Switched to update/predict naming convention.

discrete_bayes.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:4596c3be2efa3686f3281d6c9e9e897bfa114ca6945b5da108745cb466846335"
+  "signature": "sha256:65531b21c1e02194222568498f24cf6992c1fc1a78ff905904e612aa9b45fb5e"
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  "nbformat": 3,
  "nbformat_minor": 0,
@@ -44,7 +44,7 @@
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         "        color: #000000;\n",
         "        font-weight: 600;\n",
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+        "        font-size: 11pt;\n",
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         "   }\n",
@@ -250,7 +250,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x2919d50>"
+        "<IPython.core.display.HTML at 0x7f9944148f90>"
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      ],
@@ -346,7 +346,7 @@
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x2919890>"
+        "<matplotlib.figure.Figure at 0x7f9944148950>"
        ]
       }
      ],
@@ -457,7 +457,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "def measure (pos, measure, p_hit, p_miss):\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",
@@ -468,7 +468,7 @@
       "\n",
       "pos = np.array([0.2]*10)\n",
       "reading = 1 # 1 is 'door'\n",
-      "pos = measure (pos, 1, .6, .2)\n",
+      "pos = update (pos, 1, .6, .2)\n",
       "\n",
       "print(pos)\n",
       "print('sum =', sum(pos))"
@@ -498,12 +498,12 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "def normalize (p):\n",
+      "def normalize(p):\n",
       "    s = sum(p)\n",
       "    for i in range (len(p)):\n",
       "        p[i] = p[i] / s\n",
       "        \n",
-      "def sense (pos, measure, p_hit, p_miss):\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",
@@ -516,7 +516,7 @@
       "\n",
       "pos = np.array([0.2]*10)\n",
       "reading = 1 # 1 is 'door'\n",
-      "pos = sense (pos, 1, .6, .2)\n",
+      "pos = update(pos, 1, .6, .2)\n",
       "\n",
       "print('sum =', sum(pos))\n",
       "print('probability of door =', pos[0])\n",
@@ -530,12 +530,12 @@
        "stream": "stdout",
        "text": [
         "sum = 1.0\n",
-        "probability of door = 0.1875\n",
-        "probability of wall = 0.0625\n"
+        "probability of door = 0.166666666667\n",
+        "probability of wall = 0.166666666667\n"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
@@ -570,7 +570,7 @@
      "collapsed": false,
      "input": [
       "import numpy\n",
-      "def perfect_update(pos, move):\n",
+      "def perfect_predict(pos, move):\n",
       "    \"\"\" move the position by 'move' spaces, where positive is to the right, and negative\n",
       "    is to the left\n",
       "    \"\"\"\n",
@@ -581,9 +581,9 @@
       "    return result\n",
       "        \n",
       "pos = np.array([.4, .1, .2, .3])\n",
-      "print('pos before update =', pos)\n",
-      "pos = perfect_update(pos, 1)\n",
-      "print('pos after update =', pos)\n"
+      "print('pos before predict =', pos)\n",
+      "pos = perfect_predict(pos, 1)\n",
+      "print('pos after predict =', pos)\n"
      ],
      "language": "python",
