{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "MdUZzcdZd1Gc" }, "source": [ "# Lecture 2.3: Machine Learning in Python\n", "By courtesy of [Joaquin Vanschoren](https://ml-course.github.io/master/intro.html), Eindhoven University of Technology" ] }, { "cell_type": "markdown", "metadata": { "id": "Z74ZD168d1Gf" }, "source": [ "## 1. Why Python?\n", "* Many data-heavy applications are now developed in Python\n", "* Highly readable, less complexity, fast prototyping\n", "* Easy to install and import many rich libraries\n", " - numpy: efficient data structures\n", " - scipy: fast numerical recipes\n", " - matplotlib: high-quality graphs\n", " - scikit-learn: machine learning algorithms\n", " - PyTorch: neural networks\n", " - ..." ] }, { "cell_type": "markdown", "metadata": { "id": "67PTglwFd1Gf" }, "source": [ "\"ml\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 23619, "status": "ok", "timestamp": 1661689949425, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": false, "id": "-nA471vHd1Gg", "outputId": "ddef4d7a-6040-45b5-e8b5-3e16dc8a97f8" }, "outputs": [], "source": [ "# General imports\n", "%matplotlib inline\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from scipy.stats import gamma\n", "np.random.seed(4487)" ] }, { "cell_type": "markdown", "metadata": { "id": "2rHHcCsad1Gg" }, "source": [ "## 2. Numpy, Scipy, Matplotlib\n", "* We'll illustrate these with a practical example\n", "* Many good tutorials online\n", " - [Jake VanderPlas' book and notebooks](https://github.com/jakevdp/PythonDataScienceHandbook)\n", " - [J.R. Johansson's notebooks](https://github.com/jrjohansson/scientific-python-lectures)\n", " - [DataCamp](https://www.datacamp.com)\n", " - ..." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "-Oz33cVNd1Gh" }, "outputs": [], "source": [ "#print(\"pass\")\n", "\n", "def gen_web_traffic_data():\n", " '''\n", " This function generates some fake data that first shows a weekly pattern \n", " for a couple weeks before it grows exponentially.\n", " '''\n", " # 31 days, 24 hours\n", " x = np.arange(1, 31*24)\n", " \n", " # Sine wave with weekly rhythm + noise + exponential increase\n", " y = np.array(200*(np.sin(2*np.pi*x/(7*24))), dtype=np.float32)\n", " y += gamma.rvs(15, loc=0, scale=100, size=len(x))\n", " y += 2 * np.exp(x/100.0)\n", " y = np.ma.array(y, mask=[y<0])\n", "\n", " return x, y\n", "\n", "def plot_web_traffic(x, y, models=None, mx=None, ymax=None):\n", " '''\n", " Plot the web traffic (y) over time (x). \n", " \n", " If models is given, it is expected to be a list fitted models,\n", " which will be plotted as well (used later).\n", " '''\n", " plt.figure(figsize=(12,6), dpi=300) # width and height of the plot in inches\n", " plt.scatter(x, y, s=10)\n", " plt.xlabel(\"Time\")\n", " plt.ylabel(\"Hits/hour\")\n", " plt.xticks([w*7*24 for w in range(20)], \n", " ['week %i' %w for w in range(20)])\n", " \n", " if models: \n", " colors = ['g', 'r', 'm', 'b', 'k']\n", " linestyles = ['-', '-.', '--', ':', '-']\n", "\n", " if mx is None:\n", " mx = np.linspace(0, x[-1], 1000)\n", " for model, style, color in zip(models, linestyles, colors):\n", " plt.plot(mx, model(mx), linestyle=style, linewidth=2, c=color)\n", "\n", " plt.legend([\"d=%i\" % m.order for m in models], loc=\"upper left\")\n", " \n", " plt.autoscale()\n", " if ymax:\n", " plt.ylim(ymax=ymax)\n", "\n", " plt.grid()\n", " plt.ylim(ymin=0)" ] }, { "cell_type": "markdown", "metadata": { "id": "YigIIQw4d1Gh" }, "source": [ "### 2.1 Example: Modelling web traffic\n", "* We generate some artificial data to mimic web traffic data\n", " - E.g., website visits, tweets with certain hashtag, ...\n", " - Weekly rhythm + noise + exponential increase" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 824 }, "executionInfo": { "elapsed": 2265, "status": "ok", "timestamp": 1661505925949, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "zsRw8-j9d1Gi", "outputId": "4cbea476-ced8-4c3b-80bd-fb40ca2c41b8" }, "outputs": [ { "data": { "image/png": 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CBwAAAAAAqWVqOWCWFQaA8Mwrs7R4YZXuq6/V4oVVVEOEcayMAwAAkG3096K3bNEdvpOsCy2NTLoOKA0rPwT5jJhS6nvNig4AACAKJOEDAAAAAIDUMrUcMMsKAwCQXqyME73xoq3zl4f16sCgzl8eJqENAACEiv5ePDpam7Rh+SJP22xYvkgdrU0htSg/0rLyg5/PyL11VcaOP9f3mhUdAABA2EjCBwAAAAAAqcWywgAAgJVxonP2whU9fvRl3f/EM1r75LN6+FPPa+2Tz+r+J57R40dfjqTiJgAAyB/6e/GYX16m/TtWu05kLrQ0av+O1ZpfTipSEGla+cHrZ+STD7zL+Pew1P5Y0cEdJlkDAOAfZd4AAAAAAEBqOcsKd3b3+t4HywoDABCu8aKtgcERDV0bU82CctXXVhq99rIyTvhGx4rae+TMrBU5L129rs7uXnV296rQ0qiO1iaSrwAAUPj9oLygvxef+eVl2rd1ldrWL1PXiXM6eKp/0qoEddUV2t7coMKaJbEmLGfpu2Zy5YfFC81VnZ+Nl89IZUWZPvvid4wef67vdUdrk/ovDuv4Kxdc7zMvKzqcvXBFXT19OjTDe7atuUGPxvy9BgAgDbjDAAAAAAAAqVZoaQyUhM+ywgAAhCOqAX1nZZwgiSqsjDO70bGidh446TpppaunT/0Xh6mCCgDINRIbzaK/F7+ld9eofctK7d68IlHJ7ln8rpmqFP/am0ORvk9uPiOvDgwaPaab77VTrb/UpOKJ8jCpmEnWAACYQxI+AAAAAABINWdZYTeDKFPlaVlhAACiEvWAPivjhGvvkTOeqkZK0vFXLmjvkTPat3VVSK0CACCZSGwMB/295JhXZkVSXX0uWf6umVqx4ZP7e279f5STEkp9RkyvRuH2e52WFR2iwCRrAADMIgkfAAAAAACkHssKAwCQDHEN6LMyTjicyqJ+dPX0qW39sswnsQAA4CCxMVz09+DI+nfNxMoPUyVlUoLp5+b1e53UFR2ixCRrAADMSn7vEgAARGa8aOv85WG9OjCo85eHNV60424SAACAK86ywoWWRlePL7Q0pmbgDQCANAkyoB+EszKOH6yMMzu/Cfi3tj9xzlBLAABIvrj6QXlBfw+OrH/XnJUfwtLV06edB05qdKwY2jFmY/K5BfleO9X676uv1eKFVYlMwA9j3D7oJOveN4cCtwEAgKxJVCV8y7Ie9LjJPVO2Xy/Jc8/Itu3nvW4DAECWODfch2ZYei+qpQkBAACCYllhAADiFXfVdFbGMWu8aOvQqf5A+zh4ql+7N69IZFILAERtvGjntupuHsTdD8oL+nvIy3ct6MoPc4mzsrmJ55bl73WY4/YmJlm3b1kZaB8AAGRNopLwJT0nye/UPevm9l7ZSt7rAABAJEbHitp75MysN9xJWZowDxiAAQDAHJYVBgAgHnEP6Dsr45SKdUxErKO0gcGRSUkffly6el0DgyNavLDKUKsAIH0ogpMPcfeD8oL+HvLyXXNWfgj6fEuJa1JC0OeW1e912OP2TLIGACAcSU0+93K1npi0z1UeAACXRseK2nngpOtqIV09feq/OKz9O1ZnLqgRJwZgAAAIj7OsMJA0TMAEkEVJGdBnZRxzhq6NJWo/AJA2FMHJj6T0g/KC/l5+5e275mflB6/impTg57k13FWl//rzH9Lye2pDbFn4ZooLjhft0MftmWQNAEA4kpqE77cavtftkt+rBgAgJHuPnPEctIlzacKsYQAGAAAgf5iACSDLkjagz8o4wdUsMDOEZGo/AJAmFMHJl6T1g/KC/l7+5O275nXlBz/impTg9bl98oF3ae9H35fqa2SpuOA9d1bqm28Metqf13F7JlkDABCOpEU+++Q/AR8AALjk3OT7EdfShFnCAAwAAEC+MAETQB4kdUCflXH8q6+tVF11RaBEp7rqCtXXVhpsFQCkA0Vw8iWp/aC8SFt/j9Xx/Mvjd22ulR+CinNSQl5WtXATF/T7nnoZt2eSNQAA4UjUldG27XfH3QYAAPIgaLWEuJYmzAoGYAAAAPKDCZgA8oIB/eyZV2ZpW3ODOrt7fe9je3MDSWUAcociOPlDPwhusDpecHn+rs208sOFwWv65P6ewPuOe1JClle18BoX9MPtuD2TrAEACAcjeQAA5Mx40dahU/2B9nHwVL/Giyxe40fQAZjeN4cMtwgAAABhCjIBEwDSxBnQD4IB/eQptDQG237NEkMtAYD0MFEEB+lCPwiljI4VtefwaW16+rg6u3unJcA6q+M99NRz2nP4tEbHijG1NNlGx4r63S9+K/B+0v5dc1Z+uK++Vu82NGkjKZMSJj63xQurUp+AL/mLC3rldtzemWQdBJOsAQCYjiR8AAByZmBwJPAyhc7ShPCOARgAAID8YAImgDxhQD+bli26w3cifqGlkWquAHKHIjj5RD8Is3GqYLuNDXT19GnngZMk4k/hvI6fffE7gfeVpe8aE4CSLUhc0Asv4/ZMsgYAwDyS8AEAyBlTSwrGvTRhGjEAAwAAkC9MwASQNwzoZ1NHa5M2LF/kaZsNyxepo7UppBYBQHJRBCe/6AdhJqyOZ4bJauJZ+q4xASjZokjAd7gdt2eSNQAA5pGEDwBAzphaUjApSxOmCQMwAAAA+cEETAB5tOTtNdr6I/f62pYB/eSaX16m/TtWu07WKLQ0av+O1ZpfzhAUgPyhCE5+kdiIqVgdzwyT1cSz+F1jAlAymYgLeuFl3J5J1gAAmEUEFABgzHjR1vnLw3p1YFDnLw+TLJJQLE0YHwZgAAAA8oMJmADy5OyFK3r86Mu6/4lndPirr3vengH95JtfXqZ9W1fp2K6Nalu3dFpsqa66Qm3rlurYro3at3UVCfgAcosiOPlGYiMmYnU8M0wl4Gf1u8YEoGQyERd0y+u4fViTrMkVAQDkFXfvAIDAnAoEh071T7qZrKuu0LbmBj26Zgk38AniLE3Y2d3rex8sTegPAzAAAAD5wQRMAHkwOlbU3iNnAiXGFFoa1dHaRNJ2Siy9u0btW1Zq9+YVGhgc0dC1MdUsKFd9bSWxIgDQ7SI4QRLvKIKTXk5io9v+Ef2g7DK1Ot7uzSty3ccyVU385x5o1G9+NLvftY7WJvVfHNbxVy643iarkxKSIsp4np9xe2eSddv6Zeo6cU4HZ8jz2N7coIKLPA9yRQAAeUcGFwDAt7kGWi9dva7O7l51dvcSSEyYQktjoCR8lib0hwEYAACA/GACJoCsGx0raueBk54SPRxeBvSRTPPKLC1eWBV3MwAgcSiCA5OJjUgvk6vj5bnPZaqa+K/++H2ZHqNmAlDyRBnPCzJuH2SSNbkiAADcwCgeAMAXrwOtXT196r847GqpMoTPWZrQT6U6lib0jwEYAACA/GACJoCs23vkjK8E/K0ffKee+sQHubcFAGQWRXAgsXpM3rE6nhm8ju4xAShZTMQF3TA1bu91kjW5IgAA3MaVDQDgi5+B1uOvXNDeI2dCahG86mht0oblizxtw9KEwRVaGoNtzwAMAABAKjgTMINgAiaApHKWm/fj8N99V30/uGq4RQAAJIdTBMcPiuBkj5PYeF99rRYvrOIeLydYHc8MXkfvnAlAL7U/ohd2b9IXHntQL+zepJfaH1H7lpWZv8aMF22dvzysVwcGdf7ysMaLdiztMBEXnEuc4/bkigAAcBtJ+AAAz4IMtHb19Kn3zSHDLYIfztKEbgcDCi2NzE43gAEYAACA/GACJoCs8hsXurX9iXOGWgIAQDJRBAfIN6cKdhCsjsfrGETeJgCdvXBFjx99Wfc/8YzWPvmsHv7U81r75LO6/4ln9PjRl2PJTwgaF5xr33GN25MrAgDAZGTRAQA8Y6A1O5ylCY/t2qi2dUunBbLqqivUtm6pju3aqH1bV5GAbwgDMAAAAPnABEwAWTRetHXoVH+gfRw81R9bRUIAAKJAERwg31gdzwxeR8xldKyoPYdPa9PTx9XZ3atLV69P+vulq9fV2d2rh556TnsOn9boWDG0tkytwr/k7TW+44Lvvac2seP25IoAADBZftZcAgAYYWqgdffmFQQ8EsRZmnD35hUaGBzR0LUx1SwoV31tJe9TCJwBmL1HzrgKVBRaGtXR2sQADAAAQAp1tDap/+KwpyWamYAJIMkGBkemJTZ4denqdQ0MjmjxwipDrQIAIHmcIjht65ep68Q5HTzVP+kaWlddoe3NDSqsWcIEXJQ0XrQZu0mhQkujOrt7/W/P6niSeB0xu9GxonYeOOk65tbV06f+i8PGJ705leEPzXCd/+kP3qsH3v02vfjaD1zvb8PyRdq/Y7XmlVmJO/eTKwIAwHQk4QMAPGGgNducpQkRPgZgAAAA8oEJmACyZujaWKL2AwBA0lEEB36VSuzc1tygRxk/SDRndTw/VaNZHe82XkfMZu+RM56KXkjS8VcuaO+RM9q3dVXg44+OFUvG+y5dva4/+vJrkqT31N+hbw1cmXOfU+OCSRu3J1cEAIDpSMIHAHjCQCtgFgMwAAAA2ccETABZUrPAzLCCqf0AAJAWFMGBW24SOzu7e9XZ3ctE7oRjdTwzeB0xlTNJyY+unj61rV8WKAbntQr/twau6EPvvkvvu3ehDn/19dTGBckVAQBgOqLcAABPGGgFwsEADAAAQPYxARNAFtTXVqquuiJQ9bu66grV11YabBUAAEA2eE3s7OrpU//FYe3fsZpE/DmMF+3I78VZHc8MXkdM5TcB/9b2J86pfctKz9s555F9f/UNz1X4v/LaRS1/R61ean8ktXFBckUAAJiOqxoAwBMGWgEAAAAgGCZgAkizeWWWtjU3qLO71/c+tjc3pCbJAAAAIEp7j5zxnNh5/JUL2nvkjPZtXRVSq9LNqZh9aIZV6bY1N+jRkKtPszqeGbyOcIwXbR061R9oHwdP9Wv35hWu70tnO494NVcV/jgmC3lBrggAANORhA8A8ISBVgAAAAAAgHwrtDQGig0V1iwx2BoAAIBscJI8/ZgrsTOPRseKJSunX7p6XZ3dvers7o2kcnrSV8dLevKvI+mvI8I3MDgSKAlcuvH9HxgcmbNIxlznET9mqsIf92Qht8gVAQBgOpLwAQCeMdAKAAAApENaBlABAOmybNEdKrQ0+kpEKLQ0JiJ5AAAAIGmCJnnOlNiZV6NjRe08cNL1qgJdPX3qvzis/TtWh5qILyVvdby0JP9OlbTXEdEZujYWyX68nkfcmliFP2mThdwgVwQAgMlIwgcAeMZAKwAAAJBsaR1ABQCkR0drk/ovDntKSNiwfJE6WptCbBUAAEA6jRdtHTrVH2gfExM7s26uogN7j5zxnDh7/JUL2nvkjPZtXWW6uYmUxuRfQJJqFphJdZtrP37OI244VfjfXrMgsZOFSiFXBACAyUjCBwD4wkArgqIqKwAAgHkMoAIAojK/vEz7d6wued2ZiOsOAJhDbBXInoHBkUmT6P1wEjuzXB3cTdEB27Z9ryrQ1dOntvXLMp8kmuSVAoC51NdWqq66ItA5s666QvW1lbP+3TnXhGXo2ph+79lXUztZiFwRAABuIwkfAOALA63wi6qsAAAA4WAAFQAQtfnlZdq3dZXa1i9T14lzOjjDvf725gYVuNcHACOIrQLZNXRtLFH7SRovRQfee09toGN1nTin9i0rA+0j6VgpAGk2r8zStuYGdXb3+t7H9uaGkhMYw0zAl6TvXxlN9WQhckUAALiNJHwAgG8MtMILqrICAACEiwFUAEBclt5do/YtK7V78woqMwNACIitAtlXs8BM6oap/SSJ16ID33xjMNDxDp7q1+7NKzLbjw1S4TsJyb+AdCOpO0gSfmHNkln/Nl60dehUv+99z6WuukJ/feaNQPtIwmQhckUAALghe3dgAIDIMdCKuVCVFQAApNV40U5FH5cBVABAEswrs7R4YVXczQCATCG2CuRDfW2l6qorJiUwelVXXaH62kqDrUoGP0UHgrh09boGBkcy268NWuE7Ccm/wLJFd6jQ0ujr81xoaSwZBx0YHAl0Lp7Lx37kXv35V18PtI8kTRYiVwQAkHdEXgAAxjgDrffV12rxwipuqnBLkKqsAAAAcTh74YoeP/qy7n/iGa198lk9/KnntfbJZ3X/E8/o8aMvq/fNobibOImJAVQAAAAAyUNsFciHeWWWtjU3BNrH9uaGzI3NBSk6EMTQtbHIjxkFExW+D57q13jRNtQiwL+O1iZtWL7I0zYbli9SR2tTyceE/f3/yffdEzjJ35kslCTkigAA8ookfAAAEKqgVVmTluAGAACybXSsqD2HT2vT08fV2d07bUDk0tXr6uzu1UNPPac9h09rdKwYU0tvYwAVSI/xoq3zl4f16sCgzl8e5nsHAABKIrYK5EuhpTHY9muWGGpJcsSRgC9JNQvKYzlu2ExU+E5i8i/yaX55mfbvWO363FloaXS1UlCY3/9CS6PeVjPfyL6yOlkIAIC0yeadAwAASAyWtQQAAGkxOlbUzgMnXVeZ7OrpU//FYVeDN2EyOYCa1aXWgbg5CXSHTvVP+r7WVVdoW3ODHl2zpORS6AAAIJ+IrQL5smzRHSq0NPr67hdaGjN3T2Gi6IAfddUVqq+tjPy4UTCVtEvyL5JifnmZ9m1dpbb1y9R14pwOzhB32d7coIKHuEt9baXqqisCx1uncqrwf3/ompH9ZXWyEAAAacMVGQAAhMZUVdbdm1ewZB0AAAjd3iNnXCfgO46/ckF7j5zRvq2rQmrV3BhABZJrdKyovUfOzJpE46yu0dndq0JLozpam2Kd1APgtvGirYHBEQ1dG1PNgnLV11YSmwAQKWKrQD51tDap/+Kwp/iEk9iZNSaKDvixvbkhs+dNU0m7JP8iaZbeXaP2LSu1e/OKwPdx88osbWtuUGd3r7H2TYz5mEjyz/JkIQAA0oaeMQDEiME8ZF3aq7LyHQUAID+cKtV+dPX0qW39stgqzjGACiRTWlfXAPKOlSsAJEXaY6sA/JlfXqb9O1aXnMw7UZYn88ZVLKCwZkksx40Cyb/IunlllpF+T6GlMXAS/mxV+E0k+Wd5shAAAGnD6CoAxIDBPORFWquy8h0FACB//Cbg39r+xDm1b1lpqDXeMIAKJFNaV9cA8oqVKwAkTVpjqwCCm19epn1bV6lt/TJ1nTingzOMVcyU2Jk1cRQLKLQ0Zvo1DSP5l4JWyKJli+5QoaXRV8x4y/sXa88/WlHyuxA0yT/Lk4UAAEgbkvABIEIM5iFv0laVle8oAAD5NF60dehUf6B9HDzVr92bV8QyyEj1JCB50ry6BpBHrFwBIInSFlsFYN7Su2vUvmWldm9eMSnJ+e01C/T9oWsaujam85eHM5v0bKLogBcbli9SR2tTJMeKk6nkXwpaIes6WpvUf3HYU4GFDcsX6VOf+OCc94lBkvyzPlkIAIC0IToMABFxBvPc3kh19fRp54GTGh0rhtwyIDxOgDSIqKqyZuE7Ol60df7ysF4dGNT5y8MaL9pxNwkAgFQYGBwJPKB76ep1DQyOGGqRd4WWxmDbUz0JMMrE6hoAohNktuq4RQABAABJREFU5QoACEuaYqsAwjWvzNLihVUqsyx95ku9euC3vqC1Tz6rhz/1vNY++azuf+IZPX70ZfW+ORR3U41yig4E8d57al09rtDSmJsJlk7yrx+FlkbdW1elPYdPa9PTx9XZ3TstpuYUtHroqee05/DpRI2jAV7MLy/T/h2rXX9fvJ5HOlqbtGH5Ik9tystkIQAA0iT7dxAAkBAM5iGPTARIo6rKmubv6NkLV/T40Zd1/xPP5CLwDgCAaUPXxhK1Hz+CDqBSPQkwx9TqGkyqBaIRdOUK7rmRRhRySIc0xVYBhGt0rJjbpOegRQf+8NH7dWzXRrWtWzptYlNddYXa1i3VsV0btW/rqlwk4Dv8Jv/u/siK1Be0QvrE2XedX16mfVtXhXIeCTvJHwAARIP1BwEgAv0Xr7IMPXLL1LKWYQo64B7Xd3R0rKi9R87M2nYn8N7Z3atCS6M6WpsIzAAAMIOaBWbCI6b245ffJZKpngSYZXJ1jcULqwy1CsBsTKxc0b5lpaHWAOFyYmCHTvVPulbVVVdoW3ODHl2zhDh0wqQhtgogXM4qvm7v9bt6+tR/cTgziZpO0QE/fbaJRQfat6zU7s0rNDA4oqFrY6pZUK762srcTlRykn9LjTFN5IwxBSlotW/rKr/NRU4lqe+69O6aUM4jTpJ/2/pl6jpxTgdneK7bmxtUoJ8OAEBikYQPABE4+rXzgbZnMA9pZipAGqY0DrjnPfAOAIBJ9bWVqquuCJQ0W1ddofraSoOt8s7vACp9A8CsLKyuAeSFqZUrdm9ekdsELqQDhRzSKw2xVQDhIunZXNGBeWUWE50n8Jr8m9aCVkifJPddwzqPhJXkDwAAwkcEDQAi8MzL3wu0PcvQI+38LmsZRVVWUwPuUX9HgwTeAQDAZPPKLG1rbgi0j+3NDYkYEAlziWQA7mRldQ3k23jR1vnLw3p1YFDnLw9nNi5lcuUKIKmcQg5uk+a6evq088BJjY4VQ24Z3EpybBVAuIImPfe+OWS4RfFwig4UWhpdPb7Q0khBIg+c5N+X2h/RC7s36QuPPagXdm/SS+2PqH3LyluJ8yYKWgFzyXvf1Unyv6++VosXViUi3gwAAEpjJAcAIjA4MibJ/w0Sy9Aj7ZJcldXkgHtU31GqjQAAYF6hpVGd3b3+t1+zxGBrgqN6EhCfrKyugXxy7jcPzVAFc1tzgx69WQUzK1i5AnlABeX0S3JsFUC40riKb1i8Vm2Hd6UqfLOCFKQbn4Ow44z0XQEAQNqQhA8AKcFgHtIuqQHSNA64E3gHAMC8ZYvuUKGl0dd1ttDSmNgBXpZaB6LnrK4RZGJPUlbXQH6MjhVLJndeunpdnd296uzuzVRyJytXIOso5JAdSY2tAggPSc8zo+hANKYmW48X7dQVtII5UU3Wpu8KAADSiMgwAKQEg3nIiqQFSNM24E7gHQCA8HS0Nqn/4rCnaksbli9SR2tTiK0CkEZZW10D2TY6VtTOAyddX/+6evrUf3FY+3esTn0iPitXIOso5JA9SYutAghPGlfxjRJFB8IxW7J1baWZMTCKzqVL1JO16bsCAIA0SneEHABSImhggsE8pNV40db5y8N6dWBQ5y8Pa7xo3/qbEyC9r75WixdWxTZI5Ay4BxHld9Rk4B0AolbqugBMFcfnZX55mfbvWK1CS6OrxxdaGjORgAjAPGd1DT+SvLoGsmnvkTOeJqBJ0vFXLmjvkTMhtSg6zsoVQbByBZLKVCEH7tuSKSmxVQDeuY13pHEVXyTfbJ+/0bGi9hw+rU1PH1dnd++0cajBETOfI4rOpYczWdttYnxXT592Hjip0bGir+PRdwUAAGlFDxcAIvDIynfoW3/7Xd/bM5iHsExdTtJUxaSoliU0wRlwD1KlMsrvKIF3AGmUpusC4hf352V+eZn2bV2ltvXL1HXinA7O0I7tzQ0q8LkFEies+xu/WF0DaeBcd/3o6ulT2/plqb8esnIFsooKygCQLF7jHWlbxRfJVurz99MfvFcvf/ctvfjaD0JtA0Xn0iXIZO19W1d5Ph59VwAAkFbccQFABLa8f7H+IEASPoN52RZHokhYyW1RL0toSpoG3Am8A0iTtF4XEI+kfV6W3l2j9i0rtXvzikQl9QKYLu7JO7NxVtcodW6biGsh4uA3Af/W9ifOqX3LykD7iHsCjbNyhZ/XgpUrkGQUcgCAZPAb73BW8Q2SlErSM9x8/v7oy69F0haKzqVHHJO16bsCAIC0IvsKACLQcFc1g3mYJo5EkTCT25xlCd1WRejq6VP/xWHt37E69iSTNA24E3gHkBZpvi4gekn+vMwrs6ieBCRU0ibvzITVNZBk40Vbh071B9rHwVP92r15ha9kmiRNoGHlCmQRhRwAIH5B4x1pWsUXyeP18xc2is6lRxyTtem7+hfGxPa4J8sDAJAm+et9AEBMGMyDI65EkbCT26JeltC0tHxH55VZBN4BpELarwuIFp8XAF4lefLOTFhdA0k0MDgSaIK3dCOGMTA44mnCWhIn0LByBbKIQg4AEL+g8Y40reKL5PHz+QsLRefSI67J2vRdvQtjYnuSJssDAJAWRIgBICLOYF6hpdHV4wstjVSCzSAnUcRtBYGunj7tPHBSo2PFwMcOEuydS9BlCXvfHPK1rUlRfEfHi7bOXx7WqwODOn95WONF21db3bZx1u0JvAMIWf/Fq6m/LiA6WehHAIhemPc3YXJW17ivvlaLF1aRgI9YDV0bi3w/ccZF5uKsXHFs10a1rVuquuqKSX+vq65Q27qlOrZro/ZtXUXMDonnFHIIgkIOAOCfiXiHs4qvHyQ951uQz59pFJ1LF5OTtb2g7+re6FhRew6f1qanj6uzu3fa++VMbH/oqee05/BpV/fTYewTAIC8oBI+AESIZejnlvWlzeKq8ho02Nu2flnJz2QcyxKGIazvqOmqAU7g3c/rTuAdQBSOfu18oO2Tcl1ANLLSjwAQnbDvb4C8qFlgZnjAy37SsPoNK1cgS6igDADxMRXvSMsqvkiWpCTg+1lBKutjxUkXx2RtB33XuYWxMmTaVpsEACBpSMIHgBgwmDddHpY2izNRJMzktriWJQyTqe/o6Fix5FL2TtWAzu5ez4FIAu8AkuyZl78XaPukXRcQniz2IwCEj8k7gBn1tZWqq64IVOWwrrpC9bWVrh6btgk0zsoVQJpRyAEA4mEy3uGs4ltqrGEiP0nPyBYTnz8vaivLNThyO+E6KQWt4E8ck7Ud9F3nFsbE9jRMlgcAIMm48wKAGLEMvTQ8Oq5/9b/+LlVLm40XbZ2/PKxXBwZ1/vKwxou2q+1MJIr4YSrYO9vzjGtZwigE+Y46VQPcvu9dPX3aeeCk68+4E3h3uxRtoaWRigQAIjNx0MWPpF4XYF6W+xEAwhH2/Q2QJ/PKLG1rbgi0j+3NDa7vleOKiwB519HapA3LF3nahkIOABCM6XiHs4rvsV0b1bZuqeqqKyY9tq66Qm3rlurYro3at3UV4wA5Z+Lz58Xnf329Xti9SV947EG9sHuTXmp/RO1bVrpOiB4dK2rP4dOpGivOMmeydhBeJmtPRd91dkEntve+ORTJPgEAyBsq4QMAYnH2whUdeOGc/seJcxrzkMQe59JmQSowxFnl1WSwd6YKcHEuS5hkUVQNcALvbeuXqevEOR2c4bPpp9oIACRB1q4LmBn9CABehX1/A+RNoaVRnd29/rdfs8TV41j9BogPFZQBIHphxTtYaRtuRBknq6uuCFRozilo5XY8Le6x4jxwJmsHuU/0Mll7KvquswtjZUhWmwQAIDiS8AEAkRodK7q+aZ5JHEubzdVmpwJDZ3fvrDf6cSaKhJ3cFueyhEkVtGpA2/plnpLmCbwDyKIsXRcwO/oRALxi8g5g1rJFd6jQ0ujrHrbQ0uj63pUJNEC8KOQAANEKO97hrOKLZBsv2rGM2UQZJwuSbC1FU9AK3kU1WXs29F2nC2NiO5PlAQAwg1FqAEBkvFYzmI2fJGW/TFVgiDNRJOxgr7MsYZDB9CDLEiZRXFUDCLwDSIraynK9MTzue/s0XBfiGkTLGvoRALxi8g5gXkdrk/ovDnuK12xYvkgdrU2uH88EGiAZZivk8PaaBfr+0DUNXRvT+cvD3N8AQEDEO/ItyMraJpj4/LkVJNk66oJWcC+qydpzoQjZbWFMbGeyPAAAZmR/PR4AQGL4qWYwm64T54zsZy5BKjBMFGeiiBNsC6JUsNdZljCIoJUyksRU1YDxom2oRQAQvUdWviPQ9km+Lpy9cEWPH31Z9z/xjNY++awe/tTzWvvks7r/iWf0+NGX1fvmUNxNTBX6EQC8Cvv+Bsij+eVl2r9jtQotja4eX2hpnFZ8YC5MoAGSxSnkUGZZ+syXevXAb32B+xsAMIh4Rz6NjhW15/BpbXr6uDq7e6cltzoraz/01HPac/i0RseKobTDxOfPjaDJ1iYKWiE8Ha1N2rB8kadtvE7Wdsvpu95XX6vFC6tyeW4MY2I7k+UBADCDJHwAQCSCVDOYSRRJykErMEwcpIozUSSKYK/bgfpZtw+4LGGSmKwaAABpteX9iwNtn8TrQlIG0bKIfgQAL0hmAcIxv7xM+7au0rFdG9W2bum0GEZddYXa1i3VsV0btW/rKk8J+BITaICk4f4GAMJHvCNfnJW13Y4rdvX0aeeBk6FdY4N+/uYSNNmaglbJF8VkbbgXxsR2JssDAGAGvR8AQCRMJuBL0SQpm6zAEHeiSNjBXmdZQl/7NrgsYRJQNQAApIa7qjN1XUjaIFrW0I8A4BXJLEB4lt5do/YtK/VS+yN6YfcmfeGxB/XC7k16qf0RtW9Z6fu6G3dcBMBt3N8AQDSId+SLqZW1TQny+ZuLiWTr1y9epaBVCoQ9WRvuhTGxncnyAACYQQ8IAFJivGjr/OVhvTowqPOXh1M1s99ENYOZhJmkHEYFhjgTRaII9iZpWcI4UTUAAG7I0nUhaYNoWZSlzwuA8JHMAoRvXpmlxQurdF99rRYvrDKS/M4EGiAZuL8BgOj4iXesXnKXfvnBZakaB8w7kytrm+Tn81fK9vvvNZJsPTpW1GN/+vdG2kRBq2iENVkb7oUxsZ3J8gAAmEESPgAk3NkLV/T40Zd1/xPPaO2Tz+rhTz2vtU8+q/ufeEaPH305tMCMSQODI4GrGcwkzCRlE22eWoEh7kSRsJPbWJbwBqoGAMANXq8Ln3zgXXr8p5rU94OhRE04TOogWtbQjwDgFZN3gPSJOy4CgPsbAIia13iHJJ08d1EP/r/PpWocMO9MrqxtkvP52/rBdxrZ37/+iR820iffe+SMTp67aKBF4Y4Vp7lAXljCmKwN98KY2M5keQAAgqPEKgAk1OhYUXuPnJk1cHPp6nV1dveqs7tXhZZGdbQ2JTYJKYwqBGEnKZtq89T9dLQ2qf/isKdqU6YSRZxgW6nP1UR+PlfOsoRt65ep68Q5HTzVP2kyQ111hbY3N6iwZklmB8+dqgGd3b2+90HVAABZ4ea68MiKd8iW9Pmvv6HPvvidSX/b1tygR2O+ZpgYRGvfstJQa7KNfgQAL6K4vwFgXpxxEcxsvGhrYHBEQ9fGVLOgXPW1lcQkMoz7GwCI3lzxjtmkaRwwz0ytrL1784oZ+2BB+2rzy8v01Cc+qC9+c0Bvjfgf+zQ1LhtkQuBUXtrk5XV02nhohthkEuLVyC9nYruf79BsE9vD2CcAAHlDEj4AJNDoWFE7D5x0PSDZ1dOn/ovDia0GGkYVgrCTlE21eep+4k4UiSq5zVmWcPfmFbkcyC20NAZKwqdqAICsmem6ML+8TJ9+7qz+5MXkTjgMexANM8t7PwKAe0zeAdIn7rgIbiO5KH+4vwGAeE2Md7x+6ar+9Z/+vb7ymrtq4EkfB8wzkytrL15Ydet3XvtqpZLM55VZ+vjqdyWieJSpBHzJXZu8vI5ZKpCXJ3mbVBzGxHYmywMAEAxJ+ACQQHuPnPF0kyNJx1+5oL1Hzmjf1lUhtcq/+tpK1VVXBA5CTRR2krKJNs9WgSEJiSJRJbc5yxLmDVUDAGBmznUhLRMOwxpEgzt57UcA8I7JO0C6JCEukmckF+UX9zcAkAzzyiz95+NnXSfgO5I8DphnplfW9tpX27H23frTk9+ZM8k8CcWjTEwInKhUm7y+jrs/skL/4k9OJT5ejdvyOqk4jIntTJYHACAYkvABIGGCLMPX1dOntvXLEndDOa/M0rbmhkDBnYmiSFI20ea5KjAkIVGE5LbwUDUAAGaXlgmHpgfRAADh4v4GSJckxEXyJi2TYdMuqdU4ub8BgGTI4jhgnplcWdtPX83LxMq4i0eZmBDoKNUmP6/js98c0PnLI57awMSYeDCpOJyJ7UyWBwDAP5LwASBhgi7D13XinNq3rDTUGnOCVlhwRJmkHFVVCBJFsomqAQAwszQNNJocRAMAAMDMiItEJy2TYdMq6dU4ub8BgGTI6jhgXplcWfs3/vLrnvtqbjgTK3//55pjLR5laiLfh959V8k2+enzek3AdzAxJlpMKp4sjIntTJYHAMC77PUyACDFTCzDd/BUv8aLtqEWmbNs0R0qtDQG2kehpTHSm+QgbY6iWj+Sz6kacGzXRrWtW6q66opJf6+rrlDbuqU6tmuj9m1dlckAEABMZWKgMSrOIFoQziAaAAAAEKegk2F73xwy3KLsGB0ras/h09r09HF1dvdOS8JzqnE+9NRz2nP4tEbHirG0k/sbAIhflscB88pZWTuI7c0NOvf9ocBx01KOv3JBT37+G9q/Y7XrsU/T47KmJvI9/YkPzNqmIH1ev6KMV+ddkEnFWeZMbL+vvlaLF1YZSZYPY58AAGQVmV4AkCAmluG7dPW6Bgb9zdYPW0drkzYsX+Rpm/IyS7/4Y++OLUnZT5ujrNaPdHCqBrzU/ohe2L1JX3jsQb2we5Nean9E7VtWMmEDQG6kbaDR1CAaAWoAAADELU2TYdPEqcbp9vXt6unTzgMnY0nE5/4GAOKX9XHAvApchGzNkkgSx7t6+vT6peHYikeZmhB4b131rH+POgFfYmJMVJhUDAAAkookfABIEFPL8M22n/GirfOXh/XqwKDOXx6OPCAwv7zMU4WFrR98p07/5ofV0doUW5Ky1zZHXa0f6ULVAAB5l8aBRhODaAAAAIBfJuJ5aZsMmyZpq8aZpvubuGPZABCGsMcBEY+gK2s3vq06cF/NLWdiZRzFo8KeEGiiz+sHE2OiwaRiAACQVGbWewIAGGFqGb6p+3Fmhh861T8p8a2uukLbmhv06JolkSW5zy8v076tq9S2fpm6TpzTwRnatL25QYUI2zSXNLYZAJBe40VbA4MjGro2ppoF5aqvrczMpJ00DjQ6g2h+gvyFlkb6BgAAAPDFZDzP5GTYxQurAu0nS4JW42xbvyzy+4U03N8kKZYNIFxZjoHNJqxxQJjl57PZ0dqk/ovDnibnOStrm+iruXXwVL92b15x6/k4xaOiUmhpVGd3r//tS0wIjPJ1nCqPE2OiPIebmGDxpye/o3/zk++lkB4AADCOuzMASBBnGb4gAYK66grV11ZKurEc8t4jZ2YdVLl09bo6u3vV2d2rQkujOlqbIrvxdCos7N68IjVB1jS2GQCQHnlINEjrQGOQQTSEI48D9QAAIJlM90vCiOelcTJsGpioxtm+ZaWh1riX1PubJMeyAZjlJgbW+LbqTN73mx4HTKMkx3SCxGedlbVLXcsmmngti7KPFffEyjAnBMbZV83TxJg4xjFMTLB4a2RMq594Rh9f/a5MjLUAAIDkyE9PEABSwFmGL0gFAGcZvtGxonYeOOl6MKWrp0/9F4e1f8fqSAcvoq6wYEIa2wwASK48JRqkdaAxyCAazMrDZBUkX5ITBgBgqqnnrLfXLND3h65xDjMgjH5JWPG8tE6GTTIT1TinVqKNShLvb9ISywYQjJcY2PzyMo2OFW/9LSv3/SbHAdMmyTEdU/FZvytrR93HintiZVgTAuPqq6Z9YoxbcY5jmPrMvjUylpmxFgAAkBxETAEgYUwtw7f3yBlPwQtJOv7KBe09ckb7tq7yfXwAAOBe3hIN0jzQ6HcQDWZ4GeT55APv0q9s+CGNjhdJLoRRSU4YAICpZjtnWZLsCY/jHOZdmMknfuN5v/Tfv6InP/b+Wfs9aZ0Mm2QmqnHGWYk2afc3xLKB7PMaA5uYgC9lq0iFqXHAtEh6AZIw4rNeV9Y20VfzIu6JlWFNCIz6dXSkdWKMF3GPY4TxmU37WAsAAEgOkvABIGFMLMPnDLT60dXTp7b1yxh4BQAgAnlMNEj7QKPXQTQE53WQ57MvfkefffE7t34muRBBJT1hAAAmmuucZU/5mXOYN2EmnwSJ5z33D29q7ZPPqq6qQtvun97vSfNk2KQyVY0z7kq0Sbi/IZYN5IOfGNhs0p44aWIcMC3iTtx1I8z4rNuVtU301dxKysTKMCYEziuz9PCKeh186fWwmj2juOPVUYh7HCOsCRZpH2sBAADJkL67UgDIgY7WJm1YvsjTNhOX4fM7aOHoOnEu0PYAAGBuQRMNet8cMtyiaDgDjX4kaaDRGUS7r75WixdWkZAUoqAD9U5y4UNPPac9h09Pq6YHlOIkDLg9X3f19GnngZN8zgDEwus5ayrOYXMLknwyl6DxPEm6NDx7v8dvH/zW9jlILvLCVDXOuCvROuK8vyGWDWRfkBjYbNxeX5Mq6DhgWoTZdzIhSfHZoH01t5I2sdKZEPhS+yN6YfcmfeGxB/XC7k16qf0RtW9Z6ToWPDpW1J7Dp6NPwE9QvDosSfieOBNVwpDmsRYAAJAMJOEDQAI5y/C5DbgUWhpvVYUYL9o6dKo/0PEPnurXeHFqbTQAAGBSnhMN8jLQiOBMD9STXAivkp4wAAATmagwyzlsdmEmn5iI5810zIn9nqxMhk0KpxpnEEmpRBsnYtlAPphOwJ+437QmTgYZB0yLJCTuujlOoO0NxmeD9NW8SOrEyiATAoNORnYsXuitX5aXeHVSvidhfj/SPNYCAADil567NADIGWcZvmO7Nqpt3dJpgzp11RVqW7dUx3Zt1L6tq24F3gYGRwIvxXbp6nUNDI4E2gcA4Lbxoq3zl4f16sCgzl8eZnAYuU80yMNAI8wIY6Ce5EK4lYaEAQBwmJy4xjlsZmEmn5iI581kar+HybDmmKjGmbRKtHEglg1kXxgTzSZKc+Kk33FAv6KOUSclcXc2SYzP+umreZHViZUmJiNvWL5Iz/yrDcSrp0jS9yTMiSppHmsBAADxS8Y6lwCAWTnL8O3evEIDgyMaujammgXlqq+tnHGQZujamJHjmtoPAOSZkwhz6FT/pEHluuoKbWtu0KNrlmQy6I25mUw0WLywylCrouUMNLatX6auE+d0cIbvyfbmBhX4nuRWmAP1XT19alu/jM8WSjKRMNC+ZaWh1gBAaaYnrnEOm8xU8snuzStCjefNZGK/x5kMu/fIGVefmUJLozpamzKfXORXoaVRnd29/rdPaCXaKBHLBrIvrIlmjlLX17TwOg7oVRwx6rD7TiYkMT7rta/mRVYnVpqYjDyxz0u8erKkfU86WpvUf3E48KSLqdI+1gIAAOJFEn5MLMuaJ+k+SSslvVPSQknXJF2U9G1JJ23bNlpuyLKsCkk/JqlR0mJJVyR9V9JXbdt+zfCxlkr6oG48tzsknZd0TtKXbds2FmmJ8jkBcXOW4ZtLzQIzp3ZT+wGAPBodK5YMlF+6el2d3b3q7O4lqSGnSDS4LeyBRqRX2AP1JBeilDQkDACAI4yJa5zDJgs7+STsONzEfg/JReY41Tj9JH6ltRLteNE2et9GLBvIvrBjV1lKnHQ7DuhWnDHqpCXuziSp8Vm3fbVPfOhd+uMvv5b7iZVBE/A/fn+D9m1dNel3xKtvS9r3JMyJKlkYawEAAPEgKhUhy7IaJX1M0sOS1ku6s8TDxy3LekbS79m2/VcBj7tI0l5JPyPpbbM85suSPmXb9qGAx9ou6TFJa2d5yA8sy/pfkn7Dtu03AxwnsucEpE19baXqqisCBbfqqitUX1tpsFUAkB+jY0XtPHDSdSWOrp4+9V8czsXSpbiNRIPpTA80wnyCTNTCHvgguRClmEoYOH95WPPKrNR+D5EcaT+nI1xhTFzLUkKbCWEnn5iI55UyU7+H5CIz/FTjTGMl2rCqKBPLBrIvitgViZPTxR2jTlri7kySHp9101fL+8RKE5ORn/nG9zRetGfs/xKvTub3ZOpElT89+R29NRL8XJGlsRYAABAtehERsSzrTyR90sMm8yT9pKSftCzrqKQ227a/5+O4H5H0R5Lq53joj0r6UcuyuiT9stcq/JZl3SFpv6SfneOhb5P0K5I+ZlnWz9u2/ddejnPzWJE8JyCt5pVZ2tbcEGgp5O3NDQy2AYBPe4+c8bwU5vFXLmjvkTPTKq4gfdwmyJFogDDFscx4GMIe+CC5EKWYGuj/yO98SYMTBgLT9j1E/LJyTke4wkpOIqHttrCTT0zE80q5dPW6vv76JdUsKJ92n0JyUTBeq3GmrRJt2FWUiWUD2Rf2RDOJxMmZxBmjPnvhij7zJTN9mjDf27TEZ+fqq+V5YmUaVlxIuyR/T5zP/r/5yfdq9RPPBErEZ6wFAAAEwR1pdJbP8vvXJX1L0vd04/1YJukDkiZGKbdIet6yrA22bb/h9oCWZW2U9BeS5k/4tS3plKSzkuok/Yikuyf8vSDpTsuyftq27aLL48yT9L8kbZ7ypwuSvirpsqQfunks507vHZL+0rKsh23b7k7acwLSrtDSGGjgorBmicHWAEB+OElSfnT19Klt/TKSqFLKa4IciQYIQ5zLjIchioF6kgsxG1MD/YNTBgDT9j1EfLJ2Tke4wkpOIqHttiiST4LG8+byU7//5UltYSKPOVOrcWalEm1UVZSJZQPZFvZEMxInpzMRo258W7XnhO657mG8Cvu9zVp8NikTK6NcxS0NKy6kXRq+J/PLy/Tx1e9KdBsBAEC2EUWPx1cl/VdJn7dt+9tT/2hZ1r2SfkPSL0349XJJf2ZZ1oO2bdtzHcCyrAZJf67Jyep/K2mnbdvfmPC4BZJ+WdJTkipu/rpV0hOS/p3L5/P/aHIC/nVJj0n6L7Ztj0441kpJn5G09uavFkj6C8uyVtm2fT5hzwlItWWL7lChpdFXoKvQ0piqwSAASJKgAwxdJ86pfctKQ61BFIIkyJFoAJPiXmY8DGEP1EskF2J2UUwCScP3EPHI4jkd4QrjnEVC22RRJJ8Eied5xUSecGStEm1UVZSJZQPpceHKNQ2MDHo+t4U50YzEyemC9iV+9U9Oqf/SsKeVuLzew7gRxXtLfNacOFZxC3u1KtyQhu9JGtoIAACyi6hqdGxJfyXpQ7ZtN9u2/XszJeBLkm3br9u2/cuS/sWUP62T9DMuj7dX0l0Tfv6ypIcnJqvfPNY127Z/V9Inpmz/mGVZc/Y0LctaJunXp/z64zef3+jEX9q2/bKkH5f0woRfv11Sx1zHuSmS5wRkRUdrkzYsX+Rpmw3LF6mjtSmkFgFAto0XbR061R9oHwdP9Wu8OOd8SySEM7jkdmCrq6dPOw+c1OjYjcWZnEQDP0g0wFRBEmSSzO93xA2SC1GKk2wZtjR8DxG9rJ7TEZ4wzlkktE0XtF/iJrHDTzwvqKn3KQjOqUR7X32tFi+sSuV3KWgV5d43hzxtQyw7+caLts5fHtarA4M6f3mY+FVOFfb36OFPPa+1Tz6r+594Ro8ffdnV9z1IDGzONpE4OYmJGPXXv/vWtMmdzgS+h556TnsOn57Wb/BzDzOXKN5b4rPuzXYdGB0ras/h09r09HF1dvd6/uwE4UxGDoL44NzS8D1JQxsBAEB2kYQfnY/btr3Ftu2TbjewbfsPJB2a8ut/PNd2lmW9R9LPT/jVqKRfsG17pMSx/kLSH0/41QK5S47v0O1q85L0R7Zt/2WJ4wxL+oWbbXL805vJ/LOK+DkBmTC/vEz7d6x2fcNZaGmkah4ABDAwOBK48uSlq9c1MDhr9wYJYyJBjkQDmBB1gkyUwhyoJ7kQcwlzEshESf8eIlpZPqcjXKbPWSS0TRdFYocTz2t9/2Jfx/GLiTyYysRKf14Qy06usxeu6PGjL+v+J57R2ief9ZWAjXQaHSvqd7/4rVn/7jWxtqO1SQ++526jbSRxcjoTMeq5TJ3AF+QeZjZRvrc71i5Rw11VnrbJU3y21HXgNz93Ro9+psd3kZqgTExGJj7oThrGMdLQRgAAkE1EpyJi2/ZrPjf9/Sk/P+Rim5+TNG/Cz39u2/bsUZLb/sOUnz9hWdas034ty6qStH2OfUxj2/Yrkv5iwq/KdaPNpUTynICsmV9epn1bV+nYro1qW7d0WjWAuuoKta1bqmO7Nmrf1lUMWgBAAEPXxhK1H4TLVIIciQYwIeoEmaiFVRGW5ELMJcxJIFMl/XuI6GT9nI7wmDxnkdA2uygSO+aXl+k//VyzPvqBd3ptXiBM5IEjrpX+iGUnS5yVjRE/Z/XHo1877+rxbhJr55eX6TM//yG9955aI20kcXJmUcWWJ07gM52AH9V765znPvzbX1L/xWHX2+UlPuvmOvBHX35NL772A0/7NT35M4rVqsKSplVm0jCOkYY2AgCAbCqPuwGY01en/FxlWVadbduXSmyzdcrP/83NgWzb/oZlWT2SWm7+qkbST0j63CybfFhS9YSfX7Bt+5tujnWzTZ+Y8PPHJD1R4vFRPScgk5beXaP2LSu1e/MKDQyOaOjamGoWlKu+tpLZ/QBgSM0CM11rU/tBuEwkyLVvWSnpdqJB2/pl6jpxTgdP9U8a1KirrtD25gYV1iwhGQrTmEqQ2b15RWL7hc4Ayt4jZ4wN7JJcCLc6WpvUf3HY88onXiX9e4ho5OGcjnCZOGeR0Faa135JoaVRHa1NvhI7nvr4B3R5+Hro16CJJt6nIL9MrvS3eKG3ysISsewkcBKw3Z5/unr61H9xmES2DHFWf7zHw1fYSazdt3XVrI+ZX16mPyg0a9PTxwO1L8j1NeuijC139fTpF39saeB7mImiem+9nuckqeGuKv3Xn/+QlhuaSJJkfl4fL7p6+tS2fpmR2JwzGdlPzDCu+KBT4OfQDOMA25ob9GhCxwHSMI6RhjYCAIDsIcMn+Waarj5/tgdblnWPpA9M2f5vPRzvOd1OWJekj2j2hPWfnGFbt76kG21zPoM/YlnWO2zb/t7UB0b8nIBMm1dm+Rr4AADMrb62UnXVFYEGquuqK1Rfy6I9SRdWghyJBvAj7gSZqMw1gOIFyYXwIoxJIDNJw/cQ4cvLOR3hCXrOIqHNnagSO6K6Bk3ERB5IyVnpj1h2fJwEbC/cJGAjHYKu/jhXYm2QhNn33lOrP3z0fhInSzARo/ai80tnjRzrkw+8S7/04A9F9t76Oc/1XxzWH7/wWi7Oc35eH69MTv70Mxk5jvjg6FixZN/eWWWms7s30fdmaRjHSEMbAQBAdpCEn3z3Tfl5TNKbJR7/vik/f822bS9ryH55ys+l7jymHusFtwexbXvIsqzTkn5kyrGmJeHPcJwwnxMAAIAv88osbWtuUGd3r+99bG9uIACYAmEnyJFoAC+SkiATlZkGUOaXl+nTz53Vn7wYbjVa5NdcyZa1leUaHAn+HUrL9xDhyds5HeGY65xlSbInPJ5KgP5Fkdgx9f38s5e+o8vD4X3HmcgDiZX+8i7sBGwkz3jRnnQd+x8nzgXa31yJteNFW7+8YZle+d6gvvLaRdf7ffA9d+szP/8h7ufnYCJG7cXR0+eN7Oefrlsa2bmD81xpQV4fL0xO/oxytSq//Kwy8+2BK/qN1ibdVVORyOTxNIxjpKGNAAAg/YiAJd/2KT+ftG27WOLxU6Mar3o83rfn2N9EKwwca2IS/kpJz87wuCifEwAAgG+FlsZAAxyFNUsMtgZhIUEOSZLXBJmpAyi/9bFV2vkgywwjXLMlW44Xba37D8cC7z9t30OYl9dzOsIx2znr7TUL9P2ha1QCNCiKxI6p7+c/vDGovz7zhj5/+g1dGjZb6Zb7FLDSX74FTbw0WdkY4XISbQ/NMGEviNkSa2c7nhtMqPcmaIzaCxMT0qVo72E4z5UW1QpMpid/RrValV9+Vhc40fsDbf7dL0m60f5tzQ16lPgmAABA4jAik2CWZd0h6Z9O+fXhOTabWjnf613S1PIGb7cs6y7btieVIrAs622S3hbwWFMf/55ZHhfJcwIAAAgqyHLKhZZGgqcpQYIckoQEmdtYZhhRmZpsOV60+R7CCM7pCMNMCeJUAkwv5/1cvLBKG3+4Xk/89Kpb/Z6ha2P6qd+fuiisd9yngJX+8mu8aOvQqf5A+zBZ2RjhGB0rlqwWbc/4W/emJtbOdbzZuE2YnVrJnxhAsBi1H0FXh4vyHobzXGkmXh8vwpj8mcT4oInVBS5dva7O7l51dvcyMQkAACBhiKYm25OS7pnw8yVJn5ljm7opPw94OaBt21csyxqRNPFOd6GkqQnrU49z1bbtIS/HmqFtC2d53NRjhfWcPLEsq17SIo+b/dDEH65cuaK33norSDOQUENDQyV/BgBk12MbG3X5rbd00sNyyqvffZce29hIvyAlqmTrPXfNCzS4VFtZriqN6q23zFatjAt9n3j93I8s0p+fet339tt+ZJGGrgwabFH8aiypplKSrmvoSja+Z0g2vocwhc9S8tHvgSlF29b3h0Y1PDqmqvnlenvNfJVZ3hODnH5PcUEZ9ykwZtuqt+mvXjrrf/v3v50YRwpduHJNlfao7gkyV8se1WtvvKlFdyww1i6Yc33cVsfnvq6Tr1109T7fXWmX/Hk23794STXWdc/Hk6Smd96pf/PhH9Y7FlbevC6Oz3g+6b94VUe/dl7PvPy9Sde+2spyPbLyHWr9wDt1b11+Jx76iVH79ZH3vV2f//obvreP8h6G81xpRl4fD6yxEb31VtCpP7NLSnzw4Ilv654qc8/zi187p8tvvaW9H32fKuZlbzIIEBfiPQCQbleuXInt2JZth9ephX+WZW2V9OdTfv0vbNv+gzm2+3NJWyf86ldt2/49j8f+gaS7Jvzq/bZtn57ymPdL+vsJv/q+bdt3ezzOr0n6nQm/+nPbtrfN8LhInpNXlmX9pqSOIPv43d/9XTU2NgbZBQAAAAAAAAAAAAAAAAAAAJA7fX19+rVf+7WJv3qfbdtnojg26xMlkGVZH5B0YMqv/0bSH7rY/I4pP4/4aMLwHPuM8jhRHwsAAAAAAAAAAAAAAAAAAAAAZkUSfsJYltUo6a80OUn8nKRHbX/LFmRtm6iPBQAAAAAAAAAAAAAAAAAAAAC3lMfdANxmWVa9pGck3Tvh129IesS27Qsud3Nlys9VPpoydZup+4zyOFEfy4s/kPRnHrf5IUl/6fzwwAMPaMWKFQaagqQZGhrSiy++eOvnBx54QDU1NTG2CAAAmHZ93FbH576uk69ddL3N6nffpb0ffZ8q5lkhtix6cfd90vZe9F+8qqNfO69nXv6eBkfGbv2+trJcj6x8h1o/8E7dW+f9tuf6uK0/fO5VHf3a+Tkfu+X9i/UrG+/L3GcxKmG9h0g/vocwhc9ScsXd70E6/e4Xv+Xq+zzVlvcv1q/9+Hs8bZO2vjGiU7RtfX9oVMOjY6qaX66318xXmeXuPX/90rCO/v139Tcz9H9/YuU7tIX+b2Z8+vi39eenXve9/bbme/XLG35o2u/p28SraNv6+KdfmPT9dePuSls731u89fP+b5bpzZHS78t/+8UPafHCSl/Hm6i2slx/9s/W3jpP9V+8qn/yRyd976+UvH/mTPVT0vI9D3qemz/P0uj47fp/WYsFBX193PDTx02rC1euqbC/J7T9Tz1XmkLsE3lEvAcA0u0b3/hGbMcmCT8hLMt6m6QvSFo+4ddvSnrYtu1vedgVSfjBjuWabdsDkga8bGNNufm54447dOeddwZtClKgpqaG9xoAgAz6VGGt9h45o66evjkfW2hpVEdrk+aXZ39Bsqj7PnsOn9bRb1yS5H6w4eg3LmnhnX3at3VVaO2aanSsOMPn5Xab3xge17f+9rv6g7/9ru/PS/vW+1VYP6SuE+d08FS/Ll29futvddUV2t7coMKaJVp6N8FjP6J4D5F+fA9hCp+ldCDmg7mcvXBFn+l5Q176qo7P9Lyhwvr3ev6ec59yw3jR1sDgiIaujalmQbnqays1ryx/CZZnL1xRV0+fDs1wLdnW3KBHXVxL7rzzTq1ofIce+0e8plm3fc179Ad/+13/269drjvvnPx5Gh0r6lcPnNTxVy7IzbnwMz1v6FsXx7V/x+pMnpvicP7ysL51cVx+rkUTvTli6Y3h2fdRaGnUisZ3GDneG8PjGtZ8Lb7zxhDvoef7Sx47iLx/5v5N64/oWxfHb35H3dmwfJH+TeuPTHu90nAPE/Q8d0N2Y0FmXp/ZzfbZyaqaO2yNWPMnfRdMmnquDIrYJ3Ab8R4ASJc77rgjtmOThJ8AlmUtlPQ3kiZmf1zUjQr4Zzzu7vKUnxd5bMsdmp6wfsnFcaoty6qxbXvIw+HqXRxnpmOF9ZwAAAAAT+aXl2nf1lVqW78s0YNLWeYklPjR1dOntvXLInlvRseK2nkr6WBuXT196r847GsAeOndNWrfslK7N68gQcagKN9DpB/fQ5jCZwlIP7991Vvbnzin9i0rPW2T9/sUE0nnWTBzEtVtl65eV2d3rzq7e0smUc04mWEh16CsWrboDhVaGn2duwotjTN+t/YeOeMpuVeSjr9yQXuPnIl04nyWDV3zX5HerQ3LF6mjtcno8Zz9jBdtHTrVb2Sfs3HzmSs1uWvq395es0DfH7qWiv77/PIy7d+x2tgEvqTfwwQ5z83FbywoSRMHw3x98pi0Pa/M0rbmBnV294Z2DFPnXGKfAAAA/pCEHzPLsmol/R9J90/49VuSftK27b/zscupVfOXeNx+6uN/YNv2tHVrbdv+vmVZFyXdNeHXjZK8rOsw9VizVfyP5DkBAAAAfiV9cCnL4khq8iOOpIN5ZZYWL2RZYFNIHIEffA9hCp8lIJ1MJA0ePNWv3ZtX+LqvyNt9iqmk8ywwkUTFZIb86mhtUv/FYc9VsZ0E7InSMnE+62oWhJsSMPWcaup4zn4GBkdCqyI90WyfuVLnw4dXvEOS9IVvfG/S3yxJ9oR9JP3cGcYEviTfw/g5z7nlJRaU1Gutn9fnQ+++S++7d6EOf/X1XE3+dKPQ0hhqEr6pcy6xTwAAAH9Iwo+RZVk1kv63pDUTfn1F0kds237R526nJsHf53H7ZVN+fnmOY/3olGN5ScKfeqzZto3yOQEAAAC+JXlwKYviTmpyi6SD9OM9BAAAfphIGrx09boGBkcC3Wfk4T6Fyp2TBUmi6mhtYjJDzpmsip2WifNZV19bqbrqCqOJ7KUSa00cr666QvW1lZKiqeTvmPiZczO56+BLM8el7Bkem4ZzZ14m8Hk9z3k1Vywo6RMHg1wH2v/RyllXhTh/eThznyU3wlxdoLay/Na5MghinwAAAP4l784uJyzLqpJ0VNK6Cb++Kukf2bb95QC7/vqUn99vWVa1h+1/bI79lfrbWrcHuTkB4f0ujxXlcwIAAACQEiaTmsJkIukA8eI9BAAAfphKGowy+TCtgiSdZ03QJKpHP9Pjevuunj7tPHBSo2NFX8dDcjlVsY/t2qi2dUtVV10x6e911RVqW7dUx3Zt1L6tq2ZMDjU1cX68ODWdGV7NK7O0rbkh8H66drboC489qBd2b9JL7Y+ofcvKGZMuTRxve3PDrUTdsCv5T+R85pzJXWElaCf93OlM4LuvvlaLF1ZlMml6rvPcgoBJ77PFgrx+tuL6vPi9DjifnTLL0me+1KsHfusLWvvks3r4U89r7ZPP6v4nntHjR19W75tDkT6fuHW0NmnD8kXG9zs4Mqbf+Muv3/p8jBdtnb88rFcHBnX+8rDrayixTwAAAP+ohB8Dy7IqJX1O0sYJvx6R9FHbtp8Psm/bts9blvU13U5wL9eNRP+/cbmLjVN+/nyJx/4fSb9UYttS1mvy5++rtm1/b6YHRvycACCVxot2pquSAAAwkzQkNaWlWj9mx3sIAAD8MpU0GGXyYRpRuXOyoElUL772A0+PdyYz7Nu6KtBxkUxBqmInZTUQ3FBoaVRnd2+gfSy6Y4HuvLM2kuMV1iy59f9hVPKfjfOZ+71nX/U8ucsLzp3JMdN5rrJinrb8breuBUh8ny0WFGTioNvPi8kxQ6/XgaRX+Y9LmKsvdPX06ZXvDarpnQv1F3/3+qRzZV11hbY1N+jRGVYtcRD7BAAACIbIbcQsy5ov6c8lPTzh19ck/bRt2180dJjDmlxl/hflImHdsqz3SmqZ8KuhObb7a0nDkpzI11rLst5r2/Y3XbTxF6b8fHiOx0f1nAAgVZxBxkOn+j0HVQAA8GvqQE7VtEW2o5GGpKYwkg6YfBctU+/h11+/pJoF5bxnAADkiImkwbrqCtXXVnreLk99xj947tVA23edOKf2LSsNtSZeJpKo/MjiZAZM5lQ29iINE+eTIopz9rJFd6jQ0hgo+bNou4//BDleoaVx0vnEqawfdBKBW//wxluhVMCfinNnskw8z52/PKxLw+YnEYU9cTDMMUM31wGnyr/bSQZdPX3qvzis/TtW5yYRf9/WVWpbv0xdJ87p4JT3KYivvHZRX3nt4rTfu5n0wKQ5AACAYEjCj5BlWeWS/lTSRyb8+rqk7bZt/7XBQ3VJapc07+bPH7Ms6z22bX9rju3+7ZSf/9S27ZHZHmzb9lXLsg5K+sdT9vGLpQ5iWdZySVsn/GpM0p/M0bZInhMAhMlkIJ1KEjfkaUAZAJJgtoGc99w1T//yvdG3J86kJrdMJh0w+S4ept7Dn/r9L9/6f94zAADywUTS4PbmBk+xjjz1GUfHiur43Nd18KXXA+0nS5U7TSRR+ZWlyQwwIw0T5+MW9Tm7o7VJ/ReHfVd4//inX9CDTY2u2+XneBuWL1JHa9O035uo5O/W//n6jIu3hyLscydjKP6ENYko6OSO2T4vSRkzjKLKfxZMXV3g0tVR7T3ysk6c9bYakVezTXpg0hzmwrUEAIDSshu1SBjLsubpRiL5T0349Zikn7Ft+6jJY9m2/S3Lsv5Y0j+5+av5kv7Isqwfny0B3bKsn9Lk6vSjkva6ONxvSvpZSRU3f/4Fy7IO27b9uVmOUynpv91sk6PTtu1vlzpIxM8JSDVugpLHdCCdShL5GlAGgCSYayBncGRygP36eDSV8eNIavLKVLLA7x/7tg5/debkorxMvotLGAkfvGcAAORH0KTBwpolrh4XdvJV0mKOXuNjpWSpcmecyU9ZmswAM9IwcT4ucSXMzi8v0/4dq0seu5TBkTFP7fJ6vFL7NFHJ342FVeX6P19/I9RjTBTWuZMxlGDCmERkYrWamT4vSRkzDLvKfxY5qwssXlilA/+kxfe52YuZJj0waQ6z4VoCAIA79IKi818lfWLK7/6dpK9alvVuj/t6w0U19w7dqDZ/182ff1TSFyzLarNt+5vOgyzLWiDplyQ9PWX7p23bPjdXQ2zbPmtZ1u9I2jXh1wcty3pM0n+xbXt0wrFWSPrMzbY4vi/3ifGRPCcgrbgJSp6wAul5riSRlGoeAJAnfpJbOj73dX2qsDaSc3BUSU1+mUg6KC+zZk3AnyqLk+/iZuI9LIX3DACSIWkJxsiOIEmDhZZGV/G8MJOvkhpz9BMfKyUrlTvjTH7K0mQGmJGGifNxiDthdn55mfZtXaW29cvUdeKcDk45v7vltl1zHa+uukLbmxtUcHE9CVrJ343N71usz37lO6HtfyrT507GUMwIYxKRidVqZvq8JGXMMKwq/1kz233n/PIy/fufep+O/P139dZIuP3SqZMemDSHqbiWAADgDUn40dkxw+/+481/Xj0k6blSD7Btu9+yrI9J+mvdrjr/Y5JetizrJUlnJS2U1Cxp0ZTNj0r6vz205/8vqUnSR27+XCHpP0n6vy3LOiVpUNKym8eaGCkblbTVtu3zbg4S8XMCUoOboGQKK5Ce50oScQ9OAEBe+RnIOfnaxcgmf0WR1BSEiaSDsaK3lQWyMvkuKUy8h3PhPQOA+CQ1wRjZ4idpcMPyRepobXL12DCSr5IccwwSH5tNVip3hj2BdC5ZmcwAc5I+cT4OYSbMeplUuPTuGrVvWandm1doYHBE+/7qGzr6NVdDtp7bNdPx/Ex8DFrJ340Pv++eSJPwJXPnTsZQzAljEpGp93nifpIyZhhWlf8scXPfWVlRFnoCvmPipAcmzWEiriUAAHjHFTDDbNt+Tjcqx0/sHVmSVutGVf4Pa3qy+mcl/axt2+MejjN+c3//a8qf6iX9pKSPS7pfkxPwByT9lG3bX3J7nJvHek4RPCcgLZybILcBlq6ePu08cFKjY8WQW4YggfRSTFSSSKuwXlMAwOyCDuT0vjlkuEUz62ht0oblU28DSvOS1BRUoaUxkuNMFOXrnwdRvIde37Pxoq3zl4f16sCgzl8e1rjHyRoAkHejY0XtOXxam54+rs7u3mkJq06C8UNPPac9h08TS0EgTtKg2z5FoaXRdRJDGH32pMccTSdeZqlyp5NEFZesTGaAOc7EeT+imDgftbDiLGcvXNHjR1/W/U88o7VPPquHP/W81j75rO5/4hk9fvTlkvea88osDY+Oe07Ad9Ou2Y63eGGV7quv1eKFVZ6TNp3K+sd2bVTbuqWqq66Y9Pe66gq9955aT/t0FFoa9cM+tw3C1LmTMRSzgsaCpk4iMvU+T9xPUsYMTVb5zxov9537/uobkbXrz176jvovXr0VV/zkA+8KtL8sTprLK64lAAB4RxJ+xtm2/b8lvU/SpyVdLPHQE5K227b9c7Zte87UsG37im3bP6sbCfcnSjz0B5L+UNL7bNv+P16Pc/NYkTwnIA24CUqmsALppipJpDFBKy1JoACQNUkZyJlLmElNJgRJOggizZPvkiaq99DNexYkuQIAcEPSE4yRTW6SBtvWLdWxXRu1b+sq133VMPrsSY45moiPTZW1yp1x3HtIZiYzMNE0m5I+cT5Kps/ZpiYVpiX+M5FTWf+l9kf0wu5N+sJjD+qF3Zv0Uvsj+ty/XOf7M+esKBIVUxPBGEMxz/QkIhOfrYmflySNGYZR5T8LvN53+p0M5cfl4TGt+w/HbsUVt3/6hUATmLI2aS6vuJYAAOAPSfgRsW3bMvjvOY/HHrBt+1ck3SNpk6RflLRb0q9J2iZpmW3ba23bPmTgeR60bXutpGWStt88xu6bx9wkabFt2//ctm1vEfzpx4nsOQFJxU1QcoUVsM5zJYk0DgIAQNolaSDHjbCSmkzxk3RQHjARKOzXP28JMn7eQ69KvWdUbAYAc5KcYIzsK5U02L5lpacEljD67EmPOZqIj02VtcqdcU0CDjKZgYmm2Zb0ifNRMX3ONjWpMG3xn6lmqqwf5DMX9YoipiaCMYYSDpOTiEx8tiZ+XpI0ZhhGlf8s8HPfGZdLV6/rm28Met4uq5Pm8oprCQAA/mSrF4uSbNselXQsomP1SuqN4DiRPScgaUzcBLVvWWmoNXCYCljv3rxiWuA1r5UkwnxNAQCzMzmQs3hhlaFWzc1Jatq9eYUGBkc0dG1MNQvKVV9bGet1wBkA3nvkjKt+3NYPvlOH/+67gY4Z1uvvJGYdOtU/6TNSV12hbc0NenTNkkxWP/L6Hvox23vmJFe4Hbjr6ulT/8XhTCauID7jRTtR51XAr6AJxo/eX2+4RcgrJ2kwiDD67EmPOZqOa2W1cmdHa5P6Lw57Svz60Lvv0ldeK7X4cGl+JjOMjhVL9q+diaad3b0qtDSqo7WJ/m1KORPn29YvU9eJczo4w/3k9uYGFQzdTyax72r6nB1kUuG+ratCa1dSBPnMFVoa1dkd+jD3jWMZmAjGGEp4vMaC5rpWBf1sTfy8JGnM0KnyH+RcYmpViKQIct+ZFvTNsoVrCQAA/pGEDwApxE1QcoUZsM5rJYmsDgIAQNIlaSDHDxNJTaZ5GQAeLxYDJ+FLZl9/EmTmfg9NmOk9M5VcAfiR14k3yK6giRBH//67Wm6oLUBQpvvsUccc/STJmoxrZblyp9+kQb8TTv1MZmCiaT6FPXE+yX1Xk+fsoJMK29Yvu/U6pD3+Mxc/nzlnRZGwE2hNTQRjDCVcJicRBflsTf28JGnM0KnyH2SCgalVIZIiCwn4772nVm+8NRLqpDkkB9cSAAD8S1cWHgBAEjdBSRZmwDqvlSSyPggAAEmVpIGcrHEzAHz+8rCRY5l6/UmQmWym93Do2ph+6ve/HHjfU98zk8kVyJ4wK3wy8QZZZCLB+G9e/p6Wv9dQg4CATPfZo4o5BkmSNREfk6Sfe6BRv/nRdF673F7//SQN+qmg73cyAxNN8830xPk09F1NnrM/86VgVdonrlqSl/iP18+cn/OhFyYngjGGEg1Tk4hMXWuTNmZossp/2pm470yCb74xqC88tkE1C+YlamUZhINrCQAA/iX7bhgAMCNugpIrzIB1XitJ5GUQAACSJmkDOVlUagA4aa8/CTIzm/gejhftUN6zoJWzJiZXIDvCrvDJxBtklYkE48ERYilxCXPiUVqZ7jOGHXM0kSRrIj728fsb9FsfS18f1e/130vSoN8K+l6v/0w0hUlp6buaOme/vWaB0VVLkhZ/SAqv50MvTE8EYQwlWkEnEZm61iZtzNBklf+0M3HfmRT/88W+UOOK3OMlB9cSAAD8Y1QMAFKIm6DkcgLWQZQKWBdaGgPtO42VJMJ+TQEAM3MGcoJI4+SvpEjS6x80Qab3zaHAbUiDMN4zE5WzDp7q13jRDrQPJMfoWFF7Dp/WpqePq7O7d9qgrpO8+NBTz2nP4dMaHSv6Ok6QiTdIp/GirfOXh/XqwKDOXx7O7HmDYgTpdPbCFT1+9GXd/8QzWvvks3r4U89r7ZPP6v4nntHjR1/OTV9jJqb7H2HGHJ0kWbf9yq6ePu08cHLGa1nQ+Ng/f+i+QNtHzdT130kavK++VosXVs16r+BU0D+2a6Pa1i2dFperq65Q27qlOrZro/ZtXeUridTERFPAkZa+q6lz9veHrhlbtcRku7IY/3FzPvz4/Q36+P0N0/429dUwce6cDWMo6WPqWpu0McOO1iZtWL7I0zYmV4VIClP3nVvev9jIfoIIK67IPV7ycC0BAMA/si8BIIWoTJJcYVeeyGMliaRV8wCAPGEZ4Xgl5fU3XYk9yxWOgr5nP/tAo85fHr712owXbWPJFUGqtCEZoqrwSWXafAl7VYWkoRhBupiomp4HJvuMYcYcTa6slKf4WJwVvr1U0PfC1ERTp4o38i1tfVcT5+wwVi1JSvwhqdycD6fGOt5es0DfH7oWSeyDMZT0CnqtTVqfKKoVdZLO1H3nnn+0Qv/6J35YXSfO6eAM9+xbP3ivznz3Lb342g+MHG8mpuOK3OMlF9cSAAD8Y9QBAFKIm6BkCztg3dHapP6Lw54GLtNeSYJBAACIR9IGcvImCa+/yQSZc98fynyiZ5D37L331Gr7p7886bWprTQTtqHyczaYTF4sxfTEGyRTXge+TSQY3zg3j5trFGYUZ+Jx2pjsM4YVcwwjSTYv8bGorv+lOBX0Z+Jngu3A4AgTTWFM2vquJs7Z5y8PG2nLxCTRJMQf0qDU+XCmv0V5jnrwPXcbLQqQpYIJaVDqszWXpPWJnCr/beuXzZo8vr25QYUMxAFnY3Ji67wyq+REjbnu7U0wFVfkHi/5GI8HAMAfeioAkFJxLTGYlyXqJ/L6nJ2AtR9uAtZOJQm3xyi0NKY+QBH2awoAmJ2fZYRXv/uuQAM5eexvzCbuZZxNJcjs+rO/16anj6uzu3fa/pxEz4eeek57Dp/W6Fgx0PHi5uc9k6RvvjE47bUZHDEzyEXl5/QLmrzodhlvUxNv8nzeTgNn4NvtZ6qrp087D5xM/flZup1gHMRPrHyHodaglCCJx3lkss8YRszRRJLsVHmIj0V1/ffj7IUrevzoy7r/iWe09sln9fCnntfaJ5/V/U88o8ePvlzy2GFU8UY+pbXvGvSc7SR3BjHTqiVxxx/gz+hYUXsOn9bP/7ev+N6HUxTA6/kcyZDUPpFT5f+l9kf0wu5N+sJjD+qF3Zv0Uvsjat+yMtPjdibuO6dObHUmatxXX6vFC6tu/c2Z9HBs10a1rVsa+PowE1NxRe7xko/xeAAA/ElPtBEAMEnUN0FBBlbSKshzDjtgPVdQpa66Qm3rlurYro3at3VVqgYYZ8MgAADEw+tAjiTt/ej7fF178tjfmEvcA2mmElsOf/V1V4/LQqKnn+9MmGZKrkD6hJG8OBOTlWmRXHkf+A56ft7ygXcaaglmk+TE46Qy2Wc0HXMMM0k26/GxqK7/XjjJnkEm2JpK5GKiKdLadw16zg4judNEuxA9r5NrZzNTUYCsFUzIuiT3iWZLHs+6qIvpzTTpofvfPqS6KvOTtvzgHi89GI8HAMA7IlQAkGJRLDGYxyXqTTxnJ2DtdglAv6+dE1SZbRnCLInqNQUATDfXMsK1leWSxm/9XDHP2zUoj/0NL+JcxjmOxBYn0XPf1lW3fjdetFPV13Hznt1zZ6W++cZg6G2ZKbkC6WIqeXH35hVzfhaoTJt9QQe+29YvS31lMyfB2M/rUGhp1L11VXolhHbhNhOJx+1bVhpqTXqY7DOajDmaTJJdvLBqxr9nMT4W5fXfLSfZ0+3noqunT/0Xh6cl6TpVvIN8LphoCindfdeg5+xCS6M6u3t9H3+25M6p7Tp+pk8T4z21leXacn9jKPEHeOdncq0fs53PMV3c8ass9onSKuh9p99zrDPpwbHt/oZA1wtTcUXu8dKD8XgAALwjCR8AUizsmyBTAytpYvI5R5kwNzWoUkrcQcAg4kxCBADMPpBTpVEdf+45X/vMY3/DrzgG0kwkyPjhJHratq2unj4dmuGav625QY8m/Jo/23s2dG1MD3/q+Uja4LVyVhBp7mcmWRTJiw4q02YfA983BEkwHrl6JcSWIYmJx2ljos9oMuYYZZKsl/hY0kV5/XcryEoqEyfYOlW8k5AQZhr94Whloe/q95wddnKn065/uf7eSfGeP/tna1W3cKHnY8K8IJNr/ZjpfI7bnPcjKfGrLPWJ0iyKYnpzCWvSlhfc46UP4/EAAHjDiBgApFyYN0GmBlbSJIznnJTKE0kLAgaRlNcUAPJq6kDOW2/5Tw7JY38jqCgH0kwkyPj1K//jpVkrxadtdYSp79njR1+O5LhBKmd5kaV+ZhJFmbxIZdpsY+D7tiAJxiMRtC/Pkph4nFZB+4ymYo5ZSJKNg6nr/1vD12/tL0jszPRKKklICDOJ/nA83l6zQHdWluutEf/fl6T0Xf2cs6NI7iyzrJI/Iz5RJuBPPGYWVsYyidU9UUoSKorHVZF/Iu7x0ovxeAAA3MlX1BIAMsz0TVAel6gP+znHVXkiy0FAqnkAQLrlsb+RRkETZPyaLQF/qrStjmAiCdYN05WzZpLlfmaSRJm8mOXKtGDgeyoquyVTlBOP4E7QmKOJCV4Lq8o1XrT16sBgbpI+TF3/P/7pFyYlKPtNCje9kkoSEsJMoD8cj4mTHoIk4Evp7rsmIbkT8YgqrjCTrKyMZQKre0YnzSvNJOG+M+6K/NzjpR/j8QAAlEYSPgBkjKmboDwuUZ/F50wQEADSLc0DDG5k8dqbRUESZKKSptURTCTBziWK5Ar6mdGJujp91irT4jYGvmdGZbdkoWp6cvmNOZqY4DVyvah1/+HYrZ/zUF3cxPVf0rQEZT9J4WGtpBJ3QlhQ9IejN9ekBz/S3ndNQnJn1iUxNhdFXGE2WVkZywRW9wxfllaaifO+M+5JW9zjAQCArKOXAgCYJo9L1Gf1ORMEBIB0ytIAw2yyeu3NKj8JMlFLy+oIppJXayvLNTilqmmUyRX0M6MTdXX6rFSmxXQMfJdGZbdkiHriEaIRdILXtbHipJ/zUF3cxPV/Lm6TwsNaSSXuhLCg6A9Hy+ukBzey1HdlUqF5SY7NxTkpNksrYwXB6p7hyvJKM3Hdd8Y5aYt7PAAAkHXp6IkCACJlcmAlLbL4nIMGAXvfHDLcIgDAXEbHitpz+LQ2PX1cnd29065NzgDDQ089pz2HT2t0SjJKmmTx2ptlToJMoaXR1eO3fvCdIbdoZl0nzsVyXC9MJa9+/tfX64Xdm/SFxx7UC7s36aX2R9S+ZWUkg7j0M6Pn9rs36/YeK3x2tDZpw/JFnrZJUmVazMwZ+A6CgW+EzUk8DsLLxCO3xou2zl8e1qsDgzp/eVjjRdvo/rPOmeAVhq6ePu08cDLV90azCes1m8hJCi/FVLLnU3/9D9P6gU5C2LFdG9W2bum061RddYXa1i3VsV0btW/rqsQk2NEfjp6fSQ+lZLXv6iR33ldfq8ULq0jA9yENsbm4J8VmbWUsP0ys7omZOZOu3L7GWe4LhsGZtPVS+yORxhV/sumeQNuHcY8HAABgSjKiVQCARMnjEvVZfM4EAQEgXfI2wJCUay8JVe55SZB56hMfDJzo6cfBU/2Jfw9NJcEuXlgVW3IF/czoBUle9FPh0+vEm0JL45xVdBG/pCY3A1NFPfGolLMXrujxoy/r/iee0donn9XDn3pea598Vvc/8YweP/oyibQe+Jng5ZabRPI0CnPywkRzJYWbSvY8dOr1WZNW40oI84v+cLSCTHqYCX1XzCYtsTkTcYUg4p4EEDdTq3smPX4VlyArzUQp7THlKCZtTbyX+p9f+U6gfZm8xwMAADAt33dIAIAZ5XGJ+qw9Z1NBwN2bV5BgkQDjRZtlhIEcyNtS9nFfe5O8rHjSuV3mfltzgzq7eyNtWxqWRXeSYIO8NnEmwdLPjE9Ha5P6Lw57ulYEqfAZ51LlCE+hpTHQ+YeBb0TBSTz2k3DpZ+LRTEbHitp75MysbXCq4HZ296rQ0qiO1iaSOefgTPAq9boG0dXTp7b1ywK//0mLwfi5/vvRdeKc2resnPFvTrJn0JXMbh2rp0/9F4dnTIJ2EsKSjP5w9EycM+i7wo20xOZMxBX8YmUss6t7Jv2aF7WgK82Y6AvOhZjy3Oa6l/LK1D0eAABAWJKRKQgASBQTAytpC8Rl7TkTBMwGgnlAfqRhgMG0uK69JFSZM1eCTNBET7+StDLRbNKcBEs/Mz5ekxdNncPcTrxBOiQhuRn5ESSZOeqJRxM5VXDdHrtUQjEmm2uC14LyMl0LUE24VCL5XJIagwl78oKjVFJ4GMmeE5NWkzbxYS70h6NlYtLDnZXlevHfPcw5GiWlLTYXV8yFlbGSs7pnFplYacZvX3AuxJTd8XovNRdT93gAAABhIgkfADBN2qtz+pG150wQMN0I5gH5k+QBhrDEce0loSpaQRI9g0jKykSlpDkJln5mvOKsTp+GyrRwJ87k5iy6cOWaBkYGU5E4GhUTycxxTTyS0lMFN81mmuBVWTFPW363O1ASvp/q4mmIwcx1/b+zslxvjQTrW82VFB5GsmdXT5+ujRX1hW98L1ETH+ZCfzhaJiY9vDUypu8PXaMvG6K0TaaZSdpic3HFXFgZK/7VPbMqySvNEFN2z8+91GwY/wQAAGlBzx4AMKM0V+f0K0vPmSBgehHMy74sDErBrCQPMIQt6msvCVXR85PoGUSSViaaS1qTYOlnJgPV6RFEnMnNcQujL17Y36M3hm/sI+mJo2Ezncwcx8SjtFXBTbuJE7zOXx7WpeFoq4unLQYz2/X/reExffi3nw+8/1JJ4WElex58afq9cBImPpRCfzhaSZ30QHzvhqSuIuJVWmNzfuMK76yr1Gdf/I7n48VdFCApsraydlIkeaUZYsruBLmXcoRZXAIAACAsRHgAADNKc3VOv0w/5zgD8QQB04tgXnZlZVAK5iV5gCFsUfY3SKiKh9dEz/feU6tvvjHo+3hJWploLmlKgp3Yr62smKe6qopASXL0M82hOj38inNVhTiY6ouPjhX1u1/8llbNcipOeuJomMJMZo5y4lHaquBmyVvD0SfapjUGM/36P2xkv3MlhUc9wVaKf+LDTIi7Ritpkx6I792QhlVEvEhrbM5vXEGSvntpJHVFAZIi7NU98zrJJ6mTrogpuxf0XuqTH3qXnti6KhefdwAAkC0k4QMAZpXW6pxBmHjOSQjEhx0ERDgI5mVT1galYF5SBxiiElV/g4Qqf0wM/HlJ9LRtW5uePu67vUlamciNpCfBztavXRDwOkU/E0iOrK+qYLIv7iSZ/8N3BrTqg3MfO4mJo2GKIpk57IlHaa2Cm3ZOf+PPTnqvyDsTt4m2WYrBRJUU7jXZ05QkTHyYiLhrtJIy6YH43m1pW0XEjTTH5vzGFdJSFCCpwljdMwlji3FK2qQrBzFld0zcS33+zBt6IiH9PQAAAC9IwgcAzCpN1TlNCfKckxaIDyMIiHARzMueLA5KwbykDjBEJYr+BglV3oUx8Oc20TNvqzFJyUuCnatfe22sGGj/9DOB5Mniqgqm++JOkvk9Hl6mpCWOhiUrycxprYKbVnP1N/zwkmibpRhMlEnhE5M9f+/Zb+nQqdd9H9OLJJ0rJOKuUUrCpAfie5OldRWRUrIQm/MaV0h6UYCkM7m6p5exxU8+8C79yoYf0uh4MVOTp6XkTLqaiJiye9xLAQCAPMvenT8AwCgnEHds10a1rVuquuqKSX+vq65Q27qlOrZro/ZtXZWJoLKf5+wE4t0G3Lp6+rTzwEmNBkxgKsUJAvqR5iS2tDIVzBsv2oZaBBOCDEohP5wBhiDSvpR92P0Nk4MAWTc6VtSew6e16enj6uzunfa6OQN/Dz31nPYcPu2rL+Mket5XX6vFC6umDUJ1tDZpw/JFnvaZ9tWYHHO9NlHw2q/1in4mgKiY7IsHTTLvfXPI17ZpYSKZOQnSXAU3bcLqb7hNtM1iDMZvDPDW9h6TwpfeXaNdH/7hQMf0KinnCom4a9Si/nxPRXzvtv6LVzPZJ8pSbM5rXMFJ3n+p/RG9sHuTvvDYg3ph9ya91P6I2res5HxVgon4ldc+0Wdf/I4e/H+f08Ofel5rn3xW9z/xjB4/+nJiv1teOJOugjCx0sx40db5y8N6dWBQX3/9EjFll7iXAgAAeZbOUokAgMglrTpnFLw856RWf+lobVL/xWFPbctKElvaUCUie7JSDRLhS0JVt6QIq7/BIIA7Sanul8fVmJLET7/WLfqZAKJiui+epYrZpmWpOmQWquCmRVj9DbeJtlmMwZisyOuWiYq1XiTlXOEg7hqdOD7fDuJ7kx392vlA2ye1T0RsLpsrY4XNRPwqaJ8o6hW4wxbnSjOzrQpqQtZjyhL3UgAAIN/S2wMHAMQiCdU5ozbXc05yRTwnCOi2WlChpTGzy+QmHQmi2ZOVapCIRtxV3ZLGdH+DQQB3klTdL4zVESZWsjp/eThRlUuTIki/di70M5FknB+yx2RfPIsVs03K0opDWaqCm2Rh9Te8JNpmNQYT9YpSJirWepGUc4WDuGu04loxLej56r8c/3am+gDPvPy9QNsnuU9EbC4+Yd+Phbn/IPEr032iUitwp+WeN46VZuZaFdSErMeUJe6lAABAvmW/twcAQMiSXhHPCQK2rV+mrhPndHBKFYe66gptb25QYc2STFXlSRsSRLMlS9UgEY04q7rlgYkKjVkfBEhqdT8TqyPMVsmqrrpC25ob9Ch9oFuC9msXlJfp2oTBXvqZSDrOD9lkui9uKsn8/OVhNdxVHWg/SZSlZOY4q+COF+3crDwZRgK+10TbrMZg4lhRKmjFWq+ScK6YiLhrdOL4fJvoU3z2K9/R57/+hrbdn42+5eDImCT/16ekrSIyEbG56IV9Pxbl/Z6f+FUYfaKpK3Cn8Z43ypVmvK4K6kfWY8oOVhQBAAB5lqwIIQAgs7I6mJimRFsTSWwIDwmi2ZLFpe0RPpayDw+DAHNL+qRCP8uij44VSyZoJGXJ7qT0k030aysryvTFf71BI9fH6Wci0dJyfoA/pvviphI+P/I7X9InVr8rkYkuQWQtmTloQrHXKrhpTIwKwkR/Yyo/5+ksx2CiTgoPkrTqR1LOFVMRd41G1J9vE30KSbo0TN9yoqRNppmI2Fw0vNyPffKBd2nvR9/n6TsT5/2e2/hVGH0iR1dPn37+R9+tP/7ya6m8541y0pWfVUG9ynpMeaKo76UAAACSIpnRKgBAZmR9MDGNibZ+ktgQPhJEsyVL1SARnTiquuUJgwCzS9OkQre8VrLq6ulT/8Vh7d+xOrLvVNL6ySb6tZeHxzSvzNJ99bWGWgWYl4bzA4Ix3Rc3lfA5ODKW2ESXILKWzBxVFdy8TgYyldB6Z2W5PrH6Xb4TbfMQg4kyKdxP0qofSTpXzIa4azSi+nyHEZejb5ncyTQSsbkoeL0f++yL39Hzr7yp//YLH9Lye+aONaTlfs9Un2g2/+SPvqL+i8OuHpvE81IUk66CrArqRZZjylOxokgyJaUADAAAWZbcu1wAQKrlZTCRRFuYRIJodmStGiSiw1L24WEQYHZhTCqMO7jvp5LV1CW7w+K14tuvbPghjY4XQ38d6dciL5J8foAZpvviJpLMp0piootfYSczx9GnCLsKblqSw8Jgqp/wZ/9srX74njsD7SMvMZgoksK9Jq36lfSJD4he2J/vsOJyae5b1laW643hcd/bp2EyDbG5cPm5H3v90rB+4refdzWWmJb7vbBjJ24T8B1JPS+FOekqkgT8jMeUZ8KKIsmRtAIwAABkGVk9AADj8jSYSKItTCJBNDuyVg0S0WMp+3AwCDAzk8nXSQjuB6lk1dXTp7b1y0Jro5+Kb5998Tu3fg7zdaRfizxI8vkB5pjui5tIMp9JUhNd/AgjmTnOPkXYVXDTkhwWBlP9hDurKgLvgxiMWVOTVv/Xye9ocMRsgmFaJj4gO8KYiOdIa9/ykZXv0Lf+9ru+t0/TZBpic2aNF22dfO0HgRKf5xpLTNP9XhJjJ0k+L5medGViVdC55CGmPBNWFIlfXgolAgCQJFxJAQDGBRlMTBsnEB8EibaYqKO1SRuWL/K0TV6DeUnmJOoEkaZBKYTHGWC4r75WixdW8ZkIyBkEKLQ0unp8oaUxlZMEvTI18Pf7x76tTU8fV2d377QkBSe4/9BTz2nP4dMaHSsaOeZMglay6jpxzlBLpvPTT54ozNeRfi3yIMnnBy/Gi7bOXx7WqwODOn95WONFO+4mJUoYfXG3fQevunr61Pvm0Kx/T8t77SQz+zE1mXl0rKg9h0/H3qdwEoqP7dqotnVLp10j66or1LZuqY7t2qh9W1e57i8GTQ4r9XlJg6T1N4jBmOckrX7+19cb3S8THxAHE32KUpLSt/TiB1dGA22fxsk0xOaCOXvhih4/+rLuf+IZ/cx/ORF4f6XGEtN0v2eiTxSGNJ6X/DCxKmgp772nNhcx5dmEdS+FuTkFYNyeD7t6+rTzwMlQY/UAAORB8qbYAgBSY6Ylwc99fyg1lSZMCHvZdeRP0qpEzPQ95/PqTl6WtgfShmXFpzNR3a+8zNLhr77u6rFhroJkopLVwVP92r15hfHrXZCku5mYfh3p1yLrknx+cCsJq42kRdC++MDgNY2OFW+dX4NUzJ5L14lzat+yctLv0vhem1hxKIkrK5qugmsiOWzq5yVNktbfSFoMJksWL6wyVkGciQ+IU9A+RSlx9y39eO6VC5L8tZfJNPkyVyXmIGYaS0zb/V5Yq20Flcbzkh+mVgWdzRtvjWT+NXSDFUWil+dV1wAAiBNJ+AAAz0oNBt9bF2w5wDQOJpJoC9OSkCCaxqSPpIlyaXsmSwDeMQhwm4mBvzGPlXnDCu6bqGR16ep1DQyOGF3mWgqedDcT068j/VpkWZLPD3NhKXHvgibNf+7vv6vLw9cnJXc7Seb/8J0Bk02dlOiS5vfaRDJzkhMGnCq4QaQtOSwsSetvJCEGk0WmkguTdq5LC+JE5oQ5ES+uvmUcmEyTL14nVvoxdSwxjfd7YU7y8Ssv5yVTq4LOJi+vo1sm7qUwt6CrrqWtUCIAAElCEj4AwDU3g8FBg1xpHEyMMtEW+RJHgmiakz6SyEQ1yFKYLAEExyDADXEM/IUR3DdVycp0RSwTSXezMfk60q9FliX1/DCXJFYGTws/ffGJpiZ3O0nm//HIVyWZO6c7CRpvr1mQ+vc6SDJzHhIGzl8eTl1yWBiS2t9gkq55Qe8x/vs/fUDr37PIYIuyjzhROIL2KUqJum8Zh49+4J166uMfSEx/BeHzM7HSq6ljiWm83wtzkk8QeTgvmVgVdC55eB2RLHlfdQ0AgDhxtwsAcMUZ+A87GOQMJqZNR2uTNiz3NihE9Re45SSI3ldfq8ULq0JNwPfyPe/q6dPOAyc1OlYMpT1Z4CTqFFoaXT2+0NLoKolmdKyoPYdPa9PTx9XZ3TstWOxMlnjoqee05/Bp3iMAc3IG/qLWdeKc0f2ZqmRluiKWiYpspZh8HenXIquSen6YS5DK4Hnn9MVb37/Y9z66evrU++bQpH3+2o+/x0TzJhm6Npap99pJZn6p/RG9sHuTvvDYg3ph9ya91P6I2resnDEB1ETCQFKdvXBFjx99WR/5nS8Z2V8WEnqS3N+IKgaTB0HuMQotjSTge0CcKFxe43teRN23jEN97QIS8HMkyMRKL6aOJab1fs9PnyhseTgvOSv2hCkPryOSw9Sqa+MeV7sFAAA3cMcLAHAlisoVjjQOJoaVaJtG40Vb5y8P69WBQZ2/PMwNe4pkKekjSZxqkMd2bVTbuqWqq66Y9Pe66gq1rVuqY7s2at/WVa4S8JksASAMfgb+ygMmJZkO7juVrIKoq65QfW2loRbdEHb/1uTrSL8WWZXU80MpQSuDT0wez6v55WWqvzPYexZFcvf3r4xm8r12m8yc1YSBqUmxgyNm+gNZSOihv5EfSZ5wkRXEiaIxNb63sCr4uTjqvqUfRTv4tTWJ12iEJ8qq7hNjLWm835PCneTjRxrOS6aE+Zrn6XVEMpgoAJPWQokAACQBEUsAwJyiqlzhSOtgoulE27RxKrvd/8QzWvvks3r4U89r7ZPP6v4nntHjR19ObFIAbiDBJ3x+qkHOhMkSAMLideBv6wffqbGAA+mmg/smKlltb24wXvE07P6t6dcx7/1aZFNSzw+lZLkyeFSiSO6urQx2jq+rrtBfn3kj0D7S/l5nMWEgrBUls5TQQ38jH5hwET7iRNFy4nun/u+f0M9+6F2B9hV139KP7w+NBt5H0q7RCI+JvrcXE2Mtabzfc8zVJ/Li3rqqQG1Jw3nJlDBXBc3T64hkMFUAJo2FEgEASIJ0ZjkCACIVZQJ+FgYTnUD87s0rNDA4oqFrY6pZUK762spMBl1Gx4rae+TMrJ8TZ7njzu5eFVoa1dHaxEBaAv3Bc68G2r7rxDm1b1lpqDXZ5lSD9CPoZIm29ctcJ/sDSI7xoh1Zn8IZ+Gtbv0xdJ87p4Kn+SUlxddUV2t7coMKaJRovFnX4774b+Jimg/uFlkZ1dvf6337NEoOtucGpyBY0wbCUMAZJ8tavRfYl8fwwG1PJ47s3r8j199VkcvdsffhHVr5D3/pb/9fDj/3Ivfrzr77ue3sp/e91FhMGwlpRMosJPfQ3ss/LPQYxC2+IE8VnXpmlX3pwmf7nV77jex9R9i39Gh7N3jUa4THR93artrJ82lhimu73ZjJTn2h+eZk+/dxZ/cmLc5/rCy2N2rH23frwbz/vuw1xvwZR62htUv/FYeP99ry9joifqQIwaS2UCABA3LiCAgBKirpyRZYGE4Mk2qaFU9nNbYCqq6dP/ReHqWiVIKNjRXV87us6+FK+kz7SwkQ1VCZLAOnhJFQcmiFJZVtzgx4NMUnFTTLU+cvDRo5lOrjvVLLyc84stDSG8po6FdmCDAbPJcxBkjz0a5EPSTw/zCaK5PE8iCK5e8v7F+sPAiTh/+T77tF//dvXfG8vpf+9zlrCQJgrSmY5oYf+RvYx4cI84kTxSlPf0q+q+dm6RiNcUU62GBwZ02/85dcnFX3Kyndyap/otz62SjsfdD+RLQuvQVScFXtKFRrzKo+vI+JnogBMFgolAgAQF7LfAAAlRVm5Qsr2YGIWsdxxujmTKD77ov+KTQ6WFQ6fqWqo40XbUIsAhGV0rKg9h09r09PH1dndO60v5qwy89BTz2nP4dMaHSuG1hZn4O+++lotXlg1KTnGCe4HEVZwv6O1SRuWL/K0zYbli9TR2mS8LY6wlriWpr+O40Vb5y8P69WBQZ2/PMy5H5ggieeHmWSxMngcokjubrir2vc5vtDSqLfVzPfbrEnS/F4nuU/hR2gJ+CT0ICNK3WPAPeJEyZCWvqVfbzfQT0nSNRrhinqyRVdPn3YeODkpLpbV76Qzke2l9kf0wu5N+sJjD+qF3Zv0Uvsjat+yclIfMauvQVicFXuO7dqotnVLA92X5Pl1RLycAjBBZKlQIgAAUSMJHwBQUpSDuAwmpkvQ5Y573xwy3CJ45WcSRSlpTvpIA5PVUAEklzNByu01dqYBx6gkObjvVLJymxRZaGkMfaUepyJbGJzX8eyFK3r86Mu6/4lntPbJZ/Xwp57X2ief1f1PPKPHj75M/wtQMs8PM8laZfC4RJXcHSTRhfc62X0Kr8JaUZKEHgBTESdKhrT0Lf0qs4JfW5NyjUb4TPS9vZpa9Cnr30k3E9my/hqEZaaJDs//m436uQd4HZEOQePOFEoEAMA/eoAAgJKiGsRlMDF9TCx3jPgEmUQxmzQnfaQB1VCBfEjbKjNJDu7PVcmqrrpCbeuW6tiujdq3dVUkg2R+kjTd+MSH3pWY1RNgHisbmJfE88NUWasMHpeokruDJLrwXt+Q5D6FF2GsKElCD4CZECdKjjT0LeOUlGs0wmei7+3H1KJPfCez/RqEHSOZONGh8W01+q2PZfN1RPYEKQBDoUQAAIIhUwoAUJIzGGx6AHGiQkujOlqbCE6kiKnljndvXpHJKjjjRVsDgyMaujammgXlqq+tTNzzNJ2An4Wkj6SjQiaQfUFXmWlbv8xXsDzIdcsJ7vtpd1TBfaeS1e7NK2K/PjtJmnuPnDF2Lf7ZD71L+/7qG64nb3T19Kn/4jDJfCngnBMOneqfdD9WV12hbc0NenTNEgbIAkrS+WEqJ4Gls7vX9z6oOnpDoaUx0OvoNnHMSXRpW79MXSfO6eAM393tzQ0qTPnu8l7fEFefwvT9u6lk1trKcv3M6ndN+7wAgIM4UfIkuW8ZF1P3/WmIt+OGoH1vv7pOnFP7lpWTfsd3MluvQZwxkiy9jsi2jtYm9V8c9lTkh0KJAAAER2QFAFCSicHgUj5+f4P2bV0Vyr4RHpPLHS9eWGWoVfFLS6KUiUkUU2Uh6SPpTEyKYrIEkGwmVpmZOuBYionr1tkLV1Qxr0zlZZbGPFSeiiO471SyittcSZpebFi+SJYl36snhN0PJ1HCn9GxYsmJGs7KBp3dvUxoNiQp54epokoez7qok7v9JGjwXt8QZcJAWPfvppJZP//r69VwV7WRfSFf6H/lB3Gi5Epq3zJqJu770xJvx21B+t5BlCr6xHcy3a9BkmIkaX4dkQ9eC8AQVwQAwAyS8AEAcwqzcsUz3/iexos2g0Epw3LHkyUpCOiGiUkUU2Ul6SPJqJAJZFuUq8yYuG7NtY9SknAtTIKZkjTnl5fp08+d1Z+86G6QZMfad+vDv/28r+MHWT1hLiRK+Dc6VtTOAydZ2QCS0rHaSFrEUQ3OS4IG7/UNUSQMhH3/biopluQe87KenE7/K3+IEyEOW96/WJ/peWPOxwW9709bvB2T+el7B5XFok8gRgL44XeVPgAA4B9J+ACAOYVZuYLAWDqx3PFtaQwCmp78kKWkj6SjQiaQXVGtMmPiuuV1H5JUXmbpH69doh1r3801Y4qpSZq/9bFV2vmgu0GSx4++HOjYXldPmAuJEsHtPXImsSsb/H/s/X94Vdd94P9+jpBACMvIiVFMLMuGOjQgk6RgW9CCwdR0UoraUkj6Q67bdMSd9k4n81yXp3NVNNWXGsbf3mv7O82d+U5mXLUpUzVtAqUpZNIZXGMcJYADNAmGdBwXGVmJXGGbH0JICOmc+wfe+Ohwfuy911p7r7X3+/U8efKAOfvsc87ea68fn/X5IB6UEtfDhWxw/NY3mAwYiGL8TlCsfZIenE7/K92YJ7Jf0jYAffYnPyLtqz9qNKjPpfn2pP2+ugTte2/82Hw58N0h5ff11j34XZKDORIgvDBV+gAAQDjuR74BACJhMnNFUrKhpwnljt/n4iSgzs0PSQz6sBkZMoHkiqrKjI7nVphjTGZzMjGZpR3yyc8iSZTVE/xwKVDCVl6QYBgmKxsgXi4Ej7vC9mxw/NbTmQgYiGr8TlCsHdIQnE7/C8wT2SvJG4BMB/W5MN+e5N9XlyB979qaKi1B+O9cmZAvvnKG3yUhmCMB9AhSpQ8AAIRDED4AwJegi8FBJCEbetqQ2e0GVycBdWyiEBH5lYeb5f/4WfcWqV0XRYZMsgUhiWy/rqOoMqPjuZXL5Zx89rmq3CJJVNUT/HIhUMJ2quMs3ZUNYA/bg8ddY3M2OH7rW+kKGIhy/E5QbPzSEpxO/wsiVFKxTRo2AHlMBPXZPt+ept9XF79JBlTXK2ZWV8kv/rejRf8bv4ubmCMBAACAK4h6BAD4VrgY/OUTb8qlMbWsrUnJhp5GZHZzdxJQxyaKTy1vkv/wCyzYRik/gPipn2+Rz790Vv7iFb0ZMsnihCRy5bqOosqMjudWTukILIDpFFX1BD9sD5RwgW2VDWAnm4PHXWRzNjh+a/2iHL9PZXPyr9YslNf+eUS+9cYF3+9BUKw+LgWnh90sTP8LHiqp2CMtG4BMsnm+nd9XTbm+t471ionJrK9/x+/iBuZIAAAA4BKC8AEAgeUvBm/fd0r+8ltvhj5WErKhh2F7Nl4/0p7ZzfVJQNVNFP/PR+/XeDYop1wA8ZblTZIRkYPf+2elDJlkcUISuXZdm64yo+O59eUTb0pG1J5ZLIDpE0X1BL9sDpRwhW2VDWA3m4PHoRe/tR5Rjd9Ljd38sKE/mhSuBKerbham/4V8VFKxg0sbgGxk+3w7v69ZqusVQfC72I85EgAAALiEIHwAQGgzqjLy/3hkoVIQfpBs6KYD16MIjHclG69faS537PokYNo3UbjATwDxnhM3FqZ++eF75LfW/IhMTGUDt19kcUISuXpdm6wyo+O5pVoBSYQFMJ2iqJ7gh+2BEq6Is7JBEjYIA0A5psfvlcZupRAUa4btwelhNwvnP69ra2bI3hP0v3ArKqnEx5UNQDazeb6d39c8lfWKMPhd7GZT9UcAAACgEoLwAQBKogjkNR24HkVgvGvZeP1Kc7njJEwCpnkThe2CBhB/8ZU35YcXx0MFEJPFCbYLExzq6nVtsl9l06KTTefiMtPVE/yyOVDCJXFUNkjaBmEAKMXk+D3o2E1E5MF775DnPv1xufuOOoJiNbN9c2CYzcKv/fOItHx4rvzNt3+g3OfKR/8r2aikEj3bNwC5QNfzetdXvyfPffoTWtcg+H2jEWa9QgW/i71sqv4IAAAAVOJ+BBwAIHbdbS2yZtG8QK/xE8g7MZmV7ftOybpnD0tPX/8tC01e4Pqjz7wk2/edkonJbKBzMH38/PfZuvu474na3mMDsnX38dDvFzWv3PGhbWulY9UCaairmfbfG+pqpGPVAjm0ba3s2rQ0EQH4IsmYBPQ2UbS3Nvv69+2tzbFniU4LlQDiIFSzOPW/PRrqtYAfZ89fkacOnJHlOw/KyqdflMeee1lWPv2iLN95UJ46cKbk9ef6dW2qX2XTopNN5+I6v8/wkq8PUJWqlCRsTLSBV9lAhd/KBlGNgwDAFibH72HGbsfPXZD/+vJZAvAN0Lk50IQw18u33rggX/jmG1oD8D1p738BuujaADSVzWk6Izfpel4f+O6Q1vUVft/oBF2vUF2n4HexV5RzJAAAAIAqIqgAAMpMBPKaDlyPMjA+qmDauHnljk90rZcjnevkhScfkSOd6+RE13rp2rgkcVk0kzIJmNZNFDaLMoBYRxYnQDfV4FDXr2tTG6R0PLfmzq6WhtnuP/uSxKueEIbfqlSVJGFjog28ygYq/FQ2SPoGYQAoxtT43fXNn0lk8+ZAlevFlKT2v6ayORm6NCavD4/I0KUxAixhnO0bgFyh43nt0bm+wu8bLb/rFV/6VyuUx6n8LvaKao4EAAAA0CGZM2wAgMh5E2MdqxdK79Fzsufk4LSJyYa6GtmyrEnaV9zrK9hHJXB916alsR/fo7og27F6oXPB62kpd+xNAvb09Yc+hk2TgN4mis4Ni2V4ZFxGr03KnFnV0lhfa805pkVU5Y11ZXHq3LCYawTaeMGhfp/RvccGZPDC2M0g9KRc17r7VSJ6nlufWn6P5EQS8+xLijDl2v1UT/DLC5RQCUq4vbZaPjhnlpbzcVl7a7PS/eWnskFU4yAAsImp8XtUYzf4Z/PmQNsC8JO4Odabh91bZPy0eVmTPB5g/AQEYfMGIJfoeF7n07W+wu8bj0rrFa8Pj2h5H34Xe0UxRwIAAADoQCpTAIBWOrKhm84k5nKWaTI5mRfkOw6befbm6y2cBPQ2UdzfWC/z584mUDJiUZY3JosTbKRaPSZp17XuKjM6nltJfPa5zlT1BL90ZCe7PD4pD/+HF+SpA2dSnRXYdGUDMjYDSDPdfZgox27wz9aqhTquF92StDlWtZoaoMrmDUCuUX1eF9JR7ZDfN16l1iv4XZLPhuqPOrCuCwAAkHyMKgAARqhkQzedScylLNNfOv6m/O4nPyqDF66SycmwMNmyvEnAMNeUTZOAcZrK5si8n0dnAHGlNpgsTrCNjuoxU1k9wRS2Xde6qszoem7x7LOPieoJQahmJxN5Pziqp69f2lubpbutRdtGAZeYrGxAxmYAaaZ7/B7l2A3+2Vq1UMf1oltSNseqVlMDdNBRHSyJ1SnCUHleF6Oj2iG/r534XdIh7uqPKqjQAwAAkB4E4QMArKIrk1ipiVXTx8+nY4Ht8viktHT/nVyfKp4ZgWAldROTWdmx/3TJif1K37HLk4BxYgKyuCgD48kWBNvoCA79l6sXaDmXJF/XOp5bPPvsValcuym6AyWSGhzlZ/OhV9mgXP80n98xQJTjIADmsIlZjc4+jI2bmrk+blDdHGgiON22Tb5J2hyrUk1t16alhs4qOO5ft9m6AchVYZ7XpejY8Mbvayd+l3QwNUdikuqaIwAAANyT3MgCAICTTGcSczHLdKkA/EJJDVYySUe2LBcnAePEBGR5UQbGky0INtEVHPq7n/wo13UFOp5bPPvsp6t6QhA6AyVE7AyOCivo5kMTlQ3I2Ay4jU3Meujsw9i0qZnrYzobqxbatMk3SZtjdVRTi/ve4P5NDhs3ALnKe14/+aVvy4HvDikfT8c6Db+vnfhd0iHu6o9BUKEHAAAgneyZ+QMAQMxnEnMxy3QQSQpWioKubFkuTQLGiQnIyqIMjCdbEGyiKzj0ndFrXNc+6Hhu8exDoaCBjX7YEhwVlurmQ52VDWzM2AygMjYx66erD2PDpmauj9Jsq9yk43rRIWnXgY5qal0bl2g6m2C4f5PHxg1ALptZXSXbf2axliB8Hes0/L524ndJl7iqPwaRlAo9AAAACIYgfACAVUxnEnMty3QYrgcrRcVEtiwXJgHjxARkZVEHxpMtCLbQGRzKde2fjucWzz7kqxTYGEacwVEqVDYfzqjK3HI/qWafj3IcNJXN0R4AGrCJ2SzVPkzcm5q5PsqzrXKTjuslrKRujtVVTa1zw+LI+yncv8ll2wYg19mw4S0fv6+d+F3SJ47qj34koUIPAAAAwmG2BgBgFW9iVUW5iVXTx8/nLbDFoffouVje1yU6smWV4k0C3t9YL/PnziboSNQnIPvfHtV8RvZqb21We32AAGIvW1Co90l4tqCpbE6GLo3J68MjMnRpTKayubhPKdF0BodyXQen47nFsw/5vMDGV37vMbm9Vu3+3nNy0Mk2OOzmw5/9T32yfOdBWfn0i/LYcy/LyqdflOU7D8pTB84o9YeiGAedPX9Fnjpwxsj5A2mksokZ/qn0YaIcuxXi+qjM2xx4aNta6Vi14JbnYENdjXSsWiCHtq2VXZuWGg9uVr1e/Jo7u1r6/t2j8sKTj8iRznVyomu9dG1ckrhxjq5qasMj45rOyD/u3+TyNgD5vd/bW5vZXFGGjvUVndUO+X3txO8CW5hccwQAAIDdGF0AAKxiemI16onbqBbYCrkarBQVXdmy+I79YwLSv4XzbpO2j88P9dowAcTdbS2yZtG8QK9JcrYgAgjjoTs4lOsasMM7o9fk8rhapYu4gqNUqGw+/Me3Rm4JKLt49br09PXLo8+8JNv3nZKJyWzg45ocB01MZmX7vlOy7tnD0tPXb+T8gbRhE7Mb4tr8yfURjLc58ETXejnSuS624HSV6yWITy2/R5ruqEv85lid1dSixP2bfLZtAHJdnBveiuH3tRO/C+LGmiMAAEC66Uk3CACARu2tzUrlmStNrJo+fj5vgU01ADkoL1jJxpKMNtCZLYvvuDKXS4RHbWIyKzv2n5b93xkK/NqwAcRetqAd+0/7aqvaW5ulu60lcYsV3ndf6jvwAgh7+voT+x3EyQsOVXk+5weHcl0DdnA1OEqVyb5/77EBGbwwFipzn4lx0MRkVrbuPu47m6vK+QNpomMTc9fGJYFeM5XNyfDIuIxem5Q5s6qlsb428eMvHbrbWmTwwligrNaqmz/juD6SwKt6EKcw10tQugNObaWzmlqUuH/Tw9sA1LlhMc9XBSrrKyarHfL72onfBXFhzTF6jF8BAIBNCMIHAFjH9MRq1BO3USywFeNasFI+05MnaQ0Ii4vpCcikTLYFDWLLpxpA7GUL6li9UHqPnpM9Jwen/WYNdTWyZVmTtK+4N3El7EUIILSF7uDQtF/XgA10BTWNXpuU14dHnHjO69h8WMnh187Ljv2nZdempYFeZ2IctGP/6cB9l7DnD6RF1JuYvazMe