{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Report01 - 交通事故理赔审核预测\n", "\n", "* 沈键\n", "* 2021200082" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. 任务简介" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "    在交通摩擦(事故)发生后,理赔员会前往现场勘察、采集信息,这些信息往往影响着车主是否能够得到保险公司的理赔。训练集数据包括理赔人员在现场对该事故方采集的36条信息,信息已经被编码,以及该事故方最终是否获得理赔。我们的任务是根据这36条信息预测该事故方没有被理赔的概率。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. 数据分析" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "    想来分析一下所给的数据特征。使用pandas读入train.csv文件并显示前几行:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CaseIdQ1Q2Q3Q4Q5Q6Q7Q8Q9...Q28Q29Q30Q31Q32Q33Q34Q35Q36Evaluation
01000000000...0000000000
12000000000...0111100000
23000000010...1222100000
34000000000...1323100110
45000000000...1424100110
56000000000...1235100000
67000000000...0316100111
78000000000...1313100111
89000000020...0212100000
910000000000...0217100000
\n", "

10 rows × 38 columns

\n", "
" ], "text/plain": [ " CaseId Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 ... Q28 Q29 Q30 Q31 Q32 \\\n", "0 1 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n", "1 2 0 0 0 0 0 0 0 0 0 ... 0 1 1 1 1 \n", "2 3 0 0 0 0 0 0 0 1 0 ... 1 2 2 2 1 \n", "3 4 0 0 0 0 0 0 0 0 0 ... 1 3 2 3 1 \n", "4 5 0 0 0 0 0 0 0 0 0 ... 1 4 2 4 1 \n", "5 6 0 0 0 0 0 0 0 0 0 ... 1 2 3 5 1 \n", "6 7 0 0 0 0 0 0 0 0 0 ... 0 3 1 6 1 \n", "7 8 0 0 0 0 0 0 0 0 0 ... 1 3 1 3 1 \n", "8 9 0 0 0 0 0 0 0 2 0 ... 0 2 1 2 1 \n", "9 10 0 0 0 0 0 0 0 0 0 ... 0 2 1 7 1 \n", "\n", " Q33 Q34 Q35 Q36 Evaluation \n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 1 1 0 \n", "4 0 0 1 1 0 \n", "5 0 0 0 0 0 \n", "6 0 0 1 1 1 \n", "7 0 0 1 1 1 \n", "8 0 0 0 0 0 \n", "9 0 0 0 0 0 \n", "\n", "[10 rows x 38 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "train_df = pd.read_csv(\"./data/train.csv\")\n", "train_df.head(10)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 200000 entries, 0 to 199999\n", "Data columns (total 38 columns):\n", " # Column Non-Null Count Dtype\n", "--- ------ -------------- -----\n", " 0 CaseId 200000 non-null int64\n", " 1 Q1 200000 non-null int64\n", " 2 Q2 200000 non-null int64\n", " 3 Q3 200000 non-null int64\n", " 4 Q4 200000 non-null int64\n", " 5 Q5 200000 non-null int64\n", " 6 Q6 200000 non-null int64\n", " 7 Q7 200000 non-null int64\n", " 8 Q8 200000 non-null int64\n", " 9 Q9 200000 non-null int64\n", " 10 Q10 200000 non-null int64\n", " 11 Q11 200000 non-null int64\n", " 12 Q12 200000 non-null int64\n", " 13 Q13 200000 non-null int64\n", " 14 Q14 200000 non-null int64\n", " 15 Q15 200000 non-null int64\n", " 16 Q16 200000 non-null int64\n", " 17 Q17 200000 non-null int64\n", " 18 Q18 200000 non-null int64\n", " 19 Q19 200000 non-null int64\n", " 20 Q20 200000 non-null int64\n", " 21 Q21 200000 non-null int64\n", " 22 Q22 200000 non-null int64\n", " 23 Q23 200000 non-null int64\n", " 24 Q24 200000 non-null int64\n", " 25 Q25 200000 non-null int64\n", " 26 Q26 200000 non-null int64\n", " 27 Q27 200000 non-null int64\n", " 28 Q28 200000 non-null int64\n", " 29 Q29 200000 non-null int64\n", " 30 Q30 200000 non-null int64\n", " 31 Q31 200000 non-null int64\n", " 32 Q32 200000 non-null int64\n", " 33 Q33 200000 non-null int64\n", " 34 Q34 200000 non-null int64\n", " 35 Q35 200000 non-null int64\n", " 36 Q36 200000 non-null int64\n", " 37 Evaluation 200000 non-null int64\n", "dtypes: int64(38)\n", "memory usage: 58.0 MB\n" ] } ], "source": [ "train_df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "train.csv给了20w条数据,第一列为交通事故案例的Id,其意义不大,可以舍去,第2列至第37列为理赔人员在现场对该事故方采集的36条信息,并已经被编码,用整型表示,最后一列表示是否理赔。上图中显示了各项信息分数的分布。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ],\n", " 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b7/+U00RpCzkA8gCW1MFkpwLkAdipoIPf+fenKD2xu2W5jDY9rdzO8n71YMOtid+uv5WuT/1FWSzzTZaO05NS9736PCenuCxRrii3saxbX+K1iTIn5RtT/l3i3Fpfyr+s85ndWzulYOjkAMgDWPYOJjsVIA/oFjsVsKPT6OgB70OvE2jQ+m9K8aYB9ZemuHRA/S19E+L3138rxa/7DGiaHAB5AEvqYLJTAfIA7FQAAABL6mCyUwHyAAAAAGbzqNnuBAAAAIC56GACAAAAYEl0MAEAAACggwkAAACA5hjBBAAAAIAOJgAAAACaYwQTAAAAADqYAAAAAGiOEUwAAAAA6GACAAAAoDlGMAEAAACggwkAAACA5hjBBAAAAIAOJgAAAACaYwQTAAAAAEuigwkAAAAAHUwAAAAANMcIJgAAAAB0MAEAAADQHCOYAAAAANDBBAAAAEBzjGACAAAAYEl0MAEAAACggwkAAACA5hjBBAAwD5OTkxsT2xJf7Kt7cuLyxM21fFKtL96R2JK4LvHsvsccV9cvcVxf/f6JzfUx5bGTs70GAMNnWwBL6GCSQADYFsAO70usn/ZebEh8cmpqau9S1tvFoYlSV+LExNn1d9WTU5yWODBxQFnu6zAq67yi73Hr53gNGCrbAtjBtgCWMIJJAtF5flCBbQGkg+fTeRfunfZOHJ44ry6X8oi++vPzmOLKLO+abcnuKV+YuDx19ybuK8uJ9fW+Xcq65QHlsdOea9BrwLDZL6DzbAtgCR1MEgh28IOKTrMtgBmtTn7cUZfvLLfr8h6J2/rW21rrZqvfOqB+tteAobItgBnZFkCsWuS7IIHo3A+qHF3ea1p1OaK8ru+I8qbEa/uPWqe8Mo/rHbVe1ztqXR5Q5tGoR6039Y5a1/reUeuPz/Ia0Aat2hYkd8ppSCUmVq9ePbFp06bBjd5pYuKUfR+a9Q+bbqbnGpbt27c33oaF2t7BNpf/+/kelv/9K2a211jJHCia/Dy7+H0aoTaP3LZgFHOgY9+pRtkWzKzpz7KL36dRa/NiO5ha82OqsFPRLh1Kotb8oLJT0S4dyoFWbQty9zkpSkysXbt2at26Xt/sw511wcUTZ25e2Obv1mMGP9ewlM9mpr+nrTrU5rvKQYTyv7oeTNhW629P7Nm33ppaV2LdtPpNtX7NgPVne42h5UDTedCh71OjltrmUdkWjGIOdPU71QTbgpnJgaF9nxq1lDYvtoOpNT+mCjsV7dK1JGrDDyo7Fe3SoRxo1bYAGnJJolwJ7oxaXtxXf3K+txfWCb3vr9/jy7L8p30Tex+SOLWMbk3dA4mDcvuqxLGJs+Z4DWgD2wKwLYB5T/I9SO+HzsSAH1PH1kvzHtT7MZWy/Jg6pPyYqj+oyo+py+p9O35M1UvxHjvtuQa9BrTqB1VZWMDO9Uz1s+5cD3gNaAPbAjol/4ffn+KziWdkeWvihNrp84Is35zy+fV2cWnilsSWxHsSryyV9TTpNySurnF679Tpus5762O+Uk+VnpjlNaANbAvoFNsCWMIIpppA5YjzbuXHVL20bvlhc1H9YfW1xJF9P6YOqz+Mvpl4WamsR+V6P6YmBvyYKhMo71R/SPX/mBr0GtAWjlrTGbYFsOP3zNEzvA8HT6+o8/CdNGjl3LUxxcYB9dek2GdA/T2DXgOGzbYAbAtgSR1MfkyBH1RgWwCAbQEAS+pgAnS0AgAAwErMwQQAAAAAO+hgAgAAAGBJdDABAAAAoIMJAAAAgOYYwQQAAACADiYAAAAAmmMEEwAAAAA6mAAAAABojhFMAAAAAOhgAgAAAKA5RjABAAAAoIMJAAAAgOYYwQQAAACADiYAAAAAmmMEEwAAAABLooMJAAAAAB1MAAAAADTHCCYAAAAAdDABAAAA0BwjmAAAAADQwQQAAABAc4xgAgAAAGBJdDABAAAAoIMJAAAAgOYYwQQAAABAcx1Mk5OTtyY2J76QuKbWPTlxeeLmWj6p1hfvSGxJXJd4dt/zHFfXL3FcX/3+9fm31MdOLqW9sBLkAV0nB0AegG0B2BbAcoxgeu7U1NR+ibX19obEJ3N771LW28WhiVJX4sTE2b0OqRSnJQ5MHFCWe51SdZ1X9D1uvY+MlpIHdJ0cAHkAtgVgW0CHrcQpcocnzqvLpTyir/78dDwVV2Z513Qk7Z7yhYnLU3dv4r6ynFhf79ulrFseUB7b91zQdvKArpMDIA/AtgBsC+iQVUt8fOn4+UQ6g0r539MPdE7K1SnvqPffWW7X5T0St/U9dmutm61+64B6aBt5QNe1Igfy+mV0bImJ1atXT2zatGlgY1fvNDFxyr4PzfuPK2Z6rmHZvn17421YqA62ufE8WMkcKJr8PDv4fWrEqOfAfPNgFHOgo9+pRox6Hvg91C7bO5YDS+1g+vkky+35Ev9Ilst8S1/qv7OMPKrJtaIkUbt0LYnakAd2KtpFDjSzLag/4kpMrF27dmrdunUD1zvrgosnzty8sM3frccMfq5hKf+fZvp72qqDbW58W7CSOdB0HnTw+9SIUc+B+ebBKOZAR79TjRj1PPB7qF02dSwHltTBVJKnltuSKB+pcyjdVU5vK7209TS3bXX1su6efQ9fU+tKrJtWv6nWrxmw/qB22Kloka4lURvywE5Fu8iBZrYF0KQ2bAugSXIA5AEseg6m/FDaOfGE3nKKQxJfTFyS6F0JrpQX1+VSf2y9mtxBWb6/DhW8rDy2TOxdJ/cuz3NZve+Bsm69etyxfc8FrSAP6Do5APIAbAvAtgCWOoKpnDv6ke/3/ex4nr9Op9Df5PbVWb4o5Qkpv5Y4sq5/aeKwxJbENxMvK5Vlcu+s+4YslscVp5e6uvzKxPsSOyU+XgPaRB7QdXIA5AHYFoBtASy+gymdQLek+JkB9fekOHhAfTnX9KQZnmtjio0D6q9JsY/PibaSB3SdHAB5ALYFYFsASzpFDgAAAAB0MAEAAACwZEYwAQAAAKCDCQAAAIDmGMEEAAAAgA4mAAAAAJpjBBMAAAAAOpgAAAAAaI4RTAAAAAAsiQ4mAAAAAHQwAQAAANAcI5gAAAAA0MEEAAAAQHOMYAIAAABABxMAAAAAzTGCCQAAAIAl0cEEAAAAgA4mAAAAAJpjBBMAAAAAOpgAAAAAaI4RTAAAAADoYAIAAACgOUYwAQAAALAkOpgAAAAA0MEEAAAAQHOMYAIAAABgvDuYJicn1yduSmxJbGi6PdAEeUDXyQGQB2BbALYFtNuqphswx0bk0SnelXhBYmvi6tRdMjU1dUOzLYPhkQd0nRyA7uXBXhs+9rDbp+z70MTx0+qmu/WMF61kk2hY13IABpEHw9vuzIftzoh1MMUBiS3ZcNxSE+rCFIcnbEjoEnlA13UqBwb9wLFzTdfyALqeA7YFK/Mezsf71u+8DK++YjqVB4yetncw7ZG4re92OVpxYENtWXGjsCHZax7/qKe3uW3tG8ENSafzYBxyoI1tnE4OsNzfMduCZdepbUHb2RY0Qg60TNu3BWOq0TxY7L7WYj73Qa/V5f2CvRb53i/GUvYLJtP7uYxNWV7pkX1xivVp42/V2y9NcWBunzxtvRNTlCiekbhphqfcLXH3CjV3pWhz8+/zj+c799QhtWNReSAHWmfc8rb1OVDrbQvaRR4sI9uCsfs+tVVXtgWj+NkUo9jucWtz6/PA76HW2a1LOdD2EUy3J/bsu72m1j1M/sBzUpSYVZLtmqy7dvmat/K02fs8nzyQA+0ib5edbYHt11C0/H22LWjvZzOK36eBurItaPnfOaNRbLc2LzvbAjnQ6rxt+1Xkrk7snT/waYnHZvmoxCUNtwmGTR7QdXIA5AHYFoBtAS3X6hFM6TV7KB1LZbjfZYly5YiNqbu+4WbBUMkDuk4OgDwA2wKwLaD9Wt3BVDcml6YosRzmPI2uhbTZ+7yceeD75Ps0kt8N24J2fz4z0OZlZlswcuTAMut4DhSj2G5tXmYdzwNtbvn73OpJvgEAAABov7bPwQQAAABAy3Wig2lycnJ94qbElsSGptszH2nnxsS2xBebbst8pJ17Jq5I3JC4PvGqpts0H2nnDyU+l/j72u4/abpNK2XU8mDUcmBU80AOtNuo5YEcaD/bgqG8x7YFLTZqOVDYFgzlPbZP0GKj+H+1SDsfnfi7xEfL7VGQtu6a+GDiS4kbE/9+QY8f91Pkyoea4suJFyS21itQHJ2/+4ZGGzZ3u38xxfbE+WnrPk23Zx7t3T3F7mnr57P8hCxfmzhiBN7nyRQ7p53bs/iYLH8m8arcvrLhpk10PQ9GLQdGNQ/kQHs/m1HMAznQbrYFQ3ufbQtaahRzoLAtGMp7bJ+gxUbx/2qRtr4mxdrELmnrL5W6tkubz0vxt2nve7P82Cz/cJa/Pt/Hd2EE0wGJLXlTbkl8J8sXJg5vuE1zSls/neLeptuxgPbeURK+Ln8jxY2JPZpt1dzS1qLsvBWlg6nEOPa6jlwejFoOjGoeyIF2G7U8kAOtZ1swBPKg1UYuBwrbgqG8x/YJWmwU/69OTk6uSfGixHubbssC2vzEFOXg5rnldvk/uZDOpa50MJUv3m19t7e2/cs46vLF3CvFzyauarotCxi6+IUsbktcniQaiXYvkDwYslHKAznACn2v5ED72BYMmTxoHTkwZHKgdUY+B0boO/XfEn+U+JemG7IAT0v8U+Iv6ql9ZRTTzgt5gi50MDFE+QI+PsWHEq9OR80Do/Dmp53fS+yXxdLLfED+htafhkK7jVoeyAGWmxwAeQC2BXT198Xk5GQ5HW5b2lhO5RslqxLPTpydtpdOvAcTC5qrrgsdTLcn9uy7vabWsfyJ9Jia8BfkC/nhUXuD6/C/KxLrm27LCpAHQzLKeSAHWA5yoNVsC4ZEHrSWHBgSOdBaI5sDI/adek7iV9LmW+upuM/L8l813Kb5KCPatvad0fPB2uE0b13oYLo6sXc+0KfVSaqOSlzScJvGTp0Yr5yreWO+kG9tuj3zlWY/NbFrXd6pTvr4pWZbtSLkwRCMYh7IAZb5+yQH2s22YAjkQavJgSGQA602kjkwat+pqampUxNrEnvV9/hTWf7Npts1l7TxzhS35e1+Rq06OLGgidTHvoMpb9JDKU5OXFYnA7soddc326q55UN9f4rPJp6R5a2JE5pu0zx6aV9ae2e/UOOwphs1D+WKBOWSl9fVf7hlDqaRuYzkOOfBCObAqOaBHGixEcwDOdBitgVDIw9aahRzoLAtGAq/h9ptFP+vjqrfTVxQ94/LNDJ/upAHT+af6oq0CgAAAIBuGPsRTAAAAACsLB1MAAAAAOhgAgAAAKA5RjABAAAAoIMJAAAAgOYYwQQAAACADiYAAAAAmmMEEwAAAAA6mAAAAABojhFMAAAAAOhgAgAAAKA5RjABAAAAoIMJAAAAgOYYwTTmJicnj09sTnwzcWfi3Ykn1vv+PLG9L76d+EbTbYYh50HxxsTtifsTmxLP8inQoRx4XOJtiX9M3Ffve0zTbYYV/t7vk7gscXdiasBjn5z4SOLBxNcSL/GJ0LEcODlxTd0/eN/wWw/N5kH9fXRu3QZ8I/GFxKE+l9npYBpjSYBTUrw58YeJkkQHJfZKfKLsPExNTf1O4vG9SP37E/+juRbD8PMg5a8nXp74hcSTE59N/KXPgg7lwIbE2sQ+iZ9KPDvxumZaC0P73n83cVHihBme4l2J7yRWJ45JnO3gAx3LgX9MvDGxceVbC63Mg1WJ2xL/oT62/Da6KI8rj2em9zwdCzPcxSjLF3+XumF4eT7ji/rqS0fSVxN/kPrz+up3TnFn4pdS/7+H3V5oKg8S/yaxf+4/st5XRi9dm9s/5FOhIznwu4k35/4dBxjqSI1ye88GmgxD/Q2UuqenuDm3J6f9JrovsU/qv1zryoGH23O7dMjCWOfAtOcqnUxrcv/xK99yaGce9D32uhR/kvU+5LMazAim8fVzibKD/OH+yiTD9hSXJg6Ztv6vJf4p8emhtA7akwcXJn4yG4yfqkcyjkv8jQ+Ijm0L+n9QleU1veHj0IHfQNOVkXwP9TqXqr9POH2aruQAjINlzYP8Llpdtw/XL1cDx5EOpvG1W+LuJNBDA+67I/HUaXVlp/r8rG9IG13Lg1J+JnFT4p/rKXO/P7QWQvM5UDpUX5UfTk9NlBF9v1fv/2EfDh35DTRdObr9wLS6+xNPWIa2wSjkAIyD3ZYrD+pB6AsS5+X5vrRM7RtLOpjG192J3ZIM5dzR6Xav9++QdX4sxbrE+UNqG7QpD/7fxP8vsWc9yvEniU/lMXau6UoOvCnxd4kvJP5v4n/WOQnuGlYjoanfQDMoR7fLqRX9ym0XQqErOQDjYFnyII8vfSZ/WeflO3n5mjeedDCNrzJR8bcT/7G/sp5zWma/39RX/dLE/0lv7C3Dax60Jg/2S3wg3/+t5QhHolwp5UmJZ/qM6EIO5Dv/z4mTE3skfiJ199R5yP5l+M2Fof8GGqScGrcq6+/dV/czCadF0JUcgHGw5Dwol5pOcW6inB73a/ltVA7AMQsdTGMqX/7760iMs5IX68uwvjrj/UW1t7YM8es5NuHyo3Q1D65O/Ho5r7ocoUiUDtcyDHZLYw2HIeZAbu+R+NHyIyrK1VX+c+I0HwJj/r0vyqjVx5bHlOVySer6+AfrnB2nlwm/E8/J8uEJVxilEzlQb6+q9z+6RL1/0EgQGNs8iLMT/y7xy+WA3JD/hJHkKnJjLglyQp1PpsyMX5KlXCHuJUmQf6z3//sU/yvxb1Jn6Dedy4O6UTmzHt3YuXYs/XHuM9E3XcmBX6ynSP9IvRzv6anvPwgB4/i936teRajf13LfjstP5/4n18uzv6CO6tuQ+/56aI2H5nPg9QMONpSrZ5V6GPs8yH0/nuVb6yio/nmcftvvpFneb3M6d0eS5GUpTk88J5/7PzTdHmiCPKDr5ABd5HtP18kBkAdD+V+jg6lb6uk/383nXi7NDp0kD+g6OUAX+d7TdXIA5MGK/5/RwQQAAADAUpjkGwAAmM8ImD0TVyRuSFyfeFWtf3Li8sTNtXxS7wpM8Y7ElsR1iWf3Pddxdf0Sx/XV75/YXB/zjnoVpxlfA4D20MEEAADMR5no9pSpqalnpixXnTwpHT1leUPik6nfu5T19kS9FHipK3FivSJTbxL1MoH0gYkDynJfh1FZ5xV9j1tf62d6DQBaYuwuNbnbbrtN7bXXjosfPMKDDz44sfPO5SJRo0Obm3+fr7322rvzY+apQ2rKko1DDoxKO4tRaetS2llyIMWz65XGViemEuckL95edxI+kNirXmnjyNTfV484vz1xWOKbieNT//nyfPVI9evq078x9efV+v1TvC+xU+LSxKty39RMrzHueTAM3ov5vx+2Be03rt/ntvxdfTlwR7ldrj6c/883ZnGPxOGJdXXV8j99U+K1tf788r885ZVZf9fE7nXdy1N9b3lAGZGUolxGvDxul9RfWevLdueIxMdneY0Fbwva8p4u1Ci2e9zabFvQrHH7Po1jDoxdB1PZiFxzzTUD79u0adPEunW97dJo0Obm3+f8uPnakJqxLMYhB0alncWotHUp7aw50Dtq/fncfkKWr607BMfXI8pn5HY5mryh/uDvP2p9YD0ifWDfUeu1ian6PJfUDqPeUeuragfT+rpTsWGG1xjrPBgG78X83w/bgvYb1+9zW/6u6TlQL+/9s/V/9ur8j97R8RR3ltt1uXQ+3db3sK21brb6rQPqJ2Z5jentLCOlSkysXr164i1vecsj1tm+ffvE4x//+Jn+1NYaxXaPW5uf+9zn2i9oUFv+Hy7EpjFr81y/h8augwmA5Vd/1I/MUWsAVk7+R5e97w8lXp3/2w/UaZJ2qCNPy//+FTPba+Suc1KUmFi7du3UoJ2kUdzhK0ax3doM3WIOJgAWpK1HrQEYyjbgMbVz6YL8b/5wrb6rHkQo95dyW62/PbFn38PX1LrZ6tcMqJ/tNWCYHmOie5iZEUwAjMVR6+mnRZSjpjMNfZ/pvq7xXng/YCHq/HrnJm7Mv+O39t11SaLMr3dGLS/uqz85D7uwni59fzlgkNuXZflP+yb2PiRxahndmrqybTmoHsQ4NnHWHK8BwzYyUwbAsOlgAmDJR63rDsN8j1qvm1a/aT5HrQe8xoJPixjV4forxXvh/YAFek7ipYnN+X/8hVr3x7XT56LUnZCyzM9xZL3v0nqxhy31gg8vK5W1I+kNWby6rnd679TpeGXfBR/KTnWJiVleA4bpu72LlpgyAB5JBxMAc3LUGoDsUH+mbBJmeCcOnl5R5+E7adDKuWtjio0D6ssVGvYZUH/PoNeAprR1yoBxHtGtze1/nzvVwbT59vsnjt/wsQU95tYzXrRCrYHRyIFCHjBOR61tC+g62wJov70G/F47Zd+H5vwd5zfbcLR5yoD5jug+64KLJ878zIMLet2mv1+jOPJ6U8fa3KkOJgAWx1FrAID2TxkATXIVOQAAAJif2Sa6nxgw0f2xZaqBOnn9jonuU5aJ7g8pE93Xye7LRPeX1ft2THRfpyc4dtpzDXoNaA0jmAAAAGBujx+XKQNgJehgAgAAgLltL9MfzXCfie7pPKfIAQAAALAkOpgAAAAA0MEEAAAAQHOMYAIAAABABxMAAAAAzTGCCQAAAAAdTAAAAAA0xwgmAAAAAHQwAQAAANAcI5gAAAAA0MEEAAAAQHOMYAIAAABABxOstMnJyY2JbYkv9tW9PnF74gs1Duu779TElsRNiRf21a+vdeW+DX31T0tcVes/kHhsrX9cvb2l3r+XTxsAAIC2MYIJ5ud9ifUD6t82NTW1X41LS0U6gZ6Z4qjEs+pj3p26R5fI8rsShybKOkfXdYs31+d6esr7EifU+lLeV+vfVtcDAACAVtHBBPOQDp5Pp7h3nm/W4YkL85hvJ76a5S2JA2psSd0tie+Udcq66WSaTPm8xAfr489LHNH3XOX2RL3/4Lo+AAAAtMaqphsAI+7k9Pccm/KaxCnpOCqjj/ZIXNm3ztZaV9w2rf7AxFMSX89jHxqw/h69