{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "What is machine learning?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this section we will begin to explore the basic principles of machine learning.\n",
      "Machine Learning is about building programs with **tunable parameters** (typically an\n",
      "array of floating point values) that are adjusted automatically so as to improve\n",
      "their behavior by **adapting to previously seen data.**\n",
      "\n",
      "Machine Learning can be considered a subfield of **Artificial Intelligence** since those\n",
      "algorithms can be seen as building blocks to make computers learn to behave more\n",
      "intelligently by somehow **generalizing** rather that just storing and retrieving data items\n",
      "like a database system would do.\n",
      "\n",
      "We'll take a look at two very simple machine learning tasks here.\n",
      "The first is a **classification** task: the figure shows a\n",
      "collection of two-dimensional data, colored according to two different class\n",
      "labels. A classification algorithm may be used to draw a dividing boundary\n",
      "between the two clusters of points:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Start matplotlib inline mode, so figures will appear in the notebook\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 0
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Import the example plot from the figures directory\n",
      "from figures import plot_sgd_separator\n",
      "plot_sgd_separator()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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LEMq7UKVKiA6PqDZB0ofUYlIbRCRO0vC7jHdG9wqOOmpAhe+dVQ3Rj9Y2Lggn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\nlFIOoqGtlFIOoqGtlFIO8v9dSx/Qx+MLEQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106fb7e10>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This may seem like a trivial task, but it is a simple version of a very important concept.\n",
      "By drawing this separating line, we have learned a model which can **generalize** to new\n",
      "data: if you were to drop another point onto the plane which is unlabeled, this algorithm\n",
      "could now **predict** whether it's a blue or a red point.\n",
      "\n",
      "If you'd like to see the source code used to generate this, you can either open the\n",
      "code in the `figures` directory, or you can load the code using the `%load` magic command:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load figures/sgd_separator.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The next simple task we'll look at is a **regression** task: a simple best-fit line\n",
      "to a set of data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from figures import plot_linear_regression\n",
      "plot_linear_regression()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x107cfcb00>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Again, this is an example of fitting a model to data, such that the model can make\n",
      "generalizations about new data.  The model has been **learned** from the training\n",
      "data, and can be used to predict the result of test data:\n",
      "here, we might be given an x-value, and the model would\n",
      "allow us to predict the y value.  Again, this might seem like a trivial problem,\n",
      "but it is a basic example of a type of operation that is fundamental to\n",
      "machine learning tasks."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "An Overview of Scikit-learn"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Adapted from* [*http://scikit-learn.org/stable/tutorial/basic/tutorial.html*](http://scikit-learn.org/stable/tutorial/basic/tutorial.html)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "from matplotlib import pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Loading an Example Dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import datasets\n",
      "digits = datasets.load_digits()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "digits.data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([[  0.,   0.,   5., ...,   0.,   0.,   0.],\n",
        "       [  0.,   0.,   0., ...,  10.,   0.,   0.],\n",
        "       [  0.,   0.,   0., ...,  16.,   9.,   0.],\n",
        "       ..., \n",
        "       [  0.,   0.,   1., ...,   6.,   0.,   0.],\n",
        "       [  0.,   0.,   2., ...,  12.,   0.,   0.],\n",
        "       [  0.,   0.,  10., ...,  12.,   1.,   0.]])"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "digits.target"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "array([0, 1, 2, ..., 8, 9, 8])"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "digits.images[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "array([[  0.,   0.,   5.,  13.,   9.,   1.,   0.,   0.],\n",
        "       [  0.,   0.,  13.,  15.,  10.,  15.,   5.,   0.],\n",
        "       [  0.,   3.,  15.,   2.,   0.,  11.,   8.,   0.],\n",
        "       [  0.,   4.,  12.,   0.,   0.,   8.,   8.,   0.],\n",
        "       [  0.,   5.,   8.,   0.,   0.,   9.,   8.,   0.],\n",
        "       [  0.,   4.,  11.,   0.,   1.,  12.,   7.,   0.],\n",
        "       [  0.,   2.,  14.,   5.,  10.,  12.,   0.,   0.],\n",
        "       [  0.,   0.,   6.,  13.,  10.,   0.,   0.,   0.]])"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Learning and Predicting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import svm\n",
      "clf = svm.SVC(gamma=0.001, C=100.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.fit(digits.data[:-1], digits.target[:-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,\n",
        "  gamma=0.001, kernel='rbf', max_iter=-1, probability=False,\n",
        "  random_state=None, shrinking=True, tol=0.001, verbose=False)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict(digits.data[-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "array([8])"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(2, 2))\n",
      "plt.imshow(digits.images[-1], interpolation='nearest', cmap=plt.cm.binary)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.image.AxesImage at 0x10810fe10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAIcAAACMCAYAAABWILrJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEBeHE8TF4QRxcThBXBxOkP8BGQ6+Sh33LfcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1081ce160>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(digits.target[-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "8\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}