(self, X, fxs)
Evaluates a list of n functions on X.
        
        Returns a matrix, size N x sum(Q0,...Qn-1)
        with Qi the number of columns obtained by evaluating the i-th function.
       
        :param X: input points, size N x D
        :param fxs: functions, size n
        :return: tuple:
            (0) the evaluations Y, size N x sum(Q0,...Qn-1).
            (1) Not used, size N x 0. Bayesian Optimizer is gradient-free, however calling optimizer of the parent class
            expects a gradient. Will be discarded further on.
Evaluates a list of n functions on X.
        
        Returns a matrix, size N x sum(Q0,...Qn-1)
        with Qi the number of columns obtained by evaluating the i-th function.
       
        :param X: input points, size N x D
        :param fxs: functions, size n
        :return: tuple:
            (0) the evaluations Y, size N x sum(Q0,...Qn-1).
            (1) Not used, size N x 0. Bayesian Optimizer is gradient-free, however calling optimizer of the parent class
            expects a gradient. Will be discarded further on.
Evaluates a list of n functions on X . Returns a matrix size N x sum ( Q0 ... Qn - 1 ) with Qi the number of columns obtained by evaluating the i - th function . : param X : input points size N x D : param fxs : functions size n : return : tuple : ( 0 ) the evaluations Y size N x sum ( Q0 ... Qn - 1 ) . ( 1 ) Not used size N x 0 . Bayesian Optimizer is gradient - free however calling optimizer of the parent class expects a gradient . Will be discarded further on .
def _evaluate_objectives(self, X, fxs):
        """
        Evaluates a list of n functions on X.
        
        Returns a matrix, size N x sum(Q0,...Qn-1)
        with Qi the number of columns obtained by evaluating the i-th function.
       
        :param X: input points, size N x D
        :param fxs: functions, size n
        :return: tuple:
            (0) the evaluations Y, size N x sum(Q0,...Qn-1).
            (1) Not used, size N x 0. Bayesian Optimizer is gradient-free, however calling optimizer of the parent class
            expects a gradient. Will be discarded further on.
        """
        if X.size > 0:
            evaluations = np.hstack(map(lambda f: f(X), fxs))
            assert evaluations.shape[1] == self.acquisition.data[1].shape[1]
            return evaluations, np.zeros((X.shape[0], 0))
        else:
            return np.empty((0, self.acquisition.data[1].shape[1])), np.zeros((0, 0))
def _evaluate_objectives ( self , X , fxs ) : if X . size > 0 : evaluations = np . hstack ( map ( lambda f : f ( X ) , fxs ) ) assert evaluations . shape [ 1 ] == self . acquisition . data [ 1 ] . shape [ 1 ] return evaluations , np . zeros ( ( X . shape [ 0 ] , 0 ) ) else : return np . empty ( ( 0 , self . acquisition . data [ 1 ] . shape [ 1 ] ) ) , np . zeros ( ( 0 , 0 ) )