Renamed variables to improve understanding.
I used p and pos for the belief in position, which can be a bit confusing.
This commit is contained in:
Roger Labbe committed
1 parent
62f13ace77
commit
17b30bc395
1 file changed
+112
-108
@@ -1,7 +1,7 @@
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{
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"nbformat": 3,
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"nbformat_minor": 0,
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@@ -261,7 +261,7 @@
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"output_type": "pyout",
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"prompt_number": 1,
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"text": [
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"<IPython.core.display.HTML at 0x7fb632fca4e0>"
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"<IPython.core.display.HTML at 0x7ff2d98a05f8>"
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@@ -309,7 +309,7 @@
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"input": [
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"import numpy as np\n",
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"\n",
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"pos = np.array([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1])"
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"pos_belief = np.array([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1])"
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],
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"language": "python",
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"metadata": {},
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@@ -350,8 +350,8 @@
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"import bar_plot\n",
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"import numpy as np\n",
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"\n",
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"pos = np.array([0.333, 0.333, 0., 0., 0., 0., 0., 0., 0.333, 0.])\n",
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"bar_plot.plot(pos)"
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"pos_belief = np.array([1./3, 1./3, 0, 0, 0, 0, 0, 0, 1./3, 0])\n",
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"bar_plot.plot(pos_belief)"
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],
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"language": "python",
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"metadata": {},
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@@ -361,7 +361,7 @@
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"output_type": "display_data",
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truncated
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"text": [
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"<matplotlib.figure.Figure at 0x7fb61ab15940>"
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"<matplotlib.figure.Figure at 0x7ff2bccf71d0>"
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]
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}
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],
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@@ -373,15 +373,15 @@
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"source": [
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"We call this a <i>multimodal</i> distribution because we have multiple beliefs about the position of our dog. Of course we are not saying that we think he is simultaneously in three different locations, merely that so far we have narrowed down our knowledge in his position to these locations. \n",
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"\n",
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"I hand coded the pos array in the code above. How would we implement this in code? Well, hallway represents each door as a 1, and wall as 0, so we will multiply the hallway variable by the percentage, like so;"
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"I hand coded the `pos_belief` array in the code above. How would we implement this in code? Well, hallway represents each door as a 1, and wall as 0, so we will multiply the hallway variable by the percentage, like so;"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"pos = hallway * 0.3\n",
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"print('pos =', pos)"
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"pos_belief = hallway * 0.3\n",
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"print('pos_belief =', pos_belief)"
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],
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"language": "python",
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"metadata": {},
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@@ -390,7 +390,7 @@
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"pos = [ 0.3 0.3 0. 0. 0. 0. 0. 0. 0.3 0. ]\n"
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"pos_belief = [ 0.3 0.3 0. 0. 0. 0. 0. 0. 0.3 0. ]\n"
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]
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}
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],
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@@ -422,8 +422,8 @@
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"pos = np.array([0., 1., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
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"print(pos)"
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"pos_belief = np.array([0., 1., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
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"print(pos_belief)"
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],
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"language": "python",
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"metadata": {},
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@@ -465,29 +465,29 @@
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"\n",
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"The key, as with the problem above, is probabilities. We are already comfortable with assigning a probabilistic belief about the location of the dog; now we just have to incorporate the additional uncertainty caused by the sensor noise. Say we think there is a 50% chance that our dog is in front of a specific door and we get a reading of 'door'. Well, we think that is only likely to be true 0.6 of the time, so we multiply: $0.5 * 0.6= 0.3$. Likewise, if we think the chances that our dog is in front of a wall is 0.1, and the reading is 'door', we would multiply the probability by the chances of a miss: $0.1 * 0.2 = 0.02$.\n",
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"\n",
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"However, we more or less chose 0.6 and 0.2 at random; if we multiply the pos array by these values the end result will no longer represent a true probability distribution. "
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"However, we more or less chose 0.6 and 0.2 at random; if we multiply the `pos_belief` array by these values the end result will no longer represent a true probability distribution. "
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"def update(pos, measure, p_hit, p_miss):\n",
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" q = np.array(pos, dtype=float)\n",
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"def update(pos_belief, measure, p_hit, p_miss):\n",
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" q = np.zeros(len(pos_belief))\n",
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" for i in range(len(hallway)):\n",
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" if hallway[i] == measure:\n",
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" q[i] = pos[i] * p_hit\n",
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" q[i] = pos_belief[i] * p_hit\n",
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" else:\n",
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" q[i] = pos[i] * p_miss\n",
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" q[i] = pos_belief[i] * p_miss\n",
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" return q\n",
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"\n",
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"pos = np.array([0.2]*10)\n",
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"pos_belief = np.array([0.2]*10)\n",
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"reading = 1 # 1 is 'door'\n",
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"pos = update(pos, 1, .6, .2)\n",
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"pos_belief = update(pos_belief, 1, .6, .2)\n",
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"\n",
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"print(pos)\n",
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"print('sum =', sum(pos))\n",
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"bar_plot.plot(pos)"
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"print(pos_belief)\n",
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"print('sum =', sum(pos_belief))\n",
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"bar_plot.plot(pos_belief)"
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],
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"language": "python",
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"metadata": {},
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@@ -505,7 +505,7 @@
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"output_type": "display_data",
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||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHGRJREFUeJzt3Xt01/V9+PFXqjHRb4L6pUNNUFbhAKEMuqKcgqWm0opF\nd6ZTlmO91FbEns55bO1m5uatnRu241C8lENbXY/HdUI9XnYcbrXReilYtdsZt0TGxTCgNKaAuUD4\nBpL9sV9h2Q+SAIEP8n48zvEc8v1c8sr7qHn69Z1Pirq7u7sDAAAS9aGsBwAAgCwJYgAAkiaIAQBI\nmiAGACBpghgAgKQJYgAAkiaIAQBIWp9B/Nxzz8WkSZOitLQ0vvjFL/b7xg8++GCceeaZkc/n4847\n7zysIQEA4Eg5sa8TTjvttPjzP//z+OlPfxo7duzo101/8YtfxH333Revv/56nHrqqfHJT34yfv/3\nfz9mzJhx2AMDAMBA6vMd4gsvvDCuuOKKyOfz/b7pU089FVdeeWVUVVVFRUVFzJw5M5588snDGhQA\nAI6Efu8hPpjf8Lx69eoYNWpUzJs3L77+9a/HmDFj4p133jmkAQEA4EjqdxAXFRX1+6bt7e1RVlYW\n69atizVr1kR5eXm0tbUd0oAAAHAk9bmH+LcO5h3iXC4XbW1tMW/evIiIeOaZZ6KsrGy/59bV1fX7\nvgAAcKimTp2639f7HcQH8w7xyJEjo6GhYe/Hq1atitGjRx/w/Kqqqn7f+1C98MIL8bnPfe6If54P\nAmuxj7XYx1rsYy32sRb7WIt9rMU+1mKfY30t6uvrD3iszy0TXV1d0dHREbt37449e/bErl27Ys+e\nPXuPV1dXR21tbY9rZsyYEU8//XSsWrUqNm3aFI899ljU1NQcxpcAAABHRp9B/Pjjj8cpp5wSDzzw\nQDzxxBNx8sknx/3337/3eGNjYzQ1NfW4ZuLEiXHPPffEpz/96fi93/u9qKmp8cg1AACOSX1umbjh\nhhvihhtuOODx9evX7/f1W2+9NW699dZDHmygjRgxIusRjhnWYh9rsY+12Mda7GMt9rEW+1iLfazF\nPh/ktSjqPpifljsC6urqjsoeYgAA0lVfX3/AH6rr92PXAADgeCSIAQBImiAGACBpghgAgKQJYgAA\nkiaIAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgA\ngKQJYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAG\nACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaI\nAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKQJ\nYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKT1GcQbN26M6urqyOVyMWHC\nhFi5cmW/bnzXXXdFRUVFfPjDH47Pf/7z0draetjDAgDAQOsziGfNmhXjxo2LrVu3Rk1NTdTU1PR5\n02effTYef/zx+OUvfxkbNmyIrVu3xje/+c0BGRgAAAZSr0Hc0tISL774YtTW1kZJSUncdttt0djY\nGCtWrOj1pg0NDTF58uQ466yz4pRTTolLL700Vq1aNaCDAwDAQOg1iNesWROlpaWRy+ViypQpsX79\n+hg+fHg0NDT0etPPfOYz8dZbb8XGjRujra0tnn/++bjssssGdHAAABgIvQZxe3t7lJWVRWtra9TX\n18e2bduivLw82tvbe73peeedF1dffXWcc845cdppp0VxcXHcdNNNAzo4AAAMhBN7O5jL5aKtrS2G\nDh0azc3NERHR2toaZWVlvd70kUceiddeey2am5ujpKQkZs6cGbfeems88sgj+z1/zpw5e/88adKk\nmDx58sF+HQAAsNeSJUti6dKlez+ePn36Ac/tNYhHjBgRO3fujE2bNkVlZWUUCoVYu3ZtjBo1qtcB\nXnjhhbjqqqsin89HRMS1114bt99++wHP7+0YAAAcrMmTJ/d4k7W+vv6A5/a6ZWLQoEExbdq0mD17\ndnR0dMTcuXNj2LBhMXbs2L3nVFdXR21tbY/rqqqq4umnn47t27dHR0dHLFq0KD760Y8e6tcDAABH\nTJ+PXVuwYEEsX7488vl8LFq0KBYuXNjjeGNjYzQ1NfV47Z577omzzz47Ro4cGZWVlbF9+/Z46KGH\nBnZyAAAYAEXd3d3dWQ5QV1cXVVVVWY4AAMBxrr6+PqZOnbrfY351MwAASRPEAAAkTRADAJA0QQwA\nQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRAD\nAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPE\nAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIE\nMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0\nQQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASesziDdu\n3BjV1dWRy+ViwoQJsXLlyn7d+Kc//WmMHz8+ysrKYsSIEbFs2bLDHhYAAAZan0E8a9asGDduXGzd\nujVqamqipqamz5u+++67ceWVV8Zf/dVfRUtLS/z85z+Ps846a0AGBgCAgVTU3d3dfaCDLS0tMXjw\n4GhsbIyKioooFAoxePDgWLp0aYwdO/aAN7333nujoaEhnnzyyT4HqKuri6qqqkObHgAA+qG+vj6m\nTp2632O9vkO8Zs2aKC0tjVwuF1OmTIn169fH8OHDo6GhoddPuGzZsjj99NPjE5/4RJx55plxzTXX\nREtLy6F/BQAAcIT0GsTt7e1RVlYWra2tUV9fH9u2bYvy8vJob2/v9abvv/9+LFq0KBYsWBDr1q2L\nlpaWuPvuuwd0cAAAGAgn9nYwl8tFW1tbDB06NJqbmyMiorW1NcrKynq9aS6Xi4svvjjGjx8fEf+z\nD/muu+464Plz5szZ++dJkybF5MmT+/0FAADA/7VkyZJYunTp3o+nT59+wHN7DeIRI0bEzp07Y9Om\nTVFZWRmFQiHWrl0bo0aN6nWA4cOHx5YtW/Z+3N3dHb1sVY7bb7+91/sBAMDBmDx5co83Wevr6w94\nbq9bJgYNGhTTpk2L2bNnR0dHR8ydOzeGDRvW4wfqqquro7a2tsd1V1xxRSxevDhWrFgRHR0d8eij\nj8ZFF110qF8PAAAcMX0+dm3BggWxfPnyyOfzsWjRoli4cGGP442NjdHU1NTjtU996lNx9913x2c/\n+9morKyMXC4X3/jGNwZ2cgAAGAC9PnbtaPDYNQAAjrRDfuwaAAAc7wQxAABJE8QAACRNEAMAkDRB\nDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRN\nEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJ\nE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA\n0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMA\nkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0voM4o0bN0Z1\ndXXkcrmYMGFCrFy58qA+wWc+85k4++yzD3lAAAA4kvoM4lmzZsW4ceNi69atUVNTEzU1Nf2++cKF\nC6O1tTWKiooOa0gAADhSeg3ilpaWePHFF6O2tjZKSkritttui8bGxlixYkWfN25ra4sHHngg/vIv\n/zK6u7sHbGAAABhIvQbxmjVrorS0NHK5XEyZMiXWr18fw4cPj4aGhj5v/I1vfCNuvvnmGDRo0IAN\nCwAAA63XIG5vb4+ysrJobW2N+vr62LZtW5SXl0d7e3uvN62vr4+6urqYNWvWgA4LAAAD7cTeDuZy\nuWhra4uhQ4dGc3NzRES0trZGWVlZrze99dZb42/+5m/6vXd4zpw5e/88adKkmDx5cr+uAwCA/Vmy\nZEksXbp078fTp08/4LlF3b1s8G1paYl8Ph+NjY1RWVkZhUIhBg8eHEuXLo2xY8ce8Kann356vP/+\n+z0/UVFRbNu27f/bQlFXVxdVVVV9flEAAHCo6uvrY+rUqfs91uuWiUGDBsW0adNi9uzZ0dHREXPn\nzo1hw4b1iOHq6uqora3tcd22bduiq6srurq64uWXX47KysrYs2eP/cQAABxz+nzs2oIFC2L58uWR\nz+dj0aJFsXDhwh7HGxsbo6mp6YDXd3d3e+waAADHrF63TBwNtkwAAHCkHfKWCQAAON4JYgAAkiaI\nAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKQJ\nYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBp\nghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiLine truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb61a61b710>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bc7e0ba8>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -527,25 +527,25 @@
|
||||
" for i in range (len(p)):\n",
|
||||
" p[i] = p[i] / s\n",
|
||||
" \n",
|
||||
"def update(pos, measure, p_hit, p_miss):\n",
|
||||
" q = np.array(pos, dtype=float)\n",
|
||||
"def update(pos_belief, measure, p_hit, p_miss):\n",
|
||||
" q = np.zeros(len(pos_belief))\n",
|
||||
" for i in range(len(hallway)):\n",
|
||||
" if hallway[i] == measure:\n",
|
||||
" q[i] = pos[i] * p_hit\n",
|
||||
" q[i] = pos_belief[i] * p_hit\n",
|
||||
" else:\n",
|
||||
" q[i] = pos[i] * p_miss\n",
|
||||
" q[i] = pos_belief[i] * p_miss\n",
|
||||
" normalize(q)\n",
|
||||
" return q\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"pos = np.array([0.2]*10)\n",
|
||||
"pos_belief = np.array([0.2]*10)\n",
|
||||
"reading = 1 # 1 is 'door'\n",
|
||||
"pos = update(pos, 1, .6, .2)\n",
|
||||
"pos_belief = update(pos_belief, 1, .6, .2)\n",
|
||||
"\n",
|
||||
"print('sum =', sum(pos))\n",
|
||||
"print('probability of door =', pos[0])\n",
|
||||
"print('probability of wall =', pos[2])\n",
|
||||
"bar_plot.plot(pos)"
|
||||
"print('sum =', sum(pos_belief))\n",
|
||||
"print('probability of door =', pos_belief[0])\n",
|
||||
"print('probability of wall =', pos_belief[2])\n",
|
||||
