(self, objective_function, lower,
                 upper, n_restarts=10, verbosity=False, rng=None)
Interface for scipy's L-BFGS-B implementation.

        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_restarts: int
            Determines how often the local search is repeated.
        verbosity: bool
            Show scipy output.
Interface for scipy's L-BFGS-B implementation.
Interface for scipy s L - BFGS - B implementation .
def __init__(self, objective_function, lower,
                 upper, n_restarts=10, verbosity=False, rng=None):
        """
        Interface for scipy's L-BFGS-B implementation.

        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_restarts: int
            Determines how often the local search is repeated.
        verbosity: bool
            Show scipy output.

        """
        if rng is None:
            self.rng = np.random.RandomState(np.random.randint(0, 10000))
        self.n_restarts = n_restarts
        self.verbosity = verbosity
        super(SciPyOptimizer, self).__init__(objective_function,
                                             lower, upper)
def __init__ ( self , objective_function , lower , upper , n_restarts = 10 , verbosity = False , rng = None ) : if rng is None : self . rng = np . random . RandomState ( np . random . randint ( 0 , 10000 ) ) self . n_restarts = n_restarts self . verbosity = verbosity super ( SciPyOptimizer , self ) . __init__ ( objective_function , lower , upper )