lessons/notebooks/probprog/Probabilistic-Programming-3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Probabilistic Programming 3: Regression & Classification\n",
    "\n",
    "#### Goal \n",
    "  - Understand how to estimate regression parameters using Bayesian inference.\n",
    "  - Understand how to estimate classification parameters using Bayesian inference.\n",
    "  \n",
    "#### Materials       \n",
    "  - Mandatory\n",
    "    - This notebook.\n",
    "    - Lecture notes on [regression](https://nbviewer.jupyter.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Regression.ipynb).\n",
    "    - Lecture notes on [discriminative classification](https://nbviewer.jupyter.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Discriminative-Classification.ipynb).\n",
    "  - Optional\n",
    "    - Bayesian linear regression (Section 3.3 [Bishop](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf))\n",
    "    - Bayesian logistic regression (Section 4.5 [Bishop](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf))\n",
    "    - Local Variational Methods (Section 10.5 [Bishop](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf))\n",
    "    - [Cheatsheets: how does Julia differ from Matlab / Python](https://docs.julialang.org/en/v1/manual/noteworthy-differences/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "using Pkg\n",
    "Pkg.activate(\"workspace\")\n",
    "Pkg.instantiate();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem: Economic growth\n",
    "\n",
    "In 2008, the credit crisis sparked a recession in the US, which spread to other countries in the ensuing years. It took most countries a couple of years to recover. \n",
    "Now, the year is 2011. The Turkish government is asking you to estimate whether Turkey is out of the recession. You decide to look at the data of the national stock exchange to see if there's a positive trend. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "using DataFrames\n",
    "using CSV\n",
    "using ProgressMeter\n",
    "using Plots\n",
    "pyplot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data\n",
    "\n",
    "We are going to start with loading in a data set. We have daily measurements from Istanbul, from the 5th of January 2009 until 22nd of February 2011. The dataset comes from an online resource for machine learning data sets: the [UCI ML Repository](https://archive.ics.uci.edu/ml/datasets/ISTANBUL+STOCK+EXCHANGE)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"data-frame\"><p>536 rows × 2 columns</p><table class=\"data-frame\"><thead><tr><th></th><th>date</th><th>ISE</th></tr><tr><th></th><th title=\"String\">String</th><th title=\"Float64\">Float64</th></tr></thead><tbody><tr><th>1</th><td>5-Jan-09</td><td>0.0357537</td></tr><tr><th>2</th><td>6-Jan-09</td><td>0.0254259</td></tr><tr><th>3</th><td>7-Jan-09</td><td>-0.0288617</td></tr><tr><th>4</th><td>8-Jan-09</td><td>-0.0622081</td></tr><tr><th>5</th><td>9-Jan-09</td><td>0.00985991</td></tr><tr><th>6</th><td>12-Jan-09</td><td>-0.029191</td></tr><tr><th>7</th><td>13-Jan-09</td><td>0.0154453</td></tr><tr><th>8</th><td>14-Jan-09</td><td>-0.0411676</td></tr><tr><th>9</th><td>15-Jan-09</td><td>0.000661905</td></tr><tr><th>10</th><td>16-Jan-09</td><td>0.0220373</td></tr><tr><th>11</th><td>19-Jan-09</td><td>-0.0226925</td></tr><tr><th>12</th><td>20-Jan-09</td><td>-0.0137087</td></tr><tr><th>13</th><td>21-Jan-09</td><td>0.000864697</td></tr><tr><th>14</th><td>22-Jan-09</td><td>-0.00381506</td></tr><tr><th>15</th><td>23-Jan-09</td><td>0.00566126</td></tr><tr><th>16</th><td>26-Jan-09</td><td>0.0468313</td></tr><tr><th>17</th><td>27-Jan-09</td><td>-0.00663498</td></tr><tr><th>18</th><td>28-Jan-09</td><td>0.034567</td></tr><tr><th>19</th><td>29-Jan-09</td><td>-0.0205282</td></tr><tr><th>20</th><td>30-Jan-09</td><td>-0.0087767</td></tr><tr><th>21</th><td>2-Feb-09</td><td>-0.0259191</td></tr><tr><th>22</th><td>3-Feb-09</td><td>0.0152795</td></tr><tr><th>23</th><td>4-Feb-09</td><td>0.0185778</td></tr><tr><th>24</th><td>5-Feb-09</td><td>-0.0141329</td></tr><tr><th>25</th><td>6-Feb-09</td><td>0.036607</td></tr><tr><th>26</th><td>9-Feb-09</td><td>0.0113532</td></tr><tr><th>27</th><td>10-Feb-09</td><td>-0.040542</td></tr><tr><th>28</th><td>11-Feb-09</td><td>-0.0221056</td></tr><tr><th>29</th><td>12-Feb-09</td><td>-0.0148884</td></tr><tr><th>30</th><td>13-Feb-09</td><td>0.00702675</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& date & ISE\\\\\n",
       "\t\\hline\n",
       "\t& String & Float64\\\\\n",
       "\t\\hline\n",
       "\t1 & 5-Jan-09 & 0.0357537 \\\\\n",
       "\t2 & 6-Jan-09 & 0.0254259 \\\\\n",
       "\t3 & 7-Jan-09 & -0.0288617 \\\\\n",
       "\t4 & 8-Jan-09 & -0.0622081 \\\\\n",
       "\t5 & 9-Jan-09 & 0.00985991 \\\\\n",
       "\t6 & 12-Jan-09 & -0.029191 \\\\\n",
       "\t7 & 13-Jan-09 & 0.0154453 \\\\\n",
       "\t8 & 14-Jan-09 & -0.0411676 \\\\\n",
       "\t9 & 15-Jan-09 & 0.000661905 \\\\\n",
       "\t10 & 16-Jan-09 & 0.0220373 \\\\\n",
       "\t11 & 19-Jan-09 & -0.0226925 \\\\\n",
       "\t12 & 20-Jan-09 & -0.0137087 \\\\\n",
       "\t13 & 21-Jan-09 & 0.000864697 \\\\\n",
       "\t14 & 22-Jan-09 & -0.00381506 \\\\\n",
       "\t15 & 23-Jan-09 & 0.00566126 \\\\\n",
       "\t16 & 26-Jan-09 & 0.0468313 \\\\\n",
       "\t17 & 27-Jan-09 & -0.00663498 \\\\\n",
       "\t18 & 28-Jan-09 & 0.034567 \\\\\n",
       "\t19 & 29-Jan-09 & -0.0205282 \\\\\n",
       "\t20 & 30-Jan-09 & -0.0087767 \\\\\n",
       "\t21 & 2-Feb-09 & -0.0259191 \\\\\n",
       "\t22 & 3-Feb-09 & 0.0152795 \\\\\n",
       "\t23 & 4-Feb-09 & 0.0185778 \\\\\n",
       "\t24 & 5-Feb-09 & -0.0141329 \\\\\n",
       "\t25 & 6-Feb-09 & 0.036607 \\\\\n",
       "\t26 & 9-Feb-09 & 0.0113532 \\\\\n",
       "\t27 & 10-Feb-09 & -0.040542 \\\\\n",
       "\t28 & 11-Feb-09 & -0.0221056 \\\\\n",
       "\t29 & 12-Feb-09 & -0.0148884 \\\\\n",
       "\t30 & 13-Feb-09 & 0.00702675 \\\\\n",
       "\t$\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m536×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m date      \u001b[0m\u001b[1m ISE          \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m String    \u001b[0m\u001b[90m Float64      \u001b[0m\n",
       "─────┼─────────────────────────\n",
       "   1 │ 5-Jan-09    0.0357537\n",
       "   2 │ 6-Jan-09    0.0254259\n",
       "   3 │ 7-Jan-09   -0.0288617\n",
       "   4 │ 8-Jan-09   -0.0622081\n",
       "   5 │ 9-Jan-09    0.00985991\n",
       "   6 │ 12-Jan-09  -0.029191\n",
       "   7 │ 13-Jan-09   0.0154453\n",
       "   8 │ 14-Jan-09  -0.0411676\n",
       "   9 │ 15-Jan-09   0.000661905\n",
       "  10 │ 16-Jan-09   0.0220373\n",
       "  11 │ 19-Jan-09  -0.0226925\n",
       "  ⋮  │     ⋮           ⋮\n",
       " 527 │ 9-Feb-11    0.00482299\n",
       " 528 │ 10-Feb-11  -0.0176644\n",
       " 529 │ 11-Feb-11   0.00478229\n",
       " 530 │ 14-Feb-11  -0.00249793\n",
       " 531 │ 15-Feb-11   0.00360638\n",
       " 532 │ 16-Feb-11   0.00859906\n",
       " 533 │ 17-Feb-11   0.00931031\n",
       " 534 │ 18-Feb-11   0.000190969\n",
       " 535 │ 21-Feb-11  -0.013069\n",
       " 536 │ 22-Feb-11  -0.00724632\n",
       "\u001b[36m               515 rows omitted\u001b[0m"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read CSV file\n",
    "df = DataFrame(CSV.File(\"../datasets/istanbul_stockexchange.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the evolution of the stock market values over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count number of samples\n",
    "time_period = 436:536\n",
    "num_samples = length(time_period)\n",
    "\n",
    "# Extract columns\n",
    "dates_num = 1:num_samples\n",
    "dates_str = df[time_period,1]\n",
    "stock_val = df[time_period,2]\n",
    "\n",
    "# Set xticks\n",
    "xtick_points = Int64.(round.(range(1, stop=num_samples, length=5)))\n",
    "\n",
    "# Scatter exchange levels\n",
    "scatter(dates_num, \n",
    "        stock_val, \n",
    "        color=\"black\",\n",
    "        label=\"\", \n",
    "        ylabel=\"Stock Market Levels\", \n",
    "        xlabel=\"time (days)\",\n",
    "        xticks=(xtick_points, [dates_str[i] for i in xtick_points]), \n",
    "        size=(800,300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model specification\n",
    "\n",
    "We have dates $X$, referred to as \"covariates\", and stock exchange levels $Y$, referred to as \"responses\". A regression model has parameters $\\theta$, used to predict $Y$ from $X$. We are looking for a posterior distribution for the parameters $\\theta$:\n",
    "\n",
    "$$\\underbrace{p(\\theta \\mid Y, X)}_{\\text{posterior}} \\propto\\ \\underbrace{p(Y \\mid X, \\theta)}_{\\text{likelihood}} \\cdot \\underbrace{p(\\theta)}_{\\text{prior}}$$\n",
    "\n",
    "We assume each observation $Y_i$ is generated via: \n",
    "\n",
    "$$ Y_i = f_\\theta(X_i) + e$$ \n",
