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.summary.Sum;
23
24 /**
25 * <p>Computes the arithmetic mean of a set of values. Uses the definitional
26 * formula:</p>
27 * <p>
28 * mean = sum(x_i) / n
29 * </p>
30 * <p>where <code>n</code> is the number of observations.
31 * </p>
32 * <p>When {@link #increment(double)} is used to add data incrementally from a
33 * stream of (unstored) values, the value of the statistic that
34 * {@link #getResult()} returns is computed using the following recursive
35 * updating algorithm: </p>
36 * <ol>
37 * <li>Initialize <code>m = </code> the first value</li>
38 * <li>For each additional value, update using <br>
39 * <code>m = m + (new value - m) / (number of observations)</code></li>
40 * </ol>
41 * <p> If {@link #evaluate(double[])} is used to compute the mean of an array
42 * of stored values, a two-pass, corrected algorithm is used, starting with
43 * the definitional formula computed using the array of stored values and then
44 * correcting this by adding the mean deviation of the data values from the
45 * arithmetic mean. See, e.g. "Comparison of Several Algorithms for Computing
46 * Sample Means and Variances," Robert F. Ling, Journal of the American
47 * Statistical Association, Vol. 69, No. 348 (Dec., 1974), pp. 859-866. </p>
48 * <p>
49 * Returns <code>Double.NaN</code> if the dataset is empty.
50 * </p>
51 * <strong>Note that this implementation is not synchronized.</strong> If
52 * multiple threads access an instance of this class concurrently, and at least
53 * one of the threads invokes the <code>increment()</code> or
54 * <code>clear()</code> method, it must be synchronized externally.
55 *
56 * @version $Revision: 762116 $ $Date: 2009-04-05 12:48:53 -0400 (Sun, 05 Apr 2009) $
57 */
58 public class Mean extends AbstractStorelessUnivariateStatistic
59 implements Serializable {
60
61 /** Serializable version identifier */
62 private static final long serialVersionUID = -1296043746617791564L;
63
64 /** First moment on which this statistic is based. */
65 protected FirstMoment moment;
66
67 /**
68 * Determines whether or not this statistic can be incremented or cleared.
69 * <p>
70 * Statistics based on (constructed from) external moments cannot
71 * be incremented or cleared.</p>
72 */
73 protected boolean incMoment;
74
75 /** Constructs a Mean. */
76 public Mean() {
77 incMoment = true;
78 moment = new FirstMoment();
79 }
80
81 /**
82 * Constructs a Mean with an External Moment.
83 *
84 * @param m1 the moment
85 */
86 public Mean(final FirstMoment m1) {
87 this.moment = m1;
88 incMoment = false;
89 }
90
91 /**
92 * Copy constructor, creates a new {@code Mean} identical
93 * to the {@code original}
94 *
95 * @param original the {@code Mean} instance to copy
96 */
97 public Mean(Mean original) {
98 copy(original, this);
99 }
100
101 /**
102 * {@inheritDoc}
103 */
104 @Override
105 public void increment(final double d) {
106 if (incMoment) {
107 moment.increment(d);
108 }
109 }
110
111 /**
112 * {@inheritDoc}
113 */
114 @Override
115 public void clear() {
116 if (incMoment) {
117 moment.clear();
118 }
119 }
120
121 /**
122 * {@inheritDoc}
123 */
124 @Override
125 public double getResult() {
126 return moment.m1;
127 }
128
129 /**
130 * {@inheritDoc}
131 */
132 public long getN() {
133 return moment.getN();
134 }
135
136 /**
137 * Returns the arithmetic mean of the entries in the specified portion of
138 * the input array, or <code>Double.NaN</code> if the designated subarray
139 * is empty.
140 * <p>
141 * Throws <code>IllegalArgumentException</code> if the array is null.</p>
142 * <p>
143 * See {@link Mean} for details on the computing algorithm.</p>
144 *
145 * @param values the input array
146 * @param begin index of the first array element to include
147 * @param length the number of elements to include
148 * @return the mean of the values or Double.NaN if length = 0
149 * @throws IllegalArgumentException if the array is null or the array index
150 * parameters are not valid
151 */
152 @Override
153 public double evaluate(final double[] values,final int begin, final int length) {
154 if (test(values, begin, length)) {
155 Sum sum = new Sum();
156 double sampleSize = length;
157
158 // Compute initial estimate using definitional formula
159 double xbar = sum.evaluate(values, begin, length) / sampleSize;
160
161 // Compute correction factor in second pass
162 double correction = 0;
163 for (int i = begin; i < begin + length; i++) {
164 correction += (values[i] - xbar);
165 }
166 return xbar + (correction/sampleSize);
167 }
168 return Double.NaN;
169 }
170
171 /**
172 * {@inheritDoc}
173 */
174 @Override
175 public Mean copy() {
176 Mean result = new Mean();
177 copy(this, result);
178 return result;
179 }
180
181
182 /**
183 * Copies source to dest.
184 * <p>Neither source nor dest can be null.</p>
185 *
186 * @param source Mean to copy
187 * @param dest Mean to copy to
188 * @throws NullPointerException if either source or dest is null
189 */
190 public static void copy(Mean source, Mean dest) {
191 dest.incMoment = source.incMoment;
192 dest.moment = source.moment.copy();
193 }
194 }