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data-science-ipython-notebooks/kaggle/titanic.ipynb

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{
"metadata": {
"name": "",
"signature": "sha256:f3d0abb7bb59664b1bedfcbb30ca107ab65f220853e71835b425bdd11d5734ea"
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"nbformat": 3,
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kaggle Machine Learning Competition: Predicting Titanic Survivors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Competition Site\n",
"* Description\n",
"* Evaluation\n",
"* Data Set\n",
"* Setup Imports and Variables\n",
"* Explore the Data\n",
"* Feature: Passenger Classes\n",
"* Feature: Sex\n",
"* Feature: Embarked\n",
"* Feature: Age\n",
"* Feature: Family Size\n",
"* Final Data Preparation for Machine Learning\n",
"* Data Wrangling Summary\n",
"* Random Forest: Training\n",
"* Random Forest: Predicting\n",
"* Random Forest: Prepare for Kaggle Submission\n",
"* Support Vector Machine: Training\n",
"* Support Vector Machine: Predicting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Competition Site"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Description, Evaluation, and Data Set taken from the [competition site](https://www.kaggle.com/c/titanic-gettingStarted)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Description"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![alt text](http://upload.wikimedia.org/wikipedia/commons/6/6e/St%C3%B6wer_Titanic.jpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.\n",
"\n",
"One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n",
"\n",
"In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The historical data has been split into two groups, a 'training set' and a 'test set'. For the training set, we provide the outcome ( 'ground truth' ) for each passenger. You will use this set to build your model to generate predictions for the test set.\n",
"\n",
"For each passenger in the test set, you must predict whether or not they survived the sinking ( 0 for deceased, 1 for survived ). Your score is the percentage of passengers you correctly predict.\n",
"\n",
" The Kaggle leaderboard has a public and private component. 50% of your predictions for the test set have been randomly assigned to the public leaderboard ( the same 50% for all users ). Your score on this public portion is what will appear on the leaderboard. At the end of the contest, we will reveal your score on the private 50% of the data, which will determine the final winner. This method prevents users from 'overfitting' to the leaderboard."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Set"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| File Name | Available Formats |\n",
"|------------------|-------------------|\n",
"| train | .csv (59.76 kb) |\n",
"| gendermodel | .csv (3.18 kb) |\n",
"| genderclassmodel | .csv (3.18 kb) |\n",
"| test | .csv (27.96 kb) |\n",
"| gendermodel | .py (3.58 kb) |\n",
"| genderclassmodel | .py (5.63 kb) |\n",
"| myfirstforest | .py (3.99 kb) |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<pre>\n",
"VARIABLE DESCRIPTIONS:\n",
"survival Survival\n",
" (0 = No; 1 = Yes)\n",
"pclass Passenger Class\n",
" (1 = 1st; 2 = 2nd; 3 = 3rd)\n",
"name Name\n",
"sex Sex\n",
"age Age\n",
"sibsp Number of Siblings/Spouses Aboard\n",
"parch Number of Parents/Children Aboard\n",
"ticket Ticket Number\n",
"fare Passenger Fare\n",
"cabin Cabin\n",
"embarked Port of Embarkation\n",
" (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
"\n",
"SPECIAL NOTES:\n",
"Pclass is a proxy for socio-economic status (SES)\n",
" 1st ~ Upper; 2nd ~ Middle; 3rd ~ Lower\n",
"\n",
"Age is in Years; Fractional if Age less than One (1)\n",
" If the Age is Estimated, it is in the form xx.5\n",
"\n",
"With respect to the family relation variables (i.e. sibsp and parch)\n",
"some relations were ignored. The following are the definitions used\n",
"for sibsp and parch.\n",
"\n",
"Sibling: Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic\n",
"Spouse: Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored)\n",
"Parent: Mother or Father of Passenger Aboard Titanic\n",
"Child: Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic\n",
"\n",
"Other family relatives excluded from this study include cousins,\n",
"nephews/nieces, aunts/uncles, and in-laws. Some children travelled\n",
"only with a nanny, therefore parch=0 for them. As well, some\n",
"travelled with very close friends or neighbors in a village, however,\n",
"the definitions do not support such relations.\n",
"</pre>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup Imports and Variables"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import numpy as np\n",
"import pylab as plt\n",
"\n",
"# Set the global default size of matplotlib figures\n",
"plt.rc('figure', figsize=(10, 5))\n",
"\n",
"# Size of matplotlib figures that contain subplots\n",
"fizsize_with_subplots = (10, 10)\n",
"\n",
"# Size of matplotlib histogram bins\n",
"bin_size = 10"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read the data:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train = pd.read_csv('../data/titanic/train.csv')\n",
"df_train.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> Braund, Mr. Owen Harris</td>\n",
" <td> male</td>\n",
" <td> 22</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> A/5 21171</td>\n",
" <td> 7.2500</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td> female</td>\n",
" <td> 38</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> PC 17599</td>\n",
" <td> 71.2833</td>\n",
" <td> C85</td>\n",
" <td> C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> Heikkinen, Miss. Laina</td>\n",
" <td> female</td>\n",
" <td> 26</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> STON/O2. 3101282</td>\n",
" <td> 7.9250</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td> female</td>\n",
" <td> 35</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 113803</td>\n",
" <td> 53.1000</td>\n",
" <td> C123</td>\n",
" <td> S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> Allen, Mr. William Henry</td>\n",
" <td> male</td>\n",
" <td> 35</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 373450</td>\n",
" <td> 8.0500</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n",
"2 Heikkinen, Miss. Laina female 26 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n",
"4 Allen, Mr. William Henry male 35 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train.tail()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>886</th>\n",
" <td> 887</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> Montvila, Rev. Juozas</td>\n",
" <td> male</td>\n",
" <td> 27</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 211536</td>\n",
" <td> 13.00</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>887</th>\n",
" <td> 888</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> Graham, Miss. Margaret Edith</td>\n",
" <td> female</td>\n",
" <td> 19</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 112053</td>\n",
" <td> 30.00</td>\n",
" <td> B42</td>\n",
" <td> S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td> 889</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
" <td> female</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> W./C. 6607</td>\n",
" <td> 23.45</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td> 890</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> Behr, Mr. Karl Howell</td>\n",
" <td> male</td>\n",
" <td> 26</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 111369</td>\n",
" <td> 30.00</td>\n",
" <td> C148</td>\n",
" <td> C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\n",
" <td> 891</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> Dooley, Mr. Patrick</td>\n",
" <td> male</td>\n",
" <td> 32</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 370376</td>\n",
" <td> 7.75</td>\n",
" <td> NaN</td>\n",
" <td> Q</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
" PassengerId Survived Pclass Name \\\n",
"886 887 0 2 Montvila, Rev. Juozas \n",
"887 888 1 1 Graham, Miss. Margaret Edith \n",
"888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n",
"889 890 1 1 Behr, Mr. Karl Howell \n",
"890 891 0 3 Dooley, Mr. Patrick \n",
"\n",
" Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
"886 male 27 0 0 211536 13.00 NaN S \n",
"887 female 19 0 0 112053 30.00 B42 S \n",
"888 female NaN 1 2 W./C. 6607 23.45 NaN S \n",
"889 male 26 0 0 111369 30.00 C148 C \n",
"890 male 32 0 0 370376 7.75 NaN Q "
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"View the data types of each column:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train.dtypes"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"PassengerId int64\n",
"Survived int64\n",
"Pclass int64\n",
"Name object\n",
"Sex object\n",
"Age float64\n",
"SibSp int64\n",
"Parch int64\n",
"Ticket object\n",
"Fare float64\n",
"Cabin object\n",
"Embarked object\n",
"dtype: object"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Type 'object' is a string for pandas, which poses problems with machine learning algorithms. If we want to use these as features, we'll need to convert these to number representations."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get some basic information on the DataFrame:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train.info()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
"PassengerId 891 non-null int64\n",
"Survived 891 non-null int64\n",
"Pclass 891 non-null int64\n",
"Name 891 non-null object\n",
"Sex 891 non-null object\n",
"Age 714 non-null float64\n",
"SibSp 891 non-null int64\n",
"Parch 891 non-null int64\n",
"Ticket 891 non-null object\n",
"Fare 891 non-null float64\n",
"Cabin 204 non-null object\n",
"Embarked 889 non-null object\n",
"dtypes: float64(2), int64(5), object(5)"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Age, Cabin, and Embarked are missing values. Cabin has too many missing values, whereas we might be able to infer values for Age and Embarked."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate various descriptive statistics on the DataFrame:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 891.000000</td>\n",
" <td> 891.000000</td>\n",
" <td> 891.000000</td>\n",
" <td> 714.000000</td>\n",
" <td> 891.000000</td>\n",
" <td> 891.000000</td>\n",
" <td> 891.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 446.000000</td>\n",
" <td> 0.383838</td>\n",
" <td> 2.308642</td>\n",
" <td> 29.699118</td>\n",
" <td> 0.523008</td>\n",
" <td> 0.381594</td>\n",
" <td> 32.204208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 257.353842</td>\n",
" <td> 0.486592</td>\n",
" <td> 0.836071</td>\n",
" <td> 14.526497</td>\n",
" <td> 1.102743</td>\n",
" <td> 0.806057</td>\n",
" <td> 49.693429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.420000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 223.500000</td>\n",
" <td> 0.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 20.125000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 7.910400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 446.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 28.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 14.454200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 668.500000</td>\n",
" <td> 1.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 38.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 31.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 891.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 80.000000</td>\n",
" <td> 8.000000</td>\n",
" <td> 6.000000</td>\n",
" <td> 512.329200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
" PassengerId Survived Pclass Age SibSp \\\n",
"count 891.000000 891.000000 891.000000 714.000000 891.000000 \n",
"mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n",
"std 257.353842 0.486592 0.836071 14.526497 1.102743 \n",
"min 1.000000 0.000000 1.000000 0.420000 0.000000 \n",
"25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n",
"50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n",
"75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n",
"max 891.000000 1.000000 3.000000 80.000000 8.000000 \n",
"\n",
" Parch Fare \n",
"count 891.000000 891.000000 \n",
"mean 0.381594 32.204208 \n",
"std 0.806057 49.693429 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 7.910400 \n",
"50% 0.000000 14.454200 \n",
"75% 0.000000 31.000000 \n",
"max 6.000000 512.329200 "
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have a general idea of the data set contents, we can dive deeper into each column. We'll be doing exploratory data analysis and cleaning data to setup 'features' we'll be using in our machine learning algorithms.\n",
"\n",
"Plot a few features to get a better idea of each:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set up a grid of plots\n",
"fig = plt.figure(figsize=fizsize_with_subplots) \n",
"fig_dims = (3, 2)\n",
"\n",
"# Plot death and survival counts\n",
"plt.subplot2grid(fig_dims, (0, 0))\n",
"df_train['Survived'].value_counts().plot(kind='bar', \n",
" title='Death and Survival Counts')\n",
"\n",
"# Plot Pclass counts\n",
"plt.subplot2grid(fig_dims, (0, 1))\n",
"df_train['Pclass'].value_counts().plot(kind='bar', \n",
" title='Passenger Class Counts')\n",
"\n",
"# Plot Sex counts\n",
"plt.subplot2grid(fig_dims, (1, 0))\n",
"df_train['Sex'].value_counts().plot(kind='bar', \n",
" title='Gender Counts')\n",
"plt.xticks(rotation=0)\n",
"\n",
"# Plot Embarked counts\n",
"plt.subplot2grid(fig_dims, (1, 1))\n",
"df_train['Embarked'].value_counts().plot(kind='bar', \n",
" title='Ports of Embarkation Counts')\n",
"\n",
"# Plot the Age histogram\n",
"plt.subplot2grid(fig_dims, (2, 0))\n",
"df_train['Age'].hist()\n",
"plt.title('Age Histogram')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<matplotlib.text.Text at 0x10af2a5d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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zumJ7h6d0hsdHcni+32+xh6ds3/dzeXp4w4YNTE5OAjAxMcFsfNVeERXljmzq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\nnJdKut7N5fHxcRI6D/gM8KWWdYuBFS05nouBZo7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zj1JKjCrdFxsszpEysyrqJEfqPOAz\nwJeaKyQdBhwNvDQi1kv6rXz9fsBxwH7AHsDVkhZExMbCa25mZmaW2LRDexFxA/Bw2+q/AD4WEevz\nfR7I1x8DLI+I9RExDtwDLCquusVInyuSMn7K2OmvvXOkzMysSN3mSO0NvEbSTZIakg7K1+8OrGnZ\nbw1Zz5SZmZlZ5XQ0j5SkYeDyZo6UpNuBayPiPZIOBr4aES+U9Bngpoj4cr7fF4BvRcQlbcdzjtTM\no5QSo0r3xQaLc6TMrIq6nUdqDXAJQER8T9JGSc8F7gPmtey3Z77uacbGxhgeHgZgaGiIkZERRkdH\ngckhiNlSzjSA0ZZl+lBmmu3FHD/19XS5GuXm8vj4OGZmVdVtj9T/AnaPiNMkLQCujojn58nmF5Dl\nRe0BXA28qL37KXWPVKPRaGsE9WbmPVINJhsxHUeZYYxuYve/R6roaz+b4tf53ME9UmZWTdP2SEla\nDhwKPEfSvcDfAUuBpfkQ35PA2wAiYpWkC4FVwFPAKZUawzMzMzNr4WftFcA5UmbTc4+UmVWRZzY3\nMzMz61ItG1Lp59NJGT9l7PTX3vNImZlZkWrZkDIzMzMrgnOkCuAcKbPpOUfKzKrIPVJmZmZmXapl\nQyp9rkjK+Cljp7/2zpEyM7Mi1bIhZWZmZlYE50gVwDlSZtNzjpSZVZF7pMzMzMy6VMuGVPpckZTx\nU8ZOf+2dI2VmZkWatiElaamkify5eu3b/krSRkm7tKxbIuluSXdKOrLoCpuZmZkNimlzpCS9GvgF\n8KWIWNiyfh7wL8A+wIER8ZCk/YALgIOBPYCrgQURsbHtmM6RmnmUUmJU6b7YYHGOlJlV0bQ9UhFx\nA/DwFJs+CXygbd0xwPKIWB8R48A9wKJeK2lmZmY2iLrKkZJ0DLAmIm5r27Q7sKalvIasZ2qgpM8V\nSRk/Zez01945UmZmVqQ5M32DpO2ADwGva129hbdMOVY0NjbG8PAwAENDQ4yMjDA6OgpMfuD3q7xy\n5cpCj5dpAKMty2yhvHKa7ZsrM832Xst5qc/Xv67lprrEby6Pj49jZlZVHc0jJWkYuDwiFkpaSJb7\n9Hi+eU/gPuAQ4CSAiDgzf9+VwGkRcXPb8ZwjNfMopcSo0n2xweIcKTOrohkP7UXE7RExNyLmR8R8\nsuG7AyJiArgMOF7StpLmA3sDtxRbZTMzM7PB0Mn0B8uBG4EFku6VdFLbLr/pwoiIVcCFwCrgCuCU\nQex6Sp8rkjJ+ytjpr71zpMzMrEjT5khFxAnTbH9hW/kM4Iwe62VmZmY28PysvQI4R8pses6RMrMq\nquUjYszMzMyKUMuGVPpckZTxU8ZOf+2dI2VmZkWqZUPKzMzMrAjOkSqAc6TMpuccKTOrIvdImZmZ\nmXWplg2p9LkiKeOnjJ3+2jtHyszMilTLhpSZmZlZEZwjVQDnSJlNzzlSZlZFnTwiZqmkCUm3t6z7\nuKQ7JP1Q0iWSdmrZtkTS3ZLulHRkvypuZmZmllonQ3vnAUe1rbsKeElEvAxYDSwBkLQfcBywX/6e\nz0kauOHD9LkiKeOnjJ3+2jtHyszMijRtIycibgAeblu3IiI25sWbgT3z5WOA5RGxPiLGgXuARcVV\n18zMzGxwdJQjJWkYuDwiFk6x7XKyxtMFkj4D3BQRX863fQG4IiIubnuPc6RmHqWUGFW6LzZYnCNl\nZlU0p5c3S/pr4MmIuGALu035m3lsbIzh4WEAhoaGGBkZYXR0FJgcgpgt5UwDGG1Zpg9lptlezPFT\nX0+Xq1FuLo+Pj2NmVlVd90hJGgNOBl4bEU/k6xYDRMSZeflK4LSIuLnteEl7pBqNRlsjqDcz75Fq\nMNmI6TjKDGN0E7v/PVJFX/vZFL/O5w7ukTKzauqqR0rSUcD7gUObjajcZcAFkj4J7AHsDdzScy2t\nNFmjsP88hGhmZlUwbY+UpOXAocBzgQngNLJv6W0LPJTv9t2IOCXf/0PA24GngPdExLenOKZzpGYe\npSIxsjhVuv/WGfdImVkVeULOArghNfM4Vbr/1hk3pMysigZujqcypJ9PJ2X8lLHTx/c8UmZmVqRa\nNqTMzMzMiuChvQJ4aG/mcap0/60zHtozsypyj5SZmZlZl2rZkEqfK5IyfsrY6eM7R8rMzIpUy4aU\nmZmZWRGcI1UA50jNPE6V7r91xjlSZlZF7pEyMzMz61ItG1Lpc0VSxk8ZO31850iZmVmRpm1ISVoq\naULS7S3rdpG0QtJqSVdJGmrZtkTS3ZLulHRkvypuZmZmllonz9p7NfAL4EsRsTBfdzbw84g4W9IH\ngZ0jYrGk/YALgIPJHlp8NbAgIja2HdM5UjOPUpEYWZwq3X/rjHOkzKyKpu2RiogbgIfbVh8NLMuX\nlwHH5svHAMsjYn1EjAP3AIuKqaqZmZnZYOk2R2puREzkyxPA3Hx5d2BNy35ryHqmBkr6XJGU8VPG\nTh/fOVJmZlaknpPN8zG6LY3TeAzHzMzMKmlOl++bkLRrRKyVtBtwf77+PmBey3575uueZmxsjOHh\nYQCGhoYYGRlhdHQUmPzLuV/l5roij5f1tIy2LLOF8kz3b5aZZnsn5dE+H7+T+MVe/5mUR0dHS41X\n53JzeXx8HDOzqupoQk5Jw8DlbcnmD0bEWZIWA0NtyeaLmEw2f1F7ZrmTzbuKUpEYWZwq3X/rjJPN\nzayKOpn+YDlwI7CPpHslnQScCbxO0mrg8LxMRKwCLgRWAVcApwxiiyl9rkjK+Cljp4/vHCkzMyvS\ntEN7EXHCZjYdsZn9zwDO6KVSZmZmZrOBn7VXAA/tzTxOle6/dcZDe2ZWRbV8RIyZmZlZEWrZkEqf\nK5IyfsrY6eM7R8rMzIpUy4aUmZmZWRGcI1UA50jNPE6V7r91xjlSZlZF7pEyMzMz61ItG1Lpc0VS\nxk8ZO31850iZmVmRatmQMjMzMyuCc6QK4Bypmcep0v23zjhHysyqqKceKUlLJP1Y0u2SLpD0DEm7\nSFohabWkqyQNFVVZMzMzs0HSdUMqf5DxycAB+cOMtwaOBxYDKyJiAXBNXh4o6XNFUsZPGTt9fOdI\nmZlZkXrpkXoUWA9sJ2kOsB3wU+BoYFm+zzLg2J5qaGZmZjagesqRkvQO4B+AXwHfjoi3Sno4InbO\ntwt4qFlueZ9zpGYepSIxsjhVuv/WGedImVkV9TK0txfwl8AwsDuwg6S3tO6Tt5b8G9PMzMwqaU4P\n7z0IuDEiHgSQdAnwCmCtpF0jYq2k3YD7p3rz2NgYw8PDAAwNDTEyMsLo6CgwmcvRr/I555xTaLxM\nAxhtWWYL5XOAkRns3ywzzfZOyq3H6sfxO4vfaDRKu9+t5dY8obLjt9eh6vGby+Pj45iZVVXXQ3uS\nXgZ8GTgYeAL4InAL8ALgwYg4S9JiYCgiFre9N+nQXusv8SLMfGivwWQjo+MoM4zRTewyhvYawGHJ\nhvaKvvezJfYgxPfQnplVUa85Uh8ATgQ2Aj8A/gzYEbgQeD4wDrwpIta1vc85UjOPUpEYWZwq3X/r\njBtSZlZFnpCzAG5IzTxOle6/dcYNKTOrolo+Iib9fDop46eMnT6+55EyM7Mi1bIhZWZmZlYED+0V\nwEN7M49TpftvnfHQnplVkXukzMzMzLpUy4ZU+lyRlPFTxk4f3zlSZmZWpF4m5DTrWjYc2l8ewg7J\nlgAADEtJREFUPjQzs35zjlQBnCM1iHGchzVonCNlZlVUy6E9MzMzsyLUsiGVPlckZfyUsdPHd46U\nmZkVqaeGlKQhSRdJukPSKkmHSNpF0gpJqyVdJWmoqMqamZmZDZJen7W3DLg+IpZKmgNsD/w18POI\nOFvSB4GdB+2hxUVzjtQgxnGO1KBxjpSZVVHXDSlJOwG3RsQL29bfCRwaEROSdgUaEfHitn3ckJp5\nlIrEKCuOG1KDxg0pM6uiXob25gMPSDpP0g8k/