(self)
return pred.ravel() > self.minimum_pof
Returns a boolean array indicating which points are feasible (True) and which are not (False).
        
        Answering the question *which points are feasible?* is slightly troublesome in case noise is present.
        Directly relying on the noisy data and comparing it to self.threshold does not make much sense.

        Instead, we rely on the model belief using the PoF (a probability between 0 and 1).
        As the implementation of the PoF corresponds to the cdf of the (normal) predictive distribution in
        a point evaluated at the threshold, requiring a minimum pof of 0.5 implies the mean of the predictive
        distribution is below the threshold, hence it is marked as feasible. A minimum pof of 0 marks all points valid.
        Setting it to 1 results in all invalid.
    
        :return: boolean ndarray (size N)
Returns a boolean array indicating which points are feasible (True) and which are not (False).
        
        Answering the question *which points are feasible?* is slightly troublesome in case noise is present.
        Directly relying on the noisy data and comparing it to self.threshold does not make much sense.
Returns a boolean array indicating which points are feasible ( True ) and which are not ( False ) . Answering the question * which points are feasible? * is slightly troublesome in case noise is present . Directly relying on the noisy data and comparing it to self . threshold does not make much sense .
def feasible_data_index(self):
        """
        Returns a boolean array indicating which points are feasible (True) and which are not (False).
        
        Answering the question *which points are feasible?* is slightly troublesome in case noise is present.
        Directly relying on the noisy data and comparing it to self.threshold does not make much sense.

        Instead, we rely on the model belief using the PoF (a probability between 0 and 1).
        As the implementation of the PoF corresponds to the cdf of the (normal) predictive distribution in
        a point evaluated at the threshold, requiring a minimum pof of 0.5 implies the mean of the predictive
        distribution is below the threshold, hence it is marked as feasible. A minimum pof of 0 marks all points valid.
        Setting it to 1 results in all invalid.
    
        :return: boolean ndarray (size N)
        """
        pred = self.evaluate(self.data[0])
        return pred.ravel() > self.minimum_pof
def feasible_data_index ( self ) : pred = self . evaluate ( self . data [ 0 ] ) return pred . ravel ( ) > self . minimum_pof