michitakaiida/logit Secret

## logit
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import LogisticRegression"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = np.loadtxt(\"output.txt\", delimiter=',')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = data[7:,]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = data[:,6]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lists = []\n",
      "for xx in x:\n",
      "    tmp = []\n",
      "    tmp.append(xx)\n",
      "    lists.append(tmp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = data[:,4]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = LogisticRegression(C=1e5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.fit(lists ,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "LogisticRegression(C=100000.0, class_weight=None, dual=False,\n",
        "          fit_intercept=True, intercept_scaling=1, penalty='l2',\n",
        "          random_state=None, tol=0.0001)"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print (np.exp(clf.intercept_),np.exp(clf.coef_.ravel()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(array([  1.45966822e+38]), array([ 0.91710845]))\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def lr_model(clf,x):\n",
      "    return 1/ ( 1+np.exp(-(clf.intercept_ + clf.coef_ * x)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.clf()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x107a58790>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(lists, y, color='black', zorder=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.collections.PathCollection at 0x105823350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SiIh6gaYwKCkpQWJiIuLi4qDX6zF37lwUFxe3affSSy/hrrvuwogRI7QMR0REvURTGLjd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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107a58c10>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.clf()\n",
      "plt.scatter(lists, y, color='black', zorder=20)\n",
      "X_test = np.linspace(1000, 1030, 100)\n",
      "\n",
      "\n",
      "def model(x):\n",
      "    return 1 / (1 + np.exp(-x))\n",
      "loss = model(X_test * clf.coef_ + clf.intercept_).ravel()\n",
      "plt.plot(X_test, loss, color='blue', linewidth=3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "[<matplotlib.lines.Line2D at 0x107ac0fd0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UP/kEOHbMfBu1Gpg1C5g0ibORiKyNYUB254cfZCisWQNcvWq63MtLXhtp9mwgPp5nOxNZ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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10767c990>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lr_model(clf,1020)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "array([[ 0.40523615]])"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict_proba(1020)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array([[ 0.59476385,  0.40523615]])"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}