Yuk7YG5ETGR7zMBzO25lgVLnyuSMn7K2OnjO0fK\nzMyK1EtDag5wAPC5iDgA+CWwyRBe3u3kbgEzMzOrpF4m5FwDrImI7+Xli4AlwFpJu0bEWkm7AfdP\n9eaxsTGGh4cBGBoaYmRkhNHRUWDyL+d+lZvrijxe1tMy2rLMFsoz3b9ZZprtnZRH+3z8TuI31/Xj\n+K3lvNRyv0ZHR/v+8+VyVm4uj4+PY2ZWVb0mm38H+LOIWC3pdGC7fNODEXGWpMXAkJPNC4lSkRhl\nxXGO1KBxjpSZVVGv80i9G/iypB8CLwU+CpwJvE7SauDwvDxQ0ueKpIyfMnb6+M6RMjOzIvX0rL2I\n+CFw8BSbjujluGZmZmazgZ+1VwAP7Q1iHA/tDRoP7ZlZFfXUI9WLZz97Z556qr8xttoK/u3frmNk\nZKS/gczMzKyWkjWkHn/8cTZsWNvXGDvuOMqGDRuetr71G3tpNNj0G3x1id2MnzB6wnuf+ucudXwz\nsypK1pDKhnd27muErbZKeHpmZmZWeclypLbe+hls2PBEX+PstNOBXHPN5znwwAP7Gsc5UoMYxzlS\ng8Y5UmZWRb1Of2BmZmZWW7VsSKWfTydl/JSx08f3PFJmZlakWjakzMzMzIrgHKkCOEdqEOM4R2rQ\nOEfKzKqo8l9rO+igg1JXwczMzCqq56E9SVtLulXS5Xl5F0krJK2WdJWkod6r2atoe103xbpeXjPV\n6PpMepcydrnxJfX9NROpc5RSxzczq6IicqTeA6xiskWxGFgREQuAa/KyWQJTNXqLbESbmVnd9ZQj\nJWlP4IvAR4H/HRFvkHQncGhETEjaFWhExIvb3ldajtQjj/yAauQWVSVGWXGchzVonCNlZlXUa4/U\np4D3Axtb1s2NiIl8eQKY22MMMzMzs4HUdbK5pNcD90fErZJGp9onIkLSlH+yb9iwHjg9Lw0BI0w+\nA66R/9trmc1sP6fgeM11ne7fbXym2d5JufVY/Th+p/EbfTp+a5kptrduK+b4zdyj5nPsNldurut0\n/6LLZcdvLo+Pj2NmVlVdD+1JOgN4K/AU8Ezg2cAlwMHAaESslbQbcN3gDe01KPbBvTMdRuomflFD\nVVuKXcZwWAM4rIQ4mzuXBsXd+5kN7aV+aHDq+B7aM7MqKmQeKUmHAu/Lc6TOBh6MiLMkLQaGImJx\n2/7OkaptjLLiOEdq0LghZWZVVOTM5s3fKGcCr5O0Gjg8L5uZmZlVTiENqYi4PiKOzpcfiogjImJB\nRBwZEeuKiFGsRo3jp4xd7/ip53FKHd/MrIr8rD0zMzOzLlX+WXvOkRq0GGXFcY7UoHGOlJlVkXuk\nzMzMzLpU04ZUo8bxU8aud/zUOUqp45uZVVFNG1JmZmZmvXOOVCGqk/PjHKmZxXCOVOecI2VmVeQe\nKTMzM7Mu1bQh1ahx/JSx6x0/dY5S6vhmZlVU04aUmZmZWe96eWjxPOBLwPPIklE+HxGflrQL8FXg\nBcA48Kb22c2dI1XnGGXFcY7UoHGOlJlVUS89UuuB90bES4CXA++UtC+wGFgREQuAa/KyWSVJ6vvL\nzMwGV9cNqYhYGxEr8+VfAHcAewBHA8vy3ZYBx/ZayeI1ahw/Zewqxo8ZvK6b4f7F9nY5R8rMrHiF\n5EhJGgb2B24G5kbERL5pAphbRAwzMzOzQdPzPFKSdgCuBz4SEZdKejgidm7Z/lBE7NL2HudI1TZG\nWXGqE6MqeVjOkTKzKprTy5slbQNcDJwfEZfmqyck7RoRayXtBtw/1Xs3bFgPnJ6XhoARYDQvN/J/\ney0zzfaiys11/Tp+s8w02wf9+M1yc12/jt8sM832QT9+Vm4OyY2Ozq5yc3l8fBwzs6rq5Vt7IsuB\nejAi3tuy/ux83VmSFgNDEbG47b2Je6QabPpLvVcz7ZnoJn5RvR9bil1GD0sDOKyEOJs7lwbF3fty\n7ntRPVKNRuM3jZ0U3CNlZlXUS4/UK4G3ALdJujVftwQ4E7hQ0p+ST3/QUw3NzMzMBpSftVeI6uTj\nOEdq8GI4R8rMbHB5ZnMzMzOzLtW0IdWocfyUseseP2VszyNlZtYPNW1ImZmZmfXOOVKFqE4+jnOk\nBi+Gc6TMzAaXe6TMzMzMulTThlSjxvFTxq57/JSxnSNlZtYPPc1sbmb9l819239VGUI0MyuTc6QK\nUZ18HOdI1TFGFqffnwXOkTKzKqrp0J6ZmZlZ7/rSkJJ0lKQ7Jd0t6YP9iNGbRo3jp4xd9/gpYw9C\nfDOz6im8ISVpa+CfgKOA/YATJO1bdJzerKxx/Dqfe+r4dT53M7Nq6keP1CLgnogYj4j1wFeAY/oQ\npwfrahy/zueeOn6dz93MrJr68a29PYB7W8prgEP6EMfMClTWtwPNzKqkHw2pjr76s3Hjkzz72W/o\nQ/hJv/rVPZvZMt7XuNNLGT9l7LrHTxm7k/hlfAPRzKxaCp/+QNLLgdMj4qi8vATYGBFntezjCWvM\nasjTH5hZ1fSjITUHuAt4LfBT4BbghIi4o9BAZmZmZokVPrQXEU9JehfwbWBr4Fw3oszMzKyKksxs\nbmZmZlYFpc9sXuZknZKWSpqQdHvLul0krZC0WtJVkob6GH+epOsk/VjSjySdWmYdJD1T0s2SVkpa\nJeljZcbPY20t6VZJlyeIPS7ptjz+LQniD0m6SNId+fU/pIz4kvbJz7n5ekTSqSWf+5L85/52SRdI\nekaZ8c3MylJqQyrBZJ3n5bFaLQZWRMQC4Jq83C/rgfdGxEuAlwPvzM+3lDpExBPAYRExArwUOEzS\nq8qKn3sPsIrJr4SVGTuA0YjYPyIWJYj/j8C3ImJfsut/ZxnxI+Ku/Jz3Bw4EHge+XkZsAEnDwMnA\nARGxkGyI//iy4puZlansHqlSJ+uMiBuAh9tWHw0sy5eXAcf2Mf7aiFiZL/8CuINsnq0y6/B4vrgt\n2S+0h8uKL2lP4PeBLzD53ffSzr1ZjbZyWee+E/DqiFgKWe5gRDxSVvwWR5D9n7u3xNiPkv0RsV3+\n5ZPtyL54Uva5m5n1XdkNqakm69yj5DrMjYiJfHkCmFtG0Pyv9P2Bm8usg6StJK3M41wXET8uMf6n\ngPcDG1vWlXn9A7ha0vclnVxy/PnAA5LOk/QDSf8iafsS4zcdDyzPl0uJHREPAf8A/A9ZA2pdRKwo\nK76ZWZnKbkgNVGZ7ZJn2fa+TpB2Ai4H3RMRjZdYhIjbmQ3t7Aq+RdFgZ8SW9Hrg/Im5lMzMxlnD9\nX5kPb/0e2bDqq0uMPwc4APhcRBwA/JK2oax+n7+kbYE3AF9r39bP2JL2Av4SGAZ2B3aQ9Jay4puZ\nlanshtR9wLyW8jyyXqkyTUjaFUDSbsD9/QwmaRuyRtT5EXFpijoA5MNK3yTLmSkj/u8AR0v6CVmP\nyOGSzi8pNgAR8bP83wfIcoQWlRh/DbAmIr6Xly8ia1itLfHe/x7wH/n5Q3nnfhBwY0Q8GBFPAZcA\nr6DcczczK0XZDanvA3tLGs7/Wj4OuKzkOlwGnJgvnwhcuoV9eyJJwLnAqog4p+w6SHpu85tRkp4F\nvA64tYz4EfGhiJgXEfPJhpeujYi3lhEbQNJ2knbMl7cHjgRuLyt+RKwF7pW0IF91BPBj4PIy4udO\nYHJYD8r72b8TeLmkZ+X/B44g+8JBmeduZlaK0ueRkvR7wDlMTtb5sT7GWg4cCjyXLCfj74BvABcC\nzyd7+NibImJdn+K/CvgOcBuTwxhLyGZ773sdJC0kS+rdKn+dHxEfl7RLGfFb6nEo8FcRcXRZsSXN\nJ+uFgmyY7csR8bEyz13Sy8gS7bcF/hM4ieznvozz3x74b2B+czi55HP/AFljaSPwA+DPgB3Lim9m\nVhZPyGlmZmbWpdIn5DQzMzOrCjekzMzMzLrkhpSZmZlZl9yQMjMzM+uSG1JmZmZmXXJDyszMzKxL\nbkiZmZmZdckNKTMzM7Mu/X+AP0780h2GHQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10a9ede50>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we'll explore various features to view their impact on survival rates."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature: Passenger Classes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From our exploratory data analysis in the previous section, we see there are three passenger classes: First, Second, and Third class. We'll determine which proportion of passengers survived based on their passenger class."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate a cross tab of Pclass and Survived:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pclass_xt = pd.crosstab(df_train['Pclass'], df_train['Survived'])\n",
"pclass_xt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Survived</th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Pclass</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 80</td>\n",
" <td> 136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 97</td>\n",
" <td> 87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 372</td>\n",
" <td> 119</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"Survived 0 1\n",
"Pclass \n",
"1 80 136\n",
"2 97 87\n",
"3 372 119"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the cross tab:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Normalize the cross tab to sum to 1:\n",
"pclass_xt_pct = pclass_xt.div(pclass_xt.sum(1).astype(float), axis=0)\n",
"\n",
"pclass_xt_pct.plot(kind='bar', \n",
" stacked=True, \n",
" title='Survival Rate by Passenger Classes')\n",
"plt.xlabel('Passenger Class')\n",
"plt.ylabel('Survival Rate')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"<matplotlib.text.Text at 0x10a9eda10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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+FzNmZr55qJFJkiTNMtONeF1b/nl5ZV0CUf6p2ewmHPVSB/RwNECzXw/zfHRM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\nnk7w9PBiAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10a9abf90>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that passenger class seems to have a significant impact on whether a passenger survived. Those in First Class the highest chance for survival."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature: Sex"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Gender might have also played a role in determining a passenger's survival rate. We'll need to map Sex from a string to a number to prepare it for machine learning algorithms."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate a mapping of Sex from a string to a number representation:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sexes = sort(df_train['Sex'].unique())\n",
"genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\n",
"genders_mapping"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"{'female': 0, 'male': 1}"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Transform Sex from a string to a number representation:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train['Sex_Val'] = df_train['Sex'].map(genders_mapping).astype(int)\n",
"df_train.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>Sex_Val</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> Braund, Mr. Owen Harris</td>\n",
" <td> male</td>\n",
" <td> 22</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> A/5 21171</td>\n",
" <td> 7.2500</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td> female</td>\n",
" <td> 38</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> PC 17599</td>\n",
" <td> 71.2833</td>\n",
" <td> C85</td>\n",
" <td> C</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> Heikkinen, Miss. Laina</td>\n",
" <td> female</td>\n",
" <td> 26</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> STON/O2. 3101282</td>\n",
" <td> 7.9250</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td> female</td>\n",
" <td> 35</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 113803</td>\n",
" <td> 53.1000</td>\n",
" <td> C123</td>\n",
" <td> S</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> Allen, Mr. William Henry</td>\n",
" <td> male</td>\n",
" <td> 35</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 373450</td>\n",
" <td> 8.0500</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n",
"2 Heikkinen, Miss. Laina female 26 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n",
"4 Allen, Mr. William Henry male 35 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked Sex_Val \n",
"0 0 A/5 21171 7.2500 NaN S 1 \n",
"1 0 PC 17599 71.2833 C85 C 0 \n",
"2 0 STON/O2. 3101282 7.9250 NaN S 0 \n",
"3 0 113803 53.1000 C123 S 0 \n",
"4 0 373450 8.0500 NaN S 1 "
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot a normalized cross tab for Sex_Val and Survived:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sex_val_xt = pd.crosstab(df_train['Sex_Val'], df_train['Survived'])\n",
"sex_val_xt_pct = sex_val_xt.div(sex_val_xt.sum(1).astype(float), axis=0)\n",
"sex_val_xt_pct.plot(kind='bar', stacked=True, title='Survival Rate by Gender')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10a9ddd50>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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a2Zeg4dmXoOG5tmh4ri2D2PMkSZI0IoYnNVC37gIktVK37gLUEoYnSZKkEux5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\n",
"text": [
"<matplotlib.figure.Figure at 0x10adb6d90>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The majority of females survived, whereas the majority of males did not."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we'll determine whether we can gain any insights on survival rate by looking at both Sex and Pclass."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Count males and females in each Pclass:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Get the unique values of Pclass:\n",
"passenger_classes = sort(df_train['Pclass'].unique())\n",
"\n",
"for p_class in passenger_classes:\n",
" print 'M: ', p_class, len(df_train[(df_train['Sex'] == 'male') & \n",
" (df_train['Pclass'] == p_class)])\n",
" print 'F: ', p_class, len(df_train[(df_train['Sex'] == 'female') & \n",
" (df_train['Pclass'] == p_class)])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"M: 1 122\n",
"F: 1 94\n",
"M: 2 108\n",
"F: 2 76\n",
"M: 3 347\n",
"F: 3 144\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot survival rate by Sex and Pclass:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot survival rate by Sex\n",
"females_df = df_train[df_train['Sex'] == 'female']\n",
"females_xt = pd.crosstab(females_df['Pclass'], df_train['Survived'])\n",
"females_xt_pct = females_xt.div(females_xt.sum(1).astype(float), axis=0)\n",
"females_xt_pct.plot(kind='bar', \n",
" stacked=True, \n",
" title='Female Survival Rate by Passenger Class')\n",
"plt.xlabel('Passenger Class')\n",
"plt.ylabel('Survival Rate')\n",
"\n",
"# Plot survival rate by Pclass\n",
"males_df = df_train[df_train['Sex'] == 'male']\n",
"males_xt = pd.crosstab(males_df['Pclass'], df_train['Survived'])\n",
"males_xt_pct = males_xt.div(males_xt.sum(1).astype(float), axis=0)\n",
"males_xt_pct.plot(kind='bar', \n",
" stacked=True, \n",
" title='Male Survival Rate by Passenger Class')\n",
"plt.xlabel('Passenger Class')\n",
"plt.ylabel('Survival Rate')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"<matplotlib.text.Text at 0x10b610e50>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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3ZeaNq1pPk5SjAGoC81yNMFh3AKpo56rGf42IhRFxakTM7HhEkiRJk1Q79/Ea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\nggAuJN4AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10b0c3710>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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NhX8laearMM8181WY58Ojn+b6LwJPAhZSv3tPAe4ZYEySJEkz0mqnGiPi4sxc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\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10b5b5b50>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The vast majority of females in First and Second class survived. Males in First class had the highest chance for survival."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature: Embarked"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Embarked column might be an important feature but it is missing a couple data points which might pose a problem for machine learning algorithms:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train[df_train['Embarked'].isnull()]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>Sex_Val</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>61 </th>\n",
" <td> 62</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> Icard, Miss. Amelie</td>\n",
" <td> female</td>\n",
" <td> 38</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 113572</td>\n",
" <td> 80</td>\n",
" <td> B28</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>829</th>\n",
" <td> 830</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
" <td> female</td>\n",
" <td> 62</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 113572</td>\n",
" <td> 80</td>\n",
" <td> B28</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
" PassengerId Survived Pclass Name \\\n",
"61 62 1 1 Icard, Miss. Amelie \n",
"829 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) \n",
"\n",
" Sex Age SibSp Parch Ticket Fare Cabin Embarked Sex_Val \n",
"61 female 38 0 0 113572 80 B28 NaN 0 \n",
"829 female 62 0 0 113572 80 B28 NaN 0 "
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prepare to map Embarked from a string to a number representation:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Get the unique values of Embarked\n",
"embarked_locs = sort(df_train['Embarked'].unique())\n",
"\n",
"embarked_locs_mapping = dict(zip(embarked_locs, \n",
" range(0, len(embarked_locs) + 1)))\n",
"embarked_locs_mapping"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"{nan: 0, 'C': 1, 'Q': 2, 'S': 3}"
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Transform Embarked from a string to a number representation to prepare it for machine learning algorithms:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train['Embarked_Val'] = df_train['Embarked'] \\\n",
" .map(embarked_locs_mapping) \\\n",
" .astype(int)\n",
"df_train.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>Sex_Val</th>\n",
" <th>Embarked_Val</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> Braund, Mr. Owen Harris</td>\n",
" <td> male</td>\n",
" <td> 22</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> A/5 21171</td>\n",
" <td> 7.2500</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td> female</td>\n",
" <td> 38</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> PC 17599</td>\n",
" <td> 71.2833</td>\n",
" <td> C85</td>\n",
" <td> C</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> Heikkinen, Miss. Laina</td>\n",
" <td> female</td>\n",
" <td> 26</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> STON/O2. 3101282</td>\n",
" <td> 7.9250</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td> female</td>\n",
" <td> 35</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 113803</td>\n",
" <td> 53.1000</td>\n",
" <td> C123</td>\n",
" <td> S</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> Allen, Mr. William Henry</td>\n",
" <td> male</td>\n",
" <td> 35</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 373450</td>\n",
" <td> 8.0500</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n",
"2 Heikkinen, Miss. Laina female 26 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n",
"4 Allen, Mr. William Henry male 35 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked Sex_Val Embarked_Val \n",
"0 0 A/5 21171 7.2500 NaN S 1 3 \n",
"1 0 PC 17599 71.2833 C85 C 0 1 \n",
"2 0 STON/O2. 3101282 7.9250 NaN S 0 3 \n",
"3 0 113803 53.1000 C123 S 0 3 \n",
"4 0 373450 8.0500 NaN S 1 3 "
]
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the histogram for Embarked_Val:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train['Embarked_Val'].hist(bins=len(embarked_locs), range=(0, 3))\n",
"plt.title('Port of Embarkation Histogram')\n",
"plt.xlabel('Port of Embarkation')\n",
"plt.ylabel('Count')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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kD6O+A5Dmzs+WPoz6DkAtWaBJkiQNjD1oWtfsE1F79qCpPT9b1J49aJIkSeuC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\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10b0c3dd0>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the vast majority of passengers embarked in 'S': 3, we assign the missing values in Embarked to 'S': "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"if len(df_train[df_train['Embarked'].isnull()] > 0):\n",
" df_train.replace({'Embarked_Val' : \n",
" { embarked_locs_mapping[nan] : embarked_locs_mapping['S'] \n",
" }\n",
" }, \n",
" inplace=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Verify we do not have any more NaNs for Embarked_Val:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"embarked_locs = sort(df_train['Embarked_Val'].unique())\n",
"embarked_locs"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"array([1, 2, 3])"
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot a normalized cross tab for Embarked_Val and Survived:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"embarked_val_xt = pd.crosstab(df_train['Embarked_Val'], df_train['Survived'])\n",
"embarked_val_xt_pct = \\\n",
" embarked_val_xt.div(embarked_val_xt.sum(1).astype(float), axis=0)\n",
"embarked_val_xt_pct.plot(kind='bar', stacked=True)\n",
"plt.title('Survival Rate by Port of Embarkation')\n",
"plt.xlabel('Port of Embarkation')\n",
"plt.ylabel('Survival Rate')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"<matplotlib.text.Text at 0x10b7e28d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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BT5Zj3BwR/wnsBDw+QSzjnWpM4PuZ+QTwREQ8DIz2Yl3P6ivXJ3B2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\newAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10ba3e790>"
