How to use

weka.experiment.Stats

constructor

/**
 * Creates a new PairedStats object with the supplied significance level.
 *
 * @param sig the significance level for comparisons
 */
public PairedStats(double sig) {
  
 xStats = new Stats();
 yStats = new Stats();
 differencesStats = new Stats();
 sigLevel = sig;
}

/**
 * Creates a new PairedStats object with the supplied significance level.
 *
 * @param sig the significance level for comparisons
 */
public PairedStats(double sig) {
  
 xStats = new Stats();
 yStats = new Stats();
 differencesStats = new Stats();
 sigLevel = sig;
}

private Stats getStats() {
 return new Stats();
}

private Stats getStats() {
 return new Stats();
}

Stats ps = new Stats();
java.io.LineNumberReader r = new java.io.LineNumberReader(
 new java.io.InputStreamReader(System.in));

Stats ps = new Stats();
java.io.LineNumberReader r = new java.io.LineNumberReader(
 new java.io.InputStreamReader(System.in));

Stats[] stats = new Stats[resultTypes.length];
for (int i = 0; i < stats.length; i++) {
 stats[i] = new Stats();

Stats[] stats = new Stats[resultTypes.length];
for (int i = 0; i < stats.length; i++) {
 stats[i] = new Stats();

/**
 * Unweighted macro-averaged F-measure. If some classes not present in the
 * test set, they're just skipped (since recall is undefined there anyway) .
 *
 * @return unweighted macro-averaged F-measure.
 * */
public double unweightedMacroFmeasure() {
 weka.experiment.Stats rr = new weka.experiment.Stats();
 for (int c = 0; c < m_NumClasses; c++) {
  // skip if no testing positive cases of this class
  if (numTruePositives(c) + numFalseNegatives(c) > 0) {
   rr.add(fMeasure(c));
  }
 }
 rr.calculateDerived();
 return rr.mean;
}

/**
 * Unweighted macro-averaged F-measure. If some classes not present in the
 * test set, they're just skipped (since recall is undefined there anyway) .
 *
 * @return unweighted macro-averaged F-measure.
 * */
public double unweightedMacroFmeasure() {
 weka.experiment.Stats rr = new weka.experiment.Stats();
 for (int c = 0; c < m_NumClasses; c++) {
  // skip if no testing positive cases of this class
  if (numTruePositives(c) + numFalseNegatives(c) > 0) {
   rr.add(fMeasure(c));
  }
 }
 rr.calculateDerived();
 return rr.mean;
}

result.numericStats = new weka.experiment.Stats();

result.numericStats = new weka.experiment.Stats();

/**
 * Sets the format of the input instances.
 * 
 * @param instanceInfo an Instances object containing the input instance
 *          structure (any instances contained in the object are ignored -
 *          only the structure is required).
 * @return true if the outputFormat may be collected immediately
 * @throws Exception if the input format can't be set successfully
 */
@Override
public boolean setInputFormat(Instances instanceInfo) throws Exception {
 m_SelectCols.setUpper(instanceInfo.numAttributes() - 1);
 super.setInputFormat(instanceInfo);
 setOutputFormat(instanceInfo);
 m_attStats = new Stats[instanceInfo.numAttributes()];
 
 for (int i = 0; i < instanceInfo.numAttributes(); i++) {
  if (m_SelectCols.isInRange(i)
    && instanceInfo.attribute(i).isNumeric()
    && (instanceInfo.classIndex() != i) || getIgnoreClass()) {
   
   m_attStats[i] = new Stats();
  }
 }
 
 if (instanceInfo != null)
  compile();
 return true;
}

/**
 * Sets the format of the input instances.
 * 
 * @param instanceInfo an Instances object containing the input instance
 *          structure (any instances contained in the object are ignored -
 *          only the structure is required).
 * @return true if the outputFormat may be collected immediately
 * @throws Exception if the input format can't be set successfully
 */
@Override
public boolean setInputFormat(Instances instanceInfo) throws Exception {
 m_SelectCols.setUpper(instanceInfo.numAttributes() - 1);
 super.setInputFormat(instanceInfo);
 setOutputFormat(instanceInfo);
 m_attStats = new Stats[instanceInfo.numAttributes()];
 
 for (int i = 0; i < instanceInfo.numAttributes(); i++) {
  if (m_SelectCols.isInRange(i)
    && instanceInfo.attribute(i).isNumeric()
    && (instanceInfo.classIndex() != i) || getIgnoreClass()) {
   
   m_attStats[i] = new Stats();
  }
 }
 
 if (instanceInfo != null)
  compile();
 return true;
}

result.numericStats = new weka.experiment.Stats();

      new int[m_clusterInstances.attribute(i).numValues()];
} else {
  m_attStats[i].numericStats = new Stats();

  i).numValues()];
} else {
 m_attStats[i].numericStats = new Stats();

  i).numValues()];
} else {
 m_attStats[i].numericStats = new Stats();