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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*
* This is not the original file distributed by the Apache Software Foundation
* It has been modified by the Hipparchus project
*/
package org.hipparchus.stat.descriptive.moment;
import java.io.Serializable;
import org.hipparchus.exception.MathIllegalArgumentException;
import org.hipparchus.exception.NullArgumentException;
import org.hipparchus.stat.StatUtils;
import org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic;
import org.hipparchus.stat.descriptive.AggregatableStatistic;
import org.hipparchus.stat.descriptive.WeightedEvaluation;
import org.hipparchus.util.MathArrays;
import org.hipparchus.util.MathUtils;
/**
* Computes the variance of the available values. By default, the unbiased
* "sample variance" definitional formula is used:
*
* variance = sum((x_i - mean)^2) / (n - 1)
*
* where mean is the {@link Mean} and n is the number
* of sample observations.
*
* The definitional formula does not have good numerical properties, so
* this implementation does not compute the statistic using the definitional
* formula.
*
* - The
getResult method computes the variance using
* updating formulas based on West's algorithm, as described in
* Chan, T. F. and
* J. G. Lewis 1979, Communications of the ACM,
* vol. 22 no. 9, pp. 526-531.
* - The
evaluate methods leverage the fact that they have the
* full array of values in memory to execute a two-pass algorithm.
* Specifically, these methods use the "corrected two-pass algorithm" from
* Chan, Golub, Levesque, Algorithms for Computing the Sample Variance,
* American Statistician, vol. 37, no. 3 (1983) pp. 242-247.
*
*
* Note that adding values using increment or
* incrementAll and then executing getResult will
* sometimes give a different, less accurate, result than executing
* evaluate with the full array of values. The former approach
* should only be used when the full array of values is not available.
*
* The "population variance" ( sum((x_i - mean)^2) / n ) can also
* be computed using this statistic. The isBiasCorrected
* property determines whether the "population" or "sample" value is
* returned by the evaluate and getResult methods.
* To compute population variances, set this property to false.
*
* 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 increment() or
* clear() method, it must be synchronized externally.
*/
public class Variance extends AbstractStorelessUnivariateStatistic
implements AggregatableStatistic, WeightedEvaluation, Serializable {
/** Serializable version identifier */
private static final long serialVersionUID = 20150412L;
/** SecondMoment is used in incremental calculation of Variance*/
protected final SecondMoment moment;
/**
* Whether or not {@link #increment(double)} should increment
* the internal second moment. When a Variance is constructed with an
* external SecondMoment as a constructor parameter, this property is
* set to false and increments must be applied to the second moment
* directly.
*/
protected final boolean incMoment;
/**
* Whether or not bias correction is applied when computing the
* value of the statistic. True means that bias is corrected. See
* {@link Variance} for details on the formula.
*/
private final boolean isBiasCorrected;
/**
* Constructs a Variance with default (true) isBiasCorrected
* property.
*/
public Variance() {
this(true);
}
/**
* Constructs a Variance based on an external second moment.
*
* When this constructor is used, the statistic may only be
* incremented via the moment, i.e., {@link #increment(double)}
* does nothing; whereas {@code m2.increment(value)} increments
* both {@code m2} and the Variance instance constructed from it.
*
* @param m2 the SecondMoment (Third or Fourth moments work here as well.)
*/
public Variance(final SecondMoment m2) {
this(true, m2);
}
/**
* Constructs a Variance with the specified isBiasCorrected
* property.
*
* @param isBiasCorrected setting for bias correction - true means
* bias will be corrected and is equivalent to using the argumentless
* constructor
*/
public Variance(boolean isBiasCorrected) {
this(new SecondMoment(), true, isBiasCorrected);
}
/**
* Constructs a Variance with the specified isBiasCorrected
* property and the supplied external second moment.
*
* @param isBiasCorrected setting for bias correction - true means
* bias will be corrected
* @param m2 the SecondMoment (Third or Fourth moments work
* here as well.)
