[Biojava-l] unsupervised training of transition weights

[wendy wong]

Hi,

I am trying to train my HMM using unsupervised training (I don't need
to train the emission probabilities). I was wondering how I can do so
in biojava. do I have to implement the TransitionTrainer interface?

my second question is:
I implemnted getWeightImpl in my custom distribution to set up my
emission states and it works fine. but is it possible to get the
program to access it only when there's certain symbol in the observed
sequence, (instead of precalculated)? and I also found that (although
I might be wrong) the weights are calculated twice, once was when the
distribution was created, and then when I call viterbi it calls
getWeightImpl again. I am not sure what I did wrong here :(

any input would be very much appreciated!

thank you!

wendy