(self, X, Y, W, new_rule)
return X, Y, W
Cover and remove.

        After covering a learning instance, decrease its weight and
        in-turn decrease its impact on further iterations of the
        algorithm.

        Important
        ---------
        To use this function, learner parameter gamma must be set.
        Weights are adjusted inplace.

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

        Returns
        -------
        X, Y, W : ndarray
            Adjusted learning data.
Cover and remove.
Cover and remove .
def weighted_cover_and_remove(self, X, Y, W, new_rule):
        """
        Cover and remove.

        After covering a learning instance, decrease its weight and
        in-turn decrease its impact on further iterations of the
        algorithm.

        Important
        ---------
        To use this function, learner parameter gamma must be set.
        Weights are adjusted inplace.

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

        Returns
        -------
        X, Y, W : ndarray
            Adjusted learning data.
        """
        examples_to_weigh = new_rule.covered_examples.copy()
        if new_rule.target_class is not None:
            examples_to_weigh &= Y == new_rule.target_class
        if W is None:
            W = np.ones(X.shape[0])
        W[examples_to_weigh] *= self.gamma
        return X, Y, W
def weighted_cover_and_remove ( self , X , Y , W , new_rule ) : examples_to_weigh = new_rule . covered_examples . copy ( ) if new_rule . target_class is not None : examples_to_weigh &= Y == new_rule . target_class if W is None : W = np . ones ( X . shape [ 0 ] ) W [ examples_to_weigh ] *= self . gamma return X , Y , W