Sorry if I missed the discussion, but why is it out of scope?
On Mon, Apr 28, 2014 at 1:27 PM, Gael Varoquaux <
[email protected]> wrote:
> Hi Jacob,
>
> The HMMs were removed from scikit-learn because they were out of scope.
> I don't believe that we are going to re-add any HMMs in scikit-learn.
>
> If you want, you can take ownership of the HMMlearn package that I
> created to host the former HMM code of scikit-learn:
> https://github.com/hmmlearn/hmmlearn/tree/master/hmmlearn
> You could add your HMM code there.
>
> This package is currently orphan. I will not be doing any code review or
> any support of any kind with this package.
>
> Best,
>
> Gaël
>
> On Mon, Apr 28, 2014 at 12:26:55AM -0700, Jacob Schreiber wrote:
> > Hello all
>
> > I saw that HMMs will be removed in version 0.17. As a lover of HMMs and
> > sklearn, in an attempt to save them, a friend and myself have been
> working on a
> > cython-optimized HMM package which we think may be appropriate for
> sklearn.
>
> > The repo is here: https://github.com/jmschrei/yahmm
>
> > To summarize, it implements forward, backward, forward-backward, viterbi,
> > baum-welch training, and viterbi training. It allows for silent states,
> > normalizes out-edges to sum to a probability of 1., and will try to
> simplify
> > your graph structure. Forward, backward, and viterbi are implemented in
> O(n*m)
> > time instead of O(m^2), where n is the average number of edges per state
> and m
> > is the number of states.
>
> > A model is can contain states with different distribution types, instead
> of
> > being limited to each character-generating state being of the same
> distribution
> > type. Currently many distributions and kernel densities are implemented,
> but
> > the user can make their own arbitrary distribution on the fly and have
> it work
> > with this package.
>
> > The downsides are that it's hard to use its most expressive features in
> the
> > current sklearn framework of defining a classifier in one line (using
> > Model.from_matrix() ). Instead, it may take several lines to define the
> > underlying model in our package.
>
> > I'd love to hear feedback from others on the idea of merging this with
> sklearn.
>
>
>
> >
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> --
> Gael Varoquaux
> Researcher, INRIA Parietal
> Laboratoire de Neuro-Imagerie Assistee par Ordinateur
> NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
> Phone: ++ 33-1-69-08-79-68
> http://gael-varoquaux.info http://twitter.com/GaelVaroquaux
>
>
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