(self, times, config)
return 1 - mean, var
predict the loss of an unseen configuration

            Parameters:
            -----------

            times: numpy array
                times where to predict the loss
            config: numpy array
                the numerical representation of the config

            Returns:
            --------
            
            mean and variance prediction at input times for the given config
predict the loss of an unseen configuration
predict the loss of an unseen configuration
def predict_unseen(self, times, config):
        """
            predict the loss of an unseen configuration

            Parameters:
            -----------

            times: numpy array
                times where to predict the loss
            config: numpy array
                the numerical representation of the config

            Returns:
            --------
            
            mean and variance prediction at input times for the given config
        """

        assert np.all(times > 0) and np.all(times <= self.max_num_epochs)

        x = np.array(config)[None, :]

        idx = times / self.max_num_epochs
        x = np.repeat(x, idx.shape[0], axis=0)

        x = np.concatenate((x, idx[:, None]), axis=1)

        mean, var = self.model.predict(x)
        return 1 - mean, var
def predict_unseen ( self , times , config ) : assert np . all ( times > 0 ) and np . all ( times <= self . max_num_epochs ) x = np . array ( config ) [ None , : ] idx = times / self . max_num_epochs x = np . repeat ( x , idx . shape [ 0 ] , axis = 0 ) x = np . concatenate ( ( x , idx [ : , None ] ) , axis = 1 ) mean , var = self . model . predict ( x ) return 1 - mean , var