Adjusted to use upper case x for filter state.
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Roger Labbe committed
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@@ -275,7 +275,7 @@
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"Now that we understand the histogram filter and Gaussians we are prepared to implement a 1D Kalman filter. We will do this exactly as we did the histogram filter - rather than going into the theory we will just develop the code step by step. But first, let's set the book style."
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"Now that we understand the histogram filter and Gaussians we are prepared to implement a 1D Kalman filter. We will do this exactly as we did the histogram filter - rather than going into the theory we will just develop the code step by step."
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]
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},
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{
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@@ -2331,25 +2331,6 @@
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"\n",
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"If you understand this, you will be able to understand multidimensional Kalman filters and the various extensions that have been make on them. If you do not fully understand this, I strongly suggest rereading this chapter. Try implementing the filter from scratch, just by looking at the equations and reading the text. Change the constants. Maybe try to implement a different tracking problem, like tracking stock prices. Experimentation will build your intuition and understanding of how these marvelous filters work."
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"**author notes**:\n",
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" clean up the code - same stuff duplicated over and over - write a 'clean implementation' at the end.\n",
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" \n",
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" "
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@@ -1,7 +1,7 @@
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@@ -21,236 +21,22 @@
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"# use same formatting as rest of book so that the plots are\n",
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"# consistant with that look and feel.\n",
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"import book_format\n",
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"\n",
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"\n",
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"\n",
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"def update(mu1, var1, mu2, var2):\n",
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" mean = (var1*mu2 + var2*mu1) / (var1+var2)\n",
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" variance = 1 / (1/var1 + 1/var2)\n",
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" return (mean, variance)\n",
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"\n",
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"def predict(pos, variance, movement, movement_variance):\n",
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" return (pos + movement, variance + movement_variance)"
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@@ -316,8 +102,7 @@
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],
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@@ -330,13 +115,6 @@
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"import stats\n",
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"from gif_animate import animate\n",
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"\n",
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"def update(mu1, var1, mu2, var2):\n",
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" mean = (var1*mu2 + var2*mu1) / (var1+var2)\n",
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" variance = 1 / (1/var1 + 1/var2)\n",
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" return (mean, variance)\n",
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"\n",
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"def predict(pos, variance, movement, movement_variance):\n",
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" return (pos + movement, variance + movement_variance)\n",
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"\n",
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"\n",
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"sensor_error = 1.2**2\n",
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@@ -389,17 +167,7 @@
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],
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"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7f82796e75c0>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 2
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -412,6 +180,13 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"from __future__ import print_function, division\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import numpy.random as random\n",
|
||||
"import math\n",
|
||||
"import stats\n",
|
||||
"from gif_animate import animate\n",
|
||||
"\n",
|
||||
"# assume dog is always moving 1m to the right\n",
|
||||
"movement = 1\n",
|
||||
"movement_error = .05\n",
|
||||
@@ -450,7 +225,7 @@
|
||||
" plt.subplot(212)\n",
|
||||
" plt.cla()\n",
|
||||
" stats.plot_gaussian(pos[0], pos[1], xlim=[0,N*1.2])\n",
|
||||
" plt.ylim([0,1])\n",
|
||||
" plt.ylim(0, 1)\n",
|
||||
" plt.tight_layout()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
@@ -458,7 +233,17 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": []
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7f3d805b4d68>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 9
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
|
||||
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Reference in new issue
Block a user