{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python入門 〜pandasでデータ分析編〜" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TL;DR\n", "KaggleのTitanicをpandasとscikit-learnでいい感じに．\n", "春にやったこれとほぼ同じです^^; \n", "https://gist.github.com/johshisha/53ed00adabec6447642656a46a72d367" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pandasとは \n", "![Pandas](https://github.com/pandas-dev/pandas/raw/master/doc/logo/pandas_logo.png)\n", "PANel DAtaSが名前の由来 \n", "もともと統計解析用のR言語で使われていた**data.frame**という表（テーブル)みたいな \n", "データ形式をPythonに移植したモジュール（ライブラリ） \n", "**jupyter**と組み合わせるとテーブルの中身を見ながら処理できるので最強 \n", " \n", "内部的には**NumPy**というベクトルや行列といった数値計算用のモジュール \n", "（リストより効率のいい配列みたいな奴）をデータ分析用の機能でラップしたもの\n", "\n", "ただしあんまりにもでかいデータ（数GBオーダー）は苦手 \n", "代替として**Dask**や次世代数値計算ライブラリ**Blaze**を参照" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Kaggleとは\n", "![Kaggle](https://www.kaggle.com/static/images/site-logo.png) \n", "データ分析の世界的なコンペティションを行うWebサービス \n", "ユーザーはお題に沿って最も性能の良いモデルを構築することを目的とする \n", "賞金が出るお題もある \n", " \n", "今回はKaggleのチュートリアルに沿うことでデータ分析入門を目指す" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## データ分析のプロセス\n", "![Process](https://speakerd.s3.amazonaws.com/presentations/5c63dce1d2494dfc9f068fe1c58eab41/slide_6.jpg) \n", "\n", "[死にゆくアンチウイルスへの祈り](https://speakerdeck.com/ntddk/si-niyukuantiuirusuhefalseqi-ri) より\n", "\n", "データ分析は一筋縄では行かないので何段階かに分けて考える \n", "プログラミングでの大雑把な手順は \n", "\n", "1. データの加工\n", "1. 予測モデルの作成・学習\n", "1. 予測モデルの評価 \n", "\n", "の3段階で考えるのはどうでしょう（？）" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 本編" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. データの加工" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### データのロード \n", "今回の対象となるデータは，1912年4月15日に沈没したタイタニック号の乗員乗客名簿． \n", "女性，子供，1等船室の人々が生存確率が高かったことが知られている． \n", "Dead or Aliveをデータからより正確に予測することはできるか？ \n", "いわゆる二値分類問題． \n", " \n", "※[A free interactive Machine Learning tutorial in Python](https://www.datacamp.com/courses/kaggle-python-tutorial-on-machine-learning)にインタラクティブなチュートリアルがあります" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 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)\n", "memory usage: 83.6+ KB\n" ] } ], "source": [ "train_url = \"http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/train.csv\"\n", "train_df = pd.read_csv(train_url)\n", "train_df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "891人分のデータ（タイタニック号に乗っていた2224人のうちの40%）があるようだが， \n", "欠損値のある要素（Ageなど）もある\n", "\n", "- PassengerID:　乗客ID\n", "- Survived:　　　生存結果 (1: 生存, 0: 死亡)　\n", "- Pclass:　　　　客室の等級\n", "- Name:　　　　乗客の名前\n", "- Sex:　　　　　性別\n", "- Age:　　　　　年齢\n", "- SibSp:　　　　乗船している兄弟，配偶者の数\n", "- Parch:　　　　乗船している両親，子供の数\n", "- Ticket:　　　　チケット番号\n", "- Fare:　　　　　乗船料金\n", "- Cabin:　　　　部屋番号\n", "- Embarked:　　　乗船した港　Cherbourg，Queenstown，Southamptonの３種類" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
101113Sandstrom, Miss. Marguerite Rutfemale4.011PP 954916.7000G6S
111211Bonnell, Miss. Elizabethfemale58.00011378326.5500C103S
121303Saundercock, Mr. William Henrymale20.000A/5. 21518.0500NaNS
131403Andersson, Mr. Anders Johanmale39.01534708231.2750NaNS
141503Vestrom, Miss. Hulda Amanda Adolfinafemale14.0003504067.8542NaNS
151612Hewlett, Mrs. (Mary D Kingcome)female55.00024870616.0000NaNS
161703Rice, Master. Eugenemale2.04138265229.1250NaNQ
171812Williams, Mr. Charles EugenemaleNaN0024437313.0000NaNS
181903Vander Planke, Mrs. Julius (Emelia Maria Vande...female31.01034576318.0000NaNS
192013Masselmani, Mrs. FatimafemaleNaN0026497.2250NaNC
202102Fynney, Mr. Joseph Jmale35.00023986526.0000NaNS
212212Beesley, Mr. Lawrencemale34.00024869813.0000D56S
222313McGowan, Miss. Anna \"Annie\"female15.0003309238.0292NaNQ
232411Sloper, Mr. William Thompsonmale28.00011378835.5000A6S
242503Palsson, Miss. Torborg Danirafemale8.03134990921.0750NaNS
252613Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...female38.01534707731.3875NaNS
262703Emir, Mr. Farred ChehabmaleNaN0026317.2250NaNC
272801Fortune, Mr. Charles Alexandermale19.03219950263.0000C23 C25 C27S
282913O'Dwyer, Miss. Ellen \"Nellie\"femaleNaN003309597.8792NaNQ
293003Todoroff, Mr. LaliomaleNaN003492167.8958NaNS
.......................................
86186202Giles, Mr. Frederick Edwardmale21.0102813411.5000NaNS
86286311Swift, Mrs. Frederick Joel (Margaret Welles Ba...female48.0001746625.9292D17S
86386403Sage, Miss. Dorothy Edith \"Dolly\"femaleNaN82CA. 234369.5500NaNS
86486502Gill, Mr. John Williammale24.00023386613.0000NaNS
86586612Bystrom, Mrs. (Karolina)female42.00023685213.0000NaNS
86686712Duran y More, Miss. Asuncionfemale27.010SC/PARIS 214913.8583NaNC
86786801Roebling, Mr. Washington Augustus IImale31.000PC 1759050.4958A24S
86886903van Melkebeke, Mr. PhilemonmaleNaN003457779.5000NaNS
86987013Johnson, Master. Harold Theodormale4.01134774211.1333NaNS
87087103Balkic, Mr. Cerinmale26.0003492487.8958NaNS
87187211Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female47.0111175152.5542D35S
87287301Carlsson, Mr. Frans Olofmale33.0006955.0000B51 B53 B55S
87387403Vander Cruyssen, Mr. Victormale47.0003457659.0000NaNS
87487512Abelson, Mrs. Samuel (Hannah Wizosky)female28.010P/PP 338124.0000NaNC
87587613Najib, Miss. Adele Kiamie \"Jane\"female15.00026677.2250NaNC
87687703Gustafsson, Mr. Alfred Ossianmale20.00075349.8458NaNS
87787803Petroff, Mr. Nedeliomale19.0003492127.8958NaNS
87887903Laleff, Mr. KristomaleNaN003492177.8958NaNS
87988011Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)female56.0011176783.1583C50C
88088112Shelley, Mrs. William (Imanita Parrish Hall)female25.00123043326.0000NaNS
88188203Markun, Mr. Johannmale33.0003492577.8958NaNS
88288303Dahlberg, Miss. Gerda Ulrikafemale22.000755210.5167NaNS
88388402Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 3406810.5000NaNS
88488503Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500NaNS
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQ
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
\n", "

891 rows × 12 columns

\n", "
" ], "text/plain": [ " 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", "5 6 0 3 \n", "6 7 0 1 \n", "7 8 0 3 \n", "8 9 1 3 \n", "9 10 1 2 \n", "10 11 1 3 \n", "11 12 1 1 \n", "12 13 0 3 \n", "13 14 0 3 \n", "14 15 0 3 \n", "15 16 1 2 \n", "16 17 0 3 \n", "17 18 1 2 \n", "18 19 0 3 \n", "19 20 1 3 \n", "20 21 0 2 \n", "21 22 1 2 \n", "22 23 1 3 \n", "23 24 1 1 \n", "24 25 0 3 \n", "25 26 1 3 \n", "26 27 0 3 \n", "27 28 0 1 \n", "28 29 1 3 \n", "29 30 0 3 \n", ".. ... ... ... \n", "861 862 0 2 \n", "862 863 1 1 \n", "863 864 0 3 \n", "864 865 0 2 \n", "865 866 1 2 \n", "866 867 1 2 \n", "867 868 0 1 \n", "868 869 0 3 \n", "869 870 1 3 \n", "870 871 0 3 \n", "871 872 1 1 \n", "872 873 0 1 \n", "873 874 0 3 \n", "874 875 1 2 \n", "875 876 1 3 \n", "876 877 0 3 \n", "877 878 0 3 \n", "878 879 0 3 \n", "879 880 1 1 \n", "880 881 1 2 \n", "881 882 0 3 \n", "882 883 0 3 \n", "883 884 0 2 \n", "884 885 0 3 \n", "885 886 0 3 \n", "886 887 0 2 \n", "887 888 1 1 \n", "888 889 0 3 \n", "889 890 1 1 \n", "890 891 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "5 Moran, Mr. James male NaN 0 \n", "6 McCarthy, Mr. Timothy J male 54.0 0 \n", "7 Palsson, Master. Gosta Leonard male 2.0 3 \n", "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n", "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n", "10 Sandstrom, Miss. Marguerite Rut female 4.0 1 \n", "11 Bonnell, Miss. Elizabeth female 58.0 0 \n", "12 Saundercock, Mr. William Henry male 20.0 0 \n", "13 Andersson, Mr. Anders Johan male 39.0 1 \n", "14 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0 \n", "15 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 \n", "16 Rice, Master. Eugene male 2.0 4 \n", "17 Williams, Mr. Charles Eugene male NaN 0 \n", "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 1 \n", "19 Masselmani, Mrs. Fatima female NaN 0 \n", "20 Fynney, Mr. Joseph J male 35.0 0 \n", "21 Beesley, Mr. Lawrence male 34.0 0 \n", "22 McGowan, Miss. Anna \"Annie\" female 15.0 0 \n", "23 Sloper, Mr. William Thompson male 28.0 0 \n", "24 Palsson, Miss. Torborg Danira female 8.0 3 \n", "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.0 1 \n", "26 Emir, Mr. Farred Chehab male NaN 0 \n", "27 Fortune, Mr. Charles Alexander male 19.0 3 \n", "28 O'Dwyer, Miss. Ellen \"Nellie\" female NaN 0 \n", "29 Todoroff, Mr. Lalio male NaN 0 \n", ".. ... ... ... ... \n", "861 Giles, Mr. Frederick Edward male 21.0 1 \n", "862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48.0 0 \n", "863 Sage, Miss. Dorothy Edith \"Dolly\" female NaN 8 \n", "864 Gill, Mr. John William male 24.0 0 \n", "865 Bystrom, Mrs. (Karolina) female 42.0 0 \n", "866 Duran y More, Miss. Asuncion female 27.0 1 \n", "867 Roebling, Mr. Washington Augustus II male 31.0 0 \n", "868 van Melkebeke, Mr. Philemon male NaN 0 \n", "869 Johnson, Master. Harold Theodor male 4.0 1 \n", "870 Balkic, Mr. Cerin male 26.0 0 \n", "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1 \n", "872 Carlsson, Mr. Frans Olof male 33.0 0 \n", "873 Vander Cruyssen, Mr. Victor male 47.0 0 \n", "874 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 1 \n", "875 Najib, Miss. Adele Kiamie \"Jane\" female 15.0 0 \n", "876 Gustafsson, Mr. Alfred Ossian male 20.0 0 \n", "877 Petroff, Mr. Nedelio male 19.0 0 \n", "878 Laleff, Mr. Kristo male NaN 0 \n", "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 \n", "880 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 \n", "881 Markun, Mr. Johann male 33.0 0 \n", "882 Dahlberg, Miss. Gerda Ulrika female 22.0 0 \n", "883 Banfield, Mr. Frederick James male 28.0 0 \n", "884 Sutehall, Mr. Henry Jr male 25.0 0 \n", "885 Rice, Mrs. William (Margaret Norton) female 39.0 0 \n", "886 Montvila, Rev. Juozas male 27.0 0 \n", "887 Graham, Miss. Margaret Edith female 19.0 0 \n", "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", "889 Behr, Mr. Karl Howell male 26.0 0 \n", "890 Dooley, Mr. Patrick male 32.0 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 \n", "5 0 330877 8.4583 NaN Q \n", "6 0 17463 51.8625 E46 S \n", "7 1 349909 21.0750 NaN S \n", "8 2 347742 11.1333 NaN S \n", "9 0 237736 30.0708 NaN C \n", "10 1 PP 9549 16.7000 G6 S \n", "11 0 113783 26.5500 C103 S \n", "12 0 A/5. 2151 8.0500 NaN S \n", "13 5 347082 31.2750 NaN S \n", "14 0 350406 7.8542 NaN S \n", "15 0 248706 16.0000 NaN S \n", "16 1 382652 29.1250 NaN Q \n", "17 0 244373 13.0000 NaN S \n", "18 0 345763 18.0000 NaN S \n", "19 0 2649 7.2250 NaN C \n", "20 0 239865 26.0000 NaN S \n", "21 0 248698 13.0000 D56 S \n", "22 0 330923 8.0292 NaN Q \n", "23 0 113788 35.5000 A6 S \n", "24 1 349909 21.0750 NaN S \n", "25 5 347077 31.3875 NaN S \n", "26 0 2631 7.2250 NaN C \n", "27 2 19950 263.0000 C23 C25 C27 S \n", "28 0 330959 7.8792 NaN Q \n", "29 0 349216 7.8958 NaN S \n", ".. ... ... ... ... ... \n", "861 0 28134 11.5000 NaN S \n", "862 0 17466 25.9292 D17 S \n", "863 2 CA. 2343 69.5500 NaN S \n", "864 0 233866 13.0000 NaN S \n", "865 0 236852 13.0000 NaN S \n", "866 0 SC/PARIS 2149 13.8583 NaN C \n", "867 0 PC 17590 50.4958 A24 S \n", "868 0 345777 9.5000 NaN S \n", "869 1 347742 11.1333 NaN S \n", "870 0 349248 7.8958 NaN S \n", "871 1 11751 52.5542 D35 S \n", "872 0 695 5.0000 B51 B53 B55 S \n", "873 0 345765 9.0000 NaN S \n", "874 0 P/PP 3381 24.0000 NaN C \n", "875 0 2667 7.2250 NaN C \n", "876 0 7534 9.8458 NaN S \n", "877 0 349212 7.8958 NaN S \n", "878 0 349217 7.8958 NaN S \n", "879 1 11767 83.1583 C50 C \n", "880 1 230433 26.0000 NaN S \n", "881 0 349257 7.8958 NaN S \n", "882 0 7552 10.5167 NaN S \n", "883 0 C.A./SOTON 34068 10.5000 NaN S \n", "884 0 SOTON/OQ 392076 7.0500 NaN S \n", "885 5 382652 29.1250 NaN Q \n", "886 0 211536 13.0000 NaN S \n", "887 0 112053 30.0000 B42 S \n", "888 2 W./C. 6607 23.4500 NaN S \n", "889 0 111369 30.0000 C148 C \n", "890 0 370376 7.7500 NaN Q \n", "\n", "[891 rows x 12 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "行（row）が乗客一人ひとりを表し，列（column）が乗客の性質を表す \n", "「NaN」と書かれているデータは「データ無し（欠損値）」を表す \n", "891人分のデータが訓練データとして与えられている \n", " \n", "**太字**の列はindexでpandasが自動的に割り振る番号（この場合は0からスタート） \n", "基本的には重複のない整数だが，好きなカラムをindexに指定することもでき，使い方いろいろ" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 量的データ" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
10%90.0000000.0000001.00000014.0000000.0000000.0000007.550000
20%179.0000000.0000001.00000019.0000000.0000000.0000007.854200
30%268.0000000.0000002.00000022.0000000.0000000.0000008.050000
40%357.0000000.0000002.00000025.0000000.0000000.00000010.500000
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
60%535.0000000.0000003.00000031.8000000.0000000.00000021.679200
70%624.0000001.0000003.00000036.0000001.0000000.00000027.000000
80%713.0000001.0000003.00000041.0000001.0000001.00000039.687500
90%802.0000001.0000003.00000050.0000001.0000002.00000077.958300
99%882.1000001.0000003.00000065.8700005.0000004.000000249.006220
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
\n", "
" ], "text/plain": [ " 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", "10% 90.000000 0.000000 1.000000 14.000000 0.000000 \n", "20% 179.000000 0.000000 1.000000 19.000000 0.000000 \n", "30% 268.000000 0.000000 2.000000 22.000000 0.000000 \n", "40% 357.000000 0.000000 2.000000 25.000000 0.000000 \n", "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", "60% 535.000000 0.000000 3.000000 31.800000 0.000000 \n", "70% 624.000000 1.000000 3.000000 36.000000 1.000000 \n", "80% 713.000000 1.000000 3.000000 41.000000 1.000000 \n", "90% 802.000000 1.000000 3.000000 50.000000 1.000000 \n", "99% 882.100000 1.000000 3.000000 65.870000 5.000000 \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", "10% 0.000000 7.550000 \n", "20% 0.000000 7.854200 \n", "30% 0.000000 8.050000 \n", "40% 0.000000 10.500000 \n", "50% 0.000000 14.454200 \n", "60% 0.000000 21.679200 \n", "70% 0.000000 27.000000 \n", "80% 1.000000 39.687500 \n", "90% 2.000000 77.958300 \n", "99% 4.000000 249.006220 \n", "max 6.000000 512.329200 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# train_df.describe()\n", "# train_df.describe(percentiles=[.61, .62])\n", "# train_df.describe(percentiles=[.75, .8])\n", "# train_df.describe(percentiles=[.68, .69])\n", "train_df.describe(percentiles=[.1, .2, .3, .4, .5, .6, .7, .8, .9, .99])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "describe()で簡単な統計量（基本・要約・記述統計量）を一覧できる\n", "\n", "- 891人中生き残った人のデータは38%ほど\n", "- 親や子供と共に乗っていない人が75%くらい\n", "- 兄弟や配偶者と共に乗っていない人が70%くらい\n", "- \\$512も払っている人は1%未満\n", "- 65歳以上の人も1%未満" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 可視化\n", "パーセンタイルを折れ線グラフで見てみる \n", " \n", "**matplotlib**はPythonのグラフ作図ライブラリで最も有名 \n", "**seaborn**はmatplotlibの見た目をナウくする \n", "% matplotlib inline というマジックコマンドで，グラフをセル内に表示できる \n", "% matplotlib notebook ならインタラクティブな表示が可能" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可視化のツールは他にもたくさんある． \n", "それぞれ長所短所を見分けて使い分けよう． \n", "[Pythonの可視化パッケージの使い分け](http://qiita.com/alchemist/items/544d45480ce9c1ca2c16) \n", "[Pythonの可視化ツールはHoloViewsが標準になるかもしれない](http://qiita.com/driller/items/53be86cea3c3201e7e0f) \n", "[Google製可視化OSSのFacetsがめっちゃ便利だから使ってみてくれ](http://qiita.com/inoue0426/items/071c127428112f498421)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassAgeSibSpParchFare
0.190.00.01.014.00.00.07.5500
0.2179.00.01.019.00.00.07.8542
0.3268.00.02.022.00.00.08.0500
0.4357.00.02.025.00.00.010.5000
0.5446.00.03.028.00.00.014.4542
0.6535.00.03.031.80.00.021.6792
0.7624.01.03.036.01.00.027.0000
0.8713.01.03.041.01.01.039.6875
0.9802.01.03.050.01.02.077.9583
\n", "
" ], "text/plain": [ " PassengerId Survived Pclass Age SibSp Parch Fare\n", "0.1 90.0 0.0 1.0 14.0 0.0 0.0 7.5500\n", "0.2 179.0 0.0 1.0 19.0 0.0 0.0 7.8542\n", "0.3 268.0 0.0 2.0 22.0 0.0 0.0 8.0500\n", "0.4 357.0 0.0 2.0 25.0 0.0 0.0 10.5000\n", "0.5 446.0 0.0 3.0 28.0 0.0 0.0 14.4542\n", "0.6 535.0 0.0 3.0 31.8 0.0 0.0 21.6792\n", "0.7 624.0 1.0 3.0 36.0 1.0 0.0 27.0000\n", "0.8 713.0 1.0 3.0 41.0 1.0 1.0 39.6875\n", "0.9 802.0 1.0 3.0 50.0 1.0 2.0 77.9583" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.quantile([.1, .2, .3, .4, .5, .6, .7, .8, .9])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "np.arange(0, 1, .1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD8CAYAAACSCdTiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_df.quantile(np.arange(0, 1, .001)).plot(y=\"Age\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 質的データ" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameSexTicketCabinEmbarked
count891891891204889
unique89126811473
topBlackwell, Mr. Stephen WeartmaleCA. 2343G6S
freq157774644
\n", "
" ], "text/plain": [ " Name Sex Ticket Cabin Embarked\n", "count 891 891 891 204 889\n", "unique 891 2 681 147 3\n", "top Blackwell, Mr. Stephen Weart male CA. 2343 G6 S\n", "freq 1 577 7 4 644" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.describe(include=[np.object])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Nameは一意\n", "- 65%が男性\n", "- 客室には複数の人が泊まっていたり，一人が複数の客室を使用していたりする\n", "- 乗船した港は3種類だがほとんどがS\n", "- Ticketは22%も重複している" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### データ観察からの仮説\n", "参考：https://www.kaggle.com/startupsci/titanic-data-science-solutions\n", "\n", "相関 \n", "　どの指標が生死と相関があるのかを知り，モデルのたたき台にする \n", " \n", "補完 \n", "\n", "1. 生死と相関のある年齢は補完したい\n", "1. Embarkedも補完したいかもしれない\n", " \n", "修正\n", "\n", "1. 22%も重複しているTicketは分析には不適ではないか\n", "1. Cabinは欠損値が多すぎるのでつかえない\n", "1. PassengerIdはただの番号\n", "1. 名前は生死に無関係では\n", " \n", "作成\n", "\n", "1. ParchとSibSpを使えば家族数が割り出せるのでは \n", "1. 名前から敬称を取り出せる\n", "1. 年齢の連続値より年齢層として扱う方がいいかもしれない\n", "1. 料金も同様に\n", " \n", "分類\n", "\n", "1. 女性は生き残る可能性が高いか\n", "1. 子供は生き残る可能性が高いか\n", "1. 上位クラスの乗客は生き残る可能性が高いか\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 相関を見る \n", "※ Pandasのデータ操作はさまざま． \n", "[Pandasを使ったデータ操作の基本](http://cocodrips.hateblo.jp/entry/2017/07/30/185430)などを参考に，適宜「（やりたいこと） pandas」で検索． \n", "バージョン違いでAPIが変わる可能性があるので，なるべく新しい記事を参考にしよう．" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[pandas.DataFrame の列の抽出（射影）および行の抽出（選択）方法まとめ](http://akiyoko.hatenablog.jp/entry/2017/04/03/081630) \n", "![DataFrame](http://bookdata.readthedocs.io/en/latest/_images/base_01_pandas_5_0.png)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n", "
" ], "text/plain": [ " 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 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 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 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df[0:5]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Sex
0male
1female
2female
3female
4male
5male
6male
7male
8female
9female
10female
11female
12male
13male
14female
15female
16male
17male
18female
19female
20male
21male
22female
23male
24female
25female
26male
27male
28female
29male
......
861male
862female
863female
864male
865female
866female
867male
868male
869male
870male
871female
872male
873male
874female
875female
876male
877male
878male
879female
880female
881male
882female
883male
884male
885female
886male
887female
888female
889male
890male
\n", "

891 rows × 1 columns

\n", "
" ], "text/plain": [ " Sex\n", "0 male\n", "1 female\n", "2 female\n", "3 female\n", "4 male\n", "5 male\n", "6 male\n", "7 male\n", "8 female\n", "9 female\n", "10 female\n", "11 female\n", "12 male\n", "13 male\n", "14 female\n", "15 female\n", "16 male\n", "17 male\n", "18 female\n", "19 female\n", "20 male\n", "21 male\n", "22 female\n", "23 male\n", "24 female\n", "25 female\n", "26 male\n", "27 male\n", "28 female\n", "29 male\n", ".. ...\n", "861 male\n", "862 female\n", "863 female\n", "864 male\n", "865 female\n", "866 female\n", "867 male\n", "868 male\n", "869 male\n", "870 male\n", "871 female\n", "872 male\n", "873 male\n", "874 female\n", "875 female\n", "876 male\n", "877 male\n", "878 male\n", "879 female\n", "880 female\n", "881 male\n", "882 female\n", "883 male\n", "884 male\n", "885 female\n", "886 male\n", "887 female\n", "888 female\n", "889 male\n", "890 male\n", "\n", "[891 rows x 1 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(type(train_df[[\"Sex\"]]))\n", "train_df[[\"Sex\"]]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/plain": [ "0 male\n", "1 female\n", "2 female\n", "3 female\n", "4 male\n", "5 male\n", "6 male\n", "7 male\n", "8 female\n", "9 female\n", "10 female\n", "11 female\n", "12 male\n", "13 male\n", "14 female\n", "15 female\n", "16 male\n", "17 male\n", "18 female\n", "19 female\n", "20 male\n", "21 male\n", "22 female\n", "23 male\n", "24 female\n", "25 female\n", "26 male\n", "27 male\n", "28 female\n", "29 male\n", " ... \n", "861 male\n", "862 female\n", "863 female\n", "864 male\n", "865 female\n", "866 female\n", "867 male\n", "868 male\n", "869 male\n", "870 male\n", "871 female\n", "872 male\n", "873 male\n", "874 female\n", "875 female\n", "876 male\n", "877 male\n", "878 male\n", "879 female\n", "880 female\n", "881 male\n", "882 female\n", "883 male\n", "884 male\n", "885 female\n", "886 male\n", "887 female\n", "888 female\n", "889 male\n", "890 male\n", "Name: Sex, Length: 891, dtype: object" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(type(train_df[\"Sex\"]))\n", "train_df[\"Sex\"]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 male\n", "4 male\n", "5 male\n", "6 male\n", "7 male\n", "12 male\n", "13 male\n", "16 male\n", "17 male\n", "20 male\n", "21 male\n", "23 male\n", "26 male\n", "27 male\n", "29 male\n", "30 male\n", "33 male\n", "34 male\n", "35 male\n", "36 male\n", "37 male\n", "42 male\n", "45 male\n", "46 male\n", "48 male\n", "50 male\n", "51 male\n", "54 male\n", "55 male\n", "57 male\n", " ... \n", "840 male\n", "841 male\n", "843 male\n", "844 male\n", "845 male\n", "846 male\n", "847 male\n", "848 male\n", "850 male\n", "851 male\n", "857 male\n", "859 male\n", "860 male\n", "861 male\n", "864 male\n", "867 male\n", "868 male\n", "869 male\n", "870 male\n", "872 male\n", "873 male\n", "876 male\n", "877 male\n", "878 male\n", "881 male\n", "883 male\n", "884 male\n", "886 male\n", "889 male\n", "890 male\n", "Name: Sex, Length: 577, dtype: object" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df[\"Sex\"][train_df[\"Sex\"] == \"male\"]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 male\n", "4 male\n", "5 male\n", "6 male\n", "7 male\n", "12 male\n", "13 male\n", "16 male\n", "17 male\n", "20 male\n", "21 male\n", "23 male\n", "26 male\n", "27 male\n", "29 male\n", "30 male\n", "33 male\n", "34 male\n", "35 male\n", "36 male\n", "37 male\n", "42 male\n", "45 male\n", "46 male\n", "48 male\n", "50 male\n", "51 male\n", "54 male\n", "55 male\n", "57 male\n", " ... \n", "840 male\n", "841 male\n", "843 male\n", "844 male\n", "845 male\n", "846 male\n", "847 male\n", "848 male\n", "850 male\n", "851 male\n", "857 male\n", "859 male\n", "860 male\n", "861 male\n", "864 male\n", "867 male\n", "868 male\n", "869 male\n", "870 male\n", "872 male\n", "873 male\n", "876 male\n", "877 male\n", "878 male\n", "881 male\n", "883 male\n", "884 male\n", "886 male\n", "889 male\n", "890 male\n", "Name: Sex, Length: 577, dtype: object" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.loc[train_df[\"Sex\"] == \"male\", \"Sex\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "相関を見るために質的変数を量的変数に変えておく \n", "※ pandasの機能（関数）は基本的に非破壊的（＝データ自体は変わらない）． \n", "　関数の戻り値を変数へ再代入して変更を上書きする．" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Sex
00
11
21
31
40
50
60
70
81
91
101
111
120
130
141
151
160
170
181
191
200
210
221
230
241
251
260
270
281
290
......
8610
8621
8631
8640
8651
8661
8670
8680
8690
8700
8711
8720
8730
8741
8751
8760
8770
8780
8791
8801
8810
8821
8830
8840
8851
8860
8871
8881
8890
8900
\n", "

891 rows × 1 columns

\n", "
" ], "text/plain": [ " Sex\n", "0 0\n", "1 1\n", "2 1\n", "3 1\n", "4 0\n", "5 0\n", "6 0\n", "7 0\n", "8 1\n", "9 1\n", "10 1\n", "11 1\n", "12 0\n", "13 0\n", "14 1\n", "15 1\n", "16 0\n", "17 0\n", "18 1\n", "19 1\n", "20 0\n", "21 0\n", "22 1\n", "23 0\n", "24 1\n", "25 1\n", "26 0\n", "27 0\n", "28 1\n", "29 0\n", ".. ...\n", "861 0\n", "862 1\n", "863 1\n", "864 0\n", "865 1\n", "866 1\n", "867 0\n", "868 0\n", "869 0\n", "870 0\n", "871 1\n", "872 0\n", "873 0\n", "874 1\n", "875 1\n", "876 0\n", "877 0\n", "878 0\n", "879 1\n", "880 1\n", "881 0\n", "882 1\n", "883 0\n", "884 0\n", "885 1\n", "886 0\n", "887 1\n", "888 1\n", "889 0\n", "890 0\n", "\n", "[891 rows x 1 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df_select = train_df.copy()\n", "train_df_select.loc[train_df_select[\"Sex\"] == \"male\", \"Sex\"] = 0\n", "train_df_select.loc[train_df_select[\"Sex\"] == \"female\", \"Sex\"] = 1\n", "train_df_select = train_df_select.astype({\"Sex\": int})\n", "train_df_select[[\"Sex\"]]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Embarked
00
11
20
30
40
52
60
70
80
91
100
110
120
130
140
150
162
170
180
191
200
210
222
230
240
250
261
270
282
290
......
8610
8620
8630
8640
8650
8661
8670
8680
8690
8700
8710
8720
8730
8741
8751
8760
8770
8780
8791
8800
8810
8820
8830
8840
8852
8860
8870
8880
8891
8902
\n", "

