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

Class MultiStateLinTransDistributionIndepGaussians<T extends Copyable<?>>

java.lang.Object

de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.abstracts.AbstractMultiStateTransitionDistribution<T>

de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.abstracts.AbstractMultiStateTransitionDistributionIndep<T>

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

Type Parameters:

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

All Implemented Interfaces:

ConditionalDistribution<AbstractMultiState<T>>, EvaluatableDistribution<AbstractMultiState<T>>, FirstOrderMoment<AbstractMultiState<T>>, IndependentlyEvaluatableDistribution<AbstractMultiState<T>>, IndependentSamplingDistribution<AbstractMultiState<T>>, SamplingDistribution<AbstractMultiState<T>>, SecondOrderCentralMoment<Jama.Matrix[]>

public class MultiStateLinTransDistributionIndepGaussians<T extends Copyable<?>>
extends AbstractMultiStateTransitionDistributionIndep<T>
implements EvaluatableDistribution<AbstractMultiState<T>>, IndependentlyEvaluatableDistribution<AbstractMultiState<T>>, FirstOrderMoment<AbstractMultiState<T>>, SecondOrderCentralMoment<Jama.Matrix[]>

A simple multi state-transition density, which assumes independence of the single states and multivariate Gaussian process noise. Further, the noise covariance matrices are identical for each state.

Author:

Oliver Gress

Field Summary

Fields

Modifier and Type	Field and Description
protected Jama.Matrix[]	F state-transition matrix
protected GaussianDistribution[]	gaussian multivariate gaussian density object for evaluation
protected Jama.Matrix[]	Q Gaussian process noise covariance matrix

Fields inherited from class de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.abstracts.AbstractMultiStateTransitionDistribution

condX, factoryX

Constructor Summary

Constructors

Constructor and Description
MultiStateLinTransDistributionIndepGaussians(Random rand, Jama.Matrix[] F, Jama.Matrix[] Q, AbstractMultiState<T> X, AbstractMultiStateFactory<T> factoryX)
MultiStateLinTransDistributionIndepGaussians(Random rand, Jama.Matrix[] F, Jama.Matrix[] Q, MultiStateDistributionIndepGaussians<T> distribX, AbstractMultiStateFactory<T> factoryX)
MultiStateLinTransDistributionIndepGaussians(Random rand, Jama.Matrix F, Jama.Matrix Q, AbstractMultiState<T> X, AbstractMultiStateFactory<T> factoryX)
MultiStateLinTransDistributionIndepGaussians(Random rand, Jama.Matrix F, Jama.Matrix Q, MultiStateDistributionIndepGaussians<T> distribX, AbstractMultiStateFactory<T> factoryX)

Method Summary

Modifier and Type	Method and Description
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.
Jama.Matrix[]	getCovariance()
AbstractMultiState<T>	getMean()
Jama.Matrix[]	getTransitionMatrices()
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	setCondition(AbstractMultiState<T> X) Set the conditional variable

Methods inherited from class de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.abstracts.AbstractMultiStateTransitionDistribution

getCondition

Methods inherited from class java.lang.Object

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

Field Detail

F

protected Jama.Matrix[] F

state-transition matrix

Q

protected Jama.Matrix[] Q

Gaussian process noise covariance matrix

gaussian

protected GaussianDistribution[] gaussian

multivariate gaussian density object for evaluation

Constructor Detail

MultiStateLinTransDistributionIndepGaussians

public MultiStateLinTransDistributionIndepGaussians(Random rand,
                                                    Jama.Matrix F,
                                                    Jama.Matrix Q,
                                                    AbstractMultiState<T> X,
                                                    AbstractMultiStateFactory<T> factoryX)
                                             throws IllegalArgumentException

Parameters:

F - state-transition linear transform matrix

Q - Gaussian process noise covariance matrix

X - condition state

factoryX - factory to determine multi-target state layout

Throws:

IllegalArgumentException - if any dimensions of the input objects do not match

MultiStateLinTransDistributionIndepGaussians

public MultiStateLinTransDistributionIndepGaussians(Random rand,
                                                    Jama.Matrix[] F,
                                                    Jama.Matrix[] Q,
                                                    AbstractMultiState<T> X,
                                                    AbstractMultiStateFactory<T> factoryX)
                                             throws IllegalArgumentException

Parameters:

F - state-transition linear transform matrices

Q - Gaussian process noise covariance matrices

X - condition state

factoryX - factory to determine multi-target state layout

Throws:

IllegalArgumentException - if any dimensions of the input objects do not match

MultiStateLinTransDistributionIndepGaussians

public MultiStateLinTransDistributionIndepGaussians(Random rand,
                                                    Jama.Matrix F,
                                                    Jama.Matrix Q,
                                                    MultiStateDistributionIndepGaussians<T> distribX,
                                                    AbstractMultiStateFactory<T> factoryX)
                                             throws IllegalArgumentException

Parameters:

F - state-transition linear transform matrix

Q - Gaussian process noise covariance matrix

X - condition state

factoryX - factory to determine multi-target state layout

Throws:

IllegalArgumentException - if any dimensions of the input objects do not match

MultiStateLinTransDistributionIndepGaussians

public MultiStateLinTransDistributionIndepGaussians(Random rand,
                                                    Jama.Matrix[] F,
                                                    Jama.Matrix[] Q,
                                                    MultiStateDistributionIndepGaussians<T> distribX,
                                                    AbstractMultiStateFactory<T> factoryX)
                                             throws IllegalArgumentException

Parameters:

F - state-transition linear transform matrices

Q - Gaussian process noise covariance matrices

X - condition state

factoryX - factory to determine multi-target state layout

Throws:

IllegalArgumentException - if any dimensions of the input objects do not match

Method Detail

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<?>>>

Specified by:

drawSample in class AbstractMultiStateTransitionDistributionIndep<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<?>>>

Specified by:

drawSample in class AbstractMultiStateTransitionDistributionIndep<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

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

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

getMean

public AbstractMultiState<T> getMean()

Specified by:

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

getCovariance

public Jama.Matrix[] getCovariance()

Specified by:

getCovariance in interface SecondOrderCentralMoment<Jama.Matrix[]>

setCondition

public void setCondition(AbstractMultiState<T> X)

Description copied from interface: ConditionalDistribution

Set the conditional variable

Specified by:

setCondition in interface ConditionalDistribution<AbstractMultiState<T extends Copyable<?>>>

Overrides:

setCondition in class AbstractMultiStateTransitionDistribution<T extends Copyable<?>>

Parameters:

X - conditional variable

getTransitionMatrices

public Jama.Matrix[] getTransitionMatrices()