lars ill conditioned

lars_ill.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import linear_model\n",
      "import numpy as np\n",
      "from scipy import linalg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
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     "input": [
      "X, y = [[10, 10, 0], [1e-32, 0, 0], [0, 0, 1]], [5, 0, 5]\n",
      "for model in (linear_model.LassoLars(.1), linear_model.Lasso(.1)):\n",
      "    coef_ = model.fit(X, y).coef_\n",
      "    pobj = (1./ (2. * 3.)) * linalg.norm(y - np.dot(X, coef_)) ** 2 + .1 * linalg.norm(coef_, 1)\n",
      "    #print coef_\n",
      "    print('Objective function given by %s: %s' % (model.__class__.__name__, pobj))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Objective function given by LassoLars: 8.33333333333\n",
        "Objective function given by Lasso: 0.571155501407\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# fitted by R Lars\n",
      "# > X <- array(c(10, 10, 0, 1e-32, 0, 0, 0, 0, 1), dim=c(3, 3))                                                                                                                                        \n",
      "# > y <- c(5, 0, 5)\n",
      "# > lars(X, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
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   "metadata": {}
  }
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