001    /*
002     * Licensed to the Apache Software Foundation (ASF) under one or more
003     * contributor license agreements.  See the NOTICE file distributed with
004     * this work for additional information regarding copyright ownership.
005     * The ASF licenses this file to You under the Apache License, Version 2.0
006     * (the "License"); you may not use this file except in compliance with
007     * the License.  You may obtain a copy of the License at
008     *
009     *      http://www.apache.org/licenses/LICENSE-2.0
010     *
011     * Unless required by applicable law or agreed to in writing, software
012     * distributed under the License is distributed on an "AS IS" BASIS,
013     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014     * See the License for the specific language governing permissions and
015     * limitations under the License.
016     */
017    package org.apache.commons.math.stat.descriptive.moment;
018    
019    import java.io.Serializable;
020    
021    import org.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic;
022    import org.apache.commons.math.util.FastMath;
023    
024    /**
025     * Computes the sample standard deviation.  The standard deviation
026     * is the positive square root of the variance.  This implementation wraps a
027     * {@link Variance} instance.  The <code>isBiasCorrected</code> property of the
028     * wrapped Variance instance is exposed, so that this class can be used to
029     * compute both the "sample standard deviation" (the square root of the
030     * bias-corrected "sample variance") or the "population standard deviation"
031     * (the square root of the non-bias-corrected "population variance"). See
032     * {@link Variance} for more information.
033     * <p>
034     * <strong>Note that this implementation is not synchronized.</strong> If
035     * multiple threads access an instance of this class concurrently, and at least
036     * one of the threads invokes the <code>increment()</code> or
037     * <code>clear()</code> method, it must be synchronized externally.</p>
038     *
039     * @version $Revision: 1006299 $ $Date: 2010-10-10 16:47:17 +0200 (dim. 10 oct. 2010) $
040     */
041    public class StandardDeviation extends AbstractStorelessUnivariateStatistic
042        implements Serializable {
043    
044        /** Serializable version identifier */
045        private static final long serialVersionUID = 5728716329662425188L;
046    
047        /** Wrapped Variance instance */
048        private Variance variance = null;
049    
050        /**
051         * Constructs a StandardDeviation.  Sets the underlying {@link Variance}
052         * instance's <code>isBiasCorrected</code> property to true.
053         */
054        public StandardDeviation() {
055            variance = new Variance();
056        }
057    
058        /**
059         * Constructs a StandardDeviation from an external second moment.
060         *
061         * @param m2 the external moment
062         */
063        public StandardDeviation(final SecondMoment m2) {
064            variance = new Variance(m2);
065        }
066    
067        /**
068         * Copy constructor, creates a new {@code StandardDeviation} identical
069         * to the {@code original}
070         *
071         * @param original the {@code StandardDeviation} instance to copy
072         */
073        public StandardDeviation(StandardDeviation original) {
074            copy(original, this);
075        }
076    
077        /**
078         * Contructs a StandardDeviation with the specified value for the
079         * <code>isBiasCorrected</code> property.  If this property is set to
080         * <code>true</code>, the {@link Variance} used in computing results will
081         * use the bias-corrected, or "sample" formula.  See {@link Variance} for
082         * details.
083         *
084         * @param isBiasCorrected  whether or not the variance computation will use
085         * the bias-corrected formula
086         */
087        public StandardDeviation(boolean isBiasCorrected) {
088            variance = new Variance(isBiasCorrected);
089        }
090    
091        /**
092         * Contructs a StandardDeviation with the specified value for the
093         * <code>isBiasCorrected</code> property and the supplied external moment.
094         * If <code>isBiasCorrected</code> is set to <code>true</code>, the
095         * {@link Variance} used in computing results will use the bias-corrected,
096         * or "sample" formula.  See {@link Variance} for details.
097         *
098         * @param isBiasCorrected  whether or not the variance computation will use
099         * the bias-corrected formula
100          * @param m2 the external moment
101         */
102        public StandardDeviation(boolean isBiasCorrected, SecondMoment m2) {
103            variance = new Variance(isBiasCorrected, m2);
104        }
105    
106        /**
107         * {@inheritDoc}
108         */
109        @Override
110        public void increment(final double d) {
111            variance.increment(d);
112        }
113    
114        /**
115         * {@inheritDoc}
116         */
117        public long getN() {
118            return variance.getN();
119        }
120    
121        /**
122         * {@inheritDoc}
123         */
124        @Override
125        public double getResult() {
126            return FastMath.sqrt(variance.getResult());
127        }
128    
129        /**
130         * {@inheritDoc}
131         */
132        @Override
133        public void clear() {
134            variance.clear();
135        }
136    
137        /**
138         * Returns the Standard Deviation of the entries in the input array, or
139         * <code>Double.NaN</code> if the array is empty.
140         * <p>
141         * Returns 0 for a single-value (i.e. length = 1) sample.</p>
142         * <p>
143         * Throws <code>IllegalArgumentException</code> if the array is null.</p>
144         * <p>
145         * Does not change the internal state of the statistic.</p>
146         *
147         * @param values the input array
148         * @return the standard deviation of the values or Double.NaN if length = 0
149         * @throws IllegalArgumentException if the array is null
150         */
151        @Override
152        public double evaluate(final double[] values)  {
153            return FastMath.sqrt(variance.evaluate(values));
154        }
155    
156        /**
157         * Returns the Standard Deviation of the entries in the specified portion of
158         * the input array, or <code>Double.NaN</code> if the designated subarray
159         * is empty.
160         * <p>
161         * Returns 0 for a single-value (i.e. length = 1) sample. </p>
162         * <p>
163         * Throws <code>IllegalArgumentException</code> if the array is null.</p>
164         * <p>
165         * Does not change the internal state of the statistic.</p>
166         *
167         * @param values the input array
168         * @param begin index of the first array element to include
169         * @param length the number of elements to include
170         * @return the standard deviation of the values or Double.NaN if length = 0
171         * @throws IllegalArgumentException if the array is null or the array index
172         *  parameters are not valid
173         */
174        @Override
175        public double evaluate(final double[] values, final int begin, final int length)  {
176           return FastMath.sqrt(variance.evaluate(values, begin, length));
177        }
178    
179        /**
180         * Returns the Standard Deviation of the entries in the specified portion of
181         * the input array, using the precomputed mean value.  Returns
182         * <code>Double.NaN</code> if the designated subarray is empty.
183         * <p>
184         * Returns 0 for a single-value (i.e. length = 1) sample.</p>
185         * <p>
186         * The formula used assumes that the supplied mean value is the arithmetic
187         * mean of the sample data, not a known population parameter.  This method
188         * is supplied only to save computation when the mean has already been
189         * computed.</p>
190         * <p>
191         * Throws <code>IllegalArgumentException</code> if the array is null.</p>
192         * <p>
193         * Does not change the internal state of the statistic.</p>
194         *
195         * @param values the input array
196         * @param mean the precomputed mean value
197         * @param begin index of the first array element to include
198         * @param length the number of elements to include
199         * @return the standard deviation of the values or Double.NaN if length = 0
200         * @throws IllegalArgumentException if the array is null or the array index
201         *  parameters are not valid
202         */
203        public double evaluate(final double[] values, final double mean,
204                final int begin, final int length)  {
205            return FastMath.sqrt(variance.evaluate(values, mean, begin, length));
206        }
207    
208        /**
209         * Returns the Standard Deviation of the entries in the input array, using
210         * the precomputed mean value.  Returns
211         * <code>Double.NaN</code> if the designated subarray is empty.
212         * <p>
213         * Returns 0 for a single-value (i.e. length = 1) sample.</p>
214         * <p>
215         * The formula used assumes that the supplied mean value is the arithmetic
216         * mean of the sample data, not a known population parameter.  This method
217         * is supplied only to save computation when the mean has already been
218         * computed.</p>
219         * <p>
220         * Throws <code>IllegalArgumentException</code> if the array is null.</p>
221         * <p>
222         * Does not change the internal state of the statistic.</p>
223         *
224         * @param values the input array
225         * @param mean the precomputed mean value
226         * @return the standard deviation of the values or Double.NaN if length = 0
227         * @throws IllegalArgumentException if the array is null
228         */
229        public double evaluate(final double[] values, final double mean)  {
230            return FastMath.sqrt(variance.evaluate(values, mean));
231        }
232    
233        /**
234         * @return Returns the isBiasCorrected.
235         */
236        public boolean isBiasCorrected() {
237            return variance.isBiasCorrected();
238        }
239    
240        /**
241         * @param isBiasCorrected The isBiasCorrected to set.
242         */
243        public void setBiasCorrected(boolean isBiasCorrected) {
244            variance.setBiasCorrected(isBiasCorrected);
245        }
246    
247        /**
248         * {@inheritDoc}
249         */
250        @Override
251        public StandardDeviation copy() {
252            StandardDeviation result = new StandardDeviation();
253            copy(this, result);
254            return result;
255        }
256    
257    
258        /**
259         * Copies source to dest.
260         * <p>Neither source nor dest can be null.</p>
261         *
262         * @param source StandardDeviation to copy
263         * @param dest StandardDeviation to copy to
264         * @throws NullPointerException if either source or dest is null
265         */
266        public static void copy(StandardDeviation source, StandardDeviation dest) {
267            dest.setData(source.getDataRef());
268            dest.variance = source.variance.copy();
269        }
270    
271    }