    "\n",
    "where $e$ is white noise, $e \\sim \\mathcal{N}(0, \\sigma^2_Y)$, and the regression function $f_\\theta$ is linear: $f_\\theta(X) = X \\theta_1 + \\theta_2$. The parameters consist of a slope coefficient $\\theta_1$ and an intercept $\\theta_2$, which are summarized into a parameter vector, $\\theta = \\begin{bmatrix}\\theta_1 \\\\ \\theta_2 \\end{bmatrix}$. In practice, we augment the data matrix X with a 1, i.e., $\\begin{bmatrix}X \\\\ 1 \\end{bmatrix}$, so that we may define $f_\\theta(X) = \\theta^{\\top}X$. \n",
    "\n",
    "If we integrate out the noise $e$, then we obtain a Gaussian likelihood function centered on $f_\\theta(X)$ with variance $\\sigma^2_Y$:\n",
    "\n",
    "$$Y_i \\sim \\mathcal{N}(f_\\theta(X_i),\\sigma^2_Y)\\, \\ .$$ \n",
    "\n",
    "Note that this corresponds to $p(Y \\mid X, \\theta)$. We know that the weights are real numbers and that they can be negative. That motivates us to use a Gaussian prior:\n",
    "\n",
    "$$ \\theta \\sim \\mathcal{N}(\\mu_\\theta, \\Sigma_\\theta) \\, .$$\n",
    "\n",
    "Note that this corresponds to $p(\\theta)$. For now, this is all we need. We're going to specify these two equations with ForneyLab. Our prior will become the first factor node: $f_a(\\theta) = \\mathcal{N}(\\theta \\mid \\mu_\\theta, \\Sigma_\\theta)$ and our likelihood becomes the second factor node $f_b(\\theta) = \\mathcal{N}(f_\\theta(X_i),\\sigma^2_Y)$. Note that $\\theta$ is the only unknown variable, since $X$ and $Y$ are observed and $\\mu_\\theta$, $\\Sigma_\\theta$ and $\\sigma^2_Y$ are clamped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Precompiling ForneyLab [9fc3f58a-c2cc-5bff-9419-6a294fefdca9]\n",
      "└ @ Base loading.jl:1342\n"
     ]
    }
   ],
   "source": [
    "using ForneyLab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\r\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n",
       "<!-- Generated by graphviz version 2.49.3 (20211023.0002)\r\n",
       " -->\r\n",
       "<!-- Title: G Pages: 1 -->\r\n",
       "<svg width=\"402pt\" height=\"512pt\"\r\n",
       " viewBox=\"0.00 0.00 402.23 512.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 508)\">\r\n",
       "<title>G</title>\r\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-508 398.23,-508 398.23,4 -4,4\"/>\r\n",
       "<!-- 1258154917618169659 -->\r\n",
       "<g id=\"node1\" class=\"node\">\r\n",
       "<title>1258154917618169659</title>\r\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"318.23,-288 246.23,-288 246.23,-216 318.23,-216 318.23,-288\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"282.23\" y=\"-254.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">dot</text>\r\n",
       "<text text-anchor=\"middle\" x=\"282.23\" y=\"-244.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">dotproduct_1</text>\r\n",
       "</g>\r\n",
       "<!-- 14016909133886953545 -->\r\n",
       "<g id=\"node2\" class=\"node\">\r\n",
       "<title>14016909133886953545</title>\r\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"318.23,-171 246.23,-171 246.23,-99 318.23,-99 318.23,-171\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"282.23\" y=\"-137.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">𝒩</text>\r\n",
       "<text text-anchor=\"middle\" x=\"282.23\" y=\"-127.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">f_a</text>\r\n",
       "</g>\r\n",
       "<!-- 1258154917618169659&#45;&#45;14016909133886953545 -->\r\n",
       "<g id=\"edge7\" class=\"edge\">\r\n",
       "<title>1258154917618169659&#45;&#45;14016909133886953545</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M282.23,-215.87C282.23,-201.74 282.23,-185.46 282.23,-171.31\"/>\r\n",
       "<text text-anchor=\"start\" x=\"282.23\" y=\"-191.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\" fill=\"red\">θ</text>\r\n",
       "<text text-anchor=\"start\" x=\"261.23\" y=\"-173.91\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "<text text-anchor=\"start\" x=\"261.23\" y=\"-209.47\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">3 in2 </text>\r\n",
       "</g>\r\n",
       "<!-- 5983561699395971011 -->\r\n",
       "<g id=\"node5\" class=\"node\">\r\n",
       "<title>5983561699395971011</title>\r\n",
       "<polygon fill=\"lightgrey\" stroke=\"black\" points=\"225.23,-54 171.23,-54 171.23,0 225.23,0 225.23,-54\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"198.23\" y=\"-24.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">clamp_2</text>\r\n",
       "</g>\r\n",
       "<!-- 14016909133886953545&#45;&#45;5983561699395971011 -->\r\n",
       "<g id=\"edge3\" class=\"edge\">\r\n",
       "<title>14016909133886953545&#45;&#45;5983561699395971011</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M245.81,-122.32C226.51,-114.05 204.52,-100.86 193.23,-81 188.67,-72.96 188.43,-63.12 189.84,-54.11\"/>\r\n",
       "<text text-anchor=\"start\" x=\"193.23\" y=\"-74.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\" fill=\"red\">clamp_2</text>\r\n",
       "<text text-anchor=\"start\" x=\"168.84\" y=\"-56.71\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "<text text-anchor=\"start\" x=\"231.81\" y=\"-124.92\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">3 v </text>\r\n",
       "</g>\r\n",
       "<!-- 7306029012165750414 -->\r\n",
       "<g id=\"node6\" class=\"node\">\r\n",
       "<title>7306029012165750414</title>\r\n",
       "<polygon fill=\"lightgrey\" stroke=\"black\" points=\"394.23,-54 340.23,-54 340.23,0 394.23,0 394.23,-54\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"367.23\" y=\"-24.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">clamp_1</text>\r\n",
       "</g>\r\n",
       "<!-- 14016909133886953545&#45;&#45;7306029012165750414 -->\r\n",
       "<g id=\"edge5\" class=\"edge\">\r\n",
       "<title>14016909133886953545&#45;&#45;7306029012165750414</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M310.3,-99C322.01,-84.4 335.36,-67.75 346.2,-54.23\"/>\r\n",
       "<text text-anchor=\"start\" x=\"330.23\" y=\"-74.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\" fill=\"red\">clamp_1</text>\r\n",
       "<text text-anchor=\"start\" x=\"325.2\" y=\"-56.83\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "<text text-anchor=\"start\" x=\"294.3\" y=\"-92.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">2 m </text>\r\n",
       "</g>\r\n",
       "<!-- 8891380928731517020 -->\r\n",
       "<g id=\"node3\" class=\"node\">\r\n",
       "<title>8891380928731517020</title>\r\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"136.23,-405 64.23,-405 64.23,-333 136.23,-333 136.23,-405\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"100.23\" y=\"-371.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">𝒩</text>\r\n",
       "<text text-anchor=\"middle\" x=\"100.23\" y=\"-361.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">f_b</text>\r\n",
       "</g>\r\n",
       "<!-- 8891380928731517020&#45;&#45;1258154917618169659 -->\r\n",
       "<g id=\"edge1\" class=\"edge\">\r\n",
       "<title>8891380928731517020&#45;&#45;1258154917618169659</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M136.63,-345C168.47,-324.88 214.48,-295.81 246.22,-275.76\"/>\r\n",
       "<text text-anchor=\"start\" x=\"197.23\" y=\"-308.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\" fill=\"red\">variable_1</text>\r\n",
       "<text text-anchor=\"start\" x=\"225.22\" y=\"-278.36\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "<text text-anchor=\"start\" x=\"136.63\" y=\"-347.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">2 m </text>\r\n",
       "</g>\r\n",
       "<!-- 10452344320081160686 -->\r\n",
       "<g id=\"node4\" class=\"node\">\r\n",
       "<title>10452344320081160686</title>\r\n",
       "<polygon fill=\"lightgrey\" stroke=\"black\" points=\"66.23,-279 12.23,-279 12.23,-225 66.23,-225 66.23,-279\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"39.23\" y=\"-249.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">clamp_3</text>\r\n",
       "</g>\r\n",
       "<!-- 8891380928731517020&#45;&#45;10452344320081160686 -->\r\n",
       "<g id=\"edge4\" class=\"edge\">\r\n",
       "<title>8891380928731517020&#45;&#45;10452344320081160686</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M64.06,-354.36C46.9,-345.84 28.04,-333.01 18.23,-315 12.15,-303.82 15.41,-290.56 21,-279.17\"/>\r\n",
       "<text text-anchor=\"start\" x=\"18.23\" y=\"-308.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\" fill=\"red\">clamp_3</text>\r\n",
       "<text text-anchor=\"start\" x=\"0\" y=\"-281.77\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "<text text-anchor=\"start\" x=\"50.06\" y=\"-356.96\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">3 v </text>\r\n",
       "</g>\r\n",
       "<!-- 10141380729130211696 -->\r\n",
       "<g id=\"node7\" class=\"node\">\r\n",