x5T781r31/Xv7m9E6k9MUWJi9erVE5s2bRrY2NU7pZH79l5m/mZ6vpWyffv2ob/mYo1KW0elnQAAwGjSwQSLd3biDYmpWp6ZeHkTb2g6n85JUWJi7dq1U+vWrRu43lkXXDxx5uaFp/2txwx+vpVSOkJm+hvaZlTaOirtBAAARpNT5GDxnTp3Jb6X+JfcfE89Ba64PbFn36prat1M9fckds0opFXT6h/2XPX+J9b1ARgyF3wAAJiZDiZY/I7G7n03fzXRu8LcJYmj6hXgnpblvROfS1xdlusV4x5bJwK/JB1UZQTUFYkX18cfl7i477nK7Yl6/6fq+gAMnws+AADMQAcTzEM6hN6f4rOJZ2R5a6Jc3e3PUm5OXJfl5yZ+v6yb/p/rU1yUuCHxN4mT6kinMvnRyYnLEjeWdeq6xWsTr8lzbalzLJ1b60v5lFr/msQGHxhAM1zwAQBgZuZggvntVBw9oPrcWdZ/U4o3Dai/NMWlA+pv6TvFrr/+Wyl+3YcE0Gou+NAC43oxg3H9uwDoYAdTmW8gxS8ltmVnd59a9/oUr0j8U13tj+uOc7nv1BRldMf3Er+X+stq/foUb088OvHe1J9R659WL9deRm1cm3hpuYR7Ob0oy+cn9q9zzvxG6m9dlr8aAGB5uOBDS4zrxQzG9e8CoJunyJlvAABgABd8oEtMdA/AkjqYzDcAADCYCz7QMQ48A7AiczC1Yr6BIvedmKLExOrVq2c8T331Tmnovr2Xmp+mz3kfxfPutRnGj9Ol4QcXfCjnKu1WLviQ8rRyO8v7pSxX+Cyn8v92ea/KRRxS37vgw0O9Cz7UfOpd8KFMG7Bx2gUfLsz9b0z5d9Mu+PCX9YIP99arkMLQlQPP+R7uNc/VD09cmMd8O+VX6/e3N9/kljr/ZMmHMlXG4SnLBVCel3hJXee8xOvraaiH1+Xig4l3Zv1JV9YFGI8OptbMN1Bk43JOihITa9eunZrpPPWzLrh44szNC/uTbz2m2XPeR/G8e22GsT1q/c46N16/t+V/8Fv6K/Kb/5l1B/hZiR9N/K/U/VS9+12JFyTKzvnVqb8kjy874G+uz1V2rv+8zuVXtjWlvC/1T0/9UXW931iRvxDm4IIPMB4HnkfhYOigg+LzOVjetr9rFN7rhbbZQTdY5g6mMt9AX4K9J8VH683bE3v2rbqm1hWD6svk3bvmOVbVjUn/+r3nKpeEL+18Yl0fgCFz1BqAcTnwPAoHQ4/f8LFH1JXOpbkOljd9cHwU3+tFtNlBN1jODqYy30D+ed9Rb/5q4ot1+ZLEX+f+t9aj1nsnPlceUpbrFeNKx1E5Cv2SMqw1dVdk+cWJMjz2uMTFfc9Vbn+23v8pw2ABWmekjlqP6unSK2UUjyyvJO8HLNyUA8907zvvVFFYbAeT+QYAGJej1qN6uvRKGcUjyyvJ+wEL58AztO+gGzRlzl/Y+fIePaD63FnWf1OKNw2ovzTFpQPqb+mb8K+//lspfn2u9gHQDEetAbrFgWdo/0G3cR7RPYojjbd3rM1LuYocAB3mqDVAtzjwDO2fo3icR3SP4kjjTR1r86OWuS0AjO9R6zIn3jPK5dkT5epuf5Zyc+K6LD838fv1h0255Hrv8ux/07s8e/2h1Ls8e7kc9UV13d7l2V9TL2P9lGmXZ39KrX9NYsMQ/lwAgAUddOu7OX2O4qNy/+PqfMS9OYqvLsulLvHYsk5ZN7+Lygio3hzFEzPMUVyYo5hWMoIJgDk5ag0A4FRR0MEEAAAAS+SgG8zMKXIAAAAALIkOJgAAAAB0MAEAAADQHCOYAAAAANDBBAAAAEBzjGACAAAAQAcTAAAAAM0xggkAAACAJdHBBAAAAIAOJgAAAACaYwQTAAAAADqYAAAAAGiOEUwAAAAA6GACAAAAoDlGMAEAAACwJDqYAAAAANDBBAAAAEBzjGCCeZicnNyY2Jb4Yl/dkxOXJ26u5ZNqffGOxJbEdYln9z3muLp+ieP66vdPbK6PKY+dnO01AAAAoE10MMH8vC+xflrdhsQnp6am9i5lvV0cmih1JU5MnN3rLEpxWuLAxAFlua/DqKzzir7HrZ/jNQAAAKA1dDDBPKSD59Mp7p1WfXjivLpcyiP66s/PY4ors7xrOpJ2T/nCxOWpuzdxX1lOrK/37VLWLQ8oj532XINeAwAAAFpjVdMNgBG2Ov1Bd9TlO8vturxH4ra+9bbWutnqtw6on+01HiadVGWkVImJ1atXT2zatGlwg3eamDhl34fm/MOmm+n5Vsr27duH/pqLNSptHZV2QttPl07xS4lt+d+8T9/o1A8k9krcmjiyHESopzq/PXFY4puJ41P/+fqYcor06+rTvjH15/VOl64jZvPfeuLSxKvKgYeZXmMYfzMAwHzpYIJlUHcAyuijFTPba+Suc1KUmFi7du3UunXrBj7HWRdcPHHm5oWn/a3HDH6+lVI6Qmb6G9pmVNo6Ku2EliudP++sI00npp3KfEb+R5flEq+ddrr0gfVU6AP7Tpdemyj/069N3SW1w6h3uvRVtYNpfeLjs7wGAEBrOEUOFu+uenpbOepcym21/vbEnn3rral1s9WvGVA/22sAMGROlwYAWEIHk6tnwYwuSfSuBFfKi/vqj61Xkzsoy/fX09wuSxxSJvauk3sfUurqfQ+UdespFcdOe65BrwFAO7TmdGkAgCbN51wZw8HpvPT7vD9vQjm/aLcsb62nN5yRuCi3T0j5tcSR9Y26tM65saXOu/GyUlkm9866b8ji1XW900tdXX5l37wb5XSIEhOzvAYALdP06dLjNh/fQo3rXHNt+rvMQwbAkjqYynDwbEzKpJL9ypWtepN5lIkpN9W5AH5w9ayUV+ZxvatnretdPas8IHW9q2dt6l09q9b3rp718VleA4Yu39GjZ7jr4AHrlu//STM8T5kgduOA+mtS7DOg/p5BrwHDZqcCZrTjVOYywmgBp0uvm1a/aT6nSw94jbGej2+hxnWuuZb9XQ48A7Dsk3y3ajj4Sh6xa/qIUZuOWs2XNsNYslMBg/VOZT5jwOnSJ+c3yoV1ku8dp0vndjld+k/rqdIT9XTpU+so1x2nS9dJvsvp0mfN8RowVA48g4NusKJXkWt6OPhKH7Fr+mhdy45azYs2w/ixUwE7diqcLg1jcOB5FA6GDjooPp+D5W37u0bhvV5Emx10g2XuYGrNcHAAGjNyOxWjOpp1pYziD/8m348pp0vDrEblwPMoHAw9fsPHHlFXtl1zHSxv+uD4KL7XC21zvmOmkIFl7mAyHByAkdupGNXRrCtlFH/4ryTvByyKA8/QooNu43zAbRQPjG3vWJvn/IVtODgAM7BTAYADz9Cig27jfMBtFA8EbepYm+dzFTlXzwJgEDsVAB3iwDPMyEE3WI5JvgEYf3YqAHDgGWbkoBuEDiYA5mSnAgDAQTeYjQ4mAAAAmAcH3WBmj5r5LgAAAACYmw4mAAAAAJZEBxMAAAAAOpgAAAAAaI4RTAAAAADoYAIAAACgOUYwAQAAAKCDCQAAAIDmGMEEAAAAgA4mAAAAAJpjBBMAAAAAOpgAAAAAaI4RTAAAAADoYAIAAACgOUYwAQAAALAkOpgAAAAA0MEEAAAAQHOMYAIAAABABxMAAAAAzTGCCQAAAAAdTAAAAAA0xwgmWKLJyclbE5sTX0hcU+uenLg8cXMtn1Tri3cktiSuSzy773mOq+uXOK6vfv/6/FvqYyd9aAAAALSJDiZYHs+dmpraL7G23t6Q+GRu713Kers4NFHqSpyYOLvXIZXitMSBiQPKcq9Tqq7zir7HrfehAQAA0CY6mGBlHJ44ry6X8oi++vPT8VRcmeVd05G0e8oXJi5P3b2J+8pyYn29b5eybnlAeWzfcwHQEkazAgBdt6QOJj+mYIfS8fOJ5MO1iTIqqVid/qA76vKd5XZd3iNxW9/7trXWzVa/dUA9AO1jNCsA0FmrlunH1N19t3unBp2Rne2yXOK1004NOrCe9nNg36lBa+uOetlJv6SO4uidGnRV4tJ6atDHl6HNsJx+Pt/X2/O9/ZEsl/mWvtR/Zxl5lLry3V4xtWNrR+fW6tWrJzZt2jRwvdU7TUycsu9DC37+mZ5vpWzfvn3or7lYo9LWlWxnOdiQ4huJ7yUeKqeK1v/tH0jslSj3H1n+r9c5xN6eOCzxzcTxqf98fZ4y99jr6tO+MfXn9eYhS/G+xE51W/CqOqIP2q6MWl1Xl8v3eVP9TfSD0awpr8x3vDeadV1vNGt5QJnDr45m3dQbzVrre6NZ/SaiNWwLAFiODqbp/JiiU0rnUi235cfVR+ocSneVnYUyiqnuNGyrq5d19+x7+Jpad3vfTkivflOtXzNg/eltOCdFiYm1a9dOrVvX/1T/6qwLLp44c/PC0/7WYwY/30opHSEz/Q1tMyptHUI7HWyg63qjWUv53+v/5aGOZh23gw3j2uG/UCP2d9kW0Gk6Wum6VaP+YwqalO/+zikele/8N+ryIYnTE5ckymiMM2p5cX1IqT85615YR/LdXzuhLsvyn/ZN7F2e59RyFDt1DyQOqiP5jk2cNay/D5bAwQa6pvHRrON2sGFcO/wXasT/LtsCukhHK521atR/TK30EbumjxiN2FGrHTrW5tKB+pHvn/WzI5/+Ol/7v8ntq7N8UcoTUn4tcWRd/9J6atCWenrQy0pl7Uh6QxbL44rTe6dIxCv7Tg8qp0M4JYK2cbCBzmvDaFZoWCu2BfPZLxiF36qD9lnmsy/Ttr9rFN7rIbRZRyudsWocfkyt5BG7po/WjeJRqy61Od+9W1L8zID6e1IcPKC+/Og6aYbn2phi44D6a1Lss+DGwfA42DCiRvGHfxvfD6NZoT3bgvnsF4zCb9XjN3zsEXWlc2mufZmm911G8b1e5jY33tFq8EW7bB/B31pLafOiO5j8mAKgcLBhdI3iD/+Wvh9Gs9J5bdkWQNc7Wg2+aJdNI/hbayltftQSXrf8mPpMEuTvU34u8bFyalCdc+YFqb855fPr7d6pQbfUU4PeU0/72XFqUIreqUFXDzg16L31MV9JODUIoEXKwYbEE3rLdf6wL/bNQzYxYB6yY8vV5OrcYjvmIUtZ5iE7pMxDVuciK89zWb1vxzxk9Qp0x/Y9F7RCGc2a+Jkaz0q8qdbfkzg4sXfi+b3fN2UHI05K/GRi3zpStfdcGxNPr/EXffXXJPapjznZlRRpE9sCeGRHa4qHdbTWXJlvR+tM9TpaabVFj2ByahAAYeQGALYFdJ4zfGDpk3wD0GEONgBgWwA76Gil83QwAQAAwBLoaIWlzcEEAAAAADqYAAAAAFgaI5gAAAAA0MEEAAAAQHOMYAIAAABABxMAAAAAzTGCCQAAAAAdTAAAAAA0xwgmAAAAAHQwAQAAANAcI5gAAAAA0MEEAAAAQHOMYAIAAABABxMAAAAAzTGCCQAAAIAl0cEEAAAAgA4mAAAAAJpjBBMAAAAAOpgAAAAAaI4RTAAAAADoYAIAAACgOUYwAQAAALAkq5b0aAAAaIHNt98/cfyGjy34cbee8aIVaA0AdI8OJmBOew3xB3t5rVP2fWhBOwl2DgAAAJrlFDkAAAAAlsQIJqA1o54AAFaa0ykBOtrBNDk5uT7F2xOPTrx3amrqjFHYSXbKDuOUB9A0OQDyoE38PmyGbQHIA9ptVcs3ImVn+l2JFyS2Jq5O3SXZub6h2ZYN78fKfOai0Zk13rqWBzBOOTDM+csWw1H80TGqeaAjhq7nACwneUDbtbqDKQ5IbMmG45aaUBemODxhQ9Li05emd4otZbLnhRrTzjZ50OI8mO+E5GP63RwWOQDyoLN0BP+AbcEI/o5arv2CYf7Oe9/6nZe5JctKHox4Doz7fsFkOm+absOM0qH04hTr08bfqrdfmuLA3D552nonpihRPCNx0wxPuVvi7hVq7krR5ubf5x/Pd+6pQ2rHovJgDHNgVNpZjEpbl9LO1ufAmObBMHgv5v9+tD4P5MDYfp/b8ne1PgcWsC1oy3u6UKPY7nFrc+vzYMy3Bdrc8hxo+wimeckfeE6KErNKsl2TddcOoUnLRpu9z13MgVFpZzEqbR2Vdi7FuOXBMHgvxuv96HoO+LuYbx6M6ndlFNutzcM3ztsCbW7/+/yo5W7MMrs9sWff7TW1DrpEHtB1cgDkAdgWgG0BLdf2DqarE3unB+1picdm+ajEJQ23CYZNHtB1cgDkAdgWgG0BLdfqU+QyLOuhdCyV80kvS5QrR2xM3fVLeMo5hwq2kDZ3/H1e5jxo7d85zai0sxiVto5KOx/BtmBFjez3omvvR0e3BQvl7xpjcmBkv+PavIzkge9T2/O21ZN8AwAAANB+bT9FDgAAAICW08EEAAAAgA6muUxOTq5P3JTYktiwpHdsCNLGPRNXJG5IXJ94VdNtmq+09dGJv0t8tOm2zFfaumvig4kvJW5M/Pum27TS3/nUPS7xgXr/VYm92vg9T926xP2JL9T4f4fdzr623JrYXNtxzYD7i3fU9/S6xLMbaOMz+t6rEg8kXt3W93TYRm1bsJJGeTuzUkZx+7UY45gH4/597sp3c1hGMQfSzo2JbYkvNt2Wcc3JtPGHEp9L/H1t85803aaVIgeG9j7v2cU8GPs5mMpGOcWXEy9IbK1XoDg6f/cNjTZs9jbvnmL3tPHzWX5Clq9NHNHmNvekva9JsTaxS9r7S023Z55tPi/F36a9761XK/zhLH+96Xat5Hc+67wyxU+n7neyXK7O+KtZ/o22fc9LZ0iKP2jDd6l0MKVYm7bcPcP9h6X43UQpD0y8PeuWssnvQbmk84Fpx9fa+J4O0yhuC1bSKG9nVsoobr8WalzzYNy/z134bg7LqOZA2v2LKbYnzk9b92m6PeOYk+UoYYqd08btWXxMlj+TeFVuX9lw05aVHBjqe717F/OgC6fIHZDYkjfllsR3snxh4vCG2zSrtPOO8kWsy99IcWNij2ZbNbd8CdekeFHivU23ZQFtfmKKstE+t9wu35FR7lxawHe+3C4da8UHEwfXfyhDM6rf81kcXn/4TdV/wrvWDUtTDk58pb9zqeNGbluwksYw/zq3/VqkscyDcf4+d+i7OSwjmQNp66dT3Nt0O8Y5J+vvt9KJV5Qd6xLjOBJDDgzJVEfzoAsdTOVDvK3v9ta2f7DTfliUU5d+NnFV022Zh/+W+KPEvzTdkAV4WuKfEn9Rh5+XUUw7N92oIXznf7BO/ok8lOL+xFOG0rqFf8//fR2m+fHEs4bctH7ln+sn0oZrEyeOwP+aMjLt/TPc15b3dJja9vm0xohtZ1bKKG6/FmPs82AMv89d+W4Oy9jnQNuMUk7W01G/kMVticvzG7n1bV4EOdCAyQ7lQRc6mEZWPtjHp/hQ4tX5YB9ouj1ztLUM2d6Wdpahf6NkVaLMlXN22l6S/sHESJyP35Hveen1//HU/0zKsxL/c9jt6/PzaUf5rhyaOCntLiPfWqme6vkrif8x4O42vac0bJS2MytlhLdfjPn32XeTUTdqOZk2fi+xXxbLyMED0v7Wn45I+012LA+60MFU5iDZs+/2mlrXapPfP+exfBEvyAf84abbMw/PSfxKnaemDDd+Xpb/quE2zUc5crW1r2e2nC429MmZG/jO/2CdfE6lk62cKnjPUFq3gO95+SfcG6aZ8tIUj8ljdhtyM3tt2fEepiy9+R+pQ4zb+r+mdIJ9Pm29q83v6ZC16fNphRHczqyUUd1+LcbY5sGYfp+79N0clrHNgbYZ5Zys02VckVjfdFtWgBwYoskO5kEXOpjK5H1758N9Wj2qX04buaThNs2qzoVT5gS6MR/sW5tuz3yknacm1iT2qu/xp7L8m023ay5p450pbstb/oy+eWtaO/HaMn7ny+3j6vKL6+c11bbveVb5N3W9snxA/Z/VREfYznVyvh3LKQ5JfHHAe3psaW8clOX7y7nXQ25qz9EznR7Xlve0ASO3LVhJo7idWSmjuv1apLHMg3H9PnfsuzksY5kDbTOKOZkmPzWxa13eqU4E/6VmW7Ui5MCQTEYX86CMXBhrZX6ZvDknZ/GyRLlyxMbUXd9ws+ZzxOqliR2XRK91f1xHG7D8ypW/Lqg/NG5JvGwcv/OpOz3L12T5kvrP7i9Tt6VOGll+YLXie574sbKQdv557fz6T7m/zBP1z6Wdw+4Iq1YnPvL97cSO/5t/nWb8TW7/Tl9bL61XkCvv6Teb+h7VDrCyMfjtvrrfaeF7OlQjui1YSbYzHTTGeeD7zFjnQNpcDhqVq8DuluUy+v60tHvHBWpaahRzslyY5by099H14NtFae9HG27TspMDQ/WcLubBZAf2KwAAAABYQV04RQ4AAACAFaSDCQAAAAAdTAAAAAA0xwgmAAAAAJZEBxMAAAAAOpgAAAAAaI4RTAAAAADoYAIAAACgOUYwAQAAAKCDCQAAAIDmPKq5lwYAAABgHOhgAgAAAEAHEwAAAADNMYJpDE1OTh6f2Jz4ZuLOxLsTT6z37ZO4LHF3YmrAY/8qcUfigcSXE781/L8AmsuBvufYO/GtkhM+Dzq4LdhUv//ba9w0/L8Amt0WpP6oxI2JBxNfSfyCz4QObQd6//978b3EWcP/K6DRPNgrcWnivvrYdyZW+UxmpoNpzOQLf0qKNyf+MFES56DEXolP5L7HpPxu4qLECTM8xX8p609NTe2S8lcSb8zj9l/xhkN7cqDnXYmrfTB0OA9Ozrbg8TWesdJthjblQNZ5QX38yxJPSPxi4hafEl3Jgb7//4/PzX+T+OfE/xhG26FFv4fendiW2D2xX+I/JF7pE5rlPc8/jZnvZaQkSUqn0D8mXp7P9aK++rJh+GriD1J/Xq17eoqbc3tylucrOxSbEq/qfz4Y9xwoR61T/MfEDYmnZ53fHEb7oS15UEYwpfir1L/Xp0JHc+D/pjg39ecOr+XQ2n2C41KclvjJrGfnkS5tC25McUrqL623/2uKXXL7t4f0Z4wcI5jGy88lfijx4f7KJMD2FCUpDpnPk9Rhg9/M4pcSd9THQidyoG6MTk+8ZiUaCKOyLYj/UoeM/5/EumVuI7Q2B/J9f3SKtYmnZnlLYms9LWKnlWowtHQ70FM6mM7XuUQH8+C/Jcrp0j+c2CPLhyb+ZrkbOk50MI2X3RJ3J2keGnBf6Sh66nyeJI9/ZR0O/gs1Ib+9bC2E9ufAG+pR663L2jIYrTx4beInEuXH1DmJ/y8/rH5y+ZoIrc6B1Yly6sSL62+hclrEzyZet5yNhLbvExT53//j9bSgHSM9oGN58OnEsxIPJMq+wTWJ/7lsLRxDOpjGy92J3WaYeGz3ev+8JBG/l/hMFtck/tMytQ9anQN5XNmJeH7ibSvQNhiZbUH+/1+V+Ebi23X4+P9JHLbM7YS25kCZa6Y4K9//OxJl/bcm5ACd2yeIlyY+kzwopxRBl/YLHlVHK5UBFzuX50o8qc7pxAx0MI2Xz9bRRmXumIlp55mW4XxlTo2FKgnpqDVdyYF1deK/fyhXikj5B4lfy/LnV6CtMErbgjLnxozzc8A45UB2pO+rR6r755ox7wxd3Q4cmzB6iS7mwZMTP5Z4Zz3gdk+W/yLhYMMsdDCNkXzp70/xJ4mzkjjry8z45dKKuX1R7aG9ILeLci7qY8tjynLicXX5R+oleR9f5h9IvDDVRyc+2cxfBMPNgXoqUOlQLSOZSvx54mOJkgvQlW3BruX/f61blTimXkHLnAN0ZVswUXcifrf+NipHrH8/8dHh/iXQaA6Uup+rp0q7ehydy4M6erWM3PtP9ffQrnU+suua+HtGxaDhYoywJMKf5ctfelffkiiz4ZcE+d+J5+e+B2tSfXXaMPCvJfaqR+f+U92pflStf3Ued8kQ/wRoLAdyf5ncvsQOWbdMAvit1P+Tj4UObQvK3DNvTPzbxPfqBR+OyOO+PMQ/AZrMgd58fOV0iPK9/1bdIXmTj4UO5cBE3Zn+cDllekjNhrblwX+sE32/tv4m+lQ94MAMJl1pcrwlaV5Wr4j1nHzW/9B0e2DY5ADIA7AtoOvkAMiDofyv0cHUiQ1KmZzvu/msL2y6LdAEOQDyAGwL6Do5ABPyYKX/z+hgAgAAAGApTPINAAAAwJKM3STfu+2229Ree/XPTfevHnzwwYmdd955yC1qJ+/F/N+Pa6+99u6M9Hvqyn8qy0MOzJ88GM8cKOTB/MiB8c0DOTB/8mD+OZDi2YnzE6vrxWHOSV68fXJyslzO+wOJ8iP81sSRqb+vXJ4py2+vl/UuF9E4PvWfL8+Xu8oE0q+rT//G1J9X6/dP8b7ETolLE6/KfVMzvcZi8mBUP/NRbPe4tdm2oFnj9n0ayxwop8iNU+y///5lGzTQFVdcMeN9XeO9mP/7kTS5phSjEnJAHqxEDiT2TFyRuCFxff3BXzYj5Qf/5Ymba/mkWl92Kt6R2FIv5/rs3nc0jqvrlziur77sVGyuj3lH32ncA19DHiydbcGC8uDv5cB4kgcL2hbs3vt/Hk+oV9l7ZuLPEhtq/YbEm+ty6Vj6eN0mHJS4qu//+i21fFJd7m0/PlfXnayPPbTWD3yNxfwmGtXPfBTbPW5ttl/QrHH7Po1jDjhFDoD5eChxSjYcz6w//E/K0eRn1h/5n0z93qWst4tDE6WuxImJs3f0On3/CPRpiQMTB5Tl1JWdi4m6ziv6Hre+1s/0GjBscoBOy//hO3ojkOql629M7JE4PLFjBFItj6jLpf78ul9yZZZ3zf/80kn1wsTlqbu3jkIqBw/W1/t2KevWHZnzpz3XoNeAYXpMvqdXJG5IXJ94Ve/3TeLyxM213PHbpozii3cktiSuS5RRgBO9UXx1/RLH9dXvn9hcH1MeOznba0Cb6GACYE52KmDH1VjtWEOVndty7tnPJq5KrC7biXrXneV2XS6dT7f1vWlba91s9VsH1E/M8howbA42wGLnYMrGY89ROtcaVoI8gPbvVKRdZaRUiYnVq1dPbNq0aeDHtn379hnv6xrvxeLeDzkwXuTBonLg8Sk+lHh1/j8/UAdY7FB/w+84j22lzPYa89kWjOpnPortHsM2P+xgQ75v/aP41tV1yj5ueYLX9o/iS3ll1u+N4lvXG8VXHlBGJNVRfJt6o/hqfW8U38dneQ0YqUm+e6dFfD5f8HKu9bU1AY6vpyyckdvldIUN9Qvef1rEgfWUhwP7TotYm5iqz3NJ7TDqnRZxVe1gWl+TaMMMrwHDJg+g5TsVueucFCUm1q5dO7VuXe832MOVH40z3dc13ouFvx9yYPzIgwXnwGPqduCC/N/9cK2+q+w0l4MBded5W62/PVEOVvesqXUl1k2r31Tr1wxYf7bXWPC2YFQ/81Fs9zi3ua0HG6DVHUz1S7zji6yXlq6SB9D+nQpYaXKArqtnKpybuDH/k9/ad9cliXKmwhm1vLiv/uQ87MJ64Pn++r/8siz/ad8cMockTi2jOVJXDl7smBA8cWzirDleA4auzQcbxnlEtza3/32ezwim1vfSzjeJtt17/8RZFyxsW7TvHk9c0PqjYhSTsy3vRxvzYCVzoJAH42+uHBinnYrNt98/cfyGjy3oMbee8aKlvCTjo7M5UMgD4jmJl5avUb6rX6jvyB/X7+ZFqTsh5dcSR9b7Lq3TZmypU2e8rFTW7/sbsnh1Xe/03qlC8cq+qTPKGQ0lJmZ5jQWTA4zzwYb5jugu+wRnfubB+fzJrdkOjPOIuDZZSptXjUMv7YKSaPOC+tQmbj1mtL4M4/xFb8lQ2FbmwUrmQCEPxt88cmAsdipgCcr/fzlAp+X/9WdS/OuPn4c7eMD65TfLSTM818YUGwfUX5NinwH19wx6DWjAWBxsgJWwahx6aWEY5AFdZqcCJraXPv4Z3gc71gDd4GADzOJRs9w339MiJgb00h5bHld7Xnf00qYsvbSHlF7a2lNbemkvq/ft6KWtr3XstOca9BowVPIAAAA6b8fBhsRPJ/arcWkZYZc4OLF34vm90dllFF+clPjJxL51hN4PRvElnl7jL/rqr0nsUx9zch0JODHTa8CojWByWgTIAwAAAFjSVeSca03nyQMAAABYwilyAAAAADAbHUwAAAAALIkOJgAAAAB0MAEAAADQHCOYAAAAANDBBAAAAEBzjGACAAAAQAcTAAAAAM0xggkAAAAAHUwAAAAANMcIJgAAAAB0MAEAAADQHCOYAAAAAFgSHUwAAAAA6GACAAAAoDlGMAEAAACggwkAAACA5hjBBAAAAIAOJgAAAACaYwQTAAAAAEuigwkAAAAAHUwAAAAANMcIJgAAAAB0MAGwsiYnJzcmtiW+2Ff3+sTtiS/UOKzvvlMTWxI3JV7YV7++1pX7NvTVPy1xVa3/QOKxtf5x9faWev9ePmuaIg/oOjkAwGyMYAJgPt6XWD+g/m1TU1P71bi07oA8M8VRiWfVx7w7dY8ukeV3JQ5NlHWOrusWb67P9fSU9yVOqPWlvK/Wv62uB02RB3SdHKDzdLTCEjqYJBDIA0gHz6fzLtw7z3fi8MSFecy3E1/N8pbEATW2pO6WxHfKOmXdbGcmUz4v8cH6+PMSR/Q9V7k9Ue8/uK4PQycP6Do5ADvoaIUZrJqhfnoCvTNx/rT6cqT5Lf0V045a/2jif6Xup+rd5aj1CxJbE1en/pI8/oa+o9YXpu7P69Hqs/uPWqf+qLreb8yjvbAS5AEMdnL+Rx+b8prEKfmfXUYf7ZG4sm+drbWuuG1a/YGJpyS+nsc+NGD9PXqPKffnte6v69/tA