How to use

org.apache.commons.math3.stat.descriptive.moment.Kurtosis

constructor

/**
 * {@inheritDoc}
 */
@Override
public Kurtosis copy() {
  Kurtosis result = new Kurtosis();
  // No try-catch because args are guaranteed non-null
  copy(this, result);
  return result;
}

  @Override
  public Double summarize(NumericColumn<?> column) {
    double[] data = removeMissing(column);
    return new Kurtosis().evaluate(data, 0, data.length);
  }
};

@Override
public Number getExpectedValue(int start, int length)
{
  if (length < 4) {
    return null;
  }
  double[] values = new double[length];
  for (int i = 0; i < length; i++) {
    values[i] = start + i;
  }
  Kurtosis kurtosis = new Kurtosis();
  return kurtosis.evaluate(values);
}

@Override
public Number getExpectedValue(int start, int length)
{
  if (length < 4) {
    return null;
  }
  double[] values = new double[length];
  for (int i = 0; i < length; i++) {
    values[i] = start + i;
  }
  Kurtosis kurtosis = new Kurtosis();
  return kurtosis.evaluate(values);
}

  public Kurtosis() {
    super(new org.apache.commons.math3.stat.descriptive.moment.Kurtosis());
  }
}

  public Kurtosis() {
    super(new org.apache.commons.math3.stat.descriptive.moment.Kurtosis());
  }
}

/**
 * {@inheritDoc}
 */
@Override
public Kurtosis copy() {
  Kurtosis result = new Kurtosis();
  // No try-catch because args are guaranteed non-null
  copy(this, result);
  return result;
}

/**
 * {@inheritDoc}
 */
@Override
public Kurtosis copy() {
  Kurtosis result = new Kurtosis();
  // No try-catch because args are guaranteed non-null
  copy(this, result);
  return result;
}

  @Override
  public Double summarize(NumericColumn<?> column) {
    double[] data = removeMissing(column);
    return new Kurtosis().evaluate(data, 0, data.length);
  }
};

  @Override
  public double reduce(double[] data) {
    return new Kurtosis().evaluate(data, 0, data.length);
  }
};

/**
 * Calculates the kurtosis.
 *
 * @param fittedData
 *            If {@code true}, calculation is performed on polynomial fitted
 *            values, otherwise from sampled data
 *
 * @return kurtosis of intersection counts
 */
public double getKurtosis(final boolean fittedData) {
  if (fittedData)
    validateFit();
  final Kurtosis k = new Kurtosis();
  return k.evaluate(fittedData ? fCounts : inputCounts);
}

@Override
public Number getExpectedValue(int start, int length)
{
  if (length < 4) {
    return null;
  }
  double[] values = new double[length];
  for (int i = 0; i < length; i++) {
    values[i] = start + i;
  }
  Kurtosis kurtosis = new Kurtosis();
  return kurtosis.evaluate(values);
}

@Override
public Number getExpectedValue(int start, int length)
{
  if (length < 4) {
    return null;
  }
  double[] values = new double[length];
  for (int i = 0; i < length; i++) {
    values[i] = start + i;
  }
  Kurtosis kurtosis = new Kurtosis();
  return kurtosis.evaluate(values);
}

@Override
public Number getExpectedValue(int start, int length)
{
  if (length < 4) {
    return null;
  }
  double[] values = new double[length];
  for (int i = 0; i < length; i++) {
    values[i] = start + i;
  }
  Kurtosis kurtosis = new Kurtosis();
  return kurtosis.evaluate(values);
}

@Override
public Number getExpectedValue(int start, int length)
{
  if (length < 4) {
    return null;
  }
  double[] values = new double[length];
  for (int i = 0; i < length; i++) {
    values[i] = start + i;
  }
  Kurtosis kurtosis = new Kurtosis();
  return kurtosis.evaluate(values);
}

public static void main(String[] args) {
  final double[] values = new java.util.Random().doubles(5000).toArray();
  final Kurtosis stat1 = new Kurtosis();
  final org.apache.commons.math3.stat.descriptive.moment.Kurtosis stat2 = new org.apache.commons.math3.stat.descriptive.moment.Kurtosis();
  for (double value : values) {
    stat1.add(value);
    stat2.increment(value);
  }
  final double result1 = stat1.getValue();
  final double result2 = stat2.getResult();
  if (result1 != result2) {
    throw new RuntimeException("Error: " + result1 + " != " + result2);
  }
}

  return new GeometricMean();
case Kurtosis:
  return new Kurtosis();
case Max:
  return new Max();