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It appears those that embarked in location 'C': 1 had the highest rate of survival. We'll dig in some more to see why this might be the case. Below we plot a graphs to determine gender and passenger class makeup for each port:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set up a grid of plots\n",
"fig = plt.figure(figsize=fizsize_with_subplots) \n",
"\n",
"rows = 2\n",
"cols = 3\n",
"col_names = ('Sex_Val', 'Pclass')\n",
"\n",
"for portIdx in embarked_locs:\n",
" for colIdx in range(0, len(col_names)):\n",
" plt.subplot2grid((rows, cols), (colIdx, portIdx - 1))\n",
" df_train[df_train['Embarked_Val'] == portIdx][col_names[colIdx]] \\\n",
" .value_counts().plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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OrJYdwznRnhxosjz82rQhHoADBw7Q7/ePtr/t+BYHIyXqf+Nv/1dXm/kKcKLec9nOr3NL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\nRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10b30de50>"
]
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Leaving Embarked as integers implies ordering in the values, which does not exist. Another way to represent Embarked without ordering is to create dummy variables:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train = pd.concat([df_train, pd.get_dummies(df_train['Embarked_Val'], prefix='Embarked_Val')], axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature: Age"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Age column seems like an important feature--unfortunately it is missing many values. We'll need to fill in the missing values like we did with Embarked."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Filter to view missing Age values:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train[df_train['Age'].isnull()][['Sex', 'Pclass', 'Age']].head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sex</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> male</td>\n",
" <td> 3</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> male</td>\n",
" <td> 2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> female</td>\n",
" <td> 3</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> male</td>\n",
" <td> 3</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td> female</td>\n",
" <td> 3</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
" Sex Pclass Age\n",
"5 male 3 NaN\n",
"17 male 2 NaN\n",
"19 female 3 NaN\n",
"26 male 3 NaN\n",
"28 female 3 NaN"
]
}
],
"prompt_number": 24
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Determine the Age typical for each passenger class by Sex_Val. We'll use the median instead of the mean because the Age histogram seems to be right skewed."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# To keep Age in tact, make a copy of it called AgeFill \n",
"# that we will use to fill in the missing ages:\n",
"df_train['AgeFill'] = df_train['Age']\n",
"\n",
"# Populate AgeFill\n",
"df_train['AgeFill'] = df_train['AgeFill'] \\\n",
" .groupby([df_train['Sex_Val'], df_train['Pclass']]) \\\n",
" .apply(lambda x: x.fillna(x.median()))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ensure AgeFill does not contain any missing values:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(df_train[df_train['AgeFill'].isnull()])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"0"
]
}
],
"prompt_number": 26
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot a normalized cross tab for AgeFill and Survived:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set up a grid of plots\n",
"fig, axes = plt.subplots(2, 1, figsize=fizsize_with_subplots)\n",
"\n",
"# Histogram of AgeFill segmented by Survived\n",
"df1 = df_train[df_train['Survived'] == 0]['Age']\n",
"df2 = df_train[df_train['Survived'] == 1]['Age']\n",
"max_age = max(df_train['AgeFill'])\n",
"axes[0].hist([df1, df2], \n",
" bins=max_age / bin_size, \n",
" range=(1, max_age), \n",
" stacked=True)\n",
"axes[0].legend(('Died', 'Survived'), loc='best')\n",
"axes[0].set_title('Survivors by Age Groups Histogram')\n",
"axes[0].set_xlabel('Age')\n",
"axes[0].set_ylabel('Count')\n",
"\n",
"# Scatter plot Survived and AgeFill\n",
"axes[1].scatter(df_train['Survived'], df_train['AgeFill'])\n",
"axes[1].set_title('Survivors by Age Plot')\n",
"axes[1].set_xlabel('Survived')\n",
"axes[1].set_ylabel('Age')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"<matplotlib.text.Text at 0x10c4125d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
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SVldEkJmxOsc0teUsIiYDBwKbRMRTwOeBfwWuiYgTgenAkQCZ+WBEXAM8CCwB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\n",
"text": [
"<matplotlib.figure.Figure at 0x10c054390>"
]
}
],
"prompt_number": 27
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unfortunately, the graphs above do not seem to clearly show any insights. We'll keep digging further."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot AgeFill density by Pclass:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for pclass in passenger_classes:\n",
" df_train.AgeFill[df_train.Pclass == pclass].plot(kind='kde')\n",
"plt.title('Age Density Plot by Passenger Class')\n",
"plt.xlabel('Age')\n",
"plt.legend(('1st Class', '2nd Class', '3rd Class'), loc='best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"<matplotlib.legend.Legend at 0x10be093d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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89thjZ1y/d+9eOnfuzFlnnUWVKlXo0qULW7ZsyTn/3nvv0bBhw5xWwYkTJwKQ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\n",
"text": [
"<matplotlib.figure.Figure at 0x10b6d4b50>"
]
}
],
"prompt_number": 28
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When looking at AgeFill density by Pclass, we see the first class passengers were generally older then second class passengers, which in turn were older than third class passengers. We've determined that first class passengers had a higher survival rate than second class passengers, which in turn had a higher survival rate than third class passengers."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set up a grid of plots\n",
"fig = plt.figure(figsize=fizsize_with_subplots) \n",
"fig_dims = (3, 1)\n",
"\n",
"# Plot the AgeFill histogram for Survivors\n",
"plt.subplot2grid(fig_dims, (0, 0))\n",
"survived_df = df_train[df_train['Survived'] == 1]\n",
"survived_df['AgeFill'].hist(bins=max_age / bin_size, range=(1, max_age))\n",
"\n",
"# Plot the AgeFill histogram for Females\n",
"plt.subplot2grid(fig_dims, (1, 0))\n",
"females_df = df_train[(df_train['Sex_Val'] == 0) & (df_train['Survived'] == 1)]\n",
"females_df['AgeFill'].hist(bins=max_age / bin_size, range=(1, max_age))\n",
"\n",
"# Plot the AgeFill histogram for first class passengers\n",
"plt.subplot2grid(fig_dims, (2, 0))\n",
"class1_df = df_train[(df_train['Pclass'] == 1) & (df_train['Survived'] == 1)]\n",
"class1_df['AgeFill'].hist(bins=max_age / bin_size, range=(1, max_age))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10d6c22d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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f9/O3lvWmWuqpYT0YDKqqp6b1plrq6eP/v2kahsMhs5r7TUQj4lzgZ9rfFrwcuCszL4uI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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10d3a5290>"
]
}
],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the first graph, we see that most survivors come from the 20's to 30's age ranges and might be explained by the following two graphs. The second graph shows most females are within their 20's. The third graph shows most first class passengers are within their 30's."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature: Family Size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Feature enginering involves creating new features or modifying existing features which might be advantageous to a machine learning algorithm.\n",
"\n",
"Define a new feature FamilySize that is the sum of Parch (number of parents or children on board) and SibSp (number of siblings or spouses):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train['FamilySize'] = df_train['SibSp'] + df_train['Parch']\n",
"df_train.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>Sex_Val</th>\n",
" <th>Embarked_Val</th>\n",
" <th>Embarked_Val_1</th>\n",
" <th>Embarked_Val_2</th>\n",
" <th>Embarked_Val_3</th>\n",
" <th>AgeFill</th>\n",
" <th>FamilySize</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> Braund, Mr. Owen Harris</td>\n",
" <td> male</td>\n",
" <td> 22</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> A/5 21171</td>\n",
" <td> 7.2500</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 22</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td> female</td>\n",
" <td> 38</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> PC 17599</td>\n",
" <td> 71.2833</td>\n",
" <td> C85</td>\n",
" <td> C</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 38</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> Heikkinen, Miss. Laina</td>\n",
" <td> female</td>\n",
" <td> 26</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> STON/O2. 3101282</td>\n",
" <td> 7.9250</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 26</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td> female</td>\n",
" <td> 35</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 113803</td>\n",
" <td> 53.1000</td>\n",
" <td> C123</td>\n",
" <td> S</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 35</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> Allen, Mr. William Henry</td>\n",
" <td> male</td>\n",
" <td> 35</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 373450</td>\n",
" <td> 8.0500</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 35</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n",
"2 Heikkinen, Miss. Laina female 26 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n",
"4 Allen, Mr. William Henry male 35 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked Sex_Val Embarked_Val \\\n",
"0 0 A/5 21171 7.2500 NaN S 1 3 \n",
"1 0 PC 17599 71.2833 C85 C 0 1 \n",
"2 0 STON/O2. 3101282 7.9250 NaN S 0 3 \n",
"3 0 113803 53.1000 C123 S 0 3 \n",
"4 0 373450 8.0500 NaN S 1 3 \n",
"\n",
" Embarked_Val_1 Embarked_Val_2 Embarked_Val_3 AgeFill FamilySize \n",
"0 0 0 1 22 1 \n",
"1 1 0 0 38 1 \n",
"2 0 0 1 26 0 \n",
"3 0 0 1 35 1 \n",
"4 0 0 1 35 0 "
]
}
],
"prompt_number": 30
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot a histogram of FamilySize:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train['FamilySize'].hist()\n",
"plt.title('Family Size Histogram')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": [
"<matplotlib.text.Text at 0x10db78590>"
]
},
{
"metadata": {},
"output_type": "display_data",
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"text": [
"<matplotlib.figure.Figure at 0x10be12450>"
]
}
],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot a histogram of AgeFill segmented by Survived:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Get the unique values of Embarked and its maximum\n",
"family_sizes = sort(df_train['FamilySize'].unique())\n",
"family_size_max = max(family_sizes)\n",
"\n",
"df1 = df_train[df_train['Survived'] == 0]['FamilySize']\n",
"df2 = df_train[df_train['Survived'] == 1]['FamilySize']\n",
"plt.hist([df1, df2], \n",
" bins=family_size_max + 1, \n",
" range=(0, family_size_max), \n",
" stacked=True)\n",
"plt.legend(('Died', 'Survived'), loc='best')\n",
"plt.title('Survivors by Family Size')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": [
"<matplotlib.text.Text at 0x10dd85bd0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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GbW8Z6klNjS6gHU2NLkCStKNpbm6mubm52/t3N1ytioj9MnNlROwPrK62twCj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\nQn26Z51/swAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10c473890>"
]
}
],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Based on the histograms, it is not immediately obvious what impact FamilySize has on survival. The machine learning algorithms might benefit from this feature.\n",
"\n",
"Additional features we might want to engineer might be related to the Name column, for example honorrary or pedestrian titles might give clues and better predictive power for a male's survival."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Final Data Preparation for Machine Learning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Many machine learning algorithms do not work on strings and they usually require the data to be in an array, not a DataFrame.\n",
"\n",
"Show only the columns of type 'object' (strings):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train.dtypes[df_train.dtypes.map(lambda x: x == 'object')]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
"Name object\n",
"Sex object\n",
"Ticket object\n",
"Cabin object\n",
"Embarked object\n",
"dtype: object"
]
}
],
"prompt_number": 33
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Drop the columns we won't use:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train = df_train.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], \n",
" axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Drop the following columns:\n",
"* The Age column since we will be using the AgeFill column instead.\n",
"* The SibSp and Parch columns since we will be using FamilySize instead.\n",
"* The PassengerId column since it won't be used as a feature.\n",
"* The Embarked_Val as we decided to use dummy variables instead."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_train = df_train.drop(['Age', 'SibSp', 'Parch', 'PassengerId', 'Embarked_Val'], axis=1)\n",
"df_train.dtypes"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 35,
"text": [
"Survived int64\n",
"Pclass int64\n",
"Fare float64\n",
"Sex_Val int64\n",
"Embarked_Val_1 float64\n",
"Embarked_Val_2 float64\n",
"Embarked_Val_3 float64\n",
"AgeFill float64\n",
"FamilySize int64\n",
"dtype: object"
]
}
],
"prompt_number": 35
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert the DataFrame to a numpy array:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"train_data = df_train.values\n",
"train_data"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 36,
"text": [
"array([[ 0. , 3. , 7.25 , ..., 1. , 22. , 1. ],\n",
" [ 1. , 1. , 71.2833, ..., 0. , 38. , 1. ],\n",
" [ 1. , 3. , 7.925 , ..., 1. , 26. , 0. ],\n",
" ..., \n",
" [ 0. , 3. , 23.45 , ..., 1. , 21.5 , 3. ],\n",
" [ 1. , 1. , 30. , ..., 0. , 26. , 0. ],\n",
" [ 0. , 3. , 7.75 , ..., 0. , 32. , 0. ]])"
]
}
],
"prompt_number": 36
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Wrangling Summary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below is a summary of the data wrangling we performed on our training data set. We encapsulate this in a function since we'll need to do the same operations to our test set later."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def clean_data(df, drop_passenger_id):\n",
" \n",
" # Get the unique values of Sex\n",
" sexes = sort(df['Sex'].unique())\n",
" \n",
" # Generate a mapping of Sex from a string to a number representation \n",
" genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\n",
"\n",
" # Transform Sex from a string to a number representation\n",
" df['Sex_Val'] = df['Sex'].map(genders_mapping).astype(int)\n",
" \n",
" # Get the unique values of Embarked\n",
" embarked_locs = sort(df['Embarked'].unique())\n",
"\n",
" # Generate a mapping of Embarked from a string to a number representation \n",
" embarked_locs_mapping = dict(zip(embarked_locs, \n",
" range(0, len(embarked_locs) + 1)))\n",
" \n",
" # Transform Embarked from a string to dummy variables\n",
" df = pd.concat([df, pd.get_dummies(df['Embarked'], prefix='Embarked_Val')], axis=1)\n",
" \n",
" # Fill in missing values of Embarked\n",
" # Since the vast majority of passengers embarked in 'S': 3, \n",
" # we assign the missing values in Embarked to 'S':\n",
" if len(df[df['Embarked'].isnull()] > 0):\n",
" df.replace({'Embarked_Val' : \n",
" { embarked_locs_mapping[nan] : embarked_locs_mapping['S'] \n",
" }\n",
" }, \n",
" inplace=True)\n",
" \n",
" # Fill in missing values of Fare with the average Fare\n",
" if len(df[df['Fare'].isnull()] > 0):\n",
" avg_fare = df['Fare'].mean()\n",
" df.replace({ None: avg_fare }, inplace=True)\n",
" \n",
" # To keep Age in tact, make a copy of it called AgeFill \n",
" # that we will use to fill in the missing ages:\n",
" df['AgeFill'] = df['Age']\n",
"\n",
" # Determine the Age typical for each passenger class by Sex_Val. \n",
" # We'll use the median instead of the mean because the Age \n",
" # histogram seems to be right skewed.\n",
" df['AgeFill'] = df['AgeFill'] \\\n",
" .groupby([df['Sex_Val'], df['Pclass']]) \\\n",
" .apply(lambda x: x.fillna(x.median()))\n",
" \n",
" # Define a new feature FamilySize that is the sum of \n",
" # Parch (number of parents or children on board) and \n",
" # SibSp (number of siblings or spouses):\n",
" df['FamilySize'] = df['SibSp'] + df['Parch']\n",
" \n",
" # Drop the columns we won't use:\n",
" df = df.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1)\n",
" \n",
" # Drop the Age column since we will be using the AgeFill column instead.\n",
" # Drop the SibSp and Parch columns since we will be using FamilySize.\n",
" # Drop the PassengerId column since it won't be used as a feature.\n",
" df = df.drop(['Age', 'SibSp', 'Parch'], axis=1)\n",
" \n",
" if drop_passenger_id:\n",
" df = df.drop(['PassengerId'], axis=1)\n",
" \n",
" return df"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random Forest: Training"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the random forest object:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"clf = RandomForestClassifier(n_estimators=100)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 38
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit the training data and create the decision trees:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Training data features, skip the first column 'Survived'\n",
"train_features = train_data[:, 1:]\n",
"\n",
"# 'Survived' column values\n",
"train_target = train_data[:, 0]\n",
"\n",
"# Fit the model to our training data\n",
"clf = clf.fit(train_features, train_target)\n",
"score = clf.score(train_features, train_target)\n",
