diff --git a/project/.ipynb_checkpoints/ProjectDataScienceBeppeVanrolleghem-checkpoint.ipynb b/project/.ipynb_checkpoints/ProjectDataScienceBeppeVanrolleghem-checkpoint.ipynb
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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  andere 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. Door gebruik van enkele if functies kunnen we kiezen welke datasets we willen inlezen.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "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": 53,
+   "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": 54,
+   "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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N8pUrV7a4DwsL499//7XaEe/bt69F/bsTDxDWpBoTVnZW3NdhH0UGxN6GPyi1I4YjlJqqR3QOU133h2OuTVFGjRrFV199ZaVL37Fjh8WUqTi2Tt399NNPVmlz58612rhqEtZKdX+vx70NJVyXgqHEVD07O5ths+9QVe/0AYnUqnVtb6Fs2bI88sgjpKWlkZqaChimVo888ojdOmypUrt168aJEycAw874559/zvPPP28VU3zr1q1sfe+cqj6rNW25XrguBcPR+sPEm2vsf3ntkXvwjM30li1bEhERwciRI2na1LGZtb2NqNzcXD755BMuX77Mk08+abd81K3KTdJNuDIylnaEFhyKhYeHy1mzZgHX7Fr86SN22vuvUSnGWpXqiPVT/mLz2l8AeOutt3jllVfUNdq5MyQmKrKVKklSUhLR0dHme1fsqGKrzqKB0ZCxNGL6PgkhkqSUbZzl19yIoQUv5+XapajKfzLnvFkoAJ5++mnWrVvHtm3qzbzVCkVCQgLR0dEW5zy6deumut13vn1cdZnSjOYEwxWPDp4kLLymanuoeypPMl9v3bqV2rVr8+CDDxITE8P333+vqq6MjAxF+SZNmsSVK1fo08cwMrRqdW3h/e233/Lt1L/tFbVJs/a1ePlNfSFuQnOC4U8SEhJUHRsF+Gj4r2ZL140bN1pZvd53332cO3eOwkJlp+6UuBLKzMxk6tSpBAcHm9Pq1Kljsefy3pQFLIhTZ0dVO/ZPVflLM7pgFCOpzIeq8l+5VMiBXdc24Nq2bWsz38KFCwkKUjYKKZlK2TM07NOnD7/9ZnAT3KxZM26vHaOozeL0n6R+l780ogtGMWrVr+w8UzHO/9DSfD158mRq1rR2kXPw4EFGjRqlqL6srCwOHTrkNF/jxo3Jz8+3+az4RuDSpUuZ95w6Y8FOAxuxdPVCVWVKI7pgGHHF3+t/J1xzMmBvtMjKUmiq0aAB1apVo25dZcdBTb6oSlKvXj2L+zZN7lFlR5Wy5Rg9uvRVnL+0oguGkZz9p1n5cqbi/NMeSrS479GjB5s3b7aY5mzevJkHHnhAUX3nK1Viy5YtxMXFkZqa6nRN0q1bN44evWb0KKXkiy++sDrotGjRIk78X32n7V+5VMi/21qwYOwuzfm89QdK4mNUALYD5Y35V0kpJwshGgLLgRpAEvCklPKyEKI8hnga0cAp4BEpZbaX+u9Rtm80qGnr316dl5ban59fuVTI0QzrzbyuXbtSWFjIrFmzGDRoEF26WLndskJKSWZmJrfeeCMPPfQQDz30EADnzp1j7969dOrUyW7Zs2fPUrduXf744w/Onz/PE088YZVHCEFaUgZnb8y3671w+sM/knvoH1Dn+KRUo2TEuATcK6WMBKKAB4yuN6cDs6SUjYEzwDPG/M8AZ4zps4z5Aoqc/acZGbmWTTNseyxPnWt/2hUUFMSYMWOoXr26wwX3iRMnOH78OEIIizMYJqpUqUKnTp04ffo0Bw4csFlH8+bNAcNCu00b+3tWI0aMsOngefrDPzIycq1BKHQsUBIfQ0opTQcHgo0fCdwLrDKml4yPYTJJXQV0Ef7esXORDV9uZ2TkWlK2HDOnvfvodrPmxxWuXr1KVlYWN910E7VrOw8bUr16dW677TZyc3NdPpLas2dPTp8+ze71BnXs1/G/6wLhBKXezoOEEMkYPJp/BxwG8qSUV41ZTDEwoFh8DOPzfAzTrZJ1DhdC7BFC7LGnYdEKC8bu4t9tLbhyqZCcA6fo37+/S/VkZ2dTWFhoddBICWFhYVSuXJlNmzZRUFCgqqwQgvLlyzPnhS10lJP5buEfqtu/3lB0HkNKWQhECSFCgbWA256CpZTzgflgsJVytz5vM3HUGwAUFhYyYsQIVWXPnTtHXl6e27ZIZcuWpVu3bkgpOXLkiJUGyh7BwcEcOHCAyMhI/eyFQlRppaSUecAPQHsMIcRMglU8BoY5PobxeVUMi/BSQVBQEFu2KPcEmJ6eTsWKFRV/iZUghKBevXps27aNy5edq2KllDaP0erYR0l8jBuNIwVCiBDgfgxx+H4AHjZmKxkfw+RO7mFgq9SCCa8HeeGFF5zmSU1N5dy5czRp0kTxrrdaYmJiKFeuHKmpqQ7ty3Jycmweo9Wxj5IRozbwgxAiFfgV+E5KuQGYAIwVQmRgWEN8asz/KVDDmD6Wa9FcSw2OBCMtLY2zZ88SERFh10UOGN7iV69etfmFLiws5MqVK0gpKSoq4urVq+ZntsxBIiIiEEKwZ88eq2dgOBPuLeEsrSjRSqVKKVtJKSOklC2klP8zpmdKKe+UUjaWUvaXUl4ypl803jc2Ple+axYgPP7441ZTmOPHj/PXX3/RokULbrjhBqsy58+f58qVK+afQgjKlClDQUEBW7daeiuMj4+nqKiIY8eOUVRURNmyZTlz5gznz59n8uTJdvvVpk0b/vnnH86csdxjueWWW9z4ba9P9J1vF2jcuLF5M+3y5ctkZ2dTu3ZtC39PJalQoQLBwcFUrlzZbBVbpkwZKlWqRI0alkq7hg0bUr58eerWrWv2MFKtWjUqV67ssA2AmjVrUq1aNbZt28b58+dJSkryiqPk0o4uGC7y3nvvmQ3+lGibcnJy7D4raVGbmWl/kK1WrZqi/sXExFC2bFnuueceypTx37/55TdHM3N3b7+17yq6YLhIvXr1mDJlCuXKKfNQWL58ebvPStYREmI/Ll1J9zqOmDlzJmfPnlWc35NkZ2fzn3kdqB37J8Hlg5j2/mt+6Yer6ILhBi+++CJvv/22oryOFuLZ2dkW946M+OwtsEuydOlSnn32WUV5PU3kPXVZnz+GZu2veUxRe1zY35Rqh2vepnXr1lSoUIGnn37aIrKqLYKDg8nLyyM9PZ1q1arRuHFjUlNTCQkJ4aabbrLIe/vtt5OWlsaJEye46667yMjIoGzZsg5HneIsX76c6Ohoq7WLt1m6eiGnGn9tMwxbcPkgwsJrBowZiu4lxEMMHTqUxx9/nHvuuce1CqZMMXzc4N9//+WOO+5g//79PnUoEdGhodN45ybqZQ0yn1P3JQHvJSRQWbBgAZ07d+aZZ55hypQpTuNgeIqioiLy8/Pp27cvf/zxBwcOHPCZUMz7LJ7p23sqFgpQf3zYX+gjhgdx5wu5BujngT744m+Wl5fHyA+6cGesa/sjm945yYYvdni4V45RO2LoawwPotbJWXEabjnG+3YOEmmJ5yc+TvNHC1wWCoBu49SFSPMHumBohL/D7Mf31gLJycksThlN80c94+u2/m01yDmg3rY0Ye9Rn8QP19cYGuFiBe3aMnXodys/itc96gD6pWX2j+zawxsBR+2hC4aOXV77+n7mpPRl4OQIj9VZkH+Z4KQYRkaudZ65BK4EHDWt+9SuvfSplI4VJvWrWj9bzvjk+WRSd2SB+sECcD3g6IYNG1S3pY8YOmamzXqdmbt7q1K/KiH3YD7j2v4fRfmVGDZsmNl+69FHH1VVj9KAP55AHzF0yMvL46XPuhnd63h2rbNrXiFffLSV6QPs57nhhhs4cuSIU/ek47o1ZdKafRbTqZIBfzyFLhg2CMQ9FFeJfSyGrhOq2/U55SqjohKQUjLyI+d5TYaOQUFBDh3NmbRPvtBK6YJxnZKYmMiWs2/RdYJnQ41tX57JyqkpjFT5Xtm1axeFhYXExsby9ddf290stRdw1NPognEdcv+QZvSOa06tal5aXKukqKjI7Pt3/fr1xMbGsn79egDmpCj3o+uKpsseumB4kFFR6mOCmxgPzPBAHxy9qV/7+n5q1a9M77jmHmjJQEH+Zer9/TCPPTSEkUYrjyNHjlCuXDmef/55Vq9e7bSOkgep1q9fz1NPPcWSJUs81k+16ILhQdxalyQmMr1zZ4/1pSQzd/f2vPp1RAqpP1ueNszNzTW7ClIiFIsWLWLw4MFW6XPmzPFIHwGrsM9K0NW1GuL2lUdYFecde6nLOz0XEObdx7YxMnKthVA884zBdXFYmGUE2Pj4eId12RIKMBz39YSVsKsvK10wNEL61335Xvbhw3ZbnWd2AZMnRXe4cqmQtCUh5Ow/bZH+zjvv8Omnn9osExcXZ3VC0cSffzoObVY8zIG7qBUyfSqlAYr+GE2T3muhc2d+8GI7M/pvZ/xK17adZ/TfzpH0UzZDBYwbN85h2aysLJsOI5y59alTpw6oi7HpMRSPGEbHznuFEBuM9w2FELuEEBlCiK+EEOWM6eWN9xnG5w280/XSQ5lmszn123D2TKsFRfu91s6R9FNcuaQsSKaJdfH7GRm51iAURp577jlSU1Mt8tlzNJ2WlmbzVKOzo8D+Rs1UajQG15wmSm18DH8QWqcubZ56HtHlS6+283QTZZqezQvSGRm5lu8XppvTHn7Y4JF13rx5RERYGhZWrFiRBx98EICff/7ZPLdv0aKFVd2pqak8/fTTLvXfVygNAxAG9AQWGO8F10F8DF8SdPNkis7+jfzhGeeZ3SA0NJR3H9tm9/nJnPPUyxrE+jnXRq6ff/6ZEydOsGrVKou8w4cPt7hft24dK1as4K677nI4p69f33noM3+jdI0Rj0HVbvIBUwOF8TGEEKb4GBbuIYQQw4HhoCy29fVAUPO5wFyvm6Pk7D9tc+Psw+d28McvJy3SZsyYwfjx423WM3/+fKu0AQMcGEVhCO08ZMgQ8/1rr71GrVqWJ/rOnDnDf//7X4f1eBslMfh6ASellElCiM6eathefIzrdXDZeL4aUkq8t8KwZOXLmfR/uxFgiBL1T85F/v33X6t89oTCxJgxYzCd11dCcaEAePbZZ61UvLm5ufgbJVOpDkBvIUQ2hmCU9wKz8VJ8DCmlwzemLcFJT0+3qxLUKu82FsyoUIEZFSoA0LOywRHz7T5qf/vGFE7mnOf4+ls48OufNoXCREZGhs30pUuXqhKKn376SXU//YUSb+eTpJRhUsoGwEAM8S4ex0vxMTZs2GDXutWU/tJLL5Genm7+1KxZ0+1oRb5m3GEYf/Ei4y9eBOCn1m2JXGnfv603eKP3d7z96mynodMaN27MkSNHzPcffWQwmX3sscdUtXf33Xer76SfcGcfYwKwXAjxJrAXy/gYnxvjY5zGIExu07VrV6SUtGnTRrGbSi1Tvvx0KlQwWEddvDieO24IoW9758EqvcE333zjNE+9evVYuXIlffv2VR1qLRBRJRhSykQg0XidiY3tHinlRcC16I0OWL58OaDcd6vW6b3teQD2f/M6AE/uMoYCC1PmJNpT/PTTT4rf5K4G5czKynIpIKc/CZid7+rVPXtuwN+0iekKwP7JhlFjRVvPGvgppWPHjl7XgpXUOgUCuq2Un7j14d+49eHfOPByRwB+faMZQypMImHvUTpM20rDiRvpMG2rV1zDFCcmJsar9a9cuZJKlez7zCqpkbKX5mt0wfATD31hONCTJg0GedljDrAzfqDP/CaZiI2N9VrdQ4cOdXn65W8CZiq1ZMkSnnrqKX93w2MIUcfkSxUpJf0rlyE+7xRH7fhN8tZxzszMTJKSkjxe77p161iwYAH5+flOnRxokYARDJMlZkJCgl/cyHsaKSUDdp03z+/FoDXUv7kc2NinceY3yR02b97Mhx963gN5dHQ0YB1GLVAImKlUZ+PpttmzZ1vsYaSnpyOEID093XEFGmPrS1UtFtxPLdlMURnbrmu84TfJRJs2Th1/exVbu9yBsvOtKapXr05aWhppaWkW6U2aNPFTj1yjy3tnWfCQQVvzUeR0Fst5vD8ggpBgS+Hwlt8kE8uWLVOcNy8vz6U2duzwrct/TxAwUykTts4RB6IPqMlHrzC0TlmmHLvKlJQJDBLPsQN4ZXktFmR19rrfJLWsW7eOBx98kGeffZaPP/5YVdkOHTp4qVfeI+AEo7QwpU5Zi5+Lr3SBsrcixCSk/J9P+6LEwZzprMXHH3/MmTNnOHr0qM2zFvb47rvvuP/++93qpy8JuKlUqaVsX6A18uwknzftTChee80yFHG1atVo0aIFc+fOVdxGIAkF6IKhGW6fuhO4irjBxeCWLhDRoSFzUvqSmJhIz5497eZ74w3bjhSef/55VSr0H3/8UXUf/YUuGBph/6S7gbJEUNBFAAAY/UlEQVQ+Wy8lJiaavZrvqzabB94uR/uut3Ps2DGLfBkZGWbfsrZQ4xStY8eOVmn6zreOY66uNHx8xP+dtF7HPPZOEz5Ne5q64aHmtMaNG3PDDTfYVHqUdICwb98+AL799lu77f7yyy+udtmn6IKhFcr2J7WM98wzivPEiN6ENbW98RZ6UwgTV91D/0mRXL582Zz+0EMPATBpkmEN9Pnnn1OxYkWLsi1btgTggQceAODcuXNW9bdv397Kw4gW0QVDI6y8Ck+uPuGTtto+5zwGRqeBjfgg+WFatKtvMb2bOnUqP/74o9UhJdPhpeJUqVKFWbNmWU0PTS48tYwuGBrhyZl7OJjlfe9ias7UB5cP4tmPW/PCx3fz4osvmtM7duxIUJClcNk7vDRmzBg2bdpkkVatWjWWLl2qote+RxcMG/hjw/Di+DZcHG/fPGParNcRQjB0ZlvmpPRlTkpfnp/4uOp2XIlF3qx9LRo8lUnLzrVtapbeeusth+VtmbYrORa7a9cu5Z30MLpgaIzs7GyeGNGbyJhbmLDiXrMQVLo3mfeT+1hEPtqQYH+Ra4v+k9xz7Dx8djuSb5hJrwFdLNJfeeUVsrOz2bt3r3WZ4cMJCbFt6/XVV185bM+0qegP9J1vP5CYmMi7C17hUvAp2vWtZ+Wev+1zQbQl2mk9g2a0VtxmXl4enQY2Ut1XW3R75QbmpPRl5hM/krXP4C6sQYMGNGjQgN27dyOlpG3btuTm5tr0PWXikUcesftsxIgR/PXXX6oCx3gSfcTwMvHx8QghePLNaPPbf1+12XQbV8sQ1ciNmBVqyoaGhnIy57zLbdli7Bcdmb69J82ir3kWvPPOO2nbti0zZsxQNCX98kvbLkmLr2n8gdCCAV54eLgs7p8oNjY24AwD8/LymPbRRJYuXklsXDOatffNOWe14bV6Pd6JbuNreLwfu9f/SZ/mL5vVuu4QHh7OoUOHAM+FGjPZgwkhkqSUTm3tdcFwgyffjObOWMeu7L1N4Q8xxMXFqS53S7MajPvKxUj0DrDl5tMZy5cvZ+BAg5elqlWrkp+fb36mRovmzFGfGsFQ6tQ5WwixTwiRLITYY0yrLoT4TghxyPizmjFdCCHeN4YBSBVCKJ8IBxj+FgowqENd4c8/TvFk/YXsXu84eIta/jOvA2MWx6gyGhw4cCA7duxgxIgRFkIB1zxTKvl4EjVrjHuklFHFpG0isEVKGQ5sMd4DdAfCjZ/hgIJIzzqu8sQbrr93QkND+fzVJKY/7FnjvkZR1en9bmXa92nAd9995zS/EIIOHTrY3CT0F+4svou7+y8ZBmCJNLATg49b/7jY8zIpW445z+RlPDFq5R76h5GRawlOiqEg/7LzAgp57PVW/FFrLq99bRg9MjMzWbNmDYmJiVy5csWcT4vTZqXqWglsFkJI4GOjp/KbpJTHjc//Am4yXpvDABgxhQg4joZJ2HuUdzYdVHVy7tMXd7u0YaZVnns6DogjokNDs+WtJ6hVvzJzUvq6HAfcHygdMe6WUrbGME36jxDCYtVmdNqsSuyFEMOFEHuEEHtKzit9TcLeoy75c5o6c4pP+mePgvzLbF+eSXJyskfrTd2RxcjIteQe9Oz/ZdjcKGbu7s20Wa97tF5voEgwpJRHjT9PAmsx+Kw9YZoiGX+aVBHmMABGiocIKF7nfCllGyllG3+7WHln00Eu2PHn5IiJYyZ7s1sWnMw5z9LJyWx48y8i8uI4tTaSpA8qIQ+Fk5PjHS/p0wdsZdc8dTH7nBFcPohK9yYzdGZbl50rqEEIYdZsqdFwKQkcUwkoI6U8Z7zuCvyPa+7+p2EdBuAFIcRyoC2QX2zKpUns+W3ypj8nR6RsOcanL+5m4hsjefuV2YZ/aCS80ftanpL2R4MHD6ZVq1aMHj3ao3354qOvAcOXypPTxtvuvonQ0FDnGT3A+vXrzddKPS8qWWPcBKw1SltZYKmU8lshxK/ACiHEM0AOYIox9Q3QA8gACoAh1lVqizqhIRy1IQRK/DnlnbhA6E2u+X0qyL/MTyuzObwrj6XzNxIVZZzXR8KCserqWrRoEQDBwcEWC1tPYVog958U6RHTkn82N7QZGlkrOBUMo7t/K+szKeUpoIuNdAn8xyO98xHjujVl0pp9FtMppf6cVr11gKHv27drOplznh2rsilTUJWJz03jvvvus3g+wcEeW1paGnl5eezbt4+DBw8SHx/vtD9Xrlzh2LFj7Nixwyt+Y1dOTSEvL49h02NcFpB3H91OzgHFQbb8giaMCDMyMrzqXNgZJu2TWq0UQIf2HTEMjPDHLyeZO+JnHn+hB5/MWGWwKo0EejuswkxWVhbly5fnzz//JDo62uye5u6772bnzp2Kf586depw9OhRTp06RY0anjf/CA0NZeXUFJauXsiRG1dRsaq6mB5aFwrQiElImzZtpCkgjGmBpIV+qWH+/Pk0adLE7ErUHmlpafzzzz8kJiYSHBzM+PHjCQ4OBuDYsWPUqVPHZrl///3XoTv9kixcuJBt27aZp1jeJOKuRgz7SJlJ+6Z3TrLhC995JhRClFxjKDIJ0cSIUZxAEwgw+NXdunUrZcrYVvK9/vrrvPzyy5w9e9Y8CpgE6MKFC2bBqFmzJoWFhVan40CdRiUpKYkhQ4YwZMgQBg0axOLFi50XcoPUnzMBiOgcxrDZd9jNdzLnvE+Fwh10s3M3GTZsGImJiXaFAqBGjRoEBwfbnNZ88skn5uty5cqRkpJis46KFSsqciKwY8cOs6dxgMhI9w4nqSE1MZc/llax+zy6KHCWnpobMQKJFi1aKPqy3nTTTXaflZw6HT58mNatre2fioqK+Prrr4mIiLBbV0ZGhpWf2LFjx7J582a6du3qtJ+e4MPpBj9T9ZrUYPzKa5qFzQvSWT/HP1YCvXr1Ul2mVI4Yrphhq6WgoICkpCSHI4WJwkL7m2SVK1seNjKNGJcuXWLBggV88803bNq0iYsXL/Lqq6/arSc7O5vGjRvbfObsTLY3OJJ+ipGRa1kXvx+A9XP2+7wPcG1qrmYqai7o7090dLT0JLNmzfJofbbo3r274rwFBQVWaUVFRfLUqVPyvffec6sfGRkZTvNMmjTJrTZKA4avupTAHqngO1kqp1LFRwxXjAOV8O677yrOGxISwieffMJtt91GamoqgwcPJiQkhOrVqzN2rMqdvGIo9SBuCsQyc3dvfvj8MM90eUuzUamys7PJzs72SF3ONISO0Jy61l1CQ0PNNjgm48CSG3dT+7V0SzhiY2OZPXs2DRo0UDSV8gYnT55UHCb4119/pVWrVny033LD751HtpO687DPTDOUsGbNGvr16+fxer1ygi+QKG6Y5qpxoDMyMjJo1KgRUkpyc3N9HuZs06ZNqmJnnzx50kKXb2LcV534PGcIT74ZzbT3X7NR8vqlVAlGyemBt4wDTSrQoKAgwsLCaNKkCRcuXCAzM9PKW7gnycrK4vjx43Tr1k1VudTUVBZttW8JfGfsLVSKSWNOSl869LvV42bsgUipEYyoqCgSEhIs0uwZAbob7LF9+/ZWaSEhITRq1Ig6deqQl5fnccfF586do169etSubXkYcteuXXz88ccOY08sWLCA+4beqqidgZMjeOurQW71tTRQKhbf9kJluWMc6Ihy5RzbBoWGhprn7ZcuXeL48eOUKVPGHJJZDZcvX+bnn3+2uZA8ffo0bdu2pW3btua0n3/+mby8PHr06HGtjjJnVLX5yYRtqvtZ2gj4ESM0NNSuGUmfVnWZ2q8ldUNDEEDd0BC3F94AV69eVZy3fPnyNGjQgFtuuYWzZ8+yb98+ioqKFJc/efKkXe1Kyci1AHfddRdHjlw7WTxhwgRe+NR6hLNH3okLmlqM+4uAHjGKa6Ds0adVXY9HPV22bBkjR45UXe6GG26gZcuWFBYWkpuby4ULFwgPD7eZt6ioiMzMTLubdgC33XabzfRnn33WfF1QUECYCuvXlVPSec03m+SaJiBHjOzsbKZMmeKTo5HF6T8pkpffHM3EiRMd7mab2L59u81006I9PDycgoICMjMzuXDBoBBYunQpp04ZzLIdCcWhQ4eoWbOmVXrJsGDb9q1x2s/imAwCr3cCSjCSk5Pp3LkzDRo0YMqUKT5t+43ND9BpYCNqx/5JRESEedPMHj/++COdOnVyquGpWLEijRo1IiQkxByQ5fvvv3e6P2IrWhHA1q1bzdcPPPAAw2YrPyan1oNgaUZzglHSpmXRokUIIcjLyyMqKorExESf92lOSl+L46trTo6yO+8/d+4cBw8eNAdiVHPMtG3btkyePJkBAwY4zWvPCrf4/katW1Q6jM5Sprm6HtCcYBh3J833gwcPRkrplwVhXl4eM3dbH78LLh9Enym3WC36Dx8+zLlz52ja9JrW64477uDw4cOK2itXrhwTJ05UZPA2ZMgQsrKyWLx4MTt2GM44/P3339x1113mvlyocUhRuyZMlrGlhYS9R+kwbSsNJ26k7nOfOXWHVBxNLL7z8/PZsGGDv7thQf3bavDSsk4El7cdr65RVHXq316d7LRT5mnP/v376d3bWpBuvfVWUlJSaNq0KRUqVHDYbkhICLm5uYpC+jZs2JCGDRua700HnsBwOKrTi8rPZI+KSmCk/62DPEZJc6CyVWsxac0+yoTcUF1Jec2NGCZM/oAcfbzFk29G89Iy557Axy3rTJPW185T9O7d265xYWRkJF988YWi9p2tX+xRfFT9/PPPVZXVgs0coOpsuyPsmQMFVa6uSEWpWcEA5ybxniYvL483Nj+gyh/s6MXtadPxmtr0pZdeYuPGjVb59u3bxxNPPKGoTkfTxpycHFauXMn58/aDwAwfPpz3dil3LuFpj4NawJ7Zjwgqq0h3rVnB8Mcb7JWpo1zyETXog6b873/XAsr37NnTIkb20qVLCQ8PdzqNMmFP4wRw4sQJ+vfvT6VKlcjNzeX1163dXVapUsXuFNAWvyzWvtcOtdgz+5GFVxV5rVYaHyNUCLFKCPGHEOJ3IUT70hgf48PpS5jR3/begzMqP7CHFStWmO/XrjVE93nllVd45JFHLIRCSunwy29ymGAL08JeCEFYWBiTJ0/mn3/+scizMyvBVlG7bN9oW8MVyIzr1pSQYMuXQ0hwEIXnTytagSsdMWYD30opm2HwlPQ7pTQ+xpH0Uyx/Xb0BYHD5IH658CFnzhjskh555BEWLlzIW2+9ZeX1Y9KkSfz9t/2Y3iEhIVy8eNEqPSkpCVt+fjdv3my+/ueffxg42f658OuFkuZAV/NPMrVfS4ounD2tpLzTg0pCiKpAMtBIFssshDgIdJZSHjc6dU6UUjYVQnxsvF5WMp+9NrQYasyklVJLQf5l2haNs7vPkZ2dTYMGDQD4/fffad68ue16CgqoWLGiRdrRo0epW9dy7Xj16lWCgoLMyoi4uDhuHaLcyfOoqAS//62LM378eGbMmOHxer1xUKkh8DewUAixVwixwOjcWW18jJId1UwYAFvkHDjF9uXqzSMqVi3Hpz+9aPVl+/fff7ly5YpZKACaN2/O999/b/OL+fPPP1vcnzp1ykooAA4cOGChoVu9Ud1ehGmE07FEiWCUBVoDH0kpWwH/cm3aBLgWH0M6CQNgTx3rTTVtSVZOTSHvhPpDTXfG3sKDAy3PYi9fvtxin8HEfffdx7x586zSSwaN37lzJwUFBVb5du3aZb4eP348Y768W3E/dUta+ygRjFwgV0pp+g+swiAobsXHcIa94d3Xw/5rXb91qdz9L1ex2KQbMmQIc+fOtZl3xIgRVqbod9xh6dGvZ8+eVKxYkdmzZ/Pbb78BcP78eYYNG2bOI6VUpY1aOcW3R3LdofgudodpW1XtYruCU8GQUv4FHBFCmOwcugAHuBYfA6zjYzxl1E61IwDiYzhjVJQ6LY+JsWtam436ypQpY3NX3MT+/ZZ+l+y9AEaPHk3r1q1JSUmx8ku76delqvoXKJa0rka8cgelWqmRwJdCiFQgCngbQ8CY+4UQh4D7jPdgiI+RiSE+xifA8650zJdTJmdIKc2Ow9QQXD6I1emvmVWzYWFhzJ492yLPsWPHuHDhAi1btrRIL1eunE3NlInIyEheeOEF833Xrl1LrSWtt5xaOEJpqLFk43ogQkrZR0p5Rkp5SkrZRUoZLqW8T0p52phXSin/I6W8VUrZUkrpGb84fub7hemczLG/22yPZu1r0SG2qdnMY/To0UyaNAkw7IbXrFnTaj0BhhdDyf0JRzSJqOc8UzFuOO254JPexh8RrzS7861F3uj9nUuaqmGz2zLmw2u74VOnTmX58uW0bNnS4fnxAwcOKKr/vffeo1on5YHsr1wqZOrLHyrO72+85dTCEZoVDC3p1ovjqqaq08BGNI685tx54MCBTstUq1ZNUd3nzp2jVn3lZy9ebGvtY8qbuLtwtreL7a5TC0doVjC0jKuaqnHLO1tokZzhyDTERMuWLW3aSznCly8dTyycveXUwhGaOI8RiNTLGsSRhuoDsjR+8hgnTpxwGBrAhDNvJAMGDCAxMZGluUMVt597MN9GREXv4WjhrOaL7Q2nFo7Q7IihJa2ULfr06ePyzvhLn/RQ5Li4ShX7QVgWLVrEihUr2Ldvn6r29399SVV+d9FaqGilaFYwtLrGKI47O+MxPVrTvXt3l9rdtm0bgwcPBuCV959RVdbXob78sXD2BJoVDK2PGCZe6/otu9cr1wiZeGlZDJWjjvHFF18wdOhQu9Mmk8o2NzeX999/n5ycHGJiYszPd6w5zM75l8lMdm40+u6jrpnUu4M/Fs6eQLNrjEAYMUx8/moSb2x+QPUhp04DG/HKo6PJOXCK3377jfvuu4/w8HA6derEoUOH6NWrF+vWraNSpUosXryYUaNG2aznyw+vnRgMC6/JhFUdbebb9k2Sqv55AndCRfsTzQpGoDHyzuV8njNEdbl+Ewyap9atW3P6tPVbf+hQ5QtrgNxDhhHm5TdHc4SfLI7pFrfs9SVqF85r1jh2EqdEWwfQpEkTxW2WRBcMDxEaGkqNLb051fhrxWWWv57KjjXKXOuo5e1Xr5medOoZScvYyj7VRrmKN85iuIIuGB7ksYeG0KlnPP3fdu62Zkb/7RxJ981Z69J4dNXb6ItvD7N9Y4rTRe4fS6v4TCh0DKhds2pWMAKZnAOnSNliHVnpyqVCRkauLXUe/7SMqy9YfSrlJRaM3cXM3b3NB4f++OUkHz7n2z0EHQOueLnU7IgRSOpaezzdxDAyLIjbrQtFgKFZwSgNhIaGUi9rECk/ePcYpo7n0QXDy2g10LyOYzS5xli/fr3PvJ/36tXLJ+14GtOisnj87kD9XUx4439uK765EvQRQ0fHBrpg4J09k+J1+nJPJhB/Fy3uWemCgXc0YMXr9KWGLRB/Fy1qIDW5xoiNVR7bwRN46x9jehN68x9f8m8VqL+LN0cNV9YZTp06+wIhxDnAe06CnFMTUO6rpvS0fT22X19KeaOzTFoZMQ4q8UDtLYQQe/zVvj/b1tu3j77G0NGxgS4YOjo20IpgzL+O27+ef3cttG8TTSy+dXS0hlZGDB0dTeF3wRBCPCCEOGiM8jrReQmX2vhMCHFSCJFWLM0nUWeFEPWEED8IIQ4IIfYLIUb7qn0hRAUhxG4hRIqx7deN6Q2FELuMbXwlhChnTC9vvM8wPm/gzu9erB9BxjB1G/zRvks4CzLvzQ8QBBwGGgHlgBTgNi+00wlDFKi0YmkzgInG64nAdON1D+D/AAG0A3a52XZtoLXxugqQDtzmi/aNdVQ2XgcDu4x1rgAGGtPnASOM188D84zXA4GvPPT3HwssBTYY733avkt99lfDxl++PbCp2P0kYJKX2mpQQjAOArWN17Ux7KUAfAw8aiufh/qxDrjf1+0DFYHfgLYYNtTKlvwfAJuA9sbrssZ8ws12wzCEu74X2GAUVp+17+rH31MpRRFevYRbUWddwTg1aIXhze2T9o3TmGQMMRK/wzBC50kpTa4Pi9dvbtv4PB+o4WrbRuKB8YApyGANH7fvEv4WDE0gDa8or6rnhBCVgdVAnJTyrK/al1IWSimjMLy57wSaeaMdWwghegEnpZS+d4HoJv4WDI9EeHURr0adLY4QIhiDUHwppTS52fNZ+wBSyjzgBwxTl1AhhMkcqHj95raNz6sC7vj56QD0FkJkA8sxTKdm+7B9l/G3YPwKhBu1FOUwLLiUu/JzD59EnRUGs9FPgd+llDN92b4Q4kYhRKjxOgTD2uZ3DALysJ22TX16GNhqHM1cQko5SUoZJqVsgOF/u1VK+biv2ncLfyxsSizOemDQ1BwGXvFSG8uA48AVDHPaZzDMXbcAh4DvgerGvAL40NiffUAbN9u+G8M0KRVINn56+KJ9IALYa2w7DfivMb0RsBtDZN2VQHljegXjfYbxeSMP/g86c00r5fP21X70nW8dHRv4eyqlo6NJdMHQ0bGBLhg6OjbQBUNHxwa6YOjo2EAXDB0dG+iCoaNjA10wdHRs8P9AIE8v5BMVNQAAAABJRU5ErkJggg==\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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46FBctmxZyMKMg0F9fT333ntvq6nYxcXFHDt2jKVLl7Jhwwbi4+P9XhdRORAVgeG8LRPRjB+V8JfFf2X0lA84tPz/hSwa0R16vZ477rijSwsB2OYxtF1WfcaMGZw+fZqkpCSOHDkSFgukKjFQADB8+1hgCMO3j+Vn28dy2Ut7mH3TfzN8V7xmNlksFubNm6fZ9QPJ0qVLW4Ulx8XFMX78eKZOnco777yjoWUXUT4DBQAfzejL8K37+GjG4Bal59HyK1JWVsayZcs0u34g0el0/PnPf2bChAns3r2bp556ypnkpaqqSlvj7KiWgQKAxq3/3kYI4Jt3JsEPH2lkEXz88ceMGdP5al1dhT179lBQUMBzzz3XKttTUVFRWCRyVWKgACAyf2aLd98D8NPfx3Pgubu1MQjYuXNntxKDlStXsn//fkpKSlqV6/V6jEZjUFPKe4LqJigAOPdnR9/8HwhxFVJ+ybldsYirX0E+Fnp7ioqKuOeee5xDb92B/Px8ALfJUywWC4mJiRgMBs1aCUoMFICtmzAofgqN1Tsh8TEOPTsc05Hv6b/oVU3s+eGHH7j55ps1uXawGDp0KO+99167+4uKijAYDCQmJgY1aWx7qG6CArB1E2YP2k/kb24nd90SYj55iafGv8fWpXdqYk9NTQ3XXnutJtcOFpGRkTQ2tj/jMDIykqKiItLS0kJnVAtUy0ABwOu7Mtn20VxeuPlKftv7EmZsSmEY2n1BrrjiCrryQrnt8c0333S4Pzk5WZNWAagIRIXffN/Bvl4+n7W7/u89/VxCCK+zNfsbgahaBgq/+Hzrz9vdN+DuD0JoSfdizZo1JCcnt0r3HmyUGChs2MORW3HZjE4PG/iWPYfhO2/B569C6sVhM6ndqGSXJzMzk6qqKtLS0kI2SUuJgQKAuz+Y7Hx9+uvv+LLkAT56uXMxkJtS4If3aXzzQyJ+PYFPxi7l5gffBgb4Zc+sWbOor6/v8vMSWtLZkm5tKSsro6qqCpPJFJK5C0oMFABs/dlVLd5dxb9PLIGXPTnyPKX3/zu/fj2V23c/wYgpT/Ehk7j5wbfwZ8Zj7969fT42XGloaOChhx7y6hhH7EEoApLU0KICgPPv3d1qq+KXHh339/dSueuvT/PEibW8fccg5h5ZzYiFY2wzHv3gyiuv5PTp036dIxz5/vuOHK7tk5mZGWBLXFEtAwUAVyVcdDb/9EITMw54NlswaeyHbP12MzOusv2uvHhrbziymphfPIJ83Hd7hg0bxhdffOH7CcKQY8eOMXLkSK+PKysrw2AwkJyc7DIVOpColoECgO++u9+5NZ57hOqxN3h03N4L/8ldlt9zcbm0f/Dirb2Rp9znGfSUsWPHtrtWYFdl48aNjBs3zuvjIiMjsVgsFBUVBVUMVMtAAcChZ4e3ev95otGj4/ZlT+L1F27lBdMGrho+j1P/8zsG/r/VftuzaNEi+vbti9ls1ix1WqA5ceKEX47AtWvXBtWRqFoGCgDiL9nZantxs2ezk37x/Of8ZfX9rNr2OQBrs/P9tqWqqoqHHnqIu+66i48//tjv84ULU6ZM8et4vV5PWVlZ0JyJqmWgAOC7bN+mCifmreQ/7/yU3wxeCnNhZfXNrPDRBqPRSHl5OeXl5URERGC1WklISHCbdKSrYbVaefxxP5wodhwTmRzzGAKJahko/CJmy5M8Nng/VSQye9B+bl3+B5/Ok56ejtlspqamxtkt0Ol0bjMrdUX+/Oc/ByySsKysjLKystCLgRDiVSHEl0KIYy3K+goh3hFCfGr/28deLoQQeUKI40KIo0KIUQG1VhFE2uRL+OF9j47afL8J64EKZhyowHqggq1LvF8z0WQyMWTIEPLz89HpWicI6Q6tAoAXXnghYOdydBcCPdzoScugCJjUpiwH2CulvAnYa38PMBm4yb7NBXz7mVCEnL+/lwo4shh9Q8lvPIszODHyKV7nP5zbsPe9i0GuqqoiNTWVxx9/3EUIwLYGQE1NjVfnDDdqamo4f/58QPv6iYmJAZ/q3KkYSCn/Czjbpng6sMn+ehOQ3KK82J4h+gCgF0L4F5eqCAm7hhcyPPcFJoox/HTYDUzf8qVHx5WP3sp2+/bKzW+wuLja42uOGjWKG2+8kaNHj3ZY709/+lOrrERdiZqaGlauXEllZSXV1dXcddddATv32rVr0ev1AVtQ1VefwbVSys/tr/8GOFahGAS0TNxntpcpwpyR/zmamwqv4Z+Vb/OPn7/CJWajR8fNuOy0c/tt7zO8sO7zTo+xWCw8/PDD7Nmzx6Nhwzlz5vDkk096HduvNVarlZUrV2I0GgGbX6RPnz7O94EgkH4Dv0cTpJRSCOH1xHMhxFxsXQmuu+46f81Q+Mm4pk28WB/DjKsu4egv7iL9yj1s/K7z49rGJ5DYcYzByZMnycrKQq/Xe7y+4dChQ0lNTWXNmjUB8ciHim3btjF06FBnbIBOpyM/P5+77w7cdM6ALoQipex0AyKBYy3e1wED7K8HAHX21y8Ds9zV62gbPXq0dIfNPEU4812L7ZSUsnDmj9ut+8Ybb8hly5Y538fExMjq6mqPr1VbWytjYmJ8tjWUNDc3d/jZRowYIWtrawN6TUBWVFS4bMAh6cFz7ms3oRyYbX89G/hTi/JU+6jCWOBrebE7oeiG9Dq/zbkNOL+NjDd+0m7dZ555hgceeMD5vrS0lEWLFlFfX+/RtQwGA/Pnz+8S3YWFCxcSFxfX7v5Vq1aRnZ2NxWIJoVUd02k3QQixBUgE+gkhzEAu8CywVQiRDvwf4Gj37ASmAMeBZuD+INisCBqngT5403tccdWaVu/7Ly7qsH7LrkFERARr1qxxSUraEampqWRnZ7Niha+hTcGnoKCg0zoTJkygvr6ejIwMCgsLQ7aaUUd4MpowS0o5QEp5uZQyQkq5QUp5Rkp5u5TyJinlL6SUZ+11pZTyISnlECnlCCml68KGirDl0LPD2wwxenDM/t0c2l9i3/7EvufajwuIj493GSaMi4sjJyfHq1/7pqYmjx44LSgoKODIkSPk5eV1Wnf+/Pn07t07LLIpAZ75DIK9KZ9BeADIrd9ekP2fOSC/PTBLStnQ6TH/WzFLXpa1VwKS1BL5vxWzOqxfXFwsp06dKpubm1uVz5kzR+bn53tsa3Nzs5wzZ47LebSiublZ5ufn+2RPQ0ODLC8v99sGNPIZKLobPqZkXz53C39dMR6AY5tSWD634/r33Xcfo0ePZsGCBa3K8/Ly2LNnj8fxBDqdjry8PBYuXIjZ7HlLJhicPXuWhQsXcuTIEbeBU+1htVrZvXs3S5cu5YknntA8vZoSAwUAK666z/l33VX3cc03fyfmiV48l5jR4XHFn19c7XC4/X1nGI1GmpqaWpXpdDoKCgp48sknPX4odDod48aNIyUlRbMHyWQyMWnSJMaNG0dhYaHHxzkE5NChQzz00ENMmjSJ7OzsIFrqAZ40H4K9qW6C9ty69a8Shshbt/7VZesIErNtf0HKxlx52X0FHl2vqalJlpSUuJT7MnxYV1cnY2JiWg1bhoLm5mYZExPj0xBhTEyM3LRpU6uy6dOny8OHD/tsD352E9QU5p7OD3vhkmG2dOzyuNeHn9v3HAAzDnyLkR9xrtizr5Rer+eSSy5h1KhRzinLYBs+PHjwIBkZGeTl5XnU7B46dKhzNCI7O5v6+nqysrI6HNrzh+TkZPr3709WVpbHoyAOiouLKSgocDnOarVy6tQp+vTpE0hTvUKJQQ/nD9GHGHNnNjFGHxOevHc354HXARrgfANc9rOtHh2anJzMpZdeSnJyMu4yam3YsIH58+d7Zc6KFSuor6/n6aef5rPPPmP8+PEBWSnp7Nmz7Nixg4KCAtasWeOT0CxfvpwTJ07w9ttvu+zbsGEDd911F5GRkX7b6ivKZ9DD6VeTxcrJ++3vTuMylbkTrkoY49yujx/CVYu8+0olJSUxdepUl6HFvLw8jhw5QmlpqVfnczjlvvvuO/7yl78wadIk0tPTKS4u9uo8DsxmMxkZGUyaNIlvvvmGzZs3+yQEZ8+exWKxkJeX5xKGbbVa2b9/P/PmebYIbbBQuRYVToQQ9O+/gp2Z6xjz+Kf40nAU4m6k9Kxl0JIZM2aQlJTksphJfX09V155pUe/7mazmfXr17c7EchisWAymTh27BhSSurq6gDbg+p4QK1WKwaDgREjRhAZGcnTTz/Nhg2+L+5aU1PDokWL3LYm6uvrmTlzJg888ABz5871+RoO/M21qLnzUCoHYtiAD3EGbfntJ1afrl1bWytHjBjhNp4/JiZGnjlzpsPjq6urZXR0tE/X7oimpiYXR5+nlJSUyJiYGFlXV+eyr7q6WgIBiS9wgIozUAQEH+MM2vLizT/y6fIGg4G8vDzi4+Nd4gZSU1NZsmRJu1GKpaWlPPjggzz11FM+Xbsj9Ho9q1at8nqBFavVyq5duygtLXWbIm7evHnU1taSlJQUKFP9RomBAvA9ziCQJCYmUl5ezrRp01qVz58/nz59+rBmzRqXYwoKCnjxxRcpLi4O7HTeFrQnUu1htVpZuHAheXl5Lt0bq9XK8uXL2bFjR0jyJ3qFJ82HYG+qmxAedBZTEErcxQw0NDQ4YxAaGhrk1KlTQ2rT6tWrZXp6eod1YmJiZGVlpdt90dHRcvXq1cEwTUqpugmKQPHDXj6K2+DMtcgpoy0GgX9oYk5tba3LZKSIiAhSU1MxmUxMmzYtoIuEeMKiRYs6TAi7e/duXnnlFbeLuNbU1LB06VIWLVoUTBP9QomBAoD141KYPehv3Dy2L1cljGH2oL+xflwKp2q8G+cPFCtXruSll15y6av/5Cc/ca56dN9994Xcrt/97nduhykLCgpYt26d26Z/cXExDz74YFj5B9wRtkOLQohW78PBzu7Mab6nP71cyq4RP9L03u/fv5+33nqL2267jTfffJNevXqxbds2zewB23yE6Oho6urqOHDgAGaz2e1ybHPmzEFKyfPPPx+S9Qr8HVoM6whEx5ewrTAoAs+53SkwPg0uG2IrOP8Z594tArTNW5CQkMA///lPTp06xZo1axgyZIim9oBt5KO8vJyZM2dy2WWXuY0odOSIDOTip8EmbMTA3QPfsqzla9VKCDy//uVIki7N5cY7GwE4vn04u74fSeKWwCbq8IU77rhDaxNcSEpKoq6uji+//NIlorCmpoYFCxZw+PBhjazzjbARA1APuZbM+moZ2957lC+/to3lD8/sQ8LgH/HQwLD6ioQVixcvJjY2FqvV6pxQVVpaitFo9Gilo3AjrP7TnnYHlGgEnt/2voTfTuintRldjoMHD/LRRx/xhz/8gbNnz7Ju3TpSUlK0NssnwkoM3Dk/FIpwZ/jw4WG7JqM3hJUYKDTkvBsP/WUzQm+HQjOUGASIkydPUltby/Hjx5k9ezbXXHON1iZ5xd0fTHa+Pv31d3xZ8gAfvazEoCehgo4CgCMirrGxkSFDhvDLX/7SbRx9OLP1Z1c5t30T+nHNKyVam6QIMaplEACio6Oprq52zlePjY1l8ODB3HjjjWEfdebg/HutQ3ur8Cwlu6L7oMTAT1atWkVDQ0Or2WkRERHExMQwbdq0LjPycVXCxQC1n15oYsYBbVfdUYQeJQY+4MgkfMMNN7B06dJWi3Y6pq86Vra5/fbbKSkpCYv0WVFRUcTGxmIymfj1r3/N4sWLnfu++651JryB4hroIkKmCAxKDHxg6tSpPPDAA24X61y4cCFRUVHOLsOdd95Jeno6JSXa9cEtFgu5ubm89dZbREREOBfeOHnypHMBzrap1T9PNPp0rbLaRlZW1nHKYmWgXkfWxGEkRw/y8xMoQoESAy+pqKjg5ZdfdrsoZn19PZMnT24VdDJ//nxMJhNGo1GTOHVHK+b//u//nF0ZnU5HSkoKM2bMcCb9jL9kZ6vjXtw80utrldU28ljph1jPXQCg0WLlsdIPAZQgdAGUGHjBK6+8wksvveQ25rympob4+Hi3PoL8/PyQz7134GjFDBw40GVfdHQ0ubm5vPDCC3yd3XpSWy+X2p2zsrLOKQQOrOcusLKyTolBF0ANLXqAxWIhKyuL++67z0UIrFYrGRkZ3HIdbHyAAAAc+UlEQVTLLe06C3U6HRUVFYwcOTJkacAKCgoYNWoUx44dY/78+aSkpLik73r88cfJzc1l1KhR9IJWmy+csrhfo7C9ckV40akYCCFeFUJ8KYQ41qLMKIRoFEKY7NuUFvseE0IcF0LUCSEmBsvwUGEymYiPj2flypUu2X0c+fLcrYXvjlWrVnHvvfdisViCZS4WiwWj0YjJZKK8vLzVPnepzPv27Ut5eTlVVVV+X3ug3n32o/bKFeGFJy2DImCSm/I1UkqDfdsJIIS4FZiJLQfnJGCdEOLSQBkbaioqKkhNTWX16tUu+8xmszPhpqeZdydMmMADDzxARkZG0AQhIyMDs9lMfn6+y2Kczz33HMXFxS6rB0VERLBw4UK/Wy1ZE4ehu7z1v1t3+aVkTRzm13kVoaFTn4GU8r+EEJEenm868IaU8nvghBDiOBAL/I/PFmrEtGnTWLduHUePHnXZFxsbS2lpqdd59sDmUExPT2fBggWsX78esLU+LBYL9fX19Op1sZH+/fffc+rUKRITE0lMTOzwvPv372fBggVu7XXQt29fDh48SE1NDbGxsa3sP3r0KAUFBeTn5/ucNMThF1CjCV0Tf3wG84UQR+3dCEe2yEFAQ4s6ZntZl8FisTBjxgz++Mc/trvMdWlpqd/5+2bMmEFycjJCCJ588kl27txJv379uPrqq51bv3796NevHzt37kQIwYMPPug2zPm1115j0aJFHqcQi4uLIycnxyUPQXp6OkIIv0Y9kqMHUZMznhPP/pKanPFKCLoQvo4m/AF4GpD2v88D/+HNCYQQc4G5ANddd52PZgQWk8nEo48+yp133unS9HcEE/Xp08dnIaipqWH//v088cQTzJkzhxUrVrBlyxaPuhlLly6loaGBAwcOIIRg2bJl3HrrrSQnJ7Nt27ZWmYw9ISUlhYyMDAoLC51lOp2O/Px8xo0b16WW61IEBp9aBlLKL6SUF6SUPwCF2LoCAI3A4BZVI+xl7s7xipRyjJRyTP/+/X0xI+Dk5OSwZMkSt8FECQkJREVFsWLFCq/Pa7VaKS0tpaioiDFjxnDmzBkKCwsZOnSox/4GnU7H0KFDSU1N5cyZM4wZM4YNGzYQFxdHUVGRzwLV1qGo0+koKyujrKzMp/Mpui4+iYEQYkCLt3cCjpGGcmCmEKKXEOIG4CbA+461RjzyyCMua96XlpY6+9fepgevqakhOzubM2fOkJKSQmFhIRMmTPBo5KEj+vbty4QJE6ioqKCmpoavvvqK7OxsrzMKFRYWkpycTGxsbKvyiIgIfvjhB26//fagjnwowotOuwlCiC1AItBPCGEGcoFEIYQBWzfhJDAPQEr5kRBiK/AxttzeD0kpL7g7bzhSX1/vFAOr1cqGDRvYs2ePT2nBc3Nzqa+vD8kv7NChQ1mxYgUmk4nly5ezaNEij1scERERvPLKK87VfB2kpKRw6tQpzUOpFaGj05aBlHKWlHKAlPJyKWWElHKDlPI+KeUIKeVIKeU0KeXnLeovk1IOkVIOk1LuCq75gWXBggUUFBQ44weOHDnCli1bvGqCm0wmEhISuO2229iyxbukpf7iSOCRkJBAfX29V8fNnz/frUOxT58+7Ryl6G6ETRKVDz74ICym+xqNRsxms3PYzxsKCgpITk72e6QhEJjNZlJSUrwa/szIyCAqKsqlOzRq1CjWrFlDQkJCoM1UBBB/k6iocOQ2GI1Gn0SpoKCAI0eOhIUQgK35//bbb3u1UGdeXp7bbtGrr77KggULQhZKrdCGsBIDIYRHW7B5/vnnPR6zh4tCEG5r5fft25cjR454LAg6nY6CggKeffbZVuUGg4FVq1ZpntZMEVzCRgzapoduWdZ2f7DR6/WsWrXKJWy3PRxC4KnTLpTk5eVRXFzM7t27Parf0qHYknHjxrF8+XI1utCNCRsxcEeoWgLuOHr0KGfPnmXUqFGcPXvWbR2z2UxsbCyFhYVhKQRg+7U/ePAg11xzTbufoy0Gg4GysjIyMjKcxxw+fJiFCxeGxYpNiuAQtmLgrnUQapKSkrjnnntcpv6Cbfhw6dKl5OTkaGCZ9xgMBpYsWeJx/fnz5zNu3DgmTZrE8uXLiY+PJy0tLXgGKjQnbMUgXFi8eDE//vGPXcp37dpFnz59ulwqLW9iJlJTU9m/fz9vvPEG5eXlREdHB9EyhdYoMfCAF154gdtuu83pQ3C0FHwJTdaSwsJCTp065ZFD0Wq1kpWVRWpqKkePHu0yS74rfEeJgYds3ryZ+Ph46uvrqaqqYvLkyZ0fFIakp6dTXFzcqf9gwYIF/PjHP241kUnRvVFi4CEGg4Hy8nJmzpxJTk5O2DoMO0On05GTk8Prr7/ebp36+npiY2MxGo3KYdiDUAuiekFSUhImk6nLtgocTJ48mSuvvNLtxKuOFnZVdG+UGHjJH//4R373u99pbYZf6HQ66urqWpUtX76c2tpaVq5cqY0QuMsC7S0qa7RfKDHwgrKyMsaOHau1GQFh6NChzpmKZ8+e5YsvvnDmUNCCVTf7v5T84uOqNeMPSgy8wGQyMWXKlM4rdhHeffddxo8fz7Rp09zmggglWZ/5f47FnVdRdIASAy/47//+b+bMmaO1GQFj586dzJ4922VJdS3o9dz77e77fkmMR/UU/qHEwEPKysowGAxhMysxEAwcOJB9+/Z1uvJyKPguu/0ZtmKJZ/UU/qGGFj3k7Nmz3cZf4MBgMHDs2LHOKyp6BEoMPKSmpoZrr71WazMCyr/8y7/Q1NSktRmKMEGJgYe899573HLLLVqbEVAiIyP54osvtDZDESYoMfCQjz76yO9VjcONfv368eKLL2pthiJMCAsxOHPmjNYm9Ei6m7gp/CMsxKChocHjVYUUCkVwCIuhRYPBwPXXX09NTQ1xcXFam9NjMJvN3HHHHVqbYed0AOqFR2aurkpYiAHY1t5LSUkhJycnLBcMGT58uEuika5Oc3MzQ4cO1doMAA49O9zvemNyvgyUOT2SsBEDsK3C48jk420qs2Azbtw4mpubtTYjoDQ3N7skTtGKmMc8axl0VE92jRXowpawEoOIiAgOHjyI1Wr1ar3/UBAdHc2BAwfC5pc0EFRXVzNixAitzQAgt/Gc1ib0eMJKDBzodDqOHDmitRmtuOaaa6iurtbajIDywQcfMHv2bK3NAMA4MCy/ij2KsBhNcIcjIcny5cvDoin717/+leeff15rMwJKUVFRWMxLUIQHYSsGjmXFTpw4wcKFCzW1xWKxcPToUaZMmeJVQtNwZ968eVqb4AYzfL2u82pfrwMCMO9Z4SRsxcBBYWEhhYWFZGRkhLyFYDKZuO2229i8eTPr16/n5ZdfdptDoasSTjkffmgsYuW4yxFiMEL/UKf1hf4hhLiRQ8sHww9Hg29gD6BTMRBCDBZC7BNCfCyE+EgI8bC9vK8Q4h0hxKf2v33s5UIIkSeEOC6EOCqEGBUIQ6Oioli4cGHIBKGiooLU1FRWr17tHNnoTsOKJpOJyMhIrc1wsnbxfLKrz9veJK7utP5lWXuBXxLzhJm92ZOAfwTVvp6AJy2D88CjUspbgbHAQ0KIW4EcYK+U8iZgr/09wGTgJvs2F/hDIAydP38+UVFRzJo1KxCn65Tc3FyKi4uZMGFCq/LU1NSQXD/YdLQ6shY8+obtYb7svhVsrXi40/p/XTGeGQc2A/CL5z/nVHV+UO3rCQhvF78UQvwJKLBviVLKz4UQA4AqKeUwIcTL9tdb7PXrHPXaO+eYMWPkoUOH3F3L7eKcer2eqqoqDAaDV7Z3hsViISMjg969e7N+/fp26xUUFIRdHIQ3FBQUoNPpuOKKK7jvvvu0NgfAmVPznJRuh7jenHgDov/nXH/bT1oFF7XMxdnTV3QWQlBRUeFSnpSU9IGUsvNVYdpmP+5oAyKBvwI/ASwtyoXjPbADiG+xby8wpqPzjh49WrrDZp4rGzdulL1795a1tbVu9/vK+PHjZX5+fqf1AHnmzJmAXjtUnDlzRsbExMgzZ87I6OhouX37dq1NklLa7ml7/28ppUzM2+y2jqOso2N7Ci3vRZvtkPTk+fakku06XAV8AKTY31va7G+SXogBti7EIeDQdddd1+6Ha49AC8L27dtlZWWlR3Xz8/PlsmXLAnLdUJOVlSVLSkqklFLW1tbKESNGBFxUfaGzB3pLXqISg05o7x4EVAyAy4FK4JEWZXXAAPvrAUCd/fXLwCx39drbvG0ZONi4cWO7dbYfNsufP7NXRi7ZIX/+zF65/bC53fMUFxfL6OjoDq/VkubmZhkTEyPr6uo8PiYcqK2tldOnT5fNzc3OssrKSnnHHXdoaJUNxwN9oWGrm71/l4ntPPSOssvuWxEaQ8MYf8XAk9EEAWwAPpFStnTzlgOO8LXZwJ9alKfaRxXGAl/LDvwF/pCWloaUEr1eT1FRkbO8rLaRx0o/pNFiRQKNFiuPlX5IWW1jq+Pfeecdpk2bxr/+67+Sn5/v8fClTqfj4MGDrF+/PuzCpjti586dlJWVtUoNN2HCBHbv3o3RaNTOMIABNsfs7YPvpnFrLLbZiaeBz5g98CqqgFuXbuay+wpIHygoufdHLBlo9xckPoapKEsbu4OMxWIhLS0Ng8GAEAKDwYDRaKSoqIiqqqpWdaXdZyKEcG5e0ZlaAPHY1PcoYLJvU4CfYusCfArsAfrKi/6DF7FFhHxIJ/4C6UfLwEFtba3s3bu33Lhxo5RSyp8/s1dev2SHy/bzZ/a2Om7SpEmtft3z8/PlnDlzPLqmlBdbCF2BysrKVi2CtiQlJXnkLwkWs2pOSZggATkA5PvP9JfvP9Nf7n30J/Zf/9vki8cs8nXz9637wwMekrkfWzSzO1isWbNGRkVFSUDm5ubKffv2+XwuAu0zCObmrxhIeVEQpJQy0o0QXL9kh4xcsqPVMe4e/Dlz5nj1UDQ0NMiGhgaP62tBdXV1p6LV0NAgo6OjZXl5eYisas05KeXtu82SAQ9JGNDGAfZLeesfjjrrXpa1VzJkpWTCBpnb8L0m9gaDpqYmuXHjRhkVFSWnT58uN27cKJuamvw+b48TAyltgjB79myPWwbR0dEuowLNzc1y+vTpXl03JiYmbAWhoaFBxsTEeOQkrKur09SheE5Kmdvwvez/zAEJ82zbgMfkjANN8ssW9U5JKbeek3LfBU3MDAoPP/yw7N27t4yKinK2cANFjxQDKW3qevOkVHlDVlkrIbj5yV0uTsSmpiY5fvx4p3e9Jd4+4J78+oaauro6WV1d7fVx+fn5AflFUrRPU1OTfPjhhyUgZ8+eHdRr9VgxkPKiIAx99I+djiY4htfaPjSVlZUyJiamw352W6qrq316+IJBdXW1z/evurpaLly4MMAWKVrSu3dvmZCQIE+cOBH0a/VoMZDSJghRUVEe/cJt377d7dCiw6HojSDExMTITZs2eWVrIGlubpb5+fk+dV0cApiVlSUBuXLlyiBZ2XM5ceKETEhICGmwV48XAwcJCQkei0J6errLg9/c3OzVCIODkpISCXglJP7Q3NwsN23a5LbL48mx7kRv2bJlAe+/9lSamppk7969/RoV8BVPxSDspzD7i2MOQ2JiIhaLpcO6Qgg2bNjQqkyn05Gbm+v1Uu4pKSk0NDQwa9asoC8Dv3v3bhISEjh27JjXi8mazWZmzZpFYWFhq/gDgIMHD4bVzMauislkwmAwcPLkyfBeTMYTxQj2FsyWgYPZs2fLqKioDus0NTXJX/3qVwFxKDpwRP1NmzYt4K2E5uZmWVJSIrOysnyKhqytrZUxMTFuP69jZEbhH9u3b5e9e/eWubm5mtmA6ia4Mnv27E6HzRwOxbY4Rgt8eaDr6urkrbfeKgGZn5/v99BddXW1XLZsmQT8miMRExPTrsMzOjpaVlVV+XxuxcVwea27Wp6KgddTmIOBt1OY/cFoNLJ27dpOp0CPGjWKPXv2uKQgy87OZsWKFR5fz7FSUttrnTx5ku3bt1NfX89LL70EQFaWa0jt+fPnWbNmDfPmzaNfv34sW7bMr3titVrJzc2lqamJwsJCl30bNmxg+/bt7N271+drKGzrSxoMhoBPs/cFIUTgpzAHawtVy8CBY8ZjR+Tm5rbrUPRktKC5uVlmZWV5PHTU1NQk9+3b53Zr6fwsLy/3efp0Q0ODM8LS3edKT0+XKSkpKsbATzz5foUSVDehYzZu3Nhpc/2RRx5x2wzvqHktpW3NgPT09KCN1bsTKU9ozz8gpZRTp07VtF/bXQjWWhv+oMTAAzzp0504cUIOHz7cpdzhQ2j7Kw0EPb7f4ej0NJ7BEXfgjsrKSrc+EoX3nDhxIiD+AW+m33uCEgMPcXh7O/on1tbWuvXWb9q0qVUMwqZNm0L2i+AQqc4iHpctWybnzJnjtmuRn58vhw8fHla/Yl2ZzkarPGH7YbO8+cldnYbSe4OnYtDt4ww6Izk5maqqKjIzM9utYzAYmDlzJmazuVV5amoqp0+fxmq1snz5cioqKkLmMIqMjGTz5s3Ex8e3W+fs2bOALSFNW0eo1Wpl//79VFdXh4WTq6vT0ffHG1ZW1mE9d6FVmfXcBVZW1gXk/B3R48UAbA97VVVVqwVS2nLPPfdgNBpdFj9JSUlhwYIFfPbZZy7e+WBjMBgoLy93ESmwBRNNmjSJxx9/3CWYyGw2s2DBAgoLC9Hr9aEyt9vi+O6UlZX5fa5TFveL67RXHkiUGNhxDAPp9XrS0tJc9i9evJhVq1aRmppKfX09VquV4uJiZs+ezfr169mwYYMmD1ZSUhLr1693JncxmUzExsbS3NzMwYMHW9Wtqalh5MiR1NbWsn79eiUEASAzMxOj0YjFYglItOZAvc6r8kCixKAFBoMBk8mEyWRyu1+v17Nt2zYefvhhrrzySr744gv27dsXYitdyczM5MSJExQUFGA0Gtm8ebPbbNHz5s2juLiYpKQkDawMD8pqG4l79l1uyHmLuGffdVkKz1uKioo6bFF6S9bEYeguv7RVme7yS8maOCxg12gPlfq2DZGRkVRVVZGWltbuP3nXrl2hNaoT9Ho9hYWFxMfHM2XKFBchcAQaVVdX9+jWgGNtTEef3LE2JkBy9CCfzpmZmRnQ+RsOO1ZW1nHKYmWgXkfWxGE+2+cNPS4C0VMsFguJiYno9XrKyspsf2sbNfkneYMQgtraWqdTcNSoUdxzzz0sXrxYY8u0J+7Zd2l00/cepNdRkzPe6/NFRkZy8uTJAFgWXDyNQFTdhHZwZG1yiIKnKy5rTVVVFcXFxSQnJ5OcnMzSpUuVENgJpHMukF2DcEF1EzpAr9djMplIS0vjuV2ftDvkE06tg4SEBBISEpzLaIf1lNkQM1Cvc9sy8MU5ZzQatV9ePsColoEHFBUV8fnX37ndF4ohH19ITExUQtCGQDnnHEOI7kadujKqZeAhg/pcGbBfFYU2BMo5d+edd9LU1BQMEzVFiYGHZE0c1soTDaEb8lEEjuToQX5366ZPn94tR2WUGHhIy18Vc9M/uObHl/NE0oiw8hcoQkN36x44UD4DL0iOHkRNzniW3nKGT1fPJFKc1tokRYgxmUwkJydrbUZQUGLgA2lpaVgsFkwmk/fJLRVdmkBNSApHVNCRnzjGm7tr01HRmq7wnWyLp0FHymfgJ2lpaU5nkhKE7k1VVRVRUVFamxE0VDchAFgsFueMR0X3Ze3atd26m9CpGAghBgsh9gkhPhZCfCSEeNhebhRCNAohTPZtSotjHhNCHBdC1AkhJgbzA4QLjjURVOug+2Iymbp1UplOfQZCiAHAACnlYSHE1cAHQDJwN/B3KeWqNvVvBbYAscBAYA8wVErZOpa3BV3ZZ9AWx7oI3TF2vafTFb+PEMCJSlLKz6WUh+2vvwU+AToaXJ8OvCGl/F5KeQI4jk0YegRVVVXO+QyK7oPJZKJ3795amxFUvPIZCCEigWjgPXvRfCHEUSHEq0KIPvayQUBDi8PMdCwe3QrH5Ka1a9diMBg6ze+o6BqYTKZuP9fDYzEQQlwFlACZUspvgD8AQwAD8DnwvDcXFkLMFUIcEkIcOn26+wXvOKZAe5LwVRH+nDx5stsvHOtRnIEQ4nJgB1AppVztZn8ksENKeZsQ4jEAKeUz9n2VgFFK+T/tnb87+QzakpyczMmTJ6mqqlKjDV2YrhJc5u55CZjPQNjuwgbgk5ZCYHcsOrgTOGZ/XQ7MFEL0EkLcANwEtF6ZswdRVlaGyWQiMzNTtRC6OJ7kHtBycyCEcG7e4EnQURxwH/ChEMKxUujjwCwhhAGQwElgnv2GfSSE2Ap8DJwHHupoJKGnUFRURGRkJGVlZd2+udld6QqtA4eNUkqvBaFTMZBSVgPuzrizg2OWAcs8tsINjg/R8sN1dRwLjnSWAVoRfnSF71/bbrXjtaeCENYRiG2bP12doqIijEYjiYmJ7S7HrlBoRdjMTWhP1dq+7upkZmaSmZnpDEpSLQRFINmxY4fPx4Z1y6A7k5aWRmJioopUVIQNYdMyAM/7Nt2lpbB27Vruv/9+QM14VGhPWIlBRUWF1iaElLS0NNLS0jCZTOj1ejX0qNAU1U0IAzzJAq1QBBslBmGCwWAgMzNTdRcUmqHEIIyoqqqirKxMCYJCE8LKZ+ApXSESzB82bdrEpk2btDZD0cPokmLQ0xyNCoUnJCUl+XW86iYoFApAiYFCobCjxEChUABKDBQKhR0lBgqFAuiiown+ek0VCoUrXVIMustEJYUikPgbfxMWiVeFEKeBfwBfaW1LO/RD2eYt4WoX9DzbrpdS9u+sUliIAYAQ4pAnK7hqgbLNe8LVLlC2tYdyICoUCkCJgUKhsBNOYvCK1gZ0gLLNe8LVLlC2uSVsfAYKhUJbwqlloFAoNERzMRBCTBJC1AkhjgshcsLAnpNCiA+FECYhxCF7WV8hxDtCiE/tf/t0dp4A2fKqEOJLIcSxFmVubRE28uz38agQYpQGthmFEI32e2cSQkxpse8xu211QoiJQbZtsBBinxDiYyHER0KIh+3lmt+7DmzT/t5pnBvuUuAz4F+BK4AjwK0a23QS6NembAWQY3+dAzwXIlv+DRgFHOvMFmAKsAtb9quxwHsa2GYEFrupe6v9f9sLuMH+P780iLYNAEbZX18N1Ntt0PzedWCb5vdO65ZBLHBcSvkXKeU/gTeA6Rrb5I7pgGPpoU1AciguKqX8L+Csh7ZMB4qljQOAvk1y3FDY1h7TgTeklN9LKU8Ax7H974Nl2+dSysP2198CnwCDCIN714Ft7RGye6e1GAwCGlq8N9PxjQkFEtgthPhACDHXXnatlPJz++u/AddqY1qHtoTLvZxvb2q/2qI7pZltQohIIBp4jzC7d21sA43vndZiEI7ESylHAZOBh4QQ/9Zyp7S13cJiCCacbLHzB2AIYAA+B57X0hghxFVACZAppfym5T6t750b2zS/d1qLQSMwuMX7CHuZZkgpG+1/vwS2Y2uSfeFoNtr/fqmdhe3aovm9lFJ+IaW8IKX8ASjkYnM25LYJIS7H9rD9p5Sy1F4cFvfOnW3hcO+0FoP3gZuEEDcIIa4AZgLlWhkjhPixEOJqx2tgAnDMbtNse7XZwJ+0sRA6sKUcSLV7xscCX7doEoeENv3sO7HdO4dtM4UQvYQQNwA3AQeDaIcANgCfSClXt9il+b1rz7awuHfB8pp64V2dgs2j+hnwhMa2/Cs2z+0R4COHPcBPgb3Ap8AeoG+I7NmCrcl4DltfMb09W7B5wl+038cPgTEa2Paa/dpHsX2JB7So/4TdtjpgcpBti8fWBTgKmOzblHC4dx3Ypvm9UxGICoUC0L6boFAowgQlBgqFAlBioFAo7CgxUCgUgBIDhUJhR4mBQqEAlBgoFAo7SgwUCgUA/x/xUGkKmA1/IwAAAABJRU5ErkJggg==\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": 4,
+   "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": 5,
+   "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": 27,
+   "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",
+    "    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",
+    "        #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",
+    "    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, scaler=Normalizer(), l=2):\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) >= l:\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. Dit is waarom het de baseline is van deze opgave."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model score 0.053549619325076785\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: 0.11459860042677367\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "\n",
+    "xtrain, xtest, ytrain, ytest = prepTraining(d)\n",
+    "\n",
+    "\n",
+    "\n",
+    "def LinReg():\n",
+    "    xtrain, xtest, ytrain, ytest = prepTraining(d)\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": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---\n",
+      "GeneratingOptimalAlphaRBF\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.33373797e-05]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-3.67252169e-05]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimal alpha for rbf kernel 1.275\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "GeneratingOptimalAlphaDotProduct\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([84.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 78, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([348.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 48, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([20.5]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 63, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([11.125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([15.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 42, 'nit': 1, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-38.25]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 46, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.25]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([9.375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([5.35546875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 71, 'nit': 5, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([63.90625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 48, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-8.34375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 56, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([20.6875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 68, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([65.1875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-3.3125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.75]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 42, 'nit': 1, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-24.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 51, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([2.609375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 75, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.453125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.296875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 87, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.8125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 51, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([3.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 59, 'nit': 6, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([2.609375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-30.1640625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 59, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([18.078125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 46, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.71875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 46, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-23.609375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 88, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.1171875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.671875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 42, 'nit': 1, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([1.41796875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-14.09765625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 58, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([26.01953125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 54, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.10546875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-26.19140625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-16.40234375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 67, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.6796875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-6.94140625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 117, 'nit': 6, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-11.21679688]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 87, 'nit': 6, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.37890625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.22265625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([2.03515625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 99, 'nit': 5, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.69921875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.7578125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 73, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.99804688]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-4.85742188]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 62, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.59667969]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 44, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.58984375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 28, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-4.69726562]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 43, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-1.68945312]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 82, 'nit': 6, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.3359375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.52978516]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 69, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.96875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 48, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.04345703]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 43, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.72167969]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 74, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-1.03710938]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 62, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.6171875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.61621094]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 79, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.97216797]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.68652344]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 67, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.66503906]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 64, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.76708984]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 62, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.73803711]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.48022461]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 54, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.17260742]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 43, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimal alpha for dotproduct kernel 0.04\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "Generating white kernel noise level for rbf\n",
+      "\n",
+      "Optimal noise level for rbf 1\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "Generating white kernel noise for DotProd\n",
+      "\n",
+      "Optimal noise level for dot 1\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "White Kernel\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",
+      "---\n",
+      "DotProduct\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model score 0.05304424748634964\n",
+      "CrosValScore [0.08041413 0.05850993 0.26145889]\n",
+      "Mean 0.1334609854955265\n",
+      "\n",
+      "\n",
+      "Kfold:\n",
+      "Score: [0.25166842 0.00850081 0.08625134]\n",
+      "Mean: 0.11547352452721255\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "Rbf\n",
+      "Model score 0.0485491868165403\n",
+      "CrosValScore [0.06244162 0.10407524 0.32431861]\n",
+      "Mean 0.16361182171401337\n",
+      "\n",
+      "\n",
+      "Kfold:\n",
+      "Score: [ 0.26362672 -0.25184796  0.09040504]\n",
+      "Mean: 0.034061269824504775\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "DotWhite\n",
+      "Model score -0.013242764071176399\n",
+      "CrosValScore [-0.10300226 -0.03773465 -0.00324282]\n",
+      "Mean -0.04799324044247397\n",
+      "\n",
+      "\n",
+      "Kfold:\n",
