{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the [puzzlor](http://puzzlor.com/2014-04_SpyCatcher.html) website there is old puzzle that can be solved using Markov Models. The puzzle was posted on April 2014, so the answer and some solutions were already published. This problem will be used to demonstrate usage of Markov Chains and Stochastic Matrices.\n",
    "\n",
    "\n",
    "[![Open In Colab](../images/badges/colab.svg)](https://colab.research.google.com/github/izikeros/blog/blob/master/content/posts/notebooks/2019-08-10-spy-catch.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/izikeros/blog/master?labpath=content%2Fposts%2Fnotebooks%2F2019-08-10-spy-catch.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Problem to solve**:\n",
    "\n",
    ">Your government has lost track of a high profile foreign spy, and they have requested your help to track him down. As part of his attempts to evade capture, he has employed a simple strategy. Each day the spy moves from the country that he is currently in to a neighboring country.\n",
    ">The spy cannot skip over a country (for example, he cannot go from Chile to Ecuador in one day). The movement probabilities are equally distributed amongst the neighboring countries. For example, if the spy is currently in Ecuador, there is a 50% chance he will move to Colombia and a 50% chance he will move to Peru. The spy was last seen in Chile and will only move about countries that are in South America. He has been moving about the countries for several weeks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![south america map](http://safjan.com/images/spycatcher/spycatcher-map.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Question: Which country is the spy most likely hiding in and how likely is it that he is there?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Markov chains\n",
    "\n",
    "Markov's chains are mathematical models used to describe systems that change the state from one to another. For example, you can think of simple weather model with two states \"Sunny\" and \"Rainy\" and describe system assigning probabilities of transiting from one state to another and probabilities of staying in the same state."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another example can be Markov chain model of a baby's behavior, with states: \"playing,\" \"eating\", \"sleeping,\" and \"crying\". If you have transition probabilities you can predict e.g., the chance that a baby currently playing will fall asleep in the next five minutes without crying first. For getting the intuition if such model visit: [setosa project - Explained Visually](http://setosa.io/ev/markov-chains/) to see interactive models and visual explanation of Markov Chains.\n",
    "\n",
    "![Markov Chain Model with two states: 'A' and 'B' and animated transitions between them](http://safjan.com/images/spycatcher/spycatcher-mc-visually-275px.gif)\n",
    "\n",
    "*Figure 1. Sample process with two states: 'A' and 'B' and transitions between them. Probability of transition for state 'A' to 'B' is much lower than probability of transition from state 'B' to 'A'.Example captured from [Explained Visually](http://setosa.io/ev/markov-chains/)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start with importing modules we will use, and configure them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "import warnings\n",
    "import matplotlib.cbook\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=matplotlib.cbook.mplDeprecation)\n",
    "\n",
    "# limit pandas display precision for better readability\n",
    "pd.set_option(\"precision\", 2)\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solution plan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's distill important information we are given in the problem statement.\n",
    "1. We are dealing with transitions between discrete states in a finite state space.\n",
    "2. The spy's strategy is based on random moves - it is a stochastic process.\n",
    "3. Description of spy's strategy indicate that this stochastic process is memoryless - history doesn't affect next move of the spy. Only current state counts.\n",
    "\n",
    "These clued indicate that a Markov chain may be the best way to model the spy's movements.\n",
    "\n",
    "Let's begin by creating an adjacency matrix for these countries - a matrix that indicates other countries to which the spy could move from any given country. By convention, this matrix is configured row-wise, so that the matrix element $a_{ij}$ represents whether it is possible for the spy to move to country $j$ (the column) from country $i$ (the row)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create adjacency matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get South American Country names from Wikipedia using pandas *read_html* method to scrape an article:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['argentina', 'bolivia', 'brazil', 'chile', 'colombia', 'ecuador', 'french guiana', 'guyana', 'paraguay', 'peru', 'suriname', 'uruguay', 'venezuela']\n"
     ]
    }
   ],
   "source": [
    "url = \"http://en.wikipedia.org/wiki/List_of_South_American_countries_by_population\"\n",
    "south_america = pd.read_html(url, match=\"Country\")[0]\n",
    "try:\n",
    "    countries = sorted((south_america[\"Country\"][\"Country\"].str.lower())[:13])\n",
    "    countries[6] = \"french guiana\"  # shorten original name\n",
    "    print(countries)\n",
    "except:\n",
    "    # use hardcoded names if scrapping fails\n",
    "    countries = ['argentina', 'bolivia', 'brazil', 'chile', 'colombia', 'ecuador', 'french guiana', 'french guiana', 'guyana', 'paraguay', 'peru', 'suriname', 'uruguay', 'venezuela']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and describe adjacency (country neighbourhood):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the rows/cols are for the countries in alphabetical order\n",
    "adjacency = np.array(\n",
    "    [\n",
    "        [0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0],\n",
    "        [1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0],\n",
    "        [1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1],\n",
    "        [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
    "        [0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1],\n",
    "        [0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n",
    "        [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
    "        [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1],\n",
    "        [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "        [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n",
    "        [0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n",
    "        [1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "        [0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],\n",
    "    ]\n",
    ")\n",
    "\n",
    "# sanity check if adjacency matrix is symmetric\n",
