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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