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 */ 017package org.apache.commons.math4.legacy.stat.descriptive.moment; 018 019import org.apache.commons.math4.legacy.exception.MathIllegalArgumentException; 020import org.apache.commons.math4.legacy.exception.NullArgumentException; 021import org.apache.commons.math4.legacy.stat.descriptive.AbstractStorelessUnivariateStatistic; 022import org.apache.commons.math4.core.jdkmath.JdkMath; 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 */ 039public class StandardDeviation extends AbstractStorelessUnivariateStatistic { 040 /** Wrapped Variance instance. */ 041 private Variance variance; 042 043 /** 044 * Constructs a StandardDeviation. Sets the underlying {@link Variance} 045 * instance's <code>isBiasCorrected</code> property to true. 046 */ 047 public StandardDeviation() { 048 variance = new Variance(); 049 } 050 051 /** 052 * Constructs a StandardDeviation from an external second moment. 053 * 054 * @param m2 the external moment 055 */ 056 public StandardDeviation(final SecondMoment m2) { 057 variance = new Variance(m2); 058 } 059 060 /** 061 * Copy constructor, creates a new {@code StandardDeviation} identical 062 * to the {@code original}. 063 * 064 * @param original the {@code StandardDeviation} instance to copy 065 * @throws NullArgumentException if original is null 066 */ 067 public StandardDeviation(StandardDeviation original) throws NullArgumentException { 068 copy(original, this); 069 } 070 071 /** 072 * Constructs a StandardDeviation with the specified value for the 073 * <code>isBiasCorrected</code> property. If this property is set to 074 * <code>true</code>, the {@link Variance} used in computing results will 075 * use the bias-corrected, or "sample" formula. See {@link Variance} for 076 * details. 077 * 078 * @param isBiasCorrected whether or not the variance computation will use 079 * the bias-corrected formula 080 */ 081 public StandardDeviation(boolean isBiasCorrected) { 082 variance = new Variance(isBiasCorrected); 083 } 084 085 /** 086 * Constructs a StandardDeviation with the specified value for the 087 * <code>isBiasCorrected</code> property and the supplied external moment. 088 * If <code>isBiasCorrected</code> is set to <code>true</code>, the 089 * {@link Variance} used in computing results will use the bias-corrected, 090 * or "sample" formula. See {@link Variance} for details. 091 * 092 * @param isBiasCorrected whether or not the variance computation will use 093 * the bias-corrected formula 094 * @param m2 the external moment 095 */ 096 public StandardDeviation(boolean isBiasCorrected, SecondMoment m2) { 097 variance = new Variance(isBiasCorrected, m2); 098 } 099 100 /** 101 * {@inheritDoc} 102 */ 103 @Override 104 public void increment(final double d) { 105 variance.increment(d); 106 } 107 108 /** 109 * {@inheritDoc} 110 */ 111 @Override 112 public long getN() { 113 return variance.getN(); 114 } 115 116 /** 117 * {@inheritDoc} 118 */ 119 @Override 120 public double getResult() { 121 return JdkMath.sqrt(variance.getResult()); 122 } 123 124 /** 125 * {@inheritDoc} 126 */ 127 @Override 128 public void clear() { 129 variance.clear(); 130 } 131 132 /** 133 * Returns the Standard Deviation of the entries in the input array, or 134 * <code>Double.NaN</code> if the array is empty. 135 * <p> 136 * Returns 0 for a single-value (i.e. length = 1) sample.</p> 137 * <p> 138 * Throws <code>MathIllegalArgumentException</code> if the array is null.</p> 139 * <p> 140 * Does not change the internal state of the statistic.</p> 141 * 142 * @param values the input array 143 * @return the standard deviation of the values or Double.NaN if length = 0 144 * @throws MathIllegalArgumentException if the array is null 145 */ 146 @Override 147 public double evaluate(final double[] values) throws MathIllegalArgumentException { 148 return JdkMath.sqrt(variance.evaluate(values)); 149 } 150 151 /** 152 * Returns the Standard Deviation of the entries in the specified portion of 153 * the input array, or <code>Double.NaN</code> if the designated subarray 154 * is empty. 155 * <p> 156 * Returns 0 for a single-value (i.e. length = 1) sample. </p> 157 * <p> 158 * Throws <code>MathIllegalArgumentException</code> if the array is null.</p> 159 * <p> 160 * Does not change the internal state of the statistic.</p> 161 * 162 * @param values the input array 163 * @param begin index of the first array element to include 164 * @param length the number of elements to include 165 * @return the standard deviation of the values or Double.NaN if length = 0 166 * @throws MathIllegalArgumentException if the array is null or the array index 167 * parameters are not valid 168 */ 169 @Override 170 public double evaluate(final double[] values, final int begin, final int length) 171 throws MathIllegalArgumentException { 172 return JdkMath.sqrt(variance.evaluate(values, begin, length)); 173 } 174 175 /** 176 * Returns the Standard Deviation of the entries in the specified portion of 177 * the input array, using the precomputed mean value. Returns 178 * <code>Double.NaN</code> if the designated subarray is empty. 179 * <p> 180 * Returns 0 for a single-value (i.e. length = 1) sample.</p> 181 * <p> 182 * The formula used assumes that the supplied mean value is the arithmetic 183 * mean of the sample data, not a known population parameter. This method 184 * is supplied only to save computation when the mean has already been 185 * computed.</p> 186 * <p> 187 * Throws <code>IllegalArgumentException</code> if the array is null.</p> 188 * <p> 189 * Does not change the internal state of the statistic.</p> 190 * 191 * @param values the input array 192 * @param mean the precomputed mean value 193 * @param begin index of the first array element to include 194 * @param length the number of elements to include 195 * @return the standard deviation of the values or Double.NaN if length = 0 196 * @throws MathIllegalArgumentException if the array is null or the array index 197 * parameters are not valid 198 */ 199 public double evaluate(final double[] values, final double mean, 200 final int begin, final int length) throws MathIllegalArgumentException { 201 return JdkMath.sqrt(variance.evaluate(values, mean, begin, length)); 202 } 203 204 /** 205 * Returns the Standard Deviation of the entries in the input array, using 206 * the precomputed mean value. Returns 207 * <code>Double.NaN</code> if the designated subarray is empty. 208 * <p> 209 * Returns 0 for a single-value (i.e. length = 1) sample.</p> 210 * <p> 211 * The formula used assumes that the supplied mean value is the arithmetic 212 * mean of the sample data, not a known population parameter. This method 213 * is supplied only to save computation when the mean has already been 214 * computed.</p> 215 * <p> 216 * Throws <code>MathIllegalArgumentException</code> if the array is null.</p> 217 * <p> 218 * Does not change the internal state of the statistic.</p> 219 * 220 * @param values the input array 221 * @param mean the precomputed mean value 222 * @return the standard deviation of the values or Double.NaN if length = 0 223 * @throws MathIllegalArgumentException if the array is null 224 */ 225 public double evaluate(final double[] values, final double mean) 226 throws MathIllegalArgumentException { 227 return JdkMath.sqrt(variance.evaluate(values, mean)); 228 } 229 230 /** 231 * @return Returns the isBiasCorrected. 232 */ 233 public boolean isBiasCorrected() { 234 return variance.isBiasCorrected(); 235 } 236 237 /** 238 * @param isBiasCorrected The isBiasCorrected to set. 239 */ 240 public void setBiasCorrected(boolean isBiasCorrected) { 241 variance.setBiasCorrected(isBiasCorrected); 242 } 243 244 /** 245 * {@inheritDoc} 246 */ 247 @Override 248 public StandardDeviation copy() { 249 StandardDeviation result = new StandardDeviation(); 250 // No try-catch or advertised exception because args are guaranteed non-null 251 copy(this, result); 252 return result; 253 } 254 255 /** 256 * Copies source to dest. 257 * <p>Neither source nor dest can be null.</p> 258 * 259 * @param source StandardDeviation to copy 260 * @param dest StandardDeviation to copy to 261 * @throws NullArgumentException if either source or dest is null 262 */ 263 public static void copy(StandardDeviation source, StandardDeviation dest) throws NullArgumentException { 264 NullArgumentException.check(source); 265 NullArgumentException.check(dest); 266 dest.variance = source.variance.copy(); 267 } 268}