Files
college-datascience/project/.ipynb_checkpoints/test2-checkpoint.ipynb
2019-05-29 01:16:03 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Datascience project\n",
"\n",
"## Voorwoord\n",
"\n",
"Jammer genoeg heb ik niet zoveel tijd kunnen steken in deze opgave als ik wou. Dit komt namelijk omdat ik de opdracht niet goed gelezen had en de opgave verkeerd gemaakt heb voor meerendeels van de tijd die ik hierin gestoken heb. Dit project is meegegeven en kan gevonden worden in de notebook \"VoorspellenVanSignaalSterkteADVPositie\".\n",
"\n",
"## Inlezen van de data\n",
"\n",
"Er wordt begonnen met het inlezen van de data als een array van de lijnen.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Time=12/03 06:08:53& Sender=44:6E:E5:C5:8F:4F& Location=gang@0.61875;0.13758& WifiInfo=ODISEE@88-1d-fc-30-d4-40:-74,campusroam@88-1d-fc-30-d4-43:-74,ODISEE@88-1d-fc-30-d5-50:-72,eduroam@88-1d-fc-30-d4-42:-74,eduroam@88-1d-fc-30-d5-52:-72,campusroam@88-1d-fc-30-d5-53:-73,ODISEEGuest@88-1d-fc-30-d4-41:-75,ODISEEGuest@88-1d-fc-30-d5-51:-73,CiscoC5976@58-6d-8f-19-14-38:-82,rechts@58-6d-8f-19-10-fc:-59,ODISEE@88-1d-fc-41-dc-50:-81,eduroam@88-1d-fc-41-dc-52:-81,campusroam@88-1d-fc-41-dc-53:-67,eduroam@88-1d-fc-2c-c0-02:-78,campusroam@88-1d-fc-2c-c0-03:-71,ODISEE@88-1d-fc-2c-c0-00:-77,telenet-5467D@dc-53-7c-85-46-82:-87,ODISEEGuest@88-1d-fc-41-dc-51:-80,ODISEEGuest@88-1d-fc-2c-c0-01:-73,CiscoC5959@58-6d-8f-19-13-f4:-81,TELENETHOMESPOT@02-53-7c-85-46-83:-86\n",
"\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"lines = []\n",
"\n",
"if True:\n",
" with open(\"DataScienceData01.txt\",\"r\") as infile:\n",
" lines = infile.readlines()\n",
"if True:\n",
" with open(\"DataScienceData02.txt\", \"r\") as infile:\n",
" lines.extend(infile.readlines())\n",
" \n",
"\n",
"if False:\n",
" with open(\"DataScienceData03.txt\", \"r\") as infile:\n",
" lines.extend(infile.readlines())\n",
"\n",
"print(lines[1])\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"De lijnen zullen meerdere keren gesplit moeten worden om zo een uiteindelijke dataset te krijgen.\n",
"Dit gebeurt door het gebruik van de dataParse functie:\n",
"\n",
"Deze zal de data splitten en parsen naar dictionary objecten. Vorm in json:\n",
"```json\n",
"[\n",
" {\n",
" sender = '',\n",
" location = '',\n",
" time = '',\n",
" x = '',\n",
" y = '',\n",
" px = '',\n",
" py = '',\n",
" xmax = '',\n",
" ymax = '',\n",
" WifiInfo= [\n",
" {\n",
" ssid = '',\n",
" mac = '',\n",
" routerid = '',\n",
" signal = ''\n",
" },\n",
" ...\n",
" ]\n",
" },\n",
" ...\n",
"]\n",
"```\n",
"Deze worden daarna in een dataframe gestoken."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Sender Time \\\n",
"0 44:6E:E5:C5:8F:4F 1900-03-12 06:08:41 \n",
"1 44:6E:E5:C5:8F:4F 1900-03-12 06:08:53 \n",
"2 44:6E:E5:C5:8F:4F 1900-03-12 06:09:03 \n",
"3 44:6E:E5:C5:8F:4F 1900-03-12 06:09:17 \n",
"4 44:6E:E5:C5:8F:4F 1900-03-12 06:09:41 \n",
"\n",
" WifiInfo location px \\\n",
"0 [{'ssid': 'rechts', 'mac': '58-6d-8f-19-10-fc'... gang 0.65625 \n",
"1 [{'ssid': 'rechts', 'mac': '58-6d-8f-19-10-fc'... gang 0.61875 \n",
"2 [{'ssid': 'rechts', 'mac': '58-6d-8f-19-10-fc'... gang 0.26250 \n",
"3 [{'ssid': 'rechts', 'mac': '58-6d-8f-19-10-fc'... gang 0.63333 \n",
"4 [{'ssid': 'rechts', 'mac': '58-6d-8f-19-10-fc'... gang 0.63958 \n",
"\n",
" py x xmax y ymax \n",
"0 0.04449 186.37500 284 49.51737 1113 \n",
"1 0.13758 175.72500 284 153.12654 1113 \n",
"2 0.13826 74.55000 284 153.88338 1113 \n",
"3 0.31006 179.86572 284 345.09678 1113 \n",
"4 0.49555 181.64072 284 551.54715 1113 \n"
]
}
],
"source": [
"from datetime import datetime\n",
"wifiSignals = []\n",
"\n",
"def dataParse2(l):\n",
" objs = l.split(\"& \")\n",
" dic = {}\n",
" for obj in objs:\n",
" items = obj.split(\"=\")\n",
" title = items[0]\n",
" data = items[1].split(\",\")\n",
" if len(data) == 1:\n",
" data = data[0]\n",
" if title == \"Time\":\n",
" dic[title] = datetime.strptime(data, \"%d/%m %H:%M:%S\")\n",
" continue\n",
" if title == \"Location\":\n",
" temp = data.split(\"@\")\n",
" naam = temp[0].lower()\n",
" x, y = temp[1].split(\";\")\n",
" dic[\"location\"] = naam\n",
" img = plt.imread(naam+'.png')\n",
" height, width, channels = img.shape\n",
" dic[\"x\"] = float(x) * width\n",
" dic[\"y\"] = float(y) * height\n",
" dic[\"px\"] = float(x)\n",
" dic[\"py\"] = float(y)\n",
" dic[\"xmax\"] = width\n",
" dic[\"ymax\"] = height\n",
" continue\n",
" if title == \"WifiInfo\":\n",
" appendable = []\n",
" for f in data:\n",
" append = {}\n",
" temp = f.replace(\"\\n\",'').split('@')\n",
" ti = temp[0]\n",
" append[\"ssid\"] = ti\n",
" temp = temp[1].split(\":\")\n",
" append[\"mac\"] = temp[0]\n",
" append[\"routerId\"] = \"\".join(temp[0].split('-'))\n",
" append[\"routerId\"] = append[\"routerId\"][:-4]\n",
" if append[\"routerId\"] not in wifiSignals:\n",
" wifiSignals.append(append[\"routerId\"])\n",
" append[\"signal\"] = float(temp[1])\n",
" appendable.append(append)\n",
" dic[title] = sorted(appendable, key=lambda k: k[\"signal\"], reverse=True)\n",
" continue\n",
" dic[title] = data\n",
" return dic\n",
"\n",
"\n",
"data = []\n",
"for l in lines:\n",
" data.append(dataParse2(l))\n",
"\n",
"\n",
"\n",
"\n",
"d = pd.DataFrame(data)\n",
"print(d.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Selectie van de data\n",
"\n",