"bar_plot.plot(pos_belief)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -564,7 +564,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHF5JREFUeJzt3X1013Xd+PHX0rnpd0P9UqgbSgkHGBFYFEcwckqJoZ00\npR3zJktET5lH03JZ3l8WVhzCOw6a5vFYCXm86RheZdO8CUytTtxtETeNAGkuwN3A2GC7/ugXXLt+\nsA0YfJH343GO57Dv52avvY+6p1/f+yyvo6OjIwAAIFHvyfUAAACQS4IYAICkCWIAAJImiAEASJog\nBgAgaYIYAICkCWIAAJLWbRA/88wzMWbMmCgsLIwvfelLPb7x3XffHccee2xks9m48cYb92pIAADY\nVw7t7oSjjjoqvvnNb8Zvf/vb2LRpU49u+oc//CFuu+22ePXVV+PII4+Mj3/84/HhD384Jk2atNcD\nAwBAb+r2HeJTTz01zj333Mhmsz2+6RNPPBHnnXdelJWVRUlJSUyePDkef/zxvRoUAAD2hR7vId6d\n3/C8dOnSGDJkSMyYMSOuv/76GDZsWPz1r3/dowEBAGBf6nEQ5+Xl9fimzc3NUVRUFCtWrIhly5ZF\ncXFxNDU17dGAAACwL3W7h/g/ducd4kwmE01NTTFjxoyIiHjqqaeiqKhop+dWVVX1+L4AALCnxo8f\nv9PXexzEu/MO8eDBg6Ompmb7x0uWLImhQ4fu8vyysrIe33tPPffcc/HpT396n3+edwNrsYO12MFa\n7GAtdrAWO1iLHazFDtZihwN9Laqrq3d5rNstE+3t7dHS0hJbt26Nbdu2xZYtW2Lbtm3bj5eXl0dl\nZWWnayZNmhRPPvlkLFmyJNasWRMPP/xwVFRU7MWXAAAA+0a3Qfzoo4/GEUccEXfddVc89thjcfjh\nh8edd965/XhtbW3U1dV1umb06NFxyy23xGmnnRYf+tCHoqKiwiPXAAA4IHW7ZeLSSy+NSy+9dJfH\nV65cudPXr7766rj66qv3eLDeNmjQoFyPcMCwFjtYix2sxQ7WYgdrsYO12MFa7GAtdng3r0Vex+78\ntNw+UFVVtV/2EAMAkK7q6upd/lBdjx+7BgAAByNBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQx\nAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRB\nDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRN\nEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJ\nE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA\n0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACSt2yBevXp1lJeXRyaTiVGjRsXixYt7dOOb\nbropSkpK4r3vfW984QtfiMbGxr0eFgAAelu3QTxlypQYMWJErF+/PioqKqKioqLbmz799NPx6KOP\nxh//+MdYtWpVrF+/Pu64445eGRgAAHpTl0Hc0NAQzz//fFRWVkZBQUFcc801UVtbG4sWLerypjU1\nNTF27Ng47rjj4ogjjoizzjorlixZ0quDAwBAb+gyiJctWxaFhYWRyWRi3LhxsXLlyhg4cGDU1NR0\nedNPfvKT8cYbb8Tq1aujqakpnn322Tj77LN7dXAAAOgNXQZxc3NzFBUVRWNjY1RXV8eGDRuiuLg4\nmpubu7zpRz/60bjgggvihBNOiKOOOiry8/Pj8ssv79XBAQCgNxza1cFMJhNNTU3Rv3//qK+vj4iI\nxsbGKCoq6vKm9913X7zyyitRX18fBQUFMXny5Lj66qvjvvvu2+n506ZN2/7nMWPGxNixY3f36wAA\ngO3mzZsX8+fP3/7xxIkTd3lul0E8aNCg2Lx5c6xZsyZKS0ujtbU1li9fHkOGDOlygOeeey7OP//8\nyGazERFx0UUXxXXXXbfL87s6BgAAu2vs2LGd3mStrq7e5bldbpno06dPTJgwIaZOnRotLS0xffr0\nGDBgQAwfPnz7OeXl5VFZWdnpurKysnjyySdj48aN0dLSEnPmzIkPfvCDe/r1AADAPtPtY9dmzZoV\nCxcujGw2G3PmzInZs2d3Ol5bWxt1dXWdXrvlllvi+OOPj8GDB0dpaWls3Lgx7rnnnt6dHAAAekFe\nR0dHRy4HqKqqirKyslyOAADAQa66ujrGjx+/02N+dTMAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkT\nxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDS\nBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQ\nNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAA\nJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEA\nAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEnrNohXr14d5eXlkclk\nYtSoUbF48eIe3fi3v/1tjBw5MoqKimLQoEGxYMGCvR4WAAB6W7dBPGXKlBgxYkSsX78+KioqoqKi\notub/v3vf4/zzjsvvvOd70RDQ0P8/ve/j+OOO65XBgYAgN6U19HR0bGrgw0NDdG3b9+ora2NkpKS\naG1tjb59+8b8+fNj+PDhu7zprbfeGjU1NfH44493O0BVVVWUlZXt2fQAANAD1dXVMX78+J0e6/Id\n4mXLlkVhYWFkMpkYN25crFy5MgYOHBg1NTVdfsIFCxbE0UcfHSeffHIce+yxceGFF0ZDQ8OefwUA\nALCPdBnEzc3NUVRUFI2NjVFdXR0bNmyI4uLiaG5u7vKm77zzTsyZMydmzZoVK1asiIaGhrj55pt7\ndXAAAOgNh3Z1MJPJRFNTU/Tv3z/q6+sjIqKxsTGKioq6vGkmk4kzzjgjRo4cGRH/3od800037fL8\nadOmbf/zmDFjYuzYsT3+AgAA4P+aN29ezJ8/f/vHEydO3OW5XQbxoEGDYvPmzbFmzZooLS2N1tbW\nWL58eQwZMqTLAQYOHBjr1q3b/nFHR0d0sVU5rrvuui7vBwAAu2Ps2LGd3mStrq7e5bldbpno06dP\nTJgwIaZOnRotLS0xffr0GDBgQKcfqCsvL4/KyspO15177rkxd+7cWLRoUbS0tMRDDz0Up59++p5+\nPQAAsM90+9i1WbNmxcKFCyObzcacOXNi9uzZnY7X1tZGXV1dp9c+8YlPxM033xyf+tSnorS0NDKZ\nTNx+++29OzkAAPSCLh+7tj947BoAAPvaHj92DQAADnaCGACApAliAACSJogBAEiaIAYAIGmCGACA\npAliAACSJogBAEiaIAYAIGmCGACApAliAACSJogBAEiaIAYAIGmCGACApAliAACSJogBAEiaIAYA\nIGmCGACApAliAACSJogBAEiaIAYAIGmCGACApAliAACSJogBAEiaIAYAIGmCGACApAliAACSJogB\nAEiaIAYAIGmCGACApAliAACSJogBAEiaIAYAIGmCGACApAliAACSJogBAEiaIAYAIGmCGACApAli\nAACSJogBAEiaIAYAIGmCGACApAliAACSJogBAEiaIAYAIGmCGACApAliAACSJogBAEiaIAYAIGmC\nGACApAliAACSJogBAEiaIAYAIGmCGACApAliAACSJogBAEiaIAYAIGndBvHq1aujvLw8MplMjBo1\nKhYvXrxbn+CTn/xkHH/88Xs8IAAA7EvdBvGUKVNixIgRsX79+qioqIiKiooe33z27NnR2NgYeXl5\nezUkAADsK10GcUNDQzz//PNRWVkZBQUFcc0110RtbW0sWrSo2xs3NTXFXXfdFd/+9rejo6Oj1wYG\nAIDe1GUQL1u2LAoLCyOTycS4ceNi5cqVMXDgwKipqen2xrfffntcccUV0adPn14bFgAAeluXQdzc\n3BxFRUXR2NgY1dXVsWHDhiguLo7m5uYub1pdXR1VVVUxZcqUXh0WAAB626FdHcxkMtHU1BT9+/eP\n+vr6iIhobGyMoqKiLm969dVXx3e/+90e7x2eNm3a9j+PGTMmxo4d26PrAABgZ+bNmxfz58/f/vHE\niRN3eW5eRxcbfBsaGiKbzUZtbW2UlpZGa2tr9O3bN+bPnx/Dhw/f5U2PPvroeOeddzp/ory82LBh\nw/+3haKqqirKysq6/aIAAGBPVVdXx/jx43d6rMstE3369IkJEybE1KlTo6WlJaZPnx4DBgzoFMPl\n5eVRWVnZ6boNGzZEe3t7tLe3x4svvhilpaWxbds2+4kBADjgdPvYtVmzZsXChQsjm83GnDlzYvbs\n2Z2O19bWRl1d3S6v7+jo8Ng1AAAOWF1umdgfbJkAAGBf2+MtEwAAcLATxAAAJE0QAwCQNEEMAEDS\nBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQ\nNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAA\nJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAELine truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb61a757668>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bc96d8d0>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -593,7 +593,7 @@
|
||||
"\n",
|
||||
"Unfortunately, the answer is no. Even if the sensor readings perfectly match an extremely complicated hallway map we could not say that we are 100% sure that the dog is in a specific position - there is, after all, the possibility that every sensor reading was wrong! Naturally, in a more typical situation most sensor readings will be correct, and we might be close to 100% sure of our answer, but never 100% sure. This may seem head-spinningly complicated, but lets just go ahead and program the math, which as we have seen is quite simple.\n",
|
||||
"\n",
|
||||
"First let's deal with the simple case - assume the movement sensor is perfect, and it reports that the dog has moved one space to the right. How would we alter our pos array?\n",
|
||||
"First let's deal with the simple case - assume the movement sensor is perfect, and it reports that the dog has moved one space to the right. How would we alter our `pos_belief` array?\n",
|
||||
"\n",
|
||||
