GaussianMixture initialization using component parameters - sklearn

Question

I want to use sklearn.mixture.GaussianMixture to store a gaussian mixture model so that I can later use it to generate samples or a value at a sample point using score_sam

Answer 1

To understand what is happening, what GaussianMixture first checks that it has been fitted:

self._check_is_fitted()

Which triggers the following check:

def _check_is_fitted(self):
    check_is_fitted(self, ['weights_', 'means_', 'precisions_cholesky_'])

And finally the last function call:

def check_is_fitted(estimator, attributes, msg=None, all_or_any=all):

which only checks that the classifier already has the attributes.

---

So in short, the only thing you have missing to have it working (without having to fit it) is to set precisions_cholesky_ attribute:

gmix.precisions_cholesky_ = 0

should do the trick (can't try it so not 100% sure :P)

However, if you want to play safe and have a consistent solution in case scikit-learn updates its contrains, the solution of @J.P.Petersen is probably the best way to go.