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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 .
 */

/*
 *    Bagging.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.meta;

import java.util.Arrays;
import java.util.Collections;
import java.util.Enumeration;
import java.util.List;
import java.util.Random;
import java.util.Vector;
import java.util.ArrayList;

import weka.classifiers.Classifier;
import weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer;
import weka.classifiers.evaluation.Evaluation;
import weka.core.AdditionalMeasureProducer;
import weka.core.Aggregateable;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.Randomizable;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.PartitionGenerator;

/**
 
 * Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner. 
    
 * 
    
 * For more information, see
    
 * 
    
 * Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
 * 
    
 
 *
 
 * BibTeX:
 * 
     * @article{Breiman1996,
 *    author = {Leo Breiman},
 *    journal = {Machine Learning},
 *    number = {2},
 *    pages = {123-140},
 *    title = {Bagging predictors},
 *    volume = {24},
 *    year = {1996}
 * }
 * 
    
 * 
    
 
 *
 
 * Valid options are: 
    
 * 
 * 
     -P
 *  Size of each bag, as a percentage of the
 *  training set size. (default 100)
    
 * 
 * 
     -O
 *  Calculate the out of bag error.
    
 *
 * 
     -print
 *  Print the individual classifiers in the output
    
 *
 * 
     -store-out-of-bag-predictions
 *  Whether to store out of bag predictions in internal evaluation object.
    
 *
 * 
     -output-out-of-bag-complexity-statistics
 *  Whether to output complexity-based statistics when out-of-bag evaluation is performed.
    
 *
 * 
     -represent-copies-using-weights
 *  Represent copies of instances using weights rather than explicitly.
    
 * 
 * 
     -S <num>
 *  Random number seed.
 *  (default 1)
    
 * 
 * 
     -num-slots <num>
 *  Number of execution slots.
 *  (default 1 - i.e. no parallelism)
    
 * 
 * 
     -I <num>
 *  Number of iterations.
 *  (default 10)
    
 * 
 * 
     -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
    
 * 
 * 
     -W
 *  Full name of base classifier.
 *  (default: weka.classifiers.trees.REPTree)
    
 * 
 * 
     
 * Options specific to classifier weka.classifiers.trees.REPTree:
 * 
    
 * 
 * 
     -M <minimum number of instances>
 *  Set minimum number of instances per leaf (default 2).
    
 * 
 * 
     -V <minimum variance for split>
 *  Set minimum numeric class variance proportion
 *  of train variance for split (default 1e-3).
    
 * 
 * 
     -N <number of folds>
 *  Number of folds for reduced error pruning (default 3).
    
 * 
 * 
     -S <seed>
 *  Seed for random data shuffling (default 1).
    
 * 
 * 
     -P
 *  No pruning.
    
 * 
 * 
     -L
 *  Maximum tree depth (default -1, no maximum)
    
 * 
 * 
     -I
 *  Initial class value count (default 0)
    
 * 
 * 
     -R
 *  Spread initial count over all class values (i.e. don't use 1 per value)
    
 * 
 
 *
 * Options after -- are passed to the designated classifier.
    
 *
 * @author Eibe Frank ([email protected])
 * @author Len Trigg ([email protected])
 * @author Richard Kirkby ([email protected])
 * @version $Revision: 15519 $
 */
public class Bagging
  extends RandomizableParallelIteratedSingleClassifierEnhancer 
  implements WeightedInstancesHandler, AdditionalMeasureProducer,
             TechnicalInformationHandler, PartitionGenerator, Aggregateable {

  /** for serialization */
  static final long serialVersionUID = -115879962237199703L;
  
  /** The size of each bag sample, as a percentage of the training size */
  protected int m_BagSizePercent = 100;

  /** Whether to calculate the out of bag error */
  protected boolean m_CalcOutOfBag = false;

  /** Whether to represent copies of instances using weights rather than explicitly */
  protected boolean m_RepresentUsingWeights = false;

  /** The evaluation object holding the out of bag error, etc. */
  protected Evaluation m_OutOfBagEvaluationObject = null;

  /** Whether to store the out of bag predictions in the evaluation object. */
  private boolean m_StoreOutOfBagPredictions = false;

  /** Whether to output complexity-based statistics when OOB-evaluation is performed. */
  private boolean m_OutputOutOfBagComplexityStatistics;

  /** Whether class is numeric. */
  private boolean m_Numeric = false;

  /** Whether to print individual ensemble members in output.*/
  private boolean m_printClassifiers;

  /** Random number generator */
  protected Random m_random;

  /** Used to indicate whether an instance is in a bag or not */
  protected boolean[][] m_inBag;

  /** Reference to the training data */
  protected Instances m_data;

  /**
   * Constructor.
   */
  public Bagging() {
    
    m_Classifier = new weka.classifiers.trees.REPTree();
  }
  
  /**
   * Returns a string describing classifier
   * @return a description suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
 
    return "Class for bagging a classifier to reduce variance. Can do classification "
      + "and regression depending on the base learner. \n\n"
      + "For more information, see\n\n"
      + getTechnicalInformation().toString();
  }

  /**
   * Returns an instance of a TechnicalInformation object, containing 
   * detailed information about the technical background of this class,
   * e.g., paper reference or book this class is based on.
   * 
   * @return the technical information about this class
   */
  @Override
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation 	result;
    
    result = new TechnicalInformation(Type.ARTICLE);
    result.setValue(Field.AUTHOR, "Leo Breiman");
    result.setValue(Field.YEAR, "1996");
    result.setValue(Field.TITLE, "Bagging predictors");
    result.setValue(Field.JOURNAL, "Machine Learning");
    result.setValue(Field.VOLUME, "24");
    result.setValue(Field.NUMBER, "2");
    result.setValue(Field.PAGES, "123-140");
    
    return result;
  }

  /**
   * String describing default classifier.
   * 
   * @return the default classifier classname
   */
  @Override
  protected String defaultClassifierString() {

    return "weka.classifiers.trees.REPTree";
  }

  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  @Override
  public Enumeration
    
   *
   
   * Valid options are: 
    
   * 
   * 
     -P
   *  Size of each bag, as a percentage of the
   *  training set size. (default 100)
    
   * 
   * 
     -O
   *  Calculate the out of bag error.
    
   *
   * 
     -print
   *  Print the individual classifiers in the output
    
   *
   * 
     -store-out-of-bag-predictions
   *  Whether to store out of bag predictions in internal evaluation object.
    
   *
   * 
     -output-out-of-bag-complexity-statistics
   *  Whether to output complexity-based statistics when out-of-bag evaluation is performed.
    
   *
   * 
     -represent-copies-using-weights
   *  Represent copies of instances using weights rather than explicitly.
    
   * 
   * 
     -S <num>
   *  Random number seed.
   *  (default 1)
    
   * 
   * 
     -num-slots <num>
   *  Number of execution slots.
   *  (default 1 - i.e. no parallelism)
    
   * 
   * 
     -I <num>
   *  Number of iterations.
   *  (default 10)
    
   * 
   * 
     -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console
    
   * 
   * 
     -W
   *  Full name of base classifier.
   *  (default: weka.classifiers.trees.REPTree)
    
   * 
   * 
     
   * Options specific to classifier weka.classifiers.trees.REPTree:
   * 
    
   * 
   * 
     -M <minimum number of instances>
   *  Set minimum number of instances per leaf (default 2).
    
   * 
   * 
     -V <minimum variance for split>
   *  Set minimum numeric class variance proportion
   *  of train variance for split (default 1e-3).
    
   * 
   * 
     -N <number of folds>
   *  Number of folds for reduced error pruning (default 3).
    
   * 
   * 
     -S <seed>
   *  Seed for random data shuffling (default 1).
    
   * 
   * 
     -P
   *  No pruning.
    
