| /* |
| * Licensed to the Apache Software Foundation (ASF) under one or more |
| * contributor license agreements. See the NOTICE file distributed with |
| * this work for additional information regarding copyright ownership. |
| * The ASF licenses this file to You under the Apache License, Version 2.0 |
| * (the "License"); you may not use this file except in compliance with |
| * the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| package org.apache.commons.math.stat.descriptive.moment; |
| |
| import java.io.Serializable; |
| |
| import org.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic; |
| import org.apache.commons.math.stat.descriptive.WeightedEvaluation; |
| import org.apache.commons.math.stat.descriptive.summary.Sum; |
| |
| /** |
| * <p>Computes the arithmetic mean of a set of values. Uses the definitional |
| * formula:</p> |
| * <p> |
| * mean = sum(x_i) / n |
| * </p> |
| * <p>where <code>n</code> is the number of observations. |
| * </p> |
| * <p>When {@link #increment(double)} is used to add data incrementally from a |
| * stream of (unstored) values, the value of the statistic that |
| * {@link #getResult()} returns is computed using the following recursive |
| * updating algorithm: </p> |
| * <ol> |
| * <li>Initialize <code>m = </code> the first value</li> |
| * <li>For each additional value, update using <br> |
| * <code>m = m + (new value - m) / (number of observations)</code></li> |
| * </ol> |
| * <p> If {@link #evaluate(double[])} is used to compute the mean of an array |
| * of stored values, a two-pass, corrected algorithm is used, starting with |
| * the definitional formula computed using the array of stored values and then |
| * correcting this by adding the mean deviation of the data values from the |
| * arithmetic mean. See, e.g. "Comparison of Several Algorithms for Computing |
| * Sample Means and Variances," Robert F. Ling, Journal of the American |
| * Statistical Association, Vol. 69, No. 348 (Dec., 1974), pp. 859-866. </p> |
| * <p> |
| * Returns <code>Double.NaN</code> if the dataset is empty. |
| * </p> |
| * <strong>Note that this implementation is not synchronized.</strong> If |
| * multiple threads access an instance of this class concurrently, and at least |
| * one of the threads invokes the <code>increment()</code> or |
| * <code>clear()</code> method, it must be synchronized externally. |
| * |
| * @version $Revision: 1006299 $ $Date: 2010-10-10 16:47:17 +0200 (dim. 10 oct. 2010) $ |
| */ |
| public class Mean extends AbstractStorelessUnivariateStatistic |
| implements Serializable, WeightedEvaluation { |
| |
| /** Serializable version identifier */ |
| private static final long serialVersionUID = -1296043746617791564L; |
| |
| /** First moment on which this statistic is based. */ |
| protected FirstMoment moment; |
| |
| /** |
| * Determines whether or not this statistic can be incremented or cleared. |
| * <p> |
| * Statistics based on (constructed from) external moments cannot |
| * be incremented or cleared.</p> |
| */ |
| protected boolean incMoment; |
| |
| /** Constructs a Mean. */ |
| public Mean() { |
| incMoment = true; |
| moment = new FirstMoment(); |
| } |
| |
| /** |
| * Constructs a Mean with an External Moment. |
| * |
| * @param m1 the moment |
| */ |
| public Mean(final FirstMoment m1) { |
| this.moment = m1; |
| incMoment = false; |
| } |
| |
| /** |
| * Copy constructor, creates a new {@code Mean} identical |
| * to the {@code original} |
| * |
| * @param original the {@code Mean} instance to copy |
| */ |
| public Mean(Mean original) { |
| copy(original, this); |
| } |
| |
| /** |
| * {@inheritDoc} |
| */ |
| @Override |
| public void increment(final double d) { |
| if (incMoment) { |
| moment.increment(d); |
| } |
| } |
| |
| /** |
| * {@inheritDoc} |
| */ |
| @Override |
| public void clear() { |
| if (incMoment) { |
| moment.clear(); |
| } |
| } |
| |
| /** |
| * {@inheritDoc} |
| */ |
| @Override |
| public double getResult() { |
| return moment.m1; |
| } |
| |
| /** |
| * {@inheritDoc} |
| */ |
| public long getN() { |
| return moment.getN(); |
| } |
| |
| /** |
| * Returns the arithmetic mean of the entries in the specified portion of |
| * the input array, or <code>Double.NaN</code> if the designated subarray |
| * is empty. |
| * <p> |
| * Throws <code>IllegalArgumentException</code> if the array is null.</p> |
| * <p> |
| * See {@link Mean} for details on the computing algorithm.</p> |
| * |
| * @param values the input array |
| * @param begin index of the first array element to include |
| * @param length the number of elements to include |