       "<title>10141380729130211696</title>\r\n",
       "<polygon fill=\"lightgrey\" stroke=\"black\" points=\"381.73,-396 312.73,-396 312.73,-342 381.73,-342 381.73,-396\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"347.23\" y=\"-366.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">placeholder_X</text>\r\n",
       "</g>\r\n",
       "<!-- 10141380729130211696&#45;&#45;1258154917618169659 -->\r\n",
       "<g id=\"edge6\" class=\"edge\">\r\n",
       "<title>10141380729130211696&#45;&#45;1258154917618169659</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M332.49,-341.9C323.5,-326 311.89,-305.47 302.1,-288.15\"/>\r\n",
       "<text text-anchor=\"start\" x=\"317.23\" y=\"-308.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\" fill=\"red\">X</text>\r\n",
       "<text text-anchor=\"start\" x=\"281.1\" y=\"-290.75\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">2 in1 </text>\r\n",
       "<text text-anchor=\"start\" x=\"311.49\" y=\"-335.5\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "</g>\r\n",
       "<!-- 758955951018469333 -->\r\n",
       "<g id=\"node8\" class=\"node\">\r\n",
       "<title>758955951018469333</title>\r\n",
       "<polygon fill=\"lightgrey\" stroke=\"black\" points=\"134.23,-504 66.23,-504 66.23,-450 134.23,-450 134.23,-504\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"100.23\" y=\"-474.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">placeholder_Y</text>\r\n",
       "</g>\r\n",
       "<!-- 758955951018469333&#45;&#45;8891380928731517020 -->\r\n",
       "<g id=\"edge2\" class=\"edge\">\r\n",
       "<title>758955951018469333&#45;&#45;8891380928731517020</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M100.23,-449.8C100.23,-436.31 100.23,-419.68 100.23,-405.1\"/>\r\n",
       "<text text-anchor=\"start\" x=\"100.23\" y=\"-425.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\" fill=\"red\">Y</text>\r\n",
       "<text text-anchor=\"start\" x=\"79.23\" y=\"-407.7\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "<text text-anchor=\"start\" x=\"79.23\" y=\"-443.4\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "</g>\r\n",
       "</g>\r\n",
       "</svg>\r\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Start factor graph\n",
    "graph = FactorGraph();\n",
    "\n",
    "# Prior weight parameters\n",
    "μ_θ = [0., 0.]\n",
    "Σ_θ = [1. 0.; 0. 1.]\n",
    "\n",
    "# Noise variance\n",
    "σ2_Y = 1.\n",
    "\n",
    "# Add weight prior to graph\n",
    "@RV θ ~ GaussianMeanVariance(μ_θ, Σ_θ, id=:f_a)\n",
    "    \n",
    "# Define covariates\n",
    "@RV X\n",
    "\n",
    "# Define likelihood\n",
    "@RV Y ~ GaussianMeanVariance(dot(θ,X), σ2_Y, id=:f_b)\n",
    "\n",
    "# Designate observed variables\n",
    "placeholder(X, :X, dims=(2,))\n",
    "placeholder(Y, :Y)\n",
    "\n",
    "# Visualise the graph\n",
    "ForneyLab.draw(graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But this is the graph for a single observation. In reality, we have multiple observations. If we want to consume all this information at once, the likelihood part of the above graph will need to be repeated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start factor graph\n",
    "graph2 = FactorGraph();\n",
    "\n",
    "# Prior weight parameters\n",
    "μ_θ = [0., 0.]\n",
    "Σ_θ = [1. 0.; 0. 1.]\n",
    "\n",
    "# Noise variance\n",
    "σ2_Y = 1.\n",
    "\n",
    "# Add weight prior to graph\n",
    "@RV θ ~ GaussianMeanVariance(μ_θ, Σ_θ, id=:f_a)\n",
    "\n",
    "# Pre-define vectors for storing latent and observed variables\n",
    "X = Vector{Variable}(undef, num_samples) \n",
    "Y = Vector{Variable}(undef, num_samples)\n",
    "\n",
    "for i = 1:num_samples\n",
    "    \n",
    "    # Define i-th covariate\n",
    "    @RV X[i]\n",
    "    \n",
    "    # Define likelihood of i-th response\n",
    "    @RV Y[i] ~ GaussianMeanVariance(dot(θ,X[i]), σ2_Y, id=Symbol(\"f_b\"*string(i)))\n",
    "\n",
    "    # Designate observed variables\n",
    "    placeholder(X[i], :X, index=i, dims=(2,))\n",
    "    placeholder(Y[i], :Y, index=i);\n",
    "    \n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've generated the factor graph and embedded a screenshot below. It continues on for a while."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](../figures/large-factor-graph.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you'd like to see the full thing, uncomment the line below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ForneyLab.draw(graph2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's hard to tell, but each of these likelihood nodes $f_{bi}$ is connected to the prior node $f_a$ via an equality node. \n",
    "\n",
    "Now that we have our model, it is time to infer parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define and compile the algorithm\n",
    "algorithm = messagePassingAlgorithm(θ) \n",
    "source_code = algorithmSourceCode(algorithm)\n",
    "\n",
    "# Evaluate the generated code to get the step! function\n",
    "eval(Meta.parse(source_code));\n",
    "# println(source_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we iterate over time, feeding our data as it comes in and updating our posterior distribution for the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize posterior \n",
    "posterior = Dict()\n",
    "\n",
    "# Load data\n",
    "data = Dict(:X => [[dates_num[i], 1] for i = 1:num_samples],\n",
    "            :Y => stock_val)\n",
    "\n",
    "# Update posterior for θ\n",
    "step!(data, posterior);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the resulting posterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sys:1: UserWarning: The following kwargs were not used by contour: 'label'\r\n"
     ]
    }
   ],
   "source": [
    "import ForneyLab: logPdf\n",
    "\n",
    "# Define ranges for plot\n",
    "x1 = range(-1.5, length=500, stop=1.5)\n",
    "x2 = range(-2.5, length=500, stop=2.5)\n",
    "\n",
    "# Draw contour plots of distributions\n",
    "prior = ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=μ_θ, v=Σ_θ)\n",
    "p1a = contour(x1, x2, (x1,x2) -> exp(logPdf(prior, [x1,x2])), xlabel=\"θ1\", ylabel=\"θ2\", title=\"prior\", label=\"\")\n",
    "p1b = contour(x1, x2, (x1,x2) -> exp(logPdf(posterior[:θ], [x1,x2])), xlabel=\"θ1\", title=\"posterior\", label=\"\")\n",
    "plot(p1a, p1b, size=(900,300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It has become quite sharply peaked in a small area of parameter space.\n",
    "\n",
    "We can use the MAP point estimate to compute and visualize the regression function $f_\\theta$. The full predictive distribution is left for the PP Assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope coefficient = -4.320468927288263e-5\n",
      "Intercept coefficient = 0.0022453122200430577\n"
     ]
    }
   ],
   "source": [
    "# Extract estimated weights\n",
    "θ_MAP = mode(posterior[:θ])\n",
    "\n",
    "# Report results\n",
    "println(\"Slope coefficient = \"*string(θ_MAP[1]))\n",
    "println(\"Intercept coefficient = \"*string(θ_MAP[2]))\n",
    "\n",
    "# Make predictions\n",
    "regression_estimated = dates_num * θ_MAP[1] .+ θ_MAP[2];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Let's visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize observations\n",
    "scatter(dates_num, stock_val, color=\"black\", xticks=(xtick_points, [dates_str[i] for i in xtick_points]), label=\"observations\", legend=:topleft)\n",
    "\n",
    "# Overlay regression function\n",
    "plot!(dates_num, regression_estimated, color=\"blue\", label=\"regression\", linewidth=2, size=(800,300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The slope coefficient $\\theta_1$ is negative and the plot shows a decreasing line. The ISE experienced a negative linear trend from October 2010 up to March 2011. Assuming the stock market is an indicator of economic growth, then we may conclude that in March 2011 the Turkish economy is still in recession."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### $\\ast$ **Try for yourself**\n",
    "\n",
    "Change the `time period` variable. Re-run the regression and see how the results change.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recursive estimation\n",
    "\n",
    "Our graph is quite large, which means our inference algorithm is slow. But as I already said earlier, the graph is essentially a repetition of the same structure. In this case, we don't need to generate such a large graph; we can _recursively_ estimate the classification parameters. To do this, we essentially estimate parameters for a single observation, and make the resulting posterior our prior for the next observation.\n",
    "\n",
    "Let's first re-define the subgraph for a single observation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\r\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n",
       "<!-- Generated by graphviz version 2.49.3 (20211023.0002)\r\n",
       " -->\r\n",
       "<!-- Title: G Pages: 1 -->\r\n",
       "<svg width=\"424pt\" height=\"512pt\"\r\n",
       " viewBox=\"0.00 0.00 424.23 512.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 508)\">\r\n",
       "<title>G</title>\r\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-508 420.23,-508 420.23,4 -4,4\"/>\r\n",
       "<!-- 1235069440399369246 -->\r\n",
       "<g id=\"node1\" class=\"node\">\r\n",
       "<title>1235069440399369246</title>\r\n",
       "<polygon fill=\"lightgrey\" stroke=\"black\" points=\"134.23,-504 66.23,-504 66.23,-450 134.23,-450 134.23,-504\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"100.23\" y=\"-474.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">placeholder_Y</text>\r\n",
       "</g>\r\n",
       "<!-- 7881008578603509515 -->\r\n",
       "<g id=\"node6\" class=\"node\">\r\n",
       "<title>7881008578603509515</title>\r\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"136.23,-405 64.23,-405 64.23,-333 136.23,-333 136.23,-405\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"100.23\" y=\"-371.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">𝒩</text>\r\n",
       "<text text-anchor=\"middle\" x=\"100.23\" y=\"-361.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">f_b</text>\r\n",
       "</g>\r\n",
       "<!-- 1235069440399369246&#45;&#45;7881008578603509515 -->\r\n",
       "<g id=\"edge6\" class=\"edge\">\r\n",
       "<title>1235069440399369246&#45;&#45;7881008578603509515</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M100.23,-449.8C100.23,-436.31 100.23,-419.68 100.23,-405.1\"/>\r\n",
       "<text text-anchor=\"start\" x=\"100.23\" y=\"-425.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\" fill=\"red\">Y</text>\r\n",
       "<text text-anchor=\"start\" x=\"79.23\" y=\"-407.7\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "<text text-anchor=\"start\" x=\"79.23\" y=\"-443.4\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "</g>\r\n",
       "<!-- 9820295500309495691 -->\r\n",
       "<g id=\"node2\" class=\"node\">\r\n",
       "<title>9820295500309495691</title>\r\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"318.23,-171 246.23,-171 246.23,-99 318.23,-99 318.23,-171\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"282.23\" y=\"-137.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">𝒩</text>\r\n",
       "<text text-anchor=\"middle\" x=\"282.23\" y=\"-127.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">f_a</text>\r\n",
       "</g>\r\n",
       "<!-- 17066700800806923110 -->\r\n",
       "<g id=\"node5\" class=\"node\">\r\n",
       "<title>17066700800806923110</title>\r\n",
       "<polygon fill=\"lightgrey\" stroke=\"black\" points=\"224.73,-54 147.73,-54 147.73,0 224.73,0 224.73,-54\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"186.23\" y=\"-24.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">placeholder_Σ_θ</text>\r\n",
       "</g>\r\n",
       "<!-- 9820295500309495691&#45;&#45;17066700800806923110 -->\r\n",
       "<g id=\"edge2\" class=\"edge\">\r\n",
       "<title>9820295500309495691&#45;&#45;17066700800806923110</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M246.19,-105.54C237.58,-97.99 228.72,-89.55 221.23,-81 214.04,-72.79 207.18,-63 201.46,-54.09\"/>\r\n",
       "<text text-anchor=\"start\" x=\"221.23\" y=\"-74.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\" fill=\"red\">Σ_θ</text>\r\n",
       "<text text-anchor=\"start\" x=\"180.46\" y=\"-56.69\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "<text text-anchor=\"start\" x=\"232.19\" y=\"-108.14\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">3 v </text>\r\n",
       "</g>\r\n",
       "<!-- 7893419915333763050 -->\r\n",
       "<g id=\"node8\" class=\"node\">\r\n",
       "<title>7893419915333763050</title>\r\n",
       "<polygon fill=\"lightgrey\" stroke=\"black\" points=\"416.23,-54 340.23,-54 340.23,0 416.23,0 416.23,-54\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"378.23\" y=\"-24.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">placeholder_μ_θ</text>\r\n",
       "</g>\r\n",
       "<!-- 9820295500309495691&#45;&#45;7893419915333763050 -->\r\n",
       "<g id=\"edge5\" class=\"edge\">\r\n",
       "<title>9820295500309495691&#45;&#45;7893419915333763050</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M313.94,-99C327.15,-84.4 342.24,-67.75 354.48,-54.23\"/>\r\n",
       "<text text-anchor=\"start\" x=\"335.23\" y=\"-74.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\" fill=\"red\">μ_θ</text>\r\n",
       "<text text-anchor=\"start\" x=\"333.48\" y=\"-56.83\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "<text text-anchor=\"start\" x=\"297.94\" y=\"-92.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">2 m </text>\r\n",
       "</g>\r\n",
       "<!-- 16600349059624648914 -->\r\n",
       "<g id=\"node3\" class=\"node\">\r\n",
       "<title>16600349059624648914</title>\r\n",
       "<polygon fill=\"lightgrey\" stroke=\"black\" points=\"381.73,-396 312.73,-396 312.73,-342 381.73,-342 381.73,-396\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"347.23\" y=\"-366.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">placeholder_X</text>\r\n",
       "</g>\r\n",
       "<!-- 932730053641405546 -->\r\n",
       "<g id=\"node4\" class=\"node\">\r\n",
       "<title>932730053641405546</title>\r\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"318.23,-288 246.23,-288 246.23,-216 318.23,-216 318.23,-288\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"282.23\" y=\"-254.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">dot</text>\r\n",
       "<text text-anchor=\"middle\" x=\"282.23\" y=\"-244.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">dotproduct_1</text>\r\n",
       "</g>\r\n",
       "<!-- 16600349059624648914&#45;&#45;932730053641405546 -->\r\n",
       "<g id=\"edge3\" class=\"edge\">\r\n",
       "<title>16600349059624648914&#45;&#45;932730053641405546</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M332.49,-341.9C323.5,-326 311.89,-305.47 302.1,-288.15\"/>\r\n",
       "<text text-anchor=\"start\" x=\"316.23\" y=\"-308.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\" fill=\"red\">X</text>\r\n",
       "<text text-anchor=\"start\" x=\"281.1\" y=\"-290.75\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">2 in1 </text>\r\n",
       "<text text-anchor=\"start\" x=\"311.49\" y=\"-335.5\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "</g>\r\n",
       "<!-- 932730053641405546&#45;&#45;9820295500309495691 -->\r\n",
       "<g id=\"edge1\" class=\"edge\">\r\n",
       "<title>932730053641405546&#45;&#45;9820295500309495691</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M282.23,-215.87C282.23,-201.74 282.23,-185.46 282.23,-171.31\"/>\r\n",
       "<text text-anchor=\"start\" x=\"282.23\" y=\"-191.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\" fill=\"red\">θ</text>\r\n",
       "<text text-anchor=\"start\" x=\"261.23\" y=\"-173.91\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "<text text-anchor=\"start\" x=\"261.23\" y=\"-209.47\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">3 in2 </text>\r\n",
       "</g>\r\n",
       "<!-- 7881008578603509515&#45;&#45;932730053641405546 -->\r\n",
       "<g id=\"edge7\" class=\"edge\">\r\n",
       "<title>7881008578603509515&#45;&#45;932730053641405546</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M136.63,-345C168.47,-324.88 214.48,-295.81 246.22,-275.76\"/>\r\n",
       "<text text-anchor=\"start\" x=\"197.23\" y=\"-308.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\" fill=\"red\">variable_1</text>\r\n",
       "<text text-anchor=\"start\" x=\"225.22\" y=\"-278.36\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "<text text-anchor=\"start\" x=\"136.63\" y=\"-347.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">2 m </text>\r\n",
       "</g>\r\n",
       "<!-- 2581897910762599385 -->\r\n",
       "<g id=\"node7\" class=\"node\">\r\n",
       "<title>2581897910762599385</title>\r\n",
       "<polygon fill=\"lightgrey\" stroke=\"black\" points=\"66.23,-279 12.23,-279 12.23,-225 66.23,-225 66.23,-279\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"39.23\" y=\"-249.8\" font-family=\"Times New Roman,serif\" font-size=\"9.00\">clamp_1</text>\r\n",
       "</g>\r\n",
       "<!-- 7881008578603509515&#45;&#45;2581897910762599385 -->\r\n",
       "<g id=\"edge4\" class=\"edge\">\r\n",
       "<title>7881008578603509515&#45;&#45;2581897910762599385</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M64.06,-354.36C46.9,-345.84 28.04,-333.01 18.23,-315 12.15,-303.82 15.41,-290.56 21,-279.17\"/>\r\n",
       "<text text-anchor=\"start\" x=\"18.23\" y=\"-308.6\" font-family=\"Times New Roman,serif\" font-size=\"8.00\" fill=\"red\">clamp_1</text>\r\n",
       "<text text-anchor=\"start\" x=\"0\" y=\"-281.77\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">1 out </text>\r\n",
       "<text text-anchor=\"start\" x=\"50.06\" y=\"-356.96\" font-family=\"Times New Roman,serif\" font-size=\"8.00\">3 v </text>\r\n",
       "</g>\r\n",
       "</g>\r\n",
       "</svg>\r\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Start factor graph\n",
    "graph = FactorGraph();\n",
    "\n",
    "# Noise variance\n",
    "σ2_Y = 1.\n",
    "\n",
    "# Add weight prior to graph\n",
    "@RV θ ~ GaussianMeanVariance(placeholder(:μ_θ, dims=(2,)), \n",
    "                             placeholder(:Σ_θ, dims=(2,2)), id=:f_a)\n",
    "    \n",
    "# Define covariates\n",
    "@RV X\n",
    "\n",
    "# Define likelihood\n",
    "@RV Y ~ GaussianMeanVariance(dot(θ,X), σ2_Y, id=:f_b)\n",
    "\n",
    "# Designate observed variables\n",
    "placeholder(X, :X, dims=(2,))\n",
    "placeholder(Y, :Y)\n",
    "\n",
    "# Visualise the graph\n",
    "ForneyLab.draw(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define and compile the algorithm\n",
    "algorithm = messagePassingAlgorithm(θ) \n",
    "source_code = algorithmSourceCode(algorithm)\n",
    "\n",
    "# Evaluate the generated code to get the step! function\n",
    "eval(Meta.parse(source_code));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The syntax for compiling the inference algorithm is the same as before, but now we execute it differently. We feed in each sample and perform a step update for the posterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:00:01\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "# Initialize posteriors dictionary\n",
    "posterior = Dict()\n",
    "posterior[:θ] = ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=[0.,0.], v=[1. 0.;0. 1.])\n",
    "\n",
    "@showprogress for i = 1:num_samples\n",
    "    \n",
    "    # Load i-th data point\n",
    "    data = Dict(:X => [dates_num[i], 1],\n",
    "                :Y => stock_val[i],\n",
    "                :μ_θ => mean(posterior[:θ]),\n",
    "                :Σ_θ => cov(posterior[:θ]))\n",
    "\n",
    "    # Update posterior for θ\n",
    "    step!(data, posterior)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is much faster. If we now visualize the resulting posterior, we see that it is not exactly the same. Recursive estimation is not mathematically equivalent to non-recursive estimation (it's the difference between filtering and smoothing, for those familiar). In this case, it produces a less sharply peaked posterior (note the y-axis between this plot and the previous posterior plot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ForneyLab: logPdf\n",
    "\n",
    "# Define ranges for plot\n",
    "x1 = range(-2, length=500, stop=2)\n",
    "x2 = range(-3, length=500, stop=3)\n",
    "\n",
    "# Draw contour plots of distributions\n",
    "prior = ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=μ_θ, v=Σ_θ)\n",
    "p1a = contour(x1, x2, (x1,x2) -> exp(logPdf(prior, [x1,x2])), xlabel=\"θ1\", ylabel=\"θ2\", title=\"prior\", label=\"\")\n",
    "p1b = contour(x1, x2, (x1,x2) -> exp(logPdf(posterior[:θ], [x1,x2])), xlabel=\"θ1\", title=\"posterior\", label=\"\")\n",
    "plot(p1a, p1b, size=(900,300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem: Credit Assignment\n",
    "\n",
    "We will now look at a classification problem. Suppose you are a bank and that you have to decide whether you will grant credit, e.g. a mortgage or a small business loan, to a customer. You have a historic data set where your experts have assigned credit to hundreds of people. You have asked them to report on what aspects of the problem are important. You hope to automate this decision process by training a classifier on the data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data\n",
    "\n",
    "The data set we are going to use actually comes from the [UCI ML Repository](https://archive.ics.uci.edu/ml/datasets/Credit+Approval). It consists of past credit assignments, labeled as successful (=1) or unsuccessful (=0). Many of the features have been anonymized for privacy concerns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read CSV file\n",
    "df = DataFrame(CSV.File(\"../datasets/credit_train.csv\"))\n",
    "\n",
    "# Split dataframe into features and labels\n",
    "features_train = Matrix(df[:,1:7])\n",
    "labels_train = Vector(df[:,8])\n",
    "\n",
    "# Store number of features\n",
    "num_features = size(features_train,2)\n",
    "\n",
    "# Number of training samples\n",
    "num_train = size(features_train,1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the data and see if we can make sense of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAEsCAYAAAA7Ldc6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAA9hAAAPYQGoP6dpAABF10lEQVR4nO3de3xU5b3v8e+CAAlgoRjwaCGEAJMEZSYhKJTWW7kkKlqY0WoLGmpb9z7q6QFvuy3gkCDYKsXulvZV9qkKyC7bS6Z6KBKg2+3degwwdhSSQOW6tVQMWIGkAnnOH0mmyTCBXGbWmkk+79fLF86sNev5ZfLLzPqtZz3PYxljjAAAAADABj2cDgAAAABA90EBAgAAAMA2SVeAnDhxQtu2bdOJEyecDgUAAABAOyVdAVJZWamCggJVVlY6HUpS+fTTT50OAQmIvEAkcgKRyAlEIicQTXvyIukKEHTM6dOnnQ4BCYi8QCRyApHICUQiJxBNe/KCAgQAAACAbShAAAAAANgmxekAAAAAuotAIKAlJSWqrK5Wjsul+X6/vF6v02HFzf79+3X48GGnw0CcpKenKyMjo92vowABAACwQSAQkM/n0zTL0mJjtCkUks/nU1lZWZcsQvbv36/c3FxmLu3C+vbtq507d7a7CKEAAQAAsMGSkhJNsyyVGyNL0jxjVGhZWlpa2iULkMOHD+vEiRNau3atcnNznQ4HMbZz507Nnj1bhw8fpgABAABIRJXV1VrcWHxIkiWpyBgtrKpyMqy4y83N1bhx45wOAwmEQegAAAA2yHG5tMmyZBofG0nllqXc7GwnwwJsRwECAABgg/l+vzY33na1XFKhZWmLMZrv9zsdGmArChAAAAAbeL1elZWVqcbt1sLUVNW43QoEApo5c6bToQG2YgwIAACATbxeb5cccA60Bz0gAAAASHj19fU6duyY6uvrnQ4FnUQBAgAAgIQVDAZVXFys/v3767zzzlP//v1VXFysYDDodGjoIAoQAAAAJKTf/va3uvTSS7VmzRrV1tZKkmpra7VmzRpdeumlWrduncMRJg+/36/c3FxNmDDhrPvt3btX6enpcY2FAgQAAAAJp6nn49SpU5LGSHpS0vbGf8fo1KlTuu222+gJaaNHHnlEr776qt5++22nQ6EAAQAAQOJ57LHHmhUfb0uaIymv8d+3JeXq1KlT+tnPftapdizL0rFjx8KP09PTtXfvXklSZmamSkpKNGnSJI0YMUIPPfRQeL+HHnpIubm5ysvLU15envbt23fO4+3cuVOFhYVyu91yu9369a9/LUn67//+b914443h5xcuXChJ+uyzz/S9731Pl112mdxut/75n/9ZJ0+ebLX92tpa3XzzzRozZow8Ho+mTZsmSZo0aZLq6uo0efJkff/739fLL7+s8ePHh2N87733lJmZ2an3sT2YBQsAAAAJpb6+Xs8++2zjo/sl9Y/Yo3/j87frmWee0RNPPKEePeJzXf3o0aN688039fHHH2vUqFH69re/rb59+2rZsmX66KOPlJaWphMnTpyz/VOnTunrX/+6HnroIX3jG9+QJB0+fFiSNHv2bF177bV67rnnJEkff/yxJOnee+/VFVdcof/zf/6PjDH63ve+pxUrVmjOnDlR29+4caOOHDmiHTt2SJJqamokSW+++aYsy9Kbb76p/v376+WXX47HW9Vm9IAAANAFBQIBeTwFSkvrJ4+nQIFAwOmQgDY7ceJEeMxHQ69HNPmSGsaE/GPf2Js1a5YkafDgwcrKytKePXv0hS98QaNHj9bs2bO1cuVK1dTUKDU19azHqaqq0qlTp8LFh9TQO3Ls2DG9+eabmjdvXvj5wYMHS5Kef/55Pfroo8rLy1N+fr5ee+017dq1q9X2PR6PKisrdeedd+rpp59Wr1694vCOdB4FCAAAXUwgEJDP51MolK66usUKhdLl8/koQpA0+vbtq7S0tMZHwVb22i5JSktLa7Zv+/Xs2VOnT58OP66rq2uxvXlh0bNnT506dUo9e/bUH//4R82dO1d//etfNXHiRL322mttOl57GGP0/PPPKxgMKhgMqqqqSr/61a9abT8rK0s7duxQUVGR3njjDV1yySU6cuTIGcdNSUmJWYwdQQECAEAXU1KyRJY1TcaUS7pHxpTLsqaqtHSp06EBbdKjRw/ddNNNjY8elXQsYo9jjc9L3/jGNzp1+9XIkSPDA7MDgYCOHz9+ztd89tlnOnTokC6//HItXLhQX/3qV7V9+/azHi87O1u9e/dudmtZwy1Y/fv311e/+lU99thj4eebbsG64YYb9OMf/7hxLIx05MgR7d69u9X2Dx48KMuydMMNN2jZsmUyxujAgQNnxD9ixAjt2bNHn3zyiSTpqaeeavf71hkUIAAAdDHV1ZUyplCS1fiMJWOKVFW108mwgHaZN2+eUlJSJO2QdJkaZr8KNv57maSdSklJ0dy5czvVzs9+9jPddddd+spXvqJt27bp/PPPP+drPv30U3m9Xo0dO1Zut1snT55UcXHxWY+XkpKiF154Qf/2b/8Wfl1ZWZmkhgLgj3/8oy6++GJ5PB6tWLEifKyUlBTl5eXJ7XZrypQp2rt3b6vth0IhTZo0SW63W+PGjdOtt94qt9t9Rvxf+tKXdN9992n8+PG6+uqrNXDgwE69h+1lGWOMrS120rZt21RQUKCtW7dq3LhxToeTNGpqajRo0CCnw0CCIS8QiZzoGjyeAoVC6Y09IJYkI8sqlNtdo2Cwol3HIicQqa05EYtztt/+9rfNpuJtKSUlRWvWrNE3v/nNDh0bnRP5+23PZwU9IAAAdDF+/3wZs1mWVShpuSyrUMZskd8/3+nQgHb51re+pXfeeUfFxcXhcR5paWkqLi7WO++8Q/GRpChAAADoYrxer8rKyuR21yg1daHc7hoFAgHNnDnT6dCAdsvLy9OqVat07Nix8H+rVq1SXl6e06Ghg1gHBACALsjr9crr9TodBhAzPXr0UL9+/ZwOAzFADwgAAAAA21CAAAAAALANBQgAAAASXn19vY4dO6b6+nqnQ0EnUYAAAJCkAoGACjwe9UtLU4HHw0rn6JKCwaCKi4vVv39/nXfeeerfv7+Ki4sVDAadDg0dZFsBUldXpxkzZsjlcikvL09FRUXau3dveHtmZqZycnKUl5envLw8Pf3003aFBgBA0gkEAvL5fEoPhbS4rk7poZB8Ph9FCLqU3/72t7r00ku1Zs0a1dbWSpJqa2u1Zs0aXXrppVq3bp3DEdrn888/1/Tp0+V2u3XXXXeddd9Vq1bpxhtvtCmy9rN1Fqw77rhD11xzjSzL0ooVK3THHXdo8+bN4e3PPfecLrnkEjtDAgAgKS0pKdE0y1K5MbIkzTNGhZalpaWlzH6FLqGp5+PUqVMaI+l+SXlqWAv9UUk7Tp3Sbbfdptzc3G4xJe/27du1Z88evf/++06H0mm29YCkpqbq2muvlWVZkqSJEyfqgw8+sKt5AAC6lMrqahU2Fh9Sw3rnRcZoZ1WVk2EBMfPYY4+Fi4+3Jc1RQwEyp/FxrqRTp07pZz/7WYfbqK2t1c0336wxY8bI4/Fo2rRpevnllzV+/PjwPu+9954yMzPDjzds2KBLL71UHo9HeXl5evvttyVJb731li6//HJ5PB653W698MILkqRdu3bpuuuuC7/mV7/6VattN+3/la98RR6PR2PHjtWCBQu0Y8cOzZo1S3v27FFeXp7WrFmjRYsW6b777gvHtWLFCs2ZM6fD74WdHFsH5Oc//7muv/76Fs/NmjVL9fX1mjBhgh5++GENHjy41dfffffdGjBggLxer3w+X7zDTXpHjhxxOgQkIPICkciJ5DF65Eht2rFD8xqLECOp3LLkGjVKNTU1MWuHnECktubEp59+2uE26uvr9eyzz0pq6PnoH7G9f+Pzt0t65pln9MQTT6hHj/ZfVy8vL9eRI0e0Y8cOSVJNTY3+9Kc/tbp/dXW1vvOd7+jVV1+Vy+XSyZMndeLECdXU1GjmzJkKBAKaNGmS6uvrdfToUZ0+fVrf+ta39NRTTyknJ0cnTpzQxIkTNXHiRO3bt++MtqWGQuK6667Tj370o/DzgwYN0m9+8xvdd999qqiokCQtWrSo3T9vPHz66aeqqalpkReDBg0662scKUCWLl2qXbt26de//nX4uVdffVUZGRk6efKkFixYoOLiYr344outHmPFihUaN26cHeF2GedKBnRP5AUikRPJ4cHSUvl8PhValoqMUbllaYsxCpSWxvx3SE4gUltyYsCAAR0+/okTJ8JjPvJa2Se/8d/a2lrV1tZ2aJFCj8ejyspK3Xnnnbryyit17bXXnnX/LVu26Nprr5XL5ZIk9erVSwMGDNCGDRs0ZswYTZo0SVLDoomDBg3Sjh079P777+uWW24JH+Ozzz7Tjh07NGnSpKhtX3HFFbr//vt1/PhxXXnllZoyZUq7fy47DRgwIJwPbf2ssH0WrGXLlikQCGjjxo3q27dv+PmMjAxJDb/IuXPn6rXXXrM7NAAAkobX61VZWZlq3G4tTE1VjdutQCCgmTNnOh0a0Gl9+/ZVWlqapIYxH9Fsb/w3LS0tvG97ZWVlaceOHSoqKtIbb7yhSy65RD179tTp06fD+9TV1XXo2JJkjFF6erqCwWD4vz179mj27NlR2z5y5Ih8Pp/eeOMNZWdna8WKFZo+fXrUY6ekpMQsTrvZWoAsX75c69at05YtWzRw4MDw88ePH9fRo0fDj9etW6f8/PwzDwAAAMK8Xq8qgkEdr61VRTBI8YEuo0ePHrrpppskNQw4Pxax/Vjj85L0jW98o0O3X0nSwYMHZVmWbrjhBi1btkzGGNXX12vPnj365JNPJElPPfVUeP/CwkJt3LhR1dXVkqSTJ0/q008/1aRJk7Rz5069+eabkhpuIaupqVF2drb69u2rNWvWhI+xe/du1dTURG37wIED2rVrl4YMGaLbbrtNjzzyiP74xz9GjX3kyJGqqKhQfX29Tpw4obKysg69B06wrQA5ePCg7r33Xh09elRXX3218vLyNGHCBEnSoUOHdPXVV8vtdmvs2LF65ZVXWvyiAAAA0L3MmzdPKSkp2iHpMklPqqE35MnGxzvV0Aswd+7cDrcRCoU0adIkud1ujRs3TrfeequuvPJK3XfffRo/fryuvvrqFhfNR40apccff1zf/OY35Xa7ddlll6mqqkpf/OIX9bvf/U7333+/3G638vPz9frrryslJUXr16/XM888I7fbrYsvvljf/e53VVtbG7Vtt9utZ599NnyMW265pcWQheZ8Pp+GDBmiMWPGyOv1JtVMYJYxxjgdRHts27ZNBQUF2rp1K2NA2qFpABPQHHmBSOQEIpETiNTWnIjFOdtvf/vb8FS8kVJSUrRmzRp985vf7NCx0TmRv9/2fFawEjoAAAAS0re+9S298847Ki4uDo/zSEtLU3Fxsd555x2KjyTl2DS8AAAAwLnk5eVp1apVeuKJJ1RbW6u0tLQOj/lAYqAAAQAAQMLr0aNHh6baReKhfAQAAABgGwoQAAAAALahAAEAAABgGwoQAAAAALahAAEAAABgGwoQAAAAALZhGl4AAADEzc6dO50OAXHQmd8rBQgAAABiLj09XX379tXs2bOdDgVx0rdvX6Wnp7f7dRQgAAAAiLmMjAzt3LlThw8fdjoUxEl6eroyMjLa/ToKEAAAAMRFRkZGh05Q0bUxCB0AAACAbShAAAAAANiGAgQAAACAbShAAAAAANiGAgQAAACAbShAAAAAANiGAgQAAACAbShAAAAAANiGAgQA0EIgEFCBx6N+aWkq8HgUCAScDgkA0IVQgAAAwtavXy+fz6f0UEiL6+qUHgrJ5/NRhAAAYoYCBAAQ9tijj2qaZancGN0jqdwYTbUsLS0tdTo0AEAXQQECAAjbtXu3Co2R1fjYklRkjHZWVTkZFgCgC6EAAQCEjR41SpssS6bxsZFUblnKzc52MiwAQBdCAQIACJt3//3abIwKLUvLJRValrYYo/l+v9OhAQC6CAoQAEDY9ddfr7KyMtW43VqYmqoat1uBQEAzZ850OjQAQBeR4nQAAIDE4vV65fV6nQ4DANBF0QMCAAAAwDYUIAAAAABsQwECAAAAwDYUIAAAAABsQwECAAAAwDa2FCB1dXWaMWOGXC6X8vLyVFRUpL1794a379q1S5MmTZLL5dJll12mHTt22BEWAAAAAJvZ1gNyxx13qKqqSsFgUNOnT9cdd9wR3vZP//RPuuOOO1RdXa0HHnhA3/nOd+wKCwAAAICNbClAUlNTde2118qyLEnSxIkT9cEHH0iS/vrXv2rbtm2aPXu2JMnn82nPnj0tekgAAAAAdA2OLET485//XNdff70k6cCBA7rooouUktIQimVZysjI0P79+5WZmdnqMe6++24NGDBAXq9XPp/PjrCT2pEjR5wOAQmIvEAkcgKRyAlEIicQTfO8GDRo0Fn3tb0AWbp0qXbt2qVf//rX4eeaekaaGGPOeZwVK1Zo3LhxMY+vKztXMqB76mheBAIBLSkpUWV1tXJcLs33+1k9u4vgswKRyAlEIicQTVvzwtZZsJYtW6ZAIKCNGzeqb9++kqRhw4bp4MGDOnXqlKSG4uPAgQPKyMiwMzQA7RAIBOTz+ZQeCmlxXZ3SQyH5fD4FAgGnQwMAAAnOtgJk+fLlWrdunbZs2aKBAweGnx8yZIjy8/O1du1aSVJZWZkyMzPPevsVAGctKSnRNMtSuTG6R1K5MZpqWVpaWup0aAAAIMHZcgvWwYMHde+99yorK0tXX321JKlPnz56++23JUkrV67UnDlztHTpUn3hC1/Q6tWr7QgLQAdVVldrsTFqunnSklRkjBZWVTkZFgAASAK2FCBDhw4967iO7OxsvfXWW3aEAiAGclwubQqFNK+xCDGSyi1LudnZTocGoAMCgYBKSpaourpSLleO/P75jOkCEDeshA6g3eb7/dpsjAotS8slFVqWthij+X6/06EBaKemMV2hULrq6hYrFEpnTBeAuKIAAdBuXq9XZWVlqnG7tTA1VTVutwKBgGbOnOl0aADaqaRkiSxrmowpl3SPjCmXZU1VaelSp0MD0EU5sg4IgOTn9Xq5RQPoAqqrK2XMYqnZqC5jilRVtdDJsAB0YfSAAADQjblcObKsTWoYzSVJRpZVruzsXCfDAtCFUYAAANCN+f3zZcxmWVahpOWyrEIZs0V+/3ynQ+vSAoGACjwe9UtLU4HHw5gbdCsUIAAAdGNNY7rc7hqlpi6U213DmK44YzFXdHeMAQEAoJtjTJe9mi/makma1zir4NLSUn4P6BboAQEAALBRZXW1CqMs5rqTxVzRTVCAAAAA2CjH5dImy2o27J/FXNG9UIAAAADYiMVc0d1RgAAAANiIxVzR3TEIHQAAwGYM/Ed3Rg8IAAAAANtQgAAAAACwDQUIAAAAANtQgAAAAACwzRkFyIkTJ/TUU0/pJz/5idavX6/6+vozXvTBBx/o9ttvtyVAAAAAAF1Hi1mwDh06pEmTJmnPnj3q2bOnTp8+rdzcXK1du1b5+fnh/T7++GOtXr1aTzzxhO0BAwAAAEheLXpA5s+frx49eujdd9/VyZMn9frrr6tfv366/PLLtWHDBqdiBAAAANBFtChA/vCHP6ikpERjx46VJE2aNElvvvmmZs2apZkzZ+rxxx93JEgAAAAAXUOLW7AOHz6soUOHttwhJUUrV67URRddpDvuuEOHDh3S5MmTbQ0SAAAAQNfQogdk+PDhevfdd6Pu6Pf79Ytf/EIPPvigfvjDH9oSHAAAAJJDIBBQgcejfmlpKvB4FAgEnA4JCapFAfK1r33trLdZ3XnnnVq7dq3eeOONuAcGAACA5BAIBOTz+ZQeCmlxXZ3SQyH5fD6KEETV4has73//+8rJyVFNTY0GDRoU9QW33HKLLrzwQr388st2xAcAAIAEt6SkRNMsS+XGyJI0zxgVWpaWlpbK6/U6HR4STIsCZPTo0Ro9evQ5X3TllVfqyiuvjFtQAAAASB6V1dVa3Fh8SJIlqcgYLayqcjIsJKhzroS+c+dOPfXUU1q6dKn+8pe/SJJ2796tzz77LO7BAQAAIPHluFzaZFkyjY+NpHLLUm52tpNhIUGltLbhxIkT+u53v6tnnnlGkmSMUVFRkf7H//gf+uEPf6gRI0bokUcesS1QAAAAJKb5fr98Pp8KLUtFxqjcsrTFGAX8fqdDQwJqtQfkvvvu00svvaTf//73+vTTT2WMCW+79tprVV5ebkuAAAAASGxer1dlZWWqcbu1MDVVNW63AoGAZs6c6XRoSECt9oA899xzevTRR1VUVKTTp0+32JaZmam9e/fGOzYAAAAkCa/Xy4BztEmrPSDHjh3ThRdeGHXb8ePH4xYQAAAAgK6r1QLE7XarrKws6rYNGzZo/PjxcQsKQPIIBALyeAqUltZPHk8Bc74DAICzavUWrIULF+rrX/+6Tpw4oZtuukmWZen//b//p3Xr1umJJ57Qiy++aGecABJQ08JTljVNxixWKLRJPp9PZWVldMMDAICoWu0Bue666/Qf//Efev311zVjxgwZY3TnnXfq6aef1r//+79r8uTJdsYJIAGVlCxpLD7KJd0jY8plWVNVWrrU6dAAAECCitoDcurUKb377ru64oortGfPHlVXV+vw4cMaNGiQcnJy7I4RQIKqrq6UMYulZktPGVOkqqqFToYFAAASWNQekB49eujLX/6y/vSnP0mSXC6XJk2a1Oni4/vf/74yMzNlWZbee++9FtsyMzOVk5OjvLw85eXl6emnn+5UWwDiz+XKkWVtkpotPWVZ5crOznUyLAAAcA6BQEAFHo/6paWpwOOxdQxnqwVIVlaWjh49GtPGbrzxRr3++usaPnx41O3PPfecgsGggsGgbr755pi2DSD2/P75MmazLKtQ0nJZVqGM2SK/f77ToQEAgFY0jeFMD4W0uK5O6aGQfD6fbUVIq2NAfvSjH+mhhx7SRx99FLPGrrjiCg0dOjRmxwPQkt1XM5oWnnK7a5SaulBudw0LTwEAkOCWlJRommWp3BjdI6ncGE21LC0tLbWl/VZnwXr22Wd16NAhZWVlye12a8iQIbIsK7zdsiy98MILMQ1m1qxZqq+v14QJE/Twww9r8ODBre579913a8CAAfJ6vfL5fDGNoys6cuSI0yEgztavX685c+ZommVpsTHa1Hg1Y9WqVbr++uujviYWeXHVVVfpqquuavFcTU1Np48LZ/BZgUjkBCKRE8mvsqpKi41pNoJTKjJGCysrO/wd3jwvBg0adNZ9Wy1Ajh071mLMx7FjxzoUTFu9+uqrysjI0MmTJ7VgwQIVFxefdarfFStWaNy4cXGNqas5VzIguf18+fLw1QxL0jxjVGhZ+sVjj6m4uLjV15EXiEROIBI5gUjkRHLLyc7WplBI8xrPGYykcstSbk5Op363bX1tqwXIf/3Xf3W48Y7IyMiQJPXq1Utz586Vy+WytX0g2VVWV0e/mlFV5WRYAAAgwcz3++Xz+VRoWSoyRuWWpS3GKOD329J+q2NA7HT8+PEWA97XrVun/Px85wICklCOy6VNltVsPqrGqxnZ2U6GBQAAEkzTGM4at1sLU1NV43bbOoaz1R6Q0jYMQnnwwQfb1dhdd92lF154QX/5y180ZcoU9e/fX7t379ahQ4fk8/l0+vRpGWOUlZWlNWvWtOvYQHfn9NUMAACQPLxer7xeryNtW8YYE23Deeedd8ZzJ06ckDFGffr0Ua9evfS3v/0t7gFG2rZtmwoKCrR161bGgLRDTU0N92t2A4FAQEtLS7Wzqkq52dma7/ef9WqG3XkRCARUUrJE1dWVcrly5PfPd+zDD9HxWYFI5AQikROIpj150eotWJ999tkZ/9XW1mr9+vVyuVx64403YhYwgNjwer2qCAZ1vLZWFcFgQk2H2zTneCiUrrq6xQqF0m2dcxwAACSGdo0B6d27t6677jr97//9v/XP//zP8YoJQBdUUrJEljVNxpRLukfGlMuypqq0dKnToQEAABt1aBD60KFDFQwGYxwKgK6surpSxhRKzebpMqZIVVU7nQwLAADYrN0FyJ49e/STn/xEI0eOjEc8ALoolytHlrVJajZPl2WVKzs718mwAACAzVqdBeu8885rsfK5JJ08eVKff/65+vbty33bANrF758vn88nyyqUMUWyrHIZs0V+P58lAAB0J60WIPfee+8ZBUhqaqqGDh2qa665htkPALRL05zjpaVLVVW1UNnZufL77ZtzHAAAJIZWC5BFixbZGAaA7sDJOccBAEBiaHUMSFZWlt59992o29577z1lZWXFLSgAAAAAXVOrBcjevXv197//Peq2EydO6MCBA3ELCgAAAEDX1OIWrLq6uvBq55L0t7/9TTU1NYrc5/nnn9dFF11kX5QAAAAAuoQWBchPfvITlZaWSpIsy1JhYWGrL2SMCAAAAID2alGAzJgxQ5mZmTLG6Pbbb9eCBQvOWO+jd+/eys3NVV5enp1xAgAAAOgCWhQgHo9HHo9HUkMPyPTp03X++ec7EhgAAACArqfVaXiLi4vtjAMAAABAN9DqLFiS9Oqrr2r27Nm67LLL5Ha7z/gPSCSBQEAFHo/6paWpwONRIBBo13YAAADEX6sFyKZNm/S1r31Nhw8fVkVFhYYNG6b09HRVVVXp+PHjGj9+vJ1xAmcVCATk8/mUHgppcV2d0kMh+Xy+cJFxru0AAACwR6sFiN/v19y5c7VhwwZJ0uLFi/XSSy+purpavXv31te+9jXbggTOZUlJiaZZlsqN0T2Syo3RVMvS0sZZ3c61HQAAAPZotQDZuXOnrrnmGvXo0UOWZen48eOSpOHDh2vRokV66KGHbAsSOJfK6moVGiOr8bElqcgY7ayqatN2AAAA2KPVAiQ1NVX19fWyLEsXXnih/vznP4e39e/fn5XQkVByXC5tsiyZxsdGUrllKTc7u03bAQAAYI9WCxCPx6OqxqvDkydP1pIlS/T73/9emzZt0oIFCzR27FjbggTOZb7fr83GqNCytFxSoWVpizGa7/e3aTsAAADs0WoBMnfuXFlWww0rS5cu1XnnnacbbrhB11xzjT755BP98pe/tC1I4Fy8Xq/KyspU43ZrYWqqatxuBQIBzZw5s03bAQAAYA/LGGPOvZtkjNHu3btVW1urnJwc9e7dO96xRbVt2zYVFBRo69atGjdunCMxJKOamhoNGjSo08cJBAIqKVmi6upKXXDBBUpRvT46dEg5Lpfm+/3yer0dPu6SkhJVVld3+lhou1jlBboOcgKRyAlEIicQTXvy4qzrgDRnWZZGjx4tt9vtWPEBZzVNZRsKpauu7ibt27dHI/bt6/S0tkyRCwAA0H2ctQB5//33dcstt2jkyJHq06ePtm3bJkmaP3++Nm7caEuASBwlJUtkWdNkTLl6arumyNJmqdPT2to1RS4LEQIAADiv1QJky5Ytys/P1969e3XLLbfo5MmT4W29evXSr371K1sCROKorq6UMYWSLBlV6xrFZlrb1qbIfX/HjtgELnpZAAAAEkWrBcgPf/hD3XLLLfrjH/+okpKSFtvy8/O1ffv2uAeHxOJy5ciyNkkysuTSRsVmWtscl0vlEVPkvijp5MmTMSsQWIgQAAAgMbRagLz33nu69dZbJSk8G1aTgQMH6vDhw/GNDAnH758vYzbLsgp1Wvn6g4ymSp2e1na+368tptmxJP2npEukmBUILEQIAACQGFotQAYNGqQPP/ww6rbq6mpdeOGFcQsKialpKlu3u0apqc9q+PAs7cvM7PS0tl6vV7179dIuSQsl1UgKSLpNilmBwEKEAAAAiSGltQ0zZsyQ3+/XxIkTNWrUKEkNPSF/+ctftGzZMvl8PtuCROLwer1xmR73ktxcpYdCKm/spTBq6FWJVYEw3++Xz+dToWWpyBiVN/bYBFiIEAAAwFat9oA8/PDDGjx4sNxutyZMmCBJuv3225Wdna0BAwZo0aJFdsWIbiDeK5WzECEAAEBiaFGAeL1e7d69W5L0wgsvaMOGDfr1r38tl8ulKVOmKDs7Wz/96U/1+uuvq3///o4E3JUFAgF5PAVKS+snj6cgrjM02dlWZLvRpsK1o0Dwer2qCAZ1vLZWFcEgxQcAAIADWqyEnpKSojfeeEMTJkxQz5499dZbb+myyy5zMr4zdNWV0JumiW1YZ6NQlrVJxmxWWVlZTG55ar46Zbzbak1Tu9MsS4XGaJNlabMxcW8XrWM1W0QiJxCJnEAkcgLRtCcvWhQgw4cP16233qrvfve7ysrK0vPPP6+8vLxWX5yRkdHpYNurqxYgHk+BQqF0GVMuNY6CsKxCud01CgYrOn385kkR77ZaU+DxRB3nUeN2qyIYjFu7aB1fIohETiASOYFI5ASiaU9etBiEPnfuXN177716+OGHZVlWq7eoGGNkWZZOnz7d+WghqWmRv8VSs4lijSlSVdXCpG6rucrqai2OMhXuQqbCBQAA6DZaFCDz5s3Ttddeq507d8rr9Wrp0qUaPXp0TBr6/ve/r//7f/+v9u3bp1AopEsuuSS8bdeuXSouLtbhw4c1cOBArVq1SmPGjIlJu8nC5cpRKLRJxszTP3olypWdnZvUbTWX43JpUyikec16QJgKFwAAoHs5Yxre7OxsZWdnq7i4WDfffLNGjBgRk4ZuvPFGPfDAA/rqV796xrZ/+qd/0h133KE5c+boueee03e+8x299dZbMWk3Wfj98xvHZRTKmCJZVrmM2SK/P/aDw+1sqzmmwgUAAECr0/A++eSTMSs+JOmKK67Q0KFDz3j+r3/9q7Zt26bZs2dLknw+n/bs2aO9e/fGrO1k0HKRv4Vyu2viNk2snW1Fa5epcAEAALqvVhcitMuBAwd00UUXKSWlIRTLspSRkaH9+/crMzOz1dfdfffdGjBggLxeb5dZFPGqq67SVVdd1eK5mpqamBz7yJEjtrV1Nk61i+gi8wIgJxCpq+TE+vXr9dijj2rX7t0aPWqU5t1/v66//nqnw0pKXSUnEFvN8+Jcg9EdL0CkhqKjuWYTc7VqxYoVXWoWrLMJBAJaUlKiyupq5bhcmu/3d2jaWmasQDTkBSKRE4iU7DkRCAQ0Z84cTbMs3WaMNu3YoTlz5jANfCcke04gPtqaF63egmWXYcOG6eDBgzp16pSkhuLjwIEDjkzxm4ia1s5ID4W0uK5O6aGQfD6fbQsHAgCQ7JaUlGiaZancGN0jqdwYTbUsLS0tdTo0oFtyvAAZMmSI8vPztXbtWklSWVmZMjMzz3r7VXfChyYAAJ1TWV2twijTwO9kGnjAEbYVIHfddZeGDh2qgwcPasqUKRo1alR428qVK7Vy5Uq5XC79+Mc/1uOPP25XWAmPD00AADonx+XSJstS0w3eTAMPOMu2MSC//OUv9ctf/jLqtuzs7G437W5bsXYGAACdwzTwQGJx/BYsnN18v1+bjVGhZWm5pMLGD835fGgCANAmTAMPJBYKkATHhyYAAJ3n9XpVEQzqeG2tKoJBvkcBB1GAJAE+NJ0VCARU4PGoX1qaCjweZiADAADoBAoQ4CyYBhkAACC2KEBgi2TtRWAaZAAAgNiiAEHcJXMvAtMgAwAAxBYFCOIumXsRmDseAAAgtihAEHfJ3IvANMgAAACxRQGCuEvmXgSmQQZgt2QdMwcAbUUBgrhL9l4EpkEGYJdkHjMHAG1FAYK4oxcBANommcfMAUBbpTgdALoHr9crr9frdBgAkNAqq6u1OMqYuYVJMGYOANqKHhAAABKEE2PmGHMCwG4UIAAAJAi7x8wx5gSAEyhAAABIEHaPmWPMCQAnMAYEAIAEYueYOcacAHACPSAAAHRTybxOE4DkRQECAEA3lezrNAFIThQgAAB0U6zTBMAJjAEBAKAbY50mAHajBwQA0KWxzgUAJBYKkDgJBALyeAqUltZPHk9BXL/w+HJFR61fv57cQZfGOhcAkHgoQOKg6QsvFEpXXd1ihULpcfvC48sVHRUIBDRnzhxyB10a61wAQOKxjDHm3Lsljm3btqmgoEBbt27VuHHjnA4nKo+nQKFQuowpV8Os6kaWVSi3u0bBYEVM2yrweJQeCqm8cR53o4ZZTGrcblUEg+H9ampqNGjQoJi2jeTW1txB99LVPiv6paVpcV2d7mn23HJJC1NTdby21qmwkkpXywl0HjmBaNqTF/SAxEF1daWMKZSaLe1kTJGqqnbGvK3K6moVRllEaieLSOEcyB10B6xzAQCJhwIkDlyuHFnWJqnZV55llSs7Ozfmbdn95cp4k66DE7Pkw99f+7HOBQAkHgqQOPD758uYzbKsQknLZVmFMmaL/P75MW/Lzi9Xxpt0LZyYJRf+/jqGdS4AIPEwBiROAoGASkuXqqpqp7Kzc+X3z4/bF14gENDS0lLtrKpSbna25vv9Z7QVi/s1GTPQ9axevVq/eOyxs+YOEoNdf3/c241I5AQikROIpj15QQHSTcTiw6KjgzkDgYCWlJSosrpaOS6X5vv9LHqVIPgSSR52DaYmJxCJnEAkcgLRMAgdcdGRMQPcNgLEBmN2AABdBQUI2qwjYwaYgx+IDcbsAAC6CgoQtFlHBnMy1SsQGwymBgB0FSlOB4Dk4vV62zV+I8fl0qZQSPOaDZzlthGgY9r79wcAQCKiBwSSGsZqeDwFSkvrJ4+nIGZjNLhtpGNY7wEAAHRVCVOAZGZmKicnR3l5ecrLy9PTTz/tdEjdRtNA8VAoXXV1ixUKpcdsoDi3jbQfA/fRWe0tYJtfgLjiiq+RawCAuEqYaXgzMzP1+9//XpdccslZ92Ma3o4529RoHk+BQqF0GVMuNd4oZVmFcrtrFAxW2Bon7F1vhakUu56mAnaaZanQGG2yLG02RmVlZVFv32ra37KmyZhCWdYmGbO51f3R/fA5gUjkBKJhGl60S3V1pYwplJoNFTemSFVVO50Mq9ti4D46o70zz5WULGksPsol3SNjymVZU1VautTWuAEA3UdCDUKfNWuW6uvrNWHCBD388MMaPHhwq/vefffdGjBggLxer3w+n41RJqcjR460um3kyNHasWOTjJmnf/SAlGvUKJdqampsixENRo8cqU07dpwxcN81alTMfx9nywskp8qqKi2OUsAurKyMmj9VVZUyZrEiL0BUVi7k7x+S+JzAmcgJRNM8L87VE5IwBcirr76qjIwMnTx5UgsWLFBxcbFefPHFVvdfsWIFt2C1U2vJUFr6YOMtGIUypkiWVS5jtqi0NEAXqwMeLC2Vz+dToWWpyBiVNw7cD5SWxuX3we+4a8nJzo4+81xOTtTfdXZ2jkKhMy9A5OTkkhsIIxcQiZxANEl3C1ZGRoYkqVevXpo7d65ee+01hyPqPpoGirvdNUpNXSi3u4aB4g5i4D46o70zz/n982XMZllWoaTljRcitsjvn29r3ACA7iMhCpDjx4/r6NGj4cfr1q1Tfn6+cwF1Q16vV8FghWprjysYrOBk12Fer1cVwaCO19aqIhjk94E2a28BG3kBYsyYjyl4AQBxlRC3YB06dEg+n0+nT5+WMUZZWVlas2aN02EBQFJq74KFzfdndhsAQLwlRA9IVlaWtm/frj/96U8KhUJ64YUXlJmZ6XRYAGKARRUBAEBzCVGAAOiaWFQRAABEogABEDftXZMCAAB0fRQgAOKGRRUBAEAkChAAcZPjcmmTZck0Pg6vSZGd7WRYAADAQRQg6HYYFG2f9q5JAQAAuj4KEHQrDIq2F4sqAgCASAmxDghgl+aDoi1J8xqvzi8tLW3Xuglou/auSQEAALo2ekDQrTAoGgAAwFkUIIgq3uMk7BqHEdnOBRdcwKDoOAsEAvJ4CpSW1k8eTwG3twFoFZ8XQPdEAYIzxHuchF3jMKK1s2ffPgZFx1HTex4KpauubrFCoXTG2ACIis8LoPuyjDHm3Lsljm3btqmgoEBbt27VuHHjnA4nadTU1GjQoEFt2rfA41F6KBQeJ2HUcKJe43arIhjsdCzxPv652tmTkaEBAwdqZ1WVcrOzNd/v77aDotuTF23h8RQoFEqXMeVS47tuWYVyu2sUDFbErB3ET6xzAskvXjnB54U9AoGASkqWqLq6Ui5Xjvz++Z0el8fnBKJpT17QA4IzxHuchF3jMFpr58NDh1QRDOp4ba0qgsFuW3zEQ3V1pYwplJq968YUqapqp5NhAUhAfF7EH71MSFQUIDhDvBePi3b81ZJ6SjEdE2JXO/gHlytHlrVJavauW1a5srNznQyrTVgfBrBXMn9eJIuSkiWyrGmNvUz3yJhyWdZUlZYudTo0dHMUIA5LxAF48V48LvL4eZL+JGni3/8e0zEhdrWDf/D758uYzbKsQknLZVmFMmaL/P75Tod2VqwPA9gvWT8vkgm9TEhYJsls3brVSDJbt251OpROKysrM5KMZU0z0k8b/5UpKyuLeVuffPJJu2Mr8HhM39RUU+DxmEAgENN4mh//vNRUM9WyTL1kjGTqJTPVskyBx5M07US26XaPM6mpfY3bPS4uv89YaW9etEVZWZnxeApMampf4/EUxDx34mGc222m2ZAbySAeOYHkFs+cSMbPi2Tido9rPLeoNw0fb/XGsqYaj6egU8flcwLRtCcvKEAcFK8PhmgS+cOib2qq+WnjiV/Tfz+VTN/U1KRrx86iMhYSOS/OJZaFnl05mAySOScQH+RE8vrHd9LUxu+kqUZSpws9cgLRtCcvuAXLQXSNNoj3mBM72+F+W3vEemClXTkYDWNPAMSL1+tVWVmZ3O4apaYulNtdo0AgwOQrcBwFiIMYgNcg3mNO7GyHotIesS704pEbbRnfxdgTAPHm9XoVDFaotva4gsEKig8kBAoQBzk5AC+Rrro2XaGpcbu1MDVVNW53XK7Q2NEORaU9Yl3oxTo32tpDs6SkRNMsS+XG6B5J5cZoqmVpaWlph9oFACApxPFWsLjoSmNAjLFvAF7z+/Ka7gmdZlnmp43/KoHHKSSTeN1vGy/Jeh+vneOnOqKt8cVj7ElrY2PaOmYmljmRTBMyoHXJ+jmB+CEnEA2D0HGG5knBjD/xlUyzuiTrl0iiF3qpqX2N9FPTsrb4qUlN7dtiv1j/LbY2CcL999/f5skRYpUTyTYhA1qXrJ8TiB9yAtFQgHQzbbnK2DwpnJrxh6uh0ZWVlZlxbrfpm5pqxrndtr4vdn6JxPrnTORCr609IE0n6VMbeyOnNvZGdvRnaa3d1NTz2txjFKucGD58hElRf9NDqaan3EZ6LqF6qdB2nGwiEjmBaChAupG2XmV0ugeEq6HROX07XOStefEqEJ38OZ0ofNvTQxPLNXda63mRerapR8aYjp9YNH+fhw8fYSSZKY0XN6ao4fctFUdtE4mNk01EIicQDQVIN9LWK63RxoA0XXWd0ngy2KtX77idoCX6PftOcfp2uKa8iHeB6NTP6WTh60QPjVM9IE3vszTFSD81PdXfTGn8PTf9vifLMinq3+3/5pMRJ5uIRE4gGgqQbqSt95pHJkXTVdfUXr1MimQkT1xP0NoaZ3cTz9vh2nNrXrwLRKdu+0uWwjdWt6e11vPywAMPtLlHpiMnFsOHj2wsPhre5x6K/vvukUDjdNB2nGwiEjmBaFiIsBvp6LSvXq9XFcGgXLljddqaJmm74rlwXrJOTxvv6YrjtQBeexfqi/f6JU4t9JcM67LEci2Q1hYd+8lPfhLXxcj2798n6Ro1vc+WXNootfh9vygpKzOTNQgAAKIHJMm19V7z1qpSu3omEn3WomjsGLcQ60HITdp7a168ewri9XOeSzL0gDh9G16kjlzZtKxeRpra7H1+zkgykxt7Puz6fSM+uNqNSOQEouEWrE5wckaijmrLveatJUXDCdrUiBO0KVFP0Do7mDdanIk8M1a0E8PJkullWeeMtT15FMtByE3ae2ueHQViPH7Oc+VPMhS+Tt2e1pq2foE0f+979erbOAZkamPeNbzPfXv1junvu6tL1O8fTjYRiZxANBQgHeT0jETx1FpSNK0PIE1uPHGYHL5nvLl4DOZN9JmxWjsx7KGUs8aaCHnU0ckJEnVa22jamj+J/nONHD48yoBtmVGZmY7E05YvkDPf+6mNnyP9jNTHSF9MuEIv0SXC50ZrONlEJHIC0VCAdFCi3QoRS2frAWkYgF5gpL5GyjJSf2NZKS2uKMfjVhY7bo/pTA9L9B4Qy/SUJ2qsTW31sqwzTijtzqN/zErUsrBs66158Y4tFld5k+H2qrZomrJ2shpOPCc3TlmbmZllS/uRfyOrVq0652ui9ZxKU0xq6nkJW+gZk9hrESXy9w8nm4hETiAaCpAOSrRbIWKpbWNAmk5am25X+ccV5c6OFYn2xR/v8Sed7WGJHLcwObyWQeCMWJu31UMpjufRPwqQL7a4In229WHsjCsWV3kTYWa1WJzQNvwcxSZFHtNDqSZFHiPNseXn6OjfSK9eqVHf+169EvezMll7XBPh+4eTTUQiJxANBUg7ND+B6J+aaqbaeAXKzvt9zz4GpOkq8jgjRb+i3Jmrza198Q8fPiKuV7BjcYW8adxCitWwhkFDkXbmsZq31VNuM0Xxz6Oznfx25BYsO8TyKq/TPSCxOqF18ufoaNupqeeZ5tPuNvw72aSmnhf3mDvK6Xw5l9b+NkZlZjo+LoSTTUQiJxANBUgbRZ5ANNyK1LAwX7xnbom8EjxFCp+Ux+MLprWkaDlIt0+rV5Q7M5i3tS/+4cNHdfiYbRHLK+Tn+vmj9SQ13VITjzw618lvR9eHibdYXuV1eoB5rE5onfw5Ovo30qtXbxNt0HmvXr3b1K4Tg60TocfsbFqbKS4RxoVwsolI5ASioQBpo2gnEJLbpPXqbVIsy/S2LJM1fHhcPuxbG1/QcJU99l8wZ0uKpkG6DVNptryq2fyEqqODec/2xR/PAcKxvuJ5tljPbOs5k6L+JqXx6n6sTybP9bN1hx4QY5wdYB7rAteJn6OjfyNnjh0rMJKnXT2idp9UJ3oPiDFnzhSXNXx4QowL4WQTkcgJRJOUBUh1dbX58pe/bEaPHm0uvfRS8/7770fdL5YFSPQTiGJbvhxbn2EpNS5fiu2b2Sa2V2IjV0luul0jM3NUp457LnZeWbb7Kva5Tn47uz5MvDi1Hkg8JMMJ7bl0NG87k+9ODbZ2usesIxJlXAgnm4hETiCapCxArr76avPkk08aY4x59tlnzcSJE6PuF+8ekBT1t2UGo7PPsBT72wLaM7d/rK/ENs3yE3m7hh2z/Nh5ZdnOttpy8tuZ9WHiKR7rgTghGU9oo4nMk9WrV3fodW39uZ08qU70KZkjJcrMWJxsIhI5gWiSrgA5dOiQGTBggDl58qQxxpj6+npzwQUXmD179pyxb3zGgPzjBKJH45dhvL8cm9puWin4HzMslTnWAxIvTbP8tLxdw55ZfrqqWJ388iXSOcl2QtsW8c6JRDmpTgaJ0mPI5wQikROIpj15YRljjBy2detW3XrrrdqxY0f4ucsuu0zLli3TFVdc0WLfbdu2qaCgQF/+8pc1YMAAeb1e+Xy+Dre9fv16Pfroz7R7d7VGjXLpxN8+0eiDB1VujCw1jAAstCx9fPHF+s9XXulwO621XbJwofYdOKAe6q9TulGW9d8yZotWr16t6dOnx6ytI0eO6Itf/GLMjtceV1zxNe3YcYGMKZca31XLKtTFF3+sV175T0di6goic/eBB+a1O2eczAskpnjnxPr16zVnzhxNtSwVGaNyy9IWY2L+mddVrF+/Xj979FFV794t16hRmvfAA7a/T3xOIBI5gWia58WgQYPOvnPcyqB2qKioMGPGjGnx3Pjx480rr7xyxr7xXAfEGGeuONlxFdXJqxVd5VaVroirWIhkR050ldvwugs+JxCJnEA07cmLHvGthdpm2LBhOnjwoE6dOiVJMsbowIEDysjIsD0Wr9ersrIy1bjdWpiaqhq3W4FAQDNnzoxrm8FghWprjysYrIhrW05oek/d7hqlpi6U210T9/cUQOLyer2qCAZ1vLZWFcEgnwUA0M2kOB2AJA0ZMkT5+flau3at5syZo7KyMmVmZiozM9OReLxer7xeryNtd1W8pwAAAJCkhOgBkaSVK1dq5cqVcrlc+vGPf6zHH3/c6ZC6lLKyMqdDQAIiLxCJnEAkcgKRyAlE0568SJgCJDs7W2+99Zaqq6tVUVGhiy++2OmQupRAIOB0CEhA5AUikROIRE4gEjmBaNqTFwlxC1Z71NbWSpJ27tzpcCTJ5dNPP9W2bducDgMJhrxAJHICkcgJRCInEE1kXuTk5Khv375R902IaXjb49///d81e/Zsp8MAAAAA0IqtW7dq3LhxUbclXQFy+PBhbdq0SZmZmUpLS3M6HAAAAAARulQPCAAAAIDklTCD0AEAAAB0fRQgAAAAAGxDAQIAAADANhQgAAAAAGxDAdLF1NXVacaMGXK5XMrLy1NRUZH27t0b3r5r1y5NmjRJLpdLl112mXbs2OFcsLDNtGnT5Ha7lZeXp8svv1zBYDC8jZzo3kpKSmRZlt57773wc+RE95SZmamcnBzl5eUpLy9PTz/9dHgbOdF9/f3vf9fdd9+t0aNH6+KLL26xFAJ50f0cPXo0/BmRl5cnl8ullJQU1dTUSGpHThh0KbW1tWbDhg2mvr7eGGPML37xCzN16tTw9quvvto8+eSTxhhjnn32WTNx4kQnwoTNjhw5Ev7/3/3udyY/Pz/8mJzovrZu3WqKiopMRkaGCYVC4efJie5p+PDhLfKgOXKi+5o7d675X//rf4XPKz788MPwNvICjz76qJk+fXr4cVtzggKki3vnnXfMyJEjjTHGHDp0yAwYMMCcPHnSGGNMfX29ueCCC8yePXscjBB2W7VqlSkoKDDGkBPdWV1dnZk4caL54IMPWpx4khPdV2sFCDnRfR07dswMGDDAfPbZZ2dsIy9gjDFjxowxv/vd74wx7csJbsHq4n7+85/r+uuvlyQdOHBAF110kVJSUiRJlmUpIyND+/fvdzJE2OS2227TsGHDtGDBAq1evVoSOdGdPfjgg5o9e7ZGjBjR4nlyonubNWuWxo4dq+9+97v6+OOPJZET3dmf//xnnX/++XrooYc0fvx4XX755frP//xPSeQFpLfeekuffPKJpk+fLql9OUEB0oUtXbpUu3bt0pIlS8LPWZbVYh/DOpTdxpo1a3TgwAE99NBDuv/++8PPkxPdz1tvvaV33nlHd955Z9Tt5ET39Oqrr+rdd9/Vtm3bdP7556u4uDi8jZzonk6ePKkPPvhAY8aMUUVFhVasWKFbbrklXJySF93bE088odtuuy1ccEhtzwkKkC5q2bJlCgQC2rhxo/r27StJGjZsmA4ePKhTp05JakiKAwcOKCMjw8lQYbPi4mL913/9lz755BNyopt65ZVXVFlZqREjRigzM1MHDx5UYWGhNm7cSE50Y02/4169emnu3Ll67bXXJPHd0Z0NHz5cPXr00KxZsyRJHo9HI0aM0Pvvv09edHPHjx/X008/rdtvvz38XHtyggKkC1q+fLnWrVunLVu2aODAgeHnhwwZovz8fK1du1aSVFZWpszMTGVmZjoTKGzxt7/9TR9++GH48e9+9zudf/75GjRoEDnRTf3gBz/Qhx9+qL1792rv3r0aOnSoNm3apGuuuYac6KaOHz+uo0ePhh+vW7dO+fn5kvju6M7S09M1efJkbdq0SZK0b98+7dmzR9nZ2eRFN/fss8/K7XYrJycn/Fx7csIy9Jd1KQcPHtSwYcOUlZWl8847T5LUp08fvf3225KkqqoqzZkzR5988om+8IUvaPXq1br44oudDBlxduDAAfl8PtXW1qpHjx4aPHiwli1bpry8PEnkBBqmX/3973+vSy65RBI50R198MEH8vl8On36tIwxysrK0r/+67+GTxzIie7rgw8+0O23365PPvlEPXv2lN/v18yZMyWRF93Z5Zdfrttvv13f/va3Wzzf1pygAAEAAABgG27BAgAAAGAbChAAAAAAtqEAAQAAAGAbChAAAAAAtqEAAQAAAGAbChAAAAAAtqEAAQBIkh577DFlZGSoZ8+emjFjRsyOGwwGtWjRIp04cSJmx2yPxYsXa+rUqRowYIAsy1JFRYUjcQAAGlCAAABUWVmpe+65R7NmzdJrr72mRx55JGbHDgaDKikpcawAWblypT7//HNNnTrVkfYBAC2lOB0AAMB5lZWVkqTvfe97ysrKcjiaszt9+rTq6+vVq1evNu2/f/9+9ejRQy+//LLKysriHB0A4FzoAQGAbm7OnDmaOXOmJGnkyJGyLEurVq3S0aNHdeedd+rCCy9Unz59VFBQoM2bN7d47YYNGzR16lQNGTJEX/jCFzRhwgSVl5eHt69atUrf/va3JUmDBw+WZVnKzMyUJC1atEj9+/c/I57+/ftr0aJF4cdXXXWVpk+frtWrVys7O1t9+vRRMBgMtz9hwgSlpaVp8ODB+p//83/q+PHjLY7XowdfdQCQSOgBAYBubuHChcrOztaPfvQjBQIBXXjhhRoxYoSmTp2qQ4cOacmSJfrSl76ktWvX6rrrrtO2bds0duxYSdKePXt0/fXX67777lOPHj20ceNGXXvttXrppZd01VVX6brrrtOCBQv00EMPqby8XAMGDFCfPn3aHWNFRYX279+vxYsXa+DAgRo2bJiee+453Xzzzfr2t7+tkpISffTRR/rBD36gI0eO6D/+4z9i/TYBAGKEAgQAurmRI0dq9OjRkqT8/HxlZmbqySefVDAY1LvvvqsxY8ZIkgoLC1VdXa3FixfrmWeekSTdfffd4ePU19fr6quv1vvvv69/+7d/01VXXaXBgwdr5MiRkqSCggKlp6d3KMYjR46ooqJCQ4cOlSQZY3Tffffp5ptv1m9+85vwfhdccIGmT5+uhQsX6uKLL+5QWwCA+KJfGgBwhs2bN2vs2LFyuVw6depU+L/JkyfrnXfeCe938OBBFRcX60tf+pJSUlLUq1cvbd68WdXV1TGNx+12h4sPSaqurta+ffv0jW98o0V8V155JTNdAUCCowcEAHCGw4cPa/v27VEHevfs2VNSQ4/HDTfcoE8//VSlpaUaNWqU+vXrpwcffFD79++PaTxDhgw5Iz5J4bErkQ4cOBDT9gEAsUMBAgA4w6BBg+R2u/X444+3us/u3bu1fft2Pf/88/r6178efr62trZNbaSmpurkyZMtnvv73/8edbpey7LOiE+SVqxYoQkTJpyx/0UXXdSmGAAA9qMAAQCcYcqUKXrxxRd10UUXtXoy31Ro9O7dO/zcvn379MYbb8jlcoWfa9peV1fX4vVDhw7V559/rj//+c/hcSJ/+MMfZIw5Z3w5OTkaOnSoPvjgA911113t++EAAI6iAAEAnOG2227TypUrddVVV+m+++6Ty+XS0aNHtX37dn3++ed6+OGHw0XAD37wA50+fVrHjx+X3+/Xl770pRbHys3NlST98pe/1IwZM9S3b1+NHTtW11xzjfr166fvfe97+pd/+RcdPHhQ//qv/9qioGmNZVlavny5vvWtb+n48eO67rrr1K9fP+3bt08bNmzQ0qVLw0XQK6+8oo8//ljvv/++JOmll17S3r17lZmZqfHjx8f4nQMAnAsFCADgDH369NFLL72kRYsWacmSJfroo4+Unp6u/Px83XnnneF9AoGA7rrrLt10000aNmyYFixYoJdeeqnFIPD8/HwtWrRIv/nNb/TII49o2LBh2rt3r84//3yVlZXp3nvv1YwZM5SXl6ennnpKX/nKV9oU40033aSBAwdqyZIlWrt2rSQpMzNTRUVFuuCCC8L7+f1+vfLKK+HH//Iv/yJJKi4u1qpVqzr7VgEA2skybenrBgAAAIAYYBpeAAAAALahAAEAAABgGwoQAAAAALahAAEAAABgGwoQAAAAALahAAEAAABgm/8P89RqdajBum4AAAAASUVORK5CYII="
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter(features_train[labels_train .== 0, 1], features_train[labels_train .== 0, 2], color=\"blue\", label=\"unsuccessful\", xlabel=\"feature1\", ylabel=\"feature2\")\n",
    "scatter!(features_train[labels_train .== 1, 1], features_train[labels_train .== 1, 2], color=\"red\", label=\"successful\", size=(800,300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mmhh, it doesn't look like the samples can easily be separated. This will be challenging."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### $\\ast$ **Try for yourself**\n",
    "\n",
    "The plot above shows features 1 and 2. Have a look at the other combinations of features.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model specification\n",
    "\n",
    "We have features $X$, labels $Y$ and parameters $\\theta$. Same as with regression, we are looking for a posterior distribution of the classification parameters:\n",
    "\n",
    "$$\\underbrace{p(\\theta \\mid Y, X)}_{\\text{posterior}} \\propto\\ \\underbrace{p(Y \\mid X, \\theta)}_{\\text{likelihood}} \\cdot \\underbrace{p(\\theta)}_{\\text{prior}}$$\n",
    "\n",
    "The likelihood in this case will be of a Logit form:\n",
    "\n",
    "$$ p(Y \\mid X, \\theta) = \\prod_{i=1}^{N} \\ \\text{Logit}(Y_i \\mid f_\\theta(X_i), \\xi_i) \\, .$$ \n",
    "\n",
    "A \"Logit\" is short for a Bernoulli distribution with a sigmoid transfer function: $ \\sigma(x) = 1 / (1 + \\exp(-x))$. The sigmoid maps the input to the interval $(0,1)$ so that the result acts as a rate parameter to the Bernoulli. Check Bert's lecture on discriminative classification for more information.\n",
    "\n",
    "We are not using a Laplace approximation to the posterior, but rather a \"local variational method\" (see Section 10.5 of Bishop). We won't go into how that works here. All you need to know in this implementation is that there is a second parameter to the Logit, $\\xi$ (the \"local variational parameter\"), which has to be estimated just as the classification parameters $\\theta$ (but doesn't need a prior).\n",
    "\n",
    "We will use a Gaussian prior distribution for the classification parameters $\\theta$:\n",
    "\n",
    "$$ p(\\theta) = \\mathcal{N}(\\theta \\mid \\mu_\\theta, \\Sigma_\\theta) \\, .$$\n",
    "\n",
    "Note that the true posterior is still approximated with a Gaussian distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import LinearAlgebra: I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start factor graph\n",
    "graph = FactorGraph();\n",
    "\n",
    "# Parameters for priors\n",
    "μ_θ = zeros(num_features+1,)\n",
    "Σ_θ = Matrix{Float64}(I, num_features+1, num_features+1)\n",
    "\n",
    "# Define a prior over the weights\n",
    "@RV θ ~ GaussianMeanVariance(μ_θ, Σ_θ)\n",
    "\n",
    "X = Vector{Variable}(undef, num_train)\n",
    "ξ = Vector{Variable}(undef, num_train)\n",
    "Y = Vector{Variable}(undef, num_train)\n",
    "\n",
    "for i = 1:num_train\n",
    "    \n",
    "    # Features\n",
    "    @RV X[i]\n",
    "    \n",
    "    # Local variational parameter\n",
    "    @RV ξ[i]\n",
    "    \n",
    "    # Logit likelihood\n",
    "    @RV Y[i] ~ Logit(dot(θ, X[i]), ξ[i])\n",
    "    \n",
    "    # Observed \n",
    "    placeholder(X[i], :X, index=i, dims=(num_features+1,))\n",
    "    placeholder(Y[i], :Y, index=i)\n",
    "    \n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now compile an inference algorithm for this model. Since we now have two unknown variables, $\\theta$ and $\\xi$, we need to define a `PosteriorFactorization()`. With this function, we are basically telling ForneyLab that these variables need to be estimated separately (i.e., generate two `step!` functions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We specify a recognition distribution\n",
    "q = PosteriorFactorization(θ, ξ, ids=[:θ, :ξ])\n",
    "\n",
    "# Define and compile the algorithm\n",
    "algorithm = messagePassingAlgorithm() \n",
    "source_code = algorithmSourceCode(algorithm)\n",
    "\n",
    "# Bring the generated source code into scope\n",
    "eval(Meta.parse(source_code));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have compiled the algorithm, we are going to iteratively update the classification parameters and the local variational parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:00:29\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "# Initialize posteriors\n",
    "posteriors = Dict()\n",
    "for i = 1:num_train\n",
    "    posteriors[:ξ_*i] = ProbabilityDistribution(Function, mode=1.0)\n",
    "end\n",
    "\n",
    "# Load data\n",
    "data = Dict(:X => [[features_train[i,:]; 1] for i in 1:num_train],\n",
    "            :Y => labels_train)\n",
    "\n",
    "# Iterate updates\n",
    "@showprogress for i = 1:10\n",
    "    \n",
    "    # Update classification parameters\n",
    "    stepθ!(data, posteriors)\n",
    "    \n",
    "    # Update local variational parameters\n",
    "    stepξ!(data, posteriors)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict test data\n",
    "\n",
    "The bank has some test data for you as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read CSV file\n",
    "df = DataFrame(CSV.File(\"../datasets/credit_test.csv\"))\n",
    "\n",
    "# Split dataframe into features and labels\n",
    "features_test = Matrix(df[:,1:7])\n",
    "labels_test = Vector(df[:,8])\n",
    "\n",
    "# Number of test samples\n",
    "num_test = size(features_test,1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can classify test samples by taking the MAP for the classification parameters, computing the linear function $f_\\theta$ and rounding the result to obtain the most probable label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ForneyLab: unsafeMode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy = 63.0%\n"
     ]
    }
   ],
   "source": [
    "# Extract MAP estimate of classification parameters\n",
    "θ_MAP = unsafeMode(posteriors[:θ])\n",
    "\n",
    "# Compute dot product between parameters and test data\n",
    "fθ_pred = [features_test ones(num_test,)] * θ_MAP\n",
    "\n",
    "# Predict labels\n",
    "labels_pred = round.(1 ./(1 .+ exp.( -fθ_pred)));\n",
    "\n",
    "# Compute classification accuracy of test data\n",
    "accuracy_test = mean(labels_test .== labels_pred)\n",
    "\n",
    "# Report result\n",
    "println(\"Test Accuracy = \"*string(accuracy_test*100)*\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mmmhh... If you were a bank, you might decide that you don't want to automatically assign credit to your customers."
   ]
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Julia 1.8.2",
   "language": "julia",
   "name": "julia-1.8"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}