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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;

/**
 * 

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:

*
    *
  1. Initialize m = the first value
  2. *
  3. For each additional value, update using
    * m = m + (new value - m) / (number of observations)
  4. *
*

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. Note that * Double.NaN may also be returned if the input includes NaN and / or infinite * values. *

* 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 increment() or * 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. *

* 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 * @throws NullArgumentException if original is null */ public Mean(Mean original) throws NullArgumentException { copy(original, this); } /** * {@inheritDoc} *

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.

*/ @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 Double.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 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 Double.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: *

  • the values array is null
  • *
  • the weights array is null
  • *
  • the weights array does not have the same length as the values array
  • *
  • the weights array contains one or more infinite values
  • *
  • the weights array contains one or more NaN values
  • *
  • the weights array contains negative values
  • *
  • the start and length arguments do not determine a valid array
  • *

* * @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. *

* Throws MathIllegalArgumentException 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 MathIllegalArgumentException if any of the following are true: *

  • the values array is null
  • *
  • the weights array is null
  • *
  • the weights array does not have the same length as the values array
  • *
  • the weights array contains one or more infinite values
  • *
  • the weights array contains one or more NaN values
  • *
  • the weights array contains negative values
  • *

* * @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. *

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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