[SciPy-dev] PyEM, toolbox for Expectation Maximization for Gaussian Mixtures (proposal for inclusion into scipy.sandbox)

[Albert Strasheim]

Hello all

> -----Original Message-----
> From: scipy-dev-bounces at scipy.org [mailto:scipy-dev-bounces at scipy.org] On
> Behalf Of David Cournapeau
> Sent: 06 October 2006 16:36
> To: SciPy Developers List
> Subject: [SciPy-dev] PyEM, toolbox for Expectation Maximization for
> Gaussian Mixtures (proposal for inclusion into scipy.sandbox)
> 
> Hi,
> 
>     A few months ago, I posted a preliminary version of PyEM, a numpy
> package for Expectation Maximization for Gaussian Mixture Models. As it
> was developed during the various change of core functions in numpy
> (change of axis convention in mean, sum, etc...), I stopped doing public
> releases.
>     Now that this numpy API has settled, I propose the package for
> inclusion into the scipy sandbox. The package has already been used with
> success by several other people; I tried to have a coherent and easy to
> use API, and I have included some pretty plotting functions :). By
> including it to scipy, I hope also to get some feedback on usage,
> possible improvements, more testing, etc...

+1 on inclusion in the SciPy sandbox. I'd love to be able to work on David's
already excellent code.

> <snip>

>  * FUTURE:
> 
> <snip>
>
>  - Ideally, I was hoping to start a project of numpy packages for
> Machine Learning (Kalman filtering, HMM, etc...); I don't know if other
> people would be interested in developing such a package.

+1. The sandbox already has my libsvm wrapper, but it's a bit limited by
libsvm's sparse data structure -- an SVM implementation using NumPy arrays
is the next logical step. There's also PyKF[1], but it doesn't seem to be
actively maintained.

[1] http://pykf.sourceforge.net/

Cheers,

Albert