{ "metadata": { "name": "ABCtests" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## ABC" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pymc as pm" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "obs = np.array( [0,0,1] )\n", "\n", "uni = pm.Uniform(\"prop\", 0, 1)\n", "fake_obs = pm.Bernoulli(\"fake_obs\", uni, size=3 )\n", "\n", "@pm.deterministic\n", "def accept(uni = uni, fake_obs = fake_obs, obs = obs):\n", " if np.array_equal( fake_obs, obs ):\n", " return uni\n", " else:\n", " return None" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 132 }, { "cell_type": "code", "collapsed": false, "input": [ "mcmc = pm.MCMC([uni, fake_obs, accept] )" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 133 }, { "cell_type": "code", "collapsed": false, "input": [ "mcmc.sample(10000)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[****************100%******************] 10000 of 10000 complete" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 134 }, { "cell_type": "code", "collapsed": false, "input": [ "samples = mcmc.trace('accept')[:]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 135 }, { "cell_type": "code", "collapsed": false, "input": [ "hist( samples[samples > 0] )" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 136, "text": [ "(array([ 47., 118., 158., 125., 141., 95., 74., 48., 33., 10.]),\n", " array([ 0.01977077, 0.11150485, 0.20323893, 0.29497301, 0.38670709,\n", " 0.47844116, 0.57017524, 0.66190932, 0.7536434 , 0.84537748,\n", " 0.93711155]),\n", " )" ] }, { "output_type": "display_data", "png": 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