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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: 617953 $ $Date: 2008-02-02 22:54:00 -0700 (Sat, 02 Feb 2008) $
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       * @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#increment(double)
93       */
94      public void increment(final double d) {
95          if (incMoment) {
96              moment.increment(d);
97          }
98      }
99  
100     /**
101      * @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#clear()
102      */
103     public void clear() {
104         if (incMoment) {
105             moment.clear();
106         }
107     }
108 
109     /**
110      * @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#getResult()
111      */
112     public double getResult() {
113         return moment.m1;
114     }
115 
116     /**
117      * @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#getN()
118      */
119     public long getN() {
120         return moment.getN();
121     }
122 
123     /**
124      * Returns the arithmetic mean of the entries in the specified portion of
125      * the input array, or <code>Double.NaN</code> if the designated subarray
126      * is empty.
127      * <p>
128      * Throws <code>IllegalArgumentException</code> if the array is null.</p>
129      * <p>
130      * See {@link Mean} for details on the computing algorithm.</p>
131      * 
132      * @param values the input array
133      * @param begin index of the first array element to include
134      * @param length the number of elements to include
135      * @return the mean of the values or Double.NaN if length = 0
136      * @throws IllegalArgumentException if the array is null or the array index
137      *  parameters are not valid
138      */
139     public double evaluate(final double[] values,final int begin, final int length) {
140         if (test(values, begin, length)) {
141             Sum sum = new Sum();
142             double sampleSize = (double) length;
143             
144             // Compute initial estimate using definitional formula
145             double xbar = sum.evaluate(values, begin, length) / sampleSize;
146             
147             // Compute correction factor in second pass
148             double correction = 0;
149             for (int i = begin; i < begin + length; i++) {
150                 correction += (values[i] - xbar);
151             }
152             return xbar + (correction/sampleSize);
153         }
154         return Double.NaN;
155     }
156 }