#NEWLINE# def predict_proba( self , X ) : #NEWLINE# #INDENT# 'Return posterior probabilities of classification.\n\n        Parameters\n        ----------\n        X : array-like, shape = [n_samples, n_features]\n            Array of samples/test vectors.\n\n        Returns\n        -------\n        C : array, shape = [n_samples, n_classes]\n            Posterior probabilities of classification per class.\n        ' #NEWLINE# values = self . _decision_function ( X ) #NEWLINE# likelihood = np . exp ( ( self - values . max ( axis = 1 ) [ : , np . newaxis ] ) ) #NEWLINE# return ( likelihood / likelihood . sum ( axis = 1 ) [ : , np . newaxis ] )