{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from qinfer.test_models import SimplePrecessionModel\n",
      "from qinfer.derived_models import RandomWalkModel\n",
      "from qinfer.distributions import NormalDistribution, UniformDistribution\n",
      "from qinfer.smc import SMCUpdater"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prior = UniformDistribution([0, 1])\n",
      "step_dist = NormalDistribution(0, 0.01**2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = RandomWalkModel(SimplePrecessionModel(), step_dist)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "updater = SMCUpdater(model, 1000, prior)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "experiments = np.logspace(-2, 4, 200)\n",
      "true_model = prior.sample()\n",
      "for idx_exp, experiment in enumerate(experiments):\n",
      "    outcome = model.simulate_experiment(true_model, experiment[np.newaxis])\n",
      "    updater.update(outcome, experiment[np.newaxis])\n",
      "    \n",
      "print true_model, updater.est_mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.90754165]] [ 0.898664]\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}