"Nadat de data ingelezen wordt is het een goed idee om het in beeld te brengen zodat we een idee hebben van met wat we gaan werken. Dit wordt gedaan door de meetpunten te displayen in een scatterplot overheen de images. \n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Kolu3bjbLly9f3uI+JCSEf//912pHvE+fPhb174k/REijykxc1VFxX4d9HO4Xexu+oNiOGI5Qaqoe1jFEdd0fjbk5RRk1ahRff/21lS59586dFlOmgtg6dffzzz9bpc2bN89q46pRSAvV/b0V9zaUcEsKhhJT9YyMDIbNuUdVvTMGxFO9+s29hZIlS/LYY4+RnJxMUlISYJhaPfbYY3brsKVK7dKlC6dOnQIMO+NffPEFL774olVM8W3btrHt/Yuq+qzWtOVW4ZYUDEfrDxNvrbX/5bVH1uHzNtObN29OWFgYI0eOpHFjx2bW9jaisrKy+PTTT7l27RpPP/203fIRDZSbpJtwZWQs7ggtOBQLDQ2Vs2fPBm7atfjSR+z0D16nXJS1KtURG6b+zZZ1vwLw9ttv8+qrr6prtGNHiI9XZCtVmISEBCIjI833rthRRVeaTV2jIWNxxPR9EkIkSClbOcuvuRFDC17OS7VJVJX/dOYls1AAPPvss6xfv57t29WbeasViri4OCIjIy3OeXTp0kV1u+9+96TqMsUZzQmGKx4d3ElIaDXV9lAPlJ9svt62bRs1atTgkUceISoqih9++EFVXampqYryTZ48mevXr9O7t2FkaNHi5sL7u+++47tpZ+wVtUmTttV55S19IW5Cc4LhS+Li4lQdGwX4ePhvZkvXTZs2WVm9PvTQQ1y8eJG8PGWn7pS4EkpLS2PatGkEBgaa02rWrGmx5/L+1IUsjFFnR1Uj+i9V+YszumAUIKHER6ryX7+ax6HdNzfgWrdubTPfokWLCAhQNgopmUrZMzTs3bs3v/9ucBPcpEkT7q4RpajNgvSfrH6XvziiC0YBqtcp7zxTAS792Nx8PWXKFKpVs3aRc/jwYUaNGqWovvT0dI4cOeI0X8OGDcnJybH5rOBG4LJly5j/gjpjwQ4D67NszSJVZYojumAYccXf638n3nQyYG+0SE9XaKpRty6VK1emVi1lx0FNvqgKU7t2bYv7Vo0eUGVHlbj1BN079VGcv7iiC4aRzIPnWPVKmuL80/vFW9x3796dLVu2WExztmzZQteuXRXVd6lcObZu3UpMTAxJSUlO1yRdunTh+PGbRo9SSr788kurg06LFy/m1P/Vcdr+9at5/Lu9GQvH7tacz1tfoCQ+RhlgB1DamH+1lHKKEKIesAKoAvwOPC2lvCaEKI0hnkYkcBZ4TEqZ4aH+u5Udmwxq2jp3V2HcMvvz8+tX8ziear2Z17lzZ/Ly8pg9ezaDBg2iUycrt1tWSClJS0ujwW230a9fP/r16wfAxYsX2bdvHx06dLBb9sKFC9SqVYs///yTS5cu8dRTT1nlEUKQnJDKhdty7HovnPHoT2Qd+QfUOT4p1igZMa4CD0opw4EIoKvR9eYMYLYxPsZ54Dlj/ueA81LKhsBsYz6/IvPgOUaGr2PzTNsey5Pm2Z92BQQEMGbMGKpUqeJwwX3q1ClOnjyJEMLiDIaJChUq0KFDB86dO8ehQ4ds1tG0aVPAsNBu1cr+ntWIESNsOnie8ehPjAxfZxAKHQuUxMeQUkrTwYFA40cCDwKrjemF42OYTFJXA52Er3fsXGTjVzsYGb6OxK03fVK/9/gOs+bHFW7cuEF6ejq33347NWo4DxtSpUoV7rrrLrKyslw+ktqjRw/OnTvHng0Gdew3sX/oAuEEpd7OA4QQ+zF4NP8eOApkSylvGLOYYmBAgfgYxuc5gNUJfCHEcCHEXiHEXnsaFq2wcOxu/t3ejOtX88g8dJb+/fu7VE9GRgZ5eXlWB42UEBISQvny5dm8eTO5ubmqygohKF26NHNf2kp7OYXvF/2puv1bDUXnMaSUeUCEECIYWAc0tZXN+FNRfAwp5QJgARhspRT11odMGvUmAHl5eYwYMUJV2YsXL5KdnV1kW6SSJUvSpUsXpJQcO3bMSgNlj8DAQA4dOkR4eLh+9kIhqrRSUspsIB5DLL5gIYRJsArGwDDHxzA+rwScc0dntUBAQABbtyr3BJiSkkLZsmUVf4mVIISgdu3abN++nWvXnKtipZQ2j9Hq2EdJfIzbjCMFQogg4CEMcfh+BB41ZiscH8PkTu5RYJvUggmvG3nppZec5klKSuLixYs0atRI8a63WqKioihVqhRJSUkO7csyMzNtHqPVsY+SEaMG8KMQIgn4DfheSrkRmAiMFUKkYlhDfGbM/xlQ1Zg+lpvRXIsNjgQjOTmZCxcuEBYWZtdFDhje4jdu3LD5hc7Ly+P69etIKcnPz+fGjRvmZ7bMQcLCwhBCsHfvXqtnYDgT7inhLK4o0UolSSlbSCnDpJTNpJT/M6anSSnvlVI2lFL2l1JeNaZfMd43ND5XvmvmJzz55JNWU5iTJ0/y999/06xZMypWrGhV5tKlS1y/ft38UwhBiRIlyM3NZds2S2+FsbGx5Ofnc+LECfLz8ylZsiTnz5/n0qVLTJkyxW6/WrVqxT///MP585Z7LHfeeWcRfttbE33n2wUaNmxo3ky7du0aGRkZ1KhRw8LfU2HKlClDYGAg5cuXN1vFlihRgnLlylG1qqXSrl69epQuXZpatWqZPYxUrlyZ8uXLO2wDoFq1alSuXJnt27dz6dIlEhISPOIoubijC4aLvP/++2aDPyXapszMTLvPClvUpqXZH2QrV66sqH9RUVGULFmSBx54gBIlfPdvfuWt0cza08tn7buKLhguUrt2baZOnUqpUso8FJYuXdrus8J1BAXZj0tX2L2OI2bNmsWFCxcU53cnGRkZ/Gd+O2pE/0Vg6QCmf/C6T/rhKrpgFIGXX36Zd955R1FeRwvxjIwMi3tHRnz2FtiFWbZsGc8//7yivO4m/IFabMgZQ5O2Nz2mqD0u7GuKtcM1T9OyZUvKlCnDs88+axFZ1RaBgYFkZ2eTkpJC5cqVadiwIUlJSQQFBXH77bdb5L377rtJTk7m1KlT3HfffaSmplKyZEmHo05BVqxYQWRkpNXaxdMsW7OIsw2/sRmGLbB0ACGh1fzGDEX3EuImhg4dypNPPskDDzzgWgVTpxo+ReDff//lnnvu4eDBg151KBHWrp7TeOcmaqcPMp9T9yZ+7yXEX1m4cCEdO3bkueeeY+rUqU7jYLiL/Px8cnJy6NOnD3/++SeHDh3ymlDM/zyWGTt6KBYKUH982FfoI4YbKcoXci3Q1w198MbfLDs7m5EfduLeaNf2Rza/e5qNX+50c68co3bE0NcYbkStk7OC1Nt6gg/sHCTSEi9OepKmj+e6LBQAXcarC5HmC3TB0AhnQuzH99YC+/fvZ0niaJo+7h5ft3XuqkrmIdsHwRwRt++4V+KH62sMjXCljHZtmdr1bcBP4g23OoAet9z+kV17eCLgqD10wdCxy+vfPMzcxD4MnBLmtjpzc64RmBDFyPB1zjMXwpWAo6Z1n9q1lz6V0rHCpH5V62fLGZ++uJ+knemgfrAAXA84unHjRtVt6SOGjpnps99g1p5eqtSvSsg6nMP41v9Hfk45hg0bZrbfevzxx1XVozTgjzvQRwwdsrOzGfd5F6N7HfeudXbPz+PLj7cxY4D9PBUrVuTYsWNO3ZOO79KYyWsPWEynCgf8cRe6YNjAH/dQXCX6iSg6T6xi1+eUq4yKiENKyciPnec1GToGBAQ4dDRn0j55QyulC8YtSnx8PFsvvE3nie4NNbZjRRqrpiUyUuV7Zffu3eTl5REdHc0333xjd7PUXsBRd6MLxi3Iw0Oa0CumKdUre2hxrZL8/Hyz798NGzYQHR3Nhg0bAJibqNyPriuaLnvoguFGRkWojwluYgIw0w19cPSmfv2bh6lepzy9Ymx5P3KN3Jxr1D7zKE/0G8JIo5XHsWPHKFWqFC+++CJr1qxxWkfhg1QbNmzgmWeeYenSpW7rp1p0wXAjRVqXxMczo2NHt/WlMLP29HK/+nVEIkm/WJ42zMrKMrsKUiIUixcvZvDgwVbpc+fOdUsfAauwz0rQ1bUa4u5Vx1gd4xl7qWu73BcQ5r0ntjMyfJ2FUDz3nMF1cUiIZQTY2NhYh3XZEgowHPd1h5Wwqy8rXTA0Qso3ffhB9uajNtucZ3YBkyfFonD9ah7JS4PIPGjpP+/dd9/ls88+s1kmJibG6oSiib/+chzarGCYg6KiVsj0qZQGyP9zNI16rYOOHfnRg+3M7L+DCatc23ae2X8Hx1LO2gwVMH78eIdl09PTbTqMcObWp2bNmqAuxqbbUDxiGB077xNCbDTe1xNC7BZCHBFCfC2EKGVML228TzU+r+uZrhcfSjSZw9nfh7N3enXIP+ixdo6lnOX6VWVBMk2sjz3IyPB1BqEw8sILL5CUlGSRz56j6eTkZJunGp0dBfY1aqZSozG45jRRbONj+ILgmrVo9cyLiE5febSdZxsp0/RsWZjCyPB1/LAoxZz26KMGj6zz588nLMzSsLBs2bI88sgjAPzyyy/muX2zZs2s6k5KSuLZZ591qf/eQmkYgBCgB7DQeC+4BeJjeJOAO6aQf+EM8sfnnGcuAsHBwbz3xHa7z09nXqJ2+iA2zL05cv3yyy+cOnWK1atXW+QdPny4xf369etZuXIl9913n8M5fZ06zkOf+Rqla4xYDKp2kw+YqiiMjyGEMMXHsHAPIYQYDgwHZbGtbwUCms4D5nncHCXz4DmbG2cfvbCTP389bZE2c+ZMJkyYYLOeBQsWWKUNGODAKApDaOchQ4aY719//XWqV7c80Xf+/Hn++9//OqzH0yiJwdcTOC2lTBBCdDQl28jqlvgYt+rgsulSZaSUeG6FYcmqV9Lo/059wBAl6p/MK/z7779W+ewJhYkxY8ZgOq+vhIJCAfD8889bqXizsrLwNUqmUu2AXkKIDAzBKB/EMIJ4JD6GlNLhG9OW4KSkpNhVCWqV9xoKZpYpw8wyZQDoUd7giPluL7W/Y1MipzMvcXLDnRz67S+bQmEiNTXVZvqyZctUCcXPP/+sup++Qom388lSyhApZV1gIIZ4F0/iofgYGzdutGvdakofN24cKSkp5k+1atWKHK3I24w/ChOuXGHClSsA/NyyNeGr7Pu39QRv9vqed16b4zR0WsOGDTl27Jj5/uOPDSazTzzxhKr27r//fvWd9BFF2ceYCKwQQrwF7MMyPsYXxvgY5zAIU5Hp3LkzUkpatWql2E2llildegZlyhiso65cmcA9FYPo09Z5sEpP8O233zrNU7t2bVatWkWfPn1Uh1rzR1QJhpQyHkOoMYxxL6y2e6SUVwDXojc6YMWKFYBy361ap9f2FwE4+O0bADy92xgKLESZk2h38fPPPyt+k7salDM9Pd2lgJy+xG92vqtUce+5AV/TKqozAAenGEaNla3da+CnlPbt23tcC1ZY6+QP6LZSPqLBo7/T4NHfOfRKewB+e7MJQ8pMJm7fcdpN30a9SZtoN32bR1zDFCQqKsqj9a9atYpy5ez7zCqskbKX5m10wfAR/b40HOhJlgaFXcaYQ+yKHeg1v0kmoqOjPVb30KFDXZ5++Rq/mUotXbqUZ555xtfdcBtC1DT5UkVKSf/yJYjNPstxO36TPHWcMy0tjYSEBLfXu379ehYuXEhOTo5TJwdaxG8Ew2SJGRcX5xM38u5GSsmA3ZfM83sxaC117igFNvZpnPlNKgpbtmzho4/c74E8MjISsA6j5i/4zVSqo/F025w5cyz2MFJSUhBCkJKS4rgCjbFtXCWLBfczS7eQX8K26xpP+E0y0aqVU8ffHsXWLre/7HxriipVqpCcnExycrJFeqNGjXzUI9fo9P4FFvYzaGs+Dp/BEjmfDwaEERRoKRye8ptkYvny5YrzZmdnu9TGzp3edfnvDvxmKmXC1jlif/QBNeX4dYbWLMnUEzeYmjiRQeIFdgKvrqjOwvSOHvebpJb169fzyCOP8Pzzz/PJJ5+oKtuuXTsP9cpz+J1gFBem1ixp8XPJ9U5QsgFCTEbK/3m1L0oczJnOWnzyySecP3+e48eP2zxrYY/vv/+ehx9+uEj99CZ+N5UqtpTsA7REXpjs9aadCcXrr1uGIq5cuTLNmjVj3rx5itvwJ6EAXTA0w93TdgE3EBVdDG7pAmHt6jE3sQ/x8fH06NHDbr4337TtSOHFF19UpUI35UxFAAAZG0lEQVT/6aefVPfRV+iCoREOTr4fKOm19VJ8fLzZq/mBynPo+k4p2na+mxMnTljkS01NNfuWtYUap2jt27e3StN3vnUcc2OV4eMl/u+09TrmiXcb8Vnys9QKDTanNWzYkIoVK9pUehR2gHDgwAEAvvvuO7vt/vrrr6522avogqEVSvYnqYTnzDMK8tSIXoQ0tr3xFnx7EJNWP0D/yeFcu3bNnN6vXz8AJk82rIG++OILypYta1G2efPmAHTt2hWAixcvWtXftm1bKw8jWkQXDI2w6gY8veaUV9pq/YLzGBgdBtbnw/2P0qxNHYvp3bRp0/jpp5+sDimZDi8VpEKFCsyePdtqemhy4alldMHQCE/P2svhdM97F1Nzpj6wdADPf9KSlz65n5dfftmc3r59ewICLIXL3uGlMWPGsHnzZou0ypUrs2zZMhW99j66YNjAFxuGVya04soE++YZ02e/gRCCobNaMzexD3MT+/DipCdVt+NKLPImbatT95k0mnesYVOz9Pbbbzssb8u0Xcmx2N27dyvvpJvRBUNjZGRk8NSIXoRH3cnElQ+ahaDcg/v5YH9vi8hHG+PsL3Jt0X9y0Rw7D5/Thv0VZ9FzQCeL9FdffZWMjAz27dtnXWb4cIKCbNt6ff311w7bM20q+gJ959sHxMfH897CV7kaeJY2fWpbuedv/UIArYl0Ws+gmS0Vt5mdnU2HgfVV99UWXV6tyNzEPsx66ifSDxjchdWtW5e6deuyZ88epJS0bt2arKwsm76nTDz22GN2n40YMYK///5bVeAYd6KPGB4mNjYWIQRPvxVpfvsfqDyHLuOrG6IaFSFmhZqywcHBnM685HJbthj7ZXtm7OhBk8ibngXvvfdeWrduzcyZMxVNSb/6yrZL0oJrGl8gtGCAFxoaKgv6J4qOjvY7w8Ds7GymfzyJZUtWER3ThCZtvXPOWW14rZ5PdqDLhKpu78eeDX/Ru+krZrVuUQgNDeXIkSOA+0KNmezBhBAJUkqntva6YBSBp9+K5N5ox67sPU3ej1HExMSoLndnk6qM/9rFSPQOsOXm0xkrVqxg4ECDl6VKlSqRk5NjfqZGi+bMUZ8awVDq1DlDCHFACLFfCLHXmFZFCPG9MQzA90KIysZ0IYT4wBgGIEkIoXwi7Gf4WijAoA51hb/+PMvTdRaxZ4Pj4C1q+c/8doxZEqXKaHDgwIHs3LmTESNGWAgF3PRMqeTjTtSsMR6QUkYUkLZJwFZjGICtxnuAbkCo8TMcUBDpWcdVnnrT9fdOcHAwX7yWwIxH3WvcVz+iCr3eK0/b3nX5/vvvneYXQtCuXTubm4S+oiiL74Lu/guHAVgqDezC4OPWNy72PEzi1hPOM3kYd4xaWUf+YWT4OgITosjNuea8gEKeeKMFf1afx+vfGEaPtLQ01q5dS3x8PNevXzfn0+K0Wam6VgJbhBAS+MToqfx2KeVJACnlSSGEabVpDgNgxBQi4KSb+uwR4vYd593Nh1WdnPvs5T0ubZhplReejQFiCGtXz2x56w6q1ynP3MQ+LscB9wVKR4x2UsqWGKZJ/xFCOFq1KQoDIIQYLoTYK4TYW3he6W3i9h13yZ/TtFlTvdI/e+TmXGPHijT279/v1nqTdqYzMnwdWYfd+38ZNi+CWXt6MX32G26t1xMoEgwp5Qnjz9PAOgw+a0+ZpkjGnyZVhDkMgJGCIQIK1rlAStlKStnK1y5W3t18mMt2/Dk5YtKYKZ7slgWnMy+xbMp+Nr71N2HZMZxdF07Ch+WQR0LJzPSMl/QZA7axe766mH3OCCwdQLkH9zN0VmuXnSuoQQhh1myp0XApCRxTDighpbxovO4M/I+b7v6nYx0G4CUhxAqgNZBjmnJpFXt+mzzpz8kRiVtP8NnLe5j05kjeeXWO4R8aDm/2upmnsP3R4MGDadGiBaNHj3ZrX778+BvA8KVy57TxrvtvJzg42HlGN7BhwwbztVLPi0rWGLcD64zSVhJYJqX8TgjxG7BSCPEc8Bc3PZx/C3QHUoFcYIh1ldqiZnAQx20IgRJ/TtmnLhN8u2t+n3JzrvHzqgyO7s5m2YJNREQY5/XhsHCsuroWL14MQGBgoMXC1l2YFsj9J4e7xbTkny31bIZG1gpOBcPo7t/K+kxKeRboZCNdAv9xS++8xPgujZm89oDFdEqpP6fVbx9i6Af27ZpOZ15i5+oMSuRWYtIL03nooYcsnk90sFpLTk4mOzubAwcOcPjwYWJjY5325/r165w4cYKdO3d6xG/sqmmJZGdnM2xGlMsC8t7jO8g8dNZ5Rh+iCSPC1NRUjzoXdoZJ+6RWKwXQrm17DAMj/PnraeaN+IUnX+rOpzNXG6xKw4FeDqswk56eTunSpfnrr7+IjIw0u6e5//772bVrl+Lfp2bNmhw/fpyzZ89Star7zT+Cg4NZNS2RZWsWcey21ZStpC6mh9aFAjRiEtKqVStpCghjWiBpoV9qWLBgAY0aNTK7ErVHcnIy//zzD/Hx8QQGBjJhwgQCAwMBOHHiBDVr1rRZ7t9//3XoTr8wixYtYvv27eYplicJu68+wz5WZtK++d3TbPzSe54JhRCF1xiKTEI0MWIUxN8EAgx+dbdt20aJEraVfG+88QavvPIKFy5cMI8CJgG6fPmyWTCqVatGXl6e1ek4UKdRSUhIYMiQIQwZMoRBgwaxZMkS54WKQNIvaQCEdQxh2Jx77OY7nXnJq0JRFHSz8yIybNgw4uPj7QoFQNWqVQkMDLQ5rfn000/N16VKlSIxMdFmHWXLllXkRGDnzp1mT+MA4eFFO5ykhqT4LP5cVsHu88h8/1l6am7E8CeaNWum6Mt6++23231WeOp09OhRWra0tn/Kz8/nm2++ISwszG5dqampVn5ix44dy5YtW+jcubPTfrqDj2YY/EzVblSVCatuaha2LExhw1zfWAn07NlTdZliOWK4YoatltzcXBISEhyOFCby8uxvkpUvb3nYyDRiXL16lYULF/Ltt9+yefNmrly5wmuvvWa3noyMDBo2bGjzmbMz2Z7gWMpZRoavY33sQQA2zD3o9T7Azam5mqmouaCvP5GRkdKdzJ4926312aJbt26K8+bm5lql5efny7Nnz8r333+/SP1ITU11mmfy5MlFaqM4YPiqSwnslQq+k8VyKlVwxHDFOFAJ7733nuK8QUFBfPrpp9x1110kJSUxePBggoKCqFKlCmPHqtzJK4BSD+KmQCyz9vTixy+O8lyntzUblSojI4OMjAy31OVMQ+gIzalri0pwcLDZBsdkHFh4425a3+ZFEo7o6GjmzJlD3bp1FU2lPMHp06cVhwn+7bffaNGiBR8ftNzwe/exHSTtOuo10wwlrF27lr59+7q9Xo+c4PMnChqmuWoc6IzU1FTq16+PlJKsrCyvhznbvHmzqtjZp0+fttDlmxj/dQe+yBzC029FMv2D122UvHUpVoJReHrgKeNAkwo0ICCAkJAQGjVqxOXLl0lLS7PyFu5O0tPTOXnyJF26dFFVLikpicXb7FsC3xt9J+Wikpmb2Id2fRu43YzdHyk2ghEREUFcXJxFmj0jwKIGe2zbtq1VWlBQEPXr16dmzZpkZ2e73XHxxYsXqV27NjVqWB6G3L17N5988onD2BMLFy7koaENFLUzcEoYb389qEh9LQ4Ui8W3vVBZRTEOdESpUo5tg4KDg83z9qtXr3Ly5ElKlChhDsmshmvXrvHLL7/YXEieO3eO1q1b07p1a3PaL7/8QnZ2Nt27d79ZR4nzqtr8dOJ21f0sbvj9iBEcHGzXjKR3i1pM69ucWsFBCKBWcFCRF94AN27cUJy3dOnS1K1blzvvvJMLFy5w4MAB8vPzFZc/ffq0Xe1K4ci1APfddx/Hjt08WTxx4kRe+sx6hLNH9qnLmlqM+wq/HjEKaqDs0btFLbdHPV2+fDkjR45UXa5ixYo0b96cvLw8srKyuHz5MqGhoTbz5ufnk5aWZnfTDuCuu+6ymf7888+br3NzcwlRYf26amoKr3tnk1zT+OWIkZGRwdSpU71yNLIg/SeH88pbo5k0aZLD3WwTO3bssJluWrSHhoaSm5tLWloaly8bFALLli3j7FmDWbYjoThy5AjVqlWzSi8cFmz7gbVO+1kQk0HgrY5fCcb+/fvp2LEjdevWZerUqV5t+80tXekwsD41ov8iLCzMvGlmj59++okOHTo41fCULVuW+vXrExQUZA7I8sMPPzjdH7EVrQhg27Zt5uuuXbsybI7yY3JqPQgWZzQnGIVtWhYvXowQguzsbCIiIoiPj/d6n+Ym9rE4vrr29Ci78/6LFy9y+PBhcyBGNcdMW7duzZQpUxgwYIDTvPascAvub1S/U6XD6HRlmqtbAc0JhnF30nw/ePBgpJQ+WRBmZ2cza4/18bvA0gH0nnqn1aL/6NGjXLx4kcaNb2q97rnnHo4ePaqovVKlSjFp0iRFBm9DhgwhPT2dJUuWsHOn4YzDmTNnuO+++8x9uVz1iKJ2TZgsY4sLcfuO0276NupN2kStFz536g6pIJpYfOfk5LBx40Zfd8OCOndVZdzyDgSWth2vrn5EFercXYWM5LPmac/Bgwfp1ctakBo0aEBiYiKNGzemTJkyDtsNCgoiKytLUUjfevXqUa9ePfO96cATGA5HdXhZ+ZnsURFxjPS9dZDbKGwOVLJSdSavPUCJoIpVlJTX3IhhwuQPyNHHUzz9ViTjljv3BD5+eUcatbx5nqJXr152jQvDw8P58ssvFbXvbP1ij4Kj6hdffKGqrBZs5gBVZ9sdYc8cKKB8FUUqSs0KBjg3iXc32dnZvLmlqyp/sKOXtKVV+5tq03HjxrFp0yarfAcOHOCpp55SVKejaWNmZiarVq3i0iX7QWCGDx/O+7uVO5dwt8dBLWDP7EcElFSku9asYPjiDfbqtFEu+Yga9GFj/ve/mwHle/ToYREje9myZYSGhjqdRpmwp3ECOHXqFP3796dcuXJkZWXxxhvW7i4rVKhgdwpoi1+XaN9rh1rsmf3IvBuKvFYrjY8RLIRYLYT4UwjxhxCibXGMj/HRjKXM7G9778EZ5bvuZeXKleb7desM0X1effVVHnvsMQuhkFI6/PKbHCbYwrSwF0IQEhLClClT+Oeffyzy7EqPs1XULjs22dZw+TPjuzQmKNDy5RAUGEDepXOKVuBKR4w5wHdSyiYYPCX9QTGNj3Es5Swr3lBvABhYOoBfL3/E+fMGu6THHnuMRYsW8fbbb1t5/Zg8eTJnztiP6R0UFMSVK1es0hMSErDl53fLli3m63/++YeBU+yfC79VKGwOdCPnNNP6Nif/8oVzSso7PagkhKgIJAL1ZYHMQojDQEdpCAFQA4iXUjYWQnxivF5eOJ+9NrQYasyklVJLbs41WuePt7vPkZGRQd26dQH4448/aNq0qe16cnMpW7asRdrx48epVcty7Xjjxg0CAgLMyoiYmBgaDFHu5HlURJzP/9YFmTBhAjNnznR7vZ44qFQfOAMsEkLsE0IsNDp3toiPATiLj1G4o5oJA2CLzENn2bFCvXlE2Uql+Oznl62+bP/++y/Xr183CwVA06ZN+eGHH2x+MX/55ReL+7Nnz1oJBcChQ4csNHRrNqnbizCNcDqWKBGMkkBL4GMpZQvgX25Om2yhKD6GdBIGwJ461pNq2sKsmpZI9in1h5rujb6TRwZansVesWKFxT6DiYceeoj58+dbpRcOGr9r1y5yc3Ot8u3evdt8PWHCBMZ8db/ifuqWtPZRIhhZQJaU0vQfWI1BUIoUH8MZ9oZ3bw/7r3f+zqVyD79SwWKTbsiQIcybN89m3hEjRliZot9zj6VHvx49elC2bFnmzJnD77//DsClS5cYNmyYOY+UUpU2atVU7x7JLQoFd7HbTd+mahfbFZwKhpTyb+CYEMJk59AJOMTN+BhgHR/jGaN2qg1+EB/DGaMi1Gl5TIxd29Js1FeiRAmbu+ImDh609Ltk7wUwevRoWrZsSWJiopVf2s2/LVPVP3+xpHU14lVRUKqVGgl8JYRIAiKAdzAEjHlYCHEEeNh4D4b4GGkY4mN8CrzoSse8OWVyhpTS7DhMDYGlA1iT8rpZNRsSEsKcOXMs8pw4cYLLly/TvHlzi/RSpUrZ1EyZCA8P56WXXjLfd+7cudha0nrKqYUjlIYa229cD4RJKXtLKc9LKc9KKTtJKUONP88Z80op5X+klA2klM2llO7xi+NjfliUwulM+7vN9mjStjrtohubzTxGjx7N5MmTAcNueLVq1azWE2B4MRTen3BEo7DazjMVoOI59wWf9DS+iHil2Z1vLfJmr+9d0lQNm9OaMR/d3A2fNm0aK1asoHnz5g7Pjx86dEhR/e+//z6VOygPZH/9ah7TXvlIcX5f4ymnFo7QrGBoSbdeEFc1VR0G1qdh+E3nzgMHDnRapnLlyorqvnjxItXrKD978XJrax9TnqSoC2d7u9hFdWrhCM0KhpZxVVM1fkVHCy2SMxyZhpho3ry5TXspR3jzpeOOhbOnnFo4QhPnMfyR2umDOFZPfUCWhk+f4NSpUw5DA5hw5o1kwIABxMfHsyxrqOL2sw7n2Iio6DkcLZzVfLE94dTCEZodMbSklbJF7969Xd4ZH/dpd0WOiytUsB+EZfHixaxcuZIDBw6oav/gN1dV5S8qWgsVrRTNCoZW1xgFKcrOeFT3lnTr1s2ldrdv387gwYMBePWD51SV9XaoL18snN2BZgVD6yOGidc7f8eeDco1QibGLY+ifMQJvvzyS4YOHWp32mRS2WZlZfHBBx+QmZlJVFSU+fnOtUfZteAaafudG42+97hrJvVFwRcLZ3eg2TWGP4wYJr54LYE3t3RVfcipw8D6vPr4aDIPneX333/noYceIjQ0lA4dOnDkyBF69uzJ+vXrKVeuHEuWLGHUqFE26/nqo5snBkNCqzFxdXub+bZ/m6Cqf+6gKKGifYlmBcPfGHnvCr7IHKK6XN+JBs1Ty5YtOXfO+q0/dKjyhTVA1hHDCPPKW6M5xs8Wx3QLWvZ6E7UL57VrHTuJU6KtA2jUqJHiNgujC4abCA4OpurWXpxt+I3iMiveSGLnWmWuddTyzms3TU869AineXR5r2qjXMUTZzFcQRcMN/JEvyF06BFL/3ecu62Z2X8Hx1K8c9a6OB5d9TT64tvN7NiU6HSR++eyCl4TCh0DatesmhUMfybz0FkSt1ofQbl+NY+R4euKncc/LePqC1afSnmIhWN3M2tPL/PBoT9/Pc1HL3h3D0HHgCteLjU7YviTutYezzYyjAwLY/boQuFnaFYwigPBwcHUTh9E4o+ePYap4350wfAwWg00r+MYTa4xNmzY4DXv5z179vRKO+7GtKgsGL/bX38XE574n9uKb64EfcTQ0bGBLhh4Zs+kYJ3e3JPxx99Fi3tWumDgGQ1YwTq9qWHzx99FixpITa4xoqOVx3ZwB576x5jehJ78xxf+W/nr7+LJUcOVdYZTp87eQAhxEfCckyDnVAOU+6opPm3fiu3XkVLe5iyTVkaMw0o8UHsKIcReX7Xvy7b19u2jrzF0dGygC4aOjg20IhgLbuH2b+XfXQvt20QTi28dHa2hlRFDR0dT+FwwhBBdhRCHjVFeHUVqKkobnwshTgshkgukeSXqrBCithDiR2O024NCiNHeal8IUUYIsUcIkWhs+w1jej0hxG5j218LIUoZ00sb71ONz+sW5Xcv0I8AY5i6jb5o3yWcBZn35AcIAI5iiPNXCkMQzLs80E4HDFGgkgukzQQmGa8nATOM192B/8MQMq0NsLuIbdcAWhqvKwApwF3eaN9YR3njdSCw21jnSmCgMX0+MMJ4/SIw33g9EPjaTX//scAyYKPx3qvtu9RnXzVs/OXbApsL3E8GJnuorbqFBOMwUMN4XQPDXgrAJ8DjtvK5qR/rMQTa8Wr7QFngd6A1hg21koX/B8BmoK3xuqQxnyhiuyEYwl0/CGw0CqvX2nf14+uplKIIrx6iSFFnXcE4NWiB4c3tlfaN05j9GGIkfo9hhM6WUppcHxas39y28XkOUNXVto3EAhMAU5DBql5u3yV8LRiKIrx6GY/0SQhRHlgDxEgpL3irfSllnpQyAsOb+17AVmBxU/1ubVsI0RM4LaUs6ALRURua+T74WjDcEuHVRTwadbYgQohADELxlZTS5GbPa+0DSCmzgXgMa4xgIYTJHKhg/ea2jc8rAc6d4tqnHdBLCJEBrMAwnYr1Yvsu42vB+A0INWopSmFYcCl35Vc0vBJ1VhjMRj8D/pBSzvJm+0KI24QQwcbrIOAh4A/gR+BRO22b+vQosE0aJ/yuIKWcLKUMkVLWxfC/3SalfNJb7RcJXyxsCi3OumPQ1BwFXvVQG8uBk8B1DG+l5zDMXbcCR4w/qxjzCuAjY38OAK2K2Pb9GKYDScB+46e7N9oHwoB9xraTgf8a0+sDezBE1l0FlDamlzHepxqf13fj/6AjN7VSXm9f7Uff+dbRsYGvp1I6OppEFwwdHRvogqGjYwNdMHR0bKALho6ODXTB0NGxgS4YOjo20AVDR8cG/w/fWlM17k2BiQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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m/LorCzLvAmDH6kdYMH4wY25bwYn+d2HdiHVg7b2MuuPxFnJ0HXfdE8WA317Z4AQAmHIfBP0Kkdh6lS0sLIyYmBivdALOwjBVgwkTJnjaBIU1U3K4/EI/fpFWxKxp19Jj8nqWPDyL2CdvQADLLn2SF94aRtLYAYjEXKT2Ag754PfwwaaGY3fR84nX2NZ1MExr/PfPCVjCO6tyyEy4wq22eCOGcQQKY2F5kd+cYe4uO/vGHwGYFfVrm/TW9t1J94BBBI21XbX58AfdycxUTkAPhnEETScaeXP9WuF+bo5Ip7JJz4DdZdUVdjFcG4H17EOFQi9ldroH66t2Urr2Ls7kq5WtHWGYEoFC0RHqq3ayYswMrp10BXHLzC3n/sG/5ryUfIta39IRhisRKBTtIWRBMfe+NIGefRplZ6Wk8Ew9Z+tbPk9hRpUIFD5BZfZMzgDDrEYWBgJxPdR/nR6UI+gkeEO0p462Danif/tRjqCToBpgFa2hyk0KhUI5AoVCoRyBQqFAOQKFQoFyBAodzOsykJoDqYwTM6g5kOppcxQuwGsdQVVVFcmvfkjcku3UVZdjjhd3VEv9HrDE0rPIaq1kcHzrAgDqqktJL6rh/hHBTI3oRXpRDdSdwTy1to6qond58pPTLJk42CZ94nNFjFiw2fVf1AA8+lEU/dfAVnkv3f/XXsBNhSPmzJnjUKe90bWc0SPk0BEIIS4XQhQIIQ4IIb4UQjykyXsJIbYLIb7SPi/R5EII8awQokwIsVcIcW3rV2g/91z2Kc9e/jZVASEsjruOxFyg7hRCBDI4eQd13xWRmAuv3h/H0MRXGZy8o+Hc/dc9CcDyp1bS/9kYlr7zBpclLiQlKpD8f+QT1yuWuF6xdI+6mUdG9eCRLwfapG+cFcVnSzvH1On3fvkafa6fTuy2UGSB6wOOKNyPnnEEdcDDUsovhBDdgSIhxHYgCdghpVwqhFgALADmAzcBV2nbSOAF7dP59I7ll+Nm81z6/3F4yCD+5/ogvv7ZHNI8f/5v+NMTL5H9zGLOmEZxT9w0qqqqGk6N6+UPwOmew0hY9gKJt8fSt5sA5kKfCF5/5x4u7NGbQM4z9dUSZFke8xPjGtLhPNTVQEBPl3w1o/D3mnpuC/Tjttsu97QpzXj55ZfbdZ7eGJSOEEIwd+5ch3qlpaWYTCZdebYlErazvgfQONtP7wZsBG4ESoAgTRYElGj7LwJ3Wuk36LW0RUVFybZSWXVK3sJ98vrLF8uTtfUyKXOPTBsQI8/VS0ngLFlZdUoeqjol0wbEyEffP9pwTlPO1kuZNiBGzvjbgYY8ZF2tvPXJ9+VVSa/JtNnPyincJ6dwX2O6lPLWS6bJ6y55qEX7zLfWNyhZPUAu69pV5tzRQ54/9oqnzfFK9DwPM2fO1J3fDz/8YFe+efNmmy08PFxKHe91m2IWCiHCgH8CQ4AjUkqTVdopKeUlQogtwFIp5UeafAcwX0q5q0le9wL3AoSGhkZ98803bXRhxsbbYxZaU1/xLH797gPOIMQwpKz0tEmG4NZbbyUpKcmhXmVlJSEhIcTHx7eqV11dTX5+vq5rP/roo+zfv7+ZvL0xC3UPMRbmVRtygGQp5ZlWxq7bS2j2RkgpXwJeAnPwUr12KNzPDV9OpnDRO3T9ZSgFZ4552hzDsGnTJqfmZzKZWl23wBq9enrR5QiEEF0wO4HXpZSWSJDfCiGCpJRVQogg4LgmPwZYVyhDAPUX4sUcP1zF4qduBeCVgf7EVCq/7Ws4dATC/NefCRyQUq6wStoEJAJLtc+NVvIHhBBvYm4kPC2lrELhtWzvv4Dg4Ceg7hCiaixNlj4lXAjgvobjsiYrJCmMj54SwWjgHmCfEKJYky3C7AA2CCGmA0eA27S0d4GbgTLgB2CqUy1WOIWqDVE2x0G3F7WgCcFj3zHvBFyLlLc1Sz8ESPXyezUOHYHW6NdSg0CzNbC1FtKZHbRL4WJOWr34Q4RA3t7+vK7e4HitSYWxUfEIOilnn/oloxYWQv1eAu55ulXdC5ebO3zO73+D2nUrmqWvGZHJv7ROn19dkepkSxXuQDmCTsqohR9zuu4LugcEUbuuWcHOhscffRCA9FT7DsPy8u8467pluxWuRTmCTkqXKW8TMcEc4G96biBz/lbTou68H/8FwJfBAhY07zFYfuGvAHjvpxBukBtcYK3C1XjtpCNFx5j96Rzel/FkH4piaVDTfgBb/l5n3jJb6Db88qeufPlTV0I5QaKIdYW5ChejSgSdlKU7ZnEgJJWhwHG+b1X3NgdPSbYsAE4Al6AeKe9ElQg6KX4hqfwS6AqYB422zNLLYqF+BxdOzbGbHisEVfRh8r9OON9QhVtQ7ruTMr3LTUAFH9T9isX/rm5Vd94/r6bLAkFtZqjd9I/ueZqgylSm574Ev1KDSL0R5Qg6LReRWbuXHXMu5bpjZTD4/RY1/QY9T+1y83x0ew9M7Tpz0I0b0lNdYqnC9ShH4ELaMrfcwuTJk4mMjHSBNbZk1pqL+b9d8V+6CIGeiZKtPix1f4eA5qMOFd6BcgQupD2BIwoLC51viB1Sj/3MS+8dpvL/DWJxRW2rul2m5/K37g+wePSnPLZzJH/IaF78F11up0+f5Rw/HAKBd7rKbIWL8ApH4A3LdXkbqSEXkPr/Bpn3g1t/DGqf+Qkx4U5kxmWI24ciM2zTw8UMAHqcOITofggplSPwNryi10BPhBUpJYsXL25zxCVHW05OTrt09YSw8g7OQOCd7C94miq6IuXWZhplcjVSSu1TTT7yRryiRNAWtm3bRk1Ny6PkLMTExHDkyBFd9fHc3FyHOoGBrXfBeSu3f1rPhpHwS08bonApPucIxo4dq1u3V69egDkyTHFxMWFhYc10Dh48yKJFi3Tlt2rVKt3X9hY2jDTRZUq21XDkwa0OR/YE7Q0Ll5ub65RIP5GRkRQXFztWBHbu3Mno0aMd6h07doyQkJCOmqYbr6ga6CU5OVm37rFjjSG3qqurMZlMhIWFUV1t26f+yCOP6M5z1qxZunW9iZ+eOs2Xt13Gl7ddyMNvqsXHm/Loo4/q1tXjBIA2OQFnNDAbrkRgaRhsj4fXGzIamt9ok8lEeXk5AKmpqeTl5VFcXNwmO3wlWGlT/IKu50u6Ek4fFbi0BfRUH8FcJbWURFujtLTUbnBSe+jJzxGGcwTWL5OnIgGnpqaSmprasB8WFqYrWq2vcsLvuoY2Al+KzuxMnB1MNCIigoiICF26PlUiMGoXocUhxMfHk5GRYbcdwdf58J6e4HeesvV1xPztgKfNcSlGfQ4d0VHnbBhHsHlz83UEJ0wwzpJieXl5Dfsmk6lZW4Ivk/DqafNONrR9rKR34Y2lHWc4L59qLHQX1dXVpKamtmkocHhiLuF3PWYlqbHajM4Jq03hiyhH0E5SU1MbuoxSU1PJyspqVf/yI7soff3hhmPzMpKB2mZsfqIPP9GHOWOCPW2KwkUYpmrgzehpRwiddT9Tc0+RnWD8F78pz2ihyF5L/SfNQ5cqjMT48eNtji3PpiNUicCJ5OXltdiYuG5SKH2fa1wUOmfKLeQmjic3cbxdfSMx54MQ5nwQwomFqZ42RaETIUSb2g6UI3Ajr4Y2Dk5KeD4dOEhC9paWTzAIb0dt4O2oDdTamWeg8A2UI3ATA6bkULXugYZj0T2OSesOedAi/dwWYN68pR5ZRT3p0xuDqBalx2qb8UtfzsIy+U0v3vLbej1fJO7i0e2NgTtKSp5h0/6zUJULQc4djOIq8nZXkLa1hMrqcwSbujF33EDih/fztFnNmBz7Z3YUNHZHR88tBLyza9BdqBKBm3jsiwC2VH3RcHzx/g9Y8Y7Jq5zAwtx9VFSfQwIV1edYmLuPvN0VnjatGW/MOokf9Y2CAWlMyVHLsrWGcgRuIqhqB2WnX2o4/s/YJVRmeocTAEjbWsK52vM2snO150nbWuIhi1omeNKLxIqeDcdTAOatJzE83WM2GR1VNXATKU/vtDkeFuhds/gqq8+1Se5JBqTtYN1vGktffS9fgf9fPmdJnPGqMUbBMI6gaf+nr2OYG6+TYFM3Kuy89MGmbh6wpnVG97+K0JFPIGUKAMsLh3Js1lOA78WLcBYOqwZCiAuFEJ8JIfYIIb4UQjymya8UQnwqhPhKCLFeCHGBJu+qHZdp6WFtMci6/9OXGneK0sdTlD6u4ThYCErrIbfKOxYOnTtuIN26+DeT//BzneHaCba/uIbMkvcaBVPuI+S5U4hEfVOFOyN62gh+AuKklMOASOD3QohRwDJgpZTyKuAUMF3Tnw6cklKGAys1vU5PVMoWUuZuazgeOC+HYwvHc3jMZA9apZ/44f14KmEopm5dbOSnfqg1XKPhnTfCkoFDGo5zApaQlv48Mrt5m0xhYSFZWVnEx8cTExPjRiuNhcMSqjT/LVtmxnTRNgnEAZanOBtIBV4AJmr7AG8Bq4QQQur8e/elUoA1uVV17LD6bgXLEoAE4rzITcYP70fa1hKqz9mGP7c0GhqlK3F/r8lMCjrYcHz4g+6kZNoPWmN5+TtzvAnQ2WsghPAXQhQDx4HtwCGgWkppKdceAyxPQT/gKICWfhq41JlGeyMJQQE+0UXjDY2GW6cNYlll4/LsKWUFHc+0rsi8cd6hKuf32BH+pH1KkF9ru1+bj62pPwiyqkn6eZCuLXHpejallOellJFACDACGGxPTfu0N8C52d+8EOJeIcQuIcSuEyfU9FZvoaXGQSM2Glqor9oJ9Qc5k68vCK09RJdo0vcMZGqEpWRhiUfxPfCdlawGERAJWIV0k8eYGhEEwNSIK4h76mvqqg8xcdVJlky8WsvDnNe0vIsYseBfNulTI/owYuEXUHem3fY7ok1/UlLKaqAQGAWYhBCWqkUIjd/8GHA5gJbeEzhpJ6+XpJTRUsroPn36tM96B+TtrmD00nyuXPAOo5fmG6oe62osja7O3orXPU597Y8216qv/ZHidY87PNdd5FbVkZjYOMTYP/hxhP/dXNSzY7H99kWnMHrm7XyYu5YzVfmER1+LEIHsP1rLd1VVnJQmPj9haUMJ5rsq7Z9dhDBh6csAfPPdIA4ePUPGy2+zcVYUJaYR1HExT04cSh0XszbhcuI+X2GT/st7F/HZ0gnkbmp5fUq9swxbQk+vQR8hhEnb7wb8DjgAFAB/0NQSgY3a/ibtGC09X2/7gDPxppFwrsLZi71IKfn+34U8e/co+pm6IYB+pm48e/covv93ocNz3cWtlRmsOxLTcHxWbkXKXRwentKhfLPlavI+/5ZPj/nzPrew/PU0AC4NgFqgl5Bc17ux6mBvIbmZz4yj8oUJ/KL/NcB5juzMJQDJ2x8GEQDMHjqBP+/YaZN+Yt+7UHcaU79BLdrWUUegpzs7CMgWQvhjdhwbpJRbhBD/Bt4UQvwV2A1kavqZwKtCiDLMJYE7OmRhO2ltJJxRGrW8lfjh/Qx9DwOiUpBWzQKWCBARHWikyamsJbeqjg2vbeEiYP2Rn7kp4gY2H6/jkt7mbtX1R37m4gsvYPNxc9PZJX2DGs4ffvP/ADDu7ofZcryOexLGsv7Iz2wvO0teVR27Tm5j24nzjNmWx7b/2Kb/KGHLiTrGj7y6/V/AAXp6DfYCw+3ID2NuL2gq/xHw+LK43tCopfAeEoJsX5U/hl4AwPg+/s1kFi60qg1dqR0EChh/WYCNfryW99g+tuM0LOnW57gKX2jItos3NmopFJ7CZx2BvZFw3br4M3fcQA9ZpFAYF28b8q4bSx3WG+bPK/ThLfEQvBGfdQTg2UatU6dOUVpa6pFrO4ui9Fib46gUJwzMaSeWXiBLA7ClF0jhHHzaEXiSNWvWtOs8vctcuQPrF18IgexY71uH8KZ4CN6IcgSKFslfZJ4t2bPXz8TMy/GoLaoXyLX4VGOhZTVjPVgvi94abRkR15aVj7yBEZN+T89ePxOVslGbJOU5VC+Qa/EpR+BotSFr9K4//+STT+rO809/+pNuXW9gzfk7iJ5bSGJwT8fKLkb1ArkWn6sa6G2g6927t6515QcNGqQ7z+Bg31oSbFP8c0AMM4ucu55BfHw8eXl5zJs3j+XLl+s7p5VeoCeffJJ589q+POvkyd5Fvw6sAAAZTElEQVQRC8Id+JwjcEVjm9489+/f7/Rre5KjcyYxgEnEL3iKyuzFTsvXcj9HjRrVpvNa6gVatKj9swp9FUuVNioqSpe+VziCttTTH3vsMcdKbsLbg6yUpZiL3UJEgxMdgcJ1NH1XioqKdJ3nFY7AG18od067dRV1Zz6j6v0NSPmTY2WFV+MVjkDhGaa/dRkPTlnNn4MF2ZXe54w7I5s3b7Y5nj17tq7zfKrXQOFctpccJyoAFpbqCM+l8GpUiUDRIhflfEF4zhfww264aDhlZR4cWqjQRdP1QfQGLFElAkWLHFxv3g4RoJxAK+Tmena9hIKC5nNA2hoeTpUIFC2yZ5j55T9f6UBR4fUoR6BokagWno7DR7+wn6DR//JrXWCNcVm6dGmbdD/77DOHevHx8UyZMsWhXk1NDaGhoc3klp626OhoXXYpR6BwTFWuU5dvLywsBOCVV14hIcF7VoRuiQULFuj+Hnr18vLydF/fcj87gnIEihZJFOZ4BEeAW2JeJKXAOUONLf+ITbu6vJUuXbqQlJSka8j67t277dbpm3LNNdfwu9/9zqFeaWkpW7Zs0WVnayhHoGiRbOm5QCTexIQJE5gwYYJT89y7d69T83OE6jVQtMi4tQfJP1PP2tIfyZ0/zvEJCq9FOQJFi7w37t/E9ahgWsSFTFquug5awtPdh85AVQ0ULeIXdDV1ResJCO6PlPomryi8E+UIFC1S49efwCjzWAIhhFdO/lLoQzkCRYtMCE5l1o3bOLyzO/NyDnjaHIUL8d42groa6orSkUfWdzyronSQp0GeNu/bS+cnm3R5ZD3yeMe7bYzMLQMPkPDKa6z+7SyWJbS8AKfC+/FaR1B14ix/q4vlqbLhVH+9F/jJPPAF4PweOJOvKeYC50EebZQBW+eMBqC69J/sGZbCiB43EOF3HXuGpVBXcwzkf0D+h6J33mDXkIeZGN7bJn3VqXEs/My34+XNeXECwQsrKVujL8qNwnsxnCNoy2SJKU8U8MhD6+nZ50ISbx9D1cfb+GTrRkRAJEe/H0zavBkQlMDa0lq++6kfR78f3HDuuBU7AXjoyUy+OVFHIGe4N+1eogJg07bPWP3Rz6wtEkTdMpkrPnqSuzYds0mfNczE0vFXueQetIeTJ086fUGVH+r9qVwWR73fFVDjvLzj4+MBGDJkiNPyVHQMwzmCtrDrsTHIvTN5eu0n9OzWBRJWc020+eEKAI51M4cXD8v8Nb0v9LPbIDLw+vEkBAXQOxr2vfMSAP0GjWDYl0sZeWQ1yNO8f/kD3H71xTbpyGrqar5yx9fURa9evZwer3Hmv6KgZj9+QHB3x6Wf9Nv1jTWw2Pn44493xDyFFdXV1R3LQErp8S0qKkraw2yefeqllJVSyhqr41215v3j2rG1zKJjD4uORb9eSlkrpTwnpTypXaeySX7f1Ev5bUsZOrDdHTjr+mellAO4T24rOd0gO3SkSB468qH2ad5CH8mSr32yVh46UuQwz7lz50oppczJyXGKjZ7G3d/j1KlT8re//a284oor5O7du1vV1d4th++g7l4DIYQ/sAuokFKOF0JcCbwJ9AK+AO6RUv4shOgKrAOigP8Cf5RSljvKv63jpQUQ1OTYMluuj5XcegZdSxUOi451HgHadmELuqHeH5LQIfWlaynZ9Cof5gwkKPx7oIc54fwBBlx5N4eOWI8t8OPuUX9mzZZM+l/uCWt9m6ysLFJTU0lKSiI5OdkpE42saUvV4CHAug9pGbBSSnkVcAqYrsmnA6eklOHASk1P4YX4hY8jKqWAoITVCH+rNRv8B8MVD9noZt49mMs4Rtw1vd1speuoK0pv2NxJVlYWQgiSk5MbZElJSZSXl5OamorJZHL6NXU5AiFECHAL8LJ2LIA44C1NJRuI1/Ynasdo6TcIXwjp2wkJXlhCophBeHg6OSWnbdIOfWg7V77/xX7MKfinO81zOSeiUvi2b39C/69/g6wofby2xbZyZtuJiYkhLCyM4uJikpKSkFKSkZHh1Gu0ht6qQQYwD+iuHV8KVEsp67TjY4Bl5Yl+wFEAKWWdEOK0pv+ddYZCiHuBewG7gRUUnmfoq6+zbsotAIS8MwsizP495b1vAUi/6RcNums+zAf/S1nz/VU85SNVg9djhxJ7yxVsHgxgjiMQlWKuwnZkdWhLr0lycjIxMTGAc2IKdASHjkAIMR44LqUsEkLEWMR2VKWOtEaBlC8BLwFER0ersasGZOuxx8HP7N+FmIScbXYEiUeWaBrPNCr3vI6SA9+S/seL3Gyl60gp2El96Vv4RUxrnjhF/+rQWVlZZGRkEBMTQ2pqapuCjrgLPVWD0cCtQohyzI2DcZhLCCYhhMWRhACW6WnHgMsBtPSewEkn2qxwE2f8+pFbVUdRne0iM/X+PYl/8iMb3atPrWNS5AH2+VDkcyF64hd+g42s6OeDFNXVsyvTfqShvLw8hBAkJSU1yJKSkiguLiYjI8Ml9Xtn4NARSCkXSilDpJRhwB1AvpTyLqAA+IOmlghs1PY3acdo6fnS+ilSeA09gISggGaxCxMeL2muOzCaYwG/Zqh/sySvJafkOPML64GaBlnBvr5suDGk2T0xmUwUFhYSHx+PlLJNK3MbgY4MKJoPzBFClGFuA8jU5JnApZp8DrCgYyYqjMYYfiT0kdeIfbGxE2lo35EsiJ3hQaucz8b9P7IsrjsQ2CBLiQpk0htlQJ2NbnV1dUN93xtp0+xDKWUhUKjtHwZG2NH5EbjNCbYp2sDDDz/c8dFlOrn6sh8pqdrOx6kPNsj2vfgQeY/0bPW86dOn2+xnZma2ou151s1bz8Lqzxg0bUODrLToOfr1vYIqEmzGsXg7ahqyj/D000/bHK9du9Zl10op+CtXbtxuIxt63zMMCJ3PoftaPs/oL35TMgesIK4kG+vYTMeueog9B79ldvPV2b0awziCpks1KQxM9+sYdOd1NqKt//mZkZe9z3Ggv/2zvI4pS+bw6+E32sjievgRN8L4ZQHL0J2oKH0zRw0z6cgy69B6U3QMe/fUGZsfNGsUzP7fB7j26WVcdv5Ai+d5G3uGpRBhmDdEH03vdVGRvhBzhvmalskPLR0r2o6eySbt2fbVljMg9I82XYWzrqtjzuDu4D+4xfO8jagAoK4I67Fw49YeJDgxl/xFvhXV2TBVAwve+MB0Nl7fdoKvjqy1+RcZtvAZrgqNaTIRybtZmTiU/RO30NsEy+LMsq1/qKRu2GG6TAhBLmn9fE/QdNGY2bNn6zrPMCUChffwwfsfcXfob2xk9cc/59CuNDie38JZ3sfopUV88Okhcr74plHYI46AqBRkpXc1fDrCcCUChfG5+bJz9HokkfN71+F/jXny0Z4DB/DrG0pg+ASfaSwcEXQBZZaigI/jUyWC1NRUp+fZlsUrrHV9uYqTlfMjM/90Y4MTABj925lcP3CCy0cWtndyjiueDV/CpxyBwj18tesvbP3yqI2s5l/PQ/13LZyhMDo+VzXYuXMnffr0caiXlpbGmjVrHOotXbpUV5DNH374gUmTJvlUSSC3qnEY7aTgLg3f7arQUQAcOjK2IT3wVzMZEP04h3Y96l4jFU7B5xzB6NGjdemtWbOGyMhIiouLW9V74IEHdAcFzc7OdqzkRSQE1Wt7F9jIc3fl82rRf4EfAG3a8fkDHPr0Njh/ALjWjVYqnIFPVQ1SU1N1D4qZMWMGxcXFDge6XHPNNbrzvOaaa9z0Td1DbgWkF/1M/pl6m5JO6VP3MpP3aHACQMpbXVibkmoOY+ZhEnOPkph71LGiogGfKxG0tWjuSD8yMtKnivtt4fDdv2J1YRHFJf+AHo0DaBbk9Iacf3LoyMwGWfofw7n+k7uxE8LD7WQn+EiIJDfic46gPRQWFmIymYiMjPS0Ke1m1apVHDlyhFGjRjktzzlvPMXc4LF0j3kDWdnoCA4deb6ZbsqYVxgNpIxZR265/aqBpVflvffe46abbmLv3r2uac23rHgVZD94iKI5yhFgDhyZlJTkdcEkrHnggQecnufCA8MZkLaL0pQmE1e+L+fjZ19g6G+vJPBX5hgEO4MuYHTVIdI/bLmxMCHB/GJ+8sknJCQkUFNT06JuRyiqA4LiUQu16cen2gg6QlZWlmHDSHmKPj0vZEbsQKYufcxGfv2f9zHill/y9b6vG2Qfb7irVSdgj8DAQMdK7SA6dBLRXXwoVJIbUCUCK6qrq3X1JHQa/jjB/Dnzrzbij1ea5UOt2kY/PlvP/q9/pGTaGHK/9WybSs6b93F9F/ddz1LS8WaUI2hCcXExxcXFXt1e4CyunXSW7OXdeeUOx7pnHp0N+yNgbseXqW8TdVaTnALMlYHnnt3FyA2L3GuHQWga10NvG4xyBHaorq5u09BiC5988gnLly93gUWeYeDIUWTLVYjg6cjK1sdnjFv5TKvpLiNgOOHT8yjNTGio5xbs3EV9qycZAyGEy3qk2pq3cgQt4AvFvY5S8rsl9KMGWfkM5ki+rqnTd4RY4c/BXWnUF6XjF9W44oi3NH45O2CL5eVvq4Pxlvul8AC/6dGDOgJ5ODaCOgM6AYAXMycTMHA8AVHtXHbIgyxevNjpQWPaiyoRKFpkj7a+399CH+FpB7qeInja682LwfUVDSs0GRkjtUMpR6BokaiUAgCbKL5GI7CuiE/TYqC+CvzMQUVr/PrxRPgilpUZMISQFZY1EI2AqhoovJrcE8MYuXogpT80LsgaCIZ3AkZDlQh8hMjISPbs2cPcuXOdmKt54k7s/HUULJsJdGzA1bx58wAoLS1l6tSpTmmQnRQcyvnazfgFNP6nWaZPJwQZ//G2HtGat7uCtK0lVFafI9jUjbnjBhI/3D1VHOPfKYUumg6CSktL63Ce4enHzTt9fk94+iHKmg41biOu6FrdNi8Y/+nfILMbbfMGB2DBEnEpb3cFC3P3ca7WHBq6ovocC3P3AbjFGaiqgaJFSu/8hvWk8MW1OZSlDPe0OeTl5TWrV9+wbBdjf3u1jWzRuBngFSMJoLy8nNTUVNK2ljQ4AQvnas+TtrX5grOuQDkCRYtkfX8zV6UU8J+YJcyPDfGYHcnJyYSFhREfH09eXp5Nmh+wddogG9mSrasJn1/oPgM7SGFhIZXV5+ymtSR3NsoRKFrkz7PeYJaYQYQfDHzhoFuvHRkZSVJSEgAZGRmUl5e34eyjXhV9uLCwkL49utpNCzZ1c4sNyhHoID18hrYN9bQpbuXYc/BgWglVuTOYFvG9y69XXV2NEIKsrCyKi4t1TwuvyE22ORYilNz53rUSUVnuCro1mTHZrYs/c8cNdMv1dbWqCCHKgbPAeaBOShkthOgFrAfCgHLgdinlKWEeM/kMcDPmoHZJUsovnG+6+5ixfiAPfZNAZsIVDbIq7dP4y2G2H7/wcUSlmGMOWQ/a6X+582ISJiUlUVhYSHl5OSaTqc2j48LTi4B7IL3IqjEzhm8muW41aFdwYte79Im+mYg/pBi+1yBWSmkdr3oBsENKuVQIsUA7ng/cBFylbSOBF7RPr+Vs1P38Ncr88ltefF92ABbO+AXxflUdV1xa4bTJMeXl5cTHx5Oamkp8fHyHg8HY68lIG1DIb67+hR1tY3Ni17vExMTwdTvXbugIHakaTAQsYXuzgXgr+Tpp5hPAJITw6vdmcvpeJi/60Oblz00cT26iby/lPjH2GeLPruP/tjgnPyEEJpOJ4uJil46qSy6TLJr5usvydyV5eXkNbSPuRK8jkMA2IUSREOJeTfYLKWUVgPZ5mSbvh2UkipljmsxrKUgZQf8NBTaySevegb61HrKoOStXrmT69OkNm1O4ZTR+EdNgXsfHJABMmzaNhx9+mOnTp/PEE084JU97TM89yrZ1WS7L35WYTCaysrLc7gz0Vg1GSykrhRCXAduFEK01IdubV9msXKk5lHsBQkNDdZrRdpwxWis9fAaDgaKNhURNjGmQT3q1L3KZc+1tL01XvV27tuN15Bd6vQiMILNsFTWlWwmM6FgDXGameeFQS3vDhAkTmq3e6wwmbryfiVMudnq+baUjz15GRgYmk4nq6moXW2lGV4lASlmpfR4H3gZGAN9aivzapzYMjWOAdTzpEOzMW5FSviSljJZSRutZmag9WEZrVVSfQ9I4Witvd0Wb8rl20i5WDBhh4wSmMIApVe7p2vEUg6Zlkhg+Haih+0DnTfO1DIOeOnWq0/K0Jn7pY1Q/6LoShx46+uxZnEBycjIZGRmuNRYdjkAIcbEQortlHxgL7Ac2AYmaWiKwUdvfBEwRZkYBpy1VCHfjrNFaywamM3SbbXF7+7TlTJzmnlFfniS7LBORuI2Ss3s8bYpuVixIYnq0Z1dbctazl5GRQXJyMjExMU60rjl6SgS/AD4SQuwBPgPekVL+A1gK3CiE+Aq4UTsGeBc4DJQBa4D/dbrVOnHeaK2+RAbdZptHZgIJmQUt6PsGiWv3k1tVR87SW3kywnuiAsdm7uOshxelcfZIQcucBOuBVs7EYRuBlPIwMMyO/L/ADXbkEpjZVO4Jgk3dqLBz49s6WmvrtEEwbYOzzPIaloblEBQ0DeqOM6lqLN6ysmOUAeYcOevZa4plcllGRgazZ89m8eLFzQKUWsKftaXL16dHFs4dN9Blo7XqS9fCmfwO59MR8nZXMHppPlcueIfRS/Pb3PbhiKCYP1JXtB5OfIOUzm/U82Vc+eyBef6FlNJulOL2hC0zgO+0pT3ezJqmLbWTovpRcPBEh3oNzuQv4qsvPiZqzhsNUXDKwqex/9s6Enq0y8wO445pq4lZdUy8yTx8d2NwF7IrPVfctvyuFdXn6PdJvltH3bUHi23uji+wZYvtoI/Tp0/rOs9wjsDaAbQ1JLO9lyOnqIKnEoZ26AcIHBhNVPTvEP5JSLkVgMzC7xgYEggemvveWmOUsx62I39+Az7YxuGd3em76oBT8mwPznB6Lllj0QHxw/sZ2llZYxhH4Iywzq56OQq7m0fBnZeNM9qWxfVud37OwB3TVgsqlwCeD/nlDqfX2TGMI7A3sGTChAltysNVL0dcD+M1pbiqMcqIeHqufmfAeE94B2jpJfDFl8PVjVGu5NSpUwBUVuqLj9yZfldP4VOOwJtfjrYSP7wfTyUMpZ+pGwL4RWAX7hnsj+n0Vw19zkajsLCQwsJC7rrrLgoLC3Uv5d6ZfldPYZiqgTPwVEutp/Cmxiig3aPjOtvv6gl8yhGA970cCn2o39W1GNoRbN68uVm/qDuoqKho97+XJ+y1h6funTtouvS3q+no2JaOXtcVMzSb4lNtBAqFon0oR6BQKIxdNfA12jouwptxdXG2raNOO3IdI1zX1agSgR26du2KEKLN26RJk1rNtyPr3nvLZvmersZd9fWm1/HUdV2NKhHYoXfv3m5poFEojIJwt+exa4QQZwEjh/vpDXznUMszKNvaj5Htc5ZtV0gpHcYCNEqJoERKGe1pI1pCCLHLqPYp29qPke1zt22qjUChUChHoFAojOMIXvK0AQ4wsn3KtvZjZPvcapshGgsVCoVnMUqJQKFQeBCPOwIhxO+FECVCiDJtVWV3X3+tEOK4EGK/layXEGK7EOIr7fMSTS6EEM9qtu4VQrh0FQ0hxOVCiAIhxAEhxJdCiIcMZt+FQojPhBB7NPse0+RXCiE+1exbL4S4QJN31Y7LtPQwV9qnXdNfCLFbCLHFgLaVCyH2CSGKhRC7NJlnflsPj0LzBw4B/YELgD3A1W624TfAtcB+K9lyYIG2vwBYpu3fDLyHeX3HUcCnLrYtCLhW2+8OlAJXG8g+AQRq+12AT7XrbgDu0OSrgfu1/f8FVmv7dwDr3fD7zgHeALZox0ayrRzo3UTmkd/WpV9Ux424HthqdbwQWOgBO8KaOIISIEjbD8I8zgHgReBOe3pusnMj5lWlDGcfcBHwBTAS80CYgKa/MbAVuF7bD9D0hAttCgF2AHHAFu0lMoRt2nXsOQKP/LaerhoYdQl1wy35rhVVh2P+1zWMfVrRuxjzIrjbMZfwqqWUdXZsaLBPSz8NXOpC8zKAeUC9dnypgWwD8yrh24QQRcK8Ojh46Lf19MhCXUuoGwiP2CuECARygGQp5ZlWZqa53T4p5XkgUghhwrxS9uBWbHCbfUKI8cBxKWWRECJGx/U98duOllJWCiEuA7YLIQ62outS+zxdItC1hLoH6NCS785ECNEFsxN4XUqZazT7LEgpq4FCzPVXkxDC8idjbUODfVp6T+Cki0waDdwqhCgH3sRcPcgwiG0ASCkrtc/jmJ3oCDz023raEXwOXKW15F6AuZFmk4dtAoMs+S7Mf/2ZwAEp5QoD2tdHKwkghOgG/A44ABQAf2jBPovdfwDypVbhdTZSyoVSyhApZRjm5ypfSnmXEWwDEEJcLITobtkHxgL78dRv68rGEJ0NJjdjbg0/BDzigev/DagCajF73emY64Y7gK+0z16argCe12zdB0S72LZfYy7+7QWKte1mA9l3DbBbs28/8Kgm7w98BpQBfwe6avILteMyLb2/m37jGBp7DQxhm2bHHm370vLse+q3VSMLFQqFx6sGCoXCAChHoFAolCNQKBTKESgUCpQjUCgUKEegUChQjkChUKAcgUKhAP4/V8Khmhp7uRkAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"lokalen = d.location.unique().tolist()\n",
