(self, X, derivative=False, **kwargs)
Computes the LCB acquisition_functions value and its derivatives.

        Parameters
        ----------
        X: 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(N,)
            LCB value of X
        np.ndarray(N,D)
            Derivative of LCB at X (only if derivative=True)
Computes the LCB acquisition_functions value and its derivatives.
Computes the LCB acquisition_functions value and its derivatives .
def compute(self, X, derivative=False, **kwargs):
        """
        Computes the LCB acquisition_functions value and its derivatives.

        Parameters
        ----------
        X: 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(N,)
            LCB value of X
        np.ndarray(N,D)
            Derivative of LCB at X (only if derivative=True)
        """
        mean, var = self.model.predict(X)

        # RoBO maximizes the acquisition function but we want to minimize the lower confidence bound
        acq = - (mean - self.par * np.sqrt(var))
        if derivative:
            dm, dv = self.model.predictive_gradients(X)
            grad = -(dm - self.par * dv / (2 * np.sqrt(var)))
            return acq, grad
        else:
            return acq
def compute ( self , X , derivative = False , * * kwargs ) : mean , var = self . model . predict ( X ) # RoBO maximizes the acquisition function but we want to minimize the lower confidence bound acq = - ( mean - self . par * np . sqrt ( var ) ) if derivative : dm , dv = self . model . predictive_gradients ( X ) grad = - ( dm - self . par * dv / ( 2 * np . sqrt ( var ) ) ) return acq , grad else : return acq