Downloads: 1
[fda7b9]:
/
T2_Coronal.ipynb
2596 lines (2596 with data), 500.7 kB
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyNoklhjA/9cPEiZaxaqtQgC",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/Souhib-khalbous/Quantitative-Analysis-of-T2-Coronal-MRI-Data-for-Treatment-Efficiency-in-Uterine-Fibroids-/blob/master/T2_Coronal.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# **Mount Your Data**"
],
"metadata": {
"id": "XyHlZw3pQERr"
}
},
{
"cell_type": "code",
"source": [
"#Mount Google Drive when you are using Cloab\n",
"\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EPkC8-bHrRTW",
"outputId": "016a12cb-6332-4b2e-a3e3-29804df6517d"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# **Importing The Usage Libraries** 📂\n",
"\n",
"* Importing Libraries\n",
"* Reading Data\n",
"* Droping the NOT useful Data"
],
"metadata": {
"id": "tZOeFUqY1JFG"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gRakXBqYlliv"
},
"outputs": [],
"source": [
"#Libraries that alwyas we use\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, roc_auc_score, roc_curve, auc\n",
"#auc: to calculate the\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"from scipy.stats import pearsonr #for Correlation\n",
"\n",
"from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler #StandardScaler for Normaliztion.\n",
"\n",
"#to train the data by DT\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"\n",
"################################################################################\n",
"#Read the Data\n",
"Given_Data = pd.read_excel('/content/drive/MyDrive/IHIRC2/T2_Coronal_One Roi_Center Slice_1.xlsx')\n",
"\n",
"#Dropping the unnecessary columns\n",
"Given_Data = Given_Data.dropna(axis=1)\n",
"Given_Data = Given_Data.drop(['Patient_info', 'Myometrium-1_Av', 'Myometrium-2_Av', 'Muscle-1_Av', 'Muscle-2_Av','KESERCI', 'NPV_Q1_Final', 'NPV_Q2_Final','NPV_Q3_Final','NPV_Q4_Final', 'FUNAKI', 'NPV' ],axis= 1)\n",
"\n",
"#converting the Last column into integer insted of being a string\n",
"Given_Data['NPV_INTER_90'] = Given_Data['NPV_INTER_90'].apply(lambda x: 1 if x == '>90' else 0)\n",
"\n"
]
},
{
"cell_type": "markdown",
"source": [
"# **Data After Proccessing** 🔂"
],
"metadata": {
"id": "50xumd4v0yO0"
}
},
{
"cell_type": "code",
"source": [
"#Remember: The head function for the following lines. JUST the last line will be excecuted.\n",
"Given_Data.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 224
},
"id": "pdtBb5eNtVxI",
"outputId": "8f85923f-4e35-4e75-9387-8473fe5abade"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Fibroid_Av Fibroid_Av_ratio_Myometrium-1_Av \\\n",
"0 246.6 0.568989 \n",
"1 51.5 1.066253 \n",
"2 89.0 0.815018 \n",
"3 53.2 0.536290 \n",
"4 90.0 0.879765 \n",
"\n",
" Fibroid_Av_ratio_Myometrium-2_Av Fibroid_Av_ratio_Muscle-1_Av \\\n",
"0 0.627321 3.086358 \n",
"1 0.905097 1.900369 \n",
"2 0.560453 1.437803 \n",
"3 0.302445 1.886525 \n",
"4 1.243953 5.806452 \n",
"\n",
" Fibroid_Av_ratio_Muscle-2_Av NPV_INTER_90 \n",
"0 3.889590 1 \n",
"1 2.258772 1 \n",
"2 4.120370 1 \n",
"3 1.303922 1 \n",
"4 3.225806 1 "
],
"text/html": [
"\n",
" <div id=\"df-bb4b9f3c-3338-48ce-9332-d6d02a716e54\" class=\"colab-df-container\">\n",
" <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>Fibroid_Av</th>\n",
" <th>Fibroid_Av_ratio_Myometrium-1_Av</th>\n",
" <th>Fibroid_Av_ratio_Myometrium-2_Av</th>\n",
" <th>Fibroid_Av_ratio_Muscle-1_Av</th>\n",
" <th>Fibroid_Av_ratio_Muscle-2_Av</th>\n",
" <th>NPV_INTER_90</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>246.6</td>\n",
" <td>0.568989</td>\n",
" <td>0.627321</td>\n",
" <td>3.086358</td>\n",
" <td>3.889590</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>51.5</td>\n",
" <td>1.066253</td>\n",
" <td>0.905097</td>\n",
" <td>1.900369</td>\n",
" <td>2.258772</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>89.0</td>\n",
" <td>0.815018</td>\n",
" <td>0.560453</td>\n",
" <td>1.437803</td>\n",
" <td>4.120370</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>53.2</td>\n",
" <td>0.536290</td>\n",
" <td>0.302445</td>\n",
" <td>1.886525</td>\n",
" <td>1.303922</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>90.0</td>\n",
" <td>0.879765</td>\n",
" <td>1.243953</td>\n",
" <td>5.806452</td>\n",
" <td>3.225806</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb4b9f3c-3338-48ce-9332-d6d02a716e54')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-bb4b9f3c-3338-48ce-9332-d6d02a716e54 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-bb4b9f3c-3338-48ce-9332-d6d02a716e54');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-7344de40-2246-4f48-aa0e-7ee4c2ddcd53\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7344de40-2246-4f48-aa0e-7ee4c2ddcd53')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-7344de40-2246-4f48-aa0e-7ee4c2ddcd53 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"source": [
"# **Dividing the Data**\n",
"\n",
"* Separating the Features & the Target (Y).\n",
"* Encoding the target (Y).\n",
"* Splitting the Data into Training & Test sets."
],
"metadata": {
"id": "bzdrZgj90djn"
}
},
{
"cell_type": "code",
"source": [
"#In case you have just a single DATA FRAME\n",
"# Separate the features (X) and target variable (Y) for DATA 1\n",
"X=Given_Data.iloc[:,:-1 ]\n",
"Y=Given_Data.iloc[:,-1]\n",
"\n",
"X.head(10)\n",
"Y.head(10)\n",
"\n",
"# Perform one-hot encoding on the target variable\n",
"Y = pd.get_dummies(Y, drop_first=True)\n",
"\n",
"# Split the data into training and test sets\n",
"x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
"x_train.head()\n",
"#####################################################################################"
],
"metadata": {
"id": "m6zARXcdmNQO",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"outputId": "f8ab0ad4-73ea-46aa-a9e8-f3cfdfafa221"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Fibroid_Av Fibroid_Av_ratio_Myometrium-1_Av \\\n",
"73 29.8 1.683616 \n",
"61 28.3 0.234078 \n",
"55 16.0 0.583942 \n",
"40 192.6 1.059989 \n",
"9 398.8 0.512004 \n",
"\n",
" Fibroid_Av_ratio_Myometrium-2_Av Fibroid_Av_ratio_Muscle-1_Av \\\n",
"73 0.798928 1.610811 \n",
"61 0.228410 1.286364 \n",
"55 0.465116 3.809524 \n",
"40 0.603194 1.882698 \n",
"9 0.506348 2.777159 \n",
"\n",
" Fibroid_Av_ratio_Muscle-2_Av \n",
"73 2.483333 \n",
"61 1.155102 \n",
"55 1.882353 \n",
"40 1.457986 \n",
"9 4.132642 "
],
"text/html": [
"\n",
" <div id=\"df-04264485-94e1-445e-aa65-b5c756eb01a9\" class=\"colab-df-container\">\n",
" <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>Fibroid_Av</th>\n",
" <th>Fibroid_Av_ratio_Myometrium-1_Av</th>\n",
" <th>Fibroid_Av_ratio_Myometrium-2_Av</th>\n",
" <th>Fibroid_Av_ratio_Muscle-1_Av</th>\n",
" <th>Fibroid_Av_ratio_Muscle-2_Av</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>29.8</td>\n",
" <td>1.683616</td>\n",
" <td>0.798928</td>\n",
" <td>1.610811</td>\n",
" <td>2.483333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>28.3</td>\n",
" <td>0.234078</td>\n",
" <td>0.228410</td>\n",
" <td>1.286364</td>\n",
" <td>1.155102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>16.0</td>\n",
" <td>0.583942</td>\n",
" <td>0.465116</td>\n",
" <td>3.809524</td>\n",
" <td>1.882353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>192.6</td>\n",
" <td>1.059989</td>\n",
" <td>0.603194</td>\n",
" <td>1.882698</td>\n",
" <td>1.457986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>398.8</td>\n",
" <td>0.512004</td>\n",
" <td>0.506348</td>\n",
" <td>2.777159</td>\n",
" <td>4.132642</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04264485-94e1-445e-aa65-b5c756eb01a9')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-04264485-94e1-445e-aa65-b5c756eb01a9 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-04264485-94e1-445e-aa65-b5c756eb01a9');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-25e85dfc-f7b0-48fb-947e-7294985fde7c\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-25e85dfc-f7b0-48fb-947e-7294985fde7c')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-25e85dfc-f7b0-48fb-947e-7294985fde7c button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"source": [
"print(Given_Data.shape)\n",
"# Check the shape of the dataframes\n",
"print(\"\\nShape of x_train:\", x_train.shape)\n",
"print(\"\\nShape of x_test:\", x_test.shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZwwnYh1CnKqm",
"outputId": "97326f28-397a-44ed-a268-3bb28f01f070"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(79, 6)\n",
"\n",
"Shape of x_train: (63, 5)\n",
"\n",
"Shape of x_test: (16, 5)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# **Correlation** **Matrix**: 1️⃣>= X >= ➖1️⃣\n",
"\n",
"* Create the Correlation Matrix.\n",
"* Plotting the Correlation Matrix."
],
"metadata": {
"id": "TNtfIKS-zVhI"
}
},
{
"cell_type": "code",
"source": [
"# Calculate correlation matrix\n",
"correlation_matrix = Given_Data.corr()\n",
"\n",
"#Plot correlation matrix\n",
"plt.figure(figsize=(12, 12)) # Increase the figure size\n",
"sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", annot_kws={\"fontsize\": 8}) # Increase font size of annotations\n",
"plt.title('Correlation Matrix')\n",
"plt.savefig('/content/drive/MyDrive/IHIRC2.pdf', dpi=300, bbox_inches='tight')\n",
"plt.show()\n",
"\n",
"##############################################################################################################"
],
"metadata": {
"id": "UtthQZT2l4En",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "34f3498c-0f38-4ad3-cc1d-73b247df0aee"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x1200 with 2 Axes>"
],
"image/png": "iVBORw0KGgoAAAANSUhEUgAABHQAAATCCAYAAADcn7HeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAD7VElEQVR4nOzdeZyN5f/H8fd9Zt/HGDODMIx1yBIqpBBNsiTJXpYUSpFUtNmK79eW+lXazFBfgxZaKEpS2SKMKKUwCDOWYXaznfP7Y3I4zTAzOGZuXs/H4348nPu+7vu+7vucOc75nM/1uQybzWYTAAAAAAAATMNS2h0AAAAAAABAyRDQAQAAAAAAMBkCOgAAAAAAACZDQAcAAAAAAMBkCOgAAAAAAACYDAEdAAAAAAAAkyGgAwAAAAAAYDIEdAAAAAAAAEyGgA4AAAAAAIDJENABAAC4QubNmyfDMBQfH3/ZjhkfHy/DMDRv3rzLdkyza9Omjdq0aVPa3QAAwKkI6AAAAFPbs2ePhg4dqho1asjT01P+/v5q1aqVXn31VWVmZpZ29y6b2NhYzZ49u7S74WDgwIEyDEP+/v6F3us///xThmHIMAzNmDGjxMc/fPiwJkyYoLi4uMvQWwAAri6upd0BAACAi7V8+XLdd9998vDw0AMPPKAGDRooOztba9eu1VNPPaVff/1V77zzTml387KIjY3Vzp07NWrUKIf11apVU2Zmptzc3EqlX66ursrIyNAXX3yhnj17OmxbsGCBPD09dfr06Ys69uHDhzVx4kSFh4ercePGxd7v66+/vqjzAQBgJgR0AACAKe3bt0+9e/dWtWrVtHr1alWsWNG+7dFHH9Vff/2l5cuXX/J5bDabTp8+LS8vrwLbTp8+LXd3d1kspZf0bBiGPD09S+38Hh4eatWqlRYuXFggoBMbG6tOnTrpk08+uSJ9ycjIkLe3t9zd3a/I+QAAKE0MuQIAAKY0bdo0paWlae7cuQ7BnDNq1qypkSNH2h/n5uZq8uTJioiIkIeHh8LDw/Xss88qKyvLYb/w8HB17txZK1euVLNmzeTl5aW3335ba9askWEYWrRokZ5//nlVrlxZ3t7eSklJkST99NNPuvPOOxUQECBvb2/ddtttWrduXZHX8dlnn6lTp06qVKmSPDw8FBERocmTJysvL8/epk2bNlq+fLn2799vH8IUHh4u6fw1dFavXq3WrVvLx8dHgYGBuvvuu7Vr1y6HNhMmTJBhGPrrr780cOBABQYGKiAgQIMGDVJGRkaRfT+jb9+++uqrr3Tq1Cn7us2bN+vPP/9U3759C7RPSkrSmDFjdP3118vX11f+/v7q2LGjtm/fbm+zZs0aNW/eXJI0aNAg+3Wfuc42bdqoQYMG2rJli2699VZ5e3vr2WeftW87t4bOgAED5OnpWeD6o6KiVK5cOR0+fLjY1woAQFlBhg4AADClL774QjVq1FDLli2L1X7IkCGaP3++evTooSeffFI//fSTpk6dql27dmnp0qUObf/44w/16dNHQ4cO1UMPPaQ6derYt02ePFnu7u4aM2aMsrKy5O7urtWrV6tjx45q2rSpxo8fL4vFopiYGLVr104//vijbrzxxvP2a968efL19dXo0aPl6+ur1atX68UXX1RKSoqmT58uSXruueeUnJysv//+W6+88ookydfX97zHXLVqlTp27KgaNWpowoQJyszM1P/93/+pVatW2rp1qz0YdEbPnj1VvXp1TZ06VVu3btV7772nkJAQ/fe//y3Wve3evbuGDRumJUuWaPDgwZLys3Pq1q2rG264oUD7vXv36tNPP9V9992n6tWrKzExUW+//bZuu+02/fbbb6pUqZLq1aunSZMm6cUXX9TDDz+s1q1bS5LD833ixAl17NhRvXv3Vv/+/RUaGlpo/1599VWtXr1aAwYM0IYNG+Ti4qK3335bX3/9tT744ANVqlSpWNcJAECZYgMAADCZ5ORkmyTb3XffXaz2cXFxNkm2IUOGOKwfM2aMTZJt9erV9nXVqlWzSbKtWLHCoe13331nk2SrUaOGLSMjw77earXaatWqZYuKirJZrVb7+oyMDFv16tVtHTp0sK+LiYmxSbLt27fPod2/DR061Obt7W07ffq0fV2nTp1s1apVK9B23759Nkm2mJgY+7rGjRvbQkJCbCdOnLCv2759u81isdgeeOAB+7rx48fbJNkGDx7scMx77rnHVr58+QLn+rcBAwbYfHx8bDabzdajRw/b7bffbrPZbLa8vDxbWFiYbeLEifb+TZ8+3b7f6dOnbXl5eQWuw8PDwzZp0iT7us2bNxe4tjNuu+02myTbW2+9Vei22267zWHdypUrbZJsL730km3v3r02X19fW7du3Yq8RgAAyiqGXAEAANM5M8zJz8+vWO2//PJLSdLo0aMd1j/55JOSVKDWTvXq1RUVFVXosQYMGOBQTycuLs4+tOjEiRM6fvy4jh8/rvT0dN1+++364YcfZLVaz9u3c4+Vmpqq48ePq3Xr1srIyNDvv/9erOs715EjRxQXF6eBAwcqKCjIvr5hw4bq0KGD/V6ca9iwYQ6PW7durRMnTtjvc3H07dtXa9asUUJCglavXq2EhIRCh1tJ+XV3ztQdysvL04kTJ+Tr66s6depo69atxT6nh4eHBg0aVKy2d9xxh4YOHapJkyape/fu8vT01Ntvv13scwEAUNYw5AoAAJiOv7+/pPwASHHs379fFotFNWvWdFgfFhamwMBA7d+/32F99erVz3usf2/7888/JeUHes4nOTlZ5cqVK3Tbr7/+queff16rV68uEEBJTk4+7zHP58y1nDtM7Ix69epp5cqVSk9Pl4+Pj3191apVHdqd6evJkyft97ood911l/z8/LR48WLFxcWpefPmqlmzpuLj4wu0tVqtevXVV/Xmm29q3759DvWCypcvX6zzSVLlypVLVAB5xowZ+uyzzxQXF6fY2FiFhIQUe18AAMoaAjoAAMB0/P39ValSJe3cubNE+xmGUax2hc1odb5tZ7Jvpk+fft6ptc9X7+bUqVO67bbb5O/vr0mTJikiIkKenp7aunWrnnnmmQtm9lxOLi4uha632WzFPoaHh4e6d++u+fPna+/evZowYcJ5206ZMkUvvPCCBg8erMmTJysoKEgWi0WjRo0q0TVf6HkqzLZt23T06FFJ0o4dO9SnT58S7Q8AQFlCQAcAAJhS586d9c4772jDhg1q0aLFBdtWq1ZNVqtVf/75p+rVq2dfn5iYqFOnTqlatWoX3Y+IiAhJ+UGm9u3bl2jfNWvW6MSJE1qyZIluvfVW+/p9+/YVaFvcYNSZa/njjz8KbPv9998VHBzskJ1zOfXt21fR0dGyWCzq3bv3edt9/PHHatu2rebOneuw/tSpUwoODrY/Lu41F0d6eroGDRqkyMhItWzZUtOmTdM999xjn0kLAACzoYYOAAAwpaefflo+Pj4aMmSIEhMTC2zfs2ePXn31VUn5w4Ekafbs2Q5tZs2aJUnq1KnTRfejadOmioiI0IwZM5SWllZg+7Fjx86775nMmHMzYbKzs/Xmm28WaOvj41OsIVgVK1ZU48aNNX/+fIdpxHfu3Kmvv/7afi+coW3btpo8ebJef/11hYWFnbedi4tLgeyfjz76SIcOHXJYdybwdO51XKxnnnlGBw4c0Pz58zVr1iyFh4drwIABBaatBwDALMjQAQAAphQREaHY2Fj16tVL9erV0wMPPKAGDRooOztb69ev10cffaSBAwdKkho1aqQBAwbonXfesQ9z2rRpk+bPn69u3bqpbdu2F90Pi8Wi9957Tx07dlT9+vU1aNAgVa5cWYcOHdJ3330nf39/ffHFF4Xu27JlS5UrV04DBgzQ448/LsMw9MEHHxQ61Klp06ZavHixRo8erebNm8vX11ddunQp9LjTp09Xx44d1aJFCz344IP2acsDAgIuOBTqUlksFj3//PNFtuvcubMmTZqkQYMGqWXLltqxY4cWLFigGjVqOLSLiIhQYGCg3nrrLfn5+cnHx0c33XTTBWscFWb16tV68803NX78ePs06jExMWrTpo1eeOEFTZs2rUTHAwCgLCBDBwAAmFbXrl31yy+/qEePHvrss8/06KOPauzYsYqPj9fMmTP12muv2du+9957mjhxojZv3qxRo0Zp9erVGjdunBYtWnTJ/WjTpo02bNigZs2a6fXXX9djjz2mefPmKSwsTE888cR59ytfvryWLVumihUr6vnnn9eMGTPUoUOHQgMMjzzyiPr27auYmBj17dtXjz322HmP2759e61YsULly5fXiy++qBkzZujmm2/WunXrShwMcYZnn31WTz75pFauXKmRI0dq69atWr58uapUqeLQzs3NTfPnz5eLi4uGDRumPn366Pvvvy/RuVJTUzV48GA1adJEzz33nH1969atNXLkSM2cOVMbN268LNcFAMCVZNhKUu0OAAAAAAAApY4MHQAAAAAAAJMhoAMAAAAAAGAyBHQAAAAAAABMhoAOAAAAAADARfrhhx/UpUsXVapUSYZh6NNPPy1ynzVr1uiGG26Qh4eHatasqXnz5pX4vAR0AAAAAAAALlJ6eroaNWqkN954o1jt9+3bp06dOqlt27aKi4vTqFGjNGTIEK1cubJE52WWKwAAAAAAgMvAMAwtXbpU3bp1O2+bZ555RsuXL9fOnTvt63r37q1Tp05pxYoVxT4XGToAAAAAAADnyMrKUkpKisOSlZV1WY69YcMGtW/f3mFdVFSUNmzYUKLjuF6W3gC4qix3q1PaXcAVtOHdHaXdBVxBfr78138tORCfUtpdwBWUmpxZ2l3AFdT0xrDS7gKuoJFdjNLuwkUz63eLzc/10cSJEx3WjR8/XhMmTLjkYyckJCg0NNRhXWhoqFJSUpSZmSkvL69iHYdPdQAAAAAAAOcYN26cRo8e7bDOw8OjlHpTOAI6AAAAAAAA5/Dw8HBaACcsLEyJiYkO6xITE+Xv71/s7ByJgA4AAAAAAHASw828w8WcpUWLFvryyy8d1n3zzTdq0aJFiY5DUWQAAAAAAICLlJaWpri4OMXFxUnKn5Y8Li5OBw4ckJQ/fOuBBx6wtx82bJj27t2rp59+Wr///rvefPNNffjhh3riiSdKdF4COgAAAAAAABfp559/VpMmTdSkSRNJ0ujRo9WkSRO9+OKLkqQjR47YgzuSVL16dS1fvlzffPONGjVqpJkzZ+q9995TVFRUic7LkCsAAAAAAICL1KZNG9lstvNunzdvXqH7bNu27ZLOS0AHAAAAAAA4hcWVGjrOwpArAAAAAAAAkyGgAwAAAAAAYDIMuQIAAAAAAE5huJFH4izcWQAAAAAAAJMhoAMAAAAAAGAyBHQAAAAAAABMhho6AAAAAADAKZi23HnI0AEAAAAAADAZAjoAAAAAAAAmQ0AHAAAAAADAZKihAwAAAAAAnMJwo4aOs5ChAwAAAAAAYDIEdAAAAAAAAEyGIVcAAAAAAMApmLbcecjQAQAAAAAAMBkCOgAAAAAAACZDQAcAAAAAAMBkqKEDAAAAAACcgmnLnYcMHQAAAAAAAJMhoAMAAAAAAGAyBHQAAAAAAABMhho6AAAAAADAKSyu1NBxFjJ0AAAAAAAATIaADgAAAAAAgMkw5AoAAAAAADiF4cKQK2chQwcAAAAAAMBkCOgAAAAAAACYDAEdAAAAAAAAk6GGDgAAAAAAcAoLNXSchgwdAAAAAAAAkyGgAwAAAAAAYDIMuQIAAAAAAE5hWBhy5Sxk6AAAAAAAAJgMAR0AAAAAAACTIaADAAAAAABgMtTQAQAAAAAATmG4kEfiLNxZAAAAAAAAkyGgAwAAAAAAYDIEdAAAAAAAAEyGGjoAAAAAAMApLC5GaXfhqkWGDgAAAAAAgMkQ0AEAAAAAADAZhlwBAAAAAACnMCwMuXIWMnQAAAAAAABMhoAOAAAAAACAyRDQAQAAAAAAMBlq6AAAAAAAAKdg2nLnIUMHAAAAAADAZAjoAAAAAAAAmAxDrgAAAAAAgFMYDLlyGjJ0AAAAAAAATIaADgAAAAAAgMkQ0AEAAAAAADAZaugAAAAAAACnMCzkkTgLdxYAAAAAAMBkCOgAAAAAAACYDAEdAAAAAAAAk6GGDgAAAAAAcArDYpR2F65aZOgAAAAAAACYDAEdAAAAAAAAk2HIFQAAAAAAcAqLC0OunIUMHQAAAAAAAJMhoINrUps2bTRq1Cj74/DwcM2ePdsp5zIMQ59++ul5t8fHx8swDMXFxTnl/AAAAACAqw8BHVzVBg4cKMMwCizTpk3T5MmTr0gfjhw5oo4dO17240ZFRcnFxUWbN2++7McGAAAAAJRt1NDBVe/OO+9UTEyMw7oKFSrIxcXlko6bk5MjNze3ItuFhYVd0nkKc+DAAa1fv14jRoxQdHS0mjdvftnPcbWIfOU5hXZuJ+/w6/Rjs7uVsv33QttVGdRDEU89JFksOrFmo3aOmChbbm6R21D2lPeT7m3tKm8PQ6dzbFqyNk9HT9kKtGtay6LW17vIImlvglWfb8iT1SbdUNOiFpFnf+/w9zYUn2jTwu94zsui5OPx+vGTcTqdflLunn5qfe8UlQut5dAm9eQh/fjJOJ04vEt+5a5Tt8eW2rcd3rNRP6+cpdzsDMmQqtS5Tc3ueFKGhd+8yqIKgRY90NFbPl6GTmfb9MFXGTpywlqgXYsG7rrjJg8ZhrT7QK4WrcqU1SoF+Vt0f0dvVQlx0Ylkq6a+n1oKV4GSCA1y0cP3BsrP26KM01a9uzRZh44W/n586w1e6nyrrwxD2rU3W/O/SFaeVapZxU0DugRIklxcpN37s/W/5SnKzbuSV4KinDoWr28XjbW/n9/ee6qCwhzfz1OS/tbqReN0/PAu+QVdp16jPy1wHJvNps/fGqhjh37TkJf44bMsYNpy5+HTCq56Hh4eCgsLc1huv/12hyFXkpSamqo+ffrIx8dHlStX1htvvOGw3TAMzZkzR127dpWPj49efvllSdKcOXMUEREhd3d31alTRx988EGB/c4dcrVp0yY1adJEnp6eatasmbZt21bia4qJiVHnzp01fPhwLVy4UJmZmZKk3bt3yzAM/f67Y9DilVdeUURERInPczVI+GSlNrTpq4z4v8/bxiv8OtWeMFIb2vbTmrod5BESrKoP9SxyG8qmu1u6avNuq2YvzdGPO/LU/ZaCwdtyvtLtTVz03lc5mrUkRz6ehprXyf8vcetfVr3xea59Scu0aftePvWXVes/m6A6zXuqx+gVuv7WIfrxk2cLtHH38NEN7UeqTc/pBbZ5ePmrTe+Z6j5qmbo+8okSD8Tpr7jPrkTXcRH63OGldb9ka1J0qr7ZlKX7O3oXaFM+wKLOt3jqlYVpmvBeqvy8Lbqlobsk6XS2TcvWZipmefqV7jou0qC7A/Tdzxl6+tVjWr42XQ/dE1Bou+BAF917u59efu+EnnrlmPx9LWrTLP/1cSAhRxPeOq4X3jyu514/Ln8fF91+o8+VvAwUw/cfj1f9m3uq39iVuqHtEH27aFyBNu6evrqp4yi17zvjvMfZ/sM8+QdXdWZXgTKDgA7wj+nTp6tRo0batm2bxo4dq5EjR+qbb75xaDNhwgTdc8892rFjhwYPHqylS5dq5MiRevLJJ7Vz504NHTpUgwYN0nfffVfoOdLS0tS5c2dFRkZqy5YtmjBhgsaMGVOiftpsNsXExKh///6qW7euatasqY8//liSVLt2bTVr1kwLFixw2GfBggXq27dvic5ztUha+7NOH0q8YJuK3aOUuGy1shKPS5L2v7NQlXp1LnIbyh4fT6lSeUPb9+T/Yv/rfpsCfAwF+Tm2q1/Not8PWpWWHwvV5j/y1LB6wf8Srws25ONp6PcDBTN8UPoy007o+KGdimjURZIUXv8OpScnKOXEfod2Ht6BCgtvKlf3Qr78V4qUf1AVSZKrm4fKh9VV6slDzu88SszX21DVUFdt+i1bkrRtd47K+VlUIdDxb7dJbTft+CtHKRn5f7drt2epWd38gE7GaZv2HMpTds6V7Tsujp+PRdUruWn99vw3682/nlZQgItCggoG6m+s76ltv59Wclr++//qTRlq0dBLkpSdI+X9k8jl6iK5u0kS7+tlSUbqCR39e6dq39BVklSjYZTSkhOUfNzx/dzTO1AVqzeVm7tXocdJSvhT+3Z+qxvaPuT0PgNlAUOucNVbtmyZfH197Y/PV8+mVatWGjt2rKT8wMi6dev0yiuvqEOHDvY2ffv21aBBg+yP+/Tpo4EDB+qRRx6RJI0ePVobN27UjBkz1LZt2wLniI2NldVq1dy5c+Xp6an69evr77//1vDhw4t9PatWrVJGRoaioqIkSf3799fcuXN1//33S5L69eun119/3V4jaPfu3dqyZYv+97//Ffsc1xqvqhWVuf/sF7jM/YfkVaVikdtQ9gT4GErNlKznfE5PTrMp0MdQUurZlQG+hk6lnX18Mi0/8PNvTWtZFLfH6nA8lB3pyQny8qsgi0v+xxnDMOQTUFFpp47Iv3y1Eh8vI/WY4n/9Wh3un3O5u4rLoJyfRSnpjn+PSSlWlfOz6Ngpq0O7pJSzj0+kWFXOn98wzah8gEWn0qyynjOq7kRynsoHuOhokmPmZPlAFx1PPrvu+Kn8dmcEB7poVL9yCinnou27s7RqU4bT+4/iS0s+Ih9/x/dzv8CKSj15RAHBxXs/z8vL0XcfvaC2PV9m2GwZw/PhPNxZXPXatm2ruLg4+/Laa68V2q5FixYFHu/atcthXbNmzRwe79q1S61atXJY16pVqwL7ndu+YcOG8vT0PO95ixIdHa1evXrJ1TX/P7w+ffpo3bp12rNnjySpd+/eio+P18aNGyXlZ+fccMMNqlu3bqHHy8rKUkpKisOSYytYjwC41ri5StdXt2jLnwy3uhZkn07Tqg8e0fWtH1TwdQ1KuzsALrPjp/L0/BvH9di0o3J1NdQs0rPonWAqP3/9hmpc30FBoddmmQFcmwjo4Krn4+OjmjVr2peKFS8+u8LHp3THWyclJWnp0qV688035erqKldXV1WuXFm5ubmKjo6WlF+EuV27doqNjZWUnxXUr1+/8x5z6tSpCggIcFg+tCZdkespKzIPHJFXtcr2x17VKivz4JEit6HsSU63yc9LOrf2XoCvoVPpjik2yWk2BfqebVTO11Dyv9o0CLfo6CmbjiU7tcu4BD4BYcpMPSZrXn6BVJvNpvTkI/INLNn7fE5Wur6e/5Cq1munBrcMdEJPcTmcTLXK38fi8Pcd5G/RyVRrgXZB52TklPe36GQKP1SYRavGXpr8SLAmPxKs+hEeCvS16Nwf98sHuOhEcsFA+4lTeQr+V0ZOYe2ysm3auCNTLRsWPmQHpcM3oKLSUxzfz1NPHZFfueK/nx/eu1k71i7QBy+309I3+ik7K00fvNxOmWnX1udaXFsI6AD/OJPRcu7jevXqXXCfevXqad26dQ7r1q1bp8jIyPO2/+WXX3T69OnznvdCFixYoOuuu07bt293yDqaOXOm5s2bp7y8/A8u/fr10+LFi7Vhwwbt3btXvXv3Pu8xx40bp+TkZIelpyWo2H26GhxZulKhndvJIzRYklTt4T46/OHyIreh7Ek/LR1JsqlRRP5/b/WrGUpJtynpXxPZ/LrfqrpVLPL95/N88zou+mWf4xe+prUs2vInXwLLMi/f8ipfKVJ7tn8hSYr/9Wt5+4eWaLhVTla6Vs57SJVr3aLGbYs//BVXXlqGTQeP5unGyPx6OE1qu+lkqtVhuJWUX1vn+ppu8vfOj/zc0shDW/7IvuL9xcVZF5epF97ML2C8/Md0xR/JUctG+W/Wzet76mRKXoHhVpK0+bfTalLXUwG++e//7W701sYd+bV3QoJc5PLPtx4XF6lZPU8dTGTmwrLE26+8KlSO1O6tn0uS9v6yUr4BocUebiVJ9zy6QA88v1r3P7da9zy6QO4evrr/udXy8r22Ptfi2kINHeAf69at07Rp09StWzd98803+uijj7R8+YW/uD/11FPq2bOnmjRpovbt2+uLL77QkiVLtGrVqkLb9+3bV88995weeughjRs3TvHx8Zox4/xV+v9t7ty56tGjhxo0cBwOUKVKFY0bN04rVqxQp06d1L17dw0fPlzDhw9X27ZtValSpfMe08PDQx4eHg7r3IyrJ9bb4M2JCunYRh5hwbpx+VzlpqZrTb07dP3bLynxi9U6umy1Mvf9rd2TXlOL7xdKkpJ+2KQD7yyWpAtuQ9n02fpcdb/FVbdd76Ksf6Ytl6RuLV30+0Grfj9o08k0afW2PD3c0U2StC/Bqs1/nP1SGOwvVQwy9P4+AjplXau7J+qHT8Zp+5q35ebhq9b3TpEkrV3yvKrWa6eq9dopNztTH7/SUdbcbGVnpWnRf9uoZuOuahY1Wr+u/0DH/t6h3OxM7f81/707vEGUGrcdVpqXhfNY+HWG7u/orTtu8tDpbJv+tyK/DkrfO7y0Y0+OduzJ1Ylkq5avO63RffPr5/15MFc/bs8P6Li5SuMf9Jeri+TlYeilof7a9Fu2Pv/x9HnPidIV83myHr4nUF1v9VVmVv605WcMvjtA2/44rW2/Z+nYyTwtXZ2q5x8qL0n6fV+2vtuc//qIrOGuO272kdUmWSzSb3uy9dkapqwva27rMVGrF4/Tlm/flrunr9r1yn8//+7D5xVev52q12+nnOxMxf73TuXlZiv7dJrmT75NtZt2VYu7nizl3uNCmLbceQybzUapR1y1Bg4cqFOnTjlMGy5Jbdq0UePGjTV79mxJUnh4uAYPHqydO3dq+fLl8vf317hx4/T444/b9zEMQ0uXLlW3bt0cjjVnzhzNmDFDBw8eVPXq1fX888/bCxQXtt/GjRs1bNgw7dq1S5GRkXrhhRd07733atu2bWrcuPF5r2XLli1q1qyZNm3apObNmxfYftddd8nT01NLliyRJPXq1UsffvihoqOjHQo5F8dytzolag9z2/DujtLuAq4gP19+y7mWHIhPKe0u4ApKTc4s7S7gCmp6Y1hpdwFX0Mgu5g2K7OhccLIYM7h+WeEzF5clBHQAFEBA59pCQOfaQkDn2kJA59pCQOfaQkDn2kJA58ozQ0Dn6hlXAQAAAAAAcI0goAOUIcOGDZOvr2+hy7Bh1HMAAAAAYC4WF8OUixmQdw2UIZMmTdKYMWMK3ebv73+FewMAAAAAKKsI6ABlSEhIiEJCQkq7GwAAAACAMo6ADgAAAAAAcAqmLXceaugAAAAAAACYDAEdAAAAAAAAkyGgAwAAAAAAYDLU0AEAAAAAAE5hWMgjcRbuLAAAAAAAgMkQ0AEAAAAAADAZhlwBAAAAAACnYNpy5yFDBwAAAAAAwGQI6AAAAAAAAJgMAR0AAAAAAACToYYOAAAAAABwCmroOA8ZOgAAAAAAACZDQAcAAAAAAMBkCOgAAAAAAACYDDV0AAAAAACAU1BDx3nI0AEAAAAAADAZAjoAAAAAAAAmw5ArAAAAAADgFIaFPBJn4c4CAAAAAACYDAEdAAAAAAAAkyGgAwAAAAAAYDLU0AEAAAAAAE5hcWHacmchQwcAAAAAAMBkCOgAAAAAAACYDAEdAAAAAAAAk6GGDgAAAAAAcArDQg0dZyFDBwAAAAAAwGQI6AAAAAAAAJgMQ64AAAAAAIBTGBbySJyFOwsAAAAAAGAyBHQAAAAAAABMhoAOAAAAAACAyVBDBwAAAAAAOAXTljsPGToAAAAAAAAmQ0AHAAAAAADAZBhyBQAAAAAAnIIhV85Dhg4AAAAAAIDJENABAAAAAAAwGQI6AAAAAAAAJkMNHQAAAAAA4BSGhTwSZ+HOAgAAAAAAmAwBHQAAAAAAAJMhoAMAAAAAAGAy1NABAAAAAABOYViM0u7CVYsMHQAAAAAAAJMhoAMAAAAAAGAyDLkCAAAAAABOwbTlzsOdBQAAAAAAMBkCOgAAAAAAACZDQAcAAAAAAMBkqKEDAAAAAACcw2DacmchQwcAAAAAAMBkCOgAAAAAAACYDEOuAAAAAACAUxgWhlw5Cxk6AAAAAAAAJkNABwAAAAAAwGQI6AAAAAAAAJgMNXQAAAAAAIBTGBbySJyFOwsAAAAAAGAyBHQAAAAAAABMhoAOAAAAAACAyVBDBwAAAAAAOIVhMUq7C1ctMnQAAAAAAABMhoAOAAAAAACAyTDkCgAAAAAAOAXTljsPdxYAAAAAAMBkCOgAAAAAAACYDEOuABSw4d0dpd0FXEEtHrq+tLuAK6jd1y+WdhdwBQ3e1Lq0uwDAST5654fS7gKuoJFdbivtLqAMIkMHAAAAAAA4hWExTLlcjDfeeEPh4eHy9PTUTTfdpE2bNl2w/ezZs1WnTh15eXmpSpUqeuKJJ3T69Olin4+ADgAAAAAAwCVYvHixRo8erfHjx2vr1q1q1KiRoqKidPTo0ULbx8bGauzYsRo/frx27dqluXPnavHixXr22WeLfU4COgAAAAAAAJdg1qxZeuihhzRo0CBFRkbqrbfekre3t6Kjowttv379erVq1Up9+/ZVeHi47rjjDvXp06fIrJ5zEdABAAAAAABOUdpDpy52ycrKUkpKisOSlZVV6DVmZ2dry5Ytat++vX2dxWJR+/bttWHDhkL3admypbZs2WIP4Ozdu1dffvml7rrrrmLfWwI6AAAAAAAA55g6daoCAgIclqlTpxba9vjx48rLy1NoaKjD+tDQUCUkJBS6T9++fTVp0iTdcsstcnNzU0REhNq0acOQKwAAAAAAgIs1btw4JScnOyzjxo27bMdfs2aNpkyZojfffFNbt27VkiVLtHz5ck2ePLnYx2DacgAAAAAAgHN4eHjIw8OjWG2Dg4Pl4uKixMREh/WJiYkKCwsrdJ8XXnhB999/v4YMGSJJuv7665Wenq6HH35Yzz33nCyWovNvyNABAAAAAADOYbGYcykBd3d3NW3aVN9++619ndVq1bfffqsWLVoUuk9GRkaBoI2Li4skyWazFeu8ZOgAAAAAAABcgtGjR2vAgAFq1qyZbrzxRs2ePVvp6ekaNGiQJOmBBx5Q5cqV7XV4unTpolmzZqlJkya66aab9Ndff+mFF15Qly5d7IGdohDQAQAAAAAAuAS9evXSsWPH9OKLLyohIUGNGzfWihUr7IWSDxw44JCR8/zzz8swDD3//PM6dOiQKlSooC5duujll18u9jkJ6AAAAAAAAFyiESNGaMSIEYVuW7NmjcNjV1dXjR8/XuPHj7/o8xHQAQAAAAAATmEYRml34apFUWQAAAAAAACTIaADAAAAAABgMgy5AgAAAAAATmGUcApwFB93FgAAAAAAwGQI6AAAAAAAAJgMAR0AAAAAAACToYYOAAAAAABwCsPCtOXOQoYOAAAAAACAyRDQAQAAAAAAMBkCOgAAAAAAACZDDR0AAAAAAOAcFvJInIU7CwAAAAAAYDIEdAAAAAAAAEyGIVcAAAAAAMApmLbcecjQAQAAAAAAMBkCOgAAAAAAACZDQAcAAAAAAMBkqKEDAAAAAACcwjDII3EW7iwAAAAAAIDJENABAAAAAAAwGYZcAQAAAAAA52DacqchQwcAAAAAAMBkCOgAAAAAAACYDAEdAAAAAAAAk6GGDgAAAAAAcArDQh6Js3BnAQAAAAAATIaADgAAAAAAgMkQ0AEAAAAAADAZaugAAAAAAACnMCxGaXfhqkWGDgAAAAAAgMkQ0AEAAAAAADAZhlwBAAAAAADnMMgjcRbuLAAAAAAAgMkQ0AEAAAAAADAZAjoAAAAAAAAmQw0dAAAAAADgFExb7jxk6AAAAAAAAJgMAR0AAAAAAACTYcgVAAAAAABwDgt5JM7CnQUAAAAAADAZAjoAAAAAAAAmQ0AHAAAAAADAZKihAwAAAAAAnMIwmLbcWcjQAQAAAAAAMBkCOgAAAAAAACZDQAcAAAAAAMBkqKEDAAAAAACcw0IeibNwZwEAAAAAAEyGgA4AAAAAAIDJMOQKAAAAAAA4hWFh2nJnIUMHAAAAAADAZAjoAAAAAAAAmAwBHQAAAAAAAJOhhg4AAAAAAHAOgzwSZ+HOAgAAAAAAmAwBHQAAAAAAAJNhyBUAAAAAAHAOpi13msuSodOmTRuNGjXK/jg8PFyzZ8++HIcuwDAMffrpp+fdHh8fL8MwFBcX55Tzl6Z58+YpMDCwtLthekW9hgAAAAAAKOtKFNAZOHCgDMMosEybNk2TJ092Vh8dHDlyRB07drzsx42KipKLi4s2b9582Y99MQoLivXq1Uu7d+++rOcwDEOLFi0qsK1+/foyDEPz5s27bOdzljVr1sgwDJ06dapY7Z31GiqOd955R23atJG/v3+J+vxvQ4cOlYuLiz766KPL20EAAAAAgCmUeMjVnXfeqZiYGId1FSpUkIuLyyV1JCcnR25ubkW2CwsLu6TzFObAgQNav369RowYoejoaDVv3vyyn0OSbDab8vLy5Op6cSPdvLy85OXldVn7VKVKFcXExKh37972dRs3blRCQoJ8fHwu67lKW3Z2ttzd3Z3yGiqujIwM3Xnnnbrzzjs1bty4iz7GokWL9PTTTys6Olr33XffZe7l1ae8n3Rva1d5exg6nWPTkrV5OnrKVqBd01oWtb7eRRZJexOs+nxDnqw26YaaFrWIPBv/9vc2FJ9o08Lvcq/gVaA4Il95TqGd28k7/Dr92OxupWz/vdB2VQb1UMRTD0kWi06s2aidIybKlptb5DaUPfsTT+iF+Z/pVFqGfL08NWlAV9WsFOLQZvMf8RrxeqyqhZa3r3v/6cHydHeT1WrT7CXfaN1ve5SXZ1XjiCp6rm8nuble2ucaOE9YsKuG9ywvPx+LMk5b9daHSfo7MafQtm2a+6hrW39ZDOnXv7IUvTRJeVapXg0PjX2wgg4fO/u3/eLricrJLfh/A0oXz/e15bqKXnruiToK9HdTWkaupsz+Q/sOZBRoFxbioedG1VWtGr46knhag0Zucdheo5qPnhhaU+UC879fvvNBvH7YcPyKXANwJZV4yJWHh4fCwsIclttvv91hyJUkpaamqk+fPvLx8VHlypX1xhtvOGw3DENz5sxR165d5ePjo5dfflmSNGfOHEVERMjd3V116tTRBx98UGC/c4fLbNq0SU2aNJGnp6eaNWumbdu2lfSSFBMTo86dO2v48OFauHChMjMzJUm7d++WYRj6/XfHLwSvvPKKIiIiijzumcyRr776Sk2bNpWHh4fWrl2rPXv26O6771ZoaKh8fX3VvHlzrVq1yr5fmzZttH//fj3xxBP2LCip8CFXRd2vovTr10/ff/+9Dh48aF8XHR2tfv36OQSeBg8erM6dOzvsm5OTo5CQEM2dO1eSlJWVpccff1whISHy9PTULbfc4pDxdOZ+rFy5Uk2aNJGXl5fatWuno0eP6quvvlK9evXk7++vvn37KiPj7Bu31WrV1KlTVb16dXl5ealRo0b6+OOPJeUPsWvbtq0kqVy5cjIMQwMHDrTfxxEjRmjUqFEKDg5WVFSUJMfXUGHZPXFxcTIMQ/Hx8Q73fdmyZapTp468vb3Vo0cPZWRkaP78+QoPD1e5cuX0+OOPKy8v74L3e9SoURo7dqxuvvnmop6a8/roo48UGRmpsWPH6ocffrA/dykpKfLy8tJXX33l0H7p0qXy8/NzuKfXmrtbumrzbqtmL83Rjzvy1P2Wgl/UyvlKtzdx0Xtf5WjWkhz5eBpqXif/LXLrX1a98XmufUnLtGn73gs/1ygdCZ+s1IY2fZUR//d523iFX6faE0ZqQ9t+WlO3gzxCglX1oZ5FbkPZ9FLsct17yw36fNIIDbqjpV6c/3mh7aqFlteHzw+1L57u+R/yl67bpl0HE7To2Ye1dMIjMgxDC1b/dCUvASU0pHuQvv0pTaOnH9Hna1I0rGdQoe0qlHNRz6gATXwzUaP+e0QBfha1u8nXvv3wsVyNm51gX/hyXzbxfF9bnnq0lj5feUR9hm3Wgo8P6tlRdQptl56Rp3f/t08TZ+wqsM3Dw6L/PF9f7/5vn/o/8rMeGPGzfvk12dldxwUYhsWUixk4rZfTp09Xo0aNtG3bNo0dO1YjR47UN99849BmwoQJuueee7Rjxw4NHjxYS5cu1ciRI/Xkk09q586dGjp0qAYNGqTvvvuu0HOkpaWpc+fOioyM1JYtWzRhwgSNGTOmRP202WyKiYlR//79VbduXdWsWdMeLKhdu7aaNWumBQsWOOyzYMEC9e3bt9jnGDt2rP7zn/9o165datiwodLS0nTXXXfp22+/1bZt23TnnXeqS5cuOnDggCRpyZIluu666zRp0iQdOXJER44cKfS4Jb1fhQkNDVVUVJTmz58vKT/7Y/HixRo8eLBDuyFDhmjFihUOfVm2bJkyMjLUq1cvSdLTTz+tTz75RPPnz9fWrVtVs2ZNRUVFKSkpyeFYEyZM0Ouvv67169fr4MGD6tmzp2bPnq3Y2FgtX75cX3/9tf7v//7P3n7q1Kl6//339dZbb+nXX3/VE088of79++v7779XlSpV9Mknn0iS/vjjDx05ckSvvvqqfd/58+fL3d1d69at01tvvVXs+/JvGRkZeu2117Ro0SKtWLFCa9as0T333KMvv/xSX375pT744AO9/fbb9teOM82dO1f9+/dXQECAOnbsaB8W5+/vr86dOys2Ntah/YIFC9StWzd5e3s7vW9lkY+nVKm8oe17rJKkX/fbFOBjKMjPsV39ahb9ftCqtPx4rjb/kaeG1Qu+RV4XbMjH09DvB/ggWBYlrf1Zpw8lXrBNxe5RSly2WlmJ+b/U7X9noSr16lzkNpQ9SSnp+m3/YXW6qaEkqf0N9ZR4MlkHjiYVsedZu/9O1E11q8vN1UWGYeiWBjW1/KdfnNVlXCJ/H4uqX+eutdvSJUmbdmSqfKCrQssXzH6+qaG3tvyWqeS0/Pf/VRvT1LLxtfl/oVnxfF9bAgPcVLeWn77+Lv//8TXrjysk2FOVK3oWaJualqtffkvR6dMFf2DrcFuIfv0jRb/8liJJslqlUymFZ3UBZlfigM6yZcvk6+trX8433KNVq1YaO3asateurccee0w9evTQK6+84tCmb9++GjRokGrUqKGqVatqxowZGjhwoB555BHVrl1bo0ePVvfu3TVjxoxCzxEbGyur1aq5c+eqfv366ty5s5566qkSXc+qVauUkZFhz97o37+/PeNEys9gWbhwof3x7t27tWXLFvXr16/Y55g0aZI6dOigiIgIBQUFqVGjRho6dKgaNGigWrVqafLkyYqIiNDnn+f/qhgUFCQXFxf5+fnZs6AKU9L7dT6DBw/WvHnzZLPZ9PHHHysiIkKNGzd2aNOyZcsCGUAxMTG677775Ovrq/T0dM2ZM0fTp09Xx44dFRkZqXfffVdeXl4O91OSXnrpJbVq1UpNmjTRgw8+qO+//15z5sxRkyZN1Lp1a/Xo0cMelMrKytKUKVMUHR2tqKgo1ahRQwMHDlT//v319ttvy8XFRUFB+b/UhISEKCwsTAEBAfZz1apVS9OmTVOdOnVUp07hEf7iyMnJsffx1ltvVY8ePbR27VrNnTtXkZGR6ty5s9q2bVuiYNrF+PPPP7Vx40Z7EK1///6KiYmRzZYfXOjXr58+/fRTezZOSkqKli9fXqLX69UmwMdQaqZkPSf+kpxmU6CPY7X9AF9Dp9LONjqZlh/4+bemtSyK22N1OB7MxatqRWXuP2R/nLn/kLyqVCxyG8qehJPJCg7wk6tL/scZwzAUVi5ACUkFf4n9+9hJ9X75HfWd+p4WrzmbPVqvWkV9/8tupWVmKScvT19v+U2HT5y6UpeAEiof6KJTqXmyWs+uO34yV8GBBTMvgwNddfzk2S97x5JyFRx4NhAQWt5VU0aG6aXHQtWhhW+B/VH6eL6vLaHBHjqRlK28c57vxGOnFVqhYEDnQsKr+Cg7x6b/vthAMa821fP/DOECrkYlDui0bdtWcXFx9uW1114rtF2LFi0KPN61yzElrlmzZg6Pd+3apVatWjmsa9WqVYH9zm3fsGFDeXqe/SP/93mLEh0drV69etmHF/Xp00fr1q3Tnj17JEm9e/dWfHy8Nm7cKCk/2+GGG25Q3bp1i32Of19nWlqaxowZo3r16ikwMFC+vr7atWuXPUOnuEp6v86nU6dOSktL0w8//KDo6OgC2TlnDBkyxF4/KTExUV999ZW97Z49e5STk+PQHzc3N914440F+tOwYUP7v0NDQ+Xt7a0aNWo4rDt69Kgk6a+//lJGRoY6dOjgEEh8//337c/RhTRt2rSYd+HCvL29HYbZhYaGKjw8XL6+vg7rzvR7ypQpDv0t6XN7PmcCW8HBwZKku+66S8nJyVq9erX9sZubmz04+Mknn8jf31/t27c/7zGzsrKUkpLisOTmZF2W/l5t3Fyl66tbtOVPhlsBZlKvakWt/M8oLXruYb0yrKc+/nGLVv78qyTp7haN1DIyQg/Omq8HZ85XtZAguVjMkWaNixd/KFuPvnxIz76aoFnvH1f7m311c0OyOa5WPN/XFhcXqVmjQE1/fbcGjdyiYyey9eQjtUq7W4BTlLg6r4+Pj2rWrHlZTl7aRXeTkpK0dOlSe/bFGXl5eYqOjtbLL7+ssLAwtWvXTrGxsbr55psVGxur4cOHl+g8/77OMWPG6JtvvtGMGTNUs2ZNeXl5qUePHsrOzr4s11VSrq6uuv/++zV+/Hj99NNPWrp0aaHtHnjgAY0dO1YbNmzQ+vXrVb16dbVu3brE5zu3+LVhGAWKYRuGIes/P8WkpaVJkpYvX67KlSs7tPPw8CjyXEW9xiz/fGg/k+Ei5WfjXKjPxen3sGHD1LPn2boblSpVKrKvRcnLy9P8+fOVkJDgUN/ozOv19ttvl7u7u3r06KHY2Fj17t1bsbGxDgHLwkydOlUTJ050WNf67ud1a7cXLrnPZUFyuk1+XpLFOJulE+Br6FS6Y4pNcppNQf5nM3LK+RpK/lebBuEWHT1l0zGGYZta5oEj8o6oan/sVa2yMg8eKXIbyp6wcgE6npyq3DyrXF0sstlsSjiZrLCgAId2vl5n/78ILeevO5s30La/DiiqWf6MjsO7tNHwLm0kSSs271REpQpX8jJQhNY3+OiuW/PHya6PS1egn4ssFtmzNoLLuer4qYKB9uOnch2G5lQIctXxU/lFcTOzzr6/JyXnaX1chupW99DGX67denNlBc/3teXOtqHq1e06SdKqH46qfJC7XCyyZ+mEVvBU4rHTJTrm0WNZ2rbjlI4n5X+3+npNomZObFjEXnAqS8Gsd1weTvsJ6kxGy7mP69Wrd8F96tWrp3Xr1jmsW7dunSIjI8/b/pdfftHp02f/yP993gtZsGCBrrvuOm3fvt0h62jmzJmaN2+evcBtv379tHjxYm3YsEF79+51mBHqYqxbt04DBw7UPffco+uvv15hYWH2ArxnuLu7F1lgt6T360IGDx6s77//XnfffbfKlStXaJvy5curW7duiomJ0bx58zRo0CD7tjOFmc/tT05OjjZv3nxR/TkjMjJSHh4eOnDggGrWrOmwVKlSRVL+vZJU5P0qTIUK+R/az60NFBcXd9H9PSMoKMihrxc7s9m5vvzyS6Wmpmrbtm0Or9eFCxdqyZIl9sLO/fr104oVK/Trr79q9erVRQ63GjdunJKTkx2Wlp2evuT+lhXpp6UjSTY1ish/u6tfzVBKuk1JqY7tft1vVd0qFvn+M5Fc8zou+mWf1aFN01oWbfnTcR3M58jSlQrt3E4eofmZbtUe7qPDHy4vchvKniB/H9WtUtFe82bV1l0KDfRX1RDHoqnHklNl/Seim346Sz/8slt1q+QPZ87KyVVKen7xrJNpGYpeuU4D72h5Ba8CRflxa7q9kO0Xa1IVfyhbtzTJ/8Hmxuu9lJScq8QTBWei27QjQ00jvRTgm//+3/5mX23Ynv8FPtDPon/mnJCnh6Em9bwUf6h0fliDI57va8uK7xI1aOQWDRq5RQs+Oajde9J0R9tQSVKblsE6djxLh46ULKCzeu0x1a3lJ2+v/KF5NzcN0l/70i5734Gy4NK/ZZ7HunXrNG3aNHXr1k3ffPONPvroIy1ffuEPxU899ZR69uypJk2aqH379vriiy+0ZMkShxmgztW3b18999xzeuihhzRu3DjFx8eXqH7M3Llz1aNHDzVo0MBhfZUqVTRu3DitWLFCnTp1Uvfu3TV8+HANHz5cbdu2veRsi1q1amnJkiXq0qWLDMPQCy+8YM/sOCM8PFw//PCDevfuLQ8PD/sQm3OV9H5dSL169XT8+PEiC+cOGTJEnTt3Vl5engYMGGBf7+Pjo+HDh+upp55SUFCQqlatqmnTpikjI0MPPvhgiftzhp+fn8aMGaMnnnhCVqtVt9xyi5KTk7Vu3Tr5+/trwIABqlatmgzD0LJly3TXXXfJy8vLYSjUhZwJDE2YMEEvv/yydu/erZkzZ150f4uSkJCghIQE/fXXX5KkHTt2yM/PT1WrVrXXAjqfuXPnqlOnTmrUqJHD+sjISD3xxBNasGCBHn30Ud16660KCwtTv379VL16dd10000XPK6Hh0eBbCdXt6vrQ85n63PV/RZX3Xa9i7L+mbZckrq1dNHvB636/aBNJ9Ok1dvy9HDH/MyrfQlWbf7j7N9lsL9UMcjQ+/sI6JRlDd6cqJCObeQRFqwbl89Vbmq61tS7Q9e//ZISv1ito8tWK3Pf39o96TW1+D6/PlrSD5t04J3FknTBbSibXujXSS/O/0xzV6yVr6eHJg7oKkma+MEXuq1hbbVpVEffbt2lD3/YIleLRblWqzrcUE93t2wsSUrLPK0hs96XYRiy2Wzq2+5G3dbw4muuwfneW5KkYT3Lq1s7f2Vm5U9jfcZDPYK09bdMbfktU0eT8vTx18ma+Gj+l8Pf9mTp2435X+puvN5bHW72VZ5VcrFIG3dkas3P6aVyPbgwnu9ry7Q3duu5UXX1wH1VlZ6Rpymv/mHf9sxjtbX2pxNat+mEPDwsWvjWjXJzM+Tr7aolMTdr5XeJevv9fUo8lqUPPjqgt6Y3kdVq0/GkbE17fXcpXhXgPE4L6Dz55JP6+eefNXHiRPn7+2vWrFn2wsPn061bN7366quaMWOGRo4cqerVqysmJkZt2rQptL2vr6+++OILDRs2TE2aNFFkZKT++9//6t577y2yf1u2bNH27dv17rvvFtgWEBCg22+/3f4F2s/PT126dNGHH36o6OjoYl3/hcyaNUuDBw9Wy5YtFRwcrGeeeUYpKSkObSZNmqShQ4cqIiJCWVlZDkOCzijp/SpK+fLli2zTvn17VaxYUfXr1y8Q2PrPf/4jq9Wq+++/X6mpqWrWrJlWrlx53oyf4po8ebIqVKigqVOnau/evQoMDNQNN9ygZ599VpJUuXJlTZw4UWPHjtWgQYP0wAMP2Gd+Koqbm5sWLlyo4cOHq2HDhmrevLleeuml8xb7vlRvvfWWw/CmW2+9VVJ+gekz060XJjExUcuXLy8wg5WUP2zsnnvu0dy5c/Xoo4/KMAz16dNH06ZN04svvnjZr8GMjqdI73xZ8Ne8T9c7ZnX9/KdVP58nA+d4ijR5ATMklHU7Hxlf6PodQ593eHxw7kc6OPejQtteaBvKnvCwYL3/TMEfDsbf38X+795tb1TvtjcWun95f18tnfCI0/qHy+/IsVyNf6Pw2eze/dhxhrPVm9K1elPBL+5fr0/T1+v5xd4MeL6vLQcPZWrYU9sK3fbf/zsblMnKsqr7oPOPzFj53VGt/O7oZe8fLo5BbTqnMWyFRQqA80hLS1PlypUVExOj7t27l3Z34CTPz7u6MnRwYS0eur60u4ArqN3XBHqvJYO/KnmtOwDmcHDXvtLuAq6gtV/cVtpduGgZc8352cP7wUml3YUiOS1DB1cXq9Wq48ePa+bMmQoMDFTXrl1Lu0sAAAAAAFyzrurcp2HDhjlMHX3uMmzYsDJ//EuxYMGC8/atfv36JT7egQMHFBoaqtjYWEVHR1+WIr9w9O+pzs9dOnbsWNrdAwAAAACUIVf1kKujR48WqE1zhr+/v0JCQsr08S9FamqqEhMLH2/s5uamatWqXeEeoShJSUlKSkoqdJuXl1eBadudiSFX1xaGXF1bGHJ1bWHIFXD1YsjVtcXUQ66iC69xWNZ5D55YdKNSdlWnWYSEhDg1qOLs418KPz8/+fn5lXY3UAJBQUFFznQFAAAAAIB0lQ+5AgAAAAAAuBpd1Rk6AAAAAACgFDFtudNwZwEAAAAAAEyGgA4AAAAAAIDJENABAAAAAAAwGWroAAAAAAAA5zCM0u7BVYsMHQAAAAAAAJMhoAMAAAAAAGAyBHQAAAAAAABMhho6AAAAAADAKQwLeSTOwp0FAAAAAAAwGQI6AAAAAAAAJsOQKwAAAAAA4BwGeSTOwp0FAAAAAAAwGQI6AAAAAAAAJkNABwAAAAAAwGSooQMAAAAAAJzDYpR2D65aZOgAAAAAAACYDAEdAAAAAAAAkyGgAwAAAAAAYDLU0AEAAAAAAE5hGOSROAt3FgAAAAAAwGQI6AAAAAAAAJgMQ64AAAAAAIBzMG2505ChAwAAAAAAYDIEdAAAAAAAAEyGgA4AAAAAAIDJUEMHAAAAAAA4B9OWOw13FgAAAAAAwGQI6AAAAAAAAJgMQ64AAAAAAIBzGExb7ixk6AAAAAAAAJgMAR0AAAAAAACTIaADAAAAAABgMtTQAQAAAAAAzmEhj8RZuLMAAAAAAAAmQ0AHAAAAAADAZAjoAAAAAAAAmAw1dAAAAAAAgHMY5JE4C3cWAAAAAADAZAjoAAAAAAAAmAxDrgAAAAAAgHNYjNLuwVWLDB0AAAAAAACTIaADAAAAAABgMgR0AAAAAAAATIYaOgAAAAAAwDmYttxpuLMAAAAAAAAmQ0AHAAAAAADAZBhyBQAAAAAAnMNg2nJnIUMHAAAAAADAZAjoAAAAAAAAmAwBHQAAAAAAAJOhhg4AAAAAAHAOC3kkzsKdBQAAAAAAMBkCOgAAAAAAACZDQAcAAAAAAMBkqKEDAAAAAACcwzBKuwdXLTJ0AAAAAAAATIaADgAAAAAAgMkw5AoAAAAAADiHQR6Js3BnAQAAAAAATIaADgAAAAAAgMkQ0AEAAAAAADAZaugAAAAAAADnsJBH4izcWQAAAAAAAJMhoAMAAAAAAGAyDLkCAAAAAADOYRil3YOrFhk6AAAAAAAAJkOGDoAC/Hx5a7iWtPv6xdLuAq6g1XdMKu0u4AoKnr65tLuAKyglKaO0u4AryMvft7S7AKCUkaEDAAAAAABgMvwMDwAAAAAAnMMgj8RZuLMAAAAAAAAmQ0AHAAAAAADAZAjoAAAAAAAAmAwBHQAAAAAA4ByGYc7lIrzxxhsKDw+Xp6enbrrpJm3atOmC7U+dOqVHH31UFStWlIeHh2rXrq0vv/yy2OejKDIAAAAAAMAlWLx4sUaPHq233npLN910k2bPnq2oqCj98ccfCgkJKdA+OztbHTp0UEhIiD7++GNVrlxZ+/fvV2BgYLHPSUAHAAAAAADgEsyaNUsPPfSQBg0aJEl66623tHz5ckVHR2vs2LEF2kdHRyspKUnr16+Xm5ubJCk8PLxE52TIFQAAAAAAcA6LxZRLVlaWUlJSHJasrKxCLzE7O1tbtmxR+/btz7lsi9q3b68NGzYUus/nn3+uFi1a6NFHH1VoaKgaNGigKVOmKC8vr/i3tmTPBAAAAAAAwNVt6tSpCggIcFimTp1aaNvjx48rLy9PoaGhDutDQ0OVkJBQ6D579+7Vxx9/rLy8PH355Zd64YUXNHPmTL300kvF7iNDrgAAAAAAAM4xbtw4jR492mGdh4fHZTu+1WpVSEiI3nnnHbm4uKhp06Y6dOiQpk+frvHjxxfrGAR0AAAAAAAAzuHh4VHsAE5wcLBcXFyUmJjosD4xMVFhYWGF7lOxYkW5ubnJxcXFvq5evXpKSEhQdna23N3dizwvQ64AAAAAAIBT2AzDlEtJuLu7q2nTpvr222/t66xWq7799lu1aNGi0H1atWqlv/76S1ar1b5u9+7dqlixYrGCORIBHQAAAAAAgEsyevRovfvuu5o/f7527dql4cOHKz093T7r1QMPPKBx48bZ2w8fPlxJSUkaOXKkdu/ereXLl2vKlCl69NFHi31OhlwBAAAAAABcgl69eunYsWN68cUXlZCQoMaNG2vFihX2QskHDhyQxXI2p6ZKlSpauXKlnnjiCTVs2FCVK1fWyJEj9cwzzxT7nAR0AAAAAACAcxjXzsCgESNGaMSIEYVuW7NmTYF1LVq00MaNGy/6fNfOnQUAAAAAALhKENABAAAAAAAwGQI6AAAAAAAAJkMNHQAAAAAA4BzXUA2dK407CwAAAAAAYDIEdAAAAAAAAEyGgA4AAAAAAIDJUEMHAAAAAAA4hc0wSrsLVy0ydAAAAAAAAEyGgA4AAAAAAIDJMOQKAAAAAAA4B9OWOw13FgAAAAAAwGQI6AAAAAAAAJgMAR0AAAAAAACToYYOAAAAAABwDqYtdxoydAAAAAAAAEyGgA4AAAAAAIDJENABAAAAAAAwGWroAAAAAAAA57CQR+Is3FkAAAAAAACTIaADAAAAAABgMgy5AgAAAAAATmFj2nKnIUMHAAAAAADAZAjoAAAAAAAAmAwBHQAAAAAAAJOhhg4AAAAAAHAOgzwSZ+HOAgAAAAAAmAwBHQAAAAAAAJNhyBUAAAAAAHAKG0OunIY7CwAAAAAAYDIEdAAAAAAAAEyGgA4AAAAAAIDJUEMHAAAAAAA4h2GUdg+uWmToAAAAAAAAmAwBHQAAAAAAAJMhoAMAAAAAAGAy1NABAAAAAABOYTPII3EW7iwAAAAAAIDJENABAAAAAAAwGYZcAQAAAAAA52DacqchQwcAAAAAAMBkCOgAAAAAAACYDAEdAAAAAAAAk6GGDgAAAAAAcA6mLXca7iwAAAAAAIDJENABAAAAAAAwGYZcAQAAAAAAp7AxbbnTkKEDAAAAAABgMgR0AAAAAAAATIaADgAAAAAAgMlQQwcAAAAAADgH05Y7DXcWAAAAAADAZAjoAAAAAAAAmMxlC+i0adNGo0aNsj8ODw/X7NmzL9fhHRiGoU8//fS82+Pj42UYhuLi4pxy/tI0b948BQYGlnY3TK+o1xAAAAAAAGVZiQM6AwcOlGEYBZZp06Zp8uTJzuhjAUeOHFHHjh0v+3GjoqLk4uKizZs3X/ZjX4zCgmK9evXS7t27L+s5DMPQokWLCmyrX7++DMPQvHnzLtv5nGXNmjUyDEOnTp0qVntnvYaKkpSUpMcee0x16tSRl5eXqlatqscff1zJycklPlZZe70CAAAAwL/ZZJhyMYOLKop85513KiYmxmFdhQoV5OLickmdycnJkZubW5HtwsLCLuk8hTlw4IDWr1+vESNGKDo6Ws2bN7/s55Akm82mvLw8ubpeXD1qLy8veXl5XdY+ValSRTExMerdu7d93caNG5WQkCAfH5/Leq7Slp2dLXd3d6e8horj8OHDOnz4sGbMmKHIyEjt379fw4YN0+HDh/Xxxx8X+zhX6vV6tUg+Hq8fPxmn0+kn5e7pp9b3TlG50FoObVJPHtKPn4zTicO75FfuOnV7bKl92+E9G/XzylnKzc6QDKlKndvU7I4nZVgYtVoW7U88oRfmf6ZTaRny9fLUpAFdVbNSiEObzX/Ea8TrsaoWWt6+7v2nB8vT3U1Wq02zl3yjdb/tUV6eVY0jqui5vp3k5npp/8fh8ot85TmFdm4n7/Dr9GOzu5Wy/fdC21UZ1EMRTz0kWSw6sWajdo6YKFtubpHbUPZUCLSo/x2e8vUylJkl/e/rTCUkWQu0u7m+mzo0c5dhGPrzYK4Wf3daVqtU+zoXdb3FU+7/fNz8dV+uPl+bJdsVvg4UX2iQix7qHiA/b4sysmx6b0myDh0r/G/01hu81Km1jyyG9NvebL2/LEV5VimiipsGdPaXJLm6SLv35+h/X6YoN+9KXgmKo3Koh54aFq4AP1elZ+Rp+tvx2n/odIF2ocHuempouGqGeyvhWJaGPbvLvq1xpJ8e7F1ZXp4W2WzST3HJmrvokGz8oeMqdFHfRjw8PBQWFuaw3H777Q5DriQpNTVVffr0kY+PjypXrqw33njDYbthGJozZ466du0qHx8fvfzyy5KkOXPmKCIiQu7u7qpTp44++OCDAvudO1xm06ZNatKkiTw9PdWsWTNt27atxNcUExOjzp07a/jw4Vq4cKEyMzMlSbt375ZhGPr9d8cPia+88ooiIiKKPO6ZzJGvvvpKTZs2lYeHh9auXas9e/bo7rvvVmhoqHx9fdW8eXOtWrXKvl+bNm20f/9+PfHEE/YsKKnwIVdF3a+i9OvXT99//70OHjxoXxcdHa1+/fo5BJ4GDx6szp07O+ybk5OjkJAQzZ07V5KUlZWlxx9/XCEhIfL09NQtt9zikEFy5n6sXLlSTZo0kZeXl9q1a6ejR4/qq6++Ur169eTv76++ffsqIyPDvp/VatXUqVNVvXp1eXl5qVGjRvYASHx8vNq2bStJKleunAzD0MCBA+33ccSIERo1apSCg4MVFRUlyfE1VFh2T1xcnAzDUHx8vMN9X7ZsmerUqSNvb2/16NFDGRkZmj9/vsLDw1WuXDk9/vjjyss7/6eDBg0a6JNPPlGXLl0UERGhdu3a6eWXX9YXX3yh3BJ8gXDW6/Vqtf6zCarTvKd6jF6h628doh8/ebZAG3cPH93QfqTa9JxeYJuHl7/a9J6p7qOWqesjnyjxQJz+ivvsSnQdF+Gl2OW695Yb9PmkERp0R0u9OP/zQttVCy2vD58fal88//mGt3TdNu06mKBFzz6spRMekWEYWrD6pyt5CSimhE9WakObvsqI//u8bbzCr1PtCSO1oW0/ranbQR4hwar6UM8it6Fs6n27p9bvzNHk+ela9XOW+t9R8Eeu8v6GOrfw0OyPMjRpXpr8vA21apD/952RZVPMlxma8kG6psWmq3pFF90YWfSPiSg9A7v6a83PmXrmteNa/mOahnQPKLRdcKCLurfz1ZS5SXpq9nEF+FrUppm3JOlgQo4mvn1CL845oefeOCF/H4tuv9H7Sl4Gimnkg1X15XfHNWjMr1q8LEFPDQ0vtF1GZp5iPjqkKW/sLbAtNT1XL//fXg15+jc98vwu1a/lqw6tyxdyFMD8nPrz8vTp09WoUSNt27ZNY8eO1ciRI/XNN984tJkwYYLuuece7dixQ4MHD9bSpUs1cuRIPfnkk9q5c6eGDh2qQYMG6bvvviv0HGlpaercubMiIyO1ZcsWTZgwQWPGjClRP202m2JiYtS/f3/VrVtXNWvWtAcLateurWbNmmnBggUO+yxYsEB9+/Yt9jnGjh2r//znP9q1a5caNmyotLQ03XXXXfr222+1bds23XnnnerSpYsOHDggSVqyZImuu+46TZo0SUeOHNGRI0cKPW5J71dhQkNDFRUVpfnz50uSMjIytHjxYg0ePNih3ZAhQ7RixQqHvixbtkwZGRnq1auXJOnpp5/WJ598ovnz52vr1q2qWbOmoqKilJSU5HCsCRMm6PXXX9f69et18OBB9ezZU7Nnz1ZsbKyWL1+ur7/+Wv/3f/9nbz916lS9//77euutt/Trr7/qiSeeUP/+/fX999+rSpUq+uSTTyRJf/zxh44cOaJXX33Vvu/8+fPl7u6udevW6a233ir2ffm3jIwMvfbaa1q0aJFWrFihNWvW6J577tGXX36pL7/8Uh988IHefvvtEmXaSFJycrL8/f2LnbV1JV6vV5PMtBM6fminIhp1kSSF179D6ckJSjmx36Gdh3egwsKbytW94Ae88pUi5R9URZLk6uah8mF1lXrykPM7jxJLSknXb/sPq9NNDSVJ7W+op8STyTpwNKmIPc/a/XeibqpbXW6uLjIMQ7c0qKnlP/3irC7jEiSt/VmnDyVesE3F7lFKXLZaWYnHJUn731moSr06F7kNZY+vl6EqIS7avCtHkhT3V67K+RkKDnBMi29cy0079uYqNSP/5/i1O3LUtE5+0ObvY1adSMlfn5snHTpmVZA/2ZZllZ+PRdUruWn9L/k/XP38W5aC/C0KCSqYMdm8voe2/ZGl5LT8jK3VmzN08/WekqTsHCnvn0QuVxfJzc0gK6sMCvR3Ve0aPlq19oQk6cdNp1ShvLsqhXoUaJuanqdfd6frdFbBDL09+zOVcCxbkpSTY9Oe/RkKDXZ3budxQTbDYsrFDC6ql8uWLZOvr699ue+++wpt16pVK40dO1a1a9fWY489ph49euiVV15xaNO3b18NGjRINWrUUNWqVTVjxgwNHDhQjzzyiGrXrq3Ro0ere/fumjFjRqHniI2NldVq1dy5c1W/fn117txZTz31VImuZ9WqVcrIyLBnb/Tv39+ecSLlZ7AsXLjQ/nj37t3asmWL+vXrV+xzTJo0SR06dFBERISCgoLUqFEjDR06VA0aNFCtWrU0efJkRURE6PPP839JDgoKkouLi/z8/OxZUIUp6f06n8GDB2vevHmy2Wz6+OOPFRERocaNGzu0admyZYEMoJiYGN13333y9fVVenq65syZo+nTp6tjx46KjIzUu+++Ky8vL4f7KUkvvfSSWrVqpSZNmujBBx/U999/rzlz5qhJkyZq3bq1evToYQ9KZWVlacqUKYqOjlZUVJRq1KihgQMHqn///nr77bfl4uKioKAgSVJISIjCwsIUEHD215tatWpp2rRpqlOnjurUqVOi+3KunJwcex9vvfVW9ejRQ2vXrtXcuXMVGRmpzp07q23btiUKph0/flyTJ0/Www8/XOx9rsTr9WqSnpwgL78KsrjkB8wMw5BPQEWlnSo8SFqUjNRjiv/1a1Wt0+Yy9hKXS8LJZAUH+MnVJf+/N8MwFFYuQAlJBetU/X3spHq//I76Tn1Pi9eczSSsV62ivv9lt9Iys5STl6evt/ymwydOXalLwGXmVbWiMvefDcBm7j8kryoVi9yGsqecn6GUdKus53wTP5lqU5Cf5V/tLEpKPfslLynFqnJ+BT/y+nkbalzLVTv35jitz7g05f0tOpVmlfWc7+xJyXkqH1AwoFM+wEUnTp3Nkj5+yrFdcKCLJj9SXq8/E6LM01Z9uymjwDFQuioEuSvpZI7D8330RLZCyl98MKZcgKta31hOP20reb1KwAwuKqDTtm1bxcXF2ZfXXnut0HYtWrQo8HjXrl0O65o1a+bweNeuXWrVqpXDulatWhXY79z2DRs2lKen53nPW5To6Gj16tXLniHRp08frVu3Tnv27JEk9e7dW/Hx8dq4caOk/GyHG264QXXr1i32Of59nWlpaRozZozq1aunwMBA+fr6ateuXfYMneIq6f06n06dOiktLU0//PCDoqOjC2TnnDFkyBB7/aTExER99dVX9rZ79uxRTk6OQ3/c3Nx04403FuhPw4YN7f8ODQ2Vt7e3atSo4bDu6NGjkqS//vpLGRkZ6tChg0Mg8f3337c/RxfStGnTYt6FC/P29nYYthQaGqrw8HD5+voW2u8pU6Y49Pffz21KSoo6deqkyMhITZgwodj9uNyv16ysLKWkpDgsuTlZxe7PtST7dJpWffCIrm/9oIKva1Da3cElqFe1olb+Z5QWPfewXhnWUx//uEUrf/5VknR3i0ZqGRmhB2fN14Mz56taSJBcqJcEXFU83aWhXb216udsHTxa8Bd+XH2On8rTC2+e0OPTj8nV1VCzep5F7wRT8/ayaPKTNfXhsgTt3kcAD1eni6rM6+Pjo5o1a16WDpR20d2kpCQtXbrUnn1xRl5enqKjo/Xyyy8rLCxM7dq1U2xsrG6++WbFxsZq+PDhJTrPv69zzJgx+uabbzRjxgzVrFlTXl5e6tGjh7Kzsy/LdZWUq6ur7r//fo0fP14//fSTli5dWmi7Bx54QGPHjtWGDRu0fv16Va9eXa1bty7x+c4tfm0YRoFi2IZhyPpPeD4tLU2StHz5clWuXNmhnYdHwRTMfyvqNWb554ua7ZxKaTk5BX+tK6yPF+r3sGHD1LPn2VoMlSpVsv87NTVVd955p/z8/LR06dJiFQOXnPN6nTp1qiZOnOiw7vb7XlSHnuOL1aeyzicgTJmpx2TNy5XFxVU2m03pyUfkG1iyX+FzstL19fyHVLVeOzW4ZaBzOotLFlYuQMeTU5WbZ5Wri0U2m00JJ5MVFuRYc8HX6+x7R2g5f93ZvIG2/XVAUc3yZ/cb3qWNhndpI0lasXmnIipVuJKXgcso88AReUdUtT/2qlZZmQePFLkNZc/JVJv8fSyyGLJn6ZTzMxyycfLbWRUccDYIG+Rv0clz2ni4ScO7eWvH3hx9t610Pnfh/Fo18lRUy/zPbht3nFagr0UWi+xZG0EBLjqRXLBe4YnkPIUEnf1qExxYeLusbJt+2nFaLRp56qedBYvt4spqf0uQetwVKkn6bn2Sgsq5OTzfIeXddfREyf9OvTwtmvJ0La3fekqffHX0cnYZKFOc+pPjmQyBcx/Xq1fvgvvUq1dP69atc1i3bt06RUZGnrf9L7/8otOnz74h//u8F7JgwQJdd9112r59u0PW0cyZMzVv3jx7gdt+/fpp8eLF2rBhg/bu3eswI9TFWLdunQYOHKh77rlH119/vcLCwuwFeM9wd3e/YIFdqeT360IGDx6s77//XnfffbfKlStXaJvy5curW7duiomJ0bx58zRo0CD7tjOFmc/tT05OjjZv3nxR/TkjMjJSHh4eOnDggGrWrOmwVKmSX9fE3T0/FbOo+1WYChXyv6idWxsoLi7uovt7RlBQkENfz2TUpKSk6I477pC7u7s+//xzh+yyojjj9Tpu3DglJyc7LG3vGXtpF1+GePmWV/lKkdqz/QtJUvyvX8vbP1T+5asV+xg5WelaOe8hVa51ixq3LVkwF1dWkL+P6lapaK95s2rrLoUG+qtqSJBDu2PJqbL+840w/XSWfvhlt+pWyR/ampWTq5T0/HoNJ9MyFL1ynQbe0fIKXgUupyNLVyq0czt5hAZLkqo93EeHP1xe5DaUPWmZNv19LE/N6+X/CNK4pqtOpdl0PNmxGkrcnzm6voar/Lzza+vccr2btu7O/6HG3U165B5v7dqfq5WbCOaUReu2n9aLc/ILGH+5Nl3xR3LVsmF+8etmkR46mWLV0aSCn/d+/i1LTep4KMA3/+tNu+be+mlH/veDkCAX/TMSVy4uUtN6HjqYwGx2ZcGqtUka9uwuDXt2lxYvS9Rf+zLU/pb8AsatbwzU8aRsHU4sWea4p4dFU5+ppZ9/SVHspwnO6DZKyrCYczGBi5s7u5jWrVunadOmqVu3bvrmm2/00UcfafnyC39Qeuqpp9SzZ081adJE7du31xdffKElS5Y4zAB1rr59++q5557TQw89pHHjxik+Pr5E9WPmzp2rHj16qEEDx+ETVapU0bhx47RixQp16tRJ3bt31/DhwzV8+HC1bdvWIdviYtSqVUtLlixRly5dZBiGXnjhBXtmxxnh4eH64Ycf1Lt3b3l4eCg4OLjAcUp6vy6kXr16On78uLy9L1z1f8iQIercubPy8vI0YMAA+3ofHx8NHz5cTz31lIKCglS1alVNmzZNGRkZevDBB0vcnzP8/Pw0ZswYPfHEE7JarbrllluUnJysdevWyd/fXwMGDFC1atVkGIaWLVumu+66S15eXg5DoS7kTGBowoQJevnll7V7927NnDnzovt7IWeCORkZGfrf//5nH+Ik5QeWXFwuPC2yM16vHh4eBTKdXN2urvTzVndP1A+fjNP2NW/LzcNXre+dIklau+R5Va3XTlXrtVNudqY+fqWjrLnZys5K06L/tlHNxl3VLGq0fl3/gY79vUO52Zna/2v+31Z4gyg1bjusNC8L5/FCv056cf5nmrtirXw9PTRxQFdJ0sQPvtBtDWurTaM6+nbrLn34wxa5WizKtVrV4YZ6urtlY0lSWuZpDZn1vgzDkM1mU992N+q2hhdffwvO0+DNiQrp2EYeYcG6cflc5aama029O3T92y8p8YvVOrpstTL3/a3dk15Ti+/za4sl/bBJB95ZLEkX3IayadG3p9X/Dk/d0dxdp7Pzpy2XpD7tPbVjb6527s3ViRSbvtyYpSd65md5/PV3rtbuyA/otGnsrmqhLnJ3M9SoZn5gaNvuHH29meBOWTXv82Q91D1AXW71UWaWTe8tPVsLZfDd/tr2e5a2/ZGlYyfztPS7ND0/JD+Av2tftr77OX+YTWR1d3W42VtWm+RikX7dm63Pv08rlevBhc2O3q+nhoarT9cwZWTmafo78fZto4dU04atp7Rha7I83A3FzGwgN1dDPt4uiv2/67Vq7QlFLz6s7neGqE4NH3l6WHRL80BJ0g8/nVTsZwR3cPVxakDnySef1M8//6yJEyfK399fs2bNshdyPZ9u3brp1Vdf1YwZMzRy5EhVr15dMTExatOmTaHtfX199cUXX2jYsGFq0qSJIiMj9d///lf33ntvkf3bsmWLtm/frnfffbfAtoCAAN1+++2aO3euOnXqJD8/P3Xp0kUffvihoqOji3X9FzJr1iwNHjxYLVu2VHBwsJ555hn7F/szJk2apKFDhyoiIkJZWVkOQ4LOKOn9Kkr58kVP6de+fXtVrFhR9evXLxAo+M9//iOr1ar7779fqampatasmVauXHnejJ/imjx5sipUqKCpU6dq7969CgwM1A033KBnn82ffrpy5cqaOHGixo4dq0GDBumBBx7QvHnzinVsNzc3LVy4UMOHD1fDhg3VvHlzvfTSS+ct9n0ptm7dqp9+yp/++N/DFvft26fw8PDz7luar1ezC6hQXV2GLSqw/pbuL9n/7erupd7PrCl0/8ZthxG8MZHwsGC9/0zBIPL4+7vY/9277Y3q3fbGQvcv7++rpRMecVr/cPnsfKTwoaE7hj7v8Pjg3I90cO5Hhba90DaUPUdPWjVrccFaGAtXOQ6dWb8zR+t3Fhw+/fXmbII3JpNwIk+T3y18psLozxw/O3+/JVPfb8ks0G7NlkytKWQ9yp6/j2Rp5IQ/Ct02672zM5RmZdvU97EdhbaL/SyB4A2uGYatsCgBcAFpaWmqXLmyYmJi1L1799LuDpzgvx9fXRk6uLDHyy8suhGuGqvvmFTaXcAVtGL65qIb4aqRkkTh12vJ4T2Him6Eq8Y3Cy7PRC+l4eT270u7CxelXKPbSrsLRXJqhg6uLlarVcePH9fMmTMVGBiorl27lnaXAAAAAAC4Jpmj0s8lGDZsmMPU0ecuw4Zd+hAKZx//UixYsOC8fatfv36Jj3fgwAGFhoYqNjZW0dHR9iK/uHzK8usJAAAAAFB2XPVDro4ePVqgNs0Z/v7+CgkJKdPHvxSpqalKTEwsdJubm5uqVSv+LD+4MsrK64khV9cWhlxdWxhydW1hyNW1hSFX1xaGXF1bGHJ15THkqgwICQlx6pdgZx//Uvj5+cnPz6+0u4ESKMuvJwAAAAAoKZtJpgA3I+4sAAAAAACAyRDQAQAAAAAAMBkCOgAAAAAAACZz1dfQAQAAAAAApcQwSrsHVy0ydAAAAAAAAEyGgA4AAAAAAIDJMOQKAAAAAAA4BdOWOw93FgAAAAAAwGQI6AAAAAAAAJgMAR0AAAAAAACToYYOAAAAAABwCpuYttxZyNABAAAAAAAwGQI6AAAAAAAAJkNABwAAAAAAwGSooQMAAAAAAJzCZpBH4izcWQAAAAAAAJMhoAMAAAAAAGAyDLkCAAAAAADOYTBtubOQoQMAAAAAAGAyBHQAAAAAAABMhoAOAAAAAACAyVBDBwAAAAAAOIWNPBKn4c4CAAAAAACYDAEdAAAAAAAAk2HIFQAAAAAAcAob05Y7DRk6AAAAAAAAJkNABwAAAAAAwGQI6AAAAAAAAJgMNXQAAAAAAIBT2AzySJyFOwsAAAAAAGAyBHQAAAAAAABMhoAOAAAAAACAyVBDBwAAAAAAOIVNRml34apFhg4AAAAAAIDJENABAAAAAAAwGYZcAQAAAAAAp2DacufhzgIAAAAAAJgMAR0AAAAAAACTIaADAAAAAABgMtTQAQAAAAAATmEzmLbcWcjQAQAAAAAAMBkCOgAAAAAAACbDkCsAAAAAAOAUNjHkylnI0AEAAAAAADAZAjoAAAAAAAAmQ0AHAAAAAADAZKihAwAAAAAAnMJmkEfiLNxZAAAAAAAAkyGgAwAAAAAAYDIEdAAAAAAAAEyGGjoAAAAAAMApbDJKuwtXLTJ0AAAAAAAATIaADgAAAAAAgMkw5AoAAAAAADgF05Y7D3cWAAAAAADAZAjoAAAAAAAAmAwBHQAAAAAAAJOhhg4AAAAAAHAKpi13HjJ0AAAAAAAATIaADgAAAAAAgMkw5AoAAAAAADgF05Y7D3cWAAAAAADAZAjoAAAAAAAAmAwBHQAAAAAAAJOhhg4AAAAAAHAKpi13HjJ0AAAAAAAATIaADgAAAAAAgMkQ0AEAAAAAADAZaugAKOBAfEppdwFX0OBNrUu7C7iCgqdvLu0u4Aq686nmpd0FXEER3aqVdhdwBX3ee0VpdwEoFptBDR1nIUMHAAAAAADAZAjoAAAAAAAAmAxDrgAAAAAAgFPYbAy5chYydAAAAAAAAEyGgA4AAAAAAIDJENABAAAAAAAwGWroAAAAAAAAp7CRR+I03FkAAAAAAACTIaADAAAAAABgMgy5AgAAAAAATmET05Y7Cxk6AAAAAAAAl+iNN95QeHi4PD09ddNNN2nTpk3F2m/RokUyDEPdunUr0fkI6AAAAAAAAFyCxYsXa/To0Ro/fry2bt2qRo0aKSoqSkePHr3gfvHx8RozZoxat25d4nMS0AEAAAAAALgEs2bN0kMPPaRBgwYpMjJSb731lry9vRUdHX3effLy8tSvXz9NnDhRNWrUKPE5CegAAAAAAACnsMkw5ZKVlaWUlBSHJSsrq9BrzM7O1pYtW9S+fXv7OovFovbt22vDhg3nvTeTJk1SSEiIHnzwwYu6twR0AAAAAAAAzjF16lQFBAQ4LFOnTi207fHjx5WXl6fQ0FCH9aGhoUpISCh0n7Vr12ru3Ll69913L7qPzHIFAAAAAABwjnHjxmn06NEO6zw8PC7LsVNTU3X//ffr3XffVXBw8EUfh4AOAAAAAADAOTw8PIodwAkODpaLi4sSExMd1icmJiosLKxA+z179ig+Pl5dunSxr7NarZIkV1dX/fHHH4qIiCjyvAy5AgAAAAAATlHatXAudikJd3d3NW3aVN9++619ndVq1bfffqsWLVoUaF+3bl3t2LFDcXFx9qVr165q27at4uLiVKVKlWKdlwwdAAAAAACASzB69GgNGDBAzZo104033qjZs2crPT1dgwYNkiQ98MADqly5sqZOnSpPT081aNDAYf/AwEBJKrD+QgjoAAAAAAAAXIJevXrp2LFjevHFF5WQkKDGjRtrxYoV9kLJBw4ckMVyeQdJEdABAAAAAABOUdLhS2Y2YsQIjRgxotBta9asueC+8+bNK/H5qKEDAAAAAABgMgR0AAAAAAAATIaADgAAAAAAgMlQQwcAAAAAADiFzXbt1NC50sjQAQAAAAAAMBkCOgAAAAAAACZDQAcAAAAAAMBkqKEDAAAAAACcwiZq6DgLGToAAAAAAAAmQ0AHAAAAAADAZBhyBQAAAAAAnIIhV85Dhg4AAAAAAIDJENABAAAAAAAwGQI6AAAAAAAAJkMNHQAAAAAA4BTU0HEeMnQAAAAAAABMhoAOAAAAAACAyTDkCgAAAAAAOIXNxpArZyFDBwAAAAAAwGQI6AAAAAAAAJgMAR0AAAAAAACToYYOAAAAAABwCivTljsNGToAAAAAAAAmQ0AHAAAAAADAZAjoAAAAAAAAmAw1dAAAAAAAgFPYqKHjNGToAAAAAAAAmAwBHQAAAAAAAJNhyBUAAAAAAHAKm40hV85Chg4AAAAAAIDJENABAAAAAAAwGQI6AAAAAAAAJkMNHQAAAAAA4BRMW+48ZOgAAAAAAACYDAEdAAAAAAAAk2HIFQAAAAAAcAqmLXceMnQAAAAAAABMhoAOAAAAAACAyRDQAQAAAAAAMBlq6AAAAAAAAKdg2nLnIUMHAAAAAADAZAjoAAAAAAAAmAwBHQAAAAAAAJOhhg4AAAAAAHAKm40aOs5Chg4AAAAAAIDJENABAAAAAAAwGYZcAQAAAAAAp7CWdgeuYmToAAAAAAAAmMw1E9Bp06aNRo0aZX8cHh6u2bNnO+VchmHo008/Pe/2+Ph4GYahuLg4p5y/NM2bN0+BgYGl3Q2nmDBhgho3blza3QAAAAAA4OoL6AwcOFCGYRRYpk2bpsmTJ1+RPhw5ckQdO3a87MeNioqSi4uLNm/efNmPfTEKC4r16tVLu3fvvqznMAxDixYtKrCtfv36MgxD8+bNu2znuxIef/xxNW3aVB4eHpcUIKpbt648PDyUkJBw+ToHAAAAADCFq7KGzp133qmYmBiHdRUqVJCLi8slHTcnJ0dubm5FtgsLC7uk8xTmwIEDWr9+vUaMGKHo6Gg1b978sp9Dkmw2m/Ly8uTqenEvDS8vL3l5eV3WPlWpUkUxMTHq3bu3fd3GjRuVkJAgHx+fy3quK2Xw4MH66aef9Msvv1zU/mvXrlVmZqZ69Oih+fPn65lnnrnMPby6VAi06IGO3vLxMnQ626YPvsrQkRMFR/O2aOCuO27ykGFIuw/katGqTFmtUpC/Rfd39FaVEBedSLZq6vuppXAVKImwYFcN71lefj4WZZy26q0Pk/R3Yk6hbds091HXtv6yGNKvf2UpemmS8qxSvRoeGvtgBR0+lmtv++LricrJtV2py0AxVAi0qP8dnvL1MpSZJf3v60wlJBX8+765vps6NHOXYRj682CuFn93WlarVPs6F3W9xVPu//z3/uu+XH2+Nks8y2VP5CvPKbRzO3mHX6cfm92tlO2/F9quyqAeinjqIcli0Yk1G7VzxETZcnOL3Iayxy2skiqOeEqufgHKy0jXkTdmKPvv/Y6NDEMhDzwsn8bNZMvLU15qihLenq2chMOSpKC7eyqgTQfZcnNly85WYsybOv3XH6VwNShK8vF4/fDxOJ1OPyl3Tz/d2mOKyoXWcmiTevKQfvh4nE4c3iW/oOt0z2NL7dsO79mozStnKTcrQzKkKnVuU/OoJ2VYrrocBtNh2nLnuSpf3R4eHgoLC3NYbr/9dochV5KUmpqqPn36yMfHR5UrV9Ybb7zhsN0wDM2ZM0ddu3aVj4+PXn75ZUnSnDlzFBERIXd3d9WpU0cffPBBgf3OHXK1adMmNWnSRJ6enmrWrJm2bdtW4muKiYlR586dNXz4cC1cuFCZmZmSpN27d8swDP3+u+OHmldeeUURERFFHnfNmjUyDENfffWVPWtk7dq12rNnj+6++26FhobK19dXzZs316pVq+z7tWnTRvv379cTTzxhz4KSCh9yVdT9Kkq/fv30/fff6+DBg/Z10dHR6tevn0PgqbChbKdOnZJhGFqzZo0k6eTJk+rXr58qVKggLy8v1apVyyH49/fff6tPnz4KCgqSj4+PmjVrpp9++um8fXvvvfdUr149eXp6qm7dunrzzTeLvJ7XXntNjz76qGrUqFGCu+Bo7ty56tu3r+6//35FR0fb13/99dfy9PTUqVOnHNqPHDlS7dq1u+jzmV2fO7y07pdsTYpO1TebsnR/R+8CbcoHWNT5Fk+9sjBNE95LlZ+3Rbc0dJcknc62adnaTMUsT7/SXcdFGtI9SN/+lKbR04/o8zUpGtYzqNB2Fcq5qGdUgCa+mahR/z2iAD+L2t3ka99++Fiuxs1OsC8Ec8qe3rd7av3OHE2en65VP2ep/x0Ff1Qo72+ocwsPzf4oQ5PmpcnP21CrBvkRnIwsm2K+zNCUD9I1LTZd1Su66MbIon+8wZWX8MlKbWjTVxnxf5+3jVf4dao9YaQ2tO2nNXU7yCMkWFUf6lnkNpRNYUNHKXnVl9o7crCSPvtQFR8dU6CNb7MW8qpTX/vGDFP8mGHK2BGnCn0GSZI8wmuoXFQXxY97TPFPDdfJFZ8r9MFHr/RloJjWfTpBdZr31H1PrlDDW4foh4+fLdDG3cNHTTuMVJte0wtu8/JX294zde8Ty3T3o5/o6IE4/bntsyvRdaDUXJUBneKaPn26GjVqpG3btmns2LEaOXKkvvnmG4c2EyZM0D333KMdO3Zo8ODBWrp0qUaOHKknn3xSO3fu1NChQzVo0CB99913hZ4jLS1NnTt3VmRkpLZs2aIJEyZozJiC/xldiM1mU0xMjPr376+6deuqZs2a+vjjjyVJtWvXVrNmzbRgwQKHfRYsWKC+ffsW+xxjx47Vf/7zH+3atUsNGzZUWlqa7rrrLn377bfatm2b7rzzTnXp0kUHDhyQJC1ZskTXXXedJk2apCNHjujIkSOFHrek96swoaGhioqK0vz58yVJGRkZWrx4sQYPHlzsY5zxwgsv6LffftNXX32lXbt2ac6cOQoODpaU/1zddtttOnTokD7//HNt375dTz/9tKzWwuuyL1iwQC+++KJefvll7dq1S1OmTNELL7xg76ezpKam6qOPPlL//v3VoUMHJScn68cff5Qk3X777QoMDNQnn3xib5+Xl6fFixerX79+Tu1XWeXrbahqqKs2/ZYtSdq2O0fl/CyqEOj49tektpt2/JWjlIz8L+xrt2epWd38gE7GaZv2HMpTduEJHihj/H0sqn6du9Zuyw/AbdqRqfKBrgotXzDz8KaG3tryW6aS0/L/zldtTFPLxgUDfiibfL0MVQlx0eZd+X+ccX/lqpyfoeAAx18CG9dy0469uUo98/e9I0dN6+QHbf4+ZtWJlPz1uXnSoWNWBflf0x+PyqyktT/r9KHEC7ap2D1KictWKyvxuCRp/zsLValX5yK3oexx8Q+UZ41aSv7hW0lS6sYf5RZcQW5hlRwb2mwy3Nxkccv/P9vi7a3cpOP/bJPk4iqLh2f+Nh8f5Z44fqUuASWQmXZCxw/tVM3GXSRJ4Q3uUHpyglJOOGZkeXgHKiy8qdzcC/5fHVwpUv5BVSRJrm4eCqpYV2knDzm/80ApuiqHXC1btky+vmd/YT1fPZtWrVpp7NixkvIDI+vWrdMrr7yiDh062Nv07dtXgwYNsj/u06ePBg4cqEceeUSSNHr0aG3cuFEzZsxQ27ZtC5wjNjZWVqtVc+fOlaenp+rXr6+///5bw4cPL/b1rFq1ShkZGYqKipIk9e/fX3PnztX9998vKT+D5fXXX7fXCNq9e7e2bNmi//3vf8U+x6RJkxyuOygoSI0aNbI/njx5spYuXarPP/9cI0aMUFBQkFxcXOTn53fBIWYzZswo0f06n8GDB+vJJ5/Uc889p48//lgREREXVX/mwIEDatKkiZo1ayYpv0bPGbGxsTp27Jg2b96soKD8X/Nr1qx53mONHz9eM2fOVPfu3SVJ1atX12+//aa3335bAwYMKHHfimvRokWqVauW6tevL0nq3bu35s6dq9atW8vFxUW9e/dWbGysHnzwQUnSt99+q1OnTunee+91Wp/KsnJ+FqWkW2U9J7EiKcWqcn4WHTtldWiXlHL28YkUq8rxpc6Uyge66FRqns6NxR4/mavgQBclnnAcWhEc6KrjJ/Psj48l5So48Ox/jaHlXTVlZJisVpu+/zld32xIc3r/UXzl/IwCf98nU20K8rPoeHLeOe0sSko9+4I48x7wb37ehhrXctVbn2U4td9wHq+qFZW5/+wXuMz9h+RVpWKR21D2uAZXUO6pJJ37Zp5z/KjcgkPsw6kkKW3LRnk3aKSa7y6W9XSGcpNOaP/4JyVJWfv36uTyJYp4433lpaXKlpOjA/9sQ9mSnpwgb78Ksrjk/x9sGIZ8Aisq7dQR+ZevVuLjZaQeU/zOr9XhgTmXu6u4CDYx5MpZrspvK23btlVcXJx9ee211wpt16JFiwKPd+3a5bDuzBf/M3bt2qVWrVo5rGvVqlWB/c5t37BhQ3l6ep73vEWJjo5Wr1697MOL+vTpo3Xr1mnPnj2S8r/Qx8fHa+PGjZLyM0duuOEG1a1bt9jn+Pd1pqWlacyYMapXr54CAwPl6+urXbt22TN0iquk9+t8OnXqpLS0NP3www+Kjo6+qOwcSRo+fLgWLVqkxo0b6+mnn9b69evt2+Li4tSkSRN7MOdC0tPTtWfPHj344IPy9fW1Ly+99JL9eenYsaN9/Zngy+UQHR2t/v372x/3799fH330kVJT8+u69OvXT2vWrNHhw/kfdhYsWKBOnTqdd/axrKwspaSkOCx5uVmXrb+AWcUfytajLx/Ss68maNb7x9X+Zl/d3JDsnauVp7s0tKu3Vv2crYNHC8/MBFD2eEbUlkeVcP01tI/+eriP0ndsU9hDIyVJbiFh8ruplfY+NlB7hvVT0vIlqvTEc6XcYzhb9uk0ffP+I7r+1gdV4boGpd0dwKmuyoCOj4+PatasaV8qVrz4X19Ku+huUlKSli5dqjfffFOurq5ydXVV5cqVlZuba6+dEhYWpnbt2ik2NlZSfqZJSYfX/Ps6x4wZo6VLl2rKlCn68ccfFRcXp+uvv17Z2dmX58JKyNXVVffff7/Gjx+vn376qdDrs/xT8MxmO/tTbU6O4xiZjh072mv/HD58WLfffrt9CFxJijmnpeX/Sv/uu+86BA937txpD6y999579vVffvllyS74PH777Tdt3LhRTz/9tP31cPPNNysjI8M+E1jz5s0VERGhRYsWKTMzU0uXLr3g62Hq1KkKCAhwWLasfuWy9LcsOJlqlb+PRZZzfhgI8rfoZKq1QLtzh1mU97foZApf6syi9Q0+mjoqTFNHhalBLU8F+rno3BqIweVcdfxUXoH9jp/KVXC5swXzKwS56vip/CyezCybMk/nv58kJedp/f+zd9/hUdVpG8fvSUgvBAxJKKEGIaFIR1CRpiBIFemLAhYQF5CyiopSFBABwV0rhqJ0KS6oL0pHmkgJHVEhgJjQIQTSM+8fWSNjQtOcnMyc7+e65tKcczJzh4cZMs/8Ssw1VS7nZewPgjty8Yo9x/O7SIDNYTRO1nWZKnrdiJw/vwZ4eUj92/tq39E0rdttzr9zyBtJJ+LkU6Zk9tc+ZUoq6WTcLc+h4Ek/d1aFgorq+hdzj+AQpZ0743Bd4Qeb69r+GGVeuyrZ7bq8fpV8q2aNMg+of79STsQq/eIFSdLldd/Kt3JV6S9u/gHj+BUO07UrZ5WZkfVvsN1u19VLcfIPurP3cakpV/XNrKdVOrKpqt3/pAFJgYLFJRs6t+v3N97Xfx0ZGXnT74mMjNTmzZsdjm3evFlRUVE3vH7v3r1KTk6+4ePezNy5c1WqVCnt2bPHoXEwefJkzZo1SxkZWW9QevTooYULF2rr1q06evSow45Qf8XmzZv15JNPqkOHDqpWrZrCwsIUGxvrcI2np2f249/Inf553UyfPn20YcMGtWvXTkWKFMlxvlixYpLksJ7P9