Bug in train_filter

Called normalize(belief), when it should have been normalize(prior).
belief is a variable from another cell, and not used in this function.

Reported by by Shakti Gehlaut.

02-Discrete-Bayes.ipynb

@@ -17,9 +17,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -335,9 +333,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -373,9 +369,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "hallway = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])"
@@ -391,15 +385,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlwAAAEWCAYAAACzN0knAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f5d3d6a0>"
+       "<matplotlib.figure.Figure at 0x1edeae2de10>"
       ]
      },
      "metadata": {},
@@ -436,9 +428,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -472,9 +462,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "belief = np.array([0., 1., 0., 0., 0., 0., 0., 0., 0., 0.])"
@@ -514,7 +502,6 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -530,7 +517,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlwAAACeCAYAAADno9QmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f617b940>"
+       "<matplotlib.figure.Figure at 0x1edeb2c75c0>"
       ]
      },
      "metadata": {},
@@ -562,9 +549,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -607,9 +592,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -647,7 +630,6 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -664,7 +646,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f6690be0>"
+       "<matplotlib.figure.Figure at 0x1edeb3160f0>"
       ]
      },
      "metadata": {},
@@ -710,9 +692,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def scaled_update(hall, belief, z, z_prob): \n",
@@ -743,9 +723,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -798,15 +776,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f6a97860>"
+       "<matplotlib.figure.Figure at 0x1edeb355da0>"
       ]
      },
      "metadata": {},
@@ -845,9 +821,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1616,7 +1590,7 @@
     {
      "data": {
       "text/html": [
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truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1697,15 +1671,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f6a74b00>"
+       "<matplotlib.figure.Figure at 0x1edeae76390>"
       ]
      },
      "metadata": {},
@@ -1739,7 +1711,6 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1757,7 +1728,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f6aac160>"
+       "<matplotlib.figure.Figure at 0x1edebb32588>"
       ]
      },
      "metadata": {},
@@ -1785,9 +1756,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2556,7 +2525,7 @@
     {
      "data": {
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truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2672,7 +2641,6 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2680,7 +2648,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f66e4198>"
+       "<matplotlib.figure.Figure at 0x1edebb75cf8>"
