Hi Soren,
On Saturday 21 March 2009, you wrote:
> fre, 20 03 2009 kl. 19:27 +0100, skrev Levente Torok:
> > I am just wondering what if try to interface to GHMM from
> > http://www.ghmm.org/
> > to have a HMM implementation in octave-forge.
> > In general I would like to see it as a first member of Pattern
> > Recognition package.
> > What do you think of it?
>
> How general is GHMM? Does it support general Dynamic Bayes Networks?
> Does it support continuous hidden variables or only discrete ones?
Feature list:
Discrete and continous emissions
Mixtures of PDFs for continous emissions
Non-homogenous Markov chains
( I guess he thinks of time inhomogenous Markov chains )
Pair HMMs (contributed by Genoscope )
Clustering and mixture modelling for HMMs
( Not clear if it is a mixture of independent hidden state evolutions. I find
it unlikely)
I will check and test.
>
> We have some HMM code in the 'statistics' package. I've never used so I
> don't really know how good it is. If GHMM only supports the standard
> (discrete hidden variables + discrete/Gaussian output) then I think we
> might as well implement it from scratch ourselves to avoid having a
> dependency, and avoid having to write wrappers (which might be a pain to
> maintain). If GHMM does more than the basic stuff then I can see that it
> might be relevant to wrap it.
I see.
I will test both, however I suggest to move this code under Patt Recog Package.
> But that's just my thoughts,
OK. I will look through it.
Furthermore I feel that we need relate somehow to shogun toolbox.
It has so many features but very badly documented and interface changes a lot.
Levente
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