(self, X, Y, W, target_class, base_rules, domain,
                        initial_class_dist, prior_class_dist,
                        quality_evaluator, complexity_evaluator,
                        significance_validator, general_validator)
Develop a starting rule.

        Parameters
        ----------
        X, Y : ndarray
            Learning data.
        target_class : int
            Index of the class to model.
        base_rules : list of Rule
            An optional list of initial rules to constrain the search.
        domain : Orange.data.domain.Domain
            Data domain, used to calculate class distributions.
        initial_class_dist : ndarray
            Data class distribution in regard to the whole learning set.
        prior_class_dist : ndarray
            Data class distribution just before a rule is developed.
        quality_evaluator : Evaluator
            Evaluation algorithm.
        complexity_evaluator : Evaluator
            Evaluation algorithm.
        significance_validator : Validator
            Validation algorithm.
        general_validator : Validator
            Validation algorithm.

        Returns
        -------
        rules : list of Rule
            First rules developed in the process of learning a single
            rule.
Develop a starting rule.
Develop a starting rule .
def initialise_rule(self, X, Y, W, target_class, base_rules, domain,
                        initial_class_dist, prior_class_dist,
                        quality_evaluator, complexity_evaluator,
                        significance_validator, general_validator):
        """
        Develop a starting rule.

        Parameters
        ----------
        X, Y : ndarray
            Learning data.
        target_class : int
            Index of the class to model.
        base_rules : list of Rule
            An optional list of initial rules to constrain the search.
        domain : Orange.data.domain.Domain
            Data domain, used to calculate class distributions.
        initial_class_dist : ndarray
            Data class distribution in regard to the whole learning set.
        prior_class_dist : ndarray
            Data class distribution just before a rule is developed.
        quality_evaluator : Evaluator
            Evaluation algorithm.
        complexity_evaluator : Evaluator
            Evaluation algorithm.
        significance_validator : Validator
            Validation algorithm.
        general_validator : Validator
            Validation algorithm.

        Returns
        -------
        rules : list of Rule
            First rules developed in the process of learning a single
            rule.
        """
        raise NotImplementedError
def initialise_rule ( self , X , Y , W , target_class , base_rules , domain , initial_class_dist , prior_class_dist , quality_evaluator , complexity_evaluator , significance_validator , general_validator ) : raise NotImplementedError