(self, X, Y, W, target_class, predef_covered=None)
Apply data and target class to a rule.

        Parameters
        ----------
        X, Y, W : ndarray
            Learning data.
        target_class : int
            Index of the class to model.
        predef_covered : ndarray
            Built-in optimisation variable to enable external
            computation of covered examples.
Apply data and target class to a rule.
Apply data and target class to a rule .
def filter_and_store(self, X, Y, W, target_class, predef_covered=None):
        """
        Apply data and target class to a rule.

        Parameters
        ----------
        X, Y, W : ndarray
            Learning data.
        target_class : int
            Index of the class to model.
        predef_covered : ndarray
            Built-in optimisation variable to enable external
            computation of covered examples.
        """
        self.target_class = target_class
        if predef_covered is not None:
            self.covered_examples = predef_covered
        else:
            self.covered_examples = np.ones(X.shape[0], dtype=bool)
            for selector in self.selectors:
                self.covered_examples &= selector.filter_data(X)

        self.curr_class_dist = get_dist(Y[self.covered_examples],
                                        W[self.covered_examples]
                                        if W is not None else None,
                                        self.domain)
def filter_and_store ( self , X , Y , W , target_class , predef_covered = None ) : self . target_class = target_class if predef_covered is not None : self . covered_examples = predef_covered else : self . covered_examples = np . ones ( X . shape [ 0 ] , dtype = bool ) for selector in self . selectors : self . covered_examples &= selector . filter_data ( X ) self . curr_class_dist = get_dist ( Y [ self . covered_examples ] , W [ self . covered_examples ] if W is not None else None , self . domain )