{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import scipy.optimize as sc\n",
    "import scipy.sparse as sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.random.poisson(100, (10000,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = sp.csr_matrix(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.]\n",
      "[ 0.00025]\n",
      "[ 0.0005]\n",
      "[ 0.00075]\n",
      "[ 0.00125]\n",
      "[ 0.00175]\n",
      "[ 0.00275]\n",
      "[ 0.00375]\n",
      "[ 0.00575]\n",
      "[ 0.00775]\n",
      "[ 0.01175]\n",
      "[ 0.01575]\n",
      "[ 0.02375]\n",
      "[ 0.03175]\n",
      "[ 0.04775]\n",
      "[ 0.06375]\n",
      "[ 0.09575]\n",
      "[ 0.12775]\n",
      "[ 0.19175]\n",
      "[ 0.25575]\n",
      "[ 0.38375]\n",
      "[ 0.51175]\n",
      "[ 0.76775]\n",
      "[ 1.02375]\n",
      "[ 1.53575]\n",
      "[ 2.04775]\n",
      "[ 3.07175]\n",
      "[ 4.09575]\n",
      "[ 6.14375]\n",
      "[ 8.19175]\n",
      "[ 12.28775]\n",
      "[ 16.38375]\n",
      "[ 24.57575]\n",
      "[ 32.76775]\n",
      "[ 49.15175]\n",
      "[ 65.53575]\n",
      "[ 98.30375]\n",
      "[ 131.07175]\n",
      "[ 131.07175]\n",
      "[ 114.68775]\n",
      "[ 81.91975]\n",
      "[ 106.49575]\n",
      "[ 90.11175]\n",
      "[ 102.39975]\n",
      "[ 94.20775]\n",
      "[ 100.35175]\n",
      "[ 102.39975]\n",
      "[ 99.32775]\n",
      "[ 98.30375]\n",
      "[ 99.83975]\n",
      "[ 100.35175]\n",
      "[ 99.58375]\n",
      "[ 100.09575]\n",
      "[ 99.71175]\n",
      "[ 99.58375]\n",
      "[ 99.77575]\n",
      "[ 99.83975]\n",
      "[ 99.74375]\n",
      "[ 99.80775]\n",
      "[ 99.75975]\n",
      "[ 99.74375]\n",
      "[ 99.76775]\n",
      "[ 99.77575]\n",
      "[ 99.76375]\n",
      "[ 99.77175]\n",
      "[ 99.76575]\n",
      "[ 99.76975]\n",
      "[ 99.76675]\n",
      "[ 99.76875]\n",
      "[ 99.76725]\n",
      "[ 99.76825]\n",
      "[ 99.7675]\n",
      "[ 99.76725]\n",
      "[ 99.767625]\n",
      "[ 99.76775]\n",
      "[ 99.7675625]\n",
      "Optimization terminated successfully.\n",
      "         Current function value: -3594470.473058\n",
      "         Iterations: 38\n",
      "         Function evaluations: 76\n",
      "[ 99.767625]\n"
     ]
    }
   ],
   "source": [
    "def poiss_loglike(x, data, a):\n",
    "    print x\n",
    "    return -np.sum(data*np.log(x)-x)*a\n",
    "\n",
    "\n",
    "params = sc.fmin(poiss_loglike, 0, args=(data ,1))\n",
    "print params"
   ]
  }
 ],
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