samoa-api/src/main/java/org/apache/samoa/learners/classifiers/rules/distributed/AMRLearnerProcessor.java [78:127]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  @Override
  public boolean process(ContentEvent event) {
    if (event instanceof AssignmentContentEvent) {
      AssignmentContentEvent attrContentEvent = (AssignmentContentEvent) event;
      trainRuleOnInstance(attrContentEvent.getRuleNumberID(), attrContentEvent.getInstance());
    }
    else if (event instanceof RuleContentEvent) {
      RuleContentEvent ruleContentEvent = (RuleContentEvent) event;
      if (!ruleContentEvent.isRemoving()) {
        addRule(ruleContentEvent.getRule());
      }
    }

    return false;
  }

  /*
   * Process input instances
   */
  private void trainRuleOnInstance(int ruleID, Instance instance) {
    // System.out.println("Processor:"+this.processorId+": Rule:"+ruleID+" -> Counter="+counter);
    Iterator<ActiveRule> ruleIterator = this.ruleSet.iterator();
    while (ruleIterator.hasNext()) {
      ActiveRule rule = ruleIterator.next();
      if (rule.getRuleNumberID() == ruleID) {
        // Check (again) for coverage
        if (rule.isCovering(instance)) {
          double error = rule.computeError(instance); // Use adaptive mode error
          boolean changeDetected = ((RuleActiveRegressionNode) rule.getLearningNode()).updateChangeDetection(error);
          if (changeDetected) {
            ruleIterator.remove();

            this.sendRemoveRuleEvent(ruleID);
          } else {
            rule.updateStatistics(instance);
            if (rule.getInstancesSeen() % this.gracePeriod == 0.0) {
              if (rule.tryToExpand(this.splitConfidence, this.tieThreshold)) {
                rule.split();

                // expanded: update Aggregator with new/updated predicate
                this.sendPredicate(rule.getRuleNumberID(), rule.getLastUpdatedRuleSplitNode(),
                    (RuleActiveRegressionNode) rule.getLearningNode());
              }

            }

          }
        }

        return;
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



samoa-api/src/main/java/org/apache/samoa/learners/classifiers/rules/distributed/AMRulesStatisticsProcessor.java [70:118]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  @Override
  public boolean process(ContentEvent event) {
    if (event instanceof AssignmentContentEvent) {

      AssignmentContentEvent attrContentEvent = (AssignmentContentEvent) event;
      trainRuleOnInstance(attrContentEvent.getRuleNumberID(), attrContentEvent.getInstance());
    }
    else if (event instanceof RuleContentEvent) {
      RuleContentEvent ruleContentEvent = (RuleContentEvent) event;
      if (!ruleContentEvent.isRemoving()) {
        addRule(ruleContentEvent.getRule());
      }
    }

    return false;
  }

  /*
   * Process input instances
   */
  private void trainRuleOnInstance(int ruleID, Instance instance) {
    Iterator<ActiveRule> ruleIterator = this.ruleSet.iterator();
    while (ruleIterator.hasNext()) {
      ActiveRule rule = ruleIterator.next();
      if (rule.getRuleNumberID() == ruleID) {
        // Check (again) for coverage
        // Skip anomaly check as Aggregator's perceptron should be well-updated
        if (rule.isCovering(instance)) {
          double error = rule.computeError(instance); // Use adaptive mode error
          boolean changeDetected = ((RuleActiveRegressionNode) rule.getLearningNode()).updateChangeDetection(error);
          if (changeDetected) {
            ruleIterator.remove();

            this.sendRemoveRuleEvent(ruleID);
          } else {
            rule.updateStatistics(instance);
            if (rule.getInstancesSeen() % this.gracePeriod == 0.0) {
              if (rule.tryToExpand(this.splitConfidence, this.tieThreshold)) {
                rule.split();

                // expanded: update Aggregator with new/updated predicate
                this.sendPredicate(rule.getRuleNumberID(), rule.getLastUpdatedRuleSplitNode(),
                    (RuleActiveRegressionNode) rule.getLearningNode());
              }
            }
          }
        }

        return;
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -