Added animations to Discrete Bayes

Beginning of adding animations to the chapters. I wrote the
existing material not assuming animations, so it is quite
possible that I will want to revise the chapter to better
make use of this ability. For now I just animated two different
update/predict cycles.

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": [
-        "<IPython.core.display.HTML at 0x7fb6bf437cc0>"
+        "<IPython.core.display.HTML at 0x7f7040e9eba8>"
        ]
       }
      ],
@@ -359,7 +359,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb6a2f43ac8>"
+        "<matplotlib.figure.Figure at 0x7f7027259ba8>"
        ]
       }
      ],
@@ -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": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f7026d70d68>"
+       ]
       }
      ],
      "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": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f7026e9eda0>"
+       ]
       }
      ],
      "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": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAGDCAYAAAAs4AbKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb6a2be80f0>"
+        "<matplotlib.figure.Figure at 0x7f7027250f28>"
        ]
       }
      ],
@@ -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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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f7026e19d68>"
+       ]
       }
      ],
      "prompt_number": 10
@@ -719,7 +745,7 @@
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        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb6a2a3c0f0>"
+        "<matplotlib.figure.Figure at 0x7f7026c2a748>"
        ]
       }
      ],
@@ -748,7 +774,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb6a2b93be0>"
+        "<matplotlib.figure.Figure at 0x7f7026de1358>"
        ]
       }
      ],
@@ -786,7 +812,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb6a2bd6438>"
+        "<matplotlib.figure.Figure at 0x7f7026e71048>"
        ]
       }
      ],
@@ -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": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fb6a2c38ba8>"
-       ]
-      }
-     ],
-     "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",
+      " <img src=\"no_info.gif\">"
+     ]
     },
     {
      "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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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb6a2aacba8>"
+        "<matplotlib.figure.Figure at 0x7f7026e24630>"
        ]
       }
      ],
@@ -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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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb6a2b495f8>"
+        "<matplotlib.figure.Figure at 0x7f7026eae390>"
        ]
       }
      ],
@@ -947,7 +967,7 @@
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb6a29365f8>"
+        "<matplotlib.figure.Figure at 0x7f7026d36d30>"
        ]
       }
      ],
@@ -976,7 +996,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb6a29e3b38>"
+        "<matplotlib.figure.Figure at 0x7f7026ccd2b0>"
        ]
       }
      ],
@@ -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",
+      " <img src=\"simulate.gif\">"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 2,
@@ -1036,7 +1064,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb6a28e5a20>"
+        "<matplotlib.figure.Figure at 0x7f7026f0db70>"
        ]
       }
      ],
@@ -1065,7 +1093,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb6a2970438>"
+        "<matplotlib.figure.Figure at 0x7f7026d2fdd8>"
        ]
       }
      ],
@@ -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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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb6a28b74e0>"
+        "<matplotlib.figure.Figure at 0x7f7026dda6a0>"
        ]
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -1150,9 +1178,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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Chapter02_Discrete_Bayes/discrete_bayes_animations.ipynb

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+      "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": [
+      {
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+        "    blockquote{\n",
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+        "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
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+        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
+        "                \"HTML-CSS\": {\n",
+        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
+        "                }\n",
+        "        });\n",
+        "</script>\n"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 1,
+       "text": [
+        "<IPython.core.display.HTML at 0x7f77b8e02fd0>"
+       ]
+      }
+     ],
+     "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": "iVBORw0KGgoAAAANSUhEUgAAAY0AAACuCAYAAADK6FH9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f77b8e02b38>"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      " <img src=\"no_info.gif\">"
+     ]
+    },
+    {
+     "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": "iVBORw0KGgoAAAANSUhEUgAAAY0AAACuCAYAAADK6FH9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f779abe8c18>"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      " <img src=\"simulate.gif\">"
+     ]
     }
    ],
    "metadata": {}

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,