[scikit-learn] Complex variables in Gaussian mixture models?

[Rory Smith]

Hi All,

I’d like to set up a GMM using mixture.BayesianGaussianMixture to model a probability density of complex random variables (the learned means and covariances should also be complex valued). I wasn’t able to see any mention of how to handle complex variables in the documentation so I’m curious if it’s possible in the current implementation.
I tried the obvious thing of first generating a 1D array of  complex random numbers, but I see these warning when I try and fit the array X using 

dpgmm = mixture.BayesianGaussianMixture(n_components=4,
                                        covariance_type='full', n_init=1).fit(X)

~/miniconda2/lib/python2.7/site-packages/sklearn/utils/validation.py:382: ComplexWarning: Casting complex values to real discards the imaginary part
  array = np.array(array, dtype=dtype, order=order, copy=copy)


And as might be expected from the warning, the learned means are real.

Any advice on this problem would be greatly appreciated!

Best,
Rory  
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