From 2b1bc4278365a285cfe5a80c38ee952ac37a2fa3 Mon Sep 17 00:00:00 2001 From: greenxlouv Date: Sun, 29 Sep 2024 23:23:44 +0900 Subject: [PATCH 1/6] =?UTF-8?q?Week1=5F=EB=B3=B5=EC=8A=B5=EA=B3=BC?= =?UTF-8?q?=EC=A0=9C=5F=EC=9D=B4=EC=9E=AC=EB=A6=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...341\204\205\341\205\265\341\206\253.ipynb" | 2033 +++++++++++++++++ 1 file changed, 2033 insertions(+) create mode 100644 "Week1_\341\204\207\341\205\251\341\206\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.ipynb" diff --git "a/Week1_\341\204\207\341\205\251\341\206\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.ipynb" "b/Week1_\341\204\207\341\205\251\341\206\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.ipynb" new file mode 100644 index 0000000..5ce13a5 --- /dev/null +++ "b/Week1_\341\204\207\341\205\251\341\206\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.ipynb" @@ -0,0 +1,2033 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "jkGNNJY3S65Z" + }, + "source": [ + "# **1주차 복습과제**\n", + "- 1주차 복습과제는 **넘파이/판다스 연습문제**입니다.\n", + "- 코드 작성하시고, 출력 결과까지 나오도록 실행 부탁드립니다.\n", + " - 제출 시 파일명 본인 이름으로 변경해 주세요. ex) Week1_복습과제_OOO\n", + "- 교재에서 다루지 않은 메소드도 다수 포함되어 있지만 구글링이나 챗지피티 등을 활용해서라도 풀어주세요! 한 번씩 사용해 보면 좋을 것 같아 어려워도 문제에 포함했습니다 🤗" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WedDHAHPJPIA" + }, + "source": [ + "## **넘파이**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7Ue21e0fKFRI" + }, + "source": [ + "### 1. Import the numpy package under the name `np`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "60ACXMoSGe0H" + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FnHQOUj-KNiT" + }, + "source": [ + "### 2. Print the numpy version and the configuration.\n", + "\n", + "(hint: `np.__version__`, `np.show_config`)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "FgXzYXoRS-V7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.26.4\n", + "\n" + ] + } + ], + "source": [ + "print(np.__version__)\n", + "print(np.show_config)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0lB5hLlTKb1Z" + }, + "source": [ + "### 3. Create a null vector of size 10.\n", + "\n", + "#### ✅ 출력 예시\n", + "\n", + "\n", + "```\n", + "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "eK4DTjPwS_zU" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "x=np.zeros(10)\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7lSSfwh6Kj5v" + }, + "source": [ + "### 4. Create a null vector of size 10 but the fifth value which is 1.\n", + "\n", + "#### ✅출력 예시\n", + "\n", + "\n", + "```\n", + "[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "ofohgzsTTBs6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "x[4]=1\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-NeqvmpLKkdY" + }, + "source": [ + "### 5. Create a vector with values ranging from 10 to 49.\n", + "\n", + "#### ✅출력 예시\n", + "\n", + "\n", + "```\n", + "[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n", + " 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "oQ1Mo5W9TC0P" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n", + " 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]\n" + ] + } + ], + "source": [ + "x=np.arange(10,50)\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B1dFQzRNLN23" + }, + "source": [ + "### 6. Reverse a vector (first element becomes last).\n", + "\n", + "#### ✅출력 예시\n", + "\n", + "\n", + "```\n", + "[9 8 7 6 5 4 3 2 1 0]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "wrpNYd4jTDsx" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[9 8 7 6 5 4 3 2 1 0]\n" + ] + } + ], + "source": [ + "x=np.arange(0,10)\n", + "print(np.flip(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "doUDUe_NLOhe" + }, + "source": [ + "### 7. Create a 3x3 matrix with values ranging from 0 to 8.\n", + "\n", + "#### ✅출력 예시\n", + "\n", + "\n", + "```\n", + "[[0 1 2]\n", + " [3 4 5]\n", + " [6 7 8]]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "4g8WetkATEnw" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 1 2]\n", + " [3 4 5]\n", + " [6 7 8]]\n" + ] + } + ], + "source": [ + "ar1=np.arange(0,9)\n", + "ar2=ar1.reshape(3,3)\n", + "print(ar2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s-MtSq2RLzJx" + }, + "source": [ + "### 8. Find indices of non-zero elements from [1,2,0,0,4,0].\n", + "\n", + "#### ✅출력 예시\n", + "\n", + "\n", + "```\n", + "(array([0, 1, 4]),)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "jw9iUP7sTL7D" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([0, 1, 4]),)\n" + ] + } + ], + "source": [ + "a=np.array([1,2,0,0,4,0])\n", + "print(np.nonzero(a))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ihRWHFUxL6ak" + }, + "source": [ + "### 9. Create a 3x3 identity matrix.\n", + "\n", + "(hint: `np.eye`)\n", + "\n", + "#### ✅출력 예시\n", + "\n", + "\n", + "```\n", + "[[1. 0. 0.]\n", + " [0. 1. 0.]\n", + " [0. 0. 1.]]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "fFzDGJT5TNh7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 0.]\n", + " [0. 1. 0.]\n", + " [0. 0. 1.]]\n" + ] + } + ], + "source": [ + "a=np.eye(3)\n", + "print(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FvkGsY8eLPCG" + }, + "source": [ + "### 10. Create a 3x3x3 array with random values.\n", + "\n", + "#### ✅출력 예시\n", + "\n", + "\n", + "```\n", + "[[[0.07822074 0.76195472 0.95999111]\n", + " [0.69540693 0.16669248 0.97247214]\n", + " [0.7031054 0.38007668 0.61202825]]\n", + "\n", + " [[0.41082729 0.42783868 0.17188845]\n", + " [0.71563068 0.36917752 0.17016863]\n", + " [0.90248986 0.07040389 0.40558888]]\n", + "\n", + " [[0.90725777 0.19353039 0.40379553]\n", + " [0.762831 0.70087899 0.09089228]\n", + " [0.68469463 0.17365128 0.17850461]]]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "id": "084SAfnwTPpt" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[ 0.76914747 0.61144335 -0.65477999]\n", + " [ 1.22313321 -0.02336936 0.60195362]\n", + " [-1.28624018 0.47421675 0.59865626]]\n", + "\n", + " [[ 0.46623597 1.53255284 0.07861738]\n", + " [ 1.13132044 1.66308595 -0.10640485]\n", + " [-0.12428144 -0.7765752 -0.03365316]]\n", + "\n", + " [[ 0.81673685 -0.07453231 0.62982054]\n", + " [-0.76281439 -0.66558904 -0.15575494]\n", + " [-0.75735069 -0.58237448 -1.90572516]]]\n" + ] + } + ], + "source": [ + "ar_rand = np.random.randn(3,3,3)\n", + "print(ar_rand)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4-VynNt5MVYY" + }, + "source": [ + "### 11. Create a 10x10 array with random values and find the minimum and maximum values.\n", + "\n", + "#### ✅출력 예시\n", + "\n", + "\n", + "```\n", + "0.009762523436238846 0.9916129188134726\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "id": "W14PP5x6TRh6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Minimum Value: 0.0012022392074141353\n", + "Maximum Value: 0.9937820754672998\n" + ] + } + ], + "source": [ + "random_array = np.random.random((10, 10))\n", + "\n", + "# 최소값과 최대값 찾기\n", + "min_value = random_array.min()\n", + "max_value = random_array.max()\n", + "\n", + "# 결과 출력\n", + "print(\"\\nMinimum Value:\", min_value)\n", + "print(\"Maximum Value:\", max_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cQKXmfJBMVQ_" + }, + "source": [ + "### 12. Create a random vector of size 30 and find the mean value.\n", + "\n", + "#### ✅출력 예시\n", + "\n", + "\n", + "```\n", + "0.43970256642748734\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "id": "FS6ggiNJTStp" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.500897667795011\n" + ] + } + ], + "source": [ + "ar1=np.random.random(30)\n", + "num=ar1.mean()\n", + "print(num)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QZ4h-AddMVJb" + }, + "source": [ + "### 13. Create a 2d array with 1 on the border and 0 inside. (size: 10x10)\n", + "\n", + "#### ✅출력 예시\n", + "\n", + "\n", + "```\n", + "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "id": "pKEi08edTUMt" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n" + ] + } + ], + "source": [ + "array_2d=np.ones((10,10))\n", + "array_2d[1:-1,1:-1]=0\n", + "print(array_2d)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BIwX-BiSMUz_" + }, + "source": [ + "### 14. How to add a border (filled with 0's) around an existing array?\n", + "\n", + "(hint: 인덱싱 사용)\n", + "\n", + "#### ✅출력 예시\n", + "\n", + "\n", + "```\n", + "# before\n", + "[[1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]]\n", + "\n", + " # after\n", + " [[0. 0. 0. 0. 0.]\n", + " [0. 1. 1. 1. 0.]\n", + " [0. 1. 1. 1. 0.]\n", + " [0. 1. 1. 1. 0.]\n", + " [0. 0. 0. 0. 0.]]\n", + "\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "id": "A4M1huiqTWXy" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#before\n", + " [[1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]\n", + " [1. 1. 1. 1. 1.]]\n" + ] + } + ], + "source": [ + "array_2d=np.ones((5,5))\n", + "print(\"#before\\n\",array_2d)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "id": "idWhL4zzTWRO" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#after\n", + " [[0. 0. 0. 0. 0.]\n", + " [0. 1. 1. 1. 0.]\n", + " [0. 1. 1. 1. 0.]\n", + " [0. 1. 1. 1. 0.]\n", + " [0. 0. 0. 0. 0.]]\n" + ] + } + ], + "source": [ + "array_2d[[0,-1]]=0\n", + "array_2d[0:-1,[0,-1]]=0\n", + "print(\"#after\\n\",array_2d)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tggOHEUGM9PK" + }, + "source": [ + "### 15. What is the result of the following expression?\n", + "\n", + "(실행 후, 결과에 대한 주석 작성해주세요. ex. 출력 결과에 대한 이유)\n", + "\n", + "```python\n", + "0 * np.nan\n", + "np.nan == np.nan\n", + "np.inf > np.nan\n", + "np.nan - np.nan\n", + "np.nan in set([np.nan])\n", + "0.3 == 3 * 0.1\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "id": "RmjjLx8_TYNp" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nan\n", + "False\n", + "False\n", + "nan\n", + "True\n", + "False\n" + ] + } + ], + "source": [ + "print(0 * np.nan) #np.nan은 숫자가 아니기에 이를 포함하는 모든 연산결과는 nan임\n", + "print(np.nan == np.nan) #nan은 nan과 같지 않기에 false\n", + "print(np.inf > np.nan) #np.nan은 다른 수와 순서를 갖지 않기에 비교를 할 수 없어 false임\n", + "print(np.nan - np.nan) #np.nan은 숫자가 아니기에 이를 포함하는 모든 연산결과는 nan임\n", + "print(np.nan in set([np.nan])) #nna은 집합 안에 있을 때 자기 자신과 같다고 간주하기에 true\n", + "print(0.3 == 3 * 0.1) #파이썬의 부동 소수점 연산의 특징> 3*0.1은 0.3의 근삿값을 컴퓨터가 저장하므로 false" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "28iOVtXXM8gA" + }, + "source": [ + "### 16. Normalize a 5x5 random matrix.\n", + "\n", + "(hint: (x - mean) / std)\n", + "\n", + "#### ✅출력 예시\n", + "\n", + "\n", + "```\n", + "[[ 1.57158744 -0.5567505 -1.44275417 0.73527327 -1.17233846]\n", + " [ 0.63460149 -0.73078623 -0.62656662 -1.36290509 -0.44364149]\n", + " [ 0.22741386 -1.10400477 0.86652275 -0.95095209 1.38123574]\n", + " [ 0.1921141 -0.30224638 -1.24440971 0.96181116 0.75106165]\n", + " [-1.43422964 0.67492878 1.48089838 0.53420107 1.35993544]]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "id": "0L0IE4qOTZW4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1.6365804 -0.78495219 -0.28040031 -1.02942797 1.43434745]\n", + " [-1.30558856 0.58395418 1.26261761 1.20994267 0.33551885]\n", + " [ 1.4576026 -0.00363205 0.75381784 0.55643327 -1.59082124]\n", + " [-0.97737136 -0.38857923 0.36762946 -0.53444049 -0.52216966]\n", + " [ 0.33455482 -1.44675407 1.05441435 -1.50300615 -0.62027022]]\n" + ] + } + ], + "source": [ + "#z점수 공식 = (데이터-데이터의 평균)/데이터의 표준편차\n", + "ran_matrix=np.random.random((5,5))\n", + "ran_mean=np.mean(ran_matrix)\n", + "ran_std=np.std(ran_matrix)\n", + "normed_matrix=(ran_matrix-ran_mean)/ran_std\n", + "print(normed_matrix)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "asOmp4b5M8dx" + }, + "source": [ + "### 17. Multiply a 5x3 matrix by a 3x2 matrix. (real matrix product)\n", + "\n", + "#### ✅출력 예시\n", + "\n", + "\n", + "```\n", + "[[3. 3.]\n", + " [3. 3.]\n", + " [3. 3.]\n", + " [3. 3.]\n", + " [3. 3.]]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "id": "01PxOWW5Te7I" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.91141865 0.44351352]\n", + " [0.48407686 0.28875017]\n", + " [0.46589946 0.28297299]\n", + " [0.7755308 0.49283207]\n", + " [0.52439638 0.17237073]]\n" + ] + } + ], + "source": [ + "A=np.random.random((5,3))\n", + "B=np.random.random((3,2))\n", + "mul_matrix=np.dot(A,B)\n", + "print(mul_matrix)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H9zVpsYuM8aR" + }, + "source": [ + "### 18. What are the result of the following expressions?\n", + "\n", + "(실행 후, 결과에 대한 주석 작성해주세요. ex. 출력 결과에 대한 이유)\n", + "\n", + "```python\n", + "np.array(0) / np.array(0)\n", + "np.array(0) // np.array(0)\n", + "np.array([np.nan]).astype(int).astype(float)\n", + "```\n", + "이거 3번 헷갈림" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "id": "SLP--cFwTh10" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nan\n", + "0\n", + "[0.]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/2c/j3wmswps2r5gclvj1skrczqh0000gn/T/ipykernel_11986/3753971449.py:1: RuntimeWarning: invalid value encountered in divide\n", + " print(np.array(0) / np.array(0))\n", + "/var/folders/2c/j3wmswps2r5gclvj1skrczqh0000gn/T/ipykernel_11986/3753971449.py:4: RuntimeWarning: divide by zero encountered in floor_divide\n", + " print(np.array(0) // np.array(0))\n", + "/var/folders/2c/j3wmswps2r5gclvj1skrczqh0000gn/T/ipykernel_11986/3753971449.py:6: RuntimeWarning: invalid value encountered in cast\n", + " print(np.array([np.nan]).astype(int).astype(float))\n" + ] + } + ], + "source": [ + "print(np.array(0) / np.array(0)) \n", + "#np.array(0)가 numpy배열로 0을 포함하는 스칼라배열(단일값 but 배열처럼 다룰 수 있음)\n", + "#0/0이므로 정의되지 않은 연산으로 nan을 반환함\n", + "print(np.array(0) // np.array(0))\n", + "#몫을 반환하는 연산자 //, 이 경우 0나누기 0은 정의되지 않지만 numpy이므로 몫으로 0을 반환 \n", + "print(np.array([np.nan]).astype(int).astype(float))\n", + "#..?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fbi_T8LIO3yc" + }, + "source": [ + "### 19. Create a 5x5 matrix with row values ranging from 0 to 4.\n", + "\n", + "#### ✅ 출력 예시\n", + "\n", + "```\n", + "[[0. 1. 2. 3. 4.]\n", + " [0. 1. 2. 3. 4.]\n", + " [0. 1. 2. 3. 4.]\n", + " [0. 1. 2. 3. 4.]\n", + " [0. 1. 2. 3. 4.]]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": { + "id": "XHj8J0pLTjrf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 1 2 3 4]\n", + " [0 1 2 3 4]\n", + " [0 1 2 3 4]\n", + " [0 1 2 3 4]\n", + " [0 1 2 3 4]]\n" + ] + } + ], + "source": [ + "#0-4값의 1차원 배열을 세로로 5번, 가로로 1번만 쌓아서 5by5행렬 만들기\n", + "matrix = np.tile(np.arange(5),(5,1))\n", + "print(matrix)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cNRIzEzEO_Ft" + }, + "source": [ + "### 20. Create a random vector of size 10 and sort it.\n", + "\n", + "#### ✅출력 예시\n", + "\n", + "\n", + "```\n", + "[0.1738448 0.20689089 0.26989562 0.29896317 0.41140555 0.44166027\n", + " 0.48569307 0.78561734 0.79300508 0.95618918]\n", + "\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "id": "a3lZxnx-Tngw" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.02681636 0.10799395 0.28950143 0.32630225 0.36443307 0.48600552\n", + " 0.74339916 0.95306367 0.96811645 0.97362463]\n" + ] + } + ], + "source": [ + "v10=np.random.random(10)\n", + "v10.sort()\n", + "print(v10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fkX-tY1oKDq5" + }, + "source": [ + "## **판다스**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yl7zlBgszyCS" + }, + "source": [ + "\n", + "### 1. Import pandas under the alias `pd`." + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": { + "id": "ZiANPeHhz00W" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2Hk3SuquzyCU" + }, + "source": [ + "### 2. Print the version of pandas that has been imported." + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "id": "i2NtsBbjz1x2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.2.2\n" + ] + } + ], + "source": [ + "print(pd.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oOTRFQtFzyCV" + }, + "source": [ + "## 3~20번 문제는 아래 데이터프레임으로 진행됩니다.\n", + "\n", + "Consider the following Python dictionary `data` and Python list `labels`:\n", + "\n", + "``` python\n", + "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n", + " 'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n", + " 'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n", + " 'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n", + "\n", + "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m_8US0jJzyCW" + }, + "source": [ + "### 3. Create a DataFrame `df` from this dictionary `data` which has the index `labels`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": { + "id": "7CbS_gtmzyCW" + }, + "outputs": [], + "source": [ + "# 문제 풀이 전 numpy 임포트\n", + "import numpy as np\n", + "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n", + " 'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n", + " 'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n", + " 'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n", + "\n", + "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n", + "df=pd.DataFrame(data,index=labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f2shsFGyzyCW" + }, + "source": [ + "### 4. Display a summary of the basic information about this DataFrame and its data. \n", + "(hint: there is a single method that can be called on the DataFrame)" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": { + "id": "BWZpEuGtz7ky" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 10 entries, a to j\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 animal 10 non-null object \n", + " 1 age 8 non-null float64\n", + " 2 visits 10 non-null int64 \n", + " 3 priority 10 non-null object \n", + "dtypes: float64(1), int64(1), object(2)\n", + "memory usage: 400.0+ bytes\n", + "None\n" + ] + } + ], + "source": [ + "print(df.info())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AfbkaEOyzyCX" + }, + "source": [ + "### 5. Display a summary of the basic statistics about data of this DataFrame. \n", + "(hint: there is a single method that can be called on the DataFrame)" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": { + "id": "wzzc100Oz8c3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " age visits\n", + "count 8.000000 10.000000\n", + "mean 3.437500 1.900000\n", + "std 2.007797 0.875595\n", + "min 0.500000 1.000000\n", + "25% 2.375000 1.000000\n", + "50% 3.000000 2.000000\n", + "75% 4.625000 2.750000\n", + "max 7.000000 3.000000\n" + ] + } + ], + "source": [ + "print(df.describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XJ59aPcrzyCX" + }, + "source": [ + "### 6. Return the first 3 rows of the DataFrame `df`." + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": { + "id": "vZro7sh9z_rY" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Select the rows where the age is missing, i.e. it is `NaN`." + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": { + "id": "KaV4Ypkj0Ea2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " animal age visits priority\n", + "d dog NaN 3 yes\n", + "h cat NaN 1 yes\n" + ] + } + ], + "source": [ + "miss_df=df[df['age'].isna()]\n", + "print(miss_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kMH5n-UBzyCZ" + }, + "source": [ + "### 11. Select the rows where the animal is a cat *and* the age is less than 3." + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": { + "id": "8sCDh9Ez0FND", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " animal age visits priority\n", + "a cat 2.5 1 yes\n", + "f cat 2.0 3 no\n" + ] + } + ], + "source": [ + "catage_df=df[(df['animal']=='cat')&(df['age']<3)]\n", + "print(catage_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "62Y0JsYazyCZ" + }, + "source": [ + "### 12. Select the rows the age is between 2 and 4 (inclusive). \n" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": { + "id": "svjvRtgZ0G76" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " animal age visits priority\n", + "a cat 2.5 1 yes\n", + "b cat 3.0 3 yes\n", + "f cat 2.0 3 no\n", + "j dog 3.0 1 no\n" + ] + } + ], + "source": [ + "age_df=df[(df['age']>=2)&(df['age']<=4)]\n", + "print(age_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4lIGMIkPzyCZ" + }, + "source": [ + "### 13. Change the age in row 'f' to 1.5." + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": { + "id": "h4U4A6Ai0Hvk" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " animal age visits priority\n", + "a cat 2.5 1 yes\n", + "b cat 3.0 3 yes\n", + "c snake 0.5 2 no\n", + "d dog NaN 3 yes\n", + "e dog 5.0 2 no\n", + "f cat 1.5 3 no\n", + "g snake 4.5 1 no\n", + "h cat NaN 1 yes\n", + "i dog 7.0 2 no\n", + "j dog 3.0 1 no\n" + ] + } + ], + "source": [ + "df.loc['f','age']=1.5\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FZzPr9ObzyCZ" + }, + "source": [ + "### 14. Calculate the sum of all visits in `df` (i.e. the total number of visits)." + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": { + "id": "FXLAUqR40I6C" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "총 방문수 : 19\n" + ] + } + ], + "source": [ + "total=df['visits'].sum()\n", + "print(f\"총 방문수 : {total}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PO0xpJ_OzyCa" + }, + "source": [ + "### 15. Calculate the mean age for each different animal in `df`." + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": { + "id": "L63WRx_20Kta" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "animal\n", + "cat 2.333333\n", + "dog 5.000000\n", + "snake 2.500000\n", + "Name: age, dtype: float64\n" + ] + } + ], + "source": [ + "print(df.groupby('animal')['age'].mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YfVdzQXIzyCa" + }, + "source": [ + "### 16. Append a new row 'k' to `df` with your choice of values for each column. Then delete that row to return the original DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": { + "id": "Per12Ekp0Mc0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " animal age visits priority\n", + "a cat 2.5 1 yes\n", + "b cat 3.0 3 yes\n", + "c snake 0.5 2 no\n", + "d dog NaN 3 yes\n", + "e dog 5.0 2 no\n", + "f cat 1.5 3 no\n", + "g snake 4.5 1 no\n", + "h cat NaN 1 yes\n", + "i dog 7.0 2 no\n", + "j dog 3.0 1 no\n", + "k cat 3.5 2 yes\n", + " animal age visits priority\n", + "a cat 2.5 1 yes\n", + "b cat 3.0 3 yes\n", + "c snake 0.5 2 no\n", + "d dog NaN 3 yes\n", + "e dog 5.0 2 no\n", + "f cat 1.5 3 no\n", + "g snake 4.5 1 no\n", + "h cat NaN 1 yes\n", + "i dog 7.0 2 no\n", + "j dog 3.0 1 no\n" + ] + } + ], + "source": [ + "df.loc['k']=['cat',3.5,2,'yes']\n", + "print(df)\n", + "df.drop('k',axis=0,inplace=True) #inplace=True>자신의 df의 데이터 삭제\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tDkk_tHszyCa" + }, + "source": [ + "### 17. Count the number of each type of animal in `df`." + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": { + "id": "v21izXST0NTR" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "animal\n", + "cat 4\n", + "dog 4\n", + "snake 2\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "v_count=df['animal'].value_counts()\n", + "print(v_count)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cwz4s5RYzyCa" + }, + "source": [ + "### 18. Sort `df` first by the values in the 'age' in *decending* order, then by the value in the 'visits' column in *ascending* order (so row `i` should be first, and row `d` should be last).\n", + "#### ✅출력 예시" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YILpLKeqzyCa" + }, + "source": [ + " index |animal\t| age\t|visits|\tpriority\n", + "---|---|---|---|---\n", + "i|\tdog|\t7.0|\t2|\tno\n", + "e|\tdog|\t5.0|\t2|\tno\n", + "g|\tsnake|\t4.5|\t1|\tno\n", + "j|\tdog|\t3.0|\t1|\tno\n", + "b|\tcat|\t3.0|\t3|\tyes\n", + "a|\tcat|\t2.5|\t1|\tyes\n", + "f|\tcat|\t1.5|\t3|\tno\n", + "c|\tsnake|\t0.5|\t2|\tno\n", + "h|\tcat|\tNaN|\t1|\tyes\n", + "d|\tdog|\tNaN|\t3|\tyes" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": { + "id": "2l6Pb7T10Qpi" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " animal age visits priority\n", + "i dog 7.0 2 no\n", + "e dog 5.0 2 no\n", + "g snake 4.5 1 no\n", + "j dog 3.0 1 no\n", + "b cat 3.0 3 yes\n", + "a cat 2.5 1 yes\n", + "f cat 1.5 3 no\n", + "c snake 0.5 2 no\n", + "h cat NaN 1 yes\n", + "d dog NaN 3 yes\n" + ] + } + ], + "source": [ + "#by=해당 칼럼으로 작업 수행, ascending=각각 칼럼을 오름차순 내림차순으로 작업할지 f/t로 표현\n", + "df_sorted=df.sort_values(by=['age','visits'],ascending=[False,True])\n", + "print(df_sorted)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6hfE99qHzyCb" + }, + "source": [ + "### 19. The 'priority' column contains the values 'yes' and 'no'. Replace this column with a column of boolean values: 'yes' should be `True` and 'no' should be `False`. \n", + "(hint: `map`)" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": { + "id": "lPLmBRUP0SnR" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " animal age visits priority\n", + "a cat 2.5 1 True\n", + "b cat 3.0 3 True\n", + "c snake 0.5 2 False\n", + "d dog NaN 3 True\n", + "e dog 5.0 2 False\n", + "f cat 1.5 3 False\n", + "g snake 4.5 1 False\n", + "h cat NaN 1 True\n", + "i dog 7.0 2 False\n", + "j dog 3.0 1 False\n" + ] + } + ], + "source": [ + "df['priority']=df['priority'].map({'yes':True,'no':False})\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ycaIJncEzyCb" + }, + "source": [ + "### 20. In the 'animal' column, change the 'snake' entries to 'python'. " + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": { + "id": "MZelDUlE0Wag" + }, + "outputs": [], + "source": [ + "df['animal']=df['animal'].replace('snake','python')" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": { + "id": "uYFF5Jew0Xz7" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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animalagevisitspriority
acat2.51True
bcat3.03True
cpython0.52False
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gpython4.51False
