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 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "rcParams['figure.figsize'] = (10, 4) #wide graphs by default\n",
      "from __future__ import print_function\n",
      "from __future__ import division"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Machine Learning"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_iris\n",
      "\n",
      "# most examples here are based on examples from the sklearn docs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = load_iris()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "{'DESCR': 'Iris Plants Database\\n\\nNotes\\n-----\\nData Set Characteristics:\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n    :Summary Statistics:\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML iris datasets.\\nhttp://archive.ics.uci.edu/ml/datasets/Iris\\n\\nThe famous Iris database, first used by Sir R.A Fisher\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\nReferences\\n----------\\n   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...\\n',\n",
        " 'data': array([[ 5.1,  3.5,  1.4,  0.2],\n",
        "        [ 4.9,  3. ,  1.4,  0.2],\n",
        "        [ 4.7,  3.2,  1.3,  0.2],\n",
        "        [ 4.6,  3.1,  1.5,  0.2],\n",
        "        [ 5. ,  3.6,  1.4,  0.2],\n",
        "        [ 5.4,  3.9,  1.7,  0.4],\n",
        "        [ 4.6,  3.4,  1.4,  0.3],\n",
        "        [ 5. ,  3.4,  1.5,  0.2],\n",
        "        [ 4.4,  2.9,  1.4,  0.2],\n",
        "        [ 4.9,  3.1,  1.5,  0.1],\n",
        "        [ 5.4,  3.7,  1.5,  0.2],\n",
        "        [ 4.8,  3.4,  1.6,  0.2],\n",
        "        [ 4.8,  3. ,  1.4,  0.1],\n",
        "        [ 4.3,  3. ,  1.1,  0.1],\n",
        "        [ 5.8,  4. ,  1.2,  0.2],\n",
        "        [ 5.7,  4.4,  1.5,  0.4],\n",
        "        [ 5.4,  3.9,  1.3,  0.4],\n",
        "        [ 5.1,  3.5,  1.4,  0.3],\n",
        "        [ 5.7,  3.8,  1.7,  0.3],\n",
        "        [ 5.1,  3.8,  1.5,  0.3],\n",
        "        [ 5.4,  3.4,  1.7,  0.2],\n",
        "        [ 5.1,  3.7,  1.5,  0.4],\n",
        "        [ 4.6,  3.6,  1. ,  0.2],\n",
        "        [ 5.1,  3.3,  1.7,  0.5],\n",
        "        [ 4.8,  3.4,  1.9,  0.2],\n",
        "        [ 5. ,  3. ,  1.6,  0.2],\n",
        "        [ 5. ,  3.4,  1.6,  0.4],\n",
        "        [ 5.2,  3.5,  1.5,  0.2],\n",
        "        [ 5.2,  3.4,  1.4,  0.2],\n",
        "        [ 4.7,  3.2,  1.6,  0.2],\n",
        "        [ 4.8,  3.1,  1.6,  0.2],\n",
        "        [ 5.4,  3.4,  1.5,  0.4],\n",
        "        [ 5.2,  4.1,  1.5,  0.1],\n",
        "        [ 5.5,  4.2,  1.4,  0.2],\n",
        "        [ 4.9,  3.1,  1.5,  0.1],\n",
        "        [ 5. ,  3.2,  1.2,  0.2],\n",
        "        [ 5.5,  3.5,  1.3,  0.2],\n",
        "        [ 4.9,  3.1,  1.5,  0.1],\n",
        "        [ 4.4,  3. ,  1.3,  0.2],\n",
        "        [ 5.1,  3.4,  1.5,  0.2],\n",
        "        [ 5. ,  3.5,  1.3,  0.3],\n",
        "        [ 4.5,  2.3,  1.3,  0.3],\n",
        "        [ 4.4,  3.2,  1.3,  0.2],\n",
        "        [ 5. ,  3.5,  1.6,  0.6],\n",
        "        [ 5.1,  3.8,  1.9,  0.4],\n",
        "        [ 4.8,  3. ,  1.4,  0.3],\n",
        "        [ 5.1,  3.8,  1.6,  0.2],\n",
        "        [ 4.6,  3.2,  1.4,  0.2],\n",
        "        [ 5.3,  3.7,  1.5,  0.2],\n",
        "        [ 5. ,  3.3,  1.4,  0.2],\n",
        "        [ 7. ,  3.2,  4.7,  1.4],\n",
        "        [ 6.4,  3.2,  4.5,  1.5],\n",
        "        [ 6.9,  3.1,  4.9,  1.5],\n",
        "        [ 5.5,  2.3,  4. ,  1.3],\n",
        "        [ 6.5,  2.8,  4.6,  1.5],\n",
        "        [ 5.7,  2.8,  4.5,  1.3],\n",
        "        [ 6.3,  3.3,  4.7,  1.6],\n",
        "        [ 4.9,  2.4,  3.3,  1. ],\n",
        "        [ 6.6,  2.9,  4.6,  1.3],\n",
        "        [ 5.2,  2.7,  3.9,  1.4],\n",
        "        [ 5. ,  2. ,  3.5,  1. ],\n",
        "        [ 5.9,  3. ,  4.2,  1.5],\n",
        "        [ 6. ,  2.2,  4. ,  1. ],\n",
        "        [ 6.1,  2.9,  4.7,  1.4],\n",
        "        [ 5.6,  2.9,  3.6,  1.3],\n",
        "        [ 6.7,  3.1,  4.4,  1.4],\n",
        "        [ 5.6,  3. ,  4.5,  1.5],\n",
        "        [ 5.8,  2.7,  4.1,  1. ],\n",
        "        [ 6.2,  2.2,  4.5,  1.5],\n",
        "        [ 5.6,  2.5,  3.9,  1.1],\n",
        "        [ 5.9,  3.2,  4.8,  1.8],\n",
        "        [ 6.1,  2.8,  4. ,  1.3],\n",
        "        [ 6.3,  2.5,  4.9,  1.5],\n",
        "        [ 6.1,  2.8,  4.7,  1.2],\n",
        "        [ 6.4,  2.9,  4.3,  1.3],\n",
        "        [ 6.6,  3. ,  4.4,  1.4],\n",
        "        [ 6.8,  2.8,  4.8,  1.4],\n",
        "        [ 6.7,  3. ,  5. ,  1.7],\n",
        "        [ 6. ,  2.9,  4.5,  1.5],\n",
        "        [ 5.7,  2.6,  3.5,  1. ],\n",
        "        [ 5.5,  2.4,  3.8,  1.1],\n",
        "        [ 5.5,  2.4,  3.7,  1. ],\n",
        "        [ 5.8,  2.7,  3.9,  1.2],\n",
        "        [ 6. ,  2.7,  5.1,  1.6],\n",
        "        [ 5.4,  3. ,  4.5,  1.5],\n",
        "        [ 6. ,  3.4,  4.5,  1.6],\n",
        "        [ 6.7,  3.1,  4.7,  1.5],\n",
        "        [ 6.3,  2.3,  4.4,  1.3],\n",
        "        [ 5.6,  3. ,  4.1,  1.3],\n",
        "        [ 5.5,  2.5,  4. ,  1.3],\n",
        "        [ 5.5,  2.6,  4.4,  1.2],\n",
        "        [ 6.1,  3. ,  4.6,  1.4],\n",
        "        [ 5.8,  2.6,  4. ,  1.2],\n",
        "        [ 5. ,  2.3,  3.3,  1. ],\n",
        "        [ 5.6,  2.7,  4.2,  1.3],\n",
        "        [ 5.7,  3. ,  4.2,  1.2],\n",
        "        [ 5.7,  2.9,  4.2,  1.3],\n",
        "        [ 6.2,  2.9,  4.3,  1.3],\n",
        "        [ 5.1,  2.5,  3. ,  1.1],\n",
        "        [ 5.7,  2.8,  4.1,  1.3],\n",
        "        [ 6.3,  3.3,  6. ,  2.5],\n",
        "        [ 5.8,  2.7,  5.1,  1.9],\n",
        "        [ 7.1,  3. ,  5.9,  2.1],\n",
        "        [ 6.3,  2.9,  5.6,  1.8],\n",
        "        [ 6.5,  3. ,  5.8,  2.2],\n",
        "        [ 7.6,  3. ,  6.6,  2.1],\n",
        "        [ 4.9,  2.5,  4.5,  1.7],\n",
        "        [ 7.3,  2.9,  6.3,  1.8],\n",
        "        [ 6.7,  2.5,  5.8,  1.8],\n",
        "        [ 7.2,  3.6,  6.1,  2.5],\n",
        "        [ 6.5,  3.2,  5.1,  2. ],\n",
        "        [ 6.4,  2.7,  5.3,  1.9],\n",
        "        [ 6.8,  3. ,  5.5,  2.1],\n",
        "        [ 5.7,  2.5,  5. ,  2. ],\n",
        "        [ 5.8,  2.8,  5.1,  2.4],\n",
        "        [ 6.4,  3.2,  5.3,  2.3],\n",
        "        [ 6.5,  3. ,  5.5,  1.8],\n",
        "        [ 7.7,  3.8,  6.7,  2.2],\n",
        "        [ 7.7,  2.6,  6.9,  2.3],\n",
        "        [ 6. ,  2.2,  5. ,  1.5],\n",
        "        [ 6.9,  3.2,  5.7,  2.3],\n",
        "        [ 5.6,  2.8,  4.9,  2. ],\n",
        "        [ 7.7,  2.8,  6.7,  2. ],\n",
        "        [ 6.3,  2.7,  4.9,  1.8],\n",
        "        [ 6.7,  3.3,  5.7,  2.1],\n",
        "        [ 7.2,  3.2,  6. ,  1.8],\n",
        "        [ 6.2,  2.8,  4.8,  1.8],\n",
        "        [ 6.1,  3. ,  4.9,  1.8],\n",
        "        [ 6.4,  2.8,  5.6,  2.1],\n",
        "        [ 7.2,  3. ,  5.8,  1.6],\n",
        "        [ 7.4,  2.8,  6.1,  1.9],\n",
        "        [ 7.9,  3.8,  6.4,  2. ],\n",
        "        [ 6.4,  2.8,  5.6,  2.2],\n",
        "        [ 6.3,  2.8,  5.1,  1.5],\n",
        "        [ 6.1,  2.6,  5.6,  1.4],\n",
        "        [ 7.7,  3. ,  6.1,  2.3],\n",
        "        [ 6.3,  3.4,  5.6,  2.4],\n",
        "        [ 6.4,  3.1,  5.5,  1.8],\n",
        "        [ 6. ,  3. ,  4.8,  1.8],\n",
        "        [ 6.9,  3.1,  5.4,  2.1],\n",
        "        [ 6.7,  3.1,  5.6,  2.4],\n",
        "        [ 6.9,  3.1,  5.1,  2.3],\n",
        "        [ 5.8,  2.7,  5.1,  1.9],\n",
        "        [ 6.8,  3.2,  5.9,  2.3],\n",
        "        [ 6.7,  3.3,  5.7,  2.5],\n",
        "        [ 6.7,  3. ,  5.2,  2.3],\n",
        "        [ 6.3,  2.5,  5. ,  1.9],\n",
        "        [ 6.5,  3. ,  5.2,  2. ],\n",
        "        [ 6.2,  3.4,  5.4,  2.3],\n",
        "        [ 5.9,  3. ,  5.1,  1.8]]),\n",
        " 'feature_names': ['sepal length (cm)',\n",
        "  'sepal width (cm)',\n",
        "  'petal length (cm)',\n",
        "  'petal width (cm)'],\n",
        " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "        0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "        1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
        "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
        "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
        " 'target_names': array(['setosa', 'versicolor', 'virginica'], \n",
        "       dtype='|S10')}"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "sklearn.datasets.base.Bunch"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(data['DESCR'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Iris Plants Database\n",
        "\n",
        "Notes\n",
        "-----\n",
        "Data Set Characteristics:\n",
        "    :Number of Instances: 150 (50 in each of three classes)\n",
        "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
        "    :Attribute Information:\n",
        "        - sepal length in cm\n",
        "        - sepal width in cm\n",
        "        - petal length in cm\n",
        "        - petal width in cm\n",
        "        - class:\n",
        "                - Iris-Setosa\n",
        "                - Iris-Versicolour\n",
        "                - Iris-Virginica\n",
        "    :Summary Statistics:\n",
        "    ============== ==== ==== ======= ===== ====================\n",
        "                    Min  Max   Mean    SD   Class Correlation\n",
        "    ============== ==== ==== ======= ===== ====================\n",
        "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
        "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
        "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
        "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
        "    ============== ==== ==== ======= ===== ====================\n",
        "    :Missing Attribute Values: None\n",
        "    :Class Distribution: 33.3% for each of 3 classes.\n",
        "    :Creator: R.A. Fisher\n",
        "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
        "    :Date: July, 1988\n",
        "\n",
        "This is a copy of UCI ML iris datasets.\n",
        "http://archive.ics.uci.edu/ml/datasets/Iris\n",
        "\n",
        "The famous Iris database, first used by Sir R.A Fisher\n",
        "\n",
        "This is perhaps the best known database to be found in the\n",
        "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
        "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
        "data set contains 3 classes of 50 instances each, where each class refers to a\n",
        "type of iris plant.  One class is linearly separable from the other 2; the\n",
        "latter are NOT linearly separable from each other.\n",
        "\n",
        "References\n",
        "----------\n",
        "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
        "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
        "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
        "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
        "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
        "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
        "     Structure and Classification Rule for Recognition in Partially Exposed\n",
        "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
        "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
        "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
        "     on Information Theory, May 1972, 431-433.\n",
        "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
        "     conceptual clustering system finds 3 classes in the data.\n",
        "   - Many, many more ...\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['data'].shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "(150, 4)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For sklearn \"Bunches\", you can use key names as members of the object too:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.data.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "(150, 4)"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.feature_names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "['sepal length (cm)',\n",
        " 'sepal width (cm)',\n",
        " 'petal length (cm)',\n",
        " 'petal width (cm)']"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.target"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
        "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
        "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.target.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "(150,)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.target_names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "array(['setosa', 'versicolor', 'virginica'], \n",
        "      dtype='|S10')"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To find a set:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "virginicas = argwhere(data.target == list(data.target_names).index('virginica'))[:,0]\n",
      "print(virginicas)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117\n",
        " 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135\n",
        " 136 137 138 139 140 141 142 143 144 145 146 147 148 149]\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "feature = 1\n",
      "data.feature_names[feature]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "'sepal width (cm)'"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.data[virginicas].shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "(50, 4)"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.data[virginicas][:,1].mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "2.9739999999999998"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.data[virginicas][:,1].var()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "0.10192400000000007"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(data.data[virginicas][:,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f91d00d3690>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pPq0N/f4Y4DeB758FjgWer7dRFwXV/v3mTj3++HTbCRsehv/8z+ihuzFj4B3v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\nkG7FDabnFUi36t3eON3F7m5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1qERE/KCCSsQzcQoqdahERPyggkrEM3FWTN++XQWViIgP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\niIiIiIiIiIiIiIiIiIhk7f8DJpT5Rn9fP/IAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f91d013e0d0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "setosas = argwhere(data.target == list(data.target_names).index('setosa'))[:,0]\n",
      "\n",
      "plot(data.data[virginicas][:,1])\n",
      "plot(data.data[setosas][:,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f91d0114610>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wNnJsV1QCu6efq/GyeGFJwhJkFvZc6bK9aLvq/pR8DPAfgFsSb8Fbh97SfGy9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\nkiRJkiRJ0tn2f9tgRWkgjYr9AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f91d0114790>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "versicolors = argwhere(data.target == list(data.target_names).index('versicolor'))[:,0]\n",
      "\n",
      "plot(data.data[virginicas][:,1])\n",
      "plot(data.data[setosas][:,1])\n",
      "plot(data.data[versicolors][:,1])\n",
      "\n",
      "title(\"Feature: \" + data.feature_names[1])\n",
      "legend(['virginica', 'setosa', 'versicolor'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.legend.Legend at 0x7f91cfe4a7d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uKRDoRwgqCym/Wk5KVgqnfneqw2tToqbwwo4XUKvVwkflBHKrckkKSXLIufy9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\nCAQCgUAgEAgEAqf5f5dSvJ0WjhbeAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f91d0066e50>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "feature = 0\n",
      "\n",
      "plot(data.data[virginicas][:,feature])\n",
      "plot(data.data[setosas][:,feature])\n",
      "plot(data.data[versicolors][:,feature])\n",
      "\n",
      "title(\"Feature: \" + data.feature_names[feature])\n",
      "legend(['virginica', 'setosa', 'versicolor'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<matplotlib.legend.Legend at 0x7f91cfd958d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RzFQLIkcVLz8F/lt+4Mz2M4bR9UhBpdonOMPrGX6SsAqqeBUq8D9UrA+j6wm7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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f91cfdfa250>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Taking the first two features for all samples:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib.colors import ListedColormap\n",
      "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(data.data[:, 0], data.data[:, 1], c=data.target, cmap=cmap_bold)\n",
      "xlabel(data.feature_names[0])\n",
      "ylabel(data.feature_names[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<matplotlib.text.Text at 0x7fc0cd9863d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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u2KTZpSB9QK655pqgxVQVI/7yFxlptYp447rPapVbhg+PaEwqMMXFxWJz2ITV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\n1Fn+B1N9OxNt0FH3AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fc0cdaba1d0>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Baseline Classifiers"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html#sklearn.dummy.DummyClassifier"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.dummy import DummyClassifier\n",
      "\n",
      "clf = DummyClassifier(strategy='uniform')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Training set (all the samples, just the first two features\n",
      "X = data.data[:, :2]\n",
      "y = data.target"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.fit(X, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "DummyClassifier(constant=None, random_state=None, strategy='uniform')"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict((7.2, 2.5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "array([0, 1])"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict((7.2, 2.5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "array([2, 2])"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "k-Nearest Neighbor Classifiers"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import neighbors"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = data.data[:, :2]\n",
      "y = data.target"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_neighbors = 15\n",
      "clf = neighbors.KNeighborsClassifier(n_neighbors, )\n",
      "clf.fit(X, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
        "           n_neighbors=15, p=2, weights='uniform')"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict((7.2, 2.5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "array([2])"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict((5.0, 3.5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "array([0])"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(data.data[:, 0], data.data[:, 1], c=data.target, cmap=cmap_bold)\n",
      "xlabel(data.feature_names[0])\n",
      "ylabel(data.feature_names[1])\n",
      "scatter(*zip((7.2, 2.5),(5.0, 3.5)), c='purple', marker='x', lw=8)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "<matplotlib.collections.PathCollection at 0x7f91ce5a48d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VK1cGrY61a9dKss0mh7x1rAexWyx+k6ykmJhTk2Y7QXqD3HTTTUGLqSxG3n67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\nJKlR/g+eq1YrgJVqEwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f91ce6237d0>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Showing the desicion boundary"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "h = .02  # step size in the mesh\n",
      "\n",
      "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
      "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
      "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
      "                     np.arange(y_min, y_max, h))\n",
      "\n",
      "xx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "array([[ 3.3 ,  3.32,  3.34, ...,  8.84,  8.86,  8.88],\n",
        "       [ 3.3 ,  3.32,  3.34, ...,  8.84,  8.86,  8.88],\n",
        "       [ 3.3 ,  3.32,  3.34, ...,  8.84,  8.86,  8.88],\n",
        "       ..., \n",
        "       [ 3.3 ,  3.32,  3.34, ...,  8.84,  8.86,  8.88],\n",
        "       [ 3.3 ,  3.32,  3.34, ...,  8.84,  8.86,  8.88],\n",
        "       [ 3.3 ,  3.32,  3.34, ...,  8.84,  8.86,  8.88]])"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now get the prediction for each point in the mesh"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Z = clf.predict(c_[xx.ravel(), yy.ravel()])\n",
      "\n",
      "Z = Z.reshape(xx.shape)\n",
      "pcolormesh(xx, yy, Z, cmap=cmap_bold)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "<matplotlib.collections.QuadMesh at 0x7f91ce4ec9d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f91f0b94e90>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The number of neighbors affects the classification boundaries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_neighbors = [5, 10, 15, 50]\n",
      "\n",
      "X = data.data[:, :2]  # we only take the first two features. We could\n",
      "                      # avoid this ugly slicing by using a two-dim dataset\n",
      "y = data.target\n",
      "h = .02  # step size in the mesh\n",
      "sp = 1\n",
      "for n in n_neighbors:\n",
      "    # we create an instance of Neighbours Classifier and fit the data.\n",
      "    clf = neighbors.KNeighborsClassifier(n, weights='distance')\n",
      "    clf.fit(X, y)\n",
      "\n",
      "    # Plot the decision boundary. For that, we will assign a color to each\n",
      "    # point in the mesh [x_min, m_max]x[y_min, y_max].\n",
      "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
      "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
      "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
      "                         np.arange(y_min, y_max, h))\n",
      "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
      "\n",
      "    # Put the result into a color plot\n",
      "    Z = Z.reshape(xx.shape)\n",
      "    \n",
      "    subplot(1,4,sp); sp += 1 \n",
      "    pcolormesh(xx, yy, Z, cmap=cmap_bold)\n",
      "\n",
      "    xlim(xx.min(), xx.max())\n",
      "    ylim(yy.min(), yy.max())\n",
      "    title(\"k = %i\"% (n))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f91ce6437d0>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Distance can be measured in many ways!\n",
      "\n",
      "http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_neighbors = 5\n",
      "\n",
      "X = data.data[:, :2]  # we only take the first two features. We could\n",
      "                      # avoid this ugly slicing by using a two-dim dataset\n",
      "y = data.target\n",
      "h = .02  # step size in the mesh\n",
      "\n",
      "# Create color maps\n",
      "\n",
      "for weights in ['uniform', 'distance']:\n",
      "    # we create an instance of Neighbours Classifier and fit the data.\n",
      "    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)\n",
      "    clf.fit(X, y)\n",
      "\n",
      "    # Plot the decision boundary. For that, we will assign a color to each\n",
      "    # point in the mesh [x_min, m_max]x[y_min, y_max].\n",
      "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
      "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
      "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
      "                         np.arange(y_min, y_max, h))\n",
      "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
      "\n",
      "    # Put the result into a color plot\n",
      "    Z = Z.reshape(xx.shape)\n",
      "    figure()\n",
      "    pcolormesh(xx, yy, Z, cmap=cmap_bold)\n",
      "\n",
      "    # Plot also the training points\n",
      "    scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n",
      "    xlim(xx.min(), xx.max())\n",
      "    ylim(yy.min(), yy.max())\n",
      "    title(\"3-Class classification (k = %i, weights = '%s')\"\n",
      "             % (n_neighbors, weights))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TG9ZsNxc4PeD9W7C+uXl3A+sreMpeuhobiPIecPeCmwDu0hrypZUOAzfaV74x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\noA7jEklgS8pH0LkpjHrdDqS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       "text": [
        "<matplotlib.figure.Figure at 0x7f91ce161cd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HZzo4w8HzVfJ9wcEIB8s62KJKvv/nGLZm3+t5exyFYZa2qbK8Udcff3OwtoO1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\nim8gAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f91ce161ad0>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How do I ask for a prediction using 4 features?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_neighbors = 15\n",
      "\n",
      "X = data.data[:, :]  # we only take the first two features. We could\n",
      "                      # avoid this ugly slicing by using a two-dim dataset\n",
      "y = data.target\n",
      "clf = neighbors.KNeighborsClassifier(n_neighbors, weights='uniform')\n",
      "clf.fit(X, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
        "           n_neighbors=15, p=2, weights='uniform')"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict((7.2, 2.5, 3.0, 3.0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "array([1])"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict((7.2, 2.5, 5.0, 2.4))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "array([2])"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.data[120, :]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "array([ 6.9,  3.2,  5.7,  2.3])"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Generative probabilistic approaches"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Naive Bayes Classifier"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = data.data[:, :2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.naive_bayes import GaussianNB"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = GaussianNB()\n",
      "\n",
      "clf.fit(X, y)\n",
      "\n",
      "Z = clf.predict(c_[xx.ravel(), yy.ravel()])\n",
      "\n",
      "Z = Z.reshape(xx.shape)\n",
      "pcolormesh(xx, yy, Z, cmap=cmap_bold)\n",
      "scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n",
      "xlim(xx.min(), xx.max())\n",
      "ylim(yy.min(), yy.max())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "(1.0, 5.3800000000000043)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SeHXZ9quBp1pQXtb1pvX4GjD42uHtwdfCE4WZxplOKPmLsG3ldeCrCNIET7OB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\nkiRJkiRJkiRJkiRJkqSW+n+ueV+YhVi9nwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f91ce25c0d0>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.mixture import GMM"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = GMM(n_components=3)\n",
      "\n",
      "clf.fit(X)\n",
      "\n",
      "Z = clf.predict(c_[xx.ravel(), yy.ravel()])\n",
      "\n",
      "Z = Z.reshape(xx.shape)\n",
      "pcolormesh(xx, yy, Z, cmap=cmap_bold)\n",
      "scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n",
      "xlim(xx.min(), xx.max())\n",
      "ylim(yy.min(), yy.max())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "(1.0, 5.3800000000000043)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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e+/Ob3/F5aWcRmEiMlV4G7s6Nj/X5t8VXQY0blHW5zxtdVTgWKqhxpeEE/i1d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\n4rcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f91ce2f7990>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Decision Hyperplanes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://scikit-learn.org/stable/modules/classes.html#module-sklearn.svm"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.svm import SVC"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = SVC(kernel='linear')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.fit(X,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n",
        "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
        "  shrinking=True, tol=0.001, verbose=False)"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "Z = clf.predict(c_[xx.ravel(), yy.ravel()])\n",
      "\n",
      "Z = Z.reshape(xx.shape)\n",
      "pcolormesh(xx, yy, Z, cmap=cmap_bold)\n",
      "scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n",
      "xlim(xx.min(), xx.max())\n",
      "ylim(yy.min(), yy.max())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "(1.0, 5.3800000000000043)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rll8KPNaB8rKuN62H14LhHUaWh3eAR3IaxhlKjJ+JXL0trw1fRZAmeJoDXEMM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\nkiRJkiRJkiRJkiRJkiRJkjrq/wP/BkTSnXDd8wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f91ce25c5d0>"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = SVC()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.fit(data.data, data.target)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n",
        "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
        "  shrinking=True, tol=0.001, verbose=False)"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict(data.data[0:50])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0])"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict(data.data[50:100])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1])"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict(data.data[100:150])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
        "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
        "       2, 2, 2, 2])"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By: Andr\u00e9s Cabrera mantaraya36@gmail.com\n",
      "\n",
      "For Course MAT 201A at UCSB\n",
      "\n",
      "This ipython notebook is licensed under the CC-BY-NC-SA license: http://creativecommons.org/licenses/by-nc-sa/4.0/\n",
      "\n",
      "![http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png](http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}