   * 
   * 
     -L
   *  Maximum tree depth (default -1, no maximum)
    
   * 
   * 
     -I
   *  Initial class value count (default 0)
    
   * 
   * 
     -R
   *  Spread initial count over all class values (i.e. don't use 1 per value)
    
   *
   
   *
   * Options after -- are passed to the designated classifier.
    
   *
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  @Override
  public void setOptions(String[] options) throws Exception {

    String bagSize = Utils.getOption('P', options);
    if (bagSize.length() != 0) {
      setBagSizePercent(Integer.parseInt(bagSize));
    } else {
      setBagSizePercent(100);
    }

    setCalcOutOfBag(Utils.getFlag('O', options));

    setStoreOutOfBagPredictions(Utils.getFlag("store-out-of-bag-predictions", options));

    setOutputOutOfBagComplexityStatistics(Utils.getFlag("output-out-of-bag-complexity-statistics", options));

    setRepresentCopiesUsingWeights(Utils.getFlag("represent-copies-using-weights", options));

    setPrintClassifiers(Utils.getFlag("print", options));

    super.setOptions(options);
  }

  /**
   * Gets the current settings of the Classifier.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  @Override
  public String [] getOptions() {

    Vector options = new Vector();
    
    options.add("-P"); 
    options.add("" + getBagSizePercent());

    if (getCalcOutOfBag()) { 
        options.add("-O");
    }

    if (getStoreOutOfBagPredictions()) {
        options.add("-store-out-of-bag-predictions");
    }

    if (getOutputOutOfBagComplexityStatistics()) {
        options.add("-output-out-of-bag-complexity-statistics");
    }

    if (getRepresentCopiesUsingWeights()) {
        options.add("-represent-copies-using-weights");
    }

    if (getPrintClassifiers()) {
      options.add("-print");
    }

    Collections.addAll(options, super.getOptions());
    
    return options.toArray(new String[0]);
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String bagSizePercentTipText() {
    return "Size of each bag, as a percentage of the training set size.";
  }

  /**
   * Gets the size of each bag, as a percentage of the training set size.
   *
   * @return the bag size, as a percentage.
   */
  public int getBagSizePercent() {

    return m_BagSizePercent;
  }
  
  /**
   * Sets the size of each bag, as a percentage of the training set size.
   *
   * @param newBagSizePercent the bag size, as a percentage.
   */
  public void setBagSizePercent(int newBagSizePercent) {

    m_BagSizePercent = newBagSizePercent;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String representCopiesUsingWeightsTipText() {
    return "Whether to represent copies of instances using weights rather than explicitly.";
  }

  /**
   * Set whether copies of instances are represented using weights rather than explicitly.
   *
   * @param representUsingWeights whether to represent copies using weights
   */
  public void setRepresentCopiesUsingWeights(boolean representUsingWeights) {

    m_RepresentUsingWeights = representUsingWeights;
  }

  /**
   * Get whether copies of instances are represented using weights rather than explicitly.
   *
   * @return whether copies of instances are represented using weights rather than explicitly
   */
  public boolean getRepresentCopiesUsingWeights() {

    return m_RepresentUsingWeights;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String storeOutOfBagPredictionsTipText() {
    return "Whether to store the out-of-bag predictions.";
  }

  /**
   * Set whether the out of bag predictions are stored.
   *
   * @param storeOutOfBag whether the out of bag predictions are stored
   */
  public void setStoreOutOfBagPredictions(boolean storeOutOfBag) {

    m_StoreOutOfBagPredictions = storeOutOfBag;
  }

  /**
   * Get whether the out of bag predictions are stored.
   *
   * @return whether the out of bag predictions are stored
   */
  public boolean getStoreOutOfBagPredictions() {

    return m_StoreOutOfBagPredictions;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String calcOutOfBagTipText() {
    return "Whether the out-of-bag error is calculated.";
  }

