Skip to content
Permalink
master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Go to file
 
 
Cannot retrieve contributors at this time
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "3d1395e5",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1. Consent: By clicking on the \"I Agree\" button below, you are indicating that you have read and understood the information provided above, that you voluntarily agree to participate in this study, and that you are at least 18 years of age.</th>\n",
" <th>2. I see Myself as Someone Who...</th>\n",
" <th>2.1. Is talkative</th>\n",
" <th>2.2. Tends to find fault with others</th>\n",
" <th>2.3. Does a thorough job</th>\n",
" <th>2.4. Is depressed, blue</th>\n",
" <th>2.5. Is original, comes up with new ideas</th>\n",
" <th>2.6. Is reserved</th>\n",
" <th>2.7. Is helpful and unselfish with others</th>\n",
" <th>2.8. Can be somewhat careless</th>\n",
" <th>...</th>\n",
" <th>2.36. Is outgoing, sociable</th>\n",
" <th>2.37. Is sometimes rude to others</th>\n",
" <th>2.38. Makes plans and follows through with them</th>\n",
" <th>2.39. Gets nervous easily</th>\n",
" <th>2.40. Likes to reflect, play with ideas</th>\n",
" <th>2.41. Has few artistic interests</th>\n",
" <th>2.42. Likes to cooperate with others</th>\n",
" <th>2.43. Is easily distracted</th>\n",
" <th>2.44. Is sophisticated in art, music, or literature</th>\n",
" <th>3. What is your favourite dance style in the context of dance battles from the ones in the list below:</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>I agree</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>House</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>I agree</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>Hip Hop</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>I agree</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>Dancehall</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>I agree</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>Dancehall</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>I agree</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>House</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>I agree</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>Breaking</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>I agree</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>Hip Hop</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>I agree</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>Vogue</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>I agree</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>Hip Hop</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>I agree</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>Hip Hop</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>88 rows × 47 columns</p>\n",
"</div>"
],
"text/plain": [
" 1. Consent: By clicking on the \"I Agree\" button below, you are indicating that you have read and understood the information provided above, that you voluntarily agree to participate in this study, and that you are at least 18 years of age. \\\n",
"0 I agree \n",
"1 I agree \n",
"2 I agree \n",
"3 I agree \n",
"4 I agree \n",
".. ... \n",
"83 I agree \n",
"84 I agree \n",
"85 I agree \n",
"86 I agree \n",
"87 I agree \n",
"\n",
" 2. I see Myself as Someone Who... 2.1. Is talkative \\\n",
"0 NaN 4.0 \n",
"1 NaN 4.0 \n",
"2 NaN NaN \n",
"3 NaN 5.0 \n",
"4 NaN NaN \n",
".. ... ... \n",
"83 NaN 3.0 \n",
"84 NaN 1.0 \n",
"85 NaN 4.0 \n",
"86 NaN 5.0 \n",
"87 NaN 4.0 \n",
"\n",
" 2.2. Tends to find fault with others 2.3. Does a thorough job \\\n",
"0 2.0 3.0 \n",
"1 3.0 2.0 \n",
"2 NaN NaN \n",
"3 2.0 2.0 \n",
"4 1.0 3.0 \n",
".. ... ... \n",
"83 4.0 3.0 \n",
"84 2.0 3.0 \n",
"85 2.0 3.0 \n",
"86 3.0 4.0 \n",
"87 2.0 3.0 \n",
"\n",
" 2.4. Is depressed, blue 2.5. Is original, comes up with new ideas \\\n",
"0 3.0 2.0 \n",
"1 3.0 4.0 \n",
"2 NaN NaN \n",
"3 2.0 4.0 \n",
"4 NaN 3.0 \n",
".. ... ... \n",
"83 4.0 3.0 \n",
"84 4.0 2.0 \n",
"85 2.0 5.0 \n",
"86 4.0 4.0 \n",
"87 2.0 3.0 \n",
"\n",
" 2.6. Is reserved 2.7. Is helpful and unselfish with others \\\n",
"0 3.0 5.0 \n",
"1 4.0 5.0 \n",
"2 NaN NaN \n",
"3 2.0 4.0 \n",
"4 5.0 5.0 \n",
".. ... ... \n",
"83 2.0 4.0 \n",
"84 5.0 3.0 \n",
"85 2.0 5.0 \n",
"86 2.0 3.0 \n",
"87 2.0 4.0 \n",
"\n",
" 2.8. Can be somewhat careless ... 2.36. Is outgoing, sociable \\\n",
"0 2.0 ... 4.0 \n",
"1 4.0 ... 4.0 \n",
"2 NaN ... NaN \n",
"3 5.0 ... 5.0 \n",
"4 3.0 ... 3.0 \n",
".. ... ... ... \n",
"83 3.0 ... 4.0 \n",
"84 2.0 ... 1.0 \n",
"85 4.0 ... 4.0 \n",
"86 3.0 ... 5.0 \n",
"87 4.0 ... 4.0 \n",
"\n",
" 2.37. Is sometimes rude to others \\\n",
"0 3.0 \n",
"1 3.0 \n",
"2 NaN \n",
"3 3.0 \n",
"4 3.0 \n",
".. ... \n",
"83 2.0 \n",
"84 3.0 \n",
"85 3.0 \n",
"86 2.0 \n",
"87 2.0 \n",
"\n",
" 2.38. Makes plans and follows through with them \\\n",
"0 2.0 \n",
"1 3.0 \n",
"2 NaN \n",
"3 3.0 \n",
"4 3.0 \n",
".. ... \n",
"83 5.0 \n",
"84 2.0 \n",
"85 1.0 \n",
"86 3.0 \n",
"87 4.0 \n",
"\n",
" 2.39. Gets nervous easily 2.40. Likes to reflect, play with ideas \\\n",
"0 4.0 2.0 \n",
"1 4.0 4.0 \n",
"2 NaN NaN \n",
"3 1.0 3.0 \n",
"4 3.0 3.0 \n",
".. ... ... \n",
"83 4.0 4.0 \n",
"84 3.0 1.0 \n",
"85 1.0 4.0 \n",
"86 3.0 3.0 \n",
"87 2.0 2.0 \n",
"\n",
" 2.41. Has few artistic interests 2.42. Likes to cooperate with others \\\n",
"0 2.0 5.0 \n",
"1 4.0 4.0 \n",
"2 NaN NaN \n",
"3 1.0 4.0 \n",
"4 3.0 5.0 \n",
".. ... ... \n",
"83 3.0 4.0 \n",
"84 4.0 2.0 \n",
"85 5.0 4.0 \n",
"86 1.0 4.0 \n",
"87 3.0 3.0 \n",
"\n",
" 2.43. Is easily distracted \\\n",
"0 4.0 \n",
"1 4.0 \n",
"2 NaN \n",
"3 4.0 \n",
"4 3.0 \n",
".. ... \n",
"83 5.0 \n",
"84 1.0 \n",
"85 5.0 \n",
"86 3.0 \n",
"87 4.0 \n",
"\n",
" 2.44. Is sophisticated in art, music, or literature \\\n",
"0 3.0 \n",
"1 4.0 \n",
"2 3.0 \n",
"3 3.0 \n",
"4 2.0 \n",
".. ... \n",
"83 4.0 \n",
"84 1.0 \n",
"85 5.0 \n",
"86 5.0 \n",
"87 2.0 \n",
"\n",
" 3. What is your favourite dance style in the context of dance battles from the ones in the list below: \n",
"0 House \n",
"1 Hip Hop \n",
"2 Dancehall \n",
"3 Dancehall \n",
"4 House \n",
".. ... \n",
"83 Breaking \n",
"84 Hip Hop \n",
"85 Vogue \n",
"86 Hip Hop \n",
"87 Hip Hop \n",
"\n",
"[88 rows x 47 columns]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"Importing libraries and the dataset.\n",
"Personality/Dance Prefferences Dataset\n",
"6 attributes: 5 predictor attributes and a target variable.\n",
"\"\"\"\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"df = pd.read_csv('dataset.csv')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a1ab0d35",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>2.1. Is talkative</th>\n",
" <th>2.2. Tends to find fault with others</th>\n",
" <th>2.3. Does a thorough job</th>\n",
" <th>2.4. Is depressed, blue</th>\n",
" <th>2.5. Is original, comes up with new ideas</th>\n",
" <th>2.6. Is reserved</th>\n",
" <th>2.7. Is helpful and unselfish with others</th>\n",
" <th>2.8. Can be somewhat careless</th>\n",
" <th>2.9. Is relaxed, handles stress well</th>\n",
" <th>2.10. Is curious about many different things</th>\n",
" <th>...</th>\n",
" <th>2.36. Is outgoing, sociable</th>\n",
" <th>2.37. Is sometimes rude to others</th>\n",
" <th>2.38. Makes plans and follows through with them</th>\n",
" <th>2.39. Gets nervous easily</th>\n",
" <th>2.40. Likes to reflect, play with ideas</th>\n",
" <th>2.41. Has few artistic interests</th>\n",
" <th>2.42. Likes to cooperate with others</th>\n",
" <th>2.43. Is easily distracted</th>\n",
" <th>2.44. Is sophisticated in art, music, or literature</th>\n",
" <th>3. What is your favourite dance style in the context of dance battles from the ones in the list below:</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>House</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>Hip Hop</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>Dancehall</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>Dancehall</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>House</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>Breaking</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>Hip Hop</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>Vogue</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>Hip Hop</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>Hip Hop</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>88 rows × 45 columns</p>\n",
"</div>"
],
"text/plain": [
" 2.1. Is talkative 2.2. Tends to find fault with others \\\n",
"0 4.0 2.0 \n",
"1 4.0 3.0 \n",
"2 NaN NaN \n",
"3 5.0 2.0 \n",
"4 NaN 1.0 \n",
".. ... ... \n",
"83 3.0 4.0 \n",
"84 1.0 2.0 \n",
"85 4.0 2.0 \n",
"86 5.0 3.0 \n",
"87 4.0 2.0 \n",
"\n",
" 2.3. Does a thorough job 2.4. Is depressed, blue \\\n",
"0 3.0 3.0 \n",
"1 2.0 3.0 \n",
"2 NaN NaN \n",
"3 2.0 2.0 \n",
"4 3.0 NaN \n",
".. ... ... \n",
"83 3.0 4.0 \n",
"84 3.0 4.0 \n",
"85 3.0 2.0 \n",
"86 4.0 4.0 \n",
"87 3.0 2.0 \n",
"\n",
" 2.5. Is original, comes up with new ideas 2.6. Is reserved \\\n",
"0 2.0 3.0 \n",
"1 4.0 4.0 \n",
"2 NaN NaN \n",
"3 4.0 2.0 \n",
"4 3.0 5.0 \n",
".. ... ... \n",
"83 3.0 2.0 \n",
"84 2.0 5.0 \n",
"85 5.0 2.0 \n",
"86 4.0 2.0 \n",
"87 3.0 2.0 \n",
"\n",
" 2.7. Is helpful and unselfish with others 2.8. Can be somewhat careless \\\n",
"0 5.0 2.0 \n",
"1 5.0 4.0 \n",
"2 NaN NaN \n",
"3 4.0 5.0 \n",
"4 5.0 3.0 \n",
".. ... ... \n",
"83 4.0 3.0 \n",
"84 3.0 2.0 \n",
"85 5.0 4.0 \n",
"86 3.0 3.0 \n",
"87 4.0 4.0 \n",
"\n",
" 2.9. Is relaxed, handles stress well \\\n",
"0 1.0 \n",
"1 3.0 \n",
"2 NaN \n",
"3 4.0 \n",
"4 3.0 \n",
".. ... \n",
"83 1.0 \n",
"84 5.0 \n",
"85 3.0 \n",
"86 3.0 \n",
"87 3.0 \n",
"\n",
" 2.10. Is curious about many different things ... \\\n",
"0 4.0 ... \n",
"1 4.0 ... \n",
"2 NaN ... \n",
"3 5.0 ... \n",
"4 5.0 ... \n",
".. ... ... \n",
"83 5.0 ... \n",
"84 2.0 ... \n",
"85 5.0 ... \n",
"86 4.0 ... \n",
"87 3.0 ... \n",
"\n",
" 2.36. Is outgoing, sociable 2.37. Is sometimes rude to others \\\n",
"0 4.0 3.0 \n",
"1 4.0 3.0 \n",
"2 NaN NaN \n",
"3 5.0 3.0 \n",
"4 3.0 3.0 \n",
".. ... ... \n",
"83 4.0 2.0 \n",
"84 1.0 3.0 \n",
"85 4.0 3.0 \n",
"86 5.0 2.0 \n",
"87 4.0 2.0 \n",
"\n",
" 2.38. Makes plans and follows through with them \\\n",
"0 2.0 \n",
"1 3.0 \n",
"2 NaN \n",
"3 3.0 \n",
"4 3.0 \n",
".. ... \n",
"83 5.0 \n",
"84 2.0 \n",
"85 1.0 \n",
"86 3.0 \n",
"87 4.0 \n",
"\n",
" 2.39. Gets nervous easily 2.40. Likes to reflect, play with ideas \\\n",
"0 4.0 2.0 \n",
"1 4.0 4.0 \n",
"2 NaN NaN \n",
"3 1.0 3.0 \n",
"4 3.0 3.0 \n",
".. ... ... \n",
"83 4.0 4.0 \n",
"84 3.0 1.0 \n",
"85 1.0 4.0 \n",
"86 3.0 3.0 \n",
"87 2.0 2.0 \n",
"\n",
" 2.41. Has few artistic interests 2.42. Likes to cooperate with others \\\n",
"0 2.0 5.0 \n",
"1 4.0 4.0 \n",
"2 NaN NaN \n",
"3 1.0 4.0 \n",
"4 3.0 5.0 \n",
".. ... ... \n",
"83 3.0 4.0 \n",
"84 4.0 2.0 \n",
"85 5.0 4.0 \n",
"86 1.0 4.0 \n",
"87 3.0 3.0 \n",
"\n",
" 2.43. Is easily distracted \\\n",
"0 4.0 \n",
"1 4.0 \n",
"2 NaN \n",
"3 4.0 \n",
"4 3.0 \n",
".. ... \n",
"83 5.0 \n",
"84 1.0 \n",
"85 5.0 \n",
"86 3.0 \n",
"87 4.0 \n",
"\n",
" 2.44. Is sophisticated in art, music, or literature \\\n",
"0 3.0 \n",
"1 4.0 \n",
"2 3.0 \n",
"3 3.0 \n",
"4 2.0 \n",
".. ... \n",
"83 4.0 \n",
"84 1.0 \n",
"85 5.0 \n",
"86 5.0 \n",
"87 2.0 \n",
"\n",
" 3. What is your favourite dance style in the context of dance battles from the ones in the list below: \n",
"0 House \n",
"1 Hip Hop \n",
"2 Dancehall \n",
"3 Dancehall \n",
"4 House \n",
".. ... \n",
"83 Breaking \n",
"84 Hip Hop \n",
"85 Vogue \n",
"86 Hip Hop \n",
"87 Hip Hop \n",
"\n",
"[88 rows x 45 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"In this section the data is being preproccesed (cleaned)\n",
"\n",
"\"\"\"\n",
"# drop the first two columns as they are not part of the actual dataset to be analysed\n",
"df = df.iloc[:, 2:]\n",
"\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e9f6a20f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2 43\n",
"4 2\n",
"5 1\n",
"17 1\n",
"18 2\n",
"20 13\n",
"22 1\n",
"24 1\n",
"29 1\n",
"31 1\n",
"33 1\n",
"34 11\n",
"41 1\n",
"47 3\n",
"53 2\n",
"59 2\n",
"66 43\n",
"67 1\n",
"73 1\n",
"76 1\n",
"81 1\n",
"dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"SECTION 1: MISSING VALUES\n",
"NaN values affect the model accuracy and sampling\n",
"\"\"\"\n",
"# count the number of null values for each row\n",
"null_counts = df[df.isnull().any(axis=1)].isnull().sum(axis=1)\n",
"null_counts"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d61dc0ab",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>2.1. Is talkative</th>\n",
" <th>2.2. Tends to find fault with others</th>\n",
" <th>2.3. Does a thorough job</th>\n",
" <th>2.4. Is depressed, blue</th>\n",
" <th>2.5. Is original, comes up with new ideas</th>\n",
" <th>2.6. Is reserved</th>\n",
" <th>2.7. Is helpful and unselfish with others</th>\n",
" <th>2.8. Can be somewhat careless</th>\n",
" <th>2.9. Is relaxed, handles stress well</th>\n",
" <th>2.10. Is curious about many different things</th>\n",
" <th>...</th>\n",
" <th>2.36. Is outgoing, sociable</th>\n",
" <th>2.37. Is sometimes rude to others</th>\n",
" <th>2.38. Makes plans and follows through with them</th>\n",
" <th>2.39. Gets nervous easily</th>\n",
" <th>2.40. Likes to reflect, play with ideas</th>\n",
" <th>2.41. Has few artistic interests</th>\n",
" <th>2.42. Likes to cooperate with others</th>\n",
" <th>2.43. Is easily distracted</th>\n",
" <th>2.44. Is sophisticated in art, music, or literature</th>\n",
" <th>3. What is your favourite dance style in the context of dance battles from the ones in the list below:</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>Popping</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>Popping</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 45 columns</p>\n",
"</div>"
],
"text/plain": [
" 2.1. Is talkative 2.2. Tends to find fault with others \\\n",
"20 1.0 NaN \n",
"34 1.0 NaN \n",
"\n",
" 2.3. Does a thorough job 2.4. Is depressed, blue \\\n",
"20 1.0 NaN \n",
"34 1.0 NaN \n",
"\n",
" 2.5. Is original, comes up with new ideas 2.6. Is reserved \\\n",
"20 1.0 NaN \n",
"34 1.0 NaN \n",
"\n",
" 2.7. Is helpful and unselfish with others 2.8. Can be somewhat careless \\\n",
"20 1.0 NaN \n",
"34 1.0 NaN \n",
"\n",
" 2.9. Is relaxed, handles stress well \\\n",
"20 1.0 \n",
"34 1.0 \n",
"\n",
" 2.10. Is curious about many different things ... \\\n",
"20 1.0 ... \n",
"34 1.0 ... \n",
"\n",
" 2.36. Is outgoing, sociable 2.37. Is sometimes rude to others \\\n",
"20 1.0 NaN \n",
"34 1.0 NaN \n",
"\n",
" 2.38. Makes plans and follows through with them \\\n",
"20 NaN \n",
"34 1.0 \n",
"\n",
" 2.39. Gets nervous easily 2.40. Likes to reflect, play with ideas \\\n",
"20 1.0 1.0 \n",
"34 1.0 1.0 \n",
"\n",
" 2.41. Has few artistic interests 2.42. Likes to cooperate with others \\\n",
"20 1.0 1.0 \n",
"34 NaN 1.0 \n",
"\n",
" 2.43. Is easily distracted \\\n",
"20 1.0 \n",
"34 1.0 \n",
"\n",
" 2.44. Is sophisticated in art, music, or literature \\\n",
"20 1.0 \n",
"34 1.0 \n",
"\n",
" 3. What is your favourite dance style in the context of dance battles from the ones in the list below: \n",
"20 Popping \n",
"34 Popping \n",
"\n",
"[2 rows x 45 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# display rows with indices 20 and 34 to inspect as they have many NaN values\n",
"\n",
"rows_to_display = [20, 34]\n",
"df_display = df.iloc[rows_to_display]\n",
"\n",
"df_display"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "15902c3b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4 2\n",
"5 1\n",
"17 1\n",
"18 2\n",
"22 1\n",
"24 1\n",
"29 1\n",
"31 1\n",
"33 1\n",
"41 1\n",
"47 3\n",
"53 2\n",
"59 2\n",
"67 1\n",
"73 1\n",
"76 1\n",
"81 1\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# drop rows with index labels 2 and 66 as they have 43out of 44 NaN\n",
"#drop rows 20 and 34 as they seem to be faulty (25% NaN and all the other values = 1.0)\n",
"df = df.drop([2, 66, 20, 34], axis=0)\n",
"\n",
"# recount the number of null values for verification\n",
"null_counts = df[df.isnull().any(axis=1)].isnull().sum(axis=1)\n",
"null_counts\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "51881feb",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>2.1. Is talkative</th>\n",
" <th>2.2. Tends to find fault with others</th>\n",
" <th>2.3. Does a thorough job</th>\n",
" <th>2.4. Is depressed, blue</th>\n",
" <th>2.5. Is original, comes up with new ideas</th>\n",
" <th>2.6. Is reserved</th>\n",
" <th>2.7. Is helpful and unselfish with others</th>\n",
" <th>2.8. Can be somewhat careless</th>\n",
" <th>2.9. Is relaxed, handles stress well</th>\n",
" <th>2.10. Is curious about many different things</th>\n",
" <th>...</th>\n",
" <th>2.36. Is outgoing, sociable</th>\n",
" <th>2.37. Is sometimes rude to others</th>\n",
" <th>2.38. Makes plans and follows through with them</th>\n",
" <th>2.39. Gets nervous easily</th>\n",
" <th>2.40. Likes to reflect, play with ideas</th>\n",
" <th>2.41. Has few artistic interests</th>\n",
" <th>2.42. Likes to cooperate with others</th>\n",
" <th>2.43. Is easily distracted</th>\n",
" <th>2.44. Is sophisticated in art, music, or literature</th>\n",
" <th>3. What is your favourite dance style in the context of dance battles from the ones in the list below:</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>House</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>Hip Hop</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>Dancehall</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>House</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>Hip Hop</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 45 columns</p>\n",
"</div>"
],
"text/plain": [
" 2.1. Is talkative 2.2. Tends to find fault with others \\\n",
"0 4.0 2.0 \n",
"1 4.0 3.0 \n",
"3 5.0 2.0 \n",
"4 0.0 1.0 \n",
"5 1.0 1.0 \n",
"\n",
" 2.3. Does a thorough job 2.4. Is depressed, blue \\\n",
"0 3.0 3.0 \n",
"1 2.0 3.0 \n",
"3 2.0 2.0 \n",
"4 3.0 0.0 \n",
"5 5.0 1.0 \n",
"\n",
" 2.5. Is original, comes up with new ideas 2.6. Is reserved \\\n",
"0 2.0 3.0 \n",
"1 4.0 4.0 \n",
"3 4.0 2.0 \n",
"4 3.0 5.0 \n",
"5 3.0 5.0 \n",
"\n",
" 2.7. Is helpful and unselfish with others 2.8. Can be somewhat careless \\\n",
"0 5.0 2.0 \n",
"1 5.0 4.0 \n",
"3 4.0 5.0 \n",
"4 5.0 3.0 \n",
"5 3.0 4.0 \n",
"\n",
" 2.9. Is relaxed, handles stress well \\\n",
"0 1.0 \n",
"1 3.0 \n",
"3 4.0 \n",
"4 3.0 \n",
"5 3.0 \n",
"\n",
" 2.10. Is curious about many different things ... \\\n",
"0 4.0 ... \n",
"1 4.0 ... \n",
"3 5.0 ... \n",
"4 5.0 ... \n",
"5 5.0 ... \n",
"\n",
" 2.36. Is outgoing, sociable 2.37. Is sometimes rude to others \\\n",
"0 4.0 3.0 \n",
"1 4.0 3.0 \n",
"3 5.0 3.0 \n",
"4 3.0 3.0 \n",
"5 4.0 1.0 \n",
"\n",
" 2.38. Makes plans and follows through with them 2.39. Gets nervous easily \\\n",
"0 2.0 4.0 \n",
"1 3.0 4.0 \n",
"3 3.0 1.0 \n",
"4 3.0 3.0 \n",
"5 4.0 4.0 \n",
"\n",
" 2.40. Likes to reflect, play with ideas 2.41. Has few artistic interests \\\n",
"0 2.0 2.0 \n",
"1 4.0 4.0 \n",
"3 3.0 1.0 \n",
"4 3.0 3.0 \n",
"5 4.0 1.0 \n",
"\n",
" 2.42. Likes to cooperate with others 2.43. Is easily distracted \\\n",
"0 5.0 4.0 \n",
"1 4.0 4.0 \n",
"3 4.0 4.0 \n",
"4 5.0 3.0 \n",
"5 3.0 3.0 \n",
"\n",
" 2.44. Is sophisticated in art, music, or literature \\\n",
"0 3.0 \n",
"1 4.0 \n",
"3 3.0 \n",
"4 2.0 \n",
"5 5.0 \n",
"\n",
" 3. What is your favourite dance style in the context of dance battles from the ones in the list below: \n",
"0 House \n",
"1 Hip Hop \n",
"3 Dancehall \n",
"4 House \n",
"5 Hip Hop \n",
"\n",
"[5 rows x 45 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"The oher NaN values will not affect the results considerabily\n",
"Approach: will not be taken in consideration when calculating personality indexes as they are all below 3/44 NaN records each.\n",
"The formula that needs to be applied according to Personality-BigFiveInventory is a simple addition, so the NaN should be replaced by 0 in order to stay neutral.\n",
"\"\"\"\n",
"\n",
"df.fillna(value=0.0, inplace=True)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a0b307e9",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\AppData\\Local\\Temp\\ipykernel_18168\\3029970098.py:23: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n",
" 'Openness': df.iloc[:, openness_cols].sum(axis=1),\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Extraversion</th>\n",
" <th>Agreeableness</th>\n",
" <th>Conscientiousness</th>\n",
" <th>Neuroticism</th>\n",
" <th>Openness</th>\n",
" <th>Dance_Style</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>27.0</td>\n",
" <td>30.0</td>\n",
" <td>29.0</td>\n",
" <td>27.0</td>\n",
" <td>32.0</td>\n",
" <td>House</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>32.0</td>\n",
" <td>31.0</td>\n",
" <td>33.0</td>\n",
" <td>34.0</td>\n",
" <td>37.0</td>\n",
" <td>Hip Hop</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>26.0</td>\n",
" <td>30.0</td>\n",
" <td>24.0</td>\n",
" <td>29.0</td>\n",
" <td>30.0</td>\n",
" <td>Dancehall</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>22.0</td>\n",
" <td>26.0</td>\n",
" <td>18.0</td>\n",
" <td>24.0</td>\n",
" <td>33.0</td>\n",
" <td>House</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>22.0</td>\n",
" <td>34.0</td>\n",
" <td>27.0</td>\n",
" <td>31.0</td>\n",
" <td>31.0</td>\n",
" <td>Hip Hop</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Extraversion Agreeableness Conscientiousness Neuroticism Openness \\\n",
"0 27.0 30.0 29.0 27.0 32.0 \n",
"1 32.0 31.0 33.0 34.0 37.0 \n",
"3 26.0 30.0 24.0 29.0 30.0 \n",
"4 22.0 26.0 18.0 24.0 33.0 \n",
"5 22.0 34.0 27.0 31.0 31.0 \n",
"\n",
" Dance_Style \n",
"0 House \n",
"1 Hip Hop \n",
"3 Dancehall \n",
"4 House \n",
"5 Hip Hop "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"SECTION 2: FORMULA\n",
"Extraversion: 1, 6R, 11, 16, 21R, 26, 31R, 36 \n",
"Agreeableness: 2R, 7, 12R, 17, 22, 27R, 32, 37R, 42 \n",
"Conscientiousness: 3, 8R, 13, 18R, 23R, 28, 33, 38, 43R \n",
"Neuroticism: 4, 9R, 14, 19, 24R, 29, 34R, 39 \n",
"Openness: 5, 10, 15, 20, 25, 30, 35R, 40, 41R, 44\n",
"\n",
"\"\"\"\n",
"# Define the column groups\n",
"extraversion_cols = [1, 6, 11, 16, 21, 26, 31, 36]\n",
"agreeableness_cols = [2, 7, 12, 17, 22, 27, 32, 37, 42]\n",
"conscientiousness_cols = [3, 8, 13, 18, 23, 28, 33, 38, 43]\n",
"neuroticism_cols = [4, 9, 14, 19, 24, 29, 34, 39]\n",
"openness_cols = [5, 10, 15, 20, 25, 30, 35, 40, 41, 44]\n",
"\n",
"# Create a new DataFrame with the sum of the specified columns for each category\n",
"category_df = pd.DataFrame({\n",
" 'Extraversion': df.iloc[:, extraversion_cols].sum(axis=1),\n",
" 'Agreeableness': df.iloc[:, agreeableness_cols].sum(axis=1),\n",
" 'Conscientiousness': df.iloc[:, conscientiousness_cols].sum(axis=1),\n",
" 'Neuroticism': df.iloc[:, neuroticism_cols].sum(axis=1),\n",
" 'Openness': df.iloc[:, openness_cols].sum(axis=1),\n",
" 'Dance_Style': df.iloc[:,-1]\n",
"})\n",
"\n",
"category_df.head()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "582781d4",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Extraversion</th>\n",
" <th>Agreeableness</th>\n",
" <th>Conscientiousness</th>\n",
" <th>Neuroticism</th>\n",
" <th>Openness</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.193076</td>\n",
" <td>-0.258444</td>\n",
" <td>-0.031753</td>\n",
" <td>-0.976851</td>\n",
" <td>0.068336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.403400</td>\n",
" <td>-0.034637</td>\n",
" <td>0.857323</td>\n",
" <td>0.441392</td>\n",
" <td>1.093373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.048989</td>\n",
" <td>-0.258444</td>\n",
" <td>-1.143097</td>\n",
" <td>-0.571639</td>\n",
" <td>-0.341679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1.017249</td>\n",
" <td>-1.153674</td>\n",
" <td>-2.476711</td>\n",
" <td>-1.584670</td>\n",
" <td>0.273343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.017249</td>\n",
" <td>0.636786</td>\n",
" <td>-0.476290</td>\n",
" <td>-0.166427</td>\n",
" <td>-0.136672</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>0.435140</td>\n",
" <td>1.532016</td>\n",
" <td>0.857323</td>\n",
" <td>-0.369033</td>\n",
" <td>0.068336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>-1.501379</td>\n",
" <td>-2.496519</td>\n",
" <td>-0.698559</td>\n",
" <td>-3.610732</td>\n",
" <td>-1.161708</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>0.677205</td>\n",
" <td>0.189171</td>\n",
" <td>0.635054</td>\n",
" <td>0.441392</td>\n",
" <td>-0.341679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>-0.775184</td>\n",
" <td>-0.482252</td>\n",
" <td>1.079592</td>\n",
" <td>-0.774245</td>\n",
" <td>-0.341679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>-1.501379</td>\n",
" <td>0.189171</td>\n",
" <td>-0.698559</td>\n",
" <td>-1.787276</td>\n",
" <td>-1.161708</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>84 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" Extraversion Agreeableness Conscientiousness Neuroticism Openness\n",
"0 0.193076 -0.258444 -0.031753 -0.976851 0.068336\n",
"1 1.403400 -0.034637 0.857323 0.441392 1.093373\n",
"2 -0.048989 -0.258444 -1.143097 -0.571639 -0.341679\n",
"3 -1.017249 -1.153674 -2.476711 -1.584670 0.273343\n",
"4 -1.017249 0.636786 -0.476290 -0.166427 -0.136672\n",
".. ... ... ... ... ...\n",
"79 0.435140 1.532016 0.857323 -0.369033 0.068336\n",
"80 -1.501379 -2.496519 -0.698559 -3.610732 -1.161708\n",
"81 0.677205 0.189171 0.635054 0.441392 -0.341679\n",
"82 -0.775184 -0.482252 1.079592 -0.774245 -0.341679\n",
"83 -1.501379 0.189171 -0.698559 -1.787276 -1.161708\n",
"\n",
"[84 rows x 5 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"SECTION 3: Scaling the numerical features using StandardScaler\n",
"\"\"\"\n",
"X = category_df.loc[:, category_df.columns != 'Dance_Style'] # select all columns except target\n",
"y = category_df['Dance_Style'] # select only the target column\n",
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"scaler = StandardScaler()\n",
"X_scaled = scaler.fit_transform(X)\n",
"\n",
"column_names =['Extraversion','Agreeableness','Conscientiousness','Neuroticism','Openness']\n",
"df_scaled = pd.DataFrame(X_scaled, columns=column_names)\n",
"df_scaled "
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3e95c3e5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\"\"\"\n",
"SECION 4: Encoding the target variable\n",
"\"\"\"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"target_counts = y.value_counts()\n",
"sns.barplot(x=target_counts.index, y=target_counts.values)\n",
"plt.title('Class Distribution of Dance Styles')\n",
"plt.xlabel('Dance Styles')\n",
"plt.ylabel('Count')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "13d6abf5",
"metadata": {},
"outputs": [],
"source": [
"#combining Locking and Popping\n",
"y= y.replace({'Popping': 'Popping/Locking', 'Locking': 'Popping/Locking'})"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "533107a2",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Breaking</th>\n",
" <th>Dancehall</th>\n",
" <th>Hip Hop</th>\n",
" <th>House</th>\n",
" <th>Popping/Locking</th>\n",
" <th>Vogue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>84 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Breaking Dancehall Hip Hop House Popping/Locking Vogue\n",
"0 0 0 0 1 0 0\n",
"1 0 0 1 0 0 0\n",
"3 0 1 0 0 0 0\n",
"4 0 0 0 1 0 0\n",
"5 0 0 1 0 0 0\n",
".. ... ... ... ... ... ...\n",
"83 1 0 0 0 0 0\n",
"84 0 0 1 0 0 0\n",
"85 0 0 0 0 0 1\n",
"86 0 0 1 0 0 0\n",
"87 0 0 1 0 0 0\n",
"\n",
"[84 rows x 6 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#encoding the target text variables using One Hot Encoder \n",
"df_encoded = pd.get_dummies(y)\n",
"df_encoded"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "e1e72352",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Breaking</th>\n",
" <th>Dancehall</th>\n",
" <th>Hip Hop</th>\n",
" <th>House</th>\n",
" <th>Popping/Locking</th>\n",
" <th>Vogue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>84 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Breaking Dancehall Hip Hop House Popping/Locking Vogue\n",
"0 0 0 0 1 0 0\n",
"1 0 0 1 0 0 0\n",
"2 0 1 0 0 0 0\n",
"3 0 0 0 1 0 0\n",
"4 0 0 1 0 0 0\n",
".. ... ... ... ... ... ...\n",
"79 1 0 0 0 0 0\n",
"80 0 0 1 0 0 0\n",
"81 0 0 0 0 0 1\n",
"82 0 0 1 0 0 0\n",
"83 0 0 1 0 0 0\n",
"\n",
"[84 rows x 6 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#reset the index that was originally affected by the dropped columns\n",
"df_encoded = df_encoded.reset_index(drop=True)\n",
"df_encoded"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "abaa125f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Extraversion</th>\n",
" <th>Agreeableness</th>\n",
" <th>Conscientiousness</th>\n",
" <th>Neuroticism</th>\n",
" <th>Openness</th>\n",
" <th>Breaking</th>\n",
" <th>Dancehall</th>\n",
" <th>Hip Hop</th>\n",
" <th>House</th>\n",
" <th>Popping/Locking</th>\n",
" <th>Vogue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.193076</td>\n",
" <td>-0.258444</td>\n",
" <td>-0.031753</td>\n",
" <td>-0.976851</td>\n",
" <td>0.068336</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.403400</td>\n",
" <td>-0.034637</td>\n",
" <td>0.857323</td>\n",
" <td>0.441392</td>\n",
" <td>1.093373</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.048989</td>\n",
" <td>-0.258444</td>\n",
" <td>-1.143097</td>\n",
" <td>-0.571639</td>\n",
" <td>-0.341679</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1.017249</td>\n",
" <td>-1.153674</td>\n",
" <td>-2.476711</td>\n",
" <td>-1.584670</td>\n",
" <td>0.273343</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.017249</td>\n",
" <td>0.636786</td>\n",
" <td>-0.476290</td>\n",
" <td>-0.166427</td>\n",
" <td>-0.136672</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>0.435140</td>\n",
" <td>1.532016</td>\n",
" <td>0.857323</td>\n",
" <td>-0.369033</td>\n",
" <td>0.068336</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>-1.501379</td>\n",
" <td>-2.496519</td>\n",
" <td>-0.698559</td>\n",
" <td>-3.610732</td>\n",
" <td>-1.161708</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>0.677205</td>\n",
" <td>0.189171</td>\n",
" <td>0.635054</td>\n",
" <td>0.441392</td>\n",
" <td>-0.341679</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>-0.775184</td>\n",
" <td>-0.482252</td>\n",
" <td>1.079592</td>\n",
" <td>-0.774245</td>\n",
" <td>-0.341679</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>-1.501379</td>\n",
" <td>0.189171</td>\n",
" <td>-0.698559</td>\n",
" <td>-1.787276</td>\n",
" <td>-1.161708</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>84 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" Extraversion Agreeableness Conscientiousness Neuroticism Openness \\\n",
"0 0.193076 -0.258444 -0.031753 -0.976851 0.068336 \n",
"1 1.403400 -0.034637 0.857323 0.441392 1.093373 \n",
"2 -0.048989 -0.258444 -1.143097 -0.571639 -0.341679 \n",
"3 -1.017249 -1.153674 -2.476711 -1.584670 0.273343 \n",
"4 -1.017249 0.636786 -0.476290 -0.166427 -0.136672 \n",
".. ... ... ... ... ... \n",
"79 0.435140 1.532016 0.857323 -0.369033 0.068336 \n",
"80 -1.501379 -2.496519 -0.698559 -3.610732 -1.161708 \n",
"81 0.677205 0.189171 0.635054 0.441392 -0.341679 \n",
"82 -0.775184 -0.482252 1.079592 -0.774245 -0.341679 \n",
"83 -1.501379 0.189171 -0.698559 -1.787276 -1.161708 \n",
"\n",
" Breaking Dancehall Hip Hop House Popping/Locking Vogue \n",
"0 0 0 0 1 0 0 \n",
"1 0 0 1 0 0 0 \n",
"2 0 1 0 0 0 0 \n",
"3 0 0 0 1 0 0 \n",
"4 0 0 1 0 0 0 \n",
".. ... ... ... ... ... ... \n",
"79 1 0 0 0 0 0 \n",
"80 0 0 1 0 0 0 \n",
"81 0 0 0 0 0 1 \n",
"82 0 0 1 0 0 0 \n",
"83 0 0 1 0 0 0 \n",
"\n",