+      "Score: [-0.04027678 -0.00891338 -0.00084781]\n",
+      "Mean: -0.01667932332465106\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "RbfWhite\n",
+      "Model score -1.2725604583700336\n",
+      "CrosValScore [-1.79311122 -1.08885394 -1.1123998 ]\n",
+      "Mean -1.3314549845918533\n",
+      "\n",
+      "\n",
+      "Kfold:\n",
+      "Score: [-1.58380539 -1.23608991 -1.10878677]\n",
+      "Mean: -1.3095606923592664\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",
+    "    xtrain, xtest, ytrain, ytest = prepTraining(d)\n",
+    "    gp = GaussianProcessRegressor(kernel=kernel,alpha=alpha).fit(xtrain, ytrain)\n",
+    "    #print(\"Model score:{}\".format(gp.score(xtest, ytest)))\n",
+    "    return gp, cross_val_score(gp, xtest, ytest, cv = 3).mean()\n",
+    "    \n",
+    "\n",
+    "    \n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "kern = RBF()\n",
+    "pr=False #set to true for every score it gets\n",
+    "print(\"---\\nGeneratingOptimalAlphaRBF\\n\")\n",
+    "\n",
+    "for i in np.arange(0.005,5, .005):\n",
+    "    model, sc = GaussProc(alpha=i, kernel=kern)\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        if pr:\n",
+    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
+    "\n",
+    "rbfAlpha = optimal\n",
+    "print(\"Optimal alpha for rbf kernel {}\\n\\n\\n\".format(rbfAlpha))\n",
+    "\n",
+    "\n",
+    "\n",
+    "print(\"---\\nGeneratingOptimalAlphaDotProduct\\n\")\n",
+    "\n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "kern = DotProduct()\n",
+    "        \n",
+    "for i in np.arange(0.005,5, .005):\n",
+    "    model, sc = GaussProc(alpha=i, kernel=kern)\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        if pr:\n",
+    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
+    "        \n",
+    "dotAlpha = optimal\n",
+    "print(\"Optimal alpha for dotproduct kernel {} with score {}\\n\\n\\n\".format(dotAlpha, lastScore))\n",
+    "\n",
+    "\n",
+    "\n",
+    "print(\"---\\nGenerating white kernel noise level for rbf\\n\")\n",
+    "kern = RBF()\n",
+    "    \n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "\n",
+    "for i in np.arange(0.005,5, .005):\n",
+    "    model, sc = GaussProc(alpha=rbfAlpha, kernel=kern+WhiteKernel(noise_level=i))\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        if pr:\n",
+    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
+    "\n",
+    "rbfWhite = optimal    \n",
+    "\n",
+    "print(\"Optimal noise level for rbf {} with score {}\\n\\n\\n\".format(rbfWhite, lastScore))\n",
+    "\n",
+    "print(\"---\\nGenerating white kernel noise for DotProd\\n\")\n",
+    "\n",
+    "kern = DotProduct()\n",
+    "    \n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "        \n",
+    "for i in np.arange(0.005,5, .005):\n",
+    "    model, sc = GaussProc(alpha=dotAlpha, kernel=kern+WhiteKernel(noise_level=i))\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        if pr:\n",
+    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
+    "\n",
+    "dotWhite = optimal        \n",
+    "print(\"Optimal noise level for dot {} with score {}\\n\\n\\n\".format(dotWhite, lastScore))\n",
+    "\n",
+    "\n",
+    "\n",
+    "print(\"---\\nWhite Kernel\")\n",
+    "model, dump = GaussProc(alpha=1, kernel=WhiteKernel())\n",
+    "score(model)\n",
+    "print(\"\\n\\n\\n\")\n",
+    "print(\"---\\nDotProduct\")\n",
+    "model, dump = GaussProc(alpha=dotAlpha, kernel=DotProduct())\n",
+    "score(model)\n",
+    "print(\"\\n\\n\\n\")\n",
+    "print(\"---\\nRbf\")\n",
+    "model, dump = GaussProc(alpha=rbfAlpha, kernel=RBF())\n",
+    "score(model)\n",
+    "print(\"\\n\\n\\n\")\n",
+    "print(\"---\\nDotWhite\")\n",
+    "model, dump = GaussProc(alpha=dotAlpha, kernel=DotProduct() + WhiteKernel(noise_level = dotWhite))\n",
+    "score(model)\n",
+    "print(\"\\n\\n\\n\")\n",
+    "print(\"---\\nRbfWhite\")\n",
+    "model, dump = GaussProc(alpha=rbfAlpha, kernel=RBF() + WhiteKernel(noise_level = rbfWhite))\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, moest dit een klassificatie probleem zijn (dit is het jammer genoeg niet). Door dus de lokalen op hogere waarden te steken (tientallen ipv values tussen 0 en 1) kunnen we het model nog simpele decisions geven (bv tussen 0 en 10 is 1 lokaal, tussen 10 en 20 is gang etc). Jammer genoeg wordt dit niet gereflecteerd in de resultaten.\n",
+    "\n",
+    "Er was nog geen vorm van decision tree aanwezig in het project, en Random Forest had de beste resultaten voor deze opgave. 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": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.03583447235599587 en 0.15000000000000002 voor dep\n",
+      "0.08730520188582598 en 1.0 voor dep\n",
+      "0.11835171292421753 en 1.05 voor dep\n",
+      "0.12800809141118277 en 1.1 voor dep\n",
+      "0.1309682618539385 en 1.2000000000000002 voor dep\n",
+      "0.1325868840100242 en 1.8 voor dep\n",
+      "0.18389298719551705 en 2.0500000000000003 voor dep\n",
+      "0.2033019128173553 en 2.6500000000000004 voor dep\n",
+      "0.024595002239385173 en 1 voor est\n",
+      "0.1414475584387812 en 2 voor est\n",
+      "0.19844434825486879 en 6 voor est\n",
+      "0.2045974213542903 en 23 voor est\n",
+      "\n",
+      "\n",
+      "\n",
+      "Optimal Estimations 23 en optimal depth 2.6500000000000004\n",
+      "\n",
+      "\n",
+      "\n",
+      "Model score 0.05451023322649307\n",
+      "CrosValScore [0.11198684 0.02559293 0.35556681]\n",
+      "Mean 0.16299764209737205\n",
+      "\n",
+      "\n",
+      "Kfold:\n",
+      "Score: [ 0.35889054 -0.01211664  0.08784591]\n",
+      "Mean: 0.11315903629642887\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "\n",
+    "def rfor(est=5, dep=50):\n",
+    "    xtrain, xtest, ytrain, ytest = prepTraining(d, scaler=MinMaxScaler())\n",
+    "    lr =  RandomForestRegressor(n_estimators=est, max_depth=dep)\n",
+    "    lr.fit(xtrain, ytrain)\n",
+    "    return lr, cross_val_score(lr, xtest, ytest, cv = 3).mean()\n",
+    "#Calculating optimal depth\n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "for i in np.arange(0,20,.05):\n",
+    "    if i == 0:\n",
+    "        model, lastScore = rfor(dep=0.05, est=25)\n",
+    "        continue\n",
+    "    model, sc = rfor(dep=i, est=25)\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        print(\"{} en {} voor dep\".format(lastScore, optimal))\n",
+    "\n",
+    "de = optimal\n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "\n",
+    "for i in range(1,140):\n",
+    "    model, sc = rfor(est=i, dep=de)\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        print(\"{} en {} voor est\".format(lastScore, optimal))\n",
+    "\n",
+    "print(\"\\n\\n\\nOptimal Estimations {} en optimal depth {}\\n\\n\\n\".format(optimal, de))\n",
+    "optimal, sc = rfor(est=optimal, dep=de)\n",
+    "score(optimal)"
+   ]
+  },
+  {
+   "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 is de score maar een kleine verbetering op de lineare regressie die we als baseline gebruikten. \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. Meer tijd om te experimenteren met de gaussian methode zou ook betere testscores kunnen genereren."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/project/ProjectDataScienceBeppeVanrolleghem.ipynb b/project/ProjectDataScienceBeppeVanrolleghem.ipynb
new file mode 100644
index 0000000..d932355
--- /dev/null
+++ b/project/ProjectDataScienceBeppeVanrolleghem.ipynb
@@ -0,0 +1,1126 @@
+{
+ "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  andere 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. Door gebruik van enkele if functies kunnen we kiezen welke datasets we willen inlezen.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "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": 53,
+   "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": 54,
+   "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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\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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q6qm++ytM67eU93a/zxnPAnxM+L0djPe2OGLEWL7+6XuH7Tn0sl/yyq/+i4PrZnLCpF+xUxuonj+OSU9dzLW3FTGgy49idrEzG7rhsv/8B9r8HL/8cBYX/KTq8A32r2bR937K6n97nH6cxLk/38uB31zKKf0gf9Rg9ryyrU1PA/BhziCGHBX5deQcdyyDO7rzfqdxznWDeHvf9Ye9ClY/fDv3f38+j5wRiLvr/nW3Me3BG3jkxtM4ut1ttXH3OGTIwEN/FP4wZ1CCrfuehD1Sb4m8FaP1j6/R6wDFxcUAFBQUUFBQAEAwGKSwsLBXa2yj3ymcdfEYDt7+Otx7bswNB5k/q5Qni1awbPb4Dnfvtv3P88ydp/Kja59nD3e36dWqFtWx590zGVoebRkM4V8Qyov8mt965SX27FnCzGOi749OYtyyF3npxBF8FK1+x3ZeOuIk93VnOV/0SBMnTmwToNhQ1dTUEAqFCIVCrcGpq6ujuLgYEaGwsJDS0lLq6upSX2jLelZcNTAyU9eyhT+tqmXIdV+J2eAg+7ffwpMXrKWqfBLj+h3aJ+/xtzgI1Nd8winXncekbj3ykfc/D3zvFm67837mrGtsc+uttYdOWj3w2uXcERMigKJ5DYdOaj2wjLHLXmHb5SMZdsqZPLp9L7Rs4Q/LX6Xh5gu6U1yflvAUod5QVFSk1dU9/7tsVVUVlZWVVFZWEgwGKS0t7Z1ea/8S7v7SDkb/5TYu900fH8Or7/YdtyXeNsul7RShTFJcXMyDDz5IXV0djY2NFBcXU1hYSEVFRUrvt6X+NZ7+1jkU+zFEHKrPpI5Pf/U9V1FRQWNjI5WVlYRCIQBKS0tTcl/9vvgQm3+ckkM74ff6skFW9UjtBQKB1vdPoVCIgoKClPdOpm/K2h6pvdjJiFAoRGNjI6FQiEAg/lSxMV2R1T1SR0KhEJs2baKwsJCqqqp0l2OyQJ8MEkRm+MrKyggGg4k3NiaBPhskgLKyMqqqqigsLKSxsTHxDsZ0oE8HCaCwsJBNmzZRUVFBIBCgsrIy3SWZDNRnJhsSKSsrIxAIMGPGDPuMCNNlfb5HilVaWsqzzz5rU+Smy6xHaicYDLZOiafqD7gm+1iPFEd04iEQCLBp06Y0V2MygfVIHYieEVFcXGwzeiYhC1InQqFQ7/x7hsl4NrRLIDotbmdAmM5YkJIQCoUoLS21IZ7pkAUpCWVlZRQUFLT+O4Yx7VmQklRRUUFFRYUN8UxcNtmQpIKCAhobGykoKLAJCHMY65G6yIZ3Jh4LUheVlpbaKUTmMBakbrBeybRnQeqGsrKy1g+tNAYsSN1SWlrKpk2bbNLBtLIgdUMgECAYDPLggw+muxTjExakbiotLbX/pjWtkllobISIvCIi20Vkm4jc5LWHRCTcbs2k6D7lIlIrIjUiMiWVP0C6FBcXEwwGKSsrS3cpxgeS6ZEOAreo6ljgTGCOiIz1bntAVQu9r9UA3m1XAOOAqcCjIpKTgtrTrri42M50MEASQVLVXar6und5H/AmcVYpjzEdWKqqn6rqX4gsvXO6i2L9JhgMsnnz5nSXYXygS++RRKQAmACs85puFJEtIrJIRKKrT+UDsasN19N58FKucmOYs+e/zMh5z3P2/Jep3Ohubeg77rjD2bFM5ko6SCJyNJGVzctU9WPgMeAkoBDYBdzXlTsWkdkiUi0i1Xv2HLY2uDOVG8OUr9hKuLEJBcKNTZSv2OosTGld7KwLUvliYpIMkoj0JxKip1R1BYCq7lbVZlVtIbLocnT4FgZil+0e7rW1oaoLVbVIVYuGDBnS/mZnFqypoelAc5u2pgPNLFhT4+T4mRCkVL+YmORm7QT4DfCmqt4f0358zGYzgDe8yyuBK0RkgIiMBEYDf3ZXctfsbGzqUntXZcLZ4Kl+MTHJ9UhnA1cB57ab6r5XRLaKyBZgMnAzgKpuA5YB24EXgDmq2tzBsVMuL5Dbpfbu8Pvfk1L9YmKS+H8kVX0ViLf03+pO9rkLuKsHdTkzd8oYyldsbfOKnNs/h7lTxji7D7//C3peIJdwnNC4fDHp67L+zIbghHzuuXQ8+YFcBMgP5HLPpeMJTkjrRGKvmjtlDLn92/4pz/WLSV/n+/+Qja5w7kodMKPc6SEBf/9rRfRFY8GaGnY2NpEXyGXulDF96sUk1XwVpPahiX6Yvd8/1N512FMhOCHfgpNCvgjShg0bWp+M0dCISGtbJjxRTd/miyDB4b1ObKD83iP5ffrbpF7WTzb0hoKCgnSXYNLMNz3S7373u3SXYEy3WY9kjAO+6ZFKSkpaL8e+J/L7+yOTnGyfMPJFkEaNGkVtbS2qmvUPeF8WfVHMxmG8L4LUXvupcJO5+soLoy+DFO2Z+sovIdv1hZGGL4MUZT1S5sv2AEX5dtbOQpQd+srv0bdBMiaTiB9eMURkD/AJ8H/prqUDx2G1dZef60umthNUNeFnIfgiSAAiUq2qRemuIx6rrfv8XJ/L2mxoZ4wDFiRjHPBTkBamu4BOWG3d5+f6nNXmm/dIxmQyP/VIxmSstAdJRKZ6y7/Uisg8H9RT531e3yYRqfbaBovIiyLyjvd9UKLjOKxnkYg0iMgbMW1x65GIh73HcouInJqG2nyx3E8nyxGl5rFT1bR9ATnAu8CJwOeAzcDYNNdUBxzXru1eYJ53eR7ws16s5xzgVOCNRPUA04D/JvI5hGcC69JQWwi4Nc62Y73f7wBgpPd7z0lhbccDp3qXjwHe9mpIyWOX7h7pdKBWVd9T1c+ApUSWhfGb6cBi7/JiINhbd6yqfwQ+SrKe6cATGvEaEGj30dK9UVtHenW5H+14OaKUPHbpDpLvloABFPi9iGwQkdle2zBV3eVd/gAYlp7SWnVUj18eT18t99NuOaKUPHbpDpIfTVLVU4GLiKxOeE7sjRoZB/hmqtNv9dDD5X5ci7McUSuXj126g5TUEjC9SVXD3vcG4Fkiw4/d0W7e+96Qvgqhk3rS/nhqD5f7cSneckSk6LFLd5DWA6NFZKSIfI7I2rMr01WMiBwlIsdELwMXElmuZiUwy9tsFvBceips1VE9K4GrvRmoM4G9McOYXuGX5X46Wo6IVD12qZzVSXJ2ZRqRGZV3gR+muZYTicwsbQa2ResBPg+sBd4BXgIG92JNS4gMkQ4QGbdf21E9RGacHvEey61AURpqe9K77y3ek/P4mO1/6NVWA1yU4tomERm2bQE2eV/TUvXY2ZkNxjiQ7qGdMVnBgmSMAxYkYxywIBnjgAXJGAcsSMY4YEEyxgELkjEO/D+ePXpCYP9FTQAAAABJRU5ErkJggg==\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": 4,
+   "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": 5,
+   "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": 27,
+   "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",
+    "    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",
+    "        #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",
+    "    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, scaler=Normalizer(), l=2):\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) >= l:\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. Dit is waarom het de baseline is van deze opgave."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model score 0.053549619325076785\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: 0.11459860042677367\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "\n",
+    "xtrain, xtest, ytrain, ytest = prepTraining(d)\n",
+    "\n",
+    "\n",
+    "\n",
+    "def LinReg():\n",
+    "    xtrain, xtest, ytrain, ytest = prepTraining(d)\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": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---\n",
+      "GeneratingOptimalAlphaRBF\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.33373797e-05]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-3.67252169e-05]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimal alpha for rbf kernel 1.275\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "GeneratingOptimalAlphaDotProduct\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([84.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 78, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([348.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 48, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([20.5]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 63, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([11.125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([15.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 42, 'nit': 1, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-38.25]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 46, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.25]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([9.375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([5.35546875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 71, 'nit': 5, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([63.90625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 48, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-8.34375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 56, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([20.6875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 68, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([65.1875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-3.3125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.75]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 42, 'nit': 1, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-24.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 51, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([2.609375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 75, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.453125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.296875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 87, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.8125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 51, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([3.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 59, 'nit': 6, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([2.609375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-30.1640625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 59, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([18.078125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 46, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.71875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 46, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-23.609375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 88, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.1171875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.671875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 42, 'nit': 1, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([1.41796875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-14.09765625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 58, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([26.01953125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 54, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.10546875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-26.19140625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-16.40234375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 67, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.6796875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-6.94140625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 117, 'nit': 6, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-11.21679688]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 87, 'nit': 6, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.37890625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.22265625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([2.03515625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 99, 'nit': 5, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.69921875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.7578125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 73, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.99804688]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-4.85742188]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 62, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.59667969]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 44, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.58984375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 28, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-4.69726562]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 43, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-1.68945312]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 82, 'nit': 6, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.3359375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.52978516]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 69, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.96875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 48, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.04345703]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 43, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.72167969]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 74, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-1.03710938]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 62, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.6171875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.61621094]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 79, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.97216797]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.68652344]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 67, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.66503906]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 64, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.76708984]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 62, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.73803711]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.48022461]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 54, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.17260742]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 43, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimal alpha for dotproduct kernel 0.04\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "Generating white kernel noise level for rbf\n",
+      "\n",
+      "Optimal noise level for rbf 1\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "Generating white kernel noise for DotProd\n",
+      "\n",
+      "Optimal noise level for dot 1\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "White Kernel\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",
+      "---\n",
+      "DotProduct\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model score 0.05304424748634964\n",
+      "CrosValScore [0.08041413 0.05850993 0.26145889]\n",
+      "Mean 0.1334609854955265\n",
+      "\n",
+      "\n",
+      "Kfold:\n",
+      "Score: [0.25166842 0.00850081 0.08625134]\n",
+      "Mean: 0.11547352452721255\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "Rbf\n",
+      "Model score 0.0485491868165403\n",
+      "CrosValScore [0.06244162 0.10407524 0.32431861]\n",
+      "Mean 0.16361182171401337\n",
+      "\n",
+      "\n",
+      "Kfold:\n",
+      "Score: [ 0.26362672 -0.25184796  0.09040504]\n",
+      "Mean: 0.034061269824504775\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "DotWhite\n",
+      "Model score -0.013242764071176399\n",
+      "CrosValScore [-0.10300226 -0.03773465 -0.00324282]\n",
+      "Mean -0.04799324044247397\n",
+      "\n",
+      "\n",
+      "Kfold:\n",
+      "Score: [-0.04027678 -0.00891338 -0.00084781]\n",
+      "Mean: -0.01667932332465106\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "RbfWhite\n",
+      "Model score -1.2725604583700336\n",
+      "CrosValScore [-1.79311122 -1.08885394 -1.1123998 ]\n",
+      "Mean -1.3314549845918533\n",
+      "\n",
+      "\n",
+      "Kfold:\n",
+      "Score: [-1.58380539 -1.23608991 -1.10878677]\n",
+      "Mean: -1.3095606923592664\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",
+    "    xtrain, xtest, ytrain, ytest = prepTraining(d)\n",
+    "    gp = GaussianProcessRegressor(kernel=kernel,alpha=alpha).fit(xtrain, ytrain)\n",
+    "    #print(\"Model score:{}\".format(gp.score(xtest, ytest)))\n",
+    "    return gp, cross_val_score(gp, xtest, ytest, cv = 3).mean()\n",
+    "    \n",
+    "\n",
+    "    \n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "kern = RBF()\n",
+    "pr=False #set to true for every score it gets\n",
+    "print(\"---\\nGeneratingOptimalAlphaRBF\\n\")\n",
+    "\n",
+    "for i in np.arange(0.005,5, .005):\n",
+    "    model, sc = GaussProc(alpha=i, kernel=kern)\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        if pr:\n",
+    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
+    "\n",
+    "rbfAlpha = optimal\n",
+    "print(\"Optimal alpha for rbf kernel {}\\n\\n\\n\".format(rbfAlpha))\n",
+    "\n",
+    "\n",
+    "\n",
+    "print(\"---\\nGeneratingOptimalAlphaDotProduct\\n\")\n",
+    "\n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "kern = DotProduct()\n",
+    "        \n",
+    "for i in np.arange(0.005,5, .005):\n",
+    "    model, sc = GaussProc(alpha=i, kernel=kern)\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        if pr:\n",
+    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
+    "        \n",
+    "dotAlpha = optimal\n",
+    "print(\"Optimal alpha for dotproduct kernel {} with score {}\\n\\n\\n\".format(dotAlpha, lastScore))\n",
+    "\n",
+    "\n",
+    "\n",
+    "print(\"---\\nGenerating white kernel noise level for rbf\\n\")\n",
+    "kern = RBF()\n",
+    "    \n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "\n",
+    "for i in np.arange(0.005,5, .005):\n",
+    "    model, sc = GaussProc(alpha=rbfAlpha, kernel=kern+WhiteKernel(noise_level=i))\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        if pr:\n",
+    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
+    "\n",
+    "rbfWhite = optimal    \n",
+    "\n",
+    "print(\"Optimal noise level for rbf {} with score {}\\n\\n\\n\".format(rbfWhite, lastScore))\n",
+    "\n",
+    "print(\"---\\nGenerating white kernel noise for DotProd\\n\")\n",
+    "\n",
+    "kern = DotProduct()\n",
+    "    \n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "        \n",
+    "for i in np.arange(0.005,5, .005):\n",
+    "    model, sc = GaussProc(alpha=dotAlpha, kernel=kern+WhiteKernel(noise_level=i))\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        if pr:\n",
+    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
+    "\n",
+    "dotWhite = optimal        \n",
+    "print(\"Optimal noise level for dot {} with score {}\\n\\n\\n\".format(dotWhite, lastScore))\n",
+    "\n",
+    "\n",
+    "\n",
+    "print(\"---\\nWhite Kernel\")\n",
+    "model, dump = GaussProc(alpha=1, kernel=WhiteKernel())\n",
+    "score(model)\n",
+    "print(\"\\n\\n\\n\")\n",
+    "print(\"---\\nDotProduct\")\n",
+    "model, dump = GaussProc(alpha=dotAlpha, kernel=DotProduct())\n",
+    "score(model)\n",
+    "print(\"\\n\\n\\n\")\n",
+    "print(\"---\\nRbf\")\n",
+    "model, dump = GaussProc(alpha=rbfAlpha, kernel=RBF())\n",
+    "score(model)\n",
+    "print(\"\\n\\n\\n\")\n",
+    "print(\"---\\nDotWhite\")\n",
+    "model, dump = GaussProc(alpha=dotAlpha, kernel=DotProduct() + WhiteKernel(noise_level = dotWhite))\n",
+    "score(model)\n",
+    "print(\"\\n\\n\\n\")\n",
+    "print(\"---\\nRbfWhite\")\n",
+    "model, dump = GaussProc(alpha=rbfAlpha, kernel=RBF() + WhiteKernel(noise_level = rbfWhite))\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, moest dit een klassificatie probleem zijn (dit is het jammer genoeg niet). Door dus de lokalen op hogere waarden te steken (tientallen ipv values tussen 0 en 1) kunnen we het model nog simpele decisions geven (bv tussen 0 en 10 is 1 lokaal, tussen 10 en 20 is gang etc). Jammer genoeg wordt dit niet gereflecteerd in de resultaten.\n",
+    "\n",
+    "Er was nog geen vorm van decision tree aanwezig in het project, en Random Forest had de beste resultaten voor deze opgave. 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": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.03583447235599587 en 0.15000000000000002 voor dep\n",
+      "0.08730520188582598 en 1.0 voor dep\n",
+      "0.11835171292421753 en 1.05 voor dep\n",
+      "0.12800809141118277 en 1.1 voor dep\n",
+      "0.1309682618539385 en 1.2000000000000002 voor dep\n",
+      "0.1325868840100242 en 1.8 voor dep\n",
+      "0.18389298719551705 en 2.0500000000000003 voor dep\n",
+      "0.2033019128173553 en 2.6500000000000004 voor dep\n",
+      "0.024595002239385173 en 1 voor est\n",
+      "0.1414475584387812 en 2 voor est\n",
+      "0.19844434825486879 en 6 voor est\n",
+      "0.2045974213542903 en 23 voor est\n",
+      "\n",
+      "\n",
+      "\n",
+      "Optimal Estimations 23 en optimal depth 2.6500000000000004\n",
+      "\n",
+      "\n",
+      "\n",
+      "Model score 0.05451023322649307\n",
+      "CrosValScore [0.11198684 0.02559293 0.35556681]\n",
+      "Mean 0.16299764209737205\n",
+      "\n",
+      "\n",
+      "Kfold:\n",
+      "Score: [ 0.35889054 -0.01211664  0.08784591]\n",
+      "Mean: 0.11315903629642887\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "\n",
+    "def rfor(est=5, dep=50):\n",
+    "    xtrain, xtest, ytrain, ytest = prepTraining(d, scaler=MinMaxScaler())\n",
+    "    lr =  RandomForestRegressor(n_estimators=est, max_depth=dep)\n",
+    "    lr.fit(xtrain, ytrain)\n",
+    "    return lr, cross_val_score(lr, xtest, ytest, cv = 3).mean()\n",
+    "#Calculating optimal depth\n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "for i in np.arange(0,20,.05):\n",
+    "    if i == 0:\n",
+    "        model, lastScore = rfor(dep=0.05, est=25)\n",
+    "        continue\n",
