(self, model, cost_model,
                 lower, upper,
                 is_env_variable,
                 sampling_acquisition=None,
                 n_representer=50)
Information gain per unit cost as described in Swersky et al. [1] which
        computes the information gain of a configuration divided by it's cost.
        
        This implementation slightly differs from the implementation of
        Swersky et al. as it additionally adds the optimization overhead to
        the cost. You can simply set the optimization overhead to 0 to obtain
        the original formulation.
        
        [1] Swersky, K., Snoek, J., and Adams, R.
            Multi-task Bayesian optimization.
            In Proc. of NIPS 13, 2013.

        Parameters
        ----------
        model : Model object
            Models the objective function. The model has to be a
            Gaussian process.
        cost_model : model
            Models the cost function. The model has to be a Gaussian Process.
        lower : (D) numpy array
            Specified the lower bound of the input space. Each entry
            corresponds to one dimension.
        upper : (D) numpy array
            Specified the upper bound of the input space. Each entry
            corresponds to one dimension.
        is_env_variable : (D) numpy array
            Specifies which input dimension is an environmental variable. If
            the i-th input is an environmental variable than the i-th entry has
            to be 1 and 0 otherwise.
        n_representer : int, optional
            The number of representer points to discretize the input space and
            to compute pmin.
Information gain per unit cost as described in Swersky et al. [1] which
        computes the information gain of a configuration divided by it's cost.
        
        This implementation slightly differs from the implementation of
        Swersky et al. as it additionally adds the optimization overhead to
        the cost. You can simply set the optimization overhead to 0 to obtain
        the original formulation.
        
        [1] Swersky, K., Snoek, J., and Adams, R.
            Multi-task Bayesian optimization.
            In Proc. of NIPS 13, 2013.
Information gain per unit cost as described in Swersky et al . [ 1 ] which computes the information gain of a configuration divided by it s cost . This implementation slightly differs from the implementation of Swersky et al . as it additionally adds the optimization overhead to the cost . You can simply set the optimization overhead to 0 to obtain the original formulation . [ 1 ] Swersky K . Snoek J . and Adams R . Multi - task Bayesian optimization . In Proc . of NIPS 13 2013 .
def __init__(self, model, cost_model,
                 lower, upper,
                 is_env_variable,
                 sampling_acquisition=None,
                 n_representer=50):
        """
        Information gain per unit cost as described in Swersky et al. [1] which
        computes the information gain of a configuration divided by it's cost.
        
        This implementation slightly differs from the implementation of
        Swersky et al. as it additionally adds the optimization overhead to
        the cost. You can simply set the optimization overhead to 0 to obtain
        the original formulation.
        
        [1] Swersky, K., Snoek, J., and Adams, R.
            Multi-task Bayesian optimization.
            In Proc. of NIPS 13, 2013.

        Parameters
        ----------
        model : Model object
            Models the objective function. The model has to be a
            Gaussian process.
        cost_model : model
            Models the cost function. The model has to be a Gaussian Process.
        lower : (D) numpy array
            Specified the lower bound of the input space. Each entry
            corresponds to one dimension.
        upper : (D) numpy array
            Specified the upper bound of the input space. Each entry
            corresponds to one dimension.
        is_env_variable : (D) numpy array
            Specifies which input dimension is an environmental variable. If
            the i-th input is an environmental variable than the i-th entry has
            to be 1 and 0 otherwise.
        n_representer : int, optional
            The number of representer points to discretize the input space and
            to compute pmin.
        """
        self.cost_model = cost_model
        self.n_dims = lower.shape[0]

        self.is_env = is_env_variable

        super(InformationGainPerUnitCost, self).__init__(model,
                                                         lower,
                                                         upper,
                                                         sampling_acquisition=sampling_acquisition,
                                                         Nb=n_representer)
def __init__ ( self , model , cost_model , lower , upper , is_env_variable , sampling_acquisition = None , n_representer = 50 ) : self . cost_model = cost_model self . n_dims = lower . shape [ 0 ] self . is_env = is_env_variable super ( InformationGainPerUnitCost , self ) . __init__ ( model , lower , upper , sampling_acquisition = sampling_acquisition , Nb = n_representer )