*/
public Variance(boolean isBiasCorrected, SecondMoment m2) {
this(m2, false, isBiasCorrected);
}
/**
* Constructs a Variance with the specified parameters.
*
* @param m2 the SecondMoment (Third or Fourth moments work
* here as well.)
* @param incMoment if the moment shall be incremented
* @param isBiasCorrected setting for bias correction - true means
* bias will be corrected
*/
private Variance(SecondMoment m2, boolean incMoment, boolean isBiasCorrected) {
this.moment = m2;
this.incMoment = incMoment;
this.isBiasCorrected = isBiasCorrected;
}
/**
* Copy constructor, creates a new {@code Variance} identical
* to the {@code original}.
*
* @param original the {@code Variance} instance to copy
* @throws NullArgumentException if original is null
*/
public Variance(Variance original) throws NullArgumentException {
MathUtils.checkNotNull(original);
this.moment = original.moment.copy();
this.incMoment = original.incMoment;
this.isBiasCorrected = original.isBiasCorrected;
}
/**
* {@inheritDoc}
*
If all values are available, it is more accurate to use
* {@link #evaluate(double[])} rather than adding values one at a time
* using this method and then executing {@link #getResult}, since
* evaluate leverages the fact that is has the full
* list of values together to execute a two-pass algorithm.
* See {@link Variance}.
*
* Note also that when {@link #Variance(SecondMoment)} is used to
* create a Variance, this method does nothing. In that case, the
* SecondMoment should be incremented directly.
*/
@Override
public void increment(final double d) {
if (incMoment) {
moment.increment(d);
}
}
/** {@inheritDoc} */
@Override
public double getResult() {
if (moment.n == 0) {
return Double.NaN;
} else if (moment.n == 1) {
return 0d;
} else {
if (isBiasCorrected) {
return moment.m2 / (moment.n - 1d);
} else {
return moment.m2 / (moment.n);
}
}
}
/** {@inheritDoc} */
@Override
public long getN() {
return moment.getN();
}
/** {@inheritDoc} */
@Override
public void clear() {
if (incMoment) {
moment.clear();
}
}
/** {@inheritDoc} */
@Override
public void aggregate(Variance other) {
MathUtils.checkNotNull(other);
if (incMoment) {
this.moment.aggregate(other.moment);
}
}
/**
* Returns the variance of the entries in the specified portion of
* the input array, or Double.NaN if the designated subarray
* is empty. Note that Double.NaN may also be returned if the input
* includes NaN and / or infinite values.
*
* See {@link Variance} for details on the computing algorithm.
*
* Returns 0 for a single-value (i.e. length = 1) sample.
*
* Does not change the internal state of the statistic.
*
* Throws MathIllegalArgumentException if the array is null.
*
* @param values the input array
* @param begin index of the first array element to include
* @param length the number of elements to include
* @return the variance of the values or Double.NaN if length = 0
* @throws MathIllegalArgumentException if the array is null or the array index
* parameters are not valid
*/
@Override
public double evaluate(final double[] values, final int begin, final int length)
throws MathIllegalArgumentException {
double var = Double.NaN;
if (MathArrays.verifyValues(values, begin, length)) {
if (length == 1) {
var = 0.0;
} else if (length > 1) {
double m = StatUtils.mean(values, begin, length);
var = evaluate(values, m, begin, length);
}
}
return var;
}
/**
* Returns the weighted variance of the entries in the specified portion of
* the input array, or Double.NaN if the designated subarray
* is empty.
*
* Uses the formula
*
* Σ(weights[i]*(values[i] - weightedMean)2)/(Σ(weights[i]) - 1)
*
* where weightedMean is the weighted mean.
*
* This formula will not return the same result as the unweighted variance when all
* weights are equal, unless all weights are equal to 1. The formula assumes that
* weights are to be treated as "expansion values," as will be the case if for example
* the weights represent frequency counts. To normalize weights so that the denominator
* in the variance computation equals the length of the input vector minus one, use
*
* evaluate(values, MathArrays.normalizeArray(weights, values.length));
*
*
* Returns 0 for a single-value (i.e. length = 1) sample.
*
* Throws IllegalArgumentException if any of the following are true:
*
- the values array is null
* - the weights array is null
* - the weights array does not have the same length as the values array
* - the weights array contains one or more infinite values
* - the weights array contains one or more NaN values
* - the weights array contains negative values
* - the start and length arguments do not determine a valid array
*
*
* Does not change the internal state of the statistic.
*
* @param values the input array
* @param weights the weights array
* @param begin index of the first array element to include
* @param length the number of elements to include
* @return the weighted variance of the values or Double.NaN if length = 0
* @throws MathIllegalArgumentException if the parameters are not valid
*/
@Override
public double evaluate(final double[] values, final double[] weights,
final int begin, final int length)
throws MathIllegalArgumentException {
double var = Double.NaN;
if (MathArrays.verifyValues(values, weights,begin, length)) {
if (length == 1) {
var = 0.0;
} else if (length > 1) {
Mean mean = new Mean();
double m = mean.evaluate(values, weights, begin, length);
var = evaluate(values, weights, m, begin, length);
}
}
return var;
}
/**
* Returns the variance of the entries in the specified portion of
* the input array, using the precomputed mean value. Returns
* Double.NaN if the designated subarray is empty.
*
* See {@link Variance} for details on the computing algorithm.
*
* The formula used assumes that the supplied mean value is the arithmetic
* mean of the sample data, not a known population parameter. This method
* is supplied only to save computation when the mean has already been
* computed.
*
* Returns 0 for a single-value (i.e. length = 1) sample.
*
* Does not change the internal state of the statistic.
*
* @param values the input array
* @param mean the precomputed mean value
* @param begin index of the first array element to include
* @param length the number of elements to include
* @return the variance of the values or Double.NaN if length = 0
* @throws MathIllegalArgumentException if the array is null or the array index
* parameters are not valid
*/
public double evaluate(final double[] values, final double mean,
final int begin, final int length)
throws MathIllegalArgumentException {
double var = Double.NaN;
if (MathArrays.verifyValues(values, begin, length)) {
if (length == 1) {
var = 0.0;
} else if (length > 1) {
double accum = 0.0;
double dev = 0.0;
double accum2 = 0.0;
for (int i = begin; i < begin + length; i++) {
dev = values[i] - mean;
accum += dev * dev;
accum2 += dev;
}
double len = length;
if (isBiasCorrected) {
var = (accum - (accum2 * accum2 / len)) / (len - 1.0);
} else {
var = (accum - (accum2 * accum2 / len)) / len;
}
}
}
return var;
}
/**
* Returns the variance of the entries in the input array, using the
* precomputed mean value. Returns Double.NaN if the array
* is empty.
*
* See {@link Variance} for details on the computing algorithm.
*
* If isBiasCorrected is true the formula used
* assumes that the supplied mean value is the arithmetic mean of the
* sample data, not a known population parameter. If the mean is a known
* population parameter, or if the "population" version of the variance is
* desired, set isBiasCorrected to false before
* invoking this method.
*
* Returns 0 for a single-value (i.e. length = 1) sample.
*
* Does not change the internal state of the statistic.
*
* @param values the input array
* @param mean the precomputed mean value
* @return the variance of the values or Double.NaN if the array is empty
* @throws MathIllegalArgumentException if the array is null
*/
public double evaluate(final double[] values, final double mean)
throws MathIllegalArgumentException {
return evaluate(values, mean, 0, values.length);
}
/**
* Returns the weighted variance of the entries in the specified portion of
* the input array, using the precomputed weighted mean value. Returns
* Double.NaN if the designated subarray is empty.
*
* Uses the formula
*
* Σ(weights[i]*(values[i] - mean)2)/(Σ(weights[i]) - 1)
*
*
* The formula used assumes that the supplied mean value is the weighted arithmetic
* mean of the sample data, not a known population parameter. This method
* is supplied only to save computation when the mean has already been
* computed.
*
* This formula will not return the same result as the unweighted variance when all
* weights are equal, unless all weights are equal to 1. The formula assumes that
* weights are to be treated as "expansion values," as will be the case if for example
* the weights represent frequency counts. To normalize weights so that the denominator
* in the variance computation equals the length of the input vector minus one, use
*
* evaluate(values, MathArrays.normalizeArray(weights, values.length), mean);
*
*
* Returns 0 for a single-value (i.e. length = 1) sample.
*
* Throws MathIllegalArgumentException if any of the following are true:
*
- the values array is null
* - the weights array is null
* - the weights array does not have the same length as the values array
* - the weights array contains one or more infinite values
* - the weights array contains one or more NaN values
* - the weights array contains negative values
* - the start and length arguments do not determine a valid array
*
*
* Does not change the internal state of the statistic.
*
* @param values the input array
* @param weights the weights array
* @param mean the precomputed weighted mean value
* @param begin index of the first array element to include
* @param length the number of elements to include
* @return the variance of the values or Double.NaN if length = 0
* @throws MathIllegalArgumentException if the parameters are not valid
*/
public double evaluate(final double[] values, final double[] weights,
final double mean, final int begin, final int length)
throws MathIllegalArgumentException {
double var = Double.NaN;
if (MathArrays.verifyValues(values, weights, begin, length)) {
if (length == 1) {
var = 0.0;
} else if (length > 1) {
double accum = 0.0;
double dev = 0.0;
double accum2 = 0.0;
for (int i = begin; i < begin + length; i++) {
dev = values[i] - mean;
accum += weights[i] * (dev * dev);
accum2 += weights[i] * dev;
}
double sumWts = 0;
for (int i = begin; i < begin + length; i++) {
sumWts += weights[i];
}
if (isBiasCorrected) {
var = (accum - (accum2 * accum2 / sumWts)) / (sumWts - 1.0);
} else {
var = (accum - (accum2 * accum2 / sumWts)) / sumWts;
}
}
}
return var;
}
/**
* Returns the weighted variance of the values in the input array, using
* the precomputed weighted mean value.
*
* Uses the formula
*
* Σ(weights[i]*(values[i] - mean)2)/(Σ(weights[i]) - 1)
*
*
* The formula used assumes that the supplied mean value is the weighted arithmetic
* mean of the sample data, not a known population parameter. This method
* is supplied only to save computation when the mean has already been
* computed.
*
* This formula will not return the same result as the unweighted variance when all
* weights are equal, unless all weights are equal to 1. The formula assumes that
* weights are to be treated as "expansion values," as will be the case if for example
* the weights represent frequency counts. To normalize weights so that the denominator
* in the variance computation equals the length of the input vector minus one, use
*
* evaluate(values, MathArrays.normalizeArray(weights, values.length), mean);
*
*
* Returns 0 for a single-value (i.e. length = 1) sample.
*
* Throws MathIllegalArgumentException if any of the following are true:
*
- the values array is null
* - the weights array is null
* - the weights array does not have the same length as the values array
* - the weights array contains one or more infinite values
* - the weights array contains one or more NaN values
* - the weights array contains negative values
*
*
* Does not change the internal state of the statistic.
*
* @param values the input array
* @param weights the weights array
* @param mean the precomputed weighted mean value
* @return the variance of the values or Double.NaN if length = 0
* @throws MathIllegalArgumentException if the parameters are not valid
*/
public double evaluate(final double[] values, final double[] weights, final double mean)
throws MathIllegalArgumentException {
return evaluate(values, weights, mean, 0, values.length);
}
/**
* @return Returns the isBiasCorrected.
*/
public boolean isBiasCorrected() {
return isBiasCorrected;
}
/**
* Returns a new copy of this variance with the given bias correction
* setting.
*
* @param biasCorrection The bias correction flag to set.
* @return a copy of this instance with the given bias correction setting
*/
public Variance withBiasCorrection(boolean biasCorrection) {
return new Variance(this.moment, this.incMoment, biasCorrection);
}
/** {@inheritDoc} */
@Override
public Variance copy() {
return new Variance(this);
}
}