Hi Jacob, Fredrico It should be possible to treat the problem as one of having twice as many real features, but it comes at the expense of more complex code on the user's side and extra bookkeeping that would be nice to have scikit handle under the hood. I would expect that all the tricks needed to break up a Gaussian Kernel of complex variables into real and imaginary components would be relatively simple to implement within the source code. Do you think that this is worth submitting an issue to the issue tracker? (I’m not familiar with
Best, Rory > On Jan 9, 2017, at 12:43 PM, Jacob Schreiber <jmschreibe...@gmail.com> wrote: > > 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 > <mailto: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 <mailto:scikit-learn@python.org> > https://mail.python.org/mailman/listinfo/scikit-learn > <https://mail.python.org/mailman/listinfo/scikit-learn> > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn
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