de.unihalle.informatik.MiToBo.math.distributions.impl

Class GaussianDistribution

java.lang.Object

de.unihalle.informatik.MiToBo.math.distributions.impl.GaussianDistribution

All Implemented Interfaces:

EvaluatableDistribution<Jama.Matrix>, FirstOrderMoment<Jama.Matrix>, LogEvaluatableDistribution<Jama.Matrix>, LogProbabilityDensityFunction, ProbabilityDensityFunction, SamplingDistribution<Jama.Matrix>, SecondOrderCentralMoment<Jama.Matrix>, Copyable<GaussianDistribution>

public class GaussianDistribution
extends Object
implements ProbabilityDensityFunction, LogProbabilityDensityFunction, SamplingDistribution<Jama.Matrix>, FirstOrderMoment<Jama.Matrix>, SecondOrderCentralMoment<Jama.Matrix>, Copyable<GaussianDistribution>

A multivariate Gaussian distribution.

Author:

Oliver Gress

Field Summary

Fields

Modifier and Type	Field and Description
protected Jama.Matrix	cov covariance matrix
protected Jama.Matrix	icov inverse covariance matrix
protected Jama.Matrix	L
protected double	logfactor log of the normalization factor
protected Jama.Matrix	mean mean vector
protected double	normfactor normalization factor
protected Random	rand random generator for sampling

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	GaussianDistribution(int DOF) Constructor for a Gaussian distribution of dimension DOF with the zero vector as mean, the unity matrix as covariance matrix and a new random generator for sampling
	GaussianDistribution(int DOF, Random rand) Constructor for a Gaussian distribution of dimension DOF with the zero vector as mean, the unity matrix as covariance matrix and a given random generator for sampling
	GaussianDistribution(Jama.Matrix mean, Jama.Matrix covariance) Gaussian distribution with given mean, covariance and a new random generator for sampling
	GaussianDistribution(Jama.Matrix mean, Jama.Matrix covariance, Random rand) Gaussian distribution with given mean, covariance and random generator for sampling

Method Summary

Modifier and Type	Method and Description
GaussianDistribution	copy()
Jama.Matrix	drawSample() Generate a new sample from this density.
Jama.Matrix	getCovariance() Returns the covariance matrix
Jama.Matrix	getInverseCovariance()
Jama.Matrix	getMean() Returns the mean vector.
double	log_p(Jama.Matrix x) Evaluate natural logarithm of p(X) at location x. log(P(X=x))
double	mahalanobis(Jama.Matrix x)
double	p(Jama.Matrix x) Evaluate p(X) at location x.
void	setCovariance(Jama.Matrix covariance)
void	setMean(Jama.Matrix mean)

Methods inherited from class java.lang.Object

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

Field Detail

mean

protected Jama.Matrix mean

mean vector

cov

protected Jama.Matrix cov

covariance matrix

icov

protected Jama.Matrix icov

inverse covariance matrix

rand

protected Random rand

random generator for sampling

normfactor

protected double normfactor

normalization factor

logfactor

protected double logfactor

log of the normalization factor

L

protected Jama.Matrix L

Constructor Detail

GaussianDistribution

protected GaussianDistribution(int DOF)

Constructor for a Gaussian distribution of dimension DOF with the zero vector as mean, the unity matrix as covariance matrix and a new random generator for sampling

Parameters:

DOF -

GaussianDistribution

public GaussianDistribution(int DOF,
                            Random rand)

Constructor for a Gaussian distribution of dimension DOF with the zero vector as mean, the unity matrix as covariance matrix and a given random generator for sampling

Parameters:

DOF -

rand -

GaussianDistribution

public GaussianDistribution(Jama.Matrix mean,
                            Jama.Matrix covariance)
                     throws IllegalArgumentException

Gaussian distribution with given mean, covariance and a new random generator for sampling

Parameters:

mean -

covariance -

Throws:

IllegalArgumentException

GaussianDistribution

public GaussianDistribution(Jama.Matrix mean,
                            Jama.Matrix covariance,
                            Random rand)
                     throws IllegalArgumentException

Gaussian distribution with given mean, covariance and random generator for sampling

Parameters:

mean -

covariance -

rand -

Throws:

IllegalArgumentException

Method Detail

getMean

public Jama.Matrix getMean()

Returns the mean vector.

Specified by:

getMean in interface FirstOrderMoment<Jama.Matrix>

setMean

public void setMean(Jama.Matrix mean)
             throws IllegalArgumentException

Throws:

IllegalArgumentException

getCovariance

public Jama.Matrix getCovariance()

Returns the covariance matrix

Specified by:

getCovariance in interface SecondOrderCentralMoment<Jama.Matrix>

Returns:

getInverseCovariance

public Jama.Matrix getInverseCovariance()

setCovariance

public void setCovariance(Jama.Matrix covariance)
                   throws IllegalArgumentException

Throws:

IllegalArgumentException

log_p

public double log_p(Jama.Matrix x)

Description copied from interface: LogEvaluatableDistribution

Evaluate natural logarithm of p(X) at location x. log(P(X=x))

Specified by:

log_p in interface LogEvaluatableDistribution<Jama.Matrix>

Parameters:

x - realization of random variable X

Returns:

value of log(p(X)) at x

p

public double p(Jama.Matrix x)

Description copied from interface: EvaluatableDistribution

Evaluate p(X) at location x. P(X=x)

Specified by:

p in interface EvaluatableDistribution<Jama.Matrix>

Parameters:

x - realization of random variable X

Returns:

value of p(X) at x

drawSample

public Jama.Matrix drawSample()

Description copied from interface: SamplingDistribution

Generate a new sample from this density. This method should create a new object.

Specified by:

drawSample in interface SamplingDistribution<Jama.Matrix>

Returns:

new sample object

mahalanobis

public double mahalanobis(Jama.Matrix x)

copy

public GaussianDistribution copy()

Specified by:

copy in interface Copyable<GaussianDistribution>