project_lolol.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network Analytics Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Project: CIS6930 Network Analytics & Machine Learning\n",
      "Group Members: ['Pankaj Chand', 'Keith Levengood', 'Nicholas Kroeger', 'Varun Puri']\n",
      "\n",
      "Library Versions\n",
      "### Python version: 3.7.6 (default, Jan  8 2020, 13:42:34) \n",
      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "### Pandas Version: 1.2.2\n",
      "### MatPlotLib version:3.3.4\n",
      "------------\n"
     ]
    }
   ],
   "source": [
    "group_members=['Pankaj Chand', 'Keith Levengood', 'Nicholas Kroeger', 'Varun Puri']\n",
    "print('Project: CIS6930 Network Analytics & Machine Learning')\n",
    "print('Group Members:', group_members)\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from os import listdir\n",
    "from datetime import datetime\n",
    "import pickle\n",
    "\n",
    "print('\\nLibrary Versions')\n",
    "print('### Python version: ' + __import__('sys').version)\n",
    "print('### Pandas Version: ' + pd.__version__)\n",
    "print('### MatPlotLib version:' + matplotlib.__version__)\n",
    "print('------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "# Helper function to get the prefix of each APNAME \n",
    "def convertAPNameToPrefix(df):\n",
    "    prefixes = []\n",
    "    for i in range(len(df)):\n",
    "        tempPrefix = [\"\".join(x) for _, x in itertools.groupby(df.iloc[i], key=str.isdigit)][0]\n",
    "        prefixes.append(tempPrefix)\n",
    "    return prefixes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "loadPreprocessedData = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if loadPreprocessedData == False:\n",
    "    #load in csv, append APNAME to end of dataframe, save it out as .pkl\n",
    "    data = pd.read_csv('DHCP_April_2012_ANON_MAC_devType.csv')\n",
    "    print(\"loaded first dataframe\")\n",
    "    \n",
    "    print(\"Appending prefix name to table... will take a few minutes\")\n",
    "    \n",
    "    prefixes_col   = convertAPNameToPrefix(data['APNAME'])\n",
    "    data['prefix'] = prefixes_col\n",
    "    \n",
    "    print(\"Saving out new dataframe to .pkl file\")\n",
    "    data.to_pickle(\"./DHCP_April_2012_ANON_MAC_devType_wAPNAME.pkl\")\n",
    "else:\n",
    "    #load in preprecossed data\n",
    "    data = pd.read_pickle(\"./DHCP_April_2012_ANON_MAC_devType_wAPNAME.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(           userIP  userMAC                APNAME              APMAC  \\\n",
       " 0   10.131.172.58    26257  fli113-win-lap3502-1  44:e4:d9:00:ed:e0   \n",
       " 1        10.0.1.3    22699  hrb112-win-lap1231-1  00:1a:a2:09:93:90   \n",
       " 2  10.132.190.205    30264  soc1402-temp-lap3502  58:35:d9:d5:cd:c0   \n",
       " 3  10.130.146.252    72815  soc1402-temp-lap3502  58:35:d9:d5:cd:c0   \n",
       " 4   10.130.248.68    69249               unknown  00:00:00:00:00:00   \n",
       " \n",
       "     startTime     endTime deviceType   prefix  \n",
       " 0  1333238682  1333238682          f      fli  \n",
       " 1  1333238682  1333238687    unknown      hrb  \n",
       " 2  1333238685  1333238687          f      soc  \n",
       " 3  1333238688  1333238688          f      soc  \n",
       " 4  1333238690  1333238690          f  unknown  ,\n",
       " (22821939, 8))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(), data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012-04-01 00:04:41\n",
      "2012-05-01 00:05:20\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "print(pd.to_datetime(data['startTime'].min(), unit='s'))\n",
    "print(pd.to_datetime(data['endTime'].max(), unit='s'))\n",
    "data['startTime'].hist(bins=1000)\n",
    "data['endTime'].hist(bins=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load in prefix and building information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "prefix_lat_lon = pd.read_csv('prefix_lat_lon_name_category.csv')\n",
    "legalPrefixes = prefix_lat_lon.prefix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    prefix        lat        lon                             name  category\n",
      "0      aaf  29.650275 -82.345720         Academic Advising Center     admin\n",
      "1      adv  29.648792 -82.359303  Martin H. Levin Advocacy Center  academic\n",
      "2      aer  29.643272 -82.348300       Mec. Aerospace Engineering  academic\n",
      "3     aerv  29.643272 -82.348300       Mec. Aerospace Engineering  academic\n",
      "4      alf  29.642948 -82.348636   Alfred A. Ring Tennis Pavilion    sports\n",
      "..     ...        ...        ...                              ...       ...\n",
      "189   weim  29.647761 -82.347342                      Wiemer Hall  academic\n",
      "190  weimG  29.647761 -82.347342                      Wiemer Hall  academic\n",
      "191  weimg  29.647761 -82.347342                      Wiemer Hall  academic\n",
      "192     wm  29.648052 -82.346709                  Williamson Hall  academic\n",
      "193    yon  29.649968 -82.347888                         Yon Hall  academic\n",
      "\n",
      "[194 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "print(prefix_lat_lon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     aaf\n",
       "1     adv\n",
       "2     aer\n",
       "3    aerv\n",
       "4     alf\n",
       "Name: prefix, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legalPrefixes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prefixes found from our preprocessing function\n",
    "#prefixesFound = np.unique(data.prefix)\n",
    "prefixesFound = data['prefix'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prefixes in the base table that don't match in the prefix table \n",
    "#TODO: more preprocessing to keep some of these?\n",
    "unknownPrefixes = list(np.setdiff1d(prefixesFound, np.asarray(legalPrefixes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AP',\n",
       " 'APc',\n",
       " 'APe',\n",
       " 'APf',\n",
       " 'apopka',\n",
       " 'apopka-win-lap',\n",
       " 'b',\n",
       " 'cba-ftlauderdale-win-lap',\n",
       " 'ctx',\n",
       " 'dairy-win-lap',\n",
       " 'ecob',\n",
       " 'escv',\n",
       " 'flrec',\n",
       " 'hotidf-win-lap',\n",
       " 'irrec',\n",
       " 'mowv',\n",
       " 'ohl',\n",
       " 'osbs-win-lap',\n",
       " 'osbs-xxx-win-lap',\n",
       " 'osbstr',\n",
       " 'pep',\n",