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" + ], + "text/plain": [ + " animal age visits priority\n", + "a cat 2.5 1 True\n", + "b cat 3.0 3 True\n", + "c python 0.5 2 False\n", + "d dog NaN 3 True\n", + "e dog 5.0 2 False\n", + "f cat 1.5 3 False\n", + "g python 4.5 1 False\n", + "h cat NaN 1 True\n", + "i dog 7.0 2 False\n", + "j dog 3.0 1 False" + ] + }, + "execution_count": 241, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 확인용 df 출력 셀\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5YmjKvYizyCb" + }, + "source": [ + "### 21. Given a DataFrame of random numeric values:\n", + "```python\n", + "df = pd.DataFrame(np.random.random(size=(5, 3))) # this is a 5x3 DataFrame of float values\n", + "```\n", + "\n", + "how do you subtract the row mean from each element in the row?" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": { + "id": "EeAkDWv40e9J" + }, + "outputs": [], + "source": [ + "# 랜덤 시드 고정\n", + "np.random.seed(2024)\n", + "\n", + "df = pd.DataFrame(np.random.random(size=(5, 3)))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": { + "id": "Z24qNGpgKEVi" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "원본데이터\n", + " 0 1 2\n", + "0 0.588015 0.699109 0.188152\n", + "1 0.043809 0.205019 0.106063\n", + "2 0.727240 0.679401 0.473846\n", + "3 0.448296 0.019107 0.752598\n", + "4 0.602449 0.961778 0.664369\n", + "뺀 데이터\n", + " 0 1 2\n", + "0 0.096256 0.207350 -0.303606\n", + "1 -0.074488 0.086722 -0.012234\n", + "2 0.100411 0.052572 -0.152983\n", + "3 0.041629 -0.387560 0.345931\n", + "4 -0.140416 0.218913 -0.078496\n" + ] + } + ], + "source": [ + "print(\"원본데이터\\n\",df)\n", + "subtract_df=df.sub(df.mean(axis=1),axis=0)\n", + "print(\"뺀 데이터\\n\",subtract_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyN3MtLF9SsU0q9S6nVXMcF7", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 34706c4c7fede537ad4f79c0a07705a86b0b72f7 Mon Sep 17 00:00:00 2001 From: greenxlouv Date: Sun, 29 Sep 2024 23:37:48 +0900 Subject: [PATCH 2/6] =?UTF-8?q?Delete=20Week1=5F=E1=84=87=E1=85=A9?= =?UTF-8?q?=E1=86=A8=E1=84=89=E1=85=B3=E1=86=B8=E1=84=80=E1=85=AA=E1=84=8C?= =?UTF-8?q?=E1=85=A6=5F=E1=84=8B=E1=85=B5=E1=84=8C=E1=85=A2=E1=84=85?= =?UTF-8?q?=E1=85=B5=E1=86=AB.ipynb?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...341\204\205\341\205\265\341\206\253.ipynb" | 2033 ----------------- 1 file changed, 2033 deletions(-) delete mode 100644 "Week1_\341\204\207\341\205\251\341\206\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.ipynb" diff --git "a/Week1_\341\204\207\341\205\251\341\206\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.ipynb" "b/Week1_\341\204\207\341\205\251\341\206\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.ipynb" deleted file mode 100644 index 5ce13a5..0000000 --- "a/Week1_\341\204\207\341\205\251\341\206\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.ipynb" +++ /dev/null @@ -1,2033 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "jkGNNJY3S65Z" - }, - "source": [ - "# **1주차 복습과제**\n", - "- 1주차 복습과제는 **넘파이/판다스 연습문제**입니다.\n", - "- 코드 작성하시고, 출력 결과까지 나오도록 실행 부탁드립니다.\n", - " - 제출 시 파일명 본인 이름으로 변경해 주세요. ex) Week1_복습과제_OOO\n", - "- 교재에서 다루지 않은 메소드도 다수 포함되어 있지만 구글링이나 챗지피티 등을 활용해서라도 풀어주세요! 한 번씩 사용해 보면 좋을 것 같아 어려워도 문제에 포함했습니다 🤗" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WedDHAHPJPIA" - }, - "source": [ - "## **넘파이**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7Ue21e0fKFRI" - }, - "source": [ - "### 1. Import the numpy package under the name `np`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "60ACXMoSGe0H" - }, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FnHQOUj-KNiT" - }, - "source": [ - "### 2. Print the numpy version and the configuration.\n", - "\n", - "(hint: `np.__version__`, `np.show_config`)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "FgXzYXoRS-V7" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.26.4\n", - "\n" - ] - } - ], - "source": [ - "print(np.__version__)\n", - "print(np.show_config)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0lB5hLlTKb1Z" - }, - "source": [ - "### 3. Create a null vector of size 10.\n", - "\n", - "#### ✅ 출력 예시\n", - "\n", - "\n", - "```\n", - "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "eK4DTjPwS_zU" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n" - ] - } - ], - "source": [ - "x=np.zeros(10)\n", - "print(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7lSSfwh6Kj5v" - }, - "source": [ - "### 4. Create a null vector of size 10 but the fifth value which is 1.\n", - "\n", - "#### ✅출력 예시\n", - "\n", - "\n", - "```\n", - "[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "ofohgzsTTBs6" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n" - ] - } - ], - "source": [ - "x[4]=1\n", - "print(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-NeqvmpLKkdY" - }, - "source": [ - "### 5. Create a vector with values ranging from 10 to 49.\n", - "\n", - "#### ✅출력 예시\n", - "\n", - "\n", - "```\n", - "[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n", - " 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "oQ1Mo5W9TC0P" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n", - " 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]\n" - ] - } - ], - "source": [ - "x=np.arange(10,50)\n", - "print(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "B1dFQzRNLN23" - }, - "source": [ - "### 6. Reverse a vector (first element becomes last).\n", - "\n", - "#### ✅출력 예시\n", - "\n", - "\n", - "```\n", - "[9 8 7 6 5 4 3 2 1 0]\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "id": "wrpNYd4jTDsx" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[9 8 7 6 5 4 3 2 1 0]\n" - ] - } - ], - "source": [ - "x=np.arange(0,10)\n", - "print(np.flip(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "doUDUe_NLOhe" - }, - "source": [ - "### 7. Create a 3x3 matrix with values ranging from 0 to 8.\n", - "\n", - "#### ✅출력 예시\n", - "\n", - "\n", - "```\n", - "[[0 1 2]\n", - " [3 4 5]\n", - " [6 7 8]]\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "id": "4g8WetkATEnw" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0 1 2]\n", - " [3 4 5]\n", - " [6 7 8]]\n" - ] - } - ], - "source": [ - "ar1=np.arange(0,9)\n", - "ar2=ar1.reshape(3,3)\n", - "print(ar2)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s-MtSq2RLzJx" - }, - "source": [ - "### 8. Find indices of non-zero elements from [1,2,0,0,4,0].\n", - "\n", - "#### ✅출력 예시\n", - "\n", - "\n", - "```\n", - "(array([0, 1, 4]),)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "id": "jw9iUP7sTL7D" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(array([0, 1, 4]),)\n" - ] - } - ], - "source": [ - "a=np.array([1,2,0,0,4,0])\n", - "print(np.nonzero(a))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ihRWHFUxL6ak" - }, - "source": [ - "### 9. Create a 3x3 identity matrix.\n", - "\n", - "(hint: `np.eye`)\n", - "\n", - "#### ✅출력 예시\n", - "\n", - "\n", - "```\n", - "[[1. 0. 0.]\n", - " [0. 1. 0.]\n", - " [0. 0. 1.]]\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "id": "fFzDGJT5TNh7" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 0. 0.]\n", - " [0. 1. 0.]\n", - " [0. 0. 1.]]\n" - ] - } - ], - "source": [ - "a=np.eye(3)\n", - "print(a)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FvkGsY8eLPCG" - }, - "source": [ - "### 10. Create a 3x3x3 array with random values.\n", - "\n", - "#### ✅출력 예시\n", - "\n", - "\n", - "```\n", - "[[[0.07822074 0.76195472 0.95999111]\n", - " [0.69540693 0.16669248 0.97247214]\n", - " [0.7031054 0.38007668 0.61202825]]\n", - "\n", - " [[0.41082729 0.42783868 0.17188845]\n", - " [0.71563068 0.36917752 0.17016863]\n", - " [0.90248986 0.07040389 0.40558888]]\n", - "\n", - " [[0.90725777 0.19353039 0.40379553]\n", - " [0.762831 0.70087899 0.09089228]\n", - " [0.68469463 0.17365128 0.17850461]]]\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "id": "084SAfnwTPpt" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[[ 0.76914747 0.61144335 -0.65477999]\n", - " [ 1.22313321 -0.02336936 0.60195362]\n", - " [-1.28624018 0.47421675 0.59865626]]\n", - "\n", - " [[ 0.46623597 1.53255284 0.07861738]\n", - " [ 1.13132044 1.66308595 -0.10640485]\n", - " [-0.12428144 -0.7765752 -0.03365316]]\n", - "\n", - " [[ 0.81673685 -0.07453231 0.62982054]\n", - " [-0.76281439 -0.66558904 -0.15575494]\n", - " [-0.75735069 -0.58237448 -1.90572516]]]\n" - ] - } - ], - "source": [ - "ar_rand = np.random.randn(3,3,3)\n", - "print(ar_rand)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4-VynNt5MVYY" - }, - "source": [ - "### 11. Create a 10x10 array with random values and find the minimum and maximum values.\n", - "\n", - "#### ✅출력 예시\n", - "\n", - "\n", - "```\n", - "0.009762523436238846 0.9916129188134726\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "id": "W14PP5x6TRh6" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Minimum Value: 0.0012022392074141353\n", - "Maximum Value: 0.9937820754672998\n" - ] - } - ], - "source": [ - "random_array = np.random.random((10, 10))\n", - "\n", - "# 최소값과 최대값 찾기\n", - "min_value = random_array.min()\n", - "max_value = random_array.max()\n", - "\n", - "# 결과 출력\n", - "print(\"\\nMinimum Value:\", min_value)\n", - "print(\"Maximum Value:\", max_value)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cQKXmfJBMVQ_" - }, - "source": [ - "### 12. Create a random vector of size 30 and find the mean value.\n", - "\n", - "#### ✅출력 예시\n", - "\n", - "\n", - "```\n", - "0.43970256642748734\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "id": "FS6ggiNJTStp" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.500897667795011\n" - ] - } - ], - "source": [ - "ar1=np.random.random(30)\n", - "num=ar1.mean()\n", - "print(num)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QZ4h-AddMVJb" - }, - "source": [ - "### 13. Create a 2d array with 1 on the border and 0 inside. (size: 10x10)\n", - "\n", - "#### ✅출력 예시\n", - "\n", - "\n", - "```\n", - "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "id": "pKEi08edTUMt" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", - " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n" - ] - } - ], - "source": [ - "array_2d=np.ones((10,10))\n", - "array_2d[1:-1,1:-1]=0\n", - "print(array_2d)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BIwX-BiSMUz_" - }, - "source": [ - "### 14. How to add a border (filled with 0's) around an existing array?\n", - "\n", - "(hint: 인덱싱 사용)\n", - "\n", - "#### ✅출력 예시\n", - "\n", - "\n", - "```\n", - "# before\n", - "[[1. 1. 1. 1. 1.]\n", - " [1. 1. 1. 1. 1.]\n", - " [1. 1. 1. 1. 1.]\n", - " [1. 1. 1. 1. 1.]\n", - " [1. 1. 1. 1. 1.]]\n", - "\n", - " # after\n", - " [[0. 0. 0. 0. 0.]\n", - " [0. 1. 1. 1. 0.]\n", - " [0. 1. 1. 1. 0.]\n", - " [0. 1. 1. 1. 0.]\n", - " [0. 0. 0. 0. 0.]]\n", - "\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": { - "id": "A4M1huiqTWXy" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "#before\n", - " [[1. 1. 1. 1. 1.]\n", - " [1. 1. 1. 1. 1.]\n", - " [1. 1. 1. 1. 1.]\n", - " [1. 1. 1. 1. 1.]\n", - " [1. 1. 1. 1. 1.]]\n" - ] - } - ], - "source": [ - "array_2d=np.ones((5,5))\n", - "print(\"#before\\n\",array_2d)" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": { - "id": "idWhL4zzTWRO" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "#after\n", - " [[0. 0. 0. 0. 0.]\n", - " [0. 1. 1. 1. 0.]\n", - " [0. 1. 1. 1. 0.]\n", - " [0. 1. 1. 1. 0.]\n", - " [0. 0. 0. 0. 0.]]\n" - ] - } - ], - "source": [ - "array_2d[[0,-1]]=0\n", - "array_2d[0:-1,[0,-1]]=0\n", - "print(\"#after\\n\",array_2d)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tggOHEUGM9PK" - }, - "source": [ - "### 15. What is the result of the following expression?\n", - "\n", - "(실행 후, 결과에 대한 주석 작성해주세요. ex. 출력 결과에 대한 이유)\n", - "\n", - "```python\n", - "0 * np.nan\n", - "np.nan == np.nan\n", - "np.inf > np.nan\n", - "np.nan - np.nan\n", - "np.nan in set([np.nan])\n", - "0.3 == 3 * 0.1\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": { - "id": "RmjjLx8_TYNp" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nan\n", - "False\n", - "False\n", - "nan\n", - "True\n", - "False\n" - ] - } - ], - "source": [ - "print(0 * np.nan) #np.nan은 숫자가 아니기에 이를 포함하는 모든 연산결과는 nan임\n", - "print(np.nan == np.nan) #nan은 nan과 같지 않기에 false\n", - "print(np.inf > np.nan) #np.nan은 다른 수와 순서를 갖지 않기에 비교를 할 수 없어 false임\n", - "print(np.nan - np.nan) #np.nan은 숫자가 아니기에 이를 포함하는 모든 연산결과는 nan임\n", - "print(np.nan in set([np.nan])) #nna은 집합 안에 있을 때 자기 자신과 같다고 간주하기에 true\n", - "print(0.3 == 3 * 0.1) #파이썬의 부동 소수점 연산의 특징> 3*0.1은 0.3의 근삿값을 컴퓨터가 저장하므로 false" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "28iOVtXXM8gA" - }, - "source": [ - "### 16. Normalize a 5x5 random matrix.\n", - "\n", - "(hint: (x - mean) / std)\n", - "\n", - "#### ✅출력 예시\n", - "\n", - "\n", - "```\n", - "[[ 1.57158744 -0.5567505 -1.44275417 0.73527327 -1.17233846]\n", - " [ 0.63460149 -0.73078623 -0.62656662 -1.36290509 -0.44364149]\n", - " [ 0.22741386 -1.10400477 0.86652275 -0.95095209 1.38123574]\n", - " [ 0.1921141 -0.30224638 -1.24440971 0.96181116 0.75106165]\n", - " [-1.43422964 0.67492878 1.48089838 0.53420107 1.35993544]]\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": { - "id": "0L0IE4qOTZW4" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 1.6365804 -0.78495219 -0.28040031 -1.02942797 1.43434745]\n", - " [-1.30558856 0.58395418 1.26261761 1.20994267 0.33551885]\n", - " [ 1.4576026 -0.00363205 0.75381784 0.55643327 -1.59082124]\n", - " [-0.97737136 -0.38857923 0.36762946 -0.53444049 -0.52216966]\n", - " [ 0.33455482 -1.44675407 1.05441435 -1.50300615 -0.62027022]]\n" - ] - } - ], - "source": [ - "#z점수 공식 = (데이터-데이터의 평균)/데이터의 표준편차\n", - "ran_matrix=np.random.random((5,5))\n", - "ran_mean=np.mean(ran_matrix)\n", - "ran_std=np.std(ran_matrix)\n", - "normed_matrix=(ran_matrix-ran_mean)/ran_std\n", - "print(normed_matrix)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "asOmp4b5M8dx" - }, - "source": [ - "### 17. Multiply a 5x3 matrix by a 3x2 matrix. (real matrix product)\n", - "\n", - "#### ✅출력 예시\n", - "\n", - "\n", - "```\n", - "[[3. 3.]\n", - " [3. 3.]\n", - " [3. 3.]\n", - " [3. 3.]\n", - " [3. 3.]]\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": { - "id": "01PxOWW5Te7I" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.91141865 0.44351352]\n", - " [0.48407686 0.28875017]\n", - " [0.46589946 0.28297299]\n", - " [0.7755308 0.49283207]\n", - " [0.52439638 0.17237073]]\n" - ] - } - ], - "source": [ - "A=np.random.random((5,3))\n", - "B=np.random.random((3,2))\n", - "mul_matrix=np.dot(A,B)\n", - "print(mul_matrix)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "H9zVpsYuM8aR" - }, - "source": [ - "### 18. What are the result of the following expressions?\n", - "\n", - "(실행 후, 결과에 대한 주석 작성해주세요. ex. 출력 결과에 대한 이유)\n", - "\n", - "```python\n", - "np.array(0) / np.array(0)\n", - "np.array(0) // np.array(0)\n", - "np.array([np.nan]).astype(int).astype(float)\n", - "```\n", - "이거 3번 헷갈림" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": { - "id": "SLP--cFwTh10" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nan\n", - "0\n", - "[0.]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/2c/j3wmswps2r5gclvj1skrczqh0000gn/T/ipykernel_11986/3753971449.py:1: RuntimeWarning: invalid value encountered in divide\n", - " print(np.array(0) / np.array(0))\n", - "/var/folders/2c/j3wmswps2r5gclvj1skrczqh0000gn/T/ipykernel_11986/3753971449.py:4: RuntimeWarning: divide by zero encountered in floor_divide\n", - " print(np.array(0) // np.array(0))\n", - "/var/folders/2c/j3wmswps2r5gclvj1skrczqh0000gn/T/ipykernel_11986/3753971449.py:6: RuntimeWarning: invalid value encountered in cast\n", - " print(np.array([np.nan]).astype(int).astype(float))\n" - ] - } - ], - "source": [ - "print(np.array(0) / np.array(0)) \n", - "#np.array(0)가 numpy배열로 0을 포함하는 스칼라배열(단일값 but 배열처럼 다룰 수 있음)\n", - "#0/0이므로 정의되지 않은 연산으로 nan을 반환함\n", - "print(np.array(0) // np.array(0))\n", - "#몫을 반환하는 연산자 //, 이 경우 0나누기 0은 정의되지 않지만 numpy이므로 몫으로 0을 반환 \n", - "print(np.array([np.nan]).astype(int).astype(float))\n", - "#..?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "fbi_T8LIO3yc" - }, - "source": [ - "### 19. Create a 5x5 matrix with row values ranging from 0 to 4.\n", - "\n", - "#### ✅ 출력 예시\n", - "\n", - "```\n", - "[[0. 1. 2. 3. 4.]\n", - " [0. 1. 2. 3. 4.]\n", - " [0. 1. 2. 3. 4.]\n", - " [0. 1. 2. 3. 4.]\n", - " [0. 1. 2. 3. 4.]]\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "metadata": { - "id": "XHj8J0pLTjrf" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0 1 2 3 4]\n", - " [0 1 2 3 4]\n", - " [0 1 2 3 4]\n", - " [0 1 2 3 4]\n", - " [0 1 2 3 4]]\n" - ] - } - ], - "source": [ - "#0-4값의 1차원 배열을 세로로 5번, 가로로 1번만 쌓아서 5by5행렬 만들기\n", - "matrix = np.tile(np.arange(5),(5,1))\n", - "print(matrix)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cNRIzEzEO_Ft" - }, - "source": [ - "### 20. Create a random vector of size 10 and sort it.\n", - "\n", - "#### ✅출력 예시\n", - "\n", - "\n", - "```\n", - "[0.1738448 0.20689089 0.26989562 0.29896317 0.41140555 0.44166027\n", - " 0.48569307 0.78561734 0.79300508 0.95618918]\n", - "\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": { - "id": "a3lZxnx-Tngw" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.02681636 0.10799395 0.28950143 0.32630225 0.36443307 0.48600552\n", - " 0.74339916 0.95306367 0.96811645 0.97362463]\n" - ] - } - ], - "source": [ - "v10=np.random.random(10)\n", - "v10.sort()\n", - "print(v10)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "fkX-tY1oKDq5" - }, - "source": [ - "## **판다스**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yl7zlBgszyCS" - }, - "source": [ - "\n", - "### 1. Import pandas under the alias `pd`." - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "metadata": { - "id": "ZiANPeHhz00W" - }, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2Hk3SuquzyCU" - }, - "source": [ - "### 2. Print the version of pandas that has been imported." - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": { - "id": "i2NtsBbjz1x2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.2.2\n" - ] - } - ], - "source": [ - "print(pd.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oOTRFQtFzyCV" - }, - "source": [ - "## 3~20번 문제는 아래 데이터프레임으로 진행됩니다.\n", - "\n", - "Consider the following Python dictionary `data` and Python list `labels`:\n", - "\n", - "``` python\n", - "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n", - " 'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n", - " 'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n", - " 'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n", - "\n", - "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "m_8US0jJzyCW" - }, - "source": [ - "### 3. Create a DataFrame `df` from this dictionary `data` which has the index `labels`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "metadata": { - "id": "7CbS_gtmzyCW" - }, - "outputs": [], - "source": [ - "# 문제 풀이 전 numpy 임포트\n", - "import numpy as np\n", - "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n", - " 'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n", - " 'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n", - " 'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n", - "\n", - "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n", - "df=pd.DataFrame(data,index=labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "f2shsFGyzyCW" - }, - "source": [ - "### 4. Display a summary of the basic information about this DataFrame and its data. \n", - "(hint: there is a single method that can be called on the DataFrame)" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": { - "id": "BWZpEuGtz7ky" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Index: 10 entries, a to j\n", - "Data columns (total 4 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 animal 10 non-null object \n", - " 1 age 8 non-null float64\n", - " 2 visits 10 non-null int64 \n", - " 3 priority 10 non-null object \n", - "dtypes: float64(1), int64(1), object(2)\n", - "memory usage: 400.0+ bytes\n", - "None\n" - ] - } - ], - "source": [ - "print(df.info())\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AfbkaEOyzyCX" - }, - "source": [ - "### 5. Display a summary of the basic statistics about data of this DataFrame. \n", - "(hint: there is a single method that can be called on the DataFrame)" - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "metadata": { - "id": "wzzc100Oz8c3" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " age visits\n", - "count 8.000000 10.000000\n", - "mean 3.437500 1.900000\n", - "std 2.007797 0.875595\n", - "min 0.500000 1.000000\n", - "25% 2.375000 1.000000\n", - "50% 3.000000 2.000000\n", - "75% 4.625000 2.750000\n", - "max 7.000000 3.000000\n" - ] - } - ], - "source": [ - "print(df.describe())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XJ59aPcrzyCX" - }, - "source": [ - "### 6. Return the first 3 rows of the DataFrame `df`." - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "metadata": { - "id": "vZro7sh9z_rY" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " animal age visits priority\n", - "a cat 2.5 1 yes\n", - "b cat 3.0 3 yes\n", - "c snake 0.5 2 no" - ] - }, - "execution_count": 169, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head(3)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KBVp0_3ZzyCY" - }, - "source": [ - "### 7. Select just the 'animal' and 'age' columns from the DataFrame `df`." - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "metadata": { - "id": "pC6sH-1F0AJo" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " animal age\n", - "a cat 2.5\n", - "b cat 3.0\n", - "c snake 0.5\n", - "d dog NaN\n", - "e dog 5.0\n", - "f cat 2.0\n", - "g snake 4.5\n", - "h cat NaN\n", - "i dog 7.0\n", - "j dog 3.0" - ] - }, - "execution_count": 171, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[['animal','age']]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "a1giftRYzyCY" - }, - "source": [ - "### 8. Select the data in rows `[3, 4, 8]` **and** in columns `['animal', 'age']`." - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": { - "id": "tkSynFGH0BLD" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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animalage
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" - ], - "text/plain": [ - " animal age\n", - "d dog NaN\n", - "e dog 5.0\n", - "i dog 7.0" - ] - }, - "execution_count": 175, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.loc[['d','e','i'],['animal', 'age']]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "67sYTLBqzyCY" - }, - "source": [ - "### 9. Select only the rows where the number of visits is greater than 3." - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "metadata": { - "id": "d_G0WeMG0Dgd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Empty DataFrame\n", - "Columns: [animal, age, visits, priority]\n", - "Index: []\n" - ] - } - ], - "source": [ - "greater_df=df[df['visits']>3]\n", - "print(greater_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FXX4YRWgzyCY" - }, - "source": [ - "### 10. Select the rows where the age is missing, i.e. it is `NaN`." - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "metadata": { - "id": "KaV4Ypkj0Ea2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " animal age visits priority\n", - "d dog NaN 3 yes\n", - "h cat NaN 1 yes\n" - ] - } - ], - "source": [ - "miss_df=df[df['age'].isna()]\n", - "print(miss_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kMH5n-UBzyCZ" - }, - "source": [ - "### 11. Select the rows where the animal is a cat *and* the age is less than 3." - ] - }, - { - "cell_type": "code", - "execution_count": 213, - "metadata": { - "id": "8sCDh9Ez0FND", - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " animal age visits priority\n", - "a cat 2.5 1 yes\n", - "f cat 2.0 3 no\n" - ] - } - ], - "source": [ - "catage_df=df[(df['animal']=='cat')&(df['age']<3)]\n", - "print(catage_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "62Y0JsYazyCZ" - }, - "source": [ - "### 12. Select the rows the age is between 2 and 4 (inclusive). \n" - ] - }, - { - "cell_type": "code", - "execution_count": 215, - "metadata": { - "id": "svjvRtgZ0G76" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " animal age visits priority\n", - "a cat 2.5 1 yes\n", - "b cat 3.0 3 yes\n", - "f cat 2.0 3 no\n", - "j dog 3.0 1 no\n" - ] - } - ], - "source": [ - "age_df=df[(df['age']>=2)&(df['age']<=4)]\n", - "print(age_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4lIGMIkPzyCZ" - }, - "source": [ - "### 13. Change the age in row 'f' to 1.5." - ] - }, - { - "cell_type": "code", - "execution_count": 217, - "metadata": { - "id": "h4U4A6Ai0Hvk" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " animal age visits priority\n", - "a cat 2.5 1 yes\n", - "b cat 3.0 3 yes\n", - "c snake 0.5 2 no\n", - "d dog NaN 3 yes\n", - "e dog 5.0 2 no\n", - "f cat 1.5 3 no\n", - "g snake 4.5 1 no\n", - "h cat NaN 1 yes\n", - "i dog 7.0 2 no\n", - "j dog 3.0 1 no\n" - ] - } - ], - "source": [ - "df.loc['f','age']=1.5\n", - "print(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FZzPr9ObzyCZ" - }, - "source": [ - "### 14. Calculate the sum of all visits in `df` (i.e. the total number of visits)." - ] - }, - { - "cell_type": "code", - "execution_count": 219, - "metadata": { - "id": "FXLAUqR40I6C" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "총 방문수 : 19\n" - ] - } - ], - "source": [ - "total=df['visits'].sum()\n", - "print(f\"총 방문수 : {total}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PO0xpJ_OzyCa" - }, - "source": [ - "### 15. Calculate the mean age for each different animal in `df`." - ] - }, - { - "cell_type": "code", - "execution_count": 221, - "metadata": { - "id": "L63WRx_20Kta" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "animal\n", - "cat 2.333333\n", - "dog 5.000000\n", - "snake 2.500000\n", - "Name: age, dtype: float64\n" - ] - } - ], - "source": [ - "print(df.groupby('animal')['age'].mean())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YfVdzQXIzyCa" - }, - "source": [ - "### 16. Append a new row 'k' to `df` with your choice of values for each column. Then delete that row to return the original DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": 223, - "metadata": { - "id": "Per12Ekp0Mc0" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " animal age visits priority\n", - "a cat 2.5 1 yes\n", - "b cat 3.0 3 yes\n", - "c snake 0.5 2 no\n", - "d dog NaN 3 yes\n", - "e dog 5.0 2 no\n", - "f cat 1.5 3 no\n", - "g snake 4.5 1 no\n", - "h cat NaN 1 yes\n", - "i dog 7.0 2 no\n", - "j dog 3.0 1 no\n", - "k cat 3.5 2 yes\n", - " animal age visits priority\n", - "a cat 2.5 1 yes\n", - "b cat 3.0 3 yes\n", - "c snake 0.5 2 no\n", - "d dog NaN 3 yes\n", - "e dog 5.0 2 no\n", - "f cat 1.5 3 no\n", - "g snake 4.5 1 no\n", - "h cat NaN 1 yes\n", - "i dog 7.0 2 no\n", - "j dog 3.0 1 no\n" - ] - } - ], - "source": [ - "df.loc['k']=['cat',3.5,2,'yes']\n", - "print(df)\n", - "df.drop('k',axis=0,inplace=True) #inplace=True>자신의 df의 데이터 삭제\n", - "print(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tDkk_tHszyCa" - }, - "source": [ - "### 17. Count the number of each type of animal in `df`." - ] - }, - { - "cell_type": "code", - "execution_count": 225, - "metadata": { - "id": "v21izXST0NTR" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "animal\n", - "cat 4\n", - "dog 4\n", - "snake 2\n", - "Name: count, dtype: int64\n" - ] - } - ], - "source": [ - "v_count=df['animal'].value_counts()\n", - "print(v_count)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cwz4s5RYzyCa" - }, - "source": [ - "### 18. Sort `df` first by the values in the 'age' in *decending* order, then by the value in the 'visits' column in *ascending* order (so row `i` should be first, and row `d` should be last).\n", - "#### ✅출력 예시" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YILpLKeqzyCa" - }, - "source": [ - " index |animal\t| age\t|visits|\tpriority\n", - "---|---|---|---|---\n", - "i|\tdog|\t7.0|\t2|\tno\n", - "e|\tdog|\t5.0|\t2|\tno\n", - "g|\tsnake|\t4.5|\t1|\tno\n", - "j|\tdog|\t3.0|\t1|\tno\n", - "b|\tcat|\t3.0|\t3|\tyes\n", - "a|\tcat|\t2.5|\t1|\tyes\n", - "f|\tcat|\t1.5|\t3|\tno\n", - "c|\tsnake|\t0.5|\t2|\tno\n", - "h|\tcat|\tNaN|\t1|\tyes\n", - "d|\tdog|\tNaN|\t3|\tyes" - ] - }, - { - "cell_type": "code", - "execution_count": 231, - "metadata": { - "id": "2l6Pb7T10Qpi" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " animal age visits priority\n", - "i dog 7.0 2 no\n", - "e dog 5.0 2 no\n", - "g snake 4.5 1 no\n", - "j dog 3.0 1 no\n", - "b cat 3.0 3 yes\n", - "a cat 2.5 1 yes\n", - "f cat 1.5 3 no\n", - "c snake 0.5 2 no\n", - "h cat NaN 1 yes\n", - "d dog NaN 3 yes\n" - ] - } - ], - "source": [ - "#by=해당 칼럼으로 작업 수행, ascending=각각 칼럼을 오름차순 내림차순으로 작업할지 f/t로 표현\n", - "df_sorted=df.sort_values(by=['age','visits'],ascending=[False,True])\n", - "print(df_sorted)\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6hfE99qHzyCb" - }, - "source": [ - "### 19. The 'priority' column contains the values 'yes' and 'no'. Replace this column with a column of boolean values: 'yes' should be `True` and 'no' should be `False`. \n", - "(hint: `map`)" - ] - }, - { - "cell_type": "code", - "execution_count": 235, - "metadata": { - "id": "lPLmBRUP0SnR" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " animal age visits priority\n", - "a cat 2.5 1 True\n", - "b cat 3.0 3 True\n", - "c snake 0.5 2 False\n", - "d dog NaN 3 True\n", - "e dog 5.0 2 False\n", - "f cat 1.5 3 False\n", - "g snake 4.5 1 False\n", - "h cat NaN 1 True\n", - "i dog 7.0 2 False\n", - "j dog 3.0 1 False\n" - ] - } - ], - "source": [ - "df['priority']=df['priority'].map({'yes':True,'no':False})\n", - "print(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ycaIJncEzyCb" - }, - "source": [ - "### 20. In the 'animal' column, change the 'snake' entries to 'python'. " - ] - }, - { - "cell_type": "code", - "execution_count": 239, - "metadata": { - "id": "MZelDUlE0Wag" - }, - "outputs": [], - "source": [ - "df['animal']=df['animal'].replace('snake','python')" - ] - }, - { - "cell_type": "code", - "execution_count": 241, - "metadata": { - "id": "uYFF5Jew0Xz7" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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animalagevisitspriority
acat2.51True
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" - ], - "text/plain": [ - " animal age visits priority\n", - "a cat 2.5 1 True\n", - "b cat 3.0 3 True\n", - "c python 0.5 2 False\n", - "d dog NaN 3 True\n", - "e dog 5.0 2 False\n", - "f cat 1.5 3 False\n", - "g python 4.5 1 False\n", - "h cat NaN 1 True\n", - "i dog 7.0 2 False\n", - "j dog 3.0 1 False" - ] - }, - "execution_count": 241, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 확인용 df 출력 셀\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5YmjKvYizyCb" - }, - "source": [ - "### 21. Given a DataFrame of random numeric values:\n", - "```python\n", - "df = pd.DataFrame(np.random.random(size=(5, 3))) # this is a 5x3 DataFrame of float values\n", - "```\n", - "\n", - "how do you subtract the row mean from each element in the row?" - ] - }, - { - "cell_type": "code", - "execution_count": 243, - "metadata": { - "id": "EeAkDWv40e9J" - }, - "outputs": [], - "source": [ - "# 랜덤 시드 고정\n", - "np.random.seed(2024)\n", - "\n", - "df = pd.DataFrame(np.random.random(size=(5, 3)))\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 249, - "metadata": { - "id": "Z24qNGpgKEVi" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "원본데이터\n", - " 0 1 2\n", - "0 0.588015 0.699109 0.188152\n", - "1 0.043809 0.205019 0.106063\n", - "2 0.727240 0.679401 0.473846\n", - "3 0.448296 0.019107 0.752598\n", - "4 0.602449 0.961778 0.664369\n", - "뺀 데이터\n", - " 0 1 2\n", - "0 0.096256 0.207350 -0.303606\n", - "1 -0.074488 0.086722 -0.012234\n", - "2 0.100411 0.052572 -0.152983\n", - "3 0.041629 -0.387560 0.345931\n", - "4 -0.140416 0.218913 -0.078496\n" - ] - } - ], - "source": [ - "print(\"원본데이터\\n\",df)\n", - "subtract_df=df.sub(df.mean(axis=1),axis=0)\n", - "print(\"뺀 데이터\\n\",subtract_df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "authorship_tag": "ABX9TyN3MtLF9SsU0q9S6nVXMcF7", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From f9a6d83e4a432cd2e8ccc2f23b46b58c1158f60b Mon Sep 17 00:00:00 2001 From: greenxlouv Date: Tue, 15 Oct 2024 18:34:35 +0900 Subject: [PATCH 3/6] Add files via upload --- ...341\204\205\341\205\265\341\206\253.ipynb" | 3844 +++++++++++++++++ ...2\341\204\205\341\205\265\341\206\253.pdf" | Bin 0 -> 5624681 bytes 2 files changed, 3844 insertions(+) create mode 100644 "Week4_\341\204\213\341\205\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.ipynb" create mode 100644 "Week4_\341\204\213\341\205\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.pdf" diff --git "a/Week4_\341\204\213\341\205\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.ipynb" "b/Week4_\341\204\213\341\205\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.ipynb" new file mode 100644 index 0000000..ee853cc --- /dev/null +++ "b/Week4_\341\204\213\341\205\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.ipynb" @@ -0,0 +1,3844 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0fc5c12d-7bfc-4445-91b4-810e4929795c", + "metadata": {}, + "source": [ + "Ch2. 사이킷런으로 시작하는 머신러닝\n", + "05. GBM" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "60b2cd55-94b5-4d29-b4a4-39d652179560", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "전체 피처명에서 10개만 추출: ['tBodyAcc-mean()-X', 'tBodyAcc-mean()-Y', 'tBodyAcc-mean()-Z', 'tBodyAcc-std()-X', 'tBodyAcc-std()-Y', 'tBodyAcc-std()-Z', 'tBodyAcc-mad()-X', 'tBodyAcc-mad()-Y', 'tBodyAcc-mad()-Z', 'tBodyAcc-max()-X']\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "# 파일에는 피처이름 인덱스와 피처명이 공백으로 분리되어있음 > df에 로드\n", + "feature_name_df=pd.read_csv('/Users/bluecloud/Documents/대학/유런/human_activity/features.txt',sep='\\s+',header=None,names=['column_index','column_name'])\n", + "# 피처명 인덱스를 제거하고, 피처명 리스트 객체로 생성 후 샘플로 10개 추출\n", + "feature_name=feature_name_df.iloc[:,1].values.tolist()\n", + "print(\"전체 피처명에서 10개만 추출:\",feature_name[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "f6a8741a-f0ae-41d3-90ad-801cd6245c71", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "column_index 42\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# 중복된 피처명 확인\n", + "feature_dup_df=feature_name_df.groupby('column_name').count()\n", + "print(feature_dup_df[feature_dup_df['column_index']>1].count())\n", + "feature_dup_df[feature_dup_df['column_index']>1].head()\n", + "#_1,_2 붙여서 로드하는 함수\n", + "def get_new_feature_name_df(old_feature_name_df):\n", + " feature_dup_df=pd.DataFrame(data=old_feature_name_df.groupby('column_name').cumcount(),columns=['dup_cnt'])\n", + " feature_dup_df=feature_dup_df.reset_index()\n", + " new_feature_name_df=pd.merge(old_feature_name_df.reset_index(),feature_dup_df,how='outer')\n", + " new_feature_name_df['column_name']=new_feature_name_df[['column_name','dup_cnt']].apply(lambda x:x[0]+'_'+str(x[1])\n", + " if x[1]>0 else x[0],axis=1)\n", + " new_feature_name_df=new_feature_name_df.drop(['index'],axis=1)\n", + " return new_feature_name_df" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "f7cb5d62-5025-4239-a0b5-2598d85d010e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "def get_human_dataset():\n", + " feature_name_df=pd.read_csv('/Users/bluecloud/Documents/대학/유런/human_activity/features.txt',sep='\\s+',header=None,names=['column_index','column_name'])\n", + " new_feature_name_df=get_new_feature_name_df(feature_name_df)\n", + " #df에 피처명을 칼럼으로 부여하기 위해 리스트로\n", + " feature_name=new_feature_name_df.iloc[:,1].values.tolist()\n", + " # 학습 피처 데이터와 테스트 피처 데이터를 df로 로딩\n", + " X_train=pd.read_csv('/Users/bluecloud/Documents/대학/유런/human_activity/train/X_train.txt',sep='\\s+',names=feature_name)\n", + " X_test=pd.read_csv('/Users/bluecloud/Documents/대학/유런/human_activity/test/X_test.txt',sep='\\s+',names=feature_name)\n", + " #학습 레이블과 테스트 레이블을 로딩\n", + " y_train=pd.read_csv('/Users/bluecloud/Documents/대학/유런/human_activity/train/y_train.txt',sep='\\s+',header=None,names=['action'])\n", + " y_test=pd.read_csv('/Users/bluecloud/Documents/대학/유런/human_activity/test/y_test.txt',sep='\\s+',header=None,names=['action'])\n", + " return X_train,X_test,y_train,y_test\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7520afaa-9cc6-4fb7-aa2a-ba044833df9e", + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[17], line 11\u001b[0m\n\u001b[1;32m 8\u001b[0m start_time\u001b[38;5;241m=\u001b[39mtime\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 10\u001b[0m gb_clf\u001b[38;5;241m=\u001b[39mGradientBoostingClassifier(random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m---> 11\u001b[0m gb_clf\u001b[38;5;241m.\u001b[39mfit(X_train,y_train)\n\u001b[1;32m 12\u001b[0m gb_pred\u001b[38;5;241m=\u001b[39mgb_clf\u001b[38;5;241m.\u001b[39mpredict(X_test)\n\u001b[1;32m 13\u001b[0m gb_accuracy_score(y_test,gb_pred)\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/sklearn/base.py:1474\u001b[0m, in \u001b[0;36m_fit_context..decorator..wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1467\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m 1469\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m 1470\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m 1471\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m 1472\u001b[0m )\n\u001b[1;32m 1473\u001b[0m ):\n\u001b[0;32m-> 1474\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_gb.py:784\u001b[0m, in \u001b[0;36mBaseGradientBoosting.fit\u001b[0;34m(self, X, y, sample_weight, monitor)\u001b[0m\n\u001b[1;32m 781\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_resize_state()\n\u001b[1;32m 783\u001b[0m \u001b[38;5;66;03m# fit the boosting stages\u001b[39;00m\n\u001b[0;32m--> 784\u001b[0m n_stages \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_stages(\n\u001b[1;32m 785\u001b[0m X_train,\n\u001b[1;32m 786\u001b[0m y_train,\n\u001b[1;32m 787\u001b[0m raw_predictions,\n\u001b[1;32m 788\u001b[0m sample_weight_train,\n\u001b[1;32m 789\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_rng,\n\u001b[1;32m 790\u001b[0m X_val,\n\u001b[1;32m 791\u001b[0m y_val,\n\u001b[1;32m 792\u001b[0m sample_weight_val,\n\u001b[1;32m 793\u001b[0m begin_at_stage,\n\u001b[1;32m 794\u001b[0m monitor,\n\u001b[1;32m 795\u001b[0m )\n\u001b[1;32m 797\u001b[0m \u001b[38;5;66;03m# change shape of arrays after fit (early-stopping or additional ests)\u001b[39;00m\n\u001b[1;32m 798\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_stages \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimators_\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_gb.py:880\u001b[0m, in \u001b[0;36mBaseGradientBoosting._fit_stages\u001b[0;34m(self, X, y, raw_predictions, sample_weight, random_state, X_val, y_val, sample_weight_val, begin_at_stage, monitor)\u001b[0m\n\u001b[1;32m 873\u001b[0m initial_loss \u001b[38;5;241m=\u001b[39m factor \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_loss(\n\u001b[1;32m 874\u001b[0m y_true\u001b[38;5;241m=\u001b[39my_oob_masked,\n\u001b[1;32m 875\u001b[0m raw_prediction\u001b[38;5;241m=\u001b[39mraw_predictions[\u001b[38;5;241m~\u001b[39msample_mask],\n\u001b[1;32m 876\u001b[0m sample_weight\u001b[38;5;241m=\u001b[39msample_weight_oob_masked,\n\u001b[1;32m 877\u001b[0m )\n\u001b[1;32m 879\u001b[0m \u001b[38;5;66;03m# fit next stage of trees\u001b[39;00m\n\u001b[0;32m--> 880\u001b[0m raw_predictions \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_stage(\n\u001b[1;32m 881\u001b[0m i,\n\u001b[1;32m 882\u001b[0m X,\n\u001b[1;32m 883\u001b[0m y,\n\u001b[1;32m 884\u001b[0m raw_predictions,\n\u001b[1;32m 885\u001b[0m sample_weight,\n\u001b[1;32m 886\u001b[0m sample_mask,\n\u001b[1;32m 887\u001b[0m random_state,\n\u001b[1;32m 888\u001b[0m X_csc\u001b[38;5;241m=\u001b[39mX_csc,\n\u001b[1;32m 889\u001b[0m X_csr\u001b[38;5;241m=\u001b[39mX_csr,\n\u001b[1;32m 890\u001b[0m )\n\u001b[1;32m 892\u001b[0m \u001b[38;5;66;03m# track loss\u001b[39;00m\n\u001b[1;32m 893\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m do_oob:\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_gb.py:490\u001b[0m, in \u001b[0;36mBaseGradientBoosting._fit_stage\u001b[0;34m(self, i, X, y, raw_predictions, sample_weight, sample_mask, random_state, X_csc, X_csr)\u001b[0m\n\u001b[1;32m 487\u001b[0m sample_weight \u001b[38;5;241m=\u001b[39m sample_weight \u001b[38;5;241m*\u001b[39m sample_mask\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mfloat64)\n\u001b[1;32m 489\u001b[0m X \u001b[38;5;241m=\u001b[39m X_csc \u001b[38;5;28;01mif\u001b[39;00m X_csc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m X\n\u001b[0;32m--> 490\u001b[0m tree\u001b[38;5;241m.\u001b[39mfit(\n\u001b[1;32m 491\u001b[0m X, neg_g_view[:, k], sample_weight\u001b[38;5;241m=\u001b[39msample_weight, check_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 492\u001b[0m )\n\u001b[1;32m 494\u001b[0m \u001b[38;5;66;03m# update tree leaves\u001b[39;00m\n\u001b[1;32m 495\u001b[0m X_for_tree_update \u001b[38;5;241m=\u001b[39m X_csr \u001b[38;5;28;01mif\u001b[39;00m X_csr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m X\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/sklearn/base.py:1474\u001b[0m, in \u001b[0;36m_fit_context..decorator..wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1467\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m 1469\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m 1470\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m 1471\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m 1472\u001b[0m )\n\u001b[1;32m 1473\u001b[0m ):\n\u001b[0;32m-> 1474\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/sklearn/tree/_classes.py:1377\u001b[0m, in \u001b[0;36mDecisionTreeRegressor.fit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m 1347\u001b[0m \u001b[38;5;129m@_fit_context\u001b[39m(prefer_skip_nested_validation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 1348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, check_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m 1349\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Build a decision tree regressor from the training set (X, y).\u001b[39;00m\n\u001b[1;32m 1350\u001b[0m \n\u001b[1;32m 1351\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1374\u001b[0m \u001b[38;5;124;03m Fitted estimator.\u001b[39;00m\n\u001b[1;32m 1375\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1377\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m_fit(\n\u001b[1;32m 1378\u001b[0m X,\n\u001b[1;32m 1379\u001b[0m y,\n\u001b[1;32m 1380\u001b[0m sample_weight\u001b[38;5;241m=\u001b[39msample_weight,\n\u001b[1;32m 1381\u001b[0m check_input\u001b[38;5;241m=\u001b[39mcheck_input,\n\u001b[1;32m 1382\u001b[0m )\n\u001b[1;32m 1383\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/sklearn/tree/_classes.py:472\u001b[0m, in \u001b[0;36mBaseDecisionTree._fit\u001b[0;34m(self, X, y, sample_weight, check_input, missing_values_in_feature_mask)\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 462\u001b[0m builder \u001b[38;5;241m=\u001b[39m BestFirstTreeBuilder(\n\u001b[1;32m 463\u001b[0m splitter,\n\u001b[1;32m 464\u001b[0m min_samples_split,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmin_impurity_decrease,\n\u001b[1;32m 470\u001b[0m )\n\u001b[0;32m--> 472\u001b[0m builder\u001b[38;5;241m.\u001b[39mbuild(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtree_, X, y, sample_weight, missing_values_in_feature_mask)\n\u001b[1;32m 474\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_outputs_ \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m is_classifier(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 475\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_classes_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_classes_[\u001b[38;5;241m0\u001b[39m]\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "import time\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "X_train,X_test,y_train,y_test=get_human_dataset()\n", + "# GBM 수행 시간 측정을 위함. 시작 시간 설정\n", + "start_time=time.time()\n", + "\n", + "gb_clf=GradientBoostingClassifier(random_state=0)\n", + "gb_clf.fit(X_train,y_train)\n", + "gb_pred=gb_clf.predict(X_test)\n", + "gb_accuracy_score(y_test,gb_pred)\n", + "\n", + "print('GBM 정확도 : {0:.4f}'.format(gb_accuracy))\n", + "print('GBM 수행 시간 : {0:.1f}'.format(time.time()-start_time))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "95322d3c-70e6-4bd8-a4e4-5baf8f93e58e", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'X_trian' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[41], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m params\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn_estimators\u001b[39m\u001b[38;5;124m'\u001b[39m:[\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m500\u001b[39m],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlearning_rate\u001b[39m\u001b[38;5;124m'\u001b[39m:[\u001b[38;5;241m0.05\u001b[39m,\u001b[38;5;241m0.1\u001b[39m]}\n\u001b[1;32m 4\u001b[0m grid_cv\u001b[38;5;241m=\u001b[39mGridSearchCV(gb_clf,param_grid\u001b[38;5;241m=\u001b[39mparams,cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m grid_cv\u001b[38;5;241m.\u001b[39mfit(X_trian,y_train)\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m최적의 하이퍼 파라미터 :\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m,grid_cv\u001b[38;5;241m.\u001b[39mbest_params_)\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m최고 예측 정확도 :\u001b[39m\u001b[38;5;132;01m{0:.4f}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(grid_cv\u001b[38;5;241m.\u001b[39mbest_score_))\n", + "\u001b[0;31mNameError\u001b[0m: name 'X_trian' is not defined" + ] + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "X_train,X_test,y_train,y_test=get_human_dataset()\n", + "params={'n_estimators':[100,500],'learning_rate':[0.05,0.1]}\n", + "grid_cv=GridSearchCV(gb_clf,param_grid=params,cv=2,verbose=1)\n", + "grid_cv.fit(X_trian,y_train)\n", + "print('최적의 하이퍼 파라미터 :\\n',grid_cv.best_params_)\n", + "print('최고 예측 정확도 :{0:.4f}'.format(grid_cv.best_score_))" + ] + }, + { + "cell_type": "markdown", + "id": "c232f453-ccf1-4fd6-bcba-00b8339d6a58", + "metadata": {}, + "source": [ + "06. XGBoost" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "4c9095fb-56d5-4408-b5f8-94199c339cab", + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb\n", + "from xgboost import XGBClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "f1587435-9748-44a8-945b-54319e8aba81", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.1.1\n" + ] + } + ], + "source": [ + "import xgboost\n", + "print(xgboost.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "539de72b-b593-4ebc-97e0-0716fbfa17e1", + "metadata": {}, + "source": [ + "위스콘신 유방암 예측" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "eacd596f-4632-49dd-a285-531787b2efdb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
017.9910.38122.81001.00.118400.277600.30010.147100.24190.07871...17.33184.62019.00.16220.66560.71190.26540.46010.118900
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3 rows × 31 columns

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" + ], + "text/plain": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "0 17.99 10.38 122.8 1001.0 0.11840 \n", + "1 20.57 17.77 132.9 1326.0 0.08474 \n", + "2 19.69 21.25 130.0 1203.0 0.10960 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "0 0.27760 0.3001 0.14710 0.2419 \n", + "1 0.07864 0.0869 0.07017 0.1812 \n", + "2 0.15990 0.1974 0.12790 0.2069 \n", + "\n", + " mean fractal dimension ... worst texture worst perimeter worst area \\\n", + "0 0.07871 ... 17.33 184.6 2019.0 \n", + "1 0.05667 ... 23.41 158.8 1956.0 \n", + "2 0.05999 ... 25.53 152.5 1709.0 \n", + "\n", + " worst smoothness worst compactness worst concavity worst concave points \\\n", + "0 0.1622 0.6656 0.7119 0.2654 \n", + "1 0.1238 0.1866 0.2416 0.1860 \n", + "2 0.1444 0.4245 0.4504 0.2430 \n", + "\n", + " worst symmetry worst fractal dimension target \n", + "0 0.4601 0.11890 0 \n", + "1 0.2750 0.08902 0 \n", + "2 0.3613 0.08758 0 \n", + "\n", + "[3 rows x 31 columns]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import xgboost as xgb\n", + "from xgboost import plot_importance\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.datasets import load_breast_cancer\n", + "from sklearn.model_selection import train_test_split\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "dataset=load_breast_cancer()\n", + "X_features=dataset.data\n", + "y_label=dataset.target\n", + "cancer_df=pd.DataFrame(data=X_features,columns=dataset.feature_names)\n", + "cancer_df['target']=y_label\n", + "cancer_df.head(3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "73b060d2-bb1b-457d-a525-4eaddea44d8d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['malignant' 'benign']\n", + "target\n", + "1 357\n", + "0 212\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "print(dataset.target_names)\n", + "print(cancer_df['target'].value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "76e3dcf7-38f9-475b-ad44-6f45eaa13b7b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(455, 30) (114, 30)\n" + ] + } + ], + "source": [ + "X_train,X_test,y_train,y_test=train_test_split(X_features,y_label,test_size=0.2,random_state=156)\n", + "print(X_train.shape,X_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "0e260398-9396-4bcb-ba0d-7d0d84bb76d4", + "metadata": {}, + "outputs": [], + "source": [ + "dtrain=xgb.DMatrix(data=X_train,label=y_train)\n", + "dtest=xgb.DMatrix(data=X_test,label=y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "63fae937-8f08-4e99-973b-ec419a67f1bb", + "metadata": {}, + "outputs": [], + "source": [ + "params={\n", + " 'max_depth':3,\n", + " 'eta':0.1,\n", + " 'objective':'binary:logistic',\n", + " 'eval_metric':'logloss', \n", + " 'early_stoppings':100\n", + "}\n", + "num_rounds=400" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "1c79bd72-ebc8-482b-8cae-4485ae6e6f67", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0]\ttrain-logloss:0.58431\teval-logloss:0.56554\n", + "[1]\ttrain-logloss:0.51885\teval-logloss:0.50669\n", + "[2]\ttrain-logloss:0.46457\teval-logloss:0.45868\n", + "[3]\ttrain-logloss:0.41713\teval-logloss:0.41822\n", + "[4]\ttrain-logloss:0.37585\teval-logloss:0.38103\n", + "[5]\ttrain-logloss:0.34075\teval-logloss:0.35137\n", + "[6]\ttrain-logloss:0.31028\teval-logloss:0.32588\n", + "[7]\ttrain-logloss:0.28283\teval-logloss:0.30127\n", + "[8]\ttrain-logloss:0.25925\teval-logloss:0.28197\n", + "[9]\ttrain-logloss:0.23822\teval-logloss:0.26265\n", + "[10]\ttrain-logloss:0.21951\teval-logloss:0.24821\n", + "[11]\ttrain-logloss:0.20251\teval-logloss:0.23231\n", + "[12]\ttrain-logloss:0.18759\teval-logloss:0.22079\n", + "[13]\ttrain-logloss:0.17386\teval-logloss:0.20795\n", + "[14]\ttrain-logloss:0.16199\teval-logloss:0.19764\n", + "[15]\ttrain-logloss:0.15109\teval-logloss:0.18950\n", + "[16]\ttrain-logloss:0.14056\teval-logloss:0.18052\n", + "[17]\ttrain-logloss:0.13137\teval-logloss:0.17246\n", + "[18]\ttrain-logloss:0.12329\teval-logloss:0.16512\n", + "[19]\ttrain-logloss:0.11565\teval-logloss:0.15828\n", + "[20]\ttrain-logloss:0.10860\teval-logloss:0.15436\n", + "[21]\ttrain-logloss:0.10190\teval-logloss:0.14633\n", + "[22]\ttrain-logloss:0.09563\teval-logloss:0.13936\n", + "[23]\ttrain-logloss:0.09016\teval-logloss:0.13393\n", + "[24]\ttrain-logloss:0.08496\teval-logloss:0.13015\n", + "[25]\ttrain-logloss:0.08021\teval-logloss:0.12489\n", + "[26]\ttrain-logloss:0.07583\teval-logloss:0.12206\n", + "[27]\ttrain-logloss:0.07215\teval-logloss:0.11890\n", + 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"[198]\ttrain-logloss:0.00671\teval-logloss:0.08792\n", + "[199]\ttrain-logloss:0.00670\teval-logloss:0.08800\n" + ] + } + ], + "source": [ + "# train 데이터 세트는 'train', evaluation 데이터 세트는 'eval'로 명기\n", + "wlist=[(dtrain,'train'),(dtest,'eval')]\n", + "# 하이퍼 파라미터와 early stopping 파라미터를 train() 함수의 파라미터로 전달\n", + "xgb_model=xgb.train(params=params,dtrain=dtrain,num_boost_round=num_rounds,early_stopping_rounds=100,evals=wlist)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "a8d01357-3295-4ab8-a1d3-8f4fb3f1bf31", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "prdedict() 수행 결괏값을 10개만 표시, 예측 확률값으로 표시됨\n", + "[0.904 0.004 0.908 0.267 0.992 1. 1. 0.999 0.994 0. ]\n", + "예측값 10개만 표시: [1, 0, 1, 0, 1, 1, 1, 1, 1, 0]\n" + ] + } + ], + "source": [ + "pred_probs=xgb_model.predict(dtest)\n", + "print('prdedict() 수행 결괏값을 10개만 표시, 예측 확률값으로 표시됨')\n", + "print(np.round(pred_probs[:10],3))\n", + "# 0과 1이 아닌 확률값으로 나오므로 0.5 기준으로 0과 1로 표시하도록 > preds에 저장\n", + "preds=[1 if x>0.5 else 0 for x in pred_probs]\n", + "print('예측값 10개만 표시:',preds[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "69e79b05-db1d-4ce6-988d-9f41e1228462", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score, precision_score, recall_score, roc_auc_score\n", + "from sklearn.metrics import f1_score, confusion_matrix, precision_recall_curve, roc_curve\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "def get_clf_eval(y_test, pred=None, pred_proba=None):\n", + " confusion = confusion_matrix( y_test, pred)\n", + " accuracy = accuracy_score(y_test , pred)\n", + " precision = precision_score(y_test , pred)\n", + " recall = recall_score(y_test , pred)\n", + " f1 = f1_score(y_test,pred)\n", + " roc_auc = roc_auc_score(y_test, pred_proba)\n", + " print('오차 행렬')\n", + " print(confusion)\n", + " print('정확도: {0:.4f}, 정밀도: {1:.4f}, 재현율: {2:.4f},\\\n", + " F1: {3:.4f}, AUC:{4:.4f}'.format(accuracy, precision, recall, f1, roc_auc))" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "02bdea2a-60b9-44a8-ad77-977f2032eedb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "오차 행렬\n", + "[[34 3]\n", + " [ 1 76]]\n", + "정확도: 0.9649, 정밀도: 0.9620, 재현율: 0.9870, F1: 0.9744, AUC:0.9951\n" + ] + } + ], + "source": [ + "get_clf_eval(y_test,preds,pred_probs)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "703ede87-d169-4948-9267-00ec697186e9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from xgboost import plot_importance\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "fig,ax=plt.subplots(figsize=(10,12))\n", + "plot_importance(xgb_model,ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "0a1532a2-6a27-4fa3-a462-76bea5227b28", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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train-logloss-meantrain-logloss-stdtest-logloss-meantest-logloss-std
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90.2364390.0013130.2862020.021203
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" + ], + "text/plain": [ + " train-logloss-mean train-logloss-std test-logloss-mean test-logloss-std\n", + "0 0.584737 0.005961 0.592041 0.008024\n", + "1 0.517898 0.004745 0.532735 0.007335\n", + "2 0.462199 0.003589 0.483123 0.008418\n", + "3 0.415639 0.003043 0.441435 0.011510\n", + "4 0.375350 0.002036 0.406194 0.014400\n", + "5 0.340102 0.000854 0.373831 0.015171\n", + "6 0.308902 0.000900 0.348411 0.018183\n", + "7 0.281927 0.001335 0.324136 0.017817\n", + "8 0.257775 0.001182 0.304679 0.021239\n", + "9 0.236439 0.001313 0.286202 0.021203" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xgboost.cv(params,dtrain,num_boost_round=10,nfold=3,stratified=False,folds=None,metrics=(),obj=None,feval=None,maximize=False,early_stopping_rounds=None,fpreproc=None,as_pandas=True,verbose_eval=None,show_stdv=True,seed=0,callbacks=None,shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "8a59bd88-f724-4568-b1a8-8ca7315284e9", + "metadata": {}, + "outputs": [], + "source": [ + "## XGBClassifier 이용해 예측\n", + "from xgboost import XGBClassifier \n", + "xgb_wrapper=XGBClassifier(n_estimators=400,learning_rate=0.1,max_depth=3)\n", + "xgb_wrapper.fit(X_train,y_train)\n", + "w_preds=xgb_wrapper.predict(X_test)\n", + "w_pred_proba=xgb_wrapper.predict_proba(X_test)[:,1]" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "fffb3b7f-2f7a-4974-b218-e5b49b58e2bd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "오차 행렬\n", + "[[35 2]\n", + " [ 1 76]]\n", + "정확도: 0.9737, 정밀도: 0.9744, 재현율: 0.9870, F1: 0.9806, AUC:0.9947\n" + ] + } + ], + "source": [ + "get_clf_eval(y_test,w_preds,w_pred_proba)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d0395cc0-ee28-4d56-aa14-4bb472680381", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'X_test' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# 모델 정의 할 때 early_stopping_rounds 제거 \u001b[39;00m\n\u001b[1;32m 3\u001b[0m xgb_wrapper \u001b[38;5;241m=\u001b[39m XGBClassifier(n_estimators \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m400\u001b[39m, learning_rate \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.1\u001b[39m, max_depth \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m evals \u001b[38;5;241m=\u001b[39m [(X_test, y_test)]\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# 모델 학습시킬 때 인자로 early_stopping_rounds 추가 \u001b[39;00m\n\u001b[1;32m 6\u001b[0m xgb_wrapper\u001b[38;5;241m.\u001b[39mfit(X_train, y_train, early_stopping_rounds \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m, eval_metric \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlogloss\u001b[39m\u001b[38;5;124m'\u001b[39m, eval_set \u001b[38;5;241m=\u001b[39m evals, verbose \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'X_test' is not defined" + ] + } + ], + "source": [ + "from xgboost import XGBClassifier\n", + "# 모델 정의 할 때 early_stopping_rounds 제거 \n", + "xgb_wrapper = XGBClassifier(n_estimators = 400, learning_rate = 0.1, max_depth = 3)\n", + "evals = [(X_test, y_test)]\n", + "# 모델 학습시킬 때 인자로 early_stopping_rounds 추가 \n", + "xgb_wrapper.fit(X_train, y_train, early_stopping_rounds = 100, eval_metric = 'logloss', eval_set = evals, verbose = True)\n", + "ws100_preds = xgb_wrapper.predict(X_test)\n", + "ws100_pred_proba = xgb_wrapper.predict_proba(X_test)[:, 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "d0f21be8-ef00-4b6b-a96b-8135c09e101a", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Classification metrics can't handle a mix of binary and continuous targets", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[91], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m get_clf_eval(y_test,ws100_preds,ws100_pred_proba)\n", + "Cell \u001b[0;32mIn[69], line 12\u001b[0m, in \u001b[0;36mget_clf_eval\u001b[0;34m(y_test, pred, pred_proba)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_clf_eval\u001b[39m(y_test, pred\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pred_proba\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m---> 12\u001b[0m confusion \u001b[38;5;241m=\u001b[39m confusion_matrix( y_test, pred)\n\u001b[1;32m 13\u001b[0m accuracy \u001b[38;5;241m=\u001b[39m accuracy_score(y_test , pred)\n\u001b[1;32m 14\u001b[0m precision \u001b[38;5;241m=\u001b[39m precision_score(y_test , pred)\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/sklearn/utils/_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params..decorator..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 208\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m 209\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m 210\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m 211\u001b[0m )\n\u001b[1;32m 212\u001b[0m ):\n\u001b[0;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[1;32m 217\u001b[0m \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[1;32m 219\u001b[0m msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[1;32m 220\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 221\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[1;32m 223\u001b[0m )\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:319\u001b[0m, in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[38;5;129m@validate_params\u001b[39m(\n\u001b[1;32m 225\u001b[0m {\n\u001b[1;32m 226\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_true\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray-like\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 235\u001b[0m y_true, y_pred, \u001b[38;5;241m*\u001b[39m, labels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, normalize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 236\u001b[0m ):\n\u001b[1;32m 237\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Compute confusion matrix to evaluate the accuracy of a classification.\u001b[39;00m\n\u001b[1;32m 238\u001b[0m \n\u001b[1;32m 239\u001b[0m \u001b[38;5;124;03m By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}`\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;124;03m (0, 2, 1, 1)\u001b[39;00m\n\u001b[1;32m 318\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 319\u001b[0m y_type, y_true, y_pred \u001b[38;5;241m=\u001b[39m _check_targets(y_true, y_pred)\n\u001b[1;32m 320\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 321\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m is not supported\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m y_type)\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/sklearn/metrics/_classification.py:94\u001b[0m, in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m 91\u001b[0m y_type \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(y_type) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m---> 94\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 95\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClassification metrics can\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt handle a mix of \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m targets\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 96\u001b[0m type_true, type_pred\n\u001b[1;32m 97\u001b[0m )\n\u001b[1;32m 98\u001b[0m )\n\u001b[1;32m 100\u001b[0m \u001b[38;5;66;03m# We can't have more than one value on y_type => The set is no more needed\u001b[39;00m\n\u001b[1;32m 101\u001b[0m y_type \u001b[38;5;241m=\u001b[39m y_type\u001b[38;5;241m.\u001b[39mpop()\n", + "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of binary and continuous targets" + ] + } + ], + "source": [ + "get_clf_eval(y_test,ws100_preds,ws100_pred_proba)" + ] + }, + { + "cell_type": "markdown", + "id": "3d571a75-ab0c-4431-adf2-704756ef048b", + "metadata": {}, + "source": [ + "07. LightGBM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de2fcdf0-d4e4-49de-8573-237814d5e015", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Retrieving notices: ...working... done\n", + "Channels:\n", + " - conda-forge\n", + " - defaults\n", + " - anaconda\n", + "Platform: osx-arm64\n", + "Collecting package metadata (repodata.json): done\n", + "Solving environment: done\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /opt/anaconda3\n", + "\n", + " added / updated specs:\n", + " - lightgbm\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " boost-cpp-1.84.0 | hca5e981_3 16 KB conda-forge\n", + " certifi-2024.8.30 | pyhd8ed1ab_0 160 KB conda-forge\n", + " conda-24.9.1 | py312h81bd7bf_0 1.1 MB conda-forge\n", + " icu-73.2 | hc8870d7_0 11.4 MB conda-forge\n", + " khronos-opencl-icd-loader-2024.05.08| hd74edd7_0 75 KB conda-forge\n", + " libboost-1.84.0 | h17eb2be_3 1.9 MB conda-forge\n", + " libboost-devel-1.84.0 | hf450f58_3 39 KB conda-forge\n", + " libboost-headers-1.84.0 | hce30654_3 13.2 MB conda-forge\n", + " libcxx-19.1.1 | ha82da77_0 511 KB conda-forge\n", + " libexpat-2.6.2 | hebf3989_0 62 KB conda-forge\n", + " liblightgbm-4.5.0 | cpu_h7ba702d_3 1.3 MB conda-forge\n", + " libsqlite-3.46.0 | hfb93653_0 811 KB conda-forge\n", + " libzlib-1.2.13 | hfb2fe0b_6 46 KB conda-forge\n", + " lightgbm-4.5.0 | cpu_py_3 81 KB conda-forge\n", + " llvm-openmp-19.1.1 | hb52a8e5_1 273 KB conda-forge\n", + " openssl-3.3.2 | h8359307_0 2.7 MB conda-forge\n", + " python-3.12.2 |hdf0ec26_0_cpython 12.5 MB conda-forge\n", + " python_abi-3.12 | 5_cp312 6 KB conda-forge\n", + " zlib-1.2.13 | hfb2fe0b_6 76 KB conda-forge\n", + " zstandard-0.23.0 | py312h15fbf35_1 323 KB conda-forge\n", + " zstd-1.5.6 | hb46c0d2_0 396 KB conda-forge\n", + " ------------------------------------------------------------\n", + " Total: 47.0 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " khronos-opencl-ic~ conda-forge/osx-arm64::khronos-opencl-icd-loader-2024.05.08-hd74edd7_0 \n", + " libboost-devel conda-forge/osx-arm64::libboost-devel-1.84.0-hf450f58_3 \n", + " libboost-headers conda-forge/osx-arm64::libboost-headers-1.84.0-hce30654_3 \n", + " libexpat conda-forge/osx-arm64::libexpat-2.6.2-hebf3989_0 \n", + " liblightgbm conda-forge/osx-arm64::liblightgbm-4.5.0-cpu_h7ba702d_3 \n", + " libsqlite conda-forge/osx-arm64::libsqlite-3.46.0-hfb93653_0 \n", + " libzlib conda-forge/osx-arm64::libzlib-1.2.13-hfb2fe0b_6 \n", + " lightgbm conda-forge/noarch::lightgbm-4.5.0-cpu_py_3 \n", + " python_abi conda-forge/osx-arm64::python_abi-3.12-5_cp312 \n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " boost-cpp pkgs/main::boost-cpp-1.82.0-h48ca7d4_2 --> conda-forge::boost-cpp-1.84.0-hca5e981_3 \n", + " icu pkgs/main::icu-73.1-h313beb8_0 --> conda-forge::icu-73.2-hc8870d7_0 \n", + " libboost pkgs/main::libboost-1.82.0-h0bc93f9_2 --> conda-forge::libboost-1.84.0-h17eb2be_3 \n", + " libcxx pkgs/main::libcxx-14.0.6-h848a8c0_0 --> conda-forge::libcxx-19.1.1-ha82da77_0 \n", + " llvm-openmp pkgs/main::llvm-openmp-14.0.6-hc6e570~ --> conda-forge::llvm-openmp-19.1.1-hb52a8e5_1 \n", + " openssl pkgs/main::openssl-3.0.15-h80987f9_0 --> conda-forge::openssl-3.3.2-h8359307_0 \n", + " zlib pkgs/main::zlib-1.2.13-h18a0788_1 --> conda-forge::zlib-1.2.13-hfb2fe0b_6 \n", + " zstandard pkgs/main::zstandard-0.22.0-py312h1a4~ --> conda-forge::zstandard-0.23.0-py312h15fbf35_1 \n", + " zstd pkgs/main::zstd-1.5.5-hd90d995_2 --> conda-forge::zstd-1.5.6-hb46c0d2_0 \n", + "\n", + "The following packages will be SUPERSEDED by a higher-priority channel:\n", + "\n", + " certifi pkgs/main/osx-arm64::certifi-2024.8.3~ --> conda-forge/noarch::certifi-2024.8.30-pyhd8ed1ab_0 \n", + " conda anaconda::conda-24.9.1-py312hca03da5_0 --> conda-forge::conda-24.9.1-py312h81bd7bf_0 \n", + " python pkgs/main::python-3.12.4-h99e199e_1 --> conda-forge::python-3.12.2-hdf0ec26_0_cpython \n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages:\n", + "libboost-headers-1.8 | 13.2 MB | | 0% \n", + "python-3.12.2 | 12.5 MB | | 0% \u001b[A\n", + "\n", + "icu-73.2 | 11.4 MB | | 0% \u001b[A\u001b[A\n", + "\n", + "\n", + "openssl-3.3.2 | 2.7 MB | | 0% \u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "libboost-1.84.0 | 1.9 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "liblightgbm-4.5.0 | 1.3 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "conda-24.9.1 | 1.1 MB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "libsqlite-3.46.0 | 811 KB | | 0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", 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"\n", + "libboost-headers-1.8 | 13.2 MB | #4 | 4% \u001b[A\u001b[A\u001b[A\u001b[A\n", + "python-3.12.2 | 12.5 MB | 1 | 0% \u001b[A\n", + "\n", + "\n", + "openssl-3.3.2 | 2.7 MB | ######5 | 18% \u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "libboost-1.84.0 | 1.9 MB | #################8 | 48% \u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "icu-73.2 | 11.4 MB | #6 | 5% \u001b[A\u001b[A\n", + "python-3.12.2 | 12.5 MB | 2 | 1% \u001b[A\n", + "\n", + "\n", + "openssl-3.3.2 | 2.7 MB | ########4 | 23% \u001b[A\u001b[A\u001b[A\n", + "\n", + "icu-73.2 | 11.4 MB | #9 | 5% \u001b[A\u001b[A\n", + "python-3.12.2 | 12.5 MB | 4 | 1% \u001b[A\n", + "\n", + "\n", + "openssl-3.3.2 | 2.7 MB | ##########3 | 28% \u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "libboost-headers-1.8 | 13.2 MB | ## | 5% \u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "openssl-3.3.2 | 2.7 MB | ############8 | 35% \u001b[A\u001b[A\u001b[A\n", + "\n", + "icu-73.2 | 11.4 MB | ##7 | 8% \u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "libboost-headers-1.8 | 13.2 MB | ###2 | 9% \u001b[A\u001b[A\u001b[A\u001b[A\n", + "python-3.12.2 | 12.5 MB | #2 | 3% \u001b[A\n", + "\n", + "\n", + "openssl-3.3.2 | 2.7 MB | ###############9 | 43% \u001b[A\u001b[A\u001b[A\n", + "python-3.12.2 | 12.5 MB | #4 | 4% \u001b[A\n", + "\n", + "icu-73.2 | 11.4 MB | ###8 | 11% \u001b[A\u001b[A\n", + "\n", + "\n", + "libboost-headers-1.8 | 13.2 MB | ###8 | 10% \u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "libboost-1.84.0 | 1.9 MB | ##############################4 | 82% \u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "openssl-3.3.2 | 2.7 MB | ####################6 | 56% \u001b[A\u001b[A\u001b[A\n", + "\n", + "icu-73.2 | 11.4 MB | ####5 | 12% \u001b[A\u001b[A\n", + "python-3.12.2 | 12.5 MB | ##3 | 6% \u001b[A\n", + "\n", + "\n", + "\n", + "libboost-headers-1.8 | 13.2 MB | ####3 | 12% \u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "openssl-3.3.2 | 2.7 MB | ########################6 | 67% \u001b[A\u001b[A\u001b[A\n", + "python-3.12.2 | 12.5 MB | ###1 | 9% \u001b[A\n", + "\n", + "icu-73.2 | 11.4 MB | ##### | 14% \u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "libboost-headers-1.8 | 13.2 MB | ####8 | 13% \u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "liblightgbm-4.5.0 | 1.3 MB | 4 | 1% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "openssl-3.3.2 | 2.7 MB | ###########################1 | 73% \u001b[A\u001b[A\u001b[A\n", + "\n", + "icu-73.2 | 11.4 MB | #####8 | 16% \u001b[A\u001b[A\n", + "python-3.12.2 | 12.5 MB | ####3 | 12% \u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "liblightgbm-4.5.0 | 1.3 MB | ##6 | 7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "libboost-headers-1.8 | 13.2 MB | #####3 | 14% \u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "liblightgbm-4.5.0 | 1.3 MB | ########8 | 24% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "python-3.12.2 | 12.5 MB | ####9 | 13% \u001b[A\n", + "\n", + "libboost-headers-1.8 | 13.2 MB | #####6 | 15% \u001b[A\u001b[A\n", + "\n", + "\n", + "openssl-3.3.2 | 2.7 MB | ###############################9 | 86% \u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "liblightgbm-4.5.0 | 1.3 MB | ###########9 | 32% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "python-3.12.2 | 12.5 MB | #####8 | 16% \u001b[A\n", + "\n", + "libboost-headers-1.8 | 13.2 MB | ###### | 16% \u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "liblightgbm-4.5.0 | 1.3 MB | ###################5 | 53% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "openssl-3.3.2 | 2.7 MB | ###################################7 | 97% \u001b[A\u001b[A\u001b[A\n", + "python-3.12.2 | 12.5 MB | ######4 | 18% \u001b[A\n", + "\n", + "libboost-headers-1.8 | 13.2 MB | ######4 | 18% \u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "liblightgbm-4.5.0 | 1.3 MB | ##########################6 | 72% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + 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100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "liblightgbm-4.5.0 | 1.3 MB | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "libsqlite-3.46.0 | 811 KB | 7 | 2% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "python-3.12.2 | 12.5 MB | ########5 | 23% \u001b[A\n", + "\n", + "libboost-headers-1.8 | 13.2 MB | #######3 | 20% \u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "conda-24.9.1 | 1.1 MB | #############5 | 37% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "libsqlite-3.46.0 | 811 KB | #####1 | 14% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "libboost-headers-1.8 | 13.2 MB | #######6 | 21% \u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "conda-24.9.1 | 1.1 MB | #################2 | 47% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "python-3.12.2 | 12.5 MB | #########1 | 25% \u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "libsqlite-3.46.0 | 811 KB | #############1 | 36% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "icu-73.2 | 11.4 MB | #########6 | 26% \u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "conda-24.9.1 | 1.1 MB | ####################8 | 56% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "libboost-headers-1.8 | 13.2 MB | #######9 | 22% \u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "libsqlite-3.46.0 | 811 KB | #####################1 | 57% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "\n", + "icu-73.2 | 11.4 MB | ##########1 | 27% \u001b[A\u001b[A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "conda-24.9.1 | 1.1 MB | #########################5 | 69% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n", + "libboost-headers-1.8 | 13.2 MB | ########3 | 23% 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of negative: 175\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000414 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 4542\n", + "[LightGBM] [Info] Number of data points in the train set: 455, number of used features: 30\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.615385 -> initscore=0.470004\n", + "[LightGBM] [Info] Start training from score 0.470004\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No 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further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'LGBMClassifier' object has no attribute 'pred_proba'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[16], line 22\u001b[0m\n\u001b[1;32m 20\u001b[0m lgbm_wrapper\u001b[38;5;241m.\u001b[39mfit(X_train,y_train, eval_metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlogloss\u001b[39m\u001b[38;5;124m\"\u001b[39m,eval_set\u001b[38;5;241m=\u001b[39mevals)\n\u001b[1;32m 21\u001b[0m preds\u001b[38;5;241m=\u001b[39mlgbm_wrapper\u001b[38;5;241m.\u001b[39mpredict(X_test)\n\u001b[0;32m---> 22\u001b[0m pred_proba\u001b[38;5;241m=\u001b[39mlgbm_wrapper\u001b[38;5;241m.\u001b[39mpred_proba(X_test)[:,\u001b[38;5;241m1\u001b[39m]\n", + "\u001b[0;31mAttributeError\u001b[0m: 'LGBMClassifier' object has no attribute 'pred_proba'" + ] + } + ], + "source": [ + "from lightgbm import LGBMClassifier\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.datasets import load_breast_cancer\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "dataset=load_breast_cancer()\n", + "ftr=dataset.data\n", + "target=dataset.target\n", + "\n", + "#80%는 학습용으로\n", + "X_train,X_test,y_train,y_test=train_test_split(ftr,target,test_size=0.2,random_state=156)\n", + "\n", + "# 앞서 XGBoodst와 동일하게 n_estimators는 400설정\n", + "lgbm_wrapper=LGBMClassifier(n_estimators=400)\n", + "\n", + "# LightBGM도 XGBoodst와 동일하게 조기 중단 수행 가능\n", + "# early_stopping_rounds,verbose error..\n", + "evals=[(X_test,y_test)]\n", + "lgbm_wrapper.fit(X_train,y_train, eval_metric=\"logloss\",eval_set=evals)\n", + "preds=lgbm_wrapper.predict(X_test)\n", + "pred_proba=lgbm_wrapper.pred_proba(X_test)[:,1]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6458e3a2-49b2-4817-91fa-2956d368bc05", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'get_clf_eval' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m get_clf_eval(y_test,preds,pred_proba)\n", + "\u001b[0;31mNameError\u001b[0m: name 'get_clf_eval' is not defined" + ] + } + ], + "source": [ + "get_clf_eval(y_test,preds,pred_proba)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "eb04378a-d82b-4733-b2d7-58a0cd35c640", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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N14svvtjTh9Jj+nxhGB8fD09PT12HQUREREREvcRTTz2F9evXo6SkBCUlJZgyZQpmzZolFn+pqal4++23sWnTJhQXF8PGxgbTpk3DL7/8ojHOb3/7W1RVVYmvd955RxeH0yN0Xhheu3YNkZGRcHR0hEwmg1KpREhICAoKCnQdWrfatm0bnnnmGQwcOBADBw5EQEAAioqKNPp8/fXXCAkJwZAhQyCRSJCdna2bYImIiIiI+rCQkBAEBwdj5MiRGDlyJJKSkiCXy3H8+HEIgoANGzbgz3/+M55//nmMHj0aO3fuRENDA3bv3q0xjqmpKWxsbMSXhYWFjo7o8TPW5eSXLl2Cj48PFAoFUlNT4eHhAZVKhby8PCxduhTnz5/XZXjdqrCwEPPnz8fEiRPRr18/pKamIjAwEGVlZbCzswMA3L59G2PGjMErr7yCF154oUvzPZ1SALWxWXeETjokMxKQOgEYHZ+HxiaJrsOhLmAu9QvzqT+YS/3CfOqPzuTy0vqZWtubmprwySef4Pbt2/D29sbFixdx7do1BAYG/v/5ZDL4+vrim2++wZIlS8T2Xbt24cMPP8TgwYMxY8YMvPnmmxgwYEDXDq6X0umKYUREBCQSCYqKijB79myMHDkSbm5uiI6OxvHjxwEAlZWVmDVrFuRyOczNzTF37lxUV1e3Oaafnx+ioqI02kJDQ/Hyyy+L2/b29khMTERYWBjkcjmGDRuG/fv34/r16+Jc7u7uKCkpEd+zY8cOKBQK5OXlwcXFBXK5HNOnT0dVVVW7jnXXrl2IiIiAp6cnnJ2dsW3bNjQ3N2usjM6YMQOJiYl4/vnn2zUmERERERFpd/bsWcjlcshkMvz+97/Hp59+CldXV1y7dg0AMHjwYI3+gwcPFvcBwIIFC/DRRx+hsLAQcXFx2Lt3r17/nq6zFcObN28iNzcXSUlJMDNrvbKlUCggCAJCQ0NhZmaGw4cPQ61WIyIiAvPmzUNhYWGX5s/IyEBycjLi4uKQkZGBRYsWwcfHB+Hh4UhLS0NMTAzCwsJQVlYGieTeWYqGhgakp6cjKysLUqkUCxcuxKpVq7Br164Oz9/Q0ACVSgVLS8suHUdjYyMaGxvF7fr6egCATCrAyEjo0tikezKpoPGV+i7mUr8wn/qDudQvzKf+6EwuVSqVxrajoyOKi4tRV1eHffv2YfHixTh48CDUajUAQK1Wa7ynqalJY5z7F5ZGjRoFBwcH/PrXv0ZRURHGjh3bqePqaQ9+Jg+js8LwwoULEAQBzs7ObfY5ePAgzpw5g4sXL0KpVAIAsrKy4ObmhuLiYnh5eXV6/uDgYHGZeM2aNcjMzISXlxfmzJkDAIiJiYG3tzeqq6thY2MD4N4Hu3XrVjg5OQEAli1bhoSEhE7Nv3r1atjZ2SEgIKDTxwAAKSkpWLt2bav2N8Y2w9S0qUtjU++xbnyzrkOgbsJc6hfmU38wl/qF+dQfHcllTk5Om/t8fHyQl5eHP/3pT+Kq3969e+Ho6Cj2OXfuHMzMzNocRxAEGBsb45NPPmn3VYO61tDQ0O6+OisMBeFe9d+yGqdNRUUFlEqlWBQCgKurKxQKBSoqKrpUGHp4eIjftywju7u7t2qrqakRC0NTU1OxKAQAW1tb1NTUdHju1NRUcVm6X79+nYq/RWxsLKKjo8Xt+vp6KJVKJJ6WQm1i1KWxSfdkUgHrxjcjrkSKxmbeK9GXMZf6hfnUH8ylfmE+9UdncnkuPuih+//6179i8ODBeOWVVxAfH4///e9/CA4OBgDcvXsXixcvRnJystjWavxz56BWqzFjxgw888wzHTsgHWm5mrA9dFYYjhgxAhKJBBUVFQgNDdXaRxAErYVjW+0AIJVKxaKzhbYlVBMTE/H7lrG0tTU3N2t9T0ufB+d6lPT0dCQnJ+PgwYMaxWlnyWQyyGSyVu2NzRKoedO13mhslvAmej3BXOoX5lN/MJf6hfnUHx3J5f2/q7/++uuYMWMGlEolfvnlF3z88cc4fPgwcnNz8cQTTyAqKgopKSlwdnbGiBEjkJycDFNTUyxatAgmJib44YcfsGvXLgQHB+PJJ59EeXk5Vq5cibFjx8LX1xdGRn1jAebB+uVhdFYYWlpaIigoCJs3b8by5ctb3WdYW1sLV1dXVFZW4vLly+KqYXl5Oerq6uDi4qJ1XGtra42l3aamJpw7dw7+/v6P72DaKS0tDYmJicjLy8P48eMf61wnYqfCysrqsc5Bj59KpUJOTg7OxQd16D9s6n2YS/3CfOoP5lK/MJ/6o6u5rK6uxqJFi1BVVQULCwt4eHggNzcX06ZNAwD86U9/wp07dxAREYGff/4ZTz/9NL788kvxiaNPPPEECgoK8Ne//hW3bt2CUqnEzJkz8eabb/aZorCjdPrnKrZs2YKJEydiwoQJSEhIgIeHB9RqNfLz85GZmYny8nJ4eHhgwYIF2LBhg/jwGV9f3zYLqylTpiA6OhoHDhyAk5MTMjIyUFtb27MHpkVqairi4uKwe/du2Nvbi088ksvlkMvlAIBbt27hwoUL4nsuXryI0tJSWFpaYujQoTqJm4iIiIior3nvvfceul8ikSA+Ph7x8fFa9yuVShw+fPgxRNZ76fTPVTg4OODUqVPw9/fHypUrMXr0aEybNg0FBQXIzMwU/8j7wIEDMXnyZAQEBMDR0RF79uxpc8zw8HAsXrwYYWFh8PX1hYODQ69YLdyyZQvu3r2L2bNnw9bWVnylp6eLfUpKSjB27FjxKUfR0dEYO3Ys1qxZo6uwiYiIiIjIAEiEjt4kR71afX09LCwscOPGDV5KqgdaLqMIDg7mJTF9HHOpX5hP/cFc6hfmU38wl92jpTaoq6uDubn5Q/vqdMWQiIiIiIiIdI+FYTdpuVdQ2+vIkSO6Do+IiIiIiKhNOn34jD4pLS1tc5+dnV3PBUJERERERNRBLAy7yfDhw3UdAhERERERUafwUlIiIiIiIiIDx8KQiIiIiIjIwLEwJCIiIiIiMnAsDImIiIiIiAwcC0MiIiIiIiIDx8KQiIiIiIjIwLEwJCIiIiIiMnAsDImIiIiIiAwcC0MiIiIiIiIDx8KQiIiIiIjIwLEwJCIiIiIiMnAsDImIiIiIiAwcC0MiIiIiIiIDx8KQiIiIiIi0SklJgZeXFwYMGIBBgwYhNDQU3333nUafW7duYdmyZXjqqafQv39/uLi4IDMzU6OPn58fJBKJxuvFF1/syUOhR+jzhWF8fDw8PT11HQYRERERkd45fPgwli5diuPHjyM/Px9qtRqBgYG4ffu22GfFihXIzc3Fhx9+iIqKCqxYsQKRkZHYv3+/xli//e1vUVVVJb7eeeednj4ceghjXQdw7do1JCUl4cCBA7hy5QoGDRoET09PREVFYerUqboOr9ts27YNH3zwAc6dOwcAGDduHJKTkzFhwgSxT0pKCvbt24fz58+jf//+mDhxIt566y2MGjWqw/M9nVIAtbFZt8VPuiEzEpA6ARgdn4fGJomuw6EuYC71C/OpP5hL/cJ8dt2l9TM1tnNzczW2t2/fjkGDBuHkyZOYPHkyAODYsWNYvHgx/Pz8AAC/+93v8M4776CkpASzZs0S32tqagobG5vHewDUaTpdMbx06RLGjRuHr776CqmpqTh79ixyc3Ph7++PpUuX6jK0bldYWIj58+fj0KFDOHbsGIYOHYrAwEBcuXJF7NOeMzJERERERLpSV1cHALC0tBTbJk2ahM8++wxXrlyBIAg4dOgQvv/+ewQFBWm8d9euXXjyySfh5uaGVatW4ZdffunR2OnhdLpiGBERAYlEgqKiIpiZ/f/VLTc3N4SHhwMAKisrERkZiYKCAkilUkyfPh0bN27E4MGDtY7p5+cHT09PbNiwQWwLDQ2FQqHAjh07AAD29vZ47bXX8P3332Pfvn2wsrLC3/72N0ycOBGvvfYaCgoK4ODggO3bt2P8+PEAgB07diAqKgp79uxBVFQULl++jEmTJmH79u2wtbV95LHu2rVLY3vbtm34xz/+gYKCAoSFhQFo3xmZBzU2NqKxsVHcrq+vBwDIpAKMjIRHxkW9m0wqaHylvou51C/Mp/5gLvUL89l1KpWqzX2CICAqKgo+Pj4YNWqU2Pcvf/kLfv/73+Opp56CsbExpFIptm7diqefflrs8+KLL8Le3h6DBw9GWVkZ4uLiUFpain/+858PjeNh8dCjdeTz01lhePPmTeTm5iIpKUmjKGyhUCggCAJCQ0NhZmaGw4cPQ61WIyIiAvPmzUNhYWGX5s/IyEBycjLi4uKQkZGBRYsWwcfHB+Hh4UhLS0NMTAzCwsJQVlYGieTepQgNDQ1IT09HVlYWpFIpFi5ciFWrVrUq+tqjoaEBKpVK42zLg7SdkXlQSkoK1q5d26r9jbHNMDVt6nBc1DutG9+s6xComzCX+oX51B/MpX5hPjsvJyenzX0tl4empKRo9MvOzsZXX32F119/HYMGDUJZWRkiIiJw+fJljBkzBgBga2uLxsZGVFZWYsCAAVi2bBlWrVqFjRs3wsnJqc058/Pzu+/gDFBDQ0O7++qsMLxw4QIEQYCzs3ObfQ4ePIgzZ87g4sWLUCqVAICsrCy4ubmhuLgYXl5enZ4/ODgYS5YsAQCsWbMGmZmZ8PLywpw5cwAAMTEx8Pb2RnV1tXgttEqlwtatW8V/vMuWLUNCQkKn5l+9ejXs7OwQEBCgdb8gCIiOjsakSZMwevToNseJjY1FdHS0uF1fXw+lUonE01KoTYw6FRv1HjKpgHXjmxFXIkVjM++V6MuYS/3CfOoP5lK/MJ9ddy4+SGt7VFQUzp49i6NHj8LBwUFsv3PnDubMmYNPPvkEwcHBYrtarca//vUvxMbGah1PEATExsZi8ODBGu9roVKpkJ+fj2nTpsHExKSLR2W4Wq4mbA+dFYaCcG+Jv2U1TpuKigoolUqxKAQAV1dXKBQKVFRUdKkw9PDwEL9vuSzV3d29VVtNTY1YGJqammqc0bC1tUVNTU2H505NTcVHH32EwsJC9OvXT2ufZcuW4cyZMzh69OhDx5LJZJDJZK3aG5slUPOma73R2CzhTfR6grnUL8yn/mAu9Qvz2XkPFmGCICAyMhLZ2dkoLCzEiBEjNPbfuXMHKpUKTzzxhMZ7TUxMIAhCm0XduXPnoFKpoFQqH1r4mZiYsDDsgo58djorDEeMGAGJRIKKigqEhoZq7SMIgtbCsa12AJBKpWLR2ULbtbX3f0gtY2lra25u1vqelj4PzvUo6enpSE5OxsGDBzWK0/tFRkbis88+w9dff42nnnqqQ+O3OBE7FVZWVp16L/UeKpUKOTk5OBcfxB+KfRxzqV+YT/3BXOoX5rP7LV26FLt378b+/fsxYMAAXLt2DQBgYWGB/v37w9zcHL6+vvjjH/+I/v37Y9iwYTh8+DA++OADvP322wCAH374Abt27UJwcDCefPJJlJeXY+XKlRg7dix8fHx0eXh0H509ldTS0hJBQUHYvHmz1qdu1tbWwtXVFZWVlbh8+bLYXl5ejrq6Ori4uGgd19raGlVVVeJ2U1OT+CcidC0tLQ3r1q1Dbm6u+FCb+wmCgGXLlmHfvn346quvNJbpiYiIiIh6WmZmJurq6uDn5wdbW1vxtWfPHrHPxx9/DC8vLyxYsACurq5Yv349kpKS8Pvf/x4A8MQTT6CgoABBQUEYNWoUli9fjsDAQBw8eBBGRrz1qbfQ6VNJt2zZgokTJ2LChAlISEiAh4cH1Go18vPzkZmZifLycnh4eGDBggXYsGGD+PAZX19frYUVAEyZMgXR0dE4cOAAnJyckJGRgdra2p49MC1SU1MRFxeH3bt3w97eXjzbIpfLIZfLATz6jAwRERERUU9qz9VxNjY22L59e5v7lUolDh8+3J1h0WOg079j6ODggFOnTsHf3x8rV67E6NGjMW3aNBQUFCAzMxMSiQTZ2dkYOHAgJk+ejICAADg6OmqcoXhQeHg4Fi9ejLCwMPj6+sLBwQH+/v49eFTabdmyBXfv3sXs2bM1zrakp6eLfdpzRoaIiIiIiKi7SYSO3iRHvVp9fT0sLCxw48YN3mOoB1rulQgODua9En0cc6lfmE/9wVzqF+ZTfzCX3aOlNqirq4O5uflD++p0xZCIiIiIiIh0j4VhN2m5V1Db68iRI7oOj4iIiIiIqE06ffiMPiktLW1zn52dXc8FQkRERERE1EEsDLvJ8OHDdR0CERERERFRp/BSUiIiIiIiIgPHwpCIiIiIiMjAsTAkIiIiIiIycCwMiYiIiIiIDBwLQyIiIiIiIgPHwpCIiIiIiMjAsTAkIiIiIiIycCwMiYiIiIiIDBwLQyIiIiIiIgPHwpCIiIiIiMjAsTAkIiIiIiIycCwMiYiIiIiIDBwLQyIiIiIiIgNnEIVhfHw8PD09dR0GEREREVGPSklJgZeXFwYMGIBBgwYhNDQU3333nUYfiUSi9ZWWlib2uXbtGhYtWgQbGxuYmZnhV7/6Ff7xj3/09OHQY9QnCsNr164hMjISjo6OkMlkUCqVCAkJQUFBga5D61ZlZWV44YUXYG9vD4lEgg0bNug6JCIiIiLqww4fPoylS5fi+PHjyM/Ph1qtRmBgIG7fvi32qaqq0ni9//77kEgkeOGFF8Q+ixYtwnfffYfPPvsMZ8+exfPPP4958+bh9OnTujgsegyMdR3Ao1y6dAk+Pj5QKBRITU2Fh4cHVCoV8vLysHTpUpw/f17XIXabhoYGODo6Ys6cOVixYkWXxno6pQBqY7Nuiox0RWYkIHUCMDo+D41NEl2HQ13AXOoX5lN/MJf6hfkELq2fqbGdm5ursb19+3YMGjQIJ0+exOTJkwEANjY2Gn32798Pf39/ODo6im3Hjh1DZmYmJkyYAAB44403kJGRgVOnTmHs2LGP41Coh/X6FcOIiAhIJBIUFRVh9uzZGDlyJNzc3BAdHY3jx48DACorKzFr1izI5XKYm5tj7ty5qK6ubnNMPz8/REVFabSFhobi5ZdfFrft7e2RmJiIsLAwyOVyDBs2DPv378f169fFudzd3VFSUiK+Z8eOHVAoFMjLy4OLiwvkcjmmT5+Oqqqqdh2rl5cX0tLS8OKLL0Imk7X/QyIiIiIiaoe6ujoAgKWlpdb91dXVOHDgAF599VWN9kmTJmHPnj24efMmmpub8fHHH6OxsRF+fn6PO2TqIb16xfDmzZvIzc1FUlISzMxar34pFAoIgoDQ0FCYmZnh8OHDUKvViIiIwLx581BYWNil+TMyMpCcnIy4uDhkZGRg0aJF8PHxQXh4ONLS0hATE4OwsDCUlZVBIrl3VqqhoQHp6enIysqCVCrFwoULsWrVKuzatatLsbSlsbERjY2N4nZ9fT0AQCYVYGQkPJY5qefIpILGV+q7mEv9wnzqD+ZSvzCfgEqlanOfIAiIioqCj48PRo0apbXv+++/jwEDBiAkJERj/4cffogFCxbAysoKxsbGMDU1xSeffIKhQ4c+dM6uHsfjGNuQdOTz69WF4YULFyAIApydndvsc/DgQZw5cwYXL16EUqkEAGRlZcHNzQ3FxcXw8vLq9PzBwcFYsmQJAGDNmjXIzMyEl5cX5syZAwCIiYmBt7c3qqurxSV4lUqFrVu3wsnJCQCwbNkyJCQkdDqGR0lJScHatWtbtb8xthmmpk2PbV7qWevGN+s6BOomzKV+YT71B3OpXww5nzk5OW3ue+edd1BSUoKUlJQ2+23evBne3t746quvNNrfffddXLx4EWvXroW5uTlOnDiB2bNnIzk5Gfb29t15CBry8/Mf29iGoKGhod19e3VhKAj3zva0rMZpU1FRAaVSKRaFAODq6gqFQoGKioouFYYeHh7i94MHDwYAuLu7t2qrqakRC0NTU1OxKAQAW1tb1NTUdDqGR4mNjUV0dLS4XV9fD6VSicTTUqhNjB7bvNQzZFIB68Y3I65EisZmw7xXQl8wl/qF+dQfzKV+YT6Bc/FBWtujoqJw9uxZHD16FA4ODlr7HD16FFeuXEF2djbGjBkjtv/www/IycnB6dOn4ebmBgBYunQppk+fjrKyMkRERHT7cahUKuTn52PatGkwMTHp9vENRcvVhO3RqwvDESNGQCKRoKKiAqGhoVr7CIKgtXBsqx0ApFKpWHS20LbMev8/wpaxtLU1NzdrfU9Lnwfn6k4ymUzr/YiNzRKoDfSma33U2Cwx2Jvo9Q1zqV+YT/3BXOoXQ87ng7+LCoKAyMhIZGdno7CwECNGjGjzvTt37sS4ceMwfvx4jfaW35NlMpnG+MbGxlrn7E4mJiYsDLugI59dry4MLS0tERQUhM2bN2P58uWt7jOsra2Fq6srKisrcfnyZXHVsLy8HHV1dXBxcdE6rrW1tcYDYZqamnDu3Dn4+/s/voPpYSdip8LKykrXYVAXqVQq5OTk4Fx8EH8o9nHMpX5hPvUHc6lfmM/Wli5dit27d2P//v0YMGAArl27BgCwsLBA//79xX719fX45JNP8Je//KXVGM7Ozhg+fDiWLFmC9PR0WFlZITs7G/n5+fjiiy967Fjo8er1TyXdsmULmpqaMGHCBOzduxf//ve/UVFRgb/97W/w9vZGQEAAPDw8sGDBApw6dQpFRUUICwuDr69vq7MdLaZMmYIDBw7gwIEDOH/+PCIiIlBbW9uzB6bF3bt3UVpaitLSUty9exdXrlxBaWkpLly4oOvQiIiIiKgPyszMRF1dHfz8/GBrayu+9uzZo9Hv448/hiAImD9/fqsxTExMkJOTA2tra4SEhMDDwwMffPABdu7cieDg4J46FHrMevWKIQA4ODjg1KlTSEpKwsqVK1FVVQVra2uMGzcOmZmZkEgkyM7ORmRkJCZPngypVIrp06dj48aNbY4ZHh6Ob7/9FmFhYTA2NsaKFSt6xWrh1atXNf4OTHp6OtLT0+Hr69vlJ6wSERERkeFp7y1Nv/vd7/C73/2uzf0jRozA3r17uyss6oUkwuO8AY56XH19PSwsLHDjxg1eSqoHWi6JCQ4O5iUxfRxzqV+YT/3BXOoX5lN/MJfdo6U2qKurg7m5+UP79vpLSYmIiIiIiOjxYmHYg+RyeZuvI0eO6Do8IiIiIiIyUL3+HkN9Ulpa2uY+Ozu7nguEiIiIiIjoPiwMe9Dw4cN1HQIREREREVErvJSUiIiIiIjIwLEwJCIiIiIiMnAsDImIiIiIiAwcC0MiIiIiIiIDx8KQiIiIiIjIwLEwJCIiIiIiMnAsDImIiIiIiAwcC0MiIiIiIiIDx8KQiIiIiIjIwLEwJCIiIiIiMnAsDImIiIiIiAwcC0MiIiIiIiIDx8KQiIiIiIjIwLEwJCIiIiIiMnB9vjCMj4+Hp6enrsMgIiIi0iolJQVeXl4YMGAABg0ahNDQUHz33XcafQRBQHx8PIYMGYL+/fvDz88PZWVlGn1++OEHPPfcc7C2toa5uTnmzp2L6urqnjwUItJjOi8Mr127hsjISDg6OkImk0GpVCIkJAQFBQW6Dq1bbdu2Dc888wwGDhyIgQMHIiAgAEVFRRp97O3tIZFIWr2WLl2qo6iJiIioqw4fPoylS5fi+PHjyM/Ph1qtRmBgIG7fvi32SU1Nxdtvv41NmzahuLgYNjY2mDZtGn755RcAwO3btxEYGAiJRIKvvvoK//rXv3D37l2EhISgublZV4dGRHrEWJeTX7p0CT4+PlAoFEhNTYWHhwdUKhXy8vKwdOlSnD9/XpfhdavCwkLMnz8fEydORL9+/ZCamorAwECUlZXBzs4OAFBcXIympibxPefOncO0adMwZ86cDs/3dEoB1MZm3RY/6YbMSEDqBGB0fB4amyS6Doe6gLnUL8yn/nhcuby0fqb4fW5ursa+7du3Y9CgQTh58iQmT54MQRCwYcMG/PnPf8bzzz8PANi5cycGDx6M3bt3Y8mSJfjXv/6FS5cu4fTp0zA3NxfHsbS0xFdffYWAgIBui52IDJNOVwwjIiIgkUhQVFSE2bNnY+TIkXBzc0N0dDSOHz8OAKisrMSsWbMgl8vbddmEn58foqKiNNpCQ0Px8ssvi9v29vZITExEWFgY5HI5hg0bhv379+P69eviXO7u7igpKRHfs2PHDigUCuTl5cHFxQVyuRzTp09HVVVVu451165diIiIgKenJ5ydnbFt2zY0NzdrrIxaW1vDxsZGfH3xxRdwcnKCr69vu+YgIiKi3q+urg4AYGlpCQC4ePEirl27hsDAQLGPTCaDr68vvvnmGwBAY2MjJBIJZDKZ2Kdfv36QSqU4evRoD0ZPRPpKZyuGN2/eRG5uLpKSkmBm1nplS6FQQBAEhIaGwszMDIcPH4ZarUZERATmzZuHwsLCLs2fkZGB5ORkxMXFISMjA4sWLYKPjw/Cw8ORlpaGmJgYhIWFoaysDBLJvTOIDQ0NSE9PR1ZWFqRSKRYuXIhVq1Zh165dHZ6/oaEBKpVK/J/Cg+7evYsPP/wQ0dHR4vzaNDY2orGxUdyur68HAMikAoyMhA7HRb2LTCpofKW+i7nUL8yn/nhcuVSpVFrbBUFAVFQUfHx8MGrUKKhUKvz4448A7hWK97/P2toalZWVUKlUGDduHMzMzPDHP/4R69atgyAIeP3119Hc3IwrV660OZ+hafkc+Hn0fcxl9+jI56ezwvDChQsQBAHOzs5t9jl48CDOnDmDixcvQqlUAgCysrLg5uaG4uJieHl5dXr+4OBgLFmyBACwZs0aZGZmwsvLS7xsMyYmBt7e3qiuroaNjQ2Aex/s1q1b4eTkBABYtmwZEhISOjX/6tWrYWdn1+alH9nZ2aitrdVY6dQmJSUFa9eubdX+xthmmJo2aXkH9UXrxvP+EX3BXOoX5lN/dHcuc3JytLa/8847KCkpQUpKitin5daZr776SuOEcWVlJW7cuCH2W7FiBbZu3YpNmzZBIpHgmWeegaOjI3788cc25zNU+fn5ug6Buglz2TUNDQ3t7quzwlAQ7p2Ze9hqWEVFBZRKpVgUAoCrqysUCgUqKiq6VBh6eHiI3w8ePBgA4O7u3qqtpqZGLAxNTU3FohAAbG1tUVNT0+G5U1NT8dFHH6GwsBD9+vXT2ue9997DjBkzMGTIkIeOFRsbi+joaHG7vr4eSqUSiaelUJsYdTg26l1kUgHrxjcjrkSKxmbex9SXMZf6hfnUH48rl+fig1q1RUVF4ezZszh69CgcHBzEdmdnZ6xevRpubm4YO3as2P73v/8dbm5uCA4OBnDvpPaf//xn3LhxA8bGxlAoFFAqlfD19RX7GDqVSoX8/HxMmzYNJiYmug6HuoC57B4tVxO2h84KwxEjRkAikaCiogKhoaFa+wiCoLVwbKsdAKRSqVh0ttC2hHr/P7CWsbS13f+krwf/UUokklZzPUp6ejqSk5Nx8OBBjeL0fv/9739x8OBB7Nu375HjyWQyjfsNWjQ2S6DmAxH0RmOzhA+40BPMpX5hPvVHd+fy/t8ZBEFAZGQksrOzUVhYiBEjRmj0HTlyJGxsbFBYWIgJEyYAuHdLyZEjR/DWW2+1+v3D1tYWwL0VxpqaGjz33HP8xfkBJiYm/Ez0BHPZNR357HRWGFpaWiIoKAibN2/G8uXLW91nWFtbC1dXV1RWVuLy5cviqmF5eTnq6urg4uKidVxra2uNB8I0NTXh3Llz8Pf3f3wH005paWlITExEXl4exo8f32a/lqeVzZw5s80+j3IidiqsrKw6/X7qHVQqFXJycnAuPog/FPs45lK/MJ/6oydyuXTpUuzevRv79+/HgAEDcO3aNQCAhYUF+vfvD4lEgqioKCQnJ2PEiBEYMWIEkpOTYWpqipdeekkcZ/v27XBxcYG1tTWOHTuGP/zhD1ixYgVGjRr1WOImIsOi0z9XsWXLFkycOBETJkxAQkICPDw8oFarkZ+fj8zMTJSXl8PDwwMLFizAhg0bxIfP+Pr6tllYTZkyBdHR0Thw4ACcnJyQkZGB2tranj0wLVJTUxEXF4fdu3fD3t5e/J+CXC6HXC4X+zU3N2P79u1YvHgxjI11mh4iIiLqBpmZmQDuPTn9ftu3bxefJfCnP/0Jd+7cQUREBH7++Wc8/fTT+PLLLzFgwACx/3fffYfY2FjcvHkT9vb2+POf/4wVK1b01GEQkZ7TaeXh4OCAU6dOISkpCStXrkRVVRWsra0xbtw4ZGZmQiKRIDs7G5GRkZg8eTKkUimmT5+OjRs3tjlmeHg4vv32W4SFhcHY2BgrVqzoFauFW7Zswd27dzF79myN9jfffBPx8fHi9sGDB1FZWYnw8PAejpCIiIgeh/bcdiKRSBAfH6/xO8GD1q9fj/Xr13djZERE/59E6OhNctSr1dfXw8LCAjdu3OClpHqg5RKn4OBgXq7WxzGX+oX51B/MpX5hPvUHc9k9WmqDuro6mJubP7SvTv/APREREREREekeC8Nu0nKvoLbXkSNHdB0eERERERFRm/h0k25SWlra5j47O7ueC4SIiIiIiKiDWBh2k+HDh+s6BCIiIiIiok7hpaREREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERER0UOkpKTAy8sLAwYMwKBBgxAaGorvvvtOo48gCIiPj8eQIUPQv39/+Pn5oaysTNx/8+ZNREZGYtSoUTA1NcXQoUOxfPly1NXV9fThEBFpZRCFYXx8PDw9PXUdBhEREfVBhw8fxtKlS3H8+HHk5+dDrVYjMDAQt2/fFvukpqbi7bffxqZNm1BcXAwbGxtMmzYNv/zyCwDg6tWruHr1KtLT03H27Fns2LEDubm5ePXVV3V1WEREGvpEYXjt2jVERkbC0dERMpkMSqUSISEhKCgo0HVo3crPzw8SiaTVa+bMmboOjYiIyGDl5ubi5ZdfhpubG8aMGYPt27ejsrISJ0+eBHBvtXDDhg3485//jOeffx6jR4/Gzp070dDQgN27dwMARo8ejb179yIkJAROTk6YMmUKkpKS8Pnnn0OtVuvy8IiIAADGug7gUS5dugQfHx8oFAqkpqbCw8MDKpUKeXl5WLp0Kc6fP6/rELvNvn37cPfuXXH7p59+wpgxYzBnzpwOj/V0SgHUxmbdGR7pgMxIQOoEYHR8HhqbJLoOh7qAudQvzKf+aCuXl9a3fVK25fJPS0tLAMDFixdx7do1BAYG/v9xZTL4+vrim2++wZIlS9ocx9zcHMbGvf7XMSIyAL1+xTAiIgISiQRFRUWYPXs2Ro4cCTc3N0RHR+P48eMAgMrKSsyaNQtyuRzm5uaYO3cuqqur2xzTz88PUVFRGm2hoaF4+eWXxW17e3skJiYiLCwMcrkcw4YNw/79+3H9+nVxLnd3d5SUlIjv2bFjBxQKBfLy8uDi4gK5XI7p06ejqqqqXcdqaWkJGxsb8ZWfnw9TU9NOFYZERETU/QRBQHR0NCZNmoTRo0cDuHdlEwAMHjxYo+/gwYPFfQ/66aefsG7dujaLRiKintarT1HdvHkTubm5SEpKgplZ69UvhUIBQRAQGhoKMzMzHD58GGq1GhEREZg3bx4KCwu7NH9GRgaSk5MRFxeHjIwMLFq0CD4+PggPD0daWhpiYmIQFhaGsrIySCT3zjI2NDQgPT0dWVlZkEqlWLhwIVatWoVdu3Z1eP733nsPL774otZjb9HY2IjGxkZxu76+HgAgkwowMhI6PCf1LjKpoPGV+i7mUr8wn/qjrVyqVCqt/ZcvX44zZ87g0KFDYp+WS0HVarXG+5qamrSOVV9fj+DgYLi4uOD1119vcy7quJbPkp9p38dcdo+OfH69ujC8cOECBEGAs7Nzm30OHjyIM2fO4OLFi1AqlQCArKwsuLm5obi4GF5eXp2ePzg4WDyTt2bNGmRmZsLLy0tcwYuJiYG3tzeqq6thY2MD4N6Hv3XrVjg5OQEAli1bhoSEhA7PXVRUhHPnzuG99957aL+UlBSsXbu2VfsbY5thatrU4Xmpd1o3vlnXIVA3YS71C/OpPx7MZU5OTqs+7777Lk6cOIHk5GScOXMGZ86cAfD/Vwz37t0LR0dHsf+5c+dgZmamMdadO3cQHx8PmUyGV199Ffn5+Y/jcAweP1f9wVx2TUNDQ7v79urCUBDunb1rWY3TpqKiAkqlUiwKAcDV1RUKhQIVFRVdKgw9PDzE71suD3F3d2/VVlNTIxaGpqamYlEIALa2tqipqenw3O+99x5Gjx6NCRMmPLRfbGwsoqOjxe36+noolUoknpZCbWLU4Xmpd5FJBawb34y4Eikam3kfU1/GXOoX5lN/tJXLc/FB4veCICAqKgqlpaX4+uuvMWLECI0xWv5Uxf/+9z8EBwcDAO7evYvFixcjOTlZbKuvr8fMmTMxePBgfPbZZzA1Ne2BIzQsKpUK+fn5mDZtGkxMTHQdDnUBc9k9Wq4mbI9eXRiOGDECEokEFRUVCA0N1dpHEASthWNb7QAglUrForOFtmXW+/8Rtoylra25uVnre1r6PDjXozQ0NODjjz9u10qjTCaDTCZr1d7YLIGaD0TQG43NEj7gQk8wl/qF+dQfD+by/v+fR0REYPfu3di/fz8sLS3x008/AQAsLCzQv39/AEBUVBRSUlLg7OyMESNGIDk5Gaampli0aBFMTEzwyy+/YObMmWhoaMCuXbtw584d3LlzBwBgbW0NIyOezO1OJiYmLCb0BHPZNR357Hp1YWhpaYmgoCBs3rwZy5cvb3WvXW1tLVxdXVFZWYnLly+Lq4bl5eWoq6uDi4uL1nGtra01HgjT1NSEc+fOwd/f//EdTAf83//9HxobG7Fw4cJOj3EidiqsrKy6MSrSBZVKhZycHJyLD+IPxT6OudQvzKf+aE8uMzMzAdx7eN39tm/fLj647k9/+hPu3LmDiIgI/Pzzz3j66afx5ZdfYsCAAQCAkydP4sSJEwCA4cOHa4xz8eJF2Nvbd99BERF1Qq8uDAFgy5YtmDhxIiZMmICEhAR4eHhArVYjPz8fmZmZKC8vh4eHBxYsWIANGzaID5/x9fXF+PHjtY45ZcoUREdH48CBA3ByckJGRgZqa2t79sAe4r333kNoaCgLOyIiol6gPVf+SCQSxMfHIz4+Xut+Pz+/Dl9BRETUk3r9n6twcHDAqVOn4O/vj5UrV2L06NGYNm0aCgoKkJmZCYlEguzsbAwcOBCTJ09GQEAAHB0dsWfPnjbHDA8Px+LFixEWFgZfX184ODj0mtXC77//HkePHsWrr76q61CIiIiIiMhASASevtIr9fX1sLCwwI0bN7jiqAdaLnEKDg7m5Wp9HHOpX5hP/cFc6hfmU38wl92jpTaoq6uDubn5Q/v2+hVDIiIiIiIierxYGPYguVze5uvIkSO6Do+IiIiIiAxUr3/4jD4pLS1tc5+dnV3PBUJERERERHQfFoY96MHHUxMREREREfUGvJSUiIiIiIjIwLEwJCIiIiIiMnAsDImIiIiIiAwcC0MiIiIiIiIDx8KQiIiIiIjIwLEwJCIiIiIiMnAsDImIiIiIiAwcC0MiIiIiIiIDx8KQiIiIiIjIwLEwJCIiIiIiMnAsDImIiIiIiAwcC0MiIiIiIiIDx8KQiIiIiIjIwLEwJCIiIp35+uuvERISgiFDhkAikSA7O1tj/61bt7Bs2TI89dRT6N+/P1xcXJCZmal1LEEQMGPGDK3jEBHRw/X5wjA+Ph6enp66DoOIiIg64fbt2xgzZgw2bdqkdf+KFSuQm5uLDz/8EBUVFVixYgUiIyOxf//+Vn03bNgAiUTyuEMmItJLOi8Mr127hsjISDg6OkImk0GpVCIkJAQFBQW6Dq1bbdu2Dc888wwGDhyIgQMHIiAgAEVFRRp91Go13njjDTg4OKB///5wdHREQkICmpubdRQ1ERHR4zVjxgwkJibi+eef17r/2LFjWLx4Mfz8/GBvb4/f/e53GDNmDEpKSjT6ffvtt3j77bfx/vvv90TYRER6x1iXk1+6dAk+Pj5QKBRITU2Fh4cHVCoV8vLysHTpUpw/f16X4XWrwsJCzJ8/HxMnTkS/fv2QmpqKwMBAlJWVwc7ODgDw1ltvYevWrdi5cyfc3NxQUlKCV155BRYWFvjDH/7QofmeTimA2tjscRwK9SCZkYDUCcDo+Dw0NvEseF/GXOoX5rPzLq2f2aH+kyZNwmeffYbw8HAMGTIEhYWF+P777/HXv/5V7NPQ0ID58+dj06ZNsLGx6e6QiYgMgk5XDCMiIiCRSFBUVITZs2dj5MiRcHNzQ3R0NI4fPw4AqKysxKxZsyCXy2Fubo65c+eiurq6zTH9/PwQFRWl0RYaGoqXX35Z3La3t0diYiLCwsIgl8sxbNgw7N+/H9evXxfncnd31zgbuWPHDigUCuTl5cHFxQVyuRzTp09HVVVVu451165diIiIgKenJ5ydnbFt2zY0NzdrrIweO3YMs2bNwsyZM2Fvb4/Zs2cjMDCw1VlRIiIiQ/G3v/0Nrq6ueOqpp/DEE09g+vTp2LJlCyZNmiT2WbFiBSZOnIhZs2bpMFIior5NZyuGN2/eRG5uLpKSkmBm1nplS6FQQBAEhIaGwszMDIcPH4ZarUZERATmzZuHwsLCLs2fkZGB5ORkxMXFISMjA4sWLYKPjw/Cw8ORlpaGmJgYhIWFoaysTLxfoaGhAenp6cjKyoJUKsXChQuxatUq7Nq1q8PzNzQ0QKVSwdLSUmybNGkStm7diu+//x4jR47Et99+i6NHj2LDhg1tjtPY2IjGxkZxu76+HgAgkwowMhI6HBf1LjKpoPGV+i7mUr8wn52nUqkeul+tVmv0ycjIwLFjx7Bv3z4MHToUR48eRUREBKytrTF16lR8/vnn+Oqrr1BUVKTxvgfHeVQ87elLvR/zqT+Yy+7Rkc9PZ4XhhQsXIAgCnJ2d2+xz8OBBnDlzBhcvXoRSqQQAZGVlwc3NDcXFxfDy8ur0/MHBwViyZAkAYM2aNcjMzISXlxfmzJkDAIiJiYG3tzeqq6vFy1JUKhW2bt0KJycnAMCyZcuQkJDQqflXr14NOzs7BAQEiG0xMTGoq6uDs7MzjIyM0NTUhKSkJMyfP7/NcVJSUrB27dpW7W+MbYapaVOnYqPeZ9143meqL5hL/cJ8dlxOTs5D9588eRImJiYA7p38fOONN7B69WpIpVL8+OOPsLe3x69//Wu8/vrrePPNN7F9+3b88MMPePLJJzXGmTdvHlxcXJCUlNSuuPLz8zt3QNQrMZ/6g7nsmoaGhnb31VlhKAj3zrI+7OlhFRUVUCqVYlEIAK6urlAoFKioqOhSYejh4SF+P3jwYACAu7t7q7aamhqxMDQ1NRWLQgCwtbVFTU1Nh+dOTU3FRx99hMLCQvTr109s37NnDz788EPs3r0bbm5uKC0tRVRUFIYMGYLFixdrHSs2NhbR0dHidn19PZRKJRJPS6E2MepwbNS7yKQC1o1vRlyJFI3NvI+pL2Mu9Qvz2Xnn4oMeun/cuHEIDg4GcO//aWq1GhMmTMD06dPFPl988QWAeyd5f/WrX+HGjRsaY/zqV79Ceno6Zs6cCQcHh4fOp1KpkJ+fj2nTpokFKfVdzKf+YC67R8vVhO2hs8JwxIgRkEgkqKioQGhoqNY+giBoLRzbagcAqVQqFp0ttC2h3v8PrGUsbW33PxH0wX+UEomk1VyPkp6ejuTkZBw8eFCjOAWAP/7xj1i9ejVefPFFAPcK1f/+979ISUlpszCUyWSQyWSt2hubJVDzgQh6o7FZwgdc6AnmUr8wnx334P9Lb926hQsXLojbly9fRllZGSwtLTF06FD4+voiNjYWAwYMwLBhw3D48GF8+OGHePvtt2FiYtLqBHILBwcHjBw5skNx8ZdP/cF86g/msms68tnprDC0tLREUFAQNm/ejOXLl7e6z7C2thaurq6orKzE5cuXxR/65eXlqKurg4uLi9Zxra2tNR4I09TUhHPnzsHf3//xHUw7paWlITExEXl5eRg/fnyr/Q0NDZBKNZ8HZGRk1Kk/V3EidiqsrKw6HSv1DiqVCjk5OTgXH8Qfin0cc6lfmM/uU1JSovH/6JarYBYvXowdO3bg448/RmxsLBYsWICbN29i2LBhSEpKwu9//3tdhUxEpJd0+ucqtmzZgokTJ2LChAlISEiAh4cH1Go18vPzkZmZifLycnh4eGDBggXYsGGD+PAZX19frYUVAEyZMgXR0dE4cOAAnJyckJGRgdra2p49MC1SU1MRFxeH3bt3w97eHteuXQMAyOVyyOVyAEBISAiSkpIwdOhQuLm54fTp03j77bcRHh6uy9CJiIgeGz8/v4defWNjY4Pt27d3aMyOXs1DREQ6/nMVDg4OOHXqFPz9/bFy5UqMHj0a06ZNQ0FBATIzMyGRSJCdnY2BAwdi8uTJCAgIgKOjI/bs2dPmmOHh4Vi8eDHCwsLg6+sLBweHXrFauGXLFty9exezZ8+Gra2t+EpPTxf7bNy4EbNnz0ZERARcXFywatUqLFmyBOvWrdNh5EREREREpO8kAk+r6ZX6+npYWFjgxo0bvJRUD7RcrhYcHMzL1fo45lK/MJ/6g7nUL8yn/mAuu0dLbVBXVwdzc/OH9tXpiiERERERERHpHgvDbtJyr6C215EjR3QdHhERERERUZt0+vAZfVJaWtrmPjs7u54LhIiIiIiIqINYGHaT4cOH6zoEIiIiIiKiTuGlpERERERERAaOhSEREREREZGBY2FIRERERERk4FgYEhERERERGTgWhkRERERERAaOhSEREREREZGBY2FIRERERERk4FgYEhERERERGTgWhkRERERERAaOhSEREREREZGBY2FIRERERERk4FgYEhERERERGTgWhkRERERERAaOhSERERF12Ndff42QkBAMGTIEEokE2dnZGvslEonWV1pamka/Y8eOYcqUKTAzM4NCoYCfnx/u3LnTg0dCRESAHhSG8fHx8PT01HUYREREBuX27dsYM2YMNm3apHV/VVWVxuv999+HRCLBCy+8IPY5duwYpk+fjsDAQBQVFaG4uBjLli2DVNrnfz0hIupzjHUdwLVr15CUlIQDBw7gypUrGDRoEDw9PREVFYWpU6fqOrxuU1ZWhjVr1uDkyZP473//i4yMDERFRWn0iY+Px9q1azXaBg8ejGvXrnV4vqdTCqA2NutKyNQLyIwEpE4ARsfnobFJoutwqAuYS/1iiPm8tH6mxvaMGTMwY8aMNvvb2NhobO/fvx/+/v5wdHQU21asWIHly5dj9erVYtuIESO6KWIiIuoInZ6Su3TpEsaNG4evvvoKqampOHv2LHJzc+Hv74+lS5fqMrRu19DQAEdHR6xfv77V/yzv5+bmpnGG9ezZsz0YJRERUferrq7GgQMH8Oqrr4ptNTU1OHHiBAYNGoSJEydi8ODB8PX1xdGjR3UYKRGR4dLpimFERAQkEgmKiopgZvb/V7fc3NwQHh4OAKisrERkZCQKCgoglUoxffp0bNy4EYMHD9Y6pp+fHzw9PbFhwwaxLTQ0FAqFAjt27AAA2Nvb47XXXsP333+Pffv2wcrKCn/7298wceJEvPbaaygoKICDgwO2b9+O8ePHAwB27NiBqKgo7NmzB1FRUbh8+TImTZqE7du3w9bW9pHH6uXlBS8vLwDQODP6IGNj44cWjg9qbGxEY2OjuF1fXw8AkEkFGBkJ7R6HeieZVND4Sn0Xc6lfDDGfKpXqofvVanWbfd5//30MGDAAISEhYp/vv/8ewL2rZd566y14eHhg165dmDp1Kk6fPt1jK4ct8Tzq+KhvYD71B3PZPTry+emsMLx58yZyc3ORlJSkURS2UCgUEAQBoaGhMDMzw+HDh6FWqxEREYF58+ahsLCwS/NnZGQgOTkZcXFxyMjIwKJFi+Dj44Pw8HCkpaUhJiYGYWFhKCsrg0Ry7zKhhoYGpKenIysrC1KpFAsXLsSqVauwa9euLsVyv3//+98YMmQIZDIZnn76aSQnJ2tcdvOglJSUVpefAsAbY5thatrUbXGRbq0b36zrEKibMJf6xZDymZOT89D9J0+ehImJidZ9mzdvhre3N7766iux7fz58wAAf39/WFtbo6qqClOmTMH+/fuxZs0aLFq0qPuCb4f8/PwenY8eL+ZTfzCXXdPQ0NDuvjorDC9cuABBEODs7Nxmn4MHD+LMmTO4ePEilEolACArKwtubm4oLi4WV+A6Izg4GEuWLAEArFmzBpmZmfDy8sKcOXMAADExMfD29kZ1dbW4gqdSqbB161Y4OTkBAJYtW4aEhIROx/Cgp59+Gh988AFGjhyJ6upqJCYmYuLEiSgrK4OVlZXW98TGxiI6Olrcrq+vh1KpROJpKdQmRt0WG+mGTCpg3fhmxJVI0dhsGPcx6SvmUr8YYj7PxQc9dP+4ceMQHBzcqv3o0aO4cuUKsrOzMWbMGLHdxcUFq1evxm9+8xuN93344YcwNjbWOtbjoFKpkJ+fj2nTprVZ2FLfwXzqD+aye7RcTdgeOisMBeHe5Tctq3HaVFRUQKlUikUhALi6ukKhUKCioqJLhaGHh4f4fctlqe7u7q3aampqxMLQ1NRULAoBwNbWFjU1NZ2O4UH338Tv7u4Ob29vODk5YefOnRrF3/1kMhlkMlmr9sZmCdQG8kAEQ9DYLDGYB1zoO+ZSvxhSPh/1i5mxsbHWPjt37sS4cePEWzNajBgxAkOGDMEPP/yg8b4LFy5gxowZPf6LoImJCX/51CPMp/5gLrumI5+dzgrDESNGQCKRoKKiAqGhoVr7CIKgtXBsqx0ApFKpWHS20HZt7f0fUstY2tqam5u1vqelz4NzdSczMzO4u7vj3//+d4ffeyJ2apurjNR3qFQq5OTk4Fx8EH8o9nHMpX5hPoFbt27hwoUL4vbFixdRWloKS0tLDB06FMC9M9WffPIJ/vKXv7R6v0QiwR//+Ee8+eabGDNmDDw9PbFz506cP38e//jHP3rsOIiI6B6dPZXU0tISQUFB2Lx5M27fvt1qf21tLVxdXVFZWYnLly+L7eXl5airq4OLi4vWcVvuU2jR1NSEc+fOdf8B9IDGxkZUVFS06+E2REREPamkpARjx47F2LFjAQDR0dEYO3Ys1qxZI/b5+OOPIQgC5s+fr3WMqKgoxMbGYsWKFRgzZgwKCgqQn5+vcXUOERH1DJ3+uYotW7agqakJEyZMwN69e/Hvf/8bFRUV+Nvf/gZvb28EBATAw8MDCxYswKlTp1BUVISwsDD4+vq2uiSlxZQpU3DgwAEcOHAA58+fR0REBGpra3v2wLS4e/cuSktLUVpairt37+LKlSsoLS3VONu6atUqHD58GBcvXsSJEycwe/Zs1NfXY/HixTqMnIiIqDU/Pz8IgtDq1fIEcAD43e9+h4aGBlhYWLQ5zurVq3H58mXcvn0b33zzDSZNmtQD0RMR0YN0Whg6ODjg1KlT8Pf3x8qVKzF69GhMmzYNBQUFyMzMhEQiQXZ2NgYOHIjJkycjICAAjo6O2LNnT5tjhoeHY/HixWIB6eDgAH9//x48Ku2uXr0qnlmtqqpCeno6xo4di9dee03s8+OPP2L+/PkYNWoUnn/+eTzxxBM4fvw4hg0bpsPIiYiIiIhI30mEx3mTHPW4+vp6WFhY4MaNG7zHUA+03McUHBxssPcx6QvmUr8wn/qDudQvzKf+YC67R0ttUFdXB3Nz84f21emKIREREREREekeC8NuIpfL23wdOXJE1+ERERERERG1SWd/rkLflJaWtrnPzs6u5wIhIiIiIiLqIBaG3WT48OG6DoGIiIiIiKhTeCkpERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgevzhWF8fDw8PT11HQYREVGXfP311wgJCcGQIUMgkUiQnZ2tsf/ll1+GRCLReP36178W91+6dKnV/pbXJ5980sNHQ0REfY3OC8Nr164hMjISjo6OkMlkUCqVCAkJQUFBga5D61ZlZWV44YUXYG9vD4lEgg0bNrTqk5mZCQ8PD5ibm8Pc3Bze3t745z//2fPBEhFRj7t9+zbGjBmDTZs2tdln+vTpqKqqEl85OTniPqVSqbGvqqoKa9euhZmZGWbMmNETh0BERH2YsS4nv3TpEnx8fKBQKJCamgoPDw+oVCrk5eVh6dKlOH/+vC7D61YNDQ1wdHTEnDlzsGLFCq19nnrqKaxfvx7Dhw8HAOzcuROzZs3C6dOn4ebm1qH5nk4pgNrYrMtxk27JjASkTgBGx+ehsUmi63CoC5hL/dId+by0fqbG9owZMx5ZwMlkMtjY2GjdZ2Rk1Grfp59+innz5kEul3cqRiIiMhw6XTGMiIiARCJBUVERZs+ejZEjR8LNzQ3R0dE4fvw4AKCyshKzZs2CXC6Hubk55s6di+rq6jbH9PPzQ1RUlEZbaGgoXn75ZXHb3t4eiYmJCAsLg1wux7Bhw7B//35cv35dnMvd3R0lJSXie3bs2AGFQoG8vDy4uLhALpeLZ27bw8vLC2lpaXjxxRchk8m09gkJCUFwcDBGjhyJkSNHIikpCXK5XPwsiIjIsBUWFmLQoEEYOXIkfvvb36KmpqbNvidPnkRpaSleffXVHoyQiIj6Kp2tGN68eRO5ublISkqCmVnrlS2FQgFBEBAaGgozMzMcPnwYarUaERERmDdvHgoLC7s0f0ZGBpKTkxEXF4eMjAwsWrQIPj4+CA8PR1paGmJiYhAWFoaysjJIJPfOBjc0NCA9PR1ZWVmQSqVYuHAhVq1ahV27dnUpFm2amprwySef4Pbt2/D29m6zX2NjIxobG8Xt+vp6AIBMKsDISOj2uKhnyaSCxlfqu5hL/dId+VSpVA/dr1arNfpMmzYNzz33HIYOHYpLly4hPj4e/v7+OHHihNYTjtu2bYOzszO8vLweOZcha/ls+BnpB+ZTfzCX3aMjn5/OCsMLFy5AEAQ4Ozu32efgwYM4c+YMLl68CKVSCQDIysqCm5sbiouL4eXl1en5g4ODsWTJEgDAmjVrkJmZCS8vL8yZMwcAEBMTA29vb1RXV4uX5qhUKmzduhVOTk4AgGXLliEhIaHTMWhz9uxZeHt743//+x/kcjk+/fRTuLq6ttk/JSUFa9eubdX+xthmmJo2dWtspDvrxjfrOgTqJsylfulKPu+/P1CbkydPwsTERNxuuRy0srISUqkUUVFR+N3vfofExMRWJxAbGxuRlZWFuXPnPnIeuic/P1/XIVA3Yj71B3PZNQ0NDe3uq7PCUBDunWVtWY3TpqKiAkqlUiwKAcDV1RUKhQIVFRVdKgw9PDzE7wcPHgwAcHd3b9VWU1MjFoampqZiUQgAtra2D72MpzNGjRqF0tJS1NbWYu/evVi8eDEOHz7cZnEYGxuL6Ohocbu+vh5KpRKJp6VQmxh1a2zU82RSAevGNyOuRIrGZt6X1pcxl/qlO/J5Lj7oofvHjRuH4ODgh/ZJTk6Gubl5q34ffvghVCoVkpKSYG1t3an4DIVKpUJ+fj6mTZumUYhT38R86g/msnu0XE3YHjorDEeMGAGJRIKKigqEhoZq7SMIgtbCsa12AJBKpWLR2ULbEur9/8BaxtLW1tzcrPU9LX0enKurnnjiCfHhM+PHj0dxcTH++te/4p133tHaXyaTab2EqLFZAjUfcKE3GpslfGCJnmAu9UtX8vmoX3SMjY0f2uenn37C5cuX8dRTT7Xqt3PnTjz77LMYMmRIp2IzRCYmJvzlU48wn/qDueyajnx2OisMLS0tERQUhM2bN2P58uWt7jOsra2Fq6srKisrcfnyZXHVsLy8HHV1dXBxcdE6rrW1tcYDYZqamnDu3Dn4+/s/voN5jARB0LiHsL1OxE6FlZXVY4iIepJKpUJOTg7OxQfxh2Ifx1zql8eRz1u3buHChQvi9sWLF1FaWgpLS0tYWloiPj4eL7zwAmxtbXHp0iW8/vrrePLJJ/Hcc89pjHPhwgV8/fXXvISUiIg6RKd/rmLLli2YOHEiJkyYgISEBHh4eECtViM/Px+ZmZkoLy+Hh4cHFixYgA0bNogPn/H19cX48eO1jjllyhRER0fjwIEDcHJyQkZGBmpra3v2wLS4e/cuysvLxe+vXLmC0tJSyOVycYXw9ddfx4wZM6BUKvHLL7/g448/RmFhIXJzc3UZOhER9YCSkhKNk5gttwksXrwYmZmZOHv2LD744APU1tbC1tYW/v7+2LNnDwYMGKAxzvvvvw87OzsEBgb2aPxERNS36bQwdHBwwKlTp5CUlISVK1eiqqoK1tbWGDduHDIzMyGRSJCdnY3IyEhMnjwZUqkU06dPx8aNG9scMzw8HN9++y3CwsJgbGyMFStW9IrVwqtXr2Ls2LHidnp6OtLT0+Hr6ys+YbW6uhqLFi1CVVUVLCws4OHhgdzcXEybNk1HURMRUU/x8/N76O0JeXl57RonOTkZycnJ3RUWEREZCInQ3TfJkU7V19fDwsICN27c4KWkeqDlcrXg4GBeftjHMZf6hfnUH8ylfmE+9Qdz2T1aaoO6ujqYm5s/tK9O/8A9ERERERER6R4Lw24il8vbfB05ckTX4REREREREbVJp/cY6pPS0tI299nZ2fVcIERERERERB3EwrCbtDxZlIiIiIiIqK/hpaREREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgevzhWF8fDw8PT11HQYREfVSX3/9NUJCQjBkyBBIJBJkZ2dr7I+Pj4ezszPMzMwwcOBABAQE4MSJExp9fvjhBzz33HOwtraGubk55s+fj9ra2p47CCIiosdM54XhtWvXEBkZCUdHR8hkMiiVSoSEhKCgoEDXoXWrbdu24ZlnnsHAgQPFXzyKiopa9bty5QoWLlwIKysrmJqawtPTEydPntRBxERE+uH27dsYM2YMNm3apHX/yJEjsWnTJpw9exZHjx6Fvb09AgMDcf36dfH9gYGBkEgk+Oqrr/Cvf/0Ld+/eRVJSEpqbm3vyUIiIiB4bY11OfunSJfj4+EChUCA1NRUeHh5QqVTIy8vD0qVLcf78eV2G160KCwsxf/58TJw4Ef369UNqaioCAwNRVlYGOzs7AMDPP/8MHx8f+Pv745///CcGDRqEH374AQqFosPzPZ1SALWxWTcfBfU0mZGA1AnA6Pg8NDZJdB0OdQFz2XMurZ+psT1jxgzMmDGjzf4vvfSSxvbbb7+N9957D2fOnMHUqVPxr3/9C5cuXcLp06dhbm4OAPj73/+OwYMH49ChQ5g+fXr3HwQREVEP0+mKYUREBCQSCYqKijB79myMHDkSbm5uiI6OxvHjxwEAlZWVmDVrFuRyOczNzTF37lxUV1e3Oaafnx+ioqI02kJDQ/Hyyy+L2/b29khMTERYWBjkcjmGDRuG/fv34/r16+Jc7u7uKCkpEd+zY8cOKBQK5OXlwcXFBXK5HNOnT0dVVVW7jnXXrl2IiIiAp6cnnJ2dsW3bNjQ3N2usjL711ltQKpXYvn07JkyYAHt7e0ydOhVOTk7tmoOIiLrm7t27ePfdd2FhYYExY8YAABobGyGRSCCTycR+/fr1g1Qqxb/+9S9dhUpERNStdLZiePPmTeTm5iIpKQlmZq1XthQKBQRBQGhoKMzMzHD48GGo1WpERERg3rx5KCws7NL8GRkZSE5ORlxcHDIyMrBo0SL4+PggPDwcaWlpiImJQVhYGMrKyiCR3Du739DQgPT0dGRlZUEqlWLhwoVYtWoVdu3a1eH5GxoaoFKpYGlpKbZ99tlnCAoKwpw5c3D48GHY2dkhIiICv/3tb9scp7GxEY2NjeJ2fX09AEAmFWBkJHQ4LupdZFJB4yv1Xcxlz1GpVA/dr1arW/U5cOAAFi5ciIaGBtja2uKf//wnLCwsoFKpMG7cOJiZmeGPf/wj1q1bB0EQsHr1ajQ3N+Pq1auPnI96t5b8MY/6gfnUH8xl9+jI56ezwvDChQsQBAHOzs5t9jl48CDOnDmDixcvQqlUAgCysrLg5uaG4uJieHl5dXr+4OBgLFmyBACwZs0aZGZmwsvLC3PmzAEAxMTEwNvbG9XV1bCxsQFw74PdunWruIK3bNkyJCQkdGr+1atXw87ODgEBAWLbf/7zH2RmZiI6Ohqvv/46ioqKsHz5cshkMoSFhWkdJyUlBWvXrm3V/sbYZpiaNnUqNup91o3nfUz6grl8/HJych66/+TJkzAxMdFoa2xsRHp6Ourr6/Hll18iNDQUqamp4qX8K1aswNatW7Fp0yZIJBI888wzcHR0xNWrVx85H/UN+fn5ug6BuhHzqT+Yy65paGhod1+dFYaCcO+sectqnDYVFRVQKpViUQgArq6uUCgUqKio6FJh6OHhIX4/ePBgAIC7u3urtpqaGrEwNDU11bis09bWFjU1NR2eOzU1FR999BEKCwvRr18/sb25uRnjx49HcnIyAGDs2LEoKytDZmZmm4VhbGwsoqOjxe36+noolUoknpZCbWLU4diod5FJBawb34y4Eikam3lfWl/GXPacc/FBD90/btw4BAcHt7l/xYoVcHV1xeXLl8X7D4ODg/HnP/8ZN27cgLGxMczMzGBnZ4dJkyY9dCzq/VQqFfLz8zFt2rRWJwyo72E+9Qdz2T1ariZsD50VhiNGjIBEIkFFRQVCQ0O19hEEQWvh2FY7AEilUrHobKFtCfX+f2AtY2lru/+Jcw/+o5RIJK3mepT09HQkJyfj4MGDGsUpcK/QdHV11WhzcXHB3r172xxPJpNp3PfSorFZAjUfcKE3GpslfGCJnmAuH79H/QJhbGz8yD6CIECtVrfqZ2trCwD48ssvUVdXh1mzZvEXFj1hYmLCXOoR5lN/MJdd05HPTmeFoaWlJYKCgrB582YsX7681X2GtbW1cHV1RWVlJS5fviyuGpaXl6Ourg4uLi5ax7W2ttZ4IExTUxPOnTsHf3//x3cw7ZSWlobExETk5eVh/Pjxrfb7+Pjgu+++02j7/vvvMWzYsA7PdSJ2KqysrDodK/UOKpUKOTk5OBcfxB+KfRxzqTu3bt3ChQsXxO2LFy+itLQUlpaWsLKyQlJSEp599lnY2trip59+wpYtW/Djjz+KtxYAwPbt2+Hi4gJra2scO3YMf/jDHxASEoJRo0bp4pCIiIi6nU7/XMWWLVswceJETJgwAQkJCfDw8IBarUZ+fj4yMzNRXl4ODw8PLFiwABs2bBAfPuPr66u1sAKAKVOmIDo6GgcOHICTkxMyMjJ6xR8hTk1NRVxcHHbv3g17e3tcu3YNACCXyyGXywHcu3xp4sSJSE5Oxty5c1FUVIR3330X7777ri5DJyLq00pKSjRODrZcfr948WJs3boV58+fx86dO3Hjxg1YWVnBy8sLR44cgZubm/ie7777DrGxsbh58ybs7e2xevVqjBgxosePhYiI6HHRaWHo4OCAU6dOISkpCStXrkRVVRWsra0xbtw4ZGZmQiKRIDs7G5GRkZg8eTKkUimmT5+OjRs3tjlmeHg4vv32W4SFhcHY2BgrVqzoFauFW7Zswd27dzF79myN9jfffBPx8fEAAC8vL3z66aeIjY1FQkICHBwcsGHDBixYsEAHERMR6Qc/P7+HXva/b9++R46xfv16rF+/XtxuWQEmIiLSFxKhozfJUa9WX18PCwsL8cw39W0tv3wGBwfz8sM+jrnUL8yn/mAu9QvzqT+Yy+7RUhvU1dXB3Nz8oX11+gfuiYiIiIiISPdYGHaTlnsFtb2OHDmi6/CIiIiIiIjapNN7DPVJaWlpm/vs7Ox6LhAiIiIiIqIOYmHYTYYPH67rEIiIiIiIiDqFl5ISEREREREZOBaGREREREREBo6FIRERERERkYFjYUhERERERGTgWBgSEREREREZOBaGREREREREBo6FIRERERERkYFjYUhERERERGTgWBgSEREREREZOBaGREREREREBo6FIRERERERkYFjYUhERERERGTgWBgSEREREREZOBaGRESkN77++muEhIRgyJAhkEgkyM7O1tgfHx8PZ2dnmJmZYeDAgQgICMCJEyfE/ZcuXYJEItH6+uSTT3r4aIiIiHpOny8M4+Pj4enpqeswiIioF7h9+zbGjBmDTZs2ad0/cuRIbNq0CWfPnsXRo0dhb2+PwMBAXL9+HQCgVCpRVVWl8Vq7di3MzMwwY8aMnjwUIiKiHqXzwvDatWuIjIyEo6MjZDIZlEolQkJCUFBQoOvQutW2bdvwzDPPYODAgeJZ6qKiolb9tmzZAgcHB/Tr1w/jxo3DkSNHdBAtEVHfNGPGDCQmJuL555/Xuv+ll15CQEAAHB0d4ebmhrfffhv19fU4c+YMAMDIyAg2NjYar08//RTz5s2DXC7vyUMhIiLqUca6nPzSpUvw8fGBQqFAamoqPDw8oFKpkJeXh6VLl+L8+fO6DK9bFRYWYv78+Zg4cSL69euH1NRUBAYGoqysDHZ2dgCAPXv2ICoqClu2bIGPjw/eeecdzJgxA+Xl5Rg6dGiH5ns6pQBqY7PHcSjUg2RGAlInAKPj89DYJNF1ONQFzOXjc2n9zE697+7du3j33XdhYWGBMWPGaO1z8uRJlJaWYvPmzV0JkYiIqNfT6YphREQEJBIJioqKMHv2bIwcORJubm6Ijo7G8ePHAQCVlZWYNWsW5HI5zM3NMXfuXFRXV7c5pp+fH6KiojTaQkND8fLLL4vb9vb2SExMRFhYGORyOYYNG4b9+/fj+vXr4lzu7u4oKSkR37Njxw4oFArk5eXBxcUFcrkc06dPR1VVVbuOddeuXYiIiICnpyecnZ2xbds2NDc3a6yMvv3223j11Vfx2muvwcXFBRs2bIBSqURmZma75iAiokf74osvIJfL0a9fP2RkZCA/Px9PPvmk1r7vvfceXFxcMHHixB6OkoiIqGfpbMXw5s2byM3NRVJSEszMWq9sKRQKCIKA0NBQmJmZ4fDhw1Cr1YiIiMC8efNQWFjYpfkzMjKQnJyMuLg4ZGRkYNGiRfDx8UF4eDjS0tIQExODsLAwlJWVQSK5d3a/oaEB6enpyMrKglQqxcKFC7Fq1Srs2rWrw/M3NDRApVLB0tISwL0z1ydPnsTq1as1+gUGBuKbb75pc5zGxkY0NjaK2/X19QAAmVSAkZHQ4biod5FJBY2v1Hcxl4+PSqVqc59arW61f9KkSSguLsZPP/2E9957D3PnzsXRo0cxaNAgjX537tzB7t278frrr7cao2X7YXNT38Bc6hfmU38wl92jI5+fzgrDCxcuQBAEODs7t9nn4MGDOHPmDC5evAilUgkAyMrKgpubG4qLi+Hl5dXp+YODg7FkyRIAwJo1a5CZmQkvLy/MmTMHABATEwNvb29UV1fDxsYGwL0PduvWrXBycgIALFu2DAkJCZ2af/Xq1bCzs0NAQAAA4MaNG2hqasLgwYM1+g0ePBjXrl1rc5yUlBSsXbu2VfsbY5thatrUqdio91k3vlnXIVA3YS67X05OTpv7Tp48CRMTkzb3h4aGIi8vD6tXr8bs2bM19h06dAi3b9+GjY1Nm3Pk5+d3LmjqdZhL/cJ86g/msmsaGhra3VdnhaEg3Dtr3rIap01FRQWUSqVYFAKAq6srFAoFKioqulQYenh4iN+3FGPu7u6t2mpqasTC0NTUVCwKAcDW1hY1NTUdnjs1NRUfffQRCgsL0a9fP419D34egiA89DOKjY1FdHS0uF1fXw+lUonE01KoTYw6HBv1LjKpgHXjmxFXIkVjM+9L68uYy8fnXHxQm/vGjRuH4ODgh77f1NQU9vb2rfq9/fbbCAkJwfz581u9R6VSIT8/H9OmTXto4Um9H3OpX5hP/cFcdo+WqwnbQ2eF4YgRIyCRSFBRUYHQ0FCtfdoqih5WLEmlUrHobKFtCfX+f2AtY2lra25u1vqelj4PzvUo6enpSE5OxsGDBzWK0yeffBJGRkatVgdramparSLeTyaTQSaTtWpvbJZAzQdc6I3GZgkfWKInmMvud//P5lu3buHChQvi9uXLl1FWVgZLS0tYWVkhKSkJzz77LGxtbfHTTz9hy5Yt+PHHH/Hiiy9qjHPhwgUcOXIEOTk5D/2FxMTEhL+w6AnmUr8wn/qDueyajnx2OisMLS0tERQUhM2bN2P58uWt7jOsra2Fq6srKisrcfnyZXHVsLy8HHV1dXBxcdE6rrW1tcYDYZqamnDu3Dn4+/s/voNpp7S0NCQmJiIvLw/jx4/X2PfEE09g3LhxyM/Px3PPPSe25+fnY9asWR2e60TsVFhZWXU5ZtItlUqFnJwcnIsP4g/FPo657BklJSUaP+9brqhYvHgxtm7divPnz2Pnzp24ceMGrKys4OXlhSNHjsDNzU1jnPfffx92dnYIDAzs0fiJiIh0Rad/rmLLli2YOHEiJkyYgISEBHh4eECtViM/Px+ZmZkoLy+Hh4cHFixYgA0bNogPn/H19W1VWLWYMmUKoqOjceDAATg5OSEjIwO1tbU9e2BapKamIi4uDrt374a9vb24MiiXy8W/jRUdHY1FixZh/Pjx8Pb2xrvvvovKykr8/ve/12XoRER9hp+f30Ov5Ni3b1+7xklOTkZycnJ3hUVERNTr6bQwdHBwwKlTp5CUlISVK1eiqqoK1tbWGDduHDIzMyGRSJCdnY3IyEhMnjwZUqkU06dPx8aNG9scMzw8HN9++y3CwsJgbGyMFStW9IrVwi1btuDu3butHm7w5ptvIj4+HgAwb948/PTTT0hISEBVVRVGjx6NnJwcDBs2TAcRExERERGRoZAIHb1Jjnq1+vp6WFhYiJdJUd/WcvlhcHAwLz/s45hL/cJ86g/mUr8wn/qDueweLbVBXV0dzM3NH9pXp3/gnoiIiIiIiHSPhWE3ablXUNvryJEjug6PiIiIiIioTTq9x1CflJaWtrnPzs6u5wIhIiIiIiLqIBaG3WT48OG6DoGIiIiIiKhTeCkpERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgWNhSEREvdrXX3+NkJAQDBkyBBKJBNnZ2Rr74+Pj4ezsDDMzMwwcOBABAQE4ceKEuP/mzZuIjIzEqFGjYGpqiqFDh2L58uWoq6vr4SMhIiLqvfp8YRgfHw9PT09dh0FERI/J7du3MWbMGGzatEnr/pEjR2LTpk04e/Ysjh49Cnt7ewQGBuL69esAgKtXr+Lq1atIT0/H2bNnsWPHDuTm5uLVV1/tycMgIiLq1XReGF67dg2RkZFwdHSETCaDUqlESEgICgoKdB1atyorK8MLL7wAe3t7SCQSbNiwoVWfR50VJyIyRDNmzEBiYiKef/55rftfeuklBAQEwNHREW5ubnj77bdRX1+PM2fOAABGjx6NvXv3IiQkBE5OTpgyZQqSkpLw+eefQ61W9+ShEBER9VrGupz80qVL8PHxgUKhQGpqKjw8PKBSqZCXl4elS5fi/PnzugyvWzU0NMDR0RFz5szBihUrtPZpOSv+yiuv4IUXXujSfE+nFEBtbNalMUj3ZEYCUicAo+Pz0Ngk0XU41AXMZftdWj+z0++9e/cu3n33XVhYWGDMmDFt9qurq4O5uTmMjXX6v0EiIqJeQ6crhhEREZBIJCgqKsLs2bMxcuRIuLm5ITo6GsePHwcAVFZWYtasWZDL5TA3N8fcuXNRXV3d5ph+fn6IiorSaAsNDcXLL78sbtvb2yMxMRFhYWGQy+UYNmwY9u/fj+vXr4tzubu7o6SkRHzPjh07oFAokJeXBxcXF8jlckyfPh1VVVXtOlYvLy+kpaXhxRdfhEwm09rnUWfFiYhIuy+++AJyuRz9+vVDRkYG8vPz8eSTT2rt+9NPP2HdunVYsmRJD0dJRETUe+nsVOnNmzeRm5uLpKQkmJm1XtlSKBQQBAGhoaEwMzPD4cOHoVarERERgXnz5qGwsLBL82dkZCA5ORlxcXHIyMjAokWL4OPjg/DwcKSlpSEmJgZhYWEoKyuDRHLv7H5DQwPS09ORlZUFqVSKhQsXYtWqVdi1a1eXYumKxsZGNDY2itv19fUAAJlUgJGRoKuwqJvIpILGV+q7mMv2U6lUD92vVqtb9Zk0aRKKi4vx008/4b333sPcuXNx9OhRDBo0SKNffX09goOD4eLigtdff/2Rcz0qxs6+n3oP5lK/MJ/6g7nsHh35/HRWGF64cAGCIMDZ2bnNPgcPHsSZM2dw8eJFKJVKAEBWVhbc3NxQXFwMLy+vTs8fHBwsni1es2YNMjMz4eXlhTlz5gAAYmJi4O3tjerqatjY2AC498Fu3boVTk5OAIBly5YhISGh0zF0h5SUFKxdu7ZV+xtjm2Fq2qSDiOhxWDe+WdchUDdhLh8tJyfnoftPnjwJExOTNveHhoYiLy8Pq1evxuzZs8X2O3fuID4+HjKZDK+++iry8/O7HGt3jEG9A3OpX5hP/cFcdk1DQ0O7++qsMBSEe2fNW1bjtKmoqIBSqRSLQgBwdXWFQqFARUVFlwpDDw8P8fvBgwcDANzd3Vu11dTUiIWhqampWBQCgK2tLWpqajodQ3eIjY1FdHS0uF1fXw+lUonE01KoTYx0GBl1B5lUwLrxzYgrkaKxmfel9WXMZfudiw966P5x48YhODj4oX1MTU1hb28v9quvr8fMmTMxePBgfPbZZzA1Ne1SjCqVCvn5+Zg2bdpDi1Tq/ZhL/cJ86g/msnu0XE3YHjorDEeMGAGJRIKKigqEhoZq7SMIgtbCsa12AJBKpWLR2ULbEur9/8BaxtLW1tzcrPU9LX0enKunyWQyrfcsNjZLoOYDLvRGY7OEDyzRE8zloz34s/bWrVu4cOGCuH358mWUlZXB0tISVlZWSEpKwrPPPgtbW1v89NNP2LJlC3788Ue8+OKLMDExwS+//IKZM2eioaEBu3btwp07d3Dnzh0AgLW1NYyMOn8SzcTEhL+w6AnmUr8wn/qDueyajnx2OisMLS0tERQUhM2bN2P58uWt7jOsra2Fq6srKisrcfnyZXHVsLy8HHV1dXBxcdE6rrW1tcYDYZqamnDu3Dn4+/s/voPphU7EToWVlZWuw6AuUqlUyMnJwbn4IP5Q7OOYy84rKSnR+BnecpXE4sWLsXXrVpw/fx47d+7EjRs3YGVlBS8vLxw5cgRubm4A7l162vIH74cPH64x9sWLF2Fvb98zB0JERNSL6fQ53Vu2bMHEiRMxYcIEJCQkwMPDA2q1Gvn5+cjMzER5eTk8PDywYMECbNiwQXz4jK+vL8aPH691zClTpiA6OhoHDhyAk5MTMjIyUFtb27MHpsXdu3dRXl4ufn/lyhWUlpZCLpeLv6g8eFb84sWLKC0thaWlJYYOHaqTuImIdM3Pz++hV2fs27evS+8nIiIiHf+5CgcHB5w6dQr+/v5YuXIlRo8ejWnTpqGgoACZmZniH3kfOHAgJk+eLP4B4z179rQ5Znh4OBYvXoywsDD4+vrCwcGhV6wWXr16FWPHjsXYsWNRVVWF9PR0jB07Fq+99prYp6SkROwD3DsrPnbsWKxZs0ZXYRMRERERkQGQCDyNqlfq6+thYWEhXlJFfVvL5YfBwcG8/LCPYy71C/OpP5hL/cJ86g/msnu01AZ1dXUwNzd/aF+drhgSERERERGR7rEw7CZyubzN15EjR3QdHhERERERUZt0+vAZfVJaWtrmPjs7u54LhIiIiIiIqINYGHaTBx+BTkRERERE1FfwUlIiIiIiIiIDx8KQiIiIiIjIwLEwJCIiIiIiMnAsDImIiIiIiAwcC0MiIiIiIiIDx8KQiIiIiIjIwLEwJCIiIiIiMnAsDImIiIiIiAwcC0MiIiIiIiIDx8KQiIiIiIjIwLEwJCIiIiIiMnAsDImIiIiIiAwcC0MiIiIiIiIDx8KQiIg65Ouvv0ZISAiGDBkCiUSC7OxscZ9KpUJMTAzc3d1hZmaGIUOGICwsDFevXtUYo7GxEZGRkXjyySdhZmaGZ599Fj/++GMPHwkRERG16POFYXx8PDw9PXUdBhGRwbh9+zbGjBmDTZs2tdrX0NCAU6dOIS4uDqdOncK+ffvw/fff49lnn9XoFxUVhU8//RQff/wxjh49ilu3buE3v/kNmpqaeuowiIiI6D7Gug7g2rVrSEpKwoEDB3DlyhUMGjQInp6eiIqKwtSpU3UdXrfZtm0bPvjgA5w7dw4AMG7cOCQnJ2PChAla+6ekpOD111/HH/7wB2zYsKHD8z2dUgC1sVlXQqZeQGYkIHUCMDo+D41NEl2HQ13Ql3N5af1Mje0ZM2ZgxowZWvtaWFggPz9fo23jxo2YMGECKisrMXToUNTV1eG9995DVlYWAgICAAAffvghlEolDh48iKCgoMdzIERERNQmna4YXrp0CePGjcNXX32F1NRUnD17Frm5ufD398fSpUt1GVq3KywsxPz583Ho0CEcO3YMQ4cORWBgIK5cudKqb3FxMd599114eHjoIFIiou5VV1cHiUQChUIBADh58iRUKhUCAwPFPkOGDMHo0aPxzTff6ChKIiIiw6bTFcOIiAhIJBIUFRXBzOz/r265ubkhPDwcAFBZWYnIyEgUFBRAKpVi+vTp2LhxIwYPHqx1TD8/P3h6emqssoWGhkKhUGDHjh0AAHt7e7z22mv4/vvvsW/fPlhZWeFvf/sbJk6ciNdeew0FBQVwcHDA9u3bMX78eADAjh07EBUVhT179iAqKgqXL1/GpEmTsH37dtja2j7yWHft2qWxvW3bNvzjH/9AQUEBwsLCxPZbt25hwYIF2LZtGxITEx85bmNjIxobG8Xt+vp6AIBMKsDISHjk+6l3k0kFja/Ud/XlXKpUqofuV6vVbfb53//+h5iYGLz44ovo378/VCoVfvzxRzzxxBOQy+Ua7xs0aBCuXr36yPl6g5YY+0Ks9HDMpX5hPvUHc9k9OvL56awwvHnzJnJzc5GUlKRRFLZQKBQQBAGhoaEwMzPD4cOHoVarERERgXnz5qGwsLBL82dkZCA5ORlxcXHIyMjAokWL4OPjg/DwcKSlpSEmJgZhYWEoKyuDRHLvsq+Ghgakp6cjKysLUqkUCxcuxKpVq1oVfe3R0NAAlUoFS0tLjfalS5di5syZCAgIaFdhmJKSgrVr17Zqf2NsM0xNea+Ovlg3vlnXIVA36Yu5zMnJeej+kydPwsTEpFW7Wq1GamoqamtrERISIo5TWlqK5ubmVuNev34dRkZGj5yvN3nwslnqu5hL/cJ86g/msmsaGhra3VdnheGFCxcgCAKcnZ3b7HPw4EGcOXMGFy9ehFKpBABkZWXBzc0NxcXF8PLy6vT8wcHBWLJkCQBgzZo1yMzMhJeXF+bMmQMAiImJgbe3N6qrq2FjYwPgXsW9detWODk5AQCWLVuGhISETs2/evVq2NnZiffXAMDHH3+MU6dOobi4uN3jxMbGIjo6Wtyur6+HUqlE4mkp1CZGnYqNeg+ZVMC68c2IK5Gisblv3ZdGmvpyLs/FP/yev3HjxiE4OFijTaVSYf78+bhz5w7+9a9/wcrKStzXv39/ZGRkwNvbGwMHDhTb4+LiMH78+FZj9UYqlQr5+fmYNm2a1qKY+g7mUr8wn/qDueweLVcTtofOCkNBuHc5VctqnDYVFRVQKpViUQgArq6uUCgUqKio6FJheP/9ey2Xpbq7u7dqq6mpEQtDU1NTsSgEAFtbW9TU1HR47tTUVHz00UcoLCxEv379AACXL1/GH/7wB3z55ZdiW3vIZDLIZLJW7Y3NEqj72AMuqG2NzZI+98AS0q4v5vJR/0M2NjbW6KNSqbBgwQL88MMPOHToEKytrTX6P/300zAxMUFhYSHmzp0LAKiqqkJZWRnS0tL61C8AJiYmfSpeahtzqV+YT/3BXHZNRz47nRWGI0aMgEQiQUVFBUJDQ7X2EQRBa+HYVjsASKVSsehsoe3a2vs/pJaxtLU1NzdrfU9LnwfnepT09HQkJyfj4MGDGsXpyZMnUVNTg3HjxoltTU1N+Prrr7Fp0yY0NjbCyKj9K4AnYqdqnKGnvkmlUiEnJwfn4oP4Q7GP06dc3rp1CxcuXBC3L168iNLSUlhaWmLIkCGYPXs2Tp06hS+++AJNTU24du0aAMDS0hJPPPEELCws8Oqrr2LlypWwsrKCpaUlVq1aBXd3d42rKIiIiKjn6OyppJaWlggKCsLmzZtx+/btVvtra2vh6uqKyspKXL58WWwvLy9HXV0dXFxctI5rbW2NqqoqcbupqUn8ExG6lpaWhnXr1iE3N1d8qE2LqVOn4uzZsygtLRVf48ePx4IFC1BaWtqhopCI6HEqKSnB2LFjMXbsWABAdHQ0xo4dizVr1uDHH3/EZ599hh9//BGenp6wtbUVX/c/cTQjIwOhoaGYO3cufHx8YGpqis8//5w/64iIiHREp08l3bJlCyZOnIgJEyYgISEBHh4eUKvVyM/PR2ZmJsrLy+Hh4YEFCxZgw4YN4sNnfH19WxVWLaZMmYLo6GgcOHAATk5OyMjIQG1tbc8emBapqamIi4vD7t27YW9vL55Bl8vlkMvlGDBgAEaPHq3xHjMzM1hZWbVqJyLSJT8/v4deLdGeKyn69euHjRs3YuPGjd0ZGhEREXWSTv+OoYODA06dOgV/f3+sXLkSo0ePxrRp01BQUIDMzExIJBJkZ2dj4MCBmDx5MgICAuDo6Ig9e/a0OWZ4eDgWL16MsLAw+Pr6wsHBAf7+/j14VNpt2bIFd+/exezZszXOoKenp+s6NCIiIiIiMnASoaM3yVGvVl9fDwsLC9y4cYP3GOqBlvvSgoOD+/x9aYaOudQvzKf+YC71C/OpP5jL7tFSG9TV1cHc3PyhfXW6YkhERERERES6x8Kwm7TcK6jtdeTIEV2HR0RERERE1CadPnxGn5SWlra5z87OrucCISIiIiIi6iAWht1k+PDhug6BiIiIiIioU3gpKRERERERkYFjYUhERERERGTgWBgSEREREREZOBaGREREREREBo6FIRERERERkYFjYUhERERERGTgWBgSEREREREZOBaGREREREREBo6FIRERERERkYFjYUhERERERGTgWBgSEREREREZOBaGREREREREBo6FIRERERERkYHr84VhfHw8PD09dR0GERmgX375BVFRURg2bBj69++PiRMnori4WNwfHx8PZ2dnmJmZYdCgQVizZg2Kiop0GDERERGRdjovDK9du4bIyEg4OjpCJpNBqVQiJCQEBQUFug6tW23btg3PPPMMBg4ciIEDByIgIKDVL4iZmZnw8PCAubk5zM3N4e3tjX/+8586ipiIHuW1115Dfn4+srKycPbsWQQGBiIgIABXrlwBAIwcORKbNm3C2bNncejQIQwaNAjBwcG4fv26jiMnIiIi0mSsy8kvXboEHx8fKBQKpKamwsPDAyqVCnl5eVi6dCnOnz+vy/C6VWFhIebPn4+JEyeiX79+SE1NRWBgIMrKymBnZwcAeOqpp7B+/XoMHz4cALBz507MmjULp0+fhpubW4fmezqlAGpjs24/DupZMiMBqROA0fF5aGyS6Docg3dp/Uzx+zt37mDv3r3Yv38/Jk+eDODeCmF2djYyMzORmJiIl156SeyvUqkQHh6OgwcP4syZM5g6dWqPx09ERETUFp2uGEZEREAikaCoqAizZ8/GyJEj4ebmhujoaBw/fhwAUFlZiVmzZkEul8Pc3Bxz585FdXV1m2P6+fkhKipKoy00NBQvv/yyuG1vb4/ExESEhYVBLpdj2LBh2L9/P65fvy7O5e7ujpKSEvE9O3bsgEKhQF5eHlxcXCCXyzF9+nRUVVW161h37dqFiIgIeHp6wtnZGdu2bUNzc7PGymhISAiCg4MxcuRIjBw5EklJSZDL5eJnQUS9h1qtRlNTE/r166fR3r9/fxw9erRV/7t37+LLL7+EhYUFxowZ01NhEhEREbWLzlYMb968idzcXCQlJcHMrPXKlkKhgCAICA0NhZmZGQ4fPgy1Wo2IiAjMmzcPhYWFXZo/IyMDycnJiIuLQ0ZGBhYtWgQfHx+Eh4cjLS0NMTExCAsLQ1lZGSSSeys1DQ0NSE9PR1ZWFqRSKRYuXIhVq1Zh165dHZ6/oaEBKpUKlpaWWvc3NTXhk08+we3bt+Ht7d3mOI2NjWhsbBS36+vrAQAyqQAjI6HDcVHvIpMKGl9Jt1Qqlfh9v3798Otf/xoJCQkYPnw4Bg8ejI8//hgnTpzA8OHDxb4HDhzAwoUL0dDQgIEDB+Lzzz+HhYWFxljU97Tkj3ns+5hL/cJ86g/msnt05PPTWWF44cIFCIIAZ2fnNvu0XHJ18eJFKJVKAEBWVhbc3NxQXFwMLy+vTs8fHByMJUuWAADWrFmDzMxMeHl5Yc6cOQCAmJgYeHt7o7q6GjY2NgDufbBbt26Fk5MTAGDZsmVISEjo1PyrV6+GnZ0dAgICNNrPnj0Lb29v/O9//4NcLsenn34KV1fXNsdJSUnB2rVrW7W/MbYZpqZNnYqNep9145t1HQIByMnJ0dhevHgxNm3aBHt7e0ilUjg5OWHy5Mn44YcfxL6NjY1IT09HfX09vvzyS8yePRupqalQKBQ6OALqbvn5+boOgboJc6lfmE/9wVx2TUNDQ7v76qwwFIR7KyAtq3HaVFRUQKlUikUhALi6ukKhUKCioqJLhaGHh4f4/eDBgwEA7u7urdpqamrEwtDU1FQsCgHA1tYWNTU1HZ47NTUVH330EQoLC1tdhjZq1CiUlpaitrYWe/fuxeLFi3H48OE2i8PY2FhER0eL2/X19VAqlUg8LYXaxKjDsVHvIpMKWDe+GXElUjT+P/buPS6qOv8f+GsGcRRGGCFUZCe5eOEiE66iCSZiICwuSeUlQ9FYd23BC6JF7IaLBGLIBmUCq7urRVZ+d/OrfYMkpMXV/WXiEt4Y10saZAiaASmFM8z8/vDBWZHBuAwMnHk9H495OOdzPudz3ue8YfTt55wzOt5jaGpnkkPatf3qV7/C7du30djYCEdHRzz77LOwsrJCWFhYm34ajQYTJkzA+vXrUV1d3eb+Qxp4NBoNiouLERwcDEtLS1OHQz3AXIoL8ykezKVxtF5N2BkmKwzHjRsHiUQCtVqNiIgIg330er3BwrGjdgCQSqVC0dnK0BTqvT9grWMZatPpdAa3ae1z/75+SmZmJjZv3oxDhw61KU5bDR48WHj4zJQpU1BWVobXX38df/rTnwyOJ5PJIJPJ2rU36yTQ8mElotGsk/DhM/1AR38xKRQKKBQKfPfddyguLkZGRsYD/xLTarX8S04kLC0tmUuRYC7FhfkUD+ayZ7py7kxWGNrZ2SEkJATbt2/HmjVr2t1nWF9fD09PT1RVVaG6ulqYNaysrERDQwM8PDwMjuvg4NDmgTAtLS04c+YMAgMDe+9gOmnr1q1ITU1FUVERpkyZ0qlt9Hp9m3sIO+vzxMdhb2/f5e2of9FoNCgsLMSZ5BB+KPZDRUVF0Ov1mDBhAi5evIgXXngBEyZMwHPPPYfbt28jLS0NTzzxBBwdHVFbW4s333wTX3/9tXDJOhEREVF/YdKvq8jJyYGfnx+mTp2KlJQUqFQqaLVaFBcXIzc3F5WVlVCpVIiMjER2drbw8JmAgIAOC6vZs2cjPj4eBQUFcHNzQ1ZWFurr6/v2wAzIyMhAUlIS3n33XTg7O+PatWsAALlcDrlcDgD43e9+h1/84hdQKpX4/vvv8f7776O0tBQHDx40ZehE1IGGhgYkJibi66+/hp2dHZ5++mmkpaXB0tISLS0tOHfuHN566y3cuHED9vb2UCqV+Mc//tHlr58hIiIi6m0mLQxdXFxQXl6OtLQ0rF+/HjU1NXBwcMDkyZORm5sLiUSC/fv3Y/Xq1Zg5cyakUilCQ0Oxbdu2DseMjo7GyZMnERUVhUGDBmHdunX9YrYwJycHd+7cwfz589u0/+EPf0BycjIAoLa2FkuXLkVNTQ1sbW2hUqlw8OBBBAcHmyBiIvopCxcuxMKFCw2uGzJkCPbt2ycst87+dvZqASIiIqK+JNF39SY56tcaGxtha2srzFDQwNZaTISFhfFS0gGOuRQX5lM8mEtxYT7Fg7k0jtbaoKGhATY2Ng/sa9IvuCciIiIiIiLTY2FoJK33Chp6HTlyxNThERERERERdcik9xiKSUVFRYfrnJyc+i4QIiIiIiKiLmJhaCSt3z1IREREREQ00PBSUiIiIiIiIjPHwpCIiIiIiMjMsTAkIiIiIiIycywMiYiIiIiIzBwLQyIiIiIiIjPHwpCIiIiIiMjMsTAkIiIiIiIycywMiYiIiIiIzBwLQyIiIiIiIjPHwpCIiIiIiMjMsTAkIiIiIiIycywMiYiIiIiIzBwLQyIiIiIiIjPHwpCIiIiIiMjMDfjCMDk5GT4+PqYOg4jMwPfff4+4uDiMGTMGQ4cOhZ+fH8rKyoT1+/btQ0hICB566CFIJBJUVFSYLlgiIiKiLjB5YXjt2jWsXr0arq6ukMlkUCqVCA8PR0lJialDM6qzZ8/i6aefhrOzMyQSCbKzs9v1aV13/ys2NrbvAyaidlasWIHi4mLk5+fj9OnTmDNnDoKCgnD16lUAwO3bt+Hv748tW7aYOFIiIiKirhlkyp1fuXIF/v7+UCgUyMjIgEqlgkajQVFREWJjY3Hu3DlThmdUTU1NcHV1xYIFC7Bu3TqDfcrKytDS0iIsnzlzBsHBwViwYEGX9zctvQTaQdbdjpf6B5mFHhlTgYnJRWhukZg6HLNyZcvcNss//PADPvjgAxw4cAAzZ84EcPeKhf379yM3NxepqalYunTp3W2vXOnrcImIiIh6xKQzhjExMZBIJDh+/Djmz5+P8ePHw8vLC/Hx8Th27BgAoKqqCvPmzYNcLoeNjQ0WLlyI2traDsecNWsW4uLi2rRFRERg+fLlwrKzszNSU1MRFRUFuVyOMWPG4MCBA7h+/bqwL29vb5w4cULYZvfu3VAoFCgqKoKHhwfkcjlCQ0NRU1PTqWP19fXF1q1b8cwzz0Amkxns4+DggFGjRgmvjz76CG5ubggICOjUPoio92i1WrS0tGDIkCFt2ocOHYqjR4+aKCoiIiIi4zDZjOHNmzdx8OBBpKWlwdq6/cyWQqGAXq9HREQErK2tcfjwYWi1WsTExGDRokUoLS3t0f6zsrKwefNmJCUlISsrC0uXLoW/vz+io6OxdetWJCQkICoqCmfPnoVEcnempqmpCZmZmcjPz4dUKsWSJUuwYcMG7Nmzp0exGHLnzh288847iI+PF/ZvSHNzM5qbm4XlxsZGAIBMqoeFhd7ocVHfkkn1bf6kvqPRaNosDxkyBI8++ihSUlIwduxYjBw5Eu+//z4+//xzjB07tk3/1vcajabNe0Pj0sDEfIoHcykuzKd4MJfG0ZXzZ7LC8OLFi9Dr9XB3d++wz6FDh3Dq1ClcvnwZSqUSAJCfnw8vLy+UlZXB19e32/sPCwvDypUrAQAbN25Ebm4ufH19hcs2ExISMH36dNTW1mLUqFEA7p7YvLw8uLm5AQBWrVqFlJSUbsfwIPv370d9fX2bmU5D0tPTsWnTpnbtL0/SwcqqxcAWNBC9MkVn6hDMTmFhYbu2ZcuW4c0334SzszOkUinc3Nwwc+ZMXLp0qU3/1qsajh49im+++abNGMXFxb0bOPUp5lM8mEtxYT7Fg7nsmaampk73NVlhqNffnQF50GyYWq2GUqkUikIA8PT0hEKhgFqt7lFhqFKphPcjR44EAHh7e7drq6urEwpDKysroSgEAEdHR9TV1XU7hgf5y1/+gl/84hcYPXr0A/slJiYiPj5eWG5sbIRSqUTqF1JoLS16JTbqOzKpHq9M0SHphBTNOt5j2JfOJIcYbP/Vr36F27dvo7GxEY6Ojnj22WdhZWWFsLAwoU/rPYYzZswQnpqs0WhQXFyM4OBgWFpa9nb41MuYT/FgLsWF+RQP5tI4Wq8m7AyTFYbjxo2DRCKBWq1GRESEwT56vd5g4dhROwBIpVKh6GxlaAr13h+w1rEMtel0OoPbtPa5f1/G8NVXX+HQoUPYt2/fT/aVyWQG71ls1kmg5cNKRKNZJ+HDZ/rYg/4SUigUUCgU+O6771BcXIyMjIw2/VvfW1pathvHUBsNXMyneDCX4sJ8igdz2TNdOXcmKwzt7OwQEhKC7du3Y82aNe3uM6yvr4enpyeqqqpQXV0tzBpWVlaioaEBHh4eBsd1cHBo80CYlpYWnDlzBoGBgb13MEa2a9cujBgxAnPnzv3pzh34PPFx2NvbGzEqMgWNRoPCwkKcSQ7hh2I/UFRUBL1ejwkTJuDixYt44YUXMGHCBDz33HMA7t47XVVVJVw++p///AcAMGrUKP4+EhERUb9m0qeS5uTkoKWlBVOnTsUHH3yACxcuQK1W44033sD06dMRFBQElUqFyMhIlJeX4/jx44iKikJAQACmTJlicMzZs2ejoKAABQUFOHfuHGJiYlBfX9+3B2bAnTt3UFFRgYqKCty5cwdXr15FRUUFLl682KafTqfDrl27sGzZMgwaZNJvEyGi+zQ0NCA2Nhbu7u6IiorCjBkz8MknnwhF+4cffohJkyYJ/6nzzDPPYNKkScjLyzNl2EREREQ/yaSVh4uLC8rLy5GWlob169ejpqYGDg4OmDx5MnJzcyGRSLB//36sXr0aM2fOhFQqRWhoKLZt29bhmNHR0Th58iSioqIwaNAgrFu3rl/MFn7zzTeYNGmSsJyZmYnMzEwEBAS0ecLqoUOHUFVVhejoaBNESUQPsnDhQixcuLDD9cuXL+/wgVF8qhoRERH1ZxJ9b9wkRybT2NgIW1tb3Lhxg5euiUDrpaRhYWG8lHSAYy7FhfkUD+ZSXJhP8WAujaO1NmhoaICNjc0D+5r0UlIiIiIiIiIyPRaGRiKXyzt8HTlyxNThERERERERdYhPNzGSioqKDtc5OTn1XSBERERERERdxMLQSMaOHWvqEIiIiIiIiLqFl5ISERERERGZORaGREREREREZo6FIRERERERkZljYUhERERERGTmWBgSERERERGZORaGREREREREZo6FIRERERERkZkzWmFYX19vrKGIiIiIiIioD3WrMHz11Vexd+9eYXnhwoWwt7eHk5MTTp48abTgiIiIiIiIqPd1qzD805/+BKVSCQAoLi5GcXExPv74Y/ziF7/ACy+8YNQAiYiIiIiIqHcN6s5GNTU1QmH40UcfYeHChZgzZw6cnZ0xbdo0owZIREREREREvatbM4bDhw9HdXU1AODgwYMICgoCAOj1erS0tBgvOiIiIiIiIup13ZoxfOqpp/Dss89i3Lhx+Pbbb/GLX/wCAFBRUYGxY8caNUAiIiIiIiLqXd2aMczKysKqVavg6emJ4uJiyOVyAHcvMY2JiTFqgEREfU2r1eLll1+Gi4sLhg4dCldXV6SkpECn0wl9amtrsXz5cowePRpWVlYIDQ3FhQsXTBg1ERERUfd1a8bQ0tISGzZsaNceFxfX03h6RXJyMvbv34+KigpTh0JEA8Crr76KvLw8vPXWW/Dy8sKJEyfw3HPPwdbWFmvXroVer0dERAQsLS1x4MAB2NjY4LXXXkNQUBAqKythbW1t6kMgIiIi6pJuf49hfn4+ZsyYgdGjR+Orr74CAGRnZ+PAgQNGC67VtWvXsHr1ari6ukImk0GpVCI8PBwlJSVG35ep1dfXIzY2Fo6OjhgyZAg8PDxQWFho6rCIzMpnn32GefPmYe7cuXB2dsb8+fMxZ84cnDhxAgBw4cIFHDt2DLm5ufD19cWECROQk5ODW7du4b333jNx9ERERERd160Zw9zcXGzcuBFxcXFIS0sTHjijUCiQnZ2NefPmGS3AK1euwN/fHwqFAhkZGVCpVNBoNCgqKkJsbCzOnTtntH2Z2p07dxAcHIwRI0bg73//O372s5+huroaw4YN6/JY09JLoB3EWYuBTmahR8ZUYGJyEZpbJKYOR9SubJkrvJ8xYwby8vJw/vx5jB8/HidPnsTRo0eRnZ0NAGhubgYADBkyRNjGwsICgwcPxtGjR7FixYo+jZ2IiIiop7o1Y7ht2zbs3LkTv//972FhYSG0T5kyBadPnzZacAAQExMDiUSC48ePY/78+Rg/fjy8vLwQHx+PY8eOAQCqqqowb948yOVy2NjYYOHChaitre1wzFmzZrW77DUiIgLLly8Xlp2dnZGamoqoqCjI5XKMGTMGBw4cwPXr14V9eXt7CzMIALB7924oFAoUFRXBw8MDcrkcoaGhqKmp6dSx/vWvf8XNmzexf/9++Pv7Y8yYMZgxYwYeeeSRzp8wIuqxhIQELF68GO7u7rC0tMSkSZMQFxeHxYsXAwDc3d0xZswYJCYm4rvvvsOdO3ewZcsWXLt2rdO/70RERET9SbdmDC9fvoxJkya1a5fJZLh9+3aPg2p18+ZNHDx4EGlpaQbv2VEoFMK9PtbW1jh8+DC0Wi1iYmKwaNEilJaW9mj/WVlZ2Lx5M5KSkpCVlYWlS5fC398f0dHR2Lp1KxISEhAVFYWzZ89CIrk7m9PU1ITMzEzk5+dDKpViyZIl2LBhA/bs2fOT+/vwww8xffp0xMbG4sCBA3BwcMCzzz6LhISENgX4vZqbm4XZCwBobGwEAMikelhY6Ht0/GR6Mqm+zZ/UezQajfB+7969eOedd/D222/D09MTJ0+exIYNGzBixAhERUUJfX7zm9/Azs4OFhYWePzxxxEaGtpurPvHN7SOBh7mUzyYS3FhPsWDuTSOrpy/bhWGLi4uqKiowJgxY9q0f/zxx/D09OzOkAZdvHgRer0e7u7uHfY5dOgQTp06hcuXL0OpVAK4e/+jl5cXysrK4Ovr2+39h4WFYeXKlQCAjRs3CvcTLViwAMDdWYXp06ejtrYWo0aNAnD35Ofl5cHNzQ0AsGrVKqSkpHRqf19++SU+/fRTREZGorCwEBcuXEBsbCy0Wi02btxocJv09HRs2rSpXfvLk3SwsuJ3SorFK1N0P92JeuTee3nj4uLw9NNPY9iwYaiuroadnR1CQ0Pxhz/8AQ899JDQLyUlBbdv34ZWq4WtrS1eeOEFjB079oH3BRcXF/fqcVDfYj7Fg7kUF+ZTPJjLnmlqaup0324Vhi+88AJiY2Px448/Qq/X4/jx43jvvfeQnp6OP//5z90Z0iC9/u4sSetsnCFqtRpKpVIoCgHA09MTCoUCarW6R4WhSqUS3o8cORIA4O3t3a6trq5OKAytrKyEohAAHB0dUVdX16n96XQ6jBgxAjt27ICFhQUmT56Mb775Blu3bu2wMExMTER8fLyw3NjYCKVSidQvpNBaGp5lpIFDJtXjlSk6JJ2QolnHewx705nkEOG9Xq+Ht7c3wsLChLbTp0/j+PHjbdrudeHCBVy6dAnZ2dkIDg5ut16j0aC4uBjBwcGwtLQ0/gFQn2I+xYO5FBfmUzyYS+NovZqwM7pVGD733HPQarV48cUX0dTUhGeffRZOTk54/fXX8cwzz3RnSIPGjRsHiUQCtVqNiIgIg330er3BwrGjdgCQSqVC0dnK0DTrvT+ErWMZarv3u83u/8GVSCTt9tURR0dHWFpatrls1MPDA9euXcOdO3cwePDgdtvIZDLIZLJ27c06CbR8WIloNOskfPhML7v3dzc8PBxbtmyBi4sLvLy88MUXX+D1119HdHS00O9vf/sbHBwc8PDDD+P06dNYu3YtIiIiOiwc790P/4ITD+ZTPJhLcWE+xYO57JmunLsuF4ZarRZ79uxBeHg4fv3rX+PGjRvCTJex2dnZISQkBNu3b8eaNWva3WdYX18PT09PVFVVobq6Wpg1rKysRENDAzw8PAyO6+Dg0OYBES0tLThz5gwCAwONfgxd4e/vj3fffRc6nQ5S6d3nAp0/fx6Ojo4Gi8IH+Tzxcdjb2/dGmNSHNBoNCgsLcSY5hB+KfWjbtm1ISkpCTEwM6urqMHr0aKxcubLNzH1NTQ3i4+NRW1sLR0dHREVFISkpyYRRExEREXVfl59KOmjQIPz2t78VHnjy0EMP9UpR2ConJwctLS2YOnUqPvjgA1y4cAFqtRpvvPEGpk+fjqCgIKhUKkRGRqK8vBzHjx9HVFQUAgICMGXKFINjzp49GwUFBSgoKMC5c+cQExOD+vr6XjuGzvrtb3+Lb7/9FmvXrsX58+dRUFCAzZs3IzY21tShEZmVYcOGITs7G1999RV++OEHXLp0CampqW3+g2bNmjWorq7GnTt38NVXX+GVV17p8n/gEBEREfUX3fq6imnTpuGLL74wdiwGubi4oLy8HIGBgVi/fj0mTpyI4OBglJSUIDc3FxKJBPv378fw4cMxc+ZMBAUFwdXVFXv37u1wzOjoaCxbtkwoIF1cXEw+WwgASqUSn3zyCcrKyqBSqbBmzRqsXbsWL730kqlDIyIiIiIiEZPoO3sD3D3+9re/4aWXXsK6deswefLkdpd43vvQFupbjY2NsLW1xY0bN3gpqQi0XkoaFhbGS0kHOOZSXJhP8WAuxYX5FA/m0jhaa4OGhgbY2Ng8sG+3Hj6zaNEiAHcvpWrV+pAViUSClhZ+TQIREREREdFA0e0vuKeuk8vlHa77+OOP8dhjj/VhNERERERERHd1qzC8/4vtqXMqKio6XOfk5NR3gRAREREREd2jW4Xh22+//cD1UVFR3QpG7MaOHWvqEIiIiIiIiNrpVmG4du3aNssajQZNTU0YPHgwrKysWBgSERERERENIN36uorvvvuuzevWrVv4z3/+gxkzZuC9994zdoxERERERETUi7pVGBoybtw4bNmypd1sIhEREREREfVvRisMAcDCwgLffPONMYckIiIiIiKiXtateww//PDDNst6vR41NTV488034e/vb5TAiIiIiIiIqG90qzCMiIhosyyRSODg4IDZs2fjj3/8ozHiIiIiIiIioj7SrcJQp9MZOw4iIiIiIiIykW7dY5iSkoKmpqZ27T/88ANSUlJ6HBQRERERERH1nW4Vhps2bcKtW7fatTc1NWHTpk09DoqIiIiIiIj6TrcKQ71eD4lE0q795MmTsLOz63FQRERERERE1He6dI/h8OHDIZFIIJFIMH78+DbFYUtLC27duoXnn3/e6EESERERERFR7+lSYZidnQ29Xo/o6Ghs2rQJtra2wrrBgwfD2dkZ06dPN3qQRERERERE1Hu6VBguW7YMAODi4gI/Pz9YWlr2SlBERERERETUd7p1j2FAQIBQFP7www9obGxs8yIi6u+0Wi1efvlluLi4YOjQoXB1dUVKSkq7r+NRq9V44oknYGtri2HDhuHRRx9FVVWViaImIiIi6h3dKgybmpqwatUqjBgxAnK5HMOHD2/z6m+Sk5Ph4+Nj6jCIqB959dVXkZeXhzfffBNqtRoZGRnYunUrtm3bJvS5dOkSZsyYAXd3d5SWluLkyZNISkrCkCFDTBg5ERERkfF1qzB84YUX8OmnnyInJwcymQx//vOfsWnTJowePRpvv/22sWPEtWvXsHr1ari6ukImk0GpVCI8PBwlJSVG35cp7d69W3i4z72vH3/80dShEYnOZ599hnnz5mHu3LlwdnbG/PnzMWfOHJw4cULo8/vf/x5hYWHIyMjApEmT4Orqirlz52LEiBEmjJyIiIjI+Lp0j2Gr//u//8Pbb7+NWbNmITo6Go899hjGjh2LMWPGYM+ePYiMjDRagFeuXIG/vz8UCgUyMjKgUqmg0WhQVFSE2NhYnDt3zmj76g9sbGzwn//8p01bd2YnpqWXQDvI2lhhkYnILPTImApMTC5Cc0v7r4ihrrmyZa7wfsaMGcjLy8P58+cxfvx4nDx5EkePHkV2djYAQKfToaCgAC+++CJCQkLwxRdfwMXFBYmJiYiIiDDNARARERH1km7NGN68eRMuLi4A7hYyN2/eBHD3H1r//Oc/jRcdgJiYGEgkEhw/fhzz58/H+PHj4eXlhfj4eBw7dgwAUFVVhXnz5kEul8PGxgYLFy5EbW1th2POmjULcXFxbdoiIiKwfPlyYdnZ2RmpqamIioqCXC7HmDFjcODAAVy/fl3Yl7e3d5vZhd27d0OhUKCoqAgeHh6Qy+UIDQ1FTU1Np49XIpFg1KhRbV5EZHwJCQlYvHgx3N3dYWlpiUmTJiEuLg6LFy8GANTV1eHWrVvYsmULQkND8cknn+DJJ5/EU089hcOHD5s4eiIiIiLj6taMoaurK65cuYIxY8bA09MT//M//4OpU6fi//7v/6BQKIwW3M2bN3Hw4EGkpaXB2rr97JdCoYBer0dERASsra1x+PBhaLVaxMTEYNGiRSgtLe3R/rOysrB582YkJSUhKysLS5cuhb+/P6Kjo7F161YkJCQgKioKZ8+eFb7TsampCZmZmcjPz4dUKsWSJUuwYcMG7Nmzp1P7vHXrFsaMGYOWlhb4+PjglVdewaRJkzrs39zcjObmZmG59eE/MqkeFhb6Hhw99Qcyqb7Nn9QzGo1GeL9371688847ePvtt+Hp6YmTJ09iw4YNGDFiBKKiooTfq/DwcKxatQoA4OXlhaNHjyInJwd+fn7d2ve9MdDAxXyKB3MpLsyneDCXxtGV89etwvC5557DyZMnERAQgMTERMydOxfbtm2DVqvFa6+91p0hDbp48SL0ej3c3d077HPo0CGcOnUKly9fhlKpBADk5+fDy8sLZWVl8PX17fb+w8LCsHLlSgDAxo0bkZubC19fXyxYsADA3RmH6dOno7a2VpjZ02g0yMvLg5ubGwBg1apVSElJ6dT+3N3dsXv3bnh7e6OxsRGvv/46/P39cfLkSYwbN87gNunp6di0aVO79pcn6WBl1dLlY6b+6ZUpup/uRD+psLBQeB8XF4enn34aw4YNQ3V1Nezs7BAaGoo//OEPeOihh6DRaGBhYQELC4s22w0ePBinTp1q09YVxcXFPT4O6j+YT/FgLsWF+RQP5rJnmpqaOt23W4XhunXrhPeBgYE4d+4cTpw4ATc3NzzyyCPdGdIgvf7uLEnrbJwharUaSqVSKAoBwNPTEwqFAmq1ukeFoUqlEt6PHDkSAODt7d2ura6uTigMrayshKIQABwdHVFXV9ep/T366KN49NFHhWV/f3/8/Oc/x7Zt2/DGG28Y3CYxMRHx8fHCcmNjI5RKJVK/kEJradGp/VL/JZPq8coUHZJOSNGs4z2GPXUmOUR4r9fr4e3tjbCwMKHt9OnTOH78uNDW+vlxb5+//vWveOSRR9q0dYZGo0FxcTGCg4P5HbAiwHyKB3MpLsyneDCXxtGVrxLsVmF4rx9//BEPP/wwHn744Z4O1c64ceMgkUigVqs7fNiDXq83WDh21A4AUqlUKDpbGZpmvfeHsHUsQ233fu/Z/T+4Eomk3b46SyqVwtfXFxcuXOiwj0wmg0wma9ferJNAy4eViEazTsKHzxjBvb+f4eHh2LJlC1xcXODl5YUvvvgCr7/+OqKjo4V+L774IhYtWoRZs2YhMDAQBw8eREFBAUpLS7v9l5SlpSX/ghMR5lM8mEtxYT7Fg7nsma6cu24Vhi0tLdi8eTPy8vJQW1uL8+fPw9XVFUlJSXB2dsavfvWr7gzbjp2dHUJCQrB9+3asWbOm3X2G9fX18PT0RFVVFaqrq4VZw8rKSjQ0NMDDw8PguA4ODm0eCNPS0oIzZ84gMDDQKHEbi16vR0VFRZtZys76PPFx2Nvb90JU1Jc0Gg0KCwtxJjmEH4pGtm3bNiQlJSEmJgZ1dXUYPXo0Vq5ciY0bNwp9nnzySeTl5SE9PR1r1qzBhAkT8MEHH2DGjBkmjJyIiIjI+Lr1VNK0tDTs3r0bGRkZGDx4sNDu7e2NP//5z0YLDgBycnLQ0tKCqVOn4oMPPsCFCxegVqvxxhtvYPr06QgKCoJKpUJkZCTKy8tx/PhxREVFISAgAFOmTDE45uzZs1FQUICCggKcO3cOMTExqK+vN2rc3bFp0yYUFRXhyy+/REVFBX71q1+hoqICzz//vKlDIxKdYcOGITs7G1999RV++OEHXLp0CampqW0+0wAgOjoaFy5cwA8//ICKigrMmzfPRBETERER9Z5uFYZvv/02duzYgcjISFhY/Pc+NpVKZfTvFXRxcUF5eTkCAwOxfv16TJw4EcHBwSgpKUFubi4kEgn279+P4cOHY+bMmQgKCoKrqyv27t3b4ZjR0dFYtmyZUEC6uLj0i9nC+vp6/OY3v4GHhwfmzJmDq1ev4p///CemTp1q6tCIiIiIiEjEJPpu3AA3dOhQnDt3DmPGjMGwYcNw8uRJuLq6orKyElOnTsWtW7d6I1bqhMbGRtja2uLGjRu8lFQEWi8lDQsL46WkAxxzKS7Mp3gwl+LCfIoHc2kcrbVBQ0MDbGxsHti3WzOGXl5eOHLkSLv2v/3tbw/8zj0iIiIiIiLqf7r18Jk//OEPWLp0Ka5evQqdTod9+/bhP//5D95++2189NFHxo5RNORyeYfrPv74Yzz22GN9GA0REREREdFdXSoMv/zyS7i4uCA8PBx79+7F5s2bIZFIsHHjRvz85z/H//3f/yE4OLi3Yh3wKioqOlzn5OTUd4EQERERERHdo0uF4bhx41BTU4MRI0YgJCQEf/3rX3Hx4kXhy93pwcaOHWvqEIiIiIiIiNrp0j2G9z+n5uOPP0ZTU5NRAyIiIiIiIqK+1a2Hz7TqxgNNiYiIiIiIqJ/pUmEokUggkUjatREREREREdHA1aV7DPV6PZYvXw6ZTAYA+PHHH/H888/D2tq6Tb99+/YZL0IiIiIiIiLqVV0qDJctW9ZmecmSJUYNhoiIiIiIiPpelwrDXbt29VYcREREREREZCI9evgMERERERERDXwsDImIiIiIiMwcC0MiIiIiIiIzx8KQiIiIiIjIzLEwJCIiIiIiMnMsDImIiIiIiMwcC0MiIiIiIiIzx8KQiMyKVqvFyy+/DBcXFwwdOhSurq5ISUmBTqcT+ixfvhwSiaTN69FHHzVh1ERERES9q0tfcD9QJScnY//+/aioqDB1KERkYq+++iry8vLw1ltvwcvLCydOnMBzzz0HW1tbrF27VugXGhqKXbt2CcuDBw82RbhEREREfWJAFIbXrl1DWloaCgoKcPXqVYwYMQI+Pj6Ii4vD448/burwjOqDDz5AUlISLl26BDc3N6SlpeHJJ5/s8jjT0kugHWTdCxFSX5JZ6JExFZiYXITmFompwxmwrmyZK7z/7LPPMG/ePMyde7fN2dkZ7733Hk6cONFmG5lMhlGjRvVpnERERESm0u8vJb1y5QomT56MTz/9FBkZGTh9+jQOHjyIwMBAxMbGmjo8o/rss8+waNEiLF26FCdPnsTSpUuxcOFCfP7556YOjUg0ZsyYgZKSEpw/fx4AcPLkSRw9ehRhYWFt+pWWlmLEiBEYP348fv3rX6Ours4U4RIRERH1iX4/YxgTEwOJRILjx4/D2vq/M2BeXl6Ijo4GAFRVVWH16tUoKSmBVCpFaGgotm3bhpEjRxocc9asWfDx8UF2drbQFhERAYVCgd27dwO4O4uwYsUKnD9/Hvv27YO9vT3eeOMN+Pn5YcWKFSgpKYGLiwt27dqFKVOmAAB2796NuLg47N27F3FxcaiursaMGTOwa9cuODo6/uSxZmdnIzg4GImJiQCAxMREHD58GNnZ2XjvvfcMbtPc3Izm5mZhubGxEQAgk+phYaH/yX1S/yaT6tv8Sd2j0WiE9/Hx8bh58ybc3d1hYWGBlpYWpKSkYP78+UK/4OBgPPnkk3j44Ydx5coVJCcnIzAwEJ9//jlkMlmPYrg3Fhq4mE/xYC7FhfkUD+bSOLpy/vp1YXjz5k0cPHgQaWlpbYrCVgqFAnq9HhEREbC2tsbhw4eh1WoRExODRYsWobS0tEf7z8rKwubNm5GUlISsrCwsXboU/v7+iI6OxtatW5GQkICoqCicPXsWEsndy/yampqQmZmJ/Px8SKVSLFmyBBs2bMCePXt+cn+fffYZ1q1b16YtJCSkTQF7v/T0dGzatKld+8uTdLCyaunaAVO/9coU3U93og4VFhYK748cOYLdu3cjPj4eSqUSly9fRkZGBq5fv47Zs2cDAORyOYC7/+kklUoRFxeH3/zmN0hNTcX06dN7FEtxcXGPtqf+hfkUD+ZSXJhP8WAue6apqanTfft1YXjx4kXo9Xq4u7t32OfQoUM4deoULl++DKVSCQDIz8+Hl5cXysrK4Ovr2+39h4WFYeXKlQCAjRs3Ijc3F76+vliwYAEAICEhAdOnT0dtba1wL5JGo0FeXh7c3NwAAKtWrUJKSkqn9nft2rV2s5wjR47EtWvXOtwmMTER8fHxwnJjYyOUSiVSv5BCa2nR+YOlfkkm1eOVKToknZCiWcd7DLvrTHKI8H7VqlXYuHEjfvvb3wptw4cPx7vvvovMzMwOx9i8eTNsbGzaXXLaWRqNBsXFxQgODoalpWW3xqD+g/kUD+ZSXJhP8WAujaP1asLO6NeFoV5/9/K51tk4Q9RqNZRKpVAUAoCnpycUCgXUanWPCkOVSiW8by3YvL2927XV1dUJhaGVlZVQFAKAo6Njl+5Nuv9Y9Xr9A49fJpMZvLStWSeBlg8rEY1mnYQPn+mBe/9CaWpqgqWlZZu2wYMHQ6/Xd/gXz7fffovq6mr87Gc/6/FfTvfvmwY25lM8mEtxYT7Fg7nsma6cu35dGI4bNw4SiQRqtRoREREG+3RUOD2ooJJKpULR2crQ9bf3nsjWsQy13fv9Z/effIlE0m5fHRk1alS72cG6uroO75V8kM8TH4e9vX2Xt6P+RaPRoLCwEGeSQ/ihaCTh4eFIS0vDww8/DC8vL3zxxRd47bXXhHuWb926heTkZDz99NNwdHTElStX8Lvf/Q4PPfRQt54QTERERDQQ9OunktrZ2SEkJATbt2/H7du3262vr6+Hp6cnqqqqUF1dLbRXVlaioaEBHh4eBsd1cHBATU2NsNzS0oIzZ84Y/wC6aPr06e2uo/7kk0/g5+dnooiIxGfbtm2YP38+YmJi4OHhgQ0bNmDlypV45ZVXAAAWFhY4ffo05s2bh/Hjx2PZsmUYP348PvvsMwwbNszE0RMRERH1jn49YwgAOTk58PPzw9SpU5GSkgKVSgWtVovi4mLk5uaisrISKpUKkZGRyM7OFh4+ExAQIDwt9H6zZ89GfHw8CgoK4ObmhqysLNTX1/ftgRmwdu1azJw5E6+++irmzZuHAwcO4NChQzh69KipQyMSjWHDhiE7O7vDhzoNHToURUVFfRsUERERkYn16xlDAHBxcUF5eTkCAwOxfv16TJw4EcHBwSgpKUFubi4kEgn279+P4cOHY+bMmQgKCoKrqyv27t3b4ZjR0dFYtmwZoqKiEBAQABcXFwQGBvbhURnm5+eH999/H7t27YJKpcLu3buxd+9eTJs2zdShERERERGRiEn0nb0BjgaExsZG2Nra4saNG7zHUARa7zEMCwvjPYYDHHMpLsyneDCX4sJ8igdzaRyttUFDQwNsbGwe2LffzxgSERERERFR72Jh2IfkcnmHryNHjpg6PCIiIiIiMlP9/uEzYlJRUdHhOicnp74LhIiIiIiI6B4sDPvQ2LFjTR0CERERERFRO7yUlIiIiIiIyMyxMCQiIiIiIjJzLAyJiIiIiIjMHAtDIiIiIiIiM8fCkIiIiIiIyMyxMCQiIiIiIjJzLAyJiIiIiIjMHAtDIiIiIiIiM8fCkIiIiIiIyMyxMCQiIiIiIjJzLAyJiIiIiIjMHAtDIiIiIiIiM8fCkIiIiIiIyMyZRWGYnJwMHx8fU4dBRH1Mq9Xi5ZdfhouLC4YOHQpXV1ekpKRAp9MJfZKTk+Hu7g5ra2sMHz4cQUFB+Pzzz00YNREREVHfGxCF4bVr17B69Wq4urpCJpNBqVQiPDwcJSUlpg6t17z//vuQSCSIiIgwdShEA9arr76KvLw8vPnmm1Cr1cjIyMDWrVuxbds2oc/48ePx5ptv4vTp0zh69CicnZ0xZ84cXL9+3YSRExEREfWtQaYO4KdcuXIF/v7+UCgUyMjIgEqlgkajQVFREWJjY3Hu3DlTh2h0X331FTZs2IDHHnus22NMSy+BdpC1EaMiU5BZ6JExFZiYXITmFompwxkQrmyZK7z/7LPPMG/ePMyde7fN2dkZ7733Hk6cOCH0efbZZ9ts/9prr+Evf/kLTp06hccff7xvgiYiIiIysX4/YxgTEwOJRILjx49j/vz5GD9+PLy8vBAfH49jx44BAKqqqjBv3jzI5XLY2Nhg4cKFqK2t7XDMWbNmIS4urk1bREQEli9fLiw7OzsjNTUVUVFRkMvlGDNmDA4cOIDr168L+/L29m7zD8zdu3dDoVCgqKgIHh4ekMvlCA0NRU1NTaePt6WlBZGRkdi0aRNcXV07vR0RtTdjxgyUlJTg/PnzAICTJ0/i6NGjCAsLM9j/zp072LFjB2xtbfHII4/0ZahEREREJtWvZwxv3ryJgwcPIi0tDdbW7We/FAoF9Ho9IiIiYG1tjcOHD0Or1SImJgaLFi1CaWlpj/aflZWFzZs3IykpCVlZWVi6dCn8/f0RHR2NrVu3IiEhAVFRUTh79iwkkruzOU1NTcjMzER+fj6kUimWLFmCDRs2YM+ePZ3aZ0pKChwcHPCrX/0KR44c+cn+zc3NaG5uFpYbGxsBADKpHhYW+m4cNfUnMqm+zZ/00zQajfA+Pj4eN2/ehLu7OywsLNDS0oKUlBTMnz+/Tb+CggIsWbIETU1NcHR0xMcffwxbW9s2fYwVlzHHJNNhPsWDuRQX5lM8mEvj6Mr569eF4cWLF6HX6+Hu7t5hn0OHDuHUqVO4fPkylEolACA/Px9eXl4oKyuDr69vt/cfFhaGlStXAgA2btyI3Nxc+Pr6YsGCBQCAhIQETJ8+HbW1tRg1ahSAuyc/Ly8Pbm5uAIBVq1YhJSWlU/v717/+hb/85S+oqKjodIzp6enYtGlTu/aXJ+lgZdXS6XGof3tliu6nOxEAoLCwUHh/5MgR7N69G/Hx8VAqlbh8+TIyMjJw/fp1zJ49W+jX3NyMzMxMNDY24pNPPkFERAQyMjKgUCiMHl9xcbHRxyTTYT7Fg7kUF+ZTPJjLnmlqaup0335dGOr1d2dJWmfjDFGr1VAqlUJRCACenp5QKBRQq9U9KgxVKpXwfuTIkQAAb2/vdm11dXVCYWhlZSUUhQDg6OiIurq6n9zX999/jyVLlmDnzp146KGHOh1jYmIi4uPjheXGxkYolUqkfiGF1tKi0+NQ/yST6vHKFB2STkjRrOM9hp1xJjlEeL9q1Sps3LgRv/3tb4W24cOH491330VmZqbB7detWwdPT09UV1e3u/+wJzQaDYqLixEcHAxLS0ujjUumwXyKB3MpLsyneDCXxtF6NWFn9OvCcNy4cZBIJFCr1R0+nVOv1xssHDtqBwCpVCoUna0MTbPe+0PYOpahtnsffX//D65EImm3L0MuXbqEK1euIDw8XGhrHXfQoEH4z3/+06bgbCWTySCTydq1N+sk0PJhJaLRrJPw4TOddO/vYFNTEywtLdu0DR48GHq9/oF/yej1emi12l75i+j+eGhgYz7Fg7kUF+ZTPJjLnunKuevXhaGdnR1CQkKwfft2rFmzpt19hvX19fD09ERVVRWqq6uFWcPKyko0NDTAw8PD4LgODg5tHgjT0tKCM2fOIDAwsPcO5ie4u7vj9OnTbdpefvllfP/993j99dfbzIh2xueJj8Pe3t6YIZIJaDQaFBYW4kxyCD8UuyE8PBxpaWl4+OGH4eXlhS+++AKvvfYaoqOjAQC3b99GWloannjiCTg6OuLbb79FTk4Ovv76a+GScSIiIiJz0K8LQwDIycmBn58fpk6dipSUFKhUKmi1WhQXFyM3NxeVlZVQqVSIjIxEdna28PCZgIAATJkyxeCYs2fPRnx8PAoKCuDm5oasrCzU19f37YHdZ8iQIZg4cWKbttb7m+5vJ6LO2bZtG5KSkhATE4O6ujqMHj0aK1euxMaNGwEAFhYWOHfuHN566y3cuHED9vb28PX1xZEjR+Dl5WXi6ImIiIj6Tr8vDF1cXFBeXo60tDSsX78eNTU1cHBwwOTJk5GbmwuJRIL9+/dj9erVmDlzJqRSKUJDQ9t8gfX9oqOjcfLkSURFRWHQoEFYt26dSWcLiah3DBs2DNnZ2cjOzja4fsiQIdi3b1/fBkVERETUD0n0nbkBjgaMxsZG2NraCrMfNLC1XkoaFhbGS0kHOOZSXJhP8WAuxYX5FA/m0jhaa4OGhgbY2Ng8sG+//4J7IiIiIiIi6l0sDPuQXC7v8NWZL7MnIiIiIiLqDf3+HkMxedAX1zs5OfVdIERERERERPdgYdiHxo4da+oQiIiIiIiI2uGlpERERERERGaOhSEREREREZGZY2FIRERERERk5lgYEhERERERmTkWhkRERERERGaOhSEREREREZGZY2FIRERERERk5lgYEhERERERmTkWhkRERERERGaOhSEREREREZGZY2FIRERERERk5lgYEhERERERmTkWhkRERERERGaOhSEREREREZGZG/CFYXJyMnx8fEwdBhF1kbOzMyQSSbtXbGwsNBoNEhIS4O3tDWtra4wePRpRUVH45ptvTB02ERERkSiZvDC8du0aVq9eDVdXV8hkMiiVSoSHh6OkpMTUoRnV2bNn8fTTTwv/GM7Ozn5g//T0dEgkEsTFxfVJfER9raysDDU1NcKruLgYALBgwQI0NTWhvLwcSUlJKC8vx759+3D+/Hk88cQTJo6aiIiISJwGmXLnV65cgb+/PxQKBTIyMqBSqaDRaFBUVITY2FicO3fOlOEZVVNTE1xdXbFgwQKsW7fugX3LysqwY8cOqFSqbu9vWnoJtIOsu7099Q8yCz0ypgITk4vQ3CIxdTg9cmXL3DbLDg4ObZa3bNkCNzc3BAQEQCKRCIViq23btmHq1KmoqqrCww8/3OvxEhEREZkTk84YxsTEQCKR4Pjx45g/fz7Gjx8PLy8vxMfH49ixYwCAqqoqzJs3D3K5HDY2Nli4cCFqa2s7HHPWrFntZtkiIiKwfPlyYdnZ2RmpqamIioqCXC7HmDFjcODAAVy/fl3Yl7e3N06cOCFss3v3bigUChQVFcHDwwNyuRyhoaGoqanp1LH6+vpi69ateOaZZyCTyTrsd+vWLURGRmLnzp0YPnx4p8YmGuju3LmDd955B9HR0ZBIDBfADQ0NkEgkUCgUfRscERERkRkw2YzhzZs3cfDgQaSlpcHauv3MlkKhgF6vR0REBKytrXH48GFotVrExMRg0aJFKC0t7dH+s7KysHnzZiQlJSErKwtLly6Fv78/oqOjsXXrViQkJCAqKgpnz54V/qHa1NSEzMxM5OfnQyqVYsmSJdiwYQP27NnTo1juFRsbi7lz5yIoKAipqak/2b+5uRnNzc3CcmNjIwBAJtXDwkJvtLjINGRSfZs/BzKNRtPhur///e+or69HZGSkwX4//vgjEhIS8Mwzz2Do0KEPHKu/ao15IMZO7TGf4sFcigvzKR7MpXF05fyZrDC8ePEi9Ho93N3dO+xz6NAhnDp1CpcvX4ZSqQQA5Ofnw8vLC2VlZfD19e32/sPCwrBy5UoAwMaNG5GbmwtfX18sWLAAAJCQkIDp06ejtrYWo0aNAnD3xObl5cHNzQ0AsGrVKqSkpHQ7hvu9//77KC8vR1lZWae3SU9Px6ZNm9q1vzxJByurFqPFRqb1yhSdqUPoscLCwg7Xbd26FZMmTUJFRQUqKirarNNqtcjIyEB9fT3Cw8MfOM5AcP8lsjSwMZ/iwVyKC/MpHsxlzzQ1NXW6r8kKQ73+7gxIR5eNAYBarYZSqRSKQgDw9PSEQqGAWq3uUWF47/17I0eOBAB4e3u3a6urqxMKQysrK6EoBABHR0fU1dV1O4Z7VVdXY+3atfjkk08wZMiQTm+XmJiI+Ph4YbmxsRFKpRKpX0ihtbQwSmxkOjKpHq9M0SHphBTNuoF9j+GZ5BCD7V999RVOnTqF//mf/0FYWFibdRqNBosXL8YPP/yAf/3rX7C3t++LUHuFRqNBcXExgoODYWlpaepwqIeYT/FgLsWF+RQP5tI4Wq8m7AyTFYbjxo2DRCKBWq1GRESEwT56vd5g4dhROwBIpVKh6GxlaAr13h+w1rEMtel0OoPbtPa5f1/d9e9//xt1dXWYPHmy0NbS0oJ//vOfePPNN9Hc3AwLi/aFnkwmM3jPYrNOAu0Af1gJ/VezTjLgHz7T0Yf6O++8gxEjRmDevHkYNOi/H0kajQaRkZG4dOkS/vGPf7R7WM1AZWlpyb/gRIT5FA/mUlyYT/FgLnumK+fOZIWhnZ0dQkJCsH37dqxZs6bdfYb19fXw9PREVVUVqqurhVnDyspKNDQ0wMPDw+C4Dg4ObR4I09LSgjNnziAwMLD3DsYIHn/8cZw+fbpN23PPPQd3d3ckJCQYLAof5PPExwf07ArdpdFoUFhYiDPJIaL8UNTpdNi1axeWLVvWpijUarWYP38+ysvL8dFHH6GlpQXXrl0DcPezY/DgwaYKmYiIiEiUTPp1FTk5OfDz88PUqVORkpIClUoFrVaL4uJi5ObmorKyEiqVCpGRkcjOzhYePhMQEIApU6YYHHP27NmIj49HQUEB3NzckJWVhfr6+r49MAPu3LmDyspK4f3Vq1dRUVEBuVyOsWPHYtiwYZg4cWKbbaytrWFvb9+unUgsDh06hKqqKkRHR7dp//rrr/Hhhx8CAHx8fNqs+8c//oFZs2b1UYRERERE5sGkhaGLiwvKy8uRlpaG9evXo6amBg4ODpg8eTJyc3MhkUiwf/9+rF69GjNnzoRUKkVoaCi2bdvW4ZjR0dE4efIkoqKiMGjQIKxbt65fzBZ+8803mDRpkrCcmZmJzMxMBAQE9PgJq0QD1Zw5cwxeju3s7Gy0y7SJiIiI6KdJ9PzXl6g0NjbC1tYWN27c4KWkItB6KWlYWJgoLyU1J8yluDCf4sFcigvzKR7MpXG01gYNDQ2wsbF5YF+TfsE9ERERERERmR4LQyORy+Udvo4cOWLq8IiIiIiIiDpk0nsMxeT+L+W+l5OTU98FQkRERERE1EUsDI1k7Nixpg6BiIiIiIioW3gpKRERERERkZljYUhERERERGTmWBgSERERERGZORaGREREREREZo6FIRERERERkZljYUhERERERGTmWBgSERERERGZORaGREREREREZo6FIRERERERkZljYUhERERERGTmWBgSERERERGZORaGREREREREZo6FIRERERERkZljYUhEvc7Z2RkSiaTdKzY2FgCg1+uRnJyM0aNHY+jQoZg1axbOnj1r4qiJiIiIzMeALwyTk5Ph4+Nj6jCI6AHKyspQU1MjvIqLiwEACxYsAABkZGTgtddew5tvvomysjKMGjUKwcHB+P77700ZNhEREZHZMHlheO3aNaxevRqurq6QyWRQKpUIDw9HSUmJqUMzqrNnz+Lpp58WZk6ys7Pb9dFqtXj55Zfh4uKCoUOHwtXVFSkpKdDpdH0fMJEROTg4YNSoUcLro48+gpubGwICAqDX65GdnY3f//73eOqppzBx4kS89dZbaGpqwrvvvmvq0ImIiIjMwiBT7vzKlSvw9/eHQqFARkYGVCoVNBoNioqKEBsbi3PnzpkyPKNqamqCq6srFixYgHXr1hns8+qrryIvLw9vvfUWvLy8cOLECTz33HOwtbXF2rVru7S/aekl0A6yNkboZEIyCz0ypgITk4vQ3CIxdThdcmXLXIPtd+7cwTvvvIP4+HhIJBJ8+eWXuHbtGubMmSP0kclkCAgIwP/7f/8PK1eu7KuQiYiIiMyWSWcMY2JiIJFIcPz4ccyfPx/jx4+Hl5cX4uPjcezYMQBAVVUV5s2bB7lcDhsbGyxcuBC1tbUdjjlr1izExcW1aYuIiMDy5cuFZWdnZ6SmpiIqKgpyuRxjxozBgQMHcP36dWFf3t7eOHHihLDN7t27oVAoUFRUBA8PD8jlcoSGhqKmpqZTx+rr64utW7fimWeegUwmM9jns88+w7x58zB37lw4Oztj/vz5mDNnTps4iAa6/fv3o76+XvidvHbtGgBg5MiRbfqNHDlSWEdEREREvctkM4Y3b97EwYMHkZaWBmvr9jNbCoUCer0eERERsLa2xuHDh6HVahETE4NFixahtLS0R/vPysrC5s2bkZSUhKysLCxduhT+/v6Ijo7G1q1bkZCQgKioKJw9exYSyd2ZmqamJmRmZiI/Px9SqRRLlizBhg0bsGfPnh7F0mrGjBnIy8vD+fPnMX78eJw8eRJHjx41eNlpq+bmZjQ3NwvLjY2NAACZVA8LC71R4iLTkUn1bf4cSDQajcH2P//5zwgJCYGDgwM0Gg20Wi2Au5dS37tNS0vLA8cZaFqPQyzHY+6YT/FgLsWF+RQP5tI4unL+TFYYXrx4EXq9Hu7u7h32OXToEE6dOoXLly9DqVQCAPLz8+Hl5YWysjL4+vp2e/9hYWHCJWobN25Ebm4ufH19hYdhJCQkYPr06aitrcWoUaMA3D2xeXl5cHNzAwCsWrUKKSkp3Y7hfgkJCWhoaIC7uzssLCzQ0tKCtLQ0LF68uMNt0tPTsWnTpnbtL0/SwcqqxWixkWm9MmXg3WdaWFjYrq2urg4lJSVISEgQ1rfOCn7wwQdwdXUV+p45cwbW1tYGxxnIWh+8Q+LAfIoHcykuzKd4MJc909TU1Om+JisM9fq7MyCts3GGqNVqKJVKoSgEAE9PTygUCqjV6h4VhiqVSnjfegmbt7d3u7a6ujqhMLSyshKKQgBwdHREXV1dt2O43969e/HOO+/g3XffhZeXFyoqKhAXF4fRo0dj2bJlBrdJTExEfHy8sNzY2AilUonUL6TQWloYLTYyDZlUj1em6JB0Qopm3cC6x/BMcki7tpSUFIwYMQJJSUkYNOjux0/rV1X8+OOPCAsLA3D3PsRly5Zh8+bNQttAp9FoUFxcjODgYFhaWpo6HOoh5lM8mEtxYT7Fg7k0jtarCTvDZIXhuHHjIJFIoFarERERYbCPXq83WDh21A4AUqlUKDpbGZpCvfcHrHUsQ233PhH0/h9KiUTSbl898cILL+Cll17CM888A+BuofrVV18hPT29w8JQJpMZvGexWSeBdoA9rIQ61qyTDLiHz9z/+6LT6fD2229j2bJlGDp0aJt1cXFxSE9Ph7u7O8aNG4fNmzfDysoKS5cuFd1fBpaWlqI7JnPGfIoHcykuzKd4MJc905VzZ7LC0M7ODiEhIdi+fTvWrFnT7j7D+vp6eHp6oqqqCtXV1cKsYWVlJRoaGuDh4WFwXAcHhzYPhGlpacGZM2cQGBjYewdjJE1NTZBK2z4PyMLColtfV/F54uOwt7c3VmhkIhqNBoWFhTiTHDLgPxQPHTqEqqoqREdHt1v34osv4ocffkBMTAy+++47TJs2DZ988gmGDRtmgkiJiIiIzI9Jv64iJycHfn5+mDp1KlJSUqBSqaDValFcXIzc3FxUVlZCpVIhMjIS2dnZwsNnAgICMGXKFINjzp49G/Hx8SgoKICbmxuysrJQX1/ftwdmwJ07d1BZWSm8v3r1KioqKiCXyzF27FgAQHh4ONLS0vDwww/Dy8sLX3zxBV577TWD/5AmGmjmzJnT4Qy7RCJBcnIykpOT+zYoIiIiIgJg4sLQxcUF5eXlSEtLw/r161FTUwMHBwdMnjwZubm5kEgk2L9/P1avXo2ZM2dCKpUiNDQU27Zt63DM6OhonDx5ElFRURg0aBDWrVvXL2YLv/nmG0yaNElYzszMRGZmJgICAoQnrG7btg1JSUmIiYlBXV0dRo8ejZUrV2Ljxo0mipqIiIiIiMyBRG/Mm+TI5BobG2Fra4sbN27wUlIRaL2UNCwsbMBfSmrumEtxYT7Fg7kUF+ZTPJhL42itDRoaGmBjY/PAvib9gnsiIiIiIiIyPRaGRiKXyzt8HTlyxNThERERERERdcik9xiKSUVFRYfrnJyc+i4QIiIiIiKiLmJhaCStTxYlIiIiIiIaaHgpKRERERERkZljYUhERERERGTmWBgSERERERGZORaGREREREREZo6FIRERERERkZljYUhERERERGTmWBgSERERERGZORaGREREREREZo6FIRERERERkZljYUhERERERGTmWBgSERERERGZORaGREREREREZo6FIRERERERkZljYUhEPXb16lUsWbIE9vb2sLKygo+PD/7973+36aNWq/HEE0/A1tYWw4YNw6OPPoqqqioTRUxERERE9xrwhWFycjJ8fHxMHQaR2fruu+/g7+8PS0tLfPzxx6isrMQf//hHKBQKoc+lS5cwY8YMuLu7o7S0FCdPnkRSUhKGDBliusCJiIiISGDywvDatWtYvXo1XF1dIZPJoFQqER4ejpKSElOHZlQ7d+7EY489huHDh2P48OEICgrC8ePH2/RJTk6GRCJp8xo1apSJIibqnFdffRVKpRK7du3C1KlT4ezsjMcffxxubm5Cn9///vcICwtDRkYGJk2aBFdXV8ydOxcjRowwYeRERERE1GqQKXd+5coV+Pv7Q6FQICMjAyqVChqNBkVFRYiNjcW5c+dMGZ5RlZaWYvHixfDz88OQIUOQkZGBOXPm4OzZs3BychL6eXl54dChQ8KyhYVFt/Y3Lb0E2kHWPY6bTEtmoUfGVGBichGaWySmDkdwZctc4f2HH36IkJAQLFiwAIcPH4aTkxNiYmLw61//GgCg0+lQUFCAF198ESEhIfjiiy/g4uKCxMREREREmOgIiIiIiOheJp0xjImJgUQiwfHjxzF//nyMHz8eXl5eiI+Px7FjxwAAVVVVmDdvHuRyOWxsbLBw4ULU1tZ2OOasWbMQFxfXpi0iIgLLly8Xlp2dnZGamoqoqCjI5XKMGTMGBw4cwPXr14V9eXt748SJE8I2u3fvhkKhQFFRETw8PCCXyxEaGoqamppOHeuePXsQExMDHx8fuLu7Y+fOndDpdO1mRgcNGoRRo0YJLwcHh06NT2QqX375JXJzczFu3DgUFRXh+eefx5o1a/D2228DAOrq6nDr1i1s2bIFoaGh+OSTT/Dkk0/iqaeewuHDh00cPREREREBJpwxvHnzJg4ePIi0tDRYW7ef2VIoFNDr9YiIiIC1tTUOHz4MrVaLmJgYLFq0CKWlpT3af1ZWFjZv3oykpCRkZWVh6dKl8Pf3R3R0NLZu3YqEhARERUXh7NmzkEjuztQ0NTUhMzMT+fn5kEqlWLJkCTZs2IA9e/Z0ef9NTU3QaDSws7Nr037hwgWMHj0aMpkM06ZNw+bNm+Hq6trhOM3NzWhubhaWGxsbAQAyqR4WFvoux0X9i0yqb/Nnf6HRaIT3Op0OkydPxqZNmwAAEydOxOnTp5GTk4PFixcLP5/h4eFYtWoVgLsz40ePHkVOTg78/Pz6/gBMoPWc3XvuaOBiPsWDuRQX5lM8mEvj6Mr5M1lhePHiRej1eri7u3fY59ChQzh16hQuX74MpVIJAMjPz4eXlxfKysrg6+vb7f2HhYVh5cqVAICNGzciNzcXvr6+WLBgAQAgISEB06dPR21trXCfn0ajQV5ennDv1KpVq5CSktKt/b/00ktwcnJCUFCQ0DZt2jS8/fbbGD9+PGpra5Gamgo/Pz+cPXsW9vb2BsdJT08X/kF+r5cn6WBl1dKt2Kj/eWWKztQhtFFYWCi8VygUkMvlbdq0Wi0uXLiAwsJCaDQaWFhYwMLCok2fwYMH49SpU23azEFxcbGpQyAjYj7Fg7kUF+ZTPJjLnmlqaup0X5MVhnr93RmQ1tk4Q9RqNZRKpVAUAoCnpycUCgXUanWPCkOVSiW8HzlyJADA29u7XVtdXZ1QGFpZWbV5oIajoyPq6uq6vO+MjAy89957KC0tbfNUxl/84hfCe29vb0yfPh1ubm546623EB8fb3CsxMTENusaGxuhVCqR+oUUWsvu3Z9I/YdMqscrU3RIOiFFs67/3GN4JjlEeD979mx8/fXXCAsLE9o+/fRTjB8/Xmhr/V29t89f//pXPPLII23axEyj0aC4uBjBwcGwtLQ0dTjUQ8yneDCX4sJ8igdzaRytVxN2hskKw3HjxkEikUCtVnf4AAq9Xm+wcOyoHQCkUqlQdLYyNIV67w9Y61iG2nQ6ncFtWvvcv6+fkpmZic2bN+PQoUNtilNDrK2t4e3tjQsXLnTYRyaTQSaTtWtv1kmg7UcPK6GeadZJ+tXDZ+79XVi/fj38/PywdetWLFy4EMePH8ef//xn7NixQ+j34osvYtGiRZg1axYCAwNx8OBBFBQUoLS01Ow+7C0tLc3umMWM+RQP5lJcmE/xYC57pivnzmSFoZ2dHUJCQrB9+3asWbOm3X2G9fX18PT0RFVVFaqrq4VZw8rKSjQ0NMDDw8PguA4ODm0eCNPS0oIzZ84gMDCw9w6mk7Zu3YrU1FQUFRVhypQpP9m/ubkZarUajz32WJf39Xni4x1efkoDh0ajQWFhIc4kh/TbD0VfX1/87//+LxITE5GSkgIXFxdkZ2cjMjJS6PPkk08iLy8P6enpWLNmDSZMmIAPPvgAM2bMMGHkRERERNTKpF9X0frgialTpyIlJQUqlQparRbFxcXIzc1FZWUlVCoVIiMjkZ2dLTx8JiAgoMPCavbs2YiPj0dBQQHc3NyQlZWF+vr6vj0wAzIyMpCUlIR3330Xzs7OuHbtGgBALpdDLpcDADZs2IDw8HA8/PDDqKurQ2pqKhobG7Fs2TJThk70k375y1/il7/85QP7REdHIzo6uo8iIiIiIqKuMOnXVbi4uKC8vByBgYFYv349Jk6ciODgYJSUlCA3NxcSiQT79+/H8OHDMXPmTAQFBcHV1RV79+7tcMzo6GgsW7YMUVFRCAgIgIuLS7+YLczJycGdO3cwf/58ODo6Cq/MzEyhz9dff43FixdjwoQJeOqppzB48GAcO3YMY8aMMWHkREREREQkdhJ9V2+So36tsbERtra2uHHjBi8lFYHWS0nDwsL67aWk1DnMpbgwn+LBXIoL8ykezKVxtNYGDQ0NsLGxeWBfk84YEhERERERkemxMDSS1nsFDb2OHDli6vCIiIiIiIg6ZNKHz4hJRUVFh+ucnJz6LhAiIiIiIqIuYmFoJGPHjjV1CERERERERN3CS0mJiIiIiIjMHAtDIiIiIiIiM8fCkIiIiIiIyMyxMCQiIiIiIjJzLAyJiIiIiIjMHAtDIiIiIiIiM8fCkIiIiIiIyMyxMCQiIiIiIjJzLAyJiIiIiIjMHAtDIiIiIiIiM8fCkIiIiIiIyMyxMCQiIiIiIjJzLAyJiIiIiIjMHAtDIuqUq1evYsmSJbC3t4eVlRV8fHzw73//W1ifnJwMd3d3WFtbY/jw4QgKCsLnn39uwoiJiIiIqLPMojBMTk6Gj4+PqcMgGrC+++47+Pv7w9LSEh9//DEqKyvxxz/+EQqFQugzfvx4vPnmmzh9+jSOHj0KZ2dnzJkzB9evXzdd4ERERETUKYNMHUBnXLt2DWlpaSgoKMDVq1cxYsQI+Pj4IC4uDo8//ripwzOq7Oxs5ObmoqqqCg899BDmz5+P9PR0DBkypEvjTEsvgXaQdS9FSX1FZqFHxlRgYnIRmlskfbrvK1vmCu9fffVVKJVK7Nq1S2hzdnZu0//ZZ59ts/zaa6/hL3/5C06dOiW631MiIiIisen3M4ZXrlzB5MmT8emnnyIjIwOnT5/GwYMHERgYiNjYWFOHZ1R79uzBSy+9hD/84Q9Qq9X4y1/+gr179yIxMdHUoZGZ+/DDDzFlyhQsWLAAI0aMwKRJk7Bz584O+9+5cwc7duyAra0tHnnkkT6MlIiIiIi6o9/PGMbExEAikeD48eOwtv7vDJiXlxeio6MBAFVVVVi9ejVKSkoglUoRGhqKbdu2YeTIkQbHnDVrFnx8fJCdnS20RUREQKFQYPfu3QDuzoasWLEC58+fx759+2Bvb4833ngDfn5+WLFiBUpKSuDi4oJdu3ZhypQpAIDdu3cjLi4Oe/fuRVxcHKqrqzFjxgzs2rULjo6OP3msn332Gfz9/YWZF2dnZyxevBjHjx/vcJvm5mY0NzcLy42NjQAAmVQPCwv9T+6T+jeZVN/mz76k0WiE919++SVyc3Oxdu1avPDCCzhx4gTWrFkDCwsLLF26VOhXUFCAJUuWoKmpCY6Ojvj4449ha2vbZixz1XoOeC7EgfkUD+ZSXJhP8WAujaMr569fF4Y3b97EwYMHkZaW1qYobKVQKKDX6xEREQFra2scPnwYWq0WMTExWLRoEUpLS3u0/6ysLGzevBlJSUnIysrC0qVL4e/vj+joaGzduhUJCQmIiorC2bNnIZHcvcyvqakJmZmZyM/Ph1QqxZIlS7Bhwwbs2bPnJ/c3Y8YMvPPOOzh+/DimTp2KL7/8EoWFhVi2bFmH26Snp2PTpk3t2l+epIOVVUv3D576lVem6Pp8n4WFhcL7lpYWuLm5wc/PDzU1NXBycsLjjz+OjIwM2NvbC/2am5uRmZmJxsZGfPLJJ4iIiEBGRkabexHNXXFxsalDICNiPsWDuRQX5lM8mMueaWpq6nTffl0YXrx4EXq9Hu7u7h32OXToEE6dOoXLly9DqVQCAPLz8+Hl5YWysjL4+vp2e/9hYWFYuXIlAGDjxo3Izc2Fr68vFixYAABISEjA9OnTUVtbi1GjRgG4W5Xn5eXBzc0NALBq1SqkpKR0an/PPPMMrl+/jhkzZkCv10Or1eK3v/0tXnrppQ63SUxMRHx8vLDc2NgIpVKJ1C+k0FpadOu4qf+QSfV4ZYoOSSekaNb17T2GZ5JDhPejR4+Gn58fwsLChLbq6mqkp6e3abvXunXr4Onpierq6nb3H5ojjUaD4uJiBAcHw9LS0tThUA8xn+LBXIoL8ykezKVxtF5N2Bn9ujDU6+9ePtc6G2eIWq2GUqkUikIA8PT0hEKhgFqt7lFhqFKphPetl6V6e3u3a6urqxMKQysrK6EoBABHR0fU1dV1an+lpaVIS0tDTk4Opk2bhosXL2Lt2rVwdHREUlKSwW1kMhlkMlm79madBNo+flgJ9Z5mnaTPHz5z74ewv78/Lly40Kbt0qVLGDNmzAM/rFv/g4Mf6P9laWnJ8yEizKd4MJfiwnyKB3PZM105d/26MBw3bhwkEgnUajUiIiIM9tHr9QYLx47aAUAqlQpFZytD19/eeyJbxzLUptPpDG7T2uf+fXUkKSkJS5cuxYoVKwDcLUJv376N3/zmN/j9738PqbTzzwr6PPHxNpf40cCk0WhQWFiIM8khJv1QXLduHfz8/LB582YsXLgQx48fx44dO7Bjxw4AwO3bt5GWloYnnngCjo6O+Pbbb5GTk4Ovv/5amGEnIiIiov6rXz+V1M7ODiEhIdi+fTtu377dbn19fT08PT1RVVWF6upqob2yshINDQ3w8PAwOK6DgwNqamqE5ZaWFpw5c8b4B9BFTU1N7Yo/CwsL6PX6TheXRL3B19cX//u//4v33nsPEydOxCuvvILs7GxERkYCuPtzeu7cOTz99NMYP348fvnLX+L69es4cuQIvLy8TBw9EREREf2Ufj1jCAA5OTnw8/PD1KlTkZKSApVKBa1Wi+LiYuTm5qKyshIqlQqRkZHIzs4WHj4TEBAgPC30frNnz0Z8fDwKCgrg5uaGrKws1NfX9+2BGRAeHo7XXnsNkyZNEi4lTUpKwhNPPAELC94vSKb1y1/+Er/85S8NrhsyZAj27dvXxxERERERkbH0+8LQxcUF5eXlSEtLw/r161FTUwMHBwdMnjwZubm5kEgk2L9/P1avXo2ZM2e2+bqKjkRHR+PkyZOIiorCoEGDsG7dOgQGBvbhURn28ssvQyKR4OWXX8bVq1fh4OCA8PBwpKWlmTo0IiIiIiISMYme1yiKSmNjI2xtbXHjxg3eYygCrfcYhoWF8cbrAY65FBfmUzyYS3FhPsWDuTSO1tqgoaEBNjY2D+zbr+8xJCIiIiIiot7HwrAPyeXyDl9HjhwxdXhERERERGSm+v09hmJSUVHR4TonJ6e+C4SIiIiIiOgeLAz70NixY00dAhERERERUTu8lJSIiIiIiMjMsTAkIiIiIiIycywMiYiIiIiIzBwLQyIiIiIiIjPHwpCIiIiIiMjMsTAkIiIiIiIycywMiYiIiIiIzBwLQyIiIiIiIjPHwpCIiIiIiMjMsTAkIiIiIiIycywMiYiIiIiIzBwLQyIiIiIiIjPHwpCIiIiIiMjMsTAkEonk5GRIJJI2r1GjRrVZ7+7uDmtrawwfPhxBQUH4/PPPTRgxEREREfUXA74wTE5Oho+Pj6nDIOoXvLy8UFNTI7xOnz4trBs/fjzefPNNnD59GkePHoWzszPmzJmD69evmzBiIiIiIuoPBpk6gGvXriEtLQ0FBQW4evUqRowYAR8fH8TFxeHxxx83dXhGs3PnTrz99ts4c+YMAGDy5MnYvHkzpk6dKvT5/vvvkZSUhP/93/9FXV0dJk2ahNdffx2+vr5d3t+09BJoB1kbLX4yDZmFHhlTgYnJRWhukbRbf2XL3DbLgwYNajNLeK9nn322zfJrr72Gv/zlLzh16pSofteIiIiIqOtMOmN45coVTJ48GZ9++ikyMjJw+vRpHDx4EIGBgYiNjTVlaEZXWlqKxYsX4x//+Ac+++wzPPzww5gzZw6uXr0q9FmxYgWKi4uRn5+P06dPY86cOQgKCmrTh+hBLly4gNGjR8PFxQXPPPMMvvzyS4P97ty5gx07dsDW1haPPPJIH0dJRERERP2NSWcMY2JiIJFIcPz4cVhb/3d2y8vLC9HR0QCAqqoqrF69GiUlJZBKpQgNDcW2bdswcuRIg2POmjULPj4+yM7OFtoiIiKgUCiwe/duAICzszNWrFiB8+fPY9++fbC3t8cbb7wBPz8/rFixAiUlJXBxccGuXbswZcoUAMDu3bsRFxeHvXv3Ii4uDtXV1ZgxYwZ27doFR0fHnzzWPXv2tFneuXMn/v73v6OkpARRUVH44Ycf8MEHH+DAgQOYOXMmgLuXye7fvx+5ublITU01OG5zczOam5uF5cbGRgCATKqHhYX+J+Oi/k0m1bf5834ajUZ4P3nyZPz1r3/FuHHjUFdXh/T0dPj5+aGiogL29vYAgIKCAixZsgRNTU1wdHTExx9/DFtb2zbjUO9oPcc81+LAfIoHcykuzKd4MJfG0ZXzZ7LC8ObNmzh48CDS0tLaFIWtFAoF9Ho9IiIiYG1tjcOHD0Or1SImJgaLFi1CaWlpj/aflZWFzZs3IykpCVlZWVi6dCn8/f0RHR2NrVu3IiEhAVFRUTh79iwkkruX8DU1NSEzMxP5+fmQSqVYsmQJNmzY0K7o64ympiZoNBrY2dkBALRaLVpaWjBkyJA2/YYOHYqjR492OE56ejo2bdrUrv3lSTpYWbV0OS7qn16ZojPYXlhY2GZ5yJAhqK6uBnD3P16ef/55/O53v8O8efMA3P2PhMzMTDQ2NuKTTz5BREQEMjIyoFAoejV++q/i4mJTh0BGxHyKB3MpLsyneDCXPdPU1NTpviYrDC9evAi9Xg93d/cO+xw6dAinTp3C5cuXoVQqAQD5+fnw8vJCWVlZt+69axUWFoaVK1cCADZu3Ijc3Fz4+vpiwYIFAICEhARMnz4dtbW1wj1bGo0GeXl5cHNzAwCsWrUKKSkp3dr/Sy+9BCcnJwQFBQEAhg0bhunTp+OVV16Bh4cHRo4ciffeew+ff/45xo0b1+E4iYmJiI+PF5YbGxuhVCqR+oUUWkuLbsVG/YdMqscrU3RIOiFFs679PYZnkkMeuP3OnTthaWmJsLCwduvWrVsHT09PVFdXt7v/kIxPo9GguLgYwcHBsLS0NHU41EPMp3gwl+LCfIoHc2kcrVcTdobJCkO9/u6lca2zcYao1WoolUqhKAQAT09PKBQKqNXqHhWGKpVKeN96Waq3t3e7trq6OqEwtLKyEopCAHB0dERdXV2X952RkYH33nsPpaWlbWYI8/PzER0dDScnJ1hYWODnP/85nn32WZSXl3c4lkwmg0wma9ferJNAa+BhJTQwNeskBh8+86APyubmZpw7dw4zZ87ssJ9er4dWq+UHbh+ytLTk+RYR5lM8mEtxYT7Fg7nsma6cO5MVhuPGjYNEIoFarUZERITBPnq93mDh2FE7AEilUqHobGXo2tp7T1LrWIbadDqdwW1a+9y/r5+SmZmJzZs349ChQ22KUwBwc3PD4cOHcfv2bTQ2NsLR0RGLFi2Ci4tLl/YBAJ8nPi7cV0YDl0ajQWFhIc4kh/zkL/aGDRsQHh6Ohx9+GHV1dUhNTUVjYyOWLVuG27dvIy0tDU888QQcHR3x7bffIicnB19//bUwS05ERERE5stkTyW1s7NDSEgItm/fjtu3b7dbX19fD09PT1RVVQn3TAFAZWUlGhoa4OHhYXBcBwcH1NTUCMstLS3CV0SY2tatW/HKK6/g4MGDwkNtDLG2toajoyO+++47FBUVCfeHET3I119/jcWLF2PChAl46qmnMHjwYBw7dgxjxoyBhYUFzp07h6effhrjx4/HL3/5S1y/fh1HjhyBl5eXqUMnIiIiIhMz6VNJc3Jy4Ofnh6lTpyIlJQUqlQparRbFxcXIzc1FZWUlVCoVIiMjkZ2dLTx8JiAgoMPCavbs2YiPj0dBQQHc3NyQlZWF+vr6vj0wAzIyMpCUlIR3330Xzs7OuHbtGgBALpdDLpcDAIqKiqDX6zFhwgRcvHgRL7zwAiZMmIDnnnvOlKHTAPH+++93uG7IkCHYt29fH0ZDRERERAOJSb/H0MXFBeXl5QgMDMT69esxceJEBAcHo6SkBLm5uZBIJNi/fz+GDx+OmTNnIigoCK6urti7d2+HY0ZHR2PZsmWIiopCQEAAXFxcEBgY2IdHZVhOTg7u3LmD+fPnw9HRUXhlZmYKfRoaGhAbGwt3d3dERUVhxowZ+OSTT3hdNRERERER9SqJvqs3yVG/1tjYCFtbW9y4cYP3GIpA6z2GYWFh/A+CAY65FBfmUzyYS3FhPsWDuTSO1tqgoaEBNjY2D+xr0hlDIiIiIiIiMj0WhkbSeq+godeRI0dMHR4REREREVGHTPrwGTGpqKjocJ2Tk1PfBUJERERERNRFLAyNZOzYsaYOgYiIiIiIqFt4KSkREREREZGZY2FIRERERERk5lgYEhERERERmTkWhkRERERERGaOhSEREREREZGZY2FIRERERERk5lgYEhERERERmTkWhkRERERERGaOhSEREREREZGZY2FIRERERERk5lgYEhERERERmTkWhkRERERERGaOhSEREREREZGZM4vCMDk5GT4+PqYOg6hH0tPTIZFIEBcXJ7TdunULq1atws9+9jMMHToUHh4eyM3NNV2QRERERDQgDYjC8Nq1a1i9ejVcXV0hk8mgVCoRHh6OkpISU4dmVPv27cOUKVOgUChgbW0NHx8f5Ofnmzos6gdOnDiBHTt2QKVStWlft24dDh48iHfeeQdqtRrr1q3D6tWrceDAARNFSkREREQD0SBTB/BTrly5An9/fygUCmRkZEClUkGj0aCoqAixsbE4d+6cqUM0Gjs7O/z+97+Hu7s7Bg8ejI8++gjPPfccRowYgZCQkC6NNS29BNpB1r0UKfWWK1vmtmv74YcfEBUVhZ07dyI1NbXNus8++wzLli3DrFmzAAC/+c1v8Kc//QknTpzAvHnz+iJkIiIiIhKBfj9jGBMTA4lEguPHj2P+/PkYP348vLy8EB8fj2PHjgEAqqqqMG/ePMjlctjY2GDhwoWora3tcMxZs2a1uRwPACIiIrB8+XJh2dnZGampqYiKioJcLseYMWNw4MABXL9+XdiXt7c3Tpw4IWyze/duKBQKFBUVwcPDA3K5HKGhoaipqenUsc6aNQtPPvkkPDw84ObmhrVr10KlUuHo0aOdP2EkOjt27EBYWBiCgoLarZsxYwY+/PBDXL16FXq9Hv/4xz9w/vz5Lv9HAhERERGZt349Y3jz5k0cPHgQaWlpsLZuP/ulUCig1+sREREBa2trHD58GFqtFjExMVi0aBFKS0t7tP+srCxs3rwZSUlJyMrKwtKlS+Hv74/o6Ghs3boVCQkJiIqKwtmzZyGRSAAATU1NyMzMRH5+PqRSKZYsWYINGzZgz549Xdq3Xq/Hp59+iv/85z949dVXO+zX3NyM5uZmYbmxsREAIJPqYWGh78ZRkylpNJo2y++++y4uXbqEv//979BoNNDr9dDpdEK/P/7xj3j++efxs5/9DIMGDYJUKkVeXh6mTZvWbiwyrdZ8MC/iwHyKB3MpLsyneDCXxtGV89evC8OLFy9Cr9fD3d29wz6HDh3CqVOncPnyZSiVSgBAfn4+vLy8UFZWBl9f327vPywsDCtXrgQAbNy4Ebm5ufD19cWCBQsAAAkJCZg+fTpqa2sxatQoAHdPfl5eHtzc3AAAq1atQkpKSqf32dDQACcnJzQ3N8PCwgI5OTkIDg7usH96ejo2bdrUrv3lSTpYWbV0er/UPxQWFgrvr1+/jg0bNiA5ORlHjhwBAHz77be4fPmy0G///v349NNP8bvf/Q4jRozA2bNnERMTg+rqajzyyCMmOQZ6sOLiYlOHQEbEfIoHcykuzKd4MJc909TU1Om+/bow1Ovvzni1zsYZolaroVQqhaIQADw9PaFQKKBWq3tUGN77oI+RI0cCALy9vdu11dXVCYWhlZWVUBQCgKOjI+rq6jq9z2HDhqGiogK3bt1CSUkJ4uPj4erqKtxDdr/ExETEx8cLy42NjVAqlUj9QgqtpUWn90v9w5nk/14CeuDAATQ0NGD9+vXC70BLSwsqKyvx8ccf48aNG1iwYAH+9re/ISwsTNhOq9XiX//6FxITE/s8fuqYRqNBcXExgoODYWlpaepwqIeYT/FgLsWF+RQP5tI4Wq8m7Ix+XRiOGzcOEokEarUaERERBvvo9XqDhWNH7QAglUqForOVoWnWe38IW8cy1KbT6Qxu09rn/n09iFQqxdixYwEAPj4+UKvVSE9P77AwlMlkkMlk7dqbdRJoWzouqKl/uvfnJyQkBOXl5Thy5Agee+wxWFpa4rnnnoO7uzsSEhIglUqh0WgwePDgNttZWlpCr9fzQ7SfsrS0ZG5EhPkUD+ZSXJhP8WAue6Yr565fF4Z2dnYICQnB9u3bsWbNmnb3GdbX18PT0xNVVVWorq4WZg0rKyvR0NAADw8Pg+M6ODi0eSBMS0sLzpw5g8DAwN47mG7S6/Vt7iHsrM8TH4e9vX0vRER9ZdiwYZg4cSKqqqowceJEWFpawtraGvb29pg4cSIAICAgAC+88AKGDh2KMWPG4PDhw3j77bfx2muvmTh6IiIiIhpI+nVhCAA5OTnw8/PD1KlTkZKSApVKBa1Wi+LiYuTm5qKyshIqlQqRkZHIzs4WHj4TEBCAKVOmGBxz9uzZiI+PR0FBAdzc3JCVlYX6+vq+PTAD0tPTMWXKFLi5ueHOnTsoLCzE22+/zS8spw69//77SExMRGRkJG7evIkxY8YgLS0Nzz//vKlDIyIiIqIBpN8Xhi4uLigvL0daWhrWr1+PmpoaODg4YPLkycjNzYVEIsH+/fuxevVqzJw5E1KpFKGhodi2bVuHY0ZHR+PkyZOIiorCoEGDsG7dun4xW3j79m3ExMTg66+/xtChQ+Hu7o533nkHixYtMnVo1E/c/6TdUaNGYdeuXaYJhoiIiIhEQ6Lvyg1w1O81NjbC1tYWN27c4KWkIqDRaFBYWIiwsDBeXz/AMZfiwnyKB3MpLsyneDCXxtFaGzQ0NMDGxuaBffv9F9wTERERERFR72Jh2IfkcnmHr9bvqSMiIiIiIupr/f4eQzGpqKjocJ2Tk1PfBUJERERERHQPFoZ9qPX7CYmIiIiIiPoTXkpKRERERERk5lgYEhERERERmTkWhkRERERERGaOhSEREREREZGZY2FIRERERERk5lgYEhERERERmTkWhkRERERERGaOhSEREREREZGZY2FIRERERERk5lgYEhERERERmTkWhkRERERERGaOhSEREREREZGZY2FIRERERERk5lgYEvVD6enpkEgkWL9+fZt2tVqNJ554Ara2thg2bBgeffRRVFVVmShKIiIiIhKLAV8YJicnw8fHx9RhEBlNWVkZduzYAZVK1ab90qVLmDFjBtzd3VFaWoqTJ08iKSkJQ4YMMVGkRERERCQWJi8Mr127htWrV8PV1RUymQxKpRLh4eEoKSkxdWhGdfbsWTz99NNwdnaGRCJBdna2wX45OTlwcXHBkCFDMHnyZBw5cqRvAyWTunXrFiIjI7Fz504MHz68zbrf//73CAsLQ0ZGBiZNmgRXV1fMnTsXI0aMMFG0RERERCQWg0y58ytXrsDf3x8KhQIZGRlQqVTQaDQoKipCbGwszp07Z8rwjKqpqQmurq5YsGAB1q1bZ7DP3r17ERcXh5ycHPj7++NPf/oTfvGLX6CyshIPP/xwl/Y3Lb0E2kHWxgidesmVLXPbtcXGxmLu3LkICgpCamqq0K7T6VBQUIAXX3wRISEh+OKLL+Di4oLExERERET0YdREREREJEYmnTGMiYmBRCLB8ePHMX/+fIwfPx5eXl6Ij4/HsWPHAABVVVWYN28e5HI5bGxssHDhQtTW1nY45qxZsxAXF9emLSIiAsuXLxeWnZ2dkZqaiqioKMjlcowZMwYHDhzA9evXhX15e3vjxIkTwja7d++GQqFAUVERPDw8IJfLERoaipqamk4dq6+vL7Zu3YpnnnkGMpnMYJ/XXnsNv/rVr7BixQp4eHggOzsbSqUSubm5ndoHDWzvv/8+ysvLkZ6e3m5dXV0dbt26hS1btiA0NBSffPIJnnzySTz11FM4fPiwCaIlIiIiIjEx2YzhzZs3cfDgQaSlpcHauv3MlkKhgF6vR0REBKytrXH48GFotVrExMRg0aJFKC0t7dH+s7KysHnzZiQlJSErKwtLly6Fv78/oqOjsXXrViQkJCAqKgpnz56FRCIBcHfWLzMzE/n5+ZBKpViyZAk2bNiAPXv29CgWALhz5w7+/e9/46WXXmrTPmfOHPy///f/OtyuubkZzc3NwnJjYyMAQCbVw8JC3+O4qPdoNBrhfXV1NdauXYuCggJYWFhAo9FAr9ejpaUFwN2fDwAIDw/HqlWrAABeXl44evQocnJy4Ofn1/cHQF3Smu97804DF/MpHsyluDCf4sFcGkdXzp/JCsOLFy9Cr9fD3d29wz6HDh3CqVOncPnyZSiVSgBAfn4+vLy8UFZWBl9f327vPywsDCtXrgQAbNy4Ebm5ufD19cWCBQsAAAkJCZg+fTpqa2sxatQoAHdPbF5eHtzc3AAAq1atQkpKSrdjuNeNGzfQ0tKCkSNHtmkfOXIkrl271uF26enp2LRpU7v2lyfpYGXVYpTYqHcUFhYK748dO4a6ujpMmzZNaNPpdDhy5Ajy8vKwd+9eWFhYwMLCos12gwcPxqlTp9q0Uf9WXFxs6hDIiJhP8WAuxYX5FA/msmeampo63ddkhaFef3c2q3U2zhC1Wg2lUikUhQDg6ekJhUIBtVrdo8Lw3ic+thZj3t7e7drq6uqEwtDKykooCgHA0dERdXV13Y7BkPvPh16vf+A5SkxMRHx8vLDc2NgIpVKJ1C+k0FpaGDU2Mq4zySHC+8ceewwLFy5ss/7Xv/41xo0bh+nTpyMsLEz4eQ8LCxP6/PWvf8UjjzzSpo36J41Gg+LiYgQHB8PS0tLU4VAPMZ/iwVyKC/MpHsylcbReTdgZJisMx40bB4lEArVa3eHDMzoqih5ULEmlUqHobGVoCvXeH7DWsQy16XQ6g9u09rl/X9310EMPwcLCot3sYF1dXbtZxHvJZDKD9yw26yTQtnRcUJLp3fvzZGdnBzs7uzbr5XI5HBwcMGbMGFhaWuLFF1/EokWLMGvWLAQGBuLgwYMoKChAaWkpPzAHEEtLS+ZLRJhP8WAuxYX5FA/msme6cu5MVhja2dkhJCQE27dvx5o1a9rdZ1hfXw9PT09UVVWhurpamDWsrKxEQ0MDPDw8DI7r4ODQ5oEwLS0tOHPmDAIDA3vvYIxg8ODBmDx5MoqLi/Hkk08K7cXFxZg3b16Xx/s88XHY29sbM0QysSeffBJ5eXlIT0/HmjVrMGHCBHzwwQeYMWOGqUMjIiIiogHOpF9X0frQjKlTpyIlJQUqlQparRbFxcXIzc1FZWUlVCoVIiMjkZ2dLTx8JiAgAFOmTDE45uzZsxEfH4+CggK4ubkhKysL9fX1fXtgBty5cweVlZXC+6tXr6KiogJyuRxjx44FAMTHx2Pp0qWYMmUKpk+fjh07dqCqqgrPP/+8KUMnEyktLYVGo2lz/2B0dDSio6NNGBURERERiZFJC0MXFxeUl5cjLS0N69evR01NDRwcHDB58mTk5uZCIpFg//79WL16NWbOnAmpVIrQ0FBs27atwzGjo6Nx8uRJREVFYdCgQVi3bl2/mC385ptvMGnSJGE5MzMTmZmZCAgIEJ6wumjRInz77bdISUlBTU0NJk6ciMLCQowZM8ZEURMRERERkTmQ6I11kxz1C42NjbC1tcWNGzd4KakItM4YhoWF8fr6AY65FBfmUzyYS3FhPsWDuTSO1tqgoaEBNjY2D+xr0i+4JyIiIiIiItNjYWgkcrm8w9eRI0dMHR4REREREVGHTHqPoZhUVFR0uM7JyanvAiEiIiIiIuoiFoZG0vpkUSIiIiIiooGGl5ISERERERGZORaGREREREREZo6FIRERERERkZljYUhERERERGTmWBgSERERERGZORaGREREREREZo6FIRERERERkZljYUhERERERGTmWBgSERERERGZORaGREREREREZo6FIRERERERkZljYUhERERERGTmWBgSERERERGZORaGRP1Ieno6JBIJ4uLihLbXX38dgwcPhkQiEV6PPvqo6YIkIiIiItExi8IwOTkZPj4+pg6D6IHKysqwY8cOqFSqdutCQkJQU1MjvAoLC00QIRERERGJ1YAoDK9du4bVq1fD1dUVMpkMSqUS4eHhKCkpMXVoRrVz50489thjGD58OIYPH46goCAcP37c1GFRH7h16xYiIyOxc+dODB8+vN36wYMHY9SoUcLLzs7OBFESERERkVgNMnUAP+XKlSvw9/eHQqFARkYGVCoVNBoNioqKEBsbi3Pnzpk6RKMpLS3F4sWL4efnhyFDhiAjIwNz5szB2bNn4eTk1KWxpqWXQDvIupcipZ64smVuu7bY2FjMnTsXQUFBSE1Nbbf+n//8J0aMGAGFQoGAgACkpaVhxIgRfREuEREREZmBfj9jGBMTA4lEguPHj2P+/PkYP348vLy8EB8fj2PHjgEAqqqqMG/ePMjlctjY2GDhwoWora3tcMxZs2a1uYcLACIiIrB8+XJh2dnZGampqYiKioJcLseYMWNw4MABXL9+XdiXt7c3Tpw4IWyze/duKBQKFBUVwcPDA3K5HKGhoaipqenUse7ZswcxMTHw8fGBu7s7du7cCZ1OJ7qZUWrr/fffR3l5OdLT0w2unzx5Mt566y18+umn+OMf/4iysjLMnj0bzc3NfRwpEREREYlVv54xvHnzJg4ePIi0tDRYW7ef/VIoFNDr9YiIiIC1tTUOHz4MrVaLmJgYLFq0CKWlpT3af1ZWFjZv3oykpCRkZWVh6dKl8Pf3R3R0NLZu3YqEhARERUXh7NmzkEgkAICmpiZkZmYiPz8fUqkUS5YswYYNG7Bnz54u77+pqQkajeaBlw02Nze3KRAaGxsBADKpHhYW+i7vk3qfRqMR3ldXV2Pt2rUoKCiAhYUFNBoN9Ho9dDodNBoNNBoNZsyYgeDgYFhaWmLChAl45JFHMHbsWBw4cABPPvmkCY+EuqI17/fmnwYu5lM8mEtxYT7Fg7k0jq6cv35dGF68eBF6vR7u7u4d9jl06BBOnTqFy5cvQ6lUAgDy8/Ph5eWFsrIy+Pr6dnv/YWFhWLlyJQBg48aNyM3Nha+vLxYsWAAASEhIwPTp01FbW4tRo0YBuHvy8/Ly4ObmBgBYtWoVUlJSurX/l156CU5OTggKCuqwT3p6OjZt2tSu/eVJOlhZtXRrv9S77n1wzLFjx1BXV4dp06YJbTqdDkeOHMH27dvxt7/9DRYWFiguLm4zxkMPPYSCggLIZLI+i5uM4/5c0sDGfIoHcykuzKd4MJc909TU1Om+/bow1Ovvzni1zsYZolaroVQqhaIQADw9PaFQKKBWq3tUGN77dMiRI0cCALy9vdu11dXVCYWhlZWVUBQCgKOjI+rq6rq874yMDLz33nsoLS3FkCFDOuyXmJiI+Ph4YbmxsRFKpRKpX0ihtbTo8n6p951JDhHeP/bYY1i4cGGb9b/+9a8xYcIEbNiwARMmTEBxcbEwYwgA3377LW7evImAgACEhYX1aezUfRqNpl0uaeBiPsWDuRQX5lM8mEvjaL2asDP6dWE4btw4SCQSqNVqREREGOyj1+sNFo4dtQOAVCoVis5WhqZZ7/0hbB3LUJtOpzO4TWuf+/f1UzIzM7F582YcOnTI4FcX3EsmkxmcNWrWSaBt6bigJtO592fEzs6u3aXCcrkcDg4OmDRpEr777jvs2rULdnZ2UCqVuHLlCn73u9/hoYcewoIFC/hBOQBZWloybyLCfIoHcykuzKd4MJc905Vz168LQzs7O4SEhGD79u1Ys2ZNu/sM6+vr4enpiaqqKlRXVwuzhpWVlWhoaICHh4fBcR0cHNo8EKalpQVnzpxBYGBg7x1MJ23duhWpqakoKirClClTuj3O54mPw97e3oiRkSlYWFjgq6++wtNPP436+no4OjoiMDAQe/fuxbBhw0wdHhERERGJRL8uDAEgJycHfn5+mDp1KlJSUqBSqaDValFcXIzc3FxUVlZCpVIhMjIS2dnZwsNnAgICOiysZs+ejfj4eBQUFMDNzQ1ZWVmor6/v2wMzICMjA0lJSXj33Xfh7OyMa9euAbg7gySXy00cHfWVex+aNHToUCQnJyMsLIz/W0ZEREREvabff12Fi4sLysvLERgYiPXr12PixIkIDg5GSUkJcnNzIZFIsH//fgwfPhwzZ85EUFAQXF1dsXfv3g7HjI6OxrJlyxAVFYWAgAC4uLj0i9nCnJwc3LlzB/Pnz4ejo6PwyszMNHVoREREREQkYhJ9V2+Ao36tsbERtra2uHHjBi8lFQGNRoPCwkLOGIoAcykuzKd4MJfiwnyKB3NpHK21QUNDA2xsbB7Yt9/PGBIREREREVHvYmHYh1rvFTT0OnLkiKnDIyIiIiIiM9XvHz4jJhUVFR2uc3Jy6rtAiIiIiIiI7sHCsA+NHTvW1CEQERERERG1w0tJiYiIiIiIzBwLQyIiIiIiIjPHwpCIiIiIiMjMsTAkIiIiIiIycywMiYiIiIiIzBwLQyIiIiIiIjPHwpCIiIiIiMjMsTAkIiIiIiIycywMiYiIiIiIzBwLQyIiIiIiIjPHwpCIiIiIiMjMsTAkIiIiIiIycywMiYiIiIiIzBwLQ6I+lp6eDolEgri4OACARqNBQkICvL29YW1tjdGjRyMqKgrffPONaQMlIiIiIrMx4AvD5ORk+Pj4mDoMok4pKyvDjh07oFKphLampiaUl5cjKSkJ5eXl2LdvH86fP48nnnjChJESERERkTkxeWF47do1rF69Gq6urpDJZFAqlQgPD0dJSYmpQzOqs2fP4umnn4azszMkEgmys7Pb9fn+++8RFxeHMWPGYOjQofDz80NZWVnfB0u94tatW4iMjMTOnTsxfPhwod3W1hbFxcVYuHAhJkyYgEcffRTbtm3Dv//9b1RVVZkwYiIiIiIyF4NMufMrV67A398fCoUCGRkZUKlU0Gg0KCoqQmxsLM6dO2fK8IyqqakJrq6uWLBgAdatW2ewz4oVK3DmzBnk5+dj9OjReOeddxAUFITKyko4OTl1aX/T0kugHWRtjNCpG65smduuLTY2FnPnzkVQUBBSU1MfuH1DQwMkEgkUCkUvRUhERERE9F8mLQxjYmIgkUhw/PhxWFv/t4jx8vJCdHQ0AKCqqgqrV69GSUkJpFIpQkNDsW3bNowcOdLgmLNmzYKPj0+bGbmIiAgoFArs3r0bAODs7IwVK1bg/Pnz2LdvH+zt7fHGG2/Az88PK1asQElJCVxcXLBr1y5MmTIFALB7927ExcVh7969iIuLQ3V1NWbMmIFdu3bB0dHxJ4/V19cXvr6+AICXXnqp3foffvgBH3zwAQ4cOICZM2cCuHuZ7P79+5Gbm9thIdHc3Izm5mZhubGxEQAgk+phYaH/ybiod2g0mjbLe/fuxb///W989tln0Gg00Ov10Ol07foBwI8//oiEhAQ888wzGDp0qMHxaOBpzSFzKQ7Mp3gwl+LCfIoHc2kcXTl/JisMb968iYMHDyItLa1NUdhKoVBAr9cjIiIC1tbWOHz4MLRaLWJiYrBo0SKUlpb2aP9ZWVnYvHkzkpKSkJWVhaVLl8Lf3x/R0dHYunUrEhISEBUVhbNnz0IikQC4O+uXmZmJ/Px8SKVSLFmyBBs2bMCePXt6FAsAaLVatLS0YMiQIW3ahw4diqNHj3a4XXp6OjZt2tSu/eVJOlhZtfQ4LuqewsJC4f3169exYcMGJCcn49NPPwUAfPvtt7h8+XKbfsDdn4OMjAzU19cjPDwcxcXFACD8SQMfcykuzKd4MJfiwnyKB3PZM01NTZ3ua7LC8OLFi9Dr9XB3d++wz6FDh3Dq1ClcvnwZSqUSAJCfnw8vLy+UlZUJM3DdERYWhpUrVwIANm7ciNzcXPj6+mLBggUAgISEBEyfPh21tbUYNWoUgLsVd15eHtzc3AAAq1atQkpKSrdjuNewYcMwffp0vPLKK/Dw8MDIkSPx3nvv4fPPP8e4ceM63C4xMRHx8fHCcmNjI5RKJVK/kEJraWGU2KjrziSHCO8PHDiAhoYGbNiwQWhraWlBZWUlPv74Y9y6dQsWFhbQaDRYvHgxfvjhB/zrX/+Cvb09NBoNiouLERwcDEtLS1McChkJcykuzKd4MJfiwnyKB3NpHK1XE3aGyQpDvf7uZY6ts3GGqNVqKJVKoSgEAE9PTygUCqjV6h4Vhvc+FbL1slRvb+92bXV1dUJhaGVlJRSFAODo6Ii6urpux3C//Px8REdHw8nJCRYWFvj5z3+OZ599FuXl5R1uI5PJIJPJ2rU36yTQtnR8bql33fsBFhISgtOnT7dZ/9xzz8Hd3R0JCQkYMmQINBoNIiMjcenSJfzjH/+Ag4NDu/H4oSgOzKW4MJ/iwVyKC/MpHsxlz3Tl3JmsMBw3bhwkEgnUajUiIiIM9tHr9QYLx47aAUAqlQpFZytD19bee5JaxzLUptPpDG7T2uf+ffWEm5sbDh8+jNu3b6OxsRGOjo5YtGgRXFxcujzW54mPw97e3mixUfcNGzYMEydObNNmbW0Ne3t7TJw4EVqtFvPnz0d5eTk++ugjtLS04Nq1a8K2RERERES9zWRfV2FnZ4eQkBBs374dt2/fbre+vr4enp6eqKqqQnV1tdBeWVmJhoYGeHh4GBzXwcEBNTU1wnJLSwvOnDlj/APoRdbW1nB0dMR3332HoqIizJs3z9QhUS/6+uuv8eGHH+Lrr7+Gj48PHB0dhddnn31m6vCIiIiIyAyY9KmkOTk58PPzw9SpU5GSkgKVSgWtVovi4mLk5uaisrISKpUKkZGRyM7OFh4+ExAQIDwt9H6zZ89GfHw8CgoK4ObmhqysLNTX1/ftgRlw584dVFZWCu+vXr2KiooKyOVyjB07FgBQVFQEvV6PCRMm4OLFi3jhhRcwYcIEPPfcc6YMnXrBvQ9PcnZ27nDmWaPRtHtADRERERGRsZn0C+5dXFxQXl6OwMBArF+/HhMnTkRwcDBKSkqQm5sLiUSC/fv3Y/jw4Zg5cyaCgoLg5PNPEAAALMdJREFU6uqKvXv3djhmdHQ0li1bhqioKAQEBMDFxQWBgYF9eFSGffPNN5g0aRImTZqEmpoaZGZmYtKkSVixYoXQp6GhAbGxsXB3d0dUVBRmzJiBTz75hNdVExERERFRr5LojXmTHJlcY2MjbG1tcePGDd5jKAKtM4ZhYWH8D4IBjrkUF+ZTPJhLcWE+xYO5NI7W2qChoQE2NjYP7GvSGUMiIiIiIiIyPRaGRiKXyzt8HTlyxNThERERERERdcikD58Rk4qKig7XOTk59V0gREREREREXcTC0EhanyxKREREREQ00PBSUiIiIiIiIjPHwpCIiIiIiMjMsTAkIiIiIiIycywMiYiIiIiIzBwLQyIiIiIiIjPHwpCIiIiIiMjMsTAkIiIiIiIycywMiYiIiIiIzBwLQyIiIiIiIjPHwpCIiIiIiMjMsTAkIiIiIiIycywMiYiIiIiIzBwLQyIiIiIiIjPHwpCoC3Jzc6FSqWBjYwMbGxtMnz4dH3/8cZs+arUaTzzxBGxtbTFs2DA8+uijqKqqMlHEREREREQ/zSwKw+TkZPj4+Jg6DBKBn/3sZ9iyZQtOnDiBEydOYPbs2Zg3bx7Onj0LALh06RJmzJgBd3d3lJaW4uTJk0hKSsKQIUNMHDkRERERUccGmTqAzrj2/9u7/7ga7/4P4K9zSKkoWSntfCs/0m+MkKykEd1MbM02xNps7pI7sdvMmEyyGMYmNnuUr/Fg943MnUWammYhROpwY9y5rYSpqFudU5/vH76de0enFHFO57yej8d5PLo+1+f6XO/rert6ePe5ruuUlCAuLg6pqam4fv06bGxs0K9fP0RHRyMwMFDb4bUahUKB+Ph4bNmyBdevX0efPn3w6aefYvTo0S0ea3B8BpTtzZ5ClIbl6oo/qS2PGzdObTkuLg6JiYnIycmBu7s7Fi5ciODgYCQkJKj69OjR45nESkRERET0uHR+xvDq1asYMGAAfvzxRyQkJCA/Px9paWkICAhAZGSktsNrVR999BE2bdqE9evXo7CwEDNnzsSECRNw+vRpbYdGGtTW1mLHjh2orKyEj48P6urqkJqaCmdnZwQFBcHGxgaDBw9GSkqKtkMlIiIiImqSzs8YRkREQCKR4Pjx4zAz++8MmLu7O8LDwwEARUVFiIqKQkZGBqRSKUaPHo3169ejW7duGsccPnw4+vXrh7Vr16raQkJCYGlpieTkZACAo6Mj3nnnHfzzn//E7t270bVrV6xbtw5Dhw7FO++8g4yMDDg5OSEpKQkDBw4EACQnJyM6Oho7d+5EdHQ0rl27hmHDhiEpKQl2dnaPPNatW7eqZpwA4M9//jMOHDiAzz77DN9++63Gbaqrq1FdXa1arqioAAAYSwXatROP3Cc1TaFQNGjLz8+Hn58f7t+/D3Nzc/ztb39D7969cf36ddy7dw8rVqxAbGwsli1bhoMHD2LixIlIT0+Hn5/fY+9fUxzUtjCX+oX51B/MpX5hPvUHc9k6WnL+dLow/P3335GWloa4uDi1orCepaUlhBAICQmBmZkZsrKyoFQqERERgUmTJiEzM/OJ9r9mzRosX74cixYtwpo1azB16lT4+voiPDwcK1euxPz58xEWFoaCggJIJBIAQFVVFVatWoWtW7dCKpViypQpmDdvHrZt2/bI/VVXVzd4Fq1jx47Izs5udJv4+HjExsY2aP+ofx1MTWtbeMT0sP379zdoUygUWLVqFSorK/HLL79g6tSpav9GBwwYgN69e+O3336Dh4cHBg4ciNjYWMydO/ex40hPT3/sbUm3MJf6hfnUH8ylfmE+9Qdz+WSqqqqa3VenC8NLly5BCAEXF5dG+xw6dAhnz57FlStXIJPJADyYeXN3d8eJEyfg7e392PsPDg7Ge++9BwBYvHgxEhMT4e3tjdDQUADA/Pnz4ePjgxs3bsDW1hbAg6Jh48aN6NmzJwBg1qxZWLp0abP2FxQUhNWrV8PPzw89e/ZERkYG9u7di9raxgu8BQsWICYmRrVcUVEBmUyGZaelUBq1e6zjpv86tySoyfWzZ8/G6NGjcebMGaxduxbvvvsuAgMDVbO+AHDkyBEcPXpUra25FAoF0tPTMXLkSBgZGbV4e9IdzKV+YT71B3OpX5hP/cFcto76uwmbQ6cLQyEe3ApZPxuniVwuh0wmUxWFAODm5gZLS0vI5fInKgy9vLxUP9fflurp6dmgrbS0VFUYmpqaqopCALCzs0NpaWmz9vf5559jxowZcHFxgUQiQc+ePfHWW28hKSmp0W2MjY1hbGzcoL26TgJlbePnjZqnub+IFAoFzMzM4O3tjUuXLqltd/nyZTg6Oj7RLzUjIyP+UtQTzKV+YT71B3OpX5hP/cFcPpmWnDudLgx79+4NiUQCuVyOkJAQjX2EEBoLx8baAUAqlaqKznqa7r/944msH0tTW11dncZt6vs8vK/GWFtbIyUlBffv38ft27fRvXt3fPDBB3BycmrW9n90bEEgunbt2uLtqGkffvghxowZA5lMhrt372LHjh3IzMxEWloaAOD999/HpEmT4Ofnh4CAAKSlpWHfvn1PfFszEREREdHTpNNvJbWyskJQUBC+/PJLVFZWNlhfVlYGNzc3FBUV4dq1a6r2wsJClJeXw9XVVeO41tbWKC4uVi3X1tbi3LlzrX8Aj8nExAT29vZQKpXYtWsXxo8fr+2Q6P/duHEDU6dORZ8+fRAYGIhjx44hLS0NI0eOBABMmDABGzduREJCAjw9PbF582bs2rULw4YN03LkRERERESN0+kZQwDYsGEDhg4dikGDBmHp0qXw8vKCUqlEeno6EhMTUVhYCC8vL0yePBlr165VvXzG399f9bbQh40YMQIxMTFITU1Fz549sWbNGpSVlT3bA9Pg2LFjuH79Ovr164fr169jyZIlqKurw1//+ldth0b/75tvvnlkn/DwcNUbc4mIiIiI2gKdnjEEACcnJ5w6dQoBAQGYO3cuPDw8MHLkSGRkZCAxMRESiQQpKSno0qUL/Pz88NJLL6FHjx7YuXNno2OGh4dj2rRpCAsLg7+/P5ycnBAQEPAMj0qz+/fv46OPPoKbmxsmTJgAe3t7ZGdnw9LSUtuhERERERGRHpOI5j4AR21CRUUFLCwscOvWLT5jqAcUCgX279+P4OBgPnjdxjGX+oX51B/MpX5hPvUHc9k66muD8vJydO7cucm+Oj9jSERERERERE8XC8NnyNzcvNHPkSNHtB0eEREREREZKJ1/+Yw+ycvLa3Sdvb39swuEiIiIiIjoD1gYPkO9evXSdghEREREREQN8FZSIiIiIiIiA8fCkIiIiIiIyMCxMCQiIiIiIjJwLAyJiIiIiIgMHAtDIiIiIiIiA8fCkIiIiIiIyMCxMCQiIiIiIjJwLAyJiIiIiIgMHAtDIiIiIiIiA8fCkIiIiIiIyMCxMCQiIiIiIjJwLAyJiIiIiIgMHAtDIiIiIiIiA9fmC8MlS5agX79+2g6DdEh8fDy8vb3RqVMn2NjYICQkBBcuXGjQTy6X4+WXX4aFhQU6deqEIUOGoKioSAsRExERERFpl9YLw5KSEkRFRaFHjx4wNjaGTCbDuHHjkJGRoe3QWlVBQQFeeeUVODo6QiKRYO3atQ36NLegoaZlZWUhMjISOTk5SE9Ph1KpxKhRo1BZWanqc/nyZQwbNgwuLi7IzMzEmTNnsGjRIpiYmGgxciIiIiIi7WivzZ1fvXoVvr6+sLS0REJCAry8vKBQKHDgwAFERkbi/Pnz2gyvVVVVVaFHjx4IDQ3FnDlzNPapL2i8vb2hVCqxcOFCjBo1CoWFhTAzM2vR/gbHZ0DZvmXbtFVXV/xJbTktLU1tOSkpCTY2Njh58iT8/PwAAAsXLkRwcDASEhJU/Xr06PH0gyUiIiIi0kFanTGMiIiARCLB8ePH8eqrr8LZ2Rnu7u6IiYlBTk4OAKCoqAjjx4+Hubk5OnfujNdeew03btxodMzhw4cjOjparS0kJATTp09XLTs6OmLZsmUICwuDubk5HBwcsHfvXty8eVO1L09PT+Tm5qq2SU5OhqWlJQ4cOABXV1eYm5tj9OjRKC4ubtaxent7Y+XKlXj99ddhbGyssU9aWhqmT58Od3d39O3bF0lJSSgqKsLJkyebtQ/SrLy8HABgZWUFAKirq0NqaiqcnZ0RFBQEGxsbDB48GCkpKVqMkoiIiIhIe7Q2Y/j7778jLS0NcXFxGmfDLC0tIYRASEgIzMzMkJWVBaVSiYiICEyaNAmZmZlPtP81a9Zg+fLlWLRoEdasWYOpU6fC19cX4eHhWLlyJebPn4+wsDAUFBRAIpEAeDDrt2rVKmzduhVSqRRTpkzBvHnzsG3btieKpTEPFzSaVFdXo7q6WrVcUVEBADCWCrRrJ55KXLpGoVA0uk4IgejoaPj6+qJPnz5QKBQoKSnBvXv3sGLFCsTGxmLZsmU4ePAgJk6ciPT0dNWsoi6oP7amjpHaBuZSvzCf+oO51C/Mp/5gLltHS86f1grDS5cuQQgBFxeXRvscOnQIZ8+exZUrVyCTyQAAW7duhbu7O06cOAFvb+/H3n9wcDDee+89AMDixYuRmJgIb29vhIaGAgDmz58PHx8f3LhxA7a2tgAenNiNGzeiZ8+eAIBZs2Zh6dKljx1DU4QQiImJwbBhw+Dh4dFov/j4eMTGxjZo/6h/HUxNa59KbLpm//79ja7btGkTcnNzER8fr+r3+++/AwAGDBiA3r1747fffoOHhwcGDhyI2NhYzJ0795nE3RLp6enaDoFaCXOpX5hP/cFc6hfmU38wl0+mqqqq2X21VhgK8WA2q342ThO5XA6ZTKYqCgHAzc0NlpaWkMvlT1QYenl5qX7u1q0bAMDT07NBW2lpqaowNDU1VRWFAGBnZ4fS0tLHjqEps2bNwtmzZ5Gdnd1kvwULFiAmJka1XFFRAZlMhmWnpVAatXsqsemac0uCNLZHR0cjPz8f2dnZcHJyUrXX1NTg3XffRWBgIIKDg1XtR44cwdGjR9XatE2hUCA9PR0jR46EkZGRtsOhJ8Bc6hfmU38wl/qF+dQfzGXrqL+bsDm0Vhj27t0bEokEcrkcISEhGvsIITQWjo21A4BUKlUVnfU0TaH+8R9Y/Via2urq6jRuU9/n4X21hqioKHz//ff46aef8PzzzzfZ19jYWOMzi9V1EihrGy+69cnDeRFCICoqCikpKcjMzETv3r0b9Pf29salS5fUtr18+TIcHR118pePkZGRTsZFLcdc6hfmU38wl/qF+dQfzOWTacm501phaGVlhaCgIHz55ZeYPXt2g+cMy8rK4ObmhqKiIly7dk01a1hYWIjy8nK4urpqHNfa2lrthTC1tbU4d+4cAgICnt7BtJL6gmbPnj3IzMxUm+VqqWMLAtG1a9dWjK7tiIyMxPbt27F371506tQJJSUlAAALCwt07NgRAPD+++9j0qRJ8PPzQ0BAANLS0rBv374nfnaViIiIiKgt0upbSTds2IDa2loMGjQIu3btwsWLFyGXy7Fu3Tr4+PjgpZdegpeXFyZPnoxTp07h+PHjCAsLg7+/PwYOHKhxzBEjRiA1NRWpqak4f/48IiIiUFZW9mwPTIOamhrk5eUhLy8PNTU1uH79OvLy8nDp0iVVn8jISHz77bfYvn27qqApKSnBf/7zHy1G3vYkJiaivLwcw4cPh52dneqzc+dOVZ8JEyZg48aNSEhIgKenJzZv3oxdu3Zh2LBhWoyciIiIiEg7tPo9hk5OTjh16hTi4uIwd+5cFBcXw9raGgMGDEBiYiIkEglSUlIQFRUFPz8/SKVSjB49GuvXr290zPDwcJw5cwZhYWFo37495syZoxOzhb/99hv69++vWl61ahVWrVoFf39/1SxVYmIigAdfufFHSUlJal+3QU1r7u294eHhCA8Pf8rREBERERHpPol4Gg/JkdZUVFTAwsICt27dMthbSfWJQqHA/v37ERwczPvr2zjmUr8wn/qDudQvzKf+YC5bR31tUF5ejs6dOzfZV6u3khIREREREZH2sTBsJebm5o1+jhw5ou3wiIiIiIiIGqXVZwz1SV5eXqPr7O3tn10gRERERERELcTCsJX06tVL2yEQERERERE9Ft5KSkREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERm4Nl8YLlmyBP369dN2GPSMxcfHw9vbG506dYKNjQ1CQkJw4cIFtT5LliyBi4sLzMzM0KVLF7z00ks4duyYliImIiIiItJdWi8MS0pKEBUVhR49esDY2BgymQzjxo1DRkaGtkNrVQUFBXjllVfg6OgIiUSCtWvXaux3/fp1TJkyBV27doWpqSn69euHkydPPttg24CsrCxERkYiJycH6enpUCqVGDVqFCorK1V9nJ2d8cUXXyA/Px/Z2dlwdHTEqFGjcPPmTS1GTkRERESke9prc+dXr16Fr68vLC0tkZCQAC8vLygUChw4cACRkZE4f/68NsNrVVVVVejRowdCQ0MxZ84cjX3u3LkDX19fBAQE4IcffoCNjQ0uX74MS0vLFu9vcHwGlO3NnjBq3XF1xZ/UltPS0tSWk5KSYGNjg5MnT8LPzw8A8Oabb6r1Wb16Nb755hucPXsWgYGBTzdgIiIiIqI2RKszhhEREZBIJDh+/DheffVVODs7w93dHTExMcjJyQEAFBUVYfz48TA3N0fnzp3x2muv4caNG42OOXz4cERHR6u1hYSEYPr06aplR0dHLFu2DGFhYTA3N4eDgwP27t2Lmzdvqvbl6emJ3Nxc1TbJycmwtLTEgQMH4OrqCnNzc4wePRrFxcXNOlZvb2+sXLkSr7/+OoyNjTX2+fTTTyGTyZCUlIRBgwbB0dERgYGB6NmzZ7P2YcjKy8sBAFZWVhrX19TU4KuvvoKFhQX69u37LEMjIiIiItJ5Wpsx/P3335GWloa4uDiYmTWc2bK0tIQQAiEhITAzM0NWVhaUSiUiIiIwadIkZGZmPtH+16xZg+XLl2PRokVYs2YNpk6dCl9fX4SHh2PlypWYP38+wsLCUFBQAIlEAuDBrN+qVauwdetWSKVSTJkyBfPmzcO2bdueKJZ633//PYKCghAaGoqsrCzY29sjIiICM2bMaHSb6upqVFdXq5YrKioAAMZSgXbtRKvEpQsUCkWj64QQiI6Ohq+vL/r06aPWNzU1FVOmTEFVVRXs7Ozwww8/wMLCosnxdEl9nG0lXmocc6lfmE/9wVzqF+ZTfzCXraMl509rheGlS5cghICLi0ujfQ4dOoSzZ8/iypUrkMlkAICtW7fC3d0dJ06cgLe392PvPzg4GO+99x4AYPHixUhMTIS3tzdCQ0MBAPPnz4ePjw9u3LgBW1tbAA9O7MaNG1UzeLNmzcLSpUsfO4aH/frrr0hMTERMTAw+/PBDHD9+HLNnz4axsTHCwsI0bhMfH4/Y2NgG7R/1r4OpaW2rxaZt+/fvb3Tdpk2bkJubi/j4+Ab9qqursWrVKlRUVODgwYMICQlBQkLCY92eq03p6enaDoFaCXOpX5hP/cFc6hfmU38wl0+mqqqq2X21VhgK8WA2q342ThO5XA6ZTKYqCgHAzc0NlpaWkMvlT1QYenl5qX7u1q0bAMDT07NBW2lpqaowNDU1Vbut087ODqWlpY8dw8Pq6uowcOBALF++HADQv39/FBQUIDExsdHCcMGCBYiJiVEtV1RUQCaTYdlpKZRG7VotNm07tyRIY3t0dLTq5TJOTk5NjjFnzhy4ubnh2rVrDZ4/1FUKhQLp6ekYOXIkjIyMtB0OPQHmUr8wn/qDudQvzKf+YC5bR/3dhM2htcKwd+/ekEgkkMvlCAkJ0dhHCKGxcGysHQCkUqmq6KynaQr1j//A6sfS1FZXV6dxm/o+D+/rSdjZ2cHNzU2tzdXVFbt27Wp0G2NjY43PLFbXSaCsbbzobmsePvdCCERFRSElJQWZmZno3bt3s8YRQkCpVLa5XzBGRkZtLmbSjLnUL8yn/mAu9QvzqT+YyyfTknOntcLQysoKQUFB+PLLLzF79uwGzxmWlZXBzc0NRUVFuHbtmmrWsLCwEOXl5XB1ddU4rrW1tdoLYWpra3Hu3DkEBAQ8vYNpJb6+vg2+i++f//wnHBwcWjzWsQWB6Nq1a2uFpnMiIyOxfft27N27F506dUJJSQkAwMLCAh07dkRlZSXi4uLw8ssvw87ODrdv38aGDRvw73//W3W7MBERERERPaDVt5Ju2LABtbW1GDRoEHbt2oWLFy9CLpdj3bp18PHxwUsvvQQvLy9MnjwZp06dwvHjxxEWFgZ/f38MHDhQ45gjRoxAamoqUlNTcf78eURERKCsrOzZHpgGNTU1yMvLQ15eHmpqanD9+nXk5eXh0qVLqj5z5sxBTk4Oli9fjkuXLmH79u346quvEBkZqcXIdVNiYiLKy8sxfPhw2NnZqT47d+4EALRr1w7nz5/HK6+8AmdnZ4wdOxY3b97EkSNH4O7uruXoiYiIiIh0i1a/x9DJyQmnTp1CXFwc5s6di+LiYlhbW2PAgAFITEyERCJBSkoKoqKi4OfnB6lUitGjR2P9+vWNjhkeHo4zZ84gLCwM7du3x5w5c3RitvC3335D//79VcurVq3CqlWr4O/vr3rDqre3N/bs2YMFCxZg6dKlcHJywtq1azF58mQtRa27HnULr4mJCXbv3v2MoiEiIiIiatskojUfkiOtq6iogIWFBW7duqXXt5IaCoVCgf379yM4OJj317dxzKV+YT71B3OpX5hP/cFcto762qC8vBydO3dusq9WbyUlIiIiIiIi7WNh2ErMzc0b/Rw5ckTb4RERERERETVKq88Y6pO8vLxG19nb2z+7QIiIiIiIiFqIhWEr6dWrl7ZDICIiIiIieiy8lZSIiIiIiMjAsTAkIiIiIiIycCwMiYiIiIiIDBwLQyIiIiIiIgPHwpCIiIiIiMjAsTAkIiIiIiIycCwMiYiIiIiIDBwLQyIiIiIiIgPHwpCIiIiIiMjAsTAkIiIiIiIycCwMiYiIiIiIDBwLQyIiIiIiIgPHwpCIiIiIiMjAsTAkIiIiIiIycCwMiYiIiIiIDBwLQyIiIiIiIgPHwpCIiIiIiMjAsTAkIiIiIiIycO21HQC1LiEEAODu3bswMjLScjT0pBQKBaqqqlBRUcF8tnHMpX5hPvUHc6lfmE/9wVy2joqKCgD/rRGawsJQz9y+fRsA4OTkpOVIiIiIiIhIF9y9excWFhZN9mFhqGesrKwAAEVFRY9MPum+iooKyGQyXLt2DZ07d9Z2OPQEmEv9wnzqD+ZSvzCf+oO5bB1CCNy9exfdu3d/ZF8WhnpGKn3w2KiFhQUvIj3SuXNn5lNPMJf6hfnUH8ylfmE+9Qdz+eSaO1nEl88QEREREREZOBaGREREREREBo6FoZ4xNjbGxx9/DGNjY22HQq2A+dQfzKV+YT71B3OpX5hP/cFcPnsS0Zx3lxIREREREZHe4owhERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoWhntmwYQOcnJxgYmKCAQMG4MiRI9oOiR5hyZIlkEgkah9bW1vVeiEElixZgu7du6Njx44YPnw4CgoKtBgx1fvpp58wbtw4dO/eHRKJBCkpKWrrm5O76upqREVF4bnnnoOZmRlefvll/Pvf/36GR0H1HpXP6dOnN7hWhwwZotaH+dQN8fHx8Pb2RqdOnWBjY4OQkBBcuHBBrQ+vz7ahObnktdl2JCYmwsvLS/Wl9T4+Pvjhhx9U63ldahcLQz2yc+dOREdHY+HChTh9+jRefPFFjBkzBkVFRdoOjR7B3d0dxcXFqk9+fr5qXUJCAlavXo0vvvgCJ06cgK2tLUaOHIm7d+9qMWICgMrKSvTt2xdffPGFxvXNyV10dDT27NmDHTt2IDs7G/fu3cPYsWNRW1v7rA6D/t+j8gkAo0ePVrtW9+/fr7ae+dQNWVlZiIyMRE5ODtLT06FUKjFq1ChUVlaq+vD6bBuak0uA12Zb8fzzz2PFihXIzc1Fbm4uRowYgfHjx6uKP16XWiZIbwwaNEjMnDlTrc3FxUV88MEHWoqImuPjjz8Wffv21biurq5O2NraihUrVqja7t+/LywsLMTGjRufUYTUHADEnj17VMvNyV1ZWZkwMjISO3bsUPW5fv26kEqlIi0t7ZnFTg09nE8hhJg2bZoYP358o9swn7qrtLRUABBZWVlCCF6fbdnDuRSC12Zb16VLF7F582ZelzqAM4Z6oqamBidPnsSoUaPU2keNGoWjR49qKSpqrosXL6J79+5wcnLC66+/jl9//RUAcOXKFZSUlKjl1djYGP7+/syrjmtO7k6ePAmFQqHWp3v37vDw8GB+dVRmZiZsbGzg7OyMGTNmoLS0VLWO+dRd5eXlAAArKysAvD7bsodzWY/XZttTW1uLHTt2oLKyEj4+PrwudQALQz1x69Yt1NbWolu3bmrt3bp1Q0lJiZaiouYYPHgw/vd//xcHDhzA119/jZKSEgwdOhS3b99W5Y55bXuak7uSkhJ06NABXbp0abQP6Y4xY8Zg27Zt+PHHH/HZZ5/hxIkTGDFiBKqrqwEwn7pKCIGYmBgMGzYMHh4eAHh9tlWacgnw2mxr8vPzYW5uDmNjY8ycORN79uyBm5sbr0sd0F7bAVDrkkgkastCiAZtpFvGjBmj+tnT0xM+Pj7o2bMntmzZonp4nnltux4nd8yvbpo0aZLqZw8PDwwcOBAODg5ITU3FxIkTG92O+dSuWbNm4ezZs8jOzm6wjtdn29JYLnltti19+vRBXl4eysrKsGvXLkybNg1ZWVmq9bwutYczhnriueeeQ7t27Rr8taS0tLTBX15It5mZmcHT0xMXL15UvZ2UeW17mpM7W1tb1NTU4M6dO432Id1lZ2cHBwcHXLx4EQDzqYuioqLw/fff4/Dhw3j++edV7bw+257GcqkJr03d1qFDB/Tq1QsDBw5EfHw8+vbti88//5zXpQ5gYagnOnTogAEDBiA9PV2tPT09HUOHDtVSVPQ4qqurIZfLYWdnBycnJ9ja2qrltaamBllZWcyrjmtO7gYMGAAjIyO1PsXFxTh37hzz2wbcvn0b165dg52dHQDmU5cIITBr1izs3r0bP/74I5ycnNTW8/psOx6VS014bbYtQghUV1fzutQFWnjhDT0lO3bsEEZGRuKbb74RhYWFIjo6WpiZmYmrV69qOzRqwty5c0VmZqb49ddfRU5Ojhg7dqzo1KmTKm8rVqwQFhYWYvfu3SI/P1+88cYbws7OTlRUVGg5crp79644ffq0OH36tAAgVq9eLU6fPi3+9a9/CSGal7uZM2eK559/Xhw6dEicOnVKjBgxQvTt21colUptHZbBaiqfd+/eFXPnzhVHjx4VV65cEYcPHxY+Pj7C3t6e+dRBf/7zn4WFhYXIzMwUxcXFqk9VVZWqD6/PtuFRueS12bYsWLBA/PTTT+LKlSvi7Nmz4sMPPxRSqVQcPHhQCMHrUttYGOqZL7/8Ujg4OIgOHTqIF154Qe11zqSbJk2aJOzs7ISRkZHo3r27mDhxoigoKFCtr6urEx9//LGwtbUVxsbGws/PT+Tn52sxYqp3+PBhAaDBZ9q0aUKI5uXuP//5j5g1a5awsrISHTt2FGPHjhVFRUVaOBpqKp9VVVVi1KhRwtraWhgZGYn/+Z//EdOmTWuQK+ZTN2jKIwCRlJSk6sPrs214VC55bbYt4eHhqv+nWltbi8DAQFVRKASvS22TCCHEs5ufJCIiIiIiIl3DZwyJiIiIiIgMHAtDIiIiIiIiA8fCkIiIiIiIyMCxMCQiIiIiIjJwLAyJiIiIiIgMHAtDIiIiIiIiA8fCkIiIiIiIyMCxMCQiIiIiIjJwLAyJiIjakOHDhyM6OlrbYRARkZ5hYUhERHpj+vTpkEgkDT6XLl1qlfGTk5NhaWnZKmM9rt27d+OTTz7RagxNyczMhEQiQVlZmbZDISKiFmiv7QCIiIha0+jRo5GUlKTWZm1traVoGqdQKGBkZNTi7aysrJ5CNK1DoVBoOwQiInpMnDEkIiK9YmxsDFtbW7VPu3btAAD79u3DgAEDYGJigh49eiA2NhZKpVK17erVq+Hp6QkzMzPIZDJERETg3r17AB7MhL311lsoLy9XzUQuWbIEACCRSJCSkqIWh6WlJZKTkwEAV69ehUQiwXfffYfhw4fDxMQE3377LQAgKSkJrq6uMDExgYuLCzZs2NDk8T18K6mjoyOWLVuGsLAwmJubw8HBAXv37sXNmzcxfvx4mJubw9PTE7m5uapt6mc+U1JS4OzsDBMTE4wcORLXrl1T21diYiJ69uyJDh06oE+fPti6davaeolEgo0bN2L8+PEwMzPDO++8g4CAAABAly5dIJFIMH36dABAWloahg0bBktLS3Tt2hVjx47F5cuXVWPVn6Pdu3cjICAApqam6Nu3L3755Re1ff7888/w9/eHqakpunTpgqCgINy5cwcAIIRAQkICevTogY4dO6Jv3774+9//3uT5JCKiB1gYEhGRQThw4ACmTJmC2bNno7CwEJs2bUJycjLi4uJUfaRSKdatW4dz585hy5Yt+PHHH/HXv/4VADB06FCsXbsWnTt3RnFxMYqLizFv3rwWxTB//nzMnj0bcrkcQUFB+Prrr7Fw4ULExcVBLpdj+fLlWLRoEbZs2dKicdesWQNfX1+cPn0af/rTnzB16lSEhYVhypQpOHXqFHr16oWwsDAIIVTbVFVVIS4uDlu2bMHPP/+MiooKvP7666r1e/bswV/+8hfMnTsX586dw3vvvYe33noLhw8fVtv3xx9/jPHjxyM/Px9Lly7Frl27AAAXLlxAcXExPv/8cwBAZWUlYmJicOLECWRkZEAqlWLChAmoq6tTG2/hwoWYN28e8vLy4OzsjDfeeENVvOfl5SEwMBDu7u745ZdfkJ2djXHjxqG2thYA8NFHHyEpKQmJiYkoKCjAnDlzMGXKFGRlZbXofBIRGSRBRESkJ6ZNmybatWsnzMzMVJ9XX31VCCHEiy++KJYvX67Wf+vWrcLOzq7R8b777jvRtWtX1XJSUpKwsLBo0A+A2LNnj1qbhYWFSEpKEkIIceXKFQFArF27Vq2PTCYT27dvV2v75JNPhI+PT6Mx+fv7i7/85S+qZQcHBzFlyhTVcnFxsQAgFi1apGr75ZdfBABRXFysOg4AIicnR9VHLpcLAOLYsWNCCCGGDh0qZsyYobbv0NBQERwcrHbc0dHRan0OHz4sAIg7d+40egxCCFFaWioAiPz8fCHEf8/R5s2bVX0KCgoEACGXy4UQQrzxxhvC19dX43j37t0TJiYm4ujRo2rtb7/9tnjjjTeajIWIiITgM4ZERKRXAgICkJiYqFo2MzMDAJw8eRInTpxQmyGsra3F/fv3UVVVBVNTUxw+fBjLly9HYWEhKioqoFQqcf/+fVRWVqrGeRIDBw5U/Xzz5k1cu3YNb7/9NmbMmKFqVyqVsLCwaNG4Xl5eqp+7desGAPD09GzQVlpaCltbWwBA+/bt1eJxcXGBpaUl5HI5Bg0aBLlcjnfffVdtP76+vqoZQE3H1JTLly9j0aJFyMnJwa1bt1QzhUVFRfDw8NB4LHZ2dqq4XVxckJeXh9DQUI3jFxYW4v79+xg5cqRae01NDfr379+sGImIDBkLQyIi0itmZmbo1atXg/a6ujrExsZi4sSJDdaZmJjgX//6F4KDgzFz5kx88sknsLKyQnZ2Nt5+++1HvlRFIpGo3aYJaH4Ryx+Ly/rC6Ouvv8bgwYPV+tU/E9lcf3yJjUQiabTt4ds269sba3t4vRCiQVtzC+Zx48ZBJpPh66+/Rvfu3VFXVwcPDw/U1NQ88ljq4+7YsWOj49f3SU1Nhb29vdo6Y2PjZsVIRGTIWBgSEZFBeOGFF3DhwgWNRSMA5ObmQqlU4rPPPoNU+uAR/O+++06tT4cOHVTPs/2RtbU1iouLVcsXL15EVVVVk/F069YN9vb2+PXXXzF58uSWHs4TUyqVyM3NxaBBgwA8eCawrKwMLi4uAABXV1dkZ2cjLCxMtc3Ro0fh6ura5LgdOnQAALXzdPv2bcjlcmzatAkvvvgiACA7O7vFMXt5eSEjIwOxsbEN1rm5ucHY2BhFRUXw9/dv8dhERIaOhSERERmExYsXY+zYsZDJZAgNDYVUKsXZs2eRn5+PZcuWoWfPnlAqlVi/fj3GjRuHn3/+GRs3blQbw9HREffu3UNGRgb69u0LU1NTmJqaYsSIEfjiiy8wZMgQ1NXVYf78+c36KoolS5Zg9uzZ6Ny5M8aMGYPq6mrk5ubizp07iImJeVqnAsCDmbmoqCisW7cORkZGmDVrFoYMGaIqFN9//3289tpreOGFFxAYGIh9+/Zh9+7dOHToUJPjOjg4QCKR4B//+AeCg4PRsWNHdOnSBV27dsVXX30FOzs7FBUV4YMPPmhxzAsWLICnpyciIiIwc+ZMdOjQAYcPH0ZoaCiee+45zJs3D3PmzEFdXR2GDRuGiooKHD16FObm5pg2bdpjnSciIkPBt5ISEZFBCAoKwj/+8Q+kp6fD29sbQ4YMwerVq+Hg4AAA6NevH1avXo1PP/0UHh4e2LZtG+Lj49XGGDp0KGbOnIlJkybB2toaCQkJAIDPPvsMMpkMfn5+ePPNNzFv3jyYmpo+MqZ33nkHmzdvRnJyMjw9PeHv74/k5GQ4OTm1/gl4iKmpKebPn48333wTPj4+6NixI3bs2KFaHxISgs8//xwrV66Eu7s7Nm3ahKSkJAwfPrzJce3t7REbG4sPPvgA3bp1w6xZsyCVSrFjxw6cPHkSHh4emDNnDlauXNnimJ2dnXHw4EGcOXMGgwYNgo+PD/bu3Yv27R/8nfuTTz7B4sWLER8fD1dXVwQFBWHfvn3P5HwSEbV1EvHwQxFERESk15KTkxEdHY2ysjJth0JERDqCM4ZEREREREQGjoUhERERERGRgeOtpERERERERAaOM4ZEREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERk4FoZEREREREQGjoUhERERERGRgWNhSEREREREZOBYGBIRERERERm4/wP7E6URKKQybQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plot_importance()를 이용해 피처 중요도 시각화\n", + "from lightgbm import plot_importance\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "fig,ax=plt.subplots(figsize=(10,12))\n", + "plot_importance(lgbm_wrapper,ax=ax)\n" + ] + }, + { + "cell_type": "markdown", + "id": "40daddf0-384e-4560-885f-40354f1a37f8", + "metadata": {}, + "source": [ + "10. 