org.hipparchus.stat.descriptive.moment.Mean Maven / Gradle / Ivy
Show all versions of hipparchus-stat Show documentation
/*
* 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.
*/
/*
* This is not the original file distributed by the Apache Software Foundation
* It has been modified by the Hipparchus project
*/
package org.hipparchus.stat.descriptive.moment;
import java.io.Serializable;
import org.hipparchus.exception.MathIllegalArgumentException;
import org.hipparchus.exception.NullArgumentException;
import org.hipparchus.stat.StatUtils;
import org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic;
import org.hipparchus.stat.descriptive.AggregatableStatistic;
import org.hipparchus.stat.descriptive.WeightedEvaluation;
import org.hipparchus.stat.descriptive.summary.Sum;
import org.hipparchus.util.MathArrays;
import org.hipparchus.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:
*
* - Initialize
m = the first value
* - For each additional value, update using
* m = 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. 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 AggregatableStatistic, WeightedEvaluation, Serializable {
/** Serializable version identifier */
private static final long serialVersionUID = 20150412L;
/** First moment on which this statistic is based. */
protected final 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 final boolean incMoment;
/** Constructs a Mean. */
public Mean() {
moment = new FirstMoment();
incMoment = true;
}
/**
* 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 {
MathUtils.checkNotNull(original);
this.moment = original.moment.copy();
this.incMoment = original.incMoment;
}
/**
* {@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} */
@Override
public long getN() {
return moment.getN();
}
/** {@inheritDoc} */
@Override
public void aggregate(Mean other) {
MathUtils.checkNotNull(other);
if (incMoment) {
this.moment.aggregate(other.moment);
}
}
/**
* Returns the arithmetic mean of the entries in the specified portion of
* the input array, or Double.NaN if the designated subarray
* is empty.
*
* @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 (MathArrays.verifyValues(values, begin, length)) {
double sampleSize = length;
// Compute initial estimate using definitional formula
double xbar = StatUtils.sum(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
*/
@Override
public double evaluate(final double[] values, final double[] weights,
final int begin, final int length)
throws MathIllegalArgumentException {
if (MathArrays.verifyValues(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;
}
/** {@inheritDoc} */
@Override
public Mean copy() {
return new Mean(this);
}
}