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java.lang.Objectde.unihalle.informatik.MiToBo.tracking.multitarget.distributions.abstracts.AbstractMultiObservationDistribution<S,T>
de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.abstracts.AbstractMultiObservationDistributionIndep<T,T>
de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.impl.MultiObsDistributionIndepGaussians<T>
T
- class type of the observations' and states' discrete variables@ALDMetaInfo(export=ALLOWED) public class MultiObsDistributionIndepGaussians<T extends Copyable<?>>
A simple multi observation density, which assumes independence of the single observations with multivariate Gaussian noise. Further, the noise covariance matrices are identical for each object. Number of targets in observation and state must be equal!
Field Summary | |
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protected GaussianDistribution[] |
gaussian
multivariate gaussian density object for evaluation |
protected Jama.Matrix[] |
H
state-to-observation-space linear transform matrix |
protected Jama.Matrix[] |
R
Gaussian measurement noise covariance matrix |
Fields inherited from class de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.abstracts.AbstractMultiObservationDistribution |
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condX, factoryX, factoryZ |
Method Summary | |
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AbstractMultiState<T> |
drawSample()
Generate a new sample from this density. |
Jama.Matrix[] |
getCovariance()
|
AbstractMultiState<T> |
getMean()
|
int |
getNumOfIndeps()
|
Jama.Matrix[] |
getObservationMatrices()
|
double |
log_p(AbstractMultiState<T> Z)
Evaluate natural logarithm of p(X) at location x. log(P(X=x)) |
double |
log_p(AbstractMultiState<T> Z,
int i)
Evaluate the density independently for observation i in Z conditional on state i in X |
double |
log_p(AbstractMultiState<T> Z,
int i,
int j)
Evaluate the density independently for observation i in Z conditional on state j in X |
double |
logp(AbstractMultiState<T> Z)
|
double |
p(AbstractMultiState<T> Z)
Evaluate p(X) at location x. |
double |
p(AbstractMultiState<T> Z,
int i)
Evaluate the density independently for observation i in Z conditional on state i in X |
double |
p(AbstractMultiState<T> Z,
int i,
int j)
Evaluate the density independently for observation i in Z conditional on state j in X |
void |
setCondition(AbstractMultiState<T> X)
Set the conditional variable |
Methods inherited from class de.unihalle.informatik.MiToBo.tracking.multitarget.distributions.abstracts.AbstractMultiObservationDistribution |
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getCondition |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Field Detail |
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protected Jama.Matrix[] H
protected Jama.Matrix[] R
protected GaussianDistribution[] gaussian
Constructor Detail |
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public MultiObsDistributionIndepGaussians(Random rand, Jama.Matrix H, Jama.Matrix R, AbstractMultiState<T> X, AbstractMultiStateFactory<T> factoryX, AbstractMultiStateFactory<T> factoryZ) throws IllegalArgumentException
H
- state-to-observation-space linear transform matrixR
- Gaussian noise covariance matrixX
- condition statefactoryX
- factory to determine multi-target state layoutfactoryZ
- factory to determine multi-target observation layout
IllegalArgumentException
- if any dimensions of the input objects do not matchpublic MultiObsDistributionIndepGaussians(Random rand, Jama.Matrix[] H, Jama.Matrix[] R, AbstractMultiState<T> X, AbstractMultiStateFactory<T> factoryX, AbstractMultiStateFactory<T> factoryZ) throws IllegalArgumentException
H
- state-to-observation-space linear transform matricesR
- Gaussian noise covariance matricesX
- condition statefactoryX
- factory to determine multi-target state layoutfactoryZ
- factory to determine multi-target observation layout
IllegalArgumentException
- if any dimensions of the input objects do not matchpublic MultiObsDistributionIndepGaussians(Random rand, Jama.Matrix H, Jama.Matrix R, MultiStateDistributionIndepGaussians<T> distribX, AbstractMultiStateFactory<T> factoryX, AbstractMultiStateFactory<T> factoryZ) throws IllegalArgumentException
H
- state-to-observation-space linear transform matrixR
- Gaussian noise covariance matrixdistribX
- A Gaussian state distributionfactoryX
- factory to determine multi-target state layoutfactoryZ
- factory to determine multi-target observation layout
IllegalArgumentException