889 rows × 1 columns

\n", "
" ], "text/plain": [ " Embarked\n", "0 0\n", "1 1\n", "2 0\n", "3 0\n", "4 0\n", "5 2\n", "6 0\n", "7 0\n", "8 0\n", "9 1\n", "10 0\n", "11 0\n", "12 0\n", "13 0\n", "14 0\n", "15 0\n", "16 2\n", "17 0\n", "18 0\n", "19 1\n", "20 0\n", "21 0\n", "22 2\n", "23 0\n", "24 0\n", "25 0\n", "26 1\n", "27 0\n", "28 2\n", "29 0\n", ".. ...\n", "861 0\n", "862 0\n", "863 0\n", "864 0\n", "865 0\n", "866 1\n", "867 0\n", "868 0\n", "869 0\n", "870 0\n", "871 0\n", "872 0\n", "873 0\n", "874 1\n", "875 1\n", "876 0\n", "877 0\n", "878 0\n", "879 1\n", "880 0\n", "881 0\n", "882 0\n", "883 0\n", "884 0\n", "885 2\n", "886 0\n", "887 0\n", "888 0\n", "889 1\n", "890 2\n", "\n", "[889 rows x 1 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df_select.loc[train_df_select[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n", "train_df_select.loc[train_df_select[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n", "train_df_select.loc[train_df_select[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n", "train_df_select = train_df_select.dropna(subset=[\"Embarked\"])\n", "train_df_select = train_df_select.astype({\"Embarked\": int})\n", "train_df_select[[\"Embarked\"]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "とりあえずNaNの含まれるレコードを消して，相関を算出できるデータのみを取り出す" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 183 entries, 1 to 889\n", "Data columns (total 12 columns):\n", "PassengerId 183 non-null int64\n", "Survived 183 non-null int64\n", "Pclass 183 non-null int64\n", "Name 183 non-null object\n", "Sex 183 non-null int64\n", "Age 183 non-null float64\n", "SibSp 183 non-null int64\n", "Parch 183 non-null int64\n", "Ticket 183 non-null object\n", "Fare 183 non-null float64\n", "Cabin 183 non-null object\n", "Embarked 183 non-null int64\n", "dtypes: float64(2), int64(7), object(3)\n", "memory usage: 18.6+ KB\n" ] } ], "source": [ "train_df_select_drop = train_df_select.dropna()\n", "train_df_select_drop.info()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvivedPclassSexAgeSibSpParchFareEmbarked
PassengerId1.0000000.148495-0.0891360.0252050.030933-0.083488-0.0514540.029740-0.054246
Survived0.1484951.000000-0.0345420.532418-0.2540850.1063460.0235820.1342410.083231
Pclass-0.089136-0.0345421.0000000.046181-0.306514-0.1035920.047496-0.315235-0.235027
Sex0.0252050.5324180.0461811.000000-0.1849690.1042910.0895810.1304330.060862
Age0.030933-0.254085-0.306514-0.1849691.000000-0.156162-0.271271-0.0924240.088112
SibSp-0.0834880.106346-0.1035920.104291-0.1561621.0000000.2553460.2864330.015962
Parch-0.0514540.0235820.0474960.089581-0.2712710.2553461.0000000.389740-0.097495
Fare0.0297400.134241-0.3152350.130433-0.0924240.2864330.3897401.0000000.233452
Embarked-0.0542460.083231-0.2350270.0608620.0881120.015962-0.0974950.2334521.000000
\n", "
" ], "text/plain": [ " PassengerId Survived Pclass Sex Age SibSp \\\n", "PassengerId 1.000000 0.148495 -0.089136 0.025205 0.030933 -0.083488 \n", "Survived 0.148495 1.000000 -0.034542 0.532418 -0.254085 0.106346 \n", "Pclass -0.089136 -0.034542 1.000000 0.046181 -0.306514 -0.103592 \n", "Sex 0.025205 0.532418 0.046181 1.000000 -0.184969 0.104291 \n", "Age 0.030933 -0.254085 -0.306514 -0.184969 1.000000 -0.156162 \n", "SibSp -0.083488 0.106346 -0.103592 0.104291 -0.156162 1.000000 \n", "Parch -0.051454 0.023582 0.047496 0.089581 -0.271271 0.255346 \n", "Fare 0.029740 0.134241 -0.315235 0.130433 -0.092424 0.286433 \n", "Embarked -0.054246 0.083231 -0.235027 0.060862 0.088112 0.015962 \n", "\n", " Parch Fare Embarked \n", "PassengerId -0.051454 0.029740 -0.054246 \n", "Survived 0.023582 0.134241 0.083231 \n", "Pclass 0.047496 -0.315235 -0.235027 \n", "Sex 0.089581 0.130433 0.060862 \n", "Age -0.271271 -0.092424 0.088112 \n", "SibSp 0.255346 0.286433 0.015962 \n", "Parch 1.000000 0.389740 -0.097495 \n", "Fare 0.389740 1.000000 0.233452 \n", "Embarked -0.097495 0.233452 1.000000 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df_select_drop.corr()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAz8AAAJfCAYAAABR1mitAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 303, "width": 415 } }, "output_type": "display_data" } ], "source": [ "%config InlineBackend.figure_formats = {'png', 'retina'}\n", "sns.heatmap(train_df_select_drop.corr(), annot=True, cmap='RdBu_r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sex > Age > Fare > SibSp > Embarked > Pclass > Parch の順にSurvivedと相関（の絶対値）が高い \n", "ただし，EmbarkedとPclassは名義尺度なので注意 \n", "https://mathwords.net/syakudo より\n", " \n", "| | | | | | \n", "| - | - | - | - | - | \n", "| 尺度 | 例 | 大小比較 | 差 | 比 | \n", "| 名義尺度 | 電話番号 | × | × | × | \n", "| 順序尺度 | 震度 | ○ | × | × | \n", "| 間隔尺度 | 温度（℃） | ○ | ○ | × | \n", "| 比率尺度 | 長さ | ○ | ○ | ○ | \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 分布を見る" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyYAAAK8CAYAAAAavaujAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSJPXu/wPXUpDRyIGO7AAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 350, "width": 403 } }, "output_type": "display_data" } ], "source": [ "sns.pairplot(train_df_select[[\"Sex\", 'Survived']], hue='Survived')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Femaleのほうが生存率が高い" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyYAAAK7CAYAAAAHuJsbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSJLXOwESSJElS6/4HwxcmQknKAsgAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 349, "width": 403 } }, "output_type": "display_data" } ], "source": [ "sns.pairplot(train_df_select[[\"Age\", 'Survived']].dropna(), hue='Survived')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "お年寄りはだいたい亡くなっている \n", "幼児の生存率は高い" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyYAAAK7CAYAAAAHuJsbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3v1/ar8nPcSGf4EAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 349, "width": 403 } }, "output_type": "display_data" } ], "source": [ "sns.pairplot(train_df_select[[\"Pclass\", 'Survived']], hue='Survived')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1>2>3等の順で生存率が高い" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAywAAAK7CAYAAAAQmgvSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSJFWWCYskSZKkyvr/WrDEBA5bW90AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 349, "width": 406 } }, "output_type": "display_data" } ], "source": [ "sns.pairplot(train_df_select[[\"Fare\", 'Survived']], hue='Survived')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyYAAAK7CAYAAAAHuJsbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAWJQAAFiUBSVIk8AAAIABJREFUeJzs3Xl8VdW9///XhwSCkgCCA6itVJShdQTFEZXaWuy9Fq22\ntip1vNWqtV+107ejeq+37Y9vaatXbb11uMXptrZKv60DXxEt4tVr0VsnRMHihDggQ4IQJKzfH+cc\nCCGBkOxkc5LX8/HIY3P2WmftFbcc8s7aa61IKSFJkiRJeeqRdwckSZIkyWAiSZIkKXcGE0mSJEm5\nM5hIkiRJyp3BRJIkSVLuDCaSJEmScmcwkSRJkpQ7g4kkSZKk3BlMJEmSJOXOYCJJkiQpdwYTSZIk\nSbkzmEiSJEnKncFEkiRJUu4MJpIkSZJyZzCRJEmSlDuDiSRJkqTcGUw6SETcGhG35t0PSSo3fn5K\nUvdUmXcHurARo0aNGgWckndHJJWFyLsDWxE/PyW1lp+dXYgjJpIkSZJyZzCRJEmSlDuDiSRJkqTc\nGUwkSZIk5c5gIkmSJCl3BhNJkiRJuTOYSJIkScqdwUSSJElS7gwmkiRJknJnMJEkSZKUO4OJJEmS\npNyVVTCJiIERcU5E3BUR8yJiZUQsi4hHIuLsiNii7ycido2IGyNiYUTUR8SCiPh5RGzXUd+DJEmS\npI1V5t2BLfQ54DrgTWAG8CqwE/BZ4NfAsRHxuZRS2lxDETEUeBTYEZgKvACMAb4GjI+Iw1JKizvk\nu2iLy/p1YNvLOq5tSepEKSUigrr6Ndz7zJu8tbyenfpWcezeg6muqlxXLqmTpQQRUF8Hz98NtYug\nZhB89Hioql5frm6t3ILJi8BngD+nlNaWTkbEd4D/Bk6kEFJ+34q2rqUQSi5KKV3dqK3JwMXAlcB5\n2XVdktSRSqHjmhnzuHbGPFasblhXdtkfn+P8cXtwwbg9DCdSZyuFjpk/hZmTYXXd+rJ7vwVjL4Gx\nlxpOVF6PcqWUHkwp/d/GoaR4fhHwy+LLozbXTnG05BhgAXBNk+IfAiuAiRHRp719liR1jlIomXT/\n3A1CCcCK1Q1Mun8u18yYZyiROlsplEy/YsNQAoXX068olPt3s9srq2CyGR8Uj2taUXdc8TitmZBT\nC8wCtgUOzq57kqSOVFe/hmtnzNtknesemk9dfWv+mZCUmfq6wkjJpjzys0I9dWvl9ihXsyKiEvhS\n8eV9rXjL8OLxxRbKX6IwojIMmL6Za89uoWhEK/ohSd1W1p+f9z7z5kYjJU3V1a/hvmff5KTRH2rL\nJSS1xfN3bzxS0lR9LcyZCvud2jl90lapq4yY/BjYC7gnpXR/K+qXZpK3NOu7dL5/ezsmSeocby2v\nz7SepIzULsq2nrqssh8xiYiLgEsprKo1sbOvn1Ia3dz54m8CR3VydySpbGT9+blT36pM60nKSM2g\nbOupyyrrEZOIuBD4BfA8MC6l9F4r31oaEWlpDd7S+aXt6J4kqRMdu/dg+vSq2GSd6qpKxu81uJN6\nJAkoLAncq3rTdapqYOSEzumPtlplG0wi4n8BVwPPUgglWzL+N7d4HNZC+Z7FY0tzUCRJW5nqqkrO\nH7fHJut85aihVFeV/cMCUnmpqi4sCbwph19cqKdurSw/nSPiWxTmlfwP8MmU0rtb2MSM4vGYiOjR\nZE+UGuAw4H3gsSz6K0nqeCklLigGk6arb1VXVfKVo4a6j4mUh5QK+5RAcfWt2vVlVTWFUOI+JqIM\ng0lEfB+4ApgNHLOpx7cioicwFPggpTS/dD6lND8iplFYeesCCiMvJZcDfYBfpZRWdMC3IEnqABGx\nLpycfugQ7nt2/c7v4/dy53cpNxHrw8mYcwurb5V2fh85wZ3ftU5ZBZOIOJ1CKGkAZgIXNfMPzIKU\n0s3FP+8CzAFeAYY0qXc+8ChwVUQcXax3EIU9Tl4Evpv9dyBJ6kilfxOqqyqbXRLYUCLlpPR3r6q6\n+SWB/bspyiyYAB8pHiuA/9VCnYeBmzfXUHHU5AAKQWc88GngTQqT6S9PKS1pd28lSZIktUpZBZOU\n0mXAZVtQfwHQYgRPKb0GnNnefkmSJElqn7JdlUuSJElS12EwkSRJkpQ7g4kkSZKk3BlMJEmSJOXO\nYCJJkiQpdwYTSZIkSbkzmEiSJEnKncFEkiRJUu4MJpIkSZJyZzCRJEmSlDuDiSRJkqTcGUwkSZIk\n5c5gIkmSJCl3BhNJkiRJuTOYSJIkScqdwUSSJElS7gwmkiRJknJnMJEkSZKUO4OJJEmSpNwZTCRJ\nkiTlzmAiSZIkKXcGE0mSJEm5M5hIkiRJyp3BRJIkSVLuDCaSJEmScmcwkSRJkpQ7g4kkSZKk3BlM\nJEmSJOXOYCJJkiQpdwYTSZIkSbkzmEiSJEnKncFEkiRJUu4MJpIkSZJyZzCRJEmSlDuDiSRJkqTc\nGUwkSZIk5c5gIkmSJCl3BhNJkiRJuTOYSJIkScqdwUSSJElS7gwmkiRJknJnMJEkSZKUO4OJJEmS\npNwZTCRJkiTlzmAiSZIkKXcGE0mSJEm5K7tgEhEnRcTVETEzIpZHRIqIW9rQzoLie5v7WtQRfZck\nSZLUvMq8O9AG3wP2BeqA14ER7WhrGfDzZs7XtaNNSZIkSVuoHIPJxRQCyTzgSGBGO9pamlK6LItO\nSZIkSWq7sgsmKaV1QSQi8uyKJEmSpIyUXTDJWFVEnAZ8GFgBPA38JaXUkG+3JEmSpO6luweTQcCU\nJuf+HhFnppQezqNDkiRJUnfUnYPJTcBM4DmgFtgduBD4MnBvRBySUvrb5hqJiNktFLVnUr4kdXl+\nfkqSGuu2wSSldHmTU88C50VEHXApcBlwQmf3S5IkSeqOum0w2YRfUggmR7SmckppdHPni78JHJVh\nvySpS/HzU5LUWNltsNgJ3ike++TaC0mSJKkbMZhs7ODi8eVceyFJkiR1I106mEREz4gYERFDm5wf\nGREbjYhExBDg34ovb+n4HkqSJEmCMpxjEhHHA8cXXw4qHg+JiJuLf343pfT14p93AeYArwBDGjVz\nMnBpRPylWFYLDAX+AegN3AP8nw76FiRJkiQ1UXbBBNgPOL3Jud2LX1AIGl9n02YAw4H9gcMozCdZ\nCjxCYV+TKSmllFWHJUmSJG1a2QWTlNJlFJbybU3dBUA0c/5hwA0UJUmSpK1El55jIkmSJKk8GEwk\nSZIk5c5gIkmSJCl3BhNJkiRJuTOYSJIkScqdwUSSJElS7tq1XHBE3NjGt6aU0tntubYkSZKkrqO9\n+5ic0cL5RDP7hzQ6nwCDiSRJkiSg/cHkI01e9wB+BowFrgIeAhYBg4BxwFeBvwCXtPO6kiRJkrqQ\ndgWTlNIrjV9HxMUUQsmoJmVzgYcj4j+A2cAE4OftubYkSZKkriPrye9fBn7bNLCUpJT+DvyuWE+S\nJEmSgOyDyRBg6WbqLCnWkyRJkiQg+2DyLvCplgojIorlizO+riRJkqQylnUw+R2wX0T8NiI2mBhf\nfP2fwD7FoyRJkiQB7V+Vq6kfAIcDJwEnRMQbwFvATsAuQAXwBHBZxteVJEmSVMYyHTFJKdVRCCbf\nAxYAHwYOLB7/DnwXGFusJ0mSJElA9iMmpJRWA/8K/GtEVAP9gGWGkfYZsuq2Dmt7QYe1LEmSJLVO\n5sGksWIYMZBIkiRJ2qQOCSYRsQNwIjAS6JNSOqfR+Y8Az6SUVnbEtSVJkiSVn8yDSUScDVwF9AYC\nSMA5xeKdgP+isMHiDVlfW5IkSVJ5ynTye0R8ErgeeBE4AbiucXlK6VngOeD4LK8rSZIkqbxlPWLy\nLeBN4MiU0vKI2L+ZOk8Dh2R8XUmSJEllLOsNFg8A/pRSWr6JOq8DgzK+riRJkqQylnUw6QWs2Eyd\n/kBDxteVJEmSVMayDiYLgNGbqXMQMDfj60qSJEkqY1kHk6nA2Ij4XHOFEXEmsA/w+4yvK0mSJKmM\nZT35/f8DvgDcHhEnUdj1nYi4EBgLfBZ4Cbg64+tKkiRJKmOZBpOU0pKIOBL4DdB41OSq4nEmcEpK\naXPzUCRJkiR1I5lvsJhSehU4KiL2obAs8EBgGfBYSml21teTJEmSVP4yDyYlKaWnKexZspGIqEop\n1XfUtSVJkiSVl6x3fv9WK+r0Au7O8rqSJEmSylvWq3L9a0R8saXCiKgA7gSOyfi6kiRJkspY1sFk\nFnBTRIxrWlAMJf8J/CPwbxlfV5IkSVIZyzqYfAZ4GfhDROxVOhkRQWGlrs8C/55S+lrG15UkSZJU\nxjINJimlpcB4YCVwT0TsUiy6AfgiMCWldG6W15QkSZJU/rIeMSktF3wshc0V74uIXwNnAL8Dzsz6\nepIkSZLKX4csF5xS+ltx5/c/AR8FplLYWHFtR1xPkiRJUnlrVzCJiC9tpso04GDgHuDUwlSTgpTS\nb9pzbUmSJEldR3tHTG4GUjPno3i+lER+2eRcojAZXpIkSZLaHUycMyJJkiSp3doVTFJK/5FVRyRJ\nkiR1X5muyhURN0bExVm2KUmSJKnry3q54FOAHTNuU5IkSVIXl3UwWYDBRJIkSdIWyjqY3AYcGxHb\nZdyuJEmSpC4s62DyI+CvwIyI+MeI2Cnj9iVJkiR1QVnv/L6qeAwKu73TeFPFRlJKqUN2nZckSZJU\nfrIOBzNpfsPFzETEScCRwH7AvkANcGtK6bQ2tLUrcAUwHhgIvAncDVyeUlqSWaclSZIkbVKmwSSl\ndFSW7bXgexQCSR3wOjCiLY1ExFDgUQqT9acCLwBjgK8B4yPisJTS4kx6LEnqFCklIoK6+jXc+8yb\nvLW8np36VnHs3oOprqpcVy6pk6UEEVBfB8/fDbWLoGYQfPR4qKpeX65urRwfp7qYQiCZR2HkZEYb\n27mWQii5KKV0delkREwuXuNK4Lz2dVWS1FlKoeOaGfO4dsY8VqxuWFd22R+f4/xxe3DBuD0MJ1Jn\nK4WOmT+FmZNhdd36snu/BWMvgbGXGk6U+eT3DpdSmpFSeiml1OZHxoqjJcdQWN74mibFPwRWABMj\nok+bOypJ6lSlUDLp/rkbhBKAFasbmHT/XK6ZMc9QInW2UiiZfsWGoQQKr6dfUSj372a31yEjJhEx\nGDga2AWoaqZKSin9c0dcu5XGFY/TUkprGxeklGojYhaF4HIwML2zOydJ2nJ19Wu4dsa8Tda57qH5\nnH7oEKqryvGBAalM1dcVRko25ZGfwZhzC491qdvK/JM5Ii4Hvt2k7WD9pPjSn/MMJsOLxxdbKH+J\nQjAZxmaCSUTMbqGoTXNfJKm7yPrz895n3txopKSpuvo13Pfsm5w0+kNtuYSktnj+7o1HSpqqr4U5\nU2G/UzunT9oqZfooV0ScCnyfwupcJ1EIIf8BnAL8O7AWuAP4eJbXbYN+xeOyFspL5/t3Ql8kSRl4\na3l9pvUkZaR2Ubb11GVlPWLyFQoT08enlNYUn+NdkFK6A7gjIu4C/gzcnvF1c5NSGt3c+eJvAkd1\ncnckqWxk/fm5U9/mnhxuez1JGakZlG09dVlZT37fG7gnpbSm0bmK0h9SSvcD9wPfyPi6W6o0ItKv\nhfLS+aWd0BdJUgaO3XswfXpVbLJOdVUl4/ca3Ek9kgQUlgTutZm5I1U1MHJC5/RHW62sg0lPoPHe\nHyvZ+If/ZynsQ5KnucXjsBbK9yweW5qDIknaylRXVXL+uD02WecrRw114rvU2aqqC0sCb8rhFzvx\nXZk/yvUm0PhXUa8C+zSpszOwhnyV9j45JiJ6NF6ZKyJqgMOA94HH8uicJGnLpZS4oBhMrntoPnX1\n6/+pqa6q5CtHDXUfEykPKRX2KYHC6lv1tevLqmoKocR9TET2weQpYK9Grx8EvhwRE4E/AEdRmBQ/\nK+PrNisiegJDgQ9SSvNL51NK8yNiGoWVty4Arm70tsuBPsCvUkorOqOfkqT2i4h14eT0Q4dw37Pr\nd34fv5c7v0u5iVgfTsacW1h9q7Tz+8gJ7vyudbIOJn8Cro2Ij6SU/g78GDgZuLn4BfAB8L22XiAi\njgeOL74szZI6JCJK7b+bUvp68c+7AHOAV4AhTZo6H3gUuCoiji7WO4jCHicvAt9tax8lSfkohY7q\nqspmlwQ2lEg5Kf3dq6pufklg/26KjINJSulm1gcQUkqvRcSBwKUURi4WANemlJ5px2X2A05vcm73\n4hcUQsjX2YziqMkBwBXAeODTFB5F+wVweUppSTv6KEmSJGkLdPgMwOLIyYUZtncZcFkr6y6gsJdK\nS+WvAWdm0S9JkiRJbZf1qlySJEmStMU6ZMQkIg6nMBKxP4XlgpdRmBh/U0rpkY64piRJkqTylXkw\niYirKUwsb/oI1X7AGRFxTUrpoqyvK0mSJKl8ZfooV0R8lcLyu3+nMGLyEWCb4vGs4vkLIuKCLK8r\nSZIkqbxlPcfkPGAhcEBK6T9SSq+klOqLx5uBMcAiCiMqkiRJkgRkH0x2B36fUlraXGFK6T3g96xf\n2leSJEmSMg8mi4HVm6mzGng34+tKkiRJKmNZB5O7gc9ERM/mCiOiF/CZYj1JkiRJArIPJt+hsDTw\nAxFxaEQEQBQcBjwALCnWkyRJkiSgncsFR8TLzZzuBQwGZgJrIuJdYPtG13oT+B9gaHuuLUmSJKnr\naO8+Jj2A1OTcB8CrTc4tbOZ9kiRJkgS0M5iklIZk1A9JkiRJ3ZgjF5IkSZJyZzCRJEmSlLv2zjFp\nVkTsA+wL7Ao0t3RwSin9c0dcW5IkSVL5yTSYRMQAYAowvnSqhaoJMJhIkiRJArIfMfk5cCyF/Upu\nAd4A1mR8DUmSJEldTNbB5B+BR1NKx2TcriRJkqQuLOvJ7xXAoxm3KUmSJKmLyzqYPAnsnnGbkiRJ\nkrq4rIPJPwP/GBGHZ9yuJEmSpC4s0zkmKaUHI+ILwF0R8ScKIyjLWqj7myyvLUmSJKl8Zb1ccC9g\nArAdcHrxKzWtVjxnMJEkSZIEZL8q148ohJHngf8EFuJywZIkSZI2I+tg8gXgGeDAlNLqjNuWJEmS\n1EVlPfm9PzDNUCJJkiRpS2QdTOYAgzNuU5IkSVIXl3Uw+SlwfEQMy7hdSZIkSV1Y1nNM3gDuAx6P\niF8As2l5ueC/ZHxtSZIkSWUq62DyEIWlgAP4ARsvFdxYRcbXliRJklSmsg4mV7DpMCJJkiRJG8l6\n5/fLsmxPkiRJUveQ9eR3SZIkSdpi7Q4mEXFERHx4C+rvGxFfau91JUmSJHUdWYyYzADOaHwiIr4V\nEYtbqH88cFMG15UkSZLURWQRTKKZc70p7AIvSZIkSZvlHBNJkiRJuTOYSJIkScqdwUSSJElS7gwm\nkiRJknKXVTBxt3dJkiRJbZbVzu+XRcRlTU9GRENG7UuSJEnqwrIKJs0tGbwpjrBIkiRJWqfdwSSl\n5DwVSZIkSe1iqJAkSZKUO4OJJEmSpNyVZTCJiF0j4saIWBgR9RGxICJ+HhHbbUEbD0VE2sRX7478\nHiRJkiStl9Xk904TEUOBR4EdganAC8AY4GvA+Ig4LKW0eAuavLyF82va1VFJkiRJrVZ2wQS4lkIo\nuSildHXpZERMBi4GrgTOa21jKaXLsu6gJEmSpC1TVo9yFUdLjgEWANc0Kf4hsAKYGBF9OrlrkiRJ\nktqh3EZMxhWP01JKaxsXpJRqI2IWheByMDC9NQ1GxMnAR4DVwBzgwZRSfXZdliRJkrQ55RZMhheP\nL7ZQ/hKFYDKMVgYT4I4mr9+OiAtSSne25s0RMbuFohGtvL4kdUt+fkqSGiurR7mAfsXjshbKS+f7\nt6KtqcBxwK7ANhT+IfxR8b3/GRHj29FPSZIkSVug3EZMMpNS+lmTU3OB70TEQuBqCiHlvla0M7q5\n88XfBI5qbz8lqavy81OS1Fi5jZiURkT6tVBeOr+0Hdf4NYWlgveLiJp2tCNJkiSplcotmMwtHoe1\nUL5n8djSHJTNSimtAmqLL13dS5IkSeoE5RZMZhSPx0TEBn0vjm4cBrwPPNbWC0TEcGA7CuHk3ba2\nI0mSJKn1yiqYpJTmA9OAIcAFTYovpzDCMSWltKJ0MiJGRMQGK7xExEciYkDT9iNiB+Cm4ss7Ukru\n/i5JkiR1gnKc/H4+8ChwVUQcTWHvkYMo7HHyIvDdJvXnFI/R6NyRwC8j4hHgZeA94MPApynMU/kr\n8M2O+gYkSZIkbajsgklKaX5EHABcAYynECbeBH4BXJ5SWtKKZmZT2L9kNLA/0JfCo1vPAL8FfpVS\nWt0B3ZckSZLUjLILJgAppdeAM1tZN5o59wxwRsbdkiRJktRGZTXHRJIkSVLXVJYjJlJZu6ylbXiy\naHvZ5utIkiRthRwxkSRJkpQ7g4kkSZKk3BlMJEmSJOXOYCJJkiQpdwYTSZIkSbkzmEiSJEnKncFE\nkiRJUu4MJpIkSZJyZzCRJEmSlDuDiSRJkqTcGUwkSZIk5c5gIkmSJCl3BhNJkiRJuTOYSJIkScqd\nwUSSJElS7gwmkiRJknJnMJEkSZKUO4OJJEmSpNwZTCRJkiTlzmAiSZIkKXcGE0mSJEm5M5hIkiRJ\nyp3BRJIkSVLuDCaSJEmScmcwkSRJkpQ7g4kkSZKk3BlMJEmSJOXOYCJJkiQpdwYTSZIkSbkzmEiS\nJEnKncFEkiRJUu4MJpIkSZJyZzCRJEmSlDuDiSRJkqTcGUwkSZIk5c5gIkmSJCl3BhNJkiRJuavM\nuwOS1KEu69eBbS/ruLYlSepmDCZSJxuy6rYOa3tBh7UsSZLUsXyUS5IkSVLuDCaSJEmScmcwkSRJ\nkpQ7g4kkSZKk3BlMJEmSJOWuLINJROwaETdGxMKIqI+IBRHx84jYbgvbGVB834JiOwuL7e7aUX2X\nJEmStLGyWy44IoYCjwI7AlOBF4AxwNeA8RFxWEppcSvaGVhsZxjwIHAHMAI4E/iHiDgkpfRyx3wX\nkiRJkhoru2ACXEshlFyUUrq6dDIiJgMXA1cC57WinX+lEEomp5QubdTORcAvitcZn2G/JUkdLKVE\nRFBXv4Z7n3mTt5bXs1PfKo7dezDVVZXryiV1spQgAurr4Pm7oXYR1AyCjx4PVdXry9WtlVUwKY6W\nHENhH7lrmhT/EPgyMDEiLk0prdhEO9XARGAFcFmT4n8DLgE+FRG7O2oiSeWhFDqumTGPa2fMY8Xq\nhnVll/3xOc4ftwcXjNvDcCJ1tlLomPlTmDkZVtetL7v3WzD2Ehh7qeFEZTfHZFzxOC2ltLZxQUqp\nFpgFbAscvJl2Dga2AWYV39e4nbXA/U2uJ0naypVCyaT7524QSgBWrG5g0v1zuWbGPEOJ1NlKoWT6\nFRuGEii8nn5Fody/m91euQWT4cXjiy2Uv1Q8DuukdiRJW4m6+jVcO2PeJutc99B86urXdFKPJAGF\nx7dmTt50nUd+Vqinbq2sHuUC+hWPy1ooL53v30ntEBGzWygasbn3Sup4Q1bd1mFtL+iwlruHrD8/\n733mzY1GSpqqq1/Dfc++yUmjP9SWS0hqi+fv3nikpKn6WpgzFfY7tXP6pK1SuY2YSJLUrLeW12da\nT1JGahdlW09dVrmNmJRGMvq1UF46v7ST2iGlNLq588XfBI7a3PslqbvK+vNzp75VmdaTlJGaQdnW\nU5dVbiMmc4vHluZ+7Fk8tjR3JOt2JElbiWP3HkyfXhWbrFNdVcn4vQZ3Uo8kAYUlgXtVb7pOVQ2M\nnNA5/dFWq9yCyYzi8ZiI2KDvEVEDHAa8Dzy2mXYeA1YChxXf17idHhSWJG58PUnSVq66qpLzx+2x\nyTpfOWoo1VXl9rCAVOaqqgtLAm/K4RcX6qlbK6tgklKaD0wDhgAXNCm+HOgDTGm8h0lEjIiIDSZS\nppTqgCnF+pc1aefCYvv3u4eJJJWPlBIXjNuDb3xq+Ebho7qqkm98avi6fUwkdaKUCvuUHP2DwshI\nY1U1hfOlfUzUrZXjr43OBx4FroqIo4E5wEEU9hx5Efhuk/pzisemi2N/BzgKuCQi9gP+GxgJTADe\nZuPgI0naikXEunBy+qFDuO/Z9Tu/j9/Lnd+l3ESsDydjzi2svlXa+X3kBHd+1zplF0xSSvMj4gDg\nCmA88GngTeAXwOUppSWtbGdxRBxCYcf444GxwGLgJuAHKaXXO6L/kqSOUwod1VWVzS4JbCiRclL6\nu1dV3fySwP7dFGUYTABSSq8BZ7aybov/p6eU3gO+VvySJEmSlJOymmMiSZIkqWsymEiSJEnKncFE\nkiRJUu4MJpIkSZJyZzCRJEmSlDuDiSRJkqTcGUwkSZIk5c5gIkmSJCl3BhNJkiRJuSvLnd/LxJA5\nc+YwevTobFr75BXZtNOMzPqo1vFedq4y+e/95JNP3ppSOjWzBstbtp+fkrosPzu7lkgp5d2HLiki\n/g70BRZk0NyI4vGFDNpSvryXXUfW9/IF/3EtyPDz079vXYf3smvJ8n762dmFGEzKQETMBkgp+evD\nMue97Dq8l1s/71HX4b3sWryfaomPckmSJKmszZ49exvgC8AngN2Bnvn2qNv7AHgZeAC4Y/To0Stb\n8yaDiSRJkspWMZT8oqKi4siKiooBPXr02AaIvPvVzaW1a9fu0dDQcEBDQ8Mhs2fP/lprwonBRJIk\nSeXsCxUVFUdus802Ow0aNGhRdXX1+xUVFWvz7lR31tDQ0KOurm7bRYsWDVq5cuWRDQ0NXwBu2tz7\nXC5YkiRJ5ewTFRUVAwYNGrSoX79+dYaS/FVUVKzt169f3U477fRWRUXFAAqP2G2WwUSSJEnlbPce\nPXpsU11d/X7eHdGGampqVhQfrftIa+r7KFcZcNWKrsN72XV4L7d+3qOuw3vZtXTA/ewJhCMlW58e\nPXqspTDfp1er6ndsdyRJkiR1RxFbtgaBwUSSJElS7gwmkiRJknJnMJEkSZK2UlddddXAiBh91VVX\nDcy7LyUd1SeDiSRJkrqNNWvW8NOf/nT7Aw88cHi/fv32q6ysHDVgwIB9hw0b9tGTTz55t1tvvbVf\n3n3srlyVS5IkSd3CmjVr+PjHP77nzJkz+9bU1DSMGzdu2S677LJ69erV8cILL2zzxz/+ccC8efN6\nn3rqqcupeyoDAAAgAElEQVTy7mvJqaeeunTs2LHPffjDH/4g7750NIOJJEmSuoXrr79+wMyZM/sO\nHz585axZs+YOHDiwoXF5bW1tj4ceeqhPXv1rzsCBAxua9rOr8lEuSZIkdQuPPvpoNcApp5zybnM/\n7NfU1Kw97rjjakuvL7nkkp0jYvSf/vSnmqZ1586d2ysiRp944olDGp8/8cQTh0TE6Oeff77XlVde\nueOwYcM+2rt371FjxowZfv31128XEaPPPvvsDzXXv5UrV0bfvn3322GHHfb54IPCAEnT+Rzvv/9+\n1NTU7DdgwIB9S3WaOvXUUz8cEaNvv/32DR5Le+qpp3qfeOKJQwYNGrRPz549Rw0cOHDf44477iN/\n+9vfqppr59lnn6069thjd+/bt+9+22yzzf7777//iDvuuKPDHnUzmEiSJKlbGDhw4BqAF198sXdH\nX+vCCy/88I9//OOdR4wYsfKss85666CDDqo77bTTllZXVzfcfffdA5oLFbfeemv/2traihNOOOG9\nnj17Ntvutttum4477rglS5Ysqfzd7363UUhYuXJl/OlPfxowcODANSeddNK6R9LuvPPOvoceeujI\nqVOnDthnn31WnH322W8feuihy6dNm7bd4YcfPvKRRx7ZtnE7zzzzTNXYsWNH3Hfffdvtv//+dWef\nffbbgwcPXn3aaacNvfvuu7dr/3+hjfkolyRJkrqFz3/+80uuvfbaQbfddtsOdXV1FSeccMKSQw45\n5P1hw4atzvpazz777LZPPPHE8yNGjNig7eOOO27J7bffvv2dd97Z74tf/OIGc1luueWWgQDnnHPO\n4k21fdZZZ717++23b/+b3/xm4CmnnLJBG7fffnv/5cuXV5xzzjlvlcLNO++8U3HWWWft3rt377WP\nPPLI3NGjR68q1X/iiSfePPLII0d++ctf3u3555+fUzp/3nnnfXjp0qWVV1xxxWvf//73327Ux/4T\nJ04c2ob/JJvliIkkSZK6hcMOO2zldddd9/eBAwd+MHXq1AFnnHHG0OHDh+/dv3///T75yU8Ove22\n2zJ7TOmrX/3qoqahBODMM898F+A3v/nNBkvtvvrqq5WPPPJIv5EjR74/ZsyYlZtq+xOf+MSK3Xbb\nrf7BBx/s/9Zbb1U0LpsyZcpG4eaXv/zlwNra2opvfvObCxuHEoADDzxw1Re/+MV358yZs+3s2bN7\nA8yfP7/no48+2neXXXZZ/b//9/9+u3H90047bemBBx5Y17r/ClvGERNJkiR1G+ecc86SiRMnLv3z\nn/9c85e//KX66aef3vavf/1r9QMPPND/gQce6H/nnXcuvvPOOxf06NG+398fcsghK5o7/8lPfnJd\nqHjnnXcqdthhhwaAG264YWBDQwOnnHLKJkdLSr7whS+8+5Of/GSXm266acC3v/3tdwBee+21ypkz\nZ/YdOXLk+wcddNC6cPP4449XAzz99NPbXnLJJTs3bWv+/PlVAM8880zv0aNHr3r88ce3BTjwwANr\nKys3jguHH3547RNPPFHdmn5uCYOJJEmSupWqqqr02c9+dvlnP/vZ5VBYRvjmm2/e7qKLLhpy1113\nDbz11luXTpw4cWl7rrHrrru2uLxvKVTceOONA771rW+9A3D77bcPrKysTGefffZ7rWn/n/7pnxZP\nmjRpl9tuu21gKZgUw0188Ytf3CDcvPfeexUAd9xxx/abarO2trYCYOnSpRUAO+6445rm6g0aNKhD\nli72US5JkiR1a5WVlZxzzjlL/umf/uktgOnTp9cA9OjRI0EhuDS1ePHiio1ONhIRLZadc845i3v0\n6MFtt902EGDWrFnbvPTSS9sceeSRywYPHtxsGGhq6NChHxx00EHLn3nmmT5PPfVUb2g53PTt27cB\n4LHHHns+pTS7pa+vfvWriwH69+/fAPD22283O4ixaNGi5mfmt5PBRJIkSQJqamoaAFJKAGy33XYN\nAK+88kqvpnUfe+yxNu93sscee3xw0EEHLX/66af7/O1vf6u64YYbtgf40pe+1KrHuEomTpy4GODX\nv/71wEcffXSbF198cZsjjjhi2c4777xBuBkzZswKgAcffLBVj18ddNBB7wM88cQTNc2FskceeWSj\n5ZOzYDCRJElSt/CrX/1qwF133dW3oWHj/QpfffXVyilTpuwAcOSRR9bB+nkiU6ZM2b7x8r7z5s3r\nOWnSpMHt6UspVFx33XU7TJ06dUD//v3XnHzyyVu04/zEiROXVFdXN/z+978f+Otf/3p7gNNPP32j\ncHP++ee/W1NT0zBp0qSdZ8yYsW3T8oaGBhrv1TJ06NAPDj300OVvvPFGrx/96Ec7Nq57yy239O+I\n+SXgHBNJkiR1E48//nifm266acftt9/+gwMOOKBut912Ww2FEZGHHnqo36pVq3ocffTRS88444wl\nAB//+MdXHHDAAXV//etfq/fdd9+Rhx9+eO3bb7/dc/r06f2OOOKI5ffcc89GIymtddpppy395je/\n2XDDDTfsuGbNmjj99NPfrqqqSlvSRnV1dfr0pz+95Le//e32U6ZM2aF///5rPv/5z28UbgYNGtQw\nZcqU+aeeeuoeRx999MiDDz54+YgRI1ZFBG+88UbPJ598snrZsmWV9fX1T5be88tf/vLVI444YsQP\nfvCDD02fPr3vXnvttfLll1+umjZtWv9x48YtmzFjRuYbLRpMJEmS1C185zvfWbTnnnuuevDBB/vO\nmTNn25kzZ/arr6+P/v37rxkzZkztySef/N655577XuMVue699955F1544a7Tpk3rf/PNN++42267\n1f/whz98/bjjjlt+zz33tHmjwZqamrWlUAFw9tlnb9FjXCVnnXXW4t/+9rfbr1mzJj7zmc+817t3\n72bDzYQJE2pnz5793JVXXjno4Ycf7jt79uyanj17ph122GH1oYceWnviiScuaVx/7733rp85c+YL\nX//613eZNWtW38cff7xm+PDhK2+55Zb5b7/9dmVHBJMoPUMnSZIklZvZs2f/tXfv3iM/9rGPzdl8\nbXW25557buSqVavmjB49+oDN1XWOiSRJkqTcGUwkSZIk5c5gIkmSJCl3BhNJkiRJuTOYSJIkScqd\nwUSSJElS7gwmkiRJknJnMJEkSZKUO4OJJEmSpNwZTCRJkiTlzmAiSZIkKXcGE0mSJEm5M5hIkiRJ\nyp3BRJIkSVLuDCYdJCJujYhb8+6HJJUbPz8lqePNnz+/5+c+97khO+644z69evUatcsuu+x91lln\nfeidd96pyKtPlXlduBsYMWrUqFHAKXl3RFJZiLw7sBXx81NSa/nZ2QbPPfdc1RFHHDHivffeqzz6\n6KOXDhs2bNWTTz7Z56abbtpxxowZff/rv/7rhUGDBjV0dr8cMZEkSZKy1RvYERhcPPbOtzsbOvfc\ncz/83nvvVf7Lv/zLaw888MD8a6+99o3HHnvsxbPPPvutBQsW9L7kkkt2yaNfBhNJkiQpGzXAcOBj\nwIeAnYvHjxXP1+TXtYLnnnuuatasWX133nnn1d/+9rffblw2adKkhdtss83au+66a+Dy5cs7PScY\nTCRJkqT22z6lNAyorqtfw+/++hr/9uA8fvfX16irXwNQXSwfmGcn77///hqAI488cnlFxYbTSbbb\nbru1o0aNqlu1alWPGTNm9OnsvjnHRJIkSWqfmpTSbhHBNTPmce2MeaxYvX6KxmV/fI7zx+3BBeP2\nIKU0JCJWA7V5dHTu3Lm9Afbcc89VzZXvvvvu9bNmzeKFF17oPWHChE7toyMmkiRJUvvsXAolk+6f\nu0EoAVixuoFJ98/lmhnziAgoPOKVi+XLl1cA9OvXr9nJ7aXzS5cu7fTVuQwmkiRJUtv1pvj41rUz\n5m2y4nUPzV/3WBdb2YT4rUHZBZOIOCkiro6ImRGxPCJSRNzSxrZ2jYgbI2JhRNRHxIKI+HlEbJd1\nvyVJktQl9QW495k3Nxopaaqufg33PfvmBu/rbH379m0AWLZsWbMjIqXz/fv37/Tlgstxjsn3gH2B\nOuB1YERbGomIocCjFJZwmwq8AIwBvgaMj4jDUkqLM+lxW6QEEVBfB8/fDbWLoGYQfPR4qKpeX66t\nn/ey6/BeSpI2VgHw1vL6VlVuVC+XjQyHDx++CuCll15qdsTm5ZdfrgIYMWJEs3NQOlI5BpOLKQSS\necCRwIw2tnMthVByUUrp6tLJiJhcvMaVwHnt62oblX64mflTmDkZVtetL7v3WzD2Ehh7qT8ElQPv\nZdfhvZQkNa8BYKe+Va2q3Khep49IAHzqU5+qvfTSS3n44Yf7NjQ00HhlriVLlvR48sknq3v37r12\n3LhxKzq7b2X3KFdKaUZK6aWUUmprG8XRkmOABcA1TYp/CKwAJkZEpy+TBqz/4Wf6FRv+8AOF19Ov\nKJT7w8/Wz3vZdXgvJUnNWw5w7N6D6dNr04Mg1VWVjN9r8Abv62wf+9jH6g877LDlCxcu7PXjH/94\nx8Zl3/jGN3ZeuXJljxNOOGFx375913Z238oumGRkXPE4LaW0wX/0lFItMAvYFji4szsGFB4TmTl5\n03Ue+VmhnrZu3suuw3spSWreKqCuuqqS88ftscmKXzlqKNVVlVCYktDpj0qV/OpXv3p1wIABa773\nve996BOf+MTQCy64YJeDDz542A033LDTbrvtVj958uQ38uhXdw0mw4vHF1sof6l4HLa5hiJidnNf\ntHHuC1B4dr3pb2Sbqq+FOVPbfAl1Eu9l1+G9zFyHfH5KUj4WppS4YNwefONTw0vhY53qqkq+8anh\npX1MABbm0suij33sY/WPP/748yeeeOLiv/3tb32uv/76nV599dWqM8888+0nnnhizqBBg3J5zKwc\n55hkoV/xuKyF8tL5/p3Ql43VLsq2nvLjvew6vJeSpJbVRsQrKaXdLhi3B6cfOoT7nn2Tt5bXs1Pf\nKsbvNZjqqkpSSkTEAnLaXLGxPfbY44M777xzQd79aKy7BpPMpJRGN3e++Fu/UW1qtGZQtvWUH+9l\n1+G9zFyHfH5KUn7ejYh6YOfqqsrqk0Z/qGl5XUQsZCsIJVur7vooV2lEpF8L5aXzSzuhLxv76PHQ\nq3rTdapqYOSEzumP2s572XV4LyVJm1cLzAWeA16j8MjWa8XXczGUbFJ3DSZzi8eW5pDsWTy2NAel\nY1VVF5Ye3ZTDLy7U09bNe9l1eC8lSa23CngbeLN4zG2ieznpro9ylfY+OSYiejRemSsiaoDDgPeB\nx/LoHCkV9kOA4io/jcJ1VU3hhx/3SygP3suuw3spSVKH6tLBJCJ6AkOBD1JK80vnU0rzI2Iahb1M\nLgCubvS2y4E+wK9SSp2+sQxQ+KGm9EPQmHMLq/yUdpgeOcEdpsuJ97Lr8F5KktShyi6YRMTxwPHF\nl6VZpodExM3FP7+bUvp68c+7AHOAV4AhTZo6H3gUuCoiji7WO4jCHicvAt/tiP63WumHm6pq2O/U\nlsu19fNedh3eS0mSOkzZBRNgP+D0Jud2L35BIYR8nc0ojpocAFwBjAc+TeE5wF8Al6eUlmTWY0mS\nJEmbVHbBJKV0GXBZK+suAFr8FWZK6TXgzCz6JUmSJKntuuuqXJIkSZK2IgYTSZIkSbkzmEiSJEnK\nncFEkiRJUu4MJpIkSZJyZzCRJEmSlDuDiSRJktSN3HTTTdudfvrpHxo9evTw6urq/SNi9IQJEz6S\nd7/Kbh8TSZIkSW33k5/8ZPDcuXO32XbbbdfutNNOq//+97/3zrtP4IiJJEmSlLXewI7A4OJxq/jB\nv2TSpEmvPf3008/W1tY+ddVVV72ad39KHDGRJEmSslED7AxUN1NWBywEaju1R8047rjjcu9Dcwwm\nkiRJUvttT0q7EQH1dfD83VC7CGoGwUePh6rqalIaRsQCYHHend0aGUwkSZKk9qlZF0pm/hRmTobV\ndetL7/0WjL0Exl4KKQ0hYjVbwcjJ1sY5JpIkSVL77LwulEy/YsNQAoXX068olEcU6msjBhNJkiSp\n7XoD1dTXFUZKNuWRnxUe8yrMQdmqJsRvDQwmkiRJUtv1BQpzSpqOlDRVXwtzpm74Pq1jMJEkSZLa\nrgIoTHRvjfX1KjqkN2XMYCJJkiS1XQNQWH2rNdbXa+iQ3pQxg4kkSZLUdsuBwpLAvZrbvqSRqhoY\nOWHD92kdlwuWJEmS2m4VUEdVdTVjLymsvtWSwy+GqmoobLa4qnO6t7EpU6b0v/vuu/sDvP322z0B\nnnzyyT4nnnjiEICBAweuuf7661/v7H4ZTCRJkqT2WUhKwxh7aeHVIz8rTHQvqaophJLCPiYQsTCf\nbhY89dRT2/7hD38Y2Pjc66+/XvX6669XAey8886rAYOJJEmSVGZqiXiFlHZj7KUw5tzC6lulnd9H\nTiiMlBRCyQJy3lxx8uTJCydPnpxrOGqOwUSSJElqv3eJqAd2pqq6mv1ObVpeVxwpccf3FhhMJEmS\npGzUAnMpbJ7Yl8KSwA0UJrrnNqekXBhMJEmSpGytwiCyxVwuWJIkSVLuDCaSJEmScmcwkSRJkpQ7\ng4kkSZKkzKWUtqi+wUSSJEnl7AMgNTQ0+HPtVmbt2rU9gASsbk19b6AkSZLK2ctr165dWVdXt23e\nHdGGamtr+6xdu3Yl8PfW1DeYSJIkqZw90NDQ8N6iRYsGLV26tKahoaHHlj5CpOyklGhoaOixdOnS\nmrfeemunhoaG94AHWvNe9zGRJHVZQ7795w5re8GP/6HD2pa0Re5oaGg4ZOXKlUe+9tprA3r06LEL\nEHl3qptLa9euXdnQ0PBWQ0PDw8AdrXmTwUSSJElla/To0Stnz579tYaGhi80NDR8AvgI0CvvfnVz\nqyk8vvUAcMfo0aNXtuZNBhNJkiSVteIPvjcVv1SmnGMiSZIkKXdlGUwiYteIuDEiFkZEfUQsiIif\nR8R2W9jO4RExtfj+VRHxakTcExHjO6rvkiRJkjZWdsEkIoYCs4Ezgf8Gfga8DHwN+K+IGNjKdr4C\nzASOLh5/BjwMHAncGxHfzb73kiRJkppTjnNMrgV2BC5KKV1dOhkRk4GLgSuB8zbVQET0BH4ErAJG\np5TmNir7V+Ap4LsR8X9SSvXZfwuSJEmSGiurEZPiaMkxwALgmibFPwRWABMjos9mmhoA9ANebBxK\nAFJKc4AXgW2A6gy6LUmSJGkzyiqYAOOKx2kppbWNC1JKtcAsYFvg4M208zbwDjAsIvZsXBARw4A9\ngf9JKS3OpNeSJEmSNqncHuUaXjy+2EL5SxRGVIYB01tqJKWUIuIC4BZgdkTcBSwEdgFOAJ4DvtCa\nDkXE7BaKRrTm/ZLUXfn5KUlqrNyCSb/icVkL5aXz/TfXUErpdxGxELgd+FKjorcorIH9cls7KUmS\nJGnLlFswyUxEnAb8O/AH4J+BV4DdgO8D/0Zhda7Pb66dlNLoFtqfDYzKqr+S1NX4+SlJaqzc5piU\nRkT6tVBeOr90U40U55HcSOGRrYkppRdSSitTSi8AEyksR/y5iDiq/V2WJEmStDnlFkxKK2gNa6G8\nNJG9pTkoJccAPYGHm5lEvxb4S/Fls7/NkyRJkpStcgsmM4rHYyJig75HRA1wGPA+8Nhm2qkqHndo\nobx0fnVbOilJkiRpy5RVMEkpzQemAUOAC5oUXw70AaaklFaUTkbEiIhousLLzOLxpIjYp3FBROwH\nnAQk4MHsei9JkiSpJeU4+f184FHgqog4GpgDHERhj5MXge82qT+neIzSiZTSf0fETcCZwBPF5YJf\noRB4jgd6AT9PKT3Xgd+HJEmSpKKyCyYppfkRcQBwBTAe+DTwJvAL4PKU0pJWNnU2hbkkZwCfAmqA\n5cAjwL+nlO7IuOuSJEmSWlB2wQQgpfQahdGO1tSNFs4n4ObilyRJkqQcldUcE0mSJEldk8FEkiRJ\nUu4MJpIkSZJyZzCRJEmSlDuDiSRJkqTcGUwkSZIk5c5gIkmSJCl3BhNJkiRJuTOYSJIkScqdwUSS\nJElS7gwmkiRJknJnMJEkSZKUO4OJJEmSpNwZTCRJkiTlzmAiSZIkKXcGE0mSJEm5M5hIkiRJyp3B\nRJIkSVLuDCaSJEmScmcwkSRJkpQ7g4kkSZKk3BlMJEmSJOXOYCJJkiQpdwYTSZIkSbkzmEiSJEnK\nncFEkiRJUu4MJpIkSZJyZzCRJEmSlDuDiSRJkqTcGUwkSZIk5a6yPW+OiC+19b0ppd+059qSJEmS\nuo52BRPgZiA1eh1NXjenVMdgIkmSJAlofzA5s5lznwWOAx4GHgIWAYOAccARwB+Bu9p5XUmSJEld\nSLuCSUrpPxq/johPA+OBCSml/9uk+uURMQH4LfDL9lxXkiRJUteS9eT37wJ3NRNKAEgpTQXuBr6f\n8XUlSZIklbGsg8m+wLzN1JkH7NOei0TErhFxY0QsjIj6iFgQET+PiO3a0NaoiLgtIl4vtvVWRDzc\nnon9kiRJkrZMe+eYNLWaQjjZlH2BD9p6gYgYCjwK7AhMBV4AxgBfA8ZHxGEppcWtbOtC4BfAEuDP\nwBvAAGAv4NM4QV+SJEnqFFkHk+nAZ4s/8F+TUlq3QldEBHAhcCzw+3Zc41oKoeSilNLVjdqfDFwM\nXAmct7lGIuIY4Crg/wEnpZRqm5T3bEcfJUmSJG2BrB/l+jaF0YdfAC9FxM0R8ZOIuBl4Cfg58F6x\n3hYrjpYcAywArmlS/ENgBTAxIvq0orlJwErglKahBCCl1OZRHUmSJElbJtMRk5TS/Ig4mMKoxieA\n3ZtU+X/ABSmll9t4iXHF47SU/v/27jzctqK88/j3hwgqkMuQgHZQMSBgB+OAEZSIIBFRBAmaKHEA\ntDttAMFIBju2AmmN6U6rDA6xo3gVZwKItjK0ChpBoqI4RGa8ijLYoiCTF4G3/1hry3Zz9j3T2mfd\nfe738zznKc6q2lV1WM+tvd9dq6rq3pG2b01yAU3gsivN7M2MkuxEs87lE8BPk+wJ7ExzvsolwHmj\n9UuSJEmanK4f5aKqrgL2TvLbwBOAFcAtwDeq6keLrH6HNr1iTP6VNIHJ9qwhMAF+v01/THPWyu4j\n+d9OcmD7t0iSJEmasM4Dk4E2CFlsIDJqRZveMiZ/cH3TWerZsk1fQdPHfYEvAVsBbwBeAnw6yWOr\n6q41VZTk4jFZO87SB0lapzl+SpKGdb3G5FeS7Jjkj5K8dFJtLMLg734A8KKq+kxV/byqrgReBnyN\nZtbl+X11UJIkSVqXdD5jkuTxwHtoHuMaOKXNezpwFvDCcYcwzmIwI7JiTP7g+s2z1DPIv6Gqvjyc\nUVWV5EzgSTTbEH9kTRVV1c4zXW+/CXziLP2QpHWW46ckaVinMyZJtqdZs7EDzc5cZ40U+SLNrlwv\nWGATl7fp9mPyH92m49agjNYzLoD5WZs+eI79kiRJkrQIXT/KdQywAbBLVb0G+OpwZnuuyZe5b/H5\nfJ3Xpnsn+bW+J9kE2A24A7holnouotlaeJsxWwvv1KbfW2A/JUmSJM1D14HJXsDpVfXdNZS5FvgP\nC6m8qq4GzgW2AQ4fyT4O2Ag4papuH1xs17r82kLKqroDeC/wIOCN7eGPg/KPBQ4B7gb+ZSH9lCRJ\nkjQ/Xa8x2Qz44SxlQjOrslCHARcCJybZC7gU2IXmjJMrgNeNlL90qN1hr6fZJvjVwFPaM1C2Ag6k\nCVhe3QZCkiRJkias6xmTG4HtZinzuzSzJgvSBgtPAlbSBCRHA9vSrGnZtapummM9PweeBvw9sDlw\nBPBcmm2Dn1VVJyy0j5IkSZLmp+sZk88DByXZoaouH81M8vs0j3u9YzGNVNW1wKFzLDs6UzKcdxvN\nDMvoLIskSZKkJdT1jMmbadZmfDHJn9OuJUnyu+3vnwJuBf5Xx+1KkiRJmmKdzphU1eVJnk9z9sfb\n28sBvtWmNwMHVtUPumxXkiRJ0nTr/IDFqjo7yaOAg4FdgS1oDka8CHhfVf206zYlSZIkTbfOAxOA\nqrqZZjG6C8glSZIkzarrk9+fM3rwoSRJkiTNpusg4v8A1yb5n0l2mrW0JEmSJNF9YPJumsMJ/xL4\nZpKvJjkiyRYdtyNJkiRpGek0MKmqPwceBrwQOAt4HM06kx8lOT3J/kkmsq5FkiRJ0vTqfD1IVd1V\nVadW1XOBrYG/Ai4HDgDOAK5LcnzX7UqSJEmaXhNdqF5VP66qt1bV44AnACcCK4BXTbJdSZIkSdNl\nSXbQSrI98CfAgcADl6JNSZIkSdNjYus9kmwKvIjmoMUn05z8/nPgvcDKSbUrSZIkafp0Gpi0Z5g8\nmyYY2Q/YACjgczTByOlV9Ysu25QkSZI0/bqeMbkO+C2a2ZErgPcDH6iqH3XcjiRJkqRlpOvA5EHA\nPwMrq+qijuuWJEmStEx1HZhsVVWrO65TkiRJ0jLX9QGLBiWSJEmS5m1RMyZJXtb+5xlVdevQ77Oq\nqg8spm1JkiRJy8diH+VaSbPr1kXArUO/r0naMgYmkiRJkoDFByYvpwkyrm9/P3SR9UmSJGltcOyK\nCdZ9y+Tq1tRaVGBSVStHfn//onojSZIkaZ3U6eL39oBFSZIkSZqXrgOJa5P8jyS/23G9kiRJkpax\nrgOThwB/BXwryVeTHJ5k847bkCRJkrTMdB2YbAW8CDgbeDxwInBdktOS7J/kAR23J0mSJGkZ6PqA\nxbuq6uNVtS+wNfDXwBXAHwFn0AQpb0vyhC7blSRJkjTdJrZYvapurKq3VNXvATsDJ9FsLXwU8NVJ\ntStJkiRp+iz2HJM5qapvJLkNWA28eqnalSRJkjQdJhogJFlBs+bkYGCX9vKtwKmTbFeSJEnSdOk8\nMGnPMtmHJhjZD9iQ5hGuzwErgTOq6s6u25UkSZI0vToNTJK8BfhTYEsgNAvf3w+cUlU/7LItSZIk\nSctH1zMmfwHcAvwz8P6q+nLH9UuSJElahroOTA4CPlFVqzuuV5IkSdIy1vV2wf8FeF3HdUqSJEla\n5kL3kN0AAB02SURBVLoOTHbFrYAlSZIkzVPXgcmVwMM7rvN+kmyd5OQk1yVZnWRVkuOTbLaIOndP\nck+SSvLGLvsrSZIkac26DkzeA+yb5BEd1/srSbYFLgYOBb4CvA24huZE+S8n2WIBdW5Cs3vYHR12\nVZIkSdIcdR2YfAr4EnBBkiOS7JLkkUkeMfqziDbeSbMd8ZFVdUBVvbaqnkEToOwAvGkBdZ4ArADe\nvIh+SZIkSVqgrteDXENzmGJoPuyPUwtpu50t2RtYBbxjJPsY4M+AlyY5uqpun2Odz6OZfXnpQvok\nSZIkafG6/iD+AZqgY1L2bNNzq+re4YyqujXJBTSBy640J82vUZItac5c+URVfTDJIR33V5IkSdIc\ndBqYVNUhXdY3gx3a9Iox+VfSBCbbM4fAhCYoWQ945eK7JkmSJGmhpu3RpRVtesuY/MH1TWerKMnL\ngf2BF1bVjQvtUJKLx2TtuNA6JWld4PgpSRrW9eL3qZBkG+B44NSq+ni/vZEkSZLU6YxJkpPnWLSq\n6hULaGIwI7JiTP7g+s2z1HMycCdw2AL68GuqaueZrrffBD5xsfVL0nLl+ClJGtb1o1yHzJI/2LGr\ngIUEJpe36fZj8h/dpuPWoAw8kSaI+X9JZsp/XZLXAWdW1QHz7qUkSZKkeek6MHnUmOubAr8PvB64\nEHjtAus/r033TrLe8M5c7SGJu9EcknjRLPV8AHjIDNcfDewOXEJziOM3FthPSZIkSfPQ9a5c3x+T\n9X3gm0nOAb4FfBZ47wLqvzrJuTQ7bx0OnDSUfRywEfDu4TNMkuzYvvayoXqOnKn+drvg3YFPV9V/\nm2//JEmSJC3Mku7KVVXXJvkUcBQLCExah9HMupyYZC/gUmAXmjNOrgBeN1L+0jad8ZktSZIkSf3r\nY1euG7lvLci8VdXVwJOAlTQBydHAtjQnze9aVTd10EdJkiRJS2hJZ0ySPAB4BuPPIZmTqroWOHSO\nZec8U1JVK2kCHkmSJElLqOvtgndfQzsPpwkmHg+8p8t2JUmSJE23rmdMzqfZCnicAF8E/qrjdiVJ\nkiRNsa4Dk79j5sDkXuBnwFeq6isdtylJkiRpynW9XfCxXdYnSZIkad0w8cXvSfanWfAe4AtVdfqk\n25QkSZI0XRa9XXCS/ZJ8McnTZ8hbCZwBHAm8Cjg1yWmLbVOSJEnS8tLFOSb7A08E/m34YpLnAi8D\n7gDeCPwNcA1wQJKDOmhXkiRJ0jLRxaNcTwb+tap+MXL95TQL4Q+tqn8BSHIKcDXwYuAjHbQtSZIk\naRnoYsbkocC/z3B9d+Bm4FePblXVDcCngSd00K4kSZKkZaKLwGQz4K7hC0keAWwOfKmqRrcP/h6w\nRQftSpIkSVomughMbgW2Hrm2c5t+Y8xrRh/7kiRJkrQO6yIw+Tawb5KNh679Ec36ki/NUP5RwPUd\ntCtJkiRpmegiMPkQzeNcX0hyZJK30yxuvwE4b7hgkgB/AHy3g3YlSZIkLRNd7Mr1XuBA4FnA42kO\nUvwlcFRV3TNSdi+axfKf7aBdSZIkScvEogOTqro3yb7AQcBTgZuA06vqkhmK/yZwAvDJxbYrSZIk\nafnoYsaEqrqX5pGuD81S7qPAR7toU5IkSdLy0cUaE0mSJElaFAMTSZIkSb0zMJEkSZLUOwMTSZIk\nSb0zMJEkSZLUOwMTSZIkSb0zMJEkSZLUOwMTSZIkSb0zMJEkSZLUOwMTSZIkSb0zMJEkSZLUOwMT\nSZIkSb0zMJEkSZLUOwMTSZIkSb0zMJEkSZLUOwMTSZIkSb0zMJEkSZLUOwMTSZIkSb0zMJEkSZLU\nOwMTSZIkSb2bysAkydZJTk5yXZLVSVYlOT7JZnN8/UZJXpzkw0kuS3J7kluTfC3J0Uk2mPTfIEmS\nJOk+6/fdgflKsi1wIbAlcCZwGfBk4ChgnyS7VdVNs1TzNOCDwE+B84BPAJsB+wP/CzgwyV5V9YvJ\n/BWSJEmShk1dYAK8kyYoObKqThpcTPJW4C+ANwGvnKWOG4CXAKdW1V1DdfwlcD7wVOBw4C2d9lyS\nJEnSjKbqUa52tmRvYBXwjpHsY4DbgZcm2WhN9VTVJVX1oeGgpL1+K/cFI3t00WdJkiRJs5uqwATY\ns03Prap7hzPaoOIC4CHAroto45dtevci6pAkSZI0D9P2KNcObXrFmPwraWZUtgc+t8A2Xt6mZ8+l\ncJKLx2TtuMD2JWmd4PgpSRo2bTMmK9r0ljH5g+ubLqTyJEcA+wCXACcvpA5JkiRJ8zdtMyYTk+RA\n4HiahfHPr6pfzvISAKpq5zH1XQw8sbseStLy4vgpSRo2bTMmgxmRFWPyB9dvnk+lSQ4APgr8GNij\nqq5ZWPckSZIkLcS0BSaXt+n2Y/If3abj1qDcT5I/Bk4FbgSeXlWXz/ISSZIkSR2btsDkvDbdO8mv\n9T3JJsBuwB3ARXOpLMmLgY8A19EEJVd22FdJkiRJczRVgUlVXQ2cC2xDcwDisOOAjYBTqur2wcUk\nOya53w4vSQ4GPgD8ANjdx7ckSZKk/kzj4vfDgAuBE5PsBVwK7EJzxskVwOtGyl/aphlcSLInza5b\n69HMwhyaZORl3FxVx3fee0mSJEn3M3WBSVVdneRJwN/RbO37HOB64ATguKr62RyqeST3zRa9fEyZ\n79Ps0iVJkiRpwqYuMAGoqmuBQ+dY9n5TIVW1EljZba8kSZIkLdRUrTGRJEmStDwZmEiSJEnqnYGJ\nJEmSpN4ZmEiSJEnqnYGJJEmSpN4ZmEiSJEnqnYGJJEmSpN4ZmEiSJEnqnYGJJEmSpN4ZmEiSJEnq\nnYGJJEmSpN4ZmEiSJEnqnYGJJEmSpN4ZmEiSJEnqnYGJJEmSpN4ZmEiSJEnqnYGJJEmSpN4ZmEiS\nJEnqnYGJJEmSpN4ZmEiSJEnqnYGJJEmSpN4ZmEiSJEnqnYGJJEmSpN4ZmEiSJEnqnYGJJEmSpN4Z\nmEiSJEnqnYGJJEmSpN4ZmEiSJEnqnYGJJEmSpN4ZmEiSJEnqnYGJJEmSpN4ZmEiSJEnqnYGJJEmS\npN4ZmEiSJEnqnYGJJEmSpN5NZWCSZOskJye5LsnqJKuSHJ9ks3nWs3n7ulVtPde19W49qb5LkiRJ\nur/1++7AfCXZFrgQ2BI4E7gMeDJwFLBPkt2q6qY51LNFW8/2wOeBjwI7AocC+yZ5SlVdM5m/YnZV\nRRJuW303Z337em78+Wq2+o0NefZjH8bGG67/q3yt/byXy4f3UpKkyZm6wAR4J01QcmRVnTS4mOSt\nwF8AbwJeOYd6/p4mKHlrVR09VM+RwAltO/t02O85G3y4ecd5V/HO867i9rvu+VXesZ/8dw7bczsO\n33M7PwRNAe/l8uG9lCRpsqbqUa52tmRvYBXwjpHsY4DbgZcm2WiWejYGXtqWP3Yk++3A94FnJfmd\nxfd6/gYffv7xnMt/7cMPwO133cM/nnM57zjvKj/8TAHv5fLhvZQkabKmKjAB9mzTc6vq3uGMqroV\nuAB4CLDrLPXsCjwYuKB93XA99wLnjLS3pG5bfTfvPO+qNZZ51/lXc9vqu5eoR1oo7+Xy4b2UJGmy\npi0w2aFNrxiTf2Wbbr9E9ZDk4pl+aNarLMhZ377+ft/Ijrpt9d2c/Z3rF9qEloj3cvnwXnZvEuOn\nJGl6TVtgsqJNbxmTP7i+6RLVMxE3/nx1p+XUH+/l8uG9lCRpsqZx8ftapap2nul6+63fExdS51a/\nsWGn5dQf7+Xy4b3s3iTGT0nS9Jq2GZPBTMaKMfmD6zcvUT0T8ezHPoyNNnjAGstsvOH67LPTw5ao\nR1oo7+Xy4b2UJGmypi0wubxNx639eHSbjls70nU9E7Hxhutz2J7brbHMn++xLRtv6ITX2s57uXx4\nLyVJmqxpewc9r033TrLe8M5cSTYBdgPuAC6apZ6LgDuB3ZJsMrwzV5L1aLYkHm5vSVUVh7cfgEZ3\n+dl4w/X58z229byEKeG9XD68l5IkTdZUBSZVdXWSc2kCh8OBk4ayjwM2At5dVbcPLibZsX3tZUP1\n3JbkFODPaM4xOXqoniOAbYBz+jr5PcmvPgQd/NRtOPs7950wvc9OnjA9TbyXy4f3UpKkyZqqwKR1\nGHAhcGKSvYBLgV1ozhy5AnjdSPlL23T008LfAnsAr0nyeOArwGOA5wE/pgl8ejP4cLPxhuvzgp0f\nPjZfaz/v5fLhvZQkaXKmbY0JVXU18CRgJU1AcjSwLXACsGtV3TTHem4CngKcCGzX1rML8D5g57Yd\nSZIkSUtgGmdMqKprgUPnWHbsV5hV9VPgqPZHkiRJUk+mbsZEkiRJ0vJjYCJJkiSpdwYmkiRJknpn\nYCJJkiSpdwYmkiRJknpnYCJJkiSpdwYmkiRJknpnYCJJkiSpdwYmkiRJknqXquq7D8tSkpse/OAH\nb/6Yxzym765ImgJf//rXP1xVL+67H2uDLsfPm575dx30aGZb/N83TKxuaW1w8X5XTazunT+1XSf1\nOHYuLwYmE5Lke8BvAKs6qG7HNr2sg7rUL+/l8tH1vbzMN9dGh+On/96WD+/l8tLl/XTsXEYMTKZA\nkosBqmrnvvuixfFeLh/ey7Wf92j58F4uL95PjeMaE0mSJEm9MzCRJEmS1DsDE0mSJEm9MzCRJEmS\n1DsDE0mSJEm9c1cuSZIkSb1zxkSSJElS7wxMJEmSJPXOwESSJElS7wxMJEmSJPXOwESSJElS7wxM\nJEmSJPXOwESSJElS7wxM1mJJtk5ycpLrkqxOsirJ8Uk267tvmrskL0hyUpJ/TfLzJJXkg333S/OT\nZIsk/ynJGUmuSnJnkluSfCnJK5I4nk5YV2Niks3b161q67murXfrSfVd99fF/Uxyfjumjvt50CT/\nBnX7HufnHnnA4loqybbAhcCWwJnAZcCTgT2By4Hdquqm/nqouUpyCfA44Dbgh8COwIeq6iW9dkzz\nkuSVwLuA64HzgB8AWwEHAiuA04A/LgfViehqTEyyRVvP9sDnga/S/Jt8HvBj4ClVdc0k/gbdp8P7\neT7wdOC4MUXeWFV3d9Fnzayr9zg/9wiAqvJnLfwBzgEKeNXI9be21/+p7z76M+d7uSfwaCDAHu39\n+2Df/fJn3vfxGcB+wHoj1x9KE6QU8Py++7lcf7oaE4F3t+XfMnL9yPb62X3/revCT4f38/zmo0z/\nf9O6+tPVe5yfe/ypKmdM1kbttwZXAauAbavq3qG8TWi+sQ2wZVXd3ksntSBJ9qD5tt0Zk2Ukyd8C\nbwLeXlWv6rs/y01XY2KSjWlmRe4FHlZVtw7lrQdcAzyybcNZkwnp8j1uMGNSVZlYhzVnC32P83OP\nBnwmeu20Z5ueO/yPE6B9I70AeAiw61J3TNKMftmmPjIyGV2NibsCDwYuGA5K2nrupfnGdrg9TUbn\n73FJXpjktUlek+TZSTbsrrtaAn7uEWBgsrbaoU2vGJN/ZZtuvwR9kbQGSdYHXtb+enaffVnGuhoT\nHVvXDpO4Dx8F3gy8BfgM8IMkL1hY99QD/20KMDBZW61o01vG5A+ub7oEfZG0Zv8A7AR8pqrOma2w\nFqSrMdGxde3Q5X04k2bt19Y0s2E70gQomwIfS7LPIvqppeO/TQGwft8dkKRpleRI4Gia3WNe2nN3\npHVOVb1t5NLlwN8muQ44iSZIcSZTmhLOmKydBt8MrBiTP7h+8xL0RdIMkhwBnAB8F9izqn7ac5eW\ns67GRMfWtcNS3If30Kz5eny7eFprN/9tCjAwWVtd3qbjnqV8dJuOexZT0gQleTXNt7HfoQlKbui5\nS8tdV2OiY+vaYeL3oap+AQw2ONhoofVoyfhvU4CBydrqvDbde/Q06fabn92AO4CLlrpj0rouyd8A\nbwMuoQlKftxzl9YFXY2JFwF3AruNfove1rv3SHuajIm/xyXZAdiMJjj5yULr0ZLxc48AA5O1UlVd\nDZwLbAMcPpJ9HM23P6e4l7e0tJK8nmax+8XAXlXlB54lsJAxMcmOSXYcqec24JS2/LEj9RzR1n+O\nZ5hMVlf3M8mjkmw+Wn+S3wLe1/760fLk97VGkge293Lb4et+7tGAByyupdp/tBcCW9LsOnIpsAvN\nXt9XAE+tqpv666HmKskBwAHtrw8FnkVzkNu/ttd+UlV/2UffNHdJDgZWAvfQPMY10+4xq6pq5RJ2\na50x3zExSQGMHryXZIu2nu2BzwNfAR4DPI/m8MWnth+SNEFd3M8khwD/BHyJZkz9KfAI4Dk0axK+\nBjyzqlyXMEHzeY9Lsg3wPeD7VbXNSD1+7pGBydosycOBvwP2AbagOfn0DOC4qvpZn33T3CU5Fjhm\nDUXuN0Br7TOH+wjwharaY/K9WTfNZ0wcF5i0eZvT3MsDgIcBNwFnAW+oqh9O8m/QfRZ7P5M8lmZX\nvJ2B/wD8Bs2jW/8OfBx4d1XdNfm/ZN02n/e4NQUmbb6fe9ZxBiaSJEmSeucaE0mSJEm9MzCRJEmS\n1DsDE0mSJEm9MzCRJEmS1DsDE0mSJEm9MzCRJEmS1DsDE0mSJEm9MzCRJEmS1DsDE0mSJEm9MzCR\nJEmS1DsDE0mSJEm9MzCR5iHJqiSreu7DIUkqySE9tL1N2/bKpW5bkgb6HAfHWRv7JE0bAxNNlXbQ\nn+1nj777KUnTJMkDkvznJF9I8tMkv0zy4yTfSvKeJPv33UdJy9/6fXdAWqDj1pC3aqk6IUnTLskD\ngP8D7APcDHwa+CGwAfC7wJ8COwKf7KuPMzgDuAi4vu+OSOqOgYmmUlUd23cfJGmZOIgmKPkm8PSq\numU4M8lDgF366Ng4bR9vmbWgpKnio1xa1pIcO3i8K8lBSS5OckeS65K8NcmGbblnJDk/yc+T/CzJ\nKUm2WEO9K5K8PcmPkvwiyXeTHJkkM5Q9JMlpSa5JcmfbxgVJXjKm7vPbPm+Q5A1JLk+yerZ1HUk2\nS/LFJPcm+a8jeZsneXOSS9s+3JLkc0n2HlPXJu3/nx+2f99lSV6DY4a0HD21TVeOBiUAVXVHVZ03\n+H14XB0tO24dWpKV7fXfSfKq9hGxO9vx7kVt3ttm6lySDdtx+fok67fXfm09R5IHJbm5ffxsxi9d\nk7yrfc1zR67v2Pbv2iR3JbkxyYeT7DCmnu2SnNr26fYkFybZd6aykubHGROtK14FPBv4BHA+sDfw\nF8DmSc4EPkrz+ML/pnmTfgnwm+1rRm0AfBbYtH3dBsDzgROAHYDDR8q/C/h34Is0jx1sATwHOCXJ\nDlX1+jF9Pg34feCstt8/HvfHJXkEcDawHfCyqvrgUN4j2795G+Bf23IbAc8Fzk7yX6rqn4fKbwh8\nrm37m8CH2r/19cDTx/VB0tS6qU23X4K2TgCeRjPefga4h2Z8uwX40yR/VVV3j7zmeTRj0FtmyAOg\nqn6R5GPAn9GM258azm/HtRcCN9KMgYPr+wCnAw9sX3MVsDVwILBvkj2r6utD5R8NfJlmHD8LuIRm\n3P1E+7ukRTAw0VRKcuyYrF9U1T/McP0PgZ2r6tL29RsCXwdeCuwH7F1VX2jz1gPOAfZJ8viqumSk\nrocB1wA7VdXq9jXHAF8FDkvysar64lD5narq6pH+b0DzJvbaJP9UVT+aoc+PbF/7kzF/66Cux7V1\nbQQ8p6o+O1Lk/W1dB1XVR4detylNwHJikk9W1Y1t1tE0QcnpwB9X1b1t+X8ALl5TXyRNpdOBvwFe\nmWQTmvUbF1fV9yfQ1hOBJ1TV94YvDgUV+9Csdxl2cJu+f5a6V7Z1HMxIYALsD2wGvHUQ3CTZDPgI\ncAewe1V9d6g/O9GsYXlP2+eBd9AEJa+uqhOGyj+PJjiRtAg+lqFpdcyYn9eOKX/iICgBaAOKj9H8\nG/j0IChp8+4FBjMOjxtT338dBCXta34K/Pf210OHC44GJe21u2je4NYH9hrTxuvnEJQ8k2YWpGje\nWD87kv84mlmO04aDkrYPN9P8P3sQzYzPwKHAvcBfD4KStvz3gBPX1B9J06eqvkEzS3xjm54GrEpy\nU5IzkuzXYXP/czQoaQ2CjoOHLyZ5KPAs4BtV9e01VVxVXwauAPZLsvlI9kzBzctoZmKOGQ5K2rq+\nA/wz8IQk/7Hty9bAM4HvAW8fKX8m8AUkLYozJppKVXW/tRyz+NoM165r05lmAQYzGFvPkHc3cOEM\n189v0ycMX2wfs/obmgDkEcCDR1732zPUBfCVMdcHXkDzSNqVwLOr6gczlHlKm64YM8v0W236mLav\nm9A8lnDtTAEVzd94zCz9kjRlqurjSc4A9gT+gGYc+wPgAOCAJB8ADqmqWmRTM45rVXVhkkFQsVlV\n/azNejHwAJrZkLl4P/Am4EXAOwGSbMV9wc23hsoOxsfHjRkfB4+2PQb4LveN7V+qqntmKH8+Pu4q\nLYqBidYVM+3ecvcc8h44Q95Pxrwp3dCmKwYXkvwOzRvxZjQzG+e27d1Ds+bjYGDDMX2+Ycz1gae0\n/fs34NoxZQYL+J/Z/oyz8UjfbxxTbrY+SZpSVfVLmjHqXPjVNsLPB06mmV04g8U/rrSmMWQ4qHhX\ne+1g4JfAh+dY/wdoZq8Ppg1MaIKb9bn/o2CD8fE/z1Kn46O0RHyUS5q/32zfsEc9tE2HA53X0Lz5\nvaKq9qiqI6vq9e12x+esqZE5fDP5tzTnChwKnNyujRk16MtRVZU1/Bw6Un6rMW0+dMx1SctMVd1T\nVR8HBrtlPaNNB494zvTl5qazVbuGvFPaug8GSPIE4LHAZ2Z7rHWozz8EPg88OcmO7eVxwc1gvHvc\nLOPj+0fKOz5KE2JgIs3f+ty3veawPdr0G0PXtmvT02Yov9gp/9U0j3OdChwCfHCGbTIvatOnzaXC\nqrqVZlea306y7QxF9lhQTyVNs1vbdPAI7eAxq4fPUPZJC22kqq6lCSp2abfqneui91Er2/TgJI8H\nfg84q6r+30i5eY2P3De2/8GYL6f2mE8nJd2fgYm0MG9ud/YCmnNCgP/W/vq+oXKr2nSP4RcneRbw\nnxbbifbRi4NoFusfBHwsyQOH8r9G8wjZgUlePlMdSR6bZMuhS++jGRv+x/AsTJJHAUcuts+S1i5p\nznh65kyzru3i88GjToPdBgfrRA4d/jIkycOBNyyyOyvb9BU0Y9pPuP8uXbM5Hfg5zUL+Q0bqHfY+\nmpPuj0ny5NHMJOsNn9XSzsb8X+BRwBEjZZ+H60ukRXONiabSGrYLBvjEDFv8dul6mnUh30nySZp1\nHi+g2Ub4nSNbBb+T5lGrU5P8C82C+51otsT8OM2++otSVfckORj4BU2wc3qSFwztGvanNN9CvjfJ\nkTRrUm6mWdj/e21/nsJ956S8hWbB6/OBryc5h+bxjD+h+WCy/2L7LGmtsgtwFHBDki/R7DoFzQfw\nfWk27DgT+BeAqvq3JF8Edge+kuTzNI837UfziOpMMylzdQZNUPFqmrH1pPYLmDmrqjuTnEoT3BxG\nc07Lp2cod1OSF7RtXpTkczRnTlX7NzyF5lHcBw297HCac0yOT3NA7TdpZsb/iGaL4i53MJPWOQYm\nmlZr2hlqFc2hV5NyF825KH9Ps0jzN2nONfkH4KThglX1rSR7Am+keYNfn+aN7ECa4GDRgUnbzr1J\n/owmODkC+GSSA6rqzqr6YZKdaQ6ZfD737XJzA81OMycB3x6qa3WSPwSObft3FM3/0zfSvIEbmEjL\ny1todvf7Q5ovK55F82H8Jpqdpj4MfHhk3dvzgH9s01e1r/9rmoXzf7LQjlTVHUNBBcz/Ma6BlW0d\nDwQ+0m7RPlN7n0vye8Bf0vzdT6MZ46+j+ULntJHyVybZlWa8/0Oa2fBv0XyZ81sYmEiLksXv/CdJ\nkiRJi+MaE0mSJEm9MzCRJEmS1DsDE0mSJEm9MzCRJEmS1DsDE0mSJEm9MzCRJEmS1DsDE0mSJEm9\nMzCRJEmS1DsDE0mSJEm9MzCRJEmS1DsDE0mSJEm9MzCRJEmS1DsDE0mSJEm9MzCRJEmS1DsDE0mS\nJEm9MzCRJEmS1DsDE0mSJEm9+/+0B98b7BVg2wAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 349, "width": 403 } }, "output_type": "display_data" } ], "source": [ "sns.pairplot(train_df_select[[\"Embarked\", 'Survived']], hue='Survived')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyYAAAK7CAYAAAAHuJsbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFtgAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 349, "width": 403 } }, "output_type": "display_data" } ], "source": [ "sns.pairplot(train_df_select[[\"SibSp\", 'Survived']], hue='Survived')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyYAAAK7CAYAAAAHuJsbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 349, "width": 403 } }, "output_type": "display_data" } ], "source": [ "sns.pairplot(train_df_select[[\"Parch\", 'Survived']], hue='Survived')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.予測モデルの作成・学習" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((891, 12), (418, 11))" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df = pd.read_csv(\"http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/train.csv\")\n", "test_df = pd.read_csv(\"http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/test.csv\")\n", "train_df.shape, test_df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### データの整理" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pclass, Sex, Age, Fareのみで予測することにする \n", "名義尺度はダミー変数化すべし " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![axis](https://i.stack.imgur.com/DL0iQ.jpg)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PclassAgeFareSex_femaleSex_male
0322.07.250001
1138.071.283310
2326.07.925010
3135.053.100010
4335.08.050001
53NaN8.458301
6154.051.862501
732.021.075001
8327.011.133310
9214.030.070810
1034.016.700010
11158.026.550010
12320.08.050001
13339.031.275001
14314.07.854210
15255.016.000010
1632.029.125001
172NaN13.000001
18331.018.000010
193NaN7.225010
20235.026.000001
21234.013.000001
22315.08.029210
23128.035.500001
2438.021.075010
25338.031.387510
263NaN7.225001
27119.0263.000001
283NaN7.879210
293NaN7.895801
..................
388321.07.750001
38936.021.075001
390123.093.500001
391151.039.400010
392313.020.250001
393247.010.500001
394329.022.025001
395118.060.000010
396324.07.250001
397148.079.200010
398322.07.775001
399331.07.733301
400130.0164.866710
401238.021.000001
402122.059.400010
403117.047.100001
404143.027.720801
405220.013.862501
406223.010.500001
407150.0211.500001
4083NaN7.720810
40933.013.775010
4103NaN7.750010
411137.090.000010
412328.07.775010
4133NaN8.050001
414139.0108.900010
415338.57.250001
4163NaN8.050001
4173NaN22.358301
\n", "

