(self, objective_function, lower, upper, resolution=1000)
Evaluates a equally spaced grid to maximize the acquisition function
        in a one dimensional input space.

        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
        resolution: int
            Defines of how many data points the grid consists.
Evaluates a equally spaced grid to maximize the acquisition function
        in a one dimensional input space.
Evaluates a equally spaced grid to maximize the acquisition function in a one dimensional input space .
def __init__(self, objective_function, lower, upper, resolution=1000):
        """
        Evaluates a equally spaced grid to maximize the acquisition function
        in a one dimensional input space.

        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
        resolution: int
            Defines of how many data points the grid consists.
        """
        self.resolution = resolution
        if lower.shape[0] > 1:
            raise RuntimeError("Grid search works just for \
                one dimensional functions")
        super(GridSearch, self).__init__(objective_function, lower, upper)
def __init__ ( self , objective_function , lower , upper , resolution = 1000 ) : self . resolution = resolution if lower . shape [ 0 ] > 1 : raise RuntimeError ( "Grid search works just for \
                one dimensional functions" ) super ( GridSearch , self ) . __init__ ( objective_function , lower , upper )