I personally think that it would be great.

However, I must warn you that our GMMs are not the part of the codebase
that we are the most happy with. But that shouldn't deter you from this
endeavor.

Gaël

On Wed, Dec 17, 2014 at 12:21:36PM -0500, Ji Oh Yoo wrote:
> Hi,

> I wanted to learn Gaussian Mixture with "weighted samples" as I am trying to
> run the algorithm in the paper of learning mixture models with Coreset: 
> http://
> papers.nips.cc/paper/4363-scalable-training-of-mixture-models-via-coresets
> (this paper has some typos in the algorithm sections).

> If I'm not missing, I've found that the current GMM implementation does not
> support learning from weighted samples. I'm not sure whether how many people
> want this feature implemented, but I hope I can contribute to scikit by
> implementing it and use it for myself at least.

> Before starting the implementation, I want to hear opinions of developers of
> scikit about implementing this, as guided in the developers contribution 
> page. 

> Best regards,
> Ji Oh

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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
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