From e6a4f443701e145b125560bc8f4b74b7ddc77023 Mon Sep 17 00:00:00 2001 From: iorch Date: Sat, 9 Jul 2016 09:26:27 -0500 Subject: [PATCH 1/4] adding data --- .../data/saturday_testing_sample.csv" | 301 ++++++++ .../data/saturday_traning_sample.csv" | 701 ++++++++++++++++++ 2 files changed, 1002 insertions(+) create mode 100644 "5.- Aprendizaje autom\303\241tico/data/saturday_testing_sample.csv" create mode 100644 "5.- Aprendizaje autom\303\241tico/data/saturday_traning_sample.csv" diff --git "a/5.- Aprendizaje autom\303\241tico/data/saturday_testing_sample.csv" "b/5.- Aprendizaje autom\303\241tico/data/saturday_testing_sample.csv" new file mode 100644 index 0000000..5a08a87 --- /dev/null +++ "b/5.- Aprendizaje autom\303\241tico/data/saturday_testing_sample.csv" @@ -0,0 +1,301 @@ 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+240,1,24,4,2,915,0,5,4,2,1,2,2,29,2,1,1,3,1,0,1,2,0.3,0.265 +230,3,36,4,1,4210,1,3,4,1,1,2,2,26,0,1,1,3,1,0,1,2,0.8,0.21 +915,2,48,0,11,18424,1,3,1,2,1,2,3,32,2,1,1,4,1,1,0,2,0.4,0.1975 From e0e91f7f727c4a5d664bd0f16a6e52beeac69fc6 Mon Sep 17 00:00:00 2001 From: iorch Date: Sat, 9 Jul 2016 09:28:07 -0500 Subject: [PATCH 2/4] Saturday session --- .../5.- Sesi\303\263n S\303\241bado.ipynb" | 1013 +++++++++++++++++ 1 file changed, 1013 insertions(+) diff --git "a/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb" "b/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb" index e69de29..93511c8 100644 --- "a/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb" +++ "b/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb" @@ -0,0 +1,1013 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Artificial Neural Networks\n", + "\n", + "### Arquitectura típica" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+N7N+ZvZG9G9ulZndZmad44xSRAQAD/t5mO9hbQB/nAtdYw4p7wwSKfiZh0Ue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XvgDL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\nI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miTpE37txMNSVZzSc0lWkihNhIHSjpMey0wfpL0uaS5ziM0Wzp9Q1Ivr5YCHCNpqqSu\nu3bt0vDhwzVhwgQNHjxYEyZMUE5OjuR8vi+1LOtLX4NNM4CPJJ06b9489ejRQ2PGjNFNN92krVu3\nKi8vzztskqTrLMva5l+k0g033HD3M888M7KwsFBFRUUlz8+dOzfavXv3XpZlfZyUjkfqGBXrVEkn\nuY/j5agWSFolaW3MT+8zv2W/dixlCbmfe7WQ1E5SK0mHSyU1hlfJ+cx/LktzhD6ULV9f90RR4cTP\nSG9KOipX6nqSIwUFpAFGekrS+UhHnqnEvFmBDnIme1dIaus+pzlz5ugXv/iFdu/erWOOOUa//OUv\n9eyzzwqQZZW8xX6SM2g9b1nW54mIJ058T0oa/NZbb+mCCy7Qp59+ql/84heSdL9lWXfWoN0Bkl6X\nlHnLLbfo0Ucf1TvvvKP+/ftL0lJJp1mWtS4R15Ai7pX0lyocXyTn/XRDcsJJBFiSOVSyukjqKNFB\nsg50JnV4A3w8aRjt/YL42fnJGslaKkUXStZC6cy1qbuO2o+7Ov+ApKGS9Nprr+maa67REUccoZdf\nfllHHnmk5NiI/NmyrIo3+NdzgMaSHleML+7dd9+tcePGaf78+TrqqKMk57v3D5ZlPe9TmCUceOCB\nwzZs2DBekqLRaMnzhx12mFasWDHOsqxbUxaMrUxlqJ2KdbycpNMOklpr7+e9uaS8OGcWae/n3nus\nkqUlsvSVolokWxtTcg21DSOdaSQKpHP9jiWgasyUWhtps5Hya9IOZWTlLl26dJ+s3A8++ACAf/zj\nH2RkZLBgwQLv0IWJlHLLibMjUFRUVETnzp359a9/7fX/30RITzgeWhQXF3PJJZeku8FzO0mLtNfv\nq6LHHqmOm60GVJnS0m+3bt0C6beKUCprd8yYMcSTfKnA2DmF2Coj8WvYsGE/JUziDfAPI30WkT7w\nO46A6hGR7jLStrB0UFXOowyD5W3btvHkk0/Ss2dPQqEQrVq12i8rt6ioiOOOO44LLrggoROvSsQ8\nFWDixIlkZmby7bffev0nzHoE1+B59+7d9O3bN90NnnPz8vJmx/PqK/UolCMLBwTsB0HWb7WhlCHz\nCSecQLNmzUrGFC/L9+GHS/Jq4ho7p4gsSc9pbrX9AwAAIABJREFUrz/gfo977rknqOSV7kSkc4xE\nREpbH6r6TljKNdKGiDS6MscDHSjHYNmrBell5cYM7N4EbwHAa6+9hiRmz57t/W5kMq8T6AFEd+3a\nRbt27bjmmmu8fn8ggSuNlGHw/NNPP3n9pY3BM3Dgrl27ll5xxRVYllXexG+XqnjjEFC/IE7Wb7Nm\nzYKs3zIgjiFznKzdKMTN8n3Qh5CbSCpQOUlgrVu3jrN/NiDtMNKnEeldv+MIqBkRabiRts909j3s\nBxVIuUcffTSSOP744yvMygUOwbEZ4LTTTqNXr17ecduApE0egOkA48ePp1GjRrEm0An3nCSOwXPv\n3r1jM+7sRPeZaNy/+RyA7777jkaNGpW32hckdAVUCgLpt0Iox5A55kZ6AXAd1ArJ91BJ36gcq6fM\nzMxogwYN/pbCmAKSQVg6w0gYqVbXlQyoGHfVb6OJs9cP6AwsKZmdVULKdVkO3AscGafN0QAzZ85E\nEu+88453TlKsQHAkaTZv3kyrVq247bbbvP4WAkmx2cCpDPIxwIIFC2jevDnnn38+RUVFXt+1NgkC\nyMSRpfnhhx9o06YNvXr14rHHHiMjIwPLsmKlnF2SEmiPElDXoQzpd+DAgezYUbJrpF5Kv5Qh7ZZl\nyIxr7Dxv3jy/JN+OcjJb96jifcDHpSCegGRipDeN9KHfcQQkBiP91Ujrw07auyQJyMCRZ5k3bx5D\nhw6lVatWWJZFnz59ePbZZ9m6dWvsgLQNR574JVCm/QfQHKeuLmeffTZdunTx7lT3xJso1gQc6XUu\nwIgRI2jevHlsXd3fJLKvOH2XGDwbY9LG4Bn4O8CmTZvo3LkzXbp04eeffwbggQceMHIG+T1yEj+C\njMyAKkMg/e4DlZN29zNkxjF2/gF8kXz7yLFBKdfU3X0sSnIsAckmLLU3UnHEsewIqAOEpTZGKoxI\nV3vPAceCs1ctJyeHww8/nBEjRrB06dLYyV4UmAlcA8RLiY8LcCfAl19+SSgU4sUXX/Tam5TI68KR\nmFm7di25ubncd999Xj8fJbKfcvovMXh+6aWXar3BM2AD7Nq1izPOOIO2bduycuVKL95/A1mSzpS0\nVc5q38H+RhyQzlCG9DtlypTYMaZOS79UXtqNa8gMnAMpl3wvl3PzV6yKJ32FSlGJ0IAkYtzVoWlS\njt+xBCQOI71mpBJTTRzJbzPAypUrvTI7HsuBfOCI6vQFNMQtQXTFFVfQvn17b5CLAglxPMfZa/cd\nwNChQzn44IPZtm2bF//pieijknH8Amc1lNGjR2NZFhMnTvTi2IBjeOs7wPXg2NFcdtllNGvWjK+/\n/tqL8/NSE/ujJfX0K9aAugP1WPqlDGn3tddeix1rK6y1S2ol355yJnxlZu/GeXRKcAwBqWSqlGGk\nVRHpfr9jCUgsRupvJApi9mLglAna7g4i24CJVCDlVhZgCDhJItnZ2TzyyCPeYDWtpm277Q8G+P77\n78nOzuaxxx7z2n8vEe1XMZZzcRJduPnmm8nKyuL999/34lkC+LpPDqesXhHAn/70J3JycgiHw158\n3wFxE38CAhIB9Uz6pZrSbjntpVLybSVptpyJX0WTv6ikbxPcf0CqKZB+ZRwLF9/rAAYkFqSQkVYZ\nx3F/7/OQg5PkkdA7R2JW5G644QZat27Nli1bvMHqlzVsuxGwBmDgwIEceeSR7NmzBxz5w5eVA2qp\nwTNwKu6qw/jx4wmFQrFS2zqquaobEFBViCP95uXl1SnplxpKu+W0m0rJ15I0qEGDBnuysrIqknnv\nSHDfAanGSP8w0hd+xxGQHMLSQ0ZaSiVK9SUC4LcA69evJy8vj/z8fG/g+xSn3m912/0zONJHqYnM\nlETGX424RnqDcxyD52mk2OAZOBpHbuaVV14hFAoxatQoL55tOHWIAwJSBnVY+iVB0m457T/rjXvJ\nlnyBs9esWcPZZ5+NpLK8PqNySqQFpCtzpSwj/RSRhvsdS0BymCmdbCTCTnHrpENM1u3dd99Nbm4u\n69at8warX1ezzRbAJoABAwZw4oknevsTdwOHJ/oaqhibBfwDHHuZ448/3jeDZ+BAXKuemTNn0qBB\nA4YNG+bFUQgEZRgDfIEypN8TTjghLaVfEiztltNPieR79913J03yxXFmWA1w3333kZWVRW5uLqVW\n/6KSvkxUnwE+USD1NRKznI3dAXUQJMtIK8PSfSnrE84C2LJlCwcccAC33HJL7F1qlVfAcCqLEA6H\nkcS7777rtfdYEsKvMpQyeD7ssMNSbvBMjEHzl19+SdOmTbn44os9aQjgf5IdQ0BARRAj/X733Xdp\nKf0SI+1u3749YdJuOf0lXfLFvXmdO3cumZmZjB07liVLlnDyySdv1969f3sk/TkR/QX4iJHGmGCj\nZp0nIj1uUiznAx8AjBkzhuzsbJYsKfGMvqaK7bQFdkajUXr06EHfvn29drYBtcZ6BB8Nnill0Hzo\noYeWnnimbNIfEFARpLH0S5Kl3XL6fRaSI/kCZ3sTy65du3L66ad7E8uiwsLC0yRdL2mHApm3bmCk\nBSYwbK3zFEgXGCn6QQorMuBYnkR37tzJYYcdxtVXX+0NVKuqMlABTwL861//wrIsPvvsM6+dvyYz\n/uqATwbPlGPQjFNnOCX7OwMCKgtpJv1ShrR78skns2zZMu/pGku75fRfnuRbqbrsZbRbIvHec889\n5Obmxt6kj4k5tL2ki2t8IQH+EpYOMlI0Ip3vdywByWW61NRIRcYx6UwZOJINzzzzDKFQiPnz53sD\nyv9W8vyOQFFRURGdO3fmoosu8s7/L7VUCiLG4Pnll18mFArx4IMPenHvAE5LcH+VMWgOCKiVkAbS\nLymWdsuJYz/J11UUqi354kq8c+bMKZF4XRYCga9vXSMsXWyk4llSc79jCUg+EWmukVK6Jw4nw7Sw\nqKiI4447jvPPP98bVDYCFb7vgFcAnnvuOTIzM1m0aJF3/m2piL+6EGPw/NBDDyXN4Bm4Aco0aP4Y\naJSIfgICkgkx0u+uXbtqlfSLT9JuOfE8C3sl3/Hjx3sxVFnypRyJFzglWdcQ4CNGGmukr/2OIyA1\nRKRHjDQv1f0CTwO8/vrrSGL27NneQFXuvjPgFCC6a9cu2rVrx7XXXuud9wPQoLxzawPEGDzfcsst\npQ2el1JDg2diDJpvv/32wKA5IK2hlkm/lJJ2J0+eTF5eHj169EiJtFtOXAmRfKm8xBtQlzDSTCM9\n43ccAakhLF1lpMKPpESX+ykX4BB3gOS0006jV69e3uCyDTionPMMwLhx42jUqBGrV6/2zrs2lfHX\nBOAPANFolIEDB5Y2eJ5L9b29Sgyax40bFxg0B9QZqAXSL7VE2i0nvhLJt2vXrtWSfCkl8Y4bN867\nrkDirau4Fh+bI1LSMg0DahcRqaORMFLKDXyBB8HxlpPE22+/7Q0yj5ZxfH9wfPFatWrF7bffHjso\nZaQ4/BoB3OsN0v369auxwTOOLLYBYOrUqYRCIR544IHYyXRg0ByQ1uCj9EuMtPvdd9/5Lu2WE+ez\nUD3Jl0DirZ9EpMONU6YtZYXtA/wlLGUaaVdESvmKGdAM+AngnHPOoUuXLt5Aswc4stSxJQbQ99xz\nDy1atGDjxo3eoHZRqmOvKVRs8PwClcy6Jcag+YMPPiA7OzswaA6ok5Bi6ZdaKu2WBdWUfClf4n0o\nldcQkGKMdLaRmCkF+4DqEUb62kiJLvBdKYA7AL766itCoRAvvPBCyRhb6rhLAdauXUtubi5//etf\nveM+8iPuRABkAG8ArF69Op7Bc34l2sj1JsSBQXNAfYEUSL/Ucmm3LKiG5Esg8dZfjDTUSBv9jiMg\ntRjpVSO97kffQENgJcCVV15J+/btvUE1CpzoHpOF64N300030aZNm1hpJ61Xp4kxeF64cGGVDJ5x\nDJqnQZkGzSNTeS0BAamEJEq/pIm0WxZUQfIlkHjrN8bJ6E1pJYcA/zFOpZaUZ/Z6ANcBLFu2jJyc\nHCZMmOANUv92f/9HgO+//56srCwef/xx7/fv+hVzIgFauV9QRCIRGjRowHXXXeddYxFl1DLGNWje\nuHEjxx13HF27do01aP4HgUFzQB2HUtLva6+9ViPpl3Kk3Zg9uLVG2i0LKin5EkfiXbp0qXdcIPHW\nB4z0ipH+5XccAaklLN1spA1+9Y+zcvUfgBtvvJHWrVuzZcsWb/A5G1gDcNVVV3HsscdSWFgIjmzR\n1a+YEw2OwfNaqJzBMzEGzX369KFt27asWrXKO34agUFzQD2CBEi/pKm0WxY4yXDR8iRfAok3wEgz\nI9LjfscRkFoi0qVGis6VfJssAL8FWL9+PXl5eeTn53sD0EqAL774glAoxEsvveQ9P8WvWJMFMQbP\nY8eOLW3w/F9cg2diDJp/+9vfBgbNAQGqmfRLmku7ZYFTmrEsyfcCCCTeeo+RvglLQeH2ekaB1NdI\nhKUy/fOSDTFZu3/5y1/Izc1l3bp1JSPuueeeS/fu3YlGowC7gcP9ijWZEGPwfOutt8YzeP6dOzBz\n2223BQbNAQExUEXplzoi7ZYFMZLvX/7yl9KS79bY8TaQeOspRloTlmp12auAxFMgdTfOxO9YP+MA\nzgLYunUrBxxwADfffLMz6m7fTmZmJu+99543MKW0xFyqoXyD52JwVgRLGTSvJTBoDgiQtK/0u2zZ\nMk4++eR40u903L21dUHaLQtKSb6nnnpqSfLYf/7zn0Dire8YaUtE+qPfcQSkllnS0caZ+J3kdyzA\nB97EJjs7u8RLavTo0d7AtAU4wO84kw2QD44M079//30MnlevXk1WVlZg0BwQUA6UI/1u3769ZPa3\nYsWKOiPtlgVlSL7Lli2jf//+gcRbnzHSnoiUlkvaAdUnIrU1zsSvUmV9kglwknd32qFDB66++mq8\nCZBLvdiKQByD544dO5YYPM+dO9d7PQKD5oCAMqAS0u/OnTsZO3ZsnZB2y4JyJN8YJ4BA4q2PGCka\nka7wO46A1DJTOtg4E78z/I5FkoD3AJ599llCoRDz58+PHZCTVouztoFj8Py2t8rXrl07evTosc9q\nBfAHv+MMCKjt4Ei/271VLk/6nTx5MqX4D2ku7ZYFbsnLwsJCTjzxxH0kX2AZgcRbPzFOubZg4lfP\nqIUTv3cBioqKOO644zj//PO9wWkH0Nzv+FIFjs1NycbGMgyeb/Q7zoCA2g5O1m6Js3k50u9iklDr\ntzaAW9ED9kq+MXv7VlBBLd+AOoqRCgOpt/7hSb1G6u13LMApQNSb2LzxxhtIYvbs2d4AVZ+k3hcB\nNm3aVFIXtCoGzwEB9R1KZe2+/PLLsQbwNTZ8TheIMWr2pN04Wb6B1FsfMdLWILmj/uEldxjJ9wQB\nwAA89dRTJXfhPXv2pFevXt7gtA042O84kw1ucodn0NyrV6+S+rtTp04lFArFJnfsZ/AcEFDfoRxD\nZtcEHnCk3x49esSTfmtU67c2gZvcMWfOHH71q18BcSXfYsD3m/+AFGOkHyPS//odR0BqmSmdaByZ\n39e9Lbh2Lps3b6ZVq1YYYwCYNWsWknj77be9Abmu27ncCI5B86WXXlraoLkI4LHHHivT4DkgoL5D\nKUPmbt260axZM15//fXYid2bwNfeDVYZ0m+1av3WJnAl3p07d9KpUydOP/10zw+V+fPnl5Z8FxFI\nvvULI/0nIgWF3esZEemXRmKm5NtKGhAC5oMjQbRo0SI224xzzz2XLl26eKtee4Aj/Yo1mQC/9iZ3\nt956a2mD5kXAZbgGz7fddhtZWVmx/oZLAd9MuAMC/IYqGjJTTtZvjASattIvMRJvHKPmLd7zgeRb\njzHSTCPV6dWUgP0x0iWmlpRs+/HHH2ncuDH333+/NwitAPjqq68IhUI8/3zJmF4XS7adhrtKUZ5B\nM6UMnvPy8vjiiy+84+ZSR7zHAgKqAjWotUuM4fPy5cvrjPRLjMSbmZkZW7JtIU4d9Ggg+dZzjPRK\nRHrD7zgCUktEGmakDX71D2ThlBvjxhtvpE2bNrG1Nfvj1uu96qqraN++vTeIR4ET/Yo50eCYzf4X\n4Pnnn8eyLB588EHvNdjPoBmwAfbs2UP//v05+OCDY1c0ZgDZfl1LQECqIUbaXbBgAZ06daJ58+al\npd1yDZmpnOFz2ki/lJJ4+/Tps59RM65XaCD51mOMNDYizfU7joDUEpFGG2m+X/0D1wF8//33ZGVl\n8cQTT3iDz7/d3/8RnE3YOTk5PPxwiTLzrl8xJxLgIByZlunTp5OdnV1Sro5yDJqBR8HZE9mtW7d9\nDJ7dLzkr1dcSEJBKSHCtXeqI9EuMxHv33XeXlnhHxxxXYux8zz33BJJvfcRd+fmv33EEpBbj40qv\nO3CvAbjyyis59thjY+WGru4xGbgSzk033UTr1q3ZvHmzNzj90o+4EwWQiyPPMn/+fJo0acIll1xS\nksFLOQbN7uvyBpRp8Bzs1w2os1ADabcSbae19As8C2VKvDmlju1HIPnWXyLSOUbiA6ml37EEpA4j\nfRmRRld4YBIA7gD44osvCIVCvPzyy94ANbnUcZcCrF+/nry8PGzb9o77jDRd2cIxaJ4GTr3QQw45\nhL59+8aWp8