gov.sandia.cognition.statistics.distribution

Class UnivariateGaussian.SufficientStatistic

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.AbstractSufficientStatistic<java.lang.Double,UnivariateGaussian>

gov.sandia.cognition.statistics.distribution.UnivariateGaussian.SufficientStatistic

All Implemented Interfaces:

Factory<UnivariateGaussian>, SufficientStatistic<java.lang.Double,UnivariateGaussian>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

UnivariateGaussian

@PublicationReference(author="Wikipedia",
                      title="Algorithms for calculating variance",
                      year=2011,
                      type=WebPage,
                      url="http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance")
public static class UnivariateGaussian.SufficientStatistic
extends AbstractSufficientStatistic<java.lang.Double,UnivariateGaussian>

Captures the sufficient statistics of a UnivariateGaussian, which are the values to estimate the mean and variance.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected double	mean The mean of the Gaussian.
protected double	sumSquaredDifferences This is the sum-squared differences

Fields inherited from class gov.sandia.cognition.statistics.AbstractSufficientStatistic

count

Constructor Summary

Constructors

Constructor and Description
SufficientStatistic() Creates a new, empty SufficientStatistic.
SufficientStatistic(double defaultVariance) Creates a new SufficientStatistic with the given value to initialize the variance.

Method Summary

Modifier and Type	Method and Description
void	clear() Resets this set of sufficient statistics to its empty state.
UnivariateGaussian.SufficientStatistic	clone() This makes public the clone method on the Object class and removes the exception that it throws.
UnivariateGaussian.PDF	create() Creates a new instance of an object.
void	create(UnivariateGaussian distribution) Modifies the given distribution with the parameters indicated by the sufficient statistics
double	getMean() Gets the mean of the Gaussian.
double	getSampleVariance() Gets the sample variance, which may be a biased estimate.
double	getSumSquaredDifferences() Gets the sum of squared differences from the mean.
double	getVariance() Gets the variance of the Gaussian.
UnivariateGaussian.SufficientStatistic	plus(UnivariateGaussian.SufficientStatistic other) Adds this set of sufficient statistics to another and returns the combined sufficient statistics.
void	plusEquals(UnivariateGaussian.SufficientStatistic other) Adds another sufficient statistic to this one.
void	remove(double value) Removes a value to the sufficient statistics for the Gaussian.
void	update(double value) Adds a value to the sufficient statistics for the Gaussian.
void	update(java.lang.Double value) Updates the sufficient statistics from the given value

Methods inherited from class gov.sandia.cognition.statistics.AbstractSufficientStatistic

getCount, setCount, update

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

mean

protected double mean

The mean of the Gaussian.

sumSquaredDifferences

protected double sumSquaredDifferences

This is the sum-squared differences

Constructor Detail

SufficientStatistic

public SufficientStatistic()

Creates a new, empty SufficientStatistic.

SufficientStatistic

public SufficientStatistic(double defaultVariance)

Creates a new SufficientStatistic with the given value to initialize the variance. This is the initial value for the sum of squared differences. As the number of elements becomes larger, the impact of the default variance will decrease.

Parameters:

defaultVariance - The default variance to use. Must be greater than or equal to zero.

Method Detail

clone

public UnivariateGaussian.SufficientStatistic clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractSufficientStatistic<java.lang.Double,UnivariateGaussian>

Returns:

A clone of this object.

clear

public void clear()

Resets this set of sufficient statistics to its empty state.

create

public UnivariateGaussian.PDF create()

Description copied from interface: Factory

Creates a new instance of an object.

Returns:

A newly created object.

create

public void create(UnivariateGaussian distribution)

Description copied from interface: SufficientStatistic

Modifies the given distribution with the parameters indicated by the sufficient statistics

Parameters:

distribution - Distribution to modify by side effect

update

public void update(java.lang.Double value)

Description copied from interface: SufficientStatistic

Updates the sufficient statistics from the given value

Parameters:

value - Value to update the sufficient statistics

update

public void update(double value)

Adds a value to the sufficient statistics for the Gaussian.

Parameters:

value - The value to add.

remove

public void remove(double value)

Removes a value to the sufficient statistics for the Gaussian.

Parameters:

value - The value to add.

plus

public UnivariateGaussian.SufficientStatistic plus(UnivariateGaussian.SufficientStatistic other)

Adds this set of sufficient statistics to another and returns the combined sufficient statistics.

Parameters:

other - The other set of sufficient statistics.

Returns:

A combined set of sufficient statistics. The result is the same as if all of the elements added to this and other were added to one sufficient statistic.

plusEquals

public void plusEquals(UnivariateGaussian.SufficientStatistic other)

Adds another sufficient statistic to this one. Makes this one as if all the items added to the other sufficient statistics were added to this one.

Parameters:

other - The other set of sufficient statistics.

getMean

public double getMean()

Gets the mean of the Gaussian.

Returns:

The mean.

getVariance

public double getVariance()

Gets the variance of the Gaussian.

Returns:

The variance.

getSampleVariance

public double getSampleVariance()

Gets the sample variance, which may be a biased estimate. This the sum of squared deviations from the mean divided by the number of points (n) where as getVariance is divided by n - 1. If the count is less than or equal to 1, then the initial variance is returned.

Returns:

The sample variance.

getSumSquaredDifferences

public double getSumSquaredDifferences()

Gets the sum of squared differences from the mean. Used to compute the variance.

Returns:

The sum of squared differences from the mean.