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

You rock, J.P.Petersen! After seeing your answer I compared the change introduced by using fit method. It seems the initial instantiation does not create all the attributes of gmix. Specifically it is missing the following attributes,

covariances_
means_
weights_
converged_
lower_bound_
n_iter_
precisions_
precisions_cholesky_

The first three are introduced when the given inputs are assigned. Among the rest, for my application the only attribute that I need is precisions_cholesky_ which is cholesky decomposition of the inverse covarinace matrices. As a minimum requirement I added it as follow,

gmix.precisions_cholesky_ = np.linalg.cholesky(np.linalg.inv(sigma)).transpose((0, 2, 1))