi/aeeK7LpLm/VEKN6Iv2bb8+w/+Hv4Hn13JdtREE8CKajUZt6H58GbT2SBf+NynHFx1TPEt/F6m8seYmsgYnvholurJogt7fh/RqnC9N1Pqb9ByocDn2OypGOkWTJUXFv+nAYrxIsf6c9PqHpJZJUfJdcKq1PBJO7buyxqiDFR20FX1zXhBJQUK+loV+A06X6HqcbTSuG9+DbVN7IiHioWaoVy4t4Vmtl9FsB9hjxIH8XweBZSOl59SwqPa3zMT32vnwgA5F1MZ2/mnQvowWBSKnGkLKFxY8l/y5YzCgWi/zvf/9jjz32KD/mOA577LEHDz744KjjC4UCg4ODNT9LE/556Z2UivaPaCndCCcbMRNnc/Sb8CxgPEp7mT0OYHO2X86JkWKdjXOk2jVauPTpjQQp/ptg9m4Es7YhmL07wbyDCOZ8imDO7hBWNo2D+x7KlTVGY4n0VwaCYHyRPsDNtw9x30NhOqrewtlUEAZMtxWThyZ8dsM1Mcrkp5wiKUHhXmTel8GfocTGpMB7Exk4oan7BSWPJv01TOep1C1RxaBRkUq1mLhrNxQYB0GGYOAka0KYh8CzgvAqouX02p9OQLTtSdOwf2sy0jU9Ct6LSOZSnWb6IF03GbLi5DxENoX4DrV3F9teSZNkrWYpAzgQ2w6RokabQvuBYAgo6Xpnr0X6DkHy/9R5mJRGqZweIAnOKpje62p6vIn/HlJ8Aql2J1/EkGCAoP84ZPaO6mA+cAISTLw4oYWFxwpHjubOnYvv+6y8cq2768orr8zMmaP/CM4880w6OzvLP2ussWj66iwo8plC5bPaGIyz/DOkohR4hocA3fAOHuf4Q+xxAM/wUMNWI8sTXn4qXd7fjFkKq9Xyd2qKSgbV26f0X/Bf0o1M6kVB1wB3K6rJwHZbJdhyU80bTpZIf8tNY2y3VaNKofDa1l2aAhWdiqk8XiYWji2ZH0QJi6/3HLpPm7iSp9KT6v8zApL7B8HczxLM+QhB37drNmnJ34mml+zmThSNgqSr5uQr0Rw4lmDegQT9JyDZG7WpLBAEOZi7F+Su17HwgPkgc4Cw4o2KXkk0mmPc8dP45RVLfsqSRZuWVAdKcJIaUfJe0+PiO2K6fgOxD2lZevILmO4LRze2je+JafumvdeiRrrajtdWGpmrUA1Wzrpa5ylXD5o2JVS5m6mIsqmSb62EMWHxgiDDFyFzP4X0Haz/Zi5r+p4XFCKieqrCnZR75+VvVfuBFhY7VjhyNFGccMIJDAwMlH/efvvtJT2lGmz90S3KhTIiskybPDaDkc7X91BffF2N9an1QXqc+5ZzgmTonxvh4TvaCUZ6/i01CJtrNgkzF/wXqPYaSqcc7rhhdbbcNDYpIv2w/D+davSxaHV70e20wig0nHSnQeogjUSQRZ22Y2hPrOGq+ww3+pKeJyVLnoqIV2vWKfnbkcGTlTwEfVD8N9J3ZFUUwbNcI4OSADsmGftvSNbQ30sPQf4fyNDpyNCZBN4s6Du6KoVZ755tBEry9jo5cNepb0jYACa+o5aamzBdGFFRulhRepUlgInviNNzCU7vDTgdP8Y4nTa9VPlbNcZg0odgptyG6bkGM/U2TPR9qmMyjiWL7VQIbGfVtwMXgkGIbq1rFgxrpMpEMOmqr1iFO6p6v8VRcfgF2jR3USKYqw17iWrFn0nqehXubUWPlgBWOHI0ZcoUXNdl1qza0P2sWbNYZZVVRh0fj8fp6Oio+VmacPAZX2ajD65XJkgh3Ogy4f43IdQjRs3anY00ilyeCZIbCYgnAxIpH3epKsYzVAjCBCE5lHSUqFQXQW+P2xRBqod6xKjGFyn0XAI0UmE3WBnAxLbETL0bM+UfmCl/x+n4Iab7Ykz6cEz7t6HnGoiHTUtBIztVGhIZRN2ZC0AR+g9Dik9Wns5er+TJ6bDzSID3GpJX0bSJ7YBu3nkqkSsYk3A6bUBEq7nm7qLRujLqFWz4QFZ7jSX3x7R9G9PzR+05NgGYxF6YqbdD9AMoYcwpaXS6MKkD654jkiMYOEWNLGfvRNB3JOJXPKaM26s6LacLvNftXBNKgpwwkmbAFMMBNfIVWRPT9WtIfhmiG0Lsg+rzlNizcu3iYxD2fjMRwtYeUhzHAHOhUdWipXKnlKOALSxWrHDkKBaLsfXWW3PXXXeVHwuCgLvuuosPfehDS3BmC4Z0R4pf/+dn/PhP32XX/XcgGo+Q6kjR0dO2XKXYfPGaIkavoCmUeiLtegTJF6/OkcsyBDcqJFIBa2+o5G/p0RwJtbqTBUFAbaVRfYI0EZH+lpvG6xtGikel83zoSh0H73Vk4PtQelaryEwUkTyUnlGdSjCIcVfG6b4Ioh/Sc8I2J2WE9xAHuiEYRAZPQQLrPyZDVNJa+QqRGjyVYOg8iO0woqpP0GiVU/V79fXChrbhazDem6Lqs6P0BKb9eEz6YIwztmVIw9GcFKb795D+GsR3huRnMT1/wETWqXu8DP4C8n+jTBgKDyID31P/pZFweux8w1SZvbfIuvb3DJCxNgk/xDhtOB3H4/Rej9NzKSax+4jJxmrHkTAduOClnyJFJPNHgoEfEwz9qmIvUHMfK6mflY0mIkWgqJ5XZun6Ur4iYIUjRwDHHnssv//97/njH//ICy+8wFFHHUUmk+Gggw5a0lNbILiuy4f324Hjr/wW09ZZBd/zCQQS6eVHeDzAfIZRLcrJNNaO7AycZf9tpEE52f5/mAEGWHyGb4sLjoHDTn6Xqas1Q/xCz51liUiPvq/eHpfrLpmG40xMpO84cN0lq9R10lbx9chr5W2JeBHJ/QWwG1/fN5Dh8yB/E2T/iMz/CkH+boiOTObZMnUAEipydhy9jvcCMnsXgvkHQ2RjMKLl9DJYda6B7OVI7q91qvqK6OtZz4eozfKhiaZnQvFzZoLnjYZxOrQlSueZmNSXwOmqe5yIam20ei5pK+ji2hzXf2v0CfEPQ/T9aKpsSImlk4aOMzGdZ1tfpakgPjJwsrUDGGOeiY8BccoWBjIMJmkfnzi0wewxyPC5ajeQuQqZ/1XEe6P2umWrgQ3Q952novT2E6FwO5L7P8R7dYHm0MLEsVQF3RcX9t9/f+bMmcPJJ5/MzJkzef/7389tt902SqS9LMAreQz3Z+jobScSjXD8ld/k9C+ey7z3+ijml59S0C6mMJVVmcO7HIs2ka2uTqpOkWD/3Y3REabbgWPt/6eyKl0smo7bSwqOK3x4n3522GuAfNaQSI0XIZhA75mlGPPm+3zxiPfKVWnNiPSvAGYF8MUj3mvYamQ0rOAXlDQA5O+A0v/QarWoCo39mdD/batDCglWEiUvIdHIQ+Cj0anQ+Vp0rGAQottAsaqC1rQrUQgGVdDuv2vHC6hJvxgBiVOpXPP1WmP0mhsNK5wmalN7jZvNNhzBn4lkLgf/PU1npQ+B/L3I8C9t9ZoL6cMhfeiIarow6jUyvQT10n/GxKDrfBVNey+A04tJHYCJboL4c7QnXDAHiID3hlYHmhQmvmPdeZvoZtB1jjbR9Wep3UL7CZjohhNeA0BbtRQfoPz+IICgDxm+BNP189prR1aHnmu1Rx0GwYX+b9hIkyhZ7DgJk/zkgs2lhaaxQpIjgG9+85t885vfXNLTWGDMmTGPG355E3ddcz9BENDR087hv/oq9//5YYJA6Jzazrx3+vHFX9b3PQAc47C5bF9uPvtpKr42Iw0eN0ePm8VADUG6Hfg0mlSYyqpszvY4S4GnyWRCAsNq6xRw3Np0WuP+asv+m2Ok8/VERPq7UTGKrCVIcRqmAKUAJoaJ2bRWMAvVt1SbMdqeYnTaf4eo6IOqUaKsRQrJj5S0D1iNS7axAmwrZA9FxtJux/WrjguAdhuRwhKtDdRVWnJ15lB1jfJPqNmJaUptgqp+8Wch8w9UcoFA0aheKphvq9401SiZC7W9SlVUxpgoEv8QFP5tSZSj83ZXR4iDyKj5GKcN0/6d0RMp3Gv7w6UoV4BJBhn+XUNyBFZIHr9Fnc0X9jMimKV/gE40nKzlse/Uv7aJllOC0ncUeG/b+Ts696HTIbYNxh2tkW1h8rDCkqNlEbPemsOLD7/Cq0++yc0X3kZ2UL/BOhGHQqbAqZ/71RKe4aJFPYJ0DlqGPdLgcSvZhce5r0yQTkYjRsszMQIQMTz1QBu7fqaf9i4PEQ1OLF3C7AnCtNsozWi9ST1iNFGRfn2C1EYl6jISvmp+QjGxuzpaLlpEjQzD1JXtceYkICihJGYMSEbJj7HEzJ+um6IM6viE6TVHtSmlJ/U502avP0wlslUACRv6ohVvwTygw44zkiBVVXRENoLYB/U6sffr1PL3QGxbzBitQ2puJXudpv1M2pIB30a6sKX1loAFQ0jhgVEpK9PxE6T/+/YeLXkM3oN5n0Fi20HnmeMKw0UEKdyj6zGykW/pUST3V0zyc2OOMSmGkO4adg0K+tpKoMsded/455aeR/vbhaQ9qffivV7VzLeFRQEjsvTINZcFDA4O0tnZycDAwGKrXHv5f69x0bF/5IWHXkYCIfCrQssjqtRWBAQSlAlSiHrO19XVbSGWZ2JUDTfqM2WVEtvuPsiRp75HJLq0lvQ3g7D3WG1KJZMN2Hmft8clRq+gGqNDaK6c//6b1yCdCnVBIwmN7QkX2xmn50IARHxk4DjrMj0y2uRSdsxuum+Xjd6YtKZRgvlUUnNhib7YsUPDx7AarrrTvK2uKxM8Kyh32qzRYvVxhnIqK30UTvsxSP4eZPBEPdYYcNfGdF2gqZ9xEAycpJ5C1QLuoE/nYLpt1MsSutTncDp+ojOUkmqGTJf+7r0BA98Gb4YljXYN43vgdI39ZVCy1yODp1OflEa1Wm7q3aO9lCYZIoG6o+f/SfkD25mmgvRxCE4w99PgvakE2BhLwD1Mz1WY6KaLdN7LIyayfy/fO8RygFeffIMffeIMnv3PC/glv5YYsWzJaCcLjnHYnO2ZyqpA45Yg1a1GYMUhRgB+yWXW23E6ezycRerqEIfoBxflBVDTwNHmqw8/nuep57RUe7JE+k89V+ThJwSSn4PoRoz+iIwBAZQeJRj8NcHAjyBzCbSdAO5KlO0KyhodH+hH02wT6G9o2nUjrCFGoBGtkCi2o2m7aFXH+bDNByiR8KgYRII2XR2kUnnlUlPZZqZg0ochwXxk8CQIMjYyZd28B3/a3PQja9vNPDSQtGlGEwqdbc82E8XEVT0o2euRObva5rd7QfFBDIFqlsK2Jiau4xTuH7e9huT/gb4eI/VSURuRy6u30CKGMQ6m46eYjp9B6quYtu9geq5uKi1m0oepbq3sHF6A2E4q2G9hkWJZDravEPj7xXeQHbLCzzpRohU17hem2PqZSyc9uKb+Wzlm4mwruzHAfLqYskIQI4VgjJDNRHCcRRk18qwYeVHBheRXILouDP4MjcAodtk+yT57pbn59sy4In3HUfH1eCL9ffbqYJddPgGpr8L8/VGiUa0VstEWGYDsJYCLEMDwb6lEaIr28HZUa2T7rZkYyHzGJknakZ7IhlB6pM7zoZjbao5MTCvagjn28ZHVdQ5aHddf9Zg1jHTXhGBASQKiZpbdf8A4aaT4giUvKU0JASpofmaMuVch9WUo/AdKT1hiJFrCH/sIZC+CoF+F0+3fw8S3R/J3IUNn2ZRTFPzZGo3rON0OWC8NOM6bWqzHkUnZtFq47lbX5aTBqS3ICIIAx5n8zwhjXEh+csJfZk3yk2BiSPZGTbvGPohpO3Kp6P+2vKNFjpZyDMwdxIjBYGhlQGvhGIcexu+g7ZpIU8ctSzCOGcMNXR8XMTxxXzswkV5YE0UaJQCLClEoPWpbXNSmrCIRw/W/m1ZuPttIpL/lpjGuu2QaXzziPZ56rthQpL/Px6Zy/VVHE+35PpSeQPBQYhGMurYioJLaqkboJRRuxmnVHQGIy9jkKAdmqqZS6ubMw3MjgGOr3YpjjBdQIVQhLLFIfwsT2xRKz4JJI5EtMcU7CHIz9b7EUE5JiU1nmQ6kjiB6JIxJQvclkL8N/Jlanp+7SR2gBUh+Ctp/hHFsy478nSCeFaUbkKhGSkovqgbKe84KzW1FXuITmAZfiMpziO+KeC+ir0Wyah08jVi1/wBj4gRBAIPHQv5fgE9guqDrNzjx7cccf3HBJPasMalsYfGgRT+Xcqz3/vdhXFPX8doYo59xnUl6Vu1a/JNrYYlhPGIUYvX1xhEBLzTqiXsnE3nwnkb9eUaLo2MxJUj77JWmgBKdCxntfL3BurFRRpEXUiFGn95nb268aQaJKadgnLQVu7pglBBUPiqt5gjQ+x6LmISkKaP+O8Ew42uO4tD5E73v8jXrfUz7VoDdDDGtvqaNYhFTgbb3ChBF3LVh4DvI0C8hezVkrqjMXTJoxK4EwRxk4HiC4vNqfDkGjIlhkvtA8rOQuxECS7oMkP8bJvuH2vvRk2yF2nxdg8zFENsKIqHhpaPEqP2HAIj3JpK/Eyk+OirNJsn9ILZHpVTT9EJib0h/FdN9ESb5aSSYD31ftXoga3UgfdB3KIG3+BrOtrD0oUWOlnJ8/thPssUumxCJujhu5eVKtidp606TSMX50Ke25fLnfs3Ka09dMUVILTTE7p/tX9JTWOQYSZC+QUiMEtxxwybl8vyRTtrfwEaM9kpz3e/WIurfoZ3dARNZD5L7EpZ+V9BeVbI/Hqo9pHJUpwTrw0DXBZj4bmpaWNfbJ9TQOOoMHdmCCf3RmzQYO6/sH5D+45GBH8K8z9jKMGNTaVE00jJy7KI6V8/fH5n7GaT03PjXLD2h2imTtrqhpHKQwp2VacV30XsKBm11XnjPBrLXYFKfxky9F7PSfTidZ2jqL3sjMm8/dc7uOxzpOxoJhhEJCIbO1aa6xXtUFN7+I8zUu3C6zsVpPw4T2xbx3kLmfVkjkzXra+8zd23z69rCcocWOVpK0D9ngN8ffzWnfuFXXHjMH5j1lmoIEqk4p910PD++7rt893dH8sUTPktbTxqvWKKYL7L+1utwxNkHku5M891LjsCNLH891VpYMLR3e2y1y6JMeS09qCZIELYEWYfetY6rOa6aIIESo+t/N42Y3IMMnIDM/QTB0C+Rwv2aduk4FVKfheQXwFkFjLeQQr9GRMaBtu/jJHbQ5qodp4AJPZKqzjVtqpUxMUjsAZ2nNXFNl0orkqyW1TttVpgdR6NfYePa0A27uqccjN4qBPz3kP5jm2iKaurctppMivemrnf+VnvNKn2XaVdiIz5SuE+9jExSzy69hAz9wuqK0kAMSo8gwxdB7k+Q/aO9H1er5IZ/bcXtVTMY/JmKvUfNK/zvoo66trA0o6U5Wgow3J/hhI+dzoyX30UCwTiGh//xP351z6lMXb2XSDTC+luvw48+fgavPfVm+Tw34tK9UifJNtUzrL/VOiRSMTIDuQZXamFFQarN54xrXm/CIXv5QSxmuPHSadz3UI7ttkqQTpUgdwu47wO/0qqht8fl/pvX4OHH8+yyfZJIJCyFz6m3UOYPSOZKSH4S4h/HuOuC046kDsIU70EK/4Xio+jHp0+tHikss28u7akT3wM6TsSJVEwfTXw76L1a/YXyt4P3PNBG2YEbABf6jxrjWiGq05FihdjvWIF4aDFQ9bzkqKQEg8rjNXCtzcBsNay0fkh1EdtaI2H+HDQl6WuaK7o1Mu+rIP3U2gqEPc1Keg1gFDnznlNiFJa440BQgNLjqjOSwDbahdBPidIjUG1D4L1oK8GqXcxDGIi3dD4rMlqRo6UAd159HzNeeY94OkG6K02iLcH8mQPcctHtAPi+z08+84saYgTgez73//VhLvj2H8gO5Thx7zPIDS+fneZbaA5uRDAGvnDUHNbccMX75huJGHbfKUU6ZT/avMch8Sm70VU8d9Iph913SlliBOWWIHoSUIDcX6D/MGToF8jgyTDwPUjsA9GNlaQ4bXVSbKFoeCLI1hCjECayLk7boZiuc7UpKXmrW8qoD5DTC/5sKqX7zcAB/01bYh+aXI4M61RrqeqRI6Ol9eUIzzjCaKcH03UeRNay50Qg9XXw3gKZQy25rL5OwXosRTCJPUYMmrbTtvMTa0dgOhrcD6Mfd7pVBE4ntVuhgfQ3cOLbjHlfLSzfaEWOlgL0zezHGINrNUWO4yAi9M9WR9zZ0+fy+tN1Gi6iwtxbL7sLN+ry5nNvk+5MkRnIjvJDamHFgO8ZjCNs8P4sseWn7/AEUV3mHYD3Ek73+QCI/y7Sf4xWQYXNPceE9QqSDu3LNfxrTGQjBFGCIeOllEI46CbcV+e5sbu9a7+tS5Hh34I3HdzVMe3fhuLDaiMwpih8JKzRo9NtIzZQIQ+xxmOZTpvCKqBkzAdKENkEIhuMe1UT3QR6b9LUltOmVWKzPsjYlXsCTgrT9o3RTV9jO4K7DvivgzjWAiCGSX9JbQBKj9n0YETn7XRBrLb6zKSPUNIrGcr+VZGNofMXONEm3KtbWK7RIkdLAVbfcFUQoVQoEY1H8UoexhhW32AagBKdcUpnb730LqLxCNFYhEQ6Xm4t0sKKB4OhkHWWYUfs8ZDQvmP+aw2eFyg7PqNEwMK4q0LPNeBPR4rPwOAPmrieh/oERZVUtZ+oXeNLzzc/5dge2jk+84s6T9ZnsYE3A4bO0iqvyAaY9h9j3IovTxAMoGSm2UhVmLLyIbIRJrGrtvnwXq56rvpYKo/Fd8V0nqEELXej6pZi22M6TtPGr81c3Rhwe3XUoF+9osbEytbBerQA3jht0H2xVteVngWnG5M+VMv3RTDBfCRzBeCBuyqm8wyMO612jOTeYJJI7q8gGUzsg5A+BGNi2rA2fzMSDGnD2fheLW+hFQwtcrQUYPcv7cTD/3icR/75OF5J+2FtvP367PMN/ba0ytorsdbGq/HK4280HMP3fJyIYf7M/sU06xaWThiMCbj2Nyux3UcHidj+ocsVTERNEhuSI6gx/EsfXXu6bewp/qwJXNR6HfnzofQ4pvv3SPYqGD63udNL91PxRapqEksM/BdHX630hlaQhem+0lNI4T6YcjPGkj1j2hFiNGyQOwphGiyGSX9Vm6umvoiUXkHyt2iaq/AvO78RhCv/L0h9Eaf9WKTtGMCvS1qaRvGh0deogQuxLca8hnFXxtRpIWKMgbYjIX2wao2c7obExiR2xyR2r3lMvLeRvkNUT4VBxIfYzUjbkZjopuP6KzUD8V5FMlepnUJkfUz6kKb71rWweNDqrTZBLKrear7n89+/PcJ7r89mymo97PyF7YnFKx8MM9+czU8++wtef6p+eq2FFioQHBeOO/ctdv/CwCLiRgb9btVMr7DJRlh9VaIigA5TaVPQ1FWRUNti2o+ruzkGAz+G3A0Tv7xJY9q+hUkfTDD/KCjeNd4JlNuBEOi8CNBmpAG4q+JMvU19gwoPgAwhmUvBe8me71Ame6mDcTrU4ycovQzz9mHs1FSduaSOxukY3cFexFdSUHygznlxiG6K03sd4r9njSNjEN16gTZ1yd2CDBxP/bSmLffv/C1E1gZnCo4zud/jJRhGhs+D0lNgOjDpr2DiHwYg6P++kkGTRt3Oh7DOlRDfAdP5K/XCWtBrl15G+ixxQ/R9ENkM03PZIu/ztqJjIvt3K3K0lMCNuOzyhQ81fH6VtVfiwsfO4oGbHuWCb1/OvHdHaBdWwAa0LTRGEMAFP14dN2rY9dP9i+AKzTZRXRRIoqmo6lYc4Zt/GK2k0hJwcn+CyGqQOkCP8mZA8B7irASFu+w4EyxikJKWjCc+gen+DTL3APCfanCwUb2OsVVaAmW/o7LUZzsk6EP6jgbvBVuNVl29ZXVCCHivVhyq8/fSfFjQoZy+y/8FaT9qVDrMGBe6L0H6vgHF++yjdi3xwX8HKdyvvkgyrNd211Jvpiaa0dYguqWKp2WQ2khVD8Q2B2dl6D8EfY+5BMn9cTpPmdg1GkCkhPR/C0qPq68TAVJ6Ajp/iUnsZisb7bqXiZFF8QFk+AJoOxRKL6knVHSTCUXRJHO52iiU3cA9bcuSvxOSe0/KPbaw8GglUZcRiAhXnnIjZx98EYPzh3BcR38iDo6j7UVaaKEMgWLe4fenTcNbUhxmUcFJoN10R1QYEUGNC23kwWkDCZDsXxERZPgiZN5nkL7DYN7ntalqTVPWpicAUkQyV2tqpOtMaDtGr0sEbTkSzq3Dlsz7tjLKeu+UI10OlF5CBk6H0nMgMWobpUrtv8X/InM+bDfYd+01k3bMCPU/0iPaQ8xpRxu/ZiCYi0gByf5JfYYyVyLBMMbEMR0nKnEx7VoRR1Tn7q6CDPzIVsylgYRtRntqU6smUiq7WJvImpjO09XIkiTQBonPYFa+D5L7Q/5GKuTbh9y1BMO/a+o646L4qBpTkrAVhxrVk+zl+ry7GoSC++pKNxNXMlW4G5n7aaT/KKTv60jfoeq03Sxsuq4sCjQR/X0iY7SwyNGKHC0jeOLuZ/nbb/+JMYa2rjZ8z6eYK7La+tOY/sIMSoXxqm5aWDEgxFMBsZhQKhmyQy4D8yP0rrw8vD8ctPooNBUMo0Y2MiN5tJ1GQLnpKEAwT7vJ5663D4T90ELjw2pUh2AbhWOtRULWbtbDv4T0d6DzbGs2OFcNI4M+PTYogREVhgd9WoYPliz1g/ckeE/YaxUaXDOED8FMZOg3EN/RbrBJu8F7er3IRpouKt+bJWbGpkFNAiEF875YFmMLrnpC9VwO7uoQ2RxKD1nPI1eJVeILMPxzJZ7GqbwepWdG9VsT8UCKGCelKazBn0HhHn0uvium4yRM4iPqgeS9peO778MYg+SuozZVan2jcn+DtsN1DH8ulB6zwvCtm+pwX5nckK1us9ufMXqPQb/+mj4aKT5uX78QMesxlQN/hh5vbN+90lPI4FmYrrOau35kfW3WLD4Y175vDUTWaf4eWljkaJGjZQRvPP0WEgiJdjV8jEQjFLIFHMfBK43uORUikY6Tz7S8j1YMCMaBZFpJQ7FoSLT5dPYsKDEaUbG00MctJJw1VcAqQ1q+HW6eJmo3uMBOYah2KkERctdUzS9cj9DEsVqzE9Pj3NUg9mGNMvivMG5z18y5QBvEt4P2y3Ei01TonLlEXZgj60Dio9D/XZRcJaj0pavSFDW9hjnwZlXK2cPMm7uelvsTA9OGmjxmtGxfYkoI0t9QzyavuvWHC97Lan7pv6Epp/K8DLR/HxPdHBk2ds3s1iE+uF1lYiTiQ+ZiJHs1iIdE1lPiVnqEcoSucBsyWMR0nYtxuiDWNeLe6hgyIuU1ktLzmhbztYsAThd0nY2Jbdvc0kXW0ygQWZCU3o8EEN1MrxbdEHr+iGSvhdzNGmkzthGucSypSSmxwVUiXriPYPBMFbsn9sLYserBtB2JFP+n7yuxkb7k5yG2Q3Pzb2GxoEWOlhG0dacREXw/wHXVBwnUIDISjeAVPepp61vEaMVBIhVgHMgN6weuG4GDf/SeVqwtEJrdqIVakfQiQvAORLe16SRX/XWKj1gCgI1yNJrfCBdoQOcasdoXG3VxV8WkD0WcqdpDjJh6+RAF73+MLX4uqqA6OA7puQITXb9cTSXBMOJNB3cT8B6hpmGsSVvtzQThz8esdBNkr0eC9zDuakhkA+j/lt28jY4dWGPK5KdVdOx0I0M/t4OEgnZf5196WnuNmZiNRonqi3J/g8SnIbaT6pHEs9EXB5P6WmVO2SuQzO/tuI6mCrEtPhzrdi15KNyPBP1KjkYi8QkrCh/xusV21vTowAngz1XyBxAM6GNT/tmUrYCJrAvtxyFDv1KibYxWjLV9r+qYdTAdP0bShyCDZ6omyHRC/COQvZxK5SE2SpkrRyYlewN0nYOJ71j/+k439FyhlYFBH0TWhdiHayJvLSx5tMjRMoKdPrcdf/vtrcx4+V20vFRItiXpXbWbd15+j3RXiuG+zLjjtLD8YpNtsnzm0Dk8cnc7BsMHdhpi248swKbbEOHGUy+K4jM6RRWiOjKyMChVNm4MJvYFaP8WkvuH+g75rzc4b6xrGx3XREDigKfpr/7voeLuiUTD4hpN8F7QlhrRjQGQwn3IwEka9Rq1dk5VFGyikbd3kHmfh+5rcSJT9aHSSwiO3ke1lspdFafzpzqf7F+qrhWmrywRMUn0ddQIdbk1hz9LK/46f65VXsUHwcQxyc9Dcr/yZSR3iw2GpcIL27RR9XvDXk/qRzSd1H4EpedVTB/OL7YztJ8AMgC+bX1SrkBMqPFj/3cRk8TEd4DEZ8ckGya1v/pOeS8oyYrtgCnPueo4dxqm+7zK/UkRKT4MpWeAkr2HEpVInZJJGToTE/974+s7bZD8XMPnW1jyaJGjZQTpjhSn/+MErjzlBt549m16p3Wx3/c/zYM3P8Yz972A7zdOrbWwYuCd1+NstFWWzbfPkM8aYomAaHP+fE0ijZohTgQRjZb4zzG6tUZICiby3vVAImAcZPgiTHx3TVNkfj/BeYUoWeGtJQGmHRm+EPUXmghZMWg1WHiPuvGL97ZWdwUZ6kfVAsrVawsC/y3o+xpM/af+Hlkf4jtD/h4Qq18yUUz64Mo5bi+VHm3hDxptSn8FivcDeZCkni+BRjcA46Qw1kqgwYT0H7ERa7HrilfRgUkRoptasXd9OJ2nEKSP1J5ykXVwImvrqZJCt60qkik5nW/hPsAghTsx/rvQ9s2xVk7TZ9ENxzxm1DkmBt3nIYNnQPExJWjBbMoiemP79PmzEPEm7Ikk+bu0Ca+UlOQl922ZTy4htMjRMoSeVbo55uIjRjzWxa2X3UX/nMmMELSwLGLWjBh/OHMah5/8Ll29vn6xNqAf3JMRVRxg4t5GAukvq2g5mDfiuQWJJgmQs/qZQBvFmuQCjjVyXB/iu0Pur0yMsAGkwfgaJXFXBVfJhBQfg2AArYprRLbCjT7KxNbWRuT81wgCD8eJIDKE9pAL7FhxSB8KyX0rp8V2hOg2tnmurcgyceg6Hye2DZI+XLVSZHUcd2VMezNO4kDkAyrylirCZ7qso7n1aItuiOn8xbhpJOOurLqcwp1IsRMSe2KcTkgfiGR+V/EJokA5PWoMSBbJ/BGSX8K4jQnYgkJ7xdl0qT8bmbs3uo5RGzkqQWTNiROj3F+RwdMtWQcp/hv8dzDtx07yHbTQDFrkaBmG7/tc+N0ryA7lcV0XL1geKpJaaBbG0fRq9Z576zW9vPZsgtOvfZO2LnDoQIW/zaAdHaxRv7CqKMNEMHQ2uKtYkmAFzzTSBzWLIpCyJeo9VEThC4KQRToaIYlsCMV3Gxwbpqp83Yzjn1Y9iveyRkTcVTFdZ2uVlpQgew26cTZDemx6pqleaSOiCf67BP2nQvHhqvMNUIDsdZD6crlKzpgodP8WMn9ASi+og3TqSxibBjRtR0B0C6RwFwRZiO9qy9ubQPCmnVuVC7hxofsyjNhSdXetcX2BREQ1Qblr7QMBZK+E7ssgfSTG6URy/9Q1915UclcmW5ZkBvPL7UoWFYy7ErQdqd5HMqzkCEe9q0ovYqIbNTWO3u95mqYr+x/lIHstkvqKXqeFxYpWvG4ZxkuPvsYTdz5DLBEl2Z4gllwIO/8WlilEYhGMY+oGI157rp3bbtwFx8RRt+gmSLOZgn4Dt9+AJw0+yBzVdhBRXxmnnYUjMxbR7SGyiaY6TPf4xzeCSdgNKWY3u++OcXDY/sPVzSt/tRXrGi2t771Bm6yCRqC8VyY4mSLQDYx0nTaQ/BJlLVA1UXXW1B5xpceoJVZWRxTMQgr3145mkpi2o3G6f4vTeVqZGJVRehJy/wf5v8Pg8Uj/MYiMXdwh4mnvOZPSlJnTo4RM8pjgHUxkPf1pxjCx9LjVHDkqKjcp8KYjw+dhjINJHYDTew2m9wZw10DTo2J/8hpNHNFLbVHBpA/BdJ6tQnIT07lkr0bmfYZg7qeQ4qNNjFKoVMXVkLygTsS1hcWBFjlahvHWc29TzBfJDmYZmj9MMb+8uf21UA/GUUFr4NWP4vheQLJzdcb3zCmPqB/MgG5G3egHc2isOFkoaBk0MHHjxZEw0HkGSE4bh9ZEZiZIvKQIFDWlFt2aMRuiRtahopMKTQJ93ZSL92OyV1WGLb3IglXvDdlU4RQgCpGtoPsqnM5Tofti1GTSwpkGHSdbv6JE/eEQKL2I5G8n6D+GoO+bSPY6ROq/f6TwsNVwiZJGoqpDylwxzryt94+ERpe2TQpOpbKsWXhvWrFzKAwHKEL+VoKBUxB/pj5sDKbzVDt+Rn9MHNN+4mLtVabO2m9brVVoDxGA9xLS9w2k2MhB3Z5vEpqOlWqSl1Pi7k7QfbyFSUErrbaM4pXHX+cPJ/2p1uOo1T5khYAEgldspInRN8EHtr6epiJG5XPCb/M5+wEfp24D0gWCi27ow0rCBCau6amDwn+R3NXag6wmqtHsnCO6+cR2VI+c1JcwxkHGIlfe6CaxgKaOxEPyd0L6MC3RLj5I869BzUWUYBgPiGLaDsLEP4gEGYy7CjLlTrUzMElw1lFrAAmF4PUqAw34M5GBa23lWIAU7oT83dB9yWjtj/esjiFxVGfmAYIU7tOUW0MEWoEVtsfAASNa/u6uPbElcHttNZqn4wTz9f4kA/n/Q4oPQc/VGHeKvnY911hBtgex7TDRTSd2vYWEiEDpVcpkudr7S7JI7kZMbMsxzs9D2w9g4HjKae0yyWtftJNvoS5a5GgZxe9/cBW5oTyxZIxirhmNQgvLHCZc3V05+PwTV+Xn178xgXOre0iJLUsOnaQXhshEwLE+QpIEd03w1ZVZCVh+wYfOXm77YCWsRiOMfsUo621GiZzDirKSal+6L8RE3lczrIluYLvdT+DvSkpAHrwXkNk76viyICL48EUf1vtIfgHieyK5vyNDZ9rKrCim7XAk8RkY/IYVVuftT5TaXnEG6ITSg/bcKrPJ4r3IrB2RxJ6Yju9VNmHTYaM/A9T0dSs9jZSerWtwKN50ZOA48F7Tc0xCXcLju2Dav1nrnu3PVUG19xq40zQtNeI1UNH4tmoeGRTtmthKMCmC/yYyeAp0/VajR5G1tUntJEGbAP/b+hCtj4ltPebxxhjE6QI/jDqGr6M1Kg3qRyNFisjQLyB3kx7vrgWJL2JMO8S2raRoVyCIP0utMJwOmzZf2CjzgqFFjpYxZIdz3Hvdf3nliTcI/IBSsSXCXh7QOaWdob4MIoLjGHx/4aqv3n61UYqlGYTRhxwLH+FxIQh9bgT86sjLSGIUofloldg0RgCO7SkmBcIoh7aG6LaRElCCFG5Yliz5ryFzP4fE98B0nV42EDROF5L8bFW7kfFgqu7FBelnwTVVDrgbQvqrmMhamuYrPa39y6SopIMiMnQ+5P5hvZ3Cfm4Ze/9hW4yYan46ToKB46gvpp8L+T8hxbuQ2Icw0Y2RxCcsOQ61LpXXQ3I3YaKbqf7Imw4mhjgrQ/8xSnZCYbQUIbYZTsdxNVeTYEA70ntvVR4r/Ad6rlTtUOlx8GdqKqnrN5jsH5Hcn9VlvEx47etYuAeyV0H6wAVc6/qQYBDpO7LKQdxFUl/Haf/26GOlaMX4HqQPgcGfoq9BuNZRwMFEt6icU3xchfMminhvQu7vGnnEUVKQD6D3WoxJTup9LQuQ/G32vZ4jFLbT+SuMkx733MlGixwtQ3jsjqf4yWfOophraYuWNwzOGyI0OI8kY1D08MdoCzMeuqdO9D1S028DZD7NkZRx4EwFdyUVlfozxjk41KqMRfhHhtMMoVOzOjoPa1olviuS+CRkfqebV/FZtGpvZDQoA4WbkHlvQu/1FU+Z+B7223wzui07J9NGWIZdiRpI7THjwpbOJz+rKb5gSPvCySAVHU8KGNJ0oklZQgJqJhlA95XgrIxhANzVMCZJMBCSp3pQ0Tb5fyKFf0HxCUh8EnJX2+dDLVEeJGPbd3xPz8GAu5FG8EwMLeG3a5D7J0H7KThOldlW/u/WfsG23wh8COYg8w+3Uq73KJPb5Oeg/USMadfoCrYHWXk9DZK9AtMEORIp6nWJIc5qmMLtSOlpMClM8pOYqr5mkrlEnb1N2NA3r87f8Q+pBQIexkQR/12k7zvgv6onOqtA+/cge70lrUbXJLYzpNVFXLJ/QYbOoOyHJXnKBpLhWvtvqrA99oHKnLwZ2nYlyGBi71en8+UM4r2FDJxitVa2rUvxAWT4fEzH8Yt9Pi1ytIzglcdf58RPnEGwkBGFFpZOVHd+KSxwyxfdgKMx4aATZi7MbEaNucCIbgad58C8T40zTlgF1kwk1Ha1Fw8VeedRY8goRDfDdJ2vm+v8Q22PsKByTvk6I+A9hcz9FBJ5H8S2h8xFjDatrAPTpZEe7wk0ZVdNQEaso2nXCrvSf6m0Ohn59yxQvAcZOAExbZD/h41EWWNJGdZ7xqMuUcRRV2e3F5haeSq+C+T/Ms69JABHxdfpIyw5ABVFl5SARjZE+o9VEmMSeh/eyGqscF5ZLb9vO7TyVNBn18K1b3pLpoI3qu6lQ//J/RmiH4DUfuqAXnqUCumMAVEIsogEYxoliveqnfMMe+0uJIyKCUjuBui+CBPdXB8r2YhR6FMkCZAhJPsnKP4AZBiJrKuk3HvZrpsB/13I3YiZ8n8aRfPfVKuJ6FYY4yJBPzL0S6snCyMhOUYTdqH6fSGl55G+o9BCAYNkDaQPw7Qd1fCel0mUnlVyHVoZ4GpKtfjIEplOq1ptGYCI8LMvntuQGDlOqyfP8gI34ujnt2NwowuSazfs9cV5bLXLcBXhciDxZZqvEGvGVjsCqaMhtsfYxxcehtmbgW+1KI3Gaip9F3ZCj6LkwJoUlg0c98R0X6oeQwMnaVd5ivbYsIpojL8V/00V9Q79zAqAIzSuAHPR+y6B95T+K300JlN2zsFbNv03jmdU/v8gd52N4FUfJ/ae7PmSseSwoKms6JbW96kWKgYer4Q+oseIj3FXxqQPQ7eIDOBrNCmyuUaMTNLqkuppxkIC4+p9VMNdR58vV3VVpSPLyFjiBVJ8AII50HmeElGiqM1BWs+Pbjo2MZIC0v9dm8azJo3B2zZF2aYkJRhChs6unORMsXMMX0vbGid/pyV3AqUXwHta522iSqRMQlub+O9gohtjEh/HxLataGb8d4GCHmcMZTdtAghyOifJaQVipOLcLUO/gKAfSJVJlWQuQ7xG7XKWUZgk5QbSZQSwBFJq0CJHywT+fM4tvPtq40iAANFkFDfi2jLvFpZV+J7tLC+ygGk14fXnk+XPdf03AJkDiX1oLlhc/U220fspUIO+4n/HOAbUZ2m8aGcz0SLbIFV8aj+2wmsHEAxjnHYV0+ZvXYBrJSlvVvgo+are/B2gDZzVrAO2TekRRzf3RvcZt5t9QVNhTVewhdGueghJgo0oIRDbBtN5Vn3n6fjuasRJosGYrtW9FHSjd9fAtB2F6b0O0/lrTM+VmI6fYpy4PV4Y23U9rj8j+6clPqZ+QHhK7MqVkqGIHvS1DJu53oTM3QeGfwqdp4PTpVV8JgeRtTEdp44xB8B7w/ZiS1gSE5KwUJsWVI6zMOkDbRpxyLpw52wUTdSnyyQsSQ9dyENY8bqJUxfOVPQ1DduoCJVKTgBfnbW7fl1rQ+C9qfOu7iVHqYk09TKG2Actec5qBCkYAhPBJPdfItNppdWWcsx8czbXnvHXMY+RQPCLPp/51sfY5EMb8ouvnY8glPItsfayCmkUgBj7LAA8zzBqfyzcQSXd0WybCltpUxeObh5lnVAzY428qZBQNHOzHfbY6qo6qNH2hO0p/FkskJDcxGykphHikPoCTsePCAZPh+yb9jwPaLdpj3oEybeaoYm+qCE5qndeCd1Uo2paGd8DnFUwjr4WIiXI3YCUXgKnA5PcF9N9ifYEC9t7lPVRNooRtvxI7qtCcMBE1oPIeuWrSmR9jWp4LzS4V9AS/rjOMVbbmd4YB0nur9f051hhskFtA/JV9zpg/28JVv5O1Qf1/kWbvpoYRLce18tIcCxBs3OVKmItWcoVhUERGf49pu0wFU93X4Jkr9A5RjeG0vOa9qm9G6BgTRpjNtu3i0Z+6sC4UyF9EJK51JIulLB1nIqJfVCjRu600SaZ7jTw+vRDwRiUXEXAWb5cs43TBt0XaguV0rPgdmDSB2GSey+R+bTI0VKIYr7IlafeyIyX3sWNupQKJWKJKMVCqeZz0hjonNqh3YUyBbbcdTM+9KltePDmx7jvLw8tsfm3sORgHGGDLRu15gjfPM2KtUPPlnqoJt7NEJHR2pjmiRFUNssxxg4dkZ0eG6mZoCi9nO6qBxs58F7TX4tPUIkAGDTK1CgQH67VRPVbJdVfNDSlzGlEIXs9DJ8PJoGkD0WSB8Dg96Fwr91QQXI3Y3oux+m5XKcsYtOABgk8TOFWJXCRjSD+kYZ9z4yJQddvkIGTbYPael/AAh3LWUu1S1WQ3F9h8IzKeSamr5MpgtjWMk4XBHPRNc9Qfk1yf4PU1zCJjza1eiIlyFxMOQ0pWXTLs2S+bLWg6S0ZPlcJkdOLSe6H6Ty7nLKT4d8j3nOawsS1r0kY+bHpQbMmlJ5H5u6OxD6MaT9uNHlLH41x10KKDwJRTGIvTPxDY96HaT8W6fsW2p4E/eBPfr4m9ba8wLjTMN3nL+lpAGBEFuw76oqKwcFBOjs7GRgYoKOjY9LHz2fzHLzRd5gzo/YbbKojie/5FHLF8udr98pd2l8rELJDWX5wxTfZ5QsfopArcMDaRzPQaka7xBG+PhOBEzE4jotX9Ca4nwpuRLjq0RfoWamyaVX2uXoC3uXlzz8G3ZfjxD8I2M1s+BzGJ25JdB2yVCJqDpoWqkMyTTd0ngn93616vtrTZqz1DSNxzRZVuJbkNUpfRe08PSUZ4b0m97c2BFFLPsSSlakaBYpugkkfWrNxq9/OeVC4XVOF0fdDbAvI3QYyE9y1Me3fr/HdCfxBmPc5CKY3mF9CKwe7L7TVXXORuZ9UgmHSujaS0Yq65Gc12hTfHYKZSN+hhIaV5ZQqQGRTTO9fxm1aCyCZq5HhX+k6Sp7yl4Loh8EIFB9AIz5xu8ZFu6bawsO0fbdcCSdS0qrB/D9UG0QBTbGmIAgA+3ltwvdTAPEPa0qyibmOey+l55DcTSB5THRLsNWMLUwME9m/W5GjpQznHX1pmRhVb6z5TIFoLEI8ESMIBN/zGeobxnENBkO6K82G265HEAS88+pMirkibtRdqHLw5RkLQloWBNFYpNzWpdlrfvDjW9E5pZ07rryvLMI3Bj5x+J68/NirvP70Ww1f12+eMYPuqVXf5ms+l6s350WhTZtoV/lJgklB+0/LxAjQTvSmA4Z/AzJ3jJPzaHoqqeXWpdeheB815oeVC2n0YfA0+3gbSqpCYtSGmjeOJEoJNFJivWzGbLpbdV5sV0x8GyT/AJTuH3GcA/G9oXCrRpfCjTIYVPNC8SlHu0JBevAOFOdB6WGk9JR1x1YxvQyeqeJpCXRNCm9D4ZbK/P356v3Tcw0msobOwO0gmHI7zPkIyCwqkcDwHhzt91Z6CmLbWI1MlSA5bE8TzIX0wWXhskiv6rr8sC9duI4xLceXYcTvQ4bO0t51zlRM2zcw8R1qVki8F5QYOsmKZkiyENsM8G0vurRdn1BnF1VdUTCMZK8tkyNj01+kD9foV+ZSytV8jrEEySkLppECFP6jTubNNu0dAya66WJ3/V7R0SJHSxnefO5tgLKwOtxQu1fu5H2br8Vbz79N36wBAj8ob5xuxOXgM77EDb+8iTuvvo/cUG4BNSsrDhYHMQLwPSUxjuvQMaWd/lmNUiSKSCzCD674Jqn2JO/bfC0e+vv/QOATh32E3b64E9nhHF9b75v0zx4dFTQOtHf7lUiRAVOzSUvV/ycaNWoUHalGCVgZ/RZdGufYScSUu3FcrdAS8VQD4nTgpPcnSH4K5n26okcaBQEK6kRdeBT8l/QxE4fozlrxJjk0wmA3w2AuFdF2O0p6sqpNiW0FmUuo3HcKev4EuRshf5ONOkD9Nh9UzjMrYbp+gXHaMelDCPq+o0SoDKN6nVG6sPD/JWCuTfmFc4lq018pQekJ3bwTuyPBsPoPSSPiVgB6NPqUvxXaDrdrLZjCbYixRQSj5hHReQT2vepMsWtV0udEVA/kTq1xQTYmiXT+HObvRyXyF0ZkDJK7RdNlQT+YCPizkP5joPtiTGyrqilYx+9QqyP6/jdOO8S2R7I3aKqq2jspFFMbp6zBEikgw+dD4W6df2wbe29ZkBSV1GK19s7RuUtOe9jlrrfNfx1M4uOQ+MSkRJRaWHRokaOlDJ1TNdQnImpJb1nO1NV7eeOZ6QzMGSj3UzOOIdmewC/5/N9v/smMl97VtFsLE0b1Wk8mfNscVhCG+8ZvJyFBwN3X3M9HvrILnVM6ePfVmWSHclz907/QM62bqWv0MjB3qMG5hl9+e2023/5luno9NOUSRc0Pq/U9KSol7s3CAN3QdTb0H07jCFE/WnkUNt8cC2G6ZCHXPfN7AsmoP433or12HEkfqSX5/mwakxE7j+KzELyGtiKJahommA5mJWBWJSIQZNG1C80qB4CYip7bjwOnEyk9oxEN0wOdp+HENkaiJyHRLTTd5b01IpplBbgmbUnZNtBx2oieWmE1V1V5ubwHpFFhfJIykSi7gkPt2gY2MhQBySPFh3WuUlKS0pDMhlVdBpFcpWvY8HmQvULPH5W+DNcnXhZ0m8jqSPLzugYhYTJRTNu3Rl3RiW1OkPwi5MJilEDHkxIMnY6+/2J63wbUh+jPNeTIpL6A5P+u4ueQJLorQeKTGHcl6Dpbo0/+uyDWZR3HrocPcR1LBk/TdBqWYPnTNe3oPatrbxyNUkqRcnWe5LWRrLsmMvxr9Xuy/e+k+AAm6IP0V+qsdQtLC1qaowliUWuO3nh2Okdvc7zqTSzcqMtHvrwT9/35IUSkRncUjUf1S04g2pC0lUZbqmAcS7om8FcWS0bpXqmLofnDBEFAJBahlC8RT8VYf5t1+d/tT9U9z406RCIRzvjbpmy2+VVUNqwEpA6z/bUy6qkSvNnsHVCZvE09BUDu4jHOCb9BT+Z7McnY6ahG84hR9qCRvCUSIUIzQQCfSok2dsMvQfzjUPgHSigMSoaMEh+x5eZEofdqjNOLzDvAeiSB9hhrU6fn/N8hmEkl0hNW+XWg5NX+btJKspL72wiVgDMVmbu3dWIeqVuKUNHlJCG+kwqlJUBTfiNhnamlQLllhRoK2XEbkd6U6nA6z8EkdkO8t5F5n9VzTdK2iAnX1tE1MXFM+w8xqc+XRxHxtYqu+AgQwyQ/hYnvVPeKIiVk+DfaXkNylogkbATIftEwbfqaBYMQ/zBO929rxyg9jwxfoAQo8j5M2zGYyJojruND8SFk4If2/WEgsiGm67eAQeZ+zF7Lel6J1ad1X47x31BC7KymLVr8N+xrthKm61xwVrHnB1WRx2Fw0pip9y2xvmErKlqao2UY79tsTX7zn59y9qEXM+/d+XSv3MUxFx/OHVfdB2iEo3qjLRVKahgYcVvEaClEmL6LJaJEYi65ofyYKc9oIkqyLcncd+YhAh1T2skO5ijlSxRyRR6/4+mG56baU/heidWm/R+1xMSD7CX2/+NFFhNa7RXMRglJmHawlWvZK6H9JCisajf7cJMODe3CqMoE0momVU5hND6mG5yNwH9i/PHK8O184joXk1RSQBElTTEbZQm/7Wd07iZsTmsgfZCmZPK3Ue4QHxIMk7QRhyJkrkZKj9s1aUdTUUWQeZD9fdVahKnJcH4hMYIw4iaDv9TWJd7L+nB0C8Y02gwNGWMf0Oqw4v00jpLlQRI6Dwn7lMX0vsd8rRxM+lCI76q/BjPteoS6m5C0RDGdp+laRjbDRNevnapxIfUlTOpLY1zLIpgD+btA+i1ZtU2QTbwSCRJb1m4MJvb+0UsT3QTTfcGYlzHGhfiO0Pt/Gnk0CSSyARQfRLzX0PdytRmoVqiZyJqYmOqADCC912rJPz5ENlLPLe9V/b3agNNEde1DN+gWlkq0yNFSiA22WY9LnvxVzWNvPPs2d11zP/nsaEdaCQQ36tZEm1qYGCYi0DbGEIlF8Epe0+cU86WyMLsyELiuQ+BLOaUXeL5GjKyeLDuYo1iVKh3rermhHGtvHNDVO0glwhAKcpu6MyUM0c1VlCxhE1gr9jUxjUiUnoTEXpC9ESVPoBtsWD1TVV3UzDUlh34UjTFPeVf9i5y1rUvx2NqtCuxGVP6oC20EqhvVUrm+DKJpxwCi22Mi62A6f4a0H4v4Q9D3Ve2wXnbZzANFazoZppfCMu9qTUm4HiPTe9UkNgYSBeZbLx9LPIqPgzuVWoIV3k4Y7Sqojshp06at3qsN1iOhr13ZudqgonTbm679TE1bhX5PpgfajsXEt8ZE1q0M465q16yg6xXqh6LrYBJ7Nrj2xCDDv1VBM2kweZvuylnyGvaKsy1nYh9BnJXVx8kkMIlPYKIbTeh6xp0C7k5IMAB9RyDe8/Z1LqCvbac9sgiRDaj0Q7Pnm4RqzqrhrKrHBQPoellC564+6vwWli60agGXEXz8kN350D7bNNxzFrwfVwuhB15ThxpYfcNpfPuCQ0mk4wvnSC6W7FT9FfpeUNMmpjgBDVkQCDNe9Zk/OzJGdCokB/WQwLSfoBECtPu7HZlKhEGgcL8tFQ8rsEIi5thj6rWUaIRwsx+PwNlUVPAOulk1AwOpr2gkTDJABtyVoe14cHoZ7W4ctXNxIf5RFUTbKjDj9OBE18J0/EhJIhk7pu3bZtKVlNyYxHCM54xn52R1QSZuf2JKCN2NqejIQAlNGNGwBRw4mK5zLHmpsx4mXukdVkOgfXA6cVKfwln5IZjyX5jyX5yVH8JJ71dLjADjroZpO9Lekl1bJ41p/+EY9w4SDCOZqwmGztEmrDLG+9t7XSNExkHfj6E/kW0bY7qg7QeY7sshsjYMngS5GyB7JdJ3EFIc2fOtOcjwheA9j+qZ2inruWQYyIGzKqbzzKYE1cZJqYu3k9TzJQNOJ6bzZy1B9lKOluZoglhUmqO+Wf288cx04qk4a26yOm2dqVF/PKVSiX3aD6xEiCZQDGSM0VR6xKXUijDVwuj6NBsFSqRi3Dj7cr6zw4m8/nSjKqhFi1gySvgGKObsJm8g3e7xte/PZO+vzScSgebeIJb0uNMwU/6BMXGk8AAy9PNKaockuiE5GlEyKdXySKA6jfhOYDptRKLeNUdqZaKUO543RBiBKkuAoV70pC4MJPfDdJym2p3S4/pYdGuM20tQeAz6DqgzjgOd5+Mk92g4shQfRwr36r34c6DwL8q+PcHQiHuqjoiNjBolbYoldP02KPkpWbIValwK+nzvXzCFOxH/PS3X92dXkaM8uOtgem/EmAiBNx3mfholkmHlWKCNgIPZ4Idpsar7bzsGp+3ocdZ1xFoU7kWKj2NMHBJ7qaN2o2ODQWT+IbZE376Ose0xXb8d7QoNBP3fhfzdVldkVCtH1vYeWwfTdjQmthXiTUfmfY6y/kkEGAZ3PUzXL8B0qDt1kwjmfRVKT2tlH9io2BAk98UkdoPoliPE8uNDvFeh+BhqwbAjZhLK+1uYOCayf7ciR0sB/nfHUxy9zfGctM/P+e7OJ7HvSgdz4Hrf1DLuKkSjUT77rU9UohwTFPl+5ICdW73X6kE0vTUu7NL5vhCNR9jz67su0mmNhe6Vuvj+5UdTKtQS3VLB4Zpfr8z0l62uBIB2MA02B9Nu+1W12couayUR3wFnys2Ynj+p5sVJQGQdbT5qXNRYL6t6EIpQfAbyf2PsN6VQ0W6E0ag2KuQnjD6NTEdV/4wsXa9GF6SPgvafQO9tmPYfIMPnIYM/Q4qPqQ7E7dVDC/9uME4AwxdoeXsDmNhWOO3H4rQfh4l/mBr9jkmgZLP6J4yuxXS9Yzuh/dbSSo7Cru4kIPlFbUFBSVN2ktc0TPQDGHdNTPoQnI4fY3quhOhGehxFNWnsOhtjO8k7kTUx7cfYtFtMyYXTpZGv6OYoWUpT1mPhgvcm4r3Z8L7rrkV8V5z2Y9VnaAxiBKhezX8FJYX22sWHVKxeb+z0keB0aMQlsNqs+O6Yqf/C6bm0Upnmv4cSPavLCnWZ3nPIvH2RuR8nGDhNBd5SGNgbtAAAd19JREFURAr3I7mbkdLz9efp9qKvaVVloDGY+Acx8Z0mTIwATGQ9TOqLmNR+LWK0jKClOVrC6Js9wK8OvpDBeUNlTYrvCbOnz+WXB53P6f/4ERt9sCJqPPSsA0h1JPj7JXdSKpRItsWZ8/Y8gjGiHpFYBGOMpoGMWWwGiMsSQj+iMWGXbPUNpuG6LvlhNeZcEpG4gbmDnHvkJbWvo0Cx4OB5hmcf35Z13u9CdBOtVJu7J+UIUU2ZffiN3XasL6eGFCb2AUzvDZVL5P+F5G+x0Y6qCInMHmfG4TzzOo/Yjpj4hxATh8GfUoliVL8vU1RMFcMJddOwh5k7TTUppacgmKe+Mt6LhJEbyd2M9NyIE13TbtL1RMsRCF5XUXPi43pe6WUke5VGoCIbYNKHVdylEx/T5rv5f1rRsEBsOzWhHD5fNTPOmpA+EOP0gq2Uknn7qYZKipX7M46NTGyEDPzY+hgB8e0xHWfURJKNOw16rrbtTAKIrKvRmyqY9NdUG1N6XElS/KNaqdX+Q8SbbnVJ4fUdyN+OFB+Gnj9iImvXexEXCuK9rZFG2/9No2ZFxHu7Lk010Q2h5wokq6X/JroBpL5SJoBluNOo1T95aBVhWCTgQf6viNOtuqzS4zoPE4W2ozHpQ2qvm/o6UnhQ3+NibOBtE4jvMrkL0sJSjVZabYKY7LTa43c9w08++wskEAq5Qtlvx7EVaOnONKmOJJvvvDGH/+KrdPRWvrUM92c4aKPv0D+7sTjVOAY36uI4DmttsjrTn59BqjNFqeAx3Nf42/GKBtd18P16m2UtIrEIx11+NB/58s5c/4ubuPqnf1aR/GL6KzKOIRKNEIm5BL5QyI7W3xjHcOmz57LmRvoNVcRHZu+AtpkIy4kHUaKSsJEggeiHdLOIrK2VNsVHkfxtICVMbDvbTT1A+n9Q5Z68AIhsjOm9DrzXNM0iWUbpiKJbqVdQWdcD4ICzAZgA/JepDxfdEAM0qjLihYm+H6f3BoL5h0Px3jrnx3Q92n6IiW6ClJ6A4YsoewcZtIVFz+UqwAU1+SveD97bKpyO7143TVSNoPgMzP8ilao+m7Z0p2Km3KbtNgLbrNZ0LJQ+Rfy5Wj4fzMa4a0HqSyAeMvRryF2n7wljG7zKoKbnUvtC6oujCNfCIBg6R6NHpDQ9GxSBjEayYh/CpL+KcXoW7B6HL0Qyv0cjPqHjdSc48fDi4HTq/RFHyZQSKNN92ahKNyk+aRvPztN2K21HY5xOWli20SrlX4YQRnOCoCpyYSO6pYLHUN8whVyRf9/wADNfn82uX9yB6S+8Q3tPG17RIzNgS6Ab6I8kEDybeikVSuWQc7Xo13EdbXA7sppqBULQ5HeEzint3HLR7fzvjqf4yJd3JpGOk89MRIC84KgmRhtusx7PP1SfIKy+waqsseGqBN7rqkuJbq6uvoX/UNmMUUGr00NZjFx8EOb/28Zv4lSaqUaR/C0Yfzqm7Sik82cw+04m7jsEEIH2EzEmSZD7u5IO04EKXjNo5dHOWq1Vdi8O39wBBG+U5zRKMwM6jmmnUrIPFfVAAKVnCXK3QumRBvMTEBcK9yLDv9RIFD6QAicNQQlKzyC5WzGpz+oyGgfiH9Y9FxARJFBPnkYkybgr2TW2xMQYjSIF/RqhcqdhnMqHt3jTVQdjYqrTcUKz2BLk/6WtOdyVrfNypexf/DnI/AMheE9vzQCFf2O6f4eJrIIY9SPS9R/Qf/3XkOFzVXjffWHNeAsDk/oqkr9D5xIElF+f0vPgvYgU7oCeKxeMIKWPwkTWR4qPg/8OFO7RyFA1JKsfrI59XKxI2nsBRpAjE3s/JvbrcS8rUkSGLy5fzyQ+CakDWv5FywFa5GgJY4Ot12HDbdbh+QdftqRIUCNW/dBPtiWJJaKUCiWe+e8LvPjIKziuo4Fwx8FxHeLJ2Ghn7Dpk6a0XZuAYQ/+cYk06JjQqdKMuPat0UcgVyPRny+7OixNLKuU3VgDVcR3au9MMzBuif/YgmYEsrz35Js/c9wKb7bwR//nLw4tpkkpq9zl6b7bZa0tO3PuMUYcYx7Dquj3I3H1sKwyAKLR9VyuevBf0IXdl1ahEt0CGL0KGL6LcvRyoiIhD0u4imcuR2K4Y71lNUQQLQI7c92Fi2yBBv9WKiCUGjqb0JA+l19BS8pFpNqvLqelnVrU4ZdhqrxqEzxsYPB21KWjgmu10QvE/aDTHtoEgq8QoJGRDZyDuKqM6qkvpRWTgBNVumSikDoT0EaObhDoddvO2X1gQyq7PpvYbreTvQgZ/DEHGaodWg+6LwZ2mbTOK/6X8B5/7G3RfhLERQsn+UckIKXAcjaqUnlSdj7s2Fb1UkcprHdd5lB5T4pX85Og1WgAYdyr0XIVkr9H1LT1vxdZRfT38GZC9DiYoCgc0spb4KCbxUSSYj8x7wTqjx9CIqQF3TU0lhu1EwtfeLHgGQAZPVZNKu/7ivYyRYSR1oJJ9Z0pdoiTe9KrecFNsb7gdF3geLUw+WoLsJYxINMKPrz+WD++7A+09bTiuQzSu32yiiSixhP4/8AMCL8CJOCTbkyTSCQq5Al7RIxKP1OwTbsSp2QtCiC/4XjCKfPglH6/g4Zd85r/Xz+C8YSVGtoprInAiDk50wd9W2jZlgU+fOAxsscsmuG7jb3pu1CU3rKmzWCpGqiNFoi1J/+wB3nt1VmWNFvG8O1fqIBJzGe7L8P5dN+PD++4w6hgJhM23vgfxXqp6tATDZ0Pq6+q8nPgsdJyOiW6h5xTuQzf9RmTY181LitB/GDL4M+1ftiBo+y4y9Etkzu5QuBPI61gyD6QPyIG8Q/2PJqmIwZ0uKt8ARhBbyVP7YlQ3RE3ppmVSGjkr+zM5qEDZtQaHJTTVV02yqtJ0kkUGjtPKsfAq3nRk/tdU5yRFCPJI5hLIXjvqToxJWq2LKGkN5th5ObaM3F4mmI8MnqxtS0y7zt9/Bxk8BXI3W2IUswLnhBKf6uv571l9TehVFdF18GdCfHcV2ONR6flmKwhlAG0xUlsUsrAw7hSc9u9AbFsrFA9bp7jK0/zxtGtNXMPpUXfryLpgK9hM+kho/6Fqr2RIXaolq8fEP7xA1xF/JuRv13tw2mx1m0GGL0Zm74rM/QQydx+kbJ0QnjcP6TvMNqadC6XnkP7vTvpat7BwaJGjpQCdUzr4wR+/yV/n/oEbZ17Kbx88g10+/yFc18EreQR+oJEhA4mUxu4dxxCJuiTScXJDFc2LcQzxdJVOYIJBGN/zK+eEkawJIPACglLtJquEr3GQMt1ZEQGr5UCV8HQRVNe5EZcNtlmXTXfYkO9dehS/uucUjjr367Y8vhbG0datQSC4Eadm/dUM0sUNyeACBLzciIMbGT8EHwYXfC/glcdf57bL7+KZ+18YRciiMWHPfWdWn2l/fBj4gWpM8n+F/m8gOVslZKor2xohD+Q07UMSdYEO+33VzBStxGqnxhUYIP4xyFwI2ctsyix0nA7NJqGiF4pS9+NJBnSjTx0CkU3rXB80CpKD6A5V84jpnNuPsQTLV6LgdFkdlr2uaddNuzxOlNFRKIM2VJ2DDJyomq5gEPoOtgTPp+KQHSD5f9SZI5A+TMlqOWLjQDCA9B9jnZkB703dxE1S3wTG1Xl7LyDeGzqXcL5Gm7mKV2Uv4a5mI3Nh3zVL8NzVMMbBdJyG6fwVRLe29xmK9W0aM3+LpvQmGcZdQ68hNpUvnt5KZI3JGT+6Eab3L5ip92qbjrYjceIfxHSfD7HttfIy+UlNL4bi+olChin7XJUf89C/Ffu4PwPp/w5SbikDFO5SAh56Y5k2JaLZPy/w/bYw+Wil1ZYydPS209HbzrcuOITTvtDPq0++gQRCoi1BMVfEK/lE4w5BEGCMYaW1pvDW8zPK50sg5AYXjwamWai+qcHma8Ar+aQ7U6y2/iq8+sSbNXooCWob8E4GHNdwxq0n0tnbTm44x/QXZrDLF7Znty/tyE0X3Mb9f3mI/HCB922+Bjt8+oPEkzFee/JN/nb+rRqpi0Uo5Ip4nk9Hb/voti0OdHTr2MaY0c7YVYin4k0ZPRrHYWj+ML7n8/Jjr3H2oa+BgVgiVj6/a6rHd34xnfau6vlUr1sR6LYPZ5ChMyC+Cyb5Kdvrqpn2M0Wgj3IUBheSB0Lp4YpI2iQx7Sch0fdD5jKQQYjvrGmUwl0j5hbOryoaFMy11wmr1arhg7OOpl9kzhjzdKDrHIz027L9AGLbYaKbEXgvQ/4m60mEki2nR12MjUHba+Ts3MLoUTjPEe/l4n+QwZ9i3JW1+iy8djlNZudcB8YYxHuOSsQu0J9gLpK/A9O2rqbfyr3Y7Me1+OC0Y9ypiIZbbOQlsGm33so10l9H8veC/xrlpqhmJSS2g9Jm40DiIxDdEpn7cas7CglnVMlL4W6IfH2MtV4AJD8H+Tuh9D/KFXuRTdXKYJKgX7SU+EgwqJEayWHav6eVcAsLd3VwusGfS6X9iu1RFxY5kNCoYPEpSOym50mGypcWKFsP1PT9a2FJo0WOllJ0Te3krDtO4qVHXiWXKbD2Zmtw2Q+v4YGbHsUregjQvUoX897tK2+QQRDURnsaiLSXBAK//kQMhvaeNJvvvAmrrD2Vl//3+qhjJrugslTwOGDNI/no13blob//j+xgFsd1+MShH2HujPm89/psEKFvVj8v/+91zvjniWy47bo898BLvPTYa+SGcmVC9OhtT46+gGhVm+M6eCWfldeeypzpc8t2C45biRblhvJEYm5dAhiNR1RQ7/kgMrqaTqzIHjBGOPnSN1h3sxy5rEOqLahIK0KYRNUDcU0/+e9qms2bC5naljW1qNbnhKRmGEhh0vuCe6x2Kw/6ILKmpjaAoP07ULgPiKqguCGslYCE5dcOFauASHmTK3srlXt7VaPWAMwE72Gim2qUoPqojh+DuwpS+DcQxSQ/qW7K+Tsol3hLWq/v9KgdQmQjyPyeukQn92cksbe9frhOlbmY+G5171hEoFhvTQRyf0fSR4C7LsR3s3PL2Xs2ENsGSXxau9b7b0Jg04buKjV9y4zTgyR2h8xr6AbuaApz3mcIUl/DpA/EmATGnYLEdoDCHZQr9iQOJleJ7kwijIlD90WQuwnx38G4q0Dy02Wt1GRCvP9v77zDpKiyPvzequrc0xPIOYMiRlREV9eA4prDmgNmRV0D6JrTKuY1p3XNnzmsOSIgJkwIBjKSJCMwqXNX3e+PW93TzUR0YAa87/PMA9NdVfdWVU/fUyf8zmLk2hGu8KVQXrjIFYjAEXVvL21l0AhvvcnhUiZB2oji25Dlo9VnElDXOOMame7vMt+4xtWZMlEhZD/ZIgnh3f6Pn6ym2dCl/OvJhlLIbgp2xmbs/33Kgp8XUVQWZq/j/sL5O1+O40gsj0l1eRR7U2s+K5T3xBCCTNrOLfbrEijyq/BhHVhek0xKnbcwBIYhapLJGzIQBXi8FoGiAJm0TSaZRkpVQejxeZBSEquM02+HXiz9ZQXV5dU4tsROqwTPhhLHPX6LdKJm8TZMg23+OpClvyxn5cLfam2vjCWVEyYEHHrecDr07IAQgtJOJbx06xtKjdvNAyscW2J5JefdtISSdhm69k7QpVcKw8zaQpYKF8kEObVhqSrRRNsPVE8pwFl7nutlsaltBNSTvGxtjWjzap25aU5iPJRfSE2ZfrYLfH2LrU+9LywouhIysyD2ght+UCEj1Zndq7xR6xhDeVcTCCLavasW3SYgM78i156mFsXsh8Z/OCJyrWu4OsiKUUrPKEf2w2W4npD33IUw2/ML8OyMKHuyzqo16cSQK3ehbpVwP6LtewirK1ImkGtHqmrC7PkJLyJ8PgQOR0afhMwiVeEWOqXWOTur9lZGq/C5YVEnNwbeXRClDyrpgNhryKobyckhyAQIE1H6WLMv3NKpVgnJqS8BLyJ4JARPbfYqL5n+GbnmTGUQ5jBARBBt/oewuhZun1mALL8EbDdk6dsTEbk+F3qTTkx5XBMfAhI820J4NMJZipQZqLyqDg+QD9F+YoGhJaNPIqsfRP0tmGqc4lubrTJQUze6lH8zIRFL8v3YH6laW02fbXvSd/te7H9q4VNotwGdmT15HhnLwPJYm55xJCEQ9iOEIFoRrXez+gwjUHlOoNb8cEkIy2sRr4rj8Vlc9uyFXHvIrbltsuQ8NcItj/dYVMaT2BkHyxvK28Zh+qRZGJZJuDhEKpEilsrQQPMygALDSAiBYzv88Mm0enOo7IyNYRq59995dBz+oI9QSZA+2/akTefSeluVBMM2EsFDV3fBtCSl7TJc+chC+m3jVpMFzkJ4eyIrb1Rf3BI3b+eUnGEEqAarFVdB8gtql+nnaQ3lh5l8f6nbMHJiUH4RhfpF2XyWegwtUFV04XMRgcPUApr6Dux5rmdEqsRX2VAVpdvfLXhMkw0jcHNdyp5TOTZOpWpa6htec272UrAGAe+7555/zh7wHwLpGWpRla7YpjVA5bTUV85vBJFGF3B+qeNN081pcc8pM9PNO1KfTWQUWf0oInAsRtHoOo8vEx8hqx9zqwKFG1bL92p5lKRBciL4h0HgcLAXugndSRA+RNE/m98wkg6y4hLX2FPhQFn9AEKmITyy+caJv6vENMn/XnGr1GS1uld5xpGUSVX9l5mvDEkcSH6MrAogim9U21Td7ip6u6rn6e9Uo96ypxGZuUgclJGfFyoXHmo1qQ2dqkQlMwtUGNSzTe2KRk2Loo2jVkrFb5Vce+htzPtxIVJKLK/FiOuP4fALDsht8/PnM1gyZxl2xq7ptwYEIwHi1Yna5fqOEpd0pKz1sC3cBOMBO/ahuH2Er95uvsoJj8+q1eYii2EaVK+NIoTAF/DU9AlbD7wBL3sf/xcmvjKJdDJNOpXBtExOvel4vvzf18g6QnpZh2k2vynp5nMhoWp1FeHSMMl4Mjcfx85QXR7F62tY3K8usmNJRzbobcrPtcqkMlSnMsSq4lSsqqS4bQTDNHBsp1b4LRE38XgdAmEbIWDNSos7LujOQ2Nn4fEC8UeQgdeg+E6IPae8Rt7dEOFzkU5U/W6UIYxSKHkQuWp/V0+oLrwqPCeTIDyIdVSDZWIcMvqEMiZyHpE8nSGkKkV3FlMLzy4Ybf6b+1UYYSh7TMkMZGaB0R4ROk0teJm11O0SDEDRKETwhJo5yZRbGbQGrJ4I7451npkw20Po9Fop3jL1PbL8QtdblZ9A7oYAvTshvIOh7GmIvYR0lqsWEYFjEEZw3WEKxyy5UVW4FXjTvCBKwHSTk51yNy8n/7Pn6jzJclSV3TpzToxXkgLZPCMy1IQhXW+X8Kn8F0flbglhIIpGIYPHqp5xZteadivNhExPR1bdD6mJgKW0o4ShjL3YixA6p1kasirP1M0UGCnqnZp/1xV1zMxRxqHwk6uikzFIjkfKf6ES1D9AXTs3/CcNJY9hL3ANIPe6iiI1hoy729ZeaoXVx62o07RGtHHUSnn6upeY9+NCFXIyDRLRJM9c/zLb/HUgfbbtCcCDFz5JMp6iuG2ETMYm4ebCGKZBcdsIqWSaZCxJpCxMMp4iEXWf4tdZoLNhGmEKFkz/lW52XR2966dN51JWL11b+KKAotIwiVgSr99br3Hk2DVd6OsLqTWIUF6XrXcfyK6H7MQTV73A6mVrKetUSlFpiImvTFJJq/Vgp22q1lbnBDCFG94rX1WBkuCpyQXKpOsSHdywOLaDP+hjzfJy2nVry6pFq3DyktRNj4mdUUaR6f41e/2S35Z7WLnES5dernZR/A2ld5OerBJ4M7OQqUnK6MBRLRiKb0V4BiHrbQYrVHUNKTDCiKIrQIRwKseo5GbhhcS7ynAqCMvle1qEKh+PPbLOsQ2QK5SInzUAYbjeO6MMEbmqcFP/fsjqX6hbhDIKztrcU7h0osjyf9S0jMBEBk9QSblNWISllMoYk5WoPnASKFf/Gp3ANxRRdKnyDgkPhM9cL0UH4d0RWfqoqiR0ytV1MEpViMW9BhhlKlnd/o2cgSST7ut198yTsRdUrpAoAiHdY+c1wBURlMaSofR/8udkdgZz/b4DmoJMT0euPd1NhHdDt47jJuIb7udmneqv7L6ZucpItpeoPnLh82uFxApwlrs5Wl7qDOMa7dxqx3zqu3P54dt1vYbu/6UDVhclj5D8CJy8vwHfftortAmijaNWyuzvfkFKSaI6gWM7CNPATmdY8POv9Nm2J5l0hhULV2F6VOKv1zQwDEF1eZR0Iq30jKSkW/9OJKJJ4tUJTMvMGSAen+q35g2oZG7DMvEHldrz7O9qJ0UXkPddYRiCS544l4rfqrjjlAdqcn0kVK2txjANkvHaLS6aBTflI5OyqS6P8u5/x7L0l+UIIVg6dzl3nP4Q6XiqXnvGF1Tnnq8MnjOkpPKmRdoUEa9OkEqkcmP97ukK4VYXrd9+8WiCdDLN6iWrsfwe0ol0Tiw0G0ZNxEwyGYHPr/SpDEPiD2W/yCXEX0GJJ5pAkXoiTn+Dah/ih8yvyjtS9rKqwnGW1TETA4zO6ulaJtUCnJ5BLkmY7OK2LnlVaWZvSLxVxzYOZGarxdPoBCX3Ijz96tgOCJ2uKsPiz+W9mPehzMxQ3qLMQmT8ebcbutc9/wTE/ouUayByU+OLlqwExxUTzMpMyCIgjSh7DGmvRK76G1KuBSzw7ooIn6kaxa7bA6weDN9uyLYfQHoy0kmofnZmh5ozExZE/qVCUdINERkBRPF19eeoyCqUEeRWRYkSdS4ipHKJsppN/oPBW1sva0Mgo08pIUuKUBWPbosXJwbCAe+O9QsmrjndNfAEpGcg099D2bPK21cXRhtyydFYFCbv+6D4jtpjWf3A7FXTry7749vXNaQtpG93lbSebTQskyrh3+qptim+EVmecBPbUXNIfoyMPoMInfy7rpumZdDGUSvFH/KRjLkuYQGk3GRjUy0ClseipF2EVYtXI/0e5fFI2QTCfkZcfwyVq6so7VDCnCnz+OTFL/CH/cQq47nj+QI+LK9F9dpqhGkQLFJu4kwDTVQ9PmWIZeclDMF+p+7FtntuxbWH3JYzjHLeFunqHjW30nY2DzZrx0jJE1c9j51x8If8WB4zp/JdH76Al1BxKGcEJaLJmmTndQyYcHGQmCFIxlL4w34S1fHGUo5qT1kowytSFiYZS5FKpnKpM8JQzYDru07Z6x2MBJASUrFUzjOXSqZIRtX7mZRBJmUgDMmwv6+hTfv8J+asN8gG8pKZhaU8PliqhLzqFlVyXyc22Nn3HEh/6/7f3b9eEUnUeOYAKPonlJ9P3XlHfsCnRA4rLoE2r9VpYAjhQRRfh5P4EGQ2uT0/8z6MXH2Ma8Rl23+kyVXEIVWVlNkXET6tgTmjPGXC6x4nqx+mkmilUw1rT6fGM5GC1CfINZ+BaOO25rBU0nP4/AKDpxaZGciqf4O9XKmPF12C8O9XMw3fblD2f5D8Us3fu2v9xiO4velmuOE4SxnDIqjagaRngFyrjAHffs0SxmoSzm8oy90EGXG9ca6BZG2JiNxQ524y/qoyjEQ2BOcogzXxtjKU60AYpRA6XYlwymxZvZILEJFravVSA5ShWXIvsuIyyMwGDPAPRxRdWrNN5GpkRRRSbvsZq49Sms8ZqT632bEvL88ohqx+AAIHq3lpNgm0cdRKKe1QUvNL3kK8anFN1cXpt5zAnac9RKxSGQGGYXDEhQdx+AUHsGrxaj54fDzff/wjqUQax3YKusc7jqOSf4UAR+I4Doah9JPqI53McNgFB7Bm2Vq+evs70qkMn732FfN+WMjcKTU5KutdALlORZnlNZGS+pPL1zm85eY02Rk7JyiZqG5Y60lKSSqeQhgGbTqXsGTOcsQ6bnVf0Kcau7rGXqgkyGXP/IPbTr6f6rX1J4/XPZ76NxlL4fF7ci1b2nQuo7o8SrQihmmpEFm9x6AwL0lKWWeLF+kI2nXOLtjZCjED5dlZx7IskFB3VMm4MEGWop7uoUY9uooagyb/pqWonduRjwWlzyPS3yJT41WYR4bd4+XfTLeruvCpVhL28oKE2VoUX+cmfWcTvQEiSmHaXrDOHLOGUZY0VN+O46xGFI2qt0pKCA+EzkFW36N0kBBqgQ6erEKVdVbe2SBX1gwdX6zChW2erXNxlJm5bk5TDPCAvQJZcaUKr3l3qpmL1ResvvVfj/x5h89FZuaohGGSIPyIostUbpR3cJOO0ex4tlBePCfh3go36bnoEkRDTW6dcvVvTuXbACmQTkXDIczQ2QizCzL5OWAiAvshfHs2OMVcYr5cC3hrCUQKowRKHnLDdmlVIZifcC9doVThISebId3Qnr1C6SJpNgm0cdRKCYQDuTYi0nFU+MwQxCpq8ix2O2xnisrCTHz5SzKpDNv8dSu69u/EQxc9ydhnJpKMJ5VBJCGVr5Hj5tIkqhN4/BahoiDV5VGEYbhPkfUbN+8+OlbpLLl5S9HyGLO/q6Papgl43BDRusP5Aj4VSgyop7Gcx6sODNMgFAmSjCXJpDNUr43iOE6jfeFSiTSmZTLspD0YtNsAbj/lwRrDQ6rS+n479KZ99zbM/u4XvH4Pu/99KOUrKyjrWLLexhGoEKTpUYtwcdsIp918PHsf9xdOG3ghqXiKQFGARHVCtSqpg6o11QQjNTowpmVi1xPmmzVtGLQ9RXWUT7ynkkKzYntqNu65ug1AZdINuUSBMBgSnLxSdWGsc5/WR0TLUG1HSLtWYtaYWnd/2/VwuC0u3K739R7VPxyn9DGoutvVV+oFoTPW8ebks+54DsQeQwo/ougf9Q8UVN3iZeIDNUfPVuDbz9U9agyh9rEXqR5coZNqb5IY53qmXJkFVKWarP4PlAwoaEBbcDbOGhWSMjvUqogTRhhKH4L0D+BUgtUXYXWv8zgbjeBZEHsT5KqaW2EOgMDx9Vb0AQjPAGRCUJOUrj47wurf4HBCCOWtCRy8XtNU4pH1N78VQqgcvTrfDLjCkCuo8TSmAK9qDKzZZNDGUSulx8CumB4Tr8+D5bVIJdLY6Qw9tiqU199mj4Fss8dAAL5861uuPGAM0Yp4gx4IgGQiBY4k0raILv06IQzBmmXllHYoZvWyNSyeVVfOiZs07aay1NbbaTqBogAPT76Nj5+ZyKt3v0M6mUFKB2EY2Bkbj8/DnsfsythnJtbaN1t5JwyBP+TLeWHCxUGqGwilZQkVB0nFU5xw9d85+tJDsDM2Hz41gZ8+m4ltq4o127aZ/e1cZfhJiZSSp695SYW/1hVjbAQhBB6/B9MyKCoL8+gP/87JF0gpqVxdjWmZue3qM46QkIym3FYsglQiVa+XLlRchGH1QHoHI5Puok4ROcVpz84qETf5oXrP7AzBE6D6TlR+hof8hSgrVJebyHolTrml03hRoTOHmhwQQU0FGOQ8UL5hBUrP9WH4dgPfbkgnpkJT6enUGEZZpep8L9m685YQexoaMI7UInsQeIcgK0ZD9An1I0oanV++d02mvkHUZRzlG3Iy4eYLSUh9gVx9HJQ+UtBWQ8qEkmXIau2YnaH4doRny3Xm7YF6KvNaApGehCRKTUNfwP4VkfgfBI+pf8fA0ZD6VkkOyKQy1P0Hg/+A+vdpIYQQELkSWX4ZECUrmyGKLtAhtU0MbRy1Ug4+dzhTJvzMDxN+JpVIIQzBHkcNZY+jdqlze9u2eeiiJ1UIrYHQGKjcIX/ITzKWIl6dZNa3cwmXhrhz/PV06duJWHWce85+lAkvfF6wX6DIT6I6of7e85utNmWdzFtXi9sW8a83LyNUHOSHT2fkwn4I8FiC7fYaxLn3nsqkN79DCJWPg3RTpSU5g0w6SqQxlUhTVBamak11znCqD3/Ih+kxMTMm3bbojBCCeT8uZNGMJdgZu2DfdDqDk1FtWjBUGOv3tDMpaR9BGAbRyhjh4lAuv2v+z4t45c63sDM2iWgC02s22mdNGILLn72AQMjPnO/n8X//erXOPLF9T3ZL7ANHqvyJ2GvkqruMzhA+E8O3O9K5XC3GRjtAqia0qa/dpF0DlTvhhrq8e0BqigoZZT1KGChDx+1XVifZ+cXdn2wvMw+5smmnChVW84P/sCZXkwGucN8Fyjsj869F/j2y1HnIyvyrqbaR1Tj2Moz6vAHZo1VcrdpACJ/a11kNohRVTt/Y50FCciIy/hYicEjhW94hEH3SNSCzXlIBBFRvrqoxiNKa6j5Zdb+qCsQCTMgsUvo8bV5FGEWNzKPlUIarUFpVWZxqZHp6g+ExITxQ/G+li2QvV/3ivLtsvFwpUAKP6WlujzQDYZaC2bdOqQbh2xPKHkcmxgI2wjtU5YxpNim0cdRK8fo8XP/aJXzz3hR+W7KGTr3bM3i/bTGMuqtrqtdGiZbHsG2nUW+O8tIkKCoN57wu1WujfPx/n3LAmcMYc+zdzP95kQp7JdM5D1G8KpEzcrKem6YYRh6fhcfrIZPOIKXkgofPYuDQAdx8wr3MmDQrZxghVZ+1qZ/8zMJpi4lWxkjGU3Wej+kxsTwmqbiq3BI0nExuGALDDWml4il6DerOwF0HULm2ikv2vr5OkclsgrREqj6lrlHUkDRAXSRiKRzbwTQNDjl3OAALZyzmiv1voro8ipSqvUrVb9VYPqtBAy+TyvB/N7zCf3+8ix2GbcPr971HtCKuQp1SIgR4g156DnJDKPZikMXkwjugNIbWnolTfCdG4CBUibpL6b0QfRyZ/AQyCwEDrP5QdCWGd0tVGp/6CmJvKKPA7K7Kq9NTqLu0vi7iQFvwbulKC2RLyreA8IUIZ6XqWebbs/48FBdVan+5mqsIoLxS2fynXOwGrC0hfAmUj8jf2/3Xgd+ORpbcWu8iJmVcyQEIDzWNaV2PWvG9Ks8p+TVkVgKryFWD5VAhQhl9spZxJLw7QdFlSjFaAjnJhIC6VukZhZNJfkxh2NEEZwWkp4Gv7ocnmfhAKTI7a8Dqh4hcrXKYsu/bS5XRZc8FoxMifA7CM7DOY/1ehAi7xQ6OG6aVruOwcYNOCBN8f2nW+TQV6USVxzD1lfvQ4CDxKR2qknsQntrhPeEZhPAMcvd3xUwxwbMlopFwsaZ1oI2jVozlsdj10J0a3xClDO0P+agubzwXRhiCTCqD4ziYhgrnCCGIVca568yH+eWHBXj8XiyvhZ2xC6uo8r7vswt4j626smTO8hrjRIDH58E0DXxBH5Wrq3I6R6bHZM0ylej7w4SfcyXp2SoxKSWOI5ky7ifGPf9ZvR4aO22r0KGbuL1meXkDJwyd+nSgbZc2VK6pomOPdixfsIrTBlxAOpkhEWtcaqAxj1R9tOveFn/Aiz/s5+Bz9mP/0/YG4M0HPqC6PEYwEkQIgZ1Wgo/dt+jCr7OWIqUskBjIZ9H0xSyc/isde7VnvxF78tbDH2F5VK8Q6TjsvH8f2necjZOYqdoZOGuoCd1kn7YdqByjwkX55ykCSO8QiD6tcjyEpXqilZ+LbPM8wuyE8O+jmpW6SJmC+Btuf7JxqJJ5QLSh3uawoeMQ4bMh/prq+m60VWKEFZcgs0actbUKKTXUNV1WQGauMliEpU7PcY0KAup8jSIougaMDuAZCumvKDRcfCArlZHV5vUC1fAaXEVkHLW4yyg5Iyw5Vh2DJBgCZIla/J0V5EQz8ao5FXiu3FNwqtU98mzvttPwu14uCdIGs3idPdb1mNSRXJ99R6aQsZeh6k41d+GB9BTk2nOg7EXVU81Z45bKL0X1tpuFTE9Wqs/5BlRmHrJyjBJLNMoQ4ZEI//A6rlU9BA52GwavAelWnRlFiMDhTT9GCyCrH1JeK5mmJvE/Bfavymhq81q9kgoyPR1ZfrH7WRBKKqDkvoY1mjStAm0cbSaYlsnJ1x/NXWc90qA3xxvw4vFaRCtiJKJJgpFAbhHuMagbY5/5BNNj4fGqj0aBcSJA4IoPWgY9Bnbj6EsPYa/j/sLova5jzuR5WB4rZ3x17tuRtSsqlFHhGkyGKXj6upfYYdg2WN5sl3EKv9cdybRJs6heG8Xj89Tftb4JtophGviCXip/q+KRKXdSubqKi/5yDVVrqrA8FokmaDAJoRKpM/UIWdaHx+fh3s9vxLRMAkUBAqGaJ8aK3ypdT486cdNjYXktEtEEQgjCJSEyqQyVq2v6NGUNNCkl1x9xB6uXlWOYgs69O5CIJZGOw457RTnt8neg4k1qGoaa1BhHeRe7ni7gMv6mm9tR5CYIOyDXqi7qdeTMCOGF4NGI4NHKULKXIdNzofpupbSstiI/GVwEDnP3O069k/waWT7S3S4M2JD5CRn9D6Ke9hjqsG6IK18WQHjUIh88CWGWIO1lsPZ4VHjPAGMAOHPUGCIEBN1zrFLl7+budZ6j9O8H8bdRxl9eCDHx9jrnl//fDEgPCFf00LN14bV2Ysi1ZynPk8zmR0XByT5oeBChswon4/8bxJ50k7gNlChnV5Uonn/sbLgtM5MavZ8QSi5hlcrhCR4J8XddXasQGK5Hx6lGxl7KCXBKe7UyqJyVasxMhWo1I4IIX+3rVRfC7KzCTVV3K5kFswsifIFSim7NpL9xjcb8ikcJWMprai+u1dwYXMO0/BIVCswqamd+QVZegSj7v400ec3vRct2bkbsf9redB/QBdMy3aTdQoQpCEYCmB4rVzUVq4zhOA77jtiTfU7YHWEYBTk9dbXeABCGQbcBndnnhD0wDIPLnv4HPQZ2QzoSJ+PQuU9HhBCUr6hQO0iVzG0aJulkhs//9xWxqniuqkw6SltICEGHXu35ZeoC7IytxBcbQAiBYeZ9jPOMLMtjUtJehZQsr4XHZ/Hdhz9QtaaKQDjgGooNtwMp61TKdnsNonPvDvTdoRcDd+2P5TExrNp/OjlDxzKV3pLX5KoDbuH0gRdzQo+RPHb5s7lE+V6DumMYwlXdJmcAFreN5BLALa9VcD65+yJh8exlxKvixCrjzPtpIYnqBGeNacf5N00hGDZQXpO0+1NPCwVRT+NFmdXDyg4uAIFMT8epHINTeTMyOanOXYXwqtyZyqvUArjumOAmg6/z5JyZrbwkWQVoYblRq/o0l7LjBSBwiNpXuq1QyIB/OEZkNNJoC7H/oybvyQFnpvJUEVTGkcgzrhoIeYiiK13BxKxhlP/ZqevvJLugVgJxMPsgii4v3CTxrjKM8CsPlygBTKUa7d1FKWWv690Lj4TAUeQSzs1+iNL7Cjxsuf5lmfxK0ozyXKmkwZr7LKvUXHOl8u59d9bU7Jr8xK3ACro5aGGQKWT8f/Ver7oQVm+M0vsx2r6FUfpwrSTy9UFKibR/U0KcDfbb+4OIMPWLmwqUV7AO7CWq5F/4Xa+m5XruZipvoaZVoz1HmxFCCM647UTGHHs3SXextbwWZR1LWL1sLaYr4CgdhwE79uGMW09k7Ypy2ndvy4Cd+iKEYM9jd+Ojpz8hXh0nGV3HqyLJ5dtkUhmWzFEVbWOfmcgrd71F1Zpq+u7Qi6NGH4JpGdx49F0YpoFt27m8JdVOxMMbD3xAKpHGF/SRiquqK1/Qy/6n7c20L2fVrN+IBnN8gsUB0slMjXdpnYf3WGUMIQSHnvc3TNPETteE/kAlaNfXtiTStojHfr6LotLCsM745z/jwYueJFoRw07bOfmDrJfN8lpI6ZBJOiybvwKPz4OdsXnroQ+JtCni6EsP5chRB/HTZzOYPmmWUig3DPY/dS92HL49t5xwL/HKOIZl4PF5lMdKuP6RdUJ7WYNpzfJy/nV8OcOPa89F/46r9U1mlYHrWTgi/6rzZeHdAZkc5+ZXeICESnROvJu7wDL+GkSur7NMWnme4hSqErt4hyFK7ylIppUy5Savxsklgku3N1UTKtZE0WVIEVKSBTjg2wcRvli9GXuZmkUsX+Mpe0GrUbpOGaXD49mm/nGMIASPRqa/RHmb0nneuVpbk1s4g8cjvLuAd+ealiBZsuGWrNilMNV98w7BKLmj7iMLLyJyFbJolLpeosTNh4srcUt7ufL6ZX5x86NMNwwoUfckiuoJt506oDVA/SsTqBBjNZCAxFic3w5zxRkT5NS21SSUN8VpvDo0HylTkP5JfT6sAQiz7vYn9e0rqx9Q91m63eydCjUlayCU3I5oJKn+9yCCxyLTP7pjZg1jV8TUu5tKEK9zRz+1vJo4qEKEhnPpNC2PkOut2PfnprKykuLiYioqKohE6nny3gBUl0f5vxteYdZ3cyluG+Gwf/yN7fcudNFX/FbJRX+5mlWLVyOEgWPbGJbByH+fgj/s5/V73yVaEWOLIf0487YTC4UmXVKJFI9f+TwfPjmBaEUMy2vhOPWrXO99/F/4+p3vsW071xi1S79OHHfF4dx52kNYHhXCy37MhIAthvRn/k8L8YX8mK7XJ1YVp6xDCU/PuZ+jO59JMp4kk8yQykoH1IPSgpJkUqq/mHSr2kzTpH2PtoQiQUo7lLBk7jLstE3/HXvzwyfTSSVSeANe7LRNIpZUgpPZtdP917AMhh68Ixc+dGata7X0l+V8+uok3n9sHCsX/YbjyIISf8trIQSEikM5z1asMkaPgd144OtbAEin0nz1zveUryinS79ObL/P1ggh+PLNb3nhlv9RvqqSPtv2ZO/jduOTVybx7ftTavrj1YNhSC57cC17Hp5UYnvUznFR+BHtPiroXC8ziyD9M1J4ITEBshIA0kJVkuWPbYDRFtHuE4SwkDIN0SdUtZu9yFVDzoYhsguERyX6hgvL5p2q+yD6OIXtRwRQBMGjEEYZeLevt2FsQzirj3GTxdcxjswtEMEjkdlGvJ7tlXJyfe0octdoMXL1311Dwovqs1YfFogAouzpXHJurePF30RWXoeqCvS43rM4BE/DKLqgyedZE577WZ1nVr9KBNUi7VRRo5JegohchggeqfaVEll1q2oxI7MaVCbKAMyoBq2R61X/N5lxQ0QZIIUIna/apTQBJ/kZVPzLDeGZqj9f8ZhGhRlz+1feDPGX3fNLoD5fHiAAIg3WIETZM/UKev4RZPwdZPQxsH9V10iEwL8XoujKeisEVbHAxcrrli8rETgGI3JFs89R0zjrs35rz9EmQCqZ5voj7mDWt3MB9Uf302czuOblUTkDae7U+dx91n9YMmc5lsckGPFjekyiFVHGPf8Z/55wA3sf13i1h9fvZeRdp1DaoYTnxrxKsEglhpavqqxZ/PPSK756ZzJ22iZUop6I7YzNkjnLiFcnCBYFiFbGCRYHSMaSOLZDn217MeKGo7nhyDuRjgPZkJhUStcAHXu2Y96PiwgWBzFjyToryQAMU/CXw3em/459eP2+91i56Lfce6bHZMT1x7B49lJeu/sdNW1D8O0HU+kxsBurFv9GIprEMA0OOHMfxj4zkUR1MjcXUIrjkz/6gTHH3cNtH11TUGa/bN4KXvn32yRjSaV95BpXQllo2OkMoo7KwvxnEY/Xw+5HDKm1za6H7lSQiG/bNu88+nEuBFcfA3eMcuTZK+nePwmOTY06dr7Bkbt6ShzQNY5k4kO1SDsxZcGavaDkAXWrMyug6rKa/ZRWt1ICdirAbKMSdRNvuHkzGepuPCuQ9sraacPJ8ep9o8wNjbmJrwKIv4BEQNRwlZSPK7yemflqwTLagbVF7fJu399c42gdbSb/PojQCERoBOuDsLpC5Cpk5U2udyzbNsWiMHxpKGMncFQdDU7z8B+g9IpSX5IT6TT7IkKnrNe8iL/oGkZuCIe0Op5MqGMKC6QfvDspgyTPKBZCQNHl4N8XWXW7qnqjOC//qBLhLIeiy5FVd6CS3Q3w7Q9NnKeMvQiVN1KgQeU4Km+pzeuNG6Vu0r+6rgE3fAqQUdcZQ+VW2UshTxOquRCBg3LhzZoHvYalBJTm0U3IqjuVgSRM8B+ECJ/X7PPTND+bjHE0ZswY3n33XaZOnYrX66W8vLzWNosWLWLkyJFMmDCBcDjMiBEjuOWWW7CsmtP85JNPGDVqFNOmTaNbt25cffXVnHLKKRvvRH4HU8b9xKxv5+IN+LA8JlJKYpUxXr3rHbbfe2t+nbWEqw+6lcrVKsk3ncpQtbaKorIiVQnViCBkXXTfogvCFRr0+r0F62p+/zFV0l9jBBimylny+r2Memwkd535CLGqOF6fl4G7DuCqFy7C47PovmVXFk7/FTttq1YmAoaP2BOA4y4/nBuPvqsmX6keittGOPHav3PbiAcKDSPLwBCC5256lWilWuwDYZVLkkqkWTx7KXeMu45kPIWdsYlXxXnvv+NqDyAlHp+HGV/N5r5z/0vf7Xsx7OS/Egj5eezy50jFUgQjQdLJDOlktboeecaPtB2i5VH8YT+27QCCvxxe2xhal1QyjXQcfAHlel8yZzmzvv0FX9CHYyfqFKHcYnCM656cjz/g5KUKBcB7ACTfpMZAcvWJRCQXDpD2SmTl9eDEVTgGB+z5EHsWUfogMvUgNYZFvhtPglGiEp4T7wAWGH71urPaHTNrGPlBCITZzb20+QtM1ug03PFtkGvdkFHE3T+mEnl9e+UWdhl9DFn9CNleZ/gPhsi1BZ4DI3wKTmYmJF6vmYtvP4yiCxu9D/UhAgeDZxCkpyNFEFUx94vy+hhFkFkARonq3+Xbv8FFVAgPlNwLibeRmV9VpZz/0IYr9OpA2r+6tzYbnvOAdMvNpZs75N8XUXRp7dAe7n3w7og0OgIzlWGUexOQGUToePDtBpn5ypC1BjRJa0jJBNxFofCn29RVVkNmFjRiHCnD0aHms1LwZv6ZNDqfP8r66CsJI4wovn7DTUazwdhkjKNUKsVRRx3F0KFDefzxx2u9b9s2Bx54IB07duTLL79k2bJlnHzyyXg8Hm6++WYA5s+fz4EHHsg555zDc889x7hx4zjjjDPo1KkTw4evR0nqRqZqTTXSURVigCuMaFD5mwqZjH1mItGKKMFIkMrVVSop2pbEquKYpsFOw7df7zF3OXgwex27GxNe/IJ4Jo5pGDjkJU8DCNh6jy35ceJ0lWxtmSSiCfxhP/137EPXfp145PvbWTDtV4JFAfps1xPLoz5y1746mntH/peZ38wh4Pdz0Fn7ceSog7Btm9fvfx9hGrlcnfoUqS/6z9l8894UFvz8a14qhMC2HUxHUrm6CseRGEbNl5lhCjIZyZdvfcs7j3xEKpFWRlIdfdwcRxKvipNJ24x//nPGv/A5457/nJvfu5LflqzG9JiNflEalqk0jgzB/mfuw1GX1N/KIFYV5+GLn+SLN78FB7bZcyAXPnyWa9xKTNOiqCxMrDKe8yJl78XBJ/+GP+AQrTIJRYLu4hgD31D1/8RrKG+OCaIYUXJTzSKZmau8ICJITfsKU4XY4m9B9LG8WeYtRFZftU31A25OS1YDyHATdqPqWCiZAayBSP9hyIrrIDkOEEj/cPDvB9GHXW+A6S6EQh0rmyQs/UBceYnMjsjkF6rEGlAVWClIvKnyhtbxLhklt+JkLlCeBasfRjN4FoTVC6xezbIUC+FBihJIvYJ01kJqMhT9s8C70+gxjPaqoErabt6S68kMHg7BU9xxGq+/Eb5dkalPXa+Yx/1c+MDt8SbMTvW3zqiPzEKUVy2rhJ69ahnAq8rdYy+BTCJ8u0Dw5DrboUhrAKR/Rn2mXO8Ywg31pcGz7frPTaOph03GOLrhhhsAeOqpp+p8/6OPPmL69Ol8/PHHdOjQge22244bb7yRyy67jOuvvx6v18sjjzxCr169+Pe//w3Alltuyeeff87dd9/dqo2jnoO6YfksEtEk/pDqO4aU9BusykejFcrFbFom4ZIQ0Yoojq08O3sfvztH/7NQdG7+Twv5+NnPiFcn2GLnvgw7aY9a4pI/fDKN78f/pFp5+D0ccu5wpIA37n/fFTQ0Oe6Kwzn6n4dy28n3M2XcT6RTGfwhP+fffzpd+6kvqdIOJXXmNrXv1pYx71yBnVG5SlkjY8ncZcz+7hd8AS8enwfpOKytw4O01W4D2OWgwYw57h6k4+QkBrIeLdu26bFVN/xBHz9+Op1MOoMQgmQsRVFpiNfvfQ/bdrA8Zv0NblGilNncUwHM/u4XXr7jTTr26sCCnxchpcSw6l8i23Vrw20fXUMoEiBUXPuJPZ+HLnqSia9MUnlYQjD5ox+4fcQDXPe/S+jQsx1L567AF/C6SeQGO/9te7befUuevu5lSturRccf8uML+chWJAmiiJKbkPZFSsSODHh2KOyzZUSoWbjcJ3NpgxFAVt5MzRN79joJIAD+vyHXnu0aQQ6QUCXooliFxqytEMHjkPavqiO9/1Bk5bWuiKH7eYu/Cr4DIHgaxF5AdWjvB7JKlUCrEkZy3iHD1SBK/6DeI2vQqbwamZoCMo1MvKvm690LET4Lw+oMVucGr39LIZMTkRX/VNdMmJBchszMg7Jnm+5BCh4LiffdfC/3mpm9IXBUk4yiHIGjIbME4i+gJAJCKhfrj1SVGWVuDlTWsM4L8xodIPofZeAgkOlvITMPIjfVevAQxbeoBr32AjeU5q1xTHp2UJV9GyDfSPPnZJMxjhpj0qRJbL311nToUNPcb/jw4YwcOZJp06ax/fbbM2nSJIYNG1aw3/Dhw7nooos28mzXj77b9eLk647mmRteJlapKpF6bd2DETeofkT9Bvfho2cmUl0eJZ1UitGGITj28sM4+bqjC441fdIsrjv8DhJRlcfz8f9NZO6UeYy8+9Tcl9Gsb+dyxf5jcuG4VCLN6/e/zy0fXMXZd5zM6qVradO5NGdQXfPyKH6ZuoDq8hjdt+xC2871N20Ecv3LTKt2uwzHdlTaijsXYRj4wz4S0SQC1Upkq10HcN3/LuHO0x5i0lvf5gQmgdz3bygS5B8PnEGoOMiNR/2bBdNV2KGsUwlb/2VLPnvtK/whH1Vr6i+pzbZhkZKCyr03Hnifix4+i4cvfoqq8iiZeqrdALr270S4JMQb97/P4tlLKetYwiHn7U/7boVCg6lEii/f/DYnnAmQEoIZX82mfEUFV71wMTcfdw/LF6xECMGgv2zBPx48g5J2xew7Yk9Sq27DF3gDYXjca+BWfbnNOYXZtpbgY+6SGR1V+MpZlucYMlVIxl6qvEAEVQiEFGAhim9CJj5U3h4RUa/LSlS5eJXqVl48BuEZUKMM46xxk1MtciXzTkzpBHm2U93iA0dg+PdGJsYhKy5Tx5IoD1LgKDB7qv1EwF1wK3ILq8o7mYdMfuTmPglIz0bGX1ZJ5mZXRNHFiAYq0loCGXvBTfJ1daWkrcKaqc+UplETEEYplD2FjD4LznKE2QWCJ653OxEhDERkNDJ0kgqNml3/eEuS1A/UWTXp2VlV60k779wTkPgAQqfX0g4SVndo87yrGO6AtYV7TJtsxV5jyPTPKhTrLFcSCEUXbJAKN82mz2ZjHC1fvrzAMAJyvy9fvrzBbSorK4nH4wQCAdYlmUySTNYsjJWV9VX/bFiOuPBABv1lC+b9uIii0hCD99sWv7uI7nvyHnz09AR++rSmzYAwBZ+9+hVHXnRggcfiiateIBFNECgKKE9KPMVHT09k+Kl702fbngA8ec2LyjByE4ylVJVYT1/7Ejt8sQ3tuhaWV5umSf/BjQu5xari/Gf00ypsJGHIgTtw7j2nFMyvU+8OdO3fiUUzFgMy98C5+xFDuPL5i5Qgo2Xy6l1v89lrX+P1+8hk7AI9Jo/X4obXL6Wn26T3zgnXM3fKfDJpm97b9ODVu95GorSF6srHEoagtGMxFSur6nw/GU3y4VMTuOChM7jlxPsxLBNpy1o97UzL4MSr/841h9zK7O9+yYk+fvrqV9wx7jo69KgpY7ZdbyB5X/BCuKnPtkOPLbty/9e3sOyX5Zgei06922OayrAMRYIEw5ciyxdCeqobCvFA8HTVmqIBpExC+QWupk1+2bF0PU1ugrXwAhFlIFm9EYGDVKUXhjtnH8hSoAoCRyDCFyLMdcrwswnHOe+URBlxGdcTBKS+Qop7lAq38RAy/jbIBMI72PWCqOsjfXtC1b8pTIK2lV6SsMAIKqNJrlVGnwiCvRK5dmQt5ecNjbSXQnqmmoN3h9pqyk4VNdcR9X/JepfJC6MM0cQKN5mZj6z+j9LisXoqtWuzxrMmzPaN5wE1ZRwpIfYfcn30cuXwHojcBGuOUfcrd+6udISzts7jCREA7w6/by7pWaqiz4kCAjJzkOmp0OY5VRGp0eTRoiKQl19+ea51RX0/M2fObMkpcsstt1BcXJz76dat+Sshmkr/wX3Y/9S92O2wnXOGESjjREqJx2cRKgkRKgnhDfpY+stypoz/ueAYKxauKghjeXyqRchvi1fntqlaq7wp2W2y/1atbbw1SUPcf/5jjH/hC5XAnM7w6auTuOus/xQkMVseiyufv5DuW3bFyThIx2Hr3bfkgofOxPJYOU/TjK9nI6WD1+9B5GV+mB4TwzJ56Y63cq95/V4GDh3ANnsMJFwSYtdDd8KbVd6uQyYgEPYz7IQ98PisQoFJAAEev5d5PyxkwbTFCEMQaVNEURvVpw6UYeoL+hh59yksnL6Y2d/NwxfwESoO4Q/7WbuinP/d827hmCE/g3bfEjudIZ1M5366b9ElZ0R5fR56DOxG136dcoZRblpGGFH6KKL4bkTkOkTpExhFTaiKSX2jRAjFug8Ghlq0kOpp3qlWXhxhIUJnuDdrSzdZ13YNuzQIPyJwRG3DCFRFmdkTSKpFMqeFZLoCiEUqJBZ9VJ2Td2eM4hsxSu5ABI8tCJkIZxU1uScGalH1UlOGjhonJ5jlz+VByXjNZ2NDIxMfIVcfiay4GFl+DnLNKW7z0jy8g0FIZTxKB4gpY7SZe5vl5pRZjFxzmgrDpX+ExFvINach7d8a33l9x5IZsMtR9yigZAFEBISBoBI8/VDeRvfhQMbUvTJ7NP9c4i8qw0iE3ea3IdUyJfFBs4+l2fRpUc/R6NGjG60U6927tix7XXTs2JFvvvmm4LUVK1bk3sv+m30tf5tIJFKn1wjgiiuuYNSoUbnfKysrW9RAqo90Io1pWSBVp/ps7s2Lt77B4P22zbWu6NKvE+UrK3KNY1MJlUjdsVfNU2K/7Xsx+9tfcttkk36znqXfQ7QyxlfvTMa08sJG8RTfj/2R8lWVlLYvzm3bpW8n7v3iJlYsXIVpmbTv3hbDMPjm/Sl89c5kqtdWM3X8zyTjKdLJTE3jWsgZjdMnzcLO2JiWMhw/fHICX7z5DVLCrofsxKjHRnL32Y9QsbISYQiVxIyqFDt45HD6bqfO1R/2Eauoaajq8XlwbIeiNmF1fdzXTcukuG2E6vIo/Qf34ew7T2Lg0AG8eOvrqqeqq0huuArkvy1dZ4EELv7P2dw24gFmfj0HUCraVzx3Ya3QY30I4QX/Xo1uJzPzlMfA6IB0Ksk1Ai3o4+Kg+oIJVQWWmQ0ihAgeg/Dvq8YrOh+Z/gns2eTCWsHTVZVWnfMzoeQO1WvKXoQyjITKUcqKMmK4GkmNnUTM3b4kL2k7qk5BqtAfOdXkvKRwx91uIyDtZXnyCCGUZ2s6svJ2RMmtue1E6GxkepZqU0ES8CGKLvlDeT615pIY5zaeXQ141L8irK6ddFSYKfGmCmc115hOJVRcTq7dikyAjAAJZZyY3RCR65Q3z16p7qfwIYquqqe/3R/EqaAmhw333F1JC41mHVrUOGrXrh3t2jVdIbUhhg4dypgxY1i5ciXt26uFfuzYsUQiEQYOHJjb5r333ivYb+zYsQwdOrTe4/p8Pny+1q9muv0+2zD/50UkKhM13hABC35exAs3/4/TxhwPwBm3nMC1h95G1dpq19MhOOKiA+kxsMbgO/eeU/nhk2ksnr0sZxi179GW0Y+fUzBmtDLGcze9xoyvZhNpU8SBZw1jzbJyFs9eSmmHEvYd8VciZSpfIZPKqO8lQ5CMJYlXJ3AcB8M0WDRjcYFxBMqD1KVvTS7A+4+P4z+XPEM6lVEtRdxzzFWySSUj4PV7SMRS+D01Xp8XbnmdF299Hdt2EMCPn0zj6EsP5cVf/8ONR9/FlHE/Yds2UkLvrbtz7GWH4Qt6+fDJCfz0xUwMU6gEd3f9tjwmx19xBB17tee1e94hXhXH6/eSTqXx+j0cc9mhDByqVIc79elItnVKtvpOGIIufWtXIpV2KOHm965kxcJVOLZDx57tm2wYNQUpJUQfQUYfRyU4W+Db2w13xCkMq3nUYmYUIyJX1NlJXBhlUPaUyiOSFWD1b1SoUVi9oc1LKqSRWQKVV6M8SSa50n+rCR4Ta0u1uDvV5Hqj4YBvT2XIOSuoqYzyuSG8pLqH6/Q322CkZ7pGXMg14AzlMUtPKdhMGCEofVCFRJ0KsPoirObznMjkF27+lpvwLeMoHalwdgIgBdJZ26yF8LLyZkh+gWplk+1HVw4UK++mUaLapJS9oPKrZFKJcXoGNH2MzGJk1RiVh2SUIEJnIgIH1rmt8AxEJse7xrOHrKipdKsulWp3X5W/BcjMAmTlv1S/PVGCCJ/V6pvkapqPTUYhe9GiRaxZs4a33nqLO+64g88++wyAvn37Eg6HsW2b7bbbjs6dO3P77bezfPlyTjrpJM4444yCUv5BgwZx3nnncdpppzF+/HguuOAC3n333SZXq7WUQnZjJONJLt3nBmZ8pbwOhiEIlYRIJ9P03b4Xd396Y27bZfNWMOHFL0hEE2wxpB9DD96xVjJjJp3h9fvfZ9G0X+nSvzN/H3VQrgw/+/7VB9/KtC9U2NOxHVVCbxqYHhMpoXOfDtz64TWUti9GSsmlw25g+pezChKohSHo2r8T93x+U86QWpd0Ks2JPc8lVpXAMAWxSuXJUZIG5AwXf8if+3I/8uKDGHHDMSRiSU7oOZJ0MpPTOkpEE1gei/+b9yC+gJdPXvqSJXOWUdqxhH1O2J1gUSB3Td968EMWTPuVZfNXIh2H4rYR9huxZ06kcfzzn/HIJc+QiqUwvSYnXv13Dr/ggNzc7YzNrSfdx9fvfo8SLpZ037Irt3xwVb3nu6GQiQmqizgAflQIKqOEEpPj1EKeK7n2qRBZ8c0q/+f3jpnXXLfO96P/h6y+R81DKt0kfMMQnj7gP1y17Khv3+REJSIoq1Hxzu0RJXcDNqS+VyGd1NeQeItc49XA4UrVeH0quH4nMvWtynHB61ZXofKLrD4YbdevJ9kfwSkfDYmxNUnPThTVQiSgwpkyCaRVmxDfPqohrYyCZ2C9yt6NIaVErtpNGdjCNV4dN4xacgeGf/8/fF7SqUSuPk41fs3JF1iI4tvr/MxKmVRey9SXrrfUAv8RYM9ThikOiGJE8S3g2dI99jK1ncy4xx6DaIa5a1qG9Vm/Nxnj6JRTTuHpp5+u9fqECRPYc889AVi4cCEjR47kk08+IRQKMWLECG699dZaIpAXX3wx06dPp2vXrlxzzTXrJQLZWo0jgM9e+4pbT74fr9+jGqoKiFXE2HbPrRjz7pXNOtb3437i+sNvx+PzYHktkrEk0YqYCi+1i+DYDologkPO3Z8zbzsRgJWLVnHmtpcQq4ghhNIACkaCpOIpLnv6fHY/cpc6x1q9bC2nDbwQO+NgpzJk3NJ7IQTF7SLEKmOUdChGOhLLY7HfiD059vLDsDwWvy1dwxmDLsYwDLfViPLiOLbDoz/cSfvuf9xzGa+Os3rpWko7luTCc/nYGZuJL3/JkjnLKWkfYe8Tdq9zuw2NU3UvxJ5yQzy5FyFwqFJkTs9Eyig4VSoE5t1lvZ7i85HJT5Wasr1SVTxFrkLUk0gr0z8hUz9A7EWlY5QN8VkDVR5VA+Xs0v4tF/LDM7CWPo46/kwVRjQ7qmPWY6xJmVBCk4kP1Pi+vesVTWwKUqaQa06HzI8or5EDwkRErkYEjvhdx/w9OGvPgeSXrmQD4DjAWmrCjQb4hkP4H1B+rhKxFAAWomgUInjCeo+pjKM9lJEl3M+6VI2QRdmziGbIp5Lxd5EVV7ieOdPNWaoC364YpY/UM6+MKjRwVoHZQ/UITLwD+FAaWzGVFxUaCVW35nn9UOE37y4YZY/+4blrWobNsn3IU089Va/GUZYePXrUCputy5577smUKVMa3KY1kEqm+fnzmSSqE/TapjudenVodJ8d9t2Grv07sXTuchxb9UOzvBYHnrVvs8+vak01juPkcmnWbYiaVcpe+svy3Gvtu7ej58CuzJ06H38okBO1TMVlvc1fASJtwjgZp1YjXCklmVQGwzQ47PwDOGp0bYHF0vbFlLQrZvXSNarLPco4Ku1QQlmnUqSU/PLDAtYsK6dT7/Z0G1BPE8kGCIQDdO1fd84aqHykvY/ffb2P29wIEXBz0dy8i+xzkQiocJfVu1nCKjI1FVl+ifJICAsyc5Hl/4CyZxBW7apG4dka0j8incVu4rRHPalnpkP8pQbzYITZFhrLT7F6Q/ITVTIvAhD8O8L319rzrrzZ9TK5i2HiDaSsRpTcqd53YpCZpjwUnoEIo5EnT+GF0vtU+4jUZNVLLHg8+DduaEZ4d0SmvnI9RBaIBMggFJ2nqr/MruDdTWkt2QtcY8ZAqZLfo4zkOu5bg2MKgfQfpPSSZMw9XlqFQ61+zXNiMkEu100NimqGW41MT1deH7MTWFvmFZdY4KtpoyQrLkUZiNkKwiDISrAXUpiDh/v/ulsZaTY/Nhnj6M9E5eoqrj/iDuZOmY/jSPwhH+ffdxp7HrNbg/uFIkFufPMyHh71NHOnzKeoLMzRlxxS0KerPr7/+Eceu/w5Vi3+jY69OnD2nSczaLct6t2+56BueLweEtVKETubBGSY6svEcRyEIejQvXDh2nH4dsz7cSGOrcQVk9EkgbCf+T/9ytnbXUI6mWbH4dty6k3HEQgrg2PWN3MLVLlzBUiGwHEcdv/7Lhx+Qd16MKZlMuq/53Dz8fdQXa5Ko8MlQUb99xyEIbjv3P8y4YUvsG0by2Nx7OWHc8w/D230em2S+PeH2LNuYqor6igCiMAhje25XsjEe65hFHaTX6XyICTGQrjuRVZmFuMmBKkXhKW6S2R+bdRgk1JC4l1k+nsVCvQflPNMSOkgK65UY6tXkKkvofhmhL8mlC6dqKrewqyp3JMJZVTZq1WVW/k/3EUTVXlXcnejYSdhlCKKxzRyBhuY4AhIz3bFN9OAFxEZjQgeU7CZTE9HVSlmpRYC6r5l5sF6GkcAouhiJGmIvw044NlRhaXq8O79Ljxbg/C5CfYBciKlMoVccxI1odSjoOiyuj2GIgBydZ4NpNr84NnSfS+b06ZSAYS3bu+2ZvNDG0etkMeueI4538/DG/Bimibx6jgPXvAEWwzpR8eeDWuPtO/ejutevWS9xps9+RfGHH8PyVgKy2Oy4OdF3HjUv7l93HX02LJrnfv02LIrp950HE9e/QKxyhhICEYCOBlH/S4EbbuUceSoQm/OMZcdxrJ5K/jyzW9JJVKESoJst+cg3nzwfeXQMOD9x8ezelk5V794MUIIVi1WlV2RNmHSKfUlZadt2nQp49pXRtNn254N5rVss8dA7vn8JqZOmAZSsu1eW9GpVwfefuQjPn72UyyPhT/gJxVP8fzNr9F3+54M3nfb9bqGmwLC6g6ljyCrblMtHcxOKmziaaAx6u9Buh4+UfjULWWyXkNHmB1cr9Y67S/Mxj2msvpuiD3jesIcZPQZpGcQeHcF719UY1vhdRdSCbIaWf1wgXGkcq3cvnM5sqrhSWTF5W64KQAIpZlUfim0fbO2blErQkoJ8f+5/d7aKVHQ8AUITx3eG6NMhR9zFV0Z9QdplDRtLKcKEh+BLFcCjd5dMSJXI4suAzLKS9WMCE9/iFyJrLyVXDNcq7/q1SY85NrKxF9UvfDqEEEVgaOR1XepxH5huF7BrRD+vwEeZNWNbgK7odrcZGUsNJs92jhqhUz/chbCMHIJ0IFwgFhVnHk/LmzUOPo9THjhC1LxFMFIICf6GKtO8PlrX9Pj6rqNI4BDzh3OwKH9mTt1AaHiIFvvvgUf/9+nLJ69jNIOxRx0zn606aQqP1YvW8vEl74kWhlj9yN34bgrDidaEaNTnw6cve0lIATBomxz2BSTP/qBpb8sp0vfTnTo0RbTY+LYkkA4gHQk8eo4fbfrRd/tejXpHDv2bM/+pxZeu5lfz0FK8AbU4uYL+khEE8z8Zu5maRyBqtgRZbVz9/4IMjEWmfxMhTEy80GuBZLgCNeYUOGPBivZAn+H+DtgzyWnbm12V20xGho7Mx9iz6E8PpZamJGqIiwzE5ITUN4At+I02ztuXa0hUaIW1vQ0lFEk1DmYvZEE3bwmjxoDUO1KVrgiik37DLYIsWfV4p+Va0gth2qQJffWSkoX4TOR5aNV3o50zVjvLuDZvtFhpL0KufYMFZbLXr/gCYii0a6nqJm8ResgAoeBd4jybhnFyNibYM8hp8COXxnD6R8RdSnEB09EIJGx55UR5NsBUXS1MngDB4B3Z3VORgmYfdar6ey6SClVyA5zvRsLazY+2jhqhYSKgzgLVuZ+t20bwxC5KqrmJpVQKsP5oo9C1LzeEH2370Xf7WsWh7+Pqp33s3jOMq46YAxrllfkhBIPPW9/Tr/5eDKu6GG+2KJhGmQyGRJujtGWu/Rn35P/ykdPfUK8Oo6UUNwuwik3NrxwNka2ei1XUeWm42Q1oTSNI6NPI6vvdcujs58XH2qBjLv5TV5E+B8IX/1hYWGEoexJtZjbS1RLh+DxubLqerGXo7w72RBIno4FPsj84hpNcZQnwfVOrZNoLoSA4luR5Rep1h2gdHhK7gAjhMSilho3WeNvnWsiMxB7Bpn8FPAgAgeA/7A/tLD+XmTsabcK0K2MlElIfaGuyzreI+H7K5Tci4y9qBZxz/aI0FkqT6excarvdz1rwRq5gNhzSN+eSt28uc+rQOG7FyJ8LsLsiDTGuR+BvLw6SWERQh5CGBAagQiNqPv9puS0NWW+9goV3nWV4KXvr4jIDdpIasVo46gVctg/DuDekY8SrYjmEpv779iHgbv+vsqhxth69y0Z+8xEErEkHq9FOpHO9e9qDp686nnWLC/HH/ZjGAbJWJK3H/6Q3Y8cQv/Bfeg3uA/TvpiJbZmuDlKKso4ldO6jQipCCM6951QG7bYFc76fT6g4yD4n7F7QfuP3sO/Jf2XCS18Qq4xjWgaO7VDcLsJfjhzSHKe92SFlSgkJJj4ChNIUir9K7XBUGigFohA8FhE6tUkd5oVRBOGR65cUbnZBfY0lKezdlVXPFqoSK/mxMp4EYHZERK6qPX6ud9cs9YLVLyclIAN/h/iz1AhISvANU41T10FW3Qbxl5URhkSmv0U4VRA6eX3O7A+jPBXRmhwisi1qbKRTVed1Fr7dGjRic8dO/YCM/w9kFOHZ1r1mIm8s5bEhs0ApgDcjMvMrcs2pNS1GMj8iU9+qRr2Bg5HxV9yWLG541ihCBGo/tG0spLRVgULmR5SKu4Tkx8hKE1Fye4vNS9Mw2jhqhex1rPpyevOB96muiLLV0AGcfssJeH0bxjX916N3ZcG0X3nzgQ9IxVOYrsjhTvs37k5vCr/OWoowRK5RrTfgJVYZY9m8lfQf3IeLHjmLG/5+J0tmL0NKSUm7CP986rxcQjYoZek9j9mt0aT09WHATn255qVRPH3dS/y2ZA1d+3fi7DtH1GoKq1HIyltV/krWAxJ7FpXUHUaJ/ME6GfMIs32TDKPfi7C6Q+gMt+VIntdIhIGEKp0PHgmhk1SrDOEH3x719tISwg/e2iFVUXQhGH5k/B3ABt+eiPDFtbxB0l4B8dfcvKusQGkaWX2fCuFsBH2l3JyFQFpbK60nGUNpG0kVMou/hPRu/buSo2XyK2T5ha43DmTyYxCl1Bhf2XwlofKYmhkZf1kZRtkye+mo/nmJtxChU928uruULITZTeXVrdPEdqNiL1GVl/hqquKkhOQEpEy16py1PzObjM5Ra6E16xzlE62I8viVz/PDxOn4gz4OPHMYfztjnwZd+8vmrWDV4tV06NHuD3tl8rnqwJv58dPpBNywYLw6jpNxuPDhs9hvxJ7qtWiCWd/MJZO26bt9T0raFTdwRM3GRjpVyFV7o4TyXKPVyS64fpTnKOtVMdRrwkSU/rdefaNmnV9iAjL1hdIocipdD4bKezGKRtfeXqqGus1WOZU9bno2cvWh1KhzZ1XHBbT9AGMj5yfJzGLk2jPB/sV9RQAB1378B6IBmQQp0yrMmZqCah1zBMK7E87q41VuVrYa0RWRVKrqyZpxPIOUTIC9UGldhc9ploa/TsU1SnJB5ImoyigET8IoGlX/ji2EzCxErj6CAskAV4ZAtP8cIVp/B4bNhc1S50jTdOyMzZjj7+WnT6ercndb8p9LniGTtjnk3PqVwDv17kCn3o1XB60vJ113FNccehuxihipRBopJYZp8PDFT+I4kv1P3YtAyM92e/0+NV7NRkDGUIt8XjsTYYH0uf/aqK8Tt+O68CCKLt4ohhGA8O+F8O+FDF8EyY/cNhz9VcVa/mlIG6L/RcaeA5lEegYhIv9CWPUXHqwXZhdqPFgU/F/YSzd68rawuiKDJ0HVzSiD1aMMR6cKmfy8XuNISomsuBaS75PtUSeTH0PJXSoRXZh51YgeIKVEJDNzlFfH7A7JsaotBwLSM9zQ1zNNbo0iZUqFx4zSAo+bsPohEah2KFmDTCKs/r/7Om1QzC5g9VXtZAD1mUgr76M2jFotG8/Hq9lo/PLDAqZ9MROPz0MgHFAJ3o7DGw+83yLz2WLnftz+0TW06VKGMAT+sJ9I2yIyGYf//vMZls1f0fhBWhFfvvUto/a8ljO3HsUdpz3I2hXlLT2lDY/RVnkBZFIZQtJW/7d6Q+ljiPDZEL4ASu5X3qK2byrBw42MMMKIwBEqz8m3W21PaewZZPQ/bvK2A6nJyPLzlc5Rs4wfAiMrJCqpycfyK+XlFkAl/RqunEHWuJWo/Jd6yMyA5IeARylriyKlH1R9P5h9UY1k3TAaCcCD8O2GUXwjRukDCLONUqEWIdVkVoRBlqt8oEaQ0kFWP4xcuQfyt+HI3w5SCupZgse6Rm/SzS3KgP9v4D+gvkO2KEJYiJK7wLMV6uFBgndX1a5F02rRnqPNkHh1AulIjLympYZpEK+uUXfNpDNMeus7Vi1eTcee7Rly0A6YZvM1OV2XXlv3AAn+oM8VjQR/yEeiOsGiGUuapADeGpj09nfcccqDpFMZDFOwfMFKfp2xhNs+vnazrnITwoTi21WuieOqnpudVR8rT3/wbt+sTUs3FDL+hlu9la0SslSpdvoH8O3awJ5NRxRdgKy4jpo+dYZSZba2bJbjrzfeXcFsD/YqVOjL7RPWkPin8xtqIXc/0zkJhFWI0puRa88GZzXZ0KEKmdUIRUqnQr2X9fi41aBKgLQR4i8rAxZDjWkvUZ+7Ni8izI4qR6fkPtUDzlkBZjfw7rZR87nWF2F2gbJnwVkJmGC0bZHqRU3T0cbRZkj3LbsSjASIVsTwh/04tsSxHbbcRZXuppJpbjzq30z9ZFpuQdvloMFc9sw/mrUL/LpE2haxZtnaXOm8nbYxDIOisk2nnPWthz4gnUoTjATVOWRs5v+8iKnjf2bowQ13pN9QxKvjrF5WTmmH4g3as014BkCbVyD9k3rBs7WqMNuUkKm8cBCoBVjlHzUXInAoCC8y9rLKhfHshAifpwzMFkCYbaHkQWTldaqE3yhVnr6GPC1mT1S4LA4yiGvZgNlX5Q2VPe8KPsbV58A3tHBMa4Ar6plEeajSgEA0oXWITHyovFI5A9ajdKlS34JbdSaEBX+gIXJLIISh+vtpNglar6mt+d2Uti9m1H9HEowESVQnSCdS9BrUnfPuPQ2A9x79mKkTpuH1eQgUBbC8Fl+9M5mPn/10g87r6EsPxfSaxCpixCpjpJNpttt7EAN2XP/WBC1F1ZoowjByT31ZqYV4VbxF5vPxs58yov8FnL/T5Yzo9w/eeujDDTqeMIoQvl3Vz6ZmGIHbV8txw4MZt9S9FKw/3gg1H+H/G0bZkxhtXsaIXJqTBGgphGcARpsXEe2/RLQbjwge26DnQljdEeELqEm0jypvR+Ry9b7ZARE6CRE+q5ZhBLhhroOBjBKVJAXe3SF4XBNmW1dPs2xiu0azcdCeo82Unf+2PQ9+cwtzpyzAF/QycGh/fAGV/LdoxmJA5hqxenweMukMv85cskHntPsRQ/D6LN55dCzxyjgDd9uC4688YoN6q5qbgUP7sXD6r2TSGdXaJZrA6/fQZ7ueG30uP38+gwcveIJMOoPH7yUVT/H4lc/Rvntbdjmofm0ZKR0VjkCA0b5VhyOaEylTSonbXqLK28mA2QZRfJvyrmyMOdhLVWKuCKlQ5EYu416f6jwROhk8A10JhCD49kGYTatiFcKAyL+Ud8peDGYHN/TV+N+68O2NTE91pQJMZcgaEWhIYV2jaWa0cbQZ07ZLG9p2aVPr9eJ2ERCqaathKPFDJBRvhPL5IQcOZsiBza+Yu7EYccMxLJy2mBlfzyHpJPH6vZx1+0n0GNhto8/l+49/ws7YBIpU2xfLYxKvijP5ox/qNY6kvdwVpJuhXvBsBxvROGgpZOobZMUV4JQrnR//ARA8GmH1VknUG2MOiQ/dXKQ4IJS3qvSBejWXWgPCu+PvNkqEEFCXV6kxgieo/Kj4i4ANZltE5GaVt9NMSHuFkhgQJUrsU+f/aNZBG0d/Qg44cxjjn/+MNcvLc5pt7bu3Y9+T92jpqbV6QsUhbnr3Cn7+fCbV5TF6DepG1/6dW2Qu2VYsTX1dSsc1jH5CtfiQkPoOWXEVouw/G26iLYy0lyDLR6nKJuEDYUPybYSnL8K7dfONk/oWUpPVGL59C+QBnMSHUH4xKrfJBAKQnoasvB1RcmuzzWFzQAgTERmNDJ+hNKvMDs3qYZPxN5GVt6Cq7Azw7QvFN2oxRk0B2jj6E9KuaxtuG3stL9/xJisWrKJLv04cc9lhWnixiXi8Hrbfu/kW1d/LkAN34I373ydeFcfyesik0pgek6GH1POk76xwPUbeGjE6JKQnq3YSm2IOUVNITXaFIYtqkrGdKmRyfL09tdYXGXtOqTLjABKiT0LpQwjPIGRmLlRchTKMBKoKzBXPTE9plvE3R4RR3OzyBzI9E1k5xk3MDwAZJRxq9Ybw2c06lmbTRhtHf1I69mzPBQ+e2dLT0PwB+g/uwyVPnMtDFz5JdXmUcEmIM287sQHDraHcos05rJA97/xE33X7wf1+pL0MWXUPyjByK7ucCmTlGESbF9yqrvymtdkWK2nqa4ha71jS+dPkiG0Q0lOBZI26N6bSb0p9g0AbR5oatHGk0WzCDD14R4YcuANVa6oJl4Ya1qoy2oNne0h9R065WabBv+fm3R3cuxMYbVQ5OF5UPzgD4a9fLX69sBejStUD7oIryOknAVImUYaYh2xJu0IgQic2aQiZ+gFZeSPYi5BGO9UvbBMrZW8ViKwWmWsoS1eoU7RsNaGm9aEfQTSbBI7jYNt2S0+jVWIYBsVtI42KeAohEMW35SXJCvDvhYjcuOEn2YIIswOi9F6l8O0qQ4vQ2RA4+ncfU8okMjNX9S4TbVB5ROnsm0omwGivxvcOVmKIwkONKrUBgaPAf3jjY2UWIsvPU605sMFejKy4XOU4adYP3x5gdABiqhpOVqtWN4EjWnpmmlaG9hxpWjWpZJqnrn6Bcc99hp1x2H6fQZx//+kUt229TX9bM8Jsgyh9GOlUq983Z49RHsKzDbR9B+RaEOE/lHwr0z+rxHZnJSDA+xfwHwKJ1912FhKEDxG+SI3t2wPC5yGrH3adRn4InqwavzalSir5MTjVeaEgCbIamXgX4d3pd5/HnxFhlEHpI8jKmyAzC8xSRGgkwr9XS09N08rQxpGmVfPElc/z7qNjMUwlvPjVO5OpLo9y0ztXNHu7E9u2mfv9fKrLo/QY2LVOGYTNhT+LUZSPEALEHyubl04lsvxisFe6IRoHUhPBdxii6Cpk6jsQfkTgYFUGnx07dLqr+bMEjI7r1+hWptbRRMy24kj+oXP5syKs3oiyJ1p6GppWjjaONK0W27YZ9/xnGKaB3+1blk6mmTFpDkvmLKf7Fs2nexKvjnPLiffxwyfTcGwHf8jP+fedxl+Pbp5+W5rNhPQMt6FqsKaJq7Qh/Rmi5HpE8Kh6dxVmJzA7rf+Y3h0h+hgqFORDJXILhE97jTSaDYXOOdK0WqQjcTJ2QehBGAKJxE43Xy8sgGdvfI0p437CtEz8IT/xaIL7//E4i+csa9ZxNJs4wqSm2iyLROUcbaAhvTshikajErqTavzgyeA/bIONqdH82dGeI02rxfJYbL3HQCZ/9APpZBphCFKxFB17t6dLv9/xBN4AP06cBgg8PtVeIVgUIFYRY87keXRt5rE0mzCercDspRq44iGrayQCh27QYUXwePANc8Ny7dYvLKfRaNYb7TnStGoufOhMthzSDztjk0lm6NirPVc+dyFef/Oq2QaKAqrnmItjOwhD4Atq1VxNDUIEECX3gXd7QCg17ODJEDprw49ttkd4t9eGkUazEdCeI02rprRDCbd8cDWLZy8lncrQbUDnXAPd5uTAM4cx+7tfiFbEMC3Vb65r/85st9egZh9Ls2kjrK6IsqeQMgFYCKG/Rlsa6VRD6iuQUfBshbD6tvSUNJs4+q9a0+oxLXODN3b969G7kk5lePWut6laW03/wb0ZefepBIsCG3RczaaLyAkKaloSaS9Hrh0J9jylMSX8ELkCEWhcQ0qjqQ8hpZSNb6bJUllZSXFxMRUVFUQiWmtHo9FoWhKnfBQkx6FatxjKeyR8iDavIqz6H6pkeiYy8R7IpBLq9O3bNN0pzSbL+qzf2nOk0Wg0mk2X9M+AWSOtQBCIQmYu1GMcydS3yPILlEo2IOMvI0JzIXzuRpmypvWjE7I1Go1Gs+liFKt2LbkgSAYwwCiqdxdZeSvIGBBSyuMIZPQJZObXjTBhzaaANo40Go1Gs8kiQmeovnWySrVZIQmeHcGzbf07OUsBy23HAuADMuAs3/AT1mwS6LCaRqP50yClA84a1eLjT9hCZXNE+IcDFjL2gjKQPIMR4XMRwlP/TkZnsOeC4wBRlLgmyOSXan+h/QZ/drRxpNmkkFISq4rjD/mavbeaZvNGpmchKy4DezEgkIHDEUWXNryIajYJhH8fhH+fpm8fuRy59h/AWsB2X/VA7ElV7RY+e0NMU7MJoc1jzSbDz1/M5MxtRnFir3M5ocdI3vnP2JaekmYTQTWMvQAy81HtPxyIvwzR/7b01DQtgPDuBMW3oNq+eEEUg1ECCGTsWXQRt0YbR5pNgmXzVnDTMXexYsEqAKKVcf572f/x2WtftfDMNJsE6R/BWeE2jPWBCIAEmXi/pWemaSGE2V7lKomw+kwgABNkEpXUrfkzo40jzSbBdx9OJVoRI1AUwOv3EiwKYKdtPtXGkaZJSAqbxea/rvlTYvYAUaLK+aXjVryllcK2DrX+6dHGkWaTwLYd1JNdIU7Grr2xRrMunq3BaK/Kt2VKLYgC8O3X0jPTtBDCCCNKbnPDaVEgAVZPRORfLTwzTWtAJ2RrNgm23XMrfAEv8ao4Xr+XTDqDYRrsOHy7lp6aZhNAGCVQci+y4p9gLwMM8B+KCJ/T0lPTtCDCuzO0eQ0yPwMe8GyHMEItPS1NK0C3D1lPdPuQluPLN7/l/vMfI1YVxzQNDj3/b5x03VEYhnaAapqGlBlwVoEIIQz996vR/JlYn/VbG0friTaOWpZ4dZyVv66mpF2E4rb6+ms0Go2maejeaprNlkA4QI8tu7b0NDSaZkXKONhLQZQgzDYtPR2N5k+PNo40Go2mBZHJz5AV14CsBAxk4BhE0SiE0CKnGk1LoZM1NBqNpoWQmYVKtdtZC3gACfHnIPZcS09No/lTo40jjUaj2YBImUFm5iEzvyBluvDN1HfgRF0hQo8rTukgkxNbZrIajQbQYTWNRqPZYEh7ObJ8FGRmqhesPlB8N8Jy8+ZyXeElhTpeOqSm0bQk2nOk0Wg0GwAppQqZZaahnkMtSM9GVvwTKR21kXdXMEpBRlXbChkFYSIC+7fk1DWaPz3aONJoNJoNgSyH9DRUY9O8n8wspbUECLMjovQ+sLoDEkQAEb4A/Ie35Mw1mj89Oqym0Wg0G4Rsf658Kbls+KwmbCY820Cbt0BWK+NI6K9ljaal0Z4jjUaj2QAIIwy+PYGM29w0rhqbeoeCUahlJIRAGEXaMNJoWgnaONJoNJoNhIhcD779UU56E/z7IorHIETtJsoajab1oB9TNBqNZgOhOr/f6pbwS4TwtvSUNBpNE9DGkUaj0WxghPA0vpFGo2k16LCaRqPRaDQaTR7aONJoNBqNRqPJQxtHGo1Go9FoNHlo40ij0Wg0Go0mD20caTQajUaj0eShjSONRqPRaDSaPLRxpNFoNBqNRpOHNo40Go1Go9Fo8tDGkUaj0Wg0Gk0e2jjSaDQajUajyUMbRxqNRqPRaDR56N5q64mUEoDKysoWnolGo9FoNJqmkl23s+t4Q2jjaD2pqqoCoFu3bi08E41Go9FoNOtLVVUVxcXFDW4jZFNMKE0Ox3FYunQpRUVFCCFaejo5Kisr6datG7/++iuRSKSlp/OnRt+L1oW+H60LfT9aD3+2eyGlpKqqis6dO2MYDWcVac/RemIYBl27dm3padRLJBL5U3zINwX0vWhd6PvRutD3o/XwZ7oXjXmMsuiEbI1Go9FoNJo8tHGk0Wg0Go1Gk4c2jjYTfD4f1113HT6fr6Wn8qdH34vWhb4frQt9P1oP+l7Uj07I1mg0Go1Go8lDe440Go1Go9Fo8tDGkUaj0Wg0Gk0e2jjSaDQajUajyUMbRxqNRqPRaDR5aONoE2PMmDHsuuuuBINBSkpK6txm0aJFHHjggQSDQdq3b8+ll15KJpMp2OaTTz5hhx12wOfz0bdvX5566qkNP/k/CQ8++CA9e/bE7/czZMgQvvnmm5ae0mbHp59+ysEHH0znzp0RQvDGG28UvC+l5Nprr6VTp04EAgGGDRvGnDlzCrZZs2YNJ5xwApFIhJKSEk4//XSqq6s34llsHtxyyy3stNNOFBUV0b59ew477DBmzZpVsE0ikeC8886jTZs2hMNhjjzySFasWFGwTVO+tzSN8/DDD7PNNtvkhB2HDh3K+++/n3tf34umoY2jTYxUKsVRRx3FyJEj63zftm0OPPBAUqkUX375JU8//TRPPfUU1157bW6b+fPnc+CBB7LXXnsxdepULrroIs444ww+/PDDjXUamy0vvfQSo0aN4rrrruP7779n2223Zfjw4axcubKlp7ZZEY1G2XbbbXnwwQfrfP/222/nvvvu45FHHuHrr78mFAoxfPhwEolEbpsTTjiBadOmMXbsWN555x0+/fRTzjrrrI11CpsNEydO5LzzzuOrr75i7NixpNNp9ttvP6LRaG6biy++mLfffptXXnmFiRMnsnTpUo444ojc+0353tI0ja5du3LrrbcyefJkvvvuO/bee28OPfRQpk2bBuh70WSkZpPkySeflMXFxbVef++996RhGHL58uW51x5++GEZiURkMpmUUkr5z3/+U2611VYF+x1zzDFy+PDhG3TOfwZ23nlned555+V+t21bdu7cWd5yyy0tOKvNG0C+/vrrud8dx5EdO3aUd9xxR+618vJy6fP55AsvvCCllHL69OkSkN9++21um/fff18KIeSSJUs22tw3R1auXCkBOXHiRCmluvYej0e+8soruW1mzJghATlp0iQpZdO+tzS/n9LSUvnYY4/pe7EeaM/RZsakSZPYeuut6dChQ+614cOHU1lZmXtymDRpEsOGDSvYb/jw4UyaNGmjznVzI5VKMXny5IJraxgGw4YN09d2IzJ//nyWL19ecB+Ki4sZMmRI7j5MmjSJkpISdtxxx9w2w4YNwzAMvv76640+582JiooKAMrKygCYPHky6XS64H5sscUWdO/eveB+NPa9pVl/bNvmxRdfJBqNMnToUH0v1gNtHG1mLF++vOBDDeR+X758eYPbVFZWEo/HN85EN0N+++03bNuu89pmr71mw5O91g3dh+XLl9O+ffuC9y3LoqysTN+rP4DjOFx00UXstttuDBo0CFDX2uv11sqRXPd+NPa9pWk6P/30E+FwGJ/PxznnnMPrr7/OwIED9b1YD7Rx1Aq4/PLLEUI0+DNz5syWnqZGo9E0yHnnncfPP//Miy++2NJT+VMzYMAApk6dytdff83IkSMZMWIE06dPb+lpbVJYLT0BDYwePZpTTjmlwW169+7dpGN17NixVnVUthKhY8eOuX/XrU5YsWIFkUiEQCDQxFlr1qVt27aYplnntc1ee82GJ3utV6xYQadOnXKvr1ixgu222y63zbpJ8plMhjVr1uh79Ts5//zzc4ntXbt2zb3esWNHUqkU5eXlBR6L/L+LpnxvaZqO1+ulb9++AAwePJhvv/2We++9l2OOOUbfiyaiPUetgHbt2rHFFls0+OP1ept0rKFDh/LTTz8VfPGPHTuWSCTCwIEDc9uMGzeuYL+xY8cydOjQ5jupPyFer5fBgwcXXFvHcRg3bpy+thuRXr160bFjx4L7UFlZyddff527D0OHDqW8vJzJkyfnthk/fjyO4zBkyJCNPudNGSkl559/Pq+//jrjx4+nV69eBe8PHjwYj8dTcD9mzZrFokWLCu5HY99bmt+P4zgkk0l9L9aHls4I16wfCxculFOmTJE33HCDDIfDcsqUKXLKlCmyqqpKSillJpORgwYNkvvtt5+cOnWq/OCDD2S7du3kFVdckTvGvHnzZDAYlJdeeqmcMWOGfPDBB6VpmvKDDz5oqdPabHjxxRelz+eTTz31lJw+fbo866yzZElJSUHlh+aPU1VVlfvsA/Kuu+6SU6ZMkQsXLpRSSnnrrbfKkpIS+eabb8off/xRHnroobJXr14yHo/njrH//vvL7bffXn799dfy888/l/369ZPHHXdcS53SJsvIkSNlcXGx/OSTT+SyZctyP7FYLLfNOeecI7t37y7Hjx8vv/vuOzl06FA5dOjQ3PtN+d7SNI3LL79cTpw4Uc6fP1/++OOP8vLLL5dCCPnRRx9JKfW9aCraONrEGDFihARq/UyYMCG3zYIFC+Tf/vY3GQgEZNu2beXo0aNlOp0uOM6ECRPkdtttJ71er+zdu7d88sknN+6JbMbcf//9snv37tLr9cqdd95ZfvXVVy09pc2OCRMm1Pl3MGLECCmlKue/5pprZIcOHaTP55P77LOPnDVrVsExVq9eLY877jgZDodlJBKRp556au4hQ9N06roPQMF3Sjwel+eee64sLS2VwWBQHn744XLZsmUFx2nK95amcU477TTZo0cP6fV6Zbt27eQ+++yTM4yk1PeiqQgppdzo7iqNRqPRaDSaVorOOdJoNBqNRqPJQxtHGo1Go9FoNHlo40ij0Wg0Go0mD20caTQajUaj0eShjSONRqPRaDSaPLRxpNFoNBqNRpOHNo40Go1Go9Fo8tDGkUaj0Wg0Gk0e2jjSaDSbPKeccgpCCIQQuaab//rXv8hkMrltpJQ8+uijDBkyhHA4TElJCTvuuCP33HMPsVis4HiLFy/G6/UyaNCgJo3/6aefcvDBB9O5c2eEELzxxhvNeXoajWYjo40jjUazWbD//vuzbNky5syZw+jRo7n++uu54447cu+fdNJJXHTRRRx66KFMmDCBqVOncs011/Dmm2/y0UcfFRzrqaee4uijj841rG2MaDTKtttuy4MPPtjs56XRaDY+un2IRqPZ5DnllFMoLy8v8Njst99+VFVVMWnSJF5++WWOOeYY3njjDQ499NCCfaWUVFZWUlxcnPu9b9++PPTQQ0yYMIE1a9bw6KOPNnkuQghef/11DjvssOY4NY1G0wJoz5FGo9ksCQQCpFIpAJ577jkGDBhQyzACZcxkDSOACRMmEIvFGDZsGCeeeCIvvvgi0Wh0o81bo9G0PNo40mg0mxVSSj7++GM+/PBD9t57bwDmzJnDgAEDmrT/448/zrHHHotpmgwaNIjevXvzyiuvbMgpazSaVobV0hPQaDSa5uCdd94hHA6TTqdxHIfjjz+e66+/HlAGU1MoLy/nf//7H59//nnutRNPPJHHH3+cU045ZQPMWqPRtEa0caTRaDYL9tprLx5++GG8Xi+dO3fGsmq+3vr378/MmTMbPcbzzz9PIpFgyJAhudeklDiOw+zZs+nfv/8GmbtGo2ld6LCaRqPZLAiFQvTt25fu3bsXGEYAxx9/PLNnz+bNN9+stZ+UkoqKCkCF1EaPHs3UqVNzPz/88AO77747TzzxxEY5D41G0/Jo40ij0Wz2HH300RxzzDEcd9xx3HzzzXz33XcsXLiQd955h2HDhuVK+7///nvOOOMMBg0aVPBz3HHH8fTTTxfoJuVTXV2dM6YA5s+fz9SpU1m0aNFGPEuNRtNc6FJ+jUazyVNXKf+6OI7Do48+yhNPPMG0adOwLIt+/fpx8sknc+aZZ/LPf/6T8ePHM23atFr7Ll++nC5duvD6669zyCGH1Hr/k08+Ya+99qr1+ogRI3jqqaf+yKlpNJoWQBtHGo1Go9FoNHnosJpGo9FoNBpNHto40mg0Go1Go8lDG0cajUaj0Wg0eWjjSKPRaDQajSYPbRxpNBqNRqPR5KGNI41Go9FoNJo8tHGk0Wg0Go1Gk4c2jjQajUaj0Wjy0MaRRqPRaDQaTR7aONJoNBqNRqPJQxtHGo1Go9FoNHlo40ij0Wg0Go0mj/8HGEPd4vlsMkYAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pd.set_option('display.max_columns', None)\n",
"# Reducing the Dimensionality of the data to 2D\n",
"pca_2 = PCA(n_components=2)\n",
"pca_2_result = pca_2.fit_transform(temp_df_val)\n",
"print('Explained variation per principal component: {}'.format(pca_2.explained_variance_ratio_))\n",
"print('Cumulative variance explained by 2 principal components: {:.2%}'.format(np.sum(pca_2.explained_variance_ratio_)))\n",
"# fitting KMeans\n",
"kmeans = KMeans(n_clusters=2)\n",
"kmeans.fit(temp_df_val)\n",
"centroids = kmeans.cluster_centers_\n",
"centroids_pca = pca_2.transform(centroids)\n",
"\n",
"print(\"\\n\\n4. Visualizing the data\")\n",
"visualizing_results(pca_2_result[1:], kmeans.labels_[1:], centroids_pca)\n"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot = temp_df_val['smoking']\n",
"\n",
"x = pca_2_result[:, 0]\n",
"y = pca_2_result[:, 1]\n",
"\n",
"plt.scatter(x, y, c=plot, alpha=0.9, s=12.5) # plot different colors per cluster\n",
"plt.title('Original Class Clusters')\n",
"plt.xlabel('PCA 1')\n",
"plt.ylabel('PCA 2')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 175,
"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>gender</th>\n",
" <th>age</th>\n",
" <th>height(cm)</th>\n",
" <th>weight(kg)</th>\n",
" <th>waist(cm)</th>\n",
" <th>eyesight(left)</th>\n",
" <th>eyesight(right)</th>\n",
" <th>hearing(left)</th>\n",
" <th>hearing(right)</th>\n",
" <th>systolic</th>\n",
" <th>relaxation</th>\n",
" <th>fasting blood sugar</th>\n",
" <th>Cholesterol</th>\n",
" <th>triglyceride</th>\n",
" <th>HDL</th>\n",
" <th>LDL</th>\n",
" <th>hemoglobin</th>\n",
" <th>Urine protein</th>\n",
" <th>serum creatinine</th>\n",
" <th>AST</th>\n",
" <th>ALT</th>\n",
" <th>Gtp</th>\n",
" <th>dental caries</th>\n",
" <th>tartar</th>\n",
" <th>BMI</th>\n",
" <th>smoking</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.555888</td>\n",
" <td>44.015944</td>\n",
" <td>163.642252</td>\n",
" <td>63.283508</td>\n",
" <td>79.970487</td>\n",
" <td>1.011676</td>\n",
" <td>1.010563</td>\n",
" <td>1.024747</td>\n",
" <td>1.023584</td>\n",
" <td>119.858994</td>\n",
" <td>74.658695</td>\n",
" <td>95.900017</td>\n",
" <td>191.425677</td>\n",
" <td>88.943199</td>\n",
" <td>60.540608</td>\n",
" <td>113.097160</td>\n",
" <td>14.353230</td>\n",
" <td>1.075735</td>\n",
" <td>0.866235</td>\n",
" <td>22.911975</td>\n",
" <td>21.046670</td>\n",
" <td>25.763162</td>\n",
" <td>0.200299</td>\n",
" <td>0.534131</td>\n",
" <td>23.526261</td>\n",
" <td>0.287328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.775888</td>\n",
" <td>44.317159</td>\n",
" <td>166.578289</td>\n",
" <td>71.228114</td>\n",
" <td>86.090595</td>\n",
" <td>1.027114</td>\n",
" <td>1.023162</td>\n",
" <td>1.030015</td>\n",
" <td>1.021511</td>\n",
" <td>124.630315</td>\n",
" <td>78.503252</td>\n",
" <td>102.700350</td>\n",
" <td>210.784392</td>\n",
" <td>218.365683</td>\n",
" <td>48.824912</td>\n",
" <td>118.964482</td>\n",
" <td>15.170435</td>\n",
" <td>1.077539</td>\n",
" <td>0.917459</td>\n",
" <td>25.627814</td>\n",
" <td>29.541271</td>\n",
" <td>42.587794</td>\n",
" <td>0.229615</td>\n",
" <td>0.571286</td>\n",
" <td>25.567358</td>\n",
" <td>0.495248</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gender age height(cm) weight(kg) waist(cm) eyesight(left) \\\n",
"0 0.555888 44.015944 163.642252 63.283508 79.970487 1.011676 \n",
"1 0.775888 44.317159 166.578289 71.228114 86.090595 1.027114 \n",
"\n",
" eyesight(right) hearing(left) hearing(right) systolic relaxation \\\n",
"0 1.010563 1.024747 1.023584 119.858994 74.658695 \n",
"1 1.023162 1.030015 1.021511 124.630315 78.503252 \n",
"\n",
" fasting blood sugar Cholesterol triglyceride HDL LDL \\\n",
"0 95.900017 191.425677 88.943199 60.540608 113.097160 \n",
"1 102.700350 210.784392 218.365683 48.824912 118.964482 \n",
"\n",
" hemoglobin Urine protein serum creatinine AST ALT \\\n",
"0 14.353230 1.075735 0.866235 22.911975 21.046670 \n",
"1 15.170435 1.077539 0.917459 25.627814 29.541271 \n",
"\n",
" Gtp dental caries tartar BMI smoking \n",
"0 25.763162 0.200299 0.534131 23.526261 0.287328 \n",
"1 42.587794 0.229615 0.571286 25.567358 0.495248 "
]
},
"execution_count": 175,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k_means_results = pd.DataFrame(centroids)\n",
"k_means_results.columns = temp_df_val.columns\n",
"k_means_results\n",
"# 0 - Centroids for Non-Smokers\n",
"# 1 - Centroids for Smokers"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6584788029925187"
]
},
"execution_count": 177,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Measuring the performance of the model\n",
"accuracy_score(plot.values, kmeans.labels_)"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(8020, 26)"
]
},
"execution_count": 198,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_df_val.shape"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Davies Bouldin Score for K = 3 is 1.3388103748308626\n",
"Silhouette Score for K = 3 is 0.2437372075975793\n",
"Calinski Harabasz Score for K = 3 is 15956.473184539625\n",
"Davies Bouldin Score for K = 4 is 1.2976513968075036\n",
"Silhouette Score for K = 4 is 0.2346278170572996\n",
"Calinski Harabasz Score for K = 4 is 14130.467343451413\n",
"Davies Bouldin Score for K = 5 is 1.3090780443779682\n",
"Silhouette Score for K = 5 is 0.21265400533525974\n",
"Calinski Harabasz Score for K = 5 is 12751.428052540487\n",
"Davies Bouldin Score for K = 6 is 1.4412514290747653\n",
"Silhouette Score for K = 6 is 0.17693627049322924\n",
"Calinski Harabasz Score for K = 6 is 11451.264486997743\n",
"Davies Bouldin Score for K = 7 is 1.4063857760381484\n",
"Silhouette Score for K = 7 is 0.17598062146040436\n",
"Calinski Harabasz Score for K = 7 is 10569.420573687034\n",
"Davies Bouldin Score for K = 8 is 1.4330352975876717\n",
"Silhouette Score for K = 8 is 0.1674900704164932\n",
"Calinski Harabasz Score for K = 8 is 9800.53827091137\n",
"Davies Bouldin Score for K = 9 is 1.5143660231729104\n",
"Silhouette Score for K = 9 is 0.15629011062353151\n",
"Calinski Harabasz Score for K = 9 is 9031.993553054748\n",
"Davies Bouldin Score for K = 10 is 1.531445363999964\n",
"Silhouette Score for K = 10 is 0.1439139378043964\n",
"Calinski Harabasz Score for K = 10 is 8452.899231164203\n"
