(self, model, par=0.0, **kwargs)
r"""
        Computes for a given x the logarithm expected improvement as
        acquisition_functions value.

        Parameters
        ----------
        model: Model object
            A model that implements at least
                 - predict(X)
            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 0.0
r"""
        Computes for a given x the logarithm expected improvement as
        acquisition_functions value.
r Computes for a given x the logarithm expected improvement as acquisition_functions value .
def __init__(self, model, par=0.0, **kwargs):

        r"""
        Computes for a given x the logarithm expected improvement as
        acquisition_functions value.

        Parameters
        ----------
        model: Model object
            A model that implements at least
                 - predict(X)
            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 0.0
        """

        super(LogEI, self).__init__(model)

        self.par = par
def __init__ ( self , model , par = 0.0 , * * kwargs ) : super ( LogEI , self ) . __init__ ( model ) self . par = par