+    "    model, sc = rfor(dep=i, est=25)\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        print(\"{} en {} voor dep\".format(lastScore, optimal))\n",
+    "\n",
+    "de = optimal\n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "\n",
+    "for i in range(1,140):\n",
+    "    model, sc = rfor(est=i, dep=de)\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        print(\"{} en {} voor est\".format(lastScore, optimal))\n",
+    "\n",
+    "print(\"\\n\\n\\nOptimal Estimations {} en optimal depth {}\\n\\n\\n\".format(optimal, de))\n",
+    "optimal, sc = rfor(est=optimal, dep=de)\n",
+    "score(optimal)"
+   ]
+  },
+  {
+   "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 is de score maar een kleine verbetering op de lineare regressie die we als baseline gebruikten. \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. Meer tijd om te experimenteren met de gaussian methode zou ook betere testscores kunnen genereren."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/project/test2.ipynb b/project/test2.ipynb
index ec29fc5..351933c 100644
--- a/project/test2.ipynb
+++ b/project/test2.ipynb
@@ -8,16 +8,16 @@
     "\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",
+    "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  andere 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"
+    "Er wordt begonnen met het inlezen van de data als een array van de lijnen. Door gebruik van enkele if functies kunnen we kiezen welke datasets we willen inlezen.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -192,12 +192,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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evJvu3v7MxyEJOPp3H1Lyvf1Atvo8Efjpald7IjATyYbfrnY1JwIzkWw4zSk5ka0+H0Lsc0qTJ9RQM2rwgDjzYE0A9jmlLLZQCggzEejlcBnEeTJr7lJAVpMqRJxuxiNGpHv+fDUf+96X+KDjp1z5tX+uWD5uIWsgi4gyCKclL1/95Xrz1adjzcveqXhsR4fj86swn9Tc3Bx4lnZYilQtgviOF+avCgLl83nf+WQdhwj4nWp3mq7P5XK+88lEioCfqfa2trZIU+eQiRSJclPtzc3NtLS0UF9fHzmfYS7SYdbfdlvF7u4Wx+H8qT00NzeHir7ixLAW6b38QVZfd51nukMHe9j91EuB7+/kO/43s+GmFe7d8DAMa5HGf9TnK93oUcqnFpZsnpYYhrVIuWlHlZxr+PzXS84dJNy0g5O368/aYcInzw51PzeGtUh/yA+UnJOpJ5YmPHyYF3a8H/j+bqHUJp99ucs7wjGsRTpm/OAvcPv3P860JbMdUiqfOSP4gjW3Lvjoo48LfK9yRA0RcI2I7BaRX4nIz0VkvIjMEpEXzVAAD5lrlxCRceZxu3l9ZhwfoBzvHxw8WacfnMTF728bdO6pp55if1eeMF+FWxf80IF4dwqPsvq8DvgG0KiqfwaMxtgU+BbgdlWdDXQBK8y3rAC6zPO3m+kqynG5segHvy0e9/eNY/MtRw9Ks3DhQuqOn0J/iPu7dcG7nr/P5R3hiNrcjQFqzW2yJwDvAudirJ+F0hABhdI/ApwnYTdv8Fu4gT4+fPuIieYvvzWP7zY9V5JuVN9BakLc360L3vPa8+EL7UBoA6uqdorIbcDbQC/wFNAK5M19z2FwGIBiiABVHRCRbowdneNtGywMfHSYo+ZcVDw+5WttPPtIae+u6yC07+8pG4XYjUSHUjPD0SzFiOdwAjARuCBqgeIMETBmHHzw6ubi8auPbWDZN3tK0k0+ehSfrpsQKa9KEqW5Ox94U1X3q2o/sAmYjxHppFBDrWEAiiECzOuTgD/YbxpniIDugQkcNWdJ8fjpx76OHnzGIeVY4HCkvPwwFFvzvA3ME5EJ5rOlECLgOeASM409REBhAHEJ8KxWeEXxv264fVBNunD5QzS1/VVJuv/rOkSSRyNR4ji8iNEB2AHsMu+1ASPY07Ui0o7xzLnbfMvdwLHm+WuxRU+pBKu+dlXJuX8/cUfxdUdHBwBn1n7I29ueqHRxhmb6XFVvAG6wnX4DKDGEqepB4PNR8gvCmWeeyVMvbOOhN0/hqjlHzu9u+afi6+nTpwPQz0RmzLvIfovYCbOZCCS5jkfkpZde4qhDvVz12dPKptuzZw/jx3u7FcdBthOZA2MmTvRMc8opp1ShJNEY1iL5pa/P35SGX+KOF5uJBIwdO3aoi1CWTKQUkIlUAcp5sYZhWDpHOvWi/q2+nrp5Oc656y6gfI/PTrb0pUosamtjAkas0SDGmSREOB4xzd0UDAtwUOtZEiIcjxiRwpKECMeZSB4kISBUJpIHSYhwPGI6DmHJtuZJCUMd4Thr7lJAJlIKyERKAZlIKSATKQVkIqWArAtuIw6Ld9wzs5lIFpJg8XYia+4sJMHi7URWkyzEYfGOM/BTgawmWUiCxduJTCQLSbB4OzHim7vm5mZuvPFG4IhvxFBavB0pbMqUxL+GhgatFEuXLq3IfTHcKPym3a4+vocRWZPa2tpoaWmpyL3jHiPBCHwmbd26NZagTNVkxInkteliEvEUSUTuEZF9IvIry7ljRORpEfmN+X+yeV5E5MdmrIZXROQMy3suN9P/RkTiDRkSgFmzZhV3HrPuQJZk/NSkjZQuWF4LPKOqJwPPcGTV3oXAyebfSuBOMETFWGx2FsYCsxsKwlYbpwdz0vEUSVVfAP5oO22NyWCP1XC/2XnZhrHI+XhgEfC0qv5RVbuAp4lhpfpIIewzaZoe2bf8PWCa+boYq8GkEMfB7XwJcYYIGC5E7jhYxgaxoDGGCBguhBXpfbMZw/y/zzxfjNVgUojj4HY+wwdhRbLGZLDHaviK2cubB3SbzeKTwEIRmWx2GBaa5xJB0nt5nhYHEfk5sAA4TkQ6MHpp64CHRWQF8BZwqZn8CeAioB3oAa4EUNU/isj3gJfNdDepqr0zMmR4WQm8Zmut13PjYMlJhIpT5IanSKr6RZdL5zmkVeBql/vcA9wTqHQJwGu21n696yPhZ+3KbXMW0PPa87F08UecxSEoXrO1biE+67/yD7GNwUakgTUIXrO1btc78z1lw6oFWVebaJFaW1tjf6BP+OTZTD77ckYffRyHDvyerufvKwYRdPrln5CrpdNBiMJsrdv1ge79NDV9NZYyJ7q5a2hoiHV+6rEdHcz43FrGTJqKyCjGTJrKjM+t5bEdHa5Nk9dsrVuIz5+sXFi2LEFIdE2yEoc/XLnni9u93NYnAcxf9yzv5HuZVFvD+JpR5Hv6yY1TlpwUrwtYKkSKyx8urDeQ026Z1vLke/uprRnN7V+o5+IzptMG3LSi9D7WGqSqvpvyVIgUpgYUsNbAUSIccnnuxFUet6YsyuYiqRApbA2w/+KdBArjDRSmPBs3bgyUh5VEdxwKhPWHc/rFA4wWKYaIvnn56YGfH0HLk8/nA+08ZicVIp1zmrM13O18Abdf9mFV3lz3WX6x9txQD/ig/nlRBIKUiPTcr53nldzOF6iUR6pT0HanGhmXw8uwfiatWXTqoGcSxOeR6rYivWBlWLx4MW1tbZHzgRSI1LKzM3SvLAkxGOIg0SLle/q5ftOuSL2yoY7BEAeJFum9Awc5zqV35qdXZjdwOi1LcTKCNjU1hbJgV2KfCkh4x6H/kPPWBIdVU187giBJ9jsbVTP+cM1xJ5bYTvTQQF///r27hqJMITgO951tTlJVT2+bRIskIttVtXGoyxGFOD5Dopu7DINMpBSQdJE2DHUBYiDyZ0j0MynDIOk1KYNMpFSQWJFE5AIRed1ckFbxXcvCIiInishzIvKquTHyN83zzSLSKSJt5t9Flvdcb36u10VkkWcmcXrjxPWHsbHwb4GPY+yK+EtgzlCXy6WsxwNnmK8/BuwB5gDNwHUO6eeYn2ccxs6ivwVGl8sjqTXpTKBdVd9Q1T7gQYwFaolDVd9V1R3m6z8Br+Gy9spkKfCgqn6kqm9i+M2XbK9nJaki+V50liTM/dznAi+ap1aZa4fvsSw/DfzZkipS6hCRo4BHgdWqegBjvfAngHqM7cZ/GPbeSRUpVYvORKQGQ6AHVHUTgKq+r6qHVPUw8C8cadICf7akivQycLKIzBKRscBlGAvUEoe5EfLdwGuq+iPL+eMtyS4GCiEWNgOXicg4EZmFsVL/pXJ5JHLST40N7FdhrAYcDdyjqruHuFhuzAe+DOwSkYJTw3eAL4pIPcZ64r3AVQCqultEHsbY3XoAuFpVS2c2LWRmoRSQ1OYuw0ImUgrIREoBmUgpIBMpBWQipYBMpBTw/zf4nCPgGGGPAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -209,7 +209,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -221,7 +221,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -233,7 +233,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -245,7 +245,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -257,7 +257,7 @@
     },
     {
      "data": {
-      "image/png": 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ekaF8ecMUOD4+nvR018PG7t27BxjWdZYl5XyBrnw6mjGFbKlhzpw5FC5cWHU5NFcJDw8nd+7cLqeWjI/XlmDXE+jKlwNZFZNA0wmbqTR6PU0nbNZcGtsyzZ897t+/z+7du+nXr5+me7hK586d+f3337l7V10Ey/Hjx70skX105cthrIpJYMyKAyQkJSOBhKRkxqw4oEkBFy1a5PD88uXLCQoKokGDBhql1cZTTz1F/vz5+f5759bKF154we37CSHMLg1XUg7q1s4cRtMJm0lQqMRbJjSEHaOfdulaFy5coHTp0lZtSUlJbNu2jU6dOjkcO3DgQIcf1OnTp1tZO531f/fddylbtqziuR9//JFu3bo5lMcdFOTKGVWKdFzDk6WxS5cuzdWrVyle3LDLOzk5mdDQUKeKBzB+/HjCwsKc9jPRr18/h0/QVatW2VU+k+ItXLiQ3r09nwpe6cGhBn3amcPwdGnsatWqkZqayokTJwgJ8V95bTX07t2befPmmY8/+siz4WKuoitfDsPTpbGvXbvGf//7X4dp4AOJvn37ArB+/Xreeustv8qiK18OwxulscePH8+///7rOSF9QN++fVWnp/cW+povB9K5ThmP1qEvV64c27Zts1uHIdBYunQpiYmJzjt6GV35dDxC8+bNKVCgAHfu3FHV/++//7Yq55UZy5TvADExMQ5r+FlulHWEEMJqR7s/0ZVPx2PcuXOHL774gtOnT/PVV1857Pvcc8+5dG2lNPOucPPmTZ555pmAcl/paz4djzJixAi++uorKlWqxJo1a/wtDgB58+YFYOdO97Y+eRr9yafjFU6dOsWpU6fo0aMHxYsX57///S8RERE+ufeZM2f4888/mTFjBi+//DIpKSlevV+HDh00jdMjXHQ8yrS45z12rSERq5imMXFvZt6MWOWzz5O+n09Hx8e4WkpaTaGUckKILUKIQ0KIeCHEUGO7XpddR8cN1Dz57gP/k1I+CjQGBgshaqLXZdfRcQs1VYouSin3GV/fAg5hKPWs12XX0XEDl9Z8QoiKQB1gJx6oy67jfQYfvkdw8BjIUJd/U8d3qHY1CCEKAj8Bw6SUNx0sLlXVZRdCDMAwLfUrQgjNpbB8zZDaK10e8/Uj+fg67VPCRm4kcaIXhNLRjKonnxAiGIPiLZRSmnJ+u1WXXUo5U0pZX41JVkcbtcf/bX6dOLG1HyXRUUKNtVMAc4BDUkrLChh6XfYAJ+6DxvQL7ooo3Y8JYc43uOr4FjXTzqbAf4ADQohYY9s7GOqwLzPWaD8LdDee2wC0w1CX/S7Q16MS67jE+IXnGG+1BNcJFJwqn5TyD5TXcQAtFfpLYLCbcul4iGPd/sY0wdGtXoGFHuGSzUm/Ay1yC5YfvedvUXQyoQdWZ3Patoviw3MpfPzlH3z9uWvZybTwZoRrSWsdMcTD1ws0dOXL5qT9bqjUOtakePeXPzgZ1F1hhHt4NHjZWHgku6IrXzbn/k5DUcigRobilOErG5vPxRt1b1VMAhOjj3AhKZnSoSGMbFPDo2kmdJTRlS+bE9TIuihlfPdyVsemDNbJaYYiI6YM1oCugF5GN7hkc1IoyUUe5Er5PF8TPs/XxHw8MfqIWfFMJKelMzH6iM9kzKnoT75szlSjoo269ycAVbrtszrvyQzWOq6hKx9Zx6I2RIPtwaR0Jrr+cA64aT4uHRqiWLtBawZrHfXk+GmnlBIpJenp6YwbN45FixaZ2/zxc+XKFWbMmGFOo2H5o4XbO3thqWz3Ocd9bgCGJ6CnM1jrqCfHKx9AREQEhw4d4oMPPuDFF1902NdSCVzN/2g51p4ymZINrVmzhj59+rB9+3aX7pGZhNh95Ov7q/k4nbqkU5cUDAkGvJHBWkcdOT6B0qFDhwgODqZq1apO+86ePZtr165RuXJlYmNjqVGjBoULF2b9+vW0bNnSptbbpEmTyJMnD2FhYezYsYPHHnuMQoUKcenSJe7evcu7777r9J69evWie/fuPP+8tsRElwHL1LSfRRThy/hBJKR9qul6PsWiRFhWQBj9kmoTKOVo5atZsyarVq2ievXqqvpHR0fTpk0bxXODBg3im2++sWq7cuUKJUqUUOy/b98+6tZVl95GSsmyZcs8Usjxwsb2lG79EeC51DqezFhmiSezl4H3M5i5qnw5dtr5yiuvEBcXp1rxUlNTuXz5st3zzzzzjNWxlNJhenNX6hoIIXjhhRf4448/VI8xMfifG7QRgtKzDgNQuvV685RTx7/kSOWTUtK9e3eCg4NVj8mTJw8NGza0ez42NtZqDSiEICkpyW7/+Ph41fc2MWbMGK5fv+7SmAK1e3Jp2WmqDJpvbsvr8p11vEGOVL5OnTrZFOJwhpSSixft7wkOCQmxydtYqpT9vFFBQa57eX799VfeeOMN9QMyNvF52s/Eda/A7+Y1XgqQwoUd+jZLf5PjlC86OpoqVapQrFgxYmJiHCqUJWlpaezfv9/u+erVq9so3+3bt+32r1SpkjqBjaxbt448efIwduxYUlNT1Q3K1RIyNhniOzM2AbD8fl6W38/LjkbznAzW8TY5zsk+cOBAjhwxhE7VqVMHMJSfCg8PJ0+ePHbH5cmThxo1arBgwQIef/xxQkJCOHnyJI888ghpaWmK1tJbt27x7rvv0qlTJwoXLsxvv/1GgwYNOHPmDBUqVHCqgFJKfv/9d+rVq2euB1CtWjW+/vprBg9Wt1/5cq4nKdmoJZdJoSTQPce944FLjnsrXnzxRRslq1OnDvv27aNSpUoULVrU7tjWra2TENWo4dgR/cgjj/Dxxx9bHQM0atRIlazbt2+nefPmNu3R0dGqla9L8Fje6vYN05a8yBY5Q9UYHd+Q46ad9j60devWpUiRIuzdu9ehVdMXmEpZKSkeGNaXaunTbRpdFt8gD+c9IpuO58hRyvf3339Tpoz9yI1cuXJRr149SpYsSUxMDOnp6Xb7eoM9e/aQnp7u9Mk4YMAAs7+qQngxh31fW3ybTcPLMS/a//5cHWtyhPIt+slgXGjYsKHqssV16tTh5s2bxMTEeL2+24kTJ0hMTKR+/frkzp3baf/ChQsjhEAIwYhFzZkW9zxlqyk78wFaTj5H6dbrPSmyjgfI9spX/pHiXK1qqJCalpZGgQIFVI8tWrQoderUIT4+nmvXrnlFvqNHj1KlShXCwsKcdzZiivectLOjue3tH5+iQngxTp8+7WkRdbxEtlW+Dr2bMS3ueUYubQZA0y5VVLsVMlO3bl2KFSvG3r17+ffffz0i3/79+0lJSVEdYWNJlSpVKFe9OMF5rZ+SIxY1Z+2Nt6jd4sHUusfO27TYeMVteXU8T7ZTvlpNKvP+mmdoM6q4VXvPD2uRlJTEX3/9pfna9erV46GHHiImJsZh6Jgjli1bRnJyMrVq1TLXCneVX3/9lVHLm9k9/1pUQ6bFPc8zfR9hTfNvONp+kqb76HiXbBNYveineZwr+SP5i9j31Z2MvUb0Z5eJj4+3W0VUSsnKlSsJCwujSZMm5Mql/P2UlJTEqVOnePTRR8mXL59D2VJSUkhISCA0NJRixZQNJP/88w8xMTFUq1bNYRgbQMs+Neg8vKbDPpZE9djEiSM3nXd0k5SUFDp16kTLli353//+Z/d/pxoNuxpu3LjBgAEDEEKwZMkS9+7vIjkysLpCTcO6zpHiAVSOKEa7du0cPrXWrFlDq1atePLJJzlx4oTdfqGhoeb14NWrVx3e9/Dhw5QpU8au4gGsX7+e3r17c+fOHX744Qe7/a5fv+6S4gEMW9aSWpFliY2Ndd5ZIzt27ODjjz/ml19+YeTIke4rnkaKFCnC0qVLWbJkCd26dVNtYPMHWfrJ1+GlprQZqd5QARC36QJHVubh999/t2r/6quvbOImly9fTvv27cmfP7/T68bExFCqVCkefvhhwGDcWb9+PR07dnRqwUxISKBkyZJWzv9du3Zx4cIFOne23lLzeGQpBkxtnPkSTjkZe40pfba5PM4ZUkrGjh3L4MGDnRqNzp07x6ZNm6hcuTKFCxdm69at1KpVi6CgIM6ePUuNGjVo0KDBgwFCMGf2bKpWrcq1a9cQQpA/f34SExPJyMigefPmVKhQweE9165dy6RJk9i6dasH/lrH5Kj9fFr3ka2Oiue3eUfNx0uWLKFnz56Kfb/55hsGDRqk+tqXLl0iNTWVAgUKULx4cecDgE2bNtGypU3ZC+7cuWNlnXWnlmDHIlOoWLGiprH2uH//Ph9++KFVFI8jxo8fzwcffGD3fP/+/Zk1a9aDBifTzt9++41WrVqpuveIESP44osvVPXVisennUKIfEKIXUKIOCFEvBBinLG9khBipxDimBBiqRAij7E9r/H4uPF8RXf/KHtsnH3UeScFOg0L5/333zcfd+jQwa6i9+vXz6VtPCkpKezfv58iRYqo6j9s2DCaNGmieG7Hjh1Wx5auBVdIPHPb44oHMGHCBN555x3V/dPS0hyer1/ftVKNrhi9JkyYwLx5gRVMrmZingI8LaWsDUQAbY119z4DpkgpqwHXgX7G/v2A61LKqsAUYz+vsHaa63viTPx28AfzfruCBQsyZ84cxX558+Zl1Sr12c0qVKhAhw4dVG8ZGjdunGK4WHJyMk2bNjUfl6pSxMa1oJaWhZ2nq3CVCxcu0KFDB5f8ppmn0JlxZrjKjOX/xxlBQUEUKVLE4U4TX+NU+aQBk8TBxh8JPA38aGyfD5j+s52MxxjPtxQOaki7i9a0f73GRtCkSRNzCNlrr71mN6azb9++3LhxQ9V17969q1qGsWPH2n1Czpw50+qD/c4KbUVOVkfFExkZqWmsI4YOHUpERIRLY3bvdlwXftOmTS5d79dff3XeyYIuXbowevRol8Z4E7VloXMbC2MmAr8CJ4AkKaXpuX+eB+XfyoChGqPx/A3AZvEjhBgghNgjhNjjzh/gzpq1zdslqVnzgeWwaNGiVo74jIwMTp48ya1bt1RPI9UYZ0yMHTuW77//nt9++82q/cqVKwwdOtR83LKP6454E5ZrW0+xYsUKli9fzr59+1R/KYHzaWe/fv0cns+MK3sipZQcPnyYiRMnagps8AaqlE9KmS6ljMBQX70h8KhSN+NvpaecjYZ4sib7xBe0pderHFGMW+kPIlZy587Np58+yOr17bffkpqaSqFChdwV0S4vv/wyhQsXZsGCBeY2y2lup5da0Hl4uKZrb19y0m35lFi4cCFgiPw5deqUw03GlhQuXJj79+9z5swZrl27xtWrV0lMTOTChQskJSVx7NgxmzE3btww+1Rv3brF3bt3OXbsGGlpaZQrV07hLrbcuXOHxYsXU61aNUJCQrz6frqCy9ZOIcSHGMo9vw08LKW8L4R4AhgrpWwjhIg2vv5LCBEEXAJKSgc38oST/Zm+j/DcMKXvBOf8OSEfixcvBuDevXvMnTuXrl27upTkyISjjGXOWLp0Kbdu3eK1114zt2m16HrLtXDp0iWWLFnCsGHDrNrj4+MpVKgQ5cuXdzg+IyNDvQ/QaO20HGO0JqoanpyczN69e2natKnVmKSkJIKDg11ar6oT1/PWzpJCiFDj6xCgFXAI2AJ0M3brA6w2vl5jPMZ4frMjxfMUv847rHlsYvA+83ovX758DBo0SFHx0tLSmDt3rsNrlShRwumXxtKlSxXbX3jhBSvFK1Mt1JnodulTe6rmsY6YM2eO4p7I8PBwypcvz759+xwGJ2hxvluOUat4J06cICQkhCeffNJmTGhoKAkJCS7LYQ/TDhNX5AN1085SwBYhxH5gN/CrlHIdhiffcCHEcQxrOpO5cA5Q3Ng+HPDZClfrNKvz/2qyevVqh32OHTvGpEmT6N27NydPap/OjRgxgtatWzN1qnPl+N9C+/Gbjkg8c9tlY4ha1q9f79CSW7duXUJCQlRPRT3Nv//+y65du5xOSTPnWHUHrWn91Vg790sp60gpa0kpH5NSjje2n5RSNpRSVpVSdpdSphjb7xmPqxrPe2fhocDyT+M0j/1y4YeKoUj3799n5syZVK1aldGjR5M3b16HMYPJyckOv/3ee+89ihYtytChQ0lKSrLrJytVKbBcCyYyMjKcfruXLl2aWrVqERdfBov/AAAcAElEQVQX5zMlvH//PsuWLeOhhx6iYcOGDvPxAGYrd6u+1floY1vznk8trFu3zupHLdkittMSra6H/lENKVy4sFXbihUryJUrlzlQ18Q777zDgQMHFK/jaKfC1KlTCQ19MJUMDQ3lk08+4datWzbxnO+sChzXQq3IspSrbjBYOwv6tqR27drUqlWLf/75hzNnznhUJhNpaWkcPHiQoKAgevTooXrczZs32bp1K52GhRP6UAhXq66h+5jaTJgyzityKpHtlE9KyexhuzSNfXPuk4wfP958vHv3brvm8atXr5qzoFliL/XEyZMn7Yappaam8tJLL5mPn+hc0QWpH3D4r0SPuhZqNa3EtLjn6T+1gXkLU+aoGzXUrFmTChUqsG/fPo4fP+4x+Q4ePEhwcDCPPfaYy2MvXLjAppvWYXHNelamwNOxvDa5kcOEx54i2ykfwNQPF2oaVzmiGDPmTzYfjx8/npUrVyr2jYyM5OhR2w+6vZQTixcvtpsh23K92fk/T9NrXB1XxDZzI05dLKkzkpKSEELQ/xvrdWOtppU070EEw3qwQIECbk9Fr1y5wg8//KBJ6Ux07NiRsAoFFc/VblmaSdu7ez0YO0sHVjvCHdfDwW/C+Pbbb4EHyqT0oZNScuLECaucnenp6Ta7GF5//XWioqIUr7FhwwbatWtnPp686zlNa73zR27wWY/NLo/LTIWaxRmx2L6hp+G90Vy+fNmcR9Qed+/eJTU11Wqabcn+/fspVqwYZcuWtX+RTIHV9+/f58CBA+Z8q5nZunUrjRs3VhWm9tb85lSOcJx8CuDujVTKXe5Gr672M3xnXud17Ngx5+znU8Id18PJ1G3mSJe8efMqhjGlpKSwfft2m5TwSjX7OnXqxE8//WTzZXL37l2rAiulq4ZqNrI8G2Z/t4AaOrzUlGlxzztUPIABo7vw4Ycf2j2fkJDA2LFjuX37NkuXLiU5Wbm8dK1atShbtiyHDh3i1KlTDu95/fp19uzZQ1BQkF3FA0Me1YsXL7Js2TKH08bI5+qqUjyA/EXymNeDnp6KZlvlA/i8u7bIl+eGPWr1Jnfo0MGcSxMMSWuDg4Np3ry5jaP23r17Ntdr27YtvXr1YteuXVZT1enTp1tNRcf81EKTvGuiDmk2spjWdWr3Rb4W1ZB9+/YpKtXHH39M8eLFGTt2LGFhYfTr148JEyY4vN6jjz5KpUqV2Ldvn2KEy+bNmylatKjTHQ+DBw+mVKlSVKpUiR49epCammoOnLDk/ya+y3PvOXja2qFZz8o0a69tOWCPbDvtXBWTwMToIyQk3aVQ/ts8VXs3j1ZU7/WY+eZO9mw8ZZ4qTp48mUGDBjFt2jRGjhxpd1xqaqpDM/eVK1eIjY2lQYMGpKenm3e3V6/3MEPmula8xUS5U32c7hhQovwjxc0JplwhLSWdhqmjePLJJ81t27dvp1kz22udOXOG8uXLq3I+X7p0iatXr3L69GkaNmxIybAwpArXRnx8PEWKFFGcws6cOZO+ffuav+Ra9qmuOVxvSG3l9b+CfDl32rkqJoExKw6QkJQMCG7dLcTGXU9x6HRl1dcY8GUjq2Dqt956i9TUVIeKB873mJUoUYJWrVpRsGBBq7QSWhVv4+yjioq3KiaBphM2U2n0eppO2MyqGNuIju9n/KTpnsF5c9PzleesLMH2rJgVKlRgypQpqq778MMPc/v2bdq3b2/+P6pR2jt37thdOwYFBZkVb/HixZoVz9EsymtO9qzIxOgjJKdZm/zvpwfze1wDOyOU6RdVj4EDBwKGD0FmP6ASt27dUnVtS6PM45H2S4k54mTsNcU9jZZfPhJISEpmzIoDNgoYGRnJ6ihteyLf/qm5lV/t5ZdfthuyNWjQINUfykaNGrFz506H5dUsmTFjhl3fY3x8PK+++qr5+J8CjkMD7ZGWks65o47z9IBroWWQTZXvQpLyIv/WXWXTsj0eeSKMVdGLXBqjtOZzRK/+z2nKyQJwPFq