6FFWpEHqT8xRYmJ1atXT2zatGlgY1fvlEbu23uZ+Zvp+Ubd9u3bx/ZvG+J70YocmG8ejGoObB/B7+o4trl0tC7glP0fHHRL+dVyyn894DbRO+hWFlLfO+h2Yz3o9pK6TjnI9vq6f3x4Xe4ddHtnOeiW55ia/18HDXcwSSCQBzCD8mPnDSVFanlm4uVNvVsruXM9aj+Ox/mHfwvfj9bkQX6znZOixMTatWun1q1bN3C9sy64eOLMzfM5xvhwtx4z+PlGXfnMZ3qvumaR70VrcmC+eTCqOTCK39WOtbk1Ha3QlIX/Z/1XEgjkAR2WHzd39ZbzI+c9KT5ab96e2LNv1TW1rhhUf09i1zzHqvqDqn/93nNtLfenfGJdf6g7103vVKyUUfzh37b3o215AMMmB6BdHa3jfMBtFA+Mbe9Ym1eNegKNexKtlFH8orfw/WhNHjgtYnHkwdLei3zvds+OxR315q8meleYuyTx17n/rfV06b0TnysPKcvlinF1h7mc/vySMrQ7dVdk+cWJMkT8uMTFfc9Vbn+23v8pQ8FpE3lA18kBaFdH6zgfcBvFA2ObOtbmVaOeQOOeRCtlFL/obXs/2pQHTotYHHkw//ci37/3pygr7JblMlz7tHI7y/uVr2Di1sRv1+/j9am/KItlnr3ynT4pdd+rz3NyissS5YpyG8u69SVemyhz8b0x5d8lzq31pfzLOmfBvbVTChohD+g6OQAz5oaDbrDYDiYJBPKAbklH0NEDqs+dZf03pXjTgPpLU1w6oP6Wvkkv++u/leLXF9RYWCHygK6TA6CjFZbUweRIBcgDAABARyssqYPJkQqQBwAAADCbR812JwAAAADMRQcTAAAAAEuigwkAAAAAHUwAAAAANMcIJgAAAAB0MAEAAADQHCOYAAAAAFgSHUwAAAAA6GACAAAAoDlGMAEAAACggwkAAACA5hjBBAAAAIAOJgAAAACaYwQTAAAAAEuigwkAAAAAHUwAAAAANMcIJgAAAAB0MAEAAADQHCOYAAAAANDBBAAAAEBzjGACAAAAYEl0MAEAAACggwkAAACA5hjBBAAAAIAOJgBW1uTk5MbEtsQX++qenLg8cXMtn1Tri3cktiSuSzy77zHH1fVLHNdXv39ic31MeezkbK8BTZAHdJ0cAGA2RjABMB/vS6yfVrch8cmpqam9S1lvF4cmSl2JExNn9zqLUpyWODBxQFnu6zAq67yi73Hr53gNaII8oOvkAJ2noxWW0MEkgUAeQDp4Pp134d5p78ThifPqcimP6Ks/P48prszyrtmW7J7yhYnLU3dv4r6ynFhf79ulrFseUB477bkGvQYMnTyg6+QA7KCjFZYwgkkCgTyAQVZnZ+OOunxnuV2X90jc1rfe1lo3W/3WAfWzvQa0hTyg6+QAnaKjFWa2aua7/jWBcnR5r2nV5Yjyur4jypsSr+0/ap3yyjyud9R6Xe+odXlAmUejHrXe1DtqXet7R60/PstrwNDJA5gzR6byP3xqhfNw1tfIfeV0vBITq1evnti0qWw2Hmn1ThMTp+z70IJee6bnGnXbt28f27+tifej6TxYyRwoxvW7Ig/GJwfmmwejmgOj+F3tUJt1tMJ8OphGIYHsVHTjn/1KGvUNiZ2KxZEHS34v7ioHEcp3tB5M2Fbrb0/s2bfemlp3e9+Bg179plq/ZsD6s73GI2Sdc1KUmFi7du3UunX9L/Wvzrrg4okzNy9s83frMYOfa9SVz3ym96mLFvl+tCYPVjIHCnkw/kY9B+abB6OaA6P4P7uLbW66o3Wc941H8bf79o61ebEdTK1JoHq/nYoO/LNfSaO+IbFTsTjyYMnvxSWJciW4M2p5cV/9yfm+Xlgn9L6/7hRcluU/7ZvY+5DEqWV0a+oeSByU21cljk2cNcdrQFvIA7pODkCLOlrHed94FH+7b+pYmxd7FbkdX+6ysIAEmql+1gQa8BrQFvKAzsj/4fen+GziGVnemjihdvq8IMs3p3x+vV1cmrglsSXxnsQrS2U9TfoNiatrnN47dbqu8976mK/UU6UnZnkNGDp5QNfJAZizo3ViwEG3Y5M7k/VA2o6DbinLQbdDykG3euCtHHS7rN6346BbeUB57LTnGvQa0BqLHcHkSAXIAzokP3iOnuGugwesW0banTTD82xMsXFA/TUp9hlQf8+g14AmyAO6Tg7ADzpay/CO3cpBt5Sn1QNgF9UDcF9LHNl30O2wegDtm4mX1Vwqo7d7B90mBhx0Kxfa2qkecOs/6DboNWB0OpgkEMgDAABARyssqYPJkQqQBwAAALASczABAAAAgA4mAAAAAJbOCCYAAAAAdDABAAAA0BwjmAAAAADQwQQAAABAc4xgAgAAAGBJdDABAAAAoIMJAAAAgOYYwQQAAACADiYAAAAAmmMEEwAAAAA6mAAAAABojhFMAAAAACyJDiYAAAAAdDABAAAA0BwjmAAAAADQwQQAAABAc4xgAgAAAEAHEwAAAADNMYIJAAAAgCXRwQQAAACADiYAAAAAmmMEEwAAAAA6mAAAAAAY0RFMk5OTtyY2J76QuKbWPTlxeeLmWj6p1hfvSGxJXJd4dt/zHFfXL3FcX/3+9fm31MdOLqW9sBLkAV0nB0AegG0B2BbAcpwi99ypqan9Emvr7Q2JT+b23qWst4tDE6WuxImJs3sdUilOSxyYOKAs9zql6jqv6Hvceh8ZLSUP6Do5APIAbAvAtoAOW4k5mA5PnFeXS3lEX/356XgqrszyrulI2j3lCxOXp+7exH1lObG+3rdLWbc8oDy277mg7eQBXScHQB6AbQHYFtAhq5b4+NLx84l0BpXyv6cf6JyUq1PeUe+/s9yuy3skbut77NZaN1v91gH10DbygK5rRQ7k9cvo2BITq1evnti0adPAxq7eaWLilH0fmvcfV8z0XKNu+/btY/u3NfB+NJ4HK5kDxbh+V+TB+OTAfPNgVHNgFL+rHWxzK/IARrWD6eeTLLcngX4ky2W+pS/131lGHtXkWlF2Krrxz77F70fjeWCnYnHkwfjkQH2d8iOuxMTatWun1q1bN3C9sy64eOLMzQvb/N16zODnGnXlM5/pfeqiJb4fjefBSuZAIQ/G36jnwHzzYFRzYBT/Z3ewzY3nwTjvG4/ib/ftHWvzkjqYSvLUclu+yB+pcyjdVU5vK7209TS3bXX1su6efQ9fU+tKrJtWv6nWrxmw/qB22KnowD/7tr4fbcgDOxWLIw/GJwegafKArpMD0I48GOd941H87b6pY21e9BxMSY6dE0/oLac4JPHFxCWJ3pXgSnlxXS71x9aryR2U5fvrUMHLymPLxN51cu/yPJfV+x4o69arxx3b91zQCvKArpMDIA/AtgBsC2CpI5jKuaMf+X7fz47n+et0Cv1Nbl+d5YtSnpDya4kj6/qXJg5LbEl8M/GyUlkm9866b8hieVxxeqmry69MvC+RAXwTH68BbSIP6Do5APIAbAvAtgAW38GUTqBbUvzMgPp7Uhw8oL6ca3rSDM+1McXGAfXXpNjH50RbyQO6Tg6APADbArAtgCWdIgcAAAAAhQ4mAAAAAJZEBxMAAAAAOpgAAAAAaI4RTAAAAADoYAIAAACgOUYwAQAAAKCDCQAAAIDmGMEEAAAAgA4mAAAAAJpjBBMAAAAAOpgAAAAAaI4RTAAAAADoYAIAAACgOUYwAQAAALAkOpgAAAAA0MEEAAAAQHOMYAIAAABABxMAAAAAzTGCCQAAAAAdTAAAAAA0xwgmAAAAAJZEBxMAAAAAOpgAAAAAaI4RTAAAAADoYAIAAACgOa0fwTQ5Obk+cVNiS2JD0+2BJsgDuk4OgDwA2wKwLaDdVjXdgDk2Io9O8a7ECxJbE1en7pKpqakbmm0ZDI88oOvkwMrZfPv9E8dv+NiCH3frGS9agdYwjnmw1yK+X4XvGOOSA7Cc5AFt1+oOpjggsSUbjltqQl2Y4vCEDQldckBCHtBlcgDkAXRqWzCoc/aUfR+a86DAMDtn59OBPL3NOo+XrFN5wOhpewfTHonb+m6XoxUHTl8piXViihLF9nJK3QzPt1vi7oU0YPLNC1l7pCz4vRhzs70fPz7MhiwmD1YyB3Y8vzzogpHOgcK2YFH8TxijPBinbcGQtzt+E41JDiwgD1qfA4P83jza3XQb52pz29o3jnkw5r+HRvH/9W6JcWrzj49yB9O8pAf3nBQlZpXkuibrrh1Ck1rPezFe74cc6ObnvpzG4b2QB9383JfTqL8fcqCbn/tyGof3Yj55MKp/5yi2W5uHb5y3Bdrc/ve57ZN8357Ys+/2mloHXSIP6Do5APIAbAvAtoCWa3sH09WJvdOD9rTEY7N8VOKShtsEwyYP6Do5APIAbAvAtoCWa/UpchmW9VA6lk7O4mWJcuWIjam7fglPOedQwQ7xXozI+7HMedDav7Mh3o8ReC9sC7r5uTekte+HbUE3P/cGtPa9kAPt/nxmoc3LSB74PrU9byfzJV3OhgAAAADQMW0/RQ4AAACAltPBBAAAAIAOprlMTk6uT9yU2JLYsKR3bMTl79+Y2Jb4YtNtaYO8D3smrkjckLg+8aqm27RS5MHD3gt58K/vhRzoIDnQzRwobAu6+bnPV96HRyf+LvHRptuyUkYxB0btf/Yo5lfa+EOJzyX+vrb5T5pu00qRA0N7n/fsYh6M/RxMZUOZ4suJFyS21itQHJ2/+4ZGG9bc+/GLKbYnzs97sE/T7WnB+7F7it3zXnw+y0/I8rWJI8bt+yEPHvF+yIN/fS/kQAfJge7lQGFb0M3PfSHyXrwmxdrELnkvfqnp9iy3Uc2BUfufPYr5lXZOptg5bdyexcdk+TOJV+X2lQ03bVnJgaG+17t3MQ+6cIrcAYkteVNuSXwnyxcmDm+4TY3Je/DpFPc23Y4WvR93lKSvy99IcWNij2ZbtSLkQR958LD3Qg50kBzoZA4UtgXd/NznJTsTa1K8KPHeptuygkYyB0btf/Yo5lfaWZROvKLsWJcYx5EYcmBIpjqaB13oYCof4m19t7e2/YOlsR9We6X42cRVY/gZyAPmJAfoujHPgcK2oJuf+3z9t8QfJf6l6YasIDkwZKOUX/UU0S9kcVvi8uxot77NiyAHGjDZoTzoQgcTzClJ9PgUH0q8Okn0gLeMrpEDdJ0c6Caf+w/eh3I63Lb8BiqncEAn8ytt/F5ivyyW0XwHpP2tPx2R9pvsWB50oYPp9sSefbfX1DrYIUnzmJr0FySZPjymb4s8YEZygK7rSA4UtgXd/Nzn4zmJX8l7cms9bex5Wf6rhtu0EuTAkIxyfqW9X09xRWJ9021ZAXJgiCY7mAdd6GAqk/ftnQ/3aYnHZvmoxCUNt4mWqBOZnZu4MUn01qbbs4LkAQPJAbquQzlQ2BZ083OfU96DUxNrEnvV38qfyvJvNt2uFSAHhmAU8ytNfmpi17q8U50I/kvNtmpFyIEhmYwu5sHYdzDlw3woxcmJy+rEWhel7vpmW9WcfFHen+KziWdkeWvihKbb1IIjdi+tR+q+UOOwphu13OTBw8mDh5EDHSQHupcDhW1BNz93Rj8HRvB/9ijmV7niV7mk/HW1E6bMPfPRhtu07OTAUD2ni3kwmQesSMsAAAAA6IaxH8EEAAAAwMrSwQQAAACADiYAAAAAmmMEEwAAAABLooMJAAAAAB1MAAAAADTHCCYAAAAAdDABAAAA0BwjmAAAAADQwQQAAABAc4xgAgAAAGBJdDABAAAAoIMJAAAAgOYYwTSGJicnj09sTnwzcWfi3Ykn1vv2SVyWuDsxNeCx/y7xqcT9iS2JXx3+XwArmgPHJa5NPJDYmvizxKq+xz458ZHEg4mvJV7i86CDeXBy4prEtxPva+6vgOHnQMrHJc6t24BvJL6QONRnQce2A3+VuKPe/+XEbzX3l0AzedD3HHsnvlXywmcxOx1MYyZf+lNSvDnxh4mSOAcl9kp8Ivc9JuV3ExclThjw2JJMFyc+mnhy4sRE2bj81HBaD0PJgR9OvDqxW+LAxMGJP+h7inclvpNYnTgmcXYe9yyfDR3Lg39MvDGxcYjNhrbkQPk9dFviP9THvi5xUR5XHg9d2Q78l7L+1NTULil/JfHGPG7/4f0F0Io86N8/uNpnMo/3PP805l6LkZAk2aXuFLw8n+tFffWPT/HVxB+k/rxa9/QUN+f2ZN96+6S4MvGEqfrFSN0nUlyVm/95eH8JrHwO9N33mhTPTf0vZ3nnLN+X2Ce3v1zv/8sUt+f2Bp8LXciDafWlk2lN6o9f+ZZD+3Kg7/7rUvxJ7v+Qz4mu5UDue0aKTYlX9T8fdCEPUndUiv+YuCHx9Nz3m8No/6gygmm8/FzihxIf7q9MEmxPcWnikEU8Z+mAKh1PMK458IuJ6+tyGa33UK9zqfr7hBFMdCkPYNQtaw5k52J13T7IETqVA/VUom9m8UuJO+pjoTN5UDupTk+Ujifm4VHzWIfRUYb23Z2keWjAfWWj8NQ5Hn9TYlviD8uQwcQhdXh4GToIY5cD+Y6/PMXaxFtqVTmi8cC0x92feMIytxPanAcw6pYtB+opFBckzsvzlZ1s6EwO5PGvrL+BfqHupH97RVoL7c2DNyTOzXNs9SHNjw6m8XJ3YrdBE5PF7vX+GSVxyvxMRyRelLgzUc5ZLcMJJRRjlwNZ54g6v8Ch+e736ssRjXKkol+5/Y0VaCu0NQ9g1C1LDuS+8jv5L+u8fCevXHOhvduB1H0v8Zksrkn8J58VXcmD1O2X4vmJtw2hrWNDB9N4+Ww9slDOEZ2Ydp7pofXc6Vkloa5L/IfEUxIvTNVPJD63Eo2FpnIgt9eneE/il/M939y3ajk1blW5UkRf3c8knBZBl/IAJrqeA7mvTBFwbqKcHvdr9SAcdHk7UHbSf3L5mwqtzYN1dULwfyhXn6uTf/9alj/vM5uZDqYxkoQop/L8SeKskij1NLe96iik0hN7QfnBFOVc1MeWx5TlxON6z5Hln651P5z4g9q76xLVjFMOPK+UdYfhYZ2nuf1gHQJ+epnwO/GcLB9ej2BDJ/KgKEf76rbi0SXqdmHQEUAYyxyIsxP/ru5w/POw2g5tyIHc9yNlYuOyI54o24By0PnoxCd9QnRoW3BO7VQtI5lK/HniY4mSD8zAVeTGUBLlhBS/nyhXiiudR/878ZIkzT/WpCqz5vf7Wu7bcend3P9fU/xWosw58LeJ3819W4bWeFj5HLiiziXwrb6H/G3uO7Q+9sn10uwvSNyT2JD7/toHQ8fy4PUpTpv2lOUKWqUexjoHct+PZ/nWeuS7f+6O3879ZUcExj0Hytw0H6yjuMuAhK8l3pH7yigP6MzvoWnPU34DuYrcXO93vRo9YyqJ8LI68/1z8ln/Q9PtgWGTAyAPwLaArpMDIA+G8r9GB1MnNigvTfHdfNYXNt0WaIIcAHkAtgV0nRwAebDi/2d0MAEAAACwFCb5BmA+Rz33LOepJ25IXJ94Va1/cuLyxM21fFKtL96R2JK4LvHsvuc6rq5f4ri++v0Tm+tj3lGv4jTjawAAAO2hgwmA+SgT3Z6SUa/PTHlQ4qR09JTlDYlPpn7vUtbbRZkcsdSVOLFekak3iXqZPPrAxAFlua/DqKzzir7HlcvGTszyGgAAQEuM3SWHd9ttt6m99tpxQbRHePDBByd23nnnIbeonbwX838/rr322ruzY1uupjES5MD8yYMF58Ad5XaWv5FOoRuzuEfi8MS6uup5iU2J19b686e+fyWJK7P+rond67qXp/re8oAyIilFuXRsedwuqb+y1p+f4ojEx2d5jc7kgTY3/z7bFjRLDjT/Po9aDsy2LWjz90nb2v2eJQ/K75frEqsT5TfOOcmLt9cDaB9I7FWvQnlk6u+ro7Hfnjgs8c3E8an/fHmuOor7dfWp35j683ojulO8L7FT4tLEq8rvqZleYxx/D7W1bW1t1zDbNte2YOw6mEoCXXPNNQPv27Rp08S6db19lG7zXsz//cg/83Jp1pEhB+ZPHiwuB3K7/FL52cRVidXZyOzoeIo7y+26XDqfbut72NZaN1v91gH1E7O8xvR2lpFSJSZWr1498Za3vGXg37N9+/aJxz/+8QPvayttbv59fu5zn9u7nPFI7FSM27ZAm5t/n0ft99BsedDm75O2tfs9Sx5srSO6P5/lJ2T52nqw7Pg62vqM3C4jrTfUg2H9I7oPrKO1D+wb0b22blPK81xS/7f3RnRfVbcF6+sBtw0zvMbYbQva2ra2tmuYbZtrW7BqPvNupDh/VH5QwUqQB/CDXCh73x9KvDr/jx+o0yTtUP9vl+3EipntNXLXOSlKTKxdu3Zqpo1sm38czESbW/M+j8xOBQAr4ru9fdtRGNENw7ZqAfNu+EFFl8kDOi8/ch5TO5cuyDbhw/UNuav8UCojjOoPpm21/vZEOUDRs6bWlVg3rX5TrV8zYP3ZXgOGyU4FACM3orscPJlp1O5M9zWtrW3b3tJ2taltc3Yw1S/xyMy7AStBHtB1dXTquYkbkw9v7bvrkkQZnXpGLS/uqz85D7uwjt64v3YQXZblP+2b2PuQxKll25C6MiLqoPpD7djEWXO8BjSirTsVAAyHEd3dHLnd1na1qW2rxuEH1Tj00g6b92Lx70db8wBW2HMSL01sTg58odb9ce30uSh1J6Qs52QfWe+7tJ4qvaWeLv2yUlk7kt6Qxavreqf3DjzEK/tOly4HGUpMzPIaMHRt3qkY599D2ux9hrYwohuWoYOpzT+oxnnejZXivVjc+9HWPBjnnYqV5P2Y/3uRr95nUvzrF/7hDh6wfvmenjTDc21MsXFAfZmFcp8B9fcMeg0YtrbvVIzz7yFt9j5DixjRDUvpYGr7D6r52nz7/RPHb/jYgh5z6xkvWspLMkbanAfz3ak464KLJ878zIPz/ZPHPg9GcYdlpXTpvbAtoOs7FYvJgXHeFtA9coAlePy4jOj2e4iVMJ+ryJl3g86TBwCdNzY7FQAs2vZyQsEM9xnRTefNZwSTeTdAHgB0nZ0KAIAlXkXOvBt0njwAAACAmT1q5rsAAAAAYG46mAAAAABYEh1MAAAAAOhgAgAAAKA5RjABAAAAoIMJAAAAgOYYwQQAAADAkuhgAgAAAEAHEwAAAADNMYIJAAAAAB1MAAAAADTHCCYAAAAAdDABAAAA0BwjmAAAgDlNTk5uTGxLfLGv7vWJ2xNfqHFY332nJrYkbkq8sK9+fa0r923oq39a4qpa/4HEY2v94+rtLfX+vXxcAO2jgwkAAJiP9yXWD6h/29TU1H41Li0V6QR6ZoqjEs+qj3l36h5dIsvvShyaKOscXdct3lyf6+kp70ucUOtLeV+tf1tdD4CW0cEEAADMKR08n05x7zzfqsMTF+Yx3058NctbEgfU2JK6WxLfKeuUddPJNJnyeYkP1seflzii77nK7Yl6/8F1fQBaZFXTDQAAAEbayenvOTblNYlT0nFURh/tkbiyb52tta64bVr9gYmnJL6exz40YP09eo8p9+e17q/r3z29IbnvxBQlJlavXj2xadOmRzR29U5p5L69l5m/Qc+13LZv3z6U11mMtratre2CLtLBBAAALNbZiTckpmp5ZuLlTb2d6YA6J0WJibVr106tW7fuEeucdcHFE2duXvhu0K3HPPK5llvpKBnU5jZoa9va2i7oIqfIAQAAi+3QuSvxvcS/5OZ76ilwxe2JPftWXVPrZqq/J7FrRiCtmlb/sOeq9z+xrg9Ai+hgAgAAFiUdPrv33fzVRO8Kc5ckjqpXgHtalvdOfC5xdVmuV4x7bJ0I/JJ0UJURUFckXlwff1zi4r7nKrcn6v2fqusD0CJOkQMAAOaUDqH3pyjnIu2W5TJH0mnldpb3S1k6fG5N/HZZN/0/16f+oizekCgTHp1URjrV5zk5xWWJckW5jWXd+hKvTVyY+9+Y8u8S59b6Uv5l6rfUScZLpxQALaODCQAAmFM6go4eUH3uLOu/KcWbBtRfmuLSAfW39J1i11//rRS/7iMCaDenyAEAAACggwmAlTU5ObkxsS3xxb661yduT3yhxmF9951aTmVI3JR4YV/9+lpX7tvQV1/m4riq1n+gzssxUefu+ECtL/fv5bMGAID2MYIJgPl4X2L9gPq3TU1N7Vdjx+kO6QR6Zp0f41n1Me9O3aNLZPldiUMTZZ2j67rFm+tzPT3lfYkTan0p76v1b6vrAQA0wkE3mJkOJgDmlA6eT9eJVefj8MSFecy3E1/N8pY6p0aJLWWOjcR3yjpl3fxQm0z5vMQH6+PPSxzR91zl9kS9/+C6PgydnQoAwkE3WOwk3+XHVIpfSmzLDsE+te71KV6R+Ke62h/3Hbk+tR5xLleJ+L3UX9Y7LSLF2xPlCPZ7U39GrX9a3cl4SuLaxEvLjkc5LSLL5yf2T9yT+I3UlytTwNDJA5jRycmPY1Nekzgl/6fL6KM9Elf2rbO11hW3Tas/sP7//3oe+9CA9ffoPabcn9e6v65/94A8PTFFiYnVq1dPbNq0aWCDV++Uhu7be6n5mem5hmX79u2Nt2GhxrTNZafinfX3Sb8y+u4t/RXTRvL9aOJ/pe6n6t1lJN8L6nf96tSXS7Tf0DeSr1xF68/r76mz+0fypf6out5vLOFPBWAJB90WcMr+Dw66pfxqvRJibyL7HQfdykLqewfdbqwH3V5S1ykH2V5ftwWH1+XeQbd3loNueY5yBUcYmavI+TEF8gAGKT923pAoP2xKeWbi5U29Vfl9dU6KEhNr166dWreuXEn7kc664OKJMzcv7CKqtx4z+LmGpXR6zPT3tNU4ttlOBQCjcNANmjLnL2w/pkAewAzbh7t6y/mR854UH603b0/s2bfqmlpXDKovo1R3zXOsqj+o+tfvPdfWcn/KJ9b1oU3sVAB0W2sOuo3DiO62joJua7va1LaFHcJt6Y+pcUiirn4Bx+D9aEUerGQOFOP6XZEHS3sv8r3bPd/LO+rNX030rjB3SeKvc/9b66lBeyc+Vx5Sluup0aXjqJzq85IytDt1V2T5xYkyRPy4xMV9z1Vuf7be/ylDwWmZTuxUNL0tGMX/19oM3dGmg27jMKK7raOg29quNrVt1aj/mBqXJOrqF3DE34/W5MFK5kAhD8bfXDmQHzLvT1FW2C3LpRP0tHI7y/vVHChz5P12/T5en/qLsljmlCk/jk5K3ffq85yc4rI6H9/Gsm59idcmyrwzb0z5d4lza30p/7LOWXBv7ZSC1ujKTkXT24JR/N2izdAdDrrBEjqY2vRjCpoiD+jY9/3oAdXnzrL+m1K8aUB9uSDEpQPqb+mb9LK//lspfn1BjYUhslMB0C0OusEydzD5MQXyAKBr7FQA4KAbLKGDyY8pkAcAGMkHALDUq8g5LYLOkwcAAAAws0fNfBcAAAAAzE0HEwAAAABLooMJAAAAAB1MAAAAADTHCCYAAAAAdDABAAAA0BwjmAAAAABYEh1MAAAAAOhgAgAAAKA5RjABAAAAoIMJAAAAgOYYwQQAAACADiYAAAAAmmMEEwAAAABLooMJAAAAAB1MAAAAADTHCCYAAAAAdDABAAAra3JycmNiW+KLfXVPTlyeuLmWT6r1xTsSWxLXJZ7d95jj6voljuur3z+xuT6mPHZyttcAoF2MYAIAAObjfYn10+o2JD45NTW1dynr7eLQRKkrcWLi7F5nUYrTEgcmDijLfR1GZZ1X9D1u/RyvAUCL6GACAADmlA6eT6e4d1r14Ynz6nIpj+irPz+PKa7M8q7pSNo95QsTl6fu3sR9ZTmxvt63S1m3PKA8dtpzDXoNAFpkVdMNAAAARtbq9AfdUZfvLLfr8h6J2/rW21rrZqvfOqB+ttd4hHRUldFSJSZWr149sWnTpkc2eKeJiVP2fWjOP2y6Qc+13LZv3z6U11mMtratre2CLtLBBAAALFkZeZQOnjL6aMXM9Rq5+5wUJSbWrl07tW7dukesc9YFF0+cuXnhu0G3HvPI51pupaNkUJvboK1ta2u7oIucIgcAACzWXfX0tjJ6qJTbav3tiT371ltT62arXzOgfrbXAKBFdDABAACLdUmidyW4Ul7cV39svZrcQVm+v57mdlnikDKxd53c+5BSV+97oKxbrx537LTnGvQaALSIDiYA5uTS1ABkW/D+vAufTTwjy1sTJ2T5jMQLsnxzyufX28WliVsSWxLvSbyyVJbJvVO8IXF1jdNr3URd5731MV9JfLzWz/QaALSIDiYA5sOlqek8Ha10XTqCjk7snnhMYk3i3MQ9iYMTeyee3+ssqlePOynxk4l9E9f0Pc/GxNNr/EVf/TWJfepjTq5Xk5uY6TWgCbYFsIQOJgkE8gBcmhp20NEKgG0BzGDVPBPonYnz++o2JD6ZHY4z0gFVlku8NnFoYu8aBybOLmXWeXLK0xJrE+VIxLWpuySPv6+u84rEVXUo7fo6HHam14AmyAMYg0tTL/by1E1f/ngUL8E8jm0uHa35nu01rfrwRO/yReclNtXfK6X+/DoC48o8btc6OXFZ9/LeCIzUXZ5ifcryuF1Sf2WtL7+7jqi/iWZ6DQCGzLYAltDBJIFAHsC4XJp6sZenHsalqcftEswdanOrOloBaERrtgXjcMCtrQep2tquNrVtYb+wW5hA45JEXf0Cjvj70Zo8WMkcKMb1uyIPlvxe7LhsdPmOLuDS1Oum1W+az6WpB7wGtE7THa3jvC0Yxf/X2gzd1PS2YBwOuLX1IFVb29Wmti22g6k1CTQuSdTVL+C4vB