#NEWLINE# def predict_log_proba( self , X ) : #NEWLINE# #INDENT# '\n        Return log-probability estimates for the test vector X.\n\n        Parameters\n        ----------\n        X : array-like, shape = [n_samples, n_features]\n\n        Returns\n        -------\n        C : array-like, shape = [n_samples, n_classes]\n            Returns the log-probability of the samples for each class in\n            the model. The columns correspond to the classes in sorted\n            order, as they appear in the attribute `classes_`.\n        ' #NEWLINE# jll = self . _joint_log_likelihood ( X ) #NEWLINE# log_prob_x = logsumexp ( jll , axis = 1 ) #NEWLINE# return ( jll - np . atleast_2d ( log_prob_x ) . T )