5XenLJzktnYnRtn7JVzuM0XRvgINn/jS/DgoKYuLEiYr98uXLx/r16jNmuxLw7Gh7kOX+wIMHD9p1LTjjf43WOu+kgWypfKVDlQuJFMrv+i7md1e1dKkWesGCrr3B9fu7KpGBxDO3WbtIedeCvS8fpfZeXftqjol9c14TunXrZj4ePXq03dqBlgYrZ6gNek5OTlb0tQKcOnWKN99803w8Z6+6qk5KeMsuki2Vb2SbGoQEW5vsQ4Jz81Rtxzup7XEudbfT9IAmXHmjvJUawt6Xj732WW9v42SstnT4Ry7/yebNBv/iww8/zLfffmv+H9y7d49Zs2aRmJjIRx99pPqaavfphYSEMHDgQH7++WdmzHhQ/uzff/+12hT94msdVbsWMrNzhveK5WR7a+eFpGRKh4Ywsk0NhrxQy2GmZ0fUvT2CuLg4p5nMTp06pWqH9VdffYV8yrW0CSbWRB1y6Mc0rfksp54hwbn5tMvjdK6jXKUpNjaW34W2uMbPum3jZPwls5V3xYoVnD171mbPn1qio6Np06aNy0lzt27dyvXr10lJSTFno4uKiiKjyWZNX3Kfdfud88fUp9rPkUlzlehcpww7Rj/NqQnt2TH6aTrXKcOBXfb3mTnj9XdfIDw8nF9++cVhPzVGmZiYGKYv/FyzLH07vO3wfOc6Zfi0y+OUCQ1BAGVCQxwqHkBERASJZ7QlFxq+8EnGjh1rPu7SpQt9+vSxP8AJqsteZyIyMpJq1apZpYFcG/ON5tmFK4qnhWyrfEqEhoZqTjnRf2oDXurfhfDwcIfuBkfWzrS0NMLCwvhqwccMnFFPkxzbl5x0GOpkQunLxxlatyIF583NnEVfWbUpVfjdt28f/fv3Z9myZQ6v5ywPzpYtW9iwYYPi/9oy3vPHH3/0imvBU+Qo5QM4e/gqa6IOaRo7ankzypUrx8SJE+nbty9du3a18espfWsfPHiQ6dOnk5CQQGJiIhF9tK0jTsZec2vPojMiIyM150J9b3VLq1yolhw/fpwpU6ZQt25dZs2aRffu3R1+STlKwzF79mxatGhBu3btCA4OdriW3J2hbcOsWteCu2TbNZ8zPJXt+ty5cyxYsID09HT+/fdfwsLCCAkJIXfu3CxatIh27dpZbVMKtKzTSmj93ywaG8uWxYesApu3bNlCixa2YXPr16+nffv2itf5+++/ady4seKa79KlS+aU/CauXr3K999/z1tvvWVua9W1AZ0+cD2MDODwokJ8/dn3Lo9zdc2nLrNrNmTj7KO0fs31FHKdhllPY8qVK2fejX7v3j1u375NsWLFOH/+PG+99ZaN49WtrNPPVdQ01lV2rT1Lw46OEyEp0WtsBKtXrza7ZtLT0+0GVrdr144bN24opmS0FyW0fPlyunfvbtNevHhxunbtatVW4xnHO9kdoUXxtJDjpp0m3Ml2bVl80pJ8+fJRokQJcuXKpRjPWLZaiYBMDZGZaW9s4vwR9fk4Lfn4m+H89JMhbC137tzUqlVLsZ8QwrougwVKTvZ9+/bZzVCdmJhotVO+Vd/qPPKEawV0TGz8zDsViJXIscoH2lNOvBbVUFP55R9m/ajpQ+2trNP2CA0N1bxFqf+XDRnydj/z1qqyZcsyZoxyFM2IESPYsGGDTbvS0zJXrlyULl1a8TobN27kqaeeAuC7776zmZ2o5bNuv9sNXPAGOVr5rl+/rsm8nnjmtibli4yMZEzrFexae9alce6EgGklMjJSs+th6NymnD374G90ZB3OvH4DbHb8X7x4kSpVqiiOv3r1qlUKjm9XjXVR2gd427WQmRytfKGhoS5P5zZ+do2PnvtV85MoNDSUBe/tZUjtlaosi4cW51flWvAG9TK0hWTlL5KHNt2eMK/dihUrxnfffWfV5/Lly0ycOJG6devajM+cCaBUqVIUKlSI4cOH2xQ6Wb58ufn1pk2bNKfg0DoLcoccrXxg+IZ35npIS0nn8KJCDKm90qPTkrXT4nn8+lCWjFPOaXL+yA2+maAtH40n6Ny5s2bXwxtzGlvteO/QoQO3b98mIyODn3/+mZIlS9p9ItpLWjV58mQKFizI9OnTuXnzJseOHTPvOgFYd3684jg1+MPqn+OVD+ynnEhLSefz7tsZ3nCN1yxgkZGR7FhxgqfkhzbrwRerT7Yzyne4Y5ha++c8s1GlRIkS/PHHH1y6dIlnn33W4ThnpZxff/11ChcubGXQev6VSM3xm4cX+ad2g658RjJH9p8/coO6d4f7xNkKhvCuMa1XmOXwZtZpV4nbdEHTuP5RjfgoapT5uG3btnaNJpbYS7qUGcvEVcVrKbs0nLEm6pDPXAuZ0ZXPiGXkyM4Z6XzWY7NPLYxg+NAt/zSOIbVX+tS14IwvXo3WbHwZNu8pK+OLGpRC0xzRqm91ard0rtRKuJNoy1105bMgd2xThtReyQ/T1/hbFJ8rviO0GKZMBOfNTaPIx53W5rPk8GH1CrF48WK3XAv+RFc+Cwb1GeFvEQIWNYYpe7yz8mmn6zxLGjRQl+4jIyPDrdQQSq4FLxZRtkG18hmr08YIIdYZjysJIXYKIY4JIZYKIfIY2/Maj48bz1f0jug6vsbZViZHXLx9xG4q/cyorXb79ddfB1xqCFdw5ck3FENdPhOfAVOklNWA64Cppm8/4LqUsiowxdhPJxvgTsqJgd/WZ926daqKmZhqJToiOTmZk4V/1CQL+Me1kBm1NdnLAu2B2cZjATwNmP76+YApVL+T8Rjj+ZbCl89yHa+y/NM4zSkn3p8ymGXLllGnTh27AdfgOCnSrVu3aNy4MQOGvxiQqSFcQe2TLwoYBZhyoRcHkqSUpvDz84Ap2rgMcA7AeP6Gsb8VQogBQog9Qog9GmXX8RPHNmp7avSf2oAJX77Phg0bCAsLIzExUbGfvb1+d+7cITw8nI0bN5K32kXFPs5YE3UoIAxqoK4sdAcgUUq517JZoatUce5Bg5QzpZT11ex78hZqEsvq2LJu4XbNroc8jeMoVaoUt27d4t69e4SHhzNgwACrTciWTvbU1FQ+/PBDevXqxeXLlzl79ixdhzbMkq6FzKjZz9cUeE4I0Q7IBxTG8CQMFUIEGZ9uZQGTJ/Y8UA44L4QIAooAvtunoZLMSYZMiWUBVSkXcjotC7/LAVyv+x6cNzdlq5Xg/LErlC9fnvh4QwRNRkYGsbGxLFu2jGbNmrFt2zZeN7aPG/cgsdPWrVs1V5/6vPt2hniufKHbqCkLPUZKWVZKWRHoCWyWUvYGtgCmpI19AFORuTXGY4znN8tAWN1mwpXEsjq2uJXt+senbNpy5cpFREQEn3zyCW3btuX1118HsCn3lbmgpVrUpIZYFZNAmYFzfTYTcsfP9zYwXAhxHMOabo6xfQ5Q3Ng+HBjtnojewZXEsjrKuLPVqVYT9XUzLPGWa8E0EwoqEmaTYt/SXiiE8Jgv0CXlk1JulVJ2ML4+KaVsKKWsKqXsLqVMMbbfMx5XNZ7XZpv2Mq4mltWxxR3XQ8v/qkuMm5khtVcya6jryY+dTb4czYQsx7paDMUROTbCxV5W65FtavhJoqzJrLe3ubQ7Py0lnYtryzOlj/atWfu3nmdI7ZWq00CqcS34YyaUY5VPS2JZHVtCQ0OJX+24LJqJNVGHqHzhVT55z3VDjRJnD191Gp+5OipelWvBHzOhHJu9DAwKqCub+6xbuJ331zxjdz12/sgNng37gCHzIj1+b1N8Zq0mlek/vbbNecs0j44Y2aaGYop9b86EcrTy6XiOehmDOWcObDJw+K9EbsQV54fpm71+//1/GtaetSLL0n+qITDbFdeC6Uv4jZkbCS4SZq7v4c0v5yyRNNeUjNSiv91zOv6j45BwWr9WnbSUdK5srOT+9NLFQikmkpKSqNW4CmcPu74R2p3PU7ZMmpv5n5HZ+qQFpSpG+hTUPdrX6c+CcdP4+M05RL4X6Tc5QkNDNSmer8kSTz5Po6WElo4f0Pjkc++Wvnvy5Uhrpx7dohMIZIlpp6fRo1t07JH/0eY0nbDZJ8uRHPnk06NbdJRYFZNA8WeHkJCUbBNi5g1ypPLp0S06SkyMPkKuYOtAbm8uR3LktNM0jdCtnTqW+Ho5kiOVD/ToFh1bSoeGkKCgaN5ajuTIaaeOjhIj29QgI+2eVZs3lyO68unoGOlcpwxXf57ms2D7HDvt1NFR4u6hbewY/bT52JTnR41twFXnfMBGuJjo0KGDmvGeFkn1P3LdunU2bWpkzsoIIbD3fjkiODiYtm3bqv+Q+jnCRWskVLaK7cyqaT+zqtz2cOeLOjg42KV6DYGAo0goS+VT+gJWQ5ZQPk/jq6e9lJLk5GRCQnK28z4rKh543/UQsMpnGVgdCFNjezibYubPnz+g5ddCdvt77OFt14Nu7dTxOqaMX1ltGu7tSKiAefJldyOFTtZ7Yno7EioglK9evXo2bVntjdLJHmT+3HkzEkqfduro+ImAePLpZB2yavqNUaNGefya7dq1IzIyko4dO2oaryufD+kz9yBvvvwYX35/kPmvPuZvcVwmEIvLqP0y+Pzzzz1+761bt7o1Xlc+C7z9rX72vUWc2baRs9/Xh1dneOy6vsKe03nc2ni/PA0D5csgcyY9tVZdVconhDgN3ALSgftSyvpCiGLAUqAicBroIaW8bqxCOxVoB9wFXpFS7nPlj3EFTymM99/Ie2y58AnwCV3mO+0ckNhzLl+/m8b1uwYnui8VQG0ESqDiisGlhZQywiJmbTSwyViTfRMPqhE9C1Qz/gwApntK2MyYFMYT2/69n1QpH3AFSDL+ZD3UOpd9lYwqq+ficcfaaVl7PXNN9u+lgb8xFNEs5cZ97OJJhfHFG5mQUYKEjFASMkI9dk1fouR0tofS/83TTvasnotHrfJJYKMQYq8QYoCx7SEp5UUA4+8wY7u5JrsRy3rtHsWTCuOLN3Jxy+f5umVpdvZv47Fr+hKl4jKhIcGKfX2hAFk9F49a5WsqpayLYUo5WAjRzEFfVTXZhRADhBB7hBB7Ll++rFIMazypML54IytXPkerhqXp/PYLHrumr+lcpww7Rj/NqQnt2TH6acY+F+43BbD9MsjHgLyrH6z37v3D2aX9AAy/75/3ukyuoMrgIqW8YPydKIRYCTQE/hVClJJSXjROKxON3U012U1Y1mu3vOZMYCZA/fr1NYWzeLKyjC+SKnWZ85vxVdacdirh72RUlhEocbczqF2wJZeS7vFwaD4+mPszb9etym0J5V+YQ9UW3Ti+5UefyKUGp8onhCgA5JJS3jK+bg2M50Ht9QnY1mR/QwixBGgE3DBNTz2Np994bydVyiCUu4C2wsb+QY01OVCSUdUumItH+y7k0LzeAIQ/VJICjV9m1YrVdO7Sibrs97OE1qh58j0ErDQuloOARVLKX4QQu4FlQoh+wFmgu7H/BgxuhuMYXA19PS61BYHyxqshF1lP8QLBj6aWdV+8y8cZ0Vw5+jjbP36BsblGc+7r+ry2cTfTOtUl5RmvGd41ERBpJOrXry/37NnjbzG8QlYuYdZ0wmbF/WxlQkOs8pw4w9LK6dL/wo00EtuvpdOsmDrLrFa2bt1KZGSk+VgvlKLjMbKyH83biucJdOXTsUtW96P5Ei0+TF35vIi7gbf+Jqv70XyJlqVFtgqs9vT6yt3rRUZGMm3aNI9dLzPOrufu/fztRsjuZCvlC0TeeOMNf4vgFlnJmpzVCAjl27t3r+KcOataCXWscWWbTU5CX/Pp6PiJgHjyATbpx7VuzdfRySroTz4dHT+hK5+Ojp/QlU9Hx08EzJovMxIMsX1eHpOdr+fp+7mD2W4dIPIEAgGrfAINxQYDzMnu7+sFUlC3ydXgcmB1Nkafduro+ImAffLpZC8C5QkcSOhPPh0dP6Ern46On9CnnTo6bmCZ46bMwLkuJWzWlU9HRyN/XkhjwaYHOW6CioQxZsUBcoUULqZmvD7tdIP+/ftrHrsqJoGmEzZTafR6mk7YrCnFvY5/+elommLG9NwFi6nag5WtlM/TFjVn15s1axavvPKKy9fzVI0JZ/LpFkbvcvWe8v9X5A7Ko2Z8tlI+f/Ddd9+5PMb7RVl0fEHxfMpBADL9fqqa8bry+YGsnBVMC0IIq5/sQtfqwYo5btJvX1M1hdGVzw/oWcGyB01KB1vVirh/I5FPuzxORvLNa2rG69ZOj5EKF9dx/8JJguq9Cdif9nuyxkRWwHKjdHbbJG2Z40YIQefp6tfZuvJ5CCHyIqUkqJTzgGY9K5gO6MrnMW5Jydubr3ApKZlbKqyMelYw3zJ/vvpa3Nu3b1fVb9KkSVrFAXTl8xiDRQvmyy3+FkPHDn369PFKX3fQlc9DfH1kCFXFQGaOOkDksM/JVaqpv0XSseCNN94gOdmz1uT333+fihUrah4fEFWKhBC3gEBzcpUArvhbCAsCTR7QZbJHBSllSWedAuXJd0RNSSVfIoTYE0gyBZo8oMvkLrqfT0fHT+jKp6PjJwJF+Wb6WwAFAk2mQJMHdJncIiAMLjo6OZFAefLp6OQ4/K58Qoi2QogjQojjQojRPrzvXCFEohDioEVbMSHEr0KIY8bfRY3tQgjxpVHG/UKIul6Qp5wQYosQ4pAQIl4IMdSfMgkh8gkhdgkh4ozyjDO2VxJC7DTKs1QIkcfYntd4fNx4vqIn5ckkW24hRIwQYl2gyKQJKaXffoDcwAmgMoZI5Digpo/u3QyoCxy0aPscGG18PRr4zPi6HfAzhly+jYGdXpCnFFDX+LoQcBSo6S+ZjNctaHwdDOw03mcZ0NPYPgN43fh6EDDD+LonsNSL791wYBGwznjsd5k0/R1+vTk8AURbHI8Bxvjw/hUzKd8RoJTxdSkM/keAb4EXlfp5UbbVwDOBIBOQH9gHNMLgwA7K/P4B0cATxtdBxn7CC7KUBTYBTwPrjF8SfpVJ64+/p51lgHMWx+eNbf7iISnlRQDj7zBju0/lNE6P6mB42vhNJuP0LhZIBH7FMEtJklLeV7inWR7j+RtAcU/KYyQKGAVkGI+LB4BMmvC38iltaw5E86vP5BRCFAR+AoZJKW/6UyYpZbqUMgLD06Yh8KiDe3pdHiFEByBRSrnXstmfMrmDv5XvPFDO4rgscMFPsgD8K4QoBWD8nWhs94mcQohgDIq3UEq5IhBkApBSJgFbMaz5QoUQprBEy3ua5TGeLwKo2tHtAk2B54QQp4ElGKaeUX6WSTP+Vr7dQDWjtSoPhkXxGj/KswYw7Sfpg2HdZWp/2WhhbAzcME0FPYUwJDeZAxySUk72t0xCiJJCiFDj6xCgFXAI2AJ0syOPSc5uwGZpXGx5CinlGCllWSllRQyflc1Syt7+lMkt/L3oxGC1O4phPfGuD++7GLgIpGH4huyHYT2wCThm/F3M2FcAXxtlPADU94I8T2KYEu0HYo0/7fwlE1ALiDHKcxD4wNheGdgFHAeWA3mN7fmMx8eN5yt7+f2L5IG1MyBkcvVHj3DR0fET/p526ujkWHTl09HxE7ry6ej4CV35dHT8hK58Ojp+Qlc+HR0/oSufjo6f0JVPR8dP/D+bomWCq/B6rwAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -269,7 +269,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -281,7 +281,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -293,7 +293,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -305,7 +305,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -317,7 +317,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -329,7 +329,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -341,7 +341,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -375,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 289,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -408,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 290,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -440,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 356,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -460,7 +460,6 @@
     "    #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",
@@ -468,7 +467,6 @@
     "                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",
@@ -476,7 +474,6 @@
     "        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",
@@ -488,7 +485,7 @@
     "    return xtrain, xtest, ytrain, ytest\n",
     "\n",
     "\n",
-    "def prepTraining(df, l=7):\n",
+    "def prepTraining(df, scaler=Normalizer(), l=2):\n",
     "    x = []\n",
     "    y = []\n",
     "    scaler = Normalizer()\n",
@@ -497,7 +494,7 @@
     "        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",
+    "            if len(tx) >= l:\n",
     "                break\n",
     "        x.append(tx)\n",
     "        ty = (dataframe[\"px\"]+lokalen.index(dataframe[\"location\"])*10, dataframe[\"py\"]+lokalen.index(dataframe[\"location\"])*10)\n",
@@ -528,26 +525,26 @@
     "\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."
+    "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. Dit is waarom het de baseline is van deze opgave."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 312,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model score 0.053549619325076736\n",
+      "Model score 0.053549619325076785\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"
+      "Mean: 0.11459860042677367\n"
      ]
     }
    ],
@@ -560,6 +557,7 @@
     "\n",
     "\n",
     "def LinReg():\n",
+    "    xtrain, xtest, ytrain, ytest = prepTraining(d)\n",
     "    lr = LinearRegression().fit(xtrain, ytrain)\n",
     "    score(lr)\n",
     "    return lr\n",
@@ -594,29 +592,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 389,
+   "execution_count": 49,
    "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",
+      "GeneratingOptimalAlphaRBF\n",
       "\n"
      ]
     },
@@ -624,11 +608,9 @@
      "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",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.33373797e-05]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 4, '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",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-3.67252169e-05]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 4, 'warnflag': 2}\n",
       "  ConvergenceWarning)\n"
      ]
     },
@@ -636,25 +618,185 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Kfold:\n",
-      "Score: [0.25083644 0.01026288 0.08810267]\n",
-      "Mean: 0.11640066158846989\n",
+      "Optimal alpha for rbf kernel 1.275\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",
+      "GeneratingOptimalAlphaDotProduct\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([84.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 78, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([348.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 48, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([20.5]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 63, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([11.125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([15.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 42, 'nit': 1, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-38.25]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 46, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.25]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([9.375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([5.35546875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 71, 'nit': 5, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([63.90625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 48, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-8.34375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 56, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([20.6875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 68, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([65.1875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-3.3125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.75]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 42, 'nit': 1, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-24.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 51, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([2.609375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 75, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.453125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.296875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 87, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.8125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 51, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([3.