org.apache.commons.statistics.descriptive.SumOfCubedDeviations Maven / Gradle / Ivy
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package org.apache.commons.statistics.descriptive;
/**
* Computes the sum of cubed deviations from the sample mean. This
* statistic is related to the third moment.
*
* Uses a recursive updating formula as defined in Manca and Marin (2010), equation 10.
* Note that the denominator in the third term in that equation has been corrected to
* \( (N_1 + N_2)^2 \). Two sum of cubed deviations (SC) can be combined using:
*
*
\[ SC(X) = {SC}_1 + {SC}_2 + \frac{3(m_1 - m_2)({s_1}^2 - {s_2}^2) N_1 N_2}{N_1 + N_2}
* + \frac{(m_1 - m_2)^3((N_2 - N_1) N_1 N_2}{(N_1 + N_2)^2} \]
*
*
where \( N \) is the group size, \( m \) is the mean, and \( s^2 \) is the biased variance
* such that \( s^2 * N \) is the sum of squared deviations from the mean. Note the term
* \( ({s_1}^2 - {s_2}^2) N_1 N_2 == (ss_1 * N_2 - ss_2 * N_1 \) where \( ss \) is the sum
* of square deviations.
*
*
If \( N_1 \) is size 1 this reduces to:
*
*
\[ SC_{N+1} = {SC}_N + \frac{3(x - m) -s^2 N}{N + 1}
* + \frac{(x - m)^3((N - 1) N}{(N + 1)^2} \]
*
*
where \( s^2 N \) is the sum of squared deviations.
* This updating formula is identical to that used in
* {@code org.apache.commons.math3.stat.descriptive.moment.ThirdMoment}.
*
*
Supports up to 263 (exclusive) observations.
* This implementation does not check for overflow of the count.
*
*
Note that this implementation is not synchronized. If
* multiple threads access an instance of this class concurrently, and at least
* one of the threads invokes the {@link java.util.function.DoubleConsumer#accept(double) accept} or
* {@link StatisticAccumulator#combine(StatisticResult) combine} method, it must be synchronized externally.
*
*
However, it is safe to use {@link java.util.function.DoubleConsumer#accept(double) accept}
* and {@link StatisticAccumulator#combine(StatisticResult) combine}
* as {@code accumulator} and {@code combiner} functions of
* {@link java.util.stream.Collector Collector} on a parallel stream,
* because the parallel implementation of {@link java.util.stream.Stream#collect Stream.collect()}
* provides the necessary partitioning, isolation, and merging of results for
* safe and efficient parallel execution.
*
*
References:
*
* - Manca and Marin (2020)
* Decomposition of the Sum of Cubes, the Sum Raised to the
* Power of Four and Codeviance.
* Applied Mathematics, 11, 1013-1020.
* doi: 10.4236/am.2020.1110067
*
*
* @since 1.1
*/
class SumOfCubedDeviations extends SumOfSquaredDeviations {
/** 2, the length limit where the sum-of-cubed deviations is zero. */
static final int LENGTH_TWO = 2;
/** Sum of cubed deviations of the values that have been added. */
protected double sumCubedDev;
/**
* Create an instance.
*/
SumOfCubedDeviations() {
// No-op
}
/**
* Copy constructor.
*
* @param source Source to copy.
*/
SumOfCubedDeviations(SumOfCubedDeviations source) {
super(source);
sumCubedDev = source.sumCubedDev;
}
/**
* Create an instance with the given sum of cubed and squared deviations.
*
* @param sc Sum of cubed deviations.
* @param ss Sum of squared deviations.
*/
SumOfCubedDeviations(double sc, SumOfSquaredDeviations ss) {
super(ss);
this.sumCubedDev = sc;
}
/**
* Create an instance with the given sum of cubed and squared deviations,
* and first moment.
*
* @param sc Sum of cubed deviations.
* @param ss Sum of squared deviations.
* @param m1 First moment.
* @param n Count of values.
*/
SumOfCubedDeviations(double sc, double ss, double m1, long n) {
super(ss, m1, n);
this.sumCubedDev = sc;
}
/**
* Returns an instance populated using the input {@code values}.
*
* Note: {@code SumOfCubedDeviations} computed using {@link #accept(double) accept} may be
* different from this instance.
*
* @param values Values.
* @return {@code SumOfCubedDeviations} instance.
*/
static SumOfCubedDeviations of(double... values) {
if (values.length == 0) {
return new SumOfCubedDeviations();
}
return create(SumOfSquaredDeviations.of(values), values);
}
/**
* Creates the sum of cubed deviations.
*
*
Uses the provided {@code sum} to create the first moment.
* This method is used by {@link DoubleStatistics} using a sum that can be reused
* for the {@link Sum} statistic.
*
* @param sum Sum of the values.
* @param values Values.
* @return {@code SumOfCubedDeviations} instance.
*/
static SumOfCubedDeviations create(org.apache.commons.numbers.core.Sum sum, double[] values) {
if (values.length == 0) {
return new SumOfCubedDeviations();
}
return create(SumOfSquaredDeviations.create(sum, values), values);
}
/**
* Creates the sum of cubed deviations.
*
* @param ss Sum of squared deviations.
* @param values Values.
* @return {@code SumOfCubedDeviations} instance.
*/
private static SumOfCubedDeviations create(SumOfSquaredDeviations ss, double[] values) {
// Edge cases
final double xbar = ss.getFirstMoment();
if (!Double.isFinite(xbar)) {
return new SumOfCubedDeviations(Double.NaN, ss);
}
if (!Double.isFinite(ss.sumSquaredDev)) {
// Note: If the sum-of-squared (SS) overflows then the same deviations when cubed
// will overflow. The *smallest* deviation to overflow SS is a full-length array of
// +/- values around a mean of zero, or approximately sqrt(MAX_VALUE / 2^31) = 2.89e149.
// In this case the sum cubed could be finite due to cancellation
// but this cannot be computed. Only a small array can be known to be zero.
return new SumOfCubedDeviations(values.length <= LENGTH_TWO ? 0 : Double.NaN, ss);
}
// Compute the sum of cubed deviations.
double s = 0;
// n=1: no deviation
// n=2: the two deviations from the mean are equal magnitude
// and opposite sign. So the sum-of-cubed deviations is zero.
if (values.length > LENGTH_TWO) {
for (final double x : values) {
s += pow3(x - xbar);
}
}
return new SumOfCubedDeviations(s, ss);
}
/**
* Returns an instance populated using the input {@code values}.
*
*
Note: {@code SumOfCubedDeviations} computed using {@link #accept(double) accept} may be
* different from this instance.
*
* @param values Values.
* @return {@code SumOfCubedDeviations} instance.
*/
static SumOfCubedDeviations of(int... values) {
// Logic shared with the double[] version with int[] lower order moments
if (values.length == 0) {
return new SumOfCubedDeviations();
}
final IntVariance variance = IntVariance.of(values);
final double xbar = variance.computeMean();
final double ss = variance.computeSumOfSquaredDeviations();
double sc = 0;
if (values.length > LENGTH_TWO) {
for (final double x : values) {
sc += pow3(x - xbar);
}
}
return new SumOfCubedDeviations(sc, ss, xbar, values.length);
}
/**
* Returns an instance populated using the input {@code values}.
*
*
Note: {@code SumOfCubedDeviations} computed using {@link #accept(double) accept} may be
* different from this instance.
*
* @param values Values.
* @return {@code SumOfCubedDeviations} instance.
*/
static SumOfCubedDeviations of(long... values) {
// Logic shared with the double[] version with long[] lower order moments
if (values.length == 0) {
return new SumOfCubedDeviations();
}
final LongVariance variance = LongVariance.of(values);
final double xbar = variance.computeMean();
final double ss = variance.computeSumOfSquaredDeviations();
double sc = 0;
if (values.length > LENGTH_TWO) {
for (final double x : values) {
sc += pow3(x - xbar);
}
}
return new SumOfCubedDeviations(sc, ss, xbar, values.length);
}
/**
* Compute {@code x^3}.
* Uses compound multiplication.
*
* @param x Value.
* @return x^3
*/
private static double pow3(double x) {
return x * x * x;
}
/**
* Updates the state of the statistic to reflect the addition of {@code value}.
*
* @param value Value.
*/
@Override
public void accept(double value) {
// Require current s^2 * N == sum-of-square deviations
final double ss = sumSquaredDev;
final double np = n;
super.accept(value);
// Terms are arranged so that values that may be zero
// (np, ss) are first. This will cancel any overflow in
// multiplication of later terms (nDev * 3 and nDev^2).
// This handles initialisation when np in {0, 1) to zero
// for any deviation (e.g. series MAX_VALUE, -MAX_VALUE).
// Note: account for the half-deviation representation by scaling by 6=3*2; 8=2^3
sumCubedDev = sumCubedDev -
ss * nDev * 6 +
(np - 1.0) * np * nDev * nDev * dev * 8;
}
/**
* Gets the sum of cubed deviations of all input values.
*
*
Note that the sum is subject to cancellation of potentially large
* positive and negative terms. A non-finite result may be returned
* due to intermediate overflow when the exact result may be a representable
* {@code double}.
*
*
Note: Any non-finite result should be considered a failed computation.
* The result is returned as computed and not consolidated to a single NaN.
* This is done for testing purposes to allow the result to be reported.
* In particular the sign of an infinity may not indicate the direction
* of the asymmetry (if any), only the direction of the first overflow in the
* computation. In the event of further overflow of a term to an opposite signed
* infinity the sum will be {@code NaN}.
*
* @return sum of cubed deviations of all values.
*/
double getSumOfCubedDeviations() {
return Double.isFinite(getFirstMoment()) ? sumCubedDev : Double.NaN;
}
/**
* Combines the state of another {@code SumOfCubedDeviations} into this one.
*
* @param other Another {@code SumOfCubedDeviations} to be combined.
* @return {@code this} instance after combining {@code other}.
*/
SumOfCubedDeviations combine(SumOfCubedDeviations other) {
if (n == 0) {
sumCubedDev = other.sumCubedDev;
} else if (other.n != 0) {
// Avoid overflow to compute the difference.
// This allows any samples of size n=1 to be combined as their SS=0.
// The result is a SC=0 for the combined n=2.
final double halfDiffOfMean = getFirstMomentHalfDifference(other);
sumCubedDev += other.sumCubedDev;
// Add additional terms that do not cancel to zero
if (halfDiffOfMean != 0) {
final double n1 = n;
final double n2 = other.n;
if (n1 == n2) {
// Optimisation where sizes are equal in double-precision.
// This is of use in JDK streams as spliterators use a divide by two
// strategy for parallel streams.
sumCubedDev += (sumSquaredDev - other.sumSquaredDev) * halfDiffOfMean * 3;
} else {
final double n1n2 = n1 + n2;
final double dm = 2 * (halfDiffOfMean / n1n2);
sumCubedDev += (sumSquaredDev * n2 - other.sumSquaredDev * n1) * dm * 3 +
(n2 - n1) * (n1 * n2) * pow3(dm) * n1n2;
}
}
}
super.combine(other);
return this;
}
}