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  {
   "cell_type": "markdown",
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   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Learning\n",
    "\n",
    "This notebook serves as supporting material for topics covered in **Chapter 18 - Learning from Examples** , **Chapter 19 - Knowledge in Learning**, **Chapter 20 - Learning Probabilistic Models** from the book *Artificial Intelligence: A Modern Approach*. This notebook uses implementations from [learning.py](https://github.com/aimacode/aima-python/blob/master/learning.py). Let's start by importing everything from learning module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
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   "source": [
    "from learning import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review\n",
    "\n",
    "In this notebook, we learn about agents that can improve their behavior through diligent study of their own experiences.\n",
    "\n",
    "An agent is **learning** if it improves its performance on future tasks after making observations about the world.\n",
    "\n",
    "There are three types of feedback that determine the three main types of learning:\n",
    "\n",
    "* **Supervised Learning**:\n",
    "\n",
    "In Supervised Learning the agent observeses some example input-output pairs and learns a function that maps from input to output.\n",
    "\n",
    "**Example**: Let's think of an agent to classify images containing cats or dogs. If we provide an image containing a cat or a dog, this agent should output a string \"cat\" or \"dog\" for that particular image. To teach this agent, we will give a lot of input-output pairs like {cat image-\"cat\"}, {dog image-\"dog\"} to the aggent. The agent then learns a function that maps from an input image to one of those strings.\n",
    "\n",
    "* **Unsupervised Learning**:\n",
    "\n",
    "In Unsupervised Learning the agent learns patterns in the input even though no explicit feedback is supplied. The most common type is **clustering**: detecting potential useful clusters of input examples.\n",
    "\n",
    "**Example**: A taxi agent would develop a concept of *good traffic days* and *bad traffic days* without ever being given labeled examples.\n",
    "\n",
    "* **Reinforcement Learning**:\n",
    "\n",
    "In Reinforcement Learning the agent from a series of reinforcements—rewards or punishments.\n",
    "\n",
    "**Example**: Let's talk about an agent to play the popular Atari game—[Pong](http://www.ponggame.org). We will reward a point for every correct move and deduct a point for every wrong move from the agent. Eventually, the agent will figure out its actions prior to reinforcement were most responsible for it."
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Explanations of learning module goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Machine Learning Task\n",
    "## MNIST hand-written digits calssification\n",
    "\n",
    "The MNIST database, available from [this page](http://yann.lecun.com/exdb/mnist/) is a large database of handwritten digits that is commonly used for training & testing/validating in Machine learning.\n",
    "\n",
    "The dataset has **60,000 training images** each of size 28x28 pixels with labels and **10,000 testing images** of size 28x28 pixels with labels.\n",
    "\n",
    "In this section, we will use this database to compare performances of these different learning algorithms:\n",
    "* kNN (k-Nearest Neighbour) classifier\n",
    "* Single-hidden-layer Neural Network classifier\n",
    "* SVMs (Support Vector Machines)\n",
    "\n",
    "It is estimates that humans have an error rate of about **0.2%** on this problem. Let's see how our algorithms perform!\n",
    "\n",
    "Let's start by loading MNIST data into numpy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, struct\n",
    "import array\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0)\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_MNIST(path=\"aima-data/MNIST\"):\n",
    "    \"helper function to load MNIST data\"\n",
    "    train_img_file = open(os.path.join(path, \"train-images-idx3-ubyte\"), \"rb\")\n",
    "    train_lbl_file = open(os.path.join(path, \"train-labels-idx1-ubyte\"), \"rb\")\n",
    "    test_img_file = open(os.path.join(path, \"t10k-images-idx3-ubyte\"), \"rb\")\n",
    "    test_lbl_file = open(os.path.join(path, 't10k-labels-idx1-ubyte'), \"rb\")\n",
    "    \n",
    "    magic_nr, tr_size, tr_rows, tr_cols = struct.unpack(\">IIII\", train_img_file.read(16))\n",
    "    tr_img = array.array(\"B\", train_img_file.read())\n",
    "    train_img_file.close()    \n",
    "    magic_nr, tr_size = struct.unpack(\">II\", train_lbl_file.read(8))\n",
    "    tr_lbl = array.array(\"b\", train_lbl_file.read())\n",
    "    train_lbl_file.close()\n",
    "    \n",
    "    magic_nr, te_size, te_rows, te_cols = struct.unpack(\">IIII\", test_img_file.read(16))\n",
    "    te_img = array.array(\"B\", test_img_file.read())\n",
    "    test_img_file.close()\n",
    "    magic_nr, te_size = struct.unpack(\">II\", test_lbl_file.read(8))\n",
    "    te_lbl = array.array(\"b\", test_lbl_file.read())\n",
    "    test_lbl_file.close()\n",
    "\n",
    "#     print(len(tr_img), len(tr_lbl), tr_size)\n",
    "#     print(len(te_img), len(te_lbl), te_size)\n",
    "    \n",
    "    train_img = np.zeros((tr_size, tr_rows*tr_cols), dtype=np.uint8)\n",
    "    train_lbl = np.zeros((tr_size,), dtype=np.int8)\n",
    "    for i in range(tr_size):\n",
    "        train_img[i] = np.array(tr_img[i*tr_rows*tr_cols : (i+1)*tr_rows*tr_cols]).reshape((tr_rows*te_cols))\n",
    "        train_lbl[i] = tr_lbl[i]\n",
    "        \n",
    "    test_img = np.zeros((te_size, te_rows*te_cols), dtype=np.uint8)\n",
    "    test_lbl = np.zeros((te_size,), dtype=np.int8)\n",
    "    for i in range(te_size):\n",
    "        test_img[i] = np.array(te_img[i*te_rows*te_cols : (i+1)*te_rows*te_cols]).reshape((te_rows*te_cols))\n",
    "        test_lbl[i] = te_lbl[i]\n",
    "        \n",
    "    return(train_img, train_lbl, test_img, test_lbl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `load_MNIST()` loads MNIST data from files saved in `aima-data/MNIST`. It returns four numpy arrays that we are gonna use to train & classify hand-written digits in various learning approaches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_img, train_lbl, test_img, test_lbl = load_MNIST()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the shape of these NumPy arrays to make sure we have loaded the database correctly.\n",
    "\n",
    "Each 28x28 pixel image is flattened to 784x1 array and we should have 60,000 of them in training data. Similarly we should have 10,000 of those 784x1 arrays in testing data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training images size: (60000, 784)\n",
      "Training labels size: (60000,)\n",
      "Testing images size: (10000, 784)\n",
      "Training labels size: (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training images size:\", train_img.shape)\n",
    "print(\"Training labels size:\", train_lbl.shape)\n",
    "print(\"Testing images size:\", test_img.shape)\n",
    "print(\"Training labels size:\", test_lbl.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize some random images from training & testing datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "classes = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n",
    "num_classes = len(classes)\n",
    "\n",
    "def show_MNIST(dataset, samples=8):\n",
    "    if dataset == \"training\":\n",
    "        labels = train_lbl\n",
    "        images = train_img\n",
    "    elif dataset == \"testing\":\n",
    "        labels = test_lbl\n",
    "        images = test_img\n",
    "    else:\n",
    "        raise ValueError(\"dataset must be 'testing' or 'training'!\")\n",
    "        \n",
    "    for y, cls in enumerate(classes):\n",
    "        idxs = np.nonzero([i == y for i in labels])\n",
    "        idxs = np.random.choice(idxs[0], samples, replace=False)\n",
    "        for i , idx in enumerate(idxs):\n",
    "            plt_idx = i * num_classes + y + 1\n",
    "            plt.subplot(samples, num_classes, plt_idx)\n",
    "            plt.imshow(images[idx].reshape((28, 28)))\n",
    "            plt.axis(\"off\")\n",
    "            if i == 0:\n",
    "                plt.title(cls)\n",
    "\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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C4o8x3scHiT9GPU8dEn2M8T4+SPwx6nnqkOhjVOVJURRFURQlDHTxpCiKoiiK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\nERERC5o8iYiIiFj4D4tzQZGGWbuPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1188809b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# takes 5-10 secs. to execute the cell\n",
    "show_MNIST(\"training\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CBWNir+INOQ9t2rShbdu2Qe/98ssvAGzcuDHudsWDEiVKAHDRRRcBMHnyZPP6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Zk0JK5Xrx4A55xzDuDZucgpPdauaMeOHe46o6KVq666yiWYx3LVVVe5HprJxpQnwzAMwzCMAIRC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\nAQAARPgL8m0PWoQ/hegAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118460b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# takes 5-10 secs. to execute the cell\n",
    "show_MNIST(\"testing\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## kNN classifier"
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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