QskX3/dE088oTlz5mjq1Kn6+OOPJUnVq1dXTEyMLly4cMssoaGhKlGihI4ePerQPIyIiFC5cuUkSSVLlsw+VqbMnQ8VzU10dLQaNWqU4+/DkCFDFB0dnX1djx49NHfuXK1YsUJubm5q3br1De9zxIgRunz5ssOtdtMX8iRvQZB4za6TZzJULyprbn3Nuz108Uqmw3QrKWttnWoRHgr0zXpneP89Xtr5I2/snMV3u65mL1y8Yv0VxZ5K1f01sxrV9ar56MLl9BzTrSRp+75rqh3lo8L+Wf8cNr/XX1v3ZE23CQpw0//We5e3l001I30Ue4q/EwVJYpJdv57NUN3IrPVwakQU0qVEu85ddly8OuanNFUrX0gBvz+/q3lo15Gshr+nh/RcB18dOp6ub7ZTX2cXt+wbhT7aVF6hWevjlXmmm35b9NUtz6HgyUi4pJRjP6two2aSpIB7H1Da+XMO060kKfV0vHyr1shu0vjXrq+UE7H/Oxcnn0pRsv1vpLx/7fpK+e2kxM5mBY6P/126q0SUfo5ZIUmK3f+t/AqH3tF0q7SUq/pm5tMqVfF+1Wx6+8tbAM7M0u3pzZs3a+LEiWrfvr1WrVqlzz//XF99dfN/2IcPH67OnTurZs2aat68uVasWKGlS5c67AB1ve7du+uVV17R008/rREjRig2NlaTJk267YzR0dHq1KmTqlZ1HC4YHh6uESNGaOXKlWrdurU6duyo/v37q3///mrSpIlKlChxg3u8PRUrVtTSpUvVpk0b2Ww2jRw5MsfiwGXLltXGjRvVtWtXeXl5ZS8ufL07/fO6mcjISJ07d06+vrlPefDx8dG9996rCRMmqFy5cjpz5oxeffVVh2tee+011a5dW1WqVFFKSoq+/PLL7CZet27dNG7cOLVv317jx49X8eLFtXv3bpUoUSLXaXKjR4/WwIEDVbhwYbVs2VIpKSnasWOHLl68qCFDhtzw5/j555+VmJio+Ph4JSUlZTedoqKi5OnpecPvS0tL02effaYxY8bk+Pvw1FNPacqUKTpw4ICqVKmiHj16aNSoUXrzzTfVqVMneXndeFSBl5dXjvPuhVxrZMr8b6/pH4/46uH6XkpOtWvOyqw37N0f9tG+X9K075d0nb+cqa82J2tI96z1t346ma7v9mS9ufMoJL3eN1CF3CUfL5veeDZQ2w+mavl3yTd8TJjrk6UX1K/zXWrfNFBJKVnblv/u6U5FtetgknYeTNKZCxla/O1ljR4QKkk6+EuK1mzLGoFXr5qvHrrXXxmZkrubtG1fktbvYKezgmbBmmT1fNhbD9f1VHJq1rblktStubf2HU3X/qPpOp9g19fbUvRC56wm38+/pmvTvqyGTuManioT6i5PD5vuichqDO0+kqZvf6C5U9BUfX+0Qh5pLK+wYNX7KlrpV65qfeTDqvbRGzq9Yq3OfLlWScd+1ZEx76rBhvmSpAsbt+vExwsl6abnUDDFfzxNxQcM010duikj6Zri38/6HTqs3wtK3LFViTu26dLK5fIqGa5yb38oe0a6Mi5dVPzH0yRJids3yyeikspOeE/2tFRlpiTrt2kTzPyRcBP3tR+tjYtHaM/6j+Tp7a8HHhsnSfpu6asqHdlUZSKbKj01SZ9PeUSZ6alKTUnU/AmNFVGzreq2GKIDWz7T2V/3KT0tSbEHs95rlKvaQjWa9DPzx4LYttxIlm7oDB06VDt27NDo0aMVGBioKVOmZC88fCPt27fXtGnTNGnSJA0aNEjlypXTzJkz1bhx41yv9/f314oVK9SvXz/VrFlTUVFReuutt25rgdqdO3dqz549mj59eo5zhQsXVrNmzRQdHa3WrVsrICBAbdq00aJFixy2sf6rpkyZoj59+qhhw4YKDg7Wiy++qISEBIdrxowZo2effVYVKlRQSkqKw1Sn393pn9et3HXXXTc9P2PGDPXt21e1a9dWpUqVNHHiRD388MPZ5z09PbMbaz4+PnrggQeypyp5enrq22+/1dChQ9WqVSulp6crKioqx3b2v3vqqafk6+urt99+W8OHD5efn5+qVaumwYMH3zTjU089pQ0bNmR/XbNmTUnSsWPHHBZp/rPly5fr/Pnz6tChQ45zkZGRioyMVHR0tKZMmaKIiAjVq1dP27dv19SpU2+axwrOXMzU5Hk5F7Od9783fr/bsi9VW/blfBOXli69+lFCjuMouOLOpuv193LfDWf6YsdReGu3X9Xa7TkbNd9uSdS3W1gEuaA7czFTUxbmXMR4/mrHhuuW/Wnasj/nVnXf/pBK88ZJ7H/u9VyP73vW8cObk9Gf62T057lee7NzKHhSf/tVx18ZnON4/Id/TA23p6cp/qOpN7yPs/Nm6Oy8v/+7MYwXVKyc2vZfkOP4Ax3fyP7/Qp4+6vbS+ly/v0aTfjRvYDk2e27vwgFY2oBJl8yOgHx04QzNKisJLhFkdgTko5bD65odAfmoQvu8meIN57C860qzIyAf/esx510tZfNB5/yA7L4o/1tfZDLn/VsBAAAAAABgUTR0TNavXz+Hba+vv/Xr9/eHDBp9/3/H3Llzb5gtL7f5dibXb3X+59u4cePMjgcAAAAAd8Qum1PenAFTrkx25syZHGvT/C4wMFAhISEF+v7/jitXruj06dzXuPDw8MiznaGcyalTp5SUlJTruaJFi6po0aL5koMpV9bClCtrYcqVtTDlylqYcmUtTLmyFmeecrXpoHNuKnF/lJ/ZEW7J0osiFwQhISGGNlWMvv+/IyAgQAEBAWbHKFBKlixpdgQAAAAAgBOgoQMAAAAAAAyRyZwgwzjvuC0AAAAAAACLoqEDAAAAAADgZGjoAAAAAAAAOBnW0AEAAAAAAIZwli3AnREjdAAAAAAAAJwMDR0AAAAAAAAnw5QrAAAAAABgCLudKVdGYYQOAAAAAACAk6GhAwAAAAAA4GRo6AAAAAAAADgZ1tABAAAAAACGsNvNTuC6GKEDAAAAAADgZGjoAAAAAAAAOBkaOgAAAAAAAE6GNXQAAAAAAIAhMmUzO4LLYoQOAAAAAACAk6GhAwAAAAAA4GSYcgUAAAAAAAxhtzPlyiiM0AEAAAAAAHAyNHQAAAAAAACcDA0dAAAAAAAAJ8MaOgAAAAAAwBB2u9kJXBcjdAAAAAAAAJwMDR0AAAAAAAAnQ0MHAAAAAADAybCGDgAAAAAAMIRdNrMjuCxG6AAAAAAAADgZGjoAAAAAAABOhilXAAAAAADAEJlsW24YRugAAAAAAAA4GRo6AAAAAAAAToaGDgAAAAAAgJNhDR0AAAAAAGAIu51ty43CCB0AAAAAAAAnQ0MHAAAAAADAyTDlCgAAAAAAGMLOtuWGYYQOAAAAAACAk6GhAwAAAAAA4GRo6AAAAAAAADgZ1tABAAAAAACGyBTblhuFEToAAAAAAABOhoYOAAAAAACAk6GhAwAAAAAA4GRYQwcAAAAAABjCbjc7getihA4AAAAAAICToaEDAAAAAADgZJhyBQAAAAAADGG3s225URihAwAAAAAA4GRo6AAAAAAAADgZGjoAAAAAAABOhjV0AAAAAACAITLZttwwjNABAAAAAABwMjR0AAAAAAAAnAxTrgAAAAAAgCHsTLkyDCN0AAAAAAAAnAwNHQAAAAAAACdDQwcAAAAAAMDJsIYOAAAAAAAwhF02syO4LEboAAAAAAAAOBkaOgAAAAAAAE6Ghg4AAAAAAICTYQ0dAAAAAABgiEy72QlcFyN0AAAAAAAAnAwNHQAAAAAAACfDlCsAAAAAAGAIO1OuDMMIHQAAAAAAACdDQwcAAAAAAMDJMOUKQA5XLieZHQGAQRIuXDM7AvJRhfZlzI6AfPTLF8fNjoB8dKZpstkRkK98zQ6AAoiGDgAAAAAAMARr6BiHKVcAAAAAAABOhoYOAAAAAACAk2HKFQAAAAAAMESm3WZ2BJfFCB0AAAAAAAAnQ0MHAAAAAADAydDQAQAAAAAAcDKsoQMAAAAAAAzBtuXGYYQOAAAAAACAk6GhAwAAAAAA4GRo6AAAAAAAADgZ1tABAAAAAACGYA0d4zBCBwAAAAAAwMnQ0AEAAAAAAHAyTLkCAAAAAACGyGTKlWEYoQMAAAAAAOBkaOgAAAAAAAD8Te+9957Kli0rb29v1a9fX9u3b7/htdOnT9cDDzygIkWKqEiRImrevPlNr88NDR0AAAAAAIC/YeHChRoyZIhef/117dq1S/fcc49atGihM2fO5Hr9+vXr1a1bN61bt05bt25VeHi4Hn74YZ06deq2H5OGDgAAAAAAMITdbnPK252aMmWKnn76afXu3VtRUVH68MMP5evrqxkzZuR6/dy5c/Xcc8+pRo0aqly5sj755BNlZmZqzZo1t/2YNHQAAAAAAACuk5KSooSEBIdbSkpKrtempqZq586dat68efYxNzc3NW/eXFu3br2tx7t27ZrS0tJUtGjR285IQwcAAAAAAOA648ePV+HChR1u48ePz/Xac+fOKSMjQ6GhoQ7HQ0NDFR8ff1uP9+KLL6pEiRIOTaFbYdtyAAAAAACA64wYMUJDhgxxOObl5WXIY02YMEELFizQ+vXr5e3tfdvfR0MHAAAAAAAYwm43O8Ff4+XlddsNnODgYLm7u+v06dMOx0+fPq2wsLCbfu+kSZM0YcIErV69WtWrV7+jjEy5AgAAAAAA+Is8PT1Vu3ZthwWNf1/guEGDBjf8vokTJ2rs2LFauXKl6tSpc8ePywgdAAAAAACAv2HIkCF64oknVKdOHdWrV09Tp07V1atX1bt3b0lSr169VLJkyex1eN566y299tprmjdvnsqWLZu91o6/v7/8/f1v6zFp6AAAAAAAAENkOumUqzvVpUsXnT17Vq+99pri4+NVo0YNrVy5Mnuh5BMnTsjN7Y9JUh988IFSU1PVqVMnh/t5/fXXNWrUqNt6TBo6AAAAAAAAf9Pzzz+v559/Ptdz69evd/g6Njb2bz8ea+gAAAAAAAA4GRo6AAAAAAAAToYpVwAAAAAAwBDOum25M2CEDgAAAAAAgJOhoQMAAAAAAOBkmHIFAAAAAAAMwZQr4zBCBwAAAAAAwMnQ0AEAAAAAAHAyNHQAAAAAAACcDGvoAAAAAAAAQ2Syho5hGKEDAAAAAADgZGjoAAAAAAAAOBkaOgAAAAAAAE6GNXQAAAAAAIAh7KyhYxhG6AAAAAAAADgZGjoAAAAAAABOhilXAAAAAADAEJmZZidwXYzQAQAAAAAAcDI0dAAAAAAAAJwMDR0AAAAAAAAnwxo6AAAAAADAEGxbbhxG6AAAAAAAADgZGjoAAAAAAABOhilXAAAAAADAEEy5Mg4jdAAAAAAAAJwMDR0AAAAAAAAnQ0MHAAAAAADAybCGDgAAAAAAMEQma+gYhhE6AAAAAAAAToaGDgAAAAAAgJOhoQMAAAAAAOBkWEMHAAAAAAAYwm531kV0bGYHuCVG6AAAAAAAADgZGjoAAAAAAABOhilXAAAAAADAEE4748oJMEIHAAAAAADAydDQAQAAAAAAcDI0dAAAAAAAAJwMa+gAAAAAAABDZGaancB1MUIHAAAAAADAydDQAQAAAAAAcDJMuQIAAAAAAIZg23LjWGaETuPGjTV48ODsr8uWLaupU6ca8lg2m01ffPHFDc/HxsbKZrMpJibGkMc306xZsxQUFGR2DEOMGjVKNWrUMDsGAAAAAACu19B58sknZbPZctwmTpyosWPH5kuGuLg4PfLII3l+vy1atJC7u7t++OGHPL/vvyK3pliXLl105MiRPH0Mm82mBQsW5DhXpUoV2Ww2zZo1K88ez2h79uxRt27dFB4eLh8fH0VGRmratGl3fD9JSUkqWrSogoODlZKSYkBSAAAAAEBB5pJTrlq2bKmZM2c6HCtWrJjc3d3/1v2mpaXJw8PjlteFhYX9rcfJzYkTJ7RlyxY9//zzmjFjhurWrZvnjyFJdrtdGRkZKlTor/3V8PHxkY+PT55mCg8P18yZM9W1a9fsY9u2bVN8fLz8/Pzy9LGMtnPnToWEhGjOnDkKDw/Xli1b9Mwzz8jd3V3PP//8bd/PkiVLVKVKFdntdn3xxRfq0qWLgaldQ2hRdz3zWJACfN10LTlT05dd1qkz6ble26iWjx5t5C+bTTp0NFWzV1xWRqYUEe6hJ9oUliS5u0tHjqdqzlcJSs/Iz58EtyMsuJD6d75LAX5Z9f5w0QX9ejot12sb1/VT2yaBcrNJB35O0YxlF5SRKUWW99JLfYvpt7N//D157T+nlZbOuOGCJrSou57uWDjr+Z1i1ydLL+vU2Rs/v1s/4Cc3m3TwaKo+/TJBGZlShXAPPfFooCSpkLt05Hia5nzN87ug8QgroeLPD1ehgMLKuHZVce9NUuqvxx0vstkU0usZ+dWoI3tGhjKuJCj+o6lKi/9NklS0XWcVbvyQ7Onpsqem6vTM95X8848m/DS4mah3XlHoo03lW7aUvqvTTgl7Dud6XXjvTqow/GnJzU3n12/T/udHy56efstzKHiCC9vUtamn/LxtSk61a8HaVJ2+mPPf3HqV3dWkpodsNunnU5la+l2qMjOlCiXc9FRrL5259Mf3/HtpMq/jcGkuN0JHkry8vBQWFuZwa9asmcOUK0m6cuWKunXrJj8/P5UsWVLvvfeew3mbzaYPPvhAbdu2lZ+fn958801J0gcffKAKFSrI09NTlSpV0meffZbj+66fcrV9+3bVrFlT3t7eqlOnjnbv3n3HP9PMmTP16KOPqn///po/f76SkpIkSUeOHJHNZtPhw47/yL3zzjuqUKHCLe93/fr1stls+r//+z/Vrl1bXl5e2rRpk3755Re1a9dOoaGh8vf3V926dbV69ers72vcuLGOHz+uF154IXsUlJT7lKtb/XndSo8ePbRhwwadPHky+9iMGTPUo0cPh8ZTblPZLl26JJvNpvXr10uSLl68qB49eqhYsWLy8fFRxYoVHZp/v/76q7p166aiRYvKz89PderU0ffff3/DbJ988okiIyPl7e2typUr6/3337/pz9KnTx9NmzZNDz74oMqXL6+ePXuqd+/eWrp06R39mURHR6tnz57q2bOnoqOjs49//PHHKlGihDL/tDdgu3bt1KdPnzt6DFfTu11hrdtxTf+adlZfbbqqpzsUzvW64CB3PdYsQG9+cl7D3zmrQH83Na7jK0k6EZ+mUR+e08j3z+mV/5xToJ+7mtVzrqaiVTzVsajWfJ+oIW/Hafn6BPXrXDTX64oVcVfnFoU1+v3TGvxWnAoHuKlpff/s87+dTdeIqfHZN5o5BdOTbQO1fkeSXnz3nL76LlFPdbzx87tjU3+Ni76g4VPPqfB1z++T8Wka/dF5vfbBeb3y3nkF+rmpWT3f/PwxcBvCnh2sy6u/1tFBfXThv4tUfMCwHNf412kgn0pVdGxYP8UO66dr+2JUrFtvSZJX2fIq0qKNYkf8U7HD++viyuUK7Tsgv38M3Ib4Jd9oa+Puuhb76w2v8SlbSnePGqStTXpofeWH5BUSrNJPd77lORRMnR701LaD6XprfrLW7U5X16aeOa4pGmBTi3oeev+LZE2Yl6wAX+neyD/eD5y5ZNc7nydn32jmFAyZdue8OQOXbOjcrrffflv33HOPdu/erZdeekmDBg3SqlWrHK4ZNWqUOnTooH379qlPnz5atmyZBg0apKFDh2r//v169tln1bt3b61bty7Xx0hMTNSjjz6qqKgo7dy5U6NGjdKwYTl/+bgZu92umTNnqmfPnqpcubIiIiK0ePFiSdLdd9+tOnXqaO7cuQ7fM3fuXHXv3v22H+Oll17ShAkTdOjQIVWvXl2JiYlq1aqV1qxZo927d6tly5Zq06aNTpw4IUlaunSpSpUqpTFjxiguLk5xcXG53u+d/nnlJjQ0VC1atNDs2bMlSdeuXdPChQv/UoNi5MiROnjwoP7v//5Phw4d0gcffKDg4GBJWbV68MEHderUKS1fvlx79uzRv/71rxzNkd/NnTtXr732mt58800dOnRI48aN08iRI7Nz3q7Lly+raNHc32zm5pdfftHWrVvVuXNnde7cWd99952OH8/6dPLxxx/X+fPnHf58L1y4oJUrV6pHjx53lMuVBPi5qVwJD23Zk9UI/eFAsooWdldI0Zyj9upV8dbuw8m6nJhV97Xbr6lB9axRZ6lpUsb//joUcpc8PSTJSV7tLSTQz03lSnlq0+6rkqTt+5J0V1Ahhd6Vc+Rh/eq+2nkwKbveq7clqmEN3sQ7k+zn996s5/eOgykqGuiW6/O7bhUv7f4x5Y/n9w/XdG81b0k5n98eHjae3QWMe2CQvMtX1OWNayRJV7Z9J4/gYvIIK+F4od0um4eH3Dyy3gy6+foq/cK5/52T5F5Ibl5ZdXfz81P6+XP59SPgDlzYtEPJp07f9JriHVvo9JdrlXI6q4bHP56vEl0eveU5FDz+PlKpYm7adSSrA7P3aIYK+7vprkCbw3XVK7jrYGyGrmS95GvrgXTVrPj3ZmEAzswlp1x9+eWX8vf/4xPWG61nc9999+mll16SlNUY2bx5s9555x099NBD2dd0795dvXv3zv66W7duevLJJ/Xcc89JkoYMGaJt27Zp0qRJatKkSY7HmDdvnjIzMxUdHS1vb29VqVJFv/76q/r373/bP8/q1at17do1tWjRQpKyR2X84x//kJQ1guU///lP9hpBR44c0c6dOzVnzpzbfowxY8Y4/NxFixbVPffck/312LFjtWzZMi1fvlzPP/+8ihYtKnd3dwUEBNx0itmkSZPu6M/rRvr06aOhQ4fqlVde0eLFi1WhQoW/tEDxiRMnVLNmTdWpU0dS1ho9v5s3b57Onj2rH374IbvBEhERccP7ev311zV58mR17NhRklSuXDkdPHhQH330kZ544onbyrNlyxYtXLhQX3311W3/DDNmzNAjjzyiIkWKSMpaW2nmzJkaNWqUihQpokceeUTz5s1Ts2bNJEmLFy9WcHDwHf15u5q7CrvpUmKmru/Nnb+cobsKu+vMBcePbu4Kcte5y38cO3cp67rfBQe5a3CPIgop4q49R1K0evs1w/PjztwV5K5LVzIc6n3uYrqCg9x1+rzjUPvgoEI6d/GPep+9kK7goD/+aQy9q5DGDQpTZqZdG3Zc1aqtiYbnx525KzDn8/vCjZ7fhd11/tLNn9+DugdlP7/X8PwuUAoFF1P6pQu6vthp587IIzgkezqVJCXu3CbfqvcoYvpCZSZfU/qF8zr++lBJUsrxo7r41VJVeO9TZSRekT0tTSf+dw7Ox6d0cSUdP5X9ddLxU/IJL37Lcyh4CvvZlHDN7jAq4tKVTBUJsOl8wh8Hg/xtunjlj68vXrEryP+Pps9dgTYN7uQtu92uHw5naMsBptjBtbnkCJ0mTZooJiYm+/buu+/mel2DBg1yfH3o0CGHY7+/8f/doUOHdN999zkcu++++3J83/XXV69eXd7e3jd83FuZMWOGunTpkj29qFu3btq8ebN++eUXSVLXrl0VGxurbdu2ScoaOVKrVi1Vrlz5th/jzz9nYmKihg0bpsjISAUFBcnf31+HDh3KHqFzu+70z+tGWrdurcTERG3cuFEzZsz4y9OH+vfvrwULFqhGjRr617/+pS1btmSfi4mJUc2aNW9rtMzVq1f1yy+/qG/fvvL398++vfHGG9l1eeSRR7KPV6lSJcd97N+/X+3atdPrr7+uhx9++LbyZ2RkaPbs2erZs2f2sZ49e2rWrFnZI4l69OihJUuWZC+WPHfuXHXt2lVubrk/3VNSUpSQkOBwy0hnoeUbOXcpQ6++d07/nHhGhQrZVCfK+9bfBKcUeypVA948pZenxWvKp+fU/F5/3Vud0Tuu7NylDI18/7wGvn026/kdyfPbGXlXuFte4WX187Pd9PMz3XR1326FPT1IkuQREqaA+vfp6D+f1C/9eujCV0tV4oVXTE4MIC/8ejZTb3yWpKmLkzVrZarurVJI91Rg9A5cm0uO0PHz87vpyIo7vS8zXbhwQcuWLVNaWpo++OCD7OMZGRmaMWOG3nzzTYWFhalp06aaN2+e7r33Xs2bN++ORgBJOX/OYcOGadWqVZo0aZIiIiLk4+OjTp06KTU1NU9+rjtVqFAh/eMf/9Drr7+u77//XsuWLctxze8NC7v9j659WprjIqiPPPKIjh8/rq+//lqrVq1Ss2bNNGDAAE2aNOmOFnNOTMz6lH769OmqX7++w7nfF9/+5JNPstc6+vNi2gcPHlSzZs30zDPP6NVXX73tx/3mm2906tSpHIsgZ2RkaM2aNXrooYfUpk0b2e12ffXVV6pbt66+++47vfPOOze8z/Hjx2v06NEOx6o/MET3PHhnUwMLmvtq+Khlw6y/19v2JSnI301ubn98sHtXYXedv5xzYvX5SxkOUzWCg3K/LiXVrm37ktSwuo++35dszA+B2/ZALT+1ahQgSdoSc1VBAe4O9Q4uUkjnLuWs47lL6Q5TsYoVLaRzl7I+zUtK+eO15MLlDG2JuabK5by0bS+jNsx23z3eapH9/E7O8fwueqPn9+UMhRT9o943e35/vy9ZDe7x1vf7eX4XFOnnzqpQUFFdX2yP4BClnTvjcF3hB5vr2v4YZV7LmnZ5ef0qhY8cL0kKqH+/Uk7EKv3ihaxz675VWN/npUKFJBbLdTpJJ+LkW6F09tc+ZUoq6WTcLc+h4Ll81a5AX5vcbH+sXRIU4OYwGkeSLiXaHaZhFQmw6VJi1jUp1/3af/mqXTE/patccTft+YWFdMxmZw6zYVxyhM7t+n1Ey/VfR0ZG3vR7IiMjtXnzZodjmzdvVlRU1A2v37t3r5KT//iF8M+PezNz585VqVKltGfPHodRR5MnT9asWbOUkZH1AtWjRw8tXLhQW7du1dGjRx12hPorNm/erCeffFIdOnRQtWrVFBYWptjYWIdrPD09sx//Ru70z+tm+vTpow0bNqhdu3bZ042uV6xYMUlyWM/n+gWSr7/uiSee0Jw5czR16lR9/PHHkqTq1asrJiZGFy5cuGWW0NBQlShRQkePHlVERITDrVy5cpKkkiVLZh8rU6ZM9vceOHBATZo00RNPPJG90Pbtio6OVteuXR3+LsTExKhr167ZiyN7e3urY8eOmjt3rubPn69KlSqpVq1aN7zPESNG6PLlyw63qvf9845yFUSbY5I08v2sBYy/+u6qYuPS1PCerKZd3SreupiQkWM6hiT9cDBZNSt7q7B/1stj03q+2rYvqzEXUtRd7v971XR3l+pEeuvkad4AFATf7bqavXDxivVXFHsqVffXzHrDX6+ajy5cTs8x3UqStu+7ptpRPtn1bn6vv7buyWrYBAW46X/rvcvby6aakT6KPWVOUxuONu9J1msfZC1g/PWmq4qNS1fD/611VSfKSxcTMnN9fu84mKKalbz+eH7X9c1uyP75+V070ksn43l+FyQZCZeUcuxnFW6UNaU44N4HlHb+nMN0K0lKPR0v36o1spo0kvxr11fKidj/nYuTT6Uo2f43ctq/dn2l/HaSZo6Tilv2jUIfbSqv0Kz1EMs8002/LfrqludQ8CQmSafOZqrW3VkfqlUv767LiZkO060kae8vGYoq666A/30O26BKIcX8nPV6H+Ar/d7q8fKQIsu469S53NfCBFyFS47QuV2bN2/WxIkT1b59e61atUqff/75LdcyGT58uDp37qyaNWuqefPmWrFihZYuXeqwA9T1unfvrldeeUVPP/20RowYodjYWE2aNOm2M0ZHR6tTp06qWrWqw/Hw8HCNGDFCK1euVOvWrdWxY0f1799f/fv3V5MmTVSiRIkb3OPtqVixopYuXao2bdrIZrNp5MiRORYHLlu2rDZu3KiuXbvKy8sre3Hh693pn9fNREZG6ty5c/L1zX3Kg4+Pj+69915NmDBB5cqV05kzZ3KMfnnttddUu3ZtValSRSkpKfryyy+zm3jdunXTuHHj1L59e40fP17FixfX7t27VaJEiVynyY0ePVoDBw5U4cKF1bJlS6WkpGjHjh26ePGihgwZkmvG/fv3q2nTpmrRooWGDBmi+Ph4SVmjen5vSN3I2bNntWLFCi1fvjzH34devXqpQ4cOunDhgooWLaoePXro0Ucf1YEDBxymZ+XGy8tLXl5eDsfcC1296fc4o5nLL+uZDkFq28hfSSlZ25b/rk+7wtr9Y7J2H07R2YsZWrb2il59+i5J0uFjqVr3Q9Yb/Kjynnr4Xj9l2rM+ID74S6r+u/6KKT8Pbu6TpRfUr/Ndat80UEkpWduW/+7pTkW162CSdh5M0pkLGVr87WWNHhAqSTr4S4rWbMsagVevmq8eutdfGZmSu1vWSK/1O1zvueEKZi2/rKc7FlabRn5KSrHrE4fnd6B2H07R7h//9/xel6hXn8qaWnvoWKrW7fjf87ucpx6611eZ9qx6HziaquUbWDOpoIn/eJqKDximuzp0U0bSNcW/n/U7VVi/F5S4Y6sSd2zTpZXL5VUyXOXe/lD2jHRlXLqo+I+nSZISt2+WT0QllZ3wnuxpqcpMSdZv0yaY+SPhBqq+P1ohjzSWV1iw6n0VrfQrV7U+8mFV++gNnV6xVme+XKukY7/qyJh31WDDfEnShY3bdeLjhZJ003MomBZvTFWXJp5qVstDyal2LVyX9SHK4409dSA2QwdjM3Thil3f/pCmAR2ymrK//JaprQezGrLVyxdSgyqFlJmZ9Xva3l8y9MNhRufAtVm6oTN06FDt2LFDo0ePVmBgoKZMmZK98PCNtG/fXtOmTdOkSZM0aNAglStXTjNnzlTjxo1zvd7f318rVqxQv379VLNmTUVFRemtt97SY489dst8O3fu1J49ezR9+vQc5woXLqxmzZopOjparVu3VkBAgNq0aaNFixZpxowZt/Xz38yUKVPUp08fNWzYUMHBwXrxxReVkJDgcM2YMWP07LPPqkKFCkpJSXGY6vS7O/3zupW77rrrpudnzJihvn37qnbt2qpUqZImTpzosD6Np6dndmPNx8dHDzzwgBYsWJB97ttvv9XQoUPVqlUrpaenKyoqKsd29r976qmn5Ovrq7ffflvDhw+Xn5+fqlWrpsGDB98w3+LFi3X27FnNmTPHYdHqMmXK5BgB9Weffvqp/Pz8shc7vl6zZs3k4+OjOXPmaODAgWratKmKFi2qH3/88Y52O3Nl8ecyNGb6+VzPzfjvZYev1+9M0vqdSTmuW78jSet35DyOgifubLpefy/33VGmL3Ychbd2+1Wt3Z6zUfPtlkR9u4U39M4g/nyGxk7PfXTljP86/tu1YWeSNuT2/L7B8x4FS+pvv+r4K4NzHI//8I+pxfb0NMV/NPWG93F23gydnff3f1eCsfY/93qux/c96/hh3cnoz3Uy+vNcr73ZORQ8Zy/Z9Z9lOddx/Hy94+jY7w9l6PtDORs1m/ena/N+RtsVRHZn2QM8B9utLzGZzZ7bu3AAltZrJHPMrSQtJe3WF8FleHp7mh0B+WjEoV5mR0A++uWL42ZHQD5a9+5usyMgH03q77wbM0xa6pxT34Z1LPgr1BT8hAAAAAAAAHBAQ8dk/fr1c9j2+vpbv379Cvz9/x1z5869Ybbctvm2gipVqtzwz2Tu3LlmxwMAAAAAFBCWXkOnIBgzZoyGDct9e+jAwMACf/9/R9u2bXNs+f27P2/zbRVff/11jq3WfxcaGprPaQAAAADg73HaJXScAA0dk4WEhCgkJMRp7//vCAgIUEBAgNkxCpTrtzcHAAAAAOBGmHIFAAAAAADgZBihAwAAAAAADMG+2sZhhA4AAAAAAICToaEDAAAAAADgZGjoAAAAAAAAOBnW0AEAAAAAAIbIZN9ywzBCBwAAAAAAwMnQ0AEAAAAAAHAyNHQAAAAAAACcDGvoAAAAAAAAQ9hZQscwjNABAAAAAABwMjR0AAAAAAAAnAxTrgAAAAAAgCGYcmUcRugAAAAAAAA4GRo6AAAAAAAAToaGDgAAAAAAgJNhDR0AAAAAAGCITBbRMQwjdAAAAAAAAJwMDR0AAAAAAAAnQ0MHAAAAAADAybCGDgAAAAAAMIQ90+wErosROgAAAAAAAE6Ghg4AAAAAAICTYcoVAAAAAAAwhJ1tyw3DCB0AAAAAAAAnQ0MHAAAAAADAydDQAQAAAAAAcDKsoQMAAAAAAAyRybblhmGEDgAAAAAAgJOhoQMAAAAAAOBkmHIFAAAAAAAMwbblxmGEDgAAAAAAgJOhoQMAAAAAAOBkaOgAAAAAAAA4GdbQAQAAAAAAhshkCR3DMEIHAAAAAADAydDQAQAAAAAAcDI0dAAAAAAAAJwMa+gAAAAAAABD2FlExzCM0AEAAAAAAHAyNHQAAAAAAACcDFOuAAAAAACAIezMuDIMI3QAAAAAAACcDA0dAAAAAAAAJ0NDBwAAAAAAwMmwhg4AAAAAADBEJtuWG4YROgAAAAAAAE6Ghg4AAAAAAICTYcoVAAAAAAAwhJ19yw3DCB0AAAAAAAAnQ0MHAAAAAADAydDQAQAAAAAAcDKsoQMAAAAAAAxhzzQ7getihA4AAAAAAICToaEDAAAAAADgZGjoAAAAAAAAOBnW0AEAAAAAAIbItNvNjuCyGKEDAAAAAADgZGjoAAAAAAAAOBmmXAEAAAAAAEPYmXJlGEboAAAAAAAAOBkaOgAAAAAAAE6Ghg4AAAAAAICTYQ0dAAAAAABgiMxM1tAxCiN0AAAAAAAAnAwNHQAAAAAAACfDlCsAAAAAAGAIdi03DiN0AAAAAAAAnAwjdADkULtemNkRkI8+/3ij2RGQj3wC/c2OgHy0vOtKsyMgH51pmmx2BOSjJgNrmh0B+an/j2YnQAHECB0AAAAAAAAnwwgdAAAAAABgCDvblhuGEToAAAAAAABOhoYOAAAAAACAk6GhAwAAAAAA4GRYQwcAAAAAABgi084aOkZhhA4AAAAAAICToaEDAAAAAADgZJhyBQAAAAAADMG25cZhhA4AAAAAAICToaEDAAAAAADgZGjoAAAAAAAAOBnW0AEAAAAAAIZgDR3jMEIHAAAAAADAydDQAQAAAAAAcDJMuQIAAAAAAIZgxpVxGKEDAAAAAADgZGjoAAAAAAAAOBkaOgAAAAAAAE6GNXQAAAAAAIAh2LbcOIzQAQAAAAAAcDI0dAAAAAAAAP6m9957T2XLlpW3t7fq16+v7du33/T6zz//XJUrV5a3t7eqVaumr7/++o4ej4YOAAAAAADA37Bw4UINGTJEr7/+unbt2qV77rlHLVq00JkzZ3K9fsuWLerWrZv69u2r3bt3q3379mrfvr32799/249JQwcAAAAAABjCbrc75e1OTZkyRU8//bR69+6tqKgoffjhh/L19dWMGTNyvX7atGlq2bKlhg8frsjISI0dO1a1atXSf/7zn9t+TBo6AAAAAAAA10lJSVFCQoLDLSUlJddrU1NTtXPnTjVv3jz7mJubm5o3b66tW7fm+j1bt251uF6SWrRoccPrc0NDBwAAAAAA4Drjx49X4cKFHW7jx4/P9dpz584pIyNDoaGhDsdDQ0MVHx+f6/fEx8ff0fW5YdtyAAAAAABgiEwn3bZ8xIgRGjJkiMMxLy8vk9LkjoYOAAAAAADAdby8vG67gRMcHCx3d3edPn3a4fjp06cVFhaW6/eEhYXd0fW5YcoVAAAAAADAX+Tp6anatWtrzZo12ccyMzO1Zs0aNWjQINfvadCggcP1krRq1aobXp8bRugAAAAAAAD8DUOGDNETTzyhOnXqqF69epo6daquXr2q3r17S5J69eqlkiVLZq/DM2jQID344IOaPHmyWrdurQULFmjHjh36+OOPb/sxaegAAAAAAABD/JUtwJ1Rly5ddPbsWb322muKj49XjRo1tHLlyuyFj0+cOCE3tz8mSTVs2FDz5s3Tq6++qpdfflkVK1bUF198oapVq972Y9LQAQAAAAAA+Juef/55Pf/887meW79+fY5jjz/+uB5//PG//HisoQMAAAAAAOBkaOgAAAAAAAA4GaZcAQAAAAAAQ9gzrbGGjhkYoQMAAAAAAOBkaOgAAAAAAAA4GaZcAQAAAAAAQzDlyjiM0AEAAAAAAHAyNHQAAAAAAACcDA0dAAAAAAAAJ8MaOgAAAAAAwBCZdtbQMQojdAAAAAAAAJwMDR0AAAAAAAAnw5QrAAAAAABgCLYtNw4jdAAAAAAAAJwMDR0AAAAAAAAnQ0MHAAAAAADAybCGDgAAAAAAMISdbcsNwwgdAAAAAAAAJ0NDBwAAAAAAwMnQ0AEAAAAAAHAyrKEDAAAAAAAMkZnJGjpGYYQOAAAAAACAk6GhAwAAAAAA4GSYcgUAAAAAAAxhZ8qVYRihAwAAAAAA4GRo6AAAAAAAADgZGjoAAAAAAABOhjV0AAAAAACAIex21tAxCiN0AAAAAAAAnAwNHQAAAAAAACfDlCsAAAAAAGAIe2am2RFcFiN0AAAAAAAAnAwNHQAAAAAAACdDQwcAAAAAAMDJsIYOAAAAAAAwRGYm25YbhRE6AAAAAAAAToaGDgAAAAAAgJOhoQMAAAAAAOBkaOjAcE8++aRsNpsmTJjgcPyLL76QzWaTJK1fv142my37Fhoaqscee0xHjx5VamqqgoODc3z/78aOHavQ0FClpaXdNMesWbMUFBTk8LXNZlPLli0drrt06ZJsNpvWr1+ffc3NbrGxsRo1alSu5ypXrpx9v40bN84+7u3trbvvvlvjx4+X3X77c0rXrFmjhg0bKiAgQGFhYXrxxReVnp7ucM3evXv1wAMPyNvbW+Hh4Zo4ceJt3z8AAAAA5CW73e6UN2fAosjIF97e3nrrrbf07LPPqkiRIje87scff1RAQIB++uknPfPMM2rTpo327t2rnj17aubMmXrppZccrrfb7Zo1a5Z69eolDw+PO85VqFAhrV69WuvWrVOTJk1ynO/SpYtDw6djx46qWrWqxowZk32sWLFikqQqVapo9erVOe7/ek8//bTGjBmjlJQUrV27Vs8884yCgoLUv3//W2bds2ePWrVqpVdeeUWffvqpTp06pX79+ikjI0OTJk2SJCUkJOjhhx9W8+bN9eGHH2rfvn3q06ePgoKC9Mwzz9z+H4yLuXQ2VmsWvKTkqxfl6R2gZl3Hq2hYRYdrEi78qrULRujcb4cUULSUugz5Isf92O12Lf/wSZ09dVBPvfFDPqXHX1GquI9eeaGSggI9lHgtXeOm/qhjJ67luC4sxEuvDK6siuX9FXc6Wb0H7XQ4X76Mn154NkJFgrJeXz7+LFYbt57Ll58Bt69kqJeG9yurwgGFdPVaht7+KFbHTyXnuC402FPDny2riLK+ij+bon4vH8o+VyMqQH27lpSPt5vsdun7mMuKXnBKTvL7nGVcPherjYtHZL+eN+o0TkVCHV/Pr1w8pY2LR+j8/17PO/xzWfa5337Zph++maL0lGuSTQqv9KDqthgqmxufcRZEwYVt6trUU37eNiWn2rVgbapOX8z5pKxX2V1NanrIZpN+PpWppd+lKjNTqlDCTU+19tKZS398z7+XJis9Iz9/CtyOqHdeUeijTeVbtpS+q9NOCXsO53pdeO9OqjD8acnNTefXb9P+50fL/r8PN292DnBV/OuFfNG8eXOFhYVp/PjxN70uJCRExYsXV6NGjfTaa6/p4MGD+vnnn9W3b18dOXJEmzZtcrh+w4YNOnr0qPr27fuXcvn5+alPnz45GkW/8/HxUVhYWPbN09NTvr6+Dsfc3d0lZTVvrj8eFham4OBgh/v7/XvLlCmj3r17q3r16lq1atVtZV24cKGqV6+u1157TREREXrwwQc1ceJEvffee7py5Yokae7cuUpNTdWMGTNUpUoVde3aVQMHDtSUKVP+0p+Pq9iw+HVVubezerz0jWo1eUprFozIcY2nt7/qPzJYzbtPuuH97Nk4S4HBpY2MijwyfEBFLf8mTt36/aC5i0/q5cGVcr3u6rUMTZ9zTKMnHcpxzsvLTRNeraLpc46p53M71Ov5Hdp74LLR0fEXDOpbWl+vO6feww5o4ZfxGv5s2Vyvu5aUoZmfn9K4947mOHflarre/PdRPfWvg3ru1UOqUtFfDz1wl8HJcac2fzFKlep21uNDV6p6o6e0cfHLOa7x9PJT7YcGqXGXt3Oe8wlUk66T9dgLX6rdgCU6cyJGP+3+b35Ex1/Q6UFPbTuYrrfmJ2vd7nR1beqZ45qiATa1qOeh979I1oR5yQrwle6N/OMDtTOX7Hrn8+TsG82cgil+yTfa2ri7rsX+esNrfMqW0t2jBmlrkx5aX/kheYUEq/TTnW95DnBlNHSQL9zd3TVu3Dj9+9//1q+/3viF+no+Pj6SpNTUVFWrVk1169bVjBkzHK6ZOXOmGjZs6DC16U6NGjVK+/bt0+LFi//yfdwpu92u7777TocPH5anZ85fTnKTkpIib29vh2M+Pj5KTk7Wzp1Zowq2bt2qRo0aOdxnixYt9OOPP+rixYt59wM4kWtXzuvMr/t1d622kqTy1Vso8XK8Lp877nCdt2+QiperLQ9Pn1zv50L8Tzq2f41qNXna8Mz4e4IKe6hyxQB9u+60JGn9lnMKCfZWyeLeOa69kpiuvQcTlJyc8zf8hx4M0YEfE7T3YIIkKTNTupRw86mdyH9BgYV0d3k/rd50XpL03fZLKnaXp0qEeuW49srVDB04clXJKZk5zv1yPEnxZ1MlSWlpdv1y/JpCg2/v9Rn5IynxvM6d2q+IGm0kSWWrPqyrl+OVcN7x9dzLN0hhZWvLw9M3x30El4hSYNFwSVIhDy8VLV5ZiRdPGR8ed8zfRypVzE27jmS9Pu89mqHC/m66K9DmcF31Cu46GJuhK0lZX289kK6aFd3zOy7+pgubdij51OmbXlO8Ywud/nKtUk5njZQ9/vF8lejy6C3PwXz2TLtT3pwBDR3kmw4dOqhGjRp6/fXXb3ltXFycJk2apJIlS6pSpaxP1vv27avPP/9ciYmJkqQrV65o8eLF6tOnz9/KVaJECQ0aNEivvPJKjvVo7sS+ffvk7+/vcOvXr5/DNe+//778/f3l5eWlRo0aKTMzUwMHDryt+2/RooW2bNmi+fPnKyMjQ6dOncqe+hUXFydJio+PV2hoqMP3/f51fHz8X/7ZnFni5Tj5BRaTm3vWp3U2m00BQcV15WLcbd9HRkaa1n0+Ug92Gs2wfCcQGuyl8xdSlXHde/bTZ5MVWixnQ+dmyob7KTXNrrdeq6qZ02rr1f9N4ULBUqyopy5cTFPmdfU+cz5VIXf99WZMkcKF9EC9Ivp+NyOyCpKrl+PlG+D4eu4XVFyJl27/9fx6166cVez+bxVeuXEepkReKexnU8I1u65/T3XpSqaKBDg2dIL8bbp45Y+LLl6xK8j/j2vuCrRpcCdvDXrMSw2rsNqEM/MpXVxJx/9owCYdPyWf8OK3PAe4Mt6ZIF+99dZbmj17tg4dyjm9QZJKlSolPz8/lShRQlevXtWSJUuyR5t069ZNGRkZWrRokaSsKUhubm7q0qXL38714osv6uzZszlGAN2JSpUqKSYmxuF2/Vo7ktSjRw/FxMRo8+bNeuSRR/TKK6+oYcOGt3X/Dz/8sN5++23169dPXl5euvvuu9WqVStJktvfaDKkpKQoISHB4ZaelvKX788V7fj2PZWv9pCKhlYwOwrykbu7VOeeIL39nyPqPWinzp5P1dDnKt76G+HUfH3cNHZohBZ9Ga8jx3KuuwTXkJqcqFWfPqdqjfqqWKmqZseBQX49m6k3PkvS1MXJmrUyVfdWKaR7KjB6B4DroKGDfNWoUSO1aNFCI0bkXMNEkr777jvt3btXCQkJiomJUf369bPPBQYGqlOnTpo5c6akrOlWnTt3lr+//9/OFRQUpBEjRmj06NG6du2v/QLv6empiIgIh1tISIjDNYULF1ZERITq1q2rRYsW6T//+U+OhZRvZsiQIbp06ZJOnDihc+fOqV27dpKk8uXLS5LCwsJ0+rTjcNXfvw4LC8v1PsePH6/ChQs73FZ9fvO1jpyJf+HiuppwVpkZWaOv7Ha7rlyKU0CR2//U5rejP2jfprn67M2mWvZeD6WmJOqzN5sqKfGCUbFxh1o2CdXMabU1c1pt1alRRHcV9ZT7df/ChRbz1umzORfJvZkzZ1O0e98lnbuQNQ3n2/WnVaVSYF7Gxl/U/P6i+nBcpD4cF6laVQNUtIiHru9rh9zlqTPnU+/4fn283TTuXxW1ZdclLfm/M3mYGHnBr3CYrl1xfD2/eilO/kF39il8aspVfTPraZWObKpq9z9pQFLkhctX7Qr0tcntugE5QQFuDqNxJOlSot1h1E6RAJsuJWZdk5ImJaf+cX8xP6WrXHHe/jirpBNx8ilTMvtrnzIllXQy7pbnAFfGKxry3YQJE7RixQpt3bo1x7ly5cqpQoUKCggIyPV7+/btq02bNunLL7/Uli1b/vJiyLn55z//KTc3N02bNi3P7vNm/P39NWjQIA0bNuyOtsWz2WwqUaKEfHx8NH/+fIWHh6tWrVqSpAYNGmjjxo0OW7ivWrVKlSpVuuHuYiNGjNDly5cdbg89nnvDzRn5BtylYiWjdGTXcknS0b3fyL9wqAoHl7nt++gwYK56vbpW/3hlrToMmCtPL3/945W18vEvalRs3KGV606r96Cd6j1op+YuOakjvyTq4SZZ0w0bNwzW2XMpOhV3Zw2dtZvOqnLFAPn6ZH2ae2/tovr5WGKeZ8edW73pgvq9fEj9Xj6khV+e1s/Hrqn5/VkLGD9QL0jnLqTqt9N3NtLQ28tN41+sqB17EzTvC2tOUS3ofPzv0l0lovRzzApJUuz+b+VXOFSBd93+63laylV9M/Nplap4v2o2vfUOkzBPYpJ06mymat2d9Rpcvby7Lidm6nyC4+9Me3/JUFRZdwX8bwm8BlUKKebnrHV3Anyl31s9Xh5SZBl3nTqXcw0tOIe4Zd8o9NGm8grN2nSkzDPd9Nuir255DuYzey0cV15Dh4mkyHfVqlVTjx499O67797x9zZq1EgRERHq1auXKleufNvTlW6Ht7e3Ro8erQEDBvyl709PT8+xTo3NZsuxps31nn32WY0dO1ZLlixRp06dbvkYb7/9tlq2bCk3NzctXbpUEyZM0KJFi7J32urevbtGjx6tvn376sUXX9T+/fs1bdo0vfPOOze8Ty8vL3l5OS4eWsjDOV7AbteDnUZr7cIR2rnmI3l6+6tpl3GSpHWLXlXZKk1VrkpTpaUmad5bLZWRnqrU5ETNHvug7q7dVg1aDTU5Pf6Kie8d0SuDK6vX46V19VqGxk37Mfvci/+8W5u+P6/N28/Ly8tN8z+sJw8Pm/x9C2npzHv1zbrT+ujTYzp9NkWffX5CH75dU5mZdp27kKqJ/zli4k+FG5k647iGP1tW3dqG6VpSht7+ODb73JCnymjrrkvauuuyvDxtmjm5qjwK2eTn6655/66m1ZvOa8bC39SxZYgqlfeTt5eb7q8bJEna+P1FzfsvzZ2C5L72o7Vx8QjtWZ/1ev7AY1mv598tfVWlI5uqTGRTpacm6fMpjygzPVWpKYmaP6GxImq2Vd0WQ3Rgy2c6++s+paclKfZg1gjZclVbqEaTfjd7WJhk8cZUdWniqWa1PJScatfCdVnDbR5v7KkDsRk6GJuhC1fs+vaHNA3okLVO2i+/ZWrrwaxRXNXLF1KDKoWUmSm5uWU1f344zDZXBVHV90cr5JHG8goLVr2vopV+5arWRz6sah+9odMr1urMl2uVdOxXHRnzrhpsmC9JurBxu058vFCSbnoOcGU2+50MDQD+gieffFKXLl3SF198kX0sNjZWlSpVUmpqqux2u9avX68mTZro4sWLCgoKuun9jR8/Xi+//LImTpyo4cOH33aOWbNmafDgwbp06VKuX0tSRkaGqlevroMHD2rdunVq3Lixw300btxYNWrU0NSpUx2Ojxo1SqNHj87xmF5eXkpOTr7p9/br10+bNm3S3r17b7kWTtOmTbVr1y6lpKTonnvu0euvv65HHnnE4Zq9e/dqwIAB+uGHHxQcHKx//vOfevHFF296v382bQUvC1by+ccbzY6AfOQT+PenqcJ5PNSxptkRkI/OnLmzkYhwbk0G8vy2ktZpP976ogKq06CjZkf4SxZPK292hFuioQMgBxo61kJDx1po6FgLDR1roaFjLTR0rMWZGzodB/5sdoS/ZOm7EWZHuCXW0AEAAAAAAHAyNHTgMqpUqSJ/f/9cb3PnzjU73i3169fvhvn79WNuPwAAAADgDyyKDJfx9ddfO+zudL2bLUxcUIwZM0bDhg3L9VxgIFslAwAAAAD+QEMHLqNMmdvftrQgCgkJUUhIiNkxAAAAACDPOMsW4M6IKVcAAAAAAABOhoYOAAAAAACAk6GhAwAAAAAA4GRYQwcAAAAAABiCNXSMwwgdAAAAAAAAJ0NDBwAAAAAAwMkw5QoAAAAAABjCbmfKlVEYoQMAAAAAAOBkaOgAAAAAAAA4GRo6AAAAAAAAToY1dAAAAAAAgCEyMzPNjuCyGKEDAAAAAADgZGjoAAAAAAAAOBkaOgAAAAAAAE6GNXQAAAAAAIAh7Jl2syO4LEboAAAAAAAAOBkaOgAAAAAAAE6GKVcAAAAAAMAQdjvblhuFEToAAAAAAABOhoYOAAAAAACAk6GhAwAAAAAA4GRYQwcAAAAAABiCbcuNwwgdAAAAAAAAJ0NDBwAAAAAAwMkw5QoAAAAAABiCKVfGYYQOAAAAAACAk6GhAwAAAAAA4GRo6AAAAAAAADgZ1tABAAAAAACGyLRnmh3BZTFCBwAAAAAAwMnQ0AEAAAAAAHAyNHQAAAAAAACcDGvoAAAAAAAAQ9gz7WZHcFmM0AEAAAAAAHAyNHQAAAAAAACcDFOuAAAAAACAIeyZbFtuFEboAAAAAAAAOBkaOgAAAAAAAE6Ghg4AAAAAAICTYQ0dAAAAAABgCLYtNw4jdAAAAAAAAJwMDR0AAAAAAAAnw5QrAAAAAABgCLudbcuNwggdAAAAAAAAJ0NDBwAAAAAAwMnQ0AEAAAAAAHAyrKEDAAAAAAAMkcm25YZhhA4AAAAAAICToaEDAAAAAADgZGjoAAAAAAAAOBnW0AEAAAAAAIawZ2aaHcFlMUIHAAAAAADAydDQAQAAAAAAcDJMuQIAAAAAAIaws225YRihAwAAAAAA4GRo6AAAAAAAADgZGjoAAAAAAABOhjV0AAAAAACAIex2ti03CiN0AAAAAAAAnAwNHQAAAAAAACfDlCsAAAAAAGAIti03DiN0AAAAAAAAnAwNHQAAAAAAACdDQwcAAAAAAMDJsIYOAAAAAAAwhD2TbcuNwggdAAAAAAAAJ0NDBwAAAAAAwMnQ0AEAAAAAAHAyNrvdzqbwACwvJSVF48eP14gRI+Tl5WV2HBiMelsL9bYW6m0t1NtaqDfgiIYOAEhKSEhQ4cKFdfnyZQUGBpodBwaj3tZCva2FelsL9bYW6g04YsoVAAAAAACAk6GhAwAAAAAA4GRo6AAAAAAAADgZGjoAIMnLy0uvv/46C+xZBPW2FuptLdTbWqi3tVBvwBGLIgMAAAAAADgZRugAAAAAAAA4GRo6AAAAAAAAToaGDgAAAAAAgJOhoQMAAAAAAOBkaOgAAAAAAAA4GRo6AAAAAAAAToaGDgDAMpKTk82OAMAgzZs316xZs5SQkGB2FOSDp556SuvXrzc7BvLR5cuX9eOPP+rHH3/U5cuXzY4DFAg0dABYWkREhEaNGqUjR46YHQX5ICQkRE8++aRWrVqlzMxMs+PAYDy/raVKlSoaMWKEwsLC9Pjjj+u///2v0tLSzI4Fg5w9e1YtW7ZUeHi4hg8frj179pgdCQb55JNPFBUVpaJFiyoqKsrh/6Ojo82OB5iKhg4ASxswYIC++uorRUZGqm7dupo2bZri4+PNjgWDzJ49W1evXlW7du1UsmRJDR48WDt27DA7FgzC89tapk2bplOnTumLL76Qn5+fevXqpdDQUD3zzDPasGGD2fGQx/773/8qLi5OI0eO1A8//KBatWqpSpUqGjdunGJjY82Ohzzy9ttva9CgQWrXrp3WrFmj/fv3a//+/VqzZo3at2+vQYMGadKkSWbHBExjs9vtdrNDAIDZjhw5orlz52r+/Pk6duyYmjRpop49e6pXr15mR4MBrly5osWLF2v+/Plau3atypcvr549e+q1114zOxoMwPPbmpKTk7VixQq9+eab2rdvnzIyMsyOBAP9+uuvmj9/vmbMmKGffvpJ6enpZkdCHihTpozefvttde7cOdfzCxcu1PDhw3XixIl8TgYUDDR0AOBPtm3bpv79+2vv3r28AbCAgwcPqkePHtTbInh+W0N8fLwWLFigOXPmaNeuXapXr562bdtmdiwYJC0tTV999ZXmzJmjr776SkWLFtWpU6fMjoU84OPjo127dikyMjLX8wcPHlSdOnV07dq1fE4GFAxMuQKA/9m+fbsGDx6sDh066MiRI3r88cfNjgSDJCcna9GiRWrfvr1q1aqlCxcuaPjw4WbHgoF4fru+hIQEzZw5Uw899JDCw8P1wQcfqG3btvrpp59o5riodevW6emnn1ZoaKiefPJJBQYG6ssvv9Svv/5qdjTkkbp162rChAm5jrjKyMjQW2+9pbp165qQDCgYGKEDwNL+PBWjadOm6tGjhzp27Ch/f3+z4yGPffPNN5o3b56++OILFSpUSJ06dVKPHj3UqFEjs6PBADy/rcXHx0dFihRRly5d1KNHD9WpU8fsSDBQyZIldeHCBbVs2VI9evRQmzZt5OXlZXYs5LG9e/eqRYsWSktLU6NGjRQaGipJOn36tDZu3ChPT099++23qlq1qslJAXPQ0AFgaW5ubqpbt666d++url27Zv+iANfk6+urRx99VD169FCrVq3k4eFhdiQYiOe3taxatUrNmjWTm5vjAPRDhw4pOjqahVNdzPTp0/X4448rKCjI4filS5c0Z84cPf/88+YEQ567cuWK5syZo23btmUvbB8WFqYGDRqoe/fuCgwMNDkhYB4aOgAs7aefflLFihUdjtntdq1cuVLR0dFavHixSclghCtXriggIMDhWEJCgubOnavo6Gh2vHIxPL+t6+rVq1qwYIGio6O1bds2RUVFaf/+/WbHgoHWrFmj6OhoLVu2TL6+vjp//rzZkQDAcKyhA8DSrn+zd+zYMY0cOVKlS5dWhw4dlJycbGIyGOH6Zs66dev0j3/8Q8WLF9fYsWNVv359E5PBCDy/rWfz5s3q06dP9nblDRs21MGDB2nmuKiTJ09qzJgxKleunB5++GHZbDYtW7YsexQHXMfVq1e1ceNGLVy4UJ9//rl27dolxiUAjNABYHEpKSlavHixoqOjtWnTJmVkZGjSpEnq27cvQ3hd0KlTpzRr1izNnDlTly5d0sWLFzVv3jx17txZNpvN7HjIYzy/reHMmTOaNWuWZsyYocuXL6tbt27q3r27GjRooD179igqKsrsiMhDaWlp+uKLL/TJJ5/ou+++U8uWLdW9e3d169aNerugzMxMvfTSS/rPf/6jlJQUScpu5JQuXVr//ve/1aZNGzMjAqZihA4AS9q5c6eee+45hYWFaerUqWrfvr1OnjwpNzc3tWjRgjd7LmbJkiVq1aqVKlWqpJiYGE2ePFm//fab3NzcVK1aNZo5Lobnt7WUKVNG+/bt07Rp03Tq1ClNmTKFBZFdWMmSJfXvf/9bjz32mE6dOqWlS5eqU6dOZseCQV5++WV9+eWXWrRokb755hvdf//9mjBhgg4ePKhevXrp8ccf17fffmt2TMA0hcwOAABmqF+/vv75z39q27ZtqlSpktlxYLAuXbroxRdf1MKFC3OsoQPXw/PbWsqUKaNNmzapdOnSKlOmjCpXrmx2JBgoPT1dNptNNptN7u7uZseBwT799FMtXLhQDzzwgCQpMjJSlStX1qBBgzRmzBh5eHho1KhRevjhh01OCpiDEToALKlZs2aKjo7WmDFjtHLlSuZhu7i+ffvqvffeU8uWLfXhhx/q4sWLZkeCgXh+W8vhw4c1Z84cxcXFqW7duqpdu7beeecdSWL0nQv67bff9Mwzz2j+/PkKCwvTY489pmXLllFrF5WYmKiSJUtmf128eHElJydn/zv+2GOPac+ePWbFA0xHQweAJX3zzTc6cOCAKlWqpP79+6t48eIaNGiQJN4AuKKPPvpIcXFx2W8Cihcvrnbt2slutyszM9PseMhjPL+t57777tOMGTMUFxenfv366fPPP1dGRoaee+45TZ8+XWfPnjU7IvKIt7e3evToobVr12rfvn2KjIzUwIEDlZ6erjfffFOrVq1SRkaG2TGRR6pVq6b58+dnf71o0SL5+/srLCxMUtYaO15eXmbFA0zHosgAIGnVqlWaOXOmli1bpvDwcHXq1EmdOnVSrVq1zI4GA/z000+aOXOmZs+ercTERLVu3VqdOnVSx44dzY4GA/D8tqZDhw4pOjpan332mS5cuKC0tDSzI8EgmZmZ+uabbxQdHa0VK1YoICBA586dMzsW8sCaNWvUunVr3XPPPfL29taWLVv09ttva/DgwZKkSZMm6f/+7/+0Zs0ac4MCJqGhAwDXuXjxoubMmaMZM2Zo7969fMrn4jIzM/XVV18pOjpa//d//5e9gwZcE89va0pPT9fy5cuzG7YTJkxQv379FBQUZG4wGOLs2bP67LPPNGTIEEnS/Pnz1bZtW/n5+ZmcDH/Vnj17tGjRIqWkpKhFixZ66KGHzI4EFBg0dADgBnbt2pX9Cf5zzz2nMWPGKDg42ORUMMqZM2cUEhIiSWrdurU++eQTFS9e3ORUMArPb+sKDAxUTEyMypcvb3YU5APqbS28nsNqWEMHAG7g+ukYc+bMUUJCgolpYLTfmzmStHHjRiUlJZmYBkbj+W1dfJZpLdTbWng9h9XQ0AGA28AvhIDr4vkNAK6B13NYDQ0dAAAAAAAAJ0NDBwAAAAAAwMnQ0AEAAAAAAHAyNHQAAABgGQ888IB8fHzMjoF8UqZMGXl4eJgdAwAMUcjsAADgDHr27KnAwECzYyCfvPzyyypatKjZMZBPeH67tvT0dP32228qXbq0JOnrr782ORHy0/79+82OgHzE6zmsxmZnKXAAFrN3797bvrZ69eoGJkFBcfHiRa1YsUK9evUyOwryUGZmptzccg5GzszM1K+//pr9Bh+ubc+ePapVq5YyMjLMjoI88v7772vp0qUqWrSonn32WTVr1iz73Llz51SvXj0dPXrUxITIL0uXLtWoUaPu6Hc7wJUw5QqA5dSoUUM1a9bM/u/NbrCGEydOqHfv3mbHQB5JSEhQ586d5efnp9DQUL322msOb+bPnj2rcuXKmZgQwF/17rvvavjw4apcubK8vLzUqlUrjR8/Pvt8RkaGjh8/bmJC5LWPPvpInTp1Uvfu3fX9999LktauXauaNWvqH//4h+677z6TEwLmYcoVAMs5duxY9v/v3r1bw4YN0/Dhw9WgQQNJ0tatWzV58mRNnDjRrIjIYwkJCTc9f+XKlXxKgvwwcuRI7dmzR5999pkuXbqkN954Q7t27dLSpUvl6ekpSWKAsuuoVavWTc8nJSXlUxLkh48++kjTp09X9+7dJUn9+/dX+/btlZSUpDFjxpicDnltwoQJeu2111S9enUdPnxY//3vf/XKK6/o3//+twYNGqRnn31WRYoUMTsmYBqmXAGwtHr16mnUqFFq1aqVw/Gvv/5aI0eO1M6dO01Khrzk5uYmm812w/N2u102m40pGS6iTJkymj17tho3biwpawpG69atFRQUpOXLl+vSpUsqUaIE9XYR3t7e6tq16w1HXcXFxWn69OnU20X4+vrq4MGDKlu2bPax/fv3q3nz5urdu7cGDx7M89uFVKpUSS+//LKeeOIJfffdd3rwwQfVqlUrLVy4UH5+fmbHA0zHCB0AlrZv375c3wSUK1dOBw8eNCERjBAQEKBXXnlF9evXz/X8Tz/9pGeffTafU8EoZ8+eVZkyZbK/Dg4O1urVq9WiRQu1atVKn3zyiYnpkNeqVq2q+vXrq3///rmej4mJ0fTp0/M5FYwSHByskydPOjR0qlatqrVr16pp06b67bffzAuHPHfixAk1bdpUUtYOdR4eHho9ejTNHOB/aOgAsLTIyEiNHz9en3zySfZUjNTUVI0fP16RkZEmp0Ne+X1KxoMPPpjr+aCgIKbguJDSpUvr0KFDDs3agIAAffvtt3r44YfVoUMHE9Mhr91333368ccfb3g+ICBAjRo1ysdEMNL999+vpUuX6oEHHnA4HhUVpTVr1qhJkyYmJYMRUlJS5O3tnf21p6cnu1AC16GhA8DSPvzwQ7Vp00alSpXK3tFq7969stlsWrFihcnpkFe6d+9+03U0wsLC9Prrr+djIhjp4Ycf1syZM3NMpfT399c333yjhx56yKRkMMK0adNuer5ChQpat25dPqWB0V566aUbToeuUqWK1q5dqyVLluRzKhhp5MiR8vX1lZT1odsbb7yhwoULO1wzZcoUM6IBpmMNHQCWd/XqVc2dO1eHDx+WlDVqp3v37gznBZzUxYsX9dtvv6lKlSq5nr9y5Yp27dp1wxFbAICCoXHjxjddA0+SbDab1q5dm0+JgIKFhg4AwJJ+/fVXlShRQm5ubmZHQT6g3tZSrVo1ff311woPDzc7CvIB9QZgVUy5AmA5y5cv1yOPPCIPDw8tX778pte2bds2n1Ihv0VFRSkmJkbly5c3OwryAfW2ltjYWKWlpZkdA/mEegOwKho6ACynffv2io+PV0hIiNq3b3/D69jG2rUxQNVaqDcAOJ+oqCht2rQpeyHk5557TmPGjFFwcLAk6cyZMypbtqyuXbtmZkzANIw7BmA5mZmZCgkJyf7/G91o5gCAc3rggQfk4+NjdgzkE+rtug4fPqz09PTsr+fMmaOEhITsr+12u5KTk82IBhQIjNABAFjSyy+/zNanFkK9reXrr782OwLyEfW2jtxGW95q0WTAlTFCB4DlbdiwQW3atFFERIQiIiLUtm1bfffdd2bHgsFGjBihoKAgs2Mgn1Bva/jss8903333qUSJEjp+/LgkaerUqfrvf/9rcjIYgXoDsDpG6ACwtDlz5qh3797q2LGjBg4cKEnavHmzmjVrplmzZql79+4mJ0RestvtWrx4sdatW6czZ84oMzPT4fzSpUtNSgYjUG9r+eCDD/Taa69p8ODBeuONN7KnzQYFBWnq1Klq166dyQmRl6i3NdhsthwjcBiRA/yBbcsBWFpkZKSeeeYZvfDCCw7Hp0yZounTp+vQoUMmJYMRBg0apI8++khNmjRRaGhojl8KZ86caVIyGIF6W0tUVJTGjRun9u3bKyAgQHv27FH58uW1f/9+NW7cWOfOnTM7IvIQ9bYGNzc3Va1aVYUKZY1D2Lt3rypXrixPT09JUnp6ug4cOMC6h7AsRugAsLSjR4+qTZs2OY63bdtWL7/8sgmJYKTPPvtMS5cuVatWrcyOgnxAva3l2LFjqlmzZo7jXl5eunr1qgmJYCTqbQ2vv/66w9e5jbx67LHH8isOUODQ0AFgaeHh4VqzZo0iIiIcjq9evVrh4eEmpYJRChcurPLly5sdA/mEeltLuXLlFBMTozJlyjgcX7lypSIjI01KBaNQb2vo3bu3SpUqJTc3ln4FckNDB4ClDR06VAMHDlRMTIwaNmwoKWsNnVmzZmnatGkmp0NeGzVqlEaPHq0ZM2awxa0FUG9rGTJkiAYMGKDk5GTZ7XZt375d8+fP1/jx4/XJJ5+YHQ95jHpbQ7ly5RQXF6eQkBCzowAFEmvoALC8ZcuWafLkydnr5URGRmr48OEsqOiCkpKS1KFDB23evFlly5aVh4eHw/ldu3aZlAxGoN7WM3fuXI0aNUq//PKLJKlEiRIaPXq0+vbta3IyGIF6uz43NzfFx8fT0AFugIYOAMtKT0/XuHHj1KdPH5UqVcrsOMgHnTt31rp169SpU6dcF8n981x9ODfqbV3Xrl1TYmIibwItgnq7Ljc3N50+fVrFihUzOwpQINHQAWBp/v7+2r9/v8qWLWt2FOQDPz8/ffPNN7r//vvNjoJ8QL0BwLm5ubnpmWeeka+v702vmzJlSj4lAgoW1tABYGnNmjXThg0baOhYRHh4uAIDA82OgXxCvV1fzZo1c4y8uhGm2Dk/6m1N+/bty96mPDe3+3cCcEU0dABY2iOPPKKXXnpJ+/btU+3ateXn5+dwvm3btiYlgxEmT56sf/3rX/rwww9p4lkA9XZ97du3NzsC8hH1tqZly5YxnQ64AaZcAbC0m22DabPZlJGRkY9pYLQiRYro2rVrSk9Pl6+vb45Fci9cuGBSMhiBegOAc3N3d2eXK+AmGKEDwNIyMzPNjoB8NHXqVLMjIB9Rb2v54YcflJmZqfr16zsc//777+Xu7q46deqYlAxGoN7WwNgD4OYYoQMAAACnV69ePf3rX/9Sp06dHI4vXbpUb731lr7//nuTksEI1NsaZs+era5du8rLy8vsKECBREMHgOWtWbNG77zzjg4dOiRJioyM1ODBg9W8eXOTkyGvnThx4qbnS5cunU9JkB+ot7X4+/tr7969Kl++vMPxY8eOqXr16rpy5YpJyWAE6m0NGzduvK3rGjVqZHASoGBiyhUAS3v//fc1aNAgderUSYMGDZIkbdu2Ta1atdI777yjAQMGmJwQeals2bI33Q2DNZNcC/W2Fi8vL50+fTrHG/y4uDgVKsSvvK6GeltD48aNZbPZcp169fvru81mU3p6en5HAwoERugAsLRSpUrppZde0vPPP+9w/L333tO4ceN06tQpk5LBCHv27HH4Oi0tTbt379aUKVP05ptvqmPHjiYlgxGot7V069ZNcXFx+u9//6vChQtLki5duqT27dsrJCREixYtMjkh8hL1tobLly/nevzatWuaNm2a3n33XZUvX1779+/P52RAwUBDB4Cl+fv7KyYmRhEREQ7Hf/rpJ9WsWVOJiYkmJUN++uqrr/T2229r/fr1ZkdBPqDerunUqVNq1KiRzp8/r5o1a0qSYmJiFBoaqlWrVik8PNzkhMhL1NuaMjMzNWPGDI0ePVpubm4aNWqUnnjiiZvuWgq4Mho6ACyte/fuqlmzpoYPH+5wfNKkSdqxY4cWLFhgUjLkp59//ln33HOPrl69anYU5APq7bquXr2quXPnas+ePfLx8VH16tXVrVu3HFvWwzVQb2tZunSpXn75ZZ09e1YjRozQP//5TxZLhuXR0AFgOe+++272/yckJGjSpEm677771KBBA0lZa+hs3rxZQ4cO1auvvmpWTBggISHB4Wu73a64uDiNGjVKhw8fVkxMjDnBYAjqDQDOb8OGDXrxxRe1b98+DRo0SC+++GL2NDvA6mjoALCccuXK3dZ1NptNR48eNTgN8pObm1uORXLtdrvCw8O1YMGC7KYeXAP1tpbZs2crODhYrVu3liT961//0scff6yoqCjNnz9fZcqUMTkh8hL1toZWrVpp9erV6tOnj0aNGqWwsDCzIwEFCg0dAIBlbNiwweFrNzc3FStWTBEREeyK4oKot7VUqlRJH3zwgZo2baqtW7eqWbNmmjp1qr788ksVKlRIS5cuNTsi8hD1tgY3NzcVKlRIfn5+N9218MKFC/mYCig4aOgAAADA6fn6+urw4cMqXbq0XnzxRcXFxenTTz/VgQMH1LhxY509e9bsiMhD1NsaZs+efVvXPfHEEwYnAQomPp4CYDlDhgzR2LFj5efnpyFDhtz02ilTpuRTKhhl+fLleuSRR+Th4aHly5ff9Nq2bdvmUyoYhXpbl7+/v86fP6/SpUvr22+/zX599/b2VlJSksnpkNeotzXQqAFujoYOAMvZvXu3Dh8+rJo1a2r37t03vO5mQ3vhPNq3b6/4+HiFhISoffv2N7zOZrMpIyMj/4LBENTbuh566CE99dRTqlmzpo4cOaJWrVpJkg4cOKCyZcuaGw55jnoDAA0dABa0bt06ubu7Ky4uTuvWrZMkdenSRe+++65CQ0NNToe8lpmZmev/wzVRb+t677339Oqrr+rkyZNasmSJ7rrrLknSzp071a1bN5PTIa9Rb2soUqTIbX3Axho6sCrW0AFgSW5ubtmf4ktSYGCgYmJiVL58eZOTwShpaWlq2bKlPvzwQ1WsWNHsODAY9QYA58caOsDNMUIHAJS1lTFcm4eHh/bu3Wt2DOQT6m09GzduvOn5Ro0a5VMS5AfqbQ132qiZP3++2rZtKz8/P4MSAQULI3QAWJK7u7vi4+NVrFgxSVJAQID27t2rcuXKmZwMRnrhhRfk5eWlCRMmmB0F+YB6W4ubm1uOY9dP1WDNJNdCvZEbRlzDahihA8CS7Ha7nnzySXl5eUmSkpOT1a9fvxyf6CxdutSMeDBIenq6ZsyYodWrV6t27do56s2uZq6FelvLxYsXHb5OS0vT7t27NXLkSL355psmpYJRqDdyw1gFWA0NHQCW9OchvD179jQpCfLT/v37VatWLUnSkSNHTE4Do1FvaylcuHCOYw899JA8PT01ZMgQ7dy504RUMAr1BgCmXAEAAMCFHT58WHXq1FFiYqLZUZAPqLe1BQQEaM+ePUy5gmXknHwKAICL6tOnj65cuZLj+NWrV9WnTx8TEsFI1Nta9u7d63Dbs2ePVq5cqX79+qlGjRpmx0Meo94AwAgdAICFuLu7Ky4uLnu7+t+dO3dOYWFhSk9PNykZjEC9rcXNzU02my3HGhr33nuvZsyYocqVK5uUDEag3sgNI3RgNayhAwBweQkJCbLb7bLb7bpy5Yq8vb2zz2VkZOjrr7/O8aYfzot6W9OxY8ccvnZzc1OxYsUc6g/XQb2tISMjQ+7u7rd9fZkyZeTh4WFgIqBgoaEDAHB5QUFBstlsstlsuvvuu3Oct9lsGj16tAnJYATqbU1lypQxOwLyEfW2hpIlS+rJJ59Unz59cn09/7P9+/fnQyqg4GDKFQDA5W3YsEF2u11NmzbVkiVLVLRo0exznp6eKlOmjEqUKGFiQuQl6m0tn3766W1d16tXL4OTID9Qb2sZO3asZs+erWPHjqlhw4bq27evOnfuLF9fX7OjAQUCDR0AgGUcP35cpUuXls1mMzsK8gH1tgY3Nzf5+/urUKFCOdZT+Z3NZtOFCxfyORmMQL2taf369Zo5c6aWLFkid3d3de7cWU899ZTq169vdjTAVOxyBQCwjDJlymjTpk3q2bOnGjZsqFOnTkmSPvvsM23atMnkdMhr1NsaIiMj5enpqV69emnDhg26ePFijhtv7l0H9bamxo0ba/bs2YqPj9fkyZN16NAhNWjQQFWqVNGUKVPMjgeYhoYOAMAylixZohYtWsjHx0e7du1SSkqKJOny5csaN26cyemQ16i3NRw4cEBfffWVkpKS1KhRI9WpU0cffPCBEhISzI4GA1Bva/P399dTTz2lTZs2acWKFYqPj9fw4cPNjgWYhoYOAMAy3njjDX344YeaPn26wy4Y9913n3bt2mViMhiBeltH/fr19dFHHykuLk4DBw7UokWLVLx4cfXo0SO7kQfXQb2t69q1a5o1a5YefPBBtW3bVnfddZfefPNNs2MBpqGhAwCwjB9//FGNGjXKcbxw4cK6dOlS/geCoai39fj4+KhXr14aPXq06tWrpwULFujatWtmx4JBqLd1bNmyRU899ZSKFy+uAQMGqGzZslq3bp2OHDmil156yex4gGlo6AAALCMsLEw///xzjuObNm1S+fLlTUgEI1Fvazl16pTGjRunihUrqmvXrqpbt64OHDigIkWKmB0NBqDe1jBx4kRFRkbq/vvv1759+/T2228rPj5es2fPzrVhD1hNIbMDAACQX55++mkNGjRIM2bMkM1m02+//aatW7dq2LBhGjlypNnxkMeotzUsWrRIM2fO1IYNG9SiRQtNnjxZrVu3lru7u9nRYADqbS1vv/22evbsqc8//1xVq1Y1Ow5Q4LBtOQDAMux2u8aNG6fx48dnD8v38vLSsGHDNHbsWJPTIa9Rb2twc3NT6dKl1aNHD4WGht7wuoEDB+ZjKhiFeltLWlqaChUqpJ9//lmpqamqVKmSChViTALwOxo6AADLSU1N1c8//6zExERFRUXJ39/f7EgwEPV2bWXLlpXNZrvpNTabTUePHs2nRDAS9baW2NhYtWnTRgcPHpQklSpVSkuWLFGdOnVMTgYUDDR0AAAAAAAFTqdOnXTgwAG99tpr8vb21qRJk5ScnKydO3eaHQ0oEGjoAAAsIzk5Wf/+97+1bt06nTlzRpmZmQ7n2cratVBv5KZatWr6+uuvFR4ebnYU5APq7dzCwsK0ePFi3X///ZKkuLg4lSpVSgkJCfLz8zM5HWA+JiACACyjb9+++vbbb9WpUyfVq1fvlsP24dyoN3ITGxurtLQ0s2Mgn1Bv53bmzBlVrFgx++vixYvLx8dHZ86cUbly5UxMBhQMNHQAAJbx5Zdf6uuvv9Z9991ndhTkA+oNAM7NZrMpMTFRPj4+2cfc3Nx05coVJSQkZB8LDAw0Ix5gOho6AADLKFmypAICAsyOgXxCvQHAudntdt199905jtWsWTP7/202mzIyMsyIB5iOhg4AwDImT56sF198UR9++KHKlCljdhwYjHoDgHNbt26d2RGAAo2GDgDAMurUqaPk5GSVL19evr6+8vDwcDh/4cIFk5LBCNQbAJzbgw8+aHYEoECjoQMAsIxu3brp1KlTGjdunEJDQ1kk18VRbwBwbm5ubrd87bbZbEpPT8+nREDBQkMHAGAZW7Zs0datW3XPPfeYHQX5gHojNx999JFCQ0PNjoF8Qr2d27Jly254buvWrXr33XeVmZmZj4mAgoWGDgDAMipXrqykpCSzYyCfUG/r2bBhgyZNmqRDhw5JkqKiojR8+HA98MAD2dd0797drHjIY9Tb9bVr1y7HsR9//FEvvfSSVqxYoR49emjMmDEmJAMKBjezAwAAkF8mTJigoUOHav369Tp//rwSEhIcbnAt1Nta5syZo+bNm8vX11cDBw7UwIED5ePjo2bNmmnevHlmx0Meo97W89tvv+npp59WtWrVlJ6erpiYGM2ePZtF72FpNrvdbjc7BAAA+cHNLetzjD/Px2fbU9dEva0lMjJSzzzzjF544QWH41OmTNH06dOzR3HANVBv67h8+bLGjRunf//736pRo4beeusth1FYgJXR0AEAWMaGDRtuep7dNFwL9bYWLy8vHThwQBEREQ7Hf/75Z1WtWlXJyckmJYMRqLc1TJw4UW+99ZbCwsI0bty4XKdgAVbGGjoAAMvgDby1UG9rCQ8P15o1a3K8wV+9erXCw8NNSgWjUG9reOmll+Tj46OIiAjNnj1bs2fPzvW6pUuX5nMyoGCgoQMAsJRLly4pOjo6ezh+lSpV1KdPHxUuXNjkZDAC9baOoUOHauDAgYqJiVHDhg0lSZs3b9asWbM0bdo0k9Mhr1Fva+jVq9ctty0HrIwpVwAAy9ixY4datGghHx8f1atXT5L0ww8/KCkpSd9++61q1aplckLkJeptPcuWLdPkyZOzG3iRkZEaPnw40zRcFPUGYHU0dAAAlvHAAw8oIiJC06dPV6FCWYNU09PT9dRTT+no0aPauHGjyQmRl6g3AABwZTR0AACW4ePjo927d6ty5coOxw8ePKg6dero2rVrJiWDEag3AABwZayhAwCwjMDAQJ04cSLHG/yTJ08qICDApFQwCvV2fUWLFtWRI0cUHBysIkWK3HStjQsXLuRjMhiBegOAIxo6AADL6NKli/r27atJkyY5LKI5fPhwdevWzeR0yGvU2/W988472c25d955h8VTXRz1BgBHTLkCAFhGamqqhg8frg8//FDp6emSJA8PD/Xv318TJkyQl5eXyQmRl6g3AABwZTR0AACWc+3aNf3yyy+SpAoVKsjX19fkRDAS9bYGd3d3xcXFKSQkxOH4+fPnFRISooyMDJOSwQjUGwAkN7MDAACQX2bOnKmkpCT5+vqqWrVqqlatGm/uXRj1tpYbfUaZkpIiT0/PfE4Do1FvAGCEDgDAQkJDQ5WUlKTHH39cffv2zV5XBa6JelvDu+++K0l64YUXNHbsWPn7+2efy8jI0MaNGxUbG6vdu3ebFRF5iHoDwB9o6AAALCM9PV0rVqzQrFmz9H//938qX768evfurSeeeEJhYWFmx0Meo97WUK5cOUnS8ePHVapUKbm7u2ef8/T0VNmyZTVmzBjVr1/frIjIQ9QbAP5AQwcAYEmnT5/WnDlzNHv2bB0+fFgtW7ZU37591aZNG7m5MSPZ1VBv19ekSRMtXbpURYoUMTsK8gH1BgDW0AEAWFRoaKjuv/9+NWjQQG5ubtq3b5+eeOIJVahQQevXrzc7HvIY9XZ969at4829hVBvAJAKmR0AAID8dPr0aX322WeaOXOmjh49qvbt2+vLL79U8+bNdfXqVY0ZM0ZPPPGEjh8/bnZU5AHqbS2//vqrli9frhMnTig1NdXh3JQpU0xKBaNQbwBWx5QrAIBltGnTRt98843uvvtuPfXUU+rVq5eKFi3qcM2ZM2cUFhamzMxMk1Iir1Bva1mzZo3atm2r8uXL6/Dhw6patapiY2Nlt9tVq1YtrV271uyIyEPUGwAYoQMAsJCQkBBt2LBBDRo0uOE1xYoV07Fjx/IxFYxCva1lxIgRGjZsmEaPHq2AgAAtWbJEISEh6tGjh1q2bGl2POQx6g0AjNABAACACwgICFBMTIwqVKigIkWKaNOmTapSpYr27Nmjdu3aKTY21uyIyEPUGwAYoQMAsIBPP/30tq7r1auXwUmQH6i3Nfn5+WWvo1K8eHH98ssvqlKliiTp3LlzZkaDAag3ANDQAQBYwJNPPil/f38VKlRINxqYarPZeIPvIqi3Nd17773atGmTIiMj1apVKw0dOlT79u3T0qVLde+995odD3mMegMAU64AABZQpUoVnT59Wj179lSfPn1UvXp1syPBQNTbmo4eParExERVr15dV69e1dChQ7VlyxZVrFhRU6ZMUZkyZcyOiDxEvQGAhg4AwCK+//57zZgxQwsXLlRERIT69u2rHj16KDAw0OxoMAD1tpaMjAxt3rxZ1atXV1BQkNlxYDDqDQBZaOgAACwlKSlJn3/+uWbOnKnt27erffv2mjFjhry8vMyOBgNQb+vw9vbWoUOHVK5cObOjIB9QbwCQ3MwOAABAfvLx8VGvXr00evRo1atXTwsWLNC1a9fMjgWDUG/rqFq1qo4ePWp2DOQT6g0ANHQAABZy6tQpjRs3ThUrVlTXrl1Vt25dHThwQEWKFDE7GgxAva3ljTfe0LBhw/Tll18qLi5OCQkJDje4FuoNAEy5AgBYwKJFizRz5kxt2LBBLVq0UO/evdW6dWu5u7ubHQ0GoN7W5Ob2x+eUNpst+//tdrtsNpsyMjLMiAWDUG8AoKEDALAANzc3lS5dWj169FBoaOgNrxs4cGA+poJRqLc1bdiw4abnH3zwwXxKgvxAvQGAhg4AwALKli3r8Alubmw2G+sxuAjqjZt57rnnNGbMGAUHB5sdBfmAegNwZTR0AAAAYBmBgYGKiYlR+fLlzY6CfEC9AbgyFkUGAOBPqlWrppMnT5odA/mEelsLn2VaC/UG4Mpo6AAA8CexsbFKS0szOwbyCfUGAADOiIYOAAAAAACAk6GhAwAAAAAA4GRo6AAAAAAAADgZGjoAAACwjJ49eyowMNDsGMgn1BuAK6OhAwAAAKcXERGhUaNG6ciRIze97oMPPlBwcHA+pYJRqDcA0NABACCHjz76SKGhoWbHQD6h3q5hwIAB+uqrrxQZGam6detq2rRpio+PNzsWDEK9AUCy2e12u9khAADILxs2bNCkSZN06NAhSVJUVJSGDx+uBx54wORkMAL1tp4jR45o7ty5mj9/vo4dO6YmTZqoZ8+e6tWrl9nRYADqDcDKaOgAACxjzpw56t27tzp27Kj77rtPkrR582YtW7ZMs2bNUvfu3U1OiLxEvbFt2zb1799fe/fuVUZGhtlxYDDqDcBqaOgAACwjMjJSzzzzjF544QWH41OmTNH06dOzR3HANVBv69q+fbvmzZunhQsXKiEhQW3atNGCBQvMjgWDUG8AVkVDBwBgGV5eXjpw4IAiIiIcjv/888+qWrWqkpOTTUoGI1Bva/nz1JumTZuqR48e6tixo/z9/c2OhzxGvQFAKmR2AAAA8kt4eLjWrFmT4w3+6tWrFR4eblIqGIV6W0vlypVVt25dDRgwQF27dmWhaxdHvQGAhg4AwEKGDh2qgQMHKiYmRg0bNpSUtabKrFmzNG3aNJPTIa9Rb2v58ccfVbFiRYdjdrtdK1euVHR0tBYvXmxSMhiBegMAU64AABazbNkyTZ48OXv9lMjISA0fPlzt2rUzORmMQL2t6dixY5oxY4ZmzZqls2fPqnnz5vryyy/NjgWDUG8AVkVDBwAAAE4vJSVFixcvVnR0tDZt2qSMjAxNmjRJffv2VWBgoNnxkMeoNwBIbmYHAAAAAP6qnTt36rnnnlNYWJimTp2q9u3b6+TJk3Jzc1OLFi14c+9iqDcA/IE1dAAALq1o0aI6cuSIgoODVaRIEdlsthtee+HChXxMBiNQb+upX7++/vnPf2rbtm2qVKmS2XFgMOoNAH+goQMAcGnvvPOOAgICsv//Zm/w4fyot/U0a9ZM0dHROnPmjP7xj3+oRYsW1N2FUW8A+ANr6AAAAMCpnTx5UjNnztTMmTOVlJSkLl266P3339fevXsVGRlpdjzkMeoNAFlo6AAALMPd3V1xcXEKCQlxOH7+/HmFhIQoIyPDpGQwAvW2plWrVmnmzJlatmyZwsPD1alTJ3Xq1Em1atUyOxoMQL0BWBkNHQCAZbi5uSk+Pj7HG/zffvtNFSpUUFJSkknJYATqbW0XL17UnDlzNGPGDO3du5cGnouj3gCsiIYOAMDlvfvuu5KkF154QWPHjpW/v3/2uYyMDG3cuFGxsbHavXu3WRGRh6g3/mzXrl3ZIzaee+45jRkzRsHBwSanglGoNwCroKEDAHB55cqVkyQdP35cpUqVkru7e/Y5T09PlS1bVmPGjFH9+vXNiog8RL1xM4GBgYqJiVH58uXNjoJ8QL0BuDJ2uQIAuLxjx45Jkpo0aaKlS5eqSJEiJieCkag3bobPMq2FegNwZTR0AACWsW7dOrMjIB9RbwAA4Mpo6AAALOXXX3/V8uXLdeLECaWmpjqcmzJlikmpYBTqDQAAXBUNHQCAZaxZs0Zt27ZV+fLldfjwYVWtWlWxsbGy2+1sceuCqDcAAHBlbmYHAAAgv4wYMULDhg3Tvn375O3trSVLlujkyZN68MEH9fjjj5sdD3mMegMAAFdGQwcAYBmHDh1Sr169JEmFChVSUlKS/P39NWbMGL311lsmp0Neo97ITc+ePRUYGGh2DOQT6g3AlTHlCgBgGX5+ftnrqBQvXly//PKLqlSpIkk6d+6cmdFgAOrt+vbu3Xvb11avXl2S9MEHHxgVBwaj3gDgiIYOAMAy7r33Xm3atEmRkZFq1aqVhg4dqn379mnp0qW69957zY6HPEa9XV+NGjVks9lkt9tls9luem1GRkY+pYJRqDcAOKKhAwCwjClTpigxMVGSNHr0aCUmJmrhwoWqWLEiOx65IOrt+o4dO5b9/7t379awYcM0fPhwNWjQQJK0detWTZ48WRMnTjQrIvIQ9QYARza73W43OwQAAEbLyMjQ5s2bVb16dQUFBZkdBwaj3tZTr149jRo1Sq1atXI4/vXXX2vkyJHauXOnSclgBOoNACyKDACwCHd3dz388MO6ePGi2VGQD6i39ezbt0/lypXLcbxcuXI6ePCgCYlgJOoNADR0AAAWUrVqVR09etTsGMgn1NtaIiMjNX78+OyFsCUpNTVV48ePV2RkpInJYATqDQBMuQIAWMjKlSs1YsQIjR07VrVr15afn5/Deba2dS3U21q2b9+uNm3ayG63Z+9wtHfvXtlsNq1YsUL16tUzOSHyEvUGABo6AAALcXP7Y2Dq9Tuk/L5jCruiuBbqbT1Xr17V3LlzdfjwYUlZozi6d++eo5kH10C9AVgdDR0AgGVs2LDhpucffPDBfEqC/EC9AQCAK6OhAwDAnzz33HMaM2aMgoODzY6CfEC9ndfy5cv1yCOPyMPDQ8uXL7/ptW3bts2nVDAK9QYARzR0AAD4k8DAQMXExKh8+fJmR0E+oN7Oy83NTfHx8QoJCXGYYvdnTLFzDdQbABwVMjsAAAAFDZ91WAv1dl6ZmZm5/j9cE/UGAEdsWw4AAAAAAOBkaOgAAADAJWzYsEFt2rRRRESEIiIi1LZtW3333Xdmx4JBqDcAq6OhAwAAAKc3Z84cNW/eXL6+vho4cKAGDhwoHx8fNWvWTPPmzTM7HvIY9QYAFkUGACCHgIAA7dmzh0VyLYJ6u4bIyEg988wzeuGFFxyOT5kyRdOnT9ehQ4dMSgYjUG8AYIQOAAA59OzZU4GBgWbHQD6h3q7h6NGjatOmTY7jbdu21bFjx0xIBCNRbwCgoQMAsJCIiAiNGjVKR44cuel1H3zwgYKDg/MpFYxCva0lPDxca9asyXF89erVCg8PNyERjES9AYBtywEAFjJgwADNmzdPY8eOVa1atdSzZ0916dJFYWFhZkeDAai3tQwdOlQDBw5UTEyMGjZsKEnavHmzZs2apWnTppmcDnmNegMAa+gAACzoyJEjmjt3rubPn69jx46pSZMm6tmzp3r16mV2NBiAelvHsmXLNHny5Oz1UyIjIzV8+HC1a9fO5GQwAvUGYHU0dAAAlrZt2zb1799fe/fuVUZGhtlxYDDq7ZrS09M1btw49enTR6VKlTI7DgxGvQEgC2voAAAsafv27Ro8eLA6dOigI0eO6PHHHzc7EgxEvV1boUKFNHHiRKWnp5sdBfmAegNAFho6AADLOHLkiF5//XXdfffduu+++3To0CG99dZbOn36tBYsWGB2POQx6m0tzZo104YNG8yOgXxCvQGARZEBABZSuXJl1a1bVwMGDFDXrl0VGhpqdiQYiHpbyyOPPKKXXnpJ+/btU+3ateXn5+dwvm3btiYlgxGoNwCwhg4AwEJ++uknVaxY0eGY3W7XypUrFR0drcWLF5uUDEag3tbi5nbjgec2m401k1wM9QYAplwBACzk+jf3x44d08iRI1W6dGl16NBBycnJJiaDEai3tWRmZt7wxpt710O9AYApVwAAC0lJSdHixYsVHR2tTZs2KSMjQ5MmTVLfvn0VGBhodjzkMeoNAABcGSN0AAAub+fOnXruuecUFhamqVOnqn379jp58qTc3NzUokUL3ty7GOptXWvWrNGjjz6qChUqqEKFCnr00Ue1evVqs2PBINQbgNXR0AEAuLz69evLy8tL27Zt0w8//KCBAweyQK4Lo97W9P7776tly5YKCAjQoEGDNGjQIAUGBqpVq1Z67733zI6HPEa9AYBFkQEAFtCiRQtt3bpVbdq00T/+8Q+1aNFCNptNHh4e2rNnj6KiosyOiDxEva2pVKlSeumll/T88887HH/vvfc0btw4nTp1yqRkMAL1BgBG6AAALOCbb77RgQMHVKlSJfXv31/FixfXoEGDJGXthgLXQr2t6dKlS2rZsmWO4w8//LAuX75sQiIYiXoD/9/e/YRYVfdhAH/u1Rjq5qxi3DjQ5GwmIrBAmNplhCPjdSnEgNEiImKkmQSJilBSiBxowFoEEWQtHVTIjUISQxLktbE/swhdhUGL/sCASMO8ixeUcdA3Xubcwz3n81nd+flbPPBs5HvP/R4w0AGgJgYHB/P222/n2rVr+eyzz/L7779n48aN2bNnT954441cunSp7IisI33XT7vdztzc3JrzU6dOZXx8vIREFEnfAH5yBUCN/fHHHzlx4kQ++eSTLCwseNVtxem7emZnZ299/vvvv/P+++/n6aefzujoaJLk4sWLmZ+fz/T0dN58882yYrJO9A2wmoEOACS5dOlSnnjiiSTJK6+8kkOHDuWhhx4qORVF0Xc1DA0N/at7jUYjV69eLTgNRdM3wGoGOgBwh/7+/ly+fDmPPPJI2VHoAn0DAL3IDh0AuIPvOupF3wBAL9pYdgAAAPh/TE1N5fDhw2m1Wpmamrrn3ZmZmS6loij6BljNQAcAgJ7U6XSyuLiYbdu2pdPp3PWe19VXg74BVrNDBwDusGnTpnz//fd2qtSEvnvbhg0bcv369QwMDCRJ9u7dm9nZ2WzevLnkZBRB3wC32aEDAEDPuvO7ybNnz2ZpaamkNBRN3wC3GegAwB0mJibS399fdgy6RN/V4uHzetE3UGd26ABQaQsLC//67uOPP54k+eijj4qKQ8H0XT+NRmPNzhQ7VKpL3wC32aEDQKU1m800Go2srKz8z//0Ly8vdykVRdF3/TSbzYyNjaWvry9JcubMmTzzzDNptVqr7p08ebKMeKwzfQPc5gkdACrt2rVrtz53Op28/vrrOXDgQEZHR5Mk33zzTY4dO5b33nuvrIisI33Xz759+1b9PTExUVISukHfALd5QgeA2ti+fXveeeed7Nq1a9X5l19+mbfeeivfffddSckogr4BgCqzFBmA2rhy5UqGhobWnA8NDeWnn34qIRFF0jcAUGUGOgDUxsjISI4ePZqbN2/eOrt582aOHj2akZGREpNRBH0DAFXmJ1cA1Ma3336b3bt3Z2Vl5dYbjhYWFtJoNHLmzJls37695ISsJ30DAFVmoANArSwtLeXzzz/P4uJikv8+xfH888+veUMK1aBvAKCqDHQAAAAAeozXlgNQaadPn87Y2Fjuu+++nD59+p532+12l1JRFH0DAHXhCR0AKq3ZbOa3337LwMBAms27vwug0WhkeXm5i8kogr4BgLow0AEAAADoMV5bDgAAANBjDHQAqJULFy5k9+7dGR4ezvDwcNrtdr7++uuyY1EQfQMAVWWgA0BtnDhxIs8++2weeOCBTE5OZnJyMvfff3927NiRL774oux4rDN9AwBVZocOALUxMjKSl156Ka+99tqq85mZmXz88cf5+eefS0pGEfQNAFSZgQ4AtdHX15cff/wxw8PDq85/+eWXPPbYY7lx40ZJySiCvgGAKvOTKwBqY3BwMOfPn19zfu7cuQwODpaQiCLpGwCoso1lBwCAbpmens7k5GQuX76cp556KkkyPz+fTz/9NB988EHJ6Vhv+gYAqsxPrgColbm5uRw7duzW/pSRkZEcOHAge/bsKTkZRdA3AFBVBjoA1MI///yTI0eO5MUXX8yWLVvKjkPB9A0AVJ2BDgC18eCDD+aHH37Iww8/XHYUukDfAECVWYoMQG3s2LEjFy5cKDsGXaJvAKDKLEUGoDbGxsZy8ODBXLlyJU8++WRardaqf2+32yUlowj6BgCqzE+uAKiNZvPuD6Y2Go0sLy93MQ1F0zcAUGUGOgAAAAA9xg4dAAAAgB5joANArZw/fz7j4+PZunVrtm7dmvHx8Zw7d67sWBRE3wBAVRnoAFAbH374YXbu3JlNmzZl//792b9/f/r7+7Nr164cP3687HisM30DAFVmhw4AtbFly5YcPHgwr7766qrz48eP58iRI/n1119LSkYR9A0AVJkndACojT///DM7d+5cc/7cc8/lr7/+KiERRdI3AFBlBjoA1Ea73c7c3Nya81OnTmV8fLyERBRJ3wBAlW0sOwAAFGl2dvbW50cffTTvvvtuvvrqq4yOjiZJLl68mPn5+UxPT5cVkXWkbwCgLuzQAaDShoaG/tW9RqORq1evFpyGoukbAKgLAx0AAACAHmOHDgAAAECPsUMHgEqbmprK4cOH02q1MjU1dc+7MzMzXUpFUfQNANSFgQ4AldbpdLK4uJht27al0+nc9V6j0ehiKoqibwCgLuzQAaDyNmzYkOvXr2dgYCBJsnfv3szOzmbz5s0lJ6MI+gYA6sAOHQAq787vLs6ePZulpaWS0lA0fQMAdWCgA0DteDi1XvQNAFSRgQ4AlddoNNbsTLFDpbr0DQDUgaXIAFTeyspKXnjhhfT19SVJbty4kZdffjmtVmvVvZMnT5YRj3WmbwCgDgx0AKi8ffv2rfp7YmKipCR0g74BgDrwlisAAACAHmOHDgAAAECPMdABAAAA6DEGOgAAAAA9xkAHAAAAoMcY6AAAAAD0GAMdAAAAgB5joAMAAADQY/4DKeXdOrdYG9gAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# **Augmantation Data**"
],
"metadata": {
"id": "H-8OjXLYq-C8"
}
},
{
"cell_type": "code",
"source": [
"# Assuming 'Given_Data' is your original DataFrame\n",
"original_data = Given_Data.copy()\n",
"\n",
"# Define a function to perform data augmentation\n",
"def augment_data(data, num_samples=100):\n",
" augmented_samples = []\n",
"\n",
" for _ in range(num_samples):\n",
" # Randomly select a row from the original data\n",
" original_sample = data.sample(n=1)\n",
"\n",
" # Apply some augmentation operation\n",
" augmented_sample = original_sample.copy()\n",
"\n",
" # For example, add some random noise to a feature\n",
" noise = np.random.normal(0, 0.1, size=len(augmented_sample.columns) - 1)\n",
" augmented_sample.iloc[:, :-1] += noise\n",
"\n",
" augmented_samples.append(augmented_sample)\n",
"\n",
" # Concatenate the augmented samples with the original data\n",
" augmented_data = pd.concat([data] + augmented_samples, ignore_index=True)\n",
"\n",
" return augmented_data\n",
"\n",
"# Augment your data with 1000 synthetic samples (you can adjust the number)\n",
"augmented_data = augment_data(original_data, num_samples=100)\n",
"\n",
"# Now 'augmented_data' contains your original data plus synthetic samples\n"
],
"metadata": {
"id": "XIT0M8-llQzy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# **Normalizing the Features:**💢\n"
],
"metadata": {
"id": "miEgFYh4yyEN"
}
},
{
"cell_type": "code",
"source": [
"# Normalize the features\n",
"scaler = MinMaxScaler()\n",
"x_train = scaler.fit_transform(x_train)\n",
"x_test = scaler.transform(x_test)\n",
"###################################\n",
"\n",
"#RF model:\n",
"# Create a Random Forest Classifier\n",
"classifier = RandomForestClassifier(random_state= 50, n_estimators=100)\n",
"\n",
"# Fit each classifier to its respective training data\n",
"classifier.fit(x_train, y_train)\n",
"\n",
"###################################\n",
"#DT model:\n",
"\n",
"# Create a Decision Tree classifier instance (you can set hyperparameters here)\n",
"decision_tree_classifier = DecisionTreeClassifier(random_state=42) # You can set other hyperparameters as needed\n",
"\n",
"# Train the Decision Tree classifier on the training data\n",
"decision_tree_classifier.fit(x_train, y_train)\n",
"\n",
"\n",
"#########\n",
"#####################################################################################################"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 110
},
"id": "EZDQxFPouGDZ",
"outputId": "567e6a3a-b2a7-42bd-e37d-c724d1dc7366"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"<ipython-input-8-c1c47b6f11e6>:12: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" classifier.fit(x_train, y_train)\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DecisionTreeClassifier(random_state=42)"
],
"text/html": [
"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"source": [
"# **Feature Importance** *✈*\n",
"\n",
"---\n"
],
"metadata": {
"id": "ASsKQWxByCIy"
}
},
{
"cell_type": "code",
"source": [
"\n",
"# Get feature importances from your Random Forest model\n",
"feature_importances = classifier.feature_importances_\n",
"\n",
"# Extract feature names or columns from your dataset\n",
"feature_names = Given_Data.columns\n",
"\n",
"\n",
"feature_importance_dict = dict(zip(feature_names, feature_importances))\n",
"\n",
"\n",
"# Sort the features by importance, with the most important ones first\n",
"sorted_features = sorted(feature_importance_dict.items(), key=lambda x: x[1], reverse=True)\n",
"\n",
"# Extract the importance scores for visualization\n",
"importance_scores = [x[1] for x in sorted_features]\n",
"\n",
"\n",
"# Extract the top N feature names (N is the number of importance scores)\n",
"top_feature_names = [x[0] for x in sorted_features]\n",
"\n",
"\n",
"# Create a horizontal bar chart to visualize feature importance\n",
"plt.figure(figsize=(10, 6))\n",
"plt.barh(top_feature_names, importance_scores)\n",
"plt.xlabel('Feature Importance Score')\n",
"plt.ylabel('Features')\n",
"plt.title('Random Forest Feature Importance')\n",
"plt.gca().invert_yaxis() # Invert the y-axis to display the most important features at the top\n",
"plt.show()\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 564
},
"id": "SGB_o4MVwCsz",
"outputId": "9827464e-7d40-4325-bdec-a855d196a472"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# **HyperParameters Grid**"
],
"metadata": {
"id": "n_Nf13TXQgA9"
}
},
{
"cell_type": "code",
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import GridSearchCV\n",
"# Define the hyperparameter grid\n",
"param_grid = {\n",
" 'n_estimators': [100, 200, 300], # Try different values for the number of trees\n",
" 'max_depth': [None, 10, 20, 30], # Try different values for the maximum depth of trees\n",
" 'min_samples_split': [2, 5, 10], # Try different values for minimum samples to split a node\n",
" 'min_samples_leaf': [1, 2, 4] # Try different values for minimum samples in a leaf node\n",
"}\n",
"\n",
"# Create a grid search\n",
"grid_search = GridSearchCV(estimator=classifier, param_grid=param_grid, cv=5, scoring='accuracy')\n",
"\n",
"# Fit the grid search to your data\n",
"grid_search.fit(x_train, y_train)\n",
"\n",
"# Get the best hyperparameters\n",
"best_params = grid_search.best_params_\n",
"print(\"Best Hyperparameters:\", best_params)\n",
"\n",
"# Get the best model\n",
"best_rf_model = grid_search.best_estimator_\n",
"\n",
"# Evaluate the best model on your test data\n",
"accuracy = best_rf_model.score(x_test, y_test)\n",
"print(\"Test Accuracy with Best Model:\", accuracy)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4YTiYAW4yBuv",
"outputId": "61664153-7b1a-4918-f83d-ec02ae7d6240"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" estimator.fit(X_train, y_train, **fit_params)\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:909: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" self.best_estimator_.fit(X, y, **fit_params)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Best Hyperparameters: {'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 300}\n",
"Test Accuracy with Best Model: 0.75\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(\"Shape of x_train:\", x_train.shape)\n",
"print(\"Shape of x_test:\", x_test.shape)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kXRDX-taFuBR",
"outputId": "439c6969-81d3-4076-bd3b-800747391ccc"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Shape of x_train: (71, 5)\n",
"Shape of x_test: (8, 5)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# **ROC & AUC for the Classifier's Predictions:**\n",
"\n",
"* Generating a Receiver Operating Characteristic (ROC) curve\n",
"* calculating the Area Under the Curve (AUC) for the Classifier's predictions."
