samoa-api/src/main/java/org/apache/samoa/learners/classifiers/rules/distributed/AMRDefaultRuleProcessor.java [153:181]:
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  private ActiveRule newRule(int ID, RuleActiveRegressionNode node, double[] statistics) {
    ActiveRule r = newRule(ID);

    if (node != null)
    {
      if (node.getPerceptron() != null)
      {
        r.getLearningNode().setPerceptron(new Perceptron(node.getPerceptron()));
        r.getLearningNode().getPerceptron().setLearningRatio(this.learningRatio);
      }
      if (statistics == null)
      {
        double mean;
        if (node.getNodeStatistics().getValue(0) > 0) {
          mean = node.getNodeStatistics().getValue(1) / node.getNodeStatistics().getValue(0);
          r.getLearningNode().getTargetMean().reset(mean, 1);
        }
      }
    }
    if (statistics != null && ((RuleActiveRegressionNode) r.getLearningNode()).getTargetMean() != null)
    {
      double mean;
      if (statistics[0] > 0) {
        mean = statistics[1] / statistics[0];
        ((RuleActiveRegressionNode) r.getLearningNode()).getTargetMean().reset(mean, (long) statistics[0]);
      }
    }
    return r;
  }
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samoa-api/src/main/java/org/apache/samoa/learners/classifiers/rules/distributed/AMRulesAggregatorProcessor.java [261:289]:
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  private ActiveRule newRule(int ID, RuleActiveRegressionNode node, double[] statistics) {
    ActiveRule r = newRule(ID);

    if (node != null)
    {
      if (node.getPerceptron() != null)
      {
        r.getLearningNode().setPerceptron(new Perceptron(node.getPerceptron()));
        r.getLearningNode().getPerceptron().setLearningRatio(this.learningRatio);
      }
      if (statistics == null)
      {
        double mean;
        if (node.getNodeStatistics().getValue(0) > 0) {
          mean = node.getNodeStatistics().getValue(1) / node.getNodeStatistics().getValue(0);
          r.getLearningNode().getTargetMean().reset(mean, 1);
        }
      }
    }
    if (statistics != null && ((RuleActiveRegressionNode) r.getLearningNode()).getTargetMean() != null)
    {
      double mean;
      if (statistics[0] > 0) {
        mean = statistics[1] / statistics[0];
        ((RuleActiveRegressionNode) r.getLearningNode()).getTargetMean().reset(mean, (long) statistics[0]);
      }
    }
    return r;
  }
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