"[84 rows x 11 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df= pd.concat([df_scaled, df_encoded], axis=1)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "5f3f1779",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: >"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAEGEAAAw5CAYAAABPjeuGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd5TcVcE+8Gd2Z9NJI4WEIARCCyQIBAm9iJQoRQWCdAsCFn4vCYjoS1MBEcVGUVBERREVX0QFRJEiSBFDDD0VUgikkF62zu+P4MKSBMlmdyern885cw5z597vPl/+uOfuzOaZQqlUKgUAAAAAAAAAAAAAAAAAAADgv1xFuQMAAAAAAAAAAAAAAAAAAAAAbAiUMAAAAAAAAAAAAAAAAAAAAABECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAA0AYefPDBHH744Rk4cGAKhUJuv/32f7vmgQceyK677ppOnTplyy23zPe+971WzaiEAQAAAAAAAAAAAAAAAAAAAGh1y5Yty0477ZSrr776Hc2fNm1aRo0alX322SdPPvlkvvCFL+Sss87Kbbfd1moZC6VSqdRqVwcAAAAAAAAAAAAAAAAAAAB4i0KhkP/7v//LUUcdtdY55513Xu64444899xzjWNnnHFG/vnPf+aRRx5plVwVrXJVAAAAAAAAAAAAAAAAAAAA4D9adXV1Fi9e3ORRXV3dYtd/5JFHcvDBBzcZO+SQQ/LEE0+ktra2xX7OmxVb5arNUDtvarkjALRry8eeVu4IAO1acatNyh0BoH2rKJQ7AUD7Vldf7gQA7d6zN6wsdwSAdu2Wqo7ljgDQrg2v3WD+DAugXXrFNgqwXjapK3cCgPZvQWW5EwC0b2dPv7ncEYC18O+3aQuXX/2TXHLJJU3GLrroolx88cUtcv1XXnkl/fv3bzLWv3//1NXVZd68eRkwYECL/Jw387Y1AAAAAAAAAAAAAAAAAAAAsM7OP//8jBkzpslYx44t+2UQhULTL44slUprHG8pShgAAAAAAAAAAAAAAAAAAACAddaxY8cWL114s0022SSvvPJKk7E5c+akWCxm4403bpWfWdEqVwUAAAAAAAAAAAAAAAAAAABYD3vssUf+9Kc/NRm75557MmLEiFRVVbXKz1TCAAAAAAAAAAAAAAAAAAAAALS6pUuXZvz48Rk/fnySZNq0aRk/fnymT5+eJDn//PNz8sknN84/44wz8tJLL2XMmDF57rnncuONN+aHP/xhzjnnnFbLWGy1KwMAAAAAAAAAAAAAAAAAAAC87oknnsgBBxzQ+HzMmDFJklNOOSU33XRTZs+e3VjIkCSDBw/OnXfembPPPjvXXHNNBg4cmO985zv58Ic/3GoZlTAAAAAAAAAAAAAAAAAAAAAArW7//fdPqVRa6+s33XTTamP77bdfxo0b14qpmqpos58EAAAAAAAAAAAAAAAAAAAAsAFTwgAAAAAAAAAAAAAAAAAAAAAQJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQJCmWOwAAAAAAAAAAAAAAAAAAAAAtrKG+3AmgXaoodwAAAAAAAAAAAAAAAAAAAACADYESBgAAAAAAAAAAAAAAAAAAAIAoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASJIUyx0AAAAAAAAAAAAAAAAAAACAFlZqKHcCaJcqyh0AAAAAAAAAAAAAAAAAAAAAYEOghAEAAAAAAAAAAAAAAAAAAAAgShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIkxXIHAAAAAAAAAAAAAAAAAAAAoIU1NJQ7AbRLFeUOAAAAAAAAAAAAAAAAAAAAALAhUMIAAAAAAAAAAAAAAAAAAAAAECUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJkmK5AwAAAAAAAAAAAAAAAAAAANCySqWGckeAdqmi3AEAAAAAAAAAAAAAAAAAAAAANgRKGAAAAAAAAAAAAAAAAAAAAACihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIElSLHcAAAAAAAAAAAAAAAAAAAAAWlhDQ7kTQLtUUe4AAAAAAAAAAAAAAAAAAAAAABsCJQwAAAAAAAAAAAAAAAAAAAAAUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJAkKZY7AAAAAAAAAAAAAAAAAAAAAC2s1FDuBNAuVZQ7AAAAAAAAAAAAAAAAAAAAAMCGQAkDAAAAAAAAAAAAAAAAAAAAQJQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkSYrlDgAAAAAAAAAAAAAAAAAAAEALa6gvdwJolyrKHQAAAAAAAAAAAAAAAAAAAABgQ6CEAQAAAAAAAAAAAAAAAAAAACBKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiSFMsdAAAAAAAAAAAAAAAAAAAAgBZWaih3AmiXKsodAAAAAAAAAAAAAAAAAAAAAGBDoIQBAAAAAAAAAAAAAAAAAAAAIEmxuQsnTpyY+++/P3PmzElDQ0OT1y688ML1DgYAAAAAAAAAAAAAAAAAAADQlppVwnDDDTfkzDPPTJ8+fbLJJpukUCg0vlYoFJQwAAAAAAAAAAAAAAAAAAAAAO1Os0oYvvKVr+TSSy/Neeed19J5AAAAAAAAAAAAAAAAAAAAAMqiojmLFixYkGOOOaalswAAAAAAAAAAAAAAAAAAAACUTbNKGI455pjcc889LZ0FAAAAAAAAAAAAAAAAAAAAoGyKzVk0ZMiQXHDBBXn00UczbNiwVFVVNXn9rLPOapFwAAAAAAAAAAAAAAAAAAAAAG2lWSUM119/fbp165YHHnggDzzwQJPXCoWCEgYAAAAAAAAAAAAAAAAAAACg3WlWCcO0adNaOgcAAAAAAAAAAAAAAAAAAABAWVWs7wVKpVJKpVJLZAEAAAAAAAAAAAAAAAAAAAAom2JzF/7kJz/JlVdemUmTJiVJttlmm5x77rk56aSTWiwcAAAAAAAAAAAAAAAAAAAAzdDQUO4E0C41q4ThqquuygUXXJDPfOYz2WuvvVIqlfLwww/njDPOyLx583L22We3dE4AAAAAAAAAAAAAAAAAAACAVtWsEobvfve7ue6663LyySc3jh155JHZYYcdcvHFFythAAAAAAAAAAAAAAAAAAAAANqdiuYsmj17dvbcc8/Vxvfcc8/Mnj17vUMBAAAAAAAAAAAAAAAAAAAAtLVmlTAMGTIkv/zlL1cbv/XWW7P11luvdygAAAAAAAAAAAAAAAAAAACAtlZszqJLLrkko0ePzoMPPpi99torhUIhDz30UO699941ljMAAAAAAAAAAAAAAAAAAAAAbOgqmrPowx/+cB577LH06dMnt99+e37zm9+kT58+efzxx/PBD36wpTMCAAAAAAAAAAAAAAAAAAAAtLpicxfuuuuuufnmm1syCwAAAAAAAAAAAAAAAAAAAEDZvOMShsWLF6d79+6N//12/jUPAAAAAAAAAAAAAAAAAAAAoL14xyUMvXr1yuzZs9OvX7/07NkzhUJhtTmlUimFQiH19fUtGhIAAAAAAAAAAAAAAAAAAACgtb3jEoa//OUv6d27d5Lkvvvua7VAAAAAAAAAAAAAAAAAAAAAAOXwjksY9ttvvzX+NwAAAAAAAAAAAAAAAAAAAMB/gndcwvBmd999d7p165a99947SXLNNdfkhhtuyNChQ3PNNdekV69eLRoSAAAAAAAAAAAAAAAAAACAd65Uaih3BGiXKpqz6Nxzz83ixYuTJE899VTGjBmTUaNGZerUqRkzZkyLBgQAAAAAAAAAAAAAAAAAAABoC8XmLJo2bVqGDh2aJLntttty+OGH57LLLsu4ceMyatSoFg0IAAAAAAAAAAAAAAAAAAAA0BYqmrOoQ4cOWb58eZLkz3/+cw4++OAkSe/evbN48eKWSwcAAAAAAAAAAAAAAAAAAADQRorNWbTXXntlzJgx2WuvvfL444/n1ltvTZJMnDgxgwYNatGAAAAAAAAAAAAAAAAAAAAAAG2hojmLrrnmmlRVVeXXv/51rrvuumy66aZJkrvuuiuHHnpoiwYEAAAAAAAAAAAAAAAAAAAAaAvFdV1QV1eX++67L9dff30GDBjQ5LVvfvObLRYMAAAAAAAAAAAAAAAAAAAAoC1VrOuCYrGYM888MzU1Na2RBwAAAAAAAAAAAAAAAAAAAKAs1rmEIUl23333PPnkky2dBQAAAAAAAAAAAAAAAAAAAKBsis1Z9KlPfSpjx47NzJkzs+uuu6Zr165NXh8+fHiLhAMAAAAAAAAAAAAAAAAAAABoK80qYRg9enSS5KyzzmocKxQKKZVKKRQKqa+vb5l0AAAAAAAAAAAAAAAAAAAAAG2kWSUM06ZNa+kcAAAAAAAAAAAAAAAAAAAAAGXVrBKGzTffvKVzAAAAAAAAAAAAAAAAAAAA0FIaGsqdANqliuYu/OlPf5q99torAwcOzEsvvZQk+da3vpXf/va3LRYOAAAAAAAAAAAAAAAAAAAAoK0Um7Pouuuuy4UXXpj/+Z//yaWXXpr6+vokSc+ePfOtb30rRx55ZIuGhHJ5YvxT+dHPf51nn5+cufNfy7cvvyDv3XfPcscCKLsOBx6RjqOOTaHHxml4+cWs+Nm1qZ/41L9dV7n1Dul6/jfTMHNall54etMXu3RNpw9/PFUj9k6hy0ZpmDc7K2/5XuomPN5KdwFQXsURB6Vqj/ensFHPNMyZlZp7fpqG6S+scW7F5tun8yn/u9r48mvOSWn+7Deuufuhqdr1vSn06JPS8iWpf+7x1Nx7a1Jf22r3AVAuxV0PStUeo1Lo1jMNc2el5p6b0zDjbfbRk7642vjy6859Yx+tqEzVXoenOHyfFDbqlYb5s1N7762pnzqhNW8DoGyKu70vVXt+4PXz6MzU3P2TtZ9Ht9g+nU+9cLXx5VePTWney0mSTqdekMothq42p27ik6n++ddaNjzABqrvKYdmwBlHpapfr6yYOCPTL/phlj7+3Brn9jpsZPqefEi67DA4FR2qsmLijMz6xi+y+IHxbRsaoMwO+Z+jM/IjB6ZLj255afzk3HbBjXl10sy1zu+/9aAcNuaYDBq2ZXoP6pvbv/TjPHjjXU3m7Hni+7LnCQel96C+SZJXJs3MPd/5TZ6/f3xr3gpAq9r2lIOy4xmj0qVfzyyYOCuPX3Rz5jy+5t/jk6T/yO2y20UnpNc2m2b5qwvz9HW/zws//Uvj6+86bESGf/aIdN+ifwpVlVky7dU8/f07M/W2hxvnHP3oN9Nts76rXfu5m/6Ux77445a9QYA2svf/fCg7HX9AOvXomtlPTsk9F9yUeZNmve2abQ/bLfuMPTo939UvC6fPyYNX/ioT//jEGueO/NTh2f+80fn7D+/OvV+6uXG8S5/uOeDzx2WLfYelU/cumfHYC/nTRT/OghdfbdH7A2gtzqMALWPk2R/KsDedR++74KbMn/j259Ehh+2WPc85Oj3e1S+Lps/Jw1/7Vaa86Ty626cPz5BDd0vvrQakbmVNXv7HpDx0+a1ZMPWNvy09+BufzA7H7NvkurPHTc4vjrq4Re8PAOA/TbNKGL773e/mhhtuyFFHHZWvfvWrjeMjRozIOeec02LhoNxWrFiZbYdsmaNGHZyzv/iVcscB2CBUvWf/dDrhU1nxk++kfuLT6XDAB9J17OVZcv7HUnptztoXdu6azp/8fOqeHZeK7r2avlZZTNdzv5bS4oVZfvUlaXhtbip690tp5fLWvRmAMqkcOjIdDjkpNXf+KPUzJqZqlwPT6fjPZcW1n0tp8fy1rlt+9dikekXj89LyxW9cc8c90+G9o1N9xw1pmDExFRsPSIcjT0+HJDX33LyGqwG0X5VDd0+Hg09MzV03vbGPfuTcrPjeeW+/j157zlr30ar9j05xx71S84cfpmH+y6nccng6HvM/WXnTJWl49aVWvR+Atla5w8h0OPTk1PzhxtRPfyFVIw5KpxM/nxXXnJPSorfZR797dtN9dNkb++jKW69KofJNHzl03iidz/xq6p99tFXuAWBD0/uIvfKuiz+Wl75wfZb+/fn0PengbHPzBXl6/7NS8/K81eZ3Gzk0ix/8Z2Z99WepW7wsfUYfmK1v+kKe+8B5Wf7MtDLcAUDbO/CMI7Lfx0fllnOuy9xps/O+z34oZ9z8hXz1wDGpXrZyjWs6dO6Q+dPnZPydj+aoC05e45yFs+fnD1fcknkvrfoHbSM+vG8+dv05+cb7P/+2BQ8AG6otjtg977n4xDz6hZsy5+8Ts+1JB+Z9N5+b2/c/L8teXv33+G6b9c1BPz0nk35+f/762evSb7dtMvKyU7Ny/pK8dOffkyQ1C5dlwnfuyKLJL6ehti6DDto5e1/1yayctzgvP7DqCxh+N+rCVFRWNF6353aDcsgvzs9Lv/dFCkD7tPsZH8hunzgsfzjn+3lt6ivZ87NHZvTPPp8bDjg3NWs5fw7cZUiOvPozefAbv87EPz6RbQ4ZkSOv+UxuPvrLmT1+SpO5mwzfMu8+/oDMeXb1z5U+fMPZaaitz22f+GZqlq7Ibp84LMf97Pz84KDzUruiulXuF6ClOI8CtIwRZ34gu3zisNwz9vtZMPWV7H7WkfnQzz6fm/Y/N7VrOY8O2GVI3n/NZ/K3b/w6k+9+IkMOHZH3X/uZ/PLDX84rr59HB+2+ff754z/l1QlTU6iszF6fOyYfuvm8/Pi956XuTWfNaff9M/ecc33j8/qauta9YQCA/wAV/37K6qZNm5add955tfGOHTtm2bJl6x0KNhT77LFbzvrkKXnf/nuVOwrABqPDoUen5sG7UvvAnWmYPT0rf35tGl6bkw7vPfxt13U+9ezUPnJv6ic/u/o19z00hW7ds/w7F6Z+0jMpzZ+T+klPp2HG1Na6DYCyqtrjsNQ9eX/qnrw/pXkvp+aem1NaND/FEQe97brSssUpLVvU+Eip1Pha5aCt0zBjYuqf/ltKi+alfupTqX/6kVQMHNzatwPQ5qp2Pyx14+9P3fj7U5r/cmr+dHNKi+enuOt733bd2+2jxWF7p/bhO1I/5Z8pLZybunH3pn7qhFSNHNXatwPQ5qr2eH/qxt2XunH3rTqP3v2T18+j73vbdaVli1Nauqjx8eZ9NCuWNXmtcqthSW116p55rJXvBmDD0P+0IzLvF/dm3i1/zsrJMzPjohtT8/L89Dv50DXOn3HRjXnlutuz7J+TUz1tdmZ99WepnjY7Pd+3WxsnByiffT92WP58ze156o9/zysTZ+bnY69Nh84ds8uRa/98fsaEqfnd5T/L+N89krq1/JHws/eOy3P3j8/cabMzd9rs3PX1W1OzfGW22Hnr1roVgFa1w2mHZdIv7s+kW+7Poskv5/GLbs6yl+dn25PX/H7oticdmGWz5ufxi27OoskvZ9It92fyrQ9khzPeeK/zlUeey/S7n8iiyS9nyUtz8twP/5gFz81I//ds2zin+rUlWTF3UeNjs4N2zuJpr+aVR55r9XsGaA27ffzQ/O3q32bi3U9k3sSZ+cPY76eqU4cMPXLPta/52KGZ9tDTefTa3+W1KbPz6LW/y0sPP5vdPtb09/2qLh1zxLfPzF3n/TArFzX90plegzfJprtsnT9+8Ud5ZcLUvDZ1du753x+lQ9eO2f7IPVrlXgFakvMoQMvY5eOH5vGrf5vJdz+R+RNn5o9jvp9ipw7Z7qi1n0d3/viheemvT+fv1/wuC6bMzt+v+V1mPPxsdv74G+fR/zv5a3n213/N/ImzMu+56bln7PXpPqhP+g/bosm16mtqs3zuosZH9SL//g8A4N9pVgnD4MGDM378+NXG77rrrgwdOnR9MwEAG6rKYiq32CZ1Tz/RZLju6X+kOGSHtS6r2ueQVPQbkOrbf7LG14s775n6yc+m88lnZaPv/DrdLv1BOn7g+KTQrKMKwIatojIVAwanfspTTYbrpz6Vys3e/o+AO3/y0nQ+++p0Oun8VGzR9Hev+hkvpGLA4FQM3DJJUujZN5VDdkr9pPEtGh+g7P61j059uslw/dSnUzno3+yjp30lnf/f1el0wvmp2Hz7Jq8VKotJfW3TBXW1qdhsmxaJDbDBqKxMxcDBqZ8yoclw/ZQJqfw3e17n0y9P57HXptPJX1ztPPpWVTvvn7qnH0lqfYsb8J+vUFVM1+FbZdED45uML35gfLqO2O4dXqSQim6dU7dwScsHBNgA9d6sX7r365UX/vrGubS+pi5THnsuW+zacr+LFyoKeffhe6RD5455cdzEFrsuQFupqKrMxsMH5+UHmr4f+vIDT6ffiDW/H9p3161Xmz/r/qfSZ/jgFIqVa1wzYO8d0n2rTfLKo8+vNceWH9ork259oBl3AVB+PTbrm279eubFv77xOX19TV1mPPZ8Nt117Z8vDdxlSF58sOln+9MenLDamoO/fGqm/GV8Xnr4mdWuUexQTJLUVb/xOVSpoZT62vpsNsLnUMCGzXkUoGX0eFffdO3XMy892PQ8Ouux5zPwbc6jA3YZ0mRNkrz4wIS3XdNhoy5JkpULm5YsDBq5fU4fd01Ovf/KHHTFx9N54+7NuRUAgP8qxeYsOvfcc/PpT386K1euTKlUyuOPP55bbrkll19+eX7wgx/82/XV1dWprm76h5cV1dXp2LFjc+IAAG2ksFGPFCorU1q0oMl4adGCFHr0XuOaiv6bptMxp2XZpf+TNDSseU7fAanYfufUPnJvll11fir7D0qnk89KKitT/duftvRtAJRVoctGKVRUrvoG9jcpLVuUQtcea1xTWrow1b/7QRpmT0uKxRSH7Z1OJ52flT++NA3TV334WP/Mo6np0j2dPnrRqp9TWUzt3/+U2od/17o3BNDG3nYf7dZzjWtKSxam+g8/SMPsF5PK1/fRE8/Pyp9emobpLyRZVYZTtfthqX/p+ZQWzEnF4B1Suc0uisGA/ziFLt3fZh9dy3l0ycJU33FDGmZPTSqrUtxpn3Q65YtZedOX0/DS6n8MV7HpVqno/65U33F9q9wDwIam2HujFIqVqZu3sMl47byF6d6v5zu6xianH5nKLp3y2u/+1vIBATZA3fv2TJIsmdv0XLpk7qL0GtRnva8/YNvNctZvvpxix6rULF+ZH53+jbw6edZ6XxegrXXsvVEqipVZMa/pfrli3qJ0XstZs3O/HmucX1FVTKfeG2XFnIVJkqqNOufYf3w3lR2KKdU35JEv3JTZf316DVdM3nXoiHTo3iWTf/nget8TQDl0e33PXPaW8+eyeYvSfdO1nz+79e2ZZfNWX9O17xvvpW5/+Mj033GL/PiIC9d4jflTZmfRjLnZ77zRufv8H6Z2RXXe84lR6davZ7q+w/cNAMrFeRSgZXR5/f3Q5W/ZH5fPW5SN3uY82rVvzzWu6dJ3zZ/tJ8l+F56QWY+/kPkTZzaOvXj/PzPpD49n8cx56fGuvtlj7NE5+hfn5+fvvyD1NXXNuCMAgP8OzSph+OhHP5q6urp87nOfy/Lly3P88cdn0003zbe//e0cd9xx/3b95ZdfnksuuaTJ2P+ee1Yu/Nz/a04cAKCtld7yvJCk9NbBJIWKdD7jC6n+v5vS8OrM1V//l4qKlJYsyIofXZWUGtLw4qQUem6cjqOOVcIA/Adb02a6lpnzZ6du/uzG5zUzJ6fQY+NU7TEq1a+XMFRsvn2q9jkyNXf+KPWzpqSiV/90OPSkVC1dmNq/3t4K+QHKbE3nzzWNJSm9Njt1r71pH501OYXuvVM18v2pfr2Eofqen6bj+z+ezmdemaSU0oI5qfvngynutG9rpAcov9W2zMKaBldNXe08OmnVPrrnB1K9hhKG4s77p+HV6WmYNaXF4gK0B6sdRwuFtZ5R36z3kXtn4NjRmfyxy1M3f9G/nQ/QHu1y5F455rLTGp//4GNXJElKb9knC2v7zGkdzZn6cr4x6rx06t41Ox32nnzkG5/KNaMvUcQAtF/rul++df4axmuXrswdB38xVV07ZsDeO+Q9F52QpdPn5pVHnlvtclsft19m3ffPrHh1YfPyA7SxoUftmUMv+1jj81999OtJ1vS2aGFtb4s2ervf9zca0DsHXXRSbj3pitRX165xfUNdfX5zxrcz6mun5eynrk9DXX1efOiZTLlv/Du/IYBycx4FWCfbHbVn3nv5G+fR209ddR5d/c9G//159K2vF97m86cDvnxK+my3WX754S83GZ/4u8ca/3v+xJl5dcK0fPxv38rgA9+dyXc/8W8CAAD892pWCUOSnHbaaTnttNMyb968NDQ0pF+/fu947fnnn58xY8Y0GatY4oNuANjQlZYsSqm+PoWevZqMF7r3SmnxgtUXdO6c4pbbpXLzrdPppLNen1xIoaIi3W+8J8uu/Fzqnxuf0sL5KdXXJaWGxqUNs6enoufGSWUxqdewCfznKC1fklJDfQpdezYZL3Ttvtq3Eb+dhpmTUxy2V+PzDgccnboJD6XuyfuTJPVzZqSmQ8d0/MDHU/vX3+bfv1MP0D407qPdejYZL3TtsW776Kym+2iWL0n1r76VVFal0KVbSksWpOrA0SktnNsywQE2EKXli1/fR5t+M0aha/eUli5+x9dpmDk5xeF7r/5CVYcUd9wzNff9an2jArQbda8tSamuPlWvf4vRv1Rt3CO1c9/+jNr7iL2yxTc+kymnX5nFf53QiikByuuZP/8j08dPbnxe2aEqSdK9X88smbuwcbxbnx5ZMm/9C2nqa+sz76VXkyQzn5qazYZvlX0/dlh+9YUfrPe1AdpS9WtL0lBXn85vOWt22rhHVqzlrLlizqLV5/fpkYbauqxcsPSNwVIpS15ctVe+9sz09BiyaYZ95vDV/tFb1003zoB9dsx9n/jW+t4OQJuZ/KdxufHJN0piix1W/bl0t749suz1b2BPkq4bd8+ytzl/Lp27MN3e8i3Dq9asei91k2GD07Vvj5z6+zf+kVtFsTKb7b5tdj3lfbly61NTaijl1adfzI9GfTEdN+qciqpiVry2JCfffnFmPzWtJW4XoNU4jwI0z5Q/jcvsN59HO646j3Z5y3m0y8bds/xtzqPL5i5Ml7ecRztv3D3L563+2f7+l5ycrd63S355zFey9JXX3jbfsjkLs3jWvPQcvMk7uR0AgP9aFc1ZdMkll2TKlFWHwT59+qxTAUOSdOzYMd27d2/y6NixY3OiAABtqb4u9S9OTHGHXZsMF3fYNXWTn1l9/orlWfKFj2fpBZ9sfNTc97vUvzw9Sy/4ZOqnrPq2zLpJz6Si36av1yOvUtF/UBoWzFPAAPznaahPw+xpqdxyxybDlVsOS/2MSe/4MhWbbJHS0oVvDBQ7rt5u3NCQpPCmOnmA/wD/2kcHv2UfHbxj6meuxz76L/W1KS1ZkFRUprjde1I3cdx6BgbYwNTXp+HlaancaniT4cqthqV+xsR3fJmKAWveR4s7jEyKxdRNeGh9kwK0G6XauiybMCU99t2pyXj3fXfKsieeX+u63kfuncFXfTZTP31VFt37j9aOCVBW1ctWZt5LrzY+Xp00M4vnLMg2ew9rnFNZVZmtdt8+L/7jnZ9L37FCobH4AaA9aaitz/wJ0zJw36bvhw7cd8fMeWLN74fO/cek1efvt2PmTZiWUl392n9YIWvcK7cevV9WzlucmfeOX+f8AOVSs2xlFr70auNj3qRZWTpnYbbY+439saKqMpvtvl1m/WPtny+9PG5yttin6Z66xb7DGte89PAz+cH7Pp8bD/ti42P2P6fmmdv/lhsP+2JKDU0/w69esiIrXluSXlv0zybDt8yke7wfAGzYnEcBmqd22coseunVxsf8ibOybM7CbL5P0/Poprtvl5ff5jw6e9zkJmuSZPN9h6225oAvnZytDxuRXx93WRbP+PdfONOpZ7dsNKB3k0IIAABW16wShttuuy3bbLNNRo4cmauvvjpz5/pGQP4zLV++Is9PnJLnJ64qHZn18qt5fuKUzH5lTpmTAZRPzd2/Tof9RqVqn0NTMeBd6XT8manYuF9q/vK7JEnHYz6ezp88b9XkUikNs15s8igtXpjU1qRh1otJzcpV1/zLHSl0655OJ3w6Ff0HpbjT7ul4+PGpufe35blJgFZW+8hdKe5yQIrv3i+FPgPT4eATU+ixcer+cW+SpOrA0elw5BmN84u7H5rKbXdNoXf/FPpumqoDR6c49D2p/fs9jXPqJ41L1YiDUrnDyBR69k3FljumwwFHp37iuNXLGQDaudrH7kpx5/1T3GnfFDYemA7vO2HVPjru9X30gGPT4YjTG+cX33NIKrfZNYVe/VPos2mqDjg2xe3fk9q//6lxTsXArVK57YhVe+hm26bTRz6XFAqp/dvv2/r2AFpd7SN/WHUe3Xn/VefRQ05KoUef1D3x5yRJ1XuPS4cPntk4vzjysFRuNyKF3puk0HdQqt57XIpDd0/t439c7drFnQ9I/fNPJCuWrvYawH+yV2+4I30+clD6jH5vOg0ZlM0u/mg6bNonc366aq8c9PkTM/jbZzXO733k3hn87f+X6V++KUvHTUyxb88U+/ZM5UZdynULAG3uwRvvykGfPirDDtktm2wzKB/5+qdSs6I64377cOOcj3zjU3n/545rfF5ZVZmBQzfPwKGbp7KqMj36987AoZunz+b9G+eMOve4DN5tu/Qa1DcDtt0sh50zOkNGDs242xWFAe3TMzfcla0/sn+GjN43PYYMzG4Xn5Cum26cF3666v3QXT5/bPb+9hvvh77w07+k66CNs9tFJ6THkIEZMnrfbH3c/nnme3c2zhn2mcMzYJ8d0+1dfdNjqwEZ+snDMuTovTPlNw83/eGFQoaM3jdTfvXXlOob2uR+AVrL3394d/b49BHZ5pAR6bPNoLz/G6endmVNnv3t3xrnfOCq07Pf545tfP7Ej/6YwfsMy+5nfCC9txqQ3c/4QLbYa4f8/ca7k6wqe5g3cWaTR+3y6qxYsDTzJs5svM62o96Td43cPj0265ut37dLjrv585l0zxN58a9Pt93/AIBmch4FaBnjfnh3dvv0EdnqkBHZeJtBOeQbp6duZU2ev/2N8+gh3zw9e533xnn0yRv/mM33HZYRZ34gvbYakBFnfiDv2nuHPPnDuxvnHPiVU7PdB/fKnZ+9NjXLVqZL3x7p0rdHKjuuKrap6tIx+3zxIxmwy5B0H9Qng0ZunyNvHJMVC5Zm8t1PtN3/AACAdqjYnEUTJkzIM888k5/97Ge56qqrMmbMmBx00EE58cQTc9RRR6VLF38cxH+Gp5+flI999rzG51/77vVJkiMPOyiX/u/YcsUCKKvax+9fVZhw5Ekp9OydhlkvZtlV56c0f1VBTUWPjVPRu986XbP02twsu/K8dDr+zHT7yg1pWDgvNff8JtV/+EVr3AJA2dU/+2hqunRL1b4fTIduPdMwZ2ZW/vzKlBbNS5IUuvVMRY+NG+cXKoupet/xKWzUO6mrScPcmVn586+lfvI/G+fUPnh7Uko6HHBMChv1Tmn54tRPfDI1f/llW98eQKurf/ax1HTeKFX7vL6Pzp2Zlb+4MqVF85P8ax/t0zi/UFlM1UHHp7BRr9f30VlZecuVqZ/yxj6aYlU67H9MCr36JjXVqZs8PrW/vS6pXt7WtwfQ6uqfeTQ1XTZK1X4fev08OiMrf3bFG+fRjdawjx58whvn0Tkzs/JnV6R+0vgm1y1svEkqN98uK35yWVveDsAG4bU7Hk5lr40y8OxjU9WvV1a8MD0TT/pKamatKrOv6t8rHQb2bZzf98RDUlFVzBaXnZ4tLnvjD5Tn/fIvmXb2d9s8P0A5/OV7d6SqU4d8+MsfS+ceXTN9/OR8/6TLUr1sZeOcXpv2SelNJbPd+/fOOXde0fj8gNMPzwGnH57Jjz6ba4/7UpJkoz49csI3P53ufXtmxZLlmf389Fx/yuWZ+NBTbXdzAC3oxTseS8deG+XdZ38wnfv1zIIXZubPJ12ZZbNWvR/apX/PdBv4xu/xS2fMzZ9P+nrec/GJ2e6Ug7L81QV5/MKf5KU7/944p9ilY/a4/NR02aR36lfWZNGUl/PgWdflxTsea/KzB+6zQ7oN6pNJtz7QNjcL0Ioe+97vU9WpQw7+yqnp1L1LXh4/JbeeeEVq3nT+7D6wT0oNb5w/Z/1jUn772auz79hjsu/Yo7Ng+qv57WeuzuzxU9bpZ3fr1zPvveCEdO3TI0vnLMzTv3koD3/n/1rs3gBak/MoQMt44rrfp9ipQ9576anp2L1LXhk/Jb854YrUvuk8utFbzqOz/zEpd37m6ux5zjHZc+zRWfjSq7nz01fnlTedR3c6+aAkybG/+t8mP++PY76fZ3/91zTUN6TPdptl6If3TsfuXbNszsLMeOTZ/OHTVzf52QAArK5QKq3/V6I+/PDD+fnPf55f/epXWblyZRYvXrzO16idN3V9YwD8V1s+9rRyRwBo14pbbVLuCADtW0Wh3AkA2re6+nInAGj3nr3BHwkBrI9bqjqWOwJAuza8tlnfhQPA616xjQKsl03qyp0AoP1bUFnuBADt29nTby53BGAtqic+VO4I/BfouM3e5Y7Q4ipa4iJdu3ZN586d06FDh9TW1rbEJQEAAAAAAAAAAAAAAAAAAADaVLNLGKZNm5ZLL700Q4cOzYgRIzJu3LhcfPHFeeWVV1oyHwAAAAAAAAAAAAAAAAAAAECbKDZn0R577JHHH388w4YNy0c/+tEcf/zx2XTTTVs6GwAAAAAAAAAAAAAAAAAAAECbaVYJwwEHHJAf/OAH2WGHHVo6DwAAAAAAAAAAAAAAAAAAAEBZNKuE4bLLLmvpHAAAAAAAAAAAAAAAAAAAAABl1awShiSZOXNm7rjjjkyfPj01NTVNXrvqqqvWOxgAAAAAAAAAAAAAAAAAAABAW2pWCcO9996bI444IoMHD84LL7yQHXfcMS+++GJKpVJ22WWXls4IAAAAAAAAAAAAAAAAAAAA0OoqmrPo/PPPz9ixY/P000+nU6dOue222zJjxozst99+OeaYY1o6IwAAAAAAAAAAAAAAAAAAAECra1YJw3PPPZdTTjklSVIsFrNixYp069YtX/rSl3LFFVe0aEAAAAAAAAAAAAAAAAAAAACAttCsEoauXbumuro6STJw4MBMmTKl8bV58+a1TDIAAAAAAAAAAAAAAAAAAACANlRszqKRI0fm4YcfztChQ/P+978/Y8eOzVNPPZXf/OY3GTlyZEtnBAAAAAAAAAAAAAAAAAAAAGh1zSphuOqqq7J06dIkycUXX5ylS5fm1ltvzZAhQ/LNb36zRQMCAAAAAAAAAAAAAAAAAACwjhrqy50A2qV1LmGor6/PjBkzMnz48CRJly5dcu2117Z4MAAAAAAAAAAAAAAAAAAAAIC2VLGuCyorK3PIIYdk4cKFrRAHAAAAAAAAAAAAAAAAAAAAoDzWuYQhSYYNG5apU6e2dBYAAAAAAAAAAAAAAAAAAACAsmlWCcOll16ac845J7///e8ze/bsLF68uMkDAAAAAAAAAAAAAAAAAAAAoL0pNmfRoYcemiQ54ogjUigUGsdLpVIKhULq6+tbJh0AAAAAAAAAAAAAAAAAAABAG2lWCcN9993X0jkAAAAAAAAAAAAAAAAAAAAAyqpZJQyDBw/OZpttlkKh0GS8VCplxowZLRIMAAAAAAAAAAAAAAAAAAAAoC1VNGfR4MGDM3fu3NXGX3vttQwePHi9QwEAAAAAAAAAAAAAAAAAAAC0tWaVMJRKpRQKhdXGly5dmk6dOq13KAAAAAAAAAAAAAAAAAAAAIC2VlyXyWPGjEmSFAqFXHDBBenSpUvja/X19Xnsscfy7ne/u0UDAgAAAAAAAAAAAAAAAAAAALSFdSphePLJJ5MkpVIpTz31VDp06ND4WocOHbLTTjvlnHPOadmEAAAAAAAAAAAAAAAAAAAAAG1gnUoY7rvvviTJRz/60Xz7299O9+7dWyUUAAAAAAAAAAAAAAAAAAAA66HUUO4E0C5VNGfRV7/61bUWMEyYMGG9AgEAAAAAAAAAAAAAAAAAAACUQ7NKGIYNG5Y77rhjtfGvf/3r2X333dc7FAAAAAAAAAAAAAAAAAAAAEBba1YJw3nnnZfRo0fnjDPOyIoVKzJr1qwceOCBufLKK3Prrbe2dEYAAAAAAAAAAAAAAAAAAACAVtesEoaxY8fm0UcfzcMPP5zhw4dn+PDh6dy5cyZMmJAjjjiipTMCAAAAAAAAAAAAAAAAAAAAtLpmlTAkyZZbbpkddtghL774YhYvXpxjjz02/fv3b8lsAAAAAAAAAAAAAAAAAAAAAG2mWSUMDz/8cIYPH57JkydnwoQJue666/LZz342xx57bBYsWNDSGQEAAAAAAAAAAAAAAAAAAABaXbNKGA488MCMHj06jzzySLbffvt84hOfyJNPPpmZM2dm2LBhLZ0RAAAAAAAAAAAAAAAAAAAAoNUVm7PonnvuyX777ddkbKuttspDDz2USy+9tEWCAQAAAAAAAAAAAAAAAAAAALSlinWZPGrUqCxatKixgOHSSy/NwoULG19fsGBBbrnllhYNCAAAAAAAAAAAAAAAAAAAANAW1qmE4Y9//GOqq6sbn19xxRV57bXXGp/X1dXlhRdeaLl0AAAAAAAAAAAAAAAAAAAAAG1knUoYSqXS2z4HAAAAAAAAAAAAAAAAAAAAaK+K5Q4AAAAAAAAAAAAAAAAAAABAC2toKHcCaJcq1mVyoVBIoVBYbQwAAAAAAAAAAAAAAAAAAACgvSuuy+RSqZRTTz01HTt2TJKsXLkyZ5xxRrp27Zokqa6ubvmEAAAAAAAAAAAAAAAAAAAAAG1gnUoYTjnllCbPTzzxxNXmnHzyyeuXCAAAAAAAAAAAAAAAAAAAAKAM1qmE4Uc/+lFr5QAAAAAAAAAAAAAAAAAAAAAoq4pyBwAAAAAAAAAAAAAAAAAAAADYEChhAAAAAAAAAAAAAAAAAAAAAIgSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRJsdwBAAAAAAAAAAAAAAAAAAAAaGGlhnIngHapotwBAAAAAAAAAAAAAAAAAAAAADYEShgAAAAAAAAAAAAAAAAAAAAAooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACBJUix3AAAAAAAAAAAAAAAAAAAAAFpYQ0O5E0C7VFHuAAAAAAAAAAAAAAAAAAAAAAAbAiUMAAAAAAAAAAAAAAAAAAAAAFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQJCmWOwAAAAAAAAAAAAAAAAAAAAAtq1SqL3cEaJcqyh0AAAAAAAAAAAAAAAAAAAAAYEOghAEAAAAAAAAAAAAAAAAAAAAgShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIkxXIHAAAAAAAAAAAAAAAAAAAAoIWVGsqdANqlinIHAAAAAAAAAAAAAAAAAAAAANgQKGEAAAAAAAAAAAAAAAAAAAAAiBIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkSbHcAQAAAAAAAAAAAAAAAAAAAGhhDQ3lTgDtUkW5AwAAAAAAAAAAAAAAAAAAAABsCJQwAAAAAAAAAAAAAAAAAAAAAEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAkqRY7gAAAAAAAAAAAAAAAAAAAAC0sFJDuRNAu1RR7gAAAAAAAAAAAAAAAAAAAAAAGwIlDAAAAAAAAAAAAAAAAAAAAABRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkCQpljsAAAAAAAAAAAAAAAAAAAAALayhvtwJoF2qKHcAAAAAAAAAAAAAAAAAAAAAgA2BEgYAAAAAAAAAAAAAAAAAAACAKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiSFMsdAAAAAAAAAAAAAAAAAAAAgBZWaih3AmiXKsodAAAAAAAAAAAAAAAAAAAAAGBDoIQBAAAAAAAAAAAAAAAAAAAAIEmx3AH+ZfnY08odAaBd6/KNG8odAaBdq/n+xeWOANCuFYqV5Y4A0K6VCvpyAdbXivq6ckcAaNfqqkrljgDQrvnNHmD91MZ5FGB9dLSNAqy3bg2FckcAAAA2ID7/AwAAAAAAAAAAAAAAAAAAAIgSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASJIUyx0AAAAAAAAAAAAAAAAAAACAFtbQUO4E0C5VlDsAAAAAAAAAAAAAAAAAAAAAwIZACQMAAAAAAAAAAAAAAAAAAABAlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRJiuUOAAAAAAAAAAAAAAAAAAAAQAsrNZQ7AbRLFeUOAAAAAAAAAAAAAAAAAAAAALAhUMIAAAAAAAAAAAAAAAAAAAAAECUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJkmK5AwAAAAAAAAAAAAAAAAAAANDCGhrKnQDapYpyBwAAAAAAAAAAAAAAAAAAAADYEChhAAAAAAAAAAAAAAAAAAAAAIgSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEmx3AEAAAAAAAAAAAAAAAAAAABoYQ0N5U4A7VJFuQMAAAAAAAAAAAAAAAAAAAAAbAiUMAAAAAAAAAAAAAAAAAAAAABECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQJKkWO4AAAAAAAAAAAAAAAAAAAAAtKxSqb7cEaBdqih3AAAAAAAAAAAAAAAAAAAAAIANgRIGAAAAAAAAAAAAAAAAAAAAgChhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAD4/+zdd5RdZd024PtMS6+QgqGT0EKCdAjF0JsElKZ0QRFbPhBQsQAqEAT1RUAQQV5AirwiIjUExQBikBJpoSQhIT2k10mdc74/wMEhiTJhSkava61ZK+c5z2/PvfPHXvucmbkPAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAkqSiuQMAAAAAAAAAAAAAAAAAAADQwIrF5k4ALVJZcwcAAAAAAAAAAAAAAAAAAAAAWBcoYQAAAAAAAAAAAAAAAAAAAACIEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRJRXMHAAAAAAAAAAAAAAAAAAAAoIGVis2dAFqksuYOAAAAAAAAAAAAAAAAAAAAALAuUMIAAAAAAAAAAAAAAAAAAAAAECUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQJK1KGGYNGlSJk+eXPv42Wefzdlnn51f/vKXDRoMAAAAAAAAAAAAAAAAAAAAoClV1HfghBNOyJlnnpmTTz4506dPz4EHHpi+ffvm9ttvz/Tp03PhhRc2Rk4AAAAAAAAAAAAAAAAAAAA+rGKxuRNAi1RW34FXX301u+66a5Lk//7v/7Lddtvlr3/9a+68887ccsstDZ0PAAAAAAAAAAAAAAAAAAAAoEnUu4RhxYoVadWqVZLkj3/8YwYNGpQk2XrrrTNt2rSGTQcAAAAAAAAAAAAAAAAAAADQROpdwtC3b9/84he/yFNPPZXHHnsshxxySJJk6tSpWW+99Ro8IAAAAAAAAAAAAAAAAAAAAEBTqHcJw49+9KPccMMNGThwYD772c9m++23T5Lcf//92XXXXRs8IAAAAAAAAAAAAAAAAAAAAEBTqKjvwMCBAzNr1qwsWLAgXbp0qV0/88wz07Zt2wYNBwAAAAAAAAAAAAAAAAAAANBUyuo7sGTJkixbtqy2gGHChAm56qqr8uabb6Z79+4NHhAAAAAAAAAAAAAAAAAAAACgKdS7hOHII4/MbbfdliSZN29edtttt/zkJz/JUUcdleuvv77BAwIAAAAAAAAAAAAAAAAAAAA0hXqXMIwcOTJ77713kuSee+5Jjx49MmHChNx22225+uqrGzwgAAAAAAAAAAAAAAAAAAAAQFOodwlDdXV1OnTokCQZNmxYPv3pT6esrCy77757JkyY0OABAQAAAAAAAAAAAAAAAAAAAJpCvUsYevfunfvuuy+TJk3Ko48+moMOOihJMmPGjHTs2LHBAwIAAAAAAAAAAAAAAAAAAAD/Ga677rpsttlmad26dXbaaac89dRT/3L/HXfcke233z5t27bNBhtskM997nOZPXt2o+WrdwnDhRdemPPOOy+bbrppdt111+yxxx5JkmHDhmWHHXZo8IAAAAAAAAAAAAAAAAAAAABAy3f33Xfn7LPPzne+8538/e9/z957751DDz00EydOXO3+v/zlLznllFNyxhlnZNSoUfntb3+b5557Lp///OcbLWNFfQeOOeaY7LXXXpk2bVq233772vX9998/n/rUpxo0HAAAAAAAAAAAAAAAAAAAAGuhVGzuBLCKn/70pznjjDNqSxSuuuqqPProo7n++uszZMiQVfY/88wz2XTTTTN48OAkyWabbZYvfvGLueKKKxotY9naDPXs2TMdOnTIY489liVLliRJdtlll2y99dYNGg4AAAAAAAAAAAAAAAAAAABYNy1btiwLFiyo87Vs2bLV7l2+fHleeOGFHHTQQXXWDzrooPz1r39d7cyAAQMyefLkPPzwwymVSnnnnXdyzz335PDDD2/wc/mHepcwzJ49O/vvv3+23HLLHHbYYZk2bVqS5POf/3zOPffcBg8IAAAAAAAAAAAAAAAAAAAArHuGDBmSTp061fkaMmTIavfOmjUrNTU16dGjR531Hj16ZPr06audGTBgQO64444cf/zxqaqqSs+ePdO5c+dcc801DX4u/1DvEoZzzjknlZWVmThxYtq2bVu7fvzxx2fo0KENGg4AAAAAAAAAAAAAAAAAAABYN11wwQWZP39+na8LLrjgX84UCoU6j0ul0ipr//Daa69l8ODBufDCC/PCCy9k6NChGT9+fM4666wGO4cPqqjvwLBhw/Loo49mww03rLPep0+fTJgwocGCAQAAAAAAAAAAAAAAAAAAAOuuVq1apVWrVh9q7/rrr5/y8vJMnz69zvqMGTPSo0eP1c4MGTIke+65Z84///wkSf/+/dOuXbvsvffeueSSS7LBBht8tBNYjbL6DixevDht27ZdZX3WrFkf+j8HAAAAAAAAAAAAAAAAAAAA+O9RVVWVnXbaKY899lid9cceeywDBgxY7Ux1dXXKyurWIpSXlydJSqVSo+SsdwnDPvvsk9tuu632caFQSLFYzJVXXpl99923QcMBAAAAAAAAAAAAAAAAAAAA/xm+/vWv56abbsrNN9+c119/Peecc04mTpyYs846K0lywQUX5JRTTqndf8QRR+Tee+/N9ddfn3HjxuXpp5/O4MGDs+uuu+ZjH/tYo2SsqO/AlVdemYEDB+b555/P8uXL841vfCOjRo3KnDlz8vTTTzdGRgAAAAAAAAAAAAAAAAAAAKCFO/744zN79uz84Ac/yLRp07Lddtvl4YcfziabbJIkmTZtWiZOnFi7/7TTTsvChQtz7bXX5txzz03nzp2z33775Uc/+lGjZSyUSqVSfYemT5+e66+/Pi+88EKKxWJ23HHHfOUrX8kGG2yw1kHmn7r/Ws8CkLT9yY3NHQGgRVt+w8XNHQGgRStUlDd3BIAWrbSyprkjALR4L1yzpLkjALRo97Qua+4IAC3azisqmzsCQIs2oaLev8oKwD/pvaK5EwC0fIvKCs0dAaBF+8Lk25s7ArAGS/74i+aOwH+BNgec1dwRGlzF2gz17Nkz3//+9xs6CwAAAAAAAAAAAAAAAAAAAECzWasShnnz5uXZZ5/NjBkzUiwW6zx3yimnNEgwAAAAAAAAAAAAAAAAAAAAgKZU7xKGBx54ICeeeGIWL16cDh06pFAo1D5XKBSUMAAAAAAAAAAAAAAAAAAAAAAtUr1LGM4999ycfvrpueyyy9K2bdvGyAQAAAAAAAAAAAAAAAAAAMBHUSw2dwJokcrqOzBlypQMHjxYAQMAAAAAAAAAAAAAAAAAAADwH6XeJQwHH3xwnn/++cbIAgAAAAAAAAAAAAAAAAAAANBsKuo7cPjhh+f888/Pa6+9ln79+qWysrLO84MGDWqwcAAAAAAAAAAAAAAAAAAAAABNpd4lDF/4wheSJD/4wQ9Wea5QKKSmpuajpwIAAAAAAAAAAAAAAAAAAABoYvUuYSgWi42RAwAAAAAAAAAAAAAAAAAAAKBZlX2U4aVLlzZUDgAAAAAAAAAAAAAAAAAAAIBmVe8Shpqamvzwhz9Mr1690r59+4wbNy5J8r3vfS+/+tWvGjwgAAAAAAAAAAAAAAAAAAAAQFOodwnDpZdemltuuSVXXHFFqqqqatf79euXm266qUHDAQAAAAAAAAAAAAAAAAAAADSVepcw3HbbbfnlL3+ZE088MeXl5bXr/fv3zxtvvNGg4QAAAAAAAAAAAAAAAAAAAACaSr1LGKZMmZLevXuvsl4sFrNixYoGCQUAAAAAAAAAAAAAAAAAAADQ1OpdwtC3b9889dRTq6z/9re/zQ477NAgoQAAAAAAAAAAAAAAAAAAAACaWkV9By666KKcfPLJmTJlSorFYu699968+eabue222/Lggw82RkYAAAAAAAAAAAAAAAAAAADqo1Rs7gTQIpXVd+CII47I3XffnYcffjiFQiEXXnhhXn/99TzwwAM58MADGyMjAAAAAAAAAAAAAAAAAAAAQKOrWJuhgw8+OAcffHBDZwEAAAAAAAAAAAAAAAAAAABoNmXNHQAAAAAAAAAAAAAAAAAAAABgXVDxYTZ16dIlhULhQx1wzpw5HykQAAAAAAAAAAAAAAAAAAAAQHP4UCUMV111VSPHAAAAAAAAAAAAAAAAAAAAAGheH6qE4dRTT23sHNCkqvYblFaHHZdCp/VSnPp2ltxxXWpGv/Jv58r79E27C/4nxcnjs+jCL9Z9sm27tD76jFTuvFcKbTukOGtalt71i6x8+dlGOguAdd/zL76S/73znrz2xtjMnD0nPxvyvey/z4DmjgWwTqjYaf9U7n5YCu07pThzSpY/dkeKk0avdm/ZxlunzcnfXmW9+hffTGn2tPc2ladywCdT0X+vFDp0SXH29Kx4/O7UjPv397kA/wkqdtg3FbsemkL7zinNmpLlf7ozxclj1jxQXpHKAYNS3nePFNp1Smnh3KwY8WBqXnmq6UIDrEPqc3+a5N3r6N5HpWK7Ae9dR+dkxdMPZOVLTzZdaIBmtMFpB2WjLx+Zqu6ds/jNyXnrwv/Ngr+9sdq9Vd07Z/OLT037/punzeY9M+WmRzLuwlvq7Ol/78XpPKDvKrOz/zgyo04a0hinALBOOPTsYzLgs/unTaf2mfDimPz2ezdn+pjJa9zfs8+GOezrx2WjfptlvQ27594f3JrhNz+8xv0HfvmoHPGNz2b4zQ/n3h/c2hinANAktjr1gGx71mFp271z5o2ekucuuj0znn1zjft77L51dr7oxHTesleq35mXUdc/mNG/fny1ezcdtHv2uf6rmTj0+Qw/46ra9S1P2T9bnbx/2m3ULUkyf/TkvPQ/v8/UP7/coOcG0JT2OfvT2fGE/dK6U7tM+fvYDP3eLZk5Zsq/nNn60F0y8Nxj02Xj7pk7cUb+fOX/5c1Hn6+zp0OPLtn/gs9ki4Hbp7J1VWaPm54HvvHLTH/17STJoB9/Mdsfu0+dmckjx+Z/P3VRg54fQGPpfeoB2eZLh6dN986ZP3pKRl7468z8F/ej3XbfOjtefFI6bdkrS96Zl9evezBjf/2nOnsqO7ZN/28dl40O3TlVndpl0aSZ+fv378i0x19Kkmx37qfT79yj68wsmTEv9338Kw1/ggBNYJtTDsj2Zx2WNt07Z+7oKXnm4tsz/V9cS3vuvnV2v/DEdHnvtf3L1z+Y129//7V9ly17Zafzjs76/TZLh426ZcRFv86rv3q0zjEK5WXZ6eufTu9PDUib7p1T/c68jP7tk/n7z/6QlEqNdq4AAP8pPlQJwwfV1NTk97//fV5//fUUCoVss802OfLII1NRsVaHgyZVuevAtD7xy1ly29WpGf1qqvb9ZNqdOyQLLzg9pTkz1jzYpl3anPmtrHxtZMo6dqn7XHlF2p1/RUoL5qX62u+nOGdmyrp2T2lpdeOeDMA6bsmSpdmq9+Y56rCDcs53LmnuOADrjPJtdkvVgSdm+dBbUzNpTCp33DetP3NeltxwQUoLZq9xrvr6byTLltQ+LlUvqP135SeOTkW/AVn+0M0pzp6W8s37pdUx/y9Lb/1hiu9MaNTzAWhu5Vvvmsr9T8jyYb9OccqYVHx8YFod+/Usvek7KS2cs9qZqiO/nEK7jln+yP+mNPedFNp1TMrKmzg5wLphbe5PW336qym065hlD/7q/etooayJkwM0j25HDsgWP/hcxn7rxsx/7s1scPKB6Xfnd/L8Pudk2ZRZq+wvtKrMitkLMvFnv8uGZ35ytcd87fQfp1D5/s9aK7u2z05/+nFmPTCi0c4DoLkdcNag7HvG4bn9vOszc/y0HPS1T+crt38nl+x3TpYtXrramao2rTJ74jt58eFn8qnvnfIvj79x/y0y4LP7Z8rr3h8FWrZNB+2WnS8+KX/79i2Z+dzo9Dl5v+x/+/m5f+A3s3jqqq/b22/ULfv9+ryMuXN4/vK169Ntly2z22WnZenshZn48HN19rbrtV52uvCEvPPMqoVi1dPmZOSQu7Pg7XeSJFscu3f2vfnrefDg72T+6H/9B8sA66IBZ30yu3/+sNx/3i8ye9z07P21o3LiHRfkun3Py/I13H/22rF3jr72axn+k3vyxqPPZeuDd8nRP/9abjnmB5n64ltJktYd2+a0312Ut0e8lrtOvSKLZy9Il016ZNmCur8/Onb4S7n/vBtqH9csX9l4JwvQgDYetHt2/P7Jef7b/5tZz45O75P3yyfu+EYeHviNVE9Z9X603UbdMvD28/PWHX/OiK9el/V33TI7X/a5LJ29IJPfux8tqyzPvr/5VpbOWpC/nHl1qqfOSdteXbNyUd3r8bw3JuXPx79fUluqKTbuyQI0ks2P2C17XHxSnv7OLXnnudHZ+qT9csivz89v9139a/sOG3XLIbedlzfuHJ7hg69Pj122zJ6XnpYlcxbm7feupeVtWmXBxJkZ9+Cz2eOik1b7fbf/8iezzcn7Z/jZN2Tu6Mnptv1m2ecnZ2b5wiUZ9YHCBgAAVlXv34h89dVXs+WWW+bUU0/N73//+9x777059dRT06dPn7zyik9YZd1XdcgxWf7kI1nxxMMpTpuYpXdel+KcGana/4h/OdfmtHOyYsSfUjP2tVWPuc8hKbTvmOqrL0zNmFEpzZ6RmjGvpjhpXGOdBkCLsPceu2TwmafmwIF7NncUgHVK5W6HZOWLT2Tli0+kNHtqlj92R0oL5qRix/3+5Vxp8YKUFs+v/frnJuKKfntmxdMPpOatl1OaNzMrRz6emnGvpHK3Qxr7dACaXcUuB2Xly0+m5uUnU5o9LSv+dFdKC+ekYofVX1fLNtsu5RttlWW//Z8UJ7yW0oLZKU4bn+KUsU2cHGDdUN/70/LN+6V8462y9Dc/SfHtUSnNn5Xi1HGuo8B/jV5f/GSm3/V4pt/5eJaMmZJxF96SZVNmZYNTD1rt/mWTZuat7/1vZvz2yaxcuPoC75XzFmXFzHm1X1326Z+aJcsyUwkD8B/sE6cflmE//31efvTZTBs9KXec+/NUtmmVnY7ca40zE19+K38YckdGPvDXrFy+Yo37qtq2yilXfTV3feuXqZ6/qDHiAzSZbb5waMb+ZnjG3jU888dOzfMX3Z7FU2dny1P2X+3+LU/eL4unzM7zF92e+WOnZuxdwzP27ifS96zD6uwrlBWy97Vfzks//l0WTlz1g2smP/b3THn8pSwcNz0Lx03Piz/6bVYuXppuO/ZulPMEaGy7nnFI/nLtfXlj6POZOXpy/nDuL1LZuirbHTlgjTO7nX5oxv3l1Tx93f2Z/da0PH3d/Rn/9Kjsdvr7P4cf8KUjsmDa7Dxw/i8z9aVxmT95Vt5+elTmfuDaWrNsRRbPnF/7tXT+4kY7V4CGtNWZh2bcXcMz7s7hWTB2akZedHuqp85On1MOWO3+3qfsn8VTZmfkRbdnwdipGXfn8Iz7zRPZ5qzDa/ds/pmBqercPk+d/j+Z9dzoVE+ZlVnPjs681ybWOVapppilM+fXfi2bs7BRzxWgsfQ789C8+ZvhefOu4Zk3dmqeufj2LJo6O9uu4bX9Nifvl0VTZueZi2/PvLFT8+ZdwzP67ifS/4vvv7af9dK4PHvJXRl3/zOpWcN7pT126pMJw17IpMdfzKLJszL+oecy5clX0q3/Zo1yngAA/2nqXcLw+c9/Pn379s3kyZMzcuTIjBw5MpMmTUr//v1z5plnNkZGaDjlFSnfdMusfPX5OssrX30hFb37rnGscu+DU9Z9gyy777bVPl+xw4DUjH0tbU4ZnA5X35P2l96UVp88wSe/AQCwqrLylG2waWrGv1pnuWbcKynfsM+/HG3z+R+mzf+7Oq1P+GbKNtmmznOF8spk5QfeSF+5PGUbbdkgsQHWWWXlKeu5aYrjR9VZrhk/KmW9tljtSHnvHVKcPj6Vux2a1l/+aVp/YUgq9z0+qahsisQA65a1uD8t33LHFKe9nco9Dk+bwVelzVlXpGr/z7iOAv8VCpUV6dB/88wd/lKd9blPvJyOu2zVYN+n5wn7Z+Z9f02xelmDHRNgXbLeRt3TqXuXvPHUy7VrK5evzFt/ey2b7fTR39M89odnZNSf/57RT/swEaBlK6ssz3r9N8vUJ+q+bp/2xKvptvPqX7d326lPpn1g/9Thr2S9/pulUFFeu9b/nE9l6eyFGfubJ/5tjkJZIZsO2j0VbVtl5gtj1uJMAJpX5426pUP3Lhn31Pv3hzXLV2bC397Ihjut+ef0G+7YO+OefLnO2rgnX86G/3TPuuWBO2Xqy+Nz9HWD8/UXrssXHr40O3xm31WOtcnu2+TrL1yXL//5xzn88s+n7XodG+DMABpXWWV5uvbfLNOfqPv6evoTr2T9NdyPrr9Tn1X3D385Xbd//36010E7ZvYLY7LzZaflUy9dl0Mfvzzbfm1QCmWFOnMdNuuRI0demyOe+Z8MuP6rabdxtwY8O4CmUVZZnvX7bZYpT9Z9rT7lyVfTYw3X0u479lll/+Qn3i1P+OfX9v/O9OdG52N79k2nzXomSbpus3F67LJVJj3+0r+ZBAAgSSrqO/DSSy/l+eefT5cuXWrXunTpkksvvTS77LLLhzrGsmXLsmxZ3V8YWlZTTKtyf7BO4yp06JRCeXlK8+fWWS/Nn5tCp66rnSnr0Sutj/1CFl96dlIsrn5Ptw1Sts0OWTHiT1n80wtS3mPDtD5lcFJenmV/+HVDnwYAAC1YoW2HFMrKU1o0v856afGCFNp3Wu1MadG8LHvo5hSnj0/KK1PRb8+0PvGbWfrrISlOejPJu38kV7nbIamZ+GZKc2ekbLNtU77ljorBgP94tdfV6gV11kuL56fQbrvVzpR17payDbdMVq7I8t9fk7TpkKqDTk6hdbssf+TmpogNsM5Ym/vTQuduKduoT0orV2TZPVen0KZ9qg45NVVt2mf5gzc1RWyAZlPZtUMKFeVZMXNenfXlM+elS7fODfI9OuzQO+222Tijv359gxwPYF3U8b1r5oKZde9DF8ycn64bfrQ/qNjxiAHZqO9m+fGR3/5IxwFYF7Tq2iFlFeVZOqvu9XLJrPn5WPfOq51p071Tlnxg/9JZ81NWWZHWXTtkyYx56bZzn/T+7MA8eOC/vlZ23nrDHHr/xSlvVZmVi5dm+OevyvwxUz/KKQE0i/bvXTMXfeD+c/Gs+enUa/01z3XrnMWz6v4MavGsBWnf7f33Trts1C07n7R/nrnpkTz98z/kY9tvkYO/f0pqlq/Iy/f+JUkydvhLee3hv2X+5FnpvFG3DDz32Jx817dz0ye/m5rlKxvoLAEa3pruR5fOnJ/W3Vf/c6TW3Tpl6czV34+26tohS2fMS/tNuqfdntvm7d//NcNPuiIdNu+ZnS89LYWK8oz6n98nSWaPfCsjBv8iC8dNT+tuHdP3/x2VA++/OA/v+80sn7uoUc4XoDG0fu9aWv2Ba+OSmfPTZg0/W2rbvVMmD6+7v3pm3df2H8ZLP38gVR3a5NgnrkippphCeVme+9Fv89YfRqzNqQAA/NepdwnDVlttlXfeeSd9+/atsz5jxoz07t37Qx1jyJAh+f73v19n7Zv9N80FH9+8vnFg7ZQ+8LiQpPTBxSSFsrQ569tZ9vtbUnxn8pqPV1aW0sK5WfK/P01KxRTfHpNC5/XS6rDjlDAAAPDhrOmeNElpzvSsnDO99vHyKWNT6Ng1lbsfmmXvlTAse+z2tDrs9LQ560dJSinNnZGVLz2Viu33boLwAOuAD15DC4XV7/vHc6VSlj3wy2T5kiTJisd/k6qjvpw89utk5YpGDArQQvyL+9NCoZCUkmV/uD5Z9u51dPkf70qro7+a5UNvdR0F/iuscol87x6zIfT87H5Z/PrELPz72AY5HsC6YOcj98rxl32h9vENp1/+7j8+cO0sfMTraecN1sunLzw1151yWVYuc18K/OcorXK9zL++Xq7pZ06lUiratc5e13wpI86/Kcv+zR+vLXhrWh486Dup6tg2Gx+2S/a86ot59OhLFDEA67ztjhqQwy87o/bxXZ+7cvUbC4VVrrEftMrzH7gGF8rKMvWVcfnzlf+XJJk+akK6bblhdjr5gNoShtcefKZ2/8zRkzPtlfEZ/PTP0me/j+eNoc/X48wAmseq18LCqr+PX2fgA4//8fP7fxynUMjS2Qvy3Pk3pVQsZe4rb6dNjy7Z5kuH15YwTPvz+5/SPv+NZNbzY3PEiJ9ms2P3zpu/fOSjnRBAc/g395Wrbl/NewGrO86/sPmg3dP703vm8a9el7mjJ2e9vptkj4tPSvU78zLmnqc+9HEAAP5bfagShgUL3m9xveyyyzJ48OBcfPHF2X333ZMkzzzzTH7wgx/kRz/60Yf6phdccEG+/vWv11lb+uUjP2xmWGulhfNTqqlJoXOXOuuFjl1SWjB31YE2bVKx+dYp36RPWp88+L3NhRTKytLx5mFZfOU3UvP6iynNm51SzcqkVKwdLU6bmLLO6yXlFUmNpmIAAN5Vql6YUrFmlU8VLrTtmNLiBWuYWlVxythUbLfn+wvVC7Psnp8l5ZUptG2f0sK5qdz3uJTmzWyo6ADrpNrrarvVXVfnr35m0fyUFs2tLWBIkuLsqSkUylLo0DWlue80amaAdcna3J8WF81PYeHc2gKGJCnOch0F/jusmLMwpZU1qfrApw5Xrd8py2et/v6zPsraVKXbUXvm7Svu/sjHAliXvPLH5/P2i2NqH1dUVSZJOnbvnAUz59Wud1i/YxZ8hOvpRv02S8dunXP+A0Nq18oryrPFrttk71MOzte3PDGlYsOU5gA0hWVzFqa4smaVT8ZsvV6nLJm5+uvlkhmrfpJm6/U7pbhiZZbNXZTOW/VKh427Z79bzq19vlD27l9ynDTh1ty3z/lZNGFGkqS4oiYL3373df7sl8dn/Y9vnm0+f0ie+ebNDXSGAI1j9GMjM+Xvb9U+rqh699el23frlEX/9KnB7dbrmMX/4v5z0cx5ad+t7nun7dbrmEWz3n/vdOGMeZk1ZkqdPbPGTsnWh+6y5uPOmJd5U2al66Y9P9T5ADSXNd6Prt8xS9dwP7p05vy07l732tl6vY6196NJsnTGvBRX1tR5jb5gzNS06dElZZXlKa6oWeW4NUuWZf4bk9JhM9dOoGVZ+t61tO0HfrbUZv1OWbKGe9HqGfNXu7+4YmWW/ptCxX+223c/m5d+/kDG3f9uKdjcNyanQ6/18/GvHqGEAeC/TbH47/cAq/hQJQydO3d+99MG3lMqlXLcccfVrv2jXeuII45ITc2qL3g/qFWrVmnVqlWdtVJ52YcODWutZmVq3h6dir47ZeULT9cuV/TdKSv+/vSq+5dUZ+G3z6izVLX/oFRss0Oqr/1+ijPf/TTilWNGpWr3/ep8ylFZjw1TnDtLAQMAAHUVa1Kc9nbKN9suNW++ULtcvtl2WTl65Ic+TFmPTVJaNG/VJ2pWpLRwblJWnoqtd8nK1//WAKEB1mHFmhSnv52yTfumZsz719HyTbdNzZgXVztSM2VMyrfeOalslaxYliQp69IzpWIxpYVzmiI1wLpjLe5Pi5NHp2KbXepeR9dzHQX+O5RWrMzCl8elyyf6Z/Yjz9aud/5E/8we+txHPn63QQNSVlWRGb978iMfC2Bdsmzx0ixbvLTO2vwZc7PVXv0zedTbSZLyyvJssdu2uf/yO9f6+4x++tUMOei8OmsnXPmlzHhrSv74i/sVMAAtTnFFTWa/PD4f22e7TPqnT0rfYJ/tMunRF1Y7M/OFMdnwwB3rrH3sE9tl9svjU1pZk/ljp+X+/b5V5/mPf+OYVLZvk+cu/HWqp85ec6BCIWVVH+pXDgGa1fLFS7P8A/efC2fMzWZ79cv0UROSJGWV5dlkt63zp8t/s8bjTB45Npvt3S9/+9XQ2rXN9+mfyS+Mfn/PC6Oz3uYb1JnrutkGmT9l1hqP26Zz+3TaoGudQgiAdVFxRU3mvDw+PffZLpP/6X605z79MmUN96OzXhiTXh+4H+35iX6Z89K796NJMvO50dnkUwPq/O59h817pnr63NUWMCRJWVVFOvbulRl/e7MhTg2gyRRX1GTWK+PTa+/t8vY/XUt77b1dJgxb/bV0xsgx2fiAutfSXvtsl5kvv38t/TAq2lSt8p5osaZYW8YIAMC/9qF+IvLnP/+5sXNAk1k+9J60+eK3UjN+dGrGvpaqfQ9P2Xrds/zxB5IkrY49I2Vd1s+SX/4oKZVSnPJ2nfnSgnnJiuV11pc/fn9aHXBUWp/4lSx/7L6U9eyVVkeckOWP3dt0JwawDqquXpKJk6fWPp4y9Z28MfqtdOrYIRv07N6MyQCa14q/DU2rI7+Y4rTxqZk8NpU7DEyh03pZOfLxJEnlwGNT6NAlyx/4ZZKkYpeDU5o/M8WZU5LyilRsNyAV2+yapfdcXXvMso9tnkKHrim+MyGFDl1StfenkkIhK0Y83BynCNCkVj43LFWf/EKK099OcerYVGz/iRQ6rpeVL777nlblPsek0KFzlj90U5Kk5rVnUhowKFWHnZEVf7kvhTbtU7nvcal55alk5YrmPBWAZlHf+9OVr45I5V5HptURX8jyJ+9NoU2HVO33max86UnXUeC/wpQbHsxW13wtC196KwueH50NTjogrXutn2m3DUuSbPrtE9Jqg65582vX1s6067tpkqS8XetUrdcx7fpumtKKlakePbnOsXt+dr/MGvpcVtbjU4wAWqonbn44B37lqMx8e1pmjp+eA79yVFYsWZYX/vCX2j0n/eQrmf/OnDxwxV1J3i1q6NlnwyRJRWVFOvXokl7bbpJli5dm1oR3smzx0kwbPanO91m+ZGkWz1u0yjpAS/H6jY9kz599KbNfGpeZL4xNn5P2Tbte62X0r/+UJNnhW8el7QZd8vT/uyFJMvrXj2erzx2YnS86MWPu+HO67dQ7vT8zME995edJkuKyFZn3Zt370OULqpOkzvoO3zouUx5/KYunzk5l+9bZ9Mg90mOPbfKnE69oitMGaHDP/mpo9vrKoMx5e3rmjJ+evb56ZFYsXZ5X//DX2j1H/vSsLJw+N49fcfe7M/87NKf+3/cy4KxP5s3HXshWB+6Uzfbsm1uO+UHtzDM3PZLP3XtR9vzKoLz24N/S6+NbZMcT9s1DF/wqSVLZtlU+cc7Ref2RZ7Noxrx03rBb9v3GcameuyhvPPp8ANZ1b/7ykex+9Zcy5+XxmfX8mGxx0n5p22u9jLnt3fvR7S84Pm16dskz/+8XSZKxt/0pW37uwOxw0Yl5644/Z/2d+2Tzzw7MX7/8/vulY2/7Y7Y8/aDs9MOTM/rmYemwWc/0HXxk3vzVo7V7Pn7hCZkybGSqp8xO6/U7pu/ZR6WyQ5uM/z+f3A60PK/88pEM/NmXMvPlcZnxwthsfeK+ad9rvbz+3mv7Xb51XNr17JLhZ7/72v71Xz+ebU87MLtfeGLeuPPP6b5T72z1mYF5/Ks/rz1mWWV5Ovfp9d6/K9J2g67puu3GWVm9LAvefidJMvGxv+fjg4/MoimzM3f05Ky/3abpd+ahGX33E038PwAA0DJ9qBKGT3ziE42dA5rMimeHp9C+Y1ofeXIKnbumOOXtLP7pBSnNnpEkKeu0Xsq61u8Pg0tzZmbxld9M6xO+lPaX3JjivFlZPuzeLHtozQ3JAP8NXn1jTE7/2jdrH19xzbt/rHHkoQfk0u+e21yxAJpdzet/y/K27VO515Gpat85xZmTs/Q3P0lpwbufLFRo3zllndar3V8oL0/l/p9NoUOXZOXyFGdOydLf/Dg1b738/kErKlP1iaNT6NItWb4sK8e+lBX335Asq27q0wNocjVvPJsVbdqlcs9BKbTrlNKsKVn22//5p+tqpxQ6vn9dzYplWXb3lak68KS0PvXClJYsfvcYTylTBP471ff+NCuWZemdV6TVQSenzenfT2nJotS89myWP3FPM50BQNOa+Ye/pqJL+2zy9WNS1b1LFr8xKa+eeFmWTX73Ey6renRJq17r15nZ6U9X1v67w/ZbpPvRe2fppBl5dpev1K632XyDdNp9m7x83A+b5kQAmtkff3F/KltX5dgfnpG2ndplwotjc93Jl2XZP31icZde66VUKtY+7tSja7758Pt//Lv/Fwdl/y8OyphnRuWaz/wgAP+J3r7/b2nVpUP6n/OptOneOfPenJw/nXxlFk9593V7mx6d0+5j799/Lpo0M4+f/OPsfPFJ2erUA1L9ztw8d+Ftmfjwc/X6vq3X75i9rj4rbbp3zvKF1Zn3+qT86cQrMu2pVxv0/ACayl9/8WAqWlfl0EtOS5uO7TLlxbdyx0mXZ/k/3X92/Nh6dT4pePILY3Lv167NwHOPzcBzj83cie/k3q9ek6kvvlW7Z9rL4/LbM6/Kft88PvsM/lTmTZ6ZYd+/Pa/e9265Q6mmmO5bbZT+n94rrTu2y8IZ8zJhxGu59yvX1PneAOuqifc/k6ou7dP3vfvR+W9OzhMnXZnqKe++H9q6e+e07fX+z5EWT5qZ4SddmR2/f1L6nHZglrwzNyO/d1sm/9P9aPXUOfnzZy/PjhefnEP/OCTV0+fmzZuG5vWfP1C7p+0GXTPguq+mVdcOWTZ7QWaPHJthn7yo9vsCtCTjHnj3tf2OZ38qbbt3zpw3J2foKVdm0Xuv7dt275x2//SzpYWTZmboKT/OHhedlG3fe20/4sLb8vY/XUvb9uiSo4ddVvt4+7MOz/ZnHZ6pI17PQ8demiT56/duy07nH5M9LzstbdbvmOrpc/PG7Y9n5FW/b6IzBwBo2QqlUqn077etqrq6OhMnTszy5cvrrPfv33+tgsw/df+1mgPgXW1/cmNzRwBo0ZbfcHFzRwBo0QoV5c0dAaBFK62sae4IAC3eC9csae4IAC3aPa3LmjsCQIu284rK5o4A0KJNqFirX2UF4D29VzR3AoCWb1FZobkjALRoX5h8e3NHANZgySNXN3cE/gu0OXRwc0docBX1HZg5c2Y+97nP5ZFHHlnt8zU1flkYAAAAAAAAAAAAAAAAAAAAaHnq/VEWZ599dubOnZtnnnkmbdq0ydChQ3PrrbemT58+uf/++xsjIwAAAAAAAAAAAAAAAAAAAECjq6jvwOOPP54//OEP2WWXXVJWVpZNNtkkBx54YDp27JghQ4bk8MMPb4ycAAAAAAAAAAAAAAAAAAAAAI2qrL4DixcvTvfu3ZMkXbt2zcyZM5Mk/fr1y8iRIxs2HQAAAAAAAAAAAAAAAAAAAEATqXcJw1ZbbZU333wzSfLxj388N9xwQ6ZMmZJf/OIX2WCDDRo8IAAAAAAAAAAAAAAAAAAAAEBTqKjvwNlnn51p06YlSS666KIcfPDBueOOO1JVVZVbbrmlofMBAAAAAAAAAAAAAAAAAAAANIl6lzCceOKJtf/eYYcd8vbbb+eNN97IxhtvnPXXX79BwwEAAAAAAAAAAAAAAAAAAAA0lXqXMPzD8uXLM378+GyxxRbZcccdGzITAAAAAAAAAAAAAAAAAAAAH0Wx2NwJoEUqq+9AdXV1zjjjjLRt2zZ9+/bNxIkTkySDBw/O5Zdf3uABAQAAAAAAAAAAAAAAAAAAAJpCvUsYLrjggrz00ksZPnx4WrduXbt+wAEH5O67727QcAAAAAAAAAAAAAAAAAAAAABNpaK+A/fdd1/uvvvu7L777ikUCrXr2267bd56660GDQcAAAAAAAAAAAAAAAAAAADQVMrqOzBz5sx07959lfXFixfXKWUAAAAAAAAAAAAAAAAAAAAAaEnqXcKwyy675KGHHqp9/I/ihRtvvDF77LFHwyUDAAAAAAAAAAAAAAAAAAAAaEIV9R0YMmRIDjnkkLz22mtZuXJlfvazn2XUqFEZMWJEnnjiicbICAAAAAAAAAAAAAAAAAAAANDoyuo7MGDAgDz99NOprq7OFltskWHDhqVHjx4ZMWJEdtppp8bICAAAAAAAAAAAAAAAAAAAANDoKtZmqF+/frn11lsbOgsAAAAAAAAAAAAAAAAAAABAsylbm6G33nor3/3ud3PCCSdkxowZSZKhQ4dm1KhRDRoOAAAAAAAAAAAAAAAAAAAAoKnUu4ThiSeeSL9+/fK3v/0tv/vd77Jo0aIkycsvv5yLLrqowQMCAAAAAAAAAAAAAAAAAAAANIV6lzB861vfyiWXXJLHHnssVVVVtev77rtvRowY0aDhAAAAAAAAAAAAAAAAAAAAAJpKRX0HXnnlldx5552rrHfr1i2zZ89ukFAAAAAAAAAAAAAAAAAAAAB8BKVicyeAFqmsvgOdO3fOtGnTVln/+9//nl69ejVIKAAAAAAAAAAAAAAAAAAAAICmVu8ShhNOOCHf/OY3M3369BQKhRSLxTz99NM577zzcsoppzRGRgAAAAAAAAAAAAAAAAAAAIBGV+8ShksvvTQbb7xxevXqlUWLFmXbbbfNPvvskwEDBuS73/1uY2QEAAAAAAAAAAAAAAAAAAAAaHQV9dlcKpUyderU3HjjjfnhD3+YkSNHplgsZocddkifPn0aKyMAAAAAAAAAAAAAAAAAAABAo6t3CUOfPn0yatSo9OnTJ5tvvnlj5QIAAAAAAAAAAAAAAAAAAABoUmX12lxWlj59+mT27NmNlQcAAAAAAAAAAAAAAAAAAACgWdSrhCFJrrjiipx//vl59dVXGyMPAAAAAAAAAAAAAAAAAAAAQLOoqO/ASSedlOrq6my//fapqqpKmzZt6jw/Z86cBgsHAAAAAAAAAAAAAAAAAAAA0FTqXcJw1VVXNUIMAAAAAAAAAAAAAAAAAAAAgOZV7xKGU089tTFyAAAAAAAAAAAAAAAAAAAAADSrepcwLFiwYLXrhUIhrVq1SlVV1UcOBQAAAAAAAAAAAAAAAAAAANDU6l3C0Llz5xQKhTU+v+GGG+a0007LRRddlLKyso8UDgAAAAAAAAAAAAAAAAAAgLVQLDZ3AmiR6l3CcMstt+Q73/lOTjvttOy6664plUp57rnncuutt+a73/1uZs6cmR//+Mdp1apVvv3tbzdGZgAAAAAAAAAAAAAAAAAAAIAGV+8ShltvvTU/+clPctxxx9WuDRo0KP369csNN9yQP/3pT9l4441z6aWXKmEAAAAAAAAAAAAAAAAAAAAAWoyy+g6MGDEiO+ywwyrrO+ywQ0aMGJEk2WuvvTJx4sSPng4AAAAAAAAAAAAAAAAAAACgidS7hGHDDTfMr371q1XWf/WrX2WjjTZKksyePTtdunT56OkAAAAAAAAAAAAAAAAAAAAAmkhFfQd+/OMf59hjj80jjzySXXbZJYVCIc8991xef/31/O53v0uSPPfcczn++OMbPCwAAAAAAAAAAAAAAAAAAABAY6l3CcOgQYMyevToXH/99Rk9enRKpVIOPfTQ3HfffZk3b16S5Etf+lJD5wQAAAAAAAAAAAAAAAAAAABoVPUuYUiSTTbZJJdffnmSZN68ebnjjjty9NFH58UXX0xNTU2DBgQAAAAAAAAAAAAAAAAAAABoCmVrO/j444/npJNOysc+9rFce+21OfTQQ/P88883ZDYAAAAAAAAAAAAAAAAAAACAJlNRn82TJ0/OLbfckptvvjmLFy/OcccdlxUrVuR3v/tdtt1228bKCAAAAAAAAAAAAAAAAAAAANDoyj7sxsMOOyzbbrttXnvttVxzzTWZOnVqrrnmmsbMBgAAAAAAAAAAAAAAAAAAANBkKj7sxmHDhmXw4MH50pe+lD59+jRmJgAAAAAAAAAAAAAAAAAAAIAm96FLGJ566qncfPPN2XnnnbP11lvn5JNPzvHHH9+Y2QAAAAAAAAAAAAAAAAAAAFgbpWJzJ4AWqezDbtxjjz1y4403Ztq0afniF7+Y3/zmN+nVq1eKxWIee+yxLFy4sDFzAgAAAAAAAAAAAAAAAAAAADSqD13C8A9t27bN6aefnr/85S955ZVXcu655+byyy9P9+7dM2jQoMbICAAAAAAAAAAAAAAAAAAAANDo6l3C8M+22mqrXHHFFZk8eXLuuuuuhsoEAAAAAAAAAAAAAAAAAAAA0OQ+UgnDP5SXl+eoo47K/fff3xCHAwAAAAAAAAAAAAAAAAAAAGhyDVLCAAAAAAAAAAAAAAAAAAAAANDSKWEAAAAAAAAAAAAAAAAAAAAAiBIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIkFc0dAAAAAAAAAAAAAAAAAAAAgAZWLDZ3AmiRypo7AAAAAAAAAAAAAAAAAAAAAMC6QAkDAAAAAAAAAAAAAAAAAAAAQJQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkSSqaOwAAAAAAAAAAAAAAAAAAAAANrFRs7gTQIpU1dwAAAAAAAAAAAAAAAAAAAACAdYESBgAAAAAAAAAAAAAAAAAAAIAoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASJJUNHcAAAAAAAAAAAAAAAAAAAAAGlix2NwJoEUqa+4AAAAAAAAAAAAAAAAAAAAAAOsCJQwAAAAAAAAAAAAAAAAAAAAAUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJAkqWjuAAAAAAAAAAAAAAAAAAAAADSwYrG5E0CLVNbcAQAAAAAAAAAAAAAAAAAAAADWBUoYAAAAAAAAAAAAAAAAAAAAAKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgSVLR3AEAAAAAAAAAAAAAAAAAAABoYKVScyeAFqmsuQMAAAAAAAAAAAAAAAAAAAAArAuUMAAAAAAAAAAAAAAAAAAAAABECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQJKkorkDAAAAAAAAAAAAAAAAAAAA0MCKxeZOAC1SWXMHAAAAAAAAAAAAAAAAAAAAAFgXKGEAAAAAAAAAAAAAAAAAAAAAiBIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkSUVzBwAAAAAAAAAAAAAAAAAAAKCBFYvNnQBapLLmDgAAAAAAAAAAAAAAAAAAAACwLlDCAAAAAAAAAAAAAAAAAAAAABAlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJAkqWjuAP9QsUXP5o4A0KItv+Hi5o4A0KJVffHi5o4A0KItueCs5o4A0KJVbLlhc0cAaPG6dVjc3BEAWrYVHZo7AUCLVlkqNXcEgBZtbqGmuSMAtGhtij6bEeCjGlfZ3AkAAIB1iXdbAAAAAAAAAAAAAAAAAAAAAKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIklQ0dwAAAAAAAAAAAAAAAAAAAAAaWKnY3AmgRSpr7gAAAAAAAAAAAAAAAAAAAAAA6wIlDAAAAAAAAAAAAAAAAAAAAABRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkCSpaO4AAAAAAAAAAAAAAAAAAAAANLBisbkTQItU1twBAAAAAAAAAAAAAAAAAAAAANYFShgAAAAAAAAAAAAAAAAAAAAAooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACBJUtHcAQAAAAAAAAAAAAAAAAAAAGhgpVJzJ4AWqay5AwAAAAAAAAAAAAAAAAAAAACsC5QwAAAAAAAAAAAAAAAAAAAAAEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAkqSiuQMAAAAAAAAAAAAAAAAAAADQwIrF5k4ALVJZcwcAAAAAAAAAAAAAAAAAAAAAWBcoYQAAAAAAAAAAAAAAAAAAAACIEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRJRXMHAAAAAAAAAAAAAAAAAAAAoIEVi82dAFqksuYOAAAAAAAAAAAAAAAAAAAAALAuUMIAAAAAAAAAAAAAAAAAAAAAECUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJkormDgAAAAAAAAAAAAAAAAAAAEADKxWbOwG0SGXNHQAAAAAAAAAAAAAAAAAAAABgXaCEAQAAAAAAAAAAAAAAAAAAACBKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkiQVzR0AAAAAAAAAAAAAAAAAAACAhlUqlpo7ArRIZWsztGTJklRXV9c+njBhQq666qoMGzaswYIBAAAAAAAAAAAAAAAAAAAANKW1KmE48sgjc9tttyVJ5s2bl9122y0/+clPcuSRR+b6669v0IAAAAAAAAAAAAAAAAAAAAAATWGtShhGjhyZvffeO0lyzz33pEePHpkwYUJuu+22XH311Q0aEAAAAAAAAAAAAAAAAAAAAKAprFUJQ3V1dTp06JAkGTZsWD796U+nrKwsu+++eyZMmNCgAQEAAAAAAAAAAAAAAAAAAACawlqVMPTu3Tv33XdfJk2alEcffTQHHXRQkmTGjBnp2LFjgwYEAAAAAAAAAAAAAAAAAAAAaAprVcJw4YUX5rzzzsumm26a3XbbLXvssUeSZNiwYdlhhx0aNCAAAAAAAAAAAAAAAAAAAABAU6hYm6Fjjjkme+21V6ZNm5btt9++dn3//ffPpz71qQYLBwAAAAAAAAAAAAAAAAAAANBU1qqEIUl69uyZnj17JkkWLFiQxx9/PFtttVW23nrrBgsHAAAAAAAAAAAAAAAAAAAA0FTK1mbouOOOy7XXXpskWbJkSXbeeeccd9xx6d+/f373u981aEAAAAAAAAAAAAAAAAAAAACAprBWJQxPPvlk9t577yTJ73//+5RKpcybNy9XX311LrnkkgYNCAAAAAAAAAAAAAAAAAAAANAU1qqEYf78+enatWuSZOjQoTn66KPTtm3bHH744RkzZkyDBgQAAAAAAAAAAAAAAAAAAABoChVrM7TRRhtlxIgR6dq1a4YOHZrf/OY3SZK5c+emdevWDRoQAAAAAAAAAAAAAAAAAACAeioWmzsBtEhrVcJw9tln58QTT0z79u2z8cYbZ+DAgUmSJ598Mv369WvIfAAAAAAAAAAAAAAAAAAAAABNYq1KGL785S9n1113zaRJk3LggQemrKwsSbL55pvnkksuadCAAAAAAAAAAAAAAAAAAAAAAE1hrUoYkmTnnXdO//79M378+GyxxRapqKjI4Ycf3pDZAAAAAAAAAAAAAAAAAAAAAJpM2doMVVdX54wzzkjbtm3Tt2/fTJw4MUkyePDgXH755Q0aEAAAAAAAAAAAAAAAAAAAAKAprFUJwwUXXJCXXnopw4cPT+vWrWvXDzjggNx9990NFg4AAAAAAAAAAAAAAAAAAACgqVSszdB9992Xu+++O7vvvnsKhULt+rbbbpu33nqrwcIBAAAAAAAAAAAAAAAAAAAANJWytRmaOXNmunfvvsr64sWL65QyAAAAAAAAAAAAAAAAAAAAALQUa1XCsMsuu+Shhx6qffyP4oUbb7wxe+yxR8MkAwAAAAAAAAAAAAAAAAAAAGhCFWszNGTIkBxyyCF57bXXsnLlyvzsZz/LqFGjMmLEiDzxxBMNnREAAAAAAAAAAAAAAAAAAACg0ZWtzdCAAQPy9NNPp7q6OltssUWGDRuWHj16ZMSIEdlpp50aOiMAAAAAAAAAAAAAAAAAAABAo6tY28F+/frl1ltvbcgsAAAAAAAAAAAAAAAAAAAAAM1mrUsYisVixo4dmxkzZqRYLNZ5bp999vnIwQAAAAAAAAAAAAAAAAAAAFhLpeK/3wOsYq1KGJ555pmccMIJmTBhQkqlUp3nCoVCampqGiQcAAAAAAAAAAAAAAAAAAAAQFNZqxKGs846KzvvvHMeeuihbLDBBikUCg2dCwAAAAAAAAAAAAAAAAAAAKBJrVUJw5gxY3LPPfekd+/eDZ0HAAAAAAAAAAAAAAAAAAAAoFmUrc3QbrvtlrFjxzZ0FgAAAAAAAAAAAAAAAAAAAIBmU7E2Q1/72tdy7rnnZvr06enXr18qKyvrPN+/f/8GCQcAAAAAAAAAAAAAAAAAAADQVNaqhOHoo49Okpx++um1a4VCIaVSKYVCITU1NQ2TDgAAAAAAAAAAAAAAAAAAAKCJrFUJw/jx4xs6BwAAAAAAAAAAAAAAAAAAAECzWqsShk022aShcwAAAAAAAAAAAAAAAAAAAAA0q7K1Gbr11lvz0EMP1T7+xje+kc6dO2fAgAGZMGFCg4UDAAAAAAAAAAAAAAAAAAAAaCprVcJw2WWXpU2bNkmSESNG5Nprr80VV1yR9ddfP+ecc06DBgQAAAAAAAAAAAAAAAAAAABoChVrMzRp0qT07t07SXLfffflmGOOyZlnnpk999wzAwcObMh8AAAAAAAAAAAAAAAAAAAAAE1irUoY2rdvn9mzZ2fjjTfOsGHDcs455yRJWrdunSVLljRoQAAAAAAAAAAAAAAAgP/P3n1H2VnV+wP+nGlJSO+hJkBoofcOIRCkd0S6Bq4C0kGQi4CAiOIFEQHBHyIiRbyIitIJNQpSI50UQgjpldTp5/dHYGBIgjBMZhLu86w1a827z947383SvfY5553PCwAAwBdUX2ztCmC51KQQhsGDB+f444/PpptumhEjRmTvvfdOkrz++uvp169fc9YHAAAAAAAAAAAAAAAAAAAA0CJKmjLouuuuy7bbbpupU6fmT3/6U7p3754kefHFF3P44Yc3a4EAAAAAAAAAAAAAAAAAAAAALaGsKYO6dOmSa6+9dpH2iy+++EsXBAAAAAAAAAAAAAAAAAAAANAamhTC8NRTT33m6zvttFOTigEAAAAAAAAAAAAAAAAAAABoLU0KYRg4cOAibYVCoeH3urq6JhcEAAAAAAAAAAAAAAAAAAAA0BpKmjJo5syZjX6mTJmSBx98MFtuuWUefvjh5q4RAAAAAAAAAAAAAAAAAAAAYKkra8qgzp07L9I2ePDgtGnTJmeccUZefPHFL10YAAAAAAAAAAAAAAAAAAAAQEsqac7Jevbsmbfffrs5pwQAAAAAAAAAAAAAAAAAAABoEWVNGfTKK680ui4Wi5k4cWJ+8pOfZOONN26WwgAAAAAAAAAAAAAAAAAAAABaUpNCGDbZZJMUCoUUi8VG7dtss01uvvnmZikMAAAAAAAAAAAAAAAAAAAAoCU1KYRhzJgxja5LSkrSs2fPtG3btlmKAgAAAAAAAAAAAAAAAAAAAGhpTQph6Nu3b3PXAQAAAAAAAAAAAAAAAAAAQHOpr2/tCmC5VNLUgU8++WT23Xff9O/fP2uttVb222+/PP30081ZGwAAAAAAAAAAAAAAAAAAAECLaVIIw2233ZbddtstK6ywQk499dScfPLJadeuXXbdddfccccdzV0jAAAAAAAAAAAAAAAAAAAAwFJX1pRBl112Wa644oqcccYZDW2nnXZarrrqqlx66aU54ogjmq1AAAAAAAAAAAAAAAAAAAAAgJZQ0pRB77zzTvbdd99F2vfbb7+MGTPmSxcFAAAAAAAAAAAAAAAAAAAA0NKaFMKw6qqrZujQoYu0Dx06NKuuuuqXLgoAAAAAAAAAAAAAAAAAAACgpZU1ZdBZZ52VU089NcOHD892222XQqGQYcOG5ZZbbskvfvGL5q4RAAAAAAAAAAAAAAAAAAAAYKlrUgjDiSeemD59+uTKK6/MH//4xyTJeuutl7vuuiv7779/sxYIAAAAAAAAAAAAAAAAAAAA0BKaFMKQJAceeGAOPPDA5qwFAAAAAAAAAAAAAAAAAAAAoNU0OYQBlmdlW+yW8m33TqFjl9RPGZ/qh3+f+vfeXmzfkr7rpd2xP1ikff51Z6c4feLHc269R8o33zWFzj1SnD8ndW8+l+qhdyV1NUttHQCtpWzzXVO+zV4pdOic+qnjU/3I7akfN2KxfUtWWzftjv7vRdrn33Dux/toSWnKt9snZRvtkELHrqmfPik1j92VundeXZrLAFjmvTD81fz2jrvzxlujMnX6jPzi8guy607btXZZAMuE8oH7ps3XDk2hc7fUTxibyrt+lbqRr/3HcaVrDsgK37sy9RPezbxLTmz0WsWuB6Z84D4p6dYrxbmzU/Pi06m65zdJrff2wFdP2Sa7pGzLPVLo0CXFaeNT/didqR8/cskDSstSvu1+KR2wTQrtO6c4d2Zqnvl76l4bliQpdF8p5TsckJLe/VLSuUeqH7sztS8+0kKrAWh5XY/YO92OPzhlvbqlauTYTL7s11nwwuuL7dtx9+3S5Yi903a9NVKoKE/VyLGZds3tmTfspUb9Sjq2T88zj02n3bdLSecOqXl/UiZfflPmPflCSywJoFXsefoh2e7wXdOuc4eMHT4y/3vBzZk08v0l9u+z1irZ68yvZ9UNV0/3VXrlnkt+lyduvn+ROfc8/dBGbbOnzsoPtvzOUlkDQEvof+xuWe/EvdOuV5d8MGJ8Xrrw95n63OLvdUqSntusm81+eFQ6r71yFkyelTev/3tG/X5ooz7lnVbIRt//elbdc4tUdG6fueOm5uWLb8/Ex/6dJBlw8n5ZZa8t0qn/SqmrrM60F0Zm+GV/yJzRExf3TwIsl752+iHZ5vBBWaFzh4wdPip/uuDmTP6M82jvtVbJnmcemlU2XCPdVumZv1zyuzx18wON+mx31OBsd+Ru6bZKzyTJpJHv5+Fr7slbTwxfmksBWKpW/+Zu6X/SPmnbq0vmvD0+r154a6b/a8nn0e7brpsNf3h0Oq6zcionz8rI6/6Wd2/9+DxaKCvN2qful9W+vlPa9umauaMn5vUf3Zkpj7+y2PnWOmW/rH/+NzL61w/k1Qt/3+zrA2gpO55+UDY9YlDadm6fCS+PyoMX3JJpI8d/5ph19twyO591aLqu1isz35uSJ3/2x7z90MffHe14+kHZ6YyDG42ZO2VWfrHldxuuzx97+2LnHvrjO/Lsjfd9iRUBAHy1fe4Qhm7dumXEiBHp0aNHunbtmkKhsMS+M2bMaJbiYGkoHbBNKr52dKrv/23qxo1I+WaD0vaIc7Lg+nNSnD19iePmX3tWUrWg4bo4f/bHc26wXSp2PSxV9/6/1I8bkZLuK6Zi/++kIkn1w7ctzeUAtLjS9bZOxeAjU/3g71I3bmTKN9slbb9xdhbceN5n76O/OmeJ+2j5zgenbMPtUn3fzamfPjGla2yYNoeclsrfXZr6yWOX6noAlmULFlRmnf5r5IC9ds8Z5/+otcsBWGaUbbFz2h52Qipv/2XqRr2e8p33zgqnXpa5Fx2f4oypSx7YboW0G3JO6t56OYVOXRvPufWgtDn4uCy45crUjX4jJb1XSbtvnZ0kqfrjDUtzOQAtrnSdLVM+6PBUP/L71I8flbKNB6bNIWek8uYfpDhn8Z/vV+x7YgrtO6X6oVtSnDk5hRU6JSUlDa8XyitSnDU1NW+/kIpdvtFSSwFoFR332im9z/92Jv3w+sx/6Y10/caeWe2mSzJ6zxNSO3HR8+gKW26Qef94OVOvvCV1s+ely8GDs+qNF2XMoWek6o13FnYqL8tqt1yWuhmz8v4pP07NpGkpX7FH6uctWGQ+gK+K3U7YL7sct3duO/tXmTpmYnY/5aB897bz86NBZ6RqXuVix1S0a5Pp703O8PufzYEXHLPEuSe8PS7XHXVpw3Wxrr7Z6wdoKavtt002u/jovPDfv82050ak/9GDsvPt5+T+gedk/vhFv6Nvv2rPDLztexl9++N55uTr02OrtbPFj7+Vyumz8/79zydJSspLs8sfvp/KabMz7NvXZP6EGVlh5W6pnfvx/ttr23Uz8pZHM3346JSUlWajc7+eXe78fu7b+ZzULahqsfUDLC2DTtgvOx+3V+788Dw6+JSDcsJt/52fDDrzM86jFZn+3pQMv//ZHLCE8+isidNz30/vzLSxk5MkWxy8U4b8+uxcuff3PzPgAWBZtfL+22TDS47Jv79/c6Y/PyKrH71rtr3j3Azd6XtZsJjz6Aqr9cy2t5+Tsbc9nhdOvi7dt1w7G/9kSKqnz86E+xaeR9f7/qFZ9eAdMvzsmzJn5IT02mWjbH3zmXlq34vywWuN7xntsska6Xf0oHzwuntJgeXbtifsk62P3yt/O/uGzHhnUrY/5YAccft5uWGXs1O9hPPnypv1z0HXnpInr7w7bz/0fNb52pY58LpTcushl2TC8NEN/aa8PS53HHl5w/WnPw+9eouTGl2vOXDj7HPFf+Wt+59rxhUCAHz1fO4Qhp///Ofp2LFjw++fFcIAy7LybfdM7ctPpPblJ5IsDEkoXXOjlG2xW2oeu2uJ44rzZidV8xf7Wukqa6V+3IjUvfbPJEndB9NS99ozKVl5jWavH6C1lW+9R2qHP5na4U8mSaofuT2la2yYss0GpeaJ/13iuM/aR8s23D41/7g3daMXphjXvvRYStfYMOVb75Gqe29s/kUALCd23HbL7Ljtlq1dBsAyp83gg1Mz7MHUDHswSVJ11w0pW3+LVOy8b6r+fPMSx7U76vTUPPd4Ul+fsk23a/Ra2RrrpW7U66l97vEkSd30yal57vGUrr7O0lsIQCsp2+JrqX316dS9+nSSpObxO1O6+vop22SX1Dz9p0X6l/TbIKWrrpMF/+/cpHJekiwSxFg/6d3UT3p34Ws7HbJ0FwDQyroPOTCz7n44s/73oSTJ5Mt+nfY7bpauR+ydqVfeskj/yZf9utH11Kt+l467bZOOg7ZuCGHocsjuKe3SMe8edlZSW5ckqZ0wZekuBKCV7Txkrzx83Z/zykMLb/S9/azr8qMXfp3N998h/7zj0cWOee+V0XnvlYU3F+977uFLnLu+ri5zpn7Q/EUDtIJ1vr1n3rnzibxzxxNJkpcuui19Bm6UtY7ZLf++fNF7nfofs2vmjZ+ely5a+OCY2aMmpNtGa2S9E/ZuCGFY4xsDU9GlQx7Z7+IUPzx/zh8/rdE8Txx5RaPrf51xYw567YZ022j1TP3XW829TIAWt9OQPfPodX/Jqw8t3BvvOOv6XPLCjdls/+3zzB1DFztm3CvvZNwrC9/L73PuEYvt88bQlxpdP/A/d2X7owan36ZrCWEAlktrfmevjL3ziYz98Dz66oW/T6+BG2X1Y3fLGz9e9Dza75hds+D96Xn1wt8nSeaOnJAuG6+R/ifu0xDCsOohO2bEL/6SyUOHJ0ne/d2j6T1wo/Q/Ye+8ePL1DXOVrtAmW1z33Qw/66asc8YBS3WdAEvbVsftkX9c+5e8/eALSZK/nXVDTn/h+qy//3Z5+Y7HFj9myJ4ZM+y1/PP6e5Mk/7z+3qy29brZasge+cup1zX0K9bWZ95nfB766dfWHrx53n3mjcwa9xkPuwEA4POHMBx77LENv3/zm99cGrXA0ldSmpIVV0/NsL81aq5759WUrrpWaj5jaLtvX5aUlac4bXyqn/5r6t994+Px495O2Ubbp2SlNVI/4Z0UuvRMaf+NU/vK00tpIQCtpKQ0JSv2S80zf2/UXPfOqyld5T/so8dfunAfnTo+1f+4N/Vj32x4rVBantR+anRtdUpWXbsZiwcA4CuhtCwlfddK1YONb+aoff3FlK45YInDyrfbPSW9VsyC3/wkbfY+cpHXa0e9nnbb7JqSfuuk/t23U+jRJ2UbbpWafz7S7EsAaFUlpSnp0ze1z93fqLnu3ddTsnL/xQ4p7b9J6ie/m/Kt9kzpgG2TmqrUjR6emmF/XvT9PMBXXXlZ2q7fP9Nv/GOj5nnDXk67zdb7fHMUCilp3y51s+Y0NHUctHUWvPxm+lx0Ujrutk1qZ3yQ2X97ItN/fXdS7+ntwFdP91V7pXOvrnnr6Vca2mqrazP6X29k9c3XXmIIw+fVs1+fXPqvX6W2ujbvDh+Vv19xZ6aPE24DLH9KykvTbaPV8+a1je91mvTkq+mxxVqLHdNj87Uy6clXG/d/4pWsefjOKZSVplhbl5V33yzTXxyZLX78zazytc1TOX12xv75n3nzur+lWF9c7LzlnVZIklTPmtsMKwNoXd1W7ZVOvbrm7U+cR+uqazP6X2+m3+ZrLzGE4YsqlBSy8d7bpKJdm7z70ohmmROgJRXKS9Nlo9Uz8pf3Nmqf8uSr6bbl4u/v7Lb5WpnyqfPolCdeSd8jBjacR0srylJf2fg7prrK6nTfuvFDEjb+ybcy6dGXM/Xp14QwAMu1Lqv2TIdeXfPO0x/vj3XVtXnvX29llc3XWmIIw8qb9c9zv3mgUds7T72SrYbs2ait6+q9c+pz16auuibjXx6dJ664a4kBC+17dEr/QZvkb2d5UCLA/ynuO4Am+dwhDJ9UWlqaiRMnplevXo3ap0+fnl69eqWuru4zx1dVVaWqqqpRW21tXdqUlTalHPjcCit0TKGkNMV5jVPcivM+SKF958WOKc6dlaq/3ZT6iWOSsrKUbbhD2h59Xip/d1nq31uY6l73+rOpXqFT2n7rooX/TmlZap5/JDX/+Nti5wRYXjXso3M/vY/OTqHDZ+yj992c+kljktLylG24fdoeeW4qf3956se9nWRhiEP51nuk7r23U5w5JSWrD0jp2pslhZKlviYAAJYvhQ6dUigtTXH2zEbtxTkzU+jcdbFjSnqtlDYHH5f5V5y5xA+Sa59/IpUdO6f9uVclKaRQVpbqx/+W6gcXfXIHwPKs0G5Jn5HOXuJnpCVdeqZk5bWS2ppU/+XapF2HVAw+OoW27VP94G9bomyAZUZZ104plJWmdtqsRu2102amfY/Fn0c/rdtxB6XQrm1m3/9xmHf5qn2ywrYbZ/a9j2fc8Relot9K6X3RSSmUlWbatXc25xIAlgmdenZJksz+1BPYZk/9IN1W6fml5n53+KjcduZ1mTJmYjr26JKvnXJgzrjn0vx48FmZ7w+HgeVMm24dU1JWmsppjffLyqkfpG2vxb+Pb9uzcyo/tb9WTvsgJeVladOtYyqnzEqHvr3SfvsBeffP/8wTR12Rjmv0yRaXfTOFstK8/vM/L3beTX94ZKb866188LanuAPLv4/Oo3M+tV/OmfpBuq7S40vPv+I6q+bUey5NWZvyVM+vzG+/c2Umjxr/pecFaGkfnUerPrVfVk39IG16LuE82qtLpkx9ZZH+JeVlqejWMVVTZmXyE69kzRP2yrRn38q8dyen547rp8/XNk+h9ON7Rlfef9t03rBfntzjguZfGEALa9+rS5Jk3qf203nTPkinlZd8/uzQs0vmTZv9qTGz0/4Te/CE4aNz75k3ZMY7k9K+R6fscMoBOfaeH+bXg8/NgsV8HrrhwTulel5l3nrw+S+xIgCA/xuaFMJQLC4+7bqqqioVFRX/cfzll1+eiy++uFHbeQM3yPm7bNSUcqAJPv2/4cKSe06fmNrpExuuq98flULn7infdq9UfRjCUNJ3vZTvuH+q7/9t6saPTknX3qnY4+iUz52Vmqf/shTqB1jGFJIs4XxQnDEptTMmNVxXjx+VQqduKd9mz1R9GMJQ9chtabPXkLQ74adJiinOnJLafz+dso13bIHiAQBYLi1y/iws+nY/SQolafdf56Xq3ltTP3nJN7eVrr1R2ux1eCpv/2XqxryVkl4rp+1hJ6bigxmpvu/2Zi0dYJlUKCzxvf1Hr1X9/ddJ9YIkSc3jf0jF/iclj96W1NYsfhzAV9mn98zP2kc/odM+O6fnKUdm3ImXpG7GJ260KylJ3fRZmfiDXyb19al8fVTKenVP9+MPFsIAfCVssf8OOezH/9VwfeOQnyz85VN7Z+Fz7qef5c0nhjf8PvHtcXn3pRG58KlrsvXBO+fx39z3peYGaC2L3K9XWMLnoQ0DPnVdKHw0UcN15fTZef57N6VYX8zMV99Nu95ds96Jey82hGHzH38zXdZbLY8ecEmT1wDQmjbbf/sc+onz6E1Dfppk0f218Bn3QH0RU96ZkCv3OjdtO7XPxntulcOvPCnXHXaxIAZgubW4r+c/6zy62PPrJyZ69YJbs+n//Fd2G/Y/KRaLmffu5Lx315NZ7bCdkyTtVuqWDX90TP552OWpr/I9FLD8Wf+A7bLXj49ruL7rWz9bfMfP8XnoontqGo0Z/cS/G36f+nYy/qVROempq7LhITvmuZseWGS+jb++c177yz9SZ38FAPiPvlAIwzXXXJNk4ZfeN910Uzp06NDwWl1dXZ566qmsu+66/3Ge8847L2eeeWajttr/+fYXKQWapDh/Tor1dSm079KovdC+0yJPfvss9e+PStmG2zdcV+xySGpfGZbal59IktRNGZfqijZps89xqXn6r/nsbz0Blh8N+2iHxgnGhRU6pThv9hJGLap+/KiUbfDxPpr5c1J19y+S0vIUVuiQ4pyZKd/l6ynOmtpcpQMA8BVRnDs7xbq6FDp3a9Re6NglxdkzFx3Qtl1K+62Ttqv2T9vDT/6wcyGFkpJ0vOGBzL/6vNS9NTxtDjg2Nc8OTc2wB5Mk9ePfTVVF27Q9+rRU339Hs9xwB7AsKC746DPST7+375ji/MW/ty/O/SDFuTMbAhiSpH76xBQKJSl06JrirClLtWaAZUntzNkp1talrGfXRu1l3bukdvqszxzbca+dsuKPT8v4Uy/P/H8Obzzv1BlJTW1SX9/QVjV6XMp6dUvKyxa+BrAce/XRF/Lu8JEN12UV5UmSTr26ZPbUWQ3tHXt0yuxpn/+7+8+jekFVJrz1Xnqu3qdZ5wVoCVUz5qS+ti7tPnxi+0fa9uiUyqmL3y8rp36Qtr0av+9v271T6mtqUzVz4RMwK6fMSn1tXYr1H3/uOXvkhLTr3TUl5aWpr6lraN/8R8dk5d03y9ADL82CiTOaaWUALev1R1/Me8NHNVyXfuI8OucT59EOPTpnTjOcR+tq6jJt7OQkyfuvvpNVN1ozOw3ZM//73zd96bkBWtJH59FPny/b9OicqiXsl5VTZqXth098/7j/wvNo9Yfn0erpc/Kvb12VkjblqejaIZWTZmbAD76R+eMW3jPaZaM10rZn5wx8+LKGOUrKStN9m3Wz+pDdc+9qxyT1vsMHll0jH3kpN708uuG6tGLhn++179k5c6fMamhv371T5n3G+XPu1Fnp0LPxHrxwzJLv269ZUJUpb49Lt36Lfh666pbrpEf/lfLnk3/5eZcCAPB/2hcKYfj5z3+eZGGK1g033JDS0tKG1yoqKtKvX7/ccMMN/3GeNm3apE2bNo3a5pWVLqE3NKP6utRPHJPSNTZI3dsvNDSXrrFhat9+8XNPU9KnX4pzZ33cUNZm0T/GqK9PUviPSZ8Ay5X6utRPfDelq2+Quk/sm6Wrb5DaES997mlKevdtvI9+pK4mxTkzk5LSlK27ZWrf/FczFA0AwFdKXW3qx45M2XqbpfblfzQ0lw3YLLXDn1m0f+X8zL2ocfhnxcB9U7ruJllww6WpnzYpSVKoaJsU6xv1KxbrPnwihzf3wFdIfV3qJ41NSd8BqRv58Xv50r7rp27Uy4sdUjd+VErX2SIpb5PUVCVJSrr1TrG+fmE4A8D/JTW1qXx9VNpvv2nmPPLx+bP99ptmzqPPLnFYp312zoqXn57xZ1yRuU88v8jrC158I532HdjoiUcVq6+cmsnTBTAAXwlV8ypTNa+yUdsHU2ZmnR02yvuvv5skKS0vzZpbD8i9P7mjWf/tsoqy9Om/ct55/q1mnRegJdTX1GXGK2PSZ6cN8v6DH9/r1GenDTP+ocXf6zTtxZFZefBmjdr67LxhZvx7TIq1C8MVpj4/In0P3K7R+bPjGn0yf9LMxgEMlx2bVfbYIkMP+VHmjfMQBWD5tbjz6OwpM7P2DhtmfKPz6Hr5ezOfR5MkhUJD8APA8qRYU5dZr4xJz503zMQHPj6P9tx5g0x6cPHn0Rkvjkyf3RufR3sN3CizPnEe/Uh9VU0qJ81Moaw0K+29Vcbfu/Ce0alPv5ahA89p1Hezq7+TuSMnZMR1fxPAACzzqudVpvpT58+5U2Zm9R02zOTXxyZJSspLs9rW6+axn/xhifOMf2lUVt9xwzz3mwcb2tbYaaO8/+KIJY4prShLj/4rZ9xzby/y2saHDczEV97JlDff+6JLAgD4P+kLhTCMGTMmSbLLLrvknnvuSdeuXf/DCFj21DzzQNoceGLqJ45J3fsjU77ZoBQ6d0/ti0OTJOWDDkuhY9dU/3VhoEjZ1nukOGtq6qe+n5SWpWzDHVI2YKtU/vHnDXPWjXwp5dvslfpJ76Z+/OgUuvVOxS6HpG7ES56UCXzl1PzrwbTZ/zsf7qOjUr7pwIX76EuPJUnKBx66cB/926+TJGVbfi3FD6amfur4hfvoBtulbL2tUnn3NQ1zlqy0Rgodu6V+8tgUOnZNxY4HJoVCap65vzWWCLDMmD9/Qd57f0LD9fgJk/PWiNHp3KljVuzTqxUrA2hdVY/8Ke2OOyd1Y0ekbvQbKd9p75R065XqJ/+eJGlz4JAUunZP5c0/S4rF1E94t9H44pxZSW11o/bafz+bisEHpe690akb81ZKeq6Utvsfm9p/P7NIOAPA8q72hYdSsfd/Lfw8c8LolG28cwqduqX2308kScp3PHjhe/v7Fz6Vre7NZ1Pcdt9U7DkkNf/4awrtOqR856+n7tWnk9qahZOWlKbQY6WFv5eWpdChSwq9Vk2qq1KcNaUVVgmw9Ey/+c9Z+WdnZcFrI7Pg5bfS5bA9Ur5iz8y8c+HnmT3P+mbKenfPxHOuTLIwgGGlK87K5B/dmAXD30ppj4XfsRYrq1I/d36SZOYd96Xr0fum9w++k5m//1sq+q2UHid8PTNuvbd1FgnQAp68+f4M/u4BmfruxEwdMymDv3tAahZU5cW/Dmvoc9SV380Hk2fkb1fcmWThH8b1WWuVJElZeVk69+6alQf0TdW8yoYnDe//30fl9aEvZsb4aenYo3O+dvJBaduhXf71pydbfpEAzeDtXz+Qba45MTNeGZNpL4zMmkcNygord8/IWxfe67TxeYelXZ+uefa0hfc6jbp1aNb+1uBsetGRGX374+mxxVpZ4/CB+edJ1zbMOerWR7P2kN2z+aVHZ8TND6fj6n2y/qn75+3fPNTQZ4sffzN9D9wuT33rqtTOrUzbD5+8WTNnfuoqa1rwvwDA0vHUzQ9kt+8ekGnvTsrUMROz23cPTPWCqrz0149DwA+/8qTMnjwj912x8A/jSstL0/vD82hpeWk69+6WlQb0TfUnzqN7fe8befOJ4Zk1cXratm+bTfbdLv23GZBfH3t5yy8SoBmMvvH+bP7LkzLr3+9kxgsj0++oQVlh5R4Z8+F5dMB/H5a2K3bLS6f8Kkny7q1Ds8aQ3bPBD4/Ku7c/lm5brJW+hw/MCyd+/NT1rpuumbYrdssHr41NuxW7Zt2zD06hpCSjrvtbkqR2XmXmvPV+ozrq5leleubcRdoBlhfP/ebBbP/d/TLz3UmZMWZStjt5/9RUVuf1v/6zoc++V52QOZNm5okr7lo45rcP5pg/XpBtT9gnIx55MWsP3jz9tl8/tx5yScOYXc8/IiMffSkfTJie9t07ZYdTDkibDu3yyp+ebvTvV3Rol/X23ipDf7QUQscAAL6ivlAIw0cef/zx5q4DWkzdG8+meoUOKd/pwFR06JL6Ke+n8o6fpfjBtCRJoUOXlHTu3tC/UFqW8sFHpNCx28I/0Jj6firvuCJ1o/7d0Kfmqb8kxaRil0NT6NgtxfmzUzfi5VQ/9seWXh7AUlf35r8W7qM77L9wH536fir/cGWKs6cnWdw+WpryXQ9PoWPXD/fR8an8w/+kbvQrH09aVp6KnQ9OoWvPpLoqtaP+nZp7b0yq5rf08gCWKa+9NTJDTjm34fqKXy4MuNl/z91y2Q/Oaq2yAFpd7QtPprJDp7TZ58gUOndL/YSxmX/ND1KcsfCPfAtduqWk2xcLq6m67/YUU0zbA45NoUuPFOd8kNpXnk3ln3+7NJYA0Krq3n4+Ne06pHy7/VJo3znFaeNT9aerP/HevvPCz0M/UlOVqv/9n1TsemTaHn1BigvmLZxj2D0NXQoduqTdsRc3XJdstWfKt9ozde+9laq7rmixtQG0hDn3P5XJXTqmx3ePSFmvbqka8W7e+6+LUjth4Xm0rFfXlK/Us6F/l2/smUJ5Wfpc/N30ufi7De2z7nkkE89dGPpdO2laxn3rB+l9/rfT5e/XpXby9Mz43V8z/dd3t+ziAFrQozfcm/K2FTn00uOyQuf2GTt8VK4/+seNnlDcdeXuKX4iHLFz72459/6Pz5e7fme/7Pqd/TLy2dfzy28svPG4y4rdc+w1p6Z9106ZO2N23n15ZK468AeZOX5ayy0OoBm9d++zqejaIeufcWDa9eqSD95+P08e9bPM/3Bfa9urS1ZY+ePv6OeNm5onjvpZNrv4qKz1zcFZMHlmXrrg1rx///MNfeZPmJHHD/9JNvvh0dnz0cszf9LMvH3Tg3nzwz96S5K1vjk4SbLbPRc0qufZ02/MmD8+tTSXDNAiHvvwPHrwpUPSrnP7vDd8VG5c5DzaI8VPPIirU+9uOfv+nzZc7/KdfbPLd/bNqGffyPUfnkc79uicI3/+3XTq2SUL5szPxLfey6+PvTwjhr3acosDaEbj/7rwPLrumQelTa8umfPW+3nmyCuy4P0Pz6O9G59H5783Nc8ceUU2vPjorP6twamcPDOv/OB3mXDfx+fRkrblWe/7h6b9ar1SO68qkx8bnhdPvj41s90zCnx1PXPD31PWtiJ7/OibadupfcYPH507j/pJqj9x/uy8UvcU6z8+f45/cWT+fMq12fmsQ7PzWYdm5nuT8+eTf5kJw0c39OnYp1sO+OXJWaFrx8yfMTvjXx6VWw68KLM/9Xno+vtuk0KhkNfv/WcAAPh8CsVPfjr4OdXV1eWWW27J0KFDM2XKlNTXN34a4GOPPfaFC5l3yZFfeAwAn1Ba2toVACzXKr7zw9YuAWC5tuC8E1q7BIDlWtnaq7R2CQDLvbG/ntTaJQAs135V07G1SwBYrm1b3aRn4QDwoecr6lq7BIDl2k6VJa1dAsBy7/U2hdYuAWC5dv7Y21u7BGAJ5v/Cfd4sfSucdkNrl9DsmvTt32mnnZZbbrkle++9dzbYYIMUCt5oAAAAAAAAAAAAAAAAAAAAAMu3JoUw/OEPf8gf//jH7LXXXs1dDwAAAAAAAAAAAAAAAAAAAECrKGnKoIqKivTv37+5awEAAAAAAAAAAAAAAAAAAABoNU0KYTjrrLPyi1/8IsVisbnrAQAAAAAAAAAAAAAAAAAAAGgVZU0ZNGzYsDz++ON54IEHsv7666e8vLzR6/fcc0+zFAcAAAAAAAAAAAAAAAAAAEATFIutXQEsl5oUwtClS5cceOCBzV0LAAAAAAAAAAAAAAAAAAAAQKtpUgjDb3/72+auAwAAAAAAAAAAAAAAAAAAAKBVlTR1YG1tbR599NHceOONmTNnTpJkwoQJmTt3brMVBwAAAAAAAAAAAAAAAAAAANBSypoyaOzYsdljjz3y3nvvpaqqKoMHD07Hjh1zxRVXpLKyMjfccENz1wkAAAAAAAAAAAAAAAAAAACwVJU0ZdBpp52WLbbYIjNnzky7du0a2g888MAMHTq02YoDAAAAAAAAAAAAAAAAAAAAaCllTRk0bNiw/OMf/0hFRUWj9r59+2b8+PHNUhgAAAAAAAAAAAAAAAAAAABASyppyqD6+vrU1dUt0v7++++nY8eOX7ooAAAAAAAAAAAAAAAAAAAAgJbWpBCGwYMH5+qrr264LhQKmTt3bi666KLstddezVUbAAAAAAAAAAAAAAAAAAAAQIspa8qgn//859lll10yYMCAVFZW5ogjjsjIkSPTo0eP3Hnnnc1dIwAAAAAAAAAAAAAAAAAAAMBS16QQhpVWWinDhw/PnXfemZdeein19fU57rjjcuSRR6Zdu3bNXSMAAAAAAAAAAAAAAAAAAADAUtekEIYkadeuXYYMGZIhQ4Y0Zz0AAAAAAAAAAAAAAAAAAAAAreJzhzDce++92XPPPVNeXp577733M/vut99+X7owAAAAAAAAAAAAAAAAAAAAmqi+vrUrgOXS5w5hOOCAAzJp0qT06tUrBxxwwBL7FQqF1NXVNUdtAAAAAAAAAAAAAAAAAAAAAC3mc4cw1H8i6aRe6gkAAAAAAAAAAAAAAAAAAADwFVPS2gUAAAAAAAAAAAAAAAAAAAAALAuaFMJw6qmn5pprrlmk/dprr83pp5/+ZWsCAAAAAAAAAAAAAAAAAAAAaHFNCmH405/+lO23336R9u222y533333ly4KAAAAAAAAAAAAAAAAAAAAoKU1KYRh+vTp6dy58yLtnTp1yrRp0750UQAAAAAAAAAAAAAAAAAAAAAtrUkhDP3798+DDz64SPsDDzyQNdZY40sXBQAAAAAAAAAAAAAAAAAAANDSypoy6Mwzz8zJJ5+cqVOnZtCgQUmSoUOH5sorr8zVV1/dnPUBAAAAAAAAAAAAAAAAAAAAtIgmhTAMGTIkVVVVueyyy3LppZcmSfr165df/epXOeaYY5q1QAAAAAAAAAAAAAAAAAAAAICW0KQQhiQ58cQTc+KJJ2bq1Klp165dOnTo0Jx1AQAAAAAAAAAAAAAAAAAAALSokqYMWrBgQebPn58k6dmzZ6ZPn56rr746Dz/8cLMWBwAAAAAAAAAAAAAAAAAAANBSypoyaP/9989BBx2UE044IbNmzcpWW22VioqKTJs2LVdddVVOPPHE5q4TAAAAAAAAAAAAAAAAAACAz6u+2NoVwHKppCmDXnrppey4445Jkrvvvjt9+vTJ2LFjc+utt+aaa65p1gIBAAAAAAAAAAAAAAAAAAAAWkKTQhjmz5+fjh07JkkefvjhHHTQQSkpKck222yTsWPHNmuBAAAAAAAAAAAAAAAAAAAAAC2hSSEM/fv3z1/+8peMGzcuDz30UHbfffckyZQpU9KpU6dmLRAAAAAAAAAAAAAAAAAAAACgJTQphOHCCy/M2WefnX79+mXrrbfOtttumyR5+OGHs+mmmzZrgQAAAAAAAAAAAAAAAAAAAAAtoawpgw455JDssMMOmThxYjbeeOOG9l133TUHHnhgsxUHAAAAAAAAAAAAAAAAAAAA0FKaFMKQJH369EmfPn0atW211VZfuiAAAAAAAAAAAAAAAAAAAACA1tCkEIZ58+blJz/5SYYOHZopU6akvr6+0evvvPNOsxQHAAAAAAAAAAAAAAAAAAAA0FKaFMJw/PHH58knn8zRRx+dFVdcMYVCobnrAgAAAAAAAAAAAAAAAAAAAGhRTQpheOCBB3Lfffdl++23b+56AAAAAAAAAAAAAAAAAAAAAFpFSVMGde3aNd26dWvuWgAAAAAAAAAAAAAAAAAAAABaTZNCGC699NJceOGFmT9/fnPXAwAAAAAAAAAAAAAAAAAAANAqypoy6Morr8zo0aPTu3fv9OvXL+Xl5Y1ef+mll5qlOAAAAAAAAAAAAAAAAAAAAJqgWN/aFcByqUkhDAcccEAzlwEAAAAAAAAAAAAAAAAAAADQupoUwnDRRRc1dx0AAAAAAAAAAAAAAAAAAAAArapJIQwfefHFF/Pmm2+mUChkwIAB2XTTTZurLgAAAAAAAAAAAAAAAAAAAIAW1aQQhilTpuQb3/hGnnjiiXTp0iXFYjEffPBBdtlll/zhD39Iz549m7tOAAAAAAAAAAAAAAAAAAAAgKWqpCmDTjnllMyePTuvv/56ZsyYkZkzZ+a1117L7Nmzc+qppzZ3jQAAAAAAAAAAAAAAAAAAAABLXVlTBj344IN59NFHs9566zW0DRgwINddd1123333ZisOAAAAAAAAAAAAAAAAAAAAoKWUNGVQfX19ysvLF2kvLy9PfX39ly4KAAAAAAAAAAAAAAAAAAAAoKU1KYRh0KBBOe200zJhwoSGtvHjx+eMM87Irrvu2mzFAQAAAAAAAAAAAAAAAAAAALSUJoUwXHvttZkzZ0769euXNddcM/3798/qq6+eOXPm5Je//GVz1wgAAAAAAAAAAAAAAAAAAACw1JU1ZdCqq66al156KY888kjeeuutFIvFDBgwILvttltz1wcAAAAAAAAAAAAAAAAAAADQIkq+SOfHHnssAwYMyOzZs5MkgwcPzimnnJJTTz01W265ZdZff/08/fTTS6VQAAAAAAAAAAAAAAAAAAAAgKWp7It0vvrqq/Nf//Vf6dSp0yKvde7cOd/5zndy1VVXZccdd2y2AgEAAAAAAAAAAAAAAAAAAPiC6outXQEsl0q+SOd///vf2WOPPZb4+u67754XX3zxSxcFAAAAAAAAAAAAAAAAAAAA0NK+UAjD5MmTU15evsTXy8rKMnXq1C9dFAAAAAAAAAAAAAAAAAAAAEBL+0IhDCuvvHJeffXVJb7+yiuvZMUVV/zSRQEAAAAAAAAAAAAAAAAAAAC0tC8UwrDXXnvlwgsvTGVl5SKvLViwIBdddFH22WefZisOAAAAAAAAAAAAAAAAAAAAoKWUfZHOP/jBD3LPPfdk7bXXzsknn5x11lknhUIhb775Zq677rrU1dXl/PPPX1q1AgAAAAAAAAAAAAAAAAAAACw1XyiEoXfv3vnnP/+ZE088Meedd16KxWKSpFAo5Gtf+1quv/769O7de6kUCgAAAAAAAAAAAAAAAAAAALA0faEQhiTp27dv7r///sycOTOjRo1KsVjMWmutla5duy6N+gAAAAAAAAAAAAAAAAAAAABaxBcOYfhI165ds+WWWzZnLQAAAAAAAAAAAAAAAAAAAACtpqS1CwAAAAAAAAAAAAAAAAAAAABYFghhAAAAAAAAAAAAAAAAAAAAAIgQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAC+cor19X78LPWfprj++uuz+uqrp23bttl8883z9NNPf2b/qqqqnH/++enbt2/atGmTNddcMzfffHOT/u3Po2ypzQwAAAAAAAAAAAAAAAAAAADwobvuuiunn356rr/++my//fa58cYbs+eee+aNN97IaqutttgxX//61zN58uT85je/Sf/+/TNlypTU1tYutRqFMAAAAAAAAAAAAAAAAAAAAABL3VVXXZXjjjsuxx9/fJLk6quvzkMPPZRf/epXufzyyxfp/+CDD+bJJ5/MO++8k27duiVJ+vXrt1RrLFmqswMAAAAAAAAAAAAAAAAAAABfSVVVVZk9e3ajn6qqqsX2ra6uzosvvpjdd9+9Ufvuu++ef/7zn4sdc++992aLLbbIFVdckZVXXjlrr712zj777CxYsKDZ1/IRIQwAAAAAAAAAAAAAAAAAAADAF3b55Zenc+fOjX4uv/zyxfadNm1a6urq0rt370btvXv3zqRJkxY75p133smwYcPy2muv5c9//nOuvvrq3H333fnud7/b7Gv5SNlSmxkAAAAAAAAAAAAAAAAAAAD4yjrvvPNy5plnNmpr06bNZ44pFAqNrovF4iJtH6mvr0+hUMjtt9+ezp07J0muuuqqHHLIIbnuuuvSrl27L1H94glhAAAAAAAAAAAAAAAAAAAAAL6wNm3a/MfQhY/06NEjpaWlmTRpUqP2KVOmpHfv3osds+KKK2bllVduCGBIkvXWWy/FYjHvv/9+1lprraYXvwQlzT4jAAAAAAAAAAAAAAAAAAAAwCdUVFRk8803zyOPPNKo/ZFHHsl222232DHbb799JkyYkLlz5za0jRgxIiUlJVlllVWWSp1CGAAAAAAAAAAAAAAAAAAAAICl7swzz8xNN92Um2++OW+++WbOOOOMvPfeeznhhBOSJOedd16OOeaYhv5HHHFEunfvnm9961t544038tRTT+V73/tehgwZknbt2i2VGsuWyqwAAAAAAAAAAAAAAAAAAAAAn3DYYYdl+vTpueSSSzJx4sRssMEGuf/++9O3b98kycSJE/Pee+819O/QoUMeeeSRnHLKKdliiy3SvXv3fP3rX8+PfvSjpVajEAYAAAAAAAAAAAAAAAAAAACgRZx00kk56aSTFvvaLbfcskjbuuuum0ceeWQpV/Wxkhb7lwAAAAAAAAAAAAAAAAAAAACWYWWtXQAAAAAAAAAAAAAAAAAAAADNrL7Y2hXAcqmktQsAAAAAAAAAAAAAAAAAAAAAWBYIYQAAAAAAAAAAAAAAAAAAAACIEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRJWWsXAAAAAAAAAAAAAAAAAAAAQDMr1rd2BbBcKmntAgAAAAAAAAAAAAAAAAAAAACWBUIYAAAAAAAAAAAAAAAAAAAAACKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgSVLW2gUAAAAAAAAAAAAAAAAAAADQzOqLrV0BLJdKWrsAAAAAAAAAAAAAAAAAAAAAgGWBEAYAAAAAAAAAAAAAAAAAAACACGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEiSlLV2AQAAAAAAAAAAAAAAAAAAADSz+vrWrgCWSyWtXQAAAAAAAAAAAAAAAAAAAADAskAIAwAAAAAAAAAAAAAAAAAAAECEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJEnKWrsAAAAAAAAAAAAAAAAAAAAAmll9sbUrgOVSSWsXAAAAAAAAAAAAAAAAAAAAALAsEMIAAAAAAAAAAAAAAAAAAAAAECEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQJClr7QIalBRauwKA5VqhrLS1SwBYri0474TWLgFgudbu8htauwSA5Vr1L77f2iUALPc+mNe2tUsAWK51qVh2bh8AWB6NLXfvE8CX0b3o3ieAL6O8WN/aJQAs93rVec4tAADwMe8QAAAAAAAAAAAAAAAAAAAAAJJ4lAUAAAAAAAAAAAAAAAAAAMBXTbG+tSuA5VJJaxcAAAAAAAAAAAAAAAAAAAAAsCwQwgAAAAAAAAAAAAAAAAAAAAAQIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmSstYuAAAAAAAAAAAAAAAAAAAAgGZWX2ztCmC5VNLaBQAAAAAAAAAAAAAAAAAAAAAsC4QwAAAAAAAAAAAAAAAAAAAAAEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmSstYuAAAAAAAAAAAAAAAAAAAAgOZVrK9v7RJguVTS2gUAAAAAAAAAAAAAAAAAAAAALAuEMAAAAAAAAAAAAAAAAAAAAABECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQJKkrLULAAAAAAAAAAAAAAAAAAAAoJnVF1u7AlgulbR2AQAAAAAAAAAAAAAAAAAAAADLAiEMAAAAAAAAAAAAAAAAAAAAABHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQJClr7QIAAAAAAAAAAAAAAAAAAABoZvXF1q4AlkslrV0AAAAAAAAAAAAAAAAAAAAAwLJACAMAAAAAAAAAAAAAAAAAAABAhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACRJylq7AAAAAAAAAAAAAAAAAAAAAJpZsb61K4DlUklrFwAAAAAAAAAAAAAAAAAAAACwLBDCAAAAAAAAAAAAAAAAAAAAABAhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASZKy1i4AAAAAAAAAAAAAAAAAAACAZlZfbO0KYLlU0toFAAAAAAAAAAAAAAAAAAAAACwLhDAAAAAAAAAAAAAAAAAAAAAARAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAECSpKy1CwAAAAAAAAAAAAAAAAAAAKB5FeuLrV0CLJdKWrsAAAAAAAAAAAAAAAAAAAAAgGWBEAYAAAAAAAAAAAAAAAAAAACACGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEiSlLV2AQAAAAAAAAAAAAAAAAAAADSz+mJrVwDLpZLWLgAAAAAAAAAAAAAAAAAAAABgWSCEAQAAAAAAAAAAAAAAAAAAACBCGAAAAAAAAAAAAAAAAAAAAACSJGVNGTR9+vRceOGFefzxxzNlypTU19c3en3GjBnNUhwAAAAAAAAAAAAAAAAAAABAS2lSCMNRRx2V0aNH57jjjkvv3r1TKBSauy4AAAAAAAAAAAAAAAAAAACAFtWkEIZhw4Zl2LBh2XjjjZu7HgAAAAAAAAAAAAAAAAAAAIBWUdKUQeuuu24WLFjQ3LUAAAAAAAAAAAAAAAAAAAAAtJomhTBcf/31Of/88/Pkk09m+vTpmT17dqMfAAAAAAAAAAAAAAAAAAAAgOVNWVMGdenSJR988EEGDRrUqL1YLKZQKKSurq5ZigMAAAAAAAAAAAAAAAAAAABoKU0KYTjyyCNTUVGRO+64I717906hUGjuugAAAAAAAAAAAAAAAAAAAABaVJNCGF577bW8/PLLWWeddZq7HgAAAAAAAAAAAAAAAAAAAIBW0aQQhi222CLjxo0TwgAAAAAAAAAAAAAAAAAAALAsqq9v7QpgudSkEIZTTjklp512Wr73ve9lww03THl5eaPXN9poo2YpDgAAAAAAAAAAAAAAAAAAAKClNCmE4bDDDkuSDBkypKGtUCikWCymUCikrq6ueaoDAAAAAAAAAAAAAAAAAAAAaCFNCmEYM2ZMc9cBAAAAAAAAAAAAAAAAAAAA0KqaFMLQt2/f5q4DAAAAAAAAAAAAAAAAAAAAoFWVNGXQ7373u9x3330N1+ecc066dOmS7bbbLmPHjm224gAAAAAAAAAAAAAAAAAAAABaSpNCGH784x+nXbt2SZJnnnkm1157ba644or06NEjZ5xxRrMWCAAAAAAAAAAAAAAAAAAAANASypoyaNy4cenfv3+S5C9/+UsOOeSQfPvb387222+fgQMHNmd9AAAAAAAAAAAAAAAAAAAAAC2ipCmDOnTokOnTpydJHn744ey2225JkrZt22bBggXNVx0AAAAAAAAAAAAAAAAAAABACylryqDBgwfn+OOPz6abbpoRI0Zk7733TpK8/vrr6devX3PWBwAAAAAAAAAAAAAAAAAAANAiSpoy6Lrrrsu2226bqVOn5k9/+lO6d++eJHnxxRdz+OGHN2uBAAAAAAAAAAAAAAAAAAAAAC2hrCmDunTpkmuvvXaR9osvvvhLFwQAAAAAAAAAAAAAAAAAAADQGj53CMMrr7ySDTbYICUlJXnllVc+s+9GG230pQsDAAAAAAAAAAAAAAAAAACgieqLrV0BLJc+dwjDJptskkmTJqVXr17ZZJNNUigUUix+/H+8j64LhULq6uqWSrEAAAAAAAAAAAAAAAAAAAAAS8vnDmEYM2ZMevbs2fA7AAAAAAAAAAAAAAAAAAAAwFfJ5w5h6Nu372J/BwAAAAAAAAAAAAAAAAAAAPgqKGnKoMsvvzw333zzIu0333xzfvrTn37pogAAAAAAAAAAAAAAAAAAAABaWpNCGG688casu+66i7Svv/76ueGGG750UQAAAAAAAAAAAAAAAAAAAAAtrUkhDJMmTcqKK664SHvPnj0zceLEL10UAAAAAAAAAAAAAAAAAAAAQEtrUgjDqquumn/84x+LtP/jH//ISiut9KWLAgAAAAAAAAAAAAAAAAAAAGhpZU0ZdPzxx+f0009PTU1NBg0alCQZOnRozjnnnJx11lnNWiAAAAAAAAAAAAAAAAAAAABAS2hSCMM555yTGTNm5KSTTkp1dXWSpG3btjn33HNz3nnnNWuBAAAAAAAAAAAAAAAAAAAAAC2hSSEMhUIhP/3pT3PBBRfkzTffTLt27bLWWmulTZs2zV0fAAAAAAAAAAAAAAAAAAAAQItoUgjDRzp06JAtt9yyuWoBAAAAAAAAAAAAAAAAAAAAaDWfO4ThoIMOyi233JJOnTrloIMO+sy+99xzz5cuDJamss13S/m2e6XQoUvqp45P9cO3pX7c24vtW9J3vbQ7+vxF2uf/6nspTp/4YafSlG+/b8o22jGFjl1TP31iaobelbp3XlmaywBYppRtukvKttozhQ5dUpw2PtVD70j9+yOXPKC0LOXb7ZfS9bdNoX3nFOfMTM0zf0/dq0+3XNEArah84L5p87VDU+jcLfUTxqbyrl+lbuRr/3Fc6ZoDssL3rkz9hHcz75ITG71WseuBKR+4T0q69Upx7uzUvPh0qu75TVJbs7SWAbBMe2H4q/ntHXfnjbdGZer0GfnF5Rdk1522a+2yAJYJZVvtnvId9lv4GemU91P9wC2pH/vWYvuW9BuQdsf9cJH2+b84PcVpEz5uaLtCKnY7PKUDtkqhbfsUZ01J9QO/T93Il5fSKgBaT+9j98iKJ+6fil5dM3/EuIy98ObMee7NxfYt79U1fS86Nu03WjNtV18xk35zf8ZedHOjPoWy0qx0ykHpeeguqejTLQtGT8h7l/0+HzxhDwW+OnY5/aBsfvigtOvcPu8PH5W/X3BLpo4c/5ljBuyxZQaddWi6rdYrM96bkqH/88e8+dALDa+XlJZkl9MPzkYHbJcOPbtkzpRZGX73U3nyl39JsVhMSVlpdj370Kw9cJN0Xa1nKucsyDvDXssjP/1D5kyZtZRXDNC8djj9oGx8xC5p27l9Jr48Og9fcEum/Yd9dJ09t8yOZx2SLqv1yqz3puSpn/1vRnxiH/2kbU7aNwPPPSzP/+bBDL3ktob2vf/n29nw0J0a9R3/0qj8/sAffuk1AbS0HU8/KJseMShtO7fPhJdH5cHPuZfufNah6bpar8x8b0qe/Nkf8/an9tKOvbtml/O+kTUHbpzythWZ8c6k/P2cX2fSa++mpKw0O599aPrvskm6rNYzVXMWZMyw1/L4T/6Quc6kwHKs7zcHZ43v7pM2vbpk7tvv5/ULbs3Mfy3+fvw2vbpkvYuPSueNVk/7Nfrk3ZseyhsX3NqoT4d1Vsna5xySzhutkRVW65nXL7g17/76gZZYCkCrWO+Y3bLxCXulXa8umTlifJ794W2Z9Nzi99Ek6bPNutnmwiPTde2VM3/yrLzyq7/nzdsea3h9nSMGZu2Dd0zXdVZJkkx7dUye/+kfM3X4O0t9LQAsB+qLrV0BLJc+dwhD586dUygUkiSdOnVq+B2WN6UDtk7F7kel+oFbUjduRMo3G5S2h38vC244N8XZ05c4bv71ZydVCxqui/NnN/xePvCQlG2wfarv+03qp09I6Robpc2hp6fylotTP3nsUl0PwLKgdN2tUr7rEal++PepHz8yZZsMTJtDz0zlTeenOGfGYsdU7H9SCu07pfqB36Y4c3IK7TslJaUtXDlA6yjbYue0PeyEVN7+y9SNej3lO++dFU69LHMvOj7FGVOXPLDdCmk35JzUvfVyCp26Np5z60Fpc/BxWXDLlakb/UZKeq+Sdt86O0lS9ccbluZyAJZZCxZUZp3+a+SAvXbPGef/qLXLAVhmlG6wbSr2/Gaq/35T6t57O+Vb7Ja2R/93FvzyjBQ/+IzPSK8+Lama33BdnPfxZ6QpLU3bY3+Q4rzZqfrDVSl+MD2Fzt2T6sqluRSAVtF9v+3T9+JvZcx//7/Mee7N9D76a1n39h/k3wNPS/X4aYv0L6koS8302Rn/iz9lxW/vs9g5Vz33iPQ4aKe8871fZcGo8ekycJOs85tz8tr+/535r41Z2ksCWOp2OGGfbHvcXvnz2Tdk+phJ2fmUA3LsbeflmkFnp3re4s+Mq27WP4dee0oeu+ruvPnQ81nva1vm69eekt8cekneHz76w3n3zRZH7po/n3VDpox8PyttuEYO/Nm3Uzlnfp797UMpb1eRldbvlyd++edMevO9tOvcPnteeHSOuOms3LjfBS35nwDgS9n6hH2y5fF75r6zb8yMdyZlu1P2z2G3fz//b5fvLXEfXWmz/tn/2pPz1JV3Z8RDL2Ttr22R/a87ObcdcmkmfriPfqTPRmtkkyN2yZQ3Fn+f0+gn/p37z/51w3VddW3zLQ6ghWx7wj7Z+vi98rezb8iMdyZl+1MOyBG3n5cbdlnymXTlzfrnoGtPyZNX3p23H3o+63xtyxx43Sm59ZBLMuHDvbRtpxVyzJ8uythn3shdx16RedNnp2vf3qmcvfCz1PJ2FemzQb8Mu+bPmfzme2nbuX12v/DofP03Z+XmfZ1JgeXTivtvkwGXHpPXvn9zZj73dlY7Zrdsdef38+SOZ6dy/KLfNZW0KUv19NkZdfVfsvp39lrsnKXtKjJ/7JRM/Nu/MuCSo5f2EgBa1Rr7bp1tf3hU/nH+LZn8/Iise9Sg7PH77+V/dzk38yYsuo92XLVn9rj17Lx1xxN54tRfpfeWa2f7y76ZBTPm5N37n0+SrLTtehn112cy+YURqauqycYn7pM9bz83d+/6/cyfNLOllwgA8JXwuUMYfvvb3zb8fssttyyNWqBFlG+9Z2qHP5Ha4U8kSaofuS2la26Yss13Tc3jf1ziuOK82Y1uMP6ksg13SM2wv6Zu9L+TJLUvDU3pmhumfJu9UvXXXzX7GgCWNWVb7p7aV55K3StPJUlqht6Z0tU3SNmmg1Lz1N2L9C9ZfYOUrrpOFtx4TlI5L0k+MwgH4KumzeCDUzPswdQMezBJUnXXDSlbf4tU7Lxvqv588xLHtTvq9NQ893hSX5+yTRs/yb1sjfVSN+r11D73eJKkbvrk1Dz3eEpXX2fpLQRgGbfjtltmx223bO0yAJY55dvtk9qXHkvtiwufilH9wO9SutbGKdtq99Q8cucSxxXnfZBULuEz0s0GpbBCh1T+vwuS+rqF/T9Y9A+RAb4KVvz2vpl659BMvePRJMnYi25Ol4GbpPcxX8u4y29fpH/V+1Mz9sKF7/d7fWPQYufscfDOGX/N3Zn12EtJksm3PpTOAzfJit/ZL6NP+cVSWglAy9l2yB556rq/5M0Pnxh8z1k35JwXrs9G+2+XF+54bLFjthmyZ94Z9lqevv7eJMnT19+bfluvm22G7JG7T70uSbLqZmvlrUdezIjHhydJZr0/LRvut21W2nCNJEnVnAX53dE/aTTvfRf9Lifce2k6r9Q9HyzmhmaAZdGWx+2Rf17714x4cOE+et9ZN+aUF67LgP23y/Al7KNbDtkjY4a9lmev/1uS5Nnr/5bVtv7/7N13lJ1VvT/gz5k5M+m9UEKA0GvIpRN6KNKDopciIIIISlPwCvkpIjYQlIsKolxRioiIqDSJIr33EFp6QjoppJep5/dHcMKQBGWYzCT4PGuxVt599t7z3Xct933LOZ936+xy6iG5+919NEkq2rfJUT/5Uu6/8Ibsec7RK52rrqomi2bOa95FAbSwXU87JE9e85eMfHcvveeCX+QrL/w82w4emJdXsZfueuqhGf/Ea3nq3XPSp35+dzbcbavseuoh+cu7e+keXzoy86fNzr3/szysZt7k5fdGqxYsyW0nNj4n/dslN+XUe76bzuv3yHznpMBaqN+Zh2fS7x7OpFuXfU/pjYtvTq/9+mejUw7KyO//foX+SybNyhvfvDlJ0vf4/VY657xh4zLv3be1b/WN41dP4QBriO2/eGhG/v6RjLztkSTJM9/+bTbYd/tsc/IBef7yFX/XtPVJg7Jwyuw88+3fJknmjpmaXv37pf8ZhzWEMDx8TuPfLj3+9V+l3+G7ps+e22b0nU+s3gUBAHxMlTVl0KBBgzJ37twV2ufPn59Bg1b+xSFYI5SVp2y9fqkb91qj5rpxr6V8g80/cGi707+Xduddk7afHZKyjbZu9FmhvJjU1TQeUFuTsr5bNEvZAGu0svKUrbtx6se/3qi5bvzrKeuz6UqHlG/2X6mfPj4Vux2atl++Km1PvywV+x+bFCtaomKA1lVeTNlGm6f2jZcaNde+/mLKN91mlcMqBh6cst7rpeqeW1b6ee2Y11O+0eYp23hZ6EKh57opbr9raoc/13y1AwCw9isvT9n6m6RuzCuNmuvGDE953w8O8Gr35SvS7uu/TNtTLk5Zv20bT7vVTqmfNDqVR5yW9hden3Zn/ygV+3wyKRSafQkAralQUUyH/ptm7qON99G5jw5Lp523avq8lRWpr2r8rKl+aXU677r1KkYArD269e2VTr27Zczjrza01VXXZsKzI9J3p1U/p+/7X5tlzOPDG7WNeWx4Ntxx+XP4iS+MzCZ7bpse/dZNkqyz9YbZaOctM/qRYauct22ndqmvr294MzHAmq5L317p2LtrJrxvH5307Ij0+YB9dP0dN8uEx15t1Db+seErjDn4u6dk7EPD8taTjZ/5v9eGu2+dc168Nl98+Moccvlpad+jcxNXA9A6uvbtlY69u2Xc+/bSic+OyAYfsJf22XGzjHus8TnpuMeGZ4Odlp+Tbn7QTpk2fHw+9fNz85UXf57T/vr9DDhu/w+sp02ndik5JwXWUoWK8nTp3y8zH2m8P858dHi67ey78wD/SllFeXpu3y9THmv8u6Ypj72WdXZe+blp7x03X6H/5EdfTa/+/VIolq90TLFdm5RVlKdq7sLmKRwA4D9QsSmDHnnkkVRXV6/QvnTp0jz++OP/cnxVVVWqqqoatdXW1qXNKk78oLkU2ndKoax82Rvb3qO0aF4KHbuudExpwdxU3fer1E+bkJQXU9x+r7Q9cUiW3vL91E8cmSSpG/dqKnY7NHVvjUhpzoyU9ds25VvsmBSalHMCsFZp2FsXz2/UXlo0L4UO2610TFnXXinbYIuktibVf/5Z0q5TKg8+KYW2HVJ9/6rfAA/wcVDo2DmF8vKU5s9p1F5aMCeFLt1WOqas9/ppc8xpWXzF+Ul9/Ur71D7/SJZ26pIOF16VpJBCsZjqh+9J9dDbm3sJAACsxQrt3z0fXfi+e6QL56XQqetKx5QWzknVX36Z+qnjkmIxxR32SdtTLs7SX1+a+rfeTJKUdVsnhX69Ujv8iSy95bIUeqyXNkeclpSVpeaRO1f3sgBaTLF7pxSK5amZNbdRe83Meano3bXJ88579OWs98Ujs+CZN7J0wvR02bt/un1i1xTKPGsC1n4de3VNkhXeoL5o5rx03aDnB45bOLPx86eFM+enY68uDcePX3dP2nRqn3MevDKluvoUysvy4I/uyKt3P73SOYttKnLQhcfl1bueStXCJU1cEUDL6vjueeYK++iseenc54P30UWzVhzT4T376NZH7p51tts4Nx31rVXOM/aRVzLir89l3uRZ6dq3V/a+4NM5/rYhufGIi1NXXduEFQG0vA4faS9tfE66aNb8Rntpt769stOJB+TZX92fJ6+9K+vvsGkOvvTk1FXX5NU/rfjG4fI2FRl00XF57a6nUu2cFFgLVXbvnLJiearft6dWzZyXNr27rGIUAP/UtnunlBXLs/h9++iSmfPS7t17qe/XvneXTH6kcf/FM+elrKKYtt07ZcmMuSuM2WXIsVk0fU6mPLHq0EUAAD7YhwphGD58eVrhG2+8kenTpzcc19XVZejQoenTp8+/nOeyyy7LpZde2qhtyP7b5xuD+n+YcqDpSqV/ry1J6Z1pqX1nWsNx9ZQxKXTunordD0/VuyEMVX+/JW0OPy3tvnRlklJKc2ak9pXHUtxhn9VRPcCa6f376Ae96bJQSEqlVN1zfVK97GFizUO/T+XRX04euCWprVn1WICPixXOPwvJyk5JC2Vpd/qQVN19c+rfnrLK6cq36J82hx2fpbf+LHXjR6Ssd5+0PfZLqZz3Tqrvu7VZSwcA4OPg/dfxWfU90lnTUjvrPfdIJ41OoUvPVOx1ZKreDWFIoZDSovmpvuuXy+aZOj7VnbqlYq+jhDAAH08r3A9dSduHMOHiX2eTH30pOzz206SULH1rembe/lB6HTvoo9UJ0Ar6Dx6YI39wWsPxrademWRlt0QLKf3LvbPx54VCUnpP23ZH7p4djt4zfzzv2swYNSXrbbNRDv3WiVnw9pwMu7PxS0TKiuX5zM/OTqGskHsvvvHDLgugxWxz9MAc8oNTG47v+PyPkqzkMVJhFc+W3mNle+8/Gzut1z0HXnJSbj/ph6mrWvUz+hH3Ptvw71mjJmfaq+Pz5SevzqaDBmTU0Bf+1XIAWsW2Rw/MYe85J73981euvON79sVVWeGc9X33AAplZZn26rg8cuUfkiRvv/5Wem2xQXY86cAVQhjKiuX55LvnpEO/eeO/vR6ANdGKp5r/ek8F4D0+5LOm95+XNnxVfyVj+n/p8Gx69B657zPf/8BrfgAAPtiHCmEYMGBACoVCCoVCBg1a8Qs/7dq1y89+9rN/Oc+QIUNy/vnnN2qrveqMD1MKNElp8YKU6utS6Ni1UXuhQ5eUFs1b+aCVqJ8yJsXt91zesHhBqu64OimvSKF9x5QWzEnFoGNTmjuzeQoHWIM17K0dGicYF9p3XuXeWlo4L6WFcxoCGJKkfvbUFAplKXTqntKct1drzQCtqbRwfkp1dSl06d6ovdCpa0rz56w4oG27lG+8Zdr23Sxtjz/73c6FFMrK0ukX92fx1UNSN2JY2hz9udQ882BqnhiaJKmfMiFVlW3T9qTzUv3X33nICQBAkqS0+N3z0ZXdI134Ie6RTh6V4g57L593wdykvrbReWdp5pSUdeqWlJcndXUftXSANULtOwtSqq1LRa9ujdorenZJzcx/fx9dcd75GXXqD1NoU5Fit06pmf5ONvzGSama6F4psPYZ8Y+XMnnY2Ibj8splX03p2LtLFs6c29DeoWfnLJy16r1z4cy56dir8fOnDj07Z9HM5W8i/sSQE/L4dffktXueSZLMGDkpXfv0zN5fPqpRCENZsTz/fe056da3V35z/A9S5Y3DwBpszAMv5dcvL99Hi//cR3t1yaL3vNmyQ4/OWfRh99EenRve6L7u9v3SoVeXnHLvdxs+LyuWp+9uW2anzx2UKzc/JaX6FZ8vLZoxN/OmzEq3jddt0voAWsLoB17Kr15e8Zy0Q68uWdiMe2mSLJwxN7NGN36hwqwxU7LVobs0aisrludT156Trn175dbjf5Bq56TAWqr6nfmpr61Lm/ftj5U9O6fqPdfsAKzc0ncWpL62Lu17d23U3q5nlyxZxbnp4hnzVtq/vqY2S+csbNS+/RmHZcDZR+Wvx1+ed96c1JylAwD8x/lQIQzjx49PqVTKJptskueeey69evVq+KyysjK9e/dOeXn5v5ynTZs2adOmTaO2RcV/PQ4+svq61E8bn/J+26Vu5PIk9vJ+26V21Iv/9jRl626c0sK5K35QV5PSgjlJWXmKW+2a2jefXbEPwMdNfV3qp09I2cbbpm70Sw3N5Rtvk7rRw1Y6pG7K6JRvtXNS0SapqUqSlHVbN6X6+pQWvNMSVQO0nrra1L81OsWtd0zty082NBe32TG1w55esf/SxVl4yRcbNVXud2TKtxqQJb/4bupnTU+SFCrbJqX6Rv1Kpbp3444L+ZevQgIA4D9DXV3qp45L+ab9U/fm8w3N5Zv2T+2I5z9gYGNl6/VbFrzwz2knjkyx/56N3hxX6LFe6ue/I4AB+Fgp1dRm0fCx6bLPDpkzdPlzoC777JA5f3vuo89fVZOa6e+kUCxP98N2z+x7nvrIcwK0tOpFS/POoqWN2hbMmJPN9to+019/K0lSXlGejXfbKg9c/vtVzjPp5THZdK/t8/QNQxvaNtu7fya+NKrhuKJdZUrvuy9aX1+/7O2b7/pnAEOPjdfNb47/fpbMbfylZIA1TfWipal+3z66cMbcbLzXdnn73X20rKI8fXfbKo9cfvsq55n60phsvPd2ef49++jG+2yfKS+OTpK89eTr+dVBFzUac/iPvpjZY6fmmevuXWkAQ5K07doxndfr3igQAmBNs/K9dE767bV9o710w922ykMfcE465aUx6bf39nnuPXvpJvv0z+QXl5+TTnpxVLpvsl6jcd37rZd5U2Y1HP8zgKFbv3Vz63HOSYG1W6mmLvOGj0+vffvn7fuXfx+/5z7b5+2//fvfxwf4T1VfU5dZr45Pn723y4Shy/fRPntvl7f+vvJ9dMZLo7PhgTs2auuzz3aZOXx8SrXLn8f3P/Pw/Ne5g3P/iT/MrOHjV88CAAD+g3yoEIaNNtooybIH1rC2qnn2/rQZ/KXUTxuXusljUrHj/il06ZHalx5MklTs/98pdOqW6rt/mSQp7vqJlObOSv3MyUl5McXt90xx612z9I6rG+YsW3/TFDp1S/3bb6XQqXsq9/lUUiik5ql7W2OJAC2u9vm/p/KI01M/fULqp45JcYd9U+jcI7XDHk6SVOzz6RQ6dU31fb9KktS98UxKA49K5WGnpeaJv6TQrmMq9v/v1L36eFJb05pLAWgRVQ/cmXanfT11b41K3dg3UrHP4Snr3jvVjy47f2zzyVNT6NYjS399ZVIqpX7qhEbjSwvmJrXVjdprX3kmlQd9KnUTx6Zu/IiU9Vo/bQd/LrWvPL1COAPAf4rFi5dk4uSpDcdTpr6dEaPGpkvnTllv3d6tWBlA66p56t60Oeac1E8dl7pJo1Kx84EpdOmZ2uceSJJUHHR8Cp27p/rOa5MkxT0OS2nuzNS/PSkpFlPcYe8Ut909S2/7UcOctc/9PRW7H5LKw05JzTNDU9Zj3VTu+8nUPHN/q6wRYHWadv092fSn52bR8DFZ8MLIrHPiwWnTp2fevvnvSZK+Qz6bynV7ZOx5P20Y037bjZMkZR3apqJH57TfduOUqmuzZPTkJEnH/9o8Fet2z+LXJ6Ry3e7Z4IJjk7JCpv78zy2+PoDV4elfD83eZx2V2ROmZ/b46dnnrMGpWVKd4XctD5v51I/PzPy35+QfVyz7QfEzvx6aU/9wcfY684iMeODFbHXQTtlkz21zw2e+0zBm5IMvZ5+zjs68KbMzY/TkrLftxhl42qF56Y5HkyRl5WU59rrzsv62G+e3p/0oZeVlDW8yXjJ3YepqBIYBa4fnbxiaPc46KnMmvJ13xk/PHmcflZql1XnjPfvoEVedkQXT5+TRK/6QJHnhN3/LZ//wzex25hEZ/cCL2fygnbLxntvmt5/+bpJlP1CeNWpyo79Ts7gqS+YsbGivaN8me331Uxl5//NZNGNuumzQK/t8/TNZPGdhRv3thQCsTZ67YWj2POuozJkwPe+Mn56BZw9OzdLqvP6evfTIq87Mgulz8si756TP/WZoTv7DxdnjzCMy6oEXs8W7e+nNn15+Tvrcr+7P5/50SQaedVTevPfZrD9g0/zXCfvnr0NuSJIUystyzHXnZd3tNs7tp/4ohfKydHjPOWm9c1JgLTT+F/dlwDVnZe4r4zL3hVHpe9IBabdBz0y86R9Jki2/cVzartstr5xzXcOYztsu+y1KeYe2qezRKZ233Sj1NbVZOGpKkqRQUZ5OW2yQJCmrLKbtut3SeduNUrtoaRZPeLuFVwiwer16/f3Z7ydfyszh4zLjxTHZ6rP7p2OfHnnzlmW/a9rlov9Oh3W75ZGvLPtd05u3PJRtTjkou3/rsxnxu4fTe6fNsuVx++Whs69tmLP/lw7Pzl/7dB465+dZMGlW2r17zlmzaGlqF1e1/CIBAD4GPlQIw3uNHTs2V199dd58880UCoVsvfXWOe+887Lppps2Z33Q7OreeDbV7TqlYu9PprJj19TPnJylv78ypXmzkySFjl1T1qVnQ/9CeTEVB56QQqduy37oNnNKlt52ZerGvrJ80mJFKvf7TArdeiXVVakdMyw1d12XVC1u6eUBtIq6Ec+lpl2HVOx5VAoduqQ0a0qq7vjflOb/c2/tkkLnHssH1FSl6vYrU3nQiWn7uW+ltGTRsjke/1MrrQCgZdW+8GiWduycNkd8NoUu3VM/9a0s/uk3U3pnRpKk0LV7yrp/uB8HV913a0oppe3Rn0uha8+UFsxL7fBnsvTPv1kdSwBYK7w2YnROPefChuMrfnZ9kmTwoQfm+9+8oLXKAmh1da89ner2nVKx3zGp7NQt9W9PytJbLktp3rI3sxU6dlvxHuknTkqhc/ekpjr1MyZl6c2XpW70yw19SvNnZ+lN30vloZ9Lu7OuTGnBO6l5+v7UPP6Xll4ewGo3++4nU+zWKRt89b9T0btbFo+cmBEnfj/VU2YmSSp7d0ubPj0bjen/wFUN/+64w2bp+al9UjVpRl7e7cwkSaFNRfpeeELabrhO6hYvzdwHX8qYc3+SuvmeNQEfD0/84t5UtK3MEd89JW27dMiUYWNz80mXN3o7cZc+PVIqLX/r+qSXRueOc67JAV/7TAad/5nMmfh2/nD2zzJ52NiGPvddclMOuODTOeK7n0+Hnp2z4O05eeF3D+WRny575tR5ve7Z+qCdkiRn3X9Zo5p+fdz3MuGZN1fnsgGazbPv7qMHf++UtO3cPlOHjc3tJ/6w0T7aef2eKdUv30envDg6d51zTfa54DPZ54JPZ87Et3PX2ddk2nv20X+lVFefXlv2zXaf2ittO3fIwhlzM/HpN3LXWdes8IZ5gDXd07+4N8W2lTnke6ekbedl56S3nfi+c9L1e6ywl/75nGuy7wWfyb4XLDsn/fPZP8vU9+yl04aPyx+/eHX2v/DY7H3uJzN38sw8cOlv8/pfloU7dF6ve7Y4eNk56elDG5+T3nLs9zLROSmwFpp21zOp7NYpm5//qbRZp2sWjpiU50/4YZZMXvasqU3vrmn3vnukez90ecO/uw7YJH2O2SuLJ87Mw7ucmyRpu263Rn02PevIbHrWkZn95Bt55lPfbYFVAbSccfc8mzbdOmXHr3wy7Xt3zTsjJ2foyVdm4ZRl371v37trOrxnH10waWaGnvyj7HHJidnmcwdm8dtz8vS3bs6Evz7f0Gebkw9MeZuKHHT9eY3+1otX/SkvXeU7+gAATVEovfcJ9r/pb3/7W4466qgMGDAge+65Z0qlUp566qm88sorueeee3LQQQd96EIWfe/EDz0GgOUKFRWtXQLAWq12zJTWLgFgrdbusl+0dgkAa7Xqn1zU2iUArPVevaG2tUsAWKv9tbJda5cAsFZrXyq0dgkAa7W6fOivsgLwHgOW1rd2CQBrvakV5a1dAsBa7fTJv23tEoBVWHDmIa1dAv8BOv1iaGuX0OyKTRl00UUX5atf/Wouv/zyFdovvPDCJoUwAAAAAAAAAAAAAAAAAAAAALSmJoUwvPnmm/nDH/6wQvupp56aq6+++qPWBAAAAAAAAAAAAAAAAAAAwEdQKpVauwRYK5U1ZVCvXr0ybNiwFdqHDRuW3r17f9SaAAAAAAAAAAAAAAAAAAAAAFpcsSmDTj/99Hzxi1/MuHHjMnDgwBQKhTzxxBP54Q9/mAsuuKC5awQAAAAAAAAAAAAAAAAAAABY7ZoUwnDxxRenU6dO+fGPf5whQ4YkSdZff/18+9vfzrnnntusBQIAAAAAAAAAAAAAAAAAAAC0hA8dwlBbW5tbb701xx9/fL761a9mwYIFSZJOnTo1e3EAAAAAAAAAAAAAAAAAAAAALaXsww4oFov50pe+lKqqqiTLwhcEMAAAAAAAAAAAAAAAAAAAAABruw8dwpAku+22W15++eXmrgUAAAAAAAAAAAAAAAAAAACg1RSbMujLX/5yLrjggkyePDk77bRTOnTo0Ojz/v37N0txAAAAAAAAAAAAAAAAAAAAAC2lSSEMxx57bJLk3HPPbWgrFAoplUopFAqpq6trnuoAAAAAAAAAAAAAAAAAAAAAWkiTQhjGjx/f3HUAAAAAAAAAAAAAAAAAAAAAtKomhTBstNFGzV0HAAAAAAAAAAAAAAAAAAAAQKtqUgjDzTff/IGfn3zyyU0qBgAAAAAAAAAAAAAAAAAAAKC1NCmE4bzzzmt0XFNTk8WLF6eysjLt27cXwgAAAAAAAAAAAAAAAAAAANCa6kutXQGslcqaMmjOnDmN/lu4cGFGjhyZvfbaK7fddltz1wgAAAAAAAAAAAAAAAAAAACw2jUphGFlNt9881x++eU577zzmmtKAAAAAAAAAAAAAAAAAAAAgBbTbCEMSVJeXp6pU6c255QAAAAAAAAAAAAAAAAAAAAALaLYlEF33313o+NSqZRp06blmmuuyZ577tkshQEAAAAAAAAAAAAAAAAAAAC0pCaFMBx99NGNjguFQnr16pVBgwblxz/+cXPUBQAAAAAAAAAAAAAAAAAAANCimhTCUF9f39x1AAAAAAAAAAAAAAAAAAAAALSqso8yuLq6OiNHjkxtbW1z1QMAAAAAAAAAAAAAAAAAAADQKpoUwrB48eKceuqpad++fbbddttMnDgxSXLuuefm8ssvb9YCAQAAAAAAAAAAAAAAAAAAAFpCk0IYhgwZkuHDh+eRRx5J27ZtG9oPPPDA3H777c1WHAAAAAAAAAAAAAAAAAAAAEBLKTZl0F/+8pfcfvvt2X333VMoFBrat9lmm4wdO7bZigMAAAAAAAAAAAAAAAAAAABoKWVNGTRz5sz07t17hfZFixY1CmUAAAAAAAAAAAAAAAAAAAAAWFsUmzJol112yX333ZdzzjknSRqCF/7v//4ve+yxR/NVBwAAAAAAAAAAAAAAAAAAwIdXX2rtCmCt1KQQhssuuyyHHHJI3njjjdTW1uYnP/lJXn/99Tz99NN59NFHm7tGAAAAAAAAAAAAAAAAAAAAgNWurCmDBg4cmCeffDKLFy/Opptumr///e9ZZ5118vTTT2ennXZq7hoBAAAAAAAAAAAAAAAAAAAAVrtiUwduv/32uemmm5qzFgAAAAAAAAAAAAAAAAAAAIBW86FCGMrKylIoFD6wT6FQSG1t7UcqCgAAAAAAAAAAAAAAAAAAAKClfagQhj//+c+r/Oypp57Kz372s5RKpY9cFAAAAAAAAAAAAAAAAAAAAEBL+1AhDIMHD16hbcSIERkyZEjuueeefPazn813v/vdZisOAAAAAAAAAAAAAAAAAAAAoKWUNXXg1KlTc/rpp6d///6pra3NsGHDctNNN2XDDTdszvoAAAAAAAAAAAAAAAAAAAAAWsSHDmGYN29eLrzwwmy22WZ5/fXX8+CDD+aee+7JdttttzrqAwAAAAAAAAAAAAAAAAAAAGgRxQ/T+YorrsgPf/jDrLvuurntttsyePDg1VUXAAAAAAAAAAAAAAAAAAAAQIv6UCEMF110Udq1a5fNNtssN910U2666aaV9vvTn/7ULMUBAAAAAAAAAAAAAAAAAAAAtJQPFcJw8sknp1AorK5aAAAAAAAAAAAAAAAAAAAAAFrNhwphuPHGG1dTGQAAAAAAAAAAAAAAAAAAADSXUn2ptUuAtVJZaxcAAAAAAAAAAAAAAAAAAAAAsCYQwgAAAAAAAAAAAAAAAAAAAAAQIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmSYmsXAAAAAAAAAAAAAAAAAAAAQDOrL7V2BbBWKmvtAgAAAAAAAAAAAAAAAAAAAADWBEIYAAAAAAAAAAAAAAAAAAAAACKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgSVJs7QIAAAAAAAAAAAAAAAAAAABoZvWtXQCsncpauwAAAAAAAAAAAAAAAAAAAACANYEQBgAAAAAAAAAAAAAAAAAAAIAIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASJIUW7sAAAAAAAAAAAAAAAAAAAAAmlepvtTaJcBaqay1CwAAAAAAAAAAAAAAAAAAAABYEwhhAAAAAAAAAAAAAAAAAAAAAIgQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEmxtQsAAAAAAAAAAAAAAAAAAACgmdWXWrsCWCuVtXYBAAAAAAAAAAAAAAAAAAAAAGsCIQwAAAAAAAAAAAAAAAAAAAAAEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJAkKbZ2AQAAAAAAAAAAAAAAAAAAADSz+tYuANZOZa1dAAAAAAAAAAAAAAAAAAAAAMCaQAgDAAAAAAAAAAAAAAAAAAAAQIQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkSYqtXQAAAAAAAAAAAAAAAAAAAADNq1Rfau0SYK1U1toFAAAAAAAAAAAAAAAAAAAAAKwJhDAAAAAAAAAAAAAAAAAAAAAARAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAARqO1bgABAABJREFUAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAECSpNjaBQAAAAAAAAAAAAAAAAAAANDM6lu7AFg7lbV2AQAAAAAAAAAAAAAAAAAAAABrAiEMAAAAAAAAAAAAAAAAAAAAABHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQJCm2dgEAAAAAAAAAAAAAAAAAAAA0r1J9qbVLgLXSmhPCUFvX2hUArNVKhbLWLgFgrVbcYoPWLgFgrVb9k4tauwSAtVrleZe3dgkAa73ib85v7RIA1mrjS0tauwSAtdopSytauwSAtdqUon0U4KN4qa3vkAJ8VIsL9a1dAgAAsAZxtwUAAAAAAAAAAAAAAAAAAAAgQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIkxdYuAAAAAAAAAAAAAAAAAAAAgGZW39oFwNqprLULAAAAAAAAAAAAAAAAAAAAAFgTCGEAAAAAAAAAAAAAAAAAAAAAiBAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkSbG1CwAAAAAAAAAAAAAAAAAAAKB5lepbuwJYO5W1dgEAAAAAAAAAAAAAAAAAAAAAawIhDAAAAAAAAAAAAAAAAAAAAAARwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkCQptnYBAAAAAAAAAAAAAAAAAAAANLP61i4A1k5lrV0AAAAAAAAAAAAAAAAAAAAAwJpACAMAAAAAAAAAAAAAAAAAAADQIn7+85+nX79+adu2bXbaaac8/vjj/9a4J598MsViMQMGDFit9QlhAAAAAAAAAAAAAAAAAAAAAFa722+/PV/5ylfyjW98Iy+//HL23nvvHHrooZk4ceIHjps3b15OPvnkHHDAAau9RiEMAAAAAAAAAAAAAAAAAAAAwGp31VVX5bTTTssXvvCFbL311rn66qvTt2/fXHfddR847owzzsgJJ5yQPfbYY7XXKIQBAAAAAAAAAAAAAAAAAAAA+NCqqqoyf/78Rv9VVVWttG91dXVefPHFHHzwwY3aDz744Dz11FOr/Bu/+c1vMnbs2FxyySXNWvuqCGEAAAAAAAAAAAAAAAAAAAAAPrTLLrssXbp0afTfZZddttK+s2bNSl1dXdZZZ51G7euss06mT5++0jGjR4/ORRddlFtvvTXFYrHZ61+ZlvkrAAAAAAAAAAAAAAAAAAAAwMfKkCFDcv755zdqa9OmzQeOKRQKjY5LpdIKbUlSV1eXE044IZdeemm22GKLj17sv0kIAwAAAAAAAAAAAAAAAAAAAPChtWnT5l+GLvxTz549U15enunTpzdqnzFjRtZZZ50V+i9YsCAvvPBCXn755Zx99tlJkvr6+pRKpRSLxfz973/PoEGDPvoi3qes2WcEAAAAAAAAAAAAAAAAAAAAeI/KysrstNNOeeCBBxq1P/DAAxk4cOAK/Tt37pxXX301w4YNa/jvzDPPzJZbbplhw4Zlt912Wy11FlfLrAAAAAAAAAAAAAAAAAAAAADvcf755+ekk07KzjvvnD322CPXX399Jk6cmDPPPDNJMmTIkEyZMiU333xzysrKst122zUa37t377Rt23aF9uYkhAEAAAAAAAAAAAAAAAAAAABY7Y499tjMnj073/nOdzJt2rRst912+etf/5qNNtooSTJt2rRMnDixVWsslEqlUqtW8K5F3z6+tUsAWLtVVLR2BQBrtUKlfDKAj6I0f2FrlwCwVqs87/LWLgFgrffKgPNbuwSAtdpPystauwSAtdopSz2zB/gophTtowAfxVvFNeInAQBrtcUFeynAR3HZhN+1dgnAKsw6dN/WLoH/AD3vf7S1S2h2vkUBAAAAAAAAAAAAAAAAAAAAECEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJEmKrV0AAAAAAAAAAAAAAAAAAAAAzay+tQuAtVNZaxcAAAAAAAAAAAAAAAAAAAAAsCYQwgAAAAAAAAAAAAAAAAAAAAAQIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmSYmsXAAAAAAAAAAAAAAAAAAAAQPMq1bd2BbB2KmvtAgAAAAAAAAAAAAAAAAAAAADWBEIYAAAAAAAAAAAAAAAAAAAAACKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgSVJs7QIAAAAAAAAAAAAAAAAAAABoXqX61q4A1k5lrV0AAAAAAAAAAAAAAAAAAAAAwJpACAMAAAAAAAAAAAAAAAAAAABAhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACRJiq1dAAAAAAAAAAAAAAAAAAAAAM2rVN/aFcDaqay1CwAAAAAAAAAAAAAAAAAAAABYEwhhAAAAAAAAAAAAAAAAAAAAAIgQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEmxtQsAAAAAAAAAAAAAAAAAAACgmZUKrV0BrJXKWrsAAAAAAAAAAAAAAAAAAAAAgDVBk0IYhg4dmieeeKLh+Nprr82AAQNywgknZM6cOc1WHAAAAAAAAAAAAAAAAAAAAEBLaVIIw//8z/9k/vz5SZJXX301F1xwQQ477LCMGzcu559/frMWCAAAAAAAAAAAAAAAAAAAANASik0ZNH78+GyzzTZJkjvvvDNHHHFEfvCDH+Sll17KYYcd1qwFAgAAAAAAAAAAAAAAAAAAALSEsqYMqqyszOLFi5Mk//jHP3LwwQcnSbp375758+c3X3UAAAAAAAAAAAAAAAAAAAAALaTYlEF77bVXzj///Oy555557rnncvvttydJRo0alQ022KBZCwQAAAAAAAAAAAAAAAAAAABoCWVNGXTNNdekWCzmj3/8Y6677rr06dMnSXL//ffnkEMOadYCAQAAAAAAAAAAAAAAAAAAAFpCsSmDNtxww9x7770rtP/v//7vRy4IAAAAAAAAAAAAAAAAAAAAoDWUNWXQSy+9lFdffbXh+K677srRRx+d//f//l+qq6ubrTgAAAAAAAAAAAAAAAAAAACAltKkEIYzzjgjo0aNSpKMGzcuxx13XNq3b5877rgjX//615u1QAAAAAAAAAAAAAAAAAAAAICW0KQQhlGjRmXAgAFJkjvuuCP77LNPfve73+XGG2/MnXfe2Zz1AQAAAAAAAAAAAAAAAAAAALSIYlMGlUql1NfXJ0n+8Y9/5IgjjkiS9O3bN7NmzWq+6gAAAAAAAAAAAAAAAAAAAPjQSvWtXQGsncqaMmjnnXfO9773vdxyyy159NFHc/jhhydJxo8fn3XWWadZCwQAAAAAAAAAAAAAAAAAAABoCU0KYbj66qvz0ksv5eyzz843vvGNbLbZZkmSP/7xjxk4cGCzFggAAAAAAAAAAAAAAAAAAADQEopNGdS/f/+8+uqrK7RfeeWVKS8v/8hFAQAAAAAAAAAAAAAAAAAAALS0sqYOnDt3bn71q19lyJAheeedd5Ikb7zxRmbMmNFsxQEAAAAAAAAAAAAAAAAAAAC0lGJTBg0fPjwHHHBAunbtmgkTJuT0009P9+7d8+c//zlvvfVWbr755uauEwAAAAAAAAAAAAAAAAAAAGC1KmvKoPPPPz+f//znM3r06LRt27ah/dBDD81jjz3WbMUBAAAAAAAAAAAAAAAAAAAAtJQmhTA8//zzOeOMM1Zo79OnT6ZPn/6RiwIAAAAAAAAAAAAAAAAAAABoaU0KYWjbtm3mz5+/QvvIkSPTq1evj1wUAAAAAAAAAAAAAAAAAAAAQEtrUgjD4MGD853vfCc1NTVJkkKhkIkTJ+aiiy7KMccc06wFAgAAAAAAAAAAAAAAAAAAALSEJoUw/OhHP8rMmTPTu3fvLFmyJPvuu28222yzdOrUKd///vebu0YAAAAAAAAAAAAAAAAAAACA1a7YlEGdO3fOE088kYceeigvvfRS6uvrs+OOO+bAAw9s7voAAAAAAAAAAAAAAAAAAAAAWkSTQhj+adCgQRk0aFBz1QIAAAAAAAAAAAAAAAAAAADQapocwvDggw/mwQcfzIwZM1JfX9/os1//+tcfuTAAAAAAAAAAAAAAAAAAAACaplRfaO0SYK3UpBCGSy+9NN/5zney8847Z7311kuh4H+AAAAAAAAAAAAAAAAAAAAAwNqtSSEMv/jFL3LjjTfmpJNOau56AAAAAAAAAAAAAAAAAAAAAFpFk0IYqqurM3DgwOauBVpMcZeDUjHwiBQ6dU39jMmpHnpz6ieOXGnfso23TrtTvrVC++JrLkhp1tQkSdtTLk75xtus0Kd21Mup+t0VzVs8wBqquNMBqdj9sBQ6dkn9zCmpfuDW1E8ateoB5cVU7H10itsNTKFDl5QWvJOaJ+9J7SuPtVzRAK2oOGD/FHc5JIWOXVOaNSXVD92W+imjVz2gvJiKPY5K+Ta7L9s3F85JzdP3pu61J5IkhR7rp2Kvo1O2zsYp69Iz1Q/dltoXH2ih1QC0vOKuB6dir6NS6Pjutf39N6b+rREr7Vu28TZpd9q3V2hf/JOvNFzbJ0natk/lgcenfJtdU2jbIaW5M1J9/y2pG/3yaloFwJrvhWGv5je/+2PeGDEmM2e/k59cdnEO2MfzAYBeJx+adc88OhW9u2XJqEmZ9O0bsvC5N1bat+uhu6f3SYek3bb9UlZZkSWjJmbqVb/P/EeHNfRpu0Xf9PnaCWm//aZp07d3Jl5yQ2bccE8LrQag9XzyK8dm/xMOSocuHTL25dG56eL/y5TRk1bZf7/jDsxex+yXDbbcMEky/tWxueOKWzPulTENfbbcdZscfsbgbLz9pum2TvdcffrlefHvz632tQC0pD6nHJyNzjoylb27ZtHIyRl98U2Z++zK749W9u6azS89KZ36b5L2m6ybSb8amtEX39Soz3rH7pttfvrlFcY+vOGJqa+qWS1rAGhJW37uwGxz5mFp37tr5o6akucv+W1mPLfy74wmyTq7b5WdL/lsum7RJ4vfnpvXr7s3o255aKV9Nz5q9+xz3dmZOPSFPHLa1Q3thfKy7HDBp9LvkwPTrlfXLJkxN2P/8FiG/+SupFRq7iUCtIh9vvKp7HjCoLTt0iFTXh6ToRffmJmjp3zgmK0O3SX7XfCZdNuwd+ZMnJGHr/xDRv7thUZ9Oq3TLQcMOS6b7rdDKtpWZva46bnn69dn+msTGvr03Gz9HHDRcdlwt61TKCtk5qgpufOsn2b+1NmrY6kAreaArxyTXY8flHZdOmTSsDG56+LfZMYH7LW9N++Tg87/TPps3y/dNuiVe79zc5789dAWrBgA4OOrSSEMX/jCF/K73/0uF198cXPXA6td+ba7p/KQk1N9369TN3FkKnY+MG1PvChLrv1aSvNWfRNm8c++mlQtaTguLZrf8O+lt1+VQvl7/ufUrlPafeny1L3xzGpZA8Capnzr3VJ50GdTPfSm1E0anYod90/b476WJb8cktL8le+tbT51dgodOqfq3htSmvN2Ch06J4WyFq4coHWUb7lLKgYdn+oHbkn9lDEp7rBf2nz6q1n662+mtOCdlY6pPPJLKXTonOq/3bhs32zfOSlbvm8WKipTmjszNSNfSOX+x7XUUgBaRfl2e6Ty0FNSfe+vll/bn/T/suRnX/3ga/urz0uqFjccv/faPuXlafu5b6a0aH6qfn9VSvNmp9ClR1K9dHUuBWCNt2TJ0my52SY5+rCD89VvfK+1ywFYI3Q7cs/0/fapmfiNX2bh8yPS68RPZPNbLs7r+5+T6qmzVujfabdtM//xVzL5h79N3fxF6fnfB2Sz33wjbx759Sx5fXySpKxdm1RNnJ537n0yfS85taWXBNAqDj/zkzn0C0fm+q/9LNPHTcvgcz6dC2+9JF/f/+wsXbTy6/Gt99guT9/9REa/OCI1VTU5/Myj8/VbLsmQg87LnLeX3Vtt075NJr45IY/d8VDO++WFLbkkgBbRe/Ae2eK7n8vIi27I3OdGps/JB2aH24bkmb3PT9WUFe+PlrWpSM3s+Zlw9Z+z4RmHrXLe2vmL8/TArzRqE8AAfBxsfNRu2fnbJ+bZ/3djZj4/KpufNCgH/PZ/cvd+F2bRSn6427Fvrwy65WsZ/btH8sQ516XXLltktx+ckqWzF2TiX59v1LdDnx7Z6Vsn5O1nVgzC2e6sI7LFSQfkya/8MnNHTk6PHfplz6u+mOoFSzLihr+ttvUCrC4Dzzwiu3/hsNz9tV9k9rjp2fuco/PZW4fk5/t/LdWruI7vs+NmOeaac/LIj/+YEX97Plt9Ypccc+05ufHT38nUYWOTJG07t88pd16SCU+/kds+d0UWzZ6fbhutk6r5y5/td9uwdz73x29l2O2P5tH/vTNL5y9Oz837pNb5KvAxs8+ZR2av0w7NH7/2y8waPy37n/PJnPbb/5cfD7pglXttZbs2eWfijLz612dz+MUntnDFAAAfb00KYVi6dGmuv/76/OMf/0j//v1TUVHR6POrrrqqWYqD1aFij8NT+9LDqX3p4SRJ9dCbU75p/xR3Pig1D/5+leNKi+YnSxev/MMli/LeXOLidgOTmqrUvv5sM1YOsOaq2O2Q1A57NLXDHk2SVD9wa8o32T7FHQel5pE7Vuhfvsn2Kd9wyyy+9mvJ0kVJktK8Fb+cDPBxVdz5E6l99fHUvfp4kqTm4dtS3m/bFAfsn5rH71yhf9nG26W875ZZ8n8XLt833xdyUz99QuqnT1j22T6fXr0LAGhlFQOPSO1LD6X2xWVvHKq+/6aUb75DirsenJoHblvluNKieau8ti/uOCiF9h2z9P8uTurrlvV3jgqQvffYJXvvsUtrlwGwRlnni4Mz6/f/yKzb/pEkmfTtG9J53wHpdfIhmXL5b1foP+nbNzQ6nvLD36brwbum60G7NIQwLH5lTBa/+xb3DYacvJpXALBmOOS0I3LXNXfmhaHLnqv/8oKf5poXfpM9Bu+Th3/395WOue68qxsd33Dhddn10D2yzZ798+SfHkmSDH/k5Qx/5OXVWTpAq9rwzMMz9XcPZeqty+6Pjr74pvTYb4dscMrBGfv9Fe+PLp00M6O+eVOSZP3j91vlvKVSKdUz562WmgFa09anH5oxv38kY257JEnywiW/zfr7bp8tTj4gL1/+hxX6b3HSoCyaMjsvXLLsGn/emKnpsUO/bHvmYY1CGAplhex9zZfzyo/uTO/dtkxl5/aN5um10+aZ9LcXM+XBYUmSRZNnpd/gPdJjh36rZ6EAq9mupx2SJ675S0YMfSFJctcFv8j5L/w82w0emJd+99BKx+x26qEZ98RrefLndydJnvz53dlwt62y26mH5M/nXpskGfilIzN/2uzc8z/XN4ybN7nxs/r9/+e/M+bhV/LgZcvPd+dOmtms6wNYE+x56iF5+Nq78vrflp133nHBdfnGC9dlwOCBeW4Ve+3k4eMyefi4JMkhF3qBFwBAc2pSCMPw4cMzYMCAJMlrr73W6LNCofCRi4LVprw8Zev3S80TdzVqrhs7POV9t8gHZWG2O+OypFiR0swpqX7sz6mf8MYq+1b8136pfe3ppKaqeeoGWJOVladsvY1T8/S9jZrrxr2a8g02X+neWr7FjqmfNiEVexye4vYDk+rq1I1+KdWP3pnUSiYGPubKylO27kapfe6vjZrrJryesj6brXRI+WYDUv/2hFTsemjKt9kjqalK3dhhqXniz/ZN4D9PeXnK1t8kNY//pVFz3ZjhKe+75Qdf23/5imXX9jMmp/rRP6V+/OvLp91qp9RPGp3KI05LceudU1o0P7XDn1z2d0qlVU8KAMB/lEJFMR223zTTr20cojj/sWHpuPNW/+YkhZR1bJfauQtXQ4UAa4defddJ197d8trjwxraaqtrM+LZ17P5TluuMoTh/dq0q0x5RXkWzV2wmioFWLMUKsrTqf8meeunjb/79M6jr6TLzlt8pLnLO7TNwBeuSaG8LAtfn5Cxl/8hC1+b8JHmBGhtZRXl6dG/X167tvH3mqY9+lp67bz5Ssf02mnzTHu08Xejpz7yajY/bt8UiuUp1S4L8+7/1U9m6ewFGfP7R9N7ty1XmGfGc6OyxUmD0mmTdbNg3PR022bD9N51yzx/yYoBjgBruq59e6VT724Z9/irDW111bV569kR2WCnzVcZwrDBjpvl2Rvub9Q27rHh2fXUQxuOtzhop4x9dHiO+fm52Wi3rbLg7Tl54eZ/5OXfL3vhYgqFbDZoQJ7+5b054eYLs+62G2XupJl58ud3Z+TfX2z+xQK0km59e6dz724Z/fjwhra66tqMf/bNbLTTFqsMYQAAYPVpUgjDww8//JH+aFVVVaqqGv84vba2Lm2K5R9pXvhXCu07p1BWvuzNl+9RWjQvhY5dVjqmtGBuqu7+v9RPG5eUV6S4w95p+7lvZOmN3039WyNW6F/WZ9OUrbNhqu6+fiWzAXz8FNp3Wra3Lnz/3jp/lXtroWuvlPXdPKXamlT98acptOuYykM+l8p2HVN9769aomyAVlNo12kV56TzU+iw8n2zrGuvlPXZPKmtSfVfrknadUzlQSel0LZDqof+piXKBlhjFNp3TqF8JeefC+el0KnrSseUFs5J1V9+mfqp45JiMcUd9knbUy7O0l9fmvq33kySlHVbJ4V+vVI7/IksveWyFHqslzZHnJaUlaXmkTtXOi8AAP95it07pVAsT83MuY3aa2bOS0Wvbv/WHOucMTjl7dtkzj1ProYKAdYOXXt3TZLMe99+On/W3PTo0+vfnufYi07KnOnv5PUnh//rzgAfAxXdO6esWJ7qmY3vj1bNnJfu7+6tTbFozNS8ee7Ps/DNSSl2ape+px+ane/5Tp4d9PUsGT/9I1YN0HradO+UsmJ5ls5qvG8umTUv669i32zXu0uWvK//0lnzUlZRTNvunbJkxtz02nnzbHb8frn3oP+3yr/92rX3pKJTuxz96BUp1dWnUF6Wl394Rybc9fRHXhdAS+v47p658H3noYtmzUuXPj1XPa5X1yyaNf99Y+anY6/l35Hq1rdXdj7xgDzzq/vz5LV3Zf0dNs0nLj05ddU1Gf6nJ9KhZ+e06dguA790ZB750R158PLfZ9N9++czv/xKbj7u+5n47Irf5wdYG3V6d298/167cOb8dN1g1XstAACrT5NCGD6qyy67LJdeemmjtiH7bptv7Ld9a5TDf6IVXmBZWFnjsq6zp6V29rSG4+rJo1Po3D0VA49I1UpCGIr/tV/q356Y+iljm61cgLVSIat8Y3ChUEhKSdVd1yVVS5Ik1f+4LW2OOTvVQ2/yVnfgP1OhsOo3rb/7WdW91yfVy/bNmod/n8rBX07+8Vv7JvAf6n175gecf5ZmTUvtrPdc208anUKXnqnY68hUvRvCkEIhpUXzU33XL5fNM3V8qjt1S8VeRwlhAABgRe8/HS0kpVVd179H98F7Z/3zj8uYU3+Q2tnz/mV/gI+LgUfvk8//4IyG4x9//vtJVvbovrCqR/crOPyMo7P7UXvlB8d+KzVV7pEC/1lK79ssCx/0nOnfMP/F0Zn/4uiG47nPjcyu/7g8fb9wSEZ948Ymzwuwpnj/NXvhA54rvTtglfMUO7TNXj/7Up7+n1+las7CVU6x8VG7Z5Nj9szjZ/08c0dNTvdtN8oul56YxW/Pzbg7Hm/KMgBazHZHD8zhPzit4fi2z1+58o6Fwr+8L7rC5+/bgwtlZZn66rg8fOUfkiTTX38rvbbYIDuddGCG/+mJZee6SUY98FKevWFokuTtN95K3502z06fPUAIA7DWGjB4zxz9nr32plOvWPaP92+r/+rcFQCA1aZJIQyLFi3K5ZdfngcffDAzZsxIfX19o8/HjRv3geOHDBmS888/v1Fb7RVfaEop8KGUFs9Pqb5uhTezFzp0Tmnh/FWMWlH95DEp9t9rxQ8qKlPcbmCqH77jo5YKsNYoLV6w8r21feeUFq18b61fOC+FBXMaAhiSpH7W1BQKZSl06p7SnLdXa80Aram05N19s8P7981OKS1e+b5ZWjgvpYVzGgIYkqR+9rRl+2bHbinNnbFaawZYk5QWz0+pri6Fjl0btRc6dElp4b//I7b6yaNS3GHv5fMumJvU1zZ6aFmaOSVlnbol5eVJXd1HLR0AgI+B2ncWpFRbl4r3vS2z2LNLamfN/cCx3Y7cMxv96OyMO+OKLHjCG9uB/ywvPfBcxrw8quG4orIiSdK1V9fMmzGnob1zjy6Z9y/20yQ57IuDc+RZx+SHn/12Jo14q9nrBVhT1bwzP/W1dWnTq2uj9sqenVM9sxlDvkqlzB82Nu36rdt8cwK0gqp3FqS+ti7t3rdvtu3RJUtWsW8umTFvxf49u6S+pjZVcxam65Z90mnD3hl04wUNnxfKlv1A+MS3bspf9vmfLHxrRna6+Pi8ds09mXD3M0mSuSMmp8MGPbP92UcKYQDWeKMeeClTXl7+QsJi5bKfnXTs1SULZ8xtaO/Qo3MWzVr1eejCmXPTsVfj70h16NE5C2ct/47UghlzM2v0lEZ9Zo2Zkq0O3SVJsnjOgtTV1GbmCn2mpu8uW364hQGsQd74x4uZNGxMw3H5P/fa3l2yYObchvaOPTtn4QfstQAArD5NCmH4whe+kEcffTQnnXRS1ltvvYZ0wX9XmzZt0qZNm0Zti4rlTSkFPpy6utRPHZ/yTfunbsQLDc3lm26f2hEv/tvTlK23cUoL567QXtx296RYTO3wJ5qjWoC1Q31d6qdNSHm/7VI3cvleWt5vu9SOemnlQyaPSnHrXZKKNklNVZKkrMe6KdXXp7TgnRYpG6DV1NelfvpbKdtom9SNXr5Plm+0berGvLzSIXVTxqR8y50b75vd11m2by6cs9IxAB9bdXWpnzpu2bX9m883NJdv2j+1I57/gIGNla3Xb1nwwj+nnTgyxf57/vMVxkmSQo/1Uj//HQEMAAA0KNXUZtGrY9N57wGZO/TZhvbOew/I3L8/u8px3QfvnY1/fHbGnXVV5j307z+TAvi4WLpoaZYumt6obe6MOdlurx3y1uvjkyTlFcVstdu2uf3yWz5wrsPOGJzBZ386V5z83Yx/dewH9gX4uCnV1GXB8HHpvm//zLx/+f3Q7vv0z8y/vfABIz+8TttunIUjJjbrnAAtrb6mLrOHj8/6+2yXSUOX75Pr7bNdJv1t5dfnM18cnQ0O2rFR2/r7bpfZw8enVFuXeWOm5e5BFzX6fMDXP52Kju3y/LduyeKps5MkxXaVK7z9vVRX3xDYALAmq160NNWLljZqWzBjTvrttX2mv74sDLGsojwb7bZVHrz896ucZ/JLY9Jv7+3z7A1DG9o22ad/Jr+4PKhx8ouj0mOT9RqN695vvcybMivJsr186vBxK+mzbkMfgLVR9aKlmf2+vXb+jDnZfK/tM+3dvba8ojz9dts6Qy+/rTVKBAD4j9ekEIb7778/9913X/bcc8/mrgdWu5qn70ubT52V+qnjUjdpVCp2OiCFLj1T+8I/kiQVBxyXQuduqf7zdUmS4u6HpjR3ZupnTE7Kiyn23yvFbXbL0tuvWmHu4n/tvyzcYcnCFl0TQGureXZo2gw+I/XTxqdu8phU/Nd+KXTpkdqXHkqSVOz3mRQ6dUv1PdcnSWpfezoVew1OmyNPT/Vjf0qhXadUDjouta88ltTWtOJKAFpG7Qt/S+Xhp6d++oTUTx2b4g77ptC5e2pfeSRJUrH3Mcv2zb/+KklS9+YzKe1xZCoPPTU1T96VQruOqdj3v1P36uPL982y8hR6rr/s3+XFFDp2TaF336S6KqW5M1phlQCrT81T96bNMecsv7bf+cBl1/bPPZAkqTjo+BQ6d0/1ndcmSYp7HLbs2v7tSUmxmOIOe6e47e5ZetuPGuasfe7vqdj9kFQedkpqnhmash7rpnLfT6bmmftbZY0Aa4rFi5dk4uSpDcdTpr6dEaPGpkvnTllv3d6tWBlA63n7+rvS7ydfyaLhY7LoxZHp9dmDU9mnZ2be8rckSZ+LTkzFuj0y4Ss/SfJuAMPV52XSJTdk4UsjU3z3bZqlpdWpW7A4SVKoKKbt5n0b/l25Xve026Zf6hcvSdWE6SsWAfAxMPSGe3PkWcdk+oRpeXv8tBx59qdSvbQqT9/1WEOfM646N3Omz84frrg1SXL4GUfnmAuOz8/P+9/MmjwjXd7dU5cuWpqqxcu+sNymfduss/HyN7f36ts7G26zcRbNXZjZU/04A1j7TfzFfdn2mrMz/5WxmffC6PQ56YC02aBnpty07P7opt84Pm3W7Z43zrm2YUzHbTdKkpR3aJvKHp3TcduNlgWMjVr2NuF+F3w6814cncXjp6XYsV36nn5oOm63UUYOuaHlFwjQzN78v/uz50++lNmvjMvMF8dk8xP3T4c+PTLqlgeTJP910X+n/Xrd8uR5v0ySjLrloWz5+YOy8yWfzehbH06vnTbLZsftl8fPWrav1lfVZO7IyY3+RvX8Zdf3722f9MDL2f7cwVk0ZXbmjpyc7tttnG2+eGjG/P7Rllg2QLN77oah2euso/LOhOl5Z/z07HX24NQsrc5rdz3V0GfwVWdmwfQ5eeiK25eN+c3QfO4PF2fgmUdk5AMvZsuDdkq/PbfNjZ/+TsOYZ351fz7/p0uy51lH5Y17n02fAZtmxxP2z33vORd9+pf35ZhrzsnEZ0dkwtNvZNP9+meLA3fMzcd+r+X+DwDQAp789dDsd9bgzJowPbPHT89+Zw1OzZLqDHvPXvuZH38p899+J397d68tryhP7803ePffxXRep3vW22ajZSEPb73dKusAAPi4aFIIQ7du3dK9e/fmrgVaRN3rz6S6fadU7PupVHbsmvoZk7L01h+mNG/Zly0KnbqmrEvPhv6F8mIqDv5sCp26J7XVqZ8xOUtv/WHqRg9rNG+hx7op32irLLn5By25HIA1Qt2bz6a6fcdU7DV42d46c3KW/v7HKc1fluxe6Ng1ZV16LB9QU5Wlv7sibQ4+Ke1OvTSlJQtT98ZzqX70j620AoCWVTfy+dS065iKgUel0KFLSrOmpOrOq9+zb3ZZdv75TzVVqbrjR6k84LNpe9LFKS1ZtGyOJ/7U0KXQsWvafe7ShuOyXQ9Nxa6Hpm7iiFTdfkWLrQ2gJdS99vSya/v9jkllp26pf3tSlt5y2fJr+47dVry2/8RJKXTuntRUL7sXcPNlqRv9ckOf0vzZWXrT91J56OfS7qwrU1rwTmqevj81j/+lpZcHsEZ5bcTonHrOhQ3HV/xsWcDi4EMPzPe/eUFrlQXQqubc82SK3Tpn/a8cm4re3bJk5MSMPvm7qZ4yM0lS0bt72vTp1dC/14mfSFlFMRv94Ixs9IMzGtpn/eGhTDj/p8vGrNM92/79fxs+W/fMT2bdMz+ZBU+/lpGf+WYLrQygZd33iz+nsm1lTvneF9O+c4eMGzY6V5z4nSx9z9vfeqzfM6X6+objA046JBVtKnLeL77eaK4//e/t+fPVy7503K//pvnG7d9t+Oyz3zo1SfL4HQ/l+q9dszqXBNAiZtz1dCq6dUq/849Jm3W6ZeGISXnlhMuzdPKy+6OVvbumbZ8ejcbs9tDyZ0WdB2yadY/ZK0smzshTu5yTJCl2aZ+tfnR62vTumtoFi7Pg1Ql58ehvZ/7LY1tuYQCryYS7n02bbp3S/6ufTLveXTN35OQ8eNKVWTRl2fP5dut0TYf1lz9XWjhpZh466UfZ+dsnZsvPHZjFb8/J89+6ORP/+vyH+rvPffPmDPj6p7PbD05J2x6ds+TtORn124cy/H//3KzrA2gpT/3i3hTbVubQ752Sdp07ZMqwsbn1xMtT/Z7r+M7r90ipvtRwPPnF0fnTOddkvws+k/0u+EzmTHw7fzr7Z5k6bPl55rTh43LHF6/OoAuPzT7nfjJzJ8/M3y/9bV77y/IfHI/82wu57xu/zp5fPiqfuPTkzB47LXec+ZNMemFUyyweoIU89ot7UtG2MoO/+/m069Ihk4aNza9PuqzRXtu1T4+USsvvmXZap1vO/etlDcf7nHFE9jnjiIx75o3833HCagBY5j3/rwP4EAqlUqn0r7s19tvf/jZ33XVXbrrpprRv375ZCln07eObZR6A/1gVFa1dAcBarVDZpHwyAN5Vmr+wtUsAWKtVnnd5a5cAsNZ7ZcD5rV0CwFrtJ+VlrV0CwFrtlKWe2QN8FFOK9lGAj+Kt4of+SQAA77O4YC8F+Cgum/C71i4BWIWpA/dv7RL4D7D+Uw+3dgnNrkm/NPv/7N15mJ3jwT/w70xmssg6WUWEhGjtpaglaCkRSpIq1aqtaKlYk/dt61UJWrS1xFaKKK3irdZWVUr52XdJUKlssu+RPZLJNr8/6MhIQjKZmZPxfj7Xda5rzn3u+8n3yR/39ZznzHzPVVddlTFjxqRDhw7p0qVLSj/xh79DhgypkXAAAAAAAAAAAAAAAAAAAAAAdaVaJQx9+vSp4RgAAAAAAAAAAAAAAAAAAAAAhVWtEoaBAwfWdA4AAAAAAAAAAAAAAAAAAACAgiqu7sK5c+dm8ODBOf/88zN79uwkyZAhQzJ58uQaCwcAAAAAAAAAAAAAAAAAAABQV0qqs+itt97KQQcdlJYtW2bcuHH5wQ9+kNatW+eBBx7I+PHj84c//KGmcwIAAAAAAAAAAAAAAAAAAADUquLqLOrXr19OOumkjBo1Ko0bN64cP/TQQ/Pss8/WWDgAAAAAAAAAAAAAAAAAAACAulKtEobXXnstp5122mrjnTp1yrRp0zY4FAAAAAAAAAAAAAAAAAAAAEBdq1YJQ+PGjTN//vzVxkeMGJF27dptcCgAAAAAAAAAAAAAAAAAAACAulatEobevXvnkksuybJly5IkRUVFmTBhQn7605/mW9/6Vo0GBAAAAAAAAAAAAAAAAAAAAKgL1SphuPLKKzNz5sy0b98+ixcvzle/+tV069YtzZs3z6WXXlrTGQEAAAAAAAAAAAAAAAAAAABqXUl1FrVo0SLPP/98nnrqqQwZMiQrV67Ml7/85Rx00EE1nQ8AAAAAAAAAAAAAAAAAAID1VFFRVOgIUC9Vq4ThPw488MAceOCBNZUFAAAAAAAAAAAAAAAAAAAAoGCKq7vwySefzOGHH56tt9463bp1y+GHH55//vOfNZkNAAAAAAAAAAAAAAAAAAAAoM5Uq4ThhhtuSM+ePdO8efOcc845Ofvss9OiRYscdthhueGGG2o6IwAAAAAAAAAAAAAAAAAAAECtK6nOossvvzyDBg3KmWeeWTl29tlnp3v37rn00kurjAMAAAAAAAAAAAAAAAAAAADUB8XVWTR//vz07NlztfEePXpk/vz5GxwKAAAAAAAAAAAAAAAAAAAAoK5Vq4ShV69eeeCBB1Ybf+ihh3LEEUdscCgAAAAAAAAAAAAAAAAAAACAulZSnUXbbbddLr300jz99NPZe++9kyQvv/xyXnjhhfTv3z/XXXdd5dyzzz67ZpICAAAAAAAAAAAAAAAAAAAA1KJqlTDcdtttKSsry/DhwzN8+PDK8VatWuW2226rfF5UVKSEAQAAAAAAAAAAAAAAAAAAAKgXqlXCMHbs2CTJrFmzUlRUlDZt2tRoKAAAAAAAAAAAAAAAAAAAAIC6Vry+C+bOnZu+ffumbdu26dChQ9q3b5+2bdvmzDPPzNy5c2shIgAAAAAAAAAAAAAAAAAAAEDtK1mfybNnz87ee++dyZMn53vf+1622267VFRU5N///nfuuOOOPPnkk3nxxRdTVlZWW3kBAAAAAAAAAAAAAAAAAAAAasV6lTBccskladiwYcaMGZMOHTqs9lqPHj1yySWXZNCgQTUaEgAAAAAAAAAAAAAAAAAAgHVXsbLQCaB+Kl6fyQ8++GCuvPLK1QoYkmTTTTfNr3/96zzwwAM1Fg4AAAAAAAAAAAAAAAAAAACgrqxXCcPUqVOzww47rPX1HXfcMdOmTdvgUAAAAAAAAAAAAAAAAAAAAAB1bb1KGNq2bZtx48at9fWxY8emTZs2G5oJAAAAAAAAAAAAAAAAAAAAoM6tVwlDz549c8EFF2Tp0qWrvVZeXp4LL7wwPXv2rLFwAAAAAAAAAAAAAAAAAAAAAHWlZH0mX3zxxdl9992zzTbbpG/fvtl2222TJMOHD8+NN96Y8vLy3HnnnbUSFAAAAAAAAAAAAAAAAAAAAKA2rVcJw+abb56XXnopZ5xxRs4///xUVFQkSYqKinLwwQfnhhtuSOfOnWslKAAAAAAAAAAAAAAAAAAAAEBtWq8ShiTp2rVrHn300cyZMyejRo1KknTr1i2tW7eu8XAAAAAAAAAAAAAAAAAAAAAAdWW9Sxj+o6ysLF/5yldqMgsAAAAAAAAAAAAAAAAAAABAwRQXOgAAAAAAAAAAAAAAAAAAAADAxkAJAwAAAAAAAAAAAAAAAAAAAECUMAAAAAAAAAAAAAAAAAAAAAAkSUoKHQAAAAAAAAAAAAAAAAAAAICaVbGyqNARoF4qLnQAAAAAAAAAAAAAAAAAAAAAgI2BEgYAAAAAAAAAAAAAAAAAAACAKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiSlBQ6AAAAAAAAAAAAAAAAAAAAADWroqLQCaB+Ki50AAAAAAAAAAAAAAAAAAAAAICNgRIGAAAAAAAAAAAAAAAAAAAAgChhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIkpQUOgAAAAAAAAAAAAAAAAAAAAA1q2JlUaEjQL1UXOgAAAAAAAAAAAAAAAAAAAAAABsDJQwAAAAAAAAAAAAAAAAAAAAAUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJAkKSl0AAAAAAAAAAAAAAAAAAAAAGpWxcqiQkeAeqm40AEAAAAAAAAAAAAAAAAAAAAANgZKGAAAAAAAAAAAAAAAAAAAAACihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIElSUugAAAAAAAAAAAAAAAAAAAAA1KyKikIngPqpuNABAAAAAAAAAAAAAAAAAAAAADYGShgAAAAAAAAAAAAAAAAAAAAAooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACBJUlLoAAAAAAAAAAAAAAAAAAAAANSsipVFhY4A9VJxoQMAAAAAAAAAAAAAAAAAAAAAbAyUMAAAAAAAAAAAAAAAAAAAAABECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQJKkpNABAAAAAAAAAAAAAAAAAAAAqFkVFUWFjgD1UnGhAwAAAAAAAAAAAAAAAAAAAABsDJQwAAAAAAAAAAAAAAAAAAAAAEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAkqSk0AEAAAAAAAAAAAAAAAAAAACoWRUrC50A6qfiQgcAAAAAAAAAAAAAAAAAAAAA2BgoYQAAAAAAAAAAAAAAAAAAAACIEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRJSaEDAAAAAAAAAAAAAAAAAAAAULNWVhQVOgLUS8WFDgAAAAAAAAAAAAAAAAAAAACwMVDCAAAAAAAAAAAAAAAAAAAAABAlDAAAAAAAAAAAAAAAAAAAAABJkpJCB/iP4bcuKXQEgHpt8YrlhY4AUK+1a76o0BEA6rV5ixoXOgJAvVZye79CRwCo97407OpCRwCo1xrv/pNCRwCo137TeHGhIwDUa7tsPL/OClAvlaao0BEA6r3SQgcAAAA2KsWFDgAAAAAAAAAAAAAAAAAAAACwMVDCAAAAAAAAAAAAAAAAAAAAABAlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmSkkIHAAAAAAAAAAAAAAAAAAAAoGZVVBQVOgLUS8WFDgAAAAAAAAAAAAAAAAAAAACwMVDCAAAAAAAAAAAAAAAAAAAAABAlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZKSQgcAAAAAAAAAAAAAAAAAAACgZlWsLCp0BKiXigsdAAAAAAAAAAAAAAAAAAAAAGBjoIQBAAAAAAAAAAAAAAAAAAAAIEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSJCWFDgAAAAAAAAAAAAAAAAAAAEDNqqgodAKon4oLHQAAAAAAAAAAAAAAAAAAAABgY6CEAQAAAAAAAAAAAAAAAAAAACBKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkiQlhQ4AAAAAAAAAAAAAAAAAAABAzapYWVToCFAvFRc6AAAAAAAAAAAAAAAAAAAAAMDGQAkDAAAAAAAAAAAAAAAAAAAAQJQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkSUoKHQAAAAAAAAAAAAAAAAAAAICatbKiqNARoF4qLnQAAAAAAAAAAAAAAAAAAAAAgI2BEgYAAAAAAAAAAAAAAAAAAACAKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiSlBQ6AAAAAAAAAAAAAAAAAAAAADWroqKo0BGgXioudAAAAAAAAAAAAAAAAAAAAACAjYESBgAAAAAAAAAAAAAAAAAAAIAoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASJKUFDoAAAAAAAAAAAAAAAAAAAAANauiotAJoH4qLnQAAAAAAAAAAAAAAAAAAAAAgI2BEgYAAAAAAAAAAAAAAAAAAACAKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiSlBQ6AAAAAAAAAAAAAAAAAAAAADVrZUVRoSNAvVRc6AAAAAAAAAAAAAAAAAAAAAAAGwMlDAAAAAAAAAAAAAAAAAAAAABRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkCQpKXQAAAAAAAAAAAAAAAAAAAAAalZFRVGhI0C9VFzoAAAAAAAAAAAAAAAAAAAAAAAbAyUMAAAAAAAAAAAAAAAAAAAAAFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAECSpKQ6i/r167fG8aKiojRu3DjdunVL796907p16w0KBwAAAAAAAAAAAAAAAAAAAFBXqlXCMHTo0AwZMiQrVqzIF7/4xVRUVGTUqFFp0KBBtt1229x4443p379/nn/++Wy//fY1nRkAAAAAAAAAAAAAAAAAAACgxhVXZ1Hv3r1z0EEHZcqUKXnjjTcyZMiQTJ48OQcffHC++93vZvLkydl///1z3nnn1XReAAAAAAAAAAAAAAAAAAAAgFpRUp1FV1xxRZ544om0aNGicqxFixa56KKL0qNHj5xzzjkZMGBAevToUWNBAQAAAAAAAAAAAAAAAAAAWDcVFYVOAPVTcXUWzZs3LzNmzFhtfObMmZk/f36SpFWrVlm6dOmGpQMAAAAAAAAAAAAAAAAAAACoI9UqYejdu3dOPvnkPPDAA5k0aVImT56cBx54IKecckr69OmTJHn11VfzhS98oSazAgAAAAAAAAAAAAAAAAAAANSakuosuvnmm3PeeeflO9/5TpYvX/7hgUpKcuKJJ2bQoEFJkm233TaDBw+uuaQAAAAAAAAAAAAAAAAAAAAAtahaJQzNmjXLrbfemkGDBuW9995LRUVFtt566zRr1qxyzi677FJTGQEAAAAAAAAAAAAAAAAAAABqXbVKGP6jWbNm2XnnnWsqCwAAAAAAAAAAAAAAAAAAAEDBVKuEYdGiRfnlL3+ZJ598MjNmzMjKlSurvP7ee+/VSDgAAAAAAAAAAAAAAAAAAACAulKtEoZTTz01zzzzTI4//vh07NgxRUVFNZ0LAAAAAAAAAAAAAAAAAAAAoE5Vq4Th0UcfzSOPPJLu3bvXdB4AAAAAAAAAAAAAAAAAAACAgiiuzqKysrK0bt26prMAAAAAAAAAAAAAAAAAAAAAFEy1Shh+/vOfZ8CAAfnggw9qOg8AAAAAAAAAAAAAAAAAAABAQZRUZ9FVV12VMWPGpEOHDunSpUtKS0urvD5kyJAaCQcAAAAAAAAAAAAAAAAAAABQV6pVwtCnT58ajgEAAAAAAAAAAAAAAAAAAEBNWVlRVOgIUC9Vq4Rh4MCBNZ0DAAAAAAAAAAAAAAAAAAAAoKCKCx0AAAAAAAAAAAAAAAAAAAAAYGNQsq4TW7dunZEjR6Zt27YpKytLUVHRWufOnj27RsIBAAAAAAAAAAAAAAAAAAAA1JV1LmEYNGhQmjdvXvnzp5UwAAAAAAAAAAAAAAAAAAAAANQ361zCcOKJJ1b+fNJJJ6113uLFizcoEAAAAAAAAAAAAAAAAAAAAEAhFFdnUd++fdc4vmjRohx66KEbFAgAAAAAAAAAAAAAAAAAAACgEEqqs+jxxx/Pz372s/ziF7+oHFu0aFF69uxZY8GgrrU7sWc6nt4npe3LsnjkxEwYeFsWvvrvNc4tO3SvtDvhkGyyQ9cUNyzN4pETM/mq/838Z4bVbWiAAul4Uo90PqN3GrZvlUUjJmXMgNsz/5V31zi3YftW2eqiE9Ns563SZKtNM3nwo3lvwB1V5ux8/0Vptc8Oq619/59D8s5xl9fGKQAUXNmx30jrU7+VkvatUz5qfKZfeksWv/7OGuc277FPWh37jTTebqsUNSxN+ajxmXXdXVn0/JAq84qbN027fiemRY99UtyyWZZNmpbplw/Oomder4tTAqhTHU7smY4/6p2G7cvywciJGT/gd1mwlvfxpe3LsuXAE9N0563TuGvHTLvt7xk/8HdV5hSVNMhmZx2ZdkcfkIabts7iMVMy4dI7M+/poXVxOgB1rt0Jh2bTVe6HTrzotix8dfga57Y6dK+0P75nmlTeD52QKVdXvR/a+Aud0+m/js0mO22dRp3bZ8LA2zLjtofr6GwANl6vD3s7t9/9lwx/d3Rmvj87115+Yb6+/z6FjgWw0Tji3KOz/3cPyiYtm2XssFG5+8LBmTJq0lrnb7bN5unV75hsudNWabt5+/zvJbfnyd/9vcqc4gbF6XXut7Nnn/3Sol2rzJsxJy/+5ek8cv19qaioqO1TAqhTx5z73Rx8bI80bdkso4aOzK0X/jYTR01c6/yDvtMjX/vWAdnii1smSca8PTp3/frOjH5zVJVjHnPed6usmzNjTk7Z48TaOQmAAvrquUfmy8cemMYtm2by0NF59MI7MnPU5E9ds+2he+SA/kenbIv2mTNhRp664t6M+MfHn8mf/fw1adW53WrrXvvDE3n0wjtq+hQA6tS+5x6ZXY49II1bNs2UoWPy+IV3ZNZn7JtfPHSP7N//qLTaon3mTpiRZ674c0ausm/ue+6R2e+8I6usWThjbq7f48wqc7Y/Yq8036x1VixbkWlvj82zV/w5U4aNqdkTBKhlB5x7ZHb77oFp0rJpJg0bnb+tw/Xn9j33yIH9j07rLdpn9oQZefLKe/PvVfbR4gbFOeDcb2XnPvukWbtWWTBjbob95dk8c/2Da7wfesRlJ2ePY7+eRy+5My/97rEaP0cAgM+T4uosevzxx3P77bdn0KBBSZIFCxbk4IMPTlFRUR57zAUY9U/rXt2zxUUnZ8p1f8k7h/TPgleH5wt/vDANN2u7xvnN9to+8599M6OO/0XeOfS/Mv/Ft7PNHf+TTXboWsfJAepeu977ZOtLvp8J19yXNw7+cea98u/sdPcFadRpzXtmUaPSLHt/fiZce18WvTN+jXOGn3xlXtrpB5WP1796XiqWr8ish1+qzVMBKJjmh+2fDhf8MO/f9KeM7X1WFr/+TrYYfElKOq7+ixhJsskeO2bRC0Mz8dQBGdvn7Hzw8lvpfPPANNp+q48nlZZkizsuTcPN22fSWZdlTI8fZuoF12X59Pfr6KwA6k6bXt2z5cXfz+Tr7stbPfpnwSv/zrZ3/SwN13JNWtywJMven5/J196XD4aPW+Oczj85Nh2O65FxPxucN792Tmbc+Y988bYfZ5MdvdcHPn/KjuiezhednKnX/znDe/bLwleHZ5s7134/tPmeO2T+c29m1Ak/z/DD+mfBi/9Kt9svSJNV7ocWN2mU8gnTMunyP2Tp9Nl1dSoAG73Fi5fki922yv/0O6PQUQA2Oj1P752DTzk8dw+4LZf2+mnmzZyb8/54YRo1bbzWNQ2bNMqsCTNy/6/uytwZc9Zy3D7Z/3sH5+4Bt2XAQefmL5f/MYf8sFcOPOnQ2joVgIL45ulH5ohTe+fWAbfkJ0f0z9yZczLwrkvSuGmTta7Zce8d8/xfn82A71yQ87/535k1ZVYG3nlxWndoXWXehBHjc/LuJ1Q+zjvkrNo+HYA6t8/ph2evUw/LowPuyOAjLszCmfNy3F3np+GnXI9u/uVuOeqGs/LW/c/n5kPPz1v3P5+jfnNWOu2ydeWcwb0uzFW7n1H5uPPYy5Ikwx95pdbPCaA27XX64fnKqYfm8QG/zx1HDMiimXPznbt++qn7Zqcvd0ufG87Mv+5/Prcd+j/51/3Pp89vzsxmq+ybSTJzxMRct3vfysfgQ86v8vrssVPz+IDf57Ye5+eP37ok8ybNyjF3/iRNWjevlXMFqA37nn549j7lsDwy4I7c3OvD688T//jp15+dv9wtR99wVt584PnceNj5efOB5/PtG87K5qvso/uefkR2/97X88iA3+f6g/47j19+T7r/8BvZ86Qeqx1v2x67ZfNdumX+NJ/pAwCsi2qVMHTt2jX/+Mc/cumll+baa69Njx490rBhwzz66KNp2rRpTWeEWtfhB70y63+fzKx7/pkloydl4sDfZemU99P+hJ5rnD9x4O8y7aYHs+jN0SkfOzWTf3lXysdOTauD96jj5AB1r9Nph2faPU9l2t1PZfGoyXlvwB0pnzwrHU9c/UZNkpRPnJkxF96eGX9+NssXfLDGOcvnLsyymXMrH2X775wVi8szUwkD8DnV5uRvZu5fHs/cP/8jS8dMzPRLb8myaTNTduw31jh/+qW3ZPatf8mSt0dl2fgpmXn177N0/JQ0P3DPyjmtjuqRBq2aZ+KPfp7FQ4Zn+ZQZWfzG8JS/O7auTgugznT84RGZec+TmXn3P7Nk9OSM/+h9fIcTDlnj/PJJMzN+wO8y6y9PZ8X8NV+Ttv3WVzP5+vsy96khKZ8wPdP/8I/MfWZYOp7WqzZPBaAgOvywd2b97z8/vh960W1ZOmVW2q3tfuhFt2XaTQ/kg//cD/3VH1e7H/rBm6Mz6Re/z5y/Pp+Kpcvr6lQANnr77b1Hzv7hiTn4a90LHQVgo/P1k7+Rv//m/gz9x6uZMnJibu9/Qxo2aZQ9e++71jXj3hqTv1x+Z157+MUsX7psjXO2/vIX8uYTr+ft/zck70+amSGPvpx3nnszW+609RrnA9RXh5/SK/fdcG9eeeylTBg5Idf1vyaNGjfK/r33X+uaa865Oo/d+WjGDR+byWMm56af3JCi4uLs3P1LVeatWL4ic2fOrXzMnz2/tk8HoM7teUrPPHfDg3n3sdczc+SkPNT/tylt3DA79t5n7WtOPjTvPf+vvHDjX/P+mKl54ca/ZuwL72TPkz++t/rB7AVZNHNe5WObr++a2eOmZfzL/66L0wKoNXuc0jMv3vBQRj72emaNnJS/9b85pY0bZvtP2Td3P7lnxj7/r7x048OZPWZqXrrx4Yx/YXj2OLnqZ1Irl6+ssncunr2gyuvDH3op4154J3MnzsysUZPz5M/vSuMWm6T9dlvUyrkC1Ia9T+6ZZ3/zYP79j9czY+Sk3N//tylt0jA7f8o+utdH15/P3fjXzBozNc/d+Ne89+I72WuVfbTzl7fJu0+8kZH/b1jmTpqV4Y++mtHPvZ3NdtqqyrGadyjLNy4+KX855zdZsXxFrZ0nAMDnSbVKGJJkxx13zN/+9rdccMEF2WSTTRQwUG8VlZak6c5bZ94zw6qMz39mWJruvu06HqQoxc2aZPncBZ89F6AeKyotSfOdt8qcp9+sMj7nmbfSYo8v1ti/s+mxX8/MB1/Myg/Ka+yYABuN0pI03qFbFj0/pMrwoueHpsmXt1u3YxQVpbhpk6xY5fqz+YF7ZvHQf2fTgWdkm5fuStdHbkyb07+dFFf7bR/ARuk/7+PnPlP1mnTuM8PSfF3fx6/puA1Ls7K86h9vrFyyNC2+so57M0A9UVRakqY7bZ35zw6rMj7/2WFptt73QxfWfEAAAP5PaNu5fVq1L8s7z338/n750uUZ+crwbL3bhn3mNOr1d7Nt9x3ToWvHJMnm222ZbXbfNv96eshnrASoPzp07pCy9q0z7LlhlWPLly7PO6+8ky/utu73NBs2aZQGpQ2y4BO/89Sx62YZ/Ortuen5W9Pv+v9Kh84daio6wEahVed2ad6+LO8993bl2IqlyzP+lXfTebdt1rpu8y93y5hn36oyNubZt7L5bl9Y4/zi0gbZ+Zv7Zti9z9RMcIACadW5XZq1b5Wxn9g3J7zybjb/lH2z05e7Zeyzb1cZe+/Zt9LpE2vKunbIma9enx89f3V6X983rTq3W+sxi0sbZJdjD8iSeYsyY/j4ap4RQN0q++j6c/Qn9tFxn3H92XnXbhn9XNXrz9HPvpUtvvzx9eeE10dkq+47pE3XTZMkHbbbIlvu/sWMenpY5ZyioqJ8a9CP8sItf8vMUZNr6KwAAD7/StZ14q677pqioqLVxhs1apQpU6ake/ePv71lyJBP/+C6vLw85eVV/6hyacWKNCxqsK5xoMaUtG6eopIGWT5rbpXxZbPmpkX7Vut0jE1P650GmzTO7IdfrPmAABuR0o/2zGUz51YZXzpzbsrataqRf6P5rt3SdLstMrLfTTVyPICNTUlZizVefy6fNSdN25at0zFan3Jkipo0zvy/P1c5Vtp502yy95cy/6//LxNPHZiGXTZLh4FnpKikQWbdcE9NngJAQf3nffyyT76Pnzkvpev4Pn5N5j0zNB1/eEQWvDw8S8ZNS8v9dk7ZIV9JkTIb4HOmZC3v7ZfNnJfSdut2PdrhtN5psEmjzHn4hVpICADA/wUtP/pcaf7MeVXG58+clzabt92gYz9204Np0nyTXPLkNVm5YmWKGxTnwSvvyat/df0KfH60av/he/i5n3h/P3fW3LTrtPY/WPuk4396QmZPm523Xvi4FGfksBG5rt+gTHlvSlq1bZWjzvp2Lrv/1znn4DOz0BfUAJ8TzT76TGnhJ65HF86al1ad1n492qxdqyyaNb/K2KJZ89OsXcs1zt+2