(self, model, par=0.0)
r"""
        Probability of Improvement solves the following equation
        :math:`PI(X) := \mathbb{P}\left( f(\mathbf{X^+}) - f_{t+1}(\mathbf{X}) > \xi\right)`, where
        :math:`f(X^+)` is the best input found so far.

        Parameters
        ----------
        model: Model object
            Current belief of your objective function
            If you want to calculate derivatives than the model should also support
                 - predictive_gradients(X_test)

        par: float
            Controls the balance between exploration
            and exploitation of the acquisition_functions function.
r"""
        Probability of Improvement solves the following equation
        :math:`PI(X) := \mathbb{P}\left( f(\mathbf{X^+}) - f_{t+1}(\mathbf{X}) > \xi\right)`, where
        :math:`f(X^+)` is the best input found so far.
r Probability of Improvement solves the following equation : math : PI ( X ) : = \ mathbb { P } \ left ( f ( \ mathbf { X^ + } ) - f_ { t + 1 } ( \ mathbf { X } ) > \ xi \ right ) where : math : f ( X^ + ) is the best input found so far .
def __init__(self, model, par=0.0):
        r"""
        Probability of Improvement solves the following equation
        :math:`PI(X) := \mathbb{P}\left( f(\mathbf{X^+}) - f_{t+1}(\mathbf{X}) > \xi\right)`, where
        :math:`f(X^+)` is the best input found so far.

        Parameters
        ----------
        model: Model object
            Current belief of your objective function
            If you want to calculate derivatives than the model should also support
                 - predictive_gradients(X_test)

        par: float
            Controls the balance between exploration
            and exploitation of the acquisition_functions function.
        """
        super(PI, self).__init__(model)

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