(self)
return x_star
Maximizes the given acquisition function.

        Returns
        -------
        np.ndarray(N,D)
            Point with highest acquisition value.
Maximizes the given acquisition function.
Maximizes the given acquisition function .
def maximize(self):
        """
        Maximizes the given acquisition function.

        Returns
        -------
        np.ndarray(N,D)
            Point with highest acquisition value.
        """

        # Sample random points uniformly over the whole space
        rand = init_random_uniform(self.lower, self.upper,
                                   int(self.n_samples * .7))

        # Put a Gaussian on the incumbent and sample from that
        loc = self.objective_func.model.get_incumbent()[0],
        scale = np.ones([self.lower.shape[0]]) * 0.1
        rand_incs = np.array([np.clip(np.random.normal(loc, scale), self.lower, self.upper)[0]
                              for _ in range(int(self.n_samples * 0.3))])

        X = np.concatenate((rand, rand_incs), axis=0)
        y = self.objective_func(X)

        x_star = X[y.argmax()]

        return x_star
def maximize ( self ) : # Sample random points uniformly over the whole space rand = init_random_uniform ( self . lower , self . upper , int ( self . n_samples * .7 ) ) # Put a Gaussian on the incumbent and sample from that loc = self . objective_func . model . get_incumbent ( ) [ 0 ] , scale = np . ones ( [ self . lower . shape [ 0 ] ] ) * 0.1 rand_incs = np . array ( [ np . clip ( np . random . normal ( loc , scale ) , self . lower , self . upper ) [ 0 ] for _ in range ( int ( self . n_samples * 0.3 ) ) ] ) X = np . concatenate ( ( rand , rand_incs ) , axis = 0 ) y = self . objective_func ( X ) x_star = X [ y . argmax ( ) ] return x_star