9N58FK5kAhD8bfInOgd9noMwZcmvrkfF8vrKdL77g0dW6XS1P/ab0s9US9NPWp5VSh1O24NHU9XbpcmvqsOV4D2qI1Ha3jvC0Yxd8t2gyd0pptAYziVeR2fLnLwgISaKb6WRNowGtAW8gDOsOlqWFGvU7QiQEdrccmdyZr5+mOjtaUpaP1kNLRWjtbS0frZfW+HR2t5QHlsdOea9BrANAOtgWwhA4mCQTygA5xaWrQ0QqAbQHMZtU8j1qX4Xu7ZXlrvRpcOVXhotw+IeXXEkfW1ctV4A5LbEl8M/GyUllPf3hDFq+u653eu3pKvLJeoWuneqWUEhOzvAYMnTwAoHS0zvAuHDy9ol497qRBK+eujSk2Dqi/JsU+A+rvGfQaAAyfbQEs7SpyfkzRefIAAAAAlv8UOQAAAADQwQQAAADA0hnBBAAAAIAOJgAAAACaYwQTAAAAADqYAAAAAGiOEUwAAAAALIkOJgAAAAB0MAEAAADQHCOYAAAAANDBBAAAAEBzjGACAAAAQAcTAAAAAM0xggkAAACAJdHBBAAAAIAOJgAAAACaYwQTAAAAADqYAAAAAGiOEUwAAAAA6GACAAAAoDlGMAEAAACwJDqYAACAJZmcnLw1sTnxhcQ1te7JicsTN9fySbW+eEdiS+K6xLP7nue4un6J4/rq96/Pv6U+dtJHBtAuOpgAAIDl8Nypqan9Emvr7Q2JT+b23qWst4tDE6WuxImJs3sdUilOSxyYOKAs9zql6jqv6Hvceh8ZQLvoYAIAAFbC4Ynz6nIpj+irPz8dT8WVWd41HUm7p3xh4vLU3Zu4rywn1tf7dinrlgeUx/Y9FwAtoYMJAABYqtLx84l0Bl2bKKOSitXpD7qjLt9ZbtflPRK39T12a62brX7rgHoAWmRV0w0AYPTn3UjxjcT3Eg+VUyPqaQ4fSOyVKPcfWY5G1zkz3p44LPHNxPGp/3x9njLXxuvq074x9ef15t1I8b7ETolLE6+qR7ChNeQBTPx8/jXfnlz4kbwXZb6lL/W/J+X/dupW/H937dza0cG1evXqiU2bNj1indXZmpyy70MLfu5Bz7Xctm/fPpTXWYy2tq1N7bItoOuW1MEkgUAeQN+8G3f3vRu9eTfOyLaiLJd47bR5Nw6sc2oc2DfvRpm3o+yAlCPgl9RTJHrzblxVO5jKvBsf987TQvKAziqdS7Xclv/fH6lzKN1VTm8ro5jqaW7b6upl3T37Hr6m1pVYN61+U61fM2D9Qe04J0WJibVr106tW9f/dN931gUXT5y5eeG7Qbce88jnWm6lo2RQm9ugrW1rYbtsC+is5ThFzmR+IA9gOvNugDygI9J5tHPiCb3lFIckvpi4JNG7ElwpL67Lpf7YejW5g7J8fz2V7rLy2DKxd53cuzzPZfW+B8q6dSTssX3PBW3nNxGdsWqFEqjXhXxePerw2v7J/FJemW1DbzK/db3J/MoDyiVM62R+m3qT+dX63mR+jlozCuQBXZx3o5T/vR49Hvq8G/M5LWKxp0Y0PfS+TcP/56uDbW5FHkBDynf7I9/v+9mxf/HX+e7/TW5fneWLUp6Q8muJI+v6l9ZTpbfU06VfVirL/kDWfUMWy+OK03v7CPHKvtOly/6AfQLaqPFtwTj8Hmrrb4i2tqtNbVs16gk0LknU1S9gW4z6TsVK5kAxrt8VebBs70Ur5t2Yz2kRiz01YhinRYzY8P85dbDNjefBOG8LRvH/dZfanK/3LSl+ZkD9PSkOHlBfcuGkGZ5rY4qNA+qvSbHPghsHw9X4tmAcfg+19TdEW9vVpratGvUEGpck6uoXsC1GfadiJXOgkAfjbyk50JZ5N6BJbciDcd4WjOLvFm2G7mnDtgBGdg6m/gRK8bAEKvULSKCZ6iUQrScP6DLzboA8AMC2AJbUwWSnAuQB1NM/P5Ntwt+n/FziY2XejZRnJF6Q+ptTPr/e7s27cUudd+M9dU6NHfNupOjNu3H1gHk33lsf85WEeTdoG3kAgG0BnbeUU+RM5gfygI4z7wbIAwBsC2BJHUx2KkAeAAAAwJLnYAIAAAAAHUwAAAAALIkOJgAAAAB0MAEAAADQHCOYAAAAANDBBAAAAEBzjGACAAAAYEl0MAEAAACggwkAAACA5hjBBAAAAIAOJgAAAACaYwQTAAAAADqYAAAAAGiOEUwAAAAALIkOJgAAAAB0MAEAAADQHCOYAAAAANDBBAAAAEBzjGACAAAAQAcTAAAAAM0xggkAAACAJVm1pEcDAAB0wF4bPraox916xouWuSUA7WQEEwAAAABLYgQTAABAR0dZGWEFLBcjmAAAAABYEiOYAKDlR59P2fehiePnOCrtCDTA6I8q6v2/9z8dGEWtH8E0OTm5PnFTYktiQ9PtgSbIA7pODoA8ANsCsC2g3Va1fCPy6BTvSrwgsTVxdeoumZqauqHZlsHwyAO6Tg4wrCs9vW/9zq19s7uWB9M/Q6P46FoOQBvzwJUUGekOpjggsSUJc0u5keS5MMXhidZvSEywxzIa2Txou8233z/naUfTGbLeCDkA8mDkfucN6hSzDVkS24IxMMwOijHtDBnJPFjoZ1H+f64b0muNyOc+Mibz5Wy6DTNKwrw4xfq08bfq7ZemODC3T5623okpShTPSNw0w1Pulrh7hZo7arwX838/fjzfuaeu8OexpDyQA4smD8YkBzqwLdDm5t/n1ueBHGidccvb1ufAArYFbf5stK3d71nr82BMtgVtbVtb2zXMts2aA20fwTQv+QPPSVFiVkm2a7Lu2iE0qfW8F+P1fsiBbn7uy2kc3otxzgNt9j7PhxxoF3nb3jxo82ejbeP1njVhHLYFbW1bW9vVpra1fZLv2xN79t1eU+ugS+QBXScHQB6AbQHYFtBybe9gujqxd3rjnpZ4bJaPSlzScJtg2OQBXScHQB6AbQHYFtByrT5FLkO8HkrHUjmf9LJEmTF/Y+quX8JTzjlUsEO8FyPyfixzHrT272yI92ME3gvbgnZ/PrPQ5mVkW+D71PW87VAOaNt4vWfLSh40rs3ftXOabkDrJ/kGAAAAoP3afoocAAAAAC2ngwkAAAAAHUxzmZycXJ+4KbElsWFJ79iIy9+/Z+KKxA2J6xOvarpNTct78OjE3yU+2nRbVpI8eNh7sTGxLfHFxj6QlujS/4RRzIFR+66O4vcpbfyhxOcSf1/b/CdNt2kljVoejFoOFPKg3dqaA23/3rT193Las2vig4kvJW5M/Pum29R2bcqBQf/js/zkxOWJm2v5pDblZNPtm5zhd0u9MNpV9XP9QL1I2tCN/RxMeWPLJIBfTrwgsbVegeLo/N03NNqw5t6P3VPsnr//81l+QpavTRzR1fejyPvwmhRrE7vkffilptuzEuTBI96PX0yxPXF+PvN9GvhIWqMr/xNGNQdG7bs6it+ntHMyxc5p4/YsPibLn0m8KrevbLhpy24U82DUcqCQB+3V5hxo+/emrb+X067zUvxt2vTeukP9w1n+etPtaqu25cCg//Gp+7MU9+b2GbUD7ElZfm1bcjJxfJPtm4xBv1sSJUc/nPoLU//nWf77LJ89rHZ16RS5AxJb8ubekvhOli9MHN5wmxqT9+COkiR1+Rspbkzs0WyrmpPkW5PiRYn3Nt2WFSYP+uS7/+kU9zb0WbRKh/4njGQOjNp3dRS/T2lnUX7cFuWHWolxPfo2cnkwajlQyINWa20OtPl709bfy2nXE1OUDopzy+3ymepcGq0cmOF/fGlP6TgszqudOm3KyUbbNzXz75bnJT7YVLu61MFUvgS39d3e2pZ/1i34p7xXip9NXNV0Wxr03xJ/lPiXphuywuQBXf+fIAeGbJS+T/XUjy9kcVvi8vxwa32bF0keDJk8aJ2RyIEWfm/a+nv5aYl/SvxFPX2vjGLauelGtdwo5MDqbIfvqMt3lttNNmZATjbevslpv1sSX0l8Pe16qOnPtQsdTAyQL+TjU3wo8ep8ER/o6HvwSym25e8vwx2h0/xPoMvfp7Txe4n9sliO0h+Q9o/EqVi0mzxgHL43Lf+9vCrx7MTZaV/Z+X8w0Zp5tVi6MlSnFG3NyabaNzXtd0vi3w67DV3uYLo9sWff7TW1rrOSJI+pSXJBvpgfbro9DXpO4lfyftxah4c+L8t/1XCbVoo8oOv/E+TAkIzy96nO23FFYn3TbVkh8mBI5EFrtToHWvq9afPv5TJKY2veq95Irw/WDidGNAequ+r8R715kMoonTblZGvaN/Wvv1vK5PZlwvvS6dro59qFDqYycdnedVb1MvHbUYlLGm5TY+qkYOU85RvzhXxr0+1pUv7+UxNrEnvV78WnsvybTbdrhcgDuv4/QQ4MwSh+n9LkpyZ2rcs71YlPv9Rsq1aMPBgCedBqrc2Btn5v2vx7Oe0opyfdlrfuGbXq4EQrJkVvsdbmQJ/SnuPqcikvbllONtq+ycG/W26sHU0vbqpdnelgquchnpy4rL7xF6Xu+mZb1fhRiJfWow9fqHFY041iZcmDh8t3/v0pPpt4Rpa3Jk7o8HewE/8TRjUHRvC7Oorfp3L0sVyG+Lr6w7vMwdSqy3B3OQ9GMAcKedBSLc+BUfzetMHvJi6o/8PLKUN/2nB7Wq1tOTDD//gzEi/I8s0pn19vtyknm27f7jP8bilXsntN6rekfEpv8vthm0xjmnhdAAAAAMbE2I9gAgAAAGBl6WACAAAAQAcTAAAAAM0xggkAAAAAHUwAAAAANMcIJgAAAAB0MAEAAADQHCOYAAAAANDBBAAAAEBzjGACAAAAYEl0MAEAAACggwkAAACA5hjBNIYmJyePT2xOfDNxZ+LdiSfW+45LXJt4ILE18WeJVX2P3ZT4VmJ7jZua+0tg+DlQ1zkqcWPiwcRXEr/gs6Bj24LeNqAX30uc1dxfA0PPgb0Slybuq4995/RtBYx5Dvy7xKcS9ye2JH61ub8EViwPym/+m+r3fFvivMQufY99cuIjdZ/ga4mX+Cxmp4NpzORLf0qKNyf+MFES56DEXolP5L7HpPzhxKsTuyUOTByc+INpT3Py1NTU42s8Y2iNhxbkQNZ5QX38yxJPSPxi4hYfDl3Kg75twONz898k/jnxP4b6R0Czv4fendiW2D2xX+I/JF7pQ6ELOVA7mi5OfDTx5MSJib9K/U8N+c+Alc6D/5N4Tn7vlPt+IlG++2/se4p3Jb6TWJ04JnF2HvcsH8ss73nezJnvZaTU3tZ/TLw8n+tFffVlB+GriT9I/XnTHvOaFM9N/S/X25tS/FVuv3d4LYdW5cD/TXFubp/rc6GreTDtvuNSnJb4ydzvRwNd2RbcmOKU3L603v6vKXbJ7d8e0p8BjeVAlvfJ8pWJJ/T+76fuEymuys3/7KNhHPOg1peDC7ul/rDc3jnL9yX2ye0v13X+MsXtub1hiH/KSDGCabz8XOKHEh/ur0wCbE9RfiAdMuAxZXTG9dPq/kuS5+7E/0msW5GWQgtzIN/3R6dYm3hqHQ6+tZ4WsZMPjA5uC3pKB9P5OpfoWA78t0Q5deKHE3tk+dDE36xMc6H124FiMlE6nmCs8iD/43++nCKXxW8kfq3+/y/KiL2Hep1L1d8njGCahQ6m8VKGuN6dJHhowH13JJ7aX5FEenndmX5LX/Vr6/DA8mPqnMT/l/V+cmWaC63LgTL8tQyXfXHiF+ppET+beJ3Pio5tC3r3/Xg9NehhR7qhAznw6boT8UBia+KaxP9ckdZC+3LgpnqK6B+W04gSh9RtQTmtDsYqD3L/Z+opcmsSZbTqrXWdx9dtQL/76xQazEAH03i5O7HbDJNQ7l7v3yHrHJHivyQOTUL9oD7LZejrNxLfrkMGy3mph61wu6EtOVDmmSnOSt0dtf6tCTlAp7YFfV6aKD+8ylBy6EQOpO5RdbRSOeq9c91JeVKdxwPGPgdSfjdFqX9R4s5EmcfmotrZCmOXB/V7f3v9339hrSojnX4w4Xe1Sx3pxAx0MI2Xzya+nfiP/ZX1fNIytHtTvb0+xXsSv5xE2jzHc07VIbEw9jmQ5fvqj6f+eWbMOUOXtwXHJoxeoms5UCY1/rHEO+sBt3uy/BcJBxvozHYgt69L/IfEUxIvrGc4fG4YjYdh5sE0pTOqd/ZOOTVuVdbfu+/+n5njVNLO08E0RvLPvwzZ+5PEWWWDUYe07lWPOJQe2gty+3mlTPxa1n/YRiL37Zp4YeKHSk9v4ph6PrY5B+hEDlRlJ+J3s96PJMoR69+vV1GBLuVB2Sb8XD1d2tXj6FQO1FEcZdTef6q/h3atc5FdN9Q/BBrcDuT+n677BGUesj+oIz7e50NhzPLgmMSP9U0L8KbEJ+vjH6wjWU8vE34nnpPlwxNlom9m4CpyYyhf/hPqTvHTE49L/O/ES5Ik/5j7rqhzy3yr7yF/m/sOzX1PrROe/dvE9xJfSvzn3Hf5UP8AaCgH6mPLHExvL+vXdcpG6I9yf//6MNZ5UB//31P8cOrKaXLQtW3BfnWi15+pv4k+lfjd3H/XEP8EaDIHylw0v5Uov4v+tn7/t/hIGLM8eFM9gFAOKt9X94VPrSNXy2PLiNaNiRckSt2G3PfXw/8rRocOpjGXpHhZitMTz0ky/EPT7YFhkwMgD8C2gK6TAyAPhvK/Jp0Ow3gdmt2glKPP381n3ZuwDDpFDoA8ANsCuk4OgDxY8f8zOpgAAAAAWAqTfAMAAACwJDqYAAAAAFiSVfM4V3fPFOcnVifKhE3nTE1Nvb3OqP6BRLnU362JI1N/X+on6xWYDkt8M3F86j9fn6vM0P66+tRvTP15tX7/etnLnerM7a/KfVMzvcZs7d1tt92m9tqrrP5IDz744MTOO+881588trr+9y/2Pbj22mvvzveuXGFvJIxbDmhz8+/zqOXAOOZBP+1vxqjlwTjnwHLzfszvvRi1HJgtD8bxMx+3v6mtf8+o5cG4bQu0ufn3ec4cKHMwzRaxe+LZdfkJiS8nnpn4s3qZvlK/IfHmulw6lj6eKB1NByWuqvWls+iWWj6pLj+p3ve5uu5kfeyhtX7ga8wW+++/f+mbGuiKK66Y8b4u6Prfv9j3IN+7a0oxKjFuOaDNzb/Po5YD45gH/bS/GaOWB+OcA8vN+zG/92LUcmC2PBjHz3zc/qa2/j2jlgfjti3Q5ubf57lyYM5T5LLSHb0RSCm/keLGxB6JwxM7RiDV8oi6XOrPr69/ZZZ3zUik0kn1wsTlqbu3jkK6PLG+3rdLWbc2+PxpzzXoNQAAAAAYlVPk+qUzqIyv+9nEVYnVpfOp3nVnuV2XS+fTbX0P21rrZqvfOqB+YpbXmN6uE1OUmFi9evXEpk2bBrZ/+/btM97XBV3/+wvvAQAAADTYwZROnMen+FDi1en0eeD7Uy19Xxl5lNs7zmNbKbO9Ru46J0WJibVr106tW7du4HOUzpWZ7uuCrv/9S3kPRm0uMlhucgAAAFjyVeSyY/GY2rl0QXZsP1yr76qnt5X7S7mt1t+eKDvjPWtq3Wz1awbUz/YaMGwPJU7J9/+Zdb6wk/KdfGadG+yTqd+7lPV2cWii1O1dR9edXSprZ9FpiQMTB5Tl1JU5ySbqOq/oe9z6Wj/Ta8AwyQE6L/+vNya2Jb7YezOy/F8TX0pcl/hIYte++05NbEnclHhhX/36Wlfu+8H/9Cw/LXFVrf9A4rG1/nH19pZ6/+AZWwEA2tzBVEdinJu4MTu4b+2765JEGYlRlPLivvpjy+Oi7IjfX09zuyxxSNmZrjvUh5S6el8ZEXVQfa1jpz3XoNeAoTIXGV0nB2CH9/V1/veUOSX3SY78dL0Qyqmlsh6EOCrxrPqYd6fu0SWy/K56IKKsc3Rdt3hz4m15rqenLCNVT6j1pbyv1r+trgcAMHKnyD0n8dLE5vwA+kKt++PEGYmLUld+9HwtcWS979J6WtCWemrQy0plmdw7674hi1fX9U4vdXX5lX2nBpWryJWYmOU1FmXz7fdPHL/hYwt6zK1nvGgpL8kYauNcZPOdh2zbvfdPnHXBwvtp993jiQt+TJfnzRr3NrcxB1Y6D5rMgXH4fvUb5fbne/jp6aOHUveJvpvl4iYv7rtQyYW5/9spv1pGH9WRq8WW1N9Sv7cXlnVTlouoPC/xkrpOOX369XV06+F1ufhg4p3loFy9OMpQfg8VfhMxLuQAtN9eA7ZTp+z70JzbL9uqlncw5bfLZ1L864RLD3fwgPXLj52TZniujSk2Dqi/JsU+A+rvGfQa0JS2zkU233nIyk71mZsXNLf/Drce09zcXaM4d9g4t7mtObDSedBkDozD96vfqLd/Di+vc+ZN1A7S0uE0qNN0eidrOW36KYmv53v80ID1f9AxW+5PDtxf1797MZ2sq3f6/o/0hRrVjsFx7vRcbt4LAJZi4Xua0FGzzUVWRlcsYC6yddPqN81nLrIBrwFDJQdg1vz4f1KUXpsLmnqfxvlgw0oa807PBfFeALDik3xD15mLjK6TAzBrfhyf4pcSx/SdtrbQi56UUdu75rlWDTjQ8IPH1PufWNeHNkx0//rE7WUqjRqH9d1nonuADtHBBPPTm4vsedN+QJV5wl6Q5ZtTPr/e7s1FVubXKHNuvKfOM7ZjLrIUvbnIrh4wF9l762O+Mm0uskGvAcMkB2CAckW4FH+U+JX8Py9zT070XajkqHoFuKdluVwJ9HP1f//e9Ypxj60TgV9SO6au6JvD6bgZLnpS7v/UYudfghWY6H6iTk6/X43yG8hE9wAd5BQ5mAdzkdF1cgB27DC/P0U5l2q3LJc5kk6rV417XOLyOifZlcmX30lcn9sX5fYN9dS5k1L3vbJC6k+uV9ctV5TbWNat7+9rExfm/jem/Lt6Fd+ilH9ZJwq/t3ZKQSsmup9Faye6B2Bl6GACAJiH7MsePaD63FnWf1OKNw2oLyM8Lh1Qf0vfDnh//bdS/LoPiRY7Of09x6YsF+45Jd/Z+5qY6H6+k92P40T34zZB+7j9PdAVOpgAAIDFOrue/l9GE5XyzHpFxdZOdj+OE92P2wTt4/b3QFeYgwkAAFhsh85d5fTPxL/UeSd7o/BMdM842mvARPf/NfGlxHWJjyR2rfVl3X/um7/1z/ses39iczl1NPGOejGVUv/kRDnl+uZaPqnWT9b1ttTXefaw/3CYDx1MAADAomRHd/e+m7+a6O14m+iecXT3gInuL0/sk07Wn0755To3X89X+ibA/51pI/9eUS8AsXffc25IfDLrlrpP1tvFoX3rnlgfD62jgwkAAJjvRPefTTyjTHSfOCHLf1ZHYlyX5ecmfr+sWyev7010/ze9ie7rHEu9ie7LxN4XTZvo/jV1QvCnTJvo/im1/jV9O90wbNsTvStA75Dv7yf65g4r846tmUen7C55TLkoRDm19PzEEfXuw+sE98V50+rPL+uXx2V512mdu9AK5mACAADmZKJ7mFOZf+wDfbeflo6gclXQBxKvSw79bZ20vkxiPzFgQvvVWeeOunxnuT19ovtpj+mtu6CJ7kdhIvVBE/HPZ4L+tv1N21v+Pi93m3UwAQAAwBKkY+f/SVF6Py6oVaXz58fSYXRPmXMpy/8z5bPm+3xlqFLWLyOcln2i+1GYSP34DR97RF3pXJprgv62Tca/qeXv83K3WQcTAAAALFI6go5P8UuJg+tpb6Wj59spvl2Xr806X8niT9UJ8PtPo1tT64q7yqlvZRRTPQVu2xyT5kOrmIMJAAAAFiEdQWWC7j9K/Eo6hr7ZV//UxKPr8k/UCbpvqafAPZC6g+rV445NXNw3Of5xdfm4afXH1qvJHZTl+/tOpYPWMIIJAAAA5va0OtH9bmWi+5Sn1avGPS5x+ff7iyaurFeM+8XE6an7bsp/SfxO6nsThL8y8b7ETomP1yjOSFxUJ9D/WuLIWn9p4rBEmei+dGK9zIdFG+lgAgAAgLl9NZ1Ea6fV9a52+DBZ70MpPjTDfdek2GdA/T0pDh5QX067O8kHRNs5RQ4AAACAJdHBBAAAAIAOJgAAAACaYwQTAAAAADqYAABW2uTk5MbEtsQX++qenChXDrq5lk+q9cU7ElsS1yWe3feY4+r6JY7rq98/sbk+pjx2crbXAABoEyOYAADmp1xSev20ug2JT05NTe1dynq7ODRR6kqcmDi711lUL2t9YOKAstzXYVTWeUXf49bP8RoAAK2hgwkAYB7SwfPpFPdOqz48cV5dLuURffXnl0tLx5VZ3jUdSbunfGHi8tTdm7ivLCfW1/t2KevWy1GfP+25Br0GAEBrrGq6AQAAI2x1+oPuqMt3ltt1eY/EbX3rba11s9VvHVA/22s8TDqpykipEhOrV6+e2LRp0+AG7zQxccq+D835h0030/ONuu3bt4/t37ZQ3gsAlkIHEwDAMigjj9LJU0YfrZjZXiN3nZOixMTatWun1q1bN/A5zrrg4okzNy/8J+Ctxwx+vlFXOpdmeq+6Zq73osxDluKXEtvyfdun1v3XFL+c+E7iK4mX5b6vp36vLN+YuKk+vIzO+536mP3rKafp7py4NPGq+t0up5B+IFEee2viyDLSr85H9vbEYYlvJo5P/eeX968HYKmcIgcAsHh31dPbyk5zKbfV+tsTe/att6bWzVa/ZkD9bK8BbZiHrJzmuU86fH465ZcTp/bd95XU71djR+fSIucbGzinGQDtooMJAGDxLkn0rgRXyov76o+tV5M7KMv319PcLkscUib2rpN7H1Lq6n0PlHXraI1jpz3XoNeAxuchS90nEr1zLq+c1lH6CIucb2ymOc0AaJE5x0fPMBT29fWowz/V1f44911a7ytHLU5IfC/xe6m/rNavr0NbH514b+rPqPVPS3Fh4imJaxMvzX3fSf3j6ganDKG9J/EbqS9DZQEAhi6/Td6fopw/tFuWt9arwZXfMxfldvnt87XEkXX1S+vpPFvqKT0vK5Vlcu+s+4YsXl3XO73U1eVX9p029PEaE7O8BrTNy+spbj1Py/f271I+kHhdvut/u8j5xmaau6y37oLmIhvHecjGbf6scft7oCvmcwJ++aHzztrZ0+9t2QC8Zdo/9GemOCrxrMSPJv5X6n6q3v2uxAvqBuHq1F+Sx9+Q5TfX57owdX+e5RPqsNdS3pf6p6f+qLrebyzmjwQAWKr8Jjl6hrsOHrBuGZlx0gzPUw7ebRxQf02KfQbU3zPoNaBN8nv9/0lRem0uqFWl8+fHyve3zrn0P1OWfYQVndNsPnORjeM8ZOM2l9i4/T3QFY9a5CV5Z1KGr16Yx3w78dV61O6AGltSd0sZnVTWKevWIeDPS3xwhqGwvSGy5f6D6/oAAEBL5Cf68fWMh2Nq5+pE3R+4py5fWycA/6lFzjc209xlAIzJHEwn5x//deUUujqHwGIuyVtOi/t633nb/UNkf/CYev/9dX0Yuvo935b4Yl/d6xO3J75Q47C++05NbEnclHhhX/36Wlfu601cueNU0cRVtf4DicfW+sfV21vq/eWqKjB0cgCAGbYPZRqMP0r8Sn6zf7Ov/qmJR9fln6gTdN+yyPnGZprTDIAWWfjY0O8rp7CV+QPKEYpSnlnPuW7EfM61Xuz51uN07q9zmZf0HjhVlK6TAwAdN8M8ZGX+1TJ36uX1ZIMr6xXjfjFxeuq+m/JfEr+zhPnGBs5pBsAYdDBl43BXbzkbgPek+Og8hq8Oqr+nXgViVR2l1L9+77m2lvtTPrGuv6hzrRd7vnWbz7VeKOcyL/49KKeKLmD00A9OFU351TL6qJ4mOtE7VbQspL53quiN9VTRl9R1yqmhr68duYfX5d6pou8sh+56w89hWOQAADPMQ3buDNuND6X40Az3LWi+sdnmNINhm+EiWE+uE9yX/YVyYaojc999dZTe22sHaekcPT71n6+PKaP0Xlef9o2p3zE9TJ2zrNcBWzpXX1XnJBv4GsP4m2FFT5GbdlnQX018sW/46lH1tJ6n1aGwn6tXStm7ngZUTv0pk3ZfUjcWVyRePMNQ2N4Q2XL/p+xU00JOFaXr5AAA0CWl86ecGtqvTH3xyeyvlv3fT9bbxaGJUrd3PePm7L4OqTIC8MB6IPq0vmlnzq5XbO89bv0crwGtsWqRQ2HXZXm/lFO19/S3y7r5sl+f+ouyWK4OV0YknZS679XnOTnFZYlyLvbGsm59idcmyhXk3pjy7/qOgpTyL+voj3trpxS0SWtOFV3J00SbPlV0FE/t7FCbW5MDXTpdehS/X/1Gvf0AdNsMo7rLWQe90yTKSKRNdT+31J9fB0pcmcftWgdrlHUv7502mrrLU5S5Wsvjdkn9lbX+/HoRrI/P8howOh1MCxkKW9d/U4o3Dagvw/suHVB/S9/pQ/3130rx63O1D5rSplNFV/I00aZPFR3FUzu70uY25UCXTpcexe9Xv1FvPwAMsLpv4vk7y+1FXgRrj7o8vX6214CRn+QbOq8cfej7Jz/9VNG/zv1vTfmjfaeKTvZOFa07zWVU3kvqOdW9U0UvnOFU0c/W+50qSmvIAQCAh6u/7Vd0vtTZXmO+I7rbPqJ40Gjz+YxCb9vftL3l7/Nyt1kHE8yDU0XpOjkAADCju3oH3uopcNvmGNVdYt20+k21fs2A9Wd7jUWN6G77iOLjN3zsEXWlc2muUehtGnU+Cu/zcrdZBxPMg1NF6To5AAAwo95ZB2cMOBuhXBDlwjqh9/21g6jMTfynfRN7H5I4tczJlLoHEgfl9lWJYxNnzfEa0Bo6mAAAAGDxo7pLp89FuX1Cyq8ljqyrlzmID0uUC1d9M/GyUlk