       " 'presAttic-wout-lap',\n",
       " 'presStairs-win-lap',\n",
       " 'rl',\n",
       " 'rubygillhs-win-lap',\n",
       " 'treeo-win-lap',\n",
       " 'uic-win-lap',\n",
       " 'unknown',\n",
       " 'wrt']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unknownPrefixes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18867555, 8)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#keep only valid prefixes (based on the prefix_lat_lon_name_category.csv table)\n",
    "#removes about 400k rows?\n",
    "data = data[~data['prefix'].isin(unknownPrefixes)]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9179058, 8)\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userIP</th>\n",
       "      <th>userMAC</th>\n",
       "      <th>APNAME</th>\n",
       "      <th>APMAC</th>\n",
       "      <th>startTime</th>\n",
       "      <th>endTime</th>\n",
       "      <th>deviceType</th>\n",
       "      <th>prefix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>10.131.163.198</td>\n",
       "      <td>12544</td>\n",
       "      <td>gri101-wout-lap1231-1</td>\n",
       "      <td>00:1a:e2:10:ba:90</td>\n",
       "      <td>1333238683</td>\n",
       "      <td>1333238723</td>\n",
       "      <td>f</td>\n",
       "      <td>gri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>10.132.190.177</td>\n",
       "      <td>33782</td>\n",
       "      <td>soc-temp-lap3502-5</td>\n",
       "      <td>88:f0:77:f2:67:a0</td>\n",
       "      <td>1333238693</td>\n",
       "      <td>1333238733</td>\n",
       "      <td>f</td>\n",
       "      <td>soc-temp-lap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>10.132.251.191</td>\n",
       "      <td>85756</td>\n",
       "      <td>soc-temp-lap3502-2</td>\n",
       "      <td>88:f0:77:f2:5f:a0</td>\n",
       "      <td>1333238692</td>\n",
       "      <td>1333238736</td>\n",
       "      <td>f</td>\n",
       "      <td>soc-temp-lap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>10.132.190.116</td>\n",
       "      <td>59170</td>\n",
       "      <td>soc-temp-lap3502-4</td>\n",
       "      <td>88:f0:77:f2:60:e0</td>\n",
       "      <td>1333238694</td>\n",
       "      <td>1333238738</td>\n",
       "      <td>f</td>\n",
       "      <td>soc-temp-lap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>10.132.255.218</td>\n",
       "      <td>23629</td>\n",
       "      <td>soc-temp-lap3502-1</td>\n",
       "      <td>88:f0:77:f2:6b:80</td>\n",
       "      <td>1333238691</td>\n",
       "      <td>1333238741</td>\n",
       "      <td>unknown</td>\n",
       "      <td>soc-temp-lap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            userIP  userMAC                 APNAME              APMAC  \\\n",
       "32  10.131.163.198    12544  gri101-wout-lap1231-1  00:1a:e2:10:ba:90   \n",
       "41  10.132.190.177    33782     soc-temp-lap3502-5  88:f0:77:f2:67:a0   \n",
       "42  10.132.251.191    85756     soc-temp-lap3502-2  88:f0:77:f2:5f:a0   \n",
       "45  10.132.190.116    59170     soc-temp-lap3502-4  88:f0:77:f2:60:e0   \n",
       "46  10.132.255.218    23629     soc-temp-lap3502-1  88:f0:77:f2:6b:80   \n",
       "\n",
       "     startTime     endTime deviceType        prefix  \n",
       "32  1333238683  1333238723          f           gri  \n",
       "41  1333238693  1333238733          f  soc-temp-lap  \n",
       "42  1333238692  1333238736          f  soc-temp-lap  \n",
       "45  1333238694  1333238738          f  soc-temp-lap  \n",
       "46  1333238691  1333238741    unknown  soc-temp-lap  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep only sessions longer than 10 seconds. Removes ~ 7 million receords. \n",
    "data = data[data['endTime']-data['startTime'] > 30]\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3722227, 8)\n"
     ]
    }
   ],
   "source": [
    "# Remove all records with users who have had fewer than 500 sessions \n",
    "data = data.groupby('userMAC').filter(lambda x : len(x)>500)\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get information about each user (useful for prediction later)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "numUsers = data['userMAC'].unique().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prefix</th>\n",
       "      <th>name</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aaf</td>\n",
       "      <td>Academic Advising Center</td>\n",
       "      <td>admin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adv</td>\n",
       "      <td>Martin H. Levin Advocacy Center</td>\n",
       "      <td>academic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aer</td>\n",
       "      <td>Mec. Aerospace Engineering</td>\n",
       "      <td>academic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aerv</td>\n",
       "      <td>Mec. Aerospace Engineering</td>\n",
       "      <td>academic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alf</td>\n",
       "      <td>Alfred A. Ring Tennis Pavilion</td>\n",
       "      <td>sports</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  prefix                             name  category\n",
       "0    aaf         Academic Advising Center     admin\n",
       "1    adv  Martin H. Levin Advocacy Center  academic\n",
       "2    aer       Mec. Aerospace Engineering  academic\n",
       "3   aerv       Mec. Aerospace Engineering  academic\n",
       "4    alf   Alfred A. Ring Tennis Pavilion    sports"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_info = prefix_lat_lon[['prefix','name','category']]\n",
    "location_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Association Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(721,)\n"
     ]
    }
   ],
   "source": [
    "data_start = data['startTime'].min()\n",
    "data_end = data['endTime'].max()\n",
    "step = 3600\n",
    "time_slots = np.arange(data_start, data_end, step)\n",
    "#time_slots[-1] = data_end\n",
    "print(time_slots.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      " 1335744282 1335747882 1335751482 1335755082 1335758682 1335762282\n",
      " 1335765882 1335769482 1335773082 1335776682 1335780282 1335783882\n",
      " 1335787482 1335791082 1335794682 1335798282 1335801882 1335805482\n",
      " 1335809082 1335812682 1335816282 1335819882 1335823482 1335827082\n",
      " 1335830682]\n"
     ]
    }
   ],
   "source": [
    "print(data_start, data_end)\n",
    "print(time_slots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "137\n"
     ]
