gtsam::HybridSmoother Class Reference

#include <HybridSmoother.h>

Public Member Functions
std::pair< HybridGaussianFactorGraph, HybridBayesNet >	addConditionals (const HybridGaussianFactorGraph &graph, const HybridBayesNet &hybridBayesNet) const
	Add conditionals from previous timestep as part of liquefication. More...
HybridNonlinearFactorGraph	allFactors () const
	Return all the recorded nonlinear factors. More...
const DiscreteValues &	fixedValues () const
	Return fixed values: More...
HybridGaussianConditional::shared_ptr	gaussianMixture (size_t index) const
	Get the hybrid Gaussian conditional from the Bayes Net posterior at index. More...
Ordering	getOrdering (const HybridGaussianFactorGraph &factors, const KeySet &newFactorKeys)
	Get an elimination ordering which eliminates continuous and then discrete. More...
const HybridBayesNet &	hybridBayesNet () const
	Return the Bayes Net posterior. More...
	HybridSmoother (const std::optional< double > marginalThreshold={})
	Constructor. More...
Values	linearizationPoint () const
	Return the current linearization point. More...
HybridValues	optimize () const
	Optimize the hybrid Bayes Net, taking into accound fixed values. More...
void	reInitialize (HybridBayesNet &&hybridBayesNet)
void	reInitialize (HybridBayesNet &hybridBayesNet)
void	relinearize ()
void	update (const HybridNonlinearFactorGraph &graph, const Values &initial, std::optional< size_t > maxNrLeaves={}, const std::optional< Ordering > givenOrdering={})

Private Member Functions
Ordering	maybeComputeOrdering (const HybridGaussianFactorGraph &updatedGraph, const std::optional< Ordering > givenOrdering)
	Helper to compute the ordering if ordering is not given. More...
void	removeFixedValues (const HybridGaussianFactorGraph &newFactors)
	Remove fixed discrete values for discrete keys introduced in newFactors. More...

Private Attributes
HybridNonlinearFactorGraph	allFactors_
DiscreteValues	fixedValues_
HybridBayesNet	hybridBayesNet_
Values	linearizationPoint_
std::optional< double >	marginalThreshold_
	The threshold above which we make a decision about a mode. More...

Detailed Description

Definition at line 28 of file HybridSmoother.h.

Constructor & Destructor Documentation

HybridSmoother()

gtsam::HybridSmoother::HybridSmoother ( const std::optional< double >  marginalThreshold = {} )

inline

Constructor.

Parameters

removeDeadModes	Flag indicating whether to remove dead modes.
marginalThreshold	The threshold above which a mode gets assigned a value and is considered "dead". 0.99 is a good starting value.

Definition at line 46 of file HybridSmoother.h.

Member Function Documentation

addConditionals()

std::pair< HybridGaussianFactorGraph, HybridBayesNet > gtsam::HybridSmoother::addConditionals	(	const HybridGaussianFactorGraph &	graph,
		const HybridBayesNet &	hybridBayesNet
	)		const

Add conditionals from previous timestep as part of liquefication.

Parameters

graph	The new factor graph for the current time step.
hybridBayesNet	The hybrid bayes net containing all conditionals so far.
ordering	The elimination ordering.

Returns

std::pair<HybridGaussianFactorGraph, HybridBayesNet>

Definition at line 198 of file HybridSmoother.cpp.

allFactors()

HybridNonlinearFactorGraph gtsam::HybridSmoother::allFactors

(

)

const

Return all the recorded nonlinear factors.

Definition at line 311 of file HybridSmoother.cpp.

fixedValues()

const DiscreteValues& gtsam::HybridSmoother::fixedValues ( ) const

inline

Return fixed values:

Definition at line 50 of file HybridSmoother.h.

gaussianMixture()

HybridGaussianConditional::shared_ptr gtsam::HybridSmoother::gaussianMixture

(

size_t

index

)

const

Get the hybrid Gaussian conditional from the Bayes Net posterior at index.

Parameters

index

Indexing value.

Returns

HybridGaussianConditional::shared_ptr

Definition at line 267 of file HybridSmoother.cpp.

getOrdering()

Ordering gtsam::HybridSmoother::getOrdering	(	const HybridGaussianFactorGraph &	factors,
		const KeySet &	newFactorKeys
	)

Get an elimination ordering which eliminates continuous and then discrete.

