/* * 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; /** *
Computes the arithmetic mean of a set of values. Uses the definitional * formula:
** mean = sum(x_i) / n *
*where n
is the number of observations.
*
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:
*m =
the first valuem = m + (new value - m) / (number of observations)
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.
*
* Returns Double.NaN
if the dataset is empty.
*
increment()
or
* clear()
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.
* * Statistics based on (constructed from) external moments cannot * be incremented or cleared.
*/ 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, orDouble.NaN
if the designated subarray
* is empty.
*
* Throws IllegalArgumentException
if the array is null.
* See {@link Mean} for details on the computing algorithm.
* * @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, orDouble.NaN
if the designated subarray
* is empty.
*
* Throws IllegalArgumentException
if either array is null.
* 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.
*
* Throws IllegalArgumentException
if any of the following are true:
*
* Throws IllegalArgumentException
if either array is null.
* 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.
*
* Throws IllegalArgumentException
if any of the following are true:
*
Neither source nor dest can be null.
* * @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(); } }