Java tutorial
/* * 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.math.stat.descriptive.moment; import java.io.Serializable; import org.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic; import org.apache.commons.math.util.FastMath; /** * Computes the sample standard deviation. The standard deviation * is the positive square root of the variance. This implementation wraps a * {@link Variance} instance. The <code>isBiasCorrected</code> 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. * <p> * <strong>Note that this implementation is not synchronized.</strong> If * multiple threads access an instance of this class concurrently, and at least * one of the threads invokes the <code>increment()</code> or * <code>clear()</code> method, it must be synchronized externally.</p> * * @version $Revision: 1006299 $ $Date: 2010-10-10 16:47:17 +0200 (dim. 10 oct. 2010) $ */ public class StandardDeviation extends AbstractStorelessUnivariateStatistic implements Serializable { /** Serializable version identifier */ private static final long serialVersionUID = 5728716329662425188L; /** Wrapped Variance instance */ private Variance variance = null; /** * Constructs a StandardDeviation. Sets the underlying {@link Variance} * instance's <code>isBiasCorrected</code> 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 */ public StandardDeviation(StandardDeviation original) { copy(original, this); } /** * Contructs a StandardDeviation with the specified value for the * <code>isBiasCorrected</code> property. If this property is set to * <code>true</code>, 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); } /** * Contructs a StandardDeviation with the specified value for the * <code>isBiasCorrected</code> property and the supplied external moment. * If <code>isBiasCorrected</code> is set to <code>true</code>, 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} */ 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 * <code>Double.NaN</code> if the array is empty. * <p> * Returns 0 for a single-value (i.e. length = 1) sample.</p> * <p> * Throws <code>IllegalArgumentException</code> if the array is null.</p> * <p> * Does not change the internal state of the statistic.</p> * * @param values the input array * @return the standard deviation of the values or Double.NaN if length = 0 * @throws IllegalArgumentException if the array is null */ @Override public double evaluate(final double[] values) { return FastMath.sqrt(variance.evaluate(values)); } /** * Returns the Standard Deviation of the entries in the specified portion of * the input array, or <code>Double.NaN</code> if the designated subarray * is empty. * <p> * Returns 0 for a single-value (i.e. length = 1) sample. </p> * <p> * Throws <code>IllegalArgumentException</code> if the array is null.</p> * <p> * Does not change the internal state of the statistic.</p> * * @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 IllegalArgumentException 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) { 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 * <code>Double.NaN</code> if the designated subarray is empty. * <p> * Returns 0 for a single-value (i.e. length = 1) sample.</p> * <p> * 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.</p> * <p> * Throws <code>IllegalArgumentException</code> if the array is null.</p> * <p> * Does not change the internal state of the statistic.</p> * * @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 IllegalArgumentException 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) { 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 * <code>Double.NaN</code> if the designated subarray is empty. * <p> * Returns 0 for a single-value (i.e. length = 1) sample.</p> * <p> * 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.</p> * <p> * Throws <code>IllegalArgumentException</code> if the array is null.</p> * <p> * Does not change the internal state of the statistic.</p> * * @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 IllegalArgumentException if the array is null */ public double evaluate(final double[] values, final double mean) { 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(); copy(this, result); return result; } /** * Copies source to dest. * <p>Neither source nor dest can be null.</p> * * @param source StandardDeviation to copy * @param dest StandardDeviation to copy to * @throws NullPointerException if either source or dest is null */ public static void copy(StandardDeviation source, StandardDeviation dest) { dest.setData(source.getDataRef()); dest.variance = source.variance.copy(); } }