Modifier and Type | Field and Description |
---|---|
protected AbstractMultiState<MotionModelID> |
MultiTargetIMMFilter.meanX |
Modifier and Type | Method and Description |
---|---|
AbstractMultiState<T> |
MultiTargetRBMCDA.getMean() |
AbstractMultiState<MotionModelID> |
MultiTargetIMMFilter.getMean() |
Modifier and Type | Method and Description |
---|---|
MultiTargetPredictionFilter<AbstractMultiState<T>> |
MultiTargetRBMCDA.copy()
Not implemented, always returns
null |
Modifier and Type | Method and Description |
---|---|
void |
MultiTargetIMMFilter.update(AbstractMultiState<MotionModelID> observation,
DataAssociation association) |
void |
MultiTargetRBMCDA.update(AbstractMultiState<T> observation,
DataAssociation association)
The DataAssociation object may be null and is interpreted as groundtruth if given.
|
Modifier and Type | Method and Description |
---|---|
abstract AbstractMultiState<T> |
AbstractMultiState.copy()
Copy this multi-state
|
abstract AbstractMultiState<T> |
AbstractMultiStateFactory.createEmptyMultiState()
Create an empty multi state object
|
abstract AbstractMultiState<T> |
AbstractMultiStateFactory.createMultiState(double[][] continuousStateVariables,
T[] discreteStateVariables)
Create a multi state object initialized by the specified data
|
abstract AbstractMultiState<T> |
AbstractMultiStateFactory.createMultiState(Jama.Matrix[] continuousStateVariables,
T[] discreteStateVariables)
Create a multi state object initialized by the specified data
|
Modifier and Type | Method and Description |
---|---|
boolean |
AbstractMultiStateFactory.validMultiState(AbstractMultiState<T> multistate)
Test if the specified multistate is valid for this factory.
|
Modifier and Type | Class and Description |
---|---|
class |
MultiState<T extends Copyable<?>>
Multi-target state implementation.
|
Modifier and Type | Field and Description |
---|---|
private Vector<AbstractMultiState<T>> |
RBMCDASampleInfo.observations |
Modifier and Type | Method and Description |
---|---|
AbstractMultiState<T> |
MultiStateFactory.createEmptyMultiState() |
AbstractMultiState<T> |
MultiStateFactory.createMultiState(double[][] continuousStateVariables,
T[] discreteStateVariables) |
AbstractMultiState<T> |
MultiStateFactory.createMultiState(Jama.Matrix[] continuousStateVariables,
T[] discreteStateVariables) |
AbstractMultiState<T> |
RBMCDASampleInfo.getObservations(int t)
Get the observations of time t.
|
Modifier and Type | Method and Description |
---|---|
int |
RBMCDASampleInfo.addCurrentInfo(double logP_C,
DataAssociation C,
AbstractMultiState<T> observations,
Set<Short> existingTargetIDs)
Add sample info of the current time step
|
Modifier and Type | Field and Description |
---|---|
protected AbstractMultiState<T> |
AbstractMultiStateTransitionDistribution.condX
multi state condition on the density
|
protected AbstractMultiState<T> |
AbstractMultiObservationDistribution.condX
multi state condition on the density
|
protected AbstractMultiState<S> |
AbstractAssociationDistribution.Z
observations
|
Modifier and Type | Method and Description |
---|---|
abstract AbstractMultiState<T> |
AbstractMultiStateTransitionDistributionIndep.drawSample() |
abstract AbstractMultiState<T> |
AbstractMultiStateTransitionDistribution.drawSample() |
abstract AbstractMultiState<T> |
AbstractMultiStateTransitionDistributionIndep.drawSample(int i,
AbstractMultiState<T> X) |
AbstractMultiState<T> |
AbstractMultiStateTransitionDistribution.getCondition() |
AbstractMultiState<T> |
AbstractMultiObservationDistribution.getCondition() |
Modifier and Type | Method and Description |
---|---|
abstract AbstractMultiState<T> |
AbstractMultiStateTransitionDistributionIndep.drawSample(int i,
AbstractMultiState<T> X) |
abstract double |
AbstractMultiObservationDistributionIndep.log_p(AbstractMultiState<S> Z) |
abstract double |
AbstractMultiObservationDistribution.log_p(AbstractMultiState<S> Z) |
abstract double |
AbstractMultiObservationDistributionIndep.log_p(AbstractMultiState<S> Z,
int i)
Evaluate the density independently for observation i in Z conditional on state i in X
|
abstract double |
AbstractMultiObservationDistributionIndep.log_p(AbstractMultiState<S> Z,
int i,
int j)
Evaluate the density independently for observation i in Z conditional on state j in X
|
abstract double |
AbstractMultiObservationDistributionIndep.p(AbstractMultiState<S> Z) |
abstract double |
AbstractMultiObservationDistribution.p(AbstractMultiState<S> Z) |
abstract double |
AbstractMultiObservationDistributionIndep.p(AbstractMultiState<S> Z,
int i)
Evaluate the density independently for observation i in Z conditional on state i in X
|
abstract double |
AbstractMultiObservationDistributionIndep.p(AbstractMultiState<S> Z,
int i,
int j)
Evaluate the density independently for observation i in Z conditional on state j in X
|
void |
AbstractMultiStateTransitionDistribution.setCondition(AbstractMultiState<T> X) |
void |
AbstractMultiObservationDistribution.setCondition(AbstractMultiState<T> X) |
void |
AbstractAssociationDistribution.setNewObservations(AbstractMultiState<S> Z,
AbstractMultiObservationDistributionIndep<S,T> observationDistrib) |
Modifier and Type | Field and Description |
---|---|
protected AbstractMultiState<T> |
MultiStateDistributionIndepGaussians.mean |
Modifier and Type | Method and Description |
---|---|
AbstractMultiState<T> |
MultiObsDistributionIndepGaussians.drawSample() |
AbstractMultiState<T> |
MultiStateLinTransDistributionIndepGaussians.drawSample() |
AbstractMultiState<T> |
MultiObsDistributionIndepGaussMix.drawSample() |
AbstractMultiState<T> |
MultiStateDistributionIndepGaussians.drawSample() |
AbstractMultiState<T> |
MultiStateLinTransDistributionIndepGaussians.drawSample(int i,
AbstractMultiState<T> X) |
AbstractMultiState<T> |
MultiStateDistributionIndepGaussians.drawSample(int i,
AbstractMultiState<T> X) |
AbstractMultiState<T> |
MultiObsDistributionIndepGaussians.getMean() |
AbstractMultiState<T> |
MultiStateLinTransDistributionIndepGaussians.getMean() |
AbstractMultiState<T> |
MultiObsDistributionIndepGaussMix.getMean() |
AbstractMultiState<T> |
MultiStateDistributionIndepGaussians.getMean() |
Modifier and Type | Method and Description |
---|---|
AbstractMultiState<T> |
MultiStateLinTransDistributionIndepGaussians.drawSample(int i,
AbstractMultiState<T> X) |
AbstractMultiState<T> |
MultiStateDistributionIndepGaussians.drawSample(int i,
AbstractMultiState<T> X) |
double |
MultiObsDistributionIndepGaussians.log_p(AbstractMultiState<T> Z) |
double |
MultiObsDistributionIndepGaussMix.log_p(AbstractMultiState<T> Z) |
double |
MultiObsDistributionIndepGaussians.log_p(AbstractMultiState<T> Z,
int i) |
double |
MultiObsDistributionIndepGaussMix.log_p(AbstractMultiState<T> Z,
int i) |
double |
MultiObsDistributionIndepGaussians.log_p(AbstractMultiState<T> Z,
int i,
int j) |
double |
MultiObsDistributionIndepGaussMix.log_p(AbstractMultiState<T> Z,
int i,
int j) |
double |
MultiObsDistributionIndepGaussians.logp(AbstractMultiState<T> Z) |
double |
MultiObsDistributionIndepGaussians.p(AbstractMultiState<T> Z) |
double |
MultiStateLinTransDistributionIndepGaussians.p(AbstractMultiState<T> X) |
double |
MultiObsDistributionIndepGaussMix.p(AbstractMultiState<T> Z) |
double |
MultiStateDistributionIndepGaussians.p(AbstractMultiState<T> X) |
double |
MultiObsDistributionIndepGaussians.p(AbstractMultiState<T> Z,
int i) |
double |
MultiStateLinTransDistributionIndepGaussians.p(AbstractMultiState<T> X,
int i) |
double |
MultiObsDistributionIndepGaussMix.p(AbstractMultiState<T> Z,
int i) |
double |
MultiStateDistributionIndepGaussians.p(AbstractMultiState<T> X,
int i) |
double |
MultiObsDistributionIndepGaussians.p(AbstractMultiState<T> Z,
int i,
int j) |
double |
MultiObsDistributionIndepGaussMix.p(AbstractMultiState<T> Z,
int i,
int j) |
void |
MultiObsDistributionIndepGaussians.setCondition(AbstractMultiState<T> X) |
void |
MultiStateLinTransDistributionIndepGaussians.setCondition(AbstractMultiState<T> X) |
void |
AssociationDistribution.setNewObservations(AbstractMultiState<S> Z,
AbstractMultiObservationDistributionIndep<S,T> observationDistrib) |
void |
AssociationDistributionNN.setNewObservations(AbstractMultiState<S> Z,
AbstractMultiObservationDistributionIndep<S,T> observationDistrib) |
void |
MultiStateDistributionIndepGaussians.update(LinearTransformGaussNoise projector,
AbstractMultiState<T> observations) |
void |
MultiStateDistributionIndepGaussians.updateIndep(int i,
int j,
LinearTransformGaussNoise projector,
AbstractMultiState<T> observations)
Update i-th Gaussian component with j-th observation
|
void |
MultiStateDistributionIndepGaussians.updateIndep(int i,
LinearTransformGaussNoise projector,
AbstractMultiState<T> observations) |
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