x+5p3GKTDPvzsxsWGKDAmn60by76xL65aNa8tPzMfXP1NU1X2TenDBudv/W7ObPfm5qmbVtmn7P65Pj7B2bwwT/N4lUKwbsduEt633BmSps0zMIZc/O/x/0qi+coDAfqh2Yf3Q9dbR+dOS+tPuV+aLN2rbJwZtXrz4Uzq15/PnfTw2nUfJOc9eQVqVixMkUNivPklX/O2399qXLOvj86IiuXr8zLt/+jBs4GAOD/jnUuYejTp0+N/aOXX355Lr744ipjpzb7Yn7YwjcLUjgVFZ8YKCpaw+DqWvfeN5v1PyajT748y9+f95nzAT4PqrtnrotNv3tgFv17QhYMHV0jxwPYaH1y31zHvbTF4V9Nu7O+l4k/uiQrZq9y/VlcnBXvz83Un12frFyZJe+MTkn7Nmlz6reUMACfT6vto2sYWw/jLvxdtrryR/nSs9clFcmS8dMy809Ppd0xB25YToCN1RovR9flfuh+2azfdzL65MvcDwUAYJ3t2XvfHHfZaZXPrz/58g9/WMP7+w39yGmPI/bJXn32y+Bzrs2UkZPSefsuOWbASZk7fU5eus83EAP10/59vprTLjuj8vml37/ko5+qbppFRasNrVWf047Mvr32z4BjLsiy8mWV40Of/vgLmCaMGJ8RQ97Njc/ekgOOOjAPD36ouqcAUFA79tknh192SuXze75/xRrnFRUVfeZ90tVe/5TPqHY95msZ/fSbWThj7vrEBSi4Hfrsk56XnVz5/N7vX5lk9UvNoqKiz77+XO0zqapv/t97+uNveJ85YlImDxmd05+9KjsetV9eG/xo5WvjX/p3fnfoBWnSull2+e4B6XPjmfl974vywftV/zgZYGOwc+99csQq1593nfzh9eeafgf/sz+nX/29f8UqYzsesVe+1Kd7/nLObzJj5OR03H7LHDrguCyYPifD7nsuHXfskr2+f0h++40LNuSUAKjnKiqKCh0B6qV1LmEYOHBgjf2j559/fvr161dl7O1tj6ux48P6WD57QSqWr0jpJ77BvbRNyyyb+em/RNy6V/d0uerMjDntisx/7q1PnQvwebDsoz2z4Se+Ybhh25ZZOmvD//CiuEnDtOvTPeN+/acNPhbAxmr5nPmpWL4iJZ/4luGSNq2y/P25n7q2+WH7p+Nl52Ty2ZfngxeHVT3uzNnJsuXJypWVY+VjJqakfeuktOTD1wA+Bz5+H191Hy1t+9nv4z/9uPMz8uRfpahRaUrKmmfZtNnZ4oLjUz5h+oZGBtioVO6jn3hvX9K2ZZbPmvupa8uO6J4trzwz75326yx43v1QAADW3bB/vp73hn1cwF3a8MNfV2nRvlXmrfIt7i3atsz8z7gu/SxHnX98Hr3pwbz28ItJkskjJqRNp7Y59IxvKmEA6q1Xn3g1I4eOrHz+n320VbuyzJkxp3K8ZZtWmbsO+2jvH/bJt/oelYu+NyDj3x33qXPLF5dnwojx6dhls2plB9gYjHxiSG4eOqbyeclH+2izdi2rFCQ0bdNitW9sX9XCmXOrfOvwf9YsnLX6HwC37NQ2XffdMfeeds2GhQcogFFPDMmUVfbNBqvsm4tW2Tc3WYd9s+kn9s0P16y9OGHZ4vLMHDExrbt0WG18zvjpmTN+eqYMHZPTnr4yXzrmq3npxofX59QA6sS7/xySScPWsI+2b5mFq9wPbdq2RRau7/Vn2xZZNPPjffSQ84/Nczc9nH89/HKSZMaIiWnVqW32O6NXht33XLp8Zds0bdMi/V687uM8JQ1yyAXfy14n98ygfc/dkFMFAPhcKy7EP9qoUaO0aNGiyqNhUYNCRIFULFueRW+NScv9v1RlvMX+X8qi199d67rWvfdN16vPynt9r868J9+o7ZgAG4WKZcuz4K33UvbVnauMt/rqzpn/2ogNPn67XvukuGFJZtz37AYfC2CjtWx5lrwzOk2771pluGn3XbN4yL/XuqzF4V/NZr86L5P7XZGFT7+22uuL3xie0i03++grjj7UsGunLJv+vgIG4HNlbe/jW+7/pSz4lPfx63z88mVZNm12ikoapPVhe2XOP1bfcwHqs4ply7Po7TFpsd8uVcZb7LdLFn7q/dD90nXQ2Rl75tWZ95T7oQAArJ/yRUsyc/y0yseUUZMyd8acbL/vx585NSgtyRf23D5j3tiwz5waNmm02rfHrVy5MsVFvuEHqL+WLFqcaeOnVj4mjpqYOTNm50v77lI5p6S0JDvsuUNGvLH2z5uSpPdp38xRZx2Tn594cca8PfpT5yYf/qHy5t02z5wZszf0NAAKZumiJZV/uDtn/PTMHDU5C2bMyVb77lQ5p7i0Qbbcc9tMfGPUWo8zacjobLXfTlXGtt5/50x6Y+Rqc3c5ev8sen9eRj01tOZOBKCOfHLfnDVqchbOmJsu++5YOae4tEG22HPbTPqUfXPykNHput+OVca67r9TJn/KmgYNS9KmW6cqJTlrUlRUlAYNS9fthADq2NJFSzJ7/PTKx3+uP7utcv3ZoLRBunzG9efEoaOz9b5Vrz+77bdzJgxZpaixScNUVKysMmflypUp+uh+6LD7n8+NPc/PTYf9T+Vj/rTZeeGWv+UPJ/yqJk4XAOBzq6Q6i1asWJFBgwbl3nvvzYQJE7J06dIqr8+e7QMX6pfpt/41Xa89J4veHJOFb4xIu+MOTsNObTPjzn8kSTb/6XEp7dg6Y8/5sPmtde990/XaczJh4G1ZOGRkStq1SpJULFmaFQs+KNRpANSJyTf/LV+8/qwseHNM5r8+Mh2POyiNO7XN1D88niTp8j/HplHH1hlx1g2Va5ru0CVJ0qBp4zRs0yJNd+iSimXL88HISVWOvel3D8ysx17L8jkL6+x8AArh/d89kE5X9M/if43K4qHvptUxPVPasV3m3PP3JEm7/ielpEObTP3xVUk+KmD4df9M/8XNWTzs3TRo++G3v1csKc/KhR9ef865+5GUHX9EOvzstMy58+E07LJZ2p7+7cz+w18Lc5IAtWjqLQ9n6+vOzqK3RmfB6yPS4bgeadSpbaZ/dE3a+fzvpeGmbTLmnI8b3Df56Jq0uGnjlLZpkU126JKKpcuzeNSH16TNdt0mpZu2zgfvjEvDTVtn8/7HJMVFmXLjA3V+fgC1bfotD6Xrtedm0Vujs+iNEWn3vR5p2KltZn50P7TTT49L6aZtMu7ca5N8WMDQ5ZpzMnHgbVk4ZMQa74cWlZak8TadK39u2LF1mmzfNSs/WJzycdPq/iQBNhIffLA4EyZNqXw+ecr0vDtyTFq2aJ6Om7YvYDKAwnvyd4/ksL5HZsa4aZk+dmoO63tkli4uzysPPV855+Srzsyc6bPzwK/vTvJhUcNm22ye5MM/Ni7r0Cadt++SJR+VPCTJW0++kW/0PTKzJ8/KlFETs8UOXXPwKUfkhT8/VfcnCVCL/nbbX/Otvkdl6rgpmTp2So488+iULynPsw99/KUHZ199bt6fNjt3/foPSZI+px2Z7/b/Xgadc2VmTJqeVh+9x1+yaEmWfLAkSXLiBd/Pa/98NbOmzErLNi1z1FnfTpNmm+Tp++yjwOfLK7c9ln379sr746Zl9thp2ffM3lm2ZGn+9dCLlXN6X316Fkybk6d+/acP19z+WE6698Lsc/rhGfHEG/niwbula/cdcsdRl1Q9eFFRvnT0V/PWX55LxYqqfxAHUF+9dttj2advr8wZNz2zx07LPmf2yrIlSzN8lX3z8KtPy4Jpc/LMr+9Nkrx++z9y3L0/y16nH56RT7yRLxy8W7p03yF/POrnlWsOvOC7GfXPoZk/5f00bdMi+5zVO42aNcnb9z2XJClt0ij7nNk7o/75RhbOmJsmZc3z5eMPSvNNy/LuI6/U7X8CwAZ46XePZb+Prj/fHzst+/ftnWWLl+atVfbRI686PfOnz8k/P7r+fPl3j+Xkey/MvqcfnnefeCPbHrxbtuq+Q247+uPrzxFPDs3+fftk3uT3M2PUpHTcoUv2OeXQDPnzM0mSxXMXZvHcqr+bv2L5iiycOS/vvze1Ds4cAKD+qlYJw8UXX5zBgwenX79+ufDCC3PBBRdk3LhxefDBBzNgwICazgi1bvZfX0iDsubZ7Lxvp7R9WRaPmJCRx/8iSyfPTJKUdihLw83aVc5vd9whKS4tSZfLTkuXy06rHJ9171MZe971dZ4foC7NfOjFlJQ1y5b9jkrD9mVZ9O7E/Ot7l6V80qwkScMOZWnUqW2VNbs9eUXlz82/tHXaf2u/LJk4I6/u0bdyvMlWHdNyr+3y1rd/HoDPuwV/fzbTWzVP277HpqR965SPHJcJPxiY5VNmJElK2peldJXrz1bfOTRFpSXZ9OK+2fTij/fOufc/kak/GZQkWT5tViZ+/2fpcMEP0+pvv8ny6e9n9u8fyvu3/KVuTw6gDrz/1xdSUtY8m3/0Pv6DERPy7nGXVr6Pb9h+9WvSnZ+4uvLnZl/qlrZH7p/yiTMydM/TkyRFjUrT+SfHpvEWHbLigyWZ++SQjD772qyYr2wR+PyZ8/ALKSlrkc3OPabyfuioE37+8f3Q9q3TqNPq90O3vOy0bPmJ+6Hj+n1YeFPaoXV2eHxQ5Wubnv7NbHr6N7PgpX9lxNE/q6MzA9j4/OvdUTn5rJ9UPv/19bckSXofelAu/Vn/QsUC2Cg89tuHUtq4YY79+alp2rJp3hs2OoOO/0XKFy2pnNO6U9tUVFRUPm/VoSwD/v7x506HnNYrh5zWKyNefidXfueiJMndA29Ln/7fyfd+fmqat22ZudNn59m7n8jD17lXCny+PPDb+9OwcaP88Benp2mLZhk1bGQuOW5glixaXDmn7WbtsnLlx/toz+MPTWmj0vz4t+dXOdafBt2TP11zT5KkzaZt0u/6/0rzshaZP3t+Rg4dkZ9+878z86P7BgCfFy/+9m8pbdwwh/3ipDRp0TSTh43JH4/7ZZaucj3acrM2qVhlH530xqjcd9YNOaD/0Tmg/9GZPWF67jvz+kweNqbKsbfad8e02rxtht77TJ2dD0Bte/m3f0tJ44Y55BcnpXGLTTJl2Jj873G/qrJvttisbZV9c/Ibo/LgWTfkq/2Pzv79j8qcCdPz4Jk3ZMoq+2bzTVun9/V9s0lZ83wwe34mDx2d339zYOZPfj/Jh9/m3qZbx+x01DlpUtY8i+cuzNQ338sfj/5FZo2aXHf/AQAb6PmPrj8P//lJadzyw+vPPxz/ievPTm2q3A+dOGRU/nzWDfn6fx2dA/sdnTkTpufeM6/PpFX20UcG/j5f739UDv/599O0bYssmD4nr9/9VJ6+7v46PT8AgOq48cYbc8UVV2Tq1KnZYYcdcs0112S//fZb49z7778/N910U4YNG5by8vLssMMOueiii3LIIYfUWr6iilWvztbR1ltvneuuuy7f+MY30rx58wwbNqxy7OWXX87dd9+93kFe6/TN9V4DwMcWr6hWrw4AH2nXfFGhIwDUa/MWNS50BIB6raTYN2EBbKgvDbv6sycBsFZn7P6Tz54EwFq9X1Fe6AgA9douaV7oCAD1WmmKCh0BoN5bXLTef14FwCouGXdXoSMAa+Hvt6kLe0x+YL3m/+lPf8rxxx+fG2+8Md27d8/NN9+cwYMHZ/jw4dliiy1Wm3/uuedms802ywEHHJBWrVrl9ttvz5VXXplXXnklu+66a02dRhXF1Vk0bdq07LTTTkmSZs2aZd68eUmSww8/PI888kjNpQMAAAAAAAAAAAAAAAAAAAA+F66++uqccsopOfXUU7PddtvlmmuuSefOnXPTTTetcf4111yTH//4x9ljjz2yzTbb5LLLLss222yThx9+uNYyVquEYfPNN8/UqVOTJN26dcvjjz+eJHnttdfSqFGjmksHAAAAAAAAAAAAAAAAAAAAbJTKy8szf/78Ko/y8vI1zl26dGneeOON9OjRo8p4jx498uKLL67Tv7dy5cosWLAgrVu33uDsa1OtEoZvfvObefLJJ5Mk55xzTi688MJss802OeGEE3LyySfXaEAAAAAAAAAAAAAAAAAAAABg43P55ZenZcuWVR6XX375GufOmjUrK1asSIcOHaqMd+jQIdOmTVunf++qq67KokWL8u1vf3uDs69NSXUW/fKXv6z8+aijjsrmm2+eF198Md26dUuvXr1qLBwAAAAAAAAAAAAAAAAAAACwcTr//PPTr1+/KmONGjX61DVFRUVVnldUVKw2tib33HNPLrroojz00ENp3779+oddR9UqYfikvfbaK3vttVdNHAoAAAAAAAAAAAAAAAAAAACoBxo1avSZpQv/0bZt2zRo0CDTpk2rMj5jxox06NDhU9f+6U9/yimnnJI///nPOeigg6qdd10UV3fhnXfeme7du2ezzTbL+PHjkyTXXHNNHnrooRoLBwAAAAAAAAAAAAAAAAAAANR/DRs2zG677ZYnnniiyvgTTzyRffbZZ63r7rnnnpx00km5++67841vfKO2Y6akOotuuummDBgwIOeee24uvfTSrFixIknSqlWrXHPNNendu3eNhgQAAAAAAAAAAAAAAAAAAGDdrawoKnQEWE2/fv1y/PHHZ/fdd8/ee++dW265JRMmTMjpp5+eJDn//PMzefLk/OEPf0jyYQHDCSeckGuvvTZ77bVXpk2bliRp0qRJWrZsWSsZi6uz6Prrr8+tt96aCy64IA0aNKgc33333fP222/XWDgAAAAAAAAAAAAAAAAAAADg8+GYY47JNddck0suuSS77LJLnn322fz973/PlltumSSZOnVqJkyYUDn/5ptvzvLly9O3b9907Nix8nHOOefUWsaS6iwaO3Zsdt1119XGGzVqlEWLFm1wKAAAAAAAAAAAAAAAAAAAAODz54wzzsgZZ5yxxtfuuOOOKs+ffvrp2g/0CcXVWdS1a9cMGzZstfFHH30022+//YZmAgAAAAAAAAAAAAAAAAAAAKhzJdVZ9N///d/p27dvlixZkoqKirz66qu55557cvnll2fw4ME1nREAAAAAAAAAAAAAAAAAAACg1lWrhOH73/9+li9fnh//+Mf54IMPcuyxx6ZTp0659tpr853vfKemMwIAAAAAAAAAAAAAAAAAAADUuvUuYVi+fHnuuuuuHHHEEfnBD36QWbNmZeXKlWnfvn1t5AMAAAAAAAAAAAAAAAAAAACoE8Xru6CkpCQ/+tGPUl5eniRp27atAgYAAAAAAAAAAAAAAAAAAACg3lvvEoYk2XPPPTN06NCazgIAAAAAAAAAAAAAAAAAAABQMCXVWXTGGWekf//+mTRpUnbbbbc0bdq0yus777xzjYQDAAAAAAAAAAAAAAAAAAAAqCvVKmE45phjkiRnn332aq8VFRVlxYoVG5YKAAAAAAAAAAAAAAAAAAAAoI5Vq4Rh7NixNZ0DAAAAAAAAAAAAAAAAAAAAoKCqVcLQrFmztGnTJkkyceLE3HrrrVm8eHF69eqV/fbbr0YDAgAAAAAAAAAAAAAAAAAAsH4qCh0A6qni9Zn89ttvp0uXLmnfvn223XbbDBs2LHvssUcGDRqUW265JQcccEAefPDBWooKAAAAAAAAAAAAAAAAAAAAUHvWq4Thxz/+cXbaaac888wz+drXvpbDDz88hx12WObNm5c5c+bktNNOyy9/+cvaygoAAAAAAAAAAAAAAAAAAABQa0rWZ/Jrr72Wp556KjvvvHN22WWX3HLLLTnjjDNSXPxhl8NZZ52Vvfbaq1aCAgAAAAAAAAAAAAAAAAAAANSm4vWZPHv27Gy66aZJkmbNmqVp06Zp3bp15etlZWVZsGBBzSYEAAAAAAAAAAAAAAAAAAAAqAPrVcKQJEVFRZ/6HAAAAAAAAAAAAAAAAAAAAKA+KlnfBSeddFIaNWqUJFmyZElOP/30NG3aNElSXl5es+kAAAAAAAAAAAAAAAAAAAAA6sh6lTCceOKJVZ4fd9xxq8054YQTNiwRAAAAAAAAAAAAAAAAAAAAQAGsVwnD7bffXls5AAAAAAAAAAAAAAAAAAAAAAqquNABAAAAAAAAAAAAAAAAAAAAADYGShgAAAAAAAAAAAAAAAAAAAAAooQBAAAAAAAAAAAAAAAAAAAAIElSUugAAAAAAAAAAAAAAAAAAAAA1KyVFUWFjgD1UnGhAwAAAAAAAAAAAAAAAAAAAABsDJQwAAAAAAAAAAAAAAAAAAAAAEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAkqSk0AEAAAAAAAAAAAAAAAAAAACoWRUVRYWOAPVScaEDAAAAAAAAAAAAAAAAAAAAAGwMlDAAAAAAAAAAAAAAAAAAAAAARAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAECSpKTQAQAAAAAAAAAAAAAAAAAAAKhZKwsdAOqp4kIHAAAAAAAAAAAAAAAAAAAAANgYKGEAAAAAAAAAAAAAAAAAAAAAiBIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkSUmhAwAAAAAAAAAAAAAAAAAAAFCzKlJU6AhQLxUXOgAAAAAAAAAAAAAAAAAAAADAxkAJAwAAAAAAAAAAAAAAAAAAAECUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAMD/Z+++o7MsD/eBXwlhCTKUoSgqiG2dtWrrAq1bHICrap1FW6w4UVvRir9KFbTa8tU6at11VK27jtZZsa4iIoqLjSJTBAQZQvL7A5oSBcSQ5E3s53NOzuF9ct9Proc/7vM+uZ9cLwAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkSUoKHQAAAAAAAAAAAAAAAAAAAICqVVpW6ARQNxUXOgAAAAAAAAAAAAAAAAAAAABAbaCEAQAAAAAAAAAAAAAAAAAAACBKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiSlBQ6AAAAAAAAAAAAAAAAAAAAAFWrNEWFjgB1UnGhAwAAAAAAAAAAAAAAAAAAAADUBkoYAAAAAAAAAAAAAAAAAAAAAKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgSVJS6AAAAAAAAAAAAAAAAAAAAABUrbIUFToC1EnFhQ4AAAAAAAAAAAAAAAAAAAAAUBsoYQAAAAAAAAAAAAAAAAAAAACIEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRJSaEDAAAAAAAAAAAAAAAAAAAAULVKCx0A6qjiQgcAAAAAAAAAAAAAAAAAAAAAqA2UMAAAAAAAAAAAAAAAAAAAAABECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQJKkpNABAAAAAAAAAAAAAAAAAAAAqFplKSp0BKiTigsdAAAAAAAAAAAAAAAAAAAAAKA2UMIAAAAAAAAAAAAAAAAAAAAAECUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAECSpKTQAf7jrvoNCx0BoE5bVL+s0BEA6rbP1yx0AoA6rUWDWvMrBoA6aWzZvEJHAKjzGm33y0JHAKjTrhlyaaEjANRpjdt1KXQEgDqtbbudCx0BoE5rknqFjgBQ532chYWOAAAA1CLFhQ4AAAAAAAAAAAAAAAAAAAAAUBsoYQAAAAAAAAAAAAAAAAAAAACIEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIkJYUOAAAAAAAAAAAAAAAAAAAAQNUqLXQAqKOKCx0AAAAAAAAAAAAAAAAAAAAAoDZQwgAAAAAAAAAAAAAAAAAAAAAQJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmSkkIHAAAAAAAAAAAAAAAAAAAAoGqVFjoA1FHFhQ4AAAAAAAAAAAAAAAAAAAAAUBsoYQAAAAAAAAAAAAAAAAAAAACIEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRJSaEDAAAAAAAAAAAAAAAAAAAAULXKUlToCFAnFRc6AAAAAAAAAAAAAAAAAAAAAEBtoIQBAAAAAAAAAAAAAAAAAAAAIEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSJCWFDgAAAAAAAAAAAAAAAAAAAEDVKi0qdAKom4oLHQAAAAAAAAAAAAAAAAAAAACgNlDCAAAAAAAAAAAAAAAAAAAAABAlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZKSQgcAAAAAAAAAAAAAAAAAAACgapWmqNARoE4qLnQAAAAAAAAAAAAAAAAAAAAAgNpACQMAAAAAAAAAAAAAAAAAAABAlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRJSgodAAAAAAAAAAAAAAAAAAAAgKpVVugAUEcVFzoAAAAAAAAAAAAAAAAAAAAAQG2ghAEAAAAAAAAAAAAAAAAAAAAgShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIkJYUOAAAAAAAAAAAAAAAAAAAAQNUqLXQAqKOKCx0AAAAAAAAAAAAAAAAAAAAAoDZQwgAAAAAAAAAAAAAAAAAAAAAQJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmSkkIHAAAAAAAAAAAAAAAAAAAAoGqVFhUVOgLUScWFDgAAAAAAAAAAAAAAAAAAAABQGyhhAAAAAAAAAAAAAAAAAAAAAIgSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJElJoQMAAAAAAAAAAAAAAAAAAABQtcoKHQDqqOJCBwAAAAAAAAAAAAAAAAAAAACoDZQwAAAAAAAAAAAAAAAAAAAAAEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQJKkZFUHzp49e5VP2qxZs0qFAQAAAAAAAAAAAAAAAAAAACiUVS5haNGiRYqKilY6pqysLEVFRVm8ePFqBwMAAAAAAAAAAAAAAAAAAACoSatcwvDss89WZw4AAAAAAAAAAAAAAAAAAACAglrlEoZdd921OnMAAAAAAAAAAAAAAAAAAAAAFNQqlzAMHz58lU+61VZbVSoMAAAAAAAAAAAAAAAAAAAAq6+00AGgjlrlEoatt946RUVFKSsrW+m4oqKiLF68eLWDAQAAAAAAAAAAAAAAAAAAANSkVS5hGDt2bHXmAAAAAAAAAAAAAAAAAAAAACioVS5h2HDDDaszBwAAAAAAAAAAAAAAAAAAAEBBrXIJw/K8/fbbmTBhQhYuXFjheLdu3VYrFAAAAAAAAAAAAAAAAAAAAEBNq1QJw5gxY3LQQQflzTffTFFRUcrKypIkRUVFSZLFixdXXUIAAAAAAAAAAAAAAAAAAACAGlBcmUmnn356OnTokClTpmSNNdbIiBEj8vzzz2e77bbLc889V8URAQAAAAAAAAAAAAAAAAAAAKpfSWUmvfTSS3nmmWfSunXrFBcXp7i4OJ07d86AAQNy2mmn5fXXX6/qnAAAAAAAAAAAAAAAAAAAAADVqrgykxYvXpymTZsmSVq1apWPPvooSbLhhhvmvffeq7p0AAAAAAAAAAAAAAAAAAAAADWkpDKTtthiiwwfPjwdO3bM9ttvn8suuywNGjTI9ddfn44dO1Z1RgAAAAAAAAAAAAAAAAAAAIBqV6kShl/96leZO3dukuQ3v/lNDjjggHTp0iVrr7127r777ioNCAAAAAAAAAAAAAAAAAAAAFATKlXCsM8++5T/u2PHjnn77bczY8aMtGzZMkVFRVUWDgAAAAAAAAAAAAAAAAAAAKCmVKqEYXnWWmutqjoVAAAAAAAAAAAAAAAAAAAAq6G0qNAJoG6qVAnD3LlzM3DgwDz99NOZOnVqSktLK3x/zJgxVRIOAAAAAAAAAAAAAAAAAAAAoKZUqoThxBNPzD//+c8cc8wxWXfddVNUpAYFAAAAAAAAAAAAAAAAAAAAqNsqVcLw+OOP59FHH83OO+9c1XkAAAAAAAAAAAAAAAAAAAAACqK4MpNatmyZtdZaq6qzAAAAAAAAAAAAAAAAAAAAABRMpUoY+vfvn379+uWzzz6r6jwAAAAAAAAAAAAAAAAAAAAABVGyqgO/973vpaioqPz1qFGj0rZt22y00UapX79+hbFDhw6tuoQAAAAAAAAAAAAAAAAAAAAANWCVSxh69OhRjTEAAAAAAAAAAAAAAAAAAAAACmuVSxguvPDC6swBAAAAAAAAAAAAAAAAAAAAUFDFlZ04c+bM3HDDDenbt29mzJiRJBk6dGgmTpxYZeEAAAAAAAAAAAAAAAAAAAAAakpJZSYNHz48e+65Z5o3b55x48blpz/9adZaa6088MADGT9+fG677baqzgnVYp8zDs0OR+6eNZo3zfhho3LfBTdlysgPVzi+7Sbrp2ufw7L+lh2z1vqt8+BFt+b5mx6vMGano/fKTkftmbXWb50kmTzyw/zjyvvz7nPDqvNSAAqi6xmHZqcj90jj5k0zftjI3HvBTZm8knV0nU3Wz359fpT2W3bI2uu3yf0X3ZrnbnpsheP3OrlHDvzFkXnupsdy/0W3VsclABRUdayjXc84NF3POKzCsdnTZuZX3+9VLdcAUJN2O+PgbHvk7mncvEk+HDYqf7vglkwbufJC0M32/X52P+uwrLVBm8yYMDVPX35P3vn7kPLvF9crzm5nHJKteuyUpq1b5NOpMzPsr8/nn1c9mLKyshSX1MseZx+Wb/1w67TcoHXmfzovY154K09e+pd8OnVmNV8xQPU66IzDs9uP90qT5k0y+vWRufWCP2XiyA9WOP6HR+yZzof8MOt/e4Mkydg3R+fey+7ImDdGlY/59g82y/69umejLTdOy7ZrZdBPB+a1f7xa7dcCUAgHnnFYdjlyz6zRvGnGDhuZOy+4IR+t5L6+3Sbrp1ufw7Phlh3Tav02+ctFN+fpL9zXF9crTrczfpTte3RJs9YtMmvqJ3nxr8/l0avuS1lZWXVfEkCtM2TYm7n5zr/m7XdHZdrHM/J/Ay7IHrvsVOhYALVGvwv65MQTjkrLls3z6quv59TTz8/bb7+/wvHHHvOj3HTj7790vMmaHbNgwYIkSdOmTfLr//eL9Oi+b9q0WTvDho3ImX36Zchrb1TbdQAUyv5nHJbOR+6RNZo3zbhhI/OXC27MpJXc26+7yfo5sM/h2WDpnv29F92SZ5bz7FPzti1z0LlHZ/Mfbp0GjRpkythJuf0X12bCW2Or83IAatzeZxyS7Y/cI2s0b5IJw0bl/gtu/spn8ffpc2j5s/gPXXRbBn/hWfwdj94zOx61V9Zav1WSJc/iP3Xl/Xn3Oe9Hgf8N3c/4UXY9csk+/phhI/PnC27IRyvZx9/liD2z88G7Zr2l+/jj3hyT+357R8Yus48PAEDlFFdmUp8+fXL88cdn5MiRadSoUfnxrl275vnnn6+ycFCddj+pW3Y9Yb/c3+/m/L7befl02sycdPt5adik0QrnNGjcIB9PmJq/XXpnZk/9ZLljZk76OI9eeld+3+38/L7b+Rn54oj0vP7stN1k/eq6FICC2POkbtnthP1zb7+bc0W38zJ72qz0vv38r1hHG+bjCVPyyKV3ZdYK1tH/2GCrjbPTkXtk4jvjqzo6QK1QnevoR+99kPO//7Pyr4H7nF0dlwBQozqfdEB2PGG/PNrvlvyx2wWZM21Wjru9bxqsZN1sv02nHPaHU/PGAy/kmv365o0HXsiP/nBq1t9642XOe2C2O2qPPNrv1ly15zn5x4C7svPP9s/2x++dJKnfuEHabb5RnrvqgVx7wK/yl5MGZe2O6+bHN5xV7dcMUJ32P+mgdD3xwNzW70+58MBfZta0mfnlHRem0UrW1U133CIvPfxCLjmiX359UN98/NH0/OLPF6Zl27XKxzRco2EmvDMut/X7U01cBkDB7HtS9+x1wgG5s9+NubjbuZk1bWbOvP2Cr7yvnz5hau6/9I7MXMF9/b4n9cguR+2VO/vdmH57npG/Drg9+/ysW3Y/vmt1XQpArTZv3vx8u1PHnNfn5EJHAah1zjn75Jxx+s9y2hm/yg477Z/JU6blicfuStOmTVY6b9as2Vmv/dYVvv5TwJAk1//x8uy5Z5cc/5PTsvU2e+bJp/6Zvz/xl7Rrt051XxJAjdr7pO7Z44T9c3e/m3Jpt76ZPW1mTrv9V6twbz8lD1565wr37Ndo1iTn3Nc/ixctyh+OvyS/3qtP7vvNbfls9mfVdSkABbHbSQdmlxP2ywP9bs7/dTs/s6fNzM9W4Vn8GROm5rFL71rhs/izJs3IY5felUHdzs+gbudn1Isjcrxn8YH/Efud1CP7nHBg7uh3Qy7qtmQf/+zb+610H/87O2yelx9+IZceeWF+c/B5mfHRtJz9535pscw+PgAAlVOpEoZ///vf6dXry5+iut5662Xy5MmrHQpqwi49u+apqx/Mm3//dya//2HuPOuaNGjcMNt033mFcz4YPiaPDLgjwx55KYsWLlrumLefHpp3nhuWaWMnZdrYSXn88ruz8LP52eh7m1TXpQAUxK4998s/rn4gw//+aia9/0HuOOvq1G/cMNt277zCOROGj85DA+7I0EdezKKFn69wXIM1GubYQafkrnOvz2ez5lRHfICCq851tHTx4nw6bVb515wZn1bHJQDUqB177pvnr34w7/x9SKa+/2HuP+u61G/cIFt1X/GnX+7Qs2vGvPBWBl/zcKaPnpTB1zycMS+OyA499y0f036bTfLuk6/l/WeHZeaH0/P2469m1OA3027LjkmSBZ/Oy63HDMyIR1/Jx2Mm5cPXR+XRC2/Nelt1TPN2a1f7dQNUl31POCAP/eG+DHnilXz4/oT88awr06BRw+zYfZcVzrn29EF5+s9PZMLb4zJp9MTc+MtrU1xclM123qp8zPDnXs9fL78rQ554pSYuA6Bg9ui5fx67+v68/vdX89H7H+Tms/6QBo0bZvuV3NePGz46fx3w5/x7Jff1G2/zrbzx5JC8+ezQfPzhtAx9/OWMGPxGNtxy4+WOB/im67Lj93Paz47LXj9c8T4+wP+q0049MQMGXpkHH3w8I0a8l5/0PCNrrNE4Rx5x0ErnlZWVZcqUaRW+/qNRo0Y5+KD90rfvxRn8wisZPXpcLur/u4wd90FO6nVsdV8SQI3aved+eeLqBzJs6b39rWddnQaNG+b7K7m3Hz98dO4fcHuGrOTefu+fd88nH32cP59zbca/MTozPpyW9158K9MnTKmuSwEoiC49u+bpqx/MW0ufxf/LWdemQeMG+d5XPIv/twF3fuWz+O8+NyzTx07O9LGT88Tl92ThZ/Oz4fc6VdelANQae/U8IH+7+r689vdXMvH9D3LDWVelYeOG2aF7lxXOuf6M/8uzt/89H7w9LpNHT8zN516XoqKibLbzljWYHIDarjRFvnxV+9c3UaVKGBo1apTZs2d/6fh7772X1q1br3YoqG5rtW+TZm1a5r3Bw8uPLV64KKNfeScbbfutKvs5RcVF2frAHdOgccOMG/p+lZ0XoNDWbt8mzdu0zLvLrKOLFi7K6FfeTocqWEcP639CRjz7et7/15urfS6A2qi619HWG62T/q9cmwsHX5Xjrjo9a7dvs9rnBCiklu1bZ802LTNq8H/fHy5euCjjXnk37bddcelh++91yqhl1tokGfX88GywzX/X2glD3kvHnTfP2h2WfIpb2003yIbbfTsjnxu2wvM2WrNxSktLM98nFgF1VOv2bdOiTcu8NXhY+bFFCxfl3VdGZJNtv73K52nYuEHq1a+XuTOVfgH/W1q1b5MWbVpmxOA3yo8tWrgo77/ydjb+Guvo8owc8m6+s/MWadth3STJ+ptumE22+07eem7oap0XAIBvlg4dNsi667bNk0/9s/zYwoUL8/zgl7PjjtutdG7Tpk0yeuQrGTdmSB564NZsvfXm5d8rKamXkpKSzJ+/oMKc+fPmZ+edvl+1FwFQQK2W7tm//YV7+5FVcG+/1Z7bZfybY3Li1WfmsiF/ynmPXpqdj9hjdSMD1Cr/fRa/4h5+dT6LP37oyCo7L0Bt9N99/IrvUd97ZUQ6VWof3wchAgCsrpLKTOrevXsuuuii3HPPPUmSoqKiTJgwIeeee24OOeSQr5y/YMGCLFhQcaNmUdnilBTVq0wc+NqatW6RJPl02qwKxz+dNist12+12udf99vtc9r9/VPSsH4WfjY/N/e6IlNGTVzt8wLUFv9ZR2d/YR2dPW1W1lp/9QqZtjlwp7TfvEMu737eap0HoDarznV03LBRub3P1Zk6dlLWbNUi+5x6UM68v38u2eusfOaX6kAd1XTpujn3C+vm3Gmz0mIl9/FNW7fInGkVi0TnTJudpq2bl78efO0jabjmGjn16d+mbHFpiuoV5+nL782bD7+03HOWNKyfvX55RN586MUsmDOvklcEUFgt2rRIksyaNrPC8dnTZ2bt9Vb9/ejh5x6TTybPyIh/Df/qwQDfIM1Xcl+/9mruMz1x7YNpvOYauejpQSldXJriesV58PK78urD/1qt8wIA8M2yTtslBdxTpkyvcHzKlGnZcIP1VzjvvfdGpeeJZ+att95NszWb5tRTT8zzzz2UbbbbK6NGjc2cOXPz0ktDcv55p+edd0dmypRpOeKIHvnBD76XkaPGVus1AdSkFT1DWhX39q02aJNdjt4rT9/waJ645oFs9N1O+dH/+0kWLfw8r9z//GqdG6C2WHPpnvucL6yjc6roWfx1vt0+p95/Ufmz+Lf0+p1n8YFvvP/uP82scHzWtFlp9TWeKz30l0fbxwcAqCKVKmG4/PLLs99++6VNmzaZN29edt1110yePDk77rhjLr744q+cP2DAgPz617+ucGyH5ptnxxZbVCYOfKVtuu+cwy75afnrG3pemiQpKyurMK6oKMkXjlXG1DEf5Yr9fplGzZrku11/kCOvODlXH/5rv/wB6qztunfO4cuso3/sOXDJP760jhat1jraYt21c3C/43LNsZdk0YLPK30egNqmptbRJHlnmU9un/TeBxk39P30e/7KbH/Irnn2xkdX69wANWWr7jvlwEtOKH99R8/fJlnOEllU9KV7+y/78r1/2TLHtjhwh3y3x8756+lXZ+r7E7PuZhuma7+j8+mUTzLsvsEV5haX1MthV52SouKi/O2CW77uZQEUzE49dslPLulV/vqKnyz5Pf6XVtAli+Qq2b9Xj+zQrXMuObxfPncPD3zDbd+9c45eZh29queAJf/44nvR1b+tz/cP3Ck79OiSG07/v3z0/odpv9lGObzf8Zk55ZO8dN8/v/oEAAB8Ix155EG59upLy193635skuU9+7Ty35m+8urQvPLq0PLX/3rx3/n3q39P75N/kjP79EuSHPeT03LD9Vfkg/FDs2jRorz++pu56y8P5Hvf27IqLwmgRn2/e+f8+JKflb++Zum9/fLX0dX7WUVFxRn/5ug89Nu7kiQfjhiXdpu0zy5H762EAaizvtd95xx6yYnlr2/seVmSL6+jqYJ1NEmmjfkov9vv3DRu1iRbdv1Bjrji57n28Is8iw98o+zQvUuOW2b/aVDPS5Is/++cvvr5qCW69uqe7bt1zqVHXOhZfACAKlCpEoZmzZrlhRdeyDPPPJOhQ4emtLQ022yzTfbcc89Vmt+3b9/06dOnwrFfbXnCCkbD6hvx1GuZMGxU+et6DeonSZq1aZFPl2mJa9qqeT6dPuuL07+2xZ8vzvTxU5IkH745Ju232ji79Oyae8+7YbXPDVAIbz41JOOGjSx/XbLMOrps2+aarZpl9mqso+237JBmrVvknEcGlB+rV1IvG/9g03Q5dp/0+dZRKSutgt/QA9SwmlpHl2fhvAX56N0Jad1hnSo9L0B1evepoflw2Ojy1/UaLPkVVtM2zTNnmXWzSatmmbOSdXPOtJlpuvQTOJadM3fa7PLX+/T9cQZf+0jeeuTlJMnU9z5Ii/VapcvJ3SqUMBSX1MuPrj41Ldu3zs1HXpIFc+at1jUC1KShT76aUa+/X/66/tL3oy1at8isqZ+UH2+2dvPMmj7zK8+338+658Deh+TSo/5fPnh3fJXnBahthj01JGOW2Weqv/T9abM2LTJrmfenzVo1z+xVWEdX5tC+x+Txax/Mvx95MUky8b0JWXu9Vul68kFKGAAA/oc98sg/8uqrr5e/btiwQZJknXVaZ/LkqeXH27RplSlTp6/yecvKyjJkyLBs0qlD+bExY8Zn9z0PzRprNE6zZmtm8uSpufOOazNu7AdVcCUAhTH8a+zZr+4zpLOmfpLJIz+scGzy6A/zva7br9Z5AQrp7adey++W+R3pf9bRNb/0LP7K9/BX1eLPF+fjCs/id0znnvvmvvNuXO1zA9QWw576d8Ys5z1q8zYtK7X/tO9Pu+WA3ofkt0f9Oh/axwcAqBKVKmH4j9133z277777157XsGHDNGzYsGKQonqrEwVWasHc+Vkwd36FY7OnfpJvdd4yE0eMS5LUq18vG2+/af428M6qD1BUVF78AFAXLW8dnTX1k3y781b5sMI6ulkeXo119P1/vZUBe59d4diPf/vzTB09MU9d97ACBqDOqql1dHlKGpRknU7rZcy/363S8wJUp4Vz52fGF9bNT6d+kk6dt8zkEUs2CevVr5eNtv9Onhz4lxWe54PXR2XjzlvmpRufKD/WqctWmTB0mT9EbtwgZWWlFeaVlpamqKio/PV/ChjW3mid3HzkxZk3c85qXR9ATZs/d37mz51c4djMqZ9ki87fzfgRY5Mk9eqX5Dvbb567B/55pefar1f3dD/l0Fx2bP+MfXP0SscCfFMsmDs/05azjm7Weat8UH5fX5Jvbb9Z7ht4+2r9rAaNG37p04xKS0tTvMz7UwAA/vfMmTM3c+bMrXBs0qQp2XOPXTJs2IgkSf369bNLlx3S97xLvta5v/vdzfPWW1/eR/rss3n57LN5adGiefbea9ec2/fiyl8AQIEtubf/8p79pl/Ys99k+83ywMA7VutnjXntvbTt2K7CsTYd2uXjidNW67wAhbSyZ/E/+sKz+I8OvKvKf35RUVH5HycDfFOsaB9/885bZcIy+/jf3n7z3PsV+/j7/qx7DjzlkFxxXP+Ms48PAFBlKlXCcNppp6VTp0457bTTKhz/wx/+kFGjRmXQoEFVkQ2q1fM3PZ49e/fI9HGTM23spOzZ+6AsnLcgQx/6V/mYI684ObOnzMijly35g4569eul7Sbrl/+7edu10m6zDbNw7vxMX9q2ud85R+Sd54Zl5qSP06hJo2x94E7ptMNmuf64AV8OAVCH/fOmx7JX7x6ZNm5Spo2dnL1698jn8xbktYdeKB9z9BW9M2vKjDxy2ZJfqterXy/rLF1HS+qXpHnblllvsw2zYOk6umDu/Ex6v+KnZyycNz9zZ8750nGAuq461tEk6X7e0Rnx9GuZMXF61mzVPPuccnAaNW2cV3xaJlDHvXTTE+nSu1s+Hjc5H4+dnF16d8/n8xZm+EMvlo85+IqTMnvKJ3nqsruTJC/f9ER63nNBOp90QN598rV8Z69t03HnzXPjYReVz3nv6dezS+8emTXx40wd+WHW3Xyj7HRC1wy9d8m6WVyvOIdfe3rabb5Rbj/h8hTXK07T1s2TJPNmzsnizxfX4P8CQNV54sa/5cDeh2TyuEmZMnZSDjzl4CycvyAvPfR8+Zhevzstn0z+OPdctuSh4/179cghZx2Za07/faZ/ODXNW7dIsuThkAWfLXnwruEajdJ2o3XKz9G6fZtssNlGmTtzTj7+aNU/iROgtnv6pkezX++DM3Xc5EwZOyn79T44C+ctyCvL3Nf3vOKUfDJlRh64bEnhYr36JWm3zH19y7Zrp/1mG2X+3PmZNn7JQ3bDn34t+/c+ODMmTs9HIz/IBpt3yF4nHJh/3ftMzV8kQC3w2WfzMuHDj8pfT/xoSt59f3SaN1sz667TpoDJAArvyqtuyLm/PDUjR43NqFFjc+4vT81nn83LXX95oHzMzTf9Xz76aFLO/9XAJMkFvzozr7wyNCNHjU2zZmvmlN49s/V3N89pp51fPmfvvXZNUVFR3nt/dDptvFEGDrwg778/OrfceneNXyNAdXrmpseyb++DMnXpnv2+S58h/fcy9/bHXdE7M6fMyEPL7NmvW/4MaUlatF0r6y/ds5+2dM/+6RsfzTn39c++Jx+U1x59MRt9t1M6H7lH7uh7fc1fJEA1GnzT49mjd/dMHzcp08dOzu69e2ThvIV5fZln8Y+44ueZNeWTPL7cZ/GXPPvUbuk6+vHSdbTrOYfn3aXP4jds0jhbH7hjNt5hs/zpuIE1f5EANezJm/6WA3ofkilL9/EP6H1IFsxbkJcfGlw+5sQrTs3MKTPy16X7+F17dc9BfY7MH08flOkfTkuzpfv4C5bZxwcAoHIqVcJw33335eGHH/7S8Z122ikDBw5UwkCd8Mx1D6d+owY5pH/PNG7eJBOGjcofj7mkQktny/VaVfi0oWZt18rZj11a/nq3Xgdmt14HZtTLb+eaI5b8AcearZrnqN/3TrPWLTLv088y6d0Juf64AXn/hTdr7uIAasBTS9fRw/qfkDWaN8n4YaNyzZfW0bUrfKpw87Zr5ZePXVb+eo9e3bJHr24Z+fKIXHXERQH4X1Jd62iLddfOcVeeliYtm2XOjNkZ9/rI/O6gX+WTif7gDajbXrjub6nfqEEO6H98GjVvkonDRue2YwZm4TLrZvP11q5wH//B0JG599Q/ZI+zD8vufQ7LJxOm5J5TrsqHw/7b+P7ohbdmj7MOzQH9f5ImrZrl0ymfZMidz+S5K+9PkjRbd61sute2SZLej1csWLzpiN9k3MvvVOdlA1SbR697IA0aNcjxv/lZ1mjWJGOGjcxlR1+U+cusq2u3a5Wy0v++H93jmH1Tv2H9nH7dLyqc6/7f350HBi35Q4wOW22c8+/uX/69o/r1TJIMvveZXH/2H6rzkgBq1BPXPZT6jRrkx/1PTJPmTTJm2Kj8/pjfVLivX+sL+0wt2rZMv8d+W/56n17dsk+vbnnv5RG5/Ij/lyS588Ib0+OsI3JU/xOzZqvmmTllRp6/88k8cuVfa+zaAGqTt94dmZ6n/rL89WVXLfnDte5d98zFvzqrULEAaoXfXn5NGjdulD9ceUlatmyeV199PV33/3HmzJlbPmaD9u1Susy9fYvmzXPtNZdlnXVaZ9asTzNs2FvZbfdD8u8hw8rHNGveLBf3Pzfrr79uZsyYmfsfeCwX9Ls0ixYtqsnLA6h2/1h6b39k/xOzRvMmGTtsVK465uKV3ts3b7tWzl/m3n6vXt2yV69uef/lEfn9Eb9OkowfPjrX9bo8PX7x4+x3+iGZ/sHU3HvRrRXKHQC+CZ697pHUb9QgB5c/iz86f/rKZ/Fbps9j/y1T+GGvA/PDXgdm9Mtv59ojluwvNW3VPEcufRZ//qef5aN3J+RPxw3MSM/iA/8DHrvuwdRv1CDH9P9ZmjRvktHDRuaKY76wj/+FtXX3pfv4p1x3ToVzPTjo7jw06J4ayw4A8E1UVLbsO69V1KhRo7z11lvp1KlTheOjRo3KFltskfnzv35TVp+NjvjacwD4r0X52ss5AABUmRaV63kEYKmxZfMKHQGgzmtUVK/QEQDqtGuGXPrVgwBYocbtuhQ6AkCd9tN2Oxc6AkCd1iR+Pwqwuj7OwkJHAKjTbh53X6EjACtwR7ujCx2B/wFHfXR7oSNUueLKTOrUqVOeeOKJLx1//PHH07Fjx9UOBQAAAAAAAAAAAAAAAAAAAFDTKvUxlX369Mkpp5ySadOmZffdd0+SPP3007niiisyaNCgqswHAAAAAAAAAAAAAAAAAAAAUCMqVcLQs2fPLFiwIBdffHH69++fJNloo41y7bXX5thjj63SgAAAAAAAAAAAAAAAAAAAAAA1oVIlDEny85//PD//+c8zbdq0NG7cOE2bNq3KXAAAAAAAAAAAAAAAAAAAAFRSWaEDQB1V6RKG/2jdunVV5AAAAAAAAAAAAAAAAAAAAAAoqOLKTJoyZUqOOeaYtGvXLiUlJalXr16FLwAAAAAAAAAAAAAAAAAAAIC6pqQyk44//vhMmDAhF1xwQdZdd90UFRVVdS4AAAAAAAAAAAAAAAAAAACAGlWpEoYXXnghgwcPztZbb13FcQAAAAAAAAAAAAAAAAAAAAAKo7gyk9q3b5+ysrKqzgIAAAAAAAAAAAAAAAAAAABQMJUqYRg0aFDOPffcjBs3rorjAAAAAAAAAAAAAAAAAAAAABRGSWUmHX744fnss8+y8cYbZ4011kj9+vUrfH/GjBlVEg4AAAAAAAAAAAAAAAAAAACgplSqhGHQoEFVHAMAAAAAAAAAAAAAAAAAAACgsCpVwnDcccdVdQ4AAAAAAAAAAAAAAAAAAACAgqpUCcOy5s2bl88//7zCsWbNmq3uaQEAAAAAAAAAAAAAAAAAAABqVHFlJs2dOzennHJK2rRpk6ZNm6Zly5YVvgAAAAAAAAAAAAAAAAAAAADqmpLKTPrFL36RZ599Ntdcc02OPfbYXH311Zk4cWL++Mc/ZuDAgVWdEQAAAAAAAAAAAAAAAAAAgK+htKjQCaBuqlQJwyOPPJLbbrstP/zhD9OzZ8906dIlnTp1yoYbbpg77rgjRx11VFXnBAAAAAAAAAAAAAAAAAAAAKhWxZWZNGPGjHTo0CFJ0qxZs8yYMSNJ0rlz5zz//PNVlw4AAAAAAAAAAAAAAAAAAACghlSqhKFjx44ZN25ckmSzzTbLPffckyR55JFH0qJFi6rKBgAAAAAAAAAAAAAAAAAAAFBjKlXC8JOf/CRvvPFGkqRv37655ppr0rBhw5x55pk555xzqjQgAAAAAAAAAAAAAAAAAAAAQE0oqcykM888s/zfu+22W959990MGTIkG2+8cb773e9WWTgAAAAAAAAAAAAAAAAAAACAmvK1SxhKS0tzyy235P7778+4ceNSVFSUDh065NBDD81WW21VHRkBAAAAAAAAAAAAAAAAAAAAql3x1xlcVlaWbt265cQTT8zEiROz5ZZbZvPNN8/48eNz/PHH56CDDqqunAAAAAAAAAAAAAAAAAAAAADVquTrDL7lllvy/PPP5+mnn85uu+1W4XvPPPNMevTokdtuuy3HHntslYYEAAAAAAAAAAAAAAAAAAAAqG7FX2fwXXfdlfPOO+9LBQxJsvvuu+fcc8/NHXfcUWXhAAAAAAAAAAAAAAAAAAAAAGrK1yphGD58ePbdd98Vfr9r16554403VjsUAAAAAAAAAAAAAAAAAAAAQE37WiUMM2bMSNu2bVf4/bZt2+aTTz5Z7VAAAAAAAAAAAAAAAAAAAAAANa3k6wxevHhxSkpWPKVevXpZtGjRaocCAAAAAAAAAAAAAAAAAACg8koLHQDqqK9VwlBWVpbjjz8+DRs2XO73FyxYUCWhAAAAAAAAAAAAAAAAAAAAAGra1yphOO64475yzLHHHlvpMAAAAAAAAAAAAAAAAAAAAACF8rVKGG6++ebqygEAAAAAAAAAAAAAAAAAAABQUMWFDgAAAAAAAAAAAAAAAAAAAABQGyhhAAAAAAAAAAAAAAAAAAAAAIgSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSJCWFDgAAAAAAAAAAAAAAAAAAAEDVKit0AKijigsdAAAAAAAAAAAAAAAAAAAAAKA2UMIAAAAAAAAAAAAAAAAAAAAAECUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJkpJCBwAAAAAAAAAAAAAAAAAAAKBqlRYVOgHUTcWFDgAAAAAAAAAAAAAAAAAAAABQGyhhAAAAAAAAAAAAAAAAAAAAAIgSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJElJoQMAAAAAAAAAAAAAAAAAAABQtUoLHQDqqOJCBwAAAAAAAAAAAAAAAAAAAACoDZQwAAAAAAAAAAAAAAAAAAAAAEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAkqSk0AEAAAAAAAAAAAAAAAAAAACoWqWFDgB1VHGhAwAAAAAAAAAAAAAAAAAAAADUBkoYAAAAAAAAAAAAAAAAAAAAAKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgSVJS6AAAAAAAAAAAAAAAAAAAAABUrbKiQieAuqm40AEAAAAAAAAAAAAAAAAAAAAAagMlDAAAAAAAAAAAAAAAAAAAAABRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkCQpKXQAAAAAAAAAAAAAAAAAAAAAqlZpoQNAHVVc6AAAAAAAAAAAAAAAAAAAAAAAtYESBgAAAAAAAAAAAAAAAAAAAIAoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAACAGnLNNdekQ4cOadSoUbbddtsMHjx4peP/+c9/Ztttt02jRo3SsWPHXHfdddWaTwkDAAAAAAAAAAAAAAAAAAAAUO3uvvvunHHGGTn//PPz+uuvp0uXLunatWsmTJiw3PFjx47Nfvvtly5duuT111/Peeedl9NOOy333XdftWVUwgAAAAAAAAAAAAAAAAAAAABUu9/97nc54YQTcuKJJ2bTTTfNoEGD0r59+1x77bXLHX/ddddlgw02yKBBg7LpppvmxBNPTM+ePXP55ZdXW0YlDAAAAAAAAAAAAAAAAAAAAMDXtmDBgsyePbvC14IFC5Y7duHChXnttdey9957Vzi+995758UXX1zunJdeeulL4/fZZ58MGTIkn3/+edVcxBcoYQAAAAAAAAAAAAAAAAAAAAC+tgEDBqR58+YVvgYMGLDcsdOnT8/ixYvTtm3bCsfbtm2byZMnL3fO5MmTlzt+0aJFmT59etVcxBeUVMtZAQAAAAAAAAAAAAAAAAAAgG+0vn37pk+fPhWONWzYcKVzioqKKrwuKyv70rGvGr+841VFCQMAAAAAAAAAAAAAAAAAAMA3TGmhA/A/oWHDhl9ZuvAfrVq1Sr169TJ58uQKx6dOnZq2bdsud84666yz3PElJSVZe+21Kxf6KxRXy1kBAAAAAAAAAAAAAAAAAAAAlmrQoEG23XbbPPnkkxWOP/nkk9lpp52WO2fHHXf80vh//OMf2W677VK/fv1qyamEAQAAAAAAAAAAAAAAAAAAAKh2ffr0yQ033JCbbrop77zzTs4888xMmDAhJ510UpKkb9++OfbYY8vHn3TSSRk/fnz69OmTd955JzfddFNuvPHGnH322dWWsaTazgwAAAAAAAAAAAAAAAAAAACw1OGHH56PP/44F110USZNmpQtttgijz32WDbccMMkyaRJkzJhwoTy8R06dMhjjz2WM888M1dffXXatWuXK6+8Mocccki1ZVTCAAAAAAAAAAAAAAAAAAAAANSIk08+OSeffPJyv3fLLbd86diuu+6aoUOHVnOq/yqusZ8EAAAAAAAAAAAAAAAAAAAAUIspYQAAAAAAAAAAAAAAAAAAAACIEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkSUmhAwAAAAAAAAAAAAAAAAAAAFC1ygodAOqo4kIHAAAAAAAAAAAAAAAAAAAAAKgNlDAAAAAAAAAAAAAAAAAAAAAARAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAEiUMAAAAAAAAAAAAAAAAAAAAAEmUMAAAAAAAAAAAAAAAAAAAAAAkUcIAAAAAAAAAAAAAAAAAAAAAkCQpKXSA/9jq81oTBaBO0qoDsHrql5UVOgJAnTa+flGhIwDUacfPr1/oCAB13tWN5hU6AkCd1rhdl0JHAKjT5n00uNARAOq0Y7ftU+gIAHVaaZG9JoDVNb10QaEjAAAAtYjmAwAAAAAAAAAAAAAAAAAAgG+YUp+1B5Xig9MBAAAAAAAAAAAAAAAAAAAAooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAAAAAAAAAAAAAAAkihhAAAAAAAAAAAAAAAAAAAAAEiihAEAAAAAAAAAAAAAAAAAAAAgiRIGAAAAAAAAAAAAAAAAAAAAgCRKGAAAAAAAAAAAAAAAAAAAAACSKGEAAAAAAAAAAAAAAAAAAAAASKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJElJoQMAAAAAAAAAAAAAAAAAAABQtUoLHQDqqOJCBwAAAAAAAAAAAAAAAAAAAACoDZQwAAAAAAAAAAAAAAAAAAAAAEQJAwAAAAAAAAAAAAAAAAAAAEASJQwAAAAAAAAAAAAAAAAAAAAASZQwAAAAAAAAAAAAAAAAAAAAACRRwgAAAAAAAAAAAAAAAAAAAACQRAkDAAAAAAAAAAAAAAAAAAAAQBIlDAAAAAAAAAAAAAAAAAAAAABJlDAAAAAAAAAAAAAAAAAAAAAAJFHCAAAAAAAAAAAAAAAAAAAAAJBECQMAAAAAAAAAAAAAAAAAAABAkqSk0AEAAAAAAAAAAAAAAAAAAACoWqWFDgB1VHGhAwAAAAAAAAAAAAAAAAAAAADUBkoYAAAAAAAAAAAAAAAAAAAAAKKEAQAAAAAAAAAAAAAAAAAAACCJEgYAAAAAAAAAAAAAAAAAAACAJEoYAAAAAAAAAAAAAAAAAAAAAJIoYQAAAAAAAAAAAAAAAAAAAABIooQBAAAAAAAAAAAAAAAAAAAAIIkSBgAAAAAAAAAAAAAAAAAAAIAkShgAAAAAAAD+P3v3HWVlee4N+DfDwICUoVsQu2LXxN6wRI0axd7BFmtsUWOUGNSYRI3xqFGj5kQTE1vMOYnRGKOfUbEDImBFBhBFGHoZ6lDn+0McGSkBHNgzOde11l6L95nneff98Me99rvfvX8bAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkiQlhS4AAAAAAAAAAAAAAAAAAACAulVd6AKggSoudAEAAAAAAAAAAAAAAAAAAAAA9YEQBgAAAAAAAAAAAAAAAAAAAIAIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASJKUFLoAAAAAAAAAAAAAAAAAAAAA6tbCokJXAA1TcaELAAAAAAAAAAAAAAAAAAAAAKgPhDAAAAAAAAAAAAAAAAAAAAAARAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASZKSQhcAAAAAAAAAAAAAAAAAAABA3VpY6AKggSoudAEAAAAAAAAAAAAAAAAAAAAA9YEQBgAAAAAAAAAAAAAAAAAAAIAIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASJKUFLoAAAAAAAAAAAAAAAAAAAAA6lZ1oQuABqq40AUAAAAAAAAAAAAAAAAAAAAA1AdCGAAAAAAAAAAAAAAAAAAAAAAihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIElSUugCAAAAAAAAAAAAAAAAAAAAqFsLU13oEqBBKi50AQAAAAAAAAAAAAAAAAAAAAD1gRAGAAAAAAAAAAAAAAAAAAAAgAhhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIkpQUugAAAAAAAAAAAAAAAAAAAADq1sJCFwANVHGhCwAAAAAAAAAAAAAAAAAAAACoD4QwAAAAAAAAAAAAAAAAAAAAAEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAkqSk0AUAAAAAAAAAAAAAAAAAAABQt6oLXQA0UMWFLgAAAAAAAAAAAAAAAAAAAACgPhDCAAAAAAAAAAAAAAAAAAAAABAhDAAAAAAAAAAAAAAAAAAAAABJkpJVXdi/f/8MHjw4RUVF2XLLLbPzzjvXZV0AAAAAAAAAAAAAAAAAAAAAa9RKhzCMGjUqJ598cl5//fW0bt06STJ16tTsueeeeeyxx9K5c+e6rhEAAAAAAAAAAAAAAAAAAABgtSte2QVnnXVW5s2bl8GDB2fy5MmZPHlyBg8enOrq6nz3u99dHTUCAAAAAAAAAAAAAAAAAAAArHYlK7vg1VdfzRtvvJEuXbrUjHXp0iV33XVX9tprrzotDgAAAAAAAAAAAAAAAAAAAGBNKV7ZBRtssEHmzZu3xPj8+fPTqVOnOikKAAAAAAAAAAAAAAAAAAAAYE1b6RCGW265JRdffHH69++f6urqJEn//v1z6aWX5tZbb63zAgEAAAAAAAAAAAAAAAAAAADWhJKVXXDGGWdk1qxZ2W233VJS8vny+fPnp6SkJGeddVbOOuusmrmTJ0+uu0oBAAAAAAAAAAAAAAAAAAAAVqOVDmG44447VkMZAAAAAAAAAAAAAAAAAAAAAIW10iEMp59++uqoAwAAAAAAAAAAAAAAAAAAgDqysNAFQAO10iEMSbJgwYL87W9/y+DBg1NUVJStt9463bp1S6NGjeq6PgAAAAAAAAAAAAAAAAAAAIA1YqVDGIYNG5bDDjsso0ePTpcuXVJdXZ3y8vJ07tw5//jHP7LpppuujjoBAAAAAAAAAAAAAAAAAAAAVqvilV1wySWXZNNNN81nn32WAQMGZODAgRk5cmQ23njjXHLJJaujRgAAAAAAAAAAAAAAAAAAAIDVrmRlF7z88svp06dP2rZtWzPWrl273Hzzzdlrr73qtDgAAAAAAAAAAAAAAAAAAACANaV4ZReUlpZm+vTpS4zPmDEjTZo0qZOiAAAAAAAAAAAAAAAAAAAAANa0lQ5hOPzww3Puueemb9++qa6uTnV1dfr06ZPzzz8/3bp1Wx01AgAAAAAAAAAAAAAAAAAAAKx2Kx3CcOedd2bTTTfNHnvskaZNm6Zp06bZa6+9stlmm+VXv/rV6qgRAAAAAAAAAAAAAAAAAAAAYLUrWdkFrVu3zpNPPpmhQ4fmo48+SnV1dbbeeutsttlmq6M+AAAAAAAAAAAAAAAAAAAAgDVipUMYvrD55ptn8803r8taAAAAAAAAAAAAAAAAAAAAAApmhUMYLr/88hWad9ttt61yMQAAAAAAAAAAAAAAAAAAAACFssIhDAMHDqx1/Nprr2WnnXZKs2bNasaKiorqrjIAAAAAAAAAAAAAAAAAAACANWiFQxheeumlWsctW7bMo48+mk022aTOiwIAAAAAAAAAAAAAAAAAAGDVLSwqdAXQMK1wCAM0ZF1OPzDbnn9Y1urYOlPKR6ffdQ9nfL8hy5y/9u5bZpfrTk2bLTpl1ripef/epzPkoRdr/r7BoTtn+4u7pdVGa6eocaNMHzEu7//mmXz8l9dr5hzX5/a06NxhiXMPfvD59L3mD3W7QYDVrMvpB2brRX10avnovLUCfXTn605N60V99IN7n075Yn10cRt12z1d770oI5/tn97fvaNmfIvTvpUuPb6V5ot6aWX5qLxz+xOpeOndOt0bwJqw2ekHZqsLvpNmHVunsnx0Blz7UCYsp4922H3LfPP67inbolNmj5uawfc8nWEPvVBrTuNWa2X7q09I50N3TpOy5pnx2YQM/MkjGfPiO0mSrS/qlvUP2zmtNlsvC6rmZmL/oRn08z9l+vAxq3WvAKvT3t8/Jjucsn+aljXPmIHD8/96PZiJQ0cvd02XQ3fJPlccl9YbdMzUkePzyi//J+XP9V/q3N2/d0T2u+rEvPXAs3nhhodrxr9z67nZ7viuteaOHjAsDx19/dfeE0ChdDrj4Gx44RFp0rF1Zg4ZlaG9/pCpfT9a6twmHVtn85/0SMvtN8lam6yTz+5/NkN71X6Pc90T983Wd35vibUvbdA9C+fMWy17ACi0E79/cg465eA0L2uRoQPL89te9+WzoZ8tc/6BJx2c/Y7dPxt02TBJMvy9YXnklocy7J2htc554mUn11o3ZfyUfHeX01fPJgAK7Npel+fs756aNm3K0q/fwFx86TX58MPyZc4/rccJ+d0Dty8x3rzlJpkzZ06SpEWL5vnJ9T/MUUceko4d22XQoA9y2eXXpv/b76y2fQDUV/0HvZffP/q/+fCjYZkwaXJ+dVOvfKvrnoUuC6DeOPb7J+Vbpxyc5mXNM2zg0Py+128yajnX9gecdFD2OXb/rN9lgyTJiPeG5/FbHs7wxa7tD+x+SA7qfkjar98xSTJq6Mj89Vd/zju9B6zezQAUwOHfPz77nHxg1iprkRGDhuaxXvdnzNBRy5y/7ubrp9vlJ2aD7TZJ+/U75s83/D4v/O6ZWnN+/tqva3ro4nr/8dk8du0Ddb4HgEI6+bJT8u1Tvp0WZS1SPrA89/W6NyPLRy5z/sEnfzsHHHtANlx0r2nYe8Pyx1/8MUPf+fI91ftffyBrd157ibX/+MPTua/XfXW/CQCA/2BCGPiPt1G33bLr9d3T50cPZvxb5enS44Ac9PCV+dt+V2VmxaQl5rfo3CEHPvSDDH20d169+N503GWL7H7jGamaND2fPvNWkmTu1Jl5986nUjmsIgvnzc/6B34je992bqomTkvFy+8lSf5+2LUpblRcc97WW66fb/+pZz59ut+a2ThAHdmo227Z+fru6fujBzPhrfJs3uOAfOvhK/PUcvroAYv66GsX35sOu2yR3Rb10ZGL+ugXmndql52uPSXj+iz5JY9ZYyZnwE2PZ9on45Ikmx6/T/b/3eV5+tvXpLJ8+V+0A6hPNui2e775kx7p/6PfZ2K/8mzW44Ds+8gP88x+P8ys0Uv20eadO2S/h6/M8EdeypsX3ZP2u26RnW88M1WTpmXUoj5a3LhR9v/T1amaOC2vnXtnZlVMzlqd2mb+jKqa83TcY8sMffBfmTRoeIpLGmX7q07I/o9dnX/s+8MsmD1nje0foK7sdv7h2eXsQ/OPH/wmkz8emz0vPjInPnJ1frv/lZk7s2qpa9b75mY58u6L8sp//W/Kn+ufLb69c4789UV5+LifZsyg4bXmrrP9JtnxlP0z/sNPl3qu4b3fyTM/+O+a4wVz59fd5gDWsI5H7pEtfnp6hlz9QKb2G5JOpx2YHR7rmT77XJ45S3mNWlzaOPMmTcsndzyRDc47bJnnnT9tVt7c8/u1xgQwAP+pjj7/mBxx9pG56we/ypiPR+e4i0/IdY/ckIv2/16qZs5e6ppt99g2rz31Sj56+6PMmzM3R51/bK576Ce59KCLMnnc5Jp5I4d8mutP7VVzvHDBwtW+H4BCuPIH38v3Lz03Z519WYYO/Tg/6nlpnn3msWy9bdfMmDFzmesqK6dl621rhyV+EcCQJP/9m1uzzTZdcsaZl6RizLicesoxee7ZP2W7HfZPRcXY1bYfgPpo9uyqdNlskxx12MG57JqfFbocgHrliPOPzmFnd8t9P7gzYz6uyNEXH58fPfKTXL7/91K1jHtPW+2xbd546tWUL7q2P+L8o9Pzoetz5UEXZ8qia/vJYyblsV88lLGffP4DCV2P2z8/+G3P9Dzs8uUGPAA0NN8+/8gc+N3D84cf/DrjRozJYRcfm+8/3CvXHnBp5iyjjzZpVpqJI8fn7WfezAm9zljqnJu69az1Gfz1tuicyx65Nm8/8+bq2AZAwRx7wbE56uyjcscVt2f0xxU58ZITc8MjP80F+52f2cu417Td7tvllSdfzuC3B2fenHk55vxjc8PDN+TCAy/M5HGf3+u//IjLavXRDbtsmJ89+vO89o/Xl3pOAACWrfjfT4GGbZtzDs3QP/XO0Md6p3JYRfpd93BmVkxKl9O+tdT5XXockJmjJ6XfdQ+nclhFhj7WO8MefznbnP/lh4vHvjk4I5/tn8phFZn+6fgMfuC5TBn8WdbetUvNnDmTp2f2hMqaR+cDv5FpI8Zl7JuDV/ueAerSVuccmmF/6p1hi/po/0V9dItl9NEtFvXR/ov66LCl9NEkKSouyj53fy/v3PqXTB85fonzjHp+YEa/+E6mfzw20z8em0G/+J/Mn1mVDt/cbLXsE2B16XLuofn4sd75+NHemTasIgOuezizKiZl89MOXOr8zU77VmaOnpQB1z2cacMq8vGjvfPxn17OVud/p2bOJiftlyatW+TVs27PxLfKM2v0xEzsV56pH36ZgNz71Fsy4s+vZFr56Ez9cGT6XvabNF+/fdpuv/Fq3zPA6rDLdw/JG3c/mfJn+2di+aj844rfpHHTJtn6yGX/atsuZx2SEa+9nz73/D2Th49Jn3v+nk9f/zC7nHVIrXmN1ypNt19dkH9e9UCqKmct9VwL5szLzAmVNY+qymV/GQSgvtvg/O+k4tEXU/HIi5k1dHSG9vpD5oyelPXPOHip86s+m5DyH/8hY//nlcyftvQ+mSTV1dWZO6Gy1gPgP9Xh3+2Wv9z95/R99s2MLB+ZO6+4I6VNS9P1yK7LXHPHpbfl2Yf+mU8+HJHRw0fn3qvuTlFxcbbfa4da8xbMX5CpE6bWPKZNnra6twNQEJdcfHZuuvnO/O1v/8wHHwzJmWd9P2ut1Swnn3T0ctdVV1dn3LgJtR5faNq0aY45+rD07PnzvPpa3wwf/klu+OltGfHJZzn/vNNW95YA6p199tgll5x7eg7ab69ClwJQ7xz63SPyt7v/J2892yejykfm3it+lSZNS7PXcq7tf33p7Xn+oX/m0w9HpGL46Pz3VfekqLgo2+61fc2cAS+8lUEvvZ2xIyoydkRF/vzLR1I1qyqbfbPLMs8L0BB966zv5J+//msGPtcvFeWf5cEr7k6TZqXZ9ci9l7nm03eH5y83PZT+f38j8+YuPch7xuRpmTZhas1j+2/tlPGfjE15nw9X11YACqLbd4/Mn+9+PG8++2ZGln+a2y+/LaVNS7PvUfsuc81/XXprnnnomYz4cERGDR+Vu6+6K8XFxdlh7y/vNU2bPK3WfaZdvrVrKj6pyPt93lsT2wIA+I+ywiEM7777bq1HdXV1PvrooyXGoT4pbtwo7bbfOBUvv19rvOLl99Nx582XuqbDTpsvMX907/fSfvuNU1TSaKlr1t17m7TadJ2MXcovuX9RxybH7JWhj7+8CrsAKJxl9dExL7+fDsvpo2O+2nd7v5d2X+mj2192dKomTc+wP/373lhUXJSNuu2ekrVKM+HtoauwE4DCKG7cKG233zhjX6795vXYl99L+2X00fY7bb7k/N7vpu0OX/bRTgd/M5PeHpqdbzwjR79zTw598eZsfXG3FBUXLbOWxq3WSpLMnTrj62wJoCDKOndIi46t88mrX/bHBXPn57O+H6XTTkvvp0my3jc3yyev1O6pI155d4k1B//0jAx/cVA+ff2DZZ5rg923ysVv/zrnvvTLHHLzd