(self, model, lower, upper,
                 Nb=50, Nf=500,
                 sampling_acquisition=None,
                 sampling_acquisition_kw={"par": 0.0},
                 Np=50, **kwargs)
The InformationGainMC computes the asymptotically exact, sampling
        based variant of the entropy search acquisition_functions function [1] by
        approximating the distribution over the minimum with MC sampling.

        [1] Hennig and C. J. Schuler
            Entropy search for information-efficient global optimization
            Journal of Machine Learning Research, 13, 2012


        Parameters
        ----------
        model: Model object
            A model should have following methods:
            - predict(X)
            - predict_variance(X1, X2)
        lower: np.ndarray (D)
            Lower bounds of the input space
        upper: np.ndarray (D)
            Upper bounds of the input space
        Nb: int
            Number of representer points.
        Np: int
            Number of prediction points at X to calculate stochastic changes
            of the mean for the representer points
        Nf: int
            Number of functions that are sampled to approximate pmin
        sampling_acquisition: BaseAcquisitionFunction
            A function to be used in calculating the density that
            representer points are to be sampled from. It uses
        sampling_acquisition_kw: dict
            Additional keyword parameters to be passed to sampling_acquisition
The InformationGainMC computes the asymptotically exact, sampling
        based variant of the entropy search acquisition_functions function [1] by
        approximating the distribution over the minimum with MC sampling.
The InformationGainMC computes the asymptotically exact sampling based variant of the entropy search acquisition_functions function [ 1 ] by approximating the distribution over the minimum with MC sampling .
def __init__(self, model, lower, upper,
                 Nb=50, Nf=500,
                 sampling_acquisition=None,
                 sampling_acquisition_kw={"par": 0.0},
                 Np=50, **kwargs):

        """
        The InformationGainMC computes the asymptotically exact, sampling
        based variant of the entropy search acquisition_functions function [1] by
        approximating the distribution over the minimum with MC sampling.

        [1] Hennig and C. J. Schuler
            Entropy search for information-efficient global optimization
            Journal of Machine Learning Research, 13, 2012


        Parameters
        ----------
        model: Model object
            A model should have following methods:
            - predict(X)
            - predict_variance(X1, X2)
        lower: np.ndarray (D)
            Lower bounds of the input space
        upper: np.ndarray (D)
            Upper bounds of the input space
        Nb: int
            Number of representer points.
        Np: int
            Number of prediction points at X to calculate stochastic changes
            of the mean for the representer points
        Nf: int
            Number of functions that are sampled to approximate pmin
        sampling_acquisition: BaseAcquisitionFunction
            A function to be used in calculating the density that
            representer points are to be sampled from. It uses
        sampling_acquisition_kw: dict
            Additional keyword parameters to be passed to sampling_acquisition

        """

        self.Nb = Nb
        super(InformationGainMC, self).__init__(model, lower, upper)
        self.D = self.lower.shape[0]
        self.sn2 = None
        if sampling_acquisition is None:
            sampling_acquisition = LogEI
        self.sampling_acquisition = sampling_acquisition(
            model, self.lower, self.upper, **sampling_acquisition_kw)
        self.Nf = Nf
        self.Np = Np
def __init__ ( self , model , lower , upper , Nb = 50 , Nf = 500 , sampling_acquisition = None , sampling_acquisition_kw = { "par" : 0.0 } , Np = 50 , * * kwargs ) : self . Nb = Nb super ( InformationGainMC , self ) . __init__ ( model , lower , upper ) self . D = self . lower . shape [ 0 ] self . sn2 = None if sampling_acquisition is None : sampling_acquisition = LogEI self . sampling_acquisition = sampling_acquisition ( model , self . lower , self . upper , * * sampling_acquisition_kw ) self . Nf = Nf self . Np = Np