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16 package org.apache.commons.math.stat.descriptive.moment;
17
18 import java.io.Serializable;
19
20 import org.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic;
21
22 /**
23 * Computes the sample standard deviation. The standard deviation
24 * is the positive square root of the variance. This implementation wraps a
25 * {@link Variance} instance. The <code>isBiasCorrected</code> property of the
26 * wrapped Variance instance is exposed, so that this class can be used to
27 * compute both the "sample standard deviation" (the square root of the
28 * bias-corrected "sample variance") or the "population standard deviation"
29 * (the square root of the non-bias-corrected "population variance"). See
30 * {@link Variance} for more information.
31 * <p>
32 * <strong>Note that this implementation is not synchronized.</strong> If
33 * multiple threads access an instance of this class concurrently, and at least
34 * one of the threads invokes the <code>increment()</code> or
35 * <code>clear()</code> method, it must be synchronized externally.
36 *
37 * @version $Revision: 348519 $ $Date: 2005-11-23 12:12:18 -0700 (Wed, 23 Nov 2005) $
38 */
39 public class StandardDeviation extends AbstractStorelessUnivariateStatistic
40 implements Serializable {
41
42 /** Serializable version identifier */
43 private static final long serialVersionUID = 5728716329662425188L;
44
45 /** Wrapped Variance instance */
46 private Variance variance = null;
47
48 /**
49 * Constructs a StandardDeviation. Sets the underlying {@link Variance}
50 * instance's <code>isBiasCorrected</code> property to true.
51 */
52 public StandardDeviation() {
53 variance = new Variance();
54 }
55
56 /**
57 * Constructs a StandardDeviation from an external second moment.
58 *
59 * @param m2 the external moment
60 */
61 public StandardDeviation(final SecondMoment m2) {
62 variance = new Variance(m2);
63 }
64
65 /**
66 * Contructs a StandardDeviation with the specified value for the
67 * <code>isBiasCorrected</code> property. If this property is set to
68 * <code>true</code>, the {@link Variance} used in computing results will
69 * use the bias-corrected, or "sample" formula. See {@link Variance} for
70 * details.
71 *
72 * @param isBiasCorrected whether or not the variance computation will use
73 * the bias-corrected formula
74 */
75 public StandardDeviation(boolean isBiasCorrected) {
76 variance = new Variance(isBiasCorrected);
77 }
78
79 /**
80 * Contructs a StandardDeviation with the specified value for the
81 * <code>isBiasCorrected</code> property and the supplied external moment.
82 * If <code>isBiasCorrected</code> is set to <code>true</code>, the
83 * {@link Variance} used in computing results will use the bias-corrected,
84 * or "sample" formula. See {@link Variance} for details.
85 *
86 * @param isBiasCorrected whether or not the variance computation will use
87 * the bias-corrected formula
88 * @param m2 the external moment
89 */
90 public StandardDeviation(boolean isBiasCorrected, SecondMoment m2) {
91 variance = new Variance(isBiasCorrected, m2);
92 }
93
94 /**
95 * @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#increment(double)
96 */
97 public void increment(final double d) {
98 variance.increment(d);
99 }
100
101 /**
102 * @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#getN()
103 */
104 public long getN() {
105 return variance.getN();
106 }
107
108 /**
109 * @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#getResult()
110 */
111 public double getResult() {
112 return Math.sqrt(variance.getResult());
113 }
114
115 /**
116 * @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#clear()
117 */
118 public void clear() {
119 variance.clear();
120 }
121
122 /**
123 * Returns the Standard Deviation of the entries in the input array, or
124 * <code>Double.NaN</code> if the array is empty.
125 * <p>
126 * Returns 0 for a single-value (i.e. length = 1) sample.
127 * <p>
128 * Throws <code>IllegalArgumentException</code> if the array is null.
129 * <p>
130 * Does not change the internal state of the statistic.
131 *
132 * @param values the input array
133 * @return the standard deviation of the values or Double.NaN if length = 0
134 * @throws IllegalArgumentException if the array is null
135 */
136 public double evaluate(final double[] values) {
137 return Math.sqrt(variance.evaluate(values));
138 }
139
140 /**
141 * Returns the Standard Deviation of the entries in the specified portion of
142 * the input array, or <code>Double.NaN</code> if the designated subarray
143 * is empty.
144 * <p>
145 * Returns 0 for a single-value (i.e. length = 1) sample.
146 * <p>
147 * Throws <code>IllegalArgumentException</code> if the array is null.
148 * <p>
149 * Does not change the internal state of the statistic.
150 *
151 * @param values the input array
152 * @param begin index of the first array element to include
153 * @param length the number of elements to include
154 * @return the standard deviation of the values or Double.NaN if length = 0
155 * @throws IllegalArgumentException if the array is null or the array index
156 * parameters are not valid
157 */
158 public double evaluate(final double[] values, final int begin, final int length) {
159 return Math.sqrt(variance.evaluate(values, begin, length));
160 }
161
162 /**
163 * Returns the Standard Deviation of the entries in the specified portion of
164 * the input array, using the precomputed mean value. Returns
165 * <code>Double.NaN</code> if the designated subarray is empty.
166 * <p>
167 * Returns 0 for a single-value (i.e. length = 1) sample.
168 * <p>
169 * The formula used assumes that the supplied mean value is the arithmetic
170 * mean of the sample data, not a known population parameter. This method
171 * is supplied only to save computation when the mean has already been
172 * computed.
173 * <p>
174 * Throws <code>IllegalArgumentException</code> if the array is null.
175 * <p>
176 * Does not change the internal state of the statistic.
177 *
178 * @param values the input array
179 * @param mean the precomputed mean value
180 * @param begin index of the first array element to include
181 * @param length the number of elements to include
182 * @return the standard deviation of the values or Double.NaN if length = 0
183 * @throws IllegalArgumentException if the array is null or the array index
184 * parameters are not valid
185 */
186 public double evaluate(final double[] values, final double mean,
187 final int begin, final int length) {
188 return Math.sqrt(variance.evaluate(values, mean, begin, length));
189 }
190
191 /**
192 * Returns the Standard Deviation of the entries in the input array, using
193 * the precomputed mean value. Returns
194 * <code>Double.NaN</code> if the designated subarray is empty.
195 * <p>
196 * Returns 0 for a single-value (i.e. length = 1) sample.
197 * <p>
198 * The formula used assumes that the supplied mean value is the arithmetic
199 * mean of the sample data, not a known population parameter. This method
200 * is supplied only to save computation when the mean has already been
201 * computed.
202 * <p>
203 * Throws <code>IllegalArgumentException</code> if the array is null.
204 * <p>
205 * Does not change the internal state of the statistic.
206 *
207 * @param values the input array
208 * @param mean the precomputed mean value
209 * @return the standard deviation of the values or Double.NaN if length = 0
210 * @throws IllegalArgumentException if the array is null
211 */
212 public double evaluate(final double[] values, final double mean) {
213 return Math.sqrt(variance.evaluate(values, mean));
214 }
215
216 /**
217 * @return Returns the isBiasCorrected.
218 */
219 public boolean isBiasCorrected() {
220 return variance.isBiasCorrected();
221 }
222
223 /**
224 * @param isBiasCorrected The isBiasCorrected to set.
225 */
226 public void setBiasCorrected(boolean isBiasCorrected) {
227 variance.setBiasCorrected(isBiasCorrected);
228 }
229 }