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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.
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/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see .
 */

/*
 *    AggregateableEvaluation.java
 *    Copyright (C) 2011-2012 University of Waikato, Hamilton, New Zealand
 */

package weka.classifiers.evaluation;

import java.util.ArrayList;

import weka.classifiers.CostMatrix;
import weka.core.Aggregateable;
import weka.core.Instances;

/**
 * Subclass of Evaluation that provides a method for aggregating the results
 * stored in another Evaluation object.
 * 
 * @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
 * @version $Revision: 10153 $
 */
public class AggregateableEvaluation extends Evaluation implements
  Aggregateable {

  /**
   * For serialization
   */
  private static final long serialVersionUID = 8734675926526110924L;

  /**
   * Constructs a new AggregateableEvaluation object
   * 
   * @param data the Instances to use
   * @throws Exception if a problem occurs
   */
  public AggregateableEvaluation(Instances data) throws Exception {
    super(data);
  }

  /**
   * Constructs a new AggregateableEvaluation object
   * 
   * @param data the Instances to use
   * @param costMatrix the cost matrix to use
   * @throws Exception if a problem occurs
   */
  public AggregateableEvaluation(Instances data, CostMatrix costMatrix) throws Exception {
    super(data, costMatrix);
  }

  /**
   * Constructs a new AggregateableEvaluation object based on an Evaluation
   * object
   * 
   * @param eval the Evaluation object to use
   */
  public AggregateableEvaluation(Evaluation eval) throws Exception {
    super(eval.m_Header, eval.m_CostMatrix);

    m_NoPriors = eval.m_NoPriors;
    m_NumTrainClassVals = eval.m_NumTrainClassVals;
    m_TrainClassVals = eval.m_TrainClassVals;
    m_TrainClassWeights = eval.m_TrainClassWeights;
    m_PriorEstimator = eval.m_PriorEstimator;
    m_MinTarget = eval.m_MinTarget;
    m_MaxTarget = eval.m_MaxTarget;
    m_ClassPriorsSum = eval.m_ClassPriorsSum;
    m_ClassPriors = eval.m_ClassPriors;
    m_MinTarget = eval.m_MinTarget;
    m_MaxTarget = eval.m_MaxTarget;
    m_TrainClassVals = eval.m_TrainClassVals;
    m_TrainClassWeights = eval.m_TrainClassWeights;
    m_NumTrainClassVals = eval.m_NumTrainClassVals;
  }

  /**
   * Adds the statistics encapsulated in the supplied Evaluation object into
   * this one. Does not perform any checks for compatibility between the
   * supplied Evaluation object and this one.
   * 
   * @param evaluation the evaluation object to aggregate
   */
  @Override
  public AggregateableEvaluation aggregate(Evaluation evaluation) {
    m_Incorrect += evaluation.incorrect();
    m_Correct += evaluation.correct();
    m_Unclassified += evaluation.unclassified();
    m_MissingClass += evaluation.m_MissingClass;
    m_WithClass += evaluation.m_WithClass;

    if (evaluation.m_ConfusionMatrix != null) {
      double[][] newMatrix = evaluation.confusionMatrix();
      if (newMatrix != null) {
        for (int i = 0; i < m_ConfusionMatrix.length; i++) {
          for (int j = 0; j < m_ConfusionMatrix[i].length; j++) {
            m_ConfusionMatrix[i][j] += newMatrix[i][j];
          }
        }
      }
    }

    double[] newClassPriors = evaluation.m_ClassPriors;
    if (newClassPriors != null && m_ClassPriors != null) {
      for (int i = 0; i < this.m_ClassPriors.length; i++) {
        m_ClassPriors[i] = newClassPriors[i];
      }
    }

    m_ClassPriorsSum = evaluation.m_ClassPriorsSum;
    m_TotalCost += evaluation.totalCost();
    m_SumErr += evaluation.m_SumErr;
    m_SumAbsErr += evaluation.m_SumAbsErr;
    m_SumSqrErr += evaluation.m_SumSqrErr;
    m_SumClass += evaluation.m_SumClass;
    m_SumSqrClass += evaluation.m_SumSqrClass;
    m_SumPredicted += evaluation.m_SumPredicted;
    m_SumSqrPredicted += evaluation.m_SumSqrPredicted;
    m_SumClassPredicted += evaluation.m_SumClassPredicted;
    m_SumPriorAbsErr += evaluation.m_SumPriorAbsErr;
    m_SumPriorSqrErr += evaluation.m_SumPriorSqrErr;
    m_SumKBInfo += evaluation.m_SumKBInfo;
    double[] newMarginCounts = evaluation.m_MarginCounts;
    if (newMarginCounts != null) {
      for (int i = 0; i < m_MarginCounts.length; i++) {
        m_MarginCounts[i] += newMarginCounts[i];
      }
    }
    m_ComplexityStatisticsAvailable = evaluation.m_ComplexityStatisticsAvailable;
    m_CoverageStatisticsAvailable = evaluation.m_CoverageStatisticsAvailable;
    m_SumPriorEntropy += evaluation.m_SumPriorEntropy;
    m_SumSchemeEntropy += evaluation.m_SumSchemeEntropy;
    m_TotalSizeOfRegions += evaluation.m_TotalSizeOfRegions;
    m_TotalCoverage += evaluation.m_TotalCoverage;

    ArrayList predsToAdd = evaluation.m_Predictions;
    if (predsToAdd != null) {
      if (m_Predictions == null) {
        m_Predictions = new ArrayList();
      }
      for (int i = 0; i < predsToAdd.size(); i++) {
        m_Predictions.add(predsToAdd.get(i));
      }
    }

    return this;
  }

  @Override
  public void finalizeAggregation() {
    // nothing to do here
  }
}