"\"Mean accuracy of Random Forest: {0}\".format(score)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 39,
"text": [
"'Mean accuracy of Random Forest: 0.980920314254'"
]
}
],
"prompt_number": 39
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random Forest: Predicting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read the test data:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_test = pd.read_csv('../data/titanic/test.csv')\n",
"df_test.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 892</td>\n",
" <td> 3</td>\n",
" <td> Kelly, Mr. James</td>\n",
" <td> male</td>\n",
" <td> 34.5</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 330911</td>\n",
" <td> 7.8292</td>\n",
" <td> NaN</td>\n",
" <td> Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 893</td>\n",
" <td> 3</td>\n",
" <td> Wilkes, Mrs. James (Ellen Needs)</td>\n",
" <td> female</td>\n",
" <td> 47.0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 363272</td>\n",
" <td> 7.0000</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 894</td>\n",
" <td> 2</td>\n",
" <td> Myles, Mr. Thomas Francis</td>\n",
" <td> male</td>\n",
" <td> 62.0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 240276</td>\n",
" <td> 9.6875</td>\n",
" <td> NaN</td>\n",
" <td> Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 895</td>\n",
" <td> 3</td>\n",
" <td> Wirz, Mr. Albert</td>\n",
" <td> male</td>\n",
" <td> 27.0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 315154</td>\n",
" <td> 8.6625</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 896</td>\n",
" <td> 3</td>\n",
" <td> Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
" <td> female</td>\n",
" <td> 22.0</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 3101298</td>\n",
" <td> 12.2875</td>\n",
" <td> NaN</td>\n",
" <td> S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 40,
"text": [
" PassengerId Pclass Name Sex \\\n",
"0 892 3 Kelly, Mr. James male \n",
"1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n",
"2 894 2 Myles, Mr. Thomas Francis male \n",
"3 895 3 Wirz, Mr. Albert male \n",
"4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n",
"\n",
" Age SibSp Parch Ticket Fare Cabin Embarked \n",
"0 34.5 0 0 330911 7.8292 NaN Q \n",
"1 47.0 1 0 363272 7.0000 NaN S \n",
"2 62.0 0 0 240276 9.6875 NaN Q \n",
"3 27.0 0 0 315154 8.6625 NaN S \n",
"4 22.0 1 1 3101298 12.2875 NaN S "
]
}
],
"prompt_number": 40
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note the test data does not contain the column 'Survived', we'll use our trained model to predict these values."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Data wrangle the test set and convert it to a numpy array\n",
"df_test = clean_data(df_test, drop_passenger_id=False)\n",
"test_data = df_test.values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 41
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take the decision trees and run it on the test data:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Get the test data features, skipping the first column 'PassengerId'\n",
"test_x = test_data[:, 1:]\n",
"\n",
"# Predict the Survival values for the test data\n",
"test_y = clf.predict(test_x)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 42
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random Forest: Prepare for Kaggle Submission\n",
"\n",
"Create a DataFrame by combining the index from the test data with the output of predictions, then write the results to the output:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_test['Survived'] = test_y\n",
"df_test[['PassengerId', 'Survived']] \\\n",
" .to_csv('../data/titanic/results-rf.csv', index=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 43
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluate Model Accuracy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Submitting to Kaggle will give you an accuracy score. It would be helpful to get an idea of accuracy without submitting to Kaggle.\n",
"\n",
"We'll split our training data, 80% will go to \"train\" and 20% will go to \"test\":"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn import metrics\n",
"from sklearn.cross_validation import train_test_split\n",
"\n",
"# Split 80-20 train vs test data\n",
"train_x, test_x, train_y, test_y = train_test_split(train_features, \n",
" train_target, \n",
" test_size=0.20, \n",
" random_state=0)\n",
"print (train_features.shape, train_target.shape)\n",
"print (train_x.shape, train_y.shape)\n",
"print (test_x.shape, test_y.shape)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"((891, 8), (891,))\n",
"((712, 8), (712,))\n",
"((179, 8), (179,))\n"
]
}
],
"prompt_number": 44
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the new training data to fit the model, predict, and get the accuracy score:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf = clf.fit(train_x, train_y)\n",
"predict_y = clf.predict(test_x)\n",
"\n",
"from sklearn.metrics import accuracy_score\n",
"print (\"Accuracy = %.2f\" % (accuracy_score(test_y, predict_y)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Accuracy = 0.83\n"
]
}
],
"prompt_number": 45
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"View the Confusion Matrix:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| | condition True | condition false|\n",
"|------|----------------|---------------|\n",
"|prediction true|True Positive|False positive|\n",
"|Prediction False|False Negative|True Negative|"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import Image \n",
"Image(filename='../data/confusion_matrix.png', width=800)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {
"png": {
"width": 800
}
},
"output_type": "pyout",
"png": 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5nc3+9+lRWunDFNvUTd1WYMIyvw+8pau7S8bl5KhaJhyq1sSEBD2odrVhPwJ0\n0HkoKZQhDRlpaXoDVxnOmTVTpk6cJMUlpSC483H+UC2wP48x8w1VJSZ0bupNKg1OrCed70ZHRcuu\n/Hx54ZVX5U+/uldV+JSwBrvUdMz0g35WxNLpfgJlo1kE/I2ADuTINAsE6sOt22AX06a+njhIc0I0\nB1U4DBzAm2FkS+LBa96DvkYYI38UF9QxMz1d3nl/A5bIt34EF8ahcXYL7LQwE0orcLn3gQdl0YL5\nKg2kkbb3BDlGoBlwNdhPuCI1LzdHJaFGcmX6Fj8ZKCGkarqsokKefO553etu/ty5upIO8AZFUGke\nJHyUmJJw0q6PEmTzvBnMTL96+oX/yamQCo7Py5M33npLiktLVQI2Vp/LoOgEA6ykJVgDBMxGtwj4\nCwEO1JRQzT9qrqq6/oXl8zxHSQz97XCgd3xjwUdWVKQkJSbqmzKvJcTHq53RWBzMiQEn/LmQpJx0\n3HLYwzyuk5zBJY64gDSw7vEw/ieZevChv0lyYpL88Ds3Ab94VXuZidBf7Rlo+bD+JOtcpXrJBefL\nHT+/R+2r2I9oP0RfYlylSrUWDbtp53fVl74sX/72d+T0k0+SvJxs7Z/BgiPryecxGfh8+tJPyM9/\n/RvZW1iktlV85pwNlmMUz8LiEln99jq55opPKY4/+9X9GjcR947FZzLQ+naglMfKygOlJWw5LAIH\nIMABnSu4OKD/P6ySu+pLN0hFZZXayqQkJ0lNba2ShpnwVfTW2++oTdHa9e/CKDlCsmFzxKXjE7HC\niZPCWJoEzURHEnkzDI2vvuFrUg0sTj95haSmJKuRdmxMtCyE+mrbzp3y9VtulY0fbpbf3/cLSBLW\nKFk4dsHRvasOxxI2B3Shfv1kHzth2VL4DvuiXHL15+U7X/uKTEXfoTSrqqZG5s2ZI+kwbl949Hz5\n1U/uUr9j9z3wOxCsHDmOPtggJQwmDGmofu6ZZ8imLVvks9ffINdd/RmsqsxT+zVKkZcvPlb+98or\nsuadd7Hyslh2wJUI9l2W9zZulJysLEi0cvvVLjbS6EfAEqzR34a2BmMYAUoPWjGBUR3zzD//If99\n9ln5+6OPQtUAf1eQPnzqExdr7Ukkdu3JV/UX35BffmOV+oSaPHHimETH4DJ75kz5z8MPyRPPPCeP\nPPa4Sltog3bReefoyq78fYWQdM2Sc04/DasIt0tFVZXMmDpFFoEsBBMpOFQnIAaUYpFM3fCFz6Gf\nzZHnX3pJXnrtdcWSZGAuMKbtX3pqqnDV6rw5s+XDLVvljTVrlGAFE46sKx2MUmJ167e+qatUV77+\nmq7kJY7Ej4b/VF9/8erPytr163URABcSrN+wSWZOm4aVh3kqcT1Um9jzYwcBS7DGTlvamowgAjRD\noc0PP40huq+Kw0GdUobxGJhv/OJ1sIVpVnss2s6oE0MM7Fdd9kmHMIBcacA93bAX4X1jdQI0uORh\nNddXrvsCfDU1KRklJjE46I/olBOOl9NWnKQkgriQmJGAcjLk6rixio3TCfr3lxgYkkUpINWulMQw\nUA1tMOJej/ihNm9lFZUgEWl6jXiaOP3LcXTHYl0pxaLB/4XnnCVnnHqy2qexbxEv4nHmqafg/Clq\nc0U7wNKyMvkE1LDEl9gyrg1jHwFLsMZ+G9saDjMCHFC7sXJIDYIx+PY3DGRSYlyqYjgw079TLFw2\nMD9OjAzGGLlP3ijXWA+Ki2f5uxIr2A0RC7NBMdvGDRUpiQGDwWus4zLQ+hFHYsMFA+xjVEtzdSG7\nEMkEfWAxTnpaqryxeo2se+89qAt/rFLUgeY1FuITC+LCg9I/+rji00YMdTzAd3724Br7IyWnfNnx\n/evX4dFEOcf+IHB4CEb0qiVYIwq/zfzwCHiRFa+vh7/HT1c9w1ZYaJja9ES6IgXGTthSJFwHVpZC\ni3xAuTlhORMXJ6/9hyk1z/UGDOLmdg7oHMh5nYO6CTzPoGnh08RnKuY7r/siMK+eHpSpN2HnC/96\nldoXWQ0ojVC0AUtwKFw0MQ+uw4HLQApLrHrh4436Y6QRdGoQQqkKcOKqOC4i0OKhsUm40lJT5E14\ne/9wa5f6dHrgnrtlPFYeMp7pg04qI/HXIIrP/Z1z2Ati8KLvMEPcDRbm2WafJH5fuuZqXU1Ip8B6\n3zCWrrdLIQ+UA0WyJGsY4T5s0pZgHRYee3FkETDTNj4x6JtBi99HLGD0YvY0Wg2DPUorXCeUlJVI\nyb58aW5swPgeCtWTI1XiYKv/GBeDLO2mokDEIiMilSzRvQLVLrymcT2Tg6knJzr61OFvvARrcKKQ\n5JhJBac9eHid6TuJ+wgsRV3LxHIhW/7z/DZl8FFWA0zG6Q/e9T9UefrEGWAuvojOkhIzD3radmhd\n/fRF+kNKQ8vlRQDZx9i+SPT4JUvgnHWREonISJeqn6mCDQQ8nTKilAqrxz2Jpy5DwuNINx+IF+N7\n5avw4cGlc9KTjz9OxwWHkDLekRIf3HWFgGXgAWJlydXgcPTVXZZg+QpJm87wITBMg9FgCsxxi56b\nSX527t0ttW1NkgifOO4OeGl2heregdhyWZPmREqyxbdYdxf2J2uH+qCZ9j8e1YIgfphDsCJBtGKj\nYsSFFYDRkbCtAhGjj50IbJXDN2BDZvimTJUDD6bvTcz8PtkFULsMpi0D4R6FEFLBYZtxh1JJdnYG\nfBrVIftba2ub9j3v7WGciCP/N6C6pCkM8CMZZdBn1JwfbrggvcIYgSazEqzhhvpQ6VuCdShk7PkR\nQAAjj841MD6OFYmIidMyUO2WOm6C46EcAzzHJ7+TCeSp4yIGSxKf9z98X1qjwrBC7RgYmkdLF0hU\nj0quDl4yfZtlbZAI7bVUreAhX20gZ25s68JBuK6tBR5Dm6Ub6VHiFQcMoqNjYBOTLLHYrDgOq5ci\nQcCoLuRkxxVzNNZW0oWyHTR3xDvw/GAxVFIHwhcVEyupeePUBoflsKH/CLDNIuGfi58utK1DoEem\nT/e31Kbvm/iB1Obs+5GwvWMIh+E58Qyk0IvdQZ5DX5bTPNN8RuPhVgNjDEZRof6cNgX2IfUl2P1M\nyxKsfgJlo/kDAY4BGCZIHPDC1+V56+PQEArVWw/HzWEepA5XS30P9IzdDc2NkpedC3WfS/1M0WUC\n1Xn9GcfIRxyVYZhKqnSllmf8U/KFCYMGsR0gXjRsr29vktqyBpWERYS7VMoVGx0LJ5pxOEC6QHZc\nVD1C5cjAAbaLqkVs58Hv/Mf/zIIIDwVDvBI7NiRMCxNZKH4zPRsGgoCjOu5BnyYZII6BjmAgl88Z\nFpwSGjwH0hrDHddf2DEfHpSu8xuwaOdPHAw8aYOfEbAEy8+A2+yOgICODRgmYGPb1emI1cESpKMN\nW8CAxIz0mzMHr0h4tU5PSMFebMUSNwWSCBCMbqj9+CY98EAStP8u1o9HBCRUkSBOJFEMVA1StUin\nofSL1dBcJ+XVJVBNQnKGBKJg10XSlRifKAlxCfgeo8vIHUkXXRPs31pHB2Dco1gyv/3ZH/Eb8wpF\n2cJJLLHijJK7kW6TIxY6wCKwn4RhP0RKsNqx4XSgSVwCDK4jFkfx9LxcBD2eeJ7Zr+CKH29x4R0h\n4eGDGZSOiLmN0D8ELMHqH042lj8R8BCOEBiHm+BIS/CL57wZiYngp0/K20lQJk+YLOU1FVJSUSqT\nJkyRRg8Z1BVCAyofCI5KxVhXVNxTd9pudRkcPKQrHPZZtMmKBekiqSHZYVk6kXcbXBU0tzRLcU2p\n5JftlbAQEDTETYhNAOGKV9JFVSNtu3Q1IutB0oZ8eonhkcgWryM/rSO/Y/VeaCjGb6pkBlRnPzVW\nIGYD3Gi7xMUQAFPJFQkr29KGgSNgyD3xpKSWZDWY8SQeyqiAA59M9CucsjZYA+9ZvrnDEizf4GhT\nGQ4EDphzOIAGwlBBaRI9Wi+ae4ys2bBWCsP3welihrRDssS3RzPo9xsSrWffymIKduT9nkQMmeol\nYZykPddoKO+Kc0lSQqKeYfloz9UKe67m1hapKq0GmepWNWtsZIxKxYykK4aSLpAuJsY4JFzMq8+E\nb/IiCehDBNgi5Al9y+4plv04GAK9GO7HrA/WB7vHnjskAgY7pyc60cy5Q940hi/s71WspEqp+54a\nw3UPxKpZghWIrWLLFNAIkEBxZSAdWy5fuFze37ZJdjftlgl547XcvDZgktWPGjuEqi/x4m202+qh\nuMvjHot5U40ZA+P7kGSueOzW7T1o09UCwlXTUiclteXQO/aoPRftuJLik5SgxdKeC0b8qICSLEq3\nKCVTtQMzw3eSXEPueMoGi4BFIDARANnEcBAIr6WBic9wl8oSrOFG2KY/4gjwjbZXDYbSqCEsCMRQ\nAkkM7aGisKLvpOUny7p33pLN2zZjg+UpQpJCI3Xmy3j+CAdKvFTFCEkWA8vARQKUVrFsDMSj092p\nRvRULeaXFYi70A3bL6xcjI7TVYuJsOWKgzqSEq4wl+N6gnZgVEHoykXUTyVafqqjFtz+sQhYBCwC\nowQBS7BGSUPZYg4OARIJulWgRIe2QyQ97bBXcrtpMD+4NM1dJGpukJQoJLRo3jGyc/cO2YYjFduJ\nZGdma17DJc0yZTjU54GEi1KsbrejLSDh4hEO1WJCPFWL2OYDL7ksK1ctNjU3SWFVseSX5KstlxrP\nxyVqPBrdk6RFwV0EGKZ0kLziIK4m+ItUmvzsp0XAImARCEQELMEKxFYZNWUyDGX/5BpoRY+Fv6GG\nhgbZun27kgeqv8aNG6ebsnpLtQZVbpAKGnpTmtMBQ/MpEydjS5E02bT9A9mybYtMGD8B+cSrpIt5\njSTx0JbyYpQOIQKpUiEXdItoQhJGkieWmcENaRUJVwtWulU0VEoRSFcIxFcxcIiamZUrsVi5GI/4\nNJ7nvUzTGM1bwjWoHmVvsghYBMYQApZgjerGHEmCw6VvVBYxsByBR7IouVq7bp3cfe99UlNTI7k5\nOVKwd6/c9M1vyrlnn6XEQYvPGnikOuZ3/z+NGtDZjJnkZPnCZVJQuFe25W+X+IR47N2Wq/sVUqVI\n4jFYosV7+Y/qPu9A5DVdfuIYiIdtpwfhL/4zDZIqY8tF0kS1ItWEDJRwtcOOi24iKusqJB9bA3W7\nu9UDfTKkYCmJKUrOqDYNDePKQsc+TL3PI22eoGRtsPXXQvTjz4HEefBt24/MxnAUbxwthodvaG+s\nGNPidXi8guWqJVijsqUNoeGkxWB+O7+G/y8nT0hkwrFqDe4Aetw1njIwZ1Om4S/FoXLohoPNmJho\nWbV6tZx+zrly/y/vk7NOP0Pi4mLhyqBFiQTVhBwEKdEKx0HyQAI0uEDSBChASJgG06U0Kys9U7bs\n2irbtm+V9IxMyUhPV4Kh+TC+ttuRcyTxYQYkjI5acr/PLV7jORc8WDNfqgI7OlAPJTRHTvvAGFom\ndicEJVzARQPSJnEjeYqB1MqFT/oma8VBw/mqpmoprCzG5j+hsOGKleSEZDWap08u3kPXECyb488L\nKxWHkWxFQ33J/d9MuZUYor1tGBgCMfClxNWy7AeUZBJH9jEbPooAF7zo4hBcosqceLGvW7w+ilUw\nnbEEa9S1tkdyFIItIUBwerpawXUaUQtOKI5R8/4qeQ+GJD78rZ6c8GmkT/tjH/4b7zVpgFyFxYq7\noV66MZdHJMMep6f18Lf76SonAxcMsmtqauWHd9wp9993n3zp2mulDU4x3RjwqDLk2yYPTiCUbG3f\nsUOSkpJkyuTJOigOpqiGVpoBlQSOxOLY+YukvLJCtu3ZJluqKiUnJ1dSkpI1f05YbJLDES2my0mO\nBKWuvl4qKiokNSVVEhMTtKw8z213tmzdKlXV1bD9ypIJE8ZTjIU8KDEaWjD1YSrE1mAX6lGNuuBr\nKxqrFdNS0vRaGza/pg1XWX257KsoRG+D2pGEC6sUud1PQnyCOkAlWTPbBfV9+6eEa3BlZlmZ1oaN\nG6WsvFyJJxcbjIdKePasWYNu28GVZnTehSYGbrDPQ5/b9MEH8t77GyQNNoXHLVsmCQkJunjDu0+M\nzlr6rtR8PrkrQ2FRkby7fr0+oxPGj5clxx4L1Xl07wuX73K0KY0mBCzBGk2thclKiVEICA2IVWt+\nhURk4QGPiQfRAslS1ZEhTp64Wj9+x1AQAkPvsDjEbcbPNpzznNc4HCpMMDMcz5l0zHWmg733Wpsl\ncfZyicDkWfPhSkizuMqMBM/kb9Ly76dDsFyyYdNG2ZNfIGefdSY2p4WUBZKrMKyQ44RLSVBjY6P8\n4c9/gZTrTXny6WfkZ3fdJTOmTdNJmDU1CAy29JyEqG7jkZGWDgKSKkUlRbKjYKeUV5RJXk6eqtNI\nCJRoMc8DmAXrwomutrZW1r69TkpKSmVv4T655OMfl9TUo5RMkDS++OJKqayqkkkTJ8rKl1+WaVOn\nyoqTTiTHUlI02Doc7D6WUcvpAUgJFyRT2r1wLcoFCRfUihlpGVp3vslTalhWXyF7y0G4IPGMx/6K\nqUkpIJopavPlwgIEBk3LQ35NGxyIycHKxHMGq6amJvn+7XcoIZ05YwYwK5Hzzjlb5s6ZYwnWocDr\nc557bbrkmeeek3vu+6VceP758va778gTTz0td91+G8h9CuwNHSltn9uC9AcfA/ZRmiJs3LRJcrKy\n5f4HH5RXXntNbv72t9WVi5X8BWnnQLUtwRo1bc9HGZKjUEhg2pslPD5LZn/tCinfsEbqt7yF3yBZ\nKsliPE5PmPRIqEIicL4ZnzGQNrVI+952cWWDNkVys1kQLaUSjO8dzG8PuUIaIZCY9fRAXNXdjnuT\npXltqyz7/vWSAt9PT5y/UiInRSFbbG3TQ1WMKYN3mv77zgGPEozp06aqtIQTNyU9OK1SDZKWRkzE\n3XCqedM3viFHzZ2rJExXGXpK74vSGnJAu6VQEIsJeRMkKyNL9hbtld178yUyOlKysNowziNVcyRa\nfeVZTIMEhZK3M888XV5/YxXeirmFENSbICYbN26Sgn375DOfvkLSoYKkpObvjzwikydNlMmQyNHD\n+0Bssvpfb7YxSkFQ9Yvz4e6GVM4jSDU2XI5riHSHcKE8zZBw7YV0a1fRbuyfGKk+uEi46PzUOD6l\nClHJJwikcd7am5eTVZ+/JFhs4xaQ6ebmZvnNffcq0eRvqm4Gr/7tk82Y/kEMudq2orJSfnL33XL7\n978v555zjtRAMvqFL10v/33qKfniF76gBGtMAzHAylEVSBOEC887T/vvRRdeIBdd+kk587TT8KJz\nkpViDRDPsRSdM6gNowIBTGTgPSERSdKeL5J93IXysZvvlqMuv0HaP+T5BE8tSI7CJdQF0VZIlHR3\ngFxFpEsXvHpH5y6Qo793h8RNXi5u+D4KCYMNFQhbSFiKFwIgU+HpuBf+kmhnFYZ0IbHqxobDIi6k\nm4FJFXZX+BUGr+DhkGBx30BE4p+ACRTb05s5AycOHgycpCnFysrMlBu++EVZftxxkpSYqPZBGsET\nT7/76A+N0kkYlGihXDOmTJcVi0+UjIR02VuQL7vyd6nheCTsqFhu77KSdGVnZckpp6yQiRMmKFng\nddaDn3tBrkgkk5OThNKbzMwMGQ8Vxd59hRrH361CesiymfKx/CQ3+haPa7FQJ2bCNm3K5Kkydco0\nSclIk2Z3i2zdt0PWblonaze+LZt3bJEySPmoZnVBMkoVJKWOSLS3LQ1G3k1EQldZWQXpXqqMy8NK\nUah9Y6Cm4XkbjowAMQ3Dy0dVVbXE44XtmAULpAmSXqoGP335p+SNVW9qH+MLysHwP3IOYzdGVFSk\nSpsjsMdkSnIy+qvzEtf3dWns1t/W7OAIWAnWwXEJsLOcJh27p+6OOnIimXba+dJQulcyZ8yR5LNn\nS8u+LRJOf0ZddSrlatlSBqIE+yjwrE68kbZtF0mcHiELLr5CKjaukeKNIinHR4m7rlwoyIrI4HYp\nmIigemwvqJTwNBCouDTprK6S7gZch9Sro6BRutsaJWb+eEyVIC5Qf3GTY9UiKuUKDNgo+Zg0aZK8\nDbF9Nd6+M9LSpA6kykwKnPwZuBqOtIuEa7iJiKaPfGkA2wriQMJw1My5MhFSrT2F+ZK/Z49ExUZB\nxZCjb8G68g710DLj5vY2SgbRPDhnAq/Rjikcqk/aW7HePMcJkPcrIzeRR+jTYM3sWTY3DnQaLRqJ\nD43guQKRBvDOfopNUt1UIyXVpWiTEDWY5+rE1KRU3VPRBaenJKt98EHaxJXplZSWqnrrX489qtK8\nubNnq62dWXwwQjCMmmzZh+Lj42QfCPrOXbvkuOXLtV8R29LyMtjXNUsaDLpt6IsA+3ZxcbFUYrx5\n4X8vyrIlS2XZ0qV4SWjTl42+se2vYEHAEqzR0tKYl0LC4yG9KpOsMy+QrOlzZNWv7pBjrrpBpl98\nraz9/I2SsAKGz21u6Sitlxlf/brMOvtiicSKr/JtH0jxO6/J3Is/K66EFFl6w/dlzieulnW//r5k\nLT9b0qfPlg0PfBvSLpGorBmy+Fs3yZ7XnpN9DzwuE796uRz9yaslGiqcLjhN2vbCE7Ljkbsc1Cip\nGHZqMrAG4oROycc8qP2u/sxVcjtsq371i19IKt4qOXnwOifbDkhWuPInJg6eyiE5ItGi80w3rnXi\nmjcxGFgJDh+bRItpkyBQqkND2HmzjpJJeRMlv7BA9uzeI9Fx0apKpGqNZWa8CBjuUwXG1XEkZzy4\nGXZ6epoa6TMOJQ201+JAP33FCuakZCSQ2sghsviL/0q44KhV1ykCE6ryomEsT4N5xwcXN7BukuLq\nEjg9LRAXHKOSjKUlp6nTU0q2jDsIxme75ubmyE/v+pHU1zfIbXf+SNWE3//ezZIC2yFLso7QN/GC\nxWcnJztbvnnjjfLlr90ol192ma4SffmVV5XAsj/a8FEE+FLzzvr31P5qzdtvy70//5mq/r1fiD56\nlz0z1hGwBGtUtDDZFVQkeEvqLIL06uxLpB0Tz+af/FHGLfuYTDnuZHkvj4KBTmnf0ySzb7xJTvjy\nd2Tnqldkz47N4sJquc7WdqkrK5f0qR3SWFEpdSUV0lq8VbKvuFEmLFwiG34F6Qi0gOHxaTLz9Aul\nGqorav4iYLBcuXuXVO/aJnmLT5Djv/gtceOt7N2Nv9Bd6wMNPkNeSJq+A39X3/neLXLJpy6XC847\nV/1glZaVwT5pkqw48UR59PH/SBHICA3DObGQQK448QQ5ZuFC/T1cJIuYMW0eJEY8uKKRRGvy+Ekq\n0SqAgT5ttLJhNEsJ1dtvv4MVSnWyp6BA7ce4SnL+vKNkwdHzZfOWLfL0M8/K7NmzsJLpPUmHxG7K\nlMkOoQgwAnxgf/HGmJORI3lzbOWo3osFLmowD4zoDqIBvrcqCrZRZKVb+lCylYLVifQ/RpwWzJ+v\nqi3a11Gt9XmogZ+GwfYXrrnGroA7EPwDfqM7Kukl8f30py6TuehP723YKDNnTFe112uw/6MqjNdt\n6IsAyftpp54ip33sVCnDGENySrcsn7joIpX6effzvnfaX2MZAUuwRknrhoSBJDWUS8LxcTJ+wWLZ\n/uoL0oay7/rff2Ty0ntl/GXXyJ67/yxRR4ssuOxzsuftN+Wpcy8RrDeUcJCvDhCzul3rJW/Of2TD\nX34qe//5JiyqMKBiUqM/I87DOsBiYuLvro42iUwVyf/nH2Xbvj9K0olJsvP2X0rSe6tlwtITZa38\nAnfjpgAMHMxImMbl5cnvf3u/rujhcvP8gr2QGEVhEgZICCRhkZEuueqKK/R3FdShfBP1ZzADrxIt\nyHIolZk/ax6I1mQ4K82X4sIiaetsl9qGWlUdngyj2Ta0Tzt8XXEFIdU5n7j4Iiyp/xBEayu8x4+X\n+fPnqTSIaZr0/VmnwealvYmdEMGRbkG2hd88w3qQRCUmwDUJJni6g2hsapSSGqysLNuneygmxiZC\nugUXFjCWp/RvwpSpcu5ZZ8O57L5eUsB7RxMmCoYf/xAbSqkoLV26ZIksX7ZMXHCtcR+M3qdi0URS\nImxA8WzZ0BcBqlApEWeYB9s1Eqt333tPPo7PEEgGbQhOBPw7mwQnxkOsNR5OGpuHg2Dl18vUm66T\nlIlTZfrJZ8r4Te+qFCkuI1umnHqubAfBSpt6nERiIipa/7bEwVY9bsEs3A6Hd9E7JDZvNnxnRUhc\n3nRJPPpN6djhFI2DKu216CIrBGoY/g6FNKC9WmTmZ6+WpV+4UcKxuqgV0oPUCVOlrmifetOiLUyg\nBtahDXZLVMFxkDv/3HN19RNXSTFwkrjogvN1IjFqDw6StMfiNd7vz2DyU1UX1GbRGKyPmgmJVstk\nKSotFm68TNDT4PKBeweyrI4LhFb14XXKyStUEkaCyP0RRxu5OhjWBhNeU8JFuzKQRgb636Jkiwc3\nreZqQbre2F64U8LhoyshNl4idrrk6eeflSs/dYXG74hwFj1QUsYXCxO88zHngvnTsedz67PBp+C1\nlS/K1+Fy4I2XXtIVqXxe2P9scBCgQI8SLGcxRriUQyr+2qpVcirU9OxbltQHb0+xBCvg2x5PbyhW\n7bhhLIkJdsaZF0nplo2y7fnHJCYtS1prKqW+pFDGHb1IUk7OkdbSjfpAh4FItFeKRGP1X3dbpcD2\nvdcovaOpXtQVVgsIFQZKnbxwnaICN6RkOOG8xeLUMqgEGyvK5e3f/UyJ3oqb7pIwGBrr9ISiBTLJ\nosNEEg1OvJwQeJA8mQGPvrH4HaOgSknM+ZGccJk3D5abtmCRwHr65Gkyvi1PispKII0pkIKeAhCt\nNKhtUiQazkxJMOjri/XjfQwjWQctwDD8UcoLbBgOVCcmULoFScubq9+SajiPbWtvxf6TW2X+4qMk\nIT1O1r63VrfyobNTToRhsGnTfs+FGiRcHsnWWMStv01BDOj6oxKLYugjjqsx6+pq5T9PPiV//eMf\nVR3NhSFWIuMgSrz4zPHzoYf/rissMzLS1eSArlfOhf81XUDjdNn+NoNP46E/Y4CzYaQQsARrpJDv\nV76O9CrUlSatu8sk59ILJXPaTHnpxzfLxp/+QRKg+mstEklaLHLFs6Uy+8rvymvXfFVqoVqae94l\nsvOfP5Tm1wsl7oQErArE1g1w1RDJrUuS0qRtp+MErbG0ROaceaGkHX+67PrrizL96gvEBTVa/e4P\nJXIKNvaFQfG2lc/K5r+9IHnzIERx3dM7wNLIWgdbfYSHfxTpnWAxoPU3cMLkwTvUvsfzm/ebayYt\n/h5QYHy9xyFozr1Mo//lO1R+WjZcpESLB7fzmTppiq46LCkvlYLiAtmJ5fSJcEXADaapniC5Yh0H\nXI9DFSKAz/f2BZSRExwlCJzsJk2cqJIXusZYeuxiGL3nqnuMmtoaKd+LNw68GcTDMS/9bvGg2pEe\n95kGcTY2YKz6sOB4sD7GcwPo08PVLKwv+1A8FkucevLJsunDD2Qi8PzHQ3/RxQLEmD2bxQ2Y4CkM\nn8Delz2e8wOexIv9JiwsVE6GY1+qBFtb2+TzkPofs3ABtuaK87vdH5tm/+ij5UMxLckaqf5qCdZI\nId+vfDEb6GAWKl0lIrMv/ow0VVVI4fN/kPTlKRIagVVwM2Kl4eWdsu/9dTLtpNPk3SyRVT+/Wc64\n80G55NEdUo/JODw8VN66/8dS8eJzUlWwR0688VaZec4l8v7ffydbHrxNpuC+M3/0oFRdm68Ebufr\nK6VqzXPqM3T7y8/KwkuulLyFSzGwYnuZpGSpgH1W7xjb+6VfFepfpN409w8VVOlwMIO4oX9pHCoW\n0tif6qEi9fO8GcSZJoyuVe00TJqTLkx8VP2ROIzLyZMcOCitrK6UvcV7ZeeO7VgNGav+pWgUTomM\nqsE85eNEwGHXFHd/4/Wznp5omkovePjCeuPQicZ3qPa/UCwLCsVysYqsd25utowbl9tbV0oQKAUc\nlztOrxvbraLqYl2ZGAmVeVJ8sqRj5aKRbrE+XR6yxfqZKjs49r94B43pwYyJatrsz9o+B43t95Ps\nw/TFdtyypXL88mWaP0kn1dEKN8pq8PB74Q6WoeLpGRtwvQfmFNLNHuG/wGdz1owZWBQwG9hweyqY\nJ0BS3k5pn0fC5bfSEA/2KR5hoT3oswHVXH7DIUAysgQrQBri0MWIgPuESjVe3w4XCR801uvk0QOP\n6l2tNVAb4g0ckqV37r9TkmYukpjZk6