org.hipparchus.stat.descriptive.moment.StandardDeviation Maven / Gradle / Ivy
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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.
*/
package org.hipparchus.stat.descriptive.moment;
import java.io.Serializable;
import org.hipparchus.exception.MathIllegalArgumentException;
import org.hipparchus.exception.NullArgumentException;
import org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic;
import org.hipparchus.util.FastMath;
import org.hipparchus.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 final Variance variance;
/**
* Constructs a StandardDeviation. Sets the underlying {@link Variance}
* instance's isBiasCorrected property to true.
*/
public StandardDeviation() {
this(new Variance());
}
/**
* Constructs a StandardDeviation from an external second moment.
*
* @param m2 the external moment
*/
public StandardDeviation(final SecondMoment m2) {
this(new Variance(m2));
}
/**
* 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) {
this(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) {
this(new Variance(isBiasCorrected, m2));
}
/**
* Create a new instance with the given variance.
* @param variance the variance to use
*/
private StandardDeviation(Variance variance) {
this.variance = variance;
}
/**
* 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 {
MathUtils.checkNotNull(original);
this.variance = original.variance.copy();
}
/** {@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 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.
*
* 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.
*
* 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.
*
* 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();
}
/**
* Returns a new copy of this standard deviation 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 StandardDeviation withBiasCorrection(boolean biasCorrection) {
return new StandardDeviation(variance.withBiasCorrection(biasCorrection));
}
/** {@inheritDoc} */
@Override
public StandardDeviation copy() {
return new StandardDeviation(this);
}
}