"I hope after a moment's thought it is clear that we should just shift all the values one space to the right. If we previously thought there was a 50% chance of Simon being at position 3, then after the move to the right we should believe that there is a 50% chance he is at position 4. So let's implement that. Recall that the hallway is circular, so we will use modulo arithmetic to perform the shift correctly"
|
||||
]
|
||||
@@ -603,23 +603,23 @@
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"import numpy\n",
|
||||
"def perfect_predict(pos, move):\n",
|
||||
"def perfect_predict(pos_belief, move):\n",
|
||||
" \"\"\" move the position by 'move' spaces, where positive is to the right, \n",
|
||||
" and negative is to the left\n",
|
||||
" \"\"\"\n",
|
||||
" n = len(pos)\n",
|
||||
" result = np.array(pos, dtype=float)\n",
|
||||
" n = len(pos_belief)\n",
|
||||
" result = np.zeros(n)\n",
|
||||
" for i in range(n):\n",
|
||||
" result[i] = pos[(i-move) % n]\n",
|
||||
" result[i] = pos_belief[(i-move) % n]\n",
|
||||
" return result\n",
|
||||
" \n",
|
||||
"pos = np.array([.4, .1, .2, .3])\n",
|
||||
"print('pos before predict =', pos)\n",
|
||||
"bar_plot.plot (pos, title='Before prediction')\n",
|
||||
"pos_belief = np.array([.4, .1, .2, .3])\n",
|
||||
"print('belief before predict =', pos_belief)\n",
|
||||
"bar_plot.plot (pos_belief, title='Before prediction')\n",
|
||||
"\n",
|
||||
"pos = perfect_predict(pos, 1)\n",
|
||||
"print('pos after predict =', pos)\n",
|
||||
"bar_plot.plot(pos, title='After prediction')"
|
||||
"pos_belief = perfect_predict(pos_belief, 1)\n",
|
||||
"print('belief after predict =', pos_belief)\n",
|
||||
"bar_plot.plot(pos_belief, title='After prediction')"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -628,8 +628,8 @@
|
||||
"output_type": "stream",
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"pos before predict = [ 0.4 0.1 0.2 0.3]\n",
|
||||
"pos after predict ="
|
||||
"belief before predict = [ 0.4 0.1 0.2 0.3]\n",
|
||||
"belief after predict ="
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -644,7 +644,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAGDCAYAAAAs4AbKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb630755550>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bc919320>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -680,18 +680,18 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"def predict(pos, move, p_correct, p_under, p_over):\n",
|
||||
" n = len(pos)\n",
|
||||
" result = np.array(pos, dtype=float)\n",
|
||||
"def predict(pos_belief, move, p_correct, p_under, p_over):\n",
|
||||
" n = len(pos_belief)\n",
|
||||
" result = np.zeros(n)\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",
|
||||
" result[i] = (\n",
|
||||
" pos_belief[(i-move) % n] * p_correct +\n",
|
||||
" pos_belief[(i-move-1) % n] * p_over +\n",
|
||||
" pos_belief[(i-move+1) % n] * p_under) \n",
|
||||
" return result\n",
|
||||
"\n",
|
||||
"p = np.array([0,0,0,1,0,0,0,0])\n",
|
||||
"res = predict(p, 2, .8, .1, .1)\n",
|
||||
"pos_belief = np.array([0, 0, 0, 1, 0, 0, 0, 0], dtype=float)\n",
|
||||
"res = predict(pos_belief, 2, .8, .1, .1)\n",
|
||||
"print(res)\n",
|
||||
"bar_plot.plot(res)"
|
||||
],
|
||||
@@ -710,7 +710,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb61a6d80b8>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bc8e0208>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -727,8 +727,8 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"p = np.array([0, 0, .4, .6, 0, 0, 0, 0])\n",
|
||||
"res = predict(p, 2, .8, .1, .1)\n",
|
||||
"pos_belief = np.array([0, 0, .4, .6, 0, 0, 0, 0])\n",
|
||||
"res = predict(pos_belief, 2, .8, .1, .1)\n",
|
||||
"print(res)\n",
|
||||
"bar_plot.plot(res)"
|
||||
],
|
||||
@@ -747,7 +747,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGutJREFUeJzt3X9sVfX9+PFX1drqbZFV55BWyISAIAM3HBl1aCdOFF2c\nUdbo3MYEYS6OEDGj0eGvzU9QxxCZGsx0hqgBNOoSp1sQfw/8tS3hVyvywzpQrAyQtlAKtN8/lvFJ\nvx9oK1y8lPfjkZj03nPOu697EuXp5dxz81pbW1sDAAASdVSuBwAAgFwSxAAAJE0QAwCQNEEMAEDS\nBDEAAEkTxAAAJE0QAwCQtA6D+E9/+lMMHz48CgsL46c//WmnF77vvvuiR48eUVJSEjfddNNBDQkA\nAIfKMR3t0L179/jlL38ZL774Ymzfvr1Ti7711ltx++23xxtvvBEnnHBCfPvb346vf/3rMWbMmIMe\nGAAAsqnDd4jPPffcuOyyy6KkpKTTiz711FNx+eWXx4ABA6Jnz54xfvz4mDdv3kENCgAAh0KnryH+\nPN/wvGrVqujfv3/MmjUrbrzxxhg4cGC89957BzQgAAAcSp0O4ry8vE4v2tjYGEVFRbF27dpYvXp1\nFBcXR0NDwwENCAAAh1KH1xD/1+d5hziTyURDQ0PMmjUrIiKeeeaZKCoq2ue+ixYt6vS6AABwoEaO\nHLnP5zsdxJ/nHeJ+/fpFTU3N3scrV66M008/fb/7DxgwoNNrHwovvPBCXHTRRTmd4UjifGaPc5ld\nzmd2OZ/Z41xml/OZXUfK+ayurt7vtg4vmWhpaYmmpqbYvXt37NmzJ3bu3Bl79uzZu72ioiKqqqra\nHDNmzJh4+umnY+XKlbFhw4Z45JFHorKy8iBeAgAAHBodBvHcuXPj+OOPj7vuuisee+yxOO644+LO\nO+/cu722tjbq6uraHDNs2LC49dZb4zvf+U587Wtfi8rKSrdcAwDgsNThJRNjx46NsWPH7nf7unXr\n9vn8pEmTYtKkSQc82Bepb9++uR7hiOJ8Zo9zmV3OZ3Y5n9njXGaX85ldKZzPvNbP82m5Q2DRokU5\nv4YYAIAjW3V19X4/VNfp264BAMCRSBADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAA\nSRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwA\nQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRAD\nAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPE\nAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIE\nMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASeswiNevXx8VFRWRyWRi6NChsWLFik4tPG3atOjZs2ec\ndNJJcdVVV0V9ff1BDwsAANnWYRBPmDAhBg8eHJs3b47KysqorKzscNFnn3025s6dG3//+9/jww8/\njM2bN8evf/3rrAwMAADZ1G4Qb9u2LRYuXBhVVVVRUFAQkydPjtra2li+fHm7i9bU1ER5eXmccsop\ncfzxx8fFF18cK1euzOrgAACQDe0G8erVq6OwsDAymUyMGDEi1q1bF3369Imampp2Fz3//PPjnXfe\nifXr10dDQ0M899xzcckll2R1cAAAyIZ2g7ixsTGKioqivr4+qqurY8uWLVFcXByNjY3tLnrWWWfF\nlVdeGb169Yru3btHfn5+XHvttVkdHAAAsuGY9jZmMploaGiIsrKy2LRpU0RE1NfXR1FRUbuL3n//\n/fH666/Hpk2boqCgIMaPHx+TJk2K+++/f5/7z5gxY+/Pw