1309 rows × 5 columns

\n", "
" ], "text/plain": [ " Pclass Age Fare Sex_female Sex_male\n", "0 3 22.0 7.2500 0 1\n", "1 1 38.0 71.2833 1 0\n", "2 3 26.0 7.9250 1 0\n", "3 1 35.0 53.1000 1 0\n", "4 3 35.0 8.0500 0 1\n", "5 3 NaN 8.4583 0 1\n", "6 1 54.0 51.8625 0 1\n", "7 3 2.0 21.0750 0 1\n", "8 3 27.0 11.1333 1 0\n", "9 2 14.0 30.0708 1 0\n", "10 3 4.0 16.7000 1 0\n", "11 1 58.0 26.5500 1 0\n", "12 3 20.0 8.0500 0 1\n", "13 3 39.0 31.2750 0 1\n", "14 3 14.0 7.8542 1 0\n", "15 2 55.0 16.0000 1 0\n", "16 3 2.0 29.1250 0 1\n", "17 2 NaN 13.0000 0 1\n", "18 3 31.0 18.0000 1 0\n", "19 3 NaN 7.2250 1 0\n", "20 2 35.0 26.0000 0 1\n", "21 2 34.0 13.0000 0 1\n", "22 3 15.0 8.0292 1 0\n", "23 1 28.0 35.5000 0 1\n", "24 3 8.0 21.0750 1 0\n", "25 3 38.0 31.3875 1 0\n", "26 3 NaN 7.2250 0 1\n", "27 1 19.0 263.0000 0 1\n", "28 3 NaN 7.8792 1 0\n", "29 3 NaN 7.8958 0 1\n", ".. ... ... ... ... ...\n", "388 3 21.0 7.7500 0 1\n", "389 3 6.0 21.0750 0 1\n", "390 1 23.0 93.5000 0 1\n", "391 1 51.0 39.4000 1 0\n", "392 3 13.0 20.2500 0 1\n", "393 2 47.0 10.5000 0 1\n", "394 3 29.0 22.0250 0 1\n", "395 1 18.0 60.0000 1 0\n", "396 3 24.0 7.2500 0 1\n", "397 1 48.0 79.2000 1 0\n", "398 3 22.0 7.7750 0 1\n", "399 3 31.0 7.7333 0 1\n", "400 1 30.0 164.8667 1 0\n", "401 2 38.0 21.0000 0 1\n", "402 1 22.0 59.4000 1 0\n", "403 1 17.0 47.1000 0 1\n", "404 1 43.0 27.7208 0 1\n", "405 2 20.0 13.8625 0 1\n", "406 2 23.0 10.5000 0 1\n", "407 1 50.0 211.5000 0 1\n", "408 3 NaN 7.7208 1 0\n", "409 3 3.0 13.7750 1 0\n", "410 3 NaN 7.7500 1 0\n", "411 1 37.0 90.0000 1 0\n", "412 3 28.0 7.7750 1 0\n", "413 3 NaN 8.0500 0 1\n", "414 1 39.0 108.9000 1 0\n", "415 3 38.5 7.2500 0 1\n", "416 3 NaN 8.0500 0 1\n", "417 3 NaN 22.3583 0 1\n", "\n", "[1309 rows x 5 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_df = pd.concat([train_df.drop('Survived', axis=1), test_df], axis=0)\n", "all_df = pd.get_dummies(all_df[[\"Pclass\", \"Sex\", \"Age\", \"Fare\"]])\n", "all_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "groupby（集約関数）という便利機能" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AgeFareSex_femaleSex_male
Pclass
139.15993087.5089920.4458200.554180
229.50670521.1791960.3826710.617329
324.81636713.3028890.3046540.695346
\n", "
" ], "text/plain": [ " Age Fare Sex_female Sex_male\n", "Pclass \n", "1 39.159930 87.508992 0.445820 0.554180\n", "2 29.506705 21.179196 0.382671 0.617329\n", "3 24.816367 13.302889 0.304654 0.695346" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_df.groupby([\"Pclass\"]).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "groupbyで欠損値の傾向を確認する" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
0
PclassAgeFareSex_femaleSex_male
FalseFalseFalseFalseFalse1045
TrueFalseFalse1
TrueFalseFalseFalse263
\n", "
" ], "text/plain": [ " 0\n", "Pclass Age Fare Sex_female Sex_male \n", "False False False False False 1045\n", " True False False 1\n", " True False False False 263" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "missing = all_df.copy()\n", "missing = missing.isnull()\n", "pd.DataFrame(missing.groupby(missing.columns.tolist()).size())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "欠損値をどうにかする（捨てる，置き換える，補う）方法はいろいろある \n", "今回は単純に中央値を当てはめてみることにする．\n", " \n", "[Python pandas 欠損値/外れ値/離散化の処理](http://sinhrks.hatenablog.com/entry/2016/02/01/080859#欠損値) \n", "\n", "| | |\n", "| --- | --- | \n", "| リストワイズ法 | 欠損レコードを除去 | \n", "| ペアワイズ法 | 相関係数など2変数を用いて計算を行う際に、 対象の変数が欠損している場合に計算対象から除外 | \n", "| 平均代入法 | 欠損を持つ変数の平均値を補完 | \n", "| 回帰代入法 | 欠損を持つ変数の値を回帰式をもとに補完 | \n", "確率的回帰代入法，完全情報最尤推定法，多重代入法などなど" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PclassAgeFareSex_femaleSex_male
0322.07.250001
1138.071.283310
2326.07.925010
3135.053.100010
4335.08.050001
5328.08.458301
6154.051.862501
732.021.075001
8327.011.133310
9214.030.070810
1034.016.700010
11158.026.550010
12320.08.050001
13339.031.275001
14314.07.854210
15255.016.000010
1632.029.125001
17228.013.000001
18331.018.000010
19328.07.225010
20235.026.000001
21234.013.000001
22315.08.029210
23128.035.500001
2438.021.075010
25338.031.387510
26328.07.225001
27119.0263.000001
28328.07.879210
29328.07.895801
..................
388321.07.750001
38936.021.075001
390123.093.500001
391151.039.400010
392313.020.250001
393247.010.500001
394329.022.025001
395118.060.000010
396324.07.250001
397148.079.200010
398322.07.775001
399331.07.733301
400130.0164.866710
401238.021.000001
402122.059.400010
403117.047.100001
404143.027.720801
405220.013.862501
406223.010.500001
407150.0211.500001
408328.07.720810
40933.013.775010
410328.07.750010
411137.090.000010
412328.07.775010
413328.08.050001
414139.0108.900010
415338.57.250001
416328.08.050001
417328.022.358301
\n", "

