If you want to stick to python, you could use pyMC to do that.


On Mon, Feb 2, 2015 at 10:02 AM, Tom Groves <t...@technomad.org.uk> wrote:

> On 02/02/2015, Alexander Fabisch <afabi...@informatik.uni-bremen.de>
> wrote:
> > I implemented that
> > in another library (https://github.com/AlexanderFabisch/gmr, example:
> >
> https://github.com/AlexanderFabisch/gmr/blob/master/examples/plot_estimate_gmm.py
> ).
>
> I tried out your code, but it crashed when attempting to generate
> samples from the learnt GMM:
>
> /usr/local/lib/python2.7/dist-packages/gmr/gmm.py:88: RuntimeWarning:
> invalid value encountered in divide
>   r /= r.sum(axis=0)
> Traceback (most recent call last):
>   File "./GMR.py", line 37, in <module>
>     g.from_samples(npTrainingSet)
>   File "/usr/local/lib/python2.7/dist-packages/gmr/gmm.py", line 87,
> in from_samples
>     random_state=self.random_state).to_probability_density(X)
>   File "/usr/local/lib/python2.7/dist-packages/gmr/mvn.py", line 91,
> in to_probability_density
>     precision = pinvh(self.covariance)
>   File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/extmath.py",
> line 343, in pinvh
>     a = np.asarray_chkfinite(a)
>   File "/usr/lib/python2.7/dist-packages/numpy/lib/function_base.py",
> line 595, in asarray_chkfinite
>     "array must not contain infs or NaNs")
> ValueError: array must not contain infs or NaNs
>
> I hadn't even got around to testing the conditional distribution function
> yet :(
>
>
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-- 
Leon Palafox, PhD
Postdoctoral Fellow
Lunar and Planetary Laboratory, University of Arizona
Tucson, AZ
U.S.A
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