4cPj/Ly8s/7OgAAYK/FixfHkiVL9j4e\nPXr0fvdtN4j79u0bO3bsiA0bNkRpaWk0NzfHmjVron///u0O8MILL8QVV1wRJSUlERFx9dVXx5Qp\nU/a7f3vbAADg8yovL2/zJmt1dfV+9233kolu3brFqFGjYvr06dHU1BQzZ86M3r17x6BBg/buU1FR\nEVVVVW2OGzBgQDz99NOxdevWaGpqigULFsQZZ5xxoK8HAAAOmQ5vuzZnzpxYtmxZlJSUxIIFC2L+\n/PltttfW1kZdXV2b52699dY49dRTo1+/flFaWhpbt26N2bNnZ3dyAADIgrzW1tbWXA6waNGiGDBg\nQC5HAADgCFddXR0jR47c5zZf3QwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRAD\nAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPE\nAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIE\nMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0\nQQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNIEMQAASRPEAAAk\nTRADAJA0QQwAQNIEMQAASRPEAAAkTRADAJA0QQwAQNI6DOL169dHRUVFZDKZGDp0aKxYsaJTC7/4\n4osxZMiQKCoqir59+8bSpUsPelgAAMi2DoN4woQJMXjw4Ni8eXNUVlZGZWVlh4t+8MEHcfnll8ev\nfvWr2LZtW/ztb3+LU045JSsDAwBANuW1tra27m/jtm3b4sQTT4za2tro2bNnNDc3x4knnhhLliyJ\nQYMG7XfR2267LWpqamLevHkdDrBo0aIYMGDAgU0PAACdUF1dHSNHjtzntnbfIV69enUUFhZGJpOJ\nESNGxLp166JPnz5RU1PT7i9cunRpfOlLX4pvfetb0aNHj/jhD38Y27ZtO/BXAAAAh0i7QdzY2BhF\nRUVRX18f1dXVsWXLliguLo7GxsZ2F/3ss89iwYIFMWfOnFi7dm1s27YtbrnllqwODgAA2XBMexsz\nmUw0NDREWVlZbNq0KSIi6uvro6ioqN1FM5lMXHDBBTFkyJCI+M91yNOmTdvv/jNmzNj78/Dhw6O8\nvLzTLwAAAP5/ixcvjiVLlux9PHr06P3u224Q9+3bN3bs2BEbNmyI0tLSaG5ujjVr1kT//v3bHaBP\nnz6xcePGvY9bW1ujnUuVY8qUKe2uBwAAn0d5eXmbN1mrq6v3u2+7l0x069YtRo0aFdOnT4+mpqaY\nOXNm9O7du80H6ioqKqKqqqrNcZdddlk8//zzsXz58mhqaoqHH344zjvvvAN9PQAAcMh0eNu1OXPm\nxLJly6KkpCQWLFgQ8+fPb7O9trY26urq2jx3zjnnxC233BLf/e53o7S0NDKZTNxxxx3ZnRwAALKg\n3duufRHcdg0AgEPtgG+7BgAARzpBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRN\nEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJ\nE8QAACRNEAMAkDRBDABA0o7J9QAAkEubd7ZGXcOuXI9xWDi5KD9KCvJyPQZ84QQxAEmra9gVU/+y\nNtdjHBbuuvC0KCk4NtdjwBfOJRMAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQx\nAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRB\nDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRN\nEAMAkDRBDABA0gQxAABJE8QAACStwyBev359VFRURCaTiaFDh8aKFSs+1y84//zz49RTTz3gAQEA\n4FDqMIgnTJgQgwcPjs2bN0dlZWVUVlZ2evH58+dHfX195OXlHdSQAABwqLQbxNu2bYuFCxdGVVVV\nFBQUxOTJk6O2tjaWL1/e4cINDQ1x1113xc033xytra1ZGxgAALKp3SBevXp1FBYWRiaTiREjRsS6\ndeuiT58+UVNT0+HCd9xxR0ycODG6deuWtWEBACDb2g3ixsbGKCoqivr6+qiuro4tW7ZEcXFxNDY2\ntrtodXV1LFq0KCZMmJDVYQEAINuOaW9jJpOJhoaGKCsri02bNkVERH19fRQVFbW76KRJk+J//ud/\nOn3t8IwZM/b+PHz48CgvL+/UcQAAsC+LFy+OJUuW7H08evTo/e7bbhD37ds3duzYERs2bIjS0tJo\nbm6ONWvWRP/+/dsd4N13342LLrqozXNHH310bNmyZZ+XUEyZMqXd9QAA4PMoLy9v8yZrdXX1fvdt\n95KJbt26xahRo2L69OnR1NQUM2fOjN69e8egQYP27lNRURFVVVVtjtuyZUu0tLRES0tLvPzyy1Fa\nWhp79uxxPTEAAIedDm+7NmfOnFi2bFmUlJTEggULYv78+W2219bWRl1d3X6Pb21tdds1AAAOW+1e\nMhERUVZWFq+88sp+t69bt67d4ysqKuLDDz/83IMBAMAXwVc3AwCQNEEMAEDSOrxkAoDDy+adrVHX\nsCvXYxwWTi7Kj5ICn1MBDo4gBuhi6hp2xdS/rM31GIeFuy48LUoKjs31GEAX55IJAACSJogBAEia\nIAYAIGmCGACApAliAACSJogBAEiaIAYAIGmCGACApAliAACSJogBAEiaIAYAIGmCGACApALine truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb61a7169b0>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bc8c8588>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -776,7 +776,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb632fcf4e0>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bccef9e8>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -786,18 +786,18 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This is not a coincidence, or the result of a carefully chosen example - it is always true of the update step. This is inevitable; if our sensor is noisy we will lose a bit of information on every update. Suppose we were to perform the update an infinite number of times - what would the result be? If we lose information on every step, we must eventually end up with no information at all, and our probabilities will be equally distributed across the pos array. Let's try this with say 500 iterations.\n"
|
||||
"This is not a coincidence, or the result of a carefully chosen example - it is always true of the update step. This is inevitable; if our sensor is noisy we will lose a bit of information on every update. Suppose we were to perform the update an infinite number of times - what would the result be? If we lose information on every step, we must eventually end up with no information at all, and our probabilities will be equally distributed across the `pos_belief` array. Let's try this with say 500 iterations.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"pos = [1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
|
||||
"pos_belief = [1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
|
||||
"for i in range (500):\n",
|
||||
" pos = predict(pos, 1, .8, .1, .1)\n",
|
||||
"print(pos)\n",
|
||||
"bar_plot.plot(pos)"
|
||||
" pos_belief = predict(pos_belief, 1, .8, .1, .1)\n",
|
||||
"print(pos_belief)\n",
|
||||
"bar_plot.plot(pos_belief)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -814,7 +814,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb61a7a3588>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bc9cd390>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -861,9 +861,9 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"p = np.array([.1]*10)\n",
|
||||
"p = update(p, 1, .6, .2)\n",
|
||||
"print(p)"
|
||||
"pos_belief = np.array([.1]*10)\n",
|
||||
"pos_belief = update(pos_belief, 1, .6, .2)\n",
|
||||
"print(pos_belief)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -883,9 +883,9 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"p = predict(p, 1, .8, .1, .1)\n",
|
||||
"print(p)\n",
|
||||
"bar_plot.plot(p)"
