de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.impl

Class MultiStateDistributionIndepGaussians<T extends Copyable<?>>

java.lang.Object

de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.impl.MultiStateDistributionIndepGaussians<T>

Type Parameters:

T - class type of the multi-states' discrete variables

All Implemented Interfaces:

EvaluatableDistribution<AbstractMultiState<T>>, FirstOrderMoment<AbstractMultiState<T>>, IndependentlyEvaluatableDistribution<AbstractMultiState<T>>, IndependentSamplingDistribution<AbstractMultiState<T>>, SamplingDistribution<AbstractMultiState<T>>, SecondOrderCentralMoment<Vector<Jama.Matrix>>, Copyable<MultiStateDistributionIndepGaussians<T>>

public class MultiStateDistributionIndepGaussians<T extends Copyable<?>>
extends Object
implements EvaluatableDistribution<AbstractMultiState<T>>, IndependentlyEvaluatableDistribution<AbstractMultiState<T>>, SamplingDistribution<AbstractMultiState<T>>, IndependentSamplingDistribution<AbstractMultiState<T>>, FirstOrderMoment<AbstractMultiState<T>>, SecondOrderCentralMoment<Vector<Jama.Matrix>>, Copyable<MultiStateDistributionIndepGaussians<T>>

A simple multi state density, which assumes independence of the single states with multivariate Gaussian noise.

Author:

Oliver Gress

Field Summary

Fields

Modifier and Type	Field and Description
protected Vector<Jama.Matrix>	covs
protected Vector<GaussianDistribution>	gaussians
protected AbstractMultiState<T>	mean
protected Random	rand

Constructor Summary

Constructors

Modifier	Constructor and Description
	MultiStateDistributionIndepGaussians(AbstractMultiState<T> mean, Jama.Matrix covariance, Random rand) Constructor with identical covariance matrices for all states
	MultiStateDistributionIndepGaussians(AbstractMultiState<T> mean, Vector<Jama.Matrix> covariance, Random rand) Constructor with different covariance matrix for each state
protected	MultiStateDistributionIndepGaussians(int numOfIndepGaussians)

Method Summary

Modifier and Type	Method and Description
int	addIndepGaussian(GaussianDistribution stateCont, T stateDiscr) Add an independent Gaussian state distribution
MultiStateDistributionIndepGaussians<T>	copy()
AbstractMultiState<T>	drawSample() Generate a new sample from this density.
AbstractMultiState<T>	drawSample(int i, AbstractMultiState<T> X) Generate a new sample from this density by drawing only one independent variable for a given realization x.
Vector<Jama.Matrix>	getCovariance()
AbstractMultiState<T>	getMean()
double	p(AbstractMultiState<T> X) Evaluate p(X) at location x.
double	p(AbstractMultiState<T> X, int i) Evaluate p_i(X) at x_i
void	predict(LinearTransformGaussNoise predictor)
void	predictIndep(int i, LinearTransformGaussNoise predictor)
int	removeIndepGaussian(int i) Remove independent Gaussian state distribution (at index i)
void	update(LinearTransformGaussNoise projector, AbstractMultiState<T> observations)
void	updateIndep(int i, int j, LinearTransformGaussNoise projector, AbstractMultiState<T> observations) Update i-th Gaussian component with j-th observation
void	updateIndep(int i, LinearTransformGaussNoise projector, AbstractMultiState<T> observations)

Methods inherited from class java.lang.Object

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

Field Detail

mean

protected AbstractMultiState<T extends Copyable<?>> mean

covs

protected Vector<Jama.Matrix> covs

gaussians

protected Vector<GaussianDistribution> gaussians

rand

protected Random rand

Constructor Detail

MultiStateDistributionIndepGaussians

protected MultiStateDistributionIndepGaussians(int numOfIndepGaussians)

MultiStateDistributionIndepGaussians

public MultiStateDistributionIndepGaussians(AbstractMultiState<T> mean,
                                            Jama.Matrix covariance,
                                            Random rand)

Constructor with identical covariance matrices for all states

MultiStateDistributionIndepGaussians

public MultiStateDistributionIndepGaussians(AbstractMultiState<T> mean,
                                            Vector<Jama.Matrix> covariance,
                                            Random rand)

Constructor with different covariance matrix for each state

Method Detail

addIndepGaussian

public int addIndepGaussian(GaussianDistribution stateCont,
                            T stateDiscr)

Add an independent Gaussian state distribution

removeIndepGaussian

public int removeIndepGaussian(int i)

Remove independent Gaussian state distribution (at index i)

p

public double p(AbstractMultiState<T> X)

Description copied from interface: EvaluatableDistribution

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

Specified by:

p in interface EvaluatableDistribution<AbstractMultiState<T extends Copyable<?>>>

Parameters:

X - realization of random variable X

Returns:

value of p(X) at x

p

public double p(AbstractMultiState<T> X,
                int i)

Description copied from interface: IndependentlyEvaluatableDistribution

Evaluate p_i(X) at x_i

Specified by:

p in interface IndependentlyEvaluatableDistribution<AbstractMultiState<T extends Copyable<?>>>

Parameters:

X - realization of random variable X

i - i-th element in x

Returns:

value of p_i(X_i) at x_i

drawSample

public AbstractMultiState<T> 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<AbstractMultiState<T extends Copyable<?>>>

Returns:

new sample object

drawSample

public AbstractMultiState<T> drawSample(int i,
                                        AbstractMultiState<T> X)

Description copied from interface: IndependentSamplingDistribution

Generate a new sample from this density by drawing only one independent variable for a given realization x. This method should create a new object.

Specified by:

drawSample in interface IndependentSamplingDistribution<AbstractMultiState<T extends Copyable<?>>>

Parameters:

i - sample a new realization of the i-th element in x

X - realization of a random vector or finite set

Returns:

new sample object

getMean

public AbstractMultiState<T> getMean()

Specified by:

getMean in interface FirstOrderMoment<AbstractMultiState<T extends Copyable<?>>>

getCovariance

public Vector<Jama.Matrix> getCovariance()

Specified by:

getCovariance in interface SecondOrderCentralMoment<Vector<Jama.Matrix>>

predict

public void predict(LinearTransformGaussNoise predictor)

predictIndep

public void predictIndep(int i,
                         LinearTransformGaussNoise predictor)

update

public void update(LinearTransformGaussNoise projector,
                   AbstractMultiState<T> observations)

updateIndep

public void updateIndep(int i,
                        LinearTransformGaussNoise projector,
                        AbstractMultiState<T> observations)

updateIndep

public void updateIndep(int i,
                        int j,
                        LinearTransformGaussNoise projector,
                        AbstractMultiState<T> observations)

Update i-th Gaussian component with j-th observation

Parameters:

i -

j -

projector -

observations -

copy

public MultiStateDistributionIndepGaussians<T> copy()

Specified by:

copy in interface Copyable<MultiStateDistributionIndepGaussians<T extends Copyable<?>>>