    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    }