simpeg.utils.GaussianMixtureWithPrior.fit

GaussianMixtureWithPrior.fit(X, y=None, debug=False)

Estimate model parameters with the EM algorithm.

[modified from Scikit-Learn for Maximum A Posteriori estimates (MAP)] The method fits the model n_init times and sets the parameters with which the model has the largest likelihood or lower bound. Within each trial, the method iterates between E-step and M-step for max_iter times until the change of likelihood or lower bound is less than tol, otherwise, a ConvergenceWarning is raised. If warm_start is True, then n_init is ignored and a single initialization is performed upon the first call. Upon consecutive calls, training starts where it left off.

Parameters:

Xarray_like, shape (n_samples, n_features)

List of n_features-dimensional data points. Each row corresponds to a single data point.

yIgnored

Not used, present for API consistency by convention.

debugbool, default: False

If True, print debug statements

Returns:

selfobject

The fitted mixture.