7NWu1aruBuAwipq3Cgtt98kk3vXfj9/8svvpGznLb7WuRs1b5o9+9+dvQbekx0e/mFabLvR1zofQH21due106Zj2wx6dVDN2Py58/NB3w/SZaetVvg8TZqVplHjRpk+dXqt8XU3Xi/39/t97n3tt7n8rh9k7c5r11XpAPXGxhtvkHXXXTvP/+vL+0Rz587NK6/2yR577LzctS1aNM/woX3zycf98+QTf8iOO25T87eSkkYpKSlJVdWcWmuqZldlrz13qdtNAADQYHVcdG3/3leu7Qf3fT9b7LTlCp+ntFmTlDRulBnLuA9fVFycPY7YO6XNmmbogKV/vhSgIWrfuWPKOrbJh6++UzM2f+78lPf9MJvuVHehM40al2S3o/bJG39+sc7OCVAfrL3B2mnbsW0GvjKwZmz+3Pl5v+/72XIl7jWVLrrXNOMr95q+UNK4JPsfvV/+9fjzX7dkAID/k0pWdOKOO+6YoqKiVFdX14wdfvjhSVIzXlRUlAULFvzbc82ZMydz5tS+4T2vekEaFy39C+6wqkrbtkxxSaPMnlj7F9dmT6xMs46tl7qmWceypc4vblySpm1bZvb4qUmSxi2b5YS370qjJiWpXrAwb/7owYx59f2lnDHZ4JCd06TVWhn251e+9p4A1qQv+mjVUvrieivRR6u+0kc77Lx5Njt5vzx90I+W+/ytt1w/hz51fRqVNs78mVXpffYdqRxa8XW2BLBGLauPVk2oTNOOZUtd07RDWaomLL2PlrZtmarxU9Niw45pvtfW+eSJN9K7+y1puck62fnnZ6SopFE+uP2JpZ73G9efmvF9P0rlkFF1szmANajFoteeM7/SH2dOrEyrTu2Xva5D68ycuOSa5h2+7MFbHbF71t52o/yh27XLPM/w3u/ko2f6pXLUxLTu3CH7XHFcTn6sZx48vFcWzJ2/CjsCKJzGbVuluKRR5n6lp86ZUJm2y7jWXxEzh1Vk8CX3ZMbgz1LSslk6n3Nodv77Del7wA8ze8TYr1k1QP3SumObJMnUCVNrjU+dODUdOnVY4fP0uPq0TB47Oe++/uUHlcsHDcmdl9+eio8r0rp96xx38Qm58a+35NKDLlrmB+gAGqJ11u6YJBk3bmKt8XHjJmTDDdZf5rohQ4blrLMvy/vvf5RWLVvk4ovPziu9n8w3dz4ow4aNyIwZM/Pmm/1zzY8uzeCPhmbcuAk56aSjsuuu38jQYSNW654AAGg4yha9F1r5lWv7yomVab8S1/YnL7q2f3+xa/sk6dxlw9zwxM1pXNokVTOrctt5N2f0UPfqgf8crTq0TpJM+8r9pukTKtN2/WXfw19ZOx68S5q1ap43/rd3nZ0ToD5o02HRvaaJU2uNT504NR07dVzh85x+9emZNHZSBr02aKl/3/3bu6d5qxZ54X9fWNVSAQD+T1vhEIYRI+ruZvRNN92Un/zkJ7XGjmyxXY5qtX2dPQfUslh4SJIUFS05ttz5SxmfN6MqTx18TRo3L826e2+TXa87NTNGTsjYNwcvcbrNT9o3o196J7PHTV21+gEKrPpr9tHFz1PSvGn2vuuCvHnl/ZkzZfm/xj5t+Jg8ffA1adJqrWxw2C7Z647z8tyxPxPEADQ4X+2jKSpKltNGl/hbUdEXJ6o5rpo0LW9deX+qF1ZnynufpNnabbLVBd9ZagjDTjeekdZbbZB/HXXDKu8BYE3a+qg9c8iNZ9Uc/8+ZtyZZSuv8d/00S3lpWlRUM9hy3bY58LoeebzHL7JgzrxlnuOjp/vW/Hti+aiMeW9Evvf6Hdn0gB1T/mz/f7cdgHqpOl+91i9a/rX+vzHt7aGZ9vbQmuOp/YZk13/dnM5nH5Lyax5c5fMC1Addj9o35934vZrjn5/5xfX10t43XbFzHnXeMdm7W9dce+I1mbfYa9GBvQfU/HvkkE8zZMBHueeV/87+xx2Qv9//5KpuAaDgTj756Nz761/UHHc78rQkS7sHVbTk+6mL6dtvQPr2+7JXvv7GW3mr33O58Htn5rLLPw9YPP3MS3L/f/9XPvt0QObPn5+BA9/LY396It/4xnZ1uSUAABqQvY7qmrNvvKDm+JYzf5ZkGbfmV/B90iPOOzp7dtsnPz3xx7Wu7ZOk4uPRufrQy9K8VfPseugeueC/LskNJ14jiAFosHY9cu+ceuN5Ncd3n3VTkqV9Jior/B7pitjrxAPyQe+BqRw/pe5OClAA+x61Xy686cKa4xvO+Pw7dSv7/ujijjn/2HQ9ct/86ISeS7we/cJBJx6ct3u/ncnjJq9i5QAA/7etcAjDhhtuWGdP2rNnz1x++eW1xh7f8rxlzIZVN2fy9CycvyDNFqVtfqFpu7LM/kry5hdmj69ccn77siycNz9Vi39ZuLo60z8ZlySZ/MHIlG3WKdtddMQSIQzNO7XLuvtsm5fOvuPrbgdgjavrPjpnyoy07tIpLTfomAMevKLm70XFn3+5uPunf8jful6ZGZ+OT5IsnLegptdOendE2u+4SbY6+5D0uep3dbRDgNVrmX20fatULaOPVk2oTNOOZbXnt2tV00eTpGr81CycvyDVC798s33a0Io0W7tNihs3ysJ5C2rGd/rZael08DfzwtE/zewx3kgHGoZhzw/I7wYOrzkuafL5W1gtOpRl5vipNePN27XKzIlL76dJMmPC1LToULunfr5mWpJkne02TvMOZTnj6Z/W/L24pFE679YlO51+UH65+Rm1eu0XZo6fmsrRE9Nmo3VWaX8AhTRv8rQsnL8gpV95jdqkfavMXcZr1FVSXZ1pg4an2cZ6JdDw9Xu+X8oHltccN170+rR1hzaZstiHf8vatV7iF4uW5shzj8qxFx6X60+9Np9+9Mly586ZPScjh3yadTdab5VqB6gv/v73/5d+/QbWHJeWNkmSrLNOh4wdO75mvGPH9hk3fuIKn7e6ujr9+w/K5pttXDP28cef5oADj8taazVLq1YtM3bs+Dz6yL35ZMRndbATAAAaoref75dhta7tGydJWndonamLXdu3aleWyhW4tv/OuUfmyAuPy42nXpuRH326xN8XzJufcZ+OTZJ8/N7wbLLD5jnkzCPywI/u/Zo7ASiMd/7VPyMGDas5/uIeflnH1pk2YWrNeMv2ZZm2An10RbTt1D5b7bV97jv/l3VyPoBC6vd835QPHFJz3Lj089ejbZa411S2Qveajj736Bx/4fHpdeqP88ky7jV16NQhO+y9Q24698avVTsAwP9lKxzCUJdKS0tTWlpaa6xxUaNClMJ/uIXzFmTSuyOyXtdtM3KxX6Zcr+u2Gfnc20tdM+Htoel80Ddrja2377aZ+O6IVM9fsNQ1SZKipNGiN+YXt/mJ+6Zq4rSMemHQKu0BoJAW76OfLdZH1+26bT5bTh9dfyl9dNKiPlo5bEyeOuDqWn/f8YfHpXGLZnnr2ocyq2LSsgsqKkpxk4K8fAFYJQvnLcjkd0dkna7bZtRifXSdrttl9DL66MS3h6bTV/roOvtul8nvfPl6dMJb5dnw6D1r/5r7Jutk1tgptQMYfn561j9k57xw3M8y87MJdb09gNVm7syqzJ1ZVWtsxvip2WjvbTPug88/yFbcuFE677Zlet/8+DLPUzFgWDbaZ9u89cCzNWMbdd0uoxf9Uvunr3+Q+w+q/dr0O7eem0nDK9Ln3qeXGsCQJE1bt0irddvWCoQAaCiq5y3I9Hc/Ttt9t8+Ef75VM9626/aZ8Fz/5axceS232SgzPhpZp+cEKISqmbMzdubsWmNTxk/ODnvvmBEffJwkKWlckm122yYP3fyH5Z7ryPOOznEXnZCfnnZ9hr83bLlzk88/zLz+Zuvnw34frPoGAOqBGTNmZsaMmbXGxowZlwO/1TWDBn3e4xo3bpyu++yenj9auQ8F77DDNnn//Y+WGJ81a3ZmzZqd1q3LcvBB++bqnj9f9Q0AANCgVc2sStXMsbXGpoyfnO323jGffDAiSdKocUm22m3bPPZvru0PP++oHH3R8bnptJ/k4/eGL3fuF4qKimqCHwAaojkzqzLhK320cvyUbLX39vnsg0+SfN5Ht9ht6/z15ofr5Dn3PH7/TJ9UmfdeHFAn5wMopNkzZ2f2V+41TR4/OTvu8418vNi9pm132zZ/uPnB5Z7r6POOyYkXn5jrelybYe8u+17TgScclMpJlXnrxbeWOQcAgOXzLUb+433w239mn19dkInvfJwJbw/LFt33T/NO7TLkoReSJN+8+oSstW6bvHbpb5IkQx56MVueeVB2ue7UlD/yUjrstFk2P2m/vHLhr2vOud1FR2TiOyMy/dNxadS4JJ2+tWM2O27vvNnzwdpPXlSUzU7smuH/82qqFyxcU1sGqFODf/vP7PWrCzJpUR/dfFEfLV/UR7+xqI++vqiPlj/0YrqceVB2vu7UDF3URzc7ab+8uqiPLpwzL1OHjKr1HHOnzUqSWuPfuPqEjH7xncysmJTGLZpmoyP3yNp7bJUXTr1lTWwboM4M+e9/Zvc7L8jkd0dkYv+h2bT7AVmrU7sM/ePnfXSHniem2Tpt0ufS+5Ikw/74QrY486B847pTM/yRl9J+582zycn75Y3v3V1zzmF//Fe2OOvg7PTTHin/3f9Ly43XyTaXHJkhDzxXM2fnG8/IhkfvmVfOvC3zZ1Sl6aJfgp83fVYWVM1bg/8DAHXjrQeezR4XdsuUT8Zl8oix2eOibplXNTcfPvlGzZzDbzsv08dOycu3/DlJ0v/3z+XUP/84u51/eIY+/3Y2P2inbLTXNnn4uJ8m+TzsYWJ57dem82bNyewpM2rGG69Vmr0vOyZD/vlWZo6fmrL1O6TrD4/PrCkzUl7HX1YGWFNG3vePbHP3RZn2zvBU9h+aTj2+ldL122f0H55Pkmx6zckpXadtPrz4y/dEW2yzYZKkUfOmadKuVVpss2Gq583PzPLRSZKNrzgulW8PzawRY1LSolk6n3NoWmy7YYb0fGDNbxBgDXj6gady7IXHZcwnFRkzoiLHXHR85lTNyStPvlIz55Lbvp9JYyfnkVv+mCQ56rxjcvIVp+b2S2/N+FHj0rpD6ySLvggy6/MQstOvOTNv/atfJlZMTFm7shx38Qlp1mKt9P7Li2t8jwCr25133Z+rr7o4Q4eNyLBhI3L1VRdn1qzZeexPT9TM+f3vfpWKijG55sc3J0l6/fiy9O07IEOHjUirVi1z0YVnZccdtskll1xTs+bgg/ZNUVFRhpQPz2abbpSbb+6V8vLhefAPyw5yBPhPNWvW7IwcVVFzPLpiXD4qH56yVi2z7jodC1gZQOH984G/58hF1/ZjR4zJURcdl7lVc/L6Ytf2F9x2aaaMnZQ/3fL5F4qPOO/oHH/FKbn70tsyYdT4lC12bT9n0bX9iVd2z6DeAzJpzMQ0a94se3TbO1vvvk1uPu2GNb5HgNXphd/9I4deeEzGfzI240eMyaEXHpO5s+ek35Ov1cw5478uytRxk/O3Wx5N8nlQw7qbr5/k8y8bt167XdbfeqPPQx4+/TLkoaioKHset3/e/MvLWegz+MB/qKceeDLHX3h8KkZUpGJERU5YdK/p5b+9XDPnstsvz6Sxk/LHX3weFHbM+cem+xXdc+slv8y4ZdxrSj7vowcef2Be/N8X9FEAgK9BCAP/8T55qm9K27TMjpcdnWYdW2fKkFH5V49fZuboz39pfa21W6fFeu1r5s/4bEL+1ePW7Hp992x5+oGZNW5K+l37x3z6zJfpbyVrlWaPm87IWuu0zYKquakcXpFXLrk3nzzVt9Zzr7fPNmmxfvsMffzlADRUX/TR7Rf10alDRuWFxfpos7Vbp/lX+uiLPW7Nztd3T5dFffSta/+Ykc+sXIpm0/atsved56dZx9aZO31Wpg7+LC+cekvGvPp+ne4PYHUb+VSfNGnTItss6qOVQ0bl5e6/zKzRE5MkTTu2zlqd2tXMn/nZhPTu/st88yfds/kZB2X2uCkZ0OuPGbVYH51VMTkvnXxzvnl9jxz6r5sya+yUDLn/2Qz+9d9r5mx+xkFJkgP/2qtWPX2+/5uM+PMrAWho+t73dBo3bZKDf3ZGmrZaKxWDhufx7r/I3Jlf3kBstV77VC+srjke/fbQPHnx3el6xfHpesVxmTJyXJ686O6MGbRiv0qUJNULFqZDl87Z9pi907RV88wYPzUj3/wwT154d63nBmhIxj/5Zhq3aZmNLz82pWu3yYyPPss7p9ycqlGfv0Zt0rF1mi72GjVJdnvxy1DEVjtumnWO3TuzR47PG7tcnCQpKVsrW956Tko7ts786bMy/b1P8vZR12fawBXvuQANyRP3/TVNmpbm3J+dn+atWmTooPLc0P26VC32K0bt1+uQhYu9Pj2kx6FpXNo4P7yvZ61zPX77Y3n8jseSJO3WaZfL7/pBWrZplWmTp6V84JBcffSVmTB6wprZGMAa9Mtb70mzZk1z9503pk2bsvTrNzCHfueUzJgxs2bOBp3Xy8KFX35IuHVZWe6955ass06HVFZOz6BB72f/A47NW/0H1cxpVdYqP//p1Vl//XUzefLU/PWJZ9Lr2l9k/vz5a3J7APXC+x8NzVkXX1VzfMtd/50kOfLQA/PzH19RqLIA6oW/3/dEmjQtzVk/Oy/NW7XI8EHlubH79ala7P5P+/U61Lr3dNCia/vL7ruq1rn+9/Y/5S93/ClJUtahdS68/ftp3bFNZk2fmZEffZqbT7sh7732zprZGMAa8tx9T6Zx0yY55adnZ62y5hkxaFh+1eNnmbNYH23bqX2qq7/so63XbpNez/yy5vjg87rl4PO6ZUifD3LbSdfXjG+593Zpt36HvP5n4bTAf66/3PuXNGlamgt+fkFatGqR8kFDcu2p12b2YveaOqzXIdWLvT96WI/D0ri0cXr+5ke1zvXo7Y/msdsfrTnece8d03H9jnn+8edX/0YAAP6DFVUvflVbQA926l7oEgAatOJCFwDQwDWuHy+LARqsTxsXFboEgAZtl6p5hS4BoMH7ddPZ/34SAMv01Ji3C10CQIM2u+LVQpcA0KCdttPlhS4BoEFrVdS40CUANHgVC91rAvg6/j7y6UKXACzDtRudWugS+D/ghk8eKXQJda5kVReOHz8+Q4YMSVFRUbbYYot07NixLusCAAAAAAAAAAAAAAAAAABgFS2MHy2FVbHSP5w+bdq09OjRI506dcq+++6brl27plOnTunevXsqKytXR40AAAAAAAAAAAAAAAAAAAAAq91KhzCcffbZ6du3b55++ulMnTo1lZWVefrpp9O/f/+cc845q6NGAAAAAAAAAAAAAAAAAAAAgNWuZGUX/OMf/8hzzz2Xvffeu2bs29/+dn7729/mkEMOqdPiAAAAAAAAAAAAAAAAAAAAANaU4pVd0K5du5SVlS0xXlZWljZt2tRJUQAAAAAAAAAAAAAAAAAAAABr2kqHMPz4xz/O5ZdfnjFjxtSMjR07NldeeWV69epVp8UBAAAAAAAAAAAAAAAAAAAArCklK7vg3nvvzbBhw7Lhhhtmgw02SJKMHDkypaWlmTBhQn7zm9/UzB0wYEDdVQoAAAAAAAAAAAAAAAAAAACwGq10CMNRRx21GsoAAAAAAAAAAAAAAAAAAAAAKKyVDmG47rrrVkcdAAAAAAAAAAAAAAAAAAAAAAVVXOgCAAAAAAAAAAAAAAAAAAAAAOqDkhWZ1LZt25SXl6d9+/Zp06ZNioqKljl38uTJdVYcAAAAAAAAAAAAAAAAAAAAwJqyQiEMt99+e1q2bJkkueOOO1ZnPQAAAAAAAAAAAAAAAAAAAAAFsUIhDKeffvpS/w0AAAAAAAAAAAAAAAAAAED9U13oAqCBWqEQhiSZNm3aCs1r1arVKhcDAAAAAAAAAAAAAAAAAAAAUCgrHMLQunXrFBUVLfPv1dXVKSoqyoIFC+qkMAAAAAAAAAAAAAAAAAAAAIA1aYVDGF566aWaf1dXV+ewww7L/fffn06dOq2WwgAAAAAAAAAAAAAAAAAAAADWpBUOYdh3331rHTdq1Ci77757NtlkkzovCgAAAAAAAAAAAAAAAAAAAGBNKy50AQAAAAAAAAAAAAAAAAAAAAD1gRAGAAAAAAAAAAAAAAAAAAAAgHzNEIaioqK6qgMAAAAAAAAAAAAAAAAAAACgoEpWdOIxxxxT67iqqirnn39+mjdvXmv8r3/9a91UBgAAAAAAAAAAAAAAAAAAALAGrXAIQ1lZWa3j7t2713kxAAAAAAAAAAAAAAAAAAAAAIWywiEMv//971dnHQAAAAAAAAAAAAAAAAAAAAAFVVzoAgAAAAAAAAAAAAAAAAAAAADqg5JCFwAAAAAAAAAAAAAAAAAAAEDdWljoAqCBKi50AQAAAAAAAAAAAAAAAAAAAAD1gRAGAAAAAAAAAAAAAAAAAAAAgAhhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIkpQUugAAAAAAAAAAAAAAAAAAAADq1sJUF7oEaJCKC10AAAAAAAAAAAAAAAAAAAAAQH0ghAEAAAAAAAAAAAAAAAAAAAAgQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIkJYUuAAAAAAAAAAAAAAAAAAAAgLpVXegCoIEqLnQBAAAAAAAAAAAAAAAAAAAAAPWBEAYAAAAAAAAAAAAAAAAAAACACGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEiSlBS6AAAAAAAAAAAAAAAAAAAAAOrWwkIXAA1UcaELAAAAAAAAAAAAAAAAAAAAAKgPhDAAAAAAAAAAAAAAAAAAAAAARAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAECSpKTQBQAAAAAAAAAAAAAAAAAAAFC3Fqa60CVAg1Rc6AIAAAAAAAAAAAAAAAAAAAAA6gMhDAAAAAAAAAAAAAAAAAAAAAARwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkCQpKXQBAAAAAAAAAAAAAAAAAAAA1K3qQhcADVRxoQsAAAAAAAAAAAAAAAAAAAAAqA+EMAAAAAAAAAAAAAAAAAAAAABECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQJKkpNAFAAAAAAAAAAAAAAAAAAAAULcWFroAaKCKC10AAAAAAAAAAAAAAAAAAAAAQH0ghAEAAAAAAAAAAAAAAAAAAAAgQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIkJYUuAAAAAAAAAAAAAAAAAAAAgLpVnepClwANUnGhCwAAAAAAAAAAAAAAAAAAAACoD4QwAAAAAAAAAAAAAAAAAAAAAEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAkqSk0AUAAAAAAAAAAAAAAAAAAABQtxYWugBooIoLXQAAAAAAAAAAAAAAAAAAAABAfSCEAQAAAAAAAAAAAAAAAAAAACBCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkiQlhS4AAAAAAAAAAAAAAAAAAACAurUw1YUuARqk4kIXAAAAAAAAAAAAAAAAAAAAAFAflBS6gC+MrTeVADRM8yRSAXwtU4oWFLoEgAatXXWjQpcA0KCNLmlc6BIAGrwd689tL4AGae319ip0CQAN2mk7XV7oEgAatD++fVuhSwBo0P5rp2sLXQJAg7duUZNClwAAANQjxYUuAAAAAAAAAAAAAAAAAAAAAKA+EMIAAAAAAAAAAAAAAAAAAAAAECEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQJCkpdAEAAAAAAAAAAAAAAAAAAADUrepCFwANVHGhCwAAAAAAAAAAAAAAAAAAAACoD4QwAAAAAAAAAAAAAAAAAAAAAEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAkqSk0AUAAAAAAAAAAAAAAAAAAABQtxamutAlQINUXOgCAAAAAAAAAAAAAAAAAAAAAOoDIQwAAAAAAAAAAAAAAAAAAAAAEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJAkKSl0AQAAAAAAAAAAAAAAAAAAANSthYUuABqo4kIXAAAAAAAAAAAAAAAAAAAAAFAfCGEAAAAAAAAAAAAAAAAAAAAAiBAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkSUmhCwAAAAAAAAAAAAAAAAAAAKBuVae60CVAg1Rc6AIAAAAAAAAAAAAAAAAAAAAA6gMhDAAAAAAAAAAAAAAAAAAAAAARwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkCQpKXQBAAAAAAAAAAAAAAAAAAAA1K2FhS4AGqjiQhcAAAAAAAAAAAAAAAAAAAAAUB8IYQAAAAAAAAAAAAAAAAAAAACIEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRJSaELAAAAAAAAAAAAAAAAAAAAoG5Vp7rQJUCDVFzoAgAAAAAAAAAAAAAAAAAAAADqAyEMAAAAAAAAAAAAAAAAAAAAABHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAFCPTJkyJT169EhZWVnKysrSo0ePTJ06dZnz582bl6uuuirbbbddmjdvnvXWWy+nnXZaKioqVvq5hTAAAAAAAAAAAAAAAAAAAAAA9cYpp5ySQYMG5dlnn82zzz6bQYMGpUePHsucP2vWrAwYMCC9evXKgAED8te//jXl5eXp1q3bSj93ydcpHAAAAAAAAAAAAAAAAAAAAKCuDB48OM8++2z69OmT3XbbLUny29/+NnvssUeGDBmSLl26LLGmrKwszz//fK2xu+66K7vuumtGjhyZDTbYYIWfXwgDAAAAAAAAAAAAAAAAAAAAsNLmzJmTOXPm1BorLS1NaWnpKp/zzTffTFlZWU0AQ5LsvvvuKSsryxtvvLHUEIalqaysTFFRUVq3br1Sz1+8UrMBAAAAAAAAAAAAAAAAAACo9xZ6eKyBx0033ZSysrJaj5tuuilfx9ixY9OxY8clxjt27JixY8eu0Dmqqqpy9dVX55RTTkmrVq1W6vmFMAAAAAAAAAAAAAAAAAAAAAArrWfPnqmsrKz16Nmz51LnXn/99SkqKlruo3///kmSoqKiJdZXV1cvdfyr5s2bl5NOOikLFy7MPffcs9J7KlnpFQAAAAAAAAAAAAAAAAAAAMD/eaWlpSktLV2huRdddFFOOumk5c7ZaKON8u6772bcuHFL/G3ChAlZe+21l7t+3rx5OeGEEzJixIi8+OKLadWq1QrVtjghDAAAAAAAAAAAAAAAAAAAAMBq1b59+7Rv3/7fzttjjz1SWVmZfv36Zdddd02S9O3bN5WVldlzzz2Xue6LAIahQ4fmpZdeSrt27VapzuJVWgUAAAAAAAAAAAAAAAAAAABQx7baaqsccsghOeecc9KnT5/06dMn55xzTg4//PB06dKlZt6WW26ZJ554Ikkyf/78HHfccenfv38eeeSRLFiwIGPHjs3YsWMzd+7clXp+IQwAAAAAAAAAAAAAAAAAAABAvfHII49ku+22y8EHH5yDDz4422+/fR566KFac4YMGZLKysokyahRo/LUU09l1KhR2XHHHbPuuuvWPN54442Veu6SOtsFAAAAAAAAAAAAAAAAAAAAwNfUtm3bPPzww8udU11dXfPvjTbaqNbx11FcJ2cBAAAAAAAAAAAAAAAAAAAAaOCEMAAAAAAAAAAAAAAAAAAAAABECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkSUoKXQAAAAAAAAAAAAAAAAAAAAB1a2F1daFLgAapuNAFAAAAAAAAAAAAAAAAAAAAANQHQhgAAAAAAAAAAAAAAAAAAAAAIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACBJUlLoAgAAAAAAAAAAAAAAAAAAAKhb1YUuABqo4kIXAAAAAAAAAAAAAAAAAAAAAFAfCGEAAAAAAAAAAAAAAAAAAAAAiBAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAA4P+zd99RWlX3GoDfGYYmShOwgREEsRIVsWIFjVjAGo1BY4xRjCXGbhI0mhhN0yQqMV6Nxq5JiL2EIKhYsCJgo4P0DoJ05v4hGRkYUIcpkDzPWrOW3569D7991107+5x95v0AAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASJIUVXcBAAAAAAAAAAAAAAAAAAAAVKzlKa7uEmCDVFjdBQAAAAAAAAAAAAAAAAAAAACsD4QwAAAAAAAAAAAAAAAAAAAAAEQIAwAAAAAAAAAAAAAAAAAAAECSdQhhmD17du64445ceeWVmTlzZpLk7bffzoQJEyqsOAAAAAAAAAAAAAAAAAAAAICqUlSeQYMHD07nzp3ToEGDjBkzJt///vfTuHHj/POf/8zYsWNzzz33VHSdAAAAAAAAAAAAAAAAAAAAAJWqsDyDLrroopx++ukZPnx46tSpU9LepUuXvPjiixVWHAAAAAAAAAAAAAAAAAAAAEBVKVcIwxtvvJGzzz57tfatttoqkydPXueiAAAAAAAAAAAAAAAAAAAAAKpauUIY6tSpk7lz567W/tFHH6Vp06brXBQAAAAAAAAAAAAAAAAAAABAVStXCEO3bt1y7bXXZsmSJUmSgoKCjBs3LldccUWOP/74Ci0QAAAAAAAAAAAAAAAAAAAAoCqUK4Tht7/9baZNm5ZmzZplwYIFOfDAA9O6detssskmue666yq6RgAAAAAAAAAAAAAAAAAAAIBKV1SeQfXr18+AAQPy/PPP5+23387y5cuz++67p3PnzhVdHwAAAAAAAAAAAAAAAAAAAECVKFcIw38ccsghOeSQQ5Iks2fProh6AAAAAAAAAAAAAAAAAAAAWEfFKa7uEmCDVFieQb/61a/y8MMPl3z+5je/mU033TRbbbVV3n333QorDgAAAAAAAAAAAAAAAAAAAKCqlCuE4c9//nNatGiRJOnTp0/69OmTZ555Jl26dMmll15aoQUCAAAAAAAAAAAAAAAAAAAAVIWi8gyaNGlSSQjDk08+mW9+85s57LDDss0222Svvfaq0AIBAAAAAAAAAAAAAAAAAAAAqkJheQY1atQoH3/8cZLk2WefTefOnZMkxcXFWbZsWcVVBwAAAAAAAAAAAAAAAAAAAFBFisoz6Ljjjsspp5ySNm3aZMaMGenSpUuSZNCgQWndunWFFggVpeOFx+XrpxycOg3qZdI7I/Ovnndn+vAJax3TtkuH7H/xCWm4dbPMHjc1L/7mbxn23Jtl9t37B0fnoMtPyht3Ppu+195X0r5Rk/o5+IqTs80Bu6RO/Y3y8cCP0ufqv2bWmCkVOj+AynbAhcdl91MOSZ0G9TLhnRF5tufdmfYF6+j2XTrkoItPTKOtm2XWuKnp95tH8tEq6+gmmzVKpytPzrYHfT0169TKjFGT88Rlt2fy0DFJkq6/PTtfP/GAUmPGvz0idx17dYXOD6A6fOPCE7L3tw7JRg02zthBI/KPnn/JlOHj19h/szbN0+WiE9N8l1Zp3LxpHr32r3nxL8+U6rNv90Oz77c7p3HzpkmSycPH519/7J0P+w+qzKkAVIn9Lzwuu63Yk05csSf9Mvf2B660J31hDXvSg1fak84cNTlPrtiTFhbVyIGXnJjWB++ahls3zaJPFmT0gKHpd8NDmTd1diXOFqDitP1O5+zY44hs1KxhZg+bkDeuvi9TX/9ojf0323v77HH1t9Nwu63y6ZTZee9PT2bYvc+X2XebrnvngD+dl3HPvpn+3/t9SXtBjcJ8/eLj0vLYfVO3acMsmDo7Ix95MYP/8FhSXFzRUwSoFgeu8sz0mS/5zPTglfanz6+yP71gwO/TsEXT1ca9cU+fPNPz7oqeAkC1O/LCE9PxW52yUYONM2bQ8DzU885MWssz0i3aNM/RF52UrXdpmU2bN8vfrr07z//l6dX6NdisUY69ont2OmjX1KpTK1NGT8p9l/0p44aOrszpAFS54y88OZ1OOSz1GtTLiHeG566ef8744R+vsf8hJx+a/Y8/OM3bbp0kGT1kZB7+9X0Z+e7wkj6dux+eQ7sfnibNmyVJxg8fl95/eCTv9n+7cicDsB56c9CQ3PXA3/P+hyMybcbM/OH6nul0wL7VXRZAtamu9/FrblQ7B11xUtoctkfqNto4c8ZPy1t3/Svv3Ne3QucHUNkOuvC4tF/pbOmpL3G2tEOXDjlkpbOlvr95JB+utI5euIazpdfv6ZOnVzpbatJ6yxx6xcn52l47pKCwINOGTcjfzv1j5kycUWHzAwD4b1NYnkE33XRTzjvvvOy4447p06dPNt544yTJpEmT8oMf/KBCC4SKsFePo9LhzC7pc9Vf89ejr8q8abNz0v1XpFa9Omscs+XurdPtlvMytPeA/KXLjzO094B0u/W8bLHrtqv13bxdq+x6ysGZ+v7Y1X53/P/9KA23bpZ/nHlT7jrip5kzYXpOvv/K1Kxbu0LnCFCZ9u1xVPY+84g8e9XdufPonpk/bU6+ff+Va11Ht9q9dY6/5fwM6T0gt3e5MkN6D8jxt56fLVdaR+vU3yin/+PqLFuyLA9+59f5U+fL0ue6+7No7qelrjWi/7u5cY8flPw8ePqvK22uAFXlkB5dc+D3jkjvq+7KTV1/nE+mzU6P+36c2mtZW2vVrZUZ46bmyV89kLlTZ5XZZ/akGXnqVw/mpq4/yU1df5Lhr7yXM26/JJu1aV5ZUwGoEvv0OCp7nXlEnrvq7tx1dM/MmzYnp3yJPelxt5yfob0H5I4uV2Zo7wE5tow96Wn/uDrLlyzLw9/5df7c+eVv81AAANVKSURBVLL8+7r7s3DFnrRm3VrZfOdtMuCP/8ydR/40fz/799m05Rb55p0XV/qcASrCNl33yh4/654hf3w8T37jp5ny+kfpdN+lqbflpmX237hF0xxy7yWZ8vpHefIbP82Qmx9Ph2tPy9ZHdFitb72tNk37q07JlNc+XO13O597VLY7tVNe/+k9eeygy/LWdQ9mp3OOzPZnHFbhcwSoDv95ZvrMVXfnjhX70+5fsD9tvnvrnHDL+Rnce0D+3OXKDO49ICfcen62Wml/ekfXnvndHj8o+bn3lF8mSd5/amClzwmgqh3Wo1s6fe/IPHzVX/Krrldm7rTZueC+n37BM9LamT5uSh791QOZs4ZnpBvVr5dL//HzLFu6NLec/stcc+hF+ccv7smnq5w/AWzoju5xbI44s2vuuur2/OToSzN72qz8+P5rUmct6+gO++ycVx5/Kb84uWeuPvbyzJg4LVfe+7M02qxxSZ+Zk2bkwV/dm58cfUl+cvQlee+VIbnk/65M8zYtqmJaAOuVBQsWpm3rVvnxRd6NBqjO9/E7XdU9rQ78ep688E+5o9NleeOOZ3PoNaelzaG7V+gcASrTfj2Oyj5nHpGnr7o7/7fibOm0L3G2dOIt5+fd3gNyW5cr827vATlxlbOl27v2zG/3+EHJzz1lnC012rpZzvj7VZk+clLuPvkXue3wK/PCzf/M0kVLKm/CAAD/BcoVwlCzZs1ccskl+cMf/pDddtutpP3CCy/MmWeeWWHFQUXp8L3D88otj2XYs29m+rDxeeriP6dmnVrZsduaE4k7nHF4Rg8Ymtd6PZGZIyfltV5PZOzL76fDGYeX6ldzo9rp+odz8szld2bhnNIvbTRquXm22r1NnvvJXZk8eFRmjpqUf/30rtSqVzs7dNunUuYKUBn2/N7hGXDLo/nw2Tczbdj4PHbxbalZp1Z2Xss6utcZXTJqwNC83OvxzBg5KS/3ejyjX34ve620ju57ztGZO2lGnrj09kx8d1TmjJ+eMS+/l1njppa61rJFSzJ/2pySn4Vz5lfaXAGqygFndMm/b300Q557I5OHjc8DF/dKrbq1s3u3/dY45uPBo/LE9fdn0BOvZunipWX2eb/v2/mg/6BMGz0p00ZPyjO/fTiLP12YbXZrU1lTAagSe37v8Lx8y6P5aMWe9IkVe9Kd1rIn3fOMLhk9YGheWbEnfaXX4xnz8nvZc6U96T4r9qRPrrInnb1iT7rokwV5sPsN+eCpgZk5alImvjMiz13912zRrlXqr+EPmAHWJzt8v0tGPNQ/Ix7snzkjJubNq+/L/Ikzst1pncrsv92ph2T+hBl58+r7MmfExIx4sH9GPPxCdupxRKl+BYUF2f+WH+Td3/4jn6xyH58kTdu3ycfPvZUJfQdl/vjpGffUG5n4wpBs+vWWlTJPgKq21/cOz0uV8Mz005mflHoW2qbTbpk5ZnLGvvZBVUwLoEodcsYRefbWf2bQc69n4rCP89eLb02turXToVvHNY4ZO3hkel9/X9584pUsXVz2C8KHndMtsybOyL2X/ilj3x2ZmeOn5aNXhmb6uCmVNRWAatHle0fn0Vv+ljeefS3jh43Lny7+Q2rVqZ39uh2wxjG3/vCm9Ln3mYx9f3QmjpyQ2y/vlYLCguy8X7uSPm/3fSOD+r2VyaMnZvLoiXnkN/dn4acL03r3tlUxLYD1yv77dMgFZ30nhx605nN8gP8V1fU+fvLZFzAM+cdLGffaB5kzfnrefbBfpn4wLpu3a1Xh8wSoLHt/7/C8eMuj+eDZNzN12Pj8c8XZ0i5rWUf3PqNLRg4YmgG9Hs/0kZMyYMXZ0t6rnC3Nmzan5Ge7FWdLY1Y6W+p06TczvN+76XP9g5n83tjM+nhahj8/KPNnzK3UOQMAbOjKFcJwzz33rPUH1icNWjTNxs0aZsxLQ0rali1emo8Hfpit2q/5D9G23L11xrw4pFTb6BcHrzbmsJ+fnpHPD8rYl99b7RpFtYqSpFQ6XPHy4ixbsiwt9tiuXPMBqGoNWzTNJs0aZdQq6+jYgR+m+VrW0ea7t86oFweXahv14uA0b//5+rfdoe0zcfDoHN/rglz0Vq98/+nrstvJB692ra/tvUMueqtXftDvtznyhjOz0ab1K2BmANWncYtmqd+sUT566fN1ctnipRk58INs077i9okFhQXZ9eh9Uqtu7Yx5e1iFXRegqjVs0TQbl7EnHfcFe9KtvsSetM2h7TNp8Ogc1+uCXPhWr3zv6euyaxl70pXV3qRuipcvz0LfoAms5wpr1sim7Vpm4gtDS7VPemFomu5R9vrZtH2bTFql/8T+Q7Jpu5YpKKpR0tbuR8dm4YxPMuKhF8q8ztTXh2WLjjtlk1abJ0ka7bh1mu3ZNhP6vrsuUwJYL6ztmWmLL3hmOnKV/enIVfanKyusWSPtju2YQY+UvdYCbMiatGiWBs0a5f2XPt8fLl28NMMHvp9t26/bH/m267xHxg4ZlTNv/VF+/eb/5cdP/Sr7nVx2CBnAhqpZi83SqFnjDHlpUEnb0sVL88HAodmu/fZf+jq169ZKUc0amTd7Xpm/LygszD5Hd0ztunUy/O0P17VsAAA2UNX5Pn6SjH9jWNp03j0bb9YoSbL1PjukUcvNM/qFwWX2B1jfNFpxtjRylXV0zBecLbUo42xpxIuD02INZ0s1VpwtvbPS2VJBQUHaHLJrZoyelO73XJ5L3+qVMx+9Jtsf1n4dZwUA8N+vqDyDfvjDH5b6vGTJknz66aepVatWNtpoo5x22mlrHb9o0aIsWrSoVNvS4mUpKqixhhFQfhs3a5gkmT9tTqn2+dPnpP5WTdY8rmnDzJ+++ph6TRuUfN7h6L2z2c7b5K9dryrzGjNGTsqcj6flwMtPyrNX3pklCxZlzzOPyMbNGqbeiroA1nf/WUfnlbGONvjCdbR0Oub86XOz8UrraKMWTbNH90557Y5n8vKtj2XLr2+bb1xzWpYtXpLBvQckSUb0fzfvPz0wc8ZPT8MWTXPQxSfm1Ad/nDuO+mmWreFb4AHWd/WbNkySfLLK2vrJtDlp1HzNa+uXtUXbFrmg989TVLtmFn+6MHed/btMGTFhna8LUF3qrdO9/ep70nqr7Enbd++UgSvtSQ9bsScdsmJPurIatWvmkCtOztDHXsnieQvWYVYAla92401SWFQjC1d5zrlg+pxsuYbnk3WbNciCVfovnD4nhTWLUqfxJlkwdXaa7tEmrb91UJ489Mdr/LeH3vpEam5SN8e88OsUL1ueghqFeedXf8uYx15d53kBVLc1PTOdN31OGq7jM9OVbX/YHqlTf6MM+tuL61YwwHpoTc9I506bk03X8Rlpk62b5YDuh6bvHU/l2V7/zDZfb51v/uy7Wbp4SQb2tqYC/x0arNiTzpk2u1T7nOlz0mSrpl/6Ot+64rTMnDwzQ18uHZrYou3Xcu0/b0jN2rWycP7C3Hj2DZkwfPy6lg0AwAaqOt/HT5I+P7snXW44M+e9fnOWLVma4uXFeebyOzL+TV9KA2wY1raOruv7+Csr62ypXpP6qb1x3XQ85+g8/9u/5d83PJTWB7bLSX++MHeffF3GDhS6CACwJuUKYZg1a9ZqbcOHD88555yTSy+99AvHX3/99bnmmmtKtXWqv0s6N2xXnnKglB2P2TeH//KMks9/++5vkyTFq3YsKCijsbTiVX9fUFDSuMkWjdP56lPz8Km/yrJFS8ocv3zpsvTu8Ycc8evv50dDbs/ypcsyZsB7Gdlv0JefEEAV2/mYfXPkL79X8vnB7/6m7I4FBSlebaEsbbXfF6TU4lpQWJiJQ0al328eSZJMfm9smm7XPO1P7VwSwvD+k6+V9J82bHwmDRmdC17+Q9ocsms+fPbNrzAzgOqze7f9cuIvv1/y+Y4zfpVk9XWyYJV1srymjpqY3x1xeerUr5evd9kz3/rdD3LrSdcIYgA2GDsds2+OWGlP+vBa9qRftG5+mT3ppCGj0n/FnnTKij3p7qd2Xi2EobCoRo69+bwUFBbk2Z/e/aXnA1DdvvK+cw2/Ky4uTlG9Oul48zl59dI7smhW2d+SmSTbdN07rY7fLy+d2yuzh41P452+lg7XdM+nU2Zn1N9eKs80AKrNzsfsm6O+xDPTggp4Zrqy3U46KCP6v5t5U2d/lXIB1ksdunXMKb88q+RzrzOuT1LWXrVgnR+RFhQUZuyQkXnsNw8mSca/NyZbtmmRA7ofJoQB2GDtd8wBOfOX55R8/vV3f5Fk9VefvspZ09FnH5t9u+6fn5/00yxZ5d2niaMm5IouP0q9+vWyZ5d9cs7vLsi1J/1EEAMAwP+I9el9/CTZ47vfyJa7tc7fz/hd5kyYnhZ7bZ/DfnF65k2dnbEvv/clZwVQdXY5Zt8cvdLZ0v0rzpbKXkcr9mxpeP9388lKZ0sFBQVJko/6vJ3X7nw2STL5/bFp0b5N9vh2JyEMAABrUa4QhrK0adMmN9xwQ7p3754PP1z7BuzKK6/MRRddVKrtjzufXVGl8D9uRJ+385d3RpZ8Lqr12f+bb9y0QeavdCNRb9P6qyVrrmzetNmrpcN9NuazFLnNd2mZek0b5PQnf17y+8KiGmmxV9u0/86h+U2b01O8vDhTho7JXUf8JLU3qZvCmkVZMPOTnPbozzJpyOiKmC5AhRvW5+1MWMM6Oq8C1tF5K6VxfjJ1dqYPL/0HwdNHTMj2XTqs+bpTZ2f2hOlpvM3mX2o+AOuD9/79VsYNGlHyuUatmkmS+s0a5pOVvqFo4yYN8sla1tYva9mSZZk+dkqSZPyQUWnRbtsccEaX/O3Hd6zztQGqwvA+b+eOlfakNVbsSetV0J505YT4eV9yT1pYVCPH3Xp+GrZomvu/9cssnrfgK88LoKotmvlJli9dlrorvmX4P+ps2iALppW9fi6YOmf1/k0aZPmSpVk0a14att0qm2zdLIfcfXHJ7wsKP3tpo/vYv+bRAy7NvLFT077ntzL0licy5vHPwhVnfzg+9Zo3yS7nHS2EAdjgDOvzdv5cRc9M/6PBVk3SsuPOeeTs369b8QDricH/fjNjBg0v+Vy00jPSuSs9I92kSf11fkY6Z+qsTF7lj4Qnjxyf3brstU7XBahOb/V5PSPe+fxbfmuuWEcbNm2Y2VM//0Kl+ps2yJzps7/wekee1S3dzj0hv/z2VRn34djVfr9sydJMGTs5STJqyMi0+nqbHP7do3Pnj/+0jjMBAGBDsD69j1+jZlEOvPSb6X327zPy+UFJkmkffpzNdvxa9jrrSCEMwHrpo1Xex6+xlrOleRV4ttSq4855eJWzpU9nfZJlS5Zm2irvR00bMTFbd2j7ZacEAPA/qcJCGJKkRo0amThx4hf2q127dmrXrl26kIIaFVkK/8MWz1+YxfMXlmqbN3V2tum4c6a899mhYWHNGmmx1/bpf8PDa7zOxLdHZJv9d84bK5LekmSbA3bJhLc+ezFk7Mvv5Y5Dryg15sjfnpUZIyfmtT89meLlpZPlFn3y2R9nNNpms2zerlVe/N3fyz9JgEpU1jr6ydRZadlxl0xeaR392l7bp+8ND63xOuPfHpGW+++SgSuto60OaJfxb33+Ysj4t4Zl01ZblBrXuOUWmTNh+hqvW7fhxmmwRWPf/gZsUBbNX5hFq6ytc6fOynYdd8mE98YkSWrUrJFt99ohT97wQMUXUFBQEvwAsCEo+97+sz3pyvf2W++1fZ5fy550woo96etr2ZN+/NawNP6CPel/Ahgatdw89598XRbMXvM3vwOsT5YvWZYZg0dnywN2zsfPvlnSvsUBO+fj594qc8y0t4an+aG7l2rb8sCdM2Pw6BQvXZY5Iybl8UNKPxfd9bITUnPjunnjqnvz6cQZSZKiurVW+0aO4mXLSwIbADYka3pm2qqMZ6b//oJnpq1WeWa67Sr70//Y9cQDMn/GnAx//p0KmgVA9Vo0f2GmrbKWzpk6Kzt0bJfxKz0jbbPXjvnnDfev07816q2PslmrLUu1NWu5ZWZMmLZO1wWoTgvnL8zC+ZNLtc2aOjO7dNw1Y9777ItgatQsyg577ZwHb/jrWq911NnH5NjzTsz1p12TUUNGrrXvfxQUFJQEPwAA8N9vfXofv7BmUWrUKkrx8uWl+i137gSsxxbPX5iZZZwtbbvS2VKNmjWyzV7bp89azpY+fntEtt1/l7y2ytnSx2WcLe22hrOlZUuWZeLgUau9s79py83X+s4+AP9dln9xF6AM5QphePzxx0t9Li4uzqRJk3LLLbdkv/32q5DCoCK9ceez2efcrpk1Zkpmjp6cfc7rmiULF+f9x14p6XPUjWfnk8mz8sKvH0mSvHnXc/n2Iz/NXj2OyvA+b6XNoe2zzX475b4TPkvaXDx/YaYPK/3tGUs+XZQFs+aVam97xJ5ZMPOTzJkwPc22b5HOV5+a4f96M2NeGloFMweoGK/f+Ww6nts1M8dMzszRk9PxvG5ZsnBxhq60jna7sUc+mTwrz//6swfqr9/1bL7zSM/s2+OofNTnrbQ9tH1a7rdT7j7h2pIxr93xTL7b++rsd27XvP/kwGy167bZ/ZSD89SVdyZJam5UOwf+6Ph88MzrmTd1dho2b5qDL/tmPp01Lx8+92YANmQv/uWZdD73mEwfMznTRk9K53OPzeIFi/L2Yy+X9PnW736QuVNm5qlff/aQvUbNGtmsTfOS/26wWeNsuePXPtubjp2SJDni0pPzQf9BmT1pRurUq5Ndj943rffeMbd/5/qqnyRABXr9zmez37ldM2vFnnTfFXvS91bakx69Yk/af6U96WmP9Mw+PY7KsD5vZbsV9/b3rLQnff2OZ/Kd3ldn33O75oMnB2bLXbfNbqccnKdX7EkLahTm+D/9MJvvvE0ePuO3KahRmHorEuYXzJ6X5UuWVeH/FQC+ug/+75ns94dzMuPdUZn21oi06X5w6m21aYbd2zdJstsV38xGWzTKyz/8c5Jk2L3Pp+13D80eV387w+/vl6btW6f1yQflpXNvTZIsX7Qksz8q/Vx08dxPk6RU+8d93skuF3TL/AkzMvuj8Wm88zbZ8awuGfHQC1UxbYBKN3DFM9MZX+GZ6cC7ns3pX/DMNElSUJCvn3hgBv/9pRQv8yoE8N/r+b88ncPPPTZTx0zKtNGTc/iKZ6RvPDagpM93fnduZk+Zmcd+/WCSz56LblHyjLQoDTdrnOY7fu2zkIcVz0j73vlULv3Hz3P4D47NW0+9km2+3jodv9Up9195e9VPEqASPXPnE+l27gmZNGZiJo+elGPOOyGLFy7Ky4+9WNLnnBt/mFmTZ+ShX9+XJDn67GNz4sWn5JYf3php46emQdOGST4LeVj06Wd/HHLSpd0zqP/bmTFpeurWq5t9unbMjnvvlBtOu3a1GgD+23366YKMG//5l9NNmDglHw4bmQb1N8kWmzerxsoAql51vY+/eN6CjHv1gxz8429l6cIlmTNherbea/vsfHzHPP/zdQtyBKhKr935bPZf6Wxp/xVnS0NWWkePvbFH5k6elb4rnS1995Ge2W+ls6VW++2Uv6xytlRQUJBdTzww7/79pSwv42zp5T8/lRNvOT9jB36YMa++n9YHtUvbzrvn7pN+UbmTBgDYwJUrhOGYY44p9bmgoCBNmzbNIYcckt/97ncVURdUqIG3PZmadWrlsF+cnjr1N8rEQSPzcPdflUrorL9lkxQv//yb2Sa8NTyPnX9LDrj4xBxw8QmZNW5KHjvvlkwa9OUS4P9j42YN06nnt1OvSYPMmzo7Q3sPyMt//GeFzQ2gKrxy25MpqlMrXX5xeurWr5cJg0bm/u43rLKOblpqHR3/1vD0Pv+WHHTxiTno4hMza9yU9D7v5kxcaR2dNHhU/nbW73PI5SflgAuOzezx0/Kva+7L0Ec/e5hUvGx5mrVtkXbHdUyd+vXyydTZGfvq++l97s2rpSwDbGiev+3x1KxTK8f//IzUbVAv4waNyJ9P/WUWrbS+NdqqSalvD66/WeNc8vSvSj4ffPbROfjsozPitffT6+TPHqpv0qRBvn3TuanftGEWfPJpJn04Lrd/5/oMGzCk6iYHUAleXbEnPfwXp6fOij3pg6vsSRussied8Nbw/PP8W3LgxSfmwBV70n+WsSf9+1m/z8GXn5T9V+xJ+1xzX95bsSetv0XjbHdY+yTJ958tHWhz70m/yLjXPqjMaQOsszGPD0ztRpuk3Y+OTd1mDTP7o/Hpe+pvMn/CjCRJ3c0apt6WTUr6z/t4Wp4/9bfZ42fd0/Y7nfPplFl546p7Mu7pN77Sv/v6T+/JrpedkL1+eXrqbFo/C6bMyrD7ns/gmzwbBf47vLLi7OmIlZ6Z3vcF+9Pxbw3PP86/JQdffGIOvvjEzBw3Jf847+ZMWOXsqVXHndOweZO884jgGuC/279ueyw169TKt35+ZjZqUC+jB43IzadeV+oZaeNVnpE22KxxfvL0b0o+H3p21xx6dtcMe+293HTyNUmSsYNH5razf5tjLjslR/zw+Ez/eGr+du1fS4U7APw3eOK2f6ZWndo54xdnp179jTNy0LD8svvPsnCldbTJlk1L7UkPPbVLataumR/ddnmpa/39pofyj99/FgreoGnDnHvThWnYrFE+/WR+xn04Njecdm2GDHi3aiYGsB4Z+uHwnHH+52vmr2/+LNirW5fOue6nF1dXWQDVojrfx3/s/Fty4GUn5eg/nJM6DTfO3PHT8+Jv/pZ37utbYfMDqGwvr1hHj1xxtjR+0Mjc+wVnSx+/NTx/P/+WHHLxiTlkxdnS38txtvThc2/myZ/8JR1/0DVdrjktM0ZOysM9/pBxbw6rnMkCAPyXKChe+bS6Gt3wte7VXQLABm1J1ovlHGCDNavAt1gDrItNi2tUdwkAG7QWSwuquwSADd6YIs9IAdbFpIIl1V0CwAZtdvHi6i4BYIN2z1s3VncJABu037W/qrpLANjgLfQ+PsA6+dnY+6u7BGANTvraMdVdAv8DHh77aHWXUOEK1/UCxcXFWU9yHAAAAAAAAAAAAAAAAAAAAADKrdwhDPfcc0922WWX1K1bN3Xr1k27du1y7733VmRtAAAAAAAAAAAAAAAAAAAAAFWmqDyDbrzxxvTs2TPnnXde9ttvvxQXF+fll19Ojx49Mn369PzoRz+q6DoBAAAAAAAAAAAAAAAAAAAAKlW5Qhhuvvnm/OlPf8ppp51W0tatW7fstNNO+dnPfiaEAQAAAAAAAAAAAAAAAAAAANjgFJZn0KRJk7Lvvvuu1r7vvvtm0qRJ61wUAAAAAAAAAAAAAAAAAAAAQFUrVwhD69at88gjj6zW/vDDD6dNmzbrXBQAAAAAAAAAAAAAAAAAAABAVSsqz6BrrrkmJ510Ul588cXst99+KSgoyIABA9K3b98ywxkAAAAAAAAAAAAAAAAAAAAA1neF5Rl0/PHHZ+DAgWnSpEkeffTR9O7dO02aNMnrr7+eY489tqJrBAAAAAAAAAAAAAAAAAAAAKh0RV+l89y5c0v+u02bNunVq1eZferXr7/ulQEAAAAAAAAAAAAAAAAAAFAuy1Nc3SXABukrhTA0bNgwBQUFX9hv2bJl5S4IAAAAAAAAAAAAAAAAAAAAoDp8pRCGfv36lfx3cXFxjjjiiNxxxx3ZaqutKrwwAAAAAAAAAAAAAAAAAAAAgKr0lUIYDjzwwFKfa9Sokb333jutWrWq0KIAAAAAAAAAAAAAAAAAAAAAqlphdRcAAAAAAAAAAAAAAAAAAAAAsD4QwgAAAAAAAAAAAAAAAAAAAACQCghhKCgoqIg6AAAAAAAAAAAAAAAAAAAAAKpV0VfpfNxxx5X6vHDhwvTo0SP16tUr1d67d+91rwwAAAAAAAAAAAAAAAAAAACgCn2lEIYGDRqU+ty9e/cKLQYAAAAAAAAAAAAAAAAAAACgunylEIa77rqrsuoAAAAAAAAAAAAAAAAAAAAAqFaF1V0AAAAAAAAAAAAAAAAAAAAAwPpACAMAAAAAAAAAAAAAAAAAAABAhDAAAAAAAAAAAAAAAAAAAAAAJEmKqrsAAAAAAAAAAAAAAAAAAAAAKlZxiqu7BNggFVZ3AQAAAAAAAAAAAAAAAAAAAADrAyEMAAAAAAAAAAAAAAAAAAAAABHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQJCmq7gIAAAAAAAAAAAAAAAAAAACoWMuruwDYQBVWdwEAAAAAAAAAAAAAAAAAAAAA6wMhDAAAAAAAAAAAAAAAAAAAAAARwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkCQpqu4CAAAAAAAAAAAAAAAAAAAAqFjFxcXVXQJskAqruwAAAAAAAAAAAAAAAAAAAACA9YEQBgAAAAAAAAAAAAAAAAAAAIAIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASJIUVXcBAAAAAAAAAAAAAAAAAAAAVKzlKa7uEmCDVFjdBQAAAAAAAAAAAAAAAAAAAACsD4QwAAAAAAAAAAAAAAAAAAAAAEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAkqSougsAAAAAAAAAAAAAAAAAAACgYi2v7gJgA1VY3QUAAAAAAAAAAAAAAAAAAAAArA+EMAAAAAAAAAAAAAAAAAAAAABECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQJKkqLoLAAAAAAAAAAAAAAAAAAAAoGIVp7i6S4ANUmF1FwAAAAAAAAAAAAAAAAAAAACwPhDCAAAAAAAAAAAAAAAAAAAAABAhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASZKi6i4AAAAAAAAAAAAAAAAAAACAirU8xdVdAmyQCqu7AAAAAAAAAAAAAAAAAAAAAID1gRAGAAAAAAAAAAAAAAAAAAAAgAhhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIkhRVdwEAAAAAAAAAAAAAAAAAAABUrOLi4uouATZIhdVdAAAAAAAAAAAAAAAAAAAAAMD6QAgDAAAAAAAAAAAAAAAAAAAAQIQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkSYqquwAAAAAAAAAAAAAAAAAAAAAq1vLqLgA2UIXVXQAAAAAAAAAAAAAAAAAAAADA+kAIAwAAAAAAAAAAAAAAAAAAAECEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJEmKqrsAAAAAAAAAAAAAAAAAAAAAKlZxiqu7BNggFVZ3AQAAAAAAAAAAAAAAAAAAAADrAyEMAAAAAAAAAAAAAAAAAAAAABHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAkqSougv4j82XVncFABu22sXVXQHAhq3ucvlkAOuiZvHy6i4BYIP2dh37UYB1VTMF1V0CwAatXmpUdwkAG7TlBTWruwSADdrv2l9V3SUAbNAufuva6i4BYIP36/Y9q7sEAABgPeLNXgAAAAAAAAAAAAAAAAAAAIAIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEiSFFV3AQAAAAAAAAAAAAAAAAAAAFSs5Smu7hJgg1RY3QUAAAAAAAAAAAAAAAAAAAAArA+EMAAAAAAAAAAAAAAAAAAAAABECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQJKkqLoLAAAAAAAAAAAAAAAAAAAAoGIVFxdXdwmwQSqs7gIAAAAAAAAAAAAAAAAAAAAA1gdCGAAAAAAAAAAAAAAAAAAAAAAihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIElSVN0FAAAAAAAAAAAAAAAAAAAAULGWp7i6S4ANUmF1FwAAAAAAAAAAAAAAAAAAAACwPhDCAAAAAAAAAAAAAAAAAAAAABAhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASZKi6i4AAAAAAAAAAAAAAAAAAACAilWc4uouATZIhdVdAAAAAAAAAAAAAAAAAAAAAMD6QAgDAAAAAAAAAAAAAAAAAAAAQIQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkSYqquwAAAAAAAAAAAAAAAAAAAAAq1vLi4uouATZIhdVdAAAAAAAAAAAAAAAAAAAAAMD6QAgDAAAAAAAAAAAAAAAAAAAAQIQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkSYqquwAAAAAAAAAAAAAAAAAAAAAqVnF1FwAbqMLqLgAAAAAAAAAAAAAAAAAAAABgfSCEAQAAAAAAAAAAAAAAAAAAACBCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkiRF1V0AAAAAAAAAAAAAAAAAAAAAFWt5iqu7BNggFVZ3AQAAAAAAAAAAAAAAAAAAAADrAyEMAAAAAAAAAAAAAAAAAAAAABHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkCQpKs+gxx9/vMz2goKC1KlTJ61bt07Lli3XqTAAAAAAAAAAAAAAAAAAAACAqlSuEIZjjjkmBQUFKS4uLtX+n7aCgoJ07Ngxjz76aBo1alQhhQIAAAAAAAAAAAAAAAAAAABUpsLyDOrTp086dOiQPn36ZM6cOZkzZ0769OmTPffcM08++WRefPHFzJgxI5dccklF1wsAAAAAAAAAAAAAAAAAAABQKYrKM+iHP/xhbr/99uy7774lbZ06dUqdOnVy1lln5b333svvf//7nHHGGRVWKAAAAAAAAAAAAAAAAAAAAEBlKlcIw8iRI1O/fv3V2uvXr59Ro0YlSdq0aZPp06evW3UAAAAAAAAAAAAAAAAAAAB8ZctTXN0lwAapsDyD2rdvn0svvTTTpk0raZs2bVouu+yydOjQIUkyfPjwNG/evGKqBAAAAAAAAAAAAAAAAAAAAKhkReUZdOedd6Zbt25p3rx5WrRokYKCgowbNy6tWrXKY489liSZN29eevbsWaHFAgAAAAAAAAAAAAAAAAAAAFSWcoUwtG3bNh988EGee+65DBs2LMXFxdl+++1z6KGHprCwMElyzDHHVGSdAAAAAAAAAAAAAAAAAAAAAJWqXCEMSVJQUJDDDz88hx9+eEXWAwAAAAAAAAAAAAAAAAAAAFAtyh3C0Ldv3/Tt2zdTp07N8uXLS/3uL3/5yzoXBgAAAAAAAAAAAAAAAAAAAFCVyhXCcM011+Taa6/NHnvskS222CIFBQUVXRcAAAAAAAAAAAAAAAAAAABAlSpXCMNtt92Wu+++O6eeempF1wMAAAAAAAAAAAAAAAAAAABQLQrLM2jx4sXZd999K7oWAAAAAAAAAAAAAAAAAAAAgGpTrhCGM888Mw888EBF1wIAAAAAAAAAAAAAAAAAAABQbYrKM2jhwoW5/fbb8+9//zvt2rVLzZo1S/3+xhtvrJDiAAAAAAAAAAAAAAAAAAAAAKpKuUIYBg8enF133TVJMnTo0FK/KygoWOeiAAAAAAAAAAAAAAAAAAAAAKpauUIY+vXrV9F1AAAAAAAAAAAAAAAAAAAAUEGKi4uruwTYIBVWdwEAAAAAAAAAAAAAAAAAAAAA64OiL9vxuOOOy91335369evnuOOOW2vf3r17r3NhAAAAAAAAAAAAAAAAAAAAAFXpS4cwNGjQIAUFBSX/DQAAAAAAAAAAAAAAAAAAAPDf5EuHMNx1111l/jcAAAAAAAAAAAAAAAAAAADAf4PC8gy677771vi7Sy+9tNzFAAAAAAAAAAAAAAAAAAAAAFSXcoUwnHfeeXnyySdXa//Rj3601oAGAAAAAAAAAAAAAAAAAAAAgPVVuUIYHnrooXTv3j0vvvhiSdv555+fRx55JP369auw4gAAAAAAAAAAAAAAAAAAAACqSrlCGA4//PDcdtttOeaYY/Lmm2/mBz/4QXr37p1+/fpl++23r+gaAQAAAAAAAAAAAAAAAAAAACpdUXkHnnzyyZk1a1Y6duyYpk2b5oUXXkjr1q0rsjYAAAAAAAAAAAAAAAAAAACAKvOlQxguuuiiMtubNWuW3XbbLb169Sppu/HGG9e9MgAAAAAAAAAAAAAAAAAAAIAq9KVDGN55550y27fddtvMnTu35PcFBQUVUxkAAAAAAAAAAAAAAAAAAABAFfrSIQz9+vWrzDoAAAAAAAAAAAAAAAAAAACoIMtTXN0lwAapsDyD5syZk5kzZ67WPnPmzMydO3ediwIAAAAAAAAAAAAAAAAAAACoauUKYTj55JPz0EMPrdb+yCOP5OSTT17nogAAAAAAAAAAAAAAAAAAAACqWrlCGAYOHJiDDz54tfaDDjooAwcOXOeiAAAAAAAAAAAAAAAAAAAAAKpauUIYFi1alKVLl67WvmTJkixYsGCdiwIAAAAAAAAAAAAAAAAAAACoauUKYejQoUNuv/321dpvu+22tG/ffp2LAgAAAAAAAAAAAAAAAAAAAKhqReUZdN1116Vz5855991306lTpyRJ375988Ybb+Rf//pXhRYIAAAAAAAAAAAAAAAAAAAAUBUKyzNov/32y6uvvprmzZvnkUceyRNPPJHWrVtn8ODB2X///Su6RgAAAAAAAAAAAAAAAAAAAIBKV1TegbvuumseeOCBiqwFAAAAAAAAAAAAAAAAAAAAoNqUO4Rh2bJlefTRR/PBBx+koKAgO+64Y7p27ZoaNWpUZH0AAAAAAAAAAAAAAAAAAAAAVaJcIQwjRozIkUcemfHjx6dt27YpLi7OsGHD0qJFizz11FPZdtttK7pOAAAAAAAAAAAAAAAAAAAAgEpVWJ5BF1xwQVq1apWPP/44b7/9dt55552MGzcuLVu2zAUXXFDRNQIAAAAAAAAAAAAAAAAAAABUuqLyDHrhhRfy2muvpXHjxiVtm266aW644Ybst99+FVYcAAAAAAAAAAAAAAAAAAAAX11xiqu7BNggFZZnUO3atfPJJ5+s1j5v3rzUqlVrnYsCAAAAAAAAAAAAAAAAAAAAqGrlCmE46qijctZZZ2XgwIEpLi5OcXFxXnvttfTo0SNdu3at6BoBAAAAAAAAAAAAAAAAAAAAKl25Qhj++Mc/Ztttt80+++yTOnXqpE6dOtlvv/3SunXr/P73v6/gEgEAAAAAAAAAAAAAAAAAAAAqX1F5BjVs2DCPPfZYRowYkQ8++CDFxcXZcccd07p164quDwAAAAAAAAAAAAAAAAAAAKBKlCuE4T9at25dKnjh3Xffze67755ly5atc2EAAAAAAAAAAAAAAAAAAAAAVamwoi9YXFxc0ZcEAAAAAAAAAAAAAAAAAAAAqHQVHsJQUFBQ0ZcEAAAAAAAAAAAAAAAAAAAAqHRF1V0AVIW23+mcnXsckY2aNcysYRPy+tX3ZerrH62x/2Z7b58OV387jbbbKp9OmZ2hf3oyH937fMnvt+6yR9qd3zX1t9ksBTVr5JPRUzL0z09n1D9eLulzwms3ZeMWTVe79gd398nAn/y1YicIUMlaf6dzdjjnyNRt1jBzhk3I21fdm2lrWUeb7r19dv9Z9zTYbqssmDI7H/R6MiPu7VuqT836G6XdFd9Miy57pFaDepn38bS8c839mfT8u0mSnS8+LrtcfHypMQumzs6ju55b8RMEqGQtT++c1j84KnWaNcwnH03IkKvuyYyBa15HN91n++zys1OzSdutsnDK7Ay/9YmMuefzdbSgqEa2u6Brtv7mAamzeaPMGzkp7/3iwUztN7jM67U5v2t2+snJGXn7Mxly1b0VPj+A6vC10w9Nq3OPSu1mDTPvo/F5r+c9mbWGtbV2s4bZ4ZruadCuZeq12jxj7ngu7/e8p1Sfjds2z3aXnZAG7Vplo62b5r2e92TM7c9UxVQAqswBFx6X3U85JHUa1MuEd0bk2Z53Z9rwCWsds32XDjno4hPTaOtmmTVuavr95pF89NybpfpsslmjdLry5Gx70NdTs06tzBg1OU9cdnsmDx1T0qdJ6y3T6YqTs/VeO6SgsCDThk3IP879Y+ZOnFEZUwWoFB0vPC67nnJw6jSol4nvjMy/et6d6V+wjrbt0iEHXHxCGm7dLLPHTc0Lv/lbhq20jna88Ljs/6PjSo2ZN3V2bu5wXqk+Ox69dzbZsnGWLVmWyUNG58Xf/C0TB42s2AkCVIPDLjw+e32rUzZqUC/jBo1I7553Zcrw8Wvsv1mb5vnGRSek+S6t0rh50zx27T156S+l79/36d45+3z70DRu3iRJMnn4+Pz7j73zYf93K3UuANXhqAtPzP7f6pyNGmyc0YOG58Ged2TSWtbRLdo0T9eLTsrWu7RKk+bN8si1d6XvX54u1ee6AbemSfNmq43tf8+zefCqOyt8DgBVqeOFx+XrK+7tJ32Fe/v9V7q3f3GVe/uV7f2Do3PQ5SfljTufTd9r7ytpr7lR7Rx0xUlpc9geqdto48wZPy1v3fWvvHNf3zKvA/Df5M1BQ3LXA3/P+x+OyLQZM/OH63um0wH7VndZANVi/wuPy24rzuwnrjiz/zL70QNXOrN/YZUz+/0vPC4H/Kj0O/fzps7OHzp8/s59zY1q55ArTs52K+1H37jrubxtPwoAsFZfKYRh7ty5a/39J598sk7FQGXYpute2fNn3fPaj+/O1DeGpe2ph+TQ+y7NowddnvllvOC7cYum6XzvJRn+QP+8dP6f0qzDdtn7l6dn4YxPMvbpN5Iki2fPz+A/Pp45IyZm+ZKlad55t3S88awsnD43E18YkiR54oirUlijsOS6Dbdvnm88dGXGPvl61UwcoIJs3XXv7H7NqXnzx3dl+uvD0vrUQ3Lg/Zfl6YMuy6cTVl9H67VomoPuuzQj7++XV8/rlSZ7bpc9fvndLJwxN+NXrKOFNWvk4IeuyMLpczPgrD/m04kzs9FWjbN03sJS15r94cfpd9L1JZ+Lly2v3MkCVIKtuu2dXa49Le9e8ZfMeGNYWp7aKfs8cHn6HnBpFpSxjm60ddPsc/9lGXtfv7x53q3ZtMN2+foNZ2TxjLmZ+NRn6+gOV5yYFsd3zKBL7sgnwyem2cHtstdfLsqLR1+dOUPHlrpew11bZZtTD8mc98au9m8BbKi26LZ3dvz5aRl6xV8y6/WPsvVpnbPng1fkhf0vycIy1tbC2kVZPGNuRvz+0bQ8+4gyr1mjbq18OnZqJj0xMDtee2plTwGgyu3b46jsfeYRefyS2zJj1OTsf/4x+fb9V6bXwZdk8fyFZY7ZavfWOf6W89P/d3/Ph8+9ke2/0SHH33p+7j7h2pI//K1Tf6Oc/o+rM+bV9/Pgd36d+TPmptHXNsuiuZ+WXKfR1s3ynb9flUEPv5AXbvpHFs79NE3abJWli5ZUydwBKsLePY7Knmd2yZOX/DkzR03Ofud3y8n3X5HbD750revoMbeclxd/9/d89NybafuNPXLMreflvhN+XipAYdpHH+fBb99Q8nn5Ks9BZ46elH9d9dfMHjc1RXVqpcOZXXLSvZfntgMvzoKZzmeBDdfBPY7OAd87Ig9dclumj56UTucfm7Pu+3F+fchFWbSGtbVW3VqZOW5qBj89MF17ln3/PmfSzDz9qwczfezkJMkexx+Q02+/JDcdeeVaAx4ANjTf6NEtnb93VP56ya2ZMnpSjjj/+Fx4X89cdcgP17KO1s70cVPz1tOv5ps9Ty+zz/Vdryz1ztOW27XIj+6/Km89/WplTAOgyuzV46h0OLNLnlpxb7/v+d1y0v1X5P/Wcm+/5e6t023Fvf2w597Mdt/YI91W3NtPWiUccfN2rbLrKQdn6vurn813uqp7vrbPjnnywj9lzvhp2Wb/XfKNX5yeeVNmZXiftytlvgDriwULFqZt61Y55ojD8qOf/KK6ywGoNvv0OCp7nXlEnrjkthVnTcfklPuvzG1fcGZ/3C3n54Xf/T0fPfdG2n6jQ4699fzcs9KZfZJM/ejjPPDtNb9zf+hVp+Zr++yQxy7slTnjp6XV/rvk8F98N/OmzM6wPm9VzoQBAP4LFH5xl881bNgwjRo1WuPPAQccUFl1Qrnt9P0uGf5Q/wx/sH/mjJiY16++L/Mnzkjb0zqV2b/tqYdk/oQZef3q+zJnxMQMf7B/Rjz8Qnbq8fkfaUx+9YOMe/bNzBkxMZ+MnZoP7nwusz74OJvt2bakz6KZn2TBtDklPy0675a5o6dk8qsfVPqcASpS27O6ZNSD/TPqgf6ZO2Ji3r76vnw6cUbanNa5zP6tT+uU+RNm5O2r78vcERMz6oH+GfXQC9mhx5ElfVqdfFBqNdw4L51xU6a/MSyfTpie6a8Py+z3x5W6VvGy5Vk4bU7JzyIvFAMboG3PPiJjH+yfsQ/0z7zhEzPkqnuzYMKMtPxO2evoNqd1yoLxMzLkqnszb/jEjH2gf8Y+2D+tzzmqpE+LE/bPsD8+lil9B+XTcVMz5q//ztT+g9N6pbU2SWpsVDt73HpuBl18R5bMmV+p8wSoSi17HJmPH+iXj+/vl3nDJ+b9nvdk4YQZ+drph5bZf8HH0/P+T+/JhL+9lKUr/VHwyuYMGpUPr30gkx59NcsXLa3M8gGqxZ7fOzwDbnk0Hz77ZqYNG5/HLr4tNevUys7d1vxNQ3ud0SWjBgzNy70ez4yRk/Jyr8cz+uX3stcZh5f02fecozN30ow8centmfjuqMwZPz1jXn4vs8ZNLelz8KXfzIh+76bv9Q9m8ntjM/vjaRnx/KB8OmPtwc8A65MO3zs8r9zyWIY9+2amDxufJy/+c2rWqZUd17KO7nHG4Rk9YGhe7fVEZo6clFd7PZGxL7+fDiuto0myfOnyzJ82p+Rn1WCF9x97NWNefi+zP56W6cMnpO/P70+d+hul2Q5bV8pcAarK/md0Sd9bH83Q597I5GHj89DFf0qturWyW7f91jjm48Gj8uT1D2TQE69m6eKy79/f7/t2Puw/KNNHT8700ZPz7G8fyeJPF+Zru7WurKkAVItOZxyZZ27tnXeeez0Th32cuy++JbXq1s6e3TqucczYwSPzj+vvzZtPvJIli8sOR5w3c27mTptd8tOuU/tMHTM5w157v7KmAlAlVr23f+pL3Nt3WHFv/9qKe/vX1nBvX3Oj2un6h3PyzOV3ZuGc1c+ittq9dYb846WMe+2DzBk/Pe8+2C9TPxiXzdu1qvB5Aqxv9t+nQy446zs59KA13+8D/C/Y83uH5+VbHs1HK87sn1hxZr/TWvaje57RJaMHDM0rK87sX+n1eMa8/F72XGU/WrzKWdOnq5w1rboffefBfpnywbhs0a5lpcwVAOC/xVcKYejXr1+ef/75Nf785/ewviisWSObtmuZiS8MLdU+8YWhabZHmzLHNG3fZrX+E/oPSZN2LVNQVKPMMVt03Cn1t908k1/7cI11tDpuvwx/+IVyzAKg+hTWrJHG7Vpm8gtDSrVPfmFImqxhHW3Svs3q/fsPTuOvf76ObnXY7pnx1vDs8cvTc+y7vdLl+Ruy4/ldU1BYUGrcJi03S7e3b8nRr92Uff90Xupt3bQCZwdQ+Qpq1kjDdi0ztf/gUu1TXxiSxh22K3NM4/ZtMnWVdXRq/8FpuNI6WqNWUZYvLP1i3LKFi7PpXm1LtX39hu9m8r/fybSXSu9vATZkBTVrpEG7lpm2yto67YXBabRH2WsrwP+6hi2aZpNmjTLqpc/3mcsWL83YgR+mefuy7++TpPnurTPqxdLr7agXB6d5+8/X2+0ObZ+Jg0fn+F4X5KK3euX7T1+X3U4++PMBBQVpfciumTl6Uk655/Jc9FavnPHoNWl7WPuKmyBAJWvYomk2btYwo1dZR8d9wTq61e6tM/rF0vf4o14cnK1WGdOo5WY57/Wbc86AG9Pt5nPTsMWan4MW1qyRXU85OAvnzC/zmzUBNhSNWzRL/WaN8tEqa+vIgR9km/YVd39fUFiQXY/eJ7Xq1s7Yt4dX2HUBqluTFs3SoFmjvP/SuyVtSxcvzbCB72fb9m3XMvKrqVGzKHsds39eecQ7gcCGrcGKe/sxq+w/Px744Wr36SvbcvfWGbPKvf3oMu7tD/v56Rn5/KCMffm9Mq8z/o1hadN592y8WaMkydb77JBGLTfP6BcGl9kfAID/Lp+dNa1+Zv9lzpq+6Mw++eys6YLXb8m5A27KMTeft9pZ03/2o5us2I9+bZ8d07jl5hlpPwoAsFZFX6XzokWLcvDBB6dmzZrr9I8uWrQoixYtKtW2pHhZahaU/QfuUF61G2+SwqIaWTB9Tqn2BdPnpG6zhmWOqdusQZn9C2sWpU7jTbJg6uwkSc1N6uabb92cGrWKUrxseV798d2ZtIY/btv68D1Sq/5GGfHIi+s8J4Cq9J91dOEq6+LCaXNSp1mDMsfUadogC6et0n/FOlq78SZZOHV2Nv5as9Tbb8eM+ecr6d/919mk1ebZ47rTU1BUI+/d9M8kyYy3R+bVC27LJ6Mmp07T+tnph8fk0Md/lqcPvjyLZ82rlPkCVLT/rKOLVlkXF02bk9pN17CONmuYqdMGr9a/sGZRajXeJIumzs6U/oOzbY8jMv21DzN/zJQ03X+nbP6N9imo8XnO3lbd9kmDXbbJC4f3rPiJAVSjWo3rp7CoRhaXtbauYY8K8L9u4xXPQuetsnbOnz4nDbZqsuZxTRtm/vS5q4yZm41X2ss2atE0e3TvlNfueCYv3/pYtvz6tvnGNadl2eIlGdx7QOo1qZ/aG9fNvuccnf6//Vv63vBQtj2wXU7884W55+TrMm5g2cG2AOuTeivW0fnlWkdXH1NvpXV04qARefKiP2fmqEmp16RB9j3/mJza++rccegVWTD78+egrQ/ZNd1uOS8169bKvKmz81D3X2WB56TABmyTFWvhqnvUedPmpFHzNa+tX9bmbVvk/N7Xpqh2zSz+dGHuPvvGTBkxYZ2vC7C+qN+0YZJk7irr6CfT5qRxBayj/7HrYR1St369vPL3/hV2TYDqsPFa7u3rr+O9/Q5H753Ndt4mf+161Rqv0+dn96TLDWfmvNdvzrIlS1O8vDjPXH5Hxr85rByzAQBgQ7O2s6Yv3o+ufmZf+qxpZB6/6LbMHDU59ZrUT8fzj8l3ev8stx96eclZ03M/+2uOvOHMXPD6LSX70afsRwH+pxQXF1d3CbBB+kohDD169MjMmTPzjW98I926dcsRRxyRhg0bfuV/9Prrr88111xTqq3bxrvkmPrtvvK14EtZ5X8kCgpWb1tr/zLal8xbmMcP+0lq1qudLTrulD2v/nbmjZuWya9+sNrl2px8YCb0ezcLpswuX/0A1Wy1zXZBQbK2/feqvyso+M+FSj4vnDE3b1x6R4qXF2fWkDGpu1mj7HDOkSUhDJP6ff6NHXM+TKa/OSJHv3pjWp64fz66/Zl1mxBAFVtt61mQta6jZa67K11oSM97sttvv5/OA36b4uLizB8zJeMefiFbn3RgkqTulo2zyy9OyysnXZ/li5ZUzCQA1jOrbzkL1n6vD/A/ZOdj9s2Rv/xeyecHv/ubsjsWFHzhAdvqe9OUWm8LCgszccio9PvNI0mSye+NTdPtmqf9qZ0zuPeAz9bnJMP6vJ2Bdz6bJJny/ti0aN/m/9m77ygrq/NtwPcMQ+9SRBAbKChi7yiKvaDYsKMG/RR/orEkJsYYNUVj12iiJtEYNcUaNdFgjF2sgFjpRRh6R3qb7w/IwFAM4MCAua61zlpz3tn7Pc9msfba++wz98nuZx4ihAHYILU5fr8ceWPX0udPfOe2JKtag/6Xm62wxS+7bh36+tIgxgkDijOqz+B0e/P27HjyAfnw90vfB/3y3X556KhrUn2TWtnl9A45/jfd88dO12fWpLIfvAPYUO3aqV1OvvH80ucPdr0lycrfCy2P7f2EoaNzx9E/TPU6NdP2qL1y2u0X5b5TfyqIAdho7dVp/5x544Wlz+/telOSVe3by+912516cD5//aNMGz+l/G4KsB7ssNze/slV7O3/62egsrLz/qV7+9qbbZJDr+uSx7vcnIVfcza/x3eOSNNdW+aprrdn2qiJab536xz+83MzY/zUfNnz89UcFQAAG4s2x++Xo5c5s3/8a87s/9sbov/tzH7I60s/cz9hQDKqz+D835t3pO3JB+SDJWdNe37niDTbtWWe6Hpbpo2amC32bp0jf35uZoyfkuHWowAAq7RGIQxDhw7NJ598kueffz533XVXunbtmnbt2qVTp0457rjjstVWW63Wfa6++upcccUVZa493vrCVbSGtTd38ldZtGBhqi9Jf/+Pag3qZvZyCXL/MXv8tBXbN6ybRfMXZM6y3yhUUpKvho9Lkkz+fETqtmyWtt2PXSGEoWazBtnsgB3z2vl3fdPhAKx3q5xHG9bJnFXMo3MmTEu15b6BuFqDOlk0f0HmLplH54yfmkULFqZk0dI3gKYPGp3qm9ZPYeVKWTR/4Qr3XTh7bqb1H5naWzf5hqMCWH/+M48uPy9WbVg3cyeuYh4dPzXVlqQeL22/eB6dt2QenTfpq7z/nTtSWLVyqtSvlTljp2SHH5+WWSMnJEnq7bRNqjWqm4P+9YvSexQWVUqDfVpn666H5/ktzk4W+UNlYOM0b/L0LFqwMFUblZ1bqzSsk7kT/AEaQLI48GDUR0NKnxdVWXwUUKtR3cwYP7X0es0GdVb4FrdlzZgwNbWWm29rNqiTGct808ZX46dm4qCyf8g2cfCotD5qzyTJrClfZeH8BZmwQpvRab5nqzUbGMB6MujlPhm9zDxaaZl5dOYy82iN1ZhHay43jy7us+p16/zZczNhwMhsstWmK1yf8uW4TPlyXEZ/NCQXvn5bdj71wLz7m7+vydAAKswX/+6dO/oOLn1eVKVykqR243r5asLU0uu1GtbJjK+ZW1fXwvkLM+nLxef5xZ8OTfOdtsn+XY/M0z968BvfG6AifPzvXhlWZh5dvEat27hepi8zj9ZuWDfTJ05NedikWcNs326n3N9tFX8oArABG/xynzy0ivdIZ5bDe6T/2ds3abt1ajaqm3P/8bPS3xcWVUrzvVtl93MOy63bnptKlYty4PdPyTMX3pUhr/ZNkkzoPzKb7rBl9r7gGCEMAADfQoNe7pPfr+SsqWY5ndn/t7Om8QNGZpOtFn/mvqhq5XT4/ql56sI7M3jJenT8kvXoPhccI4QBAOBrFK5ph5122ik//vGP88EHH2To0KHp3LlzevToke233z4777xzfvKTn6RXr15fe4+qVaumTp06ZR6VCyqt9SBgVRbNX5hJnwxL0/Y7lrnetP2OGd9r0Er7TOg9aMX2B+6YiZ8MS8mCFf8ouFRBUmnJB0WWte2pB2bOxOkpfqXvGtcPUNEWzV+YyZ8MS5Pl5sUm7dtm4irm0Ym9B6VJ+7Zl2x/YNpM/XjqPTvhwYGpttenSb3ZPUnubJpk1dspKAxiSpLBKUeq0bJbZy7zxBLChK5m/MFM/GZZGB5adFxsduGMmfzhwpX0m9x6URgeWnXcbH7RTpn684np00dz5mTN2SgqKKqXpMXtlTI/eSZIJb32WVw66Kq8denXpY0rfISl+umdeO/RqAQzARq1k/sJM+2RYGh24U5nrDdu3zZReK59bAf7XzJs5p/QPdad8OS4TBo3KV+OnZOv9l65LCytXypZ7t05x75Xv75OkuM/gbH1A2bXsNu13SnHvpfNtce+BabDNZmXabLL1Zpk2amKSxe8tjP5k6EraNCltA7ChWX4enThoVGaMn5qt9l+6Xy+sXClb/Jd5dFSfwdn6gLJ7/K3bt82or+lTqUpRGrRsVuYDeCtTUFCw0nMpgA3V3JlzMunLcaWPcYOKM338lGy3zBq1UuVKabH39hneu/z39wUFBaXBDwAbo7kz52TCl2NLH2MGFWfa+CnZfv+l75NWqlyU7fbeIUN6DyiX19yvc4d8NWlaPn21T7ncD2B9mjdzTqZ+Oa70saq9ffO9W3/tPn10n8HZarm9/VbL7O2/7Pl5fn/YD/PQUdeUPsZ8PDSfP/tOHjrqmpQsKklh5aJUqlKUkkWLytxn0cJFKSgsCAAA3z4rP2ta8cx+9c6avv7MfnmVqhSl4TJnTUvXo2U/N7p4PbrGf1YIAPA/5Rutlpo2bZpu3brlxRdfzMSJE3Pttddm+PDhOfLII3PjjTeWV43wjXz+u39m29MPSstT26duy6bZ8/ozU7NZgwx49JUkyW4/PCX7331hafsBj76amps3yJ7XnZm6LZum5ants+1pB+Xz+18sbdO2+7HZ7IAdU2uLRqnbYrPscMFRaXny/hnyTM+yL15QkJants+QJ99KycKyb6ADbCwG/Paf2eaMDtnmtANTp2XT7Hr9WanRrEEGPbJ4Ht356lOzz93dStsPfuSV1Ny8QXa97szUadk025x2YLY5/aD0u/+FZdr8O1Xr18ruP+uS2ts0SdNDdkmbSztl0MMvl7bZ5SdnpNE+rVOzeaM02LVF9v/dd1O5dvUMe+Kt9Td4gHIw5IEXs9UZHbLF6Qem1rZNs+MNZ6VGs4YZtmQe3eFHp2a3ey4qbT/8kVdSY/OG2fH6s1Jr26bZ4vQDs+XpB2Xwff8obVN/1xbZ7Og9U2OLxmmwd6vs95cfpKCwMIN/vfjbLxfMnJOv+heXeSycNTfzpszIV/2L1+8/AMA6MOz+F9L8zA7Z/PSDUmvbptn+p11SffOGGfHHfydJWl1zWnZeZm5NkjpttkydNlumUs1qqdKgduq02TK1tmtW+vuCypVK2xRWKUq1JvVTp82WqbHcNxADbKw+eLBH9r/4uLQ6Yo802m7zdLq9W+bPmZfPnnuntE2nO7rl4KtOXdrnDz3S4oC22a9bxzRosVn269YxW7drk/cf6lHa5r3f/zPNdm2Zdhcfl/pbbpodO+2X3c7okF6PLN3jv/vAC2nTcZ/selqH1N9y0+xxzmHZ7tDdyrQB2NB9+GCP7HfxcdnuiD3ScLvN0/H2CzN/zrx8scw82vGOC3PgVaeUPu/1h5ey9QFts0+3jtmkxWbZp1vHbNWuTT5cZh49+JrT03zv1qnbvFGa7tIiJ9x3aarWqp5Pn178Pmjl6lVz4PdPSdNdW6ROswbZdMetctTN56d2k/rp/8L76+8fAGAdeOuhf+aQiztlxyP2SJPtNs+pt12UebPn5aPnlp67n3b7RTnqqtNKn1eqXClNd9gyTXfYMpUqF6XupvXTdIct02DLpfv3o75/arbes1Xqb94wTVo1z5HfOyUt9tkhfZ5d7jwfYCP3ykMv5KiLT8wuR+yVpts1z7m3XZx5s+fmg+feLm1z7u3dc/xVZ5Q+r1S5KJvvsFU232GrFFUuSr1NG2TzHbZKoy2blLl3QUFB9ju5Q959+o0s8pkn4Fviwwd7ZN9l9vbHrMHefu8le/u9l9vbz5s5JxMHFpd5zJ81N7OnzMjEgYvP5ufNmJ0R7/ZLhx+dni322T51mzdK25MPyI4n7Z+BL339l94BfBvMmjU7/QcOSf+Bi78RftTocek/cEjGjB1fwZUBrF8fPNgj7ZY5sz92yZn958usR4+9o1sOWu7MfpsD2mbfJWf2+y5Zj36wzFnTIdeckS2WOWs66b7vpmqt6vlkyVnTvBmz8+W7X+TgZdajO53cPm1POiADXvpw/f0DAABshIrK60Y1a9bMySefnJNPPjmLFi3KpEmTyuvW8I0Mf/79VK1fO7tcfkKqN66XKQOK8+8ut2bmqMX/R2tsWi+1mjYsbT9j5IT8u8tt2ev6s9L6nEMza9yUfPCTR/Lli0s3F0U1qmbfm85NjSabZOGceZk2ZHTevPS+DH++7Ifdmh7QJrU2b5hBj7+xfgYLsA6MeP69VKlfK22WzKPTBhTnjbNuzawl31ZZrXG91GjWoLT9zJET8vpZt2a3G87KtuceltnjpqTPtY+keJl5dNboyXnt9F9mt+u75Kh/35RZY6dkwO97pN+SPx5OkhqbbZL9ftM9VTepnbmTpmdSn8H5V8frSl8XYGMx6rnF82jrK05M1cb18lX/4rx75i2ZXbxkHt207Dw6a8SEvHvmLWl7Q5ds/Z3DMmfclHzy4z9m9AtL59HCapWz/Q87p+YWjbNg5tyMe7Vvenf/TeZPn7XexwdQEcY8916q1K+dba84MVU3rZcZ/UfmwzNuLp1bqzaul+rNGpbpc8Crvyz9ud4u26TZSftn1ogJeW3PS5Mk1ZrUL9OmxcXHpsXFx2ZSzy/y3ok/Ww+jAli33rn/HymqViVH/fzcVK9TM6P6Dsmfzvpl5s2cU9qmTtMGZb79orj3oDxzyb056MrOOejKzpkyYlye6X5PRvcdUtpmzCdD8+QFd+XgH5ya9peekKnFE/KvGx7LZ88u/aDIgJd65YVrHkq7/zsuR9xwdiYNGZMnu92dkb3K/xuOAdaV95bMo0f8/NxUq1Mjo/sOyV/Punm5ebRhmXl0VO9BefaSe3PglZ3T/sqTM2XEuDzb/d4y82jtJpuk0z0Xp0b92pk1eXpGfTQ4fzzhukxfco61aNGiNGi5Wdqe/N1Ur187s6fOyJiPh+axzj/PxEGj1t8/AMA68Nr9f0/lalVy4s+6pnrdmhnRd0h+1+XGzF1mbq3frGFKSpbOrXU2rZ8rXly6fz/owmNz0IXHZsh7X+S+0xbv32s1rJvT77w4dRrVy5yvZmV0/xH53Tm/zKC3P11/gwNYD166/7lUrlYlZ/zs/NSoWzPD+g7O3V1+XmYe3WS5ebTepvVz7Yu3lj4//MLjcviFx2XAe5/njtOuL73eev+2abB5o/R84tX1MhaA9eH9+/+RytWq5PBl9vaPr8be/rlL7k37Zfb2z3W/N2OW2duvjucuuTcHXnVqjr37olSrVyvTiyfmzVufzEePvVJu4wPYUH3Wf1C6XvKD0ue33PPbJEmnow7NL358ZUWVBbDevbvkrOnIn5+bakvO7P+y3Jl93eXO7Ef1HpS/LTlrOnDJmf3fljuzr91kkxx/T/cyZ00Pn3Bdpi/zmfu/XXJvOlx1ao6/+/9SrV6tTCuemNdvfSJ9rEcBAL5WQcmypyyr6Ve/+tXKb1ZQkGrVqmXbbbfNAQcckEqVKq32PR9udtaalgHAMqqu+XQOwDKqLzKPAnwTla1HAb6RPtUKK7oEgI1eUQoqugSAjdqkgoUVXQLARu2rLKjoEgA2aluXVK3oEgA2alf2/mlFlwCw0btl92srugSAjdo1X/6poksAVmGPzQ6o6BL4H9BrzFsVXUK5K1qbTnfeeWcmTJiQWbNmpX79+ikpKcnUqVNTo0aN1KpVK+PHj88222yT1157Lc2bNy/vmgEAAAAAAAAAAAAAAAAAAADK3Vp9vdqNN96YPffcM4MGDcqkSZMyefLkDBw4MHvvvXfuvvvujBgxIk2aNMnll19e3vUCAAAAAAAAAAAAAAAAAAAArBNFa9Ppxz/+cZ5++um0aNGi9FrLli1z22235aSTTsrQoUNzyy235KSTTiq3QgEAAAAAAAAAAAAAAAAAAADWpcK16TRmzJgsWLBghesLFizI2LFjkyRNmzbNV1999c2qAwAAAAAAAAAAAAAAAAAAAFhP1iqEoUOHDrnwwgvz0UcflV776KOPctFFF+Xggw9Oknz66afZeuuty6dKAAAAAAAAAAAAAAAAAAAAgHWsaG06Pfjgg+nSpUt23333VK5cOUmyYMGCHHLIIXnwwQeTJLVq1crtt99efpUCAAAAAAAAAAAAAAAAAACwWhalpKJLgI3SWoUwNGnSJC+//HL69++fgQMHpqSkJK1bt06rVq1K23To0KHcigQAAAAAAAAAAAAAAAAAAABY19YqhOE/WrdundatW5dXLQAAAAAAAAAAAAAAAAAAAAAVZq1CGBYuXJiHH344r7zySsaPH59FixaV+f2rr75aLsUBAAAAAAAAAAAAAAAAAAAArC9rFcLw3e9+Nw8//HCOOeaY7LjjjikoKCjvugAAAAAAAAAAAAAAAAAAAADWq7UKYfjrX/+aJ554IkcffXR51wMAAAAAAAAAAAAAAAAAAABQIQrXplOVKlXSsmXL8q4FAAAAAAAAAAAAAAAAAAAAoMKsVQjDlVdembvvvjslJSXlXQ8AAAAAAAAAAAAAAAAAAABAhSham05vv/12Xnvttfzzn/9MmzZtUrly5TK/f+aZZ8qlOAAAAAAAAAAAAAAAAAAAAID1Za1CGOrVq5cTTjihvGsBAAAAAAAAAAAAAAAAAAAAqDBrFcLwhz/8obzrAAAAAAAAAAAAAAAAAAAAAKhQhRVdAAAAAAAAAAAAAAAAAAAAAMCGoGh1G+6222555ZVXUr9+/ey6664pKChYZds+ffqUS3EAAAAAAAAAAAAAAAAAAACsuZKSkoouATZKqx3C0KlTp1StWjVJcvzxx6+regAAAAAAAAAAAAAAAAAAAAAqxGqHMFx33XUr/RkAAAAAAAAAAAAAAAAAAADg22C1QxhWplevXunXr18KCgqy/fbbZ/fddy+vugAAAAAAAAAAAAAAAAAAAADWq7UKYSguLs7pp5+enj17pl69ekmSqVOnZr/99stf/vKXNG/evDxrBAAAAAAAAAAAAAAAAAAAAFjnCtemU9euXTN//vz069cvkydPzuTJk9OvX7+UlJTkvPPOK+8aAQAAAAAAAAAAAAAAAAAAANa5orXp9NZbb+Wdd95Jq1atSq+1atUq99xzT9q1a1duxQEAAAAAAAAAAAAAAAAAAACsL4Vr02mLLbbI/PnzV7i+YMGCNGvW7BsXBQAAAAAAAAAAAAAAAAAAALC+rVUIwy233JJLLrkkvXr1SklJSZKkV69e+e53v5vbbrutXAsEAAAAAAAAAAAAAAAAAAAAWB+K1qbTueeem1mzZmXvvfdOUdHiWyxYsCBFRUXp2rVrunbtWtp28uTJ5VMpAAAAAAAAAAAAAAAAAAAAwDq0ViEMd911VzmXAQAAAAAAAAAAAAAAAAAAAFCx1iqE4ZxzzinvOgAAAAAAAAAAAAAAAAAAAAAq1FqFMCTJwoUL87e//S39+vVLQUFBtt9++3Tq1ClFRWt9SwAAAAAAAAAAAAAAAAAAAMrBopRUdAmwUVqrxITPPvssnTp1ytixY9OqVaskycCBA9OoUaM8//zzadu2bbkWCQAAAAAAAAAAAAAAAAAAALCuFa5Np/PPPz9t2rRJcXFx+vTpkz59+mTkyJHZaaedcsEFF5R3jQAAAAAAAAAAAAAAAAAAAADrXNHadPr444/Tq1ev1K9fv/Ra/fr184tf/CJ77rlnuRUHAAAAAAAAAAAAAAAAAAAAsL4Urk2nVq1aZdy4cStcHz9+fFq2bPmNiwIAAAAAAAAAAAAAAAAAAABY39YqhOHGG2/MpZdemqeeeirFxcUpLi7OU089lcsuuyw333xzpk+fXvoAAAAAAAAAAAAAAAAAAAAA2BgUrU2njh07JklOOeWUFBQUJElKSkqSJMcee2zp84KCgixcuLA86gQAAAAAAAAAAAAAAAAAAABYp9YqhOG1114r7zoAAAAAAAAAAAAAAAAAAAAAKtRahTAceOCB5V0HAAAAAAAAAAAAAAAAAAAAQIVaqxCGJJk6dWoefPDB9OvXLwUFBdlhhx3StWvX1K1btzzrAwAAAAAAAAAAAAAAAAAAAFgvCtemU69evdKiRYvceeedmTx5ciZOnJg77rgjLVq0SJ8+fcq7RgAAAAAAAAAAAAAAAAAAAOB/xJQpU9KlS5fUrVs3devWTZcuXTJ16tTV7n/hhRemoKAgd9111xq/9lqFMFx++eU57rjjMnz48DzzzDP529/+lmHDhqVjx4657LLL1uaWAAAAAAAAAAAAAAAAAAAAADnjjDPSt2/f9OjRIz169Ejfvn3TpUuX1er77LPP5v3330/Tpk3X6rWL1qZTr1698rvf/S5FRUu7FxUV5aqrrsoee+yxVoUAAAAAAAAAAAAAAAAAAABQPkpSUtElwFrp169fevTokffeey977713kuR3v/td9t133wwYMCCtWrVaZd9Ro0ale/fueemll3LMMces1esXrk2nOnXqZMSIEStcHzlyZGrXrr1WhQAAAAAAAAAAAAAAAAAAAAAbj7lz52b69OllHnPnzv1G93z33XdTt27d0gCGJNlnn31St27dvPPOO6vst2jRonTp0iXf//7306ZNm7V+/bUKYTj11FNz3nnn5fHHH8/IkSNTXFycv/71rzn//PNz+umnr3UxAAAAAAAAAAAAAAAAAAAAwMbhpptuSt26dcs8brrppm90z7Fjx6Zx48YrXG/cuHHGjh27yn4333xzioqKcumll36j1y9am0633XZbCgsLc/bZZ2fBggVJksqVK+eiiy7KL3/5y29UEAAAAAAAAAAAAAAAAAAAALDhu/rqq3PFFVeUuVa1atWVtr3++utzww03fO39PvzwwyRJQUHBCr8rKSlZ6fUk6d27d+6+++706dNnlW1W1xqFMMyaNSvf//738+yzz2b+/Pk5/vjj071799StWzctW7ZMjRo1vlExAAAAAAAAAAAAAAAAAAAAwMahatWqqwxdWF737t1z2mmnfW2brbbaKp988knGjRu3wu8mTJiQTTfddKX93nrrrYwfPz5bbLFF6bWFCxfmyiuvzF133ZXhw4evVo3JGoYwXHfddXn44Ydz5plnpnr16vnzn/+cRYsW5cknn1yT2wAAAAAAAAAAAAAAAAAAAAD/Qxo2bJiGDRv+13b77rtvpk2blg8++CB77bVXkuT999/PtGnTst9++620T5cuXXLooYeWuXbEEUekS5cu+c53vrNGda5RCMMzzzyTBx98sDRd4swzz0y7du2ycOHCVKpUaY1eGAAAAAAAAAAAAAAAAAAAAGBZ22+/fY488sj8v//3//LAAw8kSS644IJ07NgxrVq1Km3XunXr3HTTTTnhhBPSoEGDNGjQoMx9KleunCZNmpTpszoK16TxyJEjc8ABB5Q+32uvvVJUVJTRo0ev0YsCAAAAAAAAAAAAAAAAAAAArMyf/vSntG3bNocffngOP/zw7LTTTnn00UfLtBkwYECmTZtW7q9dtCaNFy5cmCpVqpS9QVFRFixYUK5FAQAAAAAAAAAAAAAAAAAAAP+bNtlkkzz22GNf26akpORrfz98+PC1eu01CmEoKSnJueeem6pVq5ZemzNnTrp165aaNWuWXnvmmWfWqhgAAAAAAAAAAAAAAAAAAACAirJGIQznnHPOCtfOOuuscisGAAAAAAAAAAAAAAAAAAAAoKKsUQjDH/7wh3VVBwAAAAAAAAAAAAAAAAAAAECFWqMQBgAAAAAAAAAAAAAAAAAAADZ8i0pKKroE2CgVVnQBAAAAAAAAAAAAAAAAAAAAABsCIQwAAAAAAAAAAAAAAAAAAAAAEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJAkKaroAgAAAAAAAAAAAAAAAAAAAChfJSmp6BJgo1RY0QUAAAAAAAAAAAAAAAAAAAAAbAiEMAAAAAAAAAAAAAAAAAAAAABECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQJKkqKILAAAAAAAAAAAAAAAAAAAAoHwtKimp6BJgo1RY0QUAAAAAAAAAAAAAAAAAAAAAbAiEMAAAAAAAAAAAAAAAAAAAAABECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQJKkqKILAAAAAAAAAAAAAAAAAAAAoHyVpKSiS4CNUmFFFwAAAAAAAAAAAAAAAAAAAACwIRDCAAAAAAAAAAAAAAAAAAAAABAhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASZKiii4AAAAAAAAAAAAAAAAAAACA8rWopKSiS4CNUmFFFwAAAAAAAAAAAAAAAAAAAACwIRDCAAAAAAAAAAAAAAAAAAAAABAhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASZKiii4AAAAAAAAAAAAAAAAAAACA8lWSkoouATZKhRVdAAAAAAAAAAAAAAAAAAAAAMCGQAgDAAAAAAAAAAAAAAAAAAAAQIQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkCQpqugCAAAAAAAAAAAAAAAAAAAAKF+LSkoqugTYKBVWdAEAAAAAAAAAAAAAAAAAAAAAGwIhDAAAAAAAAAAAAAAAAAAAAAARwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJAkKaroAv5jSqWKrgBg41ZrUUFFlwCwURtauaIrANi4NV4o5xHgm5hVsKiiSwDY6NnaA3wzkzKvoksA2KhNXDS3oksA2KhtVlCloksA2Kjdsvu1FV0CwEbvqt4/q+gSAACADYi/kAAAAAAAAAAAAAAAAAAAAACIEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIkRRVdAAAAAAAAAAAAAAAAAAAAAOWrJCUVXQJslAorugAAAAAAAAAAAAAAAAAAAACADYEQBgAAAAAAAAAAAAAAAAAAAIAIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASJIUVXQBAAAAAAAAAAAAAAAAAAAAlK+SkkUVXQJslAorugAAAAAAAAAAAAAAAAAAAACADYEQBgAAAAAAAAAAAAAAAAAAAIAIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASJIUVXQBAAAAAAAAAAAAAAAAAAAAlK9FKanoEmCjVFjRBQAAAAAAAAAAAAAAAAAAAABsCIQwAAAAAAAAAAAAAAAAAAAAAEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAkqSoogsAAAAAAAAAAAAAAAAAAACgfJWUlFR0CbBRKqzoAgAAAAAAAAAAAAAAAAAAAAA2BEIYAAAAAAAAAAAAAAAAAAAAACKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgSVJU0QUAAAAAAAAAAAAAAAAAAABQvhalpKJLgI1SYUUXAAAAAAAAAAAAAAAAAAAAALAhEMIAAAAAAAAAAAAAAAAAAAAAECEMAAAAAAAAAAAAAAAAAAAAAEmEMAAAAAAAAAAAAAAAAAAAAAAkEcIAAAAAAAAAAAAAAAAAAAAAkEQIAwAAAAAAAAAAAAAAAAAAAEASIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJkqKKLgAAAAAAAAAAAAAAAAAAAIDyVVJSUtElwEapsKILAAAAAAAAAAAAAAAAAAAAANgQCGEAAAAAAAAAAAAAAAAAAAAAiBAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkSVFFFwAAAAAAAAAAAAAAAAAAAED5WlRSUtElwEapsKILAAAAAAAAAAAAAAAAAAAAANgQCGEAAAAAAAAAAAAAAAAAAAAAiBAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkSVFFFwAAAAAAAAAAAAAAAAAAAED5KklJRZcAG6XCii4AAAAAAAAAAAAAAAAAAAAAYEMghAEAAAAAAAAAAAAAAAAAAAAgQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgiRAGAAAAAAAAAAAAAAAAAAAAgCRCGAAAAAAAAAAAAAAAAAAAAACSCGEAAAAAAAAAAAAAAAAAAAAASCKEAQAAAAAAAAAAAAAAAAAAACCJEAYAAAAAAAAAAAAAAAAAAACAJEIYAAAAAAAAAAAAAAAAAAAAAJIkRRVdAAAAAAAAAAAAAAAAAAAAAOWrpKSkokuAjVJhRRcAAAAAAAAAAAAAAAAAAAAAsCEQwgAAAAAAAAAAAAAAAAAAAAAQIQwAAAAAAAAAAAAAAAAAAAAASYQwAAAAAAAAAAAAAAAAAAAAACQRwgAAAAAAAAAAAAAAAAAAAACQRAgDAAAAAAAAAAAAAAAAAAAAQBIhDAAAAAAAAAAAAAAAAAAAAABJhDAAAAAAAAAAAAAAAAAAAAAAJBHCAAAAAAAAAAAAAAAAAAAAAJBECAMAAAAAAAAAAAAAAAAAAABAEiEMAAAAAAAAAAAAAAAAAAAAAEmSooouAAAAAAAAAAAAAAAAAAAAgPK1KCUVXQJslAorugAAAAAAAAAAAAAAAAAAAACADYEQBgAAAAAAAAAAAAAAAAAAAIAIYQAAAAAAAAAAAAAAAAAAAABIIoQBAAAAAAAAAAAAAAAAAAAAIIkQBgAAAAAAAAAAAAAAAAAAAIAkQhgAAAAAAAAAAAAAAAAAAAAAkghhAAAAAAAAAAAAAAAAAAAAAEgihAEAAAAAAAAAAAAAAAAAAAAgyTcIYRg8eHBeeumlzJ49O0lSUlJSbkUBAAAAAAAAAAAAAAAAAAAArG9rHMIwadKkHHroodluu+1y9NFHZ8yYMUmS888/P1deeWW5FwgAAAAAAAAAAAAAAAAAAACwPqxxCMPll1+eoqKijBgxIjVq1Ci9fuqpp6ZHjx7lWhwAAAAAAAAAAAAAAAAAAADA+lK0ph3+9a9/5aWXXsrmm29e5vq2226bL7/8stwKAwAAAAAAAAAAAAAAAAAAYO2UlJRUdAmwUSpc0w4zZ85MjRo1Vrg+ceLEVK1atVyKAgAAAAAAAAAAAAAAAAAAAFjf1jiEoX379nnkkUdKnxcUFGTRokW59dZb06FDh3ItDgAAAAAAAAAAAAAAAAAAAGB9KVrTDrfeemsOOuig9OrVK/PmzctVV12Vzz//PJMnT07Pnj3XRY0AAAAAAAAAAAAAAAAAAAAA61zhmnbYYYcd8sknn2SvvfbKYYcdlpkzZ+bEE0/MRx99lBYtWqyLGgEAAAAAAAAAAAAAAAAAAADWuaK16dSkSZPccMMN5V0LAAAAAAAAAAAAAAAAAAAAQIVZ4xCGN99882t/3759+7UuBgAAAAAAAAAAAAAAAAAAAKCirHEIw0EHHbTCtYKCgtKfFy5c+I0KAgAAAAAAAAAAAAAAAAAAAKgIhWvaYcqUKWUe48ePT48ePbLnnnvmX//617qoEQAAAAAAAAAAAAAAAAAAAGCdK1rTDnXr1l3h2mGHHZaqVavm8ssvT+/evculMAAAAAAAAAAAAAAAAAAAAID1qbC8btSoUaMMGDCgvG4HAAAAAAAAAAAAAAAAAAAAsF4VrWmHTz75pMzzkpKSjBkzJr/85S+z8847l1thUN72ufzEtD2jQ6rVrZkxHw3Ja9c+nEkDR31tn5ZH7Zn9vndy6m7RONNGjE/PW57MkJd6lf5+z4uPTcsj98wmLTbLgjnzMrr3oLx90+OZMnRMaZvDb78gbTq3L3PfMX0G56/HX1+u4wNYl7Y/+9Ds3O3oVG9cL1MGjsp71z+WsR+sOnypyT6ts89Pzkz97Zpl1rip+eS+f6TfY6+W/r7+ds2y+/dOSsO2W6d280Z597pH89mDL5W5R0Glwux+xYlpecJ+qd64XmaNm5qBT76Zj+5+LikpWWdjBVhXDrjsxOx6xsGpVrdmRn80OD2ufTgTB339erTVUXvmwCs7p/4WjTNlxPi8cesTGbDMevSAy05M+8tPKtNnxvipuXvPi0ufX/Pln1Z671du/HPee+CFbzAigIpV3mvUVmcclO1OOiD1W22eJJn46bB8ePMTmdB36DofC8CG5JDLTspepx+c6nVrZmTfwXnu2j9k/NesWxtv2yyHXdE5zdpunfqbN8o/fvpIej7UYz1WDLD+dLjsxOy+ZI4s7js4/7j24Uz4L3v7HY7cMwdf2TmbbNE4k0eMzyu3PZF+y+ztCysVpsNlJ2Wn4/dLrUb18tX4qen71Jt5455nU7KS90GPvbFr9jzjkPzzp4/mXfMt8C3U6bJTcuDph6Vm3ZoZ2ndQHr329xk9aOQq27c/7dC0O/HANGu1RZJk+KdD8/Stf8qwjwevr5IBKtTpl5+RI844IrXq1srAjwbm/mvvy4iBI1bZ/vDTj8jBJx2cLVttmSQZ/OngPHLzIxn08cDSNr/v+WA2bb7pCn1f+OM/cv+195f/IADWo4MuOzG7Lzm3H/XR4LywGnv77Y9avLf/z7n9K7c+kf7L7O0ve/uu1GveaIV+Hzzycl689uHS5w1bNs1hPzwtW+69fQoKCzJh4Kg8efGvMm30pHIbH8C6VlGff6pco2oO/uFp2e7wPVK9fq1MK56QD//wUvo89kr5DhBgA9Sr76f5w5+fyhf9B2fCpMm5+6Zrc0j7/Sq6LACA/wlrHMKwyy67pKCgYIUP/eyzzz556KGHyq0wKE97XNQxu51/VP515QOZMnRs9r60U0780w/z8EHfz/yZc1baZ7PdWuaYX3fPO7c/lcE9eqXlkXvkmN90zxMn/Sxj+w5Jkmy+9/b5+I8vZ9wnQ1NQqVLaXdU5Jz72g/zxkB9kwey5pfca9trH+df3flv6fOG8Bet2wADlaJtj986+15+Vntc8nHEfDkzrsw7OkY9+P092+EFmruQQsHbzRjnyke+l/59fz+uX3pdN99wu7X5xbmZP/irDX/wwSVKpetVMHzEhQ//xQfa97qyVvu7O/9cx23c5JK9f9kCmDCxOo523TvvbL8i8r2bn8+UCGwA2dPt265i9zz86f//e/Zk8dGzaXXJ8zvjT1bm/w/cybxXr0Wa7tcyJ916SN25/KgNe+jCtjtgzJ/z6kjxy8k8zesl6NEnGDxiZP595U+nzkoWLytznrj3+r8zzFgftnI63/L/0f/GDchwhwPq1LtaoTffdPoOfezfjeg3Mwrnzs/NFHXPUn36Qpw75YWaNnbK+hwhQIdp3Ozb7n3dUnvreA5k4bEw6XHJCznvsR7n94CtXuW6tUr1qJo8Yn09ffD/HXLvyPT7At8H+3Tpm3/OOzt++d38mDRubAy85Puc8dnV+dfCq9/bNd2uZzvdeklfveCr9Xvow2x+xZ06595I82PmnKV6yt9+/27HZ48xD8rcr78/4QcVp2nabnHDrBZnz1ay894ey74O2Pnz3bL5Ly0wfO3mdjxegIhzd7fgccd6xefB792bssNE59pKT873HfpIfHXxJ5qxirm29T5u89/zbGdxnQObPnZ+jL+yU7z36k1xz2GWZOs58CXy7nXTRSTn+/ONz15V3ZtTQ0Tn10lPz0z/9LBcd1C2zZ85eaZ+2+7TNm8+9kX69+2X+3Pk5sdtJ+eljP83Fh16cyeMWv7d6xbGXp7BSYWmfLVttmZ//+Rd5+4We62VcAOtKu24ds+/5R+fZ792fSUPHpv0lx+fsP12de77m3H7z/+ztb38q/V/6MK2P2DOdf31JHjr5pxm1ZG//2+OuLTNvNt5u85z95x/lixfeL71Wf4vG6frUT/LR42/ktTufztzps9Jw22ZZMHf+uh00QDmqyM8/HfaTLtly3+3z3GW/ybTiCdnmgLY58uffyYxxUzPw5d7rZsAAG4jZs+ekVcttcvzRh+fya35e0eUAsJFa5MtwYa0U/vcmZQ0bNixDhw7NsGHDMmzYsHz55ZeZNWtW3nnnnbRu3Xpd1Ajf2G7nHZkP7n0ug3v0yqSBxXnpigdSVK1KWh+/6vS3Xc87Ml++9Vk+/PXfM2XImHz4679nZM8vsut5R5a2+dvZt+SLp97KpIGjMrHfiPzryt+mzuYNs2nbrcrca+G8+Zk1YVrpY+60metqqADlru0FR2XAX1/PgL+8nqmDR+e96x/LjNGTssPZh6y0/fZdDs6MUZPy3vWPZerg0Rnwl9cz8PE3stOFR5e2mfjx0Hzw879k6PPvZeG8lR8mbrr7tvnyX70z8tW+mVE8McNe+DCj3vw0jXbaep2ME2Bd2uu8I9Pz3mczoEevTBhYnL9feX8qV6uSNp1WvR7dq+tRGfb2Z3nnN89n0pAxeec3z2d4z8+zV9cjy7QrWbAoMydMK33MmvxVmd8v+7uZE6Zlu8N2z/B3v8jUkRPWyVgB1od1sUZ97ZL70u+Rf2fyFyMybciYvHXV71NQWJhm7dqsr2EBVLh2XY/Ma79+Lp+/9GHGDSzOk1fel8rVq2SXr1m3Fn8yNP+86c/55O/vCp8FvtX27Xpk3vz1s+n3Uq+MH1icZ668P5WrV8lOXzNH7tP1qAx9+7O89ZvnM3HImLz1m+cz9J3Ps88ye/vmu22b/i/3zsDX+mZq8cR88c8PMvitT9O07TZl7lV70/o55oZz89R3f52FCxaus3ECVKTDunbMP379dHq/9H5GDRyZ3195T6pWr5p9Oh2wyj6/vezuvPbYSxn5xfCMHTIqf/jh/SkoKMgO7dqux8oBKsZx53XKE/c+nnd7vJsRA7/MnVfckarVqubA4w9cZZ/bv3tbXnz0xQz7YliKhxTn3h/ck8LCwuy8/86lbaZPnp6pE6aWPvY8ZK+MHj46n7336foYFsA6s895R+bNe59Nvx6L9/Z/W3Ju3/a/7O2HvP1Z3l6yt3/7N89nWM+ye/tZk7/KjAnTSh/bHbJrJg8fm+Hv9Sttc8j3T8mg1z7Oyzf9JWM//zJTRk7IoFf7Zuak6et0zADlqSI//9Rst5b59Om3MuK9fplWPDEf/eW1jOs3Ipv5PCnwP+CAfffMpReck8MOalfRpQAA/M9Z4xCGLbfcssyjefPmqVat2rqoDcpF3S0apWbjevnyzaUHgQvnLcio9/un6e7brrLfZru1LNMnSYa/8cnX9qlSu0aSZM7UsiELm++zfS7s8+uc+/qtOfTm81K9QZ21GQrAeldYuVIatt06o978rMz1UW9+lk33WPl82Hi3bVdoX/zG4vCEgqJKq/3aYz8cmKbt2qTu1k2SJJtsv0U23bNVRr768RqOAqBi1WveKLUa18/Qt8quR0e83z+bf83astluLTP0zU/KXBv65ifZfPftylyrv/WmufSDe3Px23fm+Hu6p17zRqu8Z82GddLy4F3y8eNvrOVoACre+lqjFlWvmsLKlTJ36ozyKRxgA1e/eePUaVw/g95augZdOG9Bhr3fL1sutwYF+F9Tv3mj1G5cP4OX29sPf79/mn/N3r75ri0z+K2ye/vBb36SLXZbOq+O6DUg27RrkwZL3gfddPstsuUerTLo9b6lbQoKCnLSnRel52//kQmDRpXTqAA2LI2ab5p6jevns7eWngMtmLcgA97/PC13b7Xa96lavUoqVa6UmfbzwLfcpltsmk0ab5KP3vyo9NqCeQvy2fufpfXu26/2fapWr5pKlStlxtSvVvr7ospF6XDCQfn34y9/05IBKtR/9vZD1nRvv1vLDHlzxb1981W8Z1qpcqXsdML++eiJpWfyBQUF2fbgXTJp2Jic9cgP8v3ev8n5z96Q1ofv/g1HBbD+VPTnn4o/HJhtD90ttTetnyTZct8dssnWTTLkjbL3BgAAgPJUtKYdfvWrX630ekFBQapVq5aWLVumffv2qVRp1X9kOXfu3MydO7fMtQUlC1NUsPp/mAmrq0ajekmSWROnlbk+a+K01G7WcJX9ajaqt9I+NRrVXWWfA39yZkZ9MCCTBhaXXhv++scZ9MIHmV48MXW3aJR9rzw5J//16vz5mGt9Mxywwau2Se0UFlXKrAll58PZE6al+pL5dXk1GtdN8evLzZ8TpqWwclGqbVI7s8dPXa3X/vjXf0+V2tXT+Y1bUrJwUQoqFebDm5/MkOfeXZuhAFSYmo3rJUlmLjeXzpw4LXW+Zj1aq1G9zJxY9lsvZk6cnprLrEdH9x2S56+4P5OHjk3NhnWy/yXH55xnrs9vD/tBZq/kQ8ZtT2qfeTPnpH+PD7/BiAAq1vpao+559amZOXZKRr39eXmVDrBBq71knTljufl1xoTpqbf5qtetAP8Lai1ZZ66wt58w7WvnyFqN6mXGhLJ7+xkTpqfWMnv7t+77e6rWrpFLXrm19H3QV257Mp8+v/R90P0vOjaLFizKe394qRxGA7Bhqrtkrp0+YWqZ69MmTEvDzVcdPLu8k39wVqaMnZzPe/ojDODbrX6jxX98NnXi1DLXp06cmsbNGq/2fc754TmZNHZS+r7dd6W/3+eIfVKzTq288tQra1sqwAah1tec29ddi3P7Wqv4HGnrw/dItTo10vfJN0uv1WxYJ1VrVc/+Fx2bV297Mv/+5V/T8sCdcuoDl+Xh036RL9/vv5ajAlh/KvrzTy9d/8cc88vzc+kH92bh/AUpWVSSF37w+xT3GlhOIwQAAIAVrXEIw5133pkJEyZk1qxZqV+/fkpKSjJ16tTUqFEjtWrVyvjx47PNNtvktddeS/PmzVd6j5tuuik33HBDmWuH12mbI+vutHajgGW0Pn6/HHJT19Lnz5572+IfSpZrWFCw4rXlLff7goKCpGTlnTr87Jw0bN08T5z0szLXB/79/dKfJw0szrhPhuW8d+7K1gfvksE9ev2XAgA2EMvPfQUruVamednfFRSs4j5fY5vj9knLE9vl1e6/yZSBxWnQZsvse/1ZmTVuagY99dZq3wdgfWtz/H45+sbzSp8//p1bV97wa9aW/7H8fLr8/Dvk9aXfCjdhQDKqz+D835t3pO3JB+SD3/9zhfvtfMqB+ezZnlk4d/5/HwjAhm4drlF3uuiYtDh+37zQ+RfmTOBba5dO7XL8MuvWP3a9ZfEPK7yPmjXazwN8G+zUab8cu8wc+aeui/f2K0yHBQUr7t1XsOI6tGSZazseu092Pr5dnvrurzN+4KhstsOWOeonZ+WrcVPS9+m3stmOW2Wf7xyR+4+55psMCWCDs0+nA3LOjReWPr+r641JVr5//+9z7WJHXdgpex+3f24+7bossJ8HvmUOPP6gXHzTxaXPf3ru4s/erThvrs4adbETu52U9p0OzI9OuTrzVzFvHnbq4en9eu9MHjd5LSsHqBhtj19ub7/k3H6FGbIczu2XteupB2XQ6x/nq2UCwAuWHEoNeLlP3nuwR5Jk7Bdfpvnu22aPMw8RwgBskDa0zz/t+Z0j0mzXlnmi622ZNmpitti7dY78+bmZMX5Khvf0xQoAAACsG2scwnDjjTfmt7/9bX7/+9+nRYsWSZLBgwfnwgsvzAUXXJB27drltNNOy+WXX56nnnpqpfe4+uqrc8UVV5S59kCbC1faFtbUkJf7ZMxHQ0qfF1Vd/N+8RqO6mbnMG9s1GtTJrInTlu9eauaEqamxXFpx9QZ1Mmu5NM4kOeiGs9PisN3yROefZ8bYrz90nDl+aqaPmph6WzdZneEAVKg5k7/KogULU2NJivF/VG9YN7NXMYfOGj9tpe0XzV+QOVNW/Fb2Vdn7x6fn41//PUOffy9JMqV/cWo3a5hduh8rhAHYoA16uU9+v8x6tFKVxevRmo3qZsYy69GaDepk5tesR2dMmLrCt2cs7rPievQ/5s+em/EDRmaTrVZcazbfs1Uatmyav3W/Z3WHArBBWtdr1LYXHp1duh+XF0//ZSb3G1mepQNsUL74d++M7Du49Pl/1q21GtfNV8t8+3CthnUy42vWrQDfRv3/3SfFfVfc29dqXDczlpkja/6XOXKle/uGdTJzwtK9/RFXn5G37vt7Pvv74vdBxw8YmXrNGuaA/zsufZ9+K1vt1To1G9TJFe/8amk9RZVyxDVnZp+uR+bO/S/7JkMFqDB9//1hhvYdVPq8qErlJEndxvUzbZm5tk7Dupm+3Le8r8yR/++4dLz4pNx65g0p7v9leZcLUOE+ePn9DPxoQOnzylUXz5v1G9XPlPFTSq/XbVA3U1dj3jzhghPS+eLOufbMH2d4/+ErbdOoWaPsvP/OuemCG79R7QAVYcDLfTJqJef2tVZybr/Ge/sGdTJjJef2dZs1zDb775jHL7yrzPVZU77KwvkLMmHQqDLXJwwenS32bLW6QwJYrzakzz8VVa2cDt8/NU9deGcGv9o3STK+/8hsusOW2eeCY4QwAAAAsM4UrmmHH//4x7nzzjtLAxiSpGXLlrntttty9dVXZ/PNN88tt9ySnj17rvIeVatWTZ06dco8igoqrd0IYDnzZ87JtC/HlT4mDRyVmeOnZssDdixtU1i5Uprt3Tqjew9a5X3G9Blcpk+SbNm+7Qp9Ovz07Gx71B556rQbM33khP9aX7V6tVJ7s03KBEIAbKgWzV+YiZ8OS7Pl5sNmB+yYcb1WPoeO7zNoxfbtd8yET4alZMHC1X7toupVUrKobALyooWLUlBYsIoeABuGeTPnZMqX40ofEweNyozxU7L1/m1L2xRWrpQt9m6d4q9Zj47qMzhbH9C2zLVt2u+U4t4DV9mnUpWiNGzZrMxh53/sfOpBGfPJ0IzvN2LNBwWwAVmXa9Sduh2T3b57fHp0uSUTPxlW/sUDbEDmzZyTSV+OK32MHzQq08dPybbLrFsrVa6UrffePl9+zRoU4Nto3sw5mfzluNLHhEGj8tX4KWm53By51d6tM/Jr9vYjPxqcFvuX3du3PGCnjOizdF6tXL1KSkoWlWmzaNGi0m/J7PvM2/nNkVfnvqN/VPqYPnZyev72H3nk7JvLY7gAFWLOzDkZ/+XY0sfoQSMzdfyUtNl/p9I2lSoXpdXebTK494CvuVNy5AWdcuwlJ+f2c36W4Z8O+dq2ABur2TNnZ8yXY0ofIwaOyOTxk7PLAbuWtimqXJQd994x/Xv3+9p7nXDhiTn10tNy/dnXZfAng1fZ7tBTDsu0SdPy4asflts4ANaXVe3tW6zp3r7P4LRY7ty+RfudMnIl75nu2rl9Zk6alkGvflTm+sL5CzP6k6FpsM1mZa432LpJpo2auDbDA1jnNqTPPxVWLkqlKkWr+DzpGv85DAAAAKy2ojXtMGbMmCxYsGCF6wsWLMjYsWOTJE2bNs1XX331zauDctLnwR7Z8+LjMmXYuEwdNjZ7dT8uC+bMS/9n3yltc8SdF2bG2CnpefMTSZKPHnoppzz54+xxUccM+VfvtDh892yxf5s8cdLPSvsc/PNz06rTvnn+/Dszb+ac1FiS1Dl3+qwsnDs/lWtUzT6Xn5jB//wwM8dPTZ3NG6XdVZ0ze8qMDO7Ra/3+IwCspU9/+88cdPdFmfDJ0IzvPTitz+yQWs0apN+jryRJ9vzhKanZpH5ev+yBJEm/R1/NDuceln1+cmb6//m1NN69ZVqddlBe7f7r0nsWVq6Uets2W/JzUWpstkk22WGLLJg1N9OHj0uSjHj5o+xyaafMGDUpUwYWp+GOW6XtBUdl4ONvrOd/AYBv7oMHe6TdxcdlyvCxmTxsbPbr3inz58zL588tXY8ee0e3fDV2Sl6/5fHFff7QI2c/cW327dYxA1/une0O2z1btWuTR07+aWmfQ645I4P+3SfTRk9KzQZ1sv8lx6dqrer55Om3yrx+lVrVs/0xe+WVn/95/QwYYB1bF2vUnS46Jnt87+S8eslv8tXIiam+ZI8/f+acLJg1d/0PEqAC9HyoRw66uFMmDh+bScPG5qCLO2X+7Hnpu8y6tfPtF2X6uMl5acm6tVLlSmm87eZLfi5KnU03yWY7bFka8gDwbfHuQz1ywMXHZdKSObL9kjnyk2XmyBNv75bp46bk30vmyPce6pGuT1yb/bt1TP+Xe6f1Ybtnm3Zt8mDnpXv7Aa98lPYXH59poyZl/KDibNZmq+x33lHp8+Ti90FnT52R2VNnlKll4YKFmTFhWiYNHbMeRg6w/rz80D/S8eKTMm74mIwbNiYdLz4pc2fPzXvPLX2/8/zbL8nUcZPz1C1/SpIcdWGnnHDF6Xngu3dlYvGE1GlUL0kyd+aczJ01pyKGAbDePP/gc+l8ceeMHjY6o4eNzindO2funLl549mlZ+qX33lFJo2dlEdu/mOS5MRuJ+WsK8/KbZfemnHF41Jvybw5Z+aczFlm3iwoKMihnQ/Nq0+9kkULy4aGAWys3ntw6d5+8rCxOWDJuf2ny+ztT7ijW6aPnZJXluzt3/9Dj3zniWvTrlvHDHi5d1ot2ds/tMy5fbJ43tyl84H5+Km3Vjpv9nzghXS+95J8+X7/DH/3i7Q8aKe0OnS3PHzqz9ftoAHKUUV9/mnejNn58t0vcvCPTs/8OfMybdTEbLn39ml70gH5988eW7//CAAVYNas2RlRPLr0+ajR49J/4JDUrVM7mzVpXIGVAQB8+61xCEOHDh1y4YUX5ve//3123XVxkvZHH32Uiy66KAcffHCS5NNPP83WW29dvpXCN9Drvn+kqFqVHPKLc1O1To2M7Tskz5x5c+bPXHp4WLtpwzIJmWN6D8qL3e/Nft/rnP2uPDlTvxyXFy++N2P7Lv3mjJ3PPjRJcsqTPy7zei9d8UC+WPJmesPWzbPDSfunap2amTl+aka++0VeuPjeMq8NsCEb+vf3U7V+7ex22Qmp0bheJg8oTo+zb82MUZOSJDUa10vNZg1L2381ckJ6nH1b9r3urOxwzqGZNW5K3v3JIxn+4tJvx6ixaf2c9K8bS5/v3O2Y7NztmIx+t19e6PyLJMk71z6S3b9/ctrdeG6qN6yTWWOnpP9jr6bPXX9bTyMHKD/v3r94PXrkz89NtTo1M6rvkPzlrF9m3jJrwrpNG5RZj47qPSh/u+TeHHhl5xx4ZedMGTEuf+t+T0Yvsx6t3WSTHH9P99SoXzuzJk/PqI8G5+ETrsv05b4to82x+6SgoCCfP/9OAL4N1sUadYezD02lqpVz2G+/W+a1et/xTPrc8cz6GRhABXvz/r+ncrUq6fSz76R63ZoZ2XdIHupyU5l1a71mDcp8Y3vtTevn0hdvKn3e/sKOaX9hxwx974v87jQfIAa+Pd6+/x+pXK1KOv7s3FSru3hv/0iX5fb2zRqkpGTp3n5kn0F58pJ7c8j3OufgKxbv7Z/ofk+Kl9nbv3DdH3PIlSen48++k5oN6+SrcVPS68+v5vVfWYMC/3tevP/ZVK5WJV1+dkFq1q2ZIX0H5fYuP82cZebaBs0alplrD+5yZCpXrZzu93+/zL2evevxPHfXE+utdoCK8PR9T6dKtaq56BcXpVadWhnYd0B+cuZPMnvm7NI2jZo2Ssmipfv4o7scncpVK+fqB35U5l5/vvPP+cudS8O8d9l/lzTevHFefvzldT8QgPWk55K9/TE/PzfV69RMcd8hefS/nNuP7D0oT11ybw6+snMOvrJzJo8Yl6e635NRy+ztk2Sb/XdMvc0b5qMnVv7lMv1f6pV/XPNQ9v+/43LUDWdn0pAxebzb3RnRa9XfBA+woanIzz/97ZJ70+GqU3P83f+XavVqZVrxxLx+6xPp89gr62fwABXos/6D0vWSH5Q+v+We3yZJOh11aH7x4ysrqiwAgP8JBSXLnk6vhrFjx6ZLly555ZVXUrly5STJggULcsghh+TRRx/Npptumtdeey3z58/P4Ycfvtr3vXOLs9ascgDKqOWLBwC+kfGV1mhZDMByGi8sqOgSADZqQ4ts7AG+qcqxJgX4JkZGiDzANzFx0dyKLgFgo7Z7Qd2KLgFgo1a5ogsA+Ba4qvfPKroEgI1a5YbbVHQJwCpsUnvbii6B/wGTvxpU0SWUu6I17dCkSZO8/PLL6d+/fwYOHJiSkpK0bt06rVq1Km3ToUOHci0SAAAAAAAAAAAAAAAAAAAAYF1b4xCG/1g2eKGgwDcLAQAAAAAAAAAAAAAAAAAAABu3wrXp9Mgjj6Rt27apXr16qlevnp122imPPvpoedcGAAAAAAAAAAAAAAAAAADAWigpKfHwWOePb6OiNe1wxx135Nprr0337t3Trl27lJSUpGfPnunWrVsmTpyYyy+/fF3UCQAAAAAAAAAAAAAAAAAAALBOrXEIwz333JP77rsvZ599dum1Tp06pU2bNrn++uuFMAAAAAAAAAAAAAAAAAAAAAAbpcI17TBmzJjst99+K1zfb7/9MmbMmHIpCgAAAAAAAAAAAAAAAAAAAGB9W+MQhpYtW+aJJ55Y4frjjz+ebbfdtlyKAgAAAAAAAAAAAAAAAAAAAFjfila3Yd++fbPLLrvkpz/9aU455ZS8+eabadeuXQoKCvL222/nlVdeWWk4AwAAAAAAAAAAAAAAAAAAAMDGoHB1G+62227ZfffdM27cuHzwwQdp2LBhnn322TzzzDNp2LBhPvjgg5xwwgnrslYAAAAAAAAAAAAAAAAAAACAdaZodRv27NkzDz30UH74wx9m/vz5OfHEE3Prrbfm4IMPXpf1AQAAAAAAAAAAAAAAAAAAAKwXhavbcN99983vfve7jB07Nvfdd1+Ki4tz2GGHpUWLFvnFL36R4uLidVknAAAAAAAAAAAAAAAAAAAAwDq12iEM/1G9evWcc845ef311zNw4MCcfvrpeeCBB7L11lvn6KOPXhc1AgAAAAAAAAAAAAAAAAAAAKxzaxzCsKwWLVrkhz/8Ya655prUqVMnL730UnnVBQAAAAAAAAAAAAAAAAAAALBeFa1txzfeeCMPPfRQnn766VSqVCmnnHJKzjvvvPKsDQAAAAAAAAAAAAAAAAAAAGC9WaMQhpEjR+bhhx/Oww8/nGHDhmW//fbLPffck1NOOSU1a9ZcVzUCAAAAAAAAAAAAAAAAAACwBhalpKJLgI3SaocwHHbYYXnttdfSqFGjnH322enatWtatWq1LmsDAAAAAAAAAAAAAAAAAAAAWG9WO4ShevXqefrpp9OxY8dUqlRpXdYEAAAAAAAAAAAAAAAAAAAAsN6tdgjD888/vy7rAAAAAAAAAAAAAAAAAAAAAKhQhRVdAAAAAAAAAAAAAAAAAAAAAMCGQAgDAAAAAAAAAAAAAAAA8P/bu/f4r+f7f/y3d+fzQXQiRSdJlsgkiyaSj8aMQpMmbBNhKTPnMMccZmNkK5smTEtziJAhFiIayTHZZ+2UnPdBev7+2K/Xt3en9zvFm+16vVzel0uv5/P5ejzuz1eXy/31eD4fj9f9CQAAQBRhAAAAAAAAAAAAAAAAAAAAAEiiCAMAAAAAAAAAAAAAAAAAAABAEkUYAAAAAAAAAAAAAAAAAAAAAJIowgAAAAAAAAAAAAAAAAAAAACQRBEGAAAAAAAAAAAAAAAAAAAAgCSKMAAAAAAAAAAAAAAAAAAAAAAkSWpUdQAAAAAAAAAAAAAAAAAAAABsXEVRVHUI8KVUraoDAAAAAAAAAAAAAAAAAAAAAPgiUIQBAAAAAAAAAAAAAAAAAAAAIIowAAAAAAAAAAAAAAAAAAAAACRRhAEAAAAAAAAAAAAAAAAAAAAgiSIMAAAAAAAAAAAAAAAAAAAAAEkUYQAAAAAAAAAAAAAAAAAAAABIoggDAAAAAAAAAAAAAAAAAAAAQBJFGAAAAAAAAAAAAAAAAAAAAACSKMIAAAAAAAAAAAAAAAAAAAAAkEQRBgAAAAAAAAAAAAAAAAAAAIAkijAAAAAAAAAAAAAAAAAAAAAAJElqVHUAAAAAAAAAAAAAAAAAAAAAbFzLi6KqQ4AvpWpVHQAAAAAAAAAAAAAAAAAAAADAF4EiDAAAAAAAAAAAAAAAAAAAAABRhAEAAAAAAAAAAAAAAAAAAAAgiSIMAAAAAAAAAAAAAAAAAAAAAEkUYQAAAAAAAAAAAAAAAAAAAABIoggDAAAAAAAAAAAAAAAAAAAAQBJFGAAAAAAAAAAAAAAAAAAAAACSKMIAAAAAAAAAAAAAAAAAAAAAkEQRBgAAAAAAAAAAAAAAAAAAAIAkijAAAAAAAAAAAAAAAAAAAAAAJFGEAQAAAAAAAAAAAAAAAAAAACBJUqOqAwAAAAAAAAAAAAAAAAAAAGDjKlJUdQjwpVStqgMAAAAAAAAAAAAAAAAAAAAA+CJQhAEAAAAAAAAAAAAAAAAAAAAgijAAAAAAAAAAAAAAAAAAAAAAJFGEAQAAAAAAAAAAAAAAAAAAACCJIgwAAAAAAAAAAAAAAAAAAAAASRRhAAAAAAAAAAAAAAAAAAAAAEiiCAMAAAAAAAAAAAAAAAAAAABAEkUYAAAAAAAAAAAAAAAAAAAAAJIowgAAAAAAAAAAAAAAAAAAAACQRBEGAAAAAAAAAAAAAAAAAAAAgCSKMAAAAAAAAAAAAAAAAAAAAAAkSWpUdQAAAAAAAAAAAAAAAAAAAABsXMuLoqpDgC+lalUdAAAAAAAAAAAAAAAAAAAAAMAXgSIMAAAAAAAAAAAAAAAAAAAAAFGEAQAAAAAAAAAAAAAAAAAAACCJIgwAAAAAAAAAAAAAAAAAAAAASRRhAAAAAAAAAAAAAAAAAAAAAEiiCAMAAAAAAAAAAAAAAAAAAABAEkUYAAAAAAAAAAAAAAAAAAAAAJIowgAAAAAAAAAAAAAAAAAAAACQRBEGAAAAAAAAAAAAAAAAAAAAgCSKMAAAAAAAAAAAAAAAAAAAAAAkUYQBAAAAAAAAAAAAAAAAAAAAIElSo6oDAAAAAAAAAAAAAAAAAAAAYOMqiqKqQ4AvpWpVHQAAAAAAAAAAAAAAAAAAAADAF4EiDAAAAAAAAAAAAAAAAAAAAABRhAEAAAAAAAAAAAAAAAAAAAAgiSIMAAAAAAAAAAAAAAAAAAAAAEkUYQAAAAAAAAAAAAAAAAAAAABIoggDAAAAAAAAAAAAAAAAAAAAQBJFGAAAAAAAAAAAAAAAAAAAAACSKMIAAAAAAAAAAAAAAAAAAAAAkEQRBgAAAAAAAAAAAAAAAAAAAIAkijAAAAAAAAAAAAAAAAAAAAAAJFGEAQAAAAAAAAAAAAAAAAAAACBJUqOqAwAAAAAAAAAAAAAAAAAAAGDjKlJUdQjwpVStqgMAAAAAAAAAAAAAAAAAAAAA+CJQhAEAAAAAAAAAAAAAAAAAAAAgijAAAAAAAAAAAAAAAAAAAAAAJFGEAQAAAAAAAAAAAAAAAAAAACCJIgwAAAAAAAAAAAAAAAAAAAAASRRhAAAAAAAAAAAAAAAAAAAAAEiiCAMAAAAAAAAAAAAAAAAAAABAEkUYAAAAAAAAAAAAAAAAAAAAAJIowgAAAAAAAAAAAAAAAAAAAACQRBEGAAAAAAAAAAAAAAAAAAAAgCSKMAAAAAAAAAAAAAAAAAAAAAAkSWpUdQAAAAAAAAAAAAAAAAAAAABsXEVRVHUI8KVUraoDAAAAAAAAAAAAAAAAAAAAAPgiUIQBAAAAAAAAAAAAAAAAAAAAIIowAAAAAAAAAAAAAAAAAAAAACRRhAEAAAAAAAAAAAAAAAAAAAAgiSIMAAAAAAAAAAAAAAAAAAAAAEkUYQAAAAAAAAAAAAAAAAAAAABIoggDAAAAAAAAAAAAAAAAAAAAQBJFGAAAAAAAAAAAAAAAAAAAAACSKMIAAAAAAAAAAAAAAAAAAAAAkEQRBgAAAAAAAAAAAAAAAAAAAIAkijAAAAAAAAAAAAAAAAAAAAAAJElqVHUAAAAAAAAAAAAAAAAAAAAAbFxFUVR1CPClVK2qAwAAAAAAAAAAAAAAAAAAAAD4IlCEAQAAAAAAAAAAAAAAAAAAACCKMAAAAAAAAAAAAAAAAAAAAAAkUYQBAAAAAAAAAAAAAAAAAAAAIIkiDAAAAAAAAAAAAAAAAAAAAABJFGEAAAAAAAAAAAAAAAAAAAAASKIIAwAAAAAAAAAAAAAAAAAAAEASRRgAAAAAAAAAAAAAAAAAAAAAkijCAAAAAAAAAAAAAAAAAAAAAJBEEQYAAAAAAAAAAAAAAAAAAACAJIowAAAAAAAAAAAAAAAAAAAAACRJalR1AAAAAAAAAAAAAAAAAAAAAGxcRVUHAF9S1ao6AAAAAAAAAAAAAAAAAAAAAIAvAkUYAAAAAAAAAAAAAAAAAAAAAKIIAwAAAAAAAAAAAAAAAAAAAEASRRgAAAAAAAAAAAAAAAAAAAAAkijCAAAAAAAAAAAAAAAAAAAAAJBEEQYAAAAAAAAAAAAAAAAAAACAJIowAAAAAAAAAAAAAAAAAAAAACRRhAEAAAAAAAAAAAAAAAAAAAAgiSIMAAAAAAAAAAAAAAAAAAAAAEkUYQAAAAAAAAAAAAAAAAAAAABIoggDAAAAAAAAAAAAAAAAAAAAQJKkrCiKoqqDgC+6Dz/8MBdccEFOPfXU1K5du6rDAfjSkUcBNow8CrBh5FGADSeXAmwYeRRgw8ijABtGHgXYMPIowIaRRwE2nFwKAPD5U4QBKuGdd95J48aN8/bbb6dRo0ZVHQ7Al448CrBh5FGADSOPAmw4uRRgw8ijABtGHgXYMPIowIaRRwE2jDwKsOHkUgCAz1+1qg4AAAAAAAAAAAAAAAAAAAAA4ItAEQYAAAAAAAAAAAAAAAAAAACAKMIAAAAAAAAAAAAAAAAAAAAAkEQRBqiU2rVr56yzzkrt2rWrOhSALyV5FGDDyKMAG0YeBdhwcinAhpFHATaMPAqwYeRRgA0jjwJsGHkUYMPJpQAAn7+yoiiKqg4CAAAAAAAAAAAAAAAAAAAAoKpVq+oAAAAAAAAAAAAAAAAAAAAAAL4IFGEAAAAAAAAAAAAAAAAAAAAAiCIMAAAAAAAAAAAAAAAAAAAAAEkUYYAq9+CDD6asrCxvvfVWVYcC8JmoTJ6bOHFimjRp8rnFBPBZKysry9SpUz/zfuRPgA03bNiwHHDAAZU6duHChSkrK8vcuXM/05gAAPjPdfbZZ6d79+5r3e9aH2DDVZRrK2PVewDm9YGqZpwIAMDnZWNcV38R7LHHHjnxxBPXun991goAAADw30kRBqrUsGHDUlZWttrfPvvsU6n3V3Rz5Mtg1113zeLFi9O4ceOqDgX4gnv00UdTvXr1SudIgP9kf/3rX3P88cdn6623Tu3atdOmTZsMHDgw999/f1WHliRZvHhxBgwYsFHbbNeuXa644opy2wYPHpwXX3xxo/YD8GmtuMa/8MILy22fOnVqysrKqiiq/2dtBRSuvPLKTJw4sVJttGnTJosXL85222238QMEqIQ33ngjw4cPT+vWrVOrVq20bds2J5xwQpYsWVLVoQF8qaw6P9WsWbPss88+efbZZ6s6NNf6wBfWyrmzZs2aadGiRfbaa6/88pe/zPLly6s6PIAvrbX98GvV4i8bY5y4riKz/wlrsABWVdkcC/BFsur199Zbb52TTz4577///ucWw8knn/yZrsHaaqutMn369CrPx+uzVgBgfQ0cODD9+vVb477HHnssZWVleeqppz7nqAAAWF+KMFDl9tlnnyxevLjc30033bTR2i+KIsuWLdto7a2vjz/+eJ37a9WqlZYtW34hfpACfLH98pe/zPHHH59HHnkkixYt2qC2PvnkEwvigC+thQsXZscdd8wDDzyQiy++OPPmzcv06dPTt2/fjBgxoqrDS5K0bNkytWvX/sz7qVu3bpo3b/6Z9wNQWXXq1MlFF12UpUuXfq79VnTtvS6NGzeu9BPkqlevnpYtW6ZGjRqfuj+AT+vVV1/NTjvtlBdffDE33XRTXn755fz85z/P/fffn169euXNN9+s6hABvlRWnp+6//77U6NGjey3335rPX5Dxpzrw7U+8EW2IncuXLgwd999d/r27ZsTTjgh++23X5XOyQP8NzBOBAD477Hi+vvVV1/Neeedl6uvvjonn3zy59Z/gwYN0qxZs8+k7WeffTZLlixJ3759P5P218f6rBUAWF/Dhw/PAw88kNdff321fb/85S/TvXv39OjRowoiAwBgfSjCQJWrXbt2WrZsWe6vadOmefDBB1OrVq08/PDDpWPHjRuXTTfdNIsXL86wYcPyhz/8IVdeeWWp4ufChQtLVTHvueee7LTTTqldu3YefvjhvPLKK9l///3TokWLNGjQID179sx9991XavvUU0/NLrvsslp822+/fc4666zS6wkTJqRLly6pU6dOttlmm1x99dWlfSsqxt9yyy3ZY489UqdOndx44415/fXXM3DgwDRt2jT169dP165dc9dddyVZc1Xl2267LV27dk3t2rXTrl27jBs3rlxM7dq1y49//OMceeSRadiwYbbccstcd911G/x/AXxxvf/++7nlllvy/e9/P/vtt99q1XenTZuWjh07pm7duunbt29uuOGGcrll4sSJadKkSe64445su+22qV27dl5//fV89NFHGTNmTDbffPPUr18/X/3qV/Pggw+Wa/vRRx9Nnz59Urdu3bRp0yYjR44sV1X5xhtvzE477ZSGDRumZcuWOeyww/L3v/99tXOYNWtWvvKVr6ROnTr56le/mnnz5q3znH//+99nxx13TJ06dbL11lvnnHPOKbeAr6ysLNdff32++c1vpl69eunYsWOmTZtWro3nn38+++67bxo0aJAWLVrk8MMPzz//+c/S/t/+9rfp1q1b6tatm2bNmqVfv36lc3vwwQez8847p379+mnSpEl69+69xhthwOfv2GOPTVlZWR5//PEcdNBB6dSpU7p27Zof/OAH+eMf/5gkWbRoUfbff/80aNAgjRo1yqBBg/K3v/2t1MbZZ5+d7t2759e//nXatWuXxo0b55BDDsm7775bOmZdOSL5943wFWO2Vq1a5bjjjivtKysry9SpU0uv//d//zeDBw9O06ZN06xZs+y///5ZuHBhaf+KJ3BceumladWqVZo1a5YRI0aUfuCxxx575PXXX89JJ51UGvsm/y+/r+yaa65J+/btU6tWrXTu3Dm//vWvS/vW9ISjt956K2VlZaX8v3Tp0gwZMiSbbbZZ6tatm44dO2bChAnl3j9lypT07ds39erVy1e+8pU89thj5WKo6Lvj6quvTseOHVOnTp20aNEiBx10UKU/d+CLrV+/fmnZsmUuuOCCtR5TUY5YNYcmSZMmTUpj4LVdey9fvjxjx47NFltskdq1a6d79+6ZPn16qY2tttoqSbLDDjukrKwse+yxR5LVn4K0fPnyXHTRRenQoUNq166dLbfcMueff365vlfk0crkzFtuuSVf+9rXUrdu3fTs2TMvvvhinnjiiey0005p0KBB9tlnn/zjH//4NB838F9mxIgRqVWrVu69997svvvu2XLLLTNgwIDcd999+d///d+cdtppSf597/Dcc8/NYYcdlgYNGqR169a56qqryrX19ttv55hjjknz5s3TqFGjfP3rX88zzzxT2l+Z8fIee+yRkSNHZsyYMdlkk03SsmXLnH322evVzzPPPJO+ffumYcOGadSoUXbcccc8+eSTSbLOe6oAG8PK81Pdu3fPKaeckjfeeCP/+Mc/1jrmTNY9T5Qkp5xySjp16pR69epl6623zhlnnLHOAg6vvfZaOnTokO9///tZvnz5atf6lcnJ7777boYMGZL69eunVatWufzyyz3JGPhMrMidm2++eXr06JEf/ehHuf3223P33XeXrtsvu+yydOvWLfXr10+bNm1y7LHH5r333iu1sSLP3XPPPenSpUvp2njx4sXl+lrX/deKxpkrrCt3Tp8+PbvttluaNGmSZs2aZb/99ssrr7yykT8xgI1nbePEa6+9Nm3atEm9evVy8MEHb7SnCC9dujRDhw5N06ZNU69evQwYMCAvvfTSavFMnTo1nTp1Sp06dbLXXnvljTfe2Cj9A3yeKlqvWdHc1UcffZTjjjsurVq1Sp06ddKuXbtyc2WVHb8CrLDi+rtNmzY57LDDMmTIkEydOjUffvhhRo4cmebNm6dOnTrZbbfd8sQTT5Tet2JN+p133rnWtZqVGcetGGuuUNG6piRZvHhx/ud//id169bNVlttld/85jdp165drrjiinLndvvtt6d///6VerhNRWPS5N/rUnfffffUq1cvTZs2Tf/+/df60Ijp06encePG+dWvflXuvFaozNzXCy+8kN122y116tTJtttum/vuu2+N3xMA++23X5o3b77amv8PPvggN998c4YPH17hOLSi3FqZ9aBJxevpAQBYO0UY+MJasTjs8MMPz9tvv51nnnkmp512WsaPH59WrVrlyiuvTK9evXL00UeXnlTUpk2b0vvHjBmTCy64IPPnz8/222+f9957L/vuu2/uu+++PP300+nfv38GDhxYepr8kCFDMnv27HILK5577rnMmzcvQ4YMSZKMHz8+p512Ws4///zMnz8/P/7xj3PGGWfkhhtuKBf7KaeckpEjR2b+/Pnp379/RowYkQ8//DAPPfRQ5s2bl4suuigNGjRY43nPmTMngwYNyiGHHJJ58+bl7LPPzhlnnLHaxde4ceOy00475emnn86xxx6b73//+3nhhRc2xkcPfAHdfPPN6dy5czp37pxvf/vbmTBhQoqiSPLvGygHHXRQDjjggMydOzff/e53Sz+8WNkHH3yQCy64INdff32ee+65NG/ePN/5zncya9asTJ48Oc8++2wOPvjg7LPPPqUb1fPmzUv//v1z4IEH5tlnn83NN9+cRx55pNxCt48++ijnnntunnnmmUydOjWvvfZahg0btlr/o0ePzqWXXponnngizZs3zze+8Y21Lj6+55578u1vfzsjR47M888/n2uvvTYTJ04s/fhthXPOOSeDBg3Ks88+m3333TdDhgwpPflz8eLF2X333dO9e/c8+eSTmT59ev72t79l0KBBpf2HHnpojjzyyMyfPz8PPvhgDjzwwBRFkWXLluWAAw7I7rvvnmeffTaPPfZYjjnmmNKPnoGq8+abb2b69OkZMWJE6tevv9r+Jk2apCiKHHDAAXnzzTfzhz/8ITNmzMgrr7ySwYMHlzv2lVdeydSpU3PHHXfkjjvuyB/+8IdceOGFSdadI5J/FzoYMWJEjjnmmMybNy/Tpk1Lhw4d1hjzBx98kL59+6ZBgwZ56KGH8sgjj5QWFn/00Uel42bOnJlXXnklM2fOzA033JCJEyeWxoBTpkzJFltskbFjx5bGvmvyu9/9LieccEJGjRqVP/3pT/nud7+b73znO5k5c2alP+Mzzjgjzz//fO6+++7Mnz8/11xzTTbddNNyx5x22mk5+eSTM3fu3HTq1CmHHnpoqVBORd8dTz75ZEaOHJmxY8dmwYIFmT59evr06VOpzx344qtevXp+/OMf56qrrsqf//zn1fZXZnxZWatee1955ZUZN25cLr300jz77LPp379/vvGNb5TGto8//niS5L777svixYszZcqUNbZ76qmn5qKLLirlw9/85jdp0aLFGo+tTM4866yzcvrpp+epp55KjRo1cuihh2bMmDG58sorS0UjzzzzzPU+f+C/y5tvvpl77rknxx57bOrWrVtuX8uWLTNkyJDcfPPNpXHTJZdcku233z5PPfVUTj311Jx00kmZMWNGkqQoivzP//xP/vrXv+auu+7KnDlz0qNHj+y5556la+pk3ePlFW644YbUr18/s2fPzsUXX5yxY8euVz9DhgzJFltskSeeeCJz5szJD3/4w9SsWTNJ1uueKsCGeu+99zJp0qR06NCh3BPeVh1zVmaeqGHDhpk4cWKef/75XHnllRk/fnwuv/zyNfb7pz/9Kb17987BBx+ca665JtWqrXnqtqKc/IMf/CCzZs3KtGnTMmPGjDz88MN56qmnNtKnA7BuX//61/OVr3yldJ1drVq1/OQnP8mf/vSn3HDDDXnggQcyZsyYcu/54IMPcumll+bXv/51HnrooSxatKjcEz3Xdf91Y41n33///fzgBz/IE088kfvvvz/VqlXLN7/5zSxfvvyz/LgANqqXX345t9xyS37/+99n+vTpmTt3bkaMGLFR2h42bFiefPLJTJs2LY899liKosi+++5bbo7/gw8+yPnnn58bbrghs2bNyjvvvJNDDjlko/QP8Hmp7HrNdfnJT36SadOm5ZZbbsmCBQty4403pl27dkkqP34FWJe6devm448/zpgxY3LbbbflhhtuyFNPPZUOHTqkf//+q+WTitZqfppx3LrWNSXJ0KFD85e//CUPPvhgbrvttlx33XVrfJjXtGnTsv/++1fqvCsak86dOzd77rlnunbtmsceeyyPPPJIBg4cmE8++WS1tiZPnpxBgwblV7/6VYYOHbrWPtc197V8+fIccMABqVevXmbPnp3rrrtujWt1AZKkRo0aGTp0aCZOnFhu/eOtt96ajz76KL169apwHFrZ3LouFa2nBwCgAgVUoSOOOKKoXr16Ub9+/XJ/Y8eOLYqiKD788MNihx12KAYNGlR07dq1OOqoo8q9f/fddy9OOOGEcttmzpxZJCmmTp1aYf/bbrttcdVVV5Veb7/99qW+i6IoTj311KJnz56l123atCl+85vflGvj3HPPLXr16lUURVG89tprRZLiiiuuKHdMt27dirPPPnuNMayId+nSpUVRFMVhhx1W7LXXXuWOGT16dLHtttuWXrdt27b49re/XXq9fPnyonnz5sU111xT4TkDX0677rprKbd8/PHHxaabblrMmDGjKIqiOOWUU4rtttuu3PGnnXZaudwyYcKEIkkxd+7c0jEvv/xyUVZWVvzv//5vuffuueeexamnnloURVEcfvjhxTHHHFNu/8MPP1xUq1at+Ne//rXGWB9//PEiSfHuu+8WRfH/8tzkyZNLxyxZsqSoW7ducfPNN5fia9y4cWn/1772teLHP/5xuXZ//etfF61atSq9TlKcfvrppdfvvfdeUVZWVtx9991FURTFGWecUey9997l2njjjTeKJMWCBQuKOXPmFEmKhQsXrnYOS5YsKZIUDz744BrPEag6s2fPLpIUU6ZMWesx9957b1G9evVi0aJFpW3PPfdckaR4/PHHi6IoirPOOquoV69e8c4775SOGT16dPHVr361KIpinTmiKIqidevWxWmnnbbWGJIUv/vd74qiKIpf/OIXRefOnYvly5eX9n/44YdF3bp1i3vuuacoin+Pi9u2bVssW7asdMzBBx9cDB48uPS6bdu2xeWXX16un1Xz56677locffTR5Y45+OCDi3333bcoiv83Xn366adL+5cuXVokKWbOnFkURVEMHDiw+M53vrPG81rx/uuvv760bcVnO3/+/KIoKv7uuO2224pGjRqV++xXqOhzB77YjjjiiGL//fcviqIodtlll+LII48siqIofve73xUrbkFVZny5cg5doXHjxsWECROKolj7tXfr1q2L888/v9y2nj17Fscee2y5962cA1eN+5133ilq165djB8/fo3nuGob65szb7rppiJJcf/995e2XXDBBUXnzp3X2AbACn/84x/XmB9XuOyyy4okxd/+9reibdu2xT777FNu/+DBg4sBAwYURVEU999/f9GoUaPi//7v/8od0759++Laa68tiqLi8XJR/Pve7G677VaujZ49exannHJKpftp2LBhMXHixDWe07ruqQJsqFXnp5IUrVq1KubMmVMUxdrHnBXNE63JxRdfXOy4446l12eddVbxla98pXj00UeLTTbZpLjkkkvKHb/qtX5FOfmdd94patasWdx6662l/W+99VZRr1691ebQADbEytfPqxo8eHDRpUuXNe675ZZbimbNmpVer5izevnll0vbfvaznxUtWrQovV7X/deNNZ5d1d///vciSTFv3ryiKFa/B7DqvD7AxrK2tVN16tRZbc5/1XFi9erVizfeeKO07e677y6qVatWLF68eI19rchtdevWXa2/atWqlcaPL774YpGkmDVrVum9//znP4u6desWt9xySymeJMUf//jH0jHz588vkhSzZ8/eSJ8OwIapTI6tzHrNiuaujj/++OLrX/96uTUBK1Rm/AqwslWvv2fPnl00a9asOOigg4qaNWsWkyZNKu376KOPitatWxcXX3xxURSVX6tZ0ThuxT3MlWNa17qmFe9/4oknSvtfeumlIkm5tU5//vOfi5o1axZLliwpF++arrUrMyY99NBDi969e6/1s1zxO4Of/exnRePGjYsHHnig3P5VP+uK5r7uvvvuokaNGuXG2zNmzFjnHB7w321Fflw5//Tp06c49NBDKxyHVia3VmY9aEXr6QEAWLc1P04FPkd9+/bN3Llzy/2tqMpeq1at3Hjjjbntttvyr3/9K1dccUWl291pp53KvX7//fczZsyYbLvttmnSpEkaNGiQF154IYsWLSodM2TIkEyaNCnJvysQ33TTTRkyZEiS5B//+EfeeOONDB8+PA0aNCj9nXfeeXnllVfW2ffIkSNz3nnnpXfv3jnrrLPy7LPPrjXu+fPnp3fv3uW29e7dOy+99FK5ypzbb7996d9lZWVp2bLlele1A74cFixYkMcff7xUabhGjRoZPHhwfvnLX5b29+zZs9x7dt5559XaqVWrVrnc8dRTT6UoinTq1KlcXvvDH/5Qymtz5szJxIkTy+3v379/li9fntdeey1J8vTTT2f//fdP27Zt07Bhw+yxxx5JUi6/JkmvXr1K/95kk03SuXPnzJ8/f43nPGfOnIwdO7Zcv0cffXQWL16cDz74oHTcyudTv379NGzYsJQL58yZk5kzZ5ZrY5tttkny76cffeUrX8mee+6Zbt265eCDD8748eOzdOnSUnzDhg1L//79M3DgwFx55ZVrfeo88Pkq/v+KwGVlZWs9Zv78+WnTpk3atGlT2rZiDLhy3mnXrl0aNmxYet2qVatSDllXjvj73/+ev/zlL9lzzz0rFfOcOXPy8ssvp2HDhqV8tMkmm+T//u//yo0ju3btmurVq68xnspa21hybfl2Tb7//e9n8uTJ6d69e8aMGZNHH310tWNWzr+tWrVKknL5d13fHXvttVfatm2brbfeOocffngmTZpUyu3r+tyBL5eLLrooN9xwQ55//vly2yszvqysla+933nnnfzlL3/Z4Bw4f/78fPjhh5XO8eubM1u0aJEk6datW7ltrueBDbXqOHnla/AVr1fkwzlz5uS9995Ls2bNyuXj1157rdz4dF3j5RVWznGrHlOZfn7wgx/kqKOOSr9+/XLhhReW63997qkCfBorz0/Nnj07e++9dwYMGJDXX3+9dMzKY87KzhP99re/zW677ZaWLVumQYMGOeOMM1a7V7po0aL069cvp59+erknv6/NunLyq6++mo8//rjcPeHGjRunc+fO6/+hAHxKRVGUxqIzZ87MXnvtlc033zwNGzbM0KFDs2TJkrz//vul4+vVq5f27duXXq+c1yq6/7qxxrOvvPJKDjvssGy99dZp1KhRttpqqySrz28BfB7WtHbq+uuvr/B9W265ZbbYYovS6169emX58uVZsGDBOt938803r9bfymPf+fPnp0aNGvnqV79a2tasWbPV5vhr1KhR7n3bbLPNavNxAFWtohxb2fWa6zJs2LDMnTs3nTt3zsiRI3PvvfeW9lV2/AqwsjvuuCMNGjRInTp10qtXr/Tp0yfHH398Pv7443I5q2bNmtl5551XG39VtFbz04zj1rWuacGCBalRo0Z69OhR2t+hQ4c0bdq0XBvTpk1L7969s8kmm1T4GVRmTDp37twK5/Zvu+22nHjiibn33nvTt2/fCvtd19zXggUL0qZNm7Rs2bK0f01rdQFW2GabbbLrrruW1vy/8sorefjhh3PkkUdWOA6tbG6tSEXr6QEAWLcaVR0A1K9fPx06dFjr/hU/YHjzzTfz5ptvpn79+pVud2WjR4/OPffck0svvTQdOnRI3bp1c9BBB+Wjjz4qHXPYYYflhz/8YZ566qn861//yhtvvFH60fPy5cuTJOPHjy93QydJuZtKa+r7qKOOSv/+/XPnnXfm3nvvzQUXXJBx48bl+OOPXy3ulReorLxtVTVr1iz3uqysrBQj8J/lF7/4RZYtW5bNN9+8tK0oitSsWTNLly6tdN6oW7duueOWL1+e6tWrZ86cOavlsQYNGpSO+e53v5uRI0eu1t6WW26Z999/P3vvvXf23nvv3Hjjjdlss82yaNGi9O/fv1x+XZu1/Yh6+fLlOeecc3LggQeutq9OnTqlf68rFy5fvjwDBw7MRRddtFobrVq1SvXq1TNjxow8+uijuffee3PVVVfltNNOy+zZs7PVVltlwoQJGTlyZKZPn56bb745p59+embMmJFddtmlwvMCPjsdO3ZMWVlZ5s+fnwMOOGCNx6wpL65p+7pyyLpyxKabbrpeMS9fvjw77rhjqdjXyjbbbLNKxbM+1vSdsGJbtWrVSttW+Pjjj8sdv+IHJ3feeWfuu+++7LnnnhkxYkQuvfTSNca6ou2V8++6vjtq1aqVp556Kg8++GDuvffenHnmmTn77LPzxBNPpEmTJuvMzcCXR58+fdK/f//86Ec/yrBhw0rbK8oRyb/zyqrj2VVzVbL6tfeK965sbd8Ja1O3bt1KH5t8+py56jbX80BFOnTokLKysjz//PNrHAe/8MILadq06TrHqiuP21q1apUHH3xwtWOaNGlS+ndlxqcVXZdX1M/ZZ5+dww47LHfeeWfuvvvunHXWWZk8eXK++c1vrtc9VYBPY9X5qR133DGNGzfO+PHjc9RRR5WOWaEy80R//OMfc8ghh+Scc85J//7907hx40yePDnjxo0rd/xmm22W1q1bZ/LkyRk+fHgaNWq0zljXlW/XVrByTfeIAT4r8+fPz1ZbbZXXX389++67b773ve/l3HPPzSabbJJHHnkkw4cPL3dtv6a8tiJvVXRtvrHGswMHDkybNm0yfvz4tG7dOsuXL892221XqfktgI1tTWun/vznP693OyvGhBXdE23Tps1q/a2cf9c2llzT/dY19bU+92QBPmsV5djKrLuqaO6qR48eee2113L33Xfnvvvuy6BBg9KvX7/89re/rfT4FWBlffv2zTXXXJOaNWumdevWqVmzZp555pkkn35OfEPHcZW5R7mqVbdPmzYt+++/f4WxVtTmijgrM7/fvXv3PPXUU5kwYUJ69uxZ4WdV0Xka6wLra/jw4TnuuOPys5/9LBMmTEjbtm2z5557VjgOrUxurcx60IrW0wMAsG7VqjoAWJdXXnklJ510UsaPH59ddtklQ4cOLbcwolatWpWuNvzwww9n2LBh+eY3v5lu3bqlZcuWWbhwYbljtthii/Tp0yeTJk3KpEmT0q9fv9JTKlu0aJHNN988r776ajp06FDurzI/CGvTpk2+973vZcqUKRk1alTGjx+/xuO23XbbPPLII+W2Pfroo+nUqdNqP5IG/vMtW7Ysv/rVrzJu3LhyFdmfeeaZtG3bNpMmTco222yTJ554otz7nnzyyQrb3mGHHfLJJ5/k73//+2p5bUWl3h49euS5555bbX+HDh1Sq1atvPDCC/nnP/+ZCy+8MF/72teyzTbbrPUpvn/84x9L/166dGlefPHFUiXNVfXo0SMLFixYY78rbhhVZEXs7dq1W62NFYuny8rK0rt375xzzjl5+umnU6tWrfzud78r9xmdeuqpefTRR7PddtvlN7/5TaX6Bj47m2yySfr375+f/exn5Z6ctsJbb72VbbfdNosWLcobb7xR2v7888/n7bffTpcuXSrd19pyRMOGDdOuXbvcf//9lWqnR48eeemll9K8efPV8lHjxo0rHU9lxr5dunRZ41hyxXmvKPqwePHi0v65c+eu1s5mm22WYcOG5cYbb8wVV1yR6667rtJxVvTdkfy7on6/fv1y8cUX59lnn83ChQvzwAMPJKk4NwNfHhdeeGF+//vfl4orJpXLEZtttlm5PPXSSy/lgw8+WGdfjRo1SuvWrdeZA1e0v65c2rFjx9StW7fSOX5FvJ82ZwJUVrNmzbLXXnvl6quvzr/+9a9y+/76179m0qRJGTx4cGmRxsrX4Cter7gG79GjR/7617+mRo0aq+Xi9S04ti6V7adTp0456aSTcu+99+bAAw/MhAkTSvsqe08VYGMoKytLtWrVVsuzK1RmnmjWrFlp27ZtTjvttOy0007p2LFjXn/99dXaqlu3bu64447UqVMn/fv3z7vvvvup427fvn1q1qyZxx9/vLTtnXfeyUsvvfSp2wRYHw888EDmzZuXb33rW3nyySezbNmyjBs3Lrvssks6deqUv/zlL+vVXkX3XzfGeHbJkiWZP39+Tj/99Oy5557p0qVLli5dul5xAnwRLFq0qFyefeyxx1KtWrV06tRpg9rddttts2zZssyePbu0bcmSJXnxxRfLzbUtW7as3NqEBQsW5K233lrrOgCAL6LKrNeszNxVo0aNMnjw4IwfPz4333xzbrvttrz55puf2/1Y4D/LigIybdu2LRUFWDGnvnLO+vjjj/Pkk0+uth6qorWaG3sct80222TZsmV5+umnS9tefvnlvPXWW6XX7733XmbOnJlvfOMblWqzMmPS7bffvsK5/fbt22fmzJm5/fbbN7jQ9zbbbJNFixblb3/7W2nbqmt3AVY1aNCgVK9ePb/5zW9yww035Dvf+U7KysoqHIdWJrdWZj1oZdbTAwCwdjWqOgD48MMP89e//rXctho1aqRp06Y5/PDDs/fee+c73/lOBgwYkG7dumXcuHEZPXp0kqRdu3aZPXt2Fi5cmAYNGmSTTTZZaz8dOnTIlClTMnDgwJSVleWMM85Y45MmhwwZkrPPPjsfffRRLr/88nL7zj777IwcOTKNGjXKgAED8uGHH+bJJ5/M0qVL84Mf/GCtfZ944okZMGBAOnXqlKVLl+aBBx5Y6w8AR40alZ49e+bcc8/N4MGD89hjj+WnP/1prr766rW2D/znuuOOO7J06dIMHz58tR/qHnTQQfnFL36RKVOm5LLLLsspp5yS4cOHZ+7cuZk4cWKSdVcm7tSpU4YMGZKhQ4dm3Lhx2WGHHfLPf/4zDzzwQLp165Z99903p5xySnbZZZeMGDEiRx99dOrXr5/58+dnxowZueqqq0pPNL/qqqvyve99L3/6059y7rnnrrG/sWPHplmzZmnRokVOO+20bLrppmt9iv2ZZ56Z/fbbL23atMnBBx+catWq5dlnn828efNy3nnnVeqzGzFiRMaPH59DDz00o0ePzqabbpqXX345kydPzvjx4/Pkk0/m/vvvz957753mzZtn9uzZ+cc//pEuXbrktddey3XXXZdvfOMbad26dRYsWJAXX3wxQ4cOrVTfwGfr6quvzq677pqdd945Y8eOzfbbb59ly5ZlxowZueaaa/L8889n++23z5AhQ3LFFVdk2bJlOfbYY7P77rtnp512qlQfs2fPXmuOSP49Lvze976X5s2bZ8CAAXn33Xcza9asNU7WDRkyJJdcckn233//jB07NltssUUWLVqUKVOmZPTo0dliiy0qFVO7du3y0EMP5ZBDDknt2rXXuCBj9OjRGTRoUHr06JE999wzv//97zNlypTcd999Sf79I49ddtklF154Ydq1a5d//vOfOf3008u1ceaZZ2bHHXdM165d8+GHH+aOO+5Yr+IVFX133HHHHXn11VfTp0+fNG3aNHfddVeWL1+ezp07V/i5A18u3bp1y5AhQ3LVVVeVtlWUI5Lk61//en76059ml112yfLly3PKKaes9rSJNRk9enTOOuustG/fPt27d8+ECRMyd+7cTJo0KUnSvHnz1K1bN9OnT88WW2yROnXqrDbGrlOnTk455ZSMGTMmtWrVSu/evfOPf/wjzz33XIYPH75anxuaMwHWx09/+tPsuuuu6d+/f84777xstdVWee655zJ69OhsvvnmOf/880vHzpo1KxdffHEOOOCAzJgxI7feemvuvPPOJEm/fv3Sq1evHHDAAbnooovSuXPn/OUvf8ldd92VAw44oNJj5opU1E/Xrl0zevToHHTQQdlqq63y5z//OU888US+9a1vJVm/e6oAn8bK81NLly7NT3/607z33nsZOHDgWt9T0TxRhw4dsmjRokyePDk9e/bMnXfeudbCgvXr18+dd96ZAQMGZMCAAZk+fXoaNGiw3ufRsGHDHHHEERk9enQ22WSTNG/ePGeddVaqVavmqWzARrcid37yySf529/+lunTp+eCCy7Ifvvtl6FDh2bevHlZtmxZrrrqqgwcODCzZs3Kz3/+8/XuZ133XzfGeLZp06Zp1qxZrrvuurRq1SqLFi3KD3/4w0/zkQBUqTp16uSII47IpZdemnfeeScjR47MoEGDSg9d+LQ6duyY/fffP0cffXSuvfbaNGzYMD/84Q+z+eabl3tycc2aNXP88cfnJz/5SWrWrJnjjjsuu+yyS3beeecNPTWAz01l1mtWNHd1+eWXp1WrVunevXuqVauWW2+9NS1btkyTJk0+t/uxwH+++vXr5/vf/37pPuCWW26Ziy++OB988MFqc9kVrdXc2OO4bbbZJv369csxxxyTa665JjVr1syoUaNSt27d0j3K6dOnp2PHjtl6661Xe/+8efPSsGHDctu6d+9e4Zj01FNPTbdu3XLsscfme9/7XmrVqpWZM2fm4IMPXq0g+MyZM7PHHnukRo0aueKKKz7Vee61115p3759jjjiiFx88cV59913c9pppyVZ91pd4L9bgwYNMnjw4PzoRz/K22+/nWHDhiWpeBxamdxamfWgFa2n96BYAIB1q9yjpOEzNH369LRq1arc32677Zbzzz8/CxcuLD1BsmXLlrn++utz+umnl6qznXzyyalevXq23XbbbLbZZlm0aNFa+7n88svTtGnT7Lrrrhk4cGD69++fHj16rHbcwQcfnCVLluSDDz5Y7cfBRx11VK6//vpMnDgx3bp1y+67756JEyeWnnC0Np988klGjBiRLl26ZJ999knnzp3XWlShR48eueWWWzJ58uRst912OfPMMzN27NjSxRbw3+UXv/hF+vXrt8YnpX/rW9/K3Llzs3Tp0vz2t7/NlClTsv322+eaa64p3ditXbv2OtufMGFChg4dmlGjRqVz5875xje+kdmzZ6dNmzZJ/l0p+A9/+ENeeumlfO1rX8sOO+yQM844I61atUry7wqaEydOzK233pptt902F154YS699NI19nXhhRfmhBNOyI477pjFixdn2rRppacRr6p///654447MmPGjPTs2TO77LJLLrvssrRt27bSn13r1q0za9asfPLJJ+nfv3+22267nHDCCWncuHGqVauWRo0a5aGHHsq+++6bTp065fTTT8+4ceMyYMCA1KtXLy+88EK+9a1vpVOnTjnmmGNy3HHH5bvf/W6l+wc+O1tttVWeeuqp9O3bN6NGjcp2222XvfbaK/fff3+uueaalJWVZerUqWnatGn69OmTfv36Zeutt87NN99c6T7WlSOS5IgjjsgVV1yRq6++Ol27ds1+++231idM1qtXLw899FC23HLLHHjggenSpUuOPPLI/Otf/0qjRo0qHdPYsWOzcOHCtG/fvlTBeFUHHHBArrzyylxyySXp2rVrrr322kyYMCF77LFH6Zhf/vKX+fjjj7PTTjvlhBNOWK24Ta1atXLqqadm++23T58+fVK9evVMnjy50nFW9N3RpEmTTJkyJV//+tfTpUuX/PznP89NN92Url27Vvi5A18+5557boqiKL2uKEckybhx49KmTZv06dMnhx12WE4++eTUq1evwr5GjhyZUaNGZdSoUenWrVumT5+eadOmpWPHjkn+XfDxJz/5Sa699tq0bt263GLhlZ1xxhkZNWpUzjzzzHTp0iWDBw/O3//+9zUeu6E5E2B9dOzYMU8++WTat2+fwYMHp3379jnmmGPSt2/fPPbYY+UK1I4aNSpz5szJDjvskHPPPTfjxo1L//79k/x7Edhdd92VPn365Mgjj0ynTp1yyCGHZOHChWnRosVGi7eifqpXr54lS5Zk6NCh6dSpUwYNGpQBAwbknHPOSbJ+91QBPo2V56e++tWv5oknnsitt95a7hp6VRXNE+2///456aSTctxxx6V79+559NFHc8YZZ6y1vQYNGuTuu+9OURTZd9998/7773+qc7nsssvSq1ev7LfffunXr1969+6dLl26pE6dOp+qPYC1WZE727Vrl3322SczZ87MT37yk9x+++2pXr16unfvnssuuywXXXRRtttuu0yaNCkXXHDBevezrvuvG2M8W61atUyePDlz5szJdtttl5NOOimXXHLJescJUNU6dOiQAw88MPvuu2/23nvvbLfddhvt2nnChAnZcccds99++6VXr14piiJ33XVXuR8d16tXL6ecckoOO+yw9OrVK3Xr1nV/FPjSqcx6zYrmrho0aJCLLrooO+20U3r27JmFCxfmrrvuKhVI/DzuxwL/HS688MJ861vfyuGHH54ePXrk5Zdfzj333JOmTZuudty61mp+FuO4X/3qV2nRokX69OmTb37zmzn66KPTsGHD0j3K22+/fa1z9H369MkOO+xQ7i+peEzaqVOn3HvvvXnmmWey8847p1evXrn99ttTo8bqzynt3LlzHnjggdx0000ZNWrUpzrH6tWrZ+rUqXnvvffSs2fPHHXUUaUfO7sXC6zL8OHDs3Tp0vTr1y9bbrllksqNQyvKrUnF60ErWk8PAMC6lRUrr4QHAP4jnH/++fn5z3+eN954o6pDAQAAAD5H7dq1y4knnpgTTzyxqkMBoIq8//772XzzzTNu3LjVnoIHAMB/hrPPPjtTp04tPcjm8zZx4sSceOKJeeutt6qkfwAAVvfggw+mb9++Wbp0aZo0abLGYz6vcdyf//zntGnTJvfdd1/22GOPNG/ePHfffXd23nnnz7Tfz9usWbOy22675eWXX0779u2rOhzgP9zKuXXPPfes6nAAAP4rrF7mDwD40rn66qvTs2fPNGvWLLNmzcoll1yS4447rqrDAgAAAADgM/b000/nhRdeyM4775y33347Y8eOTZK1PlkOAAAAAGBjeuCBB/Lee++lW7duWbx4ccaMGZN27dqlT58+WbJkSU466aT07NmzqsPcYL/73e/SoEGDdOzYMS+//HJOOOGE9O7dWwEG4DOxrtwKAMDnQxEGAPgP8NJLL+W8887Lm2++mS233DKjRo3KqaeeWtVhAQAAAADwObj00kuzYMGC1KpVKzvuuGMefvjhbLrpplUdFgAAAADwX+Djjz/Oj370o7z66qtp2LBhdt1110yaNCk1a9ZM8+bNc/rpp1d1iBvFu+++mzFjxuSNN97Ipptumn79+mXcuHFVHRbwH2pduRUAgM9HWVEURVUHAQAAAAAAAAAAAAAAAAAAAFDVqlV1AAAAAAAAAAAAAAAAAAAAAABfBIowAAAAAAAAAAAAAAAAAAAAAEQRBgAAAAAAAAAAAAAAAAAAAIAkijAAAAAAAAAAAAAAAAAAAAAAJFGEAQAAAAAAAAAAAAAAAAAAACCJIgwAAAAAAAAAAAAAAAAAAAAASRRhAAAAAAAAAAAAAAAAAAAAAEiiCAMAAAAAAAAAAAAAAAAAAABAEkUYAAAAAAAAAAAAAAAAAAAAAJIk/x+SunyA+2d7HQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 6000x4000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\"\"\"\n",
"SECTION 5: Checking corelation\n",
"\"\"\"\n",
"#Create a corelation matrix to check the relation between Target and other Cols\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"plt.subplots(figsize=(60, 40))\n",
"sns.heatmap(df.corr(), annot = True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "6fa78d00",
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"SECTION 6: Splitting into Train/Test\n",
"\"\"\"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"x = df.iloc[:, :5] \n",
"y = df.iloc[:, 5:] \n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "34aab1e6",
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"SECTION 7: Oversampling using Random Sampling\n",
"\"\"\"\n",
"from imblearn.over_sampling import RandomOverSampler\n",
"\n",
"ros = RandomOverSampler()\n",
"X_oversampled, y_oversampled = ros.fit_resample(X_train, y_train.to_numpy() )"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "2624cca6",
"metadata": {},
"outputs": [],
"source": [
"#Converting the array back in df format\n",
"y_oversampled_df = pd.DataFrame(y_oversampled, columns=[\"Breaking\",\"Dancehall\",\"Hip Hop\",\"House\",\"Popping/Locking\",\"Vogue\"])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "3921c8de",
"metadata": {},
"outputs": [],
"source": [
"#putting the df back together\n",
"oversampled_df = pd.concat([X_oversampled, y_oversampled_df], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "e6101873",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Checking the oversampled distribution compared to the original distribution\n",
"original_df = pd.concat([X_train, y_train], axis=1)\n",
"original_counts = original_df.iloc[:, 6:].sum()\n",
"sns.barplot(x=original_counts.index, y=original_counts.values)\n",
"plt.title('Class Distribution of Original df')\n",
"plt.xlabel('Dance Styles')\n",
"plt.ylabel('Count')\n",
"plt.show()\n",
"\n",
"oversampled_counts = oversampled_df.iloc[:, 6:].sum()\n",
"sns.barplot(x=oversampled_counts.index, y=oversampled_counts.values)\n",
"plt.title('Class Distribution of Oversampled df')\n",
"plt.xlabel('Dance Styles')\n",
"plt.ylabel('Count')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "7a3c9cb5",
"metadata": {},
"outputs": [],
"source": [
"X_train = X_oversampled\n",
"y_train = y_oversampled_df"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "7696d598",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Making sure that the test df contains all values bofore starting the processing\n",
"dfdf = pd.concat([X_test, y_test], axis=1)\n",
"counts = dfdf.iloc[:, 6:].sum()\n",
"sns.barplot(x=counts.index, y=counts.values)\n",
"plt.title('Class Distribution of Oversampled df')\n",
"plt.xlabel('Dance Styles')\n",
"plt.ylabel('Count')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "f932b1f0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.15384615384615385\n"
]
}
],
"source": [
"\"\"\"\n",
"In this section the data is being processed using Decision Tree\n",
"\"\"\"\n",
"\"\"\"\n",
"MODEL 1: Decision Tree\n",
"\"\"\"\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"dt = DecisionTreeClassifier()\n",
"dt.fit(X_train, y_train)\n",
"\n",
"# Make predictions on testing data\n",
"y_pred = dt.predict(X_test)\n",
"\n",
"# Evaluate model performance\n",
"acc = accuracy_score(y_test, y_pred)\n",
"print(\"Accuracy:\", acc)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "846846b9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.07692307692307693\n"
]
}
],
"source": [
"\"\"\"\n",
"MODEL 2: Decision Tree C4.5\n",
"\"\"\"\n",
"\n",
"dt = DecisionTreeClassifier(criterion='entropy', max_depth=5)\n",
"dt.fit(X_train, y_train)\n",
"\n",
"# Make predictions on testing data\n",
"y_pred = dt.predict(X_test)\n",
"\n",
"# Evaluate model performance\n",
"acc = accuracy_score(y_test, y_pred)\n",
"print(\"Accuracy:\", acc)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "e999ee63",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.34615384615384615\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
}
],
"source": [
"\"\"\"\n",
"MODEL 3: Decision Tree with AdaBoost\n",
"\"\"\"\n",
"from sklearn.ensemble import AdaBoostClassifier\n",
"\n",
"dt = DecisionTreeClassifier()\n",
"ada = AdaBoostClassifier(base_estimator=dt)\n",
"\n",
"# Convert one-hot encoded target variable to array of class labels\n",
"y_train_labels = np.argmax(y_train.values, axis=1)\n",
"y_test_labels = np.argmax(y_test.values, axis=1)\n",
"y_train_labels = y_train_labels.ravel()\n",
"y_test_labels = y_test_labels.ravel()\n",
"\n",
"ada.fit(X_train, y_train_labels)\n",
"y_pred_labels = ada.predict(X_test)\n",
"\n",
"acc = accuracy_score(y_test_labels, y_pred_labels)\n",
"print(\"Accuracy: \", acc)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "8e15737c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.15384615384615385\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
}
],
"source": [
"\"\"\"\n",
"MODEL 4: Decision Tree C4.5 and AdaBoost\n",
"\"\"\"\n",
"dt = DecisionTreeClassifier(criterion='entropy', max_depth=5)\n",
"ada = AdaBoostClassifier(base_estimator=dt)\n",
"\n",
"# Convert one-hot encoded target variable to array of class labels\n",
"y_train_labels = np.argmax(y_train.values, axis=1)\n",
"y_test_labels = np.argmax(y_test.values, axis=1)\n",
"y_train_labels = y_train_labels.ravel()\n",
"y_test_labels = y_test_labels.ravel()\n",
"\n",
"ada.fit(X_train, y_train_labels)\n",
"y_pred_labels = ada.predict(X_test)\n",
"\n",
"acc = accuracy_score(y_test_labels, y_pred_labels)\n",
"print(\"Accuracy: \", acc)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "72e5183a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.07692307692307693\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
}
],
"source": [
"\"\"\"\n",
"MODEL 5: OVO with Decision Tree and AdaBoost\n",
"\"\"\"\n",
"from sklearn.multiclass import OneVsOneClassifier\n",
"\n",
"dt = DecisionTreeClassifier()\n",
"ada = AdaBoostClassifier(base_estimator=dt)\n",
"ovo = OneVsOneClassifier(ada)\n",
"\n",
"# Convert one-hot encoded target variable to array of class labels\n",
"y_train_labels = np.argmax(y_train.values, axis=1)\n",
"y_test_labels = np.argmax(y_test.values, axis=1)\n",
"y_train_labels = y_train_labels.ravel()\n",
"y_test_labels = y_test_labels.ravel()\n",
"\n",
"ovo.fit(X_train, y_train_labels)\n",
"y_pred_labels = ovo.predict(X_test)\n",
"\n",
"acc = accuracy_score(y_test_labels, y_pred_labels)\n",
"print(\"Accuracy: \", acc)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "e715856b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.11538461538461539\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
}
],
"source": [
"\"\"\"\n",
"MODEL 6: OVO with Decision Tree C4.5 and AdaBoost\n",
"\"\"\"\n",
"dt = DecisionTreeClassifier(criterion='entropy', max_depth=5)\n",
"ada = AdaBoostClassifier(base_estimator=dt)\n",
"ovo = OneVsOneClassifier(ada)\n",
"\n",
"# Convert one-hot encoded target variable to array of class labels\n",
"y_train_labels = np.argmax(y_train.values, axis=1)\n",
"y_test_labels = np.argmax(y_test.values, axis=1)\n",
"y_train_labels = y_train_labels.ravel()\n",
"y_test_labels = y_test_labels.ravel()\n",
"\n",
"ovo.fit(X_train, y_train_labels)\n",
"y_pred_labels = ovo.predict(X_test)\n",
"\n",
"acc = accuracy_score(y_test_labels, y_pred_labels)\n",
"print(\"Accuracy: \", acc)\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "a2442280",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.11538461538461539\n"
]
}
],
"source": [
"\"\"\"\n",
"MODEL 6: OVO with Decision Tree\n",
"\"\"\"\n",
"dt = DecisionTreeClassifier()\n",
"ovo = OneVsOneClassifier(dt)\n",
"\n",
"# Convert one-hot encoded target variable to array of class labels\n",
"y_train_labels = np.argmax(y_train.values, axis=1)\n",
"y_test_labels = np.argmax(y_test.values, axis=1)\n",
"y_train_labels = y_train_labels.ravel()\n",
"y_test_labels = y_test_labels.ravel()\n",
"\n",
"ovo.fit(X_train, y_train_labels)\n",
"y_pred_labels = ovo.predict(X_test)\n",
"\n",
"acc = accuracy_score(y_test_labels, y_pred_labels)\n",
"print(\"Accuracy: \", acc)\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "301f9ffa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.15384615384615385\n"
]
}
],
"source": [
"\"\"\"\n",
"MODEL 6: OVO with Decision Tree C4.5\n",
"\"\"\"\n",
"dt = DecisionTreeClassifier(criterion='entropy', max_depth=5)\n",
"ovo = OneVsOneClassifier(dt)\n",
"\n",
"# Convert one-hot encoded target variable to array of class labels\n",
"y_train_labels = np.argmax(y_train.values, axis=1)\n",
"y_test_labels = np.argmax(y_test.values, axis=1)\n",
"y_train_labels = y_train_labels.ravel()\n",
"y_test_labels = y_test_labels.ravel()\n",
"\n",
"ovo.fit(X_train, y_train_labels)\n",
"y_pred_labels = ovo.predict(X_test)\n",
"\n",
"acc = accuracy_score(y_test_labels, y_pred_labels)\n",
"print(\"Accuracy: \", acc)\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "19ee093a",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best parameters: {'algorithm': 'SAMME.R', 'base_estimator': DecisionTreeClassifier(min_samples_leaf=4, min_samples_split=5), 'base_estimator__min_samples_leaf': 4, 'base_estimator__min_samples_split': 5, 'learning_rate': 0.1, 'n_estimators': 200}\n",
"Accuracy: 0.8544334975369459\n"
]
}
],
"source": [
"\"\"\"\n",
"Decision Tree with AdaBoost has the highest accuracy score\n",
"In this section Hypertuning will be performed to enchance the accuraccy\n",
"\"\"\"\n",
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"y_train_labels = np.argmax(y_train.values, axis=1)\n",
"y_test_labels = np.argmax(y_test.values, axis=1)\n",
"y_train_labels = y_train_labels.ravel()\n",
"y_test_labels = y_test_labels.ravel()\n",
"\n",
"param_grid = {\n",
" 'base_estimator': [DecisionTreeClassifier()],\n",
" 'n_estimators': [50, 100, 200],\n",
" 'learning_rate': [0.01, 0.1, 1],\n",
" 'algorithm': ['SAMME', 'SAMME.R'],\n",
" 'base_estimator__min_samples_split': [2, 5, 10],\n",
" 'base_estimator__min_samples_leaf': [1, 2, 4]\n",
"}\n",
"\n",
"grid = GridSearchCV(AdaBoostClassifier(), param_grid=param_grid, cv=5)\n",
"grid.fit(X_train, y_train_labels)\n",
"\n",
"print(\"Best parameters:\", grid.best_params_)\n",
"print(\"Accuracy:\", grid.best_score_)\n"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "de34824b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Accuracy: 0.31\n",
"\n",
"Micro Precision: 0.31\n",
"Micro Recall: 0.31\n",
"Micro F1-score: 0.31\n",
"\n",
"Macro Precision: 0.11\n",
"Macro Recall: 0.10\n",
"Macro F1-score: 0.10\n",
"\n",
"Weighted Precision: 0.36\n",
"Weighted Recall: 0.31\n",
"Weighted F1-score: 0.33\n",
"\n",
"Classification Report\n",
"\n",
" precision recall f1-score support\n",
"\n",
" Breaking 0.00 0.00 0.00 2\n",
" Dancehall 0.00 0.00 0.00 3\n",
" Hip Hop 0.67 0.57 0.62 14\n",
" House 0.00 0.00 0.00 2\n",
"Popping & Locking 0.00 0.00 0.00 1\n",
" Vogue 0.00 0.00 0.00 4\n",
"\n",
" accuracy 0.31 26\n",
" macro avg 0.11 0.10 0.10 26\n",
" weighted avg 0.36 0.31 0.33 26\n",
"\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\"\"\"\n",
"Best model fined by hypertuning - Precision, Recall and F1 score analysis \n",
"acc = accuracy_score(y_test_labels, y_pred_labels)\n",
"\"\"\"\n",
"adaboost = AdaBoostClassifier(\n",
" base_estimator=DecisionTreeClassifier(min_samples_leaf=4, min_samples_split=5),\n",
" algorithm='SAMME.R',\n",
" learning_rate=0.1,\n",
" n_estimators=200\n",
")\n",
"\n",
"# Convert one-hot encoded target variable to array of class labels\n",
"y_train_labels = np.argmax(y_train.values, axis=1)\n",
"y_test_labels = np.argmax(y_test.values, axis=1)\n",
"y_train_labels = y_train_labels.ravel()\n",
"y_test_labels = y_test_labels.ravel()\n",
"\n",
"ada.fit(X_train, y_train_labels)\n",
"y_pred_labels = ada.predict(X_test)\n",
"\n",
"# Confusion matrix evaluation\n",
"from sklearn.metrics import confusion_matrix\n",
"confusion= confusion_matrix(y_test_labels, y_pred_labels)\n",
"confusion_matrix_plot=sns.heatmap(confusion, annot=True, xticklabels=['Breaking','Dancehall','Hip Hop','House', 'Popping & Locking', 'Vogue'], yticklabels=['Breaking','Dancehall','Hip Hop','House', 'Popping & Locking', 'Vogue'])\n",
"#confusion_matrix_plot.set(xlabel='Actual Values', ylabel='Predicted Values')\n",
"\n",
"#importing accuracy_score, precision_score, recall_score, f1_score\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
"print('\\nAccuracy: {:.2f}\\n'.format(accuracy_score(y_test_labels, y_pred_labels)))\n",
"\n",
"print('Micro Precision: {:.2f}'.format(precision_score(y_test_labels, y_pred_labels, average='micro')))\n",
"print('Micro Recall: {:.2f}'.format(recall_score(y_test_labels, y_pred_labels, average='micro')))\n",
"print('Micro F1-score: {:.2f}\\n'.format(f1_score(y_test_labels, y_pred_labels, average='micro')))\n",
"\n",
"print('Macro Precision: {:.2f}'.format(precision_score(y_test_labels, y_pred_labels, average='macro')))\n",
"print('Macro Recall: {:.2f}'.format(recall_score(y_test_labels, y_pred_labels, average='macro')))\n",
"print('Macro F1-score: {:.2f}\\n'.format(f1_score(y_test_labels, y_pred_labels, average='macro')))\n",
"\n",
"print('Weighted Precision: {:.2f}'.format(precision_score(y_test_labels, y_pred_labels, average='weighted')))\n",
"print('Weighted Recall: {:.2f}'.format(recall_score(y_test_labels, y_pred_labels, average='weighted')))\n",
"print('Weighted F1-score: {:.2f}'.format(f1_score(y_test_labels, y_pred_labels, average='weighted')))\n",
"\n",
"from sklearn.metrics import classification_report\n",
"print('\\nClassification Report\\n')\n",
"print(classification_report(y_test_labels, y_pred_labels, target_names=['Breaking','Dancehall','Hip Hop','House', 'Popping & Locking', 'Vogue']))"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "f74b20a2",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:700: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross-validation scores: [0.41176471 0.35294118 0.47058824 0.29411765 0.3125 ]\n",
"Mean accuracy: 0.37\n"
]
}
],
"source": [
"\"\"\"\n",
"Evaluation using Cross Validation\n",
"\"\"\"\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"adaboost = AdaBoostClassifier(\n",
" base_estimator=DecisionTreeClassifier(min_samples_leaf=4, min_samples_split=5),\n",
" algorithm='SAMME.R',\n",
" learning_rate=0.1,\n",
" n_estimators=200\n",
")\n",
"\n",
"# Convert one-hot encoded target variable to array of class labels\n",
"y_labels = np.argmax(y.values, axis=1)\n",
"y_labels = y_labels.ravel()\n",
"\n",
"scores = cross_val_score(adaboost, X, y_labels, cv=5)\n",
"\n",
"print(\"Cross-validation scores: \", scores)\n",
"print(\"Mean accuracy: {:.2f}\".format(np.mean(scores)))\n"
]
},
{
"cell_type": "code",
"execution_count": 77,
"id": "2c1dc929",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\"\"\"\n",
"Class Importance\n",
"\"\"\"\n",
"from sklearn.ensemble import AdaBoostClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"import matplotlib.pyplot as plt\n",
"\n",
"adaboost = AdaBoostClassifier(\n",
" base_estimator=DecisionTreeClassifier(min_samples_leaf=4, min_samples_split=5),\n",
" algorithm='SAMME.R',\n",
" learning_rate=0.1,\n",
" n_estimators=200\n",
")\n",
"\n",
"adaboost.fit(X, y_labels)\n",
"\n",
"# Plot feature importances\n",
"importances = adaboost.feature_importances_\n",
"indices = np.argsort(importances)[::-1]\n",
"names = [X.columns[i] for i in indices]\n",
"\n",
"plt.figure(figsize=(10, 6))\n",
"plt.title(\"Feature Importance\")\n",
"plt.bar(range(X.shape[1]), importances[indices])\n",
"plt.xticks(range(X.shape[1]), names, rotation=90)\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 83,
"id": "0e6c8643",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n",
"C:\\Users\\DellG3\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Class 0 Feature Importances:\n",
"Neuroticism: 0.027031612170898986\n",
"Openness: 0.19728994466465\n",
"Agreeableness: 0.035932173873816586\n",
"Conscientiousness: 0.41183873993897\n",
"Extraversion: 0.3279075293516643\n",
"\n",
"Class 1 Feature Importances:\n",
"Agreeableness: 0.21865737423724554\n",
"Conscientiousness: 0.26843160287297324\n",
"Extraversion: 0.23329024798590514\n",
"Neuroticism: 0.1437961988824102\n",
"Openness: 0.13582457602146591\n",
"\n",
"Class 2 Feature Importances:\n",
"Agreeableness: 0.20950324446804217\n",
"Extraversion: 0.2824445657131312\n",
"Openness: 0.16721042018848128\n",
"Conscientiousness: 0.15161571984309513\n",
"Neuroticism: 0.1892260497872503\n",
"\n",
"Class 3 Feature Importances:\n",
"Conscientiousness: 0.0804622541926407\n",
"Agreeableness: 0.2827490156491636\n",
"Openness: 0.47265702978341395\n",
"Extraversion: 0.07748584001889186\n",
"Neuroticism: 0.08664586035588981\n",
"\n",
"Class 4 Feature Importances:\n",
"Extraversion: 0.37698863636102914\n",
"Neuroticism: 0.13387751762796277\n",
"Agreeableness: 0.11769686364015824\n",
"Conscientiousness: 0.2568327559713779\n",
"Openness: 0.11460422639947206\n",
"\n",
"Class 5 Feature Importances:\n",
"Extraversion: 0.27359230992771305\n",
"Neuroticism: 0.20728801823380436\n",
"Agreeableness: 0.14103724395780312\n",
"Openness: 0.23150254806440654\n",
"Conscientiousness: 0.1465798798162732\n",
"\n"
]
}
],
"source": [
"from sklearn.ensemble import AdaBoostClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"import numpy as np\n",
"\n",
"adaboost = AdaBoostClassifier(\n",
" base_estimator=DecisionTreeClassifier(min_samples_leaf=4, min_samples_split=5),\n",
" algorithm='SAMME.R',\n",
" learning_rate=0.1,\n",
" n_estimators=200\n",
")\n",
"\n",
"class_importances = {}\n",
"for i in range(6):\n",
" # Train a separate model for each class\n",
" y_labels_i = (y_labels == i).astype(int)\n",
" adaboost.fit(X, y_labels_i)\n",
"\n",
" # Calculate feature importances for this class\n",
" importances_i = adaboost.feature_importances_\n",
" indices_i = np.argsort(importances_i)[::-1]\n",
" names_i = [X.columns[i] for i in indices_i]\n",
"\n",
" # Store the feature importances for this class\n",
" class_importances[i] = {'importances': importances_i, 'indices': indices_i, 'names': names_i}\n",
"\n",
"# Print the feature importances for each class\n",
"for i in range(6):\n",
" print(f\"Class {i} Feature Importances:\")\n",
" for j in range(X.shape[1]):\n",
" print(f\"{class_importances[i]['names'][j]}: {class_importances[i]['importances'][j]}\")\n",
" print()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "deaf1f05",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}