gistfile1.txt

{
 "metadata": {
  "name": "Regression bugs"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "mat = array([[ 1.,0.99975145,0.92051498,0.9950906, 0.99815419],\n [ 0.92051498,0.91197251,1.,0.9537393, 0.89636339],\n [ 0.9950906, 0.99264496,0.9537393, 1.,0.9872818 ],\n [ 0.99286679,0.98997523,0.95955895,0.99979104,0.983852],\n [ 0.9840137, 0.98771506,0.84197501,0.96189652,0.99297485],\n [ 0.99815419,0.99925955,0.89636339,0.9872818, 1.],\n [ 0.85914332,0.84844105,0.98967977,0.90296462,0.82930882],\n [ 0.99679373,0.99832835,0.8881177, 0.98404465,0.99981269],\n [ 0.99975145,1.,0.91197251,0.99264496,0.99925955],\n [ 0.99482616,0.99232267,0.95451232,0.99999651,0.98686083]])\ntargets = [[ 0.04081166], [-0.95944469], [ 0.33605717], [ 0.85007786], [ 0.75169075], [-0.83124147], [-0.70947397], [ 0.32914427], [-0.99924773], [-0.9320851 ]]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from scipy.linalg import pinv, lstsq\nfrom sklearn import linear_model\nwPinv = dot(pinv(mat), targets)\nwNumpy = lstsq(mat, targets)[0]\nclf = linear_model.LinearRegression()\nwSklearn = clf.fit(mat, targets).coef_\nprint wPinv\nprint wNumpy\nprint wSklearn.T",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "[[ 58053202.46203362]\n [-71366378.49680269]\n [   124225.57851716]\n [ -5251958.64517807]\n [ 18441299.69284547]]\n[[ 58053202.4620336 ]\n [-71366378.49680264]\n [   124225.57851716]\n [ -5251958.64517807]\n [ 18441299.69284546]]\n[[ -9.16268497e+07]\n [  1.12356411e+08]\n [ -2.27788482e+05]\n [  8.44236867e+06]\n [ -2.89493001e+07]]\n"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}