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 */
17package org.apache.commons.math.stat.descriptive.moment;
18
19import java.io.Serializable;
20
21import org.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic;
22import org.apache.commons.math.stat.descriptive.WeightedEvaluation;
23import 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 */
59public 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. */
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     */
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     */
98    public Mean(Mean original) {
99        copy(original, this);
100    }
101
102    /**
103     * {@inheritDoc}
104     */
105    @Override
106    public void increment(final double d) {
107        if (incMoment) {
108            moment.increment(d);
109        }
110    }
111
112    /**
113     * {@inheritDoc}
114     */
115    @Override
116    public void clear() {
117        if (incMoment) {
118            moment.clear();
119        }
120    }
121
122    /**
123     * {@inheritDoc}
124     */
125    @Override
126    public double getResult() {
127        return moment.m1;
128    }
129
130    /**
131     * {@inheritDoc}
132     */
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
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     */
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     */
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
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     */
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