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/* * 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.math3.stat.descriptive.moment; import java.io.Serializable; import org.apache.commons.math3.exception.MathIllegalArgumentException; import org.apache.commons.math3.exception.NullArgumentException; import org.apache.commons.math3.stat.descriptive.AbstractStorelessUnivariateStatistic; import org.apache.commons.math3.stat.descriptive.WeightedEvaluation; import org.apache.commons.math3.stat.descriptive.summary.Sum; import org.apache.commons.math3.util.MathUtils; /** * <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 $Id: Mean.java 1416643 2012-12-03 19:37:14Z tn $ */ 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 * @throws NullArgumentException if original is null */ public Mean(Mean original) throws NullArgumentException { copy(original, this); } /** * {@inheritDoc} * <p>Note that when {@link #Mean(FirstMoment)} is used to * create a Mean, this method does nothing. In that case, the * FirstMoment should be incremented directly.</p> */ @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 MathIllegalArgumentException 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) throws MathIllegalArgumentException { 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 MathIllegalArgumentException if the parameters are not valid * @since 2.1 */ public double evaluate(final double[] values, final double[] weights, final int begin, final int length) throws MathIllegalArgumentException { 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>MathIllegalArgumentException</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>MathIllegalArgumentException</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 MathIllegalArgumentException if the parameters are not valid * @since 2.1 */ public double evaluate(final double[] values, final double[] weights) throws MathIllegalArgumentException { return evaluate(values, weights, 0, values.length); } /** * {@inheritDoc} */ @Override public Mean copy() { Mean result = new Mean(); // No try-catch or advertised exception because args are guaranteed non-null 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 NullArgumentException if either source or dest is null */ public static void copy(Mean source, Mean dest) throws NullArgumentException { MathUtils.checkNotNull(source); MathUtils.checkNotNull(dest); dest.setData(source.getDataRef()); dest.incMoment = source.incMoment; dest.moment = source.moment.copy(); } }