]
}
],
"source": [
"# We will choose to train 8 models with the following K values:\n",
"# K = 2, 3, 4, 5, 6, 7, 8, 9\n",
"# We will use the Davies Bouldin Score to measure the performance of the model\n",
"# The Davies Bouldin Score is a measure of how well separated the clusters are\n",
"#Implementation using GridSearch:\n",
"\n",
"params = {'n_clusters': [3,4,5,6,7,8,9,10]}\n",
"results = []\n",
"for i in params[\"n_clusters\"]:\n",
" kmeans = KMeans(n_clusters=i)\n",
" kmeans.fit(temp_df)\n",
" davies = davies_bouldin_score(temp_df, kmeans.labels_)\n",
" silehouette = silhouette_score(temp_df, kmeans.labels_)\n",
" calinski = calinski_harabasz_score(temp_df, kmeans.labels_)\n",
" print(\"Davies Bouldin Score for K = {} is {}\".format(i, davies))\n",
" print(\"Silhouette Score for K = {} is {}\".format(i, silehouette))\n",
" print(\"Calinski Harabasz Score for K = {} is {}\".format(i, calinski))\n",
" results.append([i, davies, silehouette, calinski])\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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8fLwAQOjcuXO+P5NTG4eXl5fg6Oiosq1Zs2Yqz9Wcnt9ZbQ3vt3kIgiC4u7sLtWvXVr6fMGGCYGBgoNJe8qH322vCw8OF6tWrC46OjkJISIhKuZzaCHKSWzvI+68xY8Yoy2dmZgppaWkqx3jz5o1gYWEhDBkyJNt9yOkZXdBjaGtrC2FhYcrtfn5+AgBh0qRJym35bZMyNDRUuZ68vH37Vqhdu7ZgbW0thIeHf7RsVh1HV1dXaNeunTB37txsbUmCIAgbN24UAAhLly7Nti+rLpTVHjRnzhyV/d27dxckEolKOw8AQU1NTXjw4IFK2aFDhwpWVlZCdHS0yvbevXsLhoaGOd4zopxw6i2iPOjp6SExMVH5/v21SlJTUxEdHa2ctilrCCMA9OrVC1FRUSrTS+zbtw8KhQK9evXK9XwHDhyAQqFAz549ER0drXxZWlqicuXKyiGaWSNGTp48iZSUlAJfl0wmw+nTp3H69GmcPHkSv//+O/T09NC+fXs8fvxYWe7YsWOoV6+eyrRQenp6GDFiBEJCQpRTPnyOY8eOQV1dXdm7BACkUqnKorN58fT0VOlRUrNmTRgYGODZs2cf/ZyFhQXu3LmDkSNH4s2bN1i/fj369u0Lc3Nz/Prrr8pRHrdv30ZwcDAmTpyYbURHVo+N8PBw+Pv7Y9CgQSq9KmrWrInWrVvj2LFj2c4/cuRIlff5/f6JiIg+1Lp1a/j6+qJTp064c+cOFi5cCC8vL5QvX/6Tp2HS09NTWdNMU1MT9erVU3m+Hjt2DJaWlujTp49ym4aGBsaPH4+kpKQcpyL9mNjYWJw7dw49e/ZEYmKi8lkYExMDLy8vPHnyBC9fvvyk6wHejQZp0qQJypUrp/Ks9fT0hFwux6VLlz76+U2bNmH16tVwcHDAwYMH8f3336Nq1apo1aqVSlz79++Hq6trjiM6suoOBb133bp1g5mZmfJ9Ud8rIiIqWRISEgAA+vr6+f7M+20c8fHxiI6ORrNmzfDs2bNPntb7w9+5TZo0Uak7GBkZITk5GadPn87zWGFhYWjWrBkyMjJw6dIl5ejOT/F+O8iHrw9JpVLlGiMKhQKxsbHIzMxEnTp1VNp+snz4jP6UY3Tp0gXly5dXvq9Xrx7q16+v0paQ3zYpIyMj+Pn54dWrV/m6N6NHj8a9e/ewf/9+5bTsufn555+xc+dOuLu74+TJk/jxxx9Ru3Zt1KpVS2Xa8f3798PU1DTHtp3360JSqRTjx49X2f/dd99BEAQcP35cZXuzZs1QrVo15XtBELB//3507NgRgiCo1O28vLwQHx+f470mygkTJUR5SEpKUqlkxMbGYsKECbCwsIC2tjbMzMzg4OAAACqViLZt28LQ0BC7d+9Wbtu9ezfc3Nzg5OSU6/mePHkCQRBQuXJlmJmZqbwCAgKUC2M5ODhg8uTJ+PPPP2FqagovLy+sWbMm3xUZqVQKT09PeHp6ok2bNhgxYgTOnDmD+Ph4TJ8+XVnu+fPnqFKlSrbPZ01v8fz583yd72OeP38OKysr6OnpqWzP6by5qVChQrZt5cqVw5s3b/L8rJWVFdatW4fw8HAEBgZi5cqVMDMzw8yZM/HXX38BgHL+zI9NZZJ1L3K7X9HR0UhOTlbZnvVvJ0t+v38iIqKc1K1bFwcOHMCbN29w7do1TJ8+HYmJiejevfsndW6wsbHJNu/3h8/X58+fo3LlylBTU/1p8al1hadPn0IQBMyYMSPbs3DWrFkAPm+h0CdPnuDEiRPZju3p6ZmvY6upqWHMmDG4efMmoqOj8c8//6Bdu3Y4d+4cevfurSwXFBSU5xRoBb13H9YbivpeERFRyWJgYAAAKp098+Lt7Q1PT0/lGptmZmb43//+BwCflCiRyWTZEgYf1h1Gjx4NJycntGvXDjY2NhgyZEiua4z2798fUVFRuHjxokoS4VO83w7y4SsnW7ZsQc2aNSGTyWBiYgIzMzMcPXo0x/vy4TP6U45RuXLlbNucnJxU1oLNb5vUwoULcf/+fdja2qJevXqYPXt2rh1Jf//9d2zatAmrVq3Kcf3WnPTp0weXL1/GmzdvcOrUKfTt2xe3b99Gx44dkZqaCuBdXahKlSrZ1sF93/Pnz2FtbZ0tuZffutDr168RFxeHDRs2ZKsLDR48GADrQpR/XKOE6CPCwsIQHx+PSpUqKbf17NkTPj4+mDJlCtzc3KCnpweFQoG2bduqLACqpaWFLl264ODBg1i7di0iIyPh7e2N33777aPnVCgUkEgkOH78OKRSabb97ycTlixZgkGDBuGff/7BqVOnMH78eMybNw9Xr16FjY1Nga/XxsYGVapUybMnZU5yWzztwwXbikpO9wpAtsW/PkYikcDJyQlOTk746quvULlyZezYsQPDhg0rrDCzeb83CFCw75+IiCg3mpqaqFu3LurWrQsnJycMHjwYe/fuVTae51dhPF+z5LeukFWf+v777+Hl5ZXjZ96vmxWUQqFA69at8cMPP+S4/2MdWj5kYmKCTp06oVOnTmjevDkuXryI58+ff1Zv14/Jqd4AFN29IiKiksXAwADW1tbZFgDPTVBQEFq1agVnZ2csXboUtra20NTUxLFjx7Bs2TKVNo78yq3u8D5zc3P4+/vj5MmTOH78OI4fP45NmzZhwIAB2LJli0rZr7/+Glu3bsWKFSswb968AsfzqbZv345BgwahS5cumDJlCszNzSGVSjFv3jyVhcizfPiM/pRj5Ed+26R69uyJJk2a4ODBgzh16hQWLVqEBQsW4MCBA8p15gDg2rVrmDBhAoYNG4YRI0YUOB4DAwO0bt0arVu3hoaGBrZs2QI/P79s6/UWltzqQt98802u687WrFmzSGKh0oeJEqKPyFrYKuuH55s3b3D27Fn8/PPPmDlzprLckydPcvx8r169sGXLFpw9exYBAQEQBOGj024BUC5m5eDgkK8f6i4uLnBxccFPP/0EHx8fNGrUCOvXr8ecOXPye5kqMjMzkZSUpHxvZ2eHwMDAbOUePXqk3A+86yECAHFxcSrl8tOL1M7ODmfPnkVSUpJKIiCn834pjo6OKFeuHMLDwwFAOa3X/fv3c+1tknUvcrtfpqam0NXV/eh5C/r9ExER5aVOnToAoHymFTY7OzvcvXsXCoVCZWTEp9YVHB0dAbybgiq3Z26W3JIvH9tXsWJFJCUl5XnsgqpTpw4uXryI8PBw2NnZoWLFink2VOX33uWmIPeKiIjKhg4dOmDDhg3w9fWFh4fHR8v++++/SEtLw+HDh1VmafgSUz5ramqiY8eO6NixIxQKBUaPHo3ff/8dM2bMUEnyjxs3DpUqVcLMmTNhaGiIadOmFXlswLup0x0dHXHgwAGVOkVBOp0U9Bg5tS09fvwY9vb2AAreJmVlZYXRo0dj9OjRiIqKQq1atTB37lxlouT169fo3r073NzcsGbNmnxfV27q1KmDLVu2qLSj+Pn5ISMjAxoaGjl+xs7ODmfOnEFiYqLKqJL81oXMzMygr68PuVzOuhB9Nk69RZSLc+fO4ddff4WDgwP69esH4P97RnzYi3L58uU5HsPT0xPGxsbYvXs3du/ejXr16uU6HDPL119/DalUip9//jnbeQRBQExMDIB3c49mZmaq7HdxcYGamhrS0tLyfZ3ve/z4MQIDA+Hq6qrc1r59e1y7dg2+vr7KbcnJydiwYQPs7e2Vc0NmJRLeH40il8uxYcOGPM/bvn17ZGZmYt26dSqfXbVq1SddR0H4+fllmw4LeNerIiYmRjmNVq1ateDg4IDly5dna+DJ+p6srKzg5uaGLVu2qJS5f/8+Tp06hfbt2+cZT36/fyIiog+dP38+x5EeWfNaF2RKy4Jo3749IiIiVKYbzczMxKpVq6Cnp6fsUWhnZwepVJpt5OratWtV3pubm6N58+b4/fffc0zuvH79Wvn/szogfPhsztqX0/aePXvC19cXJ0+ezLYvLi4uW/3qfRERETlOYZaeno6zZ89CTU1N2bjTrVs33LlzBwcPHsxWPut7yu+9y01B7hUREZUNP/zwA3R1dTFs2DBERkZm2x8UFIQVK1YAyLmNIz4+Hps2bSrSGD/8Xaumpqbs9Z9Te8aMGTPw/fffY/r06SrtBkUpp3vj5+en0jZS2Mc4dOiQytpi165dg5+fnzKxkd82Kblcnm1qL3Nzc1hbWyvvr1wuR+/evZGeno79+/cr11LJS0pKSq7xZ60nklXn7NatG6Kjo7F69epsZd+vC8nl8mxlli1bBolEojL6JSdSqRTdunXD/v37c+ygwroQFQRHlBDh3X/MHz16hMzMTERGRuLcuXM4ffo07OzscPjwYchkMgDvhhQ2bdoUCxcuREZGBsqXL49Tp04hODg4x+NqaGjg66+/xq5du5CcnIzFixfnGUvFihUxZ84cTJ8+HSEhIejSpQv09fURHByMgwcPYsSIEfj+++9x7tw5jB07Fj169ICTkxMyMzOxbds25UMiL5mZmdi+fTuAd0MVQ0JCsH79eigUCpXeDdOmTcPff/+Ndu3aYfz48TA2NsaWLVsQHByM/fv3K3s/Vq9eHQ0aNMD06dMRGxsLY2Nj7Nq166ONDVk6duyIRo0aYdq0aQgJCUG1atVw4MCBT144riC2bduGHTt2oGvXrqhduzY0NTUREBCAjRs3QiaTKedmVVNTw7p169CxY0e4ublh8ODBsLKywqNHj/DgwQNlY8uiRYvQrl07eHh4YOjQoXj79i1WrVoFQ0NDzJ49O8948vv9ExERfWjcuHFISUlB165d4ezsjPT0dPj4+GD37t2wt7dXztNc2EaMGIHff/8dgwYNws2bN2Fvb499+/bB29sby5cvV/YONDQ0RI8ePbBq1SpIJBJUrFgRR44cyXHe6DVr1qBx48ZwcXHB8OHD4ejoiMjISPj6+iIsLAx37twBALi5uUEqlWLBggWIj4+HlpYWWrZsCXNzc9SuXRvr1q3DnDlzUKlSJZibm6Nly5aYMmUKDh8+jA4dOmDQoEGoXbs2kpOTce/ePezbtw8hISEwNTXN8VrDwsJQr149tGzZEq1atYKlpSWioqLw999/486dO5g4caLys1OmTMG+ffvQo0cPDBkyBLVr10ZsbCwOHz6M9evXw9XVNd/37mPye6+IiKhsqFixInbu3IlevXqhatWqGDBgAGrUqKGsF+zduxeDBg0CALRp00Y5suPbb79FUlIS/vjjD5ibmxfZSFQAGDZsGGJjY9GyZUvY2Njg+fPnWLVqFdzc3JRrU3xo0aJFiI+Px5gxY6Cvr49vvvmmyOID3o3MOXDgALp27YqvvvoKwcHBWL9+PapVq6YyC0dhHqNSpUpo3LgxRo0ahbS0NCxfvhwmJibK6ULz2yaVmJgIGxsbdO/eHa6urtDT08OZM2dw/fp1LFmyBACwfv16nDt3DiNHjsw2gsjCwgKtW7fO8ZpSUlLQsGFDNGjQAG3btoWtrS3i4uJw6NAhXL58GV26dIG7uzsAYMCAAdi6dSsmT56Ma9euoUmTJkhOTsaZM2cwevRodO7cGR07dkSLFi3w448/IiQkBK6urjh16hT++ecfTJw4Udkp92Pmz5+P8+fPo379+hg+fDiqVauG2NhY3Lp1C2fOnEFsbGzeXxYRAAhEZdimTZsEAMqXpqamYGlpKbRu3VpYsWKFkJCQkO0zYWFhQteuXQUjIyPB0NBQ6NGjh/Dq1SsBgDBr1qxs5U+fPi0AECQSifDixYts+2fNmiXk9Ke4f/9+oXHjxoKurq6gq6srODs7C2PGjBECAwMFQRCEZ8+eCUOGDBEqVqwoyGQywdjYWGjRooVw5syZPK974MCBKtcNQDAwMBBatWqV4+eDgoKE7t27C0ZGRoJMJhPq1asnHDlyJMdynp6egpaWlmBhYSH873//U17/+fPnVc5vZ2en8tmYmBihf//+goGBgWBoaCj0799fuH37tgBA2LRp00fvFwBhzJgx2eKxs7MTBg4c+NF7cffuXWHKlClCrVq1BGNjY0FdXV2wsrISevToIdy6dStb+StXrgitW7cW9PX1BV1dXaFmzZrCqlWrVMqcOXNGaNSokaCtrS0YGBgIHTt2FB4+fKhSJus6Xr9+nWNceX3/REREHzp+/LgwZMgQwdnZWdDT0xM0NTWFSpUqCePGjRMiIyNVyn74jDx//ny253WzZs2E6tWrZztPTs/xyMhIYfDgwYKpqamgqakpuLi4qDy/s7x+/Vro1q2boKOjI5QrV0749ttvhfv372d73gvCu3rFgAEDBEtLS0FDQ0MoX7680KFDB2Hfvn0q5f744w/B0dFRkEqlKtcQEREhfPXVV4K+vr4AQGjWrJnyM4mJicL06dOFSpUqCZqamoKpqanQsGFDYfHixUJ6enqu9zghIUFYsWKF4OXlJdjY2AgaGhqCvr6+4OHhIfzxxx+CQqFQKR8TEyOMHTtWKF++vKCpqSnY2NgIAwcOFKKjowt074KDgwUAwqJFi3KMK7/3ioiIyo7Hjx8Lw4cPF+zt7QVNTU1BX19faNSokbBq1SohNTVVWe7w4cNCzZo1BZlMJtjb2wsLFiwQNm7cKAAQgoODleWaNWum8izNeja9/8waOHCgoKurmy2WD3/H79u3T2jTpo1gbm4uaGpqChUqVBC+/fZbITw8XFkmq73m+vXrym1yuVzo06ePoK6uLhw6dCjHY+cmt9iyfNiuoFAohN9++02ws7MTtLS0BHd3d+HIkSPZ6kEfe0Z/yjGWLFki2NraClpaWkKTJk2EO3fuqBwzP21SaWlpwpQpUwRXV1dl24Wrq6uwdu1a5XGy7ltOr/e/5w9lZGQIf/zxh9ClSxfldeno6Aju7u7CokWLhLS0NJXyKSkpwo8//ig4ODgIGhoagqWlpdC9e3chKChIWSYxMVGYNGmSYG1tLWhoaAiVK1cWFi1alK1elVvbjyC8q0+NGTNGsLW1VZ6nVatWwoYNG3K9FqIPSQThE1ZiJCIiIiIiIiIiIiIiKgW4RgkREREREREREREREZVZTJQQEREREREREREREVGZxUQJERERERERERERERGVWUyUEBERERERERERERFRmcVECRERERERERERERERlVlMlBARERERERERERERUZmlLnYAhUGhUODVq1fQ19eHRCIROxwiIqJiQRAEJCYmwtraGmpq7BtR1FgfISIiUsW6yJfFuggREZGqgtRFSkWi5NWrV7C1tRU7DCIiomLpxYsXsLGxETuMUo/1ESIiopyxLvJlsC5CRESUs/zURUpFokRfXx/Auws2MDAQORoiIqLiISEhAba2tsrnJBUt1keIiIhUsS7yZbEuQkREpKogdZFSkSjJGlJqYGDAygAREdEHOPXCl8H6CBERUc5YF/kyWBchIiLKWX7qIpwklIiIiIiIiIiIiIiIyiwmSoiIiIiIiIiIiIiIqMxiooSIiIiIiIiIiIiIiMqsUrFGSX4IgoDMzEzI5XKxQyESlYaGBqRSqdhhEBEREVEhkMvlyMjIEDsMKsP4+4KIKP/43CYqfFKpFOrq6p+9JlqZSJSkp6cjPDwcKSkpYodCJDqJRAIbGxvo6emJHQoRERERfYakpCSEhYVBEASxQ6EyjL8viIjyh89toqKjo6MDKysraGpqfvIxSn2iRKFQIDg4GFKpFNbW1tDU1Pzs7BJRSSUIAl6/fo2wsDBUrlyZPb+IiIiISii5XI6wsDDo6OjAzMyMv3FIFPx9QUSUP3xuExUNQRCQnp6O169fIzg4GJUrV4aa2qetNlLqEyXp6elQKBSwtbWFjo6O2OEQic7MzAwhISHIyMjgDxkiIiKiEiojIwOCIMDMzAza2tpih0NlGH9fEBHljc9toqKjra0NDQ0NPH/+HOnp6ZDJZJ90nDKzmPunZpKIShv2WiAiIiIqPVi3I7Hx3yARUf7xv5lERaMw2v6ZPSAiIiIiIiKiMufSpUvo2LEjrK2tIZFIcOjQoWxlAgIC0KlTJxgaGkJXVxd169ZFaGiocn9qairGjBkDExMT6OnpoVu3boiMjFQ5RmhoKL766ivo6OjA3NwcU6ZMQWZmpkqZCxcuoFatWtDS0kKlSpWwefPmorhkIiIiygUTJURERERERERU5iQnJ8PV1RVr1qzJcX9QUBAaN24MZ2dnXLhwAXfv3sWMGTNUpvSYNGkS/v33X+zduxcXL17Eq1ev8PXXXyv3y+VyfPXVV0hPT4ePjw+2bNmCzZs3Y+bMmcoywcHB+Oqrr9CiRQv4+/tj4sSJGDZsGE6ePFl0F09EREQqmCghAEBISAgkEgn8/f3FDuWzbd68GUZGRh8tM3v2bLi5uSnfDxo0CF26dCnSuIiIiIiIKG/v9+z/8HfKhQsXIJFIEBcXJ1p8VHq0a9cOc+bMQdeuXXPc/+OPP6J9+/ZYuHAh3N3dUbFiRXTq1Anm5uYAgPj4ePz1119YunQpWrZsidq1a2PTpk3w8fHB1atXAQCnTp3Cw4cPsX37dri5uaFdu3b49ddfsWbNGqSnpwMA1q9fDwcHByxZsgRVq1bF2LFj0b17dyxbtuzL3AgioiL2YVvdh+1yn6t58+aYOHFirvtLSrtfbqMb6ctgoqQYGzRoECQSCSQSCTQ0NGBhYYHWrVtj48aNUCgUhXouW1tbhIeHo0aNGoV63CxZP3CyXpqamqhUqRLmzJkDQRCK5JwFsWLFis8e2pySkoLp06ejYsWKkMlkMDMzQ7NmzfDPP/8UTpBERERERCXc69evMWrUKFSoUAFaWlqwtLSEl5cXvL29lWXCw8PRrl07EaPMn5waZQozkRMcHIy+ffvC2toaMpkMNjY26Ny5Mx49evTZx6a8KRQKHD16FE5OTvDy8oK5uTnq16+v0oB18+ZNZGRkwNPTU7nN2dkZFSpUgK+vLwDA19cXLi4usLCwUJbx8vJCQkICHjx4oCzz/jGyymQdIzdpaWlISEhQeRERFbaIiAiMGzcOjo6O0NLSgq2tLTp27IizZ89+8jG///77z/p8QeXV7vex57e9vT2WL19eZLEVZ2WtrVNd7ADo49q2bYtNmzZBLpcjMjISJ06cwIQJE7Bv3z4cPnwY6uqF8xVKpVJYWloWyrE+5syZM6hevTrS0tJw5coVDBs2DFZWVhg6dGiRn/tjDA0NP/sYI0eOhJ+fH1atWoVq1aohJiYGPj4+iImJKYQIc5aeng5NTc0iOz4RfVkKhYAMhQJa6lKxQ6FiIDVDDg2pGqRqXPCRiEqPbt26IT09HVu2bIGjoyMiIyNx9uxZlTrzl/hdUtxlZGSgdevWqFKlCg4cOAArKyuEhYXh+PHjRTqaJiMjAxoaGkV2/JIkKioKSUlJmD9/PubMmYMFCxbgxIkT+Prrr3H+/Hk0a9YMERER0NTUzDajgYWFBSIiIgC8a2B8P0mStT9r38fKJCQk4O3bt9DW1s4xxnnz5uHnn38ujMvNVUJqBgxk/DdBVFaFhISgUaNGMDIywqJFi+Di4oKMjAycPHkSY8aM+eTkvZ6eHvT09Ao52twVRrvfp5LL5ZBIJIWy2PiXVtbaOkveN1QIBEFASnqmKK+Cjp7I6mVVvnx51KpVC//73//wzz//4Pjx4yqZ0KVLl8LFxQW6urqwtbXF6NGjkZSUBABISEiAtrY2jh8/rnLsgwcPQl9fHykpKTlOvXX//n20a9cOenp6sLCwQP/+/REdHa3cv2/fPri4uEBbWxsmJibw9PREcnLyR6/HxMQElpaWsLOzQ79+/dCoUSPcunVLuV+hUOCXX36BjY0NtLS04ObmhhMnTij355Th9ff3h0QiQUhISK7nnT9/PiwsLKCvr4+hQ4ciNTVVZf+HQ/CaN2+O8ePH44cffoCxsTEsLS0xe/bsj17b4cOH8b///Q/t27eHvb09ateujXHjxmHIkCHKMmlpaZg6dSpsbW2Vi/T99ddfyv0XL15EvXr1oKWlBSsrK0ybNk1lkb/mzZtj7NixmDhxIkxNTeHl5QUg7++KiIq3xNQMbPIORsslF7DFJ0TscKiYWHr6Mb5aeRnnH0UVi9GXRFS8lYTfOHFxcbh8+TIWLFiAFi1awM7ODvXq1cP06dPRqVMnZbn8TDtx8+ZN1KlTBzo6OmjYsCECAwNV9q9btw4VK1aEpqYmqlSpgm3btin35fTbJy4uDhKJBBcuXFBu+1gde9CgQbh48SJWrFihHDUfEhKCFi1aAADKlSsHiUSCQYMGAXj3O2fevHlwcHCAtrY2XF1dsW/fvlyv78GDBwgKCsLatWvRoEED2NnZoVGjRpgzZw4aNGigLBcWFoY+ffrA2NgYurq6qFOnDvz8/PJ1H7Lu9bp169CpUyfo6upi7ty5AIB//vkHtWrVgkwmg6OjI37++edsi4+XdlmzOHTu3BmTJk2Cm5sbpk2bhg4dOmD9+vUiR/fO9OnTER8fr3y9ePGi0I4tVwhYdfYJmiw4j9CYlEI7LhG9UxKe2wAwevRoSCQSXLt2Dd26dYOTkxOqV6+OyZMnK6cYBD7eLpmT3KbEX7x4MaysrGBiYoIxY8YgIyNDWWbt2rWoXLkyZDIZLCws0L1791yPf/ToURgaGmLHjh0qxy8MeV1r1jRjhw8fRrVq1aClpYXQ0FBcv34drVu3hqmpKQwNDdGsWTOVNtEsWSNrtbW14ejomK2+MHXqVDg5OUFHRweOjo6YMWOGyn26c+cOWrRoAX19fRgYGKB27dq4ceMGgHftiu/P+PN+HSYnZa2ts0yOKHmbIUe1meIsivbwFy/oaH7ebW/ZsiVcXV1x4MABDBs2DACgpqaGlStXwsHBAc+ePcPo0aPxww8/YO3atTAwMECHDh2wc+dOlSHsO3bsQJcuXaCjo5PtHHFxcWjZsiWGDRuGZcuW4e3bt5g6dSp69uyJc+fOITw8HH369MHChQvRtWtXJCYm4vLlywX6j+2NGzdw8+ZNDBgwQLltxYoVWLJkCX7//Xe4u7tj48aN6NSpEx48eIDKlSt/0v3as2cPZs+ejTVr1qBx48bYtm0bVq5cCUdHx49+bsuWLZg8eTL8/Pzg6+uLQYMGoVGjRmjdunWO5S0tLXHs2DF8/fXX0NfXz7HMgAED4Ovri5UrV8LV1RXBwcHKP/KXL1+iffv2GDRoELZu3YpHjx5h+PDhkMlkKkmaLVu2YNSoUcrpCfL6roio+AqJTsZmnxDsuxmGpLR3FYWDt19hRNOKIkdGYkvNkOPg7Zd4nZiGwZuvo76DMaa1c4Z7hXJih0ZExVRJ+I2T1Xv00KFDaNCgAbS0tD75nD/++COWLFkCMzMzjBw5EkOGDFHWjw8ePIgJEyZg+fLl8PT0xJEjRzB48GDY2NgoExl5yauOvWLFCjx+/Bg1atTAL7/8AgAwMzPD/v370a1bNwQGBsLAwEA5EmDevHnYvn071q9fj8qVK+PSpUv45ptvlFNYfMjMzAxqamrYt28fJk6cCKk0+2jTpKQkNGvWDOXLl8fhw4dhaWmJW7duKRv483sfZs+ejfnz52P58uVQV1fH5cuXMWDAAKxcuRJNmjRBUFAQRowYAQCYNWtWAb6lks3U1BTq6uqoVq2ayvaqVaviypUrAN79BkxPT0dcXJzKqJLIyEjlyChLS0tcu3ZN5RiRkZHKfVn/m7Xt/TLv/xvKiZaW1mf9HeXl8pNoxL/NwOQ9/tj9rQdHuRIVopLw3I6NjcWJEycwd+5c6OrqZtv//n/3PtYumV/nz5+HlZUVzp8/j6dPn6JXr15wc3PD8OHDcePGDYwfPx7btm1Dw4YNERsbi8uXL+d4nJ07d2LkyJHYuXMnOnTokO/z51d+rjUlJQULFizAn3/+CRMTE5ibm+PZs2cYOHAgVq1aBUEQsGTJErRv3x5PnjxRaUecMWMG5s+fjxUrVmDbtm3o3bs37t27h6pVqwIA9PX1sXnzZlhbW+PevXsYPnw49PX18cMPPwAA+vXrB3d3d6xbtw5SqRT+/v7K0aIHDhxQro8FAGPGjMGDBw+yjWrMUtbaOstkoqQ0cHZ2xt27d5Xv358b197eHnPmzMHIkSOVf6T9+vVD//79kZKSAh0dHSQkJODo0aM4ePBgjsdfvXo13N3d8dtvvym3bdy4Eba2tnj8+DGSkpKQmZmJr7/+GnZ2dgAAFxeXPONu2LAh1NTUkJ6ejoyMDIwYMUIlUbJ48WJMnToVvXv3BgAsWLAA58+fx/Lly7FmzZr836D3LF++HEOHDlVO7zVnzhycOXMm26iSD9WsWVP5Q6By5cpYvXo1zp49m2uiZMOGDejXrx9MTEzg6uqKxo0bo3v37mjUqBEA4PHjx9izZw9Onz6tnH/2/WTN2rVrYWtri9WrV0MikcDZ2RmvXr3C1KlTMXPmTOUQvcqVK2PhwoXKz82ZM+ej35WTk1NBbxkRFSFBEHDlaTQ2e4fgXGAUsvLLjma6GNzQHl/XshE3QCoWZBpSnJ7UFOsuBGGTTwj8gmPRda0P2tWwxPdeVVDR7MsNUyciKizq6urYvHkzhg8fjvXr16NWrVpo1qwZevfujZo1axboWHPnzlUmGKZNm4avvvoKqampkMlkWLx4MQYNGoTRo0cDgLLX6+LFi/OdKMnr95CTkxM0NTWho6OjMlWYsbExAMDc3FzZgJSWlobffvsNZ86cgYeHB4B3vwOuXLmC33//PcdESfny5bFy5Ur88MMP+Pnnn1GnTh20aNEC/fr1U/6G2LlzJ16/fo3r168rz1upUiXlMfJ7H/r27YvBgwcr3w8ZMgTTpk3DwIEDlbH++uuv+OGHH8pUokRTUxN169bNNlrp8ePHyt/AtWvXhoaGBs6ePYtu3boBAAIDAxEaGqr8rj08PDB37lxERUUpF4E/ffo0DAwMlEkYDw8PHDt2TOU8p0+fVh5DDFI1CZb0dEW7FZdx4/kb/H4pCKObV8r7g0RUajx9+hSCIMDZ2TnPsnm1S+ZHuXLlsHr1akilUjg7O+Orr77C2bNnMXz4cISGhkJXVxcdOnSAvr4+7Ozs4O7unu0Ya9aswY8//oh///03x+drXmxssv8eT0lRHVWXn2vNyMjA2rVr4erqqtzWsmVLleNs2LABRkZGuHjxokpCp0ePHsqO8b/++itOnz6NVatWKY//008/qZz/+++/x65du5SJktDQUEyZMkX5vb3f8TyrvgAAy5Ytw7lz5+Dn55drUr6stXWWyUSJtoYUD3/xEu3chUEQBEgk/9+b48yZM5g3bx4ePXqEhIQEZGZmIjU1VZkYad++PTQ0NHD48GH07t0b+/fvh4GBQbYF47LcuXMH58+fz3G+wKCgILRp0watWrWCi4sLvLy80KZNG3Tv3h3lyn28p+vu3btRtWpVZGRk4P79+xg3bhzKlSuH+fPnIyEhAa9evVL+sWVp1KgR7ty58wl36Z2AgACMHDlSZZuHhwfOnz//0c99+GPNysoKUVFRuZZv2rQpnj17hqtXr8LHxwdnz57FihUr8PPPP2PGjBnw9/eHVCrN9T/UAQEB8PDwUPleGzVqhKSkJISFhaFChQoA3lXG35fXd8VECVHxkJKeiQO3XmKzTwieRv3/sNwWVcwwqJEDmlQyhRp76dF7jHQ0Mb19VQxsaI9lpx9j360wHL8fgVMPI9G7ri0mtKoMcwOZ2GESUTFRUn7jdOvWDV999RUuX76Mq1ev4vjx41i4cCH+/PNP5TRV+fF+Xd3KygrAuzUlKlSogICAAOUIiCyNGjXCihUr8n38wqxjP336FCkpKdk6XKWnp+fYyJNlzJgxGDBgAC5cuICrV69i7969+O2333D48GG0bt0a/v7+cHd3V2n0eF9+70OdOnVU3t+5cwfe3t7KabiAd/Orv//7srRISkrC06dPle+Dg4Ph7+8PY2NjVKhQAVOmTEGvXr3QtGlTtGjRAidOnMC///6rnKLN0NAQQ4cOxeTJk2FsbAwDAwOMGzcOHh4eyinS2rRpg2rVqqF///5YuHAhIiIi8NNPP2HMmDHK0SAjR47E6tWr8cMPP2DIkCE4d+4c9uzZg6NHj37xe/I+W2MdzOpYDVP23cWy04/RtLIZapQXb55/otKkJDy3CzJrTF7tkvlRvXp1lRGUVlZWuHfvHgCgdevWsLOzg6OjI9q2bYu2bduia9euKsfet28foqKi4O3tjbp16+Y79vddvnw528iJ5s2bF/haNTU1s7UrRkZG4qeffsKFCxcQFRUFuVyOlJQUhIaGqpT7MEnu4eGhMl3o7t27sXLlSgQFBSk7shsYGCj3T548GcOGDcO2bdvg6emJHj16oGJF1Vkrjh8/jmnTpuHff//9aJ2mrLV1lslEiUQi+ezpr8QWEBAABwcHAO/m2O3QoQNGjRqFuXPnwtjYGFeuXMHQoUORnp4OHR0daGpqonv37ti5cyd69+6NnTt3olevXrkuBp+UlISOHTtiwYIF2fZZWVlBKpXi9OnT8PHxwalTp7Bq1Sr8+OOP8PPzU8aVE1tbW2Uvp6pVqyIoKAgzZszIc/2PLFmZxvf/Y/3+PHyF6cNFDCUSiXIY+8c+06RJEzRp0gRTp07FnDlz8Msvv2Dq1KkfHTJdEB8Od8zruyIicYW9ScE23+f4+1ooElLfTa+lqylFjzq2GOBhB0eODKA8WBtpY1EPVwxr4ohFJx/hTEAUdviF4sCtlxjWxAEjmjpCn4usEpV5Jek3jkwmQ+vWrdG6dWvMmDEDw4YNw6xZswqUKHm/rp714zuvunqW/PymKMw6dta85UePHkX58uVV9uU1bZK+vj46duyIjh07Ys6cOfDy8sKcOXPQunXrIv198fPPP+Prr7/OVlYmK10J+hs3bqiMrpk8eTIAYODAgdi8eTO6du2K9evXY968eRg/fjyqVKmC/fv3o3HjxsrPLFu2DGpqaujWrRvS0tLg5eWl0qtYKpXiyJEjGDVqFDw8PKCrq4uBAwcqp2wDAAcHBxw9ehSTJk3CihUrYGNjgz///FM5T7uYute2wZmASJx8EIlJu/3x77jGkBVSB1CisqwkPLcrV64MiUSS54Lt+WmXzI+PtcPp6+vj1q1buHDhAk6dOoWZM2di9uzZuH79unIEp7u7O27duoWNGzeiTp06Ko3z+eXg4KAypRgAlbbT/F6rtrZ2tvMPHDgQMTExWLFiBezs7KClpQUPDw+VqbDy4uvri379+uHnn3+Gl5cXDA0NsWvXLixZskRZZvbs2ejbty+OHj2K48ePY9asWdi1axe6du0KAHj48CF69+6N+fPno02bNnmesyy1dRbvv0jK0blz53Dv3j1MmjQJwLuFDBUKBZYsWaKs9O/Zsyfb5/r164fWrVvjwYMHOHfuHObMmZPrOWrVqoX9+/fD3t4+12SKRCJBo0aN0KhRI8ycORN2dnY4ePCgsnKZH1KpFJmZmUhPT4eBgQGsra3h7e2tkon09vZGvXr1ALybqxd4t7BR1uiV97OqOalatSr8/PxUpvh6f8GpolStWjVlZtnFxQUKhQIXL17McSRP1apVsX//fpXRQt7e3tDX189x6F+W/HxXRPRlCYIAv+BYbPYOwamHEVD81w5jZ6KDgR726F7HBgZs2KYCqmKpjz8H1oXfsxjMP/EIt0PjsOrcU+zwC8XYFpXQr0EFaKmz4YKISp5q1arluXh7QVStWhXe3t7KqaOAd/XqrGmO3v9NkTWi48PfFPmpY2tqakIul2fbBkBl+/sLuX7KNCBZsqas8PHxAfBuVM2ff/6J2NjYHEeV5HUfclOrVi0EBgaqTONVWjVv3jzPHtNDhgxRWbT2QzKZDGvWrPnoVNF2dnbZptbKKZbbt29/PGARSCQS/NbVBTefx+FJVBIWnQzEjA4f/zdERKWDsbExvLy8sGbNGowfPz5bY3bW+kz5bZf8XOrq6vD09ISnpydmzZoFIyMjnDt3TpnYr1ixIpYsWYLmzZtDKpVi9erVhR7D51yrt7c31q5di/bt2wMAXrx4kePi5FevXs3WhplVX/Hx8YGdnR1+/PFH5f7nz59nO4aTkxOcnJwwadIk9OnTB5s2bULXrl0RHR2Njh07olu3bsp25YIqzW2dal/sTPRJ0tLSEBERgZcvX+LWrVv47bff0LlzZ3To0EH5R1OpUiVkZGRg1apVePbsGbZt24b169dnO1bTpk1haWmJfv36wcHBAfXr18/1vGPGjEFsbCz69OmD69evIygoCCdPnsTgwYMhl8vh5+eH3377DTdu3EBoaCgOHDiA169fKxcWyk1MTAwiIiIQFhaG48ePY8WKFWjRooVyiNiUKVOwYMEC7N69G4GBgZg2bRr8/f0xYcIE5bXa2tpi9uzZePLkCY4ePaqSNc3JhAkTsHHjRmzatAmPHz/GrFmz8ODBg49+5lM0b94cv//+O27evImQkBAcO3YM//vf/5TXZ29vj4EDB2LIkCE4dOgQgoODceHCBeV/UEePHo0XL15g3LhxePToEf755x/MmjULkydPVv7HNyd5fVdE9OWkZsix5/oLtF95Bb03XMWJB++SJI0rmeLPAXVw7rvmGNLYgUkS+iz1HU1wYFRDrP+mNhzNdBGbnI5fjjxEqyUXcej2SygU+R8iT0T0JcXExKBly5bYvn077t69i+DgYOzduxcLFy5E586dC+08U6ZMwebNm7Fu3To8efIES5cuxYEDB/D9998DeNfLs0GDBpg/fz4CAgJw8eJFlfm+gfzVse3t7eHn54eQkBBER0dDoVDAzs4OEokER44cwevXr5GUlAR9fX18//33mDRpErZs2YKgoCDcunULq1atwpYtW3K8Bn9/f3Tu3Bn79u3Dw4cP8fTpU/z111/YuHGj8l716dMHlpaW6NKlC7y9vfHs2TPs378fvr6++boPuZk5cya2bt2Kn3/+GQ8ePEBAQAB27dqV7R5R2WGip4WF3d+tSfrXlWD4PM3esEdEpdOaNWsgl8tRr1497N+/H0+ePEFAQABWrlypnCIqv+2Sn+PIkSNYuXIl/P398fz5c2zduhUKhQJVqlRRKefk5ITz589j//79KmuJFJbPudbKlStj27ZtCAgIgJ+fH/r165fjiIy9e/di48aNyjbMa9euYezYscpjhIaGYteuXQgKCsLKlStV1p9++/Ytxo4diwsXLuD58+fw9vbG9evXle213bp1g46ODmbPno2IiAjlK7f2wzLX1imUAvHx8QIAIT4+Ptu+t2/fCg8fPhTevn0rQmSfZ+DAgQIAAYCgrq4umJmZCZ6ensLGjRsFuVyuUnbp0qWClZWVoK2tLXh5eQlbt24VAAhv3rxRKffDDz8IAISZM2eqbA8ODhYACLdv31Zue/z4sdC1a1fByMhI0NbWFpydnYWJEycKCoVCePjwoeDl5SWYmZkJWlpagpOTk7Bq1apcryXr+FkvqVQq2NjYCMOHDxeioqKU5eRyuTB79myhfPnygoaGhuDq6iocP35c5VhXrlwRXFxcBJlMJjRp0kTYu3evAEAIDg4WBEEQNm3aJBgaGqp8Zu7cuYKpqamgp6cnDBw4UPjhhx8EV1dXlXvduXNn5ftmzZoJEyZMUDlG586dhYEDB+Z6jb/99pvg4eEhGBsbCzKZTHB0dBTGjx8vREdHK8u8fftWmDRpkmBlZSVoamoKlSpVEjZu3Kjcf+HCBaFu3bqCpqamYGlpKUydOlXIyMj4aFyC8PHv6kMl+W+CqLgKj3srLDrxSHD/5ZRgN/WIYDf1iFDlp2PC9AN3hcCIBNHi+tjzkQqfGPc7I1Mu7PR7LtSdc1r5b6/d8kvChcCoHJ8BRFR6lMQ6XWpqqjBt2jShVq1agqGhoaCjoyNUqVJF+Omnn4SUlBRlOQDCwYMHBUHI/jvl/Pnz2X7n3L59W+X3gCAIwtq1awVHR0dBQ0NDcHJyErZu3aoSy8OHDwUPDw9BW1tbcHNzE06dOiUAEM6fP68sk1cdOzAwUGjQoIGgra2tcv5ffvlFsLS0FCQSifL3g0KhEJYvXy5UqVJF0NDQEMzMzAQvLy/h4sWLOd6r169fC+PHjxdq1Kgh6OnpCfr6+oKLi4uwePFild+CISEhQrdu3QQDAwNBR0dHqFOnjuDn55fv+/D+vX7fiRMnhIYNGwra2tqCgYGBUK9ePWHDhg05xvqxf4usi3xZRX2/px+4K9hNPSJ4/HZGiEtJL5JzEJVWJfG5neXVq1fCmDFjBDs7O0FTU1MoX7680KlTJ5VnZl7tkh+21c2aNeuj7XKCIAgTJkwQmjVrJgiCIFy+fFlo1qyZUK5cOUFbW1uoWbOmsHv3bmXZD9vLHj58KJibmwuTJ0/O9fjvy6l+kcXOzk5YtmzZJ19rllu3bgl16tQRZDKZULlyZWHv3r3Zjg1AWLNmjdC6dWtBS0tLsLe3V7lOQRCEKVOmCCYmJoKenp7Qq1cvYdmyZcrzpaWlCb179xZsbW0FTU1NwdraWhg7dqzy3937bbPvv96vQ72vpLR1ZsWR099YQZ6Nkv9uUomWkJAAQ0NDxMfHqyxeAwCpqakIDg6Gg4NDqZtPlehT8G+CqHAIgoBboXHY7BOC4/fCkflfL/7yRtoY4GGHXnVtYaSjKWqMH3s+UuET836/TZdjo3cw1l8IQmLau7VwGlY0wbR2zqhpY/RFYyGiL4N1OiouPvZvkXWRL6uo73dyWia+WnkZITEp6OpeHst6uRX6OYhKKz63iYpWbn9jBXk2ckEDIiKiAkjPVODovVfY7B2CO2Hxyu31HIwxpJE9PKtaQF3KmS3py9LWlGJMi0roW68C1px/iq2+z+ETFINOq73RoaYVvm9TBfamunkfiIiIiCgXulrqWNrLDd3X+eDg7ZdoVdUcHWpaix0WERFRoWBLDhERUT68TkzD8jOP0WjBOUzafQd3wuKhqa6GHrVtcHR8Y+z51gNta1gxSfKFrFmzBvb29pDJZKhfvz6uXbuWa9k//vgDTZo0Qbly5VCuXDl4enp+tPzIkSMhkUiwfPnyIoi8aJXT1cRPHarh3PfN8LV7eUgkwJG74fBcehEz/7mP14lpYodIREREJVitCuUwtkUlAMCPB+8jIj5V5IiIiIgKB1tziIiIPuJeWDwm7/FHo/nnsPzME7xOTIOFgRa+b+ME32ktsaiHK6pbG4odZpmye/duTJ48GbNmzcKtW7fg6uoKLy8vREVF5Vj+woUL6NOnD86fPw9fX1/Y2tqiTZs2ePnyZbayBw8exNWrV2FtXbJ7R9qU08HSXm44Oq4JmlcxQ6ZCwFbf52i26DyWnX6MpP+m5yIiIiIqqHGtKsOlvCHi32Zgyr47KAUzuhMRETFRQkRE9KEMuQJH7r5C93U+6Lj6Cg7ceol0uQLuFYywso87rkxtibEtK8NET0vsUMukpUuXYvjw4Rg8eDCqVauG9evXQ0dHBxs3bsyx/I4dOzB69Gi4ubnB2dkZf/75JxQKBc6ePatS7uXLlxg3bhx27NgBDQ2NL3EpRa6atQE2D66HncPrw9XGECnpcqw4+wTNFp7HFp8QpGcqxA6RiIiIShgNqRqW9XKDlroaLj+Jxrarz8UOiYiI6LOVmTVK2MOB6B3+LRDl7k1yOnZeC8X2q88R/t80AhpSCb5yscKgRg5wszUSN0BCeno6bt68ienTpyu3qampwdPTE76+vvk6RkpKCjIyMmBsbKzcplAo0L9/f0yZMgXVq1fP13HS0tKQlvb/U1klJCTk8yq+vIYVTXFoTCMcvx+BRScDERydjFmHH2CjdzC+b1MFX7lYQU1NInaYRPQJWLcjsfHfYNlUyVwP/2tfFbMOP8BvxwLQsKIpKpnriR0WUbHH/2YSFY3C+Nsq9SNKsnqEpqSkiBwJUfGQnp4OAJBKpSJHQlR8PIpIwLT9d9Fg3lksOhmI8PhUmOppYnyryvCe2hLLe7szSVJMREdHQy6Xw8LCQmW7hYUFIiIi8nWMqVOnwtraGp6ensptCxYsgLq6OsaPH5/vWObNmwdDQ0Ply9bWNt+fFYNEIkF7FyucmtQUc7rUgKmeFp7HpGDc37fReY03vJ9Gix0iERVAVl0uq25HJBb+vii7+jewQ5PKpkjNUGDyHn9kyDlSlSg3fG4TFa2stv/PmR2i1I8okUqlMDIyUs5brqOjA4mEPSapbFIoFHj9+jV0dHSgrl7q//yJPkquEHAmIBKbvUPg+yxGub1GeQMMbuiADq5W0FLnD/7SZv78+di1axcuXLgAmUwGALh58yZWrFiBW7duFaiOMH36dEyePFn5PiEhodgnS4B302V808AOXd3LY+OVYPx+6RnuvYxHvz/90KSyKaa2dUaN8lx3h6i4U1dXh46ODl6/fg0NDQ2oqZX6PnBUDPH3RdmmpibBou6u8Fp+CXfD4rHq3FNMbu0kdlhExRKf20RFQxAEpKSkICoqCkZGRp/VcaNM1GQsLS0BINdFXonKEjU1NVSoUIEJQyqz4t9mYM/1F9jiG4KwN28BAFI1CdpWt8TgRvaobVeOfx/FmKmpKaRSKSIjI1W2R0ZGKp/3uVm8eDHmz5+PM2fOoGbNmsrtly9fRlRUFCpUqKDcJpfL8d1332H58uUICQnJ8XhaWlrQ0iq569ToaqljXKvK6Fu/Alaff4rtV5/j8pNoXH5yBZ3drPFd6yqoYKIjdphElAuJRAIrKysEBwfj+XOuD0Di4e+Lss3SUIa5XWtg7M7bWHP+KZpXMUOtCuXEDouo2OFzm6hoGRkZ5dkmkBeJUAomx0tISIChoSHi4+NhYGCQazm5XI6MjIwvGBlR8aOpqcmeC1QmPY1KwhafEOy/FYaUdDkAwEhHA33qVUD/BnawNtIWOcLCl9/nY0lTv3591KtXD6tWrQLwrjdrhQoVMHbsWEybNi3HzyxcuBBz587FyZMn0aBBA5V9MTExCA8PV9nm5eWF/v37Y/DgwahSpUq+4irp9zs0JgVLTgfiH/9XAN6tz9Ovvh3GtawEE72SmxAiKu0UCgWn8SBRfez3RUl/NpY0Yt7vCbtu4x//V7A30cGxCU2go1km+uUSFRif20SFT0NDI9eRJAV5NpapJ5dUKuW8qUREZYhCIeDi49fY5BOCS49fK7dXsdDH4Eb26OJeHjINPhdKmsmTJ2PgwIGoU6cO6tWrh+XLlyM5ORmDBw8GAAwYMADly5fHvHnzALxbf2TmzJnYuXMn7O3tlWuZ6OnpQU9PDyYmJjAxMVE5h4aGBiwtLfOdJCkNKpjoYEVvdwxv4ogFJx7h8pNobPYJwb6bYfi2qSOGNnFgowdRMaSmpqacSpCISCy/dKqBa8GxCIlJwdyjAZjb1UXskIiKJT63iYov/tolIqJSJyktE/tuvMAW3+cIjk4GAEgkgGdVCwxuZA8PRxNOD1GC9erVC69fv8bMmTMREREBNzc3nDhxQrnAe2hoqErP1nXr1iE9PR3du3dXOc6sWbMwe/bsLxl6iVCjvCG2Da2PK0+iMf9EAO6/TMCS04+xxfc5JnpWRq+6ttCQcmQiERER/T9DHQ0s7uGKfn/6YYdfKDyrWqCFs7nYYREREeVbmZp6i4iISrfnMcnY7BOCvTfCkJSWCQDQl6mjVx1bDPCwL3PrLfD5+GWVxvutUAg4ei8ci04GIjQ2BQDgYKqLKV5V0K6GJROORET0UaXx2VicFYf7/cu/D7HROximelo4NakpjHU1RYmDiIgI4NRbRERUhgiCAO+nMdjsE4yzj6KQlf53NNPF4Ib2+LqWDXS1+Lgj+hRqahJ0dLWGV3VL7LoeihVnniA4Ohmjd9yCq60RprV1hkdFk7wPRERERGXCD22r4PKT13gSlYTpB+5i/Te12bGCiIhKBLYcERFRifQ2XY4Dt8Ow2TsET6KSlNubVzHD4EYOaFLJFGpq/FFGVBg01dUwwONd4vGPS8/wx+VnuPMiDn3+uIrmVcwwta0zqlqxpzAREVFZJ9OQYlkvN3Rd642TDyKx/9ZLdK9tI3ZYREREeWKihIiISpSwNynYdvU5dl17gfi3GQAAXU0pute2wcCG9nA00xM5QqLSS09LHZNaO+GbBnZYde4JdvqF4kLga1x8/Bpd3cpjchsn2JQrW1PcERERkaoa5Q0x0dMJi04GYvbhB6jvYAxbY9YPiIioeGOihIiIij1BEHAtOBabfUJw8kEEFP9Nr1XBWAcDG9qjRx0bGMg0xA2SqAwx09fCL51rYEgjByw+FYgjd8Nx4PZLHLkbjgEedhjTohLKcU5yIiKiMmtks4o49ygKN5+/wXd77uDvEQ0g5WhvIiIqxpgoISKiYitTrsDB2y+xyTsED8MTlNsbVTLB4IYOaOFszh9cRCKyN9XF6r61MKJpHOYffwSfoBj8eSUYu6+/wMjmFTGkkQO0NaVih0lERERfmFRNgmU93dBuxSVcC4nFn5ef4dtmFcUOi4iIKFdqYgdARESUm9+OPcKUfXfxMDwBMg019KlXAacmNcWOYQ3gWc2CSRKiYqKmjRF2DKuPrUPqoZqVARLTMrHoZCCaLz6Pv6+FIlOuEDtEIiIi+sIqmOhgZsdqAIAlpx4j4L2OT0RERMUNEyVERFQsRcSnYvvV5wCASZ5OuDq9FeZ97QInC32RIyOinEgkEjR1MsORcY2xvJcbbMppIzIhDdMP3EOb5Zdw4n4EBEEQO0wiIiL6gnrWsYVnVQukyxWYtNsfaZlysUMiIiLKERMlRERULP1+KQjpcgXqORhjgmdlGOlwvQOikkBNTYIu7uVx9rtmmNmhGsrpaODZ62SM3H4TX6/zwbXgWLFDJCIioi9EIpFgfjcXmOhq4lFEIpaeeix2SERERDliooSIiIqd14lp2OkXCgAY37KyyNEQ0afQUpdiSGMHXPqhBca1rARtDSluh8ah5+++GLblOgIjEsUOkYiIiL4AUz0tzO9WEwCw4fIzXH0WI3JERERE2TFRQkRExc6fV54hLVMBN1sjNKpkInY4RPQZ9GUa+K5NFVyc0hz96leAVE2CMwFRaLfiEqbsvYNXcW/FDpGIiIiKWOtqFuhd1xaCAHy35w4SUjPEDomIiEgFEyVERFSsvElOxzbfd2uTjG9VCRIJF2wnKg3MDWSY29UFpyc1RXsXSygEYO/NMDRffAHzjgUgLiVd7BCJiIioCP3UoRoqGOvgZdxb/Hz4odjhEBERqWCihIiIipVN3sFISZejmpUBWlQxFzscIipkjmZ6WNuvNg6Oboj6DsZIz1Tg90vP0HThefg8jRY7PCIiIioielrqWNrTFWoSYP+tMBy/Fy52SEREREpMlBARUbGRkJqBTT4hAIBxLTmahKg0c69QDrtGNMCmwXXhbKmPhNRMjNl5i1NxERERlWJ17I0xqnlFAMD/Dt5DVEKqyBERERG9w0QJEREVG1t9QpCYmonK5nrwqm4pdjhEVMQkEglaVDHHoTGNUKO8Ad6kZGD0jltIz1SIHRoREREVkQmtnFDd+t1z/4f9dyEIgtghERERMVFCRETFQ3JaJv66EgwAGNuyEtTUOJqEqKyQaUixrl9tGMjU4f8iDr8dCxA7JCIiIioimupqWN7LDZrqargQ+Bo7/ELFDomIiIiJEiIiKh52+D3Hm5QMOJjqokNNa7HDIaIvzNZYB8t6uQEANvuE4N87r8QNiIiIiIpMZQt9TG3rDACYezQAz14niRwRERGVdUyUEBGR6FIz5Nhw6d1oklHNK0LK0SREZVKrqhYY/d+85dP238XTKDaaEBERlVaDG9qjYUUTvM2QY9KeO8iUc+pNIiISDxMlREQkul3XQhGdlIbyRtro6l5e7HCISESTWzvBw9EEyelyjNp+E8lpmWKHREREREVATU2CxT1coS9Tx50XcVhzPkjskIiIqAxjooSIiESVlinH+ovPALwbTaIh5aOJqCxTl6phZR93mOtr4UlUEv538B4XeSUiIiqlrI20MadLDQDAynNPcOdFnLgBERFRmcXWKCIiEtX+my8RkZAKCwMt9KhjI3Y4RFQMmOlrYXXfWpCqSfCP/yts5yKvREREpVYnV2t0qGkFuULApN3+eJsuFzskIiIqg5goISIi0WTIFVh74SkA4NumFaGlLhU5IiIqLuo5GGPaf4u8/vrvQ/YwJSIiKqUkEgnmdKkBCwMtPItOxrzjAWKHREREZRATJUREJJp//F8h7M1bmOppok+9CmKHQ0TFzLAmDvCqboF0uQKjd9zCm+R0sUMiIqJS5tKlS+jYsSOsra0hkUhw6NAhlf2DBg2CRCJRebVt21alTGxsLPr16wcDAwMYGRlh6NChSEpKUilz9+5dNGnSBDKZDLa2tli4cGG2WPbu3QtnZ2fIZDK4uLjg2LFjhX69xZWRjiYW93AFAGz1fY4LgVEiR0RERGUNEyVERCQKuULA2vPvRpMMa+IIbU2OJiEiVRKJBIt6uMLeRAcv495i0h5/KBRcr4SIiApPcnIyXF1dsWbNmlzLtG3bFuHh4crX33//rbK/X79+ePDgAU6fPo0jR47g0qVLGDFihHJ/QkIC2rRpAzs7O9y8eROLFi3C7NmzsWHDBmUZHx8f9OnTB0OHDsXt27fRpUsXdOnSBffv3y/8iy6mmlQ2w6CG9gCAH/bdZQcJIiL6ogqcKMmrt8WHLly4kK33hUQiQUREhEq5NWvWwN7eHjKZDPXr18e1a9cKGhoREZUgR++F41l0Mox0NPBNAzuxwyGiYspApoG1/WpDS10NFwJfY81/CVYiIqLC0K5dO8yZMwddu3bNtYyWlhYsLS2Vr3Llyin3BQQE4MSJE/jzzz9Rv359NG7cGKtWrcKuXbvw6tUrAMCOHTuQnp6OjRs3onr16ujduzfGjx+PpUuXKo+zYsUKtG3bFlOmTEHVqlXx66+/olatWli9enXRXXwxNLWtMyqa6SIqMQ0/HboPQWAHCSIi+jIKnCjJT2+LnAQGBqr0wDA3N1fu2717NyZPnoxZs2bh1q1bcHV1hZeXF6KiONSSiKg0UigErDn3rrFzSCMH6GmpixwRERVn1awNMKdLDQDA0jOPceVJtMgRERFRWXLhwgWYm5ujSpUqGDVqFGJiYpT7fH19YWRkhDp16ii3eXp6Qk1NDX5+fsoyTZs2haamprKMl5cXAgMD8ebNG2UZT09PlfN6eXnB19c317jS0tKQkJCg8irptDWlWNbLDepqEhy9F45//F+JHRIREZURBU6U5Ke3RU7Mzc1VemCoqf3/qZcuXYrhw4dj8ODBqFatGtavXw8dHR1s3LixoOEREVEJcOphJAIjE6GvpY6B/w2vJyL6mB51bNGrji0EARi/6zbC49+KHRIREZUBbdu2xdatW3H27FksWLAAFy9eRLt27SCXywEAERERKh1BAUBdXR3GxsbKmTQiIiJgYWGhUibrfV5lPpyN433z5s2DoaGh8mVra/t5F1tM1LQxwoRWlQEAM/65j5dxfOYTEVHR+2JrlLi5ucHKygqtW7eGt7e3cnt6ejpu3ryp0nNCTU0Nnp6eufacKI29JoiIygpBELD6/BMAwMCG9jDU1hA5IiIqKX7uXB3VrAwQm5yOMTtuIUOuEDskIiIq5Xr37o1OnTrBxcUFXbp0wZEjR3D9+nVcuHBB7NAwffp0xMfHK18vXrwQO6RCM6p5RbhXMEJiaia+33OHa5QREVGRK/JEiZWVFdavX4/9+/dj//79sLW1RfPmzXHr1i0AQHR0NORyeYF6TpTWXhNERGXBhcDXuP8yATqaUgxp7CB2OERUgsg0pFj/TW3oy9RxKzQO8449EjskIiIqYxwdHWFqaoqnT99NI2tpaZlt2vDMzEzExsbC0tJSWSYyMlKlTNb7vMpk7c+JlpYWDAwMVF6lhbpUDct6ukFbQwrfZzHY6B0sdkhERFTKFXmipEqVKvj2229Ru3ZtNGzYEBs3bkTDhg2xbNmyTz5mae41QURUmgmCgJXn3o0m+aaBHYx1NfP4BBGRqgomOlja0w0AsNE7GEfvhosbEBERlSlhYWGIiYmBlZUVAMDDwwNxcXG4efOmssy5c+egUChQv359ZZlLly4hIyNDWeb06dOoUqWKcmF4Dw8PnD17VuVcp0+fhoeHR1FfUrFlb6qLGR2qAQAWngxEYESiyBEREVFp9sWm3npfvXr1lL0vTE1NIZVKC9RzojT3miAiKs18gmJwOzQOWupqGNaEo0mI6NO0rmaBb5s5AgB+2HcHQa+TRI6IiIhKqqSkJPj7+8Pf3x8AEBwcDH9/f4SGhiIpKQlTpkzB1atXERISgrNnz6Jz586oVKkSvLy8AABVq1ZF27ZtMXz4cFy7dg3e3t4YO3YsevfuDWtrawBA3759oampiaFDh+LBgwfYvXs3VqxYgcmTJyvjmDBhAk6cOIElS5bg0aNHmD17Nm7cuIGxY8d+8XtSnPSpZ4uWzuZIz1Rg4m5/pGXKxQ6JiIhKKVESJf7+/sreF5qamqhdu7ZKzwmFQoGzZ8+W6Z4TRESl0ar/RpP0qVcB5voykaMhopJsSpsqqO9gjOR0OUZvv4WU9EyxQyIiohLoxo0bcHd3h7u7OwBg8uTJcHd3x8yZMyGVSnH37l106tQJTk5OGDp0KGrXro3Lly9DS0tLeYwdO3bA2dkZrVq1Qvv27dG4cWNs2LBBud/Q0BCnTp1CcHAwateuje+++w4zZ87EiBEjlGUaNmyInTt3YsOGDXB1dcW+fftw6NAh1KhR48vdjGJIIpFgfjcXGOtqIiA8AcvPPBE7JCIiKqXUC/qBpKQk5WgQ4P97WxgbG6NChQqYPn06Xr58ia1btwIAli9fDgcHB1SvXh2pqan4888/ce7cOZw6dUp5jMmTJ2PgwIGoU6cO6tWrh+XLlyM5ORmDBw8uhEskIqLi4HpILK4+i4WGVKLsCU5E9KnUpWpY1dcdX628gsDIRPx08D6W9HSFRCIROzQiIipBmjdvDkHIfaHwkydP5nkMY2Nj7Ny586NlatasicuXL3+0TI8ePdCjR488z1fWmOvL8FtXF4zcfhPrLwahpbM56tobix0WERGVMgUeUfKx3hYAEB4ejtDQUGX59PR0fPfdd3BxcUGzZs1w584dnDlzBq1atVKW6dWrFxYvXoyZM2fCzc0N/v7+OHHiRLYF3omIqORade5dkr17bVtYGWqLHA0RlQbm+jKs6uMOqZoEB26/xM5roXl/iIiIiEqctjUs0b22DQQBmLTbH4mpGXl/iIiIqAAkwse6TpQQCQkJMDQ0RHx8PNcrISIqhvxfxKHLGm9I1SS48H1z2BrriB1SmcDn45fF+y2e9ReDMP/4I2hK1bB/VEO42BiKHRIREYHPxi+ttN/vxNQMtF1+GS/j3qJnHRss7O4qdkhERFTMFeTZKMoaJUREVLas/m80SRe38kySEFGh+7apI1pXs0C6XIFRO24iLiVd7JCIiIiokOnLNLC0pyskEmDPjTCcfBAhdkhERFSKMFFCRERF6uGrBJwJiIREAoxuUVHscIioFJJIJFjcwxUVjHUQ9uYtJu+5A4WixA+aJiIiog/UdzTBiKbv1jucfuAeXiemiRwRERGVFkyUEBFRkVpz/t1okg41rVHRTE/kaIiotDLU1sDafrWgqa6Gc4+isO5ikNghERERURGY3NoJzpb6iE1Ox7T9d1EKZpQnIqJigIkSIiIqMk+jEnHsfjgAYAxHkxBREatR3hC/dq4OAFhyKhA+T6NFjoiIiIgKm5a6FMt7u0FTqoazj6Kw6/oLsUMiIqJSgIkSIiIqMmvOB0EQAK/qFnC2LH0LShJR8dOrbgX0qG0DhQCM33UbEfGpYodEREREhczZ0gBTvKoAAH498hAh0ckiR0RERCUdEyVERFQkQqKT8Y//SwDA2BaVRY6GiMqSXzrXgLOlPqKT0jF25y1kyBVih0RERESFbGhjBzRwNEZKuhyT9/gjk897IiL6DEyUEBFRkVh3IQgKAWhRxQwuNoZih0NEZYi2phTrv6kNfS113Hj+BgtPPBI7JCIiIipkamoSLO7hCn0tddwKjcN6rk9GRESfgYkSIiIqdGFvUrD/VhgAYGxLjiYhoi/P3lQXi3q4AgD+uByME/+tl0RERESlh005Hfz83/pky888wb2weJEjIiKikoqJEiIiKnS/X3yGTIWARpVMUNuunNjhEFEZ1baGJUY0dQQATNl7F8Gcv5yIiKjU6epeHu1dLJGpEDBx922kZsjFDomIiEogJkqIiKhQRSakYveNFwC4NgkRiW+KVxXUtS+HxLRMjNp+E2/T2XhCRERUmkgkEszt4gJzfS0EvU7G/OOccpOIiAqOiRIiIipUGy49Q3qmAnXty6GBo7HY4RBRGachVcPqvrVgqqeFRxGJ+OnQfQiCIHZYREREVIjK6WpiYfeaAIDNPiG48iRa5IiIiKikYaKEiIgKTXRSGnb4PQfwbm0SiUQickRERICFgQwr+7hBTQLsvxWG3ddfiB0SERERFbLmVczRv4EdAOD7vXcQn5IhckRERFSSMFFCRESF5q8rwUjNUMDVxhBNK5uKHQ4RkVLDiqb4rk0VAMDMww9w/yUXeyUiIiptprd3hqOpLiISUjHjn/tih0NERCUIEyVERFQo4lLSsdUnBABHkxBR8TSqWUW0cjZHeqYCo3bcZE9TIiKiUkZHUx1Le7lBqibB4Tuv8I//S7FDIiKiEoKJEiIiKhSbvEOQnC5HVSsDeFY1FzscIqJs1NQkWNrTDbbG2ngR+xbf7b0DhYLrlRAREZUmbrZGGNeyEgBgxqH7CI9/K3JERERUEjBRQkREny0xNQObvIMBAGNbVOJoEiIqtgx1NLCuX21oqqvhTEAkfr/0TOyQiIiIqJCNaVEJrjaGSEjNxPfsGEFERPnARAkREX22rb7PkZCaiUrmemhXw1LscIiIPqpGeUPM7lgdALDo5CP4BsWIHBEREREVJg2pGpb2coNMQw3eT2OwxTdE7JCIiKiYY6KEiIg+S0p6Jv668m40yZgWFaGmxtEkRFT89alni69rlYdCAMb9fRtRCalih0RERESFqKKZHn5sXxUAMP/4IzyJTBQ5IiIiKs6YKCEios+y0y8UscnpsDPRQcea1mKHQ0SULxKJBHO7uMDZUh/RSWkY+/dtZMoVYodFREREheibBnZo6mSGtEwFJu3xR3omn/VERJQzJkqIiOiTpWbIlfP7j25eEepSPlaIqOTQ1pRibb9a0NNSx7XgWCw6GSh2SERERFSIJBIJFnWvCSMdDdx/mYCVZ5+IHRIRERVTbNEiIqJPtufGC7xOTEN5I210dbcROxwiogJzNNPDwu41AQC/X3qGkw8iRI6IiIiICpOFgQy/dXUBAKy98BQ3n8eKHBERERVHTJQQEdEnSc9UYP2FIADAyGaO0FTnI4WISqb2LlYY2tgBAPD9njt4HpMsckRERERUmNq7WOFr93drk03afQfJaZlih0RERMUMW7WIiOiTHLgVhlfxqTDX10KPOrZih0NE9FmmtXNGbbtySEzLxMjtt5CaIRc7JCIiIipEsztXR3kjbYTGpmDO0Ydih0NERMUMEyVERFRgmXIF1v43mmREU0fINKQiR0RE9Hk0pGpY07cWTHQ1ERCegJn/3Bc7JCIiIipEBjINLO7hCokE+PvaC5x5GCl2SEREVIwwUUJERAV2+M4rhMamwFhXE33rVxA7HCKiQmFpKMPKPu5QkwB7boRhz/UXYodEREREhcijogmG/Tfd5rQDdxGdlCZyREREVFwwUUJERAUiVwhYff4pAGBYEwfoaKqLHBERUeFpVMkUk1s7AQBm/HMfD17FixwRERERFabv2lRBFQt9RCelY/qBexAEQeyQiIioGGCihIiICuT4/XA8e50MQ20N9G9gJ3Y4VEatWbMG9vb2kMlkqF+/Pq5du5Zr2T/++ANNmjRBuXLlUK5cOXh6eqqUz8jIwNSpU+Hi4gJdXV1YW1tjwIABePXq1Ze4FCqGRjevhBZVzJCWqcDoHbcQ/zZD7JCIiIiokMg0pFjWyw0aUglOP4zE3hthYodERETFABMlRESUbwqFgNXn3o0mGdzIHvoyDZEjorJo9+7dmDx5MmbNmoVbt27B1dUVXl5eiIqKyrH8hQsX0KdPH5w/fx6+vr6wtbVFmzZt8PLlSwBASkoKbt26hRkzZuDWrVs4cOAAAgMD0alTpy95WVSMqKlJsKyXG8obaeN5TAqm7L3D3qZERESlSDVrA3zXpgoA4Od/HyA0JkXkiIiISGwSoRT86ktISIChoSHi4+NhYGAgdjhERKXWqQcRGLHtJvS01OE9tSUMdZgoKc5K6/Oxfv36qFu3LlavXg0AUCgUsLW1xbhx4zBt2rQ8Py+Xy1GuXDmsXr0aAwYMyLHM9evXUa9ePTx//hwVKuS8Dk9aWhrS0v5/XuuEhATY2tqWuvtdlt0Ni0P3db5Ilyvwv/bOGNG0otghERGVKKW1LlJc8X4XjFwhoM+Gq7gWEos6duWw+1sPSNUkYodFRESFqCDPRo4oISKifBEEAav+G00ywMOOSRISRXp6Om7evAlPT0/lNjU1NXh6esLX1zdfx0hJSUFGRgaMjY1zLRMfHw+JRAIjI6Ncy8ybNw+GhobKl62tbb6vg0qGmjZGmNmxGgBgwYlA+D2LETkiIiIiKixSNQmW9HSFnpY6bjx/gw2XnokdEhERiYiJEiIiypeLj1/j3st4aGtIMbSxg9jhUBkVHR0NuVwOCwsLle0WFhaIiIjI1zGmTp0Ka2trlWTL+1JTUzF16lT06dPnoz1Opk+fjvj4eOXrxYsX+b8QKjH61a+ALm7WkCsEjP37NqISU8UOiYiIiAqJrbEOZv3XKWLp6UDcfxkvckRERCQWJkqIiChP748m6Ve/Akz0tESOiOjTzJ8/H7t27cLBgwchk8my7c/IyEDPnj0hCALWrVv30WNpaWnBwMBA5UWlj0QiwW9fu8DJQg+vE9Mw/u/byJQrxA6LiIiICkn32jbwqm6BDLmAIZuvc70SIqIyiokSIiLKk++zGNx8/gaa6moY0dRR7HCoDDM1NYVUKkVkZKTK9sjISFhaWn70s4sXL8b8+fNx6tQp1KxZM9v+rCTJ8+fPcfr0aSY+SElHUx3rvqkNXU0prj6LxZLTj8UOiYiIiAqJRCLBwm6ucLbUR1RiGvpv9OMIUiKiMoiJEiIiytOqs+9Gk/Suawtzg+y98Im+FE1NTdSuXRtnz55VblMoFDh79iw8PDxy/dzChQvx66+/4sSJE6hTp062/VlJkidPnuDMmTMwMTEpkvip5KpopocF3d8l2NZdCMLph5F5fIKIiIhKCkMdDWwZUg+2xtp4HpOCgRuvI/5ththhERHRF8RECRERfdSNkFj4PouBhlSCb5tVFDscIkyePBl//PEHtmzZgoCAAIwaNQrJyckYPHgwAGDAgAGYPn26svyCBQswY8YMbNy4Efb29oiIiEBERASSkpIAvEuSdO/eHTdu3MCOHTsgl8uVZdLT00W5RiqeOtS0xqCG9gCAyXv8OTUHEVEpcOnSJXTs2BHW1taQSCQ4dOhQrmVHjhwJiUSC5cuXq2yPjY1Fv379YGBgACMjIwwdOlRZz8hy9+5dNGnSBDKZDLa2tli4cGG24+/duxfOzs6QyWRwcXHBsWPHCuMSKZ8sDGTYNqQ+TPW0EBCegOFbbiA1Qy52WERE9IUwUUJERB+VtTZJt1o2KG+kLXI0RECvXr2wePFizJw5E25ubvD398eJEyeUC7yHhoYiPDxcWX7dunVIT09H9+7dYWVlpXwtXrwYAPDy5UscPnwYYWFhcHNzUynj4+MjyjVS8fW/9lVRq4IRElMzMWrHTTagEBGVcMnJyXB1dcWaNWs+Wu7gwYO4evUqrK2ts+3r168fHjx4gNOnT+PIkSO4dOkSRowYodyfkJCANm3awM7ODjdv3sSiRYswe/ZsbNiwQVnGx8cHffr0wdChQ3H79m106dIFXbp0wf379wvvYilP9qa62DKkLvS11HEtJBZjd97i2mRERGWERBAEQewgPldCQgIMDQ0RHx/P+cSJiArR3bA4dFrtDamaBOe+awY7E12xQ6IC4PPxy+L9Ljtexb1Fh1VXEJucjj71bDHv6+xr3hARUcl7NkokEhw8eBBdunRR2f7y5UvUr18fJ0+exFdffYWJEydi4sSJAICAgABUq1YN169fV07veeLECbRv3x5hYWGwtrbGunXr8OOPPyIiIgKampoAgGnTpuHQoUN49OgRgHcdQZKTk3HkyBHleRs0aAA3NzesX78+x3jT0tKQlpamfJ+QkABbW9sSc7+LM79nMRiw8RrSMhXoVssGi7rXhJqaROywiIiogApSF+GIEiIiytXq/0aTdHa1ZpKEiOg/1kbaWNHbDRIJ8Pe1F9h3M0zskIiIqIgoFAr0798fU6ZMQfXq1bPt9/X1hZGRkcoaaJ6enlBTU4Ofn5+yTNOmTZVJEgDw8vJCYGAg3rx5oyzj6empcmwvLy/4+vrmGtu8efNgaGiofNna2n7WtdL/q+9ogjV9a0GqJsH+W2GYeywApaCfMRERfQQTJURElKOA8AScehgJiQQY3aKS2OEQERUrTSqbYWIrJwDAT4fuISA8QeSIiIioKCxYsADq6uoYP358jvsjIiJgbm6usk1dXR3GxsaIiIhQlsmaIjRL1vu8ymTtz8n06dMRHx+vfL148aJgF0cf5VnNAgu7vRs1+teVYKy9ECRyREREVJSYKCEiohytOf9uNEl7FytUMtcTORoiouJnXMtKaOpkhtQMBUbvuIWE1AyxQyIiokJ08+ZNrFixAps3b4ZEUvymXdLS0oKBgYHKiwpXt9o2+OmrqgCARScD8fe1UJEjIiKiosJECRERZfM0KglH771bDHssR5MQEeVITU2C5b3cYG0oQ3B0Mn7Ye5fTchARlSKXL19GVFQUKlSoAHV1dairq+P58+f47rvvYG9vDwCwtLREVFSUyucyMzMRGxsLS0tLZZnIyEiVMlnv8yqTtZ/EM6yJI0Y3rwgA+PHgPRz773cSERGVLkyUEBFRNmsvPIUgAK2rWaCqFXumERHlxlhXE2u/qQ0NqQQnHkTgryvBYodERESFpH///rh79y78/f2VL2tra0yZMgUnT54EAHh4eCAuLg43b95Ufu7cuXNQKBSoX7++ssylS5eQkfH/Iw9Pnz6NKlWqoFy5csoyZ8+eVTn/6dOn4eHhUdSXSfkwxasK+tSzhUIAJu7yh/fTaLFDIiKiQsZECRERqQiNScE//q8AvJtWhoiIPs7N1ggzOlQDAMw7/gjXQ2JFjoiIiPIrKSlJmQQBgODgYPj7+yM0NBQmJiaoUaOGyktDQwOWlpaoUqUKAKBq1apo27Ythg8fjmvXrsHb2xtjx45F7969YW1tDQDo27cvNDU1MXToUDx48AC7d+/GihUrMHnyZGUcEyZMwIkTJ7BkyRI8evQIs2fPxo0bNzB27Ngvfk8oO4lEgjldXNCuhiXS5QqM2HoDd17EiR0WEREVIiZKiIhIxbqLTyFXCGjmZIaaNkZih0NEVCL0b2CHTq7WkCsEjN15C9FJaWKHRERE+XDjxg24u7vD3d0dADB58mS4u7tj5syZ+T7Gjh074OzsjFatWqF9+/Zo3LgxNmzYoNxvaGiIU6dOITg4GLVr18Z3332HmTNnYsSIEcoyDRs2xM6dO7Fhwwa4urpi3759OHToEGrUqFF4F0ufRaomwfLebmhUyQTJ6XIM2nQNT6OSxA6LiIgKiUQoBRMpJyQkwNDQEPHx8Vy8jIjoM7yMe4vmi84jQy5g30gP1LE3Fjsk+gx8Pn5ZvN+UnJaJzmu88TQqCQ0rmmDb0PqQqhW/xX+JiL4UPhu/LN7vLyMpLRN9/7iKu2HxsDaUYd+ohrA20hY7LCIiykFBno0cUUJEREobLgYhQy7Aw9GESRIiogLS1VLH+m9qQUdTCp+gGCw9HSh2SERERFTI9LTUsXlwPTia6eJVfCr6/+WH2OR0scMiIqLPxEQJEREBAKISUvH39RcAuDYJEdGnqmSuj/ndagIA1pwPwrlHkSJHRERERIXNWFcT24bWh5WhDEGvkzF40zUkpWWKHRYREX0GJkqIiAgA8MflZ0jPVKC2XTl4VDQROxwiohKrk6s1BnjYAQAm7b6DF7EpIkdEREREha28kTa2Da2HcjoauBMWj5HbbiItUy52WERE9ImYKCEiIsQkpWH71VAAwNiWlSCRcE59IqLP8eNXVeFqa4T4txkYveMWUjPYcEJERFTaVDLXx6bB9aCjKcWVp9GYtNsfckWJXwqYiKhMYqKEiIiw0TsYbzPkcClviOZOZmKHQ0RU4mmpS7G2Xy2U09HAvZfx+PXIQ7FDIiIioiLgZmuEDf3rQEMqwbF7Efjp0H0IApMlREQlDRMlRERlXHxKBrb4PAfA0SRERIWpvJE2lvVyg0QC7PALxcHbYWKHREREREWgcWVTrOjtDokE+PtaKJaceix2SEREVEBMlBARlXGbfUKQlJaJKhb