(self, X)
return probabilities
Following a decision list, for each instance, rules are tried in
        order until one fires.

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

        Returns
        -------
        res : ndarray, float
            Probabilistic classification.
Following a decision list, for each instance, rules are tried in
        order until one fires.
Following a decision list for each instance rules are tried in order until one fires .
def ordered_predict(self, X):
        """
        Following a decision list, for each instance, rules are tried in
        order until one fires.

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

        Returns
        -------
        res : ndarray, float
            Probabilistic classification.
        """
        num_classes = len(self.domain.class_var.values)
        probabilities = np.array([np.zeros(num_classes, dtype=float)
                                  for _ in range(X.shape[0])])

        status = np.ones(X.shape[0], dtype=bool)
        for rule in self.rule_list:
            curr_covered = rule.evaluate_data(X)
            curr_covered &= status
            status &= np.bitwise_not(curr_covered)
            probabilities[curr_covered] = rule.probabilities
        return probabilities
def ordered_predict ( self , X ) : num_classes = len ( self . domain . class_var . values ) probabilities = np . array ( [ np . zeros ( num_classes , dtype = float ) for _ in range ( X . shape [ 0 ] ) ] ) status = np . ones ( X . shape [ 0 ] , dtype = bool ) for rule in self . rule_list : curr_covered = rule . evaluate_data ( X ) curr_covered &= status status &= np . bitwise_not ( curr_covered ) probabilities [ curr_covered ] = rule . probabilities return probabilities