Xm7VXy5LVnyKRzPifxcDxZX1wkTfu2Syj8ir58y9Uy9fxr\nxAURdht8DXEvwRe/fo5MOu8GScjOle3P/0cK/nu/hMbj+YTZz9s/ukYqt90oKZNnSPF7a6S5rFDC\no7C33FSRD//1JwnHticR41D6bprcw0ZGwxCfad4OdWgIbGmcgL3RoLrQgYOT0RCT9yQ6tA+O4RjM\nKMHjIBoGCRMnSkr1hrd89CUFG5jwMJCJcdjbME/qsSfkPqiJi7AtTLgrXNIh0aJkxtnvr0vJFsuJ\nkrLQQ6o371aigbqGwhBa6+yDdIdUKK+bO7ESdH8/ZB9yGK9xvRGNVbFxsXHqQb8DHvGpJqaDVjo6\nxfsDbLcSJB2G8qmQ3FICEYY+yEmThJUETh8+tjPrPBg42T2QjsEuHAsttIxDaxYvBIb4VeuERQQg\npmxbp7vAtxptF3t/DzEPX97ujSfSDQeZHgk8O9jveLA8GBf47EXQ6N3P7comog0d/LhIaHdPGMqC\nx9WSLF92uYGkZQnWQNDye1w8LY6bdAmLhuH6E39Vbw2udG7yXI+HGYM/9iAMw6TQnI9l/OveERfU\nhpF56TBQL5JN37pNn++QOPh4mo5nDuqk9ord8t5XbtHzJGaM29lUKR/c+iOVWIVi5WAUVIMSyvWH\nmAii2mXbL++T7hpwnlwccHJK8uPKTJHqtc8Id8+JSMfJHm67M9SAgYFV7oGaC2MTXURowDmqyDhw\nORGc0yP/15HeUDVFgqU+mbQC/igZJn0O5Mg7FfZYaekZ6sZgb2GB5O/dLeWR4Vgmngm7pHjY1USq\n6oskgfDqRDnEIpJgcMNnJVt+q/ORC30kASL7EFdfsvyucBfU5VGwZUvtlW7RDcSesr2yY98urJhL\nARHLkVR8xqIvRkWBRCOo/Zc6S2V/ZUqK6pEL54nBMoRArRSGe5UQcFa0YdAIEE8lrEgh2PHsJVh4\noZLQHg6gfEulYabtZADB38ESLH8jPqD8SCgYYEiJfQYjc5J1QO9xg+3o88I3JpKsBnhdT4DECVKe\nzmoc8MQOtUfECfjNwRx0qtvdCEP5OhAkLGE/gTYnEKu7m7F9YCXIG84tduxQOGH2uJuQn0OYQsCo\nYmaTVfFlzPMG3+NGHjUSkZKE/FG23vI4sTTyoP5gDEB5tU4wwO+Ary+GbkyIFTDudnNPP0pNGGek\nA3ECYeHWIqnYj7EOqth2SEP8XT5FQvEIUdujNGy2HDduipRWlknxzp2yB3sDxicmqPuCKEhLuuH5\nvQvkwHPLAEdd1Bk3kkjGwF1CKxYPdEFt6Rg9B0CbDLBPON2I/c0hSVwUEQ9JTTzIKlWJJFubt6xH\nv4MNHJ6zBEhuU7CCkys6afPGZ4XqM+KpbyzM3zONHXI24z2wZUobN0Gd2tZh4YJKP53CDLAGNjrb\njnimQJrLRqiBtD6o8SQeUFlmZFrJJqUAAEAASURBVOdIdFpGM15M0Tk1jL4H1FPw0fxhCdaoaD0Q\nG9Kkrlqv0prnhSSL17CfjT5KztDe4671jPlUcXieMY0HFSOlX7q/DdLtc84rrmemINFyyBZfhMyz\nyuIwTy49ZGCepjx6Yuh/kBztmjTggySLBwMn+ZEOrLGqjIgDykPi0o2BjcxlpErHbYu4NQe9vY+H\nujcnPQv+s+qkpKpMdlXvlOjYGFUfOpsvE1OUmTYrCI4kCl8Oh62nrnoLKqkknWozio200iNVc9Zg\noAEt6DwqbEgtvhtV0VWYwIB+oNJT0vXgKs3m1mZ18LqzrAAvFN0SCyKbihWJJFwxUTF4z3C2QWKf\nIPEmEpo8MOOnQYY4s50UOwDJyZAqrUDo0wNFMBDiK8kFhmo+gAIFO56mf+lYiU6G36brBUJzBV0Z\nLMEaNU1unhPv4doU3gzn/PSOx+skJeYeE4/nnYnVia9TgVdcXj9UOuaauW5+89O3gYOFBnw4KxYx\nWZlzvbOjb/PsX2qOapClc6Q3/ETZMFGOhP1Hb5lBdPhAc7KmDQ2NldPgyoGr5UgQSrFAoqSoGGrm\nMN06hg456U+KRIsr6BgcfFkz7/bVS7yK6xqJX506s944HFrJi6MwaKW0Sig8K+ZgSBstBqpCiRX9\nj1FixU3EG5vh5LSxWvZVlUo0FpukxifpNj5UJUZAJctAyZZZbKC4Eiuc1z6Cb/qPfYbqHH0eRyl+\nrOyIBD6HzjPn9EEH2+DF04OHYqKdOARjAaCxJGtEuicytQRrpJAfdL4Hm/iY2IHnvX8f6rspxOGu\nH+6auX+4Pr3y9vrq5PaRE8NViIOmq4Ie549eJ8EgsXFKNUJlQ7YmZxrZsjwdUJ9w8omB76fp8A4/\noTNPKmqqINUql0psmRSfEC8pIGC8zvgkWpSu8J6PBmbgycH7w2T60RtG4Zn9lTEYEI/OTkd6ylWc\ndFYbHZUuGanpwBdkC0by5ZAUFtaWSyR81iXBJpKSLTo7ZVymaFSJmO4UZ7aU5qR4mjzN5yiEbcSK\nDMxMnySiQY+n6VdOg1hyNWIdUzO2BGtk8be5j1YEPsI/cOIj50a+coYk0LCbB43ix2Xl9KoPy6or\nZC/2N4yIcklqCreZSdAVUIyrhBETlknDqWBwkgCDAacvqq4p9WPgKsNUSAnTgB39RDXBbrABtmmV\npfmwMQ5Ruy0ayScBV/rbIm/toFpVg6bmEC3PGfsxCAQC8LkbRC2G5RY8w+i6VoI1LOD2I1FLsPoB\nko0yehEwJMG7BiQZwRZ6CQIIE9WHlHClwqcZj2ZsokypVnlZhZSVl0kyziVjxSnVh8RP7d+UaAG1\ng0q2gg1N4uDUmfiQWDGwX1GNSH9atOVqwZ6R3C9xT2WR9JRi30jYaqVhc2p+cmNqus3gJ9tG7fmQ\nhmknTdD+sQhYBEY1ApZgjerms4U/HAKctGiszNVznPzMREanid2YGIM1kFwxGPUht9uZlDtBxmXm\nSk19rRTDKH53dbXEwFdacnKyxGHjZBdw7IKfH6oQ6V7Ahv0IGFJ0INkibvHwudWd3g1S265bNu3F\n5tQdLW1w1tsqyVAhhoHsxmNVYgRUieyTJGb8JMIm3f052W8WAYvAaELAEqzR1Fq2rP1GgJMdiRUn\nrPUbNsq+oiJVkaVjf7WF8+dJJK7RLoaTGOMG42Rm6kx1IMBQEpoBJ6U8GpubpAxG8eUlpVIWFiKJ\niYkqmaFXdENNiRttiZyj300zpiMaTIkN+575HQXcYtOj4W0/Q1oh2erE9YKKQmmuq5OYyGhJBslK\nTSCZjYPkMFwlWo49nIO2Scef4Dnta4mePzG3eY0tBCzBGlvtaWsDBDgxRMDxZ3lVldz/xz/Lmnfe\nkeMWL8aWQeHquXvKpEmSnRWjmylTGkPp1mhU0bCevph4TRpMj1ItBrpymD5xqkyA5KUaG/6WYVPx\n3VW7JSomStJBEmITkyQC28z00FbLg7lJRxPw4x+Wm8bnatF0ACb7MSKRpqNVyIb8JIDzxoP9yxB6\nV1iE7h+ZkZYhtdWVqkqsgt1WSW2luGAknxyXCFViCozk4UmeGKPcZjUi68PgnfZwQO1gSqys+nI4\n8LVpBgcClmAFRzsHTS05MZBIcdPV2376c13F9fADv4VX8zS1O2r17KlGfzmxMdEgVjRahioRKhpu\np+ItdQhk0FhPkkgGltlXwUzcTJP/aMSdDY/wmWnp0tTcLOUwii8tLJZaEIJo5B8PNSKlWmRZRuJi\n0vBVmQ6XDskx29uQF+ZtbKJ4HzHiOYcwhGsb8/tIBAcXOILETgXtIK7sdy4QqJikGHWlwXLTHq6+\noUHK9u0UF5ybpoBs6abUILymvdWTPKWOCMOBNdNUp7QoH5HiZsYsmxsq4uHITysyyv9EoA/SHMH0\nLO1j6GccU0aqv41ySMdE8S3BGhPNaCthEOAA58Kk+sLLr8iOXbvksb/+Bc41U6UZXtY5OfROUpig\n3sLWQu+89z4MkZtl5rSp8rEVJ4F0xQQ8yeKAzeX/GzdvlsqqajnpuOVafUpJfDUBmnQcX07OxBoH\nMpUQO0kmA+RukNOtWzbJ7tIyiU2Iw7YySWpvxEmGtlq8bzilReRInPhbWlq1natq4IQXJ6dNgSuK\n8djYGav8wrBfY3VNjWzeuh2Eu1XycnNk+tQpKrFUJ7Z+kmSZvmk+mS3t4LiWkMSQnzxHv2VcxZkE\nL/k839TSLHX19VJWuEsiQLaS6f4BOCfS/QNU3MSApNaX0le2exewe231alm3/n0tx5xZM2XF8cfB\nNQVV7pZkoan6BPb5tevfk40ffgiMnB0xSPyTkxLlxOXLsfdlbMCPKX0qZH/4DIHgW07lM+hsQoGG\nAIkHJyk6gnzljVVy4TlnY/l8ivop4jUelMxwcmOcTZu3wCYmXY5dcLQ89uTT8o9HH9MqqRE4Z68A\nDKyDSuggibv3tw9KFYzRSbZ8Sa68q80Jt5dsYdJvhySDjjLzcsbJwlnz5OhJMyUpIlaqSstl5+5d\nUoItgzq7OrH/oUsJjpIsQMly+zY43tZJQEiicrOzYCwep8R6775CiQYBrISK+Mlnn9c2J7kimX7r\n7XVKsPylJjxSnYmtWXRA43YSK5VIAq4EbNg9cdx4kMapkpaVIc3SITtK98r6nR/K5t3bpQLqRbY7\n258vFaadTF8/Ut4HXud9JAYdkK6VllcIVenz586RRx57XP7yj0dUSsnrvm/LA0sy+n53gei2t7dL\nWxu28wJGr765GuYJf9GKsF183v1HH0RBWWIrwQrKZh+7leZkRbF8LSbehXjbN8TDTD4c/GjUzUnp\nmisu14kpARsix2BC/vmv75crPvFxicNETZXICAk4Dts4nNxY9rfWrYPKrkXOPu00nfA4gKPqwx6c\nyaJbt+QhRsQqCW4dxnfkYlueeimvqZD8mj3iiuYmyikq1SIhpMSIUi2W37TFUArLNJh/ZkY6yF62\n7gkZDolVeWWFLmiYPXMGFjdswvUMOf3Uk2EvFg67pxQl0rNmTNfzHSDZvijLUOrBew9GPWlPZlS/\ntC/j/oeUbPEc3T9wn8Sd5fskpLRHkqLjdK9JxmHfYHrs9zwMGepPPRmHBI/7LF56wfmq8uJzMXni\nBPnM9V/RF5bxubkw0m8LCNyGiruv7idmy2HjecLSpYoLx5iSsjI57aST4MQ3CZuwt4J0+eHh9FWF\nbDo+Q8ASLJ9BaRMKBAQ4uXCA4xs9V2tx0uBEw+1O8LV3gmccDoxtnIRwz5tr35ZlixYpYeD5QAyc\nLOk3iQP2Y089Ix8//1y4UUiUZhAt1sc/gWg5CwOIJ/fp60K5mH8mVyCmpKF88KtVC79a5ZVS1lMG\nb/EJWIGYCPVJNKQ1JLi+U2sxXxrmR+KzohLG+PBOP2/OHJUmULK1YN5R2v50/knfXjyoVs3OyvQP\nXEPIxZAiki3izMD69rp/QN9lH68H2aJkKxRNkxQTjz0SU0DI4tWOip2e/dn0aZPmkYrFlxBuT8U+\n19DYBKlpmPY9lsWGjyJA4ksHsjEwMfhgy1YdT373i0t6SW5/cf9oyvbMaEbAEqzR3Hq27H0Q4CDG\niYR2EFT7vf7WGvnMpy5Tuyq+dZMY0GibkwRJF+2x+GZJFQjVhb+483adwFR6RfYQYIGTXRTsYNa+\nu16279wpd37vu/LB5q3y0utvyOevvELtcjjQ+3MwJ2FifsSTEwy/UwIyOW+CjM/K082muQKxCGq7\nUFe4untIwr5+lLQYY23Wa7BlZr7MrxEk4LkXV8qMqVNl8qSJKsUkGeF1ps2D+XRDisbzoy0YfFgH\n08ZUv3KLI7p26M7oVmJbByniznKQrVKRREi2SLaSgTeN1kmNjkRumT7xYR7vbdwEte8eeeL55+X7\n3/6WklKqwUYjfsPe3p4+zIULz7/0spwGe85xHmmfxWvY0Q/YDEbfSBOwUNqCBQoCnLjPOf10nRzu\n/+OfdOsSkikeJFckYVRbtcDw/cE//1Vtc+6+/YeSBXUSJxAzmQVKfVgOTnw0pqWT1P8886xcdvFF\nkpuTI8WlpfIGiCRXQxpbnpEotyExzJukhjZu3XAvwM2mj5oyUxZOmyt5iRnSVtcku/bslj37CtTX\nFuvkEF1nKGI9+x0Qla4iysrL5Zn//U8mjR8vK044Tj3PM80cSKm27dipqkQaGhfDpxcXNGRlZqAP\nmO1q+p1bwEQ0/VP7MrDmfojs0zHwED8uO1emT5km2Xm54naFqBf59Ts+kA92bVW/ZvTKTw/9xKc3\nHWDujbs5v3PPHiyk+FDx5LPCF4+R7GMB0wAHKQjt50hi9xUXyyurVsmZp56iOntvXA9ymz01xhGw\nEqwx3sDBVj1ODrStyYJtzp9/fZ/8+L5fyVVfukEWQaIVHxcrhRgAr/3MVfp2efvP75Z/Pv5f+cT5\n58lf/+9fmPjz5bOXXyYnLFumRMZMNIGAIQdqlytCSePD//q3fPy8c+WOn98jnAS5rP+e3/5WPnPZ\nJ2EUPU7aAkDKYLAzfrUosRqflSu5GVkgVs2qQqwA4SmFoJBOTHnQGSfv4wpA2msxmHQObAPFAySh\nsLhEHnn8cVU9JkIV+cQzz1FQKaedvEIWH7NQ/ovfTz33gmSkpcmW7dtl2bGLVE1opEAHpjsafxuM\nKJ3CIj/FLDY6RuIh2epK71KbqYamBtkNx6a7oUqkGjE9GftOemy2WGcjTdTvIGFcyEASfxX6FKWl\nV37xy1idOV6OgZNersi1Upm+PYVtEIqXhVdXvamOjKdOnuy8rI1CaWnfmtlfQ0HAEqyhoGfvDUgE\nONhRgkJD5/t/9hO8hW+RvYWF6j5g9ikzdLLlBHvphRdiErkYb/+wn+joVEPV8bl5iOc7v1K+AigE\nqrjOTjeMZpPlqX88rCQqAoQrGsbk5bA7OgmkkMb6ajuD+gdK2D/5O5tNc+Lmaj+6I5joHofFCHVw\nYgrD9JoCCceG00kgWgmY+LkKkYbxJA0kUyYdUy+qx0gKWP8Lzz5bjdhJrCk9C4N0kpKWWEituJJ0\nDzazJik467RTJSc7W6U9Jp2x9OmNEfs3A1W4MVCh0v1IFvyZ0UCeKy+NzVZyDDzIo0+RbKkaEVgT\nV0oWKbGixGvW9OlKVrmAwBKrj/YY9jm6zeCK3mf+t1K+dcP12n+b8SJh8fooXsF0xhKsYGrtIKor\nJ5tWLJnmxLLi+OU6WbP6NNxtx3mK9JcuOqYXEcbnQWIWKKvLegvHcoMzUQ2UmJggx6YvUPUbiUpH\ne4f6Kzph+TLHfYOuWAo8zX/v5A/ceyd/kC1uy5OZmq4ONivrqqUShumVIIxRaDcapNNVAUkCJVqc\nyHC7SqiIB3+nYK9EutpQVQzbEJcZpdPTjlwFdzQM3RloH2MkanpiDP8xeDsqcSCCujNw38m47Fjp\nzuxW0lnXUCc7y2Cz1Y3tkCDxykhOQ5xo9RuWBD9OJFur4HLgpddflxs+f42HnAYOgQ+EJiTWXKVK\nVX1KcpK6tugIUFODQMArmMpgCVYwtfYYqCsnz/4G47KhHdIps0zaSEM4KDY2NX0kKb5xmsnpIxe9\nT+hM3+eEM7N/5Lx3nKF9Z7lITlRSg3xIMLi6jKpP1oVe1ftV9qEUwzSA55MT+ECnW1NGtoUhPJSe\nTMwZJ3mZOVqXKpIt+NaCElESQCqT4XIjCuSAFIr1VrKFvIkHJS0HBtOOznUa/jsxTN4HxvffbwOg\nkyN/hfQ95fuieDUQcSNRJw58+SBJp9STKz/rYSC/q2KfNNY1yIsrX5WYiGgJx6KQPQV75Vc/vkuo\n9nIWi2ipfV/OwaaI4gw3hIcqGvswV1jWY5XqqjVr1dyApN6/K3sPLN1+NNDO+38cGM3+HnYELMEa\ndohtBoNHYP/MwAnBHANJj/c45MqMM0zT+R4OA+k+ofeSidvnat8fnhmb6XP2pgqP6i8ejpilb3Rf\n/XLqE4bkaKzfDXuP+TJrxgxdHs5SD6tKok+dkRnrrMf+dhpoPRU/3ERJiRsHy09/TyRUE0Ee6xrh\nW6u6SgqxCjEMKlHaalGqRZsuirKo3jWEwaTlXYaDnfO+PhLfexRHT79hLRS+wWM40DoYTIgbyRVf\nRGivlQAP8W5ICtuy2iQZW0tt3bFdaqpq5NrTTpJ5M+aobZz62UJH66TqFu3FtEx6Ay2HT+Izf30p\nQmo9zncC6i80WXe+U1HK+k2oBil1bYdUmfZY/iqDN45Oe3heEoELCCBOWZLljZE/v1uC5U+0bV79\nQ8AzMnEA14ARzI3J1o3VUqHdcLPAEW2EA4dwlaKgLCxnF6QobqilaLtCqc7wB4CEvCOhmoiGcbcb\n+TvbLg9fzqbOrJ6pcxfq3OOTOjuNTtVtJzJgXlTnJmHST4F9ECUnlGqVV8C/FhYq0JEp7bUSQcYi\nXVGqQlTVo+kbfpxkB4q402+g7kS/odsInYn90WUOUVAuJ1C7QzQBcY/CKsOJeeNk8vgJwL1Vqmqq\n5N0dG+FrLEJSE5IkDa4fEmBDF454lKCy3/OZ5OSuwXz64TkgwXfD8zwXRvClg98ZuEG2f4LzHLKv\nZmDXCDo55kGSx/L4OygeHAvQLhygLLnydwv0zc8SrL542F8jjYCOSxisowRGz/iDoIbRKak6CfQO\n4iNdTuTPSYVvqhFY/RYLdwSRULmoBMuPZTNk01+4mDqHQ6UXhwnFTbUkpXbDEHR68hCm6GQ4MsW2\nMbNASrhHX1V9rdRgZVxJZZWE4QU9FXZcCSCaYdizb7+9ljPB+Qub/kLAyT8MKyDZd6Ji4zAXDw9+\n/S1P33h4PQBsfEkg8YoGwZ0IP1qdedwbsUkasGJ1V02phFWWKvHllkmpaRlKroyPLb/2SRCZbhAa\nPnsMiVilGoqXDq2EnvHfH9Yb257vJ5r+y7o3J/Z14hGblCIhYWFRKFMYzrEpbRgBBCzBGgHQbZaH\nQsDzxscRHnNOqEeFR7VRIlZAcQDrfTk+VBJ+PM9ickALh7oqEYM6JROBVL7hgMK7zgmYWCmF8Ued\nma+2P6VasAsahz5BqQvthgpL9sGDe6nUwQ4tAVItqhhjox2v8WwTZ4sep608MpbhgKbfabKXh2Kj\nago4wiAFCoQyHarwxJzldQHvhMRkges1tZtrbGqUGqhuNxfnS0pLo+Rk5Oh2PTHAnc8EVb5UQTrP\nLORi2kmYku8D+0Y4CCtDBIi/k5fv8xkdKToOdSNAOIE953fTvczn6KjGGCmlJVhjpCHHRjUwBnC0\nxGDe04KBvNkxQucg3YLtQHowYHPwHJ5hehAIolyU3sRgQm9DWakmRAEHkdAouoV1huQlBtKiVhj2\n8m3ZX21CZNn27A8MJN6RKMuMCVNlUs54qa6t1s2m91TsQhlDVI1FFWI0J338o4TF2Gvxfqep/N9e\nLD+JgLqh6KRj20CSYBGZj4ZOxZy406YRrjaAaTwkiu1QydXB1caGze9Ij7sHXuOT4A4iS8lWNHxx\nMVCNSHcPBNzX5IetRxIdDZUlQyvGCZYvYMYILZX//rCfU0IaGYEXvtDQZtiZGp8zwQqJ/8A/SE6W\nYB0EFHtqhBHwDAXeg7EOHJwROUiPcPFM9jrdaJlwxpSNn2M4aJ21BTyTpam3n+pMdE2/4JxPuyv+\n46SaBfVQdma2uhioqK6Qsspy2bdvHyShkIBSsgXDeforIsEx0hUla6iD/1sNOartsf9zHkxTOZg7\nZaWdXDfIKlHj3pgkVMSeKxFr62pl274dEro3RFISUvRaKtTnJFuUJLK9DEEGAPrYDKY83vdo2Tww\nOt9Hoj29SzSS3wmEOdDDrJH7SDaGWII1ovDbzPuNgGcA7Xd8P0T0LpL3dz9kPWJZaD0DpLIOl3UK\nw0mb7isYuA3MBO6FCPugJjh7rISRdllVmeyu3CUu7OWYggk/Hk5ZuV0SRR3GZov3GvLG7zYcHAEH\ncS/csfiEIRK2iHk5cNQLaVUz7OSovv1w92bYxYXCc3w61IjZSnJJyog51YhUMTMMGnenEzAFHKT/\nCDxH9h2EobfqTvMEIQKBVWVLsAKrPWxpRikCwTmcB1ZjmUmaEzwlJZRqxcAWZXLcJBCu8dIEG62y\nqnIlW2VlZRITG6MTPj2+q+d4TMq9EhZMUMEsBxlIy/biTtLUbnxsxcLHFncWcOuenzWQbJVsKZFo\nV7Rkp2VhN4UMvR4GO0tvyRbbbEChl0h5PYG95waU0piI7IXCmKjPaK+EJVijvQVt+fuFgA48+NP7\nwtuvuwI/0lit11CQ54RvJn2jQuTvODjVnAGbrKkTpmCLnlpVIdJrfGl3qRIxqhAZRyUsIGlGjWjS\nGkqZguFeJaQeyQlxp1CJ/qHisFKSbh14rh42UoXVxZJfUgDfWwmSlZYJb/7pGocYqb3WUKVawQC2\nreOoQMASrFHRTLaQg0WAqiMeNCRW8TkT0t+emWCwCY/wfaZe+saPqvClfayRR19A7E2OvMkW1YTp\nmNhppE27oXLYbFWWV0hxSYmSLG7BEwO7IeJLokVDamLO4J2mL8o4FtMwGBEzI1HkOe6lSZusdmwl\nw2169pTly67C3ZIUl6grEdNT09ROTld/enC3mI/FHhIcdbIEKzjaOShrycGdE2QU/WnhO41z6bWa\n3qu5ufNoJSS99aLBNlvWUzdjgxSUjd2PSptJn1G5vU5Hj+MYNiM9A/sZZmID7TZn8+nKMikuKha4\nAYVvLRrHYyVilMftA1ZoGckWJTRWjdgP4BHFYG+2NaL9GzFPh6qwFftn1oNsbcnfKqH5dNiZ4dhr\nAXvadZFsGR9bTMek1b+cbSyLwMghYAnWyGFvcx5GBEhC1IgZeaxavVreWrMGE2i7qizOP/ccmTN7\ntr5Fm8HafA5jkXySNOtFb/Es7xuo15ur31K1yikrVsjSxYtBHPbvu+eTDEdxIiSfh5JTEj8enLwp\nxSJRYn/JwqSflZGpfUPdPsC/VhG26YGltjoypapLJVuQiBq3D4RotPSfwTanIfJDrae5X43c8aLD\n39yAOgarP7kSkcbxxL0Y9lqxrhioELMk02OvFeoC5lAzEne2l0lL8cdvx2PXYGs49Pv4bJrgXTZz\nzn4GHwKWYAVfm4/5Ghty1dLSIr/8zf3yr8cekyuvuFwmT5okJSWlUlNTo4MzpVucVBlfbUaATCAP\njEoYMCGx3A/97WF54A9/kOuvuw6/Q6QNW8mQLOCyDR4EKK30nvQOBgzbm/8YKJniwUASm5uVqwfd\nD3AlYjkM5IsKQbaAdzyIFm22HB9bzr3E35lioZL2pKmJjZI/Bit9BtiRvAhDGPoczxvp3VCfE8XH\n01dJmKTbQSw2Jlb3muR2M3V0IltdJPmlBZIIe61suIOgcTwJLrGmvRZJFcs90uSKTcyxxGDEfULN\nBvOjpPltMYcBAUuwhgFUm+TIIcDBloMcJ8g///Vv8szzz8vzTz4hkyZOVKegHJipkqAXcMZpxjJ+\nEhYa4nKgJ9Ea6uQxXLXnknaueFu9dq388M47ZeVzz8qCBQukA5I5ql6M+mW48h816bL9cXCSZtv2\nN3i3O/sRJVsMXGHIVYh0+0AJSxUkLPSztbegoK+PLaizGNjHlGx5CIp3uhohQP+QIPDg/ohut2dn\nS5AgGv1T/Uy3ClEeT+nE1sOPhlwbb3yovneewVB1VpqGLbK4DyVViDuKdsmOvTslLSlVcjNzYc+V\nAg/u4boPJ+mZwVzT82A/5ML1MwG6BqFdGXHiytVo7JXJ3zYENwKWYAV3+4+52nNipM3Vzl275LYf\n3SkvPfecTJs6FYbMdb115YRZXV0tf/vHI/Lh5s2qRzrhuOPkkosv1sExEEkW5wuSBU5ATzz1lNz2\n/VtlwTHHSENtrU6KvpIs9II0Cr+w7YkRj4cfeUQyoeo7+8wzVLrnPYn3t2rmHmLLPsF0KbGaGOv4\n2KJkixsh0/VDfvUebJkUoTZbCfCxFQm1FyVo9GDONmMw6fU3f3/GY9ko8S3CRtrbd+yU7KxMWYT+\nRdLy3vsbZNv27SopYt2WLlks6elpHnW0r2iWU1tNDWWB+Axb8jj+tVwgL1QfUnplnJmu37ZBoiMi\nJSc9W1chpkOi6OLm02gTlpnBX3jzRe31N96ApPxxJYNpqSly5eWXy7yjjlLC5a9yaKXtn4BCwBKs\ngGoOWxhfIMC38M1btsrM6TNk8uTJkDq06OTAgZCTMAMnzDmzZ8npHztV3zRv+cEPVQJ03ec/76hB\nEMe3U4dmO+g/3P7CBQlJLQjVtu07VOJ2zz33yJ78AjnhuOVy6imnqHQhEMnhoCs9wBvZtvTUvmPn\nTrn1h7fJM0/8V1Pg+aFMcrzX3E98+Y+/SeQnjpuoTk0p2aqogvf46nLZW12DPQbDVI2YiC2FaCDP\n0AWpUFcPyRb7oNMPeX4kA7EJCw+T1pZWee75F9ROsb6+Xtog5T120SIlKzEx0bL42EXoc7Gy7p31\n8tQzz8rll30S9Y9USZfBxtf1MOmS4PLg7xhgGZcbJ11ZXXD5UC9F1SWyu2iPZJblqn+tRKgYqWYk\nyVIVoud5N2n5uoxMj3lFoi986pOXSlZmprzy2uty/Ve/Jv/394dlXF6eEnyScxuCDwFLsIKvzYOi\nxnwbT8ZSezOwmgGOvzlJpqeny7lnn60ShqikJPk2JpWf/+JeTByXYYCOcdRtiBtIgWWnGrARewDu\n2rNbzp1xluRk58h9v/6NEq2vXP8lrS+n7sAquX9QNHv6/ffJp+SzV10pixYuVPJs+oAvSuGdlpn4\nmS5J1JSJU2Ti+InwHt+kkq2K6krZW1MgISAwJFq02YqCZIuq3lBsWE3plhJ+tOtItRfrwzLw+Vi+\nbJnk5ubI+xs2yO7dexSuENibzZo5U/sV1WDccuiB3/9ByisqZApeXhxV4fCW3htzN6VTHrKVjM2n\nU6EubG1rkQao+rfmb4Gj0071Gq8qRDz/fKni804SpFijVt7paSWH+IfpLl+6VDEkRtOnTZNnIDnf\nsHGj2n3SPtKG4ETAEqzgbPexXWsMePHxcerTiAMreRLVNOEeCRYHWE6OtL+iurAFUqGNmzbJ3Dlz\nVArEewI1KHnCpHfjV74iy7BqEKO6TorXXHutXHzhBTJh/Hi1MeMm1MEU2GZU3xUU7FX14N/+9EdV\nedU3NMBjeNywQOE9UdN+jxITEpW4GMexJrfrofS0mgbysNmiGjEMNkMJIFupUHdR9cWDbcr+6JAt\nkuPhJSwHgsFNp/kc5OZmq01TW1u7ujTR5wCFa4ddEY3cWTeqECMgIaZDVq4E9HdZFRk+0AjG5hAb\nGktudq6qEevqanTz6fVb34PX+CjJzcB5GMfzpYmw0raMNmYM3u2nJ4bwh3adJHN0rEoJak1tDV5+\nspXYDSFZe+soR8ASrFHegLb4ByLgGDfT/qG4uETeXb9eLjjvPERyjGA5Sej+ZxikaYf1xNNPyxur\n3pR9WB32tz//SScaDpaMF2iBE14MSARXUVVWVuqbOSUIVEskw4GjKbd5Uw+08g9neThZkkA/+8IL\ncsZpH5Ply5fLn//ykOwpyJdbb77Za5Xl8JAX5m8mbFVNYWEcbbA4scfHedSIrTCQr66SUuyLWJCf\nD9UcbLqgWkqEv6coSMC4Zx+Jlq6qA1gmveHEzaTNPkPyQRUrVewkCy7YlNHtByVGW7duk62ww8qH\nYf9ZZ5wuGZAA04jbn2U0ZTWfmjcwJmHSsuDFQ1chwh6LbdAAcl1Qvld2F+5Rw/iczBx1ckopInHW\ndkJibKehBhLU1XAFs/Kll2Hb+Q+59Xs3qw0bn0kjWR1qHvb+0YeAJVijr81siQ+DAJdGc7ClJOeX\nv7hbLrzkUnni0X/LQqy24wqo9vYOTCSdkpaW5kgbsHpw6ZIl+v2dd9+V3Jwc/Y75BgPjYTLy8yUS\nPr6xJ0Gded45Z8v9D/5OFsyfLxkZ6fIsVkpmZmZo2bmKKRDJ4XDCReJJe6iCffvkTw89JHfddps0\nY9/BwuIixy0HJJRm9dtwlsOkzYnfdB2qpxh4jvZDkyY4+yJ24TdttvYV5kthHf1sie7Nl5iQpCSa\n8UkCWDdKuGiztT9VPeHzP1RlkdxRvc7vdXX1HqLlUslWWmqq5lmwd5+qvigxZBmV6Pi8NP1MkA8q\nkOFzz6//n73vALCjKts+M7du77vZ9EYSUkijCsgiRUH9UDARLHwq/iAKKBYIRXMj0lEQAnygFBVE\nswgKCEhdqUlITza9bJLN9t5unZn/ec69s7nZbMJms+UmOSeZnblnTn1m5swz7/ue97D9xIxtorf+\nHMxCpGF8PVyzrN6yRnidHlGQUyAdmVKSSExJtJiHodd9QeU0sh8+fJi4/FvfFGvXlYpduB+HwwYr\n6gDYviNkNerPMYKAIljHyIU+1rrJQe1rX/2qlPb8+vY7RCpUhmNGjZYqjjmXXCy+8fWvQ42YJo11\n+aL4AE47L/325TDmPWmvmg0ShUQLYfRr7iWXiLKdO8V1P/sZSNUwsQ32WL+6+WbZH1uKlWjt7u/2\nUOLCmaOU7L2IWZYvv/aq/L0TZCD1nhTx0x//GF7DcyX5HkgCGv/Cli4IQEgoQU3Di38sFkMekp0r\nZ57RuWZNQ63YjZeywG0X9bMV9SDPMihhIgk4bCLQ5UKQntgSnOUrVooNGzeiDkuSjj8/86x8HmbP\nmikmTpggpk2dKhfMfvLpP8l0/DDh/ZYIQZJQcBibhBIvW4VIb/Ajh4+QEt8WrIVY1YSJCFW7REZK\nhhgxZDhmJ+ZJj/G281NbAhx/7T6tjyF8/Jx04mxx5hmny3vsup/+TDz/4oviFz/9aYxgfVoJ6vzR\niIAiWEfjVT3G+8SBkS8ivnRpl3QGZtmVQbXRBEN2SoBGQ7rFlyzF+vxSp3Rj1KhRmNafH53xg/yJ\nGNgvqgRp/0K11ybYetDgfdLEifhaz0Lb4ak+AVWb/Y0l+8zrSInehyXvyhcajc6fW7RIEtFrrr5a\n2mFRmjSY+ERf2GQBVMdBaiLVVBH5EUA/bCOGjgDZ8osG2BHRZose5C1JttLkYsn8EOA9SwJgS7eI\n7aEQga7Xgne6TSimnzAN99IEqC6jDjNJUKjipMqQ2PE3P0pyIMny+xPfcNvGhf67DEi1ee050YBb\ndA3KJlEKw/iNOxzS3cNQeJNPT02X6SgJ40bCZpfTFbv43/R9xTGHhDMNY8zxkyaC7Nd1YhufVh0f\nOwgognXsXOtjqqccFPkS4oCXk50NA9h8vonkgMcXyh4Y69bW1UkRfk1NrXjo0UfF+PHjxFCoCPk1\n2pNBdTAAZbv4oiN5nHnCCdJ5Kn9TYpeobR4InHhNSTw5y40hGS4FUlJIDhxi7OjRUBPBuSzuh8HH\nSMpa0A4ajUePqaJiYNtoAzV86HC5kTRSslUNVWLlnko4O4cNHvqVARsjOpxlWr7USX4kSSJ3w7/e\nBn58kISwLG68x6huL8PHSSqkbS7YZG1ctlzaK54BGzfieSQEiUjs2eezQpxdMIznkkgFWIeyta1V\nElpKtbLSMmEYP1T63EryRFWg8dena39ZFq8BJ8nw3qPakTadnJH85OOPyeTEcrDuO9Qbvcm6Nlz9\nHhAEFMEaEJhVJYOBgD2okXzwRcHAwY7SjRZIfh557HG+kaT0gzN+Ftx6q/xi7w1Z6f1r7dCRYb/4\ncuMMNQb+tvt66KUdPTlopE2jbPoMY6BEkgt8k1zxJZhYGMWIFtopZ3yinQy8rjZx4ZT/4YUgW9hI\nthqaG+SSPXX4IKiIVAhvsldkwmYrjeSHsxFRBvNyz43hUPpM8mEH5nagLJK39VAbVlfXSI/uBqQ6\nX/7iF0Xh0CGS6B9K+XbZg7m328v7wZ4FSfwo1eJi35z9t2HnJniM3yqG5MCuESpEuthgIBbMx2CX\nw2OS0sVLl4r3PvgQ1yIFUuU28fvf3gf/dKfL6zaQRu4ch6JXni2T4x2aqkhWFI2B/6sI1sBjrmoc\nYAQ4GMYPiPQQTR8+d/7mNlEHKRbF+5yJx4HyUMgVBzKbWMUPagPRva59Gog6WUd8nweqzp7Wg8sc\nu84OeR0/f955kmjYL8WeljPg6WJkiPXG36dUbUUiARnHJWE4C44bPxboZJM2W/W4fysrKzvJVgpU\njXT9wED3CyQRkmwRm867VZ7e70983fJFjXaRuJ191lnygwQFSQkNJWfxZGy/ggYr4hAfQhsPEidu\nXBKoEPjmY8FvSrXqQLZ2ry4XmamZYmThCJGbk9tpq0V7OtrSETOOG5d/85uYvXqevO/yQewzQcro\n3mKgif0hQjBYV+qYqVcRrGPmUh9BHY2xFvsrnC3nYBb/+7B6g1GIHrUzYE9C2yWWTbUgDcgpTehx\nPXjhyLQc1fAyYzkYbQ+raQmfmX1mP0G12PeE7TPaRrcNeANKWyfuEyXY90wnfgdpm016SABs9RZ9\nUnGNvlwYyDO+pa1FzkishWPTKpAtt8ctjeTp/4vuH0gAbMkY62SZPUWDEkCqCu0ZhLQbC8k1GvnR\nIm+DwYWVjUAbO8U2PLbjetgy2Q8gQoyIJ/FKh1SLqlhpq0U/eVvXCvd2SBSxBmJhfqGUGrJ4fqxF\nyZlDjBwxQmJL+zpKTRmItbze8lf//9HQf/lMYjzC7AUL9XN0UmGQEFAEa5CAV9V2hwAHR8TjBa7B\nL6A7OUUm4iCVhMGur4Mt2WCV9I3DenoTHPjK9+JlJge2XpbRm3oHLQ/66IANSxJeQoncZ/vF1tvr\n2l/4sl1OGKuTGOiQmhxq++x+sX0ukIE0uCMYMXqsXMC8Be4p6hrr5FZdV48qMGMRHxIZUIElQbrF\nGYOUjEkv8nEdlHd+57277zuZnEWSGOx0LAEE2holMZ2shhGDFdByiaNsFbDEskSyH/v2oWetiz3/\nKI+5+SsJTmPT0jLE8BGjsOA0pYb1onxDpchOy8ZkmTHSVsuBa0g7LSktRC46PnUnD9KHFuEgucL9\n5bBEMu4VJ+6vqK+QnoGgUvUhAopg9SGYqqjDRYBDGgYHDpAQkvAlwEAi1AoHjbT/iNoz9GbwlEX1\n6R++6HR83XPKfXtzk4hAdXO0e1BnnynRSIP0pK2pUUQgzTja+9ynNw3vZ9zX6VA3UWLSDgxJsuR9\n35uK8CiQDsh7EWSLM/4KMBMuNykFar0szGSrEnVVNaK2olJoIEecCEDpDO0QKakhQaaUinv7qTpU\nwtebZvdlHrbdA+e7DEH4vOrT+zGObHlBnEYUDJG2j3WY5bl4RYVIg6uHgqxckZ+VIz/SOFZ1qmVB\ntjo5a192+CBl8dqZVHfCgF/zJpmYEWpf1oPkUqf6CwFFsPoLWVVu7xDgcIBBwsIs8EgwKmbnANoK\n9UcYBIaExv6a7l0FfZSLAxnsW5yUXkEy0IYv2yDWoJMvSwzKR2Ug78W1cMKhojclDX2uE0EscnxU\n97mvLyTuGwNqJQ/s/iKhsGiqquyUZh1uVbzrSLSiEh2SLcyUw1p9+Vizj0byTVAlNjQ0ih3le4QJ\nAQtnJKZDMsyVAbj8DUOUIETV8XxZ85MnoQOfQxBVupZgCMhnsH/GiEDsuZYkNidPqmybW5rE+i1r\nxRq4SMlLz8aC0wWSwHLJHLp5kIb0EscBQpL3F6RpHuCROqTQr2muI2OqZ0LfZL1vnCJYvcdO5ewv\nBGL8pHP2DQYNDqAc/LlILr/YB2i46raHbB7r1/GWoqqMLyK2j8eSAHab6+iI1HEN2FcuAqzH9dnG\n5OjoZX/2InrnSjcNDpAgEHSqmEmM+vKeZnnS/xOuFyU6Hni6HwpSNxT2Q7QbaoERdz1mJTbU1Ika\niIs9SV4p3eI6ih6ol6Qq0bClMVGh8l5U+rKle0vtzREePUkobck2pXJSMhcbQ3pT5oHy4JaXwSC2\nsNnkWJQLopWHdSVpc1WLD4515dtEmgc4w1t8Dpav8rqSoD7EzFB8jMnsaHC/oicBwdiEZxMV6bgP\nMDwpO6wDXdP+jlcEq78RVuUfBgJ7R0n58Rj9IwfUvWcOo/jDyIphS74UpcQA5bBpUlXDMmU7D6Pw\nBM4q+2iDH+un/IljOzqBmz/4TcPb1b5nCJg8ljdP3+NH4i8DypdkK6ZyJwHJhd1WHlTbdGvR2tEm\nGiGJaWxoEfVwAaFjxmJUlZgu10qkNIYfNybsudjeqBuMgVd/dXfx9tLSvfgRTj6NfR2i5UY/rqSU\nHXhEwlG3DSSlo4aNkOS1AWrfLVgDcUflLqk+pFSLa07a14Aq2c5r08eN5BWPdh9/o03r4xpUcYeC\ngCJYh4KWSqsQUAgoBI5ABOJf6CRJnDXLQLKVicWms2EETz9i9CRPVSIJV3nDLmE5NEEv83SgSbst\nqsf4BifZIumyyWJ8+UcgPL1vcozDStUqCCzxHAI3D3mwUaSUsJpG8fVVIjc1Cw5MMfsQWFKERfs7\n5uFxP8u0et83lfOwEVAE67AhVAUkMgLRL25+dUe/aDkAHrMvg0S+UKptA4qA/QzwubB9WvHZoE+4\nVNhmDcsfIv04RVWJjaIGnuQj8CTvxXI9lG6lYIavB7Z4UroFskXCYD9jdtkD2qEEqIz9Jgb0pUdc\nMjH7kFsHbEnrMEln5bZSkZmUKpflyYaDWEq9uPQVJVv2epAJ0A3VhD5EQBGsPgRTFZVYCPALkb6Q\n3HCMqMcMKKgSsV8oidVa1RqFwOAgYBMikgP6dGJgHA3f8zHbsQCLIfOZaYUBeQNVifXNUCXWwXbM\nKVLguZxSGUq3OLu007kpXQXEpDuD06vBq1USLYj56LqBx14sNj1q+EhJvOqw9NGGPdtEUqVLFMJO\nqwCSLp6XBvExFa59PQavB6rmvkJAEay+QlKVk1AI8GVBuwd6U96wabNow8uBhrBDhwyBg8YsOTU9\noRqsGqMQSAAE4l/utBWiMTeDVCVCjZidmS3dpdCom6rEJniUr2zcIwyQqaTkJDkrkdItF8iXrUok\n6eLzyH/x5SdAd/u1CXZf49WHXFA6H0bxTXDrsrOhUuys2SMKs/Ig1RoStdMCyaJEi3jZ+fu1karw\nfkVAEax+hVcVPhgIcHByQ/y+fedO8cgTT4n1mzaJcWNGiz3wBfTF888Tl1/6dflxLY1N0UAOgBzM\njpQBTb6sYgOwPRAfKW0fiPshHh+FS98gTkwpxaKineqsqCoxVaoSqRJrhbsOKd2CZKvKgDd5r0d6\nO6e60QMJjQMz7iThwrPGGXXRkBiG8oeDEHGxwz73GqJJKOODCaJpqw85ySAbswwpFaypqxXlG6tE\nQUauxDMVBJXFUqplP9/x5ajjIwcBRbCOnGulWtoDBDiIJWFw37Frl5j73e+Lyy65WFx/9VUiKzNT\nviD88FdDexFpixUrLwlT1E1MSafh7z6DZA/qG4wkJI+d681hJKaUjqqdI6Ht/Y0XX0hUbXENPV5n\n25i7v+s9GssnlrQTwo0liQHvL1vrF69KpKuJHEi2cuFsk5i3w9knjeQl4YI3ec3pgHQratuVQp9b\nSM+y6L/qSJbWEB8a/VM1yn5zs4NOFxw4t/eZJLkCQY0RL3usoXqVjl87MLmgurZGVG1ZB4P4TGkQ\nnxlbvcK+h/eWZdei9omOgCJYiX6FVPsOCQEHBjYuxPrUs8+JC887R/z8mh92viC4SC3XZ6NhKQdH\nBhKtVWvWiZSUZDF29GhJwhJ5IGN7a+vqxKp1paKquho+eHLEaSeehFle6BfsyxK57Yd0IQ8xMa8n\n1+ijvV1La5vYtHYdPGt7xITx4zqv9SEWeUwnJ54kQlt37IB6vUNMPX5SdNZbDJX4+0ySCxElF4yn\n1IrrfI6yhmNWYkDOpmuEKrER0q1qMyJcWCuRMxNpLJ+EJap4T1OKzM1+LrmPryPRLgaN2J3wjE9s\n+BymwhaN6zWSMPI+bMOSRRVV1VJaRxUpSdioEcOxNmT0Y87uG2duAjJphzVu5GgQrYDgmpKrd2wU\nGZ4UeI4fKv1pcbQiqU10XBLtOg12ewZpwaTB7raq/2hEgAO02+0S5RUV4u///Ke4+MtfguNPXbS2\ntsrBmyoO+2uQafl1XlVTI35666/kS5lfnPYAn6j4cKB+/+Ml4oOPF6OJmnjxlVfFbx95RHR0+KVB\nf6K3vz9wZZ+JCyV5zxQ/L27wLRBf+dbl4qXXX5cvab7MjkVceos1seLkkCBWTrj79w+Jrdu3y2eF\nrhm6C8TX3nieRIDXgs8apa0Fefli0pjjxMwJU8W0URNEYWqOiLQFRPnOXWLLti1i157dcsFqPpO8\njnwOnfYajZT4oD34nxCB7SAh9AcD4t33PxD//Pe/5VizcfMW2XaSKWK3vWyneP2tt0XZzt3Ab4fY\ntqNM4kH1arzqkOSKgX0nZpRM05/W+HHjhZ7iFut2bRHLN6wR9Y0NslySXgZ1P0sYEv6PkmAl/CVS\nDewpAhyDaevR1NQsv6L5JW1A9cdBm8H+arSPOVC++e5/xcmzZorpUybLF0p8Gpkpwf6QJJ5b9Fnx\npc+fJzIz0sV5Z58lLrn8O2LuRReJGdOminAH1mKzR+0Ea3t/Nke+9PD1z5f7D777v+LUE2eLMqiJ\n+f5KkHdzf3a/T8vmy5vS3o8/WSbqGxrE5z57ZlQF3akgPHh18fdfvOqM14jqsAyovoYPGSrVjvbM\nxIZq2G7FpFspeG5ph8QF2KWxPKqjZIgkxCYW8XUcvDV9fTYqWaMtFYng2WeeKdaWlkp8WJN89PCn\nobFJHD9xgvjCuefIcYV9Z6B0/UBtZzzHK2LmxDg2FK4y6E+rvqlBlO7eIlKqksSogmFSokUv8pyl\nSDwOVJ6sUP0ZVAQUwRpU+FXlfY0Avw45mLVDdG8PxvberosDNdVHldU14vl/vSR8826Q6opWiPWP\nhMGKxJFEi4NxbW2dNCbOxsxIe90zu5/Hyp7XjMQqHWqpb8+dIzIyMkCudsuX8rGCQV/1k88KiQNV\ne4vwbMz5ykWisKBArmXYm2cjPg/XFA1jY2C8C0tL5cKbPF1BUFVG2y3OSmxs48zEZmGBHdNYnupE\n6XcL0jA+23x+aTNJiZok0GhzPIlG0Xj2+wqRfcthu0mA0kAUzzj1FKny27x1q2y/3S/ei5xlOWLY\nULQ7STaGxKrrOLRvydFfbDsl0ySUfJ7Z3yHwBE+ixaV41mMpnpQqjxgJokVP/GxPCESLAMRj3V3Z\nKm7gEVAEa+AxVzX2EwIUv3OgLsjPExnwPL1zd7mYNvl44cdgZw9uHIS40TD3nffeE2PHjBazTpgm\nXoM4n/FnnnaqfFn3UxP7pFiSK37lbysrEzf/5nYpsRleWCgCwdAxP8gGKFlob5fqmL0m2X0C+zFR\nCJ8TfnwsWb5ClG7YKBbg42MV7Nk2btkCm8ZzpX0RX/69epnz2YtDkQTJCO9vuzXSGialPpyZ2AxX\nEM1NWMKntlZoUPcnQbJFO0rbWJ7FsQQ++5wVzPbbz3pcVf1wGJ1cAiAk4ZIVoG62g89nNdrbAWky\n1YX5GI+kfRZIVk8DikGIOS7F2pHE2yZa9KW1sWK72FlVLsYMHSmXPULVnbMOe1qHStf/CCgbrP7H\nWNUwQAhwEKLoviA/X1z7/64Q1998q9iweYtUF9InVjK8UEvHo/gSpqH4K/95Q3znsq/Lwe992DSt\nLV0v07C5AzNI9w4Y9mPd+g3irgceRD+/L+ZCyhD9YO+nz/beNXNQctGGhfjQvo6SGC8kINyr8OkI\n8J6XtmywvXoZ9mtXfPub4rixY2AntU0+K8SU5/vy2eAza5M1Sn5IkCmR4QcQXRmMGzFGTB8/RcwY\nO1mMzYOROBZYr6+qgV3TNrhh2SFqYBDOGXi88/ls00aJbSSVYzv7sq1dEWRdHhjs8/6yxxUurE2y\nM2PqVDEEkj+6inn+Xy9LX3xM15v22PhwkW7aeBXk5GPyxgSRnJ3eaaPVhFmbnD1r12HL9JBXDQpd\nL9wA/lYjzwCCraoaGAQ4UF904YVSBXjpFVdKAjJ29CjM+GmXg+/XQUiWrVwl/vvRx7ChOENs3oav\nwd27xQ4MhsVQi3wOdhUcOKkKsAe3gWn5p9fCAfT1d94VV1z7YzFl0iT40KkT9zy4UIwZNVL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n+gaY5vjrtW1JoTKibI9/3mzZutvLw8C0vkdJUS8XxXqVFP46KNRzvwjwH8Y69ULP44mhCyquii\n04bvMV+yCAe+hXyTYD/WLnTj374f3rOY6ez+2XnkPlpH13ZGk/TinF2H73fXZwunZ9b8a+98OyYt\njPbELjmuEXZ/fI/ccKqAKZvvmjs32/2JS7bXvUZ8pDpWCCgEFAIKAYWAQiBxEeALHeTA9D0yb+yC\nh+eVgNncBxejo7HP1RzifOFKkgssnluYZRWJIvPxxx8Pc/vhD39IckW3NyQpNLzXFy1a5LCpFckD\nJWQyLo5wxdJIwhV3LAHqLCO6UJjFtpGk+B6a97lfP3LTWfI4TpJlkxHfozdPBbn6CIX8GJMXc5Fl\ntmbqryx4+OZfs2D2j+3htghlsl0gYpyRuY9kLCrRg6QM52K4dErNmA9FkfjJfCSjLDsW2P+oStDt\nuRvq1Evttl752JVUuRIjmc/OwD3wk+VrlmM6in3y+t9dn4RFtA22Kz6dOlYIKAQUAgoBhYBC4AhC\nwH6RP/jgtZ4FC+etW7Dwpjd8D/rS47vANCQm8XFxx4ekgrPri8vfeXjQcwvnPb3g4Zse70wcI3T8\nTcmVb+G8Db6FN/097rwAuTobfepYsPBGua4vyifR6TawfweqPxbfbf9tXOyyb3t43vmoc/0dD96U\n121F0cjOsuz8jEYfXkMfFsjjLm09YMOj5am/CgGFgEJAIaAQUAgkGAKUlJiNespPIWLJrDfbZj90\n3Z3BGGEwsbc38eCDD3oaHJXXQ2l3GvhNG0Q5T8+/5o43IakRv35k3my4bRiL5XsqI8K4BarFJyGw\naYFzhw7LYU5G+ovAYX7iu8a3mcQHVV4FCVAG6i72XXPXkyQaCObdT9yQFgjov8Ca2adBL1YPkdGT\nhqVNQlmfQ70NICBParr12vwf3lVcUVhB3mFqkcDVaHvq5PyZ3yC2aLO7tLTUmP+jO95d8NC821Hy\nT3xP+Z7wfdcXgITu68LS6+BzdQqS/g/K3SZM8zbUXY7f1m8evWWYYZg3QtZ1PGROy5OSzNtvvMLX\nynJ/vfCm0xCPdYK5jq22EWsa3YF89VLSNdcnHVgbwroKcq2Xb77uLhr+C0q91teM/zGKPg+sCksb\naU/7fnTnC7H+stOUajmxRaDWfBQWY7f5Hvbd6/uRr82WzrEcJc4iCiooBBQCCgGFgELgCEAAL3Wq\nxyJsKgjKF7B786HrHgrS2B2zDammIrnqfLc36JVPQ1x1Kfw0/AlkZwvcZD1z28M3XRzLPxYyrqdA\nruaDSGyA06yVcJp1ERYmexELk5yLCv5b6Bq9Q5IUy6Qkagl42TMgabcueOim70qi8ZTP6+/QnwfX\n+jJ+/xlpPgC5coNd7EKZTfgNUmdtQD31rPOxKx+Xbcfh2SA1H2PGoAF1nAttDp17bpZsN5x2vYnz\n6cIfmsA80OpdjBmQLyH9ME2YD2F/gtD1hTx312M3ZkQM42/gbKlwRHEPzp3uD0TP+f5ww3DU+39o\n2wb4w/8DCq8PGSaIlhDnNp7LuizfYz/LBfGaAIzeYjzD+tpx6Kt1LQ7/AWL2OvayXegfT8s/kydP\npvpQCLfnI8TAdWsIBFYIOJWV53msJFhEQQWFgEJAIaAQUAgccQhoqWgypTiisrIyfk1EEgJzwcKb\nz8HuHMtlnei76q5diHsBKi2voYlbeCwsRwCLSbfj+Or5P7prK/bC9/C8FBCSFTDcjvpMYNzCeb8C\nuXh3/o/uvF+mWXjTLJCKH+D4KZCgi0E/ZomQ57j51/tIqDoDJFfn4kcYZd3LSBI/TYvaPEEa5AXp\n28n4oZVDpSSpMrZ3uK1GMyIcwhRSZQfy44DY6HVIkW6U5Tw8z4+9VD0GI/qlIHB5livp876rfB0L\nHr1ZF4b1lzsfmZdlhvTciGWOQUlP+a6+pwx5/sn8CBrwihIkyzsaGHh0p7uMJ3wLb/wsGN0lIHCf\n9f3wjjWMY7ClV9FfIGHr18v8qLMOmLWBuB6HcySGnUGyss5f6kAhoBBQCCgEFAIKgSMDAcsKQahi\n2w05Y5IrSlCk8ASkYCYkOFt8V929C+o2LztlCvNt0IVsqsHo3R1hp6j1brc7jAWnkd9axd8sL5pP\nG4KyzgaRKAHZ+i/O/w/UYkGZx7KmY7/CB3JFmzDfIp+bEil5TrMgLbJIAlmWG1ucdE2rApEp5LnC\nwgosbI2WTo6227DcWZBauaB9bOF5nAKHEht5zECHqdgFaWwPteRYHA8XYf9LaN+HlmH8BqrEYNDS\nR93yoztWodQXNUN7B+de9S28eS7zIzC/5D96CBItVGaEQiRt4HxiJrbtJFfsv+wT1IHAKUrImAhh\n/nxf/O82WNjLfkb9eUXTKIIVxUH9VQgoBBQCCgGFQMIjQJJCOx/ZUE28jz3VhALxIZ7DIe2DIJlC\n0K0AbISSeUhbJu7h393DPZfq4RI6YC/ujqQOqTZjPOfsQQUYzcPydooIpFUgGOJpLLIzVzgcvzAs\n62KncHyT6ZEjjPSSUF0HVaVvri/0+FWPh6PnwOeEJokL1mSUUipxVkzdJrQPUe7psF1KvQrpFyxY\noDEv82mG8RWUW1HgHCOJHtJRrSjbxPM6FlPknupFoZluMJ1PhCPpG1gH6Frd1K6ExOrM5qB/E9P4\nfnTX/0LdORdavBVQL/6OMxsZ35zeHMXQYXWQvjnc7iTGo/QOkjseEc+Ghpww9lKtufCZ+/73d3/6\n3bCFz/72woV/uXs208gA+y6oNSkJhGRrr4pQEawoPOqvQkAhoBBQCCgEjigE3EK7m5IfGIG/ErUl\nijafhu3yyKG9A+4wYsGj8y6xO2aY1g8hsVkd/W0kg0xYyZnJUfITjQTrigZKuUguwLlWgnmd6bv6\nvhrf1bcvve3au9fe+qPbd8tUungHdkqzfQ/OozpQhmshyYodgmOYUkpFNwZSSvVfki7Qp3zPE6BS\nFRCEvUAjeZIZxsPAnarJn4D0/Pqqq66SRI0ECM20m4VDnSQwRjKt9yFdmugUYZfvmntX/Oq6u1b6\nrr6r7P6f3u9/LCZJ8117+zIYz98KotakOfQprCejJUOWZ+qRnSg8aEZCoxkPo/8PUVUhZj9ewd9s\nFwkt247FHVdlZIZbDGFsgb1aFc/fsfCmHLC9VAjZtvF3fJBixPgIdawQUAgoBBQCCgGFQOIiQLLC\nl/7Nc++s//VDN34Jir7/0yLuVVDfrcXL3t8gKrJ8j9xyk+8Hty+GgfodpmH9DiqyC0FTRoOYpEFK\n9Y1Y75wgHXpKC+zQ7WBC/qPDzB0hKo0phn24eTOkXc/7Hr5pLSRei0F4hqGsd+dfc9e9IDPv+B65\n6UGQnD/DXcQ7YD4p4EPvIfv9mu54RRjmwySAIFMvIg1IlbCo2oP0KXTbQ7debOrGn/wBxxqUDWmV\nlQsaNR7yqZ//6kd3Pcc2MIDcgKvECBV+G5ahQ4qlz4Odle+Hd73ge+jGCyOWtRR9fAfpvMjQhlmO\n36sOZU2CHdgdyLIHpRyPsmtdbrGIZUKVR/KmkTTCRcM2S3cU4febvuvuXA/fYreijN8g/gLJwmq0\nUrR9/sK/3ndSoMPZoOvi+EjIrEX68rBmfoYyP2F46c+L5XYSQSXBIiIqKAQUAgoBhYBC4AhCwCZZ\nv4I0CWqwM6Dr+z74wpuQpCyGIdHDIuJaz+786po7f6c7xFcggSoFuXraMjznIr00aLf0yJvC1K9Y\nOmpnVKWI9A7dvM3hcP6OeSG9iWDTfdfdU26leIsQtQBUYivKelnXMasvFnw/vPOXsFD6KgjMKtRR\nomvOV3lq/tW3/wNyn6/BTmoZRENlseRStUeC+Mtrf7PDqvUUYfbfTyA1Wozzz4HwnQly9QjTSokX\n9nCjMB9iq4cYx6CHQ1AJ6pd6c71SLee79m703boM7VoJcrcY7OtpJLPaOqwymE79DdtuaBWfFF7v\nJbf8vzuq2SeqSLGXRBISrD/CsOsi3x992SwfpO3/4HT0HNT5PvjnEuD5IuPRnrPDlpkFFxEnOhza\nMMYB86uR5hXfdb4WSvxYbjRe/VUIKAQGDAGfT+iL4In4Xawhxs3Cb3zq7P1yHLCWqIoUAgqBowUB\n6VX9AJ0hkejuVCy+u7GnMw5kqfP4QOWgbCruOtPF13WAPPukJSGJz2MfM29cuXvzsK4u9R2gHruo\n/fb7pI8rC9K3Py945CZJ7PZJE1fCQ8/c9+D9f777+N8/c88Nj/79/tN/9fufXwRV4rK7774hjck6\nbeNiefY2PK4QdagQUAj0GQIaSBUGuWIxtxgfYt0Ei4PMnGJI+TF0qKAQUAgoBA4RgRgh0GFILseQ\nOQL/5szhFEH+xjIzi/RijEGMp3sBpI/aQUG3Nbd4rk5pmF2lTS7sNF3iSdhMqME0qA/jysFaiAt8\nDrt++IKyz3GJG9Qt/UPZcXaRch/fdraPQRqvy6Pon/3aBGI0pxjlxtpNSReM5DvrZy55DukWIR3b\nCr9VaPPevtvFs2z21ffoz/Mh9TsVTlhfJm6+d33O0tpSi36tcF4auT/47D3PmBHtHt1hXeLQncv2\n1JbtSHGkum+55o4VJFd2e+yyFcGykVB7hUAfI0CJFYvEXg5mlFqlhNsLRSSUHomYRotp1nzh/tIG\nu1p+sSmSZaOh9goBhYBCYGAQsElWd7WRvMWIqnjw2XuvsQz3S049fHLEilT8+NvzpN3VgfIrgtUd\noipOIXCYCCyaIxy2xGrZTbMnmZpxOYosAokaARKVhD1JVyNWMt1sGOKd6ubQ3y55fEMlSZlNyA6z\nCSq7QkAhoBBQCPQQAZIkSrm6Ss8Olp0qzmKI52KLZ++XVBGs/SBREQqBw0OAdlZzo6vVi2U3z7zR\nFNZNqR5HhtsJ808ULV3kYQ+iBZ80umgJRMS6io5vXvb4xuLfzRnu/GlxufQbc3itULkVAgoBhYBC\nYDARcA5m5apuhcDRhgAN1zVf1J7hk1tm/jHFo18RMSwRxlbfFt5d1xZZ3x42mp26lpTs0rOyU5yT\nwbSc721upjpx5MflLY3Yc0aPssc62m4O1R+FgELgiEagU11I4/ioeOqg47QiWEf05VaNTzAENDEf\nxMgHt8K3zLo9zatd0RbAwhRYzmJnffC1x/5b/epbGxtoc0UJFY1K9UtPyRtyXF7y0Hc2NpFUZazd\nZTVhz0f3oA8uzqugEFAIKAQUAgOIgG2LhRG6R+NzlIMNYANVVQqBoxUBGrGf7SuJLL1lxmfw+L3v\ncmh6GN7vVuxqfewHz277D/rtGJrhaU1NcjRaWqRtS2WIM1P4oLqxJWV4PE3NwWA1junbpUcPMNKp\noBBQCCgEFAIJiICSYCXgRVFNOvIQiM4ALJFTeeHs7mdQDYJcmWJXQ+gVkKu3Qapc4/KSK6tbwuU1\ngY6m41NFcMZpw4U3LawH6kPON7Y26s3NQXoWVurBI+/yqxYrBBQCCoH9EFAEaz9IVIRC4NARKFlQ\nBId5lF7N/Czc/X6JJbQGjapH36t8HYfm2Nzk2tW7WrfhmDZWoY+bIKEqL6eUypYi23vp0gHxKigE\nFAIKAYXAEYyAIlhH8MVTTU8cBGpLS6RKD26Nz0/xONw0at/dGFz89obGmvH5nvbNta270VqSqyC2\nePWffWzvE6dTqiUKAYWAQkAh0GsEpCPEXudWGRUCCgHBmYP0eUWP7FAVnu7ASqWBsBlcX9FRCngC\n2ane+kBA0Lg9hK1HRIoqRx/K7QrvgeK7plO/FQIKAYWAQmBwEVASrMHFX9V+FCBQIopAhErMVSu2\njMAq9ZPYJX/YrPt4e1slDoP+SJDkirZVPVb/xTy670fGDhSPslVQCCgEFAIKgQRCYL8v5ARqm2qK\nQuCIQKBocp4kQiGHlgufVilY1V5ETLMZ6kG6XAh0BJ1t2Heu9dWTTlFSVeqb4ubMxPj0lGq9eu14\nD6Vm8fHqWCGgEFAIKAQSCwE1SCfW9VCtOYIRgBO6LHCrVBNyqnBE+rqiSjBgBJ3cf6r0yiZNy66c\n7frklhlPtoddS622htk2JO/dNDPvi+GZ/85NS/3Pq7XHj2C8r2hfAmanVXuFgEJAIaAQGFwEFMEa\nXPxV7UcTAiYWBe3sj8Vny8x0uULtrqao+4bOc9EDiL32JkdUVNUII61sfYTbqX/H7dCnL9veTglW\nDnPgYPLwLPcXDNOa+OTimiREZZcGt7qw36ccplVBIaAQUAgoBAYXgX3UD4PbFFW7QuDIRsAQWiMY\nVju2VI/LkV2Q7vZUt4SMK6ePdQw9YztJkDafXZw8R+MCofizj9qwaH6JIXzwOuoNtjX79SurmgLa\nDS9sY5oh2PzBgLZna5X/x5vqAo3/WF6fjjhr/dbyPdiroBBQCCgEFAIKAYWAQuDoQoCLO7NHi66f\nkr34pukbShfMthbfNCNy39fG3o3o0diSsXUbaGsVf6LL75xkl5jtEWL86RNz0+LSDc3NcFF1OBob\nJVn7lIHfKigEFAIKAYXAICOgBuZBvgCq+qMCAbi/AsvB+lQf3Tjj3qwUx8/bg4boCJm16ys7fvvX\njxv++t62BkqaaIelrfTNyAgErM9jJZ3se7eufBzSLMMHo3Vs0k6Lhu21FbtSXtvQ4X7q/SqRBfcO\ncKDVtsg3xVG3WU95d0Ots3hllciFfVedEB0ocx9J2FGBqOqEQkAhoBA4whFQKsIj/AKq5icEAlbx\nXEqxio2VNa0PzixI/XJuimuiQzfyZo1IvXPqsJTLhBixzalDfShEXjikjS5Id02qaAo9C3L15JQ8\nSKFKp8AQvjREQ/elweYnR+emz7xoRtLXQbA2glyZ78+bluUNO14YPUqLJLlzvg2CVXPNnClOX3Gp\nIlcJcQuoRigEFAIKgX0RUARrXzzUL4VArxCYC3sqOhrV5hbvfvxb4743sTDl/7KSndM8Xl3zuPTp\nsHifTkUeRcYmpF3N/ojYUtOxGj9HpmZk+5vLm+tZ8cfh6YUuXVwWMa3IhxubuAg0BFWixq05J2Un\nO4uqWoIbrv/bNi/iMkvLm/3Yq6AQUAgoBBQCCYiAIlgJeFFUk45MBECuDLpNuPKZksXnTMm+9PJT\n8i7PT3WdmeTSh4BZkSwZwYhZ3+SP7NxaHVj92HsVOxBXuLvBvyNjeDpoF4Kpj0/x6s6a1sgH9765\nxz1jWMrQVXvaa+ACYhr9a7UEzOVNgUDGcYWpSe9tL9+FHORs0bzMr4JCQCGgEFAIJAQCimAlxGVQ\njThaEPCVlEQegx+rqx5fvuPt0ob70a9/jM5JHpqVoqW3Bgxza02AaxFS8hTGZmaluJvaGvztIi9F\n2l/hgZyOtQxFU5XJtQuTDIclVYCars3Cb1HbFq7ALiU3yTK3bFfEipiooBBQCCgEEhGBTyVYnCE1\nZw6sSzCtvGuYM6XY0nyf7kCxa76B+s0ZWSULihxFWMakt+20+w/pBF+ASlIwUBfvCK4H5CrsKxJW\njchrf2Ojv2xrVVttWb2cSUgplmNaYbLwOPXgntZIa2VToOWC8aJjfnFp2Mc+a9ZM+LkS1e3h7fhl\n1TVEaMSeBPv5E9oCpthZF5BSq2S3TqJG/1rqngQIKigEFAIKgURDgOqFwwqWz6cXl5aCf+zr0+ew\nCu2DzHjr0Onj4bx8ulO9dBfXB61VRRylCGhFRcKRFxzuCjYEnB1hQ4fBuli+vZFSKXuLwLAdKzsL\nk5KvWXnGUnwYzPjH8ror7/5P+Wak2/LSNSd4CjP0ZfAOb9322q4fvLamofKkcUm7Ptnm51qH3Tox\nRbwKCgGFgEJAITCICBxUgsUlOyL5xmg9bLnbLCvCz+8QvpuDmh70hwOtFz+8sV7z+aRqg5KeRCFZ\nlFxxyvwazLzywzjYilh1p967aktPcbbJGafLJxvNU6yw5vR7G0rP9pUF7HM9LUulO6YRsEpKSIDK\nSYK6+5iRHwDFpXIGopiQbQzThDYxZJgVS3a01caQa8lMFqfAjiu7LRBeDnJFjmY1tjsOafHoY/oq\nqM4rBBQCCoFBQEDvrk7bcaIrM1ygG+JFoWvLM1yO5Ukux9qMVH11Xqq2emRm0sqlN89448Mbp//0\nD5eNKZCzqBJkAdrlj8+WxDGsu87LT3N+ZOjWQvQzE5vT14M2lviKpONId1tDtm6KNzXN/HjzNtd0\n5NfsczhWQSFwKAiQTHXdZP6xWdvlc5iqa8eneh1J8J+1rWRzU9vE/CQSMz+WzZma4naIlqBJiVZo\nyojkMNSOVBFKgiYLUX8UAgoBhYBCIKEQ6JZg2S1sczkckAUNSfbonkZ/pKauLVxW3xGpaGqPtOAl\nkJuR5DyvMMP92ymj0t/8w3fGn0o1h03O7DIGZb88WmvYtIyWgCFa/BF6uz4OW7w37E9tmuFyYPKW\n1RoyrNDqXe1jkSH/v6W1B8XsUwtVCRQCXRBoHZomiRIkr9O9Tk20BY1tSBJyOzRKqUijptO1Q31r\niPFmukvGK9WgBEf9UQgoBBQCiYnAQVWEHW0hKzPVHQZJCT/2ftXviz+p3YFuUCWofW5yhve847Mn\nnDYm7Ypxed5pwbD5IOIvhSRrF1V0YoHQllfMdsx+fHmEtlCL4CNo7Fvb9e2NY02k2cdgfNEc4Rib\nNVuffe5YqW4U62s1gXXZqOY7EGw40SlNqi3Ns/Km1GpFTCzzLZcvn4b21jd3VWpnfbS1pRBn6DuI\n3rJlmZRkfQnt48utaHKexTpLkKDIh3p9JTJ/1dbxDaGstV9Zs7t14lubmqSxcUltqfRXhKQy0DEk\n+9m1nLNjZdjpuCculK69UrmcnrslGaX0gv0uYZ8RusvHeBWOTgR4Hwvcc7J3MHAPhE1R0SQN3MXa\nyo7W752em6xrYhpcO4hNVf5dSKcFQw7OQuQ9esDnQ5an/igEFAIKAYVAYiFgS6E+uHHKyGW3zKj+\n6Mbp5uWnDbkGrZw1NitpJPYkLMOxZd3+lTFzl9w83b/slpnWby4e9UPEpS17bLYL+85AW6bOH7ED\n+WLBcXfn7LQkL/Zx/N5uX3xc12MSqLi4zGHZSWwvJVmChC7uXLeHdvtiJ9n+EbPHZmVg32lL82lt\nl0QzVkCX8oTVDSZMyjxd08aKULujEAH7Hv/o+tOSPrl55oYlN82wfn7uiO+iq5/FNuTl644/Ds9g\nG1TxNWdPzLwIcadPH540DPv9ninEqaAQUAgoBBQCCYLAwQdpKihSoy31OuWsp+bLLyqoEaPLQmeJ\nIv2/cH9wy++b/nPOpIxtKcn6lAyvazxS55x41fLyFTfMHGq4zOE7XJFVkMqE3v3Z7Nxkl3mepVkF\nIb/j79oDyytJUCix+c/PT0jJ8jg/p1nmJFNYkY6QtvLs+1a9R5UjX0Dc23iRXNnG9B/dOG2aQ3ee\nDFqSgwSNYcNa/czbzpWPL8c0eeTh8iJuzTW1IxSuPfu3a2m/YsXKM0hiQAyLdEufBsrkwgStyna/\nsfb2lWs30DCZUjd65l68avt0IxJ2lzbVrbnq8QpKsSTBipZTEuFEgFCu+VmnsKZC4uYJG9ruxtbA\nR5pvw07hixImSuJY3gc3nJ7mdPmnmK7gZs1X0sD2eXT9PBg2j2L72/3Wx5q2upR9ZfuYh8fdBfRP\nn1w6B0m68Z/RmWGOSHRXGp1NPWYPivAhUGI63R1jMTNjYihs7i5eXlMTg6M1w+2ZnZXsStlR71/5\n7qamNsQbftNJ+6vOZ+KYhU51XCGgEFAIJDACzp62TdMlsdDS2yPacL7Yp3xCg/HAK1cP13VdpNF3\nTzAkx3xSsggcJH7H7XDc3rYzePo7P586PN1jLsxOcea1I83yqrYdSPMqyFV46c0zzwGTWJjm1SaZ\npkM4oA8BERMf3jjjXx9tb7xe8+3cYZMsm1ytvfmUgg4RuschrG9nJDs0LCtCyY/M+42i8PzHl4s7\n2Aa37vhMmkd7JRTRX8Tvb32naDT8YZUFlt04e+Qyp/lEmls/l2wJxEykOnSRijVKfjnrhOtLStZQ\n3WmWfLQlLTVFW6Q59Lz2Ru8ZiCsFKdRtv1qLb5l1qmUZD2R4HKc4UBBZmwsHLpisvfvzE3579n1r\nfoe++SmtAqGKOF2B6Wku7f3yRu2Gf183ZW2S0/lUMrx8G2g8/CIBYCPwxvVTbz3//nUPLPChSz7J\n5rolWcAeYB+MXKG1PP9pSZhMhcFDACpt4ROiPajtMSPGF9eUtyftbAwG8DXSUdcR1gMB/8qttcZX\nP97ezIfLgmPSjoZQqzJwH7wrpmpWCCgEFAI9QqDHBKvFb2ExWhH8aXE57D/ki1vajeRkOC5Jcusj\nm/2Gf3V5O71PS/WbpWkOj0sTo7Nd96a6HKdhiZCW0sr2t+rbjJq3NzalIF3KezecMBsE5HUY9jo3\nVftfKqsNrvW6tIwJBUkXjc9PusgfSWf7roAEqy4q7SqOrLkTrhdaQ/8cmuE6taw+ULVpR+ubjR2R\nGodD04dlek5u80cKkGcMti3wge30gjR1hIx8/J7wdEnZNhCTkBUyHslPc58Lm5b3NlV3vA+7FzMj\n2Zk+NN19akN7mKrEEdh2BbWwnibcXpDH9FXl/vGIqyorK2vVnhaBT3554kzLiLwG0pi5vS64eGuN\n/0NMrw8VZniOH5nt+cqILM9vXv3xlNwLf196y0P15cQKQrKIy7QcAjPFrgefym4PGtvXV7Q/DUw6\nhme7Z07IT/pqutd53wOXjS37iW/7S5PnzDHh4TVqn4MC4kNUTbregclk5IjdhgZ/RCuckGPQ8WW3\nCY6MyAP2bwCb3y3J7Yv6cf9jHgXdiqxqQnmvYSvIS3EV1raHG2cPHWqcv3BTBeL+iW1EfqYru6Yp\nVC/aBZ/FfmsTylZBIaAQUAgoBA4TARKYAweYhWMUp8m6NiTdyRl46X+6fFLO+HzIppzOEUIzvubQ\ntF94wRZW17W99pfF1ds/My7V+GhbGxe0DVOqhJmGn9lW63/h1//e88y68tZmlBHB1nb+1Ow0SG3u\nykxyOD/c1vrgd57a9DLiSSaqrzhz6NvfPz3/D7kpri/eO3fM//xi0Y5n80Ut8wl/i/PWoZmuU7dU\n+5fN//fO37+/qYXOFltj2xPYp50xITP5g81NaLhugtgJEB+SPi6au+n8wPTpTpf2xe21gS3nP7D2\n3lg+5qf65Y/Y0k4amZr6ya42K8Wjs/tB9CPcGohk8dx3n4ZPbgQzErlrWKY7c/mu9hcverj0SUS1\nYJPtWHjZ+PdPGZP6W7wof3LPJWOXXvfQVr4ggYlugsxZ6V5HYWmF/1+X/mHDQ7E8VMb+/bXrpkam\nDU+eM6LR8zX8Xvx+28o67DsJFhqD93H0xTp64tb5uum6ZrzmroX+cT+bMry1DV3X0kORyH9PGZ99\n5ZKtDa0gl3BbdsSpltDtPg29IWzM09t8+zYeqyLEB/7kRS0qKtKK0ja7P96uhz/a1VyenZ0WWp6V\nFZmTleXOyxPuTVVNHW/vafHn5mYECwoKrLy8vIM/u/GVJORxiWwV+mbV1hZpJSUllNBxU0EhoBBQ\nCBwVCBx0kNYibs3SLYM+eC6ZnXvTnNl5P3I7JUHKdOpWQVaKR1Q1B81luwLF33pi87+ASHOmNwmk\ngFzFFCkel9hY5X977mMbn0JEy5QhqXWnjk1rf+Kjyp03f37YBSAaJ+6sD6wCuWLeBmw7sTU+8X7F\n+stOyjkTBOanwzM8n0Hc21N8pduX+k4aooUjl1W1hMRLqxueA7mquHBqbotT6LuPH5IaqPW3mE9+\nWKdNzEl1fiAgENAtLOGmQ0SgkYDwBQneoac5oYaEWpEvbsZzZlaV78tDNb/f67r7re1iWLZT+wSx\n7UFTS0p2Io3pQHIazXMzl86bcZLbqZ1f1hBs/L+SSkod/BfNzK4bnp1U3VIbCVzz3NZH3vnZtFPG\n5nrnjsv3XoLzH2MrgwrITIWOsLwxuBXk6m8s66uzclrH5Trr73ujukZ3aG9FDGuOU9c5iaBw65YG\nEjaqg/YLpgHBXMTqCBqGE2TWtV8CYZkOXXc2+8NZS3Y2jMP5rdja90+XsDHyevVD63pL2Hqbb98u\ndFHZ2j9BMERJ9OODdn7R0NBga3gpseJDJepaW0VdHXn30RRK7M701zW3y1d7hYBCQCEwYAgclGCx\nFdERT75bPIZpDvdjqMcvPyQxGzZUBdYt2dG89IG3KrYgafOMId6y/LwUMJta8iuZty1o4ofouGRG\nXuU/VtXuWviDEw0QLJAvxywSHehG3E/+73FfK0zzNGWkOWvgBojMpx2VjKVtElR8I5CfKr7tZih0\nfLLLUVjbGtn+wNt7yl0u0bYtLDZt2lTT+tI62y5YiKunFbie+LgcWaKBapjYoTtkhjb7w67tcC0x\n4T8/mXp9VXP4j6nJ+vuXPAyj9Fj48VdmOP+5qkT+Qv9lXjIxBHkMQ/1ZmckuUdns3/RaaUMd7GJq\na2obdvxrZdTe6i9rqiOWKV6HbdfcdK9+PPKNxFYGh6cUVrAQqFlFIM0jKgIt9WUnj5sD6VwxbMCc\nDbQHg0TKjfPJ9Wgo9rEs8sDuh7jipZW/Ky2V71+WTRcUBwrM40rH5vN14nCgtAkRD2mOE4QjMnH6\nuLPB8H2mZgUxtdKWbrA/2DSCSeM7iQn+xOKxRzzuLBkvU8hbKhofzYvz+MdjE8XKtNF7BOVFJyRE\ngcC0C1mWrEOei+XnRcE5tA57nGX10fpwELvfULasBfGgwAi41/fpA4g/42V75QPD9JjlwfYjIZTJ\n0fqZFwHnYnXE2hpt294yYueZFzNDZIOAGs7LPrCNCEjEduGItWMfy08cNEpsTdSjWcY+edAu2U7k\nNdAu9JvtY7myTLabx6wLwaQ1ItJE8xDPaDxOyGOWTcEs4/HPwEec12FqW9ev2bIkWp76qxBQCCgE\njnwEDkqwLGcIIiCX0x8yzT99XHPvG+saKyYVeiNbaoPt22v8JAmUiISGZbo6Ml3u6lVV7Q03n1lm\nPE1cKOtBgPaQdbQGjBA/u4MwEJdnMMIOJZmACnHySWPSJpNF4AWEv9hjoz8gODPlT3ADOZcR0XoB\nlgyhZIlquo5kh6g94YS6jk2bmOzAAa8lFmkUjU5JPvve0qqXr53yk2DEemR0jvcsbi2BSOvim2Z8\n3BYy/3Dub9c8T79aBy4NLxBNK9TRVtidUerWnuJ11H/++yL4sQ/rzFW0yk54XHpZAOpJmK6nTx2e\nlgP1qEcznfJNgwTkawGYKje/+i0RLhFwXgpRhu4w0WGekv3WgU8MRUTFBTRO00qlHc42RJNJMpPd\nZtbPY3tPkubKHy/aW7Z2pkFU4obW1iiGuuUoAICf1clg0Bu8kPdvdNw9s89JO2k3+WQ5sSuBaRX7\nZNunCuSNnrUL2zfpvg8P08Slw+Hei7f3aN8Sor+YC+rsaG5WiAiQK3nyYDm7tNxubNceRSvp/Ltf\nj2Nn7NKi++7rRd5Ysm7Px8454nreWS0P9skbLcEC93S6HFiCK/QGUnw+lj4KSeyH2ikEFAIKgSMR\ngX3fEV17EOemobUj0rizIbAaGyRUUbugovHZEYcz7DcCre0lZWGqMaw5U/BmsPUesjz5QW3mOWJf\nsXF1QM0m1lcG3lu7p21pZhKENroVtgfuGCny7moINuSkOK36dtiIW5aHxAZKO5KGSHNABBYVg+/E\nlXmww93+dtPnE/qXfaUvf+vUoXtOH5/85bw09+zsZOesUdnu85OC5vkvXj35uq8+WvwHlNNp+9S1\nTM2SfQIhkqrHoBnyB+f74NoqLiHUc5iXKD/VrUAwQpxdkMjJpoLvkQDJbQEOzrLzHbBGO0F0j0Ks\nZTfNnmRoxji8oGDVFXsbxyWjBMOwNNZZ+9CO1cuLi2V/WD/rTeiwfHnUUayZpv0bVm2T+GbGtZeg\nuV3Y2zhSFMN4C7MpwDctl0v+diGONyP3sfsomt/J37jpGCBzYV7eSKRvMh2vEvPEmBKUrLyAGvz5\nyzwW8vO3zM44eQwHHcjv4DHEMJ3n7DJYnkyHm0USjNhvu0yms8vEFFqWw9tA521tx5NeRm+maPlS\nPmVA+c2rHGuPI9oetj1ajR7tB2RRFozx2GG2jemZBqRVitN0zA7BnYI4KfOKlc8+Ix0krjIPzsVw\njJaDhxQTSGSbYjQJZdj1MX4vhpCJsQyUjwc3ypBlmcjAtlOmaxgBMx3nPonWpf4qBBQCCoGjA4GD\nE6xYHzm4pqVgyIUkCrZKlaK1IlQK49Ti4gaKWhhvh9iAbP/cu8dyM9Fzk6O+m6CIqIaqgZKqxgUv\n734JKUnnaGdil8f0fFdop49JC324o9UyTKOmI6SLZI+jcExusntHXYdVwnUDY17TSZ7mI8PyCvzp\nJlRXC2tyqdBKfVPcsOla+8xisQPJqH4s+Nv/m/SNWaNSr8pMdlx3wqjkD9fs7Fjh0b14f0hp0j6l\nGYbYTfVlukfaSjlS02D//zNIXXzCqvE0y3eOrlujMSNSRCyrYWutn33Dq4yOHGKf8XElSk/ycb8P\ndMjrEBPY8PX4i+GZ7u+ReNK1RddAtxkZSQ6xpym0cvHi5C9B4FcDBgg3FZ34ds2SSL95D2ibPtxE\nG7RPkU8mUrNVWw4TAd7IvPbcVFAIKAQUAkc0Aj0iWLKHpvzgjQhvVsT3coWc0ddNz+WLsZv4zqjl\nWC4HP/DRGvmkHWRpRLbnjK/Nznv2+eW1bzz4zfEW7Hqjwd9s+kpqaeCtnTc1zQmCJVo6tLVup1WV\nk+wc8uNzC0/6yd+2/ZeJoy4LhJjrKzZ8+L3sym5YDOJH56foc4vbDVFcatDtA8yi/JtL9aqrirdv\n+deqpnrYZX3H6dByhqR6x64RHRuhGtXSY9ZNMeGSZDJ+01he1xoOw0h/2o/PHT7u92+VLxVwvLrs\nsVYHnKyyzWi1+CqlbVXNofX4RVUqjV9skQBTHHJAcZ0vnrBhLlpd3tHe5A+nGKa02YqWF5UYMKEJ\n/2QeuJ0o3727YxRO+kEAKX08UoK8l2CPtT8jPVJ6cNjtLEEJRdi43xs4827vr7472kfwfDjF7l9Q\nT9rLND1JdzgtU3kVAgoBhcCAIXBQgkUjKy80Fhj1TLzEDyngPW9SygOiIQdNLFwr968MXS65ytNL\nWt+96tTMDzHT7vSrzhxy7ZjspIrrnt36YXwl9PCek+qyTvQt74hKb9aUf3Dj9OcK0jzXnzgy5f89\n+PVxW+Cs9C/II8tmXjgRzRBjRQePIWkCqyHjiKr01tW0G6tR5q4Wl4V8TEOiSOmSuOZzQz4DezBP\nU32w7I3SBrYxGf69kJuyIJj9QqOEONNXJJzn3rduzQc3nlA8NjfpG1+alnVlU1toPRyJdrZ9ybwZ\nv8j0Oi+saA41/mt1PeOpsfI7nDowQWMR8Hu/AIsrmqQgdBpD75eGEUiiaXev/g8OSTBHYPNg6y6w\nNF7jSFYWpFeN3SVJ6DiLxu4J3cJ+b1xJv9cwCBXID5Uu9Xb7THRJo34qBBQCCoEjBoGDEqxkpxsD\noZUGVRe4waEJX2C04Un1wChWi5q5hFKj+X2+6CLHWO4m8MXjJt0Aoc7zEwuTzkxPdrx68ezsEkhp\ntsPA14QhyUigOLO1NTwP++dfu248jLS2Bl/d0HL3FyamzxyV4y1yO/U/fXjD9DlQkW2AfYsLEqPT\nI4axxXfvrquRJwwjFRdmK1KvRgJCSUhL0CM+PyTPeOiTm2f8F1rLXSBftFCZDjL1RTgsFYt3tDyP\ndJQ4WUYAu6SkVI8TtvrR/pujRo8GZmWR9ZWhBeA5MyYOSTrxB0WFL3z/rMJX3LpWBwnYKZBsndXY\nETY/2t7yxxdX1JedMzGr5e1NjRGYujijmEhC1PlCsRd6BoVz8Tz6wfZ2nsfxPoEXhVK7xsCH+na/\ntwa+vrp7YQnGG/5W8/HljaHGRknyDljmPhWoHwqB/kVA3Yf9i68qXSGgEEgABLolWFy/jobqhqGH\nTMtY7w8ZQ/1h65AkCZDE1GyvCQTbQwbVUo6OtqgEi33mWoKwB8Iagxs/uuPicXNmDIvcmJbkODvN\n6/wfWIYL+HeinyrRFjS2lWxuAbESBa9tFU3RPDuqd59c8L1LTsy5MTvFdUmaV/8S3D18iek583BP\nU7j0ta0Nw5FnPYpqrm4J1wUiVjXrRXCFQ/puEK8IvMxfDkIkJ9BjRqFAutLFZa3P//JfO5efMDyp\neU25v9VKTUkyTXMjPK4HQyFMUAej+nhtDtpexrZv/uWXxnzjtLHJv4St0xfTPc7v0T7KjzbAMH81\n2v38ba/sXDMszdmQbEWkVRiccrVVt4ZAiCz+7iRF9qxF0KSmiuZggz8coc8JNP/AIbYeo5TUHTiV\nOqMQUAgoBBQCCgGFwGAg0PmS765yatduOHt0wcqqpkkrdre5Gtojm5GuHBvJxkGD7zujvZtKA+PW\nlLeOwBI51QVCbAbLoWSoM0QJk/xZcP7krBknjkoZD3FZemswHCqrCzb8dWktiUj7qALv7p3VAdYb\njuXhF3D6aeMzZ5w1Pm1KZrIzuy1gBhdvb9nz1sbGWpyjJdeWx66c7V++bvfIFeXtY5ftam+bMjx9\nU2l5C8/lzbtgxOdg0D4Gi+uacDtR/5ePq6sQ3z4iw9VmmOGyilbRiErMX5xfkLeyPDRx2c5Wd3N7\nZAvS7MEWI4hSypQ398S8E6cOTZ7kdArvjtpQwx/er2Sa9hG5rpZwILyzqk22x6B6sUoMGb5yZ+tx\nS3a0kxxtxCY9w2Mv0F7X0rW7Ri0r7xi7end7SybOg50eSXZT7IYKfYgAn8EFC4Q2fz7uNTyt+K+k\nP32IrypKIaAQUAj0FwIHJVixSilJycDmhXFta21tVH3WwwbRRDxzWJoI72kV9jI5+2Slqqt4/XpH\ncWkpJVXJ2JKwubBJCc747KSOrQ1+Wg+RkMiXiw/Sr8KK2Q6sscd0zMON+Z3pHkzcc4umCahvOQgZ\n4pgmKzVVWG1tohkFhNFpqgtTsKViozqOkjztjLFZ/o0tjY1wlC2N0hHHQIxk/wsKRCtmIna2g2Tv\nqpeFA4tLs26WZ7dDR1kdm1obG4AX08dbsLEutkdje3AcNYrHQSzI9g5Be0HMeJ72Wyr0AwK8jyZz\n4fLDDJT4alB9H2YxndnZrvmcbbt+/3LtCR1zioupF1ZkqxM1daAQUAgoBBILgU99uURfQkKbWywH\ncw7oPRrUkYhuC7QFyIAyPjUfycpykKa1oXpHY3tEa6l1WhWtZQbIC6Vl3b682DYSLXcszxrkKYN9\nVEmJzMM6NZS7XxuY7yzM+vPXlzs2VgX0g9XFtHTtEOt/t+1g24tLpzjLRbPjYGWhPXSWpR8Ek27b\ny3wq9C0CvD8TkaDAuM6Bm20fCTFnvNaWlli4B/eJByJ8fnmfq6AQUAgoBBQCCYYAB2gVFALHJAKl\nvtOy/f5QqukMgqRQcEpRZkQ3I7plYlZHV1BMV9Ayw5oVsZxmqtvUImGnQ3fRf4keOkl8UnM4UiwS\nvuI5Am5EoiRq2c3Tz4YTznPh8Gwi/NpmQj8IcmVVY78uGDawDKd7NSS4lI4qktX1QqnfCgGFgEIg\nARBwJkAbVBMUAgOKANVsnCTQHvJfBWWxD97dKzQRls8CZnKY8MiegjmkUCNLUtP5EeII65hMKhwu\nYehGSHRoWqTFDFtwgGGsWeWZ8VUhVsUmYnQvcT1QJ0muKIeaqwljyS0zT8R0kN/Avcj5cBuieeB4\nPupdFkv9IQ0ngDT5RWSIK/BtlPf3ay8Y737ota1d1cwHqkrFKwQUAgoBhcAAIaAI1gABrapJHATs\nWbJYzWiRaRrfz0t1jYVDVoFZrKK5I1IKw/J5oDzSpg96RCxvDBcjXLwYy+ZBqpQL8jMbBoJfhjuO\nPLKvJn9kVCjopl1f74JPcihz+S2zvm5Y5pMoNxnLWHKtSwMzeHcFDbOFrkbghyQ9xaMPxXqczs3V\n7bQLHLa7so6rH4Sxdau+7l2DVC6FgELgaEeAS4UtWLBAfkD65vs4iQafcCr0JQKKYPUlmqqsIwIB\nqvJo13Sir2Tbsltnn1/XFn7T49THNPsjIRCq8dhcJ92x6k8H6wzyjcMi4Q9CynQhRqZWwyOXkjpY\nlm7P2TZXn9w640K4BPmrFx7X2rHIeYs/svWTne0vvLC8dsviHa3t+elufdbIVO85k9LH5ad5Rr2+\nvpkTMQp2VkUoveqUsnVbiYpUCCgEFAJAIEaqHKWTS/GtKJc9jZIqfOT5fD7JB7CnraciW31wx6iB\nuQ9AVEUcmQgsg1uMEx9fHv7klukn42PuDSw+nhHB0MJFyIMh6/sn3bnyiVevHe/JD2aYrUPT5IBD\nn2X2rMHFvlPSHaHgGhAs3XS7p57qW9LCSQw9tcWyVZXLfLNzzZCxJNmtj6UKsLI59P4P/rrtyd31\nAc4ipXSKazLakiq2g7Nzk7NS3fWNbaHdsfNKggUgVFAIKAS6IZ3fRgAAQABJREFUR4AECtsh+bPs\nviQV21MElASrp0ipdEcdAiRXlGSd5CtZConU3FDE+DdWBXCGqJ/TxONLb5lVc/LtK15mGiyt1Dkw\n+aYIncSLhOqTW2Y8gK/Cn4QDHYeqItRsVaUImz+AB/+x8LwvsJrAhkv/sPmJ1kC47cRRyYGGQKQ8\nw+2sT9Fcfm+ebjoChqOpw3Btq27X69pCdOHBpZ7U1+ZRd3eqDikE+g6BRZidPHdulFz5HrzhZE3X\nz8a4NRHGCZhAgzVG8F0HG8/SYEfoo+3LyzcVFxfzw45BjS1RHHr1VxGsXsGmMh0tCIA4yYW/oS58\n45NbZl6O4eSvNCZ3OTQ9Yph/XXbL9Atw7gNb2sV++6BiXHZlRkxiZH0EA60r0hw6faA1Fku/WlgG\n4VPCIswYhKTLWHrLSSNMK3Il18wEsQv+Z33j30GuWk8cle5ftrOFjn3hODcU9aW2Sw52lDrbG2tR\n4nyioIJCQCHQHQJU/WkgV8ZtD996vGFFbkOiCz1JniSX2yWNPzH0QHVoinA4wt8tw07I+y5Wcnnh\ngmsv8Lz20GtqAk13qPYwTjrz7GFalUwhcDQiYBXNL5Ge+U+6feVzptCu9bp0ETaEAbusVLhgKF76\ny+lTKO0iybIBmB1btNzp0jY7HPptAZeXajwxZ1Fxj1R1eVOKpHoeMxHPx1qfI5i3vj2y6p7Xy7ek\neRyRHfWBMkTVYKNqkNIzlsuvSe5JqhjHjXEqKAQUAgqB/RAgucJmLnjkxi8alvGBN8lzCclVMBgS\nDXWNlXVVdVvqq+t3NtY315uwj/B43enVu2vyUNAQ/24/fdcoM6L9UO15hJJg9RwrlfIoRQBG7RZs\np+RActLtKxZiIfAcLBLuaw8Y4WSPPqQjpL0Ae6tzTvQtKbclWbSzArPRNN8qLmW0iNDI3z2biaMV\nQXIm85hWkROEjmtYbq72Y/EB0TEyx11fWuGH5Er4sfWIsMmy2J5uCJdsVzfxzKOCQkAhcHQiAGIl\nba58D80rgoDqBZfb6aaUyt/h37VnR8U/Vy5et3HDqk1tQ0YOcQwfXZBy3PHjJqZlpA1fv6KUEvOh\n5evKad/JcVF9xAGE3gTFTnuDmspzVCJgG52zc1AXPpTq0a9pCxhBkC0PFvxe6nSLz88EobJn/jEd\nyUvxnDlwELqv93WeO1CwDeGX3Tg7w3AYH2d4HcdDetX81IdVd/3xg+rVs0allq3Y2VaG/D22r+Ka\nhayPZJF7Oxwo3j6v9goBhcDRh8Ac2FwVY0UI3/2+TOEKLHZ73BMNwxDNDc1LXnnujSc2rt3MDzh+\n5FFCzs1eko2mDt6UlJS69vZ2EqwWbD3+yENaFeIQUCrCODDU4bGNAEkSyRNRgLrw2rag+Vya1+Eh\nycL+5HBYe67UN8XNpWxIkpiOEqNDIVfMI7jOIELEZeRDdpal6xodiDZ+uL31/7d3HfBVVNl7Zl5/\neS89JIQWeglFQEXFgsruf13Luu4mq67uursuurp2lCLKRFcBFVEpSqxrwZWsrmLBghqRLh0SIIQk\npNf38nqdmf935uWFEIO0IBDu/eVmZm6/38y78825557biGCfTifRdCMpmR5ElnB9SEfEKkquiPRR\nQjoeFN5Cwg5ZCItgCDAEugQCmYWZ6hgg6AO3E7mCCRjO4/YW/+flD18GuWrqN6RfuEdGj4oePRL3\nDhnSo+T88zMrLrpodHVGRkapyWQqArmqARAkQT/iMahLANfJnWBThJ0MKCvu9EaAyFN01aBZF7zF\n6dclx8dof9HsCQdiTZpfOX36N9HD69UpQhCWKKk5ml7nFzaog58G5ErieQspuIdl2bur2ksSK79J\n0NLxSL4aqRx1ACx85pKbcXKOPyC/wj/8/XbqA4+VjwVzL74GaX4RDMrv8/yq/LZSOoQzxxBgCHQx\nBKIrBp9YPL17KCRNokEqHAyFdm/Z85/K0kpbv6H9giW7SmgBDel40nQgJFlVNI7wEyZMEMrKyqLj\nCkm4GMECCMfqmATrWJFj+bosAmSSgQhKplgQFCRNVrM3/JUOu9aoIxAHbYYWl5MTkRRFr4/0aK12\nqQQrGBaM2HpHR0MYFLpoMCMf0Ia1RyS9UqDASnVunD0xDtuav9I7xXhXVXOgN4IMi74oVhXyMbg+\nNay39Z8wojoU4ZpC95YYHNV8ODLHEGAIdDEEClukV2FJ+ZVWp+sLMwyc2+Xd9uE7n+1GV6Vme3MZ\njrXwUdt6NKbR8Cbn5+fTwhkaf9gCGoBwvI5JsI4XQZa/SyIwYViK+uV29pxNjo0Pn7U3yaL7RZ0z\n9Oo5s7beSh2mKUKSYh1L56NGS3ledmFej8TwBp1GiB3aPUa7q8YjJyQcXKqqR5WXJXCFecrBdYpq\nwrDkMspa45s7K1zcy9+UktJ9Rt7ayupVc8ZbJVletrHY0fPTLY37EN5/fa3bjiMNrGr/cGSOIcAQ\n6CIIkKV2OCJH+BRULtZh+6+AP8jVV9XTAhp330G97KVFFSS5onGHPuiYO4EIMIJ1AsFlRZ+eCKjT\na9l56iBFyu5JFu0ddY7Q4nOe3HI79Yim2XjxyJXa26MwgctXiVmtK1CeHmdsCMtKPPYX7HHt6MQ+\nIFhrk01WYfGkftpBsB5PRI/nqa4f1xclW5+H1jeID3OTUE8afO9Ei15rcweFFct1HjE//yGEJcP3\n7RavMy3fUkt6XkyCBRCYYwh0NQSy87JpVkqavXhKnD+knEOrXjA76Kgoqy5HOLY15ZtwpA+sIyZX\nWRjvsrKwGX22aoKm9cOMFOlRDkfK9HRkjiHAEGAI/CQCRK6iCWCuYXHFnHOVDdPPWhgNaxsfDRNb\nFN6j10dyjK7uWzvtrNxdOWOVzTNGK1/dN2LXbRPSfo/8ZH/mILfm4VE91s8Yc1nUFhdGuY5IUsLQ\n7sY+yJgyISODttOJOmtmuommDlPhmW2bKCrsyBDoYggsXbpUJT3iwqkDxAVTa596PUeZPvf+0uT0\nZJK8X9qjR4+eOLaOcUfb/ei41UG+jsajDpKdWUHHDPSZBRPr7ZmAAJGn6JY46x8e/WZqrO7makfw\n6XFPbiUpENc2vi0eIFhHPVWYl40pP0il6pvDC4R4Lgtb5cQnxmiH/Glc6sLsMSlXanlhp1bLOzWC\nkgQuNQhbSV8VlqX143K3rULdxpwJ0JHIV/UkuIJnJ9yO+YCzm13h58aLawruy+ppnJdX5t8196Lf\nKZxwjSsQfmXc9NXfI9w0L6+SLDO3foW27Qc7ZwgwBLoGAlKIS9BoOXUBDcwzeBurG2lKkBbP0PFI\nxisiTOo4kbNoahaI1aiAL7SE5+cWIpyMl+r4lMCfkSQtEPS/8uR9z9UgTCCjpohnrgUBpuTOHgWG\nABBoS542PDz6P2lW3c21ztBjUXJF5hui5KstYPRFh82azaST1Tb8cOeqSQjk+c2LO7fXNIfudPrD\nHkwTciBZ3XolGm5JjtU+E2cSchPNullpsbq/9EgwpNjcoRKUm3TZoKSkMi5D/TiiunlFmQFbXX8r\nd/hJe8tob9CqA6PCC/f0SDT8yeEJ0dShPo6Lo69bRq4AAnMMgS6NgMAb8Us3wAwMBxMNNIVHKg8B\ng8FAx8OOAZCEqeOZakdL4V+JT4h72ON290Zeg5o/0TXAbDHlgms9Vr2vgVQQNNXV1ar0DOfMtSBw\nVC8FhhpDoCsiQNNuRJ6IRG2YPnpZqlX3hzpX6JFzn9gyk/qrTstB/wCSKnyhcYJKqlq2zfnhocxU\nOSQt2MqdRYOPqvxOxyNxpEOFPQk1175Y8N6aEld2WaP/i3pXqM7mCQVhe0vxBmS50R1ylDT696wu\ndvx3yQ8Nm1Fu/5J6jy6zV6w6mGmNsX0xWZhqd4eK/rZgB32hZryRX6ZsfvKiFAyj/fY3+lzvb6i1\nUXj+tnKaNmS/eYDAHEOgSyOgYP9SnqRVPKfVaC2xCbH0QSbrdLofSZhI8hSdWoxikofNCFWn9Zqh\nOP9VfW39Bys/XU1GR/vAG6DfZXA7PSudTvdrbyx4h8ailG8qvmFjiwragX/qV/CBS3bGEDizEIhu\nfbP3hSsMG7ftXZYQo/1lnTt477lPbH0+igTtQ6iei5EvP5AsuEiYrDH0AOOaKIWFJyj0SDd7prTk\nsvM4WczK1D2wtOAbXO7OPrvb6EGphgxsIB0L21hSg1tyvr+5qdHuCdLqP6VHolYqs/l9Gr9NHSgF\nITQszmzQlrpC+72hkOG8gbHCur3OsKKR+2NpYg+PW96++KtyGvjiHbKiloFz5hgCDIEuiUCEGGFT\n50qB08Jau2LVGXTdL7js3F6fv79i/cgLM3Q33HCDZtiwAkixsrjCwkJYe/nxtF5Ucb0gtbQO59cD\nKtLf7KWHM6ebTdtW7Cpcvnw52dgjnS4zjJNqipcX/4i8Ie6MdoxgndG3/8zufJRcFYgTLPba2g9h\nrf1y2Ly6j8gVxcUNdwiVtp5S1G5VK1pjoSle4+cDAX2cxCvTMYjBTILgicS3fPm1Jj7siSLmFYRe\nuGKAftmepqalG+tXI8d2+Bh4PTx9HQojephkb1jj2lfnbhqbzrmcmQNCXF4lJwjCSNqcuskdKkY6\nDisIaTpA0mmFIfExOq6iyU/hoV5JZsV+hPa1qBzmGAIMgdMPgayspSA5PPfkvc/XzXxhyiqsIuyn\n1WqNI84eemNdVd22vFe/+DqP+4KmCeEiY5X47H2Jgt44MFXXtPm223LpY7JV/ypKtBDmS02Nq3O7\nHd7msqBvedly0uUkb01IT3DZq+1sSx2A0d4xgtUeEXZ9RiBAphbOzs0LrZ92bpIn6Hg/way5xO4N\nkwXQ32+YdtaNMidp7bUWyaI4eDn5gM4CD50rZT+neDm9XuGVVJCyVKdfqtJx4eMRjyt3Ly8OiCBC\nF07ICFRrBZ+9yauXhLDWAzvLNUG/tH2fm/YKo8EvOGV8lpzdYiZC4PmzsE8iV2X3lSKOdwVl5FBZ\n2RhMZXJ1zsBeXMpxJj7Q4GuOSOIoAXMMAYZAl0MANrAUmu7Lzs6WoDO1CNb6fm8w6M2WWMuIK//w\nfy/98tpL/ydoNDu0eo1LIwiJoFID8P2WBXt5G0GubgAg+kmTJim5uSrR4nIWTv0jxpHRHrt38dOP\nvKCOMYjn0kcm3ctJSnx5VVnua0/9p2bSpLFCbu4mZq6h3RPFCFY7QNhl10cgul0MFMST5VDo/Ri9\ncDHIlQ98xGTQCuMJAegdgK60fsh1CEoQ34GegEyfe0R+jtuJtLonv4z0qOjLkCpv79AoToFNGg19\nfG4RJ8SD7g13YG5wc4mjEnH89wU22sdQjynGUS5fmNtf76/AtWzWC/66SmadGVgwxxDo0ggQuVJX\n9E0X19/3r38+YI01LzBbzBqDydAbg8o94bCERccyp9Xr8JkIb9BzZXvLPwQoiZmZGVxRsIiMFYfE\nxaKZCwdmGc2GXjVl9e8jrAremzg0EVIxzbyQEnba6hxvIYy32/uhaLJlylxbBI7nq7ttOeycIXBS\nECCFcyJMpKBOnpTG0ZCOyInaPoqnFXzYtNkC5fT8vkmGi2kvwHiT1pRg1moNOp4zaHmOpt2MdNTS\nsWMP0wpcN6uOyk0Nh0JULxws8h2/IyJF+gztPYUfcGYuQ8NzGf6QXLJkdbUDERTv/Prh81PADYl4\n2TfuszdQuN6k7m/IvjAPoMfOOg+BQ/3eDhXeeTWzkjpEAASLdKuEeTMWvFJZWn1jY51tlavZ7fB5\nfQrMNmBrLoXzur2+xrqmsr0F+z77YdWmrSioX5PHbwx4Aup9CwRdvfEB1xPK7Ls+evujsCXJ0ocq\n0yjhTK2eLMT7N3z47+W6mJiYZOhyddiOMz2QSbDO9CfgNO4/mVbg+XzIkWBJOKJOcNjeZGWChCAt\n9KdMWA/4FVburQYrCfs4mQYV0DX8U///dFEtbw7ZHZAMCs/Ve3VakhxxhZl5avk/nfv4YqNd1fHS\nkCSrkat1BIq8Acl77gBreEOxy50cpx9pMgiJmLrctHRdLREvSfaHSDLGlFCPD3qWux0C9BKnIHqh\nt42iLVtycnLIXhIFHxTXNh07P2EIEMHiYIVdeG3e28tQy47Lr7xobEJqUl8eiu+4P2G30+ss3lVS\nX1xYQrs7SImJiVxtWa1/9NWj1XFCx+uGxlhi+Prq+vLa2iZTz75pvLvJzQlafpROp+U8Lm8N8sX0\n6pXiLyggpXnm2iPACFZ7RNj16YAAr4iYwINpBWrst+JZ8ZawnCpD43y/I1ydvaiAtoLo0PGiSjL4\n0bO2NIIk3bduxlkDeYmbuWa/99H7lhSVdJjpyAPxQjmxJAajGM9lqVtW0AzmGHhScFf1rOJNBkxV\nuiSNRhlpxR5kxTVeCg/0SjaHa206mnZkg+CR38sTkhIvPSIkP5o5GDZsmNKyoovu0Wlzn9Cfjkg7\n7Yd3oB/0wYK3+gkBlBX6UwgoeXl5oSvuukK/fP7yqq8//Z4+tmLhY+CjC2g4q9Uqm0xad329rSk9\nPd05LmmctJxbju9PbrRGq+G8Lj+NI7CnJYRw0GgEzVkSphmbmxyluFasVgtJxjt6DhB8ZjtGsM7s\n+3869h52NcEyeE7+Yeqo0fiamqqEuQskSdAhXNM7Vmd/8y9D7vrT67u/wtQhrxr0RPIcELKZMzns\n66cO9MomsmMF8ws6RRgiC8ofw4HQdwDDufeuAa4BScU0kBzkyPxCFqRTOQgdVkByLpJWccrMYVl8\nfmEDP0HMlxCovkSoee3qay0r0nQ1HZExtZyOSFm0DMqIeCqXPEfliug7nUOBdTjpWZU3+PbjSgly\nirqSEYWODksKiFewFOHhbrGGwKYS+4/6REUw97MiQBIduneHfhnRlHeeqqRMadR7/rO28AgqIwkv\n/Y7QFz2f4r8TvTH7Xa6XZk1f2IQw1Zq3uGDK9RwvjA74gq/P4ufupv3s8LJnU9RHgG8nJ1FArgLA\nP1zJVYZ8DT6vbJP1AU1Ai82g+WA4KIWaQ6Gqqir6AAuOHz+e9Lci94nncf8CXLOtuZzaVL2/2nn1\nDVcn4N4Pc7s8XG1lHel3SmGjjqTjp+SzSu0+mY4RrJOJPqv7qBGADhUWznESNmH+HTK/hym65vKm\n4KeNrlAFtpUJJlh0vbZXe9MRB7stefWkn5UDYkQkBl7dqDkrL0/Ox0bKVLnCSwFIvprtPsmMS7Mj\nEOcgKRciUY3qVD6XpWDqDwEighCnENG5hJsggMGpUjR66Yg5nKCSr6WoCySI6lNEtBflqSXhH5VJ\nemA0VRkNpzQoGDONkUFKvQYZjBKplhcaZVWGgejR8uqCpyakCbw80ekLy5tLXXVU/vpiyO8jCu5n\nNXtCXFGNlwZAJdGg6l9RO9kgCBBOolOeWDy9uyTJ6aEwPgtCIRIHKJIsKSGe89gcntpcPtebzWWr\nL7gokTmJ7e2w6pwcPJmcqHhNXkMMp3lU0Arxe4uqViPxVkwV0Yvah1/P3xKT4yfu2l5ciuuSmMwY\nAY8tI1gdInriA1vILW2TQ2SovQSVxgXVZ2ZmCkjLPTTnISsvcCO9Hl+gsqy6GvE0/jgGDs3oAXHk\nAEiwqtat3FSLMNlV42LScQDRkdN2FMjCGAKnIgKkxA5CI/0w4+zBvCK9C5tVOyfO2/EI2loFXw9P\ntliISMSN6B2HgcQBQ54H62ep10iwsZosMtCup1pkkKDOHiEfUaOi0WuMOkS0VHJF6VtfeiKlzz9A\nnFokY5RGHYrUE5weIFdUnzqIwbjoQTpjbdJEy5dVJhcto830StZSTA+ipEBYkqCP/0ytMxh6c01F\nc3qCSa62+5rXP35xD8zODPQG5YaN+xwNVGdAIzH9qxYsT9IhcgfxPxRSbof451Gsm/DwJiM23lYU\ngdPJWo4P9Egx1j86/6Hv/V7/v5+a8sK3ePCgKgOha2S67SQ1/dDVYmUaGhdsCgZDxuqq2gyktJW7\ny8twJAviTujocM31tm647tFU3aR+BOCcuZOLAI1BP0V0iXzJZovSH0SqN1Yc7tm8bgetKqSxzm2K\nNQ4ymo06Z7Nrn8vuciUlJYX8fj9Jx6lc5tohwAhWO0DY5amJAH69B3SPOOmekCRrFuVXz0Vr6/86\nPrn25l8Mr5/ATcAgIBKp8W7/q0OVHK2ZOmaAQSvfC4Y0FLzE7fRJH1w6d/u7n6RbVXKkip/wHuM1\nCq0CdG16eMxvg7I89NrXtj5fV8f5kUcldJwS/mupPfQyzxcUb5xx1rWeIBcTCIX3JMXoJmNsyQiF\nuXXnzd6a8+kdmYa0RMMMhI2RZaWqtMn/bPbi3etRtrz14VE9Agp/Y61dXtYjQbgOX4i/xmppn8MT\nfnXi8zvfh7QNL6w8ad1DI3vCTs10kDnIuRQpJCn57273PDd/ebGTyBVhwU//nsjTdHh6gWUM6Gn0\ngWC5zQbughiDNtbh9W/4aGMtDYxKdWOQESwAcdIcbhi9fXDrcDtlc2x8LGert/l8gUCZAJVhBcJL\n2CYyGfS6jITk+H5ujfbPD86+a+bTU+fPgaI4vbzIURGnlMMqNN5iBVXkFI0UCpNejzEcClM3yQm8\nwHNhKQwSyRnjfZGVaWoM+3fKItC9e7V6/2ROk2mxmDi3w1XstDs9PTJ6hKrKqoIanh+hg2kHr1fV\ny/IlpScFi3YURYbRU7ZXJ69hjGCdPOxZzUeBQB6mBrMxNShOoJWDjkvtnvDaD7Y0VZzfP7bp1dWN\nNfzqfLyA8tUSo5KuzdNGD4N+1Qp3QHZXNQfzzTqhb/d4/Ruf3T38vF+L+ST5aqQfAI0OeBvQlxvp\nME3E9M2NIFef94zlyiqdHPbxC/fXCPxDlY1+WotcjCXO44wafgqnCAXVzYFd2JdrT1qc7rZvJ484\nS8MLktMXkmqcoY3dY/XZ6fGGi+6d2PP651ZU5kN5LAGv2RkpsfzdDl94f507tA0EbXysSfOf5/7Q\n/8/Z7+UtKRUnGBuDjjfcPml4lSPwCcxFJOHVOs7tcg1F3RvoNatKNnDIuSXDsK/Ix1W4Q9XDesaG\nV+6wc4KGH2vSCxx0s/YifWB0r7jQlgoHE+EDjJPm8MpSVb7RAJCpECkwYfOj71588rU3EUREP2Q2\nmPUjzs+MO+fCs37dvVfqDSBhOX+7/0+V0Id5C570nVpfYlmQ5JIxkMLCTJV0YdsTns6RRv1oQNSP\nHOIEKNLztMdcJtIWIA+VQZa/20rI2pcdTXe48n9U4cEBpxw5PLh57CqKwIoEu/oMCZIyhsJgomEf\nDnIoHPLiqMMwOSoYCHK2huYSXEuYbKSx5ZDPHeLOaMcI1hl9+0+nztPrII/7xdm2REXRxDf7w7sR\n4Es08014f9EATl9emFEB/4AUCOfgP9xsb0iSLnl6+yRc0hRi+ef3ZD6YbNE9+ti1fbc8+mFprizI\nGhKYC7JaBgQJvAsK4kS0Unm9GToGXhjW4oOhsOyye0IgSJwF1y5otPPf73V8OOPD/f9F2L7/3Zm5\nJbO7ad66Etdbf3ptzzyElc65rs//Lhmc8NXgVMNvcb3Bg6XR4D5yjStYfu2iXTkIK4CPWTftrPz0\neN3vcb6sJmQfhPIv+mpX84NPfFa+GGHUr+S+3Y0kYdPh3YyVgmqgImaUBd96Q+0Xl7+d9nNGOK9c\nAbtYXEmDfxtdx1kEkl5FpSAUxNzPjUAbesFDsZgIFqQ7dD+dPXokV1dVNTq9AS+/Pv+HMPx3U+bc\nE5vWM/XK2PiYPyBN/tvr367CMYwnFHp+Ii9mi1Ke2ofIf/UUvw0iUR2RrKUgZNnIE0lHv6K2uenx\nOuBoa5S2sRQTvT5U+Qdys7PTGYGW6ejIc8LzwwN+EKkmRxn6pNRX1ruz/pIVi+d2CKZ+5er9VZUI\nlwSDOr60ecJPZwQ6v+2MYHU+pqzEE4iApNNEfsyY1EM1QbOsj5KHSHhOhGitnjyyG6QF51bag2QD\nxnXfxN7+eSvK3fU2/0JIjf6Rkai7FOFLFFkth7Ta1TcN8kCJXp0ubO0FL0D/XOK16hFfaxhkTF6f\nVAly9UPfbnqltD7o7pmg3wlzCZ5vdzk2ICO1xX/+4MSdMARahU2X43BtlWQtbGYpoaJ6/ze4bnpg\nQnpwbn51NUjdfkjIaPl0yu4aX2Nmmrni8iHx953bxxKMjTOuuHT25uLSGj/YI5FHpGpxosjJJc9d\nngoFfSsUpwNmIzcpyaK/qNLm37NoRekuJNPW+hRSfKdBkw2CLbidGgcSmHL+Hj36Nf397/+kTbi5\n2FiH4f775zXgNq/Ay+5KjVZLizX6YBPdRkiWwnn4cBChXP7kC9NSQrxyGZ6lwXggPP5AYOOs++d9\nT+SqPQkiiVQ2SBOV/8iCaeM0snQOxLXxkqI0hgLSpq/ezd+6aZO6cTk9Wcq/Fj7cC0r35+LXMAA0\n0IANxxukYHjHE5PnrkXZqoVwKqszHbX5SMoTZ4r0K2XP8ZGAdQxpMB1NY2pYnP9QOtQXLgn4A1xZ\nUTkpuNP9ae43uPsQi8U8yNZor1r5xdomSqs4sHiZjS2AoGPHCFbHuLDQUwwBMpFAn9Lzi7fYpg0e\n3RRn0magiZoEY1hDxCMHxGqmyCmbqsdikNgkm41CHCwV6DHVRqLtYFyClSRYXK/+vZxcqNmOLXHi\ncRnrCYL1aKMTOJRCHS1oEOcTjQpfgROoe+Fdg9KijpRmQF7O728V1u5zkZE+TgsSJuFtlxwLwzGk\nXY+XZ5xBig2GoLXAKfSC04QhvSA789iax4hrV3lKtfpixehFasw0BRTz19eKdv3vjiF3xBp0j2FF\n5Gw9L2u+mzxq4SXPbHsMBXnRCPUVQ4UiPeeXw88lWoTroWYaTLbq9TA6altfbJ+/rcTlHZxuDe4u\na44SLErO3CmCQAuj4M1miadpOGrWRbVp6lSLwWgwtmHSpMNkwrNPUlVOXDD9epCrZ2Oslu7YPYAD\nCVO3Opkxb/LSrWs23w+yUkWEBV5WTSOAXD3+0oyBWPX1rE6vvcpoiuFCwRBtEg5ZLObD/3DxvSBY\n81G0/MSL04eDUK22JlhjaRqIHjAzDEqSJOOR5x/8rKa0djLK3SV+K9J7o9OmhaitKO/wTjx8Epbi\n2BHAc9g6xkE39FVMDwbX5f9gN5lMYZ/Phw9ZxdNsd74H/cFS1OI1GAwBOPrAbc137LV3zZyMYHXN\n+9r1egXy1CLBkR56mPuiV6L+/gf+r9fZc7+o2JYbIRvKTJCP6CpAyaexcyZIdQxCCsBwD9AH1Hea\nlXPhtcInBULyOoTjQw0TiRAFtAUMbIrSRkTlOFFgYg//BUi72qTjFSi6t74YWhVkkFjNgn9uUBu9\nAScR15oX0jI1TWYDB52YiGspSLlmfLL+t4t2f4nQbRMzEwbeflHan3onGqYs/uPA7be9s3cJJ6qS\nttaysEnrGodPTgsEZX95g3P/N4WNK+d8VFKbnmjQlFW7GlAOEczWdkZqY/9PFgKQfqr3Dgsg6JHx\n5edv8nD5kU1y8SxI4tOTu4GE3yTjkXM5XEWU5pyLz9HTMntx4TRs3Kt9lwhP2d79b9ZXN+w1W0xJ\n3Xt1z0rr2S172Njh8id539yZn5/vxIa/vLon3YtTM6Rw+PP4pLh+ddUNRSV7Slf43H6bRqfRJ3ZL\nOBcv0VTU0Ru+DDPL2lAo9PW+PWWl9iaHk1ZXWKwxad3Sk67ClOWv5bCcOHhwnxvFS8VKpO8sR7bB\n8CupFtxuXxv5bLviU1M5i8cSQNrWn1q7FOzyOBGg6WEUwYt3PUVSqzvh07RabV+Qq+YBAxL5OVPn\nb0fYn+H7x8WZzQ6H11VWVsbuBwA5lGME61DIsPBTCgGMvMrS7CzIf/Ikp597IdYg33zNyIRneiUa\n3Pe+W0w8hZYpqS8vhbbQEfMbN80Ynd8z3vDbcX3j3rjpHdUeDydI8l0hWbFuKXfTyj5Ogyk7jCmY\nF1TzavBi22sxCqnTr+g56MnllSspDbTq/88nc/LuOj+JxUmcBcKikLmEVqKjAfkKg4i1ZTImTGdi\nxEKQ+lLlSRNK0dE2YG2JGmKRhjzKVhJ1UPHiVKFY3YoCe93vxyT4eyYY/oqVjX0Rb+JmYmVjm3pH\nTP6epA+vw/eEt8IbeiYbNfVOfw1k90SwSAm1tZ04Z+4kIgCSjrUOsIirF2IGDh/Y4+JfnRfu1a+H\nl5OleF4WxiqCcC+krcPqaxvK8j9f/RWaKv2w8oemWYumJgQV5WnKi61NxLcXLV2NOHq51V51/S/X\n6Q2jX4HZhOt/88crPvzoneUfp6Sk0H2np+pxWplYvq/yi/k5ua8Fg0F6hsmcCUk234KPgSVvvcvl\nMop3Pkn70d0E34vC4emF67v86glfXnD5OQus8Zbzzr7snN/s2bP/DYTTClU8WCTMPXoXNTyK6c7k\noBD4jBeS+8V241zoH57/Aw6SPMin0WmOtwS0rmmIyZ2A33d+yy4OB1Ke8DOsYlanyk5IRVlHUGoD\nPsgOJJtw4LSTz/As8LC+oK+qcnJVVc3VYUPY5/ebQyBZekOKIcZba/PUOpqdCQkJPlh+F/CsHZJH\nUFmd3LwTVhx+Bwq1d1O/TTIkxvTsH7c7JDDHXTIrgCHQyQiQDSsyHHr53Lz9H9+TeV2SUfvmhf0s\nb62eMmqyRuB2Q4/JC6I0bHlTE5GOdxp94Zw4vTDuqd/3eRcf4/+JM2ph5FG5sqje98q8r6tJOuAW\ndFqIppR4DF0ktbIEPKH3NSbtX68YkfTshEHxo+ItWq0kK38sbQq8nbexHkrvXBAvFUzhcAnYSoLy\nqAMI9qfX6zWCBeKw1t9UkA8JGk6XgHbRlKCs1UggZ5pEzLrocX3A8VycEFF69vx+hHnEneNHPygp\ncpEOlkShEPb7Bld49yur60lpneqFjaEDLgtGS2EXkNrhHZFu5UxGzrGjxuUJBjkXhcF3ykBxoEZ2\ndiwIEMNVHxTYZaDpt6SkxMv/eNt1l/EaDW41SbM0BkOM0SCDt9dU1G2Esvu7e7btrRg2alhT4bZC\nb0BWrsHKwoyqsppvQK6+QXE0DV0C7/7kP1/uHnF25pUpack3p3TvdgHCVkPiVSkumDYID0YWNvT1\nrfpy/QdEroaPGdxg4JVqa790v22PTdm+fbsGLxUi9yFIh2hqEbyc2wtPYar7+uP8onMuGXMlluff\najAbByIwGd5JdrBU/k6aUUfpYNCStnHhXFj0oZPCNdAHi4eEDMLdgwkWFQuKhV8a76urrCdpW3rg\niwBNrRO5/Lkc9Y9UFE7Yb4m+EI/O5R9d8qNPjc/ByLQ0ZXWrfBwnxeoHm7qixoehyG5XtRwoSddx\nmzqvK60vg84rkpXEEDhxCKhb34gYbsWCVVmjki7KPq/bTSY9PxHD8BB8/UpBiav/rshBz3XG/83d\nsfvlmwdf3iNRez++gcdUOwL+vXWBRyf/d986KKc3QzndodfKO+1O6dGSJm8Z8pgvmFdQ+upfB/8u\nPVY3FZOS59U6QuFKu3/2He/sWxmj4+phID2Aj+qPbV7J6/SGSC9GHXS1Wq7Q5gyJlbZQOcJU1yPZ\n6txf65tZYQsS0bE4fXIVJiRnlDb66nAtFKRg0IbDqsVnoDuVhFOjDcrzRn2oIhjmLsGLV3H4pTVv\nran/cFu5a+8tEzJ8b+SXUZZWtxRW43MuneDN58qCgeIyfm2J+mKklyO1q/Ul2ZqBnZx8BMCSoUju\nDYdkSJHCWqNJn6Y3GLiG2sYNhVv2fJD/+aoyn9vX1HdQ34b6qvrI86Tw56qSL60m7vapf/lTbEKs\nPSbGRNIovPoV6ObxvUl+qjfo+iCEnqVKSMWGm+Oshrqq+o3rv/uhMSY+pkkIG/Zt2r7dw22i74sD\nroVckZI8xyX6J0JOexakR/EQHtmw3c0GQYdPBzyt+A6gSW8z/HG9O1CP+mzO+ucs6sMN8P3giUCB\nEx7SSXqrPnbt2rWRJbOHTNapESRRkwaOGThUCHN/hPA4DHGz2nZIsmmyH+LIyKS/ek1EDIkAFeIi\nkmrcbqTHH8Ih9iYGCUHmgXjEYWkpPtfoiDxYoYxzlZCjLJ5EhOo1BOcKJ2mQF8fWeIpDGspDv3ha\npor0Egz7UZ1qXsRBQVRtVyQfnYPXU/sjAWo/WtqvtpPOpRa902jeSFmtfVLrlfBlSUMN1a9KG1F3\nyxHloAw1noqEDms0DR3D0XIg+ce12gzUF60D+8NGwtS+YZqhNU1YzRuto7Ve1BXCpwGPrwdaZ83z\noZYyD9RDdQTxGNMDTPnxnaMeI3UThiG0F0uONLyzaFsRrdw9bndcP5Ljrp0VwBA4BgR4kZNJkgWy\nVZW3rWkOilgEnwivg8fPhtMM6WF1PXaBS5P91p5SXN8FT7pY9NsSBvQwyZzJ16DU4/clbi1D2OPw\nqf0SEvx/uscu/E3cQzoId8OnwZP0SYGmsTIkhWvcWEW/ya2rEbYGvntPSBIq6HcaKScHYWkDEhO9\nxTYb3+v+tSRteg4+4aIBFu2Viwpoyu4p+OTMnrFyXp5THajPfXLrGwizXDTUbP3TK3tqcH4nPIpW\n26saCb16bLoD5IrOD3LqwMrl09f8z/lFf1Ab2MXhEcCLI+Iw1QwSxDXV275f9MSrSxDovmPGrX/I\n6N/rJp1O223T6m3VIFd7x14w1lG5r7K2sbGRyDk9Yd1DsGYLnaux8UmxY9VJM9z8SBTET2DkQehm\nIciKMOgZ4vXK8z0EiHZ9Pj+JGTwGwdB43XXX+SC1ouhWRysNyfQDbeMTDvtfBdm7goqGFW962DkT\n1m2EQ1KYVpWBOtA7g7xAhkZjrKALLS+/1gKP/IQ6QC9C+lDZAV8M3woVzts6SmfsnWqRil02kq78\nLA6SNg22/5FgF4oMbD6sktwI+G3qBwb4+2kXSRBNprLI6EVr5kjAoRkm4tW/9ikADaGjBkfiIv9b\nK1DjKMkBp22tVQ1rXyQCaZeLH8UdXAiHD0s4qid6K9Ucrf80rU1oPYnEtRRNxR2IOXAWpdkUf1Aa\nevzaJIvGUyKNDv+Ie2JfDrBetR6Kpzj1iFMtRtyoGDLSNhqCKT0lEkI6vQCBqvwxAq+BJ9dxxyJx\nh/3f0s3DpmMJGAKnFAJRSRY3U0S7RDfeA+4JEzjNHSmZGCoKpOw8Fzx+MyL9VEVobooNZKT0qkGu\nVkX4ll8Oz4kiCJJYVwJxt4jiInnwyxK5WnECpx2GMrPyCkJ8NX2doTSUORO1Ir66ko7w+HnyeVlZ\nAtpVC3KFkIhTy5rJNfO8W/2NEzHEXohNfKVTvaZUlCYH03mi6HVj7ObzstVyqiZgzLgjC3VnFoR5\nsVolY5FS2f/TFwGSKtATwxMhJklmxaJ/vSJOm3t/r+690y655Z7r73hq6gu3blqzqRyK6jIU1Vue\nE9jWxhsBG+x+UlNeW2CKwUcCDKUTDvQ1DmknZpN5Q211PZF4PPP4x5PdNMx9azQ0JR2y2WyBmTB1\nQM941Ik0LdhiIwu23uYlJMVfUVtRu7W8pGpZc5PDFRNrNlnirKndUpOvMJnj+mEKU218NP9xHNWf\nzQsv3GWwa6wXKHLYCtkL1myo5f+4WIXHnlbytn89+EwpfmwRCH+cqtNDQK4iHy6CZlU4GL6xtQK0\nQdFA4EJtgaPfP5YzQ1wCLQByWFVMv2XEqGkoPhKMaxi6I2swdK2GR5QyW8tS81AkHCRbuJeQe+M/\nZFwkjmlTZqQMxCGvOjRFy0A6EgYhU8vDhgiSe9HqnUgaFBtJE7mmeKqO+oPa1LbhMpIWJ5QWtUXS\nIGGkbzQakp1aaseB5yISR2lRJuJwgnJUxVOcqo5KonOUq8ruWsJb0kTrIVkY5W3Jg1g1D+Wjdkby\nU7uiWCJeDY+ko7yqbJCMHKp4t8mPOigeTWzFFLHYeEBKQjiR/U5x2k4phRXCEDgJCIDgYM8+kWqm\nHwnH53PhfNV2JwVFnJqGE/G7ww8qPz8s5kdjWuIxCqAMyt7qInkigwh+hRhgC9RNopdCCYP2EUSV\nstiaOnKCHyV0NCL2htpGRdp4IISI4YGrlrzUjxaH+tCWPLIJQQNAOD+vIGrpMZqEHbsAAsSG0A3/\nNTdc7lr27td1pXv33wcJ1mep6d3Ou0e8/c7nxZemrlixwgeJScTSusLVaHUazu8LNH60ZDl9YZNk\nKyLdiuBB5eH9yfEDhg/wF+8shvltuYakUCazIR3h6lifl7cUabJVs7pktLS6ezV96suPLJiSiXdO\nVmNto/Orj76dv3HVtmKEkwSWFOJdd4u3G1L0yf1QfOuzivBjdjB+KpB9LhtvwgtN/iguMd5KErLI\nhgptisXbmn6cMF3BVZfVPobTx7PwAQJjqcE2qU7kqSri2LNFlWq/eyIrYmWfkggc9G442haqP7qj\nzcTSMwROMQTUT5OfapNKgH4qQQdxEbJDzAlkpwNi1EGWTgs6lvZ2WuWsoBOHAAkk4PDA0sAtl+6s\nD0KKpIXfco9424OYhnorKTXxH3+5/+bduc/mvph+froB6fzQ2dqAff649F5plw8c3u+/e3eW7Mi6\nI0vmsLSMZr+x4a788ccfE+kQRg9N0RDBCvl8WzxurTfGGjPwd3+9ZtT7ry1bU5hVCM4WeWfwokqW\nVMIkBxWTYBKEYDDcDHLljYuL81x2zbgKe0XQCbMP/th4q7ocDL8JSn/wSyciTUBwGxeRqLQJOPiU\ntujBzwp2TMzNPpdjcmNt6WCQxxg0rc0EUCSP+jvkeV3V/uoahPRbW7m24uDSTvgV9ZcfO3bsKfe+\npJVvx9b7fDVbSoseKIT9P+0OneAY6z+ouuMt43jzH9SYzrw45R6YzuwcK4sh0BkIENnZNOOsv/vD\nnG787K2LUSb9oNUXU2eUz8ro4gjgAYpSEshjJDg8PQcsesycOVOCh0yLf/uhOfeck5qecnePXqkP\n//K3l+0U/yJ+S+js2139xYChwo6U7skjrr3xqnu2rttel7cob2Nb5MR5Ynys4gjAGjxJnrhZ0xfs\nnvHc5HdTUpP/NmT4wDuvv+26cpEX3xM5MZpNEJ95IFGcPLfR4wtW6Q2amhiLufd1f76q7wf//mTF\n/976UrUXIr4wDRuTKzf6fX51rgiZqUfQB3ODJ8aHcBrCJ8hBLzkkCGN3AaSPCJdpLrStQ18pPS/e\nJpIULhe+GzzpSUYYKE7aOUqvjU+LD1aurfyRFLhd2hNxqbRYvD8RZbMyW56pYwRCfR6PMe+hsh30\nPB8q0eHCGcE6HEIs/oxFQBQ5LFuPECnM5d+IVYOkLPxJVmaKLa+ggewIMccQODwCGKpVNqGmhPF2\ni5mmwkgypcgWmgHkFbILhWvpqSnP50x/dvLo9F6pF5194ej5DqfnhvVfr9+15MUl9rvESZOhTPJu\nekbaL6AbNe78ieO+g0X2MijWkBJKf4ULDKt2hP+GclbcBd2m+XfPD5TsLXkcEqsRyalJ50K5/t0Z\nzz14k1Yj7IVilgmZLg2HwyuR/o7nHn6uBmTs7aSUxAeHjx32yODhA4frDLoaGJq8EFzwbChHNZn1\nOmp3DLUbnvO6LbzFyiVodVqUpRrn5RIoAg6qLVZzjAmKSdCdwmWqGvqjf2o5kyZN0sFgpcehcfxo\nEUfbHO5ytwydKJLSEaljrmshoD4LXatLLfPyXa1TrD8Mgc5AoIVc0deR8uZm16/yt9X1xXnMD54G\nddudzqiDlXEGIIAniB6iiJP311TWwWBIkKRDguCm5flQsyMbb0uXYt/AbFtNdd2DmBZ7CbbR+p81\nduiNIFhPT1o8yTn/ttwvb7g9Kyvd5bvfGGO4CFuYXE2rBGF9HT4M/Sz/5k2rtpEJhW7ffP9NM6Yd\nya7V/st/feHNw8eNeAj7yF1tNBuv0tCqwLC66jAIG1mfID0915X5X62bdd740Raj2XCz2Wq+EWSO\ndL4ctZX1s70+v6P/oD4PYWsU+sig7vDmeLPEhQMboTvVJxTZMoVvtYoky3sa622j/b4gfYgI7SVY\nCGt1ubm5ESlYawg7YQh0DQQO/O67Rn9YLxgCnYYA3nx4z3HKRnFscsAv9V220Vk0Z0WJT8T0Bzyb\nIuw0pM+cgrKyzjcVVXv71ZfX9KypUFf8FaH3rdJQSJtIosXFx8f3Se2dOsSg1+q3byzYhTRYVZgV\nhmI4EbLuI84eMbJXn7Q+erM+xov5vfqKBtvWH3aQQpY3MT1xv63aVo1ziUwwYAsUelbjB2f2P6vf\nkL4DjRZTrNft8e/cvKe2qrSKLLI3DBg3oLR4fTEptFvHXTz2/NSe3QZhz0J+7Yr1Fc3N6n6b0qBR\ngzTNDc2m+up60oXaB++7efLNMZu/39ynbOe+dI/HTx8epBzvveaa8db91fYBFSVVyTaboxxhpfAk\nfWKOIXDGIMAI1hlzq1lHjxaBb7Elx6XYkmPzI6Pv94eUB95aXX3Fi9/X74FZhVB0peHRlsnSMwSA\nANlWi4uNjQ054XAeMQXQAk0LyaIpQ7JnZWibjqRcc+bMEaAPRFOMFE9l6eBpLJeTk5M9sJ1FpImm\n21TpGEmyPq7+WLMpdxOloyk+knKR6QbSdwpBod3ucDicSKdAoV2Ap3Sx8JRON2BAL6mhwWlDGhjC\n4swgf+FmsC6cR3WhqA3xKCdA5bQJp/zWxMREH0xEEIlkHyUAAY72X6T7e9yO9Pdoivm4C/qJAuj5\nodWm6YPSlWENw9S6VthXCHTNfYcV1Ue6WfdP1NFVoxjB6qp3lvXruBDA7AgJEtTBZPOM0UvsXmnk\n5c9un4RCS+Brj6twlvmMRYDIE6YDhcLCQtijEun56vDlSC+1YcOGUVqaPiRiciAdnk0oqvNcDies\nb1qv8bl8vMlqUurX1MsgXkTWDqRtgzSVicsDeepMisViCbcrn4fUS8gszOSpbEuthba0UcuEBS2q\nk2vbblV3LAtTnBHJWmt/Iu0v4AsLM6mfjFi13IcW8tzh/Wlzq47qtO1YdVQZD5OY7iGS0DTzQR8A\nP8pGq0hbxsofxZ3hAYxgneEPAOt+xwhASoXteOjrjBOuDo/ZWGbzlf1u0a4nLx9iLf16t4u292CO\nIcAQYAgcMQJEWIhsii881BNWY2+ExYtAxAAmiqCFCopC0kWovoEyqcLFtrxUkLGSM4RImOuHJJAM\nryIR+PrH4j9nFUXLpvyd4TAdDX3AiM2+xYsX6+pCZWPCnDQENk8T0UIZtjtrsAF90d41nxXm5RVg\nH3J1artTiWNn9ONkl0E3iTmGAEOgHQJ5w7Lw8ZHHXREe2QdbAvZscIa/RhLf4DRjAASrXWp2yRBg\nCDAEfhqBgmEFEYGGAabHQ8rUmDhrgs/t5bQ6HUcbgINWFaMEPWzvY86PSFar/AMLPrFbl8InxVgt\nnIQFCmRAlhYr+D1B0ocjPT6SNrVlZLg8Ntey2EKd/hUXTL21NlR6Kxozyqg3GAXUSU2j+rlQ2DX0\n4qsKJ4+5bAbE/SsmQKUiHyoVx1Zr18xFN4U5hgBDoB0CmPVQnZ4ThmKJumlPnXcfAkITUroxRd0W\nbNiBIcAQOHIEMI2qLjoQb5tTDgJ1rcfh9tAymqA/IEEChG2e+SfFf87uo8ju4W5feBinMw5t8YMV\no3EQZuJGe5zuqT6vv5pW3wR8UIlr2S7pyFvx0ykhCdNiJaskvji5W87Cqctg2uNlg8kwzhRjNIZA\n7LBPZtDrJbu3Muygma0JiXHj3A7PKJSaENocwpatKtH76UrOoFgmwTqDbjbr6pEjANUX1UHwPTIk\nye4Vu+30pRikfQEh2GKOIcAQYAgcNQJEstTpvH+KK8X5027E3nf/g/RHQ4QJJOt5ccGUfeI/56zs\noGAyyEqbnG59fNGMD0OB8Kd6o75/0BdQJU0dpD/qoMi0oBgWXxETOX/gY2OM8dwQJGuQrkkOu/s7\n7IG52WFvtmO3AT12B4hP7p48xGw09Nmzo4hE+r1ry2ppjGy7fdNRt6GrZWAEq6vdUdaf40aAZPOt\nW+Mo3CW+oNK4o8Lb2CvZHORFb6eI4Y+7kawAhgBD4LREQMSG20uHkY7TrGWYgvs7bJm9CqLFYS9K\nK+yTLRNffOiX4j+e2kDGYpNsSW2NqgqJiU2au+/4157HF027H1OFH2Gr405ZjUikL7tl02+Qq/lm\nkCvYQOOCwVDD3oJ9r7/38gcbATatIqVdAohE0VSgvmef7skwz0HnuvrS+k4jeyivSzhGsLrEbWSd\n6FQESFUTX5RkpkEONjubPOFVCPEPS9X6K8g8JHMMAYYAQ+BYEYBCVaGYiVnCPGwHOfu1nAVTk/QG\n/VOQFIUhHYqD/bE8ceHUy8U7ZxfDwKwu9zbVECvV1moSQUo2fM7V+wuxXhRK53CXqP+P55+qw4V6\nfwUDtzcGILkC2fNs31CwcNmSz7YkdkvUxcTE2CHVakRbPVq9VqqsqOQq99fsR6UGGL31u1wuIl/s\nA7TNXcBrhDmGAEPgUAjcdcUAw/zlxT1jDZzgDHA0mDAdrEOBxcIZAgyBI0ZAnSpsMWEhLpw2x2g0\nPOT3+oPQedJDL2uHojFOFP8h1h9EstqYRBAXTl8IE1gbZ94x63WUpYU/JgXz6ApAOj62cPoXBpP+\nF5BccRX7Kt96+Zk3/9enX0/B43SVNjY6qtA5snNGUjUFm18L2GxaU1xczFdWVhKxovqZFAsgRF2n\niBejhbEjQ6CrIbCh2EYDhut8iWsuiwwsXa2LrD8MAYbASUAABl0VsrRfmFeo5H+2asWFvzyvN5TJ\nz8aWRwGj2ZQeDvnHXXnpeXmz757vF78Vtfn/zpfJDlnUTbju8nUmbXDnimWrg1RWNPxoj9AB0yK/\nzKcFJkARLEfQaHiv21cBydXbzU0OnynGXFlf21SGcsmALX1gEplSampqZOwhGYatXBojyTPpFUBo\n69gqwrZosHOGQDsEsCKZpLzh/HbWttslY5cMAYYAQ+CoEWhReldVdWbeOetWn8/3oclsMoBk+Y0m\n48Ves/YdkiyJl0L5HFKqNhXw4q2ibcrfnnJhmpDGqGMmWDBoG8kr8ZfqjQaUpXC2etv60qJyW2p6\nktPR5KBtl0hyRRKqjuqhsI7CEXxmO0awzuz7z3p/GASi1twPk4xFMwQYAgyBY0KApvaIPEGSpMR4\npZtghmGlEQ7ThQGDyfibnIXTXqGCW9JF39lkIZ8MlwqgV8dMboi8kVmGSMPliwRB4LAReRgbjhcj\nzG/UGZpA+hw4PxS5imRt85/saJFxUrVtbcKxE4DQUXibJF3ulJgvcwwBhgBDgCHAEGAInEQEorpW\nZIOKk7QrjCbDCKzkC0MnSwubV3NgI2uq2rw2eljH29yWzcBVu1dcWLPGbDX3d9pc9Z8u/fyJLet2\nFg4a1LukqKi8EvUcqe7pQdK0tvpdRCCj7Y2GR6+76rGtyLGr9pH1iyHAEGAIMAQYAqc0ArRacDFW\nDd522zP1j78043cBX/ALSJT6Bv1BzmA0TBHnT/1SvGv2N2KOqBG5Y1Nobw8A7TmJ3S45jSIkSBxv\nIVkYDCt76mrUzbn9Op1AJhlaJFztc3d4rSx6Z14/iQ9neH3ybpCqapJk4Si/8PacngrPDZQD8n5c\nlyD3QWSsw9JO88CouPE07wZrPkOAIcAQYAgwBE5vBG4DyRJfF42P3P6vvRD4/EGRZSi8GzFtF3jF\n7Q+vp97NnDnzaAjPTwJS3b1ancWSJM4KuhMDyRIny1LA6/bSSkHYvYqJKrX/ZDkkkYomCCvSO9hE\n/GsuFLyAwhyOAkMkTvNMUlLSN5Ik/5aur550NVl+b80XSdO1/jOC1bXuJ+sNQ4AhwBBgCJymCEDa\noxX/Ivqp+bLCX2+MMRl8Xt9LmB78+zMPPuPJysqC1fcDU23H2830mvTItJ1Gps0QQ7R1Dy8Ixlhr\nDHGDsE6n62hlIN9evyqHy1GJ0rNLn01EEVZHs6OmcPtOUozvOW9eHpmQ0EMPPqGpwearrKglpfnu\nzRXNtLl1lyZYbIoQd5g5huC18ZkAABTESURBVABDgCHAEGAInEwEVHLVYssqZ8GU1+KT4v5ib2p+\nAeTqHmpX202YO6ud2IBaJViwe1Wn02rskJglgFR16zskI7G8rCrQd2SS5tprRQ2li+zPmsVBKb7V\n4Gm0HSIvqkTM6XF6Na7wbwq27kr4z2vvg1RxSfCepsSmoLY5NGnfrv1Jn/7vSyPCum/5fgvpdnVp\ngoX+MccQYAgwBBgCDAGGwMlCgMhVtG5xwbR35i15UsE2Ok+2hrWJbxMWWUUYDTi2YyvBeXT+lA9m\nvfKo8vhLDyt3i7d/heLOhieS9CM3a9HUBFLKbxvRdpoQ4dakJMtQHPtkZmZa2qSj6cLBWCTZj9LA\nd+lZtFZw2wDAThkCDAGGAEOAIcAQ+BkQiK4eJIICkwx5cQmxv3M5XDMfvWPWY1R9h5KrNisJKd/x\nTBtGy5/y9L1XxsSYP0FZnCSFFY/Lt9Lt8uTqY/Q7TWajW6foY3gt15sX+GtBHEybviiY9PHHH/sw\nbSnk5eWpemHzlzwzRA7L/exVtkJx2uz6zAmZQkF+gfv5N2f3RrEjbM2OfeJds/aPHJkqbN9eR3sb\nkvmHLuu6NHvssneNdYwhwBBgCDAETnsEouSKJFggV5/Exlt/57Q7p0bJFZlROGCnqk13YfvqsRen\njhZfmjaMyFU76VGbhIc/pSk/SjXnwec+hQX3uTq9lsN+g3xiSvwlqekp78RbY782aPXfafX893q9\n9pO09JRbg6FQDMiVYdCgQUlut7tV+gbFsRdNMaZPDbHGQSjSO37QeLVsgdc8Gp+Y+InZYCLFd1+f\n886lqclOU9ZHWaekOwDMKdk81iiGAEOAIcAQYAh0PQSi5OrZZ+8zOfX+z2MTrBc77e4HoHP1LEmV\nCrlCTQFXIBH5ilpbX2FfIUxMsMvZ2XkSBFf3waZCEZApzMnJoW3vjlUapBI0ImqzH5on3iP+w2OJ\ni/mHXq9LgS0uTqPVJpNUKxwKc1C456r215TDyvsa1NfXz/mbpWSJNnkOPPfyE6mQpQ33uL2uld+t\n9iCsd25ubpMoZuk5gR+DcG7v7tJ6hPdo3NFIeUiZP6Jkj5Ou6BjB6op3lfWJIcAQYAgwBE5ZBKLk\navbiKXGuMP+RRqO52GVzXQ87V+9Ro1ukVqqEh+xUtXFSLi7ExaJZCfnH43Rrm7hjPiVyBSJHOl3+\n58UXnxl/+bhV/Yb2vTQ23tKfV/gYmVPCMBVhb6qzVRRu2b2vqGBfsyXeEldeVN7wqwmjVJLEGzX9\noSifHPD5ty5f+pWQlpaWWltbWx/Xe0xPNGyg3+urXrdyI/UprdZVW4ljl1dRYgTrmB9JlpEhwBBg\nCDAEGAJHh0CUXD25YFqSP6x8aI21XOh2ut9TBE1VzqLpVyqcooGxBEmBnYb2JfNQgFL4sJUP+2+E\nhfd+sPROphC4qISrffqjuQa5otWBHDZ+DuV/nf/D6q/Xk3QsET4WPsoVVDJltZr8rmZ3I8Kd9nRj\nRHImCMPNlhiuoa6hGOF8QnqCBgQroAjcgBiL2VJf27B1x8YdZF9LFwgGiGh1aekV+tcKGp0zxxBg\nCDAEGAIMAYbACUKAdKpys3NDT742LSXkVT42Gg3joNAOZW/+Gl5Q/qAyKqIdMHXFt9eQRjiZwNJq\nIwv7aMqusx2RLJQZgOK6XO4ul92NbjI6agPh04PwCUI4hPlAOeCqd9EUn3fs2LGhPDEv0hCFH0Wc\nyWn37EOcbNAY1ClASOdG6vQ6zu3y7kV4KD09OeiXYZ6eESxAwBxDgCHAEGAIMAQYAseFAMiLIGaL\n0uPPPzgw6OM+MFvMw71uD1iLxiBoBNj5BINSSdSPBFeReluCiVhBysUZDAZa7Ue6V1xhYeEhMh1T\nkxWsCiQCRNIm0qUiqhele0T/yBMRk/tN6Sdsyt6kTi8KPDfa6/FxtVW1pZTe6XZSXqhf8aMlmIpv\ntjlU4mWOjw9UFxZT2V3eRUHr8h1lHWQIMAQYAgwBhsBJQoDHFjdETDhZq50SF28d7nN7JZArlRgR\naZLDoCGS+o9OOnQyQhVFloiN4RSCLp6m2jplipDKaeeovVQ+kSEyqUA+SrwoHMZHI+ZHY/vGdod4\nbbDf67ftKShpoLiSXSXOO8Q7LAg/y2FzcjWV1TUUDqpGki2Seql44NhlXXRetct2kHWMIcAQYAgw\nBBgCJxkBMlalNiGg5R9xN9rnKLJGCmlCMLjACXpsTyNrtDGCLFMiGfsBdiiRIkYCUqYSk7A/pOH0\nEuk7qdbV6XiynKAE+5tM1sRGd9P6H1b/4EpJT1Eaqhtc/dN79oQq+yCQwfKdGwub0D4Z0joiWKr5\nhpPV3p+rXkawfi6kWT0MAYYAQ4AhcMYjoA8qcbJB10sjwToUr5UhkvLaGqoKnxPfaD4WcMgGFq0C\nPJa8x5unMKtQrVeAnpXBqOcgwSI9q2BycnIYBMsrmDTDYLxUW9tcv29PYTFNGUqegKfLm2eI4soI\nVhQJdmQIMAQYAgwBhsCJQKCN5XXM6z1tMpquctidLsyS6UmyFZ+Uyj0874G3dmzYO8MQNjSmXZSm\nDRgCsiFgEJIMSaqeFTatkbn9nNy9e7VKamr06ZrqYLWE/K36TKTnhRWFfFvjpJGwAoRFrK2TjS27\n3Q71oE3cihV2OWqF/ai7jT6JnKi2BTphI8LhMNfc2EwSNRlb4ZASPIedqUdqdTrYz/KS/lWgz4A+\nIU+j54xQcKf+M4JFKDDHEGAIMAQYAgyBnwEBKH0LDTX1JU9PWzD13AvHhI0xJt2oc4dfkd4n9dZB\nw/uEnnl4/t3KUiWYnZct5GXntZInalo7aVUk7gB541tWAR7oBZGglo2YKZDIVlsbWwcSHv2ZmIOS\nybTD66IR036Xh4Jhrra6rholKUFPRMGd54SxwUCAszXYShEuW6wW//7i/dRulZgdfa2nVw5GsE6v\n+8VayxBgCDAEGAKnMwKYzpNkhRTGd29YtXk/jsGVX6x+f8Zzk9P0Jn02rhdAKrUHR1WBnbbE4RTe\n4rd7ixBeh3DViQsfTOMC4aDIz7MhgHS2FNqEOaBIBvHOp2vVRNDwevrNyTGeZiGBs5mrVUI0/6F0\nXqcdiiivwx3cO3fyXLJndcwuRgM7WYpSD4lc9fbNO8lSO19QsM85ZfaUOIVXMj0ur1RVUU3EixO0\nqv6V2q9jrvA0yshWEZ5GN4s1lSHAEGAIMARObwQU6HmDKBEhIl0kMhRKR0mr1QWh3U6SnXi6fnLx\ntCGPLZr2bTikLA8GQ+/qY41bpj3zwFTEqYaweF53tyRol7akVyVCAZl/Q5GF1VfdeNVAhKvO49KJ\nYUl+HeSKf3T+Q3/hec1mKRB+I+QPLzPw3PsXXT5uGBJqSboVyXFk/4msUcoNHxc0rfxuwxULn3p5\nUuHWPfZBmf2oP03dUhIyYD+rL6YO96/6ei2ROAWmG2jq8IxQcEc/2RQhgcAcQ4AhwBBgCDAEfg4E\nYFqByBDNHsVNeuCG5PR+PbUaXnczjLRf09zooH1xGu4T/5YIlaalXq8vbsv67TMaahvrLpgw7urU\nHt1m3fXoJGn+Y7lPYx/CLdB9eui3N19z2f/eWvbBnFdnDPa4Q2NB3XqkdIv9Dcp55oXPXjDYS2p+\n29zUvHbA0AGjBEGYbWuwf/bhkuV5I0YPSbbGWRMbbI4UpCXp1jEp2bfocBFR1MXGxoaTU9NCRQUl\nklbHZcZYYwSX013aWGd3J6UmSXaXnSR3Z8T0IPrJCBaBwBxDgCHAEGAIMAR+DgQgpfLFJ8YNeOS5\nh/K0Wo2Ca01QCsXbG5s/zHvtoyVoQ1V8t25/kCR5xIaVm/7+zcfff4kw2+ZV2z9+eN6DGRZrzN24\nfqto3bYvBowbWZHaM2kirj8ISfylfr+vKhQKF8UnxV+OsGedlXVjYSKh2/ZNuz4fc+GoRAjO4mHw\ns2p/0f6d8OVIw8XFxSVwGZCildHVsblJkyZpKyoqXPX19d4136yJWHbnhUs0MKDqaHZsR6nhbqkJ\noV3bi88YBXdCkulgHdvzxHIxBBgCDAGGAEPgqBGApErr83jdVWU1y1K6p4xPSk0cu2nttsf/+8qH\neZljMn1N9U1eGOccFfAH61d/uYF0tFyKstTH89mcTid8jmnACedPPHfUO+8s/0K8YNQqKI6fizQW\nGCq9xO/2F7s93iJIuv6KsFQpJP0iGAo1rVy+ugzXBeKCqe/1GdDr4elzH/ilTqNdbmu0vzn/X7n7\nlGbV1AOSHJvLzc09SBl/7utzzsM86E12W3N49469tCG1Rg5IZKbhjDAwGkWREawoEuzIEGAIMAQY\nAgyBE4wAFhHqfd5Axb/n/2chqnoeyu3/HTC4718GZmZ8ULC5oJSqx75/GlJb1+v1vkAg4J+/vJbe\n1ZJGC8ukwbAscEIMpeNl7hO9QXfpldf/6jJYgx/S3OzMq9tfs7ZHn+63X3vTry8Kh6ULvU7PJiSt\nxb6BnvLNhXdaendfpdVqrxYspmmxidbfXzzx/Jsg2dpBxuFxPNrpO9IlI6csWPL0vYrM/QZUzQVl\nrl9YYi3GsuLy3I+WfFIJu1h8RVMdzFKoBEvNcCb8Aw7MMQQYAgwBhgBDgCHwcyCALQfJmjkRExN8\nSV1FzW1Gk6Hnb//0mydwrRInWZILQa66nXPJKNKP8t3967tJdwnq4fx4bJdjW71iHW1HIwQkXz6M\nfDYMHNb3zyBT+tJ95Xu+WPZdAcjZ7oyBfW6CVKt7dWX9KqR1b9q0KfTaa8tcL4iLc5+dsfDqvYVl\nV2l12mF9hva+FvHW3NzbjlrgAlKGrBGdKmzgMwg2ryagfRP9/mBN8a7SeeJ9T/4X8TqeDzd7G70k\nwTpjFNwJmKMGlDIxxxBgCDAEGAIMAYbAMSGgx+o6HXKGbhFvMb4svvHD/Y/fcTum9V6697F/zHzu\n0RcfqK5t+iA9LXH6uZecMyutZ/eqpLS03d26WW+GUvsfairrFyBv/X33ZRmevPf5uhnzJq9PTU+5\nrbGuadX3n62pojjQmFWpPbvNgF7Xxm8/+nYHwpxPvyh284S80LZSymN4xeWz6kZKIdlrq2+2Iz4R\nYi43jkfl2kq8qiubHvZ4m1+uKa9NWfPVWrm6uo5WDJqg3N7cUNdE5iXOqBWEBCQjWIQCcwwBhgBD\ngCHAEPg5EFBkJyRUKpn5oeAHVaLz7COLFk9/5v5zserurpv+mb3mlSdfef+fM279c1xi3Ev9Bvf5\nFESmCbpU3Rtrmt789/NL3kczHUOGJKjK5JIsfwJ9rduwApGspRNZ4pSQ8m0oEJqBFYO7m5ocJO3y\n+Tj/EOh2LZENnOSCOXklKKU11DT8+9P3vtiR2idVzr0tN6KcTgUcg5s9bTbV7YVPhU/sN7iX1uf0\n2mpqmshOlwP+ID0tXHd5F50/7fIdZR1kCDAEGAIMAYbAyUbg1sk39y3bVdp/xaerSN+qDNNsMkmC\nEriEuMtuGX9xQ53Ns3L5mh8Q54pPi8+44pqJEzGlmFK+r3Lv91+uIRLVAH2qOpryo75ccdcVBp2N\nO3vn5j2mkl0lBQiqyRKz9FJp4JytG3ZS2C6EVWEij783Z9Jot8t3acAfMJUUlxet/mLdfsQ5evbs\nub+yspIkTMfsspZmaRJW+A1F9S59w94GoaCggAyKUhvJn1HK7ccMIsvIEGAIMAQYAgwBhsBxIaDL\nyMgwRkugLXCi5zgaQaB07cMQHps5IdPyEwZBTZSvTTl02lEYhZvh43uP6J3QQR6KPx5HfWnrj6cs\nlpchwBBgCDAEGAIMAYbA4REg4tSOPKmZoC/+o/Bo2uixfem0cTMRrg7Ko30C2y9i40VFFMgfqrz2\n5bNrhgBDgCHAEGAIMAQYAmcyAm0lYGcyDqzvDAGGAEOAIcAQYAgwBI4PgTmvzrGKr4utU43HVxrL\n3dkItBchdnb5rDyGAEOAIcAQYAgwBDoJAZoWpKJeeHfuaJNBKOSaA1fRdTSczpk7NRBgBOvUuA+s\nFQwBhgBDgCHAEDgsAoWFhRFr65IScDpdX21at41W6VmysrLOKCOehwWKJWAIMAQYAgwBhgBDgCFw\nVAhAIb5N+vTzz+9JVuGZYwgwBBgCDAGGAEOAIcAQOGoE2hCr+W8/9dDc1578B5XRwSrCoy6aZeh8\nBNgUYedjykpkCDAEGAIMAYZApyMg5oiq5Grh0oUWbKo82eVwjUcl5pycHFUvq9MrZAUeFwKMYB0X\nfCwzQ4AhwBBgCDAEfh4Ehg0bFpkaDHkGyJJirCitJMvt5mEzh0X0sn6eZrBajhABRrCOECiWjCHA\nEGAIMAQYAicTgZSUwgjB0mhGyuGwpnJ/zX60J5jFMQX3k3lfDlU3I1iHQoaFMwQYAgwBhgBD4BRC\noKEhIqniOWF0MBR2fPHRinI0T6K9DE+hZrKmtCCgZUgwBBgCDAGGAEOAIXBqI0CK7HC0gTIny9JZ\nAZ+/DKeNE26ZIOW/kY9T5k41BJgE61S7I6w9DAGGAEOAIcAQaIdAdl62+r5e9M6sBOxb2N/t9OxB\nEtcN59+gkq52ydnlKYAAI1inwE1gTWAIMAQYAgwBhsBPITDRPjHyvlb0gyVJSmyqbySC5U9ISGAG\nRn8KuJMYxwjWSQSfVc0QYAgwBBgCDIEjQaAmoUZVcJd5ZaQkydzeXaXFyBfKzs5mEqwjAfAkpGEE\n6ySAzqpkCDAEGAIMAYbAUSDAc4VcmNLzGn5MMBBs+v7rNVW4ZOTqKED8uZMygvVzI87qYwgwBBgC\nDAGGwNEhoIiiKM95dY6VU/gr3S7PDmRvvOWWWxjBOjocf9bUjGD9rHCzyhgCDAGGAEOAIXBkCCxd\nulS10L7g7aeueuGtp98zmzRrvF6f4YfvN3+AEnwZr2cEj6wklupkIMDMNJwM1FmdDAGGAEOAIcAQ\nOAwChYWFqn2rUFDy+4PBGKfDWbBl7fa5Xy5bsXn8+MEOkReZgvthMGTRDAGGAEOAIcAQYAgwBH6E\nAEwyRKy3c5wJkd3Jg1xZf5SQBZxyCERv3CnXMNYghgBDgCHAEGAIMARUBHjoYNH7WuBmcjKTXLGn\ngiHAEGAIMAQYAgwBhgBDgCHAEGAIMAQYAgwBhgBDgCHAEDh+BP4fO1r5PHKAccEAAAAASUVORK5C\nYII=\n",
"prompt_number": 46,
"text": [
"<IPython.core.display.Image at 0x10e2207d0>"
]
}
],
"prompt_number": 46
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the model score and confusion matrix:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"model_score = clf.score(test_x, test_y)\n",
"print (\"Model Score %.2f \\n\" % (model_score))\n",
"\n",
"confusion_matrix = metrics.confusion_matrix(test_y, predict_y)\n",
"print (\"Confusion Matrix \", confusion_matrix)\n",
"\n",
"print (\" Predicted\")\n",
"print (\" | 0 | 1 |\")\n",
"print (\" |-----|-----|\")\n",
"print (\" 0 | %3d | %3d |\" % (confusion_matrix[0, 0],\n",
" confusion_matrix[0, 1]))\n",
"print (\"Actual |-----|-----|\")\n",
"print (\" 1 | %3d | %3d |\" % (confusion_matrix[1, 0],\n",
" confusion_matrix[1, 1]))\n",
"print (\" |-----|-----|\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Model Score 0.83 \n",
"\n",
"('Confusion Matrix ', array([[98, 12],\n",
" [19, 50]]))\n",
" Predicted\n",
" | 0 | 1 |\n",
" |-----|-----|\n",
" 0 | 98 | 12 |\n",
"Actual |-----|-----|\n",
" 1 | 19 | 50 |\n",
" |-----|-----|\n"
]
}
],
"prompt_number": 47
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Display the classification report:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$Precision = \\frac{TP}{TP + FP}$$ \n",
"\n",
"$$Recall = \\frac{TP}{TP + FN}$$ \n",
"\n",
"$$F1 = \\frac{2TP}{2TP + FP + FN}$$ "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.metrics import classification_report\n",
"print(classification_report(test_y, \n",
" predict_y, \n",
" target_names=['Not Survived', 'Survived']))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
"Not Survived 0.84 0.89 0.86 110\n",
" Survived 0.81 0.72 0.76 69\n",
"\n",
" avg / total 0.83 0.83 0.82 179\n",
"\n"
]
}
],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 48
}
],
"metadata": {}
}
]
}
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