6aF3VQuxwiIhKleZVzDGuZWUAwPQD9xAYkShyRERERFQU2rtYYU6XGgCA1eef4q8rwSJHREREBcFECRFRGZaYmoGN3u8q8GNbVoKaGkeTEBEVtgmtKqNJZVOkZigwavtNJKZmiB0SERERFYF+9e0wxasKAODXIw9x4BZHkxIRlRRMlBARlWHbr4Yi/m0GHM100d7FSuxwiIhKJamaBMt7ucHKUIZn0cmYtv8e5y4nIiIqpUY3r4ghjRwAAFP23cWZh5EiR0RERPnBRAkRURmVkp6JPy8/AwCMaV4JUo4mISIqMiZ6WljdtxbU1SQ4ei8c264+FzskIiIiKgISiQQ/fVUVX7uXh1whYMzOW7gWHCt2WERElAcmSoiIyqi/r71ATHI6bI210cnNWuxwiIhKvdp25TC9fVUAwPIzT/A2XS5yRERERFQU1NQkWNC9Jlo5myMtU4GhW67j4asEscMiIqKPYKKEiKgMSs2Q4/eLQQCA0c0rQUPKxwER0Zcw0MMOtsbaiE1Ox+7roWKHQ0REREVEQ6qGNf1qoa59OSSmZmLAxmt4HpMsdlhERJQLtowREZVBe2+GISoxDVaGMnxdq7zY4RARlRnqUjWMaFoRAPDH5WBkyBUiR0RERERFRaYhxZ8D68LZUh/RSWn45i8/RCWkih0WERHlgIkSIqIyJj1TgfUX3o0mGdmsIrTUpSJHRERUtvSobQNTPS28jHuLf/xfiR0OERERFSFDbQ1sHVoPFYx18CL2LQZsvIb4lAyxwyIiog8wUUJEVMYcuv0SL+PewlRPC73q2oodDhFRmSPTkGJoYwcAwPqLQVAoBJEjIiIioqJkri/D9qH1YaavhUcRiRi65TrXKiMiKmaYKCEiKkMy5QqsufAUAPBtU0fINDiahIhIDN80qAB9mTqeRiXh1MNIscMhIiKiIlbBRAdbh9SDvkwdN56/wegdNzkFJxFRMcJECRFRGXLkbjiex6SgnI4G+tavIHY4RERllr5MAwM87AAAay88hSBwVAkREVFpV9XKABsH1YWWuhrOB77GD/vucmQpEVExwUQJEVEZoVAIWH3+3WiSYU0coaulLnJERERl2+BGDpBpqOFuWDy8n8aIHQ4RERF9AXXtjbHum1qQqklw8PZL/Hr0ITtMEBEVA0yUEBGVESceROBpVBIMZOro/18vZiIiEo+pnhZ61303um/tf9MiEhERUenX0tkCi3vUBABs8g7BmvOsBxARia3AiZJLly6hY8eOsLa2hkQiwaFDh/L9WW9vb6irq8PNzU1l++zZsyGRSFRezs7OBQ2NiIhyIQgCVp17V/ke1MgBBjINkSMiIiIAGN7UEepqEvgExcD/RZzY4RAREdEX0tXdBjM7VAMALD71GNuvPhc5IiKisq3AiZLk5GS4urpizZo1BfpcXFwcBgwYgFatWuW4v3r16ggPD1e+rly5UtDQiIgoF2cDohAQngBdTSmGNLIXOxwiIvpPeSNtdHYrDwBYy96kREREZcqQxg4Y17ISAGDGP/dx5O4rkSMiIiq7CjxBfbt27dCuXbsCn2jkyJHo27cvpFJpjqNQ1NXVYWlpWeDjEhHRx70bTfIEANDfwx5GOpoiR0RERO8b1dwRB26H4dTDSDyJTERlC32xQyIiIqIvZHJrJ8Qkp2OnXygm7faHobYGmlQ2EzssIqIy54usUbJp0yY8e/YMs2bNyrXMkydPYG1tDUdHR/Tr1w+hoaG5lk1LS0NCQoLKi4iIcnb5STTuhMVDpqGGYU0cxA6HiIg+UMlcH22qWQAA1l0MEjkaIiIi+pIkEgl+7VwDX7lYIUMu4NttN3E79I3YYRERlTlFnih58uQJpk2bhu3bt0NdPecBLPXr18fmzZtx4sQJrFu3DsHBwWjSpAkSExNzLD9v3jwYGhoqX7a2tkV5CUREJdb7o0n61rODqZ6WyBEREVFORjd/N+3GYf9XCHuTInI0RERE9CVJ1SRY2ssVTSqbIiVdjsGbr+NJZM5tYkREVDSKNFEil8vRt29f/Pzzz3Bycsq1XLt27dCjRw/UrFkTXl5eOHbsGOLi4rBnz54cy0+fPh3x8fHK14sXL4rqEoiISjS/4FhcD3kDTakaRjR1FDscIiLKhautERpVMkGmQsAfl56JHQ4RERF9YVrqUqz/pjZcbY0Ql5KB/n9dY+cJIqIvqEgTJYmJibhx4wbGjh0LdXV1qKur45dffsGdO3egrq6Oc+fO5fg5IyMjODk54enTnBe01NLSgoGBgcqLiIiyyxpN0rOuDSwNZSJHQ0REH5M1qmTX9ReITkoTORoiIiL60nS11LFpUF1UMtdDREIqBvx1DTGsExARfRFFmigxMDDAvXv34O/vr3yNHDkSVapUgb+/P+rXr5/j55KSkhAUFAQrK6uiDI+IqFS7+fwNvJ/GQF1Ngm+bVhQ7HCIiykPDiiZwtTFEWqYCm7yDxQ6HiIiIRGCsq4mtQ+rB2lCGZ9HJGLTpOpLSMsUOi4io1CtwoiQpKUmZ9ACA4OBg+Pv7Kxdfnz59OgYMGPDu4GpqqFGjhsrL3NwcMpkMNWrUgK6uLgDg+++/x8WLFxESEgIfHx907doVUqkUffr0KaTLJCIqe1b/N5qkq3t52BrriBwNERHlRSKRYNR/o0q2+j5HYmqGyBERERGRGKyNtLF1aH0Y62ri3st4jNh6A6kZcrHDIiIq1QqcKLlx4wbc3d3h7u4OAJg8eTLc3d0xc+ZMAEB4eLgyaZJfYWFh6NOnD6pUqYKePXvCxMQEV69ehZmZWUHDIyIiAPdfxuN84GuoSYDRLSqJHQ4REeVTm2oWqGSuh8TUTGy/WrA6NREREZUelcz1sHlwXehqSuETFIOJu/whVwhih0VEVGpJBEEo8f+VTUhIgKGhIeLj47leCRERgG+33cDJB5Ho7GaNFb3dxQ6HRMLn45fF+02FZd/NMHy/9w5M9bRwZWoLyDSkYodERPRJ+Gz8sni/Syefp9EYtOk60uUK9K5ri3lfu0AikYgdFhFRiVCQZ2ORrlFCRERfXmBEIk4+iAQAjOVoEiKiEqezmzXKG2kjOikNe2+GiR0OERERiahhJVOs7OMGNQmw6/oLLDwZKHZIRESlEhMlRESlzOrzTwEA7WpYorKFvsjREBFRQWlI1TC8iQMAYMOlIGTKFSJHRERERGJqW8MKv3V1AQCsuxCEPy49EzkiIqLSh4kSIqJSJOh1Eo7cfQUAGNuSo0mIiEqqXnUrwERXEy9i3+LI3XCxwyEiIiKR9a5XAT+0rQIAmHssAHtvvBA5IiKi0oWJEiKiUmTt+SAIAtDK2RzVrQ3FDoeIiD6RtqYUgxvZA3jXc1TBxVuJiIjKvFHNKipHnU47cA+nH0aKHBERUenBRAkRUSnxIjYFh/xfAuBoEiKi0qC/hz30tNQRGJmIc4+ixA6HiIiIRCaRSPC/9lXRrZYN5AoBY3bewtVnMWKHRURUKjBRQlRKpWbIMWbnLaw5/5S9UMuItReCIFcIaFLZFO4VyokdDhERfSZDbQ30a1ABALD2wlMIAp/nRESF7dKlS+jYsSOsra0hkUhw6NAhlf2zZ8+Gs7MzdHV1Ua5cOXh6esLPz0+lTGxsLPr16wcDAwMYGRlh6NChSEpKUilz9+5dNGnSBDKZDLa2tli4cGG2WPbu3QtnZ2fIZDK4uLjg2LFjhX69VPJJJBIs6OYCz6oWSM9UYPiWG7j/Ml7ssIiISjwmSohKqRP3I3D0bjgWnQzExN3+SMuUix0SFaHw+LfYd/PdHLXjWlYWORoiIiosQxs7QFNdDbdC4+AXHCt2OEREpU5ycjJcXV2xZs2aHPc7OTlh9erVuHfvHq5cuQJ7e3u0adMGr1+/Vpbp168fHjx4gNOnT+PIkSO4dOkSRowYodyfkJCANm3awM7ODjdv3sSiRYswe/ZsbNiwQVnGx8cHffr0wdChQ3H79m106dIFXbp0wf3794vu4qnEUpeqYXVfd9RzMEZiWiYGbbqG4OhkscMiIirRJEIp6JqWkJAAQ0NDxMfHw8DAQOxwiIqFsTtvqSz+2sDRGL/3rwNDbQ0Ro6KiMvvwA2z2CUE9B2Ps+dZD7HComODz8cvi/aai8uPBe9jhF4qmTmbYOqSe2OEQEeVbSXs2SiQSHDx4EF26dMm1TNY1nTlzBq1atUJAQACqVauG69evo06dOgCAEydOoH379ggLC4O1tTXWrVuHH3/8EREREdDU1AQATJs2DYcOHcKjR48AAL169UJycjKOHDmiPFeDBg3g5uaG9evX5yv+kna/6fMlpGag9+9X8TA8ATbltLF/VENYGMjEDouIqNgoyLORI0qISqEMuQIXH7/r4fRdayfoaanj6rNY9Fjvg5dxb0WOjgpbVGIq/r4WCgAYz9EkRESlzrdNK0JNAlx6/JpTaxARiSg9PR0bNmyAoaEhXF1dAQC+vr4wMjJSJkkAwNPTE2pqasopunx9fdG0aVNlkgQAvLy8EBgYiDdv3ijLeHp6qpzPy8sLvr6+ucaTlpaGhIQElReVLQYyDWwZUg/2JjoIe/MWA/66hriUdLHDIiIqkZgoISqFrofEIjE1E8a6mhjdohL2fOsBCwMtPI5MwtdrvfHwFSvQpcmfl4ORlqmAm60RGlUyETscIiIqZBVMdNDR1RoAsO5CkMjREBGVPUeOHIGenh5kMhmWLVuG06dPw9TUFAAQEREBc3NzlfLq6uowNjZGRESEsoyFhYVKmaz3eZXJ2p+TefPmwdDQUPmytbX9vAulEslMXwvbhtaHub4WAiMTMWTzdaSkZ4odFhFRicNECVEpdC4gCgDQooo5pGoSVLM2wMHRjeBkoYfIhDT0/N0Xl5+8zuMoVBLEJqdj+9XnAIDxrSpBIpGIHBERERWFUc0rAgCO3Q/Hs9dJeZQmIqLC1KJFC/j7+8PHxwdt27ZFz549ERUVJXZYmD59OuLj45WvFy9eiB0SicTWWAfbhtaHgez/2Lvv8CrKvI3j3znphBRCSEIgEHqoIbQQOhJpLoqVpihSlKICuiq7K7LqvggiYEEQLCCCYgOxIRhK6BAg1IQOoSUhlIQkpJ7z/hHImhWUlkzK/bmuuXZz5pk594yEGeY3z/PYsz3uIsM/305WjtXsWCIiJYoKJSKlUERs3k17l/r/fbPJ39OFr59uQ+uaXqRm5jDo0618u+2kWRHlDvl0/VHSs3Jp6O9O53o+f72BiIiUSEF+7nQJ8sFmgw/XHDE7johImeLq6krt2rVp3bo1H3/8Mfb29nz88ccA+Pn5/aFokpOTw/nz5/Hz88tvk5CQUKDN1Z//qs3V9dfi5OSEu7t7gUXKrnp+bnw6qCXODhbWHDjLC1/vxGot8dMSi4gUGRVKREqZw2dTOZqUhoOdQfs63gXWebjkjV96b7A/OVYbz3+9k/ciDmKz6eapJEq+nM3c9ccAeOYu9SYRESntRnTO61Xy3Y6TnEnWnGMiImaxWq1kZmYCEBYWxsWLF9m2bVv++pUrV2K1WgkNDc1vExkZSXZ2dn6bFStWUK9ePSpUqJDfJiIiosD3rFixgrCwsMI+HClFmlf3YuajzbG3GCzdeZp//7BX/94XEblBKpSIlDJXh90KrVERN2eHP6x3srdjep+mPN0x72HL2ysO8I/Fu8nJVbfckuazDce4lJlDXd/ydG1w/TfNRESkdGhe3YtWNbzIzrXx0dqjZscRESkVUlNTiY6OJjo6GoCjR48SHR1NXFwcaWlp/OMf/2DTpk0cP36cbdu28eSTT3Lq1CkefvhhAOrXr0/37t0ZOnQoW7ZsYf369YwaNYq+ffvi7583v1T//v1xdHRk8ODB7N27l0WLFvHOO+8wduzY/BzPPfccy5Yt4+233yY2NpYJEyYQFRXFqFGjivycSMnWuZ4Pbz8SDMC8jcd5N+KQyYlEREoGFUpESpnfYvK6a/9+2K3/ZbEYvNwjiNfua4jFgC+2nGDoZ1GkZWrCt5IiNTOHj9fnPSQb2bk2Fot6k4iIlAUjrsxV8sWWOC6kZZmcRkSk5IuKiiIkJISQkBAAxo4dS0hICOPHj8fOzo7Y2FgefPBB6tatS69evTh37hxr166lYcOG+ftYsGABQUFBdOnShZ49e9KuXTtmz56dv97Dw4Ply5dz9OhRmjdvzvPPP8/48eMZNmxYfps2bdqwcOFCZs+eTXBwMN988w1LliyhUaNGRXcypNS4r2kVJvRqAMC03w4wf+MxcwOJiJQAhq0U9MFLSUnBw8OD5ORkjckpZVpyejbN3lhBrtVG5N87U61iub/cZvneeJ79cgcZ2VYaV/HgkydaUsnNqQjSyu34cM1hJv4SSw1vV34b2xE7FUrkGnR9LFo631IUbDYbf3tvHXtPp/BclzqMubuu2ZFERK5L18aipfMt/2vqigO8G3EQw4B3+oZwb7C/2ZFERIrUzVwb1aNEpBRZfSCRXKuNOj7lb6hIAtC1oR8Lh7bGy9WR3aeSeWDmeg6fTS3kpHI7LmflMmdt3kS+IzrVUpFERKQMMQyDEZ1qAzB3wzH1BhUREZHrGhNeh8daV8dmg7GLolm9P9HsSCIixZYKJSKlSMSV+Um61Pe9qe2aVavAt8PbUL1iOU6cv8yDMzew7fj5wogod8CCzcdJSs2iagUXeodUMTuOiIgUse6N/Kjp7Ury5Wy+2BJndhwREREppgzD4N/3NqRXsD85VhvDP9/OtuMXzI4lIlIsqVAiUkrk5Frz3w75s/lJrqeGtyvfDm9DcIAnF9Oz6T9nM8v2nLnTMeU2ZGTn8vqP+/jPzzEADO9UCwc7/TUuIlLW2FkMnupYE4A5a4+QmZNrciIREREpriwWg7cfDqZD3Upczs7lyblbOZBwyexYIiLFjp6wiZQSUccvkJKRQ4VyDjSrVuGW9uFd3okvhoYSXt+HzBwrwxds59MrE4aLubbHXaDnO2v5eN1RbDbo2zKAPi0CzI4lIiImuT+kKn7uziSkZLJ4+ymz44iIiEgx5mhvYdajzQip5kny5Wwe+3gzJ86nmx1LRKRYUaFEpJRYGZvXm6RzPZ/bmrOinKM9sx5tzqOtq2Gzwb9/2McbP+7DarXdqahyEzKyc3nzl1gemrmBI0lp+Lo78ekTLXnzwSbYqzeJiEiZ5WhvYUj7GgDMWnOYXF2nRURE5E+Uc7Tn0ydaUsenPAkpmQz4aDPxyRlmxxIRKTb0lE2klPgtJgGAu25h2K3/ZW9n4fX7GvFi93oAfLTuKM98uYOMbA3tUZR2nbxIr/fWMWvNYaw2uD+kCstHd6Rz0O3/NxYp6WbMmEFgYCDOzs6EhoayZcuW67adM2cO7du3p0KFClSoUIHw8PA/tLfZbIwfP57KlSvj4uJCeHg4Bw8eLOzDELkt/VpVw7OcA8fOpfPzbg2XKSIiIn/Os5wj8weHEuDlQtz5dPrP2UTiJRVLRERAhRKRUuFoUhpHzqZhbzHoULfSHdmnYRiM6FSb6X2a4mBn8NOuMwz8eAsX07PuyP7l+rJyrLy9fD/3f7CBg4mpeJd35MPHmjOtT1M8yjmYHU/EdIsWLWLs2LG8+uqrbN++neDgYLp160ZiYuI1269evZp+/fqxatUqNm7cSEBAAF27duXUqf8OVzR58mTeffddZs2axebNm3F1daVbt25kZOgfjlJ8uTrZ80SbQAA+WH0Ym029SkREROTP+Xk4s3BIa6p4unAkKY0BczaTlJppdiwREdOpUCJSCkRc6U3SqoYX7s539kF675AqzBvUCjcne7YcO89DszZy8oLGMi0se08nc+/763hv5SFyrTb+1qQyy8d0pFtDP7OjiRQbU6dOZejQoQwaNIgGDRowa9YsypUrxyeffHLN9gsWLGDEiBE0bdqUoKAgPvroI6xWKxEREUBeb5Lp06fzr3/9i/vuu48mTZrw2Wefcfr0aZYsWVKERyZy855oE0g5RztizqSw+sBZs+OIiIhICRDgVY6FQ0Pxc3fmYGIqj360mQtpeilSRMo2FUpESoGImLy3qLvU9y2U/bep7c3Xw8Oo7OHMocRU7v9gA3tOJRfKd5VV2blW3o04yH3vryc2/hIVyjkwo38z3u/fDC9XR7PjiRQbWVlZbNu2jfDw8PzPLBYL4eHhbNy48Yb2kZ6eTnZ2Nl5eXgAcPXqU+Pj4Avv08PAgNDT0T/eZmZlJSkpKgUWkqHmWc6R/q2oAzFx12OQ0IiIiUlJUr+jKwqGhVHJzIjb+Eo9+vJnk9GyzY4mImEaFEpESLvlyNluPnQcg/A7MT3I9QX7ufDeiDUF+bpy9lEmfDzeyRm+u3hH74y/xwAcbmLriADlWG90a+rJ8TEfuaVLZ7GgixU5SUhK5ubn4+hYsDPv6+hIfH39D+3jppZfw9/fPL4xc3e5m9zlx4kQ8PDzyl4CAgJs5FJE7Zkj7mjjYGWw5dp6oK/cEIiIiIn+lZqXyfDE0lIqujuw9ncLATzaTkqFiiYiUTSqUiJRwkQfOkmO1UauSK9Uruhbqd1X2cOGrp8NoU6siaVm5PDl3K19FnSjU7yzNcnKtfLD6EL3eW8fuU8l4uDjwTt+mzHq0OZXcnMyOJ1Iqvfnmm3z55ZcsXrwYZ2fn29rXuHHjSE5Ozl9OnNDfh2IOPw9nHmxWFcibq0RERETkRtX2cWPB0FAqlHNg58lknvhkC6mZOWbHEhEpciqUiJRwV+cnCS+kYbf+l7uzA3MHteL+kCrkWm28+M0upv92QBPI3qTDZ1N5aNZGJi/bT1aulS5BPqwY04H7mlbBMAyz44kUW97e3tjZ2ZGQkFDg84SEBPz8/nwunylTpvDmm2+yfPlymjRpkv/51e1udp9OTk64u7sXWETM8lTHWlgMWBmbSMwZDQMnIiIiNy7Iz53Ph4Ti4eLA9riLPPnpVtKzVCwRkbJFhRKREiwn15o/cetdQYU37Nb/crS3MPWRYEZ2rgXA9N8O8tK3u8jOtRZZhpIq12rjo7VH6PnOWqJPXMTN2Z4pDwfz0eMt8HG/vbfbRcoCR0dHmjdvnj8RO5A/MXtYWNh1t5s8eTKvv/46y5Yto0WLFgXW1ahRAz8/vwL7TElJYfPmzX+6T5HipIa3Kz0a5w3ZOFO9SkREROQmNfT3YP7gVrg52bPl2HkGz43iclau2bFERIqMCiUiJdj2uItcTM/Gw8WB5tUrFOl3G4bB37sF8UbvRlgM+CrqJIPnRamL7p84lpRG39kbeeOnGDJzrHSoW4nlYzrwUPOq6kUichPGjh3LnDlzmDdvHjExMQwfPpy0tDQGDRoEwMCBAxk3blx++0mTJvHKK6/wySefEBgYSHx8PPHx8aSmpgJ5f5+NHj2aN954g6VLl7J7924GDhyIv78/vXv3NuMQRW7J8I55LzD8uOs0x8+lmZxGRERESpomVT2ZN7gVro52bDxyjmHzo8jIVrFERMoGFUpESrCI2LxhYjrXq4S9nTm/zo+2rs7sx1rg4mBH5IGz9PlwI4kpGaZkKa6sVhtz1x+l+zuRbD12AVdHOyY+0Jh5g1pS2cPF7HgiJU6fPn2YMmUK48ePp2nTpkRHR7Ns2bL8ydjj4uI4c+ZMfvuZM2eSlZXFQw89ROXKlfOXKVOm5Ld58cUXeeaZZxg2bBgtW7YkNTWVZcuW3fY8JiJFqVEVDzrWrYTVBh9GHjE7joiIiJRAzapVYO6TrXBxsGPtwSSGf76NzBwVS0Sk9DNspWBigZSUFDw8PEhOTtb44FKmhE9dw6HEVN7tF8K9wf6mZok+cZHBc7dyLi2LKp4uzHuyJbV93EzNVBycOJ/O37/ZyaYj5wFoU6sikx9qQtUK5UxOJmWBro9FS+dbioPNR87RZ/YmHO0srHups4Z1FBFT6dpYtHS+5U7acDiJQZ9uJTPHyt0NfPlgQDMcTHpBU0TkVt3MtVF/w4mUUMfPpXEoMRV7i0HHupXMjkPTAE++G9GGGt6unLp4mQdnbmTL0fNmxzKNzWZjwebjdJ8eyaYj53FxsOP1+xry+eBQFUlERKTQtKrhRfPqFcjKtfLxuqNmxxEREZESqk0tb+YMbIGjvYUV+xJ47ssd5GheUhEpxVQoESmhImISAWgZ6IWHi4PJafJUr+jKt8PbEFLNk+TL2Tz68WZ+2nXmrzcsZU5fvMzAT7bwz8V7SMvKpVWgF8tGt+exsEAsFs1FIiIihccwDEZ0ypur5PNNx0lOzzY5kYiIiJRUHepW4sNHm+NgZ/Dz7njGfrWTXGuJH5hGROSaVCgRKaGuzk/Spb6PyUkK8nJ1ZOGQ1nRt4EtWjpVRX2zno7VlY5x0m83GV1tP0G1aJGsPJuFkb+GVvzXgy2GtqV7R1ex4IiJSRtwV5EOQnxtpWbl8tvGY2XFERESkBOsc5MOM/s2wtxgs3XmaF7/ZhVXFEhEphVQoESmBLmVks/nKnBdd6vuanOaPXBztmPlocx4Pq47NBm/8FMO/f9hbqt88SUjJ4Mm5W3nx211cyswhpJonPz/XnsHtaqgXiYiIFCnDMBh+pVfJpxuOcTlLE7CKiIjIreva0I/3+oVgZzH4dvtJ/rF4t4olIlLqqFAiUgJFHkgix2qjprcrNbyLZ08FO4vBhHsb8o+eQQB8uv4YoxZuJyO7dD2ssdlsLN5xkrunrmHV/rM42ll4uUcQ3zzdhlqVypsdT0REyqh7Glemmlc5zqdl8eXWOLPjiIiISAnXo3Flpj4SjMWAL7ee4NWle7HZVCwRkdJDhRKREigipngOu/W/DMNgWIdavNsvBEc7C7/siefRjzZzIS3L7Gh3xNlLmTw1fxtjFu0kJSOHJlU9+OnZdjzdsRZ26kUiIiImsrezMKxDTQDmRB4hK0eTr4qIiMjtua9pFd56KBjDgPmbjvPaj/tULBGRUkOFEpESJtdqY9X+vInci+OwW9dyb7A/nw1uhbuzPVHHL/DgrA2cOJ9udqzb8uOu03Sdtobl+xJwsDN4oWtdvhvehjq+bmZHExERAeCh5lWp5ObE6eQMvo8+ZXYcERERKQUebF6VNx9oDOSNHPHmL7EqlohIqaBCiUgJsyPuAhfSs3F3tqd59Qpmx7lhrWtW5JvhbfD3cObI2TTu/2ADu05eNDvWTTuflsXIBdsZtXAHF9KzaVDZnaWj2jHqrjrY2+mvVBERKT6cHewY3K4GALPWHNZY4iIiInJH9GlZjTd6NwLgw8gjTF1xwOREIiK3T0/1REqYiNi83iSd6vngUMIezNf1dWPxyLbUr+xOUmomfWdvYtWV4ykJlu2Jp+u0Nfy0+wz2FoPnutRhyZXjERERKY4GhFbD3dmew2fTWL4v3uw4IiIiUko82ro6r/ZqAMB7Kw/xbsRBkxOJiNyekvWUVURKzPwk1+Pr7sxXT7WmfR1v0rNyGfJZFF9uKd6TzF5Mz2L0lzt4+vNtJKVmUc/XjSUj2zLm7ro42uuvURERKb7cnB0YGBYIwAerD2toDBEREbljBrWtwT971gdg6ooDfLD6kMmJRERunZ7wiZQgJ86ncyAhFTuLQae6JbNQAnkPbT55oiUPNqtKrtXGy9/tZury/cXy4U1ETAJ3T4tkSfRpLAaM6FSLpc+0pVEVD7OjiYiI3JBBbQNxdrCw62Qy6w+dMzuOiIiIlCJDO9Tk793qATB52X4+WnvE5EQiIrdGhRKREuRqb5IW1SvgUc7B5DS3x8HOwpSHm/BslzoAvLvyEC98vYvsXKvJyfIkX87mha93MnheFGcvZVKrkivfjWjLi92DcLK3MzueiIjIDatY3om+LasB6E1PERERueNGdq7N6PC8f9u/8VMM8zYcMzeQiMgtUKFEpAS5Oj9JSR12638ZhsHYu+vy5gONsbMYfLv9JE/O3cqljGxTc605cJbu0yP5ZttJDAOGdajJT8+2p2mAp6m5REREbtXQDjWxtxhsOHyO6BMXzY4jIiIipcxzXeowsnMtAF5dupcFm4+bnEhE5OaoUCJSQqRm5rDpSN5wGV3q+5qc5s7q26oaHw1sQTlHO9YeTOKRDzeRkJJR5DlSM3MY990uHv9kC2eSMwisWI6vnwrjHz3r4+ygXiQiIlJyVfF0oXdIFQA+WKVeJSIiInJnGYbBC13rMbR9DQD+uXgPX209YXIqEZEbp0KJSAmx9sBZsnNtBFYsR01vV7Pj3HGdg3xYNCwM7/JOxJxJ4f4Z6zmQcKnIvn/DoSS6TYvkiy15N3JPtAnkl+c60CLQq8gyiIiIFKanO9bCMGD5vgQOFuE1VkRERMoGwzD4R8/6PNEmEICXvtvF4h0nzQ0lInKDVCgRKSF+i7k67JYvhmGYnKZwNK7qweIRbahZyZXTyRk8OHMDGw8X7qSz6Vk5jP9+D/0/2sypi5cJ8HLhy2GtmXBvQ1wc1YtERERKj9o+5enWwA+AmWsOm5xGRERESiPDMHi1VwMGhFbDZoPnv9rJj7tOmx1LROQvqVAiUgLkWm2s3l+65ie5ngCvcnz7dBtaVK/ApYwcHv9kC0t3Fs5N1Zaj5+k+fS2fbcwbO3VAaDWWPdeB1jUrFsr3iYiImG3ElbHDl0af5uSFdJPTiIiISGlkGAav39eIR1pUxWqD576MZtmeeLNjiYj8KRVKREqA6BMXOZeWhZuzPS3LwFBQFVwd+XxIKD0a+ZGVa+XZL3YwO/IwNpvtjuw/IzuX13/cR5/ZG4k7n46/hzPzB7fiP/c3xtXJ/o58h4iISHHUpKon7Wp7k2O1MSfyiNlxREREpJSyWAwmPtCEB0KqkGu18cwX2/ltX4LZsURErkuFEpESYGVs3s1Ex7qVcLArG7+2zg52vN+/GU+2zZsI7v9+jmXC0r3kWm+vWLI97gI931nLx+uOYrNBnxYBLBvTgfZ1Kt2J2CIiIsXeiE55vUq+3HqCpNRMk9OIiIhIaWVnMXjr4WB6BfuTnWtjxILt+aNliIgUN2XjiatICRcRUzaG3fpfdhaD8b0a8K976mMYMG/jcYZ/vo2M7Nyb3ldGdi4Tf4nhoZkbOJKUhq+7E58Oasmkh5rg7uxQCOlFRESKp7BaFQkO8CQzx8qn64+aHUdERERKMTuLwdRHgvNHjBg2fxvrDiaZHUtE5A9UKBEp5k5eSCc2/hIWAzrVLVuFkquGtK/J+/2a4WhvYfm+BPrN2cT5tKwb3n7XyYv0em8dH645gtUGDzSrwvLRHelcr2yeTxERKdsMw8jvVfLZxuOkZGSbnEhExDyRkZH06tULf39/DMNgyZIl+euys7N56aWXaNy4Ma6urvj7+zNw4EBOny44h+L58+cZMGAA7u7ueHp6MnjwYFJTUwu02bVrF+3bt8fZ2ZmAgAAmT578hyxff/01QUFBODs707hxY37++edCOWaRouZgZ+GdviGE1/clK8fKkM+2sunIObNjiYgUoEKJSDG3MjavN0mL6l5UcHU0OY157mlSmc8Hh+Lh4sCOuIs8OHMDx8+l/ek2WTlW3l6+n/s/2MDBxFS8yzsx+7HmTH2kKR7l1ItERETKrrvr+1LHpzyXMnL4fNNxs+OIiJgmLS2N4OBgZsyY8Yd16enpbN++nVdeeYXt27fz3XffsX//fu69994C7QYMGMDevXtZsWIFP/74I5GRkQwbNix/fUpKCl27dqV69eps27aNt956iwkTJjB79uz8Nhs2bKBfv34MHjyYHTt20Lt3b3r37s2ePXsK7+BFipCjvYUZA0LoVK8SGdlWnpy7lahj582OJSKSz7DdqdmRTZSSkoKHhwfJycm4u7ubHUfkjhr4yRYiD5zl5R5BPN2xltlxTHco8RKPf7KVUxcvU9HVkU+eaElwgOcf2u09nczzX+0kNv4SAL2C/Xnt3oZlutgkZY+uj0VL51tKmm+3neT5r3fiXd6RdS/dhbODndmRRKSUKWnXRsMwWLx4Mb17975um61bt9KqVSuOHz9OtWrViImJoUGDBmzdupUWLVoAsGzZMnr27MnJkyfx9/dn5syZ/POf/yQ+Ph5Hx7x/j7z88sssWbKE2NhYAPr06UNaWho//vhj/ne1bt2apk2bMmvWrBvKX9LOt5RNGdm5DP0sirUHkyjvZM/8wa0IqVbB7FgiUkrdzLVRPUpEirG0zBw2Hc7rjhpexuYnuZ7aPm4sHtGGRlXcOZeWRd/Zm4iISchfn51r5Z3fDnLf++uJjb+El6sjHwxoxnv9QlQkERER+Z17m/pTxdOFpNQsvo46YXYcEZESITk5GcMw8PT0BGDjxo14enrmF0kAwsPDsVgsbN68Ob9Nhw4d8oskAN26dWP//v1cuHAhv014eHiB7+rWrRsbN268bpbMzExSUlIKLCLFnbODHbMfa0Hrml6kZuYw8JMt7D6ZbHYsEREVSkSKs7UHk8jKtVLNqxy1KpU3O06x4ePuzJfDwuhYtxKXr7yNsmDzcfbHX+L+D9Yz7bcD5FhtdG/ox/IxHejZuLLZkUVERIodBzsLwzrUBODDyCPk5FpNTiQiUrxlZGTw0ksv0a9fv/y3UuPj4/HxKfhSm729PV5eXsTHx+e38fX1LdDm6s9/1ebq+muZOHEiHh4e+UtAQMDtHaBIEXFxtOPjx1vSMrAClzJyePTjzew7rUKfiJhLhRKRYuxqT4ku9X0wDMPkNMVLeSd7Pnq8BY+0qIrVBv9cvId73l3LnlMpeLg48E7fpsx8tBne5Z3MjioiIlJsPdIigIqujpy8cJkfdp3+6w1ERMqo7OxsHnnkEWw2GzNnzjQ7DgDjxo0jOTk5fzlxQr0DpeRwdbLn00GtCKnmSfLlbB79eDP7rwydLSJiBhVKRIopq9XGqv15E7mH1/f9i9Zlk4OdhUkPNmF0eB0Acqw2wuv7sGJMB+5rWkXFJRERkb/g4mjHk+1qADBz9WGs1hI/faGIyB13tUhy/PhxVqxYUWCMcz8/PxITEwu0z8nJ4fz58/j5+eW3SUhIKNDm6s9/1ebq+mtxcnLC3d29wCJSkpR3smfuoFY0qerB+bQsBny0iUOJqWbHEpEySoUSkWJq58mLJKVm4eZkT8tAL7PjFFuGYTA6vC6fPdmKjwa2YM7AFvi4O5sdS0REpMR4tHV1yjvZcyAhlYjYxL/eQESkDLlaJDl48CC//fYbFStWLLA+LCyMixcvsm3btvzPVq5cidVqJTQ0NL9NZGQk2dnZ+W1WrFhBvXr1qFChQn6biIiIAvtesWIFYWFhhXVoIsWCh4sDnz3ZigaV3UlKzaL/nE0cTUozO5aIlEEqlIgUUyuvPKjoULcSjvb6Vf0rHepWIryBr3qRiIiI3CQPFwcebV0dgA9WH8JmU68SESk7UlNTiY6OJjo6GoCjR48SHR1NXFwc2dnZPPTQQ0RFRbFgwQJyc3OJj48nPj6erKwsAOrXr0/37t0ZOnQoW7ZsYf369YwaNYq+ffvi7+8PQP/+/XF0dGTw4MHs3buXRYsW8c477zB27Nj8HM899xzLli3j7bffJjY2lgkTJhAVFcWoUaOK/JyIFDXPco58PiSUer5uJF7KpP+cTZw4n252LBEpY/T0VaSY+i0mr1ByV5DPX7QUERERuT1PtgvE0d7CjriLbDpy3uw4IiJFJioqipCQEEJCQgAYO3YsISEhjB8/nlOnTrF06VJOnjxJ06ZNqVy5cv6yYcOG/H0sWLCAoKAgunTpQs+ePWnXrh2zZ8/OX+/h4cHy5cs5evQozZs35/nnn2f8+PEMGzYsv02bNm1YuHAhs2fPJjg4mG+++YYlS5bQqFGjojsZIibycs0rltSq5MqZ5Az6zt7EqYuXzY4lImWIYSsFr4ylpKTg4eFBcnKyxuSUUuHUxcu0fXMlFgOi/nU3Xq6OZkcSkRJI18eipfMtJd2/luzm801xtK/jzfzBoWbHEZFSQNfGoqXzLaVBYkoGfWbnDb9VzascXz0Vhp+HhtcWkVtzM9dG9SgRKYauDrvVrFoFFUlERESkSDzVoRZ2FoO1B5PYfTLZ7DgiIiJSBvm4O7NwaCjVvMoRdz6d/nM2kZiSYXYsESkDbrpQEhkZSa9evfD398cwDJYsWXLD265fvx57e3uaNm36h3UzZswgMDAQZ2dnQkND2bJly81GEyk1ImISALirvobdEhERkaIR4FWOXk0qAzBzzSGT04iIiEhZVdnDhYVDQ6ni6cKRpDT6f7SZpNRMs2OJSCl304WStLQ0goODmTFjxk1td/HiRQYOHEiXLl3+sG7RokWMHTuWV199le3btxMcHEy3bt1ITEy82XgiJV56Vg4bDp8DILy+r8lpREREpCwZ3qk2AL/siefw2VST04iIiEhZVbVCOb4Y2ho/d2cOJaby6EebOZ+WZXYsESnFbrpQ0qNHD9544w3uv//+m9ru6aefpn///oSFhf1h3dSpUxk6dCiDBg2iQYMGzJo1i3LlyvHJJ5/cbDyREm/dwSSycqxUreBCHZ/yZscRERGRMqSenxvh9X2w2eDDNYfNjiMiIiJlWLWK5fhiWGt83JyIjb/Eox9t5mK6iiUiUjiKZI6STz/9lCNHjvDqq6/+YV1WVhbbtm0jPDz8v6EsFsLDw9m4ceM195eZmUlKSkqBRaS0iIjJ60kVXt8XwzBMTiMiIiJlzdVeJYt3nOJM8mWT04iIiEhZVsPblYVDQ/Eu78i+MykM/GQLKRnZZscSkVKo0AslBw8e5OWXX+bzzz/H3t7+D+uTkpLIzc3F17fgEEO+vr7Ex8dfc58TJ07Ew8MjfwkICCiU7CJFzWq1sXJ/XqGki+YnERERERM0r16B0BpeZOfamBN51Ow4IiIiUsbV9nFjwZDWVCjnwK6TyTz+yRZSM3PMjiUipUyhFkpyc3Pp378///73v6lbt+4d2++4ceNITk7OX06cOHHH9i1ipt2nkjl7KRNXRzta1fAyO46IiIiUUSM65/Uq+WJLnMYDFxEREdPV83Pj8yGheLg4sCPuIoM+3UJ6loolInLnFGqh5NKlS0RFRTFq1Cjs7e2xt7fntddeY+fOndjb27Ny5Uq8vb2xs7MjISGhwLYJCQn4+fldc79OTk64u7sXWERKg4jYvN4kHepWwsnezuQ0IiIiUlZ1qONNQ393LmfnMnfDMbPjiIiIiNDQ34PPB4fi5mzP1mMXGDw3istZuWbHEpFSolALJe7u7uzevZvo6Oj85emnn6ZevXpER0cTGhqKo6MjzZs3JyIiIn87q9VKRETENSd+FynNImLyCoZ3BWnYLRERETGPYRiMuDJXybwNxzS8hYiIiBQLjat68NmTrSjvZM/GI+cYNj+KjGwVS0Tk9t10oSQ1NTW/6AFw9OhRoqOjiYuLA/KGxRo4cGDezi0WGjVqVGDx8fHB2dmZRo0a4erqCsDYsWOZM2cO8+bNIyYmhuHDh5OWlsagQYPu0GGKFH9nki+z93QKhgGdVSgRERERk3Vv5EdNb1eSL2fzxeY4s+OIiIiIABBSrQKfDmpJOUc71h5MYvjn28jMUbFERG7PTRdKoqKiCAkJISQkBMgrcoSEhDB+/HgAzpw5k180uVF9+vRhypQpjB8/nqZNmxIdHc2yZcv+MMG7SGm28sqwWyEBnniXdzI5jYiIiJR1dhaDpzvWAuCjdUf0AEJERESKjZaBXnz8eEucHSys2n+WUQt3kJ1rNTuWiJRghs1ms5kd4nalpKTg4eFBcnKy5iuREuvJuVtZGZvI37vVY+SVCVRFRG6Hro9FS+dbSqOsHCsd31rFmeQMJj7QmH6tqpkdSURKEF0bi5bOt5RF6w4m8eS8rWTlWOnZ2I93+4Zgb1eoMw2ISAlyM9dG/c0hUgxczspl/aEkALrU17BbIiIiUjw42lsY0r4mAB+uOUyutcS/YyUiIiKlSLs63nz4WHMc7Sz8vDuesV/t1P2KiNwSFUpEioH1h5LIzLFSxdOFer5uZscRERERydevVQAVyjlw7Fw6P+8+Y3YcERERkQI61/NhxoBm2FsMlu48zd+/2YlVxRIRuUkqlIgUAxGxCUBebxLDMExOIyIiIvJf5RzteaJNDQA+WH2YUjByr4iIiJQydzfw5b1+IdhZDL7bfop/LN6tYomI3BQVSkRMZrPZiIjJm8i9S31fk9OIiIiI/NHjbarj6mhHzJkUVh84a3YcERERkT/o0bgy0/o0xWLAl1tPMH7pHr3gISI3TIUSEZPtOZVC4qVMyjnaEVrDy+w4IiIiIn/gWc6R/qF5E7nPXHXY5DQiIiIi13ZvsD9THg7GMODzTXG89uM+FUtE5IaoUCJisqvDbrWv442zg53JaURERESubUj7mjjaWdhy7DxRx86bHUdERETkmh5oVpVJDzQB4NP1x/j3D/vIzrWanEpEijsVSkRMpmG3REREpCTwdXfmweZVgLy5SkRERESKq0daBvCf+xsBMHfDMR74YAOHElNNTiUixZkKJSImSkjJYPepZAwDOtfzMTuOiIiIyJ96qkMtLAasjE0k5kyK2XFERERErmtAaHVm9G+Gh4sDu08lc8+7a5m34ZgmeReRa1KhRMREK2PzepMEV/WkkpuTyWlERERE/lygtys9G1cGYKZ6lYiIiEgxd0+Tyvw6ugPt63iTmWPl1aV7efzTLcQnZ5gdTUSKGRVKREwUEZM3P0mXIPUmERERkZJheKdaAPy46zTHz6WZnEZERETkz/l5OPPZk6147b6GONlbWHswiW7TI/lh52mzo4lIMaJCiYhJMrJzWXcoCdD8JCIiIlJyNPT3oFO9Slht8GHkEbPjiIiIiPwlwzAYGBbIT8+2p0lVD5IvZ/PMFzt47ssdJKdnmx1PRIoBFUpETLLhcBIZ2Vb8PZypX9nN7DgiIiIiN2xEp9oAfBN1ksQUDV0hIiIiJUNtn/J8O7wNz3apg53F4Pvo03R/J5L1V15kFZGyS4USEZP8FpM3P8ld9X0wDMPkNCIiIiI3rlUNL1pUr0BWrpWP1h01O46IiIjIDXOwszD27rp8/XQYgRXLcSY5gwEfbebfP+wlIzvX7HgiYhIVSkRMYLPZWHmlUKJht0RERKQkGtE5b66SBZuOa8gKERERKXGaVavAz8+1Z0BoNQA+XX+Mv723jj2nkk1OJiJmUKFExAR7T6cQn5KBi4MdYTUrmh1HRERE5KZ1rudDkJ8baVm5zNt4zOw4IiIiIjetnKM9/7m/MZ8+0ZJKbk4cSkyl94z1zFh1iJxcq9nxRKQIqVAiYoKVsXm9SdrV8cbZwc7kNCIiIiI3zzAMhnfK61Xy6fqjpGflmJxIRERE5NZ0DvLh19Ed6N7Qjxyrjbd+3U+f2Zs4fi7N7GgiUkRUKBExQURMAgDh9X1MTiIiUjLNmDGDwMBAnJ2dCQ0NZcuWLddtu3fvXh588EECAwMxDIPp06f/oU1ubi6vvPIKNWrUwMXFhVq1avH6669js9kK8ShESr57Glemmlc5LqRn8+WWE2bHEREREbllXq6OzHy0GW8/HEx5J3u2Hb9Aj3fW8uWWOP27QKQMUKFEpIglpmSw82TeeJed66lQIiJysxYtWsTYsWN59dVX2b59O8HBwXTr1o3ExMRrtk9PT6dmzZq8+eab+Pn5XbPNpEmTmDlzJu+//z4xMTFMmjSJyZMn89577xXmoYiUePZ2Fp7qWBOAOWuPkJWjISpERESk5DIMgwebV2XZ6Pa0quFFelYuL3+3m6GfRXH2UqbZ8USkEKlQIlLEVu3Pe5AXXNUDH3dnk9OIiJQ8U6dOZejQoQwaNIgGDRowa9YsypUrxyeffHLN9i1btuStt96ib9++ODk5XbPNhg0buO+++7jnnnsIDAzkoYceomvXrn/aU0VE8jzYrCqV3Jw4k5zBkuhTZscRERERuW1VK5Tji6Gt+UfPIBztLPwWk0j36ZEs3xtvdjQRKSQqlIgUsd9i8goldwX5mpxERKTkycrKYtu2bYSHh+d/ZrFYCA8PZ+PGjbe83zZt2hAREcGBAwcA2LlzJ+vWraNHjx7X3SYzM5OUlJQCi0hZ5Oxgx5B2NQCYteYwuVYNTSEiIiIln53FYFiHWix9pi1Bfm6cS8ti2PxtvPjNTlIzNTebSGmjQolIEcrIzmXdwSQAumh+EhGRm5aUlERubi6+vgWLzb6+vsTH3/rbXS+//DJ9+/YlKCgIBwcHQkJCGD16NAMGDLjuNhMnTsTDwyN/CQgIuOXvFynpBrSujruzPUfOpulNSxERESlVgvzc+X5UW57qWBPDgK+iTtLjnUi2HjtvdjQRuYNUKBEpQhuPnONydi5+7s409Hc3O46IiFzx1VdfsWDBAhYuXMj27duZN28eU6ZMYd68edfdZty4cSQnJ+cvJ05oImspu8o72fN4m0AAPlh9WBOeioiISKniZG/HuB71+XJoa6p4unDi/GUe+XAjk5bFao42kVJChRKRIhQRkwDAXfV9MAzD5DQiIiWPt7c3dnZ2JCQkFPg8ISHhuhO134i///3v+b1KGjduzGOPPcaYMWOYOHHidbdxcnLC3d29wCJSlj3RJhBnBwu7TyWz7lCS2XFERERE7rjQmhVZNro9DzWvis0GM1cf5r4Z69kff8nsaCJym1QoESkiNpuNlVfmJwnXsFsiIrfE0dGR5s2bExERkf+Z1WolIiKCsLCwW95veno6FkvB2yI7OzusVr0dJnKjKpZ3om/LagB8sOqwyWlERERECoebswNTHg5m1qPN8XJ1JOZMCr3eX8dHa49g1VxtIiWWCiUiRSTmzCVOJ2fg7GChTS1vs+OIiJRYY8eOZc6cOcybN4+YmBiGDx9OWloagwYNAmDgwIGMGzcuv31WVhbR0dFER0eTlZXFqVOniI6O5tChQ/ltevXqxX/+8x9++uknjh07xuLFi5k6dSr3339/kR+fSEk2tENN7C0GG4+cY0fcBbPjiIj8qcjISHr16oW/vz+GYbBkyZIC67/77ju6du1KxYoVMQyD6OjoP+wjIyODkSNHUrFiRcqXL8+DDz74h56vcXFx3HPPPZQrVw4fHx/+/ve/k5NTcCLo1atX06xZM5ycnKhduzZz5869w0crInda90Z+LBvdnruCfMjKsfLGTzEM+Ggzpy5eNjuaiNwCFUpEisjK2Lyb5Xa1vXF2sDM5jYhIydWnTx+mTJnC+PHjadq0KdHR0Sxbtix/gve4uDjOnDmT3/706dOEhIQQEhLCmTNnmDJlCiEhIQwZMiS/zXvvvcdDDz3EiBEjqF+/Pi+88AJPPfUUr7/+epEfn0hJVsXThftDqgB5c5WIiBRnaWlpBAcHM2PGjOuub9euHZMmTbruPsaMGcMPP/zA119/zZo1azh9+jQPPPBA/vrc3FzuuecesrKy2LBhA/PmzWPu3LmMHz8+v83Ro0e555576Ny5M9HR0YwePZohQ4bw66+/3rmDFZFC4ePmzMePt+D/7m+Mi4MdG4+co/u0SBbvOKk520RKGMNWCn5rU1JS8PDwIDk5WeODS7HVe8Z6ok9cZOIDjenXqprZcUSkDND1sWjpfIvkOXw2lfCpa7DZYPmYDtT1dTM7koiYpCRdGw3DYPHixfTu3fsP644dO0aNGjXYsWMHTZs2zf88OTmZSpUqsXDhQh566CEAYmNjqV+/Phs3bqR169b88ssv/O1vf+P06dP5L3XMmjWLl156ibNnz+Lo6MhLL73ETz/9xJ49e/L33bdvXy5evMiyZcuumzkzM5PMzMz8n1NSUggICCgR51ukNDqWlMaYr6LZEXcRgHsaV+aN3o2o4OpobjCRMuxm7kXUo0SkCJy9lMnOkxcBuCtI85OIiIhI6VWrUnm6N/QDYJZ6lYhIKbZt2zays7MJDw/P/ywoKIhq1aqxceNGADZu3Ejjxo3ziyQA3bp1IyUlhb179+a3+f0+rra5uo/rmThxIh4eHvlLQEDAnTo0EbkFgd6ufP1UGC90rYu9xeCn3WfoNj2S1fsTzY4mIjdAhRKRIrBqfyI2GzSu4oGvu7PZcUREREQK1YhOtQH4fudpTpxPNzmNiEjhiI+Px9HREU9PzwKf+/r6Eh8fn9/m90WSq+uvrvuzNikpKVy+fP25DsaNG0dycnL+cuLEids9JBG5TfZ2FkbdVYfFI9pSq5IriZcyeeLTrbyyZA+Xs3LNjicif8Le7AAiZUFETN78JOpNIiIiImVB46oetK/jzdqDSTz/9U5aBXrh5mxPeWd73JwdcHOyL/BzeSd7yjvZY2cxzI4uIlJiODk54eTkZHYMEbmGxlU9+OnZ9rz5SyxzNxxj/qbjrD+UxNQ+TWka4Gl2PBG5BhVKRApZZk4uaw8mARBe3/cvWouIiIiUDiM61WbtwSS2HD3PlqPnb2gbV0e7vMKJc17hxM35yuLkcKWokve5u3PBn92cHfLbujjYYRgquIhI4fPz8yMrK4uLFy8W6FWSkJCAn59ffpstW7YU2C4hISF/3dX/vfrZ79u4u7vj4uJSiEcgIoXJ2cGOCfc2pEt9H/7+9S6OJKXx4MwNPHNXbUZ2ro2DnQb6ESlOVCgRKWSbjpwnPSsXX3cnGlXRhHoiIiJSNoTVqsh7/UKIjU8hNSOHSxk5XMrM4VJGNqmZeT9f/Twr1wpAWlYuaVm5kHLr32tnMfJ7qOQXWq70Wrnai8X99z//T6Hl6s+O9np4ISJ/rnnz5jg4OBAREcGDDz4IwP79+4mLiyMsLAyAsLAw/vOf/5CYmIiPT94IAytWrMDd3Z0GDRrkt/n5558L7HvFihX5+xCRkq19nUr8OroD//p+Dz/sPM303w6yav9Zpj0STM1K5c2OJyJXqFAiUsh+P+yW3m4UERGRsqRXsD+9gv3/sl1mTm5+4SQ1M4eUjOz8IkrqleLKpQLFlf8WWy797merDXKtNpIvZ5N8Ofu2sjvaW3D/XeHkuoWW3w0nVrOSK9Urut7W94pI0UlNTeXQoUP5Px89epTo6Gi8vLyoVq0a58+fJy4ujtOnTwN5RRDI6wHi5+eHh4cHgwcPZuzYsXh5eeHu7s4zzzxDWFgYrVu3BqBr1640aNCAxx57jMmTJxMfH8+//vUvRo4cmT9s1tNPP83777/Piy++yJNPPsnKlSv56quv+Omnn4r4jIhIYfEo58B7/UIIr+/DK0v2sPPERXq+u5Z/9qzPo62r63mRSDGgQolIIbLZbETEJALQJUjDbomIiIhci5O9HU7l7fAuf+tj7dtsNtKzcv9bWPldoSU140rx5ffFlsyCbS5dKc6kXZloNSvHSlJqFkmpWTecwTCgZ+PKPNelDnV93W75WESkaERFRdG5c+f8n8eOHQvA448/zty5c1m6dCmDBg3KX9+3b18AXn31VSZMmADAtGnTsFgsPPjgg2RmZtKtWzc++OCD/G3s7Oz48ccfGT58OGFhYbi6uvL444/z2muv5bepUaMGP/30E2PGjOGdd96hatWqfPTRR3Tr1q0wD19ETHBf0yq0quHFC1/vZP2hc7zy/V5+i0lk8kNN8HV3NjueSJlm2Gw2m9khbldKSgoeHh4kJyfj7q6hjaT4iI1Pofv0tTjZW4ge3xUXRzuzI4lIGaLrY9HS+RYpHXKttv8WTn5XWPnfQsv/9nxJvpzNvjN5Y4YZBtxzpWBSRwUTKcN0bSxaOt8iJYfVamPexmO8+UssmTlWPMs58H/3N6Zn48pmRxMpVW7m2qgeJSKF6Gpvkra1vVUkERERESkB7CwGHi4OeLg43PS2sfEpvPPbQX7ZE8+Pu87w0+4z9Griz7NdalPbRwUTERERyWOxGAxqW4N2tb0Z81U0e06lMGLBdh4IqcKE+xri7nzz9yEicns0Q6FIIbo6P0mX+j4mJxERERGRwhbk587MR5vz87Pt6d7QD5sNlu48zd3TInnuyx0cSkw1O6KIiIgUI3V83fhueFtGda6NxYDvdpyix/S1bDx8zuxoImWOCiUihSQpNZMdJy4CeRO5i4iIiEjZ0MDfnVmPNeenZ9vRtYEvNht8H32artPWMPrLHRw5q4KJiIiI5HG0t/BCt3p8/XQY1SuW49TFy/T/aBP/+WkfGdm5ZscTKTNUKBEpJKv3n8Vmg4b+7lT2cDE7joiIiIgUsYb+Hswe2IIfn2nH3Q18sdpgSfRpwqeuYeyiaI4mpZkdUURERIqJ5tW9+PnZ9vRrVQ2bDeasPcp9769n7+lks6OJlAkqlIgUkvxht9SbRERERKRMa1TFgzlXCibh9fMKJt/tOEWXt1cz9qtojqlgIiIiIoCrkz0TH2jMx4+3wLu8I/sTLtF7xnpmrj5MrtVmdjyRUk2FEpFCkJVjJfLAWQC61Pc1OY2IiIiIFAeNqnjw0eMt+GFUO7oE+eQVTLafosvUNbzw9U6On1PBRERERPKeJf06ugNdG/iSnWtj0rJY+s7eyInz6WZHEym1VCgRKQSbj54jLSuXSm5ONK7iYXYcERERESlGGlf14OMnWvL9yLbcFeRDrtXGN9tOctfba/j71zuJO6eHICIiImVdxfJOfPhYcyY/1ITyTvZsPXaB7tMj+SrqBDabepeI3GkqlIgUgoiYRADuqueDxWKYnEZEREREiqPgAE8+eaIlS0a2pVO9SuRabXy97SR3vb2al77ZpbdGRUREyjjDMHikRQC/PNeeloEVSMvK5cVvdjFs/jaSUjPNjidSqqhQInKH2Ww2ImKvzE9SX/OTiIiIiMifaxrgydxBrfhuRBs61q1EjtXGoqgTdJ6ympe/VcFERESkrAvwKseXw8J4uUcQDnYGK/Yl0H16JL/tSzA7mkipoUKJyB12MDGVE+cv42hvoV0db7PjiIiIiEgJ0axaBeY92Ypvh7ehfR1vcqw2vtyaVzAZ990uTl5QwURERKSssrMYPN2xFt+PbEc9XzeSUrMY8lkU477bRVpmjtnxREo8FUpE7rCrw261qVWRco72JqcRERERkZKmefUKzB8cyrfDw/ILJl9sySuY/GPxbk5dvGx2RBERETFJA393vh/VlqHta2AY8MWWE/R4Zy3bjp83O5pIiaZCicgdFhFzddgtX5OTiIiIiEhJ1ry6F/MHh/L102G0q+1Ndq6NhZvj6PTWKv65eDenVTAREREpk5wd7PjnPQ1YOKQ1VTxdiDufzsOzNjJ5WSwZ2blmxxMpkVQoEbmDzqdlsT3uAgB3BWl+EhERERG5fS0Dvfh8SChfPRVGm1oVyc61sWBzHJ3eWs0rS/ZwJlkFExERkbIorFZFfhndngeaVcFqgw9WH6brtEhWxSaaHU2kxFGhROQOWr0/EasN6ld2p4qni9lxRERERKQUaVXDi4VDW7NoWGvCalYkK9fK/E3H6Th5NeO/30N8cobZEUVERKSIuTs7MPWRpsx6tBl+7s7EnU9n0NytDPssSvObidwEFUpE7qCr85N0UW8SERERESkkoTUr8sWw1nwxtDWhNbzIyrXy2cbjdHhrFROW7iUhRQUTERGRsqZ7o8r89nxHhnWoib3FYPm+BMKnrmHGqkNk5VjNjidS7KlQInKHZOVYiTxwFoAu9VUoEREREZHCFVarIoueCmPh0FBaBXqRlWNl7oZjtJ+cVzBJVMFERESkTCnvZM8/etbn5+fa06qGFxnZVt76dT/d34lk/aEks+OJFGsqlIjcIVuPnedSZg7e5R0JruppdhwRERERKSPa1PJm0VOtWTgklJaBFQoUTP79gwomIiIiZU1dXzcWDWvN1EeC8S7vyJGzaQz4aDPPfLFDPU9FrkOFEpE75LeYBAA61/PBYjFMTiMiIiIiZYlhGLSp7c1XT4Xx+eBQmlevQGaOlU/X5xVMXv9xH4mX9GBERESkrDAMgweaVSXi+U480SYQiwE/7DzNXVNW89HaI2Tnajgukd9ToUTkDrDZbP+dn6S+r8lpRERERKSsMgyDdnW8+ebpMOYPbkWzap5k5lj5eN1ROkxexRs/7uPspUyzY4qIiEgR8XBxYMK9DVk6qh1NAzxJy8rljZ9i6PXeOrYeO292PJFiQ4USkTvg8NlU4s6n42hnoX0db7PjiIiIiEgZZxgG7etU4tvhbZj3ZCuaBniSkW3lo3VHaT95Jf/3cwxJqSqYiIiIlBWNqnjw3fA2vPlAYyqUcyA2/hIPz9rI81/t1D2BCCqUiNwRV3uTtK5VEVcne5PTiIiIiIjkMQyDjnUrsXhEG+YOaknwlYLJ7MgjtJ+0iok/x3BOD0dERETKBIvFoG+raqx8vhP9WgUA8O32k9w1ZTXzNx4j12ozOaGIeVQoEbkDrhZKwuv7mJxEREREROSPDMOgUz0floxow6eDWhJc1YPL2bl8GHmE9pNX8eYvsZxPyzI7poiIiBSBCq6OTHygCYtHtKGhvzspGTm88v1ees9YT/SJi2bHEzGFCiUit+lCWhZRx/PGdLwrSIUSERERESm+DMOgcz0floxsyydPtKBJVQ/Ss3KZteYw7SatZNIyFUxERETKipBqFVg6qh2v3dcQN2d7dp9K5v4P1jPuu91c0P2AlDEqlIjcpjUHzmK1QZCfG1UrlDM7joiIiIjIXzIMg7uCfPl+ZFs+frwFjaq4k56Vy8zVh2k/aSWTl8XqAYmIiEgZYGcxGBgWyMrnO/FAsyrYbPDFljjuens1i7bGYdVwXFJGqFAicpt+i0kAoIuG3RIRERGREsYwDLrU9+WHUe2YM7AFDf3dScvK5YPVh2k/eRVTft3PxXQVTEREREq7Sm5OTH2kKYuGtaaerxsX0rN56dvdPDRrA3tPJ5sdT6TQqVAichuyc62sOXAWgLuCfE1OIyIiIiJyawzD4O4Gvvz4TDtmP9acBpXdSc3M4f1Vh2g3aRVvL99Pcnq22TFFRESkkIXWrMiPz7bjnz3r4+pox/a4i/R6bx0Tlu4lJUP3AlJ6qVAichu2HjvPpYwcKro60jTA0+w4IiIiIiK3xTAMujb048dn2jHr0eYE+bmRmpnDeysP0W7SSqauOEDyZT0kERERKc0c7CwM7VCTiOc7cU+TylhtMHfDMbq8vYYlO05hs2k4Lil9VCgRuQ0RMYkAdKrng53FMDmNiIiIiMidYbEYdG/kx8/PtmfWo80I8nPjUmYO70YcpN2klUz/7QAZ2blmxxQREZFC5OfhzIz+zfh8cCg1vV05eymT0Yui6TdnEwcTLpkdT+SOUqFE5DasjM0rlIRrfhIRERERKYXyCiaV+fnZ9swc0Ix6vm5cyshh+m8H6fnOWqKOnTc7ooiIiBSydnW8+WV0e/7erR7ODhY2HTlPj3fWMvHnGNIyc8yOJ3JH3HShJDIykl69euHv749hGCxZsuRP269bt462bdtSsWJFXFxcCAoKYtq0aQXaTJgwAcMwCixBQUE3G02kSB0+m8rRpDQc7Aza161kdhwRERERkUJjsRj0aFyZX55rz/v9Q/B1d+JIUhoPf7iR137Yx+Us9S4REREpzZzs7RjZuTYrxnTk7ga+5FhtfBh5hPCpa/h59xkNxyUl3k0XStLS0ggODmbGjBk31N7V1ZVRo0YRGRlJTEwM//rXv/jXv/7F7NmzC7Rr2LAhZ86cyV/WrVt3s9FEitTKK8Nuta5ZkfJO9ianEREREREpfBaLwd+a+LN8TEceaVEVmw0+WX+UHu9EsuWoepeIiIiUdgFe5ZgzsAWfPNGCAC8XziRnMGLBdgZ+soWjSWlmxxO5ZTf9dLdHjx706NHjhtuHhIQQEhKS/3NgYCDfffcda9euZdiwYf8NYm+Pn5/fzcYRMc1vMQkAdAnSsFsiIiIiUrZ4uDgw+aFgejauzLjvdnPsXDp9Zm/k8bBAXuxej3KOepFIRESkNLsryJc2tbz5YPVhZq0+zNqDSXSbFsnTHWsyonNtnB3szI4oclOKfI6SHTt2sGHDBjp27Fjg84MHD+Lv70/NmjUZMGAAcXFx191HZmYmKSkpBRaRopScnk3U8QsAdKnva3IaERERERFzdKrnw69jOtC3ZQA2G8zdcIzu09ey6cg5s6OJiIhIIXN2sGPs3XVZPqYDHetWIivXyrsrD3H3tDVEXHnBWKSkKLJCSdWqVXFycqJFixaMHDmSIUOG5K8LDQ1l7ty5LFu2jJkzZ3L06FHat2/PpUuXrrmviRMn4uHhkb8EBAQU1WGIALD6QCK5Vht1fcsT4FXO7DgiIiIiIqZxd3bgzQebMO/JVvh7OBN3Pp2+szcx/vs9muBVRESkDAj0dmXuoJbMerQZlT2cOXH+MoPnRTFkXhQnzqebHU/khhRZoWTt2rVERUUxa9Yspk+fzhdffJG/rkePHjz88MM0adKEbt268fPPP3Px4kW++uqra+5r3LhxJCcn5y8nTpwoqsMQASDiyvwk6k0iIiIiIpKnY91K/DqmA/1aVQPgs43H6TY9kg2HkkxOJiIiIoXNMAy6N6rMb2M78lTHmthbDH6LSeDuaWt4f+VBMnNyzY4o8qeKrFBSo0YNGjduzNChQxkzZgwTJky4bltPT0/q1q3LoUOHrrneyckJd3f3AotIUcnJtbJ6/5VCieYnERERERHJ5+bswMQHGvP54FCqeLpw8sJl+n+0mX8t2U2qepdIMRQZGUmvXr3w9/fHMAyWLFlSYL3NZmP8+PFUrlwZFxcXwsPDOXjwYIE258+fZ8CAAbi7u+Pp6cngwYNJTU0t0GbXrl20b98eZ2dnAgICmDx58h+yfP311wQFBeHs7Ezjxo35+eef7/jxiogUNlcne8b1qM8vz7WndU0vMrKtTFl+gB7T17L24Fmz44lcV5HPUQJgtVrJzMy87vrU1FQOHz5M5cqVizCVyI2JOn6BlIwcKpRzIKRaBbPjiIiIiIgUO+3qePPrmA482jqvd8nnm+LoNi2S9epdIsVMWloawcHBzJgx45rrJ0+ezLvvvsusWbPYvHkzrq6udOvWjYyMjPw2AwYMYO/evaxYsYIff/yRyMhIhg0blr8+JSWFrl27Ur16dbZt28Zbb73FhAkTmD17dn6bDRs20K9fPwYPHsyOHTvo3bs3vXv3Zs+ePYV38CIihaiOrxtfDG3NO32bUsnNiSNJaTz28RZGLtjOmeTLZscT+QPDZrPZbmaD1NTU/J4eISEhTJ06lc6dO+Pl5UW1atUYN24cp06d4rPPPgNgxowZVKtWjaCgICDvbY0xY8bw7LPP8sYbbwDwwgsv0KtXL6pXr87p06d59dVXiY6OZt++fVSqVOkvM6WkpODh4UFycrJ6l0ih+89P+5iz9igPhFRhap+mZscREbkuXR+Lls63iMi1bTiUxIvf7uLkhbyHIv1DqzGuRxBuzg4mJ5PCVtKujYZhsHjxYnr37g3k9Sbx9/fn+eef54UXXgAgOTkZX19f5s6dS9++fYmJiaFBgwZs3bqVFi1aALBs2TJ69uzJyZMn8ff3Z+bMmfzzn/8kPj4eR0dHAF5++WWWLFlCbGwsAH369CEtLY0ff/wxP0/r1q1p2rQps2bNuqH8Je18i0jZkZKRzbQVB5i34RhWG5RztGN0eB0Gta2Bg50p7/FLGXEz18ab/pMYFRVFSEgIISEhAIwdO5aQkBDGjx8PwJkzZ4iLi8tvb7VaGTduHE2bNqVFixbMmDGDSZMm8dprr+W3OXnyJP369aNevXo88sgjVKxYkU2bNt1QkUSkqEXEan4SEREREZEb1aa2N7+O7sDAsOoALNyc17sk8oCG35Di7ejRo8THxxMeHp7/mYeHB6GhoWzcuBGAjRs34unpmV8kAQgPD8disbB58+b8Nh06dMgvkgB069aN/fv3c+HChfw2v/+eq22ufs+1ZGZmkpKSUmARESmO3J0deLVXQ358pj3Nq1cgPSuX//s5lnveXcvmI+fMjicCgP3NbtCpUyf+rBPK3LlzC/z8zDPP8Mwzz/zpPr/88subjSFiiqNJaRw5m4a9xaBDXW+z44iIiIiIlAiuTva8dl8jejSqzEvf7iLufDoDP9lC35YB/OOe+rird4kUQ/Hx8QD4+hZ8Sc7X1zd/XXx8PD4+BeeutLe3x8vLq0CbGjVq/GEfV9dVqFCB+Pj4P/2ea5k4cSL//ve/b+HIRETM0cDfna+fCuOb7Sd585dYDiSk0mf2Ju4PqcK4nkH4uDmbHVHKMPVtErkJETEJAITW9NJQASIiIiIiNymsVkWWjW7PE20CAfhy6wm6TYtk9f5Ec4OJlEDjxo0jOTk5fzlx4oTZkURE/pLFYvBIiwBWPt+R/qHVMAxYvOMUXaasYd6GY+Rab2qWCJE7RoUSkZsQEXNl2K0gDbslIiIiInIryjnaM+Hehnz1VBiBFctxJjmDJz7dyt+/3kny5Wyz44nk8/PzAyAhIaHA5wkJCfnr/Pz8SEwsWOjLycnh/PnzBdpcax+//47rtbm6/lqcnJxwd3cvsIiIlBSe5Rz5v/sbs2REWxpX8eBSZg6vLt3Lve+vY3vcBbPjSRmkQonIDUq+nM3WY+cB6FLf5y9ai4iIiIjIn2lVw4tfnuvA4HY1MAz4ettJuk5bw8rYhL/eWKQI1KhRAz8/PyIiIvI/S0lJYfPmzYSFhQEQFhbGxYsX2bZtW36blStXYrVaCQ0NzW8TGRlJdvZ/C4ErVqygXr16VKhQIb/N77/napur3yMiUloFB3iyZGRbXu/dCHdne/aeTuGBDzbw8re7uJCWZXY8KUNUKBG5QZEHzpJjtVHbpzzVK7qaHUdEREREpMRzcbTjlb814Ounwqjh7UpCSiZPzo3i+a92kpyu3iVS+FJTU4mOjiY6OhrIm8A9OjqauLg4DMNg9OjRvPHGGyxdupTdu3czcOBA/P396d27NwD169ene/fuDB06lC1btrB+/XpGjRpF37598ff3B6B///44OjoyePBg9u7dy6JFi3jnnXcYO3Zsfo7nnnuOZcuW8fbbbxMbG8uECROIiopi1KhRRX1KRESKnJ3F4LHW1Vn5Qiceal4VyBues/Pbq/liSxxWDcclRUCFEpEbdHV+EvUmERERERG5s1oEevHLc+0Z2j6vd8m3209y97Q1/LZPvUukcEVFRRESEkJISAgAY8eOJSQkhPHjxwPw4osv8swzzzBs2DBatmxJamoqy5Ytw9n5vxMOL1iwgKCgILp06ULPnj1p164ds2fPzl/v4eHB8uXLOXr0KM2bN+f5559n/PjxDBs2LL9NmzZtWLhwIbNnzyY4OJhvvvmGJUuW0KhRoyI6EyIi5vMu78SUh4P5+ukwgvzcuJiezbjvdvPAzA3sOZVsdjwp5QybzVbiS3IpKSl4eHiQnJysMTmlUOTkWmnxn9+4mJ7NV0+F0aqGl9mRRET+kq6PRUvnW0Tkzth2/AJ//2YnR86mAXB/SBVe7dUAz3KOJieTm6VrY9HS+RaR0iQn18q8jceZtuIAqZk5WAx4tHV1nu9aDw8XB7PjSQlxM9dG9SgRuQHb4y5yMT0bz3IONKvmaXYcEREREZFSq3n1Cvz8bHue6lgTiwGLd5wifGokv+6NNzuaiIiIFBF7OwuD29Ug4vmO3Bvsj9UGn208Tpe3V/PttpOUgnf/pZhRoUTkBlwddqtT3UrY2+nXRkRERESkMDk72DGuR32+Hd6G2j7lSUrN5Kn523j2ix2c18SuIiIiZYavuzPv9gth4ZBQalVyJSk1i+e/3kmf2ZvYH3/J7HhSiuiJ73VsO36eN3+JVXVSAIiITQSgS31fk5OIiIiIiJQdIdUq8OMz7RjRqRYWA5buPE3XaWtYtueM2dFERESkCLWp7c0vz3Xgpe5BuDjYseXoeXq+u5b//LSP1Mwcs+NJKaBCyTWcT8vi8U+2MmvNYRVLhOPn0jiUmIq9xaBD3UpmxxERERERKVOcHex4sXsQi0e0pa5veZJSs3j68+2MWridc6mZZscTERGRIuJob2F4p1r89nxHujf0I9dqY87ao3R5ezU/7jqtZ7hyW1QouQYvV0f+0bM+AB9GHuGdiIMmJxIzRcTk9SZpGeilyaJEREREREwSHODJD8+0Y1Tn2thZDH7cdYau0yL5aZd6l4iIiJQlVTxdmPVYcz4d1JLqFcuRkJLJqIU7eOzjLRw+m2p2PCmhVCi5jv6h1Rj/twYATP/tILPWHDY5kZglIjZvfpIu9X1MTiIiIlfNmDGDwMBAnJ2dCQ0NZcuWLddtu3fvXh588EECAwMxDIPp06dfs92pU6d49NFHqVixIi4uLjRu3JioqKhCOgIREbkVTvZ2vNCtHktGtCXIz41zaVmMXLidEQu2kaTeJSIiImVK53o+/Dq6A2PC6+Job2HdoSS6T49k8rJYLmflmh1PShgVSv7Ek+1q8GL3egC8+Uss8zYcMzeQFLmUjGw2HzkPaH4SEZHiYtGiRYwdO5ZXX32V7du3ExwcTLdu3UhMTLxm+/T0dGrWrMmbb76Jn5/fNdtcuHCBtm3b4uDgwC+//MK+fft4++23qVChQmEeioiI3KLGVT1YOqodz95VG3uLwc+747l76hp+2KlhN0RERMoSZwc7nguvw4oxHehcrxLZuTY+WH2Y8KlrWL43XvcFcsMMWyn405KSkoKHhwfJycm4u7vf8f1PXb6fd1ceAmDSg43p07LaHf8OKZ5+2nWGkQu3U7OSKyuf72R2HBGRm1LY10ezhIaG0rJlS95//30ArFYrAQEBPPPMM7z88st/um1gYCCjR49m9OjRBT5/+eWXWb9+PWvXrr3lXKX1fIuIFHd7TiXz9292EXMmBYBuDX15vXcjfNycTU4mujYWLZ1vESnrbDYby/cl8NoP+zh18TIAdwX5MKFXQ6pVLGdyOjHDzVwb1aPkBoy5uy5D29cA4OXvdvN99CmTE0lRiYjJG3YrXL1JRESKhaysLLZt20Z4eHj+ZxaLhfDwcDZu3HjL+126dCktWrTg4YcfxsfHh5CQEObMmfOn22RmZpKSklJgERGRoteoigffj2zL6PA62FsMft2bQNdpkXwffUpvkYqIiJQhhmHQraEfK8Z2YESnWjjYGayMTeTuaWt457eDZGRrOC65PhVKboBhGPyjZ30ebV0Nmw3GfrWTZXs0YWBpl2u1sWp/3jAudwVpfhIRkeIgKSmJ3NxcfH0LFrB9fX2Jj4+/5f0eOXKEmTNnUqdOHX799VeGDx/Os88+y7x58667zcSJE/Hw8MhfAgICbvn7RUTk9jjaWxgdXpelo9rRoLI7F9Ozee7LaIbN30ZiSobZ8URERKQIlXO058XuQfzyXAfa1q5IZo6Vab8doNv0SFbvv/aQzSIqlNwgwzB47d5GPNS8KrlWG898sYNVsfrFKs12xF3gQno27s72tKiuMepFREozq9VKs2bN+L//+z9CQkIYNmwYQ4cOZdasWdfdZty4cSQnJ+cvJ06cKMLEIiJyLQ383fl+VFuev7suDnYGK/YlcPe0SL7bflK9S0RERMqY2j7l+XxwKO/1C8HX3Ynj59J54tOtPD1/W/7QXCJXqVByEywWg0kPNuFvTSqTnWvjqc+3sf5QktmxpJD8FpNXCOtUzwd7O/2qiIgUB97e3tjZ2ZGQkFDg84SEhOtO1H4jKleuTIMGDQp8Vr9+feLi4q67jZOTE+7u7gUWERExn4OdhWe61OGHZ9rRqIo7yZezGfvVTobMiyJBvUtERETKFMMw6BXsT8TznRjSrgZ2FoNle+MJf3sNM1cfJivHanZEKSb09Pcm2VkMpvVpyt0NfMnKsTJkXhRRx86bHUsKwcrYvIdwXepr2C0RkeLC0dGR5s2bExERkf+Z1WolIiKCsLCwW95v27Zt2b9/f4HPDhw4QPXq1W95nyIiYq4gP3cWj2jL37vVw9HOQkRsIndPXcM329S7REREpKwp72TPv/7WgJ+ebUerQC8uZ+cyaVksPd9dy4bDehFeVCi5JQ52Ft7vH0KHupW4nJ3LE59uZeeJi2bHkjvoxPl0DiSkYmcx6FRXhRIRkeJk7NixzJkzh3nz5hETE8Pw4cNJS0tj0KBBAAwcOJBx48blt8/KyiI6Opro6GiysrI4deoU0dHRHDp0KL/NmDFj2LRpE//3f//HoUOHWLhwIbNnz2bkyJFFfnwiInLnONhZGNm5Nj8+247gqh6kZOTwwtc7GTR3K2eSNeSGiIhIWRPk586ip1rz9sPBeJd35FBiKv3nbObZL3ZoXrMyToWSW+Rkb8eHjzandU0vUjNzGPjJFmLOpJgdS+6QiJi83iQtqlfAo5yDyWlEROT3+vTpw5QpUxg/fjxNmzYlOjqaZcuW5U/wHhcXx5kzZ/Lbnz59mpCQEEJCQjhz5gxTpkwhJCSEIUOG5Ldp2bIlixcv5osvvqBRo0a8/vrrTJ8+nQEDBhT58YmIyJ1X19eNb4e34aXuQTjaWVi9/yxdp0by1dYT6l0iIiJSxhiGwYPNqxLxfCcGhlXHYsDSnae56+01fLzuKDm5Go6rLDJspeCuMCUlBQ8PD5KTk4t8fPDUzBwGfryZ7XEXqejqyKKnwqjtU75IM8id99jHm1l7MIl/9qzP0A41zY4jInJLzLw+lkU63yIiJcPBhEv8/ZtdRF8ZFaBD3Uq8+UBj/D1dzA1WCunaWLR0vkVEbs2eU8n8c8me/BGDgvzceKN3I1oEepkbTG7bzVwb1aPkNpV3sufTQa1oVMWdc2lZDPhoE8fPpZkdS27DpYxsNh05B8Bdmp9ERERERKRUqXOld8k/egbhaG8h8sBZuk6L5IstcepdIiIiUgY1quLB4uFtmPhAYzzLORAbf4mHZm3kha93kpSaaXY8KSIqlNwBHi4OzH8ylHq+biSkZNJ/zmZOXdR4tyXVuoNJZOfaqOHtSq1K6h0kIiIiIlLa2FkMhnWoxc/PtqdZNU9SM3MY991uBn6yhZMX0s2OJyIiIkXMYjHo16oaK5/vRN+WAQB8s+0kd01ZzfxNx8m16mWK0k6Fkjukgqsj84e0oqa3K6cuXmbAnE2aAKiE+i0mEYAuQepNIiIiIiJSmtX2Kc/XT7fhX/fUx8newtqDSXSbFsmCzcfVu0RERKQM8nJ15M0Hm/DdiDY09HcnJSOHV5bs4f4P1ucPzSWlkwold5CPmzMLhoYS4OXCsXPpDPhoM+fUPatEybXaWL0/r1CiYbdEREREREo/O4vBkPY1+eW59rSoXoG0rFz+uXgPAz7azInz6l0iIiJSFjWrVoGlo9rx73sb4uZsz66TyfT+YD3/WLybi+lZZseTQqBCyR1W2cOFhUNa4+fuzMHEVB77eAvJ6dlmx5IbFH3iIufSsnBztqelJmwSERERESkzalYqz6Knwhj/twY4O1jYcPgc3aZHMn/jMawabkNERKTMsbMYPN4mkJXPd+KBkCrYbLBwcxx3vb2Gr7ae0P1BKaNCSSEI8CrHwqGheJd3Yt+ZFAZ+uoVLGSqWlAQRMQkAdKxbCQc7/XqIiIiIiJQldhaDJ9vVYNlzHWgV6EV6Vi6vfL+XJ+Zu1WSuIiIiZVQlNyem9mnKomGtqetbnvNpWbz47S4emrWBvaeTzY4nd4ieBBeSmpXKs2BIKBXKObDzxEUGz43iclau2bHkL6yMzRt2K7y+r8lJRERERETELIHernw5rDUTejXAyd5C5IGz9HhnLesOJpkdTUREREwSWrMiPz3bnn/2rI+rox3b4y7S6711TFi6lxS9JF/iqVBSiOr5ufHZk6G4