| * @return the mean of the values or Double.NaN if length = 0 |
| * @throws IllegalArgumentException if the array is null or the array index |
| * parameters are not valid |
| */ |
| @Override |
| public double evaluate(final double[] values,final int begin, final int length) { |
| if (test(values, begin, length)) { |
| Sum sum = new Sum(); |
| double sampleSize = length; |
| |
| // Compute initial estimate using definitional formula |
| double xbar = sum.evaluate(values, begin, length) / sampleSize; |
| |
| // Compute correction factor in second pass |
| double correction = 0; |
| for (int i = begin; i < begin + length; i++) { |
| correction += values[i] - xbar; |
| } |
| return xbar + (correction/sampleSize); |
| } |
| return Double.NaN; |
| } |
| |
| /** |
| * Returns the weighted arithmetic mean of the entries in the specified portion of |
| * the input array, or <code>Double.NaN</code> if the designated subarray |
| * is empty. |
| * <p> |
| * Throws <code>IllegalArgumentException</code> if either array is null.</p> |
| * <p> |
| * See {@link Mean} for details on the computing algorithm. The two-pass algorithm |
| * described above is used here, with weights applied in computing both the original |
| * estimate and the correction factor.</p> |
| * <p> |
| * Throws <code>IllegalArgumentException</code> if any of the following are true: |
| * <ul><li>the values array is null</li> |
| * <li>the weights array is null</li> |
| * <li>the weights array does not have the same length as the values array</li> |
| * <li>the weights array contains one or more infinite values</li> |
| * <li>the weights array contains one or more NaN values</li> |
| * <li>the weights array contains negative values</li> |
| * <li>the start and length arguments do not determine a valid array</li> |
| * </ul></p> |
| * |
| * @param values the input array |
| * @param weights the weights array |
| * @param begin index of the first array element to include |
| * @param length the number of elements to include |
| * @return the mean of the values or Double.NaN if length = 0 |
| * @throws IllegalArgumentException if the parameters are not valid |
| * @since 2.1 |
| */ |
| public double evaluate(final double[] values, final double[] weights, |
| final int begin, final int length) { |
| if (test(values, weights, begin, length)) { |
| Sum sum = new Sum(); |
| |
| // Compute initial estimate using definitional formula |
| double sumw = sum.evaluate(weights,begin,length); |
| double xbarw = sum.evaluate(values, weights, begin, length) / sumw; |
| |
| // Compute correction factor in second pass |
| double correction = 0; |
| for (int i = begin; i < begin + length; i++) { |
| correction += weights[i] * (values[i] - xbarw); |
| } |
| return xbarw + (correction/sumw); |
| } |
| return Double.NaN; |
| } |
| |
| /** |
| * Returns the weighted arithmetic mean of the entries in the input array. |
| * <p> |
| * Throws <code>IllegalArgumentException</code> if either array is null.</p> |
| * <p> |
| * See {@link Mean} for details on the computing algorithm. The two-pass algorithm |
| * described above is used here, with weights applied in computing both the original |
| * estimate and the correction factor.</p> |
| * <p> |
| * Throws <code>IllegalArgumentException</code> if any of the following are true: |
| * <ul><li>the values array is null</li> |
| * <li>the weights array is null</li> |
| * <li>the weights array does not have the same length as the values array</li> |
| * <li>the weights array contains one or more infinite values</li> |
| * <li>the weights array contains one or more NaN values</li> |
| * <li>the weights array contains negative values</li> |
| * </ul></p> |
| * |
| * @param values the input array |
| * @param weights the weights array |
| * @return the mean of the values or Double.NaN if length = 0 |
| * @throws IllegalArgumentException if the parameters are not valid |
| * @since 2.1 |
| */ |
| public double evaluate(final double[] values, final double[] weights) { |
| return evaluate(values, weights, 0, values.length); |
| } |
| |
| /** |
| * {@inheritDoc} |
| */ |
| @Override |
| public Mean copy() { |
| Mean result = new Mean(); |
| copy(this, result); |
| return result; |
| } |
| |
| |
| /** |
| * Copies source to dest. |
| * <p>Neither source nor dest can be null.</p> |
| * |
| * @param source Mean to copy |
| * @param dest Mean to copy to |
| * @throws NullPointerException if either source or dest is null |
| */ |
| public static void copy(Mean source, Mean dest) { |
| dest.setData(source.getDataRef()); |
| dest.incMoment = source.incMoment; |
| dest.moment = source.moment.copy(); |
| } |
| } |