(self, objective_function, lower, upper, n_samples=500, rng=None)
Samples candidates uniformly at random and returns the point with the highest objective value.

        Parameters
        ----------
        objective_function: acquisition function
            The acquisition function which will be maximized
        lower: np.ndarray (D)
            Lower bounds of the input space
        upper: np.ndarray (D)
            Upper bounds of the input space
        n_samples: int
            Number of candidates that are samples
Samples candidates uniformly at random and returns the point with the highest objective value.
Samples candidates uniformly at random and returns the point with the highest objective value .
def __init__(self, objective_function, lower, upper, n_samples=500, rng=None):
        """
        Samples candidates uniformly at random and returns the point with the highest objective value.

        Parameters
        ----------
        objective_function: acquisition function
            The acquisition function which will be maximized
        lower: np.ndarray (D)
            Lower bounds of the input space
        upper: np.ndarray (D)
            Upper bounds of the input space
        n_samples: int
            Number of candidates that are samples
        """
        self.n_samples = n_samples
        super(RandomSampling, self).__init__(objective_function, lower, upper, rng)
def __init__ ( self , objective_function , lower , upper , n_samples = 500 , rng = None ) : self . n_samples = n_samples super ( RandomSampling , self ) . __init__ ( objective_function , lower , upper , rng )