       ]
      },
      "metadata": {},
@@ -2710,15 +2678,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f6a7b748>"
+       "<matplotlib.figure.Figure at 0x1edebb22898>"
       ]
      },
      "metadata": {},
@@ -2768,7 +2734,6 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -2776,7 +2741,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f6bf5320>"
+       "<matplotlib.figure.Figure at 0x1edebb571d0>"
       ]
      },
      "metadata": {},
@@ -2803,15 +2768,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAACcCAYAAAC9SeXkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f6c18780>"
+       "<matplotlib.figure.Figure at 0x1edebc6c4e0>"
       ]
      },
      "metadata": {},
@@ -2834,15 +2797,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f6e8d278>"
+       "<matplotlib.figure.Figure at 0x1edebc64a90>"
       ]
      },
      "metadata": {},
@@ -2865,15 +2826,13 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAACcCAYAAAC9SeXkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f6d797b8>"
+       "<matplotlib.figure.Figure at 0x1edece74940>"
       ]
      },
      "metadata": {},
@@ -2898,7 +2857,6 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -2906,7 +2864,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f6b07438>"
+       "<matplotlib.figure.Figure at 0x1edece3f710>"
       ]
      },
      "metadata": {},
@@ -2941,15 +2899,13 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f6d02d30>"
+       "<matplotlib.figure.Figure at 0x1edece55828>"
       ]
      },
      "metadata": {},
@@ -3002,7 +2958,6 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -3773,7 +3728,7 @@
     {
      "data": {
       "text/html": [
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truncated
+       "<img 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truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -3844,9 +3799,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -4615,7 +4568,7 @@
     {
      "data": {
       "text/html": [
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truncated
+       "<img src=\"data:image/png;base64,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 truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -4653,7 +4606,7 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
-    "collapsed": false
+    "scrolled": true
    },
    "outputs": [
     {
@@ -5423,7 +5376,7 @@
     {
      "data": {
       "text/html": [
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truncated
+       "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAtAAAACgCAYAAAAl6lt0AAAgAElEQVR4Xu2dD7TmxVnfP3c3bJQEyLJJPGshG6Msi8G1qCkh7SYGNgrFbDRNm6ZBTA8UaKBaCBi1CX/U1tooelpi06M0PaZEsLWaNdGiCyndYIqBgEvAJZE/Qg4bE2AJgYWF3Ht7nt3fu75c3nvfmfnNzD73N9/3nJxD9j7PM/N8v8/MfN955ze/GfQRAkJACAgBISAEhIAQEAJCIBiBmWBLGQoBISAEhIAQEAJCQAgIASGABLSKQAgIASEgBISAEBACQkAIRCAgAR0BlkyFgBAQAkJACAgBISAEhIAEtGpACAgBISAEhIAQEAJCQAhEICABHQGWTIWAEBACQkAICAEhIASEgAS0akAICAEhIASEgBAQAkJACEQgIAEdAZZMhYAQEAJCQAgIASEgBISABLRqQAgIASEgBISAEBACQkAIRCAgAR0BlkyFgBAQAkJACAgBISAEhIAEtGpACAgBISAEhIAQEAJCQAhEIOBFQL8TOB/4XuClwCHA3BJ5vAz4MHB6Z/cp4ALg6xG5y1QICAEhIASEgBAQAkJACEQj4EVAvwU4EjgU+K0AAW2C2US2CW/L4TrgKeBHoxGQgxAQAkJACAgBISAEhIAQiEDAi4AedflNwI1TBPSrgAeAjcAXOkf77zsA+9uXI/KXqRAQAkJACAgBISAEhIAQiEJgOQroLcC13W71eLLPAO8APjn+j4899tgRMzMzJsxHn4fm5uaejUJJxkJACAgBISAEhIAQEALVEVixYsUq4OhRw/Pz8zcdeeSRB/3I7nIU0GcAHwLWLmDxK8BFwMfH/3337t1bZmZmPlGdcTUoBISAEBACQkAICAEhkBWB+fn5t61evXpr1qAJwZajgI7agZaATqgKuQgBISAEhIAQEAJCwCECEtCTSQk9A31/d2PH6Ay03d7xeWDdwjPQEtAOq19dEgJCQAgIASEgBIRAAgIS0M8HbUX34KAJ6D8GDgNmATurPD8B3z/s7N/d3cLxO8CTwI8ttN29e/cJMzMzJq73fd71rndx//2mv5fv5+qrr+bEE090k8Bzzz3HF7/4xX39Wb9+PYccYhek+PnccsstnHXWWX461LMn4r8ngJnd267/a7q9jHFQ/wKwqTn/x1vtW4Zt85+f46Uiiv+6eE9rrVbtz87O8vTTTx/ozvz8/PetXr369mn9K/13L0c4fgL46JhYtn6ZcH4zYGr3buBU4OYOELsH+irgRzo7E9R2D/QTCwF79NFHX7ty5crRTjWvf/3r2blzZ2lci8a/4YYbOPnkk4u2ERP82WefZceOHftcNm7cyKpVdt7fz+fGG2/klFNO8dOhnj0R/z0BzOzedv3/GXDSAkQ/C7whM8r7w3mrfetT2/wXoXnRoOK/Lt7TWqtV+yagn3zS9kj3f2ZnZ49fs2bNXdP6V/rvXgR0sTwloItBeyBwrUGUmokEdCpyYX7e+Q/LIt3Ke/5l618Cum3+08dNiqcEdApq5Xxq1b4EdDkOl4wsAV0e+FqDKDWTsgIitVfpft4WEe/8pyMd5uk9/7L1LwHdNv9hYySXlbe5z/Lyzn8u7CfFqZW7BHRJFpeILQFdHvhagyg1k7ICIrVX6X7eFhHv/KcjHebpPf+y9S8B3Tb/YWMkl5W3uU8Cus7xTQnoXCMoMo4EdCRgCeZaQBJA6+HibRHxzn8PqINcvecvAR1EY7JR2/wnw5bk6G3uk4CWgE4q5OXiJAFdniktIOUxHm/B2yLinf/S7HjPXwK6bAW0zX9ZbBdG9zb3SUBLQNcdAZVbk4AuD7gWkPIYS0DXxTimtbbrX0c42uY/ZqT0t5WA7o9hzgi1al9HOHKyFhFLAjoCrETTWoMosXuU3YFL7VW6n7dFxDv/6UiHeXrPv2z9S0C3zX/YGMll5W3u0w60dqBz1bbLOBLQ5WnRAlIeY+1A18U4prW2618Cum3+Y0ZKf1sJ6P4Y5oxQq/a1A52TtYhYEtARYCWa1hpEid3TDnQqcIF+3vkPTCPZzHv+2oFOpjbIsW3+gyDKZiQBnQ3KLIFq1b4EdBa64oNIQMdjFutRaxDF9mtkX1ZApPYq3c/bIuKd/3Skwzy951+2/rUD3Tb/YWMkl5W3uc/y8s5/LuwnxamVuwR0SRaXiC0BXR74WoMoNZOyAiK1V+l+3hYR7/ynIx3m6T3/svUvAd02/2FjJJeVt7lPAlpnoHPVtss4EtDladECUh7j8Ra8LSLe+S/Njvf8JaDLVkDb/JfFdmF0b3OfBLQEdN0RULk1CejygGsBKY+xBHRdjGNaa7v+tQPdNv8xI6W/rQR0fwxzRqhV+zrCkZO1iFgS0BFgJZrWGkSJ3dNDhKnABfp55z8wjWQz7/lrBzqZ2iDHtvkPgiibkQR0NiizBKpV+xLQWeiKDyIBHY9ZrEetQRTbr5F9WQGR2qt0P2+LiHf+05EO8/Sef9n61w502/yHjZFcVt7mPsvLO/+5sJ8Up1buEtAlWVwitgR0eeBrDaLUTMoKiNRepft5W0S885+OdJin9/zL1r8EdNv8h42RXFbe5j4JaJ2BzlXbOeJcAZwNHA7cBpwP3LVI4B8A/gPwd4FvAtuBC4EHx+0loHPQsnQMLSDlMR5vwdsi4p3/0ux4z18CumwFtM1/WWwXRvc290lAS0DXHQGLt3YJcAFwGnAvcBlwJrAe2LPAbQb4CnAN8NPAi4H/ChwF/H0J6LqUagGpi7e3RcQ7/6XZ8Z6/BHTZCmib/7LYSkDXxTe2tVq1ryMc05m5D7gSuKozXQk8DFzUCeXxCC8DHu12n+/s/nA68LvASySgp4Od06LWIErtc1kBkdqrdD8J6HTsSni2Xf86wtE2/yVG1OIxvc192oHWDnTdETC5NTuy8ThwEnDLmMn1gAnkiye4/cfu397f7UD/JvB0t2t9wFxHOMrTqwWkPMbjLXhbRLzzX5od7/mX/QIpAd02/6VH1/Pje5v7JKAloOuOgMmt2dELO7t8HHDPmMm1wBPAORPc3gh8BDgGsCMdd3THP7429B3obdu2sWnTJg+87euDLSA7d+7c998bNmxg1apVbvpmHdm+fTubN2921ac+nRH/fdDL79t2/dcV0N5qX/Nf/vG0VETxXxfvaa3Vmvvm5ubYu3fvge7Mzs4ev2bNmsWej5vW7Wx/N+Hp4RO7A/1d3cOFP9mdfX4R8DPAu4Hv6Xai9+U1xB3orVu3snbtWg+8LYs+7Nq1iy1btiyLvoZ0UvyHoCSbEQJl67+ugFbtx9d1Wf7j+9PHQ/z3QW/5+tqm3Lp16ySgl6Bw0hnoXd3NGvaw4Pjn7cDVwOqxfzwM+DpwIvC50b9LQC/fQZOr50NaQAwTLSK5KqONOGXrXwLaexWV5b9u9pr76uLtpTUJ6OlM2Dlnu4XDHgY0MX0pcAZw7IRbOF4F/CXwU8BHgUMAOwttDxza30xI7/sMUUB7+xmr1s8400tosoWOcKQiF+bnnf+wLNKtvOdftv7rCmhvc59VTdv8p4+bFE/xn4JaOZ9ata8jHGEcXg6cC9hu8q1j90AfDdwNnArc3IWyQ60/b8dugfnuYcMPAJ8Zb2qIAtrbgxR6iCasuHNZif9cSOaJ03b91xXQ3mp/JKB37Nixr5g2btzo7hmQsg+R5hlDoVHEfyhSdexqzX26xq4Ony9oRQK6PPC1BlFqJkNaQAwDb4uId/5T6ybUz3v+ZetfArpt/kNHSR47b3PfcvgClQf5yVFq1b4EdEkWl4gtAV0e+FqDKDWTsgIitVfpft4WEe/8pyMd5uk9/7L1LwHdNv9hYySXlbe5TwJa19jlqm2XcSSgy9OiBaQ8xuMteFtEvPNfmh3v+UtAl62Atvkvi+3C6N7mPgloCei6I6ByaxLQ5QHXAlIeYwnouhjHtNZ2/WsHum3+Y0ZKf1sJ6P4Y5oxQq/Z1hCMnaxGxJKAjwEo0rTWIErtH2R241F6l+3lbRLzzn450mKf3/MvWvwR02/yHjZFcVt7mPu1Aawc6V227jCMBXZ4WLSDlMdYOdF2MY1pru/4loNvmP2ak9LeVgO6PYc4ItWpfO9A5WYuIJQEdAVaiaa1BlNg97UCnAhfo553/wDSSzbznrx3oZGqDHNvmPwiibEYS0NmgzBKoVu1LQGehKz6IBHQ8ZrEetQZRbL9G9mUFRGqv0v28LSLe+U9HOszTe/5l61870G3zHzZGcll5m/ssL+/858J+UpxauUtAl2RxidgS0OWBrzWIUjMpKyBSe5Xu520R8c5/OtJhnt7zL1v/EtBt8x82RnJZeZv7JKB1BjpXbbuMIwFdnhYtIOUxHm/B2yLinf/S7HjPXwK6bAW0zX9ZbBdG9zb3SUBLQNcdAZVbk4AuD7gWkPIYS0DXxTimtbbrXzvQbfMfM1L620pA98cwZ4Rata8jHDlZi4glAR0BVqJprUGU2D09RJgKXKCfd/4D00g2856/dqCTqQ1ybJv/IIiyGUlAZ4MyS6BatS8BnYWu+CAS0PGYxXrUGkSx/RrZlxUQqb1K9/O2iHjnPx3pME/v+Zetf+1At81/2BjJZeVt7rO8vPOfC/tJcWrlLgFdksUlYktAlwe+1iBKzaSsgEjtVbqft0XEO//pSId5es+/bP1LQLfNf9gYyWXlbe6TgNYZ6Fy17TKOBHR5WrSAlMd4vAVvi4h3/kuz4z1/CeiyFdA2/2WxXRjd29wnAS0BXXcEVG5NAro84FpAymMsAV0X45jW2q5/7UC3zX/MSOlvKwHdH8OcEWrVvo5w5GQtIpYEdARYiaa1BlFi9/QQYSpwgX7e+Q9MI9nMe/7agU6mNsixbf6DIMpmJAGdDcosgWrVvgR0GF1XAGcDhwO3AecDdy3h+h7gIuA1wJPAtcC/HreXgA4Dvo9VrUGU2seyAiK1V+l+3hYR7/ynIx3m6TLine truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -5456,15 +5409,13 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f667e6a0>"
+       "<matplotlib.figure.Figure at 0x1edeba99a58>"
       ]
      },
      "metadata": {},
@@ -5538,9 +5489,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class Train(object):\n",
@@ -5589,9 +5538,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def train_filter(iterations, kernel, sensor_accuracy, \n",
@@ -5599,7 +5546,7 @@
     "    track = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
     "\n",
     "    prior = np.array([.9] + [0.01]*9)\n",
-    "    normalize(belief)\n",
+    "    normalize(prior)\n",
     "    \n",
     "    robot = Train(len(track), kernel, sensor_accuracy)\n",
     "    for i in range(iterations):\n",
@@ -5642,7 +5589,6 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -5667,7 +5613,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f610e390>"
+       "<matplotlib.figure.Figure at 0x1edeb233cc0>"
       ]
      },
      "metadata": {},
@@ -5693,9 +5639,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -5718,7 +5662,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f6d07c88>"
+       "<matplotlib.figure.Figure at 0x1edebdc6f98>"
       ]
      },
      "metadata": {},
@@ -5744,7 +5688,6 @@
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -5752,7 +5695,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x246f6e45a20>"
+       "<matplotlib.figure.Figure at 0x1edecf3d710>"
       ]
      },
      "metadata": {},
@@ -5923,9 +5866,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }