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Java example source code file (Mean.java)

This example Java source code file (Mean.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

abstractstorelessunivariatestatistic, firstmoment, mathillegalargumentexception, mean, nullargumentexception, override, serializable, sum, weightedevaluation

The Mean.java Java example source code

/*
 * 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 n 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 m =  the first value
 * <li>For each additional value, update using 
* <code>m = m + (new value - m) / (number of observations) * </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 if the dataset is empty. Note that * Double.NaN may also be returned if the input includes NaN and / or infinite * values. * </p> * <strong>Note that this implementation is not synchronized. If * multiple threads access an instance of this class concurrently, and at least * one of the threads invokes the <code>increment() or * <code>clear() method, it must be synchronized externally. * */ 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 if the designated subarray * is empty. * <p> * Throws <code>IllegalArgumentException if the array is null.

* <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 if the designated subarray * is empty. * <p> * Throws <code>IllegalArgumentException if either array is null.

* <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 if any of the following are true: * <ul>
  • the values array is null
  • * <li>the weights array is null * <li>the weights array does not have the same length as the values array * <li>the weights array contains one or more infinite values * <li>the weights array contains one or more NaN values * <li>the weights array contains negative values * <li>the start and length arguments do not determine a valid array * </ul>

    * * @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 if either array is null.

    * <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 if any of the following are true: * <ul>
  • the values array is null
  • * <li>the weights array is null * <li>the weights array does not have the same length as the values array * <li>the weights array contains one or more infinite values * <li>the weights array contains one or more NaN values * <li>the weights array contains negative values * </ul>

    * * @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.

    * * @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(); } }

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