      "metadata": {},
@@ -592,12 +592,12 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "pos before update = [ 0.4  0.1  0.2  0.3]\n",
-        "pos after update = [ 0.3  0.4  0.1  0.2]\n"
+        "pos before predict = [ 0.4  0.1  0.2  0.3]\n",
+        "pos after predict = [ 0.3  0.4  0.1  0.2]\n"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
@@ -611,7 +611,7 @@
      "level": 2,
      "metadata": {},
      "source": [
-      "Adding Noise to the Update"
+      "Adding Noise to the Prediction"
      ]
     },
     {
@@ -629,7 +629,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "def update (pos, move, p_correct, p_under, p_over):\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",
@@ -640,7 +640,7 @@
       "    return result\n",
       "\n",
       "p = np.array([0,0,0,1,0,0,0,0])\n",
-      "res = update (p, 2, .8, .1, .1)\n",
+      "res = predict(p, 2, .8, .1, .1)\n",
       "print(res)"
      ],
      "language": "python",
@@ -654,7 +654,7 @@
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -668,7 +668,7 @@
      "collapsed": false,
      "input": [
       "p = np.array([0, 0, .4, .6, 0, 0, 0, 0])\n",
-      "res = update (p, 2, .8, .1, .1)\n",
+      "res = predict (p, 2, .8, .1, .1)\n",
       "print(res)"
      ],
      "language": "python",
@@ -682,7 +682,7 @@
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
@@ -707,11 +707,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAFyCAYAAAAQ8tcPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFldJREFUeJzt3W9snXX9//HXga7D0bFRPNhu2YJmlQVWiAvRG9TAElyh\n/EmG0awxzht6QyOLZIswlxjwjkxMjSYEFQ2RGLVx+RIMZuiqzGjjDaNNKLB01JDoEL7taTr2h8Fp\n2fa94Y/mh4x2QNcDfh6PW716Puc6717J1meuXudclQMHDpwKAAAU6JxGDwAAAI0ihgEAKJYYBgCg\nWGIYAIBiiWEAAIolhgEAKNacMfytb30rV199dW6++eY5d7Znz550d3enu7s7+/btm5cBAQDgbJkz\nhjdu3Jgf/vCHc+5oamoqfX19+cUvfpGf/OQn+eY3vzkvAwIAwNkyZwx/5CMfyfLly+fc0fDwcDo6\nOtLa2pr29va0tbVlZGRkXoYEAICzoWm+djQxMZFqtZr+/v4sW7Ys1Wo14+PjWbt27Xy9BAAAzKt5\ni+HXbN68OUkyMDCQSqUy37sHAIB5M28xXK1WU6vVZrZrtVqq1eob1h04cCBLliyZr5cFAIDTOnr0\naC677LJZ17ztGO7r60ulUsm2bduSJJ2dnRkdHc3k5GTq9XrGxsZOe4nEkiVLsn79+rf7srxDjz76\n6Bl9Mgjzz7FvLMe/sRz/xnHsG8vxb6yhoaE518wZw9/4xjcyMDCQF198Mddcc03uvvvubNiwIRMT\nE69b19zcnO3bt6e3tzdJsnPnzrc5NgAALIw5Y/iuu+7KXXfd9Ybv33PPPW/4Xk9PT3p6euZnMgAA\nOMvcga4wH/7whxs9QrEc+8Zy/BvL8W8cx76xHP93v8qBAwdOLeQLHjx40DXDAACcdUNDQ1m1atWs\na5wZBgCgWGIYAIBiiWEAAIolhgEAKJYYBgCgWGIYAIBiiWEAAIolhgEAKJYYBgCgWGIYAIBiiWEA\nAIolhgEAKJYYBgCgWGIYAIBiiWEAAIolhgEAKJYYBgCgWGIYAIBiiWEAAIolhgEAKJYYBgCgWGIY\nAIBiiWEAAIolhgEAKJYYBgCgWGIYAIBiiWEAAIolhgEAKJYYBgCgWGIYAIBiiWEAAIolhgEAKJYY\nBgCgWGIYAIBiiWEAAIolhgEAKJYYBgCgWGIYAIBiiWEAAIolhgEAKJYYBgCgWGIYAIBiiWEAAIol\nhgEAKJYYBgCgWGIYAIBiiWEAAIolhgEAKJYYBgCgWGIYAIBiiWEAAIolhgEAKJYYBgCgWGIYAIBi\niWEAAIolhgEAKNacMbxnz550d3enu7s7+/btm3XtfffdlxtvvDE33nhj7rvvvnkbEgAAzoam2R6c\nmppKX19fdu/enXq9ni1btmTDhg2nXXvw4MH86le/ym9/+9ucOHEiN9xwQzZt2pSVK1eelcEBAOCd\nmvXM8PDwcDo6OtLa2pr29va0tbVlZGTktGtbWlrS1NSUV155JfV6PYsWLcrSpUvPytAAADAfZj0z\nPDExkWq1mv7+/ixbtizVajXj4+NZu3btG9ZeeOGF2bJlS6699tqcPHkyd955Zy644IKzNjgAALxT\ns8bwazZv3pwkGRgYSKVSOe2a5557Lv39/Xn88cczPT2d3t7eXHvttalWq/M3LQAAzKNZY7haraZW\nq81s12q1N43b4eHhdHZ2pqWlJUly2WWXZf/+/bnmmmvesHbXrl0zX3d1daWrq+ttDQ8AAK8ZHBzM\n4ODgzPbGjRvnfM6sMdzZ2ZnR0dFMTk6mXq9nbGxs5hKJvr6+VCqVbNu2LUmyatWqPPnkk5mamsrJ\nkyfz9NNP57bbbjvtfnfs2HHGPxQAAJyJ/zzJOjQ0NOdzZo3h5ubmbN++Pb29vUmSnTt3zjw2MTHx\nurWdnZ35xCc+kU2bNiVJPv3pT+dDH/rQmU8PAAALrHLgwIFTC/mCBw8ezPr16xfyJQEAKNDQ0FBW\nrVo16xp3oAMAoFhiGACAYolhAACKJYYBACiWGAYAoFhiGACAYolhAACKJYYBACiWGAYAoFhiGACA\nYolhAACKJYYBACiWGAYAoFhiGACAYolhAACKJYYBACiWGAYAoFhiGACAYolhAACKJYYBACiWGAYA\noFhiGACAYolhAACKJYYBACiWGAYAoFhiGACAYolhAACKJYYBACiWGAYAoFhiGACAYolhAACKJYYB\nACiWGAYAoFhiGACAYolhAACKJYYBACiWGAYAoFhiGACAYolhAACKJYYBACiWGAYAoFhiGACAYolh\nAACKJYYBACiWGAYAoFhiGACAYolhAACKJYYBACiWGAYAoFhiGACAYolhAACKJYYBACiWGAYAoFhi\nGACAYolhAACKJYYBACiWGAYAoFhiGACAYs0Zw3v27El3d3e6u7uzb9++Wdc+8cQTufnmm9PT05Pb\nb7993oYEAICzoWm2B6emptLX15fdu3enXq9ny5Yt2bBhw2nXnjx5MnfccUfuueeerF+/PocOHTor\nAwMAwHyZ9czw8PBwOjo60tramvb29rS1tWVkZOS0a5966qm0trZm/fr1SZILL7xw/qcFAIB5NGsM\nT0xMpFqtpr+/P4899liq1WrGx8dPu/aFF17I0qVL84UvfCGbNm3Kz3/+87MyMAAAzJdZL5N4zebN\nm5MkAwMDqVQqp11Tr9czNDSUX//612lpacknP/nJfPzjH8+qVavmb1oAAJhHs8ZwtVpNrVab2a7V\naqlWq2+6ds2aNWlra0uSrFu3Ls8+++xpY3jXrl0zX3d1daWrq+ttDQ8AAK8ZHBzM4ODgzPbGjRvn\nfM6sMdzZ2ZnR0dFMTk6mXq9nbGwsa9euTZL09fWlUqlk27ZtSf4dv88//3wOHz6c973vfXnmmWey\nevXq0+53x44dZ/xDAQDAmfjPk6xDQ0NzPmfWGG5ubs727dvT29ubJNm5c+fMYxMTE69bu3Tp0uzc\nuTOf+9zn8uqrr+bmm2/OBz/4wbf0AwAAwEKqHDhw4NRCvuDBgwdnPnECAADOlqGhoTnfv+YOdAAA\nFEsMAwBQLDEMAECxxDAAAMUSwwAAFEsMAwBQLDEMAECxxDAAAMUSwwAAFEsMAwBQLDEMAECxxDAA\nAMUSwwAAFEsMAwBQLDEMAECxxDAAAMUSwwAAFEsMAwBQLDEMAECxxDAAAMUSwwAAFEsMAwBQLDEM\nAECxmho9AAD8N3jhSD3jx6YaPcZ7ysUtzWm/YHGjx6BwYhgA5sH4sal8dc/fGz3Ge8q3e9aIYRrO\nZRIAABRLDAMAUCwxDABAscQwAADFEsMAABRLDAMAUCwxDABAscQwAADFEsMAABRLDAMAUCwxDABA\nscQwAADFEsMAABRLDAMAUCwxDABAscQwAADFEsMAABRLDAMAUCwxDABAscQwAADFEsMAABRLDAMA\nUCwxDABAscQwAADFEsMAABRLDAMAUCwxDABAscQwAADFEsMAABRLDAMAUCwxDABAscQwAADFEsMA\nABRrzhjes2dPuru7093dnX379s25w2PHjqWrqysPPvjgvAwIAABnS9NsD05NTaWvry+7d+9OvV7P\nli1bsmHDhll3+IMf/CDr1q1LpVKZ10EBAGC+zXpmeHh4OB0dHWltbU17e3va2toyMjLypuufffbZ\nTE5OZt26dTl16tS8DwsAAPNp1hiemJhItVpNf39/HnvssVSr1YyPj7/p+u985zvZunXrvA8JAABn\nw6yXSbxm8+bNSZKBgYE3vfzh8ccfzyWXXJL29nZnhQEAeE+YNYar1WpqtdrMdq1WS7VaPe3a4eHh\n7N27N7///e9z6NChnHPOObn44otz0003vWHtrl27Zr7u6upKV1fX250fAACSJIODgxkcHJzZ3rhx\n45zPmTWGOzs7Mzo6msnJydTr9YyNjWXt2rVJkr6+vlQqlWzbti1Jcvvtt+f2229Pktx33305//zz\nTxvCSbJjx44z+4kAAOAM/edJ1qGhoTmfM2sMNzc3Z/v27ent7U2S7Ny5c+axiYmJtzsnAAC8K8x5\nzXBPT096enre8P177rnnTZ9z2223vbOpAABgAbgDHQAAxRLDAAAUSwwDAFCsM/qcYQDe/V44Us/4\nsalGj/GecnFLc9ovWNzoMYAGEsMA/yXGj03lq3v+3ugx3lO+3bNGDEPhXCYBAECxxDAAAMUSwwAA\nFEsMAwBQLDEMAECxxDAAAMUSwwAAFEsMAwBQLDEMAECxxDAAAMUSwwAAFEsMAwBQLDEMAECxxDAA\nAMUSwwAAFEsMAwBQLDEMAECxxDAAAMUSwwAAFEsMAwBQLDEMAECxxDAAAMUSwwAAFEsMAwBQLDEM\nAECxxDAAAMUSwwAAFEsMAwBQLDEMAECxxDAAAMUSwwAAFEsMAwBQLDEMAECxxDAAAMUSwwAAFEsM\nAwBQLDEMAECxxDAAAMUSwwAAFEsMAwBQLDEMAECxxDAAAMUSwwAAFEsMAwBQLDEMAECxxDAAAMUS\nwwAAFEsMAwBQLDEMAECxxDAAAMUSwwAAFEsMAwBQLDEMAECx5ozhPXv2pLu7O93d3dm3b9+brhsb\nG0tvb29uuumm3Hrrrfnzn/88r4MCAMB8a5rtwampqfT19WX37t2p1+vZsmVLNmzYcPodNTXl7rvv\nzqWXXprnn38+mzdvzh//+MezMjQAAMyHWc8MDw8Pp6OjI62trWlvb09bW1tGRkZOu/aiiy7KpZde\nmiRZsWJFpqenMz09Pf8TAwDAPJn1zPDExESq1Wr6+/uzbNmyVKvVjI+PZ+3atbPu9E9/+lMuv/zy\nLFq0aF6HBQCA+TRrDL9m8+bNSZKBgYFUKpVZ19Zqtdx77725//773/l0AABwFs0aw9VqNbLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3933e90>"