],
"metadata": {
"id": "Gyh8fuOqQ7cF"
}
},
{
"cell_type": "code",
"source": [
"#RF\n",
"y_pred = classifier.predict(x_test) # Predict probabilities of positive class\n",
"fpr, tpr, _ = roc_curve(y_test, y_pred)\n",
"roc_auc = auc(fpr, tpr)\n",
"plt.plot(fpr, tpr, label=f'Random Forest (AUC = {roc_auc:.2f})')\n",
"\n",
"\n",
"\n",
"####################################################################################################\n",
"#DT:\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.metrics import roc_curve, auc\n",
"\n",
"# Create a DecisionTreeClassifier with modified min_samples_split\n",
"dt_classifier = DecisionTreeClassifier(min_samples_split=5, random_state=0)\n",
"\n",
"# Fit the modified model to your training data\n",
"dt_classifier.fit(x_train, y_train) # Replace x_train and y_train with your training data\n",
"\n",
"# Predict probabilities on the test data using the modified model\n",
"y_pred_dt = dt_classifier.predict_proba(x_test)[:, 1]\n",
"\n",
"# Calculate the ROC curve and AUC for the modified Decision Tree\n",
"fpr_dt, tpr_dt, _ = roc_curve(y_test, y_pred_dt)\n",
"roc_auc_dt = auc(fpr_dt, tpr_dt)\n",
"\n",
"# Plot the ROC curve for the modified Decision Tree\n",
"plt.plot(fpr_dt, tpr_dt, label=f'Modified Decision Tree (AUC = {roc_auc_dt:.2f})')\n",
"\n",
"\n",
"\n",
"####################################################################################################\n",
"\n",
"\n",
"# Set plot properties\n",
"plt.plot([0, 1], [0, 1], 'k--')\n",
"plt.xlim([0.0, 1.0])\n",
"plt.ylim([0.0, 1.05])\n",
"plt.xlabel('False Positive Rate', fontweight='bold')\n",
"plt.ylabel('True Positive Rate', fontweight='bold')\n",
"plt.title('Receiver Operating Characteristic', fontweight='bold')\n",
"plt.legend(loc='lower right')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 489
},
"id": "9Gx6jOqCv_EA",
"outputId": "d76d040f-f604-4586-9b9b-897fc48b09bb"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x79410b9a88b0>"
]
},
"metadata": {},
"execution_count": 10
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAHHCAYAAABTMjf2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACr40lEQVR4nOzdd3iN9/vA8ffJTmSSSIJE7B1ixWoVqdDyrepQCUJIFaUVHYqWatGirdoVYiaoKi1tgxqtGrGprcROEGRIZJ7P74/8nEqTkHCSk3G/rivXlfOcZ9xnJOc+n+fz3LdGKaUQQgghhCiDjAwdgBBCCCGEoUgiJIQQQogySxIhIYQQQpRZkggJIYQQosySREgIIYQQZZYkQkIIIYQosyQREkIIIUSZJYmQEEIIIcosSYSEEEIIUWZJIiTEQ3bs2IFGo0Gj0dC/f39DhyMeYcKECbrXasmSJYYOp0DkffZ0Cvu1f7BvDw8Pve9bFD+SCIlC8/A/qwc/JiYmVKxYkS5duvDbb78ZOsQSLzU1ldmzZ/Pss89Svnx5zMzMcHV15aWXXuLnn382dHhPJS4ujgkTJjBhwoQSk+ikp6cTGhpK586dqVixIubm5ri7u+Pj48N3331HYmKioUMsNEuWLNG9XnFxcYYO55HWr1+vi/XixYuGDkcYmhKikIwfP14Bef5oNBq1fv16Q4eZTVxcnNq5c6fauXOnOnv2rKHDeaRr166pxo0bP/I57t27t0pPTzd0qE8kKipK9zjat2+f4/5Lly7pXqsbN24UfYD/cfXqVdWsWbNHvh7r1q1TSim1fft23bKAgACDxq0v7du31z2mqKioQj3W0772AQEBuli3b9+e4/4H+96/f78eohXFnUmRZFuizOvatStjxowhNjaWCRMmcPToUZRSzJo1i5deesnQ4enY2dnRrl07Q4cBQHJyMlZWVrnep9Vq6dmzJ0ePHgXA3d2dTz75BA8PD/bu3cukSZO4f/8+K1euxM3NjS+//LIoQ88hKSmJcuXK6XWf7u7uuLu763WfTyotLY3//e9/HDp0CAB7e3tGjRpFq1atSE1NZc+ePSxatMjAUT76PVUSPHgfFfZrX1z+B4giYuhMTJReD48IPfytd+3atbrltWvXzrHd0aNH1RtvvKFcXFyUqampqlSpkho4cKC6cuVKjnWTk5PVpEmTlJeXlypXrpyysrJS9evXVx9//HG29W7evKlGjhypatasqczMzJS9vb164YUX1J49e7Ktl9s39eHDh+uW/fjjj9nWnzRpku6+OXPm6JZfuHBBDRo0SLm7uyszMzPl5OSkXn/9dXXy5Mls2y9evFi3/fjx49W8efNU7dq1lYmJiVq8eHGez+3Dz6GJiYk6d+5ctvuXLFmiu9/MzExdv35dKZVzlGXfvn3q2WefVZaWlsrV1VWNGzcuxwiSVqtVoaGhqk2bNsrGxkZZWFgoT09PNWPGDJWZmZlt3apVq+r2f+nSJdWzZ09la2urPDw8lFJK/fHHH+rVV19VNWvWVHZ2dsrU1FS5urqq1157TR09elS3n4e/sf/358Ho0MPvr4efq4djiI6OVn369FH29vbK2tpavf766+r27dvZYk5OTlbvvPOOcnR0VOXKlVPdu3dXUVFR2fbzON99951uXWNjY3X48OEc6yQkJOjew/99n23btk15e3src3Nz5ebmpr799tts2/7999/Kz89P1atXTzk4OCgTExPl5OSkXnjhBfXHH39kWzc/76ng4GDVunVr5eLioszMzFS5cuWUl5eXmjZtWq4jiHv27FGvvvqqcnV1VaampsrZ2Vl17dpVHT58ONtjye3n4dGh9evXq06dOil7e3tlZmamateurSZMmKCSk5OzHe/h0aWDBw+qAQMGqAoVKuhei7xe+yNHjqj//e9/ysnJSZmYmKjy5curxo0bq8GDB6tLly5le//n9vNgdOjB7apVq2aLKyMjQ82ZM0e1atVK2draKgsLC1WzZk315ptv5vXWECWAJEKi0OSVCP3www+65c8991y2bX799Vdlbm6e6z8pFxcXdeHCBd268fHxqkmTJrmu+/A/sEuXLqkqVarkup6pqan66aefdOvmlgjt3btXt8zPzy9bvF5eXrr9xMbGKqWUOnjwoLK3t8/1eNbW1ioyMlK3/cMfWtWrV8+27qMSoT59+ujWe/XVV3Pcn56erlxcXHTrLFq0SCmVPRGqUqWKKleuXI4YBw8enG1f/fr1y/ODo1evXtnWfTh5ePjxPHg9pkyZkue+rKysdImivhKh/z6ngPL3988W80svvZRjHTc3N1W+fPl8J0IdO3bUrdu/f//Hrv/w+6xmzZrKxMQkRwxbtmzRrb9y5co8nw8jIyO1bds23br5eU/l9TcGqAEDBmSLNTQ0VBkbG+e67uLFi/OdCH388cd5rvPMM8+o1NRU3TEfToT++xjyeu1jY2OVk5NTnsfYsmXLUyVCaWlpytfXN89tRcklk6VFkbh58yZ//fUX69ev57PPPtMtHzx4sO735ORkAgICSE1NxcTEhEmTJrF582Y++OADAGJiYhg6dKhu/bFjx3LkyBEAypcvzzfffENERASzZs2ibt26uvWGDh3K1atXAejXrx8RERHMmzcPa2tr0tPTCQwMJCkpKc/Yvb29qVmzJgAbN24kNTUVgAsXLnD48GEAunTpQoUKFVBKERAQoJssOmrUKDZv3syXX36JsbEx9+7dY8CAASilchznwoUL+Pr6sn79er7//nsaNGiQZ0wnT57U/d6kSZMc95uYmGTb/uH1H7h69Spt27Zlw4YNfPbZZxgbGwPw3XffcezYMQB++OEHli1bBkCdOnVYuXIlGzZsoFWrVgCsXr2a1atX5xrjjRs3+Prrr9m8eTNjxowBoGXLlsyaNYuff/6Z7du3s2XLFt1pu+TkZL755hsg67Vds2ZNtse4c+dOdu7cyaxZs/J8Xv7r/v37rFixgrlz52JmZgbAqlWriI+PB2Dz5s389NNPAFhYWPD111+zfv16nJycuHPnTr6P8+AUJcAzzzyT7+0A/vnnH1588UU2bNjAG2+8oVv+3Xff6X6vU6cOX331FevXr2fbtm1s3bqVefPmYW5ujlarZcqUKbnuO6/31NixY1m5ciURERHs2LGDH3/8EW9vbyBr0vODv5dr164xZMgQMjMzAejRowfr1q3jhx9+ICgoCDMzM7y8vNi5c2e29+GaNWt0r5erqyv79+/X/d27urqyaNEiIiIiePHFFwHYuXOn7rX/r8uXLzN+/Hg2bdqU5zoAe/bs4datWwD07t2bLVu2sH79eqZPn0779u0xNjbG1dWVnTt30rVrV912M2fO1MXq5eWV5/5nzpzJpk2bALCysuKzzz4jIiKCkJAQWrRoked2ogQwdCYmSq9HTZauWLGiWrp0abb1161bp7u/a9euugmLO3fuVB4eHgqyJljfunVLZWZmZvvGvmnTplxjuH37ttJoNAqyRpQe3ufLL7+s2/6HH35QSuU9ifXhx/Lzzz8rpZT68ssvdctWrVqllFLq8OHDumVNmjTJdrzWrVvr7jtw4IBSKvu396pVq+Z7YnPNmjV123333Xe5rvPGG2/o1hk0aJBSKvuIkJWVlYqLi9Ot7+/vr7tv4sSJSqnsoyUzZ87UPZaQkBDd8m7duun28fBozIIFC3LElJSUpCZMmKAaNWqkrKyscrwvvLy8dOs+brJ0fkaEHkxOVkqpLl266JYfOXJEKaXUkCFDdMtGjRqlW/f06dMF+rb/8IjOb7/99tj1H36fVaxYUaWkpCillIqJicn2/nkgIyNDzZgxQ7Vo0ULZ2Njo3tMPfhwcHHTr5uc99ddff6mXXnpJubi45Doa9WCU9JtvvtEta9OmzSMf06MmS7/zzju6+8aMGaN7H23YsEG3vGHDhrnua8yYMTmOldtrHxERoVv2wQcfqMuXLyutVptrrI+bLP3w8/fAwxcm5PU3J0ommSwtDOLWrVucOHEi27KzZ8/qfv/tt99yvbxeKcXp06epXbu27hu7ubk5Pj4+uR7nn3/+0Y2+xMTE5Plt/dSpU4+Mt0+fPnz66adA1ihJ9+7d+eGHHwCwsbHhf//7X47HcOTIkUcer1mzZtmWdenSBROT/P1J2tra6n5/8C34vx5ebmdnl+P+unXrZlvesmVLwsLCgKyRBMj+eEaMGJHrcfJ67rp3755jWe/evR95Wb++L7tu37697vcKFSrkOM6DxwnoRkQgawTGwcGBu3fv5us4dnZ23L59G4Dr168XKMZWrVphbm6eZ4wAwcHBzJw5M8995PW85fae2rdvHx06dCA9Pf2x+3v49X8wevMkHt7P5MmTmTx5co51Tp8+neu2ub2PcvPMM89Qq1Ytzp07x9SpU5k6dSo2NjY0bdoUf39/Bg4ciJHRk58EefgxdOvW7Yn3I4ofOTUmikRAQADp6elERERgZWWFUoqpU6eyYcOGAu/rv6exHtQoehqPOjUGULNmTd0H5c8//8w///zD/v37AejZsyeWlpZPfTxnZ+d8b1+/fn3d7w9ODz4sIyMjW6L58Pp5edLnMK/n7r+P5/Lly7okyNramrlz57Jjxw527NihW0er1T5RDHlxcHDQ/f5wQqByOTX5NO+hxo0b637ftWtXgbZ9XIxpaWksWLBAd/8XX3zB9u3b2blzJ46OjtnW/a/c3lPz58/XJUHdunXj119/ZefOnfTr10+3jr5fh/zIyMjQnXZ+WH7/LqysrNi1axcTJ06kY8eOuLi4kJiYyB9//MGbb77J1KlT9R2yKCUkERJFxsTEBF9fX92cH4CPP/5Y93vt2rV1vwcEBKCyJvNn+0lKSsLX1xdHR0fdB0hKSgq///57rsesWbOm7gOuRo0aZGRk5NhnWloaEydOfGz8/v7+QNa35YfnKvXp0yfXx9C+ffs8H8PDc6MeKMgHcY8ePXS/r1+/PtvIBsDKlSuJiYkBwMzMLNuciAfOnDlDQkKC7nZkZKTu9+rVq+d4PNu3b8/18Zw/fz7XGP/7eK5du6b73dfXlyFDhtC+fXvdaMh/PfztvbA+mGvUqKH7/UFiC1nPTX5HgwB69eql+33ZsmW6OVYPS0xM1M29KYjbt2+TkpICZCVcH374Ic899xzVq1d/7Dym3N5TD78OU6ZMoWvXrrRr144bN27kWPfh1//XX3995LEe9Xo9vJ/Fixfn+XeR23shv38XSimcnJz4+OOP2bp1K9HR0Vy4cAFra2sAfvzxx3zFmpeHH8Mvv/ySr21EySCnxkSRGz58OFOnTiU5OZmjR4+yefNmOnfuzPPPP4+TkxO3bt1i2bJllC9fnueff57MzEwuXrzIrl27OHr0KCdPnsTIyAg/Pz/mzJkDgJ+fHx9//DF169blwoUL/Pzzz/z666+UL1+erl278uuvv3L+/Hn+97//MXDgQGxsbLh06RKHDx/mxx9/ZM+ePY8tp//GG28QHBxMRkYGW7ZsAaBSpUp07NhRt07jxo1p2LAhx48f548//qBfv3689tprmJqacvHiRfbt28e6desK9CGbm5dffpkWLVqwf/9+MjIy6NixY446Qg+MGDECV1fXHPtISkqiV69evP322xw9epRVq1bp7ntQ28nf3183mbhv376MHTuWWrVqcevWLc6dO8cvv/xC165dGT9+/GNjrlq1qu73bdu2sXLlSoyNjXUTqf/r4ZGSv//+m/Xr1+Po6KjXGjI9evRg7ty5AMyePZsqVarg7u6er8T4Yf3792f+/PkcPnyYjIwMnnvuOd577z1atmyZrY7QvHnzqFKlSoH27ezsjIWFBSkpKfz9998sWLAAZ2dnPvvssydKEB9+HaZMmUJAQAC//fabbiLww1577TVGjx5Namoqu3bt4pVXXqFfv35otVq2bNlC27ZtdV8QHn69QkJCeOGFF7C0tKR58+b4+fnx7bffAjBy5Eju3LmDp6cncXFxnD9/ns2bN1O1alVCQ0ML/Hge2L17NyNGjOCVV16hVq1aODo6cuzYMZKTkwGyjTY9HOuKFSswNjbG2Nj4kfWD+vTpo5sUP3LkSG7evEmLFi24du0aCxYsYM+ePU8cuzCwIpiHJMqovC6fV0qpYcOG6e7z8fHRLf/ll18eeWnvw5MX4+LilKen52PXe9Tl8w9+HkzufFzF365du2bbLjg4OMc6j7p8/sHPA/+t+VIQV65cUQ0bNnzkcV5//XWVlpam2+bhCchVq1ZVtra2ObZ5MLH6gUddPv/fuB9Xe+fFF1/MsX3btm1zfd2UUrlWan5wvPxMln5YXhNkc7t8vnLlygW6fF6prMrSTZs2feRzlZ/K0rk9Fw//vTz4qVWrlqpYsWKB31ORkZE5JltrNJpsk/kffj5DQkKUkZFRro/n4fVmzZr1yL/DR10+/9/n4XFVqnN77Xfu3PnI/U+ZMkW3/cOTtHP7u8wt/rS0NOXj4/PYv2lR8sipMWEQ7777rm54+vfff9ddhv7CCy9w4MAB+vbtS5UqVTA1NcXR0ZEmTZoQHByc7ZJqOzs79uzZw2effUbjxo2xtLTEysqKevXqZZvv4O7uzuHDh3n//fepW7cuFhYW2NjYULduXfr168fPP/+Mm5tbvuJ++DRYbrcBmjZtypEjR3jrrbeoXr06ZmZm2Nvb07BhQ9566y22bt1a4OcrN1WqVGH//v18++23tGvXDnt7e0xNTXF2dqZbt278+OOPrF69GlNT01y39/Dw4I8//uC5557D0tISFxcXxowZw7x587Ktt3TpUpYtW0b79u2xs7PDzMwMd3d3OnXqxMyZM7OdJnyc5cuXExAQgKOjI/b29vTt2/eR88RWrlxJly5dsn2D17eVK1cyYsQIKlSogJWVFS+++CJ//vmnbrQlv/O/KleuzN69e1m4cCE+Pj44OjpiampKpUqVaN++PXPmzKFTp05PFOP06dN59913cXV1xdramv/9739s3bq1wHPTIGtS/Lp162jUqBEWFhY0aNCANWvW0Llz51zXHzRoEDt37qRnz544Ozvr+gV27do12yXzgwcP5sMPP8Td3T3XSckTJ05k48aNulITpqamVK5cmXbt2vHFF1/oLkZ4UrVr1+bDDz+kVatWujitra1p0aIFc+bM4cMPP9St261bN6ZPn06NGjXyfYGCqakpv/32GzNnzqRly5ZYW1tjYWFBzZo1CQoKeqrYhWFplMpjlp0QotS5ePEi1apVA7LmMD08UbmsUkrlmIdy+vRp6tWrB4Cnp2e2OkFCiNJF5ggJIcq09957D0dHRzp16oSrqyunTp3i/fff193/8ERoIUTpI4mQEKJMu337Nl9//XWu9z3zzDMEBwcXcURCiKIkiZAQokzr3r07V69e5fjx49y5cwdLS0vq169P7969GTJkSJ5zrIQQpYPMERJCCCFEmSVXjQkhhBCizJJESAghhBBlVpmbI6TVarl+/To2NjZP3Z9KCCGEEEVDKUViYiKVKlV6qga6/1XmEqHr16/nu3ieEEIIIYqXK1euFLhVzaOUuUTIxsYGyHoibW1tDRyNEEIIIfIjISEBNzc33ee4vpS5ROjB6TBbW1tJhIQQQogSRt/TWmSytBBCCCHKLEmEhBBCCFFmSSIkhBBCiDJLEiEhhBBClFmSCAkhhBCizJJESAghhBBlliRCQgghhCizJBESQgghRJkliZAQQgghyixJhIQQQghRZhk0Efrzzz/p3r07lSpVQqPRsH79+sdus2PHDpo2bYq5uTk1a9ZkyZIlhR6nEEIIIUongyZCSUlJNG7cmDlz5uRr/aioKF588UU6dOjAkSNHePfddxk0aBCbNm0q5EiFEEIIURoZtOlq165d6dq1a77Xnz9/PtWqVeOrr74CoF69evz111988803+Pr6FlaYQgghhCilStQcoT179uDj45Ntma+vL3v27DFQREIIIYQoCtcPbymU/Rp0RKigYmJicHZ2zrbM2dmZhIQE7t+/j6WlZY5tUlNTSU1N1d1OSEgo9DiFEEII8fT+uXmPiOPR/Pb3dc5/0bdQjlGiEqEnMWXKFD799FNDhyGEEEKIx1BKcTI6gYjjMUQcj+HczXu6+95s48rYHy/o/ZglKhFycXHhxo0b2ZbduHEDW1vbXEeDAD766COCg4N1txMSEnBzcyvUOIUQQgiRP1qt4vCVODadyEp+Lt9JBiA15h80KfF09u1ClwYuPL/XXRKh1q1b8+uvv2ZbtmXLFlq3bp3nNubm5pibmxd2aEIIIYTIp4xMLfsu3mHT8Rg2nbhBTEKK7j5zE7D/ZxOH1s7H2saaz6cHUqVKFRL2Fc60ZoMmQvfu3eOff/7R3Y6KiuLIkSOUL18ed3d3PvroI65du8ayZcsAeOutt5g9ezYffPABgYGBbNu2je+//55ffvnFUA9BCCGEEPmQlqFl1/lYIv6OYcupG9xJStPdZ21uQqd6FWlWIZNFk9/nzx07AOjw3HN5nvHRF4MmQgcOHKBDhw662w9OYQUEBLBkyRKio6O5fPmy7v5q1arxyy+/MHLkSL799luqVKnCwoUL5dJ5IYQQohi6n5bJH2dvEXE8mq2nbpKYmqG7z8HKlOfrO9OloQttazry87ofGdxrMHfv3sXKyoqZM2cSGBiIRqMp1Bg1SilVqEcoZhISErCzsyM+Ph5bW1tDhyOEEEKUKokp6Ww7fZOI4zHsOHOL++mZuvsq2pjj28CFLg1d8K5WHhNjI7RaLYMGDWLx4sUAtGjRgrCwMGrVqpVtvwmzO2E3fJveP79L1BwhIYQQQhQ/d5PS2HLyBhEnYvjrXCxpmVrdfZXtLena0IWujVzwcnPAyCj7CI+RkRGWlpYYGRnx0UcfMX78eExNTYssdkmEhBBCCFFgNxNS2HQiht+OxxAZdYdM7b8nmKo7lctKfhq60qCSbY7TWxkZGSQkJFC+fHkApk2bRp8+fR558VNhkURICCGEEPly5U6yLvk5dPkuD0+uqe9qS9eGWae9ajnb5LmPqKgo+vTpg6mpKVu3bsXY2BgrKyuDJEEgiZAQQgghHuFBdeeIEzEcv5a9O4OXuz1dG7rg28CFqhXKPXI/SilWrFjBsGHDSExMxNbWllOnTtGwYcPCDP+xJBESQgghhM7D1Z1/Ox7DPw9VdzbSgHe1CnT5/+THxc4iX/uMi4tjyJAhrFq1CoC2bduyYsUKPDw8CuMhFIgkQkIIIUQZl1d1ZwBTYw1tazpmVXeu70wF64IVKf7jjz/o27cvV65cwdjYmAkTJjB69GhMTIpHClI8ohBCCCFEkXpUdWcLUyPa13aia0NXOtStiJ3lk13FpdVqGTFiBFeuXKFGjRqEhYXh7e2tr4egF5IICSGEEGXE46o7d6xbka4NXWhfxwkrs6dPEYyMjFi2bBlz5szh66+/xtra+qn3qW+SCAkhhBClWEGqO5ubGD/VsZRSLFy4kHv37jFy5EgAGjduzIIFC55qv4VJEiEhhBCilClodWd9iI2NJSgoiPXr12NiYkLnzp1p0KCBXvZdmCQREkIIIUqBp6nu/LQ2b95M//79iY6OxtTUlClTplCvXj29HqOwSCIkhBBClFBPU91ZH1JSUvjoo4+YMWMGAPXq1SM8PJwmTZro/ViFRRIhIYQQogTRR3VnfcjMzOTZZ59l//79AAwbNoypU6diZWVVqMfVN0mEhBBCiGJOX9Wd9cnY2Bh/f38uXrxIaGgo3bp1K7Jj65MkQkIIIUQx87jqzi2rladrQ9cCVXfWh5iYGGJjY3VtMYYPH46/vz+Ojo5FFoO+SSIkhBBCFAOFWd1ZHzZs2EBgYCD29vYcPnwYa2trjIyMSnQSBJIICSGEEAZTFNWdn1ZycjLvvfce8+bNA6BSpUrExsYWy+KIT0ISISGEEKIIFXV156dx6NAh/P39OX36NACjRo1i0qRJmJsX/YhUYZFESAghhChkRVndWR+0Wi3Tp09n3LhxpKen4+rqyrJly/Dx8TF0aHoniZAQQghRCAxR3VlfNBoN27dvJz09nZdffpmQkBAqVKhg6LAKhSRCQgghhJ4YsrqzPmRkZGBiYoJGo2Hx4sVEREQQEBBQKMUYiwtJhIQQQoincCMhhc0GrO6sD4mJiYwYMQKNRkNoaCgALi4u9O/f37CBFQFJhIQQQogCelx15y4NXehaBNWd9WHv3r34+/tz4cIFjIyMGDVqVIlolqovkggJIYQQ+VAcqzs/jYyMDCZPnszEiRPJzMzE3d2dFStWlKkkCCQREkIIIXJVXKs760NUVBR9+vRh9+7dAPTu3Zu5c+dib29v2MAMQBIhIYQQ4v8V9+rO+pCZmYmvry/nzp3D1taWuXPn4u/vb+iwDEYSISGEEGVafqo7d2noQse6zgar7qxPxsbGzJgxgylTprB8+XI8PDwMHZJBSSIkhBCizClJ1Z314c8//yQ+Pp7u3bsD8MILL9C1a9diexVbUSr5r64QQgiRD4+q7mxvZcrz9Zzp2qj4VHfWh7S0NCZMmMAXX3yBnZ0dx44dw83NDUCSoP8niZAQQohSqyRXd35aZ86cwd/fn4MHDwLQs2fPMjkZ+nEkERJCCFGqPKju/NvxaHb9c7vEVXd+WkopFi5cyLvvvktycjIODg6EhITwyiuvGDq0YkkSISGEECVeaajurA+ZmZm89tprrFu3DoCOHTuydOlSqlSpYuDIii9JhIQQQpRIV+4kE3E8hogTJb+6s74YGxvj5uaGqakpkydPJjg4GCOj0nXKT98kERJCCFFilLbqzvqQkpJCQkICFStWBOCLL75g4MCBeHp6GjiykkESISGEEMVWfqs7d27gjKudpQEjNYwTJ07g5+eHvb0927Ztw9jYGEtLS0mCCkASISGEEMXK46o7t6nhSNeGJbu689NSSjF79mzef/99UlNTcXJy4vz589SuXdvQoZU4kggJIYQwuLJW3flpxMTEMGDAACIiIgDo2rUrixcvxtnZ2cCRlUySCAkhhDCI1IxMdp+/XWaqO+vDhg0bCAwMJDY2FgsLC6ZNm8awYcNK9ZVwhU3eWUIIIYpMVnXnrAKHZaW6s75kZGQwduxYYmNj8fT0JDw8nAYNGhg6rBJPEiEhhBCFqixXd9YnExMTwsLCWL58OZ999hnm5mVzfpS+SSIkhBBC7/JT3blLQxeaupfO6s76oNVq+eqrr9BqtXz44YcANGrUiKlTpxo4stJFEiEhhBB6IdWd9efq1asEBAToLol/6aWXqFu3rqHDKpUkERJCCPHEpLqz/q1Zs4bBgwdz9+5drKys+Pbbb6lTp46hwyq1JBESQghRIFLduXAkJibyzjvvsHjxYgCaN29OWFiY1AYqZJIICSGEeCSp7lz4MjIyaNOmDcePH0ej0TBmzBjGjx+PqWnZrplUFCQREkIIkYNUdy5aJiYmvPnmm0yfPp0VK1bwzDPPGDqkMkMSISGEEMC/1Z0jjsew6UQMNxJSdfdJdWf9i4qKIj4+niZNmgDw9ttvExAQgK2trWEDK2MkERJCiDJMqjsXPaUUYWFhDB06FCcnJ44cOYKNjQ0ajUaSIAOQd7UQQpQx+a3u3KaGIxamUt1Zn+Li4hgyZAirVq0CwNPTk8TERGxs5Ko6Q5FESAghygCp7mx4f/75J3379uXy5csYGxszYcIERo8ejYmJfBQbkjz7QghRSt1JSuN3qe5scBkZGXzyySd88cUXKKWoUaMGYWFheHt7Gzo0gSRCQghRqkh15+LH2NiYo0ePopQiMDCQGTNmyKmwYkQSISGEKOGkunPxo5QiLS0Nc3NzNBoNixcv5q+//qJnz56GDk38hyRCQghRAj2o7vzb8RhOXJfqzsXJ7du3CQoKwsbGhqVLlwJQsWJFSYKKKUmEhBCiBFBKceJ6AptOSHXn4mzLli0EBAQQHR2NqakpY8eOlRYZxZwkQkIIUUzlt7qzT31nHKW6s0GlpKQwZswYvvnmGwDq1asnfcJKCEmEhBCimDl8+S7rDl+T6s4lxIkTJ/Dz8+PYsWMADB06lGnTpmFlZWXgyER+SCIkhBDFSFjkJcauO667LdWdi7eMjAy6devGxYsXcXJyIjQ0lG7duhk6LFEA8hclhBDFROhfUUzceBKALg1ceL1FFanuXMyZmJgwb948Zs2aRWhoKM7OzoYOSRSQJEJCCFEMzNtxni8jTgMw+NnqjO5aV+r8FFMbN24kLS1NdxVYly5d8PX1lderhDJ4HfU5c+bg4eGBhYUF3t7e7Nu375Hrz5gxgzp16mBpaYmbmxsjR44kJSWliKIVQgj9Ukox4/ezuiRoRKdakgQVU8nJyQwdOpTu3bsTGBjI5cuXdffJ61VyGXREaPXq1QQHBzN//ny8vb2ZMWMGvr6+nDlzhooVK+ZYPzw8nNGjRxMaGkqbNm04e/Ys/fv3R6PR8PXXXxvgEQghxJNTSjF10xnm7TgPwPu+dRjWoaaBoxK5OXToEP7+/pw+nZWwDhw4UE6DlRIGHRH6+uuvCQoKYsCAAdSvX5/58+djZWVFaGhoruvv3r2btm3b4ufnh4eHB507d6Z3796PHUUSQojiRinFxI0ndUnQuBfrSRJUDGm1WqZNm0arVq04ffo0rq6ubN68ma+++gpzcylZUBoYLBFKS0vj4MGD+Pj4/BuMkRE+Pj7s2bMn123atGnDwYMHdYnPhQsX+PXXX3nhhRfyPE5qaioJCQnZfoQQwpC0WsW49cdZvOsiAJ+91IBBz1Q3bFAih/T0dDp37swHH3xAeno6L7/8MseOHeP55583dGhCjwyWCMXGxpKZmZljaNHZ2ZmYmJhct/Hz82PixIm0a9cOU1NTatSowXPPPceYMWPyPM6UKVOws7PT/bi5uen1cQghREFkahUfrD1GWORlNBqY+oonfVt7GDoskQtTU1MaNWqElZUVISEhrF27FkdHR0OHJfTM4JOlC2LHjh1MnjyZuXPncujQIX788Ud++eUXPvvsszy3+eijj4iPj9f9XLlypQgjFkKIf6Vnahm5+gg/HLyKkQa+eb0Jr7eQL2fFSWJiItevX9fdnjJlCkePHmXQoEEyIbqUMthkaUdHR4yNjblx40a25Tdu3MDFxSXXbT7++GP69u3LoEGDAGjUqBFJSUm8+eabjB07FiOjnHmdubm5nMcVQhhcWoaWESsPE3EiBhMjDTN7e/FCI1dDhyUesnfvXvr06YOLiws7duzAxMQECwsLataUuVulmcFGhMzMzGjWrBlbt27VLdNqtWzdupXWrVvnuk1ycnKOZMfYOKvQmFKq8IIVQoinkJKeyVsrDhJxIgYzYyPm92kmSVAxkpGRoZt2cf78ea5cuSJnD8oQg14+HxwcTEBAAM2bN6dly5bMmDGDpKQkBgwYAEC/fv2oXLkyU6ZMAaB79+58/fXXeHl54e3tzT///MPHH39M9+7ddQmREEIUJ/fTMnlz+QF2novF3MSIBf2a0762k6HDEv8vKiqKPn36sHv3bgB69+7N3Llzsbe3N2xgosgYNBHq1asXt27d4pNPPiEmJoYmTZoQERGhm0B9+fLlbCNA48aNQ6PRMG7cOK5du4aTkxPdu3dn0qRJhnoIQgiRp3upGQxcsp/IqDtYmhqzqH9z2tSQybbFgVKKsLAwhg4dSmJiIjY2NsybNw9/f39DhyaKmEaVsXNKCQkJ2NnZER8fj62traHDEUKUUgkp6fQP3cehy3FYm5uwZEALmnuUN3RY4v+lp6fTokULjh49Stu2bVm+fDnVqlUzdFjiERJmd8Ju+Da9f35LrzEhhNCzuOQ0+oXu49jVeGwtTFg20JsmbvaGDks8xNTUlPDwcH788UdGjx6NiYl8HJZV8soLIYQe3b6Xiv/CSE7HJOJgZcqKQd40qGRn6LDKvPT0dCZMmIClpSXjxo0DoH79+tSvX9/AkQlDk0RICCH05GZCCn4LI/nn5j0crc0JD/KmtrONocMq886ePYu/vz8HDhzA2NiY3r17U6NGDUOHJYqJElVQUQghiqvrcfd5/bs9/HPzHi62Fnw/uJUkQQamlCIkJAQvLy8OHDiAg4MDq1evliRIZCMjQkII8ZSu3Emmd8hert69T2V7S1YGtcK9gpWhwyrTYmNjCQoKYv369QB07NiRpUuXUqVKFcMGJoodSYSEEOIpRMUm4Reyl+j4FKpWsCI8qBWV7S0NHVaZlp6eTqtWrTh//jympqZMmTKFkSNH5tp9QAh5VwghxBM6dyOR17/bQ3R8CjWcyvH94NaSBBUDpqamBAcHU69ePSIjIxk1apQkQSJP8s4QQogncPJ6Ar0W7OVWYip1XWxYPbg1zrYWhg6rzDp+/Dj79+/X3R4yZAgHDx7Ey8vLgFGJkkASISGEKKBjV+PoHbKXO0lpNKxsy8qgVjhaS3NnQ1BKMWvWLJo3b87rr79OQkICABqNBktLGZ0TjydzhIQQogAOXrpL/9B9JKZm0MTNnqWBLbGzNDV0WGVSTEwMAwYMICIiAoB69eqRlpZm4KhESSMjQkIIkU97L9ym76JIElMzaOlRnhWDvCUJMpCNGzfi6elJREQEFhYWzJo1i19++QVHR+nlJgpGRoSEECIf/jx7izeXHyAlXUvbmhUI6dccKzP5F1rU0tPTeeedd5g3bx4Anp6ehIeH06BBAwNHJkoqGRESQojH2HrqBoOWZiVBHeo4sSighSRBBmJiYsK1a9cAGDVqFPv27ZMkSDwV+UsWQohHiDgezfCVh0nPVHSu78wsPy/MTYwNHVaZotVqSUlJwcrKCo1Gw8KFCzl27BidOnUydGiiFJARISGEyMNPR64xLDwrCerm6coc/6aSBBWxK1eu4OPjw5tvvqlb5uTkJEmQ0BsZERJCiFysOXCFD9YeQyno2bQy015tjLGRxtBhlSlr1qzhzTffJC4uDisrK6KioqhWrZqhwxKljIwICSHEf4RFXuL9H7KSoN4t3ZguSVCRSkxMpH///rz++uvExcXRokULjhw5IkmQKBSSCAkhxENC/4pi7LrjAPRv48HklxthJElQkdm7dy9NmjRh6dKlGBkZMXbsWHbt2kWtWrUMHZoopeTUmBBC/L95O87zZcRpAAY/W53RXeui0UgSVFTS0tJ4/fXXuXLlCu7u7qxYsYJnnnnG0GGJUk5GhIQQZZ5Sihm/n9UlQSM61ZIkyADMzMxYtGgRfn5+HD16VJIgUSSeeEQoKSmJY8eOYWxsTMuWLfUZkxBCFBmlFFM3nWHejvMAvO9bh2Edaho4qrJBKcWKFSswNTXljTfeAOD555/n+eefN3Bkoix5ohGhzz//HGdnZ9q1a8e7777L999/T/Xq1QkPD9d3fEIIUWiUUkzceFKXBI17sZ4kQUUkLi4OPz8/+vXrx5tvvsnly5cNHZIoowqcCM2fP59PPvmE5ORklFIAdOrUiStXrrBq1Sq9ByiEEIVBq1WMW3+cxbsuAvDZSw0Y9Ex1wwZVRvzxxx94enqyatUqjI2N+eCDD6hUqZKhwxJlVIEToZkzZ2JkZMSMGTN0yypUqEDlypU5evSoPmMTQohCkalVfLD2GGGRl9FoYOornvRt7WHosEq9tLQ0xowZQ4cOHbhy5Qo1atRg165djBs3DhMTuXZHGEaBE6Hz58/ToEEDRowYkW15+fLluXHjht4CE0KIwpCRqWXk6iP8cPAqRhr45vUmvN7CzdBhlXqpqam0a9eOKVOmoJQiMDCQI0eO4O3tbejQRBlX4ETIzs6O69evk5KSolsWFxfH2bNnsbOz02twQgihT2kZWt4OP8zPR69jYqRhtl9TenhVNnRYZYK5uTnPPvssDg4O/PDDDyxatAhra2tDhyVEwROh9u3bc+fOHV0Wf/78eVq2bMn9+/fp0KGD3gMUQgh9SEnP5K0VB4k4EYOZsRHz+zTjhUauhg6rVIuNjeXKlSu625MmTeLvv//mlVdeMWBUQmRX4ETo888/x8bGhr///huNRkNsbCz//PMPtra2TJgwoRBCFEKIp3M/LZOgZQfYdvom5iZGhAQ0x6e+s6HDKtU2b95Mo0aN6NWrFxkZGUDWqFDlyjICJ4qXAidCderU4cCBAwQEBFCvXj3q1atHQEAAkZGR1K1btzBiFEKIJ3YvNYP+i/ex81wsVmbGLB7Qgva1nQwdVqmVkpLCyJEj8fX1JSYmhri4OGJiYgwdlhB5KvA0/cuXL2NjY8PixYsLIx4hhNCbhJR0+ofu49DlOKzNTVgyoAXNPcobOqxS6/jx4/j5+fH3338DMHToUKZNm4aVlZWBIxMibwUeEfLw8KBnz545lnft2hVnZxlqFkIUD3HJafRZGMmhy3HYWpiwYpC3JEGFRCnFrFmzaN68OX///TdOTk5s2LCBOXPmSBIkir0nKtzwoJDiw27evElsbOxTBySEEE/r9r1U/BdGcjomEQcrU1YM8qZBJbmqtbCkp6ezePFiUlNT6dq1K4sXL5YvxqLEyHciFBgYqPv9/Pnz2W4nJSVx9OhRuRRSCGFwNxNS8FsYyT837+FobU54kDe1nW0MHVappJRCo9FgZmZGeHg4v//+O8OGDZNmtaJEyXcitGTJEt2bOzY2lqVLl+ruezBC1KpVKz2HJ4QQ+Xc97j5+IXu5eDsZF1sLwoO8qe4kX9D0LTk5mVGjRlGxYkU+/fRTAOrWrSsXzIgSKd+J0LPPPotGo+GPP/7AxsYGLy8v3X1WVlbUrVuX9957r1CCFEKIx7lyJ5neIXu5evc+le0tWRnUCvcKMj9F3w4dOoS/vz+nT5/GxMSEwMBAqlatauiwhHhi+U6EduzYAYCRkRH169dn+/bthRWTEEIUSFRsEn4he4mOT6FqBSvCg1pR2d7S0GGVKlqtlunTpzNu3DjS09NxdXVl6dKlkgSJEq/Ak6W1Wm1hxCGEEE/k3I1E/BZGcisxlRpO5QgPaoWzrYWhwypVrly5QkBAgO4L8Msvv0xISAgVKlQwcGRCPL0numrst99+Y9WqVVy/fp3MzEzdco1Gw9atW/UWnBBCPMrJ6wn0WRTJnaQ06rrYsGKQN47W5oYOq1RJTU2lTZs2XL16FSsrK2bOnElgYKBMiBalRoETobCwMPr165dj+YOrB4QQoigcuxpH30X7iL+fTsPKtiwP9MahnJmhwyp1zM3N+fjjjwkJCSEsLIzatWsbOiQh9KrABRW//fZblFLUqFEDpRTW1ta4uLjg4OBA+/btCyNGIYTI5uClu/iHRBJ/P50mbvaEDWolSZAe7d27lz179uhuBwUFsXv3bkmCRKlU4ETo5MmTlC9fXldCvUGDBhw/fhylFAMGDNB7gEII8bC9F27Td1EkiakZtPQoz4pB3thZmho6rFIhIyODiRMn0q5dO9544w3i4uKArGkPpqbyHIvSqcCJUEZGBh4eHpibm2NsbExSUhIODg5UqlRJV09CCCEKw85zt+i/eB/JaZm0q+nIksAWWJs/0VRH8R9RUVG0b9+e8ePHk5mZSdu2bWW6gygTCvwfpHz58ty9exeAihUrcuLECYYMGcLp06extJTLVYUQhWPrqRsMCTtEWoaWDnWcmNenGRamxoYOq8RTSrFixQqGDRtGYmIitra2zJ07F39/f0OHJkSRKPCIUL169bh8+TK3bt2iQ4cOaLVaFixYgFarxdvbuzBiFEKUcRHHo3lrxUHSMrR0ru/M/L6SBOlDamoqfn5+9OvXj8TERNq2bcvRo0clCRJlSoFHhL7++msuXryIUoqvv/6aGzduEBkZiaenJ/Pnzy+MGIUQZdhPR64R/P1RMrWKbp6ufNOrCabGBf4OJ3JhZmZGSkoKxsbGTJgwgdGjR2NiIqcaRdmiUbm1ki/FEhISsLOzIz4+HltbW0OHI4R4hDUHrvDB2mMoBT2bVmbaq40xNpJ5K08jLS2N1NRUbGyyGtHGxsZy4cIFWrZsaeDIhHi0hNmdsBu+Te+f33r7WrVhwwZpuiqE0JuwyEu8/0NWEtS7pRvTJQl6amfPnqVt27YEBQXpmmU7OjpKEiTKtAIlQl9//TUvvfQSI0aM4ObNmwD89NNPNG7cmB49erB///5CCVIIUbaE/hXF2HXHAejfxoPJLzfCSJKgJ6aUIiQkBC8vLw4cOMDmzZu5evWqocMSoljI98ngTz/9lIkTJ+puHzp0iC5dujB+/Hgg6w/NwcFB/xEKIcqUeTvO82XEaQAGP1ud0V3rymXcTyE2NpagoCDWr18PQMeOHVm6dClVqlQxbGBCFBP5ToRWr16NUopy5coBsHv3bvbv349SikqVKjFy5EgGDx5caIEKIUo3pRTfbj3HjN/PATCiUy1G+tSSJOgpbNmyhYCAAKKjozE1NWXy5MkEBwdjZCSTzYV4IN+J0MWLF6lQoQJRUVFotVqqV6/O3bt3eeutt/jmm28wN5dGh0KIJ6OUYuqmM8zbcR6A933rMKxDTQNHVbKlpKQQGBhIdHQ09erVIywsDC8vL0OHJUTB3TgJe2bD9cOFsvt8J0IpKSl4enpibW0NQI0aNThw4ABfffWVJEFCiCemlGLixpMs3nURgHEv1mPQM9UNG1QpYGFhwdKlS1m7di3Tpk3DysrK0CEJkX9KwYUdsHsWnN/677JCUKCCEdevX9fNE7p+/ToA06ZNy7bOJ598oqfQhBClnVar+Pin44RFXgbgs5ca0Le1h2GDKqGUUsyePRsHBwf69OkDZM0H6tixo4EjE6IAMtLgxI+wezbcyOppisYI6nWHhgPgC/2/n/NdR8jIyChf5+ozMzOfOqjCJHWEhCgeMrWKD9ce44eDV9Fo4Muenrzews3QYZVIMTExDBgwgIiICKytrTl16pRMhhYly/04OLgEIr+DxKyBFkytwKsvtBoC5asV2ud3gUaEHpczyaRGIUR+ZGRqCf7+KD8fvY6xkYavXmtMD6/Khg6rRNqwYQOBgYHExsZiYWHBlClTqFxZnktRQsRdhr3z4dBSSLuXtczaGbwHQ7MBYFW+0EPIdyIUFRVVmHEIIcqItAwtI1YeJuJEDCZGGmb29uKFRq6GDqvESU5O5r333mPevHkAeHp6Eh4eToMGDQwcmRD5cO1Q1gToE+tB/f+ZJKd60GY4NHoVTIpu7nG+E6GqVasWZhxCiDIgJT2ToWGH2Hb6JmbGRsz1b4pPfWdDh1Xi3L9/nxYtWnDy5EkARo0axaRJk+TCFVG8abVwblPW/J9Lf/27vFp7aDMCanYCA5xZku56QogicT8tkzeXH2DnuVjMTYxY0K857Ws7GTqsEsnS0pJu3bpx9+5dli5dyvPPP2/okITIW3oKHFuVlQDdzqoThpEJNHwFWr8Nrp4GDU+argohCl1SagaBS/YTGXUHKzNjFgY0p00NR0OHVaJcvXqV9PR0qlWrBmQ1T01MTKRChQoGjkyIPCTdhv0LYd8CSI7NWmZuC836g/dbYFewuWyF9flt8PKic+bMwcPDAwsLC7y9vdm3b98j14+Li2PYsGG4urpibm5O7dq1+fXXX4soWiFEQSWkpNN3USSRUXewNjdhWWBLSYIKaM2aNXh6etK7d2/S09MBMDMzkyRIFE+x/8DGkfBNA9gxOSsJsnMD38kw8gR0/qzASVBhMuipsdWrVxMcHMz8+fPx9vZmxowZ+Pr6cubMGSpWrJhj/bS0NJ5//nkqVqzIDz/8QOXKlbl06RL29vZFH7wQ4rHiktPoF7qPY1fjsbUwYdlAb5q42Rs6rBIjMTGRd955h8WLFwNZ5Unu3LmDs7PMqxLFjFJweW/WBOjTvwD/f7LJtUnWBOj6PcC4eM7GeepTYxkZGZiYPNmD8/b2pkWLFsyePRsArVaLm5sbw4cPZ/To0TnWnz9/PtOmTeP06dOYmpo+0THl1JgQReP2vVT6LNrHqegEHKxMWTHImwaV7AwdVomxd+9e+vTpw/nz59FoNIwZM4bx48c/8f8+IQpFZgac3pA1/+fagX+X1+6SlQBVbau3CdDF6tTYH3/8Qfv27bGwsKB9+/Zs3bqVwMBAdu/ene99pKWlcfDgQXx8fP4NxsgIHx8f9uzZk+s2P//8M61bt2bYsGE4OzvTsGFDJk+eXOyLOApR1txMSKHXgr2cik7A0dqc1YNbSxKUTxkZGXz22We0a9eO8+fP4+7uzo4dO/j8888lCRLFR+q9rPo/s5rCmv5ZSZCxOTQNgGH7wG81eLQzyFVgBVXgoZwdO3bQuXNnMjIygKwii+7u7ixZsgSANm3a5Gs/sbGxZGZm5hjidXZ25vTp07luc+HCBbZt24a/vz+//vor//zzD0OHDiU9PZ3x48fnuk1qaiqpqam62wkJCfmKTwjxZK7H3ccvZC8XbyfjYmtBeJA31Z2sDR1WiaHVavnpp5/IzMykd+/ezJ07V07/i+IjMSar+vOBRZASn7XMsjy0GAQtg8A657SW4q7AidAnn3xCZmYmL7/8MuvWrQOgVq1aODs7s2vXLr0H+DCtVkvFihVZsGABxsbGNGvWjGvXrjFt2rQ8E6EpU6bw6aefFmpcQogsV+4k0ztkL1fv3qeyvSUrg1rhXkGafT6OUgqlFEZGRpiZmREWFsb+/ft1PcOEMLgHHeCPfQ/arAn7lK8OrYdBYz8wK7l/5wVOhA4cOEC1atVYu3YtRkb/nllzdXXl7Nmz+d6Po6MjxsbG3LhxI9vyGzdu4OLikus2rq6umJqaYmxsrFtWr149YmJiSEtLw8zMLMc2H330EcHBwbrbCQkJuLlJPyMh9C0qNgm/kL1Ex6dQtYIV4UGtqGxvaeiwir24uDiGDBlCjRo1+PzzzwGoU6cOderUMXBkoszLrQM8gFurrPk/dbqCkXGem5cUBU6ETExMcvQc02q1XLt2LVuC8jhmZmY0a9aMrVu30qNHD91+tm7dyttvv53rNm3btiU8PBytVqtLws6ePYurq2uuSRCAubm5VFsVopCdu5GI38JIbiWmUsOpHOFBrXC2tTB0WMXen3/+Sd++fbl8+TJmZmYMGTJE+oQJw9N1gJ8FN45nLXvQAb71cHBrYdj49KzAk6W9vLy4ePEiQUFBANy6dYvevXtz69YtmjVrVqB9BQcHExISwtKlSzl16hRDhgwhKSmJAQMGANCvXz8++ugj3fpDhgzhzp07vPPOO5w9e5ZffvmFyZMnM2zYsII+DCGEnpy8nkCvBXu5lZhKXRcbVg9uLUnQY6SlpTFmzBiee+45Ll++TI0aNfjzzz8lCRKGdT8O/poB3zaGdYOzkiBTK2g5GIYfgteXlbokCJ5gRGj06NF069aN0NBQNBoNFy5c4MKFC2g0Gt5///0C7atXr17cunWLTz75hJiYGJo0aUJERIRuAvXly5eznX5zc3Nj06ZNjBw5Ek9PTypXrsw777zDhx9+WNCHIYTQg2NX4+i7aB/x99NpWNmW5YHeOJTLfXRWZDl79iz+/v4cOJB1qXFgYCAzZszAxsbGwJGJMqsYdIA3pCeqI7R69WpGjx7NpUuXgKyGrJMnT6Z37956D1DfpI6QEPpx8NJd+ofuIzE1Ay93e5YMaImdpVze/Sj379/Hw8ODmzdv4uDgwIIFC3j11VcNHZYoq4pRB/j8KKzP7wInQkopNP9fFyA2Nqt3iKNjySmXL4mQEE9v74XbBC7ZT3JaJi09yhM6oAXW5sWzamxxs2jRIsLDw1m6dClVqlQxdDiirCmmHeDzo9gkQm5ubvTr14+AgABq166tt0CKiiRCQjydneduEbTsACnpWtrVdGRBv2ZYmUkSlJctW7ZgaWlJu3btgOyXygtRZIp5B/j8KDaJkJGRkW5EqGXLlvTv35833ngDO7uSUTVWEiEhntzWUzcYEnaItAwtHeo4Ma9PMyxMS/7ls4UhJSWFMWPG8M033+Dm5sbRo0dxcHAwdFiirNFzB3hDKjYtNsaOHUvNmjVRShEZGcnQoUNxdXWlV69e/Pbbb3oLTAhRvEQcj+atFQdJy9DSub4z8/tKEpSXEydO4O3tzTfffANA9+7dpYyHKFq6DvD1S0QHeEN64qarR44cYdWqVaxZs4aoqCgga7ToQeuN4kpGhIQouJ+OXCP4+6NkahXdPF35plcTTI3l1M5/KaWYPXs277//PqmpqTg5OREaGkq3bt0MHZooC0pwB/j8KKzP7yd+Rpo0aUKlSpWoXLkyU6ZMISYmJkehRSFEybfmwBU+WHsMpaBn08pMe7UxxkbFczKlISUnJ/PKK68QEREBQNeuXVm8eHGOfopC6F0RdoAvjQqcCN29e5cffviB1atX88cff6DVanVXkj333HOFEKIQwlDCIi8xdl1WZdneLd2Y1KMRRpIE5crS0hJra2vMzc2ZPn06w4YN082nFKJQpN6Dwytg71yIyypng7E5NH4jqweYk7RpyY8CnxozNzcnIyNDN/pTs2ZN+vXrR9++falatWqhBKlPcmpMiPwJ/SuKiRtPAtC/jQfju9eXD/b/SE5OJj09XXexyJ07d4iOjqZBgwYGjkyUaqWwA3x+FJtTYw/+6F9//XUCAgJo06aN3oIRQhQP83ac58uI0wAMfrY6o7vWlSToPw4fPoyfnx+NGjVi9erVaDQaypcvT/nypbsKrzCgUtwB3pAKnAitXLmSHj16yBUQQpRCSim+3XqOGb9n1RkZ0akWI31qSRL0EK1Wy1dffcXYsWNJT08nPj6emJgYXF1dDR2aKI3KSAd4Q8pXInT58mXMzc1xdnamdevW3LhxI8913d3d9RacEKLoKKWYuukM83acB+B93zoM61DTwFEVL1evXiUgIIBt27YB8PLLL7NgwYISVV1flBBlrAO8IeUrEfLw8KB169bs2rULDw+PPL8dajSaYn/5vBAiJ6UUEzeeZPGuiwCMe7Eeg56pbtigipkffviBN998k7t372JlZcW3337LwIEDZbRM6Nf9ODi4JGsOUOL1rGWmVuDVF1oNgfLVDBldqZTvU2MPz6mWy+SFKD20WsXHPx0nLPIyAJ/1aEjfVsX/woeilJyczMiRI7l79y7NmzcnLCysRLYYEsVYGe8Ab0j5SoS2b9+um6G9ffv2Qg1ICFF0MrWKD9ce44eDV9Fo4Muenrzews3QYRU7VlZWLFu2jN9//50JEyZgampq6JBEaVHCOsCXRgW+fP7PP//E1taWJk2aZFuemppKZmYmVlbFe9a6XD4vRJaMTC3B3x/l56PXMTbS8NVrjenhJSX3ATIyMpgyZQpubm7079/f0OGI0qYEd4A3pGJz+fxzzz2nmy/03+X79++XOUJClABpGVpGrDxMxIkYTIw0zOztxQuN5KongKioKPr27cuuXbsoV64cvr6+ckWY0I9S0AG+NHqiFhu5DSIlJSXJ3CEhSoCU9EyGhh1i2+mbmBkbMde/KT71pQ2EUoqwsDCGDh1KYmIitra2zJ07V5Ig8fRKUQf40ijfiVDHjh11v588eTLb7aSkJI4fP469vb1egxNC6Nf9tEzeXH6AnediMTcxYkG/5rSv7WTosAwuLi6OoUOHsnLlSgDatm3LihUr8PDwMGxgomSL/Qf2zoEj4ZCRkrXMzi3r6i+vvmAh0zOKg3wnQjt27ECj0aDRaEhISGDHjh051vHx8dFnbEIIPUpKzSBwyX4io+5gZWbMwoDmtKkh9W+Sk5Np2rQpUVFRGBsbM2HCBEaPHo2JScnt0i0MqJR3gC+N8v1qBAQEALB06VKcnJx44YUXdPdZWVlRt25dAgMD9R+hEOKpJaSk0z90H4cux2FtbsKSAS1o7iGX40LW/69evXqxZs0awsLC8Pb2NnRIoiSSDvAlVoGvGvPw8KBZs2asXbu2sGIqVHLVmChr4pLT6Be6j2NX47G1MGHZQG+auNkbOiyDOnv2LEZGRtSsmVU5Oy0tjdTUVGxsbAwcmShxpAN8kSk2V41dvHhRbwcXQhSu2/dS6bNoH6eiE3CwMmXFIG8aVLIzdFgGo5Ri4cKFvPvuu9SvX5/du3djamqKmZkZZmZmhg5PlCRltAN8aZSvRKh69eo0bdqUH374gerV8y67r9FoOH/+vN6CE0I8uZsJKfgvjOTczXs4WpsTHuRNbeeyO+IRGxtLUFAQ69evB8DW1paEhAQqVKhg2MBEySId4EudfCVCFy9exMXFRfd7XqTnjhDFw/W4+/gvjCQqNgkXWwvCg7yp7mRt6LAMZvPmzfTv35/o6GhMTU2ZMmUKI0eOxMjIyNChiZJAOsCXavlKhMaPH0+VKlV0vwshiq8rd5LpHbKXq3fvU9nekpVBrXCvUDa/paampvLRRx/xzTffAFCvXj3Cw8NzVMYXIlfSAb5MKPBk6ZJOJkuL0iwqNgm/kL1Ex6dQtYIV4UGtqGxvaeiwDCY9PZ22bduyf/9+hg0bxtSpU4t9GyBRDOg6wM+HxOisZdIB3uCKzWTpqKgorly5Qv369XF0dOSrr77izz//pHHjxnz88cfSjFAIAzl3IxG/hZHcSkylhlM5woNa4WxrYeiwipxSiszMTExMTDA1NSUsLIwzZ87QrVs3Q4cmijvpAF8mFTgRCg4O5ueff+b48eNs2rSJ999/H4CNGzeSlpbGF198ofcghRCPdvJ6An0WRXInKY26LjasGOSNo3XZ61odExPDgAEDaNy4se5/Ua1atahVq5aBIxPFmnSAL9MKPFPwyJEjODk5Ua9ePX755RdMTU0ZPHgwGo2mxNYWEqIkO3Y1jt4he7mTlEbDyrasDGpVJpOgDRs20KhRIyIiIpg1axY3btwwdEiiONNq4cxvsPhFCOkAx9dmJUHV2oP/Whi6B7z8JQkqAwo8IhQTE0P9+vUBOH78OM2aNWPevHns2rVLLp0XoogdvHSX/qH7SEzNwMvdniUDWmJnWbZOTycnJzNq1Cjmz58PgKenJ+Hh4Tg7SyNZkQvpAC/+o8CJULly5YiOjiY6Opp//vkHPz8/ALRaLebmkjkLUVT2XrhN4JL9JKdl0tKjPKEDWmBtXrZ6GB06dAg/Pz/OnDkDwKhRo5g0aZL8LxI5SQd4kYcC/9ds3LgxO3bs0F1O37ZtW7RaLVeuXHlksUUhhP7sPHeLoGUHSEnX0q6mIwv6NcPKrGwlQffu3eP555/nzp07VKpUiaVLl0rjZ5GTdIAXj1Hg/5yTJ0/mxRdf5M6dO7Ru3Ro/Pz927NhBYmIibdq0KYwYhRAP2XrqBkPCDpGWoaVDHSfm9WmGhWnZK+ZmbW3NV199xc8//0xISIhUiBb/etABfvcsOPMr0gFePMoT1RFSSnH37l3Kl//3UsLMzEyMjYv/P2OpIyRKsojj0QxfeZj0TEXn+s7M8vPC3KT4/93py5o1a3BycuK5554Dsv4XgVS1F/9POsCXasWmjhBk/dP5+++/OXAg643WvHlz2rdvr7eghBA5/XTkGsHfHyVTq+jm6co3vZpgalw2WkQkJiYyYsQIlixZQuXKlTl27Bjly5eXBEhkkQ7w4ikUOBFKSUmhR48ebNmyJdvyzp07s379epmkKEQhWHPgCh+sPYZS0LNpZaa92hhjo7KRBOzduxd/f38uXLiARqOhf//+2NiU3eax4iHSAV7oQYEToYkTJ7J58+Ycyzdv3sxnn33G559/rpfAhBBZwiIvMXZdVp+j3i3dmNSjEUZlIAnKyMhg8uTJTJw4kczMTNzd3VmxYgXPPPOMoUMThiYd4IUeFXhcffXq1RgZGfHNN99w48YNbty4wddffw3AqlWr9B6gEGVZ6F9RuiSofxsPJr9cNpKge/fu0b59e8aPH09mZiZ+fn4cPXpUkqCyTCk4vx2W94R5reFIWFYS5NYKeoXB2weyRoIkCRIFVODJ0ubm5tSqVYvjx49nW96gQQP++ecfUlNT9RqgvslkaVFSzNtxni8jTgMw+NnqjO5at8zMiVFK0bt3b3777Tfmzp2Lv7+/oUMShiId4MX/KzaTpW1sbLhy5QrXr1+nUqVKAFy/fp0rV65IYiGEHiil+HbrOWb8nlX1dkSnWoz0qVXqk6C4uDi0Wq1uEvS8efOIi4ujWjXp9F0mSQd4UUQKnAg9++yzrF+/nnr16umGqXfu3ElSUhKdO3fWe4BClCVKKaZuOsO8HVntat73rcOwDjUNHFXh++OPP+jbty/Nmzdn7dq1aDQaHBwccHBwMHRooqhJB3hRxAp8auzEiRO0bt2ae/fu6b6hKqWwsbFhz549uj5kxZWcGhPFlVKKiRtPsnjXRQDGvViPQc+U7mrtaWlpTJgwgS+++AKlFDVq1GD37t1UrChX+5Q50gFePEaxOTXWoEEDIiMj+eKLL3R1hFq0aMHo0aOpW7eu3gIToizRahUf/3ScsMjLAHzWoyF9W1U1cFSF68yZM/j7+3Pw4EEAAgMDmTFjhlwaX5ZotXBuU1YBxEt//bu8WntoMwJqdpICiKLQPVFl6ZJMRoREcZOpVXy49hg/HLyKRgNf9vTk9RZuhg6r0CilWLhwIe+++y7Jyck4ODgQEhLCK6+8YujQRFGRDvDiCRSLEaEZM2awbNkyAAICAnjnnXf0FogQZVFGppbg74/y89HrGBtp+Oq1xvTwKt1dsJOSkvj8889JTk6mY8eOLF26VNfEWZRy0gFeFEP5ToTCwsIIDg5Go9GglOLo0aM4OTnh5+dXmPEJUWqlZWgZsfIwESdiMDHSMLO3Fy80cjV0WIXO2tqaFStWEBkZSXBwMEZGZaNNSJkmHeBFMZbvU2Nt2rRh7969WFllFatKTk6mdevW7Nq1q1AD1Dc5NSaKg5T0TIaGHWLb6ZuYGRsx178pPvWdDR1WoUhJSWHMmDHUq1ePoKAgQ4cjiop0gBd6ZvBTY+fOncPOzo5//vkHrVZLrVq1OHv2rN4CEaKsuJ+WyZvLD7DzXCzmJkYs6Nec9rWdDB1WoTh+/Dh+fn78/ffflCtXjh49euDkVDofq/h/0gFelDD5ToRu375NixYtqFChAgB16tTRXTUmhMifpNQMApfsJzLqDlZmxiwMaE6bGo6GDkvvlFLMnj2b999/n9TUVJycnAgNDZUkqDSTDvCihCrQuGRGRgZXrlxBKUV6elajuwe3H3B3d9dvhEKUEgkp6fQP3cehy3FYm5uwZEALmnuUvuJwMTExDBgwgIiICAC6du3K4sWLcXYunaf+yjzpAC9KuHzPETIyMnpsiX+NRkNGRoZeAissMkdIGEJcchr9Qvdx7Go8thYmLBvoTRM3e0OHpXeJiYnUrl2bmJgYLCwsmDZtGsOGDSv17UHKJOkAL4qYwecIAZSxkkNC6MXte6n0WbSPU9EJOFiZsmKQNw0q2Rk6rEJhY2PDoEGD+PnnnwkPD6dBgwaGDknok1JwYUfWBOjzW/9d7tYqa/5Pna5gZGyw8IR4EvlOhMaPH1+YcQhRKt1MSMF/YSTnbt7D0dqc8CBvajuXrsrJhw8fxsrKijp1suaAfPLJJ4wbNw5zc2mJUGpIB3hRikllaSEKyfW4+/gvjCQqNgkXWwvCg7yp7mRt6LD0RqvV8tVXXzF27FgaNWrEnj17MDMzM3RYQp+kA7woRorFqTEhRP5cuZNM75C9XL17n8r2lqwMaoV7hdIzZ+Lq1asEBASwbds2AKpWrcr9+/clESotpAO8KEMkERJCz6Jik/AL2Ut0fAoeFawIC2pFZXtLQ4elN2vWrGHw4MHcvXsXKysrvv32WwYOHCgToksD6QAvyiBJhITQo3M3EvFbGMmtxFRqOJUjPKgVzrYWhg5LL5KTk3n77bdZvHgxAM2bNycsLIzatWsbODLxVKQDvCjjJBESQk9OXk+g76JIbielUdfFhhWDvHG0Lj3foM3MzDh16hQajYYxY8Ywfvx4TE1NDR2WeFLSAV4IQBIhIfTi2NU4+i7aR/z9dBpWtmV5oDcO5Ur+fJmMjAy0Wi1mZmaYmJiwYsUKrl27xrPPPmvo0MSTkg7wQmTzRIlQamoq4eHh7N27FxcXFwYOHMjFixdp2LAh5cvLJDpRthy8dJf+oftITM3Ay92eJQNaYmdZ8kdKoqKi6NOnD23btmXq1KkA1KhRgxo1ahg4MvFEpAO8ELkyKugGt2/fpnnz5gwaNIiFCxeyZcsWTp06RYcOHZg5c+YTBTFnzhw8PDywsLDA29ubffv25Wu7VatWodFo6NGjxxMdV4intffCbfouiiQxNYOWHuVZPtC7xCdBSimWL19O48aN2b17NyEhIcTGxho6LPEklIJLe2ClH8xuDgdCs5Ig1ybwyiIYcSSrErQkQaIMK3Ai9MEHH3DixAksLCx0laZ9fHywsrLit99+K3AAq1evJjg4mPHjx3Po0CEaN26Mr68vN2/efOR2Fy9e5L333uOZZ54p8DGF0Ied527Rf/E+ktMyaVfTkSWBLbA2L9lnm+Pi4vDz86Nfv34kJibStm1bDh8+jKNj6WsMW6plZsCJdbCwEyzuAmd+AVRWB/j+v8CbO7KuAjMu2e9XIfShwInQxo0bsbOz4/z587plxsbGVK1alQsXLhQ4gK+//pqgoCAGDBhA/fr1mT9/PlZWVoSGhua5TWZmJv7+/nz66adUr169wMcU4mltPXWDgUsPkJKupUMdJxYGNMfKrGR/qPzxxx94enqyatUqjI2N+eyzz9ixYwceHh6GDk3kV+q9rPo/s5rCmv5w7WBWB/imATBsH/itBo92chWYEA8p8H/uuLg46tevj4uLS7blmZmZJCYmFmhfaWlpHDx4kI8++ki3zMjICB8fH/bs2ZPndhMnTqRixYoMHDiQnTt3PvIYqamppKam6m4nJCQUKEYh/ivieDTDVx4mPVPRub4zs/y8MDcp2f2V4uPjeemll4iPj6dGjRqEhYXh7e1t6LBEfuXVAb5lUFYXeOkAL0SeCpwIVa1alRMnTvDXX//Wm9iwYQNnzpwpcD2R2NhYMjMzcXZ2zrbc2dmZ06dP57rNX3/9xaJFizhy5Ei+jjFlyhQ+/fTTAsUlRF5+OnKN4O+PkqlVdPN05ZteTTA1LvDAarFjZ2fHzJkz+eOPP5gxYwY2NqWrH1qpJR3ghXhqBf4P3rt3bzIyMmjfvj0ajYbIyEh69OiBRqOhd+/ehRGjTmJiIn379iUkJCTfcxY++ugj4uPjdT9Xrlwp1BhF6bXmwBXeXX2ETK2iZ9PKfPuGV4lNgpRShISE8Pvvv+uW9evXj0WLFkkSVNwpBee3w/KeMK81HAnLSoLcWkGvMHj7QNYokCRBQuRLgUeExo4dy4EDB3JMjPb19c12iis/HB0dMTY25saNG9mW37hxI8epN4Dz589z8eJFunfvrlum1WoBMDEx4cyZMzku7TU3N5cu2OKphUVeYuy6rK7bvVu6MalHI4yMSuY8i9jYWIKCgli/fj2urq6cOHECBwcHQ4clHkc6wAtRKAqcCJmZmfHLL7/w559/6i5zb9GiBe3bty/wwc3MzGjWrBlbt27VXQKv1WrZunUrb7/9do7169aty99//51t2bhx40hMTOTbb7/Fzc2twDEI8Tihf0UxceNJAPq38WB89/oltq/W5s2b6d+/P9HR0ZiamhIcHIydnZ2hwxKPIh3ghShUT3yZy7PPPquX6rLBwcEEBATQvHlzWrZsyYwZM0hKSmLAgAFA1nB95cqVmTJlChYWFjRs2DDb9vb29gA5lguhD/N2nOfLiKz5aoPbV2d0l7olMglKSUnho48+YsaMGQDUq1ePsLAwvLy8DBuYyFvcZdg7Dw4tkw7wQhSiAidCHTt2zPM+jUbD1q1bC7S/Xr16cevWLT755BNiYmJo0qQJERERugnUly9fxsioZM7DECWXUopvt55jxu9ZPZhGdKrFSJ9aJTIJio+P55lnntGNpg4dOpRp06ZhZSVzSIol6QAvRJHSqAdVEfPJyMgIjUbDw5s9uK3RaMjMzNR7kPqUkJCAnZ0d8fHx2NpKNVWRk1KKqZvOMG9HVq2s933rMKxDTQNH9eSUUvj7+/P7778TGhpKt27dDB2S+K+8OsBXfy5r/o90gBei0D6/Czwi1K9fv2zfiuPj49mxYweJiYm88cYbegtMCENQSvHZxlOE7ooCYNyL9Rj0TMkr2hkTE4OpqSkVKlRAo9Ewd+5cUlNTc5SqEAYmHeCFMLgCJ0JLlizJsSw2NhZPT0+qVKmij5iEMAitVvHxT8cJi7wMwGc9GtK3VVUDR1VwGzZsIDAwkLZt27Ju3To0Go1uLp0oJqQDvBDFhl56Ajg6OlKzZk2WLFnClClT9LFLIYpUplYxeu0x1hy8ikYDX/b05PUWJesqxOTkZN577z3mzZsHZHWPv3v3LuXLy6TaYkM6wAtR7BQ4EZo4cWK225mZmZw9e5a//vpLapGIEikjU8uoNUf56ch1jI00fPVaY3p4laxv5IcOHcLf319XkT04OJjJkydLDa3iQCm4vDer/s+ZX4H/n1/p2iRrAnT9HtL8VAgDKvBf34QJE/K8ckYmYYqSJi1Dy4iVh4k4EYOJkYaZvb14oZGrocPKN61Wy/Tp0xk3bhzp6em4urqydOlSnn/+eUOHJjIz4PSGrATo2sF/l9fukpUAVW0rE6CFKAYKnAi5u7tnS4Q0Gg0VK1akU6dOBa4sLYQhpaRnMjTsENtO38TM2Ii5/k3xqV+yJhPfu3ePuXPnkp6ezssvv0xISAgVKlQwdFhlW+o9OLwC9s6FuEtZy4zNofEbWT3AnOoYNj4hRDYFToQuXrxYCGEIUbTup2Xy5vID7DwXi7mJEQv6Nad9bSdDh5VvD8pV2NraEhYWxqlTpxg4cGCJrHNUakgHeCFKpALVEUpPT6du3brY2dlx8ODBEvlPV+oIiaTUDAKX7Ccy6g5WZsYsDGhOmxr5a+JraImJiYwYMYJWrVoxePBgQ4cjQDrAC1FEikUdIVNTUxITE7G2ti6RSZAQCSnp9A/dx6HLcVibm7BkQAuae5SMq6r27t2Lv78/Fy5c4IcffuC1116TK8IMRSm4sCNr/s/5h6rpu7XKmv9TpysYGRssPCFE/hW4d0X//v05c+YMx48fL4x4hCg0cclp9FkYyaHLcdhamLBikHeJSIIyMjKYOHEi7dq148KFC7i7u/PLL79IEmQIGWlwdBXMbwfLe2QlQRojqP8SDPwdBm6Cet0kCRKiBCnwHKGYmBggq+N8hw4dcHZ21o0OaTQaFi1apN8IhdCD2/dS6bNoH6eiEyhfzozlA1vSoFLx77oeFRVFnz592L17NwC9e/dm7ty5UiCxqEkHeCFKrafuNfYgCZJeY6K4upmQgv/CSM7dvIejtTnhQd7UdrYxdFiPFRcXR/Xq1bl79y42NjbMmzcPf39/Q4dVtkgHeCGKDYPPEZo4cSJVqlShffv2eju4EIXtetx9/BdGEhWbhIutBeFB3lR3sjZ0WPlib2/PiBEj+P3331m+fDnVqsmoQ5GRDvBClBn5HhEyMjKiVatWuiH6kkpGhMqOK3eS6R2yl6t371PZ3pKVQa1wr1C8r+D5888/cXJyol69ekDW/CAAExOpPFzopAO8EMWawUeEhChJomKT8AvZS3R8Ch4VrAgLakVle0tDh5Wn9PR0JkyYwJQpU2jcuDF79+7F3NxcEqCiIB3ghSjTCvRfNjU1lcuXLz9yHXd396cKSIinde5GIn4LI7mVmEoNp3KEB7XC2dbC0GHl6ezZs/j7+3PgwAEAvLy8yMjIkD5hhU06wAshKGAidOTIkUfOU9BoNLqhfCEM4eT1BPouiuR2Uhp1XWxYMcgbR+vimVAopVi4cCHvvvsuycnJODg4sGDBAl599VVDh1a6SQd4IcRDCjzuXsCLzIQoMseuxtF30T7i76fTsLItywO9cShnZuiwcpWYmEi/fv1Yv349AB07dmTp0qVUqVLFsIGVVtIBXgiRhwL95VeuXJmBAwcWVixCPLGDl+7SP3QfiakZeLnbs2RAS+wsTQ0dVp4sLS25efMmpqamTJ48meDgYIyMClzfVDyOdIAXQjxGgRKhKlWqMH78+MKKRYgnsvfCbQKX7Cc5LZOWHuUJHdACa/Pi9+0+NTUVQDcJesWKFcTFxeHl5WXgyEoh6QAvhMin4vdpIUQB7Dx3i6BlB0hJ19KupiML+jXDyqz4va1PnDiBn58fPj4+fPXVVwBSF6gwSAd4IUQB5fsTw93dHVdX18KMRYgC2XrqBkPCDpGWoaVDHSfm9WmGhWnx6vGklGL27Nl88MEHpKSkEBMTw7hx43BwcDB0aKWLdIAXQjyhfCdCFy9eLMQwhCiYiOPRDF95mPRMRef6zszy88LcpHglQTExMQQGBvLbb78B0KVLFxYvXixJkL5IB3ghhB4Uv3MIQjzGT0euEfz9UTK1iu6NK/H1640xNS5eE403btxIYGAgt27dwtzcnOnTpzNs2DBdbz7xFDLS4MSPWQnQjeNZyzRGUK97VgVotxaGjU8IUaJIIiRKlDUHrvDB2mMoBa80rcLUVz0xNipeycXdu3fp06cP8fHxeHp6Eh4eToMGDQwdVsknHeCFEIVAEiFRYoRFXmLsuqwRgN4t3ZnUoyFGxSwJAnBwcGDu3LkcPHiQyZMnS4Xop3X3UlbyIx3ghRCFIN9NV0sLabpaMoX+FcXEjScB6N/Gg/Hd6xeb00xarZavvvoKT09PfH19DR1O6XHtUNbpr5M/SQd4IYQ0XRVl17wd5/ky4jQAg9tXZ3SXusUmCbp69SoBAQFs27YNFxcXTp06hb29vaHDKrmkA7wQoohJIiSKLaUU3249x4zfszqCj+hUi5E+tYpNErRmzRoGDx7M3bt3KVeuHJMmTcLOzs7QYZVM0gFeCGEgkgiJYkkpxdRNZ5i34zwA7/vWYViHmgaOKktiYiIjRoxgyZIlALRo0YKwsDBq1apl2MBKIukAL4QwMEmERLGjlOKzjacI3RUFwLgX6zHomeoGjirLnTt3aNGiBRcuXECj0TBmzBjGjx+PqWnx7WtWLEkHeCFEMSGJkChWtFrFxz8dJyzyMgCf9WhI31ZVDRzVv8qXL0+bNm3IyMhg+fLlPPvss4YOqeSQDvBCiGJI/uuIYiNTqxi99hhrDl5Fo4Eve3ryegs3Q4dFVFQU5cqVo2LFrD5Vc+bMQavVyqTo/JIO8EKIYkwSIVEsZGRqGbXmKD8duY6xkYavXmtMDy/Dzg9RSrFixQqGDRtG+/bt+fnnn9FoNFJ2Ib+kA7wQogSQREgYXFqGlndWHea34zGYGGmY2duLFxoZtsFvXFwcQ4YMYdWqVbrbD2pYiMeQDvBCiBJEEiFhUCnpmQwLO8TW0zcxMzZirn9TfOo7GzSmP//8k759+3L58mWMjY359NNPGT16NMbG0sDzkaQDvBCiBJJESBjM/bRM3lx+gJ3nYjE3MWJBv+a0r+1ksHjS09OZMGECU6ZMQSlFjRo1CAsLw9vb22AxFXvSAV4IUcJJIiQMIik1g8Al+4mMuoOVmTELA5rTpoajQWO6f/8+K1euRCnFwIEDmTFjBtbW1gaNqdiSDvBCiFJCEiFR5BJS0ukfuo9Dl+OwNjdhyYAWNPcwTOPMB632HkyCDg8P59q1a7zyyisGiafYkw7wQohSRhIhUaTiktPoF7qPY1fjsbUwYflAbxq72RskltjYWAYNGkTnzp0ZOnQoAK1atTJILMWedIAXQpRSkgiJInP7Xip9Fu3jVHQC5cuZsXxgSxpUMsxVWJs3byYgIICYmBh27NiBv7+/XBGWG+kAL4Qo5SQREkXiZkIK/gsjOXfzHo7W5oQHeVPb2abI40hJSeGjjz5ixowZANSrV4/w8HBJgh4mHeCFEGWIJEKi0F2Pu4//wkiiYpNwsbUgPMib6k5FPwn5+PHj+Pn58ffffwMwdOhQpk2bhpWVXNYNSAd4IUSZJImQKFRX7iTTO2QvV+/ep7K9JSuDWuFeoegTj9u3b9O6dWvu3buHk5MToaGhdOvWrcjjKJakA7wQogyTREgUmqjYJPxC9hIdn4JHBSvCglpR2d7SILFUqFCBDz74gD179rB48WKcnQ1btLFYkA7wQgiBRj24friMeNAmIT4+XnpGFaJzNxLxWxjJrcRUajiVIzyoFc62FkUaw4YNG6hWrRoNGzYEIDMzEyMjIzRleX6LdIAXQpRQhfX5Lf/xhN6dvJ5A30WR3E5Ko66LDSsGeeNoXXRXFyUnJzNq1Cjmz5+Pp6cnkZGRWFhYlO0WGdIBXgghciWJkNCrY1fj6LtoH/H302lY2Zblgd44lDMrsuMfOnQIPz8/zpw5A4CPj0/ZHgHSdYCfA3GXs5ZJB3ghhNCRREjozcFLd+kfuo/E1Ay83O1ZMqAldpamRXJsrVbL9OnTGTduHOnp6bi6urJs2TJ8fHyK5PjFjnSAF0KIfJFESOjF3gu3CVyyn+S0TFp6lCd0QAuszYvm7XX37l1eeeUVtm/fDsDLL79MSEgIFSpUKJLjFyvSAV4IIQpEEiHx1Haeu0XQsgOkpGtpV9ORBf2aYWVWdG8tW1tb0tPTsbKyYubMmQQGBpat02HSAV4IIZ6YJELiqWw7fYO3VhwiLUNLhzpOzOvTDAvTwv/QTUxMxNTUVDcJOiwsjNTUVGrVqlXoxy42pAO8EEI8NUmExBOLOB7D8JWHSM9U+DZwZlbvppiZGBX6cffu3Yu/vz/du3fXtcpwd3cv9OMWG9IBXggh9EbqCIkn8tORawR/f5RMraJ740p8/XpjTI0LNwnKyMhg8uTJTJw4kczMTKpWrcqxY8fKzusoHeCFEGWY1BESxcaaA1f4YO0xlIJXmlZh6queGBsV7pycqKgo+vTpw+7duwHw8/Njzpw5ZSMJkg7wQghRaCQREgUSFnmJseuy5qP0bunOpB4NMSrEJEgpxYoVKxg2bBiJiYnY2toyd+5c/P39C+2YxYJ0gBdCiCIhiZDIt9C/opi48SQA/dt4ML57/UK/Ouv27dsMHz6cxMRE2rZty4oVK/Dw8CjUYxqUdIAXQogiJYmQyJd5O87zZcRpAAa3r87oLnWL5BJ1R0dHvvvuO86dO8fo0aMxMSmlb1npAC+EEAZRSj9VhL4opfh26zlm/J41OjGiUy1G+tQqtCQoLS2NCRMm0K5dO1544QUAevXqVSjHKhakA7wQQhhU4V/rnA9z5szBw8MDCwsLvL292bdvX57rhoSE8Mwzz+Dg4ICDgwM+Pj6PXF88OaUUUzed0SVB7/vWIfj52oWWBJ05c4Y2bdowZcoUBgwYQGJiYqEcx+CUgkt7YKUfzG4OB0KzkiDXJvDKIhhxJKsStCRBQghR6AyeCK1evZrg4GDGjx/PoUOHaNy4Mb6+vty8eTPX9Xfs2EHv3r3Zvn07e/bswc3Njc6dO3Pt2rUijrx0U0rx2cZTzNtxHoBxL9ZjWIeahXaskJAQmjZtysGDB3FwcGDu3LnY2NgUyvEMJjMDTqyDhZ1gcRc48wugsjrA9/8F3tyRdRWYsQzUCiFEUTF4HSFvb29atGjB7NmzgazmmW5ubgwfPpzRo0c/dvvMzEwcHByYPXs2/fr1e+z6Ukfo8bRaxcc/HScsMqtb+Wc9GtK3VdVCOVZsbCxBQUGsX78egI4dO7J06VKqVKlSKMczCOkAL4QQT61U1hFKS0vj4MGDfPTRR7plRkZG+Pj4sGfPnnztIzk5mfT0dMqXz72YXGpqKqmpqbrbCQkJTxd0KZepVYxee4w1B6+i0cCXPT15vYVboRzr1q1bNG7cmOjoaExNTZkyZQojR47EyMjgA5X6IR3ghRCi2DNoIhQbG0tmZibOzs7Zljs7O3P69Ol87ePDDz+kUqVK+Pj45Hr/lClT+PTTT5861rIgI1PLqDVH+enIdYyNNHz1WmN6eBXe1UpOTk507tyZffv2ERYWhpeXV6Edq0hJB3ghhCgxSvRkhC+++IJVq1axY8cOLCwscl3no48+Ijg4WHc7ISEBN7fCGeEoydIytLyz6jC/HY/BxEjDzN5evNDIVe/HOXHiBI6Ojrrkd/bs2RgZGWFlVcKTA+kAL4QQJZJBEyFHR0eMjY25ceNGtuU3btzAxcXlkdtOnz6dL774gt9//x1Pz7yLzJmbm2NuLi0IHiUlPZNhYYfYevomZsZGzPVvik9958dvWABKKWbPns37779Pp06d2LhxIxqNBmtra70ep8hJB3ghhCjRDJoImZmZ0axZM7Zu3UqPHj2ArMnSW7du5e23385zu6lTpzJp0iQ2bdpE8+bNiyja0ul+WiZvLj/AznOxmJsYsaBfc9rXdtLrMWJiYhgwYAARERG6ZUlJSSU7CZIO8EIIUSoY/NRYcHAwAQEBNG/enJYtWzJjxgySkpIYMGAAAP369aNy5cpMmTIFgC+//JJPPvmE8PBwPDw8iImJAcDa2rpkf7AaQFJqBgOX7mfvhTtYmRmzKKAFrWtU0OsxNmzYQGBgILGxsVhYWDB9+nSGDh1aJFWpC4V0gBdCiFLF4IlQr169uHXrFp988gkxMTE0adKEiIgI3RySy5cvZ7uKaN68eaSlpfHqq69m28/48eOZMGFCUYZeoiWkpDNg8X4OXrqLtbkJSwa0oLmH/j7Ek5OTGTVqFPPnzwfA09OT8PBwGjRooLdjFCnpAC+EEKWSwesIFTWpIwRxyWn0C93Hsavx2FqYsHygN43d7PV6jMTERLy8vDh//jyjRo1i0qRJJW+u1qM6wLcZDjWkA7wQQhSVUllHSBS92/dS6bNoH6eiEyhfzozlA1vSoJKdXvat1WqBrFpQNjY2rFy5kvj4+DxLGxRbeXaAfzXrEnjpAC+EEKWGJEJlyM2EFPwXRnLu5j0crc0JD/KmtrN+2lhcvXqVgIAAXnrpJUaMGAFAixYl7Iop6QAvhBBljiRCZcT1uPv4L4wkKjYJF1sLwoO8qe6kn8nla9asYfDgwdy9e5ejR48SGBhYsiauSwd4IYQosyQRKgOu3Emmd8hert69T2V7S1YGtcK9wtMXMExMTGTEiBEsWbIEyBoBCgsLKxlJkFJweW/WBOgzvwL/P1XOtUnW/J/6PaT5qRBClAHyn76Ui4pNwi9kL9HxKXhUsCIsqBWV7S2fer979+7F39+fCxcuoNFoGDNmDOPHj8fU1FQPUReizAw4vSErAbp28N/ltbtkJUBV28oEaCGEKEMkESrFzt1IxG9hJLcSU6nhVI7woFY42+beiqQgbty4QYcOHUhJScHd3Z0VK1bwzDPP6CHiQiQd4IUQQuRCEqFS6uT1BPouiuR2Uhp1XWxYMcgbR2v9XL7u7OzMxx9/zPHjx5k7dy729vZ62W+hSIiGfd/BgVDpAC+EECIHqSNUCh27GkffRfuIv59Ow8q2LA/0xqGc2RPvTynFihUraNy4sa6vm1KqeFeHlg7wQghRqkgdIZEvBy/dpX/oPhJTM/Byt2fJgJbYWT75vJ24uDiGDBnCqlWraNCgAfv378fS0rJ4JkHSAV4IIUQBSSJUiuy9cJvAJftJTsukZbXyhPZvgbX5k7/Ef/zxB3379uXKlSsYGxvzxhtvFM/J0NIBXgghxBOSRKiU2HnuFkHLDpCSrqVdTUdC+jXH0uzJRj/S0tKYMGECX3zxBUopatSoQVhYGN7e3nqO+ilJB/h8y8zMJD093dBhCCHEI5mZmWXrL1oUJBEqBbadvsFbKw6RlqGlQx0n5vVphoXpkyVBt27d4oUXXuDAgQMABAYGMmPGDGxs9FOBWi+kA3y+KaWIiYkhLi7O0KEIIcRjGRkZUa1aNczMnnxea0FJIlTCRRyPYfjKQ6RnKnwbODOrd1PMTJ48my5fvjzlypXDwcGBBQsW8Oqrr+ox2qckHeAL7EESVLFiRaysrIrn3C4hhCCrX+X169eJjo7G3d29yP5fSSJUgv189DojVx8hU6vo3rgSX7/eGFPjgidBsbGxlCtXDktLS4yNjVmxYgUAVapU0XfIBafrAD8LLu36d7l0gH+szMxMXRJUoUIFQ4cjhBCP5eTkxPXr18nIyCiyOamSCJVQPxy8ygc/HEWr4JWmVZj6qifGRgVPCDZv3kz//v155ZVXmDVrFlBMEiDpAP/UHswJsrKSUgFCiJLhwSmxzMxMSYRE3sIjLzNm3d8A9G7pzqQeDTEqYBKUkpLCmDFj+OabbwDYunUrSUlJlCtXTu/xFoh0gNc7OR0mhCgpDPH/ShKhEmbxrig+3XASgP5tPBjfvX6B3zgnTpzAz8+PY8eOATB06FCmTZtm2JED6QAvhBDCAIr2GjXxVObtOK9Lgga3r17gJEgpxaxZs2jWrBnHjh3DycmJDRs2MGfOHMMkQUrBpT2w0g9mN89qg5GRktUB/pVFMOJI1mkwSYJEEdNoNKxfv97QYZQ4t2/fpmLFily8eNHQoZQao0ePZvjw4YYOo1STRKgEUEox4/ezfBlxGoARnWoxukvdAo8E3bx5k/Hjx5OamkrXrl35+++/6datW2GE/GiZGXBiHSzsBIu7wJlfAJXVAb7/L/DmjqyrwIxlwLKs6t+/PxqNBo1Gg6mpKdWqVeODDz4gJSXF0KEVqocf98M///zzj0Fj6tGjR77WnTRpEi+99BIeHh457vP19cXY2Jj9+/fnuO+5557j3XffzbF8yZIlOXoZJiQkMHbsWOrWrYuFhQUuLi74+Pjw448/Upgdo3bs2EHTpk0xNzenZs2aLFmy5LHbKKWYPn06tWvXxtzcnMqVKzNp0iTd/dHR0fj5+VG7dm2MjIxyfQ7ee+89li5dyoULF/T4aMTD5JOmmFNKMXXTGebtOA/A+751GNah5hPty9nZmZCQEKKjoxk2bFjRn4uVDvCiALp06cLixYtJT0/n4MGDBAQEoNFo+PLLLw0dWqF68Lgf5uTk9ET7SktLK7J6LMnJySxatIhNmzbluO/y5cvs3r2bt99+m9DQUFq0eLJq73FxcbRr1474+Hg+//xzWrRogYmJCX/88QcffPABHTt2LJQm0FFRUbz44ou89dZbhIWFsXXrVgYNGoSrqyu+vr55bvfOO++wefNmpk+fTqNGjbhz5w537tzR3Z+amoqTkxPjxo3Tzdf8L0dHR3x9fZk3bx7Tpk3T+2MTgCpj4uPjFaDi4+MNHcpjabVa9enPJ1TVDzeqqh9uVCF/ni/Q9klJSWrIkCFqw4YNhRRhPsVfV2rLeKWmuCk13jbr5wsPpbZNUirxhmFjK8Xu37+vTp48qe7fv69bptVqVVJqukF+tFptvmMPCAhQL730UrZlPXv2VF5eXrrbsbGx6o033lCVKlVSlpaWqmHDhio8PDzbNu3bt1fDhw9X77//vnJwcFDOzs5q/Pjx2dY5e/aseuaZZ5S5ubmqV6+e2rx5swLUunXrdOscO3ZMdejQQVlYWKjy5curoKAglZiYmCPeSZMmqYoVKyo7Ozv16aefqvT0dPXee+8pBwcHVblyZRUaGlrgx/2wHTt2qBYtWigzMzPl4uKiPvzwQ5Wenp7t8Q4bNky98847qkKFCuq5555TSin1999/qy5duqhy5cqpihUrqj59+qhbt27ptluzZo1q2LCh7vF16tRJ3bt3T40fP14B2X62b9+ea2xr1qxRTk5Oud43YcIE9cYbb6hTp04pOzs7lZycnO3+9u3bq3feeSfHdosXL1Z2dna620OGDFHlypVT165dy7FuYmJitudCnz744APVoEGDbMt69eqlfH1989zm5MmTysTERJ0+fTpfx8jrOVBKqaVLl6oqVarkO96SLLf/Ww8U1ue3jAgVU1qt4uOfjhMWmTVy8lmPhvRtVTXf2x86dAh/f39Onz7N2rVruXDhQtFfESYd4Iud++mZ1P8k5zf2onByoi9WZk/2L+f48ePs3r2bqlX//RtISUmhWbNmfPjhh9ja2vLLL7/Qt29fatSoQcuWLXXrLV26lODgYCIjI9mzZw/9+/enbdu2PP/882i1Wnr27ImzszORkZHEx8fnOD2RlJSEr68vrVu3Zv/+/dy8eZNBgwbx9ttvZzs9sm3bNqpUqcKff/7Jrl27GDhwILt37+bZZ58lMjKS1atXM3jwYJ5//vknKlFx7do1XnjhBfr378+yZcs4ffo0QUFBWFhYMGHChGyPd8iQIezalVV3Ky4ujo4dOzJo0CC++eYb7t+/z4cffsjrr7/Otm3biI6Opnfv3kydOpWXX36ZxMREdu7ciVKK9957j1OnTpGQkKAbpSpfPvfK7Tt37qRZs2Y5liulWLx4MXPmzKFu3brUrFmTH374gb59+xbo8Wu1WlatWoW/vz+VKlXKcb+1tXWe2+7cuZOuXbs+cv/fffcd/v7+ud63Z88efHx8si3z9fXN9VTWAxs2bKB69eps3LiRLl26oJTCx8eHqVOn5vkc5qVly5ZcvXqVixcv5nraUTwdSYSKoUytYvTaY6w5eBWNBr7s6cnrLdzyta1Wq+Wrr75i7NixpKen4+rqytKlS4suCZIO8EJPNm7ciLW1NRkZGaSmpmJkZMTs2bN191euXJn33ntPd3v48OFs2rSJ77//Plsi5Onpyfjx4wGoVasWs2fPZuvWrTz//PP8/vvvnD59mk2bNuk+XCdPnpztQzM8PJyUlBSWLVum+zuaPXs23bt358svv8TZ2RnIShBmzpyJkZERderUYerUqSQnJzNmzBgAPvroI7744gv++usv3njjjcc+7ge6du3KmjVrmDt3Lm5ubsyePRuNRkPdunW5fv06H374IZ988omuP1OtWrWYOnWqbvvPP/8cLy8vJk+erFsWGhqKm5sbZ8+e5d69e2RkZNCzZ09dotmoUSPdupaWlqSmpuLi4vLI1+vSpUu5Jii///47ycnJulNIffr0YdGiRQVOhGJjY7l79y5169Yt0HYAzZs358iRI49c58HrmJuYmJgc9zs7O5OQkMD9+/extLTMsc2FCxe4dOkSa9asYdmyZWRmZjJy5EheffVVtm3bVqD4Hzyvly5dkkSoEEgiVMxkZGoZteYoPx25jrGRhq9fb8xLTfJXO+fq1asEBATo/shefvllQkJCiqaqsHSALxEsTY05OTHvOQ2FfeyC6NChA/PmzSMpKYlvvvkGExMTXnnlFd39mZmZTJ48me+//55r166RlpZGampqjisgPT2zF990dXXl5s2bAJw6dQo3N7dsH+CtW7fOtv6pU6do3Lhxti8Tbdu2RavVcubMGd0HZIMGDbI1i3R2dqZhw4a628bGxlSoUEF37Mc97gceHPfUqVO0bt0629y+tm3bcu/ePa5evYq7uztAjlGZo0ePsn379lxHTM6fP0/nzp3p1KkTjRo1wtfXl86dO/Pqq6/i4ODwyDj/6/79+1hYWORYHhoaSq9evTAxyfq46d27N++//z7nz5+nRo0a+d6/eoqJ0JaWltSs+WRzK5+UVqslNTWVZcuWUbt2bQAWLVpEs2bNOHPmDHXq5H9O5INEKzk5uVBiLeskESpG0jK0vLPqML8dj8HESMOs3l50beSar22jo6Px9PTk7t27WFlZ8e233zJw4MDCnxAtHeBLFI1G88Snp4pauXLldB9eoaGhNG7cmEWLFjFw4EAApk2bxrfffsuMGTNo1KgR5cqV49133yUtLS3bfv5bnVaj0aDVavUeb27HeZJjP/y4n8R/R3/v3bunG736L1dXV4yNjdmyZQu7d+9m8+bNzJo1i7FjxxIZGUm1avn/+3V0dOTu3bvZlt25c4d169aRnp6eLbnLzMwkNDRUdwWVra0t8fHxOfYZFxeHnZ0dkDVh3N7entOnT+c7pgee9tSYi4sLN27cyLbsxo0b2Nra5joaBFnPrYmJiS4JAqhXrx6QNXm8IInQgwnWTzppXjxayfiPWAakpGcyLOwQW0/fxMzYiLn+TfGpn/dQ7X+5urry8ssvc+zYMcLCwrL98RUK6QAvipCRkRFjxowhODgYPz8/LC0t2bVrFy+99BJ9+vQBsr6Bnz17lvr16+d7v/Xq1ePKlStER0fj6pr1pWPv3r051lmyZEm2yuu7du3SnQIrKvXq1WPt2rUopXRfcHbt2oWNjc0j5xw1bdqUtWvX4uHhoRuV+S+NRkPbtm1p27Ytn3zyCVWrVmXdunUEBwdjZmZGZmbmY+Pz8vLS9Sl8ICwsjCpVquSoybR582a++uorJk6ciLGxMXXq1GHz5s059nno0CHd/zIjIyPeeOMNli9fzvjx43Ochrt37x4WFha5PsanPTXWunVrfv3112zLtmzZkmP08GFt27YlIyMj28jX2bNnAbLNdcuP48ePY2pqSoMGDQq0ncgfqSNUDNxPyyRo2QG2nr6JuYkRIQHN85UERUZGEh0drbs9a9Ysdu/eXbhJ0LVDsGYAzPSCvXOzkiCnevDSXHj3b3hmlCRBolC89tprGBsbM2fOHCBrLsyDkYxTp04xePDgHN/aH8fHx4fatWsTEBDA0aNH2blzJ2PHjs22jr+/PxYWFgQEBHD8+HG2b9/O8OHD6du37yM/PPVt6NChXLlyheHDh3P69Gl++uknxo8fT3BwcLZTcv81bNgw7ty5Q+/evdm/fz/nz59n06ZNDBgwgMzMTCIjI5k8eTIHDhzg8uXL/Pjjj9y6dUs3euHh4cGxY8c4c+YMsbGxuh52/+Xr68uJEyeyjQotWrSIV199lYYNG2b7GThwILGxsURERAAwZMgQzp49y4gRI3TH+vrrr1m5ciWjRo3S7W/SpEm4ubnh7e3NsmXLOHnyJOfOnSM0NBQvLy/u3buXa2wPTo096sfGxibP5/Ctt97iwoULfPDBB5w+fZq5c+fy/fffM3LkSN06s2fPplOnTrrbPj4+NG3alMDAQA4fPszBgwd1k+Uf/h995MgRjhw5wr1797h16xZHjhzh5MmT2Y6/c+dOnnnmmTxHn8RT0us1aCVAcbt8/l5Kuur13W5V9cONqt7Hv6nd/8Q+dpv09HT16aefKmNjY+Xr66syMzMLN8jMTKVO/6pUaNd/L38fb6vU0v8pdW6LUgW4LFoUnUddhlrc5XUZ+ZQpU5STk5O6d++eun37tnrppZeUtbW1qlixoho3bpzq169ftu1yuyT5pZdeUgEBAbrbZ86cUe3atVNmZmaqdu3aKiIi4okvn39YbseuWrWq+uabbwr8uB/Iz+XzuV2CffbsWfXyyy8re3t7ZWlpqerWraveffddpdVq1cmTJ5Wvr69ycnJS5ubmqnbt2mrWrFm6bW/evKmef/55ZW1t/cjL55VSqmXLlmr+/PlKKaUOHDigALVv375c1+3atat6+eWXdbf37dunnn/+eeXk5KTs7OyUt7d3ttfggbi4ODV69GhVq1YtZWZmppydnZWPj49at25dgUo0FNT27dtVkyZNlJmZmapevbpavHhxtvvHjx+vqlatmm3ZtWvXVM+ePZW1tbVydnZW/fv3V7dv3862Dv8pTwDk2E+dOnXUypUrC+FRFT+GuHxeo1QhluIshhISErCzsyM+Ph5bW8O2bkhISWfA4v0cvHQXa3MTlgxoQXOPR4+mREVF0adPH3bv3g1kTTxctGhR4XxTkA7wJVpKSgpRUVFUq1Yt10msQujbL7/8wvvvv8/x48cfOUol8u+3335j1KhRHDt2LM9Tm6XJo/5vFdbnd+l/VoupuOQ0+oXu49jVeGwtTFg+0JvGbvZ5rq+UIiwsjKFDh5KYmIitrS1z587Nc3LfU5EO8EKIJ/Diiy9y7tw5rl27hptb/kp+iEdLSkpi8eLFZSIJMhR5Zg3g9r1U+izax6noBMqXM2P5wJY0qGSX5/oJCQm89dZbrFy5EsiahLd8+fICXdGRL9IBXgjxlB5VZFAU3KuvvmroEEo9SYSK2M2EFPwXRnLu5j0crc0JD/KmtnPek/Qgq/7IgQMHMDY2Zvz48Xz00Uf6+3agFFzem1X/58yvZJ2iJqsDfJvhUL+HND8VQghRasknXBGKjr+PX0gkUbFJuNhaEB7kTXWn3MvCp6enY2xsjJGREeXKlWPVqlWkp6fj7e2tn2AyM+D0hqwE6NrBf5fX7pKVAFVtC0XdlFUIIYQoYpIIFZErd5LxW7iXK3fuU9nekpVBrXCvkHuvrbNnz+Lv74+/v79umLlp06b6CUQ6wAshhBA6kggVgajYJPxC9hIdn4JHBSvCglpR2T7nVV5KKRYuXMi7775LcnIy165d480338zRMuCJJETDvu/gQCik/H8FV8vy0DIIWgwC64pPfwwhhBCihJFEqJCdu5GI38JIbiWmUsOpHOFBrXC2zXkpc2xsLEFBQboKrB07dmTp0qVPnwTdOJF1+fvfa6QDvBBCCPEfkggVopPXE+i7KJLbSWnUdbFhxSBvHK3Nc6y3efNm+vfvT3R0NKampkyePPmx1WIfSTrACyGEEPkiiVAhOXY1jr6L9hF/P51Gle1YFtgSh3JmOda7fv063bt3Jy0tjXr16hEWFoaXl9eTHVQ6wAshhBAFIqU/C8HBS3fxD4kk/n46Xu72rBjknWsSBFCpUiUmTpzI0KFDOXDgwJMlQffj4K8Z8K0nrBuclQSZWkHLwTD8ELy+TJIgIf5jx44daDQa4uLiAFiyZAn29vbZ1lmwYAFubm4YGRkxY8YMJkyYQJMmTZ7quBcvXkSj0Ty2CWhR0Wg0OZqi6mPd0mDRokV07tzZ0GGUGidPnqRKlSokJSUZOpTs9NqwowQo7F5je87Hqnof/6aqfrhRvTZ/t0pMSc92v1arVbNmzVKHDx/OtuyJ3Lmo1G+jlZpU6d/+X9NqKfXndKWSbj9+e1GqlfReY4AaPHhwjvuGDh2qgGz9wp7E9u3bFaDu3r2rlFIqOTlZ3bhxQ3d/fHy8MjU1VbNmzVLXr19XSUlJKjExUcXGPr4f4KNERUUpINv/gP9q3769ru+UmZmZqlSpkurWrZtau3btUx07N9HR0SolJUXv6z6Jhx93bj/t27cvtGP/1/3795Wrq6v666+/ctx35coVZWpqqho0aJDjvke9vrn1gjt06JB69dVXVcWKFZW5ubmqWbOmGjRokDpz5oy+HkoOWq1Wffzxx8rFxUVZWFioTp06qbNnzz5ym7lz56pGjRopGxsbZWNjo1q1aqV+/fVX3f23b99Wb7/9tqpdu7aysLBQbm5uavjw4SouLi7bfl555RU1ceLEPI9jiF5jMiKkRzvP3aL/4n0kp2XSrqYjSwe0xNr837OPMTExvPjiiwwfPhw/Pz9SUrKqN2sKWq9HOsCLMsDNzY1Vq1Zx//593bKUlBTCw8Nxd3fX+/EsLS2pWPHfqycvX75Meno6L774Iq6urlhZWWFtbU2FChX0fuzcBAUFER0dzfnz51m7di3169fnjTfe4M0339TrcVxcXDA3zzl38WnXfRI//vgj0dHRREdHs2/fPgB+//133bIff/wx2/rp6emFFssPP/yAra0tbdu2zXHfkiVLeP3110lISCAyMvKJj7Fx40ZatWpFamoqYWFhnDp1ihUrVmBnZ8fHH3/8NOE/0tSpU5k5cybz588nMjKScuXK4evrq/tMyk2VKlX44osvOHjwIAcOHKBjx4689NJLnDhxAsia5nH9+nWmT5/O8ePHWbJkCREREQwcODDbfgYMGMC8efPIyMgotMdXYHpNq0qAwsoot56KUbXG/qqqfrhR9Q+NVPfTMrLdv2HDBuXk5KQAZW5urmbNmlWwkSDpAC8KKNdvVlqtUqn3DPNTgPfogy7sDRs2VCtWrNAtDwsLU56enjk6yKekpKjhw4frOqi3bds2R9fzX375RdWqVUtZWFio5557Ti1evDjbiNDixYuVnZ2d7nf+MxoRFRWlxo8frxo3bpxtvyEhIapu3brK3Nxc1alTR82ZMyfb/ZGRkapJkybK3NxcNWvWTP3444/5GhHKrYt8aGioAtSWLVt0yy5fvqxee+01ZWdnpxwcHNT//vc/FRUVlW27RYsWqfr16+u61g8bNkx3H6Dr8p6amqqGDRumXFxclLm5uXJ3d1eTJ0/OdV2llDp27Jjq0KGDsrCwUOXLl1dBQUEqMTFRd/+D13HatGnKxcVFlS9fXg0dOlSlpaXl+dgfyG1kBVBz585V3bt3V1ZWVmr8+PFKKaXWr1+vvLy8lLm5uapWrZqaMGGCSk//dzT+7t27auDAgcrR0VHZ2NioDh06qCNHjjzy+C+++KJ67733cizXarWqevXqKiIiQn344YcqKCjosXE/8PDrmpSUpBwdHVWPHj1yPf6D96W+abVa5eLioqZNm6ZbFhcXp8zNzQvc4d7BwUEtXLgwz/u///57ZWZmlu21SE1NVebm5ur333/PdRtDjAjJZGk9iDgew/CVh0jPVPg2cGZW76aYmWQNtiUnJ/Pee+8xb948ADw9PQkPD6dBgwb523n6fTi6CvbMkQ7w4umlJ8PkSoY59pjrYFauQJsEBgayePFiXXPh0NBQBgwYwI4dO7Kt98EHH7B27VqWLl1K1apVmTp1Kr6+vvzzzz+UL1+eK1eu0LNnT4YNG8abb77JgQMHGDVqVJ7H7dWrF25ubvj4+LBv3z7c3NxwcnLKsV5YWBiffPIJs2fPxsvLi8OHDxMUFES5cuUICAjg3r17dOvWjeeff57/a+/u42q8/z+Av04351TWnW5PpJwoJWqJ5GYZbbXF+G1rZaIw29x9fdc2C7Pca8bX3dyMRTYZ44sMYzS5abmLSLe6IabQUOlGdc7790ffLo6KTlLU+/l4nAfXdT7Xdb2v8+l0vft8rs/12bx5M7KzszFlyhSVPoNHBQYG4vPPP8fOnTvh6emJiooKeHl5wd3dHcePH4eGhgbmzZsHb29vXLx4EWKxGGvWrEFwcDDCwsLw1ltvoaCgALGxsbXuf8WKFdizZw9+/fVXdOjQAdeuXcO1a9dqLVtcXCwc+8yZM7h16xY++ugjTJo0CREREUK5I0eOQCqV4siRI8jIyICfnx+cnZ0xbty4Bn0Gs2bNQlhYGJYtWwYNDQ0cP34co0aNwooVK9C/f39kZmYKrWahoaEAAF9fX2hra+P333+Hvr4+fvjhBwwaNAjp6elo27b21vMTJ05g5MiRNdYfOXIEJSUl8PT0RLt27dCnTx8sXboUbdqo9rN98OBB5OfnY+rUqbW+//j9ao/69NNPsXnz5ifu//79+7Wuz87ORl5eHjw9PYV1+vr6cHNzQ1xcHPz9/Z8au1wux/bt21FcXAx3d/c6y1XPEv/olFBisRjOzs44fvw4Bg0a9NRjNQVOhJ7Rngs38Nm2BMgVhCFOFvjPB07QVK9KgnJzczFw4ECkpqYCAIKDg7FgwYL6NS3zDPCMISAgANOmTcPVq1cBALGxsdi6datSIlRcXIw1a9YgIiICb731FgBg/fr1OHToEMLDw/Hll19izZo1sLGxwZIlSwAAdnZ2SExMxLffflvrcbW1tYUuMBMTE5ibm9daLjQ0FEuWLMG7774LAOjYsSOSk5Pxww8/IDAwEFu2bIFCoUB4eDi0tLTQtWtXXL9+HePHj2/Q56GmpgZbW1tcuXIFALBt2zYoFAr8+OOPQhf7xo0bYWBggJiYGLz55puYN28ePv/8c6UErGfP2gdP5OTkoHPnzujXrx9EIhGsrKzqjGXLli0oKyvDTz/9JCQB33//PYYMGYJvv/0WZmZmAABDQ0N8//33UFdXR5cuXeDj44Po6OgGJ0IffvghRo8eLSyPGTMGISEhCAwMBADIZDLMnTsXU6dORWhoKE6cOIHTp0/j1q1bwu/exYsXY/fu3dixY0etXY337t1DQUEBLCxq/tEQHh4Of39/qKurw9HRETKZDNu3b0dQUJBK53H5ctUftl26dFFpOwCYM2cOvvjiC5W3A6pu0QAg1E81MzMz4b26JCYmwt3dHWVlZXjllVewa9cuODg41Fo2Pz8fc+fOrfXztbCwEL7TLwJOhJ7BjvjrmLrjAhQEvOfSHove7w51tYf3+5iZmUEqlaKgoACbNm3CG2+88fSd8gzw7HnS1KlqmWmuY6vIxMQEPj4+iIiIABHBx8cHxsbGSmUyMzNRUVGhdC+HpqYmevXqhZSUFABASkpKjXn6nvSXbH0UFxcjMzMTY8eOVbqoV1ZWQl9fXzhu9+7doaX18CGqz3pcIhKSngsXLiAjIwO6usoTN5eVlSEzMxO3bt3CjRs36v2Xd1BQEN544w3Y2dnB29sbgwcPrnPUVEpKCpycnJRaQvr27QuFQoG0tDThQtu1a1eoqz98bplUKkViYqJK5/woV1dXpeULFy4gNjYW8+fPF9bJ5XKUlZWhpKQEFy5cwP3792vc21VaWorMzMxaj1F9X9qj9QZUJUg7d+7EiRMnhHUBAQEIDw9XOREiIpXKP8rU1FTpframYmdnh4SEBBQUFGDHjh0IDAzE0aNHayRDhYWF8PHxgYODA2bNmlVjP9ra2igpKWmiqJ+OE6EG2nIqB9N3VX2Zh/fqgPnDHKGmJsL169fRtm1b6OjoQE1NDZGRkdDU1Kzxy1sJzwDPmopIpHL3VHMbM2YMJk2aBABYtWpVM0fzUHXXw/r162skWY9e+BuTXC7H5cuXhRad+/fvo0ePHoiMjKxR1sTEROWHsrq4uCA7Oxu///47Dh8+jA8++ACenp7YsWNHg2PW1NRUWhaJRFAoFA3e3+NdUPfv38fs2bOFVrlHaWlp4f79+5BKpTW6U4G6u5+MjIwgEolw9+5dpfXVrWCP1jcRQaFQID09Hba2ttDTq/pjtaCgoMZ+7927JyTJtra2AIDU1FSVk+Nn6Rqrbt28efMmpFKpsP7mzZtPfTSEWCxGp06dAAA9evTAmTNnsHz5cvzwww9CmaKiInh7e0NXVxe7du2qUf8AcOfOHdjY2DzxWE2Jr64NsDE2G7N/SwYABPWxRugQB4hEImzfvh2ffPIJ/P39sXr1agBQ+kGrgWeAZ+ypvL29UV5eDpFIBC8vrxrv29jYQCwWIzY2VujKqaiowJkzZ4RJi+3t7bFnzx6l7U6ePPlMcZmZmcHCwgJZWVnCPUyPs7e3x88//4yysjKhdeFZjrtp0ybcvXsX7733HoCqxGXbtm0wNTUVLsCPs7a2RnR0NF5//fV6HUNPTw9+fn7w8/PD+++/D29vb9y5c6fGvTT29vaIiIhAcXGxkJzExsZCTU0NdnZNN3mzi4sL0tLShAt0be/n5eVBQ0MD1tbW9dqnWCyGg4MDkpOTlVrEwsPD8fnnn9do/ZkwYQI2bNiAsLAwtG3bFsbGxoiPj4eHh4dQprCwEBkZGUIC9Oabb8LY2BiLFi3Crl27asRw7969OhO1Z+ka69ixI8zNzREdHS0kPtWj31TtslUoFHjw4IGwXFhYCC8vL0gkEuzZs6dGi1q1S5cu4f33329Q/M9Fo956/RJ41rvO18RkkNVXe8nqq720YH8yKRQKKiwspNGjRwujS3r16kUlJSV176SsiChuDdFSx4ejv+aYEEVNJrqV2sAzY0zZy/4coaFDhwrLBQUFSt/Zx0eNTZkyhSwsLOj333+npKQkCgwMJENDQ7pz5w4REV29epXEYjF98cUXlJqaSpGRkWRubl7nqDEiovPnzwujxao9Pmps/fr1pK2tTcuXL6e0tDS6ePEibdiwgZYsWUJEREVFRWRsbEwBAQGUlJRE+/bto06dOtVr1Ni4ceMoNzeXrl27RnFxcTR16lTS1NSk8ePHC+WKi4upc+fONGDAADp27BhlZWXRkSNHaPLkyXTt2jUiIoqIiCAtLS1avnw5paenU3x8PK1YsULYBx4ZCbZkyRLasmULpaSkUFpaGo0dO5bMzc1JLpfXKFtcXExSqZTee+89SkxMpD///JNkMplSvTxej9V1VZ/nAdU1auzRUWtERAcOHCANDQ2aNWsWXbp0iZKTk+mXX36hGTNmEFHVKKl+/fqRk5MTHTx4kLKzsyk2NpamT59OZ86cqfP4wcHB9N577wnL1T8PKSkpNcquXr2azM3NhdFRCxYsICMjI9q8eTNlZGTQqVOnaPDgwWRtba10bdi9ezdpamrSkCFD6NChQ5SdnU1nzpyhL7/8kvz8/J76GTVUWFgYGRgYUFRUFF28eJGGDh1KHTt2VPpdMXDgQFq5cqWwHBISQkePHqXs7Gy6ePEihYSEkEgkoj/++IOIqr6jbm5u1K1bN8rIyKDc3FzhVVn5cBR1dnY2iUQiunLlSq2xNceoMU6E6kmhUNCyQ+lCErTkjzRSKBQUFxdHNjY2BIBEIhHNmDGj7qGhBTeIDoUSLbR8mACFWRP9OZ+o6Gbt2zDWQC0pEXrc44lQaWkpTZ48mYyNjescPv/bb79Rp06dSCKRUP/+/YWh6M+SCBFVDel3dnYmsVhMhoaG9Nprr9HOnTuF9+Pi4sjJyYnEYjE5OzvTf//7X5UfqCiVSmnw4MFK+62Wm5tLo0aNEs5dJpPRuHHjlH7HrV27luzs7EhTU5OkUilNnjxZeO/R5GLdunXk7OxMbdq0IT09PRo0aBCdO3eu1rJE9R8+/6jGToSIqpKhPn36kLa2Nunp6VGvXr1o3bp1wvuFhYU0efJksrCwIE1NTbK0tKQRI0ZQTk5OncdPSkoibW1t4YGAkyZNIgcHh1rL5ubmkpqaGkVFRRERUWVlJa1YsYK6detGOjo61L59e/Lz86vxWAMiojNnztC7774rPPqhU6dO9PHHH9Ply5ef+hk1VPUDFc3MzEgikdCgQYNqPMDRyspKeDwBEdGYMWPIysqKxGIxmZiY0KBBg4QkiOjhA0prez163gsWLCAvL686Y2uOREhE9Ax3bL2ECgsLoa+vLwzrqw8iwqKDaVgTU3Vj3ZdedvikvzUWLFiAOXPmQC6Xo0OHDvj555/x2muv1dwBzwDPmkFZWRmys7PRsWPHOpuoGWN18/X1hYuLC6ZNm9bcobQI5eXl6Ny5M7Zs2VLrgyqBJ//easj1uz74HqGnICLM3ZuCDbHZAICZgx0wtl9H5ObmYvny5ZDL5Rg+fDhWr16t3J/LM8AzxthL7bvvvsNvv/3W3GG0GDk5OZg+fXqdSVBz4UToCRQKwsyoS4g8lQMAmDvMESN7V92MKZVKsWHDBhQVFSEgIODhRjwDPGOMtQjW1taYPHlyc4fRYnTq1KnOm9qbEydCdZArCCH/vYjt8dchEgHfvGGF/ctDoHfTH0OHDgUA4V8AVTPAx0cAp9YCRblV6zR1qp7903s80LZjk58DY4wxxp6ME6FaVMoV+Hz7BUQl3IC6mghjZCUIDXob165dQ0xMDLy8vB72Xd69WpX8nPupavJTAHjFDHD7BOgxmic/ZYwxxl5gnAg9prxSgSlbz+P3S3lQp0r0uHUYM8NWgYhgY2ODyMjIqiTo73NV3V/JUQDJqzY2sa+6/6fb+4DG85uhmTFVtLLxEIyxl1hz/L7iROgRZRVyTIw8h+jUW8C9G1A/8T22J10EUPV02+VLl+KV3Fhg49vA1UcmLZQNqEqAbAbxAxDZC6P6ia4lJSXQ1tZu5mgYY+zpysvLATy/p7PXhhOh/yktl+Pjn8/i+OV8qJX8g9yf/o2y0hIYGhpi/ZpVeM+mDNg0kGeAZy8NdXV1GBgY4NatWwAAHR0dYY4qxhh70SgUCty+fRs6OjpKM9Y/b5wIASh+UImxm87gZNYd6IjVET7OBxEPApCRloJNE/uhffbXQArPAM9ePtXzClUnQ4wx9iJTU1NDhw4dmvSPtlafCBWWVWD0xjM4EfMnDNrJ8NOn3nB95Q5cvMXQbJcKtaQLVQV5Bnj2EhKJRJBKpTA1NUVFRUVzh8MYY08kFotVniz4Wb0QidCqVavw3XffIS8vD05OTli5ciV69epVZ/nt27dj5syZuHLlCjp37oxvv/0Wb7/9tsrHvVdSjhE/HMfxLStRdDYKnXq9CpcevwGXf4eEZ4BnLYi6unqT9rkzxtjLomnTrlps27YNwcHBCA0Nxblz5+Dk5AQvL686m/L/+usvDB8+HGPHjsX58+cxbNgwDBs2DJcuXVLpuHfuP8DgOb/g8MKxKDobBQBwVlxCRco+AFQ1A3zQPuDjmKpRYJwEMcYYYy1Os8815ubmhp49e+L7778HUHWzlKWlJSZPnoyQkJAa5f38/FBcXIy9e/cK63r37g1nZ2esXbv2qcernquki88YXD6wCXK5HCY6ImwYqoXB9q8ATv5VN0Cb2DXeSTLGGGPsmTyvucaatUWovLwc8fHx8PT0FNapqanB09MTcXFxtW4TFxenVB4AvLy86ixfl9R9GyCXy/FWJw0kftYeg8d9DXx2CXhnBSdBjDHGWCvRrP09+fn5kMvlMDMzU1pvZmaG1NTUWrfJy8urtXxeXl6t5R88eIAHDx4IywUFBQAATREw10eKTz8Lgaj7BygU6wAKAIWFz3BGjDHGGHseCv93fW7sjqwWf+PLwoULMXv27BrrKwgI2ZuLkL1TAExp+sAYY4wxprJ//vkH+vr6jba/Zk2EjI2Noa6ujps3byqtv3nzpvD8k8eZm5urVH7atGkIDg4Wlu/duwcrKyvk5OQ06gfJVFdYWAhLS0tcu3atUft7WcNwfbw4uC5eHFwXL46CggJ06NABbds27hyezZoIicVi9OjRA9HR0Rg2bBiAqpulo6OjMWnSpFq3cXd3R3R0NP79738L6w4dOgR3d/day0skEkgkNef90tfX5x/qF4Senh7XxQuE6+PFwXXx4uC6eHE09nOGmr1rLDg4GIGBgXB1dUWvXr2wbNkyFBcXY/To0QCAUaNGoV27dli4cCEAYMqUKfDw8MCSJUvg4+ODrVu34uzZs1i3bl1zngZjjDHGXkLNngj5+fnh9u3b+Oabb5CXlwdnZ2ccOHBAuCE6JydHKfvr06cPtmzZgq+//hrTp09H586dsXv3bjg6OjbXKTDGGGPsJdXsiRAATJo0qc6usJiYmBrrfH194evr26BjSSQShIaG1tpdxpoW18WLhevjxcF18eLgunhxPK+6aPYHKjLGGGOMNZdmn2KDMcYYY6y5cCLEGGOMsVaLEyHGGGOMtVqcCDHGGGOs1WqRidCqVatgbW0NLS0tuLm54fTp008sv337dnTp0gVaWlro1q0b9u/f30SRtnyq1MX69evRv39/GBoawtDQEJ6enk+tO6YaVb8b1bZu3QqRSCQ8+JQ9O1Xr4t69e5g4cSKkUikkEglsbW35d1UjUbUuli1bBjs7O2hra8PS0hKfffYZysrKmijaluvYsWMYMmQILCwsIBKJsHv37qduExMTAxcXF0gkEnTq1AkRERGqH5hamK1bt5JYLKYNGzZQUlISjRs3jgwMDOjmzZu1lo+NjSV1dXVatGgRJScn09dff02ampqUmJjYxJG3PKrWxYcffkirVq2i8+fPU0pKCgUFBZG+vj5dv369iSNvmVStj2rZ2dnUrl076t+/Pw0dOrRpgm3hVK2LBw8ekKurK7399tt04sQJys7OppiYGEpISGjiyFseVesiMjKSJBIJRUZGUnZ2Nh08eJCkUil99tlnTRx5y7N//36aMWMG7dy5kwDQrl27nlg+KyuLdHR0KDg4mJKTk2nlypWkrq5OBw4cUOm4LS4R6tWrF02cOFFYlsvlZGFhQQsXLqy1/AcffEA+Pj5K69zc3OiTTz55rnG2BqrWxeMqKytJV1eXNm3a9LxCbFUaUh+VlZXUp08f+vHHHykwMJAToUaial2sWbOGZDIZlZeXN1WIrYaqdTFx4kQaOHCg0rrg4GDq27fvc42ztalPIjR16lTq2rWr0jo/Pz/y8vJS6VgtqmusvLwc8fHx8PT0FNapqanB09MTcXFxtW4TFxenVB4AvLy86izP6qchdfG4kpISVFRUNPoEe61RQ+tjzpw5MDU1xdixY5sizFahIXWxZ88euLu7Y+LEiTAzM4OjoyMWLFgAuVzeVGG3SA2piz59+iA+Pl7oPsvKysL+/fvx9ttvN0nM7KHGun6/EE+Wbiz5+fmQy+XC9BzVzMzMkJqaWus2eXl5tZbPy8t7bnG2Bg2pi8d99dVXsLCwqPGDzlTXkPo4ceIEwsPDkZCQ0AQRth4NqYusrCz8+eefGDFiBPbv34+MjAxMmDABFRUVCA0NbYqwW6SG1MWHH36I/Px89OvXD0SEyspKfPrpp5g+fXpThMweUdf1u7CwEKWlpdDW1q7XflpUixBrOcLCwrB161bs2rULWlpazR1Oq1NUVISRI0di/fr1MDY2bu5wWj2FQgFTU1OsW7cOPXr0gJ+fH2bMmIG1a9c2d2itTkxMDBYsWIDVq1fj3Llz2LlzJ/bt24e5c+c2d2isgVpUi5CxsTHU1dVx8+ZNpfU3b96Eubl5rduYm5urVJ7VT0PqotrixYsRFhaGw4cPo3v37s8zzFZD1frIzMzElStXMGTIEGGdQqEAAGhoaCAtLQ02NjbPN+gWqiHfDalUCk1NTairqwvr7O3tkZeXh/LycojF4ucac0vVkLqYOXMmRo4ciY8++ggA0K1bNxQXF+Pjjz/GjBkzlCYJZ89XXddvPT29ercGAS2sRUgsFqNHjx6Ijo4W1ikUCkRHR8Pd3b3Wbdzd3ZXKA8ChQ4fqLM/qpyF1AQCLFi3C3LlzceDAAbi6ujZFqK2CqvXRpUsXJCYmIiEhQXi98847eP3115GQkABLS8umDL9Fach3o2/fvsjIyBCSUQBIT0+HVCrlJOgZNKQuSkpKaiQ71Qkq8dSdTarRrt+q3cf94tu6dStJJBKKiIig5ORk+vjjj8nAwIDy8vKIiGjkyJEUEhIilI+NjSUNDQ1avHgxpaSkUGhoKA+fbySq1kVYWBiJxWLasWMH5ebmCq+ioqLmOoUWRdX6eByPGms8qtZFTk4O6erq0qRJkygtLY327t1LpqamNG/evOY6hRZD1boIDQ0lXV1d+uWXXygrK4v++OMPsrGxoQ8++KC5TqHFKCoqovPnz9P58+cJAP3nP/+h8+fP09WrV4mIKCQkhEaOHCmUrx4+/+WXX1JKSgqtWrWKh89XW7lyJXXo0IHEYjH16tWLTp48Kbzn4eFBgYGBSuV//fVXsrW1JbFYTF27dqV9+/Y1ccQtlyp1YWVlRQBqvEJDQ5s+8BZK1e/GozgRalyq1sVff/1Fbm5uJJFISCaT0fz586mysrKJo26ZVKmLiooKmjVrFtnY2JCWlhZZWlrShAkT6O7du00feAtz5MiRWq8B1Z9/YGAgeXh41NjG2dmZxGIxyWQy2rhxo8rHFRFxWx5jjDHGWqcWdY8QY4wxxpgqOBFijDHGWKvFiRBjjDHGWi1OhBhjjDHWanEixBhjjLFWixMhxhhjjLVanAgxxhhjrNXiRIixVigoKAgikQgDBgxo7lAaXUxMDEQiEUQiEa5cufLU8tVlIyIinntsjLEXDydCjL1EBgwYIFy4H3/t3r27ucOr1aOJSfVLV1cXXbt2xbx581BcXNyox9PT04Obmxvc3NwgkUgAABEREcKxH1dd1sTEpFHjqIu1tbXSZ6GpqQlLS0uMGTMG+fn5Ku9v1qxZEIlEsLa2bvxgGWsFWtTs84y1FmKxGK+++qrSurZt2zZTNPUnk8lgYmKCnJwcJCcnY+bMmTh9+jT27NnTaMdwcXHByZMn611elbKNSVdXFw4ODsjNzUVOTg42btyIW7duYe/evc0SD2OtFbcIMfYSkkqlOHnypNLrtddeAwAEBgaic+fO0NXVhVgshpWVFf71r3+hsLDwifv86aef4OzsDF1dXejq6sLe3h4jR45UKrN582b07NkTOjo60NXVhbe3NxISEuod98yZM3Hy5Elcu3YNbm5uAIDffvsNd+/eBQDcuXMHEydOhKWlJTQ1NWFmZoaAgADk5OQI+8jLy8OIESMglUohkUhgbm6OgQMHYv/+/QBqdo0FBQVh9OjRwvbV782aNUtpOSIiAn///TfU1dUhEokQFRUlbBMdHS2US01NBQCkpqbC19cXJiYmEIvFsLe3x5o1a+r9WVQnbFevXkX//v0BAEePHhXeLy0txbBhw9CxY0e0adMGEokEnTt3xjfffIPy8nIAVS2Es2fPBgBcvXq1RjdfQUEBpkyZAisrK4jFYrRv3x7BwcEoKSmpd5yMtXScCDHWwkRFReHu3buwsbGBpaUlcnJysHLlSowdO7bObS5cuICgoCBcuHAB5ubmsLa2xvXr17F582ahzKJFizBy5EicPXsWlpaW0NPTw8GDB9GvXz+kpKQ8c9xlZWXw8PDA6tWrkZeXB1tbWxQWFiIyMhLu7u64ffs2AGDChAnYsmUL7t+/D0dHR4jFYsTExOD06dO17tfGxgYymUxYru4Ka9++fY2y7dq1g6enJwBg69atwvpt27YJ23bp0gWXL19G7969sWPHDigUCtjZ2SEtLQ0TJkzAnDlzGvwZdOjQQfj/gwcPEBUVhdLSUtja2sLU1BQZGRmYO3cuZsyYAQBwcHBAu3btAFS1Ej7azVdeXo4BAwZgxYoVuHXrFuzt7fHPP/9g6dKlGDJkCHiaScb+5xkni2WMNSEPD49aZ2d+9KuckJCgtM2MGTMIAGloaFBpaSkRVc3iDECYyXnHjh0EgGxtbUkulxMRUWVlJR09epSIiIqLi0lHR4cA0OzZs4moahZuV1dXAkABAQF1xvzojNIymYzc3NxIKpUK64YMGUJERBs2bBDW7dq1i4iI4uPjSU1NjQDQN998Q0REjo6OBIA2b94sHOPGjRuUkpJS43jZ2dlERLRx48Yan1O16vXVs1ZHRkYSAGrTpg0VFxdTRUUFGRkZEQBavXo1EREFBQURAHJ0dKTi4mIiIlq2bBkBIG1tbSosLKzz87CysiIApKurS25ubsJy+/bt6cSJE0K58vJySkpKUto2ICBAKFstNDSUAJCVlZVS2YiICAJAYrGY0tPTiajqZ6P6fA8fPlxnjIy1JnyPEGMvodruEap2+PBhjBgxApmZmSgrKxPWV1ZW4vbt27C0tKyxTd++fWFoaIj09HQYGRnB1tYWzs7OGDFiBAAgKSlJ6E4JDQ1FaGio0vb1vc8mKysLWVlZaNOmDRwcHODv74/g4GAAwJkzZwAAOjo6GDZsGICq7iM7OzukpKTg7NmzAIAhQ4bg0qVLCAwMRGhoKLp06QIPDw988skn9Yrhaf7v//4Penp6KCwsxN69e6Gnp4d//vkHEokE/v7+ACC0Pl26dAlt2rRR2r60tBQXL15E3759n3icoqIinDp1Slh2dXVVqlM1NTVs3rwZO3bswNWrV4XuMAC4cePGU8+jOsby8nLY2trWeP/kyZMYNGjQU/fDWEvHiRBjL6Hqe4QeFxkZiS+++EIoY2lpifz8fGRlZQEA5HJ5rfszNzdHUlISfv75Z8THxyMxMRHr1q3Djz/+iL/++gtqag970e3t7aGnp6e0vZGRUb3i3rhxI4KCgupVti7z589H3759cfDgQVy6dAnHjh3Dvn37EBMTg3379j3TvgFAW1sbvr6+CA8Px7Zt24Rzfeedd2BoaKhU1tjYGDY2NjX2oa6u/tTjeHh44PDhw9iyZQsCAwOxe/duTJs2DcuXLwcAhIWFYeHChQAAKysrmJub4/r16/j777+hUCjqfT51Jc2PnwtjrRUnQoy1INXJka6uLrKzsyGRSDB+/HisXbv2idvduHED+fn5mDp1qrDO3t4eqampOHHiBMaPHw9tbW2UlpbC29sbS5YsEYainz9/HqWlpc8ce8+ePbFmzRqUlJRg9+7dGDZsGM6dO4e0tDQAVS0mABAbGwsPDw/4+PgAqLqXZ/jw4Th27Fid+9bR0RH+X1xcXKMV53GBgYEIDw/H/v37oaWlBQBKCVzPnj2RnJwMfX197N+/Xxixl5+fj+joaPTu3bte56yhoYFRo0YhKioKO3fuxNq1azF16lS0a9dOqEtbW1ukpaVBLpfjnXfewd9//13ruZWUlICIhHrp2bMngKrkd/Xq1XBxcQFQdS/Wvn37uDWIsf/hm6UZa0G6d+8OoKrbRSaTQSaT4ddff33qdsnJyXBycoKpqSmcnZ0hk8mE0VHdunWDjo4OZs6cCQBYunQp2rdvD2dnZxgZGcHFxQV//PHHM8c+fPhwODo6AgB8fX3RtWtX9O3bFwqFAhYWFpg0aRIAICQkBEZGRujUqRN69OiBMWPGKJ17bbp06SL838HBAb1790ZsbGyd5fv16weZTIaysjLcu3cP5ubm8PLyEt6fNm0a9PT0kJmZCUtLS7z66qtCq81XX32l8rlPmzYNQFU31uLFi5XOJz09HR07doSVlVWtrYDV53b79m3Y2dmhd+/eyMrKwvDhw9G9e3fI5XL07NkTjo6OsLOzg4GBAd5//33cu3dP5TgZa4k4EWKsBRk7diyCg4NhbGyMoqIiDBgwoF6jmGQyGfz9/aGnp4f09HTcvn0bTk5OWLduHd58800AVRfrTZs2oWfPnrh79y4yMjJgamqKTz/9FO++++4zx66lpYWjR49iwoQJMDc3R3p6OnR1dTFixAjExcUJDzz08/ODq6srCgsLkZiYCAMDA/j7++OXX36pc9/du3fHzJkzYWZmhpycHJw6dUoYsl8bkUiEUaNGCcsBAQFK3V12dnaIi4uDr68vdHR0kJSUBIVCAW9vb8ydO1flc3d1dRVaaNatW4f8/HxMnz4dgYGBMDAwQGFhIfz9/TFhwoQa2w4ePBjjxo2DkZERLl++jFOnTqGkpAQSiQRHjx7Fv/71L1haWiI9PR13796Fq6sr5s+fDzMzM5XjZKwlEhHxGErGGGOMtU7cIsQYY4yxVosTIcYYY4y1WpwIMcYYY6zV4kSIMcYYY60WJ0KMMcYYa7U4EWKMMcZYq8WJEGOMMcZaLU6EGGOMMdZqcSLEGGOMsVaLEyHGGGOMtVqcCDHGGGOs1eJEiDHGGGOt1v8Df5VIDBRWRSUAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
"\n",
"accuracy = accuracy_score(y_test, y_pred)\n",
"precision = precision_score(y_test, y_pred)\n",
"recall = recall_score(y_test, y_pred)\n",
"f1 = f1_score(y_test, y_pred)\n",
"\n",
"print(f\"#The first dataset#\\naccuracy is: {accuracy}\\nPrecision is: {precision}\\nrecall is: {recall}\\nf1 is: {f1}\\n\\n\")\n",
"\n",
"###############################################\n",
"# accuracy1 = accuracy_score(y_test1, y_pred1)\n",
"# precision1 = precision_score(y_test1, y_pred1)\n",
"# recall1 = recall_score(y_test1, y_pred1)\n",
"# f1_1 = f1_score(y_test1, y_pred1)\n",
"# print(f\"#The second dataset#\\naccuracy is: {accuracy1}\\nPrecision is: {precision1}\\nrecall is: {recall1}\\nf1 is: {f1_1}\\n\\n\")\n",
"\n",
"# ###############################################\n",
"# accuracy2 = accuracy_score(y_test2, y_pred2)\n",
"# precision2 = precision_score(y_test2, y_pred2)\n",
"# recall2 = recall_score(y_test2, y_pred2)\n",
"# f1_2 = f1_score(y_test2, y_pred2)\n",
"# print(f\"#The Third dataset#\\naccuracy is: {accuracy2}\\nPrecision is: {precision2}\\nrecall is: {recall2}\\nf1 is: {f1_2}\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HnSjKSKErDst",
"outputId": "0765d334-0f4c-433c-ebcb-1b0345954c5f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"#The first dataset#\n",
"accuracy is: 0.5\n",
"Precision is: 0.42857142857142855\n",
"recall is: 1.0\n",
"f1 is: 0.6\n",
"\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"\n",
"\n",
"```\n",
"# This is formatted as code\n",
"```\n",
"\n",
"# **Confusion Matrix**"
],
"metadata": {
"id": "epbQip4cRoI2"
}
},
{
"cell_type": "code",
"source": [
"#Confusion Matrix\n",
"\n",
"labels = [\"NPV<90\",\"NPV_>90\" ]\n",
"class_names = labels\n",
"matrix = confusion_matrix(y_test, y_pred)\n",
"\n",
"# Plot confusion matrix in a beautiful manner\n",
"fig = plt.figure(figsize=(6, 4))\n",
"ax= plt.subplot()\n",
"sns.heatmap(matrix, annot=True, ax = ax, fmt = 'g'); #annot=True to annotate cells\n",
"\n",
"\n",
"# labels, title and ticks\n",
"ax.set_xlabel('Predicted Values', fontsize=10)\n",
"ax.xaxis.set_label_position('bottom')\n",
"plt.xticks(rotation=90)\n",
"ax.xaxis.set_ticklabels(class_names, fontsize = 10)\n",
"ax.xaxis.tick_bottom()\n",
"\n",
"ax.set_ylabel('Actual Values', fontsize=10)\n",
"ax.yaxis.set_ticklabels(class_names, fontsize = 10)\n",
"plt.yticks(rotation=0)\n",
"\n",
"plt.title('Non-Perfused Volume Confusion Matrix', fontsize=10)\n",
"plt.show()\n",
"\n",
"############################################################\n"
],
"metadata": {
"id": "kGDytoSPxqGU",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 458
},
"outputId": "779c32b5-7d11-45cf-b1f9-ffd969557716"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 600x400 with 2 Axes>"
],
"image/png": "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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Assuming you have a scikit-learn model or dataset\n",
"from sklearn.datasets import load_iris\n",
"\n",
"# Assuming you have a dataset named Given_Data\n",
"# Replace Given_Data with your actual dataset\n",
"\n",
"# Get the feature names\n",
"feature_names = Given_Data.columns.tolist() # Assuming your data is in a pandas DataFrame\n",
"\n",
"# Print the feature names\n",
"print(feature_names)\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sLdwDivymRbK",
"outputId": "dd1d4d02-6367-43b5-b20b-4b94d07ac107"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['Fibroid_Av', 'Fibroid_Av_ratio_Myometrium-1_Av', 'Fibroid_Av_ratio_Myometrium-2_Av', 'Fibroid_Av_ratio_Muscle-1_Av', 'Fibroid_Av_ratio_Muscle-2_Av', 'NPV_INTER_90']\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install graphviz\n",
"\n",
"from sklearn.tree import export_graphviz\n",
"import graphviz\n",
"from IPython.display import display\n",
"\n",
"# Corrected feature names to match the number of features in your model\n",
"feature_names = ['Fibroid_Av', 'Fibroid_Av_ratio_Myometrium-1_Av', 'Fibroid_Av_ratio_Myometrium-2_Av', 'Fibroid_Av_ratio_Muscle-1_Av', 'Fibroid_Av_ratio_Muscle-2_Av']\n",
"\n",
"# Visualize the Decision Tree with the corrected feature names\n",
"dot_data = export_graphviz(decision_tree_classifier,\n",
" out_file=None,\n",
" filled=True,\n",
" rounded=True,\n",
" special_characters=True,\n",
" feature_names=feature_names)\n",
"graph = graphviz.Source(dot_data)\n",
"display(graph)\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "eSpBZcqSlSWg",
"outputId": "1e567bbf-b822-4965-ffb0-df822f6f798c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (0.20.1)\n"
]
},
{
"output_type": "display_data",
"data": {
"image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.43.0 (0)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"2058pt\" height=\"893pt\"\n viewBox=\"0.00 0.00 2058.00 893.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 889)\">\n<title>Tree</title>\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-889 2054,-889 2054,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#8cc6f0\" stroke=\"black\" d=\"M1369.5,-885C1369.5,-885 1110.5,-885 1110.5,-885 1104.5,-885 1098.5,-879 1098.5,-873 1098.5,-873 1098.5,-829 1098.5,-829 1098.5,-823 1104.5,-817 1110.5,-817 1110.5,-817 1369.5,-817 1369.5,-817 1375.5,-817 1381.5,-823 1381.5,-829 1381.5,-829 1381.5,-873 1381.5,-873 1381.5,-879 1375.5,-885 1369.5,-885\"/>\n<text text-anchor=\"start\" x=\"1106.5\" y=\"-869.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Myometrium-2_Av ≤ 0.236</text>\n<text text-anchor=\"start\" x=\"1204.5\" y=\"-854.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.417</text>\n<text text-anchor=\"start\" x=\"1199\" y=\"-839.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 71</text>\n<text text-anchor=\"start\" x=\"1193\" y=\"-824.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [21, 50]</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#73baed\" stroke=\"black\" d=\"M1207,-781C1207,-781 989,-781 989,-781 983,-781 977,-775 977,-769 977,-769 977,-725 977,-725 977,-719 983,-713 989,-713 989,-713 1207,-713 1207,-713 1213,-713 1219,-719 1219,-725 1219,-725 1219,-769 1219,-769 1219,-775 1213,-781 1207,-781\"/>\n<text text-anchor=\"start\" x=\"985\" y=\"-765.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Muscle-1_Av ≤ 0.12</text>\n<text text-anchor=\"start\" x=\"1066\" y=\"-750.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.35</text>\n<text text-anchor=\"start\" x=\"1057\" y=\"-735.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 53</text>\n<text text-anchor=\"start\" x=\"1051\" y=\"-720.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [12, 41]</text>\n</g>\n<!-- 0->1 -->\n<g id=\"edge1\" class=\"edge\">\n<title>0->1</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1193.9,-816.88C1180.75,-807.44 1166.3,-797.06 1152.7,-787.29\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1154.52,-784.29 1144.36,-781.3 1150.44,-789.98 1154.52,-784.29\"/>\n<text text-anchor=\"middle\" x=\"1148.4\" y=\"-802.27\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\n</g>\n<!-- 28 -->\n<g id=\"node29\" class=\"node\">\n<title>28</title>\n<path fill=\"#ffffff\" stroke=\"black\" d=\"M1512.5,-781C1512.5,-781 1253.5,-781 1253.5,-781 1247.5,-781 1241.5,-775 1241.5,-769 1241.5,-769 1241.5,-725 1241.5,-725 1241.5,-719 1247.5,-713 1253.5,-713 1253.5,-713 1512.5,-713 1512.5,-713 1518.5,-713 1524.5,-719 1524.5,-725 1524.5,-725 1524.5,-769 1524.5,-769 1524.5,-775 1518.5,-781 1512.5,-781\"/>\n<text text-anchor=\"start\" x=\"1249.5\" y=\"-765.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Myometrium-1_Av ≤ 0.382</text>\n<text text-anchor=\"start\" x=\"1355\" y=\"-750.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n<text text-anchor=\"start\" x=\"1342\" y=\"-735.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 18</text>\n<text text-anchor=\"start\" x=\"1343.5\" y=\"-720.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [9, 9]</text>\n</g>\n<!-- 0->28 -->\n<g id=\"edge28\" class=\"edge\">\n<title>0->28</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1286.43,-816.88C1299.67,-807.44 1314.21,-797.06 1327.91,-787.29\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1330.21,-789.96 1336.32,-781.3 1326.14,-784.26 1330.21,-789.96\"/>\n<text text-anchor=\"middle\" x=\"1332.19\" y=\"-802.26\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n</g>\n<!-- 2 -->\n<g id=\"node3\" class=\"node\">\n<title>2</title>\n<path fill=\"#88c4ef\" stroke=\"black\" d=\"M926,-677C926,-677 700,-677 700,-677 694,-677 688,-671 688,-665 688,-665 688,-621 688,-621 688,-615 694,-609 700,-609 700,-609 926,-609 926,-609 932,-609 938,-615 938,-621 938,-621 938,-665 938,-665 938,-671 932,-677 926,-677\"/>\n<text text-anchor=\"start\" x=\"696\" y=\"-661.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Muscle-1_Av ≤ 0.112</text>\n<text text-anchor=\"start\" x=\"777.5\" y=\"-646.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.408</text>\n<text text-anchor=\"start\" x=\"772\" y=\"-631.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 35</text>\n<text text-anchor=\"start\" x=\"766\" y=\"-616.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [10, 25]</text>\n</g>\n<!-- 1->2 -->\n<g id=\"edge2\" class=\"edge\">\n<title>1->2</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1005.47,-712.88C976.58,-702.54 944.55,-691.08 915.04,-680.52\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"916.15,-677.2 905.55,-677.12 913.79,-683.79 916.15,-677.2\"/>\n</g>\n<!-- 21 -->\n<g id=\"node22\" class=\"node\">\n<title>21</title>\n<path fill=\"#52a9e8\" stroke=\"black\" d=\"M1227.5,-677C1227.5,-677 968.5,-677 968.5,-677 962.5,-677 956.5,-671 956.5,-665 956.5,-665 956.5,-621 956.5,-621 956.5,-615 962.5,-609 968.5,-609 968.5,-609 1227.5,-609 1227.5,-609 1233.5,-609 1239.5,-615 1239.5,-621 1239.5,-621 1239.5,-665 1239.5,-665 1239.5,-671 1233.5,-677 1227.5,-677\"/>\n<text text-anchor=\"start\" x=\"964.5\" y=\"-661.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Myometrium-1_Av ≤ 0.446</text>\n<text text-anchor=\"start\" x=\"1062.5\" y=\"-646.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.198</text>\n<text text-anchor=\"start\" x=\"1057\" y=\"-631.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 18</text>\n<text text-anchor=\"start\" x=\"1055\" y=\"-616.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 16]</text>\n</g>\n<!-- 1->21 -->\n<g id=\"edge21\" class=\"edge\">\n<title>1->21</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1098,-712.88C1098,-704.78 1098,-695.98 1098,-687.47\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1101.5,-687.3 1098,-677.3 1094.5,-687.3 1101.5,-687.3\"/>\n</g>\n<!-- 3 -->\n<g id=\"node4\" class=\"node\">\n<title>3</title>\n<path fill=\"#6ab6ec\" stroke=\"black\" d=\"M721.5,-573C721.5,-573 462.5,-573 462.5,-573 456.5,-573 450.5,-567 450.5,-561 450.5,-561 450.5,-517 450.5,-517 450.5,-511 456.5,-505 462.5,-505 462.5,-505 721.5,-505 721.5,-505 727.5,-505 733.5,-511 733.5,-517 733.5,-517 733.5,-561 733.5,-561 733.5,-567 727.5,-573 721.5,-573\"/>\n<text text-anchor=\"start\" x=\"458.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Myometrium-1_Av ≤ 0.192</text>\n<text text-anchor=\"start\" x=\"560\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.32</text>\n<text text-anchor=\"start\" x=\"551\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 30</text>\n<text text-anchor=\"start\" x=\"549\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [6, 24]</text>\n</g>\n<!-- 2->3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2->3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M741.25,-608.88C719.43,-598.81 695.29,-587.67 672.91,-577.34\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"674.32,-574.14 663.77,-573.12 671.38,-580.49 674.32,-574.14\"/>\n</g>\n<!-- 18 -->\n<g id=\"node19\" class=\"node\">\n<title>18</title>\n<path fill=\"#eca06a\" stroke=\"black\" d=\"M870,-573C870,-573 764,-573 764,-573 758,-573 752,-567 752,-561 752,-561 752,-517 752,-517 752,-511 758,-505 764,-505 764,-505 870,-505 870,-505 876,-505 882,-511 882,-517 882,-517 882,-561 882,-561 882,-567 876,-573 870,-573\"/>\n<text text-anchor=\"start\" x=\"760\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av ≤ 0.046</text>\n<text text-anchor=\"start\" x=\"785\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.32</text>\n<text text-anchor=\"start\" x=\"779.5\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n<text text-anchor=\"start\" x=\"777.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 1]</text>\n</g>\n<!-- 2->18 -->\n<g id=\"edge18\" class=\"edge\">\n<title>2->18</title>\n<path fill=\"none\" stroke=\"black\" d=\"M814.3,-608.88C814.62,-600.78 814.96,-591.98 815.3,-583.47\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"818.8,-583.43 815.69,-573.3 811.8,-583.15 818.8,-583.43\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<path fill=\"#88c4ef\" stroke=\"black\" d=\"M516,-469C516,-469 290,-469 290,-469 284,-469 278,-463 278,-457 278,-457 278,-413 278,-413 278,-407 284,-401 290,-401 290,-401 516,-401 516,-401 522,-401 528,-407 528,-413 528,-413 528,-457 528,-457 528,-463 522,-469 516,-469\"/>\n<text text-anchor=\"start\" x=\"286\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Muscle-2_Av ≤ 0.028</text>\n<text text-anchor=\"start\" x=\"367.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.408</text>\n<text text-anchor=\"start\" x=\"362\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 21</text>\n<text text-anchor=\"start\" x=\"360\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [6, 15]</text>\n</g>\n<!-- 3->4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>3->4</title>\n<path fill=\"none\" stroke=\"black\" d=\"M530.64,-504.88C512.31,-494.99 492.07,-484.07 473.22,-473.9\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"474.84,-470.79 464.38,-469.12 471.52,-476.95 474.84,-470.79\"/>\n</g>\n<!-- 17 -->\n<g id=\"node18\" class=\"node\">\n<title>17</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M629.5,-461.5C629.5,-461.5 558.5,-461.5 558.5,-461.5 552.5,-461.5 546.5,-455.5 546.5,-449.5 546.5,-449.5 546.5,-420.5 546.5,-420.5 546.5,-414.5 552.5,-408.5 558.5,-408.5 558.5,-408.5 629.5,-408.5 629.5,-408.5 635.5,-408.5 641.5,-414.5 641.5,-420.5 641.5,-420.5 641.5,-449.5 641.5,-449.5 641.5,-455.5 635.5,-461.5 629.5,-461.5\"/>\n<text text-anchor=\"start\" x=\"566\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"556.5\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 9</text>\n<text text-anchor=\"start\" x=\"554.5\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 9]</text>\n</g>\n<!-- 3->17 -->\n<g id=\"edge17\" class=\"edge\">\n<title>3->17</title>\n<path fill=\"none\" stroke=\"black\" d=\"M592.65,-504.88C592.86,-494.22 593.09,-482.35 593.3,-471.52\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"596.8,-471.59 593.5,-461.52 589.8,-471.45 596.8,-471.59\"/>\n</g>\n<!-- 5 -->\n<g id=\"node6\" class=\"node\">\n<title>5</title>\n<path fill=\"#5dafea\" stroke=\"black\" d=\"M382,-365C382,-365 156,-365 156,-365 150,-365 144,-359 144,-353 144,-353 144,-309 144,-309 144,-303 150,-297 156,-297 156,-297 382,-297 382,-297 388,-297 394,-303 394,-309 394,-309 394,-353 394,-353 394,-359 388,-365 382,-365\"/>\n<text text-anchor=\"start\" x=\"152\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Muscle-1_Av ≤ 0.048</text>\n<text text-anchor=\"start\" x=\"237\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.26</text>\n<text text-anchor=\"start\" x=\"228\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 13</text>\n<text text-anchor=\"start\" x=\"226\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 11]</text>\n</g>\n<!-- 4->5 -->\n<g id=\"edge5\" class=\"edge\">\n<title>4->5</title>\n<path fill=\"none\" stroke=\"black\" d=\"M359.49,-400.88C347.21,-391.53 333.71,-381.26 320.98,-371.57\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"322.82,-368.57 312.75,-365.3 318.58,-374.14 322.82,-368.57\"/>\n</g>\n<!-- 10 -->\n<g id=\"node11\" class=\"node\">\n<title>10</title>\n<path fill=\"#ffffff\" stroke=\"black\" d=\"M650,-365C650,-365 424,-365 424,-365 418,-365 412,-359 412,-353 412,-353 412,-309 412,-309 412,-303 418,-297 424,-297 424,-297 650,-297 650,-297 656,-297 662,-303 662,-309 662,-309 662,-353 662,-353 662,-359 656,-365 650,-365\"/>\n<text text-anchor=\"start\" x=\"420\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Muscle-2_Av ≤ 0.032</text>\n<text text-anchor=\"start\" x=\"509\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n<text text-anchor=\"start\" x=\"499.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\n<text text-anchor=\"start\" x=\"497.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 4]</text>\n</g>\n<!-- 4->10 -->\n<g id=\"edge10\" class=\"edge\">\n<title>4->10</title>\n<path fill=\"none\" stroke=\"black\" d=\"M446.51,-400.88C458.79,-391.53 472.29,-381.26 485.02,-371.57\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"487.42,-374.14 493.25,-365.3 483.18,-368.57 487.42,-374.14\"/>\n</g>\n<!-- 6 -->\n<g id=\"node7\" class=\"node\">\n<title>6</title>\n<path fill=\"#ffffff\" stroke=\"black\" d=\"M230,-261C230,-261 12,-261 12,-261 6,-261 0,-255 0,-249 0,-249 0,-205 0,-205 0,-199 6,-193 12,-193 12,-193 230,-193 230,-193 236,-193 242,-199 242,-205 242,-205 242,-249 242,-249 242,-255 236,-261 230,-261\"/>\n<text text-anchor=\"start\" x=\"8\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Muscle-1_Av ≤ 0.03</text>\n<text text-anchor=\"start\" x=\"93\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n<text text-anchor=\"start\" x=\"83.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n<text text-anchor=\"start\" x=\"81.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 2]</text>\n</g>\n<!-- 5->6 -->\n<g id=\"edge6\" class=\"edge\">\n<title>5->6</title>\n<path fill=\"none\" stroke=\"black\" d=\"M220.95,-296.88C207.05,-287.3 191.75,-276.76 177.39,-266.86\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"179.28,-263.92 169.06,-261.12 175.31,-269.68 179.28,-263.92\"/>\n</g>\n<!-- 9 -->\n<g id=\"node10\" class=\"node\">\n<title>9</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M343.5,-253.5C343.5,-253.5 272.5,-253.5 272.5,-253.5 266.5,-253.5 260.5,-247.5 260.5,-241.5 260.5,-241.5 260.5,-212.5 260.5,-212.5 260.5,-206.5 266.5,-200.5 272.5,-200.5 272.5,-200.5 343.5,-200.5 343.5,-200.5 349.5,-200.5 355.5,-206.5 355.5,-212.5 355.5,-212.5 355.5,-241.5 355.5,-241.5 355.5,-247.5 349.5,-253.5 343.5,-253.5\"/>\n<text text-anchor=\"start\" x=\"280\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"270.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 9</text>\n<text text-anchor=\"start\" x=\"268.5\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 9]</text>\n</g>\n<!-- 5->9 -->\n<g id=\"edge9\" class=\"edge\">\n<title>5->9</title>\n<path fill=\"none\" stroke=\"black\" d=\"M281.66,-296.88C285.78,-286.11 290.37,-274.11 294.55,-263.18\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"297.94,-264.11 298.24,-253.52 291.4,-261.61 297.94,-264.11\"/>\n</g>\n<!-- 7 -->\n<g id=\"node8\" class=\"node\">\n<title>7</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M100.5,-149.5C100.5,-149.5 29.5,-149.5 29.5,-149.5 23.5,-149.5 17.5,-143.5 17.5,-137.5 17.5,-137.5 17.5,-108.5 17.5,-108.5 17.5,-102.5 23.5,-96.5 29.5,-96.5 29.5,-96.5 100.5,-96.5 100.5,-96.5 106.5,-96.5 112.5,-102.5 112.5,-108.5 112.5,-108.5 112.5,-137.5 112.5,-137.5 112.5,-143.5 106.5,-149.5 100.5,-149.5\"/>\n<text text-anchor=\"start\" x=\"37\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"27.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n<text text-anchor=\"start\" x=\"25.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2]</text>\n</g>\n<!-- 6->7 -->\n<g id=\"edge7\" class=\"edge\">\n<title>6->7</title>\n<path fill=\"none\" stroke=\"black\" d=\"M102.82,-192.88C96.78,-181.89 90.04,-169.62 83.95,-158.52\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"86.89,-156.6 79.01,-149.52 80.76,-159.97 86.89,-156.6\"/>\n</g>\n<!-- 8 -->\n<g id=\"node9\" class=\"node\">\n<title>8</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M213.5,-149.5C213.5,-149.5 142.5,-149.5 142.5,-149.5 136.5,-149.5 130.5,-143.5 130.5,-137.5 130.5,-137.5 130.5,-108.5 130.5,-108.5 130.5,-102.5 136.5,-96.5 142.5,-96.5 142.5,-96.5 213.5,-96.5 213.5,-96.5 219.5,-96.5 225.5,-102.5 225.5,-108.5 225.5,-108.5 225.5,-137.5 225.5,-137.5 225.5,-143.5 219.5,-149.5 213.5,-149.5\"/>\n<text text-anchor=\"start\" x=\"150\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"140.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n<text text-anchor=\"start\" x=\"138.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 0]</text>\n</g>\n<!-- 6->8 -->\n<g id=\"edge8\" class=\"edge\">\n<title>6->8</title>\n<path fill=\"none\" stroke=\"black\" d=\"M139.51,-192.88C145.65,-181.89 152.51,-169.62 158.71,-158.52\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"161.92,-159.96 163.74,-149.52 155.81,-156.54 161.92,-159.96\"/>\n</g>\n<!-- 11 -->\n<g id=\"node12\" class=\"node\">\n<title>11</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M533.5,-253.5C533.5,-253.5 462.5,-253.5 462.5,-253.5 456.5,-253.5 450.5,-247.5 450.5,-241.5 450.5,-241.5 450.5,-212.5 450.5,-212.5 450.5,-206.5 456.5,-200.5 462.5,-200.5 462.5,-200.5 533.5,-200.5 533.5,-200.5 539.5,-200.5 545.5,-206.5 545.5,-212.5 545.5,-212.5 545.5,-241.5 545.5,-241.5 545.5,-247.5 539.5,-253.5 533.5,-253.5\"/>\n<text text-anchor=\"start\" x=\"470\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"460.5\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n<text text-anchor=\"start\" x=\"458.5\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 0]</text>\n</g>\n<!