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Statistical sampling library for use in virtdata libraries, based
on apache commons math 4
/*
* 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.math4.stat.descriptive.moment;
import java.io.Serializable;
import org.apache.commons.math4.exception.MathIllegalArgumentException;
import org.apache.commons.math4.exception.NullArgumentException;
import org.apache.commons.math4.stat.descriptive.AbstractStorelessUnivariateStatistic;
import org.apache.commons.math4.util.FastMath;
import org.apache.commons.math4.util.MathUtils;
/**
* Computes the sample standard deviation. The standard deviation
* is the positive square root of the variance. This implementation wraps a
* {@link Variance} instance. The isBiasCorrected property of the
* wrapped Variance instance is exposed, so that this class can be used to
* compute both the "sample standard deviation" (the square root of the
* bias-corrected "sample variance") or the "population standard deviation"
* (the square root of the non-bias-corrected "population variance"). See
* {@link Variance} for more information.
*
* 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 StandardDeviation extends AbstractStorelessUnivariateStatistic
implements Serializable {
/** Serializable version identifier */
private static final long serialVersionUID = 20150412L;
/** Wrapped Variance instance */
private Variance variance = null;
/**
* Constructs a StandardDeviation. Sets the underlying {@link Variance}
* instance's isBiasCorrected property to true.
*/
public StandardDeviation() {
variance = new Variance();
}
/**
* Constructs a StandardDeviation from an external second moment.
*
* @param m2 the external moment
*/
public StandardDeviation(final SecondMoment m2) {
variance = new Variance(m2);
}
/**
* Copy constructor, creates a new {@code StandardDeviation} identical
* to the {@code original}.
*
* @param original the {@code StandardDeviation} instance to copy
* @throws NullArgumentException if original is null
*/
public StandardDeviation(StandardDeviation original) throws NullArgumentException {
copy(original, this);
}
/**
* Constructs a StandardDeviation with the specified value for the
* isBiasCorrected property. If this property is set to
* true, the {@link Variance} used in computing results will
* use the bias-corrected, or "sample" formula. See {@link Variance} for
* details.
*
* @param isBiasCorrected whether or not the variance computation will use
* the bias-corrected formula
*/
public StandardDeviation(boolean isBiasCorrected) {
variance = new Variance(isBiasCorrected);
}
/**
* Constructs a StandardDeviation with the specified value for the
* isBiasCorrected property and the supplied external moment.
* If isBiasCorrected is set to true, the
* {@link Variance} used in computing results will use the bias-corrected,
* or "sample" formula. See {@link Variance} for details.
*
* @param isBiasCorrected whether or not the variance computation will use
* the bias-corrected formula
* @param m2 the external moment
*/
public StandardDeviation(boolean isBiasCorrected, SecondMoment m2) {
variance = new Variance(isBiasCorrected, m2);
}
/**
* {@inheritDoc}
*/
@Override
public void increment(final double d) {
variance.increment(d);
}
/**
* {@inheritDoc}
*/
@Override
public long getN() {
return variance.getN();
}
/**
* {@inheritDoc}
*/
@Override
public double getResult() {
return FastMath.sqrt(variance.getResult());
}
/**
* {@inheritDoc}
*/
@Override
public void clear() {
variance.clear();
}
/**
* Returns the Standard Deviation of the entries in the input array, or
* Double.NaN if the array is empty.
*
* Returns 0 for a single-value (i.e. length = 1) sample.
*
* Throws MathIllegalArgumentException if the array is null.
*
* Does not change the internal state of the statistic.
*
* @param values the input array
* @return the standard deviation of the values or Double.NaN if length = 0
* @throws MathIllegalArgumentException if the array is null
*/
@Override
public double evaluate(final double[] values) throws MathIllegalArgumentException {
return FastMath.sqrt(variance.evaluate(values));
}
/**
* Returns the Standard Deviation of the entries in the specified portion of
* the input array, or Double.NaN if the designated subarray
* is empty.
*
* Returns 0 for a single-value (i.e. length = 1) sample.
*
* Throws MathIllegalArgumentException if the array is null.
*
* Does not change the internal state of the statistic.
*
* @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 standard deviation 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 {
return FastMath.sqrt(variance.evaluate(values, begin, length));
}
/**
* Returns the Standard Deviation 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.
*
* Returns 0 for a single-value (i.e. length = 1) sample.
*
* 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.
*
* Throws IllegalArgumentException if the array is null.
*
* 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 standard deviation 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 {
return FastMath.sqrt(variance.evaluate(values, mean, begin, length));
}
/**
* Returns the Standard Deviation of the entries in the input array, using
* the precomputed mean value. Returns
* Double.NaN if the designated subarray is empty.
*
* Returns 0 for a single-value (i.e. length = 1) sample.
*
* 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.
*
* Throws MathIllegalArgumentException if the array is null.
*
* Does not change the internal state of the statistic.
*
* @param values the input array
* @param mean the precomputed mean value
* @return the standard deviation of the values or Double.NaN if length = 0
* @throws MathIllegalArgumentException if the array is null
*/
public double evaluate(final double[] values, final double mean)
throws MathIllegalArgumentException {
return FastMath.sqrt(variance.evaluate(values, mean));
}
/**
* @return Returns the isBiasCorrected.
*/
public boolean isBiasCorrected() {
return variance.isBiasCorrected();
}
/**
* @param isBiasCorrected The isBiasCorrected to set.
*/
public void setBiasCorrected(boolean isBiasCorrected) {
variance.setBiasCorrected(isBiasCorrected);
}
/**
* {@inheritDoc}
*/
@Override
public StandardDeviation copy() {
StandardDeviation result = new StandardDeviation();
// No try-catch or advertised exception because args are guaranteed non-null
copy(this, result);
return result;
}
/**
* Copies source to dest.
* Neither source nor dest can be null.
*
* @param source StandardDeviation to copy
* @param dest StandardDeviation to copy to
* @throws NullArgumentException if either source or dest is null
*/
public static void copy(StandardDeviation source, StandardDeviation dest) throws NullArgumentException {
MathUtils.checkNotNull(source);
MathUtils.checkNotNull(dest);
dest.variance = source.variance.copy();
}
}