Hi Matt,

Thanks a lot for the offer. It's heart warming to hear people proposing
improvements.

Unfortunately, across the past years, we have not been able to make the
HMM functionality respect our own guidelines. The core problem is that
HMMs are intrinsically a structured data learning task, that does not fit
in the general framework of scikit-learn. Of course, this is a limitation
of scikit-learn, but time has told that we cannot broaden the scope of
the project as the project is already very wide.

Thus, we have decided, after hesitating for a long time, to cut off the
HMM code and move it outside:
http://sourceforge.net/mailarchive/message.php?msg_id=31726218

The work of moving the HMMs outside our codebase to a new github
organisation hasn't started yet. If you want to give us a hand with this,
and merge in the new package your contribution, it would be fantastic!

Cheers,

Gaƫl

On Tue, Dec 10, 2013 at 08:02:05PM +0000, Matt McClain wrote:
> I tried using the existing HMM module for a project, but it didn't work 
> because
> it used too much memory. I implemented my own module (it currently just has a
> Multinomial HMM) that is memory-efficient and leverages numpy for fast
> vector-based operations. I'd like to contribute this module, but I wanted to
> check that I wouldn't be duplicating work in progress.

> Thanks,
> Matt

> Bazaarvoice

> Matthew McClain 
> Staff Data Scientist 
> m: 217.721.5606 
> e: [email protected] 
> Site  |  Blog  |  Twitter


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