How to use

calculateDerived

method

in

weka.experiment.Stats

/**
 * Returns the standard deviation of a numeric attribute
 *
 * @param attIndex the index of the attribute
 * @return the standard deviation
 * @throws Exception if an error occurs
 */
protected double getStandardDev(int attIndex) { //throws Exception {
  //  if (!m_clusterInstances.attribute(attIndex).isNumeric()) {
  //throw new Exception("getStandardDev: attribute is not numeric");
  // }
  m_attStats[attIndex].numericStats.calculateDerived();
  double stdDev = m_attStats[attIndex].numericStats.stdDev;
  if (Double.isNaN(stdDev) || Double.isInfinite(stdDev)) {
    return m_acuity;
  }
  return Math.max(m_acuity, stdDev);
}

xStats.calculateDerived();
yStats.calculateDerived();
differencesStats.calculateDerived();

xStats.calculateDerived();
yStats.calculateDerived();
differencesStats.calculateDerived();

/**
 * Returns the standard deviation of a numeric attribute
 * 
 * @param attIndex the index of the attribute
 * @return the standard deviation
 * @throws Exception if an error occurs
 */
protected double getStandardDev(int attIndex) throws Exception {
 if (!m_clusterInstances.attribute(attIndex).isNumeric()) {
  throw new Exception("getStandardDev: attribute is not numeric");
 }
 m_attStats[attIndex].numericStats.calculateDerived();
 double stdDev = m_attStats[attIndex].numericStats.stdDev;
 if (Double.isNaN(stdDev) || Double.isInfinite(stdDev)) {
  return m_acuity;
 }
 return Math.max(m_acuity, stdDev);
}

/**
 * Updates the counters for one more observed distinct value.
 *
 * @param value the value that has just been seen
 * @param count the number of times the value appeared
 * @param weight the weight mass of the value
 */
protected void addDistinct(double value, int count, double weight) {
 if (count > 0) {
  if (count == 1) {
   uniqueCount++;
  }
  if (value == (int)value) {
   intCount += count;
  } else {
   realCount += count;
  }
  if (nominalCounts != null) {
   nominalCounts[(int) value] = count;
   nominalWeights[(int) value] = weight;
  }
  if (numericStats != null) {
   //numericStats.add(value, count);
   numericStats.add(value, weight);
   numericStats.calculateDerived();
  }
 }
 distinctCount++;
}

/**
 * Returns the standard deviation of a numeric attribute
 * 
 * @param attIndex the index of the attribute
 * @return the standard deviation
 * @throws Exception if an error occurs
 */
protected double getStandardDev(int attIndex) throws Exception {
 if (!m_clusterInstances.attribute(attIndex).isNumeric()) {
  throw new Exception("getStandardDev: attribute is not numeric");
 }
 m_attStats[attIndex].numericStats.calculateDerived();
 double stdDev = m_attStats[attIndex].numericStats.stdDev;
 if (Double.isNaN(stdDev) || Double.isInfinite(stdDev)) {
  return m_acuity;
 }
 return Math.max(m_acuity, stdDev);
}

xStats.calculateDerived();
yStats.calculateDerived();
differencesStats.calculateDerived();

xStats.calculateDerived();
yStats.calculateDerived();
differencesStats.calculateDerived();

/**
 * Updates the counters for one more observed distinct value.
 *
 * @param value the value that has just been seen
 * @param count the number of times the value appeared
 * @param weight the weight mass of the value
 */
protected void addDistinct(double value, int count, double weight) {
 if (count > 0) {
  if (count == 1) {
   uniqueCount++;
  }
  if (value == (int)value) {
   intCount += count;
  } else {
   realCount += count;
  }
  if (nominalCounts != null) {
   nominalCounts[(int) value] = count;
   nominalWeights[(int) value] = weight;
  }
  if (numericStats != null) {
   //numericStats.add(value, count);
   numericStats.add(value, weight);
   numericStats.calculateDerived();
  }
 }
 distinctCount++;
}

 ps.calculateDerived();
 System.err.println(ps);
} catch (Exception ex) {

 ps.calculateDerived();
 System.err.println(ps);
} catch (Exception ex) {

public void addDistinct(double value, int count) {
 if (count > 0) {
  if (count == 1) {
   uniqueCount++;
  }
  if (Utils.eq(value, (double)((int)value))) {
   intCount += count;
  } else {
   realCount += count;
  }
  if (nominalCounts != null) {
   nominalCounts[(int)value] = count;
  }
  if (numericStats != null) {
   numericStats.add(value, count);
   numericStats.calculateDerived();
  }
 }
 distinctCount++;
}
 

/**
 * 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;
}

stats.calculateDerived();
subtracted1.calculateDerived();
subtracted2.calculateDerived();

stats.calculateDerived();
subtracted1.calculateDerived();
subtracted2.calculateDerived();

subtractWeightedStats(test, negativeWeights);
reference.calculateDerived();
test.calculateDerived();
checkStats(test, descr, reference, 0.0);
addWeightedStats(test, negativeWeights);
reference.calculateDerived();
test.calculateDerived();
checkStats(test, descr, reference, 0.0);
subtractWeightedStats(test, negativeWeights);
reference.calculateDerived();
test.calculateDerived();
checkStats(test, descr, reference, 0.0);
subtractWeightedStats(test, weightedValues1);
reference.calculateDerived();
test.calculateDerived();
checkStats(test, descr, reference, 0.0);

subtractWeightedStats(test, negativeWeights);
reference.calculateDerived();
test.calculateDerived();
checkStats(test, descr, reference, 0.0);
addWeightedStats(test, negativeWeights);
reference.calculateDerived();
test.calculateDerived();
checkStats(test, descr, reference, 0.0);
subtractWeightedStats(test, negativeWeights);
reference.calculateDerived();
test.calculateDerived();
checkStats(test, descr, reference, 0.0);
subtractWeightedStats(test, weightedValues1);
reference.calculateDerived();
test.calculateDerived();
checkStats(test, descr, reference, 0.0);

 && input.classIndex() != i) {
m_attStats[i].calculateDerived();

 && input.classIndex() != i) {
m_attStats[i].calculateDerived();