learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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 the module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from learning import *\n",
    "from notebook import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONTENTS\n",
    "\n",
    "* Machine Learning Overview\n",
    "* Datasets\n",
    "* Iris Visualization\n",
    "* Distance Functions\n",
    "* Plurality Learner\n",
    "* k-Nearest Neighbours\n",
    "* Naive Bayes Learner\n",
    "* Perceptron\n",
    "* Neural Network\n",
    "* Learner Evaluation\n",
    "* MNIST Handwritten Digits\n",
    "    * Loading and Visualising\n",
    "    * Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MACHINE LEARNING OVERVIEW\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 observes 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 agent. 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 learns 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATASETS\n",
    "\n",
    "For the following tutorials we will use a range of datasets, to better showcase the strengths and weaknesses of the algorithms. The datasests are the following:\n",
    "\n",
    "* [Fisher's Iris](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/iris.csv): Each item represents a flower, with four measurements: the length and the width of the sepals and petals. Each item/flower is categorized into one of three species: Setosa, Versicolor and Virginica.\n",
    "\n",
    "* [Zoo](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/zoo.csv): The dataset holds different animals and their classification as \"mammal\", \"fish\", etc. The new animal we want to classify has the following measurements: 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1 (don't concern yourself with what the measurements mean)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make using the datasets easier, we have written a class, `DataSet`, in `learning.py`. The tutorials found here make use of this class.\n",
    "\n",
    "Let's have a look at how it works before we get started with the algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intro\n",
    "\n",
    "A lot of the datasets we will work with are .csv files (although other formats are supported too). We have a collection of sample datasets ready to use [on aima-data](https://github.com/aimacode/aima-data/tree/a21fc108f52ad551344e947b0eb97df82f8d2b2b). Two examples are the datasets mentioned above (*iris.csv* and *zoo.csv*). You can find plenty datasets online, and a good repository of such datasets is [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets.html).\n",
    "\n",
    "In such files, each line corresponds to one item/measurement. Each individual value in a line represents a *feature* and usually there is a value denoting the *class* of the item.\n",
    "\n",
    "You can find the code for the dataset here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource DataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Attributes\n",
    "\n",
    "* **examples**: Holds the items of the dataset. Each item is a list of values.\n",
    "\n",
    "* **attrs**: The indexes of the features (by default in the range of [0,f), where *f* is the number of features. For example, `item[i]` returns the feature at index *i* of *item*.\n",
    "\n",
    "* **attrnames**: An optional list with attribute names. For example, `item[s]`, where *s* is a feature name, returns the feature of name *s* in *item*.\n",
    "\n",
    "* **target**: The attribute a learning algorithm will try to predict. By default the last attribute.\n",
    "\n",
    "* **inputs**: This is the list of attributes without the target.\n",
    "\n",
    "* **values**: A list of lists which holds the set of possible values for the corresponding attribute/feature. If initially `None`, it gets computed (by the function `setproblem`) from the examples.\n",
    "\n",
    "* **distance**: The distance function used in the learner to calculate the distance between two items. By default `mean_boolean_error`.\n",
    "\n",
    "* **name**: Name of the dataset.\n",
    "\n",
    "* **source**: The source of the dataset (url or other). Not used in the code.\n",
    "\n",
    "* **exclude**: A list of indexes to exclude from `inputs`. The list can include either attribute indexes (attrs) or names (attrnames)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Helper Functions\n",
    "\n",
    "These functions help modify a `DataSet` object to your needs.\n",
    "\n",
    "* **sanitize**: Takes as input an example and returns it with non-input (target) attributes replaced by `None`. Useful for testing. Keep in mind that the example given is not itself sanitized, but instead a sanitized copy is returned.\n",
    "\n",
    "* **classes_to_numbers**: Maps the class names of a dataset to numbers. If the class names are not given, they are computed from the dataset values. Useful for classifiers that return a numerical value instead of a string.\n",
    "\n",
    "* **remove_examples**: Removes examples containing a given value. Useful for removing examples with missing values, or for removing classes (needed for binary classifiers)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing a Dataset\n",
    "\n",
    "#### Importing from aima-data\n",
    "\n",
    "Datasets uploaded on aima-data can be imported with the following line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = DataSet(name=\"iris\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check that we imported the correct dataset, we can do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.1, 3.5, 1.4, 0.2, 'setosa']\n",
      "[0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print(iris.examples[0])\n",
    "print(iris.inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which correctly prints the first line in the csv file and the list of attribute indexes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When importing a dataset, we can specify to exclude an attribute (for example, at index 1) by setting the parameter `exclude` to the attribute index or name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "iris2 = DataSet(name=\"iris\",exclude=[1])\n",
    "print(iris2.inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attributes\n",
    "\n",
    "Here we showcase the attributes.\n",
    "\n",
    "First we will print the first three items/examples in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.1, 3.5, 1.4, 0.2, 'setosa'], [4.9, 3.0, 1.4, 0.2, 'setosa'], [4.7, 3.2, 1.3, 0.2, 'setosa']]\n"
     ]
    }
   ],
   "source": [
    "print(iris.examples[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we will print `attrs`, `attrnames`, `target`, `input`. Notice how `attrs` holds values in [0,4], but since the fourth attribute is the target, `inputs` holds values in [0,3]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attrs: [0, 1, 2, 3, 4]\n",
      "attrnames (by default same as attrs): [0, 1, 2, 3, 4]\n",
      "target: 4\n",
      "inputs: [0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print(\"attrs:\", iris.attrs)\n",
    "print(\"attrnames (by default same as attrs):\", iris.attrnames)\n",
    "print(\"target:\", iris.target)\n",
    "print(\"inputs:\", iris.inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will print all the possible values for the first feature/attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.7, 5.5, 6.3, 5.0, 4.9, 5.1, 4.6, 5.4, 4.4, 4.8, 5.8, 7.0, 7.1, 4.5, 5.9, 5.6, 6.9, 6.6, 6.5, 6.4, 6.0, 6.1, 7.6, 7.4, 7.9, 4.3, 5.7, 5.3, 5.2, 6.7, 6.2, 6.8, 7.3, 7.2, 7.7]\n"
     ]
    }
   ],
   "source": [
    "print(iris.values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we will print the dataset's name and source. Keep in mind that we have not set a source for the dataset, so in this case it is empty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: iris\n",
      "source: \n"
     ]
    }
   ],
   "source": [
    "print(\"name:\", iris.name)\n",
    "print(\"source:\", iris.source)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A useful combination of the above is `dataset.values[dataset.target]` which returns the possible values of the target. For classification problems, this will return all the possible classes. Let's try it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['versicolor', 'virginica', 'setosa']\n"
     ]
    }
   ],
   "source": [
    "print(iris.values[iris.target])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now take a look at the auxiliary functions found in the class.\n",
    "\n",
    "First we will take a look at the `sanitize` function, which sets the non-input values of the given example to `None`.\n",
    "\n",
    "In this case we want to hide the class of the first example, so we will sanitize it.\n",
    "\n",
    "Note that the function doesn't actually change the given example; it returns a sanitized *copy* of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sanitized: [5.1, 3.5, 1.4, 0.2, None]\n",
      "Original: [5.1, 3.5, 1.4, 0.2, 'setosa']\n"
     ]
    }
   ],
   "source": [
    "print(\"Sanitized:\",iris.sanitize(iris.examples[0]))\n",
    "print(\"Original:\",iris.examples[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently the `iris` dataset has three classes, setosa, virginica and versicolor. We want though to convert it to a binary class dataset (a dataset with two classes). The class we want to remove is \"virginica\". To accomplish that we will utilize the helper function `remove_examples`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['versicolor', 'setosa']\n"
     ]
    }
   ],
   "source": [
    "iris2 = DataSet(name=\"iris\")\n",
    "\n",
    "iris2.remove_examples(\"virginica\")\n",
    "print(iris2.values[iris2.target])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also have `classes_to_numbers`. For a lot of the classifiers in the module (like the Neural Network), classes should have numerical values. With this function we map string class names to numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class of first example: setosa\n",
      "Class of first example: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Class of first example:\",iris2.examples[0][iris2.target])\n",
    "iris2.classes_to_numbers()\n",
    "print(\"Class of first example:\",iris2.examples[0][iris2.target])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see \"setosa\" was mapped to 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we take a look at `find_means_and_deviations`. It finds the means and standard deviations of the features for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setosa feature means: [5.006, 3.418, 1.464, 0.244]\n",
      "Versicolor mean for first feature: 5.936\n",
      "Setosa feature deviations: [0.3524896872134513, 0.38102439795469095, 0.17351115943644546, 0.10720950308167838]\n",
      "Virginica deviation for second feature: 0.32249663817263746\n"
     ]
    }
   ],
   "source": [
    "means, deviations = iris.find_means_and_deviations()\n",
    "\n",
    "print(\"Setosa feature means:\", means[\"setosa\"])\n",
    "print(\"Versicolor mean for first feature:\", means[\"versicolor\"][0])\n",
    "\n",
    "print(\"Setosa feature deviations:\", deviations[\"setosa\"])\n",
    "print(\"Virginica deviation for second feature:\",deviations[\"virginica\"][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IRIS VISUALIZATION\n",
    "\n",
    "Since we will use the iris dataset extensively in this notebook, below we provide a visualization tool that helps in comprehending the dataset and thus how the algorithms work.\n",
    "\n",
    "We plot the dataset in a 3D space using `matplotlib` and the function `show_iris` from `notebook.py`. The function takes as input three parameters, *i*, *j* and *k*, which are indicises to the iris features, \"Sepal Length\", \"Sepal Width\", \"Petal Length\" and \"Petal Width\" (0 to 3). By default we show the first three features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgsAAAGMCAYAAABUAuEzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6151e4c748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgsAAAGMCAYAAABUAuEzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9IlNFhiGYRiG6dP/AUpC78h6pqibAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f614fc236d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgsAAAGMCAYAAABUAuEzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f614fd88780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "\n",
    "show_iris()\n",
    "show_iris(0, 1, 3)\n",
    "show_iris(1, 2, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can play around with the values to get a good look at the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DISTANCE FUNCTIONS\n",
    "\n",
    "In a lot of algorithms (like the *k-Nearest Neighbors* algorithm), there is a need to compare items, finding how *similar* or *close* they are. For that we have many different functions at our disposal. Below are the functions implemented in the module:\n",
    "\n",
    "### Manhattan Distance (`manhattan_distance`)\n",
    "\n",
    "One of the simplest distance functions. It calculates the difference between the coordinates/features of two items. To understand how it works, imagine a 2D grid with coordinates *x* and *y*. In that grid we have two items, at the squares positioned at `(1,2)` and `(3,4)`. The difference between their two coordinates is `3-1=2` and `4-2=2`. If we sum these up we get `4`. That means to get from `(1,2)` to `(3,4)` we need four moves; two to the right and two more up. The function works similarly for n-dimensional grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manhattan Distance between (1,2) and (3,4) is 4\n"
     ]
    }
   ],
   "source": [
    "def manhattan_distance(X, Y):\n",
    "    return sum([abs(x - y) for x, y in zip(X, Y)])\n",
    "\n",
    "\n",
    "distance = manhattan_distance([1,2], [3,4])\n",
    "print(\"Manhattan Distance between (1,2) and (3,4) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Euclidean Distance (`euclidean_distance`)\n",
    "\n",
    "Probably the most popular distance function. It returns the square root of the sum of the squared differences between individual elements of two items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Euclidean Distance between (1,2) and (3,4) is 2.8284271247461903\n"
     ]
    }
   ],
   "source": [
    "def euclidean_distance(X, Y):\n",
    "    return math.sqrt(sum([(x - y)**2 for x, y in zip(X,Y)]))\n",
    "\n",
    "\n",
    "distance = euclidean_distance([1,2], [3,4])\n",
    "print(\"Euclidean Distance between (1,2) and (3,4) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hamming Distance (`hamming_distance`)\n",
    "\n",
    "This function counts the number of differences between single elements in two items. For example, if we have two binary strings \"111\" and \"011\" the function will return 1, since the two strings only differ at the first element. The function works the same way for non-binary strings too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hamming Distance between 'abc' and 'abb' is 1\n"
     ]
    }
   ],
   "source": [
    "def hamming_distance(X, Y):\n",
    "    return sum(x != y for x, y in zip(X, Y))\n",
    "\n",
    "\n",
    "distance = hamming_distance(['a','b','c'], ['a','b','b'])\n",
    "print(\"Hamming Distance between 'abc' and 'abb' is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Boolean Error (`mean_boolean_error`)\n",
    "\n",
    "To calculate this distance, we find the ratio of different elements over all elements of two items. For example, if the two items are `(1,2,3)` and `(1,4,5)`, the ration of different/all elements is 2/3, since they differ in two out of three elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Boolean Error Distance between (1,2,3) and (1,4,5) is 0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "def mean_boolean_error(X, Y):\n",
    "    return mean(int(x != y) for x, y in zip(X, Y))\n",
    "\n",
    "\n",
    "distance = mean_boolean_error([1,2,3], [1,4,5])\n",
    "print(\"Mean Boolean Error Distance between (1,2,3) and (1,4,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Error (`mean_error`)\n",
    "\n",
    "This function finds the mean difference of single elements between two items. For example, if the two items are `(1,0,5)` and `(3,10,5)`, their error distance is `(3-1) + (10-0) + (5-5) = 2 + 10 + 0 = 12`. The mean error distance therefore is `12/3=4`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Error Distance between (1,0,5) and (3,10,5) is 4\n"
     ]
    }
   ],
   "source": [
    "def mean_error(X, Y):\n",
    "    return mean([abs(x - y) for x, y in zip(X, Y)])\n",
    "\n",
    "\n",
    "distance = mean_error([1,0,5], [3,10,5])\n",
    "print(\"Mean Error Distance between (1,0,5) and (3,10,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Square Error (`ms_error`)\n",
    "\n",
    "This is very similar to the `Mean Error`, but instead of calculating the difference between elements, we are calculating the *square* of the differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Distance between (1,0,5) and (3,10,5) is 34.666666666666664\n"
     ]
    }
   ],
   "source": [
    "def ms_error(X, Y):\n",
    "    return mean([(x - y)**2 for x, y in zip(X, Y)])\n",
    "\n",
    "\n",
    "distance = ms_error([1,0,5], [3,10,5])\n",
    "print(\"Mean Square Distance between (1,0,5) and (3,10,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Root of Mean Square Error (`rms_error`)\n",
    "\n",
    "This is the square root of `Mean Square Error`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root of Mean Error Distance between (1,0,5) and (3,10,5) is 5.887840577551898\n"
     ]
    }
   ],
   "source": [
    "def rms_error(X, Y):\n",
    "    return math.sqrt(ms_error(X, Y))\n",
    "\n",
    "\n",
    "distance = rms_error([1,0,5], [3,10,5])\n",
    "print(\"Root of Mean Error Distance between (1,0,5) and (3,10,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PLURALITY LEARNER CLASSIFIER\n",
    "\n",
    "### Overview\n",
    "\n",
    "The Plurality Learner is a simple algorithm, used mainly as a baseline comparison for other algorithms. It finds the most popular class in the dataset and classifies any subsequent item to that class. Essentially, it classifies every new item to the same class. For that reason, it is not used very often, instead opting for more complicated algorithms when we want accurate classification.\n",
    "\n",
    "![pL plot](images/pluralityLearner_plot.png)\n",
    "\n",
    "Let's see how the classifier works with the plot above. There are three classes named **Class A** (orange-colored dots) and **Class B** (blue-colored dots) and **Class C** (green-colored dots). Every point in this plot has two **features** (i.e. X<sub>1</sub>, X<sub>2</sub>). Now, let's say we have a new point, a red star and we want to know which class this red star belongs to. Solving this problem by predicting the class of this new red star is our current classification problem.\n",
    "\n",
    "The Plurality Learner will find the class most represented in the plot. ***Class A*** has four items, ***Class B*** has three and ***Class C*** has seven. The most popular class is ***Class C***. Therefore, the item will get classified in ***Class C***, despite the fact that it is closer to the other two classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "Below follows the implementation of the PluralityLearner algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def PluralityLearner(dataset):\n",
    "    \"\"\"A very dumb algorithm: always pick the result that was most popular\n",
    "    in the training data.  Makes a baseline for comparison.\"\"\"\n",
    "    most_popular = mode([e[dataset.target] for e in dataset.examples])\n",
    "\n",
    "    def predict(example):\n",
    "        \"Always return same result: the most popular from the training set.\"\n",
    "        return most_popular\n",
    "    return predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It takes as input a dataset and returns a function. We can later call this function with the item we want to classify as the argument and it returns the class it should be classified in.\n",
    "\n",
    "The function first finds the most popular class in the dataset and then each time we call its \"predict\" function, it returns it. Note that the input (\"example\") does not matter. The function always returns the same class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example\n",
    "\n",
    "For this example, we will not use the Iris dataset, since each class is represented the same. This will throw an error. Instead we will use the zoo dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mammal\n"
     ]
    }
   ],
   "source": [
    "zoo = DataSet(name=\"zoo\")\n",
    "\n",
    "pL = PluralityLearner(zoo)\n",
    "print(pL([1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output for the above code is \"mammal\", since that is the most popular and common class in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-NEAREST NEIGHBOURS CLASSIFIER\n",
    "\n",
    "### Overview\n",
    "The k-Nearest Neighbors algorithm is a non-parametric method used for classification and regression. We are going to use this to classify Iris flowers. More about kNN on [Scholarpedia](http://www.scholarpedia.org/article/K-nearest_neighbor).\n",
    "\n",
    "![kNN plot](images/knn_plot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how kNN works with a simple plot shown in the above picture.\n",
    "\n",
    "We have co-ordinates (we call them **features** in Machine Learning) of this red star and we need to predict its class using the kNN algorithm. In this algorithm, the value of **k** is arbitrary. **k** is one of the **hyper parameters** for kNN algorithm. We choose this number based on our dataset and choosing a particular number is known as **hyper parameter tuning/optimising**. We learn more about this in coming topics.\n",
    "\n",
    "Let's put **k = 3**. It means you need to find 3-Nearest Neighbors of this red star and classify this new point into the majority class. Observe that smaller circle which contains three points other than **test point** (red star). As there are two violet points, which form the majority, we predict the class of red star as **violet- Class B**.\n",
    "\n",
    "Similarly if we put **k = 5**, you can observe that there are four yellow points, which form the majority. So, we classify our test point as **yellow- Class A**.\n",
    "\n",
    "In practical tasks, we iterate through a bunch of values for k (like [1, 3, 5, 10, 20, 50, 100]), see how it performs and select the best one. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "Below follows the implementation of the kNN algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def NearestNeighborLearner(dataset, k=1):\n",
    "    \"\"\"k-NearestNeighbor: the k nearest neighbors vote.\"\"\"\n",
    "    def predict(example):\n",
    "        \"\"\"Find the k closest items, and have them vote for the best.\"\"\"\n",
    "        best = heapq.nsmallest(k, ((dataset.distance(e, example), e)\n",
    "                                   for e in dataset.examples))\n",
    "        return mode(e[dataset.target] for (d, e) in best)\n",
    "    return predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It takes as input a dataset and k (default value is 1) and it returns a function, which we can later use to classify a new item.\n",
    "\n",
    "To accomplish that, the function uses a heap-queue, where the items of the dataset are sorted according to their distance from *example* (the item to classify). We then take the k smallest elements from the heap-queue and we find the majority class. We classify the item to this class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example\n",
    "\n",
    "We measured a new flower with the following values: 5.1, 3.0, 1.1, 0.1. We want to classify that item/flower in a class. To do that, we write the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa\n"
     ]
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "\n",
    "kNN = NearestNeighborLearner(iris,k=3)\n",
    "print(kNN([5.1,3.0,1.1,0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the above code is \"setosa\", which means the flower with the above measurements is of the \"setosa\" species."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NAIVE BAYES LEARNER\n",
    "\n",
    "### Overview\n",
    "\n",
    "#### Theory of Probabilities\n",
    "\n",
    "The Naive Bayes algorithm is a probabilistic classifier, making use of [Bayes' Theorem](https://en.wikipedia.org/wiki/Bayes%27_theorem). The theorem states that the conditional probability of **A** given **B** equals the conditional probability of **B** given **A** multiplied by the probability of **A**, divided by the probability of **B**.\n",
    "\n",
    "$$P(A|B) = \\dfrac{P(B|A)*P(A)}{P(B)}$$\n",
    "\n",
    "From the theory of Probabilities we have the Multiplication Rule, if the events *X* are independent the following is true:\n",
    "\n",
    "$$P(X_{1} \\cap X_{2} \\cap ... \\cap X_{n}) = P(X_{1})*P(X_{2})*...*P(X_{n})$$\n",
    "\n",
    "For conditional probabilities this becomes:\n",
    "\n",
    "$$P(X_{1}, X_{2}, ..., X_{n}|Y) = P(X_{1}|Y)*P(X_{2}|Y)*...*P(X_{n}|Y)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classifying an Item\n",
    "\n",
    "How can we use the above to classify an item though?\n",
    "\n",
    "We have a dataset with a set of classes (**C**) and we want to classify an item with a set of features (**F**). Essentially what we want to do is predict the class of an item given the features.\n",
    "\n",
    "For a specific class, **Class**, we will find the conditional probability given the item features:\n",
    "\n",
    "$$P(Class|F) = \\dfrac{P(F|Class)*P(Class)}{P(F)}$$\n",
    "\n",
    "We will do this for every class and we will pick the maximum. This will be the class the item is classified in.\n",
    "\n",
    "The features though are a vector with many elements. We need to break the probabilities up using the multiplication rule. Thus the above equation becomes:\n",
    "\n",
    "$$P(Class|F) = \\dfrac{P(Class)*P(F_{1}|Class)*P(F_{2}|Class)*...*P(F_{n}|Class)}{P(F_{1})*P(F_{2})*...*P(F_{n})}$$\n",
    "\n",
    "The calculation of the conditional probability then depends on the calculation of the following:\n",
    "\n",
    "*a)* The probability of **Class** in the dataset.\n",
    "\n",
    "*b)* The conditional probability of each feature occuring in an item classified in **Class**.\n",
    "\n",
    "*c)* The probabilities of each individual feature.\n",
    "\n",
    "For *a)*, we will count how many times **Class** occurs in the dataset (aka how many items are classified in a particular class).\n",
    "\n",
    "For *b)*, if the feature values are discrete ('Blue', '3', 'Tall', etc.), we will count how many times a feature value occurs in items of each class. If the feature values are not discrete, we will go a different route. We will use a distribution function to calculate the probability of values for a given class and feature. If we know the distribution function of the dataset, then great, we will use it to compute the probabilities. If we don't know the function, we can assume the dataset follows the normal (Gaussian) distribution without much loss of accuracy. In fact, it can be proven that any distribution tends to the Gaussian the larger the population gets (see [Central Limit Theorem](https://en.wikipedia.org/wiki/Central_limit_theorem)).\n",
    "\n",
    "*NOTE:* If the values are continuous but use the discrete approach, there might be issues if we are not lucky. For one, if we have two values, '5.0 and 5.1', with the discrete approach they will be two completely different values, despite being so close. Second, if we are trying to classify an item with a feature value of '5.15', if the value does not appear for the feature, its probability will be 0. This might lead to misclassification. Generally, the continuous approach is more accurate and more useful, despite the overhead of calculating the distribution function.\n",
    "\n",
    "The last one, *c)*, is tricky. If feature values are discrete, we can count how many times they occur in the dataset. But what if the feature values are continuous? Imagine a dataset with a height feature. Is it worth it to count how many times each value occurs? Most of the time it is not, since there can be miscellaneous differences in the values (for example, 1.7 meters and 1.700001 meters are practically equal, but they count as different values).\n",
    "\n",
    "So as we cannot calculate the feature value probabilities, what are we going to do?\n",
    "\n",
    "Let's take a step back and rethink exactly what we are doing. We are essentially comparing conditional probabilities of all the classes. For two classes, **A** and **B**, we want to know which one is greater:\n",
    "\n",
    "$$\\dfrac{P(F|A)*P(A)}{P(F)} vs. \\dfrac{P(F|B)*P(B)}{P(F)}$$\n",
    "\n",
    "Wait, **P(F)** is the same for both the classes! In fact, it is the same for every combination of classes. That is because **P(F)** does not depend on a class, thus being independent of the classes.\n",
    "\n",
    "So, for *c)*, we actually don't need to calculate it at all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wrapping It Up\n",
    "\n",
    "Classifying an item to a class then becomes a matter of calculating the conditional probabilities of feature values and the probabilities of classes. This is something very desirable and computationally delicious.\n",
    "\n",
    "Remember though that all the above are true because we made the assumption that the features are independent. In most real-world cases that is not true though. Is that an issue here? Fret not, for the the algorithm is very efficient even with that assumption. That is why the algorithm is called **Naive** Bayes Classifier. We (naively) assume that the features are independent to make computations easier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "The implementation of the Naive Bayes Classifier is split in two; Discrete and Continuous. The user can choose between them with the argument `continuous`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discrete\n",
    "\n",
    "The implementation for discrete values counts how many times each feature value occurs for each class, and how many times each class occurs. The results are stored in a `CountinProbDist` object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the below code you can see the probabilities of the class \"Setosa\" appearing in the dataset and the probability of the first feature (at index 0) of the same class having a value of 5. Notice that the second probability is relatively small, even though if we observe the dataset we will find that a lot of values are around 5. The issue arises because the features in the Iris dataset are continuous, and we are assuming they are discrete. If the features were discrete (for example, \"Tall\", \"3\", etc.) this probably wouldn't have been the case and we would see a much nicer probability distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3333333333333333\n",
      "0.10588235294117647\n"
     ]
    }
   ],
   "source": [
    "dataset = iris\n",
    "\n",
    "target_vals = dataset.values[dataset.target]\n",
    "target_dist = CountingProbDist(target_vals)\n",
    "attr_dists = {(gv, attr): CountingProbDist(dataset.values[attr])\n",
    "              for gv in target_vals\n",
    "              for attr in dataset.inputs}\n",
    "for example in dataset.examples:\n",
    "        targetval = example[dataset.target]\n",
    "        target_dist.add(targetval)\n",
    "        for attr in dataset.inputs:\n",
    "            attr_dists[targetval, attr].add(example[attr])\n",
    "\n",
    "\n",
    "print(target_dist['setosa'])\n",
    "print(attr_dists['setosa', 0][5.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we found the different values for the classes (called targets here) and calculated their distribution. Next we initialized a dictionary of `CountingProbDist` objects, one for each class and feature. Finally, we iterated through the examples in the dataset and calculated the needed probabilites.\n",
    "\n",
    "Having calculated the different probabilities, we will move on to the predicting function. It will receive as input an item and output the most likely class. Using the above formula, it will multiply the probability of the class appearing, with the probability of each feature value appearing in the class. It will return the max result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa\n"
     ]
    }
   ],
   "source": [
    "def predict(example):\n",
    "    def class_probability(targetval):\n",
    "        return (target_dist[targetval] *\n",
    "                product(attr_dists[targetval, attr][example[attr]]\n",
    "                        for attr in dataset.inputs))\n",
    "    return argmax(target_vals, key=class_probability)\n",
    "\n",
    "\n",
    "print(predict([5, 3, 1, 0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can view the complete code by executing the next line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource NaiveBayesDiscrete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Continuous\n",
    "\n",
    "In the implementation we use the Gaussian/Normal distribution function. To make it work, we need to find the means and standard deviations of features for each class. We make use of the `find_means_and_deviations` Dataset function. On top of that, we will also calculate the class probabilities as we did with the Discrete approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.006, 3.418, 1.464, 0.244]\n",
      "[0.5161711470638634, 0.3137983233784114, 0.46991097723995795, 0.19775268000454405]\n"
     ]
    }
   ],
   "source": [
    "means, deviations = dataset.find_means_and_deviations()\n",
    "\n",
    "target_vals = dataset.values[dataset.target]\n",
    "target_dist = CountingProbDist(target_vals)\n",
    "\n",
    "\n",
    "print(means[\"setosa\"])\n",
    "print(deviations[\"versicolor\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see the means of the features for the \"Setosa\" class and the deviations for \"Versicolor\".\n",
    "\n",
    "The prediction function will work similarly to the Discrete algorithm. It will multiply the probability of the class occuring with the conditional probabilities of the feature values for the class.\n",
    "\n",
    "Since we are using the Gaussian distribution, we will input the value for each feature into the Gaussian function, together with the mean and deviation of the feature. This will return the probability of the particular feature value for the given class. We will repeat for each class and pick the max value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa\n"
     ]
    }
   ],
   "source": [
    "def predict(example):\n",
    "    def class_probability(targetval):\n",
    "        prob = target_dist[targetval]\n",
    "        for attr in dataset.inputs:\n",
    "            prob *= gaussian(means[targetval][attr], deviations[targetval][attr], example[attr])\n",
    "        return prob\n",
    "\n",
    "    return argmax(target_vals, key=class_probability)\n",
    "\n",
    "\n",
    "print(predict([5, 3, 1, 0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The complete code of the continuous algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource NaiveBayesContinuous"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examples\n",
    "\n",
    "We will now use the Naive Bayes Classifier (Discrete and Continuous) to classify items:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete Classifier\n",
      "setosa\n",
      "versicolor\n",
      "versicolor\n",
      "\n",
      "Continuous Classifier\n",
      "setosa\n",
      "versicolor\n",
      "virginica\n"
     ]
    }
   ],
   "source": [
    "nBD = NaiveBayesLearner(iris, continuous=False)\n",
    "print(\"Discrete Classifier\")\n",
    "print(nBD([5, 3, 1, 0.1]))\n",
    "print(nBD([6, 5, 3, 1.5]))\n",
    "print(nBD([7, 3, 6.5, 2]))\n",
    "\n",
    "\n",
    "nBC = NaiveBayesLearner(iris, continuous=True)\n",
    "print(\"\\nContinuous Classifier\")\n",
    "print(nBC([5, 3, 1, 0.1]))\n",
    "print(nBC([6, 5, 3, 1.5]))\n",
    "print(nBC([7, 3, 6.5, 2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the Discrete Classifier misclassified the second item, while the Continuous one had no problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PERCEPTRON CLASSIFIER\n",
    "\n",
    "### Overview\n",
    "\n",
    "The Perceptron is a linear classifier. It works the same way as a neural network with no hidden layers (just input and output). First it trains its weights given a dataset and then it can classify a new item by running it through the network.\n",
    "\n",
    "Its input layer consists of the the item features, while the output layer consists of nodes (also called neurons). Each node in the output layer has *n* synapses (for every item feature), each with its own weight. Then, the nodes find the dot product of the item features and the synapse weights. These values then pass through an activation function (usually a sigmoid). Finally, we pick the largest of the values and we return its index.\n",
    "\n",
    "Note that in classification problems each node represents a class. The final classification is the class/node with the max output value.\n",
    "\n",
    "Below you can see a single node/neuron in the outer layer. With *f* we denote the item features, with *w* the synapse weights, then inside the node we have the dot product and the activation function, *g*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![perceptron](images/perceptron.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "First, we train (calculate) the weights given a dataset, using the `BackPropagationLearner` function of `learning.py`. We then return a function, `predict`, which we will use in the future to classify a new item. The function computes the (algebraic) dot product of the item with the calculated weights for each node in the outer layer. Then it picks the greatest value and classifies the item in the corresponding class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource PerceptronLearner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the Perceptron is a one-layer neural network, without any hidden layers. So, in `BackPropagationLearner`, we will pass no hidden layers. From that function we get our network, which is just one layer, with the weights calculated.\n",
    "\n",
    "That function `predict` passes the input/example through the network, calculating the dot product of the input and the weights for each node and returns the class with the max dot product."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example\n",
    "\n",
    "We will train the Perceptron on the iris dataset. Because though the `BackPropagationLearner` works with integer indexes and not strings, we need to convert class names to integers. Then, we will try and classify the item/flower with measurements of 5, 3, 1, 0.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "iris.classes_to_numbers()\n",
    "\n",
    "perceptron = PerceptronLearner(iris)\n",
    "print(perceptron([5, 3, 1, 0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correct output is 0, which means the item belongs in the first class, \"setosa\". Note that the Perceptron algorithm is not perfect and may produce false classifications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NEURAL NETWORK\n",
    "\n",
    "### Overview\n",
    "\n",
    "Although the Perceptron may seem like a good way to make classifications, it is a linear classifier (which, roughly, means it can only draw straight lines to divide spaces) and therefore it can be stumped by more complex problems. We can extend Perceptron to solve this issue, by employing multiple layers of its functionality. The construct we are left with is called a Neural Network, or a Multi-Layer Perceptron, and it is a non-linear classifier. It achieves that by combining the results of linear functions on each layer of the network.\n",
    "\n",
    "Similar to the Perceptron, this network also has an input and output layer. However it can also have a number of hidden layers. These hidden layers are responsible for the non-linearity of the network. The layers are comprised of nodes. Each node in a layer (excluding the input one), holds some values, called *weights*, and takes as input the output values of the previous layer. The node then calculates the dot product of its inputs and its weights and then activates it with an *activation function* (sometimes a sigmoid). Its output is fed to the nodes of the next layer. Note that sometimes the output layer does not use an activation function, or uses a different one from the rest of the network. The process of passing the outputs down the layer is called *feed-forward*.\n",
    "\n",
    "After the input values are fed-forward into the network, the resulting output can be used for classification. The problem at hand now is how to train the network (ie. adjust the weights in the nodes). To accomplish that we utilize the *Backpropagation* algorithm. In short, it does the opposite of what we were doing up to now. Instead of feeding the input forward, it will feed the error backwards. So, after we make a classification, we check whether it is correct or not, and how far off we were. We then take this error and propagate it backwards in the network, adjusting the weights of the nodes accordingly. We will run the algorithm on the given input/dataset for a fixed amount of time, or until we are satisfied with the results. The number of times we will iterate over the dataset is called *epochs*. In a later section we take a detailed look at how this algorithm works.\n",
    "\n",
    "NOTE: Sometimes we add to the input of each layer another node, called *bias*. This is a constant value that will be fed to the next layer, usually set to 1. The bias generally helps us \"shift\" the computed function to the left or right."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![neural_net](images/neural_net.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "The `NeuralNetLearner` function takes as input a dataset to train upon, the learning rate (in (0, 1]), the number of epochs and finally the size of the hidden layers. This last argument is a list, with each element corresponding to one hidden layer.\n",
    "\n",
    "After that we will create our neural network in the `network` function. This function will make the necessary connections between the input layer, hidden layer and output layer. With the network ready, we will use the `BackPropagationLearner` to train the weights of our network for the examples provided in the dataset.\n",
    "\n",
    "The NeuralNetLearner returns the `predict` function, which can receive an example and feed-forward it into our network to generate a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource NeuralNetLearner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backpropagation\n",
    "\n",
    "In both the Perceptron and the Neural Network, we are using the Backpropagation algorithm to train our weights. Basically it achieves that by propagating the errors from our last layer into our first layer, this is why it is called Backpropagation. In order to use Backpropagation, we need a cost function. This function is responsible for indicating how good our neural network is for a given example. One common cost function is the *Mean Squared Error* (MSE). This cost function has the following format:\n",
    "\n",
    "$$MSE=\\frac{1}{2} \\sum_{i=1}^{n}(y - \\hat{y})^{2}$$\n",
    "\n",
    "Where `n` is the number of training examples, $\\hat{y}$ is our prediction and $y$ is the correct prediction for the example.\n",
    "\n",
    "The algorithm combines the concept of partial derivatives and the chain rule to generate the gradient for each weight in the network based on the cost function.\n",
    "\n",
    "For example, if we are using a Neural Network with three layers, the sigmoid function as our activation function and the MSE cost function, we want to find the gradient for the a given weight $w_{j}$, we can compute it like this:\n",
    "\n",
    "$$\\frac{\\partial MSE(\\hat{y}, y)}{\\partial w_{j}} = \\frac{\\partial MSE(\\hat{y}, y)}{\\partial \\hat{y}}\\times\\frac{\\partial\\hat{y}(in_{j})}{\\partial in_{j}}\\times\\frac{\\partial in_{j}}{\\partial w_{j}}$$\n",
    "\n",
    "Solving this equation, we have:\n",
    "\n",
    "$$\\frac{\\partial MSE(\\hat{y}, y)}{\\partial w_{j}} = (\\hat{y} - y)\\times{\\hat{y}}'(in_{j})\\times a_{j}$$\n",
    "\n",
    "Remember that $\\hat{y}$ is the activation function applied to a neuron in our hidden layer, therefore $$\\hat{y} = sigmoid(\\sum_{i=1}^{num\\_neurons}w_{i}\\times a_{i})$$\n",
    "\n",
    "Also $a$ is the input generated by feeding the input layer variables into the hidden layer.\n",
    "\n",
    "We can use the same technique for the weights in the input layer as well. After we have the gradients for both weights, we use gradient descent to update the weights of the network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "First, we feed-forward the examples in our neural network. After that, we calculate the gradient for each layer weights. Once that is complete, we update all the weights using gradient descent. After running these for a given number of epochs, the function returns the trained Neural Network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource BackPropagationLearner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "iris.classes_to_numbers()\n",
    "\n",
    "nNL = NeuralNetLearner(iris)\n",
    "print(nNL([5, 3, 1, 0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output should be 0, which means the item should get classified in the first class, \"setosa\". Note that since the algorithm is non-deterministic (because of the random initial weights) the classification might be wrong. Usually though it should be correct.\n",
    "\n",
    "To increase accuracy, you can (most of the time) add more layers and nodes. Unfortunately the more layers and nodes you have, the greater the computation cost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LEARNER EVALUATION\n",
    "\n",
    "In this section we will evaluate and compare algorithm performance. The dataset we will use will again be the iris one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = DataSet(name=\"iris\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes\n",
    "\n",
    "First up we have the Naive Bayes algorithm. First we will test how well the Discrete Naive Bayes works, and then how the Continuous fares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error ratio for Discrete: 0.033333333333333326\n",
      "Error ratio for Continuous: 0.040000000000000036\n"
     ]
    }
   ],
   "source": [
    "nBD = NaiveBayesLearner(iris, continuous=False)\n",
    "print(\"Error ratio for Discrete:\", err_ratio(nBD, iris))\n",
    "\n",
    "nBC = NaiveBayesLearner(iris, continuous=True)\n",
    "print(\"Error ratio for Continuous:\", err_ratio(nBC, iris))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error for the Naive Bayes algorithm is very, very low; close to 0. There is also very little difference between the discrete and continuous version of the algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## k-Nearest Neighbors\n",
    "\n",
    "Now we will take a look at kNN, for different values of *k*. Note that *k* should have odd values, to break any ties between two classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error ratio for k=1: 0.0\n",
      "Error ratio for k=3: 0.08666666666666667\n",
      "Error ratio for k=5: 0.1466666666666666\n",
      "Error ratio for k=7: 0.21999999999999997\n"
     ]
    }
   ],
   "source": [
    "kNN_1 = NearestNeighborLearner(iris, k=1)\n",
    "kNN_3 = NearestNeighborLearner(iris, k=3)\n",
    "kNN_5 = NearestNeighborLearner(iris, k=5)\n",
    "kNN_7 = NearestNeighborLearner(iris, k=7)\n",
    "\n",
    "print(\"Error ratio for k=1:\", err_ratio(kNN_1, iris))\n",
    "print(\"Error ratio for k=3:\", err_ratio(kNN_3, iris))\n",
    "print(\"Error ratio for k=5:\", err_ratio(kNN_5, iris))\n",
    "print(\"Error ratio for k=7:\", err_ratio(kNN_7, iris))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the error became larger and larger as *k* increased. This is generally the case with datasets where classes are spaced out, as is the case with the iris dataset. If items from different classes were closer together, classification would be more difficult. Usually a value of 1, 3 or 5 for *k* suffices.\n",
    "\n",
    "Also note that since the training set is also the testing set, for *k* equal to 1 we get a perfect score, since the item we want to classify each time is already in the dataset and its closest neighbor is itself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perceptron\n",
    "\n",
    "For the Perceptron, we first need to convert class names to integers. Let's see how it performs in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error ratio for Perceptron: 0.31999999999999995\n"
     ]
    }
   ],
   "source": [
    "iris2 = DataSet(name=\"iris\")\n",
    "iris2.classes_to_numbers()\n",
    "\n",
    "perceptron = PerceptronLearner(iris2)\n",
    "print(\"Error ratio for Perceptron:\", err_ratio(perceptron, iris2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Perceptron didn't fare very well mainly because the dataset is not linearly separated. On simpler datasets the algorithm performs much better, but unfortunately such datasets are rare in real life scenarios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MNIST HANDWRITTEN DIGITS CLASSIFICATION\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 and 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 different learning algorithms.\n",
    "\n",
    "It is estimated that humans have an error rate of about **0.2%** on this problem. Let's see how our algorithms perform!\n",
    "\n",
    "NOTE: We will be using external libraries to load and visualize the dataset smoothly ([numpy](http://www.numpy.org/) for loading and [matplotlib](http://matplotlib.org/) for visualization). You do not need previous experience of the libraries to follow along."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading MNIST digits data\n",
    "\n",
    "Let's start by loading MNIST data into numpy arrays."
   ]
  },
  {
   "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 going to use to train and classify hand-written digits in various learning approaches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "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 a 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": 4,
   "metadata": {},
   "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": [
    "### Visualizing MNIST digits data\n",
    "\n",
    "To get a better understanding of the dataset, let's visualize some random images for each class from training and testing datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzkAAAKqCAYAAAAZl5BAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f614fccc198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# takes 5-10 seconds to execute this\n",
    "show_MNIST(train_lbl, train_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzkAAAKqCAYAAAAZl5BAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6147f2dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# takes 5-10 seconds to execute this\n",
    "show_MNIST(test_lbl, test_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the average of all the images of training and testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of all images in training dataset.\n",
      "Digit 0 : 5923 images.\n",
      "Digit 1 : 6742 images.\n",
      "Digit 2 : 5958 images.\n",
      "Digit 3 : 6131 images.\n",
      "Digit 4 : 5842 images.\n",
      "Digit 5 : 5421 images.\n",
      "Digit 6 : 5918 images.\n",
      "Digit 7 : 6265 images.\n",
      "Digit 8 : 5851 images.\n",
      "Digit 9 : 5949 images.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzkAAACDCAYAAACuq9WXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f614c36abe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of all images in testing dataset.\n",
      "Digit 0 : 980 images.\n",
      "Digit 1 : 1135 images.\n",
      "Digit 2 : 1032 images.\n",
      "Digit 3 : 1010 images.\n",
      "Digit 4 : 982 images.\n",
      "Digit 5 : 892 images.\n",
      "Digit 6 : 958 images.\n",
      "Digit 7 : 1028 images.\n",
      "Digit 8 : 974 images.\n",
      "Digit 9 : 1009 images.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzkAAACDCAYAAACuq9WXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxxhjjDHGGFMq/h+V5nVldnlnJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f614d1b6a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Average of all images in training dataset.\")\n",
    "show_ave_MNIST(train_lbl, train_img)\n",
    "\n",
    "print(\"Average of all images in testing dataset.\")\n",
    "show_ave_MNIST(test_lbl, test_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing\n",
    "\n",
    "Now, let us convert this raw data into `DataSet.examples` to run our algorithms defined in `learning.py`. Every image is represented by 784 numbers (28x28 pixels) and we append them with its label or class to make them work with our implementations in learning module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784) (60000,)\n",
      "(60000, 785)\n"
     ]
    }
   ],
   "source": [
    "print(train_img.shape, train_lbl.shape)\n",
    "temp_train_lbl = train_lbl.reshape((60000,1))\n",
    "training_examples = np.hstack((train_img, temp_train_lbl))\n",
    "print(training_examples.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will initialize a DataSet with our training examples, so we can use it in our algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# takes ~10 seconds to execute this\n",
    "MNIST_DataSet = DataSet(examples=training_examples, distance=manhattan_distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moving forward we can use `MNIST_DataSet` to test our algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plurality Learner\n",
    "\n",
    "The Plurality Learner always returns the class with the most training samples. In this case, `1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "pL = PluralityLearner(MNIST_DataSet)\n",
    "print(pL(177))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual class of test image: 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f614c5b29b0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwg8E9X/46I56sOIdFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f614c422358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "print(\"Actual class of test image:\", test_lbl[177])\n",
    "plt.imshow(test_img[177].reshape((28,28)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is obvious that this Learner is not very efficient. In fact, it will guess correctly in only 1135/10000 of the samples, roughly 10%. It is very fast though, so it might have its use as a quick first guess."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive-Bayes\n",
    "\n",
    "The Naive-Bayes classifier is an improvement over the Plurality Learner. It is much more accurate, but a lot slower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "# takes ~45 Secs. to execute this\n",
    "\n",
    "nBD = NaiveBayesLearner(MNIST_DataSet, continuous=False)\n",
    "print(nBD(test_img[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual class of test image: 7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f614c37aba8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiL8QFKEH0iK8ANJEX4gKcIPJPVP82g/p9/JjhUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f614c422a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "print(\"Actual class of test image:\", test_lbl[0])\n",
    "plt.imshow(test_img[0].reshape((28,28)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k-Nearest Neighbors\n",
    "\n",
    "We will now try to classify a random image from the dataset using the kNN classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "# takes ~20 Secs. to execute this\n",
    "kNN = NearestNeighborLearner(MNIST_DataSet, k=3)\n",
    "print(kNN(test_img[211]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make sure that the output we got is correct, let's plot that image along with its label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual class of test image: 5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f614c93da58>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f614fce2dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "print(\"Actual class of test image:\", test_lbl[211])\n",
    "plt.imshow(test_img[211].reshape((28,28)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hurray! We've got it correct. Don't worry if our algorithm predicted a wrong class. With this techinique we have only ~97% accuracy on this dataset."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}