gtsam::Marginals Class Reference

#include <Marginals.h>

Collaboration diagram for gtsam::Marginals:

Public Types
enum	Factorization { CHOLESKY, QR }

Public Member Functions
	Marginals ()
	Default constructor only for wrappers.
	Marginals (const NonlinearFactorGraph &graph, const Values &solution, Factorization factorization=CHOLESKY)
	Marginals (const NonlinearFactorGraph &graph, const Values &solution, const Ordering &ordering, Factorization factorization=CHOLESKY)
	Marginals (const GaussianFactorGraph &graph, const Values &solution, Factorization factorization=CHOLESKY)
	Marginals (const GaussianFactorGraph &graph, const Values &solution, const Ordering &ordering, Factorization factorization=CHOLESKY)
	Marginals (const GaussianFactorGraph &graph, const VectorValues &solution, Factorization factorization=CHOLESKY)
	Marginals (const GaussianFactorGraph &graph, const VectorValues &solution, const Ordering &ordering, Factorization factorization=CHOLESKY)
void	print (const std::string &str="Marginals: ", const KeyFormatter &keyFormatter=DefaultKeyFormatter) const
GaussianFactor::shared_ptr	marginalFactor (Key variable) const
Matrix	marginalInformation (Key variable) const
Matrix	marginalCovariance (Key variable) const
JointMarginal	jointMarginalCovariance (const KeyVector &variables) const
JointMarginal	jointMarginalInformation (const KeyVector &variables) const
VectorValues	optimize () const

Protected Member Functions
void	computeBayesTree ()
void	computeBayesTree (const Ordering &ordering)

Protected Attributes
GaussianFactorGraph	graph_
Values	values_
Factorization	factorization_
GaussianBayesTree	bayesTree_

Detailed Description

A class for computing Gaussian marginals of variables in a NonlinearFactorGraph

Member Enumeration Documentation

Factorization

enum gtsam::Marginals::Factorization

The linear factorization mode - either CHOLESKY (faster and suitable for most problems) or QR (slower but more numerically stable for poorly-conditioned problems).

Constructor & Destructor Documentation

Marginals() [1/6]

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 - either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorly-conditioned problems).

Marginals() [2/6]

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 - either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorly-conditioned problems).
ordering	The ordering for elimination.

Marginals() [3/6]

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 - either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorly-conditioned problems).

Marginals() [4/6]

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 - either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorly-conditioned problems).
ordering	The ordering for elimination.

Marginals() [5/6]

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 - either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorly-conditioned problems).
ordering	An optional variable ordering for elimination.

Marginals() [6/6]

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 - either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorly-conditioned problems).
ordering	An optional variable ordering for elimination.

Member Function Documentation

computeBayesTree() [1/2]

void gtsam::Marginals::computeBayesTree ( )

protected

Compute the Bayes Tree as a helper function to the constructor

computeBayesTree() [2/2]

void gtsam::Marginals::computeBayesTree ( const Ordering &  ordering )

protected

Compute the Bayes Tree as a helper function to the constructor

jointMarginalCovariance()

JointMarginal gtsam::Marginals::jointMarginalCovariance

(

const KeyVector &

variables

)

const

Compute the joint marginal covariance of several variables

jointMarginalInformation()

JointMarginal gtsam::Marginals::jointMarginalInformation

(

const KeyVector &

variables

)

const

Compute the joint marginal information of several variables

marginalCovariance()

Matrix gtsam::Marginals::marginalCovariance

(

Key

variable

)

const

Compute the marginal covariance of a single variable

marginalFactor()

GaussianFactor::shared_ptr gtsam::Marginals::marginalFactor

(

Key

variable

)

const

Compute the marginal factor of a single variable

marginalInformation()

Matrix gtsam::Marginals::marginalInformation

(

Key

variable

)

const

Compute the marginal information matrix of a single variable. Use LLt(const Matrix&) or RtR(const Matrix&) to obtain the square-root information matrix.

optimize()

VectorValues gtsam::Marginals::optimize

(

)

const

Optimize the bayes tree

print()

void gtsam::Marginals::print	(	const std::string &	str = "Marginals: ",
		const KeyFormatter &	keyFormatter = DefaultKeyFormatter
	)		const

print

---

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

• /home/docs/checkouts/readthedocs.org/user_builds/gtsam-jlblanco-docs/checkouts/latest/gtsam/nonlinear/Marginals.h