{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 04\n", "\n", "Estimate a regression using the Capital Bikeshare data\n", "\n", "\n", "## Forecast use of a city bikeshare system\n", "\n", "We'll be working with a dataset from Capital Bikeshare that was used in a Kaggle competition ([data dictionary](https://www.kaggle.com/c/bike-sharing-demand/data)).\n", "\n", "Get started on this competition through Kaggle Scripts\n", "\n", "Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able rent a bike from a one location and return it to a different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world.\n", "\n", "The data generated by these systems makes them attractive for researchers because the duration of travel, departure location, arrival location, and time elapsed is explicitly recorded. Bike sharing systems therefore function as a sensor network, which can be used for studying mobility in a city. In this competition, participants are asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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seasonholidayworkingdayweathertempatemphumiditywindspeedcasualregisteredtotal
datetime
2011-01-01 00:00:0010019.8414.395810.031316
2011-01-01 01:00:0010019.0213.635800.083240
2011-01-01 02:00:0010019.0213.635800.052732
2011-01-01 03:00:0010019.8414.395750.031013
2011-01-01 04:00:0010019.8414.395750.0011
\n", "
" ], "text/plain": [ " season holiday workingday weather temp atemp \\\n", "datetime \n", "2011-01-01 00:00:00 1 0 0 1 9.84 14.395 \n", "2011-01-01 01:00:00 1 0 0 1 9.02 13.635 \n", "2011-01-01 02:00:00 1 0 0 1 9.02 13.635 \n", "2011-01-01 03:00:00 1 0 0 1 9.84 14.395 \n", "2011-01-01 04:00:00 1 0 0 1 9.84 14.395 \n", "\n", " humidity windspeed casual registered total \n", "datetime \n", "2011-01-01 00:00:00 81 0.0 3 13 16 \n", "2011-01-01 01:00:00 80 0.0 8 32 40 \n", "2011-01-01 02:00:00 80 0.0 5 27 32 \n", "2011-01-01 03:00:00 75 0.0 3 10 13 \n", "2011-01-01 04:00:00 75 0.0 0 1 1 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "# read the data and set the datetime as the index\n", "import zipfile\n", "with zipfile.ZipFile('../datasets/bikeshare.csv.zip', 'r') as z:\n", " f = z.open('bikeshare.csv')\n", " bikes = pd.read_csv(f, index_col='datetime', parse_dates=True)\n", "\n", "# \"count\" is a method, so it's best to name that column something else\n", "bikes.rename(columns={'count':'total'}, inplace=True)\n", "\n", "bikes.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* datetime - hourly date + timestamp \n", "* season - \n", " * 1 = spring\n", " * 2 = summer \n", " * 3 = fall \n", " * 4 = winter \n", "* holiday - whether the day is considered a holiday\n", "* workingday - whether the day is neither a weekend nor holiday\n", "* weather - \n", " * 1: Clear, Few clouds, Partly cloudy, Partly cloudy \n", " * 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist \n", " * 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds \n", " * 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog \n", "* temp - temperature in Celsius\n", "* atemp - \"feels like\" temperature in Celsius\n", "* humidity - relative humidity\n", "* windspeed - wind speed\n", "* casual - number of non-registered user rentals initiated\n", "* registered - number of registered user rentals initiated\n", "* total - number of total rentals" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(10886, 11)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bikes.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 4.1 \n", "\n", "What is the relation between the temperature and total?\n", "\n", "For a one percent increase in temperature how much the bikes shares increases?\n", "\n", "Using sklearn estimate a linear regression and predict the total bikes share when the temperature is 31 degrees " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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fNzGMAvG4AawiGFxDrTZFOn0Cv39+clvXLXw+P5a1dMvXfI0wY1HhStM00XUL\n2wa/349tQ72eZd++M035e9UPslIoQ6L4UDIbknEc56ryTwhf1SF9vVU7c+eBuK7DhQuVeZIlY2Nj\nDA46y8q6N44zv2w1kUjw67/+Eer1Ol1dXUQiEWB++KVSqTA4uB7HiVCvl/H7IxjGeiqVSmt/x3HI\n5UoIEcUw4th2mVQqy49+9BY+Xy9+f5lt20J4XoWTJy9gWUGEyFAuSz796V+kXrcJBEySyTcIhbJU\nq2M4jh/DqBOP6wSDDrFYQ83XcUp4Xh/r1kWo19P09JiY5jruu6+TixevNA0KAbHYtta9Xsz7syyL\nTMYilbq8pHCl67ps3nwv09N1LKsRSkyno/j997Z0xFQ/yMqg7rbiQ8dCGfVarTLPaCzskL7eqp2F\n80Cy2RRHjrzHJz4RbL02mVxe1n02tLawbHVgwODEiSk8L8TY2NnWk/VcAxSLxWhr8wgGowSDEarV\nEtWqRygUmifaqOsddHWtRdNMLMtgZCSAru9g1aot5PPT7N79/xGL+ZmZiWJZdXRdkk5PMTJylkCg\nCyln8PtLjI9XiUY3I6XAcSSZzCl0XUPTYuh6EE0T6PoI5XKlOR63QjRaYfXq1axda7QM+Z49k1eV\n9S68R7quc/78RSKRe5cUrjRNk/b2IN3da9F1jXK5yKFDM0QisWWPrbj9qLut+FCxWIikUjlMuTxM\nsRhatEP6emPrC5Pt0WgbUnrzlHWXknVfWP76+OMbMc0x8vk0kYjAcaKEw5vnPbVv3eq0qp9mr/vZ\nZ7fz/PNvkss1Qmmf/OQ69u49Oy+P0d9vYlkOuh6mUqnQ0REjEIBCYQKfzyEe72Hv3veoVp9EiASe\nJygUZqhW0wSDUer1PIGAhRBBvve9H1Gvx/H78zz1lEGtZuL3S2o1G9PUMU2DM2deRcoEwaDF1q2D\nLS9qtmnwerrhXddlzZp+pqfHmJqCQICrCheulPKOUa02HhI2bozjOHazD0f1g6wUypAo7mo+qNje\nbIhkZuYStVpjQerpMXn00fUt7aeFHdLXe66FyXbHsdm0KYHrniOZnJ7n2SwXWisW/dRqVvOsAtu2