"\n",
"for i in lokalen:\n",
" temp = d.loc[d[\"location\"] == i]\n",
" plt.scatter(temp.x, temp.y)\n",
" #print(np.column_stack((temp.x, temp.y)))\n",
" #print(i)\n",
" img = plt.imread(i+'.png')\n",
" #print(img)\n",
" plt.imshow(img)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zoals men kan zien zijn er sommige meetpunten die zeer dicht bij elkaar liggen. In dit geval worden deze gefilterd en moesten ze gelijkaardige wifi info hebben eruit gehaald."
]
},
{
"cell_type": "code",
"execution_count": 289,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def removeIrelevant(df, minSampleSize=50):\n",
" rdf = []\n",
" returnable = pd.DataFrame()\n",
" for i, v in df.iterrows():\n",
" rdf = CloseToOthers(v, rdf)\n",
" rdf = pd.DataFrame(rdf)\n",
" return rdf\n",
"\n",
"def CloseToOthers(i, df, SpacePerc = .1):\n",
" tdf = pd.DataFrame(df)\n",
" approved = []\n",
" if \"location\" in tdf:\n",
" l = tdf.loc[tdf[\"location\"] == i[\"location\"]]\n",
" for index, dataframe in l.iterrows():\n",
" temp = abs(dataframe.px - i.px) \n",
" temp2 = abs(dataframe.py - i.py)\n",
" if temp <= SpacePerc and temp2 <= SpacePerc and len(dataframe[\"WifiInfo\"]) > len(i[\"WifiInfo\"]):\n",
" return df\n",
" df.append(i)\n",
" return df\n",
" else:\n",
" df.append(i)\n",
" return df\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 290,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"oud: 217 \n",
"Nieuw: 203\n"
]
}
],
"source": [
"g = removeIrelevant(d)\n",
"print(\"oud: {} \\nNieuw: {}\".format(len(d), len(g)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training data:\n",
"\n",
"Nadat de data gefilterd geweest is kan er begonnen worden aan de voorbereiding van de trainings data. Er wordt ook nog een functie aangemaakt voor de modellen te evalueren.\n",
"\n",
"Voor de x waarden gebruikt deze opgave een lijst van de top 2 bereikbare modems.De y waarden werden de coordinaten van het meetpunt + een nummer die afhankelijk is van het lokaal gegeven. Dit nummer werd vermenigvuldigd zodat het niet kan samenspelen met de percentages (float 0-1). Uit testen bleek dit beter te gaan dan 3 verschillende y waarden te proberen predicten. En om dit te parsen haalt men gewoon het tiental (het eerste/eerste twee getallen) van de return values en de overblijvende nummers zijn percentages (tussen 0 en 1 ideaal) die vermenigvuldigd moeten worden met de breedte / lengte van het lokaal.\n",
"\n",
"Daarna werd er geexperimenteerd met bepaalde scalers om het beste resultaat te halen waaruit bleek dat de normalizer de beste test results gaf."
]
},
{
"cell_type": "code",
"execution_count": 356,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from sklearn.preprocessing import MaxAbsScaler\n",
"from sklearn.preprocessing import Normalizer\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.model_selection import KFold\n",
"\n",
"\n",
"def prepTrainingOLD(df, l=7):\n",
" x = []\n",
" y = []\n",
" #scaler = MinMaxScaler(feature_range=(0,1))\n",
" #scaler = StandardScaler()\n",
" #scaler = MaxAbsScaler()\n",
" scaler = Normalizer()\n",
" \n",
" for i, dataframe in df.iterrows():\n",
" tx = []\n",
" for i in sorted(dataframe[\"WifiInfo\"], key=lambda x: x[\"signal\"], reverse=True):\n",
" if i[\"routerId\"] not in tx:\n",
" tx.append(wifiSignals.index(i[\"routerId\"]))\n",
" if len(tx) >= 2:\n",
" break\n",
" \n",
" #for ij in dataframe[\"WifiInfo\"]:\n",
" # tx[ij[\"routerId\"]] = ij[\"signal\"]\n",
" #print(tx)\n",
" x.append(tx)\n",
" ty = (lokalen.index(dataframe[\"location\"])/len(lokalen),dataframe[\"px\"], dataframe[\"py\"])\n",
" #x.append(tx)\n",
" y.append(ty)\n",
" \n",
" fx = pd.DataFrame(x).fillna(0)\n",
" fy = pd.DataFrame(y)\n",
" #print(fx)\n",
" #print(fy)\n",
" xtrain, xtest, ytrain, ytest = train_test_split(fx, fy)\n",
" scaler.fit(xtrain)\n",
" xtrain = scaler.transform(xtrain)\n",
" xtest = scaler.transform(xtest)\n",
" return xtrain, xtest, ytrain, ytest\n",
"\n",
"\n",
"def prepTraining(df, l=7):\n",
" x = []\n",
" y = []\n",
" scaler = Normalizer()\n",
" for i, dataframe in df.iterrows():\n",
" tx = []\n",
" for i in sorted(dataframe[\"WifiInfo\"], key=lambda x: x[\"signal\"], reverse=True):\n",
" if i[\"routerId\"] not in tx:\n",
" tx.append(wifiSignals.index(i[\"routerId\"]))\n",
" if len(tx) >= 2:\n",
" break\n",
" x.append(tx)\n",
" ty = (dataframe[\"px\"]+lokalen.index(dataframe[\"location\"])*10, dataframe[\"py\"]+lokalen.index(dataframe[\"location\"])*10)\n",
" y.append(ty)\n",
" fx = pd.DataFrame(x).fillna(0)\n",
" fy = pd.DataFrame(y)\n",
" xtrain, xtest, ytrain, ytest = train_test_split(fx, fy, random_state=3)\n",
" scaler.fit(xtrain)\n",
" xtrain = scaler.transform(xtrain)\n",
" xtest = scaler.transform(xtest)\n",
" return xtrain, xtest, ytrain, ytest\n",
"\n",
"\n",
"def score(mod, cv=3):\n",
" kfold = KFold(n_splits=3, shuffle=True, random_state=2)\n",
" print(\"Model score {}\\nCrosValScore {}\\nMean {}\\n\\n\".format(mod.score(xtest, ytest), cross_val_score(mod, xtest, ytest, cv = cv),cross_val_score(mod, xtest, ytest, cv = cv).mean()))\n",
" print(\"Kfold:\\nScore: {}\\nMean: {}\".format(cross_val_score(mod, xtest, ytest, cv=kfold),cross_val_score(mod, xtest, ytest, cv=kfold).mean()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modellen\n",
"\n",
"![alt text](https://scikit-learn.org/stable/_images/sphx_glr_plot_classifier_comparison_001.png \"Vormen van plotting\")\n",
"\n",
"\n",
"### Lineare regressie\n",
"\n",
"Het eenvoudigste model. Dit komt vooral omdat er niet veel parameters zijn die dit model aanpassen t.o.v. andere modellen die hier gebruikt worden. Het reflecteerd ook dus zeer goed de kwaliteit van de trainings set die gebruikt wordt."