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 59, 'nit': 6, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([2.609375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-30.1640625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 59, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([18.078125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 46, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.71875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 46, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-23.609375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 88, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.1171875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.671875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 42, 'nit': 1, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([1.41796875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-14.09765625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 58, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([26.01953125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 54, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.10546875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-26.19140625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-16.40234375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 67, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.6796875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-6.94140625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 117, 'nit': 6, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-11.21679688]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 87, 'nit': 6, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.37890625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.22265625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([2.03515625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 99, 'nit': 5, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.69921875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.7578125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 73, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.99804688]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-4.85742188]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 62, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.59667969]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 44, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.58984375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 28, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-4.69726562]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 43, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-1.68945312]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 82, 'nit': 6, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.3359375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.52978516]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 69, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.96875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 48, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.04345703]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 43, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.72167969]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 74, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-1.03710938]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 62, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.6171875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.61621094]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 79, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.97216797]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 4, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.68652344]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 67, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.66503906]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 64, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.76708984]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 62, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.73803711]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.48022461]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 54, 'nit': 3, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.17260742]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 43, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimal alpha for dotproduct kernel 0.04\n",
       "\n",
       "\n",
       "\n",
+      "---\n",
+      "Generating white kernel noise level for rbf\n",
       "\n",
+      "Optimal noise level for rbf 1\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "Generating white kernel noise for DotProd\n",
+      "\n",
+      "Optimal noise level for dot 1\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "White Kernel\n",
       "Model score -1.3040243750461273\n",
       "CrosValScore [-1.80544992 -1.09822479 -1.12201902]\n",
       "Mean -1.3418979083128233\n",
@@ -667,14 +809,76 @@
       "\n",
       "\n",
       "\n",
-      "Model score -1.3040243750461273\n",
-      "CrosValScore [-1.80544992 -1.09822479 -1.12201902]\n",
-      "Mean -1.3418979083128233\n",
+      "---\n",
+      "DotProduct\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n",
+      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model score 0.05304424748634964\n",
+      "CrosValScore [0.08041413 0.05850993 0.26145889]\n",
+      "Mean 0.1334609854955265\n",
       "\n",
       "\n",
       "Kfold:\n",
-      "Score: [-1.59551739 -1.24587679 -1.11763551]\n",
-      "Mean: -1.3196765647323825\n"
+      "Score: [0.25166842 0.00850081 0.08625134]\n",
+      "Mean: 0.11547352452721255\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "Rbf\n",
+      "Model score 0.0485491868165403\n",
+      "CrosValScore [0.06244162 0.10407524 0.32431861]\n",
+      "Mean 0.16361182171401337\n",
+      "\n",
+      "\n",
+      "Kfold:\n",
+      "Score: [ 0.26362672 -0.25184796  0.09040504]\n",
+      "Mean: 0.034061269824504775\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "DotWhite\n",
+      "Model score -0.013242764071176399\n",
+      "CrosValScore [-0.10300226 -0.03773465 -0.00324282]\n",
+      "Mean -0.04799324044247397\n",
+      "\n",
+      "\n",
+      "Kfold:\n",
+      "Score: [-0.04027678 -0.00891338 -0.00084781]\n",
+      "Mean: -0.01667932332465106\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "---\n",
+      "RbfWhite\n",
+      "Model score -1.2725604583700336\n",
+      "CrosValScore [-1.79311122 -1.08885394 -1.1123998 ]\n",
+      "Mean -1.3314549845918533\n",
+      "\n",
+      "\n",
+      "Kfold:\n",
+      "Score: [-1.58380539 -1.23608991 -1.10878677]\n",
+      "Mean: -1.3095606923592664\n"
      ]
     }
    ],
@@ -683,43 +887,107 @@
     "from sklearn.gaussian_process.kernels import RBF, DotProduct, WhiteKernel\n",
     "\n",
     "def GaussProc(alpha=.08, kernel=RBF()):\n",
+    "    xtrain, xtest, ytrain, ytest = prepTraining(d)\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",
+    "    return gp, cross_val_score(gp, xtest, ytest, cv = 3).mean()\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",
+    "pr=False #set to true for every score it gets\n",
+    "print(\"---\\nGeneratingOptimalAlphaRBF\\n\")\n",
     "\n",
-    "optimal = .1\n",
+    "for i in np.arange(0.005,5, .005):\n",
+    "    model, sc = GaussProc(alpha=i, kernel=kern)\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        if pr:\n",
+    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
+    "\n",
+    "rbfAlpha = optimal\n",
+    "print(\"Optimal alpha for rbf kernel {}\\n\\n\\n\".format(rbfAlpha))\n",
     "\n",
     "\n",
-    "model, dump = GaussProc(alpha=optimal, kernel=WhiteKernel())\n",
+    "\n",
+    "print(\"---\\nGeneratingOptimalAlphaDotProduct\\n\")\n",
+    "\n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "kern = DotProduct()\n",
+    "        \n",
+    "for i in np.arange(0.005,5, .005):\n",
+    "    model, sc = GaussProc(alpha=i, kernel=kern)\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        if pr:\n",
+    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
+    "        \n",
+    "dotAlpha = optimal\n",
+    "print(\"Optimal alpha for dotproduct kernel {} with score {}\\n\\n\\n\".format(dotAlpha, lastScore))\n",
+    "\n",
+    "\n",
+    "\n",
+    "print(\"---\\nGenerating white kernel noise level for rbf\\n\")\n",
+    "kern = RBF()\n",
+    "    \n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "\n",
+    "for i in np.arange(0.005,5, .005):\n",
+    "    model, sc = GaussProc(alpha=rbfAlpha, kernel=kern+WhiteKernel(noise_level=i))\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        if pr:\n",
+    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
+    "\n",
+    "rbfWhite = optimal    \n",
+    "\n",
+    "print(\"Optimal noise level for rbf {} with score {}\\n\\n\\n\".format(rbfWhite, lastScore))\n",
+    "\n",
+    "print(\"---\\nGenerating white kernel noise for DotProd\\n\")\n",
+    "\n",
+    "kern = DotProduct()\n",
+    "    \n",
+    "lastScore = 0\n",
+    "optimal = 1\n",
+    "        \n",
+    "for i in np.arange(0.005,5, .005):\n",
+    "    model, sc = GaussProc(alpha=dotAlpha, kernel=kern+WhiteKernel(noise_level=i))\n",
+    "    if sc > lastScore:\n",
+    "        lastScore = sc\n",
+    "        optimal = i\n",
+    "        if pr:\n",
+    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
+    "\n",
+    "dotWhite = optimal        \n",
+    "print(\"Optimal noise level for dot {} with score {}\\n\\n\\n\".format(dotWhite, lastScore))\n",
+    "\n",
+    "\n",
+    "\n",
+    "print(\"---\\nWhite Kernel\")\n",
+    "model, dump = GaussProc(alpha=1, kernel=WhiteKernel())\n",
     "score(model)\n",
     "print(\"\\n\\n\\n\")\n",
-    "\n",
-    "model, dump = GaussProc(alpha=optimal, kernel=DotProduct())\n",
+    "print(\"---\\nDotProduct\")\n",
+    "model, dump = GaussProc(alpha=dotAlpha, 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",
+    "print(\"---\\nRbf\")\n",
+    "model, dump = GaussProc(alpha=rbfAlpha, kernel=RBF())\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",
+    "print(\"---\\nDotWhite\")\n",
+    "model, dump = GaussProc(alpha=dotAlpha, kernel=DotProduct() + WhiteKernel(noise_level = dotWhite))\n",
+    "score(model)\n",
+    "print(\"\\n\\n\\n\")\n",
+    "print(\"---\\nRbfWhite\")\n",
+    "model, dump = GaussProc(alpha=rbfAlpha, kernel=RBF() + WhiteKernel(noise_level = rbfWhite))\n",
     "score(model)\n",
     "#a, b = GaussProc(alpha=0.04908, kernel=DotProduct() * WhiteKernel())\n",
     "#print(b)"
@@ -731,32 +999,47 @@
    "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",
+    "Dit model werd gekozen omdat een decision tree beter past bij het voorspellen van welk lokaal een bepaalde value in komt, moest dit een klassificatie probleem zijn (dit is het jammer genoeg niet). Door dus de lokalen op hogere waarden te steken (tientallen ipv values tussen 0 en 1) kunnen we het model nog simpele decisions geven (bv tussen 0 en 10 is 1 lokaal, tussen 10 en 20 is gang etc). 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. "
+    "Er was nog geen vorm van decision tree aanwezig in het project, en Random Forest had de beste resultaten voor deze opgave. 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,
+   "execution_count": 42,
    "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",
+      "-0.03583447235599587 en 0.15000000000000002 voor dep\n",
+      "0.08730520188582598 en 1.0 voor dep\n",
+      "0.11835171292421753 en 1.05 voor dep\n",
+      "0.12800809141118277 en 1.1 voor dep\n",
+      "0.1309682618539385 en 1.2000000000000002 voor dep\n",
+      "0.1325868840100242 en 1.8 voor dep\n",
+      "0.18389298719551705 en 2.0500000000000003 voor dep\n",
+      "0.2033019128173553 en 2.6500000000000004 voor dep\n",
+      "0.024595002239385173 en 1 voor est\n",
+      "0.1414475584387812 en 2 voor est\n",
+      "0.19844434825486879 en 6 voor est\n",
+      "0.2045974213542903 en 23 voor est\n",
+      "\n",
+      "\n",
+      "\n",
+      "Optimal Estimations 23 en optimal depth 2.6500000000000004\n",
+      "\n",
+      "\n",
+      "\n",
+      "Model score 0.05451023322649307\n",
+      "CrosValScore [0.11198684 0.02559293 0.35556681]\n",
+      "Mean 0.16299764209737205\n",
       "\n",
       "\n",
       "Kfold:\n",
-      "Score: [ 0.2731529  -0.06379346  0.1188839 ]\n",
-      "Mean: 0.12751157073606273\n"
+      "Score: [ 0.35889054 -0.01211664  0.08784591]\n",
+      "Mean: 0.11315903629642887\n"
      ]
     }
    ],
@@ -765,84 +1048,39 @@
     "\n",
     "\n",
     "def rfor(est=5, dep=50):\n",
+    "    xtrain, xtest, ytrain, ytest = prepTraining(d, scaler=MinMaxScaler())\n",
     "    lr =  RandomForestRegressor(n_estimators=est, max_depth=dep)\n",
     "    lr.fit(xtrain, ytrain)\n",
-    "    return lr, lr.score(xtest, ytest)\n",
+    "    return lr, cross_val_score(lr, xtest, ytest, cv = 3).mean()\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",
+    "for i in np.arange(0,20,.05):\n",
+    "    if i == 0:\n",
+    "        model, lastScore = rfor(dep=0.05, est=25)\n",
     "        continue\n",
-    "    model, sc = rfor(dep=i)\n",
+    "    model, sc = rfor(dep=i, est=25)\n",
     "    if sc > lastScore:\n",
     "        lastScore = sc\n",
     "        optimal = i\n",
-    "        print(\"{} en {}\".format(lastScore, optimal))\n",
-    "    break\n",
+    "        print(\"{} en {} voor dep\".format(lastScore, optimal))\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",
+    "for i in range(1,140):\n",
+    "    model, sc = rfor(est=i, dep=de)\n",
     "    if sc > lastScore:\n",
     "        lastScore = sc\n",
     "        optimal = i\n",
-    "        print(\"{} en {}\".format(lastScore, optimal))\n",
+    "        print(\"{} en {} voor est\".format(lastScore, optimal))\n",
     "\n",
-    "    \n",
+    "print(\"\\n\\n\\nOptimal Estimations {} en optimal depth {}\\n\\n\\n\".format(optimal, de))\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": {},