[Biojava-l] Parameter Settings in BaumWelchTraining

[sacoca at MCB.McGill.CA]

> Hi Stephane -
>
> Within EmissionState you can set a Distribution that contains emission
> probabilities for the Symbols states emission alphabet using the
> setDistribution method. This Distribution will be your predetermined
> weights.
>
> To set the transition probabilities you can use the setWeights(State
> source, Distribution weights). The source is the state you are
> transitioning from and the weights is the probability of transitioning to
> any State that the source connects too. Because States implement Symbol
> you can put them in a Distribution.
>
> To make a Distribution of States that state 'a' could connect to use the
> following pseudo code:
>
> State a;
> Model m;
> FiniteAlphabet endPoints;
>
> endPoints = m.transitionsFrom(a);
> Distribution d =
> DistributionFactory.DEFAULT.createDistribution(endPoints);
>
> //You can then train d or set it's weights and put it back in the model
> with
>
> m.setWeights(a, d);
>
> Mark Schreiber
> Principal Scientist (Bioinformatics)
>
> Novartis Institute for Tropical Diseases (NITD)
> 1 Science Park Road
> #04-14 The Capricorn, Science Park II
> Singapore 117528
>
> phone +65 6722 2973
> fax  +65 6722 2910
>
>
>
>
>
> sacoca at mcb.mcgill.ca
> Sent by: biojava-l-bounces at portal.open-bio.org
> 03/12/2004 06:11 AM
>
>
>         To:     "Biojava Mailing List" <biojava-l at biojava.org>
>         cc:
>         Subject:        [Biojava-l] Parameter Settings in
> BaumWelchTraining
>
>
> Hi all. I'm trying to optimize the transition states probabilities for my
> HMM. I already have set them to values which I think are pretty good.
> Since I know the Baum Welch can only help with the scores and optimize
> them up to a local maxima I thought of using the parameters I calculated
> as a starting point. The problem is that I don't know how!
> I followed the example in biojava:
>
> ....
> //train the model to have uniform parameters
>     ModelTrainer mt = new SimpleModelTrainer();
>     //register the model to train
>     mt.registerModel(hmm);
>
> I want to use the values already set in my hmm  as the starting parameters
> in the BaumWelch.  I don't want to use the uniform distribution as
> indicated below!
>
>     //as no other counts are being used the null weight will cause
> everything to be uniform
>     mt.setNullModelWeight(1.0);
>     mt.train();
>
> I tried adding counts and looking up examples on the net but ended up more
> confused than I started. How do I use the addCounts to make this work!
>
> Stephane Acoca
> Master's Student
> McGill Center for Bioinformatics
>
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>
>