private RuleModel createNumericalRuleModel(ExampleSet trainingSet, Attribute attribute) {
    RuleModel model = new RuleModel(trainingSet);

    // split by best attribute
    int oldSize = -1;
    while ((trainingSet.size() > 0) && (trainingSet.size() != oldSize)) {
      ExampleSet exampleSet = (ExampleSet) trainingSet.clone();
      Split bestSplit = splitter.getBestSplit(exampleSet, attribute, null);
      double bestSplitValue = bestSplit.getSplitPoint();
      if (!Double.isNaN(bestSplitValue)) {
        SplittedExampleSet splittedSet =
            SplittedExampleSet.splitByAttribute(exampleSet, attribute, bestSplitValue);
        Attribute label = splittedSet.getAttributes().getLabel();
        splittedSet.selectSingleSubset(0);
        SplitCondition condition = new LessEqualsSplitCondition(attribute, bestSplitValue);

        splittedSet.recalculateAttributeStatistics(label);
        int labelValue = (int) splittedSet.getStatistics(label, Statistics.MODE);
        String labelName = label.getMapping().mapIndex(labelValue);
        Rule rule = new Rule(labelName, condition);

        int[] frequencies = new int[label.getMapping().size()];
        int counter = 0;
        for (String value : label.getMapping().getValues())
          frequencies[counter++] = (int) splittedSet.getStatistics(label, Statistics.COUNT, value);
        rule.setFrequencies(frequencies);
        model.addRule(rule);
        oldSize = trainingSet.size();
        trainingSet = rule.removeCovered(trainingSet);
      } else {
        break;
      }
    }

    // add default rule if some examples were not yet covered
    if (trainingSet.size() > 0) {
      Attribute label = trainingSet.getAttributes().getLabel();
      trainingSet.recalculateAttributeStatistics(label);
      int index = (int) trainingSet.getStatistics(label, Statistics.MODE);
      String defaultLabel = label.getMapping().mapIndex(index);
      Rule defaultRule = new Rule(defaultLabel);
      int[] frequencies = new int[label.getMapping().size()];
      int counter = 0;
      for (String value : label.getMapping().getValues())
        frequencies[counter++] = (int) (trainingSet.getStatistics(label, Statistics.COUNT, value));
      defaultRule.setFrequencies(frequencies);
      model.addRule(defaultRule);
    }

    return model;
  }
  private RuleModel createNominalRuleModel(ExampleSet exampleSet, Attribute attribute) {
    RuleModel model = new RuleModel(exampleSet);
    SplittedExampleSet splittedSet = SplittedExampleSet.splitByAttribute(exampleSet, attribute);
    Attribute label = splittedSet.getAttributes().getLabel();
    for (int i = 0; i < splittedSet.getNumberOfSubsets(); i++) {
      splittedSet.selectSingleSubset(i);
      splittedSet.recalculateAttributeStatistics(label);
      SplitCondition term =
          new NominalSplitCondition(attribute, attribute.getMapping().mapIndex(i));

      int labelValue = (int) splittedSet.getStatistics(label, Statistics.MODE);
      String labelName = label.getMapping().mapIndex(labelValue);
      Rule rule = new Rule(labelName, term);

      int[] frequencies = new int[label.getMapping().size()];
      int counter = 0;
      for (String value : label.getMapping().getValues())
        frequencies[counter++] = (int) splittedSet.getStatistics(label, Statistics.COUNT, value);
      rule.setFrequencies(frequencies);
      model.addRule(rule);
    }
    return model;
  }
  @Override
  public Model learn(ExampleSet exampleSet) throws OperatorException {
    Attribute label = exampleSet.getAttributes().getLabel();
    RuleModel ruleModel = new RuleModel(exampleSet);

    double pureness = getParameterAsDouble(PARAMETER_PURENESS);
    TermDetermination termDetermination = new TermDetermination(new AccuracyCriterion(), 0.5d);
    ExampleSet trainingSet = (ExampleSet) exampleSet.clone();

    for (String labelName : label.getMapping().getValues()) {
      trainingSet.recalculateAttributeStatistics(label);
      int oldSize = -1;
      while (trainingSet.size() > 0
          && trainingSet.size() != oldSize
          && trainingSet.getStatistics(label, Statistics.COUNT, labelName) > 0) {
        Rule rule = new Rule(labelName);
        ExampleSet oldTrainingSet = (ExampleSet) trainingSet.clone();

        // grow rule
        int growOldSize = -1;
        ExampleSet growSet = (ExampleSet) trainingSet.clone();
        while (growSet.size() > 0
            && growSet.size() != growOldSize
            && !rule.isPure(growSet, pureness)
            && growSet.getAttributes().size() > 0) {
          SplitCondition term = termDetermination.getBestTerm(growSet, labelName);
          if (term == null) {
            break;
          }

          rule.addTerm(term);

          Attribute splitAttribute = growSet.getAttributes().get(term.getAttributeName());
          growSet.getAttributes().remove(splitAttribute);
          growOldSize = growSet.size();
          growSet = rule.getCovered(growSet);
        }

        // add rule if not empty
        if (rule.getTerms().size() > 0) {
          growSet = rule.getCovered(trainingSet);
          growSet.recalculateAttributeStatistics(label);
          int[] frequencies = new int[label.getMapping().size()];
          int counter = 0;
          for (String value : label.getMapping().getValues()) {
            frequencies[counter++] = (int) growSet.getStatistics(label, Statistics.COUNT, value);
          }
          rule.setFrequencies(frequencies);
          ruleModel.addRule(rule);
          oldSize = trainingSet.size();

          trainingSet = rule.removeCovered(oldTrainingSet);
        } else {
          break; // no other terms found for this class --> next class
        }

        trainingSet.recalculateAttributeStatistics(label);
      }
      checkForStop();
    }

    // training set not empty? add default rule
    if (trainingSet.size() > 0) {
      trainingSet.recalculateAttributeStatistics(label);
      int index = (int) trainingSet.getStatistics(label, Statistics.MODE);
      String defaultLabel = label.getMapping().mapIndex(index);
      Rule defaultRule = new Rule(defaultLabel);
      int[] frequencies = new int[label.getMapping().size()];
      int counter = 0;
      for (String value : label.getMapping().getValues()) {
        frequencies[counter++] = (int) trainingSet.getStatistics(label, Statistics.COUNT, value);
      }
      defaultRule.setFrequencies(frequencies);
      ruleModel.addRule(defaultRule);
    }

    return ruleModel;
  }