fr.cnes.sirius.patrius.math.stat.descriptive.moment

Class Mean

java.lang.Object

fr.cnes.sirius.patrius.math.stat.descriptive.AbstractUnivariateStatistic

fr.cnes.sirius.patrius.math.stat.descriptive.AbstractStorelessUnivariateStatistic

fr.cnes.sirius.patrius.math.stat.descriptive.moment.Mean

All Implemented Interfaces:

StorelessUnivariateStatistic, UnivariateStatistic, WeightedEvaluation, MathArrays.Function, Serializable

public class Mean
extends AbstractStorelessUnivariateStatistic
implements Serializable, WeightedEvaluation

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 increment(double) is used to add data incrementally from a stream of (unstored) values, the value of the statistic that getResult() returns is computed using the following recursive updating algorithm:

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

If AbstractStorelessUnivariateStatistic.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 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.

Version:

$Id: Mean.java 18108 2017-10-04 06:45:27Z bignon $

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected boolean	incMoment Determines whether or not this statistic can be incremented or cleared.
protected fr.cnes.sirius.patrius.math.stat.descriptive.moment.FirstMoment	moment First moment on which this statistic is based.

Constructor Summary

Constructors

Constructor and Description
Mean() Constructs a Mean.
Mean(fr.cnes.sirius.patrius.math.stat.descriptive.moment.FirstMoment m1) Constructs a Mean with an External Moment.
Mean(Mean original) Copy constructor, creates a new Mean identical to the original

Method Summary

Modifier and Type	Method and Description
void	clear() Clears the internal state of the Statistic
Mean	copy() Returns a copy of the statistic with the same internal state.
static void	copy(Mean source, Mean dest) Copies source to dest.
double	evaluate(double[] values, double[] weights) Returns the weighted arithmetic mean of the entries in the input array.
double	evaluate(double[] values, double[] weights, int begin, int length) 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.
double	evaluate(double[] values, int begin, int length) Returns the arithmetic mean of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
long	getN() Returns the number of values that have been added.
double	getResult() Returns the current value of the Statistic.
void	increment(double d) Updates the internal state of the statistic to reflect the addition of the new value.

Methods inherited from class fr.cnes.sirius.patrius.math.stat.descriptive.AbstractStorelessUnivariateStatistic

equals, evaluate, hashCode, incrementAll, incrementAll

Methods inherited from class fr.cnes.sirius.patrius.math.stat.descriptive.AbstractUnivariateStatistic

evaluate, getData, getDataRef, setData, setData, test, test, test, test

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, toString, wait, wait, wait

Field Detail

moment

protected fr.cnes.sirius.patrius.math.stat.descriptive.moment.FirstMoment moment

First moment on which this statistic is based.

incMoment

protected boolean incMoment

Determines whether or not this statistic can be incremented or cleared.

Statistics based on (constructed from) external moments cannot be incremented or cleared.

Constructor Detail

Mean

public Mean()

Constructs a Mean.

Mean

public Mean(fr.cnes.sirius.patrius.math.stat.descriptive.moment.FirstMoment m1)

Constructs a Mean with an External Moment.

Parameters:

m1 - the moment

Mean

public Mean(Mean original)

Copy constructor, creates a new Mean identical to the original

Parameters:

original - the Mean instance to copy

Throws:

NullArgumentException - if original is null

Method Detail

increment

public void increment(double d)

Updates the internal state of the statistic to reflect the addition of the new value.

Note that when Mean(FirstMoment) is used to create a Mean, this method does nothing. In that case, the FirstMoment should be incremented directly.

Specified by:

increment in interface StorelessUnivariateStatistic

Specified by:

increment in class AbstractStorelessUnivariateStatistic

Parameters:

d - the new value.

clear

public void clear()

Clears the internal state of the Statistic

Specified by:

clear in interface StorelessUnivariateStatistic

Specified by:

clear in class AbstractStorelessUnivariateStatistic

getResult

public double getResult()

Returns the current value of the Statistic.

Specified by:

getResult in interface StorelessUnivariateStatistic

Specified by:

getResult in class AbstractStorelessUnivariateStatistic

Returns:

value of the statistic, Double.NaN if it has been cleared or just instantiated.

getN

public long getN()

Returns the number of values that have been added.

Specified by:

getN in interface StorelessUnivariateStatistic

Returns:

the number of values.

evaluate

public double evaluate(double[] values,
                       int begin,
                       int length)

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 Mean for details on the computing algorithm.

Specified by:

evaluate in interface UnivariateStatistic

Specified by:

evaluate in interface MathArrays.Function

Overrides:

evaluate in class AbstractStorelessUnivariateStatistic

Parameters:

values - the input array

begin - index of the first array element to include

length - the number of elements to include

Returns:

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

See Also:

UnivariateStatistic.evaluate(double[], int, int)

evaluate

public double evaluate(double[] values,
                       double[] weights,
                       int begin,
                       int length)

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 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

Specified by:

evaluate in interface WeightedEvaluation

Parameters:

values - the input array

weights - the weights array

begin - index of the first array element to include

length - the number of elements to include

Returns:

the mean of the values or Double.NaN if length = 0

Throws:

MathIllegalArgumentException - if the parameters are not valid

Since:

2.1

evaluate

public double evaluate(double[] values,
                       double[] weights)

Returns the weighted arithmetic mean of the entries in the input array.

Throws MathIllegalArgumentException if either array is null.

See 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

Specified by:

evaluate in interface WeightedEvaluation

Parameters:

values - the input array

weights - the weights array

Returns:

the mean of the values or Double.NaN if length = 0

Throws:

MathIllegalArgumentException - if the parameters are not valid

Since:

2.1

copy

public Mean copy()

Returns a copy of the statistic with the same internal state.

Specified by:

copy in interface StorelessUnivariateStatistic

Specified by:

copy in interface UnivariateStatistic

Specified by:

copy in class AbstractStorelessUnivariateStatistic

Returns:

a copy of the statistic

copy

public static void copy(Mean source,
                        Mean dest)

Copies source to dest.

Neither source nor dest can be null.

Parameters:

source - Mean to copy

dest - Mean to copy to

Throws:

NullArgumentException - if either source or dest is null