Odmz5dh5hs2PIiNbxZLi6uSFdGLjL2ExoFO9SmbHERERERERE1ksBk+0rcHSUe2o61ues5cyeeyTzUxeFkt2rtXseCIiImICBzsLQzvUJOL5TtzTpDJWG8zdcIwub69hyY5T2GwajqukUqGkkDWu6sHcJ1tSztGOtQeTGLlgO1k5uqkujq72JmlR3QvPco4mpxERERERkeKgnp8b349sR79W1bDZ4IPVh+nz4UZN9C4iIlKG+Xk4M6N/Mz4fHEpNb1fOXspk9KJo+s3ZxMGES2bHk1ugQkkRaF7di48eb4GTvYWI2ETGLIomR28gFTu/xeQVSrrU9zE5iYiIiIiIFCcujnZMfKAxM/o3w83Znu1xF+n57lp+3n3G7GgiIiJionZ1vPlldHv+3q0ezg4WNh05T4931jLx5xjSMnPMjic3QYWSItKmljcfPtYcBzuDn3af4cVvd2G1qitWcZGamcOmw+cAFUpEREREROTa7mlSmZ+fbU/TAE8uZeQwYsF2/rF4t4ZYFhERKcOc7O0Y2bk2K8Z05O4GvuRYbXwYeYTwqWv4efcZDcdVQqhQUoQ61fPh/f7NsLMYfLf9FP/6fo9+UYqJdQeTyMq1Ur1iOWpVKm92HBERERERKaYCvMrx9dNhDO9UC4CFm+O47/31HNAwGyIiImVagFc55gxswSdPtCDAy4UzyRmMWLCdgZ9s4WhSmtnx5C+oUFLEujX0Y1qfphhG3g316z/GqFhSDETEJADQJcgXwzBMTiMiIiIiIsWZg52Fl7oHMX9wK7zLO7E/4RL3vr+OL7fE6d93IiIiZdxdQb6sGNORZ7vUwdHewtqDSXSbFsnby/dzOUu9UIsrFUpMcG+wP5MebALAJ+uP8vbyAyYnKtusVhur9mt+EhERERERuTnt61Til+fa076ONxnZVl7+bjejvthBSka22dFERETERM4Odoy9uy7LR3egY91KZOVaeW/lIe6etobf9iWYHU+uQYUSkzzSIoDX72sIwPurDvH+yoMmJyq7dp68SFJqFm5O9rQM9DI7joiIiIiIlCCV3JyYN6gVL/cIwt5i8NOuM/R8Zy074i6YHU1ERERMFujtytxBLZn1aDP8PZw5eeEyQz6LYsi8rZw4n252PPkdFUpM9FhYIP/sWR+AKcsP8NHaIyYnKpsiYvJ6k3SoVwlHe/1KiIiIiIjIzbFYDJ7uWIuvnw6jagUXTl64zMOzNjJrzWGsVg3FJSIiUpYZhkH3RpX57fmOPN2xFvYWg99iEgmfuob3Ig6SmaPhuIoDPRU22dAONRl7d10A3vgphs83HTc5UdkTEXtl2K0gDbslIiIiIiK3LqRaBX56tj33NKlMjtXGm7/E8vinWzh7KdPsaCIiImKyco72vNwjiGWj2xNWsyKZOVbeXnGA7tPXEnngrNnxyjwVSoqBZ+6qzfBOtQD415I9fLPtpMmJyo5TFy8TcyYFiwGd6qlQIiIiIiIit8fDxYH3+4Xw5gONcXbIm8C1xztrWXtQD0BEREQEavu4sXBoKO/0bYqPmxNHk9IY+MkWRizYxpnky2bHK7NUKCkGDMPgxW71eKJNIAAvfrOTH3edNjdUGbHySm+SZtUq4OXqaHIaEREREREpDQzDoG+raiwd1Y66vuVJSs3ksY+38OYvsWTnWs2OJyIiIiYzDIP7mlYh4vmOPNm2BnYWg593x9Pl7TV8uOaw7hdMoEJJMWEYBq/2akC/VgFYbTD6y2hW7EswO1apFxGTd4671Pc1OYmIiIiIiJQ2dX3dWDqqHQNCqwEwa81hHvlwoyZvFREREQDcnB0Y36sBP4xqR4vqFUjPymXiL7H0fGctm46cMztemaJCSTFiGAZv9G7M/SFVyLHaGLlgu8anK0TpWTlsOJz3F054fQ27JSIiIiIid56zgx3/ub8xHwxohpuzPTviLtLz3bX8tOuM2dHkBl26dInRo0dTvXp1XFxcaNOmDVu3bs1fb7PZGD9+PJUrV8bFxYXw8HAOHjxYYB/nz59nwIABuLu74+npyeDBg0lNTS3QZteuXbRv3x5nZ2cCAgKYPHlykRyfiIiYr4G/O189FcZbDzWhoqsjBxNT6Tt7E6O/3EHipQyz45UJKpQUM3YWg7ceakKPRn5k5VoZNj9K1cNCsu5gElk5VgK8XKjtU97sOCIiIiIiUor1bFyZn59tT7NqnlzKyGHkwu2M+243l7NyzY4mf2HIkCGsWLGC+fPns3v3brp27Up4eDinTp0CYPLkybz77rvMmjWLzZs34+rqSrdu3cjI+O+DrQEDBrB3715WrFjBjz/+SGRkJMOGDctfn5KSQteuXalevTrbtm3jrbfeYsKECcyePbvIj1dERMxhsRg83CKAlc934tHW1TAMWBJ9mi5T1jBtxQHOpWaaHbFUM2w2m83sELcrJSUFDw8PkpOTcXd3NzvOHZGVY+Xpz7exMjYRV0c75g8JpVm1CmbHKlVe+mYXi6JO8ESbQCbc29DsOCIid1xpvD4WZzrfIiJyI7JzrUxbcYCZaw5js0Fd3/K8378ZdX3dzI52x5WGa+Ply5dxc3Pj+++/55577sn/vHnz5vTo0YPXX38df39/nn/+eV544QUAkpOT8fX1Ze7cufTt25eYmBgaNGjA1q1badGiBQDLli2jZ8+enDx5En9/f2bOnMk///lP4uPjcXTMmz/z5ZdfZsmSJcTGxt5Q1tJwvkVE5L92nbzIK0v2sPNkMgBO9hYeblGVIe1qEujtanK6kuFmro3qUVJMOdpb+GBAM9rV9iYtK5fHP9nCnlPJZscqNaxWGyv3503k3kXDbomIiIiISBFxsLPwYvcg5j8Zind5Jw4kpNLrvXUs2HycUvAeY6mTk5NDbm4uzs7OBT53cXFh3bp1HD16lPj4eMLDw/PXeXh4EBoaysaNGwHYuHEjnp6e+UUSgPDwcCwWC5s3b85v06FDh/wiCUC3bt3Yv38/Fy5cuGa2zMxMUlJSCiwiIlJ6NKnqyXcj2vJ+/xCaVPUgM8fK55vi6Pz2ap6ev43tcde+PsitUaGkGHN2sGP2wOa0DKzApYwcHvt4MwcSLpkdq1TYfSqZs5cyKe9kT2iNimbHERERERGRMqZdHW9+ea49HepWIjPHyj8X72Hkwu0kX842O5r8jpubG2FhYbz++uucPn2a3NxcPv/8czZu3MiZM2eIj48HwNfXt8B2vr6++evi4+Px8Sn4gp69vT1eXl4F2lxrH1fXXcvEiRPx8PDIXwICAm7/gEVEpFixsxj8rYk/349syxdDW9O5XiVsNli2N54HPtjAw7M2sGJfAlarXra4XSqUFHPlHO355ImWBFf14EJ6Nv3nbObI2dS/3lD+VERMAgAd6nrjaK9fAxERERERKXqV3JyY+0RL/tEzCHuLwc+74+n5zlq9IVrMzJ8/H5vNRpUqVXBycuLdd9+lX79+WCzm/lty3LhxJCcn5y8nTpwwNY+IiBQewzAIq1WRTwe1YvmYDjzcvCoOdgZbj11g6GdRhE9bwxdb4sjI1txnt0pPiEsAN2cH5j3ZivqV3UlKzWTAR5s5cT7d7FglWkRs3rBbdwX5/kVLERERERGRwmOxGAzrUItvhrchwMuFUxcv8/CsjcxcfVhvhxYTtWrVYs2aNaSmpnLixAm2bNlCdnY2NWvWxM/PD4CEhIQC2yQkJOSv8/PzIzExscD6nJwczp8/X6DNtfZxdd21ODk54e7uXmAREZHSr66vG289HMy6l+7i6Y61cHO258jZNMZ9t5t2k1bx/sqDXEzPMjtmiaNCSQnhWc6R+YNbUdunPGeSM+j/0SbikzPMjlUinUm+zN7TKRgGdK5Xyew4IiIiIiIiNA3w5Kdn2/O3JpXJtdqYtCyWxz/dQuIl/buvuHB1daVy5cpcuHCBX3/9lfvuu48aNWrg5+dHREREfruUlBQ2b95MWFgYAGFhYVy8eJFt27blt1m5ciVWq5XQ0ND8NpGRkWRn/3fotRUrVlCvXj0qVKhQREcoIiIlia+7My/3CGLjuC786576+Hs4k5SayZTlBwibuJIJS/fqZfuboEJJCeJd3okFQ0KpXrEcJ85fpv9Hmzh7KdPsWCXOyiu9SUICPKlY3snkNCIiIiIiInncnR14r18Ikx5sjLODhbUHk+j5zlrWHDhrdrQy7ddff2XZsmUcPXqUFStW0LlzZ4KCghg0aBCGYTB69GjeeOMNli5dyu7duxk4cCD+/v707t0bgPr169O9e3eGDh3Kli1bWL9+PaNGjaJv3774+/sD0L9/fxwdHRk8eDB79+5l0aJFvPPOO4wdO9bEIxcRkZKgvJM9Q9rXZM2LnZnepyn1K7tzOTuXuRuO0fGtVYxauJ3dJ5PNjlnsqVBSwvi6O7NgSChVPF04cjaNxz7ezIU0daW6GRExeYWSLvU17JaIiIiIiBQvhmHQp2U1fhjVjiA/N5JSs3j8ky1M/CWG7Fyr2fHKpOTkZEaOHElQUBADBw6kXbt2/Prrrzg4OADw4osv8swzzzBs2DBatmxJamoqy5Ytw9nZOX8fCxYsICgoiC5dutCzZ0/atWvH7Nmz89d7eHiwfPlyjh49SvPmzXn++ecZP348w4YNK/LjFRGRksnBzkLvkCr8/Gw7Ph8cSvs63lht8OOuM/R6fx39Zm9i1f5EbDYN7Xkthq0UnJmUlBQ8PDxITk4uM2NyHktK45EPN5J4KZPGVTxYMDQUd2cHs2MVe5ezcmn62nIyc6z8OroD9fzczI4kIlJoyuL10Uw63yIicqdlZOfyxk/7+HxTHADBAZ681zeEahXLmZzsxujaWLR0vkVE5H/tO53CnLVH+GHnaXKuzH1W17c8Q9vX5L6mVXC0L939KG7m2li6z0QpFujtysKhoVR0dWT3qWQGfbqVtMwcs2MVW+dSM/lu+0me+WI7mTlWqni6UNe3vNmxRERERERErsvZwY43ejdm5oBmuDvbs/PERe55dy0/7DxtdjQREREpARr4uzOtT1MiX+zM0PY1KO9kz4GEVP7+zS7aT17JzNWHSb6c/dc7KgPUo6SE23c6hb6zN5KSkUNYzYp8Oqglzg52ZscyndVqY/epZFbtT2TV/rPsOnmR3/9JH9W5Ni90q2deQBGRIlCWr49m0PkWEZHCdPJCOs99Gc224xcA6NsygFd7NcTFsfj++0/XxqKl8y0iIn8l+XI2X2yJ49P1R0lIyZv7uryTPX1bBvBkuxr4e7qYnPDOuplrowolpUD0iYs8+tFmUjNz6Fi3ErMHNsfJvvjeLBeW5PRsIg+eZdX+RNbsP8u5/5m7paG/O53qVaJzPR+aV6+AYRgmJRURKRpl/fpY1HS+RUSksOXkWpn+20FmrD6EzQZ1fMrzXv8QgvyK53VH18aipfMtIiI3KivHyvfRp5iz9ggHElIBsLcY9Ar2Z2j7mjTwLx3XERVKyqCtx84z8OMtXM7OpVtDX97v3wwHu9I9sprNZiPmzCVW7U9k9f5Eth2/gPV3f5rLO9nTvo43nev50LFeJXzdna+/MxGRUkjXx6Kl8y0iIkVl/aEkRi+K5uylTJzsLbzytwYMCK1W7F4G07WxaOl8i4jIzbLZbKw+cJbZa46w8ci5/M/b1/FmWIeatKvtXezuL26GCiVl1LqDSTw5bytZOVbuDfZnWp+m2FlK7h/ka0nNzGHdwSRW709k1f7E/C5iV9X1LU/nej50utJrpLRPSCQi8md0fSxaOt8iIlKUklIzef6rnaw5cBaAHo38ePOBJniUczA52X/p2li0dL5FROR27D6ZzOy1R/hp1+n8l9EbVHZnWIea3NOkcol8KV+FkjIsIiaBp+ZvI8dq4+HmVZn0YBMsJbhYYrPZOHw2lVWxeUNqbT12nuzc//6RdXGwo23tinSq50OnepWoWqGciWlFRIoXXR+Lls63iIgUNavVxsfrjjJpWSw5VhtVPF14t18IzatXMDsaoGtjUdP5FhGRO+HE+XQ+XneURVtPcDk7FwB/D2eebFeDvq2qUd7J3uSEN06FkjLu591nGLVwO1YbDAyrzr/vbViiukilZ+Ww8fA5Vu/PK46cvHC5wPoa3q75c420quGlyetFRK5D18eipfMtIiJm2XniIs98sYO48+nYWQzG3l2XpzvWMn2EAV0bi5bOt4iI3EkX07P4fNNx5m44TlJq3qg+bs72DAitzqC2gSVimgMVSoTFO04y9qud2GwwrENNxvUIKtbFkmNJaazan8iq/WfZdOQcWTnW/HWO9hZa16xI53qV6FTPhxreriYmFREpOXR9LFo63yIiYqZLGdn8Y/Eefth5GoC2tSsy7ZGm+Jj4EEPXxqKl8y0iIoUhIzuXJTtOMXvtEY6cTQPAwc6gd9MqDO1Qk7q+biYnvD4VSgSAhZvj+Mfi3QA816UOY+6ua3Ki/8rIzmXL0fNXJmI/y9GktALrq3i60Dkor9dIWK2KlHMsOV26RESKC10fi5bOt4iImM1ms/F11EleXbqXy9m5VHR15O1HgulUz8eUPLo2Fi2dbxERKUxWq42I2ERmRx5m67EL+Z93rleJYR1q0bqmV7F7Uf9mro16+lyK9Q+tRkZ2Lq/9uI93Ig7i7GDH8E61TMtz8kI6q/efZfX+RNYfOpc/xh2AvcWgZaBXfnGktk/5YveLJSIiIiIiUpwZhsEjLQNoVt2TUQt3EBt/iSc+3cqwDjV5oWs9HO1L3iSsIiIiUjxYLAZ3N/Dl7ga+bI+7wJzIIyzbG8+q/WdZtf8sTap6MLR9TXo08sO+BE78rh4lZcAHqw8xedl+ACb0asATbWsUyfdm51qJOnaB1fsTWbU/kQMJqQXW+7g50bmeD52DKtG2tjduzg5FkktEpKzQ9bFo6XyLiEhxkpGdy39+imH+puMABFf14L1+zahWsVyRZdC1sWjpfIuISFE7lpTGR+uO8HXUSTKvTKUQ4OXC4LY1eKRlgOmjBN3MtfGmSzuRkZH06tULf39/DMNgyZIlf9p+3bp1tG3blooVK+Li4kJQUBDTpk37Q7sZM2YQGBiIs7MzoaGhbNmy5WajyXWM6FSbZ++qDcCEH/bx5Za4QvuuhJQMvtp6guGfb6PZayvoN2cTH0Ye4UBCKhYDWgZW4O/d6vHzs+3Z/I8uTHqoCd0bVVaRRERERERE5A5ydrDj9d6NmPVoc9yd7dl5Mpme765l6ZU5TERERERuV6C3K2/0bsyGl+9idHgdvFwdOXH+MhN+2EebN1fy9vL9nL2UaXbMG3LTJZ20tDSCg4N58skneeCBB/6yvaurK6NGjaJJkya4urqybt06nnrqKVxdXRk2bBgAixYtYuzYscyaNYvQ0FCmT59Ot27d2L9/Pz4+5oylWtqMubsul7NzmbP2KOMW78bZwY7eIVVue7+5VhvRJy6wKvYsq/Ynsvd0SoH1FV0d6Vgvbzit9nW88SzneNvfKSIiIiIiIjemeyM/GlVxZ/SX0UQdv8CzX+xg/cEkXr23gelveYqIiEjpULG8E6PD6/JUh1p8s/0kH609wvFz6by38hAfRh7hwWZVGNK+JrUqlTc76nXd1tBbhmGwePFievfufVPbPfDAA7i6ujJ//nwAQkNDadmyJe+//z4AVquVgIAAnnnmGV5++eW/3J+6l94Ym83GK9/v4fNNcdhZDN7vF0KPxpVvej/nUjOJPHiWVbFniTx4lovp2fnrDAOaVPWk85XiSOMqHlgsmmtERMQMuj4WLZ1vEREpznJyrbwTcZD3Vx3CZoNalVx5v38z6lcuvGuWro1FS+dbRESKi1yrjeV74/kw8gjRJy4Cec+Nw+v78lSHmrQI9CqSHMV6MvcdO3awYcMG3njjDQCysrLYtm0b48aNy29jsVgIDw9n48aN19xHZmYmmZn/7bKTkpJyzXZSkGEYvHZvIzKyrXyz7STPfrmDDx0s3BXk+6fbWa02dp9KZvX+vF4jO09e5PflNQ8XBzrUrUTnepXoULcS3uWdCvlIRERERERE5GbY21l4vms9wmpWZPSiaA6fTeO+Get55Z76PNq6OoahF9xERETkzrCzGPRoXJnujfyIOn6BD9cc4beYBFbsy1uaVfNkWIea3N3AD7ti8pJ9kRVKqlatytmzZ8nJyWHChAkMGTIEgKSkJHJzc/H1Lfiw3tfXl9jY2Gvua+LEifz73/8u9MylkcViMOnBJmRk5/LjrjM8/fl2Pn2iJW1rexdol5yenddrZH8ikQfOkpSaVWB9g8rudA7K6zXSNMATe7ubnu5GREREREREilib2t788lx7nv96J6v3n+WV7/ey7lASkx8MxqOc5o4UERGRO8cwDFoGetEy0ItDial8tPYI320/xfa4izz9+XYCK5ZjSPuaPNS8Ks4OdqZmLbJCydq1a0lNTWXTpk28/PLL1K5dm379+t3SvsaNG8fYsWPzf05JSSEgIOBORS317CwG0/o0JTPHyop9CQyZF8W8J1tR3smeVfsTWb0/ke1xF8m1/rfbSHkne9rV9qZzUCU61vXBz8PZxCMQERERERGRW1WxvBOfPN6ST9YfZdKyWH7dm8CeU2t5p2/TIhsKQ0RERMqW2j7lefPBJoztWpfPNhxn/qbjHDuXzr+W7GHqigMMDKvOwLBAvFzNmeO6yAolNWrUAKBx48YkJCQwYcIE+vXrh7e3N3Z2diQkJBRon5CQgJ+f3zX35eTkhJOThne6HQ52Ft7vH8LQz7YReeAsj3z4x2HO6vqWp3M9HzrWq0SL6l442qvXiIiIiIiISGlgsRgMaV+TVjW8eOaLHRw/l06f2ZsYE16H4Z1qF5thMERERKR08XFz5oVu9RjeqRZfR53go3VHOXnhMtN/O8isNYd5uHkAr/ytQZE/izblybfVas2fY8TR0ZHmzZsTERFRYH1ERARhYWFmxCsznOzt+PDR5rSumffGkIuDHeH1fXijdyPWvdSZ5WM6Mq5nfdrU8laRREREREREpBRqUtWTH59px31N/cm12li9/yy2309KKSIiIlIIXJ3seaJtDVa/0In3+oXQuIoHGdlWYs6kmPIs+qZ7lKSmpnLo0KH8n48ePUp0dDReXl5Uq1aNcePGcerUKT777DMAZsyYQbVq1QgKCgIgMjKSKVOm8Oyzz+bvY+zYsTz++OO0aNGCVq1aMX36dNLS0hg0aNDtHp/8BRdHO+YPDmXf6RTq+bmZPhaciIiIiIiIFC03Zwem92lK+zqVCKtVUXNQioiISJGxt7PQK9ifvzWpzMYj53Ay6YX9my6UREVF0blz5/yfr84V8vjjjzN37lzOnDlDXFxc/nqr1cq4ceM4evQo9vb21KpVi0mTJvHUU0/lt+nTpw9nz55l/PjxxMfH07RpU5YtW/aHCd6lcDjYWQgO8DQ7hoiIiIiIiJjEMAweal7V7BgiIiJSRhmGQZta3uZ9v60U9KlNSUnBw8OD5ORk3N3dzY4jIiJSLOj6WLR0vkVERArStbFo6XyLiIgUdDPXRvWnFRERERERERERERGRMkuFEhERERERERERERERKbNUKBERERERERERERERkTJLhRIRERERERERERERESmzVCgREREREREREREREZEyS4USEREREREREREREREps1QoERERkRJnxowZBAYG4uzsTGhoKFu2bLlu27179/Lggw8SGBiIYRhMnz79T/f95ptvYhgGo0ePvrOhRURERERERKRYUqFERERESpRFixYxduxYXn31VbZv305wcDDdunUjMTHxmu3T09OpWbMmb775Jn5+fn+6761bt/Lhhx/SpEmTwoguIiIiIiIiIsWQCiUiIiJSokydOpWhQ4cyaNAgGjRowKxZsyhXrhyffPLJNdu3bNmSt956i759++Lk5HTd/aampjJgwADmzJlDhQoVCiu+iIiIiIiIiBQzKpSIiIhIiZGVlcW2bdsIDw/P/8xisRAeHs7GjRtva98jR47knnvuKbDvP5OZmUlKSkqBRURERERERERKHhVKREREpMRISkoiNzcXX1/fAp/7+voSHx9/y/v98ssv2b59OxMnTrzhbSZOnIiHh0f+EhAQcMvfLyIiIiIiIiLmUaFEREREyrQTJ07w3HPPsWDBApydnW94u3HjxpGcnJy/nDhxohBTioiIiIiIiEhhsTc7gIiIiMiN8vb2xs7OjoSEhAKfJyQk/OVE7dezbds2EhMTadasWf5nubm5REZG8v7775OZmYmdnd0ftnNycvrTOU9EREREREREpGQoFYUSm80GoLHBRUREfufqdfHqdbI0cHR0pHnz5kRERNC7d28ArFYrERERjBo16pb22aVLF3bv3l3gs0GDBhEUFMRLL710zSLJteh+REREpKDSeC9SnOleREREpKCbuRcpFYWSS5cuAWhscBERkWu4dOkSHh4eZse4Y8aOHcvjjz9OixYtaNWqFdOnTyctLY1BgwYBMHDgQKpUqZI/30hWVhb79u3L//+nTp0iOjqa8uXLU7t2bdzc3GjUqFGB73B1daVixYp/+PzP6H5ERETk2krbvUhxpXsRERGRa7uRe5FSUSjx9/fnxIkTuLm5YRjGHdlnSkoKAQEBnDhxAnd39zuyz+KqLB0rlK3j1bGWTjrW0utOH6/NZuPSpUv4+/vfgXTFR58+fTh79izjx48nPj6epk2bsmzZsvwJ3uPi4rBY/jsN2+nTpwkJCcn/ecqUKUyZMoWOHTuyevXqO5brTt+PlKU//2XpWKFsHa+OtXTSsZZeuhcp2XQvcnvK0vHqWEsnHWvpVZaO18x7kVJRKLFYLFStWrVQ9u3u7l7q/wBeVZaOFcrW8epYSycda+l1J4+3tL69OWrUqOsOtfW/xY/AwMCbHvLjVgoohXU/Upb+/JelY4Wydbw61tJJx1p66V6kZNK9yJ1Rlo5Xx1o66VhLr7J0vGbci1j+uomIiIiIiIiIiIiIiEjppEKJiIiIiIiIiIiIiIiUWSqUXIeTkxOvvvoqTk5OZkcpdGXpWKFsHa+OtXTSsZZeZe145c+VpT8PZelYoWwdr461dNKxll5l7Xjlz5W1Pw9l6Xh1rKWTjrX0KkvHa+axGrabHbRbRERERERERERERESklFCPEhERERERERERERERKbNUKBERERERERERERERkTJLhRIRERERERERERERESmzVCgREREREREREREREZEyS4WS/zFz5kyaNGmCu7s77u7uhIWF8csvv5gdq0i8+eabGIbB6NGjzY5yx02YMAHDMAosQUFBZscqNKdOneLRRx+lYsWKuLi40LhxY6KiosyOVSgCAwP/8N/WMAxGjhxpdrQ7Ljc3l1deeYUaNWrg4uJCrVq1eP3117HZbGZHKxSXLl1i9OjRVK9eHRcXF9q0acPWrVvNjnXbIiMj6dWrF/7+/hiGwZIlSwqst9lsjB8/nsqVK+Pi4kJ4eDgHDx40J6yYQvciuhcpDXQvonuR0kD3IroXKat0L6J7kdJA9yK6FykNSuu9CBTP+xEVSv5H1apVefPNN9m2bRtRUVHcdddd3Hfffezdu9fsaIVq69atfPjhhzRp0sTsKIWmYcOGnDlzJn9Zt26d2ZEKxYULF2jbti0ODg788ssv7Nu3j7fffpsKFSqYHa1QbN26tcB/1xUrVgDw8MMPm5zszps0aRIzZ87k/fffJyYmhkmTJjF58mTee+89s6MViiFDhrBixQrmz5/P7t276dq1K+Hh4Zw6dcrsaLclLS2N4OBgZsyYcc31kydP5t1332XWrFls3rwZ1/9v7+5Da/77OI6//M7pbJrFj9gdO8zN5j6zWnOTMsVaIuWupXH4b3EQuUtL7ooo/kBDlLuRclsalq2UEI74B0MII2W2kbudz++Pq8t1ncvvun5X/c53H+f7fT5qf/iu9Co6Peu9c5aSookTJ+rz58/tvBS20CK0SKKjRWgRt6BFaBGvokVokURHi9AibuHWFpF+0R4x+Eu///672bdvn+0ZjmlpaTH9+/c3ly5dMuPGjTPhcNj2pLirrKw0w4cPtz2jXaxYscKMGTPG9gxrwuGw6du3r4lGo7anxF1paakJhUIxz6ZNm2bKysosLXLOp0+fjM/nM+fPn495np+fb9asWWNpVfxJMqdOnfrx52g0atLT083WrVt/PGtqajJJSUnm2LFjFhbiV0GLJD5axDtoEXegRWgRxKJFEh8t4h20iDt4pUWM+XV6hHeU/A9tbW2qrq7Wx48fVVRUZHuOYyoqKlRaWqoJEybYnuKoR48eKTMzUzk5OSorK9Pz589tT3LE2bNnVVBQoOnTp6tHjx4aMWKE9u7da3tWu/j69asOHz6sUCikDh062J4Td6NGjVJtba0ePnwoSbp7966uXr2qkpISy8vi7/v372pra1NycnLM844dO7r2p54k6enTp2psbIx5Pe7cubMKCwt17do1i8tgCy3iLrSI+9Ei7kGL0CL4B1rEXWgR96NF3MOrLSLZ6xG/Y39zArt3756KoTfT4AAAB4FJREFUior0+fNnderUSadOndKgQYNsz3JEdXW1bt++7ZrPt/tvCgsLdfDgQeXm5ur169dat26dxo4dq/v37ys1NdX2vLh68uSJdu/eraVLl2r16tW6efOmFi1apEAgoPLyctvzHHX69Gk1NTVp7ty5tqc4YuXKlWpublZeXp58Pp/a2tq0ceNGlZWV2Z4Wd6mpqSoqKtL69es1cOBApaWl6dixY7p27Zr69etne55jGhsbJUlpaWkxz9PS0n58D95Ai7gPLUKLuAEtQovAO2gR96FFaBE3oEXc3yKSvR7hUPIncnNzFYlE9OHDB508eVLl5eWqr693XRS8ePFC4XBYly5d+uk66Tb/flkeNmyYCgsLFQwGdeLECc2fP9/isviLRqMqKCjQpk2bJEkjRozQ/fv3tWfPHtcHwf79+1VSUqLMzEzbUxxx4sQJHTlyREePHtXgwYMViUS0ePFiZWZmuvLf9tChQwqFQsrKypLP51N+fr5mz56tW7du2Z4GOI4WcR9ahBZxA1qEFoF30CLuQ4vQIm5Ai9AiTuKjt/5EIBBQv379NHLkSG3evFnDhw/Xjh07bM+Ku1u3bunt27fKz8+X3++X3+9XfX29du7cKb/fr7a2NtsTHdOlSxcNGDBADQ0NtqfEXUZGxk/xOnDgQNe+pfafnj17psuXL2vBggW2pzhm+fLlWrlypWbNmqWhQ4dqzpw5WrJkiTZv3mx7miP69u2r+vp6tba26sWLF7px44a+ffumnJwc29Mck56eLkl68+ZNzPM3b978+B68gRahRRIZLUKLuAUt8i+0iPfQIrRIIqNFaBG38GKLSPZ6hEPJ/yEajerLly+2Z8RdcXGx7t27p0gk8uOroKBAZWVlikQi8vl8tic6prW1VY8fP1ZGRobtKXE3evRoPXjwIObZw4cPFQwGLS1qHwcOHFCPHj1UWlpqe4pjPn36pN9+i33Z9vl8ikajlha1j5SUFGVkZOj9+/eqqanRlClTbE9yTJ8+fZSenq7a2tofz5qbm3X9+nVXfyY0/hot4j60iPvQIu5Fi9AioEXciBZxH1rEvbzUIpK9HuGjt/7DqlWrVFJSouzsbLW0tOjo0aOqq6tTTU2N7Wlxl5qaqiFDhsQ8S0lJUbdu3X56nuiWLVumyZMnKxgM6tWrV6qsrJTP59Ps2bNtT4u7JUuWaNSoUdq0aZNmzJihGzduqKqqSlVVVbanOSYajerAgQMqLy+X3+/el7XJkydr48aNys7O1uDBg3Xnzh1t375doVDI9jRH1NTUyBij3NxcNTQ0aPny5crLy9O8efNsT/tbWltbY35q6+nTp4pEIuratauys7O1ePFibdiwQf3791efPn20du1aZWZmaurUqfZGo13RIrRIoqNFaBG3oEVoEa+iRWiRREeL0CJu4dYWkX7RHjGIEQqFTDAYNIFAwHTv3t0UFxebixcv2p7VbsaNG2fC4bDtGXE3c+ZMk5GRYQKBgMnKyjIzZ840DQ0Ntmc55ty5c2bIkCEmKSnJ5OXlmaqqKtuTHFVTU2MkmQcPHtie4qjm5mYTDodNdna2SU5ONjk5OWbNmjXmy5cvtqc54vjx4yYnJ8cEAgGTnp5uKioqTFNTk+1Zf9uVK1eMpJ++ysvLjTHGRKNRs3btWpOWlmaSkpJMcXGx6/9vIxYtQou4AS3iTrQILQJvoEVoETegRdyJFnFHixjza/ZIB2OMce4MAwAAAAAAAAAA8Ovid5QAAAAAAAAAAADP4lACAAAAAAAAAAA8i0MJAAAAAAAAAADwLA4lAAAAAAAAAADAsziUAAAAAAAAAAAAz+JQAgAAAAAAAAAAPItDCQAAAAAAAAAA8CwOJQAAAAAAAAAAwLM4lAAAAAAAAAAAAM/iUAIgLubOnaupU6fGPDt58qSSk5O1bds2O6MAAIBn0CIAAMAmWgRIbH7bAwC40759+1RRUaE9e/Zo3rx5tucAAACPoUUAAIBNtAiQWHhHCYC427JlixYuXKjq6mpiAAAAtDtaBAAA2ESLAImHd5QAiKsVK1Zo165dOn/+vIqLi23PAQAAHkOLAAAAm2gRIDFxKAEQNxcuXNCZM2dUW1ur8ePH254DAAA8hhYBAAA20SJA4uKjtwDEzbBhw9S7d29VVlaqtbXV9hwAAOAxtAgAALCJFgESF4cSAHGTlZWluro6vXz5UpMmTVJLS4vtSQAAwENoEQAAYBMtAiQuDiUA4ioYDKq+vl6NjY1EAQAAaHe0CAAAsIkWARIThxIAcderVy/V1dXp7du3mjhxopqbm21PAgAAHkKLAAAAm2gRIPFwKAHgiJ49e6qurk7v3r0jCgAAQLujRQAAgE20CJBYOhhjjO0RAAAAAAAAAAAANvCOEgAAAAAAAAAA4FkcSgAAAAAAAAAAgGdxKAEAAAAAAAAAAJ7FoQQAAAAAAAAAAHgWhxIAAAAAAAAAAOBZHEoAAAAAAAAAAIBncSgBAAAAAAAAAACexaEEAAAAAAAAAAB4FocSAAAAAAAAAADgWRxKAAAAAAAAAACAZ3EoAQAAAAAAAAAAnvUHnyUBZdvU0HwAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 2000x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plotting the results\n",
"results = pd.DataFrame(results)\n",
"results.columns = [\"K\", \"Davies Bouldin Score\", \"Silhouette Score\", \"Calinski Harabasz Score\"]\n",
"#Line Plot Side by Side\n",
"fig, ax = plt.subplots(1,3, figsize=(20,5))\n",
"results.plot(x=\"K\", y=\"Davies Bouldin Score\", ax=ax[0], title=\"Davies Bouldin Score\")\n",
"results.plot(x=\"K\", y=\"Silhouette Score\", ax=ax[1], title=\"Silhouette Score\")\n",
"results.plot(x=\"K\", y=\"Calinski Harabasz Score\", ax=ax[2], title=\"Calinski Harabasz Score\")\n",
"plt.show()\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Davies Bouldin Score for K = 3 is 1.3409206073280913\n",
"Silhouette Score for K = 3 is 0.25755485130223976\n",
"Calinski Harabasz Score for K = 3 is 12492.580262713936\n",
"Davies Bouldin Score for K = 4 is 1.4638899365341889\n",
"Silhouette Score for K = 4 is 0.16489911117714118\n",
"Calinski Harabasz Score for K = 4 is 11568.421509523148\n",
"Davies Bouldin Score for K = 5 is 1.551184373838468\n",
"Silhouette Score for K = 5 is 0.1348584794345276\n",
"Calinski Harabasz Score for K = 5 is 10195.337141348047\n",
"Davies Bouldin Score for K = 6 is 1.6205041675327776\n",
"Silhouette Score for K = 6 is 0.1345520302988419\n",
"Calinski Harabasz Score for K = 6 is 9533.167288245813\n",
"Davies Bouldin Score for K = 7 is 1.6409814769591031\n",
"Silhouette Score for K = 7 is 0.10422100268976041\n",
"Calinski Harabasz Score for K = 7 is 8621.051295574664\n",
"Davies Bouldin Score for K = 8 is 1.712678849075701\n",
"Silhouette Score for K = 8 is 0.09833536962172866\n",
"Calinski Harabasz Score for K = 8 is 7911.256075982144\n",
"Davies Bouldin Score for K = 9 is 1.8029530186201133\n",
"Silhouette Score for K = 9 is 0.09026212662632332\n",
"Calinski Harabasz Score for K = 9 is 7381.9979518133\n",
"Davies Bouldin Score for K = 10 is 1.8202863009109849\n",
"Silhouette Score for K = 10 is 0.08603212018264303\n",
"Calinski Harabasz Score for K = 10 is 6957.6894338690145\n"
]
}
],
"source": [
"# Trying the same as before just with a diffrent Clustering Algorithm\n",
"# Agglomerative Clustering\n",
"\n",
"# Fitting Agglomerative Clustering In a loop an saving the results\n",
"results = []\n",
"for i in params[\"n_clusters\"]:\n",
" agg = AgglomerativeClustering(n_clusters=i)\n",
" agg.fit(temp_df)\n",
" davies = davies_bouldin_score(temp_df, agg.labels_)\n",
" silehouette = silhouette_score(temp_df, agg.labels_)\n",
" calinski = calinski_harabasz_score(temp_df, agg.labels_)\n",
" print(\"Davies Bouldin Score for K = {} is {}\".format(i, davies))\n",
" print(\"Silhouette Score for K = {} is {}\".format(i, silehouette))\n",
" print(\"Calinski Harabasz Score for K = {} is {}\".format(i, calinski))\n",
" results.append([i, davies, silehouette, calinski])\n"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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QH330UaG/k18bh5+fn3B0dFRZ16JFC5V7an737ty2htfbPIQQwsPDQzRs2FD5edy4ccLQ0FClveRNr7fXRERECBcXF+Ho6CgePnyoUi6/NoL8FNQO8voyatQoZfmsrCyRnp6uso+XL18KS0tLMXjw4DzXIb/7c1H3oaOjI54+fapcHxQUJACIL774QrmusG1SRkZGKufzLq9evRINGzYUNjY2IiIi4q1lc+s3enp6on379uL777/P05YkhBAbNmwQAMSPP/6YZ1tuPSi3PWj27Nkq23v27ClkMplKOw8AoaamJkJCQlTKDhkyRFhbW4uYmBiV9X379hVGRkb5XjOi/HA4LKrU9PX1kZSUpPz8+twgaWlpiImJUQ6llNutEAD69OmD6OholeEedu3aBYVCgT59+hR4vD179kChUKB3796IiYlRLlZWVqhVq5ay22RuT49Dhw4hNTW1yOelra2Nw4cP4/Dhwzh06BB+/vln6Ovro0OHDrhz546y3D///AMvLy+VoZr09fUxfPhwPHz4UDkEw3/xzz//QF1dXflWCADI5XKVyVvfxcfHR+VNkPr168PQ0BAPHjx46/csLS1x7do1jBgxAi9fvsTq1avx8ccfw8LCArNmzVL2zrh69SrCw8Mxfvz4PD0xct+0iIiIQHBwMPz9/VXehqhfvz7atm2Lf/75J8/xR4wYofK5sL8/ERFRftq2bYvAwEB06dIF165dw/z58+Hn54eqVau+99BI+vr6KvOIaWpqwsvLS+Ue+88//8DKygr9+vVTrtPQ0MDYsWORnJyc7xChbxMXF4djx46hd+/eSEpKUt4PY2Nj4efnh7t37+LZs2fvdT5ATi+O5s2bw8TEROV+6+Pjg+zsbJw6deqt39+4cSOWL18OBwcH7N27F19++SXq1q2LNm3aqMS1e/duuLm55dsTI7f+UNRr16NHD1SpUkX5uaSvFRERlR+JiYkAAAMDg0J/5/U2joSEBMTExKBFixZ48ODBew+1/eZzbvPmzVXqDcbGxkhJScHhw4ffua+nT5+iRYsWyMzMxKlTp5Q9Mt/H6+0gby5vksvlyjk9FAoF4uLikJWVBU9PT5W2n1xv3p/fZx9du3ZF1apVlZ+9vLzQuHFjlbaEwrZJGRsbIygoCM+fPy/UtRk5ciRu3LiB3bt3K4dKL8i3336LrVu3wsPDA4cOHcLXX3+Nhg0bokGDBipDge/evRvm5ub5tu28Xg+Sy+UYO3asyvb//e9/EELgwIEDKutbtGgBZ2dn5WchBHbv3o3OnTtDCKFSr/Pz80NCQkK+15ooP0yCUKWWnJysUoGIi4vDuHHjYGlpCR0dHVSpUgUODg4AoFJBaNeuHYyMjLB9+3bluu3bt8Pd3R21a9cu8Hh3796FEAK1atVClSpVVJawsDDlJFMODg6YMGEC1q1bB3Nzc/j5+WHFihWFrqTI5XL4+PjAx8cHvr6+GD58OI4cOYKEhARMnTpVWe7Ro0eoU6dOnu/nDjfx6NGjQh3vbR49egRra2vo6+urrM/vuAWpXr16nnUmJiZ4+fLlO79rbW2NVatWISIiArdv38bSpUtRpUoVTJ8+HevXrwcA5XiVbxtaJPdaFHS9YmJikJKSorI+9/+dXIX9/YmIiArSqFEj7NmzBy9fvsSFCxcwdepUJCUloWfPnu/18kK1atXyjLX95j320aNHqFWrFtTUVB8d3re+cO/ePQghMG3atDz3wxkzZgD4bxNv3r17FwcPHsyzbx8fn0LtW01NDaNGjcLly5cRExODP//8E+3bt8exY8fQt29fZbn79++/c1iyol67N+sOJX2tiIio/DA0NAQAlRc53+Xs2bPw8fFRzmlZpUoVfPXVVwDwXkkQbW3tPMmAN+sNI0eORO3atdG+fXtUq1YNgwcPLnBOz08//RTR0dE4efKkSoLgfbzeDvLmkp/Nmzejfv360NbWhpmZGapUqYK///473+vy5v35ffZRq1atPOtq166tMvdqYduk5s+fj5s3b8LW1hZeXl6YOXNmgS+J/vzzz9i4cSOWLVuW73yp+enXrx9Onz6Nly9fIiAgAB9//DGuXr2Kzp07Iy0tDUBOPahOnTpvnbj+0aNHsLGxyZO4K2w96MWLF4iPj8eaNWvy1IMGDRoEgPUgKjzOCUKV1tOnT5GQkICaNWsq1/Xu3Rvnzp3DxIkT4e7uDn19fSgUCrRr105lIk0tLS107doVe/fuxcqVKxEVFYWzZ8/ihx9+eOsxFQoFZDIZDhw4ALlcnmf764mCRYsWwd/fH3/++ScCAgIwduxYzJkzB+fPn0e1atWKfL7VqlVDnTp13vn2Y34KmojszcnPSkp+1wpAnom03kYmk6F27dqoXbs2OnbsiFq1amHLli0YOnRocYWZx+tvcQBF+/2JiIjeRlNTE40aNUKjRo1Qu3ZtDBo0CDt37lQ2jBdWcdxjcxW2vpBbp/ryyy/h5+eX73der58VlUKhQNu2bTFp0qR8t7/thZU3mZmZoUuXLujSpQtatmyJkydP4tGjR//pTdW3ya/uAJTctSIiovLD0NAQNjY2eSbTLsj9+/fRpk0bODk54ccff4StrS00NTXxzz//4KefflJp4yisguoNr7OwsEBwcDAOHTqEAwcO4MCBA9i4cSMGDBiAzZs3q5Tt3r07fvnlFyxZsgRz5swpcjzv67fffoO/vz+6du2KiRMnwsLCAnK5HHPmzFGZ1DvXm/fn99lHYRS2Tap3795o3rw59u7di4CAACxYsADz5s3Dnj17lHO6AcCFCxcwbtw4DB06FMOHDy9yPIaGhmjbti3atm0LDQ0NbN68GUFBQXnmxy0uBdWDPvnkkwLnea1fv36JxEIVD5MgVGnlThKV+0D58uVLHD16FN9++y2mT5+uLHf37t18v9+nTx9s3rwZR48eRVhYGIQQbx0KC4ByYigHB4dCPYC7urrC1dUV33zzDc6dO4dmzZph9erVmD17dmFPU0VWVhaSk5OVn+3s7HD79u085W7duqXcDuS82QEA8fHxKuUK8+annZ0djh49iuTkZJVG/vyOW1ocHR1hYmKCiIgIAFAOtXXz5s0C3xLJvRYFXS9zc3Po6em99bhF/f2JiIgKw9PTEwCU97XiZmdnh+vXr0OhUKj0aHjf+oKjoyOAnGGhCrrv5ioosfK2bTVq1EBycvI7911Unp6eOHnyJCIiImBnZ4caNWq8syGqsNeuIEW5VkREVPF16tQJa9asQWBgILy9vd9a9q+//kJ6ejr27dunMrpCaQzDrKmpic6dO6Nz585QKBQYOXIkfv75Z0ybNk0leT9mzBjUrFkT06dPh5GREaZMmVLisQE5w5k7Ojpiz549KvWJorxMUtR95Ne2dOfOHdjb2wMoepuUtbU1Ro4ciZEjRyI6OhoNGjTA999/r0yCvHjxAj179oS7uztWrFhR6PMqiKenJzZv3qzSjhIUFITMzExoaGjk+x07OzscOXIESUlJKr1BClsPqlKlCgwMDJCdnc16EP1nHA6LKqVjx45h1qxZcHBwQP/+/QH8/xsNb775uHjx4nz34ePjA1NTU2zfvh3bt2+Hl5dXgV0kc3Xv3h1yuRzffvttnuMIIRAbGwsgZ6zPrKwsle2urq5QU1NDenp6oc/zdXfu3MHt27fh5uamXNehQwdcuHABgYGBynUpKSlYs2YN7O3tlWMx5iYJXu9Fkp2djTVr1rzzuB06dEBWVhZWrVql8t1ly5a913kURVBQUJ4hqoCctyFiY2OVQ1s1aNAADg4OWLx4cZ6Gm9zfydraGu7u7ti8ebNKmZs3byIgIAAdOnR4ZzyF/f2JiIjyc/z48Xx7aOSOJV2UoSaLokOHDoiMjFQZBjQrKwvLli2Dvr6+8m1AOzs7yOXyPL1OV65cqfLZwsICLVu2xM8//5xv4ubFixfK/859weDN+3PutvzW9+7dG4GBgTh06FCebfHx8XnqWK+LjIzMd1ixjIwMHD16FGpqasrGmx49euDatWvYu3dvnvK5v1Nhr11BinKtiIio4ps0aRL09PQwdOhQREVF5dl+//59LFmyBED+bRwJCQnYuHFjicb45nOtmpqa8m39/Nozpk2bhi+//BJTp05VaTcoSfldm6CgIJW2keLexx9//KEyj9eFCxcQFBSkTFoUtk0qOzs7z3BbFhYWsLGxUV7f7Oxs9O3bFxkZGdi9e7dy7pJ3SU1NLTD+3Pk7cuubPXr0QExMDJYvX56n7Ov1oOzs7DxlfvrpJ8hkMpVeK/mRy+Xo0aMHdu/ene+LJ6wHUVGwJwhVeAcOHMCtW7eQlZWFqKgoHDt2DIcPH4adnR327dsHbW1tADnd/D788EPMnz8fmZmZqFq1KgICAhAeHp7vfjU0NNC9e3ds27YNKSkpWLhw4TtjqVGjBmbPno2pU6fi4cOH6Nq1KwwMDBAeHo69e/di+PDh+PLLL3Hs2DGMHj0avXr1Qu3atZGVlYVff/1VeQN4l6ysLPz2228AcroPPnz4EKtXr4ZCoVB5K2HKlCn4/fff0b59e4wdOxampqbYvHkzwsPDsXv3buUbiy4uLmjSpAmmTp2KuLg4mJqaYtu2bW9tRMjVuXNnNGvWDFOmTMHDhw/h7OyMPXv2vPckbEXx66+/YsuWLejWrRsaNmwITU1NhIWFYcOGDdDW1laOhaqmpoZVq1ahc+fOcHd3x6BBg2BtbY1bt24hJCRE2YiyYMECtG/fHt7e3hgyZAhevXqFZcuWwcjICDNnznxnPIX9/YmIiPIzZswYpKamolu3bnByckJGRgbOnTuH7du3w97eXjk2cnEbPnw4fv75Z/j7++Py5cuwt7fHrl27cPbsWSxevFj5Zp+RkRF69eqFZcuWQSaToUaNGti/f3++YzWvWLECH3zwAVxdXTFs2DA4OjoiKioKgYGBePr0Ka5duwYAcHd3h1wux7x585CQkAAtLS20bt0aFhYWaNiwIVatWoXZs2ejZs2asLCwQOvWrTFx4kTs27cPnTp1gr+/Pxo2bIiUlBTcuHEDu3btwsOHD2Fubp7vuT59+hReXl5o3bo12rRpAysrK0RHR+P333/HtWvXMH78eOV3J06ciF27dqFXr14YPHgwGjZsiLi4OOzbtw+rV6+Gm5tboa/d2xT2WhERUcVXo0YNbN26FX369EHdunUxYMAA1KtXT1kn2LlzJ/z9/QEAvr6+yh4Zn332GZKTk7F27VpYWFiUWO9RABg6dCji4uLQunVrVKtWDY8ePcKyZcvg7u6unAviTQsWLEBCQgJGjRoFAwMDfPLJJyUWH5DTo2bPnj3o1q0bOnbsiPDwcKxevRrOzs4qo2cU5z5q1qyJDz74AJ9//jnS09OxePFimJmZKYfvLGybVFJSEqpVq4aePXvCzc0N+vr6OHLkCC5evIhFixYBAFavXo1jx45hxIgReXr+WFpaom3btvmeU2pqKpo2bYomTZqgXbt2sLW1RXx8PP744w+cPn0aXbt2hYeHBwBgwIAB+OWXXzBhwgRcuHABzZs3R0pKCo4cOYKRI0fio48+QufOndGqVSt8/fXXePjwIdzc3BAQEIA///wT48ePV75w+zZz587F8ePH0bhxYwwbNgzOzs6Ii4vDlStXcOTIEcTFxb37xyICAEFUQW3cuFEAUC6amprCyspKtG3bVixZskQkJibm+c7Tp09Ft27dhLGxsTAyMhK9evUSz58/FwDEjBkz8pQ/fPiwACBkMpl48uRJnu0zZswQ+f2Z7d69W3zwwQdCT09P6OnpCScnJzFq1Chx+/ZtIYQQDx48EIMHDxY1atQQ2trawtTUVLRq1UocOXLknec9cOBAlfMGIAwNDUWbNm3y/f79+/dFz549hbGxsdDW1hZeXl5i//79+Zbz8fERWlpawtLSUnz11VfK8z9+/LjK8e3s7FS+GxsbKz799FNhaGgojIyMxKeffiquXr0qAIiNGze+9XoBEKNGjcoTj52dnRg4cOBbr8X169fFxIkTRYMGDYSpqalQV1cX1tbWolevXuLKlSt5yp85c0a0bdtWGBgYCD09PVG/fn2xbNkylTJHjhwRzZo1Ezo6OsLQ0FB07txZhIaGqpTJPY8XL17kG9e7fn8iIqL8HDhwQAwePFg4OTkJfX19oampKWrWrCnGjBkjoqKiVMq+eZ88fvx4nnt2ixYthIuLS57j5Hcvj4qKEoMGDRLm5uZCU1NTuLq6qtzDc7148UL06NFD6OrqChMTE/HZZ5+Jmzdv5rnnC5FTtxgwYICwsrISGhoaomrVqqJTp05i165dKuXWrl0rHB0dhVwuVzmHyMhI0bFjR2FgYCAAiBYtWii/k5SUJKZOnSpq1qwpNDU1hbm5uWjatKlYuHChyMjIKPAaJyYmiiVLlgg/Pz9RrVo1oaGhIQwMDIS3t7dYu3atUCgUKuVjY2PF6NGjRdWqVYWmpqaoVq2aGDhwoIiJiSnStQsPDxcAxIIFC/KNq7DXioiIKoc7d+6IYcOGCXt7e6GpqSkMDAxEs2bNxLJly0RaWpqy3L59+0T9+vWFtra2sLe3F/PmzRMbNmwQAER4eLiyXIsWLVTuo7n3pdfvVwMHDhR6enp5YnnzOX7Xrl3C19dXWFhYCE1NTVG9enXx2WefiYiICGWZ3PaaixcvKtdlZ2eLfv36CXV1dfHHH3/ku++CFBRbrjfbFRQKhfjhhx+EnZ2d0NLSEh4eHmL//v156kBvuz+/zz4WLVokbG1thZaWlmjevLm4du2ayj4L0yaVnp4uJk6cKNzc3JRtF25ubmLlypXK/eRet/yW13/nN2VmZoq1a9eKrl27Ks9LV1dXeHh4iAULFoj09HSV8qmpqeLrr78WDg4OQkNDQ1hZWYmePXuK+/fvK8skJSWJL774QtjY2AgNDQ1Rq1YtsWDBgjx1qoLafoTIqUuNGjVK2NraKo/Tpk0bsWbNmgLPhehNMiHeY9ZDIiIiIiIiIiIiIiKiMo5zghARERERERERERERUYXEJAgREREREREREREREVVITIIQEREREREREREREVGFxCQIERERERERERERERFVSEyCEBERERERERERERFRhcQkCBERERERERERERERVUjqUgdQGAqFAs+fP4eBgQFkMpnU4RAREZUJQggkJSXBxsYGamp8r6EksS5CRESUF+sipYd1ESIiorwKWxcpF0mQ58+fw9bWVuowiIiIyqQnT56gWrVqUodRobEuQkREVDDWRUoe6yJEREQFe1ddpFwkQQwMDADknIyhoaHE0RAREZUNiYmJsLW1Vd4nqeSwLkJERJQX6yKlh3URIiKivApbFykXSZDcrp6Ghoa82RMREb2BQyKUPNZFiIiICsa6SMljXYSIiKhg76qLcNBOIiIiIiIiIiIiIiKqkJgEISIiIiIiIiIiIiKiColJECIiIiIiIiIiIiIiqpDKxZwghSGEQFZWFrKzs6UOhUgycrkc6urqHJOXiIiIqALgMw5Jjc8XRESFx/s2UfErrrpIhUiCZGRkICIiAqmpqVKHQiQ5XV1dWFtbQ1NTU+pQiIiIiOg98RmHygo+XxARvRvv20QlpzjqIuU+CaJQKBAeHg65XA4bGxtoamryLRWqlIQQyMjIwIsXLxAeHo5atWpBTY0j3hERERGVN3zGobKAzxdERIXD+zZRySjOuki5T4JkZGRAoVDA1tYWurq6UodDJCkdHR1oaGjg0aNHyMjIgLa2ttQhEREREVER8RmHygo+XxARvRvv20Qlp7jqIhXmNQ6+kUKUg38LRERERBUD63VUFvD/QyKiwuG/l0Qlozj+tvjXSUREREREREREREREFRKTIEREREREREREREREVCExCVIJPHz4EDKZDMHBwVKH8p9t2rQJxsbGby0zc+ZMuLu7Kz/7+/uja9euJRoXEREREREVjkwmwx9//AEg77PKiRMnIJPJEB8fL1l8REREVHhvttW92S73X7Vs2RLjx48vcHt5afd7vf5DpY9JEIn4+/tDJpNBJpNBQ0MDlpaWaNu2LTZs2ACFQlGsx7K1tUVERATq1atXrPvNlfvgkrtoamqiZs2amD17NoQQJXLMoliyZAk2bdr0n/aRmpqKqVOnokaNGtDW1kaVKlXQokUL/Pnnn8UTJBERERFRBfDixQt8/vnnqF69OrS0tGBlZQU/Pz+cPXtWWSYiIgLt27eXMMrCya/RpTiTNOHh4fj4449hY2MDbW1tVKtWDR999BFu3br1n/dNRERUGJGRkRgzZgwcHR2hpaUFW1tbdO7cGUePHn3vfX755Zf/6ftF9a52v7fdu+3t7bF48eISi60sq2xtnepSB1CZtWvXDhs3bkR2djaioqJw8OBBjBs3Drt27cK+ffugrl48P49cLoeVlVWx7Ottjhw5AhcXF6Snp+PMmTMYOnQorK2tMWTIkBI/9tsYGRn9532MGDECQUFBWLZsGZydnREbG4tz584hNja2GCLMX0ZGBjQ1NUts/0RU+oQQkMlkUodBZUhWtgLqcr6TQkQVR48ePZCRkYHNmzfD0dERUVFROHr0qEq9uTSeTcq6zMxMtG3bFnXq1MGePXtgbW2Np0+f4sCBAyXaCyYzMxMaGholtn8qXxJeZcJIh/8/EFVWDx8+RLNmzWBsbIwFCxbA1dUVmZmZOHToEEaNGvXeSXl9fX3o6+sXc7QFK452v/eVnZ0NmUxWLBN3l7bK1tZZ/n6hdxBCIDUjS5KlqL0ect+Mqlq1Kho0aICvvvoKf/75Jw4cOKCSwfzxxx/h6uoKPT092NraYuTIkUhOTgYAJCYmQkdHBwcOHFDZ9969e2FgYIDU1NR8h8O6efMm2rdvD319fVhaWuLTTz9FTEyMcvuuXbvg6uoKHR0dmJmZwcfHBykpKW89HzMzM1hZWcHOzg79+/dHs2bNcOXKFeV2hUKB7777DtWqVYOWlhbc3d1x8OBB5fb8MrPBwcGQyWR4+PBhgcedO3cuLC0tYWBggCFDhiAtLU1l+5vd4lq2bImxY8di0qRJMDU1hZWVFWbOnPnWc9u3bx+++uordOjQAfb29mjYsCHGjBmDwYMHK8ukp6dj8uTJsLW1hZaWFmrWrIn169crt588eRJeXl7Q0tKCtbU1pkyZgqysLJW4Ro8ejfHjx8Pc3Bx+fn4A3v1bEVHZFZ2YhkMhkZh38BY+XnseLReeKBM95Eh6f1x9hrY/nsSK4/elDoWIygmpnnOKct+Kj4/H6dOnMW/ePLRq1Qp2dnbw8vLC1KlT0aVLF2W5wgwHcfnyZXh6ekJXVxdNmzbF7du3VbavWrUKNWrUgKamJurUqYNff/1VuS2/55/4+HjIZDKcOHFCue5t9Wx/f3+cPHkSS5YsUfZ4f/jwIVq1agUAMDExgUwmg7+/P4CcZ505c+bAwcEBOjo6cHNzw65duwo8v5CQENy/fx8rV65EkyZNYGdnh2bNmmH27Nlo0qSJstzTp0/Rr18/mJqaQk9PD56enggKCirUdci91qtWrUKXLl2gp6eH77//HgDw559/okGDBtDW1oajoyO+/fZblWcTqvjuRSehzaKTWHf6gdShEFU45aVtcuTIkZDJZLhw4QJ69OiB2rVrw8XFBRMmTMD58+eV5d7WLpmfgoapX7hwIaytrWFmZoZRo0YhMzNTWWblypWoVasWtLW1YWlpiZ49exa4/7///htGRkbYsmWLyv6Lw7vONXfor3379sHZ2RlaWlp4/PgxLl68iLZt28Lc3BxGRkZo0aKFSptortzesDo6OnB0dMxTV5g8eTJq164NXV1dODo6Ytq0aSrX6dq1a2jVqhUMDAxgaGiIhg0b4tKlSwBy2hVfH6nn9fpLfipbW2eF6wnyKjMbztMPSXLs0O/8oKv53y5p69at4ebmhj179mDo0KEAADU1NSxduhQODg548OABRo4ciUmTJmHlypUwNDREp06dsHXrVpUu5Vu2bEHXrl2hq6ub5xjx8fFo3bo1hg4dip9++gmvXr3C5MmT0bt3bxw7dgwRERHo168f5s+fj27duiEpKQmnT58u0j+kly5dwuXLlzFgwADluiVLlmDRokX4+eef4eHhgQ0bNqBLly4ICQlBrVq13ut67dixAzNnzsSKFSvwwQcf4Ndff8XSpUvh6Oj41u9t3rwZEyZMQFBQEAIDA+Hv749mzZqhbdu2+Za3srLCP//8g+7du8PAwCDfMgMGDEBgYCCWLl0KNzc3hIeHK/+Anz17hg4dOsDf3x+//PILbt26hWHDhkFbW1slAbN582Z8/vnnyuEC3vVbEVHZkZqRhRtPExD8JF65RCSk5Sn39OUr2Jrm/beZKpeMbAXuRicjIDQS43ze7x5IRJWLVM85RXnGyX3z848//kCTJk2gpaX13sf9+uuvsWjRIlSpUgUjRozA4MGDlXXkvXv3Yty4cVi8eDF8fHywf/9+DBo0CNWqVVMmKd7lXfXsJUuW4M6dO6hXrx6+++47AECVKlWwe/du9OjRA7dv34ahoSF0dHQAAHPmzMFvv/2G1atXo1atWjh16hQ++eQT5dASb6pSpQrU1NSwa9cujB8/HnK5PE+Z5ORktGjRAlWrVsW+fftgZWWFK1euKIdPLux1mDlzJubOnYvFixdDXV0dp0+fxoABA7B06VI0b94c9+/fx/DhwwEAM2bMKMKvROXZkbBoxCSnY/bfYVAIgeEf1pA6JKIKozy0TcbFxeHgwYP4/vvvoaenl2f763N8vK1dsrCOHz8Oa2trHD9+HPfu3UOfPn3g7u6OYcOG4dKlSxg7dix+/fVXNG3aFHFxcTh9+nS++9m6dStGjBiBrVu3olOnToU+fmEV5lxTU1Mxb948rFu3DmZmZrCwsMCDBw8wcOBALFu2DEIILFq0CB06dMDdu3dV2hGnTZuGuXPnYsmSJfj111/Rt29f3LhxA3Xr1gUAGBgYYNOmTbCxscGNGzcwbNgwGBgYYNKkSQCA/v37w8PDA6tWrYJcLkdwcLCyh+eePXuQkZGhPNaoUaMQEhICS0vLfM+1srV1VrgkSEXg5OSE69evKz+/Pg6tvb09Zs+ejREjRij/APv3749PP/0Uqamp0NXVRWJiIv7++2/s3bs33/0vX74cHh4e+OGHH5TrNmzYAFtbW9y5cwfJycnIyspC9+7dYWdnBwBwdXV9Z9xNmzaFmpoaMjIykJmZieHDh6skQRYuXIjJkyejb9++AIB58+bh+PHjWLx4MVasWFH4C/SaxYsXY8iQIcoht2bPno0jR47k6Q3ypvr16ysr+LVq1cLy5ctx9OjRApMga9asQf/+/WFmZgY3Nzd88MEH6NmzJ5o1awYAuHPnDnbs2IHDhw/Dx8cHAFQSMStXroStrS2WL18OmUwGJycnPH/+HJMnT8b06dOV3eZq1aqF+fPnK783e/bst/5WtWvXLuolI6JikK0QuBudhGv/JjuuPo7HnagkKN7IFctkQG0LA7jbGsO9ujHcbY1hbaQtTdBUpvjUtYSaDAh5nogncalMjBFRhaCuro5NmzZh2LBhWL16NRo0aIAWLVqgb9++qF+/fpH29f333yuTB1OmTEHHjh2RlpYGbW1tLFy4EP7+/hg5ciQAKN9YXbhwYaGTIO96JqpduzY0NTWhq6urMnyXqakpAMDCwkLZQJSeno4ffvgBR44cgbe3N4CcZ4EzZ87g559/zjcJUrVqVSxduhSTJk3Ct99+C09PT7Rq1Qr9+/dXPkds3boVL168wMWLF5XHrVmzpnIfhb0OH3/8MQYNGqT8PHjwYEyZMgUDBw5Uxjpr1ixMmjSJSZBK5LMPHfEqIxtLjt7FD//cQrYC+LwlEyFElcW9e/cghICTk9M7y76rXbIwTExMsHz5csjlcjg5OaFjx444evQohg0bhsePH0NPTw+dOnWCgYEB7Ozs4OHhkWcfK1aswNdff42//vor33vru1SrVi3PutTUVJXPhTnXzMxMrFy5Em5ubsp1rVu3VtnPmjVrYGxsjJMnT6oka3r16qV86X3WrFk4fPgwli1bptz/N998o3L8L7/8Etu2bVMmQR4/foyJEycqf7fXXyrPrSsAwE8//YRjx44hKChI+cLGmypbW2eFS4LoaMgR+p2fZMcuDm+OGX/kyBHMmTMHt27dQmJiIrKyspCWlqZMenTo0AEaGhrYt28f+vbti927d8PQ0FD5P+ibrl27huPHj+c7Pt/9+/fh6+uLNm3awNXVFX5+fvD19UXPnj1hYmLy1ri3b9+OunXrIjMzEzdv3sSYMWNgYmKCuXPnIjExEc+fP1f+IeVq1qwZrl279h5XKUdYWBhGjBihss7b2xvHjx9/6/fefAiztrZGdHR0geU//PBDPHjwAOfPn8e5c+dw9OhRLFmyBN9++y2mTZuG4OBgyOXyAv8RDgsLg7e3t8rv2qxZMyQnJ+Pp06eoXr06AKBhw4Yq33vXb8UkCFHpiExIQ/CTlwh+koDgJy9x42kCUjKy85SzMtSGu60x3GxzEh6u1Yygr1XhbrVUDEz1NNHI3hRB4XE4HBqFwR84SB0SEZVxUj3nFPUZp0ePHujYsSNOnz6N8+fP48CBA5g/fz7WrVunHDqqMF6vr1tbWwMAoqOjUb16dYSFhSl7LuRq1qwZlixZUuj9F2c9+969e0hNTc3zQlVGRka+jTi5Ro0ahQEDBuDEiRM4f/48du7ciR9++AH79u1D27ZtERwcDA8PD5VGjdcV9jp4enqqfL527RrOnj2rHBoLyBnT/PVnTKr4ZDIZvmhbG2oyGX46cgfzDt6CQgiMalXz3V8morcqD22TRRnt5V3tkoXh4uKi0uvR2toaN27cAAC0bdsWdnZ2cHR0RLt27dCuXTt069ZNZd+7du1CdHQ0zp49i0aNGhU69tedPn06T4+Hli1bFvlcNTU187QrRkVF4ZtvvsGJEycQHR2N7OxspKam4vHjxyrlcl+WeP3z68N3bt++HUuXLsX9+/eVL6kbGhoqt0+YMAFDhw7Fr7/+Ch8fH/Tq1Qs1aqgmsA8cOIApU6bgr7/+emt9prK1dVa4lhmZTPafh6SSWlhYGBwcchpDHj58iE6dOuHzzz/H999/D1NTU5w5cwZDhgxBRkYGdHV1oampiZ49e2Lr1q3o27cvtm7dij59+hQ4sXpycjI6d+6MefPm5dlmbW0NuVyOw4cP49y5cwgICMCyZcvw9ddfIygoSBlXfmxtbZVvJtWtWxf379/HtGnT3jnfRq7cDOHr/xC/Pu5dcXpzMkCZTKbsVv627zRv3hzNmzfH5MmTMXv2bHz33XeYPHlygVnVonqzC+K7fisiKn4p6Vm4/u+wVrk9PSIT8/Yu09OUw7WaEdxtTXJ6etgaw4q9PKgIfF2sEBQeh4DQSCZBiOidytNzjra2Ntq2bYu2bdti2rRpGDp0KGbMmFGkJMjr9fXch+t31ddzFea5ojjr2bljhf/999+oWrWqyrZ3DQlmYGCAzp07o3Pnzpg9ezb8/Pwwe/ZstG3btkSfMb799lt07949T1ltbdZlKptxPrWgLpdhwaHbWHDoNrKyBYfqJPqPysM9u1atWpDJZO+c/Lww7ZKF8bZ2OAMDA1y5cgUnTpxAQEAApk+fjpkzZ+LixYvKXpceHh64cuUKNmzYAE9PT5WG98JycHBQGeYLgErbaWHPVUdHJ8/xBw4ciNjYWCxZsgR2dnbQ0tKCt7e3yvBU7xIYGIj+/fvj22+/hZ+fH4yMjLBt2zYsWrRIWWbmzJn4+OOP8ffff+PAgQOYMWMGtm3bhm7dugEAQkND0bdvX8ydOxe+vr7vPGZlauss23+RldCxY8dw48YNfPHFFwByJgRUKBRYtGiRsjK/Y8eOPN/r378/2rZti5CQEBw7dgyzZ88u8BgNGjTA7t27YW9vX2CiRCaToVmzZmjWrBmmT58OOzs77N27FxMmTCj0ucjlcmRlZSEjIwOGhoawsbHB2bNnVTKIZ8+ehZeXF4CccXGBnEmCcnudvJ4NzU/dunURFBSkMuzW65M3lSRnZ2dlRtjV1RUKhQInT57MtwdO3bp1sXv3bpVePmfPnoWBgUG+3fFyFea3IqL3l/XvvAzBT+IR/Dge157mP6yVmgyobWkAj3+HtHK3NUFNC33I1Ype8SLK5etsiVn7Q3EhPA4vUzJgoqcpdUhERCXC2dn5nROhF0XdunVx9uxZ5XBOQE7d2tnZGYDqc0VuT4w3nysKU8/W1NREdnZ2nnUAVNa/PjHq+wzPkSt3KIlz584ByOkNs27dOsTFxeXbG+Rd16EgDRo0wO3bt1WG1qLKbVSrmlCTyTDv4C38dOQOsoXAFz613quRkYjKB1NTU/j5+WHFihUYO3Zsnobq+Ph4GBsbF7pd8r9SV1eHj48PfHx8MGPGDBgbG+PYsWPKhH2NGjWwaNEitGzZEnK5HMuXLy/2GP7LuZ49exYrV65Ehw4dAABPnjzJd6Lv8+fP52nDzK2rnDt3DnZ2dvj666+V2x89epRnH7Vr10bt2rXxxRdfoF+/fti4cSO6deuGmJgYdO7cGT169FC2KxdVRW7rZKuqhNLT0xEZGYns7GxERUXh4MGDmDNnDjp16qT8g6hZsyYyMzOxbNkydO7cGWfPnsXq1avz7OvDDz+ElZUV+vfvDwcHBzRu3LjA444aNQpr165Fv379MGnSJJiamuLevXvYtm0b1q1bh0uXLuHo0aPw9fWFhYUFgoKC8OLFC+UkPQWJjY1FZGQksrKycOPGDSxZsgStWrVSdtuaOHEiZsyYgRo1asDd3R0bN25EcHAwtmzZojxXW1tbzJw5E99//z3u3Lmjku3Mz7hx4+Dv7w9PT080a9YMW7ZsQUhIyDsnRi+qli1bol+/fvD09ISZmRlCQ0Px1VdfKc/P0NAQAwcOxODBg5WTBT169AjR0dHo3bs3Ro4cicWLF2PMmDEYPXo0bt++jRkzZmDChAnKf1jz867fKr8JFIkof0IIRCSk/f88Hk/icfNZAlLzGdbKxkgb7tWN4Vbt/4e1Kutv8lD5Y2uqi7rWhgiLSMTRW9Ho2bDgiiIRUXkQGxuLXr16YfDgwahfvz4MDAxw6dIlzJ8/Hx999FGxHWfixIno3bs3PDw84OPjg7/++gt79uzBkSNHAOS8odmkSRPMnTsXDg4OiI6OVhljGyhcPdve3h5BQUF4+PAh9PX1YWpqCjs7O8hkMuzfvx8dOnSAjo4ODAwM8OWXX+KLL76AQqHABx98gISEBJw9e1b5nPCm4OBgzJgxA59++imcnZ2hqamJkydPYsOGDZg8eTIAoF+/fvjhhx/QtWtXzJkzB9bW1rh69SpsbGzg7e39zutQkOnTp6NTp06oXr06evbsCTU1NVy7dg03b95868t0VLF93rIG5GrAD//cwtKjd6FQCPzPtzYTIUQV2IoVK9CsWTN4eXnhu+++Q/369ZGVlYXDhw9j1apVCAsLK3S75H+xf/9+PHjwAB9++CFMTEzwzz//QKFQoE6dOirlateujePHj6Nly5ZQV1fH4sWLizWO/3KutWrVwq+//gpPT08kJiZi4sSJ+fak2LlzJzw9PfHBBx9gy5YtuHDhAtavX6/cx+PHj7Ft2zY0atQoz3zPr169wsSJE9GzZ084ODjg6dOnuHjxInr06AEgZ0hSXV1dzJw5E5GRkcrvValSJd/2w0rX1inKgYSEBAFAJCQk5Nn26tUrERoaKl69eiVBZO9v4MCBAoAAINTV1UWVKlWEj4+P2LBhg8jOzlYp++OPPwpra2uho6Mj/Pz8xC+//CIAiJcvX6qUmzRpkgAgpk+frrI+PDxcABBXr15Vrrtz547o1q2bMDY2Fjo6OsLJyUmMHz9eKBQKERoaKvz8/ESVKlWElpaWqF27tli2bFmB55K7/9xFLpeLatWqiWHDhono6GhluezsbDFz5kxRtWpVoaGhIdzc3MSBAwdU9nXmzBnh6uoqtLW1RfPmzcXOnTsFABEeHi6EEGLjxo3CyMhI5Tvff/+9MDc3F/r6+mLgwIFi0qRJws3NTeVaf/TRR8rPLVq0EOPGjVPZx0cffSQGDhxY4Dn+8MMPwtvbW5iamgptbW3h6Ogoxo4dK2JiYpRlXr16Jb744gthbW0tNDU1Rc2aNcWGDRuU20+cOCEaNWokNDU1hZWVlZg8ebLIzMx8a1xCvP23yk95/ZsgKm6JrzLE2bsvxPJjd8WwzRdFo9mHhd3k/XkWl+kHRb81gWLegTBx8GaEiEooP387b7s/UvEqqWv9Y8BtYTd5vxi2+WKx7peIyrfyWp9LS0sTU6ZMEQ0aNBBGRkZCV1dX1KlTR3zzzTciNTVVWQ6A2Lt3rxAi77PK8ePH8zzrXL16VeWZQAghVq5cKRwdHYWGhoaoXbu2+OWXX1RiCQ0NFd7e3kJHR0e4u7uLgIAAAUAcP35cWeZd9ezbt2+LJk2aCB0dHZXjf/fdd8LKykrIZDLlM4RCoRCLFy8WderUERoaGqJKlSrCz89PnDx5Mt9r9eLFCzF27FhRr149oa+vLwwMDISrq6tYuHChyvPgw4cPRY8ePYShoaHQ1dUVnp6eIigoqNDX4fVr/bqDBw+Kpk2bCh0dHWFoaCi8vLzEmjVr8o31bf8/si5SekrrWq89dV9ZT557IKzA504i+n/l9b4thBDPnz8Xo0aNEnZ2dkJTU1NUrVpVdOnSReV++a52yTfb6mbMmPHWdjkhhBg3bpxo0aKFEEKI06dPixYtWggTExOho6Mj6tevL7Zv364s+2Z7WWhoqLCwsBATJkwocP+vy69ukcvOzk789NNP732uua5cuSI8PT2Ftra2qFWrlti5c2eefQMQK1asEG3bthVaWlrC3t5e5TyFEGLixInCzMxM6Ovriz59+oiffvpJebz09HTRt29fYWtrKzQ1NYWNjY0YPXq08v+719tmX19erz+9rqK0dRb2/ij79yKVaYmJiTAyMkJCQoLKZDAAkJaWhvDwcDg4OHD8UiLwb4Iqp6xsBW5HJakMa3U3Ohlv3uHkajI4WRkoJy73sDWGY5XyO6zV2+6PVLxK6lqHPk9Eh6Wnoa2hhqvTfKGjyR5+RMT6HJUtb/v/kXWR0lOa13rT2XDM/CsUAPDZh46Y0t6JPUKI3oL3baKSVRx1EY7tQURE5YoQAs/iX+HakwQEP3mJ4CfxuPEsAWmZeSdLrWqsA/fqxnCvZgz36saoZ2PERmYqU+paG6CaiQ6evnyFU3dfwM/FSuqQiIiIqJLzb+YAuZoM0/4Mwc+nHiBLIfBNx7pMhBARUbnFJAgREZVpiWmZuP4kAdeexuPq45z5PGKS0/OUM9BSV/bwcLM1hputESwM+BYOlW0ymQy+zlbYcDYcASFRTIIQERFRmfCptz3U1GT4eu9NrD8TDoUQmN7JmYkQIiIql5gEISKiMiMzW4HbkUm4+iReOYH5/Rd5h7VSV5PBydoA7rbGcLc1gbutMRzN9aBWToe1osrN18USG86G4+itKGRlK6AuL3gSOSIiIqLS0r+xHeQyGabuvYGNZx8iWyHwbRcXJkKIiKjcKXIS5NSpU1iwYAEuX76MiIgI7N27F127dn3rd7Zs2YL58+fj7t27MDIyQvv27bFgwQKYmZm9b9xERFQBRCemISg8DsH/Jj1uPEtAelbeYa1sTXXgbmsCt2pG8KhuDBcbI2hrcFgrqhg87UxgoquBl6mZuPjwJbxrsH5EREREZUNfr+pQU5Nh8u7r+CXwEbIVArM+qseXj4iIqFwp8quGKSkpcHNzw4oVKwpV/uzZsxgwYACGDBmCkJAQ7Ny5ExcuXMCwYcOKHOzblIP53YlKBf8WqLw4cTsaH8w/jjG/X8X6M+G49Ogl0rMUMNRWR/Na5hjbuibWD/TEpW98cHpSayzr54GhzR3R0M6UCZBybsWKFbC3t4e2tjYaN26MCxcuFFh27dq1aN68OUxMTGBiYgIfH5885f39/SGTyVSWdu3aqZSJi4tD//79YWhoCGNjYwwZMgTJycklcn5FpS5XQ5u6lgCAgNBIiaMhorKE9ToqC/j/IfX2tMWCnm6QyYAtQY/x9R83oFDw/wuiN/HfS6KSURx/W0XuCdK+fXu0b9++0OUDAwNhb2+PsWPHAgAcHBzw2WefYd68eUU9dL40NDQAAKmpqdDR0SmWfRKVZ6mpqQD+/2+DqCy69iQeI7dcQUaWArUt9dHE0Uw5l4eDGYe1qsi2b9+OCRMmYPXq1WjcuDEWL14MPz8/3L59GxYWFnnKnzhxAv369UPTpk2hra2NefPmwdfXFyEhIahataqyXLt27bBx40blZy0tLZX99O/fHxERETh8+DAyMzMxaNAgDB8+HFu3bi25ky0CPxcr7Lr8FAEhURxvm4j4jENlCp8vCAB6NqwGuRrwvx3X8PuFJ8hWCMztXp/1diLwvk1U0oqjLlLic4J4e3vjq6++wj///IP27dsjOjoau3btQocOHQr8Tnp6OtLT/3/S28TExALLyuVyGBsbIzo6GgCgq6vLhgOqlIQQSE1NRXR0NIyNjSGX8015KpvCY1IweNNFpGZko3ktc6wf2Aia6pwDobL48ccfMWzYMAwaNAgAsHr1avz999/YsGEDpkyZkqf8li1bVD6vW7cOu3fvxtGjRzFgwADlei0tLVhZ5T+peFhYGA4ePIiLFy/C09MTALBs2TJ06NABCxcuhI2NTXGd3ntrXsscOhpyPIt/hZDniahX1UjqkIhIQnzGobKAzxf0pm4e1aAmk+GL7cHYcekpshXA/J71IWcihCo53reJSkZx1kVKPAnSrFkzbNmyBX369EFaWhqysrLQuXPntw6nNWfOHHz77beFPkZuo0fuPzZElZmxsXGBDYFEUotOSsOADUGITcmAa1UjrPqkIRMglUhGRgYuX76MqVOnKtepqanBx8cHgYGBhdpHamoqMjMzYWpqqrL+xIkTsLCwgImJCVq3bo3Zs2cr5x4LDAyEsbGxMgECAD4+PlBTU0NQUBC6deuW5zhFeSGjOGhryPFhbXMcColCQGgUkyBExGccKjP4fEGv+8i9KuRqMozbFozdV55CIQQW9nJjIoQqPd63iUpOcdRFSjwJEhoainHjxmH69Onw8/NDREQEJk6ciBEjRmD9+vX5fmfq1KmYMGGC8nNiYiJsbW0LPIZMJoO1tTUsLCyQmZlZ7OdAVF5oaGjwDS0qs5LTszBo40U8iXsFOzNdbPBvBH2tEr8NURkSExOD7OxsWFpaqqy3tLTErVu3CrWPyZMnw8bGBj4+Psp17dq1Q/fu3eHg4ID79+/jq6++Qvv27REYGAi5XI7IyMg8Q22pq6vD1NQUkZH5z8FR1BcyioOvs1VOEiQkEhPa1i7VYxNR2cNnHCoL+HxB+elU3wZymQxjfr+KvVefIVsh8GNvN6jL+XITVV68bxOVjOKqi5R469OcOXPQrFkzTJw4EQBQv3596OnpoXnz5pg9ezasra3zfEdLSyvPWN6FIZfLWUEjIiqDMrIUGPHrZYQ8T4S5viZ+GeyFKgZF/3eeKre5c+di27ZtOHHiBLS1tZXr+/btq/xvV1dX1K9fHzVq1MCJEyfQpk2b9zpWUV/IKA6tnSwgV5PhVmQSHsemorqZbokej4jKBz7jEFFZ1N7VGstlMozeegX7rj2HQggs7uPORAhVerxvE5VNJX53Sk1NhZqa6mFy/zEojpndiYiobFMoBCbuuoYz92KgqynHRn8v2JnpSR0WScDc3BxyuRxRUVEq66Oiot7ZtXXhwoWYO3cuAgICUL9+/beWdXR0hLm5Oe7duwcgp2v6m93Ss7KyEBcXV+BxtbS0YGhoqLKUNBM9TXjZ5wzzFRCafw8VIiIiorKiXT0rrPqkITTkMuy/HoGx264iM1shdVhERER5FDkJkpycjODgYAQHBwMAwsPDERwcjMePHwPIeXPy9YlKO3fujD179mDVqlV48OABzp49i7Fjx8LLy6tMTERKREQla86BMPwZ/BzqajKs/qQhXKtxroPKSlNTEw0bNsTRo0eV6xQKBY4ePQpvb+8Cvzd//nzMmjULBw8eVJnXoyBPnz5FbGyssrept7c34uPjcfnyZWWZY8eOQaFQoHHjxv/hjIqfn0vOUGEBIVHvKElEREQkvbbOllj9SUNoytXwz41IjN56BRlZTIQQEVHZUuQkyKVLl+Dh4QEPDw8AwIQJE+Dh4YHp06cDACIiIpQJEQDw9/fHjz/+iOXLl6NevXro1asX6tSpgz179hTTKRARUVm19tQDrD0dDgBY0Ks+PqxdReKISGoTJkzA2rVrsXnzZoSFheHzzz9HSkoKBg0aBAAYMGCAysTp8+bNw7Rp07BhwwbY29sjMjISkZGRSE5OBpDzcsbEiRNx/vx5PHz4EEePHsVHH32EmjVrws/PDwBQt25dtGvXDsOGDcOFCxdw9uxZjB49Gn379i1zL2S0dcnpmXLpURxiktPfUZqIiIhIem3qWuLnAQ2hqa6GQyFRGMVECBERlTEyUQ7GpEpMTISRkRESEhJKZTgKIiL67/4MfoZx24IBAF91cMLwD2tIG1AFVF7vj8uXL8eCBQsQGRkJd3d3LF26VNkjo2XLlrC3t8emTZsAAPb29nj06FGefcyYMQMzZ87Eq1ev0LVrV1y9ehXx8fGwsbGBr68vZs2apTIBe1xcHEaPHo2//voLampq6NGjB5YuXQp9ff1CxVya17rTstO4+SwR83vUR+9GJTsPCRER0X9RXusi5VF5uNYn77zA8F8uIT1LgTZOFlj5SQNoqXNuBCIiKjmFvT8yCUJERMXu9N0XGLzpIjKzBYZ84IBvOtaFTCaTOqwKh/fH0lOa13rp0bv48fAd+NS1wLqBjUr0WERERP8F6yKlp7xc6zN3YzBk80WkZynQsk4VrP6kIbQ1mAghIqKSUdj7Y4lPjE5ERJXLzWcJGPHrZWRmC3R2s8HXHZgAISoK33/nBTl1NwYp6VkSR0NERERUeB/UMsdG/0bQ1lDDidsvMPzXy0jLzJY6LCIiquSYBCEiomLzKDYF/hsvICUjG01rmGFhr/pQU2MChKgo6lgaoLqpLjKyFDh994XU4RAREREVSdOa5tg0yAs6GnKcuvMCw365hFcZTIQQEZF0mAQhIqJiEZOcjoEbLiAmOQN1rQ3x86cNOQYw0XuQyWTw+7c3SEBIlMTREBERERVdE0czbB7sBV1NOU7/O0RWagZ7uBIRkTSYBCEiov8sJT0LgzddxMPYVFQz0cHmQY1goK0hdVhE5ZavixUA4EhYFDKzFRJHQ0RERFR0Xg6m+GWwF/S11HHufiwGb7rIoT6JiEgSTIIQEdF/kpmtwMgtV3D9aQJM9TTxy2AvWBhqSx0WUbnWoLoJzPQ0kZiWhQvhcVKHQ0REVC6dOnUKnTt3ho2NDWQyGf744w/ltszMTEyePBmurq7Q09ODjY0NBgwYgOfPn6vsIy4uDv3794ehoSGMjY0xZMgQJCcnq5S5fv06mjdvDm1tbdja2mL+/Pl5Ytm5cyecnJygra0NV1dX/PPPPyVyzmWNp70pNg/2goGWOs4/iMOgjReRzEQIERGVMiZBiIjovQkhMHn3dZy88wI6GnKsH+gJxyr6UodFVO7J1WTwqZs7JFakxNEQERGVTykpKXBzc8OKFSvybEtNTcWVK1cwbdo0XLlyBXv27MHt27fRpUsXlXL9+/dHSEgIDh8+jP379+PUqVMYPny4cntiYiJ8fX1hZ2eHy5cvY8GCBZg5cybWrFmjLHPu3Dn069cPQ4YMwdWrV9G1a1d07doVN2/eLLmTL0Ma2png16GNYaCtjgsP4+C/4QKS0jKlDouIiCoRmRBCSB3EuyQmJsLIyAgJCQkwNDSUOhwiIvrXvIO3sOrEfcjVZFg3wBOtnCykDqlS4f2x9EhxrY+GRWHI5kuwNtLGuSmtIZPJSuW4REREhVWe6iIymQx79+5F165dCyxz8eJFeHl54dGjR6hevTrCwsLg7OyMixcvwtPTEwBw8OBBdOjQAU+fPoWNjQ1WrVqFr7/+GpGRkdDU1AQATJkyBX/88Qdu3boFAOjTpw9SUlKwf/9+5bGaNGkCd3d3rF69ulDxl6drXZDrT+PxybogJKZloUF1Y2wa7AVDDqFLRET/QWHvj+wJQkRE72Xj2XCsOnEfADC3uysTIETFrFlNc+hqyhGRkIabzxKlDoeIiKjCS0hIgEwmg7GxMQAgMDAQxsbGygQIAPj4+EBNTQ1BQUHKMh9++KEyAQIAfn5+uH37Nl6+fKks4+Pjo3IsPz8/BAYGFhhLeno6EhMTVZbyrn41Y2wd1gRGOhq48jgen66/gIRX7BFCREQlj0kQIiIqsv3Xn+O7/aEAgIl+ddDL01biiIgqHm0NOVrWqQIACAjlkFhEREQlKS0tDZMnT0a/fv2Ub5JGRkbCwkL1RR91dXWYmpoiMjJSWcbS0lKlTO7nd5XJ3Z6fOXPmwMjISLnY2laM+na9qkbYOqwxTHQ1cO1JPD5dH4SEVCZCiIioZDEJQkRERXLufgwmbL8GIYCB3nYY2bKG1CERVVi+zlYAgEOcF4SIiKjEZGZmonfv3hBCYNWqVVKHAwCYOnUqEhISlMuTJ0+kDqnYuNgYYeuwJjDV08T1pwnov/484lMzpA6LiIgqMCZBiIio0EKfJ+KzXy4jI1uBDq5WmN7ZhfMUEJWgVnUsoK4mw52oZITHpEgdDhERUYWTmwB59OgRDh8+rDKeuJWVFaKjo1XKZ2VlIS4uDlZWVsoyUVFRKmVyP7+rTO72/GhpacHQ0FBlqUjqWhvi92FNYKaniZvPEvHx2iC8TGEihIiISgaTIEREVChP4lLhv/ECktKz0NjBFD/2dodcjQkQopJkpKuBJo5mAIDDHBKLiIioWOUmQO7evYsjR47AzMxMZbu3tzfi4+Nx+fJl5bpjx45BoVCgcePGyjKnTp1CZub/D+l0+PBh1KlTByYmJsoyR48eVdn34cOH4e3tXVKnVi7UsTLAtuFNYK6vhdCIRPRbex6xyelSh0VERBUQkyBERPROcSkZGLjxAqKT0uFkZYA1AzyhrSGXOiyiSsHXJWcM8YCQqHeUJCIiotclJycjODgYwcHBAIDw8HAEBwfj8ePHyMzMRM+ePXHp0iVs2bIF2dnZiIyMRGRkJDIycnok1K1bF+3atcOwYcNw4cIFnD17FqNHj0bfvn1hY2MDAPj444+hqamJIUOGICQkBNu3b8eSJUswYcIEZRzjxo3DwYMHsWjRIty6dQszZ87EpUuXMHr06FK/JmVNLcucREgVAy3cikzCx2uDEMNECBERFTMmQYiI6K1SM7IweNNFPHiRgqrGOtg0yAtGOhpSh0VUafjUzUmCXH78Ei+S2ChARERUWJcuXYKHhwc8PDwAABMmTICHhwemT5+OZ8+eYd++fXj69Cnc3d1hbW2tXM6dO6fcx5YtW+Dk5IQ2bdqgQ4cO+OCDD7BmzRrldiMjIwQEBCA8PBwNGzbE//73P0yfPh3Dhw9XlmnatCm2bt2KNWvWwM3NDbt27cIff/yBevXqld7FKMNqWuhj2/AmsDTUwu2oJPRbc551HiIiKlYyIYSQOoh3SUxMhJGRERISEircOJhERGVZVrYCw3+9jGO3omGsq4FdI7xR08JA6rDoX7w/lh6pr3WX5Wdw/WkC5nZ3RV+v6qV+fCIiovxIfX+sTCrDtQ6PSUG/NecRmZiGGlX08PuwJrAw1JY6LCIiKsMKe39kTxAiIsqXEAJf7b2BY7eioa2hhvUDGzEBQiQRP5eciVMPhXBeECIiIqqYHMz1sP2zJrAx0sb9Fynou+Y8IhPSpA6LiIgqACZBiIgoXz8evoMdl55CTQYs79cADe1MpA6JqNLydc4ZEuvsvVgkp2dJHA0RERFRybAz08P2z7xR1VgHD2JS0HdNICISXkkdFhERlXNMghARUR6/Bj7EsmP3AAA/dHOFz78NsEQkjZoW+nAw10NGtgInb7+QOhwiIiKiEmNrqottw5ugmokOHsamos/P5/EsnokQIiJ6f0yCEBGRioM3IzB9XwgAYELb2px/gKgMkMlkyt4gAaEcEouIiIgqNltTXWz/zBvVTXXxOC4VfdcE4unLVKnDIiKicopJECIiUgp6EIux24IhBNC/cXWMaV1T6pCI6F++LjlJkGO3opGRpZA4GiIiIqKSVdVYB9uGN4G9mS6exL1Cn5/P40kcEyFERFR0TIIQEREA4FZkIob+cgkZWQr4Olviu4/qQSaTSR0WEf3Lw9YE5vpaSErLQlB4rNThEBEREZU4G2MdbBvuDUdzPTyLf4W+a87jUWyK1GEREVE5wyQIERHhWfwr+G+4iKS0LDSyN8HSfh6QqzEBQlSWqKnJ0PbfIbEOhXBILCIiIqocrIy0sW14EzhW+f9EyMMYJkKIiKjwmAQhIqrk4lMzMHDDBUQmpqG2pT7WDWgEbQ251GERUT5yh8Q6HBoFhUJIHA0RERFR6bAwzEmE1LTQR0RCGvqsCcSDF8lSh0VEROUEkyBERJVYWmY2hmy+hHvRybAy1MamQV4w0tWQOiwiKkDTGmbQ05QjKjEd158lSB0OERERUamxMMhJhNS21EdUYjr6rjmPe9FMhBAR0bsxCUJEVEllZSsw5veruPzoJQy11fHLEC/YGOtIHRYRvYWWuhwtnSwAAAEcEouIiIgqGXN9Lfw+rAmcrAwQnZSTCLkblSR1WEREVMYxCUJEVAkJITDtzxAcDo2Cproa1g1shNqWBlKHRUSF4PvvvCABoVESR0JERERU+sz0tbB1WBM4WxsiJjkd/daex+1IJkKIiKhgTIIQEVVCS4/ew+8XHkNNBizt6wEvB1OpQyKiQmrlZAENuQz3opNxn2NhExERUSVkqqeJrcMaw8XGEDHJGei39jzCIhKlDouIiMooJkGIiCqZ3y88xk9H7gAAvvuoHtrVs5I4IiIqCkNtDXjXMAcABISwNwgRERFVTsa6mtg6tAnqVzNCXEoGPl57HiHPOWcaERHlxSQIEVElcjg0Cl/vvQEAGNu6Jj5pYidxRET0Pv5/SCzOC0JERESVl5GuBn4d0hhutsZ4mZqJ/uuCcPMZEyFERKSKSRAiokri8qM4jN56BQoB9PG0xRdta0sdEhG9p7b/JkGuPo5HdGKaxNEQERERScdIRwO/DvGCR3VjxKdm4uO153H9abzUYRERURnCJAgRUSVwLzoJgzddQnqWAm2cLPB9t3qQyWRSh0VE78nSUBvutsYAgMNhHBKLiIiIKjdDbQ38MtgLDe1MkJiWhf7rghD8JF7qsIiIqIxgEoSIqIKLTEjDgPUXkPAqEx7VjbH84wZQl/Off6Lyztfl3yGxOC8IEREREQy0NbB5sBca2ZsgKS0Ln64LwpXHL6UOi4iIygC2ghERVWAJrzIxcMMFPE9Ig2MVPWwY2Ag6mnKpwyKiYuDrbAUAOHc/BklpmRJHQ0RERCQ9fS11bBrkBS8HUySlZ2HA+gu4/ChO6rCIiEhiTIIQEVVQaZnZGPbLJdyOSoKFgRZ+GewFEz1NqcMiomJS00IfNaroITNb4PjtF1KHQ0RERFQm6GmpY9OgRvB2NEPyv4mQiw+ZCCEiqsyYBCEiqoCyFQJfbA/GhfA4GGipY/NgL1Qz0ZU6LCIqZr4uOb1BAkIiJY6EiIiIqOzQ1VTHBv9G+KCmOVIysjFwwwWcfxArdVhERCQRJkGIiCoYIQS+/SsEB25GQlOuhjUDPFHX2lDqsIioBPg658wLcuL2C6RnZUscDREREVHZoaMpx7qBnmheyxypGTm95CMSXkkdFhERSYBJECKiCmblifv4JfARZDLgpz7u8K5hJnVIRFRC3KoZw8JAC8npWQi8z7cbiYiIiF6nrSHH2gGecLM1RlJaFibtug4hhNRhERFRKWMShIioAtlx6QkWHLoNAJjRyRkd61tLHBERlSQ1NRna/tsbJCA0SuJoiIiIiMoebQ05FvVyg5a6Gk7fjcGWoMdSh0RERKWMSRAiogri2K0oTN1zAwAwsmUN+DdzkDgiIioNufOCHA6NgkLBNxuJiIiI3lTTQh+T2zkBAH74JwyPYlMkjoiIiEoTkyBERBXA1ccvMXLLFWQrBHo0qIaJfnWkDomISom3oxkMtNTxIikdV5/ESx0OERERUZnk39QeTRxNkZqRjS93XkM2Xx4hIqo0mAQhIirn7r9IxuBNF5GWqUDLOlUwt4crZDKZ1GERFWjFihWwt7eHtrY2GjdujAsXLhRYdu3atWjevDlMTExgYmICHx8flfKZmZmYPHkyXF1doaenBxsbGwwYMADPnz9X2Y+9vT1kMpnKMnfu3BI7x9Kkqa6GVk4WAICA0EiJoyEiIiIqm9TUZFjQ0w16mnJcfPgS6888kDokIiIqJUyCEBGVY1GJaRiw/gJepmbCrZoRVvZvAA05/2mnsmv79u2YMGECZsyYgStXrsDNzQ1+fn6Ijo7Ot/yJEyfQr18/HD9+HIGBgbC1tYWvry+ePXsGAEhNTcWVK1cwbdo0XLlyBXv27MHt27fRpUuXPPv67rvvEBERoVzGjBlToudamnxd/p0XJCSKk30SERERFcDWVBfTOjkDABYeuoM7UUkSR0RERKWBLWVEROVUYlom/DdexLP4V3Aw18MG/0bQ1VSXOiyit/rxxx8xbNgwDBo0CM7Ozli9ejV0dXWxYcOGfMtv2bIFI0eOhLu7O5ycnLBu3TooFAocPXoUAGBkZITDhw+jd+/eqFOnDpo0aYLly5fj8uXLePxYddJLAwMDWFlZKRc9Pb0SP9/S0qJ2FWjK1RAek4L7L5KlDoeIiIiozOrTyBat6lRBRrYCE3YEIzNbIXVIRERUwpgEISIqh9KzsjHi18sIi0hEFQMt/DLYC2b6WlKHRfRWGRkZuHz5Mnx8fJTr1NTU4OPjg8DAwELtIzU1FZmZmTA1NS2wTEJCAmQyGYyNjVXWz507F2ZmZvDw8MCCBQuQlZVV4D7S09ORmJiospRlBtoaaFrTDABwKCRK4miIiIiIyi6ZTIa5PerDSEcDN58lYvmxe1KHREREJYxJECKickahEPjfjms4dz8W+lrq2OjfCLamulKHRfROMTExyM7OhqWlpcp6S0tLREYWbi6LyZMnw8bGRiWR8rq0tDRMnjwZ/fr1g6GhoXL92LFjsW3bNhw/fhyfffYZfvjhB0yaNKnA48yZMwdGRkbKxdbWtlDxScnX2QoAEBDKJAgRERHR21gaamNW13oAgOXH7+HG0wSJIyIiopLEJAgRUTkihMCsv0Ox/3oENOQy/PxpQ9SraiR1WESlYu7cudi2bRv27t0LbW3tPNszMzPRu3dvCCGwatUqlW0TJkxAy5YtUb9+fYwYMQKLFi3CsmXLkJ6enu+xpk6dioSEBOXy5MmTEjmn4uTjbAGZDLj2JB6RCWlSh0NERERUpnVxs0HH+tbIVghM2BGMtMxsqUMiIqISwiQIEVE5subUA2w8+xAAsKi3O5rVNJc2IKIiMDc3h1wuR1SUak+FqKgoWFlZvfW7CxcuxNy5cxEQEID69evn2Z6bAHn06BEOHz6s0gskP40bN0ZWVhYePnyY73YtLS0YGhqqLGWdhYE2GlQ3AQAcDi1czxoiIiKiymzWR/Vgrq+Fu9HJ+PHwHanDISKiEsIkCBFRObHnylPMOXALAPBNx7ro4mYjcURERaOpqYmGDRsqJzUHoJzk3Nvbu8DvzZ8/H7NmzcLBgwfh6emZZ3tuAuTu3bs4cuQIzMzM3hlLcHAw1NTUYGFh8X4nU0b5OucMNcYhsYiIiIjezVRPE3O7uwIA1p5+gAvhcRJHREREJYFJECKicuDknReYtOs6AGD4h44Y2txR4oiI3s+ECROwdu1abN68GWFhYfj888+RkpKCQYMGAQAGDBiAqVOnKsvPmzcP06ZNw4YNG2Bvb4/IyEhERkYiOTkZQE4CpGfPnrh06RK2bNmC7OxsZZmMjAwAQGBgIBYvXoxr167hwYMH2LJlC7744gt88sknMDExKf2LUIJ8XXJ61ATej0XCq0yJoyEiIiIq+3ycLdGrYTUIAXy58xpS0rOkDomIiIoZkyBERGXc9afx+Py3y8hSCHR1t8GUdk5Sh0T03vr06YOFCxdi+vTpcHd3R3BwMA4ePKicLP3x48eIiIhQll+1ahUyMjLQs2dPWFtbK5eFCxcCAJ49e4Z9+/bh6dOncHd3Vylz7tw5ADlDW23btg0tWrSAi4sLvv/+e3zxxRdYs2ZN6V+AEuZgrodaFvrIUgicuB0tdThERERE5cL0zs6oaqyDx3Gp+OGfMKnDISKiYiYTQgipg3iXxMREGBkZISEhoVyMyU1EVFwexqSgx6pziE3JQPNa5lg/sBE01Zm/phy8P5ae8nStFxy6hRXH76OjqzVW9G8gdThERFSBlaf7Y3nHa13yzt2LwcfrggAAmwd7oUXtKhJHRERE71LY+yNb0oiIyqgXSekYsOECYlMyUK+qIVZ90pAJECJ6J79/h8Q6cTsaaZnZEkdDREREVD40rWkO/6b2AIDJu64jIZVDixIRVRRsTSMiKoOS07MwaNMFPI5LRXVTXWz094K+lrrUYRFROeBa1QhWhtpIycjGufsxUodDREREVG5MbucEB3M9RCamYeZfIVKHQ0RExYRJECKiMiYjS4HPf7uMm88SYaaniV8Ge6GKgZbUYRFROSGTyeDrkjPHSkBIlMTREBEREZUfOppyLOrtBjUZsPfqMxy8GfHuLxERUZnHJAgRURmiUAhM2nUNp+/GQFdTjo2DGsHeXE/qsIionPF1zhkS60hYFLIVZX76NyIiIqIyo0F1E4xoUQMA8PXem4hJTpc4IiIi+q+YBCEiKiMyshT4ctc1/BH8HOpqMqz6pCHqVzOWOiwiKocaO5rCQFsdMckZuPr4pdThEBEREZUr43xqwcnKALEpGfhqzw0IwZdKiIjKMyZBiIjKgKS0TAzZfBF7rjyDXE2GRb3d0KJ2FanDIqJySkOuhjZOFgCAgFAOiUVERERUFFrqcvzY2x0achkCQqOw9+ozqUMiIqL/gEkQIiKJRSWmoffP55VDYK0f6ImP3KtKHRYRlXO+LjlDYh0KieTbi0RERERF5GxjiHFtagEAZuwLQUTCK4kjIiKi98UkCBGRhO5GJaH7ynMIi0iEub4Wtg/3Rss6FlKHRUQVQIvaVaCproZHsam4E5UsdThERERE5c6IFjXgZmuMpLQsTNp1nS+WEBGVU0yCEBFJJOhBLHqsOodn8a/gWEUPe0c2hWs1I6nDIqIKQk9LHc1rmgMAAkIiJY6GiIiIqPxRl6thUS83aKmr4fTdGGwJeix1SERE9B6YBCEiksD+68/x6foLSEzLQkM7E+we0RS2prpSh0VEFYyviyUAzgtCRERE9L5qWuhjcjsnAMAP/4ThUWyKxBEREVFRFTkJcurUKXTu3Bk2NjaQyWT4448/3lre398fMpksz+Li4vK+MRMRlWvrTj/A6K1XkZGtQDsXK2wZ2hgmeppSh0VEFVCbupaQyYAbzxLwPJ7jWBMRERG9D/+m9mjiaIrUjGz8b8c1ZCs4LBYRUXlS5CRISkoK3NzcsGLFikKVX7JkCSIiIpTLkydPYGpqil69ehU5WCKi8kyhEPjur1DM/jsMQE5FekX/BtDWkEscGRFVVOb6WvC0MwEAHGZvECIiIqL3oqYmw4KebtDXUselRy+x/swDqUMiIqIiKHISpH379pg9eza6detWqPJGRkawsrJSLpcuXcLLly8xaNCgIgdLRFRepWVmY/TvV7DhbDgA4OsOdTGjszPkajKJIyOiis7X2QoAEBDKeUGIiIiI3petqS6mdaoLAFh46A7uRCVJHBERERVWqc8Jsn79evj4+MDOzq7AMunp6UhMTFRZiIjKq/jUDHy6Pgj/3IiEplwNS/t5YNiHjpDJmAAhopKXOy/I+QdxiE/NkDgaIiIiovKrt6ctWjtZICNbgQk7gpGZrZA6JCIiKoRSTYI8f/4cBw4cwNChQ99abs6cOTAyMlIutra2pRQhEVHxehKXih6rzuHiw5cw0FbH5sFe6OJmI3VYRFSJ2JnpwcnKANkKgWO3oqUOh4iIiKjckslkmNvdFUY6Grj5LBHLj92TOiQiIiqEUk2CbN68GcbGxujatetby02dOhUJCQnK5cmTJ6UTIBFRMbr5LAHdV53D/RcpsDbSxu7Pm8K7hpnUYRFRJeTrnNMbJCCE84IQERER/RcWhtqY1bUeAGD58Xu48TRB4oiIiOhdSi0JIoTAhg0b8Omnn0JTU/OtZbW0tGBoaKiyEBGVJ6fuvECfnwPxIikdTlYG2DuyGWpbGkgdFhFVUr4uOfOCnLzzAmmZ2RJHQ0RERFS+dXGzQcf61shWCEzYEcz6FRFRGVdqSZCTJ0/i3r17GDJkSGkdkohIEjsvPcHgTReRkpGNZjXNsGOEN6yMtKUOi4gqMRcbQ9gYaeNVZjbO3I2ROhwiIqJScerUKXTu3Bk2NjaQyWT4448/VLbv2bMHvr6+MDMzg0wmQ3BwcJ59pKWlYdSoUTAzM4O+vj569OiBqCjVnpWPHz9Gx44doaurCwsLC0ycOBFZWVkqZU6cOIEGDRpAS0sLNWvWxKZNm4r5bKm0zf6oHsz1tXA3Ohk/Hr4jdThERPQWRU6CJCcnIzg4WFk5CA8PR3BwMB4/fgwgZyirAQMG5Pne+vXr0bhxY9SrV++/RUxEVEYJIbDs6F1M3HUdWQqBru422OjvBUNtDalDI6JKTiaTKXuDBIRGShwNERFR6UhJSYGbmxtWrFhR4PYPPvgA8+bNK3AfX3zxBf766y/s3LkTJ0+exPPnz9G9e3fl9uzsbHTs2BEZGRk4d+4cNm/ejE2bNmH69OnKMuHh4ejYsSNatWqF4OBgjB8/HkOHDsWhQ4eK72Sp1JnoaWJeD1cAwNrTD3AhPE7iiIiIqCDqRf3CpUuX0KpVK+XnCRMmAAAGDhyITZs2ISIiQpkQyZWQkIDdu3djyZIl/zFcIqKyKStbgWl/3sTvF3LmMPq8ZQ1M8qsDmUwmcWRERDl8XSyx6dxDHAmLRla2AuryUp0ajoiIqNS1b98e7du3L3D7p59+CgB4+PBhvtsTEhKwfv16bN26Fa1btwYAbNy4EXXr1sX58+fRpEkTBAQEIDQ0FEeOHIGlpSXc3d0xa9YsTJ48GTNnzoSmpiZWr14NBwcHLFq0CABQt25dnDlzBj/99BP8/PyK96SpVLWpa4nentWw49JTfLnzGg6Maw49rSI3tRERUQkr8tNvy5YtIYTIs+R25dy0aRNOnDih8h0jIyOkpqZi2LBhxREzEVGZkpqRheG/XsbvF55ATQbM+sgFk9s5MQFCRGWKl70pjHQ0EJeSgcuPXkodDhERUZl3+fJlZGZmwsfHR7nOyckJ1atXR2BgIAAgMDAQrq6usLS0VJbx8/NDYmIiQkJClGVe30dumdx95Cc9PR2JiYkqC5VN0zo5o6qxDh7HpeKHf8KkDoeIiPLBVwCJiP6DF0np6LvmPI7dioa2hhpWf9IQn3rbSx0WEVEe6nI1tKlrAQAICI16R2kiIiKKjIyEpqYmjI2NVdZbWloiMjJSWeb1BEju9txtbyuTmJiIV69e5XvsOXPmwMjISLnY2toWxylRCTDQ1sCCnvUBAFuCHuPknRcSR0RERG9iEoSI6D09eJGMHqvO4frTBJjqaWLrsCbKMfeJiMoiX+f/nxdECCFxNERERFSQqVOnIiEhQbk8efJE6pDoLZrWNId/U3sAwORd15GQmiltQEREpIJJECKi93Dl8Uv0WHUOj+NSUd1UF7s/b4oG1U2kDouI6K0+rG0OLXU1PIl7hVuRSVKHQ0REVKZZWVkhIyMD8fHxKuujoqJgZWWlLBMVFZVne+62t5UxNDSEjo5OvsfW0tKCoaGhykJl2+R2TnA010NkYhpm/hUidThERPQaJkGIiIooICQS/dacx8vUTLhVM8KekU3hYK4ndVhERO+kq6mO5rWqAAACQjgkFhER0ds0bNgQGhoaOHr0qHLd7du38fjxY3h7ewMAvL29cePGDURHRyvLHD58GIaGhnB2dlaWeX0fuWVy90EVg46mHAt7u0FNBuy9+gwHb0ZIHRIREf2LSRAioiL4NfAhRvx2GelZCrR2ssDvw5vAXF9L6rCIiArNzyVnTPJDIZESR0JERFSykpOTERwcjODgYABAeHg4goOD8fjxYwBAXFwcgoODERoaCiAnwREcHKycy8PIyAhDhgzBhAkTcPz4cVy+fBmDBg2Ct7c3mjRpAgDw9fWFs7MzPv30U1y7dg2HDh3CN998g1GjRkFLK+c5YcSIEXjw4AEmTZqEW7duYeXKldixYwe++OKLUr4iVNIaVDfBiBY1AABf772JmOR0iSMiIiKASRAiokJRKATmHriFaX+GQCGAfl62WPNpQ+hqqksdGhFRkbSpawk1GRAakYgncalSh0NERFRiLl26BA8PD3h4eAAAJkyYAA8PD0yfPh0AsG/fPnh4eKBjx44AgL59+8LDwwOrV69W7uOnn35Cp06d0KNHD3z44YewsrLCnj17lNvlcjn2798PuVwOb29vfPLJJxgwYAC+++47ZRkHBwf8/fffOHz4MNzc3LBo0SKsW7cOfn5+pXEZqJSN86kFJysDxKZk4Ks9NzgPGxFRGSAT5eBf48TERBgZGSEhIYHjYBJRqcvIUmDSrmv4I/g5AOB/bWtjdOuakMlkEkdGlR3vj6Wnol3rPj8HIig8DtM7OWPwBw5Sh0NEROVURbs/lmW81uVL6PNEfLTiDDKzBX7s7YbuDapJHRIRUYVU2Psje4IQEb1FYlom/DdewB/Bz6GuJsOCnvUxpk0tJkCIqFzzdcmZqDUglENiERERERU3ZxtDjPepDQCYsS8Ez+NfSRwREVHlxiQIEVEBIhJeoffqQJy7Hws9TTk2+DdCL09bqcMiIvrPfJ1z5gW5EB6HlykZEkdDREREVPF89qEj3G2NkZSWhcm7r3NYLCIiCTEJQkSUj9uRSei+8hxuRSahioEWdozwxoe1q0gdFhFRsbA11UVda0MoBHD0VrTU4RARERFVOOpyNSzq7QYtdTWcvhuD34IeSx0SEVGlxSQIEdEbAu/Houfqc4hISENNC33sHdkULjZGUodFRFSs/FxyeoMcCuGQWEREREQloUYVfUxu5wQA+OHvMDyKTZE4IiKiyolJECKi1/wZ/AwDN1xAUloWvOxNsWuEN6qZ6EodFhFRsfN1zpkX5PTdF3iVkS1xNEREREQVk39Te3g7muFVZjb+t+MashUcFouIqLQxCUJEBEAIgZ9P3se4bcHIyFagg6sVfhniBWNdTalDIyIqEXWtDVDNRAdpmQqcuvtC6nCIiIiIKiQ1NRnm96wPfS11XHr0EuvPPJA6JCKiSodJECKq9LIVAt/+FYo5B24BAAY3c8Dyfg2grSGXODIiopIjk8mUvUECQqIkjoaIiIio4rI11cW0TnUBAAsP3cGdqCSJIyIiqlyYBCGiSi0tMxsjt1zGpnMPIZMB33Ssi+mdnaGmJpM6NCKiEuf777wgR29FIStbIXE0RERERBVXb09btHayQEa2AhN2BCOTdS8iolLDJAgRVVovUzLQf10QDoVEQVOuhuX9GmBoc0epwyIiKjWediYw0dVAfGomLj58KXU4RERERBWWTCbD3O6uMNbVwM1niVh+7J7UIRERVRpMghBRpfQkLhU9Vp3D5UcvYaitjl+HeKFjfWupwyIiKlXqcjX41M3pDXIoJFLiaIiIiIgqNgtDbcz6qB4AYPnxe7j+NF7agIiIKgkmQYio0rnxNAHdVp7Dg5gUVDXWwe7Pm6Kxo5nUYRERScLXJWdekMOhURBCSBwNERERUcXW2c0GHetbI1shMGHHNaRlZksdEhFRhcckCBFVKsdvR6PPmkDEJKejrrUh9oxsilqWBlKHRUQkmea1zKGjIcez+FcIeZ4odThEREREFd7sj+qhioEW7kUnY1HAbanDISKq8JgEIaJKY/vFxxi6+RJSM7LRvJY5dnzWBJaG2lKHRVTprFixAvb29tDW1kbjxo1x4cKFAsuuXbsWzZs3h4mJCUxMTODj45OnvBAC06dPh7W1NXR0dODj44O7d++qlImLi0P//v1haGgIY2NjDBkyBMnJySVyfuWNtoYcH9Y2BwAEhEZJHA0RERFRxWeip4m53V0BAOvOhONCeJzEERERVWxMghBRhSeEwE+H72Dy7hvIVgh0b1AV6wc2goG2htShEVU627dvx4QJEzBjxgxcuXIFbm5u8PPzQ3R0dL7lT5w4gX79+uH48eMIDAyEra0tfH198ezZM2WZ+fPnY+nSpVi9ejWCgoKgp6cHPz8/pKWlKcv0798fISEhOHz4MPbv349Tp05h+PDhJX6+5YWvc86QWAGcF4SIiIioVLSpa4nentUgBPDlzmtISc+SOiQiogpLJsrB4M+JiYkwMjJCQkICDA0NpQ6HiMqRzGwFvt57AzsuPQUAjG5VE//zrQ2ZTCZxZET/XXm8PzZu3BiNGjXC8uXLAQAKhQK2trYYM2YMpkyZ8s7vZ2dnw8TEBMuXL8eAAQMghICNjQ3+97//4csvvwQAJCQkwNLSEps2bULfvn0RFhYGZ2dnXLx4EZ6engCAgwcPokOHDnj69ClsbGzeedzyeK2LIj41Aw1nH0G2QuDUxFaobqYrdUhERFQOVPT7Y1nCa10xJaVlot3i03gW/wr9G1fH991cpQ6JiKhcKez9kT1BiKjCSknPwtDNl7Dj0lOoyYDvu9XDl351mAAhkkhGRgYuX74MHx8f5To1NTX4+PggMDCwUPtITU1FZmYmTE1NAQDh4eGIjIxU2aeRkREaN26s3GdgYCCMjY2VCRAA8PHxgZqaGoKCgvI9Tnp6OhITE1WWisxYVxONHXKuaUAoe4MQERERlQYDbQ0s6FUfALAl6DFO3nkhcURERBUTkyBEVCFFJ6Whz5pAnLzzAtoaaljzqSf6N7aTOiyiSi0mJgbZ2dmwtLRUWW9paYnIyMI1vE+ePBk2NjbKpEfu9962z8jISFhYWKhsV1dXh6mpaYHHnTNnDoyMjJSLra1toeIrz3ydc65hQAjnBSEiIiIqLU1rmMO/qT0AYPKu60hIzZQ2ICKiCohJECKqcO6/SEb3ledw81kizPQ0sW24N3ycLd/9RSIq0+bOnYtt27Zh79690NbWLtFjTZ06FQkJCcrlyZMnJXq8sqCtS868IJcexSEmOV3iaIiIiIgqj8ntnOBorofIxDTM/CtE6nCIiCocJkGIqEK59DAOPVadw9OXr2BvpovdnzeFu62x1GEREQBzc3PI5XJERan2NIiKioKVldVbv7tw4ULMnTsXAQEBqF+/vnJ97vfetk8rK6s8E69nZWUhLi6uwONqaWnB0NBQZanoqhrroF5VQygEcCws/4nqiYiIiKj46WjKsbC3G9RkwN6rz3DwZoTUIRERVShMghBRhXHwZgT6rwtCfGom3GyNsfvzprA315M6LCL6l6amJho2bIijR48q1ykUChw9ehTe3t4Ffm/+/PmYNWsWDh48qDKvBwA4ODjAyspKZZ+JiYkICgpS7tPb2xvx8fG4fPmyssyxY8egUCjQuHHj4jq9CsHXOScpxHlBiIiIiEpXg+omGNGiBgDgq7032TOXiKgYMQlCRBXCprPh+HzLFaRnKeBT1wLbhjWBmb6W1GER0RsmTJiAtWvXYvPmzQgLC8Pnn3+OlJQUDBo0CAAwYMAATJ06VVl+3rx5mDZtGjZs2AB7e3tERkYiMjISycnJAACZTIbx48dj9uzZ2LdvH27cuIEBAwbAxsYGXbt2BQDUrVsX7dq1w7Bhw3DhwgWcPXsWo0ePRt++fWFjY1Pq16As83XJGTrw1N0YpKRnSRwNERERUeUyzqcWnKwMEJeSga/23IAQQuqQiIgqBCZBiKhcUygE5vwThpl/hUIIoH/j6lj9SUPoaMqlDo2I8tGnTx8sXLgQ06dPh7u7O4KDg3Hw4EHlxOaPHz9GRMT/d/9ftWoVMjIy0LNnT1hbWyuXhQsXKstMmjQJY8aMwfDhw9GoUSMkJyfj4MGDKvOGbNmyBU5OTmjTpg06dOiADz74AGvWrCm9Ey8n6lgawM5MFxlZCpy680LqcIiIiIgqFS11OX7s7Q4NuQwBoVHYe/WZ1CEREVUIMlEO0sqJiYkwMjJCQkJCpRiTm4gKJz0rG1/uvI6/rj0HAExqVweft6gBmUwmcWREpYP3x9JTma7193+HYu3pcHTzqIqf+rhLHQ4REZVhlen+KDVe68plxfF7WHDoNgy01XFo/IewMdaROiQiojKpsPdH9gQhonIp4VUmBm64gL+uPYe6mgw/9nbDyJY1mQAhIvqPfF1y5gU5GhaFzGyFxNEQERERVT6ffegId1tjJKVlYfLu6xwWi4joP2IShIjKnefxr9Br9TmcfxAHfS11bBrkhe4NqkkdFhFRhdCgugnM9DSRmJaFC+FxUodDREREVOmoy9WwqLcbtDXUcPpuDH4Leix1SERE5RqTIERUroRFJKL7ynO4E5UMS0Mt7PjMGx/UMpc6LCKiCkOuJoNP3Zw5WgJCIiWOhoiIiKhyqlFFH5PbOQEAfvg7DI9iUySOiIio/GIShIjKjbP3YtB7dSAiE9NQy0Ife0Y2g7MNx8MlIipuvi7/JkFCozj8AhEREZFEBnrbw9vRDK8ys/G/HdeQrWC9jIjofTAJQkTlwh9Xn8F/4wUkpWehsYMpdo1oiqqcHI6IqEQ0q2kOXU05IhLScONZgtThEBEREVVKamoyLOhVH/pa6rj06CXWn3kgdUhEROUSkyBEVCZlZStw7Uk8Vp+8j4EbLmD89mBkZgt0qm+NX4Z4wUhXQ+oQiYgqLG0NOVrWqQIACAiJkjgaIiIiosqrmokupndyBgAsPHQHd6KSJI6IiKj8UZc6ACIiAMhWCIQ+T8T5B7EIfBCLi+FxSErPUikzrLkDpravCzU1mURREhFVHr7OVvjnRiQCQiPxpV8dqcMhIiIiqrR6eVbDwZBIHLsVjQk7grF3ZDNoyPleMxFRYTEJQkSSUCgEwiITEXg/FucfxCIoPA5JaapJD0NtdTR2NEMTRzN8UNMcdawMJIqWiKjyaVXHAupqMtyJSkZ4TAoczPWkDomIiIioUpLJZJjb3RW+i0/h5rNELD92D1+0rS11WERE5QaTIERUKhQKgdtRSTk9Pe7nJD0SXmWqlDHQUoeXgym8a+QkPupaG0LOXh9ERJIw0tVAE0cznLkXg8OhkRj+YQ2pQyIiIiKqtCwMtTHro3oY8/tVLD9+D23qWqB+NWOpwyIiKheYBCGiEiGEwN3oZGVPj/MPYvEyVTXpoacpRyMHU3g7msG7hhlcbIyY9CAiKkN8XSxx5l4MAkKimAQhIiIiklhnNxscConE/usRmLDjGvaP+QDaGnKpwyIiKvOYBCGiYiGEwP0XyQh8EIfz/yY+YlMyVMroasrhaZ+T9GjiaArXqkZQ5zimRERlVltnS0z/MwSXH7/Ei6R0VDHQkjokIiIiokpt1kf1EBQeh3vRyVgUcBtfd3SWOiQiojKPSRAiei9CCITHpCDwQSzOP4jD+QexeJGUrlJGW0MNnnb/P7xV/WpGnLyNiKgcsTbSgVs1I1x7moAjYVHo51Vd6pCIiIiIKjUTPU3M7e6KIZsvYd2ZcLR1toKXg6nUYRERlWlMghBRoQgh8Cg2NWdOj3+Ht4pKVE16aKmroaGdCZr8O7yVWzVjaKoz6UFEVJ75uljh2tMEBIREMglCREREVAa0qWuJ3p7VsOPSU3y58xoOjGsOPS028RERFYT/QhJRgZ7EpSrn9Ah8EIuIhDSV7ZpyNXhUN4Z3DTN4O5rBzdaY45ESEVUwvs6WWHDoNs7ei0Vyehb0+YBNREREJLlpnZxx9l4sHsel4od/wvB9N1epQyIiKrP4FEtESk9fpuL8gzhl4uNZ/CuV7RpyGTxsTdDE0RRNapihQXUTJj2IiCq4mhb6cDDXQ3hMCk7efoGO9a2lDomIiIio0jPQ1sCCXvXx8dogbAl6DF8XK7SoXUXqsIiIyiQmQYgqsYiEVyo9PZ7EqSY91NVkcLM1/nciczM0tDOBjiaTHkRElYlMJoOvsyV+PvUAAaGRTIIQERERlRFNa5jDv6k9Np17iEm7riFgfAsY6WpIHRYRUZnDJAhRJRKVmJaT8Pg38fEwNlVlu1xNBteqRsrhrRramXBcUSIigq9LThLk2K1oZGQpON8TERERURkxuZ0TTt15gQcxKZj5Vwh+6uMudUhERGUOWzeJKrDopDScfxCH8w9icf5+LB7EpKhsV5MBrlWN0KRGTk+PRvamHOudiIjy8LA1gbm+FmKS03H+QSw+5FALRERERGWCjqYcC3u7oeeqc9h79Rn8XCzRrh577hIRvY6tnUQVSGxyes6cHg9icP5BHO5FJ6tsl8kAFxtDeDuawbuGGTztTWGoza6yRET0dmpqMrR1tsTvFx4jIDSSSRAiIiKiMqRBdRN83rIGVhy/j6/23oSnvSnM9bWkDouIqMxgEoSoHItLycCF8JzhrQIfxOJOVN6kR10rQzT5N+nh5WAKIx0mPYiIqOh8XXKSIIdDo/Bdl3pQU5NJHRIRERER/Wtsm1o4GhaNW5FJmLL7BlZ/0gDqcg5hSkQEMAlCVC6lpGdh8u7r2H89Is82JysDNPl3IvMmjqYw1tWUIEIiIqpomtYwg56mHFGJ6bj+LAHutsZSh0RERERE/9JSl+OnPu7osvwMjoRFoefqQCzu4w57cz2pQyMikhyTIETlTFRiGgZvuoiQ54kAgNqW+jk9PRzN0NjRDKZ6THoQEVHx01KXo6WTBf6+HoGAkEgmQYiIiIjKmLrWhljWrwEm7rqG4Cfx6LD0NGZ2dkEvz2qQydiLl4gqLyZBiMqRkOcJGLLpEiIT02Cmp4k1AxqioZ2p1GEREVEl4etsmZMECY3CpHZOUodDRERERG9oV88KrtWMMGF7MILC4zBp93UcuxWNOd1dYcKXJomokuLggETlxLFbUei1OhCRiWmoaaGPvSObMQFCRESlqpWTBTTkMtyLTsb9F8nv/gIRERERlbqqxjrYOqwJJrdzgrqaDAdDItFuySmcuRsjdWhERJJgEoSoHNh0NhxDN19CakY2mtU0w+7Pm6K6ma7UYRERUSVjqK0B7xrmAICAkCiJoyEiIiKigsjVZPi8ZQ3sHdkMjlX0EJWYjk/WB2HW/lCkZWZLHR4RUaliEoSoDMtWCMzcF4KZf4VCIYA+nrbYNMgLRjoaUodGRESVlK+zJQAgIDRS4kiIiIiI6F1cqxnh7zHN0b9xdQDA+jP/1959h0dR7m0c/+6m9xDSIRBCC51ICUHAAhIRPaJYEKQXC6CIlaOiHvWg6PEgFpAiIEXBAiooiCggEBIIhN4JPYUA6aTuvn+AOeYFBDTJJJv7c11zSWaenb1niMyz+5t5nkR6fbSefclZBicTEak4KoKIVFLZ+UUM/2wzszccAeCFHuG81bsFDnb631ZERIxz28UiyNZj6aRm5hmcRkRERESuxsXRjjfvacGMAW2p6ebI3uQs7vpwHZ+uS8RisRodT0Sk3OnbVJFKKCnjPPdPjeGXvak42ZuZ0u8GHr2pPiaTyehoIiJSzQV4OtM6xBuAlXs0JJaIiIhIVdGtaQDLx3ThlsZ+FBRZ+NfS3QyavUk3toiIzVMRRKSS2XEig7s/XM+epEx83Z1Y+EgUPVoEGR1LRESkRPdmF4fE0rwgIiIiIlWKn4cTnw5qx7/uboaTvZm1+08TPWktK3ZpqFMRsV0qgohUIj/tSuaBT2JIzcqnUYA7S0Z2LLnbVkREpLKIbhYIwIZDaWTmFRqcRkRE5PLWrl3LXXfdRXBwMCaTiSVLlpTabrVaGT9+PEFBQbi4uNCtWzcOHDhQqs3Zs2fp168fnp6eeHt7M3ToULKzs0u12b59O507d8bZ2ZmQkBAmTpx4SZYvv/yS8PBwnJ2dadGiBT/88EOZH6/ItTKZTAyICmXp6E40DfLkXG4hj8yNZ9w328ktKDI6nohImVMRRKQSsFqtzPjtMI/Mi+d8YTGdG/ry1WMdqV3D1ehoIiIil6jv5059PzcKi62s3nfa6DgiIiKXlZOTQ6tWrfjoo48uu33ixIlMnjyZqVOnEhsbi5ubG9HR0eTl/W9ooH79+rFr1y5WrlzJ0qVLWbt2LSNGjCjZnpmZSffu3albty7x8fG88847vPrqq0ybNq2kzYYNG3jooYcYOnQoW7dupVevXvTq1YudO3eW38GLXIOGAR4sHtmRR7qEYTLB53HH6Tl5HduOpxsdTUSkTJmsVmulnwEpMzMTLy8vMjIy8PT0NDqOSJkqKrbwyne7mB97DIC+kXX41z+aYa8J0EXkKnR9rDg615d6e/lepqw+xJ0tg/iw7w1GxxEREQNUpeujyWRi8eLF9OrVC7hwI1pwcDBPP/00zzzzDAAZGRkEBAQwe/Zs+vTpw549e2jatCmbNm2ibdu2ACxfvpw77riDEydOEBwczJQpU3jxxRdJTk7G0dERgBdeeIElS5awd+9eAB588EFycnJYunRpSZ4OHTrQunVrpk6dek35q9K5lqppw6E0nl60jaSMPOzNJsZ0a8hjNzfAzqy5SUWk8rrW6+N1f8t6tcdJLyc/P58XX3yRunXr4uTkRGhoKJ9++un1vrWIzcnKK2TInM3Mjz2GyQQv9WzCm72aqwAiIiKVXvemF+YFWb3vNPlFxQanERERuT6JiYkkJyfTrVu3knVeXl5ERkYSExMDQExMDN7e3iUFEIBu3bphNpuJjY0tadOlS5eSAghAdHQ0+/bt49y5cyVt/vg+v7f5/X0uJz8/n8zMzFKLSHnqWN+X5U92oWfLIIosVt79aT99psVw/Gyu0dFERP626/6m9WqPk17OAw88wKpVq5g5cyb79u3j888/p3Hjxtf71iI25cS5XO6bEsPa/adxcbBj6sNtGNY5DJNJd1mIiEjl16q2N/4eTmTnFxFz6IzRcURERK5LcvKFSaADAgJKrQ8ICCjZlpycjL+/f6nt9vb2+Pj4lGpzuX388T2u1Ob37ZczYcIEvLy8SpaQkJDrPUSR6+bl6sCHD0Xwn/tb4e5kz6Yj57jj/d9YvPUEVWAgGRGRK7K/3hf06NGDHj16XHP75cuXs2bNGg4fPoyPjw8AoaGh1/u2IjZl2/F0hs7ZTFp2Pv4eTswc2I4Wtb2MjiUiInLNzGYTtzUNYH7sMX7ancLNjf2v/iIRERG5JuPGjWPs2LElP2dmZqoQIhXCZDLRu01t2tfzYczCBOKPnuOphdv4Ze9p3ujVHC8XB6Mjiohct3Ifc+e7776jbdu2TJw4kVq1atGoUSOeeeYZzp8/f8XX6LFPsWXLdybx4LQY0rLzCQ/0YMnIG1UAERGRKim6WSAAK3enYLHo7kAREak6AgMvXMNSUlJKrU9JSSnZFhgYSGpqaqntRUVFnD17tlSby+3jj+9xpTa/b78cJycnPD09Sy0iFSnEx5WFIzow9rZG2JlNfL/tFD0mrWXjYT0BLCJVT7kXQQ4fPsy6devYuXMnixcvZtKkSXz11Vc8/vjjV3yNHvsUW2S1WvlkzSEenbeFvEILNzf246vHOhLs7WJ0NBERkb+kQ1hNPJzsOZ2Vz9bj6UbHERERuWb16tUjMDCQVatWlazLzMwkNjaWqKgoAKKiokhPTyc+Pr6kzS+//ILFYiEyMrKkzdq1ayksLCxps3LlSho3bkyNGjVK2vzxfX5v8/v7iFRW9nZmnujakK8ejaJuTVdOZeTx0PSNvPXjXgqKLEbHExG5ZuVeBLFYLJhMJubPn0/79u254447eO+995gzZ84VnwYZN24cGRkZJcvx48fLO6ZIuSostvDPxTuY8ONeAAZG1WXGgLa4O133iHQiIiKVhqO9mVvCLwyD9dPuK49rLiIiYoTs7GwSEhJISEgALkyGnpCQwLFjxzCZTIwZM4Y33niD7777jh07djBgwACCg4Pp1asXAE2aNOH2229n+PDhxMXFsX79ekaNGkWfPn0IDg4GoG/fvjg6OjJ06FB27drFwoULef/990sNZfXkk0+yfPly/vOf/7B3715effVVNm/ezKhRoyr6lIj8JRF1avDDE515sG0IVitMXXOIe6es52BqttHRRESuSbkXQYKCgqhVqxZeXv8b7qdJkyZYrVZOnDhx2dfosU+xJRnnCxk8axOfxx3HbIJX7mrKa3c3x96u3P/3ExERKXfdm12Y6PWnXSmaMFNERCqVzZs3ExERQUREBABjx44lIiKC8ePHA/Dcc88xevRoRowYQbt27cjOzmb58uU4OzuX7GP+/PmEh4fTtWtX7rjjDjp16sS0adNKtnt5efHTTz+RmJhImzZtePrppxk/fjwjRowoadOxY0cWLFjAtGnTaNWqFV999RVLliyhefPmFXQmRP4+Nyd73r6vJVMfvgFvVwd2nszkzg9+Y+7Go+oDikilZ7L+jX+pTCYTixcvLrlL4nKmTZvGmDFjSE1Nxd3dHYBvv/2We++9l+zsbFxcrj4UUGZmJl5eXmRkZKggIlXK8bO5DJm9iQOp2bg62vHBQxF0bRJgdCwRsRG6PlYcnesry8orpM3rP1NQbOHnsV1o4O9hdCQREakguj5WHJ1rqUxSMvN45stt/HYgDYCu4f68fV9LfN2dDE4mItXNtV4fr/tW9D97nBQuDGU1YMCAkvZ9+/alZs2aDB48mN27d7N27VqeffZZhgwZck0FEJGqasuxc/T6aD0HUrMJ9HRm0SNRKoCIiAAfffQRoaGhODs7ExkZSVxc3BXb7tq1i969exMaGorJZGLSpEmXtPl92/9fRo4cWdLm5ptvvmT7o48+Wh6HV+14ODvQsUFNAFbsSrlKaxERERGp6gI8nZkzuD0v39kURzszq/amcvuktfy6N9XoaCIil3XdRZCrPU6alJRUUhABcHd3Z+XKlaSnp9O2bVv69evHXXfdxeTJk8voEEQqn2Xbk3ho2kbO5BTQLNiTJSNvpHktr6u/UETExi1cuJCxY8fyyiuvsGXLFlq1akV0dDSpqZf/wJSbm0tYWBhvvfUWgYGBl22zadMmkpKSSpaVK1cCcP/995dqN3z48FLtJk6cWLYHV41FN7vwd/PTLs0LIiIiIlIdmM0mhnaqx7ejbqRxgAdp2QUMnr2Jl5fs5HxBsdHxRERK+VvDYVUUPfYpVYXVauXj1Yd4Z8U+ALo18ef9PhG4aQJ0ESkHVfH6GBkZSbt27fjwww8BsFgshISEMHr0aF544YU/fW1oaChjxoxhzJgxf9puzJgxLF26lAMHDmAymYALT4K0bt36sk+SXIuqeK4rUmpWHpH/XoXVCjHjbiXIS0/7iohUB7o+Vhyda6nM8gqLeXv5XmatPwJAA393Jj3YWjeDiki5K7fhsETk8gqKLDz31faSAsiQG+vxSf+2KoCIiFxUUFBAfHw83bp1K1lnNpvp1q0bMTExZfYe8+bNY8iQISUFkN/Nnz8fX19fmjdvzrhx48jNzb3ifvLz88nMzCy1yJX5ezhzQ50aAPy8W0NiiYiIiFQnzg52vHJXMz4b0h4/DycOpmZzz8frmbrmEBZLpb/3WkSqARVBRMpARm4hAz+N48v4E5hN8PrdzRh/V1PszKarv1hEpJpIS0ujuLiYgIDS8yMFBASQnFw2wygtWbKE9PR0Bg0aVGp93759mTdvHr/++ivjxo1j7ty5PPzww1fcz4QJE/Dy8ipZQkJCyiSfLeve9MLf608qgoiIiIhUS10a+bFiTBe6Nw2gsNjKWz/upe+MjZxKP290NBGp5lQEEfmbjp7J4Z4p64k5fAY3RztmDmpH/6hQo2OJiFRLM2fOpEePHgQHB5daP2LECKKjo2nRogX9+vXjs88+Y/HixRw6dOiy+xk3bhwZGRkly/HjxysifpXW/eK8IDGHzpBxvtDgNCIiIiJiBB83Rz7p34a37m2Bi4MdGw+f5fZJa/l+2ymjo4lINaYiiMjfsPnIWe75eAOHT+cQ7OXMV4915JbG/kbHEhGplHx9fbGzsyMlpfSTAikpKVec9Px6HD16lJ9//plhw4ZdtW1kZCQABw8evOx2JycnPD09Sy3y5+r5utHQ350ii5XV+y4/0b2IiIiI2D6TyUSf9nX44cnOtKrtRWZeEaM/38rYhQlk5elmGRGpeCqCiPxF3yacpO/0WM7mFNCilhdLRt5IkyB9SSYiciWOjo60adOGVatWlayzWCysWrWKqKiov73/WbNm4e/vT8+ePa/aNiEhAYCgoKC//b7yP9EXnwZZsatshjcTERERkaqrnq8bXz3WkdG3NsBsgm+2nuSOyb+x+chZo6OJSDWjIojIdbJarUxedYAnv0igoNhCdLMAFj7SAX9PZ6OjiYhUemPHjmX69OnMmTOHPXv28Nhjj5GTk8PgwYMBGDBgAOPGjStpX1BQQEJCAgkJCRQUFHDy5EkSEhIueYLDYrEwa9YsBg4ciL29falthw4d4vXXXyc+Pp4jR47w3XffMWDAALp06ULLli3L/6Crke7NLswLsnrfafIKiw1OIyIiIiJGc7Az83T3xix8JIraNVw4fvY8D3wSw3s/7aOw2GJ0PBGpJuyv3kREfpdfVMy4r3fwzdaTAIzoEsYLt4dj1gToIiLX5MEHH+T06dOMHz+e5ORkWrduzfLly0smSz927Bhm8//u0Th16hQRERElP7/77ru8++673HTTTaxevbpk/c8//8yxY8cYMmTIJe/p6OjIzz//zKRJk8jJySEkJITevXvz0ksvld+BVlMtankR6OlMcmYeGw6lcWt4gNGRRERERKQSaBfqww9PdubVb3fxzdaTTP7lIGsOpPH+g60J9XUzOp6I2DiT1Wq1Gh3iajIzM/Hy8iIjI0NjcothzuUU8Mi8eOISz2JnNvH63c3pG1nH6FgiUo3p+lhxdK6v3fhvd/JZzFH6tAvhrd560kZExJbp+lhxdK7Flny/7RQvLt5BZl4Rro52vHJXUx5oG4LJpBtMReT6XOv1UcNhiVyDxLQc7p2ygbjEs3g42TNrUDsVQERERC6je9ML84L8vCeFYkulv9dGRERERCrYXa2CWT6mCx3CfMgtKOb5r3fw6Lx4zuUUGB1NRGyUiiAiVxF7+Az3fLyexLQcanm78PXjHenSyM/oWCIiIpVSZJgPHs72pGUXsPXYOaPjiIiIiEglFOztwvxhHXihRzgOdiZW7EohetJafjtw2uhoImKDVAQR+RPfbDnBwzNjSc8tpHWIN0tG3kijAA+jY4mIiFRaDnZmuob7A7BiV7LBaURERESksrIzm3j0pvosfvxG6vu5kZqVT/+Zcby+dDd5hcVGxxMRG6IiiMhlWK1W3lu5n7GLtlFYbOWOFoF8MaIDfh5ORkcTERGp9KKbXRgS66fdKVSB6edERERExEDNa3mxdHRn+neoC8DMdYn0+mg9+5KzDE4mIrZCRRCR/yevsJgnv0hg8qoDADx2c30+fOgGnB3sDE4mIiJSNXRp5IejvZmjZ3LZn5JtdBwRERERqeRcHO14vVdzPh3UFl93R/YmZ3HXh+v4dF0iFs0zJyJ/k4ogIn9wJjufh2fE8t22U9ibTbzduwXP3x6O2WwyOpqIiEiV4eZkT+cGvgD8pCGxREREROQa3RoewI9PduHWcH8Kiiz8a+luBs6KIzUzz+hoIlKFqQgictHB1Gzu+XgDm4+ew8PZnjlD2vNguzpGxxIREamSujcLAC4MiSUiIiIicq38PJyYObAtr/dqjrODmd8OpBE9aa3mmxORv0xFEBFgw6E07v14PcfO5hLi48Lixzty48U7WEVEROT6dW0SgMkEO05mcCr9vNFxRERERKQKMZlM9O9Ql6WjO9Es2JNzuYU8MjeeF77eTk5+kdHxRKSKURFEqr0vNx9nwMw4MvOKuKGON0sev5EG/h5GxxIREanSfN2daFu3BqAhsURERETkr2ng78Hix2/k0ZvqYzLBF5uO03PybyQcTzc6mohUISqCSLVlsVh5Z8Venv1qO0UWK3e1CmbB8A7UdHcyOpqIiIhNiG4WCGhILBERERH56xztzbzQI5wFwzoQ5OXMkTO59J6ygQ9WHaBYk6aLyDVQEUSqpbzCYkZ/sZWPfj0EwOhbG/D+g61xdrAzOJmIiIjtuK3phXlBYhPPkp5bYHAaEREREanKourXZPmTXbizZRDFFiv/WbmfBz+J4XRWvtHRRKSSUxFEqp207Hwemr6RZduTcLAz8e79rXi6e2PMZpPR0URERGxK3ZpuhAd6UGyx8sveVKPjiIiIiEgV5+XqwAcPRfDeA61wd7Jn89Fz9JkWQ0pmntHRRKQSUxFEqpUDKVn0+mg9W4+l4+XiwNyhkdzXprbRsURERGxW94tPgyzbnmRwEhERERGxBSaTiXtvqM33ozsR7OXModM5PPhJDKfSzxsdTUQqKRVBpNpYdyCNe6ds4MS584TWdGXx4x3pEFbT6FgiIiI27Y6WQQCs2pvKB6sOGJxGRERERGxFPV83Fj4SRe0aLhw5k8sDn8Rw/Gyu0bFEpBJSEUSqhS/ijjFoVhxZeUW0C63BN4/fSJifu9GxREREbF54oCcv3tEEgP+s3M+0tYcMTiQiIiIitiLEx5VFj0QRWtOVE+fO8+AnMRxJyzE6lohUMiqCiE2zWKxM+HEPL3yzgyKLlXsiajFvWCQ+bo5GRxMREak2hncJ4+nbGgHw7x/2MmfDEWMDiYiIiIjNCPZ2YeEjUYT5uXEqI48Hp8VwMDXb6FgiUomoCCI263xBMY/P38Inaw4DMKZbQ957oBVO9nYGJxMREal+RndtyMhb6gPwyne7+CLumMGJRERERMRWBHg6s3BEFI0C3EnJzKfPtI3sS84yOpaIVBIqgohNSs3Ko8+0GJbvSsbRzsykB1szplsjTCaT0dFERESqrWe6N2ZYp3oAjFu8g8VbTxicSERERERshZ+HE58P70CTIE/SsvN5aPpGdp/KNDqWiFQCKoKIzdmXnMU9H21g24kMarg6MG9YJL0iahkdS0REpNozmUy82LMJ/TvUxWqFpxdtY9n2JKNjiYiIiIiNqOnuxOfDI2lZ24uzOQU8NH0jO05kGB1LRAymIojYlDX7T9N7ygZOpp8nzNeNxY/fSPt6PkbHEhERkYtMJhOv/aMZD7StjcUKT36xlZW7U4yOJSIiIiI2wtvVkXnDIomo403G+UL6ztjIlmPnjI4lIgZSEURsxup9qQybs4ns/CI6hPnwzeMdCfV1MzqWiIiI/D9ms4kJ97bk7tbBFFmsjJy/hTX7TxsdS0RERERshKezA3OHRtIutAZZeUX0nxHLpiNnjY4lIgZREURswuYjZ3l0XjyFxVZ6tgzisyGReLs6Gh1LRERErsDObOI/97eiR/NACootjPhsMzGHzhgdS0RERERshLuTPXOGtCcqrCY5BcUMmBnHhkNpRscSEQOoCCJV3u5TmQyevYm8Qgs3N/Zj0oOtcbTXr7aIiEhlZ29n5v0+EXQN9ye/yMLQOZuIP6o79ERERESkbLg62vPpoHZ0bujL+cJiBs/axFo9gSxS7eibYqnSjqTlMODTOLLyimhbtwZT+rXBwU6/1iIiIlWFo72Zj/rdQOeGvuQWFDPo001sP5FudCwRERERsREujnZMH9CWWy/eeDPss838ujfV6FgiUoH0bbFUWckZeTw8M5a07HyaBHkyc1A7XBztjI4lIiIi18nZwY5p/dvSvp4PWflF9J8Zx+5TmUbHEhEREREb4exgx9SH2xDdLICCIgsj5m5mxa5ko2OJSAVREUSqpHM5BfSfGcuJc+cJrenKZ0Pa4+XiYHQsERER+YtcHO34dFA7Iup4k3G+kP4zYzmQkmV0LBERERGxEY72Zj7sewM9WwZRWGxl5PwtLNueZHQsEakAKoJIlZOTX8Sg2Zs4kJpNoKczc4dG4ufhZHQsERER+ZvcneyZPbg9zWt5ciangH4zYklMyzE6loiIiIjYCAc7M+8/2Jp7ImpRZLEy+vMtLNl60uhYIlLOVASRKiW/qJgRczez7Xg63q4OzB3anhAfV6NjiYiISBnxcnFg7pBIwgM9SM3Kp9/0jRw/m2t0LBERERGxEfZ2Zt69vxX3t6mNxQpPLUrgy83HjY4lIuVIRRCpMoqKLTz5eQLrD57BzdGO2YPb0zDAw+hYIiIiUsZquDkyd2gk9f3cOJWRR98ZG0nKOG90LBERERGxEXZmE2/3bknfyDpYrfDsV9tZEHvM6FgiUk5UBJEqwWq18s/FO1i+KxlHOzPTB7SldYi30bFERESknPh5ODF/WAfq1nTl+Nnz9JseS2pWntGxRERERMRGmM0m3uzVnEEdQwH45+IdzNlwxNBMIlI+VASRSs9qtfLvH/awaPMJzCb4oG8EHRv4Gh1LREREylmglzPzh0VSy9uFw2k5PDwjlrM5BUbHEhEREREbYTKZeOWupozoEgbAK9/tYsZvhw1OJSJlTUUQqfQ+Xn2I6b8lAvB275ZENws0OJGIiIhUlNo1XFkwPJIATyf2p2TTf2YsGbmFRscSERERERthMpkY1yOcUbc0AOCNZXv46NeDBqcSkbKkIohUavM2HuWdFfsAeKlnE+5vG2JwIhEREalodWu6MX9YB3zdHdl1KpMBs+LIylMhRERERETKhslk4pnoxoy9rREA76zYx6Sf92O1Wg1OJiJlQUUQqbS+23aKl7/dCcDoWxswrHOYwYlERETEKA383Zk3LBJvVwe2HU9nyOxN5BYUGR1LRERERGzIE10b8tztjQGY9PMB3lmxT4UQERugIohUSr/uS2XswgSsVujfoW5JJV5ERESqr/BAT+YNjcTD2Z5NR84x/LPN5BUWGx1LRERERGzI4zc34KWeTYALQ7S/uWyPCiEiVZyKIFLpbD5ylsfmxVNksXJ362Be+0czTCaT0bFERESkEmhey4s5Q9rj5mjH+oNneGxePPlFKoSIiIiISNkZ1jmMf93dDIAZ6xJ59btdKoSIVGEqgkilsvtUJoNnbyKv0MItjf149/5WmM0qgIiIiMj/3FCnBp8Oaoezg5lf951m9IKtFBZbjI4lIiIiIjZkQFQoE+5tgckEc2KO8s/FO7FYVAgRqYpUBJFKIzEthwGfxpGVV0S70Bp83K8NDnb6FRUREZFLRYbVZMaAdjjam/lpdwpjF22jWB9KRURERKQMPdS+Du/c1wqTCT6PO8ZzX29Xn1OkCtI3zFIpJGfk8fCMWNKy82ka5MmMge1wcbQzOpaIiIhUYp0a+jL14RtwsDPx/bZTPPfVdt2dJyIiIiJl6r42tZn0YGvszCa+ij/B2EUJFOkpZJEqRUUQMdy5nAL6z4zlZPp56vm6MWdIe7xcHIyOJSIiIlXAreEBfPBQBHZmE19vOcFL3+7UeM0iIiIiUqbubl2LDx6KwN5s4tuEUzz5RYKGYxWpQlQEEUNl5xcxaPYmDqRmE+jpzNyh7fHzcDI6loiIlKOPPvqI0NBQnJ2diYyMJC4u7optd+3aRe/evQkNDcVkMjFp0qRL2rz66quYTKZSS3h4eKk2eXl5jBw5kpo1a+Lu7k7v3r1JSUkp60MTg9zePIj3HrgwTMGC2GP8a+luFUJEREREpEzd0SKIj/tdeAp52Y4kRs7fQn5RsdGxROQaqAgihskrLGbEZ5vZdjydGq4OzB3anto1XI2OJSIi5WjhwoWMHTuWV155hS1bttCqVSuio6NJTU29bPvc3FzCwsJ46623CAwMvOJ+mzVrRlJSUsmybt26Utufeuopvv/+e7788kvWrFnDqVOnuPfee8v02MRYd7euxcTeLQGYtf4IE1fsUyFERESuKisrizFjxlC3bl1cXFzo2LEjmzZtKtlutVoZP348QUFBuLi40K1bNw4cOFBqH2fPnqVfv354enri7e3N0KFDyc7OLtVm+/btdO7cGWdnZ0JCQpg4cWKFHJ+IlK3uzQKZ1r9tybx0j86NJ69QhRCRyk5FEDFEUbGFJz7fyoZDZ3BztGP24PY0DPAwOpaIiJSz9957j+HDhzN48GCaNm3K1KlTcXV15dNPP71s+3bt2vHOO+/Qp08fnJyu/KSgvb09gYGBJYuvr2/JtoyMDGbOnMl7773HrbfeSps2bZg1axYbNmxg48aNZX6MYpz724bweq/mAExZfYjJqw4anEhERCq7YcOGsXLlSubOncuOHTvo3r073bp14+TJkwBMnDiRyZMnM3XqVGJjY3FzcyM6Opq8vLySffTr149du3axcuVKli5dytq1axkxYkTJ9szMTLp3707dunWJj4/nnXfe4dVXX2XatGkVfrwi8vfdEu7PzIFtcXYw8+u+0wz/bDPnC1QIEanMVASRCmexWHnhmx38tDsFR3sz0we2pVWIt9GxRESknBUUFBAfH0+3bt1K1pnNZrp160ZMTMzf2veBAwcIDg4mLCyMfv36cezYsZJt8fHxFBYWlnrf8PBw6tSpc8X3zc/PJzMzs9QiVUP/DnV5qWcTAP77836mrjlkcCIREamszp8/z9dff83EiRPp0qULDRo04NVXX6VBgwZMmTIFq9XKpEmTeOmll7j77rtp2bIln332GadOnWLJkiUA7Nmzh+XLlzNjxgwiIyPp1KkTH3zwAV988QWnTp0CYP78+RQUFPDpp5/SrFkz+vTpwxNPPMF7771n4NGLyN/RuaEfswa1x9XRjt8OpDF4dhw5+UVGxxKRK1ARRCqU1Wrl3z/s4av4E9iZTXz4UAQd6/te/YUiIlLlpaWlUVxcTEBAQKn1AQEBJCcn/+X9RkZGMnv2bJYvX86UKVNITEykc+fOZGVlAZCcnIyjoyPe3t7X/L4TJkzAy8urZAkJCfnL+aTiDescxrPRjQF468e9zFqfaHAiERGpjIqKiiguLsbZ2bnUehcXF9atW0diYiLJycmlbqTw8vIiMjKy5EaKmJgYvL29adu2bUmbbt26YTabiY2NLWnTpUsXHB0dS9pER0ezb98+zp07d9lsuiFDpPKLql+Tz4a0x93Jno2HzzJoVhxZeYVGxxKRy1ARRCrUx6sPMWPdhS8i3u7dku7Nrjy+u4iIyLXo0aMH999/Py1btiQ6OpoffviB9PR0Fi1a9Jf3OW7cODIyMkqW48ePl2FiqQgjb2nAE7c2AOC173ezIPbYVV4hIiLVjYeHB1FRUbz++uucOnWK4uJi5s2bR0xMDElJSSU3S/zZDRzJycn4+/uX2m5vb4+Pj0+pNpfbx+/bLkc3ZIhUDW1DfZg7tD0ezvZsOnKO/jPjyDivQohIZaMiiFSYuRuP8s6KfQC8fGdT7mtT2+BEIiJSkXx9fbGzsyMlJaXU+pSUlD+d9Px6eXt706hRIw4evDAfRGBgIAUFBaSnp1/z+zo5OeHp6VlqkarnqdsaMaJLGAAvLtnBN1tOGJxIREQqm7lz52K1WqlVqxZOTk5MnjyZhx56CLPZ2K9LdEOGSNURUacGnw/vgLerAwnH03l4RizpuQVGxxKRP1ARRCrEtwknGf/tTgCeuLUBQzvVMziRiIhUNEdHR9q0acOqVatK1lksFlatWkVUVFSZvU92djaHDh0iKCgIgDZt2uDg4FDqffft28exY8fK9H2l8jGZTIzrEc7AqLpYrfDMl9tYuv2U0bFERKQSqV+/PmvWrCE7O5vjx48TFxdHYWEhYWFhJTdL/NkNHIGBgaSmppbaXlRUxNmzZ0u1udw+ft92ObohQ6RqaV7LiwXDOuDj5siOkxk8ND2WM9n5RscSkYtUBJFy9+veVJ5etA2rFQZE1eWp2xoZHUlERAwyduxYpk+fzpw5c9izZw+PPfYYOTk5DB48GIABAwYwbty4kvYFBQUkJCSQkJBAQUEBJ0+eJCEhoeQpD4BnnnmGNWvWcOTIETZs2MA999yDnZ0dDz30EHBh7O6hQ4cyduxYfv31V+Lj4xk8eDBRUVF06NChYk+AVDiTycQrdzWjT7sQLFYY80UCP+3663PQiIiIbXJzcyMoKIhz586xYsUK7r77burVq0dgYGCpGykyMzOJjY0tuZEiKiqK9PR04uPjS9r88ssvWCwWIiMjS9qsXbuWwsL/DZGzcuVKGjduTI0aNSroCEWkvDUN9uSLER3wdXdiT1ImD03fSGpWntGxRAQVQaScbTpylkfnxVNksXJ362BevasZJpPJ6FgiImKQBx98kHfffZfx48fTunVrEhISWL58ecm42MeOHSMpKamk/alTp4iIiCAiIoKkpCTeffddIiIiGDZsWEmbEydO8NBDD9G4cWMeeOABatasycaNG/Hz8ytp89///pc777yT3r1706VLFwIDA/nmm28q7sDFUGaziTfvacE9EbUoslgZtWArq/elXv2FIiJi81asWMHy5ctJTExk5cqV3HLLLYSHhzN48GBMJhNjxozhjTfe4LvvvmPHjh0MGDCA4OBgevXqBUCTJk24/fbbGT58OHFxcaxfv55Ro0bRp08fgoODAejbty+Ojo4MHTqUXbt2sXDhQt5//33Gjh1r4JGLSHloFODBwkc6EODpxP6UbPpM20hyhgohIkYzWa1Wq9EhriYzMxMvLy8yMjL0CGgVsutUBn2mbSQrr4hbw/35pH8bHOxUdxMRKSu6PlYcnWvbUFRs4ckvEli2IwknezOzBrWjYwNfo2OJiFRZtnB9XLRoEePGjePEiRP4+PjQu3dv3nzzTby8vACwWq288sorTJs2jfT0dDp16sTHH39Mo0b/G+Hg7NmzjBo1iu+//x6z2Uzv3r2ZPHky7u7uJW22b9/OyJEj2bRpE76+vowePZrnn3/+mnPawrkWqU6Onsmh7/RYTqafp25NVxYM70AtbxejY4nYnGu9PqoIIuUiMS2H+6duIC27gPahPswZ0h4XRzujY