Hi Tom,

as far as I know this is not implemented in sklearn. I implemented that
in another library (https://github.com/AlexanderFabisch/gmr, example:
https://github.com/AlexanderFabisch/gmr/blob/master/examples/plot_estimate_gmm.py).
The implementation is very simple and not as efficient and usable as
sklearn. Maybe we can integrate something like this? The problem is that
it does not really work with the standard sklearn interface.

On 02/02/2015 02:25 PM, Tom Groves wrote:
> I'm using scikit-learn to fit a multivariate Gaussian Mixture Model to
> some data (which works brilliantly). But I need to be able to get a
> new GMM conditional on some of the variables, and the scikit toolkit
> doesn't seem to be able to do that, which surprised me because it
> seems like a pretty basic thing to want to do.
> 
> Wikipedia has a good explanation of what I'm trying to do (for a
> single Gaussian, not a GMM), and it's just possible I might be able to
> implement it myself, but my matrix maths isn't great and I can see it
> taking a long time.
> 
> Is there a really easy way of doing this (in which case, could it be
> added as an example in the documentation?) or might it get added to
> sklearn?
> 
> Thanks in advance,
> Tom
> 
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look and join the conversation now. http://goparallel.sourceforge.net/
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