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 import org.apache.commons.math.stat.descriptive.WeightedEvaluation; 23 import org.apache.commons.math.stat.descriptive.summary.Sum; 24 25 /** 26 * <p>Computes the arithmetic mean of a set of values. Uses the definitional 27 * formula:</p> 28 * <p> 29 * mean = sum(x_i) / n 30 * </p> 31 * <p>where <code>n</code> is the number of observations. 32 * </p> 33 * <p>When {@link #increment(double)} is used to add data incrementally from a 34 * stream of (unstored) values, the value of the statistic that 35 * {@link #getResult()} returns is computed using the following recursive 36 * updating algorithm: </p> 37 * <ol> 38 * <li>Initialize <code>m = </code> the first value</li> 39 * <li>For each additional value, update using <br> 40 * <code>m = m + (new value - m) / (number of observations)</code></li> 41 * </ol> 42 * <p> If {@link #evaluate(double[])} is used to compute the mean of an array 43 * of stored values, a two-pass, corrected algorithm is used, starting with 44 * the definitional formula computed using the array of stored values and then 45 * correcting this by adding the mean deviation of the data values from the 46 * arithmetic mean. See, e.g. "Comparison of Several Algorithms for Computing 47 * Sample Means and Variances," Robert F. Ling, Journal of the American 48 * Statistical Association, Vol. 69, No. 348 (Dec., 1974), pp. 859-866. </p> 49 * <p> 50 * Returns <code>Double.NaN</code> if the dataset is empty. 51 * </p> 52 * <strong>Note that this implementation is not synchronized.</strong> If 53 * multiple threads access an instance of this class concurrently, and at least 54 * one of the threads invokes the <code>increment()</code> or 55 * <code>clear()</code> method, it must be synchronized externally. 56 * 57 * @version $Revision: 1006299 $ $Date: 2010-10-10 16:47:17 +0200 (dim. 10 oct. 2010) $ 58 */ 59 public class Mean extends AbstractStorelessUnivariateStatistic 60 implements Serializable, WeightedEvaluation { 61 62 /** Serializable version identifier */ 63 private static final long serialVersionUID = -1296043746617791564L; 64 65 /** First moment on which this statistic is based. */ 66 protected FirstMoment moment; 67 68 /** 69 * Determines whether or not this statistic can be incremented or cleared. 70 * <p> 71 * Statistics based on (constructed from) external moments cannot 72 * be incremented or cleared.</p> 73 */ 74 protected boolean incMoment; 75 76 /** Constructs a Mean. */ Mean()77 public Mean() { 78 incMoment = true; 79 moment = new FirstMoment(); 80 } 81 82 /** 83 * Constructs a Mean with an External Moment. 84 * 85 * @param m1 the moment 86 */ Mean(final FirstMoment m1)87 public Mean(final FirstMoment m1) { 88 this.moment = m1; 89 incMoment = false; 90 } 91 92 /** 93 * Copy constructor, creates a new {@code Mean} identical 94 * to the {@code original} 95 * 96 * @param original the {@code Mean} instance to copy 97 */ Mean(Mean original)98 public Mean(Mean original) { 99 copy(original, this); 100 } 101 102 /** 103 * {@inheritDoc} 104 */ 105 @Override increment(final double d)106 public void increment(final double d) { 107 if (incMoment) { 108 moment.increment(d); 109 } 110 } 111 112 /** 113 * {@inheritDoc} 114 */ 115 @Override clear()116 public void clear() { 117 if (incMoment) { 118 moment.clear(); 119 } 120 } 121 122 /** 123 * {@inheritDoc} 124 */ 125 @Override getResult()126 public double getResult() { 127 return moment.m1; 128 } 129 130 /** 131 * {@inheritDoc} 132 */ getN()133 public long getN() { 134 return moment.getN(); 135 } 136 137 /** 138 * Returns the arithmetic mean of the entries in the specified portion of 139 * the input array, or <code>Double.NaN</code> if the designated subarray 140 * is empty. 141 * <p> 142 * Throws <code>IllegalArgumentException</code> if the array is null.</p> 143 * <p> 144 * See {@link Mean} for details on the computing algorithm.</p> 145 * 146 * @param values the input array 147 * @param begin index of the first array element to include 148 * @param length the number of elements to include 149 * @return the mean of the values or Double.NaN if length = 0 150 * @throws IllegalArgumentException if the array is null or the array index 151 * parameters are not valid 152 */ 153 @Override evaluate(final double[] values,final int begin, final int length)154 public double evaluate(final double[] values,final int begin, final int length) { 155 if (test(values, begin, length)) { 156 Sum sum = new Sum(); 157 double sampleSize = length; 158 159 // Compute initial estimate using definitional formula 160 double xbar = sum.evaluate(values, begin, length) / sampleSize; 161 162 // Compute correction factor in second pass 163 double correction = 0; 164 for (int i = begin; i < begin + length; i++) { 165 correction += values[i] - xbar; 166 } 167 return xbar + (correction/sampleSize); 168 } 169 return Double.NaN; 170 } 171 172 /** 173 * Returns the weighted arithmetic mean of the entries in the specified portion of 174 * the input array, or <code>Double.NaN</code> if the designated subarray 175 * is empty. 176 * <p> 177 * Throws <code>IllegalArgumentException</code> if either array is null.</p> 178 * <p> 179 * See {@link Mean} for details on the computing algorithm. The two-pass algorithm 180 * described above is used here, with weights applied in computing both the original 181 * estimate and the correction factor.</p> 182 * <p> 183 * Throws <code>IllegalArgumentException</code> if any of the following are true: 184 * <ul><li>the values array is null</li> 185 * <li>the weights array is null</li> 186 * <li>the weights array does not have the same length as the values array</li> 187 * <li>the weights array contains one or more infinite values</li> 188 * <li>the weights array contains one or more NaN values</li> 189 * <li>the weights array contains negative values</li> 190 * <li>the start and length arguments do not determine a valid array</li> 191 * </ul></p> 192 * 193 * @param values the input array 194 * @param weights the weights array 195 * @param begin index of the first array element to include 196 * @param length the number of elements to include 197 * @return the mean of the values or Double.NaN if length = 0 198 * @throws IllegalArgumentException if the parameters are not valid 199 * @since 2.1 200 */ evaluate(final double[] values, final double[] weights, final int begin, final int length)201 public double evaluate(final double[] values, final double[] weights, 202 final int begin, final int length) { 203 if (test(values, weights, begin, length)) { 204 Sum sum = new Sum(); 205 206 // Compute initial estimate using definitional formula 207 double sumw = sum.evaluate(weights,begin,length); 208 double xbarw = sum.evaluate(values, weights, begin, length) / sumw; 209 210 // Compute correction factor in second pass 211 double correction = 0; 212 for (int i = begin; i < begin + length; i++) { 213 correction += weights[i] * (values[i] - xbarw); 214 } 215 return xbarw + (correction/sumw); 216 } 217 return Double.NaN; 218 } 219 220 /** 221 * Returns the weighted arithmetic mean of the entries in the input array. 222 * <p> 223 * Throws <code>IllegalArgumentException</code> if either array is null.</p> 224 * <p> 225 * See {@link Mean} for details on the computing algorithm. The two-pass algorithm 226 * described above is used here, with weights applied in computing both the original 227 * estimate and the correction factor.</p> 228 * <p> 229 * Throws <code>IllegalArgumentException</code> if any of the following are true: 230 * <ul><li>the values array is null</li> 231 * <li>the weights array is null</li> 232 * <li>the weights array does not have the same length as the values array</li> 233 * <li>the weights array contains one or more infinite values</li> 234 * <li>the weights array contains one or more NaN values</li> 235 * <li>the weights array contains negative values</li> 236 * </ul></p> 237 * 238 * @param values the input array 239 * @param weights the weights array 240 * @return the mean of the values or Double.NaN if length = 0 241 * @throws IllegalArgumentException if the parameters are not valid 242 * @since 2.1 243 */ evaluate(final double[] values, final double[] weights)244 public double evaluate(final double[] values, final double[] weights) { 245 return evaluate(values, weights, 0, values.length); 246 } 247 248 /** 249 * {@inheritDoc} 250 */ 251 @Override copy()252 public Mean copy() { 253 Mean result = new Mean(); 254 copy(this, result); 255 return result; 256 } 257 258 259 /** 260 * Copies source to dest. 261 * <p>Neither source nor dest can be null.</p> 262 * 263 * @param source Mean to copy 264 * @param dest Mean to copy to 265 * @throws NullPointerException if either source or dest is null 266 */ copy(Mean source, Mean dest)267 public static void copy(Mean source, Mean dest) { 268 dest.setData(source.getDataRef()); 269 dest.incMoment = source.incMoment; 270 dest.moment = source.moment.copy(); 271 } 272 } 273