\ncd3QvKf2uZ3e8wsCtvCVr/RRKBQIhULs3Xt2npEaGxvmk59cz549w7huhHA4QzAYYfXqGEIINM3H\n1FSFXA7KZRMI4Hl+IMLAgEkk4hEIdBCJrONb33qDVav+D2KxHgqFafbs+XM8T9Ld/QsMDAwwM3OG\n06e/zapf0MCGAAAgAElEQVRV7Ujpo163OHFiinL5ikTJ9XbDN+65Tb0uEcKkXrfw+ZyrjMLCUt5i\nsXjD/SBKtPHWoe6e4q7lRsT2dF1neHiETOZB/P526vUMqdQIn/3stkUlSD7ouRYuik8+eT/BYHDJ\njvTFQmuaVmHv3ikmJkJoWju2naNYPElHx4bWhMTZTm8QZDIzhEIxbNvfanYMBAKUy+VFjFSIp5/e\nTkfHBVKpCp2d9yJlF3/xF3+DZXVgmmm++MX1PP+8JBTahGF04ThdFIv/m76+IMEgdHa2kc1eIBZb\nhd8/Qz4/g2lCJNJFR4dOsXiay5fHsO0s9XqYUOiX6ejYQLF4iT17/pRSqUQ4HG4t0tcq651FCND1\nMJrWmHXfGFF0NXMfAG5UI02N6L21KEOiuGuYKxFiGMYNie1ZloXn+fD5qghRwOer4nm+eb0WsyzX\nfT17rrlVS7PyJ48/fl/rOiuVKu+8MzJP/mNhaXGlcmBeaG3Dhg6+9a0Jenoew+8PUq9XmZw8we7d\nP0bTOlud7Xv2nOall07geQ3V3I9+NIZp3tu6/vkTExthPJ+vjt9vNsUPQ82ZJRk+8YnfaoaAfJw8\n+TKdnWFSqcNYVgTDKBGJOHzjG3+HEEOY5gz/+T9/FE3LMjY2g+tG0PUS/f1Fenp68Ps9HMdPqVRq\n9rIYVCoz6LpHONzL5ORlTp1KtRbpoaEYZ85klux+n83tBAKd7NixlnK5TDi8ikJh7LryHbdD6l/x\nwVB3TXFXsFAi5Jln7rtusb1yudwKWzW2hxgY2IQQGlJ6pFJTV51v4WCphd3XySQUi0VKpStVS52d\n1XmjXjXt4lXz0PfuPYIQYSIRX2tEbCDQyaOPrmpdY7lcbl6bAMBxPHI5j507d9LWlsB1Pc6cGebs\n2SRC3I+mhRGizNmzwziOMy80t3Bi4he+sI133jnFmTNg2xqWVeDgwYsEAiex7QCmWae7WyccrjI5\nOYXnJXCcKSyrxKZN/4W2tm4qlQJ//dd/RyhU4uzZn+C6fej6ZVavNujpuZfDhy9gWSE0LU9vr4tp\nTiFlFE2rsHq1ZHraIpHY0mpI/Pu//z6x2AZ8vrZFu99nCxCq1RQjI3bTI7nEPffkse0+zp07N6+S\n7Wa5Hql/xQdD3TXFilOr1a6SCHnxxVfZtu2ea4rtVasp9u6ttCYEXqmIGl5SyG/hXHPLqjM8/Abt\n7dmWjIhpOoyMzBCLPdCqIhobe5VIRBCLbWtKpGQ4efJturoE1WoVn8+HEGGKxcucOVNpnf+ee+qE\nw/e2FqlwOMzGjXEuXBilUolSraZYtSpOV1dva5/Ll20yGUEyaTdLfW1M06BQKGAYRsuTujIxseE1\nnTx5hN27hxkfb8e2I3hehqNHj7N69ceJxYbI5yeYmjpBMBgiHtfwPEG9blEuh6jVSqRSOppWJ5v1\nOHeuxsDAL9FYJhyOH3+RSGQSn28QzzMxzQ58Po3h4b/GcfoJBKb5L//lMQyjndmQnJSSCxfqPPLI\nRqLRBPV6lRMnXkaIETo7P9IywIcODeN5DrPd9lDlzJlRXnzxIPV6oqVI/OCDD97039uNyOErlkcZ\nEsWKMRvayOfzWFZ4nkRIOh1j3bpEqx9hMbG92R6FudVOsxVRodD4kkJ+s3PN0+kr3kYwqPHeey8j\nZaLlEZXLa0gmk2SzOQzDZtWqXizLmNO1HaNarfHOO4ebWlNFBgbS+Hx+IMjsiFioz3vfhmHw5JP3\ns2fPaQqFcrNqa1Wzea8RogoEPM6cOUNb21P09g409a9eplR6jGPHLi/wpK4Mmpqc9Hj//Qvo+v34\nfG2USjqVigCmqdV0dL1AIBClUoHNmz+PbZdx3Qf46U9PUSyadHevoVCYpFqdwbYNAoEHCQS6qdVm\nSKe/z7Fj54lGfx6/v4dsdpzTp1/kmWf+E/F4F7Zts2fPD9mx4wRHjhzB89qo1ycplTJomoFl1XBd\nrzkB0jevuCCZFOh6jIcf3kKlUkDKLv7kT37Cfff9JqtXD5LPT/ONb3yXP/mT9TftmdzKQWaKBurO\nKVaEuaElKQu4bpJiMUs02nj6N83yvH6ExcT2lupRCIVCPPXUA0smYHVd58KFCSKRp0gkEuRySY4e\nfYvnnvvVVl5hcnKUWEynq6uvtbhbVhGgmZPQmj0YFobhAjWgim3bRKN9PPbYJmzbxucbJJs9fVXY\nREowDJ1AQMfvh/7+AD/5yf+mUvETCtV5+ukhNm0a4tKld7l82Y/fX2fDhkEOHTpPZ+fDmKaBbVsM\nD78+z5OSsozjCAqFDFKW8LwCuh5g06Z7SSQGcF2HqakT1OuTHDjwFp7XCVyiu9sgnf4JyeT7+P05\nfuEXHuDddy9QLr9PqRRCiAo9PSaGEUMIG9dN47oFXLedaLSbcLgDwzAYHY0yPDyBpn0cny+GpnWS\nybzAgQOvImUcn6/CY48FiEaNq7zNQqHAgQNH0LQYyeQparUg7e19AMTjPSSTcZLJ5C0Jcd3MIDPF\n1ai7p7jjLJbsXLcuzcTEq6TTsZZEyGxyfCmxvdkehVwui+e5aJp+XbPWXddlaGiw5W24bp6urgEC\ngRDBYMMoVash7r8/yshIo2dh1iPKZnOtnIQQWVav7mHz5kexbQufzySVOgmUcRy7NSJ2qd4S09xA\nKNQwCC+88ALh8Fri8TBCWJw7lyMalbS3J/A8P5pWJxzOU6sZDA9f6f4OBo15ntTHPz6A55UpFNJo\nWheuWyQarVMuv0u93otpWjzxxCD/+I/jBIOyWR0lKJfrPPHEvwLCBAJ1QqFTPP30IN/97rvYdgc+\nX5pnnhnk9GmLy5cncJwAQuQJBlOcPTtKJDJAvZ7BMMYxjA0A5PMzRKMmjhOgVptB1yWaVkHXO3jo\nodXzGkKvCEk2PLlQqAefbz+53Azt7avI56cxzTxdXV237O9QiTbeOtRdVNxxFkt2dnYO8cwz3biu\nu2yJ6FwMwyCRkPzFX/xDcz7G4pP94Op+kETCpKOjG8fxECLKoUNnse1Gb8ds9VNvby+9vb3zpE0O\nHhxvSaLYtsWBA69TKhUxTf91d83P9rpMTp6jXLbQNIfR0QJPPLGFaLSder3K6OjLxOM6Bw9OtmaP\nbNkS4eLFy8Tj98/xpMbneVIXL+7D5wsQi61tTj+MAoew7UmqVYtYrM7AwGaE8JHJHMG2E2jaNIGA\nDyHKxGJRhJDYtkDKAE899WkcJ4hhVDGMUwQCY1y69B6O04umXaanx2Jm5g2mpvrw+Wb47Ge72Ldv\nmNOndYToxnHGgLPs3LkTz/Njmgajo6N87nP+edVvrusSCHTy2GP3tjy5SOQyBw9+m0yms/XZ3qqE\nu+LWogyJ4o5wPXPN4/H4B3pCrNVqvP76WbZt+yLBYJhqtcyrr7551WS/xXoGGtVOr7eqnR55pIt9\n+678PFd8cfbfK30bjZxEMBikszPBe++9hJQdrdd1dXXx+OPRJfsmdF1n//4DnD4dRde7qNXGKRYn\n8TyBZVVxXQ/btpmagjVrNmNZYJr9TE6eYuPGLiqVcbLZqaYntWqeJ3Xpkk5vbz/9/X04joEQOidP\nmgQCW4lGB4AKb7xxhLNnpwiHv0ow2EWpdJ5k8o+bjYDg9wexLItgsJtHH310TtnwJGfPlonHn6Eh\nP7+aVCrDv/t3X0LTfIRCEWZmXmV8/BjpdB4wkDJNrZbjyJFRTLMbKUt0d58jmUwxNpajUmmIXz70\n0OrW34SuN8Jemzat59/+26fJZrO3tGpLcetRhkRx27mRuebXQ6FQaCbpOwEwzSC5XJhCodD6fSgU\nWrJHZLbaybLq7Nv3Btu3P9XKNSwmvrjQAJZKRVKpLI899guYZqAl2hgOJzl8eLy1SM72TcxSLpe5\nfLlGIPAEhtGOrveQyZziwIFXMYweDKPItm3w9ttpHCeEpkXJ54tkMkl27BhkzZq1res+eHCi2cDY\nqGZrbw+yaVOUTCaF50WoVKaRsoRt91IstiGlxvnzGaLRDlKpw6RSQXS9RCBgcP78YRKJjbhukrVr\nywSDMSqVYitv5TgpSiUfwWAHEMJxDGZmXEZHT5NIrEaIJNVqmpkZD8NYjZQ6jiOpViXZrEY4HMV1\na0g5zVtvnaBYXNNqPqxUTrF5cy8vvPDKPGMeiUTw+XzLVlSpDvWVR911xW1l6eava881vxaxWCOf\nsjBJn0ym+eY3d7fyGP39XWzatKXV19GoYpJ0dFypdmosXoHWk/1i4ovzZeRB06qsWdNPJBLFtm0C\ngSCplMbrrx/i8uVOPM9E0ywqleN89rMfndc0aRgJBgaG8DwPKSNcvhzGdUsYRgyoIYTEssqk04fR\ntA48L01XV5UHHhhkdHSYfN4hHjd49tntjI2NkUw2DPJjj22gt9fkz/7sNQoFE9NME4sFMYyNhMNd\nlEpJymVJtZokGu3A50tgWUlyuSpr1mwlEGjHMDoIh8/wmc/cx6uvvkE63VjYf+mXHuCVV46TyZTw\n+cJYVg3IMzMjKZfB88p0dJSpVGbwvCS63k+9PonngaZ1ABF03cVxTEZG0mzY8KmmjH6NEydeQdME\n27c/hefZaJqPw4ePc/p0plXavVj3uepQvztQhkRxW1mu+et65povRyAQ4Nlnt/Od77zK5cuNaqfP\nf34rP/7xcKsnJZdL8cYbf0My2WgMbCjtVohGY3MGVHmYZnnek/1SfQVXZOQFhuEnn0/x7rvH8LwA\nmlaju3uGkydT1GptaFoQz3PI5yd44ok8gUCgqWDbTk+Pw6lTe5Ayjm1PI4TkkUc+TyAQwPMkFy/+\nGF3X6ei4D00L4XmdmOZ0c7LiGXI5j7Y2jY0bu+cZZIBjxybx+7uIRoMI4aOtzaFUOk4uF8AwLDZt\n6sXzSoyOvofrtqNp08TjDaNq2xaapgM6Q0NDfOUrG1shunK5zMBAG8PDB7HtNqScJh5vY2CgDSHq\nmGYcz4sSDMao1yVQxjAa+l6x2Cri8T5sO4zr+hFCmyej7zg1MpkaExPjOI4fISrMzFziySd/no6O\nxSXiVYf63cOK320hRBz4a+B+wAN+CxgFvgPcA5wHnpNS5pv7f7W5jwP8rpTylRW4bMV1crubv+Lx\nNrZtW0Oh4BKL6ei6vqAnpQ3HCVGreWgaaJofXa9fVTW08Ml+sVDb/GqrRmjpxIlDZLMpXDeGz1cm\nENCZnMzS03MvoVCCSiXLmTO7+elPj2IY8VY+YM2aGMeOHcay4mhakvZ2ME0/gUCYer2Kpkm6u9sZ\nH59p5khgcDDOP/zDW1y40IuUnQiRpVz+CV/72pdbDZfpdJo337xAd/evEAolKJVSjI7+P4TDI0A3\nfn+BNWsCHD8eoKdnO/V6CE0bIJs9zbFjp/D77wGSPPxwHtM0CQQC88b4hsMhBgbuoV7XESJANnua\nyclJNK0Dn69CZ6fF6tWrqVY34Xk6nhcmmz1EW9t5fL4y4XCZoaHVbN3aTSp1RUZ/06Z2zp3LEA4/\nSCgUJpebYXLyfYLBxvtarPv8g3Soq/DX7eVuuKN/BrwkpfxlIYQBhIH/BrwmpfwjIcR/Bb4K/J4Q\nYjPwHLAJGABeE0LcK6VcevSaYkW52eav5RaA2YU9Ht9KT0/DSJ05cwzDKJDLJQkEQuTzSTRNEgrF\nsCybcNiH45iEQqGrQmuDg8svNgsbGW07x+nTOVat2oCux9E0i7Gx43R1xbDtSxQKOVw3R71uc/Zs\nkFAogecVSSYPcvGizfr1O3EcDU2rMz39Bun0AcLhXjyvwJYtXezePUkyOdY0Gik6O1OcPu0QCDyB\nEHGkzLNv34vMzMywevXq1jU2vKMA9bqFlAaVip977+0hEukDEqRSR7FtqFQiCNFFtepgWX56ezcR\nDvfjuj1kMm9TLpcBWh7JLJpWxDCCQJlKpUgm4xEKRXCcErpe4KMf7eLs2Ut4XhzIcM89Cbq6ilSr\nNuGwzUMPreJTn3qYQ4euNI1u3ryRcnmUw4ePUK8HMYwS7e1+qtVyczrk1Q8g1/uQosJft58VNSRC\niBjwhJTyywBSSgfICyE+DzzZ3O2bwC7g94BfBL7d3O+8EOI08Aiw9w5fuuIDcLsUWmefSBu6Vg2J\nEk2L8sQTA3zzm9/GsuLoeopw2GF83MDvb2d6OkMu11ADXthHcK2+gtlGxmDwSUKhCKnUZS5eTDI0\ntJl4vIdyOcfMzCF27uxnZqZGve7iullmZsK0tW3BMHy4bjenTp1kfDxFX99niMUaOQK//wDr17sY\nRmNhXbt2DT/60VEGBx9B0+J4Xp50+kUymTKBQBhNM/C8MI7TaMycZTZsNjz8DpCgVhsnGPQTi62m\nVLJoa+ukWg1iGD76+sLNJktBrRZjcHCI9vZepPSYmDjGyMgo77wz2Up+P/30EPW6Qy5n4rpBXLeM\nlG309Azh84UwjPUIMc1zzz3I66+fIZOpEI87aNp6du/OUK3qBIMu5XJDEfjjH4/OM1Kp1F7Wr38C\nTTPxPItM5jLV6mizyfTqWe/X85Ciwl93hpW+k0NASgjxt8ADwAHgPwI9UsppACnllBCiu7l/P7Bn\nzusnmtsUdzmLLdLX420stwCYprlA6K8hUaJpMb74xV/DcTxqtQrf/e53CIXKCGHMUwOe1ayaPf+1\n5pG4rktnZ6L11AwZ+vp6sO0pCgUHKSsMDHSybVs/L710kmJRx+crkEgYnD49iRARpCyhaVXa2gzO\nnXsfKeMIkWdoKMwnPvFQK48yMzPTTMhvaiafV3Hu3H6EuMjExJtoWjeeN8PgYLElBDl73WvWxDh+\n/ASOk0DKacrl87z++kGE6ESIYe65ZwRdr3Dq1D/jeV0IMU04nMRxLlKrQb2eYmBAsmvXeWKxn6O9\nvTGN8Qc/eIFUqgRogEAIH5ZVwranEKIXz6sSDJbp6OjkgQdcUqkKfr/FN795ksuXu5sqwjleeuk4\nTz+9nQsXSi3V5Pvu66CzM87hw/uo10P4/RXuvbetmbfyLfl3da2HFCXQeGdY6TtpANuB/yClPCCE\n+FMansfCUNUNha6+9rWvtb7fuXMnO3fuvLGrVNxyrtfbuNYCIATMFfqzbRvb9hOPR7BtG03TMIwE\n69atIRCIttSAM5kse/eeaUmbr1/fzrlzhavmkezdO9qqkHrwwdWkUlnWrn0cn8+gVrMol0/S21tB\nyjQ+X521a7s5cyZDIDCI3x/AcXKMjLxNd/cUwWA39XqKaNTFNE08TwAuIAiH9XkhmYZnUWdk5G00\nrRPPS9HfXyMej6FpA0iZQAg/hnGKPXtOEYmswuers359G7lcmM985jfwvDqlUon/+T/347p5dD2K\nlAUMI02pVKNWu6/p7WhEImdobz9FvT5Odzd86lPb+MEPLlEslnEcC8NwKBahVCqTTptImQDqSFln\ndPQEPp+Fpk3zsY/VOHjwPBMTQTyvnVTqNIcPX6K9/TlMc4B6fYJjx77B228PMzS0syXRf+zYMDMz\nGVav/ij1uoNhaBw9+kM2bPgYoVAE13WaMv5RHMeZ16OznCepBBqvza5du9i1a9dNHWOlDckl4KKU\n8kDz5+/RMCTTQogeKeW0EKIXmGn+fgIYnPP6gea2RZlrSBR3D9frbVxrAZidYdHohr4iUVIoTHHm\njETTojhOjp4em3r9LFK243kFNmyI8v7755qDpWLYdoY9e95m584v0NYWbfWaTExMcuCA3ZwHMs3E\nxASJRIgjR96iVgsQCNTYsqWbev0sth1D12usX38fL710gc7OT6BpBtVqHl0fZvVqDSEqBAIh2trW\nUSqVSKWyFIsNKZSOjjZ27TqOYcRbKsb33z9IPt/QzjKMMhs29DAxIfD5IjiORNOCSNmG37+Wrq4h\narUqR44cbDYQ6ggRZnr6ArVagqGhXyYYjGLbFhMTJyiXzeaY3QhCaBSLYXK5DKFQGNcVBAIBkslL\nRCKPEA63US7nSKcnKRQcgsE2NM3EtiPk8yHWrfsYoVAPur6FfP5V3nvvLEJsw/MEqZRHrRbEdV1q\ntUsAeF6EVKpAqTSKZemYpks8XqFer/DDHz6Pbbeh6ynWrRMMD0+g63EMw6ary+LkyVO8/PLIvF6T\noaGhJf/Wlgt/qQR8g4UP2V//+tc/8DFW9O41DcVFIcQGKeUo8DRwovn1ZeAPgd8AXmi+5IfAPzY9\nl35gPbDvjl+44qa4Hm/jeuLfs8amoWsVbhkbv99g1kvRtDr33z9IR4eBZTXyDxs2DPGtbx2ls/Ph\n5mKf4Pz5ESzLol63CIUi5HI2u3aN0dPzJUwziGVVefvtb9PdrQMPEQ7Hse0cJ07s5otf/DUMQ0fT\nfFy4cIJSqUI6fR4hIrhuDl332LhxDYFAEE3TqddHOXp0hMnJITwvTqGQJZs9wKZNH6etLdGaa5JI\nDPLFL64ll8vQ1tZOOn2KQGCctrYAEMFx0lQqgmi0o5Uj0vUYPT1THD78Ip7XRrF4hlDIASo4jh/X\nLRAMamQyFaLRj2Ga91CrjZHN/jMzMxvp7OynWnX5znf2cd99Xbz99vdbM9u3bm3n2LEJLl8+hOe1\nIWUKw7DQNAAPTWs0IF64MIXP9wCaZlIqJZByhunpN4FuhEiyZk2BqakMlqWh61Fct0AkMsLBg5dY\nterLhEIxCoUUBw78GQ899BydnaspFrOMjr7P6Kggkfj51riB559/g3//77sQQixpEBYLf6kE/K3l\nbjDD/ycN4+ADxoDfBHTgu0KI3wIu0KjUQko5LIT4LjBMQ5/7d1TF1s8e1xtuWCr+PXeS4kJjs3lz\nD8ePh3jwwUHy+STx+CDV6kV27Lii42VZFtVqjeHhc4APz6uSTl/g29/+CbregWGU2LTJolaTXLx4\nCscxMAwH267S1pbANDsQIoTneZTLBsPDZ/H7OzCMOvG4ixDl5kx2DXBpb3c4evRtLCtMOFzjk59c\nx/nzBQoFH5rmw3F0cjmLcrlAuVwgHu9AiDCFwhSjo06zCitFX1+GLVt6+fGPX6RajWKaeTZuNNm/\nf4RgsLOVI2pv72DVKkE2W6O3dxPJ5Bjnz38Pz0ug6wUeeiiKbRtkMu9SqZxAyssIEeDEiVF8viq6\nnqO/f5rt2wfZufM5DMOH49ikUj+mVMrj96/CMPqo10Pk8z/kzJnX0PVuNK3AmjVJ/P4eZmbSaJrE\nsgp4Xo16HTRNICVYVhXL0piamkHKKkIUSSQq+HwhcrmDJJP+pu5XG9XqKbLZMoZRp7u7jfFxm1Ao\nimXVCIWiXLqk8eKL72GaiXnTFxcyN/ylEvC3nhW/a1LKI8CORX71ySX2/wPgD27rRSluKx+kJHhh\n/HvhJMVnn91+VUPeG28cZe/eI82Q1F62bhVIuQbLMgiFYOvWVdTreS5cSKJpndTr00xPp4CH8fs7\n8DxJMHiZSmWS8+fPoOs9uO40a9ZM09a2mp6eLjxP4ro+Tp8uA6tJJBpPzefODZNIRDh+fD+1WhjT\nLNDd7aLra4jFYmiaxfvvnyeXsxkcfKCpa1Vm//4f8Md//DcI0UswWOVzn1v1/7P3pkFyXeeZ5nPu\nnvtSmbWiqlAAARRAEATABaRIiaRIetHSlmUN6U12T8e0OzwxPY52RIc9Ef3DHTETHaGwZ6aj1e72\njNwzVnuRTFm2LEviJhKgCBACQBAgCKBQQO1rZlZmVu53P/Mjk0WCIinYoqRxq94IRFTdupV5cCrv\n/e4537sQi8W5cWNuK2o3HoerV9cZGPgQQlgEQZvFxec4cKAFxHmzRzQzU+Lv/m4N2+4Wm2w2IBqt\n9f7/dQ4e3MXS0jU8TyUMO4RhiOO0cd0H0fUJOp1Vrl79j3zsY3cBFWwbLAv6+/NkMqOUShE8r40Q\nAlXNUC4HKIpAUTz6+lwGBlT6+kYIApN6vYTr5jGMXQgRQYgUxeIUS0ur7Nv3JIYRx3WbrKxcotEo\nAUfQ9SyOs0EQNDh48Laej1cEz7NZXLzKuXOXUNUkvr9Jsfg6yeRDaJpAiO91EXgTb3/4CIJguwH/\nAWN71rbxY8E/hBL8ZpJiJPLQFpPoK185wb/8l0NbgjzbtpmbK6MoB9G0OEFQ4/jxp7nrrkEsq58w\nrFMqvYamxRgfjyEltNshi4tZ9uy5n2g0RhhKZmfnkNIkmx1FiDRSGljWOv39IRcv/h2+n0TKMrfd\nlkbXaxQKV7EsGBsb4sUXX2Vi4peIxzOUy8ucO/fHHDkySTKZwXFs5uenSacDPO8ykKPTWaFWW6XV\n2oWipBHCJwxPc//9D7J//6O4bhvDiDI39y3q9ZB2O4qUScIwxPOSTE6OEY0miUYnWF4+y1NPnURR\n/hmx2Bi12mWmps6xb99vEIlk0bQm58+/RF9fwPXrL+D7/Ug5i2FoeF6ZWi1AyjqxWAopG3ieDVi4\nrk0yKdE0nz179qNpUer1a5w/b5JIfApdH0TKMgsL/4kdOzwWFl7H8yK02wsEQQfPy6Ioo0hZwvNq\npFLD2PY0zaaKpgWMjGRQ1ZBz5yq9OOE6e/dm+eu/fgoYwjRr/MZv3M/wcISLF4/j+32E4SrQYnU1\n1itSgs3NKR55pEUqldr63Lzz4eNTnzq03YD/gLFdSLbxY8PfNw+iXq+zualQqbzFJFIUhXq9vqW+\n7ho5WpimT6dTRVUd1tcFur6TTGYc1+1w7drfoKoqBw7cjhAK9XqW8+ePEwQOhpGj3a4iZZNEYoAD\nB+7C81x0fTeFwhye5yOEBahoWhRVreG6NkLEcV2bIKgzMDBGENRoNmtoWotoNI3rtoAMQkgsy+Sx\nx/bx6qtz2PYKMIfrJkiljqCqScJwiKmpKwwOXufy5cwWRTifL9Nu++Ry+1AUiyAYYHr6Gc6fv04i\nsQNFWUSIGwRBhljMAlroekink6LRSBGP9+G6MTY2Nns5Jb+OosRx3WWKxT8jmewnEhkmCDaBkEjE\nIghcNM0iDF0iEYM77shw9uyf4Ps5bPtKzwYmQxDECUMfx9HZ2CgDRzDNNK7rEoZVXPdbqOoIUhbQ\n9YVD6/oAACAASURBVAaDgzFef/0ytq1jWR6PPqoxM6OQzU4QBFGEaDAzc5Ynn/xlMpkBPM/jm998\nHiEsHn/8s7huB8dx+PKX/wjLcrEsCEMfXS/h+/5WI11K+baHj65D9N/8zQl+9Vc/xJUr2wmJHxS2\nZ24b/2gQjUYplZZJJj9EKjVArVagXl8mGo3edM7y8jyt1jiG0Ue7vUatVkAIa8ui3TSjTEwYlErd\nXHfY5MEHB7DtE6yuZlGUTX7qp/YwNbXO9PSLQBaoMDHRolZLc/DgIziOg65rnDnzFwwM1InH44Sh\nSzQaRdcrNJs1VDVFEDgMDnp43hzVaqOnWs9x4MAg6+un2NhoE4t17VtsO4VhDOK6Att22djYwDAk\nmibwfUm1WmVkJMq5c8/hulE0rUV/v8f8/BxQwzQ73H67gmk6mGYUw4j3Vk4FKpWr1Go1hNjAMNZw\n3QyJxC4URUFREuh6FNv+Oq47hmFscu+9Q6TTI0xO3km73SQanaBcvkY6nWLHjgFs28P376BcPo6m\nTaOqGRRlE1WtI+UYltXA9wW6XkcIDSkHgG7vwrJMFheL1GoG0IfjbHLtWpWNDR9VDTBNlU7Hp15v\ns77eYnPTRtN8XFcgZZ1abQ0h4rRa6zhOi42NTQwjQhhuks+3qFY3uXBhGc8zaLUKrK42sawKvt9A\n0zwUpSsu/UFNQ7fxFt5z9oQQl3h3/YYApJTy0A9tVNvYxrtACMH99+/jwoWXWV2NYRgt7r9/H6Ir\nJgG66YepVIKFhVmCoIIQVXK5GJcuvYSuj6Aom9x/v8njj9/1NosOg5//+V9gaqpIreaRSmW4++5d\nuO4LXL26iOfV0PUaY2MplpaqXLp0GikTBEEVVfW4557DWJa1RT8eG6tTLq/i+20UZZNHHtnLyIhC\ns1kmldK5++5Jnn76VdbW+vD9BO22hq5P43mrBEFAGJZIpTQymSGWliq4bgvDcNi7t5/Z2Ys0m23C\ncABYpdNZIJ2+B0VJoigKi4tlPvKRJF/5yucJgkGknCcSaeC6c6iqTRiuoetNTFOnXj8F5JCygKq2\niMezCKFjmlkSiQZB0OTcueme2HOVXG4d2xY0m5v4foowlMTjbXz/GTxvAEUpsWePRRia5PMPYZpR\nSqUZNO1F4vFdqOogUtYQ4gyLi7Bnz2fR9Sie12Z6+n9HVV1KpfPYtoGudwgCB8cRpNNd+nGtVmBw\nMEm97qMoPq7rommCwcEomqYSBBa6rnPp0jL9/feSTkfQtH6mp4+zb99jZDIjvYePVaLR6N97RbyN\n98b7zeInfmSj2MY2bgGmaTI2NsDY2D1bVuOwiKqqW8pu3/dptyX33PNRVNXC89q8/vpVRkcTxONR\nhBDE4z6ZTIaPfjRz0xPp0NDQVkPWcRyq1Qgf//ivEYYhiqKwvv4ca2vTBMEdSJkCdGq104RhuEU/\nlrLF0NAe9uyZoFYrk0r1USi8QRgGWFYUVe1mk584sUg6/fOoqopljZBKnSWZbBKGGrpuc/Dgfubm\nCuRyn8Y0kzhOnevX/5BCQWKa9yNEAtcdoFi8ARxlcHA/zWaJN974Qw4dyvCxj92H40CnM8i3v20T\njz9AGAo0bTe6voFpbtJqNRAiQRA0AId4PI6m9QE1VlfXsO0ON4s9bWZmKoyN/XMikT5qtRXm50+R\nydyHovQhRJN0+hzDwzkuXz6L40SQco1MJkEkoiFEdysymcyjaRGEMND1CL7vo6oRCoUbrK6mgQGk\n3CCRqLKy8jwrK1cwjBof/vAg0WiCS5emaTQ0VHWTiYkR+vtTCKGhaRkikQHC0ELTdDqdFppmMDm5\nh2bzJSqVCPG45EMf2n/Tw8c2fnC8ZyGRUi78KAeyjW28G94pGuuyvRYJgm6O+cREkpMnr23pAXbv\nTrFjR4Z6fRbfjxCGdfr78xw7dgRVFUSjSRqNuZtYXkDPnn12K4xq374cQijoemQrM8P3PRQlyvT0\nGzhOBNPscPhwnnr9DcJwEF13OXZsNy+/PMWrr15CSpMwXKTdnufhhz9FPN4VO547d4pyuczc3FmC\nII4QNdJpjY9+NI6ux4hGYwwMGLiu5Pz5r+N5CXS9we7dFrOzHtHoEGEoevqNJGFYpdksAm1isQRC\n5Ln33o/Q6dRptZq88MJ3qFbXkDKLptUZGnIZGdlHKnUbjgNSjjM/P8bw8IOkUhN4XouVlf8IxLjv\nvru2xJ43brxCOp3GdVeo1xeQsoVljTI5uadHfw4IwyLt9hrVaoDv96EoJXI5m2h0Ac/bxDCa3Hvv\nDizLZHb2ZZrNrmr/ttsEMzNxYrFjaFofvl+k0bjCoUMPkkwOoes6tn2SarWEohwgnU7heWVUdR7H\nWUSIDEHQ4MiRLFK2OXnyu4ShhZRtfH+dMBwCFFy3g6ZtN9Y/aHzfdZ0Q4j7gP9B13DXoajxaUsrk\n+/7iNrbx98Q7i8Z7icbeZHupqsrJk9du0gNMT19hYiLBK68UcBwTTbPJ5wOuXLmBrmcJwznGx11a\nrRynT9/A8wwUpU2xWKJcHt7y7Go05tizJ8by8jTtdvfY3r0Z/vZvv0On82vo+hCdzhqXLv0pv//7\nv0wikdi6ObVaLa5f9/H9BFKWyGYllvUW1VTTkpRKq9Rqe+leTi6JhMOdd0YJQ5VUSmP//jv40z99\nmVTqQTQthu+32NiYQVFKLC+f6KnSy5jmKtlsg0RiBSk77NyZApq8+upJIEarVaDdXgQqqKqF4xSp\nVNaYnBzDMOJ0Og6KIigWVVy3QKslCMMmAwNJ4nHlJrFnNmtgmlWOHz+BlP1IuUIkUmT37jFisSyu\n22RpyWZhoUNf3wRhKBEizvr6aRRlCs+LoygOu3bdzic/+RGeeupVGo0WiYTPHXcc4oUXGgwP30kY\n+oRhntnZDKdOnSAS2Y1ltXjggSiNhr6l41EUgabFGRnR8DybRCJCPK7QaDSZmek6EoRhiVKpysjI\nA6TTA7juBjMzUz1m2Pa21geFW5nJzwO/CDwF3A38GrD3hzmobfzk4Z0rgm5eyOp7isY0TdvKUO+6\n/3bTDz3PwPMcpPToGgu2EQJ8X8W2bQxDIQgCLlxYJBY7QDodoVRa5/jxV7n33g8TjXaT/65ff55f\n/MWDaNoy6+tLDA6mGBzsIwxjaNpyT9VtEwQRVlZWyGaz5PN5giBgerqOad6Oomioapq1tePUahX6\n+vqx7Q6+X0dVNTY2zuP7fWhamUxGJQh8IhELVYV2u91LXCwhZYAQFXI5iyCwEWIecFCUNSIRD887\nR6UyRCzW4Od+7gFOnLjMM898F9dN4PuLKIqFaaYAFSHSCJFjc/Mqly7NEoY7UJQlRkcbeN4s1Wq3\nH/SJT4zx4Q8fuCmzZXKyn8XFGqnUXlS1nyBQ8LyLVKvfpF7Poest7rorxeXL82xsnMP3kwhRoV6v\nks3uRlH6se0qzz9/lV//9U/x27/9qa2tRNu26ev7BrOzf9UzpFwDlonHP0MsNoDnNTh//jkmJ/cy\nNtbV8fi+xvS0x7lzcwRBH5bVFTW2WjqHD/8MiqKzuVlgdnaFAwcmMQwTTRtlaalMvV7/HtPObfzD\ncUuzJ6W8IYRQpZQB8P8IIV6jmxGyjW3ckmfR93P6PXHiDRYXU1srgkrlNRKJofcVjb3l/lvfCkjK\n5cosLLjE43cSBBphWGdmZpFWq4TvR4lEAuLxAFVN0Nf31msHgcBxOqiqQhj6SBkyP7/A179+gXY7\nTjR6g099ajdStggCjyDQUFUPzyvwuc99DVUdJxpt8iu/coi5uTU6nT0oiuj1V9o0m1OEYbl3Q86x\nstIiGv1FNC2F51W5ceM/oarj5HLjBIHP5ctncF2VvXsfR9MMfN9lZeUyitLP8PA/ByJI2aJe/30e\nffSnGRvbhaYpLC1d5NSpZUxzP5oWx3ESuO4MAwP3YFlpPM+hUnmR5eUIg4P/A4oSJwgalMt/yM/+\nbJ5IZADTzDEwIMlkMhw7FqFUKpHPj7KwsEAYDjM4eAe2XcWy7mRzc4pEoo6uW2SzcN99B/jc557H\ndR9GVRPYdoFO53UM45dIJsdxnCqvv/7vKBQKHDhwYIu2DbB/f561tRmCoIGirGIYGo3GCtXqBrru\nk8sZZDIuFy9+kyCIEwQllpYWmJz8N2Sz42xuLvPss5/nvvuOIqUCQCSSRNM6NBqb9PUN0GhsYpot\nfD/gxInL2xYpHxBupZC0hRAGcEEI8Tlgja73wza2cUueRd/vnFarxbVrNfL5e7b6ETMzc9xxR/37\nisaEgDA08P1u+mEQwOpqhVyun1gsSqNR5OrVGVT1URKJcUqlAqdOfYsnnnhg67WFUOjv97h06VuE\nYQpNq3PXXRZ/9mcXiEZ/nkQiQhB0eOqpv0RV65TLs0A/UhZQlBLt9j8lHh+hWt3gP//nF2g0Wiwv\nn+ZN2vCuXW2OHduNruskk0nm5+cRwqRWmwa64U+qavHqq9e4cmWDVCpGNgsTExmuXHkFz4ui620m\nJnJUKiG+30AIjzBsYFkZstkskUgEXdeZn3eZnq5iWbejaSnCcBBF+QaVypfRtF0IUWRiIqRazeG6\ngiBoI4QkCAYYGoqh6wGjo3uAClevXuMb37hMu92NMX7ssV1Uq1PU61bPIqaBolwnk/lNhIihaSFn\nzkwjhEGjcR0pU3TTICyq1XM0GgUUpY1hWNi2/Y7PSAUhxvi5n/tparUCum7xta/9rywultC0caQs\n43lLCDGG5+m0WnaP5JBHUTZYX18jGrXIZMZJpzu89tqL+H4MTWvxwANZpqe/hudltoSNV68Wty1S\nPkDcyqx9lm7h+J+Af0XXfffTP8xBbeMfB27Fs+hWfY2kDG/K8FYUwZ137uDGjfcWjXXNH00URe+l\n7+lAlEQiYG7uOaRMY9tzaFoU03QRooxp2vh+lF27MqytdV9bUdpMTOSp1yEMVYQwaTTqrKz4rK+/\ngevqGIZHJtPG8+IIMYrvRxFCwXFyNBpKr7Gbplr1CII2irIbVc0QBBmKxRlOnLiAoqRIpXSGhy18\nv4VhDKEoeXzfotFY5/nnL6BpO1HVMkeOuMRiCTIZ0fPa6pDPRxkbE1y9+hpS5oEi/f11rlxZxbIc\nFMUmEqkiZUC93vW68v0qlmUwMpImCMCyshw8uI/nn38D151D03J43gbt9nn+4A/awAiG8RWeeGIM\nKZNUq7tQ1STVap2nnjqDotisrs4g5SCwTDbrcu5cHV1XUFWHeHyFSqWMpu1FiEF8fxnff4VOx8Ky\nRgiCRQyjwvDw8E2rVNM0KZdXOX/+BYTI4jiLuK5AiMPo+iiet8Hm5hucPTvH7OwAtq2jaQHr6zeo\nVL6DlGk0rcW+fUvE43cTBFU8zwGaNJuSJ574ZYQQKIpKoXANMLctUj5A3MqsfUpK+e8BG/i3AEKI\n36IbkbuNn2DciovvzSmGb/Ux3n5OLBZj//4MCwtvz/DOMDIywsjIyHtuiamqypUr16hUDmMYfRQK\nFZLJWZLJGGE4jqIkabUUVlauMTIyQSyWwnXbbGycY2BggL17UziOg23bvPrqCkePPoKi6IShx9zc\nX3H58nl8/xC6PoznrVIqXcK2DVT1LnQ9j++v0mo9i23rpNODtNslWq0q8XiKRGIQIUzC0GBtzeWb\n31zoCQA32bu3Q19fkpWVOXy/hJRVpNQolfZhmrsJwyLnzj3NAw/cwcTErt5KLoOmNRkdHaRe93Dd\nBrruEYtFuH79BrCBYTgcOuSRTErW1maQsoyUa6iqT7vt4jhNfN/FtkPy+QhTU1eAQaRcwvdVguAY\n0egeXHeRp576JpOTk0Sjd/e2G7vhXDduuMDPoKpxfL9CqfQFisUmqdQwQeAyNzeNEN0+E1QAF4gT\njc6g6xaKssng4BDFYonLlwtbq9Rdu5LU6w1gJ7o+jG2HgEkiEQFsolEDIVKcOvU6ivLTaFo/jjPP\n2toKinIDRRkFVkmny8zM7OttbZoEQZ3FxTMYhkEi0bVNWV+PIWVr2yLlA8StFJJf53uLxj99l2Pb\n+AnDrbj4vlsfY3zcxTT3bJ2jaRoPPXSQs2dntzK877ln71ZmxHuhm0+uo+sdhKij6x18X2Fycpxm\nM41tg64PEYnsptl8jk5noCcQHCeVSuH7Pq1WCyklQigoit7bWgPb9lBVi3b7Oq5bBipYloXruijK\nDEJUCcMSqioplb5OvX4DTStz550DNBohlcpqT7RYpNNpks1+nHx+nHa7yne/+38DLpYlkFLD9x02\nN00MYx+6nkXKDGtrz9Jud9Xz3YAujTAUdDomH/rQE6iqoNVq8eKL/yea1gY0TDNgamqTaFQg5Q3C\nMEsYrtJo1Ol0+hFimGq1gON8FyFS3HbbryOEQbN5g7m5ZfL5h7GsPoS4nYWF06ytFZmYGMKyUrTb\nNYrFCu1219FYCEnXd9ticXGVRCINNLAs8H0b0BDCQkoV8BkdPUwsNoauqwixyhtvrDE+/uGtVeqp\nU8cZGNjN8HAfnU4TRRmkXAbXvUE0ugffX0PXS3henFjsblQ1SxCEuO4gAwO/1vNRazE7+/sMDCyz\nf/8v9KjeVWZmTlCvV9E0jSAIsayQQ4d230Qk2LZI+cHwfsr2XwJ+GZgQQvzt236UpPuosY2fcNxK\naJCqqr0Uwwhg0XX/d7/ntboCwTu/h/7bZXLxnhbhlhVlx479CKEgZUihsEImYzA2NoTvh2iawtjY\nYTRNodmUpFIZ7rtvLwsLS1tGfppWJ5NRaTbfovredlsCKU1SqbsRQkdKD9c9RyTS7inWDYKgjWUF\nHD36IUwzSzS6l4GB61Sr66yvXyIMU3jeGpFIjFisrzdnUYLAQtMEiUQaKRO4bgYpq7TbiwSBgu8X\nUZQKrVaR69fPEoYpFKXGkSMbjI8P4zgVwjBOEBSp16usrvZhmjvx/QLF4jl03WR09DG6gskZVlau\nAzFU1SAMIxQKIWNjARsbV/D9FFKuoCgBGxurxOMRHKeIotjs2bOD+flXemmMVYaGLF57rQ4ESBkD\nSkCLbtFwCYKQdttDUVzCsIYQUaRsAh1qtTNI6SBlgYmJGpFIX0802M1RiUb7UZTzNBpr+L4kDH2y\nWUkmU+7NW51du4Y5c6aJ77eRUiEIWgiRRtczmGYez4vRaBhkMhqOs4JtlxHC5rbbUly48BKK0r/l\nGJ3P53noocw2a+sDwvvN3im6jfUc8AdvO94AXv9hDmob/3jw/UKDfL+G75vcd98BPM9D10epVq+/\n6370OzMjukwuY2sl806L8De3xGZnX+/1EWwOHswxOdnPl7/8d1SrIZmMwq/+6gOMjo5ujdH3fb76\n1RduMvJbX/8Gd93Vxvd9olHJ+PgRvvCF48zPnwb6gSIjIwLTjLOxMUMYJhGijBAa9foSYdgkGrXZ\nty9BvR6jvz9CpwOm2U+lUqFavYRtDxIENXbsENTrQ3Q6dk9tLolGo/j+dwiCRYQoMTIiKBY7tNtu\nr3/ksrra4P77M6ysLNNo+MTjm6iqga7vR9PSQIx6XSGfVymXr2HbOq47CyiE4QhgImUE0Gi11vH9\nAori4/tNLKtEo/Etms0dCLHOww8nGB/vJ5FI0JWPJajXFbq3jFlgE1gHNnHdMrVaEk3bIJkM0PUk\nhtHXY05l8P0kkUgHKdeIRkNGR3fQbm9w+vSVLZbejh0NcjmX55//Ep43iKquMjmp8elP/wLNZpVU\nKk+1epJK5VVmZ5eBDJFIC9Ncp1J5kXp9lDBcZWCgxeHDO7h8+XVcN45hNMlmBQ888ElM0yIIfGZn\nZxkd9bctUj5AfD9l+wJwvxBigLcyQ65KKd97v2EbP3F4v9CgZrPB1avPks/v31J238p+dKvVYmqq\nysDA4xhGBNftcPXqczdZhGuaxqFDO7h27SytlkUsZnP77Ud47rnznD69iuMke3kgZ/iVX8nTbrdR\nVZV6vU61CmtrSzSbDvG4SSQS59ChYSzLIplMUqvVsKxI770sIEUkEmH37gSNRhXbrqHrDmBgGEew\nrAGCoMrZs09j2z612l0IkcZ1N4nFpqnXv02l0kci0eGznz3K668/Szq9H8sapNmcY33dJJMZRUoN\nXd9BOt2gUFAYHPx5DMPEdR1WVz+PojQ4fvwVGo04ul4gHg+RsoBtN3thUCYbG4usrIwDGTxPBwp4\n3lfpFsQSplkgkRhkc3OZIKjT3baLsX9/nE6nTjabY2Skj9tu28nmZpRWyycWi1IqjSHEDaQcoZt/\n4gFZhDiEqu4kDKsEwRSJhE2rtYkQ3Yx4RXF62ScBYeiyuNjm2DGft9uvVKtVjh8vkkz+MpBGyk3m\n5r7AN7/5Z+j6GLpe47HHEjz22BGee26BdruEYdTR9Sj1+grgoihl9u7tIxqNoaoBmgZhGBAEOpYV\nQcquMLRa3W6sf9C4FWX7fwf8PnCcrmHjfxBC/Gsp5Vd+yGPbxj9CvLMBH48n2LlzhFrtItVqZGuL\n6lYu4u52VdcTSUqBEDezzn3fZ26uzn33/SyqqhEEPhcvnuErX7mIovwCpjmI46zzJ3/yRVZWWhjG\nMKbZ4md+Zi9XrrzK9etFpMwiRIWdO6d57bUBpIwSjUqGhgwsK8XY2I5eZnoCKa+ztrZBp7OXIOha\nhUhZo9FwaLcbCGETiahUKg0saz