+        "<matplotlib.figure.Figure at 0x7f9928afeed0>"
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -726,7 +726,7 @@
      "input": [
       "pos = [1.0,0,0,0,0,0,0,0,0,0]\n",
       "for i in range (500):\n",
-      "    pos = update(pos, 1, .8, .1, .1)\n",
+      "    pos = predict(pos, 1, .8, .1, .1)\n",
       "print(pos)\n",
       "bar_plot.plot(pos)"
      ],
@@ -745,11 +745,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAFyCAYAAAD7xWwMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3918690>"
+        "<matplotlib.figure.Figure at 0x7f992877ae10>"
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",
@@ -763,8 +763,8 @@
      "collapsed": false,
      "input": [
       "pos = [1.0,0,0,0,0,0,0,0,0,0]\n",
-      "for i in range (100):\n",
-      "    pos = update(pos, 1, .8, .1, .1)\n",
+      "for i in range(100):\n",
+      "    pos = predict(pos, 1, .8, .1, .1)\n",
       "print(pos)\n",
       "bar_plot.plot(pos)"
      ],
@@ -784,11 +784,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x392f690>"
+        "<matplotlib.figure.Figure at 0x7f99287ab050>"
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 28
     },
     {
      "cell_type": "heading",
@@ -816,9 +816,9 @@
      "collapsed": false,
      "input": [
       "p = np.array([.1]*10)\n",
-      "p = sense(p, 1, .6, .2)\n",
+      "p = update(p, 1, .6, .2)\n",
       "print(p)\n",
-      "p = update(p, 1, .8, .1, .1)\n",
+      "p = predict(p, 1, .8, .1, .1)\n",
       "print(p)\n",
       "bar_plot.plot(p)"
      ],
@@ -829,22 +829,22 @@
        "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"
+        "[ 0.16666667  0.05555556  0.16666667  0.05555556  0.05555556  0.16666667\n",
+        "  0.05555556  0.16666667  0.05555556  0.05555556]\n",
+        "[ 0.06666667  0.14444444  0.07777778  0.14444444  0.06666667  0.06666667\n",
+        "  0.14444444  0.07777778  0.14444444  0.06666667]\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAFyCAYAAAD7xWwMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3928e90>"
+        "<matplotlib.figure.Figure at 0x7f992883c110>"
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 29
     },
     {
      "cell_type": "markdown",
@@ -857,7 +857,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "p = sense(p, 1, .6, .2)\n",
+      "p = update(p, 1, .6, .2)\n",
       "print(p)\n",
       "bar_plot.plot(p)"
      ],
@@ -868,20 +868,20 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[ 0.15671642  0.31343284  0.10447761  0.04477612  0.03731343  0.03731343\n",
-        "  0.03731343  0.03731343  0.13432836  0.09701493]\n"
+        "[ 0.12676056  0.0915493   0.14788732  0.0915493   0.04225352  0.12676056\n",
+        "  0.0915493   0.14788732  0.0915493   0.04225352]\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAFyCAYAAAD7xWwMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x394e710>"
+        "<matplotlib.figure.Figure at 0x7f9928afedd0>"
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 30
     },
     {
      "cell_type": "markdown",
@@ -894,8 +894,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "p = update(p, 1, .8, .1, .1)\n",