Expects factors to already have the necessary conditionals which were connected to the variables in the newly added factors. Those variables should be in newFactorKeys.

Parameters

factors	All the new factors and connected conditionals.
newFactorKeys	The keys/variables in the newly added factors.

Returns

Ordering

Definition at line 38 of file HybridSmoother.cpp.

hybridBayesNet()

const HybridBayesNet & gtsam::HybridSmoother::hybridBayesNet

(

)

const

Return the Bayes Net posterior.

Definition at line 273 of file HybridSmoother.cpp.

linearizationPoint()

Values gtsam::HybridSmoother::linearizationPoint

(

)

const

Return the current linearization point.

Definition at line 306 of file HybridSmoother.cpp.

maybeComputeOrdering()

Ordering gtsam::HybridSmoother::maybeComputeOrdering ( const HybridGaussianFactorGraph &  updatedGraph, const std::optional< Ordering >  givenOrdering  )

private

Helper to compute the ordering if ordering is not given.

Definition at line 65 of file HybridSmoother.cpp.

optimize()

HybridValues gtsam::HybridSmoother::optimize

(

)

const

Optimize the hybrid Bayes Net, taking into accound fixed values.

Definition at line 278 of file HybridSmoother.cpp.

reInitialize() [1/2]

void gtsam::HybridSmoother::reInitialize

(

HybridBayesNet &&

hybridBayesNet

)

Re-initialize the smoother from a new hybrid Bayes Net.

Definition at line 28 of file HybridSmoother.cpp.

reInitialize() [2/2]

void gtsam::HybridSmoother::reInitialize

(

HybridBayesNet &

hybridBayesNet

)

Re-initialize the smoother from a new hybrid Bayes Net (non rvalue version).

Definition at line 33 of file HybridSmoother.cpp.

relinearize()

void gtsam::HybridSmoother::relinearize

(

)

Relinearize the nonlinear factor graph with the latest linearization point.

Definition at line 295 of file HybridSmoother.cpp.

removeFixedValues()

void gtsam::HybridSmoother::removeFixedValues ( const HybridGaussianFactorGraph &  newFactors )

private

Remove fixed discrete values for discrete keys introduced in newFactors.

Definition at line 89 of file HybridSmoother.cpp.

update()

void gtsam::HybridSmoother::update	(	const HybridNonlinearFactorGraph &	graph,
		const Values &	initial,
		std::optional< size_t >	maxNrLeaves = {},
		const std::optional< Ordering >	givenOrdering = {}
	)

Given new factors, perform an incremental update. The relevant densities in the hybridBayesNet will be added to the input graph (fragment), and then eliminated according to the ordering presented. The remaining factor graph contains hybrid Gaussian factors that are not connected to the variables in the ordering, or a single discrete factor on all discrete keys, plus all discrete factors in the original graph.

Note

If maxNrLeaves is given, we look at the discrete factor resulting from this elimination, and prune it and the Gaussian components corresponding to the pruned choices.

Parameters

graph	The new factors, should be linear only
maxNrLeaves	The maximum number of leaves in the new discrete factor, if applicable
givenOrdering	The (optional) ordering for elimination, only continuous variables are allowed

Prune

Definition at line 99 of file HybridSmoother.cpp.

Member Data Documentation

allFactors_

HybridNonlinearFactorGraph gtsam::HybridSmoother::allFactors_

private

Definition at line 30 of file HybridSmoother.h.

fixedValues_

DiscreteValues gtsam::HybridSmoother::fixedValues_

private

Definition at line 36 of file HybridSmoother.h.

hybridBayesNet_

HybridBayesNet gtsam::HybridSmoother::hybridBayesNet_

private

Definition at line 33 of file HybridSmoother.h.

linearizationPoint_

Values gtsam::HybridSmoother::linearizationPoint_

private

Definition at line 31 of file HybridSmoother.h.

marginalThreshold_

std::optional<double> gtsam::HybridSmoother::marginalThreshold_

private

The threshold above which we make a decision about a mode.

Definition at line 35 of file HybridSmoother.h.

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The documentation for this class was generated from the following files:

• HybridSmoother.h
• HybridSmoother.cpp