diff --git a/Chapter02_Discrete_Bayes/discrete_bayes.ipynb b/Chapter02_Discrete_Bayes/discrete_bayes.ipynb index 33d28a2..a10df4a 100644 --- a/Chapter02_Discrete_Bayes/discrete_bayes.ipynb +++ b/Chapter02_Discrete_Bayes/discrete_bayes.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:964af9c2a922f3d503dcd4209ea814d6fc6464278774fa2fcf5aa057b4052977" + "signature": "sha256:8e8231b3997b1d22f43983521ca3cca0ced5a69e74e0edeb1b3c2676b1f81c87" }, "nbformat": 3, "nbformat_minor": 0, @@ -259,7 +259,7 @@ "output_type": "pyout", "prompt_number": 1, "text": [ - "" + "" ] } ], @@ -359,7 +359,7 @@ "output_type": "display_data", "png": 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ZsSl3xc+yewAAAABJRU5ErkJggg==\n", "text": [ - "" + "" ] } ], @@ -484,7 +484,8 @@ "pos = update (pos, 1, .6, .2)\n", "\n", "print(pos)\n", - "print('sum =', sum(pos))" + "print('sum =', sum(pos))\n", + "bar_plot.plot(pos)" ], "language": "python", "metadata": {}, @@ -496,6 +497,14 @@ "[ 0.12 0.12 0.04 0.04 0.04 0.04 0.04 0.04 0.12 0.04]\n", "sum = 0.64\n" ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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AgKQJYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaI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\nB2kSX4v2hY6CAAAAAElFTkSuQmCC\n", + "text": [ + "" + ] } ], "prompt_number": 7 @@ -533,7 +542,8 @@ "\n", "print('sum =', sum(pos))\n", "print('probability of door =', pos[0])\n", - "print('probability of wall =', pos[2])" + "print('probability of wall =', pos[2])\n", + "bar_plot.plot(pos)" ], "language": "python", "metadata": {}, @@ -546,6 +556,14 @@ "probability of door = 0.1875\n", "probability of wall = 0.0625\n" ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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ASJogBgAgaYIYAICkCWIAAJImiAEASJogBgAgaYIYAICkCWIAAJImiAEASJogBgAgaYIY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\nq7IAAAAASUVORK5CYII=\n", + "text": [ + "" + ] } ], "prompt_number": 8 @@ -597,10 +615,9 @@ "print('pos before predict =', pos)\n", "bar_plot.plot (pos, title='Before prediction')\n", "\n", - "\n", "pos = perfect_predict(pos, 1)\n", "print('pos after predict =', pos)\n", - "bar_plot.plot (pos, title='After prediction')\n" + "bar_plot.plot (pos, title='After prediction')" ], "language": "python", "metadata": {}, @@ -625,7 +642,7 @@ "output_type": "display_data", "png": 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YAAAARiOIAQAAYDSCGAAAAEYjiAEAAGA0ghgAAABGI4gBAABgNIIYAAAARiOIAQAAYDSC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\nH7WokSOa/dtCAAAAAElFTkSuQmCC\n", "text": [ - "" + "" ] } ], @@ -673,7 +690,8 @@ "\n", "p = np.array([0,0,0,1,0,0,0,0])\n", "res = predict(p, 2, .8, .1, .1)\n", - "print(res)" + "print(res)\n", + "bar_plot.plot (res)" ], "language": "python", "metadata": {}, @@ -684,6 +702,14 @@ "text": [ "[ 0. 0. 0. 0. 0.1 0.8 0.1 0. ]\n" ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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"text": [ - "" + "" ] } ], @@ -796,40 +822,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", "After 500 iterations we have lost all information, even though we were 100% sure that we started in position 1. Feel free to play with the numbers to see the effect of different number of updates. For example, after 100 updates we have a small amount of information left.\n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "pos = [1.0,0,0,0,0,0,0,0,0,0]\n", - "for i in range(100):\n", - " pos = predict(pos, 1, .8, .1, .1)\n", - "print(pos)\n", - "bar_plot.plot(pos)" - ], - "language": "python", + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 0.10407069 0.10329322 0.10125784 0.09874205 0.09670682 0.09592945\n", - " 0.09670682 0.09874205 0.10125784 0.10329322]\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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AACRNEAMAkLT/D65RX42ZHrtJAAAAAElFTkSuQmCC\n", - "text": [ - "" - ] - } - ], - "prompt_number": 14 + "source": [ + "And, if you are viewing this on the web or in IPython Notebook, here is an animation of that output.\n", + " " + ] }, { "cell_type": "heading", @@ -858,7 +861,26 @@ "input": [ "p = np.array([.1]*10)\n", "p = update(p, 1, .6, .2)\n", - "print(p)\n", + "print(p)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[ 0.1875 0.1875 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.1875\n", + " 0.0625]\n" + ] + } + ], + "prompt_number": 14 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ "p = predict(p, 1, .8, .1, .1)\n", "print(p)\n", "bar_plot.plot(p)" @@ -870,8 +892,6 @@ "output_type": "stream", "stream": "stdout", "text": [ - "[ 0.1875 0.1875 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.1875\n", - " 0.0625]\n", "[ 0.0875 0.175 0.175 0.075 0.0625 0.0625 0.0625 0.0625 0.075\n", " 0.1625]\n" ] @@ -881,7 +901,7 @@ "output_type": "display_data", "png": 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IAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBp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\nLK688sq4/fbbz7s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"text": [ - "" + "" ] } ], @@ -891,7 +911,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "So after the first sense we have assigned a high probability to each door position, and a low probability to each wall position. The update step shifted these probabilities to the right, smearing them about a bit. Now lets look at what happens at the next sense." + "So after the first update we have assigned a high probability to each door position, and a low probability to each wall position. The update step shifted these probabilities to the right, smearing them about a bit. Now lets look at what happens at the next sense." ] }, { @@ -918,7 +938,7 @@ "output_type": "display_data", "png": 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"text": [ - "" + "" ] } ], @@ -989,6 +1009,14 @@ "Here things have degraded a bit due to the long string of wall positions in the map. We cannot be as sure where we are when there is an undifferentiated line of wall positions, so naturally our probabilities spread out a bit." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, for those viewing this in a Notebook or on the web, here is an animation of that algorithm.\n", + " " + ] + }, { "cell_type": "heading", "level": 2, @@ -1036,7 +1064,7 @@ "output_type": "display_data", "png": 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"text": [ - "" + "" ] } ], @@ -1087,7 +1115,7 @@ "for i,m in enumerate(measurements):\n", " pos = update(pos, m, .6, .2)\n", " pos = predict(pos, 1, .8, .1, .1)\n", - " plt.subplot(3,2, i+1)\n", + " plt.subplot(3, 2, i+1)\n", " bar_plot.plot(pos, title='step{}'.format(i+1))" ], "language": "python", @@ -1096,13 +1124,13 @@ { "metadata": {}, "output_type": "display_data", - "png": 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FlixZgrCwMMjlcrz88ssdO3MiImoVc5uIyDKSvLy8to9lEIBKpYKvr6+YUyAieiC5ublw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\niRMnkJSUhKlTp4pe2+Fw4MyZM/Dz80NYWBiee+450WsCwK1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WrMHy5csRHh4OuVyO559/vntnTkREHWJuExGZR1JYWNj5uQwCUCqV8PPzE3MKRET3JT8/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\nFV5++WWsWrVKsn/nDz/8EF1dXdixYwfWrl2LadOmQa/Xi15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"text": [ - "" + "" ] } ], diff --git a/Chapter02_Discrete_Bayes/discrete_bayes_animations.ipynb b/Chapter02_Discrete_Bayes/discrete_bayes_animations.ipynb index 70dbc73..7451462 100644 --- a/Chapter02_Discrete_Bayes/discrete_bayes_animations.ipynb +++ b/Chapter02_Discrete_Bayes/discrete_bayes_animations.ipynb @@ -1,20 +1,415 @@ { "metadata": { "name": "", - "signature": "sha256:fea22f31f443e23e2d336a0742e6bf9a559c588029a2d3c2e8e69066e08c2fe5" + "signature": "sha256:47d64bacc2f597ef2e39688a14b58dc93224617c01ee343a7bb71c0cc97e6efa" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Discrete Bayes Animations" + ] + }, { "cell_type": "code", "collapsed": false, - "input": [], + "input": [ + "from __future__ import division, print_function\n", + "import matplotlib.pyplot as plt\n", + "import sys\n", + "sys.path.insert(0,'../code') # allow us to format the book\n", + "import book_format\n", + "book_format.load_style()" + ], "language": "python", "metadata": {}, - "outputs": [] + "outputs": [ + { + "html": [ + "\n", + "\n" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 1, + "text": [ + "" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook creates the animations for the Discrete Bayesian filters chapter. It is not really intended to be a readable part of the book, but of course you are free to look at the source code, and even modify it. However, if you are interested in running your own animations, I'll point you to the examples subdirectory of the book, which contains a number of python scripts that you can run and modify from an IDE or the command line. This module saves the animations to GIF files, which is quite slow and not very interactive. " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from matplotlib import animation\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "%matplotlib inline\n", + "\n", + "def barplot(pos, ylim=(0,1)):\n", + " \"\"\" implement plotting of bar plots in a way that plays nicely with the\n", + " animations.\"\"\"\n", + " \n", + " plt.cla()\n", + " ax = plt.gca()\n", + " x = np.arange(len(pos))\n", + " ax.bar(x, pos)\n", + " if ylim:\n", + " plt.ylim(ylim)\n", + " plt.xticks(x+0.4, x)\n", + " plt.grid()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# the predict algorithm of the discrete bayesian filter\n", + "def predict(pos, move, p_correct, p_under, p_over):\n", + " n = len(pos)\n", + " result = np.array(pos, dtype=float)\n", + " for i in range(n):\n", + " result[i] = \\\n", + " pos[(i-move) % n] * p_correct + \\\n", + " pos[(i-move-1) % n] * p_over + \\\n", + " pos[(i-move+1) % n] * p_under \n", + " return result\n", + "\n", + "\n", + "def normalize(p):\n", + " s = sum(p)\n", + " for i in range (len(p)):\n", + " p[i] = p[i] / s\n", + " \n", + "# the update algorithm of the discrete bayesian filter\n", + "def update(pos, measure, p_hit, p_miss):\n", + " q = np.array(pos, dtype=float)\n", + " for i in range(len(hallway)):\n", + " if hallway[i] == measure:\n", + " q[i] = pos[i] * p_hit\n", + " else:\n", + " q[i] = pos[i] * p_miss\n", + " normalize(q)\n", + " return q" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pos = [1.0,0,0,0,0,0,0,0,0,0]\n", + "def bar_animate(nframe):\n", + " global pos\n", + " barplot(pos)\n", + " pos = predict(pos, 1, .8, .1, .1)\n", + "\n", + "\n", + "fig = plt.figure(figsize=(6.5, 2.5))\n", + "anim = animation.FuncAnimation(fig, bar_animate,\n", + " frames=100, interval=75)\n", + "anim.save('no_info.gif', writer='imagemagick')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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haq0n6f1ufRH4Mr0dkGNd5YHTlwA/UWv9ry5zwOnZ4GfpzTAO0jvnc7TbVAD8XSnlCL2r\nSt96MTbobUQkSc0mfqYhSZocloYkqZmlIUlqZmlIkppZGpKkZpaGJKmZpSFJavb/A9CC7H8FLJ4A\nAAAASUVORK5CYII=\n", + "text": [ + "" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pos = np.array([.1]*10)\n", + "hallway = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])\n", + "\n", + "def bar_animate(nframe):\n", + " global pos\n", + " if nframe == 0:\n", + " return\n", + "\n", + " barplot(pos, ylim=(0,1.0))\n", + " if nframe % 2 == 0:\n", + " pos = predict(pos, 1, .8, .1, .1)\n", + " else:\n", + " x = (nframe/2) % len(hallway)\n", + " z = hallway[x]\n", + " pos = update(pos, z, .6, .2)\n", + " \n", + "\n", + "fig = plt.figure(figsize=(6.5, 2.5))\n", + "anim = animation.FuncAnimation(fig, bar_animate,\n", + " frames=60, interval=200)\n", + "anim.save('simulate.gif', writer='imagemagick')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] } ], "metadata": {} diff --git a/Chapter02_Discrete_Bayes/no_info.gif b/Chapter02_Discrete_Bayes/no_info.gif new file mode 100644 index 0000000..8ac5202 Binary files /dev/null and b/Chapter02_Discrete_Bayes/no_info.gif differ diff --git a/Chapter02_Discrete_Bayes/simulate.gif b/Chapter02_Discrete_Bayes/simulate.gif new file mode 100644 index 0000000..dcf571c Binary files /dev/null and b/Chapter02_Discrete_Bayes/simulate.gif differ diff --git a/code/bar_plot.py b/code/bar_plot.py index fa930a6..fc724be 100644 --- a/code/bar_plot.py +++ b/code/bar_plot.py @@ -7,16 +7,18 @@ Created on Fri May 2 12:21:40 2014 import matplotlib.pyplot as plt import numpy as np -def plot(pos, ylim=(0,1)): +def plot(pos, ylim=(0,1), title=None): plt.cla() ax = plt.gca() x = np.arange(len(pos)) ax.bar(x, pos) if ylim: - plt.ylim([0,1]) + plt.ylim(ylim) plt.xticks(x+0.4, x) plt.grid() - plt.show() + if title is not None: + plt.title(title) + if __name__ == "__main__": p = [0.2245871, 0.06288015, 0.06109133, 0.0581008, 0.09334062, 0.2245871,