(X, Y, W, new_rule)
return X[examples_to_keep], Y[examples_to_keep], W
Cover and remove.

        After covering a learning instance, remove it from further
        consideration.

        Parameters
        ----------
        X, Y, W : ndarray
            Learning data.
        new_rule : Rule
            Remove learning instances covered by this rule.

        Returns
        -------
        X, Y, W : ndarray
            Learning data subset.
Cover and remove.
Cover and remove .
def exclusive_cover_and_remove(X, Y, W, new_rule):
        """
        Cover and remove.

        After covering a learning instance, remove it from further
        consideration.

        Parameters
        ----------
        X, Y, W : ndarray
            Learning data.
        new_rule : Rule
            Remove learning instances covered by this rule.

        Returns
        -------
        X, Y, W : ndarray
            Learning data subset.
        """
        examples_to_keep = new_rule.covered_examples.copy()
        if new_rule.target_class is not None:
            examples_to_keep &= Y == new_rule.target_class
        examples_to_keep = np.logical_not(examples_to_keep)
        W = W[examples_to_keep] if W is not None else None
        return X[examples_to_keep], Y[examples_to_keep], W
def exclusive_cover_and_remove ( X , Y , W , new_rule ) : examples_to_keep = new_rule . covered_examples . copy ( ) if new_rule . target_class is not None : examples_to_keep &= Y == new_rule . target_class examples_to_keep = np . logical_not ( examples_to_keep ) W = W [ examples_to_keep ] if W is not None else None return X [ examples_to_keep ] , Y [ examples_to_keep ] , W