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java.lang.Objectde.unihalle.informatik.MiToBo.tracking.multitarget.distributions.impl.MultiStateDistributionIndepGaussians<T>
T
- class type of the multi-states' discrete variablespublic class MultiStateDistributionIndepGaussians<T extends Copyable<?>>
A simple multi state density, which assumes independence of the single states with multivariate Gaussian noise.
Field Summary | |
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protected java.util.Vector<Jama.Matrix> |
covs
|
protected java.util.Vector<GaussianDistribution> |
gaussians
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protected AbstractMultiState<T> |
mean
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protected java.util.Random |
rand
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Constructor Summary | |
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MultiStateDistributionIndepGaussians(AbstractMultiState<T> mean,
Jama.Matrix covariance,
java.util.Random rand)
Constructor with identical covariance matrices for all states |
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MultiStateDistributionIndepGaussians(AbstractMultiState<T> mean,
java.util.Vector<Jama.Matrix> covariance,
java.util.Random rand)
Constructor with different covariance matrix for each state |
protected |
MultiStateDistributionIndepGaussians(int numOfIndepGaussians)
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Method Summary | |
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int |
addIndepGaussian(GaussianDistribution stateCont,
T stateDiscr)
Add an independent Gaussian state distribution |
MultiStateDistributionIndepGaussians<T> |
copy()
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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. |
java.util.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)
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void |
predictIndep(int i,
LinearTransformGaussNoise predictor)
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int |
removeIndepGaussian(int i)
Remove independent Gaussian state distribution (at index i) |
void |
update(LinearTransformGaussNoise projector,
AbstractMultiState<T> observations)
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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)
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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 java.util.Vector<Jama.Matrix> covs
protected java.util.Vector<GaussianDistribution> gaussians
protected AbstractMultiState<T extends Copyable<?>> mean
protected java.util.Random rand
Constructor Detail |
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public MultiStateDistributionIndepGaussians(AbstractMultiState<T> mean, Jama.Matrix covariance, java.util.Random rand)
public MultiStateDistributionIndepGaussians(AbstractMultiState<T> mean, java.util.Vector<Jama.Matrix> covariance, java.util.Random rand)
protected MultiStateDistributionIndepGaussians(int numOfIndepGaussians)
Method Detail |
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public int addIndepGaussian(GaussianDistribution stateCont, T stateDiscr)
public MultiStateDistributionIndepGaussians<T> copy()
copy
in interface Copyable<MultiStateDistributionIndepGaussians<T extends Copyable<?>>>
public AbstractMultiState<T> drawSample()
SamplingDistribution
drawSample
in interface SamplingDistribution<AbstractMultiState<T extends Copyable<?>>>
public AbstractMultiState<T> drawSample(int i, AbstractMultiState<T> X)
IndependentSamplingDistribution
drawSample
in interface IndependentSamplingDistribution<AbstractMultiState<T extends Copyable<?>>>
i
- sample a new realization of the i-th element in xX
- realization of a random vector or finite set
public java.util.Vector<Jama.Matrix> getCovariance()
getCovariance
in interface SecondOrderCentralMoment<java.util.Vector<Jama.Matrix>>
public AbstractMultiState<T> getMean()
getMean
in interface FirstOrderMoment<AbstractMultiState<T extends Copyable<?>>>
public double p(AbstractMultiState<T> X)
EvaluatableDistribution
p
in interface EvaluatableDistribution<AbstractMultiState<T extends Copyable<?>>>
X
- realization of random variable X
public double p(AbstractMultiState<T> X, int i)
IndependentlyEvaluatableDistribution
p
in interface IndependentlyEvaluatableDistribution<AbstractMultiState<T extends Copyable<?>>>
X
- realization of random variable Xi
- i-th element in x
public void predict(LinearTransformGaussNoise predictor)
public void predictIndep(int i, LinearTransformGaussNoise predictor)
public int removeIndepGaussian(int i)
public void update(LinearTransformGaussNoise projector, AbstractMultiState<T> observations)
public void updateIndep(int i, int j, LinearTransformGaussNoise projector, AbstractMultiState<T> observations)
i
- j
- projector
- observations
- public void updateIndep(int i, LinearTransformGaussNoise projector, AbstractMultiState<T> observations)
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