uvRBuNgI/AMXhu0aJFYPAcUOchAdJuJfpIS+kXuADKl3jjnBNIvvWVGdIRRqJA6ul3\nLAGpASlkpB1hKeWlv4CWwM8A55xzDj169PBsBnYDh5c61gI+AUeSyM3NZe3atd7gdFGqY68pxBg0\nb9y4kU6dOtG1a9fYTOa/V3ZCC7RyB2hmzpwZz+A57V6fgIB4UEranTRpUo2k3Ur0l1bSL5WUeOOc\nt5/ku2jRIu+8QPKty7iTgK1GGuJ3LAGpoUA6xjjl2uLeCSYT4EGAgoICJPH+++97A82jZRzfH2Dr\n1q0ccMABDBs2LPauNDPF4dcI4F7vrvz000+nbdu2rFq1yrueN4CMKrbXASfzl3/9619kZGQEBs8B\ndQoqJ+1+TRJq7ZIm0i9lS7wLqKAWL4HkW38x0odGetrvOAJSg5EuN1LRe1LjVPYLtAV2RqNRTj75\nZPr16+cNUFuAA8o5bzrAuHHjyM7OZsmSJd5516Yy/ppAHIPmBQsWeNfxEdComu2e5H05eQbPzz33\nnNduYPAckLaQAmm3EjHUaumX8iXeHpVsI5B86yNGetj4mOEZkFoi0jgjfZ3qfoGnAV5//XUsy2LO\nnDneAFNuLV53chPdvXs3HTp0YNCgQd55q6s7YUolxBg0X3/99fEMmlvVsP1zcA2eb7/99sDgOSCt\noQxp95RTTkmKtFuJeGql9Es1Jd447TShbMl3TDKvIcBHwtJlRir6RGridywByScifWKkJ1PZJ9AJ\nKCosLOSYY47h4osv9gaW/1IJqQR4BeC5554jFAoxf/587/zbUhF/dSHGoPmhhx4iFArx0ksvebGX\nGDQnoJ9rwTF4HjRoUGmD588JDJ4D0gB8lHYrEVutkn5xJd7PPvusyhJvnLYCybe+MVs6xDh1W8/x\nO5aA5DJbyjPSHiMNTGW/7oDIM888Q1ZWVuwdZaXqRANHA3uKi4s5/vjjOe+887zzNwItkh1/daB8\ng+atwEkJ7m8/g+dly5Z5/QUGzwG1GmqBtFuJGGuF9AtcCI7E27Fjx2pJvHHa3EfyHTt2bOz1BJJv\nXcRI3xpprN9xBCSXsDTASMyQDk1Vn8CpADt27ODQQw9l8ODB3oCyAmhQhXaeBCeRQRLGGK+dvyYx\n/GpBjEHz+++/H8+gOSk3WZRv8DyJNLXBCai7UMuk3YqglPT7+uuv06xZM7p165YS6RdH4l0DNZN4\n47TbxB2TK5J8D5TUr+ZXEuA7RppgpIV+xxGQXMLS/5kU7+8DIgBjxoyhUaNGrF692htMrqliO4e4\nXwD07NmTXr16ee3sANokK/6qQoxB87x586pk0JyAvjOA16FMg+dy91MGBKQS4ki7lmXVCmm3IvBJ\n+qWUxBtT1ajKEm+ctksk3+7du8eTfC+X9LOkqKS2ibieAB8JS//POHLv4RUeHJC2GGmZkR5IVX/A\nWQA///wzLVu2ZPjw4bGDVJXsS9z2RgHMmjULSbz11ltee48lIfwqA2TjyKosX748nkGznYIYGuIa\nPH/zzTe0aNGC8847L9bg+aZkxxAQUBGkgbRbEaRY+iUJEm+cPvaTfH/88Uf69u27Tc6ED0mFkm5N\nRH8BPjJNyjHSZhP8MessEekE40zuU+LvBoSA+eBIEi1atGDTpk3eYPjrarbZDPgJYMCAAXTu3Nkb\n+PYARyX6GqoYW0UGzXG9CpMUS4nB8yeffEKjRo344x//6MURGDwH+AZpJu1WBGVIv506dYq1bKqx\n9Ev5Eu+DCbyeEsn3jjvuICcnh4YNG5KZmYmcSR9yJoBfJqrPAB8x0gsR6RO/4whIDkb6m5FWIqVk\nnxfwW4Aff/yRxo0bM2rUKG+Q+rCG7f4Z4KuvviIUCvH88yXfIVMSFXs14xrp3Y0nwqA5AfHsZ/Ac\n8zfYAQTVegJSCmks7VYESZZ+SaLEG6evfitWrIj26dMndrIX73FkIvsN8AEjnWekaDj4Y9Y5kCwj\nLYtI4/Z53lmlOiwJA0c2bnLDDTfcQJs2bdi5c6c3UNXIHgBHylwJMHDgQNq1a+d9aUSB7om6hirG\ndCNAUVER559/Ps2bN0+IQXMC4ioxeH788cfjGTwf60dcAfUP6oC0WxEkSfolBRJvDCFJw7KzswtL\nrfKVfuyRdEeC+w5INXOlLCOti0gj/Y4lILEY6UzjyLwneM8BpwPr3MFoGzAROBMI1bQ/YAjA4sWL\nycrK4sknn/QGvWk1bdttfzDAsmXLyMnJib37fS8R7VcxlovcAZghQ4aQk5MTm3FcY4PmBMRXYvD8\npz/9KTB4DkgpxJF2c3Nz01barQjiSL/NmzevtvRLjMR71113JU3idWkm6V2Vv8oX+0h5IYCAJBCR\nRhtpTVhKqzqoAeUTll4y0ufe/3GyP38C2LJlC6VYDuQDHarTlzvQrwG44oor6NixY2xmWNdEXI8b\n/zcAQ4cOpVWrVmzevNmLv28i+qhkHCUGzaNHj45n0Fyt1zDREGPwfPXVV8czeM6LOfxQSV38ijWg\n7kAdlnYrggRJv8BzkBqJV9IZcvbveUkclXkECmG6M0s62kjRAuk3fscSkBg+kA400u6INNh7DjgW\nnGzbnJwcTjjhBMaPH8+GDRtiB6UojhXL70tNDMoFuBPg888/x7Ispk6d6rU3KZHXBVwCsGHDBpo0\naYJt214/n5ECvzpiDJonTpyIZVmMHj3aiyHhBs01hRiD57POOqssg+dfSNooaYckX1cqA9Ib6oG0\nWxHUUPoltRKvx0WSdsnJ3K1o0rdH0j1JiiMglRjpPSNF/I4jIDGEJdtIG9+TGnvPAZnAzwCRSISB\nAwfSqFEjsrOzufDCC3njjTfYs2dP7ABdKSkYaOm1e/bZZ3PKKacQjUYBdgMJtQrC2Z/4CcCIESPI\nzc1l7dq1XrxJvXGhlEFzVlZWSgyaawrwf+Cs8p5wwgn7GDz//e9/D8sZ8IvlDPqj/I02IB2hnkm7\nFUE1pV9SK/GW5hQ5N4B7VPHkb1GSYwlIBZ6nn3Hu/gPSmI+khkZab+J49wHHA8tKRp6dO5k6dSoD\nBgwgMzOTpk2bMnDgQKZPn+5N3jzWuANZtzhtjgaYMWMGkpg+fbp3zv8l4/pwDEfZunUrBx54IMOG\nDfP6WwQkZbsCjkHz51CmQfO1yeg3ERBj8LxmzRrat29Pjx49GDNmDKFQqPSAvlvSIb4GHJBWUI+l\n3YqglPR7yimnkJeXx6RJk2LH1hLpl9RKvPE4UtIyVW7yd1wK4glINkb6PCK94XccATXDSEONtDMs\nxd3ADzTFkWQ+jB19Vq9ezfjx4+natSuSOOaYYxgxYkSsNAh7peDfuZOhtsDOaDTKySefTL9+/bzj\ntgAHJOsagekA48ePJzs7myVLlnj9JrxCBrXAoLmmEGPwPG/ePLKzs8sazAsVlHEMqCSUIe2+8cYb\nsWNGnZZ2K4LKS783Q8ol3ngcJOkLlSP7ZmVlRRs0aPC3FMYUkCwi0kXG2evniz1GQM1xV/vWhKVK\nrbYBhwLDge9iR+q5c+cydOhQWrZsSSgUomfPnjz55JNs27Yt9rBduKafr7/+OpZlMXfuXO93Sc0S\nB7oD0d27d9OhQwcGDRpUMn8lgTYqONLyJICffvrJV4PmmgK02rFjx6KLLroo3kpf6T08wapfQJlQ\nStp98cUX67W0WxFUTvqNgi8SbzwaS3pHUpHKGCdatmy52Ye4AhKN6/v2VcRJ7Q5IQ4z0JyPtjFSj\npqI7mXoSZ7UO7w61MlLw+vXrmTZtmvffdVQhMaS6AFPBSbIIhULMmzfP6//2BPZxn3cX3rt3bw47\n7LBYg+bXSbFBcw1p2qhRo88yMjIqknAKVcr7MSDAgzKk3eHDh8fuEa6X0m5FUAnp99VXX+Xxxx/3\n/psqiTceGZIeUznZviNHjvy9T7EFJJKIdI6RKJBSZo8RkBg+kFoaaWNEGl2Tdty700uAt3C96qDS\nUjA4RsvDSLKXHXAUsKe4uJhu3boxYMAAr/9NQIsEtH8TlGvQ3DAR15EiDpRTbqkyWXtI2umeExBQ\nAoG0W2OonPQLjhVWKiXeshhmWRaWZe0zRmRnZzNs2LCf0mwcrLdUaHlhpPclHbBe6n6pk+0XkAYY\n6WFJVyAddab0cyLaBA6VdJWkPyjGu+nzzz/X888/r8mTJ2vjxo069dRTNWjQIF155ZVq3LgkkXi3\npOmSnpf0umVZRYmIqVR8T0oa/NZbb+n8889XOBzWGWecIUn3W5Z1Zw3avUjSVEkZQ4YM0bPPPqv3\n3ntPffr0kaTvJPW0LOu/CbiEVHGfpLuqcHyRnPfTbckJJ1HMbC0VdZJCHSSrg8RBcjwJD5RjTGu5\nPz1QyWfD2iSxRtKPkrVGYqkUXSpZC6Xd70vnAAAgAElEQVQzE/L5qSsAjSU9LmmgJE2aNEnXXXed\nOnfurJdeeknt2rWTHDugIZZlPe9jqGkB0EBO8t1QSXrjjTd0zTXX6OCDD9bUqVN13HHHSc5n8DbL\nsh72MVRJ0oEHHjj4p59+ehJQcfHeKUHbtm21fPnycRkZGbemNKD71E7FOlpSB6EOklpLOsz9mSvH\nkzhWdSqStNX9+bOklZLWy9JKSUtlaamiWihbO1J5GamkwolfWOpsSfMt6YY+0pOpCCqgZhRIx4Wk\neZZ0Yx/pqWT0gVMabbCky+V+qHbv3q0333xTzz//vN599101atRIF1xwgQYNGqS+ffvKskrebj9K\nekXSM5ZlJazQN3CwpO8lNTrzzDNVWFio2bNnS86K1dGWZa2qRps95UxYG44ePVp//vOfNXnyZP32\nt7+VpHWSTrMsa2miriFF5MmxahkiR7qpjES9R85gui6JcVWBT5pIu0+R1Fuil6SO2rsquVvSMsla\nLbFJ0kbnZ2iTpE1724iGJKu5JO/Rwv3ZVlI7SVnugaslLZSYJWm2lPeZdFKd/VIoD6CLnJugY3fs\n2KHrrrtOL774ov70pz9p5MiRysrKkqQFki61LOs/vgabZgCzJPWSpBUrVuiyyy7TwoUL9cQTT+iK\nK67wDntN0jWWZfm6p27o0KG3PfPMM6N3796twsLCkufnzp0b7d69+5mWZc1MSsd/1YEq0ilCZ8qx\nnDlGe2/mtklaImeM2ijns75JljYKbS1pw1KWULzPfXtJbeSUrCuSMyH8UpaM0MeSvpCthC9Y1FqM\nNMZIm2ZKB/sdS0D5IIUi0mwjzcF5Aye3PzgPHFNgtzrHPlLw8ccfjySOPvpoRowYEbtJ2WNuIqVg\nYBTArFmzkMSbb77p9fN4Ndo6ljQyaK4Gp8hZsSxWBXJvZmZmNCsry8fklakZUkEPyfxFMrMkUygZ\nJPOtZF6QwrdJBedKsw+Tal5yUApnSpHDJfNrKfxnybwsmeVunzslM10yt0sFx0vJNwuvDZSWdjt2\n7BhIuwkC16h5165dfPvtt8RKv5Zl1bjWbzIwxrx6yCGHlLgCZGVlcfvttwMscVeFa46tBrLVT7Ye\nlK35shWVrSLZmidbT8nWUOXrTP01QVtRJihHI9VR+bpctvJl6y3ZWidbyNbPsvWabF0nW7WiIlNS\nCUu5RvrBOHd6AbUYIw0xUlEqsrGBkLc/5a677qJz587s2LGj9MSuJCu4VatWFWUFv4Wzn7Da/ntA\nM9xSdOeddx6dO3f2JqSFwLFVaKfEoPm9994jKyuLW265xYu11ho0V4MsSX+Ws9+v3D1/GRkZxQ0b\nNqxyolD1ISQV9JUij0tmvTvpWiGZJ6XIpdKMQ1MXi8esDlL4KslMlMxaN6aVkhkrmVNTH0/yoZys\n3RUrVnhPB1m71QRoQYxRc6nykzWu9ZvEuJssXrx49VFHHUVOTg6SaNOmjZfoV30bqAnKka2LZWuS\nbG13J11fy9Zo5etc2arxnu0qgSzZ6upO+F6Trc0xMdmyVenvlbTDSGcbKRqWLvM7loD4zJCOMNJW\nI6Uk5R+4DBwj4MaNG/PAAw94g9KHlJEV/Oabb3LJJZeQmZlJkyZNKjKIrtZdLY4lDV9//TUZGRlM\nnDjRa/elSp7fFPgKHJ+7vLw8rrrqqtgYa61Bcw3oKifho8zVv6ysLM4666w5yQ9l5sHuyt5id2K1\nQIqMlMKdk993VcCSwie5k77v3Vi/lMwt0qzmfkeXCAiydpMOpYyaJ0yY4L2uq6jY8PlVKqj1m+TY\nB2zdupWzzjqrZJz4+OOPwUkEPKFKjdk6VrbGuitsxbL1sfL1v7qv6q4USeVJZbmrkM/K1o/uKmRY\n+bpathr4HV7CMdJTRto429nrE1CLCEuZRvrYSAumSUlP+ccxMl4KcP3119O2bVt27tzpDUa9Y46L\nmxW8Zs2afaTgww47jOHDhydECnb7/AFg0KBBtGvXzqsaEMXZm1jRdZVn0DyiBi9bbSdT0nBJezIy\nMuLaNmRlZfHRRx/9JTndzzxZCr8omd2S+cmZUM3smpy+kkHkNGc10myVzHYp/IQ0s5PfUVUXAmk3\n6VBBLV7iZP1WQvq1JI2RdHeKruHpnTt3cskllyDJk3sBKk5EsRWSrXNk6113ArVctu6RnSZzjKnK\nUL7Ok61XZGuPbK2Xrftk1yHvU1fyXWSkWWHnSyKglmCkvxqnQsd+ZdSSATAEYPHixWRlZfHUU095\nH/Zp5ZzTBmc1bvE+M7vKS8FTcfYUVvjeAwZ7k7ecnBzGjx/vtfNeOefsY9DcsWNHunbtGiu7JKXs\nXC2kc1ZW1telLRvkTvyGDBkSxcl0ThDh/yeZD90VszlS5Appml9+ZQngvcZSZLBkvpFMVDLvpZMM\nTCDtpgRiJN4777yztFHzAzHHVbXW7zDt/cxenYLraA6sLi4uJj8/nylTpngxvVDmSbYyZet3srXI\nnfBNU776y07+vvSkYesA5esu2VojW7tl6wXZe90v0pqw1M1IO02c2q8B/mAcGb7YOFmaScf9YlgD\ncPnll9OxY0dvH10xTtZfZdqoiRS82h0Iy1wNwqlJ+w3AsGHDSu+bietLiWvQvH37dnr06JHuBs01\nJfOoo466Nzs7O5qZmbnf5G/FihU7gV4168L0l8xnzoQv8oEUOT0BcdcisJyEk9hrLKgNXmxlQjnS\nbmFhofdZCKTdBABMhLgSb1yjZuIYPufm5u4j/f7rX/8y2ne7xh5JJ6fgWs4GSrR/nBXLc/c70Fnh\nGyRbS90kjRc1UnXrvWQrW/n/n73zDpOiSr/wqZ5EGrIoQUBRwLQKKKuyriiKOaxxMYeFn3nNmKkB\nFUyYA+YccM0KssD0bUBgEVBYQQEVVkBQSQISJr2/P6pqaIbJ093V3VPv8/SDQnXdU90z1bfvud/5\ndLF7jcXuBDD1i0Ei0qUm2O+XFESkPY201khvJGpM4DaAWbNmYVkW7777rvfLXmMNbLOCx+O2KYJt\nVvABBxywnRUc1YfXo0IrGDgD4LfffqNp06YMGTLEe86XsH0lJlEBzSeddFKqBzTHjJEjR55y8MEH\nF0Wv/rmrfuBUPNdiY3O4u2Q+SZXJUGwoneSWSOZVKZJc+5a0vbX73//+N7B24whVWLyVPK9C63fp\n0qXssssuhEKh6G0aRXLis3ZKwDUdAbwLfFTBpO+vsjWrdDI0TF3irclXbGUqT+fL1mJ3BXC4Rsi3\n/ZgxwUijjLQxLKV6pEXKMllqYaR5RpozU4pZT9rKAFoB6wCOOeYYDj74YG81bgvQuY7nLtcK/uab\nbxg8ePAOVvCGDRuiD9vBCsaxbqcB2LZNkyZNWLlypXf8GVHjnoazWslll11GTk4OxhjvuO/Km1TW\nJ4qKio5/5plnihs0aIC3+peZmel1aVmMk59YDWY2ksz9kimQzH+lSP+4Ck86sJxqZLPY3QN4izQz\nq+rnxVlVYO0mFKpp8Vby/O2s39dee40mTZrQrFkzyq7Ou49CSZPk1/YsW7vI1ltuJex4DZXvUTQJ\nxVa2bF0rW2tla6XydK7fkmrNTCkrIk0w0srJSoNlzBQjLDUwUsRIy2vTi7e2AA8ATJw4EUnRE6TH\nYzxOpVZwVlZWtaxg4HCADRs2sPPOO3PNNdd4xywAMoE+uKscI0aMIBQK8fbbb3vHrASCn205ezrn\nz5/PgQceWPqBcuWVV3qv03tVnyF8lDvhWSdF/pkMEx7/mNlICttuHuBcpzLYHwis3YRDDS3eSs5T\nav3ecsstVbVdLJYT2p5Y8nSpO+FZIltnVP2ENOYe7Sxbz7l7GselTAFLWaZKLY0030gLv5Da+K2n\nvjBayohI/zLSRiMdlKhxgY7A5pKSEg466CCOPvpo74a1HoiLlUBsrODvAB599FGys7Ojj7kTN6D5\n5ZdfxrIsHnzwQe/fNlBFBXB9A7irqKiIhx56iJycHLKzs1m1ahU4uYYVVLGNayxFHnctzo+kKelT\n7VZnJnd1w6gLJJOX6MkwgbWbcIC/Qc0t3krO1238+PGLMjIyqtNzu0TSmbG7mkqw1U62PnYnOY/K\nVvAz5DFUh8nWQtlaqzxd7LecWhGWOhhpiZFmTZea+q0n3UGyjGOzb41ICbXKgOcB3nvvPSzLYubM\nmd6Hw9AEje9Zwd9HfzJ5VvBOO+1UmRXM1q1b6dKlC+eff773V0VQYUDzcYm4plQCxzp/BZxq7muu\nuSZ6Vajrjs+Y9Ce3snW9FIl7dWFqMjpDitzgRNhEpkvhzvEekcDa9QXqaPFWQCvLslaU2ddX2WOT\npPhGDOXpODfW5H/K0xFxHStVsdVEth5zJ8ajU3Lvn1tg8LORpoW3b4IeEEOQrLD0hJEKI1IM4zSq\nMTbsDRQVFhbSrVs3zjrrLO+G9QuQW/UZYq6nSiu4YcOGnHnmmYwfP7508vfqq68SCoX46quvAKe1\nXD0JaI4JQBYwlu2ZsOOR4Ysks8lZ0QqnR6RBXIn0cPY9mjVS+MR4jUJg7foGZSzexx9/3Hu9a2Tx\nluFdVdFxJ/oRCoVKQqHQAsVjT7hTsXuvO5n5l+5Vq5iPkW44nUhWyNb3Kbn3cZK0t3Emf19OUPCG\nx5rRUoZxVvoK/KimBt4HeP7558nKymLhwoXeTeu6RGspo6sh5VjBK1as2M4K/utf/0pRURHFxcUc\ncMABnHjiiYBTxfv888/Xl4DmmIATcn2Vu2p0CxD1ITIzSzKPOtZu5AGn521A9ZjZyG0FVySZ2xTj\nHsAE1q5vEGOL16WjHPu2SNWc+EkiIyOjpE2bNmPrek3bMVwt3L62m2VrUEzPne7co7ayFZGtDbJ1\nlt9yaoy78rfUSPMTWXCQ7oyRcoz0rpG2hqVTEz0+cIi3QtCuXTsuu+wy70PiRyA70XoqAtiVSqzg\nl19+GYBPPvkESYTD4TILVyyimgHRAeUxvakbVrxZipztt5rUJfJPd/L3mjSvzr9fVGDtHnLIIYG1\nmwCo3OKta8HFwZJelbRVzgSwwpaL0Q83AqYaRVnVwFZn2ZovWys1VPUgmikOOIHWT8tWifJ0u99y\naswUqaNxIkaWRqRqhfkGVExYam6kfCP9HpaO8kMDEAF44IEHaNy4MT///LN307rIDz3VgXKs4K1b\nt3pB0/Tt25c+ffoAUFxcXFFVcPDzW22mtJPM15L52c8q1fQh/xjJ/C6ZfGl8rff/EFi7vkN8LN6y\n5EoaJGmutgU3V2X78vrrr0+hLr1+bfV0rcqvZKtDHa8hIE9XusHWz8pOse5ok6UWEckYaUNEOtlv\nPamKu4K60EjL8uWP/w8cC7BmzRpatGjBrbfeGn3TSvpOFmyzgr+LntlNnz4dy7L4+OOPS1dBBg8e\nzPffb7dYCNsCooPtCxWS300ySySzUIrs5rea9CHSQzIrJTNHmlTNrMRtEFi7vkN8LN6q6CXpWUmb\nJRWX13pR7sSvRYsWLF68+Ae27/VbPfLUT7bWy1a+7KCwM2bYOtW1zD/SSKVW4wDXnnzRSCVGysNp\nHB1QTcLSiUZab6Qvw9r+mxTwV3cycng8NQAh3JT42267jVatWrF27VrvA+OUeI4dS4gKaP7kk0+8\nGy8nn3wy++67b+nev0MPPbSyquDN1KBXcP0hv5tklktmtjRhZ7/VpB/hPSTzo1MdXb3JH4G1mxQQ\nX4u3OjSVNMiyrPmS03FHZSZ/WVlZ9OzZky1btkT3+q0ap6/uJtn6QLYaxEV9fcZWX9n6XbbGptzk\nT5LC0lVGKjDSuIlSe7/1JDthqUFYetA4E+ZXx0jbWQGAXWZF6nmI7SbwqLEGACxdupSGDRty//33\ne2N+EY/x4gHwF2/VY/jw4YRCIebMmYO3CpKRkcErr7wCOJZvRVXBZazgZcAIyo0wqU+E93CsXTNV\nCgfV/HFjYnvJfOs8vqg0L5Uoa3fjxo2BtesjJMbirS69DjjggPycnBxCoRDRq4CZmZkMHDjQ0/Ye\nVVm/zkrfZrcHbfAlOF7YOtgNv/5cjynRPy91x0iHGOl/RvotLMUtqiDVyZe6GWm2kTYZaWDZfwea\nAoUlJSW88cYbbNy40ftlfTLWWnAqN38EuPzyy9l1113ZvHmzN95hsR4vHgDdcQOaX3rppbIBzSUA\nF154IZ06dWLLli3bzaZXr17NqFGj6NGjB5LYddddAyt4OyK7uvburLrsQQuoLhPbuyt/X1c0yQZ6\n4LZTrMTarfqDPaDOUMbi7du3byIs3ip5/fXX+91xxx2/dezYEUnk5OSUTgDfeOMN72fkP1Rk/zuT\nkQ2yNVqjlfRbfVIeW71dO/29lHy93b6ybxkJI70U5P1tw41qud5IW4w0OyztW95xwE5A4dq1a2nW\nrBknnnhi9Lf4K2OpCbgCYOHChWRlZfHcc89543wWy3HiBdAWp48sn3/+eXkBzY8CLFmyhJycHB5+\n+GHv3xZSQVVwmzZtkESvXr0YNWoU69evjz6sHlnBE1q5wczfVbUCFRBLwnu4e/4mS1N3sH+AMQAf\nffQRzZo1Y//99+e770q3tgbWboIgyuK99dZbadKkidfXGhJj8Valr0FRUdFjY8eO5ZRTTsHr+tGg\nQQPmzp3r6bxihycO1T6ytdptN5Y0aQ5pz7YV1hdEim6ZC0tnGOk34/SYvaC+7/3Ll/oY6T9GKohI\nw8JVNNIGPgKnOKFRo0YMGDDAsyGLgL/FQhOQixPMzN///nf22msvrxq2mBSodAWaA3MBZs+eXTag\nuQQ4F8gA5gNce+21tG7dmt9//9276R2NYxGPwu2FCU5VcA2t4D39fi1iz7xsyYQls1QKB1V8CWdS\nT8mslcxbZXP+cC3es846i8suu4xNmzZ5P48/AEGldYLA3V+ZJBZvhQCDgM3Lli0jLy+PTp06RfcN\nfmC7g221ka2lsvVlSu45S3VsnSZbxbJ1l99Sak1Yam2kF4xUbKTIJKm3z5ISzhSpnXFeg5KINGWS\n9KfqPA9oD/wE8PHHH5ORkRFdabsVOLqu2oDbAWbOnIllWfzrX//yzv96Xc8db4AcIB9g8eLFtG3b\nlqOOOio6oPmuqGPPAPjtt99o2rQpQ4YM8Y75EnffJBUERHtWcM+ePZFEhw4dqrKCW/r2osQU85Jk\nNjgVpwH+EOkvmULJbNcqEXiw7A8fgbWbUEhSi7cigP2BBWV+ZkqAba3WHlOObE2TrR9laxcf5dZv\nbF3vdkU5w28pdSJf+rORphpnAvhqWOrsq6AEMEXKjUhDjFOx+z8jnV/TcwD7AmsBnnnmGSTx6KOP\ner+0v1Ob0vxt526Fu0+of//+HHLIId55twCda3veRIDTS/ZNgF9++YU99tiD/fffP3ol7/Fyjp8G\nkJeXR5MmTVi5cqV37A6/XGwLiP4h+i5Zf6xgc4VkiiUTk5XlgLoQvsrpjpJfWl2P007vKWANzpeX\nf/ipsL5BGYs3Nzc3qSzeisBxeB4EluJsc7l4uwOcTLkNssvfghSQQGw9574X8e23nAgi0rlGWmyc\n6t9XwlLa9fYcLzULS7aRVhtn0pcXVu3L4IG+7mSMwYMHEwqFeO+997ybzHKgUy3P+yDAhAkTkEQk\nEil30pSMAPeCU83Yu3dvOnbsyPLlyz3971NO7iBwOMCGDRvYeeedueaaa7zjF1Q0QcOJudnBCi4q\nKmL8+PGlVnCDBg3SyAqe1FsyW6VI0NIuaTDPObbvxC5+KwnYZvH+5z//KWvx/pcksnhrRJ4udLtJ\npEx8V1ozSlmy9YVsfStbqZ+/OU/KNtLFRlpgpKKI9K98qY+vomJARNotIj1snMne2rBkh6XWsTg3\ncDZQXFJSwoUXXkjDhg2ZPHmyd7OZB7So4fk6AptLSko46KCD6N+/v3eu9cBOsdAcL4BrAAoLCznx\nxBNp0aIF33zzjaf/C6DCfSnAvwEee+wxsrOzo+3aHSqry3luuVbwmjVryrWCFy1aRBlqYgX7FIcU\nbi6ZxZIZLxHyR0PAjkxtKJm5UmSmNCY1JxZpAlEWb/fu3ctavKm5lWmY9pKtjbI10m8pAVHcrV1l\na5Vsvea3lJgxWspwC0AmGacCeK6RbvpCSpnqwbDUwEjnG+nfRioy0g9Gum66Yp9uDtwEUFBQwDHH\nHEOrVq349ttvvUlFBKj2qiLwAsC//vUvQqEQX3/9tXeevFjrjiXA6bgBzYMGDSInJyd6pfI7qoha\nwdnnUlxQUECXLl0477zzvOcuBxrVQEesrODyyvb/LKch+/uSErxf0IyWzK+16R4REG8m7S2ZPyTz\nkN9K6iukqMVbKbYayNYc2ZoZVPAmIXk6RbaQrfSr1J8k9TROAcTvxrGBJxrp4vFS0m1WDkuZEel4\nI71hnNW9QiONiUgnI8V1lQR4GGD9+vX06NGDDh06sHTpUu/GMxqqXqUB9gGKCgsL6dq1K2effbb3\n/F9I4pZOOJbrZoB7772XUCjEO++842lfAVSrhRjwNsBrr71GKBTiq6++8s4xuBaaqrSCs7OzS63g\njz/+uLSHsEt5VvBzcpqwF0laKalvTXXVjshZkkEyJyVmvICa4+29zE95hyQVoYzF+8QTT3i/x6lr\n8dq6R7b+0DB181tKQAXYGiVba2Srnd9S4sI4qXFY+kdEmmCcrLstRhofkW6YJP1ptPwJNpwidTTS\nxWHpbePs3Ssx0ldGurNsm7V44k40RgP8/PPPdO7cmf322y+6vdpT1TjH+wDPPfcc2dnZ0a2FrkvE\nNdQGnIDm1bAtoPmhhx7ydG8AetXgXHsCBcXFxfTo0YMTTjjBO89a6lCRixO6fQEVWMG9evWq0gpe\nunTp1XL6bnphq8Xun89KqvaKZM2Z0MpZ6TMvxG+MgLqDJZlxklkghYP2WQmEMhbv0Ucf7e3nLQB6\n+q2vVtjqKVtFytPVfksJqARbTWTrB9n6yG8pccctkjjPSK8ZaaVx7OANRsoPS3eEpWMnS7vHetxJ\n0k750uFGutpI7xlphTv2RiN9ZqRr/KxKxum0MQFg0aJF7LTTThxxxBHRHSluqOS5hwL88ccftGvX\njssvv9x7zg9AUi7zAx1wqtAqCmg+rhbnfBrg008/RRLhcNg73/AYae5IJVbwzjvvXK4V/MorrxAK\nhcprtF4kaYGkWldxV074eScweHKN9ooG+MHk3R3LNyi+SRRUbvHG5J6RcGyFZGuGbE2XHV+nKiAG\n2OrvWr6n1uRpKR2ejGQZaf+QdDjSgZL+IqmjHGv1d0k/SPpJ0q+W9BPSWqQ1SGstZ9WkLM0tqYWk\nFkjtLPeB1EUqzS/6TdJ/JM2wpC/+kL44Xtoa72utDjh5XZMk/WnGjBk68sgjdfLJJ+uNN96Q25vx\nQsuydtgQCkySdNgDDzygvLw8LVq0SG3btpWkiy3LejmhF1ENgOZyrnO/2bNnq2/fvjr11FP1yiuv\neNd5vmVZb9TivG0lLZLU+Mgjj9SmTZs0bdo0WZa1WVJXy7KWxUh/SNKhcqJ8zpGc6qwtW7bogw8+\n0CuvvKIJEyaoSZMmOvvsszVjxgzNnTtXJSUl5Z2uSM7ev5slPSZnQhgDwgdL1hcS50pHvB2bcwbE\nl/CNkjVMythHOuxHv9WkO8Crks6fMWOG+vTpo0ceeURXXnmlJH0j6UDLspLic6FG2Bok6UlJPWTr\nG7/lBFQDp8jjMEl7y9Ymv+X4whQpN186PCJdaaSHjPSBkWYYaZFxCi2oxmOpkb6KSJ8b6amwdGNY\nOnGSlPSb26k84LmAMgHPwLHgBBI3b96c2267rXQhivKLDHyFqIBmb2WzTEDznXU8/73g7NexLIuP\nPvrIO++oWF1DmfHKtYKXLVvG8OHD6dKlS3krfeU9SiRNUEx+RglJZoZkInU/V0DimJnl2L3mA7+V\npDukp8XbXLZ+k61H/ZYSUANstXOz/Wy/pSQ146Vmk6UWZR/xLsJIFDiFGmugwoDnA9zjQsAcz6po\n1aoV69at84472efL2AFqGNBcyzGaAasATjnlFPbdd1+v8KII2CsW11HJ2DtYwUOGDCEzM7Nak7+M\njIySrKys9Tk5OXUsxDDnO8UCk1LzA6xeY05yinHCff1Wkq6kpcUrSbYecIsF0qTLUD0iT3fK1h+6\n26/Ir4CkACeYeIt3cwqFQtGt15YDnYBzAJYuXUrDhg154IEHvH+f4rf+8gCGQ80Cmms5zk0A3377\nLZmZmbz88sveGKNjcf5qjB8C/lJUVPRsu3btSlS9FT8kEQqFsCyLfv36fTV16tRa9FUekyOZJVI4\n6dvzBVRExDgrtqT0Vp5khXSs4r1bnWRri/J0k99SAmrBA2osW8tlKyjEq+8QFfB80UUX0aBBg+iA\n5/nAEoDLLruMXXfdlc2bN3v/9he/tZeFcgKa582b5+mtNKC5FmM1wLXLL7roIjp16uQVyZSQ2H6b\nR6gGk77oR0ZGBl27dmXatGnzcJqx51ZvyPBlkilwigUCUpPIoe6q34l+K0k3SEeLV5JsPS1byzRS\nMbuPBiSYPF0uWwUapmpFmAWkMcCNsC3guWXLltEBz3z33XdkZmby/PPPe3/1qd+ay0KZgOYGDRrU\nKKC5lmP+A2DJkiXk5OTw8MMPe+ONj/VYlfCipELVYfLXsGFD772tKiBa7h6xJZJ5Nr6XFRB/zBgp\nMt1vFekEURbvLbfckj4W791qL1tblacr/ZYSUAdsZcvWEtmKy370gBSDqIDnnj17bhfwPHPmTPbb\nbz9vH1sxUAt7MH4QFdB899131zqguRbjZrirolx33XW0bt06ei/hUfEYsxzmyqk+3yKncrzGkz+3\nypnTTjuN1atXe/pnlv+6RS6UTKEUCb4xpjzeql/kSL+VpAvAa+BYvBkZGelh8UqSrZGy9bMeU+pe\nQ4CDs+q3JdjrF7BdwPOvv/7KHnvssV3A8w8/lNYSJFXvP2Av3IDmF198EcuyGDlypKe1RgHNtRz/\ndIDffvuNpk2bctddd3ljfwkJ2VuFfCAAACAASURBVD/VTdLFku6U9ISkDyVNl/SzpAJtv7evpGHD\nhjRq1IisrKxyJ4A777wz+fn53jX8yg4ZjWaOZN5JwHUFJAQzVTKf+a0iHSBdLd4RaiZb65Wn2/2W\nEhADbDVy+/je67eUgCSAMgHPbdq0KRvwXAh09lunB07f26UAY8eOJSsri+uvv97TuhVIyEoGMBVg\n6NChNGnShJUrV3oazkzE+FWwk6R9JR0j6cLu3bs/esIJJ3x10kknbTnooINo37492dnZOxR/RNlT\nUfsV8w9394Ud7MeFBMQDc6ZTnR3ew28lqQzpavFKUp7+KVubgkreNMJpt/drsIIbIKk0M+5Xz65o\n3LgxAwYM8L65AiRFw2eguWufMGvWLHJzczn//PM9nSXAuQnU8ldwKol32WUXrr76au+1WghkJUpH\nTWD7XsEb16xZwzfffMP48eP58MMPvdexmO32Rpq3JPO1f6oDYs/MLMn8LEUe8FtJKkO6WrySZGue\nbL3st4yAGOJUaBfJ1jl+SwlIAnBy4kqX+D755BMyMzO55ZZbvL/aIeDZB42lAc0LFy4sL6D5Dh80\njQN4/PHHycrK4vvvv/e0DEq0lpoCtMapiJ7N9tyy7agJrSSzRTID/VMaEB8iwyTzizMJDKgpwGlQ\nocXbw299dcLWoW67r95+SwmIMbY+kq18v2UEJAHAiwDjxo1jzZo1AIwaNarCgGcf9IWAt8AJaO7S\npUvMA5prqWt/oLigoIAuXbpw3nnneXp+Bhr5oak24AR7/w0oY/2ZKySzSZre1B9lAfFjYhfJlEiR\npAtkT3ZwLN4VkIYWryTZGiVb8/yWERAHbJ0mW8W6W538lhLgI+6HflFhYSFdu3YlLy+vdOnntttu\nKzfg2QeNI8CxVQ866KCyAc3v4WMLOeBtgNdff51QKMTs2aULaIP90hQ7TEQy7/qtIiBemKmSqXHv\n6voOZSzeJ5980vudTweLN1O2VgVFHWnKY8qRrbVBIHc9B/gA4NlnnyU7O5sff/zRu4mVlJSUcPHF\nF5cNeJ4HJGzDL/BPcAKaTzjhhLgGNNdS355AQXFxMT169OCEE07wtK1N5OsUeya1dQsAzvBbSUC8\nCF8rmd+lcAO/laQKpLPFK0m2+rs2b1D4k67Yekm2ZvgtI8AngEMB/vjjD9q1a8cVV1zhTVp+AG4G\nJ+D52GOPpWXLlsyfP9/790lA3D8sgDNwA5oHDhyYkIDm2gA8DfDZZ58hKToaZYTf2mpP+DLH5h3X\n2G8lAfFiYienYjv/BL+VpAJUbvGmR0yG06ljrt8yAuKIrZNlq0S2OvgtJcAH3Akc9913H7m5ufzy\nyy/eTexC999HQvkBz8BHxNFiJSqgediwYQkLaK4NQFtgI8ARRxxB7969vVWAzcCufuurHebjIOut\nPmDmSuZJv1WkAqSzxevhdHhIj0lsQPk4/Xs3KU//57eUgAQDHAewevVqmjdvzu233+7dxL4GQu4x\nlnez+/XXX9lzzz23C3gGno6Ttv2AdQAvvPAClmVF32Q3kITBqMA93oeCZVl8+OGHnt4UbJMzL1sy\nGyVztd9KAuKNuU8yP/qtItmhjMXbv3//9LJ4JcnW3rKFhuowv6UExBlb42Trfb9lBCQQnCrZuZ5l\n0apVK9atW+dNVE4uc2xpwPP3339fXsBzTDeJkiQBzTUFaAasAjj11FPp3r07hYWFAEXAXn7rqxnh\nv7ihzd39VhIQb8zRzns9MajyqwDqg8UrSXm6WrY2aJSCiJ90x9aNsrVGtkJ+SwlIEMA5AEuXLqVh\nw4Y8+OCD3k1sSgXHN3VXApkxYwaNGzfm73//e3RwckwCnokKaJ42bRqNGjUqG9Cc1MGTwE0A3333\nHZmZmbz00kve6zrab201w9wmmRV+qwhIBDMbSaZAiiRFSHsyArzureanrcUrSbbela3P/ZYRkABs\nHegW8fzJbykBCQAnCHkxwP/93//RsWNHNm/e7N3I/lLJ89oD/4P4BDy7usKwLaD56KOPjg5oTvp4\nAaAB8BPAxRdfTKdOnbyV0RIghVqemTGSec9vFQGJwsyQTApuSYg/1AeL18PWMtm6y28ZAQlglLJk\n6w/l6XK/pQQkAOAqgPnz55ORkcELL7zgTaw+qcZz9wHWgBP/ohgFPBMV0Lxy5cryApofq815/QC4\nFGDZsmU0bNiQkSNHetdg/NZWfcxvkglynuoN5jHJzPZbRbJBlMU7ePDg9LV4JelutXdXgPr7LSUg\nQdiaJFsv+S0jIM4Aubg9ec866yz23ntvioqKwIlM2a+a5zgct73b7bffHpOAZ8oENHfq1ClpAppr\nCpCBk3XI9ddfT+vWraMnsL62vase4Q5uxEc/v5UEJApzsWS2Bu3btoeKLd65pJPFK0m2TpQtdK92\n8ltKQIKw9YhsfeW3jIA4A9wJMH36dCzL4v333/duZK/W8DxnAcUVBDzPpwbBxVQe0DwFnwOaawOu\nPbRq1SqaNWvGnXfe6V3PTMDyW1/lmOOcid+k4AOg3jCpp1vMs6/fSpIF6pPFK0l5ulW2lvktIyCB\n5Oli2doiW5l+SwmIE0AbYD3AUUcdxaGHHupNRrYAnWtxvuuhwoDn6VSjVy1RAc3/+Mc/ygY0f0uS\nBDTXBmAqOBmEjRs3ZsWKFd51neW3tsqJ3CCZX/1WEZBIZjZyurREkvxnMzFQnyxeD1uvytZ4v2UE\nJBBbB7v2fle/pQTECeAhgPHjxyOJSZMmeTeyR+twzpHgdP44+OCD6dChAz/99JN33koDnoHDcAOa\nhw4dSigUYvTo0d5zkyqguTYAfwXHvt5ll1246qqrvGtbCCSxpRZ+RoqUW90dkM6Y/0mRpC+gSgS4\nFu+UKVMIhULpbfF62JouW0/4LSMggQxXC9lCeQo696QjQEdgc0lJCQceeCDHHnusdyP7Hai1pYcT\n8PwqbAt43nfffasMeAb+RJmA5qeeesp7zjqqud8w2QE+B3jiiSfIysri+++/964xiRPTzTjJvOa3\nioBEEzFS+Hm/VfgN9c3i9bC1UrZu9FtGQIKxtUZ5CoL60xHgJYDRo0cTCoWYM2eONwGxY3DubGA8\nVBjwfHOZ43cFlgF8+OGHZGRkcMMNN3jHJm1Ac20A9geKCwoK2GOPPTj33HO96/yZaljh/mDmSSY9\n7ayASjCvSZF6neFGfbR4JclWtmyVKE9n+y0lIMHYmitb9/stIyDG4ESwFBUUFNC1a1cGDBjg3chW\nAk1iNEa5Ac/FxcUQFfBMVEDz1KlTUy6guTbgxtS88cYbhEIhZs+e7b3+t/itrXzMGil8ld8qAhKN\nuU8yc/xW4SeUsXijXAi/LN4pkiZKullSTylOXRZsdQxatdVTbI2VrRoVdwakAMCHAKNGjSI7O5sf\nf/zRu5n9M8bjtMMNeP7000/JzMxk8ODB3lgFwAm4Ac0LFiygdevWKRfQXBuA3YCtJSUl9OzZk+OP\nP9673rXUoPo5MYQzJVMiRYJv/vWO8I2S+dlvFX6Ba/Fu2rQpmSzeEvdRIAlJ6yS9K2mgpNjtgR6q\nXu4m/6BFY33D1muyNdZvGQExBDgUnOKLtm3bcuWVV3qTjh+A7DiMtzdlAp4feeQRb8xC2BbQfMAB\nB6RkQHNtAJ4CGDNmDJLIz8/3rvs+v7VtT7i1m+F3jN9KAhJN+B+S2eS3Cj8AdsLNNy3H4r3HR2kl\nciZ80Y8ibZsIrpYzERwkqWOtR8lTP3fit0tdBQekGLYek61p3v8GuS7pwQhJevzxx7Vx40bddVdp\nN548y7IKYj2YZVnzgVMljRs4cGCDpUuX6vrrr1f79u11xhlnZAIaOHCgioqK9Nlnn6lp06aS9L6k\na2OtJckYJumC4447rvGRRx6pW265RdOnT5dlWdcAT1iWtdRvgQ6hXOfzJGOD30oCEk1ovURDZ9X3\niCLvb4F9JJ0mqYN/2uLOkZJ2Wrlypbp06aLp06erc+fOkrRG0s6AL+3sLMsqL/Mzw31IUsuMjIzT\nSkpKTgesVq1abezdu/fSI488cvk555yzrF27dmXv8Uj6WtJ7lmX9FvW3uZKkxgp+7+sfGyT3/Q9I\nfYDjAVavXk3z5s254447vG+wXwHx2SuybezSgOdLLrlku4DnBQsW8N1333laUjKguTYA94DTBcCy\nLD788EPvNXjWb23biOzlrPhF0rd6MaACzEluiHPpvl/gONwuPQGJx7Kssqt9VT5ycnKQRCgUYv/9\n92fw4MGMHz8+utgO4HugXelbn6cBsoVQkofLB8QcW3fI1g/e/wYrfikMzsRuhCTdf//9ysjI0I03\nllbq32VZVkk8x7csazTQwbKsh5599lmtXr1aJ554oiZPnqz99itNalko6RTLsjbHU0sScb+k/+vd\nu3erU089VYMHD9YJJ5ygzMzMS4CHLcv61m+BkpXlfH6UFPqtJCDhuKt8mdEZk7aknLfeektz5tTr\nug9fAGr8nK1bt0qSSkpKNHfuXH3zzTe677771LRpU/Xr109333239t577y6SLpXjREgoS1KRLNV8\nwIBUp0hR871g4pfaDJC039KlS/Xoo4/qnnvuUbNmzSTJWJb1SYI0fCmpKCMjI/PNN99Uv379dPzx\nx2vq1KnaddddJWmpnM3K9QLLsn4HRkh6YPjw4dp33331+uuv66KLLsqQNFTSmT5LjKIWnzgBKQ5I\nlqSM6FWfVpI0btw4ffPNN/7ICqg1gIqLi2VZltavX6/PPvtMp59+uvbee2/JfW+jD/dBYoDfWCJ4\n59MAIAdYDDBo0CA6duzI5s2bvSX+vyRIQ2lAs2fr/vbbb3Tt2rVaAc/pCtAAt/L5kksuoWPHjp4F\nUwIc7Lc+KX8ft0/vn/xWEpBo8k9w3vvxzby/AYbG3L8MqDa1sXolkZmZWfrn4YcfzogRI5g5cyZF\nRUXeqQuAPqVvfZ7Ola3iBP2gBSQTtm6TrcXe/wYrfqnLQEmdv/32W73wwgt67rnn1KBBA0n61LKs\nuLfiAnaVNEZSs48++kgDBgzQnDlztOeee2rMmDHq06eP/va3v+nzzz9XTk7OZcASy7KSrLo1PliW\ntQUYKun5oUOH6q233tJTTz2l6667zpJjzff1WeFW9z8a+CojwA/c97xwS9TfDZG0RdIZkprt+JSU\nJUNOsUrGmjVrtH79enXo0EGZmZmS40Ks8VXdNnavzkEZGRkqKXF273Tt2rXgmGOO2dS/f//Nf/3r\nX7fk5uaWXc9ZImmkZVlfRP3dFkkhPaYcXaOtCqhPNJDz/gekKkAubizBmWeeyT777ON9yysG9k3A\n+JUFNAMVBjxfGG9tyQKQAcwDuOGGG2jdujXr1q3zXp6j/VU3qa1b3JE23VMCqkvkQsnUi72dwBtQ\nYVBzzGOu6kB5cS5IKpazNwtJKyQ9K2erSJtajWLrWNlC9+5g/wakO7ZGytZMv2UE1AHgToBp06Zh\nWRYffPCBd0OLezI3jsUchu0DmgsKCjwNI6g84Ll/vDUmC7hhsWvWrKFFixbceeed3uswE/Cxsm5q\nQ3fid5p/GgL8wVwjmVV+q4g3wOmQdEHNFRE98dvs/rlFse7mYetgN8evWiuMAWmErRdka6LfMgJq\nCdAG2ABw+OGHc+ihh3qTiS1ApziPHcJtTbZy5Up23333sgHNj7rHlQY8P/fcc2UDntcn4Y03bgBT\nAe6++24aN27MihUrvNfhLH+VmT+cMN+A+oXJk8x3fquIJ0QFNd98883k5uayZMkS7/fOz6DmivhD\nzuRvrqThkvopHtswhmlPt2XbQTE/d0ByY+tD2XrHbxkBtQQYCfDvf/8bSUyaNMm7oT2SgLHvB9iw\nYQMHHnggnTp1Yvny5d747xGVGwj8FdgMcNdddxEKhXj33Xe9Y38GOsdbbzLgvg5s3LiRtm3bctVV\nV3mvwUIgq+ozxAvzo2Tu9G/8AH8wz0om4reKeIJr8U6ePDnZLV6PPSTFv62jrabuit/JcR8rILmw\n9R/ZSuvOWWkLsDtuP9hevXpx3HHHeTe034HWcR77WoDCwkKOP/54WrRowbx587zxJ1NOQDNwJm7A\n86WXXkqDBg2iJ6rzSboetvEBGAvw5JNPkpWVxffff++9Bpf5p8pMlowvnQoC/MR8Jpk3/VYRL4iy\neLt165bsFm/isbVetq7wW0ZAgrG1THka7LeMgFoAvATwzjvvEAqFmDt3rjeBsOM87pk4hSOlE7hI\nJOKN/S1Q4WZh4AqAoqIiTjnlFJo1axatezrQKJ7akwGc2JvigoIC9thjD84999zolc/G/qgKvy6Z\ncf6MHeAfZp4UGe63inhA5Rbv3X7rSwps/Ve26kW6QoDLY8qRrWLl6Wy/pQTUEGAfoKigoIA999yT\nc845x7uhrQSaVH2GWo/bD9gKMGTIEDIyMvjoo4+iJy6dq3GOBwH++OMPDjnkEDp06MBPP/3kneNj\nIKOqc6Q6wJsAb775JqFQiNmzZ3vXf6s/isK2ZH6o8rCANGJ0hmQ2S5FL/VYSDyhj8T799NPe71iy\nWryJx9YHsvWe3zICEoitvd29nb38lhJQQ4CPAJ555hlycnJYvHixd1O7Jo5jlgY0P/HEE0iK3i+z\nFtivypM457GAV6HCgOdn4nUNyQKwG65N37Nnz2ibfm1lK6bxI3K2ZIqdCt+A+kF4D7ea+1C/lcQa\nAou3etgaLlvz/ZYRkEDydLpslchW3BaIAuIA0MdbMStTIPBDvL7JArsCywA++OADMjIyuPHGG71x\ntwBH1PB82cC/AX744Qd23nln+vbtG91UPO33HwBPAnz++edIIj8/37v2+xOvprR7R8/Ejx3gD/mn\nSKYkumtHOoCTdBBYvNXB6d5RoMeU47eUgARh6y7Z+tFvGQE1BKd4guHDh5Obm8uvv/7q3dTOj9N4\nLYBvwAlobtiwYXRAcwkwoJbnbQp8DfDll1/SpEmTehXw7H5ArQc48sgj6d27t/eabsbphJJAZmY5\ntl8Q6VJ/MHlONXd6QWDxVp+h2ieIdKln2PpItj7wW0ZADQBOAFi9ejXNmzePDgH+iqj4lBiOlwMY\ncPrvlhPQXKc9aUA7YAnAZ599RmZmJjfffLN37gLgmFhdSzIC3A1OZ5My4dvPJV5NZLoT7xFQPzBj\nJPOu3ypiCWUs3mOOOSaweCtjtDJka4PydKXfUgIShK2flafb/ZYRUE1wApPnehZGmzZtWL9+vTdR\nODFO470NlQc0x2Cc0oDn559/vl4FPAPNgFUAp512Gt26daOwsBCgCNgrsWoiD0vmv4kdM8AfCElm\nrWSu91tJrCCweGuHrXzZet1vGQEJwFZn2UJ56ue3lIBqApwH8NNPP9GgQQNGjhzp3dTCcRrvAXAC\nmnv16lU2oPlfsVxhJCrgeciQIfUq4Bm4wVtRzczM5MUXXyx9jROrJP90p8AjHNcMyIBkIH9/d09n\nb7+VxArcSvnA4q0htobK1k9+ywhIALYukK2CoLAjRcCxXBcDDBw4kN12242tW7d6N7a/xGG80oDm\n4447jpYtW1YZ0ByDMUvzAa+44oqyAc+LgJ1iPWYyADTA7Wd86aWX0r59ezZt2uRd98GJUzKhlTPx\ni/jcPi4g/oSvlcw6KZzpt5JYQGDx1h5bfd0OHnv4LSUgzth6Vbam+C0joJoAVwPMnz+fjIwMXnrp\nJW9i8HEcxirtsHHeeeeVnYB9Sxw7bACXgxPwfOqpp9KsWTPmzJnjjf0f0jTgGbgEYPny5TRq1IiH\nHnrIu2aTWCXmS8n4sL8wILGYsZJ5328VsYAoi/emm24KLN6a4gT6rpetq/yWEhBHkOXu7xvit5SA\nagDkeje2M844g3322YeioiJw9oHtG+OxSgOa77rrrrIBzUuBDrEcrwIND3rf3g855BDat2+f9gHP\nQAZu5fSNN95Iq1atWLdunXfN/ROnxORJZrmElbgxAxJLuEk6VXATWLx1x9aHsjXWbxkBcWSoegUV\n3CkEcBfAtGnTsCyLDz/80LuxvRLjcWIS0BwDHfUy4Bn4G8CaNWto0aIFd9xxh3e9XxOHiu3ymdTT\n2fsVTqDFHJBYwme4ezl38VtJXaFyi/cAv/WlDLYuka0tGqG0ynQMiMLW3bK1TCj4Up/suDbGBoDD\nDz+cPn36eJOBLUCnGI7TETeg+f33369zQHMM9GRRecDzLYnUkyiALwDuueceGjduzIoVK7zrTWBf\nRfO9ZB5K3HgBicW8I0WM3yrqCoHFGztstZStAuUpLlmwAUmArQWyFZMkjoA4AzwMMG7cOCQxefJk\n78b2SAzHKA1o/uKLL2jYsCEXXHBBnQOaY6CrKU4+YUUBzxf5oSueAId5KxgdOnTgyiuv9N7vhUBW\nYlSER0hmmRP5EZBeTMmVzB9SOOX3cxFYvLHF1ueB3ZumbLN5D/NbSkAVALsDW4uLi9l///05/vjj\nvRvb70BMIjcoE9DcqlWrsgHNvq6sERXwPGbMmHoR8AyMBXjqqafIysri+++/9673shidvz1wKzAP\nKARmA1ENu8P7unbvUbEYLyCZiFwima2pHtlDYPHGHlvnyFah7lFbv6UExBhbj8rWD4HNmwIALwO8\n/fbbhEIh5s6d600AYlKVQ1RA84oVK9htt93iEtBcV3ACnlfDtoDnhx9+2NOYdgHPOHstiwsKCthz\nzz0555xzvGv9GWhcy3M2AAYAn+MUBZVlFRDVrzMyXYokOEcwIP6k/vtK5RbvML/1pSwj1VC21srW\nHX5LCYghthrJ1prgfU0BgP2jP/zPPfdc78a2EohJ+CJu9WwiAprrCo4FuhnAtu20D3jG7Tf61ltv\nYVkWs2bN8q71tmqeopGkPS+//PLLRowYMf7BBx/cdPPNN3P++efTt29funTpQm5uLllZWdEfmlEF\nHZFLJFMgTQq+/acNkR7OSq452m8ldYEyFu8zzzzj/fwGFm9dcVaGftJopV1yQr0lTxe7oc3t/JYS\nUAXARwBPP/00OTk5LF682Lu5XROj818HlQY0N4jFOLEEONlbrbryyivTOuAZ2A3YWlJSQq9evTju\nuOMAKCkp+f3ss88+WNLhks6VdJ2khyS9KWm6pCWWZW2WRPQjFAoRCoW2+zvLsrjqqqu8PZPFbJfP\nOLWhZFZLkWAFJW0IvyyZ71I5qofA4o0vw7SXbJXI1hl+SwmIEbZmytZov2UEVAHQB2Djxo20bduW\nq6++2pvcfB+Lb7Q4rd9KSkpKOPfcc8tOoOYTx4DmukLVAc+1skKTEeAJ2FbYM3HiRADuv//+6Alc\ngfsoVpnJXkWPrKwsWrZsyaeffuq9bluAS3ZUEBkumVXSzLQMza5fTGnn7O0zV/itpLYQWLyJwdYY\n2Zrmt4yAGGDrSLeoo4/fUgKqwF1x49577yU3N5dff/3Vu7nVudSeqIDmO++8s2xA808kIKC5ruD2\nEN60aROHHnpo2YDnT4B0aUPVBmcPI/369aN3796UlJSwefNmdt1112pN8qIflmUhiWOOOYaVK1cC\n/AKMBPYqX0F4F8lskSL/jPvFBsSZ8IOS+dVZyU1NCCzexDBUh7st3Pr6LSWgjtgaH7RoSwGAEwBW\nrVpFs2bNuOuuu7yb21fUcc8dzr7B3wEef/xxJEVHIKwlxl1A4gVOwPMr4AQ8d+vWrWzA8yi/NcYK\nYBjAjBkzsCyLDz74AIDnnnuuRpO+zMxMGjZsyIsvvrgFGAX8pXo/T5HHJfNzKk8YAibs7ES4RAb7\nraS2EFi8icXWJNkyfssIqANDdZhsoTylXfJFWoFTZTvXszLatGnDhg0bvMnMCXU89w4BzTfddFO0\n1ZfQgOa6QlTA848//lhewPOtfmuMBUAznIpbTj/9dLp160ZhYSFFRUXsvffe1Zr0hUIh9thjj/Xv\nvvvuYKBpzRSEOzjtvcx1Mb2wgARiRkrmN6dVW+pBlMV74403BhZvIshTP3fS0M9vKQG1xFZYtqb7\nLSOgCoDzAX766ScaNGgQHVkSruN5KwtoLgb+HqtrSCREBTzPnDkz7QKe3ZXNo3BatrFgwQIyMzN5\n8cUXAXjvvfcqnfBlZGRgWVbJbrvt9rikOtjf4QedicPUpN37GVARE7s4E/fUteuBtyCweBOOrX/L\n1izZSpp0h4BqYuvkmtj1KVvtVRVfSG0KpS6WtBtSe0ktJO0i579DkppZ2vYDjrRezob5VZa0DGmt\nJf2C9GOx9ONqadlZzr/HBJwMte8kdR44cKDy8/P17bffKjs7G0mHWJb1nzqcd5ykwxcsWKA+ffqo\nZ8+e+uyzz5SVlSVJt1iWdV+sriPRAO0kTZXUaezYsTr55JN13XXX6f7775ekQkknWZY1zleRNQTo\nImmgnKrd7fZcDhw4UGPHjtWiRYvUsGFDHXrooZo2bcd92JZlFQOrJJ0jKb9uiia3kIoXSXpD6puy\nE4j6iXlf0r7STvtK+xT4raamAKdL+tfmzZvVo0cPde7cWWPHjpVlWYWSeluW9bXfGtOWodpfJZot\nS//QEL3kt5yAajJKWVqhuZIWytYpfstJGFOkdhHpZCMNNdIYI/1iJKIea4z0tZHCRnovLD0fkR4I\nSyO8R0R62kjvGOnfRvrSSMvLnGODkSZHpIfD0nlhqTuq/Tcj4CKAefPmkZGRwcsvv+x9qx1Th3OG\ngHegwoDmmLV98xOiAp5ff/11LMsqG/Dc02+NVQHkAoOAmUTx+++/8/zzz3PvvfcCsHz5cho1asSD\nDz4IQCQSKbvSV+L++Y6k5rFTaK5xcv0iFRSCBCQfkSOd3L7IaX4rqQ1lLd6mTZsGFm+isfWibP0s\nWzXcIhLgG7audXP7uvstJa5MkXIj0slh6QkjLTTOxKzESN8Y6d2INDgi9Z8odZkp1anf6RSpnZEO\nM9IVRnoxIk030hZ3zF+N9GZYumiKahaWiBvYe+utt9K9e3cKCws9G7bWG5epPKD5XZIooLmuEBXw\nPHToUEKhEKNHj/au9VdgG7xQuQAAIABJREFUD781liXKyh0NbPLEFhQUMHr0aE488USys7Np3Lgx\nl112GX/88Qfg7P9s0aIFa9asAeCYY47xJn2FkjZJuiD2asOZkvlaMpNSOQeu/hBu4GT2RT73W0lt\nqcTinRNYvAnCVhu3m8cTfksJqAZ3a1fZ+l227vdbSlyYJO0Ukf7PSJ8YaauRiiLS9Ig0LF86YZKU\nsDDfmVIjIx0WkW4wzgrjH8aZCP7HSLeFVfXMG7gHYOnSpdFhzS/XVhNuQPPWrVs58sgjUyKgua4Q\nFfB81VVXkZ2dzYQJE7xrXgS08Vuj5Fi5wAic+JxSZs+ezaBBg2jZsiWWZXHUUUfxyiuvRK/QArB2\n7VpatmzJHXfcAcDXX39NKBQqljRHUrf4Kc//s2SKJTMwfmMExAYz1Knknby730pqA3AGBFW8SUGe\nBspWsWwdXPXBAb5i6yPZ+kEPKG3ybIUUCkvHGukNI200UqFxJlrnh6WkaToelhrkS8cY6SUj/W6k\n4og0wUgXz3Raae0A0B4noNnjv0CtromogOZzzjmHRo0aMX36dO+8c4Fmtb+65Aa4DJyA57/97W9J\nE/AMNKEcK/eXX35hxIgRpRW6u+22G0OGDGHBggXRh5UA44ELgOngZDw2btyYFStWAHDbbbd9LikB\nkSuRhyXzuxTuHP+xAmpHfi/HljfX+62kNuBYvL9BYPEmBciSrYmy9a3s8j+/ApIAWxfIVknaVGJP\nkXKNdI3ZZuPOiEiDJkg7+6usaqZKDfOlU4z0sZEKjLO/8P6JUqeyxwLNgVOBk4Dc2oyHYx1uBbjj\njjtSMqC5rgD3e6sFfgY8UwMrd9CgQUyePNmrSPZYDNhAt6hzHuZdW4cOHbjiiiu8Y38kIfbXzEaS\nWehYvqODfp5Jx7jGkvlWMl9IqbmVg8DiTT6GaTfZ2iBbT/otJaAcnPdnnWylfobtJGmnsDTCOKtm\n68PSI2EpZZf53f2BNxtpiZGKwtLbYSlmoclEBTQ/9thjKRvQXFeICnhetWoV3bp1Y5999klYwDMV\nWLmzZs2qjpW7EXjVnTCW+8ENjAGnj3NWVhaLFi3ynnt5PK9rG+EDnfZfkSGJGS+g+pjnJLNOiuzm\nt5LaQGDxJi95Ot+NCDnVbykBUdjKlK2psvVtSlu8X0htwtIjRtpkpN+MdPN4KW3sybCUGZHONtJ/\njVOE8vEk6U91OSdOQPNycPLdyglo7hsL7akCZQKed9lll7IBz7fFeDzPyp2CU5QDlG/ljhgxgv/9\n73/Rk71oK7fKn3NgP6C4sLCQ7t27M2DAAO88P5MwKzt8rWSKJJNSwd/pTeRcp4o3fIbfSmoDlVu8\nQ/3WFyDJ1kuytUa2OvstJcDF1v2ytUl23eYQvjFGyjHSdUZaa6Rfw9K145TCM9gqcPcsnmqkuUYq\nNtKTYSdbsGbniQponjJlStoENNcVygl4Pvvss2MW8EwVVu5RRx1FRkYGTZo0qbaVW4Ox3wB4++23\nsSyLWbNmxWVCW4kCSzLvSmZlsN8vGZjUUzIbnD2YqQllLN5Ro0Z5P9OBxZtggH1wtswMx8kVdbDV\nSLbmyNbslF5dShdsnSVbJbJ1kd9SaoVbtPGdcfbB3T9FqtUet1QEKRSRLjXO6ua6iPTPcDU7LQAN\ngAjAt99+S6tWrejfvz8FBQXeTTNl+3PGAqAdsARg4sSJZGdnR6+EFgA17mNInK3camroDGwtKSmh\nV69eHHvssd751wGtanvemjElVzLzJfOV898B/hDeRTJLJBORZtYprsovCCzepADIcL+MFkXds1YA\nnUsPsrWHG/EyOujq4SND1Uu2NsrWM35LqTGTpRZGGm0kItLnYSnp8tYSxXipWUR62DjVyl9Vx/51\nJyDbBTSvX7/e+4V9Nv6qkx9gL+oY8Ew1rdzdd9+9zlZuDa7rCYB///vfSGLixIneeA/EaoyqmdhF\nMqsk87GT9ReQWMY1lsw0yfwoTUpYhFUsIbB4kwJgN2AGOLFiw4YNwxjjvQ93bnew08u3SLbu9Ulu\n/cbJ61vm9uNNrdXwiHSakX420tKIdLLPcpKGfGmfiDTFOMHQd1bUEQRntW91YWEhf/7zn2nfvn30\nhCOtAprrCvBn4A+AYcOGVSvgGR+t3Gpe007uxJV+/frRu3dvb5VkM9AxHmOWT7iv0w82/HwQ7pxI\n5mVL5jPJrJHCKVu4RcUW7ywSVIFf3wH6FRUVrfjkk0847rjjCIVCSOK1117z3osdC7lsXeYWe1yT\neMX1mHu1k2zNc+N1kibGrkrcwOMXjERYenuqFDR/L8NoKSMi3W6krRFpSlg7bqbFsTFZv349p556\nanRe3XQgyFsqA9UMeAZ2pxwrd+bMmQwaNIgWLVpUZuX+QQys3Bpc01CAL7/8EsuyeP/99z0dz8d7\n7O0xf3OLPR5K7Lj1FUKSecsJaY4c6rea2kIZi/fYY48NLN4EAmTMmjVr5JAhQ4p32mknLMvCsiwk\ncfHFF3v3kvUVfnm1dYebHXdhYpXXU+5Trmx9KVs/yVYCv9zXkbDU3UjzjLTOSOf7LCfpyZd6GWfv\n49p87dhwGShdi3dZCKSk5ZMIiAp4Pu2008oGPC8GplENKzcqFxDiZOVW83pygV8AzjjjDLp16+a1\n+ysC9k6kFilygdPZI2wndtz6BiE3tmWLFOnvt5raQpTFe8MNN9C0adNoxyKweONLdvfu3Qf26NFj\nrWVZZGVllfb7zsrK4uCDD/b2iS8Beld6JlsPyFahbKVkNXnK8IAay5aRrZUaFs8OTTEmXzrBOBW7\nX5YXYBxQPlOlhmHpZeNU/m5n/QLNgMdx9p49ASR9qLXfAPd5qwx9+vQpG/DM1q1bq2vlLsGxcn1t\nhg1cD7BgwQIyMzN54YUXPH3vJV6NGei2dbs/sH3jQThTMq85kz5zkt9q6gLwNgQWb4LpImlEVlbW\nBsuySi1d75GRkUHr1q29jkBvUp1GAk5nj0dlqzBY+YsTw9XCzer7RUO1n99yqo2RrjfOxOXVitqW\nBVSOcbqXFBppdFhKu167iQKnKve/sH3A85o1awB4+eWXS63c0aNHs2lT6fY+SLCVWx1w9nr+D2Dg\nwIG0b98+WvMhiVcU/odr+z4XdPeIJVMbOkU0ZnMaTPoCizdxZEk6U5KRVJKRkVGsqMmeykz8Jk6c\nuAW4oMaj2HrEtX2vi5nyAOketZWtubK1QnbsGj7EFaSQkR41zqTvJqRgFaAO5Ev9jLQmIk0J9kZW\nHyqoyoVtAc+HH354acDzunXrog8pcZ83iCTtcwxcBLB8+XIaNWrEgw8+6GmP+KMocpozQYl86ExY\nAurG1JZSZIpTyGEO81tNXaCMxdu8eXOWLVvm/bwGFm/s6CJphKQ1kkokVTjhk4RlWQwdOvQ3oEet\nR7R1m5srd58IPuvrzDB1k60fZWuhbO3ut5xqMVPKMk5Uy9awVC9DhONBvrSPkZYaaf5Eqb3fepIV\ntlXlvorbyg62Wbknn3wyX331leMtzZpFbm5udMAzwCqSwMqtDjj5W98A3HzzzbRo0aJ0BRM41h9V\n4b+4US+zpHDa94iOH5P2lsz3TlZf/j5+q6krBBZvPNludU9SgSqZ7HmPzMxMDjvssJ+oZY/47bB1\ngWxtla33g5DnOmCrv9slZZruVWrs2x8j5USkD4200UhH+60n3ZgodTLSAiMtDPZLbg/bqnK3C9Ob\nPHnyDlW5H3/8sWcxlQY833jjjd5TCvybNNUc4BSAtWvX0rJlS26//XbvOubgmy09uatkvpPM8lSu\nPvUPc5Jk1kqR6dKktn6rqStUbvHu77e+FKe1pJ/kTOaKVI0Jn9xJX25u7q+KZVtUW0fK1m+yNVvD\nlJJ9o33F1rVuwcw7GqnUcEzmSdkR6VMjrc+X+vitJ12ZIO1snArpxRFpV7/1+AkVWLkrV67criq3\nS5cu5VXlAhQCvPHGG7UKeE4WgC8Ahg8fTuPGjfn555+96xjgn6rpTSXziWQKJHN9UPRRHWZmSeER\nkimRzItOZl9qQ2DxxpvGkn6UVKhqTvrcAo8tkmK/kny3Ornt3dbIDnJ6q4Wt5rL1rmwVK0+DU8Yu\nHy1lGMfe3RCWDvZbT7oTllobZ/K34Aupjd96EglVWLk1qcolKuD57rvvLi/geU+/r7c6AId5Kyod\nOnTgiiuu8K7hR3ztd0pIMrdJplAy49Jh9Sp+TO7qtMEzm5xCmfQA1+KdNGlSYPHGj30kbVE1J35y\n7OD4Tcqc3r4vuPv+ng2s30oYqsNla7lsrZSto/yWU22QLCM9Z6StJrB3E8ZEqb2Rlhhp9nSpqd96\n4g1Oa6FqWbk1qcoFTsINeL766qvLBjx/jxvwnOwAnwE888wzZGVlsWjRIu8arvBbm5TfRzI/OPal\nCXI8t4OQFL7WmfCZOemwn8+DKIu3a9eugcUbJ4DswYMHT1D1Jn3FkoYnRFieBrgrf4tl668JGTNV\ncCbHj8pWsWyN0T1KrUg2I91qpOKgkCPxuMHYq4w0Niyl3bdn6m7lelW5zasY5zKA4uLi8gKeZwBJ\n/40V2A8oLioqYq+99mLAgP9v787jrC7r/o+/vmfYEQQ0NRQXRBL3NXeT26U0bReX0uy+E7Xu3LrL\n8r7TsRXTrPv+WSmWG5VB2h5p6JzrAGIlmimgorihoBKyLwJz3r8/vufoYZyBAc451zlz3s/HYx7j\nA4c578Ns13zf3+tznVXM/5rKcfP2FruvL2RvKtSYf4Kc7wEity+EB9MrotlvplVv16ANV7zXxM7X\nVajkrN2zzz77HTP62rysBf5CB8eBVkQ6luTewtW/H/EdBlbtsWtVuoHjWZpZSjP/HjvOJgtweoB8\nDr4YO0ujaoGjAqwM8KPYWcpBnaxy+/Xrp9GjR2v69OltF3ubNWBZ6dXE9QY8l5wo8AfVQS0l6WeS\nNH78eCVJokceeaSY/79jZ3tb9rjCxo/VkPtGuiBsNNMGQfhhYe7h3yG3+aM0apTaVLxjx44tf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SfNWvE1RHFa9c7ZrVv6nQL6QbAr4XO4ttnhb4YEhHuBy5KX9P0t6SxhSvChWVVsGZ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\nDDwBPNkXHjsEVpYnuTU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+ "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import SVG, display, Image\n", + "%matplotlib inline\n", + "Image('https://upload.wikimedia.org/wikipedia/commons/thumb/4/46/Colored_neural_network.svg/638px-Colored_neural_network.svg.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Función de activación típica\n", + "#### Sigmoide\n", + "$$ S(t) = \\frac{1}{1+e^{-t}}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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AakqqOXOspDOLRn8dmx97Xqg8AAAMFwoAAABQM1IfzIyU27eKtv67WfyRm256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\nev7wrq7try8OMR8AAACKGwUAAAhZU9Pj1R3xDVe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+ "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image('https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1024px-Logistic-curve.svg.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `sklearn`\n", + "\n", + "En `sklearn`sólo se incluye un tipo de ANN, llamadas **Bernoulli Restricted Boltzmann machines**. [http://scikit-learn.org/stable/modules/neural_networks.html#rbm](http://scikit-learn.org/stable/modules/neural_networks.html#rbm)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[BernoulliRBM] Iteration 1, pseudo-likelihood = -1.11, time = 0.02s\n", + "[BernoulliRBM] Iteration 2, pseudo-likelihood = -1.23, time = 0.01s\n", + "[BernoulliRBM] Iteration 3, pseudo-likelihood = -1.14, time = 0.01s\n", + "[BernoulliRBM] Iteration 4, pseudo-likelihood = -1.24, time = 0.01s\n", + "[BernoulliRBM] Iteration 5, pseudo-likelihood = -1.03, time = 0.01s\n", + "[BernoulliRBM] Iteration 6, pseudo-likelihood = -1.15, time = 0.01s\n", + "[BernoulliRBM] Iteration 7, pseudo-likelihood = -1.20, time = 0.01s\n", + "[BernoulliRBM] Iteration 8, pseudo-likelihood = -1.18, time = 0.01s\n", + "[BernoulliRBM] Iteration 9, pseudo-likelihood = -1.16, time = 0.01s\n", + "[BernoulliRBM] Iteration 10, pseudo-likelihood = -1.06, time = 0.01s\n", + "[BernoulliRBM] Iteration 11, pseudo-likelihood = -1.19, time = 0.01s\n", + "[BernoulliRBM] Iteration 12, pseudo-likelihood = -1.15, time = 0.01s\n", + "[BernoulliRBM] Iteration 13, pseudo-likelihood = -1.13, time = 0.01s\n", + "[BernoulliRBM] Iteration 14, pseudo-likelihood = -1.07, time = 0.01s\n", + "[BernoulliRBM] Iteration 15, pseudo-likelihood = -1.08, time = 0.01s\n", + "[BernoulliRBM] Iteration 16, pseudo-likelihood = -1.11, time = 0.01s\n", + "[BernoulliRBM] Iteration 17, pseudo-likelihood = -1.16, time = 0.01s\n", + "[BernoulliRBM] Iteration 18, pseudo-likelihood = -1.20, time = 0.01s\n", + "[BernoulliRBM] Iteration 19, pseudo-likelihood = -1.15, time = 0.01s\n", + "[BernoulliRBM] Iteration 20, pseudo-likelihood = -1.11, time = 0.01s\n", + "[BernoulliRBM] Iteration 21, pseudo-likelihood = -1.07, time = 0.01s\n", + "[BernoulliRBM] Iteration 22, pseudo-likelihood = -1.17, time = 0.01s\n", + 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"data": { + "text/plain": [ + "Pipeline(steps=[('rbm', BernoulliRBM(batch_size=10, learning_rate=1, n_components=10, n_iter=100,\n", + " random_state=0, verbose=True)), ('logistic', LogisticRegression(C=100.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", + " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", + " verbose=0, warm_start=False))])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import linear_model\n", + "from sklearn.neural_network import BernoulliRBM\n", + "from pandas import read_csv as read_csv\n", + "from sklearn.pipeline import Pipeline\n", + "\n", + "df_training = read_csv('data/saturday_traning_sample.csv',header=0)\n", + "df_testing = read_csv('data/saturday_testing_sample.csv',header=0)\n", + "\n", + "varnames =[\"random_forest\",\n", + " \"nearest_neighbors\"]\n", + "training_data = df_training.as_matrix(columns=varnames)\n", + "training_target = df_training.as_matrix(columns=['is_good'])\n", + "# Models we will use\n", + "logistic = linear_model.LogisticRegression()\n", + "rbm = BernoulliRBM(random_state=0, verbose=True)\n", + "\n", + "classifier = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)])\n", + "\n", + "###############################################################################\n", + "# Training\n", + "\n", + "# Hyper-parameters. These were set by cross-validation,\n", + "# using a GridSearchCV. Here we are not performing cross-validation to\n", + "# save time.\n", + "rbm.learning_rate = 1\n", + "rbm.n_iter = 100\n", + "# More components tend to give better prediction performance, but larger\n", + "# fitting time\n", + "rbm.n_components = 10\n", + "logistic.C = 100.0\n", + "\n", + "# Training RBM-Logistic Pipeline\n", + "classifier.fit(training_data, training_target.ravel())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "testing_data = df_testing.as_matrix(columns=varnames)\n", + "df_testing['neural_net']=classifier.predict_proba(testing_data)[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Unnamed: 0checking_acc_numerictime_credit_acccredit_history_numericpurpose_numericamountsavings_acc_numericp_employment_time_numericinstallment_ratemarita_status_sex_numeric...foreign_worker_numericis_goodlinnear_scoredecision_treedecision_tree_predrandom_forestrandom_forest_prednearest_neighborsnearest_neighbors_predneural_net
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"75% 163000.000000 175750.000000 187000.000000 222250.000000 \n", + "max 325000.000000 361000.000000 340000.000000 406000.000000 \n", + "\n", + " 8 9 10 11 \\\n", + "count 100.000000 100.000000 100.000000 100.000000 \n", + "mean 169540.000000 158740.000000 160600.000000 195520.000000 \n", + "std 104215.144598 97942.636272 92562.509591 82057.319213 \n", + "min -53000.000000 -47000.000000 -77000.000000 -14000.000000 \n", + "25% 91000.000000 91000.000000 99250.000000 144250.000000 \n", + "50% 160000.000000 146500.000000 166000.000000 188500.000000 \n", + "75% 236500.000000 235000.000000 220750.000000 253750.000000 \n", + "max 400000.000000 373000.000000 355000.000000 394000.000000 \n", + "\n", + " 12 13 14 15 \\\n", + "count 100.000000 100.00000 100.000000 100.000000 \n", + "mean 130120.000000 74230.00000 -28160.000000 -199820.000000 \n", + "std 78994.729398 67671.79485 52958.096643 50046.923436 \n", + "min -59000.000000 -71000.00000 -155000.000000 -293000.000000 \n", + "25% 76000.000000 30250.00000 -56750.000000 -233750.000000 \n", + "50% 124000.000000 73000.00000 -27500.000000 -203000.000000 \n", + "75% 176500.000000 115000.00000 1750.000000 -167000.000000 \n", + "max 391000.000000 277000.00000 124000.000000 -32000.000000 \n", + "\n", + " 16 17 18 \n", + "count 100.000000 100.000000 100.000000 \n", + "mean -307190.000000 -400280.000000 -464660.000000 \n", + "std 38465.699006 23890.355604 14792.995886 \n", + "min -392000.000000 -452000.000000 -500000.000000 \n", + "25% -335000.000000 -416000.000000 -476000.000000 \n", + "50% -309500.000000 -398000.000000 -464000.000000 \n", + "75% -283250.000000 -380000.000000 -452000.000000 \n", + "max -182000.000000 -344000.000000 -422000.000000 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from bootstrap import bootstrapped_utility\n", + "from pandas import DataFrame\n", + "bs_f = bootstrapped_utility(df_testing=df_testing,varname='neural_net', bins = nn_scores_range)\n", + "bs_df = DataFrame(bs_f)\n", + "bs_df = bs_df.transpose()\n", + "for n in range(0,100):\n", + " bs_f = bootstrapped_utility(df_testing=df_testing,varname='neural_net', bins = nn_scores_range)\n", + " bs_df_tmp = DataFrame(bs_f)\n", + " bs_df_tmp = bs_df_tmp.transpose()\n", + " bs_df.loc[n]=bs_df_tmp.loc[0] \n", + "bs_results=bs_df.describe()\n", + "bs_results" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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fbQNyNAWMs7Ot2snChXDGGbHph0tvhwxaqrpeREYCXwua/hnLChki8ldsxNNV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\nWmZmNmC40jIzswHj/wFa8R33YZkqUwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bs_mean = bs_results.loc['mean',]\n", + "bs_std = bs_results.loc['std',]\n", + "plt.plot(nn_scores_range[:(len(bs_mean))], bs_mean, 'k', color='blue')\n", + "plt.fill_between(nn_scores_range[:(len(bs_mean))], bs_mean-bs_std, bs_mean+bs_std,\n", + " alpha=0.4, edgecolor='blue', facecolor='blue')\n", + "plt.xlabel('Valor del Neural Net')\n", + "plt.ylabel('Utilidad seleccionando x como valor mínimo, con fluctuaciones al 68% NC')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 92d0b65cf7bb984d9b1c672676eb30f418cde0d7 Mon Sep 17 00:00:00 2001 From: iorch Date: Sat, 9 Jul 2016 09:34:15 -0500 Subject: [PATCH 3/4] Adding challenges --- .../5.- Sesi\303\263n S\303\241bado.ipynb" | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git "a/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb" "b/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb" index 93511c8..07a3915 100644 --- "a/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb" +++ "b/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb" @@ -987,6 +987,16 @@ "plt.xlabel('Valor del Neural Net')\n", "plt.ylabel('Utilidad seleccionando x como valor mínimo, con fluctuaciones al 68% NC')" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retos:\n", + "\n", + "* Entrenar una red neuronal con variables no categóricas. (Hay que redefinirlas para que vayan de (0,1))\n", + "* Encontrar el monto promedio del préstamo por bin del modelo de NN, y con eso recalcular las ganancias.\n" + ] } ], "metadata": { From 973f0bba1540c18027be3f77ef7b64d0d058ad15 Mon Sep 17 00:00:00 2001 From: iorch Date: Sat, 9 Jul 2016 09:46:33 -0500 Subject: [PATCH 4/4] rename columns --- .../5.- Sesi\303\263n S\303\241bado.ipynb" | 395 +++++++++--------- 1 file changed, 198 insertions(+), 197 deletions(-) diff --git "a/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb" "b/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb" index 07a3915..8d498dd 100644 --- "a/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb" +++ "b/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb" @@ -86,12 +86,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[BernoulliRBM] Iteration 1, pseudo-likelihood = -1.11, time = 0.02s\n", + "[BernoulliRBM] Iteration 1, pseudo-likelihood = -1.11, time = 0.03s\n", "[BernoulliRBM] Iteration 2, pseudo-likelihood = -1.23, time = 0.01s\n", "[BernoulliRBM] Iteration 3, pseudo-likelihood = -1.14, time = 0.01s\n", "[BernoulliRBM] Iteration 4, pseudo-likelihood = -1.24, time = 0.01s\n", "[BernoulliRBM] Iteration 5, pseudo-likelihood = -1.03, time = 0.01s\n", - "[BernoulliRBM] Iteration 6, pseudo-likelihood = -1.15, time = 0.01s\n", + "[BernoulliRBM] Iteration 6, pseudo-likelihood = -1.15, time = 0.00s\n", "[BernoulliRBM] Iteration 7, pseudo-likelihood = -1.20, time = 0.01s\n", "[BernoulliRBM] Iteration 8, pseudo-likelihood = -1.18, time = 0.01s\n", "[BernoulliRBM] Iteration 9, pseudo-likelihood = -1.16, time = 0.01s\n", @@ -115,12 +115,12 @@ "[BernoulliRBM] Iteration 27, pseudo-likelihood = -1.23, time = 0.01s\n", "[BernoulliRBM] Iteration 28, pseudo-likelihood = -1.14, time = 0.01s\n", "[BernoulliRBM] Iteration 29, pseudo-likelihood = -1.21, time = 0.01s\n", - "[BernoulliRBM] Iteration 30, pseudo-likelihood = -1.21, time = 0.01s\n", + "[BernoulliRBM] Iteration 30, pseudo-likelihood = -1.21, time = 0.00s\n", "[BernoulliRBM] Iteration 31, pseudo-likelihood = -1.12, time = 0.01s\n", "[BernoulliRBM] Iteration 32, pseudo-likelihood = -1.16, time = 0.01s\n", "[BernoulliRBM] Iteration 33, pseudo-likelihood = -1.17, time = 0.01s\n", - "[BernoulliRBM] Iteration 34, pseudo-likelihood = -1.13, time = 0.01s\n", - "[BernoulliRBM] Iteration 35, pseudo-likelihood = -1.20, time = 0.01s\n", + "[BernoulliRBM] Iteration 34, pseudo-likelihood = -1.13, time = 0.00s\n", + "[BernoulliRBM] Iteration 35, pseudo-likelihood = -1.20, time = 0.00s\n", "[BernoulliRBM] Iteration 36, pseudo-likelihood = -1.13, time = 0.01s\n", "[BernoulliRBM] Iteration 37, pseudo-likelihood = -1.12, time = 0.01s\n", "[BernoulliRBM] Iteration 38, pseudo-likelihood = -1.11, time = 0.01s\n", @@ -150,7 +150,7 @@ "[BernoulliRBM] Iteration 62, pseudo-likelihood = -1.18, time = 0.01s\n", "[BernoulliRBM] Iteration 63, pseudo-likelihood = -1.12, time = 0.01s\n", "[BernoulliRBM] Iteration 64, pseudo-likelihood = -1.19, time = 0.01s\n", - "[BernoulliRBM] Iteration 65, pseudo-likelihood = -1.16, time = 0.01s\n", + "[BernoulliRBM] Iteration 65, pseudo-likelihood = -1.16, time = 0.00s\n", "[BernoulliRBM] Iteration 66, pseudo-likelihood = -1.13, time = 0.01s\n", "[BernoulliRBM] Iteration 67, pseudo-likelihood = -1.16, time = 0.01s\n", "[BernoulliRBM] Iteration 68, pseudo-likelihood = -1.06, time = 0.01s\n", @@ -261,7 +261,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -272,7 +272,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -282,7 +282,7 @@ "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", - "from numpy import arange, array\n", + "from numpy import arange, array, delete\n", "nn_scores_range = arange(0.67,0.71,0.002)\n", "df_good = df_testing[df_testing['is_good']==1] \n", "df_bad = df_testing[df_testing['is_good']!=1]\n", @@ -676,35 +676,35 @@ " \n", " \n", " \n", - " 0\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", - " 8\n", - " 9\n", - " 10\n", - " 11\n", - " 12\n", - " 13\n", - " 14\n", - " 15\n", - " 16\n", - " 17\n", - " 18\n", + " 0.67\n", + " 0.672\n", + " 0.674\n", + " 0.676\n", + " 0.678\n", + " 0.68\n", + " 0.682\n", + " 0.684\n", + " 0.686\n", + " 0.688\n", + " 0.69\n", + " 0.692\n", + " 0.694\n", + " 0.696\n", + " 0.698\n", + " 0.7\n", + " 0.702\n", + " 0.704\n", + " 0.706\n", " \n", " \n", " \n", " \n", " count\n", + " 100.00000\n", " 100.000000\n", " 100.000000\n", " 100.000000\n", - " 100.000000\n", - " 100.000000\n", + " 100.00000\n", " 100.000000\n", " 100.000000\n", " 100.000000\n", @@ -722,212 +722,212 @@ " \n", " \n", " mean\n", - " -6050.000000\n", - " 3220.000000\n", - " 22300.000000\n", - " 58210.000000\n", - " 70240.000000\n", - " 96040.000000\n", - " 110200.000000\n", - " 147850.000000\n", - " 169540.000000\n", - " 158740.000000\n", - " 160600.000000\n", - " 195520.000000\n", - " 130120.000000\n", - " 74230.00000\n", - " -28160.000000\n", - " -199820.000000\n", - " -307190.000000\n", - " -400280.000000\n", - " -464660.000000\n", + " 11800.00000\n", + " 18910.000000\n", + " 35380.000000\n", + " 70930.000000\n", + " 82000.00000\n", + " 107410.000000\n", + " 122860.000000\n", + " 157210.000000\n", + " 176770.000000\n", + " 165010.000000\n", + " 168910.000000\n", + " 201490.000000\n", + " 136210.000000\n", + " 85210.00000\n", + " -18350.000000\n", + " -198110.000000\n", + " -301880.000000\n", + " -397520.000000\n", + " -463220.000000\n", " \n", " \n", " 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-280250.000000 -386000.000000 -452000.000000 \n", + "max -194000.000000 -332000.000000 -428000.000000 " ] }, "execution_count": 7, @@ -945,9 +945,10 @@ " bs_f = bootstrapped_utility(df_testing=df_testing,varname='neural_net', bins = nn_scores_range)\n", " bs_df_tmp = DataFrame(bs_f)\n", " bs_df_tmp = bs_df_tmp.transpose()\n", - " bs_df.loc[n]=bs_df_tmp.loc[0] \n", + " bs_df.loc[n]=bs_df_tmp.loc[0] \n", "bs_results=bs_df.describe()\n", - "bs_results" + "bs_results.columns=delete(nn_scores_range,-1)\n", + "bs_results\n" ] }, { @@ -960,7 +961,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -969,9 +970,9 @@ }, { "data": { - "image/png": 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fbQNyNAWMs7Ot2snChXDGGbHph0tvhwxaqrpeREYCXwua/hnLChki8ldsxNNV\nRDZgSRM/B54WkeuB9cBlQV8+EpG/Ax8B5cCUiKKAtwKPAm2Al1V1dtD+CPB4kLSxE5gcvNZuEfkJ\nlkGowD2quidWP1dz9fnnUFRkx7MPHmwVKoYM8bWpRFO1k67btYNbbrGj7Q/Hqada4HrttdgGriFD\n4LvftRHX/v028j6UI46AN9+0oBuLE5Ndeosm5X0qcCPwXNB0MfBHVX2w/mdlFk95b5iqHVo5b55l\n/Z10kh2y6OWUkqO83NYMjznGKks09oNe1Q7OfOMNW4eKZbmyrVttH9dZZ0U3gtq40QLpWWfFrg8u\n/pJSxinY9Huiqu4LbucAC+K8ppVSPGjV7eBBOwJk3jz7gBs/3r4NxyJl2jXOF1/Y/rbzzrNU8aYG\nmlAI/vEPWLDARlyxDFw7d1rgGjvWjjZpaDReXm4p8Hfe6Qk76SRZ+7QEqIy4XUkjM/BcZti5s2oK\ncOBAS5UeOtSnAJNt82ZbA7rpptgdyZKVZQGlosKy+Pr1i93fc9eutnn4gQdsqvCyy+p/bDiT8a23\nLCC75iuaoPUXYJGIPB/cnoitE7lmpLTUDukLp6yfcALcdRd0757snrmKCjuufuhQmDTJ0tpjKSvL\nEjnKy62MVt++sQtcHTrA7bfDz34Gw4bB8OH1PzY/374onXSSHZ7qmqdoEjF+LSJFWOo7wHWq6lXB\nMlhpKaxfb5cNG+yyb5/t3Rk9Gq691tPVU0F5uf297NplBzB+7WvxqxjSooUFxIoKWLnS/i3ESrt2\nVRubhw6tP82+ZUsLoP/8p22bcM1TNGtaJwAfqure4HYH4GhVXZSA/qWETF7TKi21oBQZpMIBqm9f\nu/TpYyMqPzesypdfWip5y5Y2dRX+M3y9qVQtKJWV2faBsrKqw0bB1pratLFklwkTrCZjIhw8CDNn\n2mg7loEL7DyuoUMbTraorLQ9Z3fcYeenudSWrESMZVghWQ1uZwFLVLUglh1JZZkStCIDVPjPyAAV\n/tMDVP1KSy2tv0MHOP54W4sJn2i9b5/df+CABZfIKbTwAaJZWdWDm6oFpIMH7XHhE7BV7T26dLG/\nj27dbEosN9eyAXNzk7fn7cABeOIJ+/fTu3fsXnfbNrj3XjtqpqECu5s3256viy+O3Xu7+EhW0HpP\nVUfVaFvu2YPpY+tWeOopq0jRp0/1UZQHqOiUlNg0XLducPbZcPTR9Y+oKiurRkeRI6WyMgtse/da\nlt/evRZcp7NyAAAgAElEQVS8unWzv4cOHaoCUk5O7KpRxENZGTz2mAWQukpCNdazz9rv5dpr639M\nZaWlwN9+u2+rSHXJClrPYVXWHw6apgCnq+rEWHYklaVr0Cors3OnwhlXp5/uVdIP165dFrB69rRp\nq8GD/XcY9uWX8OijsH374RXCbUhZmY20br7ZSn3VZ8sW24f2jW/E5n1dfCQraOVhJZHGY5UjXge+\np6rbG3xiBkm3oKUK774LTz9tFQgmTTr884yaM1WbAiwttRTvM86wNSMfkdZWWgp//rMF91gFrnD5\nrx/8oP7feShkU9xTp9pJxy41JSVoufQKWlu22EL53r0webIFLRedUMgq0e/fb7+300+3qVTff9aw\nvXvt39zatVYuqqnTmqGQncF1yikNn6W1dav9PU2e3LT3c/GTrM3FLg2UlcFLL9k+lvPOszJKPo0V\nncpKm+I6cMD2CZ12WmzXaTJdbi5cd51tOH/tNcjLa1qNwKwsuPxy+O1vGz5LKy8Pli+3v68jjmj8\n+7n04iOtKKTySEsVliyxBeyjjrLD/XwqMDqVlfZtvaLC9p+dcop9ELrG+/RT+Nvf7Hfb1EDy+OOW\n1n/ppfU/Zts2W/u68sqmvZeLDx9puWo2b7ZpmX374NvftlN/3aGFQva7U7XKHiee6Ht+YmXgQDuh\n+NlnrYjykUc2/jDJiy6Ce+6xLxP1BcC8PPjoI9u7Fcv0e5e6DnukJSIXAVt9c3HylJVVFTH9+tdt\nesSnAqOzb59NBZ54ov3efFQaH5WVVrlizhyrMdjYIrevvw4ffGCBsL61xR07bDq3oTR5lxypMtIa\nBxwrIi1VNREHRLqAqhUtffZZ2yc0bZpXvI6WqiWptGxpH27RnpzrGqdFC1tX7dcP/vpXG9keccTh\nJ7UUFlrwe//9+g+N7NbNRnUbNsS+SodLPb6mFYVUGGlt3mxrBfv328GKAwcmtTtp5cABKC62fT0X\nXeSBPtH27oXnn7dpvMZMF65caRU4pk+v/7mff26p7zfc0OTuuhhKykhLRFoBtwCnBk1vAL9X1fJY\ndsTVVlJi3zCXLbNvkeefb1Navl8oejt22HTqJZdY2SX/3SVebq4lSvzrX7bZvXPnw5uWPfpoC3av\nvlr/sSTdulkSyObNsdsv5lJTNJuL/xdoBTwWNF0FVKrqt+Pct5SRyJHWjh3w3nsWqMK7/keNslRs\nr6wevXCpn549rYK4ZwWmhg0bbMbgyy/t7yba6cLPP4f/9//gRz+qP2mmuNiSNvx049SRrIoY76vq\nyEO1pSsROQe4D8gCHlHVe+t4TNyClqp9O3z3XQtWJSVWDHTUKKt43djMq+YsfHrvGWfYyNR/h6ll\n3z74v/+DFSss4y/awr8vvmhJNN+u5+tyWZlV6LjrLh9Rp4pkJWJUishAVf006MQAqp9knLaCivUP\nAWcAm4HFIvKCqn4cz/cNhexAxfCIKhSyIDV5sq1V+X+4xgmnsufkxPb0XhdbOTlV67KzZtkaY+fO\nh37eOedY8tHq1XVXemnTxvZtbdrkCRmZLJqg9e/AfBFZCwjQF7gurr1KnLHAGlVdDyAiM4GLgJgH\nrcpKy3B67z275ORYoPq3f7P5ei8V1DT799t06ujRtg2gXbtk98g1JCvL9sgdeaRlF27aZNOFDX1h\ny862ArlPPQU//GHd2zxatbIUeQ9amSuak4tfF5HBQDhJeJWqHohvtxKmF7Ax4vYmLJA1SUWFrU1t\n3Vr1ze+jj2xdZdQoO8DOi3zGztatNsq64go49lj/ApBOevWCW2+1qb9ogk1BgZWLeustm/qtqVs3\nWLrUjo9J5aNdXONFkz3YApgA9Asef2YwT/nrOPct5ZWW2gdm+LJtm/25a5ctFvfoAfn5lv00aVJ0\nUyAueuXllmwxaJD/ftNZu3aW3bl1q50G3dDfo4hNo//mNzaqrlnjMDvb1rY2bmz4aBOXvqL5LjIL\nKANWAKH4difhioHI73a9g7Za/vSn6ZSW2iJyZWUhJSWFhEIWlMLB6aST7Hr37r74H2+7d1vSyte/\nbr93rwiS3rKzrcbgb39rgaih/z+9elnAevFFG13X1Lq1FdL1oJV4RUVFFBUVxfU9oskezNhTioNR\n5CosEWML8A7wTVVdWeNxOny4fhWcwpfcXJ+KSqTKSgtUJSX2xWDyZN+Tk2nmz4e5cw8dcPbts6SM\nqVNtXSxSebmlyP/wh/7lMdmSlT34ioicrapzY/nGqUBVK0XkO8BcqlLeV9b12O9+N6Fdc4GyMptu\nLS+3RfpBg2DCBBg2zL5Ru8zyta/Z+u+OHfbFpD45OXDhhVYw+s47q395bNUKDh6E9eu9iHQmiiZo\nLQSeD9LDy7EMQlXVjCiGo6qzqUoycUmmamV/9uyx6zk5MGaM1Qo88kgPVJmuZUubJnzgAQs8De3h\nOuUUePNNO5pnzJjq97VpY1m6HrQyTzRB69fAicAK9UKFLg7Kyy1IffmlfWPu1cvWqQYMsDVCn4Jt\nXvLyrFzTrFkNTxNmZdkU8f/+L4wYUf0LTdeutnn5ggv8i06miSZobQQ+8IDlYmnfPkumqKy0b9PD\nhlnJqj59mnbqrcsM48bZNOGWLQ1vDxk0CAYPhldegYkTq9pbtrStJ+vWeUX/TBNN0FoLFInIK8BX\n+7M85d0drvJy2xYQCtmWgMJC+9Dp2dP31LjqWrSwNPj77rN1zYbqbl5yCfzkJ3DyydXXwdq1s/Jo\nHrQySzQfFZ8Fl+zg4txhOXDA9uC0agWnnmobRP2kYHcoXbrYUTJPP23ThPVNE3fuDGeeCS+8UL0u\nYZcuNlrbvx/atk1Mn138RVMR455EdMRlnv37rcBp69ZWN270aC+v5A5PQYEFnrVr7RDJ+px2mqW4\nRwaoFi2q6nwOG5aY/rr4qzdoich9qvo9EZkF1FrPUtUL49ozl7b27bOU5fbtLS155Eg/VsU1joj9\nG7r/fkvUqe9LT06OFdF97z048cSq9txcO+3bg1bmaGik9Xjw5y8T0RGX/r74wvZUde5sacvDh/vm\nTtd0HTvCxRfDk09a5f76iuqOHQsLFlQPWp06wZo19kUqJych3XVxVm/QUtWlwZ9vJK47Lh3t3m0p\n6z16wLe+ZeeAeVklF0vDh9tU4Qcf2BlcdRkxwgLb3r02wgILcKrwySc24nfp75AnN4nIySLyqois\nFpG1IvJZcEyJa8ZUrVTOunX2AXHddXDbbZa27gHLxZqI1Zls3doKVdeldWur8r90afX2Dh1sitBl\nhmiyBx8Bvg8sJUMOf3SNFwpZsNq3z9LVv/lNm7LxDcAu3nJy4LLL4M9/trWtuqYJx4yB2bNtO0VY\nx46WjPHFFxbAXHqLJmiVqOorce+JS0mhkGVk7d1rZXVCIRtNnXZa/dM0zsXL4MF2eOTixXWfvTVs\nGDz6KOzcaVUxwIKbiK1tjR6d0O66OIgmaM0Xkf8BnqP65uJ349YrlxShkGVolZZagBKxacAePWw9\noU8fSztuqJCpc/E2YQKsXm3V/jt2rH5fy5b2b3XxYttmEdaxI7zzjgetTBBN0BoX/Hl8RJsC42Pf\nHZcooZBN8UUGKBE7cmXoUAtQ3brZt1XPAHSppE0by079/e9tW0XNNdSxY+Gpp6oHrQ4dYMOGQx8y\n6VJfnUFLRE4AlqnqAVU9PcF9cnEQCtlG3wMHqgJUz542nRIZoLyckksH/frZutVbb9WeJhw0yL6M\nbd5cdd5a+N/86tVW19Clr/o+oloDL4rIDcBldT3Aaw+mj337rObfqFGWFtytm5W48QDl0tn48fDx\nx7VHT1lZlpCxeLGVgQrr3NmmCD1opbc6U96DvVnXAsOA3HouLsWpQnGxfeu8+mo7xmHYMDv6wQOW\nS3fZ2TZNWFJixZgjjRljASrybIrcXKuB+fnnie2ni62GNhdvwY6gz7gTi5uD/fvtWIcRI+D88z3V\n12WmXr3grLNg7tzqZ2/16WNrXevWVW/PyoJVq2y2waWnQ37fFpH+wHeBfpGP99qDqUnVvk0CXHGF\nbbb0PVQuk33ta1ZUd8eOqsxWkarRVmTQ6tIFFi2yQ0b9/0V6OmRFDOD/gHXAg8CvIi4uxZSV2SbK\n/v1h6lQbZfl/TJfpWra0acL9+6tPE44ZA0uWWBJSWE6O7eHavj3x/XSxEU3QKlPVB1R1vqq+Eb40\n9Y1F5Bsi8oGIVIpIQY377haRNSKyUkTOjmgvEJHlQUmp+yLas0VkZvCcBSLSJ+K+a4LHrxKRqyPa\n+4nIwuC+v4lIWq/ybN1q/xkvvRSuusoKhTrXXOTlWaHcyGCUn2//D1atqv7YrCxYuTKx/XOxE03Q\nul9EponIiUHQKKgZZBppBXAxUC0AisjRWMbi0cC5wO9EvhovPAzcoKpDgCEiMiFovwHYpaqDgfuA\nXwSv1Rn4MTAG2282TUTC2xHvBX4VvNae4DXSzsGDdtZQ7942uho92kdXrnkaMQIqKqq3hbMII3Xr\nVjtJw6WPaILWscCNwM+pmhps8nElqrpKVdcANT9iLwJmqmqFqq4D1gBjRSQfyFXV8D/BGcDEiOc8\nFlx/hqqNzxOAuapaoqp7sKSS8JbD8cCzwfXHsACaVrZts8vFF8O11/ppwK5569XLEo72769qGzPG\nztiKnDZs29YyDrdsSXwfXdNFMyV2KTBAVQ/GuzOBXsCCiNvFQVsFsCmifVPQHn7ORgBVrRSREhHp\nEtke+Voi0hXYraqhiNfqGesfJF4OHrRU9n79YNIkz4RyDmzab+xYmDcPjjzS2jp3tg3GH35o+xTD\nWra0tp5p87/ehUUz0voAaNQKSXCkyfKIy4rgzwsa83qH89YxekzK2bHDviFecAF8+9sesJyLdMwx\nUFnjLIqxY2tPEXbtam2RSRouPUQz0uoEfCwii6leMPeQKe+qelYj+lQMHBlxu3fQVl975HM2i0gL\noIOq7hKRYqCwxnPmq+pOEekoIlnBaCvyteo0a9b0r64PGVLIUUcV1vvYeCgvt9FVr15w44228Oyc\nqy4vzwo8Rx4EWVAAzz5r2bVt2lhbmzY2tb5pU93V4l3jFBUVUVRUFNf3ED3EaqSInFZXe6xONBaR\n+cCd4ZOSRWQY8CSWONELeBUYrKoqIguB24DFwEvAA6o6W0SmAMNVdYqITAYmqurkIBFjCVCAjSqX\nAKNVdY+IPAU8p6pPicjDwPuq+vt6+qh/+EPiV21DIZt7/+IL2yh5xhlw8slezcK5hixaBLNmVQ9G\nDz1k61uRJZw2b7ZR2HnnJb6PzYWIoKoxndU65MdfrIJTTSIyEdv71Q34h4i8p6rnqupHIvJ34COg\nHJiiVZH1VuBRoA3wsqrODtofAR4XkTXATmBy0PfdIvITLFgpcE+QkAFwFzAzuH9Z8BopobQUdu2y\n7KaBA+0/1aBBtoDsnGvYUUfBiy/al77wQZHhjcaRQatbNzvl+Oyz/YtgOjnkSMslZqRVVmY10Sor\nbYrjhBPsiJCa5wU55w7tT3+yL37hQrplZXDXXfDTn9pxJmHr19vacGTVDBc7SRlpufgpL7dAdfCg\n/Uc67TQYPtzm5J1zjTdunJ2pFQ5abdpYksbSpfb/LKx1a1i+3INWOmkwaAVJDTNU9VsJ6k/GC4Xs\nG2BpqVWpPu44S8U98siqqQznXNMMGmT/nyorqw6JHDsWXn21etDq2tX2cZ13nh92mi4aDFrBnqe+\nIpKdwH1aGUfVkil277b/SEOH2hx7//4WuJxzsdWuHRx9tNXiDBfRHTYMHnvMvjSGN+K3amUzHevX\nW6BzqS+a6cG1wNsi8iKwL9zoh0DWrbLSRlH79tn0X7ikUq9elv03ZEj1OXXnXHwcfzx88EFV0GrV\nymY2liyx5IuwNm1stOVBKz1EE7Q+DS5Z+OGP1ezfb8Fp//6qOmYtW1qAGjnSdtt37WoXH1E5l1j9\n+tn/u/Lyqqm/MWPgmWeqB62uXWHFCtuw37p1UrrqDkM0Ke/3AIhI++B2abw7lYq++MIC1MGDNsUX\nCtki78CBth8kL8/+8Xfo4GtTzqWC7GzbWPzuu1XlmoYMsf/LW7daFXiwL5oVFXZg5FFHJa27LkrR\nHAI5HHgc6BLc/hy4WlU/jHPfUkpurmUf9e5twalLl6rd9c651DRihG02DsvKsmnDd96BCyNq+rRr\nZ8HNg1bqi2Z68I/A7ao6H0BECoE/ASfFsV8p56abkt0D59zh6tPHDn6MLOE0diw88ohNB4bXnLt0\nsdOP9+/3TfypLpqJrJxwwAJQ1SIgJ249cs65GAlXfv/886q2vn1tDXr9+qq2Fi1syv+zzxLfR3d4\noglaa0XkP4OTfvuJyI+wjELnnEt5w4dXr/wuUnfl99zc2m0u9UQTtK4HugPPBZfuQZtzzqW8Hj1s\nHbo0IoVszBhLfY88mqRTJ1i9GrZvT3wfXfQOGbRUdbeq3qaqBcFlqqruTkTnnHOuqUSslueuXVVt\nRxxh+yXXrKlqy8qyjMM34lIi3MVKvYkYIjILq4xep2jO03LOuVQwdCi89JKtZYWTL8aOtSzCyIzB\nHj1g2TIr9eRn1qWmhrIHf5mwXjjnXBx16WL1PUtKbBoQbIrwpz+Fb36z6miSrCzLMiwqgssuS1p3\nXQPqDVrxOkfLOeeS4YQT4Omnq4JWly42Tfjhh1bBJiwvz8o6nXaan7iQig65piUig0XkGRH5SETW\nhi+J6JxzzsXKoEE2NRiZfDFmTO2MwcjRlks90WQP/gV4GKgATgdmAE/Es1POORdr7dvb+lVkQsbo\n0VZU98CB6o/Ny4P337dyTy61RBO02qrq69gpx+tVdTrw9fh2yznnYm/MmOqp77m5MGCABahIPtpK\nXdEErQMikgWsEZHviMjFgB+u4ZxLO/37W8X3ioqqtro2GoONtpYvhy1bEtc/d2jRBK2pQDvgNmA0\ncCVwTVPfWER+ISIrReQ9EXlWRDpE3He3iKwJ7j87or1ARJaLyGoRuS+iPVtEZgbPWSAifSLuuyZ4\n/CoRuTqivZ+ILAzu+5uIRFOH0TmXxlq3tpPCI8s6jRplm4r37av+2Kwsq0M4fz4uhUQTtCpVtVRV\nN6nqdao6SVUXxuC95wLHqOooYA1wN4CIDAMuA44GzgV+JxLeWcHDwA2qOgQYIiITgvYbgF2qOhi4\nD/hF8FqdgR8DY4BxwDQR6Rg8517gV8Fr7QlewzmX4UaOrL6G1aaNneDw7ru1H9u9u615bd6cuP65\nhkUTtH4VjHh+EhxTEhOq+pqqhvN4FgK9g+sXAjNVtUJV12EBbayI5AO5qhoeyM8AJgbXLwIeC64/\nA4wPrk8A5qpqiaruwQLlOcF944Fng+uPARfH6mdzzqWuvn1tBBUZuMaMsY3GNfloK/VEU8bpdCxr\ncAfwBxFZERTNjaXrgZeD672AjRH3FQdtvYBNEe2bgrZqz1HVSqBERLrU91oi0hXYHRE0NwE9Y/bT\nOOdSVosWFqQipwiHD4dNm2B3HQXqune3vVw+2koNUZ2xq6pbVfUB4GbgPWzK7ZBE5NVgDSp8WRH8\neUHEY/4DKFfVvzXmB6jvrWP0GOdcBho+vHoyRqtWcNxx8NZbtR8bHm29/nri+ufqF83JxUcDlwOT\ngJ3AU8Ad0by4qp51iNe+FjiPquk8sNHQkRG3ewdt9bVHPmeziLQAOqjqLhEpBgprPGe+qu4UkY4i\nkhWMtiJfq07Tp0//6nphYSGFhYX1PtY5l9p69rTKGF9+aacWA5x7LvzsZ3DqqdCxY/XH5+XBypVQ\nXAy9etV+PWeKioooivM+AVGttyauPUBkATATeFpVYzZAFpFzgF8Bp6rqzoj2YcCTWOJEL+BVYLCq\nqogsxLIYFwMvAQ+o6mwRmQIMV9UpIjIZmKiqk4NEjCVAATaqXAKMVtU9IvIU8JyqPiUiDwPvq+rv\n6+mrHur35JxLL2+9BXPmWE3CsGeesdOLr7qq9uO3b7fHXtPk3OnmQ0RQ1ZjOakWzpnWiqt4fy4AV\neBDb7/WqiLwrIr8L3u8j4O/AR9g615SIiHEr8AiwGlijqrOD9keAbiKyBvgecFfwWruBn2DBahFw\nT5CQQfCY20VkNdAleA3nXDMxdGj1wyHBRlvvv28jqpq6d4ePP7a1L5c8hxxpOR9pOZepfvtbG1l1\n6FDVNm8erFgBU6fWfvyOHTY9eO21CetiWkvKSMs55zLVuHG1MwZPPdUyCz/8sPbju3WDVatg48ba\n97nEiDpoiUh7EfHyTc65jDFkiP0ZWfm9ZUuYNMnWtyLbwarEt28Pr72WuD666qI5muRYEVkGfAh8\nJCJLY7nJ2DnnkqVDBxg8GPbsqd4+ciTk5MDbb9d+TrduVvZpw4bE9NFVF81I6w/A7araV1X7YOnu\nf4xvt5xzLjHGjIG9e6u3icA3vgGzZkFZWe37cnN9tJUs0QStHFX9qoiJqhYBOXHrkXPOJdDAgVYl\no2YmYb9+lmE4Z07t53TtCmvWwPr1CemiixBN0ForIv8ZVEXvF5Rw8pOLnXMZoU0bOPbY6mWdwiZO\nhDfeqJ2s4aOt5IkmaF0PdAeeCy7dgzbnnMsIxx1XexoQoEsXyyb8v/+rfV+3bvDJJ7BuXdy75yJE\ns7l4t6repqoFwWVqsGnXOecyQr9+dtbWwYO17zvnHPjoo7qnAnNz4dVXwbdxJk402YPHi8hzQdWK\nr4rfJqJzzjmXCC1bwujRdU8RtmkDF1xgKfA1g1O3brB2rY+2Eima6cEngUexgrkXRFyccy5jjBgB\n5eV133fyyZZh+P77te/r0AHmzvXRVqJEE7R2qOqLqvqZqq4PX+LeM+ecS6BevaBPHyvVVFOLFpYC\n/9xztbMMu3a1kdZnnyWkm81eNEFrmoj8r4h8U0QuCV/i3jPnnEugrCy46CI7riTyrK2wY46xxIw3\n3qh9X8eOvraVKNEEreuAUdgx9eGpwfPj2SnnnEuG/Hw4/fS6TykObzh++WULbJG6dPHRVqJEc57W\nKlU9KkH9SUle5d255uPAAXjgAQtSkdXfw2bMsIMjv/GN6u27dtnBkjfdZM91yavy/q/gYEbnnMt4\nrVvDJZfAzp21C+aCTSH+61+1Mw27dLG0+LVeeiGuoglaJwDviciqIN19hae8O+cy2cCBUFAAW7bU\nvq9jRxg/3pIyaurUyaYP6wp2LjaiCVrnAIOBs6laz/KUd+dcRjvnHEvO2L+/9n1nnQWffmqXSJ07\n23rYBx8kpo/NUTQVMdYDnahKwujkKe/OuUyXm2ubirdurX1f69ZWl/Dpp2tnDOblwUsv2dqYi71o\nKmJMxTYY5wWXJ0Tku/HumHPOJduoUTBgAGzfXvu+ceMsNX7p0urtOTm2EfmddxLTx+YmmunBG4Bx\nqvpjVf0xtsZ1Y1PfWET+S0TeF5FlIjJbRPIj7rtbRNaIyEoROTuivSBYV1stIvdFtGeLyMzgOQtE\npE/EfdcEj18lIldHtPcTkYXBfX8TkZZN/Zmcc5klvHdr//7a1TKysuDSS+H552vf17MnvP46fPFF\n4vraXEQTtASI3ANeGbQ11S9UdaSqHge8BEwDCDIVLwOOBs4FfifyVQLpw8ANqjoEGCIiE4L2G4Bd\nqjoYuA/4RfBanYEfA2OAcdhG6Y7Bc+4FfhW81p7gNZxzrpq8PDjjjLr3bh11lAWo+fOrt2dnWzJG\nXRuRXdNEE7T+AiwSkekiMh1YCDzS1DdW1dKImzlAON/mQmCmqlao6jpgDTA2GInlquri4HEzgInB\n9YuAx4LrzwDjg+sTgLmqWqKqe4C5WGIJwWOeDa4/Blzc1J/JOZeZTjnFMgNLSmrfN2mSHRRZWlq9\n/YgjYMEC2LYtMX1sLqJJxPg1VhVjV3C5TlXva/hZ0RGRn4rIBuAKbEQE0AvYGPGw4qCtF7Apon1T\n0FbtOapaCZSISJf6XktEugK7VTUU8Vo9Y/EzOecyT3a2Baddu2qns+fnw/HHwz/+Ub29RQto2xZm\nz05cP5uDQ67jiMgJwIeq+m5wu4OIjFPVRVE891WgR2QToMB/qOosVf0R8CMR+QHwXWB6I36GOt86\nRo/5yvTp07+6XlhYSGFh4eH1yDmX1gYMsOC0fLkV1410/vkwbRoUFloQC8vLg5UrbcPxgAEJ7W5S\nFBUVUVRUFNf3iKaM0zKgIFzHSESygCWqWhCzTogcCbykqiNE5C5AVfXe4L7Z2HrXemC+qh4dtE8G\nTlPVW8KPUdVFItIC2KKqecFjClX15uA5vw9e4ykR2Q7kq2ooCMzTVPXcevrnZZycc+zdC7/5jW0w\nbtu2+n1z5ti+rSlTqrfv2WOPnTLFRl/NSbLKOFX7xA6m1JqcaScigyJuTgQ+Dq6/CEwOMgL7A4OA\nd1R1KzbtNzZIzLgaeCHiOdcE1y8F5gXX5wBniUjHICnjrKANYH7wWILnhl/LOefqlJsLF15Yd6WM\n8eOhuBg+/LB6e6dOlsSxYkVi+pjpoglaa0XkNhFpFVymArGorvXzIH39PeBMYCqAqn4E/B34CHgZ\nmBIRNG/FkkBWA2tUNTxb/AjQTUTWAN8D7gpeazfwE2AJsAi4J0jIIHjM7SKyGuhCDJJLnHOZb8QI\nGDSo9t6tVq3giivgySehrKz6fT16WHmnmu3u8EUzPZgHPIBl2ynwOvA9Va1ju11m8ulB51yk7dut\nEvwRR1iwivSXv1gV+Msvr96+YYOVfzrttMT1M9mSMj2oqttVdbKq5qlqD1W9ojkFLOecqykvD848\ns+69W5deCkuW1K72np9vG47rSpt30YtmetA551wNJ51kx5HUDELt28Nll8Hjj1c/ATk7287ZqrkR\n2R0eD1rOOdcIDe3dOv546NoVXnmlent+vtUkrKsIr4uOBy3nnGukfv1gzJja2YQi8K1vQVFR9SnE\nFi1svWv27NrV4V10oqny/nhEvT5EpK+IvB7fbjnnXHo4+2xo2bL2uVudO1t6/IwZ1Udi3bvDqlW1\nz3ElgC8AABoSSURBVOJy0YlmpPUWVnvwPBG5EXgVK0rrnHPNXvv29e/d+trXbHQVWSRCxNbC/vEP\nqKys/RzXsGiyB/8AfBvbfPtfwKmqOiveHXPOuXRx7LEweHDt4rhZWXDVVRagdu6sau/Y0dLm338/\nsf3MBNFMD14F/BmrQPEo8LKIjIxzv5xzLm2Ez906cKD22Vr5+ZYe/8QT1dexevSwRA3fcHx4opke\nnAScoqp/U9W7gZux4OWccy7QrZutb23aVPu+CRPsQMhFEWXG27a1dbAFCxLXx0wQzfTgxMjNxKr6\nDnagonPOuQgnnQT9+9dOaW/RwqYJn3mm+mnGRxwB8+ZZUV0XnUalvKvqwVh3xDnn0l3LlraxWKT2\noZD9+sEJJ8Df/17V1qqVBbR583BR8n1azjkXQ5062R6t7durV8QAyzJct87O5ArLz7eyT3WVhHK1\nedByzrkYGzAAzjkHNm6s3p6dDVdeCX/9a9W+rqwsyMmxpAzfcHxo0WQPdhSR34jIkuDyq8jNxs45\n52o79VQYOrT2CGroUBg2DJ5/vqqte3f45BNYsyaxfUxH0Yy0/gx8AVwWXL4A/hLPTjnnXLrLyrLa\nhG3a1C6qO2mS7dGKDFJdu8KsWbWnFF110QStgao6TVXXBpd7gAHx7phzzqW79u1tfWv3bjgYkb6W\nk2PnbT3+eNW+rg4dbAPye+8lp6/pIpqgtV9ETgnfEJGTgf0NPN4551zgyCPhggtsfStyzaqgAHr2\nhJdeqmoLbzj+8svE9zNdRBO0bgF+KyLrRGQ98BC2wdg551wUTjgBjjsOiourt3/zm/DWW1UJG23b\n2sjr5Zc9KaM+Eu0x8iLSAUBVvzjUYzONiGi0vyfnnKvL/v3wu99ZUOrSpar9rbfgjTfgrrtsz1Yo\nBJ99BuefbwV305mIoKoSy9esd6QlIrdHXrCiud+OuB0TInKHiIREpEtE290iskZEVorI2RHtBSKy\nXERWi8h9Ee3ZIjIzeM4CEekTcd81weNXicjVEe39RGRhcN/fRKRlrH4m55yrqW1bW98qLa1eb/Dk\nk+2+14MDn7KyoE8fmzZcvTo5fU1lDU0P5gaX47Epwl7B5WagIBZvLiK9gbOA9RFtR2NZikcD5wK/\nE5FwpH4YuEFVhwBDRGRC0H4DsEtVB2PHpvwieK3OwI+BMVjpqWkR6fr3Ar8KXmtP8BrOORc3+flw\nySU2TRg+Y0vESjzNng07dlhbq1a2vvXXv9omZVel3qClqvcEmYK9gQJVvUNV7wBGA33qe95h+g3w\n7zXaLgJmqmqFqq4D1gBjRSQfyFXVxcHjZgATI57zWHD9GWB8cH0CMFdVS1R1DzAXOCe4bzzwbHD9\nMeDiGP1MzjlXr4ICW+OK3HjcvbttRn788aq1rJwc24z8xBOemBEpmkSMHkBkrcGDQVuTiMiFwEZV\nXVHjrl5A5D7yYqpGeZH1kzcFbdWeo6qVQEkw3Vjna4lIV2C3qoYiXqtnU38m55yLxnnn2UgqPLIC\nOOMMW/d6++2qtm7dbI/XM8/4gZFh0azjzADeEZHw/u2JVI1qGiQir1I9wAmgwI+AH2JTg/EQzcLf\nYS0OTp8+/avrhYWFFBYWHl6PnHMu0Lo1XHEFPPigjaLatbMkjKuvhvvuszWtPsF8Vq9e8PHH8Npr\ndsRJKisqKqIo8pjmOIgqe1BERgPhvVpvquqyJr2pyHDgNeBLLHj0xkZBY4HrAVT158FjZwPTsHWv\n+ap6dNA+GThNVW8JP0ZVF4lIC2CLquYFjylU1ZuD5/w+eI2nRGQ7kK+qIRE5IXj+ufX017MHnXMx\n99FHMGMG9O1rQQvg3Xfhb3+DO+6wNTCwUdb69ZYiPzKNjuBNaPZgDe8BTwPPAzsjs/MaQ1U/UNV8\nVR2gqv2x6bnjgnO7XgQuDzIC+wODgHdUdSs27Tc2SMy4GngheMkXgWuC65cC4UL/c4CzgvqJnbGR\n3ZzgvvnBYwmeG34t55xLiGHD4LTTqq9vFRTAxIlw//2wa5e1tWhhG5GffrruQyabk0OOtETku9hI\nZxtQSTDFp6ojYtYJkbXA8aq6K7h9N5bNVw5MVdW5Qfto7NTkNsDLqjo1aG8NPA4cB+wEJgdJHIjI\ntcB/YNOSP1XVGUF7f2Am0BlYBlypqjUOyv6qfz7Scs7FRUUFPPIIbNtWNbICmw588024804r8QS2\nvlVRAVOmQMc0KFsej5FWNEHrE2Ccqu6M5RunEw9azrl42rPH1rdycqxeYdiLL9rZW3fcYXu5wE5F\nzsuD66+37MJUlqzpwY1AySEf5ZxzrlE6dbLEjJoHR15wAQwcCA89VFVwNz/fpgj/8Y/mWeopmpHW\nI8BRwEvAgXC7qv46vl1LHT7Scs4lwrx5MHeuHSIZFgrBX/5i6fC33FJV6mndOjsJ+aSTktbdQ0rW\nSGsD8CqQTVWVjNxYdsI555wlZQwdWj0xIysLrr3WKmf85S8WsLKyrHr8rFnw6adJ625SRF0wtznz\nkZZzLlHKyizl/ZNPbK9WuIjdwYO27nXEEZb6LmJ1DPfuhVtvtY3IqSZZiRjdgf8POAbL2gNAVcfX\n+6QM40HLOZdI5eXw7LN2unHfvjayApsi/PWv4ZhjLC0e4PPPbXPyTTdVJWukimRNDz4JfAz0B+4B\n1gGLG3qCc865xmvVCi691GoUrl9fVcKpbVuYOhWWLbO1L7AR1q5dFuSaQ6mnaIJWV1V9BChX1f+/\nvXsPt3u68zj+/khDJCSSugtxTSmTilvdh6JaVYxLRRmCKfO006Y8VWNqRqfVUTo8bU1bo1rEQ2SK\nElrEJXGphpaQZKaUVoqk7qppXFJ854+1ds4v2778Ts7ZlxOf1/Ps5+y9fr/129+9ss9Z+a3rXRFx\nAj0L0pqZWQsMGpRGD+6zTxp08dc8i3S11eCLX4SZM9NeXJCWepo3D2bM6FS07VOm0qpMuP2jpE9I\nGg+MapTBzMz6bqWVYN994eCD4amn4M08fnvkyHTHNW0aPPhg6t8aMyZNSJ5bvQT5CqZMn9aBwD3A\nhsCFwHDg3yNiWuvD6w7u0zKzTps9G6ZOTQMxKn1XTz+dlns64YS0JNTrr6eV4z/72bTsU6d1ZCCG\nudIys+7w6KNpf61Ro3pWznjiCbjoojSHa7PN0uoaEXDKKZ1fMaOTowc/A2xMYSuT3Lf1nuBKy8y6\nxfz5ab7WsGFpJQ1I/VmXXZb6ukaPTk2Jp53W+fUJOzV68AZgBGkrkZ8VHmZm1mYbb5yGty9Zkoa7\nA2yzDUyYkOZxPf98R8NruTJ3Wg9HxLZtiqcr+U7LzLrNiy+mO6433ki7IAPccw/cckuafPz1r793\n77RuknRAf76pmZn1zZprwkknpW1LFi5MaXvsAXvu2TMUfkVU5k5rETAMWELP8PeIiOEtjq1r+E7L\nzLrV4sVwxRVp5feN8va88+fD6ae/R++0ImL1iFgpIobk56u/lyosM7NuNmxYWlB37NhUWVUW1F1R\nva/5KSDpIGDP/HJmRNzUupDMzKw3hgxJ/VjXX58mGw8a1OmIWqdpfSzpm8Ak4P/yY5Kkc1odmJmZ\nlTd4MBx6KOy+e8/K8CuiMn1ac4BtI+Kd/HoQMDsixrUhvq7gPi0zGygi4LHHUnNhp5sJOzV6EGCN\nwvN+6dqTdJakZyQ9lB8fKxw7Q9Ljkn4j6aOF9O0kzZH0W0nfLqSvLOnqnOeXkjYqHDsun/+YpGML\n6RtLmpWPTZFUqqm0m82cObPTIZQyEOIcCDGC4+xvK0KcUtpIstMVVquU+VjnALMlXSbpcuBB4Bv9\n9P4XRMR2+XELgKStgE8BWwEfB74vLb3Z/QFwYkSMBcZK2j+nnwi8HBFbAN8GzsvXGgn8G7Aj8GHg\nLEmVSvdc4Px8rT/lawxoK8IvXLcYCDGC4+xvjrP7lRk9OAXYGbgOuBbYJSKm9tP717ptPBi4OiLe\nioj5wOPATpLWBVaPiMpeXpOBQwp5Ls/Pr6Fn65T9gekR8WpE/AmYDlTu6D6SPw8579/1z0cyM7NW\nqVtpSdoy/9wOWA94Jj/Wz2n94Z8kPSzpksId0AbA04VzFuS0DfL7VzyT05bJExFvA69KGlXvWpLe\nD7xS6aerfK5++kxmZtYidQdiSLo4Ik6SVGtbsYiIphtBSroNWKeYBATwFWAW8GJEhKSzgXUj4h8k\nXQj8MiKuyte4BPg58AfgnIj4aE7fHfhyRBwkaS6wf0QszMeeAHYCjgdWiYj/yOlnAq+R7qxm5eZE\nJI0Gfl5vcIkkj8IwM1sO/T0Qo+7gg4g4Kf/ce3kvHhH7lTz1h8CN+fkC0t5dFaNzWr30Yp6FeXTj\n8Ih4WdICYK+qPDMi4iVJIyStlO+2iteq9TlW4AGkZmYDR5l5Wp+TtEbh9UhJn+3rG+c+qopDgXn5\n+TRgQh4RuAmwOfBARDxLavbbKQ/MOJa0An0lz3H5+RHAnfn5rcB+uYIaCeyX0wBm5HPJeSvXMjOz\nLrVcq7xLmh0R4/v0xtJkYFvgHWA+cHJEPJePnUEazfdXYFJETM/p2wOXAUNIzXmTcvoqwBXAeOAl\nYEIexIGkiaTmyADOjojJOX0T4GpgJDAbOCYiKmsrmplZFypTac0FxlVm1+bmtzkRsXUb4jMzM1uq\nzDytW4CpkvaRtA8wJacNSJI+JunRPKn49Drn7CVptqR5lYEoksbmtIfyz1clfSEfGylpep7AfGth\nJGS3xVl3Qne748zpp+S0OZKulLRyTu+a8mwSZ7+WZx9jnCRpbn58oZDebWVZjHNSIb3t301JXyr8\nrsyV9JZyV0i9vJ0oz+WMs9vK80eSnlNaYamYp/flGRENH6SK7R9J85+uAU4GBjXL142P/FmeAMYA\ng4GHgS2rzhkB/C+wQX69Zp3rLARG59fnkkYyApwOfLNL4zwLOLUbypM0xeD3wMr59VTg2G4rzyZx\n9lt59jHGrYE5wCrAIOA2YNMuLMtGcbb9u1l1/oHA7c3ydqI8lzPOrinP/Hp3UnfQnKrzel2eZe60\nVgV+GBGHR8ThwCX5SzcQ7QQ8HhF/iNR/dTVpYnLRp4FrI2IBQES8WOM6+wK/i4jKvLHi5ObL6Zn0\n3G1xQu0J3Z2KcxAwTGkJraH0jODstvKsjnNh4Vh/lWdfYtwKuD8i3ow0T/Eu0uAm6K6ybBQntP+7\nWXQUqRWpWd5OlOfyxAndU55ExL3AKzXO63V5lqm07iBVXBWrAreXyNeNqicbFycoV4wFRkmaIelX\nkv6+xnWOpPAPAqwdeRBJpFGOa3dpnFB7Qnfb44w0p+584ClSZfWniLgj5+ma8qwTZ/H731/l2Zd/\n83nAHrmpZShwAD3TQ9bplrJsEie0/7sJgKRVSSvlVFbIaZS3E+W5PHFC95RnI73+XS9TaQ2JiL9U\nXuTnQ0vkG6jeB2xHWvfwY8C/Stq8clDSYOAg4CcNrtGOycjLE+f3Sc0x2wLPAhd0Ks7c1n0wqblh\nfWA1SZ+uc42OlWeTONtdnjVjjIhHSc0st5Em4s8G3q5zjY6VZZM4O/HdrPgkcG+kpd56q50LD/Qm\nzhW2PMtUWotVWLZJadj568sRTDdYAGxUeF1rUvEzwK0R8UZEvATcDXyocPzjwIMR8UIh7TlJ68DS\n+WfPd2OcEfFC5MZj0oTuHTsY577A7yPi5dxUdB2wa87TTeVZN85+Ls8+/ZtHxKURsUNE7EVaAPq3\nOc+zXVSWdePs0HezYgLLtkg0ytuJ8ux1nF1Wno30/ne9WacX6cP+DrgHuJfUGbd9s3zd+CD1TVQ6\nE1cmdSZuVXXOlqT/CQ4i3VHOBT5YOD4FOK4qz7nA6dF/nbOtinPdwvNTgKs6FSepjXwuac6dSPPv\nPtdt5dkkzn4rz77+mwNr5Z8bkTZrHd5tZdkkzrZ/N/N5I0hzO1ctk7cT5bmccXZNeRaObQzMrUrr\ndXmWDXgwsE1+DO7Lh+/0g9Rc8Rhp9fh/zmknAycVzvkSafTTHODzhfShwAuk1eaL1xxF6ud7jLSS\n/BpdGufkfO7DwPWk9vlOxnkW8Jucfnnlu9WF5Vkvzn4tzz7GeDepz2g2sFcXfzfrxdmp7+Zx1PiD\nXitvh8uzt3F2W3leRRrA9Capf/j45S3PMpOLhwKnAmMi4jOStgA+EBE3NcxoZmbWz8r0aV0KLAF2\nya8XAGe3LCIzM7M6ylRam0XEeaR1AImI1+jf8f9mZmallKm0luRx9wEgaTNSu6SZmVlb1d1Pq+As\n0lqDG0q6EtgNmNjKoMzMzGppOhADQGl7+p1JzYKzovaSQWZmZi1Vt9IqTiiuJSIeaklEZmZmdTTq\n0zq/weM/Wx+aWetIulPSflVpkyR9r0m+Rf0Yw3GSLlyec3L625K2KaTNlbRR9bn9SdIYpT32aqW/\nI+lzhbQLJR3b5HoHS9qyFbHaiqlun1ZE7N3OQMza7CrSStS3FdImkCbFNlJ6rTlJgyIt/dTX69U7\n52nSrtxH9Ta2RkrEXe99ngcmSfrviHir5NsdAtwEPNqbGO29q+noQUlDJZ0p6eL8egtJB7Y+NLOW\nuhY4IG83gqQxwHoR8QtJwyTdLunXkh6RdFCtC0j6Vr67eUTSp3La30q6W9INpBUhqvMcnze8m0Ua\n1FRJX1PSNZLuz49dqvPW8DNg6zzhHwpTUSTtJ+m+/Bmm5kUCkPSkpFH5+fbq2Tz0LEmTJd0LTM53\nTnfn/L+WtHOJeF4g7Qoxscbn3lTSzXnV97uUNivdhbSo83lKGwduUuI97D2uzOjBS4EH6VnMdAFp\n5XCviGEDVkS8IukB0sLCN5Lusv4nH34DOCQi/pIHIc0CphXzSzoMGBcRfyNpbeBXku7Kh8cDW0fE\nU1V51gW+mo//GZgJVPqGvwNcEBH3SdoQuJW09mEjbwPnke62Jhbe5/3AmcA+EfG6pC+TVrU5m3ff\nJRVfbwXsFhFLJA0B9s3PNyetZdls0dUgrSV3i6QfVR27GDg5In4naSfgBxGxj6RpwI0RcV2Ta5sB\n5SqtzSLiSElHQZpcLMmTi21FcDWpsqpUWifkdAHnSNoTeAdYX9LaEVFcgXo38krWEfG8pJmkP+qL\ngAeqK6zsw8CMiHgZQNJUoHKXtC+wVeF3a7XK3VETU4CvSNq4kLYzqcL7Rb7eYOC+wmerZ1pELMnP\nVwb+S9K2pMpxi/rZekTE/HwXeXQlTdIw0n96f1L4fIPLXM+sWplKy5OLbUV1A3CBpPGkValn5/Sj\ngTWB8RHxjqQnSau8N1KsDBaXPK86/cORdoXtSWzy/8OIeFvS+aQVsit3TQKmR8TRNbK8RU+3QPVn\nKsZ9CvBsRIyTNIjebUd0DnAN6U6S/H6vRETDEclmZZRZEaN6cvEdwJdbGpVZG0TEYtIf1h+z7P4/\nI4Dnc4W1N2k7hopKLXIPcKSklSStBewBPNDkLe8H9lTauXcwcETh2HRg0tI3kT5UnbmBy0l3amvl\n17OA3fJ/MCv90pU7pSeB7fPzwxpccwTwx/z8WNLWFEvDq5NHABHxGGnbkYPy60XAk5IOX3qiNC4/\nXQQMb/ThzIqaVloRcRtwKKnNfAqwQ0TMbG1YZm0zBRjHspXWlcCOkh4BjiFtS1IRABHxU9LWD4+Q\ntlY4rar58F0ibSf+VVKlcg/pD3vFJGCHPKhjHmnLh1Ly3dl3yVuV58n/E4Ep+TPcB3wgn/414Lu5\nP6/RCL/vAxMlzQbGsuxdWL3Rg8X0b7DsduzHACcqbf8+j1yhkZpoT5P0oAdiWBlltibZDXg4IhZL\nOoa0jfZ3IuIP7QjQzMysokzz4A+A13JzxamkXYwntzQqMzOzGspUWm9Fuh07GPheRHwPWL21YZmZ\nmb1bmdGDiySdQWqT3lPSSni4qpmZdUCZO60jSUPcT8wdyaOBb7U0KjMzsxpKbU1iZmbWDcrcaZmZ\nmXUFV1pmZjZguNIyM7MBo+7oQaWN3up2eEXEuHrHzMzMWqHRkPfKnlmVnUivyD9rLcJpZmbWcmWW\ncZodEeOr0h7yis1mZtZuZfq0lNcfrLzYtWQ+MzOzflVmRYwTgR9LGkHaeuAVejbLMzMza5vSk4tz\npUVEvNrSiMzMzOooVWlJ+gSwNYWdTiPiay2My8zM7F2a9k1Juoi0/uDnSc2DR7DsTq5mZmZtUWb0\n4JyIGFf4uRpwc0Ts0Z4QzczMkjKjAF/PP1+TtD7wV2C91oVkZmZWW5nRgzdJWoO0HclDpFUyLmlp\nVGZmZjX0amsSSasAQzyC0MzMOqFupSXp0EYZI+K6lkRkZmZWR6PmwU/mn2sDuwJ35td7A/cBrrTM\nzKyt6lZaEXE8gKTpwAcj4o/59XrAZW2JzszMrKDM6MENKxVW9hywUYviMTMzq6vM6ME7JN0KTMmv\njwRub11IZmZmtZVdxulQoDKZ+O6I+GlLozIzM6uhV0PezczMOqlu86CkeyNid0mLSBOKlx4CIiKG\ntzw6MzOzAt9pmZnZgNHoTmtUo4wR8XL/h2NmZlZfoxUxniQ1C6rG4YiITVsZmJmZWTU3D5qZ2YDR\nqHlwy4h4VNJ2tY5HxEOtC8vMzOzdGjUPXhwRJ0maUeNwRMRHWhuamZnZssrsXDwkIt5olmZmZtZq\nZdYevK9kmpmZWUs16tNaF9gAWFXSeHpGEQ4HhrYhNjMzs2U0WjB3f2AiMBq4oJD+Z+BfWhiTmZlZ\nTWX6tA6LiGvbFI+ZmVldjUYPnlqVFMCLwL0R8WSrAzMzM6vWaCDG6lWP4cAOwM2SJrQhNjMzs2X0\nekWMvCbh7RFRc9KxmZlZq5QZ8r6MvFBurfUIzczMWqrXlZakvYFXWhCLmZlZQ43mac1l2c0fAUYB\nC4FjWxmUmZlZLY1GD46pSgrgpYhY3PKozMzMavDWJGZmNmD0uk/LzMysU1xpmZnZgOFKy8zMBgxX\nWmZmNmC40jIzswHj/wFa8R33YZkqUwAAAABJRU5ErkJggg==\n", 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syktG0Nqb77WrgEFRfLiI9BKRt0XkCxH5TERuCtrbi8hUEVkkIlNEpG3Ca+4SkcUislBE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\nJgHb504dj5CWfCglX519j7xUeR78PxGYlj/DfcBH8+FfB76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