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 2015-05-24 17:03:13 -07:00
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01_g-h_filter.ipynb
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@ -1357,7 +1357,7 @@
"source": [
"In the example above, I explicitly coded this to solve the weighing problem that we've been discussing throughout the chapter. For example, the variables are named \"weight_scale\", \"gain\", and so on. I did this to make the algorithm easy to follow - you can easily see that we correctly implemented each step. But, that is code written for exactly one problem, and the algorithm is the same for any problem. So let's rewrite the code to be generic - to work with any problem. Use this function signature:\n",
"\n",
" def g_h_filter(data, x0, dx, g, h):\n",
" def g_h_filter(data, x0, dx, g, h, dt):\n",
" \"\"\"\n",
" Performs g-h filter on 1 state variable with a fixed g and h.\n",
"\n",

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README.md
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@ -96,7 +96,7 @@ Personally, I use the Anaconda Python distribution in all of my work, [available
Finally, you will need to install FilterPy, described in the next section.
Installation of all of these packages is described in the Installation appendix, which you can read online [here](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/Appendix_A_Installation/Appendix_Installation.ipynb).
Installation of all of these packages is described in the Installation appendix, which you can read online [here](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/Appendix_A_Installation.ipynb).
Provided Libraries and Modules