  /**
   * Set whether the out of bag error is calculated.
   *
   * @param calcOutOfBag whether to calculate the out of bag error
   */
  public void setCalcOutOfBag(boolean calcOutOfBag) {

    m_CalcOutOfBag = calcOutOfBag;
  }

  /**
   * Get whether the out of bag error is calculated.
   *
   * @return whether the out of bag error is calculated
   */
  public boolean getCalcOutOfBag() {

    return m_CalcOutOfBag;
  }
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String outputOutOfBagComplexityStatisticsTipText() {

    return "Whether to output complexity-based statistics when out-of-bag evaluation is performed.";
  }

  /**
   * Gets whether complexity statistics are output when OOB estimation is performed.
   *
   * @return whether statistics are calculated
   */
  public boolean getOutputOutOfBagComplexityStatistics() {

    return m_OutputOutOfBagComplexityStatistics;
  }

  /**
   * Sets whether complexity statistics are output when OOB estimation is performed.
   *
   * @param b whether statistics are calculated
   */
  public void setOutputOutOfBagComplexityStatistics(boolean b) {

    m_OutputOutOfBagComplexityStatistics = b;
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String printClassifiersTipText() {
    return "Print the individual classifiers in the output";
  }

  /**
   * Set whether to print the individual ensemble classifiers in the output
   *
   * @param print true if the individual classifiers are to be printed
   */
  public void setPrintClassifiers(boolean print) {
    m_printClassifiers = print;
  }

  /**
   * Get whether to print the individual ensemble classifiers in the output
   *
   * @return true if the individual classifiers are to be printed
   */
  public boolean getPrintClassifiers() {
    return m_printClassifiers;
  }

  /**
   * Gets the out of bag error that was calculated as the classifier
   * was built. Returns error rate in classification case and
   * mean absolute error in regression case.
   *
   * @return the out of bag error; -1 if out-of-bag-error has not be estimated
   */
  public double measureOutOfBagError() {

    if (m_OutOfBagEvaluationObject == null) {
      return -1;
    }
    if (m_Numeric) {
      return m_OutOfBagEvaluationObject.meanAbsoluteError();
    } else {
      return m_OutOfBagEvaluationObject.errorRate();
    }
  }
  
  /**
   * Returns an enumeration of the additional measure names.
   *
   * @return an enumeration of the measure names
   */
  @Override
  public Enumeration enumerateMeasures() {
    
    Vector newVector = new Vector(1);
    newVector.addElement("measureOutOfBagError");
    return newVector.elements();
  }
  
  /**
   * Returns the value of the named measure.
   *
   * @param additionalMeasureName the name of the measure to query for its value
   * @return the value of the named measure
   * @throws IllegalArgumentException if the named measure is not supported
   */
  @Override
  public double getMeasure(String additionalMeasureName) {
    
    if (additionalMeasureName.equalsIgnoreCase("measureOutOfBagError")) {
      return measureOutOfBagError();
    }
    else {throw new IllegalArgumentException(additionalMeasureName 
					     + " not supported (Bagging)");
    }
  }

  /**
   * Returns a training set for a particular iteration.
   * 
   * @param iteration the number of the iteration for the requested training set.
   * @return the training set for the supplied iteration number
   * @throws Exception if something goes wrong when generating a training set.
   */
  @Override
  protected synchronized Instances getTrainingSet(int iteration) throws Exception {

    Random r = new Random(m_Seed + iteration);

    // create the in-bag indicator array if necessary
    if (m_CalcOutOfBag) {
      m_inBag[iteration] = new boolean[m_data.numInstances()];
      return m_data.resampleWithWeights(r, m_inBag[iteration], getRepresentCopiesUsingWeights(), m_BagSizePercent);
    } else {
      return m_data.resampleWithWeights(r, null, getRepresentCopiesUsingWeights(), m_BagSizePercent);
    }
  }

  /**
   * Returns the out-of-bag evaluation object.
   *
   * @return the out-of-bag evaluation object; null if out-of-bag error hasn't been calculated
   */
  public Evaluation getOutOfBagEvaluationObject() {

    return m_OutOfBagEvaluationObject;
  }

  /**
   * Bagging method.
   *
   * @param data the training data to be used for generating the
   * bagged classifier.
   * @throws Exception if the classifier could not be built successfully
   */
  @Override
  public void buildClassifier(Instances data) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(data);

    // Has user asked to represent copies using weights?
    if (getRepresentCopiesUsingWeights() && !(m_Classifier instanceof WeightedInstancesHandler)) {
      throw new IllegalArgumentException("Cannot represent copies using weights when " +
              "base learner in bagging does not implement " +
              "WeightedInstancesHandler.");
    }

    // get fresh Instances object
    m_data = new Instances(data);

    super.buildClassifier(m_data);

    m_random = new Random(m_Seed);

    m_inBag = null;
    if (m_CalcOutOfBag)
      m_inBag = new boolean[m_Classifiers.length][];

    for (int j = 0; j < m_Classifiers.length; j++) {
      if (m_Classifier instanceof Randomizable) {
        ((Randomizable) m_Classifiers[j]).setSeed(m_random.nextInt());
      }
    }

    m_Numeric = m_data.classAttribute().isNumeric();

    buildClassifiers();

    // calc OOB error?
    if (getCalcOutOfBag()) {
      m_OutOfBagEvaluationObject = new Evaluation(m_data);

      for (int i = 0; i < m_data.numInstances(); i++) {
        double[] votes;
        if (m_Numeric)
          votes = new double[1];
        else
          votes = new double[m_data.numClasses()];

        // determine predictions for instance
        int voteCount = 0;
        for (int j = 0; j < m_Classifiers.length; j++) {
          if (m_inBag[j][i])
            continue;

          if (m_Numeric) {
            double pred = m_Classifiers[j].classifyInstance(m_data.instance(i));
            if (!Utils.isMissingValue(pred)) {
              votes[0] += pred;
              voteCount++;
            }
          } else {
            voteCount++;
            double[] newProbs = m_Classifiers[j].distributionForInstance(m_data.instance(i));
            // sum the probability estimates
            for (int k = 0; k < newProbs.length; k++) {
              votes[k] += newProbs[k];
            }
          }
        }

        // "vote"
        if (m_Numeric) {
          if (voteCount > 0) {
            votes[0] /= voteCount;
            m_OutOfBagEvaluationObject.evaluationForSingleInstance(votes, m_data.instance(i), getStoreOutOfBagPredictions());
          }
        } else {
          double sum = Utils.sum(votes);
          if (sum > 0) {
            Utils.normalize(votes, sum);
            m_OutOfBagEvaluationObject.evaluationForSingleInstance(votes, m_data.instance(i), getStoreOutOfBagPredictions());
          }
        }
      }
    } else {
      m_OutOfBagEvaluationObject = null;
    }

    // save memory
    m_inBag = null;
    m_data = new Instances(m_data, 0);
  }

  /**
   * Calculates the class membership probabilities for the given test
   * instance.
   *
   * @param instance the instance to be classified
   * @return preedicted class probability distribution
   * @throws Exception if distribution can't be computed successfully 
   */
  @Override
  public double[] distributionForInstance(Instance instance) throws Exception {

    double[] sums = new double[instance.numClasses()], newProbs;

    double numPreds = 0;
    for (int i = 0; i < m_NumIterations; i++) {
      if (m_Numeric) {
        double pred = m_Classifiers[i].classifyInstance(instance);
        if (!Utils.isMissingValue(pred)) {
          sums[0] += pred;
          numPreds++;
        }
      } else {
        newProbs = m_Classifiers[i].distributionForInstance(instance);
        for (int j = 0; j < newProbs.length; j++)
          sums[j] += newProbs[j];
      }
    }
    if (m_Numeric) {
      if (numPreds == 0) {
        sums[0] = Utils.missingValue();
      } else {
        sums[0] /= numPreds;
      }
      return sums;
    } else if (Utils.eq(Utils.sum(sums), 0)) {
      return sums;
    } else {
      Utils.normalize(sums);
      return sums;
    }
  }

  /**
   * Returns description of the bagged classifier.
   *
   * @return description of the bagged classifier as a string
   */
  @Override
  public String toString() {
    
    if (m_Classifiers == null) {
      return "Bagging: No model built yet.";
    }
    StringBuffer text = new StringBuffer();
    text.append("Bagging with " + getNumIterations() + " iterations and base learner\n\n" + getClassifierSpec());
    if (getPrintClassifiers()) {
      text.append("All the base classifiers: \n\n");
      for (int i = 0; i < m_Classifiers.length; i++)
        text.append(m_Classifiers[i].toString() + "\n\n");
    }
    if (m_CalcOutOfBag) {
      text.append(m_OutOfBagEvaluationObject.toSummaryString("\n\n*** Out-of-bag estimates ***\n", getOutputOutOfBagComplexityStatistics()));
    }

    return text.toString();
  }
  
  /**
   * Builds the classifier to generate a partition.
   */
  @Override
  public void generatePartition(Instances data) throws Exception {
    
    if (m_Classifier instanceof PartitionGenerator)
      buildClassifier(data);
    else throw new Exception("Classifier: " + getClassifierSpec()
			     + " cannot generate a partition");
  }
  
  /**
   * Computes an array that indicates leaf membership
   */
  @Override
  public double[] getMembershipValues(Instance inst) throws Exception {
    
    if (m_Classifier instanceof PartitionGenerator) {
      ArrayList al = new ArrayList();
      int size = 0;
      for (int i = 0; i < m_Classifiers.length; i++) {
        double[] r = ((PartitionGenerator)m_Classifiers[i]).
          getMembershipValues(inst);
        size += r.length;
        al.add(r);
      }
      double[] values = new double[size];
      int pos = 0;
      for (double[] v: al) {
        System.arraycopy(v, 0, values, pos, v.length);
        pos += v.length;
      }
      return values;
    } else throw new Exception("Classifier: " + getClassifierSpec()
                               + " cannot generate a partition");
  }
  
  /**
   * Returns the number of elements in the partition.
   */
  @Override
  public int numElements() throws Exception {
    
    if (m_Classifier instanceof PartitionGenerator) {
      int size = 0;
      for (int i = 0; i < m_Classifiers.length; i++) {
        size += ((PartitionGenerator)m_Classifiers[i]).numElements();
      }
      return size;
    } else throw new Exception("Classifier: " + getClassifierSpec()
                               + " cannot generate a partition");
  }
  
  /**
   * Returns the revision string.
   * 
   * @return		the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 15519 $");
  }

  /**
   * Main method for testing this class.
   *
   * @param argv the options
   */
  public static void main(String [] argv) {
    runClassifier(new Bagging(), argv);
  }
  
  protected List m_classifiersCache;

  /**
   * Aggregate an object with this one
   * 
   * @param toAggregate the object to aggregate
   * @return the result of aggregation
   * @throws Exception if the supplied object can't be aggregated for some
   *           reason
   */
  @Override
  public Bagging aggregate(Bagging toAggregate) throws Exception {
    if (!m_Classifier.getClass().isAssignableFrom(toAggregate.m_Classifier.getClass())) {
      throw new Exception("Can't aggregate because base classifiers differ");
    }
    
    if (m_classifiersCache == null) {
      m_classifiersCache = new ArrayList();
      m_classifiersCache.addAll(Arrays.asList(m_Classifiers));
    }
    m_classifiersCache.addAll(Arrays.asList(toAggregate.m_Classifiers));
    
    return this;
  }

  /**
   * Call to complete the aggregation process. Allows implementers to do any
   * final processing based on how many objects were aggregated.
   * 
   * @throws Exception if the aggregation can't be finalized for some reason
   */
  @Override
  public void finalizeAggregation() throws Exception {    
    m_Classifiers = m_classifiersCache.toArray(new Classifier[1]);
    m_NumIterations = m_Classifiers.length;
    
    m_classifiersCache = null;
  }
}




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