(self, X_test, derivative=False)
return acquisition_values.mean(axis=0)
Integrates the acquisition_functions function over the GP's hyperparameters by
        averaging the acquisition_functions value for X of each hyperparameter sample.

        Parameters
        ----------
        X_test: np.ndarray(N, D), The input point where the acquisition_functions function
            should be evaluate. The dimensionality of X is (N, D), with N as
            the number of points to evaluate at and D is the number of
            dimensions of one X.

        derivative: Boolean
            If is set to true also the derivative of the acquisition_functions
            function at X is returned

        Returns
        -------
        np.ndarray(1,1)
            Integrated acquisition_functions value of X
        np.ndarray(1,D)
            Derivative of the acquisition_functions value at X (only if derivative=True)
Integrates the acquisition_functions function over the GP's hyperparameters by
        averaging the acquisition_functions value for X of each hyperparameter sample.
Integrates the acquisition_functions function over the GP s hyperparameters by averaging the acquisition_functions value for X of each hyperparameter sample .
def compute(self, X_test, derivative=False):
        """
        Integrates the acquisition_functions function over the GP's hyperparameters by
        averaging the acquisition_functions value for X of each hyperparameter sample.

        Parameters
        ----------
        X_test: np.ndarray(N, D), The input point where the acquisition_functions function
            should be evaluate. The dimensionality of X is (N, D), with N as
            the number of points to evaluate at and D is the number of
            dimensions of one X.

        derivative: Boolean
            If is set to true also the derivative of the acquisition_functions
            function at X is returned

        Returns
        -------
        np.ndarray(1,1)
            Integrated acquisition_functions value of X
        np.ndarray(1,D)
            Derivative of the acquisition_functions value at X (only if derivative=True)
        """
        acquisition_values = np.zeros([len(self.model.models), X_test.shape[0]])

        # Integrate over the acquisition_functions values
        for i in range(len(self.model.models)):
            acquisition_values[i] = self.estimators[i].compute(X_test, derivative=derivative)

        return acquisition_values.mean(axis=0)
def compute ( self , X_test , derivative = False ) : acquisition_values = np . zeros ( [ len ( self . model . models ) , X_test . shape [ 0 ] ] ) # Integrate over the acquisition_functions values for i in range ( len ( self . model . models ) ) : acquisition_values [ i ] = self . estimators [ i ] . compute ( X_test , derivative = derivative ) return acquisition_values . mean ( axis = 0 )