-      "p = sense(p, 0, .6, .2)\n",
+      "p = predict(p, 1, .8, .1, .1)\n",
+      "p = update(p, 0, .6, .2)\n",
       "bar_plot.plot(p)"
      ],
      "language": "python",
@@ -904,13 +904,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAFyCAYAAAD7xWwMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3b56d50>"
+        "<matplotlib.figure.Figure at 0x7f9945e1af50>"
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 31
     },
     {
      "cell_type": "markdown",
@@ -923,8 +923,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "p = update(p, 1, .8, .1, .1)\n",
-      "p = sense(p, 0, .6, .2)\n",
+      "p = predict(p, 1, .8, .1, .1)\n",
+      "p = update(p, 0, .6, .2)\n",
       "bar_plot.plot(p)"
      ],
      "language": "python",
@@ -933,13 +933,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAFyCAYAAAD7xWwMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAFyCAYAAAD7xWwMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAF+ZJREFUeJzt3W1s3XX9//HXYV03xq6oHmi3jABZZcoGYSEaQw3O4Arl\nIplX2WKcMXpDg0Sz+QOcMeodQUyNJogwDPkZozYuEoxm6KrMi8UbRhupSDpH8GLAsp6mk20MTrut\nvxv86f64rmNbtwP7PB63es75cPruN+zb5z77nnMq27dvHwsAABTinEYPAAAAZ5IABgCgKAIYAICi\nCGAAAIoigAEAKIoABgCgKMcN4K997Wu55pprcvPNNx/3yTZv3pzOzs50dnZm69atUzIgAABMpeMG\n8MqVK/PAAw8c94lGRkbS3d2dH/3oR/nf//3ffPWrX52SAQEAYCodN4CvuuqqzJ8//7hP1N/fn/b2\n9rS0tKStrS2tra0ZGBiYkiEBAGCqNE3VEw0NDaVaraanpyfz5s1LtVrN4OBglixZMlXfAgAATtmU\nBfArVq9enSTp7e1NpVKZ6qcHAIBTMmUBXK1WU6vVxm/XarVUq9Wj1v3zn//MtGnTpurbAgDAhOr1\nehYvXnzU/ScdwN3d3alUKlm3bl2SZNmyZdmxY0eGh4dTr9eze/fuCS9/mDZtWpYvX36y3/aE/Oxn\nP3tN715RAsfiCMfiCMfiCMfiCMfiCMfiCMfiZY7DEW+EY9HX1zfh/ccN4K985Svp7e3Nf/7zn1x7\n7bX58pe/nBUrVmRoaOhV65qbm7N+/fqsWbMmSbJhw4YpGBsAAKbWcQP4S1/6Ur70pS8ddf9dd911\n1H1dXV3p6uqamskAAOA0OKs/Ce4tb3lLo0d43XAsjnAsjnAsjnAsjnAsjnAsjnAsXuY4HPFGPhaV\n7du3j53Jb7hz584zdg0wAADl6uvry6JFi466/6zeAQYAgP8mgAEAKIoABgCgKAIYAICiCGAAAIoi\ngAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICi\nCGAAAIoigAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCg\nKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICiCGAAAIoigAEA\nKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICiCGAA\nAIoigAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICiHDeAN2/enM7OznR2dmbr1q2T\nrr333ntz44035sYbb8y99947ZUMCAMBUaZrswZGRkXR3d2fTpk2p1+tZu3ZtVqxYMeHanTt35qc/\n/Wl++ctf5tChQ7nhhhuyatWqLFy48LQMDgAAJ2PSHeD+/v60t7enpaUlbW1taW1tzcDAwIRrZ8+e\nnaamprz00kup1+uZPn165syZc1qGBgCAkzXpDvDQ0FCq1Wp6enoyb968VKvVDA4OZsmSJUetPf/8\n87N27dq8+93vzuHDh3PHHXdk7ty5p21wAAA4GZMG8CtWr16dJOnt7U2lUplwzTPPPJOenp489thj\nGR0dzZo1a/Lud7871Wp16qYFAIBTNGkAV6vV1Gq18du1Wu2YQdvf359ly5Zl9uzZSZK3ve1tefLJ\nJ3Pttdcetfbuu+8e/7qjoyMdHR0nNTwAALxi27Zt2bZt2/jtlStXTrhu0gBetmxZduzYkeHh4dTr\n9ezevXv88ofu7u5UKpWsW7cuSbJo0aL89a9/zcjISA4fPpy//e1v+fSnPz3h8955550n9UMBAMCx\n/PfGal9f34TrJg3g5ubmrF+/PmvWrEmSbNiwYfyxoaGhV61dtmxZ3vve92bVqlVJkg996EO59NJL\nT256AAA4TSrbt28fO5PfcOfOnVm+fPmZ/JYAABSor68vixYtOup+nwQHAEBRBDAAAEURwAAAFEUA\nAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEUR\nwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBR\nBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQ\nFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAA\nFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAA\nAEU5bgBv3rw5nZ2d6ezszNatWydd+/jjj+fmm29OV1dXPvvZz07ZkAAAMFWaJntwZGQk3d3d2bRp\nU+r1etauXZsVK1ZMuPbw4cO5/fbbc9ddd2X58uXZs2fPaRkYAABOxaQ7wP39/Wlvb09LS0va2trS\n2tqagYGBCdc+8cQTaWlpyfLly5Mk559//tRPCwAAp2jSAB4aGkq1Wk1PT08effTRVKvVDA4OTrh2\n165dmTNnTj7xiU9k1apV+eEPf3haBgYAgFMx6SUQr1i9enWSpLe3N5VKZcI19Xo9fX19+fnPf57Z\ns2fn/e9/f971rndl0aJFUzctAACcokkDuFqtplarjd+u1WqpVqvHXLt48eK0trYmSZYuXZqnn356\nwgC+++67x7/u6OhIR0fHSQ0PAACv2LZtW7Zt2zZ+e+XKlROumzSAly1blh07dmR4eDj1ej27d+/O\nkiVLkiTd3d2pVCpZt25dkpeD97nnnsvzzz+fc889N3//+99z0UUXTfi8d95550n9UAAAcCz/vbHa\n19c34bpJA7i5uTnr16/PmjVrkiQbNmwYf2xoaOhVa+fMmZMNGzbkox/9aA4ePJibb745l1xyyUn/\nAAAAcDpUtm/fPnYmv+HOnTvH3ykCAABOl76+vgkvx/VJcAAAFEUAAwBQFAEMAEBRBDAAAEURwAAA\nFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAA\nAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEM\nAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUA\nAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEUR\nwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQlOMG8ObN\nm9PZ2ZnOzs5s3br1uE+4f//+dHR05KGHHpqSAQEAYCo1TfbgyMhIuru7s2nTptTr9axduzYrVqyY\n9Anvv//+LF26NJVKZUoHBQCAqTDpDnB/f3/a29vT0tKStra2tLa2ZmBg4Jjrn3766QwPD2fp0qUZ\nGxub8mEBAOBUTRrAQ0NDqVar6enpyaOPPppqtZrBwcFjrv/GN76R2267bcqHBACAqTLpJRCvWL16\ndZKkt7f3mJc2PPbYY7n44ovT1tZm9xcAgNetSQO4Wq2mVquN367VaqlWqxOu7e/vz5YtW/LrX/86\ne/bsyTnnnJMLLrggN91001Fr77777vGvOzo60tHRcbLzAwBAkmTbtm3Ztm3b+O2VK1dOuK6yffv2\nY27XjoyM5IYbbhh/EdxHP/rRbNmyJUnS3d2dSqWSdevWHfXf3XvvvTnvvPPysY997KjHdu7cmeXL\nl5/wDwQAACeir68vixYtOur+SXeAm5ubs379+qxZsyZJsmHDhvHHhoaGpnhEAAA4/SbdAT4d7AAD\nAHAmHGsH2CfBAQBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEM\nAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUA\nAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEUR\nwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBR\nBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQ\nFAEMAEBRBDAAAEVpavQAAMeya289g/tHGj3GlLpgdnPa5s5o9BjAWcy58/gEMPC6Nbh/JP+z+alG\njzGlvt61WAADp5Vz5/G5BAIAgKIcN4A3b96czs7OdHZ2ZuvWrcdct3v37qxZsyY33XRT3ve+9+UP\nf/jDlA4KAABTYdJLIEZGRtLd3Z1NmzalXq9n7dq1WbFixcRP1NSUL3/5y7nsssvy3HPPZfXq1fnd\n7353WoYGAICTNekOcH9/f9rb29PS0pK2tra0trZmYGBgwrVvetObctlllyVJFixYkNHR0YyOjk79\nxAAAcAom3QEeGhpKtVpNT09P5s2bl2q1msHBwSxZsmTSJ/3973+fyy+/PNOnT5/SYQEA4FS9pneB\nWL16dZKkt7c3lUpl0rW1Wi333HNP7rvvvlOfDgAAptikAVytVlOr1cZv12q1VKvVY66v1+Line truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3916690>"
+        "<matplotlib.figure.Figure at 0x7f99287e4d50>"
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 32
     },
     {
      "cell_type": "markdown",
@@ -974,8 +974,8 @@
       "measurements = [1,0,1,0,0]\n",
       "\n",
       "for m in measurements:\n",
-      "    pos = sense(pos, m, .6, .2)\n",
-      "    pos = update(pos, 1, .8, .1, .1)\n",
+      "    pos = update(pos, m, .6, .2)\n",
+      "    pos = predict(pos, 1, .8, .1, .1)\n",
       "bar_plot.plot(pos)\n",
       "print(pos)"
      ],
@@ -987,7 +987,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAFyCAYAAAD7xWwMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x391fa50>"
+        "<matplotlib.figure.Figure at 0x7f992877a550>"
        ]
       },
       {
@@ -999,7 +999,7 @@
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 33