- if any dimensions of the input objects do not matchpublic MultiObsDistributionIndepGaussians(Random rand, Jama.Matrix[] H, Jama.Matrix[] R, MultiStateDistributionIndepGaussians<T> distribX, AbstractMultiStateFactory<T> factoryX, AbstractMultiStateFactory<T> factoryZ) throws IllegalArgumentException
H
- state-to-observation-space linear transform matricesR
- Gaussian noise covariance matricesX
- condition statefactoryX
- factory to determine multi-target state layoutfactoryZ
- factory to determine multi-target observation layout
IllegalArgumentException
- if any dimensions of the input objects do not matchpublic MultiObsDistributionIndepGaussians(Random rand, Jama.Matrix H, Jama.Matrix R, MultiStateLinTransDistributionIndepGaussians<T> transdistribX, AbstractMultiStateFactory<T> factoryX, AbstractMultiStateFactory<T> factoryZ) throws IllegalArgumentException
H
- state-to-observation-space linear transform matrixR
- Gaussian noise covariance matrixtransdistribX
- A Gaussian state distributionfactoryX
- factory to determine multi-target state layoutfactoryZ
- factory to determine multi-target observation layout
IllegalArgumentException
- if any dimensions of the input objects do not matchpublic MultiObsDistributionIndepGaussians(Random rand, Jama.Matrix[] H, Jama.Matrix[] R, MultiStateLinTransDistributionIndepGaussians<T> transdistribX, AbstractMultiStateFactory<T> factoryX, AbstractMultiStateFactory<T> factoryZ) throws IllegalArgumentException
H
- state-to-observation-space linear transform matricesR
- Gaussian noise covariance matricesX
- condition statefactoryX
- factory to determine multi-target state layoutfactoryZ
- factory to determine multi-target observation layout
IllegalArgumentException
- if any dimensions of the input objects do not matchMethod Detail |
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public double logp(AbstractMultiState<T> Z) throws IllegalArgumentException
IllegalArgumentException
public double p(AbstractMultiState<T> Z) throws IllegalArgumentException
EvaluatableDistribution
p
in interface EvaluatableDistribution<AbstractMultiState<T extends Copyable<?>>>
p
in class AbstractMultiObservationDistributionIndep<T extends Copyable<?>,T extends Copyable<?>>
Z
- realization of random variable X
IllegalArgumentException
public double p(AbstractMultiState<T> Z, int i)
AbstractMultiObservationDistributionIndep
p
in interface IndependentlyEvaluatableDistribution<AbstractMultiState<T extends Copyable<?>>>
p
in class AbstractMultiObservationDistributionIndep<T extends Copyable<?>,T extends Copyable<?>>
Z
- realization of random variable Xi
- i-th element in x
public double p(AbstractMultiState<T> Z, int i, int j)
AbstractMultiObservationDistributionIndep
p
in class AbstractMultiObservationDistributionIndep<T extends Copyable<?>,T extends Copyable<?>>
public double log_p(AbstractMultiState<T> Z)
LogEvaluatableDistribution
log_p
in interface LogEvaluatableDistribution<AbstractMultiState<T extends Copyable<?>>>
log_p
in class AbstractMultiObservationDistributionIndep<T extends Copyable<?>,T extends Copyable<?>>
Z
- realization of random variable X
public double log_p(AbstractMultiState<T> Z, int i)
AbstractMultiObservationDistributionIndep
log_p
in interface LogIndependentlyEvaluatableDistribution<AbstractMultiState<T extends Copyable<?>>>
log_p
in class AbstractMultiObservationDistributionIndep<T extends Copyable<?>,T extends Copyable<?>>
Z
- realization of random variable Xi
- i-th element in x
public double log_p(AbstractMultiState<T> Z, int i, int j)
AbstractMultiObservationDistributionIndep
log_p
in class AbstractMultiObservationDistributionIndep<T extends Copyable<?>,T extends Copyable<?>>
public AbstractMultiState<T> getMean()
getMean
in interface FirstOrderMoment<AbstractMultiState<T extends Copyable<?>>>
public AbstractMultiState<T> drawSample()
SamplingDistribution
drawSample
in interface SamplingDistribution<AbstractMultiState<T extends Copyable<?>>>
public Jama.Matrix[] getCovariance()
getCovariance
in interface SecondOrderCentralMoment<Jama.Matrix[]>
public void setCondition(AbstractMultiState<T> X)
ConditionalDistribution
setCondition
in interface ConditionalDistribution<AbstractMultiState<T extends Copyable<?>>>
setCondition
in class AbstractMultiObservationDistribution<T extends Copyable<?>,T extends Copyable<?>>
X
- conditional variablepublic Jama.Matrix[] getObservationMatrices()
public int getNumOfIndeps()
getNumOfIndeps
in class AbstractMultiObservationDistributionIndep<T extends Copyable<?>,T extends Copyable<?>>
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