I'm not too familiar with how complex values are traditionally treated, but is it possible to make the complex component a real valued component and treat it just as having twice as many features?
On Mon, Jan 9, 2017 at 11:34 AM, Rory Smith <smit...@ligo.caltech.edu> wrote: > 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 > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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