]
},
{
"cell_type": "code",
"execution_count": 312,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model score 0.053549619325076736\n",
"CrosValScore [0.08107485 0.05643525 0.26221046]\n",
"Mean 0.1332401864126653\n",
"\n",
"\n",
"Kfold:\n",
"Score: [0.25199567 0.00721587 0.08458426]\n",
"Mean: 0.11459860042677378\n"
]
}
],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"\n",
"\n",
"xtrain, xtest, ytrain, ytest = prepTraining(d)\n",
"\n",
"\n",
"\n",
"def LinReg():\n",
" lr = LinearRegression().fit(xtrain, ytrain)\n",
" score(lr)\n",
" return lr\n",
"\n",
"model = LinReg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gaussian process\n",
"\n",
"Dit was een model waar er meer geexperimenteerd werd in de notebook (eerder vermeld). De mogelijkheid om kernels te kiezen die het model zou gebruiken leek mij zeer interessant om de resultaten hiervan te kunnen zien.\n",
"\n",
"\n",
"#### White kernel\n",
"\n",
"Op zich zelf is deze kernel redelijk onbruikbaar. Het is een white noise kernel, wat betekend dat het willekeurige en onverwachte resultaten zal geven, maar dit is handig als je ze in gebruik zet met andere kernels om een meer gevarieerd resultaat te geven.\n",
"\n",
"\n",
"#### DotProduct en RBF kernels\n",
"\n",
"De dotproduct kernel is een kernel die meer decision tree achtige resultaten geeft, terwijl de RBF kernel meer gevarieerd zal zijn. Dit is ook te zien in de notebook waar ze oorspronkelijk geimplementeerd werden, maar het leek interessant om ze in deze opgave ook te implementeren.\n",
"\n",
"Jammer genoeg is het niet gelukt om de White noise die deze kernel genereerd te onderdrukken. (((***Goede***))) resultaten zijn bereikbaar via de RBF en DotProduct kernel op zichzelf, maar van zodra dat de white noise erbij komt is dit teveel. Dit is zichtbaar door de testscores die exact dezelfde zijn als die van de whiteKernel op zichzelf.\n",
"\n",
"![alt text](https://scikit-learn.org/stable/_images/sphx_glr_plot_gpc_xor_001.png \"Kernels\")\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 389,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model score -1.3040243750461273\n",
"CrosValScore [-1.80544992 -1.09822479 -1.12201902]\n",
"Mean -1.3418979083128233\n",
"\n",
"\n",
"Kfold:\n",
"Score: [-1.59551739 -1.24587679 -1.11763551]\n",
"Mean: -1.3196765647323825\n",
"\n",
"\n",
"\n",
"\n",
"Model score 0.05289226283071731\n",
"CrosValScore [0.07935586 0.06096812 0.25956421]\n",
"Mean 0.13329606013862721\n",
"\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\gaussian_process\\gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the state: {'grad': array([76.25]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 53, 'nit': 5, 'warnflag': 2}\n",
" ConvergenceWarning)\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\gaussian_process\\gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the state: {'grad': array([-3.46875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 3, 'warnflag': 2}\n",
" ConvergenceWarning)\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\gaussian_process\\gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the state: {'grad': array([-3.46875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 3, 'warnflag': 2}\n",
" ConvergenceWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Kfold:\n",
"Score: [0.25083644 0.01026288 0.08810267]\n",
"Mean: 0.11640066158846989\n",
"\n",
"\n",
"\n",
"\n",
"Model score 0.04847369359864713\n",
"CrosValScore [0.04184544 0.12240472 0.30573872]\n",
"Mean 0.15666296007411418\n",
"\n",
"\n",
"Kfold:\n",
"Score: [0.23477509 0.06686579 0.10696049]\n",
"Mean: 0.13620045723071283\n",
"\n",
"\n",
"\n",
"\n",
"Model score -1.3040243750461273\n",
"CrosValScore [-1.80544992 -1.09822479 -1.12201902]\n",
"Mean -1.3418979083128233\n",
"\n",
"\n",
"Kfold:\n",
"Score: [-1.59551739 -1.24587679 -1.11763551]\n",
"Mean: -1.3196765647323825\n",
"\n",
"\n",
"\n",
"\n",
"Model score -1.3040243750461273\n",
"CrosValScore [-1.80544992 -1.09822479 -1.12201902]\n",
"Mean -1.3418979083128233\n",
"\n",
"\n",
"Kfold:\n",
"Score: [-1.59551739 -1.24587679 -1.11763551]\n",
"Mean: -1.3196765647323825\n"
]
}
],
"source": [
"from sklearn.gaussian_process import GaussianProcessRegressor\n",
"from sklearn.gaussian_process.kernels import RBF, DotProduct, WhiteKernel\n",
"\n",
"def GaussProc(alpha=.08, kernel=RBF()):\n",
" gp = GaussianProcessRegressor(kernel=kernel,alpha=alpha).fit(xtrain, ytrain)\n",
" #print(\"Model score:{}\".format(gp.score(xtest, ytest)))\n",
" return gp, gp.score(xtest, ytest)\n",
" \n",
"\n",
" \n",
"lastScore = 0\n",
"optimal = 1\n",
"kern = RBF()\n",
"#for i in np.arange(0.00001,1, .0001):\n",
"# model, sc = GaussProc(alpha=i, kernel=DotProduct())\n",
"# if sc > lastScore:\n",
"# lastScore = sc\n",
"# optimal = i\n",
"# print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
"\n",
"optimal = .1\n",
"\n",
"\n",
"model, dump = GaussProc(alpha=optimal, kernel=WhiteKernel())\n",
"score(model)\n",
"print(\"\\n\\n\\n\")\n",
"\n",
"model, dump = GaussProc(alpha=optimal, kernel=DotProduct())\n",
"score(model)\n",
"print(\"\\n\\n\\n\")\n",
"optimalRbf = 2\n",
"model, dump = GaussProc(alpha=optimalRbf, kernel=RBF())\n",
"score(model)\n",
"\n",
"print(\"\\n\\n\\n\")\n",
"\n",
"model, dump = GaussProc(alpha=optimal, kernel=DotProduct() * WhiteKernel(noise_level = 1e-5, noise_level_bounds=(1e-10, 1e+1)))\n",
"score(model)\n",
"print(\"\\n\\n\\n\")\n",
"optimalRbf = 2\n",
"model, dump = GaussProc(alpha=optimalRbf, kernel=RBF() * WhiteKernel(noise_level = 1e-5, noise_level_bounds=(1e-10, 1e+1)))\n",
"score(model)\n",
"#a, b = GaussProc(alpha=0.04908, kernel=DotProduct() * WhiteKernel())\n",
"#print(b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Random Forest\n",
"\n",
"Dit model werd gekozen omdat een decision tree beter past bij het voorspellen van welk lokaal een bepaalde value in komt. Door dus de lokalen op hogere waarden te steken (tientallen ipv values tussen 0 en 1) wordt een decision tree nuttiger. Jammer genoeg wordt dit niet gereflecteerd in de resultaten.\n",
"\n",
"Er moest voor deze opgave ook een vorm van decision tree aanwezig zijn, en degene met beste resultaten is voor deze opgave random forest. Door for loops te maken kan er gekeken worden wat de beste waarden zijn voor de n_estimators en max_depth. "
]
},
{
"cell_type": "code",
"execution_count": 294,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.05731817367540793 en 2\n",
"0.08226970663928608 en 4\n",
"0.11383447594132909 en 6\n",
"0.1356945475227735 en 18\n",
"Model score -0.10544783760114482\n",
"CrosValScore [0.04500586 0.04377591 0.0086799 ]\n",
"Mean 0.11149634984717412\n",
"\n",
"\n",
"Kfold:\n",
"Score: [ 0.2731529 -0.06379346 0.1188839 ]\n",
"Mean: 0.12751157073606273\n"
]
}
],
"source": [
"from sklearn.ensemble import RandomForestRegressor\n",
"\n",
"\n",
"def rfor(est=5, dep=50):\n",
" lr = RandomForestRegressor(n_estimators=est, max_depth=dep)\n",
" lr.fit(xtrain, ytrain)\n",
" return lr, lr.score(xtest, ytest)\n",
"#Calculating optimal depth\n",
"lastScore = 0\n",
"optimal = 1\n",
"for i in np.arange(1,80,.05):\n",
" if i == 1:\n",
" model, lastScore = rfor(dep=i)\n",
" continue\n",
" model, sc = rfor(dep=i)\n",
" if sc > lastScore:\n",
" lastScore = sc\n",
" optimal = i\n",
" print(\"{} en {}\".format(lastScore, optimal))\n",
" break\n",
"\n",
"de = optimal\n",
"lastScore = 0\n",
"optimal = 1\n",
"\n",
"for i in range(1,80):\n",
" if i == 1:\n",
" model, lastScore = rfor(dep=i)\n",
" continue\n",
" model, sc = rfor(est=i, dep=6.85)\n",
" if sc > lastScore:\n",
" lastScore = sc\n",
" optimal = i\n",
" print(\"{} en {}\".format(lastScore, optimal))\n",
"\n",
" \n",
"optimal, sc = rfor(est=optimal, dep=de)\n",
"score(optimal)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Neural net\n",
"\n",
"{{}}"
]
},
{
"cell_type": "code",
"execution_count": 372,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model score -0.020048317987887438\n",
"CrosValScore [0.08638929 0.07721733 0.23919267]\n",
"Mean 0.13494499844986285\n",
"\n",
"\n",
"Kfold:\n",
"Score: [0.24753026 0.0192626 0.10087002]\n",
"Mean: 0.12344449081010432\n"
]
}
],
"source": [
"from sklearn.neural_network import MLPRegressor\n",
"\n",
"\n",
"def neuralNet():\n",
" lr = MLPRegressor(hidden_layer_sizes =100, solver=\"sgd\",alpha=20, max_iter=500, momentum=.9)\n",
" lr.fit(xtrain, ytrain)\n",
" return lr\n",
"\n",
"model = neuralNet()\n",
"score(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conclusie\n",
"\n",
"Uit deze vindingen kunnen we afleiden dat meeste modellen redelijk gelijkaardige scores halen voor deze trainingsdata. Hoewel theoretisch random forest het beste van de bovenstaande modellen zou zijn krijgt ze een gelijkaardige score t.o.v. de andere modellen.\n",
"\n",
"## Post-mortem/wat kon beter\n",
"\n",
"Er moest zeker meer tijd gestoken worden in het selecteren van de trainings data en features. Deze hebben de rest van het project sterk beinvloed en gezorgd voor lage en gelijkaardige scores."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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