org.apache.commons.statistics.descriptive.StandardDeviation Maven / Gradle / Ivy
/*
* 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.
*/
package org.apache.commons.statistics.descriptive;
/**
* Computes the standard deviation of the available values. The default implementations uses
* the following definition of the sample standard deviation:
*
* \[ \sqrt{ \tfrac{1}{n-1} \sum_{i=1}^n (x_i-\overline{x})^2 } \]
*
*
where \( \overline{x} \) is the sample mean, and \( n \) is the number of samples.
*
*
* - The result is {@code NaN} if no values are added.
*
- The result is {@code NaN} if any of the values is {@code NaN} or infinite.
*
- The result is {@code NaN} if the sum of the squared deviations from the mean is infinite.
*
- The result is zero if there is one finite value in the data set.
*
*
* The use of the term \( n − 1 \) is called Bessel's correction. Omitting the square root,
* this provides an unbiased estimator of the variance of a hypothetical infinite population. If the
* {@link #setBiased(boolean) biased} option is enabled the normalisation factor is
* changed to \( \frac{1}{n} \) for a biased estimator of the sample variance.
* Note however that square root is a concave function and thus introduces negative bias
* (by Jensen's inequality), which depends on the distribution, and thus the corrected sample
* standard deviation (using Bessel's correction) is less biased, but still biased.
*
*
The {@link #accept(double)} method uses a recursive updating algorithm based on West's
* algorithm (see Chan and Lewis (1979)).
*
*
The {@link #of(double...)} method uses the corrected two-pass algorithm from
* Chan et al, (1983).
*
*
Note that adding values using {@link #accept(double) accept} and then executing
* {@link #getAsDouble() getAsDouble} will
* sometimes give a different, less accurate, result than executing
* {@link #of(double...) of} with the full array of values. The former approach
* should only be used when the full array of values is not available.
*
*
Supports up to 263 (exclusive) observations.
* This implementation does not check for overflow of the count.
*
*
This class is designed to work with (though does not require)
* {@linkplain java.util.stream streams}.
*
*
Note that this instance 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 instance 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:
*
* - Chan and Lewis (1979)
* Computing standard deviations: accuracy.
* Communications of the ACM, 22, 526-531.
* doi: 10.1145/359146.359152
*
- Chan, Golub and Levesque (1983)
* Algorithms for Computing the Sample Variance: Analysis and Recommendations.
* American Statistician, 37, 242-247.
* doi: 10.2307/2683386
*
*
* @see Standard deviation (Wikipedia)
* @see Bessel's correction
* @see Jensen's inequality
* @see Variance
* @since 1.1
*/
public final class StandardDeviation implements DoubleStatistic, StatisticAccumulator {
/**
* An instance of {@link SumOfSquaredDeviations}, which is used to
* compute the standard deviation.
*/
private final SumOfSquaredDeviations ss;
/** Flag to control if the statistic is biased, or should use a bias correction. */
private boolean biased;
/**
* Create an instance.
*/
private StandardDeviation() {
this(new SumOfSquaredDeviations());
}
/**
* Creates an instance with the sum of squared deviations from the mean.
*
* @param ss Sum of squared deviations.
*/
StandardDeviation(SumOfSquaredDeviations ss) {
this.ss = ss;
}
/**
* Creates an instance.
*
* The initial result is {@code NaN}.
*
* @return {@code StandardDeviation} instance.
*/
public static StandardDeviation create() {
return new StandardDeviation();
}
/**
* Returns an instance populated using the input {@code values}.
*
*
Note: {@code StandardDeviation} computed using {@link #accept(double) accept} may be
* different from this standard deviation.
*
*
See {@link StandardDeviation} for details on the computing algorithm.
*
* @param values Values.
* @return {@code StandardDeviation} instance.
*/
public static StandardDeviation of(double... values) {
return new StandardDeviation(SumOfSquaredDeviations.of(values));
}
/**
* Updates the state of the statistic to reflect the addition of {@code value}.
*
* @param value Value.
*/
@Override
public void accept(double value) {
ss.accept(value);
}
/**
* Gets the standard deviation of all input values.
*
*
When no values have been added, the result is {@code NaN}.
*
* @return standard deviation of all values.
*/
@Override
public double getAsDouble() {
// This method checks the sum of squared is finite
// to provide a consistent NaN when the computation is not possible.
// Note: The SS checks for n=0 and returns NaN.
final double m2 = ss.getSumOfSquaredDeviations();
if (!Double.isFinite(m2)) {
return Double.NaN;
}
final long n = ss.n;
// Avoid a divide by zero
if (n == 1) {
return 0;
}
return biased ? Math.sqrt(m2 / n) : Math.sqrt(m2 / (n - 1));
}
@Override
public StandardDeviation combine(StandardDeviation other) {
ss.combine(other.ss);
return this;
}
/**
* Sets the value of the biased flag. The default value is {@code false}. The bias
* term refers to the computation of the variance; the standard deviation is returned
* as the square root of the biased or unbiased sample variance. For further
* details see {@link Variance#setBiased(boolean) Variance.setBiased}.
*
*
This flag only controls the final computation of the statistic. The value of
* this flag will not affect compatibility between instances during a
* {@link #combine(StandardDeviation) combine} operation.
*
* @param v Value.
* @return {@code this} instance
* @see Variance#setBiased(boolean)
*/
public StandardDeviation setBiased(boolean v) {
biased = v;
return this;
}
}