(self, model, par=1.0)
r"""
        The lower confidence bound that computes for a
        test point the acquisition value by:

        .. math::

        LCB(X) := - (\mu(x) - \kappa\sigma(x))

        Note: We want to find the minimum of and thus minimize the lower confidence bound.
        But RoBO always maximizes the acquisition function

        Parameters
        ----------
        model: Model object
            A model that implements at least
                 - predict(X)
                 - getCurrentBestX().
            If you want to calculate derivatives than it should also support
                 - predictive_gradients(X)

        par: float
            Controls the balance between exploration
            and exploitation of the acquisition_functions function. Default is 1
r"""
        The lower confidence bound that computes for a
        test point the acquisition value by:
r The lower confidence bound that computes for a test point the acquisition value by :
def __init__(self, model, par=1.0):
        r"""
        The lower confidence bound that computes for a
        test point the acquisition value by:

        .. math::

        LCB(X) := - (\mu(x) - \kappa\sigma(x))

        Note: We want to find the minimum of and thus minimize the lower confidence bound.
        But RoBO always maximizes the acquisition function

        Parameters
        ----------
        model: Model object
            A model that implements at least
                 - predict(X)
                 - getCurrentBestX().
            If you want to calculate derivatives than it should also support
                 - predictive_gradients(X)

        par: float
            Controls the balance between exploration
            and exploitation of the acquisition_functions function. Default is 1
        """
        self.par = par
        super(LCB, self).__init__(model)
def __init__ ( self , model , par = 1.0 ) : self . par = par super ( LCB , self ) . __init__ ( model )