## Wednesday, March 24, 2010

### Kalman Filter in Python

The attached Kalman filter code is based on Python example found in book Machine Learning: An Algorithmic Perspective by Stephen Marsland. We simply turned the code into a class which is able to keep its state across invocations, and therefore work in an online fashion. The example code was assuming all data is available before everything started, and ran everything in one mainline.

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Hi.. i was looking at the kalman filter code.. I was looking to use kalman filters to predict the next data point from a dataset, given n data points.. Is it possible to extend your code to do that?

ReplyDeleteSure it is possible. Train the filter with n points, than "push" the filter to n+1 using the transition equations, one for hidden state x at n+1, the other for observation y at n+1.

ReplyDeleteThanks! ill give it a try!

DeleteI'm getting some errors while doin this.. So i train it by initializing mu to my n-point array right?

DeleteAnd then how do I push my filter to n+1?

The __main__ does it already. k.update trains with each data point. For pushing to n+1, look at the two main eqns of kalman filtering, one is time transition (for n+1) the other is observation (y_{n+1}). Once you have a \mu and a covariance, just apply the transition on x_n getting you a x_{n+1}

ReplyDeletemerhaba ,

ReplyDeletekalman filtreyi python'dak bir yazılıma adapte etmeye çalışıyorum ama başarılı olamadım.

accx.plot( pos=(t, words[3]) )

accy.plot( pos=(t, words[4]) )

accz.plot( pos=(t, words[5]) )şeklinde 3 ayrı eksen için ivme bilgilerini çizdiriyorum. Altına birde bunların kalman filtresinden geçmiş şeklini çizdirmem gerekiyor fakat uyguladıklarım olmadı.

Yardımcı olabilir misiniz ? oguzgora@yahoo.com

Suradaki pdf belgeye de bir bakin

Deletehttps://github.com/burakbayramli/classnotes/tree/master/app-math-tr/filter

her veri noktasini filtreye teker teker update ile gecmek lazim. kalman.py icinde bir __main__ var, orada ornegi gosteriliyor.