    "assert np.all(adjacency.T == adjacency)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The adjacency matrix can be turned into graph using *networkx* module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "\n",
    "\n",
    "def make_label_dict(labels):\n",
    "    l = {}\n",
    "    for i, label in enumerate(labels):\n",
    "        l[i] = label\n",
    "    return l\n",
    "\n",
    "\n",
    "G = nx.Graph(adjacency)\n",
    "\n",
    "labels = make_label_dict(countries)\n",
    "\n",
    "# layout the graph\n",
    "pos = nx.spring_layout(G)\n",
    "\n",
    "# draw graph\n",
    "ax = nx.draw(G, pos, node_size=500, with_labels=False)\n",
    "\n",
    "# draw node labels\n",
    "for p in pos:  # raise text positions\n",
    "    pos[p][1] += 0.10\n",
    "nx.draw_networkx_labels(G, pos, labels=labels)\n",
    "f = 0.1\n",
    "x = plt.xlim()[1] - plt.xlim()[0]\n",
    "y = plt.ylim()[1] - plt.ylim()[0]\n",
    "plt.xlim(plt.xlim()[0] - f * x, plt.xlim()[1] + f * x)\n",
    "plt.ylim(plt.ylim()[0] - f * x, plt.ylim()[1] + f * x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Figure 2. Neighbouring relations visualized as graph created from adjacency matrix*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate stochastic matrix (transition matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have figured out which countries are adjacent (that is, which other countries the spy could feasibly move to from each country), we can turn that into a stochastic matrix $A$ (also called the transition matrix) representing how likely each transition is conditioned on the spy being in a certain country.\n",
    "\n",
    "In other words, we are normalizing the row vectors so that they represent probabilities, which is very easy in this problem because the spy is equally likely to move to any of the adjacent countries. That means we can normalize each row just by dividing it by its sum. To be concrete, we are taking each row of the adjacency matrix such as this one showing the countries we could move to from Argentina:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjacency[0, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the knowledge that each adjacent country is an equally likely transition, we are turning this binary row of possibilities into a probability mass function of probabilities representing chance of being in each of the adjacent countries in the subsequent day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7521c_row0_col0, #T_7521c_row0_col4, #T_7521c_row0_col5, #T_7521c_row0_col6, #T_7521c_row0_col7, #T_7521c_row0_col9, #T_7521c_row0_col10, #T_7521c_row0_col12, #T_7521c_row1_col1, #T_7521c_row1_col4, #T_7521c_row1_col5, #T_7521c_row1_col6, #T_7521c_row1_col7, #T_7521c_row1_col10, #T_7521c_row1_col11, #T_7521c_row1_col12, #T_7521c_row2_col2, #T_7521c_row2_col3, #T_7521c_row2_col5, #T_7521c_row3_col2, #T_7521c_row3_col3, #T_7521c_row3_col4, #T_7521c_row3_col5, #T_7521c_row3_col6, #T_7521c_row3_col7, #T_7521c_row3_col8, #T_7521c_row3_col10, #T_7521c_row3_col11, #T_7521c_row3_col12, #T_7521c_row4_col0, #T_7521c_row4_col1, #T_7521c_row4_col3, #T_7521c_row4_col4, #T_7521c_row4_col6, #T_7521c_row4_col7, #T_7521c_row4_col8, #T_7521c_row4_col10, #T_7521c_row4_col11, #T_7521c_row5_col0, #T_7521c_row5_col1, #T_7521c_row5_col2, #T_7521c_row5_col3, #T_7521c_row5_col5, #T_7521c_row5_col6, #T_7521c_row5_col7, #T_7521c_row5_col8, #T_7521c_row5_col10, #T_7521c_row5_col11, #T_7521c_row5_col12, #T_7521c_row6_col0, #T_7521c_row6_col1, #T_7521c_row6_col3, #T_7521c_row6_col4, #T_7521c_row6_col5, #T_7521c_row6_col6, #T_7521c_row6_col7, #T_7521c_row6_col8, #T_7521c_row6_col9, #T_7521c_row6_col11, #T_7521c_row6_col12, #T_7521c_row7_col0, #T_7521c_row7_col1, #T_7521c_row7_col3, #T_7521c_row7_col4, #T_7521c_row7_col5, #T_7521c_row7_col6, #T_7521c_row7_col7, #T_7521c_row7_col8, #T_7521c_row7_col9, #T_7521c_row7_col11, #T_7521c_row8_col3, #T_7521c_row8_col4, #T_7521c_row8_col5, #T_7521c_row8_col6, #T_7521c_row8_col7, #T_7521c_row8_col8, #T_7521c_row8_col9, #T_7521c_row8_col10, #T_7521c_row8_col11, #T_7521c_row8_col12, #T_7521c_row9_col0, #T_7521c_row9_col6, #T_7521c_row9_col7, #T_7521c_row9_col8, #T_7521c_row9_col9, #T_7521c_row9_col10, #T_7521c_row9_col11, #T_7521c_row9_col12, #T_7521c_row10_col0, #T_7521c_row10_col1, #T_7521c_row10_col3, #T_7521c_row10_col4, #T_7521c_row10_col5, #T_7521c_row10_col8, #T_7521c_row10_col9, #T_7521c_row10_col10, #T_7521c_row10_col11, #T_7521c_row10_col12, #T_7521c_row11_col1, #T_7521c_row11_col3, #T_7521c_row11_col4, #T_7521c_row11_col5, #T_7521c_row11_col6, #T_7521c_row11_col7, #T_7521c_row11_col8, #T_7521c_row11_col9, #T_7521c_row11_col10, #T_7521c_row11_col11, #T_7521c_row11_col12, #T_7521c_row12_col0, #T_7521c_row12_col1, #T_7521c_row12_col3, #T_7521c_row12_col5, #T_7521c_row12_col6, #T_7521c_row12_col8, #T_7521c_row12_col9, #T_7521c_row12_col10, #T_7521c_row12_col11, #T_7521c_row12_col12 {\n",
       "  background-color: #f7fbff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7521c_row0_col1, #T_7521c_row0_col2, #T_7521c_row0_col3, #T_7521c_row0_col8, #T_7521c_row0_col11, #T_7521c_row1_col0, #T_7521c_row1_col2, #T_7521c_row1_col3, #T_7521c_row1_col8, #T_7521c_row1_col9, #T_7521c_row2_col0, #T_7521c_row2_col1, #T_7521c_row2_col4, #T_7521c_row2_col6, #T_7521c_row2_col7, #T_7521c_row2_col8, #T_7521c_row2_col9, #T_7521c_row2_col10, #T_7521c_row2_col11, #T_7521c_row2_col12, #T_7521c_row3_col0, #T_7521c_row3_col1, #T_7521c_row3_col9, #T_7521c_row4_col2, #T_7521c_row4_col5, #T_7521c_row4_col9, #T_7521c_row4_col12, #T_7521c_row5_col4, #T_7521c_row5_col9, #T_7521c_row6_col2, #T_7521c_row6_col10, #T_7521c_row7_col2, #T_7521c_row7_col10, #T_7521c_row7_col12, #T_7521c_row8_col0, #T_7521c_row8_col1, #T_7521c_row8_col2, #T_7521c_row9_col1, #T_7521c_row9_col2, #T_7521c_row9_col3, #T_7521c_row9_col4, #T_7521c_row9_col5, #T_7521c_row10_col2, #T_7521c_row10_col6, #T_7521c_row10_col7, #T_7521c_row11_col0, #T_7521c_row11_col2, #T_7521c_row12_col2, #T_7521c_row12_col4, #T_7521c_row12_col7 {\n",
       "  background-color: #08306b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7521c_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >0</th>\n",
       "      <th class=\"col_heading level0 col1\" >1</th>\n",
       "      <th class=\"col_heading level0 col2\" >2</th>\n",
       "      <th class=\"col_heading level0 col3\" >3</th>\n",
       "      <th class=\"col_heading level0 col4\" >4</th>\n",
       "      <th class=\"col_heading level0 col5\" >5</th>\n",
       "      <th class=\"col_heading level0 col6\" >6</th>\n",
       "      <th class=\"col_heading level0 col7\" >7</th>\n",
       "      <th class=\"col_heading level0 col8\" >8</th>\n",
       "      <th class=\"col_heading level0 col9\" >9</th>\n",
       "      <th class=\"col_heading level0 col10\" >10</th>\n",
       "      <th class=\"col_heading level0 col11\" >11</th>\n",
       "      <th class=\"col_heading level0 col12\" >12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7521c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7521c_row0_col0\" class=\"data row0 col0\" >0.00</td>\n",
       "      <td id=\"T_7521c_row0_col1\" class=\"data row0 col1\" >0.20</td>\n",
       "      <td id=\"T_7521c_row0_col2\" class=\"data row0 col2\" >0.20</td>\n",
       "      <td id=\"T_7521c_row0_col3\" class=\"data row0 col3\" >0.20</td>\n",
       "      <td id=\"T_7521c_row0_col4\" class=\"data row0 col4\" >0.00</td>\n",
       "      <td id=\"T_7521c_row0_col5\" class=\"data row0 col5\" >0.00</td>\n",
       "      <td id=\"T_7521c_row0_col6\" class=\"data row0 col6\" >0.00</td>\n",
       "      <td id=\"T_7521c_row0_col7\" class=\"data row0 col7\" >0.00</td>\n",
       "      <td id=\"T_7521c_row0_col8\" class=\"data row0 col8\" >0.20</td>\n",
       "      <td id=\"T_7521c_row0_col9\" class=\"data row0 col9\" >0.00</td>\n",
       "      <td id=\"T_7521c_row0_col10\" class=\"data row0 col10\" >0.00</td>\n",
       "      <td id=\"T_7521c_row0_col11\" class=\"data row0 col11\" >0.20</td>\n",
       "      <td id=\"T_7521c_row0_col12\" class=\"data row0 col12\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7521c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_7521c_row1_col0\" class=\"data row1 col0\" >0.20</td>\n",
       "      <td id=\"T_7521c_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
       "      <td id=\"T_7521c_row1_col2\" class=\"data row1 col2\" >0.20</td>\n",
       "      <td id=\"T_7521c_row1_col3\" class=\"data row1 col3\" >0.20</td>\n",
       "      <td id=\"T_7521c_row1_col4\" class=\"data row1 col4\" >0.00</td>\n",
       "      <td id=\"T_7521c_row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
       "      <td id=\"T_7521c_row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
       "      <td id=\"T_7521c_row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
       "      <td id=\"T_7521c_row1_col8\" class=\"data row1 col8\" >0.20</td>\n",
       "      <td id=\"T_7521c_row1_col9\" class=\"data row1 col9\" >0.20</td>\n",
       "      <td id=\"T_7521c_row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
       "      <td id=\"T_7521c_row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
       "      <td id=\"T_7521c_row1_col12\" class=\"data row1 col12\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7521c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_7521c_row2_col0\" class=\"data row2 col0\" >0.10</td>\n",
       "      <td id=\"T_7521c_row2_col1\" class=\"data row2 col1\" >0.10</td>\n",
       "      <td id=\"T_7521c_row2_col2\" class=\"data row2 col2\" >0.00</td>\n",
       "      <td id=\"T_7521c_row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
       "      <td id=\"T_7521c_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
       "      <td id=\"T_7521c_row2_col5\" class=\"data row2 col5\" >0.00</td>\n",
       "      <td id=\"T_7521c_row2_col6\" class=\"data row2 col6\" >0.10</td>\n",
       "      <td id=\"T_7521c_row2_col7\" class=\"data row2 col7\" >0.10</td>\n",
       "      <td id=\"T_7521c_row2_col8\" class=\"data row2 col8\" >0.10</td>\n",
       "      <td id=\"T_7521c_row2_col9\" class=\"data row2 col9\" >0.10</td>\n",
       "      <td id=\"T_7521c_row2_col10\" class=\"data row2 col10\" >0.10</td>\n",
       "      <td id=\"T_7521c_row2_col11\" class=\"data row2 col11\" >0.10</td>\n",
       "      <td id=\"T_7521c_row2_col12\" class=\"data row2 col12\" >0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7521c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_7521c_row3_col0\" class=\"data row3 col0\" >0.33</td>\n",
       "      <td id=\"T_7521c_row3_col1\" class=\"data row3 col1\" >0.33</td>\n",
       "      <td id=\"T_7521c_row3_col2\" class=\"data row3 col2\" >0.00</td>\n",
       "      <td id=\"T_7521c_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
       "      <td id=\"T_7521c_row3_col4\" class=\"data row3 col4\" >0.00</td>\n",
       "      <td id=\"T_7521c_row3_col5\" class=\"data row3 col5\" >0.00</td>\n",
       "      <td id=\"T_7521c_row3_col6\" class=\"data row3 col6\" >0.00</td>\n",
       "      <td id=\"T_7521c_row3_col7\" class=\"data row3 col7\" >0.00</td>\n",
       "      <td id=\"T_7521c_row3_col8\" class=\"data row3 col8\" >0.00</td>\n",
       "      <td id=\"T_7521c_row3_col9\" class=\"data row3 col9\" >0.33</td>\n",
       "      <td id=\"T_7521c_row3_col10\" class=\"data row3 col10\" >0.00</td>\n",
       "      <td id=\"T_7521c_row3_col11\" class=\"data row3 col11\" >0.00</td>\n",
       "      <td id=\"T_7521c_row3_col12\" class=\"data row3 col12\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7521c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_7521c_row4_col0\" class=\"data row4 col0\" >0.00</td>\n",
       "      <td id=\"T_7521c_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
       "      <td id=\"T_7521c_row4_col2\" class=\"data row4 col2\" >0.25</td>\n",
       "      <td id=\"T_7521c_row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
       "      <td id=\"T_7521c_row4_col4\" class=\"data row4 col4\" >0.00</td>\n",
       "      <td id=\"T_7521c_row4_col5\" class=\"data row4 col5\" >0.25</td>\n",
       "      <td id=\"T_7521c_row4_col6\" class=\"data row4 col6\" >0.00</td>\n",
       "      <td id=\"T_7521c_row4_col7\" class=\"data row4 col7\" >0.00</td>\n",
       "      <td id=\"T_7521c_row4_col8\" class=\"data row4 col8\" >0.00</td>\n",
       "      <td id=\"T_7521c_row4_col9\" class=\"data row4 col9\" >0.25</td>\n",
       "      <td id=\"T_7521c_row4_col10\" class=\"data row4 col10\" >0.00</td>\n",
       "      <td id=\"T_7521c_row4_col11\" class=\"data row4 col11\" >0.00</td>\n",
       "      <td id=\"T_7521c_row4_col12\" class=\"data row4 col12\" >0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7521c_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_7521c_row5_col0\" class=\"data row5 col0\" >0.00</td>\n",
       "      <td id=\"T_7521c_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
       "      <td id=\"T_7521c_row5_col2\" class=\"data row5 col2\" >0.00</td>\n",
       "      <td id=\"T_7521c_row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
       "      <td id=\"T_7521c_row5_col4\" class=\"data row5 col4\" >0.50</td>\n",
       "      <td id=\"T_7521c_row5_col5\" class=\"data row5 col5\" >0.00</td>\n",
       "      <td id=\"T_7521c_row5_col6\" class=\"data row5 col6\" >0.00</td>\n",
       "      <td id=\"T_7521c_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
       "      <td id=\"T_7521c_row5_col8\" class=\"data row5 col8\" >0.00</td>\n",
       "      <td id=\"T_7521c_row5_col9\" class=\"data row5 col9\" >0.50</td>\n",
       "      <td id=\"T_7521c_row5_col10\" class=\"data row5 col10\" >0.00</td>\n",
       "      <td id=\"T_7521c_row5_col11\" class=\"data row5 col11\" >0.00</td>\n",
       "      <td id=\"T_7521c_row5_col12\" class=\"data row5 col12\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7521c_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_7521c_row6_col0\" class=\"data row6 col0\" >0.00</td>\n",
       "      <td id=\"T_7521c_row6_col1\" class=\"data row6 col1\" >0.00</td>\n",
       "      <td id=\"T_7521c_row6_col2\" class=\"data row6 col2\" >0.50</td>\n",
       "      <td id=\"T_7521c_row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
       "      <td id=\"T_7521c_row6_col4\" class=\"data row6 col4\" >0.00</td>\n",
       "      <td id=\"T_7521c_row6_col5\" class=\"data row6 col5\" >0.00</td>\n",
       "      <td id=\"T_7521c_row6_col6\" class=\"data row6 col6\" >0.00</td>\n",
       "      <td id=\"T_7521c_row6_col7\" class=\"data row6 col7\" >0.00</td>\n",
       "      <td id=\"T_7521c_row6_col8\" class=\"data row6 col8\" >0.00</td>\n",
       "      <td id=\"T_7521c_row6_col9\" class=\"data row6 col9\" >0.00</td>\n",
       "      <td id=\"T_7521c_row6_col10\" class=\"data row6 col10\" >0.50</td>\n",
       "      <td id=\"T_7521c_row6_col11\" class=\"data row6 col11\" >0.00</td>\n",
       "      <td id=\"T_7521c_row6_col12\" class=\"data row6 col12\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7521c_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_7521c_row7_col0\" class=\"data row7 col0\" >0.00</td>\n",
       "      <td id=\"T_7521c_row7_col1\" class=\"data row7 col1\" >0.00</td>\n",
       "      <td id=\"T_7521c_row7_col2\" class=\"data row7 col2\" >0.33</td>\n",
       "      <td id=\"T_7521c_row7_col3\" class=\"data row7 col3\" >0.00</td>\n",
       "      <td id=\"T_7521c_row7_col4\" class=\"data row7 col4\" >0.00</td>\n",
       "      <td id=\"T_7521c_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
       "      <td id=\"T_7521c_row7_col6\" class=\"data row7 col6\" >0.00</td>\n",
       "      <td id=\"T_7521c_row7_col7\" class=\"data row7 col7\" >0.00</td>\n",
       "      <td id=\"T_7521c_row7_col8\" class=\"data row7 col8\" >0.00</td>\n",
       "      <td id=\"T_7521c_row7_col9\" class=\"data row7 col9\" >0.00</td>\n",
       "      <td id=\"T_7521c_row7_col10\" class=\"data row7 col10\" >0.33</td>\n",
       "      <td id=\"T_7521c_row7_col11\" class=\"data row7 col11\" >0.00</td>\n",
       "      <td id=\"T_7521c_row7_col12\" class=\"data row7 col12\" >0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7521c_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_7521c_row8_col0\" class=\"data row8 col0\" >0.33</td>\n",
       "      <td id=\"T_7521c_row8_col1\" class=\"data row8 col1\" >0.33</td>\n",
       "      <td id=\"T_7521c_row8_col2\" class=\"data row8 col2\" >0.33</td>\n",
       "      <td id=\"T_7521c_row8_col3\" class=\"data row8 col3\" >0.00</td>\n",
       "      <td id=\"T_7521c_row8_col4\" class=\"data row8 col4\" >0.00</td>\n",
       "      <td id=\"T_7521c_row8_col5\" class=\"data row8 col5\" >0.00</td>\n",
       "      <td id=\"T_7521c_row8_col6\" class=\"data row8 col6\" >0.00</td>\n",
       "      <td id=\"T_7521c_row8_col7\" class=\"data row8 col7\" >0.00</td>\n",
       "      <td id=\"T_7521c_row8_col8\" class=\"data row8 col8\" >0.00</td>\n",
       "      <td id=\"T_7521c_row8_col9\" class=\"data row8 col9\" >0.00</td>\n",
       "      <td id=\"T_7521c_row8_col10\" class=\"data row8 col10\" >0.00</td>\n",
       "      <td id=\"T_7521c_row8_col11\" class=\"data row8 col11\" >0.00</td>\n",
       "      <td id=\"T_7521c_row8_col12\" class=\"data row8 col12\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7521c_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_7521c_row9_col0\" class=\"data row9 col0\" >0.00</td>\n",
       "      <td id=\"T_7521c_row9_col1\" class=\"data row9 col1\" >0.20</td>\n",
       "      <td id=\"T_7521c_row9_col2\" class=\"data row9 col2\" >0.20</td>\n",
       "      <td id=\"T_7521c_row9_col3\" class=\"data row9 col3\" >0.20</td>\n",
       "      <td id=\"T_7521c_row9_col4\" class=\"data row9 col4\" >0.20</td>\n",
       "      <td id=\"T_7521c_row9_col5\" class=\"data row9 col5\" >0.20</td>\n",
       "      <td id=\"T_7521c_row9_col6\" class=\"data row9 col6\" >0.00</td>\n",
       "      <td id=\"T_7521c_row9_col7\" class=\"data row9 col7\" >0.00</td>\n",
       "      <td id=\"T_7521c_row9_col8\" class=\"data row9 col8\" >0.00</td>\n",
       "      <td id=\"T_7521c_row9_col9\" class=\"data row9 col9\" >0.00</td>\n",
       "      <td id=\"T_7521c_row9_col10\" class=\"data row9 col10\" >0.00</td>\n",
       "      <td id=\"T_7521c_row9_col11\" class=\"data row9 col11\" >0.00</td>\n",
       "      <td id=\"T_7521c_row9_col12\" class=\"data row9 col12\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7521c_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_7521c_row10_col0\" class=\"data row10 col0\" >0.00</td>\n",
       "      <td id=\"T_7521c_row10_col1\" class=\"data row10 col1\" >0.00</td>\n",
       "      <td id=\"T_7521c_row10_col2\" class=\"data row10 col2\" >0.33</td>\n",
       "      <td id=\"T_7521c_row10_col3\" class=\"data row10 col3\" >0.00</td>\n",
       "      <td id=\"T_7521c_row10_col4\" class=\"data row10 col4\" >0.00</td>\n",
       "      <td id=\"T_7521c_row10_col5\" class=\"data row10 col5\" >0.00</td>\n",
       "      <td id=\"T_7521c_row10_col6\" class=\"data row10 col6\" >0.33</td>\n",
       "      <td id=\"T_7521c_row10_col7\" class=\"data row10 col7\" >0.33</td>\n",
       "      <td id=\"T_7521c_row10_col8\" class=\"data row10 col8\" >0.00</td>\n",
       "      <td id=\"T_7521c_row10_col9\" class=\"data row10 col9\" >0.00</td>\n",
       "      <td id=\"T_7521c_row10_col10\" class=\"data row10 col10\" >0.00</td>\n",
       "      <td id=\"T_7521c_row10_col11\" class=\"data row10 col11\" >0.00</td>\n",
       "      <td id=\"T_7521c_row10_col12\" class=\"data row10 col12\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7521c_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_7521c_row11_col0\" class=\"data row11 col0\" >0.50</td>\n",
       "      <td id=\"T_7521c_row11_col1\" class=\"data row11 col1\" >0.00</td>\n",
       "      <td id=\"T_7521c_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
       "      <td id=\"T_7521c_row11_col3\" class=\"data row11 col3\" >0.00</td>\n",
       "      <td id=\"T_7521c_row11_col4\" class=\"data row11 col4\" >0.00</td>\n",
       "      <td id=\"T_7521c_row11_col5\" class=\"data row11 col5\" >0.00</td>\n",
       "      <td id=\"T_7521c_row11_col6\" class=\"data row11 col6\" >0.00</td>\n",
       "      <td id=\"T_7521c_row11_col7\" class=\"data row11 col7\" >0.00</td>\n",
       "      <td id=\"T_7521c_row11_col8\" class=\"data row11 col8\" >0.00</td>\n",
       "      <td id=\"T_7521c_row11_col9\" class=\"data row11 col9\" >0.00</td>\n",
       "      <td id=\"T_7521c_row11_col10\" class=\"data row11 col10\" >0.00</td>\n",
       "      <td id=\"T_7521c_row11_col11\" class=\"data row11 col11\" >0.00</td>\n",
       "      <td id=\"T_7521c_row11_col12\" class=\"data row11 col12\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7521c_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_7521c_row12_col0\" class=\"data row12 col0\" >0.00</td>\n",
       "      <td id=\"T_7521c_row12_col1\" class=\"data row12 col1\" >0.00</td>\n",
       "      <td id=\"T_7521c_row12_col2\" class=\"data row12 col2\" >0.33</td>\n",
       "      <td id=\"T_7521c_row12_col3\" class=\"data row12 col3\" >0.00</td>\n",
       "      <td id=\"T_7521c_row12_col4\" class=\"data row12 col4\" >0.33</td>\n",
       "      <td id=\"T_7521c_row12_col5\" class=\"data row12 col5\" >0.00</td>\n",
       "      <td id=\"T_7521c_row12_col6\" class=\"data row12 col6\" >0.00</td>\n",
       "      <td id=\"T_7521c_row12_col7\" class=\"data row12 col7\" >0.33</td>\n",
       "      <td id=\"T_7521c_row12_col8\" class=\"data row12 col8\" >0.00</td>\n",
       "      <td id=\"T_7521c_row12_col9\" class=\"data row12 col9\" >0.00</td>\n",
       "      <td id=\"T_7521c_row12_col10\" class=\"data row12 col10\" >0.00</td>\n",
       "      <td id=\"T_7521c_row12_col11\" class=\"data row12 col11\" >0.00</td>\n",
       "      <td id=\"T_7521c_row12_col12\" class=\"data row12 col12\" >0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f94daa01220>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = adjacency.astype(float)\n",
    "\n",
    "# normalize by row\n",
    "for i in range(len(adjacency)):\n",
    "    A[i, :] /= A[i, :].sum()\n",
    "\n",
    "np.set_printoptions(precision=2)\n",
    "pd.DataFrame(A).style.background_gradient(cmap=\"Blues\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Table 1. Transition matrix - value in each matrix element $a_{ij}$ represents probability that the spy will move to country $j$ (the column) from country $i$ (the row).*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add these probabilities to our graph representation of the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "\n",
    "G = nx.Graph(A)\n",
    "\n",
    "edge_labels = dict(((u, v), f'{d[\"weight\"]:.2f}') for u, v, d in G.edges(data=True))\n",
    "\n",
    "# layout the graph\n",
    "# TODO: use capital coordinates to layout the graph\n",
    "pos = nx.spring_layout(G)\n",
    "\n",
    "# add edge labels\n",
    "nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)\n",
    "\n",
    "# draw graph\n",
    "ax = nx.draw(G, pos, node_size=500, with_labels=False)\n",
    "\n",
    "# draw node labels\n",
    "for p in pos:  # raise text positions\n",
    "    pos[p][1] += 0.10\n",
    "nx.draw_networkx_labels(G, pos, labels=labels)\n",
    "plt.tight_layout()\n",
    "f = 0.1\n",
    "x = plt.xlim()[1] - plt.xlim()[0]\n",
    "y = plt.ylim()[1] - plt.ylim()[0]\n",
    "plt.xlim(plt.xlim()[0] - f * x, plt.xlim()[1] + f * x)\n",
    "plt.ylim(plt.ylim()[0] - f * x, plt.ylim()[1] + f * x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Figure 3. Neighbouring relations and transitions probabilities visualized as graph created from transition matrix*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example fourth row of our transition matrix represents Chile. Knowing that the spy was in Chile on day $t$, what are the probabilities that he will be in any given country on day $t+1$?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_f998f_row0_col0, #T_f998f_row0_col1, #T_f998f_row0_col9 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f998f_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >country</th>\n",
       "      <th class=\"col_heading level0 col0\" >argentina</th>\n",
       "      <th class=\"col_heading level0 col1\" >bolivia</th>\n",
       "      <th class=\"col_heading level0 col2\" >brazil</th>\n",
       "      <th class=\"col_heading level0 col3\" >chile</th>\n",
       "      <th class=\"col_heading level0 col4\" >colombia</th>\n",
       "      <th class=\"col_heading level0 col5\" >ecuador</th>\n",
       "      <th class=\"col_heading level0 col6\" >french guiana</th>\n",
       "      <th class=\"col_heading level0 col7\" >guyana</th>\n",
       "      <th class=\"col_heading level0 col8\" >paraguay</th>\n",
       "      <th class=\"col_heading level0 col9\" >peru</th>\n",
       "      <th class=\"col_heading level0 col10\" >suriname</th>\n",
       "      <th class=\"col_heading level0 col11\" >uruguay</th>\n",
       "      <th class=\"col_heading level0 col12\" >venezuela</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f998f_level0_row0\" class=\"row_heading level0 row0\" >p(country)</th>\n",
       "      <td id=\"T_f998f_row0_col0\" class=\"data row0 col0\" >0.33</td>\n",
       "      <td id=\"T_f998f_row0_col1\" class=\"data row0 col1\" >0.33</td>\n",
       "      <td id=\"T_f998f_row0_col2\" class=\"data row0 col2\" >0.00</td>\n",
       "      <td id=\"T_f998f_row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
       "      <td id=\"T_f998f_row0_col4\" class=\"data row0 col4\" >0.00</td>\n",
       "      <td id=\"T_f998f_row0_col5\" class=\"data row0 col5\" >0.00</td>\n",
       "      <td id=\"T_f998f_row0_col6\" class=\"data row0 col6\" >0.00</td>\n",
       "      <td id=\"T_f998f_row0_col7\" class=\"data row0 col7\" >0.00</td>\n",
       "      <td id=\"T_f998f_row0_col8\" class=\"data row0 col8\" >0.00</td>\n",
       "      <td id=\"T_f998f_row0_col9\" class=\"data row0 col9\" >0.33</td>\n",
       "      <td id=\"T_f998f_row0_col10\" class=\"data row0 col10\" >0.00</td>\n",
       "      <td id=\"T_f998f_row0_col11\" class=\"data row0 col11\" >0.00</td>\n",
       "      <td id=\"T_f998f_row0_col12\" class=\"data row0 col12\" >0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f94daa78d30>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chile_idx = countries.index(\"chile\")\n",
    "df = pd.DataFrame(A[chile_idx], index=countries, columns=[\"p(country)\"])\n",
    "df.index.name = \"country\"\n",
    "df.T.style.highlight_max(color=\"lightgreen\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are told that the spy started out in Chile on day $t=0$, so let's encode that in a linear algebra friendly way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial state probability vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.zeros((len(countries), 1), dtype=float)\n",
    "x[chile_idx] = 1\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## State probability at time $t$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like each row of the transition matrix, this column vector $x$ is a stochastic vector which represents probabilities. By setting the element corresponding to Chile equal to one, we are saying that we know the spy started there. However, if the problem were different, we could just as easily encode our beliefs about different probabilities with appropriate fractions.\n",
    "\n",
    "To get from there to the probability on the next day, $t+1$, we can do the following:\n",
    "\n",
    "$$x^T A$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_71a1c_row0_col0, #T_71a1c_row0_col1, #T_71a1c_row0_col9 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_71a1c_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >argentina</th>\n",
       "      <th class=\"col_heading level0 col1\" >bolivia</th>\n",
       "      <th class=\"col_heading level0 col2\" >brazil</th>\n",
       "      <th class=\"col_heading level0 col3\" >chile</th>\n",
       "      <th class=\"col_heading level0 col4\" >colombia</th>\n",
       "      <th class=\"col_heading level0 col5\" >ecuador</th>\n",
       "      <th class=\"col_heading level0 col6\" >french guiana</th>\n",
       "      <th class=\"col_heading level0 col7\" >guyana</th>\n",
       "      <th class=\"col_heading level0 col8\" >paraguay</th>\n",
       "      <th class=\"col_heading level0 col9\" >peru</th>\n",
       "      <th class=\"col_heading level0 col10\" >suriname</th>\n",
       "      <th class=\"col_heading level0 col11\" >uruguay</th>\n",
       "      <th class=\"col_heading level0 col12\" >venezuela</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_71a1c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_71a1c_row0_col0\" class=\"data row0 col0\" >0.33</td>\n",
       "      <td id=\"T_71a1c_row0_col1\" class=\"data row0 col1\" >0.33</td>\n",
       "      <td id=\"T_71a1c_row0_col2\" class=\"data row0 col2\" >0.00</td>\n",
       "      <td id=\"T_71a1c_row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
       "      <td id=\"T_71a1c_row0_col4\" class=\"data row0 col4\" >0.00</td>\n",
       "      <td id=\"T_71a1c_row0_col5\" class=\"data row0 col5\" >0.00</td>\n",
       "      <td id=\"T_71a1c_row0_col6\" class=\"data row0 col6\" >0.00</td>\n",
       "      <td id=\"T_71a1c_row0_col7\" class=\"data row0 col7\" >0.00</td>\n",
       "      <td id=\"T_71a1c_row0_col8\" class=\"data row0 col8\" >0.00</td>\n",
       "      <td id=\"T_71a1c_row0_col9\" class=\"data row0 col9\" >0.33</td>\n",
       "      <td id=\"T_71a1c_row0_col10\" class=\"data row0 col10\" >0.00</td>\n",
       "      <td id=\"T_71a1c_row0_col11\" class=\"data row0 col11\" >0.00</td>\n",
       "      <td id=\"T_71a1c_row0_col12\" class=\"data row0 col12\" >0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f94d687a5e0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs = np.dot(x.T, A)\n",
    "pd.DataFrame(pd.Series(probs.ravel(), index=countries)).T.style.highlight_max(\n",
    "    color=\"lightgreen\", axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about on day $t+2$? We advance the probabilities by multiplying by $A$ again:\n",
    "\n",
    "$$x^T A A = x^T A^2$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c03ba_row0_col2, #T_c03ba_row0_col3 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c03ba_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >country</th>\n",
       "      <th class=\"col_heading level0 col0\" >argentina</th>\n",
       "      <th class=\"col_heading level0 col1\" >bolivia</th>\n",
       "      <th class=\"col_heading level0 col2\" >brazil</th>\n",
       "      <th class=\"col_heading level0 col3\" >chile</th>\n",
       "      <th class=\"col_heading level0 col4\" >colombia</th>\n",
       "      <th class=\"col_heading level0 col5\" >ecuador</th>\n",
       "      <th class=\"col_heading level0 col6\" >french guiana</th>\n",
       "      <th class=\"col_heading level0 col7\" >guyana</th>\n",
       "      <th class=\"col_heading level0 col8\" >paraguay</th>\n",
       "      <th class=\"col_heading level0 col9\" >peru</th>\n",
       "      <th class=\"col_heading level0 col10\" >suriname</th>\n",
       "      <th class=\"col_heading level0 col11\" >uruguay</th>\n",
       "      <th class=\"col_heading level0 col12\" >venezuela</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c03ba_level0_row0\" class=\"row_heading level0 row0\" >p(country)</th>\n",
       "      <td id=\"T_c03ba_row0_col0\" class=\"data row0 col0\" >0.07</td>\n",
       "      <td id=\"T_c03ba_row0_col1\" class=\"data row0 col1\" >0.13</td>\n",
       "      <td id=\"T_c03ba_row0_col2\" class=\"data row0 col2\" >0.20</td>\n",
       "      <td id=\"T_c03ba_row0_col3\" class=\"data row0 col3\" >0.20</td>\n",
       "      <td id=\"T_c03ba_row0_col4\" class=\"data row0 col4\" >0.07</td>\n",
       "      <td id=\"T_c03ba_row0_col5\" class=\"data row0 col5\" >0.07</td>\n",
       "      <td id=\"T_c03ba_row0_col6\" class=\"data row0 col6\" >0.00</td>\n",
       "      <td id=\"T_c03ba_row0_col7\" class=\"data row0 col7\" >0.00</td>\n",
       "      <td id=\"T_c03ba_row0_col8\" class=\"data row0 col8\" >0.13</td>\n",
       "      <td id=\"T_c03ba_row0_col9\" class=\"data row0 col9\" >0.07</td>\n",
       "      <td id=\"T_c03ba_row0_col10\" class=\"data row0 col10\" >0.00</td>\n",
       "      <td id=\"T_c03ba_row0_col11\" class=\"data row0 col11\" >0.07</td>\n",
       "      <td id=\"T_c03ba_row0_col12\" class=\"data row0 col12\" >0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f94d68f3220>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs = np.dot(x.T, np.dot(A, A))\n",
    "pd.Series(probs.ravel(), index=countries)\n",
    "\n",
    "df = pd.DataFrame(probs.ravel(), index=countries, columns=[\"p(country)\"])\n",
    "df.index.name = \"country\"\n",
    "df.T.style.highlight_max(color=\"lightgreen\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily generalize this by saying that our probability vector at any given day $t$ (having started in Chile on day $t=0$) is equal to:\n",
    "\n",
    "$$x^T A^t$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def probability_vector(t):\n",
    "    \"\"\"Calculate probability vector of being in any country on day t\"\"\"\n",
    "    probabilities = np.dot(x.T, np.linalg.matrix_power(A, t))\n",
    "    return pd.Series(probabilities.ravel(), index=countries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spy has been moving around for \"several weeks\" - let's see what the probabilities are after $3$ weeks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: make animated gif\n",
    "plt.figure(figsize=(10, 8))\n",
    "# calculate the probabilities for 3 weeks of wandering\n",
    "probs = probability_vector(3 * 7)\n",
    "\n",
    "# make a bar plot\n",
    "pos = np.arange(len(countries))\n",
    "rects = plt.barh(pos, probs)\n",
    "\n",
    "# label the bars with their actual values\n",
    "# see: http://matplotlib.org/examples/pylab_examples/barchart_demo2.html\n",
    "for i, rect in enumerate(rects):\n",
    "    plt.text(\n",
    "        rect.get_width() - 0.0015,\n",
    "        rect.get_y() + rect.get_height() / 2.0,\n",
    "        str(round(probs[i], 4)),\n",
    "        color=\"white\",\n",
    "        horizontalalignment=\"right\",\n",
    "        verticalalignment=\"center\",\n",
    "    )\n",
    "rects[2].set_color(\"r\")\n",
    "\n",
    "# annotate the plot\n",
    "plt.yticks(pos, countries, fontsize=16)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.xlim(0, 0.25)\n",
    "plt.ylim(-0.5, 13)\n",
    "plt.xlabel(r\"$p(\\mathrm{spy\\ in\\ country\\ on\\ day\\ }t=21)$\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Figure 4. Probability distribution of spy being in given country after 21 days.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We might worry that we are not being exact here in interpreting \"several weeks\" as exactly $21$ days. What saves us is that this Markov chain converges to a steady state (called a stationary distribution) after a sufficient number of transitions.\n",
    "\n",
    "For instance, here are the probabilities after different numbers of days:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "#T_c99bb_row5_col8 {\n",
       "  background-color: #dbe9f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c99bb_row5_col9 {\n",
       "  background-color: #97c6df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c99bb_row5_col12 {\n",
       "  background-color: #dfebf7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c99bb_row6_col0, #T_c99bb_row6_col1, #T_c99bb_row6_col9, #T_c99bb_row7_col0, #T_c99bb_row7_col1, #T_c99bb_row7_col9 {\n",
       "  background-color: #9dcae1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c99bb_row6_col4 {\n",
       "  background-color: #c6dbef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c99bb_row7_col4 {\n",
       "  background-color: #c7dbef;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c99bb_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >day</th>\n",
       "      <th class=\"col_heading level0 col0\" >argentina</th>\n",
       "      <th class=\"col_heading level0 col1\" >bolivia</th>\n",
       "      <th class=\"col_heading level0 col2\" >brazil</th>\n",
       "      <th class=\"col_heading level0 col3\" >chile</th>\n",
       "      <th class=\"col_heading level0 col4\" >colombia</th>\n",
       "      <th class=\"col_heading level0 col5\" >ecuador</th>\n",
       "      <th class=\"col_heading level0 col6\" >french guiana</th>\n",
       "      <th class=\"col_heading level0 col7\" >guyana</th>\n",
       "      <th class=\"col_heading level0 col8\" >paraguay</th>\n",
       "      <th class=\"col_heading level0 col9\" >peru</th>\n",
       "      <th class=\"col_heading level0 col10\" >suriname</th>\n",
       "      <th class=\"col_heading level0 col11\" >uruguay</th>\n",
       "      <th class=\"col_heading level0 col12\" >venezuela</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c99bb_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_c99bb_row0_col0\" class=\"data row0 col0\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row0_col1\" class=\"data row0 col1\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row0_col2\" class=\"data row0 col2\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row0_col3\" class=\"data row0 col3\" >1.00</td>\n",
       "      <td id=\"T_c99bb_row0_col4\" class=\"data row0 col4\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row0_col5\" class=\"data row0 col5\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row0_col6\" class=\"data row0 col6\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row0_col7\" class=\"data row0 col7\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row0_col8\" class=\"data row0 col8\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row0_col9\" class=\"data row0 col9\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row0_col10\" class=\"data row0 col10\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row0_col11\" class=\"data row0 col11\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row0_col12\" class=\"data row0 col12\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c99bb_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_c99bb_row1_col0\" class=\"data row1 col0\" >0.33</td>\n",
       "      <td id=\"T_c99bb_row1_col1\" class=\"data row1 col1\" >0.33</td>\n",
       "      <td id=\"T_c99bb_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row1_col4\" class=\"data row1 col4\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row1_col9\" class=\"data row1 col9\" >0.33</td>\n",
       "      <td id=\"T_c99bb_row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row1_col12\" class=\"data row1 col12\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c99bb_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_c99bb_row2_col0\" class=\"data row2 col0\" >0.07</td>\n",
       "      <td id=\"T_c99bb_row2_col1\" class=\"data row2 col1\" >0.13</td>\n",
       "      <td id=\"T_c99bb_row2_col2\" class=\"data row2 col2\" >0.20</td>\n",
       "      <td id=\"T_c99bb_row2_col3\" class=\"data row2 col3\" >0.20</td>\n",
       "      <td id=\"T_c99bb_row2_col4\" class=\"data row2 col4\" >0.07</td>\n",
       "      <td id=\"T_c99bb_row2_col5\" class=\"data row2 col5\" >0.07</td>\n",
       "      <td id=\"T_c99bb_row2_col6\" class=\"data row2 col6\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row2_col7\" class=\"data row2 col7\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row2_col8\" class=\"data row2 col8\" >0.13</td>\n",
       "      <td id=\"T_c99bb_row2_col9\" class=\"data row2 col9\" >0.07</td>\n",
       "      <td id=\"T_c99bb_row2_col10\" class=\"data row2 col10\" >0.00</td>\n",
       "      <td id=\"T_c99bb_row2_col11\" class=\"data row2 col11\" >0.07</td>\n",
       "      <td id=\"T_c99bb_row2_col12\" class=\"data row2 col12\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c99bb_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_c99bb_row3_col0\" class=\"data row3 col0\" >0.19</td>\n",
       "      <td id=\"T_c99bb_row3_col1\" class=\"data row3 col1\" >0.16</td>\n",
       "      <td id=\"T_c99bb_row3_col2\" class=\"data row3 col2\" >0.15</td>\n",
       "      <td id=\"T_c99bb_row3_col3\" class=\"data row3 col3\" >0.05</td>\n",
       "      <td id=\"T_c99bb_row3_col4\" class=\"data row3 col4\" >0.07</td>\n",
       "      <td id=\"T_c99bb_row3_col5\" class=\"data row3 col5\" >0.03</td>\n",
       "      <td id=\"T_c99bb_row3_col6\" class=\"data row3 col6\" >0.02</td>\n",
       "      <td id=\"T_c99bb_row3_col7\" class=\"data row3 col7\" >0.02</td>\n",
       "      <td id=\"T_c99bb_row3_col8\" class=\"data row3 col8\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row3_col9\" class=\"data row3 col9\" >0.16</td>\n",
       "      <td id=\"T_c99bb_row3_col10\" class=\"data row3 col10\" >0.02</td>\n",
       "      <td id=\"T_c99bb_row3_col11\" class=\"data row3 col11\" >0.03</td>\n",
       "      <td id=\"T_c99bb_row3_col12\" class=\"data row3 col12\" >0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c99bb_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "      <td id=\"T_c99bb_row4_col0\" class=\"data row4 col0\" >0.13</td>\n",
       "      <td id=\"T_c99bb_row4_col1\" class=\"data row4 col1\" >0.12</td>\n",
       "      <td id=\"T_c99bb_row4_col2\" class=\"data row4 col2\" >0.18</td>\n",
       "      <td id=\"T_c99bb_row4_col3\" class=\"data row4 col3\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row4_col4\" class=\"data row4 col4\" >0.08</td>\n",
       "      <td id=\"T_c99bb_row4_col5\" class=\"data row4 col5\" >0.04</td>\n",
       "      <td id=\"T_c99bb_row4_col6\" class=\"data row4 col6\" >0.03</td>\n",
       "      <td id=\"T_c99bb_row4_col7\" class=\"data row4 col7\" >0.04</td>\n",
       "      <td id=\"T_c99bb_row4_col8\" class=\"data row4 col8\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row4_col9\" class=\"data row4 col9\" >0.12</td>\n",
       "      <td id=\"T_c99bb_row4_col10\" class=\"data row4 col10\" >0.04</td>\n",
       "      <td id=\"T_c99bb_row4_col11\" class=\"data row4 col11\" >0.04</td>\n",
       "      <td id=\"T_c99bb_row4_col12\" class=\"data row4 col12\" >0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c99bb_level0_row5\" class=\"row_heading level0 row5\" >10</th>\n",
       "      <td id=\"T_c99bb_row5_col0\" class=\"data row5 col0\" >0.10</td>\n",
       "      <td id=\"T_c99bb_row5_col1\" class=\"data row5 col1\" >0.10</td>\n",
       "      <td id=\"T_c99bb_row5_col2\" class=\"data row5 col2\" >0.20</td>\n",
       "      <td id=\"T_c99bb_row5_col3\" class=\"data row5 col3\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row5_col4\" class=\"data row5 col4\" >0.08</td>\n",
       "      <td id=\"T_c99bb_row5_col5\" class=\"data row5 col5\" >0.04</td>\n",
       "      <td id=\"T_c99bb_row5_col6\" class=\"data row5 col6\" >0.04</td>\n",
       "      <td id=\"T_c99bb_row5_col7\" class=\"data row5 col7\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row5_col8\" class=\"data row5 col8\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row5_col9\" class=\"data row5 col9\" >0.10</td>\n",
       "      <td id=\"T_c99bb_row5_col10\" class=\"data row5 col10\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row5_col11\" class=\"data row5 col11\" >0.04</td>\n",
       "      <td id=\"T_c99bb_row5_col12\" class=\"data row5 col12\" >0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c99bb_level0_row6\" class=\"row_heading level0 row6\" >100</th>\n",
       "      <td id=\"T_c99bb_row6_col0\" class=\"data row6 col0\" >0.10</td>\n",
       "      <td id=\"T_c99bb_row6_col1\" class=\"data row6 col1\" >0.10</td>\n",
       "      <td id=\"T_c99bb_row6_col2\" class=\"data row6 col2\" >0.20</td>\n",
       "      <td id=\"T_c99bb_row6_col3\" class=\"data row6 col3\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row6_col4\" class=\"data row6 col4\" >0.08</td>\n",
       "      <td id=\"T_c99bb_row6_col5\" class=\"data row6 col5\" >0.04</td>\n",
       "      <td id=\"T_c99bb_row6_col6\" class=\"data row6 col6\" >0.04</td>\n",
       "      <td id=\"T_c99bb_row6_col7\" class=\"data row6 col7\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row6_col8\" class=\"data row6 col8\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
       "      <td id=\"T_c99bb_row6_col10\" class=\"data row6 col10\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row6_col11\" class=\"data row6 col11\" >0.04</td>\n",
       "      <td id=\"T_c99bb_row6_col12\" class=\"data row6 col12\" >0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c99bb_level0_row7\" class=\"row_heading level0 row7\" >1000</th>\n",
       "      <td id=\"T_c99bb_row7_col0\" class=\"data row7 col0\" >0.10</td>\n",
       "      <td id=\"T_c99bb_row7_col1\" class=\"data row7 col1\" >0.10</td>\n",
       "      <td id=\"T_c99bb_row7_col2\" class=\"data row7 col2\" >0.20</td>\n",
       "      <td id=\"T_c99bb_row7_col3\" class=\"data row7 col3\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row7_col4\" class=\"data row7 col4\" >0.08</td>\n",
       "      <td id=\"T_c99bb_row7_col5\" class=\"data row7 col5\" >0.04</td>\n",
       "      <td id=\"T_c99bb_row7_col6\" class=\"data row7 col6\" >0.04</td>\n",
       "      <td id=\"T_c99bb_row7_col7\" class=\"data row7 col7\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row7_col8\" class=\"data row7 col8\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row7_col9\" class=\"data row7 col9\" >0.10</td>\n",
       "      <td id=\"T_c99bb_row7_col10\" class=\"data row7 col10\" >0.06</td>\n",
       "      <td id=\"T_c99bb_row7_col11\" class=\"data row7 col11\" >0.04</td>\n",
       "      <td id=\"T_c99bb_row7_col12\" class=\"data row7 col12\" >0.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f94daa786d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_state = {0: probability_vector(0)}\n",
    "comparison = pd.DataFrame(initial_state)\n",
    "\n",
    "days = [1, 2, 3, 5, 10, 100, 1000]\n",
    "for t in days:\n",
    "    comparison[t] = probability_vector(t)\n",
    "comparison.index.name = \"day\"\n",
    "comparison.T.style.background_gradient(cmap=\"Blues\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Table 2. Probability of spy being in given country after arbitrary number of days: $1, 2, 3, 5, 10, 100$ and $1000$. Stationary distribution emerges after day $10$.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's pretty clear that, after a while, the probabilities converge to a steady state. Let's visualize this process. In the following graph, the x-axis will represent the number of transitions since the spy starts out in Chile on day $t=0$, and the y-axis will represent the probability of being in each country after that number of transitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.4)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = np.arange(20)\n",
    "\n",
    "for i, country in enumerate(countries):\n",
    "    if i < 1:\n",
    "        pm = np.array([probability_vector(t)[i] for t in ts])\n",
    "    else:\n",
    "        pv = np.array([probability_vector(t)[i] for t in ts])\n",
    "        pm = np.vstack((pm, pv))\n",
    "\n",
    "pm_df = pd.DataFrame(pm)\n",
    "\n",
    "pm_df.T.plot(\n",
    "    legend=False, figsize=(10, 6), marker=\"\", color=\"grey\", linewidth=1, alpha=0.4\n",
    ")\n",
    "# Now re-do the interesting curve, but bigger with distinct color\n",
    "plt.plot(ts, pm_df.iloc[2, :], marker=\"\", color=\"red\", linewidth=4, alpha=0.7)\n",
    "\n",
    "plt.xlabel(\"$t [days]$\", fontsize=20)\n",
    "plt.xticks(ts, fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.ylabel(\"$p(\\mathrm{country})$\", fontsize=20)\n",
    "plt.ylim(0, 0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Figure 5. Evolution of probabilities that spy is being in given country at given day. Probabilities for Brasil is marked in orange other countries in red.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probabilities converge to the stationary distribution, with Brazil as the highest probability state at any given time. This should probably come as no surprise given that Brazil has the most adjacency with other nodes in this graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rects = pd.DataFrame(adjacency.sum(axis=1), index=countries).plot(\n",
    "    kind=\"barh\", figsize=(10, 6), legend=None, fontsize=16\n",
    ")\n",
    "plt.xlabel(\"number of neighbours\", fontsize=20)\n",
    "plt.ylabel(\"country\", fontsize=20)\n",
    "plt.show()"
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  },
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   "source": [
    "*Figure 6. Number of adjacent countries (neighbour count)*"
   ]
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   "source": [
    "With this data we can state that most probably the spy is on territory of Brasil  $p(Brazil)=0.2$ after several weeks hiding in South America countries."
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