|
||||
"pos_belief = predict(pos_belief, 1, .8, .1, .1)\n",
|
||||
"print(pos_belief)\n",
|
||||
"bar_plot.plot(pos_belief)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -903,7 +903,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb61a7a8d68>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bc9e7a58>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -920,9 +920,9 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"p = update(p, 1, .6, .2)\n",
|
||||
"print(p)\n",
|
||||
"bar_plot.plot(p)"
|
||||
"pos_belief = update(pos_belief, 1, .6, .2)\n",
|
||||
"print(pos_belief)\n",
|
||||
"bar_plot.plot(pos_belief)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -940,7 +940,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb61a53d908>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bc85b748>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -957,9 +957,9 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"p = predict(p, 1, .8, .1, .1)\n",
|
||||
"p = update(p, 0, .6, .2)\n",
|
||||
"bar_plot.plot(p)"
|
||||
"pos_belief = predict(pos_belief, 1, .8, .1, .1)\n",
|
||||
"pos_belief = update(pos_belief, 0, .6, .2)\n",
|
||||
"bar_plot.plot(pos_belief)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -969,7 +969,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHMNJREFUeJzt3XtwlXed+PFPbNOkPQltg9I2oUULw00EV5SRVGwsKl3a\nHa0tZrSXrZaCo91OV9xttru96VapyiCtlUG3rNPxUtDpxenSVaTV1i3V6u4st0TkYlhATCPQXCCc\nQPL7w5+wcckFOPAA39drxhlynud88znPTODd4zfPKeru7u4OAABI1OuyHgAAALIkiAEASJogBgAg\naYIYAICkCWIAAJImiAEASJogBgAgaf0G8dNPPx2TJ0+O0tLS+NjHPjbghR966KG48MILo6KiIu66\n665jGhIAAI6XM/s74bzzzou///u/jx//+MexZ8+eAS3685//PO6///742c9+Fueee268613vir/4\ni7+IGTNmHPPAAABQSP2+Q3z55ZfHNddcExUVFQNe9Pvf/35ce+21MWbMmKisrIyZM2fG448/fkyD\nAgDA8TDgPcRH8gnP69evj1GjRsWCBQviM5/5TIwdOzZ+/etfH9WAAABwPA04iIuKiga8aHt7e5SV\nlcWmTZtiw4YNUV5eHm1tbUc1IAAAHE/97iH+kyN5hziXy0VbW1ssWLAgIiKefPLJKCsrO+y5K1as\nGPC6AABwtKZOnXrYxwccxEfyDvHIkSOjoaHh4Nfr1q2L0aNH93r+mDFjBrz20Xr22WfjL//yL4/7\n9zkVuBaHuBaHuBaHuBaHuBaHuBaHuBaHuBaHnOzXor6+vtdj/W6Z6Orqio6Ojti/f38cOHAg9u3b\nFwcOHDh4vKamJurq6no8Z8aMGfHEE0/EunXrYtu2bbF48eKora09hpcAAADHR79B/Nhjj8U555wT\nDz74YHzrW9+Ks88+Ox544IGDxxsbG6OpqanHcyZNmhT33ntvvOc974m3vOUtUVtb65ZrAACclPrd\nMnHzzTfHzTff3OvxzZs3H/bx22+/PW6//fajHqzQRowYkfUIJw3X4hDX4hDX4hDX4hDX4hDX4hDX\n4hDX4pBT+VoUdR/Jb8sdBytWrDghe4gBAEhXfX19r79UN+DbrgEAwOlIEAMAkDRBDABA0gQxAABJ\nE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA\n0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMA\nkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QA\nACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQx\nAABJE8QAACRNEAMAkDRBDABA0gQxAABJE8QAACRNEAMAkDRBDABA0gQxAABJ6zeIt27dGjU1NZHL\n5WLixImxdu3aAS189913R2VlZbz+9a+Pj370o9Ha2nrMwwIAQKH1G8SzZs2K8ePHx86dO6O2tjZq\na2v7XfSpp56Kxx57LH71q1/Fli1bYufOnfG5z32uIAMDAEAh9RnELS0tsXz58qirq4uSkpK44447\norGxMdasWdPnog0NDVFdXR0XXXRRnHPOOXHVVVfFunXrCjo4AAAUQp9BvGHDhigtLY1cLhdTpkyJ\nzZs3x/Dhw6OhoaHPRd/73vfGK6+8Elu3bo22trZ45pln4uqrry7o4AAAUAh9BnF7e3uUlZVFa2tr\n1NfXx65du6K8vDza29v7XPTtb397fOQjH4lLLrkkzjvvvCguLo5bb721oIMDAEAhnNnXwVwuF21t\nbTF06NBobm6OiIjW1tYoKyvrc9FHHnkkXnzxxWhubo6SkpKYOXNm3H777fHII48c9vx58+Yd/PPk\nyZOjurr6SF8HAAAc9NJLL8XKlSsPfj19+vRez+0ziEeMGBF79+6Nbdu2RVVVVeTz+di4cWOMGjWq\nzwGeffbZuO6666KioiIiIm644YaYM2dOr+f3dQwAAI5UdXV1jzdZ6+vrez23zy0TgwYNimnTpsXc\nuXOjo6Mj5s+fH8OGDYtx48YdPKempibq6up6PG/MmDHxxBNPxO7du6OjoyOWLl0ab37zm4/29QAA\nwHHT723XFi1aFKtXr46KiopYunRpLFmypMfxxsbGaGpq6vHYvffeGxdffHGMHDkyqqqqYvfu3fHw\nww8XdnIAACiAou7u7u4sB1ixYkWMGTMmyxEAADjN1dfXx9SpUw97zEc3AwCQNEEMAEDSBDEAAEkT\nxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDS\nBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQ\nNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAA\nJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEA\nAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQtH6D\neOvWrVFTUxO5XC4mTpwYa9euHdDCP/7xj2PChAlRVlYWI0aMiFWrVh3zsAAAUGj9BvGsWbNi/Pjx\nsXPnzqitrY3a2tp+F/3tb38b1157bfzTP/1TtLS0xH/8x3/ERRddVJCBAQCgkIq6u7u7ezvY0tIS\ngwcPjsbGxqisrIx8Ph+DBw+OlStXxrhx43pd9L777ouGhoZ4/PHH+x1gxYoVMWbMmKObHgAABqC+\nvj6mTp162GN9vkO8YcOGKC0tjVwuF1OmTInNmzfH8OHDo6Ghoc9vuGrVqjj//PPjne98Z1x44YVx\n/fXXR0tLy9G/AgAAOE76DOL29vYoKyuL1tbWqK+vj127dkV5eXm0t7f3uehrr70WS5cujUWLFsWm\nTZuipaUl7rnnnoIODgAAhXBmXwdzuVy0tbXF0KFDo7m5OSIiWltbo6ysrM9Fc7lcvP/9748JEyZE\nxB/3Id999929nj9v3ryDf548eXJUV1cP+AUAAMCfe+mll2LlypUHv54+fXqv5/YZxCNGjIi9e/fG\ntm3boqqqKvL5fGzcuDFGjRrV5wDDhw+PHTt2HPy6u7s7+tiqHHPmzOlzPQAAOBLV1dU93mStr6/v\n9dw+t0wMGjQopk2bFnPnzo2Ojo6YP39+DBs2rMcv1NXU1ERdXV2P511zzTWxbNmyWLNmTXR0dMSj\njz4aV1xxxdG+HgAAOG76ve3aokWLYvXq1VFRURFLly6NJUuW9Dje2NgYTU1NPR5797vfHffcc0+8\n733vi6qqqsjlcvHZz362sJMDAEAB9HnbtRPBbdcAADjejvq2awAAcLoTxAAAJE0QAwCQNEEMAEDS\nBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQ\nNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAA\nJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEA\nAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEM\nAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEkTxAAAJE0QAwCQNEEMAEDSBDEAAEnrN4i3bt0a\nNTU1kcvlYuLEibF27doj+gbvfe974+KLLz7qAQEA4HjqN4hnzZoV48ePj507d0ZtbW3U1tYOePEl\nS5ZEa2trFBUVHdOQAABwvPQZxC0tLbF8+fKoq6uLkpKSuOOOO6KxsTHWrFnT78JtbW3x4IMPxj/+\n4z9Gd3d3wQYGAIBC6jOIN2zYEKWlpZHL5WLKlCmxefPmGD58eDQ0NPS78Gc/+9mYPXt2DBo0qGDD\nAgBAofUZxO3t7VFWVhatra1RX18fu3btivLy8mhvb+9z0fr6+lixYkXMmjWroMMCAEChndnXwVwu\nF21tbTF06NBobm6OiIjW1tYoKyvrc9Hbb789Pv/5zw947/C8efMO/nny5MlRXV09oOcBAMDhvPTS\nS7Fy5cqDX0+fPr3Xc4u6+9jg29LSEhUVFdHY2BhVVVWRz+dj8ODBsXLlyhg3blyvi55//vnx2muv\n9fxGRUWxa9eu/7OFYsWKFTFmzJh+XxQAAByt+vr6mDp16mGP9bllYtCgQTFt2rSYO3dudHR0xPz5\n82PYsGE9Yrimpibq6up6PG/Xrl3R1dUVXV1d8fzzz0dVVVUcOHDAfmIAAE46/d52bdGiRbF69eqo\nqKiIpUuXxpIlS3ocb2xsjKampl6f393d7bZrAACctPrcMnEi2DIBAMDxdtRbJgAA4HQniAEASJog\nBgAgaYIYAICkCWIAAJImiAEASJogBgAgaYIYAICkCWIAAJImiAEASJogBgAgaWdmPQD8uZ37uqOp\nrTPrMQpuSFlxVJQUZT0GAPBnBDEnnaa2zrjz3zdlPUbBPXjlpVFRclbWYwAAf8aWCQAAkiaIAQBI\nmiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKQJYgAAkiaIAQBImiAGACBpghgAgKLine truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb61a5e1780>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bc939ef0>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -986,9 +986,9 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"p = predict(p, 1, .8, .1, .1)\n",
