diff --git a/02-Discrete-Bayes.ipynb b/02-Discrete-Bayes.ipynb index fe52fad..5d6687c 100644 --- a/02-Discrete-Bayes.ipynb +++ b/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": 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jcAEAABhG4QIAADCMwgUAAGAYhQsAAMAwChcAAIBhFC4AAADDKFwAAACGUbgAAAAMo3AB\nAAAY5nPhKisrU0JCgiIjI5Wamqrq6uoL7t22bZtmzJihgQMHqmfPnkpOTlZFRYVfAgMAAAQanwpX\nZWWl8vLytHTpUtXX1ys9PV1Tp07V0aNH291fU1Ojyy67TC+99JLef/993XXXXVqwYIE2btzo1/AA\nAACBINyXTcXFxcrJyVFOTo4kqaSkRK+//rrKy8tVVFTUZn9BQUGrv+fm5qqqqkovvfSSZs+e7YfY\nAAAAgaPDM1zNzc2qq6tTZmZmq/UpU6aopqbG5yc6efKk+vbte+kJAQAAAlyHZ7iOHz+u8+fPy+Fw\ntFp3OBx66623fHqS1157TW+//fYlFTQAAIBg4dNbip3xzjvv6LbbbtOqVauUkpJi+ul+VE1NTaqt\n/di3vT0GGE7jf5cyn2m1tbV2RzAq2OeTgn9G5gt8ds8YiK8TUtd5rbD75zd8+PCLfr/DtxT79++v\nsLAweTyeVusej0cxMTEXPba6ulrXX3+9li9frgULFvgQFwAAIPh0eIYrIiJCKSkpcjqdmjlzZsu6\n0+nUrFmzLnjc9u3bdcMNN6iwsFALFy70T9oupnfv3kpNGuzT3upPv5D0jdlAfnYp85ny3b9YUlNT\nbc1hSrDPJwX/jMwX+LrKjIH4OiHZ/1rRVX5+jY2NF/2+Tx8LkZ+fr7Vr12rNmjXav3+/Fi9eLJfL\npdzcXEnf/lbi5MmTW/Zv3bpV119/ve666y7Nnj1bHo9HHo9Hx48f78QoAAAAgcmna7iysrJ04sQJ\nFRUVyeVyKSkpSZs3b1ZcXJwkye126/Dhwy37161bp6+//lorV67UypUrW9aHDBmiQ4cO+XkEAACA\nrs3ni+Zzc3Nbzmh93/c/Rb6iooJPlgcAAPj/uJciAACAYRQuAAAAwyhcAAAAhlG4AAAADKNwAQAA\nGEbhAgAAMIzCBQAAYBiFCwAAwDAKFwAAgGEULgAAAMMoXAAAAIZRuAAAAAyjcAEAABhG4QIAADCM\nwgUAAGAYhQsAAMAwChcAAIBhFC4AAADDKFwAAACGUbgAAAAMo3ABAAAYRuECAAAwjMIFAABgGIUL\nAADAMAoXAACAYRQuAAAAwyhcAAAAhlG4AAAADKNwAQAAGEbhAgAAMIzCBQAAYBiFCwAAwDAKFwAA\ngGEULgAAAMMoXAAAAIZRuAAAAAyjcAEAABjmc+EqKytTQkKCIiMjlZqaqurq6ovub2ho0KRJkxQV\nFaX4+HgVFhZ2OiwAAEAg8qlwVVZWKi8vT0uXLlV9fb3S09M1depUHT16tN39TU1NyszMVGxsrOrq\n6vT000/rqaeeUnFxsV/DAwAABAKfCldxcbFycnKUk5OjESNGqKSkRLGxsSovL293/4YNG/T1119r\n3bp1GjVqlH71q1/p/vvv15///Ge/hgcAAAgEHRau5uZm1dXVKTMzs9X6lClTVFNT0+4xu3bt0jXX\nXKNu3bq1rF133XU6duyYPv30005GBgAACCwdFq7jx4/r/PnzcjgcrdYdDofcbne7x7jd7nb3W5Z1\nwWMAAACCVbjdAf7X1l