7kt5Qr7ZenN6b8DteWa9Ut1Od3LvExCyvAa2hgwkAAAAWP6q7OHjAumWepJNmeJ6NKTYOqL8mxT4D6u8Z9BrQJo9qugEAAAAAjDYdTAAAAADoYAIAaNLk5OTvJ65PfLHMz5H4ocTTElcltiQ+kHhsXfdx9faWev9efc9zaq2/KfHCvvr1ta7ct6GJvxEAYDZGMAEALEE6fPZI8XuJtVNTU2XejEcnjkq8OfG21D095X2JMjFrUcr7av3b6nrleZ5ZH/esxPrEu1P36BJZflfi0ERZ5+i6LgBAa+hgAgBYngun7JSOn1L+cOKOxPMSH6z3n5c4oi4fXm9P1PsPzuMma/2F6Xj6duKr9apDB9TYkrpbEt8p69R1AQBaw1XkAACWIJ0+t6d/6C1Z/IfEPyc+kbg28fXc91BdrVzKuox0Kkp5W33sQ3ns/Vl8Sq2/su+p+x9z27T6A6e3I89zYooSE6tXr57YtGnTwPau3mli4pR9e82av5meb9Rt3759bP+25X4v8h0rV7z6pcS2Olqv1D05xQcS5VTPWxNH5r77aqfp2+sl2svl2Y9P/efrY45L8br6tG9M/Xm1fv++y7OXy7u/qlyFa6bXWNY/HoAl08EEALAE2fl9Uh1R9LTE1xP/o57iNlTZ4T4nRYmJtWvXTq1bt27gemddcPHEmZsX/hPw1mMGP9+oKx0qM71XXTOP96J0/rwzcX5fXZkT7JP5/p1R5wcr8dp6SufeNUqH6NmlrJ1FpyXWJsol3K9N3SW1w6is84rEVbWDqeTRx2d5DQBaxClyAABL8/zEV7Pz+0+J72b5w4nnJHatp8wVaxK31+VS7lkW6v1PTNzTXz/tMTPVw1Dl+/3pFPdOq+4/5XP6qaDnlxFIcWXNh91TlsnrL0/dvbVT6fLE+nrfLmXd8oDy2GnPNeg1AGgRI5gAAJamnBp3UHaQf7ieIndw4prEFYkX1zmTyilBF9f1L6m3P1vv/1Q9DajU/3XKt6b80Try43OJcqrR3uWqdLVjqUwE/hIfGi2xOl/fMudYcWe5Pf1U0GmnfM5Wv3VA/WyvMbGYU0XH8TTRcTvVc9z+HugKHUwAAEuQHd+rslNbJusu88uUvda/q6eqfSxxYe57Y607tz6klH+Z+i11NMhR9XmuT91FWbyhPs9JqfteuS/1J6e4LFGuKLexrOtDo21qR2kZfdTYa8znVNFxPE103E71HLe/B7pCBxMAwBJlp7bMKVOi3y31CnDT1/1Wil+f4XnelOJNA+rLfDQloG3uKqe3lRFG9TS3bbV+tlM+102r31Tr1wxYf7bXAKBFzMEEAAAsVu+Uz4kBp4IeW64mFwdl+f56mlsZiXdImRy/TpB/SKmr9z1Q1q1XoDt22nMNeg0AWsQIJgAAYE7p93l/HX20W5a31lF7ZyQuyu0TUn4tcWRdvYy4OyxRTgX9ZuJlpbJM7p1135DFq+t6p5e6uvzKeqW6nerV40pMzPIaALSIDiYAAGBO6Qg6eoa7Dh6wbpkn6aQZnmdjio0D6svk+PsMqL9n0GsA0C5OkQMAAABABxMAAAAAzTGCCQAAAAAdTAAAANCEycnJZyS+0BflioivTrw+cXtf/WF9jzk1sSVxU+KFffXra125b0Nf/dMSV9X6DyQeO+y/E+ZiBBMAAAAs0tTU1E2J/Urk5v71yokfqXe/rXdf4tLaWfTMFEclnpVYn3h36h5dIsvvShyaKOscXdct3lyf6+kp70uUqypCq+hgAgAAgOVRrnj4lXQEfW2WdQ5PXJh1vp34apa3JA6osSV1tyS+U9Yp66aTaTLl8xIfrI8/L3GED4y2WdV0AwAAAGBMlJFJ7++7fXL6h45NeU3ilHQcldFHeySu7Ftna60rbptWf2DiKYmv57EPDVj/YfJaJ6YoMbF69eqJTZs2DWzk9u3bZ7yvDU7Zt/en/qvVOw2u79e2v2l7y9/n5W6zDiYAAABYojov0q8kTq1VZyfekJiq5ZmJl6/kG51OqHNSlJhYu3bt1Lp16wauVzoQZrqvDY7f8LFH1JXOpTM3z96Fcesx7fqbNrX8fV7uNj9qHkmyMbEt8cW+uicnLk/cXMsn1friHXXisesSz+57zHF1/RLH9dXvn9hcH/OOOvxvxtcAAACAFipzJ30+nTx3lRulTHwv8S+5+Z56Clxxe2LPvsetqXUz1d+T2DX7xKum1cPIzcH0vjrxWL8ym/0nkyh7l7Le7iVUqdu7Dss7u9dZlOK0OryvJNVpfR1GZZ1X9D1u/RyvAQAAAG1zdP/pcdnn3b3vvl9N9AZtXJI4Kvc/rlwdLstln/dziavLcr1i3GPr6XaXZJ+4jIC6IvHi+vgyYOPiFf1LYCU6mPJd/nSKewdMSlYmFps+wVipP78kQFxZe1lLUpXLLl6eunvrOaeXJ9bX+3Yp69akOX/acw16DRg6I/noOjkAc+ZI+c3zwcSXEjcm/v0wRnwD0A75t7xzihckPtxX/Wf1f/d1WX5u4vdLZXZ9r09xUeKGxN8kTqojncoEQycnLkvcWNb5/7d3N1BSlXeex6uDLyEaxZdMHwLs4I7EXaOr0T7ArtlsRyI26oqzMQR1BRxHZ1dQMzIbGzdnzahkcDdKlBgzRDpABoPE6MIoSoixT467AQElIhDHDuLQLIoioq0THUjv79/+y1zKW9XV9Xbfvp9z/ue59dx7q27drqdv3aeeF9/W3Ki4wa4DPibTgga8LaAhYzA164O+y5dfsce+PCxkUDLLK5XfHZJf6jUqHsisnEHBChV7riRK4gBjMToH1pLvu14JmitoZTdHn0Fbbvd//MGWfGO8ld6YQEu+FoVVqG5Q3gqvdM235FurWOkt+R4r8RpAo1EGgNLuUjyu/9cX+6/On1Dc1IDrBAAgBvS/+h2v+AnmXV5i+9lKZofk2//4lSH52wJd7IBYqnqQb2t5pC8/9iWobvp7jXIHMpu3ZHm/g4LFfZCwrA0wFpdzYC359BkcWZBtrexaA63sOv3G4cOWfErX+K/aQ33bvpZ8toP9mu0t+TrzLfk8P9+S77ESrwE0FGUAKE7/t49W8gXFNC8vNrX0+8pvxHUCAAAg0RVMr9oXIWth5F+IdpcxWFlrQX6n5w8P2b7UawBxEZuWfPVsxWeibPmWxJZ3GTrm2JSBLLVmTeLnKyjpx1+EjZ/xmuKH+hyepnSD4voGlREgcvrcn6TkgUDWv1T8D8UQb3ln5cPc5K0zbB+bZetKxQHFdcpf5flt3iJwkOI+awHo+VbOlnoLEStjl3tlLgAg4RVMNiiZjQswp2CAMcufoQvAUm/yvc8riOyC8a3AwN7jFbPsFzrlvaUY602+pyjm9fMaQOxE3ZKvnq34om7Jl8SWd1k85qjLQJZasybx8xWU9OMvwj5QNo7StfocrtXn9K7CyUkaUUbS/GNDPaW00rOh50If7xeUnO6fw0H+g/HDiisUc7X+28Httc3JPnjxZxWfVvxceZ/x1ff4ODZWkbrOu4naODW3+3MtVd73vXKqb0IhAEA89PsNW//AbRR8+yZ4vJa7fWwAq/RZpsf2j/1lxSTf3H6ROE9hA4+96xeVnFck3eqj4ptb8s2/5Rof22OwN/XON/cu9hpAXNCSD1lHGQA+YN+Puq1yyR8/6BVMjSgjmfixoZ5SWukZ5bkYp/itPo8vlxiL3rqJLtU27yl9yQctzo8t0+VjzeT8R+uJNnC+0rMVlwa6nH5TQQUTAMRIv98u9A/eplosdvEo3NZ+mZte5Hk6lHSE5K9XckpI/p6w1wBihJZ8yDrKAPDBd5ZXdAO8w7oJeUsO+/5iLS62NKDFNxA3k4PTtPtn3T6z9p1/pg9ab108+8YUC+n2WdhNdIx3i3vTZ9gq3H7ALfnS2IovbS3x0vZ+gKyoepBvIAtoyYesowwA/bpWscRnkNvmrbg/1oAW37E2sv3RivbbPuf8Gh8JGsE//xcqbHylnLcwss+0/Qht6R2KP6vnMZTTki+NrfjS1hIvbe8HyAoqmIAy0JIPWUcZAPotIxuVtDS6xTcQMxMUz+jz+qo9yKde+fQDJY/0003UhOVbzwabbfEQb8UU2k0UABAt+2UNAAAAAKp1SbB7nI89lveniucD3UQna/3hPjvcKMXT3npvlOV5ayjrbrfCK2WfVFzs+zMBEADEEC2YAAAAAFRFFUJH+OxvfxHI/p/Kt9nlrIJoe36d6os2K3+Zj1NmLZKmK++AP88MJTYemc1G12Hb+nPdqLAZ5G5T+qxiAX8yAIgXKpgAAAAAVEUVQe/4YNzBvMtLbD9byeyQfBujbGVI/rbATHMAgBiiixwAAAAAAACoYAIAAAAAAEB0aMEEAAAAAACAqlDBBAAAAAAAACqYAAAAAAAAEB1aMAEAAAAAAIAKJgAAAAAAotTU1LRdsUmxUbHe845VrFa86Okxnm/uVnQpnlOcEXieqb69xdRA/pn+/F2+b1Pj3yVQHC2YAAAAAACojS/29vaermjxx+2KJ/R4lKX+2ExQWJ7F1Yp78xVSSm5WjFGMtuV8pZRvc1Vgvzb+aIgTKpgAAAAAAKiPiYpFvmzpRYH8xap4Mmu0PEQVSUOVnqtYrbw3FHttWdHm646ybW0H2zfwXEAsHBL1AQAAAAAAkAJW8fMzVQZZ+reqB5qvtFnpLl//ij325WGKHYF9uz2vVH53SP5B9NrWGsoi19zcnOvs7Aw90J6enqLr4mDmqfs/ktc8ODw/KG7vqSfm57nWx0wFEwAAAAAA1fu8KpN2qpLnj7Rs4y39JrjSWh555VPdeKWWRa6lpaW3tbU1dDurQCi2Lg6mtT/6kTyrXLpjU+kqjO2Xxes9dcb8PNf6mOkiBwAAUCXdMAxSPKt4xB+foFjrA7E+oDjM8w/3x12+fmTgOWZ5/guKcwP5bZ5n6/JjdwAAYsYqlzzdreRhH0PpVe/eZv/PLbV1xrYdEdh9uOeVyh8ekg/EBhVMAAAA1btesTXw+HbFXN1knKjUxtC40vMt3ev5c307u+k4WclkxWd90NbveaXVIC3f44PB2jaX+LYAgBjR/+YjFJ/MLysZr3hesUKRnwnO0uW+bPlTfDa5sVre513pVtm+NrC3D+5tz7PK171l2/rscVMCzwXEAhVMAAAAVdD3fPsV+XzFff7YvvifrXiwyKCu+cFebf04397yl+oG4j3FS1ru8l++LbqUt03xvm3j2wKxwvTsQN/YSk+pLPxa6dOKR/V/+3GlcxTnKP9FpV/yx2alYpv/v/+B4hrLtMG9ldyqWOdxi+flfJv7fJ/fKh7jvCNOGIMJAACgOt9RfF3R98u1HKd4UzcE+0MGYv1w8FZbrxuOfb695dssQrmQfQoHe7Wpq4G4Ts/+euBxfnr2Od690+LGgunZx/jU62MC07Pb9O42Ts0G5a3wmbTy07Ov9Rtza+nHzTViw34IUHJaSP4eJeNC8u0zPr3Ic3Uo6QjJX6/klKoPFqgTKpgAAAAqpJvfC5Ts1pd+uxGOdBTPcmcOKmcWnjDFnq+USl6n0tfK0gw/CToX1touXy6s5V6nVzB9OD270jX67OanZ2/NT89uOygvPz17Z356ds/PT89OBRMAxAgVTAAAAJU7S3GhbnjPU/pxxVGKuxR2w3yIt2IKDsSaH7y129YrPVqxp8SgrqZYfkUzB81bsrzfWXhqNTNP2CxA9XqtLM3wE9NzEfn07OVWtDaykrVR0lZRmrb3A2QFFUwAAAAV0s3zLCWz/MbW7sz/SnmXafknWr7Yx0wqHNTVHv/K1//Cp622/PuV3qn00951yMbwsPGZRtmsdF6xZAOBX8ofDDEU+fTs5Va0NrKStVHSVlGatvcDZAWDfAMAANSedQO6QTfUXT7G0gLPt/Q4z7/Bx6Sxm+LNSpYptihsUNjpyjvgLaBm+KxCNkvdMt8WiBWmZwcAUMEEAABQmxvsTsUF+cFeFaMVJyq+YrPDef7v/PGJvn5bYP/Zij9RnKT4cGwZLa9UfMbXzeaPhbhhenYAgKGLHAAAAIBq2NhKD6uiKX9/cb9Nz67HNsX6MqVXKn1ZMcm3t1ngbNwya8n3ruIKy7TBvbVtfnr2XMj07AsVgxVWAcsA3wAiNbKMcQZnnrr/I+MRbp9zfr0OKdkVTLoAbFfytuKAYr8uAC0+vegDipEKWz/JphZt+uCKc5dfTOxCMk35z/jz2FgE3/CnvU35izz/zMCFxC5E1/tsEwAAAABigOnZAQC16iL3RV1UTrfKJX9sYwk8occ2OOUT/thMUFjeKJ/Z4V7L9AqpmxVjFKNtWXnH+D62zVWB/dr4syFurKJVsUmxUbHe8461qXUVL3ra95m2ila528beUDynOCPwPFN9+xe90jWff6Y/f5fv2/fzIBAXlAEAAAAA9RiDaaKirwWSpxcF8hdbCyRZ49P3DlV6rmK1NX+1lk62rGjzdUfZtt5qaXHguYC4oaIVWUcZAAAAADKs2gomq/j5mSqDNiisVZJpVn3QLl9+xR778jDFjsC+3Z5XKr87JB9IAipakXWUAQAAACBDqh3k+/M2Jakql/5Iy9YN6DfBldbySHl1HzPJK7f6Kriam5tznZ2dods1D/5gkK2BKPZcSdTT05Oq9xOjc5CvaLX0b/Wxn6+UilZkCWUAAAAAyLiqKpiscsnT3bq5ftjHUHrVurdZKybv5rbbN7dtRwR2H+55Fq0F+Z2ePzxk+7DjsBt6i1xLS0tva2vw6f5g3pLluTs2Dewtb78s/LmSyCpWip2brKjTOYi8orWelawmyorJJFaMZvCYIy8DWfqxIYmfr6CkHz8AAABqXMGkL/JHKPmY7hve9uXxilsUKxQ2QPEcT5f7LpY/Q9su9QG993kl1CotfyswsLc9zyyfpvQtxVg9XquYophX6fEC9RKHitZ6VrJGXdGaxIrRrB1zHMpAln5sSOLnKyjpxw8AAIDaj8FkYys9pRuHXyt9WvGovtw/7hVL59hMWEq/5I/NSsU2RZfiB4prLNMqkpTcqljncYvn5Xyb+3yf3yoeq+J4gZqzylXFJ/PLXkH6fKCiNRdS0TrFZ5Mbm69oVWoVreOtotUrW+15Vvm6vopWnz1uSuC5gMhRBgAAAABU1YJJN75WWXRaSP4eJeNC8q17xPQiz9WhpCMk36Z8P4U/FWLMKlof/qDup6883W8VrXpslaXLlF6p9GXFpEBF63leafqu4grL9BZ7+YrWXEhF60LFYK9kpaIVcUIZAAAAAFD1IN9AplHRiqyjDAAAAACotoscAAAAAACZ1tTUNELxpGKLYrPies//psImQtnocV5gn1mKLsULinMD+W2eZ+vaA/knKNZ6/gOKwxr7LoH+0YIJAAAAAPoxsv3Ris7R9jnnc27Tz6annamW3c/4+KwblK72dXOV/+3gxlp3spLJis8qPq34ufI+46vvUZyj6FasU/4K7b9Fy7f7cy1V3ve1bENx3Fv3dwYMAC2YAAAAAACokE3MY5VLvvy2kq2KYSV2mahYqm3fU7zk47PaLLwWXTYEgeJ928a29cl+zlY86PsvUlzEHwxxQwUTAABAfbpGHGu/YNvMup4e4/nmbu/m8JzijMBzTfXtLaYG8s9UbPJ9bN++2SUAAPGif88jlXxOsdazZvj/+o7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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "train_df.iloc[:, 1:37].hist(figsize=(20, 20))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这是一个二分类的问题,并且数据集较大,当特征量并不多,考虑使用全连接神经网络解决。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. 全连接神经网络" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "    全连接神经网络,顾名思义,就是相邻两层之间任意两个节点之间都有连接。全连接神经网络是最为普通的一种模型(比如和CNN相比),由于是全连接,所以会有更多的权重值和连接,因此也意味着占用更多的内存和计算。其网络结构如下图所示:" ] }, { "attachments": { "%E5%85%A8%E8%BF%9E%E6%8E%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%BB%93%E6%9E%84.png": { "image/png": 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} }, "cell_type": "markdown", "metadata": {}, "source": [ "![%E5%85%A8%E8%BF%9E%E6%8E%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%BB%93%E6%9E%84.png](attachment:%E5%85%A8%E8%BF%9E%E6%8E%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%BB%93%E6%9E%84.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "其由输入层,隐藏层和输出层组成。当给定输入后,输入层通过一组线性关系公式传给隐藏层,到达隐藏层后,又通过一组线性关系输出给输出层,最后由输出层输出最终的预测值。其示意图如下:" ] }, { "attachments": { "%E5%85%A8%E8%BF%9E%E6%8E%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E8%BF%90%E7%AE%97%E7%A4%BA%E6%84%8F%E5%9B%BE.jpg": { "image/jpeg": 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} }, "cell_type": "markdown", "metadata": {}, "source": [ "![%E5%85%A8%E8%BF%9E%E6%8E%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E8%BF%90%E7%AE%97%E7%A4%BA%E6%84%8F%E5%9B%BE.jpg](attachment:%E5%85%A8%E8%BF%9E%E6%8E%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E8%BF%90%E7%AE%97%E7%A4%BA%E6%84%8F%E5%9B%BE.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "其中$x$表示前一层传入的输出值,$w$表示系数矩阵,$b$表示偏置项。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "但光靠上面的结构,其输入值和输出值之间仍然保持线性关系,与普通的逻辑回归模型可以证明是等价的。为了增加非线性特性,数据在从隐藏层和输出层输出前,会通过一层激活函数的计算,由此引入非线性特性,增加模型的表达能力。常见的激活函数有:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sigmoid激活函数:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\sigma(x)=\\frac{1}{1+e^{-x}}\n", "$$" ] }, { "attachments": { "sigmoid.jpg": { "image/jpeg": 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} }, "cell_type": "markdown", "metadata": {}, "source": [ "![sigmoid.jpg](attachment:sigmoid.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "tanh激活函数:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\tanh (x)=2 \\sigma(2 x)-1\n", "$$" ] }, { "attachments": { "tanh.jpg": { "image/jpeg": 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} }, "cell_type": "markdown", "metadata": {}, "source": [ "![tanh.jpg](attachment:tanh.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ReLU激活函数:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\operatorname{Re} L U(x)=\\max (0, x)\n", "$$" ] }, { "attachments": { "relu.jpg": { "image/jpeg": 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} }, "cell_type": "markdown", "metadata": {}, "source": [ "![relu.jpg](attachment:relu.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "如果各层之间的系数矩阵和偏置项已知的话,给定输入值,就可以求出输出值来,这个过程叫做前向传播。但实际情况是,在训练前,模型的系数矩阵和偏置项的中的值是随机的,此时需要根据预测结果与真实值的误差大小修正模型的系数矩阵和偏置项,这一过程称为反向传播。首先,我们定义一个损失函数,用于评估预测结果与真实值之间的误差,本次任务中选用的损失函数为Pytorch中提供的交叉熵函数(CrossEntropyLoss),其由log_softmax和nll_loss实现。log_softmax为对数softmax函数,其计算公式为:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\text { log_softmax }=\\log \\left(\\frac{\\exp \\left(x_{i}\\right)}{\\sum_{j} \\exp \\left(x_{j}\\right)}\\right)\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "nll_loss的计算方式就是将上面输出的值与对应的Label中的类别拿出来去掉负号,用计算公式表示为:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\text { nll_loss }(x, \\text { class })=-x[\\text { class }]\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "再计算出了预测值与输出值之间的损失函数,开始反向传播,逐层求出目标函数对各神经元权值的偏导数,构成目标函数对权值向量的梯量,作为修改权值的依据,网络的学习在权值修改过程中完成。误差达到所期望值时,网络学习结束。Pytorch中内置的数据结构Tensor支持自动微分,使得我们不需要手动给出某一项参数的具体的求导公式,给我们带来了极大的便利。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. 模型训练" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "使用Pytorch搭建的全连接神经网络代码如下:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import logging\n", "import pickle\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from torch.utils.data import TensorDataset\n", "from torch.utils.data import DataLoader\n", "from torch.utils.data import random_split\n", "\n", "class Insurance_Model(nn.Module):\n", " def __init__(self, input_dim, hidden_dim, num_classes,\n", " act_func=F.sigmoid):\n", " super().__init__()\n", " # hidden layer\n", " self.linear_layer1 = nn.Linear(input_dim, hidden_dim)\n", " # output layer\n", " self.linear_layer2 = nn.Linear(hidden_dim, num_classes)\n", " # activation function\n", " self.act_func = act_func\n", "\n", " def forward(self, inputs):\n", " outputs = self.linear_layer1(inputs)\n", " outputs = self.act_func(outputs)\n", " outputs = self.linear_layer2(outputs)\n", " return outputs\n", "\n", " @staticmethod\n", " def compute_accuracy(outputs, labels):\n", " _, preds = torch.max(outputs, dim=1)\n", " return torch.tensor(torch.sum(preds == labels).item() / len(preds))\n", "\n", " @staticmethod\n", " def log_epoch_loss_and_acc(prefix, epoch, epoch_loss, epoch_acc, interval=5):\n", " if epoch % interval == 0:\n", " logging.info(f'{prefix}_Epoch [{epoch}], loss: {epoch_loss:.4f},'\n", " f' acc: {epoch_acc:.4f}.')\n", "\n", " def evaluate(self, batch, loss_func, need_acc=False, no_grad=False):\n", " if no_grad:\n", " with torch.no_grad():\n", " inputs, labels = batch\n", " outputs = self(inputs)\n", " loss = loss_func(outputs, labels)\n", " else:\n", " inputs, labels = batch\n", " outputs = self(inputs)\n", " loss = loss_func(outputs, labels)\n", "\n", " if need_acc:\n", " acc = self.compute_accuracy(outputs, labels)\n", " return {'loss': loss, 'acc': acc}\n", " else:\n", " return {'loss': loss}\n", "\n", " def compute_epoch_loss_and_acc(self, dataloader, loss_func):\n", " results = [self.evaluate(batch, loss_func, need_acc=True, no_grad=True)\n", " for batch in dataloader]\n", " batch_losses = [r['loss'] for r in results]\n", " epoch_loss = torch.stack(batch_losses).mean()\n", " batch_accs = [r['acc'] for r in results]\n", " epoch_acc = torch.stack(batch_accs).mean()\n", " return {'epoch_loss': epoch_loss, 'epoch_acc': epoch_acc}\n", "\n", " def epoch_postprocess(self, prefix, data_loader, epoch,\n", " history, loss_func, log_interval):\n", " loss_and_acc = self.compute_epoch_loss_and_acc(data_loader, loss_func)\n", " epoch_loss = loss_and_acc['epoch_loss']\n", " epoch_acc = loss_and_acc['epoch_acc']\n", " history.append({'epoch_loss': epoch_loss,\n", " 'epoch_acc': epoch_acc})\n", " self.log_epoch_loss_and_acc(prefix, epoch,\n", " epoch_loss,\n", " epoch_acc,\n", " log_interval)\n", "\n", " def train(self, train_loader, val_loader, num_epochs, lr,\n", " loss_func=F.cross_entropy, opt_func=torch.optim.SGD,\n", " log_interval=5):\n", " optimizer = opt_func(self.parameters(), lr)\n", " self.history_train = [] # history of train set\n", " self.history_val = [] # history of validation set\n", "\n", " # initial loss and accuracy of training dataset\n", " self.epoch_postprocess('Train', train_loader, 0,\n", " self.history_train, loss_func, log_interval)\n", "\n", " # initial loss and accuracy of validation dataset\n", " self.epoch_postprocess('Val', val_loader, 0,\n", " self.history_val, loss_func, log_interval)\n", "\n", " # iteration\n", " for epoch in range(num_epochs):\n", " for batch in train_loader:\n", " loss = self.evaluate(batch, loss_func, need_acc=False)['loss']\n", " loss.backward()\n", " optimizer.step()\n", " optimizer.zero_grad()\n", "\n", " # training dataset loss and accuracy\n", " self.epoch_postprocess('Train', train_loader, epoch+1,\n", " self.history_train, loss_func, log_interval)\n", "\n", " # validation dataset loss and accuracy\n", " self.epoch_postprocess('Val', val_loader, epoch+1,\n", " self.history_val, loss_func, log_interval)\n", "\n", " def predict(self, inputs):\n", " outputs = self(inputs)\n", " _, preds = torch.max(outputs, dim=1)\n", " return [preds[i].item() for i in range(len(preds))]\n", "\n", " def save_model(self, save_file):\n", " torch.save(self.state_dict(), save_file)\n", " pickle.dump(self.history_train, open('insurance_history_train.pkl', 'wb'))\n", " pickle.dump(self.history_val, open('insurance_history_val.pkl', 'wb'))\n", "\n", "\n", " def recover_model(self, save_file):\n", " self.load_state_dict(torch.load(save_file))\n", " self.history_train = pickle.load(open('insurance_history_train.pkl', 'rb'))\n", " self.history_val = pickle.load(open('insurance_history_val.pkl', 'rb'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "分割训练集,按5:1的比例划分训练集和验证集。" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# delete CaseId (because it has no meaning)\n", "train_df.drop('CaseId', axis=1, inplace=True)\n", "\n", "# convert pandas dataframe to numpy array\n", "train_data = train_df.to_numpy()\n", "\n", "# convert numpy array to tensor\n", "inputs = torch.from_numpy(train_data[:, :36]).type(torch.float)\n", "labels = torch.from_numpy(train_data[:, 36]).type(torch.long)\n", "\n", "dataset = TensorDataset(inputs, labels)\n", "train_ds, val_ds = random_split(dataset, [166666, 33334])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "使用gpu加速计算,Pytorch中使用gpu计算十分简单,只需要将训练数据和模型参数转移到显存中即可(前提是配置好cuda驱动)。" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def to_device(data, device):\n", " \"\"\"Move tensor(s) to chosen device\"\"\"\n", " if isinstance(data, (list,tuple)):\n", " return [to_device(x, device) for x in data]\n", " return data.to(device, non_blocking=True)\n", "\n", "class DeviceDataLoader():\n", " \"\"\"Wrap a dataloader to move data to a device (default: cpu)\"\"\"\n", " def __init__(self, dl, device):\n", " self.dl = dl\n", " self.device = device\n", "\n", " def __iter__(self):\n", " \"\"\"Yield a batch of data after moving it to device\"\"\"\n", " for b in self.dl:\n", " yield to_device(b, self.device)\n", "\n", " def __len__(self):\n", " \"\"\"Number of batches\"\"\"\n", " return len(self.dl)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "logging.basicConfig(format='%(asctime)s %(levelname)s:%(message)s', \\\n", " level=logging.INFO, datefmt='%m/%d/%Y %I:%M:%S %p')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "选用Simoid作为激活函数,学习速率选为0.01,迭代步数为100时:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "03/07/2022 10:07:18 AM INFO:Initializing NN model.\n", "03/07/2022 10:07:20 AM INFO:Start training...\n", "c:\\users\\sj2050\\miniconda3\\lib\\site-packages\\torch\\nn\\functional.py:1806: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n", " warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n", "03/07/2022 10:07:22 AM INFO:Train_Epoch [0], loss: 1.0106, acc: 0.1578.\n", "03/07/2022 10:07:22 AM INFO:Val_Epoch [0], loss: 1.0096, acc: 0.1587.\n", "03/07/2022 10:08:01 AM INFO:Train_Epoch [10], loss: 0.2757, acc: 0.8635.\n", "03/07/2022 10:08:01 AM INFO:Val_Epoch [10], loss: 0.2757, acc: 0.8634.\n", "03/07/2022 10:08:37 AM INFO:Train_Epoch [20], loss: 0.2434, acc: 0.8928.\n", "03/07/2022 10:08:37 AM INFO:Val_Epoch [20], loss: 0.2435, acc: 0.8927.\n", "03/07/2022 10:09:14 AM INFO:Train_Epoch [30], loss: 0.2261, acc: 0.9054.\n", "03/07/2022 10:09:15 AM INFO:Val_Epoch [30], loss: 0.2264, acc: 0.9049.\n", "03/07/2022 10:09:52 AM INFO:Train_Epoch [40], loss: 0.2175, acc: 0.9079.\n", "03/07/2022 10:09:52 AM INFO:Val_Epoch [40], loss: 0.2177, acc: 0.9075.\n", "03/07/2022 10:10:30 AM INFO:Train_Epoch [50], loss: 0.2168, acc: 0.9067.\n", "03/07/2022 10:10:31 AM INFO:Val_Epoch [50], loss: 0.2170, acc: 0.9062.\n", "03/07/2022 10:11:08 AM INFO:Train_Epoch [60], loss: 0.2263, acc: 0.9001.\n", "03/07/2022 10:11:09 AM INFO:Val_Epoch [60], loss: 0.2265, acc: 0.8996.\n", "03/07/2022 10:11:46 AM INFO:Train_Epoch [70], loss: 0.2059, acc: 0.9108.\n", "03/07/2022 10:11:47 AM INFO:Val_Epoch [70], loss: 0.2059, acc: 0.9107.\n", "03/07/2022 10:12:25 AM INFO:Train_Epoch [80], loss: 0.2027, acc: 0.9124.\n", "03/07/2022 10:12:25 AM INFO:Val_Epoch [80], loss: 0.2023, acc: 0.9122.\n", "03/07/2022 10:13:02 AM INFO:Train_Epoch [90], loss: 0.2006, acc: 0.9120.\n", "03/07/2022 10:13:03 AM INFO:Val_Epoch [90], loss: 0.1999, acc: 0.9121.\n", "03/07/2022 10:13:41 AM INFO:Train_Epoch [100], loss: 0.2240, acc: 0.8994.\n", "03/07/2022 10:13:42 AM INFO:Val_Epoch [100], loss: 0.2236, acc: 0.8997.\n", "03/07/2022 10:13:42 AM INFO:Training finished.\n", "03/07/2022 10:13:42 AM INFO:Save model.\n" ] } ], "source": [ "x_dim = 36 # input dimension\n", "y_dim = 2 # label dimension\n", "hidden_dim = 24 # hidden layer dimension\n", "act_func = F.sigmoid # activation function\n", "batch_size = 128\n", "num_epochs = 100\n", "learning_rate = 0.01\n", "device = torch.device('cuda')\n", "\n", "train_loader = DataLoader(train_ds, batch_size, shuffle=True)\n", "val_loader = DataLoader(val_ds, batch_size)\n", "# move dataloader to gpu\n", "train_loader = DeviceDataLoader(train_loader, device)\n", "val_loader = DeviceDataLoader(val_loader, device)\n", "\n", "# initialize linear regression model\n", "logging.info(\"Initializing NN model.\")\n", "insurance_model = Insurance_Model(x_dim, hidden_dim, y_dim, act_func)\n", "# move model parameters to gpu\n", "to_device(insurance_model, device)\n", "logging.info(\"Start training...\")\n", "insurance_model.train(train_loader, val_loader, num_epochs,\n", " learning_rate, log_interval=10, opt_func=torch.optim.SGD)\n", "logging.info(\"Training finished.\")\n", "\n", "logging.info(\"Save model.\")\n", "insurance_model.save_model('report01-insurance_model.pth')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "训练过程中的损失函数值和准确率变化:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "history_train = pickle.load(open('insurance_history_train.pkl', 'rb'))\n", "history_val = pickle.load(open('insurance_history_val.pkl', 'rb'))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Loss vs. No. of epochs')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "train_losses = [float(x['epoch_loss']) for x in history_train]\n", "val_losses = [float(x['epoch_loss']) for x in history_val]\n", "plt.plot(train_losses, '-x', label='train')\n", "plt.plot(val_losses, '-x', label='val')\n", "plt.xlabel('epoch')\n", "plt.ylabel('loss')\n", "plt.legend()\n", "plt.title('Loss vs. No. of epochs')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Accuracy vs. No. of epochs')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "train_accs = [float(x['epoch_acc']) for x in history_train]\n", "val_accs = [float(x['epoch_acc']) for x in history_val]\n", "plt.plot(train_accs, '-x', label='train')\n", "plt.plot(val_accs, '-x', label='val')\n", "plt.xlabel('epoch')\n", "plt.ylabel('accuracy')\n", "plt.legend()\n", "plt.title('Accuracy vs. No. of epochs')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "选用ReLU作为激活函数,学习速率选为0.01,迭代步数为100时:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "03/07/2022 10:43:29 AM INFO:Initializing NN model.\n", "03/07/2022 10:43:29 AM INFO:Start training...\n", "03/07/2022 10:43:32 AM INFO:Train_Epoch [0], loss: 0.9338, acc: 0.1584.\n", "03/07/2022 10:43:32 AM INFO:Val_Epoch [0], loss: 0.9337, acc: 0.1592.\n", "03/07/2022 10:44:25 AM INFO:Train_Epoch [10], loss: 0.2281, acc: 0.9040.\n", "03/07/2022 10:44:26 AM INFO:Val_Epoch [10], loss: 0.2285, acc: 0.9018.\n", "03/07/2022 10:45:19 AM INFO:Train_Epoch [20], loss: 0.4112, acc: 0.8306.\n", "03/07/2022 10:45:19 AM INFO:Val_Epoch [20], loss: 0.4156, acc: 0.8270.\n", "03/07/2022 10:46:11 AM INFO:Train_Epoch [30], loss: 0.2760, acc: 0.8954.\n", "03/07/2022 10:46:11 AM INFO:Val_Epoch [30], loss: 0.2749, acc: 0.8941.\n", "03/07/2022 10:47:01 AM INFO:Train_Epoch [40], loss: 0.1933, acc: 0.9181.\n", "03/07/2022 10:47:02 AM INFO:Val_Epoch [40], loss: 0.1944, acc: 0.9163.\n", "03/07/2022 10:47:52 AM INFO:Train_Epoch [50], loss: 0.2034, acc: 0.9143.\n", "03/07/2022 10:47:52 AM INFO:Val_Epoch [50], loss: 0.2034, acc: 0.9133.\n", "03/07/2022 10:48:44 AM INFO:Train_Epoch [60], loss: 0.2081, acc: 0.9059.\n", "03/07/2022 10:48:44 AM INFO:Val_Epoch [60], loss: 0.2101, acc: 0.9029.\n", "03/07/2022 10:49:34 AM INFO:Train_Epoch [70], loss: 0.2219, acc: 0.9050.\n", "03/07/2022 10:49:34 AM INFO:Val_Epoch [70], loss: 0.2222, acc: 0.9034.\n", "03/07/2022 10:50:25 AM INFO:Train_Epoch [80], loss: 0.2025, acc: 0.9154.\n", "03/07/2022 10:50:25 AM INFO:Val_Epoch [80], loss: 0.2023, acc: 0.9141.\n", "03/07/2022 10:51:18 AM INFO:Train_Epoch [90], loss: 0.1857, acc: 0.9202.\n", "03/07/2022 10:51:18 AM INFO:Val_Epoch [90], loss: 0.1867, acc: 0.9181.\n", "03/07/2022 10:52:09 AM INFO:Train_Epoch [100], loss: 0.2087, acc: 0.9052.\n", "03/07/2022 10:52:09 AM INFO:Val_Epoch [100], loss: 0.2113, acc: 0.9026.\n", "03/07/2022 10:52:09 AM INFO:Training finished.\n", "03/07/2022 10:52:09 AM INFO:Save model.\n" ] } ], "source": [ "x_dim = 36 # input dimension\n", "y_dim = 2 # label dimension\n", "hidden_dim = 24 # hidden layer dimension\n", "act_func = F.relu # activation function\n", "batch_size = 64\n", "num_epochs = 100\n", "learning_rate = 0.01\n", "device = torch.device('cuda')\n", "\n", "train_loader = DataLoader(train_ds, batch_size, shuffle=True)\n", "val_loader = DataLoader(val_ds, batch_size)\n", "# move dataloader to gpu\n", "train_loader = DeviceDataLoader(train_loader, device)\n", "val_loader = DeviceDataLoader(val_loader, device)\n", "\n", "# initialize linear regression model\n", "logging.info(\"Initializing NN model.\")\n", "insurance_model = Insurance_Model(x_dim, hidden_dim, y_dim, act_func)\n", "# move model parameters to gpu\n", "to_device(insurance_model, device)\n", "logging.info(\"Start training...\")\n", "insurance_model.train(train_loader, val_loader, num_epochs,\n", " learning_rate, log_interval=10, opt_func=torch.optim.SGD)\n", "logging.info(\"Training finished.\")\n", "\n", "logging.info(\"Save model.