daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Merge branch 'master' of https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

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Roger Labbe 2015-05-09 10:28:48 -07:00
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4
05_Kalman_Filters.ipynb
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@ -474,7 +474,7 @@
"\n",
"> **Note**: NumPy uses a random number generator to generate the normal distribution samples. The numbers I see as I write this are unlikely to be the ones that you see. If you run the cell above multiple times, you should get a slightly different result each time. I could use `numpy.random.seed(some_value)` to force the results to be the same each time. This would simplify my explanations in some cases, but would ruin the interactive nature of this chapter. To get a real feel for how normal distributions and Kalman filters work you will probably want to run cells several times, observing what changes, and what stays roughly the same.\n",
"\n",
"So the output of the sensor should be a wavering dotted red line drawn over a straight green line. The green line shows the actual position of the dog, and the dotted red line is the noisy signal produced by the simulated RFID sensor. Please note that the red dotted line was manually plotted - we do not yet have a filter that recovers that information! \n",
"So the output of the sensor should be a wavering dotted red line drawn over a straight black line. The black line shows the actual position of the dog, and the dotted red line is the noisy signal produced by the simulated RFID sensor. Please note that the red dotted line was manually plotted - we do not yet have a filter that recovers that information! \n",
"\n",
"If you are running this in an interactive IPython Notebook, I strongly urge you to run the script several times in a row. You can do this by putting the cursor in the cell containing the Python code and pressing CTRL+Enter. Each time it runs you should see a different sensor output.\n",
"\n",
@ -957,7 +957,7 @@
"Kalman filters are supposed to be hard! But this is very short and straightforward. All we are doing is multiplying the Gaussian that reflects our belief of where the dog is with the new measurement. Perhaps this would be clearer if we used more specific names:\n",
"\n",
" def update_dog(dog_pos, dog_variance, measurement, measurement_variance):\n",
" return multiply(dog_pos, dog_sigma, measurement, measurement_variance)"
" return multiply(dog_pos, dog_variance, measurement, measurement_variance)"
]
},
{

2
README.md
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@ -124,7 +124,7 @@ Some chapters introduce functions that are useful for the rest of the book. Thos
There is an undocumented directory called **exp**. This is where I write and test code prior to putting it in the book. There is some interesting stuff in there, and feel free to look at it. As the book evolves I plan to create examples and projects, and a lot of this material will end up there. Small experiments will eventually just be deleted. If you are just interested in reading the book you can safely ignore this directory.
The directory **styles** contains a css file containing the style guide for the book. The default look and feel of IPython Notebook is rather plain. Work is being done on this. I have followed the examples set by books such as [Probabilistic Programming and Bayesian Methods for Hackers](http://nbviewer.ipython.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter1_Introduction/Chapter1_Introduction.ipynb). I have also been very influenced by Professor Lorena Barba's fantastic work, [available here](https://github.com/barbagroup/CFDPython). I owe all of my look and feel to the work of these projects.
The directory **styles** contains a css file containing the style guide for the book. The default look and feel of IPython Notebook is rather plain. Work is being done on this. I have followed the examples set by books such as [Probabilistic Programming and Bayesian Methods for Hackers](http://nbviewer.ipython.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter1_Introduction/Chapter1.ipynb). I have also been very influenced by Professor Lorena Barba's fantastic work, [available here](https://github.com/barbagroup/CFDPython). I owe all of my look and feel to the work of these projects.
License
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