4v0lTYva5LH6SuVFfXmTza5uy3o9RKUyYBT23jV1FTmm/KrqUWB+zedbNgA5Lo4Nrz5xe9rJ34XRI01x+gwfgUAADYiCB8AYCXTE6tRTtyaXmArJepMTjqUmjy5vbZa1i/5kPziQ/fI8ns/oLzQ5Gq2LFeZmoBM2mRbmCA2EZG2j8/XFsSW1mxBBBDawdQmubRe14ANdARHZUTk5/7zN2/+2fbnvI7Nh370HhuQjtULI9kgXGoc5PXFwgh7/kAaRJVFkUpQ+kS5+ZMsm+7zc71s+sTdcvqHl+WVN971fdyoAk5tobuaWhS4f4FwqHYIwHasOZrH+BUAANiMXhwAwEqmJ1ajnrgtXGD70vE35fK4uYwGUWdyUlVp8uTy+KTsPfkD2XvyBzKzukp+5eFm+bUfvy/04rWL2bJcpnsCMomTbSpBbPu/MyRPrh/VGsSWpmxBBBDaxeQmOZPXdVIqcniS9nkQHx3BUbmCP9v+nI8ya1nv0XPStXFJoNfoHAepbjAOc/5JQ3uLYqLIokglKDOi2PxJls146Wy3K10vleY+8tnYJ4qC7mpqpnH/AuFR7bA8xhVAvFhzNIvxKwAAsB1B+AAAa5meWI1j4tZbYPvdT35UHtx50FggftSZnFQEnTyZmMzKF775hnzhm2+EXmR0MVuWy3ROQCZ1so0gtvjw3dvFtexmSavIkbTPg2BMLdqrBkeVo+M5r/tzR5m1bM/JQencsDjw+eoYB01lc7L35GAs558EtLf+pDWYKIosilSCMsvk5k+ybMbDZLtd6noh4LQyU9XUTOH+BdRR7XA6xhWAHVhzNIvxKwAAsB0zNQAA65meWI1j4nZmdZV86sF7jAUkRZ3JSUWYyROPSuCVa9myXKZzAvL3v/Jq4ibbCGKLD9+9nVwINklaRY6kfR4EY3rRXiU4yo+wz3lTn1vH5kO/Ll69LsMj46EDPVXGQcMj48qfUfX8XUR760/ag4k+OGeW3F5brbRpv1wWRSpBuY0sm9Gyod0m4LQ8k9XUdOP+BfRJUxXPYmx4PgGYjjVHMxi/AgAAFzDaAgA4w5tYvb+xXubPna19ocn08Qu1tzYbO64rEwoqkyceL/AqKC8gLAyXvmNbqF7v7SvuVZ5s6397VOkcTNEZxIZgdH33Q5fGNJ0R8nnBJie61suRznXywpOPyJHOdXKia710bVwSawD+1t3HfbdHvccGZOvu4zIxmTV8ZuEk7fPAv4nJrGzfd0rWPXtYevr6b2kPvUX7R595SbbvO6X0m3e3tciaRfNUT7mkIM9505/b23wYldFr6pW1woyDdLyvzuO4gPa2sijbJRudPX9FnjpwRh7+Dy8oV80rl0VRRyUoxEfHc4Ysm/7Y1m5HPW/pCq+amt95p/bW5tiqJXL/AtDBtucTgBtYczSD8SsAAHABQfgAAMREZUKmlLgyOYWlKyNq2ADrMAFhrn3HttAxAZnUyTaC2OKj6zv76T/6ujx14Iy1Gz1cZ1uwiUr5Wxsl7fPAn6gX7YMGR4Xh5zkf1ec2+TkLzZkVT5FLXe8b1/nHgfa2vDQHE1XafBBGqSyKuipBTWVzSseAGh2b3FEZ7bZeU9mcDF0ak9eHR2To0pjWdsSrpnZo21rpWLVAGupqpv33hroa6Vi1QA5tWyu7Ni2NNRs09y8AVTyfAHux5qgX41cAAOAKgvABAIiRzqygcWZyCkPH5Em+MAHWLmXLSoIw1/uKBR+Q9tZ7ZfDCVdl7IpmTbQSxxUfXdzYyPpnorKx4X9IqciTt88C/OBbtKwVHqW6v8fOcj+pzm9hsW0xDXY001tcaf59iGutrb/kNg4rz/KNGe1tZWoOJgm4+8KNcFkWqcCUDWTbVVQoIp93Wx6vysXznQVn59Ivy2HMvy8qnX5TlOw9q38xuazW1fDbcvyY3RAAwi+cTYDfWHPVi/AoAAFxBpA4AAAqmsjkZHhmX0WuTMmdWtTTW1wbK0OtNyOzYfzrU5GnD7BrZsrxJ2lfca8VCUhA6Jk/y7Tk5KJ0bFgfOkOwFhHWsXii9R8/JnpOD086roa5Gtixz8zu2TdDrfWZ1lRztf1c2fO7rWt7fm2ybP3e2luOVE6Rt8ILYVO6HNAWx6aTjuy/Ue2xABi+MMXmeUDoqcnRtXKLpbNQl7fPAH9VF+47VC5X6RF5wVOeGxTeflaPXJuXn/vM3Qx9TpPJzPurP3d3WIoMXxgIHFQexZVmT9uogfvswM6oysnlZk/T09Yd+LxPnbyva2/LibpfiFGbzQTmVsihShSs5wjxnyLL5fnuzt8jcz+ZlTfL4e3M/tNvqJiazZed/Ll69Lj19/dLT1y/trc3S3daibQztVVOzVVz3r5/rv/kDdUpz3VEIMx+vOocP2ILnE2A/1hz1YfwKAABcQRA+AAAh+F2088ObkPm1H79PfuML35LBC2O+z+NjTXPldz/5UScDPXVPeqgGWBcLCGNRRr9KE5Azq6tuZhE3kU3c9GRbmLaBILb46Pjui/Gysu7atFTrcREvXeVvw2wYMyFpnwf+2bJonx8c9frwiPLxRMo/56P+3Kqbbf1oX3FvqNcVC0I6985o4D5Me2uz0jM07Pm7hva2MlvapaipbD4oxk8QK1W4kiPoc0Z3kLNrggSE//LD98jXTr2l9H5Jb7cr8ap8+A0yT9tm9qjv3yDXf/68nEi4uW5Twsy56ZzDB+LGuAJwC2uO6hi/AgAAV9DbAAAgAJNZnP7sm28ECsAXEXn5+287G+hpYtJDR4C17dmykqJwAvLi1QnZsf+MHD37rtH3NTXZpto2EMQWH9XvvhTXs7LiVjrL39rwnEna54E/ti7am15Ui+tz+8l+dtfttfKPbwXfhNDe2hz4GVMqCKkw2CpfuT7Mwnm3SXtrc6gg4jDn7yra2/JsbZeioCMAP2gWRapwJQtZNv0JGhD+xVfeVH7PJLfbfoSp8uHqZvawGdajun+DXv+FfUKTFQv8CjPnJiKxVWJAvJJc9YBxBeAm1hzDY/wKAABcQRA+AAA+mczipJIBz9VATx2TJ4XIZuAebwLyP734uvEAfFOTbTrahqQEsbm40KXy3VfialZWFJe08rdJ+jwutj1xsXXR3vSiWtyfu1z2s6lsLlA/QkRkzaJ5N4OL/KgUuOS3+lCxPkx3W4sMXhgzev6uS1J7a8LQpTEr2yXTdGw+uL22Wl75vccCBQ1ShSuZyLJZXpiAcB2S2m5XkpY5Tl0Z1k3fvzqv/zgqFoSZcxt496qIiHz9+2/7fs0/DV+R329rkTvm1NB2OioNVQ8YVwBIG8avAADAFUSqAQDgk8ksTqrBny4GeuqYPMlHNgN3qSzQBmFqsk1X2+ByEJvrC11hvns/XM3KqsP5K9dkeHwkUcE/SSt/m4TPY7LtSWpgv62L9qYX1Wz53MWyn82oysjzTzxYNkg+X9BMnUEDlyop7MPMrK4yev5JkIT21gSvDf/ScfWM0yLuBRPp2Bx0eXxS3hm9FnjzAVW4kossm7eKar6hmKS1234lfY7TVJVUE/evies/6ooFYebc/Abf5zva/65s+NzXRcSduSw/kjquzWeycrFtGFcASCPGrwAAwAWMsgAA8MFkFicdGfBcDfRUnTzJRzYDd0W1IG5isk1n2+BiEFtSFrqCfvd+uZiVVZf254/JW2M32uSkLGAnrfyty5/HZNvj+qaiSmxetDe5qGbz5xa58RzatWmpdKxeKL1Hz8meItfflmVN0h7i+jOR/bdYH8bU+SeBy+2tCZXa8LBcCyaKc3NQUqpwwV42BX3GFYCfpHY7iKTPcZqskmqCqes/qooFcW2icWUuq5ykj2s9rt2TqhhXAEgjxq8AAMAFbq1OAAAQE5NZnHRkwLt49boMXRqTpjvqlI4TNZXJk0K2ZjOwafHZRjoWaP0wNdmmu21wKYgtaQtdhd/9Xx1/U0bG1YOjXMvKGsbEZFY+9/ffl6UlftYkLGCLJK/8raufx1Tbk5RNRZXYvGhvclHN5s+db8Gdc6Rr4xLp3LBYS//RZODSfz/yhmx9ZOG0c9R9/knhantrgu7KDB4Xg4ni3hzkchUu2Mu2oM+o5huKSUq7HZSuOU4/m9njmG/7nS9/21iVVN1MX/9RVCyIaxNN4TnYPJdVKC3jWo/JysU2YlwBIK0YvwIAANsRhA8AQAWmszjpCtD86T/6unz6wXuMLGqaXNgKM3lSyMZsBrYtPttKxwJtJaYm20y2DS4EsSV1ocv77n/9J+6TVX94SPl4rmVlDcoLpvvfbw7L0k9U/veuLWAXSlr5Wxc/j4m2J2mbisqxfdHe5KLaJ1vukr/81puhzy3KYIUZVRktVVRMBi796TfekD/5xhs3/1zYx01jFZhyXGxvTTBRmUHEzWCiuDcHuViFC/ayNegzivmGUpLSbgcVRZWPOObbJiaz8jtf+rbs/+5QqNdHlTk+n+nr33TFgjg30RRyYS5LJF3jWhGzlYttxrgCQBoxfgUAALaj1wEAQAU6szgVoytAc2R8Unr6+uXRZ16S7ftOycRkVvmYZ89fkacOnJHlOw/KyqdflMeee1lWPv2iLN95UJ46cEb63x5Vfg9v8qS9tTnU623LZjAxmZXt+07JumcPS09f/y3Xjrf4rPN3cpnpLOGbfuxuY4tJptsGkfeD8O5vrJf5c2dbE1ykutClo+0wbf7c2dJQV6N0DBezsgalEhDtIi9Tdxg2bhhz7fOYanvSdh2H/c1vvt7gon3QfmF7a3PZ53x+X1YlAF/EvWAF04FLuYI/08ctz7X21gSTlRlcuz9F3t8UpUJ184FXCerQtrXSsWrBLX3fhroa6Vi1QA5tWyu7Ni0lgAFFeUGffu/v3mMD8kTPMTnzw8sydGlMprKFTxR94qpKlpR2OwyTVT7imm/zrvGwAfie3qPntJyPX6av/0rzWari3ERTjAtzWWkb1+qoTuoixhUA0orxKwAAsFmy0zICAKCB6SxOOjLgFVLNZBN1FjNv8qRj9ULZ/c03pPeVAV+LZVFmM/BTDSBtGYd0MJ0l/F8/+iPGvtsoMrzZSsdCl+my5apszxZtg7RmHUta+VuXPo+JtieN17G3aB/mc0exaJ/fL+w9ek72FMlyumVZk7SXyXJaqS8blIvBCnEGLtHHLc6l9tYEYwH4Dt6fHlsymbpQhQv2ChP0ebT/Xdnwua+LiNns5XFUJUtSux2GqSofcc636ari8qXjb8rvfvKjkfWNorj+Tc5n2ThXZvNcVtrGtaYrF9su7eMKAOnG+BUAANiIlTAAgLWmsjkZujQmrw+PGM+OVY7JLE4iejLgFRM2k02YLGZbdx/XkmFqwZ1zpPtnW+R7f/BJ+dK/WiFblt8tt9dO/96izmYQpBpA2jIO6eAt0JpictHRdNtgK10LXXG16UHYnC3aBmnNOqY7U3fcXPk8ptqetF7H3W0tsmbRvECviXrR3ltUO9G1Xo50rpMXnnxEjnSukxNd66Vr45KyAfhB+rKVuBqsEHfgUtr7uMW40t6aYKoyg6v3p8e2TKa2VuGCvXRUuDBZSUXHfMPc2dXyKw9H127bMhcalqkqH3HNt+ms4nJ5fFIe1FhZtBLT820ibsy56WTzXJaN41qT7VkU1UltluZxBQB4GL8CAACb2DeLAQBIPW+BY2+RrJemsmOVYyqLUz7VDHilhMlko7KwtWvT0qCnWNSMqow8vOCD8vCCD8rU5soZ6E0IWg3giZX3pSrjkC46so2XUum+UxVF22AjXQtdQ5fGpOmOOk1nZYbt2aLjlPasYzoyddvEhc+jc5F9/tzZIpLu69hbtPebLT7K6kOFvEU1v3RlKxWJ93OrsiFwKc193FJcaG9NMFGZweX7Mx+ZTOEy3RUudFdS0THf8Knl90jXxiWy9RGz7bZtc6EqdFf5iDPDt+5r/PL4pNbKouWYnG8TcWPOTbfC8aQtbBvXRtGepbk6qSet4woAAAAAsFH8K3IAALwnaNBzVAvuOhYtimVxyqcS6FlJkFK5NpauDRp4pUOYMtcv/W+1YC+bSxqbZmoTSqX7TlUUbYONdC1Q/fQffV0+/eA91gcT2BYYNZWNZ2NSIRMB0S5KWvlbmz+PiUX2tF/HSVy015Gt1MXPXYwtgUtp7uOWY3N7a4KuNry+tlp+8cF7nL8/87m0KQrIZ6rChe4kD7oCwlXb7VLjOFvnQlXo3syuI8N3mL6IqWvco3vTSTGm5ttE3JhzM8HGoG1bxrVRtmdprU5aTNrGFQAAAABgI/dHlwCARAgT9Gx6oSKf7ixOxYQJ9PQjSCabuBa2bBMmg+oPLo4pvaermXR1MLUJxc99p/weEbQNttG1QDUSYQY4FbYERtmWGZGsY9PFsWHMJBs/j4lFdq7jG5K0aK/al/jlh+6RnZuWOve5i7ElcCnNfVw/bGxvTdDVhn/t3662vpJSGEncFIXkM1HhwqMzyYPugPCg7Xa5cdzPf+JuOfPDy/LKG+/6OlbUc6EqdG1mjzPDt8lr3KN700khk0lfXJhzM8HGoG0bxrVRr+2ktTppOWkZVwAAAACAjeyeqQMApEaYoGdvoSIK3qJFGMUW7YrxAj3Dvk8pXiabSnQtbE1lc0rHiJuODKph+P2dkqq7rUXWLJqn7Xh+7ztVUbQNtvEWunTqPTYgW3cfl4nJrNbj6uIFRh3atlY6Vi245fM31NVIx6oFcmjbWtm1aanWgIiJyaxs33dK1j17WHr6+m9ZYPQyiT36zEuyfd+pyL5Dso4hajransJF9iRdx1PZnAxdGpPXh0dk6NJYqP6Yt2h/f2O9zJ8727mgaR192a+dfkvT2dhB97gijLT3cXGDrjbclsCibC5X9s9heZuiTnStlyOd6+SFJx+RI53r5ETXeunauMTJsQOSy/QmxN6j57QdK8x8wyMfuVOpupmfcdwXvvmG7wB8T5RzoSqCznG2tzYXDcbVmeE7qKg22vYeG5D+t0eNHV/3fJuIG3NuJtgatG3DuDbqtR1vw7EKm6qT6hjPAwAAAADiQxA+ACB2KkHPphcq8oVZtCiWxamcwkDP+lo9k+h+Fo7iXNgKwvSkdBwB+B7XM+mq0LkJJeh9pyqKtsEmOha6itEZTGCqnYg6MMrLJOa3XYpyM4OJgGigHBOL7Em4js+evyJPHTgjy3celJVPvyiPPfeyrHz6RVm+86A8deBMZP1kG7jSl42SLYFLae7j4oakBEp5be6nPn9k2t9/6vNHtLa5rm+KQjqY3oSoM8lDmPmG7w5ekj/8u38MdV8HHccFFeVcqAodm9njzPAd5UZbnZtOCulO+uLCnNvqj9wpqz9yp/ZzsaEvUkzc49q41nZUr2kbqpMyngcAAACAZCAIHwAQO9VFqSALFSqBmbqyOPnhBXp+7d+uDvzaYvwsHNlQurYclUlpv7+7jgyqKmzIpBunSgu0fqjcd2FF2TbYwlRAn2owQVSLV1EFRtlcJSYpwXRwi+5FdpevY1urZMTJ9r5sXExkPw0q7X1c3OByoFRhmzsyPr2dGBmfTF2bCzTMnml0TKt7Y1zhfMPc2eWfTRfHwvelwozjgjIZtK2bymb2ODN8m6gCWIrpyqKV5tv83suuzLn1/NpD0vNrD2mfu7IhaLuYuMe1Ua7t5FPZcPzRu+pDvU4XxvMAAAAAkCysggEAYqUj6HnPyUHp3LC47ESxl5Fl78nBaZOaDXU1snlZkzy+4l5fGYy9RYuO1Qul9+g52VPkeFuWNUm7z+NVMn/ubGmoq1HK6uk3k40NpWuLmZjMyo79p0tO6HuT0j19/dLe2izdbS03F4OC/u46MqiGVfg7TWVzMjwyLqPXJmXOrGpprK9NTbCqt0DbuWHxtO9g9NqU/OUrA8bvuzCibhvi5i10mcjs13v0nHRtXBLoNSrthK1UM4l1rF5o/Fprb22Wnr7+8K+PcQE7zW2sy1TanvbW5qL3hIvXsZdd1W9wV++xARm8MOb8BrBKbO3Lxs0LXCr3nCz89/lBHhkRUQkJi7taBOxhog2PAm0ucKuJyaz85p+fMB4UaGJj3II758jvfvKj8trwFXnZwH2tMo4Lws9cqG28zexBeIHwUcyLFvICm1XGCn55m06Cfj9BlZpva6yvlYF3r1o7nxV2zq3ca4KKsy/iR1zj2qjWdkrpbmuRwQtjgTc+/eNbI/LoMy/FMkdI3xIAAAAAkidZK4sAAOfoCHout1BhKjCz3KKFzgUwHQs+fjPZxLmwVUrYSen//CvL5OmvfS/w7x5n5lPvd/KzcaD5A3WpCB4ttkAbxX2nIqq2IQhTwcZhF7oqCbr4ltTFKx2ZxIJuZgjKxWA6XZvyEJ8wbc+aRfOku62l6H9z8TpWqZKxa9NSQ2cVPxv7srYIErhU2M98/uWz8iffeCP0e1P1BPl0t+FRoM0FbhVFpncRcxvjduw/7TsA3+P3vo4iAF8kuqDtuE1lc3LX7bVK/TuVvohqYHMQUc5JFptvs3E+q1CYcyx8zcWrE7Jj/xk5evZd3+8bd1/Ej7jGtabXdioJuuG4UBxzhPQtAQAAACB5CMIHAMRK1wJDseNEEZgZJotTUFFlsoky4N+vsJPS6/+vwzJ0yV/Z8vzfPc7Mp59+6B7Zvu+Ur40DhRlK0xY8GsV9p8qGczQdbKy60FVK0MW3JC5exZ1JLAgvmO5/vzns+zVxLGAnsVpCWgVte/z8ni4FhbpQJSMuNvZlbeM3cCn/Gfz4inuVgvDjrHoC+5how02izQVuFVWmd1Mb40ze1zrGcUHEmUgiCt687j++NaJ0HJW+iMkqgIVsqcZkw3xWJWHO0XvN/LmzZfdvtDrTFwkijnGtybUdv/I3HP/Wn58I3GZEOUdI3xIAAAAAksn+WQMAQKLpWmAodhyVwEybeAs+YQTNZBP2fW6+XmOQjcqktN8AfI/3u3sZVKP2Sw/dI7u+Wjpzf6HCku9e8Oijz7wk2/edMl4SHnabmMzK9n2nZN2zh6Wnr/+WjFQ6rxdvoevQtrXSsWqB1NfqadP9Lr6pLl71vz0a6rWm6cwkZpoXTLfxY/N9/fv21ubIqxB4wRt+r5XeYwOydfdx2lKLFbY9hc/uhroa6Vi1QA5tWyu7Ni2teL1517HfflAc17FHR5WMJLOpL2szLwjp/sZ6mT93dtmNB1GORZAOuttwk2hzgRuB5UOXxuT14REZujQmfx7RdW1qY5zJ+1rHOC4IW4K2TdFRcUFHX6S7rUXWLJqndIxKklqNyVYu9UWCiGNca3JtJ6hcLhd6005Uc4T0LQEAAAAgmZI9SwcAsJ4X9KyySFVsoSJpWUWiymQTV+naYqIq4Z3/fh2rFypnUG26Y7YMXhjz/e/XLJonmYxoK+UeRxld2COKCiDFeJl1f/0n7pNVf3go9HE8fhffdCxedW1conQME2zIJBbEzOoq+exPfkQOHSqe9bGhrka2LGuS9piqdSSxWgJu8JvV24/87Hm9R8/JniJVROK8jkXcqpIRF5v6skniUrWIfFPZnHLbAHN0tuEm0OYi7UpVVovqajaxMc70fR1lZvqkB23rqLigqy9iqgpgvqRXY7KV7X2RMKIe15pa2wnD9jlC+pYAAAAAkFwE4QMAYjWjKqMc9FxsocL2Sdeggi74qJTKtSHIJuoS3p7eo+ekvbVZ6Xr8k19/SP7sm2/4/p2eWHmf/Iv/+HLo9yuG4NH0ijvYeP7c2ZEtviV58cqmTGJh9W5tlVx1bewL2EnblKdTkoJTvazeOtgciKGzSoau78tGNvRlkybKsYgOpQJHG+pqZPOyJnk8xs00uJXONlwn2lyk1cRktmx7n4vgHExtjDN9X0c5/kp60LbqvO5H76rXmpyiUmBzfW21jIyH34SRlmpMtrK1L6IiqnGtqbWdoFyYI6RvCQAAAADJRRA+ACB2qkHPhQsVLky6hhFVJhsbgmyiLuHt8X53lQyqiz5UH+h3eurAGZ0f4aakB4/iVjYEG0e5+JbkxSubMomFNe+2WXL77fWxvb8naZvydCA41R8bAzFcq5IRFxv6sknkQrWISoGjF69el56+funp6+d3R0W62srLY9ete54ApQStrGZC/sY43ZtGTfeldIzj/Epy0LaOed23Lo8bmdMtF9j8+195NbXVmJK0wTtpohjX6l7bCcOFOULG8wAAAACQXAThAwBit3DebUpBz4ULFS5MuqqIIpNN3EE2cU0me7+7jgyqfn4n0xn/kxg8itJsCTaOavEtyYtXtmQSc11SN+WFRXCq+5JQJSMqcfdlk8zWahFBA0d7jw3I4IUxrVlykSy62spPff6IfOrBe9jkBieEqaymk9cHHbxw1cimUdN9KR3jOD+SELRdjgvzusUCm9NYjYkN3hDRv7YThgtzhIznAQAAACC5GKkBAKygc6HChUlXHaLIZBNXkE2ck8mj1yZl/tzZ2jKolvudTGf8T1LwKMqzKdg4qsW3pC9e2ZBJzHUuBG9EheDUZEhClYyo2RowngS2VYsIEzh6+LXzsmP/adm1aamhs4LLdGW0vjw+ySY3OEGlspqqpjtmy5/82kNy351zjG4ajaIvpTqOq8T1oG0/XJ3XTVM1JjZ4o1Dcm1BcmCNkPA8AAAAAycWsBwDACt5CRXtrs69/397aXDIwzIVJV9d4QTb3N9bL/LmzjQcteZPScfB+dy+D6qFta6Vj1YJbzqehrkY6Vi2QQ9vWyq5NS0MtJpleEPSCR5F8OoONdehua5E1i+YFek3QxTcd7YTNi1feZoYwkp4Z0S9XgzdMUAlOhT287Koq0lolI+q+LKKlEjjae2xA+t8e1XxGSAIdbW6h3mMDsnX3cZmYzGo9LvSZyuZk6NKYvD48IkOXxmQqm4v7lCITVwD+Lz98j7z4O2vlvjvnyNbdx32fR5j7KYq+lMo4rpJyc6FJ4vK8bhRziXHzNnibvFfjluZnQVg613bCcGGOkPE8AAAAACQX0YUAAGt4CxUdqxdK79FzsqdIKdsty5qkvUIpW7KKuC+qEt6Fiv3uJjOoRrEgmITgUVRmW7BxFBngdLQTti9exZ1JbCqbczpztMvBGyoKf7fRa5NKwakdqxeyqcMiVMkAbqUaONp79Jx0bVyi6WyQJCYyWlOBwU7eZp69ReahNi9rkscrzEO5TkdltSCKze9t33cqkoomUfSlwozjHrrvDnng7rmy7x9+EGouVLc4x4JJmNdNcjWmJFcfsvlZ4ML8jK61nTBcmSNkPA8AAAAAyeRWNAEAIBVUFyrinHR1YULcFaZLeBdT7nf3MqjqpGNhsRLXgkcRjo3BxlEsviV98SqucvY2LzwHkYTgjSBK/W6zFK8HglPt4mVXDRN0TJUMJJGOwNE9Jwelc8Nixm24hUqbW46uTW7MP6ibmMyW7WtfvHpdevr6paevX1tf20Y6Kqv5sfFj82X7zyy+5VpVrWgS5H6Koi+lMo7r+pklsd7XNowFXQmm9cPEXGKcorxXo2Tzs8CGezKouDahuDBHyHgeAAAAAJKJqCwAgLVUFiqinnR1cULcdqYCHsqJOiDXdMZ/l4JHocbmYGOTi29pWLyKMpOYzQvPYSQpeKOcSr/btcms0vEJTrVP3FUyAJvoCBy9ePW6DI+MJypIDvqEaXP9UNnkxvyDHhOTWdm6+7jv37b32IAMXhiT55940Oo+cBhRVNBbs2iePPfpTxT97qKuaBJFXyrsOC6uoG3bxoIuBNOmURKrD9n6LLDtngwj6vbMlTlCxvMAAAAAkDx2jcgBANDEm3QNo721WZo/UCdDl8bk9eERGbo0JlPZXNF/OzGZle37Tsm6Zw9LT1//LQEg3oT4o8+8JNv3nZIJxUC4tOlua5E1i+YFes38ueGCiOMKyA17nfrhQvAo9PCCjVWYvl68xbf7G+tl/tzZ2t4rTDvh4uKVt5nhRNd6OdK5Tl548hE50rlOTnStl66NS7QE4G/dfdz3YmXvsQHZuvu49c811TbW9uCNoL9bGF5wKuzhZVf1e323tzYnMmAQENEXOBpFACrcFLTN9WvPycGS8wylMP+g1479pwNvrjj82nnZsf+0oTOKj+kKeuX6IroqmgS5n6LsS5kex+lg41hQdV7Xhu81aeK4V6Ng47PAxnvSFS7METKeBwAAAIDkYcQGAEisMJOuD913h9TMqJLlOw/KyqdflMeee1lWPv2iLN95UJ46cEb63x69+W+ZEDcvzKT0wf/XGusn2/OpLCxWYnvwKPRKerBxKWlbvDK1mcHGhWcdkh68EeZ3C4PgVPt42VUPbVsrHasWSENdzbT/3lBXIx2rFsihbWtl16alzrZ5QCW6AkdNB6DCbYVt7m2zZigfM+gmN+Yf9PKqCYTRe2xg2txQEniV1VQUjkr89kV0VjQJIuq+lKlxnA62jgVdCKZNk7juVZNsfRbYek+6wJU5QsbzAAAAAJAsrDABABLLm3QtV7o130cab5NvvXFBvvXGhVv+W7ESryoT4rs2LQ30ujQLU8I7yO9uQ8neMGVoK3EheBR6uVJ22YQw7QTep7rw3LF6oXxwpuaT0iippb5VfregCE4NZyqbk+GRcRm9NilzZlVLY32t9oArL7tq54bFxt/LBlF8p3CLFziqEhTWUFcjjfXhqmkhXbw292eX3CH9p15RPl6QTW7MP+il2ofqPXpOujYu0XQ28fMqq/X09Yc+xmd+4j7Z+sjCwM/ouCuapK0vVUjHWNDUGDvovK4N83tJFve9aoKNzwKb70lXuDRHmPZnEAAAAAAkBSvpAIBE8zPpuukTd8vpH16WV95419cxe48NyGv/PFI0WN/v65kQDy7IpLRLk+0iwRcWK3EheBRmJDXY2C8Wr8LRsfD82UeaNJ2NfkkN3ogqAJ/g1OC8wIm9Rfofm5c1yeMG+h9edtWkiuM7hRt0BI5uWdZEPwGBzKmNtgIDAXl6TWVzsvfkoNIx9pwclM4NixPVdrS3Niu1pb+68r5QfRFbKpokvS9Vio1ByPlcm99LMlvuVV1sfRbYfk+6xKU5wrQ+gwAAAAAgKeyY7QAAwLByk66//5VXfQfge8IG4HuYEA8vyKS0S5PtlRYWZ1ZXycRktuJxXAkehRlJDTYOisUr/3QtPP/26rs1nZEZSQve0PG7+UVwqn8Tk9my7W+xykpJa3914zuFH6qBo+0r7tV4NkiK/MobtTUzRERk/PqUzJlVLQ2z1UsABdnkRkBeaWEqpAyPjCtVzxC58fwZHhlP1JgjrspqVDSJj61ByMW4NL+XVEm7V218Frh0T7qEOUIAAAAAgGkE4QMAEq3Ygmz+pKtKRjkVTIhHy6XJ9nILiwPvXk1E8CjMSlqwMczStfD8zuiEpjMyKynBGzp+N7/SEpwaJogv38RkVrbuPu67EknvsQEZvDAmzz/xIEHjJfCdwq+4AkeRTKUqb+T7yB0z5Lc/qvY+fje5EZBXnEqFlNFrk1rOQddxbBJHZTUqmsTHxiDkSlya30uapN2rNj4Lor4nVcfASAauAwAAAABQRxA+ACCR/C7IxhGAL5LMrGnQq9jCYlKCRxENrhf4oWvBeGzCrSAk14M3ogr6SkNwqkoQX74d+08HClgTETn82nnZsf+07Nq0NPB562LzgrsN36nN3w+miyNwFMlSqfJGvpFx9eew301uLgbJmqSjQsqcWXqWRHQdxyZxVVajokk8bAxCht2SdK/a+CyI6p7UNQaG27gOAAAAAECf5M0UAwC0cyn4JMiC7C8/fI987dRbEZ/h+1ikQliuB48iWlwvKEfXgvHsmQwtoxRF0FfSg1N1BPF5VCor9R4bkI7VCyNf3LZ9wT3u79T27we3iitwFMkQtPKGqiCb3AiSfZ+uCimN9bXSMLtGLo6F39zQUFcjjfW1oV9vszgqq1HRJB42BiHDbkm6Vxvra6WhrkZpo5vuZ4Hpe1LnGBju4joAAAAAAP2YHQMAlORa8EnQBdkvvvKm4TMqj0UqAEDcdC08f3DOTI1nhUp0/G7lJH2hVVcQX/5/V9F79Jx0bVyidAy/XFlwj+s7deX7QXFxBI7azqUN9XEKU3kjrKCb3AiSfZ+OCinePNfY9Smlc9myrCnx91LUldWoaBI9G4OQYb+k3KszqjKyeVmTUmZ/3c8Ck/ekyhgYyaF7LgRAaYzFAQAA0sX92XcAgHauBp9EuXCuikUqAIANdC08V2VYRIiSjt9tVnWVXJvM3vxzmoJTdQTxeaayOdl7clDpfPacHJTODYuNL8a5suAe13fqyveDyqIOHLWRaxvq46RSeSOoMPMnBMneoFoh5dd+/D75s2++oe23bl9xr5bjuCCqympUNFETJtDLxiBk2C9J92p7a7PS9a/7WWDynlQZA/+7n0zPMy9fEgNodc6FACiOsTgAAEA6EYQPAJjG1eCTKBfOdWCRCgBgCz0Lz2rZRBGc6u/21c+uljmzZiRqQdkP1SC+jtULpy2WDY+MK1ckuHj1ugyPjBsPcHNlwT2u79SV7wf+RRU4ahNXN9THSec8wgMfvl0GL45prcBAkOwNqr/Tb3zhWzJ4YUzLubS3NhM4YwgVTYJTDfSyLQgZbkjKvbpw3m3S3toc6hlj6llg4p5UHQM/vrwx9Pm4KKkBtLrnQgBMx1gcAAAg3QjCBwBM42rwiUsB+CIsUgGmJDFLEWCajoXny5cvGzgzlKP6u93feJuBs1ITRRuu2mfsPXpOujYuufnn0WuTqqek9TiluLTgHsd36tL3A5Ti6ob6OOmovJFv8OKYvPJ7j8k7o9e0PsvSHiSr43fSFYC/ZtE86W5r0XIslEZFk8p0BXrZGIQMdyThXu1ua5HBC2OB1kNMPgtM3JOqY+AD3/mhLFI6ghuSHkCrey4EwPsYiwMAAIAgfADATa4Gn+heODeNRSpAv6RmKQKiYtvCM/xJyu8WVRuuo8+45+SgdG5YfDOoZM4sPdMquo5TiksL7nF8py59P0Aprm6o1yHsJi4dlTfyXbx6Xd4Zvaa9AkPag2R1/05huRhw57o0VjTxQ3egV1LGFIiPjns1rqQaM6ur5PknHiwbfJ0vimeBzntSxxj4f535Z1n0UaVDWC/pAbQm5kIAvC/NY3EAAADcQBA+AOAmV4NP4lyQffi+D8grb7zr+9+zSGUOGdDTKelZipAecbdhNi48ozLXf7eo23AdfcaLV6/L8Mj4zSCTxvpaaairUTpuQ12NNNbXKp1XOa4tuEf9nbr2/SC8uJ+1Jrm6oV5VqU1cc2dXy08/MF8++cBd8qN31Zf8rU1UITFV2STNQbKmq8WUM6u6Sn51xb3SzqZuWER3oJfrYwq4pbA/NnptUr74ypuxJtWYWV0luzYtlY7VC6X36DnZU+RctixriuxZoPOe1DEGHhmP7zkclaQH0JqYCwFwQ1rH4gAAAJiOIHwAgIi4HXwS14KsN8HNIlW8yICeXknPUoR0sKkNs23hGf64+rvF0Ybr6jPmH2dGVUY2L2uSnr7+0MfbsqzJaP/ZtQV3Hd/pT7fc5fvfuvb9IDibnrWmuLqhPqxKm7gujU3KX37rTfnLb70pIiINs2tk8/Jbf2sTVUhMVTZJc5Cs6Wox5dTWVLHJClYxFejl6pgC7ijVHysljqQaC+6cI10bl0jnhsWxb9zUdU/GuZEtn82bYdMQQGtiLgTADWkbiwMAAKA4gvABACLidvBJHAuyXkY5FqniQwZ0JD1LEZLN5jbMpoVn+Ofa7xZHG66rz1h4nPbWZqWA8fYV96qeUlkuLrirfqdf/Nab8rXTb/kKsHbx+4E/pp+1tgQTubyhPoygm7hERC6OFf+tdVTeyGe6skla5x90/05BXBqbZJMVrGI60Mu1MQXsV6k/5kfUSTVmVGWsafdV78k4N7KJuLEZNg0BtKbmQoC0S9tYHAAAAKUxWgIAiIjbwSc6FmTnzq6Wn1n6YfmLV8JllGORKlpkQEcashQhuVxpw2xaeIZ/LvxucbXhOvqMxQIsF867Tdpbm0N9pvbWZuPPIxcX3FW+U0+pAOvCwOnamhlazpmABLuYfNbaFkzk8ob6MMJs4spX+FurVt7IZ7qyiSdt8w86KqSoYJMVbBFloJcLYwrTCvuMH5wzS94ZvZb4NlenMBvnSkl7Uo2w96SOMXB9bbWITAV6jc2JJ/KlJYDW1FwIkHZpG4sDAACgNFYIASDBgmTmczE4x6NjQfZTy++Rro1LZOsjahnlWKSKBhnQYUuWIlsyoMIttGFIu7jacB19xlIBlt1tLTJ4YSzQve1VVjLN1QX3MN9pKb3HBuS1fx6Rlg/Plb/59g+mfRdzZ1fLrOoquTaZDX18AhLsY+JZa2swkcsb6oNS2cSVL/+3Vq28kc90ZZNCaZp/0Pk7BcUmK9iCQK9olNpslxGRXN6/symTt61UN84VIqlGcDrGwD+15EMiWf/9L1cST4ikp101ORcCpFmaxuIAAAAoj1SwAJBAZ89fkacOnJHlOw/KyqdflMeee1lWPv2iLN95UJ46cEb63x695TVecI4Kv8EnU9mcDF0ak9eHR2To0phMZXMVX1NJe2uz2uvfWyz3Msqd6FovRzrXyQtPPiJHOtfJia710rVxCZP8FlDNnlvs+odbdGUpUml7wrSzgAhtGBB3G66rz1hoZnWVPP/Eg76P397aHFmQgbfgriKOBfeg32kl33rjgnzhm2/cEmRxaWxSKQBfhIAE25h41nrBRH6P23tsQLbuPi4TiteWHy5vqA9KRwB+/rH63x69WXlDVRSVTdJM1+8UFJusYBMCvcyamMzK9n2nZN2zh6Wnr/+WPmPh6MPbfPfoMy/J9n2nInnmu0TXxrlCvUfPaT9m0qk+Pzd+/MOB/r3KZtiopaldNTUXAqRZmsbiAAAAKI8gfABIkEqLBeUWB6IIzjEZtKqyIFtssdzLKHd/Y73MnzuboBqL6MieC7fpzFIUlEo7C4jQhgFxtuEi+vuM+WZWV8muTUvl0La10rFqwS0bXBvqaqRj1QI5tG2t7Nq0NNIsf64uuFf6Tm1BQIJdTDxrbQ4minJDfZx0bOIq5P3W3W0tsmbRvNDHefC+OyKpbJJ2YX6nuxvUstKyyQo2IdDLnKCb7QqF2XxnIkmNTUwE4IuoJ9VII9UxcJBnqWuJJ9LUrpqcCwHSKi1jcQAAAFRGED4AJISOzHymgnOiCloNsyC7ZtE8FssdEnf2XNghrixFNmdAhRtowwA7Ms2Z7jPaWFnJ9QX3/O/0lx66J9ZzKWTD94P3mXjW2h5M5Gq1i6B0bOIq5P3WqpU3dvzsA5FurEqrMFVn/uTXH1J6TzZZwSYEepkTZrNdIb+b79JQWdHExjmPyobsNItq3cS1xBNpa1dZPwP0SstYHAAAAJWxOgAACaEjM5+J4Jwog1bDLMg+/8SDiVwsT2o2pbiz58Isv9dtXFmKbM6ACjfQhrknqc/TqOV/j7qC8FWeBVH1GW2rrJSUBfe/O/1W3Kdwk43fT9qZeNa6EEzkarWLIHQ9P/Ll/9ZBKm/U105/BtXMIGgiKkGrzvzoXfVOb0ID8hHoZYbKZrtC5TbfpamyoomNc/lM9An8cnWOIIoxsIuJJ9LWrrJ+BuiXhrE4AAAAKrO/PhoAoCLVzHwdqxfeXFjsbmuRwQtjgQJNywWfqASt7tq0NNDrRN5fkO1YvVB6j56TPScHp036N9TVyJZlTdK+4t5ELqZ618LeIp9787Imedzxz21D9txiprI5GR4Zl9FrkzJnVrU01tc6M/lug6DXrZelSGVBL2iWIp3tLNLL1jYMt0r68zQqpb7HjIioLKvryDSXxj6jt+C+Y/9pX8+09tZm6W5rsWrB3XRATxA2fj/Q/6zVFUzUuWGx0fGBt6E+TH/VlUBjXRtxCxVeM17ljc4Ni2+O8WprZoiIyPj1KZkzq1pmy4QcfuklI+cDf4r9TqXG4rrnuYA4tbc2S09ff/jXE+h1C10B+DePd/ScdG1cMu3vvCQ1ftuh3mMDMnhhzNngV9NzFqb6BOUkYY7A9BhY52bY+XNnKx0niLS1q2mcCwFMSsNYHAAAAJURhA8ACaAjM5+3OKAzOCfKoNXCIOjmD9T5XpBNgonJbNnfzMum1NPX73TAUFwZ0EtJwgJMnMJet16WIpUFkqBZinS2s0gv29qwKLi2SSktz1PTKn2PqnntdGaaCxLElwSuL7hHuQlpVnWVzK6ZIRfH3Pl+oP9Z61IwkalAY1ue5To24hZT6prxqpkUc/myHZuBUP538iRhExrgsT3Qy5Znhl86NtsVKrb5LuokNXEzOWehY0N2EEmcIzA1BnY18YTt7aopaZsLAUxi0y8AAADcid4AABRlIjOfruCcKIJWCYJOVzYlExnQwywQJnEBJmqq122UWYpcyYAKdaYDBuKo4hCXwQtXZe/Lg049n9P0PDUp6PcYholMc36C+JLEz4K7jUFUUW5CujaZlb//nTUyoypj1Xfgl42/XxR0P2tdCibSHWhs21hbx0bcQq70q6DO9U1oQD4bA71se2b4ZaLKUuHmO9UkNZ/5iQUyZ9YMp/p0pjbOiejdkF1J0ucIdI+BXUw84Y2ZHl/RLP90/oocPfuu79cmJYA2bXMhgAls+gUAAABB+ADgOJOZ+VSyYZgOWiUI+n1pyqakMwN62AXCpC/AREX1uo0yS5FLGVARTlQBA3FUcYjLb3zhuLw1dut52vx8TtPz1KQw32MQLmeas1GxBXebg6hMBvQUM359Su5vrI/kvXSx+feLgu5nrWvBRDoCjW0ea6tuxC3kSr8K+pD1FUlgU6CXzc8MP0xtkss/rmqSmp/53Nfl2mT25p9d6NOZ2DjnMbEhuxTmCIJxKfFEqTHTzOoqmci730qxsT0DEC82/QIAAKQbQfgA4LgoMvOFyYZhMmiVIOj3qWZT6li90LkJH9XAi08/dI9s33cq9AIhCzDqdF23UWV/cykDKoKJI2AgqioOYTMgR5k52abncxqfpyaofI9+JCXTnK1cCKIyGdBTTJRZGFW58PtFReez1qVgonxhA41tH2urbMQtJspAPtiFrK9wnQ2BXrY/M/ww1dfzjqsjSc21goDgKPt0KvMDujfOeceMatzNHEFwLiSeqDRmyg/ALwzIJ4AWgB9s+gUAAEgnd1YTAQBF2ZqZz2TQKkHQ71MNPug9ek66Ni7RdDbRUAm8+KWH7pFdX/1e6AVCFmD00HXdRpX9zdZ2FmriChgwXcUhbAbkuDIn2/J8TuPz1ASTAfhJDxqOm0tBVCYCeoqJI3A6LJd+vyjofNa6EExUTtBAYxfG2mE24hZDZRUASRBnoJcLz4xKTFRZyu9D6khSU46pPp2O+QHdG+ei3pDNHEE4USWeCCPomGliMisrFnxAfr+tRe6YU0MALYBA2PQLAACQLslbaQOAlPEWC1SYCDAxFbSqGgTd//aojtOygo5sSntODspUNqfpjKLT3dYiaxbNC/SaNYvmSSYjoRcIRfQswKSd7uvWy/52aNta6Vi14Jb2sKGuRjpWLZBD29bKrk1LQy1KfnDOLLm9Vq1NcymQLwpT2ZwMXRqT14dHZOjSWCztkErAgKqwbVi5BeeJyaxs33dK1j17WHr6+m9Z6Pey5T36zEuyfd+pm9m8wr6u0OCFq4E+T764n89pfp7qpON7LFzO1tGGw58428SgvIAe0+IMnA7Kpd8vKjqftarXmyvZ1l0Za3sbcVV+l7sbZsuvrbxP30mlnA19eyDtvECv+xvrZf7c2cb7MK48MyrxNtvplN+HjKIios4+na75AU+Y/lgx7a3NkW4eZY4gPJWxmukNkmHGTEf735XeY+ciaVcBAAAAAO4iHScAOE5HZr6fbrlL4xndoCOTULGgVbLQvE9HNqWLV6/L8Mi4cxkZwmRAf2LlffIv/uPLod6v99iAfOYnFmhZgOncsDjVk/amrlsT2d/yM39dHldbOHUpkM+kuLKtlzqPMHRUtdBdxSFsBuT//CvL5F//xUktmZMPfHdIftTXUUocO8bn8w8uXE3t81QnHe17TkS+8q9/XObMqqZUc4TibhPD0JUJuxxbA6ensrlpfZ3Ra5PO/X5R0PmsNV3FxhYujbW9jbgdqxdK79Fz8uUTb8qlMf/99R9cHJOf+o8vU2VFkS19ewDRc+mZUYnuKkv5fcioKiLq6NOZqKwUtD+Wr6GuRrYsa5L2GJ4laZ5z1yHMWM10pQMXx7wAAAAAAHewwgAACaCame+L33pTlu88KE8dOKMtE5GOTEKFQatkoZlOVzalKLIymRA0A/qXjr+p9H49Xz+rbQEmzUxftzqyv1XK/BWGrYF8UdGdTU2VDVUtdFZxCJsBedP//Q0tmZOnsjk5eOafAx2nUFzP54nJrDz5pe9oOZarz1NddH3+ObOqI8vgiRtsaBOD0pEJuxwbA6fPnr8iTx04I8t3HpSVT78ojz33sqx8+kX5mc/1KR03yZWadD5rTVSxsYmrY21vI+7Jf/9TcqRznfzxE8tl/lz/1ad6jw3I1t3Hjfc9k8a2vj2AaLn6zChFZ5Wlwj6kjgq2fqn26UxVVvLbH3vhyTVypHOdvPDkI3Kkc52c6FovXRuXxNInT/ucu6qgY7UoKh24OOYFAAAAALiDTPgAkAAqmfk83iJpT1+/tmxwqpmECoNWyUIzna5sSlFlZTLFTwZ0HQuEB04N6Tjd1C7AeGy/boNm/vLDxkC+KJnIpqZCV8CArqoWqlUcVLJ5fX/4SqjXFWYBGx4ZlxHFahFxPZ937D8tx89d0HIs15+nqmxv31GcbW1iEIWZsPcUycS86RN3y+kfXpZX3njX93FtC5yemMyWzR56TTG4NQ2VmnRUTNJdxcY2ro+1vY24/+nF12XoUrBNz14A4a5NSw2dXbLY1rcHbFJYrSapVZ1cf2YUo6PKUrE+pI4Ktn6p9OmiyBJuooKlinL3qy1jW5fbFD9jtagqHbg85gUAAAAAuIHVbQBICB2LBR5di6QqmwOKBa2ShWY6L5uSysJXQ12NNNb7zxRoMy/wohgdC4SqAaaetAcX2n7dhsn8VY5tgXxxUMmmZiIYytaAgXJtWDmq2bzC6j16Tro2LhERd5/PKoEGhZL0PA3L9vYdxdnaJgZRKaCnUhB7PtsCp01sDiwU9+8XpbDPWo9NwUS6ufoszxdFACHs69sDNvDan71FngublzXJ4w4+F8pJwjOjUNDNdoXK9SFVk9T4pdKn05El3JsfqES1P6bKz/3a/IG6WMe2SWpTbNh8kYQxL5BULm82AgAAAPKlOwoMABJEdbGgkK5F0jCbA0oFrdqShcYWOrIpbVnWFNmkVpwTaroW9uprq5WC8QkutPu61RmQK2JfIF8cbAyGSlLAgI5sXmHlZwGL+vms63mi836P8nlqK5vb96i4uHhoY5sY9nssFdDjcuC07s2BpdjwTHOJDcFEuiVhrB1lAGFa2di3B+JUaaOfiaqjNkjCM6OYSn3GjIjk8v693z6kjgq2foXp06UlS3jQ+/XnP3G3fOGbb4R+vzBj2yS3KXFuvrBxzAukXZI2GwEAAAAiBOEDgFVUA3cqLRYEpWORNOjmgHITyI31tXJ7bbVc1hQE7WKgVCHVbErtK+7VeDbF2TChpmth72eWzpe//NaboV/venChLrZetzoWRG0O5IuDjcFQSQoY0JHNK6yLV6/L0KUxabqjThrra6W+tlpEpkIfz88mJZ3PE90bGKJ4nrrA1vbdNBv6OmHZ1Caa/h5dC5zWvTmwHBueaS6KO5OrTq5XM0lLAGHcbOzbA3EJWq1GV9VRG7j+zKikVJ/xg3NmyTuj10L1IXVWsC0nTJ8uDVnCw9yvD913h9J7Bh3bprlNMc2mMS+QdknebAQAAIB0Y8QIABbQHXCSv1iwfd8ppaBhHYukOrJPepMzKgH4IjeCoM+9M+psoFQhlWxK7a3NRj+nTRNquhYIO1YvULqfXA0u1M3G61ZH4M7ttdXyyu89xsTwe2wNhkpSwEDcWbh++o++Lp9+8B55fMW9sn7Jh0Sy4QOzym1SMvE80bmBwfTz1CU2tu8m2dTXCcuGNjHq79GVwOmoAvBteaYhXq5XM0lDAGHcbO3bA3EJU61GV9XRuLn+zPCrWJ8x7DNCdwXbYsL26dKQJTzM/fqtNy7IRxpvk+8PXwn8fmHGtmluU0yzYcwLgM1GAAAASDZ6rAAQo4nJrGzfd0rWPXtYevr6b5kI9AJOHn3mJdm+75RMTGYDv8ffnX5L6Rz3nByUqWyu8j/0wdsccKJrvRzpXCcvPPmIHOlcJye61kvXxiVlA/C37j6uZZFieOSa0e87Dt1tLbJm0bxAr1mzaJ50t7UYOqPgv1nvsQHZuvu4se/cWyBUsWVZk9zfWC/trc2hXu9icKGKqWxOhi6NyevDIzJ0aeyWdsS261ZH4M7l8Ul5Z/SapjNyn85gKJ10tQc2BAzEnYVrZHzy5nPz3SsTSscqtUnJ1PNEV4DAQ/fdYfR56iLb2ndTbOvrhBV3m5iU71E33dU6yrHlmYb4hR3n3Hx9jBuO0xBAGDdb+/ZAHFSq1fQeG5D+t0c1n1H0XH5mxMVLUnNo21rpWLVAGupqpv33WYoBhmH7dEnPEq5yv35/+Io8fN8HAr0mzNiWNsWsuMe8AG5Q2WwEAAAA2I4gfACISRQBJ7YuknqZhO5vrJf5c2dXnMAMMzlTzPy5tfK33/mhr3/rUoCPl03J7wJYe2uz8ewRNk6o6VogTEtwYVhnz1+Rpw6ckeU7D8rKp1+Ux557WVY+/aIs33lQnjpw5ubCkG3XLYE7+tn8nSYlYMDL5mWDlxSe0+U2KZl6nugKEHj20x8nG1MB29p3U+Lo61Ta4BZWnG2ijX1GG+is1lGJLc80xM+rZhJG3BuOkx5AaAOb+/ZA1FSThPQePafpTOLj8jMjbqWS1Hz1s6uUjhu2T6djXsGmLOGFY6Y/V7zfWj58u/GxLW2KeUmZBwRcxWYjAAAAJJ1bq9wAkCBRBJwkYZFUZXIm3/y5tTJ0KdhmApcCfCplU2qoq5GOVQvk0La1smvTUqOBbrZOqOlaIExLcGFQYSp72HTdpiVwx1QAZzE2f6dJCRjQkc0rbuU2KZl8nugKNLi7oU7pGEllU/tuQtR9Hb8b3MKKq020tc9og6jGXzY902AHVzccJy2A0EY29+2BKOmoVqOz6micXH1m2KIwSU1c1TeTkiW81JjpT7/xhtJx9337B/IHP/eAsbGtjjblyyfelMELVyOZ63NVUuYBAVex2QgAAABJx6w3AMRANeCkY/VCXxN/SVgk1RGA3/ax+bL/u0Oh39/v920DL5tS54bFMjwyLqPXJmXOrGpprK+NbDFEx4Ra18Ylms5muu62Fhm8MBZoA0yxBUIvuLBj9ULpPXpO9pwcnBZ03lBXI1uWNUn7inuduXZUeJU9/H6vvccGZPDC2M3NCTZct17gjkrWWZsDd7znzt4i1+rmZU3yuIFr1fbvVFd7ELf21mbp6euP+zRCaW9tlu62lpKL1CafJ16ggcp3Z0OgQRBT2VzkbawN7bsJUfV1JiazsmP/6ZLv521w6+nrr3g/VRJHm2hznzFuUYy/bHymIX7ehuNybU8+1bZHlzQ+16Nme98eiIrOqqPz587WdFbxcPWZYbO45ilU5xXizBJeacykGo7u3a+mxrY62pRLY5Oy6g8P3fyzybk+lyVlHhBwja4NjJ0bFjNeAwAAgLWY7QKAGES169/1bHA6Jmfqa6tlXv0spWO4mGWhMJtSVJNTtmcE053FvlQJ6RNd66Vr45LULHToquwR13XrvXcSMn8VClOhQBfbv9OkVLVQyeZl0p9+5iGlLHFRPE/SUo7cdBZ1P+Js33WLqq/jbXDzO27oPTYgW3cfD92OR90m2t5njJuOcVw5tj7TYAdXq5mk5bkeF9v79kBUklB1VCdXnxm2imuewtUs4UHHTGHl36+6x7Ym2gKTc30uS8o8IOAanRsYAQAAAFuRCR8AIhblrn/Xs8HpmJwZGZ+UPSfIshAVFzKCmchi7y3ApFFUlT2i4HLmr2JUKxToYPt3mpSqFmGyeZk2f26tUpa4KJ4nXqBBmDbMhXLkUWZRT5Oo+joqG9x2bVoa6ryibBNd6DPGScc4blZ1lVzLC7ix5ZkWR1UOhONaNZOkP9dtYHvfHohCEqqOmuDaM8Nmcc1TuJglPMyYKQyT96vptsDEXJ/LkjIPCLiEDYwAAABIg2TN9AGAA6IOOHF5kVTXpMrlcbXjJDnARzeXJtRYINRDR2WPro1LNJ2NmqQF7sQRwFnIle/U9fbAy+b17/Z8R/Z9+4dxn46IiLwzOiENc8NvUorqeRJHoEEUAag2bMJJqiiuzbg3uEXRJrrUZ4yL6jjuq59dLXNmzbDmmeZd13uLBLpsXtYkjzsS6JLGTQQubTh2MYDQJa707ZF8cbbFXrUalbndOKuOmubSM8N2Uc9TePMK5TZy54t7I7fKmCkI0/erjjalEt1zfUng+jwg4BI2MAIAACAN6K0CQMSiDjhxeZHUpkmVJAf46KTrN6utmaHlOH6wQBhelJU9opKUwJ24AzjzufSdutwezKyukn+97n5rgvDHJtSem1Et0EQZaBBlAKoNm3CSKopr05YNbibbRBZhK1Mdx93feJuBswouKVU5krKJIOlcCyB0kUt9eySPDW2x61VH4Z4o5ylcyhIeRQC+iPn7VUeb4odt1Uht4fI8IOAKNjACAAAgDVhhAICIxRFw0t3WImsWzQt0fBsWSb3JGRW31xLgEyUdv5mIyMbP9clTB85I/9ujGs4Kpuis7GELL3CnvbXZ179vb222MnO0jgBOXZLynbrApmfV7Jlq56LjeeJ3gcYLNDi0ba10rFpwy/s21NVIx6oFcmjbWtm1aWnga3NiMivb952Sdc8elp6+/lvaTS8A9dFnXpLt+07JxGQ20PELqW7C4dlbnulrU9cGt6lsTukYpkV5j7vMpXHcVDYnQ5fG5PXhERm6NCZT2dzNqhx+26TeYwOydfdx5XZQp6jbcKgz/VxPO/r2iINtbbHf67/k62OsOgr44WUJP9G1Xo50rpMXnnxEjnSukxNd66Vr45LYA7l1jJn8iuJ+VW1T/NI51wcAfnmbjVSwgREAAAC2sydKAwBSIo5d/65mg9OSXWp5k/z1P/yALAsR0ZW95+KYGxkx0y7qyh5RcSnzVzE2Vihw/Tt1hY4+xtzZ1ZKRjFwcU9tg88E5M5VeH0eGSZ3lyKeyufc2Kk3Ijv1n5OjZd329rvfYgAxeGFMKVrMli3pSmb42dW5wszmjH1lk/XFhHFcuK/Fdt9fKP741Euh4NlXl8DYR+M34raMNhz46n+uYjr49omRDW+z17b125N4PznG26ijiVXgt2f5MsjVLuI4xkx9R3a8qFbCCsK0aKYD0aG9tVpr/YQMjAAAAbEcQPgBELK6AE1cXSVUnZ3515X2SyWQI8ImQ6m9WiGAWe8VR2SNKrgbu2BzA6ep36godfYxPLb9HciLK7XhVRv33jGuBRiXQoFQwahCHXzsvT37p27L9ZxYHvjds3ISTRCavzaRucCuGRVh/bB3HTUxmy24OuHj1euh2sPfYgHSsXhj7uHTH/tO+gz49Nm0iwA22BhAmAX17RCHOtrjcRrOf/8Td8vB9H5BX3vC32VbEjqqjiEe5a2nzsiZ53LL5eNtFMdaJ+n7tbmuRwQtjgdu7IFzYrA0gmVQ2G7GBEQAAAC6wM+IJABIuzoAT1xZJdUzOEOBjls6MYKUQzGKnOCp7xMG1wB0XAjhd+05douOZl8vltG6mCsulBZpKwahBHfjukBz47lDgoAybN+EkiclrM+kb3PK5dI/bwKZxXNCsxGHEXZXDC5YLw5ZNBEBU6NvDlLjaYj8bzb7wzTdEROQjjbfJ94evVDwmVR7Tyc+1RCXQ4EyPdeL4LYJWwArLhc3aAJIpzGYjNjACAADAFczmAEAMvICTMIoFnExlczJ0aUxeHx6RoUtjMpXNVTyOt0h6f2O9zJ8728oAfE93W4usWTQv0GvyJ2d0f9+44ez5K/LUgTOyfOdBWfn0i/LYcy/LyqdflOU7D0rNjCp5+L4PaH2/3mMD0v/2qJZjhblnkizs9+Fl3VZBpQn90hTAiVvpeOapHEM31T5AFLxgVBML5V5QxqPPvCTb952Sicls2X/vwiacpDB1bXob3FS4sMHN48I97keUfUsbxnFhshIHtefkYKx9dNU2vffoOU1nAiAsxv3ui6MtDtq3//7wFXnovjvkMz9x3y19uIa6GulYtUAObVsruzYtJbg6ZYJeS73HBmTr7uMVx3wuMN3+6hgzFfagbbhfvQpYh7atlY5VC5Q/YzHM9QGIi7fZyO+cc3trM9WxAQAA4AxG2wAQEx27/tNSyjZoJphi2WrIsqCPiYxgfqlmxEzLPeOXju+DShP20VGhoL622pkATtxKxzMvilLofujoA5gWRTCqyI2gjMELY2UXoNiEEx1T16a3wU3l2erSBjcX7vFy0ti3VMlKHEScVTmmsjnZe3JQ6Rh7Tg5K54bFztyLQJKksW1Oorja4jB9+2+9cUEWfaheTnStj71aDYorrOIZxW8T5lpyvRJoVO2vjjHTZ37iPtn6yMKK10Qc105hBaxdX/2eHPjukPJxXdqsDSCZvM1GHasXSu/Rc7KnyPNiy7Imaae/DgAAAMdkcjkywABRyWQyLSLyqvfnV199VVpaCPBNosuXL8uhQ4du/vnRRx+V22+//ZZ/VymYOV9+wEnY1yVB/9ujoSdn0vy96eJlcQqyiPTQfXfIA3fPlb8+OSiXxtQy6zbU1ciJrvWBFzv47afT/X1s33cqVDBWe2uzswuLtnvqwBmlxUiRdNwLcTK9kKvjPg9yjI7Wu2Rp1ftBMqX6PipU+gCmnD1/RdY9ezjS9yzXdk5lc7J850GlTThhn7VppvvaVL2uDm1b6+RipY33eClp7lvq6GP49cKTj8j9jfWRvFe+oUtjsvLpF5WPc6RzXSybCGwRR9BaVPzO+cQpyd9/KWlum5MojrY4rX2wpCjW7p17ZzSWTTlpu5biaH9Nf8e2bOjSOefQsWpBqOQ2LvR7ALgpjWMWAHaj3wMAbjt9+rQ88MAD+X/1QC6XOx3Fe5PeDgBiFGbXf9AgaD9ZU11SmAkmyOQMWRbUqWQE++pnV8uqPzxU+QVlhMmImfZ7ppCJ74NKE/ZRrVAgkvx7IS5RLeTqeOYFOcYdNZNy+KX3g/CzBjZ7q/QBTIkiG3Sx9+xYvbDo75a2LOq20H1tLpx3m7S3Nofe4OZqP9bGe7yYNPctdWQlDiKuqhyj19Q2Dus+jmtsCVpLq7R+/2lum5MqjrZYtW+vWr0R4ZRq97xENsVcvHpdevr6paev38imnDRdS3G1v6bGTH4qwJq8dgrpnHMIUo00PzA2M3lN2zkAQL4ZVZlUb1wHAABAcpAJH4gQmfDTI+wuWT+7/sk4rQ9ZFoJRzbzzhc88JL/+p99SPo+gGTG5Z6Yz9X2QddA+YX/rQkm9F6IW9z2i45lXLrPgy6cH5Lc/+v7C7H/6x1nySEtzYgO9RPRknQ+rXAa7tGVdTKow1YfWLJoXSTBh2vvQae5b6spK7EecVTnIhB9O3H2dKNmYGS1N338xaW6bkyrqtpiKUu4J0u5VorMfnbZrKc72V/eYybYxmM45h/bWZvmDn3ug4jiu2KaWu2bnpPMTUzf/jQ39HgAAABNsnO8BAPgXZyb85My0A0ACeLv+72+sl/lzZ5ecBA2j99iA9L89quM0E6PS943pVBe1/uerb2k5jyAZMblnpjP5fXgZsw9tWysdqxZIQ13NtP/eUFcjHasWyKFta2XXpqWJCviwVXdbi6xZNE/5OEm7F6ayORm6NCavD4/I0KUxmcqa35TsLeT6vf96jw3I1t3HS2btC0PHMy//GB+cM0t+/yuvyrpnD0tPX7+MjE/PMDkyPik9ff3y6DMvyfZ9p7R+FlsMj4zHEoAvIrLn5GDJa9fLCBiGy1nUk2ZmdZU8/8SDvn/L9tZm4wH4Z89fkacOnJHlOw/KyqdflMeee1lWPv2iLN95UJ46cCZRz4pS0t63jDKze5CqHLqf7Y31tbf0ZYNqqKuRxvpapWO4xIa+Tpql/fs//L+HU902J1XUbbGOvr1XvRHmBW33Kjn82nnZsV/PmmyarqW4+8a6x0xhKsDqvHYK6Zpz+HjTXKmZUVV2HDcxmZXt+07dnOMp976f+/vvJ6YPAQAAAACADvHUdQYAhJKmUrawy1Q2J3tPDiod43+8OiQNs2vk4phaJqggwSzcM9NF8X0suHOOdG1cIp0bFqc6S64NvMVIHZnhknAvlCpR31BXI5uXNRnN2K6ykGsiM6dqFuu4ys3bJspg1EJeUEaprJ7dbS0yeGEscAa/7jaqdNnE2+DWsXqh9B49J3uKtF9bljVJu+GKE5WyjF68el16+vqlp68/kdmV86X9eRpkM6yq9hX3Vvw3pp7tM6oysnlZk/T09Qd+rSfIJoIksK2vkzZp/f51ZcF2vW1OqqjbYl19+zjHCGkSpt2rpPfYgHSsXqjcr07TtWRD+6trzKS6oUDHtVNI1zXwncFL8p3BS7f8ff44bv7cWhm65G/jx4HvDsn3L0wlbo4HAAAAAICwGB0DgCN0BEGXy5oKlKMj886lsUn55AN3KR0jaEZM7pn36fg+vnziTRm8cNVXllEqTdhhZnWV/MHPPSC316oFzbl8L1TK5uUtOprK2B53ZrjCc9GRxdq27HBxiTIYtZhyC/I2ZlFHeN4GtxNd6+VI5zp54clH5EjnOjnRtV66Ni4xHoCf5uzK+ehb6slK7EelqhxRPNvDVhS5+XofmwiSwqa+Thql9fvXmQXb9bY5yaJsi3X17eMeI6SBSrtXSe/Rc8rHSMu1ZFvfWHXMpGNDgW5RXgN+A/A9SZzjAQAAAAAgLFbYAcARaSplC/voyrzzyQc+pPT6IAuo3DPT6dpIseoPD4UO3EU8hkfG5fK42j3s6r1gQ/CoDQu5OoMV0xroVUxUwailVFqQ9zICHtq2VjpWLbjlXBvqaqRj1QI5tG2t7Nq0lAB8B8SxwY1NN++jb/l+VmKTKlXliOrZvnDebaGDPyttIkgaG/o6aZbW719nFmzX2+Yki7It1tG3D1q9EeGYCsAXEdl99JyMTUwpHSMt15KtfeMwYybbNhR44p5zqCRpczwAAAAAAITFKjsAOCJNpWxhH12Zd370rtsjW0B14Z6ZyuZk6NKYr8zyqkx8DtMZxKGHC/eCKXEHj9qwkKs7WDGtgV7FRBGMWkqQoIw4s6jDbWy6mS7Nz9N8qlmJKx27UlWOKJ/t3W0tsmbRvECvKbWJIMp+f5Rs6OukWVq/fxNZsF1vm5NMZ1tcjo6+fZDqjQhHR7tXzsRkVj7zhVeU5rdsvJZM9EOS1De2eUNBXHMOfiVpjgcAAAAAgLAIwgcAR6SllC3spDOLU1QLqDbfM2fPX5GnDpyR5TsPysqnX4wks7zpe99EBnHoYfO94DGxIGxD8KgNC7k6gxXTGuhVjslg1HLCBGXEkUUdbmPTzXQuPE+joJKV+KN31StV5Yj62T6zukqef+JB35+32CaCOPr9UbKhr5Nmaf3+TWTBdr1tTjIdbbFfqn37INUbEY6Odq+So2ffVd6Yb8u1ZLIfkqS+sc0bCuKac/AraXM8AAAAAACEEf/sBgDAFy8IWmWhwYVStrCTl3mnp68/9DG8gMEZVRl5/okHZcf+074Wz9tbm6W7rSXwAqqN98zEZLbs5/Yyy/f09Yf+3PmmsjkZHhmX0WuTUlszQxpm18jFMXOLlV7g7q5NS429B4Kz8V7weMF0e08OTju/hroa2bysSR5fcW/oLN06gke7Ni5ROkbcC7mqwYodqxdO+/51BnrNnztb6Ti28IJRTQSDlUOAD0zTtemmc8PixGz4sPl5GrXuthYZvDAWaJPXmkXz5PknHpQZVZmb/dM5s6qlsb7W9zUSx7N9ZnWV7Nq0VDpWL5Teo+dkT5E+y5ZlTdJe0GeJut8fl7j7OmmXxu/fRBbspLTNSRa2LQ5KpW8ftHojwomqvSo2Hg4i7mspin5IkvrGNm8oiGvOwa+kzfEAAAAAABAGQfgA4AidQdBAGO2tzUrXX37AYBQLqLbdMxOTWdm6+7jvYKXeYwMyeGEsVAa3UoHNsyII7FFdqIR+tt0LIuYXhE0Ej+ZvavEbNBj3Qq7uYMU0Bnr5ESYYdcWCD8gHbpsp/+PUW4HfL2kBPmHuLZjHpptb2fg8jUKxe9TLShx2U22YayLujSEL7pwjXRuXSOeGxRXbrCj7/XGLu6+Tdmn8/k1kwXaxbU6rIG1xWGE3mgWt3ohwomyvVDfmx3UtRdUPSVLf2PYNBWGupSglbY4HAAAAAICg3JlhBwBoDYIGgjKRxcn0AqpN98yO/acDL5YEzSxfKbD52mQ20PuHpSODOPSy6V6IYkFYZ/Do2MRU6Gz9DbNnyszqKplQuPeCLOQWVr/Ye0JvsGIaA738CBuMKiIyes3/vSCSrAAfk5UwoI5NN8XZ9Dw1zc89GkVWYo8tG0NmVGUqvj6Kfr8tbA9aS7o0fv8mnisutc24wU9bHJbqRjOYpaPd80u1qpPKtaSyUTnKfkhS+sa2bygIei1FLWlzPAAAAAAABMXIGAAcEncpW8BUFidTC6i23DNeEFMYfjPLBw1sNkl1oRL62XIviESzIKwrOGfXV78nB747VPS/VcrWPzGZld/88xNKAfgi/hZySwVKqioMVkxjoJdfYSu8pDHAx3QlDOjBppvibHqemhLmHjWdlVjEnY0hUfT7bWJ70FrSpfH71/1ccaVtRrRMV2+kElR4Oto9v3Rs3gt6LaluVI66H6LSNxYR+eOvn7VmvGf7hgK/19JPtXxIPv1fjxo9l3xJneMBAAAAACCIZK2GAkAKUBYZcXIxI5gN94xqliI/meXDBDabomOhEvrZcC9EtSCsKzinVAB+oWLZ+nXdk+UWcisFSuqQH6yYxkCvoIJWeDEd4GObKCphQA823ZRmw/PUFNV71GTfz5WNIVH0+21je9Ba0qXt+9eZBduVthnx0V29kUpQeqi2e0Ho2rxX6VqamMzK9n2nlDcqx9EPCdM3vvl+Fo33XNlsW+lamsrmIqsWIZL8OR4AAAAAAPxgFRsAHOMFQbe3Nvv69+2tzVZMZCM5vIDBQ9vWSseqBdJQVzPtvzfU1UjHqgVyaNta2bVpaezXXtz3zFQ2J3tPDiodY8/JQZnK5kr+d5XAZlNMZxlFcHHfCyJ6FoT98IJzouRl6xfRd0+WW8j1AiVN3/uFwYp+r59SXAv0Csur8HJ/Y73Mnzu74qK0t5B+omu9HOlcJy88+Ygc6VwnJ7rWS9fGJYkJxlGphIFoeZtuVCQ1IMOG56kpNt+jOp7tpjeGRNHvt5EXtBYGWcjVpe371/F8EnGrbUb8gvbtC3kB1uuePSw9ff23BMd6AdaPPvOSbN93SrmaWtKptHtB6d68V+xaCjq27z02IFt3H7/lOomrHxK0b1zIpvFed1uLrFk0L9Br4trQVapd0vWc9CstczwAAAAAAJTDLDMAOMi1IGgkk0sBg3HeM8Mj48rZh7zM8qWoBuHOMtBGmM4yinDivBeiXBCOetHR03tsQPrfHtUSGF9pITeK6hfFghXTFugVNdUAH5upVsLof3tU8xmhEjbdlJbE8Zjt96gLG0Oi6PfbyqWgtSRK2/ev+nz67//yYWfaZvgzlc3J0KUxeX14RIYujVm1mUlXgDWmC9PuBRVVVSddmyDj7IfMrK6Sf7lqQej3tWW8l5TNtlFtUmGOBwAAAACAG4iOAgCH6S6LDIThBQy6II57RldG+FLH0RHYXFtTJX//O2tk/PqU1NbMkI2f65OLY+EX7m6vrZYPzpmldE4wK457QeeCsJ82J8oS9fn++5E35K//4QdKx5hZXSWff3x5yYXcqKpflApWDFNu3uVAL+ihoxJG18Ylms4GfnibbsL8dmkJyEjSeMyFe1T12W56Y8jlMbP9fpt5QWs79p/2dS21tzZLd1uLdUFrrkrb96/6fFr9EbOBu7pMZXPOP1tM88ZFe08OThtrNtTVyOZlTfL4intj74+oBFjv2rTU0Fm5L2i7F0YUVZ1UN0F2rF548xo3Pf/o53xU2DLe8zbbdqxeKL1Hz8meIu3LlmVN0m5B+1KKynPSL+Z4AAAAAAB4H0H4AJAAuoOgWehDEpS7jqPcOKArI3yp4+gIbL40NikzqjJyf2O9iIhsXt6kFOB0eXxSHv4PL1iz6I3SorwXol4QjmLRsZg9Jwbl8rjaZ52YzMrFsQmZPbP4bxPVZyoVrJi2QC+o01UJo3PDYvqkEWPTjT/5z1MXx1Ku3KO2bgzxAui+fPxNLcczWVHK5PWZhKA1l6Xt+0/y88mFwPK4TUxmy45FLl69Lj19/dLT1x/rWERngDVuVandq6+tlhGFsXkUVZ10Bq6bnn8sx5W+ZBCub7YN85ycP7dWhi5VroSw8WPz5Xfbfow5HgAAAAAA3kMQPgDgJhb6ksnFQCAVtl3HjfW10lBXoxQoX64EtonAZh0ZxG1Z9IY94lgQDrPoqEo1AN9jsvqFH5WCFQsDHg6fHhCRqZv/vb62WjYub05MoBfURF0JA/rYvunGpn6ubX3QIFy6R20KvK0UBBpGuX6/iiivT9eD1lyXlu/f9udTGK4ElsdtYjIrW3cf9/0c6D02IIMXxuT5Jx6M/PtKSmZw25Vr937/K69at3nPoztw3fT8Yzku9SWDcqkCbL6wz8kfXBwruqkl32d/8iOpfP4AAAAAAFAKQfgAABb6EsrlQKAwbL2OZ1RlZPMytczy5Upgmwhs1p1BPM5Fb9gjjgXhKErUm2Ky+kUlQYIVvYCH3159txx+6aWbf//l31wpDXPnGjpDuCbqShjQy8bsyjb1c23tgwbh0j1qS+Bt0CBQv8r1+8OI8/p0NWgtKdLw/dv4fAorjsBymzayBbFj/+nAbe/h187Ljv2nZdempYbO6lZJzAxuu2Ltnk2b9wrpDlw3Pf9Yjkt9yTQJ85wstqklMzkur37rm3F9DAAAAAAArEcQPgCknEsZpOBPEgKBgrL9OlbNLF+uBLapwGbdGcTjWPSGXeJaEPYWHUfGJ+Vvv/PD0O8dxO211UoZ8aOoflFK2OdCVSZT9s9ItzgqYUA/G7Ir29bPtb0P6pcL92hhsOgf/NwDsQbehgkC9aNcvz+opFyf8MfVgGodbHg+qYoysNymjWxBeeceRu+xAelYvTCyz5bkzOAusWXzXjE2VrYM2w9xoS+ZZmGek/mbWi5fzkV5ugAAAAAAOIcZDQBIOVcySMGftAZa2H4dq2SWr1QC21Rgs4kM4lEvesM+cS0Inz1/JbIA/Ia6GvmFH7tb/uQbb4Q+RhTVL/K5lCUUboqjEgbMiSu7so39XNv7oH7ZfI/6CRaNOvBWJQi0nEr9/qCScn2iPJcDqnVzNft/VIHltm1kC0O17e09ek66Ni7RdDblkRncHrZWzbCtsqVKP8TmviTe5+pzEgAAAAAA29k1iwoAKTCVzcnQpTF5fXhEhi6NyVQ2vkwiqgt9/W+Paj4jqFIJtHCVK9dxd1uLrFk0L9Br/JbAbm9tDntaN15fIrDZW6g8tG2tdKxaIA11NUrvI3Jj0Rvp5S0Ih6GyIGwiUK6ULe8FO6nwU/1CxdzZ1dL37x6VF558RI50rpMTXeula+OS1ARoIXrehjEVYSphIFls6+e60gf1w8Z7dGIyK9v3nZJ1zx6Wnr7+W4K6vGDRR595SX7/K6/KB+fMkvsb62X+3NnG2woT/Qq//X6/knR9orgg98j2fadkYjIb05miEh2B5ZV4G9n8vlfvsQHZuvu4VdfNVDYne08OKh1jz8nByOZhyQxuHy8b+Imu9XKkc13s42EdY/tSlS1NzT+WYmNfEgAAAAAAICoE4QNARM6evyJPHTgjy3celJVPvyiPPfeyrHz6RVm+86A8deDMLYvcUQTrR7HQh+ikNdDClevYyyzvNwC5vbXZd+ZW04HN3kLlK7/3mNxeq7YAHOWiN+wU9YKwjmCNINpX3Gv0ntSxuP2p5fdI0x11kQUrAiLmNowhHWzs57rSB/XLpns0imDRsONtE/2KIP1+v5J2fWK6JARU44aoAstt28gWxvDIuFKWbZEbm1OGR8Y1nVF5pgKsoc7LBh73eNhU4LrJ+cdKx1F6PeM9AAAAAADgKILwASACn/v77/vOTva/3xoJFKwflmsZpFBZGgMtXLuOK2WWb6irkY5VC+TQtrWya9PSQAtgUQQ2vzN6TS6Pq5VCj3LRG3aKekFYR7CGX/kB9C5WvwBMiqsSBpLBtn6ua31QP2y6R00GiwbdHF9IV7/i9trq0P3+SpJ4fWK6JARU44YoAstt3MgWxug1tbkI3cephMzg8COuypYq84+l2NSXBAAAAAAAiBK1LAEgAge+OyQilRdNeo8NlFwY84L1e/r6pb21WbrbWpQmyHUu9M2fO1vpOFCnK9Cic8NiXwt8U9mcDI+My+i1SZkzq1oa62tjWRh09Tr2Mst3blis7Xv0Apt37D/ta4E9TDvi2qI37OUtCHesXii9R8/JnpOD0+7lhroa2bKsSdpX3Ku8EBvV9VYYQG/ynvQWt8ME07C4jTh1t7XI4IWxQIGDKpUwkAxR93P9cLUPWokN96hqsGjH6oVFn3MTk9myz2S/421d/Yov/+ZK+dG7btdyrEJJvT5xg6l7BPGIYoytYyNb18YlSsfQYc4sPUtZuo7jR3trs/T09Yd/PZunE8/02N7E/GM5NvQlAQAAAAAAokYQPgA4qPfYgAxeGFPKDEwwbbJEFWjhLfjvLRIwu3lZkzyuIWA2CNevY68Eti6mA5tdXPSG3aJYEI7ieisVrGfynmRxGy6KYsMYksfGgGJb+6CqG2VtuEdNBItOTGZl6+7jvp+Z5cbbuvoVt8+uqfyPQrL1+oQeSQmoxg2mx9g2bmQLq7G+VhrqapT6BA11NdJYX6vxrMpj8zT8iGJsr3v+sRQb+pIAAAAAAABRIwIKABzllRLftWlpqNcTTJsspgMtdGWO1I3ruDhTgc0uLnrDDSYXhHVct7Oqq2R2zQy5OBYugN7V6heACVFWwkAy2BhQbFsfVOdG2TjvUVPBojv2nw4U2CZSerztQn/YtusT+iQpoBo3mG5TbNzIFtaMqoxsXtaklFl+y7KmyK99Nk+jkqSN7RnvAQAAAACAtGE1BQAcplJK3IXgAfhnMtBCZ+ZI3biOy9Md2OzqojfSTcd1+6sr7tUSQO9a9QvApCgqYSAZbAwotqUPanKjbBz3qIlgUW+DQhjFxtsu9IdtuT6hX5ICqnGD6TbFxo1sKtpbm5W+q/YV92o8G3+SFmANM5I4tme8BwAAAAAA0oIgfAAwIJvLRfZeYUuJuxA8AP9MBlrozBypG9dx9Fxc9AZ0XLdRlW8Pg8VtuMzmewt2sDGg2IY+aFQbZaO8R00Ei4YNwL/5+iLjbdv7wzZcnzAjaQHVrpvK5rT0vU22KTZuZFOxcN5t0t7aHKptb29tji14OWyAta5rDO5I4tg+bF+S6x8AAAAAALjCjtlTAEiYd0YnInsvlVLitgcPwD9TgRa6M0eawHUcLVcXvVFZkhc403LdEswMIIlsDSiOuw9q80bZsHQFeXqbDKayOdl7clDpWMXG2y70K+K+PmFG0gKqXeXNk+wtEki9eVmTPB4wU7XJNsXGjWyquttaZPDCWKBn4JpF86S7rcXgWfnjN8Ba9zUG96R5bM/1DwAAAAAAXENNSwAwYGwiuqxiXinxMLyFvjBcCkpMi7C/5c3XFwm00JE50jSu4+h1t7XImkXzAr3GlkVv3Ors+Svy1IEzsnznQVn59Ivy2HMvy8qnX5TlOw/KUwfOSP/bo3GfohZctwDgLhP9XFVx9kFVN8ra+mz3gkVVdf3NaZmYzMrwyLhS4KlI6fG27f0KxkjJpOMesS2g2iUTk1nZvu+UrHv2sPT09d/Svly8el16+vrl0Wdeku37TsnEZNb3sU21Kd5GNhW2VcaYWV0lzz/xoO82rr21OXAVGNO8AOv7G+tl/tzZN79fk9cYYDuufwAAAAAA4Cp7Zh4BIEFmz4w2q5hKKXHbgwfgn+5AC12ZI6eyOaVj+MF1HK0kLHojfQucXLcA4C5bA4rj6oO6sFE2DB3BoiIiL7+X8V9lnJyv2HFc6FcwRkqeJAZUu2JiMitbdx/33f72HhuQrbuP+x5DmWxTbNzIpmpmdZXs2rRUDm1bKx2rFtyyOaWhrkY6Vi2QQ9vWyq5NS50Y05m+xgCbcf0DAAAAAACX2T/7CAAO+uCcmZG+n0opcReCB+CfzkALk5kjdeM6jl7YRe+pbE6GLo3J68MjMnRpLJJNGrhVWhc4kxisAQBpYWNAcRx9UJc2yoahGizq6T02IO+OTmg5Vqnxtu39iqDX5y8/fE/g65O+ffRsCahO22+/Y/9pOfza+UCvOfzehiC/TLUptm5k02HBnXOka+MSOdG1Xo50rpMXnnxEjnSukxNd66Vr4xKrz71QFNcYYCuufwAAAAAA4LJoUzUDQEpUZaLLKqajlLi30NexeqH0Hj0ne04OTgu+bqirkS3LmqR9xb1OLWClkRdosWP/aV/Bte2tzdLd1lJ08dZk5kgTuI7j4S16d25YLMMj4zJ6bVLmzKqWxvraaRkWz56/Ir3HBmRvkd9l87ImeZzfJVIqC5y7Ni01dFbR8XvdAgDsobOfq/u8ouyD6twoO3/ubOXz0c0LFlXN9i8i8nevviUNdTVK35ef8bbN/YpK12e+r736ltTNrPbVL6dvr24qmwt1vajcIzoCqtP423ufOYzeYwPSsXphoO/ERJvS3dYigxfGAo0BXaqMMaMqY+Uzza+orzHAJlz/AAAAAADAdQThA4DjdJYStzl4AP7pCgRSqbBg4jh+cR3Ho9Si98Rktmyw3MWr16Wnr196+vojC5YrJmwQjItY4Hyf68EaAJA2Nm+6jKoP6tpG2TC621rkzXevysvff1vpOH/9Dz+QX/ixu+VPvvFG6GMEGW/b3K9YcOcc+d1PflSuTkzJX7wSvl/uSt/eZjqC2OMIqE7zb6+6Kaj36Dnp2rgk8Ot0tim2bmTDDXFdY0geF+e2uP4BAAAAAIDrCMIHAMfpKiWez+bgAfinGgjUWF8bSeZIU7iO4zcxmZWtu4/7Dg7pPTYggxfG5PknHoxssT+NmRxZ4AQAuM7mTZem+6CubpQNYmZ1lez8+Qfkkf/vS0rHuXj1uvyLlruUgvBNjLfjoKNf7kLf3mY6g9ijDqhO828/lc3J3pODSsfYc3JQOjcsjv35ZPNGtjRL0jWG+Lg6t8X1DwAAAAAAksDeFTcAQEU6Sokj+cIGAs2oysjmZU3S09cf+r11VmqAe3bsPx0oO6OIyOHXzsuO/adl16alhs7qhrRmcmSBEwCQJGncdOn6Rlm/JqayWo7zwdtmSntrc6hNiEkab+vol9vct7ediSD2KAOq0/zbD4+MK7W3IjfGlsMj49Y8r2zeyJZGSbzGEB3X57a4/gEAAAAAQBLYM9sCAAm28WPztR9TtZQ44Ed7a7Pa6xOSORLBeVm4wug9NiD9b49qPqP3eUEwfs+v99iAbN19XCYm9QSDxUnnAicAANBnKpuToUtj8vrwiAxdGpOpbK7ov/M2yqpwYaOszoz/3W0tsmbRvECvS9J4W0e/3Ja+vd/7xDYqQeyVeAHVJ7rWy5HOdfLCk4/Ikc51cqJrvXRtXKIcgG/Lbx+X0WuTVh1HJ28j2/2N9TJ/7mzrnwtJleRrDGYlYW6L6x8AAAAAACQBmfABIAKf/cmPSPvqj5bNTvbph+6RP/vmG5GUEgf8WjjvNjJHIpSwgRo3X3/0nHRtXKLpbKZLcybHKBc4p7I5MisCAFCBF+C6t8g4cfOyJnm8SBbr9tZmpWpVLmyU1Znxf0ZVRp5/4sGymWLz/fLD98hvrfkRGXh3NBF9GB39ctVQd9W+fZj7xBaqQewdqxf6+mxhK4NU6rPbPK6Lgs4NQUAxXGMIKwlzW1z/AAAAAAAgCZiZAICI+Cn3HFUpcSCI7rYWGbwwFmhhJ0mZI21nY6DzVDYne08OKh1jz8lB6dywWPtniSoIxlZRLHC6HCQFAEi3KPtVE5PZskHhF69el56+funp679lE3YaNsp6Gf9VNhvkZ/yfWV1Vcby9fvGHJCciX3v1LfniK29O+2+u9mF09Mu/fOJNyYjafRC2b69yn9jC1iB2P3325g/UWTuui4rODUFAMVxjCCMpc1tc/wAAAAAAIAkIwgeAiFXKTuYnWB+I0szqqkCZI20NfkgamwOdh0fGlRbQRG4E1AyPjIfK5liOrUEwUTG5wJmEICkAQDpF3a+amMzK1t3HfW9y7T02IIMXxuT5Jx68+exMw0ZZExn/i423Z1ZXyedfOit/8Ury+jA6+uWXxtQrKYXp2+u4T+KmYxPE7qPn5Hd+6kdl9swZWs4pSJ990yc+bO24Liq6NwQBhbjGEEZS5ra4/u1MrgIAAAAAAIKxY0UCAHALL1j//sZ6mT93NpOviJWXOfLQtrXSsWqBNNTVTPvvDXU10rFqgRzatlZ2bVpqTdBDEk1MZmX7vlOy7tnD0tPXf0tQhBc08egzL8n2fadkYjIb+TmOXlMP1NF5HI+uDP1T2ZymM4qet8CpotgCpxck5XchuPfYgGzdfTyW6xMAAE9c/aod+08HCp4XETn82nnZsf/0zT97G2XbW5t9vb69tdmq4GQ/vIz/YVTK+O+Nt5s/MEf+/d+cLhmAX8i1Pozu/rSKoOei4z6Jm45NEBOTWfnMF17Rcs0F7bPv+/YPld9TxK7rMIyw7dDN1xfZEATk4xpDEEmb20rr9X/2/BV56sAZWb7zoKx8+kV57LmXZeXTL8rynQflqQNnpP/t0bhPEQAAAAAA+OTOyhsAAIidlznyRNd6OdK5Tl548hE50rlOTnStl66NS6woZZxkrgQ6z5mlp9iSjuNMZXMydGlMXh8ekVd/cFFbJkeXmVjgTEKQFAAgXeLqV3lZ98PoPTYwLSAnDRtlu9taZM2ieYFeEyTjf9L7MLr65ToEORed90mcdAWfHz37rpZrLsz1roNN12EYJjcEASJcYwhGZ/VJG6Tt+nchuQoAAAAAAAjGvdU3AAAqyA+6Hbo0Zk1mH9eU+x6p1BAPV4KEGutrbwkCC6qhrkYa62tDv75YRqmf+8/fVDonj+uZHHUvcCYlSAoAkC5x9avCPjNvvv7ouVv+LskbZU1m/E9DH0ZHv3yGhqFe0L69ifskDjqDz1WvOZXrXYXquM4WpjcEAVxj8MvW6pMq0nL9u5JcBQAAAAAABEMQPgAgMSjjqgffo51cChKaUZWRzcualI6xZVlTqM0dlTJK6eB6JkcRvQucSQmSAgCkR1z9qqlsTvaeHAz1Ws+ek4MlNxkndaOsqYz/aejD6OiXT2nY0x6kb2/6PomSjk0Q+VSuuTgC8EXCj+tsY3JDECDCNQb/bKo+qUtarn9XkqsAAAAAAIBg3JqhAACgCMq46sH3aDfXgoTCZlq/+foV9wZ+TdCMUmEkJZOjrgXOJAVJAaZQoQewT1z9quGRceUNghevXpfhkXGlY7hKZ8b/NPVhVPvlWs4hQN8+SfeJjk0Q+cJeczqu97DCjOtsZWpDEODhGoMfNlSfNCHp179LyVUAAAAAAEAw9qQ6AAAgBC/o1m8Wmd5jAzJ4YczJbDkm8T3aTVeQUOeGxZFlIVw47zZpb20OtcDU3tocKIjKEyajVFDlMjlOZXMyPDIuo9cmZc6sammsr7U666O3wNmxeqH0Hj0ne04OTgt4aqirkS3LmqR9xb0lfw+dQVLz584u+t9d+16jxHdjN2+RfW+Re2vzsiZ5vMy9BcCcOPtVo9cmld5X93Fc5WX8VxFFH8YWKv1yHYL27ZN2n7S3NktPX7+WY4W95nRc72GEHdfZztsQ1LlhMX1xGME1hnK8DV4qzxabq5Qk9frXsQm4a+MSTWcDAAAAAAB0IggfAOA0lTKuuzYtNXRW7uF7tJurQULdbS0yeGEs0LW1ZtE86W5rCfxeKhmlgvilh5tl6NLYtEXAc++MWhVoGzQwW2WB02SQlEsBzFEHw7v03aTRxGRWduw/XbJN8irL9PT1S3trs3S3tbChDYhQnP2qObP0TMPpOk6aJS3Qu5Iw/fLqqoxMKmb6fyRE3z5p94nuTRBhrrk4rtOw4zqX6NgQBJTDNYZSVDd4uVClJEnXv4vJVQAAAAAAgH92rEYAABCCahnXjtULCVIUvkcXuBokNLO6Sp5/4sGywaj5VIJRowjA/+hd9bLl89+cFrg3s7pKJiazRf991IG2qoHZYRY4TQRJBQlg3vSJD8vWR35E7phTE0tWtKiD4QnuvpVt1QCoLAPYL85+VWN9rTTU1ShtAmiou/HMg5qkBXpXErRfvukTH5Z93/6h8vvu/PngfZEk3ifdbS3yT8NX5Gj/u8rHCnPN6bpON/3Y3bLvH35Q8d+lpR8KAHGJo/okwnM1uQoAAAAAAPDHjZUiAACKoIyrHnyP9nM5SGhmdZXs2rRUOlYvlN6j52RPkWDlLcuapF0hWFlHRik//vGtkVv+rlQAfiGTgbZxBmbrDpIKGsC879s/vBkgFmUW+Di+c4K7p7O1GgCVZQD7xdmvmlGVkc3LmpQyl25Z1kQGTA2SGOhdSZB++VQ2qyUI329fudAnW+6Sv/zWm6Hf17b7ZGZ1lfzpZx6Wj//B/wr9nYiEv+Z0Xe/PfOrj8tmf/IixcR0AwL8oq09CjavJVQAAAAAAgD8E4QMAnEQZVz34Ht2QhCChBXfOka6NS6Rzw2LtWat1ZJSKgolA27gDs3UHE4YJYPZElQU+ru+c4O4bbK4GQGUZwA1x96vaW5uVnpvtK+4N/Vq8L80bIvz0ywcvXNXyXkE2q5TaYBeGjffJ7Jkz5FdX3BvLNafzejc5rgMA+Bdl9UmocTm5CgAAAAAAqIzZFgCAk3SWcU0zvkc3eEETKmwJEppRlZH5c2fL/Y31Mn/ubC3nZCITVENdjXz0rnrtx+09NiD9b49qO55KYLYu7a3Naq9/L0hKJYC5UO+xAdm6+7hSptFS4vjOVYO7dV5zcfI2QPj9LkxeB6XeT+n1R89pOhMA5cTdr1o477bQz8721mY262ikqw/jqmL98rPnr8hTB87Iz3zu68rH97tZZWIyK9v3nZJ1zx6Wnr5+9QB8i++TOK853e9tYlwHAAjGq3JzaNta6Vi1QBrqaqb994a6GulYtUAObVsruzYtdTYAfyqbk6FLY/L68IgMXRqTqWwu7lMKxNsErCLu5CoAAAAAAKA0ts0DAJxEGVc9+B7dQdbU0nRlgvrKv/5xmTOrWubMqpbRa5Py2HMvazluod6j56Rr4xLl49iSddsLJgxzLvlBUroC8D0mssDH9Z3rCO7Wcc3FzeZqAFSWAdwSd7+qu61FBi+MBWrT1iyaJ91tLUrvi+l09WGSoFKlmTD8bFYJWmGoEtvvkzivOa53AEiupFYpKVUlp6GuRjYva5LHV9zrxPMpzRWYAAAAAABIAzfTHgCAY7I5t7O12IgyrnrwPbqDrKml6coo9cDdDTczOX7xlTc1nd2t9pwc1PIcsCnrdndbi6xZNC/Qa/KDpHQEMBejOwt8HN+5ruBu1/setlcDoLIM4Ja4+1Uzq6vk+Sce9H0O7a3N8vwTDzqbvdRmqn2YJAhaacYvP5tVwmywK/l+jtwncV5zXO8AkGxJqVJSqUrOxavXpaevXx595iXZvu9UZNXvVKS9AhMAAAAAAElm96oEACTEpz5/RFY+/aI89tzLsvLpF2X5zoPy1IEzxgPCkowyrnrwPbrFtqAJW8pBexmlVORnlDIVEO7REWhrW2C2ajChjgDmUnRtNojrOye4+wabNp0UQ2UZwD1x96tmVlfJrk1L5dC2tdKxasEtffKGuhrpWLVADm1bK7s2LVUOLLal32YbNkToDYT3+NmsorLBzqP7PolCnNcc1zsAwHZBNwf2HhuQrbuPWx+IH/cmYAAAAAAAYA5pawHAgOtT0wMaRsYnReT9zDNetpaevn5pb22W7rYWFjUDooyrHnyPbvGCJnbsP+1rMcpU+2JjOej21mal6zg/o5TJgHCPaqCtzsDs+XNnKx3H4wUTdqxeKL1Hz8meItfHlmVN0l7k+jAZeLzn5KB0blis3E7F9Z0T3K1vA4SO66AUKssA7rGlX7XgzjnStXGJdG5YLMMj4zJ6bVLmzKqWxvpaLW2Wjf0226j0YVynIxC+kN/NKqrv+8sP3SM7Ny11ciwa5zWX5usdAApNZXNG+l8IL8zmwMOvnZcd+0/Lrk1LDZ2VHt1tLTJ4YSzQ56MiDQAAAAAA9mOFHwA0m5jMSvffvirrG/z9+95jAzJ4YYzsYiHoDLpNM75Ht8QZNDExmS0bqBbnBiMvo1SYYJ7CjFJRBCurBtraHJgdJpjQZOCxrs0GcX3nBHfbuemkkFdZRuU8qSwDRM+mYNQZVRmtbZTN/TZbmd4QYSPdAfh+ryUdG+y+dvot2Wl5sF0lcV5zabzeAcDDJkU7qWwO7D02IB2rF1r9u9myCRgAAAAAAOjlbiQGAFhqx/7TcvyNC7L+E/5f40q2FtvoDLpNM75HN0UdNOGVg/abrSmODUa6MkqZDlYuF2jrNwubC4HZQYIJdQQwl6MjgD6u75zgbrs3nXioLAO4LWnBqC7022ymY0OEC5l1dQTCi4g0zK6RLcuDbVZxYYNdlHRvwnHlvQEgamxStJvq5sDeo+eka+MSTWdjhk2bgAEAAAAAgB4E4QOARl62lrtCrF+6kK3FRpRx1YPv0V1RBU24UA5aV0Yp0wHhxQJtg2ZhS1pgto4A5nJ0BNDr+M7nzq4O/J0T3O3GphMRKssASZCUYFQX+m1J5VJmXR2B8CIiBz67SpruqAv0Ghc22AEAkoVNinbTsTlwz8lB6dyw2In5j6RtAgYAAAAAIM2YOQIAjXRka0EwXtBte2uzr3/f3trM4kkRfI8oR7UcdP/bo5rPqDQvo9ShbWulY9UCaairmfbfG+pqpGPVAjm0ba3s2rS06DXsBT2bkh9oOzGZle37Tsm6Zw9LT1//LYFQXha2R595SbbvOyUTk1lt52hbYLbf9icoXZsNdHzn49ez8h/+x/cC3xOq343rwd3eBggVUWw68SrLhEFlGaTBVDYnQ5fG5PXhERm6NCZT2Vzcp5RILvXbkiRsny5OugLYx69PBX6NKxvsAADJobJJEebprJLjEm8T8P2N9TJ/7myr5ukAAAAAAIA/rFSkRCaTqRGRnxCRZhGZLyJXROSHIvIPuVzuDc3vtUBEPiEiHxaR20RkSETOicg3c7mctpSyUX4mwA/Xs7VMZXPOZl2hjKsefI8oxcVy0KoZpVQzWpc7rnf/qGZhS1rWbS+AWfV6K6Rzs4Hqd35tMhuqtL3Kd5OE4G6XqgFQWQZ+udz3DsqlzOBJ4GK/zXWuZtY1EQjvt21LWlUnAIDdVDcpUr3WPKrkAAAAAAAAVxGEH5NMJrNQRB4SkQff+/9lIlKf90/O5XK5+zS8zzwR2SEivygiHyjxb74pIs/lcrm9iu+1RUSeFJGVJf7Ju5lM5q9E5PdzudzbCu8T2WcCgtCZrWX+3NmazqqyJAXFUMZVD75H5HN9g5GXUSooEwHhhYG2KlnYdm1amsjA7DABzJXo3Gyg87oIGoCX9uBuVzadeJVlduw/7es6CbIZA8mQpL53JROT2bL3gpcZPOjGJJTmer/NVap9urjoDIQP2ra5tMEOAOA+Ninajyo5AAAAAADAVaxuRiiTyazNZDL/M5PJvCMi/yQifyki20RkjUwPwNf1fj8tIq+KyG9JiWD19/y4iOzJZDJ/nslkAq/2ZzKZ2zKZzBdF5MtSOgBf3juH3xKRVzOZzL8I+j7vvVcknwkIw7VsLROTWdm+75Sse/aw9PT137Lw7gXFPPrMS7J93ymZmMxGcl46UMZVD75HiKS3HLTIjaDnNYvmaTlWe2vztGBr1Sxs/W+Phj5HmwOzvQDm9tZmLcczsdlA53URpLR90O+m8JpznbcBIoyoN514lWUObVsrHasWSENdzbT/3lBXIx2rFsihbWtl16alifmNUF6S+97FeJnB/T7reo8NyNbdx53/3HFLc78tLrr6dHHwAuFVbPrE3fL7X3k1VNum2t+zraoTAMBOujYpTmVzms4IxXibA1VQJQcAAAAAAMSB1f5ofUJEfkrKB49rkclk1orI34hIY95f50TkhNwIlj8oIoXZ6NtF5IuZTMb3dZHJZGaIyF+JyC8V/KfzIvK/3nuvk++9t+dDIvKVTCazyu/7vPdeayWCzwSE5VK2FoJiAPjl2gYjnYIGPRcG0pYLtNWRhS3MOboQmF0pgNkvU5sNdG8UCBKAl/bgbtc2nXiVZU50rZcjnevkhScfkSOd6+RE13rp2rgkMdnO02gqm5OhS2Py+vCIDF0aqxiQk8a+t0pmcISX5n5bXHT16eKi2p85/cPLods2lzbYAQDcxSZFN+jYHEiVHAAAAAAAEAfq8tnhmogMisiP6DhYJpNpEpG/FpGZeX/9DRHZmsvlvpf372aJyL8SkWdExIvgaRORnSLyez7f7v8UkQ15f74uIk+KyH/L5XITee+1RET+WN7PlD9LRP4mk8kszeVyQ5Z9JiAUnaXcTVMJitm1aamhswJgI5c2GJngBT13rF4ovUfPyZ6Tg9Pa+Ya6GtmyrEnaV9wrzR+ok+GRcRm9NilzZlVLY31t0cU/XVnYOjcslhlVmUDn6FKwkhfA3Llh8XuL5hPy317ul33/8IOKr21vbZbuthZjQeiF3/l/P3pOrikEywYtbV/43VS65pLC2wCxY/9pXwF/pq8Dv7zKMnCfl/F6b5F2dvOyJnm8RDubtr63ambwjtULnXpe2STt/bao6e7TxcELhA9zz36k8TZ55Y13A72msG3rbmuRwQtjgdpIm6s6AQDswyZFd7S3NktPX3/411MlBwAAAAAAxIBVtehdF5HTInJcRL713v+fEpGfEJFDmt5jh4jckffnb4rIY7lcblqqjlwud01EPpfJZAZEZF/ef3oyk8n811wuVzYdVyaTWSgi/7bgrz+Vy+W+Uvhvc7ncmUwm85Mi8vfyfiD+B0WkW0R+05bPBKjwsrWoTBRHka2FoBgAQbi0wcgkv0HPfgJtdWZhy3+/pAZmewHM8+fOlv/rFz8hn/3Jj1iz2WDBnXOkc8Ni2XNiUCkIP2wAXhqDu5O66QR2m5jMlt38cfHqdenp65eevv5bNn+kse+tIzN4kI1JeB/9tmiZ6tNFLUwg/EP33SHfeuNCqPfLb9tc3WAHAHAHmxTdobI5kCo5AAAAAAAgLswaRevPROTzhYHjIiKZjJ7AqEwm8xER+bW8v5oQkV8v9p6eXC73N5lM5s/yXjdLbgTH/0aFt+uW97PNi4h8oVgAft77jGUymV+XG5sOvIz2/zKTyfx/crnc2VKvi/gzAUpcyNZCUAyAIFzZYBQVHUHPprOwJT0w27bNBsMj43JxzP0APNfYdh0guSYms7J193Hfwam9xwZk8MKYPP/EgzKzuip1fW9bMoNPZXOpbBvot0UrKZl1wwTC18yoCh2ELzK9bWODHQDAJDYpuoUqOQAAAAAAwDWkDYpQLpe7UC5wXJNfEZEZeX/+61wu930fr/vDgj9/OpPJlJxVzGQys0VkS4Vj3CKXy70mIn+T91fVcuOcy4nkMwE6eNlawogiW4uuoJipbE7TGQFwQdh27ebrKQc9DVnY9PA2G9zfWC/z586OLWAwKQF4rrLlOkBy7dh/OlAAiIjI4dfOy479p1PZ99aZGTyMs+evyFMHzsjynQdl5dMvymPPvSwrn35Rlu88KE8dOCP9b48qnZsL6LdFJ0l9Oi8Q/tC2tdKxaoE01NVM++8NdTXSsWqBHNq2Vv7g5x6Qv/n2D5Ter1jb5m2wO9G1Xo50rpMXnnxEjnSukxNd66Vr4xIC8AEAoXibFFWwSTE63uZAv33a9tbmmxugAQAAAAAA4hD/Kg9021Tw5z/186JcLve9TCZzTERa3/urOSLyUyLytyVe8i9EpC7vz0dyudw/+jzHPxWRT+f9+RdEZGeZfx/VZwK06G5rkUuXL4vI275fE1W2Fp1BMWTrBdKDctB6kYUtWZIUgD/jVdEAAF7HSURBVAdgurPnr4TOZN97bEB+7hMfTl3fO66NSROT2bJZvC9evS49ff3S09cv7a3N0t3WkthAHfpt0Ulin85PpZmhS2NG27akV3UCAETPheq1eB9VcgAAAAAAgEuI9EiQTCZzl4h8PO+vJkXkGwEO8ZK8H7AuIvLTUjpg/ZNFXuvX1+XGuXnX349lMpkP5XK5fy78hxF/JkCLmdVVsuNnH5C+l1/y9e+jDAIhWy+AsCgHrY+XhU1lAZgsbPZIYgAegBvCBuB7/upbb2o5D5f63nFsTJqYzMrW3cd991F6jw3I4IWxRGfMpN8WjST36coFwjOvAABwDZsU3eRncyAAAAAAAEDckrnamF4PFPz5u7lcLkit9W8W/Lnc6mvhex3x+ybvndMpn+8V5WcCtKmZMX0SuL52ehBJfin3XZuWRhb8QbZeAGFRDlovv99jydeThc0alLYHkmkqm5O9JweVjnHwzC37zENxqe/tbUxSEXRj0o79pwMFm4uIHH7tvOzYfzroqTmDflt0bOzTTWVzMnRpTF4fHpGhS2Mylc1pPT7zCgAAF3W3tciaRfMCvYZNinbwNgfe31gv8+fOZv4EAAAAAABYhdWOZFlS8OfXA77+nyocL99iDe/1YwXv9WKRfxflZwKM+fJvrpQxmRl7thay9QJQ4Xo56KlszprMWWRhSxZK2wPJMzwyrtRnFhG5PD4pt9dWy+Xx8NmeXet7R50Z/Oz5K6ErFvQeG5CO1QsT+0x1vd/mCpv6dN79sLfIb715WZM8rum3Zl4BAOAib5Pijv2nfT23o6xeCwAAAAAAAHcRhJ8s9xf8OegK4LmCP38wk8nckcvlLuT/ZSaT+YCIfEDxvQr//UdK/LtIPhNgWlUmI/NvL17KPUpRB8UASCbXykFHFZAUVHdbiwxeGAuUvdfLwmbThgLYFYCH5PB7n9MemDF6LXzgfL71Sz4ke0/+IPTrXex7R7kxKWwA/s3XHz0nXRuTvVfftX6bi1T6dDpMTGbLBhRevHpdevr6paevX0tAIfMKAABXsUnxVownAQAAAAAA1BCEnywNBX8eDvLiXC53JZPJjItIfiqquSJSGLBe+D5Xc7ncaJD3KnJuc0v8u8L3MvWZAstkMo0iEqx+qciP5P/hypUrcvnyZdVTgYVGR0fL/jlOm5d+QL564mz413/sg1y3AG6akxGZUysicl1Gr6hlDNbt+lRO/stLr8uB7w6JyI3OwF35+6FyE/LVE2flqyfOysaPzZffWnu/1MyIdqHxuU0/Kv/lpRk3z7GcjR+bLz/78Q/Lf/zad+TgmX+WkbzMyvW11bJ+yYek7eMflrsb4t/0lUZPrm2WS5cvy/E3/HczH7zvDnlybXMinqs2931cM3jhqhz47lDF+9zvv0M4mclrctfsnPJxNj3wAfnG9wZDv97Fvveds0Q6Wu/y9WwrtPFj8+WDM6d8feZsLicvnx5Q+p0Onx6Q3159t1Rl0hFoZHO/zXVB+3S/tfZ+Gb96RcYV3/f6VE5+/29flRNvXJjezy3h7797Ti5dviw7fvYBpX5v2ucV6PcAgNs+OFPks480yW+vvlveGZ2QsYlJmT2zWj44Z+Z7/UJ//VGXMZ6EX/R7AABAWtDvAQC3XblyJbb3zuRy6ovKUJfJZNaKyKG8vzqXy+XuC3iMvxaRTXl/9W9yudx/CniMd0Xkjry/+lgulztV8G8+JiLfyfurd3K53J0B3+ezIvJHeX/117lcbnORfxfJZwojk8n8HyLSrXKMz33uc9Lc3Kx6KgAAAAAAAAAAAAAAAAAAAECqDAwMyGc/+9n8v3ogl8udjuK9w9ceho1uK/hzmIRaYxWOGeX7RP1eAAAAAAAAAAAAAAAAAAAAAFAWQfjJFqbMgc2vifq9AAAAAAAAAAAAAAAAAAAAAGCa6rhPAFpdKfjz7BDHKHxN4TGjfJ+o3yuo/1tEvhzwNT8iIl/x/vDwww/L4sWLNZ0ObDI6OiqvvPLKzT8//PDDMmfOnBjPqLQfXByTA9/5ofyvM/8sI+OTN/++vrZafmrJh2Tjxz8sdzeEufUAIB6fP/xP8tcnfxD69ZuX3S3/as2PaDyj8AYvXJXf+MLx0K//0888pL0Nvz6Vk//y0uty4LtDFf/txo/Nl99ae7/UzMhoPQeEc30qJ91/+6ocf+OC79c8eN8dsuNnH6j4G8bR9zH5eaKkep/7ZaI9SIPP/f33fbV3hTZ+bL589ic/csvfp7nvnc3l5J3RCRmbmJTZM6vlg3NmSlUm/L2YzeXkU58/Mu17DKq+tlq+/Jsri54HzzvYKGybVEy56z+otLVtLs35AEHpfl4DsION80twA/0eAACQFvR7AMBt3/ve92J7b4Lwk4UgfLX3CiSXyw2LyHCQ12QKFixuu+02uf3223WcDiw3Z84ca3/r22+/XRY3f0ie/JmcDI+My+i1SZkzq1oa62tlRhWLbADcMpXNyV/8w3m5OBa+/er9h/Py5M98woo2cO/Lg/KWwmfZ+913pGvjEm3nMzGZlX+z+7gcfu28iFQ+rz8+9pZ8/8KUPP/EgzKzmiJccdu+75Qc+N5F8fPbeQ5876LMvX1Adm1aGui9ouj7RPl5TFK9z32/j+b2IC1+t+3H5PsXpt5r9/xZs2ie/G7bjxVt95LY957K+v8sDXP1vvcjLc3S09cf+vUblzdLw9xbT4rnHWx09vwV+eNjb0mQ5145b41NyZjMlPm3qwfUJbFtC8LmOR/Ar7Pnr0jvsQHZe3JQLl69fvPvG+pqZPOyJnl8xb2y4E6CDwBX2Ta/BHfR7wEAAGlBvwcA3HLbbbfF9t6sDCbLpYI/zwvy4kwmc5vcGrB+0cf71GUymaAz8I0+3qfYe5n6TEDqzajKyPy5s+X+xnqZP3d2KhbKASTP8Mj4tICBMC5evS7DI+Oazii8qWxO9p4cVDrGnpODMpXNaTojkR37TwcKRBUROfzaedmx/7S2c0A4XlBNGL3HBqT/7VHNZ6QmKZ9Hx33ul+72IC1mVlfJ8088KO2tzb7+fXtrs69A7CT0vc+evyJPHTgjy3celJVPvyiPPfeyrHz6RVm+86A8deBMJPeZ39+l5OtX3Fv073ne6TWVzcnQpTF5fXhEhi6N0RaFFPa5V87otfCVJIqJsm3jugL0mJjMyvZ9p2Tds4elp6//lvH0xavXpaevXx595iXZvu+UTExmYzpTAGHZOL8EAAAAAACQFGTCT5bvF/y5+GpyaYX//t1cLneh8B/lcrl3MpnMBRG5I++vm0UkSE2HwvcqPPdSf2/kMwEAgGTQFUikOyApDJ0bCubPVc9wqhr03LF6IZkTY6QauNd79JxVWe+S8nl03Od+6WwP0mZmdZXs2rRUOlYvlN6j52RPkQyxW5Y1SXtKMsROTGZlx/7TJe9DL1ivp69f2lubpbutxVh2+IXzbpP21uZQbUJ7a3PR34vnnT5kVdbH1KatObPcmxrmugL0mZjMytablV8q6z02IIMXxqj8AjjGtvklAAAAAACAJHFvpQXlFAbB3x/w9QsL/nymwnv9eMF7BQnCL3yvUq+N8jMBAADH6QoksiEgybYNBUkJek4jXVnvOjcstiJbd5yfZyqbk+GRcRm9NilzZlVLY32t0ncS9YYfGzYYuWzBnXOka+MS6dywWOt14BIbg/W621pk8MJYoMz1axbNk+62lqL/jeedOps2aiSFiU1bDXU10lhfq/WYJnFdAfqpVH7ZtWmpobMCoJtt80sAAAAAAABJwkpEsrxa8OePZTKZugCv/4kKxyv331b6fZNMJjNHRD7m872i/EwAAMBxjfW10lBXo3QMWwKSbNpQQOlyt+nMemeDOD7P2fNX5KkDZ2T5zoOy8ukX5bHnXpaVT78oy3celKcOnJH+t0dDnUfUG35s2GCUBDOqMjJ/7my5v7Fe5s+dnZoAfBG1YD1TZlZXyfNPPCjtrc2+/n17a3PJTQE879R5GzX8bmboPTYgW3cfl4nJrOEzc5uJoLcty5qcab+4rgD9VCu/hO3/AoieTfNLAAAAAAAASUMQfoLkcrkhEflu3l9Vi8iqAIdYW/Dnr5X5t39X4bXlrJbpVRj+IZfL/XOxfxjxZwIAAI6bUZWRzcualI5hS0CSTRsKkhbEnTZJy3oX5eeZmMzK9n2nZN2zh6Wnr/+W+8DLuvvoMy/J9n2nAgf76bjP/bJlgxHcZXOw3szqKtm1aakc2rZWOlYtuOW+aqirkY5VC+TQtrWya9PSktmxed6ps3GjRhKYCHprX3Gv9mOawnUF6Kej8gsAN9g0vwQAAAAAAJA0BOEnz76CP3/Gz4symcxHRaQ1769GReR/lXnJ/xSRsbw/r3zvGH78esGfC8+5UFSfCQAAJIDfTLglX29JQJJNGwqSFsSdNknLehfV54ki666O+9wvWzYYwV0uBOstuHOOdG1cIie61suRznXywpOPyJHOdXKia710bVwiC+6cU/b1PO/U2LxRw3W6N221tzZXvB9swXUF6EflFyBdbJpfAgAAAAAASBqC8JOnV0Sm8v78C5lM5iM+XvfvCv78pVwuVzJ1Wy6Xuyoieyoc4xaZTGaRiGzK+6tJEfmLCi+L5DMBAIBkWDjvttCB+LYFJNmyoSBpQdxpk7Ssd1F9nqiy7qre577fx5INRnCTa8F6M6oyMn/ubLm/sV7mz53tO2CI550aFzZquErnpq01i+ZJd1uLlmNFgesK0I/KL0D62DK/BAAAAAAAkDQE4SdMLpf7voj8Wd5fzRSRL2QymZIRJplM5udkenb6CRHZ4ePt/g8RyZ+t//VMJvOzZd6nVkT+9L1z8vTkcrl/KvcmEX8mAACQAN1tLbJm0bxAr7ExIMmWDQVJC+JOm6RlvYvi80SZdVflPvfLtg1GcE9agvV43oXn2kYNF+l4VrS3NsvzTzwoM6vdmBLmugLMoPILkD62zC8BAAAAAAAkjRsrLgmSyWSaMpnMfYX/E5G7Cv5pdbF/997/7qzwNt0iciHvzz8uIi9kMpmPFpzLrEwm829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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x, y = gen_web_traffic_data()\n", "plot_web_traffic(x, y)" ] }, { "cell_type": "markdown", "metadata": { "id": "338J3crrd1Gi" }, "source": [ "### 2.2 Use numpy to fit some polynomial lines\n", "* `polyfit` fits a polynomial of degree d\n", "* `poly1d` evaluates the function using the learned coefficients\n", "* Plot with matplotlib\n", "\n", "```python\n", "f2 = np.poly1d(np.polyfit(x, y, 2))\n", "f10 = np.poly1d(np.polyfit(x, y, 10))\n", "f50 = np.poly1d(np.polyfit(x, y, 50))\n", "\n", "mx = np.linspace(0, x[-1], 1000)\n", "plt.plot(mx, f2(mx))\n", "```" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 860 }, "executionInfo": { "elapsed": 1597, "status": "ok", "timestamp": 1661505927538, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "NGmUcPU1d1Gj", "outputId": "82c58b26-721a-4490-a5a7-7bd9bb8c621d" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3437: RankWarning: Polyfit may be poorly conditioned\n", " exec(code_obj, self.user_global_ns, self.user_ns)\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f2 = np.poly1d(np.polyfit(x, y, 2))\n", "f10 = np.poly1d(np.polyfit(x, y, 10))\n", "f50 = np.poly1d(np.polyfit(x, y, 50))\n", "plot_web_traffic(x, y, [f2,f10,f50])" ] }, { "cell_type": "markdown", "metadata": { "id": "huJP46WUd1Gj" }, "source": [ "### 2.3 Evaluate\n", "* Using root sum squared error: $\\sqrt{\\sum_i (f(x^{(i)}) - y^{(i)})^2}$\n", "* The degree of the polynomial needs to be tuned to the data\n", "* Predictions don't look great. We need more sophisticated methods." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 876, "referenced_widgets": [ "d53df7e7fc2145e985784431b3864bed", "e310d6f2ef0343118f9c6d6925d0243a", "6e8df99808d94fe593d39455a910d232", "af2bed9679ce434eada7a836483084c3", "8796abd4fac2414594919c49a939e539", "729f811a6c66423c93c669557b6fc099", "57f356a576d141fbbdc20b4824468db2" ] }, "executionInfo": { "elapsed": 1249, "status": "ok", "timestamp": 1661505928782, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "vhyMIdCFd1Gk", "outputId": "2bec9e01-ab3f-4c65-d41f-73af8d766acf" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8d591d236664493187dd3eec02f73ab0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(IntSlider(value=15, description='degree', max=30, min=1, step=2), Output()), _dom_classe…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import ipywidgets as widgets\n", "from ipywidgets import interact, interact_manual\n", "\n", "mx = np.linspace(0, 6 * 7 * 24, 100)\n", "\n", "def error(f, x, y):\n", " return np.sqrt(np.sum((f(x)-y)**2))\n", "\n", "@interact\n", "def play_with_degree(degree=(1,30,2)):\n", " f = np.poly1d(np.polyfit(x, y, degree))\n", " plot_web_traffic(x, y, [f], mx=mx, ymax=10000)\n", " print(\"Training error for d=%i: %f\" % (f.order, error(f, x, y)))" ] }, { "cell_type": "markdown", "metadata": { "id": "fu4S47yHd1Gk" }, "source": [ "## 3. scikit-learn\n", "One of the most prominent Python libraries for machine learning:\n", "\n", "* Contains many state-of-the-art machine learning algorithms\n", "* Builds on numpy (fast), implements advanced techniques\n", "* Wide range of evaluation measures and techniques\n", "* Offers [comprehensive documentation](http://scikit-learn.org/stable/documentation) about each algorithm\n", "* Widely used, and a wealth of [tutorials](http://scikit-learn.org/stable/user_guide.html) and code snippets are available \n", "* Works well with numpy, scipy, pandas, matplotlib,...\n", "\n", "**Note**: We'll repeat most of the material below in the lectures and tutorials on model selection and data preprocessing, but it's still very useful to study it beforehand.\n", "\n", "See the [Reference](http://scikit-learn.org/dev/modules/classes.html)" ] }, { "cell_type": "markdown", "metadata": { "id": "VczBVe2Nd1Gk" }, "source": [ "__Supervised learning:__\n", "\n", "* Linear models (Ridge, Lasso, ...)\n", "* Support Vector Machines\n", "* Nearest neighbors\n", "* Neural networks \n", "* Feature selection" ] }, { "cell_type": "markdown", "metadata": { "id": "-qrfd17hd1Gk" }, "source": [ "__Unsupervised learning:__\n", " \n", "* Clustering (KMeans, ...)\n", "* Matrix Decomposition (PCA, ...)\n", "* Manifold Learning (Embeddings)\n", "* Density estimation\n", "* Outlier detection" ] }, { "cell_type": "markdown", "metadata": { "id": "Jz8gSpj1d1Gk" }, "source": [ "__Model selection and evaluation:__\n", "\n", "* Cross-validation\n", "* Grid-search\n", "* Lots of metrics" ] }, { "cell_type": "markdown", "metadata": { "id": "5gO6ROEwd1Gl" }, "source": [ "### 3.1 Data import\n", "Multiple options:\n", "\n", "* A few toy datasets are included in `sklearn.datasets`\n", "* Import 1000s of datasets via `sklearn.datasets.fetch_openml`\n", "* You can import data files (CSV) with `pandas` or `numpy`\n", "\n", "```python\n", "from sklearn.datasets import load_iris, fetch_openml\n", "iris_data = load_iris()\n", "dating_data = fetch_openml(name=\"SpeedDating\")\n", "```" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "SG0bkFzcd1Gl" }, "outputs": [], "source": [ "from sklearn.datasets import load_iris, fetch_openml\n", "iris_data = load_iris()\n", "dating_data = fetch_openml(\"SpeedDating\", version=1)" ] }, { "cell_type": "markdown", "metadata": { "id": "yWZHqffrd1Gl" }, "source": [ "These will return a `Bunch` object (similar to a `dict`)\n", "\n", "``` python\n", "print(\"Keys of iris_dataset: {}\".format(iris_dataset.keys()))\n", "print(iris_dataset['DESCR'][:193] + \"\\n...\")\n", "```" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 541, "status": "ok", "timestamp": 1661505940679, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "id": "XVVDrZCQd1Gl", "outputId": "2c4ed58d-b208-4414-a75c-4ce2bf56dac1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Keys of iris_dataset: dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])\n", ".. _iris_dataset:\n", "\n", "Iris plants dataset\n", "--------------------\n", "\n", "**Data Set Characteristics:**\n", "\n", " :Number of Instances: 150 (50 in each of three classes)\n", " :Number of Attributes: 4 numeric, pre\n", "...\n" ] } ], "source": [ "print(\"Keys of iris_dataset: {}\".format(iris_data.keys()))\n", "print(iris_data['DESCR'][:193] + \"\\n...\")" ] }, { "cell_type": "markdown", "metadata": { "id": "ODQFXgHxd1Gl" }, "source": [ "* Targets (classes) and features are lists of strings\n", "* Data and target values are always numeric (ndarrays)\n", "\n", "``` python\n", "print(\"Targets: {}\".format(iris_data['target_names']))\n", "print(\"Features: {}\".format(iris_data['feature_names']))\n", "print(\"Shape of data: {}\".format(iris_data['data'].shape))\n", "print(\"First 5 rows:\\n{}\".format(iris_data['data'][:5]))\n", "print(\"Targets:\\n{}\".format(iris_data['target']))\n", "```" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 538, "status": "ok", "timestamp": 1661505945226, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "id": "yriDr2xKd1Gm", "outputId": "0487975a-0ad4-4a95-f6c5-f013e327c57d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Targets: ['setosa' 'versicolor' 'virginica']\n", "Features: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n", "Shape of data: (150, 4)\n", "First 5 rows:\n", "[[5.1 3.5 1.4 0.2]\n", " [4.9 3. 1.4 0.2]\n", " [4.7 3.2 1.3 0.2]\n", " [4.6 3.1 1.5 0.2]\n", " [5. 3.6 1.4 0.2]]\n", "Targets:\n", "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2]\n" ] } ], "source": [ "print(\"Targets: {}\".format(iris_data['target_names']))\n", "print(\"Features: {}\".format(iris_data['feature_names']))\n", "print(\"Shape of data: {}\".format(iris_data['data'].shape))\n", "print(\"First 5 rows:\\n{}\".format(iris_data['data'][:5]))\n", "print(\"Targets:\\n{}\".format(iris_data['target']))" ] }, { "cell_type": "markdown", "metadata": { "id": "55nlCkP7d1Gm" }, "source": [ "### 3.2 Building models\n", "All scikitlearn _estimators_ follow the same interface" ] }, { "cell_type": "markdown", "metadata": { "id": "2geYnHx2d1Gm" }, "source": [ "```python\n", "class SupervisedEstimator(...):\n", " def __init__(self, hyperparam, ...):\n", "\n", " def fit(self, X, y): # Fit/model the training data\n", " ... # given data X and targets y\n", " return self\n", " \n", " def predict(self, X): # Make predictions\n", " ... # on unseen data X \n", " return y_pred\n", " \n", " def score(self, X, y): # Predict and compare to true\n", " ... # labels y \n", " return score\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "nqfD1U9Qd1Gm" }, "source": [ "### 3.3 Training and testing data\n", "To evaluate our classifier, we need to test it on unseen data. \n", "`train_test_split`: splits data randomly in 75% training and 25% test data.\n", "\n", "``` python\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " iris_data['data'], iris_data['target'], random_state=0)\n", "```" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 3, "status": "ok", "timestamp": 1661505948934, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": false, "id": "Xcbk3LzDd1Gm", "outputId": "c5338131-7ef8-4ee0-c36a-6a3b0b22da35" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_train shape: (112, 4)\n", "y_train shape: (112,)\n", "X_test shape: (38, 4)\n", "y_test shape: (38,)\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " iris_data['data'], iris_data['target'], \n", " random_state=0)\n", "print(\"X_train shape: {}\".format(X_train.shape))\n", "print(\"y_train shape: {}\".format(y_train.shape))\n", "print(\"X_test shape: {}\".format(X_test.shape))\n", "print(\"y_test shape: {}\".format(y_test.shape))" ] }, { "cell_type": "markdown", "metadata": { "id": "gqcSSkkMd1Gm" }, "source": [ "### 3.4 Fitting a model" ] }, { "cell_type": "markdown", "metadata": { "id": "j6Nr4WzXd1Gn" }, "source": [ "The first model we'll build is a k-Nearest Neighbor classifier. \n", "kNN is included in `sklearn.neighbors`, so let's build our first model\n", "\n", "``` python\n", "knn = KNeighborsClassifier(n_neighbors=1)\n", "knn.fit(X_train, y_train)\n", "```" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 2, "status": "ok", "timestamp": 1661505950561, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": false, "id": "W5Rb7ttgd1Gn", "outputId": "52436e1d-4ef5-4bca-c8d2-bdd75068404e" }, "outputs": [ { "data": { "text/plain": [ "KNeighborsClassifier(n_neighbors=1)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "knn = KNeighborsClassifier(n_neighbors=1)\n", "knn.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": { "id": "MTc7WVU-d1Gn" }, "source": [ "### 3.5 Making predictions\n", "Let's create a new example and ask the kNN model to classify it\n", "\n", "``` python\n", "X_new = np.array([[5, 2.9, 1, 0.2]])\n", "prediction = knn.predict(X_new)\n", "class_name = iris_data['target_names'][prediction]\n", "```" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 499, "status": "ok", "timestamp": 1661505955254, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": false, "id": "EwTywTU4d1Gn", "outputId": "55672878-23c9-4b66-dc32-0f9542efd317" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prediction: [0]\n", "Predicted target name: ['setosa']\n" ] } ], "source": [ "X_new = np.array([[5, 2.9, 1, 0.2]])\n", "prediction = knn.predict(X_new)\n", "print(\"Prediction: {}\".format(prediction))\n", "print(\"Predicted target name: {}\".format(\n", " iris_data['target_names'][prediction]))" ] }, { "cell_type": "markdown", "metadata": { "id": "GjEaXSIHd1Gn" }, "source": [ "### 3.6 Evaluating the model\n", "Feeding all test examples to the model yields all predictions\n", "\n", "``` python\n", "y_pred = knn.predict(X_test)\n", "```" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 512, "status": "ok", "timestamp": 1661505959642, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "P_-LgDMfd1Gn", "outputId": "eaaa8cfc-a8bc-43bc-bdce-305ee9bab0d2", "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test set predictions:\n", " [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n", " 2]\n" ] } ], "source": [ "y_pred = knn.predict(X_test)\n", "print(\"Test set predictions:\\n {}\".format(y_pred))" ] }, { "cell_type": "markdown", "metadata": { "id": "5Bc2BZXbd1Go" }, "source": [ "The `score` function computes the percentage of correct predictions\n", "\n", "``` python\n", "knn.score(X_test, y_test)\n", "```" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 3, "status": "ok", "timestamp": 1661505962288, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "NfQwHtAId1Go", "outputId": "b2fb0cad-898a-4376-c017-601085a67e47" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Score: 0.97\n" ] } ], "source": [ "print(\"Score: {:.2f}\".format(knn.score(X_test, y_test) ))" ] }, { "cell_type": "markdown", "metadata": { "id": "I1m6Tj2Nd1Go" }, "source": [ "## 4. Cross-validation\n", "\n", "- More stable, thorough way to estimate generalization performance\n", "- _k-fold cross-validation_ (CV): split (randomized) data into _k_ equal-sized parts, called _folds_\n", " - First, fold 1 is the test set, and folds 2-5 comprise the training set\n", " - Then, fold 2 is the test set, folds 1,3,4,5 comprise the training set\n", " - Compute _k_ evaluation scores, aggregate afterwards (e.g. take the mean)" ] }, { "cell_type": "markdown", "metadata": { "id": "kjfU8UbVd1Go" }, "source": [ "### 4.1 Cross-validation in scikit-learn\n", "\n", "* `cross_val_score` function with learner, training data, labels\n", "* Returns list of all scores\n", " * Does 3-fold CV by default, can be changed via `cv` hyperparameter\n", " * Default scoring measures are accuracy (classification) or $R^2$, coefficient of determination (regression)\n", "* Even though models are built internally, they are not returned\n", "\n", "``` python\n", "knn = KNeighborsClassifier(n_neighbors=1)\n", "scores = cross_val_score(knn, iris.data, iris.target, cv=5)\n", "print(\"Cross-validation scores: {}\".format(scores))\n", "print(\"Average cross-validation score: {:.2f}\".format(scores.mean()))\n", "print(\"Variance in cross-validation score: {:.4f}\".format(np.var(scores)))\n", "```" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 518, "status": "ok", "timestamp": 1661505965505, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "CA1n2Qwfd1Go", "outputId": "0c8839f4-ff07-4df4-f1de-c77d9f250426" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cross-validation scores: [0.96666667 0.96666667 0.93333333 0.93333333 1. ]\n", "Average cross-validation score: 0.96\n", "Variance in cross-validation score: 0.0006\n" ] } ], "source": [ "from sklearn.model_selection import cross_val_score\n", "from sklearn.datasets import load_iris\n", "\n", "iris = load_iris()\n", "knn = KNeighborsClassifier(n_neighbors=1)\n", "\n", "scores = cross_val_score(knn, iris.data, iris.target)\n", "print(\"Cross-validation scores: {}\".format(scores))\n", "print(\"Average cross-validation score: {:.2f}\".format(scores.mean()))\n", "print(\"Variance in cross-validation score: {:.4f}\".format(np.var(scores)))" ] }, { "cell_type": "markdown", "metadata": { "id": "rI9RDyHud1Go" }, "source": [ "### 4.2 More variants\n", "* Stratified cross-validation: for inbalanced datasets\n", "* Leave-one-out cross-validation: for very small datasets\n", "* Shuffle-Split cross-validation: whenever you need to shuffle the data first\n", "* Repeated cross-validation: more trustworthy, but more expensive\n", "* Cross-validation with groups: Whenever your data contains non-independent datapoints, e.g. data points from the same patient\n", "* Bootstrapping: sampling with replacement, for extracting statistical properties" ] }, { "cell_type": "markdown", "metadata": { "id": "m1qACAGCd1Go" }, "source": [ "### 4.3 Avoid data leakage\n", "- Simply taking the best performing model based on cross-validation performance yields optimistic results\n", "- We've already used the test data to evaluate each model!\n", "- Hence, we don't have an independent test set to evaluate these hyperparameter settings\n", " - Information 'leaks' from test set into the final model\n", "- Solution: Set aside part of the training data to evaluate the hyperparameter settings\n", " - Select best model on validation set\n", " - Rebuild the model on the training+validation set\n", " - Evaluate optimal model on the test set" ] }, { "cell_type": "markdown", "metadata": { "id": "1LbffNLQd1Go" }, "source": [ "### 5. Pipelines\n", "* Many learning algorithms are greatly affected by _how_ you represent the training data\n", "* Examples: Scaling, numeric/categorical values, missing values, feature selection/construction\n", "* We typically need chain together different algorithms\n", " - Many _preprocessing_ steps\n", " - Possibly many models\n", "* This is called a _pipeline_ (or _workflow_)\n", "* The best way to represent data depends not only on the semantics of the data, but also on the kind of model you are using." ] }, { "cell_type": "markdown", "metadata": { "id": "BJ-U-9OUd1Gp" }, "source": [ "### 5.1 Example: Speed dating data\n", "* Data collected from speed dating events\n", "* See https://www.openml.org/d/40536\n", "* Could also be collected from dating website or app\n", "* Real-world data:\n", " - Different numeric scales\n", " - Missing values\n", " - Likely irrelevant features\n", " - Different types: Numeric, categorical,...