daviddoji/Kalman-and-Bayesian-Filters-in-Python
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PEP-8 compliance.

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This commit is contained in:
Roger Labbe committed 2014-12-01 09:18:41 -08:00
1 parent 5f1cc41067
commit 4755c46714
1 file changed
+52 -50
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02_Discrete_Bayes/discrete_bayes.ipynb
+52 -50
View File
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
"signature": "sha256:8e8231b3997b1d22f43983521ca3cca0ced5a69e74e0edeb1b3c2676b1f81c87"
"signature": "sha256:95c1c7e7d8e05c5083e2c87d6ec8af7a1d7b140105ad8ac41a8f558311c8e42f"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -32,7 +32,7 @@
"from __future__ import division, print_function\n",
"import matplotlib.pyplot as plt\n",
"import sys\n",
"sys.path.insert(0,'../code') # allow us to format the book\n",
"sys.path.insert(0, '../code') # allow us to format the book\n",
"import book_format\n",
"book_format.load_style()"
],
@@ -43,6 +43,8 @@
"html": [
"<style>\n",
"@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
"@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
"@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
"\n",
" div.cell{\n",
" width: 850px;\n",
@@ -129,9 +131,9 @@
" white-space: nowrap;\n",
" }\n",
" div.text_cell_render{\n",
" font-family: 'Open sans',verdana,arial,sans-serif;\n",
" font-family: 'Arimo',verdana,arial,sans-serif;\n",
" line-height: 135%;\n",
" font-size: 110%;\n",
" font-size: 125%;\n",
" width:750px;\n",
" margin-left:auto;\n",
" margin-right:auto;\n",
@@ -141,12 +143,12 @@
" div.output_subarea.output_text.output_pyout {\n",
" overflow-x: auto;\n",
" overflow-y: scroll;\n",
" max-height: 300px;\n",
" max-height: 50000px;\n",
" }\n",
" div.output_subarea.output_stream.output_stdout.output_text {\n",
" overflow-x: auto;\n",
" overflow-y: scroll;\n",
" max-height: 300px;\n",
" max-height: 50000px;\n",
" }\n",
" code{\n",
" font-size: 70%;\n",
@@ -259,7 +261,7 @@
"output_type": "pyout",
"prompt_number": 1,
"text": [
"<IPython.core.display.HTML at 0x7f7040e9eba8>"
"<IPython.core.display.HTML at 0x7fb632fca4e0>"
]
}
],
@@ -349,7 +351,7 @@
"import numpy as np\n",
"\n",
"pos = np.array([0.333, 0.333, 0., 0., 0., 0., 0., 0., 0.333, 0.])\n",
"bar_plot.plot (pos)"
"bar_plot.plot(pos)"
],
"language": "python",
"metadata": {},
@@ -357,9 +359,9 @@
{
"metadata": {},
"output_type": "display_data",
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7027259ba8>"
"<matplotlib.figure.Figure at 0x7fb61ab15940>"
]
}
],
@@ -470,7 +472,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
"def update (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",
@@ -481,7 +483,7 @@
"\n",
"pos = np.array([0.2]*10)\n",
"reading = 1 # 1 is 'door'\n",
"pos = update (pos, 1, .6, .2)\n",
"pos = update(pos, 1, .6, .2)\n",
"\n",
"print(pos)\n",
"print('sum =', sum(pos))\n",
@@ -501,9 +503,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7026d70d68>"
"<matplotlib.figure.Figure at 0x7fb61a61b710>"
]
}
],
@@ -560,9 +562,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7026e9eda0>"
"<matplotlib.figure.Figure at 0x7fb61a757668>"
]
}
],
@@ -617,7 +619,7 @@
"\n",
"pos = perfect_predict(pos, 1)\n",
"print('pos after predict =', pos)\n",
"bar_plot.plot (pos, title='After prediction')"
"bar_plot.plot(pos, title='After prediction')"
],
"language": "python",
"metadata": {},
@@ -640,9 +642,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7027250f28>"
"<matplotlib.figure.Figure at 0x7fb630755550>"
]
}
],
@@ -691,7 +693,7 @@
"p = np.array([0,0,0,1,0,0,0,0])\n",
"res = predict(p, 2, .8, .1, .1)\n",
"print(res)\n",
"bar_plot.plot (res)"
"bar_plot.plot(res)"
],
"language": "python",
"metadata": {},
@@ -706,9 +708,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7026e19d68>"
"<matplotlib.figure.Figure at 0x7fb61a6d80b8>"
]
}
],
@@ -726,7 +728,7 @@
"collapsed": false,
"input": [
"p = np.array([0, 0, .4, .6, 0, 0, 0, 0])\n",
"res = predict (p, 2, .8, .1, .1)\n",
"res = predict(p, 2, .8, .1, .1)\n",
"print(res)\n",
"bar_plot.plot(res)"
],
@@ -743,9 +745,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7026c2a748>"
"<matplotlib.figure.Figure at 0x7fb61a7169b0>"
]
}
],
@@ -764,7 +766,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
"bar_plot.plot (res)"
"bar_plot.plot(res)"
],
"language": "python",
"metadata": {},
@@ -772,9 +774,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7026de1358>"
"<matplotlib.figure.Figure at 0x7fb632fcf4e0>"
]
}
],
@@ -791,7 +793,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
"pos = [1.0,0,0,0,0,0,0,0,0,0]\n",
"pos = [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",
@@ -810,9 +812,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7026e71048>"
"<matplotlib.figure.Figure at 0x7fb61a7a3588>"
]
}
],
@@ -899,9 +901,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7026e24630>"
"<matplotlib.figure.Figure at 0x7fb61a7a8d68>"
]
}
],
@@ -936,9 +938,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7026eae390>"
"<matplotlib.figure.Figure at 0x7fb61a53d908>"
]
}
],
@@ -965,9 +967,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7026d36d30>"
"<matplotlib.figure.Figure at 0x7fb61a5e1780>"
]
}
],
@@ -994,9 +996,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7026ccd2b0>"
"<matplotlib.figure.Figure at 0x7fb61a7a1d68>"
]
}
],
@@ -1038,9 +1040,9 @@
"cell_type": "code",
"collapsed": false,
"input": [
"hallway = [1,0,1,0,0,1,0,1,0,0]\n",
"hallway = [1, 0, 1, 0, 0, 1, 0, 1, 0, 0]\n",
"pos = np.array([.1]*10)\n",
"measurements = [1,0,1,0,0]\n",
"measurements = [1, 0, 1, 0, 0]\n",
"\n",
"for m in measurements:\n",
" pos = update(pos, m, .6, .2)\n",
@@ -1062,9 +1064,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7026f0db70>"
"<matplotlib.figure.Figure at 0x7fb61a590f28>"
]
}
],
@@ -1091,9 +1093,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7026d2fdd8>"
"<matplotlib.figure.Figure at 0x7fb61a6a3898>"
]
}
],
@@ -1110,7 +1112,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
"measurements = [0,1,0,1,0,0]\n",
"measurements = [0, 1, 0, 1, 0, 0]\n",
"\n",
"for i,m in enumerate(measurements):\n",
" pos = update(pos, m, .6, .2)\n",
@@ -1124,9 +1126,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7026dda6a0>"
"<matplotlib.figure.Figure at 0x7fb61a4fd828>"
]
}
],
@@ -1178,9 +1180,9 @@
{
"metadata": {},
"output_type": "display_data",
"png": 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truncated
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truncated
"text": [
"<matplotlib.figure.Figure at 0x7f7026dcaa58>"
"<matplotlib.figure.Figure at 0x7fb61a4f1da0>"
]
}
],