{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Mining\n", "\n", "## Lab 6: Dimensionality Reduction\n", "\n", "Pada modul ini, Anda akan mempelajari tentang bagaimana kita dapat melakukan reduksi dimensi pada data dengan dimensi yang sangat besar, e.g. dari teks atau gambar. Dalam kasus ini, kita akan menggunakan dataset MNIST. Salah satu metode yang akan dibahas secara mendalam dalam modul ini adalah *Principal Component Analysis* (PCA)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function\n", "from sklearn.decomposition import PCA\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "%matplotlib inline\n", "\n", "np.random.seed(538)\n", "plt.style.use('ggplot')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pengenalan PCA" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.datasets import load_digits\n", "from sklearn.model_selection import train_test_split\n", "\n", "mnist = load_digits()\n", "\n", "img_rows, img_cols = 8, 8\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(mnist.data, mnist.target, test_size=0.33, random_state=1945)\n", "\n", "# Mengubah dimensi data menjadi n x pixels\n", "X_train = X_train.reshape(X_train.shape[0], img_rows * img_cols)\n", "X_test = X_test.reshape(X_test.shape[0], img_rows * img_cols)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sekarang, kita ingin melihat nilai rata-rata dari masing-masing pixel, untuk kemudian dilihat hasil visualisasinya." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(X_train.mean(axis=0).reshape(img_rows, img_cols), cmap='Greys')\n", "plt.xticks(range(8))\n", "plt.yticks(range(8))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perhatikan bahwa rata-rata untuk pixels di pojok kiri dan pojok kanan berwarna putih. Artinya, pixels tersebut **tidak pernah berubah warnanya** untuk semua data yang kita miliki. Dengan kata lain, pixels tersebut *tidak berguna* untuk menentukan gambar digit apakah yang akan kita klasifikasi tersebut. PCA bertujuan untuk \"membuang\" atribut-atribut yang tidak berguna tersebut." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1203, 64)\n", "(594, 64)\n" ] } ], "source": [ "print(X_train.shape)\n", "print(X_test.shape)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pca = PCA(2) # kita akan mengambil dua komponen paling pentingnya saja\n", "X_train_pca = pca.fit_transform(X_train)\n", "X_test_pca = pca.transform(X_test)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1203, 2)\n", "(594, 2)\n" ] } ], "source": [ "print(X_train_pca.shape)\n", "print(X_test_pca.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Karena dimensi hasil proyeksi hanya terdiri dari dua atribut, maka kita bisa memetakannya ke dalam diagram cartesian." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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Ral17B117B1uZRaAhsVDsGADhCpW2dSZG9gwn3lxPdWw1j3z0bpqbmxkbG8Nr\nbqU1uBnqDvGtb/4rI+cyhINBmjubiUQiDA0NUVNTw9ycYH66l3Qmh2HYeL1+Ar4G1qxZR0NDA1AM\nkfvCF74AwJ49e0ilUrS1tTEwMMD4+LhTv9nr9VIoFKitraW+vp5oNEqhUCCdThMIBOjo6HBE+OGH\nHy6zynVd5wc/+AFvv/22UyMkEok4ZVUzmQzvvPMOXV1dzroupKOjg8HBwSXjpYvL1XLnnXcyNDTE\njHU+TV0Iwaq7ii6rk5zkIxSFOiuz9NJLRmZoFs2sYQ2KcCNEbgTe5L73/TVvmFDv3LmTnTt3Lhn/\n2te+dgNW8/5jSYsfjf4XE/lJoFg46cz4GZLobK8q/9FbyiiSrejafgzPO0hRQEgfHvNWvMZ2xDKx\n05ayfLsnSxlHV48ipA9dexeETfFrIAANS5lAoCFkiHBFFdvuXsP2O+oIFR5f0txW4MNLG3XV66mr\nLn+duro6hoeH8fkCzE0r2HYEIXWMAkhLY2hoyOlluLi40YYNGzhy5AjpdJqhoSGnWFKpCYEQgsbG\nRmpqahgZGaGl5fyFLRAIkEqlyGazTphfKZnmJz/5CUePHiWfzzvlUfP5PPX19U6iTCgUYmBgYFmh\nTiaTTup5qWUYFLvU7Nq165r8yc3NzTz++OOc3H+S1HQKf4Wfpq1NVLYXXX42xbDAUTnKs/JZdFm8\n8B6UB1klVvEJPoFHuHkG7ze+P3j/O9Lf8M3E31T6Uv2OSBcp/uCOx6fpCkWp9J33TQqCGNq76N7z\nV3IpCuietxDSh9e8DVskEdKD4Hx0hy3mEDKEoBgRYSpDWMoYpjKOrYwhRQbFrkAoIaRIIUUShF6s\nnYFEigy2Mo5UI5jqKTzW0tC2i4WR+f1+Nm3axO7du53U8FAohGmazM7OoigK/f39dHR0cPvttzvP\na25uprm5me9///tMTRU7oWcyGaLRKLOzs/j9fubn553KePl8vqwpLRQ3Cp999llno87n8zmF/2dm\nZpw1G4ZBLpcjFAo51f2W6zb+2muvcfDgQaSUSCnRdZ2NGzfS2NjIhg0bLttB+lI0NTXxscc+Rp/s\nW/K3Top3Pr+Qv3BEusSwHOaIOMJWtl7za7v898EV6hvEWH6i7LEigygygpQFJnKZ80ItBR5zM3nv\n7iXHkEhy3h+R9f0IKTKodh2a2YUt5jDUfiztDEiBatejyBiWOogkj63aSJHCFvNIkUYz1wEGtjoP\nKAjpL7puVB7bAAAgAElEQVRWZAxbmcG2G7CUOJ5l3LD19fVUVlY6jWcXU1NTQ1tbm5PeXcoenJ6e\nZn5+ns7OThRF4d///d959NFHWbVqFdPT04yNjdHW1uaUMFUUxamUl8lkSCQSdHV10dzczKFDh2hs\nbHSKJBmGwdTUVJlvenBwkPHxcTZt2sTExISTdm7bNrqus2bNGifZZP368uYIAwMDHDhwwHkshMDn\n8zE3N8ejjz56ZR/2ZdghdjDBBAl5vqZITMT4kPgQcRknLuNl80tFqAbkAFvF8kK9uOzre4nzdvn1\nsHfvXl577TXn8cDAAN/73vcuOt8V6htERFtaPF6z1qGKEUJa0boWMohP34FqN2KLVNlcicRQezC1\nYwgZASQGCtL7IgKBYjdgk0YqKSx1CCEDCBkAgkgMkD4QAikKSJFDs4ruB0uZQrEbUFCRWEh0DO0w\ntq1jakdJ2R9BshbB+YJIH/3oR/nxj3/sJKNAsfFtIBCgtraW6eliJk2pU3gikaCurq7M7bB7926e\nfPJJjh8/zsjICBMTE068tJTSsaBL1nVtbS1VVVV0dHQwNDREJBKhoqKCUCi0pPuK3+8nn8+TSqXY\ntGkTo6OjCCGYmZlh9erVTtLKzp07icViZc/t7e1d9vObnZ1lcnKS+vr6K//QL0JURPkdfoc+0Udc\nxqkSVXTRhSY05uT5C2BSJhliiCRJNKmRI4clLVRxXoillM5Ga4nSuXOTaFYO999/P/fffz9Q/I7t\n23dpv7cr1DeI7kgXv4q/g2Gf/0EJ6aU1cD9dyt2QL6DYtYiF6naq3YSljjhzpZjD1E5iiywKAomN\nFGkQOsgwEguECTKAYtcgyWEpkwvHUwABUgFhYSspFLMR8KHadUiRxBJ5JBbHDiY5/laEXFyjpnaU\nex+YIxbczFBvE0II1q5dS1NTE08++SSnTp0il8vR2tpKfX09o6OjHDx4kJaWFk6cOMH8/LwTVhcI\nBOjt7WXjxo1omkYmk2FkZIREIsHQ0BBQTCZJJpNO01uPx0NDQwN+v59EIkFDQwN1dXVOJ/M//uM/\n5rnnniuzgAFisRjBYNAJ/Wxvb6e9vR2/38+OHTtQVZX29vZlK+JdKpqjVGb1euARHjaxaUmplkpR\nSYNo4Ix9hhOcwKK4HhOTJEn2yD08KB505peaMFz4HvL5vFvxb4Xyox/96LI5JK5Q3yAinjCfbP4t\n9ky+yqxevLVtCTbz+c7/i+xsFi4oL+ExtqFrb2OpEyBVbGUaW0wDyoK1bS2E1pkg0thKHoG/uNEo\nY8VNQ1FAohTD8JAgLIQdRbM68esfxZDvYKh9WGoeUDn2dpp9v7ARUkFFZ2Ya/uX/7EdR3qI28iEE\nHl577TXuv/9+GhsbGRsbY35+nmw2SyAQoLm5mZtuugnTNBkeHkZRlGJaeSjkWMdTU1NOnHOpe3gp\n68/r9RIKhZxb+KamJidue7EvuZTlGgwGl01qEELQ3d1Na2srU1NTSClpb2/n3nvvvWys/po1axgY\nGFgyHgqFLhodUkJKiY6OF+97smYfEg/xv8T/wpLnLxp1C//rpZe75d2ERdHVc+HFw7Ztx7fu9/td\nq3qF0d/fT3V19ZI7uQtxhfoG0hJo5ourf5s5fR5NaEQ8YWK+GFmyzhxTGaLg2UfB+wtMZQgpDKTI\nIEkBEscEEwXOq7sFQiKxQWqAhZBehB3CVpIgMkgkAg2FGgKFR/AbO9HsFozQ36HIGNKu4ODLGUYG\nBKn5LB5tjMrKasaG0gRCKjUbCgg82LbNM888QygUcsRzeHiYnp4ennjiCR544AFCoRBDQ0MoioKq\nqk65UihGUzQ1NREIBGhpaaFQKDg1oL1eL6qqYhiG49oolUGtra11fLVQDJ2DorC2tLQwMnL+7gPg\n5ptv5uGHH3bqUi+3abgcGzdu5NSpU07MNRQvDDt37ryk7/eQPMTb8m3SMk1URLmDO2ji2jLjqkQV\n29iGT/jQ0YkQIUTRdWZLm4RIEKYo1KWLXKmR7+KCUrlcznWBrDD27t17ydj9Eq5QrwAqvctfTXXt\nIAXvqxjqUUz1DLZIU7SYi2VGEdai0qU2jnDLorW8sN2IkGHAXAjrKya0CFF0tYCOZtwMgCKjeMxb\nkGSZj+d4940zFApFC03IAmNDUyhCQSj+smzJs2fPUldXV2bN5nI5fvWrX/HQQw85CShwvlJcyW/t\n8XhQVZVdu3ahaRqdnZ1s3bqVwcFBzp49i9frxbIsCoWCs8k4NTXllMetrKxk+/btbN68ufgeFIVP\nfvKTHD58mIGBAYQQbNiwwanXcbUba6VGvQMDA5w7d45AIMDGjRsvaQEdlofZa+91Hidlkl/KX9Ko\nN1LL1WUvlqgTdVTJpXWwVaFSRXG8ZDmXOq0vRgjhlH1d3G7M5cbS09PD7/3e7112nivUKxSbLBnf\n9zDVYayFFG+BwfnabipIi/MCvajqm2MwFd0hqtWErcwjGEGxVxWfiwFoKLICy3MazahFsWtQpA8p\nBKd7Z1GkHygsHLsocKlEnoaGZiem2jAMxx1xIcPDw85GYHV1NbOzs45fu76+nvn5eR5++GHuvvtu\nJ0pDVVU+8YlP8B//8R8MDAxQU1ODpmnU1dURi8WYmJigq6uLmZkZTNN0kl4W3/J7PB62bdvGtm3b\n3vPnAEXxX7t27ZIqeosZkSMckAeYYYZj8hiVspKoiJbN2VfYx6NcW6TIFrGFHnooyPKO7zdzMwER\ncDZebdt2fPqltZfCIwFXqFcQ8Xgcv99/RXd3rlCvQGwxTyL018XswtImIXmkUECWfmQC8FEUUmXh\nX8kVUlJqDfBjeA7jzz+ErUwDJXH3IaSCZrVhKxMLR/ThNe6k4H2NydE0jasqONtf9HsL6ScQEGST\nKjWVnc5aS92+l+t6kslk+O53v0sikcAwDObm5pxO4g0NDTz++OPLlh1tbGzklltuIZFIYNs20WgU\nTdNIJpPE43Hq6+tpbW115mezWU6cOOFY7e83g3KQH8sfY0sbiWRUjjLGGFvNrTTZTZjCJKEliNvx\nyx/sIsREjMd5nH3sY5hhggTZLDY7cdS6rju+/FKFwVLXdUVR0HXdaebrsjKYn5+nomJpx6TlcIV6\nBZLz/gxLHQRYCINTFzwcRXeHkP5iVAcm4F0QbwkYTg3qIgpCeor/JbSwCVm0zCGIZm4o+rDt85af\n19yGIiuJhtLU1SdRFIuJEZ1CXiUWaaCjtY6a6vM1rUthbYsr6UFRPDOZjOMjLXVkqays5KGHHqKu\nru6SbohSB5iy85LLOX+7kOXiuN8v3pJvYcvimgSCsB1mbXYdzTRTL4rhe416I56wZ0k/x6uhVtTy\nqFjeIl98R1HaCzBN0xHrEoVCwYl+cbmxrFmzhr/8y7+8ormuUK8wLJnE1E5S/GiKPmRkgKIIL2wY\nSi+K9CCFjSQPIgeoxXC8kitEegA/Ai+WmCXv/TFSMUDkkNKDFCl07wFUqwlFRjGtTjR7NQCa1cmW\nzTvp6TlOTV0FNaWMdmmwdfPdbNvyAP39/UCxqH4oFOKXv/wlfX192LaNpmlUVlYSDAa5kLm5OTRN\nu6yveM2aNRw7dqxsLBgMoqrqslZITU3NkrH3iwkmsKXNjJwhd1Bl3cB2OmpXgd9GtqiIGouwFaZr\ntou0lXas3lIi0KWQUpZtgErksjU+LjxOaRN28eaiEMJpLVZRUXHJz6D0PHfjcWXgCvX7TMpIkzAT\nVHpihC5IepFIDDmJXAjDUu1qbDG7kNYdAami2vVo1kYEHkz1HLYyjI1B0aL2UHRtCMCLIkPF6A6R\nxVYyqLIeG7EQTw3ISlS7BYRNzvdfhPK/hyKL6dCVq07z0cfW8sbecyQTeRRFsGZtFTsfC6MkK5f4\nfx9++GF27NhBMpmkqqqKX/ziF5w+vbTRLhRrOF+uJGhHRwfr16+nr+98anU0GmX79u1LLOpYLEZX\nV9dlzvzFScs0s8xSQQUxcekwqeUIyzD72Id1MED0jWaqVlcgkWh5DavfQ0jx0x5pw2/7sWXRoi7F\nOl+Y/r6YUvyzbducEWd423ibOc8cMTXGreLWsqxETdPKNhAvFNhSezQpJYZhkMlk8Hq9S16/lDBT\nssZVVcXn87nZjTcYV6jfAzOFWd6c/RXnssP4VT83VWzk9spbl7V4LGnxy8mXOZE6hS1tFKFyU0U3\n99fuKO7IK6Pkvb9A2AaWNoJkHgihyHpsmUZR4gi7uZhBKAxUsxNJGlsZXShPutBUViwksywgMUH6\nEEKCVBAygCLDgESxIyhyweoVJqbai9e8A4mJrczSsb6aNeuqmJ/L4/drBIIetMAsdnL58xEOh51N\nwaampjKhltiAjap4Lxt/DEVheeSRR9i4cSNnzpzB4/HQ3d1NRUUFb731Fr29vViWRWdnJ3fdddc1\n3cpLKXlJvsQxjjkC2ik7eUg8dFXFjmIiRtJKUvfO6uLapUBIgSUsBAqZMYNTG06xQW4gyPm7DMMw\nlrWqZ+UsE3KCilwFUTvKsDLM8+rzxabCJiREglfFq9jCZou1pdg0WCaZUWbw236qZBWaKP60S8Wi\nFl/cSoJdEmTbtjFN08ksLblnoHixyOVyBINBt5/jDcQV6mskaaT4/0Z+TN4qfrkN2+DNmf0kjSS7\n6u9fMv/NmV/RkzzpPLalxaG5oxQsnS1V6whXPAeKDkTQrDUYahZbmUPKKFJJIGQIr3kbqjxfWU8z\nuzA8x4rZhjJYbEuID5BgexAyiip0bJFCksEWM8XUcQAEUuQwlTEEowgZQxNzC38pVs+TIoMQgsqq\n8xltKpUX5uIsy6ZNmzh69CjxuVks5Ry2MoUUFtvu2IQ3PAN262WPIYRwsggXs2PHDnbs2HEFq7g0\nBznIEXnEeSyl5DSnCRG6qjrQXry0Ge1YOR8e20MiniMSCYMUZKWOJ6eSJ88x5Rjb7G0onBfPUqam\nEALNo7FH3cMJeYKgFaTD6iAkQpziFCnDIGNLvEJQteA6eUt/i3X6Og6JQ+wT+7CxCYgAVWoV93rv\npdKsRNf1JX0gSy6QUkx6idJcVVXRNM0R65IVvrgbjsv7iyvU18jhxDFHpBfTkzzJ9qrbiXjOFwWS\nUnIsWV4zYk5PcCZzlkOJY0zLKtrFOGvDHUSIIGQA1VoNeFCsGCjVqNZ6FBbdfgobzVqDZq3FUkYB\nuVDLQ2Ars2hWJ1LJI2UeIQyQKlLkKLpGLKQwQPqRSmohsC+N6TmCNO5F4Mdr3EbB+xoXEhZ3MRCP\nc+7cOXw+H52dnctas36/n89+9rPsO/ItzpxL4Q9E2LC5lvUbo+TkswTzny82270GTNOkp6eHs2fP\n4vF42LhxI542Dy9nXuaEfYIIEW4Tt9Etlm9kW+K4PL7seA893Cfvu+J6z168NPka0aMVmHOCkfg8\nWlSlobIKEOT8JtI2mPOeo0FvoF22O1bs4o3F0+ZpDngPMKaNscpexSpWoZsmr5jniNsmYqGgbZ1p\n0eK3UGSOcWOc49pxbLV4+cyRY0JM8Jz9HL/r/V2EIcqs6VJxrJIlXbpIGIbhzCsVdFJVtWxsMbZt\nO88pbV6WjnclexAuV4cr1NfIbGH5UCtb2sSNuXKhRpaJum7rnEqfxpI2qlBRtSxZe4zjmTN4ol5M\nX4JiRIeGooRBerGUCVSrFVVWO6VLFVmDr3Afed+LSCUJaBSyCvn524hGK9GCUwgZxrZnkUoai6li\nKVO8SGksRJQUQ/oUuwKJiqEdw2tuw2NuBSS65x2kyCLsCD7jTl7fM8YvfvELx0rz+/089thjyzaO\n9QUtbr9P4XZxU/kfhIXheRdVX77DfDabJZVKEYvFllhxlmXxzDPPlGUeHhg+QPqhNOtC68jJHDly\nvCBfoKAU2CK2XOQThDxLL7RQbI1lCcuxfC9Gn+zjHfkO5+Q5BhigZds6rBersbHpPTfIxPQcLeEW\n5tfMcNKe5FY1iI7uvA9VVR2Rjss4R+VRKo1KjmpHGVVH8Qs/4fQ2EoTRvVNoqNSoHlRgTjdp1aJk\n7AztZjt9oo+sUoxnF7YACcMM06A0OK9XYnFaeYkLH5fEuiTQJQH2eDyOO6Q0f3EtFiEEuq7j8Xgu\n6X93uTpcob5CDNvAlBYBtfjlq/JWciYzuGSeEIJKT/mGlCIUWgJNjOSKzTmnC7NYC+FcFZ4wCXOC\nKpFCUVKkDQufaoAQIDVsqYA6BTKwYDkrKLIKzWrBW9gOqo1qN2JYFlPT04z1x+h5uY3ObT20r43R\n1taCpAFDHAPFRhBCNduxlVkQecBEs9ah2PUIBJZSXKNA4DVvXxDsAuBn8Owg+/b9suwHnc/n+dnP\nfsaTTz65xIcpRWKhMcFSbJFYMmaaJi+99BK9vb3Ytu0krmzfvt2Z09fXtyQ9fKxpjPhInNWdq8vG\nfyV/xc3cfFHLuJVWTnJyyXizaL6sj/qIPMIeu9ixXJUqQgomus7gkxbiiAclDf4mP+Mbh5mvT6Db\ngOqlhRZHAAt2gVE5SkJJMM44GTJ4bA9zco5pMc3rntfZnL+LarubUc80Utj4FtzZeVtyk1FsVScQ\n1Fl1DCqD1Fl11FuNaFJFWMKpD7LYwr0wa7EUWQLnNyGXs8Lz+bzj2y5Z0oujUizLKkus0TTtilP1\nXS6NexYvQ94q8PL065xMncaWFvX+Ou6tvZubY5s4muihYBVImEmSRhqvovGhmu1EPUsLyX+oZjvP\njD63IPjFmFdN0WgOe0gUNJK6pDlgYiM5X7PDi1QyRaNXmV0YU7GIY8scBL6DZq9Ds9s4c6rA7Ezx\nR1azapzUbJSx4CiBQGChTZRVbBIgg6jWelCOggwjpIpiNyAQ5HMGp94dY35sN9FolJtuumkhFK7o\no14cgbGYdDrN6Ogoq1atKhsXViXSVhDKUrFW7aXlQV9//XWOHz/vjjAMg3379hGJRJwU8FJlvcVk\ngpliF+9EAv+ihgsZmSEjMkRYvrD/drGdIYbIyvOZlR7h4cPiw45fthTiVop+EEKgmzqv269jYDCn\nzDEoBjGFyRZ7C8qqZtY2dOPR0uRFsQ/ivJlln3KWh6IPEslEsBUbqUhOcIK8LEZ1FJQCWbKkRIo5\n5jAw6NP60NUjtJk3syp1L/PBYwgtS8SqpKOwmS5PN3mRZkJM4MFD1KqgTm9GkRoBGcBrRbGEjbZg\nuWua5vimL6QkzKU7mNJjRVGcQlmGYZT5vEviX3p8oXvENE1XqK8T7lm8DLsnfs5g5rw4TOaneHb0\nZ3yh7Qk+2fwx/k///8tA5iyqUKn31TKUHWE8P0mjv1yImgONfL71cQ7PHyWkhZBAg78OVTuHjaA/\nHsDvybKmwqbo9pALcdOl2h6Ak+QikOoIBWUC05pG6E3MxIdhoVperGGGM+92E62bYXKqn4qms1jq\nGUBBsQVSG0DYQaSSRYpipmI6afLM93vJza5DWSgKdejQIT71qU85bo0rbe6al3lek69xghPo+UFa\nPFnuVtuoVIqCL2QAj3nLkudfGDdd4vDhw45QL3c77c/7yfvzS0TBJ3wEuHhpzypRxf/gf3CEI0wz\nTYwYN4ubqRSVTmJIiVL9DEVRSFpJklqSAgVOcYq8kkcIQb+nnx3KOuplhLDRRFqdIynmUWQNv6ve\nxofVrQzZxe/SkDp03h0mi5EiCDihnXBacCEgHj5CjdFJxKqkOrWdu/UqfGj4NBtFtYnYEerteqa0\nKaJGDbl8JSPZELoZoF+1uSVg0+oTaKrq1AG5HKWmCoCTMFPyR5fC/BRFcc6Hy68fV6gvwUxhtkyk\nSxi2wdH5HlSh4lW83FKx2XGJ6LbOzyf38sW2zy55XpU3xv11O7iv9sM8N/4C/ekzmE53cQ2fqMKn\nFjDsAkVRXii+hOC8lb0g2gtlSy3lCKY5jqVmiFZrBIJ+bMNPes7PqWMmXVtnsNRcsVnAQiElS8yg\nyEoUuxIwEKgcfHOKbLwVlfOx3YZh8Morr/C5z30OKCa3jI6OLnlfpcp3JX4if8KoLM4T1iqG5SQ/\nUc/xOe/NBO12fMZ2FFleB0PX9SV1lEtkMpniO5cS/01+TqVOIaWkeraa6ng1TeNNFBoKxGIx0qnz\n3bxv5VYnTO1ihEWYu8XdZWOldlsXUipq5Nf8+PDRL/oZE2NOaF9cjdPh76DVWk3W8KGYVfipptmj\n0e73ljVWmNAmGFFGqDfr8UovOjpHPUfpV/uJ2THmlXkQEI0NU8hNoOU1CrLAGcNLu8+DNzBDrzpK\nt+imnXZ8VgXTWYvpgo9qKRi3JSOWxahu8VsVXlYHlCVZiheiLoj54vNwobgvdo2UXB8l8b5wA3Hx\nhfNsVudYOkfBlrT5vdwc9eNbEHkpJWdzOhMFk6imsC7kx6u4iTaLcYX6EiSMYlq0YRtMFWbIWFkC\nip86Xw1vzO5nIHOWeT2BEIIqbyWdoTWoQmG2MEtcn6PKu3ytYyEEH2v8KL2pPk6lj2P63mBNLEpb\n1SQwTrHgEguWdGnDbzFy0X9tNJ9B/SoDoeRBmuipMLFVJ/HWZInWiOLxUJFiFlNNItCw1EFUcx3R\nzF+g2euZOPUsqlwaID0xMeGUx1y7di2zs7Nl3SgWV76DYiPWkkhD0X+q2g0YdgND1t0X3dwLBAJO\n4aZ0KE3BWyCcCePTfc5F4Ofy5/RU9FB5eyWDg4PMVs1SN13Hnck7ebTzUUbUEdKkCYogt4pbuZ3b\nl32tyzFmjTEtpqmVtUSJMi/nGWWUjMxgCYsm0cQGewMvaC+ct34lSCHRhY7un2FVoJJKuxJNCObE\nNL1M8kphL1KRbLW3EpMxjmhHmFVn0dAYZphT2iksLGxZtJRNYSLVAs3NRxjIvEMw28qMGiTmB7+s\nIonGGeUMG+VGQlYNI2YGv5AENYip0Js38QvB6YJFe/Dyaesl33np7mixS6MUnVL6V9qQBByXymLr\n2uv1Ot+JA/NZXps7fwEdyumczOR5orH4+/jxZIKR/PkL4xtzGT7TEKPauzLlqeC5si7k195Jcykr\n80ysEOp8NRRsnWOJXnT7/BfpVLqf1cHWsvTc2UIcj/CwJtTmjF0KRShsim5gU3QDlnI3Oe+PycvZ\noj7bMYQMIEUeSRrUNEs6CZQVYDKpblCIT1kYuk4yNY+lZIhUW7Sui1BMLy8sCH8WudB13FSHSAf/\nhXD+ixdNGCn5KKF4gfnUpz5FS0sLZ8+eJRAI0NXVVdbcdY6L19yIy/iSDiaL2XbPNv533/8mES5u\nNAopWDW3ii9u/yLjcpwe2QNAbU0t1dXVpFKp4oUisotG0cjHIx/nXPIcGtplRWk5cjLHc/I5hhlG\nV3UEgjarjWpZjZACG5spZYp31HfosrtYZa0iFzdR4z5UoRKo9pCoSpA1s1RSQUBVGJbDjMgRbGEz\n451hRpvhnH2OB80HSZBgXpknKINUU02VrKIgCzTIBgKy6LKZUqfYwDpu04IEQgHmxTyTYpI+USwT\nG7fjdJvdhAVk7eInKwGfArcHNQoSlAVhLW0KXsqqXs7qLj1vsRCXHvt8PqfG9eKO8SXrOm/Z7JvP\nLHmdad2kJ50nb9llIg2QsWz2zKZ5vPHqs0TfD2ZXX1kX8utZ1MAV6ksQ8YT/f/bePMiS8yzz/X1f\nrmet5dS+9d7Vi7pbllq7LFkC2ZYFlrEtG3vgmvFcw8AEOGK4QHAxhIkAY4IYYC4WDEyMHRdsX9AY\ny8hgeZEsya2ltav3fanu6tpPbWfN7fvuH3nOqTpd1d2SLIEs/CgU0Z2VJzM76+Sb7/e+z/s8CERT\nkAYoRRXKqkKn3cFCsJSFTnsz5Ow25oNF/ur0FzGlwU3t1/POjptWMA80EaFxgsg4RyCPoGUBO9yN\nZQ9Q1Mcxo3WgbXz7yZj5IeqUveWIs2Whs7hOifZOyfQFSUubQyJboaNXYJoesad4rEMdp35GTabU\nIDJO41lPsmXH1Tz52MyKezA8PIxlNTMgBgcHVzQO68iRu+T97BCX/+qeXHOSNbk1jI+P43keqVSK\njms7uJC4QIVK075SSFqysebHOc7RSy/Aa5oovBiP6EcY1aNNAe1Z41m26q30nVtH6ShMRB5qbYL9\n2/az8dvXY9DBdPcYQkjEvGBscZbEQIKEThDqkDFiFs2UNYWSsQ7KXDTH/xD/AxubeTnPOOOkVZod\n4Q4qVPBF/H1L6AQ3eTcxGA4ilMDUJq20kpVZjlpH0UKTiTIopbANA1dKfKUQQLuUSKGphpqEXJoy\nvBSWB+HVdD7qDcXlCYhpmiQSicZnV3vZj3sh4SWSlnMVn/lw9WsarfqUQkXK/HENHH4cqK+ItJli\nXWoNU940oY5otVqQQlIMS2xPb2ExXGSmxqmuRlWOFU9gC4tAniBphfx/Y89ypHCc/7z+PzaOqQmp\nOP9IZIyixCKBebAmOboZR66hrEdRchwZbkZoF0mE0mmQi8TBtp5Jy9gTUadQwsdJeAxuSGOqXkIZ\nO8HEk4iqRpNbloXrmk0XCiXn2HG9wcLMDg4ePNh4GAcHB7njjjte0/3qFb2sEWsY0SNN21tEC1vZ\nesnPVXSFU5winUqzaWOz7vMhfeiywysur42vq5Ti8OHDnDhxAiEEmzdvZt2WdZzkZGOfunaGH/mc\nGD2H/OYARaNIJGw4a8IzCSjbrDW2kpvvYa59GqEFucM9bHr/NpycQyEskNd55qw5SkaJtE4TRREn\n9UmmxBRpmY5dwpVBUifpUB1cpa5iVsTfp7aoDSKwtIWpTUJCEjpBv+5nSkyRt/JsijY1hlPWuian\nKgFojSOhosAQgl7HbMqk67Xo5ZlzvTl4MeqljnoDcbmUaiqVumIzMVCKvB/iGpKU0bxv0pAsXCJQ\nx+e+8u/y3wt+HKivAMdw6HW7m1gcSivm/Ln4IU9vpNctUggKlFVcqtjac5y0XWuMCUW+/CX2l1Jc\nlf5JpG4nMA80jGpVbWxbC0VonEboIcxoG5EcAekjVT9KjiGFEWfWOqx5I0qEjsV/lCzVFPRixogW\nJblLpkYAACAASURBVKTKoQwPsGKtD6Vqk4kCdKbhIg4uaAMpJe9+97u54YYbmJ6eJpvN1mh9rx3v\nF+/nGZ7hCEcICdnABm4Vt2KLS+txBASXLBd5eAwzzA/ED/B18+rGFjab2UylUmHPnj3s27ePdDrN\nrl27LukQ/s///M9NOiSnTp1iw4UNqDuXcYcRmIbJoiqweEHTadYGnHT8s+TJHB1uO5Pdo7TNd9I2\nH4tMdYkukqOt2L02WSvLZDRJW9BGh99B6IUshAucc84hECTCBIg4GE6LaRIiwQ3RDfihwfHjWQ4X\nNLesdyAR4ToCj2o8nagFa/QadoQ7GGIIRVwv7nNtNIJJP8QUYAkYdg2yhmjUn+tGAhdLrbquS6VS\nWbXsUa9B14N1fdvyzy+3/hJCYJgm380XOVL0uOCFFMOIVstgc8rBFPGM5c6MS9aUTPsrmSiDrk3S\n+HE2XcePA/UVsD27hb35ZlfrgUQfKTOuIc7588wHCziGw1BiAJX4TiNIx/6GFdpScCL6KuvcUezg\nZiJjOXNi2ZJT+CgKSJ1BRlfheO8lNE5QTvwdWi+ijSoIE3QCQ/VjRP3xgIqIkGqYyDiNFh5KLGCo\ntRhBH0pOIXU7QmUIzZfRIlx2ThupWuIBmnAzAC0tLa9azPxSsIXN7eJ2buf2V/2ZrMiSEznyOr/i\nZ+tYhytcPsAH+BbfoqjjxlRGZLhb3I2uaL761a+ilGroYh86dIh77rmHzZs3Nx3r/Pnzq6r6ndp/\nCvc6l2qmeVqxs9CDN7vELa6/THJmG8m5DFd1bmfGmEGjaaOdNtqwUvFjldRJbqrcxJyai00FtGZY\nDfO0+TSe8GJlw7qVmoALepHHp1O8cDqDX4jodvPIqB9VtPCCkEzawlIWWmj6g34Ste/g8sA5IAR9\nroUfRgi11BRc7i9Z/0woJBUNCZqZHsv/XB91r+t/QFxCKZfLeJ6HbdvYto3v+00Z+Yv5RQ6WI6QU\nDKccTpQ85oOIkYrPjkyC29vTdDsWOcvkfDVgpLL0As6YBj/ZsTTZ+3bFnj17eOihh5BS8tGPfpRr\nrrnmkvv+OFBfhIMLh3lpfj+FsEiP28X1bdcyn1ngWPFE40u8PbuFd3bczP86+3dMetM4hkOb1crZ\n8mmuzs1DraUTZ7BxsSHrFkFofPspZLSUqRqqg0iOLuNKG4BCaBsr2giighGuxbdeAq3QQoPwiYwR\n0E7N53AXAkmk2wlqhgNSu5jRVRAGWOG1GLqViPuoJL5CKM8giEWbrGgYO9jd0KL+t8Sd4k4e5MHG\nQBBAm2jjOhFLqg6KQT7FpxgX4wD00osUkh+88AMWFhaamppKKR5//HE2btzYtDxfbqx7MdaeXcup\nnacI9BJNcKOxkZbxDUzpGaq6Gut66F462jspBgFO5LDOWNcIglbSpGNTTHH0fZ9NehMjYoSJaAKF\nIqmT3OndySPuI+jafwALISSq7SyWW4lklUU3IBIZotDAMSWBZxElIlwRly20aK4Vu67bcHaBeAip\nWq2u6p0IMBaEFELdqPwPCknrKgua+sRhfdz9Yk2QKIooFouNTLuOwyWfSGmktHCk4KqMSyWKy3b/\naaAdu/Y7MaXgwz2tnK/R8zKmZGPSwXyb0/MKhQJf+9rX+PznP0+1WuWBBx546wbqc+fO8Sd