4mI2BRdHyuOzrXtKCy28Ni8eH7ek4qLgx1zh7anbaiP0bFERKokXR8rjs61SNVz/GwufWds5PjZ89Su4cLnwzsQ4uNqdCwRm3Kt10fdli9lLinjPA/PiCUtu4CmQZ7MGNRWBRARERGpFBzszHzU7wa6NPLjfGExg2ZtIuF4utGxRERERMTGhPi4snBEFPV83Thx7jwPfBLDkbQco2OJVEsqgkiZOptTQP+ZcZxMP0+YrxufDW2Pp7OD0bFERERESjjZ2/HJw23oEOZDdn4RA2bGsutUhtGxRERERMTGBHu7sHBEB+r7uZGUkccDn8RwMDXb6Fgi1c51F0HWrl3LXXfdRXBwMCaTiSVLlvxp+9WrV2MymS5ZkpOT/2pmqaSy84sYNCuOg6nZBHk589nQ9vi6OxkdS0REROQSLo52zBzYjjZ1a5CZV0T/mXHsT8kyOpaIiIiI2Bh/T2e+GBFF4wAPUrPy6TMthn3J6neKVKTrLoLk5OTQqlUrPvroo+t63b59+0hKSipZ/P39r/etpRLLKyxmxGeb2X4igxquDswd2p7aNTTOoYiIiFRebk72zBrcjpa1vTibU0C/GbEkaogCERERESljfh5OfD6iA02DPEnLLqDPtBg9iSxSga67CNKjRw/eeOMN7rnnnut6nb+/P4GBgSWL2ayRuGxFUbGFJz7fyoZDZ3BztGPOkPY08PcwOpaIiIjIVXk6O/DZkPaEB3pwOiufvtM3cvxsrtGxRERERMTG+Lg5smB4JC1re3Eut5C+02PZfiLd6Fgi1UKFVSJat25NUFAQt912G+vXr//Ttvn5+WRmZpZapHKyWKy88M0OftqdgqO9mekD29KytrfRsURERESumberI/OHRdLA352kjDwemr6RU+nnjY4lIiIiIjbG29WRecMiuaGONxnnC+k3PZYtx84ZHUvE5pV7ESQoKIipU6fy9ddf8/XXXxMSEsLNN9/Mli1brviaCRMm4OXlVbKEhISUd0z5C6xWK28s28NX8SewM5v48KEIOtb3NTqWiIiIyHWr6e7EgmGRhNZ05cS58/SbEUtqZp7RsURERETExng6O/DZ0Eja1/MhK7+I/jNiiUs8a3QsEZtW7kWQxo0b88gjj9CmTRs6duzIp59+SseOHfnvf/97xdeMGzeOjIyMkuX48ePlHVP+gg9/Ocin6xMBmNi7Jd2bBRqcSEREROSv8/d0Zv7wDtTydiExLYd+M2I5k51vdCwRERERsTHuTvbMHtyOjvVrklNQzMBP49hwMM3oWCI2y5CJOdq3b8/BgwevuN3JyQlPT89Si1Quc2OO8J+V+wEYf2dTerepbXAiERERkb+vlrcLnw/vQKCnMwdSs+k/M4703AKjY4mIiIiIjXF1tOfTQe3o0siP84XFDJ69ibX7TxsdS8QmGVIESUhIICgoyIi3ljLwbcJJxn+3C4AnujZkSKd6BicSERERKTt1arqyYHgkvu5O7E7KZOCncWTmFRodS0RERERsjLODHdP6t6FruD/5RRaGzdnML3tTjI4lYnOuuwiSnZ1NQkICCQkJACQmJpKQkMCxY8eAC0NZDRgwoKT9pEmT+Pbbbzl48CA7d+5kzJgx/PLLL4wcObJsjkAq1C97U3h60TasVhgYVZenujU0OpKIiIhImQvzc2f+sEhquDqw7UQGQ2ZtIie/yOhYIiIiImJjnB3smPJwG25vFkhBsYVH5sazfGey0bFEbMp1F0E2b95MREQEERERAIwdO5aIiAjGjx8PQFJSUklBBKCgoICnn36aFi1acNNNN7Ft2zZ+/vlnunbtWkaHIBUlLvEsj83bQpHFSq/WwbxyVzNMJpPRsURERETKReNAD+YOjcTT2Z7NR88xbM5m8gqLjY4lIiIiIjbG0d7MB30juLNlEIXFVkYu2MLS7aeMjiViM0xWq9VqdIiryczMxMvLi4yMDM0PYpCdJzN4aNpGsvKL6Bruz9T+bXCwM2Q0NRERuUjXx4qjc129bT12jv4z48jOL+KmRn5MG9AGJ3s7o2OJiBhO18eKo3MtUj0UFVt47qvtfLP1JGYT/OeBVtwToXl4Ra7kWq+P+hZbrurw6WwGfhpHVn4R7ev58FG/G1QAERERkWojok4NZg1uh4uDHWv2n2bUgq0UFluMjiUiIiIiNsbezsw797figba1sVhh7KJtLNp83OhYIlWevsmWP5WUcZ7+M+M4k1NAs2BPZgxsi7OD7nwUERGR6qVdqA8zBrbF0d7Myt0pjFmYQJEKISIiIiJSxuzMJt66tyUPd6iD1QrPfbWd+bFHjY4lUqWpCCJXdDangIdnxHIy/Txhvm7MGdIeT2cHo2OJiIiIGOLGBr580r8NDnYmlm1P4rmvtmOxVPqRZUVERESkijGbTbx+d3MG3xgKwIuLdzJ7faKxoUSqMBVB5LKy8goZNCuOQ6dzCPJyZu6wSHzdnYyOJSIiImKoWxr782HfG7Azm/hm60leXLKTKjDFnoiIiIhUMSaTifF3NuWRLmEAvPr9biYu36unkUX+AhVB5BJ5hcWM+Cye7Scy8HFzZO7QSGp5uxgdS0RERKRSiG4WyKQHW2M2wedxx3jt+90qhIiIiIhImTOZTLzQI5wnbm0AwMerD/HwzFhSM/MMTiZStagIIqUUFVsY/flWYg6fwd3JnjmD29PA393oWCIiIiKVyl2tgpl4XysAZm84wlvL96oQIiIiIiJlzmQyMbZ7YyY/FIGbox0bD5/ljsnr2HAozehoIlWGiiBSwmKx8vzXO1i5OwVHezPTB7SlRW0vo2OJiIiIVEr3tanNm/c0B+CTNYeZ9PMBgxOJiIiIiK36R6tgvhvdicYBHqRl5/PwjFg+WHVAc9SJXAMVQQQAq9XK68t28/WWE9iZTXzU9wai6tc0OpaIiIhIpdYvsi7j72wKwPurDjBl9SGDE4mIiIiIrarv586SkTdyf5vaWKzwn5X7GTR7E2ey842OJlKp2RsdQCqHD345yKz1RwB4576W3NY0wNhAIiIiIlXEkE71yC+y8Pbyvby9fC8pmXmE+LhiZwKz2YTZZMLObMJs4g9/NmE2m7AzXVx/8c92ZhMmE9hd/Nl0cZ2d+cJQCH/axmTCbP5/73GxXUmOP7a5mEFEREREqg4XRzveub8V7er5MP7bnazdf5qek9fxYd8I2ob6GB1PpFJSEUSYs+EI763cD8D4O5ty7w21DU4kIiIiUrU8dnN98gqLeX/VAWZvOGJ0nOtSUii5XPHkcsWaP7Sp4+PK672aU7uGq9GHISIiIlKtPNA2hJa1vXh8/hYOn87hwWkbef72xgzvHIbJpBtdRP5IRZBqbsnWk7zy3S4AnuzakCGd6hmcSERERKRqGtOtIbVquBBz6AzFFivFVitWq/XCny0Xhh8tvviz1UpJG4vFisVqpdjK//78h/9arVz2df/btxWLlVKvs1xsdy0sVrAUW4HrH0/60Okc+s2IZdEjUQR4Ol/360VERETkrwsP9OS7UZ0Y980Ovt92in//sJe4xHP85/5WeLk6GB1PpNJQEaQaW7Unhae/3AbAoI6hjOnW0OBEIiIiIlWXyWTigbYhPNA2xOgoJSy/F1qsViwW/vDnS4snlxZc/le0sVgutisp2kBeYTEvLtnB0TO59JsRy8IRHajp7mT0IYuIiIhUK+5O9kzu05rIej786/vd/LwnhZ4f/MZHfW+gVYi30fFEKgUVQaqp2MNneHz+FootVu6JqMX4O5vqUTkRERERG2M2mzBTfn28BcM6cP/UGA6mZtN/ZhyfD++guw5FREREKpjJZOLhDnVpHeLN4/O3cOxsLvdPjeGlO5vQv0Ndfecn1Z7Z6ABS8XaezGDYnM3kF1no1sSfife11KSYIiIiInLdQnxcmT88El93R3YnZTJodhzZ+UVGxxIRERGplprX8uL70Z2IbhZAQbGF8d/uYtTnW8nKKzQ6moihVASpZg6fzmbgp3Fk5RfRvp4PH/a9AQc7/RqIiIiIyF9T38+duUMj8XJxYOuxdIbN2UReYbHRsURERESqJS8XB6Y+3IaX72yKvdnEsu1J/OPD9ew+lWl0NBHD6NvvauRU+nn6z4zjTE4BzWt5MmNgW5wd7IyOJSIiIiJVXJMgTz4b0h53J3s2Hj7LI3PjyS9SIURERETECCaTiaGd6rHo0SiCvZxJTMvhno/Xs3DTMaxWq9HxRCqciiDVxJnsfPrPjOVk+nnC/NyYM7g9ns4ar1lEREREykarEG9mDW6Hi4Mda/af5onPt1JUbDE6loiIiEi1dUOdGix7ojM3N/Yjv8jC81/v4Okvt5FboOFLpXpREaQayMorZNCsTRw6nUOQlzNzh0ZS093J6FgiIiIiYmPahfowfUBbHO3NrNiVwtNfbqPYorsNRURERIxSw82RTwe249noxphN8M2Wk9z94XoOpmYZHU2kwqgIYuPyi4oZ/tlmdpzMwMfNkblDI6nl7WJ0LBERERGxUZ0a+vJx3xuwN5v4NuEULy7eoWEXRERERAxkNpsYeUsDFgzvgJ+HEwdSs7nrg/Us2XrS6GgiFUJFEBv3waqDbDx8Fncne+YMbk8Df3ejI4mIiIiIjevWNIBJfVpjNsEXm47z2ve7VQgRERERMViHsJr88ERnbmxQk/OFxYxZmMC4b3aQV6i53MS2qQhiw3aezGDKmkMAvHNfS1rU9jI4kYiIiIhUF3e2DGbifa0AmL3hCO+s2GdwIhERERHx83DisyGRPNG1ISYTfB53jHs/3sCRtByjo4mUGxVBbFRBkYVnLo7B3LNFED1aBBkdSURERESqmfva1Ob1u5sB8PHqQ3z4ywGDE4mIiIiIndnE2NsaMWdwe3zcHNmdlMmdH6zjxx1JRkcTKRcqgtioj1cfZG9yFjVcHXjt4gdPEREREZGK1j8qlH/eEQ7Auz/tZ+a6RIMTiYiIiAhAl0Z+/PBEZ9qF1iA7v4jH5m/hte93UVBkMTqaSJlSEcQG7UnK5MNfDgLw6j+a4evuZHAiEREREanORnSpz5huDQF4feluPo87ZnAiEREREQEI9HJmwfAOPHJTGACz1h/h/k9iOHEu1+BkImVHRRAbU1Rs4dmvtlFksXJb0wD+0SrY6EgiIiIiIjzZtSGPdLnw4fqfi3eweOsJgxOJiIiICICDnZlxPZowY0BbvFwc2HY8nZ6T17FqT4rR0UTKhIogNuaTtYfZeTITLxcH3uzVHJPJZHQkERERERFMJhMv9AhnQFRdrFZ45svtLN+pcadFREREKotuTQNYOroTrWp7kXG+kKFzNvPWj3spKtbwWFK1qQhiQw6kZPH+zxcmmxx/Z1P8PZ0NTiQiInKpjz76iNDQUJydnYmMjCQuLu6KbXft2kXv3r0JDQ3FZDIxadKkS9pMmDCBdu3a4eHhgb+/P7169WLfvn2l2tx8882YTKZSy6OPPlrWhyYiV2EymXj1rmbc16Y2xRYroz/fyq97U42OJSIiIiIXhfi48uWjHRnUMRSAqWsO0Xd6LMkZecYGE/kbVASxEcUWK89+tZ2CYgu3NPbj3htqGR1JRETkEgsXLmTs2LG88sorbNmyhVatWhEdHU1q6uW/BM3NzSUsLIy33nqLwMDAy7ZZs2YNI0eOZOPGjaxcuZLCwkK6d+9OTk5OqXbDhw8nKSmpZJk4cWKZH5+IXJ3ZbOLt3i25s2UQhcVWHp0Xz4ZDaUbHEhEREZGLHO3NvPqPZnzU9wbcneyJO3KWnpN/47cDp42OJvKXqAhiI2auO0zC8XQ8nOz5970tNAyWiIhUSu+99x7Dhw9n8ODBNG3alKlTp+Lq6sqnn3562fbt2rXjnXfeoU+fPjg5OV22zfLlyxk0aBDNmjWjVatWzJ49m2PHjhEfH1+qnaurK4GBgSWLp6dnmR+fiFwbO7OJ/z7Ymm5N/MkvsjBszmbij541OpaIiIiI/EHPlkF8P7oTTYI8OZNTwIBP4/jvyv0UW6xGRxO5LiqC2IDDp7P5z0/7AXixZxOCvFwMTiQiInKpgoIC4uPj6datW8k6s9lMt27diImJKbP3ycjIAMDHx6fU+vnz5+Pr60vz5s0ZN24cubm5V9xHfn4+mZmZpRYRKVsOdmY+7HsDnRv6kltQzKBPN7HzZIbRsURERETkD+r5urH48Y481L4OViu8v+oAAz6N5XRWvtHRRK6ZiiBVnMVi5bmvtpNfZKFzQ18ebBdidCQREZHLSktLo7i4mICAgFLrAwICSE5OLpP3sFgsjBkzhhtvvJHmzZuXrO/bty/z5s3j119/Zdy4ccydO5eHH374ivuZMGECXl5eJUtIiK6vIuXB2cGOT/q3oV1oDbLyi+g/M5Z9yVlGxxIRERGRP3B2sGPCvS3474OtcHGwY/3BM/Sc/Buxh88YHU3kmqgIUsXNiTnC5qPncHO88I+RhsESEZHqbOTIkezcuZMvvvii1PoRI0YQHR1NixYt6NevH5999hmLFy/m0KFDl93PuHHjyMjIKFmOHz9eEfFFqiVXR3s+HdSOVrW9OJdbyMMzY0lMy7n6C0VERESkQt0TUZvvRt1IA393UrPyeWj6Rj5efRCLhseSSk5FkCrs6JkcJi7fB8ALdzShdg1XgxOJiIhcma+vL3Z2dqSkpJRan5KScsVJz6/HqFGjWLp0Kb/++iu1a9f+07aRkZEAHDx48LLbnZyc8PT0LLWISPnxcHZgzpD2hAd6cDorn37TN3Li3JWHrBMRERERYzQM8OC7UTdyb0QtLFaYuHwfQ+ds4lxOgdHRRK5IRZAqymKx8vzX2zlfWEyHMB/6ta9jdCQREZE/5ejoSJs2bVi1alXJOovFwqpVq4iKivrL+7VarYwaNYrFixfzyy+/UK9evau+JiEhAYCgoKC//L4iUra8XR2ZNyySMD83TmXk0W9GLCmZeUbHEhEREZH/x9XRnv880Iq37m2Bo72ZX/edpufk39hy7JzR0UQuS0WQKmp+3DE2Hj6Li4Mdb/duidmsYbBERKTyGzt2LNOnT2fOnDns2bOHxx57jJycHAYPHgzAgAEDGDduXEn7goICEhISSEhIoKCggJMnT5KQkFDqCY6RI0cyb948FixYgIeHB8nJySQnJ3P+/HkADh06xOuvv058fDxHjhzhu+++Y8CAAXTp0oWWLVtW7AkQkT/l6+7EgmEdCPFx4eiZXPrNiOVMtibdFBEREalsTCYTfdrXYfHjHQmt6cqpjDwemBrDzHWJWK0aHksqFxVBqqAT53J564c9ADwb3Zi6Nd0MTiQiInJtHnzwQd59913Gjx9P69atSUhIYPny5SWTpR87doykpKSS9qdOnSIiIoKIiAiSkpJ49913iYiIYNiwYSVtpkyZQkZGBjfffDNBQUEly8KFC4ELT6D8/PPPdO/enfDwcJ5++ml69+7N999/X7EHLyLXJNDLmQXDOhDk5czB1Gz6z4wjI7fQ6FgiIiIichnNgr34fnQnerYIoshi5fWlu3ls3hYy89R/k8rDZK0CpbnMzEy8vLzIyMio9mNyW61WBnwax28H0mhbtwaLHonSUyAiItWUro8VR+dapOIdOp3Ng5/EkJZdQOsQb+YNi8Tdyd7oWCLyB7o+VhydaxGp7KxWK5/FHOWNZbspLLZSx8eVj/vdQPNaXkZHExt2rddHPQlSxSzafJzfDqThZG9m4n0aBktEREREbFN9P3fmDo3Ey8WBhOPpDJ29ifMFxUbHEhEREZHLMJlMDOwYypePdqSWtwvHzuZy75QNzI89quGxxHAqglQhSRnneWPphWGwnu7eiDA/d4MTiYiIiIiUnyZBnnw2pD3uTvbEJp7lkXnx5BepECIiIiJSWbUO8WbZE53o1sSfgiILLy7eyZiFCeTkFxkdTaoxFUGqCKvVyj+/2UFWfhGtQrwZ2inM6EgiIiIiIuWuVYg3swa3w8XBjrX7TzN6wVYKiy1GxxIRG1BcXMzLL79MvXr1cHFxoX79+rz++uul7li2Wq2MHz+eoKAgXFxc6NatGwcOHCi1n7Nnz9KvXz88PT3x9vZm6NChZGdnl2qzfft2OnfujLOzMyEhIUycOLFCjlFExAjero5MH9CWf94Rjp3ZxLcJp/jHh+vYl5xldDSpplQEqSIWbz3Jr/tO42hn5t37WmKnYbBEREREpJpoF+rD9AFtcbQ389PuFJ75chvFFg2rICJ/z9tvv82UKVP48MMP2bNnD2+//TYTJ07kgw8+KGkzceJEJk+ezNSpU4mNjcXNzY3o6Gjy8vJK2vTr149du3axcuVKli5dytq1axkxYkTJ9szMTLp3707dunWJj4/nnXfe4dVXX2XatGkVerwiIhXJZDIxokt9Fo7oQKCnM4dO53D3R+v4Kv6E0dGkGtLE6FVAamYet/13LRnnC3k2ujEjb2lgdCQREakEqvv1sSLpXItUDqv2pPDI3HiKLFb6tAthwr0tMJl0c5CIUar69fHOO+8kICCAmTNnlqzr3bs3Li4uzJs3D6vVSnBwME8//TTPPPMMABkZGQQEBDB79mz69OnDnj17aNq0KZs2baJt27YALF++nDvuuIMTJ04QHBzMlClTePHFF0lOTsbR0RGAF154gSVLlrB3795rylrVz7WIVG9nsvMZszCB3w6kAfBA29q89o/muDjaGZxMqjpNjG4jrFYrLy3ZScb5QprX8mREFw2DJSIiIiLVU9cmAbzfJwKzCb7YdJzXvt+tiTZF5C/r2LEjq1atYv/+/QBs27aNdevW0aNHDwASExNJTk6mW7duJa/x8vIiMjKSmJgYAGJiYvD29i4pgAB069YNs9lMbGxsSZsuXbqUFEAAoqOj2bdvH+fOnbtstvz8fDIzM0stIiJVVU13J2YPbs/Y2xphNsGizSe45+P1HDqdffUXi5QBFUEque+3J/HT7hQc7Ey8c18rHOz0VyYiIiIi1VfPlkFMvK8VALM3HOGdFfsMTiQiVdULL7xAnz59CA8Px8HBgYiICMaMGUO/fv0ASE5OBiAgIKDU6wICAkq2JScn4+/vX2q7vb09Pj4+pdpcbh9/fI//b8KECXh5eZUsISEhf/NoRUSMZWc28UTXhswbGomvuyN7k7P4xwfr+H7bKaOjSTWgb9QrsbTsfF75dicAI29pQJMgPfIqIiIiInJfm9q83qs5AB+vPsSHvxy4yitERC61aNEi5s+fz4IFC9iyZQtz5szh3XffZc6cOUZHY9y4cWRkZJQsx48fNzqSiEiZ6NjAlx+e6ExkPR9yCooZ/flWXl6yk/yiYqOjiQ1TEaQSe+XbXZzLLSQ80IPHb9Y8ICIiIiIiv+vfoS4v3tEEgHd/2s/MdYkGJxKRqubZZ58teRqkRYsW9O/fn6eeeooJEyYAEBgYCEBKSkqp16WkpJRsCwwMJDU1tdT2oqIizp49W6rN5fbxx/f4/5ycnPD09Cy1iIjYCn9PZ+YPi2TkLfUBmLvxKPdNieH42VyDk4mtUhGkkvpxRxLLdiRhZzbx7v2tcLTXX5WIiIiIyB8N7xLGU90aAfD60t0siD1mcCIRqUpyc3Mxm0t/1razs8NisQBQr149AgMDWbVqVcn2zMxMYmNjiYqKAiAqKor09HTi4+NL2vzyyy9YLBYiIyNL2qxdu5bCwsKSNitXrqRx48bUqFGj3I5PRKQys7cz82x0OLMGt8Pb1YEdJzO4Y/Jv/LTr8sMEivwd+ma9EjqXU8DLF4fBevSmMJrX8jI4kYiIiIhI5fRE1wY80iUMgBeX7GDx1hMGJxKRquKuu+7izTffZNmyZRw5coTFixfz3nvvcc899wBgMpkYM2YMb7zxBt999x07duxgwIABBAcH06tXLwCaNGnC7bffzvDhw4mLi2P9+vWMGjWKPn36EBwcDEDfvn1xdHRk6NCh7Nq1i4ULF/L+++8zduxYow5dRKTSuKWxPz880ZmIOt5k5RUxYm48byzdTWGxxehoYkPsjQ4gl3rt+12kZRfQ0N+dJ7o2NDqOiIiIiEilZTKZeKFHOOcLi/ks5ihPL9qGs70dPVoEGR1NRCq5Dz74gJdffpnHH3+c1NRUgoODeeSRRxg/fnxJm+eee46cnBxGjBhBeno6nTp1Yvny5Tg7O5e0mT9/PqNGjaJr166YzWZ69+7N5MmTS7Z7eXnx008/MXLkSNq0aYOvry/jx49nxIgRFXq8IiKVVbC3CwtHRDFx+V5mrEtkxrpE1h1M46WeTenU0NfoeGIDTFar1Wp0iKvJzMzEy8uLjIwMmx8H8+fdKQz7bDNmE3z9WEci6ujRWBERubzqdH00ms61SOVnsVh57uvtfBV/Agc7E9P6t+WWcH+jY4nYNF0fK47OtYhUF8t3JvPCN9tJz70whGDXcH/G3dGEBv7uBieTyuhar48aDqsSycgt5J+LdwAwrHOYCiAiIiIiItfIbDbxdu+W3NkyiMJiK4/Mi2fDwTSjY4mIiIjIdbi9eSCrn7mZwTeGYm82sWpvKrdPWsur3+3iXE6B0fGkilIRpBJ5Y9luUrPyCfN1Y+xtjYyOIyIiIiJSpdiZTfz3wdZ0axJAQZGFYZ9tJv7oWaNjiYiIiMh18HZ15JW7mrHiqS50axJAkcXK7A1HuOmdX5nx22EKijRfiFwfFUEqidX7Uvky/gQmE0y8ryXODnZGRxIRERERqXIc7Mx82DeCzg19yS0oZtCnm9h5MsPoWCIiIiJyner7uTNjYFvmD4skPNCDzLwi3li2h+hJa/lpVzJVYJYHqSRUBKkEsvIKGffNhWGwBnUMpW2oj8GJRERERESqLmcHO6b1b0v7UB+y8ovoPzOWfclZRscSERERkb/gxga+LHuiM2/3boGvuxOJaTmMmBtP3+mx7Dqlm13k6lQEqQT+/cNekjLyqOPjyrPRjY2OIyIiIiJS5bk42jFzUFtahXhzLreQfjNiSUzLMTqWiIiIiPwFdmYTD7arw+pnb2bkLfVxtDcTc/gMd36wjue+2kZqZp7REaUSUxHEYOsPpvF53DEA3u7dEldHe4MTiYiIiIjYBg9nB+YMbkd4oAdp2fn0m76RE+dyjY4lIiIiIn+Ru5M9z0aH88vTN3FXq2CsVli0+QQ3v7uaD385QF5hsdERpRJSEcRAOflFPP/1dgAe7lCHqPo1DU4kIiIiImJbvF0dmTcskjA/N05l5NF3eiwpulNQREREpEqrXcOVDx6K4OvHOhJRx5vcgmLe/Wk/t767mm8TTmq+EClFRRADTVy+lxPnzlPL24UXejQxOo6IiIiIiE3ydXdiwbAOhPi4cOxsLv1mxHImO9/oWCIiIiLyN7WpW4NvHuvI+31aU8vbhVMZeTz5RQL3fLyB+KNnjY4nlYSKIAaJPXyGOTFHAXirdwvcnTQMloiIiIhIeQn0cmbBsA4EeTlzMDWbh2fGkZFbaHQsEREREfmbTCYTd7euxaqnb+LZ6Ma4OdqRcDyd3lNiGLVgC8fPajjU6k5FEAOcLyjmuYvDYPVpF0Lnhn4GJxIRERERsX0hPq7MGxaJr7sje5IyGTgrjuz8IqNjiYiIiEgZcHawY+QtDfj12Zvp0y4EkwmWbk+i63treHv5XrLydANMdaUiiAHe/WkfR8/kEujpzD97ahgsEREREZGKUt/PnXnDIvF2dSDheDpDZ2/ifIEm0BQRERGxFf4ezrzVuyXLRnemY/2aFBRZmLL6ELe8u5rP445RbNF8IdWNiiAVLP7oWT5dnwjAhHtb4OnsYHAiEREREZHqJTzQk8+GtMfDyZ7YxLM8Mi+e/CIVQkRERERsSdNgT+YPi2T6gLbU83UjLbuAcd/soOfk31h3IM3oeFKBrrsIsnbtWu666y6Cg4MxmUwsWbLkml+7fv167O3tad269fW+rU3IKyzm2a+2Y7XCvTfU4pZwf6MjiYiIiIhUSy1rezNrcDtcHOxYu/80oxdspbDYYnQsERERESlDJpOJ25oGsGJMF8bf2RQvFwf2Jmfx8MxYhs7exKHT2UZHlApw3UWQnJwcWrVqxUcffXRdr0tPT2fAgAF07dr1et/SZkz6+QCHT+fg5+HE+DubGh1HRERERKRaaxvqw4yBbXG0N/PT7hSeXrRNwyOIiIiI2CBHezNDOtVjzbM3M6hjKPZmE6v2phL937W8+t0uzuUUGB1RytF1F0F69OjBG2+8wT333HNdr3v00Ufp27cvUVFR1/uWNmHb8XSmrT0EwJu9muPt6mhwIhERERERubGBL1P63YC92cR3207xz292YFEhRERERMQmebs68uo/mrHiqS50a+JPkcXK7A1HuPnd1cxcl0hBkZ4MtkUVMifIrFmzOHz4MK+88so1tc/PzyczM7PUUpXlFxXz7FfbsFjhH62C6d4s0OhIIiIiIiJyUdcmAbzfJwKzCRZuPs6/lu7GalUhRERERMRW1fdzZ8bAdswfFkl4oAcZ5wt5feluoiet5addyeoL2phyL4IcOHCAF154gXnz5mFvb39Nr5kwYQJeXl4lS0hISDmnLF8f/nKQ/SnZ1HS7UGkUEREREZHKpWfLICbe1wqA2RuOMHHFPn34FREREbFxNzbwZdkTnXnr3hb4ujuRmJbDiLnx9J0ey65TGUbHkzJSrkWQ4uJi+vbty2uvvUajRo2u+XXjxo0jIyOjZDl+/Hg5pixfO09m8PHqC8Ng/evu5vi4aRgsEREREZHK6L42tXm9V3MApqw+xIe/HDQ4kYiIiIiUNzuziT7t67D62Zt5/Ob6ONqbiTl8hjs/WMdzX20jNTPP6IjyN13boxl/UVZWFps3b2br1q2MGjUKAIvFgtVqxd7enp9++olbb731ktc5OTnh5ORUntEqRGGxhee+2k6xxUqP5oH0bBlkdCQREREREfkT/TvUJa+gmDd/2MN/Vu7HxdGOYZ3DjI4lIiIiIuXM3cme524Pp29kHd5evo/vt51i0eYTLN2exOM312dY5zCcHeyMjil/Qbk+CeLp6cmOHTtISEgoWR599FEaN25MQkICkZGR5fn2hpuy+hC7kzKp4erAv+5ubnQcERERERG5BsO7hPFUtwtPsr+xbA8LYo8ZnEhEREREKkrtGq588FAEXz/WkdYh3uQWFPPuT/u59d3VfJtwUkOmVkHX/SRIdnY2Bw/+77HwxMREEhIS8PHxoU6dOowbN46TJ0/y2WefYTabad689Jf//v7+ODs7X7Le1uxNzuSDXw4A8Oo/muHnUfWfbBERERERqS6e6NqA3MIiPllzmBeX7MDF0cw9EbWNjiUiIiIiFaRN3Rosfrwj3207xds/7uVURh5PfpHArPVHePnOprSpW8PoiHKNrrsIsnnzZm655ZaSn8eOHQvAwIEDmT17NklJSRw7Vr3vlCoqtvDsl9spLLbSrUkA/2gVbHQkERERERG5DiaTiRduDyevoJg5MUd5etE2Fm06QcvaXjSv5UXL2l7U8XHFZDIZHVVEREREyonJZOLu1rWIbhbIzHWJfPzrQRKOp9N7ygbubBnECz3CqV3D1eiYchUmaxV4ficzMxMvLy8yMjLw9PQ0Os5Vfbz6IBOX78PT2Z6VY28iwNPZ6EgiImKDqtr1sSrTuRapviwWKy8u2cHncccv2ebl4kCLWl60qO1Fy4v/reXtosKIVBu6PlYcnWsRkcohNSuP/6zYz6L441it4GhvZlinejx2c308nB2MjlftXOv1UUWQMnYwNYs7Jq+joMjCO/e15P62IUZHEhERG1WVro9Vnc61iOxLzmLb8XS2n0xnx4kM9iRlUVBsuaSdj5sjLS4+KXLhv94EeDqpMCI2SdfHiqNzLSJSuew+lckby3az4dAZAHzdHXm6e2MeaBuCnVn9voqiIogBii1W7pu6ga3H0rm5sR+zBrXThx0RESk3VeX6aAt0rkXk/ysosrA/JYvtJzLYcTKdHScz2JuURZHl0o9Xfh5OF54Y+b04UtsLfw89LS5Vn66PFUfnWkSk8rFarfy8J5V//7CHxLQcAMIDPXipZ1M6NfQ1OF31cK3Xx+ueE0SubNb6RLYeS8fdyZ5/39NCBRARERERERvlaG+mea0L84NAHQDyCovZl5zF9pMZ7DiRzvYTGRxIzeZ0Vj6/7E3ll72pJa8P9HQuNYxWi1pe1HR3MuhoREREROR6mUwmbmsawE2N/Ji78Sjv/7yfvclZPDwzlq7h/vyzZxPq+7kbHVNQEaTMJKbl8M6KfQC82LMJwd4uBicSEREREZGK5OxgR6sQb1qFeAN1AThfUMzupMwLRZGTGew4kcHB09kkZ+aRvDuPlbtTSl5fy9ul5EmRlrW8aVHLCy9XjS0tIiIiUpk52psZ2qke90bU4v1VB5i38Sir9qayZv9pHu5QlzHdGuLt6mh0zGrNbHQAW2CxWHn+q+3kF1m4sUFN+rTTPCAiIiJX8tFHHxEaGoqzszORkZHExcVdse2uXbvo3bs3oaGhmEwmJk2a9Jf2mZeXx8iRI6lZsybu7u707t2blJSUy+5LRKQsuTja0aZuDQbdWI/3HmjNyrE3sfPVaL58NIqX72xKr9bBhPm5AXAy/Tw/7kxm4vJ9PDwzllb/+okuE39l5IItfLLmEBsOpZGZV2jwEYmIiIjI5dRwc+TVfzRjxVNd6NbEnyKLldkbjnDTO6uZuS6RgqJL55OTiqEnQcrA3I1HiTtyFldHO966t6WGwRIREbmChQsXMnbsWKZOnUpkZCSTJk0iOjqaffv24e/vf0n73NxcwsLCuP/++3nqqaf+8j6feuopli1bxpdffomXlxejRo3i3nvvZf369eV6vCIil+PmZE+7UB/ahfqUrMvMK2TXyUx2nEy/OM9IBkfP5HLs7IVl2fakkrZhvm4lQ2i1rO1Ns2BP3Jz00U5ERESkMqjv586Mge1YdyCNN5btZm9yFq8v3c28jUf55x1N6NbEX98fVzBNjP43HT+bS/SkteQWFPOvu5sxICrU6EgiIlJNVObr45VERkbSrl07PvzwQwAsFgshISGMHj2aF1544U9fGxoaypgxYxgzZsx17TMjIwM/Pz8WLFjAfffdB8DevXtp0qQJMTExdOjQ4aq5q+K5FpGqLz23gJ0nM9l+Mp2dJzPYfiKDE+fOX9LOZLrwYfv3+UVa1vaiaZAXLo52BqSW6kTXx4qjcy0iUjUVW6x8ufk47/60n7TsfAA61q/Jiz2b0CzYy+B0VZ8mRq8AVquV57/eTm5BMZH1fHg4sq7RkURERCqtgoIC4uPjGTduXMk6s9lMt27diImJKbd9xsfHU1hYSLdu3UrahIeHU6dOnSsWQfLz88nPzy/5OTMz8y/lExH5O7xdHenU0JdODX1L1p3NKWDHHyZe33Eyg6SMPA6mZnMwNZtvtp4EwGyCRgEeF58W8aJFbW/CAz1wdlBhRERERKSi2JlN9GlfhztbBfPxrweZsS6RDYfOcOcH63igTQhPd2+Ev6ez0TFtnoogf8OCuGNsOHQGZwczb/duidmsx5hERESuJC0tjeLiYgICAkqtDwgIYO/eveW2z+TkZBwdHfH29r6kTXJy8mX3O2HCBF577bW/lElEpDz5uDlyUyM/bmrkV7IuNSuv5EmRHScy2H4yg9NZ+exNzmJvchZfxp8AwN5sonGgBy1re9G81oXJ1xsHeuBor6kiRURERMqTu5M9z90ezkPt6/D28r0s3Z7Ews3H+X77KYZ3DmNgx1B83DR5enlREeQvOpl+ngk/XPhy5dnocEJ93QxOJCIiImVl3LhxjB07tuTnzMxMQkJCDEwkInJl/h7O3BruzK3hFwrCVquVlMz8/z0xcrFAcjangF2nMtl1KhM4DoCjnZnwoD88MVLLm4YB7jjYqTAiIiIiUtZCfFz5sO8NDL7xHK8v3U3C8XTeX3WAT9Ye4r42tRnaKYx6+p65zKkI8hdYrVbGfbOD7PwibqjjzaCOoUZHEhERqfR8fX2xs7MjJSWl1PqUlBQCAwPLbZ+BgYEUFBSQnp5e6mmQP3tfJycnnJyc/lImERGjmUwmAr2cCfRy5ram/yuMnMrIKzWM1vYTGWScL2T7iQt/nh974fVO9mbahtagS0M/bmrsR+MAD03eKSIiIlKG2tStwTePdeSHnUlMXXOInSczmbfxGPNjj9G9aQAjuoTRpq6P0TFthoogf8GX8SdYu/80jvZmJt7XCjsNgyUiInJVjo6OtGnThlWrVtGrVy/gwiTmq1atYtSoUeW2zzZt2uDg4MCqVavo3bs3APv27ePYsWNERUX97eMSEakKTCYTtbxdqOXtwu3Ng4ALhZHjZ8+z/WT6xadGLixZ+UWsP3iG9QfPMOHHvQR4OtG5oR9dGvnRuYEvNTRUg4iIiMjfZjabuLNlMD1bBLHx8Fmm/3aYX/amsmJXCit2pXBDHW9GdAnjtqaB+v75b1IR5DolZ+Tx+tLdAIy9rREN/N0NTiQiIlJ1jB07loEDB9K2bVvat2/PpEmTyMnJYfDgwQAMGDCAWrVqMWHCBODCxOe7d+8u+fPJkydJSEjA3d2dBg0aXNM+vby8GDp0KGPHjsXHxwdPT09Gjx5NVFTUZSdFFxGpLkwmE3VqulKnpit3tgwGwGKxcjgtm7X701h74DQbD58hJTOfr+JP8FX8CUwmaFnbm5sa+tKlkR+tQ7yx19BZIiIiIn+ZyWQiqn5NourX5EBKFjN+S2Tx1pNsOZbOo/O2ULemK8M61eO+NiG4ONoZHbdKMlmtVqvRIa4mMzMTLy8vMjIy8PT0NCyH1Wpl2JzNrNqbSqvaXnz9WEd1+EVExDCV5fp4vT788EPeeecdkpOTad26NZMnTyYyMhKAm2++mdDQUGbPng3AkSNHqFev3iX7uOmmm1i9evU17RMgLy+Pp59+ms8//5z8/Hyio6P5+OOPr3kYrqp6rkVE/q68wmI2HznHmv2prN2fxr6UrFLbPZzt6dTgQkGkSyM/anm7GJRUjKDrY8XRuRYRqV5Ss/L4bMNR5m48Ssb5QgBquDrQv0NdBnQMxdddwzfDtV8fVQS5Dou3nuCphdtwsDOx7InONArwMCyLiIhIZbk+Vgc61yIiFyRn5LH2wGnW7D/NugNpJR/Kf1ffz42bGvnTpZEvHcJq4uyguxVtma6PFUfnWkSkesotKOLLzSeYse4wx8+eB8DR3kzvG2ozrHM96vtV71GKVAQpY6lZedz23loyzhfy9G2NGN21oSE5REREflcZro/Vhc61iMilii1Wtp9ILxk6a+uxc1j+8OnS0d5MZD0fbrr4lEhDf3dNsG5jdH2sODrXIiLVW7HFyopdyXyy9jDbjqeXrO/W5MIk6u1Ca1TLfpaKIGXIarXy6Lx4VuxKoVmwJ0tG3oiDhsESERGDGX19rE50rkVEri4jt5D1h9JYu/80a/ef5lRGXqntQV7OdG7oy02N/OnUwBcvVweDkkpZ0fWx4uhci4gIXPieevPRc0xbe5if96Tw+zf7rUK8GdE5jOhmAdVq+oZrvT5qYvRrsGxHEit2pWBvNvHOfa1UABEREREREfl/vFwduKNFEHe0CMJqtXIwNZs1+0+z9kAasYfPkJSRx6LNJ1i0+QRm04UP610a+nFTYz9a1fbGzlz97l4UERERuR4mk4l2oT60C/Xh0OlsZq5L5Kv4E2w7ns7IBVsI8XFh6I31uL9tCG5O+ur/d3oS5CrOZOdz23/XcjangCe6NmTsbY0q9P1FRESuRHcEVhydaxGRvyevsJjYxLMlT4kcSM0utd3LxeHiBOsXJlkP8tIE61WBro8VR+daRESuJC07n7kxR/ks5gjnci/M1+bl4sDDHeowMCoUf09ngxOWHw2HVUZGLdjC0u1JhAd68N2oTjja6ykQERGpHPRhuOLoXIuIlK1T6ecvFEQOXJhgPTOvqNT2RgHudGl4YS6R9vV8NMF6JaXrY8XRuRYRkas5X1DM11tOMOO3wxw5kwuAo52ZXhHBDOscRqMAD4MTlj0VQcrA8p3JPDovHjuziSWP30iL2l4V9t4iIiJXow/DFUfnWkSk/BQVW9h2IoO1+0+zZv9ptp9ILzXBurODmch6NenSyI+bGvlS308TrFcWuj5WHJ1rERG5VsUWKyt3pzD9t8PEHz1Xsv6Wxn4M7xJGVFhNm+lLqQjyN6XnFtDtvbWkZefz2M31ef728Ap5XxERkWulD8MVR+daRKTipOcWsO5gWklRJCUzv9T2Wt4uF4bNauhHxwa+eLlognWj6PpYcXSuRUTkr4g/epbpaxNZsTu5ZBL15rU8Gd45jDtaBFX5ua9VBPmbxi5M4JutJ2ng787S0Z30+LWIiFQ6+jBccXSuRUSMYbVa2Z+SXTJ0VmziWQqKLCXb7cwmIkK86dLowtBZLWp5aYL1CqTrY8XRuRYRkb/jSFoOM9cl8mX8cfIKL/Slanm7MPjGUPq0r4N7FZ1EXUWQv+GXvSkMmb0Zswm+eqwjN9SpUe7vKSIicr30Ybji6FyLiFQO5wuK2Zh4pmSC9UOnc0pt93a9MMH6TReLIgE2PBFoZaDrY8XRuRYRkbJwNqeAeRuPMmfDEc7kFADg4WxP38g6DO5Yj0CvqtV3UhHkL8o4X0j3/64hJTOf4Z3r8WLPpuX6fiIiIn+VPgxXHJ1rEZHK6cS5XNbuvzB01vqDaWTll55gPTzQ4+JcIn60Da2Bk72e8C9Luj5WHJ1rEREpS3mFxSzeepLpvx3m8MWbSuzNJv7ROpjhncNoElQ1rjUqgvxFz321jUWbT1DP140fn+ysYbBERKTS0ofhiqNzLSJS+RUVW0g4ns6ai0+JbD+ZwR8/7bo42NEhzKekKFLP181mJgU1iq6PFUfnWkREyoPFYuWXvalM++0wcYlnS9Z3bujLiC5hdGrgW6n7S9d6fayag32Vk7X7T7No8wlMJni7d0sVQERERERERKoIezszbUN9aBvqw9PdG3M258IE62v2XZhP5HRWPr/uO82v+04DULuGC10a+XFDnRrU83Wlnq87NVwdKvUHfREREZGyZDab6NY0gG5NA0g4ns703w7z444kfjuQxm8H0ggP9GBElzDubBmMo33VnURdT4JclJ1fRPR/13Iy/TyDOoby6j+alcv7iIiIlBXdEVhxdK5FRKo2q9XK3uQs1u4/zZr9p9l85BwFxZZL2nk621PP1416vm6E/v7fmhf+7OXiYEDyyk3Xx4qjcy0iIhXl+NlcPl2fyMJNx8ktKAYg0NOZwTeG8lBkHTydK0+fSMNhXacXF+9gfuwxQnxcWDGmC66OekhGREQqN30Yrjg61yIitiW3oIiNh8/w24E09qdkkXg6h1MZeX/6mppujiWFkd+LIxeKJa7V9vOjro8VR+daREQqWkZuIfPjjjJr/RFOZ+UD4O5kT592IQzuVI9a3i4GJ1QR5LpsOJhG3xmxACwYHknH+r5l/h4iIiJlTR+GK47OtYiI7csrLObomVwS07JJTMvlSFoOiWk5JJ7JKfngfyUBnk6XFEfq+bpRp6arTU/GrutjxdG5FhERo+QXFfNtwimmrz3MgdRsAOzMJu5sGcTwzmE0r+VlWDbNCXKNcvKLeP6b7QD0i6yjAoiIiIiIiEg15OxgR+NADxoHelyyLTu/qKQo8sfiyJG0HM7lFpKSmU9KZj4bD58t9TqTCWp5u5QaVivs4lBbtWu44GBXdcfWtgWhoaEcPXr0kvWPP/44H330EXl5eTz99NN88cUX5OfnEx0dzccff0xAQEBJ22PHjvHYY4/x66+/4u7uzsCBA5kwYQL29v/7umX16tWMHTuWXbt2ERISwksvvcSgQYMq4hBFRET+Nid7Ox5oG8L9bWqzev9ppq89zIZDZ/g24RTfJpyiY/2aDO8Sxs2N/Crt3GrVvgjyzop9HD97nlreLoy7o4nRcURERERERKSScXeyp3ktr8ve6ZieW3ChOHImh8S03JJCyZG0HLLyizhx7jwnzp3ntwNppV5nbzYR4uNKaE3XUsWR0JpuBHu7YGeunF8i2JJNmzZRXFxc8vPOnTu57bbbuP/++wF46qmnWLZsGV9++SVeXl6MGjWKe++9l/Xr1wNQXFxMz549CQwMZMOGDSQlJTFgwAAcHBz497//DUBiYiI9e/bk0UcfZf78+axatYphw4YRFBREdHR0xR+0iIjIX2QymbilsT+3NPZn58kMpv92mKXbk9hw6AwbDp2hUYA7wzqHcXfr4Er3JGy1Hg4rLvEsD3wSA8CcIe25qZFfme1bRESkvGlYhIqjcy0iItfLarWSll1wsThS+imSI2dyyCu8dGL23znam6nr43rZOUgCPJ0qzV2WtnZ9HDNmDEuXLuXAgQNkZmbi5+fHggULuO+++wDYu3cvTZo0ISYmhg4dOvDjjz9y5513curUqZKnQ6ZOncrzzz/P6dOncXR05Pnnn2fZsmXs3Lmz5H369OlDeno6y5cvv+ZstnauRUTENpxMP8+sdYl8HneMnIuTqPt5ODGoYygPR9bFy7V8J1HXcFhXcb6gmOe/vjAM1gNta6sAIiIiIiIiImXGZDLh5+GEn4cT7UJ9Sm2zWKykZOX9v+LIhflIjp3NpaDIwoHU7JJxt//I1dGOujXdqOfreskcJD5ujpWmQFLVFBQUMG/ePMaOHYvJZCI+Pp7CwkK6detW0iY8PJw6deqUFEFiYmJo0aJFqeGxoqOjeeyxx9i1axcRERHExMSU2sfvbcaMGfOnefLz88nP/99cNJmZmWVzoCIiImWolrcLL93ZlNFdG/JF3DFmrT9CcmYe76zYx0e/HuSBtiEM7VSPEB9XQ3NW2yLIeyv3kZiWQ4CnEy/2bGp0HBEREREREakmzGYTQV4uBHm5XDIvZbHFyqn08xz+4/wjF58eOXHuPLkFxexJymRP0qVfins421/y5MjvT5N4uZTvnZhV3ZIlS0hPTy+ZqyM5ORlHR0e8vb1LtQsICCA5ObmkzR8LIL9v/33bn7XJzMzk/PnzuLi4XDbPhAkTeO211/7uYYmIiFQILxcHHrmpPoNvrMfS7aeYtvYwe5OzmL3hCJ/FHKFHiyBGdA6jVYi3IfmqZRFky7FzzFyXCMCEe1uoMygiIiIiIiKVgt3FuUJCfFwvGbGgoMjCiXO5pQojF54kyeVUxnmy8orYfiKD7ScyLtmvj5sjoTVd+e+Dralb062iDqfKmDlzJj169CA4ONjoKACMGzeOsWPHlvycmZlJSEiIgYlERESuztHezL031OaeiFqsO5jGtLWH+e1AGsu2J7FsexLt6/nwyl1NaRZ86Txr5alaFkG+iDuGxQr3RtTi1vCAq79ARERERERExGCO9mbC/NwJ83O/ZFteYTFHz+T+rzhyOofEMxeeJknNyudsTgFncwrwcNZNgP/f0aNH+fnnn/nmm29K1gUGBlJQUEB6enqpp0FSUlIIDAwsaRMXF1dqXykpKSXbfv/v7+v+2MbT0/OKT4EAODk54eTk9LeOS0RExCgmk4nODf3o3NCPPUmZTP/tMN8lnGLTkbM4O1T8pOnVsgjy1r0taVnbmztbBhkdRURERERERORvc3awo3GgB40DPS7Zlp1fxJG0HI6dzaVGOU9QWhXNmjULf39/evbsWbKuTZs2ODg4sGrVKnr37g3Avn37OHbsGFFRUQBERUXx5ptvkpqair+/PwArV67E09OTpk2blrT54YcfSr3fypUrS/YhIiJi65oEefLeA615Nrox6w6kUf8yN3OUt2pZBDGbTTzcoa7RMURERERERETKnbuTPc1redG8VsUOPVEVWCwWZs2axcCBA7G3/99XJF5eXgwdOpSxY8fi4+ODp6cno0ePJioqig4dOgDQvXt3mjZtSv/+/Zk4cSLJycm89NJLjBw5suQpjkcffZQPP/yQ5557jiFDhvDLL7+waNEili1bZsjxioiIGCXIy4X72xoztGO1LIKIiIiIiIiIiPz8888cO3aMIUOGXLLtv//9L2azmd69e5Ofn090dDQff/xxyXY7OzuWLl3KY489RlRUFG5ubgwcOJB//etfJW3q1avHsmXLeOqpp3j//fepXbs2M2bMIDo6ukKOT0RERMBktVqtRoe4mszMTLy8vMjIyMDT09PoOCIiIpWCro8VR+daRETkUro+VhydaxERkUtd6/XRXIGZREREREREREREREREKoyKICIiIiIiIiIiIiIiYpNUBBEREREREREREREREZukIoiIiIiIiIiIiIiIiNgkFUFERERERERERERERMQmqQgiIiIiIiIiIiIiIiI2SUUQERERERERERERERGxSSqCiIiIiIiIiIiIiIiITVIRREREREREREREREREbJKKICIiIiIiIiIiIiIiYpNUBBEREREREREREREREZukIoiIiIiIiIiIiIiIiNgke6MDXAur1QpAZmamwUlEREQqj9+vi79fJ6X8qC8iIiJyKfVFKo76IiIiIpe61r5IlSiCZGVlARASEmJwEhERkconKysLLy8vo2PYNPVFRERErkx9kfKnvoiIiMiVXa0vYrJWgVs2LBYLp06dwsPDA5PJVCb7zMzMJCQkhOPHj+Pp6Vkm+6ysdKy2Scdqu6rT8epY/x6r1UpWVhbBwcGYzRrhsjypL/L36Fhtk47VdlWn49Wx/j3qi1Qc9UX+Hh2rbapOxwrV63h1rLbJyL5IlXgSxGw2U7t27XLZt6enp83/gv1Ox2qbdKy2qzodr471r9NdlxVDfZGyoWO1TTpW21WdjlfH+tepL1Ix1BcpGzpW21SdjhWq1/HqWG2TEX0R3aohIiIiIiIiIiIiIiI2SUUQERERERERERERERGxSdW2COLk5MQrr7yCk5OT0VHKnY7VNulYbVd1Ol4dq1Rn1el3Qsdqm3Sstqs6Ha+OVaqz6vQ7oWO1TdXpWKF6Ha+O1TYZeaxVYmJ0ERERERERERERERGR61VtnwQRERERERERERERERHbpiKIiIiIiIiIiIiIiIjYJBVBRERERERERERERETEJqkIIiIiIiIiIiIiIiIiNqlaFUGmTJlCy5Yt8fT0xNPTk6ioKH788UejY1WIt956C5PJxJgxY4yOUi5effVVTCZTqSU8PNzoWOXm5MmTPPzww9SsWRMXFxdatGjB5s2bjY5V5kJDQy/5ezWZTIwcOdLoaGWuuLiYl19+mXr16uHi4kL9+vV5/fXXsVqtRkcrF1lZWYwZM4a6devi4uJCx44d2bRpk9GxysTatWu56667CA4OxmQysWTJklLbrVYr48ePJygoCBcXF7p168aBAweMCSsVTn0R9UVshfoi6otUdeqLqC9SXakvor6IrVBfRH2Rqk59kYrti1SrIkjt2rV56623iI+PZ/Pmzdx6663cfffd7Nq1y+ho5WrTpk188skntGzZ0ugo5apZs2YkJSWVLOvWrTM6Urk4d+4cN954Iw4ODvz444/s3r2b//znP9SoUcPoaGVu06ZNpf5OV65cCcD9999vcLKy9/bbbzNlyhQ+/PBD9uzZw9tvv83EiRP54IMPjI5WLoYNG8bKlSuZO3cuO3bsoHv37nTr1o2TJ08aHe1vy8nJoVWrVnz00UeX3T5x4kQmT57M1KlTiY2Nxc3NjejoaPLy8io4qRhBfRH1RWyB+iLqi9gC9UXUF6mu1BdRX8QWqC+ivogtUF+kgvsi1mquRo0a1hkzZhgdo9xkZWVZGzZsaF25cqX1pptusj755JNGRyoXr7zyirVVq1ZGx6gQzz//vLVTp05GxzDEk08+aa1fv77VYrEYHaXM9ezZ0zpkyJBS6+69915rv379DEpUfnJzc612dnbWpUuXllp/ww03WF988UWDUpUPwLp48eKSny0WizUwMND6zjvvlKxLT0+3Ojk5WT///HMDEkploL6IbVBfpHpQX8Q2qC+ivoiUpr6IbVBfpHpQX8Q2qC9S8X2RavUkyB8VFxfzxRdfkJOTQ1RUlNFxys3IkSPp2bMn3bp1MzpKuTtw4ADBwcGEhYXRr18/jh07ZnSkcvHdd9/Rtm1b7r//fvz9/YmIiGD69OlGxyp3BQUFzJs3jyFDhmAymYyOU+Y6duzIqlWr2L9/PwDbtm1j3bp19OjRw+BkZa+oqIji4mKcnZ1LrXdxcbHZO5V+l5iYSHJycql/k728vIiMjCQmJsbAZGIE9UVsj/oitk19Eduhvoj6InKB+iK2R30R26a+iO1QX6Ti+yL25bbnSmrHjh1ERUWRl5eHu7s7ixcvpmnTpkbHKhdffPEFW7ZssZnx5P5MZGQks2fPpnHjxiQlJfHaa6/RuXNndu7ciYeHh9HxytThw4eZMmUKY8eO5Z///CebNm3iiSeewNHRkYEDBxodr9wsWbKE9PR0Bg0aZHSUcvHCCy+QmZlJeHg4dnZ2FBcX8+abb9KvXz+jo5U5Dw8PoqKieP3112nSpAkBAQF8/vnnxMTE0KBBA6Pjlavk5GQAAgICSq0PCAgo2Sa2T30R26S+iPoiVZ36IuqLqC9SfagvYpvUF1FfpKpTX0R9kfLsi1S7Ikjjxo1JSEggIyODr776ioEDB7JmzRqbu+AfP36cJ598kpUrV15SVbRFf6wKt2zZksjISOrWrcuiRYsYOnSogcnKnsVioW3btvz73/8GICIigp07dzJ16lSbvtjPnDmTHj16EBwcbHSUcrFo0SLmz5/PggULaNasGQkJCYwZM4bg4GCb/HudO3cuQ4YMoVatWtjZ2XHDDTfw0EMPER8fb3Q0kXKnvohtUl9EfZGqTn0R9UWk+lBfxDapL6K+SFWnvoj6IuWp2g2H5ejoSIMGDWjTpg0TJkygVatWvP/++0bHKnPx8fGkpqZyww03YG9vj729PWvWrGHy5MnY29tTXFxsdMRy5e3tTaNGjTh48KDRUcpcUFDQJZ3TJk2a2OxjrgBHjx7l559/ZtiwYUZHKTfPPvssL7zwAn369KFFixb079+fp556igkTJhgdrVzUr1+fNWvWkJ2dzfHjx4mLi6OwsJCwsDCjo5WrwMBAAFJSUkqtT0lJKdkmtk99EfVFqjr1RWyT+iLqi6gvUn2oL6K+SFWnvohtUl9EfZHy7ItUuyLI/2exWMjPzzc6Rpnr2rUrO3bsICEhoWRp27Yt/fr1IyEhATs7O6Mjlqvs7GwOHTpEUFCQ0VHK3I033si+fftKrdu/fz9169Y1KFH5mzVrFv7+/vTs2dPoKOUmNzcXs7n0P8l2dnZYLBaDElUMNzc3goKCOHfuHCtWrODuu+82OlK5qlevHoGBgaxatapkXWZmJrGxsTY9DrP8OfVFbJP6IrZFfRHbpb6I+iKivoitUl/EtqgvYrvUF6mYvki1Gg5r3Lhx9OjRgzp16pCVlcWCBQtYvXo1K1asMDpamfPw8KB58+al1rm5uVGzZs1L1tuCZ555hrvuuou6dety6tQpXnnlFezs7HjooYeMjlbmnnrqKTp27Mi///1vHnjgAeLi4pg2bRrTpk0zOlq5sFgszJo1i4EDB2Jvb7v/ZN111128+eab1KlTh2bNmrF161bee+89hgwZYnS0crFixQqsViuNGzfm4MGDPPvss4SHhzN48GCjo/1t2dnZpe62SkxMJCEhAR8fH+rUqcOYMWN44403aNiwIfXq1ePll18mODiYXr16GRdaKoz6IuqL2AL1RWyT+iLqi6gvUj2oL6K+iC1QX8Q2qS+ivki59kWs1ciQIUOsdevWtTo6Olr9/PysXbt2tf70009Gx6owN910k/XJJ580Oka5ePDBB61BQUFWR0dHa61atawPPvig9eDBg0bHKjfff/+9tXnz5lYnJydreHi4ddq0aUZHKjcrVqywAtZ9+/YZHaVcZWZmWp988klrnTp1rM7OztawsDDriy++aM3Pzzc6WrlYuHChNSwszOro6GgNDAy0jhw50pqenm50rDLx66+/WoFLloEDB1qtVqvVYrFYX375ZWtAQIDVycnJ2rVrV5v//Zb/UV9EfRFbob6I7VFfRH0RqR7UF1FfxFaoL2J71BdRX6Q8maxWq7X8SiwiIiIiIiIiIiIiIiLGqPZzgoiIiIiIiIiIiIiIiG1SEURERERERERERERERGySiiAiIiIiIiIiIiIiImKTVAQRERERERERERERERGbpCKIiIiIiIiIiIiIiIjYJBVBRERERERERERERETEJqkIIiIiIiIiIiIiIiIiNklFEBERERERERERERERsUkqgoiIiIiIiIiIiIiIiE1SEURErsmgQYPo1atXqXVfffUVzs7O/Oc//zEmlIiIiFQb6ouIiIiIkdQXEam67I0OICJV04wZMxg5ciRTp05l8ODBRscRERGRakZ9ERERETGS+iIiVYeeBBGR6zZx4kRGjx7NF198oQu9iIiIVDj1RURERMRI6ouIVC16EkRErsvzzz/Pxx9/zNKlS+natavRcURERKSaUV9EREREjKS+iEjVoyKIiFyzH3/8kW+//ZZVq1Zx6623Gh1HREREqhn1RURERMRI6ouIVE0aDktErlnLli0JDQ3llVdeITs72+g4IiIiUs2oLyIiIiJGUl9EpGpSEURErlmtWrVYvXo1J0+e5PbbbycrK8voSCIiIlKNqC8iIiIiRlJfRKRqUhFERK5L3bp1WbNmDcnJybrgi4iISIVTX0RERESMpL6ISNWjIoiIXLeQkBBWr15Namoq0dHRZGZmGh1JREREqhH1RURERMRI6ouIVC0qgojIX1K7dm1Wr15NWlqaLvgiIiJS4dQXERERESOpLyJSdZisVqvV6BAiIiIiIiIiIiIiIiJlTU+CiIiIiIiIiIiIiIiITVIRREREREREREREREREbJKKICIiIiIiIiIiIiIiYpNUBBEREREREREREREREZukIoiIiIiIiIiIiIiIiNgkFUFERERERERERERERMQmqQgiIiIiIiIiIiIiIiI2SUUQERERERERERERERGxSSqCiIiIiIiIiIiIiIiITVIRREREREREREREREREbJKKICIiIiIiIiIiIiIiYpP+DzrU0TMuKzF5AAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 2000x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plotting the results\n",
"results = pd.DataFrame(results)\n",
"results.columns = [\"K\", \"Davies Bouldin Score\", \"Silhouette Score\", \"Calinski Harabasz Score\"]\n",
"#Line Plot Side by Side\n",
"fig, ax = plt.subplots(1,3, figsize=(20,5))\n",
"results.plot(x=\"K\", y=\"Davies Bouldin Score\", ax=ax[0], title=\"Davies Bouldin Score\")\n",
"results.plot(x=\"K\", y=\"Silhouette Score\", ax=ax[1], title=\"Silhouette Score\")\n",
"results.plot(x=\"K\", y=\"Calinski Harabasz Score\", ax=ax[2], title=\"Calinski Harabasz Score\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 183,
"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>gender</th>\n",
" <th>age</th>\n",
" <th>height(cm)</th>\n",
" <th>weight(kg)</th>\n",
" <th>waist(cm)</th>\n",
" <th>triglyceride</th>\n",
" <th>HDL</th>\n",
" <th>hemoglobin</th>\n",
" <th>serum creatinine</th>\n",
" <th>ALT</th>\n",
" <th>Gtp</th>\n",
" <th>dental caries</th>\n",
" <th>tartar</th>\n",
" <th>BMI</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16671</th>\n",
" <td>1</td>\n",
" <td>30.0</td>\n",
" <td>165.0</td>\n",
" <td>65.0</td>\n",
" <td>82.0</td>\n",
" <td>175.0</td>\n",
" <td>45.0</td>\n",
" <td>15.2</td>\n",
" <td>0.9</td>\n",
" <td>15.0</td>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23.875115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3695</th>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>165.0</td>\n",
" <td>60.0</td>\n",
" <td>78.0</td>\n",
" <td>145.0</td>\n",
" <td>45.0</td>\n",
" <td>15.8</td>\n",
" <td>1.0</td>\n",
" <td>38.0</td>\n",
" <td>56</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>22.038567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3694</th>\n",
" <td>1</td>\n",
" <td>60.0</td>\n",
" <td>165.0</td>\n",
" <td>75.0</td>\n",
" <td>97.1</td>\n",
" <td>142.0</td>\n",
" <td>70.0</td>\n",
" <td>16.0</td>\n",
" <td>1.0</td>\n",
" <td>40.0</td>\n",
" <td>65</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>27.548209</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14201</th>\n",
" <td>0</td>\n",
" <td>55.0</td>\n",
" <td>155.0</td>\n",
" <td>55.0</td>\n",
" <td>73.0</td>\n",
" <td>98.0</td>\n",
" <td>58.0</td>\n",
" <td>14.8</td>\n",
" <td>0.6</td>\n",
" <td>11.0</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>22.892820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29776</th>\n",
" <td>1</td>\n",
" <td>70.0</td>\n",
" <td>160.0</td>\n",
" <td>60.0</td>\n",
" <td>79.5</td>\n",
" <td>143.0</td>\n",
" <td>36.0</td>\n",
" <td>15.2</td>\n",
" <td>0.9</td>\n",
" <td>29.0</td>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23.437500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17289</th>\n",
" <td>1</td>\n",
" <td>40.0</td>\n",
" <td>165.0</td>\n",
" <td>75.0</td>\n",
" <td>95.0</td>\n",
" <td>139.0</td>\n",
" <td>54.0</td>\n",
" <td>15.9</td>\n",
" <td>1.1</td>\n",
" <td>33.0</td>\n",
" <td>65</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>27.548209</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5192</th>\n",
" <td>0</td>\n",
" <td>55.0</td>\n",
" <td>165.0</td>\n",
" <td>70.0</td>\n",
" <td>85.0</td>\n",
" <td>117.0</td>\n",
" <td>51.0</td>\n",
" <td>14.5</td>\n",
" <td>0.9</td>\n",
" <td>18.0</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>25.711662</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12172</th>\n",
" <td>1</td>\n",
" <td>60.0</td>\n",
" <td>160.0</td>\n",
" <td>65.0</td>\n",
" <td>88.0</td>\n",
" <td>98.0</td>\n",
" <td>57.0</td>\n",
" <td>15.2</td>\n",
" <td>1.0</td>\n",
" <td>11.0</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>25.390625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>235</th>\n",
" <td>1</td>\n",
" <td>45.0</td>\n",
" <td>170.0</td>\n",
" <td>85.0</td>\n",
" <td>95.0</td>\n",
" <td>177.0</td>\n",
" <td>40.0</td>\n",
" <td>13.9</td>\n",
" <td>1.1</td>\n",
" <td>42.0</td>\n",
" <td>26</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>29.411765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29733</th>\n",
" <td>1</td>\n",
" <td>50.0</td>\n",
" <td>175.0</td>\n",
" <td>80.0</td>\n",
" <td>91.0</td>\n",
" <td>43.0</td>\n",
" <td>47.0</td>\n",
" <td>14.7</td>\n",
" <td>1.0</td>\n",
" <td>17.0</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>26.122449</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>25660 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
" gender age height(cm) weight(kg) waist(cm) triglyceride HDL \\\n",
"16671 1 30.0 165.0 65.0 82.0 175.0 45.0 \n",
"3695 1 35.0 165.0 60.0 78.0 145.0 45.0 \n",
"3694 1 60.0 165.0 75.0 97.1 142.0 70.0 \n",
"14201 0 55.0 155.0 55.0 73.0 98.0 58.0 \n",
"29776 1 70.0 160.0 60.0 79.5 143.0 36.0 \n",
"... ... ... ... ... ... ... ... \n",
"17289 1 40.0 165.0 75.0 95.0 139.0 54.0 \n",
"5192 0 55.0 165.0 70.0 85.0 117.0 51.0 \n",
"12172 1 60.0 160.0 65.0 88.0 98.0 57.0 \n",
"235 1 45.0 170.0 85.0 95.0 177.0 40.0 \n",
"29733 1 50.0 175.0 80.0 91.0 43.0 47.0 \n",
"\n",
" hemoglobin serum creatinine ALT Gtp dental caries tartar \\\n",
"16671 15.2 0.9 15.0 17 0 0 \n",
"3695 15.8 1.0 38.0 56 1 0 \n",
"3694 16.0 1.0 40.0 65 0 1 \n",
"14201 14.8 0.6 11.0 12 0 1 \n",
"29776 15.2 0.9 29.0 17 0 0 \n",
"... ... ... ... ... ... ... \n",
"17289 15.9 1.1 33.0 65 1 0 \n",
"5192 14.5 0.9 18.0 16 0 0 \n",
"12172 15.2 1.0 11.0 23 0 1 \n",
"235 13.9 1.1 42.0 26 1 1 \n",
"29733 14.7 1.0 17.0 40 1 1 \n",
"\n",
" BMI \n",
"16671 23.875115 \n",
"3695 22.038567 \n",
"3694 27.548209 \n",
"14201 22.892820 \n",
"29776 23.437500 \n",
"... ... \n",
"17289 27.548209 \n",
"5192 25.711662 \n",
"12172 25.390625 \n",
"235 29.411765 \n",
"29733 26.122449 \n",
"\n",
"[25660 rows x 14 columns]"
]
},
"execution_count": 183,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train_new"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mission 4 - Unsupervised Learning"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on Test: \n",
"DT accuracy: 0.6935785536159601 \n",
"NN accuracy: 0.7591957605985037\n"
]
}
],
"source": [
"\n",
"\n",
"\n",
"# Train a DT & NN model on the training data\n",
"\n",
"nn = MLPClassifier(random_state=1,\n",
" hidden_layer_sizes=(10, 10,10,2),\n",
" max_iter=200,\n",
" activation='relu', \n",
" verbose=False,\n",
" solver='adam',\n",
" alpha=0.000005,\n",
" learning_rate_init=0.005)\n",
"\n",
"classifier_2.fit(X_train_new, y_train_new)\n",
"nn.fit(X_train_new_s, y_train_new)\n",
"\n",
"# Make predictions on the test data using the DT & NN model\n",
"dt_predictions = classifier_2.predict(X_valid_new_s)\n",
"nn_predictions = nn.predict(X_valid_new_s)\n",
"\n",
"# Compare the performance of the models using accuracy\n",
"dt_accuracy = accuracy_score(y_val_new, dt_predictions)\n",
"nn_accuracy = accuracy_score(y_val_new, nn_predictions)\n",
"\n",
"print(f'Accuracy on Test: \\nDT accuracy: {dt_accuracy} \\nNN accuracy: {nn_accuracy}')"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [],
"source": [
"def create_subtitle(fig: plt.Figure, grid: SubplotSpec, title: str):\n",
" \"Sign sets of subplots with title\"\n",
" row = fig.add_subplot(grid)\n",
" # the '\\n' is important\n",
" row.set_title(f'{title}\\n', fontweight='semibold', fontsize = 18)\n",
" # hide subplot\n",
" row.set_frame_on(False)\n",
" row.axis('off')"
]
},
{
"cell_type": "code",
"execution_count": 228,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>gender</th>\n",
" <th>age</th>\n",
" <th>height(cm)</th>\n",
" <th>weight(kg)</th>\n",
" <th>waist(cm)</th>\n",
" <th>triglyceride</th>\n",
" <th>HDL</th>\n",
" <th>hemoglobin</th>\n",
" <th>serum creatinine</th>\n",
" <th>ALT</th>\n",
" <th>Gtp</th>\n",
" <th>dental caries</th>\n",
" <th>tartar</th>\n",
" <th>BMI</th>\n",
" <th>smoking</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>30.0</td>\n",
" <td>175.0</td>\n",
" <td>70.0</td>\n",
" <td>82.0</td>\n",
" <td>96.0</td>\n",
" <td>70.0</td>\n",
" <td>14.2</td>\n",
" <td>1.2</td>\n",
" <td>19.0</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>22.857143</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>40.0</td>\n",
" <td>155.0</td>\n",
" <td>65.0</td>\n",
" <td>82.5</td>\n",
" <td>47.0</td>\n",
" <td>69.0</td>\n",
" <td>16.7</td>\n",
" <td>0.8</td>\n",
" <td>15.0</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>27.055151</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>50.0</td>\n",
" <td>150.0</td>\n",
" <td>50.0</td>\n",
" <td>72.4</td>\n",
" <td>89.0</td>\n",
" <td>65.0</td>\n",
" <td>13.3</td>\n",
" <td>1.0</td>\n",
" <td>11.0</td>\n",
" <td>18</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>22.222222</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>40.0</td>\n",
" <td>180.0</td>\n",
" <td>70.0</td>\n",
" <td>84.0</td>\n",
" <td>54.0</td>\n",
" <td>80.0</td>\n",
" <td>13.2</td>\n",
" <td>1.2</td>\n",
" <td>22.0</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>21.604938</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>60.0</td>\n",
" <td>155.0</td>\n",
" <td>55.0</td>\n",
" <td>78.0</td>\n",
" <td>192.0</td>\n",
" <td>48.0</td>\n",
" <td>13.6</td>\n",
" <td>0.8</td>\n",
" <td>21.0</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>22.892820</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8015</th>\n",
" <td>1</td>\n",
" <td>30.0</td>\n",
" <td>165.0</td>\n",
" <td>85.0</td>\n",
" <td>95.0</td>\n",
" <td>177.0</td>\n",
" <td>55.0</td>\n",
" <td>15.4</td>\n",
" <td>0.8</td>\n",
" <td>37.0</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>31.221304</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8016</th>\n",
" <td>1</td>\n",
" <td>40.0</td>\n",
" <td>170.0</td>\n",
" <td>70.0</td>\n",
" <td>82.2</td>\n",
" <td>190.0</td>\n",
" <td>49.0</td>\n",
" <td>16.2</td>\n",
" <td>1.0</td>\n",
" <td>30.0</td>\n",
" <td>89</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>24.221453</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8017</th>\n",
" <td>1</td>\n",
" <td>20.0</td>\n",
" <td>170.0</td>\n",
" <td>65.0</td>\n",
" <td>83.8</td>\n",
" <td>78.0</td>\n",
" <td>63.0</td>\n",
" <td>17.4</td>\n",
" <td>0.9</td>\n",
" <td>16.0</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>22.491349</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8018</th>\n",
" <td>1</td>\n",
" <td>65.0</td>\n",
" <td>160.0</td>\n",
" <td>55.0</td>\n",
" <td>74.0</td>\n",
" <td>59.0</td>\n",
" <td>72.0</td>\n",
" <td>13.8</td>\n",
" <td>0.9</td>\n",
" <td>16.0</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>21.484375</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8019</th>\n",
" <td>1</td>\n",
" <td>55.0</td>\n",
" <td>160.0</td>\n",
" <td>50.0</td>\n",
" <td>75.8</td>\n",
" <td>133.0</td>\n",
" <td>60.0</td>\n",
" <td>16.4</td>\n",
" <td>0.9</td>\n",
" <td>22.0</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>19.531250</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8020 rows × 15 columns</p>\n",
"</div>"
],
"text/plain": [
" gender age height(cm) weight(kg) waist(cm) triglyceride HDL \\\n",
"0 1 30.0 175.0 70.0 82.0 96.0 70.0 \n",
"1 1 40.0 155.0 65.0 82.5 47.0 69.0 \n",
"2 0 50.0 150.0 50.0 72.4 89.0 65.0 \n",
"3 1 40.0 180.0 70.0 84.0 54.0 80.0 \n",
"4 0 60.0 155.0 55.0 78.0 192.0 48.0 \n",
"... ... ... ... ... ... ... ... \n",
"8015 1 30.0 165.0 85.0 95.0 177.0 55.0 \n",
"8016 1 40.0 170.0 70.0 82.2 190.0 49.0 \n",
"8017 1 20.0 170.0 65.0 83.8 78.0 63.0 \n",
"8018 1 65.0 160.0 55.0 74.0 59.0 72.0 \n",
"8019 1 55.0 160.0 50.0 75.8 133.0 60.0 \n",
"\n",
" hemoglobin serum creatinine ALT Gtp dental caries tartar \\\n",
"0 14.2 1.2 19.0 16 0 1 \n",
"1 16.7 0.8 15.0 24 1 1 \n",
"2 13.3 1.0 11.0 18 0 0 \n",
"3 13.2 1.2 22.0 29 1 1 \n",
"4 13.6 0.8 21.0 27 0 1 \n",
"... ... ... ... ... ... ... \n",
"8015 15.4 0.8 37.0 60 0 1 \n",
"8016 16.2 1.0 30.0 89 0 1 \n",
"8017 17.4 0.9 16.0 21 0 0 \n",
"8018 13.8 0.9 16.0 14 0 1 \n",
"8019 16.4 0.9 22.0 20 0 1 \n",
"\n",
" BMI smoking \n",
"0 22.857143 0 \n",
"1 27.055151 1 \n",
"2 22.222222 0 \n",
"3 21.604938 0 \n",
"4 22.892820 0 \n",
"... ... ... \n",
"8015 31.221304 0 \n",
"8016 24.221453 1 \n",
"8017 22.491349 1 \n",
"8018 21.484375 0 \n",
"8019 19.531250 1 \n",
"\n",
"[8020 rows x 15 columns]"
]
},
"execution_count": 228,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_df_val[wanted]"
]
},
{
"cell_type": "code",
"execution_count": 238,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Explained variation per principal component: [0.82068415 0.07027603]\n",
"Cumulative variance explained by 2 principal components: 89.10%\n"
]
},
{
"data": {
"text/html": [
"<style>#sk-container-id-25 {color: black;background-color: white;}#sk-container-id-25 pre{padding: 0;}#sk-container-id-25 div.sk-toggleable {background-color: white;}#sk-container-id-25 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-25 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-25 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-25 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-25 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-25 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-25 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-25 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-25 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-25 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-25 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-25 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-25 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-25 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-25 div.sk-item {position: relative;z-index: 1;}#sk-container-id-25 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-25 div.sk-item::before, #sk-container-id-25 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-25 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-25 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-25 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-25 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-25 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-25 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-25 div.sk-label-container {text-align: center;}#sk-container-id-25 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-25 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-25\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-53\" type=\"checkbox\" checked><label for=\"sk-estimator-id-53\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2)</pre></div></div></div></div></div>"
],
"text/plain": [
"KMeans(n_clusters=2)"
]
},
"execution_count": 238,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Reducing the Dimensionality of the data to 2D\n",
"X_val_2 = temp_df_val[wanted].drop(['smoking'], axis=1)\n",
"y_val_2 = temp_df_val[wanted]['smoking']\n",
"pca_2 = PCA(n_components=2)\n",
"pca_2_result = pca_2.fit_transform(temp_df_val[wanted])\n",
"print('Explained variation per principal component: {}'.format(pca_2.explained_variance_ratio_))\n",
"print('Cumulative variance explained by 2 principal components: {:.2%}'.format(np.sum(pca_2.explained_variance_ratio_)))\n",
"# fitting KMeans\n",
"kmeans = KMeans(n_clusters=2)\n",
"kmeans.fit(temp_df_val)"
]
},
{
"cell_type": "code",
"execution_count": 239,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 2000x1800 with 15 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots((3),(2),figsize = (20,18))\n",
"# Create grid\n",
"grid = plt.GridSpec(3, 3)\n",
"create_subtitle(fig, grid[0, ::], 'K-Means')\n",
"create_subtitle(fig, grid[1, ::], 'Neural Network')\n",
"create_subtitle(fig, grid[2, ::], 'Decision Tree')\n",
"fig.tight_layout()\n",
"fig.set_facecolor('w')\n",
"\n",
"dict_model = {'model':[], 'report':[], 'pred':[], 'cm':[], 'acc':[],\"f1_score\": []}\n",
"for model in ([kmeans, nn, classifier_2]):\n",
" if model is not kmeans:\n",
" y_pred = model.predict(X_val_2)\n",
" else:\n",
" y_pred = model.labels_\n",
" \n",
" \n",
" dict_model['model'].append(model)\n",
" dict_model['report'].append(classification_report(y_val_2,y_pred, output_dict=True))\n",
" dict_model['pred'].append(y_pred)\n",
" dict_model['cm'].append(confusion_matrix(y_val_2,y_pred))\n",
" dict_model['acc'].append(accuracy_score(y_val_2, y_pred))\n",
" dict_model['f1_score'].append(f1_score(y_val_2, y_pred))\n",
"\n",
"colors = ['Blues', 'Greens', 'Reds']\n",
"for i in range(3):\n",
" #Heat map plot\n",
" sns.heatmap(pd.DataFrame(dict_model['report'][i]).iloc[:-1, :].T, annot=True, cmap = colors[i], ax = ax[i][0])\n",
" ax[i][0].set_title('Heat Map for Classification report')\n",
" #Confusion Matrix plot\n",
" cm = confusion_matrix(y_val_2,y_pred)\n",
" disp = ConfusionMatrixDisplay(confusion_matrix=dict_model['cm'][i], display_labels=dict_model['model'][1].classes_)\n",
" disp.plot(cmap=colors[i], ax=ax[i][1])\n",
" ax[i][1].set_title('Confusion Matrix (1-smoking,0-not)')\n"
]
},
{
"cell_type": "code",
"execution_count": 240,
"metadata": {},
"outputs": [],
"source": [
"dict_model['model'] = ['K-Means', 'Neural Network', 'Decision Tree']"
]
},
{
"cell_type": "code",
"execution_count": 241,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.41958041958041953, 0.5068008198248556, 0.6852192176424258]"
]
},
"execution_count": 241,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dict_model[\"f1_score\"]"
]
},
{
"cell_type": "code",
"execution_count": 242,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.6584788029925187, 0.3399002493765586, 0.7009975062344139]"
]
},
"execution_count": 242,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dict_model[\"acc\"]"
]
},
{
"cell_type": "code",
"execution_count": 243,
"metadata": {},
"outputs": [],
"source": [
"res_df = pd.DataFrame({'Model': dict_model['model'], 'Accuracy': dict_model[\"acc\"], 'F1 Score': dict_model[\"f1_score\"]})"
]
},
{
"cell_type": "code",
"execution_count": 245,
"metadata": {},
"outputs": [],
"source": [
"x = df.drop(['smoking'], axis=1)\n",
"y = df['smoking']\n",
"x_train_final, x_test_final, y_train_final, y_test_final = train_test_split(x, y, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 285,
"metadata": {},
"outputs": [],
"source": [
"wanted = [x for x in df_new.columns if x not in ['smoking']]\n",
"x_train_final = x_train_final[wanted]\n",
"x_test_final = x_test_final[wanted]\n",
"scaler = StandardScaler()\n",
"x_test_final = scaler.fit_transform(x_test_final)\n",
"x_train_final = scaler.fit_transform(x_train_final)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 286,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(32076, 14)"
]
},
"execution_count": 286,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train_final.shape"
]
},
{
"cell_type": "code",
"execution_count": 287,
"metadata": {},
"outputs": [],
"source": [
"# Train a neural network and a decision tree on the training set\n",
"\n",
"nn = MLPClassifier(random_state=1,\n",
" hidden_layer_sizes=(10, 10,10,2),\n",
" max_iter=200,\n",
" activation='relu', \n",
" verbose=False,\n",
" solver='adam',\n",
" alpha=0.000005,\n",
" learning_rate_init=0.005)"
]
},
{
"cell_type": "code",
"execution_count": 288,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-35 {color: black;background-color: white;}#sk-container-id-35 pre{padding: 0;}#sk-container-id-35 div.sk-toggleable {background-color: white;}#sk-container-id-35 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-35 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-35 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-35 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-35 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-35 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-35 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-35 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-35 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-35 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-35 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-35 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-35 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-35 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-35 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-35 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-35 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-35 div.sk-item {position: relative;z-index: 1;}#sk-container-id-35 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-35 div.sk-item::before, #sk-container-id-35 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-35 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-35 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-35 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-35 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-35 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-35 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-35 div.sk-label-container {text-align: center;}#sk-container-id-35 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-35 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-35\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(alpha=5e-06, hidden_layer_sizes=(10, 10, 10, 2),\n",
" learning_rate_init=0.005, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-73\" type=\"checkbox\" checked><label for=\"sk-estimator-id-73\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(alpha=5e-06, hidden_layer_sizes=(10, 10, 10, 2),\n",
" learning_rate_init=0.005, random_state=1)</pre></div></div></div></div></div>"
],
"text/plain": [
"MLPClassifier(alpha=5e-06, hidden_layer_sizes=(10, 10, 10, 2),\n",
" learning_rate_init=0.005, random_state=1)"
]
},
"execution_count": 288,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#fitting nn\n",
"nn.fit(x_train_final, y_train_final)"
]
},
{
"cell_type": "code",
"execution_count": 289,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(32076, 14)"
]
},
"execution_count": 289,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train_final.shape"
]
},
{
"cell_type": "code",
"execution_count": 290,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'class_weight': 'balanced',\n",
" 'criterion': 'gini',\n",
" 'max_depth': 10,\n",
" 'max_features': 20,\n",
" 'min_samples_leaf': 1000,\n",
" 'min_samples_split': 500,\n",
" 'splitter': 'best'}"
]
},
"execution_count": 290,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid_search_2.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 291,
"metadata": {},
"outputs": [],
"source": [
"# fitting Decision Tree\n",
"classifier_2 = DecisionTreeClassifier(criterion = 'gini', max_depth = 10,max_features=4, min_samples_leaf = 500, min_samples_split = 1000, splitter = 'best',class_weight='balanced')"
]
},
{
"cell_type": "code",
"execution_count": 292,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-36 {color: black;background-color: white;}#sk-container-id-36 pre{padding: 0;}#sk-container-id-36 div.sk-toggleable {background-color: white;}#sk-container-id-36 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-36 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-36 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-36 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-36 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-36 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-36 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-36 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-36 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-36 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-36 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-36 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-36 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-36 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-36 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-36 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-36 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-36 div.sk-item {position: relative;z-index: 1;}#sk-container-id-36 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-36 div.sk-item::before, #sk-container-id-36 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-36 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-36 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-36 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-36 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-36 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-36 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-36 div.sk-label-container {text-align: center;}#sk-container-id-36 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-36 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-36\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(class_weight='balanced', max_depth=10, max_features=4,\n",
" min_samples_leaf=500, min_samples_split=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-74\" type=\"checkbox\" checked><label for=\"sk-estimator-id-74\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(class_weight='balanced', max_depth=10, max_features=4,\n",
" min_samples_leaf=500, min_samples_split=1000)</pre></div></div></div></div></div>"
],
"text/plain": [
"DecisionTreeClassifier(class_weight='balanced', max_depth=10, max_features=4,\n",
" min_samples_leaf=500, min_samples_split=1000)"
]
},
"execution_count": 292,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"classifier_2.fit(x_train_final, y_train_final)"
]
},
{
"cell_type": "code",
"execution_count": 293,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([9.06942447e-01, 1.10055542e-02, 4.82571945e-04, 0.00000000e+00,\n",
" 1.80883516e-03, 2.44890767e-02, 6.56223216e-03, 2.41193981e-03,\n",
" 2.70241959e-03, 6.57880461e-04, 2.79037572e-02, 6.05425927e-03,\n",
" 6.81133299e-03, 2.16769307e-03])"
]
},
"execution_count": 293,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"classifier_2.feature_importances_"
]
},
{
"cell_type": "code",
"execution_count": 294,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-37 {color: black;background-color: white;}#sk-container-id-37 pre{padding: 0;}#sk-container-id-37 div.sk-toggleable {background-color: white;}#sk-container-id-37 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-37 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-37 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-37 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-37 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-37 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-37 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-37 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-37 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-37 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-37 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-37 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-37 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-37 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-37 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-37 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-37 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-37 div.sk-item {position: relative;z-index: 1;}#sk-container-id-37 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-37 div.sk-item::before, #sk-container-id-37 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-37 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-37 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-37 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-37 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-37 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-37 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-37 div.sk-label-container {text-align: center;}#sk-container-id-37 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-37 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-37\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[('dt',\n",
" DecisionTreeClassifier(class_weight='balanced',\n",
" max_depth=10,\n",
" max_features=4,\n",
" min_samples_leaf=500,\n",
" min_samples_split=1000)),\n",
" ('nn',\n",
" MLPClassifier(alpha=5e-06,\n",
" hidden_layer_sizes=(10, 10, 10, 2),\n",
" learning_rate_init=0.005,\n",
" random_state=1))],\n",
" voting='soft', weights=tensor([0.6557, 0.3443]))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-75\" type=\"checkbox\" ><label for=\"sk-estimator-id-75\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[('dt',\n",
" DecisionTreeClassifier(class_weight='balanced',\n",
" max_depth=10,\n",
" max_features=4,\n",
" min_samples_leaf=500,\n",
" min_samples_split=1000)),\n",
" ('nn',\n",
" MLPClassifier(alpha=5e-06,\n",
" hidden_layer_sizes=(10, 10, 10, 2),\n",
" learning_rate_init=0.005,\n",
" random_state=1))],\n",
" voting='soft', weights=tensor([0.6557, 0.3443]))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>dt</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-76\" type=\"checkbox\" ><label for=\"sk-estimator-id-76\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(class_weight='balanced', max_depth=10, max_features=4,\n",
" min_samples_leaf=500, min_samples_split=1000)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>nn</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-77\" type=\"checkbox\" ><label for=\"sk-estimator-id-77\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(alpha=5e-06, hidden_layer_sizes=(10, 10, 10, 2),\n",
" learning_rate_init=0.005, random_state=1)</pre></div></div></div></div></div></div></div></div></div></div>"
],
"text/plain": [
"VotingClassifier(estimators=[('dt',\n",
" DecisionTreeClassifier(class_weight='balanced',\n",
" max_depth=10,\n",
" max_features=4,\n",
" min_samples_leaf=500,\n",
" min_samples_split=1000)),\n",
" ('nn',\n",
" MLPClassifier(alpha=5e-06,\n",
" hidden_layer_sizes=(10, 10, 10, 2),\n",
" learning_rate_init=0.005,\n",
" random_state=1))],\n",
" voting='soft', weights=tensor([0.6557, 0.3443]))"
]
},
"execution_count": 294,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Implement Voting Classifier and create ensemble model\n",
"\n",
"dt = classifier_2\n",
"nn = nn\n",
"#Create ensemble model\n",
"ensemble = VotingClassifier(estimators=[('dt', dt), ('nn', nn)], voting='soft',weights=weights)\n",
"ensemble.fit(x_train_final, y_train_final)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 297,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7613466334164588"
]
},
"execution_count": 297,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res = nn.predict(x_test_final)\n",
"accuracy_score(y_test_final, res)"
]
},
{
"cell_type": "code",
"execution_count": 339,
"metadata": {},
"outputs": [],
"source": [
"x_test_real = pd.read_csv('X_test.csv')"
]
},
{
"cell_type": "code",
"execution_count": 301,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <th></th>\n",
" <th>ID</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>height(cm)</th>\n",
" <th>weight(kg)</th>\n",
" <th>waist(cm)</th>\n",
" <th>eyesight(left)</th>\n",
" <th>eyesight(right)</th>\n",
" <th>hearing(left)</th>\n",
" <th>hearing(right)</th>\n",
" <th>systolic</th>\n",
" <th>relaxation</th>\n",
" <th>fasting blood sugar</th>\n",
" <th>Cholesterol</th>\n",
" <th>triglyceride</th>\n",
" <th>HDL</th>\n",
" <th>LDL</th>\n",
" <th>hemoglobin</th>\n",
" <th>Urine protein</th>\n",
" <th>serum creatinine</th>\n",
" <th>AST</th>\n",
" <th>ALT</th>\n",
" <th>Gtp</th>\n",
" <th>oral</th>\n",
" <th>dental caries</th>\n",
" <th>tartar</th>\n",
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" <td>63.0</td>\n",
" <td>100.0</td>\n",
" <td>242.0</td>\n",
" <td>101.0</td>\n",
" <td>51.0</td>\n",
" <td>170.0</td>\n",
" <td>13.3</td>\n",
" <td>1</td>\n",
" <td>0.6</td>\n",
" <td>22.0</td>\n",
" <td>16.0</td>\n",
" <td>20</td>\n",
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" <td>0</td>\n",
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" <td>145.0</td>\n",
" <td>55.0</td>\n",
" <td>85.0</td>\n",
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" <td>127.0</td>\n",
" <td>75.0</td>\n",
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" <td>242.0</td>\n",
" <td>89.0</td>\n",
" <td>66.0</td>\n",
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" <td>0</td>\n",
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" <td>60.0</td>\n",
" <td>155.0</td>\n",
" <td>65.0</td>\n",
" <td>85.2</td>\n",
" <td>1.0</td>\n",
" <td>0.9</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>110.0</td>\n",
" <td>70.0</td>\n",
" <td>88.0</td>\n",
" <td>214.0</td>\n",
" <td>124.0</td>\n",
" <td>60.0</td>\n",
" <td>129.0</td>\n",
" <td>14.1</td>\n",
" <td>1</td>\n",
" <td>0.8</td>\n",
" <td>17.0</td>\n",
" <td>15.0</td>\n",
" <td>22</td>\n",
" <td>Y</td>\n",
" <td>0</td>\n",
" <td>N</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
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" <td>55.0</td>\n",
" <td>180.0</td>\n",
" <td>85.0</td>\n",
" <td>99.0</td>\n",
" <td>0.7</td>\n",
" <td>0.7</td>\n",
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" <td>74.0</td>\n",
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" <td>0</td>\n",
" <td>N</td>\n",
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" <td>F</td>\n",
" <td>60.0</td>\n",
" <td>150.0</td>\n",
" <td>55.0</td>\n",
" <td>79.0</td>\n",
" <td>0.4</td>\n",
" <td>0.4</td>\n",
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" <td>1.0</td>\n",
" <td>143.0</td>\n",
" <td>77.0</td>\n",
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" <td>176.0</td>\n",
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],
"text/plain": [
" ID gender age height(cm) weight(kg) waist(cm) eyesight(left) \\\n",
"0 40139 F 60.0 150.0 55.0 80.0 0.6 \n",
"1 35862 F 50.0 145.0 55.0 85.0 1.5 \n",
"2 8918 F 60.0 155.0 65.0 85.2 1.0 \n",
"3 40190 M 55.0 180.0 85.0 99.0 0.7 \n",
"4 16914 F 60.0 150.0 55.0 79.0 0.4 \n",
"\n",
" eyesight(right) hearing(left) hearing(right) systolic relaxation \\\n",
"0 0.6 1.0 1.0 103.0 63.0 \n",
"1 1.5 1.0 1.0 127.0 75.0 \n",
"2 0.9 1.0 1.0 110.0 70.0 \n",
"3 0.7 1.0 1.0 108.0 74.0 \n",
"4 0.4 1.0 1.0 143.0 77.0 \n",
"\n",
" fasting blood sugar Cholesterol triglyceride HDL LDL hemoglobin \\\n",
"0 100.0 242.0 101.0 51.0 170.0 13.3 \n",
"1 100.0 242.0 89.0 66.0 158.0 15.1 \n",
"2 88.0 214.0 124.0 60.0 129.0 14.1 \n",
"3 119.0 110.0 155.0 37.0 42.0 13.8 \n",
"4 101.0 176.0 156.0 58.0 89.0 13.5 \n",
"\n",
" Urine protein serum creatinine AST ALT Gtp oral dental caries tartar \n",
"0 1 0.6 22.0 16.0 20 Y 0 N \n",
"1 1 0.8 18.0 15.0 17 Y 0 Y \n",
"2 1 0.8 17.0 15.0 22 Y 0 N \n",
"3 1 1.0 21.0 24.0 35 Y 0 N \n",
"4 1 0.7 42.0 22.0 28 Y 0 Y "
]
},
"execution_count": 301,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_test_real.head()"
]
},
{
"cell_type": "code",
"execution_count": 340,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Already deleted\n",
"Y 11132\n",
"Name: oral, dtype: int64\n",
"1.0 10496\n",
"2.0 363\n",
"3.0 196\n",
"4.0 58\n",
"5.0 21\n",
"Name: Urine protein, dtype: int64\n",
"col_0 Prior Probabity\n",
"Urine protein \n",
"1.0 0.942444\n",
"2.0 0.032594\n",
"3.0 0.017599\n",
"4.0 0.005208\n",
"5.0 0.001886\n"
]
}
],
"source": [
"\n",
"l_1 = list(x_test_real[\"waist(cm)\"])\n",
"misplaced_1 = [ind for ind,x in enumerate(l_1) if isfloat(x) == False]\n",
"# The Identified problematic rows\n",
"\n",
"#Deleting rows with misplaced data ( Run only once)\n",
"try:\n",
" x_test_real.drop([misplaced_1[0]],axis=0,inplace = True)\n",
" print(\"Sample # \",misplaced_1[0] ,\"was deleted\")\n",
"except:\n",
" print(\"Already deleted\")\n",
"\n",
"x_test_real[\"waist(cm)\"] = x_test_real[\"waist(cm)\"].astype(float)\n",
"\n",
"#Identified as useless, because all of the samples are 1 and the only 12 \n",
"# different samples are misformatted\n",
"#Fixing oral values\n",
"x_test_real = x_test_real[(x_test_real[\"oral\"] != \"yes\") & ((x_test_real[\"oral\"] != \"12\"))]\n",
"x_test_real[\"oral\"] = x_test_real[\"oral\"].astype(object)\n",
"print(x_test_real[\"oral\"].value_counts())\n",
"\n",
"#Removing the row with the yes value and merging the float and the integers to one value \n",
"x_test_real = x_test_real[x_test_real[\"Urine protein\"] != \"yes\"]\n",
"x_test_real[\"Urine protein\"] = x_test_real[\"Urine protein\"].astype(float)\n",
"print(x_test_real[\"Urine protein\"].value_counts())\n",
"print(pd.crosstab(x_test_real[\"Urine protein\"],\"Prior Probabity\")/len(x_test_real))\n",
"\n",
"x_test_real[\"weight(kg)\"].astype(float)\n",
"x_test_real = x_test_real[x_test_real[\"waist(cm)\"] != \"ok\"]\n",
"x_test_real[\"waist(cm)\"] = x_test_real[\"waist(cm)\"].astype(float)\n",
"\n",
"#removing the null values\n",
"x_test_real.dropna(inplace = True)\n",
"#Removing all unreasonable values - removing the entire tuple if one of the values is unreasonable\n",
"# Fixing systolic values (there were negative values and values above 250)\n",
"x_test_real = x_test_real[(x_test_real[\"systolic\"]> 0) & (x_test_real[\"systolic\"]< 250)]\n",
"# Fixing fasting blood sugar values (there were values above 400 )\n",
"x_test_real = x_test_real[(x_test_real[\"fasting blood sugar\"]> 0) & (x_test_real[\"fasting blood sugar\"]< 200)]\n",
"# Fixing triglyceride values (there were values above 500 )\n",
"x_test_real = x_test_real[(x_test_real[\"triglyceride\"]> 0) & (x_test_real[\"triglyceride\"]< 500)]\n",
"# Fixing HDL cholesterol values (there were values above 300 )\n",
"x_test_real = x_test_real[(x_test_real[\"HDL\"]> 10) & (x_test_real[\"HDL\"]< 300)]\n",
"# Fixing LDL cholesterol values (there were values above 1800 )\n",
"x_test_real = x_test_real[(x_test_real[\"LDL\"]> 0) & (x_test_real[\"LDL\"]< 300)]\n",
"# Fixing serum creatinine values (there were values above 1.5 )\n",
"x_test_real = x_test_real[(x_test_real[\"serum creatinine\"]> 0) & (x_test_real[\"serum creatinine\"]< 1.5)]\n",
"# Fixing AST values (there were values above 50 )\n",
"x_test_real = x_test_real[(x_test_real[\"AST\"]> 0) & (x_test_real[\"AST\"]< 50)]\n",
"# Fixing ALT values (there were values above 100 )\n",
"x_test_real = x_test_real[(x_test_real[\"ALT\"]> 0) & (x_test_real[\"ALT\"]< 100)]\n",
"# Fixing Gtp values (there were values above 100 )\n",
"x_test_real = x_test_real[(x_test_real[\"Gtp\"]> 0) & (x_test_real[\"Gtp\"]< 100)]\n",
"#Deriving a new feature based on existing features\n",
"x_test_real[\"BMI\"] = x_test_real[\"weight(kg)\"]/((x_test_real[\"height(cm)\"]/100)**2)"
]
},
{
"cell_type": "code",
"execution_count": 341,
"metadata": {},
"outputs": [],
"source": [
"\n",
"x_test_real = x_test_real.drop(['oral'],axis=1)\n",
"# dummies for gender\n",
"x_test_real['gender'].replace(['M', 'F'], [1,0], inplace=True)\n",
"# dummies for age\n",
"x_test_real['age'].replace(['Young', 'Middle', 'Old'], [2,1,0], inplace=True)\n",
"# dummies for height\n",
"x_test_real['height(cm)'].replace(['Middle', 'Tall', 'Short'], [2,1,0], inplace=True)\n",
"# dummies for weight\n",
"x_test_real['weight(kg)'].replace(['Underweight', 'Normal', 'Overweight'], [2,1,0], inplace=True)\n",
"# dummies for fasting blood sugar\n",
"x_test_real['fasting blood sugar'].replace(['Pre-Diabetes', 'Diabetes', 'Normal'], [2,1,0], inplace=True)\n",
"# dummies for tartar\n",
"x_test_real['tartar'].replace(['Y', 'N'], [1,0], inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 342,
"metadata": {},
"outputs": [],
"source": [
"x_test_real = x_test_real.drop(['ID'],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 343,
"metadata": {},
"outputs": [],
"source": [
"x_test_real = x_test_real[wanted]"
]
},
{
"cell_type": "code",
"execution_count": 344,
"metadata": {},
"outputs": [],
"source": [
"x_test_real[\"gender\"] = x_test_real[\"gender\"][x_test_real[\"gender\"] != \"ok\"]\n",
"x_test_real[\"tartar\"] = x_test_real[\"tartar\"][x_test_real[\"tartar\"] != \"yes\"]\n",
"x_test_real[\"tartar\"] = x_test_real[\"tartar\"][x_test_real[\"tartar\"] != \"no\"]\n"
]
},
{
"cell_type": "code",
"execution_count": 345,
"metadata": {},
"outputs": [],
"source": [
"x_test_real.dropna(inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 346,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <th>height(cm)</th>\n",
" <th>weight(kg)</th>\n",
" <th>waist(cm)</th>\n",
" <th>triglyceride</th>\n",
" <th>HDL</th>\n",
" <th>hemoglobin</th>\n",
" <th>serum creatinine</th>\n",
" <th>ALT</th>\n",
" <th>Gtp</th>\n",
" <th>dental caries</th>\n",
" <th>tartar</th>\n",
" <th>BMI</th>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>24.444444</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>50.0</td>\n",
" <td>145.0</td>\n",
" <td>55.0</td>\n",
" <td>85.0</td>\n",
" <td>89.0</td>\n",
" <td>66.0</td>\n",
" <td>15.1</td>\n",
" <td>0.8</td>\n",
" <td>15.0</td>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>26.159334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>60.0</td>\n",
" <td>155.0</td>\n",
" <td>65.0</td>\n",
" <td>85.2</td>\n",
" <td>124.0</td>\n",
" <td>60.0</td>\n",
" <td>14.1</td>\n",
" <td>0.8</td>\n",
" <td>15.0</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>27.055151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>55.0</td>\n",
" <td>180.0</td>\n",
" <td>85.0</td>\n",
" <td>99.0</td>\n",
" <td>155.0</td>\n",
" <td>37.0</td>\n",
" <td>13.8</td>\n",
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" <td>26.234568</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>60.0</td>\n",
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" <td>79.0</td>\n",
" <td>156.0</td>\n",
" <td>58.0</td>\n",
" <td>13.5</td>\n",
" <td>0.7</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>24.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gender age height(cm) weight(kg) waist(cm) triglyceride HDL \\\n",
"0 0 60.0 150.0 55.0 80.0 101.0 51.0 \n",
"1 0 50.0 145.0 55.0 85.0 89.0 66.0 \n",
"2 0 60.0 155.0 65.0 85.2 124.0 60.0 \n",
"3 1 55.0 180.0 85.0 99.0 155.0 37.0 \n",
"4 0 60.0 150.0 55.0 79.0 156.0 58.0 \n",
"\n",
" hemoglobin serum creatinine ALT Gtp dental caries tartar BMI \n",
"0 13.3 0.6 16.0 20 0 0 24.444444 \n",
"1 15.1 0.8 15.0 17 0 1 26.159334 \n",
"2 14.1 0.8 15.0 22 0 0 27.055151 \n",
"3 13.8 1.0 24.0 35 0 0 26.234568 \n",
"4 13.5 0.7 22.0 28 0 1 24.444444 "
]
},
"execution_count": 346,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_test_real.head()"
]
},
{
"cell_type": "code",
"execution_count": 347,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 5664\n",
"0 4373\n",
"Name: tartar, dtype: int64"
]
},
"execution_count": 347,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_test_real[\"tartar\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 348,
"metadata": {},
"outputs": [],
"source": [
"scaler = StandardScaler()\n",
"x_test_real = scaler.fit_transform(x_test_real)\n",
"final_result = nn.predict(x_test_real)"
]
},
{
"cell_type": "code",
"execution_count": 349,
"metadata": {},
"outputs": [],
"source": [
"final_result = pd.DataFrame(final_result)"
]
},
{
"cell_type": "code",
"execution_count": 351,
"metadata": {},
"outputs": [],
"source": [
"final_result.columns = [\"Target\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x_test_real = pd.read_csv(\"smoking_g25_25.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 352,
"metadata": {},
"outputs": [],
"source": [
"final_result.to_csv(\"smoking_G25_ytest.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 364,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7613466334164588"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0.7297794117647058"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0.627370417193426"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0.6747110808973487"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cm = confusion_matrix(y_test_final, res)\n",
"disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=nn.classes_)\n",
"disp.plot(cmap=\"Blues\")\n",
"display(accuracy_score(y_test_final, res))\n",
"display(recall_score(y_test_final, res))\n",
"display(precision_score(y_test_final, res))\n",
"display(f1_score(y_test_final, res))"
]
}
],
"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": "3.10.8"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "6331f6627766c5d902399042081938523872e004e4aa9b68eb97fccba61701b4"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}