    }
   ],
   "source": [
    "numLocations = location_info['name'].unique().shape[0]\n",
    "print(numLocations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encounter_length(sa, ea, sb, eb):\n",
    "    smax = sa if sa > sb else sb\n",
    "    emin = ea if ea < eb else eb\n",
    "    return emin - smax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ncls import NCLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create dict mapping prefix to building name\n",
    "name_prefix_codes = dict(zip(location_info.prefix, location_info.name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateAssociationMatrices():\n",
    "    ass_mats = [0]*numUsers\n",
    "    usermacs = [0]*numUsers\n",
    "    inds = np.arange(0, len(time_slots))\n",
    "    #print(\"indx\", inds)\n",
    "    #print(time_slots)\n",
    "    int_tree = NCLS(time_slots.astype(np.int64), (time_slots + step).astype(np.int64), inds.astype(np.int64))\n",
    "\n",
    "    counter = 0\n",
    "    for user in data['userMAC'].unique():\n",
    "        if (counter % 5 == 0):\n",
    "            print(\"User \", counter, '/', numUsers)\n",
    "        #ass_mat = np.zeroes((time_slots.shape[0], numLocations))\n",
    "        ass_mat = pd.DataFrame(0, index=time_slots, columns=location_info['name'].unique())\n",
    "        userSessions = data[data['userMAC'] == user]\n",
    "        \n",
    "        for session in userSessions.iterrows():\n",
    "            start = session[1]['startTime']\n",
    "            end =  session[1]['endTime']\n",
    "            overlaps = int_tree.find_overlap(start, end)\n",
    "            for o in overlaps:\n",
    "                enc_len = encounter_length(start, end, o[0], o[1])\n",
    "                prefix = session[1]['prefix']\n",
    "                #sessionBuildingName = location_info[location_info['prefix'] == prefix] ['name']\n",
    "                sessionBuildingName = name_prefix_codes[prefix]\n",
    "                ass_mat.loc[o[0]][sessionBuildingName] += enc_len\n",
    "        \n",
    "        ass_mats[counter] = ass_mat\n",
    "        usermacs[counter] = user\n",
    "        counter+=1\n",
    "\n",
    "    return ass_mats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateBuildingVectors():\n",
    "    bldg_vecs = [0]*numUsers\n",
    "    usermacs  = [0]*numUsers\n",
    "    inds = np.arange(0, len(time_slots))\n",
    "    #print(\"indx\", inds)\n",
    "    #print(time_slots)\n",
    "    int_tree = NCLS(time_slots.astype(np.int64), (time_slots + step).astype(np.int64), inds.astype(np.int64))\n",
    "    np.warnings.filterwarnings('ignore', category=np.VisibleDeprecationWarning)\n",
    "    counter = 0\n",
    "    for user in data['userMAC'].unique():\n",
    "        if (counter % 5 == 0):\n",
    "            print(\"User \", counter, '/', numUsers)\n",
    "        if counter > 2500:\n",
    "            break\n",
    "        #ass_mat = np.zeroes((time_slots.shape[0], numLocations))\n",
    "        ass_mat = pd.DataFrame(0, index=time_slots, columns=location_info['name'].unique())\n",
    "        userSessions = data[data['userMAC'] == user]\n",
    "        \n",
    "        for session in userSessions.iterrows():\n",
    "            start = session[1]['startTime']\n",
    "            end =  session[1]['endTime']\n",
    "            overlaps = int_tree.find_overlap(start, end)\n",
    "            for o in overlaps:\n",
    "                enc_len = encounter_length(start, end, o[0], o[1])\n",
    "                prefix = session[1]['prefix']\n",
    "                #sessionBuildingName = location_info[location_info['prefix'] == prefix] ['name']\n",
    "                sessionBuildingName = name_prefix_codes[prefix]\n",
    "                ass_mat.loc[o[0]][sessionBuildingName] += enc_len\n",
    "        ass_mat = np.asarray(ass_mat)\n",
    "        \n",
    "        tempBldgVec = np.zeros((len(time_slots), 1))   \n",
    "        for i in range(len(time_slots)): #loop all the time slots\n",
    "            if np.sum(ass_mat[i]) == 0: #havent been in any buildings for this time slot\n",
    "                if counter == 0: #first iter\n",
    "                    tempBldgVec[i] = np.random.randint(numLocations) #randomly pick a location \n",
    "                else: #still not in a building\n",
    "                    tempBldgVec[i] = bldg_vecs[counter-1][i] #take prev building\n",
    "            else:\n",
    "                tempBldgVec[i] = np.argmax(ass_mat[i]) #get building index\n",
    "        bldg_vecs[counter] = tempBldgVec\n",
    "        usermacs[counter]  = user\n",
    "        counter+=1\n",
    "\n",
    "    return bldg_vecs, usermacs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "User  2500 / 4567\n"
     ]
    }
   ],
   "source": [
    "#mats = generateAssociationMatrices()\n",
    "#Save association matrices (takes a long time to make them)\n",
    "#mats.to_pickle(\"./AssociationMatrices.pkl\")\n",
    "\n",
    "    \n",
    "bldgHistoryPerPerson, usermacs = generateBuildingVectors()\n",
    "with open('BldgHistoryPerPerson.npy', 'wb') as f:\n",
    "    np.save(f, bldgHistoryPerPerson)\n",
    "with open('userMacInfo_BldgHistory.npy', 'wb') as f:\n",
    "    np.save(f, usermacs)\n",
    "\n",
    "#DESCRIPTION: Each entry in the list is a user. Each user has an array over time of \n",
    "#             their most used building in that time slot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Normalize per row\n",
    "# def normalizeAssMat(mats):\n",
    "#     for i in range(0, len(mats)):\n",
    "#         mats[i] = mats[i].div(mats[i].sum(axis=1), axis=0)\n",
    "#         mats[i].fillna(value=0, inplace=True)\n",
    "#     return mats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalizedMats = normalizeAssMat(mats[:5])\n",
    "# print(normalizedMats[1].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA \n",
    "def ClusterWithAssMats():\n",
    "    #Clustering with ass_mats\n",
    "\n",
    "    numTimeSlots = len(time_slots)\n",
    "    numLocs = mats[0].shape[1]\n",
    "    X = [0]*100 #np.zeros((numUsers*numTimeSlots, numLocs))\n",
    "    for i in range(100):\n",
    "        X[i] = mats[i]\n",
    "    X = np.asarray(X)\n",
    "    print(X.shape)\n",
    "    X = X.reshape(100*numTimeSlots, numLocs)\n",
    "    print(X.shape)\n",
    "    \n",
    "    kmeans = KMeans(n_clusters = 5, random_state = 0).fit(X)\n",
    "    clusterCenters = kmeans.cluster_centers_\n",
    "    labels = kmeans.labels_ \n",
    "    print(\"labels\", labels)\n",
    "    \n",
    "    MC_Cluster = labels.reshape(100, 2881).T\n",
    "    unique, counts = np.unique(labels, return_counts=True)\n",
    "    print(unique, counts)\n",
    "    \n",
    "    #Try PCA on data and cluster it\n",
    "    pca = PCA(n_components = 3)\n",
    "    X_pca = pca.fit_transform(X)\n",
    "    print(pca.explained_variance_ratio_.cumsum())\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(projection='3d')\n",
    "    ax.scatter(X_pca[:,0], X_pca[:,1], X_pca[:,2])\n",
    "    plt.show()\n",
    "\n",
    "    kmeans_pca = KMeans(n_clusters = 5, random_state=5).fit(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2500, 721)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cluster the building time-series vector for each person\n",
    "#Temporarily use part of this array\n",
    "X = np.stack(bldgHistoryPerPerson[:2500],axis=1).squeeze().T\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(137, 137)"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(BldgsCoords), len(bldg_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " lon   -82.343903\n",
       " Name: 180, dtype: float64,\n",
       " lat    29.633778\n",
       " lon   -82.366198\n",
       " Name: 182, dtype: float64,\n",
       " lat    29.650447\n",
       " lon   -82.346867\n",
       " Name: 183, dtype: float64,\n",
       " lat    29.648549\n",
       " lon   -82.351042\n",
       " Name: 184, dtype: float64,\n",
       " lat    29.649455\n",
       " lon   -82.341582\n",
       " Name: 185, dtype: float64,\n",
       " lat    29.648418\n",
       " lon   -82.348606\n",
       " Name: 186, dtype: float64,\n",
       " lat    29.648399\n",
       " lon   -82.349265\n",
       " Name: 187, dtype: float64,\n",
       " lat    29.647761\n",
       " lon   -82.347342\n",
       " Name: 189, dtype: float64,\n",
       " lat    29.648052\n",
       " lon   -82.346709\n",
       " Name: 192, dtype: float64,\n",
       " lat    29.649968\n",
       " lon   -82.347888\n",
       " Name: 193, dtype: float64]"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BldgsCoords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('BldgsCoords.npy', 'wb') as f:\n",
    "    np.save(f, BldgsCoords)\n",
    "with open('BldgDict.npy', 'wb') as f:\n",
    "    np.save(f, bldg_dict)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Custom distance metric (based on building location)\n",
    "BldgsCoords = [0]*numLocations\n",
    "unique_bldgs = prefix_lat_lon['name'].unique()\n",
    "\n",
    "for i in range(numLocations):\n",
    "    BldgsCoords[i] = prefix_lat_lon[prefix_lat_lon.name == unique_bldgs[i]][['lat','lon']].iloc[0]\n",
    "\n",
    "\n",
    "from haversine import haversine, Unit\n",
    "import math\n",
    "import pyclustering.utils.metric\n",
    "def building_dist_metric(a, b):\n",
    "    coords_a = [BldgsCoords[int(x)] for x in a]\n",
    "    coords_b = [BldgsCoords[int(x)] for x in a]\n",
    "    \n",
    "    totalDist = 0\n",
    "    \n",
    "    for i in range(0,len(coords_a)):\n",
    "        totalDist += haversine(coords_a[i], coords_b[i])\n",
    "        \n",
    "    return math.sqrt(totalDist)\n",
    "bldg_dist_func = lambda point1, point2: building_dist_metric(point1, point2)\n",
    "bldg_dist_metric = distance_metric(type_metric.USER_DEFINED, func=bldg_dist_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "object of type 'numpy.int64' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m--------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-185-9fd6d8163c11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# Run cluster analysis and obtain results.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mkmedians_instance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mclusters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkmedians_instance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clusters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mmedoids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkmedians_instance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_medoids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pyclustering/cluster/kmedians.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m             \u001b[0;31m# Check for dimension\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__pointer_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__medians\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mNameError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Dimension of the input data and dimension of the initial medians must be equal.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'numpy.int64' has no len()"
     ]
    }
   ],
   "source": [
    "from pyclustering.cluster.kmedians import kmedians\n",
    "# Initialize initial medoids using K-Means++ algorithm\n",
    "initial_medoids = kmeans_plusplus_initializer(X, 4).initialize(return_index=True)\n",
    " \n",
    "# Create instance of K-Medoids (PAM) algorithm.\n",
    "kmedians_instance = kmedians(X, initial_medoids, metric = bldg_dist_metric)\n",
    "\n",
    "# Run cluster analysis and obtain results.\n",
    "kmedians_instance.process()\n",
    "clusters = kmedians_instance.get_clusters()\n",
    "medoids = kmedians_instance.get_medoids()\n",
    " \n",
    "# Print allocated clusters.\n",
    "print(\"medoids:\", medoids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished elbow\n"
     ]
    }
   ],
   "source": [
    "from pyclustering.cluster.kmeans import kmeans\n",
    "from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer\n",
    "from pyclustering.cluster.elbow import elbow\n",
    "from pyclustering.utils import read_sample\n",
    "from pyclustering.samples.definitions import SIMPLE_SAMPLES\n",
    " \n",
    "# create instance of Elbow method using K value from 1 to 10.\n",
    "kmin, kmax = 1, 15\n",
    "elbow_instance = elbow(X, kmin, kmax)\n",
    " \n",
    "# process input data and obtain results of analysis\n",
    "elbow_instance.process()\n",
    "amount_clusters = elbow_instance.get_amount()  # most probable amount of clusters\n",
    "wce = elbow_instance.get_wce()  # total within-cluster errors for each K\n",
    "print(\"finished elbow\")\n",
    "manhattan_metric = distance_metric(type_metric.MANHATTAN)\n",
    "\n",
    "# perform cluster analysis using K-Means algorithm\n",
    "\n",
    "centers = kmeans_plusplus_initializer(X, amount_clusters,\n",
    "                      amount_candidates=kmeans_plusplus_initializer.FARTHEST_CENTER_CANDIDATE,\n",
    "                      metric = bldg_dist_metric).initialize()\n",
    "kmeans_instance = kmeans(X, centers)\n",
    "kmeans_instance.process()\n",
    "\n",
    "# obtain clustering results and visualize them\n",
    "clusters = kmeans_instance.get_clusters()\n",
    "means    = kmeans_instance.get_centers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means = kmeans_instance.get_centers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "roundedMeans = [np.rint(x) for x in means]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZEEnnA8cDvwGLgPPM7HVJOxH6P5YTktmNZnZ3phhLS0tt/PjxWb2ffNlhB5g/H6ZODfdPOOdcoZM0IWraX3VbjCTRBPjVzJZFyyVAIzMrmNmeCyFJ/PvfoX+irAx23z3RUJxzLiuZkkSc5qZXgDVSltcAXq5OYLXREUfAOuuE6U2dc67YxUkSjc1sQflC9HzN3IdU3NZYA048EUaMgK+/Tjoa55yrnjhJYmHUsQyApM6EPgRXQf/+sGwZ3Hln0pE451z1xEkSZwGPSXpN0uvAo8AZeYmqyG2yCeyzTyj699tvSUfjnHOrL05ZjnHAFsBpQH/gD2Y2IV+BFbNrr4Wddw7NTU9HZQnLysJ655wrJvVj7t8FaB8d10kSZlan55SoTJcuoQN73XVDPad11gnLw4YlHZlzzsWTdZKQ9ACwCTAJWBatNur4xEOV6d49JIReveDVV2HChNCR3b170pE551w8ca4kSoGOmW6ecyt07w5nnBGamJYsgY02Sjoi55yLL07H9QfA+vkKpLYpK4N77gmlOn79FfbYI9yJ7ZxzxSTOlURLYJqkdwmTDwFQyJMBJaWsbEUfRPfu0L49DBoURjy99looLe6cc8UgTpK4NF9B1Dbjxq1IEADnnQdffAG33gqXXAJXXplsfM45l62sk4SZjclnILXJoEGrrrv55jAH9j/+AVttFcqKO+dcoYszM11XSeMkLZC0RNIySatMIOQqJ4UriV13DWU7xo1LOiLnnKtanNbxW4GjgY8Jxf1OBryMXQwNG8Ljj8P660Pv3l7byTlX+GJ1oZrZJ0CJmS0zs3uBffITVu3VqlW4C3v+fDjoIFjk1a+ccwUsTpL4RVJDYJKkayX9JebxLrL11mGa0/Hj4aSTwO88cc4Vqjgf8sdF+58BLATaAofkI6i6oHfvMMrp4Yfh6quTjsY55yoXJ0kcZGa/mtk8M7vMzM4GeuUrsLrgggvgmGPg4ovhqaeSjsY551YVJ0n0rWTdCZkOkNRWUpmkaZKmShpYyT69JU2WNEnS+Ghe7PJtfSV9HD0qe/2iJsFdd0FpKfTpA1OmJB2Rc86trMo5riUdDRwD7Ay8lrKpObDMzHpkOHYDYAMzmyipGTCBcEUyLWWfpsBCMzNJ2wDDzGwLSesA4wk1oyw6trOZ/Zju9QphjuvV8fXXoXJsw4bw7ruhc9s552pKpjmus7mZ7k1gNqEsx79S1s8HJmc60MxmR8diZvMlTQdaA9NS9lmQckgTQkIA2BsYZWY/RG9iFGE01cNZxFxUfv97ePLJcA/FoYfCyy+HhOGcc0mrsrnJzGaa2WhgT+C16M7r2UAbQNm+kKT2wPbAO5VsO1jSh8BzwInR6tbAlym7zYrWVTy2X9RMNX7u3LnZhlNwunQJBQFfey1Uj/URT865QhCnT2Is0FhSa2AkYbTTfdkcGDUpDQfOMrNV7tI2sxFmtgVwEHBFjJgws6FmVmpmpa2KvJ3m6KPhoovC3Ni33pp0NM45Fy9JyMx+IQx7HWxmhwNbVnmQ1ICQIB4ysycy7WtmY4GNJbUEviIMsy3XJlpXq11xRRgee9ZZMGpU0tE45+q6WElC0o5AH0KzEEBJVQcAdwPTzez6NPt0iPZDUiegEfA98BLQU1ILSS2AntG6Wq1ePXjgAdhyy1BufMaMpCNyztVlcUqFnwVcCIwws6mSNgbKqjimG6FZaoqkSdG6i4B2AGY2BDgUOF7Sb8Ai4Mho9rsfJF0BlJfCu7y8E7u2a9YslO7o0gUOOADeeQfWXjvpqJxzdVGVQ2CLSbEOgU1n7FjYc88wq92zz0L9OCndOeeylGkIbJXNTZJujH4+I+npio8cx+pS7LorDB4ML71U+RwVzjmXb9l8N30g+nldPgNxlTv55HAn9g03hMmKTjyx6mOccy5XqkwSZjYh+ukz0yXkX/+C6dOhf3/YbDPYeeeqj3HOuVyoMklImsKKu6BXYWbb5DQit4r69eHRR0OC6NUL3n8fNtwwbCsrC7PceXOUcy4fshkC2ws4AHgxevSJHi8Az+cvNJeqRQu47jqYNy90ZC9YEBLEEUeEUVDOOZcP2TQ3zQSQtJeZbZ+y6XxJE4EL8hWcW1nfvvDtt3D++bDttvDTT2E61O7dk47MOVdbxb2ZrlvKwk4xj3c5MGgQ7LcffPppSBJPPAHffJN0VM652irOh/xJwGBJn0v6HBjMimJ8roaUlYVy4gMHhkqxgwfDxhuHq4vvv086OudcbZN1kjCzCWa2LbAtsK2ZbWdmE8u318ZJgQpNeR/EsGFw443w/POw1lqw007wz3+GZHH55TB/ftKROudqi9jNRWb2s5n9XMmmVWadc7k1blxIEOV9EN27w/Dh0LMnTJ4cOrT//nfYaKMwbHbRomTjdc4Vv5yV5ZD0XoWO7RpX28pyrI5x4+Cvf4WRI8NkRn/9K5x0kk9i5JxLr1plOWKoPUWgiliXLqGMx+jR4YpiwADYYgu4/35Ytizp6JxzxSaXSSLrWepc/u22W5jl7vnnQwXZvn1h661D81QtqunonMuzXCaJN3J4LpcDEuy7L4wfD489FtYddhiUlsILL3iycM5Vrco+CUlnZ9qebjKhJHifRGbLlsFDD4XO7c8/DzWgrrwS3n47NFOl3pSXi3If116bn/M653Krun0SzaJHKXAa0Dp69Ac65SpIl38lJXD88fDRR+H+iv/+NzRLPfYYHHJI+ACH3JX76NIlnCfX53XO1ZysRzdJGgvsb2bzo+VmwHNmtmse44vFryTiWbQoJIurrgo34jVsCPvsEz7MTzstdHibrXjAysuVPSruM2NG6DQ/7TS4996Vh/A65wpDpiuJOEniI2AbM1scLTcCJpvZ5jmLtJo8SayeefPCzXlXXglLluTvdS6+GP7v//J3fufc6smUJOJMiHk/8K6kEdHyQcC/q3jhttFx6xGGyA41s5sq7NMHOJ8wOmo+cJqZvR9t+zxatwxYmu5NuOpp3hx22SXMrX300fCf/8Att4Q7uaXKH5B+W+o+r78OffqEqrU33AA9eviVhHNFxcyyfhD6IAZGj+2z2H8DoFP0vBkwA+hYYZ+dgBbR832Bd1K2fQ60zDa+zp07m4vv1VfNWrYMPytbru55X3nFrE8fM8lsrbWqf17nXG4B4y3N52rcIbBrAvMsXA3MkrRRFQlotkX1nSz0ZUwndHqn7vOmmf0YLb4NtIkZk6umysp9DBsW1ufivHvsAUOGhEmTSkpWdGQ75wpfnD6JvxNGOG1uZptJ+j3wmJl1q+LQ8uPbA2OBrcxsXpp9zgW2MLOTo+XPgB8JTVV3mNnQSo7pB/QDaNeuXeeZM2dm9X5czZs8GXbYITRtvfgi1PNC884VhFyV5TgYOBBYCGBmXxOakLIJoCkwHDgrQ4LoTihHfn7K6p3NrBOhGep0SauMpDKzoWZWamalrVq1ivF2XE3bZhu4+WYYNSqMqHLOFb44SWJJ1HZlAJKaZHOQpAaEBPGQmT2RZp9tgLuA3mb2v1kRzOyr6OccYATwxxjxugJ08slwzDHwt7/BmDFJR+Ocq0qcJDFM0h3A2pJOAV4G7sx0gCQBdwPTLc2d2ZLaAU8Ax5nZjJT1TaJ7McoTUk/ggxjxugIkhf6JDh3CSKo5c5KOyDmXSVZDYKMP+0eBLYB5wObA38xsVBWHdgOOA6ZImhStuwhoB2BmQ4C/Ab8jzHoHK4a6rgeMiNbVB/5jZi9m/c5cwWrWLHRo77ADHHdcqCPl/RPOFaY4HddTzGzrPMdTLX4zXXEZOhROPTXcxHfRRUlH41zdlauO64mSvOqOy5lTTglNTpdcAmPHJh2Nc64ycZLEDsBbkv4rabKkKZIm5yswV/tJcMcdsMkmIVnMnZt0RM65iuKU5dg7b1G4Oqu8f6Jr19A/8fzz3j/hXCHJ+s/RzGaa2UxgEWEY7P+GwzpXHdttBzfdFKZdveaapKNxzqXKOklIOlDSx8BnwBhCXaUX8hSXq2P69YMjjwz9E6+9lnQ0zrlycS7srwC6AjPMbCOgB6HWknPVJoXRThtt5P0TzhWSOEnit+hu6HqS6plZGaGWk3M50bx56J/47rswg97y5UlH5JyLkyR+imowjQUeknQTUR0n53Jl++3DBEgvvgj//GfS0Tjn4iSJ3oRO678ALwL/BQ7IR1Cubjv11DAX9sUXh0mLnHPJyfqO62Lgd1zXHvPmQadO8OuvMGkStGyZdETO1V45ueNa0nxJ86LHr5KWSaq07Ldz1dW8OTz2WOjA7tvX+yecS0qc+ySamVlzM2sOrAEcCgzOW2Suztt++zAv9vPPw3XXJR2Nc3XTat3bGk2L+iR+F7bLs9NOg8MPDwUA33gj6Wjiu/baVadrLSsL650rBnGamw5JeRwm6Wrg1zzG5hwS3HkntG8PRx0F339f5SEFpUuX0AlfnijKysJyFy+V6YpEnNpNqSOZlhLuuO6d02icq8Raa4X7J3bcMfRPPP108dR36t4dHnkEDjwQDj44zJ0xbFhY71wxyDpJmNmf8hmIc5l06gTXXw9nnAH/+hecd17SEWVn7txwv8eCBfDAA+EqwhOEKyZxJh26OdN2M/tzTiKqBh8CW7uZwbbbwtSpob7TTjuF9WVlMG4cDBqUbHwVvf56aCKbMwcaNgxXRF9/DZddFub4dq5Q5GrSocZAJ+Dj6LEd0BCYED2cyyspzGIHcNBBoX+iENv4ly8PVw+77x4SW5Mm8MwzMG0a/OEP8Pe/w6WXJh2lc1kys6wehGJ+9VOWGwBvV3FMW6AMmAZMBQZWsk8fYDIwBXgT2DZl2z7AR8AnwAVVxdi5c2dztd+QIWZg1qGDWcuWZq++mnREK3z/vVmvXiG+ww4zu+yyleP7+WezLbc0k8weeSS5OJ1LBYy3NJ+rcTquWwDNgR+i5abRukyWAueY2URJzYAJkkaZ2bSUfT4DdjOzHyXtCwwFdpBUAtwG7AXMAsZJerrCsa4OOvVUeOqp0Am89tqhIKBZuNJI0jvvhKua2bPh5ptD/0nFmJo3h7ffhv33h2OOgWXLwk/nClWc5qargfck3Sfp38BE4B+ZDjCz2WY2MXo+H5gOtK6wz5tm9mO0+DbQJnr+R+ATM/vUzJYAj+CjqRwr+iD69IH588MH8847w7vvJhOPWZg0aZddQlJ44w0488z0Satp03CD4K67htn4HnigZuN1Lo44d1zfS5jnegTwBLCjmf072+MltQe2B97JsNtJrJjIqDXwZcq2WVRIMNF5+0kaL2n8XJ+EoNYr74MYNgwefDBUi23aFKZPhx12gGOPhS++qLl4fvoJDjsMzjoL9t0X3nsvu/6RJk3guefCSKe+feG++/IcqHOrKc7NdN2A+Wb2FNAMGCRpwyyPbQoMB84ys0rrPUnqTkgS52cbE4CZDTWzUjMrbdWqVZxDXREaN27l+wz23DPcN/GXv4SqscOHw+abhxnuFizIbywTJ0LnzuH1r7sOnnwSWlTVAJtizTVDh/Zee8GJJ8Jdd+UtVOdWX7rOiooPQueygG0JTU2nA2OyOK4B8BJwdoZ9tiGUHt8sZd2OwEspyxcCF2Z6Le+4djNnmh1zTOg4Xn99s7vvNlu6NLevsXy52eDBZg0bmrVpY/bGG9U736JFZvvsE2IeMiQ3MToXBxk6ruP0SSyNTtYbuM3MbiNcUaQlScDdwHQzuz7NPu0IzVfHmdmMlE3jgE0lbSSpIXAU8HSMeF0d1K4dPPQQvPVWmAr1pJPCt/1XX83N+efPD9OrDhgAPXqE5qXy+zVWV+PG4Spk//2hf38Y7GUzXQGJkyTmS7oQOBZ4TlI9wlVCJt2A44A9JE2KHvtJ6i+pf7TP34DfAYOj7eMBzGwpcAbhKmQ6MMzMpsaI19VhXbuGDuRHHgn9Bj16QO/eMGNGlYemNXkylJaGEub/+Ac8+2zu5rlo1Cg0lR14IJx+ehgd5VxBSHeJUfEBrA+cDewSLbcDjs/2+Jp4eHOTq8yiRWZXX23WrJlZ/fpmAweG+xmytXy52V13mTVubLbBBmajR+ctVFu82Ozgg0PT0/XX5+91nEtFLpqbzOwbM7vezF6Llr8ws/vLt0t6K5fJy7lcadwYzj8fPv44ND/dcgt06BDm0l6yJPOxCxfCCSfAySeHYbbvvQe77Za/WBs2hEcfDSOmzj7b5/l2yctlLc3GOTyXczm33nowZEiYDrW0NIyI2mqrMDrJKilhNm0a/PGP4T6GSy8Nw23XWy//cTZoAA8/DEceGepRXX11/l/TuXRymSRqz2TZrlbbemt46aVwn0JJSeir6NAhzFtR7sEHQ+XZmTNh5MhQb6mkpOZirF8/xHDMMXDhhfB//1dzr+1cqjhlOZyrNSTYb79wj8Kdd4YP4n79YMQI+P3v4e67wzf6e+8N92IkoX59uP/+kJwuuQSWLg3JKunyI65uqTJJSGpkZouzOJf/13VFp0GDMJz1mGNCTahhw8L6NdYIzVBJJYhyJSUhUZWUhBLjy5bB5Zd7onA1J5vmprcAJFVVYea46ofjXDLWXjt0GJ95Zlg+99zkE0S5kpJwZXPyyaHZ6eKLK+9DcS4fsmluaijpGGAnSYdU3GhmT0Q/P8h1cM7VpLKy0GF8ySVw++2h9EehzCJXrx7ccUdIGFddFZqerrnGryhc/mWTJPoT5nxYm5XnuYbQWf1EjmNyrsalFg4sTw6py4WgXr2QvOrXD0NjP/ssxFeeKAp1hj5X3KpMEmb2OvC6pPFmdncNxORcjatYOLB797A8blzhJAkICeGWW8I0qI8/DoceGu7UHj16RVJzLpfizHHdkHBVsWu0agwwxMx+y1Nssfkc166uMAtJ4fHHYccdw42ChXTV44pLrua4Hgx0jn4OJsx3fXv1w3POxSWFpNC5cyhm2LmzJwiXH3GSRBcz62tmr0aPPwEFNP28c3XL6NHhZr/NNw83B152WdIRudooTpJYJmmT8gVJGwPLch+Sc64qqR3t770HW24ZSofcdFPSkbnaJk6SOA8okzRa0hjgVeCc/ITlnMsktaN9jTVgzBho2xYuuACmekF9l0NZd1xDuPsa2Dxa/Cj1TmxJe5nZqBzHF4t3XLu6bObM0IldUhL6Kdq0SToiVyxy1XGNmS02s8nRo2KpjmtWO0LnXLVtuCE8/zz8/DPsu2+YbMm56splFVi/99O5hG23XShS+NFHcPDBsDibqmvOZeClwp2rZXr0CEUBR4+G44+H5cuTjsgVs1wmiVVIaiupTNI0SVMlDaxkny0kvSVpsaRzK2z7XNKU1LmvnXNV69MHrr02dG6fe27V+zuXTi7nk/i8knVLgXPMbKKkZsAESaPMbFrKPj8AfwYOSnPe7mb2XQ7jdK5OOPdcmDULbrghdGKffXbSEblilHWSkFQC7A+0Tz3OzK6PflZWIXY2MDt6Pl/SdKA1MC1lnznAHEn7r95bcM5VRgoJYvZsOOecMJnSUUclHZUrNnGuJJ4BfgWmALFbOSW1B7YH3olxmAEjJRlwh5kNjfu6ztVl9eqF2e2+/Tb0T6y7LuyxR9JRuWISJ0m0MbNtVudFJDUFhgNnmdm8GIfubGZfSVoXGCXpQzMbW+Hc/YB+AO3atVud8Jyr1Ro3hiefhF12CSOexo6FbbdNOipXLOJ0XL8gqWfcF5DUgJAgHiqfoChbZvZV9HMOMAL4YyX7DDWzUjMrbdWqVdzwnKsTWrSAF16AZs3CPRQzZyYdkSsWcZLE28AISYskzZM0X1LGqwJJAu4Gppf3XWRLUpOosxtJTYCegM9+59xqatsWXnwRfvklJIoffkg6IlcM4jQ3XQ/sCEyx7Gt5dCPMfT1F0qRo3UVAOwAzGyJpfWA80BxYLuksoCPQkpCUyuP8j5m9GCNe51wFW20FTz0FPXvCgQfCqFGh9pNz6cRJEl8CH8RIEOWz2mW8E9vMvgEqqzIzD/CWU+dybLfd4IEHwkinPn3gscdCvSfnKhMnSXwKjJb0AvC/m/3jNiM555J3xBFhaOxZZ8Gf/wy33rpirmznUsVJEp9Fj4bRwzlXxAYODDfbXXfdijLjzlWUdZIwM5/3yrla5ppr4Kuv4MILw812xx+fdESu0MS547qMSor4mZnfmuNckapXLxQD/PZbOOkkWH/90KntXLk4zU2pZcIaA4cSajM554pYo0ahvPgWW0Dv3vDGG9CpU9hWVhZmwRs0KNkYXXLiNDdNqLDqDUnv5jge51wCmjcP82MfdVQoNT5hQrjhrnwebVd3xWluWidlsR5QCqyV84icc4k4/HBYuBBOPBG23x6WLIFTTw1NUnPnghc0qJuynuNa0mes6JNYSigNfnl0L0RB8Dmunau+E08M/RQNG4ZEUa5VK+jYEbbcMjzKn3vyKH6Z5riu8kpCUhfgSzPbKFruS+iP+JyUkt/OueJXVgbPPAOXXAK33w633QZrrQXTpsHUqeHx4IMwL6UgT8uWqyaOjh1DxVkIkx916QLdu6/8Ot7XURyyaW66A9gTQNKuwFXAmcB2wFDgsHwF55yrOWVlK/oguncPj/Llv/xlxX5mYdhseeIo/5kueay1FlxxBVx2WRhBNXGi93UUkyqbmyS9b2bbRs9vA+aa2aXR8iQz2y7fQWbLm5ucW33V/cZvBl9/vXLiKH/+889hn5ISaNAg3OF90kn5eR8uvkzNTdkkiQ+A7cxsqaQPgX7lczpI+sDMtsp5xKvJk4Rzhac8eQwaBP/5D9SvD0uXhmT05z/DAQd47aikZUoS2ZQKfxgYI+kpYBHwWnTSDsDPOYvSOVcrSTBjBowcGfo61loL+vWDTz4JkyB16AD/+hf8+GPSkbrKVJkkzOxK4BzgPsJMceWXHvUIfRPOOZdWal/H5ZeHqrNPPAH33AOPPw7t2sG550KbNjBgAEyfnnTELlVWkw6Z2dtmNsLMFqasm2FmE/MXmnOuNhg3bkVnOISfw4aFDuxDD4UxY8LzI48MiaNjx1Aa5LnnYPnyZGN3Me6TKAbeJ+FccZs7F4YOhcGDQz9Ghw5w5plwwgnhrnCXH9Xtk3DOuRrRqhVcfDF8/jk8/HBYHjgwNEUNHAgff5x0hHWPJwnnXMFp0CDUkXrzTXj33VB48PbbYbPNYP/9Qyf4NdeE/o5UZWVhKK/LnbwmCUltJZVJmiZpqqSBleyzhaS3JC2WdG6FbftI+kjSJ5J8ShTn6qAuXcJ0q198AZdeGooP7r13uBv8wAPh+efDfuUd5F26JBpurZPXPglJGwAbmNlESc2ACcBBZjYtZZ91gQ2Bg4Afzey6aH0JMAPYC5gFjAOOTj22Iu+TcK72W7w4jJC66SYYPz4Msd1113DjXmoHucteYn0SZja7fASUmc0HpgOtK+wzx8zGAb9VOPyPwCdm9qmZLQEeAXrnM17nXOFr1AiOPTY0Q735ZmiCGjMmjIRa6jPc5FyN9UlIag9sD7yT5SGtgS9TlmdRIcE45+ouCX79Fb7/Hvr0CaU/evYMQ2m//jrp6GqPGkkSkpoCw4GzzGxeVfvHPHc/SeMljZ87d24uT+2cK2CpN+k9+GC4r2LNNVfMsnfjjX5lkQt5TxKSGhASxENm9kSMQ78C2qYst4nWrcTMhppZqZmVtvLC9s7VGRVv0tt7b3j22VCxduedw8/SUnjrrWTjLHb5Ht0k4G5gupldH/PwccCmkjaS1BA4Cng61zE654rToEGrdlJ37x6Gxj73HAwfHpqidtoJTjklPHfx5ftKohtwHLCHpEnRYz9J/SX1B5C0vqRZwNnAXyXNktTczJYCZwAvETq8h5nZ1DzH65yrBSQ45JBQB+rcc8NMe5tvHsp+eKmPeLwsh3Ou1psyJRQPfP31cGVx++2wzTZJR1U4vCyHc65O23rrMEz23ntD2fJOneCcc2D+/KQjK3yeJJxzdUK9eqFQ4Ecfwcknww03wB/+EMqV16IGlZzzJOGcq1PWWQeGDAk34rVqBYcfDvvuGyZBcqvyJOGcq5O6dg3DaG+6KSSMrbaCyy6DK6/0woGpPEk45+qs+vXDPNsffRSmUr300jCXxcEHr0gUdb1woCcJ51ydt8EGYf6KUaOgadNQ4mOffaB//xV3ddfVwoGeJJxzLrLnnjB5MlxxBSxbBnfcAY0bw1dfhTpRdZEnCeecS9GoEXTrBmuvHZLG11/DcceF2fHOO6/uzY7nScI551KU90E89lhofho5EtZaCzp2DMNmN9sM9torlP34reIEB7WQJwnnnEtRsXBgjx6hsmyvXmF2vCuuCB3dhx0GG24If/sbfPll5nMWMy/L4ZxzMS1bFqZNHTIEXngh1Iraf3847bQwp0VJSdIRxuNlOZxzLodKSuCAA0K12U8/hQsugHfegf32gw4d4Kqr4Ntvk44yNzxJOOdcNbRvH27A+/JLePRR2GgjuOgiaNs2zJI3enQoX16sN+h5knDOuRxo2DB0eL/6aihRfsYZoeO7e3e47bZw5fF0NCNOMd2g50nCOedybIst4Prrw/0V990HrVvDwoXQu3cYJbX//mG+i5kzQ5PVu+/CZ5/BggXxig1ee23+r1C849o552rApEmh+uyECaFPY9myyvdbY41QeDD1se66q65r1Qo+/DBUti0fjZU673ecO8QzdVzXj/9WnXPOxfXjj+HK4ZJLwqRH990XSpXPmQNz5676KF8/bVr4uWhR5edt0CDc9LfppmGK1lyXEPEk4ZxzeVbxG3737vG/8S9cuHLySH2MHAnvvx8SUK5rTHmScM65PKt4g1737mF53LjsP9SbNAmP9u1XXl9WFmbcK79CKU9CuZLXPglJbYH7gfUAA4aa2U0V9hFwE7Af8AtwgplNjLYtA6ZEu35hZgdmej3vk3DO1SUVr1CKsU9iKXCOmU2U1AyYIGmUmU1L2WdfYNPosQNwe/QTYJGZbZfnGJ1zrijl4gqlKnlNEmY2G5gdPZ8vaTrQGkhNEr2B+y1c0rwtaW1JG0THOuecS2PQoFXX5bq5qcbuk5DUHtgeeKfCptZAanmsWdE6gMaSxkt6W9JBac7bL9pn/Ny5c3MctXPO1W01kiQkNQWGA2eZ2bwYh24YtZMdA9woaZOKO5jZUDMrNbPSVq1a5Shi55xzUANJQlIDQoJ4yMyeqGSXr4C2KcttonWYWfnPT4HRhCsR55xzNSSvSSIauXQ3MN3Mrk+z29PA8Qq6Aj+b2WxJLSQ1is7TEujGyn0Zzjnn8izfo5u6AccBUyRNitZdBLQDMLMhwPOE4a+fEIbA/ina7w/AHZKWE5LZ1RVGRTnnnMuzWlW7SdJcYGY1TtES+C5H4eRbMcUKxRVvMcUKxRVvMcUKxRVvdWLd0Mwq7dStVUmiuiSNT3dDSaEpplihuOItplihuOItplihuOLNV6xeKtw551xaniScc86l5UliZUOTDiCGYooViiveYooViiveYooViivevMTqfRLOOefS8isJ55xzaXmScM45l5YnCUDSPpI+kvSJpAuSjicTSW0llUmaJmmqpIFJx1QVSSWS3pP0bNKxVCWqQvy4pA8lTZe0Y9IxpSPpL9H/gQ8kPSypcdIxpZJ0j6Q5kj5IWbeOpFGSPo5+tkgyxnJpYv1n9P9gsqQRktZOMMSVVBZvyrZzJFlUqaLa6nySkFQC3EaY16IjcLSkjslGlVH5HB0dga7A6QUeL8BAYHrSQWTpJuBFM9sC2JYCjVtSa+DPQKmZbQWUAEclG9Uq7gP2qbDuAuAVM9sUeCVaLgT3sWqso4CtzGwbYAZwYU0HlcF9rBpv+URvPYEvcvVCdT5JAH8EPjGzT81sCfAIYY6LgmRms8tn7jOz+YQPsdaZj0qOpDbA/sBdScdSFUlrAbsS6o1hZkvM7KdEg8qsPrCGpPrAmsDXCcezEjMbC/xQYXVv4N/R838DB9VkTOlUFquZjTSzpdHi24TiowUhze8W4AZgEGEm0JzwJJF5PouClmGOjkJyI+E/7fKE48jGRsBc4N6oeewuSU2SDqoyUYXk6wjfGGcTCmOOTDaqrKyXMqHYN4SpjYvBicALSQeRiaTewFdm9n4uz+tJokhVY46OGiOpFzDHzCYkHUuW6gOdgNvNbHtgIYXTHLKSqC2/NyGx/R5oIunYZKOKJ5qNsuDH4Eu6mNDM+1DSsaQjaU1C8dS/5frcniQyzGdRqLKYo6NQdAMOlPQ5oRlvD0kPJhtSRrOAWWZWfmX2OCFpFKI9gc/MbK6Z/QY8AeyUcEzZ+FbSBgDRzzkJx5ORpBOAXkAfK+ybyjYhfGF4P/p7awNMlLR+dU/sSQLGAZtK2khSQ0Ln39MJx5RWlnN0FAQzu9DM2phZe8Lv9VUzK9hvu2b2DfClpM2jVT0o3DlMvgC6Sloz+j/RgwLtZK/gaaBv9Lwv8FSCsWQkaR9CU+mBZvZL0vFkYmZTzGxdM2sf/b3NAjpF/6erpc4niahj6gzgJcIf2TAzm5psVBmVz9Gxh6RJ0WO/pIOqRc4EHpI0GdgO+Eey4VQuutp5HJgITCH8LRdUCQlJDwNvAZtLmiXpJOBqYC9JHxOuhq5OMsZyaWK9FWgGjIr+zoYkGmSKNPHm57UK+wrKOedckur8lYRzzrn0PEk455xLy5OEc865tDxJOOecS8uThHPOubQ8STiXZ5LaV1at07li4EnCOedcWp4knKtBkjaOigd2SToW57JRP+kAnKsronIfjwAn5LpSp3P54knCuZrRilCn6BAzK9R6UM6twpubnKsZPxOK8u2cdCDOxeFXEs7VjCXAwcBLkhaY2X+SDsi5bHiScK6GmNnCaCKmUVGiKNiS9M6V8yqwzjnn0vI+Ceecc2l5knDOOZeWJwnnnHNpeZJwzjmXlicJ55xzaXmScM45l5YnCeecc2n9P93VppJ2UEyzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(amount_clusters)\n",
    "plt.plot(np.arange(len(wce)), wce, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Sum_of_squared_distances')\n",
    "plt.title('Elbow Method For Optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Markov Chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predict for each user, where will they go next?\n",
    "# *** predict across all users, where will an arbitrary person go next?\n",
    "\n",
    "#Question: constrain the prediction of each user to be based only on where they've been before?\n",
    "#Question: if not, assume that being in each building is equally likely?\n",
    "\n",
    "#TODO: 5 min sampling of time bins\n",
    "#TODO: 0 pad when they're not connected\n",
    "#TODO: make transition matrix look pretty?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MakeTransitionMatrix(history, NumComponents):\n",
    "#      Input: DataArray - array of length N of integers that belong to 1 of C classes\n",
    "#      Output: TransMat - is a right stochastic matrix (each row sums to 1)\n",
    "#                         Each entry of TransMat(i,j) represents the probability\n",
    "#                         of going from label i to label j\n",
    "    NumTimeSteps  = len(history)\n",
    "    \n",
    "    #Pre-allocate transition matrix\n",
    "    TransMat = np.ones((NumComponents+1, NumComponents+1)) #+1 because 0-indexed\n",
    "\n",
    "    for i in range(NumTimeSteps-1):\n",
    "        Current = history[i]\n",
    "        Next    = history[i+1]\n",
    "        TransMat[Current, Next] = TransMat[Current, Next] + 1\n",
    "        \n",
    "    #Normalize rows\n",
    "    TransMat = TransMat/np.sum(TransMat,1)[:,None] #broadcast vector for normalization by rows\n",
    "    return TransMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FindNextState(TransMat, state, loc_dict, plotNextStateProb=False):\n",
    "    #assume state is 0-indexed\n",
    "    rand = np.random.random()\n",
    "    \n",
    "    #Make cdf of transitioning out of a given state\n",
    "    cdf = np.cumsum(TransMat[state])    \n",
    "\n",
    "    #find first state above random number on cdf\n",
    "    nextState = np.where(cdf > rand)[0][0]\n",
    "    if plotNextStateProb: #plot cdf and probability of transitioning\n",
    "        StateSize = len(TransMat)\n",
    "        plt.figure(dpi=100)\n",
    "        plt.plot(np.insert(cdf, 0, 0))\n",
    "        plt.title(\"Cumulative Distribution function\" + \" - Chose state \" + str(nextState) + ' ' + loc_dict[nextState])\n",
    "        plt.axhline(rand, c='red')\n",
    "        for i in range(StateSize):\n",
    "            plt.axvline(i, c='k')\n",
    "        plt.show()\n",
    "    \n",
    "    return nextState"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PredictFutureSteps(TransMat, MostRecentState, bldg_dict, NumFutureSteps, PlotCDF=False):\n",
    "    nextStates  = []\n",
    "    nextBldgs   = []\n",
    "    currState   = MostRecentState\n",
    "    for i in range(NumFutureSteps):\n",
    "        nextState = FindNextState(TransMat, currState, bldg_dict, PlotCDF)\n",
    "        #print(\"\\nNext state\", bldg_dict[nextState])\n",
    "        nextStates.append(nextState)\n",
    "        nextBldgs.append(bldg_dict[nextState])\n",
    "        currState = nextState\n",
    "\n",
    "    #print(\"Next states array:\", nextStates)\n",
    "    #print(\"Next buildings array:\", nextBldgs)\n",
    "    return nextStates, nextBldgs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Toy example for Markov Chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TransMat:\n",
      " [[0.375      0.375      0.125      0.125     ]\n",
      " [0.22222222 0.22222222 0.44444444 0.11111111]\n",
      " [0.2        0.2        0.2        0.4       ]\n",
      " [0.14285714 0.28571429 0.42857143 0.14285714]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([3, 3, 0, 3, 1], ['lib west', 'lib west', 'Not Connected', 'lib west', 'hub'])"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# demo of a Markov chain predictor\n",
    "\n",
    "#Toy data\n",
    "bldg_dict = {0: 'Not Connected', \n",
    "             1: 'hub',\n",
    "             2: 'reitz',\n",
    "             3: 'lib west'}\n",
    "\n",
    "personHistory = np.array([0,0,0,1,1,2,2,1,2,3,2,3,2,3,1,2,0,1,0])\n",
    "\n",
    "#Make transition matrix\n",
    "TransMat = MakeTransitionMatrix(personHistory,3)\n",
    "print(\"TransMat:\\n\", TransMat)\n",
    "\n",
    "#Predict future steps based on their most recent history\n",
    "NumFutureSteps  = 5\n",
    "MostRecentState = personHistory[-1]\n",
    "nextSteps, nextBldgs = PredictFutureSteps(TransMat, MostRecentState, bldg_dict, NumFutureSteps, PlotCDF=False)\n",
    "nextSteps, nextBldgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "#map the buildings to an index\n",
    "bldg_dict = {i: location_info['prefix'].iloc[i] for i in range(len(location_info))}\n",
    "keyOfBldg = list(bldg_dict.values()).index('aer')\n",
    "#TODO: apply MC to actual data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'aaf',\n",
       " 1: 'adv',\n",
       " 2: 'aer',\n",
       " 3: 'aerv',\n",
       " 4: 'alf',\n",
       " 5: 'and',\n",
       " 6: 'ans',\n",
       " 7: 'ansc',\n",
       " 8: 'apl',\n",
       " 9: 'arch',\n",
       " 10: 'aud',\n",
       " 11: 'bar',\n",
       " 12: 'barB',\n",
       " 13: 'bcf',\n",
       " 14: 'bcftr',\n",
       " 15: 'ben',\n",
       " 16: 'bgh',\n",
       " 17: 'blk',\n",
       " 18: 'bro',\n",
       " 19: 'broEastOIR-win-lap',\n",
       " 20: 'broEastOIR',\n",
       " 21: 'broOIR-EB',\n",
       " 22: 'broOIR',\n",
       " 23: 'brt',\n",
       " 24: 'brtb',\n",
       " 25: 'brttr',\n",
       " 26: 'bry',\n",
       " 27: 'bwc',\n",
       " 28: 'bwcbookstore-win-lap',\n",
       " 29: 'bwcbookstore',\n",
       " 30: 'bwcg',\n",
       " 31: 'bwcp',\n",
       " 32: 'bwctr',\n",
       " 33: 'car',\n",
       " 34: 'carATTIC-win-lap',\n",
       " 35: 'carATTIC-wout-lap',\n",
       " 36: 'carATTIC',\n",
       " 37: 'cbd',\n",
       " 38: 'cenx',\n",
       " 39: 'che',\n",
       " 40: 'clb',\n",
       " 41: 'con',\n",
       " 42: 'conG',\n",
       " 43: 'crec',\n",
       " 44: 'crecxxxrxx-win-lap',\n",
       " 45: 'crecxxxrxx',\n",
       " 46: 'cris',\n",
       " 47: 'criv',\n",
       " 48: 'crr',\n",
       " 49: 'crrB',\n",
       " 50: 'cse',\n",
       " 51: 'csee',\n",
       " 52: 'csev',\n",
       " 53: 'csfl',\n",
       " 54: 'cwp',\n",
       " 55: 'dauxxx-win-lap',\n",
       " 56: 'dau',\n",
       " 57: 'dinefreshfood-win-lap',\n",
       " 58: 'dinefreshfood',\n",
       " 59: 'edb',\n",
       " 60: 'ehs',\n",
       " 61: 'elm',\n",
       " 62: 'elmv',\n",
       " 63: 'eyn',\n",
       " 64: 'faa',\n",
       " 65: 'fab',\n",
       " 66: 'fac',\n",
       " 67: 'facB',\n",
       " 68: 'fad',\n",
       " 69: 'ff',\n",
       " 70: 'ff1',\n",
       " 71: 'fif',\n",
       " 72: 'fifv',\n",
       " 73: 'flg',\n",
       " 74: 'fli',\n",
       " 75: 'flih',\n",
       " 76: 'flo',\n",
       " 77: 'fpof',\n",
       " 78: 'fsf-win-lap',\n",
       " 79: 'fsf',\n",
       " 80: 'ger',\n",
       " 81: 'gerc',\n",
       " 82: 'gri',\n",
       " 83: 'harn',\n",
       " 84: 'harng',\n",
       " 85: 'harntr',\n",
       " 86: 'her',\n",
       " 87: 'hgs',\n",
       " 88: 'hol',\n",
       " 89: 'hrb',\n",
       " 90: 'hub',\n",
       " 91: 'hume',\n",
       " 92: 'iblc',\n",
       " 93: 'ihlc',\n",
       " 94: 'imag',\n",
       " 95: 'impl',\n",
       " 96: 'inf',\n",
       " 97: 'infb',\n",
       " 98: 'jen',\n",
       " 99: 'lar',\n",
       " 100: 'lbw',\n",
       " 101: 'lit',\n",
       " 102: 'mae',\n",
       " 103: 'maec',\n",
       " 104: 'mat',\n",
       " 105: 'mcca',\n",
       " 106: 'mccaG',\n",
       " 107: 'mccb',\n",
       " 108: 'mccbG',\n",
       " 109: 'mccc',\n",
       " 110: 'mccctr',\n",
       " 111: 'mccd',\n",
       " 112: 'mccdG',\n",
       " 113: 'mcg',\n",
       " 114: 'mcgB',\n",
       " 115: 'mcgC',\n",
       " 116: 'mcgCG',\n",
       " 117: 'mckl',\n",
       " 118: 'mcsb',\n",
       " 119: 'meb',\n",
       " 120: 'mebv',\n",
       " 121: 'meh',\n",
       " 122: 'msl',\n",
       " 123: 'mub',\n",
       " 124: 'mus',\n",
       " 125: 'nano',\n",
       " 126: 'neb',\n",
       " 127: 'npb',\n",
       " 128: 'npbB',\n",
       " 129: 'nrg',\n",
       " 130: 'nrn',\n",
       " 131: 'nrna',\n",
       " 132: 'nrnag',\n",
       " 133: 'nsc',\n",
       " 134: 'nzh',\n",
       " 135: 'nzhB',\n",
       " 136: 'ohl1',\n",
       " 137: 'ohl2',\n",
       " 138: 'pcpa',\n",
       " 139: 'pdc',\n",
       " 140: 'phl',\n",
       " 141: 'plsl',\n",
       " 142: 'pow',\n",
       " 143: 'pres',\n",
       " 144: 'psf',\n",
       " 145: 'pst',\n",
       " 146: 'psy',\n",
       " 147: 'pugh',\n",
       " 148: 'rcd',\n",
       " 149: 'reiOrangeBrew-win-lap',\n",
       " 150: 'rei',\n",
       " 151: 'reiB',\n",
       " 152: 'reiG',\n",
       " 153: 'reiOrangeBrew',\n",
       " 154: 'reiR',\n",
       " 155: 'rfc',\n",
       " 156: 'rhn',\n",
       " 157: 'rhnb',\n",
       " 158: 'rl1',\n",
       " 159: 'rl2',\n",
       " 160: 'rla',\n",
       " 161: 'rlab',\n",
       " 162: 'rnk',\n",
       " 163: 'rnktr',\n",
       " 164: 'rog',\n",
       " 165: 'rol',\n",
       " 166: 'sbh',\n",
       " 167: 'sbhG',\n",
       " 168: 'sis',\n",
       " 169: 'sma',\n",
       " 170: 'soc-temp-lap',\n",
       " 171: 'soc',\n",
       " 172: 'socgate',\n",
       " 173: 'sta',\n",
       " 174: 'stz',\n",
       " 175: 'stzWlkwy-wout-lap',\n",
       " 176: 'stzWlkwy',\n",
       " 177: 'swc',\n",
       " 178: 'tge',\n",
       " 179: 'tig',\n",
       " 180: 'tur',\n",
       " 181: 'turl',\n",
       " 182: 'urbe',\n",
       " 183: 'ust',\n",
       " 184: 'van',\n",
       " 185: 'wal',\n",
       " 186: 'wei',\n",
       " 187: 'weiclocktower-wout-lap',\n",
       " 188: 'weiclocktower',\n",
       " 189: 'weim',\n",
       " 190: 'weimG',\n",
       " 191: 'weimg',\n",
       " 192: 'wm',\n",
       " 193: 'yon'}"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bldg_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00729927 0.00729927 0.00729927 ... 0.00729927 0.00729927 0.00729927]\n",
      " [0.00729927 0.00729927 0.00729927 ... 0.00729927 0.00729927 0.00729927]\n",
      " [0.00729927 0.00729927 0.00729927 ... 0.00729927 0.00729927 0.00729927]\n",
      " ...\n",
      " [0.00729927 0.00729927 0.00729927 ... 0.00729927 0.00729927 0.00729927]\n",
      " [0.00729927 0.00729927 0.00729927 ... 0.00729927 0.00729927 0.00729927]\n",
      " [0.00729927 0.00729927 0.00729927 ... 0.00729927 0.00729927 0.00729927]]\n"
     ]
    }
   ],
   "source": [
    "cluster1History = roundedMeans[0].astype(int)\n",
    "nBuildings = 137\n",
    "TransMat = MakeTransitionMatrix(cluster1History, nBuildings-1)\n",
    "print(TransMat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([99, 61, 112, 18, 58, 8, 83, 89, 124, 16],\n",
       " ['Phelps Lab',\n",
       "  'The Hub',\n",
       "  'Research Laboratory 2',\n",
       "  'Bryan Hall',\n",
       "  'Hough Hall Business School',\n",
       "  'School of Architecture',\n",
       "  'Mehrhof Hall',\n",
       "  'Physics',\n",
       "  'SW Recreation Center',\n",
       "  'Broward Hall'])"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predict future steps based on their most recent history\n",
    "NumFutureSteps  = 10\n",
    "MostRecentState = cluster1History[-1]\n",
    "bldgs = location_info['name'].unique()\n",
    "bldg_dict = dict(zip(np.arange(len(bldgs)), bldgs))\n",
    "nextSteps, nextBldgs = PredictFutureSteps(TransMat, MostRecentState, bldg_dict, NumFutureSteps, PlotCDF=False)\n",
    "nextSteps, nextBldgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[99, 61, 112, 18, 58, 8, 83, 89, 124, 16]"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('cluster1History.npy', 'wb') as f:\n",
    "    np.save(f, cluster1History)\n",
    "with open('nextSteps.npy', 'wb') as f:\n",
    "    np.save(f, nextSteps)\n",
    "    \n",
    "cluster1History\n",
    "nextSteps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "721"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cluster1History)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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