gtsam::HybridSmoother Class Reference

Public Member Functions
void	update (HybridGaussianFactorGraph graph, const Ordering &ordering, std::optional< size_t > maxNrLeaves={})
Ordering	getOrdering (const HybridGaussianFactorGraph &newFactors)
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.
const HybridBayesNet &	hybridBayesNet () const
	Return the Bayes Net posterior.

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>

update()

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

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
ordering	The ordering for elimination, only continuous vars are allowed
maxNrLeaves	The maximum number of leaves in the new discrete factor, if applicable

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

• /home/docs/checkouts/readthedocs.org/user_builds/gtsam-jlblanco-docs/checkouts/latest/gtsam/hybrid/HybridSmoother.h