#NEWLINE# def predict_quantiles( self , X , quantiles = ( 2.5 , 97.5 ) , Y_metadata = None , kern = None , likelihood = None ) : #NEWLINE# #INDENT# '\n        Get the predictive quantiles around the prediction at X\n\n        :param X: The points at which to make a prediction\n        :type X: np.ndarray (Xnew x self.input_dim)\n        :param quantiles: tuple of quantiles, default is (2.5, 97.5) which is the 95% interval\n        :type quantiles: tuple\n        :param kern: optional kernel to use for prediction\n        :type predict_kw: dict\n        :returns: list of quantiles for each X and predictive quantiles for interval combination\n        :rtype: [np.ndarray (Xnew x self.output_dim), np.ndarray (Xnew x self.output_dim)]\n        ' #NEWLINE# ( m , v ) = self . _raw_predict ( X , full_cov = False , kern = kern ) #NEWLINE# if ( self . normalizer is not None ) : #NEWLINE# #INDENT# ( m , v ) = ( self . normalizer . inverse_mean ( m ) , self . normalizer . inverse_variance ( v ) ) #NEWLINE# #UNINDENT# if ( likelihood is None ) : #NEWLINE# #INDENT# likelihood = self . likelihood #NEWLINE# #UNINDENT# return likelihood . predictive_quantiles ( m , v , quantiles , Y_metadata = Y_metadata )