/8ifc\nc889vPe972VmZoYvfOELDXePX/3VX13RyHozEOkIQxi8MPcKT0w/2dh+tnSO85Uxfnbgg9yUu568\nn6fNaqXDyXFg4TALwQKtVgstVpaE4TKQ7CNQPpY0auWJGGnTIrdsUENgLftzAjPaQGicRpLEECmE\nDnC9exA4GKoXLecwdDtKWyhjDE0YE/NEHkEHdacXQ/WDdmvWWhI7uBY73N3QmAZwC7cQGEeJjLMI\nlcOKNmHo1UsE/9oYEkN8gk+wn/0UKdIjetjOdhyxpPchhaSfZl2R1aYVIX4Y5ubmyOWWGpyJxKUH\nYNaYa7hV3Mp+9lOgQLfoZnvrdg5kJ+mbGiBQAQaxjKywYNfP9TL+0iJhSWMYgkS7zZa7uzGsOAhF\nUfy9Ws96enUvkY6QUfyzF9WLzMq4nBJpUEGWwcr16NAkSwu2kBRUgYMLVbZlBS2mjRUksW0jHu/H\nbuhv1P+vlzPqYk+2beN53oosuRBpVKRJSUhqmNMwHoGtFIlaPfpi1IPyaoyR1Zzdfa1RqjmTT9RK\nGWIV6s9gwmYw8e/HdebAgQPs2LGDRCJBIpHgl37ply67/79ZoK5Wq3zpS19qTJwBPPDAA7znPe/h\npptu4qtf/SqPPfYY73736sI9bwROFk/zVP45ZrwZkkaS85UL5C7iPkcq5Pm5l/jp3vfSXnMLn6xO\n8zdn/l/GKnFmJ4Sgx+lmXWoIR2/EkpNERAghSVkWOcelz1liSQjVBmKCwHoBMJC6C9e/Eyu8hrbU\nFnQl2QjmUvVQN5bVshhrT6MQ2sXQXbGDixxrZMSGzmFEOcxwI26wshEocLCjXRDtamwL5Wl86zmU\nnEGqHHZ4HWYU+yJWKhX27t3L8ePHAbj55pvZvHnzmyZ52SpauU28NgnTSwVfIcSKScYtW7bw5JNP\nrhhoSSQSbN68GVvYvFO8c9lBYPu9vRx4cJTFyThIma5kzTvb6NySomt7ClU0MC2TdGfzPVmeYVoy\nbggGUcBatZat4VZm5Aye8AhVktlKJ/2VaykKmKtC2lCkkzbfLlapmppBU3FNRhOIgASJRoNPCNHE\nvLj4vMuHWOr145KKqACGjkegOiUUtaaqNAlj9Uy24W6uQaGxasyY+vkuDu5rHJMj5ZUDTAOujfU2\nz5ZfDaampvA8jz/+4z+mVCpx3333sWPHjkvu/28WqC3L4rd/+7f5xje+0dh26NAhPvWpTwGwe/du\nHnrooTctUJ8tneOh8YcbX7A5f45jhROsSQ7Sn+ht2nfGW6qbaq35l4nvEKigadt4dYIWK4Ndvo7b\nu4uU9QQVcYKMZdHldJLUNX41AaF5EASY0TaUWERgYUTDOOENJEQfokbpiiGwwqsIjMNE8iyaEEFc\n8hCYmNFQXDpZDm1iBzc2/hqGIUeOHOHMmTO4rsv27dsbY+GhPEXF+adG6SU0RvCspzFULzLq4onH\nL3DslQRaxS+LZ555hgMHDvCxj33sLWPTtGPHDs6ePbti+4YNG0ilUqA1jIzA9DSJtjZ+5t57efg7\n32FxMaZW1n0cL8UlT7bbXPcf1zJ1Zo7IV6S67UbWbBgGmd7kqveirjRX3880TYIgIKMyfND7IEfM\nIxRFESvs5PDCOgqBxTQhu9qgLakxTEiIFsbKEUUTJpRgk3BxDKsRpC/mN9fPtVyitH5tQggKSvNc\nKWAsULQZgp0Jk1ZT4uual/0q2bQQghDBqXJAIYqPmTElG9MmaWhMKC7HDRmXsUBR0CDQmELgSMnt\n7akVx//3ikKhwG/8xm8wPT3N7//+7/OXf/mXl3ym/s0C9XJSfB2e5zVKHdlslvn5+Ssep6+v73Wd\n/xRnG24kACmdIlVNkddzbE5vQi67Yetb1zXOc6E0RjgRsbZ9DYuzzYauRVmmp3Unt/d+Eo/DlNTz\nlNmPqdsRNR61IdqJdF0iNUOdFi/SZ+iS2VX/TbPRtUxHL6BwoD5ybsyTYJCkXIPBTizRS8gslugl\nLW7GqpmqhmHIl7/85aZAdvbsWd773vdy4403Mh39C6aO74PWijIvInQJKTwqs5DqPsaWG9oYPbiU\ngReLRSqVChs3bqxd3ywPVx4mr/Jst7Zzo3MjKfmv90D29fVhGAY/+MEPGnXqDRs28KEPfYiElJS/\n8hWiZbXp7V1dXPtf/yuThUK8GurpuexLR4ch0dgY7evbKSyzvLIsi7a2tssKDy0uLlIqlRqBzLZt\nwjCklVY22vH9i6KIUwt5jocR17cIUhkoeAEtBuQMyaArCUwItWBBm3QsW82k02k6OjpWrHCUUiwu\nLpLP5xvNwMmqzzdmSuQDTTGCYqQYCwLuzDo4DphuAlMt1aDrLwPDMBiLBAuGRVVCuwGRkJwO4PqW\nFE6t9LJ8lVINNT0ywfh8iXIUsTmd4pc3D9CbeP0rsdf7rL8V0dLSwvDwMIZh0NPTQyKRYHFx8ZKN\n/B/5ZuLY2NiVd7oIfX19nJ46SyFoDrRtooXRyhjzi3NYMn5hCCFY37KmcZ6xyhiFQgELg06Z40J1\nfEmXV0a8M3kTkxN5oBv4KQxxO4FxDFCY0UYq1h5Cs/m8dQTVfQz13LLi31SxygSugzJslCgBPiAp\n6hP4QY5U9ReIopjdEAIVIqhl5QcPHlxV7OjBBx+kq6uLoO0EiDjwRGKasEFD85meSuJ7HmZygkh2\nUl7IkMlkKBQKHDx4kGQyyX61n/+m/1uDiWFisk1s49Py06/Lf/D1YsOGDezevZtnXnqZtmyW7lw7\n8/PzzD/2GBxuNm2gUGDh7/8e7rknXg2Nj684nq999rGP2cNPM/j4CXoqLbSLdnR3N+p974PaAzU1\nNXXFa6s35Pr6+jh79mwj0Ncn+ZRSuGFALorIaAPQbE0JMobEEOAIQVUp5pWm6AdUa/mNlJJiMb7v\nl3rROI7T4Eg/u1ilGkU4QlDRUSOD3l/yeJ9jktU19xdgMVKgdSx9G2kipUkowYSCcgg9UmMKOJ1X\nrG/NNGrYURRRVvD3Ewv4WtMGtAkoFQt85dBJPt63uqzCldDX1/e6nvWLj/FWwa5du7j//vu59957\nKZVKVKvVpmb4xbhkoK5UKvzTP/0To6OjbNy4kfe9731NS8PPfe5zr9rq/NXCdd2Glf3s7Cxtba/v\nl/pq0GG3sxg0a1sMJvpxjUQs9K88BhL93JK7gcHkUvOqx+0mZSYphWWGkgN0u10sBAuYwuSjgx+k\nx23mHkudwQ6XTEjFRWJEzfuufJsqUcC3H4ttsVQrwiiiMREYaFFGi/lY9/kSGBkZWXV7GIZcuHCB\nnta22KIL0GJJ7lPgNtV3E5ki5YWlL1JraytKK/5K/1UjSAOEhBzQB/ie+h73Gfdd8rreaJwse+wv\nzHCGJEYhYpsucEd7GuvQIZiZiacnWluhvoo7fhzuvhuWeQrWKWZKKL5nfo/J2YPs/PZ+5pVmnnGG\nGGJgEoyHHoJPfOJVX1udPrda5l1X5QPNJtegyxKkhcAgbjIC6BrX2hBVbOGgamWoerZ7udVAfcwb\nYEaU4qZjpGi3TIIwbkrPR5quhI0OAjTwgi9IacgZAq2hECmmQkXGMumRUNKAEJQBT6mGTrVt2xiG\nwUvzJfxVSijjXsCFakC/++YTBN7qaG9v58Ybb+R3fud3APjkJz952eGhSwbqv/7rvwZg+/btvPLK\nK+zdu5fPfOYzjXLB9PT0G3ndQFxr3Lt3L7fddht79+7l6quvvvKHXgMiHTHlzWBXHa5vv5az5fMo\nvURfGqtOYEsbS1hoNBkztaJebQiDu7ru5JsT3yZSIY606XI62ZBex1XZS0/e1WGHuwjMfY0stg4z\nWofU7U3bNB4V5x+I5DRa+LVAKkEvlYyUKFCxH8IKdyJYuay8XNPPdV3s4DqqzsNoPJSYR4k5hLYx\no/W053I458/jVav4laWg3dbWxoYNGzihTzClV2aUGs3zPM99vPGBerXx5nEv4JuTi6Qy8Xcz0poD\nhQrukSPc9swzUFetM03YtAkuSgCUUpTL5caxJ/Uk6SBN52gvmfVJhDAIFicZnRmhW3Vj5fMwNgar\nuNpcCfW6dT3LrjM01romzxV8klqQsCWIOFBHQCTKzFjjTIkZ8u4JrtW72B1dx8iUxZkJheuEDA9J\n+ns1zy5UOFn2cARsS1hsT7u4duy8kjIMPFNjGJowjKBmXmELgSUEPrHOxkKkiKQgrBE8fA3zocY2\nNBkJCQFRjbmRNuPvYr1sKYRgMby0lshCGNHP6w/USmsOFz1OlT2EgC0pl82pH00vx7vuuou77rrr\nVe17yUB99uxZ/vzP/xyAu+++m69//ev84R/+IZ/97GffkI7/6dOn+du//Vump6cxDIO9e/fya7/2\na9x///088sgjdHR0cPvtr35g4nKY9ef55vi3eTr/LFJINk1toJtO3t19B4cWjzJZncJTPoYw0CjO\nVS6QMpNQOst3Jx/j3r67m463Ib2W/7Tm5zhcOEolqjKUHGBdcs2raq4pUUaqTgLzFcBAqk7saBuO\nv5KhEZiHUXIBQ/Wg5BzgoUQVRBGNhcAhYhwty1TtR0j496w4Rt1k9uJmT3t7O319fYionygYo+z+\nLUoWgACwiYxJLN3Btm3bOHk0T2muHSkF27ZtY9euXRiGQahCJHJJQW4ZVqNg/TBQSjVcs4GGkL9h\nGLyyWKkZLizBWVzE/N53CbItWPVAHYZw4gRccw1s2dLIpuvmAHUssEA2zNKa2YpKHgc0RiKLleli\n8fQsOdqg0qw98mpR18zwfZ8ZP+R4JUBLiSUEVa15oRyy1nYQgClAGRUWjTwFFukyTWbtiJfFyxx5\nvo/qmbjGbRohR84Z5FvLdG7x6BKaLqmZLQcc8D22ZRK4rsvOjMuj+SLFUCE02DJuOG5LWA1qX1EL\nTCGItG4IaNlSkDAkodLYUlKu3eukIclZS0lDGIZYlkW3bbK6sjj0/BCKeFprvjm1yMmy19h2ouSx\nK5PgJzveSK26tx4uedeEEHie1wjKH/zgB6lWq/zRH/0Rv/Vbv/VDn3j9+vV89rOfXbH9d3/3d3/o\nYy/HlDfNl858hefnX248jIV8kfX2WgIV8Ik1MXvhy+ce4Pm5l6hG1YaGcdJMorTirvB2kmay6bgZ\nK80N7btXnO9y8M3n8Ow98UQaLkrkgXYs/50IXELjJHPqaSr2NGa0gUhOAiB1G2a0Hs+cAkqxboeO\nJwyVHEcrj8A4jMt7amJLS+jp6eGuu+7i8ccfbzR7crkc73//+xsvlsgYxYp2YUYKJSYJzMNE8gwY\nASnrZ7hh+NNct95FCEFLfwsPjj3IWXU25s5i4uGtCMw3iBte0725HLTWK8ab6759qVRq1Qyu48xJ\nUIrqwABWsQDlWlknDCEI4F3vajrWcjjawVAGyg0QpoUO/XhUP5NBtEgo8rqyaYgzTyEExwN4bN4n\nUgqIKESKVkNQIJYnXeuYJKRg3phmUczhGoKSFUvqlmZTvDxSoC4Yq4FZP+LEWejoh+62pZfOfBix\nGIRAFRPBRDXgXNVHaUgKzfvaktzS5pJIJOLyhQqpasWihpTQjaZ6zpJ02RaVKG5pt1kGfY7V1HSv\nf5+2pl1eWqwwGzSPhm9Nu7RfIVCXQoUlxap61OeqQVOQrmNfocI7som3rCzqG4FL/stuueUWPvOZ\nz/Arv/IrrFu3DoCPf/zjPPDAA/zmb/4m5XL5Uh99S+Hp/HOMVseajTu14lxllBYrw4XqGFkzy9cv\n/DOjlXi02xIWbXYrhHCuPEolqq4I1K8VijKe9RQaTWAcQMsFYvW6SbQoYoU7icwRTJUhNAuE5slY\n6A7d0HQ2VC9KTtWWrEZtqi0COUckp5gNzvPS7FnK4hhdqSobk8Pk5A3s2LGD4eFhxsbGcByH3t6l\nco4S87F/IqDkFKFxBkEiFnMSZkOLxDRNqrrKF4tf5LxeYlB00slZzmLVlrMCwVXiKt7P+3+o+7Uc\nlxq0qNtD9TjmCqlMIwgwBCQcG3buhNnZOFi7blybXtZdv3gl1KE7mBST+EkTEpIFPPIZD6TkZMs8\nQ84Q1x09GjcpwxDWr4frrouPfRnUr7cSKZ5crDZUh+IXHkwGChOo6niJH2pBGR8EeEbAjDUBwMJU\nKwEBCoVEIoWgVHvZOGUBF7V2SlH8Yn9mtkKHVLQmDDwNFoKS0lg16qAQghbb4sBMhfPVABfNOluy\nM2XR71gMZ5PYtk1lldVEvV4OcQb+0d5Wnp0vc7riYQnB1rTLtdlLDxudrfg8Plsk74dIIdiScrgz\nl26YCwBNY+YXY6Ti//sM1B/+8IcZGBhYMRn4kY98hN27d/PII4+86Rf3RmC0MkagVoq+FIICCk0p\nKPPdycdZDJcai4EOmPHy9LhdlKJSHLR/SETGOZScIzD3EclxQCC0g9TtRMYFIuMUZtQsqq9FQMzj\nqGk6IBHaQcuw1mGqaYpoTdV4ln8Zb6MldYGW5AwFYJ/3LNsyz9OpPoxtb2Pt2rUrL0xboAVaRERy\nKQDH5zLRskhgvoQT3MZBDjKvmimTa8Qa2mhjPesJCNghdrBb7MYQxsVnet24kn7yO7IJDhebNTrm\nBtdw0/GDmELEAbGjJrEqBNRohXVYltXkYpIQCdppZ8KY4NxWm3JZ4FZd0jLDVJfB2HPPMXHkCD0i\ndvhmZgZ18jTT1/40vido6XPJ9q8MSvVk4ZwXEur4UoSIFeYCIiwB3abJWtugpAWB0qRJkRcFxlMn\ncWVctDatkKRO1oK0REiJW/OlNMyVTTxXSsarHmHoE0Xxiz9Ra1DmI8UFP2JYSCzL4tF8mTbLpBAp\nZoOIY76mLBX/d28LSScmEziOg+ctZbb1waLlL7ykIbkjl+YOrqzZMeOHPDi5gKoP5mjN4WIVT2k+\n0L30Qk1cRqTpcj97O+Cyr6Abb7xx1e3r16/nF3/xF9+UC3qjkTQStFgZpr2Y2RCqkIpfJQojymEF\njSbv5cmaGcphpaEzoVCUowq7U++4pIv15RCqkMWwQNJI4hoOGo/AOIIS9UCn0aJKJKZBG2jhY6gS\nsJwVotDaiMsbogjKQsp2tHJQogg6NgAASSmqsLFnL6GyUbVmY7xyOE828yhmZSOClQMdkhSGGiIw\nDtdeDMt+pmJFuNj9nFUbhwBZstwsb2aT2LTqz1e/QbV6cT4P7e2weXPc7FsFF/Ptm65RSrKmwcd6\n2zhpuOyrlklKyc6rNjNQmIT9+5s/cPPNjWw6r/Mc0AcoyRJrnDUM+UMIHQ+RZMwMGZFhllncdBIr\nbRGJCK98ns7D00yIEj3EgdovhUw+f4Kpo3spdcX3oHNzhi33dCOWLeHrwylmveSkNfmozHgQkg8V\nUkRc3ZpGChOpNQUtaFNtdFhzCNHKorGIEILONXOk9vdjKgNpGAigwzYYD0FnmwN10pBkDcG5IGSh\nTiNBx2yOMGSsoikhWUcZVS5xruKTMQ2GL2rQna6GXFsL1HX3lnrJ6GIlvdeKVxYrjSDddM6yz1wQ\n0Varg29LuTw9V1qhb500Yn2QtzPevmuFGna17CDvzTJpTjPpTTHrz2MYBkmRYDaY48n8XqSQdDo5\nlFaUwnKtsSjpT/Txzo6bXvM5X5h9hWfnXqAaVTGkyVXZrdzcp5s0PgC08IACkbRAlAnlMZSOdSk0\nVQLzAFL1YqoBtFZISugoQBkzIErUtaUNlWPR07h2kTCyKftLWUghKKCFTyRHMdX6Va/X9d+DcmYJ\n9H4QsaKb1F1IHVMNhY6HV7JcmlrYwmtQ3CsW4YEHYG6ZG8zevfCRj0B6ZQZWn+q72Ji14T6Tz9P6\n4ov8pFLsNgy49lpIu3DXXVQGBzn36KMslEpYV13F8M6dJIGT+iTf1N9E1ZgPR42jdCW6+DAfxpVu\nvEqpViGM9aArRoUL9gXSZ+MXbZWlDH7mRAkVKJziDKXuTRiOYOZUgYmDCXp3tqC1ZoQRJv1JhCMY\nCl1cKTjpLXDe05QigYdiyDSIRIQWHpsSGbK2TafdQkWkOKFcTogTtBltXJO8hvDWDh7eqyhXNdKQ\ntKQk/9c7E+xRPk8UqrRKzfaUzdaUUxtckU0t30qkOVwJMKXEFPDszAIji0XKkaLPtVjj2nQ5S+Fh\nLmiu4188ETnuBRwoVClHigHXYkfGbSpbXA4LQcSkFzLth0Ra01qrf1sSCuFSoE6Zknu7W/jOdIFi\n7SXRahm8rzP7th9Lf9sH6mtad1IKS2hgYnoK13DoSnWyyV1Pzm5nojqFpzzWJocoR9WmJfuOlq3c\n2H7dFc+hxByBeYRCOMe/nD/CN0cPIBB0OjmGkoPsmz9AayZgk70FbZaWmc36sVa0dgGFlh5VjgLr\niIwLaCKMWlYrkAgyWNEwITJW5tMGUmcQuPhBgKaMIZuDmSnrv+JL/6qlzpCqfgq0RWgeQ+p0XKcG\n0KLhGL5T7OSUOEWB5oGdQTFIl3gN2tV79jQHaYj/vmdPXD9eBXWOfT1Ym6aJbduIsTH42tcgDAkz\nGSgU4MgRuO8+8rbNA489RrlajfnTR47w7Llz3PeR+3i09VEiFTHOOBNMEBCQIUNO5nifeB9CCJLJ\nJPkwz4yeIZDxasPKxJlbpuaIF1QignJ8TbIzTW6ogjs9ik4aLF6QJK7SPMiDzOpZMuUMBVFge3o7\nd6ub+a3zJ5mOUhgIEJqEGWFKiwohrZZJd41vnCHDjfJGbpe3x1TCShndFvCJ98BYTd1gTY/Jo8UA\nv6RpcR2UUhyoajKOZnfGYX0y5EA1ZNKPGPUjTnkh80FEwjT41nRsaRZEIb7SOL5krFqm0zbosE0G\nXIuOy9R/DxWrfGe60FACPFX2OFSs8tGeVqb9kKfny4x7AS2mwTuyCa6+qFY97oecWtYkLEWKuSDi\n6myCzovOuzZh86nBdsa9ECliFslbRcrgzcQVA/X09DSdnZ0rth8/fnyF1u9bEUIIbuu8mXa7jUpU\nwTEc2rNtDd3ihOFiSxtDSHZltzMXzFONPIZSA/yX9f/nskC3OgLjMFX7O4Q64GBxP6nWEW6VJs+c\n72OyOk05qrIju5WDc3NkXIkhr6E9fYbAOFYbXJEI7WKG21HGJBGzaPrQeFjR5hXcaEmWVOUXKSf/\nF0qUEdrBUH20Ss24dxLTaC5f9LjdCJXGUANcDgJByvsYVf1dQuNkLbNO4vi3YqohINaM/vnUz/P3\nhb9nXI8jhWSYYe4Qr80FhlX0oIGlIZTVrk8IHMdZSQ196qm4jLIcQQBPPcUe01zR9C6VSjz85MMU\n7ylyjnNcYEkbfJ55vq6/zg36Bs5znhf0C5wVZzkvzjOgB2gX7SwOZql0ptk+E9/P+ipcpBySfl9P\nlgAAIABJREFUaZ/E97+GqAUs4/jz/GBtitnNzTX2Qxwil+mlkj1INRqknTQdhoUnBGXlIKRHWTUv\n7+u9ouVUQilhoPZoni5VOFYMaoMxQK1m+/2FKotKY4WK2zI231/0OOmFuFKQNAwqGopRRFpIJAJP\nKSa8gIQhma19lSpK022vXn4KleaJfLERpOuY8UO+PrnAU3MlKkqRNiQ9jslsPn4ZXN8aN+cXw4hi\nqLBrNmJ1lCNFu2WsWnuWQvy7G5q5YqD+3Oc+x5/92Z81batWq3z+85/ni1/84pt2YW80XMMlba6u\nP3FN6y663U4OLx6lNYwn7trtNp7OP8eu1qtosVZf8ms8PPuR2MXFP08meRZTzdOV1nSkKjw/2svo\nIpwvX+DF+SlGKxopPdodlzuG+mlLz4AW8bLUGEHoNhKsR3n3xBzqJoOB+kkNnPAmdLVEYB5p0OL6\nXE01cDicD8mlzyGloNvpZMDZQML/6WWOLpeGwCXhvx9FCS3KseEAzQ/ogDnAx+XHqegKJubr8yi8\nVAb0KpfKTbiwyj0CGB3lzCUcYy6cuQAaJphY+UMND+gHKOs4wLfRhoHBmBijlVY2y81s++D7afne\nARgZwU4a6O5uKps20n5ib/OxnCqZh19GrL0efVFmeFoeZY2ZY96qUqBKSUsMJJPeNO9xB5ckQWuN\nOrlsgnI1nPPCFeYAZ8o+415AKVK0S80LtQbhJtciGyieD/1GePUiRaJmeG9KGnX0Lsdk0LXYV6jS\n69p4SnG85FEI4xKJLaCyyjXNBRGP5osNv8aFIGLKD7kq7fLCQplrWxIYQjDhhVgSdmRcRqsBC2GE\nJQRdtkm389YMxs+oV+dCPvwGnvOSgfrRRx/ly1/+MuVymY997GNNP9NaMzz8Rl7Gm4+1yUESRoJK\ntJJatDW7mf5EL31uD/8w+iCVqEIhLDBSPse+hYPcN/CBFaPhAKFxLnZxQaOtkxhRiKENlA5pcTx2\n9EyTLxscK54kZ7fhRluJuMCsN8q3Ror87FawRBsgCHTEeHmEmblpxi+sZUfOZX2nWtHItKJtsVSp\n925emp5jX2EvlchjyN3E7a2/zHCuk/nwAml3HldmMavrVnCrrwRJCvTlRZUS4tJUqytieBhW0R/h\n9XynEgkorXS5JpXC9v24znwRWpwWDGEQ6ZV+fZ108op+hc0srRaztf96FwfYceAW8nmfSuft9Nxk\nYtgh2cDG+Mdvxy2DWuQzXQMjqTD9iLYzc8wON69KQ0J+tf02frXwJJXIQAkVN7AxKOsMg60tWFKs\nEOS/1DLfushaayGIOFf1kQhMAaZp4RNyuuRzdcqiJ2liVGrWWcTSpUoLLBmr3PU6ZqwRXctcJ2o1\n5K9NzDPlBYx5IeVI0e2YpKQkazW/0EcqPqHSsKx2HCjNhWqcrZciRdY0SNdeSI4UbEg2N7szb1Em\nx4HSq3Mhp/3Ku7xaXPIJ/omf+AnuuOMO/uAP/oBf/uVfbvqZYRhvqg7HmwFTmtzT+24eGnu4sU0I\nwU3t1zfGxJ/MP7sikPvKZ8/MM9w3cO+KY4oau0KLBRwDShE40iHUUY39DK4zh+l3MZgcABGi5Axg\nUAokZ4tV1rdMIqJOjs7PUQ5DXDNDUZ/l+2MZ5qIE1/fUmo5aYkbDjQnGx6ef5pX5eWALAOc8eKD4\nbX5+6KP0OP2EMtYBUSL/ljEGaOCd74SpKZicXNrW1RVvvwhKKxZYwMVtvBz2n1S8cFQxX4ROfSc3\nlfewMXmR0uKuXWwrlXjppZdWHHPbtm1sFVt5iqco6LgEJhD000+raGVezzem8urQkyaTX1OMzoyg\nCgvMO1VezKVZ/6FWhjp6aRl0YNoiyrYhUwksv4xRLMba0ask9hvEBnYlB/mbNT/B/zN+kJFqQIYk\nd6YH+XBvP65lEkWaw2c15ydDko5m05BBV6tFpVJpMC7qDdXtmQQH/XhCc8oLeaVQYS6IMIQg5xm0\nmAaOZZKyTEpC0mWZrE1YTHhh7E0vJG2mZDGMedCuIZumCLOmwXemFzlcqHKwWEUDSUNQjuKZ0KvS\nLi21YB0oTTlSdNgmXi3bDpRmMYybhqVI8/JChVvaUvS5Fj2OxYTXXLIzhWBH5odIBt5muGyqJaXk\n937v95idnSWfzzctu6anp3/ksuo1yUF+cd0vsJgsMDY5ztrUUFNZ41x5dZum85XRFctKAEOtQegk\nWsyQMBKYskCoQlJGkpkwhSWh3Wmlz9hJi5WJKXAsmd4GQRtSR+S9CtXAwdDtSJJYhkeFDC9PwlXu\nh8i5EqHTcaYLlMIS+xcOX3yZeJHHi4vf5/qB0RqjJIaMeoinGCdq1l3XYIfvWPH5fzUkEvAf/gOc\nORPT83I5WLduRUnkkD7Ek/pJirqIFJKteivtJ9/FnheXsreplrU8NA8f8J5iZyaIm4a7dsF113Fr\nGLKwsMCpU6ca+29cv56be3sxT87xiaGf5QlrLwEBadLY2BgYbBKbah6HIGsZoXoySfLsHMWR58jr\nPHZrL0axhVNPFJj70BTbNw/gKkUkDHxPU0FgpgoMzXg8u64FltVwe0QP76jNFa5PtPKf13cwE82R\no53txgCmiIP0Pz4RoaMqramIalWz/zhs6te0Z0XDEbzua9iTTfMeafL1iXlOlX0irTFEPFE4H0ac\nLPtsSTsMJexGXX3AtZjyI1KGZCiTgjAgYQg6LZOkKTlT8UkYkh7bYnPK5i9GZhr8ZoByBBlD0+Na\nLIaqEahdKdmQdEgYkkOFKqFWTAex6YAtBTnb4IXFMoVI8VNdWT7Q1cJ38wXOlOMJ0JxlckcuTav1\nxnHxf9RxxTXx3/3d3/Hwww/T0tKywkXiC1/4wpt6cW8GHMNmV24H6XKSyeo0oQrJOe21nzn4auX0\nky3tVZecAgPX+ykqztcIjZN02h0sBAXyZZdWq41NqRzX2j/Fty8cQ6NQy1TuJA59KRDaZLFcZGFO\n4FVnsawiIxf6aW2Pk7rJ6hxdzram884F801iUnVoNOPRE2ix1DhUYhHPeQYr2ozUOZSYx7O/D/jY\n4Rs35v2aIUQ80bd+dcrgOX2O7+jvLLmTaMVBDjI6FzDIe5uOo9eu4/nWQW66O0Fxbi42CXj6aayB\nAT5w773M5PPMzs6SC0Nye/bAQw8BcLtpkHjfRl7ZUKQ8PULXqQIbjyY4N20w2jqGn0mR39ZJ66Y0\n0eES9kSRce3j4dFiD0LkIw8I1ActZhMBrXYCubCIrPpILfBEkoXsu/lZtZ2T8hi2Y+OUHIYZxhAG\nc5VxHpj9G4pGORaKMi1e1s/zET7C6REX3/PItYTMhRHlSJO0FFMFTSppkXSW/BGFEERRxLa0y7F0\ngopSeMriTHnpuzwXRHhK022b/FRXlufmy+xfrJKzJJY0SJgGplZ8rLeVx2dLjFTiYO9KSYtpUAgV\nM37cCFyOQqTo1pqhhM3/0d9GKVL0OCY/mC2xv1BhS9rhQKGK0nGWvDnp0F+rPR8vecwGIe2Wyc90\nt1COFIHSjYD/YyzhioH6ySef5P777/+RK3VcDt8fe4Jvn3mUIPKZ9edImSnu6n4Xw+mNvDD38or9\nd2S3cXDhMC/M72MxWKTb7eKm9usYSg5gqkHSlf+CEQ3hW8/SI1vpTcfMBDNah8Od7LUmOFw4iqE8\nWh0bEOxsHaLNniMISoydWaCo4pfgyESGI8fP09cbsnbtWrLWSrGZVqsVKWSDA1yHFkVaneYHKXYp\n1yiZR0ZL3oG+9SJWeO1rrl//a+Fl/fIKIalIwahzjB7jNqyoeaR/tmqBUvCVr8CyqTnWraPj3nvp\naGuD//k/m+rZMoy48ZtT3LhrFxMvn+K0Gkd/z2HLQon+tgqFtSbeKxOcvvkGklMVElVBkZhholVE\nZEhs7VGenOOgamFhOCRVVHSNKHILKdp1Ar+3j8IrETfcegN9iT7GRE1T+fBhnjjz3ym2zNQuRsKG\nDeQ74Cmewpu4k7ZMGAfH2n2wTPCVZrTos9E2kULgK0U1VCgjosWy8LWm3Vr6nZ6txCyROivjnq4s\nQ67N/67OU1LxqkErGHAsOiz47kyRQdeiyzYJta7VvuGJuRIZ08AQ4YqBE0sK0qakwzZrNhjwrvY0\nvtIcL3n0OCa2FPQ5FkOJOEhrDXNhyN65Eje0psjZJmcqPs8vlJkPIjptkxtak2/7QZZXiys+pblc\n7m0TpL3I54X5l3ileggvrHK4cIxiGD+4I+VR3tG6g6HkAOcrFwhVyIQ3hS1tnsk/T96fpdvtRCAY\nLV/gHyvjfGTgA/Qn+hDYJP0P4YQ3Nib8zGgdOljP18f/hVl/DgOD6Yqkqkp8dN0Wdua60GE/p4/t\nJ5pqZ9HxODedZHI+BwSMT0ywc90OhhIraXVpM8XWzGYOLR5t2m5Lkx3tzU2ruhP6xdCighbly+pj\nr4a6OPzFTa43GkWKK7YZEmxH45ulFYG6PQvVb3yjOUhDXF45cACy2dWbjlFE9Vvf4EzvOOYJjbsQ\n75OZS+B6Nm25DayfUBzJFglINVg2fmUWN92NWp/nUf8guxK3I7Sg5CjOrg2ZXlRsGDdI+iWKUxcF\nm0KB6LsPc3r3ksUbSsHJk5DNcso5xXrzTsq+atJ19mssRI1mxg8phHFJQQOjiz59iQqtKuSU5yOE\noMs0yGUN5gJFQkp+bU0HadPgsXyRQ4XqkuGF1pwrVYkMzbgX0GmbJAyBvez3O+OHbEjaTPtxU7GO\nhJR02xbvuIgbbUnBPV1Zbg8jHssXOVysNpzFq5HmSKlKJVK4UnCk5JExJIth1PhOTXgBD00u8oHu\nLOt/HKyvHKjvvPNO/uIv/oJbb72VZLL54fhRqVFrrdmTf4aX5/ezb/4gvhHgeV5TRjoXzLEYFsg5\n7Xxyzc/xlfP/m5xuxxE2L86/gq8CCmGRTel4qa604rnZl/iZ/iXXCEP1x07gNTw5u5eR0jmkkHS7\nXXTRRmAeZrRcZGeuC4HFyMF1nD3cQsn0mEudh3SAANq8LDdx7SWD4V3dd5A0kxxYOIynPPoTfdya\nu4EW+yE0SwFJ6ARaVAjDVibLBZKmRZvjIrRbM8q9zH0jqjmdOxBlqFarTE5OUi6XG4L0l7Oh+mHQ\nS28TfU5pxSijVPrmOVh9gLbCeoambsYNWuKmcO8c0amZ1Q92/Hhct14Nvk+xPIXWGne6CsumR40g\nokIFpxSirxGkgzzBTIZQh3jlKdJDFcaun2RMKobUIu1RGuHHPYhypkh+oR1npER660XN3OPHEVE8\nAaqXdxq1hnwe2dfB9nWSh1+SiGVEiHIAE6GiHEToeUWXLQmUZi7SjEeKiarP1WmbrClZDFVNdtSk\n2zH5iVyGtGmgtOZgsRpP8l1UPZv0QkwZs0QuRr9jIUWcKR8uVpnwYpOBjGlwT2eW3ZcQXEqbBnfk\n4s8Uw7gefrLsUamZF9RpiE/MluhzTXqWUfI0mucWym/LQH3o0CH+9E//lMHB2PR6aGiIT37yk5fc\n/4pP2YMPPgjA0aPN2duPUo36xfl9PD8bd/9DHaK04kJlvEF9U1rhSIdSWGK0fIELlTGqURVXOvgq\nwK8Z2U57M/QlekgZcYCb8WdXP2ENx4rNgx0CCyvcxejcIrLzdlx66UicYIQXSYdJdiwM42iXUqGI\nqU162mpaEtrHwGiamjSEwW0dN3Nbx80ovUTjC727qTr/1NDtMFQfZxdLfP/8GH7NmHQgleGnuz52\n2bJHYBzFsx9HizjoR5UepLoTiP/tSqmGzOjlnCleL3aL3RzjWIPPfJKTzDDDQKoPf+0Ec/k5iulR\n7pr7eW7bmmLIvUxdUwgYGoobjRdJmmJZeJ0tQB7L8QmWBWplSjQaG5vC9d1k+vez/tgYM1UPz81T\naQs4nkwTVjo4PP881yWuxZUuoQgJZciUt0CL18fmqy8ar1cKiWA4n+NIx0UGHFqzhS30dwoG+mzO\nTfsYIg5axwyPBRUQFePkYz7SuIYkQBApRacpSUvBR7rS7Cv5jHkhKUtyXa6VdTXqm6cUY1UfL9LM\nBRFJQxBpWKj66DDi+tYEEs20H1EIFaaALtvklrY0QwmL784UWJewCbTGizRbUg5nqj6bfYdu2+R8\nNSDSmsGa2/ikF/Ct6QKe1pyvhhTCiGqkGEouUf+UhqpSzPhRU6AGmPFX9mLeLti2bRu//uu//qr2\nvWKgvv/++3/oC/q3xr6FJc5ui5Ulr+aoRh4VVSFj1rQlFJwonubq1h2MV5doY6YwsKTVcB0vBMVG\noG6/gqreakIzAhC6BSvciSFtdu5M8Morr8SuH2hKskzBKrGtewsqp/gH9Q+M6lEMYTCsh3mXeNcK\nDvNyrrWp1pCqfIrAPIqmyljR4NHz3yMyzoMoIrTNxGKOR4MFPnwJSeVITFK1H451r4mDcihHkKnv\nAL/QtG8QBG+IkcTFyIosH+fjPM/znNAnCAhwcRljLPYEzkCa82ySu1gvbwHakS0tsQjT/HwclLu6\noLs7NglIJOC221CPPtpgL0kpkcPDpDKb4MUvE/RLzDGf0LNRSGbdFhKVLOWOLm7adQ/fu1GTGpkl\nM14gH/UzZXYzcFYynz5M0SzzePEJaNeUkkUqqsq63Ha237yeZPtFYlgbNsCePbxrZC35RIWpVK3M\nIwQDrVdxo4jF0N5zlctXRioU50MWRMCUCqlogS0lY9WAYqSwwrhBqLRmMohIeiEpQ3JzNpZc9bTm\nUNXn+7MFJHHgG636VFTsDH6uEiCFIGlBhylIG5KXCh7BMoaXpzT9rsX6pMMv9Fv82dlpNiRtMoaB\nEJD3Q740OkuLYVCtrVIdKbmjPcWeuRKlSJEyJFvTDpVIsb9Qpc+xMBqrRU2oYSaIa/LtlrFMB/vH\njUV4lVofL730Es899xye5/HpT3+affv2sWXLljflAX0zUAqXxoj73F4Wq0UUsR0SNUPPVqulEYxb\nlwVgKf5/9t4zyI7zvPf8vR1PjpNnMDMY5EgQBCMoJomkAkVR6dqWZFu27HLZdV3XJXvL3lp98Vap\nXN4qe6XrvU63dFf2ylmRFK1AkSJBEQwASICIgzQzmBzOmZNP53c/9MyZGQxAUCJly5L/n2ZO9+l+\nu0/38z7vE/5/hZ5IF2NLpXvGKtHbA9lrl7jN2fMcLrzC2eow89YCnZGOsJV7aXt/bAOGEr68uVyO\nxx57jMcPPcFL/quImCDemSC5aY7/a/5PqLp1alYdUzeYTxeoJCv8nPi5N7xeQbRVfvd68VsoMo3i\nrfXqxupXKLuVa3ZdutrrLSMNK/ScgTaFH8wDK7zLb0RB+laRFmneJd5Fv+xn2B9mnLXlk2XKPCef\n4yAHwbKQ9XpY7reshl2rhcx8u3YBYO3ciZdKoQwPI1yXYHAQdds2MqZJSp1h