|
||||
"p = update(p, 0, .6, .2)\n",
|
||||
"bar_plot.plot(p)"
|
||||
"pos_belief = predict(pos_belief, 1, .8, .1, .1)\n",
|
||||
"pos_belief = update(pos_belief, 0, .6, .2)\n",
|
||||
"bar_plot.plot(pos_belief)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -998,7 +998,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb61a7a1d68>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bc9d62b0>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1041,14 +1041,15 @@
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"hallway = [1, 0, 1, 0, 0, 1, 0, 1, 0, 0]\n",
|
||||
"pos = np.array([.1]*10)\n",
|
||||
"pos_belief = np.array([.1]*10)\n",
|
||||
"measurements = [1, 0, 1, 0, 0]\n",
|
||||
"np.set_printoptions(precision=3)\n",
|
||||
"\n",
|
||||
"for m in measurements:\n",
|
||||
" pos = update(pos, m, .6, .2)\n",
|
||||
" pos = predict(pos, 1, .8, .1, .1)\n",
|
||||
"bar_plot.plot(pos)\n",
|
||||
"print(pos)"
|
||||
" pos_belief = update(pos_belief, m, .6, .2)\n",
|
||||
" pos_belief = predict(pos_belief, 1, .8, .1, .1)\n",
|
||||
"bar_plot.plot(pos_belief)\n",
|
||||
"print(pos_belief)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -1057,8 +1058,7 @@
|
||||
"output_type": "stream",
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"[ 0.2245871 0.06288015 0.06109133 0.0581008 0.09334062 0.2245871\n",
|
||||
" 0.06288015 0.06109133 0.0581008 0.09334062]\n"
|
||||
"[ 0.225 0.063 0.061 0.058 0.093 0.225 0.063 0.061 0.058 0.093]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1066,7 +1066,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb61a590f28>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bc782390>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1083,9 +1083,9 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"pos = update(pos, m, .6, .2)\n",
|
||||
"pos = predict(pos, 1, .8, .1, .1)\n",
|
||||
"bar_plot.plot(pos)"
|
||||
"pos_belief = update(pos_belief, m, .6, .2)\n",
|
||||
"pos_belief = predict(pos_belief, 1, .8, .1, .1)\n",
|
||||
"bar_plot.plot(pos_belief)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -1095,7 +1095,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAF2CAYAAABpv45gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb61a6a3898>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bc92be48>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1115,10 +1115,10 @@
|
||||
"measurements = [0, 1, 0, 1, 0, 0]\n",
|
||||
"\n",
|
||||
"for i,m in enumerate(measurements):\n",
|
||||
" pos = update(pos, m, .6, .2)\n",
|
||||
" pos = predict(pos, 1, .8, .1, .1)\n",
|
||||
" pos_belief = update(pos_belief, m, .6, .2)\n",
|
||||
" pos_belief = predict(pos_belief, 1, .8, .1, .1)\n",
|
||||
" plt.subplot(3, 2, i+1)\n",
|
||||
" bar_plot.plot(pos, title='step{}'.format(i+1))"
|
||||
" bar_plot.plot(pos_belief, title='step{}'.format(i+1))"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -1128,7 +1128,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb61a4fd828>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bc8ccc50>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1170,9 +1170,9 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"p = [0.2245871, 0.06288015, 0.06109133, 0.0581008, 0.09334062, 0.2245871,\n",
|
||||
" 0.06288015, 0.06109133, 0.0581008, 0.09334062]\n",
|
||||
"bar_plot.plot(p) "
|
||||
"pos_belief = [0.2245871, 0.06288015, 0.06109133, 0.0581008, 0.09334062, \n",
|
||||
" 0.2245871, 0.06288015, 0.06109133, 0.0581008, 0.09334062]\n",
|
||||
"bar_plot.plot(pos_belief) "
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -1182,7 +1182,7 @@
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fb61a4f1da0>"
|
||||
"<matplotlib.figure.Figure at 0x7ff2bc7425f8>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1249,13 +1249,17 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
" * [1] D. Fox, W. Burgard, and S. Thrun. \"Monte carlo localization: Efficient position estimation for mobile robots.\" In *Journal of Artifical Intelligence Research*, 1999.\n",
|
||||
" http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume11/fox99a-html/jair-localize.html\\\n",
|
||||
" \n",
|
||||
" http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume11/fox99a-html/jair-localize.html\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" * [2] Dieter Fox, et. al. \"Bayesian Filters for Location Estimation\". In *IEEE Pervasive Computing*, September 2003.\n",
|
||||
" \n",
|
||||
" http://swarmlab.unimaas.nl/wp-content/uploads/2012/07/fox2003bayesian.pdf\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" * [3] Sebastian Thrun. \"Artificial Intelligence for Robotics\".\n",
|
||||
" \n",
|
||||
" https://www.udacity.com/course/cs373\n",
|
||||
" \n",
|
||||
" "
|
||||
|
||||
Reference in new issue
Block a user