public void trainOnInstanceImpl()

in samoa-api/src/main/java/org/apache/samoa/learners/classifiers/rules/centralized/AMRulesRegressorProcessor.java [196:262]
40 lines of code
11 McCabe index (conditional complexity)

  public void trainOnInstanceImpl(Instance instance) {
    /*
    AMRules Algorithm

    For each rule in the rule set
       If rule covers the instance
           if the instance is not an anomaly 
               Update Change Detection Tests
               Compute prediction error
               Call PHTest
               If change is detected then
                   Remove rule
               Else
                   Update sufficient statistics of rule
                   If number of examples in rule  > Nmin
                       Expand rule
                   If ordered set then
                       break
       If none of the rule covers the instance
           Update sufficient statistics of default rule
           If number of examples in default rule is multiple of Nmin
               Expand default rule and add it to the set of rules
               Reset the default rule
    */
    boolean rulesCoveringInstance = false;
    Iterator<ActiveRule> ruleIterator = this.ruleSet.iterator();
    while (ruleIterator.hasNext()) {
      ActiveRule rule = ruleIterator.next();
      if (rule.isCovering(instance)) {
        rulesCoveringInstance = true;
        if (!isAnomaly(instance, rule)) {
          // Update Change Detection Tests
          double error = rule.computeError(instance); // Use adaptive mode error
          boolean changeDetected = ((RuleActiveRegressionNode) rule.getLearningNode()).updateChangeDetection(error);
          if (changeDetected) {
            ruleIterator.remove();
          } else {
            rule.updateStatistics(instance);
            if (rule.getInstancesSeen() % this.gracePeriod == 0.0) {
              if (rule.tryToExpand(this.splitConfidence, this.tieThreshold)) {
                rule.split();
              }
            }
          }
          if (!this.unorderedRules)
            break;
        }
      }
    }

    if (!rulesCoveringInstance) {
      defaultRule.updateStatistics(instance);
      if (defaultRule.getInstancesSeen() % this.gracePeriod == 0.0) {
        if (defaultRule.tryToExpand(this.splitConfidence, this.tieThreshold)) {
          ActiveRule newDefaultRule = newRule(defaultRule.getRuleNumberID(),
              (RuleActiveRegressionNode) defaultRule.getLearningNode(),
              ((RuleActiveRegressionNode) defaultRule.getLearningNode()).getStatisticsOtherBranchSplit()); // other branch
          defaultRule.split();
          defaultRule.setRuleNumberID(++ruleNumberID);
          this.ruleSet.add(this.defaultRule);

          defaultRule = newDefaultRule;

        }
      }
    }
  }