> 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 > > > > > > [EMAIL PROTECTED] > Sent by: [EMAIL PROTECTED] > 03/12/2004 06:11 AM > > > To: "Biojava Mailing List" <[EMAIL PROTECTED]> > 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 > > _______________________________________________ > Biojava-l mailing list - [EMAIL PROTECTED] > http://biojava.org/mailman/listinfo/biojava-l > > >
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