8bXv01Gcw5tWuNzY\nBPUEjWSKsZs/gPbdXj54/6eYHTzLhL9Ax7NR9s2mkeWXqe6e50xXmkJvmUaiBggyQRebDvbzvPoM\n3bKNvlWVOORycOedxA4f5hOn9jCWLrMYadK+4x76kis83kIIPrwhw/OJOl+dcWgGgpyh0h/RmbDC\nZ9WVEk9KlKVum9UxbV9Kvl5oUF7qSl1wPM7XbUxFQRFhk0lEVQgk9Mej7I4oFFwfNwjYEjNRhCCq\nCmKqwnPFGptjBpeaDoYiMBR11Xng9UqTDVG95RHbQcDfTZeIKwoRdSWWElUVTEWw4Hj0RHScYJnS\nNMCXYSVIVFXYlQhJnW7NvDUe+J9kTExM8Md//MfUajU++tGPsnfv3uvu+6ZCHy+99BL33nsv3/pW\n2Cxy6dIlDh06xG//9m+/faP+MaI32s1o/QoQGtpt6c1cLF6i4TcpuWVM1STmN8kZWfqiPexMbuXF\nwis4gYMbeHSY7QghWHRKZIwMHZF27szdxkBsw7pzFexFvjDyJbzApcNoZ8EuMFIfw2ae3oQgpisc\n7B4ioIGyFEbYMLAB4+4Eu+u7yWQyNJtNLnqXeO3y63Sk8uiqjuU5lJs13MDjnsw9dIvudee+Fqxr\ndGIuo+lbLUO9zNbnK1O42ikkQWt8q0MbYShkxVC/EQXp24UsWea5tkZngUIY+jl1Cul5cPPNoUiA\n44QJxEgEajX8eDxU/e7sxO9ciRkHnoduGOy+9zeZPfgBzs6e4Xt/Uqa5sEjdNFlM7qBjwqAn6XH0\neIx33bWfxjNX0BcqdJ96AtVp8sGFHXTsvsQT3UUyMklnZJAd3TswFQUpJa/x2lpDDXDnnTA0hDh/\nnkFgcOvW0Pu/ClFV4aG2JDUvYKQZJkp9KUmqCpa/IoKmKAIhoWsVJ8fFpstisNKZX3LDve0gYHPc\nZLThEFFCvcStySjRwGVxqaTPCiQboivmoez5LLg+jWuo6Sy6Hq6UuFeV7jlBQNMP6FPXhjOGYia1\npeNcaTpLHY467brKjONh+ZKS5/OpvsxPbdVHd3c3H/3oR7nzzjuZnZ3lD//wD/mzP/uz6+Z8bmio\nn376af70T/8UwzB46qmnAPjgBz/Ipz/96bd35D9GHMzfwWRzuuUxl5wKRa/UCnv40qfiVtkY0+kw\n24lpMR5ov4e/Gv0i4/UJ6n6DuBbn7vwdtBt52ow8VmDhBd4a0qaxxjh/dvF/MlIfBSCuxRiKDdIQ\nozTkNAe7trEz14aunKEZzBCzPoYgwmh9nLpXX5Ocm2rOEEhJw26Sjq086BPFaYrp4ps21BtifUw0\np9Z9HtOitJthuV5Ag2bkHwiUsLsvUIp4yhi6vx1FZlvqHYGnUHQ1rshhJJKMmmGjtnHdsT3Payl6\nK0pISL8sfPqjoF20kxZpLBm2g3tOgFV2wVJwF3XGKTKw3OWoqrCKRMyXHsrcLH7/QPi/G+BaPpqp\nohlKa7yqqtKp9fClZ8/zvYyBn1g2rAvMyhr+6A7OBlEudDSYijvcMXqKTreJCrSV47zj1BYKGySp\nDQm4ZQes8jivZhtsobPzmsb5WtgaN1qGWhWCfKs8T9JuhA0qHZrCziXjqqoqZUVDUVZivKscWwIp\nGYgarVK7mKaC47Y6vq9FG6oJQc9VZEhVL+Byw2HW9oipClk9wJdhMWBSVVlw14t2pDSFn+/KUPUD\nxi2XrXGTnK6hCOheOr4uxH9Y0do3g1wux113ha3oXV1dZDIZisUiHR3XZqG8oaFWVXWd13R1fetP\nOroiHXx8w0c5VjpBwSlyuniOvJFrNbcsJ+lGmlfYkdyKG7g8Nfcstmcz7yzgBj4Nv8mXJ75Bd7SL\n3mgXg7F+TkXP8OHeR9EVnZpX5+tTT7LgrJRc1b0Gw7VhdrU7ZESC7qRA6GdxRBWBhgTi1iexg/U6\ncIG/onaxGrbnkvCSLGsA2L6DEzgktPg1DeHNmb0MVy9QdFZoRYUQ3NN2sHXdrn6kZaQBlKADoczh\nqaPoXgaBQNM0hp0Us5zAURyqapWCXmCAAT4oP9hS1PZ9H9d1W+V7y6K0Usq3FCp7D+/h23ybaWeW\n+pxD3ErQWe6hY6GPsXOLRBKQWHX587E6z/WPcSVdRu1w2Mp+No/uwpkJSfOFgHibQW5o5b5dWXB5\nyTiEL/biyvD3URDUhMVwY5KcP4SqKyi6gMYcV6KSoUZYsJeqmsQCPWTyazYhvsKrvSww8FawMxHh\nctPhQj18VgaiOnYg2ZcySWnhhBNVFO7vzJBcav3Oug1g5dlqNzRmbA+JxFQUkprCWFOgCkHW0Kk5\nFu2GRtULyOsq1pIhLXk+eV3jQt3mQDrKlrjJhbpNyfU5Ww/j2aoiWHR9vrtQJaerGEp4X3YmotiB\nxFxl+IdiJtsTJkIIXi431rDmLeOnnbr0+eefZ3FxkUcffZRSqUS5XCaXuz45yA0N9U033cQf/dEf\n8dBDD+E4Dq+++ipPP/00N12v3OknFHkzx0Od9+MGLs+VX6DDbKPklql7DdzARREKpjDQhc7JyhmO\nLB6j4Cy2Kj4KThFVqGT8JtPWLN2RLiab05yqnOXmzF7OVobxAo+IYq7xoGxZp+T4tEd1IuZFlvpa\nkLg4+ktoQR8DsYMoQl3Tbdim5Cn4BSL6WuPWpXTSo3Vj+w7PzB/iXPUCgfTJGVnuaTvIpsTgmv2j\naoRf2PARXi+fYqI5RUyNsie9q8VvAuCpY2u+I9DQvV0EygxqkEcJctS9Pg4Fz5FITq65vhE5wpgz\nRt4NvXPXdQmCoMVBsfzCua4b8kf/iC/gPco9TMtpEmNTNAqhmK7hmey9EvKFj1V7GcqGXnVdd/jn\nHaexNA9SKbx4hJemjnHOH+NBGdKoSgm1eQdFemwwroBhcNKLUtUtOrmEnA096lIiQiVmUNcrbOn2\nycxNkqpMYdgWtiKpq4KED5pUecfiAC/2TYKxkjyMiii3iFvWXc+snOVl+TIzzJAmzS3iFjaLtRJh\nvpTM2h6GImgzNB7tSDNhOYw3XWKqQoehcbZuUXID2g2VfakoSW3FqdqRiPByuUFjqdonoSlsXKqF\nzmjhRHprOoYqVrr3N8VMekyNYxWL0YaDpkCHodNt6jy/WGPR9XmkPcXJiMUXJ4ukVIVcVMfyA46W\nmwSEYRJFhIb6TK3JxqhBQlPZnYywNW6yM7Ei27UtbnKyuj48d7XCzE8bDhw4wOc//3mOHj2K53n8\n2q/92huWut7QUP/SL/0SX//61/n617+Opmk88cQTHDhwgIcffvhtHfi/FTShkTWzzFcXSGkpLN8m\nEEGo7uw3eGruGZzAxfJtbD/0uH0ZJh496WH7NjE1StWtYZo5Rupj3JzZy7Q1y5XmJIEM8KTXUjIH\nFS9w2ZaVXE2hK6SBq54mrt3N3fnbObSwQp/YG+0GARFdp0QJFZV20c6jmfehKzrfmPoWF2srHBZF\nZ5HHp7/Fx/s/Qoe5tukloprsz9xETI0y0rjSapRZNtZCrn8pBDqq30/M+nkEUc7LE9dcSWmBRtWp\nkhdLyjTLicclHorlF1JKueb/WTnLPPNkydIrbqzonRZpfpFf5CuXnmbMvYJtueTnuigvas2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xVkJIIcGiJWXjq+ftXjqWukIhr3K++nGlSo+iVyWgfHzk7isz6BOXumysZ72sj0x5g7W8VTPF4d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\n0WM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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Kode dari http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.09-Principal-Component-Analysis.ipynb\n", "plt.scatter(X_train_pca[:, 0], X_train_pca[:, 1],\n", " c=y_train, edgecolor='none', alpha=0.5,\n", " cmap=plt.cm.get_cmap('spectral', 10))\n", "plt.xlabel('component 1')\n", "plt.ylabel('component 2')\n", "plt.colorbar();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dari *scatter plot* tersebut, kita dapat melihat bahwa angka 1, 4, dan 7 berada berdekatan (lihat bagian bawah kiri). Ini sesuai dengan intuisi kita bahwa angka 1, 4, dan 7 memiliki kemiripan dari sisi tarikan garis lurus. Inilah yang dicoba digambarkan sebagai \"komponen prinsipil\" dari gambar angka yang kita miliki." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Menentukan Jumlah Komponen Prinsipil\n", "\n", "Dalam mencari jumlah komponen prinsipil, kita ingin mempertahankan sebanyak mungkin variansi dari data yang sebenarnya. Angka yang lumrah digunakan adalah 90% atau 95%. Nilai variansi ini bersesuaian dengan $m$ nilai eigen terbesar pertama.\n", "$$\n", "\\frac{\\sum_{i=1}^{m} \\lambda_i}{\\sum_{i=1}^{d} \\lambda_i} \\le 1\n", "$$\n", "dengan $d$ adalah jumlah dimensi (atribut) asli data dan $m \\ll d$. Nilai ini dapat juga dipetakan sebagai total kumulatif nilai eigen yang terurut tersebut sehingga dapat terlihat seperti di bawah ini." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pca = PCA().fit(X_train)\n", "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n", "plt.axhline(.90, linestyle='--', c='b')\n", "plt.axhline(.95, linestyle='--', c='y')\n", "plt.xlabel('number of components')\n", "plt.ylabel('cumulative explained variance');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dari grafik tersebut, bisa kita lihat bahwa untuk mencapai 90% variansi, dibutuhkan sekitar 20 komponen prinsipil, sedangkan untuk 95% variansi, dibutuhkan ~30 komponen prinsipil." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Menggunakan PCA untuk Memperbaiki Klasifikasi\n", "\n", "Dalam beberapa algoritma, penggunaan PCA bisa meningkatkan akurasi. Namun, beberapa algoritma yang lain mendapat keuntungan dari klasifikasi berupa proses yang lebih cepat. Dalam modul ini, kita akan melihat dampak PCA pada hasil klasifikasi dengan Naive Bayes dan k-Nearest Neighbours." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Naive Bayes" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 2.84 ms per loop\n", "Akurasi: 0.833333333333\n" ] }, { "data": { "image/png": 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Q+iJzsjpgycnRLMumhP5B5dXWA0s0yQlu/b4mOQCbD36iWZaW78Fyx8omQaXI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\nRK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.metrics import accuracy_score, confusion_matrix\n", "\n", "clf = GaussianNB()\n", "%timeit -n 1 clf.fit(X_train, y_train)\n", "y_pred = clf.predict(X_test)\n", "\n", "print('Akurasi:', accuracy_score(y_test, y_pred))\n", "sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt='d');" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 9.7 ms per loop\n", "1 loop, best of 3: 2.1 ms per loop\n", "Akurasi: 0.93265993266\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pca = PCA(.9) # PCA dengan komponen yang menjelaskan 90% variansi\n", "%timeit -n 1 pca.fit(X_train)\n", "X_train_pca = pca.transform(X_train)\n", "X_test_pca = pca.transform(X_test)\n", "\n", "clf = GaussianNB()\n", "%timeit -n 1 clf.fit(X_train_pca, y_train)\n", "y_pred = clf.predict(X_test_pca)\n", "\n", "print('Akurasi:', accuracy_score(y_test, y_pred))\n", "sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt='d');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ingat bahwa Naive Bayes memanfaatkan asumsi bahwa setiap atribut *conditionally independent* jika diberikan kelasnya? PCA menghasilkan proyeksi dengan memanfaatkan vektor eigen yang saling tegak lurus satu sama lain sehingga hasil klasifikasi dengan Naive Bayes bisa lebih baik karena sesuai dengan asumsi yang diberikan di awal tersebut." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### k-Nearest Neighbours" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 83.2 ms per loop\n", "1 loop, best of 3: 90.2 ms per loop\n", "1 loop, best of 3: 88.3 ms per loop\n", "1 loop, best of 3: 90.5 ms per loop\n", "1 loop, best of 3: 95.2 ms per loop\n", "1 loop, best of 3: 99.1 ms per loop\n", "1 loop, best of 3: 91.9 ms per loop\n", "1 loop, best of 3: 92.4 ms per loop\n", "1 loop, best of 3: 101 ms per loop\n", "1 loop, best of 3: 92.9 ms per loop\n", "Akurasi terbaik 0.991582491582\n" ] }, { "data": { "image/png": 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ObWEs+t9ehl3foBcXoM19HDFsZLfHoijK5RPSldNjL4Mj2xDf2XOCf+Sc4NfX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\npMHgx4rN+/n6SD1LtxxjkL8nsxMCGT/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "train_acc = []\n", "acc = []\n", "\n", "for k in range(1, 11):\n", " clf = KNeighborsClassifier(k)\n", " clf.fit(X_train, y_train)\n", " y_train_pred = clf.predict(X_train)\n", " %timeit -n 1 clf.predict(X_test) # diulang di bawah hanya untuk mengetahui proses pencarian data\n", " y_pred = clf.predict(X_test)\n", "\n", " train_acc.append(accuracy_score(y_train, y_train_pred))\n", " acc.append(accuracy_score(y_test, y_pred))\n", "print('Akurasi terbaik', max(acc))\n", "plt.plot(range(1, 11), train_acc)\n", "plt.plot(range(1, 11), acc)\n", "plt.legend(['train', 'test']);" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 9.94 ms per loop\n", "1 loop, best of 3: 26.6 ms per loop\n", "1 loop, best of 3: 17.3 ms per loop\n", "1 loop, best of 3: 18.5 ms per loop\n", "1 loop, best of 3: 19.6 ms per loop\n", "1 loop, best of 3: 20.4 ms per loop\n", "1 loop, best of 3: 22.2 ms per loop\n", "1 loop, best of 3: 22.1 ms per loop\n", "1 loop, best of 3: 23.1 ms per loop\n", "1 loop, best of 3: 23.3 ms per loop\n", "1 loop, best of 3: 29.3 ms per loop\n", "Akurasi terbaik 0.989898989899\n" ] }, { "data": { "image/png": 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uu+02CgoKWLRoEcuWLQPgnnvuITo62rFb5SBnWyzO+1haLLoy5W/F9P+exnhp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\nEOXLodNVpOZV2ilaITonSfq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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pca = PCA(.9) # PCA dengan komponen yang menjelaskan 90% variansi\n", "%timeit -n 1 pca.fit(X_train)\n", "X_train_pca = pca.transform(X_train)\n", "X_test_pca = pca.transform(X_test)\n", "train_acc = []\n", "acc = []\n", "\n", "for k in range(1, 11):\n", " clf = KNeighborsClassifier(k)\n", " clf.fit(X_train_pca, y_train)\n", " y_train_pred = clf.predict(X_train_pca)\n", " %timeit -n 1 clf.predict(X_test_pca)\n", " y_pred = clf.predict(X_test_pca)\n", "\n", " train_acc.append(accuracy_score(y_train, y_train_pred))\n", " acc.append(accuracy_score(y_test, y_pred))\n", "print('Akurasi terbaik', max(acc))\n", "plt.plot(range(1, 11), train_acc)\n", "plt.plot(range(1, 11), acc)\n", "plt.legend(['train', 'test']);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perhatikan dalam kasus k-NN bahwa waktu yang dibutuhkan untuk mengklasifikasikan data menjadi lebih cepat. Jika Anda coba untuk dataset yang berukuran lebih besar, Anda akan melihat efek yang lebih drastis pada perubahan kecepatan jika dibandingkan dengan pengorbanan akurasi -- dan terkadang kita membutuhkan ini." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }