m_SumPredicted += predictedValue; m_SumSqrPredicted += predictedValue * predictedValue;
m_SumPredicted += instance.weight() * predictedValue; m_SumSqrPredicted += instance.weight() * predictedValue * predictedValue;
  protected void updateStatsForPredictor(double predictedValue,				       Instance instance)        throws Exception {    if (!instance.classIsMissing()){      // Update stats      m_WithClass += instance.weight();      if (Instance.isMissingValue(predictedValue)) {	m_Unclassified += instance.weight();	return;      }      m_SumClass += instance.weight() * instance.classValue();      m_SumSqrClass += instance.weight() * instance.classValue()      *	instance.classValue();      m_SumClassPredicted += instance.weight()       * instance.classValue() * predictedValue;      m_SumPredicted += predictedValue;      m_SumSqrPredicted += predictedValue * predictedValue;      if (m_ErrorEstimator == null) {	setNumericPriorsFromBuffer();      }      double predictedProb = Math.max(m_ErrorEstimator.getProbability(				      predictedValue 				      - instance.classValue()),				      MIN_SF_PROB);      double priorProb = Math.max(m_PriorErrorEstimator.getProbability(	                          instance.classValue()),				  MIN_SF_PROB);      m_SumSchemeEntropy -= Utils.log2(predictedProb) * instance.weight();      m_SumPriorEntropy -= Utils.log2(priorProb) * instance.weight();      m_ErrorEstimator.addValue(predictedValue - instance.classValue(), 				instance.weight());      updateNumericScores(makeDistribution(predictedValue),			  makeDistribution(instance.classValue()),			  instance.weight());    } else      m_MissingClass += instance.weight();  }