+6nsZ1K6ytPc/999+JZeXQdZ/5+Sq33z7JykoZ296kr69JKhVD\nVR2EGEBVywjhY1kWlcpSLw9lE8OQfOlLr1GrPYKUEXx/k2r1S8TjK0AfilKnr89hcVFHVdOASRDE\n6a4oBnv/dOANXFfFso4QBCEwQql0kRs3fFR1iGKxhhArHD48RqfTxHW7XmOJhIGuO3jeFYQYIAyn\ne++h43l+7+8UYWCgzdWrV3qBXFWgRqPRj6btRMo1rl9/A0V5/Cb7ldOnlygWNaLRvQgRIQz7qNUs\nbDuCoiRQVZ2lpQLZbB+PPvqLSClpt+u88EKHj3zkozQabTKZezCM68zPuxw58gkUReB5PidPfpFv\nf/sUmpZA1yV79jiY5p7tzPYPELcye/8GuEdKWQQQQuSB54HtQrIN4Gax4bs14DXNQ9NMXFfw7obS\nXbw9xS4WizE5mWJh4a2+xeRkaivACt5etBJbx6anHSoVjZGRPei6ThiaLCyAEIfZufMojUaVp576\nK5aXy1Qqo4COlAGOU+L8eR/T1NB1l0JhCU1zmJm5iu/raJrHzp0NVlYkQXAI39eQMkuzOUU8niWZ\n7CcIchQKz5JOJ+h0ajhOQBhuADrx+O309Q0Thh5LS88QidjkcimCQMdxJFJKcrlHMM0cvl/D85YY\nHAwpleZRlAxhWCWV6jA15WPbDaQMCMMWjYZCELwKDGOaTXQ9xLZBVRO9RrcGpDHNIyhKAiF2EoYX\naDardDrXEaIf31/EcSAM92AYe/C8NaamLrK4OM+5cxLfz6FpGxw54pNOxymXFbokzgjdBruOYcSQ\n0sG265RKm4ThXiDZa7xHcd1DhOEOYIBm83Vsu4xtd1BVDdvukEhoSFmhWp1FUYbwvDlct0WplETX\nVXRdR8oqjz46wPz8VVw3gqI0SCZVpPTJ5boZMJbVwrL60DQVw4ggZYNGo8CpU21UdQhNK5NMDlOp\nVLlwYXHLSeG9Inq3cWu4lUKivFlEeiizLUjcRg/vJjZ8ewNeUboWJanUHVuF5d10JDMzczz11Nkt\n8duTT97zNiZXtXexH7zpd94sWs1mA1VVCIKQTMYkmWyztnYGVe3HcRbQ9Q75/DgAiUSGK1dCNjZM\n0unHUZQInldgff1VTp5skEgkUNUOw8PLrK3NUauBEMNIuUqxuEqxCEIsI0SWMCzj+xtMTX0Jw9iN\nphU5erRbGCyrjmEYNJtVqtWApaUGpVIZKdtYlk8+r3Hp0kl8P43nLZFOJ1FVH9/vbgWlUv3cfXeS\ns2cLtFoNYrE2+/dP8PLLJ1DVOJo2jON4tFpFUqmHsawRpKxx48bT+H6ZTucGQgzheZtAgyCoIoRG\nENTQtA5CxInHjxIEUXw/QadzgWh0HMtKAjGazTjnzq2zZ8//jKp2rVmmpr5ANBql1UoShlGktHFd\nFbiI562gKE1UtUOrFUVRPo6mpXCcJaScIQiqWFY/vu/heTA6GuPMmWe3UgsfeGCIZDJgff1ppOxD\nyhV8v4LvZ7GsETqdAlNT8xw5MsCePY8QicRpt+uUy2eRsoDjyB61eph4PM7ycpdKXq+v0GrB4cP/\nPZFIEtdt8t3vfgnLOsPm5uhWj+a9Inq3cWu4lVn7lhDiGeAvet8/CXzzhzekbfxjwXuLDW/fCg3y\nfZ9XXlm9ybTwnVoT27b5r//1JarVXZhmjlptgy9+8SV+53ee/B4m19uhaRoTE0n+6q/euiF94hMH\nOHp0mDNnrhMERSKRMvG4RhB4ADQaVSIRByECwEfXI728cpt2ewjDmMB1N1ha+hsqFZ1Y7DGkNBHi\nAJuba3heAUUZRlV34HkaUmqMjd1NX98egqCO531nqwBubFwkm430hIkNIInnbaLrdTY2TCYm7sdx\nBEKM8corXyWRUDHNDEFQJhp1EMLiox/9BLqu43kec3NfRtMMbHsNx2kRBGtAlFYriudFgQBFkcRi\nKarVfqRM09UPC3z/DWA3YbhKLFZFVUeoVF4DUki5gRA2tn2SMBxFiAbRaB0YpVCo0e2FtGm1NFQV\nkskEQRBFSo9q1aPV6qCqbaRs0dfXRsoEsNFbjbQAB99fo9OJoSgNMhmfUing3nt/ausBYH7+5d6q\ntcvICsNOzwurhefVEaJJJKKSy2VQ1RXabYMgqLN79z4+/OGP4Lo2qVQfnc4SBw8mSCQKtNttOh2P\ny5d3oOtRXNdBUSxcV2NqqszExEM9G5s8U1PHvyeidxu3jlspJBL4I+DB3vf/F3DfD21E2/j/Fd5v\nH/n9BImxWGyLBvzOVcObzfY3X7tc7vpjjY7eja5HsKwO8/NTVCpdBfS7eXO9SS2em6v3bkhalK/9\nRwAAIABJREFUj5FzmYMHD+A4Tep1STI5xK5dOZrNb9No9GGaLZ544n6mp0vMzr5MGPahqjPEYqDr\nm4ThEkLUkTKg1fKQcolus91GCId4PI+UDrCGrrdwnBFGR/uIx3UikSF0fZIbNy5z8mQBx+lD12c5\ncsRi3z4D369imgrR6BjPPnuFMEwCBr6vYpo6nc6L1GpR4nGfBx/cST4/Qrm8TrHokE6b7No1jmGc\nwffp5WlIoKvW7kbetnFdl0gkTiYzAej4vqBSyZFO9yGlh66PEIvtplBYAXaiacN43hJSPsvGxjqK\nYqIoazz0UHccrVYdTQPfr6PrLTRNw7Ja+H4baPa8xDSEiKGqds/ev4LvLwPDdImfq4ThFJ1OE00r\nYRguqppA03Ta7TrRaJJWK2RtrY2mfRZFyeO6c3jen5BMZojFEgSBQMo4w8NpBgd3oaoKjuPy8svT\nXLo0hRBxFGWZXbtCBgf3kMvlqNfrGMZuTp++wUsvPdMzgyxy6FADsJievrzFCOwWzm38Q3ErheRx\nKeXvAF9984AQ4t8Cv/NDG9U2fiy4VRv3N3ErgsR3WzV85jNHqdcbWxoR36/ieTa2bSMlOI6Nqsp3\nZXa9k1ps2woTE2/1SGo1k0Khyu23P04kEqfTadLpnOBf/IuHaDQa5PN5LMviM58p8vzzK1Sr61iW\nxtxcEt9foFpdxTACslkdKWvYdhlFGSEMy0QiVcbGduG6KmGoAAaNRgfw8Lw2YVghkSjyzDPzeN5n\nsawduO4yZ89+gSefzJBIpFAUnXq9QbtdY27uDGGY6DnkLuH7/YDAtj3m55dRVXjuuRlcN4lh1Hnw\nQZ9MJooQfXQZWS6eJwlDieN0UBSbZDKCqro4ziJSphFilTCs4HkeqhrD8yo4TpVIJIfndVc1ur6J\n5+VIp+/GsgZRlL0sLX2Nw4cjHD/+DRwng2l2+xNTU9MUCnNAliBYBEx0/UFUNYOULRznMpYlabXa\nCLFOGG4CWRznoyjKDjyvQqn056ysTPPiizNbuS59fWuEYRpV1YBuA17KCK3WZRynQiTi8MADYxw7\ntoeZmVk6ne626cCAwcWLRTyvia636e+Pva3/AZpmI4RLPt8ETKBJLKajaSHNpkDTFHxfkEgE28r2\nHwDvJ0j8TeB/BHYJId6uG0kAJ3/YA9vGjxbvLBqHDg2/r407vL8g8U28yax6+6rh+vVp6vV5Vlai\nKEoSz7OJxZpcufLX+H4STWvwyU/2fc82w61QixXFYffunVSrFdrtbkZ3Lpfh9OkbaFqK2dkZjh4d\nY2QkzsrKHK2WiWm2SKc9rl27tGXrfvhwm0RiB7p+ECkjCJEhErnGQw/lOXHiBI6TQNdr3HGHydTU\naXw/h66XOXbMo9HIkUhMEoYQjU5Srfbz/PNfJpc7SDTq8sgj45RKFVqtIaS08P06lUqIEJOY5hiN\nRoHTp59hYaFEvX4MRclj2/DKK6cZHh6i0ynjOE1McxPDSCDlKkJIwrBKMmmg6z6VygxSZlGUVRSl\ngaIMoardQCwpfcLQQVVH0PV4jxQg8P0ctt2PEC0cx+fq1QL33PObWFYc225y/fr/S7kc4rr3AXnC\ncJww/DK6DpZlEQQB7baHacaBOGGo9FyVk6hqClWNImVItWpw8uQFisUDhGEERekwPl4GNnEcUBQT\n3zcIwzUUZYEw9PG8GooSZXBwkMHBQer1OlJm+c53rhGJjGGaURSlzdTUDeAc5XIWRUlSq21QLJp8\n/OO/gOu2iUSSzM8/TTYrKZU2qFQKJBIq4+MjBEHwI7ve/lvD+61I/hz4FvDvgN992/GGlPIDVbaL\nLq/zHLAspfwnQogM8GVgHJgHnpBS1nrn/i/APwN84LeklM9+kGP5ScS79TrOnLkIxL6vsd27CRLf\njndjVm1sSC5fLjE6+kkMI0Kn06BSeYF8XiKlgmnqxGKR7xnne1GLy+XXWFvTSSZVjh3bzeuvrzI0\nNISqaniew7lzlxgc3I/r2ihKjpdfvspf/uUpotFHsSwV297g4sWvks//EoqSRYhN1ta+imUFeN4m\nvt9EVX3icZNkMk8q1aFel0QicYrFDjt2fBQpIyiKy/z8M7TbNwiCBUxzBNteptmcJ5n8DJY1gKr6\nXLgwx+amj+OkUZQ0jrOB46TJZg9hWQnCcCfr688TBIJcbrKn0O+jUrlEtTrL5uY+hBggCEJ8v0ou\nN4Ki9ANxPO8CAwNZXHcE3+/qb6LRcUwzIAg2iEQkmcwuHGeZtbVzwBC+PwsUkHIDXU/guit0Oivk\n84cwzRaO08I0u6r8alUH8nRvHYOARaPxVTqdcYRoMDyssbJSwnHqqOoIrlsAigTBBooSIwiKSFni\nxo0YAwP3ATGgxfz8HLpeotP5c8JwiDBcBHyazUlUtR/b3uS11y4wP7/AyoqN5xl0OgXm50uMjHyM\nWKyb6764eAYhBBMTD2MYETRtiErlu9RqZTKZAdrtJpbVYWmpzPr6DoTI0GpVmZpaQlU/9EFdUj9x\neD9BYg2oAb/0IxjHbwFX6NqvQLdwPS+l/JwQ4nfoZp/8rhDiAPAE3bTGHcDzQog9Usr35pRu4/vi\n3XodcOvGdu+nEL6ZWdVdkei6g6aZWxoR225Trxs8+OAniMeThKHP0tKLtFo3Nz/fbSut06mwsFCh\n1dJIJCT79+c5enSMV165Sr0eYBgevt/kD//wL2g2TeJxh6NHLa5cKdBozOF5UYLgBrVahP7+YQwj\n0ru5JHDdNyiVVrZYW/H4CidOaMzP7yEMc4ThAo1GA8OwejbqUXxfsHNnyPT0H1GvDwBrJJNNarVh\nHCcN2MzPr9LpSGKxrmZCUUzC8GnW1k6h62NIWcCyNmm1BFDGMAZx3XVarRkaje52DdSQsoPvq3Q6\n11GUDqraIJmMomkxUqldeJ7A9+Osr5/FMNIYRh9B0KHRWGd4eIyhoUPYtk8YHuLy5e8QBM/QaORQ\nlAZjY92gqE6ngaYl6HQaCFHH80rADRQlTxiuA0Uc57beFp2D77cwzX4cJ4eUoKr9+L4JXEEID0VZ\nA1o4jkq9bmIYKVzXp1broKopLOsQYdjVprhukyCYRNfzPR+tc7z00kV2734cVfVQ1QhSXsJ11wmC\nFlK2GRzMoig6vh8ipY2q6uzZ08/Fi39OEPRvZcb88R+vEon0oes5PE9heXmGVqt1Uz7KNm4dP3au\nmxBiB/Ax4H8Dfrt3+OeAh3pf/wldMeTvAv8E+FJPWT8vhLgO3At890c55v/W8G436A/K2O7NHslT\nT31rS33+6U8fIQzDLY1Is7lKPh8lEoljmhEcx/4e8eGbr/X2rTQp61y+vEi7vR9VTdFq1fjiF1/i\nM5+5l9dem6HREKhqg6efPku5/AhCpJCyxurqiywsVIAPI0QG123jOCcpFr+LaY4SBKuY5iIbGyqR\nSBohDKRMs74uqdcL5HL/CsvK02gsUK+/wsWLL/SyPkrs2LHC2NhtaNp+PE9BiJ2srZ1ifd0iHk/i\n+yG23SYWg7W1k0iZBQqYpo/vLyGEIAxLDAxAJpNibu5VpBxAiAL9/RrFoiAI9iOEge/3EYYvoGmH\nyWTuwrZL1OuvMThosrk5QxAkCcMCmlahVPoqijKCEOvs36+Qzapsbi4RBCGK4mFZMaLRg6hqH0K0\nSCQucuDAKBcvLmLbcTStyfh4ii6Dq0gYakA3ayQIjvaKbZVS6RK+3yQIKkCC7vOoRSwWR1UDIMeO\nHbcTj2vY9jyOUwYaJBIhxWIUVR1F05I4jgacw/NMhEgThhJosrRU4NKl57DtCIbRJJOBgYFNNE2i\nKDZjY6M0my3OnDlBGKaBCoZhc/vt9xGGBroeMje3gapmGB8/iqJAGI6xunoDx3H+4RfRTzh+7IUE\n+D+Af0034edNDMhuIg5SynUhRH/v+AjwytvOW+kd28YPgPfqdXS3rX4wYzvf97l4cRkp+1CUEClj\nXL68zgMPTGKas9RqDSYmYHR0D+XyDI7z7uLDN/H2rbRyucwXv3iG7sfDAkyKxfP8l//yLL5/FNPM\nsbBwnqtXq+h6raeUr9NqtXDdgHa7iJR2z2BQousBhhEgpUDXIQwTxOMfQYju6qzZ/DZB0O0/QAnf\nLxGGHorSQtPahGGHcnmDXbt2Mjp6F6qapNNZp1h8DQjpqsp1FAWCoIOuDyNEhiDwUJQsfX2D+L7A\nsgbI59vEYgoHDtxNq1UlFhtFURq88cYpXPcSQuQJgmUUpYGmzdBud9C0Bjt2DLOxsUazWcbzPIQo\n0Wr5ZLM/har2AXWKxa+Tz1e5cuUCQZBGiCLxuE4qNQ5kUdU6fX1r6Hqa8fEEGxsVcrkd2PYmQvQj\n5X10G9dZ4DpCRNG0DGEYo1aTSFnq/TxH16Bzk5ERCIKQZFJy7NhOOh2PM2fO4XkJdL3Bvn1xVlfr\nNJtJIIWiZIAmvv8UUo4Da8TjTS5dWkHXH8ayBtjcLKAoX+Wxx3yk7PQimm/n5Mkpdu/WkNLEcSJc\nuRJw8ODdRCIJXLfD8vI3GB4O2Ny8wpuMvJ07NbLZ7N/7872NLn6shUQI8XGgIKW8IIR4+H1O/Qdt\nXf3e7/3e1tcPP/wwDz/8fm/xk4336nXcirHd+1GEW60WL788xeJiosciKlMuNzh8eAxV1XqqaMkD\nDwzzxhtrlMsb9PVZPPjgwZtchN9tTM1mk2KxihBgGDFc18a2iwiRZ+/eN6nETRzHQ9P2oyh5pKzS\nbD6N4zRR1QSKMoAQdcIwzeTkDiKRJJbVh20vs7p6Ds+7gqrmCYISpuliGC6dzjyKYuE4VcAlFtuF\nEClU1SIMM+RyUZJJgZQunqdQLucYHOwGTem6h+tGuHEji6qu4ftNhCgThg7tdhxdn8C2S1SrRSIR\nk0uXvk4QDKCqBQ4d6mCaOrZ9HiljSFlDCJ/bbrubXG4M33coly9TLPoUizqg4PsKUmbw/bFeEajj\neRavvrpOo/E40E8YLuM4L3PnnTkUJUkkkkGIWV5++WWuXcv2/m7XyeUuoygBQaDS7W006PZWvoVt\n76ZL9V2hW2AidL20IkCC5eXzaNpeKpUyDz44SLmskMkcQFGyhGGFdvsskYjEtl8jDFOo6hqaZhCJ\n3IWqWggxRCrVtbQxDA3fr2EYGlIOcMcdXRFiMpkkCAIsK8exYxO023WkHOH69UU8zycS6VrtGIbF\n0aMjfP7zX6PVihOLNXnyyZ/6id3WOv7/sfdeQZJd553n71ybPiszK8u2R3cDjYZ3JEiRAAlSlCgN\nKWkoilqNuNqZiIl1EYrYfdiZeVPoQbv7sEYRmliNdlZmhyuJRiQlUhQokvBAA90AGt3orvamfFZW\nenP9OftwbnYTJChCoIYO9UVkdFX2vefce+re738+9/+efJInn3zyBxrjR22RvBf4mBDio6RPnBDi\n/wU2hRCzSqmGEGIOmFTWr6ErrCayK/3uTeXbgWRHvr+8HTbU75ci7Ps+Z89uUqt9nGJxhsFgi1On\n/j0nTlyhXn+AXE7HTZ555hlWV/v4fo5GY5s77phHKd6URn4CLgC1mkOzuUYQeEjZplbLYJpGmkos\niOMEKR0Gg1PoIHGbbBYsy8EwpjGMHHG8CIQMh+spZXzAwYNFhsMqzz33LaScwTC2ePjhLM2m5OLF\nr6HUArCMaUZ43jSOs0gQbJHLKR56aC/9/oAwHGEYEaZZwfOaRFFMkvQ5fLjC44+fo1D4GWy7zmh0\njXb7CQyjh2luoVSXft9jPPYol//1Dc6wS5f+L6IowTRvQQhNPS/Edba3v0qvtw/X7fLAA3WOHVsh\nimYxjHKapvwahrGZupYikqTH9raL47wbyyoTx3vx/edZXj7JzMy76PU2qVY3WVkZMR7fjmlOkyQZ\nVlZeRakW8Hdoa2MFXXB4e7q2ebSXOUCTXxSAMRCSJPfjOLeh1IDPf/5xHnzwXezadQ9hKHCcPQTB\nCklyHikbGIaHlBsoJRiNLqfAMmD37iymOWQ8XiWOTSwrwbKanDy5Si5Xx7Y3uOuuBTxvm7Nnuyjl\nIuWI2dmAILhGp9NFygEHDmQ4fnyZffseRUoHwwh54omrPPTQQ+9IMPnOTfbv/M7v/KPH+JECiVLq\n3wH/DkAI8QjwPyqlfjPtDf9bwP+Cbqz15fSUvwY+I4T439EurYPASz/s694RLW+ljW6SJFQqdYTw\nGQ63EMKnXK7Qavk0mxvEsY1SQ44fv8j99/8qe/YsMBh0+Oxn/559+6osL7tEkYFtS8bj13nPe27j\n9Ol1osghDFvs3VujXjcZj1vkchZTUweo1eCJJ75MFJUZjc5gGGOkPIplzZIkTaLoq2QyNoaxgZRD\nLKtHFI3Z3Hye7e0mlrXJ3XcvEAQO9fpukkRgmrsZDlusrwsymf8O06wSRct43h9jGF2ggGX5zM3V\nuPvuXTz55FV8P4Pretx++zyDwYEbLXuLxRWmp0u026eIoiqwguMUGY8ThsNtbNukVgPT1MWY43GD\nTMYCZkmSLmF4K0LUUKqFbR/jttvuoFLZj2FItrf/ljCM0YAhARPYYjB4HsvajxCbVKsddEX7mDjO\nI6UHSLLZPo7ToFiMqFbLXLzok8vdilIutl2j33867TUi0icgAiposNhAJ1KW0BTzHtqtNQYgCG4H\nbkPKLcZjRbe7gVJbKFVGiB7FYh/bLqUxKhvLMkiS8yTJIYTYTZKsce7ci+zdm+GVV44jZT2N97Sp\nVO6iVJrC9z1effUs/X6fixfVjZ7t99xTZ2HBYzTyKZVM9u1b4ItfPJOuYQ4hxvT7Z2i328zMzOwQ\nOb4N+XFdqf8Z+KwQ4l+iqew/CaCUOiuE+Cw6wysC/tudjK0fnbyVVrvVapVbb83TarUxzTJJ0qNc\nLtJq9ahU6lQqFdbWLtFshhSLuvlTsVhhbc3iqadOs7Y2Sxi6OE5AozHAMAS12v036kiC4BhXr54g\nSaZwnB5Hjy6QyxWYm+vgeQHgYdsZbHsVKTsYxgilpqhUemxtDVAqj5Q9HCfDXXf9NoXCFHGc8Mwz\nf8DaWkgYZtGdDgdsbIyJohpKVUiSDEkyAziY5nUgxnEipqayXLs2uMFIPB4PePbZJyiXBWHok8tl\nMM0SBw7MsmvXHsJQEMf7WF//KwaDdUxzD6PRNYRYpVgM6XavMWkslc9fIIo05YpmyJ1CSpc4HhKG\nA3I5hyQRaIugj/b/t4AsmcytOM4CpjmLbS9j203C8CyGMY+U65imRyYzRxwnWFYWx3GI44C1tQGm\n6ZAkAxynT6FwCCk/mHZrrJEkS+l8s8BqOl8RTerYSf/NI0QZw6ihFMSxRRRtMxicQVsyTbLZFp4H\nvd4YqBJFw/T6YwxjgJQGnudy/nyfYvHniCKJadZZX38B3w+xbd0gazBQLC21yGTuQcdpAi5depWZ\nmZBMpohl6RbNmuE4g+NUCEOJUj36/QFLS83vaV3vyPeWHxsgUUo9BTyV/twGPvQ9jvs9dG3LjvyI\n5c1IE78zRTiTyfDpT/8Mn/3szZ4hv/ALD7G0NGR7ezmtLO9Tr5v0+9uUSlU8b4Rh9HjllWtsb9cR\nooRSDRqNJe6//wiLixq4LMum07GYnb2POLbJZmFl5QwQ4TgHiGOfSuV+hHiNJPExTRelIlx3TLk8\nw3g8Io6vIcSIMKzQ7Xbo98dYlst4bNHr9SgUPoBtV4miNr3e14njBkIEGMYcutf6JuPxHK5bJQi2\nuHZtCfgohmHSbrfJ53MsLa3geRLLmgE2OHx4k4cfXuDxx18lCIqE4TVMM0sc34OUJZSq4nkXUOoy\n29ufQ6ldCLGKYawB00h5FcOYR6kNkmSb48dPk8mMsO0hu3f30UF9TSOvlXqZJPGJ4yFSekiZY9eu\nMsvLDaTsYhg+rpvQaFzCNENMs0UUrVEsCnq9q0RRD9NsUyo5BEHEYBAhRCHtZVIEDqNrSvLAs8AQ\n7XXOo91bX0epEwRBH9gil4ux7VlqtRxxHKddIx16PY8kmUeIIlLG6NIyH6UkSkVAxNZWB6VOAwWU\n6uE413nmmdeYnz+IlAMqlS0ajT5zc7vJ5SoMh9ucOvVlPvCBx5id3YXve5w9+yLVqsP29jZBIJGy\nzfS0w9LSBjMz73qDdf3e92ZJkmTHQvk+srMyO/K25XvRn3znC7d//35++7fnb1DEW5ZFo3GGmZkD\nNwDIda/x8sufJwwruG6Pj350N5/5jMRx3oPj1AnDJs3mMcKwcyNNud1u0Gx6rK6uEUUujhNy990D\nms0VNjd7aWbTOrY9ZDR6FcOYRakme/cmdLtjwjBK+a4GeN4lXn/9K9j2LEp1mJ+/Qi6XZWvrG2hl\nOKRazZPJbDMc/nmakrsKSGz7AKa5C8PIMR5f5vTpF/nMZy4QhlMotcnW1hLFYgWluhjGgCtXGuzf\nX6VerzIYwHicIwhslLKABHAYDiOGwxKO85vYdkwUWbRa/x7dwGkDITwmHa/H49uBuxgO1xgOX07H\naKGN9i7QIwhmEOIoSbKClE3m54tUKhCGJkKEeJ7BeLwXqGFZWRqN60SRgZQ9pBwghMQ0s5TLXfr9\nZ1CqjmG8jpQm2r2l0ImXRXTnxK8D88A62r21iWFkEKJPoRChVIa5ufdimiZJknDlylMEQUIcd9Bq\nyUvvI0C39e0AQ0YjC8NQWFaJKOoRBFvpWviAh2UJFher+P5FBoMiQdCmUpkil9MFsZlMFimz3HLL\nLAsLgjgeYVmCbHYGKXNvIBddW/P45jdPYprlHQvl+8gOkOzI25Y3oz/59jam3y6ZTOYNgUydbnyT\nM2luboFf/dUP4vtjMpkcq6vPk8/ncN0hSiU4jkcYFrj11llGI52m7PvrnD//OrZ9N7ncfsbjqzz7\n7JcoFqdw3Q/jugv0eufw/WeoVm9DiClMc4HxeESr1cJx3o3rLuB5F1HqGTyvQhDMYBiKKLIZj5up\n8iuiVEwY9piZmSGKyiSJJEmyGEad6ekPpR0SfTqdl3jxxcv4/qMYRgXP67Ky0mdhYRooYRg5wvB1\nXnjhCpcv7yEIcml/kTbwOrAH2Eh5rHYDW8RxHmgjpUuhYDAe1wFNNwJFHOcgprmIYRQZDvPo3JQR\n2iIYAwGWtY1pXsEwegiRpdMZYJp3k89XCYIr+P452u06llVDqQyeFxHHy4ThTNofpMHGxiWq1T0U\ni3MEgYth7GI4vApcBfahg+8baGD5LbS7ax3tqdYpwUqNKZenmJ93WFr6InFcxbLaLCxkiKIEuAXL\nmiaOHTSAt7CsKxjGiNnZXfR6kjh+H1LWMIy9CLHE/v2z7N1bI5fbT7d7kTvusFhft5BSAg7V6hRJ\nIvG8EUkiKRYtHnxwH9evC6Q0MQzB3r37KRbNG5uU4XDA9etrPPDAY9i2S5LEvPLKle9qf7AjWnZW\nZEfetrwZ/Umz+dbamH57unEcxzz++BIbG+vEsYNldSkUCiwshCwtPY1SNYRoceedMXfccQeZTIYg\nCDh3bkw2O4MQEIYdbBuSpIDrTlOrGSTJFlImwBRTU++jVDpKEGyzufkEmUydIPAJgmWkbAILFArv\nwranEOIIjcYSSRKSJGvoIHIDsIhjn2KxgqZlUrTbEf3+GUajLrBNvZ4wGtXIZg8SRR62vYfRqMDm\nZoLrFkiSmHy+m2YQ3YVlzdJq9dCvokTvrAXaLdUnSWx0i9xVDGOI42SQUqKUiZQxvp+QyUyRy7kk\nSYleLwByaNfShCiihBAOQpgYxhS2LRGixGDQJ0l8pPQBgyQJMAwdZxkM+jhOiUzmXegOjXuIoiVW\nVlr4/kIKiBINVlfRcZIJc1I1/b2V3k8RIQ5g23eiVJNm85uMxwmHDn0Qw5hCyi6dzv+NZWWJ4x5x\nbKAB0OC2245QLh9NY1tL+L6gWKxiGDmSpMZwKLhw4SpbWxLbjjl0CB577B5efXWZ8TgglzOo1+/k\nG9/41hus5qmpA2mvG8jlMjz44O0AHD9+lmYThPCYnq5w7tzWjcZm9Xqw06L3e8jOiuzI25a3wv77\nD8kk3dj3fa5fX6NQ+CCVSoXBoMPKyhKLiyWuX19P4xttdu0qv+Elnp6eJp/PkssdSiuU6/T7Febn\nY7rda0CRKFohkwmI4+v0epsA1GpTbG83sG0T3Vd9gzDs0utdwDB0trnjDPC8DIXCb6QKzmM0ukit\n5tFonCBJKhhGi1LJI59/BSHWse0ed99d4fjxZfr9V9EMudeJogZCjFBqgGFopmPPy5DPL5IkRXTT\n0SIaAAy0iyiDbReIYwchQpRycN0iti0JgjZKRQgxwDQHbG7+Wbo732R+vk+vV0rHyqXjBnjeKwRB\nCyG2mZlpMRxmGI3iNB5hoC2fM6kbq4njjLCsWaTMIWWMYeRS110TKcP0Gg10POZ+tBUSAyeBbTQQ\nOGiWpQFC3IO2Wsr4/nNUq1VmZzNEUYhtZ+h2b+eFF/4epTIYhoWUOYQIqNeXMM0uxWLEe97zMzz+\n+AmWlp4gSaYxjAZzcxGvvNICBJnMiFKpTKXyMO9/f5F+v08ul+PFFy9/l9X8yCO7v6vXTbvdSZ8s\ngWlaLC9fZmbm3hvP5NWrpzHNu/7xL8o7QHaAZEfetrwV9t+3IkmSsH//bprNJp1OF8uKqNXKnD1b\n52Mf+xRR5GPbGba2vsjly1dYW/MYj8F1Yx54wOZzn/t/SJJ5THODT396irm5Op///DP4fgXHaaUt\nch8nSWax7S3e8x64eDHD+fNfRalZlLoMBCRJHaUWUErvSLNZl+HwJFoZDygWE65c6SDlL2IYu4nj\nZdrtP+XOO6tks1Ucp0g+38T3rzAYrAMK3Wo3IJNxsCyJEA6m6SBlyMbGayhVJQiuoXfuPvqV7AEj\nTLMMaLZeIQS2bRPHY2x7EaVKKJUjihwqlYNpjKaKEJeYxCS0VdBMxzWQUq93vx8wGnnAybQeZgUQ\nJMksUk6jVB7TPIFtd2m3r6U0+muUSuspNUqCLkY00C60a+h4TC+dew34j+gM/SvACMMLsAT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kj3Xv+7v3uO69f33qghaDS+RrMZk8k8gG3XiKIcKyvn2dwcEASatmI8DpmaytNunwKmEWLA3JxJ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\nKtWiWi1Tqcyi1BBazzA5eZQzZ36ezs5ep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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Pandas scatter plot\n", "bikes.plot(kind='scatter', x='temp', y='total', alpha=0.2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 04.2\n", "\n", "Evaluate the model using the MSE" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 04.3\n", "\n", "Does the scale of the features matter?\n", "\n", "Let's say that temperature was measured in Fahrenheit, rather than Celsius. How would that affect the model?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Exercise 04.4\n", "\n", "Run a regression model using as features the temperature and temperature$^2$ using the OLS equations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 04.5\n", "\n", "\n", "Estimate a regression using more features ['temp', 'season', 'weather', 'humidity'].\n", "\n", "How is the performance compared to using only the temperature?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 04.6\n", "\n", "Split the data in train and test\n", "\n", "Which of the following models is the best in the testing set?\n", "* ['temp', 'season', 'weather', 'humidity']\n", "* ['temp', 'season', 'weather']\n", "* ['temp', 'season', 'humidity']\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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 }