\n", " - Input errors (e.g., 'lawyer' vs 'Lawyer')\n", " \n", "```\n", "dating_data = fetch_openml(\"SpeedDating\")\n", "```" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "-ZDEEEVOd1Gp" }, "outputs": [], "source": [ "dating_data = fetch_openml(\"SpeedDating\", version=1)" ] }, { "cell_type": "markdown", "metadata": { "id": "hHvDS7gTd1Gp" }, "source": [ "### 5.2 Scaling\n", "\n", "When the features have different scales (their values range between very different minimum and maximum values), one feature will overpower the others. Several scaling techniques are available to solve this: \n", "- `StandardScaler` rescales all features to mean=0 and variance=1\n", " - Does not ensure and min/max value\n", "- `RobustScaler` uses the median and quartiles\n", " - Median m: half of the values < m, half > m\n", " - Lower Quartile lq: 1/4 of values < lq\n", " - Upper Quartile uq: 1/4 of values > uq\n", " - Ignores _outliers_, brings all features to same scale\n", "- `MinMaxScaler` brings all feature values between 0 and 1\n", "- `Normalizer` scales data such that the feature vector has Euclidean length 1\n", " - Projects data to the unit circle\n", " - Used when only the direction/angle of the data matters" ] }, { "cell_type": "markdown", "metadata": { "id": "KVX2PH8ed1Gp" }, "source": [ "#### Applying scaling transformations\n", "- Lets apply a scaling transformation _manually_, then use it to train a learning algorithm\n", "- First, split the data in training and test set\n", "- Next, we `fit` the preprocessor on the __training data__\n", " - This computes the necessary transformation parameters\n", " - For `MinMaxScaler`, these are the min/max values for every feature\n", "- After fitting, we can `transform` the training and test data \n", "\n", "```python\n", "scaler = MinMaxScaler()\n", "scaler.fit(X_train)\n", "X_train_scaled = scaler.transform(X_train)\n", "X_test_scaled = scaler.transform(X_test)\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "7NTD15xPd1Gp" }, "source": [ "### 5.3 Missing value imputation\n", "* Many sci-kit learn algorithms cannot handle missing value\n", "* `Imputer` replaces specific values\n", " * `missing_values` (default 'NaN') placeholder for the missing value\n", " * `strategy`:\n", " - `mean`, replace using the mean along the axis\n", " - `median`, replace using the median along the axis\n", " - `most_frequent`, replace using the most frequent value\n", "* Many more advanced techniques exist, but not yet in scikit-learn\n", " * e.g., low rank approximations (uses matrix factorization)" ] }, { "cell_type": "markdown", "metadata": { "id": "lMisYiUSd1Gp" }, "source": [ "``` python\n", "imp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n", "imp.fit_transform(X1_train)\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "rR35stICd1Gp" }, "source": [ "### 5.4 Feature encoding\n", "* scikit-learn classifiers only handle numeric data. If your features are categorical, you need to encode them first\n", "* `LabelEncoder` simply replaces each value with an integer value\n", "* `OneHotEncoder` converts a feature of $n$ values to $n$ binary features\n", " * Provide `categories` as array or set to 'auto'\n", " \n", "```python\n", "X_enc = OneHotEncoder(categories='auto').fit_transform(X)\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "NlVW-j1fd1Gp" }, "source": [ "* `ColumnTransformer` can apply different transformers to different features\n", "* Transformers can be pipelines doing multiple things\n", "\n", "```python\n", "numeric_features = ['age', 'pref_o_attractive']\n", "numeric_transformer = Pipeline(steps=[\n", " ('imputer', SimpleImputer(strategy='median')),\n", " ('scaler', StandardScaler())])\n", "\n", "categorical_features = ['gender', 'd_d_age', 'field']\n", "categorical_transformer = Pipeline(steps=[\n", " ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n", " ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n", "\n", "preprocessor = ColumnTransformer(\n", " transformers=[\n", " ('num', numeric_transformer, numeric_features),\n", " ('cat', categorical_transformer, categorical_features)])\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "m_G_or2Wd1Gp" }, "source": [ "### 5.5 Building Pipelines\n", "* In scikit-learn, a `pipeline` combines multiple processing _steps_ in a single estimator\n", "* All but the last step should be transformer (have a `transform` method)\n", " * The last step can be a transformer too (e.g. Scaler+PCA)\n", "* It has a `fit`, `predict`, and `score` method, just like any other learning algorithm\n", "* Pipelines are built as a list of steps, which are (name, algorithm) tuples\n", " * The name can be anything you want, but can't contain `'__'`\n", " * We use `'__'` to refer to the hyperparameters, e.g. `svm__C`\n", "* Let's build, train, and score a `MinMaxScaler` + `LinearSVC` pipeline:" ] }, { "cell_type": "markdown", "metadata": { "id": "5GDtxHOQd1Gq" }, "source": [ "``` python\n", "pipe = Pipeline([(\"scaler\", MinMaxScaler()), (\"svm\", LinearSVC())])\n", "pipe.fit(X_train, y_train).score(X_test, y_test)\n", "```" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 2, "status": "ok", "timestamp": 1661505972506, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "HdUgqzopd1Gq", "outputId": "17e6f9e7-eec0-46be-d1f8-b6f168279200" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test score: 0.97\n" ] } ], "source": [ "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.svm import LinearSVC\n", "from sklearn.datasets import load_breast_cancer\n", "\n", "cancer = load_breast_cancer()\n", "\n", "pipe = Pipeline([(\"scaler\", MinMaxScaler()), (\"svm\", LinearSVC())])\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target,\n", " random_state=1)\n", "pipe.fit(X_train, y_train)\n", "print(\"Test score: {:.2f}\".format(pipe.score(X_test, y_test)))" ] }, { "cell_type": "markdown", "metadata": { "id": "9Xe_CDTtd1Gq" }, "source": [ "* Now with cross-validation:\n", "``` python\n", "scores = cross_val_score(pipe, cancer.data, cancer.target)\n", "```" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 488, "status": "ok", "timestamp": 1661505973558, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "gMHLpAUJd1Gq", "outputId": "96a8aad3-2dac-4431-ef20-90b370e60a4e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cross-validation scores: [0.98245614 0.97368421 0.96491228 0.96491228 0.99115044]\n", "Average cross-validation score: 0.98\n" ] } ], "source": [ "from sklearn.model_selection import cross_val_score\n", "scores = cross_val_score(pipe, cancer.data, cancer.target)\n", "print(\"Cross-validation scores: {}\".format(scores))\n", "print(\"Average cross-validation score: {:.2f}\".format(scores.mean()))" ] }, { "cell_type": "markdown", "metadata": { "id": "YzmPVkBLd1Gq" }, "source": [ "* We can retrieve the trained SVM by querying the right step indices\n", "``` python\n", "pipe.steps[1][1]\n", "```" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 2, "status": "ok", "timestamp": 1661505974802, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "fG-n07and1Gq", "outputId": "dfd1145d-d506-46be-a183-b5dd61649d58" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SVM component: LinearSVC()\n" ] } ], "source": [ "pipe.fit(X_train, y_train)\n", "print(\"SVM component: {}\".format(pipe.steps[1][1]))" ] }, { "cell_type": "markdown", "metadata": { "id": "_nccYQJLd1Gq" }, "source": [ "* Or we can use the `named_steps` dictionary\n", "``` python\n", "pipe.named_steps['svm']\n", "```" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 2, "status": "ok", "timestamp": 1661505977903, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "bNbBLyXAd1Gq", "outputId": "f10aa383-38e0-4a78-d1c8-2f853350c75a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SVM component: LinearSVC()\n" ] } ], "source": [ "print(\"SVM component: {}\".format(pipe.named_steps['svm']))" ] }, { "cell_type": "markdown", "metadata": { "id": "pIt5X6NSd1Gr" }, "source": [ "* When you don't need specific names for specific steps, you can use `make_pipeline`\n", " * Assigns names to steps automatically\n", "``` python\n", "pipe_short = make_pipeline(MinMaxScaler(), LinearSVC(C=100))\n", "print(\"Pipeline steps:\\n{}\".format(pipe_short.steps))\n", "```" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 2, "status": "ok", "timestamp": 1661505979921, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "DCP8qSCLd1Gr", "outputId": "ff5060fb-e4c9-4abb-a80f-b2ab84c2fa39" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pipeline steps:\n", "[('minmaxscaler', MinMaxScaler()), ('linearsvc', LinearSVC(C=100))]\n" ] } ], "source": [ "from sklearn.pipeline import make_pipeline\n", "# abbreviated syntax\n", "pipe_short = make_pipeline(MinMaxScaler(), LinearSVC(C=100))\n", "print(\"Pipeline steps:\\n{}\".format(pipe_short.steps))" ] }, { "cell_type": "markdown", "metadata": { "id": "47Vk1MWtd1Gr" }, "source": [ "## 6. Model selection and Hyperparameter tuning\n", "* There are many algorithms to choose from\n", "* Most algorithms have parameters (hyperparameters) that control model complexity\n", "* Now that we know how to evaluate models, we can improve them selecting by `tuning` algorithms for your data" ] }, { "cell_type": "markdown", "metadata": { "id": "s1QyQ0CCd1Gr" }, "source": [ "We can basically use any optimization technique to optimize hyperparameters:\n", " \n", "- __Grid search__\n", "- __Random search__\n", "\n", "More advanced techniques:\n", "\n", "- Local search\n", "- Racing algorithms\n", "- Bayesian optimization\n", "- Multi-armed bandits\n", "- Genetic algorithms" ] }, { "cell_type": "markdown", "metadata": { "id": "rg9Zx7f_d1Gr" }, "source": [ "Grid vs Random Search\n", "\"ml\"" ] }, { "cell_type": "markdown", "metadata": { "id": "wjCaBvE_d1Gr" }, "source": [ "### 6.1 Grid Search\n", "\n", "- For each hyperparameter, create a list of interesting/possible values\n", " - E.g. For kNN: k in [1,3,5,7,9,11,33,55,77,99]\n", " - E.g. For SVM: C and gamma in [$10^{-10}$..$10^{10}$]\n", "- Evaluate all possible combinations of hyperparameter values\n", " - E.g. using cross-validation\n", "- Split the training data into a training and validation set\n", "- Select the hyperparameter values yielding the best results on the validation set" ] }, { "cell_type": "markdown", "metadata": { "id": "KCqaQVDgd1Gr" }, "source": [ "#### Grid search in scikit-learn\n", "- Create a parameter grid as a dictionary\n", " - Keys are parameter names\n", " - Values are lists of hyperparameter values\n", " \n", "``` python\n", "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],\n", " 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n", "print(\"Parameter grid:\\n{}\".format(param_grid))\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 595, "status": "ok", "timestamp": 1661506008342, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "WG_oNS_Fd1Gr", "outputId": "4b534448-0c8a-4f42-ca33-0ed831570420" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameter grid:\n", "{'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n" ] } ], "source": [ "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],\n", " 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n", "print(\"Parameter grid:\\n{}\".format(param_grid))" ] }, { "cell_type": "markdown", "metadata": { "id": "ZjSiMqc6d1Gr" }, "source": [ "- `GridSearchCV`: like a classifier that uses CV to automatically optimize its hyperparameters internally\n", " - Input: (untrained) model, parameter grid, CV procedure\n", " - Output: optimized model on given training data\n", " - Should only have access to training data\n", " \n", "``` python\n", "grid_search = GridSearchCV(SVC(), param_grid, cv=5)\n", "grid_search.fit(X_train, y_train)\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 2, "status": "ok", "timestamp": 1661506008970, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "4ULBknx8d1Gr", "outputId": "e1c51b71-c9d8-465d-b202-b6340e78412a" }, "outputs": [ { "data": { "text/plain": [ "GridSearchCV(cv=5, estimator=SVC(),\n", " param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100],\n", " 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]})" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import GridSearchCV \n", "from sklearn.svm import SVC\n", "grid_search = GridSearchCV(SVC(), param_grid, cv=5)\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " iris.data, iris.target, random_state=0)\n", "grid_search.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": { "id": "zXlo1sogd1Gs" }, "source": [ "The optimized test score and hyperparameters can easily be retrieved:\n", "\n", "``` python\n", "grid_search.score(X_test, y_test)\n", "grid_search.best_params_\n", "grid_search.best_score_\n", "grid_search.best_estimator_\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 3, "status": "ok", "timestamp": 1661506009482, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "S4RTymcDd1Gs", "outputId": "ccca35f4-1bcf-44c2-b777-7f8b6f7a17f9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test set score: 0.97\n", "Best parameters: {'C': 10, 'gamma': 0.1}\n", "Best cross-validation score: 0.97\n", "Best estimator:\n", "SVC(C=10, gamma=0.1)\n" ] } ], "source": [ "print(\"Test set score: {:.2f}\".format(grid_search.score(X_test, y_test)))\n", "print(\"Best parameters: {}\".format(grid_search.best_params_))\n", "print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))\n", "print(\"Best estimator:\\n{}\".format(grid_search.best_estimator_))" ] }, { "cell_type": "markdown", "metadata": { "hide_input": false, "id": "6DDlcMf4d1Gs" }, "source": [ "#### Nested cross-validation\n", "\n", "- Note that we are still using a single split to create the outer test set\n", "- We can also use cross-validation here\n", "- Nested cross-validation:\n", " - Outer loop: split data in training and test sets\n", " - Inner loop: run grid search, splitting the training data into train and validation sets\n", "- Result is a just a list of scores\n", " - There will be multiple optimized models and hyperparameter settings (not returned)\n", "- To apply on future data, we need to train `GridSearchCV` on all data again" ] }, { "cell_type": "markdown", "metadata": { "id": "GU_sCT5nd1Gs" }, "source": [ "``` python\n", "scores = cross_val_score(GridSearchCV(SVC(), param_grid, cv=5),\n", " iris.data, iris.target, cv=5)\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 1766, "status": "ok", "timestamp": 1661506012912, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "eJle3xohd1Gs", "outputId": "37a45440-8d29-45e5-b854-881054c65212" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cross-validation scores: [0.96666667 1. 0.96666667 0.96666667 1. ]\n", "Mean cross-validation score: 0.9800000000000001\n" ] } ], "source": [ "scores = cross_val_score(GridSearchCV(SVC(), param_grid, cv=5),\n", " iris.data, iris.target, cv=5)\n", "print(\"Cross-validation scores: \", scores)\n", "print(\"Mean cross-validation score: \", scores.mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "vQBKfPudd1Gs" }, "source": [ "### 6.2 Random Search\n", "\n", "- Grid Search has a few downsides:\n", " - Optimizing many hyperparameters creates a combinatorial explosion\n", " - You have to predefine a grid, hence you may jump over optimal values\n", "- Random Search:\n", " - Picks `n_iter` random parameter values\n", " - Scales better, you control the number of iterations\n", " - Often works better in practice, too\n", " - not all hyperparameters interact strongly\n", " - you don't need to explore all combinations" ] }, { "cell_type": "markdown", "metadata": { "id": "RRb2t-rjd1Gs" }, "source": [ "- Executing random search in scikit-learn:\n", " - `RandomizedSearchCV` works like `GridSearchCV`\n", " - Has `n_iter` parameter for the number of iterations\n", " - Search grid can use distributions instead of fixed lists\n", " \n", "``` python\n", "param_grid = {'C': expon(scale=100), \n", " 'gamma': expon(scale=.1)}\n", "random_search = RandomizedSearchCV(SVC(), param_distributions=param_grid,\n", " n_iter=20)\n", "random_search.fit(X_train, y_train)\n", "random_search.best_estimator_\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 654, "status": "ok", "timestamp": 1661506014081, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "7mzIm_Sdd1Gs", "outputId": "77ed4915-e244-4df4-f69d-b98a8dc41294" }, "outputs": [ { "data": { "text/plain": [ "SVC(C=228.02745029445418, gamma=0.00951014484718497)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import RandomizedSearchCV\n", "from scipy.stats import expon\n", "\n", "param_grid = {'C': expon(scale=100), \n", " 'gamma': expon(scale=.1)}\n", "random_search = RandomizedSearchCV(SVC(), param_distributions=param_grid,\n", " n_iter=20)\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " iris.data, iris.target, random_state=0)\n", "random_search.fit(X_train, y_train)\n", "random_search.best_estimator_" ] }, { "cell_type": "markdown", "metadata": { "id": "ckrliPChd1Gs" }, "source": [ "### Using Pipelines in Grid-searches\n", "* We can use the pipeline as a single estimator in `cross_val_score` or `GridSearchCV`\n", "* To define a grid, refer to the hyperparameters of the steps\n", " * Step `svm`, parameter `C` becomes `svm__C`\n", " \n", "```python\n", "param_grid = {'svm__C': [0.001, 0.01, 0.1, 1, 10, 100],\n", " 'svm__gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n", "pipe = pipeline.Pipeline([(\"scaler\", MinMaxScaler()), (\"svm\", SVC(C=100))])\n", "grid = GridSearchCV(pipe, param_grid=param_grid, cv=5)\n", "grid.fit(X_train, y_train)\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hide_input": true, "id": "66rJ5fBId1Gs" }, "outputs": [], "source": [ "param_grid = {'svm__C': [0.001, 0.01, 0.1, 1, 10, 100],\n", " 'svm__gamma': [0.001, 0.01, 0.1, 1, 10, 100]}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 1018, "status": "ok", "timestamp": 1661506021281, "user": { "displayName": "Hanwei", "userId": "13726710393687068958" }, "user_tz": -480 }, "hide_input": true, "id": "dLAIkmBfd1Gt", "outputId": "bcdf8a24-859d-4b07-a4b2-e9e17d640bec" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best cross-validation accuracy: 0.96\n", "Test set score: 0.97\n", "Best parameters: {'svm__C': 1, 'svm__gamma': 10}\n" ] } ], "source": [ "from sklearn import pipeline\n", "from sklearn.svm import SVC\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "\n", "pipe = pipeline.Pipeline([(\"scaler\", MinMaxScaler()), (\"svm\", SVC(C=100))])\n", "grid = GridSearchCV(pipe, param_grid=param_grid, cv=5)\n", "grid.fit(X_train, y_train)\n", "print(\"Best cross-validation accuracy: {:.2f}\".format(grid.best_score_))\n", "print(\"Test set score: {:.2f}\".format(grid.score(X_test, y_test)))\n", "print(\"Best parameters: {}\".format(grid.best_params_))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "e3trmcnbed48" }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Slideshow", "colab": { "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": "3.11.5" }, "latex_metadata": { "author": "Joaquin Vanschoren", "title": "Introduction" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "57f356a576d141fbbdc20b4824468db2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": 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