8OsDvCJWtsbPRp39ifhAb1fKYMHz68S+QwJdjnk4J/RuYLfF1lxkB9nZDs\n/W/XVX5+HenwDFf//v0VFhYmj8fTat3j8SgmJqbdY2JiYtrdHxIScsFjAAAAglWHhSsiIkIpKSly\nOp2t1p1OpzIyMto9Zvz48dqxY4fOnj3bsrZlyxYNHDhQQ4YM6WRkAACAwBJiWZbV0aYXX3xRc+fO\nVWlpqTIyMlReXq6Kigp98MEHiouLU0FBgXbv3q0333xTknTy5EmNHDlSkyZN0kMPPaQDBw5o/vz5\nWrZsmfLy8owPBQAA0JX4dA1XVlaWTpw4oaKiIrlcLiUlJWnz5s2Ki4uT9O1F8ocPH27Z36dPHzmd\nTt1zzz1KS0tT3759tWTJEsoWAAD4P8mnM1wAAAD44YL2XoqXeiuiQLNjxw7deOONiouLU2hoqJ5/\n/nm7I/nNE088oSuvvFLR0dEaMGCAfvnLX+r999+3O5ZflZWVKTk5WdHR0YqOjlZ6ero2bdpkdyxj\nnnjiCYWGhmrRokV2R/GbZcuWKTQ0tNWfgQMH2h3Lr9xut7KzszVgwABFRkYqKSlJO3bssDuWXwwb\nNqzNzy80NFTTp0+3O5rfeL1ePfzwwy2vhQkJCXr44Yfl9XrtjuY3p06dUl5enoYOHaqoqChNmDBB\ntbW1dsdqV1AWrku9FVEgOnXqlMaOHauSkhJFRUXZHcevtm/frt/97nfauXOnqqqqFB4ersmTJ+vL\nL7+0O5rfxMfH68knn9S7776ruro6/eIXv9CMGTPU0NBgdzS/27Vrl1avXq3k5GS7o/jdyJEj5fF4\n5Ha75Xa79d5779kdyW8aGxuVkZGhkJAQbd68Wfv379eqVas0YEBgfmzB99XW1rb83Nxut/bs2aOQ\nkBDdcsstdkfzmxUrVqi8vFzPPPOMDhw4oJKSEpWVlemJJ56wO5rf3HHHHXI6nVq/fr0aGhqUmZmp\nyZMny+Vy2R2tLSsIXXXVVdadd97Zam348OHWgw8+aFMis3r16mWtW7fO7hjGnDp1ygoLC7Nee+01\nu6MY1a9fP+uvf/2r3TH86ssvv7QSExOtrVu3WpMmTbIWLlxodyS/eeyxx6yxY8faHcOYgoICa8KE\nCXbH+NEsX77c6tu3r3XmzBm7o/jNDTfcYGVnZ7damzdvnjV9+nSbEvnX119/bYWHh1uvvvpqq/WU\nlBTr4YcftinVhQXdGa4fcisidG0nT56U1+tV37597Y5ihNfr1caNG3X69Gmlp6fbHcevFixYoKys\nLE2cONHuKEYcOnRIgwYNUkJCgubMmdPql4cC3csvv6yrrrpKs2fPlsPh0BVXXKHS0lK7Yxnzt7/9\nTbfffru6d+9udxS/mTBhgqqqqnTgwAFJ0gcffKC3335b06ZNszmZf5w7d07nz59v8zOLjIzskpcR\nBV3h+iG3IkLXtnjxYo0bN07jx4+3O4pfNTQ0qHfv3urevbvuvvtu/fOf/9SYMWPsjuU3q1ev1qFD\nh7R8+XK7oxhx9dVXa+3atXrjjTf03HPPye12Kz09XV988YXd0fzi0KFDKisrU2JiorZs2aK8vDw9\n8MADKisrszua323ZskWffPKJfvvb39odxa/uv/9+/frXv9bo0aPVrVs3jR07VtnZ2brzzjvtjuYX\nvXr10vjx47V8+XIdO3ZMXq9XGzZs0M6dO7vkW4pd6tY+wPfl5+erpqZG77zzjkJCQuyO41cjR47U\n3r171djYqH/84x+aO3eutm3bptGjR9sdrdM+/PBDPfTQQ3rnnXcUGhp0/66TJF133XWt/n711Vdr\n2LBhWrduXVB8BI7X69WVV16poqIiSVJycrI+/PBDlZaW6u6777Y5nX+tXr1aaWlpSkpKsjuKX23c\nuFHr16/Xxo0bNXr0aNXX12vRokUaNmyY5s+fb3c8v9iwYYNycnIUFxen8PBwjRs3Trfeeqvq6urs\njtZG0P2f8Ifcighd07333qvKykpVVVUF5R0KwsPDlZCQoCuuuEJFRUW6/PLLVVxcbHcsv9i5c6f+\n+9//avTo0YqIiFBERIS2bdum0tJSdevWTc3NzXZH9LuoqCiNGTNGBw8etDuKX8TGxmrUqFGt1kaN\nGqUjR47YlMiMzz//XK+88ooWLFhgdxS/u++++7RkyRLNmjVLY8aM0W233ab8/Pygumh+2LBhqqqq\n0unTp/XZZ59p165dOnv2rBISEuyO1kbQFa4fcisidD2LFy9uKVvf3Zg02Hm9Xn3zzTd2x/CLm266\nSe+995727t3b8ic1NVVz5szR3r17FRERYXdEvztz5oz279+v2NhYu6P4RUZGRsu1P985cOBA0P3j\np6KiQj169NDs2bPtjuJ3X331VZszzKGhoUH1sRDfiYyMlMPh0BdffKE33nhDM2bMsDtSG0H5lmJ+\nfr7mzp2rtLS0llsRuVyuoHnfWpJOnz6tjz76SJZlyev16siRI9q7d6/69eun+Ph4u+N1yj333KMN\nGzbo5ZdfVnR0dMvZyl69eqlnz542p/OPgoICTZs2TfHx8WpqatLf//53bdu2LWg+i6tPnz5t3hrt\n2bOn+vXr1+asSaBasmSJpk+frsGDB8vj8aiwsFBfffWV5s2bZ3c0v7j33nuVkZGhxx9/XLfccov2\n7NmjVatWacWKFXZH86s1a9Zozpw5QffxOpI0ffp0rVixQkOHDtWYMWO0Z88eFRcXKzs72+5ofrNl\nyxZ5vV6NHDlSBw8e1H333afRo0d3zRnt/jVJU8rLy61hw4ZZPXr0sFJTU63q6mq7I/nV1q1brZCQ\nECs0NLTVn/nz59sdrdPamys0NNRatmyZ3dH8Jjs72xo6dKjVo0cPy+FwWJmZmZbT6bQ7llHXXntt\nUH0sxOzZs61BgwZZ3bt3t+Li4qybb77Z2rdvn92x/GrTpk1WcnKyFRkZaY0YMcJ65pln7I7kV1VV\nVVZoaKhVW1trdxQjTp06Zd17773W0KFDraioKCsxMdFaunSp9c0339gdzW9efPFFKzEx0erRo4c1\ncOBAa9GiRdbJkyftjtUubu0DAABgWNBdwwUAANDVULgAAAAMo3ABAAAYRuECAAAwjMIFAABgGIUL\nAADAMAoXAACAYRQuAAAAwyhcAAAAhv0/XwnOJz+IimQAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "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": 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"text/plain": [ - "" + "" ] }, "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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"text/plain": [ - "" + "" ] }, "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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fIyIiUFNTg3fffddpizIioq5gnhFRT7K5KPP09IRKpUJdXZ1Ze11dHby9vTvd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\nlUqFuro6s/a6ujp4e3tb3cb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"text/plain": [ - "" + "" ] }, "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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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2941,15 +2899,13 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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UlEYbysvL2y0P1tP9M3PmTPj6+mLmzJkICgrCN998g7Vr1/IjJOBJQOnWrRtC\nQkLw0UcfYfny5XBxcQEALFmyBBUVFQgJCcGaNWswfvx4/jh/f3/4+flh8uTJGD9+vEZCwFGjRoEx\nhpEjR/J3VletWqWz/+u1Z/+Qzq1LprVhjCErK0vtck6IxGIxevXq1ea3/VvSP/XLIVryREB7aa/+\nIZ1flxxxqVQqVFZWdnQzWq2urq5dns2j/iFC1yUDF2Osy1xCtMeAmPqHCF2XDFwAGn2RqJC01xez\nuf0zaNCgTnWZWI8C17OpywaurrKiur1+D+ofImRdMnBxHEc5yhtB/UOErksuQOU4rkXvLbx27Ro2\nbdqElJQUiMVi9OzZE++//z6Sk5Pxyy+/YOfOne3Q2sa1R4Ch/iFC12UDl1QqbdartcrLy7F48WIs\nX74co0aNglKpxNWrV/kveEdckkgkErVHbNoK9Q8Rui75z1VLRhTp6engOA6jR48Gx3GQSCR48cUX\nYWRkhE8//RQ3btzAsGHD+MWmSqUSX375JUJDQxEcHIzPPvuMfwTlypUrGDt2LL777juMGjUKEyZM\nQHR0dLN/D7lcDolE0uzjmkL9Q4SuSwYuAM3+Yrq4uEAkEmHVqlW4ePEi/05BNzc3fPDBB/D19cXZ\ns2fxxx9/AHiS5TQjIwMHDhzAL7/8gtzcXOzYsYOvr2EKmI8//hhr167lX7WlL6lU2m4jGeofImRd\nNnCZmJg0619juVyOnTt3guM4rF27FqNGjcL777+PgoICreV/+eUXvPfee1AoFJBKpQgLC+PT3gC6\nU8A0h7Gxcbt9Mal/iJB1yTku4Mn8h7m5OZ+2WR9ubm74+OOPAQBpaWlYuXIl1q9fD39/f7VyhYWF\nqKqq4p+rA56si2q4pkhXChh9icXidp2/of4hQtZlA5dIJIJCoWjWF7MhV1dXhIaG4siRIxpfTAsL\nC5iamuLgwYOwsbHReryuFDD6srKyatdn8Kh/iJB12UtFAFAoFFAoFHqVTU1NRVRUFP9uwUePHuHk\nyZPw8fGBtbU1cnNz+UR7HMdh4sSJWL9+PX9nLjc3F5cvX+br05YCZtSoUXq3vT5baHui/iFC1WVH\nXMCTORBLS0u9Xmcll8tx+/Zt/kUNZmZmGDp0KBYsWACJRAJ3d3cEBwdDJBLh9OnTmD9/Pnbs2IGw\nsDAUFxfDzs4Or776Kv+SiIYpYKRSKZYvX66WlK8xZmZmegeU1qD+IULVJdPaNFReXo7k5GSDZhFo\nbQoYFxecLSB8AAAOaElEQVQX2NjYGGTimfqHCFGXH2vLZLJGc8J3NpaWljA3NzfYl5L6hwhRlw9c\nHMfBwsIC1tbWHd2UJonFYlhbWxt0USX1DxGiLn+pWK+0tBQPHjzo1InnevToAVtb2w4ZTVD/ECHp\n8iOuegqFolNfEllaWsLCwqLDvpTUP0RInpnAxXEcrKys1N6u3FmYmZnxr+TqKNQ/REiemcAFPJkj\nsbGxQffu3Tu6KTy5XA5HR8dOsZiS+ocIxTMzx9VQbW0t8vLyGn2noSF069YNjo6OnW5NEvUP6eye\nycAFPHlDTEFBAbKzsztkQtrOzg7W1taQyWQGP7c+qH9IZ/bMBi7gyWMnZWVlyM/P13jZaHsxMTGB\no6MjLCwsIBaLDXLOlqL+IZ3VMx246imVShQVFbXr6ILjONja2sLKygoymUxQd8eof0hnQ4Hr/2OM\noaKiAuXl5SgsLNTr+T19GBsbw9raGnK5HGZmZoIdRVD/kM6EAtdTGGNQKpUoKytDWVkZiouL+ZTD\n+hKLxZDJZLCwsIBcLu9SIwjGGKqqqlBZWUn9QzoMBa5GqFQq1NTUoLq6GkqlEkqlEpWVlVAqlXxi\nPLFYDJFIBKlUColEAmNjY/4lDmKxmP9C5uXloaamBtbW1oJNgFddXY0LFy6guroaISEhYIy1Wf8Q\n0hwUuJqBMab2qVf/nsLGvoQlJSU4ePAgVCoVP8po+JFKpXwdvXv37nRf6KysLMTGxvKXiEOGDMFz\nzz2nVqY1/UNIc1DgMqDLly/jxo0bjZbx8vLC8OHDDdSippWWluLy5ct48OCB2naO4zBu3Dg4ODh0\nUMvIs6xLJxJsT9u3bwcADB06FH379tXrmIEDB+LevXuorq7Wul8sFmPQoEFt1sbWqK2txbVr13D9\n+nXU1dVp7Le0tKRHcEiHocBlQCYmJhg4cKBaCuOGJBIJcnJyIJPJOiwtsUqlQkpKCuLi4nTeOfT1\n9cULL7xAdwBJh6HAZWD9+vXDnTt3UFJSorGvsrISMTExsLCwgK+vL9zd3Q02qqmqqkJCQgLu3Lmj\nM2DZ2toiICAA9vb2BmkTIbpQ4DIwsViMYcOG4fjx4zrLFBUV4ezZs7h48SJ69uwJLy8vODo6tvnk\nNmMM+fn5uHXrFpKTk7VeEgJPXrz6wgsvwMvLiybYSadAgasDdO/eHf369cPt27cBAEZGRqirq8PT\n90lqa2uRmJiIxMREKBQKeHh4wMHBAba2ti1+hq+iogIPHz5EVlYWsrKyGl1IynEcfHx88Pzzz9N8\nFulUKHB1kCFDhiA9PR2lpaXw8/ND9+7dcfXqVSQlJWkEMAAoKyvD9evXcf36dQBPEv/Z2dnBxsZG\nY42UsbExlEolysvL1T4FBQU63zzdkEgkgoeHBwYOHAgLC4s2/90JaS0KXB3E2NgYw4cPx7lz59Cn\nTx+IRCKMGDECgwYNQkJCAhITE1FeXq7z+PqV/SkpKW3WJrlcDm9vb/Tp0wdSqbTN6iWkrVHg6kDd\nu3fH2LFj1e4gduvWDUOGDMHgwYPx8OFD3L9/Hw8ePNA5/9RaHMfB0dER/fr1g6urK71klQgCLUBt\npkuXLuHmzZtq26RSKd544w0YGxu3yzlramqQmpqKnJwc5ObmoqCgQOvlpL4sLS3h5OQEJycnODo6\n0vwVERwKXM1UUVGB/fv3q42A/Pz84Ovra7A2KJVK5OXlITc3F0VFRaipqeE/SqUSNTU1MDY2hkwm\n4x8vksvlkMvlcHBwoOR8RPDoUrGZZDIZvL29+VGXVCrVe+V8WzE2Noajo2OnfisPIe2JJjRaYMCA\nAfyq8QEDBrTbJSIhRDsKXC1QP+rqiNEWIYQCV4uEh4fjn//8J55//nnBjLaKiorg4OCgkeWhXl5e\nnt51TZkyBRs2bGirphHSfKyFHj9+zGbPns3c3NyYiYkJs7e3ZyNHjmS///47Y4yxwMBANn/+/GbX\n29LjtAkLC2McxzGRSMQ4juM//v7+rWpLSUkJKy4uZiqVqk3a2dzzt0RERASbPn261n2HDh1i06ZN\nYz///LNedd28eZNZWVmxkpKSNmkbIc3V4sn5yZMno6qqCt999x08PDyQm5uL2NhYg70NRl+jR49G\nVFSU2vKB1t7+NzMza22zDKqyshK7du3CiRMnNPalpKRAoVBgz549+PXXX5GWlgZXV9dG6+vfvz/c\n3d0RFRWF2bNnt1ezCdGtJdGuqKiIcRzHYmJitO5/eqQjEolYWloaY4yx6OhoNnToUGZpacmsrKxY\ncHAwu3v3bpPHMcbY559/zjw8PJhUKmW+vr4sKiqq0XaGhYWxcePGNVomNjaW+fn5MYVCwczNzdmL\nL77Ibt++3Whbnq43MDCQzZ49m73//vvMysqK2drask2bNrHq6mo2d+5cZmFhwVxcXNj333/PH9NY\nPzTVF83th0OHDjFra+tGyzTXmjVr2NChQ9u0TkL01aLAVVtby8zMzNjChQtZVVWVxv7i4mIWEBDA\nZsyYwXJzc1lOTg5/WfXTTz+xn3/+mSUnJ7ObN2+y//qv/2Kenp5MqVQ2etzy5ctZnz592KlTp1hq\nairbv38/UygU7Ndff9XZzqYCV21tLbO0tGRLlixhDx48YPfu3WP79+9nCQkJrKSkRGdbtAUuc3Nz\ntnr1apaUlMQ2bNjAOI5jL7/8Mtu0aRNLTk5mK1euZCYmJuzRo0dN9oOuPqyrq2tRPyxcuJCNGTNG\n5/6WiI6OZiYmJlr//glpby2e4/r555+ZtbU1MzU1Zf7+/iwiIoL99ddf/H5952fKysqYWCxmFy5c\n0HlceXk5k0ql7Pz582rbFy1axMaOHauz7rCwMGZkZMQUCgX/MTMzY8uWLWOMMVZQUMBEIhE7e/as\n1uN1/Q7aAldAQIBaGVtbWzZhwgT+Z6VSySQSCfvpp5+0nuvpftB2/pb2w8SJE1lYWJjati+++IK5\nu7szkUjEoqKiWGhoKHNycmIzZsxgjDEWGhrKevbsqbPOGzduMJFIxFJSUnSWIaS9tPiu4qRJk/Dw\n4UMcP34cr7zyCi5dugQ/Pz989tlnjR6XkpKC//7v/4anpyfMzc3h4OAAxhjS09N1HnPnzh1UVVUh\nJCQEZmZm/Gfr1q1NPmQ8fPhw3Lhxg8+scO3aNURGRgJ48ujLtGnTMGbMGISGhmLjxo3IyMhofmcA\nGivn7ezs4OPjw/9sZGQES0tL5ObmGrwfKisrYWpqqrYtMjISx44dg4mJCRITE8EYQ0xMDHbu3AkA\niIiIaPT3lUqlYIyhsrKy0XKEtIdWrZyXSCQYOXIkRo4ciQ8//BBvv/02Vq1a1ej/9GPHjoWLiwu2\nb98OJycnGBkZoW/fvo2+m0+lUgEAjh8/jh49eqjta2o5gkwmQ8+ePXXu3717NxYvXozo6GgcO3YM\nK1aswNGjRzF69OhG633a0+3gOE7rtvrfxZD9YGNjg8LCQo3t/fr1w7p16/Dee+/h008/hZeXV+O/\nZAMFBQX826cJMbQ2XcfVt29f1NbWoqqqChKJRCOjQUFBAe7du4fly5cjKCgIXl5eKC4uRm1tLV9G\n23He3t4wMTFBamoq3N3d1T5Pf4FbwsfHB5GRkThz5gwCAwOxd+9enW1pC/r0g7bzt7QfBg4ciDt3\n7mjdN2DAAMjlcuzduxdVVVUa+x8+fAhnZ2d89dVXattv3boFJycnClykQ7RoxFVQUIDXX38d06dP\nh6+vL8zMzPCf//wH//rXvzBq1CgoFAq4ubkhLi4OaWlpUCgUsLa2hqWlJWxsbLBjxw44OzsjMzMT\nS5YsURstaDtOoVAgIiICERERUKlUGDZsGMrKynD58mWIxWLMnDlTZ1urq6