1309 rows × 5 columns

\n", "
" ], "text/plain": [ " Pclass Age Fare Sex_female Sex_male\n", "0 3 22.0 7.2500 0 1\n", "1 1 38.0 71.2833 1 0\n", "2 3 26.0 7.9250 1 0\n", "3 1 35.0 53.1000 1 0\n", "4 3 35.0 8.0500 0 1\n", "5 3 28.0 8.4583 0 1\n", "6 1 54.0 51.8625 0 1\n", "7 3 2.0 21.0750 0 1\n", "8 3 27.0 11.1333 1 0\n", "9 2 14.0 30.0708 1 0\n", "10 3 4.0 16.7000 1 0\n", "11 1 58.0 26.5500 1 0\n", "12 3 20.0 8.0500 0 1\n", "13 3 39.0 31.2750 0 1\n", "14 3 14.0 7.8542 1 0\n", "15 2 55.0 16.0000 1 0\n", "16 3 2.0 29.1250 0 1\n", "17 2 28.0 13.0000 0 1\n", "18 3 31.0 18.0000 1 0\n", "19 3 28.0 7.2250 1 0\n", "20 2 35.0 26.0000 0 1\n", "21 2 34.0 13.0000 0 1\n", "22 3 15.0 8.0292 1 0\n", "23 1 28.0 35.5000 0 1\n", "24 3 8.0 21.0750 1 0\n", "25 3 38.0 31.3875 1 0\n", "26 3 28.0 7.2250 0 1\n", "27 1 19.0 263.0000 0 1\n", "28 3 28.0 7.8792 1 0\n", "29 3 28.0 7.8958 0 1\n", ".. ... ... ... ... ...\n", "388 3 21.0 7.7500 0 1\n", "389 3 6.0 21.0750 0 1\n", "390 1 23.0 93.5000 0 1\n", "391 1 51.0 39.4000 1 0\n", "392 3 13.0 20.2500 0 1\n", "393 2 47.0 10.5000 0 1\n", "394 3 29.0 22.0250 0 1\n", "395 1 18.0 60.0000 1 0\n", "396 3 24.0 7.2500 0 1\n", "397 1 48.0 79.2000 1 0\n", "398 3 22.0 7.7750 0 1\n", "399 3 31.0 7.7333 0 1\n", "400 1 30.0 164.8667 1 0\n", "401 2 38.0 21.0000 0 1\n", "402 1 22.0 59.4000 1 0\n", "403 1 17.0 47.1000 0 1\n", "404 1 43.0 27.7208 0 1\n", "405 2 20.0 13.8625 0 1\n", "406 2 23.0 10.5000 0 1\n", "407 1 50.0 211.5000 0 1\n", "408 3 28.0 7.7208 1 0\n", "409 3 3.0 13.7750 1 0\n", "410 3 28.0 7.7500 1 0\n", "411 1 37.0 90.0000 1 0\n", "412 3 28.0 7.7750 1 0\n", "413 3 28.0 8.0500 0 1\n", "414 1 39.0 108.9000 1 0\n", "415 3 38.5 7.2500 0 1\n", "416 3 28.0 8.0500 0 1\n", "417 3 28.0 22.3583 0 1\n", "\n", "[1309 rows x 5 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_df = all_df.fillna(all_df.median())\n", "all_df" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((891, 5), (418, 5))" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train, test = all_df[:train_df.shape[0]], all_df[train_df.shape[0]:]\n", "train.shape, test.shape" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "t_train = train_df[\"Survived\"].values\n", "x_train = train.values\n", "x_test = test.values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### scikit-learn\n", "![Scikit](http://scikit-learn.org/stable/_static/scikit-learn-logo-small.png) \n", "Pythonで最もメンテナンスされている機械学習ライブラリ． \n", "わかりやすいAPIと豊富な機能が売り． " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Cheat](http://scikit-learn.org/stable/_static/ml_map.png) \n", "膨大な手法があるので，このチートシートを頼りにどれを使うかの参考にするといい． \n", "この図に載っているのはごく一部だけれど．" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 決定木で予測" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "import sklearn.tree\n", "clf_decision_tree = sklearn.tree.DecisionTreeClassifier(max_depth=2, random_state=0)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\n", " max_features=None, max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n", " splitter='best')" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_decision_tree.fit(x_train, t_train)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n", " 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,\n", " 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0,\n", " 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n", " 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n", " 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0,\n", " 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n", " 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n", " 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0,\n", " 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0,\n", " 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n", " 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n", " 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,\n", " 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n", " 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1,\n", " 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0,\n", " 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1,\n", " 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,\n", " 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0,\n", " 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train = clf_decision_tree.predict(x_train)\n", "y_train" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "accuracy（正解数÷データ数）はライブラリを使うまでもないですが" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.79573512906846244" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(t_train == y_train) / len(t_train)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0.79573512906846244" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sklearn.metrics.accuracy_score(t_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "長いimportはこれで短縮（名前空間を汚染するので諸刃の剣） " ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.79573512906846244" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import accuracy_score\n", "accuracy_score(t_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "クラス数が多い分類はconfusion matrixを見ると傾向がつかみやすい" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[532, 17],\n", " [165, 177]])" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "confusion_matrix(t_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "横軸が正解，縦軸が予測値． \n", "（左上）個が正しく死亡と判定，（左下）個が誤って死亡と判定 \n", "（右上）個が誤って生存と判定，（右下）個が正しく生存と判定" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.76 0.97 0.85 549\n", " 1 0.91 0.52 0.66 342\n", "\n", "avg / total 0.82 0.80 0.78 891\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report\n", "print(classification_report(t_train, y_train))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 決定木の可視化 \n", "graphvizを別途インストールしていないと動きません^^; " ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "import pydotplus\n", "from sklearn.externals.six import StringIO\n", "dot_data = StringIO()\n", "sklearn.tree.export_graphviz(clf_decision_tree, out_file=dot_data\n", " , feature_names=train.columns, filled=True, rounded=True)\n", "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n", "\n", "# PDFファイルに出力\n", "# graph.write_pdf(\"graph.pdf\")" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAFYCAYAAABAjHTHAAAAAXNSR0IArs4c6QAAQABJREFUeAHsnQV8U0vTh/+4W7HiWhyKu7u7u7vbxeXi7hd3d3d3d7fiUqRY0QJ9ZxZO3iRN2tSTZub7hZyzZ3fP7nP63XcyZySUJwlEhIAQEAJCQAgIASEgBISA7RHYEtr21iwrFgJCQAgIASEgBISAEBACfwiIMit/CUJACAgBISAEhIAQEAI2S0CUWZt9dLJwISAEhIAQEAJCQAgIgbCCQAgIASHgFwLPnz/H3bt38e7dO3h4ePhlChljRwRChQqFqFGjwtHREenTp0eECBHsaPeyVSEgBAKTgCizgUlX5hYCIYzAsWPHsHjxYmzfvg0vXrwMYbuT7QQVgTBhwiB//nyoUaMmGjdujFixYgXVreU+QkAIhEACoSSbQQh8qrIlIRDABHbt2oV+/fri4sVLyJbBCRWL5kGB7BmRJkViOESPhnDh5HdxACMPkdO5f/mKZ65vcOnmfew9cQFbD57Cz1+/0aFDB/Tt2xexY8cOkfuWTQkBIRCoBLaIMhuofGVyIWDbBF69eoV27dphw4YNKE8KbL/W9UiZTW3bm5LVWw0BVm6XbNqLcfPX4hdCYfr0Gahbt67VrE8WIgSEgE0QEGXWJh6TLFIIBAOBU6dO0Wvg6giL35gxqBOK580aDKuQW9oDgU+fv2DA5EWYt3YH2rRujWnTp5O1P5w9bF32KASEgP8JiDLrf4YygxAIeQS2b9+OmjVromiuzJg/ogdiRo8a8jYpO7I6Alv2n0CrQZNRoEABbNi4CZEjR7a6NcqChIAQsDoCosxa3SORBQmBYCawb98+lC9fHvUrFsO0AR3AwToiQiCoCFy8cQ9VOgxGzly5sXXbdrHQBhV4uY8QsF0CUgHMdp+drFwIBDyBO3fuoFq1aqheqoByLRBFNuAZy4zeE2Cf7M0zhuL48ePoSIFhIkJACAgBnwhIAJhPhOS6ELATAj9+/ECOHNkRET+xd+FohPelz+Lj56+wfNt+3H/0HJ+/fkPyRI4okS8bSubPbicE/2zzxWs37D52TmV7cEqWyKb3/vv3b4QOHTy1dbYeOIm63UdgxYoVqFevnk1zlMULASEQqATEzSBQ8crkQsCGCAwdOhRjx4zG2XXTlSLqm6Uv2rgH3UfNxPcfHogbKwY4Qf4rt/dqitxZ0mHzf/8ielT78H88cvYKyrXqhxmDO6NptdK+wWgVfe8+eobZq7Zh26FT+Oj+BfmypkfHhlVRLI/PAYAcwDVz5VaT+xjRrTnKFspl8pq5xq4j/8OG/adw69ZtxIkTx1w3aRcCQsC+CYibgX0/f9m9EPhD4OXLlxgzZgz6tanna0X2xr1H6DriP6ROmhBXtszBw4PL8eDAMjw+tAKNq5TCmSu30HbwZEFtAwS+fvuOWp3/VemySuXPgVa1yuMeWdprUtux89d83MG5a3fw9OUbxKEfNMafCOF9n51gWJemCBsKGDlypI/3lg5CQAjYLwHJdG6/z152LgR0BCZOnIhY0aKgXb1KujZLDzjxvcfPn+hDOWhTkUKrSeyY0fHfkM44cPoSdh09i58/fyFsWAkm0/hY4/eQ6UvAltkN04egTMGcaontG1RGnpod0XrgJNzYMd/bZd9//AJF8zhj9aQB3vaz9GK0KJHRu0Ut9Jv8H/r37y9FFSwFJ/2EgJ0RCB5nKDuDLNsVAtZMgH1lFyyYjxY1yyJihPC+XupT19dqTNqUib2MZXeDUd1bKCX5I+US1ZdtpARXbDMAyYrVR4G6XdB3wnz1Wlvrs3rHIZRs2huj5qzUmtT3oTOXUbZlX4ydt9qg3aeT05dvqvmOnruq8pmWavYP0pZths7DZ+A5VaVyefICdboNR4riDZGrRgdK5L/Gy5TsQsCvvrNUbg2n0k3Q5J+xaq5fv3556Wvc4NN+jftbes7Kp6m1Wjpev9+yzfuRySm5TpHla/Fjx0KpAjnw6Lkrzl69rd/dy/H9x8+QJrnXvwMvHX3R0KhqKUQIH1aVUfbFMOkqBISAHREQZdaOHrZsVQiYInDkyBG8fetGqbiKm7rsY1u2dH8qgrUZOBms7BkrdtVLFwT7SzrEiKaba/ScVUpx5EAxfpWdPlVSzFmzHSWa9MKLV29Vv6olC+D9J3eMmLkCpy7dVG1cMYpdFi7fuo96FYrp5rPkwO2DO05euoE+4+eh97i5SJIgLqJGjoj563aidtfh6t48b8ViefHdwwNDpi0B+wJrcvjMFVQg5XvdriMqqK1p9TJgRb4LuVgMmrpY62by25L9mhxoppHdAVZuO4gyLfoga5U2mLlii5melje/efdB8TblG5v6byDbhet3zU7Iz9L17XukTpYQ3I8re3HeWO15mh3ow4UokSKiSvF82LB+nQ895bIQEAL2SkDcDOz1ycu+hcBfAgcOHECaFEmRNGE8PzFpVKUkth48iT3Hz6vAp2hRIiF/tozIS4FDbNHLlv6PsqtNfvvBE4ycvUJZ/9ZPG6yCxfha/UolUKntAExbtgkjyZrLPpZzh3dH0UY90G7IFJxaM42qRC3EkxevVSGHJAn8tt6Xb9xwa9dCxHOICU9PTxRr3FNZHJuRcjqV8upy9P7DZy+RsUJLHCIXCS2Ia+2uwwgbJjSubZunKyLRo1lNZCjfHDsOn1EKu7ZH/W9L96s/xtzxldsuWLhhN9hq/eHTZ+WnPKRTYzSgHyKsjM5Zs8PcUF171RL5kSF1Mt25dnD34TN16BjXQWvSfWtZGV7/DerTXdA7uP/4uTob/t9yPP/7g4QbwocLi39a1SU3FL+XqWX/3aZ9x+HLly9SSEGPuRwKASHwh4Aos/KXIATsnMDVK1eQNV0KP1MIR8oK+1hyOipOp3SILJh8zJ+h05eC84aOJMts4VxZ1D3mksL169dvtK5TQafI8gUul8tK0xqyfLIyy8KKMCtBrCDV7zGCfG/PoUbpQqjrS6usmuzvP00owwArsizsBsGv1fn1efMaZXVpqDitGFtub7k8+TsK6NSoKtqST7F+NbQfHj/V+QeK+jcnvtmvqTk4o8DanYexcONucEEB/rHADBpWLol82TLohvBaR8xcrjs3d+BEllNTyuz9J3+U0VgxvFZ7S/b3h857UqDNCbtpsLAyzD9C+FluP3Qas1dvw7D/liFe7JiKsbnx3rU7p0sJThN248YN5Mz5x5fXu/5yTQgIAfsiIMqsfT1v2a0Q8ELg+fPnKOKc0ku7bxpYKeS0S1rqJde375S1csXWAzhx8Tq9xh+mLKusJN55+FRNvXTzPizfut/gNl/o9Tm/lv72/YfOf7dX89rYeeSsUmQTxIuNKWQ99Y8kSxTfYLgWZc9z60uY0GHwg9wNNEmbIgnevv+IKUs24PTlW+C8uvfIR/TT569KgdP6GX/7dr/640fMWoHJi9aDuRShHwOsJLJlNTK9ejeWtCkS482p9cbNXs7ZUmpKNA7vyB3DWNiFgCWWN2WNC2TPhF3zRiFHRifd+vgHS2VarzP5GE9YsM7Pymxix7jq/px1Q0QICAEhYEzA9H/VjHvJuRAQAiGWgLu7O6KQ76hfxIMskzuOnEHi+HGQI1Ma3RQcNMSv7fnTtM9YrCVr6y5SSNmy6fb+k7KAasqTbhAdFMyRSZ2yFU5fjM/1r/n2mH0wTQkr5N7JJFIq2cLI6y6YIzOKkSW5d8vamLJ0I7kluJod6pf9apMdO39VKbL5smZAj+a1KNers856rPXRvnn9kSJG0E59/c3PjOXBU68Ko9uHT+oap9syJ3EdYiCuQ2Yvlx3jxELW9KlUaq8vpBSbUsS9DDJq4MBEdv/gv1URISAEhIAxAVFmjYnIuRCwMwKenr8Riv7PL8KptloNmIiEZNW8tHm2ySnYJYCV2dduH9T15IkdcfHmPfRqUVsFfukPYgsguyDoKzxjKGsBv14vTf637JfLxRkWjuqlPyzQj3ntA6csUgUhrmydQ6/6/18AYuw8r1kP9Bfk2/3qj10wsheWbt6LxRv3onK7gUhEPxoaVCquXAz006DxmJdv3mG0UeYH/bm04yZVSyvXD+1c+9b8Yh+aUGav3XmguuXKnFbr7uV774nz5IMM9Zz0L7JfMiv7USNHMniu+n0sOWZlnecSEQJCQAgYE5BsBsZE5FwICAGLCbCCkZ1eK3N6qOMXrpkcpyXbZ99ZljxUEYxlJ1l09YUDmtJRqqx6VMJUE1Zix8xdBa4ixsFi5ciVYQ35j27Yc0zrEiTfT168UooUvzLXV2SfvnyNyxSU5Z34Zr/G8yQg/9PeLetQ0NlcbJs9nKpxZcDkxRtUajBOLcYZAzjDA8sHyvywaMMeHz+ab6uXe9EPEraMH6PnqN+Hre+riTm7YWjP0Hgsn89buxPVOw5Rac70r3PRDOaUX8+/V/+6HAsBISAE/EtALLP+JSjjhYCdExj/TxsUqt9V5YztSa/Cc5Hiya+W+XX1ZkrNxJH3rPDya3kW9qPkNFzsQ8mWxrzO6VWKK7Z8coCRFvXOfrMt+o9H6FCh8R+VhuXXzNMGdkSOGu0pHdYM5M+eUd0nKPA7JU8Edk9Yv/sIZWHIoXKpnqR0Yf/OWIroZKX9TAol+8aayrFq6X692wf/aOCUWfxhv132RV5EAWGc5aHH6FloULkEJvdrj/fnNnk3jY/X2FpevdMQNOo1Gr1b1UHMaFExceE69Sz1M0/wRKkpz64rWYM/XfiTFoxdSlQuXcpI8W/nppSiKxEF0D1Gnwnz1LMb0qmJj/eXDkJACAgBvxAQZdYv1GSMEBACOgKcDWDHnJHoNXYOpdwyLHDAvo4NSdEa1aOlUgZ5EPucbp01jBTVCWjeb7xuHlYEV0/qj0I5//hdDp62GLcfPEW/tvV17ghsHRzTs5XKNdth6BSy1g7RjQ/MA7bGzhraVd23Vpdh6lYcDDWmVyu1L3a1yElK9sfzXvO9WrpfS9fPldU4swJ/OP8uK7V7j1+wdLi3/Urmz67SnrUfMpWyR4xUfWNQZbgxPVsaFFLgC7+pUIS+LzMH/zGjgZQ+jYtPaMLBWzvnjgRnJBARAkJACAQGgVDkgyROSIFBVuYUAjZCII1TatQtnU9nEfXrsvk/JZxr9B59OJ1UYsc4yJAqmUEqK/25tf6ch9UhRnRyJUiLMGGsu9wtW0Uv33JRFmEu9MAWUxZuf//R3aCcr/5e+Tgw98tFFPwT/GW8Vi49fOHGXVJWPZErcxpfPRdey417j+D28ZNKz5WU8gGzVd2/Ej1HFSxduhT16tXz71QyXggIgZBFYItYZkPWA5XdCIFgI8CKHb9a1qpF+bQQ3/Y3NR9X37JE6lcshjzkzuBfYaso58M1Fm7nj3cSEPs1N39AKrJ8Dw7sYz9lvwivRT+zhV/mkDFCQAgIAd8QEGXWN7SkrxAQAlZFgHOvWiKOcbxWtbJknPQRAkJACAgB6ycgyqz1PyNZoRAQAmYIVC9d0MwVaRYCQkAICAF7IeB/RyZ7ISX7FAJCQAgIASEgBISAELA6AqLMWt0jkQUJASHgGwKcw3TRxj0q+Mw347gvp5Jav/uob4f5q79+BgC/TvTp8xcVdObX8frjvv/4f8le/XY5FgJCQAjYCgFxM7CVJyXrFAJCwCSBq3ceosPQqZgzrJu32QRMDR5NBRm43GyNMoVMXQ7QtgXrd2HTvuM4eu4qUidNhOL5sqp8rKbK+np3Y86ckLtWR8SIGhkXNs7Sda1LxSbuUq5b74RTZ3EVLy5Py/lpT1y8oQoacD7ZolQqd3DHRiZz5Xo3p1wTAkJACAQ3AVFmg/sJyP2FgBDwF4G0KRJjaOcmyJo+la/naVu3Irg4Q2ALV+rqNGw6cmZKg55UmOC2yxPMWL5FFSNYMb6fyh5g6Rq4UMLL125KmdUfE5bSmoULa/o/6Y+eu6p0aZylgK26FVr3A/8IqF2uiFJeuUrblgMnKW/tDZxcMw3xHGLqTy3HQkAICAGrJmD6v3xWvWRZnBAQAkLg/wRSJkkArjzmF2lYuaRfhvlqDLtB9B43R5Wi5eIB4cL9+c9u2pmJVZGJVTsOUmEJy9Yxlyqn7T1+HlywwViWjetj3KTOHz57iTy1OqF/uwbIlj41tlBVtiu3H2BK//ZoWau8bkx3stTOXrUNW/efRIta5XTtciAEhIAQsHYC4jNr7U9I1icE7JjA1TsPwK/P05dvDq68tWzLPhw4dQkNeo7S+Yyeu3obVdsPUu2M6vTlmyjZtDcu3riHhRt2o1jjnkhcuK5qYx9ZfeFX7W0GTdJvCvBjtnh++vwVnalil6bI8k24BC3Lul1H1LdP/3Ahgj4T5mN412ZwjGtZqjH2z+VKa9kypEYfKk/Lwq4FLDXKFFbf2j/1KhRTh65u77Um+RYCQkAI2AQBUWZt4jHJIoWA/RHgV9+siJ64cB35s2VQ1sjuo2YpX0/2PeVKUyyv333E3hMX8OL1W3Xu9sEdJ+l1ec+xs/HPuLmqjGrNsoVx8/5jNOg1Chdv3lP9+J/TV26RD+s13XlgHNx99ExNW8yo2AJXxgpPVtoLpHT7JOwK0bTPWBTInhHt61f2qbvu+vgFa3GWlP1JfdvpqnA1rV4Gh5dO8GLdZd4s5agsrYgQEAJCwJYIiJuBLT0tWasQsBMCbFHsOWY2IoQLh2MrJiNpwnhq550bV0PBel0touDy+AXOrpuBZIniq/4l8mZTVt7jpLTx63ZL5M27D5izZoePXauWyI8MqZOZ7Hf34TNEpqpY0aJENrjOJV7ZReL2g6f49euXtyVj+01aQMq6GzbPHKYroWswmYmTa3cfYsSsFWheoyy49K4m6VIm0Q5xhpT5w2ev4NLN+9hM7gdsnWUrrogQEAJCwJYIiDJrS09L1ioE7ITA5VsuFKD0AD2a1dQpsrz1TE7JUZMyD6zcftBHEi1rl9cpsty5QI5Magy/rrdU3pDVd8TM5T52d0qW0Kwye//Jc8SKEc3kHMkSxsctCgb7SG4IpvxgedCOI2eUL+vKCf2QwEL3Ah43hjI1sBI9gHxlzQkrsv/OWAb+8cDldnk9Hh4/DdwhzI2VdiEgBISAtRAQZdZanoSsQwgIAR0Blycv1LFT8sS6Nu0gfSrTFlDtuvbNr/H1RVMW3b9802/29pgzJbw5td7bPnyR3QXMCVuXn7/74wJh3Ofz129KiYweJZLxJXXO1ti2gyajabXSqEzWX0vl/uPn2Lj3uPLTjRMrhtlhvSizArstsIV25baD4FRl7z65Y2KftmbHyAUhIASEgLURMP9fYGtbqaxHCAgBuyHw/qO72mvsmF4tmr9+/7KIQ6SI4S3q510ntlZGIuumfyR+nFhgv9lXFFhlnPKKc9w6kNU2DKXVMiXzyMWB88p+cP9iEKj2/NVbeHp6qrbUyRKBlVJ9mbx4g7rehJRgY9GssLw3liiRIqJYnqwomtsZ7De7/dBpUWaNocm5EBACVk1AlFmrfjyyOCFgnwSSJvpjVT156SbKF8ljAIFdEIJKXr55h9FzVvp4uyZVS5v1NU1D1mVWEh8+fWmgzLJV9gGlzSqSK7PZ+eM4xECWtCmoutmfIDKtI1ftYqX0ym0XhA5lGMfL83LWhzzO6ZA2xf/9Y3ksj4mTtwYyOiXH0eWGWRxYuXWgHw/s3iGuBhpp+RYCQsAWCIgyawtPSdYoBOyMQEZyJQgTJjQOnLyIYV2a6nb/gBTCg6cv6c4D++ADvXJftGGPj7cplCOzWWW2FmVS4OpfXDghd5Z0urm4jC5nZKhQJK+uzfigXb1K4I+xFKjbBV+/f8fJ1dOML6kKYz/I77VG6UJernHQWQZiywFfXAWMrcKa8I8ETmeWOU0K8ZnVoMi3EBACNkFAlFmbeEyySCFgXwQSxo+DDvWrYOrSjWg9cJIqN8t+oLMoqX9QCls235/b5K9bFsqZGfzhnLfxY8dCucK5VDquvhPnq1RbjaqU1M2funQTuJI1+NOFLbo23x5oyn5BUrBNSXcKqmvUezQqtu6Pfm3rq6Cy3VSIYcXW/ar7wPbmA8ZMzSdtQkAICIHgJiDKbHA/Abm/EBACJgmwRTZGtChU9nUzlpOixVbEOuWLIia1jZqzykuqK5OTWEEjv75fO2Uganb+VwVYcZAVSw4qbbtsfF8DK+hvStHFrgD+kYNUVIKzGGRyMh0oV710QTx/1RIDpyxCnW7DdbeKS4Fic4Z1Q4Wi5i3Fus5yIASEgBCwIgKhKIjA04rWI0sRAkIgiAmkcUqNuqXzoU/rukF8Z8tv944CwrRsBFx2dSelq7q5Y4HlE1hJT85OcPnWfeWSwFba4JSPFFR2y+WxsgRzLl727Y0Ywf9Bc4G1p+g5qmDp0qWoV69eYN1C5hUCQsA2CWwRy6xtPjhZtRAI0QTYl7Rcq37Kx3Rsr1Y6RZaDm/ZRta8saVPa5P45T6xvcsUG5iajR41s4MMbmPeSuYWAEBACgUlAlNnApCtzCwEh4CcCnA6L3QpmrtyKj+6fyc80N9g6u3TzXnpF/hb/De7sp3llkBAQAkJACIQ8AqLMhrxnKjsSAiGCwMJRvTBu/hqV0WDp5n0qH2rW9KmwfupgFPxbzStEbFQ2IQSEgBAQAv4iIMqsv/DJYCEgBAKLAAd/De/aDKDPh0+fKeArEji1VFDKrqNn8enzF9QqWyQobxug93L/8hVRI5uuMKZ/I8ktq09DjoWAELAlAqLM2tLTkrUKATslwIptcMikRevBpXVtTZll5X/wtMXYsOeYqiDG2Q2K5MqCUT1bwokqhmnCinrvsXOx58R5FQiWLUNqlMqfXQUDhqcyvOYkc6VWKEzpxmaIu4c5RNIuBIRAEBIIWjNHEG5MbiUEhIAQsEcCnKCmdtdhmEulcFMmSYB+beohe0Yn7Dp2DqWb/QOuasbCltgSTXphCfkhF8yeCX2pH7eNmbsaPcbMNouOXT5YwRcRAkJACFgLAbHMWsuTkHUIASEgBAKAwNFzV1X53HoVimHeiB5qxv7078jZKzFi5nJV6rZn81qqKtn1e4/Qu2VtDO7Y+E8/KqLQa+wc/LdiC8oUyImKxf7knH3m+gYjZ63A+et3VbnbAFimTCEEhIAQCDACoswGGEqZSAgIAe8InL16G0OmLaHqV3dVt/SpkuKfVnVRpmBOg2FHzl7Bhr3HcICS/3OKrvzZMlIFrUxoVr0MlbgNo/qevnwT/SctJCWsEW4/eILVOw7j8YtXaq4+rerg2w8PcIWtM5dvIQ4VA6hdvgh6taituw9XwOKyrYVzZsGMFZtx+MwVxHWIgfoVS6Bb0+o++uZuO3hKVSO7escFiePHRWF6hc+WTU53pYml+9X6B9T3k5ev1VQFyQ1AX4rldlbKrPvnr6p5/6mL6rtO+WL63cBKMCuzR89f1Smzn2jM3UfP1P7YynuBlFoRISAEhIC1EBA3A2t5ErIOIRCCCdxyeaLyxrq+fYeODasqxfLXr9+o3nEI9pK/piasVFZoMwDrdh1BSfLdbEoK7FPX1+gy4j8MmrpY6wa3D+44eekG+oyfh97j5iJJgrgU5BQR89ftpFfsw9Xrcy5OwJbF7x4eSoletHGPbvyh05exZNNeVOs4GD/o1XrzGmXB6cAGTV2Ejv9O0/UzdTCaqo9x5SzOeduqVnmwUj5nzXZ1zxeUNozF0v2amt+/bWUL5UK4sGGxmPb7iyqKsfA3l9NlKV8kt/rmAg4cGJY2RWJ1rv3DGSM40O4GWW01SZcyCfYsGKM+iyjLhIgQEAJCwJoIiGXWmp6GrEUIhFACa3ceVlZWfu2dNV0qtctOpNQ6lW6CFVsPUNBRDtW2dtdhhA0TGte2zUPM6FFVW49mNZGhfHPsOHwGI7o1NyD08o0bbu1aiHgOMcG+osUa9wRbRNmKO3VAB6WUPXz2EhkrtMSh05fQtFpp3fgHT19idI+W6NSoqmob1KEhKdL9sZiU3Fa1K6gqXbrOfw/YCjxy9gplAV4/bTC4VC1L/UolUKntAExbtgkju7eApfv9O63u6827D6QY79CdmzuoWiI/MqQ2Xa42dszoGNKpsVLgU5ZsRGnMMuMUKf7sK9u6TgXkypxWTZsycQJlYWUrK5fW1YQtsFxS98b9x1qTfAsBISAErJqAKLNW/XhkcUIgZBD47flbbWT+2p0YQxH1kSNFRLhwYZUiql9RmxXLtvUq6RRZHsSWU1ZsP1D5VWNpQsopK7IsrFhmckqulFm2tGppvJInclSWW7aW6gtnSOjYsIquifv3alEHR85exb6TF0wqsxxUxRZlVgo1RZYnKJ43q8oSsIYsyqzMWrpf3c3/Hrx591G5Ahi3G587JUtoVpnlvqmTJiRLc3jwfGyFfv/JXU3x+7en+lHB/GuXK4x1u49gOPnRDurQSO2XFVu2dLP8pn2KCAEhIARsgYAos7bwlGSNQsDGCbSoWQ5rdh5RQUerdxxCgewZSQHMhsrF8yFZovi63aVNkUSlkpqyZANOk7/r4+evcO/xM8r1+hWOVArWWPTH8rUI4f+kk0oQL7ZB1zChw5BS7GHQlooUPn2FlC+yywDLAzPR+ncePlXXOaJ/+db96lj75wv597KbwbfvP2DpfrWx2je/8n9zar12avY7PP0QMCeb9h1Hg56jFGO2ZLMlli3K4+evxby1O2jPwOR+7VGhaF50aFAFM5Zvxp7j51XFNbcPn5SPMvsTcwU2ESEgBISALRAQn1lbeEqyRiFg4wQSO8bFxU0zsXx8X5QqkENFxfeZMA+ZKF8p53LVhI/Z9YD9Uj1+/kIxsnjO+bcb8mZNr3Ux+I5CFkZTYqykmurjGMercqzNFyFCeFND4Pb+k7L4stLMfqn6H65KVqd8UfWK3tL9Gt+E182+uz59tEA44/F8vn7PUdU8sH1DnUsB/0gY1aOFWu+q7Qd1w8b2aoX9i8Ypt4RGVUpiwcie2DprGJ68eK1T7HWd5UAICAEhYKUEzP+8t9IFy7KEgBCwPQIfyUUgDPnCVi1ZQH3YJ/PY+Wto8s9Yldy/bd2KcP/yDQOnLEJcyj5wZescqvj1/8wAY+etCfBNuzx57mXOR89dVVua5IZBUVrH5IkdcfHmPRXApllxtWscEMYuCPwK35L9ssJqLOzXOnrOSuNmL+dNqpY26QbBHa/deaiU1txZ0hmM46wOzulSUjaJe8pK/ZZcED5RdTD+oaD/Y4Gtz+yWkDF1coPxciIEhIAQsFYCosxa65ORdQmBEESAg6Pevv+oArt4W+yfyumsOPKek/azUvWUrIHsP1uZgpv0FdmnlGrq8m0XxIv9xzc2oLBwoNP9x8/B7gaasPsAS5a0KbQmg+88pCBupLRhO4+cMbBccsWtTBVb0riU2D5nhAoG82m/ppTZD6RELtrw/6wLBjfXOylEQV1crcuUcOYBVkh3HD6NaqUK6ro8euaqLOJpkicCV/eaSFZwTsF1afNsg6pg05dtRkSyTLNVXEQICAEhYAsERJm1hackaxQCNk6gcvH8Ku3VYEqvxf6kESk4ifPJrtpxUCllHMQViRQofs2/noKSyhTMAbaOnrx0E//OWIroZKX9TAovK2nmrKa+RcRWVK6UNZiCn1JTidfNB04o5a566YLkb5rJ5HQc+MVpuCYsWIdE8eMgr3N6lTqMLcrvSaHt07quGmfJfk3dgN0B3p/bZOqSxW3t61fGtkOn0X3ULKW8sl8yZyZYuH6X+rHAuX1ZqtCPBlZmOw2brnxoOQsCpyvjFF7je7dGCrJCiwgBISAEbIGAKLO28JRkjULAxgl0piwF1+89xPgFa9VH20629Kmh5S1la+ysoV3RdvBk1OoyTHWJRVkMxpBfJyu5rQZMRM4a7fHx/BZtuL++i+XJioQUKFafgqW0jAqFqNAAB0eZE/aVZZ/SFv0noHm/8bpurGCvntSfijtkVm2W7Fc3OIAPeA1LxvRGnwnzlT+y5pMcmdwa2EeW/XpZ2MeXzzl/b47q7VQbF31oWaucytagGuQfISAEhIANEAhF/xH3tIF1yhKFgBAIJAJpnFKjbul8OqtiIN1GTcu5Xdm6yhH/nInAmV7LGwdr8ev5y7dc4BgnlnqVr13n9vcf3Q3cAvy61iRF6iEHVbLa9N+/eEdzckoqVmyN/WDNzc//2WQXBc4S4BAjOnJnSaurTqY/xpL96vcPyGPO3sD+sw/JvcAxbixkSJXMIOWZdi/ObXv97iOVTqwgWaQ5ZZo1SvQcVbB06VLUq1fPGpcnaxICQiD4CGyxzv9qBR8QubMQEAKBSIBfXfv0+ppfd3PeVmPhdv4EtLD1t0S+bL6alhVsdk3gj3diyX69G++fa+wXy6Vn+eOdcGBYkdxZvOsi14SAEBACVk1AUnNZ9eORxQkBISAEhIAQEAJCQAh4R0CUWe/oyDUhIARCJIEEVIAhdqyAt/KGSFiyKSEgBISAlRMQNwMrf0CyPCEgBAKewJl1MwJ+UplRCAgBISAEgoWAKLPBgl1uKgSEgEZg19GzVK72C2qVLaI12cT3gnW78JqCp1g4tyunuvKPcFAZzxcjahRdWV7j+X5SVTQuPqEFxRlf98v59x8eZu9naj5+Vj88fnrrv/yFCkhw5gfjSmX7TlxQ6cJ43kiUnq1zo2qmbiFtQkAICAFfERBl1le4pLMQEAIBTYBTR7k8eWFzyuyMFZvx6PkrsMtCaSrRy8osVzbLV6czfv765QVTsoTxsWH6EC/tWsOEhevAeXiXjPkHNcoU0prVNyv8w2Yso3yxj8Dps4rkclbpszi9ll/E7cMn9Bg9Cycu3gAXpYgZLSqK5nHG4I6NvM3jyxklctfqSAp3ZFzYOMvg1rz3odOXYjsVa7jl8gRhw4RRSv6wrk1RKn8O1ffstdtYsfUAXr19T1XKwogya0BQToSAEPArAfGZ9Ss5GScEhIDdEyiYPSOubp2LCX3aKhbPXN/g2t2HCEMVzjhLgP4nVoyoZnmdu3pbFYcw1WHNzsOo0WmoSiHWrWkNVTWNK5DV7DwUXMXMt8KW1Qqt+2HtriNUHCIjBrZvqApXbDlwEuVa9sUrt/dmp2w3ZApevnYzeb0l5QHmPMJxHWKoOSsUzaNSl1VtP1hVI+NBfVvXU7y4kIOIEBACQiCgCIhlNqBIyjxCQAjYPYH7ZGFmmTeihyptawkQVi6b9h1HSmBML4oi54rtN2mBKhpxYtUUXZ7YYV2bwal0EzTuPRonV0+z5Da6PgdPXcKV2w8wpX97KpBQXtfenSy1s1dtw9b9J9GCCicYy1yqfLb3+HlwKjNj4VK5q3ccQo3ShbCYCjZobhAnLl5HqWb/gCuklS+Sx3iYnAsBISAEAoSAWGYDBKNMIgTsgwArPCWb9sYLE9a5jv9OQ6W2A8if0kPB+EDlXacu3Ygq7QYhYaE6KN6kJwZMXoirdx54C6ulUXUtrTNb/fje7DeqL9sOnkLFNgOQrFh9FKjbBX2p8tVH9y/6XYLs+P6j5+peTj7kn9VfULeRM5VbAltIjeXm/Sd48eotyhTKqVNkuQ+X/y1JuXFZKWXOvhF2LWCpUaawwbB6FYqpc1cTltkb9x6pimLDSYl2JLcKYzl1+aZqalC5hE6R5Yb82TIiZZIEyu3AncoRiwgBISAEAoOAKLOBQVXmFAIhlEAqUkxOXrqBLftPGOyQFa5FG/eQ1S4aOFk/S73uI5Ri+ZmCgXo2r4X0KZNiwfpdKN38H6WgGUygd3Lx5j1cvHFPr+XPISuKfG/9ooWj56xCnW7DwfdoRVZGruA1hyyIJZr08vYeXiYPoIb7T54jSYK4cP/yDTvIFYCZnLp0E79M+NDyLdmauYo+88mSy36oxvLi9VvVlDNTGuNLyJEprWq7ef+xl2veNTStXgaHl07wYmE9dv6aGlauUC6D4VytrWmfscoloX39ygbXtJOokSMqH17jdXIgmNv7T4gYITwi0UdECAgBIRAYBMTNIDCoypxCwIYIhAoVGp70f5ZI7XJF0XfifGzcdxxt6lbUDVm/56hSMhtVLanaWLk9fPYKujeriWFdmur6ZUidDL3HzcVxev1c08gyqOtk4QGXkh05ewXKFMyJ9dMG6yyC9SuVUBbiacs2YWT3FiZn4xKuc9bsMHlNv7EqBXXxmi0VdjP45P4V6cs3x9dv33XDsmVIjXnDe6iAKK3x4bOX6DJihlL0C1AZ2Y17j2mXdN9s1WQ5fOYKujSurmvng1t/lVgOCsubNb3BNe9OOPOCJmeu3FLP6dLN+9hMP1DYOstr1Rd2c2BL/OaZw3SM9a/zcYWiedXHuH3Gii14/8ldPWvjzAbGfX065x8xmvuCT33luhAQAvZFQJRZ+3reslsh4IVA1KhR8ZksiZYIB/eULpATu4+dU4FC/LqbhYOJEsSLjRJ5/5SFjUZWxoNLxsMpeWKDaSNFjKDOA8INYC4po79+/VYWQX0lh0vh8mv+NbQm88rsR4yYudxgbaZOnJIl9JUy6/KYlNkvXzCkY2NwkNObdx+xbMs+LN60F7W7DiP/1qnK/5VdJZr2GafK4fZvW9/UrVVb6qQJlXJ58PQlLNywm5TCQpQxwROrth/Ehr/KL2cR8KvwD45/KUsCz8EMOeOCB6XdChfuz/80sHWZ/WhXTuinsjZYep93H91VtgS2PKehv4Hx/7SxdKjJfmwd5jXy36qIEBACQsCYgCizxkTkXAjYGYGECROCo/AtlYbkF8nR9FqgEAf/nLt2R1kYQ1MUP0vUyJGQO0s6HD13lZTKw2Al79FzVzx4+tLS2/jY787Dp6rP0s37sHzrfoP+X8gqytZhVoL4FbexpE2RGG9OrTdu9nIe/q9S5+WCmYY5w7ohfPiwyJg6ueqRmpRqtprGiBYFkxdvUNbP+hWLY8Ss5SrrASu3muJoakrmOWtoV5W5gH2Se42do5Q6Vmib1SgDznXLrhV+lV4taoNdB9hCu3LbQYyeuwrvyJI6kbIzsDW27aDJaFqtNCpbmEOXrafz1+1UCjKn/2pdpwL+7dwE0aJ4daHwzZo5fRiLo6Ojb4ZJXyEgBOyEgCizdvKgZZtCwByBzFmyYP0qn62U2vhyhXOrvKTsasBR7+t2H1WXGlYpqXVRilDltgNVXtRMTsmRK3NaFcQUnQoCdBg6VdfPNwduHz8ZdGdfTFb2ODm/sWj5V81ZLdkKqVmJjcf659z4Fb02F7tCsDLLgVRs+Rw3fy1SJHbE+PlrtC54/OKVOp67dgf2HD+Hbk1rKrcE5neWKpat33NMuRZwABZbn/mHAkv6VJa7QXB/zQqrWbOjRIqIYnmyomhuZ7Df7PZDp5UyO48s35xX9gMF07UZNImHKnlOPxJYaeU2VtZZIWZ57fYBLfqPx/6TF1E4V2aM7tEKzulSqmv+/efyLRf1rDNkyODfqWS8EBACIZCAKLMh8KHKloSAbwgUL14co0aNwmMqAJA0YTwfh7LyyEn9F23crZSddbsPI49zOvVqXxvMShr7crK/LPvNasKvrX0SVrJ+/zbMWMBj7v61xGr+vclJGeRgMVamjK2THBDGLgiRSVEzJS/fvMPoOStNXTJoa1K1tBcfUoMOeidsPWQLdY6MThQEZsjxAfnHsrCbBkvmNMnV95XbLuqb//lIvrYsbMH+QNZRd1VpywMPyfIdO2Z0ZSFVHf7+M2HBOjjGiQWHGNH0m709ZkU2Tt4ayEgK8tHl/1dQeRBzd4gZTWWbYIU7Dq01S9oUuP/4mcGcXDGM5+G1hyZ/axZ2m2A3Ct7/tAEd0bxmWYMx/j1h5T5f3jyIHDmyf6eS8UJACIRAAqLMhsCHKlsSAr4hULhwYcSO7YAV2w6gT+u6Fg1lVwN+nTyRqlZxeqjpgzoZjOPgJhZO1aQvOw/7rMwmI4V6/8lLBr6bbNHUcrhq8+UhNwYOmmKXB31lllNVZarYUuV53T5nhNbd4JuVxUUb9hi0mToplCOzxcosv1Zv0HMUmtcoi2kDOxpMt/6v9boApapitwJTuWF3UOWsWl2GYXiXZroKYO/J9zRb1bZUHa0wFo3urZuTFedN+4+jcdVSujZLDtiSnYEsuRzwxevVV4TZ+slZJDKnSaHW2K5eJfDHWDj92dfv3w32wFW/2FWBizoEtCLLP0y2HDiFwUP/NV6KnAsBISAEFAFRZuUPQQjYOYHw4cOjefMWmL94Ibo2qW7Sx9QYEfvDpqLgpKlLN6nX9ZwsX1+ypU+NXUfPqfKsXZvUgCtZQtfsPIRN+/6k9OLytayoxTSRgD8nuSTw2Nb0GrsZpZFiJZaV5hjkosCvvTVhf0xOw8UWykTx4yCvc3o8dX2tEvS/J4XWO8U8bYokeH9ukzZVgHyzEshcOP0YK4laeduVFKzFr975nPfmG2E+RXJlUdkjim3aQ0Fl+cn/+Dk6DpuGRPHiYGS35gbTJShYW6UF+3Rhi0G7/glbyhtRsYWKrfujHwWfcTne3VQMYcVfv+OB7Rvod7fo+PiF66of55LlPL+mZGjnxrq0baaum2tbSsFz38lS3KRJE3NdpF0ICAE7JyDKrJ3/Acj2hQAT6N69O6ZPn46ZK7cq65olVDiQadh/y0hJy4foRjlSWWHi5PwcnMUffoXNmQ4ubJqp8s9OWrReBQX906qOl1t1aVQNZy7fIuX3sPpwloT6fxP6TyClVhN2d9g6axj5aU5A837jtWYVPb96Un8UyplZ1xYUB7zH1ZMHoP2QqaqsKxd50IQrbY3uYTpNmNbH3DcHgDWhPK88L39YsqZLRZbaXl4Cq9i1wpyfsDZ/9dIF8fxVS6X0c45eTeJS+V0OYOM0W76VE3+VWc4wYU4GkJKs5SA218e4naujjSX/4vbt29Pbg9jGl+VcCAgBIaAIhCJHfssSTAowISAEQjSBoUOHYuyY0RRsNB3JEwVM1Dj7VXJ6quyUu1TfCsuJ/rm4AGc9MCccUMTBRuy3yYqiOeH/hN0nayXnnXWIEZ2so2nh35ym5u6l356jejskcYyLTf95ff3N/secbSEmZTFIS3ld/RvNz3u8dvchHlI2iKzpU3nxydXWxYps3tqdcIYCxnwSTo92y+WxsponSxRf/QgwlfnBp3n8cr3VgInKPeTpkVXeDu868j9s2H8Kt27dRpw4cbztKxeFgBCwWwJbxDJrt89eNi4EDAn07dsX69atReN/xmHfwtG+tqIZzvbnLEvalKaaDXxcTXagRg6W0gKmzPXhdlZ0OaqeP9YiHEhnSTCdpevlPbIbA3+8k4FTFiF/9ozeddFdY2s6u0VYq2w9cBJs6V2xYoUostb6kGRdQsBKCIS2knXIMoSAEAhmAuw7u379Btx++AxtB08xKBsbzEuz2tuz5blhr9GYRr7D1iDsOzzBnwUKAnMfS8j/lXkdPf8nrZi5e3EgWsuBk9C6VSvUq1fPXDdpFwJCQAgoAmKZlT8EISAEdATSpEmDjRs3onz58ioQbNqADkHyyl63ABs6KJEvOzirAL/a19KFBffyuQCCNQtzYl7ZMziR64VpFxNWZKt0GIwCBQpg+gyf3SWseb+yNiEgBIKGgPjMBg1nuYsQsCkC27dvR82aNVGUkt/PH9HDwN/VpjYii7UpAlv2n0ArqjrGiuyGjZskr6xNPT1ZrBAINgJbxM0g2NjLjYWA9RKoUKECDh48iCv3nyIf5RU9cOqS9S5WVmbzBDhrQZcR/6Fej5Fo0KAhtm7bLoqszT9V2YAQCDoCYpkNOtZyJyFgcwRevXqFdu3aYcOGDShfNA/6ta5ncREBm9usLDjICXBeWvaj5fK+vxCK0sPNQN26lhXuCPLFyg2FgBCwVgJbRJm11kcj6xICVkRg165d6NevLy5evETKrBMqkmJbgKLm06RIDIfo0VTFKCtarizFSgmw8vrM9Y2qQLb3xAVsPXgKPyk3bocOHcDZNCSXrJU+OFmWELBuAqLMWvfzkdUJAesicPToUSxZsgTbt2/Dixd/StZa1wplNbZAgPMA58+fDzVqUDWyRo3g4OBgC8uWNQoBIWCdBESZtc7nIqsSAtZP4Pnz57h79y7evXsHDw8Pq1rw58+f0bNnT+TIkQMtW7a0qrUF5mLc3NzQsWNH9aq+cmXrymzAuXKjRo0KR0dHpE+fHhEiRAhMFDK3EBAC9kNAiibYz7OWnQqBgCWQMGFC8McapU6dOkpZ4oT79vbqmn9cDB48GD169EDWrFmt8fHImoSAEBACAUpAfGYDFKdMJgSEQHATWLx4MZo1a4bdu3ejVKlSwb2cIL8/53EtXrw4OHjv/PnziBTJdD7XIF+Y3FAICAEhEDgEJDVX4HCVWYWAEAgOAi4uLujUqRO6detml4osMw8dOjSWLl1KPs0vlKtFcDwHuacQEAJCICgJiGU2KGnLvYSAEAg0Ar9+/UKhQoXw5csXnD592u59MlevXq18Z7dt2wbOGywiBISAEAihBMRnNoQ+WNmWELA7AsOGDaPUYRdx7tw5u1dk+eGz3/COHTvQvHlzXLlyBfHjx7e7vwnZsBAQAvZBQCyz9vGcZZdCIEQTOHnypLLKTp48WUXzh+jN+mJznz59grOzs8oewCWKRYSAEBACIZCApOYKgQ9VtiQEfCSwd+9ePH782Md+3KFGjRqIGTOmRX2Do5MobN5T96uiz5kgvn796u3k7NaRJk0ab/voX2Q/XrYW+3ac/hxyLASEgBAwIiDKrBEQORUCdkGAc5Bu3brVor1ev34dGTJksKhvcHRq0qQJuEKZvEo3T3/IkCEYM2aMcsHImDGj+Y56VxIkSICXL70vjLFgwQKVOUJvmLeHhw4dQrFixTBv3jy0aNHC275yUQgIASFgIQHxmbUQlHQTAiGKwMSJEzFo0CDdnu7cuYMGDRqgdOnSGDFihK6dD1KmTGlwbk0nHOTEFck4yEl8Qs0/mYEDB2LPnj3qGfsmOC5WrFjYsGGD2YnTpUtn9ppcEAJCQAgEFYGwQXUjuY8QEALWQyB16tQGiwkXLpw657KiOXPmNLhmrSdPnjxB27Zt0b59e4nW9+EhcfnYZcuWqSIK/fr1w4QJE3wY8edy+PDhUbRoUYv6SichIASEQHARCB1cN5b7CgEhYBsELl26pHwcjx8/DvajzJMnD0aOHKkW37hxYzRs2NDLRkaPHq3G/Pz5U3ft/fv3SvHMlCmTKmlavXp15T+p6+CLAy4M0KhRI/Cr8PHjx/tipP12ZQv7tGnTMGnSJOzbty/AQbALQYcOHZQPbZIkSVCvXj3MmjULnDLNO2FLccmSJZVfNvtmFyhQADt37vQyJCD/frxMLg1CQAjYNAFRZm368cnihUDgE2Al4tixY5gyZYp6Tf3gwQOlRPKducIUf4zl7t27aoynp6e69PTpU2TLlk25BBQuXFj5WT58+BCVKlUCZyDwrYwdOxYc2MTKtVS4spwe+xfXqlUL/P327VvLB/rQ8+DBg0ohXbVqFcqUKYOWLVuCLeft2rVD3759zY6+efOmqlbGvrlc6IKtxqz8cl5cruCmSUD//WjzyrcQEAIhhAD9j42IEBACdk6ArK+sdXrWrVvXCwlSVNS1sGHDepJ/que3b990fSgwzJP8JnXn2gHlNlVjfvz4oZrIH1ednzp1Suvi+f37d08KBvKkV9mepFjp2n06oDyynuQW4Tlu3Difusp1EwTevXvnSZZTz2rVqpm4+v8mR0dHzwgRInhWrVrV5Gft2rW6zq1atVJ9eW5NKBOCJ8+h//eh/S1RAJjqRr686u/iwoUL2jBP/puJFy+eZ/369XVtAfn3o5tUDoSAEAgpBDaLZTaE/CiRbQiBwCbAwWH8ap8UHF/dys3NTVlQc+XKpVwUtMHsj9m6dWuQ8uJtkJHWn7+5uhcpOWDrbo8ePfQvybGFBPhVPgfNbd68WWUV8G6Yh4cHTpw4YfLD1lJNunfvjrNnzxqkcOPnygFkHz9+1Lp5+WZ3ERZ2R+Bny8L+248ePdKtLSD/ftQN5B8hIARCHAEJAAtxj1Q2JAQCh4BfS6Levn0b9PMf7u7uqiqV/uo0Ref+/fv6zWaPu3btijdv3uDAgQMIFSqU2X5ywXsCHNTVq1cvMM8iRYrAycnJ5IC4ceP6mJ6LB3JWA3Zb4MAydv9gFxJ2NeHnmzBhQpNzc2ObNm2wcuVKzJkzR/3g4fyzpUqVAlmNkTx5cjUuIP9+1ITyjxAQAiGOgFhmQ9wjlQ0JgcAh4BuLLFvTNNF8M3k8W930P7Fjx1Z+uJbkPt20aRPmzp2rFJ9EiRJp08u3Hwlw+V9WQtnSzRZY/wi5fCBx4sTgOXkuDuhatGiRCubybl4OFGO/2XXr1qFs2bLKustWXs62wXOyBNTfj3frkGtCQAjYNgGxzNr285PVC4FgJcDWUe1Vsf5C2JrGwhZZLU8tW/84PZS+cLAPV/CKHDmyfrOXY64cRX6ZIF9cVZHMSwdp8DUB/lGxfPlyZM+eHYMHD9ZlqPDtRK9fv0afPn3AVly2xkaLFk03hXHOYt2FvwdsueW0YVxljj/8t3TkyBGQ77YKBuvYsWOA/P0Y31fOhYAQCFkExDIbsp6n7EYIBCkBfhXMr5T1LXtcMezevXu6dbCVjRUdjk7X78cdRo0apfwqz5w5o+tvfMAKcdOmTZU/JmdUEAk4AmnTplWpurg6GCuRfhH2b2UllFOt6SuynM2A07p5J+yH7ezsrOsSOnRosAsEu7RwWjf+oePfvx/d5HIgBIRAiCUgymyIfbSyMSEQ+AQ45ywH+rCyyXlGuUwpRb8jRowYuptzoBcrrWyF45y0FLmulF32rxw+fLjykeTcouaEU3exjyxbEaNGjWqum7T7kQAH4XGKNA7u4zRsvhVWiPm5cDU2LpHM1ll2McifPz+iR4+ufKU1S73x3Owby/7SnJKLleJXr16pefhZc/EOymoA//79GN9TzoWAEAiBBEJKXgbZhxAQAn4nYElqLi2dkv5dPn/+7FmuXDmVXon+8+hJvqye9MpZffic029pMnXqVM+IESPq+tJrbk+q4OVtWq7Lly+rlE+k9GrTyHcgECBXAU8qQGGQmo3TalGJYIvutmbNGk9SaHXPlirJeS5evNiTfGE9o0SJ4slp3ViMU3NxGi4t7Rb/vWifHDlyeFKJZYN7++Xvx2ACORECQiCkEtgcindG/wEREQJCQAj4mQD7TT579ky9MvYuywC/Nr548aKy1mXOnBkcAGROKJ+tss5xeqfDhw+DX0GLBB6BPXv2qCAsUkKVlda3d+JALX62XJWN8g/rsk1wO+WfVe4C5uZ0cXEBW28pNy1SpEihyu6a+jvyzd+PuXtJuxAQAiGOwBZRZkPcM5UNCYGQQaBz584qHypZZ5EsWbKQsSkr3wVX4VqwYIHydWWlUkQICAEhYAMERJm1gYckSxQCdkdg586dKF++vPKT5dRRIkFDgNxCwMUtOJCLA8I404CIEBACQsDKCWyR93ZW/oRkeULA3giwy0KzZs1U/llRZIP26XMu4BUrVqggPZ/SagXtyuRuQkAICAHzBMTNwDwbuSIEhEAwEODI+qtXr4LdC/SzIgTDUuz2lhRspcoFHz16FHnz5rVbDrJxISAEbIKAuBnYxGOSRQoBOyEwc+ZMdOrUSaX5KliwoJ3s2vq2yXHB7ObBabY4qEs/f6z1rVZWJASEgJ0TEDcDO/8DkO0LAashcOvWLWUN7Nu3L0SRDd7HwpkEFi5cqHIDcyCeiBAQAkLAmgmIm4E1Px1ZmxCwEwJceIFfZ3OJ1ePHj4PyktrJzq17m1wEoXLlyqA8sqhVq5Z1L1ZWJwSEgL0SEMusvT552bcQsCYCAwYMUK+0ufKTKLLW82TYf5kKW6BNmzZ4+vSp9SxMViIEhIAQ0CMgllk9GHIoBIRA0BPgUrUlS5ZUpXCbN28e9AuQO3pL4MuXL6CKXKoYwr59+6R4hdxi7ewAAEAASURBVLe05KIQEALBQEAss8EAXW4pBITAXwJubm5o3LgxqlevDlFkrfPPInLkyCpdF7t/jB8/3joXKasSAkLArglInlm7fvyyeSEQNATYJ9aU8Otrljlz5pi6LG1WQiBbtmwYNmwY2B3kwoULJldl7hmb7CyNQkAICIEAJCDKbADClKmEgBDwSoCzFOTOnRs3btwwuMjR8uvXr1clax0cHAyuyYn1EejZs6fKMsGFLNj1QJPfv3+DCyxwoQsRISAEhEBwEBBlNjioyz2FgB0R2LFjhyqAwNa9GTNmqJ3fv38fnPKpR48eKF68uB3RsN2thg4dWv3wePXqFbp376428ujRIxQoUEBZbLds2YJfv37Z7gZl5UJACNgsAQkAs9lHJwsXArZBoGjRojhy5Ag4ET/nLy1VqhTevHkDtuidPn0a4cOHt42NyCoVgbVr16J27dpKoZ09eza+f/+Onz9/qmtcMUxyBMsfihAQAkFMQALAghi43E4I2BUBd3d3lTeWFVkW/ubsBdeuXVPpnkSRtb0/hzJlyiBt2rSYOHEiPn/+rFNkOUcwW+FFhIAQEAJBTUDcDIKauNxPCNgRgf379+uUHW3bbMXz8PBAu3bt0KFDB3z9+lW7JN9WTuDYsWPIkCED2E3EWPiZbtq0ybhZzoWAEBACgU5AlNlARyw3EAL2S2D79u2qqpcxAc1Sy6+pnZ2dlU+tcR85tx4C/ANk4MCBKFy4MF68eOHlB4q20ps3b+L58+faqXwLASEgBIKEgCizQYJZbiIE7JMABwWxxc6ccMDQ3bt30alTJ2/7mRsv7UFDgN0HxowZo27Gvs7mhIPEdu7cae6ytAsBISAEAoWAKLOBglUmFQJC4OrVq3B1dTULghUfDgjj3KXsR8s+lyLWSaBy5co4e/YsnJycECZMGLOL5Oe5detWs9flghAQAkIgMAiIMhsYVGVOISAElIUubNiwJklwe4IECcDR75yM31w/k4OlMVgIaO4gbdu2VfdnxdVY2NK+Z88esbIbg5FzISAEApWAKLOBilcmFwL2S2Dz5s1m847WqlVLFVHgHKUitkMgYsSImD59uspaECtWLJM/QjigjwPFRISAEBACQUVAlNmgIi33EQJ2RODDhw84deqUSsWlbZutr1GiRMGKFSvUJ3r06Nol+bYxAuXKlQNXduOcwcYWWknRZWMPU5YrBEIAAVFmQ8BDlC0IAWsjsHfvXgNFlv1jtZK29erVs7blynr8QCBu3LjKQstV3ThfsOYqwgF/bJUXEQJCQAgEFQFRZoOKtNxHCNgRAU7JxRY7Dhbiz/Dhw5V/bNKkSe2Ign1slfMFX758GenTp9cFh3GGisePH9sHANmlEBACwU5AlNlgfwSyACEQsghwDlmOaOcUTqy8srtB3759wdZZkZBJIF26dDh37hx69OihczuQamAh81nLroSANRIIRf/D86fOpDWuTtYkBHxBgK1B/Hrz4KHDuEJpod68foVvUl3KFwRDXtfwESLAwSE2MpDVsGDBAqhUqRJy5sxpkxt1c3NTPxL2UraAixfO4+mzZ/j4yd0m9yKLDloC/EMyRvRoSJE8ObLnzIWyZcuifPnyiBQpUtAuRO4mBAKHwBZRZgMHrMwahAQ4FdDwESNx9MhhRI3pgGTZiiB+qkyIGjs+wkeMHIQrkVsxAZdzhxAhagwkSpct2IH8/PEdn9+9xqsHN/H40lG8c32KjJky45/evdCgQQObsBbzj7Thw4dh9arV5If8G/nSJEC25HGQNG40RI0YDqFNpMgKbvCfv3tgzfE7aFgkPcKFEYt8cD+PX7898fHLd7i4fsS5B29w7t4LRI8WFS1atsI///yDePHiBfcS5f5CwD8ERJn1Dz0ZG7wEXFxc0Lp1G+zfvw9p8pZCzmqtkSpnMYSS19nB+mA8yb3AWp/Bs5vncXbTPFzbtxbpM2TEgvnzVGBasAIzc/MvX76oghJTp05FKseYaFMyI6rnSa0UWDNDrK75NylRoUN7zUdrdQu1swW9/vgVq47dxuz91/Hp2y8MI5/2Ll266Hye7QyHbNf2CYgya/vP0D53wOmdWrZqhRiOyVG263gkzZzXPkHIrv1E4M3ju9g9rTdczh9RRRv69eun8/X004QBPOjatWuoXq0qXr98joE1cqFBobQIIz/SApiyTPf1x09M33kZk7ZfRJ48ebBm7TpVzETICAEbIyDKrI09MFkuERg4cKCKjs9Tsx1KthmCMGGlDKr8YfiNwJkNc7B35gBUr14Dy5YuUSmm/DZTwI1it5lqVasiS1IHzG1dDI6xogTc5DKTEDBB4MbTt2g+8wC+Ijz27N2HjBkzmuglTULAagmIMmu1j0YWZpJAz549MWnSJFTsOQVZyzUw2UcahYBvCDy8dAxrBjRA8aKFsXnTJl2+VN/MEVB9d+7cicqVK6F67tSY0qwwwoq/aUChlXl8IMA+tQ2n7cUtV3ccPXZcFFofeMllqyIgyqxVPQ5ZjLcEJk6cCFZmq/WfjUwlanrbVy4KAd8QeHrjHJb3rIr6detiwYL5vhkaYH3Pnz+PQgULomrO5EqRNa6sFWA3komEgBkC7HZQZ9IuPPzwE+cvXBSXAzOcpNnqCIgya3WPRBZkksCRI0dQrFgxFG81CPnrdjbZx6+NV/etw8/vX70MjxA1OuKlSI84SdN4ueZTw91Te/D9yydkKl7Dp652c93j2xeECRcBoamIgjXKnZN7sKpfXcyZMwetyB87KOXdu3fI6pwFKaKHwupuZa3WP/blu8/Yd/UJwocNjdr5ff//F0HJNLDuxdksP3z5gZhRIgTWLYJ1XrbQlh6xBY4p0uLwkaMSFBasT0NubiEBUWYtBCXdgpEAR3VnyJgJEROlQ53hywN8JROqp6P0Ta/MzpuuYAVU7TcT4SNFNdvH+MLirhXh9uwhuq29ZnzJrs45s8GB+cNx58QuvH50mxTZsIibLA1KtB6C1LlLeMuCx85uVRi/f/3y0i+mY1LUH73aS7t/G/bP/RcXN8/FjevXkSxZMv9OZ/H4Zs2aYufm9Tg8pDpiR7Pe3J8j1p/BZAoWYjk4pCYyJY1t8R5tveP7z98xdM0prD99D2zBjEJp0UpmToIxDQv6+MwWHbyOuftN/7dgSO18KJXFuirj3XzqhpLDNmLEyFHqbZitPztZf4gnsCV0iN+ibNDmCYwbNw6v3rxF+W4TAm0vkaLHQtsFx3Wf1nMPo8ag+UietSBuHduOvbOGBNq9Q/LEG0e1xfEVkxElZhwUa9YPafOXA2cSWPFPLdwmBdc7+fj6OV653KDUTmFofGyDT6RoMb0b6udrRZr8g6hxEtL/gPfy8xy+HXj69GksWrQYo+vn81Ep8u3cAdmf02ytPnFHlxpszck7ATm9Vc/14+cv1J20A8uO3qL0aKkwqWkR1KA0aZvPuqDR1N0+rv3Cg9d45vYZceiHivEnAlm5rU3SJ3ZA94pZMXjQILi6ulrb8mQ9QsALgbBeWqRBCFgRgffv32P8hAnIW6cLosV2DLSVscWQXQr0xTF1ZqQtUB7jq6XB9QPrUaHbeP3L6pithtb62tzLYoO44f3Lx5TPdR0yFKuGGgPn6VJfPb56Cos6l8f+OUNJuS1rdlVuz1zUtar9ZsExdSaz/QLyQtjwEVC05WCsGdgQV65cQZYsWQJyepNzDRzQH3nSJETFHClNXreWxsM3nuIFuRkMrZMX03ZcwoZT9zC4Vh6rdYkISG6rqADEeZdXGFo7L9qXdVZTNyycTn0vOXwTlx68QtYU5gsPPHD9gMLpE2FJpzIBuaxAnat9GWcsPnwbo0ePVkG3gXozmVwI+JOAKLP+BCjDA5fAkiVL4PHzN3JXD1ofRm1XrNzEjJ+EKkjdUD6wESJHU6+9jywZi+sHN+Lt0/tkMYyLDEWroFjzfohIla/MCUfN3zi0SVXI8vj+TeXGTZY1P7JXaGKgEHMw0kF6Nf/89iU1Vdzk6VCoUQ845Smlm/rnj284tnwSruxdA7ZgxoiXGCmyF0apdv+C1+hX+eXxA7eObkMUh3jKKu3XeXjck2tn1HDnMnV1iiw3cE7gWAlT4A25Hfz46m7WfcON2LLETpJKfQfVP+kKlkf85Gkxbdp0zJ07J1Bve+fOHezdtx8rupYL1PsExOSrjt8mxTUUaudLg4evPmLhwRs4cuMZimVK4mX660/eYuzmc7j66A0yJIlNinoKJIgZhZSjGxjfuDAcokbUjdl58SHm0St4HpMoVlQUSJ8QvSrnQLRI4XV9/HJw7+V7bDv/AF0r+L8S3bqTd8miGhEtSxr+qOpWMRvyODn6aFF3IWW2TurA+zHuFz4+jYkUPqwq1DGRCosMp6IKUaJIijifmMn14CNgfe83go+F3NkKCSxZugzpi1b1l4Lmn22xEsufyDFi69awsm9dHFkyDg6JUqJwo56kSCbC2Y1zsWF4a7O3enDxKJb2qIprBzYgVa7ipMA2IiX0GXZM6gn209SE/UqXdK8Cd7dXyEt5dAs26AbP37+wsk8d3DuzX+uG7TTu6LIJSJYlP0q1/RdOVAHt8u5VWN7Lb1keXj+8hd0z+mNizfRYP6wl3pIrgH8lfKQoyFm1BZW1zWEwFQeCff3ohrDhI9LHvH+o27MHiBE/MSm8n3Hn5G5c3L6UFOTTJn1oDW4QACfO5Rth9Zo1+PnzZwDMZn4KLv6RMHZ0lDChEJofFfRXOCho+/mHKEE+onGiR0I1esXOsoaUPGM5cfs5yo3YhFN3XpLFOQFiRo6APsuOoc/y49h67oHyN9XGTNh6Ho2n7caX7z/RrFgGpE0UCwsPXEf5kZvAwWa+FfZlXUuuEFXGbEG+fqsxd+9V305hsj8royUyJ6XAtzBKkd9FCvjlR6/hSAo6B8IliWP+BySX9n1FFbdSOcZQFtwV5KqwnZRsv+zP5OICsbFugTRw//wZ27dvD8S7yNRCwP8ExDLrf4YyQyARePv2Lc6fO4vaw7oE0h3+P+2vnx54cfeKruGXx3dlWbxKlk+WHJWbqe+bR7bi/tn9Skkr32WcaivarC8pgK2UKwK/Gmcl11iu7V+vgp86r7ios94WqN8FU+pmVcFRpdoOVUO4H2dW4FfrCZz+vOLOW6s9JtXKiCt7VqugqZ8/voPX5ZS3NKr0maG7FVs7d0/vi7dP7pE184+yobto4oCVRLYUX9i2BE9vnKVMA+GVW4Vz2XpUFrg4bhzeAlZyvRNW8nORwmpK0hYoR/N5tTieXj8b39w/IGOx6gYWaeM5mOX3z5+IkbNBtokEabKqgLy4ydIaDwmw8zTk27vnvwE4deoUChYsGGDzGk+0c/s2lMqc2OpLvm48cx/fyW+0FlllWfKSNdIxZmTsIKXsc2MPRIkQTrWzX22/FcdVtoN9g6rrlLz2ZbOg5NANqo/2z90X7zBu83kVRMWWaS0VWR1SDmtO2I6Ze66QS0M+rbu339cev8XSIzfBFtSPX38gVfwY6F8jN3iut5++YsGBG96O54uVcqZAukQOXvq5f/OA64cviBsjEhpM2Yk9lx/r+qSmMsPTWhRFzlTxdW3GBw9cP6qmMZvOKTcN7TpnhOheKTt6VDL8saddt4ZvDkbM7ZQQu3btQu3ata1hSbIGIWCSgCizJrFIozUQuHTpz2v2JBlzBfpyvn16T9WWipq8T9byDVGoYXd17dLOP9kU8tfpZNC3cOOeiJUgKfj1vynJV7s9uUq01imy3Idf6XMg07fPf/7Hjts8PX/zF85vWYgyHUYgXMTIqsJZ19VX6JqnusaWWpaHl44rBVxTenNXa4VstFa2eHonz25dUFZOVpz5NT8rh+U6j6XcvTXAgXCasKLLH++ElWZzyqzxuK/EeOfU3sqPNnYSJ5TtPNq4i8E5W2a/f3FH8ZYDkK5QRXx5/waXdq3EpZ3LsLp/A3CQHlt/A0McEqVA1JgOuHDhQqAps7/I3/ry1ato1DB/YGwhQOdceey2eu1fNtufDA+seFajwg6scLKVUUvTdfXxG3IXcEPn8ll1iiwvJEPi2KiaOxXW6lly2U3hFym/zUtk0imy3LdIxsTKirmBsgZ4p8x+IqWV+yw9cguXH75WgWlVc6VC3YJp1at/novlzvN3yuXhz5n5f9lyakqZffDqgxo0h6y8KeLFwKgGBZArdXycufsS/649TQFgu3BkWG3EJYu1KdHGx48RGTNaFlOK9q5LjzD/wDWM3niOxkVG4yLpTQ21iracKePgyNk/LkNWsSBZhBAwQUCUWRNQpMk6CLi4uCBilGiIEituoC8oQpToqNhjksF92P+V/VWjx02oa2cFKzz5pMYgP1p9YSth8ZYD9ZsMjjlX7ZcPbji5erqygnJw1NunLvhBuWij6gW25ajUFKxkXti2GJz/NlmWfEiZs6hS5jgdFQsruBx1f3DBCKWA89zJsxVE6jylyXJb3Ky1k9e+dnBjuN6/jsiUXSB7xcZURa0+Bb5lMFirdlKNrMP6ll+tXf9bs6bptxkfsxJ+futC8gMeia+f3imrdgnKF+yTby/fO2z48Lr1xU6cCkky5VE/CE6unqZ8e7OUrmN8uwA75/vx32BgyYsXL/D9+w+kJCuiNQtbUDn4iZW9Gbsu65b6miyeLGvotb6mzD4gX1oWtlgaSzpyIdCXuy/eq9OVx25hNfnj6stXcjt4+f4Lvnn8RMRwXv9niv1xZ+y8jC/kVlAwXUKlJLJfbuS/FmL9uZwSxMTjWabfHuj3Y0upKeGUXCw/yHd/YYdScErwZx/OyeLiNbkPTNp2ERtJqW5dKrOp4chHrhabeldCthRxdetrXjwjKtB68/Rdhak7Llq1Mst/n4uOnDO5N2kUAtZCwOt/JaxlZbIOuyfw8eNHRI4WNP9Dz4FeGSnq3if58uENZVWIb2BJ8mkMXz+xaiopn6NIOYuAZM75kSJHURRs2AOn1szAuxePdFNwIFeHxadV2qrr5F/LQWP3zuyj1GCDwAqgVjCCA8IyFq9OfrIrce/0XpwjS+65zQvgQApY0ynbENXB62vPT29eKEWWLbd5qrdB1vINvM0QwW4HYXQr89vBZ7KmbhzRhoLeDqqAstJkbeYsEZZIwrRZTXZj/2BWZl89uGnyekA1ho8SAx8+fAio6bzMo80dI5J1J99feexPCi72G+VX5cZy5OYzUjw/K//RD+Rby+IQ1eue2AqrL+/cvyE0WXjZD9VY8qVNoJrYbcGUnLj9QimyHHzVhQK8OFNAaApOMyX8g4uDmfwqHLjGkiNlPJ0iq81VxjmZUmbv/FXMtXb9b/Yx5o+xsKXWOVkcqL2QX60pRdx4THCc89+n++cv+E15n0OHNq3wB8e65J5CQJ+A3/8/XH8WORYCgUCAg29CUY5RaxK2jrJv7deP7wxeybPV89axbSqPqrG/Kit0+ygNFeda7bjsnIFF8hgFcenLd3I54D1nKFJZfbhwwKMrJ7D+3xYUKDYMuciVgPOuepBfLa+FMyjwx93NFUeXTsDZTfNwZsMck1ZiziJQf8wa5WJwePEYHFo0iiy5JeFctr5KkcXKq75c3LEML+5c0m/ycsxKc+HGvby0c8PvXz+VO8CzW+dRoftEsNXZUvnw6ime3bxAwWPZVRCY/rh3zx+qU+YZmBKKKpWxK0BgiTZ3mDCmlbDAuq9v5v1Ff3+cTzZBrCi4NK6BF4XxP7LUDqZCApymi1NWaYFQp++6okzW5Aa3YhcEfUkWNzoFUb1BtwrZVeCX/jUOmmJF1pyCN6t1cbDrw3IKpqpF/rUJaX11KFipboG0Xizd7O86Yct5/elNHjcolA7Oyb2+BUoUO6rq/9OEYv3N48/fR3RvMi8cuPZEuQhxAJm+8BuLR68/qeIL5vap3z+4jjmDBYsos8H1BOS+lhAQZdYSStJHCPwlkJSyB7y4cxkPLx9HevLj1ISrXN2gVF0ZilTVmnTfH1yfsDMsuQpUMlBkWWF7ee8quVH8Pz/lsl41yB3hLTotv6DGhyJLCBdu4GAv9tf9QT6kL+5epqIDtVWQWJZSf4IyWKlkqy0rs18/mbYm8lysvPLn87vXyqp7gTIErBvSVCnmmUvWIreDBjrL6YMLRygIbLNuH6YOYidObVaZvX18p3Kp4HX5RpHl+3z9+F6tK3vFJl7cPzglGktScsEQCVwCh64/hSu97u9YztmLIst3rkJ+sKzMssLLymx6CqBi5ecwjQPloNWEU3kdvv5MO1XfHDS15ZwLBVQ9MlBmOXNCzn9WUnWxONjQ6///P6Y/mLMIdKuYXaXdOkqW4WXkN8suEPzKn4PT6pHfbGXyn41KVbp4Pr7uk+RPm9CkMstWXXZlOHbrOe6TdZqDyzTZceGBOsxNPrTmZBH5Bu+6RNkPxjdUPwq0fufuu1IhBXeVIUJrk28hIAT8RkCUWb9xk1F2SoBTZbHFcudkskaSgsr+tOzjyoFSaagAQExHQ19axsSW2nARo1Be2g3k11oScZI6qRRT7EPKfqOcVYCrYnE7l87lVF38yVGpGcJFiIiHlNbr6r61KlCL/YfZb5R9Xo8sHqvu75g6C5XOdYFm5WXF1yfheVjJ5M+jyyeUj+75rYuVVTd+qkxUbW08qg+Yoz4+zWXu+uMrJ9UlDjLbM9O0P3EJ8jNmi/D5rYuwY3JPUox7kz9wb8RPlRGJM+RS6+KgtPSFK1GKst8qry67LPB5ovTWGwVujomttbP1k4Vzy5qSRA5RkYuU0rOkmN14+lYFerHv6MzdV9Bx3kEV9MXuCfNNlHJlv9GFVOZ1CvmMJnSIQkFVjkq5G0ZBVR++/KAo/+ymbmnQxi4EhTMkVh83cltYTcUNlh29iS4LD6MvpQJja+3YRoXwfG4rg3G+PRlEinlpKu/a8r+9KksC75uV6MWHbqpgs7LZkuumzNx9KV59+ArX+a1VWyMK7uJcujXGb8PAmnmUMnybgtIGrT6p3Cw464I1CyEWEQJWT0CUWat/RLJAayLAr7abTduJtYMaUzBVE93S0heujIo9J+vO9Q9YYa3yzzRsHtOJXrvXV5ciUhaDMh1GqmCuzaPbY2az/Bi4/zXy1u4AV8pryyVg+aNJgjTOqD5wrjrl+VjR3DSqPZZ0q6x1IaUwAoq1GIA0+XxWZnWD6IB9ePlTrstYpSxe3L4ErveuIUlG//2P7OOrf5RZ9uU1J0Wb9lHKLP8wYGWVv1lYSakzfBm2juvshUWOys1Ruv0wc1NKewAR4MAnVsIyJnEAlzc1J2ydZWV2zYm7GFI7NgaRwhYjcnjM3nNVlb+NFSUCauZzUm3jt1xANLKWskQIFwZre1RA+7kH0HbOAd30HDy2pFNpFCBrqG+ECzG0K5NFfc7ce6mssfyKPyAkG1X3WknpwzovOIR6k3fqpiyTNRmmNS+qO+cDdo/4/ffvmM9LZUmKqc2LUuaDUyqnLrexsEK8sXdFZCYLtIgQEAL+IxCK/HZMe9j7b14ZLQT8TWDMmDEYN3UmOiy/6O+5AmMC9t389PalyisblSpm+SSczeDlvSsqOIuzJGiZALj9m/t7g/y0PPebJ3cpv+o3xEyQTL361/pr9+HiA64u1/HB9akq6sDleAMq8wP75IaL4DVoRbt3UH6/f/mEcufeVVkM4iRLY+CqEZjrWNW/HvKkigeuQhcYwuVynZ2dcXJkHZPR/4Fxz6CekxXimKTMsnDhBHYpuEC+t/rC/xPkQm4Id8laGYsU0pzEPEwABRpxEQX/BH/pr5OPPSjX7o1nbnD79E0p+OzuYKnwWm7R2HfEhF0VksSOZtJ1w9L5gqofp15rOmMPPDw8EDas2L+Cirvcx1cEtshfpq94SWch8H8CsRImp7Ksyf/f4MNR5BgOSJmjqJde3M4ffbFkbk7Rxa/i+RPQYi2KLO+LXTdMuW8E9J5lPv8TYIWt2titqojA8Hr5dYosB3QdJCsp+8EaC/9IY+VO3xfVuI9fzwNSkeU1hKPMC5ySyy/Ca2ELr62JuBnY2hOzz/WKMmufz112LQSEgBAIcAKssLFbwdx911QlrtLOSfH+8w/KPHALLyiQbFKzIgF+T5lQCAgBISDKrPwNCAEhIASEQIARmN2mBCZtv6gyGnAAWZQIYZGFrJkrupQFZwwQEQJCQAgENAFRZgOaqMwnBISAELBjAtEjR8DgWnkpNRdUWqyoEcPbhG+oHT8y2boQsHkCUs7D5h+hbMAaCHDO2IuUs5VTZPlWbh3bQWm7/uRO9e1Yv/ZXmQP8OJiLIfy2sJgAp+WyRHwzpyXzSZ+gIcDZDjaduW/2ZqzYmqrMxflVOfcrp+0KSjFXUcySNXz/WyDBkr6W3od9iblkr4gQEAL+IyCWWf/xk9FCQBFwvX8DW8d3QZU+/xlkJbAEz9Gl46lIgJtF5XQtmc+7Phe2LaZCCFsot+xxWmcqpMxZVJXJ5TK7PsnVvWtVUQaugMbKp0OiFMhdrTVyVmlOVcv+/7v4m/tHHJj3Lynom9S+OFCNCz+Ubj9c5dzVv4+lc+qPkWPrITBh6wVwWdqqlJ7LN3L9yVt0W3QY01sU81KxyzfzWNL33sv3WHDgukoz9pHy13IJ3LalM6vctD6N5731oXy1p+++VDlwOeVY4QyJ0K9abqROENNguG/us4t+BAxbfxp3nr+nrCaUHi9OdAyomRtVqNCDiBAQAr4nIMqs75nJCCHghQAXPCjeahAVNsji5ZpPDbmpRK3H/9i7Drimri986sQtQ8AtirgVFffeo05cddU9627dWuuodbRurbtq3X/3xL1wiyIq4lYURcWBA1DU/s938cUQkpAAYST39Jfm5b377vheJN8775zvfAyNrlmsj6OC2K6/BotiA5XbDabn/jfp7OaF9PrJfWo1fiUlS677z8HlfesJeri2OfNTuZa9WTIslK4f30l75wyjUJYVq9rxFzE/yCxtGNNOFGLIXsiN8jXvTg+8PenW2QP0+MYl6rX0uJAmQ2ND+xQdy/8lSgS61ypCYaxgYKzlZyI4hosFFM8dVd3A2L70tYe6Qoc5HhT46j21KJ9fJKftYqmp9rM9aMOQhnpjeN+FfiT36bvoGheDcC/nTJjzqRtPaLfXfTrH5XqPjG9JWTJGyNcZMw682Z3m7RMkfhwXY0jFCgkoKtH974OsxZuaqhfJoW9J8phEQCKgBQHdv15aGstdEgGJgHYE4KWs3G6Q9oPR7C1Rv200LWJ/GGEQHvNGcfWw8vTjzO2UPEWEcP1RJuGoJAYPqb55nN44j2xy5OMf3AOUOl1GMaFKbQfS7LaudH7rUhWZhccXFcWKcZnd5qMWqiZ+jMc4tmIKeXusU+FkaJ+qTuRGokPgh0oFYjQnJ/tMNPD7kjE615iTJm85R3cCg0XBg9pcvACGCmXVfv0f9V92lLymRRQx0dbnMd8Ausoe5OkdK1PnGkVEk58bR+jlLmNPL0rZdqpeWOw3dBzo1I5ae1JozO4e2ZRsM0SQ4cZuecn1lzW0+MAVSWa1XQy5TyIQDQLfng1G01AelghYKgKIDz2xegYt7V1LVOrav2AMvXrygMMKBpFS3SrguhetGdaK7nodVcG0c/pA2jN7KL0NekKbJ/agWW2K05x2JbkSWD9RwlZpuHfOcPZ6/qR8NMm734nd9DHkLVcY66sishioRL0IIn318Bad4yJs4Nm966IUr0Jk0TiDXVZyKlmVQt++os+fwsX5KOAAy1OiknhX/udUqqrYxBxgxvQpTpD/i1cEPnM1tpm7LlKdCVuo8piN9Ov60/Tg+RsawqEB/zCRUwwlY/svO6J8pMFcRnY4F0eAJ7TXooOCoLkNXysqZyE+VLGLd59Rmxl7hOKBss8U71BTKMzVyxQiizHsM6WlmsVykn/QW/LiymW6DKEFsGZlnSM1acXVzGAoWauYoeOgz0cv3glCrRBZ9JHVOp2oevYjl76VJhGQCBiPgPTMGo+ZPMPCENgwpj3dOrOfH8+7UZ6SVYTnEQlbH96/5RKsnwUa71+/oDvnD1HRWi1U6ATevkIhwS/oBid4ZXbMTUVrulPA9Qt02WMtn/uGWk+IqCz16Np5EVuqOtEEGy8e3Ra95i0dWeczs0NOUU4WIQC6LFny5NR59u4oBSJASFGBLJ9bDRVBzl++LiVjr++lPavZ09uOQxeSi2Sxi1wiF+ZSsb54N6ZPcYL8X7wi0HHOPjrg40+l89pTlULZ6dTNJ7T13G16GxZOn7lcq2IoY4u4UsWusCfz5dtQ4bXMnSUjNS+Xj0BcQfYQr7qiX0Sp5SBug1KzeHxvKnvBYwTzmO0qR31sn88hIt7V+/5zKp3PQesUOlQtKGKBlQpmSqPTjAUMGrowY8ZREt4alMxDIPc+D4IohN9RTKKeax7Rn/yfREAiYDwCkswaj5k8w4IQQFwoiGy5ln2o3k+/i5VDCWDrH73p6sFN0SIR/PQhVeTH8bU4nhaVjnDukt416d7F49Geq94ASVvP7/up74qynTaTLZVp1i3Kfux44X+bkIiVOm2GSMeRuGWdzYmCOH4WHmiQTE1LlSYd5SrGUktf7cymvymYS8wCF5D5yu0HK4dEJbOa3cdwAtgkmtGiIOVmD+3Dq2fp3cun5MZzww0BzJg+VZ3LjXhBYJfXXUFkkSQ18YeKYkxk5/ddepg2n4m4KdI3kYfseezfwJXGckITvvM4Fx7e49cD9J0W5RhI4vLDvlH2a+5o7OZEBbNHrqCHNrc5vADmkDmteFf/Xz7HTOIjSLUuc8lmrTp0gUm7p99jJp/PCTG38M6WyBNRCcyYce6wekPyZN+RL5P+XosOUcjXeGPs61azCI1vU4FSJJcPTFXAyw2JgIEISDJrIFCymWUigKQppBvX7DZaBQAIYI0uowwisylSWVH1zsPFjzo6wLk5i5ajQFYEePM8gDJmya7qV9+G79FthJc+s83prJPMQjIsTYZvP87q/WR2zEVBD27QBw4BSJMhwmOlflxzG0QVCWCwLHkKCpKs3gYqCSiHC6/0vYvHOKQgglT8x6QG54FUa1p0fWq2l59NhwC8qMiwH9m8jGoQyGuNaFbGIDJrlTI5DWtWWvWdx7llWUHAxz+IHrMkVzab9Kp+9W28eBtG07Zf0NdEHAMx1UZm7z2L+N5pelZxUi67iJs6eG4NMRDZP7acpy+c4AhscD7iX1He1phxFM9sj4UHOSHNmUmxC336/IVm7/GmxVw1LRNXTxvWNOKGz5B5yTYSAYlABAKSzMpvgkRADwKvHt+nTPbZoxAw62x5CEQ1OktnbRelnUIYP4a+j+501XEkUzUdMV/1WdsGvGC6DNJbb55HPB7VbBMexvPgczW9tprtlM+jPALoxaM79PDKGTq0ZKKIJR608YpQKYAHedNvnSlX8QpUu9d4ylHYTXh9PdfMJK8dywURaDjoT6Ur1bu+PlWN5Ea8IHDv2RvKzoQzbeqUkcbLY5+RQFSjMzvO8LdKGfmnJXO6VOI09bjZ6PqBeoD/Qu1PGtTPTZVCuycTKgGw1+8/qDcX2yEfPon3zKweYIgN4mS1HrWLihjbjaduESTJ0O+UDpWFGgH6MGQctEGYRhO3PDSrS3XV0Ah1KDBghUgAk2RWBYvckAgYjID2vwIGny4bSgTMG4HQt68pTcaojzDxSB4yVNFZilQR2crRtYvuePKUqYS3Ex5PXS995Dq9jT2BtL5/9TzKUCHBr4TXVluIARpjnZpFFmxZ2cC1QXuq3XOc0Jy9deaA6FfxHldnzzWILMwul4vQmEUsrQ+rJsCM6VOcIP8XbwgEM+GySRf1Rg1JYQZ85aMQ2ZhOHDdnaVKliPaVXE3jWH0sB070gt3nxDVNe/k1ztc2Q9R1Km0RHqH+bzwdk/uqhXPQ3G7VhWfWw/uBaGrMOEj0gv1QObIKRHqrlFS5YHYR4/ssOES0kf+TCEgEDEcg8u2z4efJlhIBi0AAj+CRHIVH8OqeS8SYfg6P6vExFShIqHpy01tv9+ltHKjqj0O1toE+LCSzXj25T+mss6jawDuMfU6c2KbLTq6dxTGwE6ntH+sJCV7qlobjdGFvnkXEQz69c00kgClEVmmLeN6s+YsLLD+Hf6TTG+cb3KfSh3yPHwRy8iN0JEZBZzV9mgiPKka+9eQ1feBH6/FlT5nU/bXDK9rh2lcpqIpfVW+czyEiLvYBe5o1zZe1Y2FIcNNmILK5ei+jQqyEcOBX90hNQLKt01sRCj8g1MCYcXLYRoQ3ILRA01AJDA9XQGylSQQkAsYhIMmscXjJ1haGgCMTMCgQ3Pc+SQW+ZuIDAlTSik9Dwpjvse16h7TN4ayTzEJlAXO+tGcNe0zLqPqBJxVxrIrKgOqA2oZ93ggtzbsXjkYhs5d2R+Dg4FxUnJElTwF68fAW3TztQYWrNVX18jrQnwL8LpIdk2p4mY3pU9WJ3IgXBIpxIQMvViA4eeNxpAz7Vceux8v4yiBvQj6IkrfKZ13vFQtk00pmHdkLWrFAVoL6AOJaoW0LAwFFIpsjJ4YpSVyafSPOt2B2a6E2ALUGkFfFrnDs72Um+0Vy2oiYWWPGqcMKCHP3eovx65bIrXRJ8BSfuRlIRXPaRgnvUDWSGxIBiYBOBCSZ1QmNPCARICaHv3BBgY20mytnhbwO4mx9W5HFL3RZ4UaJJ3Mfs5jwiqlBVQCvSyyRhZADlwr12EvqTQf+HiviWxEyoJjXzhW0Z9YvvPZhVK3TMMpfrg7ZOxWmc1sXk1X6TJSvbE0RfwtyfeOUB2UrWEr0h/PLuveiGyf30N7Zw+ix3yUqWOV7en7Pj4Q0Fz+jrvK1UpgxfSrzku/xg8DPjUvR/07fop9XnqAgTsKyZSIHma4tZ28Lz2H8zIK44pY1PV7SI1bDIda17ay91H3BQRrcuCQhRnYOJ1sh9GDtwAa8nm//hosN+Vdoxz5d1lOM2b+hq6jK1eLPXTSUk7JAfg/6PKSNp26K40iIU8zQcSq4ZKU6XLxh+/k7wqPboFQeIVmGRDckl/3a6ptqiNK3fJcISASiR0CS2egxki0sGIEMto7UZe5eJndDuRTsIIGEvVMh+nHGNpH4pF5EIDHDhB/tHyavpfWj2tKJf/8UL8wXRLTVbytUOrFiDUqM7NcASSgwtJm0mrb+3ouOrZwqXspaC1ZpRPX7T1GVws3jWola/LqM9i8YS6fWzxEvtIWCQb2fJlOx2i3Fqcb0qYwl3+MHAcfM6QjVqVD8YMiKCAm5QjmsaevQRlRn4lbKmPZb6EH8zCjmo9QompMW9Kgpijl0mR8R152RQycgOaZeSAEjILQAhFKxpmXy0RMu/jBx01n6ce4+ZTfZcZztvG41qH7JPKp9xoyzqFctGsbYTucQCrxgmNOyPnVk9S8VonJDImAcAt9xgPu3f73GnStbSwRMisDUqVNp+py/6ac1l0w6jqGdf/oYJhJCImSnXtKfzZyp6fD5esvAGtp3fLZ7+yJQSINldXEVXlpDx0YSGCqfBfnf4iQ0K4IUWMYs2bSejrhYFFR4ze3T8w0BbgDg1dU0Y/rUPNfUn9ePbkvl8tnTqlWrTDKUj48PlShRgk5PbkPOjtFLoplkEtF0ijhO/EIgEQuPwgsMWCkSoGJaxjaa4Ux2GDGqiAMGWUWcrK6kMW0TeMuxwzcev2KvbQgnfmUk56yZdCa5GTMOYoIRd2vHJW0LsKZtagOUIrTNz9T7RNneefspPDycUqSQ/i9T4y37jxECO+Q3M0a4yZMsBYHzW5fS5f3rmbTOE5qqyrqvH98hNh2ciym7ksw7vM14GWvwptpkdxKv6M5FXGy2AiXFS19bY/rU1488FncILDt0lTbwo/S5XWtQAY4bVWzXhbtis2hOO2VXknlHIQI3HZW+oltEBvaaGnquMeNABUFRQohuDvK4REAioB8BSWb14yOPWjgCji7FyWPeCPrfuM7CA4siAQG+F+gkP0IvUKmB8DhaOERy+WaGQHFOABu19hR1WbCf2lYqQKiEhZK0SFyq75qbM/y/EVwzW7pcjkRAIpBEEZBkNoleODnt+EEgZ5GyHC+6li57rKGT62ZTGOvOomBCqe87ihhQXdqs8TM7OYpEIO4RKOPsSP8OqEeoBDaHCSyE/vNkyUgdqhakSW0rGvWIPu5nJ3uUCEgEJAJREZBkNiomco9EIBICLhXqcrZ+XVE44EPIO479zBjpuPwgETA3BCAbhReSot6FfeSkL8MqZZkbDnI9EgGJQNJAQFYASxrXSc4yESCA+M6EILJI2Lq0+1/Wb72dCFCI2RQ+fTRNgQnkr6Kqman6j9lqzecs6K0mFJFV5MDMB025EomARMBUCEjPrKmQlf1KBOIIAZDYnX8OpMa/zBYKAnHUrcm7CX3zivbOGU7+V85whbBHQs3AqVQ1qtFtNJe4za9z/LntS1Me18rUeOhsnW2UAwj9OLxkgpADK1KjubJbvpsBAvM4xOEeV+9yL+ecZFYDT3aN3zbRJ1b+0LRcXP1r3eCGYnenefvoduBrzSaRPs/pUp3efwinkWtPRtqv+aFE7ixCfkxzv/wsEbAkBCSZtaSrLdcqEYgnBFD+d9WQpkKeC9XH7HK50AOuoubnuYseXj1LvZYej1RWV5mWt8daevX4niCzyj5d7wHXvejIst91HZb7JQLxjsDjV+/I99FLKpTdhquGRQ7NyKxWRSwFP+VJyQoL2sw/6B1BDixFimT03cfvdLYLC/9MdwKDKe/Xymba+pL7JAKWgoAks5ZypeU6JQLxiMBdr2P09M5Vajj4T3Jr0jViZK7+hcpg57ctJb8Tu6h0ky5i/5vnAXRsxTSuSHaRz7lm0CxBlrdM6iEI8TsOw5AmEUgMCMCTDEOhhqK5bHVOaVnfOlqPPeDKZNXGbaJhXHEMHlfY0fGttLYdwYUX3oWG05+dqmo9LndKBCwJAUlmLelqy7VybGUYea6ZST5covbN88eUyT4HOZWqSnX6TKDUaTOoELrv7Um+R7fR3QtHKfxDGOUqVp5yu1ZkFYNOXO0quWj38No5OrjwN/HYPMj/Bl09uIleP30kyr9W6fiziONEudhHvue5DK6dqH5Vuf1g1Ribxnclh3xF2QtZic5uXkT3LkZ4K0vUbUMVfxhAiNHVZ36ee+j81iWCNGa0z055Slahaj8OJfWqZIauV984MTn2kEMLYEVquEc6vVid1oLMvuM4V8WQVPfi0W0xb2jTPr4RfZEMVGT78vkT1egyUoRgKH3J96gIoPDB7N2XRInaxy/fUw7b9FSlUHYa37o8pWcNVcXehHyg1Sf86OjVR+TFUlwQ8i/n4kgty+enIjm/EbPB/xyjcC5CMKRxKS4Ne4kOc/u8DpmofeUC1KqiCy3Yd5k2n75NAeylBCGb3L6SKN2KcfZ536flh33Fvs1cMnef9wPyf/GW3LiQAapyOWfVXzwimOc4afM5On3jiSjiUMbZgVUWCokSsco68O515yn9vuW8KJSAzwV5LZivZtUvHItLu/s0WHSXzzFqgZDoxkGIQt8lhxkzO0JJYX126Io/LT9yjTb/0khq1eoDSh6zGAQkmbWYSy0XCgR2z/yFfLgIQvE6bcgxf3HxSNtr50p6dteXus6PKFl579IJWv1Lc0GuitVqyUTUlu56HaU9fO6rxw+oTu/xAszQN6/5kfkZLt06mp7fv0GFqjWmEI4T9dr5j/AygiynSJWa9Wgb0n3u8/DSiZQusx2VZFkv2L2Lx+jJTW9R8hUxoqUbd6I754/QIY4BfRFwl5oMnSPaafvfcS5Je3T5ZMpRuAy5Ne0qKnNd2LaM7pw7TB2mb6IMdlnFaYasV1v/sd2HNSKGNU2GyOTE3+eU6Dp/+bqqIbLkLkCdZ+8Wn1/yuud1cFMd07ZxhW8arhz8H3WatYuTv55payL3qSEw7F9P2nDyJrWumJ+K5bKj++z9W3X0ungcvnd0M1XLTlzlydPvMZXL70iDvi9JIGZot5Jfpya1JkfrdKLtFa5a9eTlOzrq+4gycWnbygWz0bZzd+gkn7v57G06eu2RII057NLTgcv+1GL6Lro4rR0hmezhi3dMfh9SZ44Z/cCPyRuUzENBb0Npz8X7VGv8Zjo4zp3yZ7VWzUl94zGP2eiP7fTibRi1ruQiSsAe4b7az95LE9pUoN51i4vmN7laV3MeM5ddBt5XjNKkTEG7Lt6jtrP20oYhDakml7g1lQEz3Cy8Dwun474B9PxNKOv0Zjao6thsvjHATcQx9sQCK12GSmwDlx+j5mWdxU2JrnZyv0TAkhCQZNaSrraFrxUZ71fYIwsi1XTEfBUa1tmcaN+8kUItACVarx7azN7XFDRg7SVVCdZK7QbS7B9c6eYpDxWZVTp49+IpDdrgIx55I7t++U/1KOD6BSatP1KjITOEhxVlYOe2Y4LA3leFzOL8V4/vU92+k6h8q76iuxpdR9O/Pzcj7z2rxeP5bAVclWFU70H+N/mx/FRyLleH2v6xnr77LuKHr0S9H5iEu9OZ//0tPM2GrlfVsdqG77EdTND91PZE3QTJL9OsW9QDvAcEVTF4pu9f8mTifpmun9hJ8M5qW5fSXt87cNw982eq3G4Q5S5egXyPbdfX3OKPgTD+jz2gdUrk4jK0NVR4QDd29LpTIgkJpXQDX70XRLZ/A1f6tVU5VbuCOWxoDLc7feuJIE/KgWdM0kY2LyO8ndiHJC2QRRBaTya+SnnefkuPiGpid58Fq/ah/Vt+PH58QkuVUsIxJsCtZuym3zaepTUD66NJFJuw6awgwx5MwEt/reY1vJkbtZ6xhyb87yy1Ya+wNcelbmFCHfrxk3jUjwIQMJDa4j+vFqReF5l9waQaXuPorLGbExXkmFhthjADxLuWGrZWzEFpUyIPJ2l1ryEKUCj71N99H72gadu96MdqhSJVXVNvo2wP55sTeKjHtiyr7JLvEgGLR0CSWYv/ClgOAP99+SwWe58TkZ7c8qGs7JmFlW3eg0o27MBeVCvxuULrvlTWvaeKyGLn5/CPwssY9v6NaKP+P5ybzjoivg3E0j5vYUFm4WlVQgWss+amTA45KOhBZIKIkIByLfuoukP7yh2GEMIc7l44opX0Xdi+nDVvPwsiqRBZdJC3dHWhdnD18GZBZg1dr2pwtQ2EWOClz0D8dZFZ9fNAZI8s/13o9DLzpsyOuejzp3BKniKlerNotxFWsGViD7LNkY+qdR4RbXvZgL+3X7PqQTJ9HgSRQu661ypK7bkIglXKiJAZhBvAS6uQUAW7NKkifiJAPtUtGV/Hfg1KqHYV/RqGgPAF9T4qsdcWpXHhLVXf36tOMRWRRSfViuSgMkxQQWpxQ6j+vcbxV+yN3HzmNrkyKVSILPanSpFcEECsD97Xjhxy8IXPh6086suhCxUobeqUlJLbXfqzPfctDmn9Hzy+07Zf0HpMfSdCCHST2WDW5Q2n0e5lqWFpJ/Yih9J6Lj6x5sQN6jjHgw6Pb0npeD6aNmPnRQLWIOf6zC/gJW07f4cGNyrJHuBvYVH6zpHHJAKWgIAks5ZwleUaBQIprdJStU7DBbFa0rO6yLDPU7IyezjrknPZmqpYWGTehwS/pNMb5ol419eB/hzTeZc+ctJRelvHKGhmZqKqbggtgCmP+pVj3yVLzqQ4MikAMdP84UbJXNhLzurXZkH+t8Ru771cmWzf+khNwsNC6W3QExEbbOh6I3Xw9UPzUQsjea+1tdGct7Y22Ic4YdwcPOIywD4HNtAJDpFAJbUGA6fpOkXr/qMrpgh1hF5LjhlNhLV2aAE7QeSGNilNf2w9Lx7j58+aWYQFIHYUHsrkX+Oy01ulJDcmkyCF8GzCw/gw6K0ISdAGk2PmtIJIKsdSfyXFWb+GIij7k399XP7xU2SpKm2xsSCI524/pSfsJc5mk17pQrwrMlaQqur+94FIxxSiff9r8lWn6oXFGlYdu84E+BaVd8lK1QrnoO+ZXCL0QJcBG/+F2p80qJ+TilUGdNk89n6DYBdijzYsH8cSl+WKatDqne9xmXZ73eNwD5dIpyM0YceFu9SHwyRsM6SJdEzzA0oKY3y0lSYRkAh8Q0CS2W9YyC0LQACJWUVqujMJXEe3zx6gCzv+IXg6bZhUdp69i9LbOIgY1iPL/xDxrrlLVCQn9nhW7vAzndk4X8SmasKUikmyNjOE7KW3dYhyqtKfQoo1G4S+eSk8vslTfUveUdrkKlFBbP731SNnyHqVc9Xfk6dMRRE+O/W9hm+L8dl7p2CQKk069hxXE8l2/pdP0Q0O1zCGzMKTi8Q966x5xLsyk+CnD8UmruHtswepYtsBkUIclHaW/I7Ep+bl8olH7Ad9/GkFeyz/OeIriNb2EU1EAlHg6/fU6q/d5BfwigozESvNCVm1i+fkuNTUNHjFsSjwgSTHxhwyRf03kzZ1xM+RQozV+3/5LqLoRmomiik0JK0ggdWivLNI8sI52ZkIn/q9jUgu23ruNhP0J3ToykMOYTgjHs3341AKbYbvquKJ1nbckH0IJ9BmdfjmAWT2OntWNQ374TFGuWB99ogT5eCdblw6rwin0NdWHpMIWBoCksxa2hW34PUiVCD8Q6h4zF2j6yjC693Lp+wp/Etk2J/bslg88j+4eLxI1Oq3+kIkhQPP1X/FOXovA6J6X18HRhA0u5z5tY4HQof40yrth5DixVUafgx9L0IQ4JU1ZL01u49VTo30foljdpGcps9A/KuyeoKmgchOrp+dHDjcovvCQ5EOC8KQ0UYoMBgbauCQt4joK/D2FVWfkOiCwXse9i6YvefvVMfkBtHHT59F7CY8kiM4xhWvp8EhNJMfay87fI2WHrxKo1uUZbUDb0Fkf21Zjvo3/Eb2oDZgCrvHMbRKyIPSvz97gjOnS63VO4kYXxhUExb2rKWcIt4RSoFH+woRRcwqEqgau+UVL6gEnL75hHosPCiUELpxiIXSVr0j4PLXDi/1XVq321cpSNpIawAnqF3kBK6STlmihAAg6Q6WRcPzCk/z+pM3RIiFrsQ3ZRLwNH/mtSA8RJpEQCIQGQFJZiPjIT+ZMQL3Lh2ntcNbUzN+hF6ck5BgIGSQwYL2aejbYBKePnaTFKzSOBKRDeYKViBR6azt4xQhVPdCBr9N9ryqfr33rhHbDs7FVPvUN3IUKSMSn26e3heJzIa9e0Nz25ckRz6v41/byJD1qvervg2ZsOiSq2xzOGsls4j7tXcqyHHJlwlVwNJk/JadDgxBkh3yFTEqVADxtSi0oGnw8G4Y3Y5q9/pNqCdoHrf0zyeuB9APM/eK5CPIZsHgFUW8K8jsa04kgkHfFNaGVQLUbf9l05BZqBw0LZNPNRSI5CGfhwSpLW3m5JCRSa6VUEIIZ4KOGFjFZrHs2JStF2gne5kRUtCSPcwvOf71/NS2ogmILWJ34R1dy/Gr6sRX6QPvQprsuJ/6Lq3bFQtk00pmX7H3uOuCAyKG9y8N7VeoPcAwP3U7xRJjCMFoWvYbFurH1bePsAQayH5VjkuWJhGQCERGQJLZyHjIT2aMQM6i5SgtS2MdXzmNMmbJxqSvuCCSiscVKgdIakpplY6uHdnCsbS1RdlVVKw6smyyILfwfCJmVV85VmMgRJLW+tHthVYt4mdRTODslkVUuHozka2vrS+3pt2YfC+jk2tn8TqyU86iZSn4WQAdYo8yvJNVOkZ4Sw1Zr7b+sc99zGLx0nU8uv2V2g4k6OhCmaEqxylnsHMUYQCQRYNV7zIqui7k8ThAoBzHa9oxCfyTPY6IQy3GQv6Ih52x66LoHQQPViKPHR3gEARouParX4KeMbnEI+2dXnfFcXhSkUGfiWM/48KQFObAcbdNyuSl4Pecmb/+tEjcgtasNkMc6pgW5UTIQx/WYoX3OINVKvK4dJ8GWz43AABAAElEQVSQPFWtcHYhKYZzvy+VhyZuOkeTWP0A8bNWnFjl6RdAmzh+Fh7VLBm1x6XCM/p4SQ9twxu0r0hOGxF3DA+qNZPORhyjy45UoSYBuTJ8LsXhG+p2nOXNYBULRCa56m2w/ZoxuvzgOdUrkVuvbJfmefKzRMBSEJBk1lKutFynIKMgadv+6EurBjdRIZI8ZWomk2PIpUJdsa/p8Lm0fWp/4fHDDivWSq3302QmuWlp+5S+9HeXijT20DfRf1VHMdhwKlWNMmTJSv8b14mUVOvcJSpRw0F/6uwNsbQd/txC2yb3pq2/91S1s+WwhNYTV4siDNiJIhCGrFfVQRxugIzX5UQ0EOyNYzuoesbNRNMRC1h7t4Fqn9wwHQJQKcBj+Z+WHaFm03aqBkLs6Sj3MlSXyREMklxnbgbSOvZc4sXho1Sdk6YQe9qZ9Wfn7b0syCPib+PCoAuLZKY5eyJCWZCABm+mvqpZiCkN/RhO41m+a/v5CJKN+Fk89h/Na1His5EchZKys7lvvBRDMYJFvWopH+P8HeOv6l+PBnFRCc2xO9coLLRwNQc9di2A0jLZRpyyPgMZR1ytLs+1vnPlMYmAJSDwHcug8D8RaRKBxIfA1KlTafqcv+mnNdFXhDJm9uFhISIrPpirdUEr1d6pkEpaS+kHagaBt31EGALiUpUfSuwPe/c6UliAco6x79Ob5mPprVLUftr/OMThtah8lZGLHWjGwerqF/90EaIQ9OCmeJSPAgpKdTL1cwxZr3r7uNz+wFJmzx/wo92XzzhWOTd7tJ1VEmhxOY4p+lo/ui2Vy2dPq1atMkX35OPjQyVKlKDTk9tEkq0yxWAhHJsJgveIixbgcT2UA7R5KK/6vxByUq4c96nuhb3BiWHZuRgASGdsbOmhqzRyzUmCViy8lNcevhQeX3iMkfFviL3jmFgf/yB6/+GTIIFI+NJmUDeACgIqoOWyyyi80sq/Y23t43IflCAwNjB0YZUE9UprcTlOfPS1hyXPUFAjnJVYUqSQ/q/4wFyOYTQCO+Q302jM5AlJHQF4WEH88NJlaTPZCN1WzePYj1dcGypl5XOrYVS3+GFGaAJe+syQ9eo7PzbHoKOrD+fY9C3PNRwBqA9AegsvfabLM1og+7e4Z33nG3MM319d4+nrB8QQcavRWR77jIRXQlhOTrjDS5pEQCIQPwjoFsyLn/HlKBIBiYBEQCIgEZAISAQkAhKBGCMgyWyMoZMnSgRih0AG26wm8fLGblbybImA6RBA9SsUXFBXIzDdaLJniYBEwFIQkGEGlnKl5TqNRuDti0C6feYA5SpeQagcGN1BNCf0Xu4ZTYuYHQ7yv0nXj31L9indpLOIDVbv7WPoO0qVRnusoXo7U20bMj7K1xJ9pzUOWHNenz5+EEUuNPffOX+YHvtFxFynSG1FFVr/pNnEYj+jUMJBlsMq7+Jo8phdBeS2lQsQXrGxW09e0c4L91RddKpeSKs2LRogvvbj5y9kk95K1V59A3HnwSEfheSV+n5t25D0im3MsLZ+DdkHrVxIjEVnn3itqLqmGRuMuOEFHj6q02sWzUGuTpGVFVQH5YZEIAkiIMlsErxocsrxgwA0YHf+OZAa/zLbJGTWVKt4dtdXlOzNYJdNELwiNZsLMgsd2sNLJ7Ds2DbWf30p1BnyuFamun0n6V3f3PalWSGhMjUeOjtWU0ahh0NLJjC5vCgkxNJZZ2FVg4ZUp/d4QmytYlcO/E/o/j655UMgtDbZnahs857k1rSrqHymtIOG7d45w8n/yhl6wzrAVukzcYWxakLmTJFOC7juJUr+vn/1nJJx8ookswp6RHcCg4XU1czO1eKNzH4bPeZbSGRDeV6UzrXiMrrNWKNVWxnYl+/CqOqv/+MqZqmEKoP6iJC6Gs8VwTZz6d7Qj58oHSe21S6Wk6Z2qBypL2jPQq4M6gnoD8oDlQtlo/GsxuDsmFm9yxht/48lyvouPUI+f3UQ69HsBDJfO7nU7akbj0XBiOpFcgiJMs0qaZBVm7LlPPk9fkkZeL1VCmanLjULq2KLP4azBCAXZwhnsoskwAxpUkoyqwm2/JykEZBhBkn68snJSwR0I9Bq/Arqv8ZLKC/AA7VhTLuI0r1coKFqp2GspOBKt7ik74qB34tKaNp68vZYS68ef/OCaWtjyL7HNy7RqiFNRcGEorVasBbuL4LAXty1krVom3PVsi+im8v71tPWyb2EukO5lr2pTLNuBG3fvXOG0Yk1M1RDofIX+rt6eDN7zstTda7mltXFlfw8dwnZNZBXGCqUAYOCVb5XnSs3zAOBf/rWoXNT2gqSp21FA5cfpaevQ6IcQlW0H2buodUn/Midy/yCzLco5ywIa8c5+1Tt8W+m49x9ovSvEyeSDW1SmglgFqHH22TKDlFJTdU4BhuoVDaPS9nqsrU8v59XHqc33G7g9yW5XK8NLTpwhbr/fYDggVUMesDtZu0VBTD61XcVxSFQ7KL9bA+hqIB2UIoAVtuGNVZOk+8SAbNCQHpmzepyysVIBLQj8ODySXpw+RQV48pnzbkCmmLHuIDEsRVTyNtjHVVuN0jsfvM8gPdNY6mwi1x29prSNFbv57YuEaWEu/99UFQoQ2coJ4yiCqg2dv34DlEo4vTGeWTD6gz4wVa8tSjAMLutK53fupSqMgmG3fU6JkriNhz8J7k16Sr2ER/bO3uY8Oqi+ETpJl0i9sv/WxwC/3B1s8NXH2oNH1h/8iZ5cdnZ8a3LU18uEAGDhi0MnlDve8+E1xLVufBqVSE/LehRUxzH/6Zv96Jp2y/QetbjBck01jDGASabJ/we03sOXdBmKI07et0pUQhi69BGqhjj/Nsy03QugIECED9UKiDKFf+28TSlS52CDv/WQiWn9mur8lT859XUg/+9HRnfUtsQcp9EwKwQkJ5Zs7qclrsYPML+p39DOrH6m/dOQePhtXPi2GUmbIrd9/akPbN+oXkd3Ghmq6K0eUJ3urBjOT/W/qw0ifKOczAGiJS6wQuI/V47V6jvFo/Sd8/8RRRZ+Mu9ABcP6Ei3zuyP1Ca+PkBTF5aHCzKom1OpquLjR/Z0KvYh5B29eHRbkMlsBYz/sVb6UX9/dPWcILEotaturvXbi48B1xF68Iae3bsuKq8pRBYHM7D2rlPJquytfUWfP0X8+D/k0AJYkRru4l35H8g67N1Xz6yyP6m/o/pToz+208yvlbvU13P+dqA4hsfIip1kojTs3xNUbsQ6QWp6LjxIK45co89fPeBKO/V3nIMxlKpUyrHnb0LF/lVHfZVd4h0VwYbyGJXHbKTCg1axFuk+4bWM1CgBPvgFvKRfN5wmEDqU7tW0Tae5gh9r7XavXTTSocGNStL87jVUYQZ4HA/TlAGrytXGYIihjYndfYpqah+peC47MQ9tfez2uif6RwEI9WQ5pdzw1rMR5XFvPH5Fgex9rs2V3NR1gaEhXINDEq4+fCHK9GobQ+6TCJgTApLMmtPVtOC1OOQrQi8e3qJzXApWeWStwIFH1w+vnqHshUuLXfcunRAewauHt1C+MjWp1PcdCd7IPUw8EdOpy0JeB4l+QoNfRGryKfyD2B/89KFqP/pb1L0qoXxrruIVCaTtdaA/rRvVls5s+lvVLr42UKo3WYqUdGnPahVhB3G/uHuVmIJLxfqqqWTJXYA6z94tXu5jl6j2x3QDBBQ4l20etVRoMOMES5PRWiR6YVx4YtUNJPfp3WtChzc5rwFWkq9ZtwUHCPq86ubvc0p8xHrNyYrksBWPjJccvEpIBlI3eBrP3gqk0l9LpXpeDyD36bto67k7VKNoTuF1hKdv6L+eosyr+rnq20FvQ0U/Lzg2VN3wWB79P/xK7nDsMfdXY9wm2shjV+BSrEjqQqGA9rP30sL93xKN1PuJj20kOvVceIiT2rJSTw2yqowPMlmrWC5CiVwUVkBJXNwsOGZOR60ruqj0YeuUyEUpucLYGn7cr9wE4P1f9qzC6rnmVro06v039gjvGNFEvKox4dRmd3iOMJTpVbecthl43snI+35EGA2ILKyUlmSukl/3+THhlSYRMHcEZJiBuV9hC1lfsuQpqGjtlnRu8yJ64HNaVdIVCUTXj+2g7IXcuPqUi0Dj6qHNTJxS0IC1l0TSEHZWasePsn9wpZunPERCUmxhO8hlXEFuu87fz0UD3ER31buMoDXDWtLBRb9Ribo/CAKnbRxfnu/z+37aDqn2oXIZ4kkNNRR6qNl9DCeATaIZLQoSSuY+vHpWxMq6cT/Ax1QGAtpg4LQo3cOjjdABXIv8FeqxukI6ylWsvKodSH9w4EPhzf7vy2eq3H6w6hgIt2KPfM/T/UueHI97ma6f2ClCKRAPbE6Gsq2I61zMZPb0zSdUqWBE0QDETu5iLx6IbP6s1mLJWzipKUXy7+jC1LYqb92Ahq5Ueuha2uf9gECmYmsTNp0V5BaVvEp/LcQwvJkbtZ6xhyb87yy1YVJorUNBAAlNflxRTJ+hSlnXmkX0NdF67LcNZ9hT+Z42/twwSkY/ToA39WlwCGXJlEYQ7/2X/VX9IKFrbrfqqsISUEAY3aIs/c4JYEUG/8se2qx07tZTcT7mptw8qDqIww1UD0OymWblMCgaoBDErSevBcF2yhKROHmCb2CUkAllGje/klhgXdbZUdkt3yUCZomAJLNmeVnNY1FWVlYEr6ehBoIIMnv92HYVmb3rdVRk7rt2G63qpkLrvlTWvaeKyOLA5/CPwssXxuVXY2vIsr96cBMnWJVUEVn0mTxlKirVuBMhXAGkq9T3P2odyvfoNsJLn9nmdDaKzKIvm+z5KGXqNBTCnuV7F4+JMAjs/489fZ8+hAp1A3yOD7t5eh/tmNaf4O2u1+8PcshbOMqwIN6YFwwlflHJTJuByB5Z/nuER56rSmV2zCXCERQvrrZzDNn3meW+8B00lSl9f+BMc0OsdSUXQWZBBhUye8z3kciyH+X+rZpdn3rFxSN09cfO4Z++UKZ0qUUykSFj6Wvzij23SDpyzZNFRWTRHp7OH6sVIoQr7OISqB2rFtLazfbzd0SyldaDX3fmc8xkNJkFUV/GsbIrfqorvKza+r/3LMLjuZgTqZzsM9Ef7StRGWcHJqmBgoR3nONBxye2VpX6zeuQiayYVL54G8bhFwEiPAD9fuHkMKggoLKaKewee2Yz8/XSZrnYO3vz8Wt6Gxoukt9K8HU4zmT23+PXqXmZfGJu/+NQiu0XIkIRvugJLdHWv+a+MP5+JkuWTJay1QRGfk5UCEgym6guh5yMOgI2Njb0/vVLQVK+4z+m0VlWlxJMegoxUdxF9QdMFZ6Za4e3sjyVFRWp2UJ1Ojy0IcEv6fSGeQSvHh7/v3h0lxA3mt429h6MIA53gCELf9P4rqpxsfHhfURs6quA+5H2q39AglbTEfPVd0XZ1tSRjNJAYwe8vZt+6yw0c2v3Gi9INvRoPdfMJC+OFWYOSA0H/alxVtx/fBlwj/bPH0Ugs9bZnMh9zGKtZYMx8iiPAL4udwjxsYeWTKSlvWvRoI1XKL1N5JKs8Nji5uSR7wXyObCBTvz7J4W9fa3VG2zMihBOYmNTzphTjGqL7zfsBT/eN8RK5M5CBbmsLIgiSBi+A9s4lADyVM1ZnkoxeGghI7WAM+XP33kqHv/jsTW8kg5csCC2Bq8h7P2HcJGop94fCBYMj+91GZKp5naroeuw2I/vozEGb+yA5UepQ5WC9H1pJ52nQpIL9pHJ/T8/1VF5s4EtYoNn7rpEW9mz3bNOMSGJ1ZVDWSpwyMK4VuUEcYfG7azdlzj+2JcVkImmdawi+ovr/6Xiaxr46r3WbkOYRAMfyGvBUzunazWhXDBkxXEavfaUCEMB2cbNBJLNCmSP+J5p7cyAnfh+2lhnMqClbCIRSDgEomcICTc3ObKFI1CoUCH2zH6klwF3DUaiRL029I6LHeAROoT0/Tx3syxTI/bCftMxPbV+Did9FaHjTHoQz+lUurogjzmLxoy4hL6J+HFXJgnPLAyeWGibqr/SZLIW4RBZnAoqzaO84zx4UPW9QNCNMcXTW73LKJW3GKQeGrOIpfVhbVdTm8+BjSKO+L73Sard6zfq88+pSEQWUkia8c62rGzg2qA91e45TmjO3uIiFjC0Q3vFEKKQt3Q1ajp8vvDM3uBwkdgY4omfP7hJ+A6ayuzs7MjWxpquR/PIXX18PL6H3NS5208JHt3dF+9Tw1JOQnpJaTdvrzcVH7Ka/tx5UeiKVi2cg+YxeSzLHsiY2Kt3kZ+OvPz6OTV7YhH+oP6yTp+aWpR3ZhmpiJAHbePBg5uGvZ36XlYpjfOzgFyCwEPGqv+yI6rXEyaEiCvFPpDQrBwXC1MPy1DmWK9ERAzsTX6ED4MHGYbwCSWUAjcKE1hjFrG08H6aypC49v7DJ0GwNcfAOq3TWXFxhIif78IcT31iQishMda5emEayV76/WPdyeXrNdB3LTT71vYZYQqFCkV9cqKtrdwnEUgoBIz7i5FQs5TjWiQCxYsXpzRp03I85Am9ov7q4BSr3YoQr3r9+E7hff3AYQMl6rdVNXnPj7VxPF1mO+q3+gKlTptBdcxz9V+qba0bX91F//33TeMR7ZB4BlPIlXXWiB9FEDH30YvFMeV/IEmofgWiqsuQpPXkpreuw2I/vJPQUDXUILEF0qrE7yrnIfY2a/7iLMN1SYRagEibwuCJ3Ta5N+UoUpZajF1KmRyiJr6cXDuLY3onUts/1pNmAlcanifszbMAQWQn188uQhO6c7KPusFbmSajjZDtwo1KTEMN4LEP55uhChUqqHcf59sVK1YkTz9f6sWeQEOsZfn8hHjVXV4RIv7QKlWvqBXE3kUct8uQhs798UOkmEttSgjqY3LdKPFR86m04olVvt95vsZp4hH8wp611LsQcZzwAIOo6jIkVF3+msCkq409k7lfWNfVUEOMbdGctoTkLnVD8hq8lFf9X1Ay/m5kt42oevdJI4kO5+BxOgxFFmDXHnJhESatbl9jgsVO/h8KNBTLbccSXs+FNBbIeVwb4nchC/bg+RtVyAPGgDf8wfO3VPlrzDTW58+fbXj9iryYMpc5ey4JNQddsctKu+jePW8+pVY/NomumTwuEUhQBHT/xUnQacnBJQIcg5cqFdWtW5euHN9usGYoZJzyudUQZBblaDNmyU55uSqUYkJxgH/cClZpHInIBnMFqcDbVyidtb3SNMo7YjFhLx5GeGyUBjdO7lU2xbsNFyUASbzNpVQ1CZXn2pl0dPlk6jxnT6RkJ/UOoLvqy3G/+sw2h7NRZDZLngKCdN887UGFqzVVdY0QiwCuyGWXM7/wJKsOxPHGYQ4TgNwWCjlk0BHKYf81bvbuhaNRyOyl3SvFjByci4oqYPbs2X5y6zLHQ7+KlEiHa4gbAahbxJTIYiDgn885P7m4RCQNxjEcqu4aN2lKA/vvF/JJELaPzhy56lV19rSinGvgqxDKxp+rFsquOu3Ri7d8U0XiUbt68hDUDK4woUPiky7LZRdB9O48jfykYS9n+6ubk0NGJnRWQsc1nMmUunQUvJ9Ttl6gnZytD0UBbYbYU8T96jPEzBpDZnvULkZ4aVqt3zZTKCscqGutggh6clwvQi/yMSFXbA+Hb8AUD3YB9myCyCMWt0mZvEoz8mfVhkusRQvCaQoii4HcOdkPIQIonKBOprdzWAlidet/VVLAdoXRG0Txh0W9aqvmiOu9kxMD27HKRGwMNwF3n7ykxo0bx6Ybea5EwOQISDJrcojlALFBoHu3buIPaZA/a0Pmym9QV8U5EWzr7z3petBjVikYFKkEKhKnUlql45KuW4SeKfpESMKRZZMFuUWcq66xHPIyQUqZms5uXiiqaqW1tiO/E7vpDpNWdYN3s1aPX0Up3K2/9xJSU6nTZaAbnntEPGdeDmvQF9KAOFK84tLKuveiGyf3iKICj/0uiYpYz+/5RUhzMftBRa6Y2NRGeYSneeyhIJ2nh3L86rN7vuTIHuDTG7XHAqNcbv5ydcjeqTCd27pYJOflK1uTJdOeCGKJsIFsBUuRC6sewCDfhXhkFF2o2mk4a9E60u2zB4UUGo4jnCKmBs/5lX3radyYkTHtwuDz2rRpQ4MGDqR1LHFlqHcWWqO9Fx8mPEIf+L2riJtUBgTBgoA+YmlRntWZH4sjuQnlXxFjCZH+2/wY3TlrZuUU1Xth9mwidADJUXk5OcqOtUp3M8E7eu2Rqg02QODGtCgnSuH2WXKY+rNSQgarVELiagaHNkBOqlx+3bHni3rVIrwSyn7l+Ne6E7dSd46HhVpBdpv0BDWAlUevi3nXL5lHTK0HS3uByI9Y40mX7j+j7zmcA4/ckWiFG4afG5dSLeHvfT70G5fHBQEf2tRwj7KqA40NKCfg9e9xP4KXui6HQECOaxzr5yKGV/HGI8kP5HwH39xULewn5ogkN8TP4kYHhSFiY/+wNrFL/nxUqVKl2HQjz5UImBwBSWZNDrEcIDYINGzYkFwKFKTjK6eSOz+eNsRQujRVmvSCZLnWbxfpFIQVNB0+l7ZP7U8bRkccs2Kt0no/TRbZ8tun9BVFDsYeeh7pPHwASYVncfOEbqLkKpLS8rhWoTaTVtOKAQ0jtYcOajhn4h9YOE6lTAAJqpINOwqJLGMTuCJ1HoMPeVwrUYtfl9H+BWMJMcN4waAQgLUXY1mzmBjCJjTjXDX7wc0CLPCWj3hpHsdnPOIGUQWWuAE4xtcbL8UQ91y//xQh44V9has3o7pBT+gQh4xsHNtBaUZpOXyk6YgFVKBSA9U+YzfObFpIyb/7Qj169DD2VKPbZ8yYkXr26kVzVy7lhJ2CBmXHI0Y2nVUEMUUVKHWDN3Z21+qEUq4dvpZmRVb8pB8qct8pqN/SI1R57EYKXNpT/TSxDZK6jEvEosACSCoey1culI1W9a8niiaon4BH2qEfw2n8xrMqZQLEzrbnBKzRHLMZ399v9blFtw391XWDGoiEsbazvj1VgW7s3K7VVadDMWJJ79qiAMO8vZcJLxgksya1rcixwd9urhGCgXAG/BcXBvzWDKhP7Vld4S++QcALVpLL6eIaqXvD53StTj0XHaJB/xwTL7RDQQbcMKh757HfGIMHGjdZ8xf8bcxpsq1EIEEQ+I7/EcbNv74Emb4c1BIQ2LZtGzVv3pw6ztjOlaCqxMmSoWYQeNtHZMZD9kn58cX+sHevhedV10DQrkVyUHobexF7q6sd9n9ghYTAW1cEscZj9Ez2UWNF9Z0fk2NI9oLXUl3jVukHEmQoQPD6yQOh3GDvVCiSRJnSztB3EFkUh+i93NPQU6Jthz5f8fzgIU+Z2krES2fMkk3reYiJfv7gBuvlPuPEr9zsvXcW6hWajbf90UcoKAzbof/xNsIuFnWtSGNHj6LRo0drdmOSz8+e8SPrfHmpc5X8XLWqXJyMgSShKw+ChHoBHpcr32/sR0Y/4l11GbRrb3LWvn3GtMI7q6sd9r/jmF0f/yCRrFQ4h43wcuprHxfHkJiFssjqGrcx6RchEr5cLewly24V4rmjaII2Q1yqL1fSesDkDolZaKsue6acA6907iwZRQKcsi8u3qHUgBCREhynCy+tNsPPuO+jlyLGtji3y8HyXdoMMbhuw9fR5HYVtYZlqJ+Dm6F775PTNV8/KculDozcTowI7JCe2cR4WeScIiHQrFkzQmzh7ukDqNuiI1GqPkVqbOAHFBHA435Nw3689Bk8rNp0UbWdA09w7hIVtR1KkH3wLkP/Nq7K1CKZLlfx2D3K1AQCHm+b7E7ipXlM8zPicHMULqO5O0af4WXeOfUnypM7Nw0danhyXYwGUzvJ3t6epv/5F/Xt24cfJ+fSGWuqdkq0mxD811ZdCvvx0mfwsCJD3hCD50+z3Ksh5yWGNvBuQpIrOoPH2pW9uXjpMiSerfX0o+3D4z5RCiRbF9FW5oOblSIcJoJXXNhqDm/Y532fjh07JolsXAAq+zA5AlKay+QQywHiAoFlS5dQ6u8+0bZJPYREU1z0ae59QBlg029dRKleU60VHtP6/b+FA5hqnJj26713jcDgweWT0Xaxf8FoeuznRRs3rBfJh9GeEIcNevbsSU04yab7osOEJC5p0SMwiStzoaQxSusmtN1nj+eaAQ3ixTMdk7VCYQJYjVgd/ROUC6xNPGLNSRo5ciRVrVo1JsPJcyQC8Y6AJLPxDrkcMCYIZMmShbZv20oB187Q9j/6MqGNkNGJSV/mfk4GJpiFqjYWHmxNGbG4Xnu5Fr04jjXupYniap54/AoM4IkuUFF3HO2xldO4tO4SWrVyJUESLr4NnrWVq/4l++y5qdVMD1GSNb7nkFTGQ2JTIy6MgFhgxKkmBqtZNCcXJ7BODFPROQdghWpmwA7Vz7QZ1Avazt5HderWo4kTJ2prIvdJBBIlAjJmNlFeFjkpXQgcOnSIGrEHK3fJqtR8zBKR6KWrrdwvEYgOAdwU7f97DJ3fspgWLlxI8JAmpAUGBlL1alUp9PVzWjegrkr4PiHnJMe2DAQ8WdGh0/yD5FauPO3avYfSpEljGQuXqzQHBHZIz6w5XEYLWkOtWrXoMBPaoJsXaXmfWqw16mNBq5dLjUsEgp8+ojW/NKPLrGG7bt26BCeyWJujoyOdPHWasuctQHUnbaMNnE0uTSJgSgSQ8IfktRZ/7ab63zemPXs9JJE1JeCyb5MgIMmsSWCVnZoSAVRluux9iQrkzkrLmNB6zBtJqOwlTSJgCALhYSHkydXGFnapQClCgujsmTMEvdfEYra2tnTs+Anq0asP9Vt2hFrO2MPZ7PL7nViujznNAxrCdSZtpxm7L9Nff82gDRs3UurU0RfvMCcM5FrMAwEZZmAe19EiV4F4yKVLl9LoMWMp+M0bKlzDnYrWainUA2JT/ckiwTTzReO78pgrnfke3U4++9bSf58+0DBWLBgxYkSi/vE+deoUDRzQny54cTGCormobUVnqlUsl4gXNfNLJpdnIgQCudiGBysVrPa8RZfvPaUG9evRrNlzTF7tzkTLkd1KBIDADklm5RchySMQEhJCK1asoIWLFtMVn8uUguWnHLjcaTobB0qeKo1KY1NZKLy4abhQAiS2LMVA5hStUUtZM9b56WMYhQUH0bP7Nygs5B3lyp2HenTvRr179yY7O7skA8Xu3btpwfx5tP/AAfrMj4XzOFgTys9mSJ1cFDdIMguRE40zBILehlJmrgAGKbXo7NOX/+ht2Ce6++wNBQQFUxorK2ru3pz69x9A5cvHrbRedHORxyUCJkBAklkTgCq7TEAEHjx4ILQRr169Ss+fP6f379+rZgNCd+3aNbp+/TqVLFmSnJ2dVcfMeePEiRMiBs7Nzc2cl6l1bVb8o43H9oUKFaKKFStS0aJFtbZLKjuDg4Pp6NGj5O3tTY8ePaK3b9/SFy4yYekWGhpKu3btopo1a4rrbe54fObExb179woNWJDRzJmjlidWxyA5K45kypSJnJycqFSpUlSlShXCvw1pEgEzQUCSWTO5kHIZ0SDw8OFDateuHXl5edHMmTOpF5cQtRRzd3cXP1xr1661lCXLdVoYAjdv3qQCBQoIkl+iRAmLWH1AQID4m3bu3DmaMWMG9enTxyLWLRcpEdCCgFQz0AKK3GVmCOzYsYNcXV3pxYsXdPbsWYsisriU6dKli+ShNrPLK5cjEVB9v/FdtxTLnj07HT58WFSr69evH7Vq1YrguZcmEbBEBKIPtrFEVOSazQKBjx8/0qBBg6hp06aEkrgXLlygYsWKmcXajFmEJLPGoCXbJkUElHAiSyKzuE4IH5gwYQIdPHiQTp48KW7accMuTSJgaQhIMmtpV9xC1nv79m0RI7l8+XJavXo1LVu2jNKmTWshq4+8TElmI+MhP5kfApZKZpUrWaNGDbp8+TIVLFhQxMNOnz6dK98ljupoyhzlu0TAlAhIMmtKdGXfCYIABPCR5IA/5oiRbd++fYLMI7EMKslsYrkSch6mQsDSySxwRcnvPXv20O+//06jR4+mhg0biiRYU2Eu+5UIJCYEJJlNTFdDziVWCECiq3v37iIpokuXLnT69GnKnz9/rPo0h5MlmTWHqyjXoA8BkFmI/eOxuyUb5PeGsn4yFEz8/PwIyXBHjhyxZEjk2i0EAUlmLeRCm/syIcVVpkwZ2rp1K23bto1mz55NqVKlMvdlG7Q+SWYNgkk2SsIIgMziey4tAoFy5crRpUuXRKhV7dq1ady4caxP/FnCIxEwWwQkmTXbS2s5C1uyZAmVLVuWrK2thTQPEr6kfUNAktlvWMgt80RAktmo1xXas5s2baJ58+bRtGnThAYv5LykSQTMEQFJZs3xqlrImt5wCdsffvhBVHMaPHiwEJPPmTOnhaze8GVKMms4VrJl0kRAklnd1w36s2fOnKGnT5+KsANUk5MmETA3BCSZNbcraiHrgcwWqnihGtK+fftE0kOKFJZTntaYywwyi2Q4VEmSJhEwRwQkmdV/VRE7i2TYRo0aUePGjennn3+m8PBw/SfJoxKBJISAJLNJ6GLJqZIgZajghdKk+fLlE3I0iAmTphsBJZYQP/jSJALmiIAks9FfVfwdWLFiBa1cuZIWL15MlSpVort370Z/omwhEUgCCEgymwQukpxiBAKo4NWkSRMaNmwYjR8/XnhkHRwcJDzRICDJbDQAycNJHgFJZg2/hB07dhReWnhm8XRrw4YNhp8sW0oEEikCkswm0gsjpxUZgePHj4t4Lx8fHzp27BiNHDmSIEMjLXoEJJmNHiPZImkjIMmscdfPxcVFxNGC2CLvoFevXjIMyTgIZetEhoAks4nsgsjpREbgy5cvolxjzZo1hfSWt7e3CDGI3Ep+0oeAJLP60JHHzAEBSWaNv4rQ5YXSwZYtW2jjxo1CEcbX19f4juQZEoFEgIAks4ngIsgpaEfgyZMnhHjYyZMnE+JkoSEL+S1pxiEgyaxxeMnWSQ8BSWZjfs2aN28uJA0zZMggHAYo/S1NIpDUEJBkNqldMQuZr4eHhwgrePTokajk1b9/fwtZedwvU5LZuMdU9pi4EJBkNnbXI3fu3IRQLvyd7dGjh6ii+Pbt29h1Ks+WCMQjApLMxiPYcqjoEUBSAhK8UFe8bt26dPHiRZGkEP2ZsoUuBNKkSUPJkiWjd+/e6Woi90sEkjQCkszG/vJB2nDKlCkER8KhQ4eoVKlS4u9v7HuWPUgETI+AJLOmx1iOYCAC9+/fpypVqtCCBQsIj7pWr15N6dOnN/Bs2UwfAmnTpqWQkBB9TeQxiUCSRUCS2bi7dHAiXL58meCtrVChgigNHne9y54kAqZBQJJZ0+AqezUSgc2bNwsPLAjX+fPnqUuXLkb2IJvrQwChBvjBlyYRMEcEJJmN26vq6OhI+/fvp7Fjx4oCCygR/vLly7gdRPYmEYhDBCSZjUMwZVfGIxAWFkZ9+/alli1bComYc+fOUaFChYzvSJ6hFwFJZvXCIw8mcQQkmY37C4jQpDFjxogqiwj3cnV1JU9Pz7gfSPYoEYgDBCSZjQMQZRcxQ8DPz4/KlStHa9euFdIwf//9N1lZWcWsM3mWXgRAZmXMrF6I5MEkigDk+3BTrCQ6JtFlJNppV65cWYQdgMxWr15dlA4H5tIkAokJAUlmE9PVsKC5oKSim5ubIK+XLl2iVq1aWdDq43+p0jMb/5jLEeMHASV8RpJZ0+FtY2NDO3bsoL/++kvofiOuNjAw0HQDyp4lAkYiIMmskYDJ5rFDAN7BH3/8UcTE9unTRzy2cnJyil2n8uxoEZBkNlqIZIMkioAks/F34QYOHEinTp2iBw8eCOlExNVKkwgkBgQkmU0MV8FC5oDqXaVLl6a9e/fS7t27afr06ZQyZUoLWX3CLlOS2YTFX45uOgQkmTUdttp6xt9wxNDWqlWL6tevL0qLf/r0SVtTuU8iEG8ISDIbb1Bb9kDz58+n8uXLU7Zs2UT8VYMGDSwbkHhevSSz8Qy4HC7eEJBkNt6gVg2EamHIdViyZAnNmTOHqlatSv7+/qrjckMiEN8ISDIb34hb2HivXr0id3d3wuOpkSNHCjFuEFpp8YuAJLPxi7ccLf4QkGQ2/rDWHKlbt25CSvHNmzdC7QAlx6VJBBICAUlmEwJ1Cxnz9OnTQjsWcluoKDNu3DhRicpClp+olinJbKK6HHIycYiAJLNxCGYMuipcuLAgtJBXhOMCJXE/fPgQg57kKRKBmCMgyWzMsZNn6kDgv//+o6lTp4pHT0WKFCHEylarVk1Ha7k7PhCQZDY+UJZjJAQCkswmBOqRx0TJ7MWLF9P69etp1apVIqTs5s2bkRvJTxIBEyIgyawJwbXErp89eyaSAlA5BnW+d+3aRXZ2dpYIRaJasySziepyyMnEIQIgsylSpKBUqVLFYa+yq5gg0KZNG4LUIq4HEsVQklyaRCA+EJBkNj5QtpAxEEpQokQJunXrFp08eVKUQfzuu+8sZPWJe5mSzCbu6yNnF3MEQGbx/ZaWOBDImzev+Pvfo0cP6tixI3Xu3FmW0k4cl8asZyHJrFlf3vhZ3OfPn0XZQwhpV6lSRdyZlylTJn4Gl6MYhIAkswbBJBslQQQkmU18Fw1e8hkzZtDOnTvF0zkUyPHx8Ul8E5UzMhsEJJk1m0uZMAt59OiRKHGIP1wLFiwQZWkzZcqUMJORo+pEQJJZndDIA0kcAUlmE+8FbNSokciZsLe3F6XLUbJcmkTAFAikMEWnsk/LQADlDbt06UIODg509uxZKlasmGUsPAmuEmQWHvSgoCCCwDkIAF6oaV+2bNkkuCI5ZUtE4PHjx4QXvs/KKzg4WIYZJOIvQ44cOejw4cM0fvx46tevn9heunQpSadHIr5oSXBq33Hm+X9JcN5yygmIwMePH2nYsGE0e/Zs6tq1K82dO5fSpk2bgDOSQ6sjgB/32rVr08uXLwVhDQ0NpZCQEEFi1dthG2Ehx48f19wtP0sEEiUCR44coZo1a0aZG2LzraysCFn1+FuUPn16QszmkCFDorSVOxIOAVy/9u3bU+rUqYXyQbly5RJuMnJkc0JghwwzMKfLGQdrefLkCS1atEhnT7dv36aKFSvS8uXLac2aNbRs2TJJZHWilTAH4PGwtrame/fu0dOnTwmC5trKTSZLloyaNm2aMJOUo0oEYoAAbr5AVDUNPhnctOEGDqFPfn5+VKlSJc1m8nMCI1CjRg1RAbJgwYLiRholzXX503AtUV1MmkTAEAQkmTUEJQtqg8zTn376SYQNaC573bp1VKpUKfHHx8vLi9q1a6fZRH5OJAj07dtX54+EMsUvX75Q48aNlY/yXSKQ6BGA5BPiMPGuy+ClBVmSXj9dCCXs/ixZstCePXto0qRJNGrUKGrYsCE9f/48yqR+/PFHGjx4MKH4jjSJQHQISDIbHUIWdBxxTPv37xckqEWLFvT69Wuxejyi7t69uyCviJHFH5f8+fNbEDJJb6n4wY9O3zdPnjzk4uKS9BYnZ2zRCOBpAuK/dRnILGIzpSVeBHCNEKrm6ekpvOiQdEQIgmIIYQPhhcFpgt8gaRIBfQhIMqsPHQs6dv/+fRowYIBYMTx2eDwN4nr16lWCzBZqbm/btk3EyUpx8sT/xYDnqlevXpQyZUqtk8V+lJ+UJhFIagjUr1+fQIZ0Gb77HTp00HVY7k9ECMB7jiILCF1DnD9Knp8/f15olCP8AL9FCBsB8ZUmEdCHgEwA04eOhRzDH42qVavSmTNnosRWQgA7a9ashBCDnDlzWggi5rFM3KDg+umKSTt27Ji47uaxWrkKS0IAf6/g1dP8buMmDZ68FStWWBIcZrFWyHYhrAAx/y9evIjifT948CDVqlXLLNYqFxHnCMgEsDiHNAl2iEc6qNilLUnI399fBOFLIpv0LizCCJD5nTx58iiTz5Ahg0yQiYKK3JFUEHB3d9f6vQ4PD6fevXsnlWXIeaoh0KdPH6pXr55I4tMMI0GyKqqJIZlVmkRAGwIyzEAbKha078aNGzR8+PAoHg4FAng+WrduLcsRKoAksXck82n+MOAxLBK/tJHcJLY8OV0LRQDfX2033wUKFKDy5ctbKCpJe9lQyIF2ubbrinADJIn1798/aS9Szt5kCEgyazJoE3/HIDlt27YVcUm6Zos2Dx48INw1S0t6COBH39bWNtLEcU2lJFckSOSHJIZAvnz5yNnZOdKscXMmE78iQZJkPvj6+hIUWPQZSO6qVatEiVx97eQxy0RAklnLvO5i1VOmTBGaf9ruhNVhwfF///1XvNT3y+3Ej4C2RDA8skMSjTSJQFJGAIor6gmOILMy8SvpXVHoAyMZ9cOHD9FOHol/nVk+EjG10iQC6ghIMquOhgVtX758WWSO4vGNLlN+KODZQzUdlCWUlvQQgKwaYglh+DGA8HzGjBmT3kLkjCUCagg0adJE9b3G36o2bdpQ5syZ1VrIzaSAAMgsfl+gaIC/T3jpCoFC2BviZnv27JkUlibnGI8ISDWDeAQ7sQyFcrSurq5069atSPFJyh8ReGLxCK9Vq1bicXTZsmX1SuEklnXJeehGAFnAR48eFddxxowZKhk23WfIIxKBxI0AbsRxo63oYSOJFYRIWtJFAB7X3bt305YtW8jDw0N4a3GjotyMq68MCjs//PCD+i65bbkI7JBk1gIvPqqu/PHHH2LluAPG3S5e0PzD4x7EU2rGo1kgTGa15M2bN6t0ZVHmNg8rHUiTCCR1BDp16iTiKFH8A8ms0swHgbCwMDp06JDQNwe5RXlbdWILRRZcc0hHSrN4BOKGzELUGJqVV65cEWL779+/t3hkEysAuPM9fPiwmB5iJx0dHSl79uziD0Lq1Kn1TjtNmjTCE1KkSBEh64SSkUnVUFEG31kIdiPBLTg4WG8iXFJdpzJveLF27txJKHjRoEEDZbfZvyNmGI+ekTDk5uYmPHdK+Iw5L/7JkyfCE4+/yYGBgfTu3TuzXC5+e1CREE+azKkqYdq0aUUFP/ytrVy5cpJeG757eCrk7e1NkHpEmIC+8DZtX1Q4W0BmHz9+TA8fPlSp6zg4OEitbG2AJeJ9+JsMLWFooJcuXVp8v+OgEFPMySyCtZFZuGDhIvK+6EXJU6SkjDlcKEUmB/ouVRoE5yViOC1zav99+Uyv/c5S8jTpKHVmR0qVyY6+SxZVg1QnOuFh9OlNEL0NuEnhYSHklM+ZevXoLuKXrK2tdZ6WmA6AwM6fP18QO9z5w0MJooN/XLritBLT/GMzFxAbWLFixWLTTZI6F48nX716JTw4IHWIFcajSWS9mxsOWOuaNWvo74UL6dxZ/nfOPxrZnQpQpixZKRX/m0cYkbnZZw6J8vHcR8Uq1aEUKVOZzfLCP4TS25fP6NHt6/TxQxi5sOOgF8eVIv49qcS7HzhwgObPm0d79u7hMIFPlNshM+W2TU8ZrZJTslh+F9+FhVPg6xAKDA6hXNxnLrsMZnPtzX0hn75w3HPYJ7od+IYCX72ldGnTsPxnG+rHsmulSpWK6fJjRmZBYoePHEVBQUGUpUwjcqzgTtaFKlKylPo9ezGdpTwvbhD4/DGMkqVIxQQ2dnl//7GXL/iOFz09u5OendxIKbm7MaNH0ZAhQyJlF8fNrOOmFx8fHxo0aJCo/424um7dulGjRo3I3t4+bgZIAr3cvXtX/JtFDLQlGsIrtm/fTkuWLKHr16+LhKHp06ebRWLjxo0bueTncAp4HEClajah8g3bUMEy1SiVFTsWzNzOevyPytVvZZar/MIyere9z9C5/Zvp7J71ZJU6Ff06dqzQW02sN98XLlyggQP606nTZ6hiwWzUtkI+ql0sB9lmsDLJNQr58InSpk5hkr5lp6ZF4NGLd+Rx2Z9We94h34dB1IxDHGfOmiWcTEaObByZffbsGbXv0JEOHTxA2Wt0IKdmQ9jD52DkmLK5OSHwKfQd+XssIv/d88nFJT9t2riBChUqlGiWiMdZv//+O02cOFE80gB5wWM7aZaNAGLwRowYIcKi5s6dSz/++GOSBASPXrt06cpi89upUpMO1LT3KLJxlKojSfJiRjPpkLevyWPFLDqwZp54qrBxwwbxVCma0+LtMPSrxzLRnjp1KpXP70i/tihFpZyyxNv4cqCkjcABn4c0YctFevgylGbMnEm9evUyZkGGk1nEFjb4vhG9/5KSCvWaR5mcSxszkGxr5giEPL1Pfov7U2iAH21Yv05UmEroJSN2G9XLUNN72rRpwpuBOGFpEgEggDCT0aNHE9QdUCltzpw5lJS+HxCab9CgIb3/+Im6TlxCBUrLmzRL+GY/vnuDlo/tTi8C7tHmTZuoTp06Cb5s5Bw0b9aUTp86RZPalKGOVVzMMqwlwYE28wl8/PSZ/tzpTbP3XqHOnX6kxUuWEmJsDTDDyOwp/oLWrVef0jqVpMJ9F1HKdJkM6Fs2sTQEvnwKpxurRtGT4+tEPHX79u0TDAIkGdStW5fwaBklEqHUIE0ioA0BKD1AbB+6pWvXrk0SsdMXL16kWrVrk32egtTnz7WUwTpylTdt65T7zAeBcI6jXTmhH3kd3Eob2EPbvHnzBFsckopr1qhOzwMe0Jp+NaloTpsEm4sc2DwQOHjlIXVfdJxq8t+4LVu3GRK+GD2ZRdJIpcpVKG3+ClTkp4Ui5tI84JKrMBUCt9ZPpIf7FtP2bdtEXKqpxtHVL5IT69WrJ3R0kUVrTlnOutYs98cOASQGNmzYUJR3Xrp0aew6M/HZ0IeuwHHfjvmL009/rbOIuFgTQ5oku0eG/5opP9PJbSuFJmvNmjXjfR1QhalerSoFPrhN236uSzk5GUuaRCAuEPC6+5xazTpA3zduQuvWb4jO06+fzEKM2rVUaXqX2p5KDF0nE7zi4gpZSB++S4dQ8MXddNHrAsfSusTrqvv06SM8bBBRL1q0aLyOLQdLughArB3e2VmcgNCfM2sToyF0xq1MGfqQLA39sngPpU6TNjFOU84pnhAAoV08sgvdvnCULrG3Pnfu3PE0csQwHTu0p907ttLe4Q0or4N8Yhuv4FvAYJ5+T6g1E9rxEyYQ9PH12A69AYR9f+pHz4PfU5F+i5MUkX3m5cGZ9jv0rFv7obAXARRwbC2FPL2nvYGJ9kIdwNysYOcplMohH7X+oS0hMSC+DNnqC1ma6J9//klSRBbaicuWLaPbt28bDRXWjGz2+DRjdSLjc24xHev777+nCfxH85dffqFr167FtBuTnjdkyM8U8OQZhxasiVci+zLwEZ3Ytoqe+t8xen2Xju6m8/u3GH1ebE6Ii+9n2Pu39O71i2inAUL55uVzCv/4Idq2hvYZbUdfG0BurfO4BZTOxoE6dOwoit8Yem5s20EGbvWatfR318pJisjuvfSAtp83/jc+4OU7WnPiJt199ia20Bl1/heWsoqpQenhswH84tPnLxTKsfeJzSoXzEq/tSwtEguhkqHPdFYAQ9IMAstLDF5JWUomfIC5vkVoHjs7rj6Fv31FlWec1Tyk93OQ90HynvEjFe45m7JVNq3Uy/snd+jRwRX0/KIHfQp9S5nzl6Fc9XuSTZEqeueoefDJyc10bVF/qjzLi6xsElcllPePb9G5sXXor+nTaODAgZpTj/PPeOQFLzBKt65cuTLO+zdlh/AKQioM8zY2s74Me+oQtwbpLVMbZK02cdIJHssjfAN/I1BNLrqCGyAXJUuWjFQ+WZlrHtb6xfoTg2GekG5D0oGnp2dimJJqDigOgLn1nLycytZvqdofHxs+JzxozsDW1HXCQqrYqJ1RQ07sUI3ev35JU3ZF6BwbdbKRjY9vWUEXDm6jm16eZJ8rHxUpX5Pc+/9GKVMZJxsJEjuuTQVKmz4jTdys/0d0zz8zaMvc36jXlBVUpq67zhkb06fOTnQc8Pe7TL93rE6LFi0SsoM6msXZbiR85cvrRE2KO9DU9hXirN/46KjOpB306v0HuvCHcb/xyLZvP/cgze1ShdpUdDbpVO8EBtOyI9fJw5uLTISGU1lne+pdpwhVLZQt2nFBfv/YdlFIXt188ppScNKzS9ZMNLaFG9UsmiPS+UeuBYjvt9/jV/Tp838iTKRP3SLUtXohToZNHLrUuFl0n3GA3qWyoUvel3WFG+xI/htbpNV9/dCiVWv64lCY8rr/ou1wot4HLdVM+UpRxrwljJvnd8lYasyeNXMrUKqMdsada0Trzx9DyWtyC3p94zTZl25I1gXK0+tb5+jR4X8pc4FylMYu8hdOV9cgwdeX/UwfuZBBrvq9KEWaxCUcnSqDLRP1d7RvzQLq91NfUX1K11riYj+y0iHUDWKULl26uOgy3vqAZiTKMlarVs1o7VsQSSS4ocKVKQ3eboi229nZsRxUF/FHZfny5QQNX5RB1qcEAM/zyJEjRcU5nI/ro7xQhc7dXTcJMOWaNPuGpwvVpCAxhOo0BQoU0GySYJ8hi5iW/za0GjI53ucAbepMWRyFYkJGmyxGjY9iBnmLulGeIqWMOs/Yxp7b/6WVE/tT+sy2VLlpB/H9xL5Ht65S6drN9H4/NcdaPLIrPfC9JBLrarbpqXlY9fnu1Qu0dEx34RF14zGy59MtS2hon6rOjdjIZOdIwS+e0aZ/l/Df2p8MzQA3YoTITSF3ePbkCVr9Uy1KndKIwjuRu0mQT6lSJBOSYa55jPuNR6EHh8xpqWIBR8qSMY3J5g4PadPpe+nMrafUsFRuqujiSGdvP6WVx26w5JlDtHHJfZcdp3+O+lHBbJmpfWUXypg2JZ2784w2nL5DxXPbkrNjRDjI8euPqfXM/fSBFQRalucKiXmz0LWHL2mn1wPiAvcEr2hiMPxNLpnHlqZsPC7+HusodnNDq2cWXpfq1atT2QkelDFP8cSwHrOaw80148h/3xJy/Xk12ZWICNr/EPyczoyqSclTp43WoxxwZDUFXT5EL3096XPYe4FNYvTMYmLh717RycFuNOuv6UL+yFQX8hNXAsqVK5fwak6ZMsVUw1hsvyCjiD8uXry4KIeslITFvfD48eNpxYoV1KlTJ534oIQyPOYoaVmihJE3mTp7Nd0BxM5CEQMJhInBvLy8xM3K0CV7pASXlguCMIhfW5WjHPmL0C+LdnM1sJSi1faFk2nn4inUZfzfVKmxYeoqR/63lDb8OYLDONJRRtssOj2zCBkY37YyV+gKpeDngXo9s4b2qWVpBu96zXMY/n1hWs7hSsY+3TF4EG4ISbusjg7Up4YzDf4+8f9bNmZtiaHt2A1nadFBX1o3oA7V4mITsGdvQqn6b9tEcQh9HmX/oLfkNnITNXVzosU9q6m8mCDGTabtER5azwkRjoOm0/fQ6ZtP6ezvLcjJPqMYB5XVig/dwOWG/6M7c9tTcr6JTSzWe+lxuv8xA3ldvKRtStpjZpcuXUY2zuzZTGREFuVY7+2YTefGNaDTI6vTzbW/Uehzf/JdPpQeHfr2WPnGv2Po2uJBqgX7LvuF/FaOog+vAunKgr7kObgMnfy5PF1bMpg+fwhRtQu+c4kuTW9HL64eV+0zxcbjExspfc5CKiKLMVJnykJ2xWtQWNBDrq51Ue+wiOkNf/+aMuQuSinZ+5mYLWV6a7Iv15QW83fKlObh4SFq0CP5K7EZPJfwPDo5OVFTrnCCUAKE8bRq1UqEB2C+586dY83QBmI/PuORcpUqVQgkBtn1eLxsa2sr9iFGVt0GDBggPKXq++J6e+vWrYLcaVZ5Uwjs+vXr9Q6JDHzcYcd3MqDeSek5CN1Z3NTfuWN8jKiebmN8CB7wHOz1M4WWLKpM7V72J03qUJ1+bVmWNswYxTJL94WXEyQMBg/krH7u5Hv2iGoNkIZCNv3rZ084CakrDWtYA3jTPgAAQABJREFUmEY0Lk7/Z+8qwKO4uuj9kRAIcSSQoBHc3d29uJZiLVDcpXihUKxYgaKF4u7u7hIIHiFOBIhCgP73vM1sdjezm02yGxKay7fszJtn87LZnLnv3HPXTxtEH6IUD9movGXeGFo31bi/l3fOHiSAy8Y9hiiBLMau3kpBibh5fDdOEzSfl260g++/w7AZZJlDd0KgzXzvnz9/orYDJ+vsNzF96uwogYtW7DkvX68V/WVkNQ58F4SHR1AP1pJNbfbIO4R6rzhNFcbvpJ7LTtG2K8/p/GNf6rvyDIWER4vpTtx6jYauv6ic+k32Wraae5juewbR5otPqfmcQ+Qy7B9RBn6tZHfc31CXxSdEf1KZMd63XXlBxe2tlUAWY+RiT3D9kvbkFRROiPLXZrgXWBemQeD7VjJ4dAvlNKdnfu8IgBXmGxJBeayzKYEsyrKbZmavdQ6KYQ5tdMxnFKUa+6FuEbpz957YCZSbVDzYDc7YQd6mta3YQq7+Vy27v6g3vdw1lyhDRrIpXpNCn16jW7PaUsC1ffTe/b5ybm9f3KLQJ1eV52FeruzJPEU3pjWnDyG+lJvBVRbbvOR3cTu5Mt9UspiwYAp+eI4+vA2Qigz+/pHH+BT5TpYbm83OUYynei9yE3Du8gtVnLRXvGxL1pGrkqrKclVuRQ/u3SVfX1+jzevgwYME7mhKR/MmdEMXLlygatWq0cWLF0XmMWtra/r5559FtDy4p+D5wt68eSPkdaQ1QmYncDbBNR4xYoTgm3bp0kUEJgEEQ2dUMgBfAC9j2rNnz0T3DVn3T9Ww3qA5JETOR2AbPOfh4eF06NAhEewG/eqUDA5UnXdCx/AiW1lZibkmVDclru/bf4DKNWhjlKGWjuhCe5fP4K/VjFS0ch16ducyze3ThG4wAPTgrXZYeGgQuV45RW/f+Cnn4PXsIT24eJxmMVczJMCbKjdpTza57enygc209pe4rfmXD24IDquyoREOAlgaCla8Sl3xLv1nmyc/g1sTvo+43xfpmuY7tFuhDOBcrjo16KobfF87uoPTy26nfpysIivzarVZYvrU1kdiykGnuMIqLqGhoYlplqi6Bw8eoKq89W3MrfZETSi28tVn/gKIwgtZhTmmVtmy0IQt12gCg1dsnUsBTgB8V7iuZG+ZP3v9RSBN2nqdftl+g0rlt6V2lQvTE9+31HfVWXrAIBcWHBZNZ5hj6v82zgEm9WGod4zxLvIj1S4enxvrGKsWcc9DMR+5Mc2yZKY+9YpSOQakqoZAsBC+T1OmhGQ1UdBCmpcrQH6hkXT6obey6gvm6l564i8oBugrNVllx1yU2zo74W+9nGXSLIQH5V1oCBUpXkPz0lc9D7x5RGyt52/Sn1y6TxdzgQrAo1VDyf/qngTnFh3kTQVaDCanThPFEwvawsMb+uhSgm1VKwCMep/aqFoke5yrUgvK7hCfbxfJgV8wuTTA2fIUFtfAgf2WzJp5wHhKBOhq3769UW4NfUMnNDUZHgzhNZXAngS0R40apTe/FSAQWs8FCxYUt4aAKwikAySXL68/BxGJARKK0AeXddCgQbJL+PTpU8FxNTdX52WDJ+vo6EhPnjwRwFRbvnjcB7btcR8SgMdA4Pn+/fffqSoFMuaFADB4xvG5SongRYypzXx8fMjXx5u6V6qtrUqSy2+fPkAPLx2nRt0HU+dRc0Q/+NwCjF5nwJaQBft5UdPew6n9kOnidxxtZ3HAl9uN8wk1Vbt++9R+8nnlplameWLOXNh6nfprFotzf4/nrLebjUzN4n8+czoUIn+PZwQPNAC7Ntu5eDKDdX8asXyfmldLsz681ptnj6BmP4wkl/LVRcCZZh3pXN8+pfrJfS/KnxH8DLDTA61tY9jVy5eoa3ndXmtjjKurT2yLw+NqkikjnZzcWskrRTBTo1ny4EezP3dWKTg/rS3lz6H4DNVhQNl7xRmxFV+6gDo41GwrnQOMgq+akLVkLmxR9r5q2ouAd6Iot2VWzUvkmFvx0BQUFhXvmlTQtGx+wkvTVp9+JEBy20qFlNSBfg2K00WWvuq65CQBKIL7fOmpH9kxL3hiO/3/tmiOZaxzYIgaLrk4y9xl2SHigVk3N8UXilne1LWF4HuRtzH5Zhw7jFPeCIISCrcfoxeYzZDZlAq3G6X8kkJbK5dKFOb5kKLZW2tqE/9JSDmQykHM+xB6tXe+Son8IYCpLJgN8BANMplZxWtomiOfKPsU8T7etbRcAB6wuV1Bkj5bhr4XRDsCTI0dO9bQXSerP6SAvn//Po0fP17NYwwCe+fOnWnTpk0J9g/aBACgZLVrKwCNq6urVKTXO6S7EpLvQrCTNjALMGpjYyM7FuaH1KoAq/A8yxnah4WFEQJHAMbhiQbPFtvnoF5grVJb0B44wqlBZUH6vdEVXCS35vqUwYuKPxJtB/2irI4HlLYDJ+kFZjNnMaU2PyocBOgAbZ3KViVE14cE+AhPrbJjHQc3T+6hWyf36qhBlLuAs1YwG/j6FZlZyn/2cuRlD5T7U4ri71UzC/k69y8cpTPbV9MgljzDdr02+8zc/L8m9uW5OFFrvm9dpm+fuvpI7DUEv1kxPQLfh8YAs1FRUeTh5U1FW6YufPDwdTA98g6loU1LKYEs1q64gw0BwO28pnAi6VrP3nWLKoEs6lVj7zPMjSP99bVgpjLMO6DYzdDVBsBUDswCUMOszbLEay4lpIDnVl+D1xme6d3XX4nAr9ldqyqbWmY1IQdOcuHKQV932dubOWMGDmTkB3n+HZaoCMrKqeSgaB4r2npX/m9fPDALiR+TrNkpo4lpKpm+YhqRgR4CcAIYqVq2XAVYAzfhuZpY2Ma7p0yxaXmlICrVfrUdZ8vrRPXWJPyLAUUFOcvAW16wT8x51TSJvyvNS/N6Wj4Htxdb58YwbF3HxMRQzpw5jdF9kvuU+JZyEfElSpTQq1/JmytVlsAi7jkxBuAM8KjLVDlWmvXgXYaHUM4g5A8QY2Gh8BzI1cHY6ENKYgFZL/CAsZX/+++/0549e6gn62SmJsPnyVif2cTcJ76T8bMxs5R/mEhMX5p133i/Ims7h3iatfBmAqgmZOasbKBZz8xc8aD+ITKON5tQP/1m/UV9pq/UWU3X5zMTS2+9DfSVbQ/+LtpmNZP/fMIbu37aQKrVtheVr99Ktg+p8MCq2UIdYcrWS2rcXOm69J6YPqU2hnqHBxufGWOYRF+wMU/4s2GM8bX16REYJi45xkbqq9YrwlH9+hiAnapZZVP8rY6I/qRarPPYmcf3XJ7w9xgUFeQsC3uWYZAO07TIWB1YKxmgq1kXDh6oH/y2/47oC9SDyd9VFJxYqW4rDghz8wkV0mrtGPDDM3va1ZtGbrxM3dhbe3F6OzVwL7X7mu+2/LkLDpF/uIgHZhGpmJG/GFKbfYp4x1vz8Z+YERRGLCORkGUwie+2T6iN3HV8KWZMRl8mlrlEt1GBnvG6R+Q/DMD7W7P/MbjHZ8sYJvVrapq6vmClL34EbmmavlzRrFkN87k1MZF/uNKcl7ZzyGeBahAYGBhPOgx/OOG11UYxQJ+QuZIzUEMAZhPraZbry9Bl+DxJny1D952Y/jAH6KTqAnOJ6U+1bsT7t+yJjC/Bgy15/EFMyEz0ALwJ9YHr4LVSZn1qytextM1FAZ7PRfICTemw8HchwmurjWJwjoPcwlkHNyr8vVqgWigHtuFvC4LX4Ilt0nMoHVm3gAD0j6xbqJwIqBYw9PPw8klq+v1wunFsp159tugzStmPoQ4y80OjsT63Ur/gXqYmexupAH822eNjF32TDkhc0uTcl3hoMokHq/TuMlcsvcDzTVi8NiHhinu0za7771wQUx0GrTlP5zjwrQbLiM3oVFnwgFU7hP4sgCxkv35gj7RkLcsXpBvMH1558hEdvuNJAxuXlC6lincA7mhOVy9nSV91ud6MWIYt+DAO8oJuaSb2HEsW4fuCvsTI35xUx5DvH94Gkvv+RQl2mbd2V7IoVDpePTM7BS82klUYNC389WNRZFm4vOal9PM0uAIFY+kBSKvbqpW6xwfb6ilp2M6HMoIuA2CFvqqcwbuMIDMkZsiVS/FAhnrwyqKsXr16cs1EGWS9wOFDgB6CwFRNSvSg2qfq9fRjsKv+Z7RlwBY8gqOgBKDKN/VjjuknPTJaGWpilzi7mIfbPZ3dQV2gVf84mplqZbuCziJw7Y23O6mCWXhlg3w8qEhF7Xxjc+sclM+lVLzsZp/47wpiK14/fUCgpcEcnBV/3FEmWRSvHSzI14siw95RdGQY69Pq36fUT/p70ldA2oIHEGtSRv07BhSElLKAd5G08ND9BIfrVtOZysjwcKUgL09WLdC0x6zUAKvAerDaDJm8erGKwx33IJrfszr1ql1EtqrUFzRzNQ1cYYDZt4mgM2j28TXO0wyYtWAZqvcsWRX65ApnJGusXCtorqakQYnA59yWBIe0LlpNFsxmsbYjq6KcJOHJNU6b60HZchcUfX35FEP+V/YSrpvLgOAEB0yvkOpWAFvqCCRCIgdV7VsAOEhzpaSdPn1aZO7SNSZks7SB2a5du9Lq1asFx7Vq1arKbsDDRUAXdFm1GbbqkVRhwIABIkORar3t27eLUwRbpZv8CujjIZVvmXBp/iKl6dXDm/Tk1kUqWycugPLC7vUJNzZgDQSMIXOXLoN3VBuYrdy0IyH7F5IkOJaurOwGaXQ/Rkep3ZvyYuxBg64/sXrBT5rFNKNbLYphDdmp2y4rr6keS4X3zh+hZawIATkvKQMYkkTo26fUT/p70legGAdTZeSMVfBG/tI+rh8P9nCefwwPe8rYewaAmznlbUIGj6gcmEXwVTWX3Bx05k/gz0r6rzGfvtAe5r3iehlOfKDNjt33olss3fVzk5JagSzaujD3FHbwtgeNbV1OHEv/HYhN9Qt5sLRkaQbMFmo7gvyu7Ca3dWPpYwfm9ZrbUNC90+TPslzsukixNTfL60wN1nsma7xCrYbSvQU96eGyH6lQ62EEjqzH4eWsmetJZUduUvPEXBxWnqXCAqnhRu9kjZneOOVXwN7eXkTCL1iwgHr37i2CvqAWsmzZshSfDPKo45VUQ2ayunXrCs1beHCRehdyXKNHjyYEpSEjmGQODg5C8xeJLGBItAB5MqTCBeUCmruIuN68eTOdOHFCKFxUrhwHQKR+0t8VK2BMz2xL9nReZYmpTb8OE/JbCCB6wOoGkOUy5riaP9v+s9cSXkk16O/idXHvRrK0zU2lazcVGbx2LJpEzqw4UKN1D2XX5xmob54zgloNGE+t+ZVuaX8F8lib0QCOzv+TPYpD1l0UQV+vGAyuOaMIaE+pO3RmkOiz8vtkDTe8eRnBWe236hwnpSgtJMaWHntIHpwQYcuQRmq/l0hwEPguivxX9xZjQpYMFsFSXFN33BDHmv9N+q4CZwezprrsgQX478zaucgABu/2EdbV3XPDncAzblZO3cOt2U9qO08zYBZSVhUn76enf09kQDtGrGN2h6JCHPnGtGapLpWrrh+0bam6VOKnpfR4zSh6sLSfqJopmwW5dJumlkgBFwQn+N8vurpLv5aKVwAeWQQ5LV68WCRLAJjr3r27KJsxY4bOoKnUdFsANgcOHBB0iZkzZxJeKAN1YOfOnSRlBMOcwQdW5QSj3r59+0Qq3Dlz5hBekkGtAWA/3b7OCiByf/y6E5z8YCRtnDVUTMLesTiNXnlQ6Mdm06Gh+nVmLD8qPmNDFm+nJcM60aE188QLNQuVqEAD521SC9aCpxv0ARG6Ld9demkaXIFf2lckSw7aQvas7VdfCEWA9lUKi7IFvPVvbpq8uIGUWpJ6JexpRd/aNIIDsfr8eVYMa8HKAzOZ+yplBJPmAj7wFxVuuwRmdcmDjW1TTkiYrRpQV+jw7rnxis6yfq5kSLDwR++aoo5Ulhbe46WzXb58OY2ZNJVqLH2Yauf/+SMCif4VgVgfw0LowuCSVLz/Yspbq1OqnbPcxL58/iR4wP8yWLV0LM+8rNRFqpebc1LL7v3WgTrWKUMrVqxIahda20HmCZxLpB6FBzG1GgLCJDWCIUOGCEF+d3f31DpdrfPy8/MTUloI6sqdO7fWenIXPD09RSAZAH6xYsVIU7dWrs3XKvvzzz9pypQpQkbsa80B40KHt/+AH2nF1UCjTgMC/wB6JqZZOXgpmIbXL5SoNLBGnVwiOoeSgNfT+1SgWDn20uZKRMtvo+rsnnWoffOGNG/ePIPfEChS0JU+ObmV7Da5wQdMYoeQpJKi/pE44cSD13T7t45J7O3rNAP/FQkSAFbBkzVWallkAnvC8mPI+AU1Bid+4eEwNdoOfkgZufkaffgQT57sQJrxzL4+tZ78Lu0UoDW7vYtynQNvHRHH5gX0kzpSNkwFBxkyZiJLpwqpYCbpUzDGCkCTsX79+gSO6aJFi5RAFkFTx48fp7JlyxpjWKP3mSdPHsIrKQapMU25saT0k97GMCsAbdWrh7YK0Jq3cFxUM5IpwMCpTWsGb7Murdi0dj/p8014BZDd67sFxwTom9W5ihLIRnyIEVm7SuYzvKxdwrNKXo1MrPtakZMZGNvy2pgRXmnd0gyYNS9Qip5u+kVsy+et1ZmQ1AEBYR6HV1DO8k04QUHcF3Fa/6Gkz//bWAHIakGyaunSpfTu3TvBM4V3dv369UKzdY2Rc6h/G6uYfhfGXIECRcvQ1t/H0p9jelD1Vj0ob+Ei9Mr1Fh3bsFgETYFykG7pK5DaVyAry2Ehfe2a024UxkFYjcrko3cRH2nr5eecfjaCFn+fujKapvb1TIvzSzNg1sq5IpUdsYF8L2wjj0PLRdKBrJwwwb5uN5He9lveok+LH6z0OStWYMuWLTR79myhaIDEAchyhTS0hw4dEoFT6euUvgJfcwUcy1ShIYu20SXOBHZswyKKeB8qdFRrtfueuoz+TWf616857/Sx01dAcwVW9a9Di4/cF0FNW6+8ILMsmYS+6j8cNCVl89Jsk37+7axAmgGzWPIcZRuKF8j7n6NZb5aDpgxhH94GUND905zetgqZ5XE0RJffRB9YZ0lf8Zu4oa9wE5aWljR37lzxgncWHFFkyzKEHTlyRKSQ7dKliyG6S/V9gM8JfjTWFNnEdBlS5378+FGoJ+iql36N1SZqNRUvKExAbzabueVXXxbwXh9ePkHO5aqRHaexTasWHRlOptnidNF13QfW/lPMR4KiRLolfgUsOPhrSodKNIWbQiIru2lm/q41Hvfz1MPXFBYVQ+0qF078ZL/RFuD5gm5rLH6vrmVLU2BWuhEALEMBWfQZ4feS3NaOpmJ956dZMOt3eTc9WjWEai6+zWl/4/iMIY8uMj1jsrR0su/QtS3541LlNWj3Btw8zJq+V1kHtxDZlqxNTp0mctpg3QBC2UH6gewKAIQZ0hDggZS5aRnMIq0tJL8g25WQ4aFgwoQJBG3aTp06aa2OjGSQA8N6P378WGu99AvqK4CHrNQAZDErf87mtXHGz/T9lGVpDsx6cvKH3UunkcejO5xE4a1I4lC2bgvqOHwWZdWiDoGAu6mdqxHUI2buvqX+g0k/S/QKANga24RcFuvYpiUwC/WD+jP20yeoeWhYPltz2jqskbJ0w7knLG0m//05rWMlalgqn7Lurmsvad1ZN3roFSL6LpjTnPrVLy6yixnzgUI5AT4wjItItcf04xRfgU9RYeR5ZIX8uPyY9L+MmWVf8LxG+D6nz5xVTTLQONzWj6VPkSzY3GoIc5GLkNcJTtO4/CeC+kK6pa+AoVYAtIsXL17o1R0yiGlL6KDZQd++fcnX11ezOP08fQWMvgIAsPN/bEkAtJWbdqCW/cYygLUUCR0W/NRa6CvLTWLD9MH0jr3R6Za+AsZcAd/QCHrMaWzhObU1N1V7WZupPwDcdX9DPqx0oFkP5yaZ4pSXtjOlY9DaCyJj2ICGCgALndsJW68J2ocx70e17zTpmVW9gf/yMTyooEeEPL7EtIsI2aWwKV6Tqv56Svbak78ncXrgMCrae664Hh3sQ083T2G6RWUqP34nZciUWZRny+NE7vsWcoayPWlO/kz2xtMLv9oKeHt70/Tp0+nmzZt0/37CaR8xUVAGunXrJuTXEgKpkNM6duyYCLz7ajeZPvB/dgWgDoGMY5M2nVUqQbQdNJnm/9SKnnCWszun91PFRu3U1ufszjXkeuUUmVmkrYxLajeRfpImVgBZxWDLWcc2IYWHV+x1rl0sL20c3EDnvf15wpUK57ag4xNbkjnr4cKGNistcgCsO/uERrZMGdUeg4NZaMB6HFpK/rztHR3iR6a29gRA5dx1Cic2iOMOhbhdocAbBynY9QJ94TYAUEgBa1+vu1Jv9e3zW/Ri+yxybD9OeBD9r+6l6GBvsi1TnzNnDacvnDv7+dYZ9O7FLcpsbkt21duxN1Eh/I0FfcAZtszzFyfrYtXJ6/gaCn18mUwsbClPzY5UoPmgBPmggbePkTdLgoV5PeKte3uyLl6dCrcdqZagQd/7xXwMbZEB7hQT8ZbMOdVvhO8LignTPwd10IOz5H16A5Uft52yWOUSUwu8fVRwkfM3HaAEsrgA/V6A2QDOtpbWtHz1WfPo6Ggh5I+MVABb+fPnF5Ja8+fPV9NBBed17dq1ImvV9evXqXjx4lSzZk2RBAHb2pL179+fYmJiaPLkySKNLWS4sJ3ep08f6tGjBy1cuFBk43r9+jVBq3XJkiXiOtofPHhQaPGiDBm7cO7h4SHkvdCuSJEi0jCy72/fvqWJEyfShQsXKCgoiKpXry6SFTRv3lytPuY/adIkkcULF0qUKCHm26xZM7V6hj4BMH327JmgACDhAkBtQjZ48GBCNjEkmejXr5/W6o8ePaJRo0YJfjJoC+CAphtxStZoOrJugcj0FRrgQ7Z58lHRSrWp04hfydTMXLlEkWHv6NK+v+nRtTOc4vYWKxsUZc5qVarSrDPlcymprIft/8/882jRbwwdXb+QHl09TbnyO1LNNj2pWosudGLTUrp2dAeF+HtTweLlqOvYeZQ7v5Noj9Sv5xi8oezakR10/8JRCvb1osKlKlHnUbPJrqCLchy5A2zb71k6nZ7duSy0cBHAhmC10jWbKKvre7/KBgY8eHH/ugCxmpJmNTkDGcDsK9fbamDW56Ub7Vg4UaTCRTpeaI7/1yw65hP9ceQBYavaNzSSHFgmqmaxPDS9Y2XBe5XWAzzYfy49EwFetzllK7JUVXHKRe05e1UJhzjpLSQbAHdzRMsytOToA5EQoHAuC+pW04U6cl2Ar12cGtY3JFxo5c7uWoXBmIICdpzTwa5nAIYy1Dl+/zW95sxbFQrnopmdKwsNVmk+cu/vIj/Qr3tu01XOxBUSFk2VeH49armobcmjHeY/e+9tuucZJLpBRq6RLcrES4ogN0ZyypAVDebI4DMhcw94T5WrK35vtdXFz8SNNWpBKZCALOoi7W6tonno4hM/QirezJmMTwIwOJh9snGC0IPNU6MDmRcsSVEBnuRzbjOFe7tRpSkHxZqEMKi8M7ez4L3aVWsnUtMC1D7ZOF6kdHXu8ouo94mB2ttnN+jZlmkM1p5RrootKCY8hHzObKL3r+7ThxBfwePMWb4phbhdppc7f2OwmoPs63QT7UPZYxnm8YA8Wb7LulgNBso9GDyfoxc7ZlOkvzsV76c989Cr/Yvp1e55QgfWocH3PC8vBrYbKfjBOSo/ditlsbYTY+hzv6KiEf6T1gldu/75M/lf3aPXKEg08fivEWRXta140JAaRfq9Eoc2JWpLReLd1NaB/pfJhN67P1Ar/1ZOBg0aJITpe/bsSeXKlRM81NWrV9PDhw/pypUryttEGtYzZ84IAAvuJlLTot6qVasENzNv3ryi7r179wQoPnnypMj0Va9ePcHzRFIHqBugHOASequHDx+mhg0bEpIngLOIpALwLGIsgOy2bdtSYGCgyKAF4IsUskWLysvQAYjXqlVLBEn16tVLAEYA6VatWoksW8OHDxfzc3NzE2C9UKFCNGLECIKE2J49e6hFixZ09OhRatIkDhgob95AB0iUcP78edEbKAYA+boM6wVQj7ULCFCkapSrj7Xq2rWruP+hQ4fqxcGV6+dbLNs8ZyRdObSFgWZXyl+0NL157U7n96wn7xePaeKGuF2bFaO705ObF8ipbFVq3mcUBXq9oPO7N/BrPc3cdZOscim4+F7PHhJA8aPrZ5jjaUlFGBjf5PS3T29dpOtHd9JjLi/F4BKg+cHF44Tt9d8OuYrPd7Dfa+GFXD6qO336+IHK1m1JYSFv6O65QzSjW2365Z8LlKeQPKAN4THn9mlCYaH8kNayq+CfujKQXspZvzqNnE2Nug8WPz5979fQP+tP/ABbsnoDkXVMs2/MHWZmGed9BehePeEHfmCoTg26DhRUBM12/4XzcZuvcsaul9SpmqNQH4D3cNOFZ+TmHUpHJrRULkHvP8/QJQZHALDDmpemVwy2/uZ6Gy88pcszvhMACpVdXweTH4Pic499RPavmkXy0L6b7nT5qT/tvv6SznMK14alHDh9qxmdfOBN7Rcep9tzOopgsdfB4UKXtveKM/SBkwg0K1eAgsKsOM2rFzWceUAki0DKWjlD8oFW845QMINY3Auydp3hzFrdl56iGQzMf2xUQjR75veWNXGPUv4c5vRTwxJkyrJih+94UNclJ2kbc1brl3SQ694gZe6BYeJhATSAi25+9OZ9FDnnsYyXlAH6vIF8DaAXiRtATbDk+0HyBgBVyTJm5MyQY5tTQb4XVQPIfeQdQnVL5E0RIIuxDQpm4Sn1v7JbKA6UGLBYeW9ZcxegZ7x9jUArqAX4s4fvf5wwoMb8q5TZTPFEVKDFYLo8qgq9uXuSVEEaOoHaQM1FNwVQRUTzzRmthMZs3rrdqRhvkSMgDGDz8qiqFPLokhLMom1UoCc5d5tGBdjbCHNsP5aBdCeW+NpKAKkWHPykaeCRuu9dIDzAZUduUmbDCK7Rke7O60Kex1aTC3ua9b1fzf5xjgCrCO+ncpeUZZnNbShfw97Kc0MdICUwOLFOHSeodRnp/5IymGRV86CjAtY3G8ugRfi9EOl1vyUZtA8fPhA8sgBy0H+VDFluhg0bJryILi4ugoMJIDtu3DjhbZXqlSxZkgASL168SJ07d5aKyd/fn2bNmiW8nygE0AKABSiDBxF9wnr37i3S3ALYSWUof//+PT148EAAUpyfOnVKgMyxY8eKtLIo07Tx48cLL+61a9eoSpUq4jK29Js2bSrmDYAL3dutW7dSZGQkbdq0SYB3VASodXBwEKBeG5iFp1efDG7t27cXnl7N+SX2HAAf6W5xXwDpu3bt0trFmDFjxM8I4D21Zq/ROnkjXohhwHjtyDahVtBn+p/KkXLmK0Tbfh8nAq2gFvA20E8A2aa9h1OHoTOU9eyditO2+ePp2d0rVLlJe2X5++BAajvoF+aEKlKLV2F+6B9DOtDT2xdpxq4byqCtdVN+EkA68PVLZRk6QeT+tO0cYBqrnPD4+llaNKgt7frjF5GWVjmQysHuJVMp2M+LJm48LTy5uNRm4CRaNLgdt5tC1RjgZslqptf9qnSrPAwLDaazOxMORKzQoA3ZOxZTtpMOMmXOTN3GzZdOle/vGayfZfpBxkyZqAwrR0i2c/FkgmrDiOX89zCVZlyS5mqsdwDGnQwwG5V2oCU/1FIOU4g9qZO2XaeX/u/IkTNS+b+NFEB2SNNShLS1khWzt6LJ22/Qtef+1LZSYalYALEJbcvTCPZ2wr5jxQGAxSsMaC9Nbyf6RPmQdRdF6lsAaIwjWVh0DJ2f2pakQDIA4E6Lj9P0Xbdo85CGUjW195l7bhHA8FEG4AB9MKSO7bz4BM3goL5O7OW0NstCe9jjG/Xxs0hZWyq/QrkCoLbM2O20g0G9NjALkKwrTa00mZblC1BR+7iHJqkc77hP3FuF8TvEHKRrZQrYCuqBSyxQB+iFzT1wVzwYSPVM2MMKD7JEHTDLkpkfLnJLl2nVyUf0mj3eeEhAsNkwphuklBkUzP775bOYdyhTCN57PCSLgqXEeb6GfQTAlKLhASzzN+qjBLKo9O/nGMrEwPZTpGIRRcPY/+zrdBVAFqf4pc+er5gAsw51eyipAllz5heUhgifZ6pNhfc3f5P+yjIAs4JMRcAc4aWVA7Pep/8WoC1fgx/UvmQQ1Z/NzlFstwPM6nu/ysFVDgKuHxA0C5WieIcYy9BgNpwBNMYu2HoomeZQfwIEbSFzdvlfAtOc+QTVAxzbzGbyT6bxbiANFHz+rPjMAmTevXtXCe5+/vlnQiCRqampuAsLCwu6evVqvG3+bNkUT6kAn6qWMWNGAsCSrEwZxZcqMoKpgta6desKMIuoe9VyAGRV9QN4b6tVqya8unig0/zjFxISIry+2LqXgCzGNjExoQEDBtDZs2eF9xXb9NL2+8qVK0VmMtxDZv5DDK8w+tZmkMWaOnWqtsvKctwHaAvJMdAKwJNFX9OmTdPZFTR7ly1bJu4vqZnJdA6Qhi9+if18w2vq9eQ+e2ZjP4edf6RabXtRZhPF59s0uzlNYC+tXUF1T7mJqeLzDfCpavgebfr9MGVRPmfFd33RSnXUQGuRijUFmPV79VStvGG3QUogi06KV6lHjqUrC4qD3Gcw/F0Ie313MG2hvBLIol2mzCZU57sfhFf4zpmDVKVpRxSLc133Kypp/AeP74GVszVK45/aMWVCDszGr0mCRoHgLvTdZcxccnBW/F6AXgF+7aD5//yns5V9ZsADg9f0oVew8MzivG+9YkwLcCbTzIpAI8hsHRnfIt42P5IlwCCRpWoZGCcMblJSWVQiNgNYTd76VgWt1YvYCTD7lL2lquU/MriUgCw6qVM8L1ViqgFArdz3byinz93NILVswRxKIIt2CJTqVbuIuD94X3vUKiLS0+LahvNPaGanKpSN9XCxDX9nbkfuG1fkLTg8muYxuEzI4E3VBWbDGcxObFeBWpTLz17naNrGAVxbLj2nnstO05kprVmfN7MAvRgnt2U2WsYPGY5MwzjGFAwoFvy2/y7ltMhKPfm+NG32vttKkAwaiPTz0axnjHODgtmMWbIxp3QUvdw9l25MaULZ8jqRDW