kolmogorov_smirnov_test.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:3228696d7209af74c8987a359c5aca247f9b5dd596b9f33915a81987f32077d1"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Kolmogorov Smirnov Test\n",
      "\n",
      "- **Author:** [Chris Albon](http://www.chrisalbon.com/), [@ChrisAlbon](https://twitter.com/chrisalbon)\n",
      "- **Date:** -\n",
      "- **Repo:** [Python 3 code snippets for data science](https://github.com/chrisalbon/code_py)\n",
      "- **Note:**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Import required packages\n",
      "from scipy import stats\n",
      "import scipy as sp\n",
      "import numpy as np\n",
      "import math\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create x, which is uniformly distributed\n",
      "x = np.random.uniform(size=1000)\n",
      "\n",
      "# Plot x to double check its distribution\n",
      "plt.hist(x)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/T979rsnMlBdqAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107b94a90>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create y, which is NOT uniformly distributed\n",
      "y = x**4\n",
      "\n",
      "# Plot y to double check its distribution\n",
      "plt.hist(y)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNc6gl6TGGfSS1DiDXpIaZ9BLUuP+DxhcET9mjLGeAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107dd0450>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Run kstest on x. We want the second returned value to be \n",
      "# not statistically significant, meaning that both come from \n",
      "# the same distribution.\n",
      "stats.kstest(x, 'uniform', args=(min(x),max(x)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "(0.043685470092267642, 0.042698030459771275)"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Run kstest on y. We want the second returned value to be \n",
      "# statistically significant, meaning that likely do not both \n",
      "# come from the same distribution.\n",
      "stats.kstest(y, 'uniform', args=(min(y),max(y)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "(0.48969825580402526, 0.0)"
       ]
      }
     ],
     "prompt_number": 19
    }
   ],
   "metadata": {}
  }
 ]
}