gtsam::Marginals Class Reference

Detailed Description

A class for computing Gaussian marginals of variables in a NonlinearFactorGraph.

Public Member Functions
	Marginals ()
	Default constructor only for wrappers.
	Marginals (const NonlinearFactorGraph &graph, const Values &solution, Factorization factorization=CHOLESKY)
	Construct a marginals class from a nonlinear factor graph.
	Marginals (const NonlinearFactorGraph &graph, const Values &solution, const Ordering &ordering, Factorization factorization=CHOLESKY)
	Construct a marginals class from a nonlinear factor graph.
	Marginals (const GaussianFactorGraph &graph, const Values &solution, Factorization factorization=CHOLESKY)
	Construct a marginals class from a linear factor graph.
	Marginals (const GaussianFactorGraph &graph, const Values &solution, const Ordering &ordering, Factorization factorization=CHOLESKY)
	Construct a marginals class from a linear factor graph.
	Marginals (const GaussianFactorGraph &graph, const VectorValues &solution, Factorization factorization=CHOLESKY)
	Construct a marginals class from a linear factor graph.
	Marginals (const GaussianFactorGraph &graph, const VectorValues &solution, const Ordering &ordering, Factorization factorization=CHOLESKY)
	Construct a marginals class from a linear factor graph.
	Marginals (GaussianBayesTree &&bayesTree, const VectorValues &solution, Factorization factorization=CHOLESKY)
	Construct a marginals class from a precomputed Bayes tree.
void	print (const std::string &str="Marginals: ", const KeyFormatter &keyFormatter=DefaultKeyFormatter) const
	print
GaussianFactor::shared_ptr	marginalFactor (Key variable) const
	Compute the marginal factor of a single variable.
Matrix	marginalInformation (Key variable) const
	Compute the marginal information matrix of a single variable.
Matrix	marginalCovariance (Key variable) const
	Compute the marginal covariance of a single variable.
JointMarginal	jointMarginalCovariance (const KeyVector &variables) const
	Compute joint marginal covariance with blocks in variables order.
JointMarginal	jointMarginalInformation (const KeyVector &variables) const
	Compute joint marginal information with blocks in variables order.
void	deleteCachedShortcuts ()
	Delete cached Bayes tree shortcuts created while computing marginals.
VectorValues	optimize () const
	Optimize the bayes tree.

Public Types
enum	Factorization { CHOLESKY , QR }
	The linear factorization mode - either CHOLESKY (faster and suitable for most problems) or QR (slower but more numerically stable for poorly-conditioned problems).

Protected Member Functions
GaussianFactorGraph::Eliminate	eliminationFunction () const
	Select the elimination rule that matches the requested marginal factorization.
void	computeBayesTree ()
	Compute the Bayes Tree as a helper function to the constructor.
void	computeBayesTree (const Ordering &ordering)
	Compute the Bayes Tree as a helper function to the constructor.

Protected Attributes
GaussianFactorGraph	graph_
Values	values_
Factorization	factorization_
GaussianBayesTree	bayesTree_

Constructor & Destructor Documentation

Marginals() [1/7]

gtsam::Marginals::Marginals	(	const NonlinearFactorGraph &	graph,
		const Values &	solution,
		Factorization	factorization = CHOLESKY )

Construct a marginals class from a nonlinear factor graph.

Parameters

graph	The factor graph defining the full joint density on all variables.
solution	The linearization point about which to compute Gaussian marginals (usually the MLE as obtained from a NonlinearOptimizer).
factorization	The linear decomposition mode: CHOLESKY|QR

Marginals() [2/7]

gtsam::Marginals::Marginals	(	const NonlinearFactorGraph &	graph,
		const Values &	solution,
		const Ordering &	ordering,
		Factorization	factorization = CHOLESKY )

Construct a marginals class from a nonlinear factor graph.

Parameters

graph	The factor graph defining the full joint density on all variables.
solution	The linearization point about which to compute Gaussian marginals (usually the MLE as obtained from a NonlinearOptimizer).
factorization	The linear decomposition mode: CHOLESKY|QR
ordering	The ordering for elimination.

Marginals() [3/7]

gtsam::Marginals::Marginals	(	const GaussianFactorGraph &	graph,
		const Values &	solution,
		Factorization	factorization = CHOLESKY )

Construct a marginals class from a linear factor graph.

Parameters

graph	The factor graph defining the full joint density on all variables.
solution	The solution point to compute Gaussian marginals.
factorization	The linear decomposition mode: CHOLESKY|QR

Marginals() [4/7]

gtsam::Marginals::Marginals	(	const GaussianFactorGraph &	graph,
		const Values &	solution,
		const Ordering &	ordering,
		Factorization	factorization = CHOLESKY )

Construct a marginals class from a linear factor graph.

Parameters

graph	The factor graph defining the full joint density on all variables.
solution	The solution point to compute Gaussian marginals.
factorization	The linear decomposition mode: CHOLESKY|QR
ordering	The ordering for elimination.

Marginals() [5/7]

gtsam::Marginals::Marginals	(	const GaussianFactorGraph &	graph,
		const VectorValues &	solution,
		Factorization	factorization = CHOLESKY )

Construct a marginals class from a linear factor graph.

Parameters

graph	The factor graph defining the full joint density on all variables.
solution	The solution point to compute Gaussian marginals.
factorization	The linear decomposition mode: CHOLESKY|QR
ordering	An optional variable ordering for elimination.

Marginals() [6/7]

gtsam::Marginals::Marginals	(	const GaussianFactorGraph &	graph,
		const VectorValues &	solution,
		const Ordering &	ordering,
		Factorization	factorization = CHOLESKY )

Construct a marginals class from a linear factor graph.

Parameters

graph	The factor graph defining the full joint density on all variables.
solution	The solution point to compute Gaussian marginals.
factorization	The linear decomposition mode: CHOLESKY|QR
ordering	An optional variable ordering for elimination.

Marginals() [7/7]

gtsam::Marginals::Marginals	(	GaussianBayesTree &&	bayesTree,
		const VectorValues &	solution,
		Factorization	factorization = CHOLESKY )

Construct a marginals class from a precomputed Bayes tree.

Parameters

bayesTree	The precomputed Gaussian Bayes tree representing the factorization of the linear system.
solution	The solution point at which to compute Gaussian marginals.
factorization	The linear decomposition mode: CHOLESKY|QR

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

• /tmp/gtsam-4.3.0-doxygen.rsXPUS/source/gtsam/nonlinear/Marginals.h
• /tmp/gtsam-4.3.0-doxygen.rsXPUS/source/gtsam/nonlinear/Marginals.cpp