-- 10->11 -->\n<g id=\"edge11\" class=\"edge\">\n<title>10->11</title>\n<path fill=\"none\" stroke=\"black\" d=\"M524.34,-296.88C520.22,-286.11 515.63,-274.11 511.45,-263.18\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"514.6,-261.61 507.76,-253.52 508.06,-264.11 514.6,-261.61\"/>\n</g>\n<!-- 12 -->\n<g id=\"node13\" class=\"node\">\n<title>12</title>\n<path fill=\"#9ccef2\" stroke=\"black\" d=\"M794,-261C794,-261 576,-261 576,-261 570,-261 564,-255 564,-249 564,-249 564,-205 564,-205 564,-199 570,-193 576,-193 576,-193 794,-193 794,-193 800,-193 806,-199 806,-205 806,-205 806,-249 806,-249 806,-255 800,-261 794,-261\"/>\n<text text-anchor=\"start\" x=\"572\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Muscle-2_Av ≤ 0.05</text>\n<text text-anchor=\"start\" x=\"649.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n<text text-anchor=\"start\" x=\"647.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\n<text text-anchor=\"start\" x=\"645.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 4]</text>\n</g>\n<!-- 10->12 -->\n<g id=\"edge12\" class=\"edge\">\n<title>10->12</title>\n<path fill=\"none\" stroke=\"black\" d=\"M585.05,-296.88C598.95,-287.3 614.25,-276.76 628.61,-266.86\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"630.69,-269.68 636.94,-261.12 626.72,-263.92 630.69,-269.68\"/>\n</g>\n<!-- 13 -->\n<g id=\"node14\" class=\"node\">\n<title>13</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M616.5,-149.5C616.5,-149.5 545.5,-149.5 545.5,-149.5 539.5,-149.5 533.5,-143.5 533.5,-137.5 533.5,-137.5 533.5,-108.5 533.5,-108.5 533.5,-102.5 539.5,-96.5 545.5,-96.5 545.5,-96.5 616.5,-96.5 616.5,-96.5 622.5,-96.5 628.5,-102.5 628.5,-108.5 628.5,-108.5 628.5,-137.5 628.5,-137.5 628.5,-143.5 622.5,-149.5 616.5,-149.5\"/>\n<text text-anchor=\"start\" x=\"553\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"543.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n<text text-anchor=\"start\" x=\"541.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 3]</text>\n</g>\n<!-- 12->13 -->\n<g id=\"edge13\" class=\"edge\">\n<title>12->13</title>\n<path fill=\"none\" stroke=\"black\" d=\"M651.23,-192.88C639.35,-181.23 626.01,-168.14 614.17,-156.53\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"616.61,-154.02 607.02,-149.52 611.71,-159.02 616.61,-154.02\"/>\n</g>\n<!-- 14 -->\n<g id=\"node15\" class=\"node\">\n<title>14</title>\n<path fill=\"#f2c09c\" stroke=\"black\" d=\"M917.5,-157C917.5,-157 658.5,-157 658.5,-157 652.5,-157 646.5,-151 646.5,-145 646.5,-145 646.5,-101 646.5,-101 646.5,-95 652.5,-89 658.5,-89 658.5,-89 917.5,-89 917.5,-89 923.5,-89 929.5,-95 929.5,-101 929.5,-101 929.5,-145 929.5,-145 929.5,-151 923.5,-157 917.5,-157\"/>\n<text text-anchor=\"start\" x=\"654.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Myometrium-2_Av ≤ 0.123</text>\n<text text-anchor=\"start\" x=\"752.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n<text text-anchor=\"start\" x=\"750.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n<text text-anchor=\"start\" x=\"748.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 1]</text>\n</g>\n<!-- 12->14 -->\n<g id=\"edge14\" class=\"edge\">\n<title>12->14</title>\n<path fill=\"none\" stroke=\"black\" d=\"M718.44,-192.88C727.62,-183.8 737.67,-173.85 747.2,-164.4\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"749.73,-166.82 754.37,-157.3 744.81,-161.85 749.73,-166.82\"/>\n</g>\n<!-- 15 -->\n<g id=\"node16\" class=\"node\">\n<title>15</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M766.5,-53C766.5,-53 695.5,-53 695.5,-53 689.5,-53 683.5,-47 683.5,-41 683.5,-41 683.5,-12 683.5,-12 683.5,-6 689.5,0 695.5,0 695.5,0 766.5,0 766.5,0 772.5,0 778.5,-6 778.5,-12 778.5,-12 778.5,-41 778.5,-41 778.5,-47 772.5,-53 766.5,-53\"/>\n<text text-anchor=\"start\" x=\"703\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"693.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n<text text-anchor=\"start\" x=\"691.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 0]</text>\n</g>\n<!-- 14->15 -->\n<g id=\"edge15\" class=\"edge\">\n<title>14->15</title>\n<path fill=\"none\" stroke=\"black\" d=\"M768.06,-88.95C762.77,-80.17 757.03,-70.66 751.7,-61.82\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"754.69,-59.99 746.52,-53.24 748.69,-63.61 754.69,-59.99\"/>\n</g>\n<!-- 16 -->\n<g id=\"node17\" class=\"node\">\n<title>16</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M879.5,-53C879.5,-53 808.5,-53 808.5,-53 802.5,-53 796.5,-47 796.5,-41 796.5,-41 796.5,-12 796.5,-12 796.5,-6 802.5,0 808.5,0 808.5,0 879.5,0 879.5,0 885.5,0 891.5,-6 891.5,-12 891.5,-12 891.5,-41 891.5,-41 891.5,-47 885.5,-53 879.5,-53\"/>\n<text text-anchor=\"start\" x=\"816\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"806.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"start\" x=\"804.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n</g>\n<!-- 14->16 -->\n<g id=\"edge16\" class=\"edge\">\n<title>14->16</title>\n<path fill=\"none\" stroke=\"black\" d=\"M807.59,-88.95C812.74,-80.26 818.3,-70.86 823.5,-62.09\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"826.66,-63.62 828.75,-53.24 820.64,-60.06 826.66,-63.62\"/>\n</g>\n<!-- 19 -->\n<g id=\"node20\" class=\"node\">\n<title>19</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M760.5,-461.5C760.5,-461.5 689.5,-461.5 689.5,-461.5 683.5,-461.5 677.5,-455.5 677.5,-449.5 677.5,-449.5 677.5,-420.5 677.5,-420.5 677.5,-414.5 683.5,-408.5 689.5,-408.5 689.5,-408.5 760.5,-408.5 760.5,-408.5 766.5,-408.5 772.5,-414.5 772.5,-420.5 772.5,-420.5 772.5,-449.5 772.5,-449.5 772.5,-455.5 766.5,-461.5 760.5,-461.5\"/>\n<text text-anchor=\"start\" x=\"697\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"687.5\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"start\" x=\"685.5\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n</g>\n<!-- 18->19 -->\n<g id=\"edge19\" class=\"edge\">\n<title>18->19</title>\n<path fill=\"none\" stroke=\"black\" d=\"M787.13,-504.88C776.81,-493.45 765.26,-480.63 754.94,-469.19\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"757.31,-466.6 748.02,-461.52 752.12,-471.29 757.31,-466.6\"/>\n</g>\n<!-- 20 -->\n<g id=\"node21\" class=\"node\">\n<title>20</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M873.5,-461.5C873.5,-461.5 802.5,-461.5 802.5,-461.5 796.5,-461.5 790.5,-455.5 790.5,-449.5 790.5,-449.5 790.5,-420.5 790.5,-420.5 790.5,-414.5 796.5,-408.5 802.5,-408.5 802.5,-408.5 873.5,-408.5 873.5,-408.5 879.5,-408.5 885.5,-414.5 885.5,-420.5 885.5,-420.5 885.5,-449.5 885.5,-449.5 885.5,-455.5 879.5,-461.5 873.5,-461.5\"/>\n<text text-anchor=\"start\" x=\"810\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"800.5\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n<text text-anchor=\"start\" x=\"798.5\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 0]</text>\n</g>\n<!-- 18->20 -->\n<g id=\"edge20\" class=\"edge\">\n<title>18->20</title>\n<path fill=\"none\" stroke=\"black\" d=\"M823.82,-504.88C826.01,-494.22 828.46,-482.35 830.69,-471.52\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"834.16,-472.02 832.75,-461.52 827.3,-470.61 834.16,-472.02\"/>\n</g>\n<!-- 22 -->\n<g id=\"node23\" class=\"node\">\n<title>22</title>\n<path fill=\"#45a3e7\" stroke=\"black\" d=\"M1153,-573C1153,-573 927,-573 927,-573 921,-573 915,-567 915,-561 915,-561 915,-517 915,-517 915,-511 921,-505 927,-505 927,-505 1153,-505 1153,-505 1159,-505 1165,-511 1165,-517 1165,-517 1165,-561 1165,-561 1165,-567 1159,-573 1153,-573\"/>\n<text text-anchor=\"start\" x=\"923\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Muscle-2_Av ≤ 0.026</text>\n<text text-anchor=\"start\" x=\"1004.5\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.111</text>\n<text text-anchor=\"start\" x=\"999\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 17</text>\n<text text-anchor=\"start\" x=\"997\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 16]</text>\n</g>\n<!-- 21->22 -->\n<g id=\"edge22\" class=\"edge\">\n<title>21->22</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1079.17,-608.88C1074.31,-600.33 1069.01,-591.01 1063.92,-582.07\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1066.92,-580.26 1058.93,-573.3 1060.84,-583.72 1066.92,-580.26\"/>\n</g>\n<!-- 27 -->\n<g id=\"node28\" class=\"node\">\n<title>27</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M1266.5,-565.5C1266.5,-565.5 1195.5,-565.5 1195.5,-565.5 1189.5,-565.5 1183.5,-559.5 1183.5,-553.5 1183.5,-553.5 1183.5,-524.5 1183.5,-524.5 1183.5,-518.5 1189.5,-512.5 1195.5,-512.5 1195.5,-512.5 1266.5,-512.5 1266.5,-512.5 1272.5,-512.5 1278.5,-518.5 1278.5,-524.5 1278.5,-524.5 1278.5,-553.5 1278.5,-553.5 1278.5,-559.5 1272.5,-565.5 1266.5,-565.5\"/>\n<text text-anchor=\"start\" x=\"1203\" y=\"-550.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"1193.5\" y=\"-535.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"start\" x=\"1191.5\" y=\"-520.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n</g>\n<!-- 21->27 -->\n<g id=\"edge27\" class=\"edge\">\n<title>21->27</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1141.18,-608.88C1156.67,-597.01 1174.09,-583.65 1189.44,-571.88\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1191.92,-574.38 1197.72,-565.52 1187.66,-568.83 1191.92,-574.38\"/>\n</g>\n<!-- 23 -->\n<g id=\"node24\" class=\"node\">\n<title>23</title>\n<path fill=\"#9ccef2\" stroke=\"black\" d=\"M1151,-469C1151,-469 925,-469 925,-469 919,-469 913,-463 913,-457 913,-457 913,-413 913,-413 913,-407 919,-401 925,-401 925,-401 1151,-401 1151,-401 1157,-401 1163,-407 1163,-413 1163,-413 1163,-457 1163,-457 1163,-463 1157,-469 1151,-469\"/>\n<text text-anchor=\"start\" x=\"921\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Muscle-2_Av ≤ 0.025</text>\n<text text-anchor=\"start\" x=\"1002.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n<text text-anchor=\"start\" x=\"1000.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n<text text-anchor=\"start\" x=\"998.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 2]</text>\n</g>\n<!-- 22->23 -->\n<g id=\"edge23\" class=\"edge\">\n<title>22->23</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1039.35,-504.88C1039.19,-496.78 1039.02,-487.98 1038.85,-479.47\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1042.35,-479.23 1038.65,-469.3 1035.35,-479.37 1042.35,-479.23\"/>\n</g>\n<!-- 26 -->\n<g id=\"node27\" class=\"node\">\n<title>26</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M1271,-461.5C1271,-461.5 1193,-461.5 1193,-461.5 1187,-461.5 1181,-455.5 1181,-449.5 1181,-449.5 1181,-420.5 1181,-420.5 1181,-414.5 1187,-408.5 1193,-408.5 1193,-408.5 1271,-408.5 1271,-408.5 1277,-408.5 1283,-414.5 1283,-420.5 1283,-420.5 1283,-449.5 1283,-449.5 1283,-455.5 1277,-461.5 1271,-461.5\"/>\n<text text-anchor=\"start\" x=\"1204\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"1191\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 14</text>\n<text text-anchor=\"start\" x=\"1189\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 14]</text>\n</g>\n<!-- 22->26 -->\n<g id=\"edge26\" class=\"edge\">\n<title>22->26</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1103.93,-504.93C1125.59,-493.63 1149.9,-480.85 1172,-469 1173.55,-468.17 1175.12,-467.32 1176.7,-466.47\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1178.74,-469.35 1185.87,-461.5 1175.41,-463.19 1178.74,-469.35\"/>\n</g>\n<!-- 24 -->\n<g id=\"node25\" class=\"node\">\n<title>24</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M1016.5,-357.5C1016.5,-357.5 945.5,-357.5 945.5,-357.5 939.5,-357.5 933.5,-351.5 933.5,-345.5 933.5,-345.5 933.5,-316.5 933.5,-316.5 933.5,-310.5 939.5,-304.5 945.5,-304.5 945.5,-304.5 1016.5,-304.5 1016.5,-304.5 1022.5,-304.5 1028.5,-310.5 1028.5,-316.5 1028.5,-316.5 1028.5,-345.5 1028.5,-345.5 1028.5,-351.5 1022.5,-357.5 1016.5,-357.5\"/>\n<text text-anchor=\"start\" x=\"953\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"943.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n<text text-anchor=\"start\" x=\"941.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2]</text>\n</g>\n<!-- 23->24 -->\n<g id=\"edge24\" class=\"edge\">\n<title>23->24</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1019.49,-400.88C1013.35,-389.89 1006.49,-377.62 1000.29,-366.52\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1003.19,-364.54 995.26,-357.52 997.08,-367.96 1003.19,-364.54\"/>\n</g>\n<!-- 25 -->\n<g id=\"node26\" class=\"node\">\n<title>25</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M1129.5,-357.5C1129.5,-357.5 1058.5,-357.5 1058.5,-357.5 1052.5,-357.5 1046.5,-351.5 1046.5,-345.5 1046.5,-345.5 1046.5,-316.5 1046.5,-316.5 1046.5,-310.5 1052.5,-304.5 1058.5,-304.5 1058.5,-304.5 1129.5,-304.5 1129.5,-304.5 1135.5,-304.5 1141.5,-310.5 1141.5,-316.5 1141.5,-316.5 1141.5,-345.5 1141.5,-345.5 1141.5,-351.5 1135.5,-357.5 1129.5,-357.5\"/>\n<text text-anchor=\"start\" x=\"1066\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"1056.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"start\" x=\"1054.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n</g>\n<!-- 23->25 -->\n<g id=\"edge25\" class=\"edge\">\n<title>23->25</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1056.18,-400.88C1062.22,-389.89 1068.96,-377.62 1075.05,-366.52\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1078.24,-367.97 1079.99,-357.52 1072.11,-364.6 1078.24,-367.97\"/>\n</g>\n<!-- 29 -->\n<g id=\"node30\" class=\"node\">\n<title>29</title>\n<path fill=\"#eca572\" stroke=\"black\" d=\"M1496,-677C1496,-677 1270,-677 1270,-677 1264,-677 1258,-671 1258,-665 1258,-665 1258,-621 1258,-621 1258,-615 1264,-609 1270,-609 1270,-609 1496,-609 1496,-609 1502,-609 1508,-615 1508,-621 1508,-621 1508,-665 1508,-665 1508,-671 1502,-677 1496,-677\"/>\n<text text-anchor=\"start\" x=\"1266\" y=\"-661.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Muscle-1_Av ≤ 0.244</text>\n<text text-anchor=\"start\" x=\"1347.5\" y=\"-646.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.346</text>\n<text text-anchor=\"start\" x=\"1345.5\" y=\"-631.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 9</text>\n<text text-anchor=\"start\" x=\"1343.5\" y=\"-616.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [7, 2]</text>\n</g>\n<!-- 28->29 -->\n<g id=\"edge29\" class=\"edge\">\n<title>28->29</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1383,-712.88C1383,-704.78 1383,-695.98 1383,-687.47\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1386.5,-687.3 1383,-677.3 1379.5,-687.3 1386.5,-687.3\"/>\n</g>\n<!-- 34 -->\n<g id=\"node35\" class=\"node\">\n<title>34</title>\n<path fill=\"#72b9ec\" stroke=\"black\" d=\"M1764,-677C1764,-677 1538,-677 1538,-677 1532,-677 1526,-671 1526,-665 1526,-665 1526,-621 1526,-621 1526,-615 1532,-609 1538,-609 1538,-609 1764,-609 1764,-609 1770,-609 1776,-615 1776,-621 1776,-621 1776,-665 1776,-665 1776,-671 1770,-677 1764,-677\"/>\n<text text-anchor=\"start\" x=\"1534\" y=\"-661.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Muscle-2_Av ≤ 0.015</text>\n<text text-anchor=\"start\" x=\"1615.5\" y=\"-646.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.346</text>\n<text text-anchor=\"start\" x=\"1613.5\" y=\"-631.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 9</text>\n<text text-anchor=\"start\" x=\"1611.5\" y=\"-616.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 7]</text>\n</g>\n<!-- 28->34 -->\n<g id=\"edge34\" class=\"edge\">\n<title>28->34</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1470.01,-712.88C1496.95,-702.63 1526.78,-691.28 1554.33,-680.79\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1555.87,-683.95 1563.97,-677.12 1553.38,-677.41 1555.87,-683.95\"/>\n</g>\n<!-- 30 -->\n<g id=\"node31\" class=\"node\">\n<title>30</title>\n<path fill=\"#e99355\" stroke=\"black\" d=\"M1435,-573C1435,-573 1329,-573 1329,-573 1323,-573 1317,-567 1317,-561 1317,-561 1317,-517 1317,-517 1317,-511 1323,-505 1329,-505 1329,-505 1435,-505 1435,-505 1441,-505 1447,-511 1447,-517 1447,-517 1447,-561 1447,-561 1447,-567 1441,-573 1435,-573\"/>\n<text text-anchor=\"start\" x=\"1325\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av ≤ 0.055</text>\n<text text-anchor=\"start\" x=\"1346.5\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.219</text>\n<text text-anchor=\"start\" x=\"1344.5\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\n<text text-anchor=\"start\" x=\"1342.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [7, 1]</text>\n</g>\n<!-- 29->30 -->\n<g id=\"edge30\" class=\"edge\">\n<title>29->30</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1382.68,-608.88C1382.6,-600.78 1382.51,-591.98 1382.43,-583.47\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1385.92,-583.26 1382.33,-573.3 1378.92,-583.33 1385.92,-583.26\"/>\n</g>\n<!-- 33 -->\n<g id=\"node34\" class=\"node\">\n<title>33</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M1548.5,-565.5C1548.5,-565.5 1477.5,-565.5 1477.5,-565.5 1471.5,-565.5 1465.5,-559.5 1465.5,-553.5 1465.5,-553.5 1465.5,-524.5 1465.5,-524.5 1465.5,-518.5 1471.5,-512.5 1477.5,-512.5 1477.5,-512.5 1548.5,-512.5 1548.5,-512.5 1554.5,-512.5 1560.5,-518.5 1560.5,-524.5 1560.5,-524.5 1560.5,-553.5 1560.5,-553.5 1560.5,-559.5 1554.5,-565.5 1548.5,-565.5\"/>\n<text text-anchor=\"start\" x=\"1485\" y=\"-550.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"1475.5\" y=\"-535.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"start\" x=\"1473.5\" y=\"-520.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n</g>\n<!-- 29->33 -->\n<g id=\"edge33\" class=\"edge\">\n<title>29->33</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1425.21,-608.88C1440.35,-597.01 1457.37,-583.65 1472.37,-571.88\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1474.77,-574.45 1480.47,-565.52 1470.45,-568.94 1474.77,-574.45\"/>\n</g>\n<!-- 31 -->\n<g id=\"node32\" class=\"node\">\n<title>31</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M1401.5,-461.5C1401.5,-461.5 1330.5,-461.5 1330.5,-461.5 1324.5,-461.5 1318.5,-455.5 1318.5,-449.5 1318.5,-449.5 1318.5,-420.5 1318.5,-420.5 1318.5,-414.5 1324.5,-408.5 1330.5,-408.5 1330.5,-408.5 1401.5,-408.5 1401.5,-408.5 1407.5,-408.5 1413.5,-414.5 1413.5,-420.5 1413.5,-420.5 1413.5,-449.5 1413.5,-449.5 1413.5,-455.5 1407.5,-461.5 1401.5,-461.5\"/>\n<text text-anchor=\"start\" x=\"1338\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"1328.5\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"start\" x=\"1326.5\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n</g>\n<!-- 30->31 -->\n<g id=\"edge31\" class=\"edge\">\n<title>30->31</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1376.81,-504.88C1375.13,-494.22 1373.27,-482.35 1371.57,-471.52\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1375.01,-470.86 1370,-461.52 1368.1,-471.94 1375.01,-470.86\"/>\n</g>\n<!-- 32 -->\n<g id=\"node33\" class=\"node\">\n<title>32</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M1514.5,-461.5C1514.5,-461.5 1443.5,-461.5 1443.5,-461.5 1437.5,-461.5 1431.5,-455.5 1431.5,-449.5 1431.5,-449.5 1431.5,-420.5 1431.5,-420.5 1431.5,-414.5 1437.5,-408.5 1443.5,-408.5 1443.5,-408.5 1514.5,-408.5 1514.5,-408.5 1520.5,-408.5 1526.5,-414.5 1526.5,-420.5 1526.5,-420.5 1526.5,-449.5 1526.5,-449.5 1526.5,-455.5 1520.5,-461.5 1514.5,-461.5\"/>\n<text text-anchor=\"start\" x=\"1451\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"1441.5\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 7</text>\n<text text-anchor=\"start\" x=\"1439.5\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [7, 0]</text>\n</g>\n<!-- 30->32 -->\n<g id=\"edge32\" class=\"edge\">\n<title>30->32</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1413.49,-504.88C1424.47,-493.34 1436.79,-480.39 1447.75,-468.86\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1450.38,-471.18 1454.73,-461.52 1445.3,-466.35 1450.38,-471.18\"/>\n</g>\n<!-- 35 -->\n<g id=\"node36\" class=\"node\">\n<title>35</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M1674.5,-565.5C1674.5,-565.5 1603.5,-565.5 1603.5,-565.5 1597.5,-565.5 1591.5,-559.5 1591.5,-553.5 1591.5,-553.5 1591.5,-524.5 1591.5,-524.5 1591.5,-518.5 1597.5,-512.5 1603.5,-512.5 1603.5,-512.5 1674.5,-512.5 1674.5,-512.5 1680.5,-512.5 1686.5,-518.5 1686.5,-524.5 1686.5,-524.5 1686.5,-553.5 1686.5,-553.5 1686.5,-559.5 1680.5,-565.5 1674.5,-565.5\"/>\n<text text-anchor=\"start\" x=\"1611\" y=\"-550.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"1601.5\" y=\"-535.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"start\" x=\"1599.5\" y=\"-520.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n</g>\n<!-- 34->35 -->\n<g id=\"edge35\" class=\"edge\">\n<title>34->35</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1647.1,-608.88C1645.85,-598.22 1644.45,-586.35 1643.18,-575.52\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1646.65,-575.04 1642,-565.52 1639.69,-575.86 1646.65,-575.04\"/>\n</g>\n<!-- 36 -->\n<g id=\"node37\" class=\"node\">\n<title>36</title>\n<path fill=\"#55abe9\" stroke=\"black\" d=\"M1943,-573C1943,-573 1717,-573 1717,-573 1711,-573 1705,-567 1705,-561 1705,-561 1705,-517 1705,-517 1705,-511 1711,-505 1717,-505 1717,-505 1943,-505 1943,-505 1949,-505 1955,-511 1955,-517 1955,-517 1955,-561 1955,-561 1955,-567 1949,-573 1943,-573\"/>\n<text text-anchor=\"start\" x=\"1713\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Muscle-2_Av ≤ 0.036</text>\n<text text-anchor=\"start\" x=\"1794.5\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.219</text>\n<text text-anchor=\"start\" x=\"1792.5\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\n<text text-anchor=\"start\" x=\"1790.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 7]</text>\n</g>\n<!-- 34->36 -->\n<g id=\"edge36\" class=\"edge\">\n<title>34->36</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1709.12,-608.88C1726.24,-599.12 1745.12,-588.37 1762.77,-578.31\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1764.91,-581.12 1771.87,-573.12 1761.45,-575.03 1764.91,-581.12\"/>\n</g>\n<!-- 37 -->\n<g id=\"node38\" class=\"node\">\n<title>37</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M1769.5,-461.5C1769.5,-461.5 1698.5,-461.5 1698.5,-461.5 1692.5,-461.5 1686.5,-455.5 1686.5,-449.5 1686.5,-449.5 1686.5,-420.5 1686.5,-420.5 1686.5,-414.5 1692.5,-408.5 1698.5,-408.5 1698.5,-408.5 1769.5,-408.5 1769.5,-408.5 1775.5,-408.5 1781.5,-414.5 1781.5,-420.5 1781.5,-420.5 1781.5,-449.5 1781.5,-449.5 1781.5,-455.5 1775.5,-461.5 1769.5,-461.5\"/>\n<text text-anchor=\"start\" x=\"1706\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"1696.5\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n<text text-anchor=\"start\" x=\"1694.5\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 5]</text>\n</g>\n<!-- 36->37 -->\n<g id=\"edge37\" class=\"edge\">\n<title>36->37</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1798.83,-504.88C1787.96,-493.34 1775.78,-480.39 1764.93,-468.86\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1767.42,-466.4 1758.02,-461.52 1762.32,-471.2 1767.42,-466.4\"/>\n</g>\n<!-- 38 -->\n<g id=\"node39\" class=\"node\">\n<title>38</title>\n<path fill=\"#9ccef2\" stroke=\"black\" d=\"M2038,-469C2038,-469 1812,-469 1812,-469 1806,-469 1800,-463 1800,-457 1800,-457 1800,-413 1800,-413 1800,-407 1806,-401 1812,-401 1812,-401 2038,-401 2038,-401 2044,-401 2050,-407 2050,-413 2050,-413 2050,-457 2050,-457 2050,-463 2044,-469 2038,-469\"/>\n<text text-anchor=\"start\" x=\"1808\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fibroid_Av_ratio_Muscle-1_Av ≤ 0.212</text>\n<text text-anchor=\"start\" x=\"1889.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n<text text-anchor=\"start\" x=\"1887.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n<text text-anchor=\"start\" x=\"1885.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 2]</text>\n</g>\n<!-- 36->38 -->\n<g id=\"edge38\" class=\"edge\">\n<title>36->38</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1860.84,-504.88C1869.22,-495.89 1878.39,-486.04 1887.11,-476.68\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1889.73,-479 1893.99,-469.3 1884.61,-474.23 1889.73,-479\"/>\n</g>\n<!-- 39 -->\n<g id=\"node40\" class=\"node\">\n<title>39</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M1903.5,-357.5C1903.5,-357.5 1832.5,-357.5 1832.5,-357.5 1826.5,-357.5 1820.5,-351.5 1820.5,-345.5 1820.5,-345.5 1820.5,-316.5 1820.5,-316.5 1820.5,-310.5 1826.5,-304.5 1832.5,-304.5 1832.5,-304.5 1903.5,-304.5 1903.5,-304.5 1909.5,-304.5 1915.5,-310.5 1915.5,-316.5 1915.5,-316.5 1915.5,-345.5 1915.5,-345.5 1915.5,-351.5 1909.5,-357.5 1903.5,-357.5\"/>\n<text text-anchor=\"start\" x=\"1840\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"1830.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"start\" x=\"1828.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n</g>\n<!-- 38->39 -->\n<g id=\"edge39\" class=\"edge\">\n<title>38->39</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1906.49,-400.88C1900.35,-389.89 1893.49,-377.62 1887.29,-366.52\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1890.19,-364.54 1882.26,-357.52 1884.08,-367.96 1890.19,-364.54\"/>\n</g>\n<!-- 40 -->\n<g id=\"node41\" class=\"node\">\n<title>40</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M2016.5,-357.5C2016.5,-357.5 1945.5,-357.5 1945.5,-357.5 1939.5,-357.5 1933.5,-351.5 1933.5,-345.5 1933.5,-345.5 1933.5,-316.5 1933.5,-316.5 1933.5,-310.5 1939.5,-304.5 1945.5,-304.5 1945.5,-304.5 2016.5,-304.5 2016.5,-304.5 2022.5,-304.5 2028.5,-310.5 2028.5,-316.5 2028.5,-316.5 2028.5,-345.5 2028.5,-345.5 2028.5,-351.5 2022.5,-357.5 2016.5,-357.5\"/>\n<text text-anchor=\"start\" x=\"1953\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"start\" x=\"1943.5\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n<text text-anchor=\"start\" x=\"1941.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2]</text>\n</g>\n<!-- 38->40 -->\n<g id=\"edge40\" class=\"edge\">\n<title>38->40</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1943.18,-400.88C1949.22,-389.89 1955.96,-377.62 1962.05,-366.52\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1965.24,-367.97 1966.99,-357.52 1959.11,-364.6 1965.24,-367.97\"/>\n</g>\n</g>\n</svg>\n",
"text/plain": [
"<graphviz.sources.Source at 0x79756eb07a00>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# ** **Get a Report **"
],
"metadata": {
"id": "4o1Jb3d9R5Q4"
}
},
{
"cell_type": "code",
"source": [
"# print(classification_report(y_test,y_pred ),\"\\n\\n\\n\\n\\n\\n\")\n",
"# print(classification_report(y_test1,y_pred1 ), \"\\n\\n\\n\\n\\n\\n\")\n",
"# print(classification_report(y_test2,y_pred2 ))\n",
"\n",
"print(y_test, \"\\n\\n\\n\")\n",
"print(y_pred)\n"
],
"metadata": {
"id": "ZtZKu6zN4FPf",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0e054d2f-ee9d-47e5-e218-e001470eaa55"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" 1\n",
"30 0\n",
"0 1\n",
"22 1\n",
"31 0\n",
"18 0\n",
"28 0\n",
"10 1\n",
"70 0 \n",
"\n",
"\n",
"\n",
"[1 1 1 1 1 1 1 0]\n"
]
}
]
}
]
}