\")\n", "insurance_model.save_model('report01-insurance_model.pth')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "训练过程中的损失函数值和准确率变化:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "history_train = pickle.load(open('insurance_history_train.pkl', 'rb'))\n", "history_val = pickle.load(open('insurance_history_val.pkl', 'rb'))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Loss vs. No. of epochs')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "train_losses = [float(x['epoch_loss']) for x in history_train]\n", "val_losses = [float(x['epoch_loss']) for x in history_val]\n", "plt.plot(train_losses, '-x', label='train')\n", "plt.plot(val_losses, '-x', label='val')\n", "plt.xlabel('epoch')\n", "plt.ylabel('loss')\n", "plt.legend()\n", "plt.title('Loss vs. No. of epochs')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Accuracy vs. No. of epochs')" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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XCjMYQ0dgNM59/a8xmlG9JxajhBtawBkPvbwe7X69G7WzJNLikN7Uc+yPnnW+fW8M3XiUvxlE9Hudu6n3Q+9RauZJSnx7IHWnjeq4OZG/fAQq2EheBiPJBjnJ3pTq5+9UTrTlFdfRz5++RA9lf0TZFKqup71w5o07X0PTP32bbk95BWY2GG4+RxnspfaudJkjwXPsftt6kLpMa0uVLDmUY9gpxOL6/HYutnncVoNqOdLokC2aDEcORREcBa6R/9V5hroOvJNOfXoDdbXyd3v4Fbd14FRPU3Uji45ZqtIpGPg6lKGOLzvO3PBoOpjtUPeR46+f0KfffE+POFzX9QGKoJooioQijyctlehIZGeKO7xQzZtouYla3/l6gYKOPzX2cu0I9GcJ34X4k3+TsK3XfObz5/Y+gSL1V4ruxDLJ5alGUJTB9zbgbiPMRbVpjc6UXg7W7UtvH2yrbk7a/QfduOsZ+piG0HV1syn2wFxVFV3V5X06tXMpXZv+Ka23taUWjm34HQutrX0jtU6bdZaB8DayLbtdR/PGj6H+hz9WpbcolOr4xthtqasuNG/DXP/X++kkheASdMWxM6kyVccyXJLhK8DqdePzJfGjswfNbfgUddz1Id1rnU37qDbFUprHyG22NKWmT6+gIDufWqK5U9+l3ttfoJXVr6U2WQs9257z/RQauHGkWuYYtnkE5aF49XEs13bY4PDNdoSqUqQli742+lDMsP+pi373qp8p6ufhtMDZnvqEbC/gIO47OZKqhNrpldzXVcnv++Dr6NZT09X+bKt8OTXI/pNyraFUzenaXzcOHNs83PQHqnWkBpkr1H6nGVVVKZA5bFSnaJQUf4l6UIV2VKkUGV0aey91T+ZL1aCFcaOoy75JqhTN+V+LEnId6wmKdSZTSlB9ym72V9qdsIL+QstpCbWlyyiRskPrUp1TOws1sCvw213ve6tAGm9z3MTxNCqVPxpGtBzLdNPLzP7gMboG1wvvCx/EN6s/Q/8Y0plOfzWM/nvyWmoZlEqdnJuovpU/ZIbjQfUoGkd4f2Qvapb2C0qzri9COnC0cjG1NWoINT84k3KsYVTDmYF9aEiV8w7R1phbqXnKDKxfn7rSZlpGbagV7SanNRjLHaXp1mvoUPcXKCzY6ir5XnaSliYepjtOfOY6TzCOf171KXXsPYT2HM6iSuNaUy2+5nANnTaCaNvVn9CSFctp1MkPKc8SQulVmlPt7O1wZA4KQ8namzw4qWcc91FTmODhtjnqvPGhZMcxq/YDlJ6dS7dnT8EVnqeOcSq2VANXXCiml8X8ja64/21lPGdOfpteow/oSN3eVPnQarWtdTWvpfYZs/F7FloWcRN1TfuaQoNsZG/Sl6xbf1DX6SH8WjTOd74lmOwoEOQs/RC1W4MWVL2B+hz7Ti0f0nEYOVd9RFbDQUdRO6gBVzXL2ZVO2arQLTRP3WeWNjdTzpbZat2VETdQxyMzVUHrCZpCyfb6FG07RiPzHvXUhJk9M19WTt63dsw1jQaDny7fjkB/75U/M4gAMCXrT+QNxfa2eC3Dn8qbhbTP+fulGL8H43eVJ0fgMbx9USXHBVafY4n4W305SoibvqJepxbCwHIJJFddgOlUjYKNHFrc4V26rm1dOjX1LnVztqiSQ3HZCdTZus3zpXYu7eTgRgy38HfOizYQviUCzlPcrw+oi7MJDq3buBy21aEgxyl1AyyLuQcX/zue30p6/jJqbOyhVFuMMgMxjhTaixvegQu7Ye422hTSgY7mWKgXzNoJI4Qqo8TkS5olgioZx+lwUBw1yEui1fYO1PFfv9FvC2ZT+yUPoHrLXx0kWlz7Tur58DjatT+Fqk3qAoeTTYtr3UQdjvzkumG6PUj5KybQ3pwq1MiaSjOd3Wlg6GZy5J4kG26ihyxP0y0330GJ3z5HI52ub6vzzdLyrjddDgJV9BhU0fNQvgjBcTdg0OzqU8u4yBqNos7DXqblU56nLolv09pq/ajJsWX0JzWmnrQBJbdgWtDhfbpuyG20ZfG31GTB/apUn2aPUutH5vOXFl0GJhGur4FyWhbaYDRCNT5RORpEd9XZc9jCKLjHIzAK49SNvTzyJro8/XuVVw57ZdTtSRG7f6T/0p3U467n6PjGmdR7I8Ie2B47iC7GZrifUDIsVhVC8Q7vzF2VQF1+7k/VLdmUC4OabQlXNTCmya/34LzZKHzgC2TM+acqyT/vvBfnbg1dbV2vrgeE4hHzC0UNakCBPBxNmE99141SIQzvY+E+Xgkh7ah1zgZa2eRxMrqNpCWTn1NGKqUGQoEZq9TvXHN1X2qx4G/K4K20tKZ5eZepku8qGLYrD3yG4kI1cka0oOgjy1XYaX//j1Cjm0uDMibT9haPUuWUxVQ3awOdxrVvw9XINTi+h/aiFlzp6Baqv/ZVWhbWm9qdWq4KNsdCY+BEd6n9YnOVb7Gr+2ZXrd7UKn2OpwbIzsLAPgd3H6XOiZGfqxxDlqUyJfebQLvSsqn/xsco1DhFWzu/SrGrXkRIz0Lzat9DvVI/Uff14uh7qV/aZ3AQTuRnIrUIzSTLT6PUeecCRNDd31PCgSxlB3jdb6veSTce/9KzPGew6fzh2BuHOgfuvCkn0O8ltJ808Kw7t81bZD20ia5JHUep9np0Ks9BDYd9SI4Zw+Hcx1LP/n/1hPpy4eQfOPkAZfE1lb7K1Y5w2+fFCiGdyxEEsvsoF52TsOFdUK7+UPgQn2X4I+m/6fGFhcwvFbik726Yco+zeJxL3cuqDkRD2d10Yu4rZDfyKMjIp/ar/k69Ty/EDRqGamYuJQa3oERUYbnEXQWXeOKa3+iJH1A9Pt2InrF/SX899Y1yAqmW2qo0szuoMR20xygnsBvVdI5ZJ9QeRNkwwhxCSIP5bJb8tcoHN/5xCYBLAhknTtOGlb9jG9kqnppii8UWKymnEeo4RmHGadeBTXGty6gagXM/th1JlRxHqaojQy1fIy+VInP30dyIYVTv9Fbqat9O1POfZAsOoyWOVmrdNUZz/H5lWl21H9VyHqGNjUdSg7GraRfyfHn+OtryYmfqvmS4qinwTcrqeugrWrfoRzr06TBVM9lY92ainBPKCfANkxDajva3fZTirQdptfUyus66jJLbPEzpdboro/yu5b+05dtX6EHHV8rIMXfQL55jMHnxVgpGlZ6r9wfhnLjkz0ZiHcIs7AR4fzm0w8YsPbwhTaQbqAdCDymhTclqD1KhJP4dpy2YTqMkuim4HYXkZ1Fo/jFagdIjn4vDtihqxrFf/DBvq4t1O+WjxJwRVEftq8MaQsF3zYDhbEu5uOND0OiYE9udlkXf7SrdtX6Qdlnq0d6OY5VT4DaWbxNQOwkKU/m4gjbRPKMz1YChX1z3PlXY6LX9JbWPc/48QJE/3wNzmk0LcN8G45j8UeMGVyx8xVRcXXZKjbqKqMsDZPR8SoWIXrT9Dw2Q61EEqKSOVy4c/NY+k87KA++7JSiEDtrqeo7FrB+neY4XN7LzkKePzn9brcfrHz3l9PzOriOnyXbHdHLCgXWFaePre8XpehSfMgthHSvVDDEouv9ohD3CaStCgkd3rKB2R+ZQhrUGNbv5OYp58Hs6ZquuSv025P0UjDo7Aab62g/UdhrH1lYOnn8v/coXaEHr15VD5PPM98fv7d+hto9Mo+19PlE1D8awBlHwndM958Rqcy2/AqV8PnadsxfSFntLFcrJSk9VhjvkzmnUJme9GudwLI+HoK2HpzkEuPU0F2PCKDM4Csc9WDkBTneve3utXZ7l929aQu/9tpOsQWioj+9FucHVaa7DZXdnoRiyLGpogXUHRxyk61pFqv09FlQLNeUU2hfeUhn4EbZZrus9KZ1mn2hCH+YOoI9tr9PLBx+gd6zvFNsJlGWvoRj9tSg3XCvo4rPMRuivOnx0A1QFXqsWHAd/GL3UQj7uhs/lu9eRkZ5I9XdOQinNoJ1dXqG1r/ala3LXYBoGzuKpzODSyKPE0DYUcXqPMqotk6eqquXaiMHULu0netyGBqYsCNfoSZS82OD/it3vRFsKLL+6an/qmDVP3XxV4juQc94ClEbzKRyllt19J53pWYESwJjJJ+ivxm90pwX5RB52hLWjxic3eta1JH+lYtXMn/H3Uxu9T3xTc1joSBiq/Cc3qZKPEVpNbZ/H84Kqeoz0vOONaHv+EPq77QvaUakTdcheq0qBbcIr0crabdRvJSzvQK2eWkb7XulArR07KBMlrqrGCVrR5O9U89hWanZ4DrX+7R5lwHaEt6P41DmU0+QaCrniK0rEjcRhtRiE1fgGuDxrNe2uNlYZgMz2D5Hz0CI67bDSA5ap2AvkLxg9jYJqkyM7nd6zvUPvzQ2j4VkfIjxhJeuVj1PksgmqND43chh1Tv/B08jLoaluOLczf5hGj2eMpb2tx9KhjBMUc2U/GofQ0tz56dQi9Qt1U/IRs+iaXuWWfSghrLoqHa+u1p+aHfsD84NpM8IkLVOmwVlk0d5ql1NN9Hza4WUUuBFycMo6oohI2l3XtS1PQ3PrbtT9109oOHqsBN32Fe3/Yx7l1riJ+iWMp9wc3IJp21SN07r5W3Wev/h6FQ207UBIrBP1DNtJJ/IqU1hmIu2/GjWPldOpGmL+jvauMtOK+vfDUP2OavcKmu24nK4KTSSK7UVByevovQVJCCeMpnguVfrkIYqNCEqafCyWJHb1HC83CUs7UJu145XBiedlBz3p2Rfez2XWvqjuD6Lh9CMi41XoKtufanaONZyCh06hFdjHiFrdqFPaQvpP5vXU05pOO2Juoe1TnlPHJfSOLyln8g2qRpcQewd11aFXd0hR3Z9cwgZsYL8/HEVXwokeVGG8LOXAarZGbzC7y5mn26pRZUdWwXOCnlq5k1EIyU5Tv5uVuJJicn+ngzXbFegEUP9RL4Oqj0HreBwDDPn+YydVWFuYQhtjXp5rHONwrdlvm0rLnC1Ry3pB3Ufb61xLAzKX00gYdj4frd2xfr0uVqV9tRqS7df7aN/WVdSgz7VUE3GUcdOG0ejPMmmAZSU9ZluuavItrPtpogPtCPj9K0pu/spd99EnoA9g/IdjyJ8WTIFcdXwvMH8EBiyqV6/eRdmwd1e3xpcPpJUr0bCV+JYrzKKroO1WPK6WTQxrQ7VP76btMTdSq5TpynBsqXolDPj8AkaYDUlI+1tobtpfqO/6R5UhdN+ch2Ovob66it4SjUPu5Z2oyrpLYDuO9lYX8Kn136oQQ0TTTpRidfXKiccN+ebnN6gyEudtD5fSMrNppbVPgXWdx9LQAwP5Dq6kLthwNHJu7/o6dV3xEO3Diqr6CjjdPc43PJdq2EhXWjaVHg1eQZZeL1HImtm0tzlKgeiNMSfqVep2/W3KQHB+LLgRYp9cRQnv46I8vkQZzW53/htnL59S3sCxzdmpGu0iERd3Nyq7b5gVbKg73epJ4xshoWZL9bsxvcZSrUUvo9SHHh82VPNv/4oOzvmIWp6cRT+F3kp9D39O9WBUUhtcT/sSdlBLr9J4Svv+Z92oXOoibcx4OwwbtzY/vVbA6LiPBTup5nBSfE648d7tLI2wGp7x451Ho9ZwdkO+943t3pY7vWUrOAnUNni8q7skB6Oa+d0T1AvOJinqHbq8xyCaN/Ut+tv252mfM5K6BSchr5PpwJJp1CPpa1pbsw0dc/6IK8BGNS+7xhMrHheyg7ajbWbgodn0Vv4w6tbjWboCjZps5OdsiiZqdNtZeXDni4/FVezAfBqq1T75pLnXYSPn2u4fKNr9k8KWT6SfT3ema22r0FtqoDJSVRtlUeS8T3A/GfRY5uuUYwmi2vvnUK4ObyUezEBJMYTWx96la75XFTDO3uNuA+vrwLydeVGGen/1LmjLW0eRHT+nrVUbU4M971NSfTY35+eELlB4rl3ehk4v7Ni4rzU+RukogDwa/JO6j5qhlx2qQQXPhw/RLboSSop0cs9aTF2rfmNLx5fog6WjlAPghm2uqVu7PUT3rJyknIq7y/XFIJBtBOgvRc/h9/+ip1WrF6ZfLWL5yhhsw/zY0mojULH2eQ+i4cqOhr9MFbPkxqWE4Mtg0PJQ2t1Ca8O6U8NTmzx9od1tBFvhsp01G5/Vu8Rdennf+l86YIlCLH0nvWUZRi3rhFHzmJqqi9uSoK4U2e12tXxh3RU3/zaVWi1+iP4c+D217cJNJ4RSWzp1mNJC1Sxm0pUUcddnBdoM3OuS00k5L9SmFZE3U6+Rrptuw6pF1G72YNrWcxw173PnuQ/KErQrxHQoWO3EjUdsLHo8dtbx4xtve+zN6mZ23zSOrAOU9kE/ispLLrQh9FwsS0qj4CmDVc3J3YOiauoy9G2/mw6hOx93fzSCq5Iz7ySO+1XquHNp3J2/i/nAVlHdbQs7ZyVl5tef0uDNj9GncS9TRLuB1PjHIVTfckidZ/f+p/65kG7ceB99WPMp6p/+BQXXjKXkQV/R4l+/ozGZr7rCBLz/COvkLXqT5rR4lQbDaRd13i4UrmUN3MqG+fMzJV8DNcg611BDLvnqxk5+dqPJr8NVzxxuHE7q/+l5e5n59pw61zW5F878eMeHzvlsy+6fXqf4ta/Q4usWU9aOJTRox78oe9ivVKmhb3DiIlOM+0gBO5z5Qn1aH9aFrvonN52iF827z9LfMjhYQqpQtbPfJ6591d2DPee5nDcW23Vj8dW6pM+Nxbdje5u9lmFLdhRpToy/jHEHxp8tLUfA+578fHOKo4OUGNScIvJSYNRu8YRtNscOpbb7J9PGJqNUadeffv7eN0lJb86Dieso6suraFm7N+iK6x9QJb/pkycgfvYfSq3VlSJO7DirZ4E3yS+2Rs+PetTlqdlqevH3E6nnxn9Q2p0LKLJxoddBsTnXzcwU5iDOh6eEyw1hXe6jPC756P2MXvNfit/qcmwcw3eXAi919vzwItVYP56WG63oCMIed9jmI5SIgEl0B6qUsV3t/73xR6hJ0mfKEbaw7qN1LcfShG0h9FrUQoQQnvDf2ATAyHmu915jUAjhkm+HAkZqw/h7qN2h72h91M3U/sFJF/UJb3/ITdlIwf/rSd/Ve4bC0v+kq07OpdD/gzmyBV3U7VwMEt/sT/nHDlGzf2+gJWgbqDa5P7Wx7SFrfA/KT15PDzvGnLnnS3Cez+UIAhYawgbzseFRGJ0LcWvOJ+wEkPYCxtdgfCaG3Jn+VaQZOjTk6mdYSiz74ln0Xz5ISbZG1Ch3+1lhnsotrqJdED9pySGRAhepV3XRu5roXT1036BB0W1d8WPGK70oIuKaqmFe+i41TN80n96wjVdBsxPtR1B0TOQ5q5lZYbFUM/tML1xHepIa1optXswjVPxqs/dDZBz35Sq/99Om54L307s3RFD8lZ79vOKml+jEK1NVHJgdNP92RaDBZb0o/8/3qZ9jlXoAKRe10+BgtCn95V9qPu//KkTiq9pyqaZzn0r7eutJGhf8EQX1K6TB0I/r64LxMj4FrncNnz++3lUnhUPzVa3Q3fGBr4HCjH2RoagLJDga4TRrNQpPWUr18ndTatXWFF8OnQCTjza4xsfWoKvtUdq5ZAYNt+6iQ3HXUtTdU8muw2Heob5LpbGYnQEXSWf7pHlK/Bj/BgNWqZOw+EfqsusDSrdUp/S4v6D7YGiBWHuBUr8e9+tCLcxDF/Ok2UMrI181KChrj+dm21j3BroMtZM6DdsS1W3iudkKI7dqA4o/vo5O5eRTWIhd/c4RS02qhd+9WBR1M/vG/88XV/XmLKOiY9jKqKyYgy6vTvTquccTU64INQLeR9vA18jy8+N03AijIHiD4NunnilEYP+7Y/8dTV8hmjNGPfPwf/bPVdtBwA2+PxRxvXPvGncNsbgFgovJhD92U/cqHahT5lo0Mh+nvXHXlehhrNKgcnwnCkJHlX3b1lL7zPmqvbJm39Fn3QuBoKwbi0sV7ypp3qqPVbe7HTF/JSvCNxz64VK/AYPluVCLKPWXBkeDY6jKqWTPzZaxfoTqvlY1quF5nYstshGFH8ihxP27qUnjJlT11H46GhJDtUoh3xdU2juvURmvjcrVZWJUAsXyGoPoBM2g/pblNNHw6RGiz/NKtJ3E0RsUh/aDQPQaudgUt6E1UHDX68VZ4dTGckxNZ0R2KpOXuvlDVHO0W8wnOrZrDTp9HKdMeyRVj7u8VGp7pnoNtaen0O9fU1z2n7SbYqlFyteeuH9R1day4GSlehSZd0C1YzCVj++ig0FoR7e6+kyfiyrRzdTwyP5tlI9SdJ38A3SqCj/EfWlSmFFxv+PoUke1i0ydQleFuJ7huCf4N8/zEr7LRIXkFblMeYPvI18nXRb3V4M2V9LQ4CVqnHtbfbp4hwo/cnp54+MEJwoElYj2LqcrjA10rMFfaNnuo6XySmpT1QjcpZJ49NetZDmtngDe1798Njoa1etT1NHZdDgjkyJrVKc6Ofsos0Zrv9aNrO9qCzh5MJFSDx9BKTKTDtfUNYlLkNKMKZc252oXcbf/+LOMUAQqpDKZnF8MoXSE1d6wf1h+wmo+DDg2XXUbHmhZqh6sPFrvL6VWezFVjcBtQHaGup6a3RJ9fbl0AkxIHVf88tC+HXTwKPe5PkzOCFcj8vmoXLuhKv0YR1GL2Ot6CC4s2r91hdKF20UKPCXqjgVze0kxlhGKZhl6ZP2Efin8Lq4pjr6qy2t5pAFqKU1sqcoJZFqq0oSFpVd7MZ0j4J4LjU9zvYCo2UHXqyTKI9VjXOGdrJQddCApQb2oKzy6hX8r2+yUZqtDYcf30okDO3SPIT/XFUoXbhcprOePd3uJP8sI5+ySfE3IxvIfVovvSdkdHlCjyY4a9Jbt/Yv+KomiMJUjcPd131Szv5re1/MdV5tBOXQGkfVc4Z3ctJ2Utd9Vqo9s2Mbv9bPC4qjG6WTKT3fFF6vHNrn4mRSEck6BsFqfp9WQpzm9PJJU/2b1YGtr695Srb2YyhG4Gx2d1VwPL7fsUX4bHYOqRKqXXdky95Dz8Db1DqHqsf5fFNyFNMZIJQvCQxnoImsJrRbA3ApC+eRSCqst40+3/rCAbCFVSr32YqrGYs9nALctUkObLaj8NjqiE3F6UF2qlL2fck4fUW/DjApyfQTEH+wRjajqgVNU72QCHQ2PQR9qQTAhF+G5ntKiLDsFmKpG4MHIV6+ytfKjnOWYE5XiqFZuCkWc3kuZ4QVeY3ZeKtd1NQ43tabQ6SoNApE9QRAqSO3FVDUCD04HOdCrprzvvKMawjsZf5ATTiuxZq9irevuQqqodel2HRUE09Cj7Gov5btIHCgceeqDF+WdoMiG6lXW/DWpkKjmxXqCelfSVvUpQiY8qonnQzuCIAi+lH9rGAgM1AjUlzTLL/w0YZq9rme6VoO2qtHIn6cM+UnpugtGUxb6IjOnso6q3lHuJ6gFQRDI7I7A4sxXoaHyDL8jZfaKTZ7pnc5o1YOA08+H+xUMVZyubwjXXf+m36+CFgTBfIgjKKfw04QvhnyhGrWPUjWa9O1PxXrKkI3+ocquh8i2xd4iTkAQhCIxqSNwNRaXa1SPgS/Uh+5PGkHF/mA1twlUyt5La+re7nkXvCAIQmGYtvtoeW8jYPipwtnUg2It6cV6yvDM18LGU6cR41VYqLw+QS0IQtljSkdghSNwlvMaATcMc5vAX0ISiv2UYUV+bbMgCBefgHalt1gsAzDgry+z1Z1kGMZrPvPrYYB4B1XXyzylv2oW+Mbicl4juJCnDCvya5sFQbiEagQw8mxpx0EDIY5pDEWab2zjGWgGjH97DNm6ub58HmAs3H20nD9Odim9I0UQhEubQFpD7rSeBCOvvsAOJ8D9HodArldpuuDPb7k6uxO6xhAdCGB+CjgCZzmvEVxK70gRBOHSJpBtBDHQfq/pZJ3mzXPQnXASPI9DQo8U9kOYPwJaw0pLS7vgjFkRGir3jkAQBMEkjcVDoc9Qa+D3Ql8DTYaxPytPmD8R6sSKjIy8OI3F4ggEQRBcNlH9DwwpUJzXdKxO8+ZeaAaPwMgvx4DfsxwRwDydCQ2V8zYCQRCEiuAIVkNNUMKPh4J1Y/BMn2X2QVfzCJZpoR3Bhcd+zoP1UmgjEARBuNQdAUr4+RiMguZCW3XvoM0w+C9Ag/ViY6D7Mb0Rw6+g4ViGG5AD7wis4ggEQRCYgMZH9DMBs33SnvUa5x5E3Uv7VFiJ2wgkNCQIguCyiSaEawSGhIYEQRBcNlH9Nxk25QikRiAIgmBaR2AlOAJpIxAEQdA20YTY0I4toSFBEARTOwInGouDyjobgiAI5QLThoZIQkOCIAjaJpoQm2ojkMZiQRAE0zoCO+UTSfdRQRAEc7cRGFZpIxAEQTCvI5Duo4IgCB5M6whI2ggEQRDM6wjs5CSLOAJBEARzOgJnfj5ZLYa0EQiCIJjVEeTn57lGbPIaakEQBFM6AofbEUhoSBAEwWUO1X8TkacdgbQRCIIgmNQROPNyXSNSIxAEQXCZQ/U/QFgslgHQdigJeqqQ+W9DG7R2QJmBzA+T73C3EcgDZYIgCEzAXrgDo86tseOgflAytBppM/XnKRUYf9xr+UcwaF8avYbU9mzyriFBEIRA1wg6Q0kw9rsgjsdMg4acY/mh+gP2pdJrSNoIBEEQAu8IYqD9XtPJOu0sUBuoj0E89FsR80dAa1hpaWkXlClnvquNwCo1AkEQBJc9VP/Lntugb1BzcBQ2E+kToU6syMjIC9qQQ4eGpI1AEAQh8I4gBYrzmo7VaUU5goCHhRinbiyWGoEgCELgHcFqqAnCOfFQsDb2M30XwrzmGNSAlgcwL2c5Auk+KgiCoM2ha3DxQRiHYzCjoLnQVmgG0jbD8L8ADfZalB3ENMwzApWXwkJDNrv0GhIEQWACag1h22djMNsn7Vmf6edK81Q4Pb2G5DkCQRCE8tRYXGoY7jYCuzgCQRAEZQ/Ndhgc2hFY5MliQRAEczoCw+FqI5BeQ4IgCKZ1BK4agU1CQ4IgCOZ0BE5PjUDaCARBEJQ9NG1jsbxiQhAEwWUP1X8T4XTqGoGEhgRBEFz2UP03EYbngTIJDQmCIJjTETjdjcX81gtBEATBdI6ApPuoIAiCuR2BodsI7BIaEgRBMKkjkBqBIAiC2R2BbiMIkjYCQRAEUzoCktCQIAhCAUzrCKT7qCAIgkkdgeF0fRZZGosFQRCK4QgsFst30LXQJe84LO42AputjHMiCIJQPvDXsH8I3Q4lwhm8BjXzZyUsNwDaDiVBTxWxzC3QFog/YznVz/yUHCOfcg0bWayXvE8TBEEovU9VGoYxH4P5MNTVMByqx/dj+D9oCubrL8KfAfO5yD0O6gclQ6uRNhPLbvFapgkGY6HuSM/AdO0L3iM/2ggcJLUBQRAEN34Xi2Gka2EwHLoPWg+9C3WA5hWxSmcoCQZ+F5SL8WnQEJ9l7ofGsRPgCQwP+5ufEoM2AnEEgiAIxW8j+B6DP6BwaBAM9mBoOvQIpisXsVoMxLUGN8k6zZumLPz+UmgFh5KK2P4IaA0rLS3NnywXvS9cI7j0mzoEQRBKNzQE3oPRX1jYDKR3usDtc3ioNxQLLYaxb4PfzPTZxkQMWNSpUyfjArYnoSFBEAQf/C0at4SBru6ewHgN6OHzrJMCxXlNx+o08qklcLtBHrQb4zu0YwgYFjQW5/vt/wRBECo+/jqC+71L6Tqmz/H9c7EaagKHEQ/x+xxug2b6LPODrg2wc4nQoaJdfuapRFjQRuA03+MTgiAIReKvRbTBUFt8egSd82U9cBb8CO8oaC60FZqBNO4i+gI0WC/G845w91EMOfT0DyxzxM88XUAbgfQaEgRBcONvjOQXaDoM9kd6+gGddk5g1GdjMNsn7VmvcY73/12rVODQkFO6jwqCIBTbETypjf9Depq7jE7yc91yhcVwUL5F2ggEQRCK+0CZE4PxWpc0VoSGpEYgCIJQTEegnwB+FWoJhXo5iIb+rF/eagROaSMQBEEodmPxp7o2wA3AV0FfQFP8XLdcYeU2AnEEgiAIZ+yiZ+zchKH0vwBDC4Z7oecwfq2f65bDGoG0EQiCILjx1yLm6FdQ89tHR+kHw4p6tUS5xiqhIUEQhIJ2scBU0YzW7xl6FOoI3Qnd7ee65QqrOAJBEITi1Qj0w2O3Ihz0BIYnoHvOt075byOoVNbZEARBuHRqBHAA/G3HHqWQl1KrERjSWCwIglDsNoL1/FEZDL+Gsr2cxHd+rl9usEljsSAIQokcAT87wO8A6uOVxq+HuOQcgZVQI7DKu4YEQRCK+2TxJd0ucHZoSLqPCoIgFPfJYn6gzCjEQfzNn/XLEzbKlxqBIAiCF/4WjWf5hIlugA74uW65ayOQGoEgCELxQ0Pf+tQQvsJgiT/rljdsqo1AQkOCIAhuSvqpLn4JXe0Srlum2MgpoSFBEIQStBEc92kjOKi/UUCXYhsBSWOxIAhC8WoECA1Vgap6qalvuKgIBzIA2g4lQU8VMn84lAZt0LrPn/xcCHZuI5DQkCAIQvEcAQz0DVA1r+nq0PXnWYc764+DBurvGAxFGg99mQ6n0k5rUmmEhkgcgSAIQrHbCP4NI53lnsB4JqedZ53OUBKW3QXlYnwaNMTP7QW0sVgcgSAIQvEdgbUE7Qsx0H6v6WSd5suNqCn8CX0DxRX2Q0gfAa1hpaWl+ZfjQjCcTrJbpEYgCIJQEkfARvgtqJHWW0hb6+e65+InqAFqDG0xnAd9XthCmD8R6sSKjIws8cYc+XmuEXnFhCAIQrEdwSMQh3em6xDPaWjkedbhj9d4l/BjdZoHGPYjUI6enKS/dRAw8t2OwBYUyM0IgiBUyAfK+I2jZ/X6OQ+roSaoPcRrB3AbdLv3ApgXjd9O1ZODoa3F3EYJawTyQJkgCEJxew3N455CXtM1oLnnWgcGnj90z5+1nKsN/AykbcZ6L0Bs9JlHMc5pG3kcGu5PfkpKfn6+K//iCARBEDz4WzSO0D2FFBjPgPE+75PFWG42BrN90p71Gh+LAatUcOS5olAWm9QIBEEQittG4IThr+eewHiDwt5GWt5xOlw1ArJKG4EgCIIbf4vGT0NL4AAWYWiBroRG+LluucHdRiA1AkEQhOI3Fv8CJ9BJG//10A/QKX/WLU84PDUCCQ0JgiAU96Vz/A6g0boL6AaoK7Tc59OV5R5Hfq5+jEAcgSAIQnHbCNgJXA7tRe3gKgzbQ57G40sFp4SGBEEQSuwITsMBnNa1gxCMb8NoMz/XLXehIYs8UCYIguDB3xhJsn6OgNsG+JmCDAz3+rluOawRSK8hQRCE4jYW8zeKmefgBBZiyK+k/sWfdctjryGrNBYLgiB4KHarKZwCdyG9JDEc2hHYpbFYEAThQr9ZfEnifqBMQkOCIAgmdwRWaSMQBEEwqSNwtxHIcwSCIAjmdASeNgKpEQiCIJjTETidOjQUJN1HBUEQTOkIDN1GYJMagSAIgrkdgbQRCIIgmNQROHUbgc0uoSFBEIRScQQWi2UAtB1Kgor85jHm3QgZ+lXXgcPjCOSBMkEQhIA7Ahh1GwbjoIFQS2go0loWslwV/XbTlYHKixtDNxbb7CGB3pQgCMIlQyBrBJ2hJMMwdkH8IYBp0JBClnsReh1SbzctDUcgr5gQBEEoHUcQA+33mk7Wad61gQ4YxMFR/HyuH8JyI6A1rLS0tAtuLLZLryFBEISybyyGUedtvwWNOd+ycBQToU6syMjIkm/T3UYgzxEIgiCUiiNIgeK8pmN1mhtuG2gN/Q6nsEd//nJmIBuMDadDDe324EBtQhAE4ZIjkI5gNdQEhj0eYst7GzTTPROl+ywoAmrAQtIKaDDG1wQsR57GYuk+KgiCEHBHAIPOVncUNBfaCs1A2mY4hRegwYHarj+OwC7dRwVBEDwEtEM9DP9sDGb7pD1bxLK9A5kXhTOPHIaFbDbu2SoIgiCY7sliQhuBg8QJCIIgmNgR5FOeOAJBEARzOwKpEQiCIJjYEVjYEag3XwiCIAjmdQQSGhIEQTCzI5DGYkEQBFM7AjIkNCQIgmBqR2AxHOSU0JAgCIJ5HYGVHyiTxmJBEISCtrHAlAlqBI7APkwtCIJwyWE6R+CUGoEgCIJ5HYEV3UfFEQiCIPjYxoKTFRsreg1JY7EgCIKPbSw4WbGR0JAgCILJHYFVtRFIY7EgCEIB2+g9YQpHYJV3DQmCIBSwjd4TFR0rOo8a0mtIEATBxzYGEIvFMgDaDiVBTxUy/0FoE7QBWgK1DHhjsYSGBEEQSscRwKhzDGYcNBBiAz+0EEM/1TCMNlA7jL8BvRWo/DA2hIYMcQSCIAilViPoDCXByO+CcjE+DRrivQDSj3lNVuKkQIeGpI1AEAShIIHsQhMD7feaToa6+C6EWsJIDP4OBUN9CvshLDMCAxbVq1fvghqLSdoIBEEQCtrGAlNlAGoF46BGGH0SeqaIZSZCnViRkZEl3pZduo8KgiCUqiNIgeK8pmN1WlFw6Oj6QIeGyCrPEQiCIBS0jYFjNdQEYZ14iMM+t0EzvRdAehOvyWuhxADmh2zcfVQcgSAIQgECVjxGGCcfhn4URudC3IPoE6RtRtoLGF+DcXYKozDdF8M8KAO6O1D58TgCaSMQBEEoQEDjJDD2szGY7ZP2rNf46EBuv7DuoxIaEgRBKGeNxaWJXdoIBEEQzO0IpI1AEATB5I7ATk4JDQmCIJxlG02C0+Egq8WAI5C3jwqCGcnLy6Pk5GQ6ffp0WWcloISGhlJsbCwFBQX5vY5pHEF+Xq56dFkaiwXBnLATqFKlCjVo0IC7rpd1dgICOuDQkSNH1L7Gx8f7vZ5pQkOOfO6hCmz+e0lBECoOXBOoVatWhXUCDO8b72Nxaz2mcQT52hFY5IEyQTAtFdkJXMg+msYROB26RiCOQBAEwZyOIA9tBIzUCARBOB8TFu2kZTvTC6TxNKeXlMzMTPrwww+Lvd4111yj1g0kpnEEzvx8NbTYTNM+LghCCWkbW41GTV3vcQY85GlOv9iOIF/bpqKYPXs2Va9evcTb9QfTWEWHOzQkjcWCYHqe/2kzbTng/V2ss6ldJYSGfbyK6lQNoUPHcqhx7cr07vxEpcJoWbcq/XtQq0LnMU899RTt3LmT2rVrp7p2cjfPGjVq0LZt22jHjh10/fXX0/79+1VD7+jRo2nECPUJFtXLac2aNXTixAkaOHAg9ejRg5YtW0YxMTH0448/UlhYWJHb9BcT1QiksVgQBP+pFhaknEBK5mk15OkL4bXXXqNGjRrRhg0b6D//+Q+tW7eO3n33XeUEmE8++YTWrl2rjP57772nuoH6kpiYSCNHjqTNmzerWsK33357UU6paWoE7uqXhIYEQThXyd2NOxz0aJ/GNGXlPhrdtwld0SjCPfuC6dy5c4G+/mz8v//+ezXONQM2+twV1BtenmsUTMeOHWnPnj0XJS+mcQTOfFdjsVXaCARB8NMJfHB7e2X8uzaqVWD6YlCpEn+m3cXvv/9O8+fPp+XLl1N4eDj17t270GcBQkJCPOM2m41OnTp1UfJintCQtBEIguAnfyZnFTD6PORpTi8p/FTz8ePHC52XlZWl2gvYCXCbwYoVK0r1XJmnRuBwhYakRiAIwvl4sBd/Rr0g7AwupDbAYZ7u3btT69atVQNvnTp1PPMGDBhAEyZMoBYtWlCzZs2oa9eupXqS7GZ7xYRFeg0JglBGTJ06tdB0DvnMmTOn0HnudoCIiAhKSEjwpD/xxBMXLV/WAD/qPADaDiVBTxUy/+/QFuhPaAFUP1B5MbQjkBqBIAhCKTkCGHV+3/M4aCDUEhqKNB56sx7qZBhGWwy/gd4I9HMEVrt6B6kgCIJQCjWCzlASjPwuiLvsTIOGeC+A9IXQST3JrSOxgcqM4ZQ2AkEQhNJ2BDHQfq/pZJ1WFPdChQbJUJMYAa1hpaWllSgz0lgsCIJQjruPwsDfiUEn6D+FzUetYSLEIaROkZGRF9hGIN8jEARBKK1eQylQnNd0rE7zdQJ9MXga6gVDnxOozBi6jcBmF0cgCIJQWjWC1VATGPp4iFtob4Nmei+A9PYYfAQNhhM4HMC8kNPdRiCOQBCE87HkHaLdiwum8TSnlxKVK1e+9B0BDDtb3lHQXGgrNANpm2H8X4AG68U4FMR7+zXSNkAFHMVFRR4oEwTBX2I6wCoNP+MMeMjTnF4BCegDZTD8szGY7ZP2rNc4h4VKBXdjsc0m3UcFwfTMeYro4KZzH4Yq0USTb3ANj6cSRTYn+v11lwojqg3RwNfO+RrquLg49fZQ5rnnniO73U4LFy6kjIwMysvLo5deeomGDCnQudI8jcWlgruNIMg0D1MLgnAhhFZ3OYGs/a4hT18At956K82YMcMzzeN33323euMov5KaHcKYMWO4gFzq5800VtH9HIFdHigTBOEcJXcP7nBQz38SrfmYqPeTRPE9PbOLS/v27enw4cN04MAB4m7w/JK5qKgoevzxx2nx4sVktVopJSWFDh06pNJLkwrvCFZ88X9UuVFn9Bo680BZwtKf6MTOVdR12ItlnDtBEMolu7UTuPkzl/GPv7LgdAm5+eab6ZtvvqGDBw+qGsKXX36pnAJ/kIa/WsZfIyvs9dOBpsKHhtgJxMx7mIy07Wp674ZFaprTBUEQCiVlXUGjz0Oe5vQLgI3/tGnTlDNgp8Cvn65du7ZyAhwa2rt37wX9fkmp8DWC1t0HEb+vr+2v96K/KlGDZf+glH7jVbogCEKh9Hjs7DRVMyh5bYBp1aqV+iYBf284Ojqa7rjjDho0aBC1adOGOnXqRM2bo0G6DKjwjoBho79sZR+64tgc2lb3RuomTkAQhDJi06YzvZX41dL8VbLC4I/VlxYVPjTEcJtAi2NLaUXsvdT8wHdqWhAEQTBJjYCNPrcJpPT7kLpymGjpVWqaw0USHhIEQTCBIziB3kHsBNxG391mwOkkISJBMBXcR99iQWNhBcYowXMIFd4RFNZFVDkFcQKCYCpCQ0PpyJEj6tvBFdUZGHACvI+8r8WhwjsCQRAEJjY2lpKTk1W//YpMKJwA72txEEcgCIIp4L768fHxZZ2Ncokpeg0JgiAIRSOOQBAEweSIIxAEQTA5Fm5lvpRAaz+39JT0hRwRUPpFzM6lgOyzOZDzbA4iLsCG1Ye9j6wQjuACncga7G+nss5HaSL7bA7kPJsDS4BsmISGBEEQTI44AkEQBJNjNkcwsawzUAbIPpsDOc/mYGIgftRUbQSCIAjC2ZitRiAIgiD4II5AEATB5FhN1O1qALQdSoKeKuv8BALsVxy0ENoCbYZG6/Sa0DwoUQ9rlHVeLybYHxu0Hpqlp+OhlfpcT4eCyzqPFxPsT3XoG2gbtBXqZoJz/Li+phOgr6DQinaeLRbLJ9BhKMErrdDziiHznt73P6EOF7JtUzgCHCQbBuOggVBLaCjSeFjRyIfGoN2H960rNFLvJzu+BUhvwkM9XZFgh7fVa/p16G3sb2MMM6B7yyRXgeNd6BfsH3/g9jK97xX2HOMajsHgUagT9q81hnw/31YBz/Nn0ACftKLOK9syTmONgMZfyIZN4QhAZygJB3MXlIvxadCQMs7TRQf7lgqt0+PHtYGI0fv6uV6Mh9eXTQ4DYiT4fbvXQpP0NL9ovg/0TQXd32oY8BfUP+Zpvp6hzIp8jr3elByG/edhOJRa0c6zYRiLMTjqk1zUeeX0L7AOswLjXEuMLum2zeII2Bju95pO1mkVFlwUDTBoD62E6uBi4RuHOcjTZZaxi8870D8hp56uBWVif7l2VBHPNb9HmV+z8qkOh02CKlXkc4z9SsHgv9A+iPcxC1pbwc+zm6LO60W1aWZxBKYChqEyBt9Cj+EiOuY9j4sPPCiTjF38/bwOg8PYJTYKZoFLxBwPHo/9Zkef7RsGqkjnmNFx8SHaCdaFKhUSQqnwGIAHgfhtszgCLlHEeU3H6rQKB26aIO0EvsR1851OPuSuNurh4bLK30WmOzQY+7RHh/v66Pg5V5PtFfRcc8kvGeeWa3qkQyMdKvA5ZvpCu7HPaVAexr/T574in2c3RZ3Xi2rTzOIIVkNNdC+DYN3QNLOM83TR0fFxjh1vxQ3zltcs3te79TgPfyztvAUC7ONYKBZqoM/pbxi/A8OF0E0VbX8Z7B+HB/bjVDfTSVdDWyrqOdZwSKgr9jlcX+Pufa6w59mLos4rpw/TvYe4Y0iWVwip+HBtwwwC10A7oJ3Q0xV0H3vwKYX+hDZoXaPj5tzjIBGaD9WsgPveG5qlxxtCq6Ak6GsopILtaztojT7PP0A1Kvo5Bs9D2yDuWjmZz2lFO8/gK4iNeZ6u+d1b1HkFFt0Tku3ZJt2jqsTblldMCIIgmByzhIYEQRCEIhBHIAiCYHLEEQiCIJgccQSCIAgmRxyBIAiCyRFHIAilCPp893a/JVUQygviCARBEEyOOAJBKASU2u+EVkEboI/4VebQCeht/V78BVCkXrYdtEK/F/57r3fGN4bmQxuhdVAj/fOVvb4n8KV+WlYQygxxBILgA+xyCwxuhbobhsFP8TqgO/TLztYgrRWGi6B/61W+gJ5Eelv9lKc7/UtoHNL5mwFXQO5XAPDL4h6DWuqnY/m9OYJQZrhf2CQIwhn4XTYdodW6sB6mX/bFr7qerpeZAn2nvw9QHcaeHYP7nfFfI70KhjFI/54TMTzNQ/17qzCdrKf5NSD8rqQlcgKEskIcgSCcDVvrz2GsxxZItFj+z2e5kr4SOMdrnGsbch8KZYqEhgThbPglXzfB8Nf2+m5sfX2/uN92eTu0BM6CP5KSgflX6vS7oEX6C3HJSFdflMIwhN+eKQdbKI9ISUQQfIAR3wKj/QxGf8XQqt8GOVJ/BKaznndYtyO4Xw88QRv6XdA9Xk6BG5pf0L9xsxxsoTwibx8VBH9vFvQagpPgr78JQoVCQkOCIAgmR2oEgiAIJkdqBIIgCCZHHIEgCILJEUcgCIJgcsQRCIIgmBxxBIIgCCbn/wGx2/VQWGWs5wAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "train_accs = [float(x['epoch_acc']) for x in history_train]\n", "val_accs = [float(x['epoch_acc']) for x in history_val]\n", "plt.plot(train_accs, '-x', label='train')\n", "plt.plot(val_accs, '-x', label='val')\n", "plt.xlabel('epoch')\n", "plt.ylabel('accuracy')\n", "plt.legend()\n", "plt.title('Accuracy vs. No. of epochs')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "选用tanh作为激活函数,学习速率选为0.01,迭代步数为100时:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "03/07/2022 10:20:06 AM INFO:Initializing NN model.\n", "03/07/2022 10:20:06 AM INFO:Start training...\n", "c:\\users\\sj2050\\miniconda3\\lib\\site-packages\\torch\\nn\\functional.py:1795: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n", " warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n", "03/07/2022 10:20:08 AM INFO:Train_Epoch [0], loss: 0.7299, acc: 0.3735.\n", "03/07/2022 10:20:08 AM INFO:Val_Epoch [0], loss: 0.7296, acc: 0.3737.\n", "03/07/2022 10:20:45 AM INFO:Train_Epoch [10], loss: 0.2216, acc: 0.9048.\n", "03/07/2022 10:20:45 AM INFO:Val_Epoch [10], loss: 0.2220, acc: 0.9038.\n", "03/07/2022 10:21:23 AM INFO:Train_Epoch [20], loss: 0.2235, acc: 0.9028.\n", "03/07/2022 10:21:23 AM INFO:Val_Epoch [20], loss: 0.2231, acc: 0.9029.\n", "03/07/2022 10:22:00 AM INFO:Train_Epoch [30], loss: 0.2048, acc: 0.9060.\n", "03/07/2022 10:22:00 AM INFO:Val_Epoch [30], loss: 0.2037, acc: 0.9056.\n", "03/07/2022 10:22:39 AM INFO:Train_Epoch [40], loss: 0.2216, acc: 0.9017.\n", "03/07/2022 10:22:39 AM INFO:Val_Epoch [40], loss: 0.2223, acc: 0.8999.\n", "03/07/2022 10:23:20 AM INFO:Train_Epoch [50], loss: 0.2089, acc: 0.9097.\n", "03/07/2022 10:23:20 AM INFO:Val_Epoch [50], loss: 0.2084, acc: 0.9088.\n", "03/07/2022 10:23:57 AM INFO:Train_Epoch [60], loss: 0.2126, acc: 0.9048.\n", "03/07/2022 10:23:57 AM INFO:Val_Epoch [60], loss: 0.2136, acc: 0.9030.\n", "03/07/2022 10:24:34 AM INFO:Train_Epoch [70], loss: 0.1915, acc: 0.9177.\n", "03/07/2022 10:24:34 AM INFO:Val_Epoch [70], loss: 0.1924, acc: 0.9145.\n", "03/07/2022 10:25:13 AM INFO:Train_Epoch [80], loss: 0.1916, acc: 0.9195.\n", "03/07/2022 10:25:13 AM INFO:Val_Epoch [80], loss: 0.1922, acc: 0.9180.\n", "03/07/2022 10:25:49 AM INFO:Train_Epoch [90], loss: 0.2093, acc: 0.9069.\n", "03/07/2022 10:25:49 AM INFO:Val_Epoch [90], loss: 0.2096, acc: 0.9054.\n", "03/07/2022 10:26:26 AM INFO:Train_Epoch [100], loss: 0.1877, acc: 0.9205.\n", "03/07/2022 10:26:26 AM INFO:Val_Epoch [100], loss: 0.1887, acc: 0.9184.\n", "03/07/2022 10:26:26 AM INFO:Training finished.\n", "03/07/2022 10:26:26 AM INFO:Save model.\n" ] } ], "source": [ "x_dim = 36 # input dimension\n", "y_dim = 2 # label dimension\n", "hidden_dim = 24 # hidden layer dimension\n", "act_func = F.tanh # activation function\n", "batch_size = 128\n", "num_epochs = 100\n", "learning_rate = 0.01\n", "device = torch.device('cuda')\n", "\n", "train_loader = DataLoader(train_ds, batch_size, shuffle=True)\n", "val_loader = DataLoader(val_ds, batch_size)\n", "# move dataloader to gpu\n", "train_loader = DeviceDataLoader(train_loader, device)\n", "val_loader = DeviceDataLoader(val_loader, device)\n", "\n", "# initialize linear regression model\n", "logging.info(\"Initializing NN model.\")\n", "insurance_model = Insurance_Model(x_dim, hidden_dim, y_dim, act_func)\n", "# move model parameters to gpu\n", "to_device(insurance_model, device)\n", "logging.info(\"Start training...\")\n", "insurance_model.train(train_loader, val_loader, num_epochs,\n", " learning_rate, log_interval=10, opt_func=torch.optim.SGD)\n", "logging.info(\"Training finished.\")\n", "\n", "logging.info(\"Save model.\")\n", "insurance_model.save_model('report01-insurance_model.pth')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "训练过程中的损失函数值和准确率变化:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "history_train = pickle.load(open('insurance_history_train.pkl', 'rb'))\n", "history_val = pickle.load(open('insurance_history_val.pkl', 'rb'))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Loss vs. No. of epochs')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "train_losses = [float(x['epoch_loss']) for x in history_train]\n", "val_losses = [float(x['epoch_loss']) for x in history_val]\n", "plt.plot(train_losses, '-x', label='train')\n", "plt.plot(val_losses, '-x', label='val')\n", "plt.xlabel('epoch')\n", "plt.ylabel('loss')\n", "plt.legend()\n", "plt.title('Loss vs. No. of epochs')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Accuracy vs. No. of epochs')" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "train_accs = [float(x['epoch_acc']) for x in history_train]\n", "val_accs = [float(x['epoch_acc']) for x in history_val]\n", "plt.plot(train_accs, '-x', label='train')\n", "plt.plot(val_accs, '-x', label='val')\n", "plt.xlabel('epoch')\n", "plt.ylabel('accuracy')\n", "plt.legend()\n", "plt.title('Accuracy vs. No. of epochs')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以看到,选用tanh激活函数的模型最终训练出来的精度最佳,在验证集上可以达到91.84%的准确度。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. 小结" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "    在本次任务中,我们使用了全连接神经网络进行了交通事故理赔审核预测。这是我第一次使用Pytorch进行实战开发,在开发中遇到了不少问题,例如从训练样本文件中读入输入值和标签值时,需考虑读入的类型,例如标签label需要为整型,如果类型不当,Pytorch将会爆出RuntimeError: expected scalar type Long but found Float的错误。如果需要使用gpu加速计算,需要安装对应的显卡驱动,并且gpu版的Pytorch和普通版本的Pytorch并不一致,需要卸载掉原有的Pytorch再重新安装gpu版的Pytorch才能使用到gpu加速功能。同时,在开发中,也感受到了Pytorch的魅力,其所有操纵都十分简洁,但功能齐全,与平时作业手写神经网络相比,大大提升了开发效率。此外,在该任务中,我们还测试了不同的激活函数下的训练精度和效果,结果发现tanh激活函数取得效果最好。" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" }, "main_language": "python" }, "nbformat": 4, "nbformat_minor": 2 }