(self, X_test, n_funcs=1)
Samples F function values from the current posterior at the N
        specified test points.

        Parameters
        ----------
        X_test: np.ndarray (N, D)
            Input test points
        n_funcs: int
            Number of function values that are drawn at each test point.

        Returns
        ----------
        function_samples: np.array(F, N)
            The F function values drawn at the N test points.
Samples F function values from the current posterior at the N
        specified test points.
Samples F function values from the current posterior at the N specified test points .
def sample_functions(self, X_test, n_funcs=1):
        """
        Samples F function values from the current posterior at the N
        specified test points.

        Parameters
        ----------
        X_test: np.ndarray (N, D)
            Input test points
        n_funcs: int
            Number of function values that are drawn at each test point.

        Returns
        ----------
        function_samples: np.array(F, N)
            The F function values drawn at the N test points.
        """

        if self.normalize_input:
            X_test_norm, _, _ = normalization.zero_one_normalization(X_test, self.lower, self.upper)
        else:
            X_test_norm = X_test

        if not self.is_trained:
            raise Exception('Model has to be trained first!')

        funcs = self.gp.sample_conditional(self.y, X_test_norm, n_funcs)

        if self.normalize_output:
            funcs = normalization.zero_mean_unit_var_unnormalization(funcs, self.y_mean, self.y_std)

        if len(funcs.shape) == 1:
            return funcs[None, :]
        else:
            return funcs
def sample_functions ( self , X_test , n_funcs = 1 ) : if self . normalize_input : X_test_norm , _ , _ = normalization . zero_one_normalization ( X_test , self . lower , self . upper ) else : X_test_norm = X_test if not self . is_trained : raise Exception ( 'Model has to be trained first!' ) funcs = self . gp . sample_conditional ( self . y , X_test_norm , n_funcs ) if self . normalize_output : funcs = normalization . zero_mean_unit_var_unnormalization ( funcs , self . y_mean , self . y_std ) if len ( funcs . shape ) == 1 : return funcs [ None , : ] else : return funcs