GaussianMixtureWithPrior.fit_predict(X, y=None, debug=False)[source]#

Estimate model parameters using X and predict the labels for X.

[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. After fitting, it predicts the most probable label for the input data points.

X(n_samples, n_features) array_like

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


Not used, present for API consistency by convention.

debugbool, default: False

If True, print debug statements

(n_samples) array

Component labels.