uRk5Ojtk0sFsPGxgap\nqanYtm0bxo8fDycnJyQnJ+PGjRuYO3euzra0BX36Qdv5raysWtQPwcHBWLZsGQoLC2Fpaclvz83N\nxZo1axAfHw9/f39ERkbi66+/5vczxrBjxw7s27cPw4YNU6vz3LlzCA4ObpP+IKTZWjIxVl1dzVas\nWMGGDBnCrKysmFwuZ71792YRERGssLCQMcbY/fv3WUBAAJPJZGq38s+cOcN8fHyYVCplPj4+7NSp\nU8zMzIzt3bu30eMYY+ybb75h/fr1Y6ampszOzo6NGTOGX/CqTVhYGBOJRBqfHj16MMYYy8nJYZMn\nT2bOzs7M1NSUubq6smXLlrHa2tpG2/L05PyIESM0JvF9fHzY6tWr1bY5OjqyzZs369UPjZ2/uf3A\nGGMBAQFsy5Yt/M8rVqxgvXv3Zr6+vqy0tJT16tWLicViNmnSJJadnc3+/PNPZmJiwuzs7NiZM2fU\n6qqqqmLm5uYsLi6u0XMS0l5afFeRCEt0dDTr06eP3qv9//zzT9a/f3924MAB5uvrq3bc5s2bWXBw\ncHs1lZAm0bOKz4jg4GDMnTsXmZmZepXfs2cPcnJyUFxcjPv37+ONN97g90kkErVLSkIMjRIJEkIE\nh0ZchBDBocBFCBEcClyEEMGhwEUIERwKXIQQwaHARQgRHApchBDBocBFCBEcClyEEMGhwEUIERwK\nXIQQwaHARQgRHApchBDBocBFCBEcClyEEMGhwEUIERwKXIQQwaHARQgRHApchBDBocBFCBEcClyE\nEMGhwEUIERwKXIQQwaHARQgRHApchBDBocBFCBEcClyEEMGhwEUIERwKXIQQwaHARQgRHApchBDB\nocBFCBEcClyEEMGhwEUIERwKXIQQwaHARQgRHApchBDBocBFCBEcClyEEMGhwEUIERwKXIQQwaHA\nRQgRHApchBDBocBFCBEcClyEEMGhwEUIERwKXIQQwaHARQgRHApchBDBocBFCBEcClyEEMGhwEUI\nERwKXIQQwaHARQgRHApchBDBocBFCBEcClyEEMGhwEUIERwKXIQQwaHARQgRHApchBDB+X/SFcVR\nxSSLKAAAAABJRU5ErkJggg==\n", 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7vM8Up13ey/Ms5yprlE6Avg5Y2nreS0QMpsutklaQHVpZO7K/mVmNOO8VYPsL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\nuP3HZEdBrA/5ELBVIiIeIks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"text/plain": [ - "" + "" ] }, "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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"text/plain": [ - "" + "" ] }, "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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"text/plain": [ - "" + "" ] }, "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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"text/plain": [ - "" + "" ] }, "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 }