#include <HybridSmoother.h>

Public Member Functions
std::pair< HybridGaussianFactorGraph, HybridBayesNet >	addConditionals (const HybridGaussianFactorGraph &graph, const HybridBayesNet &hybridBayesNet, const Ordering &ordering) const
	Add conditionals from previous timestep as part of liquefication. More...

GaussianMixture::shared_ptr	gaussianMixture (size_t index) const
	Get the Gaussian Mixture from the Bayes Net posterior at `index`. More...

Ordering	getOrdering (const HybridGaussianFactorGraph &newFactors)

const HybridBayesNet &	hybridBayesNet () const
	Return the Bayes Net posterior. More...

void	update (HybridGaussianFactorGraph graph, std::optional< size_t > maxNrLeaves={}, const std::optional< Ordering > given_ordering={})

Private Attributes
HybridBayesNet	hybridBayesNet_

HybridGaussianFactorGraph	remainingFactorGraph_

Detailed Description

Definition at line 27 of file HybridSmoother.h.

Member Function Documentation

◆ addConditionals()

std::pair< HybridGaussianFactorGraph, HybridBayesNet > gtsam::HybridSmoother::addConditionals	(	const HybridGaussianFactorGraph &	graph,
		const HybridBayesNet &	hybridBayesNet,
		const Ordering &	ordering
	)		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 93 of file HybridSmoother.cpp.

◆ gaussianMixture()

GaussianMixture::shared_ptr gtsam::HybridSmoother::gaussianMixture ( size_t index ) const

Get the Gaussian Mixture from the Bayes Net posterior at index.

Definition at line 139 of file HybridSmoother.cpp.

◆ getOrdering()

Ordering gtsam::HybridSmoother::getOrdering ( const HybridGaussianFactorGraph & newFactors )

Definition at line 27 of file HybridSmoother.cpp.

◆ hybridBayesNet()

const HybridBayesNet & gtsam::HybridSmoother::hybridBayesNet ( ) const

Return the Bayes Net posterior.

Definition at line 145 of file HybridSmoother.cpp.

◆ update()

void gtsam::HybridSmoother::update	(	HybridGaussianFactorGraph	graph,
		std::optional< size_t >	maxNrLeaves = `{}`,
		const std::optional< Ordering >	given_ordering = `{}`
	)

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 Gaussian mixture 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 maxComponents 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
given_ordering	The (optional) ordering for elimination, only continuous variables are allowed

Prune

Definition at line 59 of file HybridSmoother.cpp.

Member Data Documentation

◆ hybridBayesNet_

HybridBayesNet gtsam::HybridSmoother::hybridBayesNet_

private

Definition at line 29 of file HybridSmoother.h.

◆ remainingFactorGraph_

HybridGaussianFactorGraph gtsam::HybridSmoother::remainingFactorGraph_