     },
     {
      "cell_type": "markdown",
@@ -1012,8 +1012,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "pos = sense(pos, m, .6, .2)\n",
-      "pos = update(pos, 1, .8, .1, .1)\n",
+      "pos = update(pos, m, .6, .2)\n",
+      "pos = predict(pos, 1, .8, .1, .1)\n",
       "bar_plot.plot(pos)"
      ],
      "language": "python",
@@ -1024,11 +1024,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAFyCAYAAAD7xWwMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3b58150>"
+        "<matplotlib.figure.Figure at 0x7f9945e1af50>"
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 35
     },
     {
      "cell_type": "markdown",
@@ -1044,8 +1044,8 @@
       "measurements = [0,1,0,1,0,0]\n",
       "\n",
       "for m in measurements:\n",
-      "    pos = sense(pos, m, .6, .2)\n",
-      "    pos = update(pos, 1, .8, .1, .1)\n",
+      "    pos = update(pos, m, .6, .2)\n",
+      "    pos = predict(pos, 1, .8, .1, .1)\n",
       "bar_plot.plot(pos)"
      ],
      "language": "python",
@@ -1056,11 +1056,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAFyCAYAAAD7xWwMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAF9VJREFUeJzt3W9snXX9//HXYV03x/5RPNhuGUGzyoQNwkI0hhqY0RUK\nmAz/ZItxxugNjRLJ5hdwxqh3BDU1mCDKMORrjNq4SDSYoasy/yzeMNJIBdI5gn8GLOtpOtjG4LTb\n+r3Bj+6H6zq2dTuwz+Nxa9c5H8559wq5+ty161ynsn379rEAAEAhzmn0AAAAcCYJYAAAiiKAAQAo\nigAGAKAoAhgAgKIIYAAAinLcAP7GN76Rq666KjfeeONxX2zz5s3p7OxMZ2dntm7dOiUDAgDAVDpu\nAK9cuTL33nvvcV9oZGQk3d3d+elPf5r//d//zde//vUpGRAAAKbScQP4iiuuyPz584/7Qv39/Wlv\nb09LS0va2trS2tqagYGBKRkSAACmStNUvdDQ0FCq1Wp6enoyb968VKvVDA4OZsmSJVP1FgAAcMqm\nLIBfsXr16iRJb29vKpXKVL88AACckikL4Gq1mlqtNr5dq9VSrVaPWvevf/0r06ZNm6q3BQCACdXr\n9SxevPiox086gLu7u1OpVLJu3bokybJly7Jjx44MDw+nXq9n9+7dE17+MG3atCxfvvxk3/aEPPjg\ng6/p7hUlsC+OsC+OsC+OsC+OsC+OsC+OsC9eZj8c8UbYF319fRM+ftwA/trXvpbe3t4899xzufrq\nq/PVr341K1asyNDQ0KvWNTc3Z/369VmzZk2SZMOGDVMwNgAATK3jBvBXvvKVfOUrXznq8TvuuOOo\nx7q6utLV1TU1kwEAwGlwVn8T3Nvf/vZGj/C6YV8cYV8cYV8cYV8cYV8cYV8cYV+8zH444o28Lyrb\nt28fO5NvuHPnzjN2DTAAAOXq6+vLokWLjnr8rD4DDAAA/00AAwBQFAEMAEBRBDAAAEURwAAAFEUA\nAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEUR\nwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBR\nBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQ\nFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEURwAAA\nFEUAAwBQFAEMAEBRBDAAAEURwAAAFEUAAwBQFAEMAEBRBDAAAEU5bgBv3rw5nZ2d6ezszNatWydd\ne/fdd+f666/P9ddfn7vvvnvKhgQAgKnSNNmTIyMj6e7uzqZNm1Kv17N27dqsWLFiwrU7d+7ML3/5\ny/zmN7/JoUOHct1112XVqlVZuHDhaRkcAABOxqRngPv7+9Pe3p6Wlpa0tbWltbU1AwMDE66dPXt2\nmpqa8tJLL6Ver2f69OmZM2fOaRkaAABO1qRngIeGhlKtVtPT05N58+alWq1mcHAwS5YsOWrteeed\nl7Vr1+aaa67J4cOHc9ttt2Xu3LmnbXAAADgZkwbwK1avXp0k6e3tTaVSmXDN008/nZ6enjz88MMZ\nHR3NmjVrcs0116RarU7dtAAAcIomDeBqtZparTa+XavVjhm0/f39WbZsWWbPnp0kueSSS/LEE0/k\n6quvPmrtnXfeOf7njo6OdHR0nNTwAADwim3btmXbtm3j2ytXrpxw3aQBvGzZsuzYsSPDw8Op1+vZ\nvXv3+OUP3d3dqVQqWbduXZJk0aJF+fvf/56RkZEcPnw4jz/+eD73uc9N+Lq33377Sf1QAABwLP99\nYrWvr2/CdZMGcHNzc9avX581a9YkSTZs2DD+3NDQ0KvWLlu2LO9///uzatWqJMlHPvKRvO1tbzu5\n6QEA4DSpbN++fexMvuHOnTuzfPnyM/mWAAAUqK+vL4sWLTrqcd8EBwBAUQQwAABFEcAAABRFAAMA\nUBQBDABAUQQwAABFEcAAABRFAAMAUBQBDABAUQQwAABFEcAAABRFAAMAUBQBDABAUQQwAABFEcAA\nABRFAAMAUBQBDABAUQQwAABFEcAAABRFAAMAUBQBDABAUQQwAABFEcAAABRFAAMAUBQBDABAUQQw\nAABFEcAAABRFAAMAUBQBDABAUQQwAABFEcAAABRFAAMAUBQBDABAUQQwAABFEcAAABRFAAMAUBQB\nDABAUQQwAABFEcAAABRFAAMAUBQBDABAUQQwAABFEcAAABRFAAMAUBQBDABAUQQwAABFEcAAABRF\nAAMAUBQBDABAUQQwAABFEcAAABRFAAMAUBQBDABAUQQwAABFEcAAABRFAAMAUBQBDABAUQQwAABF\nOW4Ab968OZ2dnens7MzWrVsnXfvoo4/mxhtvTFdXV2655ZYpGxIAAKZK02RPjoyMpLu7O5s2bUq9\nXs/atWuzYsWKCdcePnw4t956a+64444sX748e/bsOS0DAwDAqZj0DHB/f3/a29vT0tKStra2tLa2\nZmBgYMK1jz32WFpaWrJ8+fIkyXnnnTf10wIAwCmaNICHhoZSrVbT09OThx56KNVqNYODgxOu3bVr\nV+bMmZNPfepTWbVqVX7yk5+cloEBAOBUTHoJxCtWr16dJOnt7U2lUplwTb1eT19fX371q19l9uzZ\n+eAHP5j3vOc9WbRo0dRNCwAAp2jSAK5Wq6nVauPbtVot1Wr1mGsXL16c1tbWJMnSpUvz1FNPTRjA\nd9555/ifOzo60tHRcVLDAwDAK7Zt25Zt27aNb69cuXLCdZMG8LJly7Jjx44MDw+nXq9n9+7dWbJk\nSZKku7s7lUol69atS/Jy8D777LN5/vnn86Y3vSn/+Mc/cuGFF074urfffvtJ/VAAAHAs/31ita+v\nb8J1kwZwc3Nz1q9fnzVr1iRJNmzYMP7c0NDQq9bOmTMnGzZsyMc//vEcPHgwN954Y9761ree9A8A\nAACnQ2X79u1jZ/INd+7cOX6nCAAAOF36+vomvBzXN8EBAFAUAQwAQFEEMAAARRHAAAAURQADAFAU\nAQwAQFEEMAAARRHAAAAURQADAFAUAQwAQFEEMAAARRHAAAAURQADAFAUAQwAQFEEMAAARRHAAAAU\nRQADAFAUAQwAQFEEMAAARRHAAAAURQADAFAUAQwAQFEEMAAARRHAAAAURQADAFAUAQwAQFEEMAAA\nRRHAAAAURQADAFAUAQwAQFEEMAAARRHAAAAURQADAFAUAQwAQFEEMAAARRHAAAAURQADAFAUAQwA\nQFEEMAAARRHAAAAURQADAFAUAQwAQFEEMAAARRHAAAAURQADAFAUAQwAQFEEMAAARRHAAAAURQAD\nAFAUAQwAQFEEMAAARRHAAAAURQADAFAUAQwAQFEEMAAARRHAAAAURQADAFAUAQwAQFGOG8CbN29O\nZ2dnOjs7s3Xr1uO+4P79+9PR0ZH7779/SgYEAICp1DTZkyMjI+nu7s6mTZtSr9ezdu3arFixYtIX\n/P73v5+lS5emUqlM6aAAADAVJj0D3N/fn/b29rS0tKStrS2tra0ZGBg45vqnnnoqw8PDWbp0acbG\nxqZ8WAAAOFWTBvDQ0FCq1Wp6enry0EMPpVqtZnBw8Jjrv/3tb+fmm2+e8iEBAGCqTHoJxCtWr16d\nJOnt7T3mpQ0PP/xwLrroorS1tTn7CwDA69akAVytVlOr1ca3a7VaqtXqhGv7+/uzZcuW/O53v8ue\nPXtyzjnn5IILLsgNN9xw1No777xz/M8dHR3p6Og42fkBACBJsm3btmzbtm18e+XKlROuq2zfvv2Y\np2tHRkZy3XXXjX8I7uMf/3i2bNmSJOnu7k6lUsm6deuO+u/uvvvunHvuufnEJz5x1HM7d+7M8uXL\nT/gHAgCAE9HX15dFixYd9fikZ4Cbm5uzfv36rFmzJkmyYcOG8eeGhoameEQAADj9Jj0DfDo4AwwA\nwJlwrDPAvgkOAICiCGAAAIoigAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICiCGAA\nAIoigAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIY\nAICiCGAAAIoigAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoA\nBgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICiCGAAAIoi\ngAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICiCGAAAIoigAEAKIoABgCgKAIYAICi\nNDXiTR99dl8j3va0umB2c9rmzmj0GMBZatfeegb3jzR6jCnluAk0SkMC+H82P9mItz2tvtW12IEc\nOG0G94+cdcdOx02gUVwCAQBAUQQwAABFEcAAABTluAG8efPmdHZ2prOzM1u3bj3mut27d2fNmjW5\n4YYbctNNN+XPf/7zlA4KAABTYdIPwY2MjKS7uzubNm1KvV7P2rVrs2LFiolfqKkpX/3qV3PxxRfn\n2WefzerVq/PHP/7xtAwNAAAna9IzwP39/Wlvb09LS0va2trS2tqagYGBCdeef/75ufjii5MkCxYs\nyOjoaEZHR6d+YgAAOAWTngEeGhpKtVpNT09P5s2bl2q1msHBwSxZsmTSF/3Tn/6USy+9NNOnT5/S\nYQEA4FS9pvsAr169OknS29ubSqUy6dparZZvfvObueeee059OgAAmGKTBnC1Wk2tVhvfrtLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x3b59c10>"
+        "<matplotlib.figure.Figure at 0x7f99287b9e90>"
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 36
     },
     {
      "cell_type": "markdown",
@@ -1104,17 +1104,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x3b56110>"
-       ]
-      }
-     ],
-     "prompt_number": 22
+     "outputs": []
     },
     {
      "cell_type": "markdown",