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.math3.stat.descriptive.moment; 018 019 import java.io.Serializable; 020 021 import org.apache.commons.math3.exception.MathIllegalArgumentException; 022 import org.apache.commons.math3.exception.NullArgumentException; 023 import org.apache.commons.math3.stat.descriptive.AbstractStorelessUnivariateStatistic; 024 import org.apache.commons.math3.stat.descriptive.WeightedEvaluation; 025 import org.apache.commons.math3.stat.descriptive.summary.Sum; 026 import org.apache.commons.math3.util.MathUtils; 027 028 /** 029 * <p>Computes the arithmetic mean of a set of values. Uses the definitional 030 * formula:</p> 031 * <p> 032 * mean = sum(x_i) / n 033 * </p> 034 * <p>where <code>n</code> is the number of observations. 035 * </p> 036 * <p>When {@link #increment(double)} is used to add data incrementally from a 037 * stream of (unstored) values, the value of the statistic that 038 * {@link #getResult()} returns is computed using the following recursive 039 * updating algorithm: </p> 040 * <ol> 041 * <li>Initialize <code>m = </code> the first value</li> 042 * <li>For each additional value, update using <br> 043 * <code>m = m + (new value - m) / (number of observations)</code></li> 044 * </ol> 045 * <p> If {@link #evaluate(double[])} is used to compute the mean of an array 046 * of stored values, a two-pass, corrected algorithm is used, starting with 047 * the definitional formula computed using the array of stored values and then 048 * correcting this by adding the mean deviation of the data values from the 049 * arithmetic mean. See, e.g. "Comparison of Several Algorithms for Computing 050 * Sample Means and Variances," Robert F. Ling, Journal of the American 051 * Statistical Association, Vol. 69, No. 348 (Dec., 1974), pp. 859-866. </p> 052 * <p> 053 * Returns <code>Double.NaN</code> if the dataset is empty. 054 * </p> 055 * <strong>Note that this implementation is not synchronized.</strong> If 056 * multiple threads access an instance of this class concurrently, and at least 057 * one of the threads invokes the <code>increment()</code> or 058 * <code>clear()</code> method, it must be synchronized externally. 059 * 060 * @version $Id: Mean.java 1416643 2012-12-03 19:37:14Z tn $ 061 */ 062 public class Mean extends AbstractStorelessUnivariateStatistic 063 implements Serializable, WeightedEvaluation { 064 065 /** Serializable version identifier */ 066 private static final long serialVersionUID = -1296043746617791564L; 067 068 /** First moment on which this statistic is based. */ 069 protected FirstMoment moment; 070 071 /** 072 * Determines whether or not this statistic can be incremented or cleared. 073 * <p> 074 * Statistics based on (constructed from) external moments cannot 075 * be incremented or cleared.</p> 076 */ 077 protected boolean incMoment; 078 079 /** Constructs a Mean. */ 080 public Mean() { 081 incMoment = true; 082 moment = new FirstMoment(); 083 } 084 085 /** 086 * Constructs a Mean with an External Moment. 087 * 088 * @param m1 the moment 089 */ 090 public Mean(final FirstMoment m1) { 091 this.moment = m1; 092 incMoment = false; 093 } 094 095 /** 096 * Copy constructor, creates a new {@code Mean} identical 097 * to the {@code original} 098 * 099 * @param original the {@code Mean} instance to copy 100 * @throws NullArgumentException if original is null 101 */ 102 public Mean(Mean original) throws NullArgumentException { 103 copy(original, this); 104 } 105 106 /** 107 * {@inheritDoc} 108 * <p>Note that when {@link #Mean(FirstMoment)} is used to 109 * create a Mean, this method does nothing. In that case, the 110 * FirstMoment should be incremented directly.</p> 111 */ 112 @Override 113 public void increment(final double d) { 114 if (incMoment) { 115 moment.increment(d); 116 } 117 } 118 119 /** 120 * {@inheritDoc} 121 */ 122 @Override 123 public void clear() { 124 if (incMoment) { 125 moment.clear(); 126 } 127 } 128 129 /** 130 * {@inheritDoc} 131 */ 132 @Override 133 public double getResult() { 134 return moment.m1; 135 } 136 137 /** 138 * {@inheritDoc} 139 */ 140 public long getN() { 141 return moment.getN(); 142 } 143 144 /** 145 * Returns the arithmetic mean of the entries in the specified portion of 146 * the input array, or <code>Double.NaN</code> if the designated subarray 147 * is empty. 148 * <p> 149 * Throws <code>IllegalArgumentException</code> if the array is null.</p> 150 * <p> 151 * See {@link Mean} for details on the computing algorithm.</p> 152 * 153 * @param values the input array 154 * @param begin index of the first array element to include 155 * @param length the number of elements to include 156 * @return the mean of the values or Double.NaN if length = 0 157 * @throws MathIllegalArgumentException if the array is null or the array index 158 * parameters are not valid 159 */ 160 @Override 161 public double evaluate(final double[] values,final int begin, final int length) 162 throws MathIllegalArgumentException { 163 if (test(values, begin, length)) { 164 Sum sum = new Sum(); 165 double sampleSize = length; 166 167 // Compute initial estimate using definitional formula 168 double xbar = sum.evaluate(values, begin, length) / sampleSize; 169 170 // Compute correction factor in second pass 171 double correction = 0; 172 for (int i = begin; i < begin + length; i++) { 173 correction += values[i] - xbar; 174 } 175 return xbar + (correction/sampleSize); 176 } 177 return Double.NaN; 178 } 179 180 /** 181 * Returns the weighted arithmetic mean of the entries in the specified portion of 182 * the input array, or <code>Double.NaN</code> if the designated subarray 183 * is empty. 184 * <p> 185 * Throws <code>IllegalArgumentException</code> if either array is null.</p> 186 * <p> 187 * See {@link Mean} for details on the computing algorithm. The two-pass algorithm 188 * described above is used here, with weights applied in computing both the original 189 * estimate and the correction factor.</p> 190 * <p> 191 * Throws <code>IllegalArgumentException</code> if any of the following are true: 192 * <ul><li>the values array is null</li> 193 * <li>the weights array is null</li> 194 * <li>the weights array does not have the same length as the values array</li> 195 * <li>the weights array contains one or more infinite values</li> 196 * <li>the weights array contains one or more NaN values</li> 197 * <li>the weights array contains negative values</li> 198 * <li>the start and length arguments do not determine a valid array</li> 199 * </ul></p> 200 * 201 * @param values the input array 202 * @param weights the weights array 203 * @param begin index of the first array element to include 204 * @param length the number of elements to include 205 * @return the mean of the values or Double.NaN if length = 0 206 * @throws MathIllegalArgumentException if the parameters are not valid 207 * @since 2.1 208 */ 209 public double evaluate(final double[] values, final double[] weights, 210 final int begin, final int length) throws MathIllegalArgumentException { 211 if (test(values, weights, begin, length)) { 212 Sum sum = new Sum(); 213 214 // Compute initial estimate using definitional formula 215 double sumw = sum.evaluate(weights,begin,length); 216 double xbarw = sum.evaluate(values, weights, begin, length) / sumw; 217 218 // Compute correction factor in second pass 219 double correction = 0; 220 for (int i = begin; i < begin + length; i++) { 221 correction += weights[i] * (values[i] - xbarw); 222 } 223 return xbarw + (correction/sumw); 224 } 225 return Double.NaN; 226 } 227 228 /** 229 * Returns the weighted arithmetic mean of the entries in the input array. 230 * <p> 231 * Throws <code>MathIllegalArgumentException</code> if either array is null.</p> 232 * <p> 233 * See {@link Mean} for details on the computing algorithm. The two-pass algorithm 234 * described above is used here, with weights applied in computing both the original 235 * estimate and the correction factor.</p> 236 * <p> 237 * Throws <code>MathIllegalArgumentException</code> if any of the following are true: 238 * <ul><li>the values array is null</li> 239 * <li>the weights array is null</li> 240 * <li>the weights array does not have the same length as the values array</li> 241 * <li>the weights array contains one or more infinite values</li> 242 * <li>the weights array contains one or more NaN values</li> 243 * <li>the weights array contains negative values</li> 244 * </ul></p> 245 * 246 * @param values the input array 247 * @param weights the weights array 248 * @return the mean of the values or Double.NaN if length = 0 249 * @throws MathIllegalArgumentException if the parameters are not valid 250 * @since 2.1 251 */ 252 public double evaluate(final double[] values, final double[] weights) 253 throws MathIllegalArgumentException { 254 return evaluate(values, weights, 0, values.length); 255 } 256 257 /** 258 * {@inheritDoc} 259 */ 260 @Override 261 public Mean copy() { 262 Mean result = new Mean(); 263 // No try-catch or advertised exception because args are guaranteed non-null 264 copy(this, result); 265 return result; 266 } 267 268 269 /** 270 * Copies source to dest. 271 * <p>Neither source nor dest can be null.</p> 272 * 273 * @param source Mean to copy 274 * @param dest Mean to copy to 275 * @throws NullArgumentException if either source or dest is null 276 */ 277 public static void copy(Mean source, Mean dest) 278 throws NullArgumentException { 279 MathUtils.checkNotNull(source); 280 MathUtils.checkNotNull(dest); 281 dest.setData(source.getDataRef()); 282 dest.incMoment = source.incMoment; 283 dest.moment = source.moment.copy(); 284 } 285 }