gt:       int compareCol = Integer.parseInt(compareColStr) - 1;
context: /*
   *   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 <http://www.gnu.org/licenses/>.
   */
  
  /*
   *    PairedCorrectedTTester.java
   *    Copyright (C) 2003-2012 University of Waikato, Hamilton, New Zealand
   *
   */
  
  
  package weka.experiment;
  
  import java.io.BufferedReader;
  import java.io.FileReader;
  import java.util.Enumeration;
  
  import weka.core.Attribute;
  import weka.core.FastVector;
  import weka.core.Instance;
  import weka.core.Instances;
  import weka.core.Option;
  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;
  
  /**
   * Behaves the same as PairedTTester, only it uses the corrected
   * resampled t-test statistic.<p/>
   *
   * For more information see:<p/>
   *
   <!-- technical-plaintext-start -->
   * Claude Nadeau, Yoshua Bengio (2001). Inference for the Generalization Error. Machine Learning..
   <!-- technical-plaintext-end -->
   *
   * <p/>
   * 
   <!-- technical-bibtex-start -->
   * BibTeX:
   * <pre>
   * &#64;article{Nadeau2001,
   *    author = {Claude Nadeau and Yoshua Bengio},
   *    journal = {Machine Learning},
   *    title = {Inference for the Generalization Error},
   *    year = {2001},
   *    PDF = {http://www.iro.umontreal.ca/\~lisa/bib/pub_subject/comparative/pointeurs/nadeau_MLJ1597.pdf}
   * }
   * </pre>
   * <p/>
   <!-- technical-bibtex-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -D &lt;index,index2-index4,...&gt;
   *  Specify list of columns that specify a unique
   *  dataset.
   *  First and last are valid indexes. (default none)</pre>
   * 
   * <pre> -R &lt;index&gt;
   *  Set the index of the column containing the run number</pre>
   * 
   * <pre> -F &lt;index&gt;
   *  Set the index of the column containing the fold number</pre>
   * 
   * <pre> -G &lt;index1,index2-index4,...&gt;
   *  Specify list of columns that specify a unique
   *  'result generator' (eg: classifier name and options).
   *  First and last are valid indexes. (default none)</pre>
   * 
   * <pre> -S &lt;significance level&gt;
   *  Set the significance level for comparisons (default 0.05)</pre>
   * 
   * <pre> -V
   *  Show standard deviations</pre>
   * 
   * <pre> -L
   *  Produce table comparisons in Latex table format</pre>
   * 
   * <pre> -csv
   *  Produce table comparisons in CSV table format</pre>
   * 
   * <pre> -html
   *  Produce table comparisons in HTML table format</pre>
   * 
   * <pre> -significance
   *  Produce table comparisons with only the significance values</pre>
   * 
   * <pre> -gnuplot
   *  Produce table comparisons output suitable for GNUPlot</pre>
   * 
   <!-- options-end -->
   *
   * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
   * @version $Revision: 8034 $
   */
  public class PairedCorrectedTTester 
    extends PairedTTester
    implements TechnicalInformationHandler {
    
    /** for serialization */
    static final long serialVersionUID = -3105268939845653323L;
  
    /**
     * 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
     */
    public TechnicalInformation getTechnicalInformation() {
      TechnicalInformation 	result;
      
      result = new TechnicalInformation(Type.ARTICLE);
      result.setValue(Field.AUTHOR, "Claude Nadeau and Yoshua Bengio");
      result.setValue(Field.YEAR, "2001");
      result.setValue(Field.TITLE, "Inference for the Generalization Error");
      result.setValue(Field.JOURNAL, "Machine Learning");
      result.setValue(Field.PDF, "http://www.iro.umontreal.ca/~lisa/bib/pub_subject/comparative/pointeurs/nadeau_MLJ1597.pdf");
  
      return result;
    }
  
    /**
     * Computes a paired t-test comparison for a specified dataset between
     * two resultsets.
     *
     * @param datasetSpecifier the dataset specifier
     * @param resultset1Index the index of the first resultset
     * @param resultset2Index the index of the second resultset
     * @param comparisonColumn the column containing values to compare
     * @return the results of the paired comparison
     * @throws Exception if an error occurs
     */
    public PairedStats calculateStatistics(Instance datasetSpecifier,
  					 int resultset1Index,
  					 int resultset2Index,
  					 int comparisonColumn) throws Exception {
  
      if (m_Instances.attribute(comparisonColumn).type()
  	!= Attribute.NUMERIC) {
        throw new Exception("Comparison column " + (comparisonColumn + 1)
  			  + " ("
  			  + m_Instances.attribute(comparisonColumn).name()
  			  + ") is not numeric");
      }
      if (!m_ResultsetsValid) {
        prepareData();
      }
  
      Resultset resultset1 = (Resultset) m_Resultsets.elementAt(resultset1Index);
      Resultset resultset2 = (Resultset) m_Resultsets.elementAt(resultset2Index);
      FastVector dataset1 = resultset1.dataset(datasetSpecifier);
      FastVector dataset2 = resultset2.dataset(datasetSpecifier);
      String datasetName = templateString(datasetSpecifier);
      if (dataset1 == null) {
        throw new Exception("No results for dataset=" + datasetName
  			 + " for resultset=" + resultset1.templateString());
      } else if (dataset2 == null) {
        throw new Exception("No results for dataset=" + datasetName
  			 + " for resultset=" + resultset2.templateString());
      } else if (dataset1.size() != dataset2.size()) {
        throw new Exception("Results for dataset=" + datasetName
  			  + " differ in size for resultset="
  			  + resultset1.templateString()
  			  + " and resultset="
  			  + resultset2.templateString()
  			  );
      }
  
      // calculate the test/train ratio
      double testTrainRatio = 0.0;
      int trainSizeIndex = -1;
      int testSizeIndex = -1;
      // find the columns with the train/test sizes
      for (int i=0; i<m_Instances.numAttributes(); i++) {
        if (m_Instances.attribute(i).name().toLowerCase().equals("number_of_training_instances")) {
  	trainSizeIndex = i;
        } else if (m_Instances.attribute(i).name().toLowerCase().equals("number_of_testing_instances")) {
  	testSizeIndex = i;
        }
      }
      if (trainSizeIndex >= 0 && testSizeIndex >= 0) {
        double totalTrainSize = 0.0;
        double totalTestSize = 0.0;
        for (int k = 0; k < dataset1.size(); k ++) {
  	Instance current = (Instance) dataset1.elementAt(k);
  	totalTrainSize += current.value(trainSizeIndex);
  	totalTestSize += current.value(testSizeIndex);
        }
        testTrainRatio = totalTestSize / totalTrainSize;
      }
      PairedStats pairedStats =
        new PairedStatsCorrected(m_SignificanceLevel, testTrainRatio);
  
      for (int k = 0; k < dataset1.size(); k ++) {
        Instance current1 = (Instance) dataset1.elementAt(k);
        Instance current2 = (Instance) dataset2.elementAt(k);
        if (current1.isMissing(comparisonColumn)) {
  	System.err.println("Instance has missing value in comparison "
  			   + "column!\n" + current1);
  	continue;
        }
        if (current2.isMissing(comparisonColumn)) {
  	System.err.println("Instance has missing value in comparison "
  			   + "column!\n" + current2);
  	continue;
        }
        if (current1.value(m_RunColumn) != current2.value(m_RunColumn)) {
  	System.err.println("Run numbers do not match!\n"
  			    + current1 + current2);
        }
        if (m_FoldColumn != -1) {
  	if (current1.value(m_FoldColumn) != current2.value(m_FoldColumn)) {
  	  System.err.println("Fold numbers do not match!\n"
  			     + current1 + current2);
  	}
        }
  
        double value1 = current1.value(comparisonColumn);
        double value2 = current2.value(comparisonColumn);
        pairedStats.add(value1, value2);
      }
      pairedStats.calculateDerived();
      return pairedStats;
    }
  
    /**
     * Test the class from the command line.
     *
     * @param args contains options for the instance ttests
     */
    public static void main(String args[]) {
      
      try {
        PairedCorrectedTTester tt = new PairedCorrectedTTester();
        String datasetName = Utils.getOption('t', args);
        String compareColStr = Utils.getOption('c', args);
        String baseColStr = Utils.getOption('b', args);
        boolean summaryOnly = Utils.getFlag('s', args);
        boolean rankingOnly = Utils.getFlag('r', args);
        try {
  	if ((datasetName.length() == 0)
  	    || (compareColStr.length() == 0)) {
  	  throw new Exception("-t and -c options are required");
  	}
  	tt.setOptions(args);
  	Utils.checkForRemainingOptions(args);
        } catch (Exception ex) {
  	String result = "";
  	Enumeration enu = tt.listOptions();
  	while (enu.hasMoreElements()) {
  	  Option option = (Option) enu.nextElement();
  	  result += option.synopsis() + '\n'
  	    + option.description() + '\n';
  	}
  	throw new Exception(
  	      "Usage:\n\n"
  	      + "-t <file>\n"
  	      + "\tSet the dataset containing data to evaluate\n"
  	      + "-b <index>\n"
  	      + "\tSet the resultset to base comparisons against (optional)\n"
  	      + "-c <index>\n"
  	      + "\tSet the column to perform a comparison on\n"
  	      + "-s\n"
  	      + "\tSummarize wins over all resultset pairs\n\n"
  	      + "-r\n"
  	      + "\tGenerate a resultset ranking\n\n"
  	      + result);
        }
        Instances data = new Instances(new BufferedReader(
  				  new FileReader(datasetName)));
        tt.setInstances(data);
        //      tt.prepareData();
  