스태킹 앙상블" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7dbb9a89-618b-48a5-948c-124626976fea", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.ensemble import AdaBoostClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "from sklearn.datasets import load_breast_cancer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "cancer_data=load_breast_cancer()\n", + "\n", + "X_data=cancer_data.data\n", + "y_label=cancer_data.target\n", + "\n", + "X_train,X_test,y_train,y_test=train_test_split(X_data,y_label,test_size=0.2,random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "f96e5a45-67af-4e24-ac84-51481fdaeee7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KNN 정확도: 0.9211\n", + "랜덤 포레스트 정확도: 0.9649\n", + "결정 트리 정확도: 0.8947\n", + "에이다부스트 정확도: 0.9561\n" + ] + } + ], + "source": [ + "# 개별 ML모델 생성\n", + "knn_clf = KNeighborsClassifier(n_neighbors = 4)\n", + "rf_clf = RandomForestClassifier(n_estimators = 100, random_state = 0)\n", + "dt_clf = DecisionTreeClassifier()\n", + "ada_clf = AdaBoostClassifier(n_estimators = 100)\n", + "\n", + "# 스태킹으로 만들어진 데이터 세트를 학습, 예측할 최종 모델\n", + "lr_final = LogisticRegression(C = 10)\n", + "\n", + "# 개별 모델들을 학습\n", + "knn_clf.fit(X_train, y_train)\n", + "rf_clf.fit(X_train, y_train)\n", + "dt_clf.fit(X_train, y_train)\n", + "ada_clf.fit(X_train, y_train)\n", + "\n", + "# 학습된 개별 모델들이 각자 반환하는 예측 데이터 세트를 생성하고 개별 모델의 정확도 측정\n", + "knn_pred = knn_clf.predict(X_test)\n", + "rf_pred = rf_clf.predict(X_test)\n", + "dt_pred = dt_clf.predict(X_test)\n", + "ada_pred = ada_clf.predict(X_test)\n", + "#gbm_pred = gbm_clf.predict(X_test)\n", + "\n", + "print('KNN 정확도: {0:.4f}'.format(accuracy_score(y_test, knn_pred)))\n", + "print('랜덤 포레스트 정확도: {0:.4f}'.format(accuracy_score(y_test, rf_pred)))\n", + "print('결정 트리 정확도: {0:.4f}'.format(accuracy_score(y_test, dt_pred)))\n", + "print('에이다부스트 정확도: {0:.4f}'.format(accuracy_score(y_test, ada_pred)))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "2a4c4b6b-8d45-4d64-ba22-d578412fd763", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4, 114)\n", + "(114, 4)\n" + ] + } + ], + "source": [ + "# 피처 값 생성 > 예측값의 칼럼 레벨 여픙로 붙이기\n", + "pred = np.array([knn_pred, rf_pred, dt_pred, ada_pred])\n", + "print(pred.shape)\n", + "\n", + "# transpose를 이용해 행과 열의 위치 교환, 칼럼 레벨로 각 알고리즘의 예측 결과를 피처로 만듬\n", + "pred = np.transpose(pred)\n", + "print(pred.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "7fb30804-d4df-4bd6-b40c-eec9ee61a45f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "최종 메타 모델의 예측 정확도: 0.9737\n" + ] + } + ], + "source": [ + "# 로지스틱 회귀 학습\n", + "lr_final.fit(pred, y_test)\n", + "final = lr_final.predict(pred)\n", + "\n", + "print('최종 메타 모델의 예측 정확도: {0:.4f}'.format(accuracy_score(y_test, final)))" + ] + }, + { + "cell_type": "markdown", + "id": "5ffb311d-a3d2-4b03-b164-3c789359182a", + "metadata": {}, + "source": [ + "CV 세트 기반 스태킹" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "37cad482-e0ba-4b0e-8ddb-c23901d2edd3", + "metadata": {}, + "outputs": [], + "source": [ + "#step1 부분을 코드로 구현\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.metrics import mean_absolute_error\n", + "\n", + "# 개별 기반 모델에서 최종 메타 모델이 사용할 학습 및 테스트용 데이터를 생성하기 위한 함수\n", + "def get_stacking_base_datasets(model, X_train_n, y_train_n, X_test_n, n_folds):\n", + " # 지정된 n_folds 값으로 KFold 생성\n", + " kf = KFold(n_splits = n_folds, shuffle = False)\n", + " # 추후에 메타 모델이 사용할 학습 데이터 반환을 위한 넘파이 배열 초기화\n", + " train_fold_pred = np.zeros((X_train_n.shape[0], 1))\n", + " test_pred = np.zeros((X_test_n.shape[0], n_folds))\n", + " print(model.__class__.__name__, 'model 시작')\n", + "\n", + " for folder_counter, (train_index, valid_index) in enumerate(kf.split(X_train_n)):\n", + " #입력된 학습 데이터에서 기반 모델이 학습/예측할 폴드 데이터 세트 추출\n", + " print('\\t 폴드 세트: ', folder_counter, '시작')\n", + " X_tr = X_train_n[train_index]\n", + " y_tr = y_train_n[train_index]\n", + " X_te = X_train_n[valid_index]\n", + "\n", + " # 폴드 세트 내부에서 다시 만들어진 학습 데이터로 기반 모델의 학습 수행\n", + " model.fit(X_tr, y_tr)\n", + " # 폴드 세트 내부에서 다시 만들어진 검증 데이터로 기반 모델 예측 후 데이터 저장\n", + " train_fold_pred[valid_index, :] = model.predict(X_te).reshape(-1,1)\n", + " # 입력된 원본 테스트 데이터를 폴드 세트 내 학습된 기반 모델에서 예측 후 데이터 저장\n", + " test_pred[:, folder_counter] = model.predict(X_test_n)\n", + "\n", + " # 폴드 세트 내에서 원본 테스트 데이터를 예측한 데이터를 평균하여 테스트 데이터로 생성\n", + " test_pred_mean = np.mean(test_pred, axis = 1).reshape(-1,1)\n", + "\n", + " # train_fold_pred는 최종 메타 모델이 사용하는 학습 데이터, test_pred_mean은 테스트 데이터 \n", + " return train_fold_pred, test_pred_mean\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "a1bef2fb-ab11-4f9b-ab59-96a520b39fa1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KNeighborsClassifier model 시작\n", + "\t 폴드 세트: 0 시작\n", + "\t 폴드 세트: 1 시작\n", + "\t 폴드 세트: 2 시작\n", + "\t 폴드 세트: 3 시작\n", + "\t 폴드 세트: 4 시작\n", + "\t 폴드 세트: 5 시작\n", + "\t 폴드 세트: 6 시작\n", + "RandomForestClassifier model 시작\n", + "\t 폴드 세트: 0 시작\n", + "\t 폴드 세트: 1 시작\n", + "\t 폴드 세트: 2 시작\n", + "\t 폴드 세트: 3 시작\n", + "\t 폴드 세트: 4 시작\n", + "\t 폴드 세트: 5 시작\n", + "\t 폴드 세트: 6 시작\n", + "DecisionTreeClassifier model 시작\n", + "\t 폴드 세트: 0 시작\n", + "\t 폴드 세트: 1 시작\n", + "\t 폴드 세트: 2 시작\n", + "\t 폴드 세트: 3 시작\n", + "\t 폴드 세트: 4 시작\n", + "\t 폴드 세트: 5 시작\n", + "\t 폴드 세트: 6 시작\n", + "AdaBoostClassifier model 시작\n", + "\t 폴드 세트: 0 시작\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t 폴드 세트: 1 시작\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t 폴드 세트: 2 시작\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t 폴드 세트: 3 시작\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t 폴드 세트: 4 시작\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t 폴드 세트: 5 시작\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t 폴드 세트: 6 시작\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# 개별 모델 생성 후 함수를 호출해 데이터 세트 반환\n", + "knn_train, knn_test = get_stacking_base_datasets(knn_clf, X_train, y_train, X_test, 7)\n", + "rf_train, rf_test = get_stacking_base_datasets(rf_clf, X_train, y_train, X_test, 7)\n", + "dt_train, dt_test = get_stacking_base_datasets(dt_clf, X_train, y_train, X_test, 7)\n", + "ada_train, ada_test = get_stacking_base_datasets(ada_clf, X_train, y_train, X_test, 7)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "7fc15e9f-b7d3-44e0-8cb2-369552af0e49", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "원본 학습 피처 데이터 Shape: (455, 30) 원본 테스트 피처 Shape: (114, 30)\n", + "스태킹 학습 피처 데이터 Shape: (455, 4) 스태킹 학습 피처 데이터 Shape: (114, 4)\n" + ] + } + ], + "source": [ + "# step 2 구현 : 학습 데이터와 테스트 데이터 합치기 \n", + "Stack_final_X_train = np.concatenate((knn_train, rf_train, dt_train, ada_train), axis = 1)\n", + "Stack_final_X_test = np.concatenate((knn_test, rf_test, dt_test, ada_test), axis = 1)\n", + "print('원본 학습 피처 데이터 Shape:', X_train.shape, '원본 테스트 피처 Shape:', X_test.shape)\n", + "print('스태킹 학습 피처 데이터 Shape:', Stack_final_X_train.shape,\n", + " '스태킹 학습 피처 데이터 Shape:', Stack_final_X_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "90a5a299-ed0e-45ab-8f9d-f9f86fd3820a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "최종 메타 모델의 예측 정확도: 0.9737\n" + ] + } + ], + "source": [ + "# 정확도 측정하기\n", + "lr_final.fit(Stack_final_X_train, y_train)\n", + "stack_final = lr_final.predict(Stack_final_X_test)\n", + "\n", + "print('최종 메타 모델의 예측 정확도: {0:.4f}'.format(accuracy_score(y_test, stack_final)))" + ] + }, + { + "cell_type": "markdown", + "id": "14cae68e-bad1-407a-8526-d6908cc6b6c2", + "metadata": {}, + "source": [ + "4.8 파머완 2장" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "aa416157-8341-4e1e-9a0d-91099d1533ab", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "params={\n", + " 'max_depth':[10,20,30,40,50], 'num_leaves':[35,45,55,65],\n", + " 'colsample_bytree':[0.5,0.6,0.7,0.8,0.9], 'subsample':[0.5, 0.6, 0.7, 0.8, 0.9],\n", + " 'min_child_weight':[10,20,30,40], 'reg_alpha':[0.01, 0.05, 0.1]\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7b5265ac-4ee3-4f8a-a611-559a33669420", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting hyperopt\n", + " Downloading hyperopt-0.2.7-py2.py3-none-any.whl.metadata (1.7 kB)\n", + "Requirement already satisfied: numpy in /opt/anaconda3/lib/python3.12/site-packages (from hyperopt) (1.26.4)\n", + "Requirement already satisfied: scipy in /opt/anaconda3/lib/python3.12/site-packages (from hyperopt) (1.13.1)\n", + "Requirement already satisfied: six in /opt/anaconda3/lib/python3.12/site-packages (from hyperopt) (1.16.0)\n", + 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"code", + "execution_count": 13, + "id": "ca3c98a7-1600-46c6-9d95-dc7f22cdc1c2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|████████████| 5/5 [00:00<00:00, 751.45trial/s, best loss: -2199023255552.0]\n", + "best: {'x': 4.0, 'y': -2.0}\n" + ] + } + ], + "source": [ + "from hyperopt import STATUS_OK\n", + "import numpy as np\n", + "\n", + "# 목적 함수 생성. 변숫값과 변수 검색 공간을 갖는 딕셔너리를 인자로, 특정 값 반환\n", + "def objective_func(search_space):\n", + " x=search_space['x']\n", + " y=search_space['y']\n", + " retval=x**20*y\n", + "\n", + " return retval\n", + "from hyperopt import fmin, tpe, Trials\n", + "# 입력 결괏값을 저장한 Trials 객체 생성\n", + "trial_val=Trials()\n", + "\n", + "# 목적 함수의 최솟값 반환하는 최적 입력 변숫값을 5번의 입력값 시도로 찾아냄 (max_evals=5)\n", + "best_01=fmin(fn=objective_func, space=search_space, algo=tpe.suggest, max_evals=5, trials=trial_val, rstate=np.random.default_rng(seed=0))\n", + "print('best:', best_01)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4967cd5a-0c29-4fa8-b96a-e6c3a65efa45", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|███| 20/20 [00:00<00:00, 427.81trial/s, best loss: -1.2157665459056928e+20]\n", + "best: {'x': -9.0, 'y': -10.0}\n", + "[{'loss': 1.828079220031488e+16, 'status': 'ok'}, {'loss': 10995116277760.0, 'status': 'ok'}, {'loss': -2199023255552.0, 'status': 'ok'}, {'loss': 13194139533312.0, 'status': 'ok'}, {'loss': 1.2157665459056929e+19, 'status': 'ok'}, {'loss': 15728640.0, 'status': 'ok'}, {'loss': 7e+20, 'status': 'ok'}, {'loss': -1.2157665459056928e+20, 'status': 'ok'}, {'loss': 0.0, 'status': 'ok'}, {'loss': -0.0, 'status': 'ok'}, {'loss': -0.0, 'status': 'ok'}, {'loss': 2.0, 'status': 'ok'}, {'loss': 4.8630661836227715e+19, 'status': 'ok'}, {'loss': 3.656158440062976e+16, 'status': 'ok'}, {'loss': 3.647299637717079e+19, 'status': 'ok'}, {'loss': 3145728.0, 'status': 'ok'}, {'loss': -14680064.0, 'status': 'ok'}, {'loss': -8796093022208.0, 'status': 'ok'}, {'loss': 8.77714929273732e+17, 'status': 'ok'}, {'loss': -0.0, 'status': 'ok'}]\n" + ] + } + ], + "source": [ + "\n", + "trial_val=Trials()\n", + "\n", + "# max_evals=20\n", + "best_02=fmin(fn=objective_func, space=search_space, algo=tpe.suggest, max_evals=20, trials=trial_val, rstate=np.random.default_rng(seed=0))\n", + "print('best:', best_02)\n", + "\n", + "print(trial_val.results)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "34c0b246-db42-4b22-bf60-4ec1958fa7b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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xylosses
0-6.05.01.828079e+16
1-4.010.01.099512e+13
24.0-2.0-2.199023e+12
3-4.012.01.319414e+13
49.01.01.215767e+19
52.015.01.572864e+07
610.07.07.000000e+20
7-9.0-10.0-1.215767e+20
8-8.00.00.000000e+00
9-0.0-5.0-0.000000e+00
10-0.0-3.0-0.000000e+00
111.02.02.000000e+00
129.04.04.863066e+19
136.010.03.656158e+16
149.03.03.647300e+19
152.03.03.145728e+06
16-2.0-14.0-1.468006e+07
17-4.0-8.0-8.796093e+12
187.011.08.777149e+17
19-0.0-0.0-0.000000e+00
\n", + "
" + ], + "text/plain": [ + " x y losses\n", + "0 -6.0 5.0 1.828079e+16\n", + "1 -4.0 10.0 1.099512e+13\n", + "2 4.0 -2.0 -2.199023e+12\n", + "3 -4.0 12.0 1.319414e+13\n", + "4 9.0 1.0 1.215767e+19\n", + "5 2.0 15.0 1.572864e+07\n", + "6 10.0 7.0 7.000000e+20\n", + "7 -9.0 -10.0 -1.215767e+20\n", + "8 -8.0 0.0 0.000000e+00\n", + "9 -0.0 -5.0 -0.000000e+00\n", + "10 -0.0 -3.0 -0.000000e+00\n", + "11 1.0 2.0 2.000000e+00\n", + "12 9.0 4.0 4.863066e+19\n", + "13 6.0 10.0 3.656158e+16\n", + "14 9.0 3.0 3.647300e+19\n", + "15 2.0 3.0 3.145728e+06\n", + "16 -2.0 -14.0 -1.468006e+07\n", + "17 -4.0 -8.0 -8.796093e+12\n", + "18 7.0 11.0 8.777149e+17\n", + "19 -0.0 -0.0 -0.000000e+00" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "import pandas as pd\n", + "\n", + "losses=[loss_dict['loss'] for loss_dict in trial_val.results]\n", + "\n", + "result_df=pd.DataFrame({'x': trial_val.vals['x'], 'y': trial_val.vals['y'], 'losses': losses})\n", + "result_df" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5774176e-4829-4def-99d8-d08c04a24349", + "metadata": {}, + "outputs": [], + "source": [ + "from hyperopt import hp\n", + "\n", + "xgb_search_space={\n", + " 'max_depth':hp.quniform('max_depth', 5,20,1),\n", + " 'min_child_weight':hp.quniform('min_child_weight', 1,2,1),\n", + " 'learning_rate':hp.uniform('learning_rate', 0.01, 0.2),\n", + " 'colsample_bytree':hp.uniform('colsample_bytree', 0.5,1)\n", + "}\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "bf038494-3ef9-4078-9ff2-334fd3beedbe", + "metadata": {}, + "outputs": [], + "source": [ + "#목적 함수 objective_func() 생성\n", + "from sklearn.model_selection import cross_val_score\n", + "from xgboost import XGBClassifier\n", + "from hyperopt import STATUS_OK\n", + "\n", + "# XGBClassifier 정수형 하이퍼 파라미터는 정수형 변환해야 함\n", + "# -1*정확도 -> 큰 정확도 값일 수록 최소가 되도록 변환\n", + "def objective_func(search_space):\n", + " # n_estimators=100으로 축소\n", + " xgb_clf=XGBClassifier(n_estimators=100, max_depth=int(search_space['max_depth']),\n", + " min_child_weight=int(search_space['min_child_weight']),\n", + " learning_rate=search_space['learning_rate'],\n", + " colsample_bytree=search_space['colsample_bytree'],\n", + " eval_metric='logloss')\n", + " accuracy=cross_val_score(xgb_clf, X_train, y_train, scoring='accuracy', cv=3)\n", + "\n", + " # accuracy는 cv=3 수만큼 roc-auc 결과를 리스트로 가짐. 평균해서 반환하되, -1 곱합\n", + " return {'loss': -1*np.mean(accuracy), 'status':STATUS_OK}" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "97ffa344-da7e-4dd6-8efa-a5c18166893e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0%| | 0/50 [00:00 6\u001b[0m best\u001b[38;5;241m=\u001b[39mfmin(fn\u001b[38;5;241m=\u001b[39mobjective_func,\n\u001b[1;32m 7\u001b[0m space\u001b[38;5;241m=\u001b[39mxgb_search_space,\n\u001b[1;32m 8\u001b[0m algo\u001b[38;5;241m=\u001b[39mtpe\u001b[38;5;241m.\u001b[39msuggest,\n\u001b[1;32m 9\u001b[0m max_evals\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m50\u001b[39m,\n\u001b[1;32m 10\u001b[0m trials\u001b[38;5;241m=\u001b[39mtrial_val, rstate\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mdefault_rng(seed\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m9\u001b[39m))\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbest:\u001b[39m\u001b[38;5;124m'\u001b[39m, best)\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/hyperopt/fmin.py:540\u001b[0m, in \u001b[0;36mfmin\u001b[0;34m(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[1;32m 537\u001b[0m fn \u001b[38;5;241m=\u001b[39m __objective_fmin_wrapper(fn)\n\u001b[1;32m 539\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m allow_trials_fmin \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(trials, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfmin\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 540\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m trials\u001b[38;5;241m.\u001b[39mfmin(\n\u001b[1;32m 541\u001b[0m fn,\n\u001b[1;32m 542\u001b[0m space,\n\u001b[1;32m 543\u001b[0m algo\u001b[38;5;241m=\u001b[39malgo,\n\u001b[1;32m 544\u001b[0m max_evals\u001b[38;5;241m=\u001b[39mmax_evals,\n\u001b[1;32m 545\u001b[0m timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[1;32m 546\u001b[0m loss_threshold\u001b[38;5;241m=\u001b[39mloss_threshold,\n\u001b[1;32m 547\u001b[0m max_queue_len\u001b[38;5;241m=\u001b[39mmax_queue_len,\n\u001b[1;32m 548\u001b[0m rstate\u001b[38;5;241m=\u001b[39mrstate,\n\u001b[1;32m 549\u001b[0m pass_expr_memo_ctrl\u001b[38;5;241m=\u001b[39mpass_expr_memo_ctrl,\n\u001b[1;32m 550\u001b[0m verbose\u001b[38;5;241m=\u001b[39mverbose,\n\u001b[1;32m 551\u001b[0m catch_eval_exceptions\u001b[38;5;241m=\u001b[39mcatch_eval_exceptions,\n\u001b[1;32m 552\u001b[0m return_argmin\u001b[38;5;241m=\u001b[39mreturn_argmin,\n\u001b[1;32m 553\u001b[0m show_progressbar\u001b[38;5;241m=\u001b[39mshow_progressbar,\n\u001b[1;32m 554\u001b[0m early_stop_fn\u001b[38;5;241m=\u001b[39mearly_stop_fn,\n\u001b[1;32m 555\u001b[0m trials_save_file\u001b[38;5;241m=\u001b[39mtrials_save_file,\n\u001b[1;32m 556\u001b[0m )\n\u001b[1;32m 558\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trials \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(trials_save_file):\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/hyperopt/base.py:671\u001b[0m, in \u001b[0;36mTrials.fmin\u001b[0;34m(self, fn, space, algo, max_evals, timeout, loss_threshold, max_queue_len, rstate, verbose, pass_expr_memo_ctrl, catch_eval_exceptions, return_argmin, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[1;32m 666\u001b[0m \u001b[38;5;66;03m# -- Stop-gap implementation!\u001b[39;00m\n\u001b[1;32m 667\u001b[0m \u001b[38;5;66;03m# fmin should have been a Trials method in the first place\u001b[39;00m\n\u001b[1;32m 668\u001b[0m \u001b[38;5;66;03m# but for now it's still sitting in another file.\u001b[39;00m\n\u001b[1;32m 669\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfmin\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fmin\n\u001b[0;32m--> 671\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fmin(\n\u001b[1;32m 672\u001b[0m fn,\n\u001b[1;32m 673\u001b[0m space,\n\u001b[1;32m 674\u001b[0m algo\u001b[38;5;241m=\u001b[39malgo,\n\u001b[1;32m 675\u001b[0m max_evals\u001b[38;5;241m=\u001b[39mmax_evals,\n\u001b[1;32m 676\u001b[0m timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[1;32m 677\u001b[0m loss_threshold\u001b[38;5;241m=\u001b[39mloss_threshold,\n\u001b[1;32m 678\u001b[0m trials\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 679\u001b[0m rstate\u001b[38;5;241m=\u001b[39mrstate,\n\u001b[1;32m 680\u001b[0m verbose\u001b[38;5;241m=\u001b[39mverbose,\n\u001b[1;32m 681\u001b[0m max_queue_len\u001b[38;5;241m=\u001b[39mmax_queue_len,\n\u001b[1;32m 682\u001b[0m allow_trials_fmin\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;66;03m# -- prevent recursion\u001b[39;00m\n\u001b[1;32m 683\u001b[0m pass_expr_memo_ctrl\u001b[38;5;241m=\u001b[39mpass_expr_memo_ctrl,\n\u001b[1;32m 684\u001b[0m catch_eval_exceptions\u001b[38;5;241m=\u001b[39mcatch_eval_exceptions,\n\u001b[1;32m 685\u001b[0m return_argmin\u001b[38;5;241m=\u001b[39mreturn_argmin,\n\u001b[1;32m 686\u001b[0m show_progressbar\u001b[38;5;241m=\u001b[39mshow_progressbar,\n\u001b[1;32m 687\u001b[0m early_stop_fn\u001b[38;5;241m=\u001b[39mearly_stop_fn,\n\u001b[1;32m 688\u001b[0m trials_save_file\u001b[38;5;241m=\u001b[39mtrials_save_file,\n\u001b[1;32m 689\u001b[0m )\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/hyperopt/fmin.py:586\u001b[0m, in \u001b[0;36mfmin\u001b[0;34m(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)\u001b[0m\n\u001b[1;32m 583\u001b[0m rval\u001b[38;5;241m.\u001b[39mcatch_eval_exceptions \u001b[38;5;241m=\u001b[39m catch_eval_exceptions\n\u001b[1;32m 585\u001b[0m \u001b[38;5;66;03m# next line is where the fmin is actually executed\u001b[39;00m\n\u001b[0;32m--> 586\u001b[0m rval\u001b[38;5;241m.\u001b[39mexhaust()\n\u001b[1;32m 588\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_argmin:\n\u001b[1;32m 589\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(trials\u001b[38;5;241m.\u001b[39mtrials) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/hyperopt/fmin.py:364\u001b[0m, in \u001b[0;36mFMinIter.exhaust\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mexhaust\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 363\u001b[0m n_done \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrials)\n\u001b[0;32m--> 364\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_evals \u001b[38;5;241m-\u001b[39m n_done, block_until_done\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39masynchronous)\n\u001b[1;32m 365\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrials\u001b[38;5;241m.\u001b[39mrefresh()\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/hyperopt/fmin.py:300\u001b[0m, in \u001b[0;36mFMinIter.run\u001b[0;34m(self, N, block_until_done)\u001b[0m\n\u001b[1;32m 297\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpoll_interval_secs)\n\u001b[1;32m 298\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 299\u001b[0m \u001b[38;5;66;03m# -- loop over trials and do the jobs directly\u001b[39;00m\n\u001b[0;32m--> 300\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mserial_evaluate()\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrials\u001b[38;5;241m.\u001b[39mrefresh()\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrials_save_file \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/hyperopt/fmin.py:178\u001b[0m, in \u001b[0;36mFMinIter.serial_evaluate\u001b[0;34m(self, N)\u001b[0m\n\u001b[1;32m 176\u001b[0m ctrl \u001b[38;5;241m=\u001b[39m base\u001b[38;5;241m.\u001b[39mCtrl(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrials, current_trial\u001b[38;5;241m=\u001b[39mtrial)\n\u001b[1;32m 177\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 178\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdomain\u001b[38;5;241m.\u001b[39mevaluate(spec, ctrl)\n\u001b[1;32m 179\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 180\u001b[0m logger\u001b[38;5;241m.\u001b[39merror(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mjob exception: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mstr\u001b[39m(e))\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/hyperopt/base.py:892\u001b[0m, in \u001b[0;36mDomain.evaluate\u001b[0;34m(self, config, ctrl, attach_attachments)\u001b[0m\n\u001b[1;32m 883\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 884\u001b[0m \u001b[38;5;66;03m# -- the \"work\" of evaluating `config` can be written\u001b[39;00m\n\u001b[1;32m 885\u001b[0m \u001b[38;5;66;03m# either into the pyll part (self.expr)\u001b[39;00m\n\u001b[1;32m 886\u001b[0m \u001b[38;5;66;03m# or the normal Python part (self.fn)\u001b[39;00m\n\u001b[1;32m 887\u001b[0m pyll_rval \u001b[38;5;241m=\u001b[39m pyll\u001b[38;5;241m.\u001b[39mrec_eval(\n\u001b[1;32m 888\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexpr,\n\u001b[1;32m 889\u001b[0m memo\u001b[38;5;241m=\u001b[39mmemo,\n\u001b[1;32m 890\u001b[0m print_node_on_error\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrec_eval_print_node_on_error,\n\u001b[1;32m 891\u001b[0m )\n\u001b[0;32m--> 892\u001b[0m rval \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfn(pyll_rval)\n\u001b[1;32m 894\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(rval, (\u001b[38;5;28mfloat\u001b[39m, \u001b[38;5;28mint\u001b[39m, np\u001b[38;5;241m.\u001b[39mnumber)):\n\u001b[1;32m 895\u001b[0m dict_rval \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mloss\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mfloat\u001b[39m(rval), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus\u001b[39m\u001b[38;5;124m\"\u001b[39m: STATUS_OK}\n", + "Cell \u001b[0;32mIn[25], line 15\u001b[0m, in \u001b[0;36mobjective_func\u001b[0;34m(search_space)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mobjective_func\u001b[39m(search_space):\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# n_estimators=100으로 축소\u001b[39;00m\n\u001b[1;32m 10\u001b[0m xgb_clf\u001b[38;5;241m=\u001b[39mXGBClassifier(n_estimators\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m, max_depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mint\u001b[39m(search_space[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax_depth\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m 11\u001b[0m min_child_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mint\u001b[39m(search_space[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin_child_weight\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m 12\u001b[0m learning_rate\u001b[38;5;241m=\u001b[39msearch_space[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlearning_rate\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 13\u001b[0m colsample_bytree\u001b[38;5;241m=\u001b[39msearch_space[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolsample_bytree\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 14\u001b[0m eval_metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlogloss\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 15\u001b[0m accuracy\u001b[38;5;241m=\u001b[39mcross_val_score(xgb_clf, X_train, y_train, scoring\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m, cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m# accuracy는 cv=3 수만큼 roc-auc 결과를 리스트로 가짐. 평균해서 반환하되, -1 곱합\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mmean(accuracy), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstatus\u001b[39m\u001b[38;5;124m'\u001b[39m:STATUS_OK}\n", + "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined" + ] + } + ], + "source": [ + "# fmin() 이용하여 최적 하이퍼 파라미터 도출하기\n", + "\n", + "from hyperopt import fmin, tpe, Trials\n", + "\n", + "trial_val=Trials()\n", + "best=fmin(fn=objective_func,\n", + " space=xgb_search_space,\n", + " algo=tpe.suggest,\n", + " max_evals=50,\n", + " trials=trial_val, rstate=np.random.default_rng(seed=9))\n", + "print('best:', best)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "81a618c8-7f0e-4b84-8c37-1b390a630830", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'best' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[29], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# 최적 하이퍼 파라미터로 XGBClassifier 재학습 및 성능 평가\u001b[39;00m\n\u001b[1;32m 2\u001b[0m xgb_wrapper\u001b[38;5;241m=\u001b[39mXGBClassifier(n_estimators\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m400\u001b[39m,\n\u001b[0;32m----> 3\u001b[0m learning_rate\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mround\u001b[39m(best[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlearning_rate\u001b[39m\u001b[38;5;124m'\u001b[39m],\u001b[38;5;241m5\u001b[39m),\n\u001b[1;32m 4\u001b[0m max_depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mint\u001b[39m(best[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax_depth\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m 5\u001b[0m min_child_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mint\u001b[39m(best[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin_child_weight\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m 6\u001b[0m colsample_bytree\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mround\u001b[39m(best[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolsample_bytree\u001b[39m\u001b[38;5;124m'\u001b[39m],\u001b[38;5;241m5\u001b[39m),\n\u001b[1;32m 7\u001b[0m early_stopping_rounds\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m50\u001b[39m, eval_metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlogloss\u001b[39m\u001b[38;5;124m'\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 8\u001b[0m evals\u001b[38;5;241m=\u001b[39m[(X_tr, y_tr), (X_val, y_eval)]\n\u001b[1;32m 9\u001b[0m xgb_wrapper\u001b[38;5;241m.\u001b[39mfit(X_tr, y_tr, eval_set\u001b[38;5;241m=\u001b[39mevals)\n", + "\u001b[0;31mNameError\u001b[0m: name 'best' is not defined" + ] + } + ], + "source": [ + "# 최적 하이퍼 파라미터로 XGBClassifier 재학습 및 성능 평가\n", + "xgb_wrapper=XGBClassifier(n_estimators=400,\n", + " learning_rate=round(best['learning_rate'],5),\n", + " max_depth=int(best['max_depth']),\n", + " min_child_weight=int(best['min_child_weight']),\n", + " colsample_bytree=round(best['colsample_bytree'],5),\n", + " early_stopping_rounds=50, eval_metric='logloss', verbose=True)\n", + "evals=[(X_tr, y_tr), (X_val, y_eval)]\n", + "xgb_wrapper.fit(X_tr, y_tr, eval_set=evals)\n", + "preds=xgb_wrapper.predict(X_test)\n", + "pred_proba=xgb_wrapper.predict_proba(X_test)[:,1]\n", + "\n", + "get_clf_eval(y_test, preds, pred_proba)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fed9d139-1cdc-463c-8563-b39b10bdb624", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/Week4_\341\204\213\341\205\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.pdf" "b/Week4_\341\204\213\341\205\250\341\204\211\341\205\263\341\206\270\341\204\200\341\205\252\341\204\214\341\205\246_\341\204\213\341\205\265\341\204\214\341\205\242\341\204\205\341\205\265\341\206\253.pdf" new file mode 100644 index 0000000000000000000000000000000000000000..9fbf5db6c1be4d4b0909f791c11ad3c63c62aeeb GIT binary patch literal 5624681 zcmd42Wmp~Cwk^8wFwx+_6N0-t1b26Lmq~Ef;1+_rTkzm+!QCy$M1s5ft*pK7+xt8B zocrVbd9S|tF^j5EHAjundmp{G){0zFRGglffel1HbGQ!yF_SWpf{m>~e0+=|j%G&A zU`J9a5lcs~ot29nHHcBs5p3#WVg?*kv9U5@1g<5uV`kiqqvjH+ht&ZLY|c19Lv!vB1W{PQiP2VxX+ca~6b{xbl;r4p>9 ze;z;?=qjT)*xvciF>y9hwm-*!BkZK?|Lk#)a{RN$Ny_=p9v3OsKYQGy-2dz`Gm!$H zzwh|xp2EUlcT$}{2Us~-N!gfz-Q)&Fn3lElF9Jxda4AotyzHumibgL@Nqe 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