How to use

add

method

in

weka.experiment.Stats

/**
 * Add an observed pair of values.
 *
 * @param value1 the value from column 1
 * @param value2 the value from column 2
 */
public void add(double value1, double value2) {
 xStats.add(value1);
 yStats.add(value2);
 differencesStats.add(value1 - value2);
 xySum += value1 * value2;
 count ++;
}
 

/**
 * Adds a value to the observed values<p>
 * 
 * It's equivalent to <code>add(value, 1)</code><p>
 *
 * @param value the observed value
 */
public void add(double value) {
 add(value, 1);
}

/**
 * Add an observed pair of values.
 *
 * @param value1 the value from column 1
 * @param value2 the value from column 2
 */
public void add(double value1, double value2) {
 xStats.add(value1);
 yStats.add(value2);
 differencesStats.add(value1 - value2);
 xySum += value1 * value2;
 count ++;
}
 

/**
 * Adds a value to the observed values<p>
 * 
 * It's equivalent to <code>add(value, 1)</code><p>
 *
 * @param value the observed value
 */
public void add(double value) {
 add(value, 1);
}

private void addWeightedStats(Stats stats, double... values) {
 assert values.length % 2 == 0;
 for (int i = 0; i < values.length; i += 2) {
  stats.add(values[i], values[i + 1]);
 }
}

private void addWeightedStats(Stats stats, double... values) {
 assert values.length % 2 == 0;
 for (int i = 0; i < values.length; i += 2) {
  stats.add(values[i], values[i + 1]);
 }
}

/**
 * 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++;
}

private void addWeightedStats(Stats stats, long... values) {
 assert values.length%2 == 0;
 
 for (int i = 0; i < values.length; i += 2) {
  stats.add(Double.longBitsToDouble(values[i]),
    Double.longBitsToDouble(values[i + 1])
    );
 }
 
}

/**
 * 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++;
}

private void addWeightedStats(Stats stats, long... values) {
 assert values.length%2 == 0;
 
 for (int i = 0; i < values.length; i += 2) {
  stats.add(Double.longBitsToDouble(values[i]),
    Double.longBitsToDouble(values[i + 1])
    );
 }
 
}

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++;
}
 

add(value, -weight);
return;

 double value = Double.longBitsToDouble(adds[j]);
 double weight = Double.longBitsToDouble(adds[j + 1]);
 stats.add(value, weight);
stats.add(value, weight);
b.add(value); b.add(weight);

/**
 * 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;
}

 && (!instance.isMissing(i))) {
m_attStats[i].add(instance.value(i), instance.weight());

stats.add(value, weight);
checkStatsInvalidState(stats);
stats.add(value, weight);
checkStatsInvalidState(stats);
stats.add(value, weight);
checkStatsInvalidState(stats);

stats.add(value, weight);
checkStatsInvalidState(stats);
stats.add(value, weight);
checkStatsInvalidState(stats);
stats.add(value, weight);
checkStatsInvalidState(stats);

 stats.add(values[i], values[i + 1]);
} else {
 break;

 stats.add(values[i], values[i + 1]);
} else {
 break;