(self, model, par=0.0)
r"""
        Computes for a given x the expected improvement as
        acquisition_functions value.
        :math:`EI(X) :=
            \mathbb{E}\left[ \max\{0, f(\mathbf{X^+}) -
                f_{t+1}(\mathbf{X}) - \xi\right] \} ]`, with
        :math:`f(X^+)` as the incumbent.

        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 0.0
r"""
        Computes for a given x the expected improvement as
        acquisition_functions value.
        :math:`EI(X) :=
            \mathbb{E}\left[ \max\{0, f(\mathbf{X^+}) -
                f_{t+1}(\mathbf{X}) - \xi\right] \} ]`, with
        :math:`f(X^+)` as the incumbent.
r Computes for a given x the expected improvement as acquisition_functions value . : math : EI ( X ) : = \ mathbb { E } \ left [ \ max \ { 0 f ( \ mathbf { X^ + } ) - f_ { t + 1 } ( \ mathbf { X } ) - \ xi \ right ] \ } ] with : math : f ( X^ + ) as the incumbent .
def __init__(self, model, par=0.0):

        r"""
        Computes for a given x the expected improvement as
        acquisition_functions value.
        :math:`EI(X) :=
            \mathbb{E}\left[ \max\{0, f(\mathbf{X^+}) -
                f_{t+1}(\mathbf{X}) - \xi\right] \} ]`, with
        :math:`f(X^+)` as the incumbent.

        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 0.0
        """

        super(EI, self).__init__(model)
        self.par = par
def __init__ ( self , model , par = 0.0 ) : super ( EI , self ) . __init__ ( model ) self . par = par