1 /*
2 * Licensed to the Apache Software Foundation (ASF) under one or more
3 * contributor license agreements. See the NOTICE file distributed with
4 * this work for additional information regarding copyright ownership.
5 * The ASF licenses this file to You under the Apache License, Version 2.0
6 * (the "License"); you may not use this file except in compliance with
7 * the License. You may obtain a copy of the License at
8 *
9 * http://www.apache.org/licenses/LICENSE-2.0
10 *
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
16 */
17 package org.apache.commons.math.stat.descriptive.moment;
18
19 import java.io.Serializable;
20
21 import org.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic;
22
23 /**
24 * Computes the sample standard deviation. The standard deviation
25 * is the positive square root of the variance. This implementation wraps a
26 * {@link Variance} instance. The <code>isBiasCorrected</code> property of the
27 * wrapped Variance instance is exposed, so that this class can be used to
28 * compute both the "sample standard deviation" (the square root of the
29 * bias-corrected "sample variance") or the "population standard deviation"
30 * (the square root of the non-bias-corrected "population variance"). See
31 * {@link Variance} for more information.
32 * <p>
33 * <strong>Note that this implementation is not synchronized.</strong> If
34 * multiple threads access an instance of this class concurrently, and at least
35 * one of the threads invokes the <code>increment()</code> or
36 * <code>clear()</code> method, it must be synchronized externally.</p>
37 *
38 * @version $Revision: 762116 $ $Date: 2009-04-05 12:48:53 -0400 (Sun, 05 Apr 2009) $
39 */
40 public class StandardDeviation extends AbstractStorelessUnivariateStatistic
41 implements Serializable {
42
43 /** Serializable version identifier */
44 private static final long serialVersionUID = 5728716329662425188L;
45
46 /** Wrapped Variance instance */
47 private Variance variance = null;
48
49 /**
50 * Constructs a StandardDeviation. Sets the underlying {@link Variance}
51 * instance's <code>isBiasCorrected</code> property to true.
52 */
53 public StandardDeviation() {
54 variance = new Variance();
55 }
56
57 /**
58 * Constructs a StandardDeviation from an external second moment.
59 *
60 * @param m2 the external moment
61 */
62 public StandardDeviation(final SecondMoment m2) {
63 variance = new Variance(m2);
64 }
65
66 /**
67 * Copy constructor, creates a new {@code StandardDeviation} identical
68 * to the {@code original}
69 *
70 * @param original the {@code StandardDeviation} instance to copy
71 */
72 public StandardDeviation(StandardDeviation original) {
73 copy(original, this);
74 }
75
76 /**
77 * Contructs a StandardDeviation with the specified value for the
78 * <code>isBiasCorrected</code> property. If this property is set to
79 * <code>true</code>, the {@link Variance} used in computing results will
80 * use the bias-corrected, or "sample" formula. See {@link Variance} for
81 * details.
82 *
83 * @param isBiasCorrected whether or not the variance computation will use
84 * the bias-corrected formula
85 */
86 public StandardDeviation(boolean isBiasCorrected) {
87 variance = new Variance(isBiasCorrected);
88 }
89
90 /**
91 * Contructs a StandardDeviation with the specified value for the
92 * <code>isBiasCorrected</code> property and the supplied external moment.
93 * If <code>isBiasCorrected</code> is set to <code>true</code>, the
94 * {@link Variance} used in computing results will use the bias-corrected,
95 * or "sample" formula. See {@link Variance} for details.
96 *
97 * @param isBiasCorrected whether or not the variance computation will use
98 * the bias-corrected formula
99 * @param m2 the external moment
100 */
101 public StandardDeviation(boolean isBiasCorrected, SecondMoment m2) {
102 variance = new Variance(isBiasCorrected, m2);
103 }
104
105 /**
106 * {@inheritDoc}
107 */
108 @Override
109 public void increment(final double d) {
110 variance.increment(d);
111 }
112
113 /**
114 * {@inheritDoc}
115 */
116 public long getN() {
117 return variance.getN();
118 }
119
120 /**
121 * {@inheritDoc}
122 */
123 @Override
124 public double getResult() {
125 return Math.sqrt(variance.getResult());
126 }
127
128 /**
129 * {@inheritDoc}
130 */
131 @Override
132 public void clear() {
133 variance.clear();
134 }
135
136 /**
137 * Returns the Standard Deviation of the entries in the input array, or
138 * <code>Double.NaN</code> if the array is empty.
139 * <p>
140 * Returns 0 for a single-value (i.e. length = 1) sample.</p>
141 * <p>
142 * Throws <code>IllegalArgumentException</code> if the array is null.</p>
143 * <p>
144 * Does not change the internal state of the statistic.</p>
145 *
146 * @param values the input array
147 * @return the standard deviation of the values or Double.NaN if length = 0
148 * @throws IllegalArgumentException if the array is null
149 */
150 @Override
151 public double evaluate(final double[] values) {
152 return Math.sqrt(variance.evaluate(values));
153 }
154
155 /**
156 * Returns the Standard Deviation of the entries in the specified portion of
157 * the input array, or <code>Double.NaN</code> if the designated subarray
158 * is empty.
159 * <p>
160 * Returns 0 for a single-value (i.e. length = 1) sample. </p>
161 * <p>
162 * Throws <code>IllegalArgumentException</code> if the array is null.</p>
163 * <p>
164 * Does not change the internal state of the statistic.</p>
165 *
166 * @param values the input array
167 * @param begin index of the first array element to include
168 * @param length the number of elements to include
169 * @return the standard deviation of the values or Double.NaN if length = 0
170 * @throws IllegalArgumentException if the array is null or the array index
171 * parameters are not valid
172 */
173 @Override
174 public double evaluate(final double[] values, final int begin, final int length) {
175 return Math.sqrt(variance.evaluate(values, begin, length));
176 }
177
178 /**
179 * Returns the Standard Deviation of the entries in the specified portion of
180 * the input array, using the precomputed mean value. Returns
181 * <code>Double.NaN</code> if the designated subarray is empty.
182 * <p>
183 * Returns 0 for a single-value (i.e. length = 1) sample.</p>
184 * <p>
185 * The formula used assumes that the supplied mean value is the arithmetic
186 * mean of the sample data, not a known population parameter. This method
187 * is supplied only to save computation when the mean has already been
188 * computed.</p>
189 * <p>
190 * Throws <code>IllegalArgumentException</code> if the array is null.</p>
191 * <p>
192 * Does not change the internal state of the statistic.</p>
193 *
194 * @param values the input array
195 * @param mean the precomputed mean value
196 * @param begin index of the first array element to include
197 * @param length the number of elements to include
198 * @return the standard deviation of the values or Double.NaN if length = 0
199 * @throws IllegalArgumentException if the array is null or the array index
200 * parameters are not valid
201 */
202 public double evaluate(final double[] values, final double mean,
203 final int begin, final int length) {
204 return Math.sqrt(variance.evaluate(values, mean, begin, length));
205 }
206
207 /**
208 * Returns the Standard Deviation of the entries in the input array, using
209 * the precomputed mean value. Returns
210 * <code>Double.NaN</code> if the designated subarray is empty.
211 * <p>
212 * Returns 0 for a single-value (i.e. length = 1) sample.</p>
213 * <p>
214 * The formula used assumes that the supplied mean value is the arithmetic
215 * mean of the sample data, not a known population parameter. This method
216 * is supplied only to save computation when the mean has already been
217 * computed.</p>
218 * <p>
219 * Throws <code>IllegalArgumentException</code> if the array is null.</p>
220 * <p>
221 * Does not change the internal state of the statistic.</p>
222 *
223 * @param values the input array
224 * @param mean the precomputed mean value
225 * @return the standard deviation of the values or Double.NaN if length = 0
226 * @throws IllegalArgumentException if the array is null
227 */
228 public double evaluate(final double[] values, final double mean) {
229 return Math.sqrt(variance.evaluate(values, mean));
230 }
231
232 /**
233 * @return Returns the isBiasCorrected.
234 */
235 public boolean isBiasCorrected() {
236 return variance.isBiasCorrected();
237 }
238
239 /**
240 * @param isBiasCorrected The isBiasCorrected to set.
241 */
242 public void setBiasCorrected(boolean isBiasCorrected) {
243 variance.setBiasCorrected(isBiasCorrected);
244 }
245
246 /**
247 * {@inheritDoc}
248 */
249 @Override
250 public StandardDeviation copy() {
251 StandardDeviation result = new StandardDeviation();
252 copy(this, result);
253 return result;
254 }
255
256
257 /**
258 * Copies source to dest.
259 * <p>Neither source nor dest can be null.</p>
260 *
261 * @param source StandardDeviation to copy
262 * @param dest StandardDeviation to copy to
263 * @throws NullPointerException if either source or dest is null
264 */
265 public static void copy(StandardDeviation source, StandardDeviation dest) {
266 dest.variance = source.variance.copy();
267 }
268
269 }