/v5yjTgGxK1WXgqfhgmuV1Jmx1ex5ZKfiuUUHevO3/SvA1TaziUL50w6YMVFUtY6xEVBYbxVa/dA39f/msnrMX0lKaf/gRoQ8DBULO4IGE+V7cRr6XdqhV+fwxij6E+hO4svrer1oHsSclf1pK/6p4r+XqMHSXL05Gqefh5YIyUKDpj/F6yZApC9+bX7xyFHz+EIknCTW+sGzFNFYIIAeABn4rkhlgGxy0AHhR4aEEKIVlz55d8FaxRQ7PJjyp4LJCGkvOQDkAkJRMAsX29vZSkXiX+ocmqqrJcWMBEC9fviyyh8GLqmpPnz4VX7Lh4eFqHmLUkYC2NNcff/xR3AMoEtjGh9ezUaNG1K5dOypYsKBqt2rHoDfAo5uQqd53QnW1XQeAhWcaDxgA2trMz8+PfvjhB8GlxfzTTX0FsmTNRq0GTKB9K2byNn4tsYVfpGJt5pg2phLVGiqTIkALFWDy6e1LdOPYTgp4/UpwWd94u6t3GHtmlTOPAJLSxUxZsohD61gqglSeIfY7H/qpqqYJmnEtL3s7wTkNDfRVrSqOAzyei/cPrLyyctz3atcloP2G56zv/ap1EHsCesOKK9qpLFIbAOiELJDnsn3BBAFmc+UrTP1nrxWAHe3gjV0/baB4mChfv1VCXX3T1wHkxrQqR3P23aEGvI3vzN5RAE7QAOqVsFfqlQLMIrUrPKt7brwSYAteUA8OUpIzbIWrRtxniQXFeazjtsjRLmOsDi14narmZBefU1qEvcA3XgYKT6Vm+tcX7EGGRTBQ7LfqrGpXFB4LtD1ivZ3f1ylCe/keQKWAl7aKc26qW9yemjO4BPVAm2FtPJf31HZZWQ7vqTZb2qeWWJdisZ5bcIUrs2fVkqXMlh93ZbqDJ1MknESSiX2jmwlwjp8RrA8/YLRgr2/VSbsFF1kOzHou78X0j3d07UUgzWbucJPZB+ku74QDFBvbDApmMdlCbYZR7qptmDe7gyPtz5D3mb850GgjezQLU4WJe0SwEVLQvtrzOwcZmZAVB33ZlKjFAV1DyfPoKkEX0LxpgEZ5SxjsyYFjqT/JU6zZd0x4KAO3DDw/xRe06nXrIlUVp7FEfX3uV7W9dIx7T2mL5ocGBNHlqtRC1gNrYpWTHype0sf3QUpPuDTHmLBQ9shaKx9IpPJv4R2BUNBq3bhxIyERATyW2E6HVxDg1c7OjgCaGjduTK6urkLDFN5PAF54T+WCkpDpS840H6zk6qBMzsMo9SkBY9W20FaFIZmAJviztbUVQWqStzRfvnwE3iyCy7Zt20bwSoMrC0/ynDlz1DzKqmNg7uDXGtsQPId5FC5cWLxL48FzDINiAeYLygUeLEB/QHAeQK1kPj4+AtyjDD9HcJz/qwYqACgCVw5uEYkIzu9aK4KwEJQ1ds1RssyRWwCshYPakC8HJMF7WKhkRSpdozFlZRoAAr40DaBRzvT9fFvlUHdEoC9QBGCSt1icxP4HzywsEzsyMsaqrMReYh6qDQepdRJgGGX63K/UVvUdczcxTf7n++rhbbR59gjx8N9h2AzBh81sEve3BAFw4W9DKCr8Pa2bOlA5hVCmevAepSjLXcCJWjBv+b9goAK0rVSIIPF0ytVbeCyxnY4I+f1jmgkgFMA0g46LjtMT37dUnIFYed7GB+AFL3XE35fjLZMEwDQvsFClZpHsuRz4yhbrBZaAsWrD0PAP4hTXMmdUB5PW2bNQ+yqFqUgsgLS3yU6XZ34ngsv23XwlvLZnXH1o2s6bNPm7CvQzUynkTHz/xs5B7ro+ZWUK5JCthrUEmH3C3FhYDpbfysFea03DupRmSsLVZwEUybzbrCYZhTdZVUsWABkvPCcMXX+JTj/0FsF3mn0Z+tygYPbLp4/swYuirLwlDW4qXh/eBpL7gT+EKsDrk+sIW/4vdvzKQV+2VP33K2r8TPcDSwx9f+x9je9ZiHrzWoyTjfm7cpY1V34ROIat+Oz2LmpV4KEEvQCAWJ/7deo4Xq29dOJzfqsYQzqXezexzMWebv5SNJB5cyAe5p6XaRtyZmbnRG+fXBM8YwTSSYZ7BifZhlUhvjWDRxTeRngkES2PF/iuv/76q9i6Xrp0qTgGuAKQhXA/QJRk2OI2hsHzi2A0VYMnGJzXHDnifjbSdQA/GAKqwAFWNVApoCKgSomARxjcVrxAO4D6AQA9lBBAsZADrViXmTNnqnYtewzVBgSrJcckdQgE00kmeZixDlBtwD3lzJmTypYtK4LxpHp4Bxca94X2hsq4ptp/WjmGR/RjdCTlyFuAOa6TxetdUAAdXvu72OY+vW0lfffzVDqyfoEAsu2ZL9uMebOSYTvcGAbPpUR5kPoP8vUU8lTm1rZSkfI9p0MhcZybVRP6/7pGWY4DUCmimZ4GSoS+96vWQewJ1uXgX3PlLqmVQbUBKg1yhvVa+8sA9nJXoQFz1okgOM165tY5WB2iFAV4qe/qfOKYE2h/v376QEmf02z7rZ1//PSZt6U/sUcyO41njiteAe8iafHhB7SWt7TXnHGjSbwl/sfRBwLIgi8L3qxkJ1htwBgGbqnEZ5X6hyfYij2Y0FnVtAKcJAAG1YQ/+9VRu/yZf6bY2pe23MOiPopgs1YVChJe4JZeZc7vgFXn6FdWOOhbv5iyrmpHWJeFh+6rFskeg54hB1p9mMt6xz2IyjEVwsE2u1pbz1gPdw6mD8DO8EPFv/zeQCMYDRQLr6BwkSoYDwxIHzx77x3aMrRhPMUG2+yKdYJWbUqYQcEsVAruze9OJTibVJ4a7cX8IftUsPlAAWahTgDvIKB8rorN1YAsNE7DPF3JxFJBnDbUzYO+AC4o6AaS+V7cLg7NC8jz+iydKjKf9RAF3TupBmYRNHV5dDXKnr+EIFDrc7/SmJrvUFoI4DF0GbzZhgSzIQ/PMy/ZihUMaskOm7taW6E84cOJEyyd4sAIOLZfmF6Ro3wT2XZpuRBBXZCjQjAUZLNg8MTCSwkeZmio4kn11atX4tr3338v3qX/4N00hkHloGNHBf8P/QNIwhsJqS05c3JyEsAOAVDwbKp6ZwHEkXAAQWqQEoOHGd5MAGYYwF5d5u4iCG7dunUCJMqBWQDINWvUgYTcXOrUqZMsMIu5g16gaVjr1q1biwcKKQNY5cqVaciQIZpVxfhRUVGy/cSr/A0XuLEc1B9D2lPfmauFbBZuFZ7YJr2GCTAb+f6tuPs33h7ivXqrbuJd+s9YYBYqB6p6qwCSDy+fJCeW2pIzbNUjzSvUC6AagGArySA7tu/PWTRu7XEGteF63a/UVvUd0mQX925ULZI9LlKhplYwu2fZdJEkYeDvm7RyYRt0/Ym9tT/F6xs0kBh2Bk3dFt/TGK/yN1IA6aauf5wUwUeQzYLB+we+K8DsO+aiwiQ6QWcNqajjD7zEdUP/hwCm1hXjMAOA5OmHPkJqS26sQrnMyZY9sFAv0JSiguwYeKaI+q/KlAJ4mEPYk3tjdgfRFbyaNVhxoWHpfLT18nM14Ks6FhQCNl98ploke1zdxU4WzIZGfKS+K88y19WFFvSsodYWag8wzA+28fxTwZG9xxSBPNZxu4y3WFIM4LR+SQVdrri9jah/jrnEqhnBULgpdq4SX1lUNOJ/BgWzVs6VhN7rK9YkzcIpVS1Y/zQywEN4ZnEPOco2JHhD4dX0Z4AEvVgzFuR/+/wmvdw1T/AxIf4PzirKDWHwRN5f1JscO4wTwVuBt47Q6xNrKFflVqSkDGgMlI9VDrxPbSCPg8vI1DoPWfJ9RYf4sObtbNZ1fUeF2yi8pfrcr0bXytOSA5cTXill0KN9zzJlOcs11vrUD51fvHzP/UNZ+KEiR9lGLMd1X2j5WhWpwhqznVNquik2To0aNYQYPzyy4KHCGwqQB88sDAAPBk8jACa2qwF0AS7BN929e7e4jjYAe1ZWhgmO+/vvvwXVAIAWgHrkyJHC0witWTkDTxWgFZQHgHKoLiBobf/+/UJVAZxY3CsM3FIoBMALC/4sgCsCxCCBVbFiRbEecmOAMwuPZ7qlnRVwZpkteAIPrp5L1rnthQYqvKLwzMJK11I8oBYsVpYeXjrOGq7TBNB9HxwgAq5unz4g6kGNABqv2cwN8/mGVBhANQAtAPX2hROEV7LzqDliPM3/wFNtP3S6oDysmdyPvccjhEbuvfOH6dCaeYKPCkmxDwxm9blfzf5xDs7sqhvBcpf0Kot4H0o+LHcGjzO0duUMAXFlajeTu/SfLEMkPLa05x+8R3mZz1oqny25v3lPiw4rPJCNGODBEG1/irerf2VVgMHsmQ18FyUCrg7d8RDX4UmFxqtltiziPLn/bb/6gkF1Vga0BUVmqynbb4jArRmdKst2DX7u5O8qCsrDwLXnaSjPMTtTII7d86KFfC91OPkAJMVgzVnuaxbzSaFH24v5swhyu/zUT8iG4T4RXCVnkATzWanuTJGrp62shIM1VWR6Bri6Nmamgv/6hR2L0PcFGIUKQvlCCmciAO9RnnsHli0D9QHBcU+Z4jF1xw2mD/xPeMsxDugJ4N+uOe0mpLvAc/ZjSsiBWx5Mo/ASXuDGsT9DbfMyVLlBwSySIgCgPVo9jO7MUTx1YKLgpgJMAszCivdfJHROATJh8Ba6dJ8uQO6jVUPp2oR61GCDYbYPwMeFJuyDpf2FRxjjAbAV/V7+SxPXMd/y47aR68oh/IrjiyETVpnh61izthqqCc+yPvcrKn/l/0IfXxH3r+px1ZwSODllRmyke4t6kTvr7OIFsyhclkoP+UstkYJm27R6bm5uLkAcPK4I/JIMvFQAWgnMAhxeunRJyHdBwgveTABEcE+hCTtv3jwBHsG/NYQtWLBAeCB/++030R2AKfRsJVUEuTGgvgDKBGgQO3bsEFXg5UQ57kXiMwIYQ0MX4BcvyQDYAdDT7dtZASRFQPDR2ik/0vwBigcz3F0m5nC2GzxFSHbhvCmDw+f3rtLlA5vFC5+V4lXrE+SGVrAm7LGNfzB4tFBKcaFNcgy6sMc2LKKj/IJhnr0m/yG237X1C/WFj1GRtJPlu26d3CuqQe6qJpfjXjBnfe9X2xjJKX9x75poDhUFvGQN37HpYFa5NAjswrb8z+suULv5x5TlWRgcTmxbniW7FGB2SNPSdJ2DirYyrxYvXkYRNAXu6Q+sCbuM+Z4Q7ZekuJQdJfEAoHXp0Qe05NhD0QPmOb9ndZ1Zs7pzcoRIpkzMYPkugDlYJtZh7c7JGqAeIH3/DmxUktyYmwrqBF6SgYu6qn8d6dTg7xgf2byQVEJz7N4MqqerAHV4Wf/oXZNm8sPD97y+ktlzQou9o5sqKRjwKqPPQWvO0+/8QIKXZC0YtCP5RCYNDrF03dDv/2MOxL+qnS5fvpzGTJpKNZYqfoiq1/Q9Bscy/LUbZ+vyYU+tDWV3KBovoAhqBqAVZGH1AjPmpUo/aJR/inynRgvQd1zNeucHsbxGoTJUbswWkSkLXsYs7GnV5MFqtpPOsTTg3EJ3FpJVAIKSIoNUB+/63K9q/bRwDG3fMA/+gmAdXnhpk2v3futAHeuU0UunNLFjQTIqV65cIpAJW9xJMYBARM97eXkJTir0Y9GnpiEFK8aDB1PVCwtpLWQOg+pBcgzUBmybQysWW+iYEzyz8BirSnXpGgNcUmzTQ9mgVKlShIAvOQN1AioI2I4vxAkUwD2Vfg/l6v/XyhBoNmXKFPHz/pr3Di99/wE/0oqrgUmexgcGgd7PXUVWLmzXQz/Wwib+7/VrTogAGSlwQlW9sL6vnpCNnQNB9SA5dnrbKto6b4zQikWQGeYU8f6t8BhLmrMJ9Q/1Aq8nDyialQ0cnEqIeWm20fd+Ndt9C+eze9ah9s0bigdsQ98PvjOgwX1ycivZreykjodgoscsso8tbBveroe0lJyH0vV1iEhKULagrZoXFl5DAC2AzuTYmjOcSGTrdaEVW75QDhb+DxUeX3iMVaW6dI0BfixkxpB4AF5LBHzJGagT0NGN4gxoBVjFAOllU+r7F/xfKDBAxQDastrWDXxmBIVBegzyXPmYa6sa7CXdF3i/npwpDX3C0+zEnlxVeoJUL7nvO9hjPnLzNd4h/KjZ1QGDemal3kEjAPDT5QU0YZAL3VZNQzlehjZoo9qWTBzQwQcLvFW8dJk+96urfWq8hoeMLGUV/JnUOD9DzwnBUVWrVhUvXX1r84wita2hDZ8/bePpGgve5tq14/9uabZB0JgUOKZ5Lf3821oBqA9AegsvXYbAJDlDaltDGz7f2sbTNRa8ry4V1Dl/mvX1vV/NdunnX2cFEEwE6a2KCtqs1kkA8MkZNE0Nbfh8ahtP11gAhtWYt5qQFeSgMby+hgGU4pWQIWitXCz1QFddAFwkusDra1mGrzVw+rjpK5C+AukrkL4C6SuQvgLpK5C+AukrkNwV+KbBLLyLoDmkW/oKpJUVAE0BiRUMkXggrdxz+jz/Oytgms2Mo/zVEy78d+4+/U5T+wog+5Ui4cI3DY1S+48hSfMzCs0gSTMxQqOqs+OIy0boPr3L9BUw+Ar07t2b8Eq39BX4FlegRusehFe6pa9AalyBrjWcCa90S3srkOrALAKPgu6fJiuXKizPlQCBJhWtN4LEAm8eUc7Ivn4PkRhCWcAHXz5/EgFk4OKktCGYDYF14A4nxZC+14sztElmW6ouqxyUkU7/8+/IHAZRfyQeSEuGlLYIaIMhlS+UGZJqCKJDBjIpRW9S+0lKO21jnzhxgm7evCm6hAQZlBzSLXErgPSrDy+fIOdy1ciuQNr6Q39+93oR0IY7zlOoCFVo0Frt5j9/+kSfP8UYJPOXWsd6nOA7GXJnZhbWarVjPkTTcRVpr5LVGlDBEuXV6vxXT5AJDBJd0EOFXFRasr8vPBXBa5izcx5LlsIqKDt9BJAhmYRNbNIBuUoILkMqXtPMuiEcArPkArbk+tS3TFNHV1u7T5+/cEAbUgare7mjOeDtzxOPlM0g51WWEzkk13SvRHJ7T0L7CL+X5LZ2NBXrOz9Ngdlw7yf0cvdcoZYAaa/cVVsrwWzQvdN8bR6F+zwVcl42xWuSQ4PeLBFWNQkrpN7E7/JuerRqCNVcfJtMWdtX06Av+3zbLJHGFokPMpqakW3p+ixNNls5P802cn3+y9ndkGzi388xQqUiI8uwpYPZuJWDNNfLly/THJj9448/CBm18ubNK5JHQDFh6NChcTcmcwQJL0TYw5Bla/LkyXTgwAEhU5aJpZIAipEprUmTJjKtdRcBTENhQZchsQPSCesz9vXr18VcAwICRCKJdDCra2Xlr/l7Phf6rt9PWZbmwOypLSso2M9LUBtK1mikBLOPOPnCriVTOevZYwaznzhTV35q3HMI1evUX8juya+E/qUT25QlJFfAmmka9Gh3Lf6Frh/bydnZoigLK0SU4rl1H7+QNXJtKebjB049/A99YpAd4veasnLAWzqYVazii4B3Qst1Ua8aaQ7Mrj71iKAiYMdJIRqwPqscmA0Jj6Y60/aJNL2QHdM06NbOYrm8Z37vBFCECgJ0YFUTPKDNJgbOB2970JVn/iIrWZ3ieYUOrlwqXs0x5M4BsCdtuy4ygwW+jxJKFgCho1qWIWjsqhp0a9dxwouHXqxMxX8fEOTWr35x+qFuUQGsP8Z8oW2cHCKGwa43q1cgYM4QYFYdMqvOKP04SStQeuhfVGP+FaW0mP/VvXRvYU8hDYZMaDnKNKQ3d09yWS9ODvEySWNIjT5FhZHnkRXSabx3pNu9O78H+Z7fQnZV24oHBLuq7Ti72UFOJPFDvPoo0NZnpmwW4r4qTFQkCZBtnF6YJlcAygfPnz+nJUuWCGkYaNPKvZAWFxJkUlpZ3GyvXr2EVi1kzJB4Ahm6njx5Qk2bNqWkZEcDGJYbG2U+Pj4CMONY37GR+Qz3hkQRadU01BPT6m18tXm7lK9Bs/ffo25jfxdzcLt+jhYNbkfBvl6C8gAAi5S/kAw7uCpOdzmpE4ZWLxJTyBnS7f4xpANd3Pc3VW7SQYDdKk07CN3cZSMUuzqQKMN8x6w+LNdFelkaXoFqznZ0nTN/ze4q78gazhqwAZwQQs6O3vVkzdfT9Jm9rVM4re+szlXE93W/Vec46YGPsskWBoqjNl2h95w2d1iz0lQkrzWtPv2Y+q8+R/CWJtbgiW3x22GRnQwqDaNbluUsZ59FYgvImKnadtYAHrT2gkg0MaChAsBGsOzahK3XROpb1IXEGdZg72jDJg9JdZ5Z1YVJ68cAk8+3zRTJIKrMPMFb/IptEecuk+nisPL0cPlPVHXWyUTfps/ZzYKKEcIpcZExTZv5XdxB71/eIecuU6hAc0X6RPs6nK6SXf/o4/2r+0rvqr59ahsrvfzbWIH69evTvXtxwteqdwX9WwDZlStXimJ4dJE1rHPnzrR161bxxYoLSC5Rq1YtkWWsVatWql0keCwle9Cs6O7uLmTKpk2bRuXLlxfeZEOPrTlmajn/GrSk1HLvxpjHwb/mim4nbz5HSJMLaz9kGo1uWlRs77caMJ4yZFT3NolKOv4LCfARQNj90R2hnaut6pWDW+jVw5vUccQsatJTsQOCZBD4GYMS4cHt072w2lbv2y5ff+6J8HxaMdjTNABKeEYhp3VoXAuy5axpMGTtKjduJ60+9VgkkfAJCafJXA/ZxvaMakaZMyn8lc4HLEWWtV3XefeweuKoQqBHIMnD8OalRfIHjDumdTkxn78YJDfg1NGpZHQAACh5SURBVLbNOEEC7M8TrlQ4twUdn9hSJLFA2VAG1BXG72Rv7RMayUDYWJZsz+yTvyfRrVltCVxXTXu8bgzdmduFAOpgIW5X6MnGCXR5TA0GcxUYzA0k79N/cxrDz5pNledog/5DHl1UluHg4/sgUe7NoEzVkG72yYbxdHVCXbrwc2m6/0dfwjb/17AIn2f0IdSf0/Y2UAJZzMPEIofQvA33esQ81veJnlokJ3IAfcCc0wVnNrfV2t7vym5xPV/jPmp1CrUaSiUGLKHMFnFt9e1TraM0egJQBrDl5+cX7w4GDBhAjRs3po8fFZ/Zd+/eEVLIwtNobW0tUsIiGxiSGegyeCyRVlbTkNELY3/irU1VQ9pZZBSDhxPb+KNHj1bzgKrW/RrHx44dEwkvNm/eTHZ2dmIKV65wVjk2ZE9TBVw1a9YkJycn4UVF4obkGugEPXv2FOsCSgMspcZO7ty/Rvstc0fTb30aE7iumrZx5hBaOLANwUMIiwx7J1KvwlM5pHY+mtO7Ee3iDFuvn7lqNlU7X/PLAPprUj+1MpwcWb9QjI2te8nAC908ewRN6VCZRjZ0pOWjutEDTp37tQzA0zpXXiWQxTygXVuoRAXBn43h+IDEWnREOPl7vaCs5haccEI7v/XqkW0i3W6DLgrngjRO8z6jqe+MVeKaVPYtvE/Yco1azT1M4Lpq2si/L1NHTpcKfigMKV3H/XOVqkzaTWXGbqcBq8/RBgZ4n/n3X5cNZk/gQM5ApWnIcoWxVb2RSHk7dvMVqjV1L5UYuZV6s6fz1MPXmk1T/ByJCZAqFh7X3FbZ4o1//UWA2JIf0KC4EsiiEhITbBxcn3rVLiLaHLnrRaAE/MRZxiQgiwudqzmJ6/tuuIv3xPx33s1XVO9Q1VGtWcfYc1AZYO8jP5KbbyiDWwclkEU51CFqFc0jEi8AlBvLkg1ms+Xm3MXPblDgrbjgJ0wWIA7b25mzW3EaVBMKeXyZ7vzWifyv7ScED9nX7UbRIb4MbsfTix2ztd5fTFiw6B+ZwVTtS8wHUR4d5K0sRn/Xf2lEvpd2knWRqpS3dheKDnot0rN6HVutrJdSBx9CFQDfsnC5eENaxJaBR5tYc+7yC1WctFe8dCWCiPR3Z1pDfbH+kYGe9ObOcXrv8YBMOL1vnpodKGsOB+XQ+vapbJCGDwC04D3cs2eP2l34+vrS2rVrycaGE3eYKJ6OweEcNWoURURE0IQJEwjJERA0ha151Ndmt2/fJrw0DVveGFt163jWrFnUtm1bMcbAgQOpRIkSAjjWqFFD5xiafRvrPDg4mPr06SP4wPDcSgYZscGDB4tMZVIZ3hGMhTZICYygq+QaHgDAfUV2QqQRhqXU2MmduyHaq35W9OkP3kakVr1z5oBa9beBfnSJt7fNLK0pU2bF53vF6O60Y9Ek+hAVQc37jKK8hYuwh3ADzevXlFBfm3k+vkuebnfjXQ7weinGluYM4Di9S026cmgrYbsfSgZBvL2/dFgnOvnP8njtU6KgfL1WFBroK4LapPH8PZ7Rk1sXqGil2pQlq5lUrPc71m3cmmPihfTB2iyQ16dUjcZi/d94u9O9c4d5He8JTm+1ll3JNm9+bU3TZHmhXOYiDe2hO55q8/dncPvPpWdkZZZFcC4vPfGj9guO094br6g+czF7cApYX+ZTjmVwO2tP/O9R1c7uewbRfc9g1SJx/CrgvRib4+yEob/6Mw7QjqsvqRoHkEG1wCsonLovPUWrTsYFJMXryMgFCIj68a/zIqitP4NVOXsVqHB6NS2bX2QWu8YA8rSrN1MSIqlJmfyEcthL5hTDwJFVNXh0TdhLe4/XKrGGn5UZJ7Vw1gi4K53fljLwjsITn7eiy4ycuvfA2OY0tGkptSEAch9xdre6JfKqAWy1SgY4yaTZh/CwSD99zYsy53bVvqPnW2cwD/MQ5WsYx8MMuM5fpNxP3lqdRSv/a/vofxkzMe/yqtJLWaDFYLo8qorgkAJMJddebJ/N4NWbKk09RJaOiqfjwt+Npru/d6Pn239lANdRpKWVGyfg5mGK8NYNLKFZm69hb7nmsmVZcytc7yFul5Tb/FLFCN9n4hBjWjlXkooN9v6J6Qcf3wWyFzin4OcG3Tul7DtbHicq0X+RzgxtysoGPFD13hmwW6VXUPoDmlDf3bp1E57PXbt2CTAm1ccWN7yAP/yg+BwDrJ45c4bgiQWgkgypbocPH04XL14UW+xSeVLewS+dPn06NW/enA4dOqS8F3h24aldtGgR/f67guun2X9QUJBe6YHbt28vALJme33PAVjfvn0ruLGqbcCPxUvTEFSGFLxQdkiussHDhw8J1AJ4zFWzrKXE2Pg8Geszq7lmus4xB30/2+inSrNOAqDePrWP6nceoOz65sk9oh9JGgtg9cnNC9S093DqMHSGsh5S3W6bP56e3b3CvM72yvKkHOzmICsEYE3ceJoKl1J8z7UZOElwVnf9MYUA4LJb2sh2HRYaTGd3/iV7TbWwQoM2ZO9YTLVI53GDLj+S241zgrvqVIZzx2cxpae8DtC/bTd4is62ybkYHRlO74ICyMI2Jy1hMP/g4jFld3YFnemHaX8mmKFN2UDHgTE/t9Lvg74Q4bsqjjR1500RjNS3ftzPaP9Nd8ADBpQKj+EeBrGZGAzdnNNBmaZ2SLNSVHH8Ljp+/zVN7ZD8v5EzOXAKAVhHJ7SkCoVzihUc26YcdV58gmbsvkWdqjuRNYNrOUMwFVLm6jKk4e1TL+4eddVVvTZ95y0CYNwxvLHW7xsAc6gXPOb0uj8x8I3kNLMwlGHM6R0r8fplEOlks3HWLs30tFA0QCDWc043C0+3psKA6nw0j9HunkeQeKlmAwNw/sI/xCfsjYVBo7eKU1zWUDwgvGbaw8kH3vx39V/B39XsO7Hn+MxIn0HNtvHALDweH/kpXV8z4a1qRMdDTgtb/9hCh8EDm4U9gDaxKWQLNB1A+Rv1UQJZ1EFkfCbmkX6KDMNpsiwmPJQj9veQRaEySiCLDuEVtq/Xg0KZrgDvsX3d7rLjAHwjMEqXZbNzTBSYzZa7MJkXKs0UiUvkc+4fyl2lDd/0F8L2f0DsWP8msIWiaz66rkUxFQHmdWINB6MVpCI9Z5Glc0X2Zt+kFwzs7y3qTdXmnFX+vHT1ZYhrX6LDhTfNEH1p9oHPLEzfLe2cOXOKyH3IaQUGBoqtfbTftm2bSFgAEAmzsLCgq1evUpEiii0cUcj/IfUtTDUQShQk4b8///xTUA4AGFV/SRs2bCjGBRdVG5iFpNbUqVMTHNXFxSXJYPbRo0cEkD9x4kTKn1+31wgAFhQOcFmLFi0qAsoSnFwCFeC1xnoD8OsyY4yNz5P02dI1trGvIT0xItyxdZ8xU7yv7HjDm1vnEN4/bOW/D3lDFjaKP9w3ju8WgK14VYV33TS7OU3YcIoApFTNxFTx+Y6OSN73cvi7ELp+dIfYdpeALMaBV7jOdz/Q01sX2Xt8kGq3+151eOVxWGgQHVg5W3mu7cAuv1OiwGxWDrCyzZOPqRQPCRzXjJkyC5CfgZ0tAJzGMiko7NSWPwXFoSsHpAFMw4u+a8kUQgDY9J3XlT+vpM7jQyRHiMd+Jya1D23tpH4hDaWP5WBuJ7adIaf1hqPgc1oodmr23nwltp/rFrcX3QxsVIIj3ospgSwKsSVtyfxRBDIl10IjPtDu669E1LwEZNEnIvGxRX/5qT8dvuNBPWqpf9dL4+6/5U4HbnlIp7LvjswVTSyYPcFAfS1H/q8fWF+WXiANJHlmQb34rkphwhb/p8//0hKmUoC3Cp4teKzugWHC2y21U33PZ2suVBDComK01lGtLx1/V7kw7eOHj7kH7tKEtuWFkgHA7S/br4sqAKpyNnvfbYr6qKCQIN0wUuMm1/C5MzeT3zmJ13uePHnoM2/hY1vfRM/sWXlqdWJe6kkGi0fJoX5PinrzmoOL7lLBlkNYV1WxLWiW11n06XlkJb17cYui2IMa6f+KA5jCyYQzdSXXJGWATx8i6MGyH9W6+8xR/7Ao3mrXZiV/Wkr/Dlis7XJs+f8SuK5+Gfdeot8i4Rl1Y/7w08381M9gFgAWoBpBV2YOLuqNDHQGTi0Mklqlh/xFWH+YRcHS/NDxhjwOLOEHjn2Uv3E/UW7s/6JD/AifLWMYtrMhKfX6tf7cJ/A8EW2/d+9e+vHHH0VAEbayQSVQ3cquWrUqnT9/XgQ4vXjxQtSDBJehDJ5Z2Pr162njxo1q3WK7HhH80dHRYste7SKfADCiTkImUSYSqid3HZJjaK9L0gqeoFWrVhGUA0JCQoS3e86cOQQQlhzDesN7jrFz5FA8JGv2Z6yxMQ4+TxI/WHPclDyX5vCWt8b13Yau3rIb3b9wVIDFuh368Na+J7m73qLmP4xUfr5NWRLKsXRlenr7Et1gmagAjsBHhD+2vw1hAR7PRTcfosJp5Th1wCoB5Tdaov7RME8hF1pxJSDBqUiUiQQrxlaY27cJ+bx4TN0nLBSe58wmpkw5OEl/M594ydAONGPXDcqRt4C+3eldL+KdwoMFvvLA3zeL+0PjAsXK8kNHIB1eO59uHN9FDbsO1LtPzYr4fQjhz4mxvmsVFKzM5Bua8PeONLfO7PE88eA1HUE0fp2ivLUfRnfcg4SnTtJAdc5jRZClWsEBRLdeBpIXe1DhjQT/M7dlVqmrJL+/YI8kLIL767fqrFo/4QzuYB4MBLXZir61aekPtbRdFuW8gZIoA4946IaL1J0pFS04kEuXAYxDxaBVhfy0+PuayqoA5kVHbBFqBQCzoBLAyytnkR9j2GFCzGfNLHdZaxkoDFAmQJDZGVfmm7P3GvOpwlSNEg7WZJ3dVLat5/Je/DN8R9deBNJspoo0mX2Q7s7txD9PxcOybKMECv34c2dnJ48X44HZUqUUfIcwz4ciSCmBvsXlnGUbUqZslpw04JAAswHX94tygFzJPA6voFd7fheeUqui1cimRC0q1HooebIQf9QbL6ma3u8SWJMawDMLy5ApC0eiqv+wMmS3IdAhzOzln7oU7RQcMtGJAf/Lnq8YVZ19lj2xBwgBYUixa1OytvAUY5jsOuaUnGnAKw6zYLqFBGSl/nKWayzALBI9pISBPx359g1Jny1jjFm6dGm6e/eu3l23bNlSBHQBLAHMbt++XbRVzb6FADEEg7m6uhL6h7Yp6AAAzv36Je0hAGBP1cAtxVY8kg1oGni5MFAf5AyeXENwUuX6RpmXlxdt2bKFQFPAHzE5g3cYgW5ITlCvXj0RLFe2bFm5qokug0ca9963b1/ZtsYcGwPi84Sf+9c20Fpgnk/u6w1mS9duStnMrQhUA4DZm8cV/PDqrbsrbwcBYgsHtWGtVTdycC5BhUpWpNLM54TncuOMn5X1EnMQwd5YyeCZhWVi3W14P1XNjKkFoEPk1UEPwOfbxDT5QEZ1XN9XTwWQhQZsvY5xv8NIpvDi/jU6uXkZ3Tl9QGjOqrYzxLF1LsXDPLzUAOqqVqZ2MwFm/dx1U91U28gdB3AQWjR7Zo31XYvvqmK8U/WAuZdSAJDcPFTLGpfOJzys2KoHmIWXD9aFQa5ky449pLn775JJ5gxU3cWO6hTLSyOalxHR8Z4MfpNibxlwSRYarjiGzmpm3o5XNWumB7Rnb2cRe2vVYrVjTS1VtYtJPIF6QQjPK4w9z0PXX1T2AsD2L/9DGZQBhvM65IkNClNdMzQAnaBmkTx05pEPQf81FwP/l/wQoOoFlzrGGgCIJoZiILWFDFjrCgWFbu3biI9UKr+NAOClRm8nyHXB8CAFGoD0gIKywrktxYtZDnw/l+g0e+i7MXhPqj14HUJlyir+Lmr2EQ/Mwgvg5FKUgh+c1RvMSkkCfM9tEd5XUAwsnSoqkx58fB/MQV6/CpH+6r9fEYkDpIm4s4dQt8U+7rBHU9UiJY1WrB5b1lyKJ5tsdoWo5MBlqlWFWsIn9g5kzKL9i9Hn/FYK4+AoXWZimYsKtx2hq4raNag4AKhnZjAtJLFUrnocXEboL3N27b9AKtUTfWhqay/a/PtFwa1R7QDZvGCZslqoFhvtOIg/SyYM1qpXr260MRCYBDF9fQ3gEZJSa9asEcFKoBhgftiSlwzeRQBZJAAYO3asVCy4rcoTLQf4QywHQqWEAPjFhxUuXFgEimEbX5UTimsIOoO2q0RrQJmq+fv708yZM1WLZI8RvAWFhMQaAt2guqANTOJamzZt6MaNG8IzC16roQz3Dk81fibwQGuaMcfGWKAtwFOfUAIJzXkZ4xwe7goVK5HrlZNUvn4rvYbIbJKFKjX5ji7u3Ujhb4OFx8+xdBW1pAdH1i8QQLY982WbMW9WMnh0EzLx+eZtTk0L4AQLCvuXcjoUEoe58ztS/1/XqFX9wp/raKaXSZQGtYuxJ+CXSjJactelspptejKVoZx0qvPd+7mruO7CYFbTilepJ8BsBKsvGMNs7PKJbj9zJkhN+8hZv2BZsyfvO9n1yikyZ3pUUn7fNeek7bxh4yZ0cNsGbZfjlQNAtq1UiDZffCa8rwCzlRxzKZMeBIVFE/isOdjDB/1RVb7noiP34/WnWaDtu1byxgIYFmDeJ6xwLgv6s18dtS7AIYUHWNc2+BYOVpMLMlPtCEByVCKkpyCvVTKfDUkUAqkvqDuAi+rKwA33BkMAFwzeWU2L4gAyVMvOnFUnDtK6+iyAPN+EKSkdqI/teTwU1GDgm1iDpxfrU5n5sHhJhvV9x8FdRWMfAqAeMXvvHdoytCE1LKX4rEt1bWO9tz4chJdUi2KeMO5t+YgGsl3EA7Oo1aVTB1q44i9y6jyZvZyyVeJ1lrdmJ/I5s4k8Dy8nSE4V6xMXtCIUB/iHk6ticzUgGx3sQ2GergzqFJyueJ1yQdYcikUBJUHVEJmvauCFIkAr+ME5lgKLYQ9tnCfA/eBSesUZuKAAYFWkimoz5XEoa7YGcBCbLstmVzhRYPbzhyi6Oq42ZwNrS6UGrVB2jfsOvHVYqC0oCw18kNEkK1kXq8Ee4MuCzoG5S/bm9jFxaMUc2pSwN1d3UxOWtjKmFxGqA+BVghJQp476l5W2ewTVAJqpAKvQVv3rr7/Uqr56pfjMoZ6q6ZMMoGDBgnTy5EmKiYkRSQDQHvxTbJ2rWrVq1Wjnzp0CIKuCWUiCOTo6Erycp06dUm2iPEZQFsB4Qob1SMofN3hb4ZFt0ED+ywNZv8ApBtA3JJDF/eDn+OHDB+rUKW53R/U+jTk2xgFXGfQKSLKlBuvYoT3NmjOXuo6ZR5k5YEkfA9Xg/K51dHTDYsEP7fXLUrVmb7w9xHn1Vt3UyvUBs7a8Df/42hmW+OK4h9gkFj7s4ZV4oegQqgrZrWzJlbNtqdbDtSPrFtC+P2fRuLXHRZpclGkaZMMAxhMyeFn1BbN5CysejOCxbvPTRLWub53cK84dnEqolRvqBF5mqCUg6A4e1NzM9ZXs7lnF3x4nfuBIjl0/vE08YCL5iLEM37ULFixgcBck+JP6jANpqI3nn9JS9sACpC3sFefY8GZKAZ7tsdWuCmShmerKWaRyWuj+vAPonX/sKzi2khwVpK7c37xXTg2qCrbsgYUHE1xcqR4q/HHkAf3GXmFE4iNNrpxdcPMTQWxy16QycGYTA2ahXCCnXtBg5gGKZuB2ZkobqWtqxBnDsHbg/TZiT7dkoGZcfx7I2/02lI0VB8Bv3XThGSFxQkV+YJBsP/N9wV+VVA+kcn3eMS54uVc5I5lqCuFVnNHMlB9UarMXHVbc3ka8n+OfhSaY3cQPMrASDN6TavDsI6MYdlXlTPYTjy3U2bNnE+gCeaq3l2sXr8zSqQJlzV1I0AYyMJDKXaW1sk62PI4icYA/B1nZslSUGUfTv31+k17umsfg1lwI/0f4vRDlykaxB9nzFyN4fr2Or+H+C4qApTfMzQ1+eF6tKgK9nDpNFKlwXVf+zHzdwaLvN7ePk/v+xUxrqE2WLpXV2qielBy4nD26y1WLkn2MJAkAlKBf+JyvxWC+GUUGeBD4s1ms84hkBtIgniwdBlWIwm1HUuF2I6XiZL07dZ5EN6c1Fxxip44TyNQ2L0ukcTDa2U1kxWuRs3yTZPWvT2M8rAQ9vkKDFyoAtD5tklIH28HgtyL6X18wi/rOzs5iaxzeT03gBAB4+PBhwaMdM2YMwROKbffdu3eLKQKYAlBaWVnFmzIoCWgL2kL//v0FiAVoBkUBKgSSDRo0SCgSQC3BwcFBeCLB1Rw/frzoGzxUbQaPJQCfMQyeSUiLIemBxCHWHAdqDvAcIFAKurhyhu8RgEL8XLCGU6ZMES+5uqplEoDX9rNMzNiq/epzDG/40qVLhRoDggBTg0FhA5+Fyyy6D9qAPgY+bK58jnRi81KxXV+pUTu1ZgWZq/mQg8T2LJ1GTXoNo/fBASJg6zZvs8MCX79kHVp5L2XhUhVF2/VTf6Ja3/UWIPbYhkXCsxj+NkS0B5e1/dDpgrKwZnI/9v6OEHqu984fpkNr5hE8oU5lq4q6cv9hK37VjWC5S0kuA60BAXAA4tDWrdq8s+DH3jl7UKSXBdgtVy/uj+WQ2g4iKOyvW/LrkNiJYD1+7VmPVo79nr4bMpVscjuQ283zLIe2TqxF2botEtulsv7zu1fJnSXTNq1Wf2hRVjDQAXZLShQvSitPPmYvZ229egWwKgSv6IlH7AHNSG0qFlK2gzcR0k/w2CLNKySgrjPP8rf9dwS/Exmk4AVEPTkDbxQBZkN4W75nbRcOgnpPS48+FClhsY0PA01g8ncVRSrcgWvPC/mo7FlNCOlhFx6+L2gNSDSgzVb2r0N4fS2rylv5DXltEIgG6kEz5rEikGseB2XBizulg8IxBYoGXvCC5+Jgu8Zl8gkVgmmsKAGg3lUlYcJKVhtA+ehWZcVL270hMQPALLKKzevBFFH2ssJTDdD8a5cqQiUBbTG/YuylXXPajSx5bZHu1o+9ugicO37fi8oVzEGgnCTFsJO5+swT+o4zOUKLXc5kwWyBAgWoe4+etG/fAspdqaUAk3KNNcsgfQUPKEAbQKpkmbJmp+IsBfX4rxGcRrW3KM5kZkUu3acLkPto1VC6NqEeNdjwWmqifAdILfXzapEt69HKIazLkIFsitegMsPXiaQJyop8gK18eENfbJ+lVCaAHFjeOl3JqcN4pctetY2xj3HfrisGMsgeJV4Yz7xAKeGpxbooDbxIQaXgR1QDmWXhslR21GZe9+F0b0EPZa85mDMLaa6UsFc7Z1O5ChWpSRPjA2f8sW/RooXwFsLjqY9BAgvt4G3QBC6Q5IImLIKz8AKog9KBm5ubqI/gKLSZNGlSvKGgTQuvJcAvXvb29kL4HxVVZb5Ad4AHFEkBunfvruwHQBXBadrAnLKikQ7Onj0raBK61hGAEl8yK1as0DoLeMsBZkG5AEiU6BVaG8ReAJg146hVbdy/xIyd0Fia10FvwIMKvL+pxfAFDm73ljVzqVqLznproVZn6St4QMvXbx1vC7spg8vn964S0q/ihQcTAD1s+a4Y1Z2ObfyDwaeFLOBs3GMIvbx/QwDA6xw8Blmrai26iOU6yqBWMmS3+hgVSTs5EYPk+YQiQ00uhwyWtI0q1Tf2O36HB8xZR1vnjhFzf8ReY8mcy1enH6auEGoLUhnoEIZUnEFihmFLd9H6qQOFNJg0Tpk6zVmaS/vvkVRP1/veZdOodp26IrGLrnqGuPbLlGnUtWtX+rlpSeEV1KfPztUchQe0BWeLMmewIxm8sX/0rknDNlyinssUPw9E589knia8jUPWXRRJDvxW9ZaaqL0P5AQBNzloDPJeeEGkvxNH+8OWsFdRsu61XISk1Yxdt5TKBJADQwDWxHYVUvyzKM1L3/dVDKaRVGL+wXvihXYWvI5rfqwnsn/hHL9Pm4c0ZO3ckwKkA6jDACTX/lRPzSMNEIxXQt/J4MTO6lxZ6P3WnLJX9Icgst51i7B6g2KnA4Xgym4c3IAGcQKL33mOeEmGn/nsriyDp8FXlq4n9A5A/MDjDW3a/4vWqv/jG5FFT/AOFSlajOwa9iPHDuO0dpCYC1BIgKcOQVBm9i7KDw/KP0W+YwmpuKc1zX6/MM8okgOWwDOFHJguAz8W4yDVa/Z8Rdkjaa+rukGuQWrrIasoqGrcSh1jicNfuzF/1pMsGMiaqiQrkOrg3X3/H8z9zc/BauoeFNU6STkG7SLc241ieJ0RkIb1T6qBA3x5VFVy6TGTlRD66uwGagmuKwbRhQsXRNYrnZUNdBFgEx5UeBUBogxh9+/fJwQbVaxYUc0L+/jxYyFXJcnVyI2FdlAkKFOmjPLzLlcPnxEAKABlW1tb4WVOrkar3DiaZUjQAMmto0ePal4y+Pmvv/4qOML4I5jSBqoIdHwRcKfLINUGugceLKCXm5oMc3Ph4JtKzbtRl9G/GWxqkKiCDBa26hE0JpnvqydkY+dAUD3QZmiHBAT5XErp/HxDvcDryQOK5u9mbOOj35SwX9pXEjJcw5ftiTccEjog+C2G+ap2BV2ERJkmuMZD2PQu1Wn6jmvx2ienALQLnxePBJ/ZntfDKqddvO7e+HjQhFalBbWkQdef4l1XLbjAdIzNvw4T/PWkUIpU+9LnGN9X1atVpQ/8N+3IuKZJCirSHAdb5g+ZVgD1Akg5ST8LlL9lbib4rroM3Fu/0AjBQ5XaytUH//OhV7DgkcKTaG+j/fMt1z6pZTWn7CEHHmsb68kmx5Ao4RFTNSBz5sIqEOAkyxnUEh6+DqbSBXIIL61cnUUMdgvkMBdyX3LXVcuCeX2R1hYAuJqznRowVq0HqS7wc+FNBw0BHnVkKtM08HorTdwlvLtydAupPrK21Zx2gJq1ac9OpQ1Sseb7AVnPLGrly5eP5v8+j35m3Ujr4jWFN1SzdWLPIfVly5H8mobyhGTAwN0FENPH4PG0Lqp9+0qfPgxZB79Y5vmLi5e2fpFO1vfCVqowcbe2KkkuB38YklwpaaBTPOfsbj/9NDDFgCzuDxJRAI7wjGKr2BCG/uRMleMqdx1l0LTFKyHDZwSUB7y+RQNQR4DeuXPnUu3tAbhAmQHedgDv1Gbwzi5hgA1gXrxKXSpdq6lBpgggKmcSv1TumlQGTVu8EjKki3WpUCOhail63Sa3PW/z63Z0IOmDc7nqBp8XeMaQ5DKE4aFj+/xxguqTEkAWc8b31dp166l8+XI0h4N+JnMa1uQatq81M1ehT5TjlZBB0xavhAyeYCkCP6G6qfE6pK30kbdCWly51LjSPSHwDHSBfWOaSUU63xGwVpPT0iZk8NCCUoJXcg0PTUM2XKYMWcyYDhi34yPXr7pGhUYN8Pmw/fp4xY+UUjJOGlNIc6cvdsxhjuoAkao3MZOPYvBXZuTfKeJFTsy8pLrIKIb7evL3RKlI6zs87a6Lv6cizo7MlVyotZ4xLkAdAClply1bpnP72xhjp9U+EfwGvjB4rcYyaPMicA4PySlpoIfg3hBQlpANGzZM7CIgIE+Xtz2hfox5HbSY3syfXTOpH3k9fWDMob6ZvrFOK8f2Yu7wskTfk3XuvNSNkxukpCFxA+a75bfRCQ4LxYdlwztR+XLlCAlGUtLwML/i/+1df0yVVRh+N6IUV6IsbbUxXCy6Ij/GSk3vlga0ZTRpawXYf/qX0fKPHK0fphC23KJlP4jKP2IXisIJpKQiUSshnIGxMbS0cW/EhhpDFru3u9HpfU5hd3AvXi6X77vfve/ZGPd+fOc773k4OzvfOe/zPO9X66P8hs6LRjZt2bZAftvxQQfnG/eb3odBXsw6ns03bGd6qsPYGQcGL9T/MHUp4O9K1qeFg9jnXzRqKc2AN/IfAu7MTlWq5fyxzbl59NOBpyhrdwOnB0TnztFUf0P9DUIX1Bp0Qf7rHEtS5uY51jDhdu5XXPwi3c9AKSGQYet7s4Rui/PSV8eO+hX7X+jIsXjB4gluVPG8AwIClhT/CEBHFylF2JXEW/BCFSNypv3Fjj6hb0gRmc3EAQQ2uLHB7cyo3S1/8QZzrYYVOJxOF73NBKbn3jtCyWmZwVSLyXvSH8il0RG20wQfIYTxPR8Dg/kAjnjj2Qwmh/VvVyT/r0Tj+0zYEb/1zFbOm4yn5qYj11VTfO9Z6M+Q/cOpy64Db2hr1Sf+y1Vd6Hat+PxNTIgaZmmqYPJUjejfQ+zMZlYBBiACglwWKH0E5DbkPNfW1pLdbr9hqAFzZn1rgrH9yKMF1NvXT6t3VnOqgHmsPt+45HNkIfDn0HnqP7idlvFJT0f7KZ0faWaE5eXl2u4VGq7QYw3EyjczRmnbXASgZ4uXHZhngPhlRj5vKAhAQWJrYSF1dZ+h7RUfUTYTiKTEDgLOgXNU/fw2Slq6hE6xBCCUUMwskOeDyQnsTndtybye72pmTNK2NRHwsG5uWV03NXRdJGidB9I5n9a7lri9XKZdnPEVdqHbSorp5/MD1P7hPsKRc2La2hlOWzMqyoWYQABs399Ofkz91TspO/0eam87afhxsj+goQQAchPUCsCOx9sdyFVSBAEg0NnZqTULYYwB5QgYQFilgNxYXFTEO7SDVPP6S5pIBDb+XK1drdJfifNfBKCucLz2IB16ZQetvS+HjjNxc8ru2EyMQLyF5fSedz5hWa0rtJ5lrpYmzHQ1NDNGaTvyEYB28dPvdlCPa4z11xu1PGKQUV+YNWfW9yEQvP+0vk6vlEe/r6fuMjsNdTho0uv2vU0+xxAC6u9JNn9opbN78ulSQwW9WLabvvv2G1q5MnS1hHDDh2MwSGSNj48TbEGRF4ljdSmxiwAWr9iBxcsNZAh7enooLy/PcoBgQXuITTMcDgf1tjXSy4XZ1P5ZDf3lnrBcXyTg2RGYZLe9MycO094n19GXNZW0j/eg2ljSL5JezktLS+n06U66PLmE7K82aw1TMOqlCAI3QgDKB6UswfZw5VG6fZWNenrPBTRHCPSsoNIMpleG9FE5H9vCMSnulgRKytlCSRkPsn7qGjYDuENfm15HvlsfAby4ICcWMmMwXxj98RhNXB2mx5kkuJ/Z3/5sRyOl17A/xXgFSWJkZETrxcJJBOYGcNqCmYGkIUTKfyt8ceD/DgMI2AhjJ7apqUm/3IC8gtQTEFyjocCIAwoMyPuNY5OZ7E0FtGZDHiXfm0mJK+6cVV4rGvofbX3wetx0jU0shn7pp4HuDur9upnGrl5mC+4insMq9JwVqX32er16HO6vfI3+GB2lXM7NzM+4S2udwlb21kU3a03SSI1f4lpYBGAfPDbhpUsj1+jsr1foRN/v1HVhmFLvXsXrykq9Gwu1jDmWFpA+Qi68KFDsaqTuX7de8UIA7BH5iREM0mzpinNRFSf/hzx+zKjIE61iko/iRYxiQpCM1xgZr5ibWNpKsRqAYpthzoxhCkIUFl7UqqqqKrVh40bFOsUyvqNgfGdkZSl2zlODg4OWGrEej0fV1dWpxwoKVMLixTIWo2AshnuNt3xZoiopKVatra2KTXXmM76bQ9qZ9bdidrvdBBF5CHuDVMFR+btNrlkYAaSaIC/KZrPpnUwLd0WHDpY7VA+cTqdOQ4BDlZToQgBqFth1T01NjYg8biPR5cWENuHASQQIYzInG4n+/NqCvTbmWpwgzKbCMb9WjKuNuRWqBy6XS8+1mHulxCYCN7EDIOZkyGimpKSEC4SWsC1mwxWRPEcQEAQEAUFAEBAEBAFBQBAIEoGWoAlgQT5QbhMEBAFBQBAQBAQBQUAQEAQMQ0AWs4ZBLQ0JAoKAICAICAKCgCAgCIQbAVnMhhtReZ4gIAgIAoKAICAICAKCgGEI/AMdKh/pdrWZ2AAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(graph.create_png())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Random Forestで予測\n", "Random Forestは欠損値に強い，次元数の多さに強い \n", "木が十分な数なら過学習しない（？） \n", "OOBエラーによる評価なのでCross Varidation要らず（？）" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "# n_estimatorは木の数 \n", "clf_random_forest = RandomForestClassifier(n_estimators=100, random_state=0)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n", " oob_score=False, random_state=0, verbose=0, warm_start=False)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_random_forest.fit(x_train, t_train)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.977553310887\n" ] } ], "source": [ "y_train = clf_random_forest.predict(x_train)\n", "print(accuracy_score(t_train, y_train))" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.98 0.99 0.98 549\n", " 1 0.98 0.96 0.97 342\n", "\n", "avg / total 0.98 0.98 0.98 891\n", "\n" ] } ], "source": [ "print(classification_report(t_train, y_train))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[541 8]\n", " [ 12 330]]\n" ] } ], "source": [ "print(confusion_matrix(t_train, y_train))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.予測モデルの評価\n", "何をもってよいモデルだとするかは問題設定による \n", "accuracy？precision？recall？f1-score？ \n", "PR曲線のAUC？ROC曲線のAUC？\n", "\n", "#### モデルを良くする \n", "- もっといいパラメータがあるのでは？ \n", "パラメータをいろいろ試す必要がある．→ Grid Search\n", " \n", " \n", "- 他のデータでもうまく行く保証はあるのか？ \n", "過学習（overfitting） を防ぎたい＝汎化性能を高める．→ Cross Varidation \n", "\n", "![Cross](https://image.slidesharecdn.com/random-150204215702-conversion-gate01/95/-54-1024.jpg)\n", "[機械学習によるデータ分析まわりのお話](https://www.slideshare.net/canard0328/ss-44288984) より \n", " \n", "両方skit-learnでできます！" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", "from sklearn.svm import SVC\n", "tuned_parameters = [\n", " {'C': [1, 1e1, 1e2, 1e3, 1e4], \n", " 'kernel': ['rbf'], \n", " 'gamma': [1e-2, 1e-3, 1e-4]},\n", "]\n", "clf_SVC_cv = GridSearchCV(\n", " SVC(random_state=0),\n", " tuned_parameters,\n", " cv=5,\n", " scoring=\"f1\",\n", " n_jobs=-1\n", ")" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GridSearchCV(cv=5, error_score='raise',\n", " estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n", " max_iter=-1, probability=False, random_state=0, shrinking=True,\n", " tol=0.001, verbose=False),\n", " fit_params=None, iid=True, n_jobs=-1,\n", " param_grid=[{'C': [1, 10.0, 100.0, 1000.0, 10000.0], 'kernel': ['rbf'], 'gamma': [0.01, 0.001, 0.0001]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n", " scoring='f1', verbose=0)" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_SVC_cv.fit(x_train, t_train)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
mean_fit_timemean_score_timemean_test_scoremean_train_scoreparam_Cparam_gammaparam_kernelparamsrank_test_scoresplit0_test_score...split2_test_scoresplit2_train_scoresplit3_test_scoresplit3_train_scoresplit4_test_scoresplit4_train_scorestd_fit_timestd_score_timestd_test_scorestd_train_score
00.0223020.0046120.5004320.59943910.01rbf{'C': 1, 'gamma': 0.01, 'kernel': 'rbf'}120.348624...0.5370370.5813950.4693880.5931030.5964910.5653210.0008820.0006820.0863230.025639
10.0177710.0033630.4782820.49860210.001rbf{'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}130.336283...0.5094340.5136360.4375000.4328360.5636360.4941720.0005850.0000740.0832510.041543
20.0153370.0032250.3732770.40132210.0001rbf{'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}150.222222...0.3333330.3680000.3913040.3548390.3913040.3819630.0007190.0000570.0992990.047146
30.0300710.0029180.6801140.808045100.01rbf{'C': 10.0, 'gamma': 0.01, 'kernel': 'rbf'}90.666667...0.6764710.8022390.6386550.7977940.7338130.7911280.0037100.0000440.0309940.014921
40.0230250.0031890.6968230.736891100.001rbf{'C': 10.0, 'gamma': 0.001, 'kernel': 'rbf'}70.671053...0.7050360.7395260.6511630.7581230.7067670.7198580.0021230.0001110.0339280.012797
50.0164980.0033380.4427280.442583100.0001rbf{'C': 10.0, 'gamma': 0.0001, 'kernel': 'rbf'}140.330275...0.4210530.4226800.4583330.3905010.4536080.4164520.0015080.0001200.0709800.047659
60.1153080.0028670.6850990.8510021000.01rbf{'C': 100.0, 'gamma': 0.01, 'kernel': 'rbf'}80.652778...0.7246380.8514490.6560000.8430660.7259260.8451730.0293870.0003200.0330260.006237
70.0491890.0024600.7106310.7710081000.001rbf{'C': 100.0, 'gamma': 0.001, 'kernel': 'rbf'}50.701987...0.7246380.7631580.6451610.7723130.7555560.7537310.0099580.0003000.0368650.012765
80.0411630.0030290.7044710.7137951000.0001rbf{'C': 100.0, 'gamma': 0.0001, 'kernel': 'rbf'}60.733813...0.6956520.7191010.6250000.7245840.7230770.7111110.0129300.0001380.0429100.007052
90.5729570.0025420.6697560.87049110000.01rbf{'C': 1000.0, 'gamma': 0.01, 'kernel': 'rbf'}100.652778...0.7042250.8628160.6386550.8666670.6962960.8713240.2420910.0000940.0256980.005719
100.3115190.0024860.7156290.79421810000.001rbf{'C': 1000.0, 'gamma': 0.001, 'kernel': 'rbf'}20.716216...0.7164180.7854410.6721310.7881040.7794120.7800370.0823250.0001400.0357280.013975
110.1743070.0038530.7127100.72242610000.0001rbf{'C': 1000.0, 'gamma': 0.0001, 'kernel': 'rbf'}40.739130...0.7058820.7221170.6451610.7388060.7230770.7169810.0754240.0026720.0369030.008729
124.1240180.0016040.6491240.888971100000.01rbf{'C': 10000.0, 'gamma': 0.01, 'kernel': 'rbf'}110.638298...0.6865670.8814810.6115700.8901300.6564890.8872181.2396000.0000480.0244710.004438
131.4607510.0015530.7135170.814629100000.001rbf{'C': 10000.0, 'gamma': 0.001, 'kernel': 'rbf'}30.722222...0.7299270.7984790.6504070.8111110.7703700.8123860.2187360.0000390.0396920.010188
140.5055280.0021300.7249730.755406100000.0001rbf{'C': 10000.0, 'gamma': 0.0001, 'kernel': 'rbf'}10.753425...0.7205880.7565540.6504070.7697970.7538460.7269300.2160260.0004310.0391890.015732
\n", "

15 rows × 23 columns

\n", "
" ], "text/plain": [ " mean_fit_time mean_score_time mean_test_score mean_train_score param_C \\\n", "0 0.022302 0.004612 0.500432 0.599439 1 \n", "1 0.017771 0.003363 0.478282 0.498602 1 \n", "2 0.015337 0.003225 0.373277 0.401322 1 \n", "3 0.030071 0.002918 0.680114 0.808045 10 \n", "4 0.023025 0.003189 0.696823 0.736891 10 \n", "5 0.016498 0.003338 0.442728 0.442583 10 \n", "6 0.115308 0.002867 0.685099 0.851002 100 \n", "7 0.049189 0.002460 0.710631 0.771008 100 \n", "8 0.041163 0.003029 0.704471 0.713795 100 \n", "9 0.572957 0.002542 0.669756 0.870491 1000 \n", "10 0.311519 0.002486 0.715629 0.794218 1000 \n", "11 0.174307 0.003853 0.712710 0.722426 1000 \n", "12 4.124018 0.001604 0.649124 0.888971 10000 \n", "13 1.460751 0.001553 0.713517 0.814629 10000 \n", "14 0.505528 0.002130 0.724973 0.755406 10000 \n", "\n", " param_gamma param_kernel params \\\n", "0 0.01 rbf {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'} \n", "1 0.001 rbf {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'} \n", "2 0.0001 rbf {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'} \n", "3 0.01 rbf {'C': 10.0, 'gamma': 0.01, 'kernel': 'rbf'} \n", "4 0.001 rbf {'C': 10.0, 'gamma': 0.001, 'kernel': 'rbf'} \n", "5 0.0001 rbf {'C': 10.0, 'gamma': 0.0001, 'kernel': 'rbf'} \n", "6 0.01 rbf {'C': 100.0, 'gamma': 0.01, 'kernel': 'rbf'} \n", "7 0.001 rbf {'C': 100.0, 'gamma': 0.001, 'kernel': 'rbf'} \n", "8 0.0001 rbf {'C': 100.0, 'gamma': 0.0001, 'kernel': 'rbf'} \n", "9 0.01 rbf {'C': 1000.0, 'gamma': 0.01, 'kernel': 'rbf'} \n", "10 0.001 rbf {'C': 1000.0, 'gamma': 0.001, 'kernel': 'rbf'} \n", "11 0.0001 rbf {'C': 1000.0, 'gamma': 0.0001, 'kernel': 'rbf'} \n", "12 0.01 rbf {'C': 10000.0, 'gamma': 0.01, 'kernel': 'rbf'} \n", "13 0.001 rbf {'C': 10000.0, 'gamma': 0.001, 'kernel': 'rbf'} \n", "14 0.0001 rbf {'C': 10000.0, 'gamma': 0.0001, 'kernel': 'rbf'} \n", "\n", " rank_test_score split0_test_score ... split2_test_score \\\n", "0 12 0.348624 ... 0.537037 \n", "1 13 0.336283 ... 0.509434 \n", "2 15 0.222222 ... 0.333333 \n", "3 9 0.666667 ... 0.676471 \n", "4 7 0.671053 ... 0.705036 \n", "5 14 0.330275 ... 0.421053 \n", "6 8 0.652778 ... 0.724638 \n", "7 5 0.701987 ... 0.724638 \n", "8 6 0.733813 ... 0.695652 \n", "9 10 0.652778 ... 0.704225 \n", "10 2 0.716216 ... 0.716418 \n", "11 4 0.739130 ... 0.705882 \n", "12 11 0.638298 ... 0.686567 \n", "13 3 0.722222 ... 0.729927 \n", "14 1 0.753425 ... 0.720588 \n", "\n", " split2_train_score split3_test_score split3_train_score \\\n", "0 0.581395 0.469388 0.593103 \n", "1 0.513636 0.437500 0.432836 \n", "2 0.368000 0.391304 0.354839 \n", "3 0.802239 0.638655 0.797794 \n", "4 0.739526 0.651163 0.758123 \n", "5 0.422680 0.458333 0.390501 \n", "6 0.851449 0.656000 0.843066 \n", "7 0.763158 0.645161 0.772313 \n", "8 0.719101 0.625000 0.724584 \n", "9 0.862816 0.638655 0.866667 \n", "10 0.785441 0.672131 0.788104 \n", "11 0.722117 0.645161 0.738806 \n", "12 0.881481 0.611570 0.890130 \n", "13 0.798479 0.650407 0.811111 \n", "14 0.756554 0.650407 0.769797 \n", "\n", " split4_test_score split4_train_score std_fit_time std_score_time \\\n", "0 0.596491 0.565321 0.000882 0.000682 \n", "1 0.563636 0.494172 0.000585 0.000074 \n", "2 0.391304 0.381963 0.000719 0.000057 \n", "3 0.733813 0.791128 0.003710 0.000044 \n", "4 0.706767 0.719858 0.002123 0.000111 \n", "5 0.453608 0.416452 0.001508 0.000120 \n", "6 0.725926 0.845173 0.029387 0.000320 \n", "7 0.755556 0.753731 0.009958 0.000300 \n", "8 0.723077 0.711111 0.012930 0.000138 \n", "9 0.696296 0.871324 0.242091 0.000094 \n", "10 0.779412 0.780037 0.082325 0.000140 \n", "11 0.723077 0.716981 0.075424 0.002672 \n", "12 0.656489 0.887218 1.239600 0.000048 \n", "13 0.770370 0.812386 0.218736 0.000039 \n", "14 0.753846 0.726930 0.216026 0.000431 \n", "\n", " std_test_score std_train_score \n", "0 0.086323 0.025639 \n", "1 0.083251 0.041543 \n", "2 0.099299 0.047146 \n", "3 0.030994 0.014921 \n", "4 0.033928 0.012797 \n", "5 0.070980 0.047659 \n", "6 0.033026 0.006237 \n", "7 0.036865 0.012765 \n", "8 0.042910 0.007052 \n", "9 0.025698 0.005719 \n", "10 0.035728 0.013975 \n", "11 0.036903 0.008729 \n", "12 0.024471 0.004438 \n", "13 0.039692 0.010188 \n", "14 0.039189 0.015732 \n", "\n", "[15 rows x 23 columns]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(clf_SVC_cv.cv_results_)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'C': 10000.0, 'gamma': 0.0001, 'kernel': 'rbf'}" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_SVC_cv.best_params_" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.72497257558382566" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_SVC_cv.best_score_" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.84 0.87 0.85 549\n", " 1 0.77 0.73 0.75 342\n", "\n", "avg / total 0.81 0.82 0.81 891\n", "\n" ] } ], "source": [ "y_train = clf_SVC_cv.predict(x_train)\n", "print(classification_report(t_train, y_train))" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.81593714927\n" ] } ], "source": [ "print(accuracy_score(t_train, y_train))" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[476 73]\n", " [ 91 251]]\n" ] } ], "source": [ "print(confusion_matrix(t_train, y_train))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### モデルの保存 \n", "pickleで保存してもいいが，ここではjoblibで保存することにする． \n", "compressをつけておくと，本来は複数ファイルのモデルをひとまとめにできる．" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['svc.pkl.cmp']" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.externals import joblib\n", "joblib.dump(clf_SVC_cv.best_estimator_, 'svc.pkl.cmp', compress=True)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python01.ipynb \u001b[1m\u001b[36m_templates\u001b[m\u001b[m predict_result.csv\r\n", "Python02.ipynb conf.py siritori.pkl\r\n", "\u001b[1m\u001b[36m_static\u001b[m\u001b[m index.rst svc.pkl.cmp\r\n" ] } ], "source": [ "!ls" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "classifier = joblib.load('svc.pkl.cmp')" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.81593714927\n" ] } ], "source": [ "y_train = clf_SVC_cv.predict(x_train)\n", "print(accuracy_score(t_train, y_train))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Kaggleへテストの結果を提出 \n", "問題分を読んで回答に合う形式で提出" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "y_test = clf_SVC_cv.predict(x_test)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PassengerIdSurvived
000
111
220
330
441
550
661
770
881
990
10100
11110
12121
13130
14141
15151
16160
17170
18181
19191
20200
21210
22221
23230
24241
25250
26261
27270
28280
29290
.........
3883880
3893890
3903900
3913911
3923920
3933930
3943940
3953951
3963960
3973971
3983980
3993990
4004001
4014010
4024021
4034030
4044040
4054050
4064060
4074070
4084081
4094091
4104101
4114111
4124121
4134130
4144141
4154150
4164160
4174170
\n", "

418 rows × 2 columns

\n", "
" ], "text/plain": [ " PassengerId Survived\n", "0 0 0\n", "1 1 1\n", "2 2 0\n", "3 3 0\n", "4 4 1\n", "5 5 0\n", "6 6 1\n", "7 7 0\n", "8 8 1\n", "9 9 0\n", "10 10 0\n", "11 11 0\n", "12 12 1\n", "13 13 0\n", "14 14 1\n", "15 15 1\n", "16 16 0\n", "17 17 0\n", "18 18 1\n", "19 19 1\n", "20 20 0\n", "21 21 0\n", "22 22 1\n", "23 23 0\n", "24 24 1\n", "25 25 0\n", "26 26 1\n", "27 27 0\n", "28 28 0\n", "29 29 0\n", ".. ... ...\n", "388 388 0\n", "389 389 0\n", "390 390 0\n", "391 391 1\n", "392 392 0\n", "393 393 0\n", "394 394 0\n", "395 395 1\n", "396 396 0\n", "397 397 1\n", "398 398 0\n", "399 399 0\n", "400 400 1\n", "401 401 0\n", "402 402 1\n", "403 403 0\n", "404 404 0\n", "405 405 0\n", "406 406 0\n", "407 407 0\n", "408 408 1\n", "409 409 1\n", "410 410 1\n", "411 411 1\n", "412 412 1\n", "413 413 0\n", "414 414 1\n", "415 415 0\n", "416 416 0\n", "417 417 0\n", "\n", "[418 rows x 2 columns]" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result_df = pd.DataFrame({\n", " \"PassengerId\": test.index,\n", " \"Survived\": y_test\n", "})\n", "result_df" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "result_df.to_csv('predict_result.csv', index=False)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PassengerId,Survived\r\n", "0,0\r\n", "1,1\r\n", "2,0\r\n", "3,0\r\n", "4,1\r\n", "5,0\r\n", "6,1\r\n", "7,0\r\n", "8,1\r\n" ] } ], "source": [ "!head predict_result.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## おわりに\n", "コンペ的にはここからがスタート，トライアンドエラーが基本． \n", "最後にデータ分析における格言？を載せておきます．" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ノーフリーランチ定理 \n", "ただ(=事前の知見なし)の飯(=予測や探索での改善)はない(no free lunch)」 \n", "![FreeLunch](https://upload.wikimedia.org/wikipedia/ja/f/f2/NoFreeLunch.gif)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 醜いアヒルの子の定理 \n", "特徴量を同等に扱っていれば，アヒルの子とガチョウの子の類似度はアヒルの子同士の類似度と同じ．　 \n", "あらゆる特徴を使うのではなく，目的あった特徴を選ぶべき． \n", "![Duck](http://images.slideplayer.com/20/6219656/slides/slide_28.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 見せかけの回帰\n", "シミュレーションで発生させた互いに独立なランダムウォークを回帰したもの． \n", "独立なのにも関わらず非常に高いt検定統計量の値となっている．\n", "![Spurious](https://upload.wikimedia.org/wikipedia/commons/0/0e/Spurious_Regression.svg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### テキサスの狙撃兵の誤謬（クラスター錯覚）\n", "納屋の壁に向かって何発も発砲した後で，最も弾痕が集中した箇所に標的を描き， \n", "自分は狙撃兵（射撃の名手）だと主張するテキサス人がいたというジョーク． \n", "仮説の構築と検証を同じデータで行ってはいけない． \n", "![Texas](https://hilbertthm90.files.wordpress.com/2015/04/sharpshooter.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ハンマー釘病\n", "If all you have is a hammer, Everything looks like a nail.\n", "![Hummer](http://www.sixhills-consulting.com/blog-wp/wp-content/uploads/2014/10/Hammer.png)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }