fabgoos/sm_mutual_information.ipynb

## sm_mutual_information.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:eedafd2216eed6d1de0545bd9fb897c1e1221355214603fb6cb3a384436916c3"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import scipy.stats as st\n",
      "import scipy.linalg as la\n",
      "import pylab as pl\n",
      "import sklearn.datasets as ds\n",
      "%matplotlib inline\n",
      "\n",
      "def calc_contingence(centroids, X, y):\n",
      "    k = centroids.shape[0]\n",
      "    cont_mat = np.zeros((k,2))\n",
      "    n = X.shape[0]\n",
      "    for i in xrange(n):\n",
      "        idx = 0\n",
      "        min_dist = la.norm(X[i] - centroids[0,:])\n",
      "        for j in xrange(1, k):\n",
      "            dist = la.norm(X[i] - centroids[j,:])\n",
      "            if dist < min_dist:\n",
      "                idx = j\n",
      "                min_dist = dist\n",
      "        cont_mat[idx, int(y[i])] += 1\n",
      "    return cont_mat\n",
      "\n",
      "def calc_mi(cont_mat):\n",
      "    n = np.sum(cont_mat)\n",
      "    p = cont_mat / n\n",
      "    p_class = np.sum(p, axis = 0)\n",
      "    p_clust = np.sum(p, axis = 1)\n",
      "    (k, m) = p.shape\n",
      "    result = 0.0\n",
      "    for i in xrange(k):\n",
      "        for j in xrange(m):\n",
      "            if p[i, j] <> 0:\n",
      "                result += p[i,j]*np.log2(p[i,j] / (p_clust[i] * p_class[j]))\n",
      "    return result\n",
      "    \n",
      "(X,y) = ds.make_classification(n_samples=100, n_features=10,n_informative=10, n_redundant=0, n_classes=2)\n",
      "#print X, y\n",
      "pl.plot(X[y==0,0], X[y==0,1],'.',X[y==1,0], X[y==1,1],'+' )\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10e03eed0>,\n",
        " <matplotlib.lines.Line2D at 0x10e04c150>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10cae4fd0>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vals = []\n",
      "for i in xrange(100):\n",
      "    cont_mat = calc_contingence(X[0:i+1,:], X, y)\n",
      "    mi = calc_mi(cont_mat)\n",
      "    print  mi\n",
      "    vals.append(mi)\n",
      "pl.plot(range(100),vals)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0\n",
        "0.119461581841\n",
        "0.124831467316\n",
        "0.102487750533\n",
        "0.138369400911\n",
        "0.182268611029"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.224728084934\n",
        "0.263701234736"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.292429852373\n",
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       ]
      },
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.424112732432\n",
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       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.448285584829\n",
        "0.524826893777"
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        "\n",
        "0.53514062202\n",
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        "0.636327280607\n",
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       ]
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        "\n",
        "0.721921583078\n",
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       "text": [
        "<matplotlib.figure.Figure at 0x10cc6f8d0>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}