(self, X)
return self.unordered_predict(X)
In contrast to the classic CN2 algorithm, all applicable rules
        are taken into account even though CN2SD induces ordered rules.

        For each instance, all rules are tried and those that fire are
        collected. If a clash occurs, class probabilities (predictions)
        of all collected rules are summed and weighted.

        Notes
        -----
        If no other rules fire, the default rule prediction is used.
        Any other empty rules (a fallout most common in the domain of
        weighted covering algorithms) are ignored.

        Parameters
        ----------
        X : ndarray
            Classify this data.

        Returns
        -------
        res : ndarray, float
            Probabilistic classification.
In contrast to the classic CN2 algorithm, all applicable rules
        are taken into account even though CN2SD induces ordered rules.
In contrast to the classic CN2 algorithm all applicable rules are taken into account even though CN2SD induces ordered rules .
def predict(self, X):
        """
        In contrast to the classic CN2 algorithm, all applicable rules
        are taken into account even though CN2SD induces ordered rules.

        For each instance, all rules are tried and those that fire are
        collected. If a clash occurs, class probabilities (predictions)
        of all collected rules are summed and weighted.

        Notes
        -----
        If no other rules fire, the default rule prediction is used.
        Any other empty rules (a fallout most common in the domain of
        weighted covering algorithms) are ignored.

        Parameters
        ----------
        X : ndarray
            Classify this data.

        Returns
        -------
        res : ndarray, float
            Probabilistic classification.
        """
        return self.unordered_predict(X)
def predict ( self , X ) : return self . unordered_predict ( X )